2022-05-26 15:07:31,889 INFO [train.py:906] (1/4) Training started
2022-05-26 15:07:31,889 INFO [train.py:916] (1/4) Device: cuda:1
2022-05-26 15:07:31,893 INFO [train.py:934] (1/4) {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'encoder_dim': 512, 'nhead': 8, 'dim_feedforward': 2048, 'num_encoder_layers': 12, 'decoder_dim': 512, 'joiner_dim': 512, 'model_warm_step': 3000, 'env_info': {'k2-version': '1.15.1', 'k2-build-type': 'Debug', 'k2-with-cuda': True, 'k2-git-sha1': 'ecfe7bd6d9189964bf3ff043038918d889a43185', 'k2-git-date': 'Tue May 10 10:57:55 2022', 'lhotse-version': '1.1.0', 'torch-version': '1.10.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'streaming-conformer', 'icefall-git-sha1': '364bccb-dirty', 'icefall-git-date': 'Thu May 26 10:29:08 2022', 'icefall-path': '/ceph-kw/kangwei/code/icefall_reworked2', 'k2-path': '/ceph-kw/kangwei/code/k2/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-hw/kangwei/dev_tools/anaconda3/envs/rnnt2/lib/python3.8/site-packages/lhotse-1.1.0-py3.8.egg/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-3-0307202051-57dc848959-8tmmp', 'IP address': '10.177.24.138'}, 'world_size': 4, 'master_port': 13498, 'tensorboard': True, 'num_epochs': 50, 'start_epoch': 2, 'start_batch': 0, 'exp_dir': PosixPath('streaming_pruned_transducer_stateless4/exp'), 'bpe_model': 'data/lang_bpe_500/bpe.model', 'initial_lr': 0.003, 'lr_batches': 5000, 'lr_epochs': 6, 'context_size': 2, 'prune_range': 5, 'lm_scale': 0.25, 'am_scale': 0.0, 'simple_loss_scale': 0.5, 'seed': 42, 'print_diagnostics': False, 'save_every_n': 8000, 'keep_last_k': 20, 'average_period': 100, 'use_fp16': False, 'dynamic_chunk_training': True, 'causal_convolution': True, 'short_chunk_size': 32, 'num_left_chunks': 4, 'delay_penalty': 0.0, 'return_sym_delay': False, 'full_libri': True, 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 300, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'blank_id': 0, 'vocab_size': 500}
2022-05-26 15:07:31,893 INFO [train.py:936] (1/4) About to create model
2022-05-26 15:07:32,315 INFO [train.py:940] (1/4) Number of model parameters: 78648040
2022-05-26 15:07:32,315 INFO [checkpoint.py:112] (1/4) Loading checkpoint from streaming_pruned_transducer_stateless4/exp/epoch-1.pt
2022-05-26 15:07:44,537 INFO [train.py:955] (1/4) Using DDP
2022-05-26 15:07:44,729 INFO [train.py:963] (1/4) Loading optimizer state dict
2022-05-26 15:07:45,478 INFO [train.py:971] (1/4) Loading scheduler state dict
2022-05-26 15:07:45,478 INFO [asr_datamodule.py:391] (1/4) About to get train-clean-100 cuts
2022-05-26 15:07:51,841 INFO [asr_datamodule.py:398] (1/4) About to get train-clean-360 cuts
2022-05-26 15:08:18,717 INFO [asr_datamodule.py:405] (1/4) About to get train-other-500 cuts
2022-05-26 15:09:03,841 INFO [asr_datamodule.py:209] (1/4) Enable MUSAN
2022-05-26 15:09:03,841 INFO [asr_datamodule.py:210] (1/4) About to get Musan cuts
2022-05-26 15:09:05,307 INFO [asr_datamodule.py:238] (1/4) Enable SpecAugment
2022-05-26 15:09:05,307 INFO [asr_datamodule.py:239] (1/4) Time warp factor: 80
2022-05-26 15:09:05,307 INFO [asr_datamodule.py:251] (1/4) Num frame mask: 10
2022-05-26 15:09:05,308 INFO [asr_datamodule.py:264] (1/4) About to create train dataset
2022-05-26 15:09:05,308 INFO [asr_datamodule.py:292] (1/4) Using BucketingSampler.
2022-05-26 15:09:10,523 INFO [asr_datamodule.py:308] (1/4) About to create train dataloader
2022-05-26 15:09:10,524 INFO [asr_datamodule.py:412] (1/4) About to get dev-clean cuts
2022-05-26 15:09:10,813 INFO [asr_datamodule.py:417] (1/4) About to get dev-other cuts
2022-05-26 15:09:10,972 INFO [asr_datamodule.py:339] (1/4) About to create dev dataset
2022-05-26 15:09:10,985 INFO [asr_datamodule.py:358] (1/4) About to create dev dataloader
2022-05-26 15:09:10,985 INFO [train.py:1082] (1/4) Sanity check -- see if any of the batches in epoch 1 would cause OOM.
2022-05-26 15:09:21,056 INFO [distributed.py:874] (1/4) Reducer buckets have been rebuilt in this iteration.
2022-05-26 15:09:26,260 INFO [train.py:1023] (1/4) Loading grad scaler state dict
2022-05-26 15:09:38,910 INFO [train.py:842] (1/4) Epoch 2, batch 0, loss[loss=0.4062, simple_loss=0.4045, pruned_loss=0.204, over 7194.00 frames.], tot_loss[loss=0.4062, simple_loss=0.4045, pruned_loss=0.204, over 7194.00 frames.], batch size: 26, lr: 2.06e-03
2022-05-26 15:10:18,182 INFO [train.py:842] (1/4) Epoch 2, batch 50, loss[loss=0.3454, simple_loss=0.3838, pruned_loss=0.1535, over 7239.00 frames.], tot_loss[loss=0.3483, simple_loss=0.3826, pruned_loss=0.1571, over 312457.70 frames.], batch size: 20, lr: 2.06e-03
2022-05-26 15:10:57,100 INFO [train.py:842] (1/4) Epoch 2, batch 100, loss[loss=0.3209, simple_loss=0.3677, pruned_loss=0.1371, over 7440.00 frames.], tot_loss[loss=0.3498, simple_loss=0.3822, pruned_loss=0.1587, over 560857.63 frames.], batch size: 20, lr: 2.05e-03
2022-05-26 15:11:35,988 INFO [train.py:842] (1/4) Epoch 2, batch 150, loss[loss=0.271, simple_loss=0.3303, pruned_loss=0.1059, over 7323.00 frames.], tot_loss[loss=0.3438, simple_loss=0.3785, pruned_loss=0.1546, over 751250.89 frames.], batch size: 20, lr: 2.05e-03
2022-05-26 15:12:14,713 INFO [train.py:842] (1/4) Epoch 2, batch 200, loss[loss=0.2675, simple_loss=0.3217, pruned_loss=0.1067, over 7166.00 frames.], tot_loss[loss=0.3419, simple_loss=0.3775, pruned_loss=0.1531, over 901061.57 frames.], batch size: 19, lr: 2.04e-03
2022-05-26 15:12:53,664 INFO [train.py:842] (1/4) Epoch 2, batch 250, loss[loss=0.3496, simple_loss=0.3934, pruned_loss=0.1529, over 7379.00 frames.], tot_loss[loss=0.3423, simple_loss=0.378, pruned_loss=0.1533, over 1015262.24 frames.], batch size: 23, lr: 2.04e-03
2022-05-26 15:13:32,394 INFO [train.py:842] (1/4) Epoch 2, batch 300, loss[loss=0.2973, simple_loss=0.3499, pruned_loss=0.1223, over 7265.00 frames.], tot_loss[loss=0.3432, simple_loss=0.3796, pruned_loss=0.1534, over 1104877.00 frames.], batch size: 19, lr: 2.03e-03
2022-05-26 15:14:11,459 INFO [train.py:842] (1/4) Epoch 2, batch 350, loss[loss=0.3656, simple_loss=0.3922, pruned_loss=0.1695, over 7217.00 frames.], tot_loss[loss=0.3404, simple_loss=0.3776, pruned_loss=0.1516, over 1173794.80 frames.], batch size: 21, lr: 2.03e-03
2022-05-26 15:14:50,194 INFO [train.py:842] (1/4) Epoch 2, batch 400, loss[loss=0.3633, simple_loss=0.3977, pruned_loss=0.1644, over 7141.00 frames.], tot_loss[loss=0.3396, simple_loss=0.3769, pruned_loss=0.1511, over 1230767.28 frames.], batch size: 20, lr: 2.03e-03
2022-05-26 15:15:28,836 INFO [train.py:842] (1/4) Epoch 2, batch 450, loss[loss=0.3692, simple_loss=0.3873, pruned_loss=0.1756, over 7149.00 frames.], tot_loss[loss=0.3396, simple_loss=0.3776, pruned_loss=0.1508, over 1276106.99 frames.], batch size: 19, lr: 2.02e-03
2022-05-26 15:16:07,192 INFO [train.py:842] (1/4) Epoch 2, batch 500, loss[loss=0.301, simple_loss=0.3459, pruned_loss=0.128, over 7154.00 frames.], tot_loss[loss=0.3372, simple_loss=0.3761, pruned_loss=0.1491, over 1307949.85 frames.], batch size: 18, lr: 2.02e-03
2022-05-26 15:16:46,580 INFO [train.py:842] (1/4) Epoch 2, batch 550, loss[loss=0.2845, simple_loss=0.3437, pruned_loss=0.1127, over 7354.00 frames.], tot_loss[loss=0.3378, simple_loss=0.3764, pruned_loss=0.1496, over 1332890.86 frames.], batch size: 19, lr: 2.01e-03
2022-05-26 15:17:25,161 INFO [train.py:842] (1/4) Epoch 2, batch 600, loss[loss=0.3332, simple_loss=0.381, pruned_loss=0.1427, over 7371.00 frames.], tot_loss[loss=0.3398, simple_loss=0.3781, pruned_loss=0.1507, over 1353855.73 frames.], batch size: 23, lr: 2.01e-03
2022-05-26 15:18:04,045 INFO [train.py:842] (1/4) Epoch 2, batch 650, loss[loss=0.2894, simple_loss=0.3414, pruned_loss=0.1186, over 7282.00 frames.], tot_loss[loss=0.3379, simple_loss=0.3763, pruned_loss=0.1497, over 1367603.58 frames.], batch size: 18, lr: 2.01e-03
2022-05-26 15:18:42,827 INFO [train.py:842] (1/4) Epoch 2, batch 700, loss[loss=0.3948, simple_loss=0.4187, pruned_loss=0.1855, over 5024.00 frames.], tot_loss[loss=0.3341, simple_loss=0.374, pruned_loss=0.1471, over 1379671.48 frames.], batch size: 53, lr: 2.00e-03
2022-05-26 15:19:21,652 INFO [train.py:842] (1/4) Epoch 2, batch 750, loss[loss=0.3098, simple_loss=0.3651, pruned_loss=0.1272, over 7249.00 frames.], tot_loss[loss=0.3362, simple_loss=0.3758, pruned_loss=0.1483, over 1391080.18 frames.], batch size: 19, lr: 2.00e-03
2022-05-26 15:20:00,307 INFO [train.py:842] (1/4) Epoch 2, batch 800, loss[loss=0.3148, simple_loss=0.3604, pruned_loss=0.1346, over 7067.00 frames.], tot_loss[loss=0.3359, simple_loss=0.376, pruned_loss=0.148, over 1400775.99 frames.], batch size: 18, lr: 1.99e-03
2022-05-26 15:20:39,196 INFO [train.py:842] (1/4) Epoch 2, batch 850, loss[loss=0.4027, simple_loss=0.4221, pruned_loss=0.1916, over 7339.00 frames.], tot_loss[loss=0.3329, simple_loss=0.374, pruned_loss=0.1459, over 1408503.68 frames.], batch size: 20, lr: 1.99e-03
2022-05-26 15:21:17,859 INFO [train.py:842] (1/4) Epoch 2, batch 900, loss[loss=0.3962, simple_loss=0.4121, pruned_loss=0.1902, over 7445.00 frames.], tot_loss[loss=0.3346, simple_loss=0.3752, pruned_loss=0.1471, over 1413182.49 frames.], batch size: 20, lr: 1.99e-03
2022-05-26 15:21:56,745 INFO [train.py:842] (1/4) Epoch 2, batch 950, loss[loss=0.3007, simple_loss=0.3464, pruned_loss=0.1275, over 7261.00 frames.], tot_loss[loss=0.3337, simple_loss=0.3744, pruned_loss=0.1465, over 1415120.82 frames.], batch size: 19, lr: 1.98e-03
2022-05-26 15:22:35,347 INFO [train.py:842] (1/4) Epoch 2, batch 1000, loss[loss=0.3656, simple_loss=0.4103, pruned_loss=0.1605, over 6832.00 frames.], tot_loss[loss=0.3325, simple_loss=0.3736, pruned_loss=0.1457, over 1416869.14 frames.], batch size: 31, lr: 1.98e-03
2022-05-26 15:23:14,146 INFO [train.py:842] (1/4) Epoch 2, batch 1050, loss[loss=0.3532, simple_loss=0.3893, pruned_loss=0.1586, over 7442.00 frames.], tot_loss[loss=0.3332, simple_loss=0.374, pruned_loss=0.1462, over 1418270.17 frames.], batch size: 20, lr: 1.97e-03
2022-05-26 15:23:52,766 INFO [train.py:842] (1/4) Epoch 2, batch 1100, loss[loss=0.3093, simple_loss=0.3519, pruned_loss=0.1334, over 7171.00 frames.], tot_loss[loss=0.3368, simple_loss=0.3763, pruned_loss=0.1486, over 1420016.04 frames.], batch size: 18, lr: 1.97e-03
2022-05-26 15:24:32,111 INFO [train.py:842] (1/4) Epoch 2, batch 1150, loss[loss=0.401, simple_loss=0.4124, pruned_loss=0.1948, over 7235.00 frames.], tot_loss[loss=0.3353, simple_loss=0.3749, pruned_loss=0.1478, over 1423385.29 frames.], batch size: 20, lr: 1.97e-03
2022-05-26 15:25:10,578 INFO [train.py:842] (1/4) Epoch 2, batch 1200, loss[loss=0.3816, simple_loss=0.4138, pruned_loss=0.1747, over 7081.00 frames.], tot_loss[loss=0.3358, simple_loss=0.3758, pruned_loss=0.1479, over 1422500.34 frames.], batch size: 28, lr: 1.96e-03
2022-05-26 15:25:49,495 INFO [train.py:842] (1/4) Epoch 2, batch 1250, loss[loss=0.2851, simple_loss=0.3359, pruned_loss=0.1171, over 7272.00 frames.], tot_loss[loss=0.3354, simple_loss=0.3757, pruned_loss=0.1476, over 1422555.59 frames.], batch size: 18, lr: 1.96e-03
2022-05-26 15:26:28,038 INFO [train.py:842] (1/4) Epoch 2, batch 1300, loss[loss=0.3211, simple_loss=0.3799, pruned_loss=0.1311, over 7224.00 frames.], tot_loss[loss=0.3339, simple_loss=0.3745, pruned_loss=0.1466, over 1416769.82 frames.], batch size: 21, lr: 1.95e-03
2022-05-26 15:27:06,800 INFO [train.py:842] (1/4) Epoch 2, batch 1350, loss[loss=0.3981, simple_loss=0.3986, pruned_loss=0.1988, over 7283.00 frames.], tot_loss[loss=0.3325, simple_loss=0.3733, pruned_loss=0.1459, over 1420359.49 frames.], batch size: 17, lr: 1.95e-03
2022-05-26 15:27:45,158 INFO [train.py:842] (1/4) Epoch 2, batch 1400, loss[loss=0.3565, simple_loss=0.3956, pruned_loss=0.1587, over 7214.00 frames.], tot_loss[loss=0.3315, simple_loss=0.3725, pruned_loss=0.1453, over 1418929.46 frames.], batch size: 21, lr: 1.95e-03
2022-05-26 15:28:24,279 INFO [train.py:842] (1/4) Epoch 2, batch 1450, loss[loss=0.366, simple_loss=0.4074, pruned_loss=0.1623, over 7181.00 frames.], tot_loss[loss=0.3313, simple_loss=0.3727, pruned_loss=0.1449, over 1422632.00 frames.], batch size: 26, lr: 1.94e-03
2022-05-26 15:29:02,872 INFO [train.py:842] (1/4) Epoch 2, batch 1500, loss[loss=0.319, simple_loss=0.3768, pruned_loss=0.1306, over 6385.00 frames.], tot_loss[loss=0.3304, simple_loss=0.3722, pruned_loss=0.1444, over 1422762.33 frames.], batch size: 37, lr: 1.94e-03
2022-05-26 15:29:41,827 INFO [train.py:842] (1/4) Epoch 2, batch 1550, loss[loss=0.3175, simple_loss=0.3676, pruned_loss=0.1337, over 7435.00 frames.], tot_loss[loss=0.3299, simple_loss=0.3718, pruned_loss=0.144, over 1425485.27 frames.], batch size: 20, lr: 1.94e-03
2022-05-26 15:30:20,570 INFO [train.py:842] (1/4) Epoch 2, batch 1600, loss[loss=0.266, simple_loss=0.3156, pruned_loss=0.1082, over 7179.00 frames.], tot_loss[loss=0.3281, simple_loss=0.3701, pruned_loss=0.143, over 1424658.15 frames.], batch size: 18, lr: 1.93e-03
2022-05-26 15:30:59,432 INFO [train.py:842] (1/4) Epoch 2, batch 1650, loss[loss=0.2711, simple_loss=0.3262, pruned_loss=0.108, over 7425.00 frames.], tot_loss[loss=0.3267, simple_loss=0.3692, pruned_loss=0.1421, over 1426054.27 frames.], batch size: 20, lr: 1.93e-03
2022-05-26 15:31:38,106 INFO [train.py:842] (1/4) Epoch 2, batch 1700, loss[loss=0.3571, simple_loss=0.3998, pruned_loss=0.1572, over 7402.00 frames.], tot_loss[loss=0.3261, simple_loss=0.3686, pruned_loss=0.1417, over 1424029.02 frames.], batch size: 21, lr: 1.92e-03
2022-05-26 15:32:16,712 INFO [train.py:842] (1/4) Epoch 2, batch 1750, loss[loss=0.2617, simple_loss=0.3239, pruned_loss=0.09972, over 7276.00 frames.], tot_loss[loss=0.3269, simple_loss=0.3699, pruned_loss=0.142, over 1423178.80 frames.], batch size: 18, lr: 1.92e-03
2022-05-26 15:32:55,310 INFO [train.py:842] (1/4) Epoch 2, batch 1800, loss[loss=0.3722, simple_loss=0.3994, pruned_loss=0.1724, over 7365.00 frames.], tot_loss[loss=0.3279, simple_loss=0.3706, pruned_loss=0.1426, over 1424544.45 frames.], batch size: 19, lr: 1.92e-03
2022-05-26 15:33:34,114 INFO [train.py:842] (1/4) Epoch 2, batch 1850, loss[loss=0.2946, simple_loss=0.3544, pruned_loss=0.1173, over 7342.00 frames.], tot_loss[loss=0.3257, simple_loss=0.369, pruned_loss=0.1412, over 1425236.14 frames.], batch size: 20, lr: 1.91e-03
2022-05-26 15:34:12,761 INFO [train.py:842] (1/4) Epoch 2, batch 1900, loss[loss=0.3423, simple_loss=0.3541, pruned_loss=0.1653, over 7011.00 frames.], tot_loss[loss=0.3241, simple_loss=0.3685, pruned_loss=0.1398, over 1428801.81 frames.], batch size: 16, lr: 1.91e-03
2022-05-26 15:34:51,940 INFO [train.py:842] (1/4) Epoch 2, batch 1950, loss[loss=0.5381, simple_loss=0.4612, pruned_loss=0.3075, over 7271.00 frames.], tot_loss[loss=0.3267, simple_loss=0.3702, pruned_loss=0.1416, over 1428408.12 frames.], batch size: 18, lr: 1.91e-03
2022-05-26 15:35:30,335 INFO [train.py:842] (1/4) Epoch 2, batch 2000, loss[loss=0.3089, simple_loss=0.3662, pruned_loss=0.1258, over 7116.00 frames.], tot_loss[loss=0.3283, simple_loss=0.3718, pruned_loss=0.1424, over 1423430.06 frames.], batch size: 21, lr: 1.90e-03
2022-05-26 15:36:09,252 INFO [train.py:842] (1/4) Epoch 2, batch 2050, loss[loss=0.317, simple_loss=0.3751, pruned_loss=0.1294, over 7116.00 frames.], tot_loss[loss=0.3267, simple_loss=0.3706, pruned_loss=0.1415, over 1424728.97 frames.], batch size: 28, lr: 1.90e-03
2022-05-26 15:36:48,131 INFO [train.py:842] (1/4) Epoch 2, batch 2100, loss[loss=0.309, simple_loss=0.3562, pruned_loss=0.1309, over 7399.00 frames.], tot_loss[loss=0.3263, simple_loss=0.3703, pruned_loss=0.1412, over 1425593.23 frames.], batch size: 18, lr: 1.90e-03
2022-05-26 15:37:27,071 INFO [train.py:842] (1/4) Epoch 2, batch 2150, loss[loss=0.3924, simple_loss=0.4163, pruned_loss=0.1843, over 7407.00 frames.], tot_loss[loss=0.3256, simple_loss=0.3701, pruned_loss=0.1406, over 1424073.31 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 15:38:05,656 INFO [train.py:842] (1/4) Epoch 2, batch 2200, loss[loss=0.3901, simple_loss=0.4086, pruned_loss=0.1858, over 7116.00 frames.], tot_loss[loss=0.3227, simple_loss=0.3674, pruned_loss=0.139, over 1423270.60 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 15:38:44,471 INFO [train.py:842] (1/4) Epoch 2, batch 2250, loss[loss=0.2629, simple_loss=0.332, pruned_loss=0.09691, over 7215.00 frames.], tot_loss[loss=0.323, simple_loss=0.3674, pruned_loss=0.1393, over 1424622.57 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 15:39:23,297 INFO [train.py:842] (1/4) Epoch 2, batch 2300, loss[loss=0.3357, simple_loss=0.3761, pruned_loss=0.1477, over 7220.00 frames.], tot_loss[loss=0.3221, simple_loss=0.3671, pruned_loss=0.1385, over 1425004.73 frames.], batch size: 22, lr: 1.88e-03
2022-05-26 15:40:02,239 INFO [train.py:842] (1/4) Epoch 2, batch 2350, loss[loss=0.3548, simple_loss=0.3874, pruned_loss=0.1611, over 7242.00 frames.], tot_loss[loss=0.3226, simple_loss=0.3674, pruned_loss=0.1389, over 1424214.70 frames.], batch size: 20, lr: 1.88e-03
2022-05-26 15:40:40,747 INFO [train.py:842] (1/4) Epoch 2, batch 2400, loss[loss=0.4162, simple_loss=0.4434, pruned_loss=0.1945, over 7318.00 frames.], tot_loss[loss=0.3249, simple_loss=0.3693, pruned_loss=0.1402, over 1423777.74 frames.], batch size: 21, lr: 1.87e-03
2022-05-26 15:41:19,586 INFO [train.py:842] (1/4) Epoch 2, batch 2450, loss[loss=0.2873, simple_loss=0.3436, pruned_loss=0.1155, over 7324.00 frames.], tot_loss[loss=0.3245, simple_loss=0.3696, pruned_loss=0.1396, over 1426409.27 frames.], batch size: 21, lr: 1.87e-03
2022-05-26 15:41:58,100 INFO [train.py:842] (1/4) Epoch 2, batch 2500, loss[loss=0.3464, simple_loss=0.3998, pruned_loss=0.1466, over 7142.00 frames.], tot_loss[loss=0.3248, simple_loss=0.3699, pruned_loss=0.1398, over 1426587.40 frames.], batch size: 26, lr: 1.87e-03
2022-05-26 15:42:36,847 INFO [train.py:842] (1/4) Epoch 2, batch 2550, loss[loss=0.265, simple_loss=0.3118, pruned_loss=0.1091, over 6982.00 frames.], tot_loss[loss=0.3235, simple_loss=0.3687, pruned_loss=0.1392, over 1426158.51 frames.], batch size: 16, lr: 1.86e-03
2022-05-26 15:43:15,431 INFO [train.py:842] (1/4) Epoch 2, batch 2600, loss[loss=0.3835, simple_loss=0.4127, pruned_loss=0.1772, over 7147.00 frames.], tot_loss[loss=0.3214, simple_loss=0.3669, pruned_loss=0.138, over 1428080.53 frames.], batch size: 26, lr: 1.86e-03
2022-05-26 15:43:54,064 INFO [train.py:842] (1/4) Epoch 2, batch 2650, loss[loss=0.4056, simple_loss=0.4366, pruned_loss=0.1873, over 6309.00 frames.], tot_loss[loss=0.3197, simple_loss=0.3657, pruned_loss=0.1369, over 1426330.87 frames.], batch size: 38, lr: 1.86e-03
2022-05-26 15:44:32,725 INFO [train.py:842] (1/4) Epoch 2, batch 2700, loss[loss=0.3082, simple_loss=0.3613, pruned_loss=0.1276, over 6704.00 frames.], tot_loss[loss=0.318, simple_loss=0.3644, pruned_loss=0.1358, over 1426283.71 frames.], batch size: 31, lr: 1.85e-03
2022-05-26 15:45:11,867 INFO [train.py:842] (1/4) Epoch 2, batch 2750, loss[loss=0.3008, simple_loss=0.3577, pruned_loss=0.1219, over 7307.00 frames.], tot_loss[loss=0.3165, simple_loss=0.3633, pruned_loss=0.1349, over 1423289.44 frames.], batch size: 24, lr: 1.85e-03
2022-05-26 15:45:50,242 INFO [train.py:842] (1/4) Epoch 2, batch 2800, loss[loss=0.2732, simple_loss=0.3397, pruned_loss=0.1033, over 7191.00 frames.], tot_loss[loss=0.3176, simple_loss=0.3638, pruned_loss=0.1357, over 1425636.50 frames.], batch size: 23, lr: 1.85e-03
2022-05-26 15:46:29,149 INFO [train.py:842] (1/4) Epoch 2, batch 2850, loss[loss=0.3066, simple_loss=0.3613, pruned_loss=0.1259, over 7283.00 frames.], tot_loss[loss=0.3166, simple_loss=0.3629, pruned_loss=0.1352, over 1425314.41 frames.], batch size: 24, lr: 1.84e-03
2022-05-26 15:47:07,593 INFO [train.py:842] (1/4) Epoch 2, batch 2900, loss[loss=0.3267, simple_loss=0.3685, pruned_loss=0.1424, over 7239.00 frames.], tot_loss[loss=0.319, simple_loss=0.3647, pruned_loss=0.1366, over 1420496.11 frames.], batch size: 20, lr: 1.84e-03
2022-05-26 15:47:46,393 INFO [train.py:842] (1/4) Epoch 2, batch 2950, loss[loss=0.2711, simple_loss=0.3385, pruned_loss=0.1019, over 7232.00 frames.], tot_loss[loss=0.3207, simple_loss=0.3662, pruned_loss=0.1376, over 1422258.47 frames.], batch size: 20, lr: 1.84e-03
2022-05-26 15:48:24,996 INFO [train.py:842] (1/4) Epoch 2, batch 3000, loss[loss=0.2226, simple_loss=0.2726, pruned_loss=0.08627, over 7272.00 frames.], tot_loss[loss=0.3188, simple_loss=0.3652, pruned_loss=0.1363, over 1426040.14 frames.], batch size: 17, lr: 1.84e-03
2022-05-26 15:48:24,997 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 15:48:34,574 INFO [train.py:871] (1/4) Epoch 2, validation: loss=0.2365, simple_loss=0.3276, pruned_loss=0.07266, over 868885.00 frames.
2022-05-26 15:49:14,145 INFO [train.py:842] (1/4) Epoch 2, batch 3050, loss[loss=0.2508, simple_loss=0.3116, pruned_loss=0.09496, over 7282.00 frames.], tot_loss[loss=0.3194, simple_loss=0.3652, pruned_loss=0.1368, over 1422425.08 frames.], batch size: 18, lr: 1.83e-03
2022-05-26 15:49:52,548 INFO [train.py:842] (1/4) Epoch 2, batch 3100, loss[loss=0.5564, simple_loss=0.5177, pruned_loss=0.2975, over 4991.00 frames.], tot_loss[loss=0.3188, simple_loss=0.3648, pruned_loss=0.1364, over 1421773.17 frames.], batch size: 52, lr: 1.83e-03
2022-05-26 15:50:31,851 INFO [train.py:842] (1/4) Epoch 2, batch 3150, loss[loss=0.2518, simple_loss=0.3114, pruned_loss=0.09608, over 7198.00 frames.], tot_loss[loss=0.3187, simple_loss=0.3651, pruned_loss=0.1362, over 1424244.07 frames.], batch size: 16, lr: 1.83e-03
2022-05-26 15:51:10,258 INFO [train.py:842] (1/4) Epoch 2, batch 3200, loss[loss=0.4211, simple_loss=0.4296, pruned_loss=0.2063, over 5077.00 frames.], tot_loss[loss=0.3204, simple_loss=0.3668, pruned_loss=0.137, over 1413875.93 frames.], batch size: 52, lr: 1.82e-03
2022-05-26 15:51:49,126 INFO [train.py:842] (1/4) Epoch 2, batch 3250, loss[loss=0.3425, simple_loss=0.3942, pruned_loss=0.1454, over 7202.00 frames.], tot_loss[loss=0.3219, simple_loss=0.3677, pruned_loss=0.1381, over 1415946.87 frames.], batch size: 23, lr: 1.82e-03
2022-05-26 15:52:27,663 INFO [train.py:842] (1/4) Epoch 2, batch 3300, loss[loss=0.3395, simple_loss=0.3938, pruned_loss=0.1426, over 7212.00 frames.], tot_loss[loss=0.3186, simple_loss=0.3653, pruned_loss=0.1359, over 1420173.97 frames.], batch size: 22, lr: 1.82e-03
2022-05-26 15:53:06,349 INFO [train.py:842] (1/4) Epoch 2, batch 3350, loss[loss=0.4492, simple_loss=0.4568, pruned_loss=0.2208, over 7190.00 frames.], tot_loss[loss=0.32, simple_loss=0.3667, pruned_loss=0.1367, over 1423303.23 frames.], batch size: 26, lr: 1.81e-03
2022-05-26 15:53:45,051 INFO [train.py:842] (1/4) Epoch 2, batch 3400, loss[loss=0.2637, simple_loss=0.3162, pruned_loss=0.1056, over 7149.00 frames.], tot_loss[loss=0.3191, simple_loss=0.3657, pruned_loss=0.1362, over 1424889.50 frames.], batch size: 17, lr: 1.81e-03
2022-05-26 15:54:23,705 INFO [train.py:842] (1/4) Epoch 2, batch 3450, loss[loss=0.3515, simple_loss=0.3905, pruned_loss=0.1562, over 7277.00 frames.], tot_loss[loss=0.3201, simple_loss=0.3666, pruned_loss=0.1368, over 1427001.61 frames.], batch size: 24, lr: 1.81e-03
2022-05-26 15:55:02,071 INFO [train.py:842] (1/4) Epoch 2, batch 3500, loss[loss=0.3241, simple_loss=0.3701, pruned_loss=0.1391, over 6446.00 frames.], tot_loss[loss=0.3189, simple_loss=0.3659, pruned_loss=0.136, over 1423880.87 frames.], batch size: 37, lr: 1.80e-03
2022-05-26 15:55:40,848 INFO [train.py:842] (1/4) Epoch 2, batch 3550, loss[loss=0.3766, simple_loss=0.4194, pruned_loss=0.1668, over 7297.00 frames.], tot_loss[loss=0.3194, simple_loss=0.3661, pruned_loss=0.1364, over 1424116.89 frames.], batch size: 25, lr: 1.80e-03
2022-05-26 15:56:19,292 INFO [train.py:842] (1/4) Epoch 2, batch 3600, loss[loss=0.4043, simple_loss=0.4225, pruned_loss=0.1931, over 7229.00 frames.], tot_loss[loss=0.3184, simple_loss=0.3659, pruned_loss=0.1355, over 1424877.61 frames.], batch size: 20, lr: 1.80e-03
2022-05-26 15:56:58,175 INFO [train.py:842] (1/4) Epoch 2, batch 3650, loss[loss=0.2785, simple_loss=0.3219, pruned_loss=0.1176, over 6786.00 frames.], tot_loss[loss=0.3178, simple_loss=0.3652, pruned_loss=0.1352, over 1427115.47 frames.], batch size: 15, lr: 1.79e-03
2022-05-26 15:57:36,613 INFO [train.py:842] (1/4) Epoch 2, batch 3700, loss[loss=0.2627, simple_loss=0.3303, pruned_loss=0.09755, over 7153.00 frames.], tot_loss[loss=0.3158, simple_loss=0.3644, pruned_loss=0.1336, over 1428663.75 frames.], batch size: 19, lr: 1.79e-03
2022-05-26 15:58:15,387 INFO [train.py:842] (1/4) Epoch 2, batch 3750, loss[loss=0.2939, simple_loss=0.3488, pruned_loss=0.1195, over 7288.00 frames.], tot_loss[loss=0.3161, simple_loss=0.3645, pruned_loss=0.1338, over 1429381.81 frames.], batch size: 24, lr: 1.79e-03
2022-05-26 15:58:54,099 INFO [train.py:842] (1/4) Epoch 2, batch 3800, loss[loss=0.2655, simple_loss=0.3289, pruned_loss=0.101, over 7006.00 frames.], tot_loss[loss=0.3179, simple_loss=0.3651, pruned_loss=0.1353, over 1430339.70 frames.], batch size: 16, lr: 1.79e-03
2022-05-26 15:59:32,924 INFO [train.py:842] (1/4) Epoch 2, batch 3850, loss[loss=0.3221, simple_loss=0.3792, pruned_loss=0.1325, over 7215.00 frames.], tot_loss[loss=0.3164, simple_loss=0.3645, pruned_loss=0.1341, over 1431449.95 frames.], batch size: 22, lr: 1.78e-03
2022-05-26 16:00:11,509 INFO [train.py:842] (1/4) Epoch 2, batch 3900, loss[loss=0.3901, simple_loss=0.4232, pruned_loss=0.1785, over 6483.00 frames.], tot_loss[loss=0.3159, simple_loss=0.3644, pruned_loss=0.1337, over 1433510.22 frames.], batch size: 39, lr: 1.78e-03
2022-05-26 16:00:50,508 INFO [train.py:842] (1/4) Epoch 2, batch 3950, loss[loss=0.337, simple_loss=0.3847, pruned_loss=0.1447, over 7321.00 frames.], tot_loss[loss=0.3154, simple_loss=0.3635, pruned_loss=0.1336, over 1431203.48 frames.], batch size: 21, lr: 1.78e-03
2022-05-26 16:01:29,041 INFO [train.py:842] (1/4) Epoch 2, batch 4000, loss[loss=0.4379, simple_loss=0.4527, pruned_loss=0.2115, over 5132.00 frames.], tot_loss[loss=0.3161, simple_loss=0.3642, pruned_loss=0.134, over 1431838.16 frames.], batch size: 52, lr: 1.77e-03
2022-05-26 16:02:07,537 INFO [train.py:842] (1/4) Epoch 2, batch 4050, loss[loss=0.3106, simple_loss=0.3666, pruned_loss=0.1273, over 6854.00 frames.], tot_loss[loss=0.3163, simple_loss=0.3645, pruned_loss=0.1341, over 1427521.45 frames.], batch size: 31, lr: 1.77e-03
2022-05-26 16:02:46,191 INFO [train.py:842] (1/4) Epoch 2, batch 4100, loss[loss=0.3464, simple_loss=0.387, pruned_loss=0.153, over 7116.00 frames.], tot_loss[loss=0.318, simple_loss=0.365, pruned_loss=0.1355, over 1429439.32 frames.], batch size: 28, lr: 1.77e-03
2022-05-26 16:03:25,059 INFO [train.py:842] (1/4) Epoch 2, batch 4150, loss[loss=0.3494, simple_loss=0.4025, pruned_loss=0.1481, over 7214.00 frames.], tot_loss[loss=0.3164, simple_loss=0.3641, pruned_loss=0.1344, over 1425798.07 frames.], batch size: 26, lr: 1.76e-03
2022-05-26 16:04:03,615 INFO [train.py:842] (1/4) Epoch 2, batch 4200, loss[loss=0.237, simple_loss=0.289, pruned_loss=0.09246, over 6994.00 frames.], tot_loss[loss=0.3149, simple_loss=0.3631, pruned_loss=0.1334, over 1424456.48 frames.], batch size: 16, lr: 1.76e-03
2022-05-26 16:04:42,388 INFO [train.py:842] (1/4) Epoch 2, batch 4250, loss[loss=0.3219, simple_loss=0.3693, pruned_loss=0.1372, over 7212.00 frames.], tot_loss[loss=0.3141, simple_loss=0.3627, pruned_loss=0.1328, over 1423279.55 frames.], batch size: 22, lr: 1.76e-03
2022-05-26 16:05:21,012 INFO [train.py:842] (1/4) Epoch 2, batch 4300, loss[loss=0.3279, simple_loss=0.3729, pruned_loss=0.1414, over 7343.00 frames.], tot_loss[loss=0.3119, simple_loss=0.3607, pruned_loss=0.1315, over 1425516.91 frames.], batch size: 22, lr: 1.76e-03
2022-05-26 16:05:59,699 INFO [train.py:842] (1/4) Epoch 2, batch 4350, loss[loss=0.324, simple_loss=0.3671, pruned_loss=0.1405, over 7156.00 frames.], tot_loss[loss=0.3123, simple_loss=0.3612, pruned_loss=0.1317, over 1422042.46 frames.], batch size: 19, lr: 1.75e-03
2022-05-26 16:06:38,216 INFO [train.py:842] (1/4) Epoch 2, batch 4400, loss[loss=0.2703, simple_loss=0.3455, pruned_loss=0.09758, over 7288.00 frames.], tot_loss[loss=0.3131, simple_loss=0.3621, pruned_loss=0.132, over 1423078.40 frames.], batch size: 24, lr: 1.75e-03
2022-05-26 16:07:17,566 INFO [train.py:842] (1/4) Epoch 2, batch 4450, loss[loss=0.2428, simple_loss=0.3094, pruned_loss=0.08811, over 7409.00 frames.], tot_loss[loss=0.3098, simple_loss=0.359, pruned_loss=0.1303, over 1423818.66 frames.], batch size: 18, lr: 1.75e-03
2022-05-26 16:07:56,070 INFO [train.py:842] (1/4) Epoch 2, batch 4500, loss[loss=0.2793, simple_loss=0.341, pruned_loss=0.1088, over 7330.00 frames.], tot_loss[loss=0.31, simple_loss=0.359, pruned_loss=0.1305, over 1425560.44 frames.], batch size: 20, lr: 1.74e-03
2022-05-26 16:08:34,947 INFO [train.py:842] (1/4) Epoch 2, batch 4550, loss[loss=0.4139, simple_loss=0.4397, pruned_loss=0.1941, over 7267.00 frames.], tot_loss[loss=0.3087, simple_loss=0.3581, pruned_loss=0.1296, over 1425960.53 frames.], batch size: 18, lr: 1.74e-03
2022-05-26 16:09:13,358 INFO [train.py:842] (1/4) Epoch 2, batch 4600, loss[loss=0.3579, simple_loss=0.4017, pruned_loss=0.1571, over 7212.00 frames.], tot_loss[loss=0.3103, simple_loss=0.3594, pruned_loss=0.1306, over 1421177.68 frames.], batch size: 22, lr: 1.74e-03
2022-05-26 16:09:52,125 INFO [train.py:842] (1/4) Epoch 2, batch 4650, loss[loss=0.3173, simple_loss=0.3751, pruned_loss=0.1297, over 7303.00 frames.], tot_loss[loss=0.3081, simple_loss=0.3582, pruned_loss=0.1291, over 1424565.12 frames.], batch size: 25, lr: 1.74e-03
2022-05-26 16:10:30,647 INFO [train.py:842] (1/4) Epoch 2, batch 4700, loss[loss=0.3766, simple_loss=0.3995, pruned_loss=0.1768, over 7317.00 frames.], tot_loss[loss=0.3085, simple_loss=0.3585, pruned_loss=0.1292, over 1425531.30 frames.], batch size: 21, lr: 1.73e-03
2022-05-26 16:11:09,333 INFO [train.py:842] (1/4) Epoch 2, batch 4750, loss[loss=0.3092, simple_loss=0.3657, pruned_loss=0.1263, over 7404.00 frames.], tot_loss[loss=0.3098, simple_loss=0.3596, pruned_loss=0.13, over 1418397.88 frames.], batch size: 21, lr: 1.73e-03
2022-05-26 16:11:47,765 INFO [train.py:842] (1/4) Epoch 2, batch 4800, loss[loss=0.287, simple_loss=0.3543, pruned_loss=0.1099, over 7273.00 frames.], tot_loss[loss=0.3105, simple_loss=0.36, pruned_loss=0.1305, over 1416319.58 frames.], batch size: 24, lr: 1.73e-03
2022-05-26 16:12:26,413 INFO [train.py:842] (1/4) Epoch 2, batch 4850, loss[loss=0.4377, simple_loss=0.4321, pruned_loss=0.2217, over 7159.00 frames.], tot_loss[loss=0.3107, simple_loss=0.3604, pruned_loss=0.1305, over 1416238.64 frames.], batch size: 18, lr: 1.73e-03
2022-05-26 16:13:04,893 INFO [train.py:842] (1/4) Epoch 2, batch 4900, loss[loss=0.2601, simple_loss=0.3131, pruned_loss=0.1036, over 7292.00 frames.], tot_loss[loss=0.3069, simple_loss=0.358, pruned_loss=0.1279, over 1419836.00 frames.], batch size: 17, lr: 1.72e-03
2022-05-26 16:13:43,579 INFO [train.py:842] (1/4) Epoch 2, batch 4950, loss[loss=0.3047, simple_loss=0.3603, pruned_loss=0.1245, over 7238.00 frames.], tot_loss[loss=0.3077, simple_loss=0.3589, pruned_loss=0.1283, over 1422628.70 frames.], batch size: 20, lr: 1.72e-03
2022-05-26 16:14:22,294 INFO [train.py:842] (1/4) Epoch 2, batch 5000, loss[loss=0.2783, simple_loss=0.3248, pruned_loss=0.1159, over 7268.00 frames.], tot_loss[loss=0.3092, simple_loss=0.3595, pruned_loss=0.1294, over 1424933.16 frames.], batch size: 17, lr: 1.72e-03
2022-05-26 16:15:00,738 INFO [train.py:842] (1/4) Epoch 2, batch 5050, loss[loss=0.2975, simple_loss=0.3597, pruned_loss=0.1176, over 7422.00 frames.], tot_loss[loss=0.3135, simple_loss=0.3626, pruned_loss=0.1323, over 1418193.00 frames.], batch size: 21, lr: 1.71e-03
2022-05-26 16:15:39,321 INFO [train.py:842] (1/4) Epoch 2, batch 5100, loss[loss=0.295, simple_loss=0.3515, pruned_loss=0.1192, over 7160.00 frames.], tot_loss[loss=0.3114, simple_loss=0.361, pruned_loss=0.1309, over 1420987.11 frames.], batch size: 19, lr: 1.71e-03
2022-05-26 16:16:18,368 INFO [train.py:842] (1/4) Epoch 2, batch 5150, loss[loss=0.391, simple_loss=0.4263, pruned_loss=0.1779, over 7221.00 frames.], tot_loss[loss=0.3108, simple_loss=0.3611, pruned_loss=0.1303, over 1421963.02 frames.], batch size: 21, lr: 1.71e-03
2022-05-26 16:16:56,920 INFO [train.py:842] (1/4) Epoch 2, batch 5200, loss[loss=0.3143, simple_loss=0.3756, pruned_loss=0.1265, over 7296.00 frames.], tot_loss[loss=0.3105, simple_loss=0.3614, pruned_loss=0.1298, over 1423068.99 frames.], batch size: 25, lr: 1.71e-03
2022-05-26 16:17:35,686 INFO [train.py:842] (1/4) Epoch 2, batch 5250, loss[loss=0.3577, simple_loss=0.412, pruned_loss=0.1517, over 6828.00 frames.], tot_loss[loss=0.3104, simple_loss=0.361, pruned_loss=0.1299, over 1425247.73 frames.], batch size: 31, lr: 1.70e-03
2022-05-26 16:18:14,232 INFO [train.py:842] (1/4) Epoch 2, batch 5300, loss[loss=0.3287, simple_loss=0.3918, pruned_loss=0.1328, over 7387.00 frames.], tot_loss[loss=0.3093, simple_loss=0.3606, pruned_loss=0.129, over 1422362.95 frames.], batch size: 23, lr: 1.70e-03
2022-05-26 16:18:53,032 INFO [train.py:842] (1/4) Epoch 2, batch 5350, loss[loss=0.2395, simple_loss=0.3085, pruned_loss=0.08522, over 7358.00 frames.], tot_loss[loss=0.3072, simple_loss=0.3584, pruned_loss=0.128, over 1419303.25 frames.], batch size: 19, lr: 1.70e-03
2022-05-26 16:19:31,681 INFO [train.py:842] (1/4) Epoch 2, batch 5400, loss[loss=0.2757, simple_loss=0.3354, pruned_loss=0.108, over 6506.00 frames.], tot_loss[loss=0.3065, simple_loss=0.3576, pruned_loss=0.1277, over 1420439.86 frames.], batch size: 38, lr: 1.70e-03
2022-05-26 16:20:10,831 INFO [train.py:842] (1/4) Epoch 2, batch 5450, loss[loss=0.2862, simple_loss=0.3362, pruned_loss=0.1181, over 7320.00 frames.], tot_loss[loss=0.3053, simple_loss=0.3566, pruned_loss=0.1271, over 1422173.27 frames.], batch size: 16, lr: 1.69e-03
2022-05-26 16:20:49,331 INFO [train.py:842] (1/4) Epoch 2, batch 5500, loss[loss=0.2153, simple_loss=0.2845, pruned_loss=0.07303, over 7133.00 frames.], tot_loss[loss=0.3018, simple_loss=0.3543, pruned_loss=0.1247, over 1423436.71 frames.], batch size: 17, lr: 1.69e-03
2022-05-26 16:21:28,411 INFO [train.py:842] (1/4) Epoch 2, batch 5550, loss[loss=0.2343, simple_loss=0.2965, pruned_loss=0.086, over 7002.00 frames.], tot_loss[loss=0.3019, simple_loss=0.3541, pruned_loss=0.1248, over 1424203.09 frames.], batch size: 16, lr: 1.69e-03
2022-05-26 16:22:06,838 INFO [train.py:842] (1/4) Epoch 2, batch 5600, loss[loss=0.2996, simple_loss=0.3684, pruned_loss=0.1154, over 7291.00 frames.], tot_loss[loss=0.3012, simple_loss=0.3539, pruned_loss=0.1243, over 1424858.62 frames.], batch size: 24, lr: 1.69e-03
2022-05-26 16:22:45,455 INFO [train.py:842] (1/4) Epoch 2, batch 5650, loss[loss=0.3152, simple_loss=0.3664, pruned_loss=0.1319, over 7175.00 frames.], tot_loss[loss=0.3024, simple_loss=0.355, pruned_loss=0.1249, over 1425511.99 frames.], batch size: 23, lr: 1.68e-03
2022-05-26 16:23:24,164 INFO [train.py:842] (1/4) Epoch 2, batch 5700, loss[loss=0.227, simple_loss=0.2957, pruned_loss=0.07912, over 7276.00 frames.], tot_loss[loss=0.3039, simple_loss=0.3557, pruned_loss=0.126, over 1423980.66 frames.], batch size: 18, lr: 1.68e-03
2022-05-26 16:24:03,282 INFO [train.py:842] (1/4) Epoch 2, batch 5750, loss[loss=0.3625, simple_loss=0.415, pruned_loss=0.155, over 7321.00 frames.], tot_loss[loss=0.3049, simple_loss=0.3564, pruned_loss=0.1267, over 1421969.94 frames.], batch size: 21, lr: 1.68e-03
2022-05-26 16:24:41,893 INFO [train.py:842] (1/4) Epoch 2, batch 5800, loss[loss=0.3388, simple_loss=0.3873, pruned_loss=0.1452, over 7177.00 frames.], tot_loss[loss=0.3045, simple_loss=0.3565, pruned_loss=0.1262, over 1426553.54 frames.], batch size: 26, lr: 1.68e-03
2022-05-26 16:25:20,725 INFO [train.py:842] (1/4) Epoch 2, batch 5850, loss[loss=0.2758, simple_loss=0.3488, pruned_loss=0.1014, over 7406.00 frames.], tot_loss[loss=0.3042, simple_loss=0.3563, pruned_loss=0.126, over 1421142.48 frames.], batch size: 21, lr: 1.67e-03
2022-05-26 16:26:08,846 INFO [train.py:842] (1/4) Epoch 2, batch 5900, loss[loss=0.2607, simple_loss=0.3068, pruned_loss=0.1073, over 7261.00 frames.], tot_loss[loss=0.3027, simple_loss=0.3552, pruned_loss=0.125, over 1423487.43 frames.], batch size: 17, lr: 1.67e-03
2022-05-26 16:26:47,916 INFO [train.py:842] (1/4) Epoch 2, batch 5950, loss[loss=0.3479, simple_loss=0.3844, pruned_loss=0.1557, over 7217.00 frames.], tot_loss[loss=0.3043, simple_loss=0.3569, pruned_loss=0.1258, over 1422676.12 frames.], batch size: 22, lr: 1.67e-03
2022-05-26 16:27:26,493 INFO [train.py:842] (1/4) Epoch 2, batch 6000, loss[loss=0.2716, simple_loss=0.343, pruned_loss=0.1001, over 7416.00 frames.], tot_loss[loss=0.3035, simple_loss=0.3561, pruned_loss=0.1255, over 1419730.99 frames.], batch size: 21, lr: 1.67e-03
2022-05-26 16:27:26,494 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 16:27:35,906 INFO [train.py:871] (1/4) Epoch 2, validation: loss=0.2259, simple_loss=0.3198, pruned_loss=0.06603, over 868885.00 frames.
2022-05-26 16:28:15,158 INFO [train.py:842] (1/4) Epoch 2, batch 6050, loss[loss=0.2959, simple_loss=0.3562, pruned_loss=0.1178, over 7212.00 frames.], tot_loss[loss=0.3038, simple_loss=0.3562, pruned_loss=0.1257, over 1424218.81 frames.], batch size: 23, lr: 1.66e-03
2022-05-26 16:28:53,640 INFO [train.py:842] (1/4) Epoch 2, batch 6100, loss[loss=0.3275, simple_loss=0.3778, pruned_loss=0.1385, over 7372.00 frames.], tot_loss[loss=0.3045, simple_loss=0.3568, pruned_loss=0.1261, over 1425806.81 frames.], batch size: 23, lr: 1.66e-03
2022-05-26 16:29:42,211 INFO [train.py:842] (1/4) Epoch 2, batch 6150, loss[loss=0.3294, simple_loss=0.3926, pruned_loss=0.1331, over 7009.00 frames.], tot_loss[loss=0.3016, simple_loss=0.3549, pruned_loss=0.1241, over 1426870.30 frames.], batch size: 28, lr: 1.66e-03
2022-05-26 16:30:39,583 INFO [train.py:842] (1/4) Epoch 2, batch 6200, loss[loss=0.42, simple_loss=0.441, pruned_loss=0.1995, over 6716.00 frames.], tot_loss[loss=0.3035, simple_loss=0.3564, pruned_loss=0.1253, over 1424467.16 frames.], batch size: 31, lr: 1.66e-03
2022-05-26 16:31:18,943 INFO [train.py:842] (1/4) Epoch 2, batch 6250, loss[loss=0.2796, simple_loss=0.3404, pruned_loss=0.1094, over 7100.00 frames.], tot_loss[loss=0.3049, simple_loss=0.3569, pruned_loss=0.1264, over 1427813.57 frames.], batch size: 21, lr: 1.65e-03
2022-05-26 16:31:57,549 INFO [train.py:842] (1/4) Epoch 2, batch 6300, loss[loss=0.2872, simple_loss=0.3617, pruned_loss=0.1064, over 7208.00 frames.], tot_loss[loss=0.3038, simple_loss=0.3563, pruned_loss=0.1256, over 1431609.00 frames.], batch size: 26, lr: 1.65e-03
2022-05-26 16:32:36,120 INFO [train.py:842] (1/4) Epoch 2, batch 6350, loss[loss=0.2748, simple_loss=0.3364, pruned_loss=0.1066, over 6453.00 frames.], tot_loss[loss=0.3061, simple_loss=0.3584, pruned_loss=0.1269, over 1430192.59 frames.], batch size: 38, lr: 1.65e-03
2022-05-26 16:33:14,715 INFO [train.py:842] (1/4) Epoch 2, batch 6400, loss[loss=0.3054, simple_loss=0.3579, pruned_loss=0.1264, over 6839.00 frames.], tot_loss[loss=0.3065, simple_loss=0.358, pruned_loss=0.1275, over 1425581.06 frames.], batch size: 31, lr: 1.65e-03
2022-05-26 16:33:53,455 INFO [train.py:842] (1/4) Epoch 2, batch 6450, loss[loss=0.2769, simple_loss=0.32, pruned_loss=0.1169, over 7404.00 frames.], tot_loss[loss=0.3049, simple_loss=0.3568, pruned_loss=0.1265, over 1425040.87 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 16:34:31,952 INFO [train.py:842] (1/4) Epoch 2, batch 6500, loss[loss=0.3125, simple_loss=0.3728, pruned_loss=0.1261, over 7214.00 frames.], tot_loss[loss=0.3042, simple_loss=0.3558, pruned_loss=0.1263, over 1423429.10 frames.], batch size: 22, lr: 1.64e-03
2022-05-26 16:35:10,754 INFO [train.py:842] (1/4) Epoch 2, batch 6550, loss[loss=0.2812, simple_loss=0.3417, pruned_loss=0.1103, over 7071.00 frames.], tot_loss[loss=0.3038, simple_loss=0.3558, pruned_loss=0.1259, over 1420340.13 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 16:35:49,305 INFO [train.py:842] (1/4) Epoch 2, batch 6600, loss[loss=0.2706, simple_loss=0.3272, pruned_loss=0.1069, over 7265.00 frames.], tot_loss[loss=0.3032, simple_loss=0.3555, pruned_loss=0.1255, over 1420075.71 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 16:36:27,747 INFO [train.py:842] (1/4) Epoch 2, batch 6650, loss[loss=0.4145, simple_loss=0.4409, pruned_loss=0.1941, over 7200.00 frames.], tot_loss[loss=0.3042, simple_loss=0.356, pruned_loss=0.1261, over 1413321.53 frames.], batch size: 23, lr: 1.63e-03
2022-05-26 16:37:06,266 INFO [train.py:842] (1/4) Epoch 2, batch 6700, loss[loss=0.2554, simple_loss=0.3129, pruned_loss=0.09895, over 7280.00 frames.], tot_loss[loss=0.3011, simple_loss=0.3541, pruned_loss=0.124, over 1418939.83 frames.], batch size: 17, lr: 1.63e-03
2022-05-26 16:37:45,041 INFO [train.py:842] (1/4) Epoch 2, batch 6750, loss[loss=0.262, simple_loss=0.3291, pruned_loss=0.09745, over 7227.00 frames.], tot_loss[loss=0.3006, simple_loss=0.3541, pruned_loss=0.1235, over 1421636.09 frames.], batch size: 20, lr: 1.63e-03
2022-05-26 16:38:23,580 INFO [train.py:842] (1/4) Epoch 2, batch 6800, loss[loss=0.3159, simple_loss=0.3878, pruned_loss=0.122, over 7109.00 frames.], tot_loss[loss=0.3002, simple_loss=0.3541, pruned_loss=0.1231, over 1423999.24 frames.], batch size: 21, lr: 1.63e-03
2022-05-26 16:39:05,221 INFO [train.py:842] (1/4) Epoch 2, batch 6850, loss[loss=0.2637, simple_loss=0.335, pruned_loss=0.09626, over 7324.00 frames.], tot_loss[loss=0.3002, simple_loss=0.3545, pruned_loss=0.1229, over 1421362.63 frames.], batch size: 20, lr: 1.63e-03
2022-05-26 16:39:43,799 INFO [train.py:842] (1/4) Epoch 2, batch 6900, loss[loss=0.2761, simple_loss=0.3391, pruned_loss=0.1066, over 7440.00 frames.], tot_loss[loss=0.3003, simple_loss=0.3542, pruned_loss=0.1232, over 1420272.06 frames.], batch size: 20, lr: 1.62e-03
2022-05-26 16:40:22,605 INFO [train.py:842] (1/4) Epoch 2, batch 6950, loss[loss=0.2384, simple_loss=0.3059, pruned_loss=0.08549, over 7296.00 frames.], tot_loss[loss=0.2983, simple_loss=0.3527, pruned_loss=0.122, over 1420680.74 frames.], batch size: 18, lr: 1.62e-03
2022-05-26 16:41:01,156 INFO [train.py:842] (1/4) Epoch 2, batch 7000, loss[loss=0.2843, simple_loss=0.3429, pruned_loss=0.1128, over 7315.00 frames.], tot_loss[loss=0.2992, simple_loss=0.3532, pruned_loss=0.1226, over 1423478.93 frames.], batch size: 21, lr: 1.62e-03
2022-05-26 16:41:40,651 INFO [train.py:842] (1/4) Epoch 2, batch 7050, loss[loss=0.3208, simple_loss=0.3578, pruned_loss=0.1419, over 5203.00 frames.], tot_loss[loss=0.2983, simple_loss=0.3521, pruned_loss=0.1223, over 1426442.08 frames.], batch size: 52, lr: 1.62e-03
2022-05-26 16:42:19,187 INFO [train.py:842] (1/4) Epoch 2, batch 7100, loss[loss=0.2991, simple_loss=0.3646, pruned_loss=0.1168, over 7109.00 frames.], tot_loss[loss=0.2991, simple_loss=0.3529, pruned_loss=0.1226, over 1425269.89 frames.], batch size: 21, lr: 1.61e-03
2022-05-26 16:42:57,895 INFO [train.py:842] (1/4) Epoch 2, batch 7150, loss[loss=0.2528, simple_loss=0.3245, pruned_loss=0.09058, over 7409.00 frames.], tot_loss[loss=0.3021, simple_loss=0.3554, pruned_loss=0.1244, over 1422172.35 frames.], batch size: 21, lr: 1.61e-03
2022-05-26 16:43:36,581 INFO [train.py:842] (1/4) Epoch 2, batch 7200, loss[loss=0.2938, simple_loss=0.3452, pruned_loss=0.1211, over 7011.00 frames.], tot_loss[loss=0.3031, simple_loss=0.3562, pruned_loss=0.125, over 1420222.24 frames.], batch size: 16, lr: 1.61e-03
2022-05-26 16:44:15,854 INFO [train.py:842] (1/4) Epoch 2, batch 7250, loss[loss=0.298, simple_loss=0.3562, pruned_loss=0.1199, over 7244.00 frames.], tot_loss[loss=0.3018, simple_loss=0.3554, pruned_loss=0.1241, over 1425371.76 frames.], batch size: 20, lr: 1.61e-03
2022-05-26 16:44:54,476 INFO [train.py:842] (1/4) Epoch 2, batch 7300, loss[loss=0.2995, simple_loss=0.3587, pruned_loss=0.1201, over 7224.00 frames.], tot_loss[loss=0.3019, simple_loss=0.3554, pruned_loss=0.1242, over 1428222.94 frames.], batch size: 21, lr: 1.60e-03
2022-05-26 16:45:33,712 INFO [train.py:842] (1/4) Epoch 2, batch 7350, loss[loss=0.364, simple_loss=0.3841, pruned_loss=0.172, over 4860.00 frames.], tot_loss[loss=0.3001, simple_loss=0.3532, pruned_loss=0.1234, over 1424073.58 frames.], batch size: 52, lr: 1.60e-03
2022-05-26 16:46:12,362 INFO [train.py:842] (1/4) Epoch 2, batch 7400, loss[loss=0.3037, simple_loss=0.3436, pruned_loss=0.1319, over 7423.00 frames.], tot_loss[loss=0.3003, simple_loss=0.3534, pruned_loss=0.1236, over 1424290.41 frames.], batch size: 17, lr: 1.60e-03
2022-05-26 16:46:51,031 INFO [train.py:842] (1/4) Epoch 2, batch 7450, loss[loss=0.2615, simple_loss=0.3168, pruned_loss=0.1031, over 7360.00 frames.], tot_loss[loss=0.3038, simple_loss=0.3558, pruned_loss=0.1259, over 1420248.30 frames.], batch size: 19, lr: 1.60e-03
2022-05-26 16:47:29,545 INFO [train.py:842] (1/4) Epoch 2, batch 7500, loss[loss=0.2707, simple_loss=0.3349, pruned_loss=0.1032, over 7204.00 frames.], tot_loss[loss=0.3066, simple_loss=0.3576, pruned_loss=0.1278, over 1421331.81 frames.], batch size: 21, lr: 1.60e-03
2022-05-26 16:48:08,287 INFO [train.py:842] (1/4) Epoch 2, batch 7550, loss[loss=0.375, simple_loss=0.4126, pruned_loss=0.1687, over 7404.00 frames.], tot_loss[loss=0.3035, simple_loss=0.3565, pruned_loss=0.1252, over 1421833.76 frames.], batch size: 21, lr: 1.59e-03
2022-05-26 16:48:46,895 INFO [train.py:842] (1/4) Epoch 2, batch 7600, loss[loss=0.3866, simple_loss=0.4136, pruned_loss=0.1798, over 4971.00 frames.], tot_loss[loss=0.3016, simple_loss=0.355, pruned_loss=0.1241, over 1421461.68 frames.], batch size: 52, lr: 1.59e-03
2022-05-26 16:49:26,111 INFO [train.py:842] (1/4) Epoch 2, batch 7650, loss[loss=0.3198, simple_loss=0.3665, pruned_loss=0.1365, over 7409.00 frames.], tot_loss[loss=0.2996, simple_loss=0.3536, pruned_loss=0.1228, over 1422514.16 frames.], batch size: 21, lr: 1.59e-03
2022-05-26 16:50:04,695 INFO [train.py:842] (1/4) Epoch 2, batch 7700, loss[loss=0.3638, simple_loss=0.4024, pruned_loss=0.1626, over 7353.00 frames.], tot_loss[loss=0.3001, simple_loss=0.3538, pruned_loss=0.1232, over 1422853.62 frames.], batch size: 22, lr: 1.59e-03
2022-05-26 16:50:43,502 INFO [train.py:842] (1/4) Epoch 2, batch 7750, loss[loss=0.3569, simple_loss=0.3985, pruned_loss=0.1577, over 6967.00 frames.], tot_loss[loss=0.2998, simple_loss=0.3541, pruned_loss=0.1228, over 1423902.57 frames.], batch size: 28, lr: 1.59e-03
2022-05-26 16:51:22,001 INFO [train.py:842] (1/4) Epoch 2, batch 7800, loss[loss=0.2807, simple_loss=0.345, pruned_loss=0.1082, over 7154.00 frames.], tot_loss[loss=0.3016, simple_loss=0.3553, pruned_loss=0.124, over 1423680.66 frames.], batch size: 20, lr: 1.58e-03
2022-05-26 16:52:00,789 INFO [train.py:842] (1/4) Epoch 2, batch 7850, loss[loss=0.2775, simple_loss=0.34, pruned_loss=0.1075, over 7319.00 frames.], tot_loss[loss=0.3, simple_loss=0.354, pruned_loss=0.123, over 1424945.42 frames.], batch size: 21, lr: 1.58e-03
2022-05-26 16:52:39,363 INFO [train.py:842] (1/4) Epoch 2, batch 7900, loss[loss=0.4029, simple_loss=0.4299, pruned_loss=0.188, over 5200.00 frames.], tot_loss[loss=0.301, simple_loss=0.3545, pruned_loss=0.1238, over 1426734.28 frames.], batch size: 53, lr: 1.58e-03
2022-05-26 16:53:18,137 INFO [train.py:842] (1/4) Epoch 2, batch 7950, loss[loss=0.2906, simple_loss=0.3264, pruned_loss=0.1274, over 7166.00 frames.], tot_loss[loss=0.2989, simple_loss=0.3528, pruned_loss=0.1225, over 1428202.45 frames.], batch size: 18, lr: 1.58e-03
2022-05-26 16:53:56,770 INFO [train.py:842] (1/4) Epoch 2, batch 8000, loss[loss=0.3298, simple_loss=0.3967, pruned_loss=0.1315, over 7213.00 frames.], tot_loss[loss=0.2991, simple_loss=0.3536, pruned_loss=0.1223, over 1426259.03 frames.], batch size: 21, lr: 1.57e-03
2022-05-26 16:54:35,602 INFO [train.py:842] (1/4) Epoch 2, batch 8050, loss[loss=0.3098, simple_loss=0.3627, pruned_loss=0.1284, over 6629.00 frames.], tot_loss[loss=0.2993, simple_loss=0.3537, pruned_loss=0.1224, over 1424867.78 frames.], batch size: 37, lr: 1.57e-03
2022-05-26 16:55:14,314 INFO [train.py:842] (1/4) Epoch 2, batch 8100, loss[loss=0.2767, simple_loss=0.3417, pruned_loss=0.1058, over 7166.00 frames.], tot_loss[loss=0.2988, simple_loss=0.3531, pruned_loss=0.1223, over 1427863.37 frames.], batch size: 26, lr: 1.57e-03
2022-05-26 16:55:53,018 INFO [train.py:842] (1/4) Epoch 2, batch 8150, loss[loss=0.3001, simple_loss=0.3369, pruned_loss=0.1316, over 7061.00 frames.], tot_loss[loss=0.2968, simple_loss=0.3516, pruned_loss=0.121, over 1429146.93 frames.], batch size: 18, lr: 1.57e-03
2022-05-26 16:56:31,565 INFO [train.py:842] (1/4) Epoch 2, batch 8200, loss[loss=0.2748, simple_loss=0.3426, pruned_loss=0.1034, over 7279.00 frames.], tot_loss[loss=0.2989, simple_loss=0.3532, pruned_loss=0.1224, over 1424686.70 frames.], batch size: 18, lr: 1.57e-03
2022-05-26 16:57:10,921 INFO [train.py:842] (1/4) Epoch 2, batch 8250, loss[loss=0.2865, simple_loss=0.3585, pruned_loss=0.1072, over 7088.00 frames.], tot_loss[loss=0.2979, simple_loss=0.352, pruned_loss=0.1219, over 1423788.16 frames.], batch size: 28, lr: 1.56e-03
2022-05-26 16:57:49,463 INFO [train.py:842] (1/4) Epoch 2, batch 8300, loss[loss=0.3126, simple_loss=0.3658, pruned_loss=0.1297, over 7149.00 frames.], tot_loss[loss=0.2983, simple_loss=0.3522, pruned_loss=0.1222, over 1421814.22 frames.], batch size: 20, lr: 1.56e-03
2022-05-26 16:58:28,234 INFO [train.py:842] (1/4) Epoch 2, batch 8350, loss[loss=0.471, simple_loss=0.4567, pruned_loss=0.2426, over 4996.00 frames.], tot_loss[loss=0.2959, simple_loss=0.3511, pruned_loss=0.1203, over 1420484.41 frames.], batch size: 52, lr: 1.56e-03
2022-05-26 16:59:06,606 INFO [train.py:842] (1/4) Epoch 2, batch 8400, loss[loss=0.2779, simple_loss=0.3309, pruned_loss=0.1124, over 7125.00 frames.], tot_loss[loss=0.2972, simple_loss=0.3519, pruned_loss=0.1212, over 1419141.64 frames.], batch size: 17, lr: 1.56e-03
2022-05-26 16:59:45,118 INFO [train.py:842] (1/4) Epoch 2, batch 8450, loss[loss=0.3269, simple_loss=0.3784, pruned_loss=0.1377, over 7190.00 frames.], tot_loss[loss=0.2981, simple_loss=0.353, pruned_loss=0.1215, over 1415019.40 frames.], batch size: 22, lr: 1.56e-03
2022-05-26 17:00:23,697 INFO [train.py:842] (1/4) Epoch 2, batch 8500, loss[loss=0.2855, simple_loss=0.3288, pruned_loss=0.1211, over 7128.00 frames.], tot_loss[loss=0.298, simple_loss=0.3529, pruned_loss=0.1216, over 1419228.82 frames.], batch size: 17, lr: 1.55e-03
2022-05-26 17:01:02,433 INFO [train.py:842] (1/4) Epoch 2, batch 8550, loss[loss=0.2208, simple_loss=0.2973, pruned_loss=0.07211, over 7371.00 frames.], tot_loss[loss=0.2961, simple_loss=0.3524, pruned_loss=0.1199, over 1424390.75 frames.], batch size: 19, lr: 1.55e-03
2022-05-26 17:01:41,158 INFO [train.py:842] (1/4) Epoch 2, batch 8600, loss[loss=0.3035, simple_loss=0.362, pruned_loss=0.1225, over 6545.00 frames.], tot_loss[loss=0.2953, simple_loss=0.3512, pruned_loss=0.1197, over 1422670.81 frames.], batch size: 38, lr: 1.55e-03
2022-05-26 17:02:19,992 INFO [train.py:842] (1/4) Epoch 2, batch 8650, loss[loss=0.2402, simple_loss=0.3162, pruned_loss=0.08204, over 7145.00 frames.], tot_loss[loss=0.2941, simple_loss=0.3501, pruned_loss=0.119, over 1424632.41 frames.], batch size: 20, lr: 1.55e-03
2022-05-26 17:02:58,638 INFO [train.py:842] (1/4) Epoch 2, batch 8700, loss[loss=0.2537, simple_loss=0.3055, pruned_loss=0.101, over 7076.00 frames.], tot_loss[loss=0.2918, simple_loss=0.3482, pruned_loss=0.1177, over 1423112.95 frames.], batch size: 18, lr: 1.55e-03
2022-05-26 17:03:37,059 INFO [train.py:842] (1/4) Epoch 2, batch 8750, loss[loss=0.2744, simple_loss=0.32, pruned_loss=0.1144, over 7176.00 frames.], tot_loss[loss=0.2924, simple_loss=0.3489, pruned_loss=0.1179, over 1421954.42 frames.], batch size: 18, lr: 1.54e-03
2022-05-26 17:04:15,673 INFO [train.py:842] (1/4) Epoch 2, batch 8800, loss[loss=0.3157, simple_loss=0.3773, pruned_loss=0.1271, over 7328.00 frames.], tot_loss[loss=0.2947, simple_loss=0.3502, pruned_loss=0.1196, over 1414223.20 frames.], batch size: 22, lr: 1.54e-03
2022-05-26 17:04:54,409 INFO [train.py:842] (1/4) Epoch 2, batch 8850, loss[loss=0.3245, simple_loss=0.3646, pruned_loss=0.1422, over 7279.00 frames.], tot_loss[loss=0.2965, simple_loss=0.3512, pruned_loss=0.1208, over 1412495.88 frames.], batch size: 24, lr: 1.54e-03
2022-05-26 17:05:32,690 INFO [train.py:842] (1/4) Epoch 2, batch 8900, loss[loss=0.3042, simple_loss=0.3577, pruned_loss=0.1254, over 6868.00 frames.], tot_loss[loss=0.2973, simple_loss=0.3521, pruned_loss=0.1213, over 1403116.12 frames.], batch size: 31, lr: 1.54e-03
2022-05-26 17:06:11,210 INFO [train.py:842] (1/4) Epoch 2, batch 8950, loss[loss=0.3007, simple_loss=0.3673, pruned_loss=0.117, over 7113.00 frames.], tot_loss[loss=0.2979, simple_loss=0.3525, pruned_loss=0.1216, over 1402563.69 frames.], batch size: 21, lr: 1.54e-03
2022-05-26 17:06:49,708 INFO [train.py:842] (1/4) Epoch 2, batch 9000, loss[loss=0.3266, simple_loss=0.3641, pruned_loss=0.1445, over 7262.00 frames.], tot_loss[loss=0.2985, simple_loss=0.353, pruned_loss=0.122, over 1397513.40 frames.], batch size: 18, lr: 1.53e-03
2022-05-26 17:06:49,709 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 17:06:59,110 INFO [train.py:871] (1/4) Epoch 2, validation: loss=0.2204, simple_loss=0.3177, pruned_loss=0.06159, over 868885.00 frames.
2022-05-26 17:07:37,639 INFO [train.py:842] (1/4) Epoch 2, batch 9050, loss[loss=0.2413, simple_loss=0.3018, pruned_loss=0.09045, over 7281.00 frames.], tot_loss[loss=0.2993, simple_loss=0.3533, pruned_loss=0.1226, over 1382619.49 frames.], batch size: 18, lr: 1.53e-03
2022-05-26 17:08:15,227 INFO [train.py:842] (1/4) Epoch 2, batch 9100, loss[loss=0.2652, simple_loss=0.3349, pruned_loss=0.09777, over 5088.00 frames.], tot_loss[loss=0.305, simple_loss=0.3572, pruned_loss=0.1264, over 1328667.83 frames.], batch size: 52, lr: 1.53e-03
2022-05-26 17:08:52,761 INFO [train.py:842] (1/4) Epoch 2, batch 9150, loss[loss=0.2951, simple_loss=0.3483, pruned_loss=0.121, over 5041.00 frames.], tot_loss[loss=0.3146, simple_loss=0.3635, pruned_loss=0.1328, over 1257980.71 frames.], batch size: 52, lr: 1.53e-03
2022-05-26 17:09:46,532 INFO [train.py:842] (1/4) Epoch 3, batch 0, loss[loss=0.2386, simple_loss=0.3034, pruned_loss=0.08693, over 7276.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3034, pruned_loss=0.08693, over 7276.00 frames.], batch size: 17, lr: 1.50e-03
2022-05-26 17:10:25,892 INFO [train.py:842] (1/4) Epoch 3, batch 50, loss[loss=0.4947, simple_loss=0.479, pruned_loss=0.2552, over 7291.00 frames.], tot_loss[loss=0.3024, simple_loss=0.3539, pruned_loss=0.1255, over 321276.12 frames.], batch size: 25, lr: 1.49e-03
2022-05-26 17:11:04,574 INFO [train.py:842] (1/4) Epoch 3, batch 100, loss[loss=0.2634, simple_loss=0.3057, pruned_loss=0.1105, over 6989.00 frames.], tot_loss[loss=0.2984, simple_loss=0.3518, pruned_loss=0.1225, over 568673.67 frames.], batch size: 16, lr: 1.49e-03
2022-05-26 17:11:43,655 INFO [train.py:842] (1/4) Epoch 3, batch 150, loss[loss=0.3339, simple_loss=0.3817, pruned_loss=0.143, over 6832.00 frames.], tot_loss[loss=0.2974, simple_loss=0.351, pruned_loss=0.1219, over 762080.15 frames.], batch size: 31, lr: 1.49e-03
2022-05-26 17:12:22,314 INFO [train.py:842] (1/4) Epoch 3, batch 200, loss[loss=0.3141, simple_loss=0.3545, pruned_loss=0.1368, over 6803.00 frames.], tot_loss[loss=0.2964, simple_loss=0.3511, pruned_loss=0.1209, over 901174.35 frames.], batch size: 15, lr: 1.49e-03
2022-05-26 17:13:00,913 INFO [train.py:842] (1/4) Epoch 3, batch 250, loss[loss=0.2756, simple_loss=0.3321, pruned_loss=0.1095, over 7368.00 frames.], tot_loss[loss=0.2956, simple_loss=0.3509, pruned_loss=0.1201, over 1011204.74 frames.], batch size: 19, lr: 1.49e-03
2022-05-26 17:13:39,439 INFO [train.py:842] (1/4) Epoch 3, batch 300, loss[loss=0.3022, simple_loss=0.3492, pruned_loss=0.1276, over 6793.00 frames.], tot_loss[loss=0.2971, simple_loss=0.3523, pruned_loss=0.121, over 1100472.25 frames.], batch size: 31, lr: 1.49e-03
2022-05-26 17:14:18,349 INFO [train.py:842] (1/4) Epoch 3, batch 350, loss[loss=0.3449, simple_loss=0.3973, pruned_loss=0.1462, over 7318.00 frames.], tot_loss[loss=0.2962, simple_loss=0.352, pruned_loss=0.1202, over 1170794.54 frames.], batch size: 21, lr: 1.48e-03
2022-05-26 17:14:56,913 INFO [train.py:842] (1/4) Epoch 3, batch 400, loss[loss=0.3083, simple_loss=0.3601, pruned_loss=0.1282, over 7278.00 frames.], tot_loss[loss=0.2947, simple_loss=0.3505, pruned_loss=0.1194, over 1221848.78 frames.], batch size: 24, lr: 1.48e-03
2022-05-26 17:15:35,596 INFO [train.py:842] (1/4) Epoch 3, batch 450, loss[loss=0.3105, simple_loss=0.3719, pruned_loss=0.1246, over 7189.00 frames.], tot_loss[loss=0.2931, simple_loss=0.3494, pruned_loss=0.1184, over 1263078.54 frames.], batch size: 22, lr: 1.48e-03
2022-05-26 17:16:14,216 INFO [train.py:842] (1/4) Epoch 3, batch 500, loss[loss=0.2621, simple_loss=0.3008, pruned_loss=0.1117, over 6984.00 frames.], tot_loss[loss=0.2909, simple_loss=0.3476, pruned_loss=0.1171, over 1300966.04 frames.], batch size: 16, lr: 1.48e-03
2022-05-26 17:16:53,095 INFO [train.py:842] (1/4) Epoch 3, batch 550, loss[loss=0.3532, simple_loss=0.3947, pruned_loss=0.1559, over 7218.00 frames.], tot_loss[loss=0.2917, simple_loss=0.3481, pruned_loss=0.1176, over 1331006.06 frames.], batch size: 21, lr: 1.48e-03
2022-05-26 17:17:31,919 INFO [train.py:842] (1/4) Epoch 3, batch 600, loss[loss=0.3066, simple_loss=0.3729, pruned_loss=0.1201, over 7288.00 frames.], tot_loss[loss=0.29, simple_loss=0.3464, pruned_loss=0.1168, over 1352922.87 frames.], batch size: 25, lr: 1.47e-03
2022-05-26 17:18:10,652 INFO [train.py:842] (1/4) Epoch 3, batch 650, loss[loss=0.337, simple_loss=0.3818, pruned_loss=0.1461, over 7357.00 frames.], tot_loss[loss=0.29, simple_loss=0.3463, pruned_loss=0.1169, over 1367589.72 frames.], batch size: 19, lr: 1.47e-03
2022-05-26 17:18:49,281 INFO [train.py:842] (1/4) Epoch 3, batch 700, loss[loss=0.2639, simple_loss=0.3354, pruned_loss=0.0962, over 7225.00 frames.], tot_loss[loss=0.2884, simple_loss=0.3456, pruned_loss=0.1156, over 1377506.25 frames.], batch size: 21, lr: 1.47e-03
2022-05-26 17:19:28,522 INFO [train.py:842] (1/4) Epoch 3, batch 750, loss[loss=0.3377, simple_loss=0.3926, pruned_loss=0.1414, over 7191.00 frames.], tot_loss[loss=0.2903, simple_loss=0.3472, pruned_loss=0.1167, over 1391243.06 frames.], batch size: 23, lr: 1.47e-03
2022-05-26 17:20:07,106 INFO [train.py:842] (1/4) Epoch 3, batch 800, loss[loss=0.2964, simple_loss=0.3622, pruned_loss=0.1153, over 7211.00 frames.], tot_loss[loss=0.2891, simple_loss=0.3467, pruned_loss=0.1158, over 1402150.33 frames.], batch size: 23, lr: 1.47e-03
2022-05-26 17:20:46,502 INFO [train.py:842] (1/4) Epoch 3, batch 850, loss[loss=0.3307, simple_loss=0.3755, pruned_loss=0.143, over 7319.00 frames.], tot_loss[loss=0.2873, simple_loss=0.3449, pruned_loss=0.1149, over 1410529.99 frames.], batch size: 25, lr: 1.47e-03
2022-05-26 17:21:24,954 INFO [train.py:842] (1/4) Epoch 3, batch 900, loss[loss=0.2688, simple_loss=0.3332, pruned_loss=0.1023, over 7077.00 frames.], tot_loss[loss=0.2896, simple_loss=0.3471, pruned_loss=0.116, over 1412762.61 frames.], batch size: 18, lr: 1.46e-03
2022-05-26 17:22:03,738 INFO [train.py:842] (1/4) Epoch 3, batch 950, loss[loss=0.3486, simple_loss=0.3883, pruned_loss=0.1544, over 7146.00 frames.], tot_loss[loss=0.2916, simple_loss=0.3484, pruned_loss=0.1174, over 1417697.34 frames.], batch size: 20, lr: 1.46e-03
2022-05-26 17:22:42,271 INFO [train.py:842] (1/4) Epoch 3, batch 1000, loss[loss=0.3183, simple_loss=0.374, pruned_loss=0.1313, over 6774.00 frames.], tot_loss[loss=0.2927, simple_loss=0.3494, pruned_loss=0.118, over 1417248.48 frames.], batch size: 31, lr: 1.46e-03
2022-05-26 17:23:20,999 INFO [train.py:842] (1/4) Epoch 3, batch 1050, loss[loss=0.2447, simple_loss=0.3011, pruned_loss=0.09418, over 7271.00 frames.], tot_loss[loss=0.2938, simple_loss=0.35, pruned_loss=0.1188, over 1415336.97 frames.], batch size: 18, lr: 1.46e-03
2022-05-26 17:23:59,526 INFO [train.py:842] (1/4) Epoch 3, batch 1100, loss[loss=0.3034, simple_loss=0.3692, pruned_loss=0.1188, over 7234.00 frames.], tot_loss[loss=0.2921, simple_loss=0.3495, pruned_loss=0.1173, over 1420108.59 frames.], batch size: 21, lr: 1.46e-03
2022-05-26 17:24:38,358 INFO [train.py:842] (1/4) Epoch 3, batch 1150, loss[loss=0.3108, simple_loss=0.3709, pruned_loss=0.1253, over 7228.00 frames.], tot_loss[loss=0.2899, simple_loss=0.3474, pruned_loss=0.1161, over 1421417.42 frames.], batch size: 20, lr: 1.45e-03
2022-05-26 17:25:16,893 INFO [train.py:842] (1/4) Epoch 3, batch 1200, loss[loss=0.2581, simple_loss=0.3253, pruned_loss=0.09539, over 7429.00 frames.], tot_loss[loss=0.2907, simple_loss=0.3477, pruned_loss=0.1169, over 1424933.74 frames.], batch size: 20, lr: 1.45e-03
2022-05-26 17:25:55,757 INFO [train.py:842] (1/4) Epoch 3, batch 1250, loss[loss=0.2215, simple_loss=0.2998, pruned_loss=0.07165, over 7417.00 frames.], tot_loss[loss=0.2907, simple_loss=0.3476, pruned_loss=0.1169, over 1425733.77 frames.], batch size: 21, lr: 1.45e-03
2022-05-26 17:26:34,361 INFO [train.py:842] (1/4) Epoch 3, batch 1300, loss[loss=0.2777, simple_loss=0.3548, pruned_loss=0.1003, over 7324.00 frames.], tot_loss[loss=0.2876, simple_loss=0.3459, pruned_loss=0.1147, over 1426360.04 frames.], batch size: 21, lr: 1.45e-03
2022-05-26 17:27:13,049 INFO [train.py:842] (1/4) Epoch 3, batch 1350, loss[loss=0.4096, simple_loss=0.4209, pruned_loss=0.1992, over 7430.00 frames.], tot_loss[loss=0.2888, simple_loss=0.3469, pruned_loss=0.1153, over 1425810.97 frames.], batch size: 20, lr: 1.45e-03
2022-05-26 17:27:51,769 INFO [train.py:842] (1/4) Epoch 3, batch 1400, loss[loss=0.1949, simple_loss=0.2758, pruned_loss=0.05701, over 7167.00 frames.], tot_loss[loss=0.2882, simple_loss=0.3467, pruned_loss=0.1148, over 1422570.29 frames.], batch size: 19, lr: 1.45e-03
2022-05-26 17:28:30,419 INFO [train.py:842] (1/4) Epoch 3, batch 1450, loss[loss=0.2875, simple_loss=0.341, pruned_loss=0.117, over 7149.00 frames.], tot_loss[loss=0.2867, simple_loss=0.3454, pruned_loss=0.1139, over 1420138.81 frames.], batch size: 17, lr: 1.44e-03
2022-05-26 17:29:08,826 INFO [train.py:842] (1/4) Epoch 3, batch 1500, loss[loss=0.3651, simple_loss=0.4119, pruned_loss=0.1592, over 7319.00 frames.], tot_loss[loss=0.2884, simple_loss=0.3466, pruned_loss=0.1151, over 1418892.87 frames.], batch size: 21, lr: 1.44e-03
2022-05-26 17:29:47,547 INFO [train.py:842] (1/4) Epoch 3, batch 1550, loss[loss=0.2708, simple_loss=0.3435, pruned_loss=0.09907, over 7165.00 frames.], tot_loss[loss=0.2888, simple_loss=0.3468, pruned_loss=0.1154, over 1422968.40 frames.], batch size: 19, lr: 1.44e-03
2022-05-26 17:30:26,313 INFO [train.py:842] (1/4) Epoch 3, batch 1600, loss[loss=0.2792, simple_loss=0.3356, pruned_loss=0.1114, over 7153.00 frames.], tot_loss[loss=0.2888, simple_loss=0.3469, pruned_loss=0.1154, over 1424347.74 frames.], batch size: 19, lr: 1.44e-03
2022-05-26 17:31:05,611 INFO [train.py:842] (1/4) Epoch 3, batch 1650, loss[loss=0.3067, simple_loss=0.3533, pruned_loss=0.1301, over 7423.00 frames.], tot_loss[loss=0.2875, simple_loss=0.3454, pruned_loss=0.1148, over 1426623.49 frames.], batch size: 20, lr: 1.44e-03
2022-05-26 17:31:43,982 INFO [train.py:842] (1/4) Epoch 3, batch 1700, loss[loss=0.2872, simple_loss=0.3509, pruned_loss=0.1118, over 7143.00 frames.], tot_loss[loss=0.2859, simple_loss=0.3445, pruned_loss=0.1137, over 1417318.44 frames.], batch size: 20, lr: 1.44e-03
2022-05-26 17:32:23,197 INFO [train.py:842] (1/4) Epoch 3, batch 1750, loss[loss=0.2905, simple_loss=0.3605, pruned_loss=0.1102, over 7236.00 frames.], tot_loss[loss=0.2883, simple_loss=0.3466, pruned_loss=0.115, over 1424387.15 frames.], batch size: 20, lr: 1.43e-03
2022-05-26 17:33:01,597 INFO [train.py:842] (1/4) Epoch 3, batch 1800, loss[loss=0.3307, simple_loss=0.3828, pruned_loss=0.1393, over 7122.00 frames.], tot_loss[loss=0.2885, simple_loss=0.3466, pruned_loss=0.1153, over 1417043.43 frames.], batch size: 21, lr: 1.43e-03
2022-05-26 17:33:40,966 INFO [train.py:842] (1/4) Epoch 3, batch 1850, loss[loss=0.24, simple_loss=0.3202, pruned_loss=0.07989, over 7412.00 frames.], tot_loss[loss=0.2885, simple_loss=0.3463, pruned_loss=0.1154, over 1418605.95 frames.], batch size: 21, lr: 1.43e-03
2022-05-26 17:34:19,495 INFO [train.py:842] (1/4) Epoch 3, batch 1900, loss[loss=0.2743, simple_loss=0.3302, pruned_loss=0.1092, over 7157.00 frames.], tot_loss[loss=0.2882, simple_loss=0.3462, pruned_loss=0.1151, over 1416287.64 frames.], batch size: 18, lr: 1.43e-03
2022-05-26 17:34:58,248 INFO [train.py:842] (1/4) Epoch 3, batch 1950, loss[loss=0.2921, simple_loss=0.3526, pruned_loss=0.1158, over 6782.00 frames.], tot_loss[loss=0.2861, simple_loss=0.3446, pruned_loss=0.1139, over 1417230.99 frames.], batch size: 31, lr: 1.43e-03
2022-05-26 17:35:36,921 INFO [train.py:842] (1/4) Epoch 3, batch 2000, loss[loss=0.2617, simple_loss=0.3272, pruned_loss=0.09807, over 7157.00 frames.], tot_loss[loss=0.2844, simple_loss=0.3439, pruned_loss=0.1125, over 1422190.86 frames.], batch size: 19, lr: 1.43e-03
2022-05-26 17:36:15,522 INFO [train.py:842] (1/4) Epoch 3, batch 2050, loss[loss=0.2749, simple_loss=0.3362, pruned_loss=0.1068, over 5121.00 frames.], tot_loss[loss=0.288, simple_loss=0.347, pruned_loss=0.1146, over 1422124.05 frames.], batch size: 53, lr: 1.42e-03
2022-05-26 17:36:54,225 INFO [train.py:842] (1/4) Epoch 3, batch 2100, loss[loss=0.2879, simple_loss=0.3541, pruned_loss=0.1108, over 7318.00 frames.], tot_loss[loss=0.2898, simple_loss=0.3477, pruned_loss=0.1159, over 1424970.02 frames.], batch size: 21, lr: 1.42e-03
2022-05-26 17:37:33,229 INFO [train.py:842] (1/4) Epoch 3, batch 2150, loss[loss=0.2716, simple_loss=0.3495, pruned_loss=0.09687, over 7239.00 frames.], tot_loss[loss=0.2885, simple_loss=0.347, pruned_loss=0.115, over 1425957.79 frames.], batch size: 20, lr: 1.42e-03
2022-05-26 17:38:11,814 INFO [train.py:842] (1/4) Epoch 3, batch 2200, loss[loss=0.289, simple_loss=0.36, pruned_loss=0.109, over 7146.00 frames.], tot_loss[loss=0.2878, simple_loss=0.3464, pruned_loss=0.1146, over 1425363.61 frames.], batch size: 20, lr: 1.42e-03
2022-05-26 17:38:50,518 INFO [train.py:842] (1/4) Epoch 3, batch 2250, loss[loss=0.2771, simple_loss=0.3338, pruned_loss=0.1102, over 7331.00 frames.], tot_loss[loss=0.2885, simple_loss=0.347, pruned_loss=0.115, over 1425771.67 frames.], batch size: 20, lr: 1.42e-03
2022-05-26 17:39:29,088 INFO [train.py:842] (1/4) Epoch 3, batch 2300, loss[loss=0.2493, simple_loss=0.3195, pruned_loss=0.08961, over 7365.00 frames.], tot_loss[loss=0.2889, simple_loss=0.3468, pruned_loss=0.1155, over 1414270.32 frames.], batch size: 19, lr: 1.42e-03
2022-05-26 17:40:07,874 INFO [train.py:842] (1/4) Epoch 3, batch 2350, loss[loss=0.2524, simple_loss=0.3149, pruned_loss=0.09496, over 7270.00 frames.], tot_loss[loss=0.2891, simple_loss=0.3467, pruned_loss=0.1157, over 1416011.53 frames.], batch size: 19, lr: 1.41e-03
2022-05-26 17:40:46,291 INFO [train.py:842] (1/4) Epoch 3, batch 2400, loss[loss=0.2491, simple_loss=0.315, pruned_loss=0.09162, over 7263.00 frames.], tot_loss[loss=0.2859, simple_loss=0.3448, pruned_loss=0.1134, over 1418539.28 frames.], batch size: 19, lr: 1.41e-03
2022-05-26 17:41:25,122 INFO [train.py:842] (1/4) Epoch 3, batch 2450, loss[loss=0.2803, simple_loss=0.3398, pruned_loss=0.1104, over 7239.00 frames.], tot_loss[loss=0.2884, simple_loss=0.3472, pruned_loss=0.1148, over 1415718.16 frames.], batch size: 20, lr: 1.41e-03
2022-05-26 17:42:03,917 INFO [train.py:842] (1/4) Epoch 3, batch 2500, loss[loss=0.2713, simple_loss=0.3233, pruned_loss=0.1096, over 7167.00 frames.], tot_loss[loss=0.2872, simple_loss=0.3458, pruned_loss=0.1143, over 1414283.54 frames.], batch size: 19, lr: 1.41e-03
2022-05-26 17:42:42,675 INFO [train.py:842] (1/4) Epoch 3, batch 2550, loss[loss=0.3233, simple_loss=0.3818, pruned_loss=0.1324, over 7220.00 frames.], tot_loss[loss=0.2863, simple_loss=0.3444, pruned_loss=0.1141, over 1412722.19 frames.], batch size: 21, lr: 1.41e-03
2022-05-26 17:43:21,458 INFO [train.py:842] (1/4) Epoch 3, batch 2600, loss[loss=0.3264, simple_loss=0.3653, pruned_loss=0.1437, over 7280.00 frames.], tot_loss[loss=0.287, simple_loss=0.3452, pruned_loss=0.1144, over 1419683.78 frames.], batch size: 18, lr: 1.41e-03
2022-05-26 17:44:00,643 INFO [train.py:842] (1/4) Epoch 3, batch 2650, loss[loss=0.2533, simple_loss=0.3203, pruned_loss=0.0931, over 7322.00 frames.], tot_loss[loss=0.2859, simple_loss=0.3441, pruned_loss=0.1139, over 1418407.38 frames.], batch size: 20, lr: 1.41e-03
2022-05-26 17:44:39,139 INFO [train.py:842] (1/4) Epoch 3, batch 2700, loss[loss=0.2964, simple_loss=0.3497, pruned_loss=0.1215, over 7069.00 frames.], tot_loss[loss=0.2857, simple_loss=0.3445, pruned_loss=0.1134, over 1419701.35 frames.], batch size: 18, lr: 1.40e-03
2022-05-26 17:45:17,850 INFO [train.py:842] (1/4) Epoch 3, batch 2750, loss[loss=0.3062, simple_loss=0.3674, pruned_loss=0.1225, over 7151.00 frames.], tot_loss[loss=0.2847, simple_loss=0.3438, pruned_loss=0.1127, over 1419004.10 frames.], batch size: 26, lr: 1.40e-03
2022-05-26 17:45:56,566 INFO [train.py:842] (1/4) Epoch 3, batch 2800, loss[loss=0.4374, simple_loss=0.4414, pruned_loss=0.2167, over 5142.00 frames.], tot_loss[loss=0.2838, simple_loss=0.343, pruned_loss=0.1123, over 1418259.72 frames.], batch size: 53, lr: 1.40e-03
2022-05-26 17:46:35,465 INFO [train.py:842] (1/4) Epoch 3, batch 2850, loss[loss=0.269, simple_loss=0.3386, pruned_loss=0.0997, over 7223.00 frames.], tot_loss[loss=0.285, simple_loss=0.3438, pruned_loss=0.1131, over 1420673.08 frames.], batch size: 21, lr: 1.40e-03
2022-05-26 17:47:13,970 INFO [train.py:842] (1/4) Epoch 3, batch 2900, loss[loss=0.2692, simple_loss=0.3395, pruned_loss=0.09948, over 6306.00 frames.], tot_loss[loss=0.2877, simple_loss=0.3453, pruned_loss=0.115, over 1417114.73 frames.], batch size: 38, lr: 1.40e-03
2022-05-26 17:47:52,724 INFO [train.py:842] (1/4) Epoch 3, batch 2950, loss[loss=0.3068, simple_loss=0.3648, pruned_loss=0.1244, over 7199.00 frames.], tot_loss[loss=0.2881, simple_loss=0.3455, pruned_loss=0.1154, over 1415709.02 frames.], batch size: 26, lr: 1.40e-03
2022-05-26 17:48:31,449 INFO [train.py:842] (1/4) Epoch 3, batch 3000, loss[loss=0.3558, simple_loss=0.3962, pruned_loss=0.1577, over 7336.00 frames.], tot_loss[loss=0.2858, simple_loss=0.3436, pruned_loss=0.114, over 1419044.62 frames.], batch size: 22, lr: 1.39e-03
2022-05-26 17:48:31,450 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 17:48:40,684 INFO [train.py:871] (1/4) Epoch 3, validation: loss=0.2137, simple_loss=0.3102, pruned_loss=0.05856, over 868885.00 frames.
2022-05-26 17:49:19,943 INFO [train.py:842] (1/4) Epoch 3, batch 3050, loss[loss=0.3128, simple_loss=0.3709, pruned_loss=0.1274, over 7416.00 frames.], tot_loss[loss=0.2848, simple_loss=0.3438, pruned_loss=0.1129, over 1424462.70 frames.], batch size: 21, lr: 1.39e-03
2022-05-26 17:49:58,623 INFO [train.py:842] (1/4) Epoch 3, batch 3100, loss[loss=0.2312, simple_loss=0.2887, pruned_loss=0.08681, over 7266.00 frames.], tot_loss[loss=0.2822, simple_loss=0.3418, pruned_loss=0.1113, over 1427900.40 frames.], batch size: 18, lr: 1.39e-03
2022-05-26 17:50:37,604 INFO [train.py:842] (1/4) Epoch 3, batch 3150, loss[loss=0.303, simple_loss=0.3728, pruned_loss=0.1165, over 7231.00 frames.], tot_loss[loss=0.282, simple_loss=0.3417, pruned_loss=0.1112, over 1422810.78 frames.], batch size: 21, lr: 1.39e-03
2022-05-26 17:51:16,011 INFO [train.py:842] (1/4) Epoch 3, batch 3200, loss[loss=0.2775, simple_loss=0.343, pruned_loss=0.106, over 7383.00 frames.], tot_loss[loss=0.2821, simple_loss=0.3423, pruned_loss=0.1109, over 1425603.40 frames.], batch size: 23, lr: 1.39e-03
2022-05-26 17:51:54,685 INFO [train.py:842] (1/4) Epoch 3, batch 3250, loss[loss=0.2324, simple_loss=0.2983, pruned_loss=0.0832, over 7168.00 frames.], tot_loss[loss=0.2807, simple_loss=0.3411, pruned_loss=0.1102, over 1426805.09 frames.], batch size: 19, lr: 1.39e-03
2022-05-26 17:52:33,226 INFO [train.py:842] (1/4) Epoch 3, batch 3300, loss[loss=0.3002, simple_loss=0.3644, pruned_loss=0.1179, over 7146.00 frames.], tot_loss[loss=0.2814, simple_loss=0.3417, pruned_loss=0.1105, over 1429080.97 frames.], batch size: 26, lr: 1.39e-03
2022-05-26 17:53:11,850 INFO [train.py:842] (1/4) Epoch 3, batch 3350, loss[loss=0.2562, simple_loss=0.3029, pruned_loss=0.1047, over 7268.00 frames.], tot_loss[loss=0.2841, simple_loss=0.3437, pruned_loss=0.1123, over 1426398.90 frames.], batch size: 18, lr: 1.38e-03
2022-05-26 17:53:50,293 INFO [train.py:842] (1/4) Epoch 3, batch 3400, loss[loss=0.1991, simple_loss=0.2817, pruned_loss=0.05825, over 7406.00 frames.], tot_loss[loss=0.2849, simple_loss=0.3444, pruned_loss=0.1127, over 1424474.34 frames.], batch size: 18, lr: 1.38e-03
2022-05-26 17:54:29,389 INFO [train.py:842] (1/4) Epoch 3, batch 3450, loss[loss=0.2593, simple_loss=0.3286, pruned_loss=0.09503, over 7264.00 frames.], tot_loss[loss=0.2853, simple_loss=0.3448, pruned_loss=0.1129, over 1421297.78 frames.], batch size: 19, lr: 1.38e-03
2022-05-26 17:55:07,992 INFO [train.py:842] (1/4) Epoch 3, batch 3500, loss[loss=0.2893, simple_loss=0.358, pruned_loss=0.1103, over 7306.00 frames.], tot_loss[loss=0.2838, simple_loss=0.3437, pruned_loss=0.1119, over 1422070.27 frames.], batch size: 25, lr: 1.38e-03
2022-05-26 17:55:46,662 INFO [train.py:842] (1/4) Epoch 3, batch 3550, loss[loss=0.2975, simple_loss=0.3557, pruned_loss=0.1197, over 7219.00 frames.], tot_loss[loss=0.2844, simple_loss=0.3442, pruned_loss=0.1123, over 1420589.33 frames.], batch size: 21, lr: 1.38e-03
2022-05-26 17:56:25,355 INFO [train.py:842] (1/4) Epoch 3, batch 3600, loss[loss=0.2561, simple_loss=0.3309, pruned_loss=0.09063, over 7277.00 frames.], tot_loss[loss=0.2824, simple_loss=0.3426, pruned_loss=0.1111, over 1421273.46 frames.], batch size: 24, lr: 1.38e-03
2022-05-26 17:57:04,493 INFO [train.py:842] (1/4) Epoch 3, batch 3650, loss[loss=0.3276, simple_loss=0.3776, pruned_loss=0.1388, over 7377.00 frames.], tot_loss[loss=0.2824, simple_loss=0.3418, pruned_loss=0.1115, over 1421939.18 frames.], batch size: 23, lr: 1.37e-03
2022-05-26 17:57:43,111 INFO [train.py:842] (1/4) Epoch 3, batch 3700, loss[loss=0.2313, simple_loss=0.3019, pruned_loss=0.08034, over 7408.00 frames.], tot_loss[loss=0.2837, simple_loss=0.3429, pruned_loss=0.1123, over 1416832.71 frames.], batch size: 18, lr: 1.37e-03
2022-05-26 17:58:22,047 INFO [train.py:842] (1/4) Epoch 3, batch 3750, loss[loss=0.2602, simple_loss=0.3238, pruned_loss=0.09825, over 7288.00 frames.], tot_loss[loss=0.2839, simple_loss=0.3429, pruned_loss=0.1124, over 1423380.31 frames.], batch size: 18, lr: 1.37e-03
2022-05-26 17:59:00,550 INFO [train.py:842] (1/4) Epoch 3, batch 3800, loss[loss=0.2187, simple_loss=0.2837, pruned_loss=0.07688, over 7165.00 frames.], tot_loss[loss=0.2818, simple_loss=0.3412, pruned_loss=0.1112, over 1423867.44 frames.], batch size: 18, lr: 1.37e-03
2022-05-26 17:59:39,275 INFO [train.py:842] (1/4) Epoch 3, batch 3850, loss[loss=0.2276, simple_loss=0.3151, pruned_loss=0.07007, over 7337.00 frames.], tot_loss[loss=0.2825, simple_loss=0.3415, pruned_loss=0.1117, over 1423005.20 frames.], batch size: 22, lr: 1.37e-03
2022-05-26 18:00:17,886 INFO [train.py:842] (1/4) Epoch 3, batch 3900, loss[loss=0.2882, simple_loss=0.3438, pruned_loss=0.1163, over 7337.00 frames.], tot_loss[loss=0.2826, simple_loss=0.3413, pruned_loss=0.1119, over 1424601.18 frames.], batch size: 20, lr: 1.37e-03
2022-05-26 18:00:57,120 INFO [train.py:842] (1/4) Epoch 3, batch 3950, loss[loss=0.3251, simple_loss=0.3861, pruned_loss=0.132, over 7325.00 frames.], tot_loss[loss=0.2839, simple_loss=0.3424, pruned_loss=0.1127, over 1421177.22 frames.], batch size: 21, lr: 1.37e-03
2022-05-26 18:01:35,680 INFO [train.py:842] (1/4) Epoch 3, batch 4000, loss[loss=0.3416, simple_loss=0.4004, pruned_loss=0.1414, over 7331.00 frames.], tot_loss[loss=0.2825, simple_loss=0.3414, pruned_loss=0.1118, over 1425760.11 frames.], batch size: 22, lr: 1.36e-03
2022-05-26 18:02:15,094 INFO [train.py:842] (1/4) Epoch 3, batch 4050, loss[loss=0.2626, simple_loss=0.336, pruned_loss=0.0946, over 7437.00 frames.], tot_loss[loss=0.2817, simple_loss=0.3409, pruned_loss=0.1112, over 1425701.62 frames.], batch size: 20, lr: 1.36e-03
2022-05-26 18:02:53,426 INFO [train.py:842] (1/4) Epoch 3, batch 4100, loss[loss=0.2178, simple_loss=0.2875, pruned_loss=0.07408, over 7460.00 frames.], tot_loss[loss=0.2823, simple_loss=0.3415, pruned_loss=0.1115, over 1417118.99 frames.], batch size: 19, lr: 1.36e-03
2022-05-26 18:03:32,197 INFO [train.py:842] (1/4) Epoch 3, batch 4150, loss[loss=0.2494, simple_loss=0.3259, pruned_loss=0.08646, over 7328.00 frames.], tot_loss[loss=0.2819, simple_loss=0.3417, pruned_loss=0.1111, over 1421724.96 frames.], batch size: 25, lr: 1.36e-03
2022-05-26 18:04:10,665 INFO [train.py:842] (1/4) Epoch 3, batch 4200, loss[loss=0.272, simple_loss=0.3422, pruned_loss=0.1009, over 7210.00 frames.], tot_loss[loss=0.2818, simple_loss=0.3416, pruned_loss=0.111, over 1420871.41 frames.], batch size: 22, lr: 1.36e-03
2022-05-26 18:04:49,675 INFO [train.py:842] (1/4) Epoch 3, batch 4250, loss[loss=0.2586, simple_loss=0.3171, pruned_loss=0.1, over 7259.00 frames.], tot_loss[loss=0.2817, simple_loss=0.3415, pruned_loss=0.111, over 1425126.83 frames.], batch size: 19, lr: 1.36e-03
2022-05-26 18:05:28,269 INFO [train.py:842] (1/4) Epoch 3, batch 4300, loss[loss=0.2267, simple_loss=0.2979, pruned_loss=0.07778, over 6809.00 frames.], tot_loss[loss=0.2805, simple_loss=0.3407, pruned_loss=0.1101, over 1424402.36 frames.], batch size: 15, lr: 1.36e-03
2022-05-26 18:06:07,403 INFO [train.py:842] (1/4) Epoch 3, batch 4350, loss[loss=0.255, simple_loss=0.3162, pruned_loss=0.09695, over 7358.00 frames.], tot_loss[loss=0.2782, simple_loss=0.3392, pruned_loss=0.1086, over 1426896.06 frames.], batch size: 19, lr: 1.35e-03
2022-05-26 18:06:45,887 INFO [train.py:842] (1/4) Epoch 3, batch 4400, loss[loss=0.3241, simple_loss=0.3738, pruned_loss=0.1372, over 7424.00 frames.], tot_loss[loss=0.2805, simple_loss=0.3407, pruned_loss=0.1102, over 1427802.92 frames.], batch size: 20, lr: 1.35e-03
2022-05-26 18:07:24,817 INFO [train.py:842] (1/4) Epoch 3, batch 4450, loss[loss=0.2764, simple_loss=0.3278, pruned_loss=0.1125, over 7006.00 frames.], tot_loss[loss=0.279, simple_loss=0.3394, pruned_loss=0.1093, over 1431665.83 frames.], batch size: 16, lr: 1.35e-03
2022-05-26 18:08:03,443 INFO [train.py:842] (1/4) Epoch 3, batch 4500, loss[loss=0.3117, simple_loss=0.3671, pruned_loss=0.1282, over 7193.00 frames.], tot_loss[loss=0.2785, simple_loss=0.3389, pruned_loss=0.109, over 1427099.85 frames.], batch size: 23, lr: 1.35e-03
2022-05-26 18:08:42,201 INFO [train.py:842] (1/4) Epoch 3, batch 4550, loss[loss=0.2456, simple_loss=0.3251, pruned_loss=0.08308, over 7328.00 frames.], tot_loss[loss=0.2775, simple_loss=0.3379, pruned_loss=0.1086, over 1426500.65 frames.], batch size: 20, lr: 1.35e-03
2022-05-26 18:09:20,905 INFO [train.py:842] (1/4) Epoch 3, batch 4600, loss[loss=0.2246, simple_loss=0.2877, pruned_loss=0.08072, over 7434.00 frames.], tot_loss[loss=0.2776, simple_loss=0.3384, pruned_loss=0.1084, over 1427871.61 frames.], batch size: 18, lr: 1.35e-03
2022-05-26 18:10:00,052 INFO [train.py:842] (1/4) Epoch 3, batch 4650, loss[loss=0.2799, simple_loss=0.3309, pruned_loss=0.1144, over 6995.00 frames.], tot_loss[loss=0.2788, simple_loss=0.3397, pruned_loss=0.109, over 1429241.14 frames.], batch size: 16, lr: 1.35e-03
2022-05-26 18:10:38,693 INFO [train.py:842] (1/4) Epoch 3, batch 4700, loss[loss=0.246, simple_loss=0.3212, pruned_loss=0.0854, over 7248.00 frames.], tot_loss[loss=0.2778, simple_loss=0.3389, pruned_loss=0.1083, over 1433423.72 frames.], batch size: 19, lr: 1.34e-03
2022-05-26 18:11:17,511 INFO [train.py:842] (1/4) Epoch 3, batch 4750, loss[loss=0.3155, simple_loss=0.3732, pruned_loss=0.1289, over 7057.00 frames.], tot_loss[loss=0.2785, simple_loss=0.3395, pruned_loss=0.1087, over 1433318.90 frames.], batch size: 28, lr: 1.34e-03
2022-05-26 18:11:55,920 INFO [train.py:842] (1/4) Epoch 3, batch 4800, loss[loss=0.2328, simple_loss=0.2871, pruned_loss=0.08928, over 7286.00 frames.], tot_loss[loss=0.2773, simple_loss=0.3388, pruned_loss=0.1079, over 1432659.83 frames.], batch size: 17, lr: 1.34e-03
2022-05-26 18:12:34,711 INFO [train.py:842] (1/4) Epoch 3, batch 4850, loss[loss=0.2593, simple_loss=0.3347, pruned_loss=0.09192, over 7318.00 frames.], tot_loss[loss=0.2778, simple_loss=0.3392, pruned_loss=0.1082, over 1429798.87 frames.], batch size: 21, lr: 1.34e-03
2022-05-26 18:13:13,117 INFO [train.py:842] (1/4) Epoch 3, batch 4900, loss[loss=0.2463, simple_loss=0.3238, pruned_loss=0.08442, over 7227.00 frames.], tot_loss[loss=0.2781, simple_loss=0.3398, pruned_loss=0.1082, over 1426778.22 frames.], batch size: 20, lr: 1.34e-03
2022-05-26 18:13:51,825 INFO [train.py:842] (1/4) Epoch 3, batch 4950, loss[loss=0.3089, simple_loss=0.3658, pruned_loss=0.126, over 7102.00 frames.], tot_loss[loss=0.2789, simple_loss=0.3405, pruned_loss=0.1086, over 1425394.47 frames.], batch size: 21, lr: 1.34e-03
2022-05-26 18:14:30,280 INFO [train.py:842] (1/4) Epoch 3, batch 5000, loss[loss=0.2914, simple_loss=0.3467, pruned_loss=0.1181, over 7146.00 frames.], tot_loss[loss=0.2794, simple_loss=0.3407, pruned_loss=0.1091, over 1419467.18 frames.], batch size: 19, lr: 1.34e-03
2022-05-26 18:15:09,119 INFO [train.py:842] (1/4) Epoch 3, batch 5050, loss[loss=0.2194, simple_loss=0.2978, pruned_loss=0.07053, over 7319.00 frames.], tot_loss[loss=0.2751, simple_loss=0.3372, pruned_loss=0.1065, over 1418816.52 frames.], batch size: 21, lr: 1.33e-03
2022-05-26 18:15:47,551 INFO [train.py:842] (1/4) Epoch 3, batch 5100, loss[loss=0.2461, simple_loss=0.3211, pruned_loss=0.08557, over 7357.00 frames.], tot_loss[loss=0.2731, simple_loss=0.3358, pruned_loss=0.1052, over 1422066.85 frames.], batch size: 19, lr: 1.33e-03
2022-05-26 18:16:26,355 INFO [train.py:842] (1/4) Epoch 3, batch 5150, loss[loss=0.2129, simple_loss=0.287, pruned_loss=0.06943, over 7152.00 frames.], tot_loss[loss=0.2719, simple_loss=0.3346, pruned_loss=0.1046, over 1422279.09 frames.], batch size: 18, lr: 1.33e-03
2022-05-26 18:17:04,912 INFO [train.py:842] (1/4) Epoch 3, batch 5200, loss[loss=0.3022, simple_loss=0.3606, pruned_loss=0.1219, over 7204.00 frames.], tot_loss[loss=0.2744, simple_loss=0.336, pruned_loss=0.1064, over 1422572.16 frames.], batch size: 22, lr: 1.33e-03
2022-05-26 18:17:43,696 INFO [train.py:842] (1/4) Epoch 3, batch 5250, loss[loss=0.3193, simple_loss=0.361, pruned_loss=0.1388, over 7335.00 frames.], tot_loss[loss=0.276, simple_loss=0.3374, pruned_loss=0.1073, over 1422924.66 frames.], batch size: 20, lr: 1.33e-03
2022-05-26 18:18:22,095 INFO [train.py:842] (1/4) Epoch 3, batch 5300, loss[loss=0.2995, simple_loss=0.3663, pruned_loss=0.1163, over 7320.00 frames.], tot_loss[loss=0.2775, simple_loss=0.3394, pruned_loss=0.1078, over 1421099.49 frames.], batch size: 25, lr: 1.33e-03
2022-05-26 18:19:00,849 INFO [train.py:842] (1/4) Epoch 3, batch 5350, loss[loss=0.3147, simple_loss=0.3697, pruned_loss=0.1298, over 7410.00 frames.], tot_loss[loss=0.2802, simple_loss=0.3423, pruned_loss=0.109, over 1416899.65 frames.], batch size: 21, lr: 1.33e-03
2022-05-26 18:19:39,499 INFO [train.py:842] (1/4) Epoch 3, batch 5400, loss[loss=0.2828, simple_loss=0.3495, pruned_loss=0.108, over 7067.00 frames.], tot_loss[loss=0.2813, simple_loss=0.3429, pruned_loss=0.1098, over 1416013.15 frames.], batch size: 18, lr: 1.33e-03
2022-05-26 18:20:18,486 INFO [train.py:842] (1/4) Epoch 3, batch 5450, loss[loss=0.2557, simple_loss=0.3182, pruned_loss=0.09662, over 7161.00 frames.], tot_loss[loss=0.2806, simple_loss=0.3417, pruned_loss=0.1098, over 1417269.50 frames.], batch size: 18, lr: 1.32e-03
2022-05-26 18:20:57,211 INFO [train.py:842] (1/4) Epoch 3, batch 5500, loss[loss=0.3489, simple_loss=0.3935, pruned_loss=0.1521, over 7221.00 frames.], tot_loss[loss=0.2785, simple_loss=0.3398, pruned_loss=0.1086, over 1419463.23 frames.], batch size: 21, lr: 1.32e-03
2022-05-26 18:21:35,992 INFO [train.py:842] (1/4) Epoch 3, batch 5550, loss[loss=0.2599, simple_loss=0.3118, pruned_loss=0.104, over 6769.00 frames.], tot_loss[loss=0.2774, simple_loss=0.3389, pruned_loss=0.1079, over 1418280.68 frames.], batch size: 15, lr: 1.32e-03
2022-05-26 18:22:14,559 INFO [train.py:842] (1/4) Epoch 3, batch 5600, loss[loss=0.2858, simple_loss=0.3395, pruned_loss=0.1161, over 7161.00 frames.], tot_loss[loss=0.2765, simple_loss=0.3383, pruned_loss=0.1073, over 1423075.47 frames.], batch size: 26, lr: 1.32e-03
2022-05-26 18:22:55,852 INFO [train.py:842] (1/4) Epoch 3, batch 5650, loss[loss=0.2469, simple_loss=0.3103, pruned_loss=0.09177, over 7279.00 frames.], tot_loss[loss=0.2743, simple_loss=0.3367, pruned_loss=0.106, over 1424302.39 frames.], batch size: 17, lr: 1.32e-03
2022-05-26 18:23:34,214 INFO [train.py:842] (1/4) Epoch 3, batch 5700, loss[loss=0.2672, simple_loss=0.3341, pruned_loss=0.1002, over 6373.00 frames.], tot_loss[loss=0.275, simple_loss=0.3376, pruned_loss=0.1062, over 1422903.59 frames.], batch size: 38, lr: 1.32e-03
2022-05-26 18:24:13,057 INFO [train.py:842] (1/4) Epoch 3, batch 5750, loss[loss=0.2919, simple_loss=0.345, pruned_loss=0.1194, over 5449.00 frames.], tot_loss[loss=0.2736, simple_loss=0.3364, pruned_loss=0.1053, over 1420467.68 frames.], batch size: 53, lr: 1.32e-03
2022-05-26 18:24:51,644 INFO [train.py:842] (1/4) Epoch 3, batch 5800, loss[loss=0.2457, simple_loss=0.2984, pruned_loss=0.09649, over 7136.00 frames.], tot_loss[loss=0.2743, simple_loss=0.3369, pruned_loss=0.1058, over 1425217.45 frames.], batch size: 17, lr: 1.31e-03
2022-05-26 18:25:30,406 INFO [train.py:842] (1/4) Epoch 3, batch 5850, loss[loss=0.3751, simple_loss=0.4063, pruned_loss=0.1719, over 5106.00 frames.], tot_loss[loss=0.2748, simple_loss=0.3372, pruned_loss=0.1062, over 1424169.48 frames.], batch size: 52, lr: 1.31e-03
2022-05-26 18:26:08,877 INFO [train.py:842] (1/4) Epoch 3, batch 5900, loss[loss=0.2781, simple_loss=0.352, pruned_loss=0.1021, over 7235.00 frames.], tot_loss[loss=0.2757, simple_loss=0.3379, pruned_loss=0.1068, over 1426161.19 frames.], batch size: 21, lr: 1.31e-03
2022-05-26 18:26:47,679 INFO [train.py:842] (1/4) Epoch 3, batch 5950, loss[loss=0.2925, simple_loss=0.3559, pruned_loss=0.1145, over 7383.00 frames.], tot_loss[loss=0.2768, simple_loss=0.3386, pruned_loss=0.1075, over 1423092.95 frames.], batch size: 23, lr: 1.31e-03
2022-05-26 18:27:26,257 INFO [train.py:842] (1/4) Epoch 3, batch 6000, loss[loss=0.256, simple_loss=0.3193, pruned_loss=0.09639, over 7277.00 frames.], tot_loss[loss=0.276, simple_loss=0.3378, pruned_loss=0.107, over 1421412.06 frames.], batch size: 17, lr: 1.31e-03
2022-05-26 18:27:26,258 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 18:27:36,052 INFO [train.py:871] (1/4) Epoch 3, validation: loss=0.2084, simple_loss=0.3064, pruned_loss=0.05526, over 868885.00 frames.
2022-05-26 18:28:14,674 INFO [train.py:842] (1/4) Epoch 3, batch 6050, loss[loss=0.3015, simple_loss=0.3537, pruned_loss=0.1247, over 7160.00 frames.], tot_loss[loss=0.2767, simple_loss=0.339, pruned_loss=0.1072, over 1421312.10 frames.], batch size: 20, lr: 1.31e-03
2022-05-26 18:28:53,190 INFO [train.py:842] (1/4) Epoch 3, batch 6100, loss[loss=0.2401, simple_loss=0.311, pruned_loss=0.08459, over 7160.00 frames.], tot_loss[loss=0.2781, simple_loss=0.3397, pruned_loss=0.1083, over 1419004.96 frames.], batch size: 26, lr: 1.31e-03
2022-05-26 18:29:31,953 INFO [train.py:842] (1/4) Epoch 3, batch 6150, loss[loss=0.2333, simple_loss=0.3098, pruned_loss=0.07838, over 7416.00 frames.], tot_loss[loss=0.2763, simple_loss=0.3381, pruned_loss=0.1072, over 1419284.68 frames.], batch size: 21, lr: 1.31e-03
2022-05-26 18:30:10,411 INFO [train.py:842] (1/4) Epoch 3, batch 6200, loss[loss=0.3309, simple_loss=0.3838, pruned_loss=0.139, over 5170.00 frames.], tot_loss[loss=0.2763, simple_loss=0.3379, pruned_loss=0.1073, over 1417147.10 frames.], batch size: 53, lr: 1.30e-03
2022-05-26 18:30:49,611 INFO [train.py:842] (1/4) Epoch 3, batch 6250, loss[loss=0.3019, simple_loss=0.3611, pruned_loss=0.1213, over 6802.00 frames.], tot_loss[loss=0.2749, simple_loss=0.3364, pruned_loss=0.1067, over 1418769.60 frames.], batch size: 31, lr: 1.30e-03
2022-05-26 18:31:28,263 INFO [train.py:842] (1/4) Epoch 3, batch 6300, loss[loss=0.3748, simple_loss=0.3966, pruned_loss=0.1765, over 7153.00 frames.], tot_loss[loss=0.2766, simple_loss=0.3374, pruned_loss=0.1079, over 1413915.98 frames.], batch size: 18, lr: 1.30e-03
2022-05-26 18:32:07,490 INFO [train.py:842] (1/4) Epoch 3, batch 6350, loss[loss=0.246, simple_loss=0.3052, pruned_loss=0.09345, over 7286.00 frames.], tot_loss[loss=0.2774, simple_loss=0.3371, pruned_loss=0.1089, over 1419002.83 frames.], batch size: 17, lr: 1.30e-03
2022-05-26 18:32:46,019 INFO [train.py:842] (1/4) Epoch 3, batch 6400, loss[loss=0.258, simple_loss=0.3333, pruned_loss=0.09138, over 7306.00 frames.], tot_loss[loss=0.2783, simple_loss=0.3378, pruned_loss=0.1094, over 1419442.83 frames.], batch size: 25, lr: 1.30e-03
2022-05-26 18:33:24,832 INFO [train.py:842] (1/4) Epoch 3, batch 6450, loss[loss=0.2493, simple_loss=0.3302, pruned_loss=0.0842, over 7110.00 frames.], tot_loss[loss=0.2765, simple_loss=0.3369, pruned_loss=0.108, over 1419416.19 frames.], batch size: 21, lr: 1.30e-03
2022-05-26 18:34:03,177 INFO [train.py:842] (1/4) Epoch 3, batch 6500, loss[loss=0.2205, simple_loss=0.2949, pruned_loss=0.0731, over 7283.00 frames.], tot_loss[loss=0.2759, simple_loss=0.3369, pruned_loss=0.1074, over 1417965.73 frames.], batch size: 18, lr: 1.30e-03
2022-05-26 18:34:42,299 INFO [train.py:842] (1/4) Epoch 3, batch 6550, loss[loss=0.2623, simple_loss=0.3388, pruned_loss=0.09292, over 7144.00 frames.], tot_loss[loss=0.2765, simple_loss=0.3377, pruned_loss=0.1077, over 1422209.49 frames.], batch size: 20, lr: 1.30e-03
2022-05-26 18:35:21,036 INFO [train.py:842] (1/4) Epoch 3, batch 6600, loss[loss=0.3085, simple_loss=0.3743, pruned_loss=0.1214, over 7105.00 frames.], tot_loss[loss=0.2748, simple_loss=0.3364, pruned_loss=0.1066, over 1422428.99 frames.], batch size: 21, lr: 1.29e-03
2022-05-26 18:36:00,219 INFO [train.py:842] (1/4) Epoch 3, batch 6650, loss[loss=0.2391, simple_loss=0.3157, pruned_loss=0.08131, over 7123.00 frames.], tot_loss[loss=0.2754, simple_loss=0.3367, pruned_loss=0.107, over 1422923.74 frames.], batch size: 21, lr: 1.29e-03
2022-05-26 18:36:38,884 INFO [train.py:842] (1/4) Epoch 3, batch 6700, loss[loss=0.3425, simple_loss=0.3995, pruned_loss=0.1427, over 7218.00 frames.], tot_loss[loss=0.2756, simple_loss=0.3369, pruned_loss=0.1071, over 1420132.04 frames.], batch size: 21, lr: 1.29e-03
2022-05-26 18:37:17,621 INFO [train.py:842] (1/4) Epoch 3, batch 6750, loss[loss=0.2258, simple_loss=0.294, pruned_loss=0.07886, over 6843.00 frames.], tot_loss[loss=0.2761, simple_loss=0.338, pruned_loss=0.1072, over 1420336.87 frames.], batch size: 15, lr: 1.29e-03
2022-05-26 18:37:56,016 INFO [train.py:842] (1/4) Epoch 3, batch 6800, loss[loss=0.2733, simple_loss=0.3466, pruned_loss=0.1, over 7146.00 frames.], tot_loss[loss=0.2757, simple_loss=0.3375, pruned_loss=0.107, over 1421850.06 frames.], batch size: 26, lr: 1.29e-03
2022-05-26 18:38:34,911 INFO [train.py:842] (1/4) Epoch 3, batch 6850, loss[loss=0.2589, simple_loss=0.3237, pruned_loss=0.09704, over 7386.00 frames.], tot_loss[loss=0.2745, simple_loss=0.3366, pruned_loss=0.1062, over 1422843.75 frames.], batch size: 23, lr: 1.29e-03
2022-05-26 18:39:13,434 INFO [train.py:842] (1/4) Epoch 3, batch 6900, loss[loss=0.3267, simple_loss=0.3521, pruned_loss=0.1507, over 7128.00 frames.], tot_loss[loss=0.2743, simple_loss=0.3365, pruned_loss=0.1061, over 1426162.75 frames.], batch size: 17, lr: 1.29e-03
2022-05-26 18:39:52,129 INFO [train.py:842] (1/4) Epoch 3, batch 6950, loss[loss=0.2793, simple_loss=0.3487, pruned_loss=0.105, over 7212.00 frames.], tot_loss[loss=0.2745, simple_loss=0.3367, pruned_loss=0.1061, over 1427571.14 frames.], batch size: 21, lr: 1.29e-03
2022-05-26 18:40:30,500 INFO [train.py:842] (1/4) Epoch 3, batch 7000, loss[loss=0.269, simple_loss=0.341, pruned_loss=0.09848, over 7311.00 frames.], tot_loss[loss=0.2744, simple_loss=0.3366, pruned_loss=0.1061, over 1425729.79 frames.], batch size: 21, lr: 1.28e-03
2022-05-26 18:41:09,292 INFO [train.py:842] (1/4) Epoch 3, batch 7050, loss[loss=0.2611, simple_loss=0.3324, pruned_loss=0.09489, over 7182.00 frames.], tot_loss[loss=0.2754, simple_loss=0.3374, pruned_loss=0.1067, over 1424399.16 frames.], batch size: 22, lr: 1.28e-03
2022-05-26 18:41:47,891 INFO [train.py:842] (1/4) Epoch 3, batch 7100, loss[loss=0.2061, simple_loss=0.2969, pruned_loss=0.05766, over 7242.00 frames.], tot_loss[loss=0.2761, simple_loss=0.338, pruned_loss=0.1071, over 1423992.27 frames.], batch size: 20, lr: 1.28e-03
2022-05-26 18:42:26,617 INFO [train.py:842] (1/4) Epoch 3, batch 7150, loss[loss=0.2831, simple_loss=0.339, pruned_loss=0.1136, over 7162.00 frames.], tot_loss[loss=0.275, simple_loss=0.3364, pruned_loss=0.1067, over 1422760.18 frames.], batch size: 19, lr: 1.28e-03
2022-05-26 18:43:05,183 INFO [train.py:842] (1/4) Epoch 3, batch 7200, loss[loss=0.2942, simple_loss=0.3453, pruned_loss=0.1215, over 5096.00 frames.], tot_loss[loss=0.2771, simple_loss=0.3382, pruned_loss=0.108, over 1416719.74 frames.], batch size: 52, lr: 1.28e-03
2022-05-26 18:43:43,951 INFO [train.py:842] (1/4) Epoch 3, batch 7250, loss[loss=0.2502, simple_loss=0.3067, pruned_loss=0.09684, over 7274.00 frames.], tot_loss[loss=0.2761, simple_loss=0.3378, pruned_loss=0.1072, over 1419413.26 frames.], batch size: 17, lr: 1.28e-03
2022-05-26 18:44:22,273 INFO [train.py:842] (1/4) Epoch 3, batch 7300, loss[loss=0.2815, simple_loss=0.3487, pruned_loss=0.1072, over 7315.00 frames.], tot_loss[loss=0.278, simple_loss=0.3396, pruned_loss=0.1082, over 1421872.98 frames.], batch size: 21, lr: 1.28e-03
2022-05-26 18:45:01,013 INFO [train.py:842] (1/4) Epoch 3, batch 7350, loss[loss=0.3294, simple_loss=0.3773, pruned_loss=0.1407, over 7281.00 frames.], tot_loss[loss=0.2775, simple_loss=0.3391, pruned_loss=0.108, over 1423637.54 frames.], batch size: 24, lr: 1.28e-03
2022-05-26 18:45:39,604 INFO [train.py:842] (1/4) Epoch 3, batch 7400, loss[loss=0.2515, simple_loss=0.3101, pruned_loss=0.09645, over 7238.00 frames.], tot_loss[loss=0.2773, simple_loss=0.3387, pruned_loss=0.108, over 1426741.67 frames.], batch size: 19, lr: 1.27e-03
2022-05-26 18:46:18,836 INFO [train.py:842] (1/4) Epoch 3, batch 7450, loss[loss=0.2276, simple_loss=0.3105, pruned_loss=0.07241, over 7406.00 frames.], tot_loss[loss=0.2752, simple_loss=0.3366, pruned_loss=0.1069, over 1426216.28 frames.], batch size: 21, lr: 1.27e-03
2022-05-26 18:46:57,505 INFO [train.py:842] (1/4) Epoch 3, batch 7500, loss[loss=0.2573, simple_loss=0.3189, pruned_loss=0.09785, over 7135.00 frames.], tot_loss[loss=0.2727, simple_loss=0.3349, pruned_loss=0.1053, over 1429218.37 frames.], batch size: 17, lr: 1.27e-03
2022-05-26 18:47:36,159 INFO [train.py:842] (1/4) Epoch 3, batch 7550, loss[loss=0.2805, simple_loss=0.346, pruned_loss=0.1075, over 7302.00 frames.], tot_loss[loss=0.2724, simple_loss=0.3343, pruned_loss=0.1052, over 1427654.79 frames.], batch size: 24, lr: 1.27e-03
2022-05-26 18:48:14,669 INFO [train.py:842] (1/4) Epoch 3, batch 7600, loss[loss=0.2452, simple_loss=0.3269, pruned_loss=0.08174, over 7341.00 frames.], tot_loss[loss=0.2737, simple_loss=0.3354, pruned_loss=0.106, over 1424443.30 frames.], batch size: 22, lr: 1.27e-03
2022-05-26 18:48:53,735 INFO [train.py:842] (1/4) Epoch 3, batch 7650, loss[loss=0.2147, simple_loss=0.2728, pruned_loss=0.07832, over 6999.00 frames.], tot_loss[loss=0.2725, simple_loss=0.334, pruned_loss=0.1055, over 1418070.13 frames.], batch size: 16, lr: 1.27e-03
2022-05-26 18:49:32,393 INFO [train.py:842] (1/4) Epoch 3, batch 7700, loss[loss=0.305, simple_loss=0.3715, pruned_loss=0.1192, over 7012.00 frames.], tot_loss[loss=0.2732, simple_loss=0.3349, pruned_loss=0.1058, over 1418813.72 frames.], batch size: 28, lr: 1.27e-03
2022-05-26 18:50:11,093 INFO [train.py:842] (1/4) Epoch 3, batch 7750, loss[loss=0.3358, simple_loss=0.3584, pruned_loss=0.1566, over 6788.00 frames.], tot_loss[loss=0.2738, simple_loss=0.3358, pruned_loss=0.1059, over 1424061.77 frames.], batch size: 15, lr: 1.27e-03
2022-05-26 18:50:49,654 INFO [train.py:842] (1/4) Epoch 3, batch 7800, loss[loss=0.2276, simple_loss=0.3088, pruned_loss=0.07314, over 7367.00 frames.], tot_loss[loss=0.2726, simple_loss=0.3351, pruned_loss=0.1051, over 1423898.22 frames.], batch size: 23, lr: 1.27e-03
2022-05-26 18:51:28,484 INFO [train.py:842] (1/4) Epoch 3, batch 7850, loss[loss=0.2534, simple_loss=0.332, pruned_loss=0.08738, over 7173.00 frames.], tot_loss[loss=0.2695, simple_loss=0.333, pruned_loss=0.103, over 1424211.66 frames.], batch size: 26, lr: 1.26e-03
2022-05-26 18:52:07,137 INFO [train.py:842] (1/4) Epoch 3, batch 7900, loss[loss=0.272, simple_loss=0.3429, pruned_loss=0.1006, over 7325.00 frames.], tot_loss[loss=0.2692, simple_loss=0.3326, pruned_loss=0.1029, over 1425332.36 frames.], batch size: 20, lr: 1.26e-03
2022-05-26 18:52:45,883 INFO [train.py:842] (1/4) Epoch 3, batch 7950, loss[loss=0.2772, simple_loss=0.3606, pruned_loss=0.09689, over 7230.00 frames.], tot_loss[loss=0.2699, simple_loss=0.3333, pruned_loss=0.1032, over 1424612.43 frames.], batch size: 20, lr: 1.26e-03
2022-05-26 18:53:24,293 INFO [train.py:842] (1/4) Epoch 3, batch 8000, loss[loss=0.4017, simple_loss=0.4148, pruned_loss=0.1943, over 7256.00 frames.], tot_loss[loss=0.2714, simple_loss=0.3344, pruned_loss=0.1042, over 1420433.70 frames.], batch size: 26, lr: 1.26e-03
2022-05-26 18:54:03,073 INFO [train.py:842] (1/4) Epoch 3, batch 8050, loss[loss=0.2337, simple_loss=0.3129, pruned_loss=0.07728, over 7151.00 frames.], tot_loss[loss=0.2717, simple_loss=0.3344, pruned_loss=0.1045, over 1418777.87 frames.], batch size: 26, lr: 1.26e-03
2022-05-26 18:54:41,743 INFO [train.py:842] (1/4) Epoch 3, batch 8100, loss[loss=0.3409, simple_loss=0.3887, pruned_loss=0.1465, over 7204.00 frames.], tot_loss[loss=0.2718, simple_loss=0.3346, pruned_loss=0.1045, over 1419558.72 frames.], batch size: 22, lr: 1.26e-03
2022-05-26 18:55:20,583 INFO [train.py:842] (1/4) Epoch 3, batch 8150, loss[loss=0.2244, simple_loss=0.304, pruned_loss=0.0724, over 7246.00 frames.], tot_loss[loss=0.2717, simple_loss=0.3343, pruned_loss=0.1045, over 1413038.65 frames.], batch size: 19, lr: 1.26e-03
2022-05-26 18:55:59,064 INFO [train.py:842] (1/4) Epoch 3, batch 8200, loss[loss=0.2409, simple_loss=0.3332, pruned_loss=0.07435, over 7328.00 frames.], tot_loss[loss=0.2713, simple_loss=0.3339, pruned_loss=0.1044, over 1415425.28 frames.], batch size: 20, lr: 1.26e-03
2022-05-26 18:56:37,967 INFO [train.py:842] (1/4) Epoch 3, batch 8250, loss[loss=0.2689, simple_loss=0.3363, pruned_loss=0.1008, over 7257.00 frames.], tot_loss[loss=0.2687, simple_loss=0.3321, pruned_loss=0.1027, over 1419173.16 frames.], batch size: 19, lr: 1.26e-03
2022-05-26 18:57:16,523 INFO [train.py:842] (1/4) Epoch 3, batch 8300, loss[loss=0.2233, simple_loss=0.3047, pruned_loss=0.07094, over 7106.00 frames.], tot_loss[loss=0.2698, simple_loss=0.3331, pruned_loss=0.1033, over 1420333.03 frames.], batch size: 21, lr: 1.25e-03
2022-05-26 18:57:55,090 INFO [train.py:842] (1/4) Epoch 3, batch 8350, loss[loss=0.3029, simple_loss=0.3516, pruned_loss=0.1271, over 4819.00 frames.], tot_loss[loss=0.2719, simple_loss=0.3347, pruned_loss=0.1045, over 1416683.53 frames.], batch size: 52, lr: 1.25e-03
2022-05-26 18:58:33,823 INFO [train.py:842] (1/4) Epoch 3, batch 8400, loss[loss=0.2455, simple_loss=0.3135, pruned_loss=0.08873, over 7253.00 frames.], tot_loss[loss=0.2705, simple_loss=0.3339, pruned_loss=0.1035, over 1419036.61 frames.], batch size: 19, lr: 1.25e-03
2022-05-26 18:59:12,599 INFO [train.py:842] (1/4) Epoch 3, batch 8450, loss[loss=0.253, simple_loss=0.3238, pruned_loss=0.09109, over 7109.00 frames.], tot_loss[loss=0.2726, simple_loss=0.3356, pruned_loss=0.1048, over 1419031.08 frames.], batch size: 28, lr: 1.25e-03
2022-05-26 19:00:01,255 INFO [train.py:842] (1/4) Epoch 3, batch 8500, loss[loss=0.2373, simple_loss=0.3096, pruned_loss=0.08253, over 7147.00 frames.], tot_loss[loss=0.2727, simple_loss=0.3358, pruned_loss=0.1048, over 1420998.60 frames.], batch size: 19, lr: 1.25e-03
2022-05-26 19:00:39,792 INFO [train.py:842] (1/4) Epoch 3, batch 8550, loss[loss=0.2741, simple_loss=0.3468, pruned_loss=0.1007, over 6176.00 frames.], tot_loss[loss=0.2732, simple_loss=0.3363, pruned_loss=0.1051, over 1417663.93 frames.], batch size: 37, lr: 1.25e-03
2022-05-26 19:01:18,433 INFO [train.py:842] (1/4) Epoch 3, batch 8600, loss[loss=0.258, simple_loss=0.3097, pruned_loss=0.1031, over 7298.00 frames.], tot_loss[loss=0.2716, simple_loss=0.3347, pruned_loss=0.1043, over 1418596.67 frames.], batch size: 17, lr: 1.25e-03
2022-05-26 19:01:57,308 INFO [train.py:842] (1/4) Epoch 3, batch 8650, loss[loss=0.2751, simple_loss=0.3203, pruned_loss=0.115, over 7267.00 frames.], tot_loss[loss=0.2715, simple_loss=0.3341, pruned_loss=0.1044, over 1417309.50 frames.], batch size: 18, lr: 1.25e-03
2022-05-26 19:02:35,785 INFO [train.py:842] (1/4) Epoch 3, batch 8700, loss[loss=0.3025, simple_loss=0.3684, pruned_loss=0.1183, over 7050.00 frames.], tot_loss[loss=0.2721, simple_loss=0.3349, pruned_loss=0.1047, over 1417172.29 frames.], batch size: 28, lr: 1.24e-03
2022-05-26 19:03:14,520 INFO [train.py:842] (1/4) Epoch 3, batch 8750, loss[loss=0.253, simple_loss=0.3318, pruned_loss=0.08705, over 7106.00 frames.], tot_loss[loss=0.2712, simple_loss=0.3348, pruned_loss=0.1038, over 1418517.50 frames.], batch size: 28, lr: 1.24e-03
2022-05-26 19:03:53,199 INFO [train.py:842] (1/4) Epoch 3, batch 8800, loss[loss=0.2146, simple_loss=0.2898, pruned_loss=0.06966, over 7278.00 frames.], tot_loss[loss=0.2714, simple_loss=0.3346, pruned_loss=0.1041, over 1417937.32 frames.], batch size: 18, lr: 1.24e-03
2022-05-26 19:04:31,978 INFO [train.py:842] (1/4) Epoch 3, batch 8850, loss[loss=0.2423, simple_loss=0.3216, pruned_loss=0.08157, over 7342.00 frames.], tot_loss[loss=0.2707, simple_loss=0.334, pruned_loss=0.1037, over 1419083.33 frames.], batch size: 22, lr: 1.24e-03
2022-05-26 19:05:10,562 INFO [train.py:842] (1/4) Epoch 3, batch 8900, loss[loss=0.306, simple_loss=0.3657, pruned_loss=0.1232, over 7008.00 frames.], tot_loss[loss=0.2705, simple_loss=0.334, pruned_loss=0.1035, over 1417546.14 frames.], batch size: 28, lr: 1.24e-03
2022-05-26 19:05:59,433 INFO [train.py:842] (1/4) Epoch 3, batch 8950, loss[loss=0.2582, simple_loss=0.3067, pruned_loss=0.1049, over 7280.00 frames.], tot_loss[loss=0.2732, simple_loss=0.3361, pruned_loss=0.1052, over 1412176.64 frames.], batch size: 17, lr: 1.24e-03
2022-05-26 19:06:48,324 INFO [train.py:842] (1/4) Epoch 3, batch 9000, loss[loss=0.3495, simple_loss=0.398, pruned_loss=0.1505, over 4905.00 frames.], tot_loss[loss=0.2747, simple_loss=0.3373, pruned_loss=0.1061, over 1401798.43 frames.], batch size: 52, lr: 1.24e-03
2022-05-26 19:06:48,325 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 19:07:08,221 INFO [train.py:871] (1/4) Epoch 3, validation: loss=0.2052, simple_loss=0.3029, pruned_loss=0.0537, over 868885.00 frames.
2022-05-26 19:07:46,541 INFO [train.py:842] (1/4) Epoch 3, batch 9050, loss[loss=0.2537, simple_loss=0.3288, pruned_loss=0.0893, over 7283.00 frames.], tot_loss[loss=0.2788, simple_loss=0.3407, pruned_loss=0.1084, over 1387740.17 frames.], batch size: 25, lr: 1.24e-03
2022-05-26 19:08:23,990 INFO [train.py:842] (1/4) Epoch 3, batch 9100, loss[loss=0.3009, simple_loss=0.3507, pruned_loss=0.1255, over 4940.00 frames.], tot_loss[loss=0.2857, simple_loss=0.3461, pruned_loss=0.1126, over 1355969.71 frames.], batch size: 52, lr: 1.24e-03
2022-05-26 19:09:01,539 INFO [train.py:842] (1/4) Epoch 3, batch 9150, loss[loss=0.3694, simple_loss=0.391, pruned_loss=0.1739, over 5026.00 frames.], tot_loss[loss=0.2924, simple_loss=0.3505, pruned_loss=0.1171, over 1297625.02 frames.], batch size: 52, lr: 1.24e-03
2022-05-26 19:09:53,249 INFO [train.py:842] (1/4) Epoch 4, batch 0, loss[loss=0.2612, simple_loss=0.3279, pruned_loss=0.09724, over 7227.00 frames.], tot_loss[loss=0.2612, simple_loss=0.3279, pruned_loss=0.09724, over 7227.00 frames.], batch size: 23, lr: 1.20e-03
2022-05-26 19:10:32,538 INFO [train.py:842] (1/4) Epoch 4, batch 50, loss[loss=0.2122, simple_loss=0.2855, pruned_loss=0.06945, over 7276.00 frames.], tot_loss[loss=0.269, simple_loss=0.3329, pruned_loss=0.1026, over 317691.23 frames.], batch size: 17, lr: 1.20e-03
2022-05-26 19:11:11,263 INFO [train.py:842] (1/4) Epoch 4, batch 100, loss[loss=0.2879, simple_loss=0.3303, pruned_loss=0.1227, over 7267.00 frames.], tot_loss[loss=0.2678, simple_loss=0.3308, pruned_loss=0.1024, over 564177.96 frames.], batch size: 17, lr: 1.20e-03
2022-05-26 19:11:50,055 INFO [train.py:842] (1/4) Epoch 4, batch 150, loss[loss=0.2283, simple_loss=0.3088, pruned_loss=0.07385, over 7342.00 frames.], tot_loss[loss=0.2693, simple_loss=0.3322, pruned_loss=0.1033, over 755195.52 frames.], batch size: 22, lr: 1.20e-03
2022-05-26 19:12:28,786 INFO [train.py:842] (1/4) Epoch 4, batch 200, loss[loss=0.3133, simple_loss=0.3772, pruned_loss=0.1247, over 7188.00 frames.], tot_loss[loss=0.2721, simple_loss=0.334, pruned_loss=0.1051, over 904206.34 frames.], batch size: 23, lr: 1.19e-03
2022-05-26 19:13:07,620 INFO [train.py:842] (1/4) Epoch 4, batch 250, loss[loss=0.2595, simple_loss=0.3372, pruned_loss=0.09095, over 7322.00 frames.], tot_loss[loss=0.2713, simple_loss=0.3344, pruned_loss=0.1041, over 1016923.86 frames.], batch size: 22, lr: 1.19e-03
2022-05-26 19:13:46,329 INFO [train.py:842] (1/4) Epoch 4, batch 300, loss[loss=0.2698, simple_loss=0.3442, pruned_loss=0.09767, over 7392.00 frames.], tot_loss[loss=0.2694, simple_loss=0.3337, pruned_loss=0.1026, over 1111340.56 frames.], batch size: 23, lr: 1.19e-03
2022-05-26 19:14:25,598 INFO [train.py:842] (1/4) Epoch 4, batch 350, loss[loss=0.2403, simple_loss=0.3223, pruned_loss=0.07914, over 7309.00 frames.], tot_loss[loss=0.2666, simple_loss=0.3318, pruned_loss=0.1007, over 1182547.38 frames.], batch size: 21, lr: 1.19e-03
2022-05-26 19:15:04,154 INFO [train.py:842] (1/4) Epoch 4, batch 400, loss[loss=0.2252, simple_loss=0.3083, pruned_loss=0.07103, over 7222.00 frames.], tot_loss[loss=0.2677, simple_loss=0.3319, pruned_loss=0.1017, over 1232637.41 frames.], batch size: 20, lr: 1.19e-03
2022-05-26 19:15:42,983 INFO [train.py:842] (1/4) Epoch 4, batch 450, loss[loss=0.2679, simple_loss=0.3423, pruned_loss=0.09675, over 7148.00 frames.], tot_loss[loss=0.267, simple_loss=0.3311, pruned_loss=0.1015, over 1274230.93 frames.], batch size: 20, lr: 1.19e-03
2022-05-26 19:16:21,291 INFO [train.py:842] (1/4) Epoch 4, batch 500, loss[loss=0.2813, simple_loss=0.3533, pruned_loss=0.1046, over 7148.00 frames.], tot_loss[loss=0.2679, simple_loss=0.3322, pruned_loss=0.1018, over 1304330.35 frames.], batch size: 19, lr: 1.19e-03
2022-05-26 19:17:00,279 INFO [train.py:842] (1/4) Epoch 4, batch 550, loss[loss=0.2143, simple_loss=0.2834, pruned_loss=0.07264, over 7163.00 frames.], tot_loss[loss=0.2696, simple_loss=0.3329, pruned_loss=0.1031, over 1329201.02 frames.], batch size: 18, lr: 1.19e-03
2022-05-26 19:17:38,805 INFO [train.py:842] (1/4) Epoch 4, batch 600, loss[loss=0.3566, simple_loss=0.402, pruned_loss=0.1556, over 6396.00 frames.], tot_loss[loss=0.2702, simple_loss=0.3334, pruned_loss=0.1035, over 1346819.59 frames.], batch size: 37, lr: 1.19e-03
2022-05-26 19:18:17,888 INFO [train.py:842] (1/4) Epoch 4, batch 650, loss[loss=0.2019, simple_loss=0.2845, pruned_loss=0.05964, over 7427.00 frames.], tot_loss[loss=0.2673, simple_loss=0.3317, pruned_loss=0.1014, over 1367925.18 frames.], batch size: 20, lr: 1.18e-03
2022-05-26 19:18:56,793 INFO [train.py:842] (1/4) Epoch 4, batch 700, loss[loss=0.2475, simple_loss=0.3236, pruned_loss=0.08565, over 7291.00 frames.], tot_loss[loss=0.2645, simple_loss=0.3297, pruned_loss=0.09966, over 1384874.85 frames.], batch size: 24, lr: 1.18e-03
2022-05-26 19:19:35,628 INFO [train.py:842] (1/4) Epoch 4, batch 750, loss[loss=0.2982, simple_loss=0.3559, pruned_loss=0.1202, over 7283.00 frames.], tot_loss[loss=0.2661, simple_loss=0.3307, pruned_loss=0.1008, over 1393251.76 frames.], batch size: 24, lr: 1.18e-03
2022-05-26 19:20:14,162 INFO [train.py:842] (1/4) Epoch 4, batch 800, loss[loss=0.2699, simple_loss=0.3307, pruned_loss=0.1045, over 7259.00 frames.], tot_loss[loss=0.2651, simple_loss=0.3302, pruned_loss=0.1, over 1397664.17 frames.], batch size: 19, lr: 1.18e-03
2022-05-26 19:20:53,471 INFO [train.py:842] (1/4) Epoch 4, batch 850, loss[loss=0.1975, simple_loss=0.2742, pruned_loss=0.06038, over 7074.00 frames.], tot_loss[loss=0.2642, simple_loss=0.3297, pruned_loss=0.09936, over 1407728.76 frames.], batch size: 18, lr: 1.18e-03
2022-05-26 19:21:32,125 INFO [train.py:842] (1/4) Epoch 4, batch 900, loss[loss=0.2805, simple_loss=0.3452, pruned_loss=0.1079, over 7450.00 frames.], tot_loss[loss=0.2618, simple_loss=0.3282, pruned_loss=0.09771, over 1415779.41 frames.], batch size: 22, lr: 1.18e-03
2022-05-26 19:22:10,976 INFO [train.py:842] (1/4) Epoch 4, batch 950, loss[loss=0.2629, simple_loss=0.3369, pruned_loss=0.09444, over 7179.00 frames.], tot_loss[loss=0.264, simple_loss=0.3298, pruned_loss=0.09911, over 1420803.74 frames.], batch size: 26, lr: 1.18e-03
2022-05-26 19:22:49,558 INFO [train.py:842] (1/4) Epoch 4, batch 1000, loss[loss=0.2375, simple_loss=0.2994, pruned_loss=0.08779, over 7268.00 frames.], tot_loss[loss=0.2643, simple_loss=0.3293, pruned_loss=0.09969, over 1421272.57 frames.], batch size: 18, lr: 1.18e-03
2022-05-26 19:23:28,206 INFO [train.py:842] (1/4) Epoch 4, batch 1050, loss[loss=0.2861, simple_loss=0.3365, pruned_loss=0.1178, over 6837.00 frames.], tot_loss[loss=0.2678, simple_loss=0.3317, pruned_loss=0.102, over 1419760.57 frames.], batch size: 31, lr: 1.18e-03
2022-05-26 19:24:06,796 INFO [train.py:842] (1/4) Epoch 4, batch 1100, loss[loss=0.2787, simple_loss=0.3399, pruned_loss=0.1088, over 7414.00 frames.], tot_loss[loss=0.2687, simple_loss=0.3326, pruned_loss=0.1024, over 1420942.78 frames.], batch size: 21, lr: 1.18e-03
2022-05-26 19:24:45,530 INFO [train.py:842] (1/4) Epoch 4, batch 1150, loss[loss=0.2694, simple_loss=0.3509, pruned_loss=0.09396, over 7325.00 frames.], tot_loss[loss=0.2692, simple_loss=0.3338, pruned_loss=0.1023, over 1418048.19 frames.], batch size: 21, lr: 1.17e-03
2022-05-26 19:25:24,176 INFO [train.py:842] (1/4) Epoch 4, batch 1200, loss[loss=0.286, simple_loss=0.3507, pruned_loss=0.1107, over 7321.00 frames.], tot_loss[loss=0.2706, simple_loss=0.335, pruned_loss=0.1031, over 1415981.38 frames.], batch size: 21, lr: 1.17e-03
2022-05-26 19:26:03,119 INFO [train.py:842] (1/4) Epoch 4, batch 1250, loss[loss=0.2461, simple_loss=0.2956, pruned_loss=0.09827, over 6819.00 frames.], tot_loss[loss=0.2698, simple_loss=0.3341, pruned_loss=0.1027, over 1414308.25 frames.], batch size: 15, lr: 1.17e-03
2022-05-26 19:26:45,114 INFO [train.py:842] (1/4) Epoch 4, batch 1300, loss[loss=0.2921, simple_loss=0.3495, pruned_loss=0.1174, over 7193.00 frames.], tot_loss[loss=0.2687, simple_loss=0.3331, pruned_loss=0.1021, over 1416793.58 frames.], batch size: 23, lr: 1.17e-03
2022-05-26 19:27:23,951 INFO [train.py:842] (1/4) Epoch 4, batch 1350, loss[loss=0.269, simple_loss=0.3376, pruned_loss=0.1002, over 7220.00 frames.], tot_loss[loss=0.2681, simple_loss=0.3327, pruned_loss=0.1017, over 1415941.35 frames.], batch size: 20, lr: 1.17e-03
2022-05-26 19:28:02,845 INFO [train.py:842] (1/4) Epoch 4, batch 1400, loss[loss=0.3157, simple_loss=0.3782, pruned_loss=0.1266, over 7218.00 frames.], tot_loss[loss=0.2677, simple_loss=0.3321, pruned_loss=0.1016, over 1419268.27 frames.], batch size: 22, lr: 1.17e-03
2022-05-26 19:28:42,067 INFO [train.py:842] (1/4) Epoch 4, batch 1450, loss[loss=0.247, simple_loss=0.327, pruned_loss=0.08355, over 7305.00 frames.], tot_loss[loss=0.2673, simple_loss=0.3323, pruned_loss=0.1012, over 1421889.61 frames.], batch size: 24, lr: 1.17e-03
2022-05-26 19:29:23,406 INFO [train.py:842] (1/4) Epoch 4, batch 1500, loss[loss=0.2777, simple_loss=0.3544, pruned_loss=0.1005, over 7279.00 frames.], tot_loss[loss=0.2668, simple_loss=0.3317, pruned_loss=0.101, over 1418206.34 frames.], batch size: 24, lr: 1.17e-03
2022-05-26 19:30:02,815 INFO [train.py:842] (1/4) Epoch 4, batch 1550, loss[loss=0.389, simple_loss=0.4237, pruned_loss=0.1771, over 4989.00 frames.], tot_loss[loss=0.2659, simple_loss=0.331, pruned_loss=0.1004, over 1417422.18 frames.], batch size: 52, lr: 1.17e-03
2022-05-26 19:30:42,104 INFO [train.py:842] (1/4) Epoch 4, batch 1600, loss[loss=0.3222, simple_loss=0.3819, pruned_loss=0.1313, over 7304.00 frames.], tot_loss[loss=0.2663, simple_loss=0.3315, pruned_loss=0.1005, over 1413765.12 frames.], batch size: 25, lr: 1.17e-03
2022-05-26 19:31:21,044 INFO [train.py:842] (1/4) Epoch 4, batch 1650, loss[loss=0.2403, simple_loss=0.3137, pruned_loss=0.08342, over 7322.00 frames.], tot_loss[loss=0.2663, simple_loss=0.3315, pruned_loss=0.1006, over 1415991.55 frames.], batch size: 20, lr: 1.17e-03
2022-05-26 19:31:59,646 INFO [train.py:842] (1/4) Epoch 4, batch 1700, loss[loss=0.2927, simple_loss=0.3591, pruned_loss=0.1131, over 7150.00 frames.], tot_loss[loss=0.2671, simple_loss=0.3326, pruned_loss=0.1009, over 1419750.15 frames.], batch size: 20, lr: 1.16e-03
2022-05-26 19:32:38,147 INFO [train.py:842] (1/4) Epoch 4, batch 1750, loss[loss=0.2506, simple_loss=0.3213, pruned_loss=0.08993, over 7209.00 frames.], tot_loss[loss=0.2659, simple_loss=0.3316, pruned_loss=0.1, over 1420206.97 frames.], batch size: 22, lr: 1.16e-03
2022-05-26 19:33:16,539 INFO [train.py:842] (1/4) Epoch 4, batch 1800, loss[loss=0.2525, simple_loss=0.3292, pruned_loss=0.08786, over 7229.00 frames.], tot_loss[loss=0.2671, simple_loss=0.333, pruned_loss=0.1006, over 1421810.36 frames.], batch size: 21, lr: 1.16e-03
2022-05-26 19:33:55,179 INFO [train.py:842] (1/4) Epoch 4, batch 1850, loss[loss=0.2329, simple_loss=0.3051, pruned_loss=0.08037, over 7127.00 frames.], tot_loss[loss=0.2694, simple_loss=0.3347, pruned_loss=0.1021, over 1420148.13 frames.], batch size: 17, lr: 1.16e-03
2022-05-26 19:34:33,650 INFO [train.py:842] (1/4) Epoch 4, batch 1900, loss[loss=0.2448, simple_loss=0.3137, pruned_loss=0.08798, over 7161.00 frames.], tot_loss[loss=0.2686, simple_loss=0.3339, pruned_loss=0.1016, over 1423053.66 frames.], batch size: 19, lr: 1.16e-03
2022-05-26 19:35:12,557 INFO [train.py:842] (1/4) Epoch 4, batch 1950, loss[loss=0.287, simple_loss=0.3365, pruned_loss=0.1188, over 6389.00 frames.], tot_loss[loss=0.2673, simple_loss=0.333, pruned_loss=0.1008, over 1427689.73 frames.], batch size: 37, lr: 1.16e-03
2022-05-26 19:35:51,048 INFO [train.py:842] (1/4) Epoch 4, batch 2000, loss[loss=0.2517, simple_loss=0.3296, pruned_loss=0.0869, over 7113.00 frames.], tot_loss[loss=0.268, simple_loss=0.3335, pruned_loss=0.1013, over 1424611.43 frames.], batch size: 21, lr: 1.16e-03
2022-05-26 19:36:29,972 INFO [train.py:842] (1/4) Epoch 4, batch 2050, loss[loss=0.2801, simple_loss=0.3357, pruned_loss=0.1122, over 6717.00 frames.], tot_loss[loss=0.268, simple_loss=0.3334, pruned_loss=0.1013, over 1420837.86 frames.], batch size: 31, lr: 1.16e-03
2022-05-26 19:37:08,666 INFO [train.py:842] (1/4) Epoch 4, batch 2100, loss[loss=0.2188, simple_loss=0.3022, pruned_loss=0.06763, over 7311.00 frames.], tot_loss[loss=0.2669, simple_loss=0.3327, pruned_loss=0.1005, over 1419662.39 frames.], batch size: 21, lr: 1.16e-03
2022-05-26 19:37:47,569 INFO [train.py:842] (1/4) Epoch 4, batch 2150, loss[loss=0.2433, simple_loss=0.3147, pruned_loss=0.086, over 7332.00 frames.], tot_loss[loss=0.2658, simple_loss=0.3316, pruned_loss=0.09995, over 1422034.06 frames.], batch size: 22, lr: 1.16e-03
2022-05-26 19:38:26,053 INFO [train.py:842] (1/4) Epoch 4, batch 2200, loss[loss=0.2534, simple_loss=0.3289, pruned_loss=0.08892, over 7217.00 frames.], tot_loss[loss=0.2642, simple_loss=0.3303, pruned_loss=0.09905, over 1424902.05 frames.], batch size: 21, lr: 1.15e-03
2022-05-26 19:39:04,828 INFO [train.py:842] (1/4) Epoch 4, batch 2250, loss[loss=0.314, simple_loss=0.3603, pruned_loss=0.1338, over 4746.00 frames.], tot_loss[loss=0.2619, simple_loss=0.3294, pruned_loss=0.09725, over 1426542.85 frames.], batch size: 52, lr: 1.15e-03
2022-05-26 19:39:43,435 INFO [train.py:842] (1/4) Epoch 4, batch 2300, loss[loss=0.3185, simple_loss=0.3679, pruned_loss=0.1346, over 7160.00 frames.], tot_loss[loss=0.2612, simple_loss=0.3289, pruned_loss=0.09679, over 1429371.87 frames.], batch size: 19, lr: 1.15e-03
2022-05-26 19:40:22,291 INFO [train.py:842] (1/4) Epoch 4, batch 2350, loss[loss=0.2435, simple_loss=0.3086, pruned_loss=0.08922, over 7329.00 frames.], tot_loss[loss=0.2609, simple_loss=0.3283, pruned_loss=0.09673, over 1431172.08 frames.], batch size: 20, lr: 1.15e-03
2022-05-26 19:41:00,756 INFO [train.py:842] (1/4) Epoch 4, batch 2400, loss[loss=0.2482, simple_loss=0.3137, pruned_loss=0.09138, over 7297.00 frames.], tot_loss[loss=0.263, simple_loss=0.3304, pruned_loss=0.09781, over 1433785.63 frames.], batch size: 25, lr: 1.15e-03
2022-05-26 19:41:39,550 INFO [train.py:842] (1/4) Epoch 4, batch 2450, loss[loss=0.2345, simple_loss=0.3187, pruned_loss=0.07518, over 7391.00 frames.], tot_loss[loss=0.2618, simple_loss=0.3293, pruned_loss=0.09713, over 1436492.58 frames.], batch size: 23, lr: 1.15e-03
2022-05-26 19:42:18,062 INFO [train.py:842] (1/4) Epoch 4, batch 2500, loss[loss=0.2784, simple_loss=0.3334, pruned_loss=0.1117, over 7157.00 frames.], tot_loss[loss=0.2609, simple_loss=0.3286, pruned_loss=0.09655, over 1434333.84 frames.], batch size: 19, lr: 1.15e-03
2022-05-26 19:42:56,656 INFO [train.py:842] (1/4) Epoch 4, batch 2550, loss[loss=0.2169, simple_loss=0.2891, pruned_loss=0.07238, over 7409.00 frames.], tot_loss[loss=0.2616, simple_loss=0.3289, pruned_loss=0.09712, over 1426688.92 frames.], batch size: 18, lr: 1.15e-03
2022-05-26 19:43:35,180 INFO [train.py:842] (1/4) Epoch 4, batch 2600, loss[loss=0.2088, simple_loss=0.2905, pruned_loss=0.06353, over 7223.00 frames.], tot_loss[loss=0.2615, simple_loss=0.3289, pruned_loss=0.09703, over 1426731.67 frames.], batch size: 20, lr: 1.15e-03
2022-05-26 19:44:13,867 INFO [train.py:842] (1/4) Epoch 4, batch 2650, loss[loss=0.3047, simple_loss=0.3373, pruned_loss=0.136, over 7000.00 frames.], tot_loss[loss=0.2627, simple_loss=0.3298, pruned_loss=0.09775, over 1419475.74 frames.], batch size: 16, lr: 1.15e-03
2022-05-26 19:44:52,274 INFO [train.py:842] (1/4) Epoch 4, batch 2700, loss[loss=0.2356, simple_loss=0.2913, pruned_loss=0.08993, over 6787.00 frames.], tot_loss[loss=0.2643, simple_loss=0.3314, pruned_loss=0.09858, over 1418052.94 frames.], batch size: 15, lr: 1.15e-03
2022-05-26 19:45:31,378 INFO [train.py:842] (1/4) Epoch 4, batch 2750, loss[loss=0.2315, simple_loss=0.3162, pruned_loss=0.07343, over 7267.00 frames.], tot_loss[loss=0.2652, simple_loss=0.3321, pruned_loss=0.09914, over 1421325.73 frames.], batch size: 19, lr: 1.14e-03
2022-05-26 19:46:09,937 INFO [train.py:842] (1/4) Epoch 4, batch 2800, loss[loss=0.321, simple_loss=0.3636, pruned_loss=0.1392, over 7165.00 frames.], tot_loss[loss=0.2656, simple_loss=0.3322, pruned_loss=0.09951, over 1424292.05 frames.], batch size: 19, lr: 1.14e-03
2022-05-26 19:46:48,829 INFO [train.py:842] (1/4) Epoch 4, batch 2850, loss[loss=0.2627, simple_loss=0.3273, pruned_loss=0.09908, over 4869.00 frames.], tot_loss[loss=0.2651, simple_loss=0.3315, pruned_loss=0.09931, over 1422881.18 frames.], batch size: 52, lr: 1.14e-03
2022-05-26 19:47:27,312 INFO [train.py:842] (1/4) Epoch 4, batch 2900, loss[loss=0.299, simple_loss=0.366, pruned_loss=0.116, over 6782.00 frames.], tot_loss[loss=0.264, simple_loss=0.3308, pruned_loss=0.09862, over 1423491.71 frames.], batch size: 31, lr: 1.14e-03
2022-05-26 19:48:06,168 INFO [train.py:842] (1/4) Epoch 4, batch 2950, loss[loss=0.2568, simple_loss=0.3295, pruned_loss=0.09202, over 7072.00 frames.], tot_loss[loss=0.2644, simple_loss=0.3308, pruned_loss=0.09903, over 1427247.82 frames.], batch size: 28, lr: 1.14e-03
2022-05-26 19:48:45,195 INFO [train.py:842] (1/4) Epoch 4, batch 3000, loss[loss=0.3145, simple_loss=0.3597, pruned_loss=0.1346, over 7144.00 frames.], tot_loss[loss=0.2647, simple_loss=0.3311, pruned_loss=0.09911, over 1425519.73 frames.], batch size: 20, lr: 1.14e-03
2022-05-26 19:48:45,197 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 19:48:54,321 INFO [train.py:871] (1/4) Epoch 4, validation: loss=0.2021, simple_loss=0.3, pruned_loss=0.05209, over 868885.00 frames.
2022-05-26 19:49:33,155 INFO [train.py:842] (1/4) Epoch 4, batch 3050, loss[loss=0.2499, simple_loss=0.3258, pruned_loss=0.08701, over 7119.00 frames.], tot_loss[loss=0.2654, simple_loss=0.3312, pruned_loss=0.09979, over 1421673.07 frames.], batch size: 21, lr: 1.14e-03
2022-05-26 19:50:11,911 INFO [train.py:842] (1/4) Epoch 4, batch 3100, loss[loss=0.2943, simple_loss=0.3725, pruned_loss=0.1081, over 7284.00 frames.], tot_loss[loss=0.2655, simple_loss=0.3311, pruned_loss=0.09996, over 1418670.28 frames.], batch size: 24, lr: 1.14e-03
2022-05-26 19:50:51,322 INFO [train.py:842] (1/4) Epoch 4, batch 3150, loss[loss=0.343, simple_loss=0.3958, pruned_loss=0.1451, over 7277.00 frames.], tot_loss[loss=0.2643, simple_loss=0.3301, pruned_loss=0.09925, over 1423272.61 frames.], batch size: 25, lr: 1.14e-03
2022-05-26 19:51:30,072 INFO [train.py:842] (1/4) Epoch 4, batch 3200, loss[loss=0.2046, simple_loss=0.2781, pruned_loss=0.06554, over 7066.00 frames.], tot_loss[loss=0.2633, simple_loss=0.3291, pruned_loss=0.09871, over 1424265.91 frames.], batch size: 18, lr: 1.14e-03
2022-05-26 19:52:09,194 INFO [train.py:842] (1/4) Epoch 4, batch 3250, loss[loss=0.3269, simple_loss=0.3713, pruned_loss=0.1413, over 7254.00 frames.], tot_loss[loss=0.2638, simple_loss=0.3291, pruned_loss=0.09921, over 1424480.26 frames.], batch size: 19, lr: 1.14e-03
2022-05-26 19:52:47,482 INFO [train.py:842] (1/4) Epoch 4, batch 3300, loss[loss=0.2781, simple_loss=0.3355, pruned_loss=0.1104, over 7211.00 frames.], tot_loss[loss=0.2635, simple_loss=0.3292, pruned_loss=0.09894, over 1423656.20 frames.], batch size: 23, lr: 1.13e-03
2022-05-26 19:53:26,307 INFO [train.py:842] (1/4) Epoch 4, batch 3350, loss[loss=0.3047, simple_loss=0.3614, pruned_loss=0.124, over 6157.00 frames.], tot_loss[loss=0.2628, simple_loss=0.3287, pruned_loss=0.09848, over 1422013.15 frames.], batch size: 37, lr: 1.13e-03
2022-05-26 19:54:04,823 INFO [train.py:842] (1/4) Epoch 4, batch 3400, loss[loss=0.2133, simple_loss=0.2777, pruned_loss=0.07444, over 7007.00 frames.], tot_loss[loss=0.2639, simple_loss=0.3294, pruned_loss=0.09924, over 1421608.45 frames.], batch size: 16, lr: 1.13e-03
2022-05-26 19:54:43,735 INFO [train.py:842] (1/4) Epoch 4, batch 3450, loss[loss=0.206, simple_loss=0.2862, pruned_loss=0.0629, over 7166.00 frames.], tot_loss[loss=0.2623, simple_loss=0.3281, pruned_loss=0.09821, over 1426541.21 frames.], batch size: 18, lr: 1.13e-03
2022-05-26 19:55:22,252 INFO [train.py:842] (1/4) Epoch 4, batch 3500, loss[loss=0.3001, simple_loss=0.3555, pruned_loss=0.1223, over 7374.00 frames.], tot_loss[loss=0.2613, simple_loss=0.3274, pruned_loss=0.0976, over 1428352.83 frames.], batch size: 23, lr: 1.13e-03
2022-05-26 19:56:01,286 INFO [train.py:842] (1/4) Epoch 4, batch 3550, loss[loss=0.2691, simple_loss=0.3414, pruned_loss=0.09837, over 7313.00 frames.], tot_loss[loss=0.2622, simple_loss=0.3279, pruned_loss=0.09827, over 1429356.64 frames.], batch size: 24, lr: 1.13e-03
2022-05-26 19:56:39,768 INFO [train.py:842] (1/4) Epoch 4, batch 3600, loss[loss=0.2446, simple_loss=0.3099, pruned_loss=0.0897, over 6993.00 frames.], tot_loss[loss=0.2628, simple_loss=0.3285, pruned_loss=0.09853, over 1428021.45 frames.], batch size: 16, lr: 1.13e-03
2022-05-26 19:57:18,390 INFO [train.py:842] (1/4) Epoch 4, batch 3650, loss[loss=0.2118, simple_loss=0.2822, pruned_loss=0.07069, over 7132.00 frames.], tot_loss[loss=0.2627, simple_loss=0.3282, pruned_loss=0.09859, over 1428009.97 frames.], batch size: 17, lr: 1.13e-03
2022-05-26 19:57:56,885 INFO [train.py:842] (1/4) Epoch 4, batch 3700, loss[loss=0.2301, simple_loss=0.2966, pruned_loss=0.08181, over 7013.00 frames.], tot_loss[loss=0.2634, simple_loss=0.3286, pruned_loss=0.09908, over 1427460.20 frames.], batch size: 16, lr: 1.13e-03
2022-05-26 19:58:35,882 INFO [train.py:842] (1/4) Epoch 4, batch 3750, loss[loss=0.2961, simple_loss=0.354, pruned_loss=0.1191, over 7436.00 frames.], tot_loss[loss=0.2613, simple_loss=0.3267, pruned_loss=0.09795, over 1425550.60 frames.], batch size: 20, lr: 1.13e-03
2022-05-26 19:59:14,459 INFO [train.py:842] (1/4) Epoch 4, batch 3800, loss[loss=0.3477, simple_loss=0.369, pruned_loss=0.1632, over 7062.00 frames.], tot_loss[loss=0.2625, simple_loss=0.3275, pruned_loss=0.09874, over 1421946.93 frames.], batch size: 18, lr: 1.13e-03
2022-05-26 19:59:53,452 INFO [train.py:842] (1/4) Epoch 4, batch 3850, loss[loss=0.198, simple_loss=0.2764, pruned_loss=0.05983, over 7391.00 frames.], tot_loss[loss=0.2611, simple_loss=0.3268, pruned_loss=0.09772, over 1425303.49 frames.], batch size: 18, lr: 1.12e-03
2022-05-26 20:00:32,075 INFO [train.py:842] (1/4) Epoch 4, batch 3900, loss[loss=0.4036, simple_loss=0.4348, pruned_loss=0.1862, over 5218.00 frames.], tot_loss[loss=0.2624, simple_loss=0.3279, pruned_loss=0.09846, over 1427033.35 frames.], batch size: 52, lr: 1.12e-03
2022-05-26 20:01:10,952 INFO [train.py:842] (1/4) Epoch 4, batch 3950, loss[loss=0.2231, simple_loss=0.2848, pruned_loss=0.08072, over 6760.00 frames.], tot_loss[loss=0.2614, simple_loss=0.3267, pruned_loss=0.09806, over 1425956.58 frames.], batch size: 15, lr: 1.12e-03
2022-05-26 20:01:49,427 INFO [train.py:842] (1/4) Epoch 4, batch 4000, loss[loss=0.3328, simple_loss=0.3847, pruned_loss=0.1404, over 7222.00 frames.], tot_loss[loss=0.2621, simple_loss=0.3276, pruned_loss=0.09833, over 1417368.94 frames.], batch size: 21, lr: 1.12e-03
2022-05-26 20:02:28,194 INFO [train.py:842] (1/4) Epoch 4, batch 4050, loss[loss=0.2506, simple_loss=0.3299, pruned_loss=0.08564, over 7417.00 frames.], tot_loss[loss=0.2611, simple_loss=0.3271, pruned_loss=0.09756, over 1419072.92 frames.], batch size: 21, lr: 1.12e-03
2022-05-26 20:03:06,976 INFO [train.py:842] (1/4) Epoch 4, batch 4100, loss[loss=0.2385, simple_loss=0.295, pruned_loss=0.091, over 7407.00 frames.], tot_loss[loss=0.2625, simple_loss=0.3282, pruned_loss=0.09841, over 1423655.39 frames.], batch size: 18, lr: 1.12e-03
2022-05-26 20:03:45,843 INFO [train.py:842] (1/4) Epoch 4, batch 4150, loss[loss=0.2671, simple_loss=0.3124, pruned_loss=0.111, over 7221.00 frames.], tot_loss[loss=0.2622, simple_loss=0.3276, pruned_loss=0.09838, over 1426317.74 frames.], batch size: 16, lr: 1.12e-03
2022-05-26 20:04:24,275 INFO [train.py:842] (1/4) Epoch 4, batch 4200, loss[loss=0.2428, simple_loss=0.3029, pruned_loss=0.09134, over 7273.00 frames.], tot_loss[loss=0.2618, simple_loss=0.3279, pruned_loss=0.09779, over 1426545.26 frames.], batch size: 17, lr: 1.12e-03
2022-05-26 20:05:03,078 INFO [train.py:842] (1/4) Epoch 4, batch 4250, loss[loss=0.2236, simple_loss=0.3043, pruned_loss=0.07143, over 7238.00 frames.], tot_loss[loss=0.2624, simple_loss=0.3287, pruned_loss=0.09805, over 1424283.22 frames.], batch size: 20, lr: 1.12e-03
2022-05-26 20:05:41,574 INFO [train.py:842] (1/4) Epoch 4, batch 4300, loss[loss=0.2322, simple_loss=0.3005, pruned_loss=0.0819, over 7258.00 frames.], tot_loss[loss=0.2608, simple_loss=0.3277, pruned_loss=0.09688, over 1422778.04 frames.], batch size: 19, lr: 1.12e-03
2022-05-26 20:06:20,306 INFO [train.py:842] (1/4) Epoch 4, batch 4350, loss[loss=0.3023, simple_loss=0.3528, pruned_loss=0.1259, over 7217.00 frames.], tot_loss[loss=0.2597, simple_loss=0.3271, pruned_loss=0.09617, over 1421675.04 frames.], batch size: 23, lr: 1.12e-03
2022-05-26 20:06:58,788 INFO [train.py:842] (1/4) Epoch 4, batch 4400, loss[loss=0.2501, simple_loss=0.3306, pruned_loss=0.08474, over 7233.00 frames.], tot_loss[loss=0.2594, simple_loss=0.3272, pruned_loss=0.09582, over 1421956.85 frames.], batch size: 20, lr: 1.12e-03
2022-05-26 20:07:40,547 INFO [train.py:842] (1/4) Epoch 4, batch 4450, loss[loss=0.2235, simple_loss=0.2911, pruned_loss=0.078, over 7364.00 frames.], tot_loss[loss=0.2595, simple_loss=0.3264, pruned_loss=0.09634, over 1424299.61 frames.], batch size: 19, lr: 1.11e-03
2022-05-26 20:08:19,166 INFO [train.py:842] (1/4) Epoch 4, batch 4500, loss[loss=0.266, simple_loss=0.3439, pruned_loss=0.094, over 7139.00 frames.], tot_loss[loss=0.2578, simple_loss=0.3252, pruned_loss=0.09516, over 1425989.81 frames.], batch size: 21, lr: 1.11e-03
2022-05-26 20:08:58,295 INFO [train.py:842] (1/4) Epoch 4, batch 4550, loss[loss=0.2377, simple_loss=0.3, pruned_loss=0.08773, over 7430.00 frames.], tot_loss[loss=0.2572, simple_loss=0.3245, pruned_loss=0.09494, over 1424180.68 frames.], batch size: 18, lr: 1.11e-03
2022-05-26 20:09:36,942 INFO [train.py:842] (1/4) Epoch 4, batch 4600, loss[loss=0.2563, simple_loss=0.3399, pruned_loss=0.08636, over 7402.00 frames.], tot_loss[loss=0.258, simple_loss=0.3247, pruned_loss=0.09568, over 1424899.51 frames.], batch size: 21, lr: 1.11e-03
2022-05-26 20:10:15,674 INFO [train.py:842] (1/4) Epoch 4, batch 4650, loss[loss=0.2695, simple_loss=0.3341, pruned_loss=0.1025, over 7409.00 frames.], tot_loss[loss=0.257, simple_loss=0.324, pruned_loss=0.09505, over 1424657.72 frames.], batch size: 21, lr: 1.11e-03
2022-05-26 20:10:54,165 INFO [train.py:842] (1/4) Epoch 4, batch 4700, loss[loss=0.2743, simple_loss=0.3439, pruned_loss=0.1024, over 6772.00 frames.], tot_loss[loss=0.2597, simple_loss=0.3258, pruned_loss=0.09676, over 1424454.83 frames.], batch size: 31, lr: 1.11e-03
2022-05-26 20:11:33,091 INFO [train.py:842] (1/4) Epoch 4, batch 4750, loss[loss=0.2602, simple_loss=0.3389, pruned_loss=0.09076, over 7119.00 frames.], tot_loss[loss=0.2577, simple_loss=0.3244, pruned_loss=0.09546, over 1425898.46 frames.], batch size: 21, lr: 1.11e-03
2022-05-26 20:12:11,638 INFO [train.py:842] (1/4) Epoch 4, batch 4800, loss[loss=0.2584, simple_loss=0.3001, pruned_loss=0.1084, over 7133.00 frames.], tot_loss[loss=0.2573, simple_loss=0.3247, pruned_loss=0.09496, over 1425760.33 frames.], batch size: 17, lr: 1.11e-03
2022-05-26 20:12:51,095 INFO [train.py:842] (1/4) Epoch 4, batch 4850, loss[loss=0.2318, simple_loss=0.2885, pruned_loss=0.08758, over 6807.00 frames.], tot_loss[loss=0.2554, simple_loss=0.3226, pruned_loss=0.09409, over 1426407.84 frames.], batch size: 15, lr: 1.11e-03
2022-05-26 20:13:29,712 INFO [train.py:842] (1/4) Epoch 4, batch 4900, loss[loss=0.28, simple_loss=0.349, pruned_loss=0.1055, over 7289.00 frames.], tot_loss[loss=0.2572, simple_loss=0.3238, pruned_loss=0.09536, over 1425539.80 frames.], batch size: 24, lr: 1.11e-03
2022-05-26 20:14:08,460 INFO [train.py:842] (1/4) Epoch 4, batch 4950, loss[loss=0.2653, simple_loss=0.3423, pruned_loss=0.09415, over 7109.00 frames.], tot_loss[loss=0.2575, simple_loss=0.3243, pruned_loss=0.09542, over 1424884.80 frames.], batch size: 21, lr: 1.11e-03
2022-05-26 20:14:46,911 INFO [train.py:842] (1/4) Epoch 4, batch 5000, loss[loss=0.2378, simple_loss=0.3132, pruned_loss=0.08122, over 7325.00 frames.], tot_loss[loss=0.2601, simple_loss=0.3264, pruned_loss=0.09691, over 1424185.96 frames.], batch size: 20, lr: 1.11e-03
2022-05-26 20:15:25,474 INFO [train.py:842] (1/4) Epoch 4, batch 5050, loss[loss=0.2773, simple_loss=0.3536, pruned_loss=0.1006, over 7156.00 frames.], tot_loss[loss=0.2617, simple_loss=0.3281, pruned_loss=0.09765, over 1424522.80 frames.], batch size: 26, lr: 1.10e-03
2022-05-26 20:16:04,087 INFO [train.py:842] (1/4) Epoch 4, batch 5100, loss[loss=0.2808, simple_loss=0.349, pruned_loss=0.1063, over 7086.00 frames.], tot_loss[loss=0.2604, simple_loss=0.3274, pruned_loss=0.09675, over 1422676.11 frames.], batch size: 28, lr: 1.10e-03
2022-05-26 20:16:43,109 INFO [train.py:842] (1/4) Epoch 4, batch 5150, loss[loss=0.1937, simple_loss=0.2656, pruned_loss=0.06087, over 7300.00 frames.], tot_loss[loss=0.2573, simple_loss=0.3254, pruned_loss=0.09464, over 1427645.26 frames.], batch size: 17, lr: 1.10e-03
2022-05-26 20:17:21,940 INFO [train.py:842] (1/4) Epoch 4, batch 5200, loss[loss=0.2439, simple_loss=0.3122, pruned_loss=0.08779, over 7362.00 frames.], tot_loss[loss=0.2581, simple_loss=0.3263, pruned_loss=0.09497, over 1428194.89 frames.], batch size: 19, lr: 1.10e-03
2022-05-26 20:18:00,745 INFO [train.py:842] (1/4) Epoch 4, batch 5250, loss[loss=0.2546, simple_loss=0.3307, pruned_loss=0.0893, over 7115.00 frames.], tot_loss[loss=0.2607, simple_loss=0.3278, pruned_loss=0.09681, over 1426692.70 frames.], batch size: 21, lr: 1.10e-03
2022-05-26 20:18:39,346 INFO [train.py:842] (1/4) Epoch 4, batch 5300, loss[loss=0.2116, simple_loss=0.2841, pruned_loss=0.06953, over 7064.00 frames.], tot_loss[loss=0.2591, simple_loss=0.3265, pruned_loss=0.09587, over 1429526.35 frames.], batch size: 18, lr: 1.10e-03
2022-05-26 20:19:18,363 INFO [train.py:842] (1/4) Epoch 4, batch 5350, loss[loss=0.235, simple_loss=0.2998, pruned_loss=0.08513, over 7274.00 frames.], tot_loss[loss=0.2591, simple_loss=0.3261, pruned_loss=0.096, over 1431778.90 frames.], batch size: 17, lr: 1.10e-03
2022-05-26 20:19:56,807 INFO [train.py:842] (1/4) Epoch 4, batch 5400, loss[loss=0.2443, simple_loss=0.3116, pruned_loss=0.08857, over 7287.00 frames.], tot_loss[loss=0.2593, simple_loss=0.3265, pruned_loss=0.09612, over 1431245.90 frames.], batch size: 17, lr: 1.10e-03
2022-05-26 20:20:35,588 INFO [train.py:842] (1/4) Epoch 4, batch 5450, loss[loss=0.2975, simple_loss=0.3612, pruned_loss=0.1169, over 7182.00 frames.], tot_loss[loss=0.2599, simple_loss=0.3267, pruned_loss=0.09657, over 1430342.97 frames.], batch size: 23, lr: 1.10e-03
2022-05-26 20:21:14,031 INFO [train.py:842] (1/4) Epoch 4, batch 5500, loss[loss=0.2397, simple_loss=0.3117, pruned_loss=0.08385, over 7156.00 frames.], tot_loss[loss=0.2607, simple_loss=0.3275, pruned_loss=0.09694, over 1428166.38 frames.], batch size: 26, lr: 1.10e-03
2022-05-26 20:21:53,000 INFO [train.py:842] (1/4) Epoch 4, batch 5550, loss[loss=0.3199, simple_loss=0.3691, pruned_loss=0.1353, over 6875.00 frames.], tot_loss[loss=0.259, simple_loss=0.3256, pruned_loss=0.09613, over 1422380.42 frames.], batch size: 31, lr: 1.10e-03
2022-05-26 20:22:31,508 INFO [train.py:842] (1/4) Epoch 4, batch 5600, loss[loss=0.3223, simple_loss=0.3512, pruned_loss=0.1467, over 7264.00 frames.], tot_loss[loss=0.259, simple_loss=0.3256, pruned_loss=0.09623, over 1425689.91 frames.], batch size: 18, lr: 1.10e-03
2022-05-26 20:23:10,156 INFO [train.py:842] (1/4) Epoch 4, batch 5650, loss[loss=0.3118, simple_loss=0.3807, pruned_loss=0.1214, over 7186.00 frames.], tot_loss[loss=0.2595, simple_loss=0.3262, pruned_loss=0.09644, over 1421014.82 frames.], batch size: 23, lr: 1.09e-03
2022-05-26 20:23:48,771 INFO [train.py:842] (1/4) Epoch 4, batch 5700, loss[loss=0.2445, simple_loss=0.3208, pruned_loss=0.08405, over 7236.00 frames.], tot_loss[loss=0.2606, simple_loss=0.3272, pruned_loss=0.09698, over 1421560.90 frames.], batch size: 20, lr: 1.09e-03
2022-05-26 20:24:27,446 INFO [train.py:842] (1/4) Epoch 4, batch 5750, loss[loss=0.3604, simple_loss=0.3972, pruned_loss=0.1618, over 7283.00 frames.], tot_loss[loss=0.2606, simple_loss=0.3273, pruned_loss=0.09701, over 1420861.19 frames.], batch size: 25, lr: 1.09e-03
2022-05-26 20:25:06,085 INFO [train.py:842] (1/4) Epoch 4, batch 5800, loss[loss=0.2439, simple_loss=0.3291, pruned_loss=0.07936, over 7319.00 frames.], tot_loss[loss=0.2619, simple_loss=0.3281, pruned_loss=0.09787, over 1420726.97 frames.], batch size: 21, lr: 1.09e-03
2022-05-26 20:25:44,933 INFO [train.py:842] (1/4) Epoch 4, batch 5850, loss[loss=0.2618, simple_loss=0.3329, pruned_loss=0.09539, over 6304.00 frames.], tot_loss[loss=0.2614, simple_loss=0.3275, pruned_loss=0.09766, over 1417042.73 frames.], batch size: 37, lr: 1.09e-03
2022-05-26 20:26:23,775 INFO [train.py:842] (1/4) Epoch 4, batch 5900, loss[loss=0.2368, simple_loss=0.3202, pruned_loss=0.07674, over 7322.00 frames.], tot_loss[loss=0.2589, simple_loss=0.3259, pruned_loss=0.0959, over 1422821.15 frames.], batch size: 21, lr: 1.09e-03
2022-05-26 20:27:02,270 INFO [train.py:842] (1/4) Epoch 4, batch 5950, loss[loss=0.2685, simple_loss=0.332, pruned_loss=0.1025, over 7168.00 frames.], tot_loss[loss=0.2611, simple_loss=0.3277, pruned_loss=0.09727, over 1421324.90 frames.], batch size: 19, lr: 1.09e-03
2022-05-26 20:27:41,337 INFO [train.py:842] (1/4) Epoch 4, batch 6000, loss[loss=0.2976, simple_loss=0.3577, pruned_loss=0.1187, over 7202.00 frames.], tot_loss[loss=0.261, simple_loss=0.3273, pruned_loss=0.09737, over 1420828.85 frames.], batch size: 23, lr: 1.09e-03
2022-05-26 20:27:41,338 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 20:27:50,628 INFO [train.py:871] (1/4) Epoch 4, validation: loss=0.1997, simple_loss=0.2983, pruned_loss=0.05051, over 868885.00 frames.
2022-05-26 20:28:29,524 INFO [train.py:842] (1/4) Epoch 4, batch 6050, loss[loss=0.3623, simple_loss=0.4191, pruned_loss=0.1527, over 7227.00 frames.], tot_loss[loss=0.2594, simple_loss=0.3264, pruned_loss=0.09617, over 1416415.83 frames.], batch size: 20, lr: 1.09e-03
2022-05-26 20:29:08,214 INFO [train.py:842] (1/4) Epoch 4, batch 6100, loss[loss=0.2871, simple_loss=0.3551, pruned_loss=0.1095, over 7125.00 frames.], tot_loss[loss=0.2594, simple_loss=0.3265, pruned_loss=0.09611, over 1413251.05 frames.], batch size: 21, lr: 1.09e-03
2022-05-26 20:29:47,421 INFO [train.py:842] (1/4) Epoch 4, batch 6150, loss[loss=0.2341, simple_loss=0.3203, pruned_loss=0.07396, over 7311.00 frames.], tot_loss[loss=0.259, simple_loss=0.3261, pruned_loss=0.0959, over 1418246.87 frames.], batch size: 25, lr: 1.09e-03
2022-05-26 20:30:26,272 INFO [train.py:842] (1/4) Epoch 4, batch 6200, loss[loss=0.2106, simple_loss=0.2871, pruned_loss=0.06707, over 7151.00 frames.], tot_loss[loss=0.2563, simple_loss=0.3236, pruned_loss=0.09448, over 1420195.58 frames.], batch size: 17, lr: 1.09e-03
2022-05-26 20:31:05,444 INFO [train.py:842] (1/4) Epoch 4, batch 6250, loss[loss=0.2226, simple_loss=0.2942, pruned_loss=0.07555, over 7428.00 frames.], tot_loss[loss=0.2554, simple_loss=0.323, pruned_loss=0.09384, over 1418401.81 frames.], batch size: 20, lr: 1.08e-03
2022-05-26 20:31:44,044 INFO [train.py:842] (1/4) Epoch 4, batch 6300, loss[loss=0.2624, simple_loss=0.3277, pruned_loss=0.09853, over 7324.00 frames.], tot_loss[loss=0.2548, simple_loss=0.3227, pruned_loss=0.09341, over 1422160.41 frames.], batch size: 21, lr: 1.08e-03
2022-05-26 20:32:22,637 INFO [train.py:842] (1/4) Epoch 4, batch 6350, loss[loss=0.2509, simple_loss=0.3161, pruned_loss=0.09279, over 7434.00 frames.], tot_loss[loss=0.2562, simple_loss=0.324, pruned_loss=0.0942, over 1418708.17 frames.], batch size: 20, lr: 1.08e-03
2022-05-26 20:33:01,276 INFO [train.py:842] (1/4) Epoch 4, batch 6400, loss[loss=0.282, simple_loss=0.3502, pruned_loss=0.1069, over 7383.00 frames.], tot_loss[loss=0.2575, simple_loss=0.3251, pruned_loss=0.09501, over 1418772.94 frames.], batch size: 23, lr: 1.08e-03
2022-05-26 20:33:40,218 INFO [train.py:842] (1/4) Epoch 4, batch 6450, loss[loss=0.3076, simple_loss=0.3638, pruned_loss=0.1257, over 7318.00 frames.], tot_loss[loss=0.2569, simple_loss=0.3244, pruned_loss=0.09475, over 1421129.52 frames.], batch size: 21, lr: 1.08e-03
2022-05-26 20:34:18,855 INFO [train.py:842] (1/4) Epoch 4, batch 6500, loss[loss=0.1815, simple_loss=0.254, pruned_loss=0.05449, over 7156.00 frames.], tot_loss[loss=0.2574, simple_loss=0.3248, pruned_loss=0.09499, over 1422478.41 frames.], batch size: 16, lr: 1.08e-03
2022-05-26 20:34:57,618 INFO [train.py:842] (1/4) Epoch 4, batch 6550, loss[loss=0.2513, simple_loss=0.3091, pruned_loss=0.09679, over 7366.00 frames.], tot_loss[loss=0.2576, simple_loss=0.325, pruned_loss=0.09507, over 1426388.90 frames.], batch size: 19, lr: 1.08e-03
2022-05-26 20:35:36,114 INFO [train.py:842] (1/4) Epoch 4, batch 6600, loss[loss=0.2761, simple_loss=0.3449, pruned_loss=0.1036, over 7202.00 frames.], tot_loss[loss=0.2571, simple_loss=0.3248, pruned_loss=0.09468, over 1421078.72 frames.], batch size: 22, lr: 1.08e-03
2022-05-26 20:36:14,974 INFO [train.py:842] (1/4) Epoch 4, batch 6650, loss[loss=0.2727, simple_loss=0.3448, pruned_loss=0.1003, over 7336.00 frames.], tot_loss[loss=0.2574, simple_loss=0.325, pruned_loss=0.09491, over 1423140.28 frames.], batch size: 22, lr: 1.08e-03
2022-05-26 20:36:53,544 INFO [train.py:842] (1/4) Epoch 4, batch 6700, loss[loss=0.2196, simple_loss=0.2857, pruned_loss=0.07676, over 7138.00 frames.], tot_loss[loss=0.2588, simple_loss=0.3264, pruned_loss=0.0956, over 1421327.42 frames.], batch size: 17, lr: 1.08e-03
2022-05-26 20:37:32,287 INFO [train.py:842] (1/4) Epoch 4, batch 6750, loss[loss=0.2352, simple_loss=0.3024, pruned_loss=0.08394, over 7211.00 frames.], tot_loss[loss=0.2586, simple_loss=0.3263, pruned_loss=0.09548, over 1421142.05 frames.], batch size: 23, lr: 1.08e-03
2022-05-26 20:38:11,166 INFO [train.py:842] (1/4) Epoch 4, batch 6800, loss[loss=0.2436, simple_loss=0.3179, pruned_loss=0.08464, over 7413.00 frames.], tot_loss[loss=0.2577, simple_loss=0.3254, pruned_loss=0.095, over 1423952.59 frames.], batch size: 21, lr: 1.08e-03
2022-05-26 20:38:50,331 INFO [train.py:842] (1/4) Epoch 4, batch 6850, loss[loss=0.2943, simple_loss=0.3612, pruned_loss=0.1137, over 7322.00 frames.], tot_loss[loss=0.2599, simple_loss=0.3264, pruned_loss=0.09673, over 1421947.32 frames.], batch size: 25, lr: 1.08e-03
2022-05-26 20:39:29,146 INFO [train.py:842] (1/4) Epoch 4, batch 6900, loss[loss=0.2383, simple_loss=0.3163, pruned_loss=0.08014, over 7215.00 frames.], tot_loss[loss=0.2574, simple_loss=0.3246, pruned_loss=0.09511, over 1423665.48 frames.], batch size: 22, lr: 1.07e-03
2022-05-26 20:40:08,045 INFO [train.py:842] (1/4) Epoch 4, batch 6950, loss[loss=0.2261, simple_loss=0.2983, pruned_loss=0.07695, over 7257.00 frames.], tot_loss[loss=0.2588, simple_loss=0.3251, pruned_loss=0.09627, over 1421837.89 frames.], batch size: 19, lr: 1.07e-03
2022-05-26 20:40:46,542 INFO [train.py:842] (1/4) Epoch 4, batch 7000, loss[loss=0.2966, simple_loss=0.3486, pruned_loss=0.1223, over 7150.00 frames.], tot_loss[loss=0.2586, simple_loss=0.3249, pruned_loss=0.09614, over 1419332.86 frames.], batch size: 19, lr: 1.07e-03
2022-05-26 20:41:25,596 INFO [train.py:842] (1/4) Epoch 4, batch 7050, loss[loss=0.28, simple_loss=0.3483, pruned_loss=0.1059, over 7320.00 frames.], tot_loss[loss=0.259, simple_loss=0.3257, pruned_loss=0.09615, over 1417648.68 frames.], batch size: 21, lr: 1.07e-03
2022-05-26 20:42:04,324 INFO [train.py:842] (1/4) Epoch 4, batch 7100, loss[loss=0.282, simple_loss=0.3434, pruned_loss=0.1103, over 7325.00 frames.], tot_loss[loss=0.257, simple_loss=0.3238, pruned_loss=0.09511, over 1420980.09 frames.], batch size: 21, lr: 1.07e-03
2022-05-26 20:42:43,529 INFO [train.py:842] (1/4) Epoch 4, batch 7150, loss[loss=0.2832, simple_loss=0.3568, pruned_loss=0.1047, over 7141.00 frames.], tot_loss[loss=0.2564, simple_loss=0.3236, pruned_loss=0.0946, over 1418664.60 frames.], batch size: 20, lr: 1.07e-03
2022-05-26 20:43:22,290 INFO [train.py:842] (1/4) Epoch 4, batch 7200, loss[loss=0.27, simple_loss=0.3391, pruned_loss=0.1004, over 7224.00 frames.], tot_loss[loss=0.2568, simple_loss=0.3239, pruned_loss=0.09491, over 1418235.37 frames.], batch size: 21, lr: 1.07e-03
2022-05-26 20:44:01,302 INFO [train.py:842] (1/4) Epoch 4, batch 7250, loss[loss=0.2628, simple_loss=0.3392, pruned_loss=0.09321, over 7413.00 frames.], tot_loss[loss=0.2542, simple_loss=0.3218, pruned_loss=0.09323, over 1418627.19 frames.], batch size: 21, lr: 1.07e-03
2022-05-26 20:44:39,867 INFO [train.py:842] (1/4) Epoch 4, batch 7300, loss[loss=0.2531, simple_loss=0.3212, pruned_loss=0.0925, over 7336.00 frames.], tot_loss[loss=0.256, simple_loss=0.3232, pruned_loss=0.09442, over 1421056.24 frames.], batch size: 22, lr: 1.07e-03
2022-05-26 20:45:18,995 INFO [train.py:842] (1/4) Epoch 4, batch 7350, loss[loss=0.2582, simple_loss=0.3375, pruned_loss=0.08943, over 7312.00 frames.], tot_loss[loss=0.2544, simple_loss=0.3218, pruned_loss=0.09349, over 1421391.83 frames.], batch size: 21, lr: 1.07e-03
2022-05-26 20:45:57,673 INFO [train.py:842] (1/4) Epoch 4, batch 7400, loss[loss=0.2034, simple_loss=0.2808, pruned_loss=0.06305, over 7326.00 frames.], tot_loss[loss=0.2551, simple_loss=0.3226, pruned_loss=0.09379, over 1421849.46 frames.], batch size: 20, lr: 1.07e-03
2022-05-26 20:46:36,599 INFO [train.py:842] (1/4) Epoch 4, batch 7450, loss[loss=0.3085, simple_loss=0.3753, pruned_loss=0.1208, over 7281.00 frames.], tot_loss[loss=0.2567, simple_loss=0.3237, pruned_loss=0.09485, over 1418558.26 frames.], batch size: 25, lr: 1.07e-03
2022-05-26 20:47:15,384 INFO [train.py:842] (1/4) Epoch 4, batch 7500, loss[loss=0.2126, simple_loss=0.2953, pruned_loss=0.06494, over 7374.00 frames.], tot_loss[loss=0.2564, simple_loss=0.3235, pruned_loss=0.09462, over 1421224.29 frames.], batch size: 19, lr: 1.07e-03
2022-05-26 20:47:54,208 INFO [train.py:842] (1/4) Epoch 4, batch 7550, loss[loss=0.2724, simple_loss=0.3369, pruned_loss=0.1039, over 6525.00 frames.], tot_loss[loss=0.2547, simple_loss=0.3224, pruned_loss=0.09355, over 1424169.08 frames.], batch size: 38, lr: 1.07e-03
2022-05-26 20:48:32,794 INFO [train.py:842] (1/4) Epoch 4, batch 7600, loss[loss=0.217, simple_loss=0.2881, pruned_loss=0.07293, over 7149.00 frames.], tot_loss[loss=0.2556, simple_loss=0.3228, pruned_loss=0.09423, over 1423860.63 frames.], batch size: 17, lr: 1.06e-03
2022-05-26 20:49:11,716 INFO [train.py:842] (1/4) Epoch 4, batch 7650, loss[loss=0.2722, simple_loss=0.3285, pruned_loss=0.108, over 7272.00 frames.], tot_loss[loss=0.2554, simple_loss=0.3229, pruned_loss=0.09397, over 1427411.48 frames.], batch size: 19, lr: 1.06e-03
2022-05-26 20:49:50,449 INFO [train.py:842] (1/4) Epoch 4, batch 7700, loss[loss=0.2555, simple_loss=0.324, pruned_loss=0.09349, over 7153.00 frames.], tot_loss[loss=0.2546, simple_loss=0.3222, pruned_loss=0.0935, over 1427937.66 frames.], batch size: 19, lr: 1.06e-03
2022-05-26 20:50:29,423 INFO [train.py:842] (1/4) Epoch 4, batch 7750, loss[loss=0.2938, simple_loss=0.3574, pruned_loss=0.1151, over 6395.00 frames.], tot_loss[loss=0.2542, simple_loss=0.3225, pruned_loss=0.09298, over 1429875.43 frames.], batch size: 38, lr: 1.06e-03
2022-05-26 20:51:08,062 INFO [train.py:842] (1/4) Epoch 4, batch 7800, loss[loss=0.2065, simple_loss=0.2887, pruned_loss=0.0622, over 7326.00 frames.], tot_loss[loss=0.2518, simple_loss=0.3204, pruned_loss=0.0916, over 1428035.04 frames.], batch size: 20, lr: 1.06e-03
2022-05-26 20:51:46,897 INFO [train.py:842] (1/4) Epoch 4, batch 7850, loss[loss=0.2754, simple_loss=0.3483, pruned_loss=0.1013, over 6425.00 frames.], tot_loss[loss=0.2529, simple_loss=0.3212, pruned_loss=0.09228, over 1425658.86 frames.], batch size: 38, lr: 1.06e-03
2022-05-26 20:52:25,502 INFO [train.py:842] (1/4) Epoch 4, batch 7900, loss[loss=0.2309, simple_loss=0.2896, pruned_loss=0.08607, over 7418.00 frames.], tot_loss[loss=0.2528, simple_loss=0.3209, pruned_loss=0.09238, over 1427027.95 frames.], batch size: 18, lr: 1.06e-03
2022-05-26 20:53:04,277 INFO [train.py:842] (1/4) Epoch 4, batch 7950, loss[loss=0.2453, simple_loss=0.3078, pruned_loss=0.09145, over 7160.00 frames.], tot_loss[loss=0.2532, simple_loss=0.3209, pruned_loss=0.09279, over 1425979.40 frames.], batch size: 18, lr: 1.06e-03
2022-05-26 20:53:42,677 INFO [train.py:842] (1/4) Epoch 4, batch 8000, loss[loss=0.2836, simple_loss=0.3632, pruned_loss=0.102, over 6283.00 frames.], tot_loss[loss=0.2553, simple_loss=0.323, pruned_loss=0.09379, over 1424410.36 frames.], batch size: 38, lr: 1.06e-03
2022-05-26 20:54:21,452 INFO [train.py:842] (1/4) Epoch 4, batch 8050, loss[loss=0.232, simple_loss=0.3028, pruned_loss=0.08064, over 7321.00 frames.], tot_loss[loss=0.2565, simple_loss=0.324, pruned_loss=0.09456, over 1425277.69 frames.], batch size: 21, lr: 1.06e-03
2022-05-26 20:54:59,917 INFO [train.py:842] (1/4) Epoch 4, batch 8100, loss[loss=0.278, simple_loss=0.3412, pruned_loss=0.1074, over 7142.00 frames.], tot_loss[loss=0.2574, simple_loss=0.325, pruned_loss=0.09486, over 1426263.35 frames.], batch size: 28, lr: 1.06e-03
2022-05-26 20:55:38,676 INFO [train.py:842] (1/4) Epoch 4, batch 8150, loss[loss=0.2406, simple_loss=0.3039, pruned_loss=0.08864, over 7425.00 frames.], tot_loss[loss=0.2585, simple_loss=0.326, pruned_loss=0.09549, over 1427704.95 frames.], batch size: 20, lr: 1.06e-03
2022-05-26 20:56:17,359 INFO [train.py:842] (1/4) Epoch 4, batch 8200, loss[loss=0.2579, simple_loss=0.3367, pruned_loss=0.0896, over 7176.00 frames.], tot_loss[loss=0.2559, simple_loss=0.3242, pruned_loss=0.09377, over 1429941.78 frames.], batch size: 23, lr: 1.06e-03
2022-05-26 20:56:55,973 INFO [train.py:842] (1/4) Epoch 4, batch 8250, loss[loss=0.2642, simple_loss=0.3308, pruned_loss=0.0988, over 7301.00 frames.], tot_loss[loss=0.2578, simple_loss=0.3256, pruned_loss=0.09501, over 1420633.37 frames.], batch size: 25, lr: 1.05e-03
2022-05-26 20:57:34,475 INFO [train.py:842] (1/4) Epoch 4, batch 8300, loss[loss=0.2858, simple_loss=0.354, pruned_loss=0.1088, over 7204.00 frames.], tot_loss[loss=0.2588, simple_loss=0.3264, pruned_loss=0.09565, over 1422411.32 frames.], batch size: 26, lr: 1.05e-03
2022-05-26 20:58:13,599 INFO [train.py:842] (1/4) Epoch 4, batch 8350, loss[loss=0.2769, simple_loss=0.3423, pruned_loss=0.1058, over 7158.00 frames.], tot_loss[loss=0.2574, simple_loss=0.3249, pruned_loss=0.09491, over 1420015.46 frames.], batch size: 26, lr: 1.05e-03
2022-05-26 20:58:52,462 INFO [train.py:842] (1/4) Epoch 4, batch 8400, loss[loss=0.2146, simple_loss=0.2908, pruned_loss=0.06918, over 7057.00 frames.], tot_loss[loss=0.2545, simple_loss=0.3228, pruned_loss=0.09314, over 1421738.37 frames.], batch size: 18, lr: 1.05e-03
2022-05-26 20:59:31,365 INFO [train.py:842] (1/4) Epoch 4, batch 8450, loss[loss=0.2336, simple_loss=0.3034, pruned_loss=0.08189, over 7361.00 frames.], tot_loss[loss=0.2545, simple_loss=0.3223, pruned_loss=0.09334, over 1422365.35 frames.], batch size: 19, lr: 1.05e-03
2022-05-26 21:00:09,906 INFO [train.py:842] (1/4) Epoch 4, batch 8500, loss[loss=0.3026, simple_loss=0.3669, pruned_loss=0.1192, over 7259.00 frames.], tot_loss[loss=0.2551, simple_loss=0.3232, pruned_loss=0.09353, over 1422172.36 frames.], batch size: 19, lr: 1.05e-03
2022-05-26 21:00:48,717 INFO [train.py:842] (1/4) Epoch 4, batch 8550, loss[loss=0.2609, simple_loss=0.3201, pruned_loss=0.1009, over 7422.00 frames.], tot_loss[loss=0.2564, simple_loss=0.3235, pruned_loss=0.09471, over 1416234.69 frames.], batch size: 18, lr: 1.05e-03
2022-05-26 21:01:27,364 INFO [train.py:842] (1/4) Epoch 4, batch 8600, loss[loss=0.2866, simple_loss=0.349, pruned_loss=0.1121, over 7148.00 frames.], tot_loss[loss=0.2566, simple_loss=0.3233, pruned_loss=0.09492, over 1417183.98 frames.], batch size: 26, lr: 1.05e-03
2022-05-26 21:02:06,596 INFO [train.py:842] (1/4) Epoch 4, batch 8650, loss[loss=0.2703, simple_loss=0.3354, pruned_loss=0.1026, over 7376.00 frames.], tot_loss[loss=0.2562, simple_loss=0.3232, pruned_loss=0.09465, over 1417107.34 frames.], batch size: 23, lr: 1.05e-03
2022-05-26 21:02:45,051 INFO [train.py:842] (1/4) Epoch 4, batch 8700, loss[loss=0.2623, simple_loss=0.3373, pruned_loss=0.09366, over 7182.00 frames.], tot_loss[loss=0.2556, simple_loss=0.3225, pruned_loss=0.09432, over 1414637.88 frames.], batch size: 26, lr: 1.05e-03
2022-05-26 21:03:23,718 INFO [train.py:842] (1/4) Epoch 4, batch 8750, loss[loss=0.2644, simple_loss=0.325, pruned_loss=0.1019, over 4875.00 frames.], tot_loss[loss=0.2542, simple_loss=0.3211, pruned_loss=0.0936, over 1403406.96 frames.], batch size: 52, lr: 1.05e-03
2022-05-26 21:04:02,235 INFO [train.py:842] (1/4) Epoch 4, batch 8800, loss[loss=0.2478, simple_loss=0.3267, pruned_loss=0.08447, over 6773.00 frames.], tot_loss[loss=0.2529, simple_loss=0.3197, pruned_loss=0.09311, over 1402000.51 frames.], batch size: 31, lr: 1.05e-03
2022-05-26 21:04:40,828 INFO [train.py:842] (1/4) Epoch 4, batch 8850, loss[loss=0.1986, simple_loss=0.27, pruned_loss=0.06362, over 7173.00 frames.], tot_loss[loss=0.2525, simple_loss=0.3191, pruned_loss=0.09292, over 1395817.09 frames.], batch size: 18, lr: 1.05e-03
2022-05-26 21:05:19,461 INFO [train.py:842] (1/4) Epoch 4, batch 8900, loss[loss=0.2706, simple_loss=0.3212, pruned_loss=0.11, over 7178.00 frames.], tot_loss[loss=0.2531, simple_loss=0.3191, pruned_loss=0.09355, over 1392265.82 frames.], batch size: 18, lr: 1.05e-03
2022-05-26 21:05:58,254 INFO [train.py:842] (1/4) Epoch 4, batch 8950, loss[loss=0.2523, simple_loss=0.3109, pruned_loss=0.09683, over 7358.00 frames.], tot_loss[loss=0.2539, simple_loss=0.3189, pruned_loss=0.0944, over 1391705.15 frames.], batch size: 19, lr: 1.04e-03
2022-05-26 21:06:36,751 INFO [train.py:842] (1/4) Epoch 4, batch 9000, loss[loss=0.258, simple_loss=0.3231, pruned_loss=0.09643, over 7159.00 frames.], tot_loss[loss=0.2555, simple_loss=0.3209, pruned_loss=0.09508, over 1379268.41 frames.], batch size: 19, lr: 1.04e-03
2022-05-26 21:06:36,752 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 21:06:46,019 INFO [train.py:871] (1/4) Epoch 4, validation: loss=0.1922, simple_loss=0.292, pruned_loss=0.04621, over 868885.00 frames.
2022-05-26 21:07:24,215 INFO [train.py:842] (1/4) Epoch 4, batch 9050, loss[loss=0.2844, simple_loss=0.3494, pruned_loss=0.1097, over 5146.00 frames.], tot_loss[loss=0.2567, simple_loss=0.3223, pruned_loss=0.09555, over 1363705.65 frames.], batch size: 54, lr: 1.04e-03
2022-05-26 21:08:01,816 INFO [train.py:842] (1/4) Epoch 4, batch 9100, loss[loss=0.3184, simple_loss=0.3746, pruned_loss=0.1311, over 6564.00 frames.], tot_loss[loss=0.262, simple_loss=0.3265, pruned_loss=0.09875, over 1342166.42 frames.], batch size: 38, lr: 1.04e-03
2022-05-26 21:08:39,509 INFO [train.py:842] (1/4) Epoch 4, batch 9150, loss[loss=0.3426, simple_loss=0.3793, pruned_loss=0.1529, over 4991.00 frames.], tot_loss[loss=0.269, simple_loss=0.3318, pruned_loss=0.1031, over 1284002.98 frames.], batch size: 52, lr: 1.04e-03
2022-05-26 21:09:32,022 INFO [train.py:842] (1/4) Epoch 5, batch 0, loss[loss=0.3042, simple_loss=0.371, pruned_loss=0.1187, over 7219.00 frames.], tot_loss[loss=0.3042, simple_loss=0.371, pruned_loss=0.1187, over 7219.00 frames.], batch size: 23, lr: 1.00e-03
2022-05-26 21:10:11,440 INFO [train.py:842] (1/4) Epoch 5, batch 50, loss[loss=0.3059, simple_loss=0.3743, pruned_loss=0.1188, over 7332.00 frames.], tot_loss[loss=0.2485, simple_loss=0.3196, pruned_loss=0.08868, over 320302.10 frames.], batch size: 22, lr: 1.00e-03
2022-05-26 21:10:50,259 INFO [train.py:842] (1/4) Epoch 5, batch 100, loss[loss=0.2826, simple_loss=0.3613, pruned_loss=0.102, over 7341.00 frames.], tot_loss[loss=0.2544, simple_loss=0.3231, pruned_loss=0.09284, over 566064.41 frames.], batch size: 22, lr: 1.00e-03
2022-05-26 21:11:29,042 INFO [train.py:842] (1/4) Epoch 5, batch 150, loss[loss=0.3423, simple_loss=0.3869, pruned_loss=0.1488, over 5486.00 frames.], tot_loss[loss=0.2584, simple_loss=0.3259, pruned_loss=0.09544, over 754993.05 frames.], batch size: 52, lr: 1.00e-03
2022-05-26 21:12:07,504 INFO [train.py:842] (1/4) Epoch 5, batch 200, loss[loss=0.1957, simple_loss=0.2713, pruned_loss=0.06, over 7161.00 frames.], tot_loss[loss=0.2563, simple_loss=0.3249, pruned_loss=0.09381, over 903717.31 frames.], batch size: 19, lr: 1.00e-03
2022-05-26 21:12:46,181 INFO [train.py:842] (1/4) Epoch 5, batch 250, loss[loss=0.2395, simple_loss=0.3134, pruned_loss=0.08274, over 7341.00 frames.], tot_loss[loss=0.2591, simple_loss=0.3274, pruned_loss=0.0954, over 1021448.56 frames.], batch size: 22, lr: 1.00e-03
2022-05-26 21:13:24,990 INFO [train.py:842] (1/4) Epoch 5, batch 300, loss[loss=0.2121, simple_loss=0.2861, pruned_loss=0.06905, over 7277.00 frames.], tot_loss[loss=0.2553, simple_loss=0.3244, pruned_loss=0.09314, over 1114001.79 frames.], batch size: 17, lr: 1.00e-03
2022-05-26 21:14:03,868 INFO [train.py:842] (1/4) Epoch 5, batch 350, loss[loss=0.2096, simple_loss=0.2912, pruned_loss=0.06402, over 7151.00 frames.], tot_loss[loss=0.2553, simple_loss=0.3237, pruned_loss=0.09345, over 1181932.62 frames.], batch size: 19, lr: 1.00e-03
2022-05-26 21:14:42,416 INFO [train.py:842] (1/4) Epoch 5, batch 400, loss[loss=0.235, simple_loss=0.3102, pruned_loss=0.07996, over 7083.00 frames.], tot_loss[loss=0.2557, simple_loss=0.3238, pruned_loss=0.09386, over 1233084.95 frames.], batch size: 28, lr: 9.99e-04
2022-05-26 21:15:21,495 INFO [train.py:842] (1/4) Epoch 5, batch 450, loss[loss=0.2652, simple_loss=0.3409, pruned_loss=0.09476, over 7161.00 frames.], tot_loss[loss=0.2557, simple_loss=0.3242, pruned_loss=0.0936, over 1274702.53 frames.], batch size: 28, lr: 9.99e-04
2022-05-26 21:16:00,451 INFO [train.py:842] (1/4) Epoch 5, batch 500, loss[loss=0.2198, simple_loss=0.297, pruned_loss=0.07135, over 7306.00 frames.], tot_loss[loss=0.2547, simple_loss=0.3234, pruned_loss=0.09296, over 1309684.69 frames.], batch size: 21, lr: 9.98e-04
2022-05-26 21:16:39,268 INFO [train.py:842] (1/4) Epoch 5, batch 550, loss[loss=0.2664, simple_loss=0.3369, pruned_loss=0.0979, over 6743.00 frames.], tot_loss[loss=0.2526, simple_loss=0.3216, pruned_loss=0.09173, over 1334155.05 frames.], batch size: 31, lr: 9.97e-04
2022-05-26 21:17:17,940 INFO [train.py:842] (1/4) Epoch 5, batch 600, loss[loss=0.2979, simple_loss=0.3338, pruned_loss=0.131, over 7000.00 frames.], tot_loss[loss=0.2515, simple_loss=0.3205, pruned_loss=0.09124, over 1356065.99 frames.], batch size: 16, lr: 9.97e-04
2022-05-26 21:17:56,779 INFO [train.py:842] (1/4) Epoch 5, batch 650, loss[loss=0.2037, simple_loss=0.2933, pruned_loss=0.05706, over 7326.00 frames.], tot_loss[loss=0.2524, simple_loss=0.3213, pruned_loss=0.09179, over 1370167.29 frames.], batch size: 20, lr: 9.96e-04
2022-05-26 21:18:35,190 INFO [train.py:842] (1/4) Epoch 5, batch 700, loss[loss=0.2646, simple_loss=0.3347, pruned_loss=0.09729, over 7337.00 frames.], tot_loss[loss=0.2534, simple_loss=0.3221, pruned_loss=0.09231, over 1379284.27 frames.], batch size: 25, lr: 9.95e-04
2022-05-26 21:19:14,035 INFO [train.py:842] (1/4) Epoch 5, batch 750, loss[loss=0.1841, simple_loss=0.2575, pruned_loss=0.05538, over 7048.00 frames.], tot_loss[loss=0.2519, simple_loss=0.3206, pruned_loss=0.09163, over 1384565.91 frames.], batch size: 18, lr: 9.95e-04
2022-05-26 21:19:52,740 INFO [train.py:842] (1/4) Epoch 5, batch 800, loss[loss=0.2017, simple_loss=0.2741, pruned_loss=0.06463, over 7063.00 frames.], tot_loss[loss=0.2505, simple_loss=0.3189, pruned_loss=0.09108, over 1396517.23 frames.], batch size: 18, lr: 9.94e-04
2022-05-26 21:20:31,416 INFO [train.py:842] (1/4) Epoch 5, batch 850, loss[loss=0.267, simple_loss=0.3248, pruned_loss=0.1046, over 7064.00 frames.], tot_loss[loss=0.2503, simple_loss=0.3185, pruned_loss=0.09105, over 1394998.11 frames.], batch size: 18, lr: 9.93e-04
2022-05-26 21:21:09,957 INFO [train.py:842] (1/4) Epoch 5, batch 900, loss[loss=0.2124, simple_loss=0.2922, pruned_loss=0.06627, over 7336.00 frames.], tot_loss[loss=0.2495, simple_loss=0.3184, pruned_loss=0.09035, over 1401809.26 frames.], batch size: 21, lr: 9.93e-04
2022-05-26 21:21:48,890 INFO [train.py:842] (1/4) Epoch 5, batch 950, loss[loss=0.2769, simple_loss=0.3502, pruned_loss=0.1018, over 7067.00 frames.], tot_loss[loss=0.2525, simple_loss=0.3204, pruned_loss=0.09227, over 1405868.76 frames.], batch size: 28, lr: 9.92e-04
2022-05-26 21:22:27,556 INFO [train.py:842] (1/4) Epoch 5, batch 1000, loss[loss=0.1821, simple_loss=0.2643, pruned_loss=0.04994, over 7060.00 frames.], tot_loss[loss=0.2508, simple_loss=0.3192, pruned_loss=0.09116, over 1410818.50 frames.], batch size: 18, lr: 9.91e-04
2022-05-26 21:23:06,450 INFO [train.py:842] (1/4) Epoch 5, batch 1050, loss[loss=0.2413, simple_loss=0.3125, pruned_loss=0.08509, over 7291.00 frames.], tot_loss[loss=0.2509, simple_loss=0.3197, pruned_loss=0.09106, over 1416736.71 frames.], batch size: 24, lr: 9.91e-04
2022-05-26 21:23:44,738 INFO [train.py:842] (1/4) Epoch 5, batch 1100, loss[loss=0.2919, simple_loss=0.3548, pruned_loss=0.1145, over 6439.00 frames.], tot_loss[loss=0.2518, simple_loss=0.3208, pruned_loss=0.09139, over 1413200.83 frames.], batch size: 38, lr: 9.90e-04
2022-05-26 21:24:23,626 INFO [train.py:842] (1/4) Epoch 5, batch 1150, loss[loss=0.2841, simple_loss=0.3515, pruned_loss=0.1084, over 7426.00 frames.], tot_loss[loss=0.2519, simple_loss=0.3214, pruned_loss=0.09127, over 1415234.41 frames.], batch size: 20, lr: 9.89e-04
2022-05-26 21:25:02,154 INFO [train.py:842] (1/4) Epoch 5, batch 1200, loss[loss=0.3753, simple_loss=0.4077, pruned_loss=0.1715, over 6345.00 frames.], tot_loss[loss=0.2517, simple_loss=0.3211, pruned_loss=0.0911, over 1417286.89 frames.], batch size: 37, lr: 9.89e-04
2022-05-26 21:25:41,004 INFO [train.py:842] (1/4) Epoch 5, batch 1250, loss[loss=0.2347, simple_loss=0.3006, pruned_loss=0.08439, over 7275.00 frames.], tot_loss[loss=0.2509, simple_loss=0.3203, pruned_loss=0.09079, over 1412899.56 frames.], batch size: 19, lr: 9.88e-04
2022-05-26 21:26:19,423 INFO [train.py:842] (1/4) Epoch 5, batch 1300, loss[loss=0.2308, simple_loss=0.3107, pruned_loss=0.07541, over 7321.00 frames.], tot_loss[loss=0.2518, simple_loss=0.321, pruned_loss=0.09125, over 1416223.35 frames.], batch size: 20, lr: 9.87e-04
2022-05-26 21:26:58,325 INFO [train.py:842] (1/4) Epoch 5, batch 1350, loss[loss=0.2416, simple_loss=0.3163, pruned_loss=0.08346, over 7146.00 frames.], tot_loss[loss=0.2521, simple_loss=0.3215, pruned_loss=0.09141, over 1423564.07 frames.], batch size: 17, lr: 9.87e-04
2022-05-26 21:27:36,967 INFO [train.py:842] (1/4) Epoch 5, batch 1400, loss[loss=0.259, simple_loss=0.3297, pruned_loss=0.0941, over 7240.00 frames.], tot_loss[loss=0.2542, simple_loss=0.323, pruned_loss=0.0927, over 1419783.16 frames.], batch size: 20, lr: 9.86e-04
2022-05-26 21:28:15,793 INFO [train.py:842] (1/4) Epoch 5, batch 1450, loss[loss=0.1962, simple_loss=0.2729, pruned_loss=0.05974, over 7008.00 frames.], tot_loss[loss=0.2533, simple_loss=0.3225, pruned_loss=0.09202, over 1420416.65 frames.], batch size: 16, lr: 9.86e-04
2022-05-26 21:28:54,583 INFO [train.py:842] (1/4) Epoch 5, batch 1500, loss[loss=0.2702, simple_loss=0.3335, pruned_loss=0.1035, over 7326.00 frames.], tot_loss[loss=0.2507, simple_loss=0.3205, pruned_loss=0.09052, over 1423091.52 frames.], batch size: 20, lr: 9.85e-04
2022-05-26 21:29:33,897 INFO [train.py:842] (1/4) Epoch 5, batch 1550, loss[loss=0.3087, simple_loss=0.3632, pruned_loss=0.1271, over 7371.00 frames.], tot_loss[loss=0.2503, simple_loss=0.3201, pruned_loss=0.09023, over 1425302.50 frames.], batch size: 23, lr: 9.84e-04
2022-05-26 21:30:12,547 INFO [train.py:842] (1/4) Epoch 5, batch 1600, loss[loss=0.3375, simple_loss=0.3813, pruned_loss=0.1469, over 7320.00 frames.], tot_loss[loss=0.2511, simple_loss=0.3205, pruned_loss=0.09083, over 1424421.47 frames.], batch size: 25, lr: 9.84e-04
2022-05-26 21:31:01,988 INFO [train.py:842] (1/4) Epoch 5, batch 1650, loss[loss=0.2191, simple_loss=0.2924, pruned_loss=0.07289, over 7114.00 frames.], tot_loss[loss=0.2509, simple_loss=0.3204, pruned_loss=0.09065, over 1422236.40 frames.], batch size: 21, lr: 9.83e-04
2022-05-26 21:31:40,731 INFO [train.py:842] (1/4) Epoch 5, batch 1700, loss[loss=0.2519, simple_loss=0.3199, pruned_loss=0.09196, over 7337.00 frames.], tot_loss[loss=0.2488, simple_loss=0.3185, pruned_loss=0.0896, over 1423742.02 frames.], batch size: 22, lr: 9.82e-04
2022-05-26 21:32:19,659 INFO [train.py:842] (1/4) Epoch 5, batch 1750, loss[loss=0.238, simple_loss=0.3275, pruned_loss=0.07431, over 7293.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3182, pruned_loss=0.08948, over 1423713.22 frames.], batch size: 24, lr: 9.82e-04
2022-05-26 21:32:58,261 INFO [train.py:842] (1/4) Epoch 5, batch 1800, loss[loss=0.2624, simple_loss=0.331, pruned_loss=0.09693, over 7318.00 frames.], tot_loss[loss=0.2513, simple_loss=0.3203, pruned_loss=0.09117, over 1426054.89 frames.], batch size: 21, lr: 9.81e-04
2022-05-26 21:33:37,263 INFO [train.py:842] (1/4) Epoch 5, batch 1850, loss[loss=0.2602, simple_loss=0.3269, pruned_loss=0.0968, over 6279.00 frames.], tot_loss[loss=0.2508, simple_loss=0.32, pruned_loss=0.0908, over 1426628.14 frames.], batch size: 37, lr: 9.81e-04
2022-05-26 21:34:15,879 INFO [train.py:842] (1/4) Epoch 5, batch 1900, loss[loss=0.2979, simple_loss=0.3729, pruned_loss=0.1114, over 7093.00 frames.], tot_loss[loss=0.2495, simple_loss=0.3194, pruned_loss=0.08979, over 1427611.12 frames.], batch size: 21, lr: 9.80e-04
2022-05-26 21:34:55,031 INFO [train.py:842] (1/4) Epoch 5, batch 1950, loss[loss=0.1873, simple_loss=0.2622, pruned_loss=0.05621, over 7155.00 frames.], tot_loss[loss=0.248, simple_loss=0.3183, pruned_loss=0.0889, over 1428676.21 frames.], batch size: 18, lr: 9.79e-04
2022-05-26 21:35:33,834 INFO [train.py:842] (1/4) Epoch 5, batch 2000, loss[loss=0.2711, simple_loss=0.344, pruned_loss=0.09909, over 7316.00 frames.], tot_loss[loss=0.2467, simple_loss=0.3174, pruned_loss=0.08794, over 1426510.51 frames.], batch size: 25, lr: 9.79e-04
2022-05-26 21:36:12,766 INFO [train.py:842] (1/4) Epoch 5, batch 2050, loss[loss=0.2807, simple_loss=0.3502, pruned_loss=0.1056, over 7283.00 frames.], tot_loss[loss=0.2467, simple_loss=0.3169, pruned_loss=0.08828, over 1431162.33 frames.], batch size: 24, lr: 9.78e-04
2022-05-26 21:36:51,527 INFO [train.py:842] (1/4) Epoch 5, batch 2100, loss[loss=0.2027, simple_loss=0.2735, pruned_loss=0.06594, over 7417.00 frames.], tot_loss[loss=0.2459, simple_loss=0.3167, pruned_loss=0.08761, over 1434436.89 frames.], batch size: 18, lr: 9.77e-04
2022-05-26 21:37:30,132 INFO [train.py:842] (1/4) Epoch 5, batch 2150, loss[loss=0.2022, simple_loss=0.2864, pruned_loss=0.05905, over 7060.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3169, pruned_loss=0.08731, over 1432983.60 frames.], batch size: 18, lr: 9.77e-04
2022-05-26 21:38:08,839 INFO [train.py:842] (1/4) Epoch 5, batch 2200, loss[loss=0.3063, simple_loss=0.366, pruned_loss=0.1233, over 7329.00 frames.], tot_loss[loss=0.2462, simple_loss=0.3167, pruned_loss=0.08789, over 1435091.52 frames.], batch size: 22, lr: 9.76e-04
2022-05-26 21:38:47,518 INFO [train.py:842] (1/4) Epoch 5, batch 2250, loss[loss=0.2547, simple_loss=0.3309, pruned_loss=0.08929, over 7371.00 frames.], tot_loss[loss=0.247, simple_loss=0.3173, pruned_loss=0.08837, over 1432581.17 frames.], batch size: 23, lr: 9.76e-04
2022-05-26 21:39:26,225 INFO [train.py:842] (1/4) Epoch 5, batch 2300, loss[loss=0.2221, simple_loss=0.2941, pruned_loss=0.07505, over 7292.00 frames.], tot_loss[loss=0.247, simple_loss=0.3173, pruned_loss=0.0884, over 1430332.01 frames.], batch size: 17, lr: 9.75e-04
2022-05-26 21:40:05,048 INFO [train.py:842] (1/4) Epoch 5, batch 2350, loss[loss=0.2114, simple_loss=0.2734, pruned_loss=0.0747, over 7414.00 frames.], tot_loss[loss=0.2463, simple_loss=0.3169, pruned_loss=0.08781, over 1433501.97 frames.], batch size: 18, lr: 9.74e-04
2022-05-26 21:40:53,739 INFO [train.py:842] (1/4) Epoch 5, batch 2400, loss[loss=0.2355, simple_loss=0.3187, pruned_loss=0.07609, over 7216.00 frames.], tot_loss[loss=0.2455, simple_loss=0.3159, pruned_loss=0.08756, over 1434791.95 frames.], batch size: 21, lr: 9.74e-04
2022-05-26 21:41:32,664 INFO [train.py:842] (1/4) Epoch 5, batch 2450, loss[loss=0.2232, simple_loss=0.3013, pruned_loss=0.07252, over 7292.00 frames.], tot_loss[loss=0.2475, simple_loss=0.3175, pruned_loss=0.08872, over 1434286.39 frames.], batch size: 18, lr: 9.73e-04
2022-05-26 21:42:21,525 INFO [train.py:842] (1/4) Epoch 5, batch 2500, loss[loss=0.2234, simple_loss=0.3177, pruned_loss=0.06458, over 7210.00 frames.], tot_loss[loss=0.2487, simple_loss=0.3185, pruned_loss=0.08944, over 1431765.97 frames.], batch size: 22, lr: 9.73e-04
2022-05-26 21:43:11,051 INFO [train.py:842] (1/4) Epoch 5, batch 2550, loss[loss=0.269, simple_loss=0.3463, pruned_loss=0.0959, over 7149.00 frames.], tot_loss[loss=0.249, simple_loss=0.3188, pruned_loss=0.08963, over 1432544.06 frames.], batch size: 20, lr: 9.72e-04
2022-05-26 21:43:49,607 INFO [train.py:842] (1/4) Epoch 5, batch 2600, loss[loss=0.2711, simple_loss=0.3342, pruned_loss=0.104, over 7330.00 frames.], tot_loss[loss=0.2494, simple_loss=0.3194, pruned_loss=0.08975, over 1431628.27 frames.], batch size: 21, lr: 9.71e-04
2022-05-26 21:44:28,551 INFO [train.py:842] (1/4) Epoch 5, batch 2650, loss[loss=0.1947, simple_loss=0.2613, pruned_loss=0.06405, over 6999.00 frames.], tot_loss[loss=0.2488, simple_loss=0.3186, pruned_loss=0.08953, over 1429342.06 frames.], batch size: 16, lr: 9.71e-04
2022-05-26 21:45:07,108 INFO [train.py:842] (1/4) Epoch 5, batch 2700, loss[loss=0.2554, simple_loss=0.3149, pruned_loss=0.09792, over 7272.00 frames.], tot_loss[loss=0.2468, simple_loss=0.3172, pruned_loss=0.08826, over 1431632.80 frames.], batch size: 18, lr: 9.70e-04
2022-05-26 21:45:46,200 INFO [train.py:842] (1/4) Epoch 5, batch 2750, loss[loss=0.2334, simple_loss=0.3014, pruned_loss=0.0827, over 7353.00 frames.], tot_loss[loss=0.2482, simple_loss=0.3176, pruned_loss=0.08941, over 1432295.18 frames.], batch size: 19, lr: 9.70e-04
2022-05-26 21:46:24,823 INFO [train.py:842] (1/4) Epoch 5, batch 2800, loss[loss=0.2186, simple_loss=0.2917, pruned_loss=0.07274, over 7144.00 frames.], tot_loss[loss=0.2476, simple_loss=0.317, pruned_loss=0.08911, over 1433045.00 frames.], batch size: 17, lr: 9.69e-04
2022-05-26 21:47:03,558 INFO [train.py:842] (1/4) Epoch 5, batch 2850, loss[loss=0.2397, simple_loss=0.3179, pruned_loss=0.08078, over 6710.00 frames.], tot_loss[loss=0.2482, simple_loss=0.318, pruned_loss=0.08926, over 1430322.26 frames.], batch size: 31, lr: 9.68e-04
2022-05-26 21:47:41,770 INFO [train.py:842] (1/4) Epoch 5, batch 2900, loss[loss=0.2262, simple_loss=0.3067, pruned_loss=0.07285, over 7300.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3185, pruned_loss=0.08932, over 1428151.67 frames.], batch size: 24, lr: 9.68e-04
2022-05-26 21:48:20,713 INFO [train.py:842] (1/4) Epoch 5, batch 2950, loss[loss=0.2227, simple_loss=0.3079, pruned_loss=0.06875, over 7337.00 frames.], tot_loss[loss=0.2485, simple_loss=0.3184, pruned_loss=0.08933, over 1428259.23 frames.], batch size: 22, lr: 9.67e-04
2022-05-26 21:48:59,374 INFO [train.py:842] (1/4) Epoch 5, batch 3000, loss[loss=0.2826, simple_loss=0.3336, pruned_loss=0.1158, over 7148.00 frames.], tot_loss[loss=0.2481, simple_loss=0.318, pruned_loss=0.08909, over 1424578.64 frames.], batch size: 26, lr: 9.66e-04
2022-05-26 21:48:59,375 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 21:49:08,664 INFO [train.py:871] (1/4) Epoch 5, validation: loss=0.1927, simple_loss=0.2922, pruned_loss=0.04663, over 868885.00 frames.
2022-05-26 21:49:47,980 INFO [train.py:842] (1/4) Epoch 5, batch 3050, loss[loss=0.247, simple_loss=0.3209, pruned_loss=0.08658, over 7200.00 frames.], tot_loss[loss=0.248, simple_loss=0.318, pruned_loss=0.08901, over 1428212.60 frames.], batch size: 22, lr: 9.66e-04
2022-05-26 21:50:26,472 INFO [train.py:842] (1/4) Epoch 5, batch 3100, loss[loss=0.2462, simple_loss=0.3287, pruned_loss=0.08189, over 7228.00 frames.], tot_loss[loss=0.2498, simple_loss=0.3191, pruned_loss=0.09021, over 1427113.66 frames.], batch size: 20, lr: 9.65e-04
2022-05-26 21:51:05,264 INFO [train.py:842] (1/4) Epoch 5, batch 3150, loss[loss=0.2513, simple_loss=0.33, pruned_loss=0.08634, over 7298.00 frames.], tot_loss[loss=0.2491, simple_loss=0.3189, pruned_loss=0.08968, over 1427453.28 frames.], batch size: 25, lr: 9.65e-04
2022-05-26 21:51:43,972 INFO [train.py:842] (1/4) Epoch 5, batch 3200, loss[loss=0.2076, simple_loss=0.2839, pruned_loss=0.06564, over 7371.00 frames.], tot_loss[loss=0.2488, simple_loss=0.3187, pruned_loss=0.08947, over 1428609.32 frames.], batch size: 19, lr: 9.64e-04
2022-05-26 21:52:25,513 INFO [train.py:842] (1/4) Epoch 5, batch 3250, loss[loss=0.1943, simple_loss=0.2716, pruned_loss=0.05851, over 7178.00 frames.], tot_loss[loss=0.2491, simple_loss=0.3188, pruned_loss=0.08966, over 1427739.31 frames.], batch size: 18, lr: 9.64e-04
2022-05-26 21:53:04,083 INFO [train.py:842] (1/4) Epoch 5, batch 3300, loss[loss=0.2939, simple_loss=0.3513, pruned_loss=0.1183, over 7154.00 frames.], tot_loss[loss=0.249, simple_loss=0.3188, pruned_loss=0.08958, over 1422499.06 frames.], batch size: 26, lr: 9.63e-04
2022-05-26 21:53:43,086 INFO [train.py:842] (1/4) Epoch 5, batch 3350, loss[loss=0.2487, simple_loss=0.3282, pruned_loss=0.08454, over 7121.00 frames.], tot_loss[loss=0.2511, simple_loss=0.3206, pruned_loss=0.09073, over 1424974.64 frames.], batch size: 21, lr: 9.62e-04
2022-05-26 21:54:21,689 INFO [train.py:842] (1/4) Epoch 5, batch 3400, loss[loss=0.1942, simple_loss=0.2797, pruned_loss=0.05432, over 7246.00 frames.], tot_loss[loss=0.2509, simple_loss=0.3209, pruned_loss=0.09042, over 1425994.15 frames.], batch size: 20, lr: 9.62e-04
2022-05-26 21:55:00,654 INFO [train.py:842] (1/4) Epoch 5, batch 3450, loss[loss=0.3107, simple_loss=0.3538, pruned_loss=0.1338, over 7213.00 frames.], tot_loss[loss=0.2478, simple_loss=0.3184, pruned_loss=0.08861, over 1426717.03 frames.], batch size: 23, lr: 9.61e-04
2022-05-26 21:55:39,263 INFO [train.py:842] (1/4) Epoch 5, batch 3500, loss[loss=0.3392, simple_loss=0.3889, pruned_loss=0.1448, over 7318.00 frames.], tot_loss[loss=0.2484, simple_loss=0.319, pruned_loss=0.08889, over 1428216.94 frames.], batch size: 21, lr: 9.61e-04
2022-05-26 21:56:18,076 INFO [train.py:842] (1/4) Epoch 5, batch 3550, loss[loss=0.2158, simple_loss=0.2993, pruned_loss=0.06621, over 7321.00 frames.], tot_loss[loss=0.2487, simple_loss=0.3192, pruned_loss=0.08908, over 1424202.90 frames.], batch size: 20, lr: 9.60e-04
2022-05-26 21:56:56,514 INFO [train.py:842] (1/4) Epoch 5, batch 3600, loss[loss=0.2203, simple_loss=0.2936, pruned_loss=0.07345, over 7323.00 frames.], tot_loss[loss=0.2494, simple_loss=0.3194, pruned_loss=0.08971, over 1420258.44 frames.], batch size: 20, lr: 9.59e-04
2022-05-26 21:57:35,229 INFO [train.py:842] (1/4) Epoch 5, batch 3650, loss[loss=0.2522, simple_loss=0.3028, pruned_loss=0.1008, over 7061.00 frames.], tot_loss[loss=0.2506, simple_loss=0.3201, pruned_loss=0.09055, over 1413223.77 frames.], batch size: 18, lr: 9.59e-04
2022-05-26 21:58:13,850 INFO [train.py:842] (1/4) Epoch 5, batch 3700, loss[loss=0.2946, simple_loss=0.3723, pruned_loss=0.1085, over 7211.00 frames.], tot_loss[loss=0.2499, simple_loss=0.3192, pruned_loss=0.09024, over 1418524.09 frames.], batch size: 21, lr: 9.58e-04
2022-05-26 21:58:52,983 INFO [train.py:842] (1/4) Epoch 5, batch 3750, loss[loss=0.444, simple_loss=0.4323, pruned_loss=0.2278, over 4852.00 frames.], tot_loss[loss=0.2491, simple_loss=0.3186, pruned_loss=0.08983, over 1418138.48 frames.], batch size: 52, lr: 9.58e-04
2022-05-26 21:59:31,658 INFO [train.py:842] (1/4) Epoch 5, batch 3800, loss[loss=0.2013, simple_loss=0.273, pruned_loss=0.06474, over 6801.00 frames.], tot_loss[loss=0.2509, simple_loss=0.3202, pruned_loss=0.09081, over 1419015.93 frames.], batch size: 15, lr: 9.57e-04
2022-05-26 22:00:10,485 INFO [train.py:842] (1/4) Epoch 5, batch 3850, loss[loss=0.2398, simple_loss=0.3043, pruned_loss=0.08763, over 7395.00 frames.], tot_loss[loss=0.2494, simple_loss=0.319, pruned_loss=0.08995, over 1419225.06 frames.], batch size: 18, lr: 9.56e-04
2022-05-26 22:00:49,040 INFO [train.py:842] (1/4) Epoch 5, batch 3900, loss[loss=0.2297, simple_loss=0.3028, pruned_loss=0.07834, over 7355.00 frames.], tot_loss[loss=0.2497, simple_loss=0.3191, pruned_loss=0.09013, over 1416572.70 frames.], batch size: 19, lr: 9.56e-04
2022-05-26 22:01:28,108 INFO [train.py:842] (1/4) Epoch 5, batch 3950, loss[loss=0.1943, simple_loss=0.2818, pruned_loss=0.05338, over 7261.00 frames.], tot_loss[loss=0.2492, simple_loss=0.3185, pruned_loss=0.08998, over 1414231.13 frames.], batch size: 19, lr: 9.55e-04
2022-05-26 22:02:06,776 INFO [train.py:842] (1/4) Epoch 5, batch 4000, loss[loss=0.2656, simple_loss=0.3358, pruned_loss=0.09773, over 7341.00 frames.], tot_loss[loss=0.2482, simple_loss=0.318, pruned_loss=0.08923, over 1417761.17 frames.], batch size: 22, lr: 9.55e-04
2022-05-26 22:02:46,095 INFO [train.py:842] (1/4) Epoch 5, batch 4050, loss[loss=0.1964, simple_loss=0.2848, pruned_loss=0.05402, over 7284.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3173, pruned_loss=0.08846, over 1419618.36 frames.], batch size: 18, lr: 9.54e-04
2022-05-26 22:03:24,843 INFO [train.py:842] (1/4) Epoch 5, batch 4100, loss[loss=0.2873, simple_loss=0.3533, pruned_loss=0.1107, over 7140.00 frames.], tot_loss[loss=0.2463, simple_loss=0.3163, pruned_loss=0.08809, over 1420637.31 frames.], batch size: 26, lr: 9.54e-04
2022-05-26 22:04:03,584 INFO [train.py:842] (1/4) Epoch 5, batch 4150, loss[loss=0.2169, simple_loss=0.2979, pruned_loss=0.068, over 7168.00 frames.], tot_loss[loss=0.2469, simple_loss=0.317, pruned_loss=0.08837, over 1416174.62 frames.], batch size: 26, lr: 9.53e-04
2022-05-26 22:04:42,301 INFO [train.py:842] (1/4) Epoch 5, batch 4200, loss[loss=0.2046, simple_loss=0.2832, pruned_loss=0.06297, over 7271.00 frames.], tot_loss[loss=0.2456, simple_loss=0.3166, pruned_loss=0.08727, over 1420378.24 frames.], batch size: 18, lr: 9.52e-04
2022-05-26 22:05:21,090 INFO [train.py:842] (1/4) Epoch 5, batch 4250, loss[loss=0.3237, simple_loss=0.3775, pruned_loss=0.1349, over 7212.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3173, pruned_loss=0.08846, over 1420220.12 frames.], batch size: 22, lr: 9.52e-04
2022-05-26 22:05:59,625 INFO [train.py:842] (1/4) Epoch 5, batch 4300, loss[loss=0.3062, simple_loss=0.3516, pruned_loss=0.1304, over 7177.00 frames.], tot_loss[loss=0.2452, simple_loss=0.3151, pruned_loss=0.08763, over 1421011.17 frames.], batch size: 18, lr: 9.51e-04
2022-05-26 22:06:38,355 INFO [train.py:842] (1/4) Epoch 5, batch 4350, loss[loss=0.2413, simple_loss=0.3177, pruned_loss=0.08246, over 7130.00 frames.], tot_loss[loss=0.2438, simple_loss=0.3144, pruned_loss=0.08663, over 1422533.12 frames.], batch size: 26, lr: 9.51e-04
2022-05-26 22:07:17,028 INFO [train.py:842] (1/4) Epoch 5, batch 4400, loss[loss=0.2835, simple_loss=0.351, pruned_loss=0.108, over 7151.00 frames.], tot_loss[loss=0.2445, simple_loss=0.315, pruned_loss=0.08704, over 1423543.79 frames.], batch size: 20, lr: 9.50e-04
2022-05-26 22:07:55,981 INFO [train.py:842] (1/4) Epoch 5, batch 4450, loss[loss=0.2511, simple_loss=0.3128, pruned_loss=0.0947, over 7243.00 frames.], tot_loss[loss=0.2448, simple_loss=0.3156, pruned_loss=0.08699, over 1425477.58 frames.], batch size: 26, lr: 9.50e-04
2022-05-26 22:08:34,439 INFO [train.py:842] (1/4) Epoch 5, batch 4500, loss[loss=0.2388, simple_loss=0.3234, pruned_loss=0.07715, over 7376.00 frames.], tot_loss[loss=0.2449, simple_loss=0.3163, pruned_loss=0.08677, over 1424680.44 frames.], batch size: 23, lr: 9.49e-04
2022-05-26 22:09:13,382 INFO [train.py:842] (1/4) Epoch 5, batch 4550, loss[loss=0.2512, simple_loss=0.3163, pruned_loss=0.09305, over 7046.00 frames.], tot_loss[loss=0.2452, simple_loss=0.3161, pruned_loss=0.08716, over 1426521.82 frames.], batch size: 28, lr: 9.48e-04
2022-05-26 22:09:51,887 INFO [train.py:842] (1/4) Epoch 5, batch 4600, loss[loss=0.2699, simple_loss=0.3468, pruned_loss=0.09648, over 7207.00 frames.], tot_loss[loss=0.2456, simple_loss=0.3163, pruned_loss=0.08748, over 1422737.41 frames.], batch size: 22, lr: 9.48e-04
2022-05-26 22:10:31,342 INFO [train.py:842] (1/4) Epoch 5, batch 4650, loss[loss=0.2558, simple_loss=0.333, pruned_loss=0.08929, over 7318.00 frames.], tot_loss[loss=0.2446, simple_loss=0.3152, pruned_loss=0.08698, over 1424676.78 frames.], batch size: 21, lr: 9.47e-04
2022-05-26 22:11:09,880 INFO [train.py:842] (1/4) Epoch 5, batch 4700, loss[loss=0.2351, simple_loss=0.31, pruned_loss=0.08011, over 7328.00 frames.], tot_loss[loss=0.2439, simple_loss=0.3147, pruned_loss=0.08657, over 1424603.55 frames.], batch size: 20, lr: 9.47e-04
2022-05-26 22:11:48,510 INFO [train.py:842] (1/4) Epoch 5, batch 4750, loss[loss=0.2076, simple_loss=0.2898, pruned_loss=0.06275, over 7327.00 frames.], tot_loss[loss=0.2434, simple_loss=0.3146, pruned_loss=0.08603, over 1424262.31 frames.], batch size: 20, lr: 9.46e-04
2022-05-26 22:12:26,977 INFO [train.py:842] (1/4) Epoch 5, batch 4800, loss[loss=0.2666, simple_loss=0.3421, pruned_loss=0.09556, over 7340.00 frames.], tot_loss[loss=0.2447, simple_loss=0.3158, pruned_loss=0.08679, over 1423162.23 frames.], batch size: 22, lr: 9.46e-04
2022-05-26 22:13:05,802 INFO [train.py:842] (1/4) Epoch 5, batch 4850, loss[loss=0.1946, simple_loss=0.2745, pruned_loss=0.05735, over 7420.00 frames.], tot_loss[loss=0.2443, simple_loss=0.3157, pruned_loss=0.0864, over 1425748.42 frames.], batch size: 18, lr: 9.45e-04
2022-05-26 22:13:44,304 INFO [train.py:842] (1/4) Epoch 5, batch 4900, loss[loss=0.2866, simple_loss=0.345, pruned_loss=0.1141, over 7217.00 frames.], tot_loss[loss=0.246, simple_loss=0.3171, pruned_loss=0.08746, over 1425737.08 frames.], batch size: 23, lr: 9.45e-04
2022-05-26 22:14:23,577 INFO [train.py:842] (1/4) Epoch 5, batch 4950, loss[loss=0.2726, simple_loss=0.3351, pruned_loss=0.1051, over 7380.00 frames.], tot_loss[loss=0.246, simple_loss=0.3169, pruned_loss=0.08759, over 1428124.66 frames.], batch size: 23, lr: 9.44e-04
2022-05-26 22:15:02,123 INFO [train.py:842] (1/4) Epoch 5, batch 5000, loss[loss=0.2564, simple_loss=0.3305, pruned_loss=0.09113, over 7022.00 frames.], tot_loss[loss=0.2488, simple_loss=0.319, pruned_loss=0.08935, over 1425809.14 frames.], batch size: 28, lr: 9.43e-04
2022-05-26 22:15:41,364 INFO [train.py:842] (1/4) Epoch 5, batch 5050, loss[loss=0.2823, simple_loss=0.3401, pruned_loss=0.1123, over 7420.00 frames.], tot_loss[loss=0.2485, simple_loss=0.3185, pruned_loss=0.0893, over 1425603.07 frames.], batch size: 21, lr: 9.43e-04
2022-05-26 22:16:20,175 INFO [train.py:842] (1/4) Epoch 5, batch 5100, loss[loss=0.217, simple_loss=0.3074, pruned_loss=0.06333, over 7335.00 frames.], tot_loss[loss=0.2489, simple_loss=0.3197, pruned_loss=0.08908, over 1421371.79 frames.], batch size: 22, lr: 9.42e-04
2022-05-26 22:16:59,118 INFO [train.py:842] (1/4) Epoch 5, batch 5150, loss[loss=0.2699, simple_loss=0.3351, pruned_loss=0.1023, over 7322.00 frames.], tot_loss[loss=0.2465, simple_loss=0.3179, pruned_loss=0.0876, over 1423872.50 frames.], batch size: 20, lr: 9.42e-04
2022-05-26 22:17:37,805 INFO [train.py:842] (1/4) Epoch 5, batch 5200, loss[loss=0.2383, simple_loss=0.3104, pruned_loss=0.08307, over 7437.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3194, pruned_loss=0.08893, over 1422278.20 frames.], batch size: 20, lr: 9.41e-04
2022-05-26 22:18:16,852 INFO [train.py:842] (1/4) Epoch 5, batch 5250, loss[loss=0.2199, simple_loss=0.3051, pruned_loss=0.06737, over 7214.00 frames.], tot_loss[loss=0.2485, simple_loss=0.3193, pruned_loss=0.08888, over 1423137.42 frames.], batch size: 21, lr: 9.41e-04
2022-05-26 22:18:55,417 INFO [train.py:842] (1/4) Epoch 5, batch 5300, loss[loss=0.2456, simple_loss=0.2992, pruned_loss=0.096, over 7257.00 frames.], tot_loss[loss=0.2494, simple_loss=0.3194, pruned_loss=0.08964, over 1418593.86 frames.], batch size: 16, lr: 9.40e-04
2022-05-26 22:19:34,406 INFO [train.py:842] (1/4) Epoch 5, batch 5350, loss[loss=0.2502, simple_loss=0.3109, pruned_loss=0.09474, over 7414.00 frames.], tot_loss[loss=0.2517, simple_loss=0.3209, pruned_loss=0.09127, over 1422365.64 frames.], batch size: 20, lr: 9.40e-04
2022-05-26 22:20:12,914 INFO [train.py:842] (1/4) Epoch 5, batch 5400, loss[loss=0.2121, simple_loss=0.2834, pruned_loss=0.07045, over 7273.00 frames.], tot_loss[loss=0.2503, simple_loss=0.3198, pruned_loss=0.09042, over 1420606.66 frames.], batch size: 18, lr: 9.39e-04
2022-05-26 22:20:51,938 INFO [train.py:842] (1/4) Epoch 5, batch 5450, loss[loss=0.2396, simple_loss=0.3178, pruned_loss=0.08069, over 7335.00 frames.], tot_loss[loss=0.2496, simple_loss=0.3189, pruned_loss=0.09017, over 1424373.33 frames.], batch size: 22, lr: 9.38e-04
2022-05-26 22:21:30,507 INFO [train.py:842] (1/4) Epoch 5, batch 5500, loss[loss=0.2226, simple_loss=0.2986, pruned_loss=0.07327, over 7239.00 frames.], tot_loss[loss=0.2506, simple_loss=0.3199, pruned_loss=0.09061, over 1417711.70 frames.], batch size: 20, lr: 9.38e-04
2022-05-26 22:22:09,557 INFO [train.py:842] (1/4) Epoch 5, batch 5550, loss[loss=0.2567, simple_loss=0.3222, pruned_loss=0.09556, over 7306.00 frames.], tot_loss[loss=0.2498, simple_loss=0.3191, pruned_loss=0.09021, over 1419869.69 frames.], batch size: 25, lr: 9.37e-04
2022-05-26 22:22:48,044 INFO [train.py:842] (1/4) Epoch 5, batch 5600, loss[loss=0.2239, simple_loss=0.2969, pruned_loss=0.07547, over 7202.00 frames.], tot_loss[loss=0.2518, simple_loss=0.3213, pruned_loss=0.09114, over 1417377.61 frames.], batch size: 22, lr: 9.37e-04
2022-05-26 22:23:26,827 INFO [train.py:842] (1/4) Epoch 5, batch 5650, loss[loss=0.2507, simple_loss=0.3046, pruned_loss=0.09844, over 7418.00 frames.], tot_loss[loss=0.25, simple_loss=0.3202, pruned_loss=0.08992, over 1416213.50 frames.], batch size: 18, lr: 9.36e-04
2022-05-26 22:24:05,335 INFO [train.py:842] (1/4) Epoch 5, batch 5700, loss[loss=0.2781, simple_loss=0.3441, pruned_loss=0.1061, over 7181.00 frames.], tot_loss[loss=0.2473, simple_loss=0.318, pruned_loss=0.08824, over 1419260.22 frames.], batch size: 26, lr: 9.36e-04
2022-05-26 22:24:44,528 INFO [train.py:842] (1/4) Epoch 5, batch 5750, loss[loss=0.2376, simple_loss=0.3106, pruned_loss=0.0823, over 7171.00 frames.], tot_loss[loss=0.2464, simple_loss=0.3175, pruned_loss=0.08771, over 1424137.78 frames.], batch size: 18, lr: 9.35e-04
2022-05-26 22:25:23,055 INFO [train.py:842] (1/4) Epoch 5, batch 5800, loss[loss=0.2582, simple_loss=0.3131, pruned_loss=0.1016, over 4885.00 frames.], tot_loss[loss=0.2464, simple_loss=0.3171, pruned_loss=0.08783, over 1421273.16 frames.], batch size: 52, lr: 9.35e-04
2022-05-26 22:26:01,751 INFO [train.py:842] (1/4) Epoch 5, batch 5850, loss[loss=0.2276, simple_loss=0.3128, pruned_loss=0.07118, over 7146.00 frames.], tot_loss[loss=0.247, simple_loss=0.3177, pruned_loss=0.08811, over 1417982.77 frames.], batch size: 20, lr: 9.34e-04
2022-05-26 22:26:40,295 INFO [train.py:842] (1/4) Epoch 5, batch 5900, loss[loss=0.2586, simple_loss=0.331, pruned_loss=0.09311, over 6756.00 frames.], tot_loss[loss=0.248, simple_loss=0.3186, pruned_loss=0.08871, over 1419999.30 frames.], batch size: 31, lr: 9.34e-04
2022-05-26 22:27:19,122 INFO [train.py:842] (1/4) Epoch 5, batch 5950, loss[loss=0.2063, simple_loss=0.283, pruned_loss=0.06477, over 7165.00 frames.], tot_loss[loss=0.2478, simple_loss=0.318, pruned_loss=0.08884, over 1421444.17 frames.], batch size: 19, lr: 9.33e-04
2022-05-26 22:27:58,406 INFO [train.py:842] (1/4) Epoch 5, batch 6000, loss[loss=0.2723, simple_loss=0.342, pruned_loss=0.1012, over 7240.00 frames.], tot_loss[loss=0.2457, simple_loss=0.3164, pruned_loss=0.0875, over 1423914.95 frames.], batch size: 20, lr: 9.32e-04
2022-05-26 22:27:58,407 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 22:28:07,775 INFO [train.py:871] (1/4) Epoch 5, validation: loss=0.1919, simple_loss=0.2921, pruned_loss=0.04582, over 868885.00 frames.
2022-05-26 22:28:46,652 INFO [train.py:842] (1/4) Epoch 5, batch 6050, loss[loss=0.2062, simple_loss=0.2853, pruned_loss=0.06353, over 7156.00 frames.], tot_loss[loss=0.2441, simple_loss=0.3157, pruned_loss=0.08624, over 1424658.51 frames.], batch size: 18, lr: 9.32e-04
2022-05-26 22:29:25,228 INFO [train.py:842] (1/4) Epoch 5, batch 6100, loss[loss=0.3214, simple_loss=0.366, pruned_loss=0.1384, over 4959.00 frames.], tot_loss[loss=0.2446, simple_loss=0.3161, pruned_loss=0.08655, over 1420333.76 frames.], batch size: 53, lr: 9.31e-04
2022-05-26 22:30:04,159 INFO [train.py:842] (1/4) Epoch 5, batch 6150, loss[loss=0.2477, simple_loss=0.3156, pruned_loss=0.08991, over 7164.00 frames.], tot_loss[loss=0.2446, simple_loss=0.316, pruned_loss=0.08662, over 1423801.51 frames.], batch size: 18, lr: 9.31e-04
2022-05-26 22:30:42,871 INFO [train.py:842] (1/4) Epoch 5, batch 6200, loss[loss=0.1678, simple_loss=0.2396, pruned_loss=0.04794, over 7411.00 frames.], tot_loss[loss=0.2424, simple_loss=0.3142, pruned_loss=0.08534, over 1426430.53 frames.], batch size: 18, lr: 9.30e-04
2022-05-26 22:31:21,504 INFO [train.py:842] (1/4) Epoch 5, batch 6250, loss[loss=0.2053, simple_loss=0.303, pruned_loss=0.05385, over 7106.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3167, pruned_loss=0.08747, over 1426916.93 frames.], batch size: 21, lr: 9.30e-04
2022-05-26 22:32:00,103 INFO [train.py:842] (1/4) Epoch 5, batch 6300, loss[loss=0.2793, simple_loss=0.3548, pruned_loss=0.1019, over 7374.00 frames.], tot_loss[loss=0.2452, simple_loss=0.3164, pruned_loss=0.08702, over 1428775.43 frames.], batch size: 23, lr: 9.29e-04
2022-05-26 22:32:38,984 INFO [train.py:842] (1/4) Epoch 5, batch 6350, loss[loss=0.1994, simple_loss=0.2789, pruned_loss=0.0599, over 7157.00 frames.], tot_loss[loss=0.2461, simple_loss=0.3174, pruned_loss=0.08733, over 1428834.81 frames.], batch size: 18, lr: 9.29e-04
2022-05-26 22:33:17,672 INFO [train.py:842] (1/4) Epoch 5, batch 6400, loss[loss=0.2934, simple_loss=0.3589, pruned_loss=0.114, over 7326.00 frames.], tot_loss[loss=0.245, simple_loss=0.3163, pruned_loss=0.08688, over 1428615.31 frames.], batch size: 20, lr: 9.28e-04
2022-05-26 22:33:56,749 INFO [train.py:842] (1/4) Epoch 5, batch 6450, loss[loss=0.2793, simple_loss=0.3197, pruned_loss=0.1195, over 6810.00 frames.], tot_loss[loss=0.2478, simple_loss=0.3179, pruned_loss=0.0888, over 1431309.21 frames.], batch size: 15, lr: 9.28e-04
2022-05-26 22:34:35,281 INFO [train.py:842] (1/4) Epoch 5, batch 6500, loss[loss=0.2262, simple_loss=0.2971, pruned_loss=0.07765, over 7159.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3176, pruned_loss=0.08848, over 1429324.63 frames.], batch size: 18, lr: 9.27e-04
2022-05-26 22:35:13,831 INFO [train.py:842] (1/4) Epoch 5, batch 6550, loss[loss=0.2376, simple_loss=0.3108, pruned_loss=0.08226, over 7324.00 frames.], tot_loss[loss=0.2465, simple_loss=0.317, pruned_loss=0.08795, over 1423428.80 frames.], batch size: 21, lr: 9.27e-04
2022-05-26 22:35:52,501 INFO [train.py:842] (1/4) Epoch 5, batch 6600, loss[loss=0.2821, simple_loss=0.3487, pruned_loss=0.1078, over 6511.00 frames.], tot_loss[loss=0.2467, simple_loss=0.3174, pruned_loss=0.088, over 1423967.19 frames.], batch size: 38, lr: 9.26e-04
2022-05-26 22:36:31,330 INFO [train.py:842] (1/4) Epoch 5, batch 6650, loss[loss=0.1909, simple_loss=0.2721, pruned_loss=0.05481, over 7141.00 frames.], tot_loss[loss=0.2464, simple_loss=0.317, pruned_loss=0.08792, over 1423636.66 frames.], batch size: 20, lr: 9.26e-04
2022-05-26 22:37:09,822 INFO [train.py:842] (1/4) Epoch 5, batch 6700, loss[loss=0.3186, simple_loss=0.372, pruned_loss=0.1326, over 6754.00 frames.], tot_loss[loss=0.2478, simple_loss=0.3181, pruned_loss=0.08874, over 1423122.63 frames.], batch size: 31, lr: 9.25e-04
2022-05-26 22:37:49,189 INFO [train.py:842] (1/4) Epoch 5, batch 6750, loss[loss=0.2446, simple_loss=0.3144, pruned_loss=0.08738, over 7312.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3167, pruned_loss=0.08746, over 1426887.30 frames.], batch size: 21, lr: 9.25e-04
2022-05-26 22:38:27,816 INFO [train.py:842] (1/4) Epoch 5, batch 6800, loss[loss=0.2353, simple_loss=0.326, pruned_loss=0.07226, over 7307.00 frames.], tot_loss[loss=0.2465, simple_loss=0.3175, pruned_loss=0.08774, over 1424080.05 frames.], batch size: 24, lr: 9.24e-04
2022-05-26 22:39:06,770 INFO [train.py:842] (1/4) Epoch 5, batch 6850, loss[loss=0.2681, simple_loss=0.3337, pruned_loss=0.1012, over 7332.00 frames.], tot_loss[loss=0.2454, simple_loss=0.3168, pruned_loss=0.087, over 1425951.18 frames.], batch size: 20, lr: 9.23e-04
2022-05-26 22:39:45,101 INFO [train.py:842] (1/4) Epoch 5, batch 6900, loss[loss=0.2599, simple_loss=0.3316, pruned_loss=0.09411, over 7195.00 frames.], tot_loss[loss=0.2464, simple_loss=0.3179, pruned_loss=0.08751, over 1428007.29 frames.], batch size: 23, lr: 9.23e-04
2022-05-26 22:40:24,007 INFO [train.py:842] (1/4) Epoch 5, batch 6950, loss[loss=0.2798, simple_loss=0.3411, pruned_loss=0.1092, over 7119.00 frames.], tot_loss[loss=0.2467, simple_loss=0.3181, pruned_loss=0.08767, over 1429299.29 frames.], batch size: 21, lr: 9.22e-04
2022-05-26 22:41:02,650 INFO [train.py:842] (1/4) Epoch 5, batch 7000, loss[loss=0.1929, simple_loss=0.2684, pruned_loss=0.05873, over 7066.00 frames.], tot_loss[loss=0.2463, simple_loss=0.3176, pruned_loss=0.08746, over 1432513.27 frames.], batch size: 18, lr: 9.22e-04
2022-05-26 22:41:41,557 INFO [train.py:842] (1/4) Epoch 5, batch 7050, loss[loss=0.2514, simple_loss=0.3269, pruned_loss=0.088, over 7149.00 frames.], tot_loss[loss=0.2459, simple_loss=0.3168, pruned_loss=0.08744, over 1425226.74 frames.], batch size: 20, lr: 9.21e-04
2022-05-26 22:42:20,187 INFO [train.py:842] (1/4) Epoch 5, batch 7100, loss[loss=0.255, simple_loss=0.3261, pruned_loss=0.09195, over 7240.00 frames.], tot_loss[loss=0.2466, simple_loss=0.317, pruned_loss=0.08815, over 1429034.69 frames.], batch size: 20, lr: 9.21e-04
2022-05-26 22:42:59,027 INFO [train.py:842] (1/4) Epoch 5, batch 7150, loss[loss=0.1957, simple_loss=0.2693, pruned_loss=0.06111, over 7272.00 frames.], tot_loss[loss=0.2444, simple_loss=0.3158, pruned_loss=0.08655, over 1429142.86 frames.], batch size: 17, lr: 9.20e-04
2022-05-26 22:43:37,766 INFO [train.py:842] (1/4) Epoch 5, batch 7200, loss[loss=0.2273, simple_loss=0.2922, pruned_loss=0.08119, over 7216.00 frames.], tot_loss[loss=0.2452, simple_loss=0.3164, pruned_loss=0.08696, over 1427695.30 frames.], batch size: 16, lr: 9.20e-04
2022-05-26 22:44:16,575 INFO [train.py:842] (1/4) Epoch 5, batch 7250, loss[loss=0.1962, simple_loss=0.2794, pruned_loss=0.05653, over 7006.00 frames.], tot_loss[loss=0.2445, simple_loss=0.3158, pruned_loss=0.08665, over 1424306.33 frames.], batch size: 16, lr: 9.19e-04
2022-05-26 22:44:55,174 INFO [train.py:842] (1/4) Epoch 5, batch 7300, loss[loss=0.2652, simple_loss=0.3473, pruned_loss=0.0915, over 6814.00 frames.], tot_loss[loss=0.2449, simple_loss=0.316, pruned_loss=0.0869, over 1422756.32 frames.], batch size: 31, lr: 9.19e-04
2022-05-26 22:45:33,961 INFO [train.py:842] (1/4) Epoch 5, batch 7350, loss[loss=0.2681, simple_loss=0.3426, pruned_loss=0.09681, over 7032.00 frames.], tot_loss[loss=0.2455, simple_loss=0.3168, pruned_loss=0.08713, over 1422378.05 frames.], batch size: 28, lr: 9.18e-04
2022-05-26 22:46:12,482 INFO [train.py:842] (1/4) Epoch 5, batch 7400, loss[loss=0.2779, simple_loss=0.3393, pruned_loss=0.1082, over 7262.00 frames.], tot_loss[loss=0.2455, simple_loss=0.3163, pruned_loss=0.08731, over 1416697.95 frames.], batch size: 19, lr: 9.18e-04
2022-05-26 22:46:51,145 INFO [train.py:842] (1/4) Epoch 5, batch 7450, loss[loss=0.2039, simple_loss=0.271, pruned_loss=0.06842, over 7410.00 frames.], tot_loss[loss=0.2456, simple_loss=0.3159, pruned_loss=0.08763, over 1417998.20 frames.], batch size: 18, lr: 9.17e-04
2022-05-26 22:47:29,613 INFO [train.py:842] (1/4) Epoch 5, batch 7500, loss[loss=0.2274, simple_loss=0.3077, pruned_loss=0.0735, over 7284.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3164, pruned_loss=0.08761, over 1420826.14 frames.], batch size: 18, lr: 9.17e-04
2022-05-26 22:48:08,437 INFO [train.py:842] (1/4) Epoch 5, batch 7550, loss[loss=0.3006, simple_loss=0.3621, pruned_loss=0.1196, over 7341.00 frames.], tot_loss[loss=0.2464, simple_loss=0.317, pruned_loss=0.08786, over 1419948.25 frames.], batch size: 22, lr: 9.16e-04
2022-05-26 22:48:46,904 INFO [train.py:842] (1/4) Epoch 5, batch 7600, loss[loss=0.3135, simple_loss=0.3779, pruned_loss=0.1245, over 7211.00 frames.], tot_loss[loss=0.2467, simple_loss=0.3175, pruned_loss=0.08794, over 1419885.28 frames.], batch size: 22, lr: 9.16e-04
2022-05-26 22:49:25,701 INFO [train.py:842] (1/4) Epoch 5, batch 7650, loss[loss=0.2048, simple_loss=0.2894, pruned_loss=0.06012, over 7431.00 frames.], tot_loss[loss=0.2444, simple_loss=0.3154, pruned_loss=0.08663, over 1419398.12 frames.], batch size: 20, lr: 9.15e-04
2022-05-26 22:50:04,136 INFO [train.py:842] (1/4) Epoch 5, batch 7700, loss[loss=0.2021, simple_loss=0.2818, pruned_loss=0.06113, over 7148.00 frames.], tot_loss[loss=0.243, simple_loss=0.3144, pruned_loss=0.08579, over 1420823.51 frames.], batch size: 20, lr: 9.15e-04
2022-05-26 22:50:43,134 INFO [train.py:842] (1/4) Epoch 5, batch 7750, loss[loss=0.2023, simple_loss=0.2631, pruned_loss=0.07079, over 7415.00 frames.], tot_loss[loss=0.242, simple_loss=0.3135, pruned_loss=0.08522, over 1422743.62 frames.], batch size: 18, lr: 9.14e-04
2022-05-26 22:51:21,852 INFO [train.py:842] (1/4) Epoch 5, batch 7800, loss[loss=0.2108, simple_loss=0.2853, pruned_loss=0.06812, over 7328.00 frames.], tot_loss[loss=0.2411, simple_loss=0.3128, pruned_loss=0.08472, over 1425842.42 frames.], batch size: 20, lr: 9.14e-04
2022-05-26 22:52:00,606 INFO [train.py:842] (1/4) Epoch 5, batch 7850, loss[loss=0.3232, simple_loss=0.3637, pruned_loss=0.1414, over 7256.00 frames.], tot_loss[loss=0.2448, simple_loss=0.3158, pruned_loss=0.08691, over 1428128.54 frames.], batch size: 19, lr: 9.13e-04
2022-05-26 22:52:39,116 INFO [train.py:842] (1/4) Epoch 5, batch 7900, loss[loss=0.2215, simple_loss=0.2803, pruned_loss=0.08136, over 7268.00 frames.], tot_loss[loss=0.2456, simple_loss=0.3159, pruned_loss=0.0876, over 1429550.45 frames.], batch size: 17, lr: 9.13e-04
2022-05-26 22:53:18,009 INFO [train.py:842] (1/4) Epoch 5, batch 7950, loss[loss=0.2996, simple_loss=0.3572, pruned_loss=0.121, over 7107.00 frames.], tot_loss[loss=0.2453, simple_loss=0.3155, pruned_loss=0.08759, over 1427825.60 frames.], batch size: 28, lr: 9.12e-04
2022-05-26 22:53:56,513 INFO [train.py:842] (1/4) Epoch 5, batch 8000, loss[loss=0.23, simple_loss=0.2919, pruned_loss=0.08409, over 7133.00 frames.], tot_loss[loss=0.2447, simple_loss=0.3151, pruned_loss=0.08718, over 1428144.92 frames.], batch size: 17, lr: 9.12e-04
2022-05-26 22:54:35,460 INFO [train.py:842] (1/4) Epoch 5, batch 8050, loss[loss=0.2272, simple_loss=0.3049, pruned_loss=0.07477, over 7364.00 frames.], tot_loss[loss=0.2449, simple_loss=0.3154, pruned_loss=0.08724, over 1428644.86 frames.], batch size: 19, lr: 9.11e-04
2022-05-26 22:55:14,028 INFO [train.py:842] (1/4) Epoch 5, batch 8100, loss[loss=0.2548, simple_loss=0.3172, pruned_loss=0.09622, over 7038.00 frames.], tot_loss[loss=0.2447, simple_loss=0.3151, pruned_loss=0.08712, over 1429068.32 frames.], batch size: 28, lr: 9.11e-04
2022-05-26 22:55:52,781 INFO [train.py:842] (1/4) Epoch 5, batch 8150, loss[loss=0.2172, simple_loss=0.2978, pruned_loss=0.06828, over 7180.00 frames.], tot_loss[loss=0.2459, simple_loss=0.3164, pruned_loss=0.08768, over 1423121.28 frames.], batch size: 26, lr: 9.10e-04
2022-05-26 22:56:31,180 INFO [train.py:842] (1/4) Epoch 5, batch 8200, loss[loss=0.2846, simple_loss=0.3448, pruned_loss=0.1122, over 7236.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3165, pruned_loss=0.0876, over 1420210.11 frames.], batch size: 20, lr: 9.10e-04
2022-05-26 22:57:10,101 INFO [train.py:842] (1/4) Epoch 5, batch 8250, loss[loss=0.2482, simple_loss=0.3119, pruned_loss=0.09228, over 7294.00 frames.], tot_loss[loss=0.2469, simple_loss=0.3171, pruned_loss=0.08839, over 1421542.29 frames.], batch size: 18, lr: 9.09e-04
2022-05-26 22:57:48,664 INFO [train.py:842] (1/4) Epoch 5, batch 8300, loss[loss=0.2645, simple_loss=0.3329, pruned_loss=0.09803, over 7024.00 frames.], tot_loss[loss=0.2471, simple_loss=0.317, pruned_loss=0.08863, over 1424966.31 frames.], batch size: 28, lr: 9.09e-04
2022-05-26 22:58:27,290 INFO [train.py:842] (1/4) Epoch 5, batch 8350, loss[loss=0.2346, simple_loss=0.313, pruned_loss=0.07811, over 7420.00 frames.], tot_loss[loss=0.2484, simple_loss=0.3179, pruned_loss=0.08946, over 1421815.74 frames.], batch size: 21, lr: 9.08e-04
2022-05-26 22:59:05,771 INFO [train.py:842] (1/4) Epoch 5, batch 8400, loss[loss=0.2288, simple_loss=0.3015, pruned_loss=0.07807, over 7226.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3169, pruned_loss=0.08863, over 1422626.00 frames.], batch size: 20, lr: 9.08e-04
2022-05-26 22:59:44,478 INFO [train.py:842] (1/4) Epoch 5, batch 8450, loss[loss=0.2609, simple_loss=0.3128, pruned_loss=0.1045, over 7126.00 frames.], tot_loss[loss=0.2479, simple_loss=0.3175, pruned_loss=0.08918, over 1416718.09 frames.], batch size: 17, lr: 9.07e-04
2022-05-26 23:00:23,193 INFO [train.py:842] (1/4) Epoch 5, batch 8500, loss[loss=0.2308, simple_loss=0.2882, pruned_loss=0.08674, over 7293.00 frames.], tot_loss[loss=0.2463, simple_loss=0.3163, pruned_loss=0.08819, over 1419976.14 frames.], batch size: 17, lr: 9.07e-04
2022-05-26 23:01:02,283 INFO [train.py:842] (1/4) Epoch 5, batch 8550, loss[loss=0.2744, simple_loss=0.3307, pruned_loss=0.109, over 7249.00 frames.], tot_loss[loss=0.2458, simple_loss=0.316, pruned_loss=0.08778, over 1422223.09 frames.], batch size: 19, lr: 9.06e-04
2022-05-26 23:01:41,075 INFO [train.py:842] (1/4) Epoch 5, batch 8600, loss[loss=0.2336, simple_loss=0.2984, pruned_loss=0.08438, over 7357.00 frames.], tot_loss[loss=0.2457, simple_loss=0.3163, pruned_loss=0.08751, over 1423960.12 frames.], batch size: 19, lr: 9.06e-04
2022-05-26 23:02:19,898 INFO [train.py:842] (1/4) Epoch 5, batch 8650, loss[loss=0.2692, simple_loss=0.3359, pruned_loss=0.1012, over 7223.00 frames.], tot_loss[loss=0.2447, simple_loss=0.3155, pruned_loss=0.08692, over 1419303.53 frames.], batch size: 21, lr: 9.05e-04
2022-05-26 23:02:58,459 INFO [train.py:842] (1/4) Epoch 5, batch 8700, loss[loss=0.2573, simple_loss=0.3224, pruned_loss=0.09614, over 7231.00 frames.], tot_loss[loss=0.244, simple_loss=0.315, pruned_loss=0.0865, over 1417208.19 frames.], batch size: 20, lr: 9.05e-04
2022-05-26 23:03:37,420 INFO [train.py:842] (1/4) Epoch 5, batch 8750, loss[loss=0.3078, simple_loss=0.3687, pruned_loss=0.1235, over 7173.00 frames.], tot_loss[loss=0.2423, simple_loss=0.3141, pruned_loss=0.08524, over 1418086.08 frames.], batch size: 26, lr: 9.04e-04
2022-05-26 23:04:16,073 INFO [train.py:842] (1/4) Epoch 5, batch 8800, loss[loss=0.3094, simple_loss=0.3695, pruned_loss=0.1246, over 7267.00 frames.], tot_loss[loss=0.2448, simple_loss=0.3161, pruned_loss=0.08678, over 1417438.47 frames.], batch size: 24, lr: 9.04e-04
2022-05-26 23:04:55,062 INFO [train.py:842] (1/4) Epoch 5, batch 8850, loss[loss=0.2834, simple_loss=0.3515, pruned_loss=0.1077, over 5155.00 frames.], tot_loss[loss=0.2432, simple_loss=0.3144, pruned_loss=0.08603, over 1412213.05 frames.], batch size: 54, lr: 9.03e-04
2022-05-26 23:05:33,495 INFO [train.py:842] (1/4) Epoch 5, batch 8900, loss[loss=0.2101, simple_loss=0.3016, pruned_loss=0.05929, over 6231.00 frames.], tot_loss[loss=0.2438, simple_loss=0.3152, pruned_loss=0.08618, over 1411481.08 frames.], batch size: 37, lr: 9.03e-04
2022-05-26 23:06:11,785 INFO [train.py:842] (1/4) Epoch 5, batch 8950, loss[loss=0.2618, simple_loss=0.3394, pruned_loss=0.0921, over 7211.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3173, pruned_loss=0.08716, over 1402403.89 frames.], batch size: 23, lr: 9.02e-04
2022-05-26 23:06:49,920 INFO [train.py:842] (1/4) Epoch 5, batch 9000, loss[loss=0.2552, simple_loss=0.3292, pruned_loss=0.09056, over 6460.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3202, pruned_loss=0.08847, over 1395673.69 frames.], batch size: 38, lr: 9.02e-04
2022-05-26 23:06:49,921 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 23:06:59,289 INFO [train.py:871] (1/4) Epoch 5, validation: loss=0.191, simple_loss=0.2904, pruned_loss=0.04585, over 868885.00 frames.
2022-05-26 23:07:37,138 INFO [train.py:842] (1/4) Epoch 5, batch 9050, loss[loss=0.238, simple_loss=0.3111, pruned_loss=0.08242, over 5017.00 frames.], tot_loss[loss=0.2524, simple_loss=0.3231, pruned_loss=0.09087, over 1362964.83 frames.], batch size: 52, lr: 9.01e-04
2022-05-26 23:08:14,674 INFO [train.py:842] (1/4) Epoch 5, batch 9100, loss[loss=0.2609, simple_loss=0.3218, pruned_loss=0.09997, over 4918.00 frames.], tot_loss[loss=0.2596, simple_loss=0.328, pruned_loss=0.09555, over 1296499.10 frames.], batch size: 52, lr: 9.01e-04
2022-05-26 23:08:52,435 INFO [train.py:842] (1/4) Epoch 5, batch 9150, loss[loss=0.2814, simple_loss=0.3529, pruned_loss=0.105, over 5333.00 frames.], tot_loss[loss=0.2657, simple_loss=0.3319, pruned_loss=0.09981, over 1235403.38 frames.], batch size: 52, lr: 9.00e-04
2022-05-26 23:09:43,993 INFO [train.py:842] (1/4) Epoch 6, batch 0, loss[loss=0.2332, simple_loss=0.3011, pruned_loss=0.08267, over 7168.00 frames.], tot_loss[loss=0.2332, simple_loss=0.3011, pruned_loss=0.08267, over 7168.00 frames.], batch size: 19, lr: 8.65e-04
2022-05-26 23:10:23,200 INFO [train.py:842] (1/4) Epoch 6, batch 50, loss[loss=0.2429, simple_loss=0.3113, pruned_loss=0.08727, over 4968.00 frames.], tot_loss[loss=0.2457, simple_loss=0.3186, pruned_loss=0.08639, over 317956.89 frames.], batch size: 53, lr: 8.64e-04
2022-05-26 23:11:01,569 INFO [train.py:842] (1/4) Epoch 6, batch 100, loss[loss=0.1944, simple_loss=0.2792, pruned_loss=0.05477, over 7144.00 frames.], tot_loss[loss=0.245, simple_loss=0.3177, pruned_loss=0.08617, over 561207.41 frames.], batch size: 20, lr: 8.64e-04
2022-05-26 23:11:40,516 INFO [train.py:842] (1/4) Epoch 6, batch 150, loss[loss=0.2543, simple_loss=0.3144, pruned_loss=0.09714, over 6854.00 frames.], tot_loss[loss=0.2451, simple_loss=0.3169, pruned_loss=0.08665, over 750212.67 frames.], batch size: 31, lr: 8.63e-04
2022-05-26 23:12:19,111 INFO [train.py:842] (1/4) Epoch 6, batch 200, loss[loss=0.2733, simple_loss=0.3272, pruned_loss=0.1097, over 7399.00 frames.], tot_loss[loss=0.2448, simple_loss=0.317, pruned_loss=0.08634, over 899401.82 frames.], batch size: 18, lr: 8.63e-04
2022-05-26 23:12:57,963 INFO [train.py:842] (1/4) Epoch 6, batch 250, loss[loss=0.2412, simple_loss=0.3252, pruned_loss=0.07855, over 7325.00 frames.], tot_loss[loss=0.2432, simple_loss=0.316, pruned_loss=0.0852, over 1019370.12 frames.], batch size: 22, lr: 8.62e-04
2022-05-26 23:13:36,480 INFO [train.py:842] (1/4) Epoch 6, batch 300, loss[loss=0.2079, simple_loss=0.2963, pruned_loss=0.05977, over 7233.00 frames.], tot_loss[loss=0.2413, simple_loss=0.3139, pruned_loss=0.08433, over 1112298.62 frames.], batch size: 20, lr: 8.62e-04
2022-05-26 23:14:15,762 INFO [train.py:842] (1/4) Epoch 6, batch 350, loss[loss=0.1925, simple_loss=0.2836, pruned_loss=0.05071, over 7329.00 frames.], tot_loss[loss=0.2398, simple_loss=0.3124, pruned_loss=0.08364, over 1184740.81 frames.], batch size: 20, lr: 8.61e-04
2022-05-26 23:14:54,195 INFO [train.py:842] (1/4) Epoch 6, batch 400, loss[loss=0.2631, simple_loss=0.3272, pruned_loss=0.09947, over 7377.00 frames.], tot_loss[loss=0.2411, simple_loss=0.3137, pruned_loss=0.08428, over 1236397.76 frames.], batch size: 23, lr: 8.61e-04
2022-05-26 23:15:33,154 INFO [train.py:842] (1/4) Epoch 6, batch 450, loss[loss=0.2139, simple_loss=0.2889, pruned_loss=0.06948, over 7212.00 frames.], tot_loss[loss=0.241, simple_loss=0.3139, pruned_loss=0.08408, over 1279612.74 frames.], batch size: 16, lr: 8.61e-04
2022-05-26 23:16:11,617 INFO [train.py:842] (1/4) Epoch 6, batch 500, loss[loss=0.2694, simple_loss=0.3309, pruned_loss=0.1039, over 4839.00 frames.], tot_loss[loss=0.2422, simple_loss=0.3151, pruned_loss=0.0846, over 1309705.41 frames.], batch size: 53, lr: 8.60e-04
2022-05-26 23:16:50,470 INFO [train.py:842] (1/4) Epoch 6, batch 550, loss[loss=0.2189, simple_loss=0.3063, pruned_loss=0.06571, over 6188.00 frames.], tot_loss[loss=0.2412, simple_loss=0.3143, pruned_loss=0.08406, over 1333791.86 frames.], batch size: 37, lr: 8.60e-04
2022-05-26 23:17:29,196 INFO [train.py:842] (1/4) Epoch 6, batch 600, loss[loss=0.2701, simple_loss=0.3333, pruned_loss=0.1035, over 7146.00 frames.], tot_loss[loss=0.2398, simple_loss=0.3125, pruned_loss=0.08358, over 1352599.28 frames.], batch size: 20, lr: 8.59e-04
2022-05-26 23:18:08,035 INFO [train.py:842] (1/4) Epoch 6, batch 650, loss[loss=0.2677, simple_loss=0.3454, pruned_loss=0.09502, over 7396.00 frames.], tot_loss[loss=0.2385, simple_loss=0.3117, pruned_loss=0.08267, over 1367024.92 frames.], batch size: 21, lr: 8.59e-04
2022-05-26 23:18:46,484 INFO [train.py:842] (1/4) Epoch 6, batch 700, loss[loss=0.2303, simple_loss=0.2859, pruned_loss=0.08738, over 7250.00 frames.], tot_loss[loss=0.2395, simple_loss=0.3125, pruned_loss=0.08324, over 1378429.94 frames.], batch size: 16, lr: 8.58e-04
2022-05-26 23:19:25,318 INFO [train.py:842] (1/4) Epoch 6, batch 750, loss[loss=0.2468, simple_loss=0.3238, pruned_loss=0.08491, over 7216.00 frames.], tot_loss[loss=0.2424, simple_loss=0.3148, pruned_loss=0.08502, over 1387422.51 frames.], batch size: 21, lr: 8.58e-04
2022-05-26 23:20:03,927 INFO [train.py:842] (1/4) Epoch 6, batch 800, loss[loss=0.2126, simple_loss=0.2944, pruned_loss=0.06545, over 7226.00 frames.], tot_loss[loss=0.2414, simple_loss=0.314, pruned_loss=0.08442, over 1397685.89 frames.], batch size: 21, lr: 8.57e-04
2022-05-26 23:20:42,688 INFO [train.py:842] (1/4) Epoch 6, batch 850, loss[loss=0.2366, simple_loss=0.3252, pruned_loss=0.07399, over 7204.00 frames.], tot_loss[loss=0.2391, simple_loss=0.3123, pruned_loss=0.08294, over 1402502.26 frames.], batch size: 23, lr: 8.57e-04
2022-05-26 23:21:21,250 INFO [train.py:842] (1/4) Epoch 6, batch 900, loss[loss=0.2562, simple_loss=0.3256, pruned_loss=0.09342, over 7420.00 frames.], tot_loss[loss=0.2394, simple_loss=0.3125, pruned_loss=0.08313, over 1404687.93 frames.], batch size: 21, lr: 8.56e-04
2022-05-26 23:21:59,950 INFO [train.py:842] (1/4) Epoch 6, batch 950, loss[loss=0.1851, simple_loss=0.2649, pruned_loss=0.05266, over 7135.00 frames.], tot_loss[loss=0.2389, simple_loss=0.3118, pruned_loss=0.08305, over 1406072.27 frames.], batch size: 17, lr: 8.56e-04
2022-05-26 23:22:38,498 INFO [train.py:842] (1/4) Epoch 6, batch 1000, loss[loss=0.3107, simple_loss=0.3614, pruned_loss=0.13, over 7414.00 frames.], tot_loss[loss=0.239, simple_loss=0.3118, pruned_loss=0.08311, over 1407050.41 frames.], batch size: 21, lr: 8.56e-04
2022-05-26 23:23:17,202 INFO [train.py:842] (1/4) Epoch 6, batch 1050, loss[loss=0.2358, simple_loss=0.3096, pruned_loss=0.08103, over 7327.00 frames.], tot_loss[loss=0.2395, simple_loss=0.3119, pruned_loss=0.08355, over 1411998.04 frames.], batch size: 20, lr: 8.55e-04
2022-05-26 23:23:55,808 INFO [train.py:842] (1/4) Epoch 6, batch 1100, loss[loss=0.2464, simple_loss=0.3173, pruned_loss=0.08774, over 7326.00 frames.], tot_loss[loss=0.2417, simple_loss=0.3135, pruned_loss=0.08494, over 1407525.62 frames.], batch size: 21, lr: 8.55e-04
2022-05-26 23:24:34,775 INFO [train.py:842] (1/4) Epoch 6, batch 1150, loss[loss=0.2137, simple_loss=0.312, pruned_loss=0.05766, over 7145.00 frames.], tot_loss[loss=0.2424, simple_loss=0.3149, pruned_loss=0.08493, over 1412392.85 frames.], batch size: 20, lr: 8.54e-04
2022-05-26 23:25:13,269 INFO [train.py:842] (1/4) Epoch 6, batch 1200, loss[loss=0.2463, simple_loss=0.3138, pruned_loss=0.08937, over 7204.00 frames.], tot_loss[loss=0.2414, simple_loss=0.3141, pruned_loss=0.08438, over 1413569.71 frames.], batch size: 26, lr: 8.54e-04
2022-05-26 23:25:52,102 INFO [train.py:842] (1/4) Epoch 6, batch 1250, loss[loss=0.2398, simple_loss=0.3292, pruned_loss=0.07514, over 7137.00 frames.], tot_loss[loss=0.2419, simple_loss=0.3145, pruned_loss=0.0847, over 1413006.28 frames.], batch size: 20, lr: 8.53e-04
2022-05-26 23:26:30,734 INFO [train.py:842] (1/4) Epoch 6, batch 1300, loss[loss=0.2129, simple_loss=0.2905, pruned_loss=0.06761, over 7358.00 frames.], tot_loss[loss=0.2425, simple_loss=0.3145, pruned_loss=0.0852, over 1411799.20 frames.], batch size: 19, lr: 8.53e-04
2022-05-26 23:27:09,697 INFO [train.py:842] (1/4) Epoch 6, batch 1350, loss[loss=0.2463, simple_loss=0.3193, pruned_loss=0.08671, over 7016.00 frames.], tot_loss[loss=0.2414, simple_loss=0.3135, pruned_loss=0.08471, over 1414365.02 frames.], batch size: 28, lr: 8.52e-04
2022-05-26 23:27:48,130 INFO [train.py:842] (1/4) Epoch 6, batch 1400, loss[loss=0.175, simple_loss=0.2695, pruned_loss=0.04027, over 7329.00 frames.], tot_loss[loss=0.2398, simple_loss=0.3126, pruned_loss=0.0835, over 1418248.31 frames.], batch size: 20, lr: 8.52e-04
2022-05-26 23:28:26,823 INFO [train.py:842] (1/4) Epoch 6, batch 1450, loss[loss=0.3177, simple_loss=0.3757, pruned_loss=0.1299, over 7440.00 frames.], tot_loss[loss=0.2409, simple_loss=0.3137, pruned_loss=0.0841, over 1419516.83 frames.], batch size: 20, lr: 8.52e-04
2022-05-26 23:29:05,419 INFO [train.py:842] (1/4) Epoch 6, batch 1500, loss[loss=0.1854, simple_loss=0.2736, pruned_loss=0.04861, over 7155.00 frames.], tot_loss[loss=0.2415, simple_loss=0.3143, pruned_loss=0.08432, over 1419727.79 frames.], batch size: 20, lr: 8.51e-04
2022-05-26 23:29:44,049 INFO [train.py:842] (1/4) Epoch 6, batch 1550, loss[loss=0.251, simple_loss=0.3148, pruned_loss=0.0936, over 7297.00 frames.], tot_loss[loss=0.2415, simple_loss=0.3144, pruned_loss=0.08428, over 1422069.91 frames.], batch size: 17, lr: 8.51e-04
2022-05-26 23:30:22,450 INFO [train.py:842] (1/4) Epoch 6, batch 1600, loss[loss=0.262, simple_loss=0.3246, pruned_loss=0.09969, over 7440.00 frames.], tot_loss[loss=0.2418, simple_loss=0.3148, pruned_loss=0.0844, over 1416014.50 frames.], batch size: 20, lr: 8.50e-04
2022-05-26 23:31:01,146 INFO [train.py:842] (1/4) Epoch 6, batch 1650, loss[loss=0.2871, simple_loss=0.366, pruned_loss=0.1041, over 7286.00 frames.], tot_loss[loss=0.2411, simple_loss=0.3139, pruned_loss=0.08413, over 1416108.06 frames.], batch size: 25, lr: 8.50e-04
2022-05-26 23:31:39,550 INFO [train.py:842] (1/4) Epoch 6, batch 1700, loss[loss=0.2276, simple_loss=0.3199, pruned_loss=0.06762, over 7213.00 frames.], tot_loss[loss=0.2412, simple_loss=0.3139, pruned_loss=0.08425, over 1414281.46 frames.], batch size: 22, lr: 8.49e-04
2022-05-26 23:32:18,315 INFO [train.py:842] (1/4) Epoch 6, batch 1750, loss[loss=0.2044, simple_loss=0.2784, pruned_loss=0.06527, over 7278.00 frames.], tot_loss[loss=0.2397, simple_loss=0.313, pruned_loss=0.08323, over 1411130.02 frames.], batch size: 18, lr: 8.49e-04
2022-05-26 23:32:56,831 INFO [train.py:842] (1/4) Epoch 6, batch 1800, loss[loss=0.3063, simple_loss=0.357, pruned_loss=0.1278, over 4811.00 frames.], tot_loss[loss=0.2393, simple_loss=0.3128, pruned_loss=0.08283, over 1413536.36 frames.], batch size: 52, lr: 8.48e-04
2022-05-26 23:33:35,704 INFO [train.py:842] (1/4) Epoch 6, batch 1850, loss[loss=0.1856, simple_loss=0.2725, pruned_loss=0.04938, over 7160.00 frames.], tot_loss[loss=0.2378, simple_loss=0.3115, pruned_loss=0.08209, over 1416302.44 frames.], batch size: 18, lr: 8.48e-04
2022-05-26 23:34:14,184 INFO [train.py:842] (1/4) Epoch 6, batch 1900, loss[loss=0.2379, simple_loss=0.2963, pruned_loss=0.08978, over 7128.00 frames.], tot_loss[loss=0.2384, simple_loss=0.312, pruned_loss=0.08239, over 1415326.98 frames.], batch size: 17, lr: 8.48e-04
2022-05-26 23:34:52,998 INFO [train.py:842] (1/4) Epoch 6, batch 1950, loss[loss=0.2344, simple_loss=0.3078, pruned_loss=0.08048, over 7112.00 frames.], tot_loss[loss=0.239, simple_loss=0.3126, pruned_loss=0.08268, over 1420147.71 frames.], batch size: 21, lr: 8.47e-04
2022-05-26 23:35:31,650 INFO [train.py:842] (1/4) Epoch 6, batch 2000, loss[loss=0.2552, simple_loss=0.3228, pruned_loss=0.09384, over 7286.00 frames.], tot_loss[loss=0.2382, simple_loss=0.3118, pruned_loss=0.0823, over 1423933.68 frames.], batch size: 18, lr: 8.47e-04
2022-05-26 23:36:13,258 INFO [train.py:842] (1/4) Epoch 6, batch 2050, loss[loss=0.2664, simple_loss=0.3426, pruned_loss=0.09504, over 7087.00 frames.], tot_loss[loss=0.239, simple_loss=0.313, pruned_loss=0.08249, over 1424314.20 frames.], batch size: 28, lr: 8.46e-04
2022-05-26 23:36:51,795 INFO [train.py:842] (1/4) Epoch 6, batch 2100, loss[loss=0.2701, simple_loss=0.3359, pruned_loss=0.1022, over 6438.00 frames.], tot_loss[loss=0.239, simple_loss=0.3129, pruned_loss=0.08255, over 1425559.49 frames.], batch size: 37, lr: 8.46e-04
2022-05-26 23:37:30,931 INFO [train.py:842] (1/4) Epoch 6, batch 2150, loss[loss=0.2698, simple_loss=0.3512, pruned_loss=0.09419, over 7151.00 frames.], tot_loss[loss=0.2383, simple_loss=0.3125, pruned_loss=0.08203, over 1430873.29 frames.], batch size: 20, lr: 8.45e-04
2022-05-26 23:38:09,306 INFO [train.py:842] (1/4) Epoch 6, batch 2200, loss[loss=0.248, simple_loss=0.3364, pruned_loss=0.07978, over 7140.00 frames.], tot_loss[loss=0.2389, simple_loss=0.3128, pruned_loss=0.0825, over 1427536.85 frames.], batch size: 20, lr: 8.45e-04
2022-05-26 23:38:48,207 INFO [train.py:842] (1/4) Epoch 6, batch 2250, loss[loss=0.2085, simple_loss=0.2875, pruned_loss=0.06474, over 7361.00 frames.], tot_loss[loss=0.2378, simple_loss=0.3119, pruned_loss=0.08178, over 1425928.48 frames.], batch size: 19, lr: 8.45e-04
2022-05-26 23:39:26,674 INFO [train.py:842] (1/4) Epoch 6, batch 2300, loss[loss=0.199, simple_loss=0.2875, pruned_loss=0.0553, over 7283.00 frames.], tot_loss[loss=0.2388, simple_loss=0.3124, pruned_loss=0.08261, over 1423352.79 frames.], batch size: 24, lr: 8.44e-04
2022-05-26 23:40:05,772 INFO [train.py:842] (1/4) Epoch 6, batch 2350, loss[loss=0.228, simple_loss=0.3077, pruned_loss=0.07415, over 7211.00 frames.], tot_loss[loss=0.2388, simple_loss=0.312, pruned_loss=0.08277, over 1423234.91 frames.], batch size: 21, lr: 8.44e-04
2022-05-26 23:40:44,303 INFO [train.py:842] (1/4) Epoch 6, batch 2400, loss[loss=0.2158, simple_loss=0.3045, pruned_loss=0.06358, over 7320.00 frames.], tot_loss[loss=0.2384, simple_loss=0.3113, pruned_loss=0.08279, over 1423412.84 frames.], batch size: 20, lr: 8.43e-04
2022-05-26 23:41:23,354 INFO [train.py:842] (1/4) Epoch 6, batch 2450, loss[loss=0.2056, simple_loss=0.2672, pruned_loss=0.07204, over 6780.00 frames.], tot_loss[loss=0.2379, simple_loss=0.3102, pruned_loss=0.08274, over 1422788.59 frames.], batch size: 15, lr: 8.43e-04
2022-05-26 23:42:01,782 INFO [train.py:842] (1/4) Epoch 6, batch 2500, loss[loss=0.2186, simple_loss=0.3045, pruned_loss=0.0664, over 7327.00 frames.], tot_loss[loss=0.2395, simple_loss=0.3116, pruned_loss=0.08366, over 1422079.80 frames.], batch size: 22, lr: 8.42e-04
2022-05-26 23:42:40,570 INFO [train.py:842] (1/4) Epoch 6, batch 2550, loss[loss=0.1814, simple_loss=0.2495, pruned_loss=0.05662, over 6761.00 frames.], tot_loss[loss=0.238, simple_loss=0.3105, pruned_loss=0.08275, over 1423507.15 frames.], batch size: 15, lr: 8.42e-04
2022-05-26 23:43:19,153 INFO [train.py:842] (1/4) Epoch 6, batch 2600, loss[loss=0.2469, simple_loss=0.3229, pruned_loss=0.08549, over 7316.00 frames.], tot_loss[loss=0.239, simple_loss=0.3115, pruned_loss=0.08324, over 1426184.51 frames.], batch size: 21, lr: 8.42e-04
2022-05-26 23:43:57,917 INFO [train.py:842] (1/4) Epoch 6, batch 2650, loss[loss=0.2795, simple_loss=0.35, pruned_loss=0.1045, over 7297.00 frames.], tot_loss[loss=0.24, simple_loss=0.3127, pruned_loss=0.08368, over 1423655.69 frames.], batch size: 25, lr: 8.41e-04
2022-05-26 23:44:36,529 INFO [train.py:842] (1/4) Epoch 6, batch 2700, loss[loss=0.1986, simple_loss=0.2723, pruned_loss=0.06247, over 6756.00 frames.], tot_loss[loss=0.2396, simple_loss=0.3124, pruned_loss=0.08342, over 1425656.60 frames.], batch size: 15, lr: 8.41e-04
2022-05-26 23:45:15,205 INFO [train.py:842] (1/4) Epoch 6, batch 2750, loss[loss=0.2291, simple_loss=0.3144, pruned_loss=0.07193, over 7235.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3121, pruned_loss=0.08252, over 1423757.84 frames.], batch size: 20, lr: 8.40e-04
2022-05-26 23:45:53,698 INFO [train.py:842] (1/4) Epoch 6, batch 2800, loss[loss=0.1824, simple_loss=0.2566, pruned_loss=0.0541, over 7274.00 frames.], tot_loss[loss=0.2359, simple_loss=0.31, pruned_loss=0.0809, over 1421432.69 frames.], batch size: 18, lr: 8.40e-04
2022-05-26 23:46:32,467 INFO [train.py:842] (1/4) Epoch 6, batch 2850, loss[loss=0.2103, simple_loss=0.2805, pruned_loss=0.07012, over 7284.00 frames.], tot_loss[loss=0.2359, simple_loss=0.31, pruned_loss=0.08091, over 1418511.25 frames.], batch size: 17, lr: 8.39e-04
2022-05-26 23:47:10,995 INFO [train.py:842] (1/4) Epoch 6, batch 2900, loss[loss=0.2837, simple_loss=0.3468, pruned_loss=0.1103, over 6720.00 frames.], tot_loss[loss=0.2366, simple_loss=0.3103, pruned_loss=0.08145, over 1420600.32 frames.], batch size: 31, lr: 8.39e-04
2022-05-26 23:47:50,224 INFO [train.py:842] (1/4) Epoch 6, batch 2950, loss[loss=0.2306, simple_loss=0.3232, pruned_loss=0.06899, over 7136.00 frames.], tot_loss[loss=0.2359, simple_loss=0.31, pruned_loss=0.08094, over 1420898.29 frames.], batch size: 20, lr: 8.39e-04
2022-05-26 23:48:28,896 INFO [train.py:842] (1/4) Epoch 6, batch 3000, loss[loss=0.2285, simple_loss=0.3178, pruned_loss=0.06961, over 7235.00 frames.], tot_loss[loss=0.2368, simple_loss=0.3106, pruned_loss=0.08147, over 1419991.03 frames.], batch size: 20, lr: 8.38e-04
2022-05-26 23:48:28,896 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 23:48:38,152 INFO [train.py:871] (1/4) Epoch 6, validation: loss=0.1895, simple_loss=0.2892, pruned_loss=0.04494, over 868885.00 frames.
2022-05-26 23:49:17,577 INFO [train.py:842] (1/4) Epoch 6, batch 3050, loss[loss=0.2671, simple_loss=0.3445, pruned_loss=0.09484, over 7211.00 frames.], tot_loss[loss=0.2361, simple_loss=0.31, pruned_loss=0.08107, over 1425696.17 frames.], batch size: 23, lr: 8.38e-04
2022-05-26 23:49:56,215 INFO [train.py:842] (1/4) Epoch 6, batch 3100, loss[loss=0.2252, simple_loss=0.3009, pruned_loss=0.07478, over 7329.00 frames.], tot_loss[loss=0.2354, simple_loss=0.3089, pruned_loss=0.08099, over 1423692.44 frames.], batch size: 22, lr: 8.37e-04
2022-05-26 23:50:35,001 INFO [train.py:842] (1/4) Epoch 6, batch 3150, loss[loss=0.2152, simple_loss=0.2894, pruned_loss=0.07045, over 7195.00 frames.], tot_loss[loss=0.2365, simple_loss=0.3098, pruned_loss=0.08161, over 1423788.10 frames.], batch size: 23, lr: 8.37e-04
2022-05-26 23:51:13,523 INFO [train.py:842] (1/4) Epoch 6, batch 3200, loss[loss=0.2754, simple_loss=0.3443, pruned_loss=0.1033, over 7228.00 frames.], tot_loss[loss=0.2376, simple_loss=0.3111, pruned_loss=0.08205, over 1425552.42 frames.], batch size: 21, lr: 8.36e-04
2022-05-26 23:51:52,211 INFO [train.py:842] (1/4) Epoch 6, batch 3250, loss[loss=0.2443, simple_loss=0.302, pruned_loss=0.09331, over 7350.00 frames.], tot_loss[loss=0.2374, simple_loss=0.3108, pruned_loss=0.08195, over 1425418.31 frames.], batch size: 19, lr: 8.36e-04
2022-05-26 23:52:30,644 INFO [train.py:842] (1/4) Epoch 6, batch 3300, loss[loss=0.2339, simple_loss=0.3209, pruned_loss=0.07347, over 7206.00 frames.], tot_loss[loss=0.2377, simple_loss=0.3114, pruned_loss=0.08194, over 1421235.21 frames.], batch size: 23, lr: 8.36e-04
2022-05-26 23:53:09,710 INFO [train.py:842] (1/4) Epoch 6, batch 3350, loss[loss=0.212, simple_loss=0.2912, pruned_loss=0.06641, over 7264.00 frames.], tot_loss[loss=0.2373, simple_loss=0.311, pruned_loss=0.08177, over 1425675.24 frames.], batch size: 19, lr: 8.35e-04
2022-05-26 23:53:48,173 INFO [train.py:842] (1/4) Epoch 6, batch 3400, loss[loss=0.2372, simple_loss=0.3175, pruned_loss=0.07844, over 7287.00 frames.], tot_loss[loss=0.2364, simple_loss=0.3105, pruned_loss=0.0812, over 1424738.19 frames.], batch size: 24, lr: 8.35e-04
2022-05-26 23:54:27,002 INFO [train.py:842] (1/4) Epoch 6, batch 3450, loss[loss=0.2287, simple_loss=0.3022, pruned_loss=0.0776, over 7414.00 frames.], tot_loss[loss=0.2378, simple_loss=0.3119, pruned_loss=0.08179, over 1426661.30 frames.], batch size: 21, lr: 8.34e-04
2022-05-26 23:55:05,851 INFO [train.py:842] (1/4) Epoch 6, batch 3500, loss[loss=0.2538, simple_loss=0.3229, pruned_loss=0.09234, over 7212.00 frames.], tot_loss[loss=0.2374, simple_loss=0.3109, pruned_loss=0.08193, over 1423752.09 frames.], batch size: 22, lr: 8.34e-04
2022-05-26 23:55:44,590 INFO [train.py:842] (1/4) Epoch 6, batch 3550, loss[loss=0.1779, simple_loss=0.2661, pruned_loss=0.04482, over 7323.00 frames.], tot_loss[loss=0.2351, simple_loss=0.3092, pruned_loss=0.08053, over 1427001.82 frames.], batch size: 21, lr: 8.33e-04
2022-05-26 23:56:22,921 INFO [train.py:842] (1/4) Epoch 6, batch 3600, loss[loss=0.2165, simple_loss=0.2834, pruned_loss=0.07478, over 7168.00 frames.], tot_loss[loss=0.2338, simple_loss=0.3082, pruned_loss=0.07973, over 1428018.95 frames.], batch size: 18, lr: 8.33e-04
2022-05-26 23:57:01,910 INFO [train.py:842] (1/4) Epoch 6, batch 3650, loss[loss=0.2319, simple_loss=0.31, pruned_loss=0.07688, over 7412.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3086, pruned_loss=0.08025, over 1427487.21 frames.], batch size: 21, lr: 8.33e-04
2022-05-26 23:57:40,539 INFO [train.py:842] (1/4) Epoch 6, batch 3700, loss[loss=0.216, simple_loss=0.2928, pruned_loss=0.06961, over 7231.00 frames.], tot_loss[loss=0.2338, simple_loss=0.3074, pruned_loss=0.08004, over 1426202.31 frames.], batch size: 20, lr: 8.32e-04
2022-05-26 23:58:19,406 INFO [train.py:842] (1/4) Epoch 6, batch 3750, loss[loss=0.2808, simple_loss=0.3544, pruned_loss=0.1036, over 7375.00 frames.], tot_loss[loss=0.2365, simple_loss=0.3095, pruned_loss=0.08177, over 1425117.99 frames.], batch size: 23, lr: 8.32e-04
2022-05-26 23:58:57,953 INFO [train.py:842] (1/4) Epoch 6, batch 3800, loss[loss=0.244, simple_loss=0.3191, pruned_loss=0.08443, over 7295.00 frames.], tot_loss[loss=0.2351, simple_loss=0.3082, pruned_loss=0.08105, over 1421614.35 frames.], batch size: 24, lr: 8.31e-04
2022-05-26 23:59:36,793 INFO [train.py:842] (1/4) Epoch 6, batch 3850, loss[loss=0.3313, simple_loss=0.3898, pruned_loss=0.1364, over 7329.00 frames.], tot_loss[loss=0.2348, simple_loss=0.3085, pruned_loss=0.08061, over 1419517.45 frames.], batch size: 22, lr: 8.31e-04
2022-05-27 00:00:15,375 INFO [train.py:842] (1/4) Epoch 6, batch 3900, loss[loss=0.2256, simple_loss=0.2939, pruned_loss=0.07858, over 7284.00 frames.], tot_loss[loss=0.2336, simple_loss=0.3072, pruned_loss=0.07996, over 1424639.17 frames.], batch size: 18, lr: 8.31e-04
2022-05-27 00:00:54,254 INFO [train.py:842] (1/4) Epoch 6, batch 3950, loss[loss=0.2666, simple_loss=0.3507, pruned_loss=0.09121, over 6855.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3077, pruned_loss=0.08007, over 1424812.02 frames.], batch size: 31, lr: 8.30e-04
2022-05-27 00:01:33,004 INFO [train.py:842] (1/4) Epoch 6, batch 4000, loss[loss=0.3317, simple_loss=0.387, pruned_loss=0.1382, over 7385.00 frames.], tot_loss[loss=0.2352, simple_loss=0.3093, pruned_loss=0.08062, over 1426958.94 frames.], batch size: 23, lr: 8.30e-04
2022-05-27 00:02:11,860 INFO [train.py:842] (1/4) Epoch 6, batch 4050, loss[loss=0.2517, simple_loss=0.3217, pruned_loss=0.09088, over 7162.00 frames.], tot_loss[loss=0.2357, simple_loss=0.3093, pruned_loss=0.08106, over 1429836.16 frames.], batch size: 19, lr: 8.29e-04
2022-05-27 00:02:50,515 INFO [train.py:842] (1/4) Epoch 6, batch 4100, loss[loss=0.2652, simple_loss=0.3446, pruned_loss=0.09291, over 7375.00 frames.], tot_loss[loss=0.2368, simple_loss=0.3102, pruned_loss=0.08166, over 1427099.19 frames.], batch size: 23, lr: 8.29e-04
2022-05-27 00:03:29,573 INFO [train.py:842] (1/4) Epoch 6, batch 4150, loss[loss=0.2464, simple_loss=0.3067, pruned_loss=0.09302, over 7131.00 frames.], tot_loss[loss=0.2359, simple_loss=0.3095, pruned_loss=0.08111, over 1426332.98 frames.], batch size: 17, lr: 8.29e-04
2022-05-27 00:04:18,771 INFO [train.py:842] (1/4) Epoch 6, batch 4200, loss[loss=0.2291, simple_loss=0.2931, pruned_loss=0.08255, over 7400.00 frames.], tot_loss[loss=0.2368, simple_loss=0.3099, pruned_loss=0.08188, over 1428817.53 frames.], batch size: 18, lr: 8.28e-04
2022-05-27 00:04:57,589 INFO [train.py:842] (1/4) Epoch 6, batch 4250, loss[loss=0.2138, simple_loss=0.2958, pruned_loss=0.06592, over 7294.00 frames.], tot_loss[loss=0.2377, simple_loss=0.3108, pruned_loss=0.08237, over 1428341.60 frames.], batch size: 24, lr: 8.28e-04
2022-05-27 00:05:36,202 INFO [train.py:842] (1/4) Epoch 6, batch 4300, loss[loss=0.3169, simple_loss=0.3664, pruned_loss=0.1337, over 7332.00 frames.], tot_loss[loss=0.2364, simple_loss=0.31, pruned_loss=0.08142, over 1429468.60 frames.], batch size: 22, lr: 8.27e-04
2022-05-27 00:06:15,200 INFO [train.py:842] (1/4) Epoch 6, batch 4350, loss[loss=0.2564, simple_loss=0.3178, pruned_loss=0.09752, over 7063.00 frames.], tot_loss[loss=0.2358, simple_loss=0.3093, pruned_loss=0.08114, over 1430608.70 frames.], batch size: 18, lr: 8.27e-04
2022-05-27 00:06:53,754 INFO [train.py:842] (1/4) Epoch 6, batch 4400, loss[loss=0.209, simple_loss=0.2956, pruned_loss=0.06118, over 7245.00 frames.], tot_loss[loss=0.2366, simple_loss=0.31, pruned_loss=0.08164, over 1427960.72 frames.], batch size: 20, lr: 8.26e-04
2022-05-27 00:07:32,834 INFO [train.py:842] (1/4) Epoch 6, batch 4450, loss[loss=0.2222, simple_loss=0.3023, pruned_loss=0.07106, over 7242.00 frames.], tot_loss[loss=0.2357, simple_loss=0.3091, pruned_loss=0.08118, over 1429105.02 frames.], batch size: 20, lr: 8.26e-04
2022-05-27 00:08:11,512 INFO [train.py:842] (1/4) Epoch 6, batch 4500, loss[loss=0.2717, simple_loss=0.3367, pruned_loss=0.1033, over 5219.00 frames.], tot_loss[loss=0.2338, simple_loss=0.3074, pruned_loss=0.08007, over 1428411.03 frames.], batch size: 52, lr: 8.26e-04
2022-05-27 00:08:50,476 INFO [train.py:842] (1/4) Epoch 6, batch 4550, loss[loss=0.1958, simple_loss=0.2715, pruned_loss=0.06007, over 7062.00 frames.], tot_loss[loss=0.2361, simple_loss=0.309, pruned_loss=0.08156, over 1428055.33 frames.], batch size: 18, lr: 8.25e-04
2022-05-27 00:09:28,907 INFO [train.py:842] (1/4) Epoch 6, batch 4600, loss[loss=0.248, simple_loss=0.3221, pruned_loss=0.0869, over 7064.00 frames.], tot_loss[loss=0.2358, simple_loss=0.3088, pruned_loss=0.08133, over 1428819.29 frames.], batch size: 28, lr: 8.25e-04
2022-05-27 00:10:07,613 INFO [train.py:842] (1/4) Epoch 6, batch 4650, loss[loss=0.3253, simple_loss=0.3795, pruned_loss=0.1356, over 7121.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3111, pruned_loss=0.08304, over 1418210.86 frames.], batch size: 21, lr: 8.24e-04
2022-05-27 00:10:46,050 INFO [train.py:842] (1/4) Epoch 6, batch 4700, loss[loss=0.2496, simple_loss=0.3139, pruned_loss=0.09266, over 7004.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3114, pruned_loss=0.08295, over 1424642.47 frames.], batch size: 16, lr: 8.24e-04
2022-05-27 00:11:25,083 INFO [train.py:842] (1/4) Epoch 6, batch 4750, loss[loss=0.1517, simple_loss=0.2401, pruned_loss=0.03164, over 7431.00 frames.], tot_loss[loss=0.2375, simple_loss=0.3105, pruned_loss=0.08227, over 1426879.01 frames.], batch size: 18, lr: 8.24e-04
2022-05-27 00:12:03,902 INFO [train.py:842] (1/4) Epoch 6, batch 4800, loss[loss=0.2009, simple_loss=0.2764, pruned_loss=0.06269, over 7267.00 frames.], tot_loss[loss=0.2381, simple_loss=0.3106, pruned_loss=0.08282, over 1424966.73 frames.], batch size: 19, lr: 8.23e-04
2022-05-27 00:12:42,812 INFO [train.py:842] (1/4) Epoch 6, batch 4850, loss[loss=0.2181, simple_loss=0.308, pruned_loss=0.0641, over 7400.00 frames.], tot_loss[loss=0.2388, simple_loss=0.3115, pruned_loss=0.08301, over 1426455.03 frames.], batch size: 21, lr: 8.23e-04
2022-05-27 00:13:21,337 INFO [train.py:842] (1/4) Epoch 6, batch 4900, loss[loss=0.199, simple_loss=0.2928, pruned_loss=0.05263, over 7337.00 frames.], tot_loss[loss=0.239, simple_loss=0.312, pruned_loss=0.08298, over 1430611.90 frames.], batch size: 22, lr: 8.22e-04
2022-05-27 00:14:00,189 INFO [train.py:842] (1/4) Epoch 6, batch 4950, loss[loss=0.2475, simple_loss=0.3288, pruned_loss=0.08312, over 7136.00 frames.], tot_loss[loss=0.2384, simple_loss=0.3113, pruned_loss=0.08273, over 1427623.99 frames.], batch size: 28, lr: 8.22e-04
2022-05-27 00:14:38,742 INFO [train.py:842] (1/4) Epoch 6, batch 5000, loss[loss=0.205, simple_loss=0.2898, pruned_loss=0.06011, over 6729.00 frames.], tot_loss[loss=0.2393, simple_loss=0.3118, pruned_loss=0.08341, over 1426611.32 frames.], batch size: 31, lr: 8.22e-04
2022-05-27 00:15:17,433 INFO [train.py:842] (1/4) Epoch 6, batch 5050, loss[loss=0.2416, simple_loss=0.2976, pruned_loss=0.09274, over 7133.00 frames.], tot_loss[loss=0.2385, simple_loss=0.3114, pruned_loss=0.08277, over 1423250.35 frames.], batch size: 17, lr: 8.21e-04
2022-05-27 00:15:55,972 INFO [train.py:842] (1/4) Epoch 6, batch 5100, loss[loss=0.2376, simple_loss=0.3074, pruned_loss=0.08387, over 7068.00 frames.], tot_loss[loss=0.2385, simple_loss=0.3111, pruned_loss=0.08293, over 1424815.27 frames.], batch size: 18, lr: 8.21e-04
2022-05-27 00:16:34,571 INFO [train.py:842] (1/4) Epoch 6, batch 5150, loss[loss=0.1959, simple_loss=0.2737, pruned_loss=0.05908, over 7276.00 frames.], tot_loss[loss=0.2389, simple_loss=0.3115, pruned_loss=0.08316, over 1423019.40 frames.], batch size: 17, lr: 8.20e-04
2022-05-27 00:17:13,245 INFO [train.py:842] (1/4) Epoch 6, batch 5200, loss[loss=0.2445, simple_loss=0.3213, pruned_loss=0.08384, over 7360.00 frames.], tot_loss[loss=0.237, simple_loss=0.3099, pruned_loss=0.08204, over 1427358.15 frames.], batch size: 23, lr: 8.20e-04
2022-05-27 00:18:02,476 INFO [train.py:842] (1/4) Epoch 6, batch 5250, loss[loss=0.271, simple_loss=0.3535, pruned_loss=0.09423, over 7281.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3088, pruned_loss=0.08103, over 1428305.54 frames.], batch size: 25, lr: 8.20e-04
2022-05-27 00:19:01,439 INFO [train.py:842] (1/4) Epoch 6, batch 5300, loss[loss=0.2387, simple_loss=0.3118, pruned_loss=0.08277, over 7113.00 frames.], tot_loss[loss=0.2375, simple_loss=0.3106, pruned_loss=0.08222, over 1418205.59 frames.], batch size: 21, lr: 8.19e-04
2022-05-27 00:19:40,586 INFO [train.py:842] (1/4) Epoch 6, batch 5350, loss[loss=0.2432, simple_loss=0.3191, pruned_loss=0.08358, over 7419.00 frames.], tot_loss[loss=0.2365, simple_loss=0.3102, pruned_loss=0.08144, over 1423003.53 frames.], batch size: 21, lr: 8.19e-04
2022-05-27 00:20:19,204 INFO [train.py:842] (1/4) Epoch 6, batch 5400, loss[loss=0.2249, simple_loss=0.2824, pruned_loss=0.08375, over 7281.00 frames.], tot_loss[loss=0.2371, simple_loss=0.3099, pruned_loss=0.08212, over 1420542.19 frames.], batch size: 18, lr: 8.18e-04
2022-05-27 00:20:58,438 INFO [train.py:842] (1/4) Epoch 6, batch 5450, loss[loss=0.2576, simple_loss=0.3282, pruned_loss=0.09345, over 7332.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3098, pruned_loss=0.08233, over 1424532.25 frames.], batch size: 22, lr: 8.18e-04
2022-05-27 00:21:37,368 INFO [train.py:842] (1/4) Epoch 6, batch 5500, loss[loss=0.2307, simple_loss=0.3037, pruned_loss=0.07883, over 7159.00 frames.], tot_loss[loss=0.2369, simple_loss=0.3096, pruned_loss=0.08209, over 1419889.18 frames.], batch size: 18, lr: 8.18e-04
2022-05-27 00:22:16,104 INFO [train.py:842] (1/4) Epoch 6, batch 5550, loss[loss=0.2255, simple_loss=0.3064, pruned_loss=0.07225, over 7196.00 frames.], tot_loss[loss=0.2382, simple_loss=0.3105, pruned_loss=0.08297, over 1417145.17 frames.], batch size: 22, lr: 8.17e-04
2022-05-27 00:22:54,571 INFO [train.py:842] (1/4) Epoch 6, batch 5600, loss[loss=0.2803, simple_loss=0.3476, pruned_loss=0.1065, over 7029.00 frames.], tot_loss[loss=0.2363, simple_loss=0.3093, pruned_loss=0.08166, over 1419412.90 frames.], batch size: 28, lr: 8.17e-04
2022-05-27 00:23:33,374 INFO [train.py:842] (1/4) Epoch 6, batch 5650, loss[loss=0.2458, simple_loss=0.316, pruned_loss=0.08786, over 7215.00 frames.], tot_loss[loss=0.2347, simple_loss=0.308, pruned_loss=0.08072, over 1415802.36 frames.], batch size: 22, lr: 8.17e-04
2022-05-27 00:24:12,123 INFO [train.py:842] (1/4) Epoch 6, batch 5700, loss[loss=0.2031, simple_loss=0.2866, pruned_loss=0.05974, over 7121.00 frames.], tot_loss[loss=0.2347, simple_loss=0.3077, pruned_loss=0.08088, over 1416915.97 frames.], batch size: 21, lr: 8.16e-04
2022-05-27 00:24:50,733 INFO [train.py:842] (1/4) Epoch 6, batch 5750, loss[loss=0.2473, simple_loss=0.3184, pruned_loss=0.08804, over 7165.00 frames.], tot_loss[loss=0.2352, simple_loss=0.3085, pruned_loss=0.08091, over 1418259.69 frames.], batch size: 19, lr: 8.16e-04
2022-05-27 00:25:29,349 INFO [train.py:842] (1/4) Epoch 6, batch 5800, loss[loss=0.2265, simple_loss=0.3097, pruned_loss=0.07162, over 7217.00 frames.], tot_loss[loss=0.2349, simple_loss=0.3083, pruned_loss=0.08079, over 1418446.85 frames.], batch size: 21, lr: 8.15e-04
2022-05-27 00:26:08,348 INFO [train.py:842] (1/4) Epoch 6, batch 5850, loss[loss=0.2598, simple_loss=0.3157, pruned_loss=0.102, over 7402.00 frames.], tot_loss[loss=0.2358, simple_loss=0.3089, pruned_loss=0.08134, over 1423840.77 frames.], batch size: 18, lr: 8.15e-04
2022-05-27 00:26:46,774 INFO [train.py:842] (1/4) Epoch 6, batch 5900, loss[loss=0.2943, simple_loss=0.3537, pruned_loss=0.1175, over 7412.00 frames.], tot_loss[loss=0.2378, simple_loss=0.3106, pruned_loss=0.08249, over 1424056.76 frames.], batch size: 21, lr: 8.15e-04
2022-05-27 00:27:25,976 INFO [train.py:842] (1/4) Epoch 6, batch 5950, loss[loss=0.2221, simple_loss=0.2878, pruned_loss=0.07822, over 7350.00 frames.], tot_loss[loss=0.2384, simple_loss=0.3108, pruned_loss=0.08302, over 1424710.75 frames.], batch size: 19, lr: 8.14e-04
2022-05-27 00:28:04,708 INFO [train.py:842] (1/4) Epoch 6, batch 6000, loss[loss=0.2129, simple_loss=0.3008, pruned_loss=0.06256, over 7340.00 frames.], tot_loss[loss=0.2388, simple_loss=0.3111, pruned_loss=0.08321, over 1423720.54 frames.], batch size: 22, lr: 8.14e-04
2022-05-27 00:28:04,709 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 00:28:13,973 INFO [train.py:871] (1/4) Epoch 6, validation: loss=0.1847, simple_loss=0.2853, pruned_loss=0.04201, over 868885.00 frames.
2022-05-27 00:28:52,837 INFO [train.py:842] (1/4) Epoch 6, batch 6050, loss[loss=0.2209, simple_loss=0.2936, pruned_loss=0.07411, over 7294.00 frames.], tot_loss[loss=0.2369, simple_loss=0.3098, pruned_loss=0.08198, over 1427390.62 frames.], batch size: 18, lr: 8.13e-04
2022-05-27 00:29:31,594 INFO [train.py:842] (1/4) Epoch 6, batch 6100, loss[loss=0.2287, simple_loss=0.2878, pruned_loss=0.08479, over 6837.00 frames.], tot_loss[loss=0.2363, simple_loss=0.309, pruned_loss=0.08179, over 1424564.43 frames.], batch size: 15, lr: 8.13e-04
2022-05-27 00:30:10,445 INFO [train.py:842] (1/4) Epoch 6, batch 6150, loss[loss=0.2349, simple_loss=0.33, pruned_loss=0.06992, over 7111.00 frames.], tot_loss[loss=0.2358, simple_loss=0.3087, pruned_loss=0.08141, over 1416463.43 frames.], batch size: 21, lr: 8.13e-04
2022-05-27 00:30:48,855 INFO [train.py:842] (1/4) Epoch 6, batch 6200, loss[loss=0.2204, simple_loss=0.3044, pruned_loss=0.06816, over 6465.00 frames.], tot_loss[loss=0.2387, simple_loss=0.3109, pruned_loss=0.08329, over 1413336.58 frames.], batch size: 38, lr: 8.12e-04
2022-05-27 00:31:27,498 INFO [train.py:842] (1/4) Epoch 6, batch 6250, loss[loss=0.2437, simple_loss=0.3149, pruned_loss=0.0863, over 6511.00 frames.], tot_loss[loss=0.237, simple_loss=0.3103, pruned_loss=0.08188, over 1416996.87 frames.], batch size: 38, lr: 8.12e-04
2022-05-27 00:32:06,071 INFO [train.py:842] (1/4) Epoch 6, batch 6300, loss[loss=0.2072, simple_loss=0.2838, pruned_loss=0.06529, over 7333.00 frames.], tot_loss[loss=0.2361, simple_loss=0.3093, pruned_loss=0.08139, over 1419159.28 frames.], batch size: 20, lr: 8.11e-04
2022-05-27 00:32:44,924 INFO [train.py:842] (1/4) Epoch 6, batch 6350, loss[loss=0.1843, simple_loss=0.2571, pruned_loss=0.0557, over 7410.00 frames.], tot_loss[loss=0.2344, simple_loss=0.3084, pruned_loss=0.08018, over 1421030.58 frames.], batch size: 18, lr: 8.11e-04
2022-05-27 00:33:23,421 INFO [train.py:842] (1/4) Epoch 6, batch 6400, loss[loss=0.2611, simple_loss=0.3262, pruned_loss=0.09801, over 7030.00 frames.], tot_loss[loss=0.2333, simple_loss=0.3075, pruned_loss=0.0795, over 1419681.94 frames.], batch size: 28, lr: 8.11e-04
2022-05-27 00:34:02,225 INFO [train.py:842] (1/4) Epoch 6, batch 6450, loss[loss=0.2046, simple_loss=0.2722, pruned_loss=0.0685, over 7273.00 frames.], tot_loss[loss=0.2331, simple_loss=0.3072, pruned_loss=0.07953, over 1418849.25 frames.], batch size: 17, lr: 8.10e-04
2022-05-27 00:34:40,744 INFO [train.py:842] (1/4) Epoch 6, batch 6500, loss[loss=0.2271, simple_loss=0.3144, pruned_loss=0.06983, over 7290.00 frames.], tot_loss[loss=0.2323, simple_loss=0.3065, pruned_loss=0.07908, over 1419619.86 frames.], batch size: 25, lr: 8.10e-04
2022-05-27 00:35:19,831 INFO [train.py:842] (1/4) Epoch 6, batch 6550, loss[loss=0.2497, simple_loss=0.3127, pruned_loss=0.09339, over 7115.00 frames.], tot_loss[loss=0.2337, simple_loss=0.3075, pruned_loss=0.07993, over 1417998.06 frames.], batch size: 21, lr: 8.10e-04
2022-05-27 00:35:58,636 INFO [train.py:842] (1/4) Epoch 6, batch 6600, loss[loss=0.2219, simple_loss=0.2945, pruned_loss=0.0746, over 7300.00 frames.], tot_loss[loss=0.2342, simple_loss=0.3079, pruned_loss=0.08027, over 1420057.63 frames.], batch size: 24, lr: 8.09e-04
2022-05-27 00:36:37,422 INFO [train.py:842] (1/4) Epoch 6, batch 6650, loss[loss=0.1955, simple_loss=0.2782, pruned_loss=0.05646, over 7148.00 frames.], tot_loss[loss=0.2345, simple_loss=0.308, pruned_loss=0.08051, over 1419681.33 frames.], batch size: 20, lr: 8.09e-04
2022-05-27 00:37:16,105 INFO [train.py:842] (1/4) Epoch 6, batch 6700, loss[loss=0.2315, simple_loss=0.3172, pruned_loss=0.07287, over 7200.00 frames.], tot_loss[loss=0.2342, simple_loss=0.3075, pruned_loss=0.08041, over 1419210.06 frames.], batch size: 23, lr: 8.08e-04
2022-05-27 00:37:54,964 INFO [train.py:842] (1/4) Epoch 6, batch 6750, loss[loss=0.1893, simple_loss=0.2821, pruned_loss=0.04821, over 7381.00 frames.], tot_loss[loss=0.2344, simple_loss=0.3077, pruned_loss=0.08051, over 1417571.11 frames.], batch size: 23, lr: 8.08e-04
2022-05-27 00:38:33,404 INFO [train.py:842] (1/4) Epoch 6, batch 6800, loss[loss=0.2508, simple_loss=0.3305, pruned_loss=0.08558, over 7324.00 frames.], tot_loss[loss=0.2343, simple_loss=0.308, pruned_loss=0.08026, over 1418784.08 frames.], batch size: 20, lr: 8.08e-04
2022-05-27 00:39:12,304 INFO [train.py:842] (1/4) Epoch 6, batch 6850, loss[loss=0.2156, simple_loss=0.2886, pruned_loss=0.07124, over 7449.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3077, pruned_loss=0.08002, over 1420511.04 frames.], batch size: 19, lr: 8.07e-04
2022-05-27 00:39:50,891 INFO [train.py:842] (1/4) Epoch 6, batch 6900, loss[loss=0.2787, simple_loss=0.3484, pruned_loss=0.1045, over 7147.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3091, pruned_loss=0.08096, over 1421972.61 frames.], batch size: 20, lr: 8.07e-04
2022-05-27 00:40:29,640 INFO [train.py:842] (1/4) Epoch 6, batch 6950, loss[loss=0.2736, simple_loss=0.3351, pruned_loss=0.1061, over 7323.00 frames.], tot_loss[loss=0.237, simple_loss=0.3105, pruned_loss=0.0818, over 1426660.83 frames.], batch size: 20, lr: 8.07e-04
2022-05-27 00:41:08,369 INFO [train.py:842] (1/4) Epoch 6, batch 7000, loss[loss=0.2382, simple_loss=0.3244, pruned_loss=0.07599, over 7230.00 frames.], tot_loss[loss=0.2361, simple_loss=0.3092, pruned_loss=0.08151, over 1428926.60 frames.], batch size: 20, lr: 8.06e-04
2022-05-27 00:41:47,469 INFO [train.py:842] (1/4) Epoch 6, batch 7050, loss[loss=0.2286, simple_loss=0.3185, pruned_loss=0.06932, over 7332.00 frames.], tot_loss[loss=0.2354, simple_loss=0.3085, pruned_loss=0.08117, over 1425552.17 frames.], batch size: 22, lr: 8.06e-04
2022-05-27 00:42:26,212 INFO [train.py:842] (1/4) Epoch 6, batch 7100, loss[loss=0.2834, simple_loss=0.3297, pruned_loss=0.1185, over 7271.00 frames.], tot_loss[loss=0.2345, simple_loss=0.3075, pruned_loss=0.08073, over 1423979.10 frames.], batch size: 17, lr: 8.05e-04
2022-05-27 00:43:05,031 INFO [train.py:842] (1/4) Epoch 6, batch 7150, loss[loss=0.2118, simple_loss=0.2927, pruned_loss=0.06546, over 7411.00 frames.], tot_loss[loss=0.234, simple_loss=0.3072, pruned_loss=0.08039, over 1426300.46 frames.], batch size: 21, lr: 8.05e-04
2022-05-27 00:43:43,850 INFO [train.py:842] (1/4) Epoch 6, batch 7200, loss[loss=0.2571, simple_loss=0.3284, pruned_loss=0.0929, over 7281.00 frames.], tot_loss[loss=0.2329, simple_loss=0.3064, pruned_loss=0.07971, over 1427804.07 frames.], batch size: 24, lr: 8.05e-04
2022-05-27 00:44:22,619 INFO [train.py:842] (1/4) Epoch 6, batch 7250, loss[loss=0.2233, simple_loss=0.3034, pruned_loss=0.07159, over 7312.00 frames.], tot_loss[loss=0.2334, simple_loss=0.307, pruned_loss=0.07993, over 1424428.86 frames.], batch size: 20, lr: 8.04e-04
2022-05-27 00:45:01,198 INFO [train.py:842] (1/4) Epoch 6, batch 7300, loss[loss=0.2211, simple_loss=0.303, pruned_loss=0.06956, over 7146.00 frames.], tot_loss[loss=0.2321, simple_loss=0.306, pruned_loss=0.07908, over 1424991.74 frames.], batch size: 20, lr: 8.04e-04
2022-05-27 00:45:40,186 INFO [train.py:842] (1/4) Epoch 6, batch 7350, loss[loss=0.1656, simple_loss=0.2534, pruned_loss=0.03885, over 6817.00 frames.], tot_loss[loss=0.2315, simple_loss=0.3054, pruned_loss=0.07884, over 1425476.46 frames.], batch size: 15, lr: 8.04e-04
2022-05-27 00:46:18,814 INFO [train.py:842] (1/4) Epoch 6, batch 7400, loss[loss=0.1864, simple_loss=0.2767, pruned_loss=0.04805, over 7437.00 frames.], tot_loss[loss=0.2323, simple_loss=0.3065, pruned_loss=0.0791, over 1431170.55 frames.], batch size: 20, lr: 8.03e-04
2022-05-27 00:46:57,728 INFO [train.py:842] (1/4) Epoch 6, batch 7450, loss[loss=0.1841, simple_loss=0.253, pruned_loss=0.05761, over 7258.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3074, pruned_loss=0.08019, over 1426123.39 frames.], batch size: 17, lr: 8.03e-04
2022-05-27 00:47:36,453 INFO [train.py:842] (1/4) Epoch 6, batch 7500, loss[loss=0.2808, simple_loss=0.343, pruned_loss=0.1092, over 7211.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3063, pruned_loss=0.0796, over 1422680.51 frames.], batch size: 22, lr: 8.02e-04
2022-05-27 00:48:15,420 INFO [train.py:842] (1/4) Epoch 6, batch 7550, loss[loss=0.2096, simple_loss=0.2888, pruned_loss=0.06521, over 7341.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3078, pruned_loss=0.08072, over 1422197.99 frames.], batch size: 20, lr: 8.02e-04
2022-05-27 00:48:53,871 INFO [train.py:842] (1/4) Epoch 6, batch 7600, loss[loss=0.2597, simple_loss=0.3371, pruned_loss=0.09114, over 7416.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3102, pruned_loss=0.08209, over 1420669.75 frames.], batch size: 21, lr: 8.02e-04
2022-05-27 00:49:32,700 INFO [train.py:842] (1/4) Epoch 6, batch 7650, loss[loss=0.2732, simple_loss=0.3357, pruned_loss=0.1054, over 7296.00 frames.], tot_loss[loss=0.2371, simple_loss=0.3097, pruned_loss=0.08232, over 1417427.29 frames.], batch size: 25, lr: 8.01e-04
2022-05-27 00:50:11,220 INFO [train.py:842] (1/4) Epoch 6, batch 7700, loss[loss=0.2341, simple_loss=0.3086, pruned_loss=0.07983, over 7063.00 frames.], tot_loss[loss=0.2363, simple_loss=0.3088, pruned_loss=0.08186, over 1419086.94 frames.], batch size: 18, lr: 8.01e-04
2022-05-27 00:50:49,907 INFO [train.py:842] (1/4) Epoch 6, batch 7750, loss[loss=0.2429, simple_loss=0.3128, pruned_loss=0.08645, over 7155.00 frames.], tot_loss[loss=0.2375, simple_loss=0.31, pruned_loss=0.08248, over 1412890.07 frames.], batch size: 26, lr: 8.01e-04
2022-05-27 00:51:28,325 INFO [train.py:842] (1/4) Epoch 6, batch 7800, loss[loss=0.3042, simple_loss=0.345, pruned_loss=0.1317, over 7137.00 frames.], tot_loss[loss=0.2356, simple_loss=0.3089, pruned_loss=0.08112, over 1412880.56 frames.], batch size: 17, lr: 8.00e-04
2022-05-27 00:52:07,397 INFO [train.py:842] (1/4) Epoch 6, batch 7850, loss[loss=0.2886, simple_loss=0.3512, pruned_loss=0.113, over 7410.00 frames.], tot_loss[loss=0.2358, simple_loss=0.3091, pruned_loss=0.08123, over 1411706.02 frames.], batch size: 21, lr: 8.00e-04
2022-05-27 00:52:46,291 INFO [train.py:842] (1/4) Epoch 6, batch 7900, loss[loss=0.2001, simple_loss=0.2783, pruned_loss=0.06097, over 7250.00 frames.], tot_loss[loss=0.2343, simple_loss=0.3078, pruned_loss=0.08042, over 1413876.91 frames.], batch size: 16, lr: 7.99e-04
2022-05-27 00:53:25,065 INFO [train.py:842] (1/4) Epoch 6, batch 7950, loss[loss=0.2412, simple_loss=0.3217, pruned_loss=0.08037, over 7304.00 frames.], tot_loss[loss=0.2358, simple_loss=0.3086, pruned_loss=0.08147, over 1419675.88 frames.], batch size: 25, lr: 7.99e-04
2022-05-27 00:54:03,684 INFO [train.py:842] (1/4) Epoch 6, batch 8000, loss[loss=0.2506, simple_loss=0.3407, pruned_loss=0.0802, over 7328.00 frames.], tot_loss[loss=0.2356, simple_loss=0.3091, pruned_loss=0.08105, over 1421079.27 frames.], batch size: 21, lr: 7.99e-04
2022-05-27 00:54:42,716 INFO [train.py:842] (1/4) Epoch 6, batch 8050, loss[loss=0.413, simple_loss=0.4399, pruned_loss=0.193, over 5160.00 frames.], tot_loss[loss=0.2378, simple_loss=0.3106, pruned_loss=0.08251, over 1418480.08 frames.], batch size: 52, lr: 7.98e-04
2022-05-27 00:55:21,175 INFO [train.py:842] (1/4) Epoch 6, batch 8100, loss[loss=0.1919, simple_loss=0.2767, pruned_loss=0.05355, over 7323.00 frames.], tot_loss[loss=0.238, simple_loss=0.3109, pruned_loss=0.08251, over 1422031.87 frames.], batch size: 20, lr: 7.98e-04
2022-05-27 00:55:59,916 INFO [train.py:842] (1/4) Epoch 6, batch 8150, loss[loss=0.3351, simple_loss=0.3703, pruned_loss=0.1499, over 7205.00 frames.], tot_loss[loss=0.2389, simple_loss=0.3119, pruned_loss=0.08294, over 1418883.32 frames.], batch size: 26, lr: 7.98e-04
2022-05-27 00:56:38,270 INFO [train.py:842] (1/4) Epoch 6, batch 8200, loss[loss=0.2316, simple_loss=0.3171, pruned_loss=0.07311, over 7333.00 frames.], tot_loss[loss=0.2382, simple_loss=0.3116, pruned_loss=0.08246, over 1419833.21 frames.], batch size: 22, lr: 7.97e-04
2022-05-27 00:57:17,228 INFO [train.py:842] (1/4) Epoch 6, batch 8250, loss[loss=0.2476, simple_loss=0.3289, pruned_loss=0.08318, over 6434.00 frames.], tot_loss[loss=0.2362, simple_loss=0.3101, pruned_loss=0.08114, over 1420857.35 frames.], batch size: 37, lr: 7.97e-04
2022-05-27 00:57:55,647 INFO [train.py:842] (1/4) Epoch 6, batch 8300, loss[loss=0.249, simple_loss=0.3342, pruned_loss=0.08188, over 7390.00 frames.], tot_loss[loss=0.2361, simple_loss=0.3102, pruned_loss=0.08098, over 1424360.01 frames.], batch size: 23, lr: 7.97e-04
2022-05-27 00:58:34,561 INFO [train.py:842] (1/4) Epoch 6, batch 8350, loss[loss=0.2009, simple_loss=0.3047, pruned_loss=0.04851, over 7334.00 frames.], tot_loss[loss=0.2352, simple_loss=0.3098, pruned_loss=0.08033, over 1425362.33 frames.], batch size: 22, lr: 7.96e-04
2022-05-27 00:59:13,025 INFO [train.py:842] (1/4) Epoch 6, batch 8400, loss[loss=0.2049, simple_loss=0.266, pruned_loss=0.07187, over 7296.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3105, pruned_loss=0.08194, over 1422337.84 frames.], batch size: 17, lr: 7.96e-04
2022-05-27 00:59:52,158 INFO [train.py:842] (1/4) Epoch 6, batch 8450, loss[loss=0.2509, simple_loss=0.3086, pruned_loss=0.09655, over 7263.00 frames.], tot_loss[loss=0.2364, simple_loss=0.3102, pruned_loss=0.08127, over 1423825.68 frames.], batch size: 17, lr: 7.95e-04
2022-05-27 01:00:31,033 INFO [train.py:842] (1/4) Epoch 6, batch 8500, loss[loss=0.194, simple_loss=0.2871, pruned_loss=0.05041, over 7111.00 frames.], tot_loss[loss=0.2353, simple_loss=0.3089, pruned_loss=0.08085, over 1423586.37 frames.], batch size: 21, lr: 7.95e-04
2022-05-27 01:01:10,183 INFO [train.py:842] (1/4) Epoch 6, batch 8550, loss[loss=0.2491, simple_loss=0.3192, pruned_loss=0.08949, over 7377.00 frames.], tot_loss[loss=0.2347, simple_loss=0.3085, pruned_loss=0.0805, over 1426915.06 frames.], batch size: 23, lr: 7.95e-04
2022-05-27 01:01:48,822 INFO [train.py:842] (1/4) Epoch 6, batch 8600, loss[loss=0.3113, simple_loss=0.3668, pruned_loss=0.1279, over 7219.00 frames.], tot_loss[loss=0.2363, simple_loss=0.3094, pruned_loss=0.08159, over 1427099.54 frames.], batch size: 21, lr: 7.94e-04
2022-05-27 01:02:27,486 INFO [train.py:842] (1/4) Epoch 6, batch 8650, loss[loss=0.1952, simple_loss=0.2771, pruned_loss=0.05672, over 7333.00 frames.], tot_loss[loss=0.2366, simple_loss=0.3096, pruned_loss=0.08183, over 1424389.14 frames.], batch size: 20, lr: 7.94e-04
2022-05-27 01:03:06,083 INFO [train.py:842] (1/4) Epoch 6, batch 8700, loss[loss=0.175, simple_loss=0.2486, pruned_loss=0.05067, over 7124.00 frames.], tot_loss[loss=0.235, simple_loss=0.3084, pruned_loss=0.08076, over 1419079.50 frames.], batch size: 17, lr: 7.94e-04
2022-05-27 01:03:44,889 INFO [train.py:842] (1/4) Epoch 6, batch 8750, loss[loss=0.1984, simple_loss=0.2776, pruned_loss=0.05962, over 7137.00 frames.], tot_loss[loss=0.2349, simple_loss=0.3086, pruned_loss=0.08056, over 1417793.74 frames.], batch size: 17, lr: 7.93e-04
2022-05-27 01:04:23,735 INFO [train.py:842] (1/4) Epoch 6, batch 8800, loss[loss=0.2136, simple_loss=0.2931, pruned_loss=0.06704, over 7130.00 frames.], tot_loss[loss=0.2353, simple_loss=0.3084, pruned_loss=0.08108, over 1417567.70 frames.], batch size: 17, lr: 7.93e-04
2022-05-27 01:05:02,674 INFO [train.py:842] (1/4) Epoch 6, batch 8850, loss[loss=0.2031, simple_loss=0.271, pruned_loss=0.06759, over 7265.00 frames.], tot_loss[loss=0.2356, simple_loss=0.3084, pruned_loss=0.08144, over 1418405.84 frames.], batch size: 17, lr: 7.93e-04
2022-05-27 01:05:41,180 INFO [train.py:842] (1/4) Epoch 6, batch 8900, loss[loss=0.2179, simple_loss=0.3014, pruned_loss=0.0672, over 7201.00 frames.], tot_loss[loss=0.2347, simple_loss=0.3071, pruned_loss=0.08114, over 1413572.90 frames.], batch size: 26, lr: 7.92e-04
2022-05-27 01:06:20,558 INFO [train.py:842] (1/4) Epoch 6, batch 8950, loss[loss=0.213, simple_loss=0.2905, pruned_loss=0.06777, over 7349.00 frames.], tot_loss[loss=0.2345, simple_loss=0.3066, pruned_loss=0.08123, over 1407795.53 frames.], batch size: 19, lr: 7.92e-04
2022-05-27 01:06:58,912 INFO [train.py:842] (1/4) Epoch 6, batch 9000, loss[loss=0.1936, simple_loss=0.2654, pruned_loss=0.06091, over 7275.00 frames.], tot_loss[loss=0.236, simple_loss=0.3082, pruned_loss=0.08192, over 1400958.73 frames.], batch size: 17, lr: 7.91e-04
2022-05-27 01:06:58,913 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 01:07:08,313 INFO [train.py:871] (1/4) Epoch 6, validation: loss=0.186, simple_loss=0.2866, pruned_loss=0.04271, over 868885.00 frames.
2022-05-27 01:07:46,581 INFO [train.py:842] (1/4) Epoch 6, batch 9050, loss[loss=0.1672, simple_loss=0.2464, pruned_loss=0.04401, over 7280.00 frames.], tot_loss[loss=0.2377, simple_loss=0.3095, pruned_loss=0.08295, over 1368466.18 frames.], batch size: 17, lr: 7.91e-04
2022-05-27 01:08:24,101 INFO [train.py:842] (1/4) Epoch 6, batch 9100, loss[loss=0.2154, simple_loss=0.3044, pruned_loss=0.06322, over 7211.00 frames.], tot_loss[loss=0.2406, simple_loss=0.3125, pruned_loss=0.08436, over 1341918.15 frames.], batch size: 21, lr: 7.91e-04
2022-05-27 01:09:01,943 INFO [train.py:842] (1/4) Epoch 6, batch 9150, loss[loss=0.3363, simple_loss=0.3848, pruned_loss=0.1439, over 5202.00 frames.], tot_loss[loss=0.2481, simple_loss=0.3184, pruned_loss=0.08886, over 1293234.19 frames.], batch size: 53, lr: 7.90e-04
2022-05-27 01:09:54,889 INFO [train.py:842] (1/4) Epoch 7, batch 0, loss[loss=0.1727, simple_loss=0.253, pruned_loss=0.0462, over 7407.00 frames.], tot_loss[loss=0.1727, simple_loss=0.253, pruned_loss=0.0462, over 7407.00 frames.], batch size: 18, lr: 7.58e-04
2022-05-27 01:10:33,938 INFO [train.py:842] (1/4) Epoch 7, batch 50, loss[loss=0.1801, simple_loss=0.2591, pruned_loss=0.05049, over 7426.00 frames.], tot_loss[loss=0.2328, simple_loss=0.3069, pruned_loss=0.0793, over 322361.59 frames.], batch size: 18, lr: 7.58e-04
2022-05-27 01:11:12,625 INFO [train.py:842] (1/4) Epoch 7, batch 100, loss[loss=0.2225, simple_loss=0.305, pruned_loss=0.06994, over 7147.00 frames.], tot_loss[loss=0.2298, simple_loss=0.3049, pruned_loss=0.07735, over 567337.29 frames.], batch size: 19, lr: 7.57e-04
2022-05-27 01:11:51,442 INFO [train.py:842] (1/4) Epoch 7, batch 150, loss[loss=0.2484, simple_loss=0.3204, pruned_loss=0.08815, over 7160.00 frames.], tot_loss[loss=0.2363, simple_loss=0.3095, pruned_loss=0.08152, over 757355.85 frames.], batch size: 19, lr: 7.57e-04
2022-05-27 01:12:30,102 INFO [train.py:842] (1/4) Epoch 7, batch 200, loss[loss=0.2584, simple_loss=0.3334, pruned_loss=0.09168, over 7366.00 frames.], tot_loss[loss=0.235, simple_loss=0.3091, pruned_loss=0.08041, over 905995.44 frames.], batch size: 23, lr: 7.57e-04
2022-05-27 01:13:09,012 INFO [train.py:842] (1/4) Epoch 7, batch 250, loss[loss=0.2336, simple_loss=0.3093, pruned_loss=0.07896, over 7143.00 frames.], tot_loss[loss=0.2338, simple_loss=0.308, pruned_loss=0.07985, over 1020595.83 frames.], batch size: 20, lr: 7.56e-04
2022-05-27 01:13:47,490 INFO [train.py:842] (1/4) Epoch 7, batch 300, loss[loss=0.141, simple_loss=0.2208, pruned_loss=0.03064, over 7218.00 frames.], tot_loss[loss=0.2327, simple_loss=0.307, pruned_loss=0.07924, over 1107026.23 frames.], batch size: 16, lr: 7.56e-04
2022-05-27 01:14:26,502 INFO [train.py:842] (1/4) Epoch 7, batch 350, loss[loss=0.2281, simple_loss=0.3035, pruned_loss=0.07639, over 7116.00 frames.], tot_loss[loss=0.2323, simple_loss=0.3069, pruned_loss=0.07887, over 1178070.18 frames.], batch size: 21, lr: 7.56e-04
2022-05-27 01:15:04,814 INFO [train.py:842] (1/4) Epoch 7, batch 400, loss[loss=0.2001, simple_loss=0.2873, pruned_loss=0.05645, over 7164.00 frames.], tot_loss[loss=0.2312, simple_loss=0.3063, pruned_loss=0.07806, over 1230716.35 frames.], batch size: 18, lr: 7.55e-04
2022-05-27 01:15:43,929 INFO [train.py:842] (1/4) Epoch 7, batch 450, loss[loss=0.2363, simple_loss=0.3182, pruned_loss=0.07719, over 7360.00 frames.], tot_loss[loss=0.2316, simple_loss=0.3065, pruned_loss=0.07838, over 1276389.88 frames.], batch size: 19, lr: 7.55e-04
2022-05-27 01:16:22,232 INFO [train.py:842] (1/4) Epoch 7, batch 500, loss[loss=0.2306, simple_loss=0.3136, pruned_loss=0.07383, over 6402.00 frames.], tot_loss[loss=0.2319, simple_loss=0.3071, pruned_loss=0.07841, over 1305325.60 frames.], batch size: 38, lr: 7.55e-04
2022-05-27 01:17:01,067 INFO [train.py:842] (1/4) Epoch 7, batch 550, loss[loss=0.2557, simple_loss=0.3193, pruned_loss=0.09602, over 7103.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3052, pruned_loss=0.07733, over 1330138.37 frames.], batch size: 21, lr: 7.54e-04
2022-05-27 01:17:39,723 INFO [train.py:842] (1/4) Epoch 7, batch 600, loss[loss=0.2766, simple_loss=0.3324, pruned_loss=0.1104, over 7095.00 frames.], tot_loss[loss=0.2316, simple_loss=0.307, pruned_loss=0.0781, over 1348159.38 frames.], batch size: 28, lr: 7.54e-04
2022-05-27 01:18:18,873 INFO [train.py:842] (1/4) Epoch 7, batch 650, loss[loss=0.2587, simple_loss=0.324, pruned_loss=0.09676, over 5119.00 frames.], tot_loss[loss=0.2326, simple_loss=0.3068, pruned_loss=0.07922, over 1364874.53 frames.], batch size: 53, lr: 7.54e-04
2022-05-27 01:18:57,445 INFO [train.py:842] (1/4) Epoch 7, batch 700, loss[loss=0.1872, simple_loss=0.2624, pruned_loss=0.05601, over 7159.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3054, pruned_loss=0.07808, over 1378833.17 frames.], batch size: 18, lr: 7.53e-04
2022-05-27 01:19:36,361 INFO [train.py:842] (1/4) Epoch 7, batch 750, loss[loss=0.2271, simple_loss=0.2948, pruned_loss=0.07968, over 6731.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3049, pruned_loss=0.07776, over 1391708.49 frames.], batch size: 31, lr: 7.53e-04
2022-05-27 01:20:15,033 INFO [train.py:842] (1/4) Epoch 7, batch 800, loss[loss=0.2205, simple_loss=0.2988, pruned_loss=0.07116, over 7332.00 frames.], tot_loss[loss=0.2314, simple_loss=0.3056, pruned_loss=0.07854, over 1391747.34 frames.], batch size: 20, lr: 7.53e-04
2022-05-27 01:20:56,569 INFO [train.py:842] (1/4) Epoch 7, batch 850, loss[loss=0.2312, simple_loss=0.3088, pruned_loss=0.07681, over 7262.00 frames.], tot_loss[loss=0.2316, simple_loss=0.3057, pruned_loss=0.07879, over 1398439.39 frames.], batch size: 24, lr: 7.52e-04
2022-05-27 01:21:35,043 INFO [train.py:842] (1/4) Epoch 7, batch 900, loss[loss=0.247, simple_loss=0.3267, pruned_loss=0.08362, over 7377.00 frames.], tot_loss[loss=0.2315, simple_loss=0.3058, pruned_loss=0.07864, over 1404384.16 frames.], batch size: 23, lr: 7.52e-04
2022-05-27 01:22:13,826 INFO [train.py:842] (1/4) Epoch 7, batch 950, loss[loss=0.2097, simple_loss=0.3023, pruned_loss=0.05858, over 7379.00 frames.], tot_loss[loss=0.2316, simple_loss=0.3063, pruned_loss=0.07839, over 1408144.94 frames.], batch size: 23, lr: 7.52e-04
2022-05-27 01:22:52,370 INFO [train.py:842] (1/4) Epoch 7, batch 1000, loss[loss=0.2722, simple_loss=0.3438, pruned_loss=0.1003, over 7379.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3055, pruned_loss=0.07796, over 1409354.35 frames.], batch size: 23, lr: 7.51e-04
2022-05-27 01:23:31,597 INFO [train.py:842] (1/4) Epoch 7, batch 1050, loss[loss=0.2299, simple_loss=0.303, pruned_loss=0.07836, over 7161.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3051, pruned_loss=0.07756, over 1416322.94 frames.], batch size: 19, lr: 7.51e-04
2022-05-27 01:24:10,629 INFO [train.py:842] (1/4) Epoch 7, batch 1100, loss[loss=0.2367, simple_loss=0.3194, pruned_loss=0.07701, over 7293.00 frames.], tot_loss[loss=0.2313, simple_loss=0.3063, pruned_loss=0.07812, over 1420469.52 frames.], batch size: 25, lr: 7.51e-04
2022-05-27 01:24:49,509 INFO [train.py:842] (1/4) Epoch 7, batch 1150, loss[loss=0.196, simple_loss=0.2668, pruned_loss=0.06263, over 7129.00 frames.], tot_loss[loss=0.2313, simple_loss=0.3063, pruned_loss=0.0782, over 1418045.81 frames.], batch size: 17, lr: 7.50e-04
2022-05-27 01:25:28,099 INFO [train.py:842] (1/4) Epoch 7, batch 1200, loss[loss=0.1908, simple_loss=0.2604, pruned_loss=0.06053, over 6821.00 frames.], tot_loss[loss=0.2306, simple_loss=0.3053, pruned_loss=0.07797, over 1412577.33 frames.], batch size: 15, lr: 7.50e-04
2022-05-27 01:26:07,103 INFO [train.py:842] (1/4) Epoch 7, batch 1250, loss[loss=0.2033, simple_loss=0.2939, pruned_loss=0.05635, over 7236.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3048, pruned_loss=0.07782, over 1413894.62 frames.], batch size: 20, lr: 7.50e-04
2022-05-27 01:26:45,633 INFO [train.py:842] (1/4) Epoch 7, batch 1300, loss[loss=0.1917, simple_loss=0.2645, pruned_loss=0.05946, over 7289.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3048, pruned_loss=0.07769, over 1415125.55 frames.], batch size: 17, lr: 7.49e-04
2022-05-27 01:27:24,588 INFO [train.py:842] (1/4) Epoch 7, batch 1350, loss[loss=0.2328, simple_loss=0.3167, pruned_loss=0.07444, over 7419.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3055, pruned_loss=0.07801, over 1420415.68 frames.], batch size: 21, lr: 7.49e-04
2022-05-27 01:28:02,936 INFO [train.py:842] (1/4) Epoch 7, batch 1400, loss[loss=0.2338, simple_loss=0.3091, pruned_loss=0.07925, over 7158.00 frames.], tot_loss[loss=0.2311, simple_loss=0.306, pruned_loss=0.07808, over 1418398.34 frames.], batch size: 19, lr: 7.49e-04
2022-05-27 01:28:41,841 INFO [train.py:842] (1/4) Epoch 7, batch 1450, loss[loss=0.2272, simple_loss=0.304, pruned_loss=0.07524, over 6771.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3056, pruned_loss=0.07806, over 1418596.25 frames.], batch size: 31, lr: 7.48e-04
2022-05-27 01:29:20,399 INFO [train.py:842] (1/4) Epoch 7, batch 1500, loss[loss=0.2379, simple_loss=0.3214, pruned_loss=0.07722, over 7416.00 frames.], tot_loss[loss=0.23, simple_loss=0.305, pruned_loss=0.07753, over 1423083.28 frames.], batch size: 21, lr: 7.48e-04
2022-05-27 01:29:59,203 INFO [train.py:842] (1/4) Epoch 7, batch 1550, loss[loss=0.2315, simple_loss=0.3119, pruned_loss=0.07555, over 7098.00 frames.], tot_loss[loss=0.2298, simple_loss=0.3048, pruned_loss=0.07741, over 1416158.28 frames.], batch size: 26, lr: 7.48e-04
2022-05-27 01:30:37,808 INFO [train.py:842] (1/4) Epoch 7, batch 1600, loss[loss=0.304, simple_loss=0.365, pruned_loss=0.1215, over 7107.00 frames.], tot_loss[loss=0.2299, simple_loss=0.305, pruned_loss=0.07741, over 1423367.90 frames.], batch size: 21, lr: 7.47e-04
2022-05-27 01:31:16,722 INFO [train.py:842] (1/4) Epoch 7, batch 1650, loss[loss=0.2406, simple_loss=0.3085, pruned_loss=0.0863, over 7073.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3051, pruned_loss=0.07758, over 1416379.63 frames.], batch size: 18, lr: 7.47e-04
2022-05-27 01:31:55,645 INFO [train.py:842] (1/4) Epoch 7, batch 1700, loss[loss=0.2167, simple_loss=0.3026, pruned_loss=0.06535, over 7188.00 frames.], tot_loss[loss=0.2309, simple_loss=0.3055, pruned_loss=0.07818, over 1416062.86 frames.], batch size: 22, lr: 7.47e-04
2022-05-27 01:32:34,541 INFO [train.py:842] (1/4) Epoch 7, batch 1750, loss[loss=0.2499, simple_loss=0.3241, pruned_loss=0.0879, over 7321.00 frames.], tot_loss[loss=0.2311, simple_loss=0.3053, pruned_loss=0.07841, over 1411188.98 frames.], batch size: 22, lr: 7.46e-04
2022-05-27 01:33:12,937 INFO [train.py:842] (1/4) Epoch 7, batch 1800, loss[loss=0.2697, simple_loss=0.3464, pruned_loss=0.09645, over 7262.00 frames.], tot_loss[loss=0.232, simple_loss=0.3071, pruned_loss=0.0785, over 1414245.00 frames.], batch size: 25, lr: 7.46e-04
2022-05-27 01:33:51,793 INFO [train.py:842] (1/4) Epoch 7, batch 1850, loss[loss=0.1898, simple_loss=0.2589, pruned_loss=0.06039, over 7015.00 frames.], tot_loss[loss=0.2307, simple_loss=0.306, pruned_loss=0.0777, over 1416324.85 frames.], batch size: 16, lr: 7.46e-04
2022-05-27 01:34:30,338 INFO [train.py:842] (1/4) Epoch 7, batch 1900, loss[loss=0.2103, simple_loss=0.2882, pruned_loss=0.06623, over 7075.00 frames.], tot_loss[loss=0.2304, simple_loss=0.3055, pruned_loss=0.07764, over 1413829.59 frames.], batch size: 18, lr: 7.45e-04
2022-05-27 01:35:09,638 INFO [train.py:842] (1/4) Epoch 7, batch 1950, loss[loss=0.1888, simple_loss=0.2665, pruned_loss=0.05553, over 7266.00 frames.], tot_loss[loss=0.2291, simple_loss=0.3042, pruned_loss=0.07703, over 1417533.60 frames.], batch size: 18, lr: 7.45e-04
2022-05-27 01:35:48,204 INFO [train.py:842] (1/4) Epoch 7, batch 2000, loss[loss=0.2366, simple_loss=0.3118, pruned_loss=0.08068, over 7283.00 frames.], tot_loss[loss=0.2283, simple_loss=0.3036, pruned_loss=0.07648, over 1418504.47 frames.], batch size: 25, lr: 7.45e-04
2022-05-27 01:36:27,082 INFO [train.py:842] (1/4) Epoch 7, batch 2050, loss[loss=0.2269, simple_loss=0.2984, pruned_loss=0.07767, over 7301.00 frames.], tot_loss[loss=0.23, simple_loss=0.3051, pruned_loss=0.0775, over 1416301.41 frames.], batch size: 24, lr: 7.44e-04
2022-05-27 01:37:05,455 INFO [train.py:842] (1/4) Epoch 7, batch 2100, loss[loss=0.2042, simple_loss=0.2801, pruned_loss=0.06415, over 6989.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3047, pruned_loss=0.07752, over 1419829.34 frames.], batch size: 16, lr: 7.44e-04
2022-05-27 01:37:44,432 INFO [train.py:842] (1/4) Epoch 7, batch 2150, loss[loss=0.2204, simple_loss=0.2956, pruned_loss=0.07259, over 7417.00 frames.], tot_loss[loss=0.2314, simple_loss=0.3062, pruned_loss=0.07832, over 1424905.52 frames.], batch size: 21, lr: 7.44e-04
2022-05-27 01:38:22,863 INFO [train.py:842] (1/4) Epoch 7, batch 2200, loss[loss=0.2097, simple_loss=0.2677, pruned_loss=0.07585, over 7137.00 frames.], tot_loss[loss=0.2313, simple_loss=0.306, pruned_loss=0.07826, over 1422503.87 frames.], batch size: 17, lr: 7.43e-04
2022-05-27 01:39:01,915 INFO [train.py:842] (1/4) Epoch 7, batch 2250, loss[loss=0.2081, simple_loss=0.282, pruned_loss=0.0671, over 7290.00 frames.], tot_loss[loss=0.2313, simple_loss=0.3062, pruned_loss=0.07824, over 1417022.08 frames.], batch size: 17, lr: 7.43e-04
2022-05-27 01:39:40,338 INFO [train.py:842] (1/4) Epoch 7, batch 2300, loss[loss=0.2859, simple_loss=0.3641, pruned_loss=0.1038, over 7192.00 frames.], tot_loss[loss=0.2305, simple_loss=0.3057, pruned_loss=0.07759, over 1420440.89 frames.], batch size: 23, lr: 7.43e-04
2022-05-27 01:40:19,212 INFO [train.py:842] (1/4) Epoch 7, batch 2350, loss[loss=0.2096, simple_loss=0.3034, pruned_loss=0.05795, over 7417.00 frames.], tot_loss[loss=0.2306, simple_loss=0.3056, pruned_loss=0.07781, over 1418351.79 frames.], batch size: 21, lr: 7.42e-04
2022-05-27 01:40:57,861 INFO [train.py:842] (1/4) Epoch 7, batch 2400, loss[loss=0.1933, simple_loss=0.2645, pruned_loss=0.06104, over 7275.00 frames.], tot_loss[loss=0.2296, simple_loss=0.3049, pruned_loss=0.07717, over 1421795.39 frames.], batch size: 18, lr: 7.42e-04
2022-05-27 01:41:36,508 INFO [train.py:842] (1/4) Epoch 7, batch 2450, loss[loss=0.2141, simple_loss=0.2869, pruned_loss=0.07062, over 7404.00 frames.], tot_loss[loss=0.2307, simple_loss=0.306, pruned_loss=0.0777, over 1416942.70 frames.], batch size: 21, lr: 7.42e-04
2022-05-27 01:42:14,983 INFO [train.py:842] (1/4) Epoch 7, batch 2500, loss[loss=0.2188, simple_loss=0.3132, pruned_loss=0.06215, over 7313.00 frames.], tot_loss[loss=0.2313, simple_loss=0.3069, pruned_loss=0.0779, over 1416471.55 frames.], batch size: 21, lr: 7.42e-04
2022-05-27 01:42:53,974 INFO [train.py:842] (1/4) Epoch 7, batch 2550, loss[loss=0.223, simple_loss=0.3053, pruned_loss=0.07035, over 7432.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3075, pruned_loss=0.07805, over 1423135.42 frames.], batch size: 20, lr: 7.41e-04
2022-05-27 01:43:32,348 INFO [train.py:842] (1/4) Epoch 7, batch 2600, loss[loss=0.1832, simple_loss=0.2705, pruned_loss=0.04798, over 7162.00 frames.], tot_loss[loss=0.2325, simple_loss=0.3077, pruned_loss=0.07858, over 1417942.86 frames.], batch size: 18, lr: 7.41e-04
2022-05-27 01:44:11,316 INFO [train.py:842] (1/4) Epoch 7, batch 2650, loss[loss=0.1976, simple_loss=0.276, pruned_loss=0.05963, over 7164.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3072, pruned_loss=0.07865, over 1417763.05 frames.], batch size: 18, lr: 7.41e-04
2022-05-27 01:44:49,767 INFO [train.py:842] (1/4) Epoch 7, batch 2700, loss[loss=0.221, simple_loss=0.2943, pruned_loss=0.07382, over 6829.00 frames.], tot_loss[loss=0.2323, simple_loss=0.3075, pruned_loss=0.07856, over 1419564.12 frames.], batch size: 15, lr: 7.40e-04
2022-05-27 01:45:28,486 INFO [train.py:842] (1/4) Epoch 7, batch 2750, loss[loss=0.2316, simple_loss=0.302, pruned_loss=0.08062, over 7415.00 frames.], tot_loss[loss=0.2331, simple_loss=0.3083, pruned_loss=0.07899, over 1419727.80 frames.], batch size: 18, lr: 7.40e-04
2022-05-27 01:46:06,986 INFO [train.py:842] (1/4) Epoch 7, batch 2800, loss[loss=0.1889, simple_loss=0.2653, pruned_loss=0.05623, over 6995.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3072, pruned_loss=0.07851, over 1418015.22 frames.], batch size: 16, lr: 7.40e-04
2022-05-27 01:46:46,090 INFO [train.py:842] (1/4) Epoch 7, batch 2850, loss[loss=0.2261, simple_loss=0.3055, pruned_loss=0.07339, over 7326.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3053, pruned_loss=0.07755, over 1422741.26 frames.], batch size: 21, lr: 7.39e-04
2022-05-27 01:47:24,784 INFO [train.py:842] (1/4) Epoch 7, batch 2900, loss[loss=0.2665, simple_loss=0.3313, pruned_loss=0.1008, over 4999.00 frames.], tot_loss[loss=0.2298, simple_loss=0.3051, pruned_loss=0.07719, over 1424372.69 frames.], batch size: 53, lr: 7.39e-04
2022-05-27 01:48:03,874 INFO [train.py:842] (1/4) Epoch 7, batch 2950, loss[loss=0.3563, simple_loss=0.3941, pruned_loss=0.1593, over 7254.00 frames.], tot_loss[loss=0.2319, simple_loss=0.3068, pruned_loss=0.07846, over 1424450.09 frames.], batch size: 25, lr: 7.39e-04
2022-05-27 01:48:42,429 INFO [train.py:842] (1/4) Epoch 7, batch 3000, loss[loss=0.2515, simple_loss=0.3228, pruned_loss=0.09017, over 7170.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3066, pruned_loss=0.07854, over 1425416.06 frames.], batch size: 26, lr: 7.38e-04
2022-05-27 01:48:42,430 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 01:48:51,662 INFO [train.py:871] (1/4) Epoch 7, validation: loss=0.1805, simple_loss=0.2812, pruned_loss=0.03987, over 868885.00 frames.
2022-05-27 01:49:30,624 INFO [train.py:842] (1/4) Epoch 7, batch 3050, loss[loss=0.2517, simple_loss=0.3209, pruned_loss=0.09128, over 7204.00 frames.], tot_loss[loss=0.2313, simple_loss=0.3061, pruned_loss=0.07828, over 1425813.13 frames.], batch size: 26, lr: 7.38e-04
2022-05-27 01:50:09,082 INFO [train.py:842] (1/4) Epoch 7, batch 3100, loss[loss=0.286, simple_loss=0.361, pruned_loss=0.1055, over 7152.00 frames.], tot_loss[loss=0.2311, simple_loss=0.3061, pruned_loss=0.07803, over 1422721.28 frames.], batch size: 26, lr: 7.38e-04
2022-05-27 01:50:47,932 INFO [train.py:842] (1/4) Epoch 7, batch 3150, loss[loss=0.244, simple_loss=0.3249, pruned_loss=0.0816, over 7049.00 frames.], tot_loss[loss=0.2316, simple_loss=0.3066, pruned_loss=0.07828, over 1426344.77 frames.], batch size: 28, lr: 7.37e-04
2022-05-27 01:51:26,322 INFO [train.py:842] (1/4) Epoch 7, batch 3200, loss[loss=0.2507, simple_loss=0.3223, pruned_loss=0.0895, over 7332.00 frames.], tot_loss[loss=0.2324, simple_loss=0.3072, pruned_loss=0.07878, over 1422432.61 frames.], batch size: 22, lr: 7.37e-04
2022-05-27 01:52:05,325 INFO [train.py:842] (1/4) Epoch 7, batch 3250, loss[loss=0.2579, simple_loss=0.3325, pruned_loss=0.09161, over 7102.00 frames.], tot_loss[loss=0.2312, simple_loss=0.3059, pruned_loss=0.07819, over 1421164.69 frames.], batch size: 28, lr: 7.37e-04
2022-05-27 01:52:43,667 INFO [train.py:842] (1/4) Epoch 7, batch 3300, loss[loss=0.309, simple_loss=0.3621, pruned_loss=0.128, over 7148.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3069, pruned_loss=0.07872, over 1417374.07 frames.], batch size: 20, lr: 7.36e-04
2022-05-27 01:53:22,489 INFO [train.py:842] (1/4) Epoch 7, batch 3350, loss[loss=0.2333, simple_loss=0.3027, pruned_loss=0.08196, over 7157.00 frames.], tot_loss[loss=0.2315, simple_loss=0.3066, pruned_loss=0.07822, over 1418932.97 frames.], batch size: 19, lr: 7.36e-04
2022-05-27 01:54:01,079 INFO [train.py:842] (1/4) Epoch 7, batch 3400, loss[loss=0.2438, simple_loss=0.3202, pruned_loss=0.08376, over 7125.00 frames.], tot_loss[loss=0.231, simple_loss=0.3061, pruned_loss=0.07793, over 1421708.84 frames.], batch size: 21, lr: 7.36e-04
2022-05-27 01:54:39,836 INFO [train.py:842] (1/4) Epoch 7, batch 3450, loss[loss=0.2501, simple_loss=0.3155, pruned_loss=0.09235, over 7311.00 frames.], tot_loss[loss=0.2316, simple_loss=0.3065, pruned_loss=0.07832, over 1419963.94 frames.], batch size: 24, lr: 7.36e-04
2022-05-27 01:55:18,274 INFO [train.py:842] (1/4) Epoch 7, batch 3500, loss[loss=0.2028, simple_loss=0.2939, pruned_loss=0.0559, over 7221.00 frames.], tot_loss[loss=0.2314, simple_loss=0.3068, pruned_loss=0.07802, over 1422011.30 frames.], batch size: 21, lr: 7.35e-04
2022-05-27 01:55:57,334 INFO [train.py:842] (1/4) Epoch 7, batch 3550, loss[loss=0.2484, simple_loss=0.3219, pruned_loss=0.08742, over 7400.00 frames.], tot_loss[loss=0.2291, simple_loss=0.305, pruned_loss=0.07661, over 1423645.19 frames.], batch size: 23, lr: 7.35e-04
2022-05-27 01:56:36,208 INFO [train.py:842] (1/4) Epoch 7, batch 3600, loss[loss=0.2681, simple_loss=0.345, pruned_loss=0.09559, over 7224.00 frames.], tot_loss[loss=0.2285, simple_loss=0.3046, pruned_loss=0.07619, over 1425623.28 frames.], batch size: 21, lr: 7.35e-04
2022-05-27 01:57:15,157 INFO [train.py:842] (1/4) Epoch 7, batch 3650, loss[loss=0.2828, simple_loss=0.348, pruned_loss=0.1087, over 7052.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3056, pruned_loss=0.07742, over 1422142.41 frames.], batch size: 28, lr: 7.34e-04
2022-05-27 01:57:53,803 INFO [train.py:842] (1/4) Epoch 7, batch 3700, loss[loss=0.2631, simple_loss=0.3324, pruned_loss=0.0969, over 7429.00 frames.], tot_loss[loss=0.2284, simple_loss=0.3041, pruned_loss=0.07641, over 1422966.47 frames.], batch size: 20, lr: 7.34e-04
2022-05-27 01:58:32,561 INFO [train.py:842] (1/4) Epoch 7, batch 3750, loss[loss=0.2507, simple_loss=0.3265, pruned_loss=0.08747, over 4910.00 frames.], tot_loss[loss=0.2282, simple_loss=0.3035, pruned_loss=0.07646, over 1423432.75 frames.], batch size: 52, lr: 7.34e-04
2022-05-27 01:59:10,981 INFO [train.py:842] (1/4) Epoch 7, batch 3800, loss[loss=0.2404, simple_loss=0.3153, pruned_loss=0.08272, over 7356.00 frames.], tot_loss[loss=0.2303, simple_loss=0.3051, pruned_loss=0.07777, over 1421126.65 frames.], batch size: 19, lr: 7.33e-04
2022-05-27 01:59:50,091 INFO [train.py:842] (1/4) Epoch 7, batch 3850, loss[loss=0.1813, simple_loss=0.2596, pruned_loss=0.05151, over 7138.00 frames.], tot_loss[loss=0.2308, simple_loss=0.305, pruned_loss=0.07827, over 1424083.99 frames.], batch size: 17, lr: 7.33e-04
2022-05-27 02:00:28,931 INFO [train.py:842] (1/4) Epoch 7, batch 3900, loss[loss=0.2042, simple_loss=0.2842, pruned_loss=0.06209, over 7429.00 frames.], tot_loss[loss=0.2298, simple_loss=0.3045, pruned_loss=0.07757, over 1424762.03 frames.], batch size: 20, lr: 7.33e-04
2022-05-27 02:01:07,928 INFO [train.py:842] (1/4) Epoch 7, batch 3950, loss[loss=0.2058, simple_loss=0.2828, pruned_loss=0.06443, over 7277.00 frames.], tot_loss[loss=0.2285, simple_loss=0.303, pruned_loss=0.07701, over 1424223.77 frames.], batch size: 18, lr: 7.32e-04
2022-05-27 02:01:46,441 INFO [train.py:842] (1/4) Epoch 7, batch 4000, loss[loss=0.2376, simple_loss=0.3172, pruned_loss=0.07898, over 7346.00 frames.], tot_loss[loss=0.2297, simple_loss=0.305, pruned_loss=0.07725, over 1430476.56 frames.], batch size: 22, lr: 7.32e-04
2022-05-27 02:02:25,562 INFO [train.py:842] (1/4) Epoch 7, batch 4050, loss[loss=0.2022, simple_loss=0.2901, pruned_loss=0.05716, over 7342.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3048, pruned_loss=0.0778, over 1433094.90 frames.], batch size: 22, lr: 7.32e-04
2022-05-27 02:03:03,999 INFO [train.py:842] (1/4) Epoch 7, batch 4100, loss[loss=0.2246, simple_loss=0.3079, pruned_loss=0.0707, over 6722.00 frames.], tot_loss[loss=0.2294, simple_loss=0.3044, pruned_loss=0.07723, over 1428427.40 frames.], batch size: 31, lr: 7.32e-04
2022-05-27 02:03:42,706 INFO [train.py:842] (1/4) Epoch 7, batch 4150, loss[loss=0.2101, simple_loss=0.2818, pruned_loss=0.06925, over 7257.00 frames.], tot_loss[loss=0.2289, simple_loss=0.3041, pruned_loss=0.07684, over 1427517.17 frames.], batch size: 19, lr: 7.31e-04
2022-05-27 02:04:21,261 INFO [train.py:842] (1/4) Epoch 7, batch 4200, loss[loss=0.2222, simple_loss=0.3108, pruned_loss=0.06684, over 6410.00 frames.], tot_loss[loss=0.2285, simple_loss=0.3038, pruned_loss=0.07662, over 1428813.45 frames.], batch size: 37, lr: 7.31e-04
2022-05-27 02:05:00,151 INFO [train.py:842] (1/4) Epoch 7, batch 4250, loss[loss=0.2709, simple_loss=0.3444, pruned_loss=0.09873, over 7101.00 frames.], tot_loss[loss=0.2263, simple_loss=0.3022, pruned_loss=0.07523, over 1430792.01 frames.], batch size: 21, lr: 7.31e-04
2022-05-27 02:05:38,648 INFO [train.py:842] (1/4) Epoch 7, batch 4300, loss[loss=0.2728, simple_loss=0.3456, pruned_loss=0.1, over 6740.00 frames.], tot_loss[loss=0.2262, simple_loss=0.3016, pruned_loss=0.07539, over 1425981.09 frames.], batch size: 31, lr: 7.30e-04
2022-05-27 02:06:17,403 INFO [train.py:842] (1/4) Epoch 7, batch 4350, loss[loss=0.1957, simple_loss=0.2864, pruned_loss=0.05248, over 7432.00 frames.], tot_loss[loss=0.2275, simple_loss=0.303, pruned_loss=0.07596, over 1421338.01 frames.], batch size: 20, lr: 7.30e-04
2022-05-27 02:06:55,815 INFO [train.py:842] (1/4) Epoch 7, batch 4400, loss[loss=0.1917, simple_loss=0.272, pruned_loss=0.05572, over 7414.00 frames.], tot_loss[loss=0.2273, simple_loss=0.3027, pruned_loss=0.07593, over 1416601.09 frames.], batch size: 18, lr: 7.30e-04
2022-05-27 02:07:34,687 INFO [train.py:842] (1/4) Epoch 7, batch 4450, loss[loss=0.196, simple_loss=0.2884, pruned_loss=0.0518, over 7150.00 frames.], tot_loss[loss=0.2285, simple_loss=0.3038, pruned_loss=0.07662, over 1418398.41 frames.], batch size: 20, lr: 7.29e-04
2022-05-27 02:08:13,209 INFO [train.py:842] (1/4) Epoch 7, batch 4500, loss[loss=0.2397, simple_loss=0.2993, pruned_loss=0.09007, over 7282.00 frames.], tot_loss[loss=0.2296, simple_loss=0.3046, pruned_loss=0.07728, over 1421343.79 frames.], batch size: 17, lr: 7.29e-04
2022-05-27 02:08:52,139 INFO [train.py:842] (1/4) Epoch 7, batch 4550, loss[loss=0.202, simple_loss=0.2808, pruned_loss=0.06163, over 6831.00 frames.], tot_loss[loss=0.2289, simple_loss=0.3042, pruned_loss=0.07676, over 1419644.06 frames.], batch size: 15, lr: 7.29e-04
2022-05-27 02:09:30,773 INFO [train.py:842] (1/4) Epoch 7, batch 4600, loss[loss=0.2707, simple_loss=0.3447, pruned_loss=0.09832, over 7423.00 frames.], tot_loss[loss=0.2288, simple_loss=0.3038, pruned_loss=0.07693, over 1416211.90 frames.], batch size: 21, lr: 7.28e-04
2022-05-27 02:10:09,714 INFO [train.py:842] (1/4) Epoch 7, batch 4650, loss[loss=0.2091, simple_loss=0.2784, pruned_loss=0.06989, over 7163.00 frames.], tot_loss[loss=0.2281, simple_loss=0.3033, pruned_loss=0.07647, over 1420638.39 frames.], batch size: 18, lr: 7.28e-04
2022-05-27 02:10:48,231 INFO [train.py:842] (1/4) Epoch 7, batch 4700, loss[loss=0.2431, simple_loss=0.316, pruned_loss=0.08506, over 7308.00 frames.], tot_loss[loss=0.2278, simple_loss=0.3033, pruned_loss=0.07617, over 1421449.68 frames.], batch size: 24, lr: 7.28e-04
2022-05-27 02:11:27,363 INFO [train.py:842] (1/4) Epoch 7, batch 4750, loss[loss=0.2288, simple_loss=0.3091, pruned_loss=0.07418, over 7349.00 frames.], tot_loss[loss=0.2277, simple_loss=0.3031, pruned_loss=0.07615, over 1421942.00 frames.], batch size: 19, lr: 7.28e-04
2022-05-27 02:12:05,929 INFO [train.py:842] (1/4) Epoch 7, batch 4800, loss[loss=0.2208, simple_loss=0.2884, pruned_loss=0.07655, over 7272.00 frames.], tot_loss[loss=0.2257, simple_loss=0.3014, pruned_loss=0.075, over 1420669.52 frames.], batch size: 18, lr: 7.27e-04
2022-05-27 02:12:44,841 INFO [train.py:842] (1/4) Epoch 7, batch 4850, loss[loss=0.232, simple_loss=0.3088, pruned_loss=0.07761, over 7415.00 frames.], tot_loss[loss=0.2257, simple_loss=0.3015, pruned_loss=0.07494, over 1418718.30 frames.], batch size: 21, lr: 7.27e-04
2022-05-27 02:13:23,596 INFO [train.py:842] (1/4) Epoch 7, batch 4900, loss[loss=0.2641, simple_loss=0.3406, pruned_loss=0.09374, over 7200.00 frames.], tot_loss[loss=0.2263, simple_loss=0.3022, pruned_loss=0.07525, over 1418882.29 frames.], batch size: 23, lr: 7.27e-04
2022-05-27 02:14:02,741 INFO [train.py:842] (1/4) Epoch 7, batch 4950, loss[loss=0.2438, simple_loss=0.3119, pruned_loss=0.08785, over 7317.00 frames.], tot_loss[loss=0.2242, simple_loss=0.3001, pruned_loss=0.07413, over 1421772.91 frames.], batch size: 21, lr: 7.26e-04
2022-05-27 02:14:41,335 INFO [train.py:842] (1/4) Epoch 7, batch 5000, loss[loss=0.2123, simple_loss=0.3022, pruned_loss=0.06118, over 7187.00 frames.], tot_loss[loss=0.2242, simple_loss=0.3004, pruned_loss=0.074, over 1422670.31 frames.], batch size: 23, lr: 7.26e-04
2022-05-27 02:15:20,262 INFO [train.py:842] (1/4) Epoch 7, batch 5050, loss[loss=0.2325, simple_loss=0.3201, pruned_loss=0.07241, over 7330.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3003, pruned_loss=0.07397, over 1413748.42 frames.], batch size: 25, lr: 7.26e-04
2022-05-27 02:15:58,648 INFO [train.py:842] (1/4) Epoch 7, batch 5100, loss[loss=0.178, simple_loss=0.2655, pruned_loss=0.04527, over 7153.00 frames.], tot_loss[loss=0.2246, simple_loss=0.3005, pruned_loss=0.07437, over 1416089.22 frames.], batch size: 18, lr: 7.25e-04
2022-05-27 02:16:37,763 INFO [train.py:842] (1/4) Epoch 7, batch 5150, loss[loss=0.2416, simple_loss=0.3081, pruned_loss=0.08753, over 7417.00 frames.], tot_loss[loss=0.2247, simple_loss=0.3001, pruned_loss=0.07467, over 1417688.79 frames.], batch size: 18, lr: 7.25e-04
2022-05-27 02:17:16,404 INFO [train.py:842] (1/4) Epoch 7, batch 5200, loss[loss=0.2091, simple_loss=0.2878, pruned_loss=0.06523, over 7321.00 frames.], tot_loss[loss=0.2267, simple_loss=0.3019, pruned_loss=0.07573, over 1420061.67 frames.], batch size: 20, lr: 7.25e-04
2022-05-27 02:17:55,304 INFO [train.py:842] (1/4) Epoch 7, batch 5250, loss[loss=0.2476, simple_loss=0.3197, pruned_loss=0.08773, over 7323.00 frames.], tot_loss[loss=0.2265, simple_loss=0.3016, pruned_loss=0.07568, over 1417327.04 frames.], batch size: 20, lr: 7.25e-04
2022-05-27 02:18:33,909 INFO [train.py:842] (1/4) Epoch 7, batch 5300, loss[loss=0.1672, simple_loss=0.2433, pruned_loss=0.04549, over 6994.00 frames.], tot_loss[loss=0.2265, simple_loss=0.3016, pruned_loss=0.07576, over 1420597.43 frames.], batch size: 16, lr: 7.24e-04
2022-05-27 02:19:12,840 INFO [train.py:842] (1/4) Epoch 7, batch 5350, loss[loss=0.2408, simple_loss=0.3014, pruned_loss=0.09007, over 7240.00 frames.], tot_loss[loss=0.227, simple_loss=0.3018, pruned_loss=0.07615, over 1422539.56 frames.], batch size: 20, lr: 7.24e-04
2022-05-27 02:19:51,742 INFO [train.py:842] (1/4) Epoch 7, batch 5400, loss[loss=0.29, simple_loss=0.3476, pruned_loss=0.1163, over 4748.00 frames.], tot_loss[loss=0.2285, simple_loss=0.303, pruned_loss=0.07704, over 1414895.97 frames.], batch size: 52, lr: 7.24e-04
2022-05-27 02:20:30,646 INFO [train.py:842] (1/4) Epoch 7, batch 5450, loss[loss=0.2423, simple_loss=0.3266, pruned_loss=0.07893, over 7305.00 frames.], tot_loss[loss=0.2286, simple_loss=0.3035, pruned_loss=0.07687, over 1416062.18 frames.], batch size: 24, lr: 7.23e-04
2022-05-27 02:21:09,282 INFO [train.py:842] (1/4) Epoch 7, batch 5500, loss[loss=0.2877, simple_loss=0.3407, pruned_loss=0.1173, over 7149.00 frames.], tot_loss[loss=0.2281, simple_loss=0.3031, pruned_loss=0.07653, over 1418084.89 frames.], batch size: 19, lr: 7.23e-04
2022-05-27 02:21:48,183 INFO [train.py:842] (1/4) Epoch 7, batch 5550, loss[loss=0.2614, simple_loss=0.328, pruned_loss=0.09744, over 7287.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3047, pruned_loss=0.07754, over 1418626.74 frames.], batch size: 24, lr: 7.23e-04
2022-05-27 02:22:26,589 INFO [train.py:842] (1/4) Epoch 7, batch 5600, loss[loss=0.234, simple_loss=0.3186, pruned_loss=0.07466, over 7196.00 frames.], tot_loss[loss=0.2297, simple_loss=0.3045, pruned_loss=0.07745, over 1418332.82 frames.], batch size: 23, lr: 7.22e-04
2022-05-27 02:23:05,533 INFO [train.py:842] (1/4) Epoch 7, batch 5650, loss[loss=0.1622, simple_loss=0.2442, pruned_loss=0.04015, over 7264.00 frames.], tot_loss[loss=0.2301, simple_loss=0.305, pruned_loss=0.07759, over 1418203.47 frames.], batch size: 17, lr: 7.22e-04
2022-05-27 02:23:44,699 INFO [train.py:842] (1/4) Epoch 7, batch 5700, loss[loss=0.2367, simple_loss=0.3106, pruned_loss=0.08144, over 7142.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3057, pruned_loss=0.07783, over 1421949.68 frames.], batch size: 20, lr: 7.22e-04
2022-05-27 02:24:23,475 INFO [train.py:842] (1/4) Epoch 7, batch 5750, loss[loss=0.2299, simple_loss=0.3083, pruned_loss=0.07573, over 7321.00 frames.], tot_loss[loss=0.2312, simple_loss=0.3057, pruned_loss=0.07842, over 1421585.41 frames.], batch size: 25, lr: 7.22e-04
2022-05-27 02:25:01,795 INFO [train.py:842] (1/4) Epoch 7, batch 5800, loss[loss=0.2827, simple_loss=0.3533, pruned_loss=0.106, over 6382.00 frames.], tot_loss[loss=0.2296, simple_loss=0.3045, pruned_loss=0.07732, over 1419301.57 frames.], batch size: 37, lr: 7.21e-04
2022-05-27 02:25:40,556 INFO [train.py:842] (1/4) Epoch 7, batch 5850, loss[loss=0.1861, simple_loss=0.2564, pruned_loss=0.05793, over 7415.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3056, pruned_loss=0.07799, over 1415980.60 frames.], batch size: 18, lr: 7.21e-04
2022-05-27 02:26:19,107 INFO [train.py:842] (1/4) Epoch 7, batch 5900, loss[loss=0.2231, simple_loss=0.2876, pruned_loss=0.0793, over 7280.00 frames.], tot_loss[loss=0.229, simple_loss=0.3038, pruned_loss=0.07715, over 1416404.15 frames.], batch size: 17, lr: 7.21e-04
2022-05-27 02:26:58,451 INFO [train.py:842] (1/4) Epoch 7, batch 5950, loss[loss=0.2331, simple_loss=0.2978, pruned_loss=0.08418, over 7411.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3051, pruned_loss=0.07734, over 1420479.92 frames.], batch size: 20, lr: 7.20e-04
2022-05-27 02:27:37,190 INFO [train.py:842] (1/4) Epoch 7, batch 6000, loss[loss=0.2337, simple_loss=0.2997, pruned_loss=0.08383, over 7162.00 frames.], tot_loss[loss=0.229, simple_loss=0.304, pruned_loss=0.077, over 1419565.89 frames.], batch size: 18, lr: 7.20e-04
2022-05-27 02:27:37,190 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 02:27:46,474 INFO [train.py:871] (1/4) Epoch 7, validation: loss=0.1828, simple_loss=0.2835, pruned_loss=0.04102, over 868885.00 frames.
2022-05-27 02:28:25,433 INFO [train.py:842] (1/4) Epoch 7, batch 6050, loss[loss=0.1745, simple_loss=0.2698, pruned_loss=0.03963, over 7327.00 frames.], tot_loss[loss=0.2264, simple_loss=0.3023, pruned_loss=0.07526, over 1423317.29 frames.], batch size: 20, lr: 7.20e-04
2022-05-27 02:29:04,007 INFO [train.py:842] (1/4) Epoch 7, batch 6100, loss[loss=0.2091, simple_loss=0.2709, pruned_loss=0.07371, over 6811.00 frames.], tot_loss[loss=0.2269, simple_loss=0.3025, pruned_loss=0.07558, over 1423365.38 frames.], batch size: 15, lr: 7.20e-04
2022-05-27 02:29:42,985 INFO [train.py:842] (1/4) Epoch 7, batch 6150, loss[loss=0.2028, simple_loss=0.2764, pruned_loss=0.06454, over 7406.00 frames.], tot_loss[loss=0.2272, simple_loss=0.3027, pruned_loss=0.07579, over 1424176.90 frames.], batch size: 18, lr: 7.19e-04
2022-05-27 02:30:21,587 INFO [train.py:842] (1/4) Epoch 7, batch 6200, loss[loss=0.2391, simple_loss=0.3027, pruned_loss=0.08768, over 7274.00 frames.], tot_loss[loss=0.2295, simple_loss=0.3043, pruned_loss=0.07736, over 1423109.94 frames.], batch size: 18, lr: 7.19e-04
2022-05-27 02:31:00,694 INFO [train.py:842] (1/4) Epoch 7, batch 6250, loss[loss=0.2054, simple_loss=0.2819, pruned_loss=0.06443, over 7192.00 frames.], tot_loss[loss=0.2291, simple_loss=0.3038, pruned_loss=0.07715, over 1423478.21 frames.], batch size: 16, lr: 7.19e-04
2022-05-27 02:31:39,570 INFO [train.py:842] (1/4) Epoch 7, batch 6300, loss[loss=0.1949, simple_loss=0.2724, pruned_loss=0.05869, over 7355.00 frames.], tot_loss[loss=0.2284, simple_loss=0.3032, pruned_loss=0.07685, over 1414517.04 frames.], batch size: 19, lr: 7.18e-04
2022-05-27 02:32:18,507 INFO [train.py:842] (1/4) Epoch 7, batch 6350, loss[loss=0.1607, simple_loss=0.2431, pruned_loss=0.03916, over 7289.00 frames.], tot_loss[loss=0.2272, simple_loss=0.3025, pruned_loss=0.07595, over 1419559.80 frames.], batch size: 18, lr: 7.18e-04
2022-05-27 02:32:57,027 INFO [train.py:842] (1/4) Epoch 7, batch 6400, loss[loss=0.282, simple_loss=0.3375, pruned_loss=0.1132, over 5004.00 frames.], tot_loss[loss=0.2292, simple_loss=0.3047, pruned_loss=0.07686, over 1422227.74 frames.], batch size: 52, lr: 7.18e-04
2022-05-27 02:33:36,093 INFO [train.py:842] (1/4) Epoch 7, batch 6450, loss[loss=0.225, simple_loss=0.2989, pruned_loss=0.07551, over 7205.00 frames.], tot_loss[loss=0.2279, simple_loss=0.3035, pruned_loss=0.07612, over 1427117.24 frames.], batch size: 23, lr: 7.18e-04
2022-05-27 02:34:14,493 INFO [train.py:842] (1/4) Epoch 7, batch 6500, loss[loss=0.2583, simple_loss=0.3367, pruned_loss=0.08998, over 7029.00 frames.], tot_loss[loss=0.2281, simple_loss=0.3036, pruned_loss=0.07628, over 1427320.08 frames.], batch size: 28, lr: 7.17e-04
2022-05-27 02:34:53,304 INFO [train.py:842] (1/4) Epoch 7, batch 6550, loss[loss=0.2436, simple_loss=0.3172, pruned_loss=0.08499, over 7328.00 frames.], tot_loss[loss=0.229, simple_loss=0.3045, pruned_loss=0.07675, over 1422792.98 frames.], batch size: 25, lr: 7.17e-04
2022-05-27 02:35:31,825 INFO [train.py:842] (1/4) Epoch 7, batch 6600, loss[loss=0.2359, simple_loss=0.3095, pruned_loss=0.08121, over 7420.00 frames.], tot_loss[loss=0.2274, simple_loss=0.3033, pruned_loss=0.0758, over 1420779.01 frames.], batch size: 21, lr: 7.17e-04
2022-05-27 02:36:10,719 INFO [train.py:842] (1/4) Epoch 7, batch 6650, loss[loss=0.2041, simple_loss=0.2851, pruned_loss=0.06153, over 7063.00 frames.], tot_loss[loss=0.2268, simple_loss=0.3026, pruned_loss=0.07553, over 1420545.34 frames.], batch size: 18, lr: 7.16e-04
2022-05-27 02:36:49,522 INFO [train.py:842] (1/4) Epoch 7, batch 6700, loss[loss=0.2069, simple_loss=0.2896, pruned_loss=0.0621, over 7065.00 frames.], tot_loss[loss=0.2265, simple_loss=0.3024, pruned_loss=0.07531, over 1423692.80 frames.], batch size: 18, lr: 7.16e-04
2022-05-27 02:37:28,481 INFO [train.py:842] (1/4) Epoch 7, batch 6750, loss[loss=0.2323, simple_loss=0.3108, pruned_loss=0.07691, over 7166.00 frames.], tot_loss[loss=0.2258, simple_loss=0.3018, pruned_loss=0.07492, over 1425253.70 frames.], batch size: 19, lr: 7.16e-04
2022-05-27 02:38:06,902 INFO [train.py:842] (1/4) Epoch 7, batch 6800, loss[loss=0.2435, simple_loss=0.324, pruned_loss=0.08148, over 7294.00 frames.], tot_loss[loss=0.2284, simple_loss=0.3037, pruned_loss=0.0765, over 1422333.11 frames.], batch size: 25, lr: 7.16e-04
2022-05-27 02:38:45,851 INFO [train.py:842] (1/4) Epoch 7, batch 6850, loss[loss=0.1711, simple_loss=0.2475, pruned_loss=0.04734, over 7225.00 frames.], tot_loss[loss=0.2306, simple_loss=0.3055, pruned_loss=0.07781, over 1419620.66 frames.], batch size: 16, lr: 7.15e-04
2022-05-27 02:39:35,204 INFO [train.py:842] (1/4) Epoch 7, batch 6900, loss[loss=0.2282, simple_loss=0.3084, pruned_loss=0.07407, over 7330.00 frames.], tot_loss[loss=0.2303, simple_loss=0.3051, pruned_loss=0.07779, over 1420762.29 frames.], batch size: 22, lr: 7.15e-04
2022-05-27 02:40:14,038 INFO [train.py:842] (1/4) Epoch 7, batch 6950, loss[loss=0.2283, simple_loss=0.3184, pruned_loss=0.06911, over 7413.00 frames.], tot_loss[loss=0.2289, simple_loss=0.3042, pruned_loss=0.07677, over 1417080.11 frames.], batch size: 21, lr: 7.15e-04
2022-05-27 02:40:52,453 INFO [train.py:842] (1/4) Epoch 7, batch 7000, loss[loss=0.2464, simple_loss=0.3178, pruned_loss=0.0875, over 6163.00 frames.], tot_loss[loss=0.2294, simple_loss=0.3049, pruned_loss=0.07698, over 1418981.06 frames.], batch size: 37, lr: 7.14e-04
2022-05-27 02:41:31,357 INFO [train.py:842] (1/4) Epoch 7, batch 7050, loss[loss=0.2519, simple_loss=0.3241, pruned_loss=0.08988, over 7317.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3058, pruned_loss=0.07732, over 1423326.94 frames.], batch size: 25, lr: 7.14e-04
2022-05-27 02:42:09,959 INFO [train.py:842] (1/4) Epoch 7, batch 7100, loss[loss=0.1875, simple_loss=0.2813, pruned_loss=0.04686, over 7178.00 frames.], tot_loss[loss=0.2305, simple_loss=0.306, pruned_loss=0.07754, over 1424616.11 frames.], batch size: 23, lr: 7.14e-04
2022-05-27 02:42:49,027 INFO [train.py:842] (1/4) Epoch 7, batch 7150, loss[loss=0.1686, simple_loss=0.2379, pruned_loss=0.04965, over 7280.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3072, pruned_loss=0.07905, over 1422307.63 frames.], batch size: 17, lr: 7.14e-04
2022-05-27 02:43:27,675 INFO [train.py:842] (1/4) Epoch 7, batch 7200, loss[loss=0.2328, simple_loss=0.3092, pruned_loss=0.0782, over 7360.00 frames.], tot_loss[loss=0.2323, simple_loss=0.3068, pruned_loss=0.07887, over 1414480.87 frames.], batch size: 19, lr: 7.13e-04
2022-05-27 02:44:06,734 INFO [train.py:842] (1/4) Epoch 7, batch 7250, loss[loss=0.2927, simple_loss=0.3531, pruned_loss=0.1161, over 7192.00 frames.], tot_loss[loss=0.2331, simple_loss=0.3075, pruned_loss=0.07932, over 1417394.67 frames.], batch size: 28, lr: 7.13e-04
2022-05-27 02:44:45,252 INFO [train.py:842] (1/4) Epoch 7, batch 7300, loss[loss=0.3072, simple_loss=0.3693, pruned_loss=0.1225, over 7118.00 frames.], tot_loss[loss=0.2338, simple_loss=0.3083, pruned_loss=0.07969, over 1420432.32 frames.], batch size: 26, lr: 7.13e-04
2022-05-27 02:45:24,103 INFO [train.py:842] (1/4) Epoch 7, batch 7350, loss[loss=0.2007, simple_loss=0.283, pruned_loss=0.05923, over 6978.00 frames.], tot_loss[loss=0.2341, simple_loss=0.3081, pruned_loss=0.08004, over 1421110.51 frames.], batch size: 16, lr: 7.12e-04
2022-05-27 02:46:02,471 INFO [train.py:842] (1/4) Epoch 7, batch 7400, loss[loss=0.2226, simple_loss=0.2964, pruned_loss=0.07439, over 7067.00 frames.], tot_loss[loss=0.2333, simple_loss=0.3075, pruned_loss=0.07953, over 1417483.54 frames.], batch size: 18, lr: 7.12e-04
2022-05-27 02:46:41,239 INFO [train.py:842] (1/4) Epoch 7, batch 7450, loss[loss=0.2505, simple_loss=0.3161, pruned_loss=0.09245, over 7292.00 frames.], tot_loss[loss=0.2351, simple_loss=0.3089, pruned_loss=0.08067, over 1417326.28 frames.], batch size: 17, lr: 7.12e-04
2022-05-27 02:47:19,875 INFO [train.py:842] (1/4) Epoch 7, batch 7500, loss[loss=0.2135, simple_loss=0.2946, pruned_loss=0.06616, over 7134.00 frames.], tot_loss[loss=0.2324, simple_loss=0.3067, pruned_loss=0.07909, over 1418815.06 frames.], batch size: 20, lr: 7.12e-04
2022-05-27 02:47:58,788 INFO [train.py:842] (1/4) Epoch 7, batch 7550, loss[loss=0.2322, simple_loss=0.3201, pruned_loss=0.07219, over 7312.00 frames.], tot_loss[loss=0.2313, simple_loss=0.3058, pruned_loss=0.07838, over 1417876.26 frames.], batch size: 21, lr: 7.11e-04
2022-05-27 02:48:37,264 INFO [train.py:842] (1/4) Epoch 7, batch 7600, loss[loss=0.2075, simple_loss=0.2907, pruned_loss=0.06219, over 7318.00 frames.], tot_loss[loss=0.2303, simple_loss=0.3053, pruned_loss=0.07767, over 1421358.00 frames.], batch size: 22, lr: 7.11e-04
2022-05-27 02:49:16,248 INFO [train.py:842] (1/4) Epoch 7, batch 7650, loss[loss=0.28, simple_loss=0.3381, pruned_loss=0.111, over 5066.00 frames.], tot_loss[loss=0.228, simple_loss=0.3033, pruned_loss=0.07637, over 1423634.02 frames.], batch size: 52, lr: 7.11e-04
2022-05-27 02:49:54,713 INFO [train.py:842] (1/4) Epoch 7, batch 7700, loss[loss=0.2141, simple_loss=0.2969, pruned_loss=0.0657, over 7117.00 frames.], tot_loss[loss=0.2275, simple_loss=0.3027, pruned_loss=0.07614, over 1419938.05 frames.], batch size: 21, lr: 7.10e-04
2022-05-27 02:50:33,581 INFO [train.py:842] (1/4) Epoch 7, batch 7750, loss[loss=0.2699, simple_loss=0.3463, pruned_loss=0.09673, over 7226.00 frames.], tot_loss[loss=0.227, simple_loss=0.3023, pruned_loss=0.0759, over 1424495.48 frames.], batch size: 20, lr: 7.10e-04
2022-05-27 02:51:12,172 INFO [train.py:842] (1/4) Epoch 7, batch 7800, loss[loss=0.2034, simple_loss=0.2788, pruned_loss=0.06406, over 7440.00 frames.], tot_loss[loss=0.2271, simple_loss=0.3025, pruned_loss=0.07581, over 1424872.51 frames.], batch size: 20, lr: 7.10e-04
2022-05-27 02:51:51,061 INFO [train.py:842] (1/4) Epoch 7, batch 7850, loss[loss=0.2007, simple_loss=0.276, pruned_loss=0.06267, over 6834.00 frames.], tot_loss[loss=0.2257, simple_loss=0.3017, pruned_loss=0.07483, over 1423911.94 frames.], batch size: 15, lr: 7.10e-04
2022-05-27 02:52:29,404 INFO [train.py:842] (1/4) Epoch 7, batch 7900, loss[loss=0.2074, simple_loss=0.2867, pruned_loss=0.0641, over 7263.00 frames.], tot_loss[loss=0.2269, simple_loss=0.3026, pruned_loss=0.07565, over 1422189.87 frames.], batch size: 19, lr: 7.09e-04
2022-05-27 02:53:07,966 INFO [train.py:842] (1/4) Epoch 7, batch 7950, loss[loss=0.2318, simple_loss=0.3127, pruned_loss=0.07551, over 7188.00 frames.], tot_loss[loss=0.2272, simple_loss=0.3024, pruned_loss=0.07599, over 1415968.74 frames.], batch size: 23, lr: 7.09e-04
2022-05-27 02:53:46,561 INFO [train.py:842] (1/4) Epoch 7, batch 8000, loss[loss=0.2488, simple_loss=0.3183, pruned_loss=0.08969, over 4959.00 frames.], tot_loss[loss=0.2312, simple_loss=0.3054, pruned_loss=0.07852, over 1413663.99 frames.], batch size: 52, lr: 7.09e-04
2022-05-27 02:54:25,451 INFO [train.py:842] (1/4) Epoch 7, batch 8050, loss[loss=0.2484, simple_loss=0.312, pruned_loss=0.09242, over 7418.00 frames.], tot_loss[loss=0.2312, simple_loss=0.3055, pruned_loss=0.07846, over 1414818.92 frames.], batch size: 21, lr: 7.08e-04
2022-05-27 02:55:24,507 INFO [train.py:842] (1/4) Epoch 7, batch 8100, loss[loss=0.2376, simple_loss=0.3081, pruned_loss=0.08361, over 6740.00 frames.], tot_loss[loss=0.2324, simple_loss=0.3069, pruned_loss=0.0789, over 1414773.25 frames.], batch size: 31, lr: 7.08e-04
2022-05-27 02:56:13,876 INFO [train.py:842] (1/4) Epoch 7, batch 8150, loss[loss=0.1968, simple_loss=0.284, pruned_loss=0.05484, over 7322.00 frames.], tot_loss[loss=0.231, simple_loss=0.3054, pruned_loss=0.07831, over 1419396.73 frames.], batch size: 21, lr: 7.08e-04
2022-05-27 02:56:52,243 INFO [train.py:842] (1/4) Epoch 7, batch 8200, loss[loss=0.2578, simple_loss=0.3201, pruned_loss=0.09772, over 7368.00 frames.], tot_loss[loss=0.2311, simple_loss=0.3055, pruned_loss=0.07837, over 1419780.18 frames.], batch size: 19, lr: 7.08e-04
2022-05-27 02:57:31,161 INFO [train.py:842] (1/4) Epoch 7, batch 8250, loss[loss=0.2123, simple_loss=0.3008, pruned_loss=0.06187, over 6826.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3053, pruned_loss=0.07748, over 1421276.47 frames.], batch size: 31, lr: 7.07e-04
2022-05-27 02:58:09,610 INFO [train.py:842] (1/4) Epoch 7, batch 8300, loss[loss=0.1962, simple_loss=0.2759, pruned_loss=0.05824, over 7429.00 frames.], tot_loss[loss=0.2287, simple_loss=0.3039, pruned_loss=0.07674, over 1416705.63 frames.], batch size: 17, lr: 7.07e-04
2022-05-27 02:58:48,414 INFO [train.py:842] (1/4) Epoch 7, batch 8350, loss[loss=0.2184, simple_loss=0.3034, pruned_loss=0.06675, over 7212.00 frames.], tot_loss[loss=0.2276, simple_loss=0.3034, pruned_loss=0.07591, over 1420903.80 frames.], batch size: 21, lr: 7.07e-04
2022-05-27 02:59:26,860 INFO [train.py:842] (1/4) Epoch 7, batch 8400, loss[loss=0.202, simple_loss=0.2792, pruned_loss=0.06239, over 7219.00 frames.], tot_loss[loss=0.2259, simple_loss=0.302, pruned_loss=0.07496, over 1422419.88 frames.], batch size: 20, lr: 7.06e-04
2022-05-27 03:00:05,684 INFO [train.py:842] (1/4) Epoch 7, batch 8450, loss[loss=0.2265, simple_loss=0.3019, pruned_loss=0.07552, over 7408.00 frames.], tot_loss[loss=0.226, simple_loss=0.3023, pruned_loss=0.07482, over 1422117.58 frames.], batch size: 21, lr: 7.06e-04
2022-05-27 03:00:44,405 INFO [train.py:842] (1/4) Epoch 7, batch 8500, loss[loss=0.2302, simple_loss=0.3094, pruned_loss=0.07548, over 7329.00 frames.], tot_loss[loss=0.2272, simple_loss=0.3031, pruned_loss=0.0756, over 1422489.42 frames.], batch size: 22, lr: 7.06e-04
2022-05-27 03:01:22,956 INFO [train.py:842] (1/4) Epoch 7, batch 8550, loss[loss=0.2146, simple_loss=0.2959, pruned_loss=0.0666, over 7168.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3023, pruned_loss=0.07449, over 1416281.38 frames.], batch size: 19, lr: 7.06e-04
2022-05-27 03:02:01,348 INFO [train.py:842] (1/4) Epoch 7, batch 8600, loss[loss=0.2486, simple_loss=0.3094, pruned_loss=0.09393, over 7151.00 frames.], tot_loss[loss=0.2279, simple_loss=0.3043, pruned_loss=0.07577, over 1417562.02 frames.], batch size: 18, lr: 7.05e-04
2022-05-27 03:02:40,398 INFO [train.py:842] (1/4) Epoch 7, batch 8650, loss[loss=0.1888, simple_loss=0.2862, pruned_loss=0.04568, over 7325.00 frames.], tot_loss[loss=0.226, simple_loss=0.3026, pruned_loss=0.07466, over 1417628.66 frames.], batch size: 22, lr: 7.05e-04
2022-05-27 03:03:18,983 INFO [train.py:842] (1/4) Epoch 7, batch 8700, loss[loss=0.2137, simple_loss=0.2855, pruned_loss=0.07097, over 7426.00 frames.], tot_loss[loss=0.2266, simple_loss=0.3033, pruned_loss=0.0749, over 1419172.00 frames.], batch size: 18, lr: 7.05e-04
2022-05-27 03:03:57,742 INFO [train.py:842] (1/4) Epoch 7, batch 8750, loss[loss=0.2692, simple_loss=0.3479, pruned_loss=0.09528, over 7221.00 frames.], tot_loss[loss=0.2296, simple_loss=0.3059, pruned_loss=0.07666, over 1419123.46 frames.], batch size: 21, lr: 7.05e-04
2022-05-27 03:04:35,968 INFO [train.py:842] (1/4) Epoch 7, batch 8800, loss[loss=0.2739, simple_loss=0.3334, pruned_loss=0.1072, over 4891.00 frames.], tot_loss[loss=0.2301, simple_loss=0.306, pruned_loss=0.07708, over 1414529.37 frames.], batch size: 52, lr: 7.04e-04
2022-05-27 03:05:17,312 INFO [train.py:842] (1/4) Epoch 7, batch 8850, loss[loss=0.1847, simple_loss=0.2672, pruned_loss=0.05111, over 7406.00 frames.], tot_loss[loss=0.2284, simple_loss=0.3044, pruned_loss=0.07621, over 1417436.31 frames.], batch size: 18, lr: 7.04e-04
2022-05-27 03:05:55,607 INFO [train.py:842] (1/4) Epoch 7, batch 8900, loss[loss=0.229, simple_loss=0.2927, pruned_loss=0.08268, over 7167.00 frames.], tot_loss[loss=0.2282, simple_loss=0.3038, pruned_loss=0.07632, over 1412824.44 frames.], batch size: 18, lr: 7.04e-04
2022-05-27 03:06:34,502 INFO [train.py:842] (1/4) Epoch 7, batch 8950, loss[loss=0.2057, simple_loss=0.2868, pruned_loss=0.06232, over 7338.00 frames.], tot_loss[loss=0.2291, simple_loss=0.3049, pruned_loss=0.07664, over 1408246.55 frames.], batch size: 22, lr: 7.03e-04
2022-05-27 03:07:12,744 INFO [train.py:842] (1/4) Epoch 7, batch 9000, loss[loss=0.2668, simple_loss=0.3356, pruned_loss=0.09898, over 7224.00 frames.], tot_loss[loss=0.231, simple_loss=0.3067, pruned_loss=0.07764, over 1401328.87 frames.], batch size: 21, lr: 7.03e-04
2022-05-27 03:07:12,744 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 03:07:22,108 INFO [train.py:871] (1/4) Epoch 7, validation: loss=0.1806, simple_loss=0.2819, pruned_loss=0.03969, over 868885.00 frames.
2022-05-27 03:08:00,673 INFO [train.py:842] (1/4) Epoch 7, batch 9050, loss[loss=0.3316, simple_loss=0.3817, pruned_loss=0.1407, over 5213.00 frames.], tot_loss[loss=0.231, simple_loss=0.3068, pruned_loss=0.07764, over 1396292.00 frames.], batch size: 52, lr: 7.03e-04
2022-05-27 03:08:38,276 INFO [train.py:842] (1/4) Epoch 7, batch 9100, loss[loss=0.2414, simple_loss=0.3148, pruned_loss=0.08402, over 7325.00 frames.], tot_loss[loss=0.2338, simple_loss=0.3097, pruned_loss=0.07893, over 1379627.44 frames.], batch size: 25, lr: 7.03e-04
2022-05-27 03:09:15,873 INFO [train.py:842] (1/4) Epoch 7, batch 9150, loss[loss=0.2252, simple_loss=0.3044, pruned_loss=0.07297, over 6252.00 frames.], tot_loss[loss=0.236, simple_loss=0.3116, pruned_loss=0.08014, over 1341085.44 frames.], batch size: 37, lr: 7.02e-04
2022-05-27 03:10:09,057 INFO [train.py:842] (1/4) Epoch 8, batch 0, loss[loss=0.225, simple_loss=0.3214, pruned_loss=0.06426, over 7326.00 frames.], tot_loss[loss=0.225, simple_loss=0.3214, pruned_loss=0.06426, over 7326.00 frames.], batch size: 22, lr: 6.74e-04
2022-05-27 03:10:47,712 INFO [train.py:842] (1/4) Epoch 8, batch 50, loss[loss=0.2255, simple_loss=0.2915, pruned_loss=0.07969, over 7122.00 frames.], tot_loss[loss=0.2306, simple_loss=0.3073, pruned_loss=0.07695, over 321122.93 frames.], batch size: 17, lr: 6.73e-04
2022-05-27 03:11:26,528 INFO [train.py:842] (1/4) Epoch 8, batch 100, loss[loss=0.273, simple_loss=0.3377, pruned_loss=0.1041, over 7303.00 frames.], tot_loss[loss=0.2273, simple_loss=0.3056, pruned_loss=0.07452, over 569618.69 frames.], batch size: 25, lr: 6.73e-04
2022-05-27 03:12:05,190 INFO [train.py:842] (1/4) Epoch 8, batch 150, loss[loss=0.2344, simple_loss=0.3192, pruned_loss=0.07481, over 7112.00 frames.], tot_loss[loss=0.228, simple_loss=0.3051, pruned_loss=0.07541, over 758854.59 frames.], batch size: 21, lr: 6.73e-04
2022-05-27 03:12:43,898 INFO [train.py:842] (1/4) Epoch 8, batch 200, loss[loss=0.2499, simple_loss=0.3257, pruned_loss=0.087, over 7202.00 frames.], tot_loss[loss=0.2265, simple_loss=0.3037, pruned_loss=0.07465, over 907305.80 frames.], batch size: 22, lr: 6.73e-04
2022-05-27 03:13:22,334 INFO [train.py:842] (1/4) Epoch 8, batch 250, loss[loss=0.2396, simple_loss=0.3161, pruned_loss=0.08155, over 7443.00 frames.], tot_loss[loss=0.2254, simple_loss=0.303, pruned_loss=0.07386, over 1020852.22 frames.], batch size: 22, lr: 6.72e-04
2022-05-27 03:14:01,062 INFO [train.py:842] (1/4) Epoch 8, batch 300, loss[loss=0.2593, simple_loss=0.3351, pruned_loss=0.09178, over 7068.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3028, pruned_loss=0.07419, over 1106502.08 frames.], batch size: 18, lr: 6.72e-04
2022-05-27 03:14:39,758 INFO [train.py:842] (1/4) Epoch 8, batch 350, loss[loss=0.2195, simple_loss=0.3075, pruned_loss=0.06572, over 7111.00 frames.], tot_loss[loss=0.2242, simple_loss=0.301, pruned_loss=0.07367, over 1178260.55 frames.], batch size: 21, lr: 6.72e-04
2022-05-27 03:15:18,907 INFO [train.py:842] (1/4) Epoch 8, batch 400, loss[loss=0.2657, simple_loss=0.3242, pruned_loss=0.1036, over 4897.00 frames.], tot_loss[loss=0.2231, simple_loss=0.3001, pruned_loss=0.07302, over 1231148.21 frames.], batch size: 52, lr: 6.72e-04
2022-05-27 03:15:57,545 INFO [train.py:842] (1/4) Epoch 8, batch 450, loss[loss=0.2382, simple_loss=0.3008, pruned_loss=0.08777, over 6816.00 frames.], tot_loss[loss=0.2225, simple_loss=0.2995, pruned_loss=0.07278, over 1271946.59 frames.], batch size: 15, lr: 6.71e-04
2022-05-27 03:16:36,540 INFO [train.py:842] (1/4) Epoch 8, batch 500, loss[loss=0.233, simple_loss=0.3126, pruned_loss=0.07668, over 7207.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2972, pruned_loss=0.07107, over 1304888.71 frames.], batch size: 23, lr: 6.71e-04
2022-05-27 03:17:15,288 INFO [train.py:842] (1/4) Epoch 8, batch 550, loss[loss=0.271, simple_loss=0.334, pruned_loss=0.104, over 7194.00 frames.], tot_loss[loss=0.223, simple_loss=0.2999, pruned_loss=0.07307, over 1332606.64 frames.], batch size: 23, lr: 6.71e-04
2022-05-27 03:17:54,028 INFO [train.py:842] (1/4) Epoch 8, batch 600, loss[loss=0.2157, simple_loss=0.2962, pruned_loss=0.06761, over 7222.00 frames.], tot_loss[loss=0.2248, simple_loss=0.3015, pruned_loss=0.07401, over 1352273.05 frames.], batch size: 21, lr: 6.71e-04
2022-05-27 03:18:32,600 INFO [train.py:842] (1/4) Epoch 8, batch 650, loss[loss=0.2056, simple_loss=0.2891, pruned_loss=0.06099, over 7270.00 frames.], tot_loss[loss=0.2238, simple_loss=0.3007, pruned_loss=0.07349, over 1367285.04 frames.], batch size: 19, lr: 6.70e-04
2022-05-27 03:19:11,326 INFO [train.py:842] (1/4) Epoch 8, batch 700, loss[loss=0.2954, simple_loss=0.3798, pruned_loss=0.1054, over 5146.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3024, pruned_loss=0.07433, over 1375767.40 frames.], batch size: 52, lr: 6.70e-04
2022-05-27 03:19:49,829 INFO [train.py:842] (1/4) Epoch 8, batch 750, loss[loss=0.2046, simple_loss=0.2868, pruned_loss=0.06125, over 7361.00 frames.], tot_loss[loss=0.2251, simple_loss=0.3021, pruned_loss=0.07406, over 1384479.04 frames.], batch size: 19, lr: 6.70e-04
2022-05-27 03:20:28,428 INFO [train.py:842] (1/4) Epoch 8, batch 800, loss[loss=0.2299, simple_loss=0.319, pruned_loss=0.07037, over 6520.00 frames.], tot_loss[loss=0.2269, simple_loss=0.3041, pruned_loss=0.07484, over 1390325.69 frames.], batch size: 38, lr: 6.69e-04
2022-05-27 03:21:07,286 INFO [train.py:842] (1/4) Epoch 8, batch 850, loss[loss=0.2336, simple_loss=0.2903, pruned_loss=0.08845, over 7413.00 frames.], tot_loss[loss=0.2243, simple_loss=0.3014, pruned_loss=0.07363, over 1399423.53 frames.], batch size: 18, lr: 6.69e-04
2022-05-27 03:21:46,354 INFO [train.py:842] (1/4) Epoch 8, batch 900, loss[loss=0.23, simple_loss=0.3047, pruned_loss=0.07767, over 6775.00 frames.], tot_loss[loss=0.225, simple_loss=0.3015, pruned_loss=0.07427, over 1398888.09 frames.], batch size: 31, lr: 6.69e-04
2022-05-27 03:22:24,856 INFO [train.py:842] (1/4) Epoch 8, batch 950, loss[loss=0.2032, simple_loss=0.29, pruned_loss=0.0582, over 7240.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3002, pruned_loss=0.07337, over 1404508.60 frames.], batch size: 20, lr: 6.69e-04
2022-05-27 03:23:03,656 INFO [train.py:842] (1/4) Epoch 8, batch 1000, loss[loss=0.2611, simple_loss=0.3296, pruned_loss=0.09623, over 7217.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2997, pruned_loss=0.07242, over 1409352.95 frames.], batch size: 21, lr: 6.68e-04
2022-05-27 03:23:42,254 INFO [train.py:842] (1/4) Epoch 8, batch 1050, loss[loss=0.2012, simple_loss=0.2664, pruned_loss=0.06799, over 7125.00 frames.], tot_loss[loss=0.2232, simple_loss=0.3007, pruned_loss=0.07281, over 1407794.77 frames.], batch size: 17, lr: 6.68e-04
2022-05-27 03:24:21,162 INFO [train.py:842] (1/4) Epoch 8, batch 1100, loss[loss=0.2174, simple_loss=0.2952, pruned_loss=0.06981, over 7196.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2989, pruned_loss=0.07181, over 1411656.94 frames.], batch size: 22, lr: 6.68e-04
2022-05-27 03:24:59,654 INFO [train.py:842] (1/4) Epoch 8, batch 1150, loss[loss=0.2246, simple_loss=0.292, pruned_loss=0.0786, over 4728.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2993, pruned_loss=0.07163, over 1417026.21 frames.], batch size: 52, lr: 6.68e-04
2022-05-27 03:25:38,516 INFO [train.py:842] (1/4) Epoch 8, batch 1200, loss[loss=0.2415, simple_loss=0.3174, pruned_loss=0.08281, over 7149.00 frames.], tot_loss[loss=0.2206, simple_loss=0.2985, pruned_loss=0.07136, over 1420838.42 frames.], batch size: 20, lr: 6.67e-04
2022-05-27 03:26:16,968 INFO [train.py:842] (1/4) Epoch 8, batch 1250, loss[loss=0.245, simple_loss=0.3191, pruned_loss=0.08545, over 7282.00 frames.], tot_loss[loss=0.2203, simple_loss=0.2981, pruned_loss=0.07124, over 1419389.09 frames.], batch size: 18, lr: 6.67e-04
2022-05-27 03:26:55,520 INFO [train.py:842] (1/4) Epoch 8, batch 1300, loss[loss=0.2605, simple_loss=0.334, pruned_loss=0.09346, over 7154.00 frames.], tot_loss[loss=0.222, simple_loss=0.2995, pruned_loss=0.07223, over 1416594.42 frames.], batch size: 20, lr: 6.67e-04
2022-05-27 03:27:34,028 INFO [train.py:842] (1/4) Epoch 8, batch 1350, loss[loss=0.2111, simple_loss=0.2851, pruned_loss=0.06856, over 7162.00 frames.], tot_loss[loss=0.2232, simple_loss=0.3005, pruned_loss=0.07289, over 1414988.11 frames.], batch size: 19, lr: 6.67e-04
2022-05-27 03:28:12,695 INFO [train.py:842] (1/4) Epoch 8, batch 1400, loss[loss=0.1791, simple_loss=0.2604, pruned_loss=0.04891, over 7274.00 frames.], tot_loss[loss=0.2237, simple_loss=0.301, pruned_loss=0.07325, over 1415721.44 frames.], batch size: 18, lr: 6.66e-04
2022-05-27 03:28:51,239 INFO [train.py:842] (1/4) Epoch 8, batch 1450, loss[loss=0.1806, simple_loss=0.2627, pruned_loss=0.0492, over 7157.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2998, pruned_loss=0.07206, over 1415833.97 frames.], batch size: 18, lr: 6.66e-04
2022-05-27 03:29:30,460 INFO [train.py:842] (1/4) Epoch 8, batch 1500, loss[loss=0.2136, simple_loss=0.2922, pruned_loss=0.06755, over 7419.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2993, pruned_loss=0.07264, over 1415348.88 frames.], batch size: 18, lr: 6.66e-04
2022-05-27 03:30:09,007 INFO [train.py:842] (1/4) Epoch 8, batch 1550, loss[loss=0.2087, simple_loss=0.3019, pruned_loss=0.05778, over 7211.00 frames.], tot_loss[loss=0.2246, simple_loss=0.3011, pruned_loss=0.07407, over 1420451.83 frames.], batch size: 22, lr: 6.66e-04
2022-05-27 03:30:48,025 INFO [train.py:842] (1/4) Epoch 8, batch 1600, loss[loss=0.2148, simple_loss=0.3042, pruned_loss=0.06269, over 6482.00 frames.], tot_loss[loss=0.2264, simple_loss=0.3028, pruned_loss=0.075, over 1420675.43 frames.], batch size: 38, lr: 6.65e-04
2022-05-27 03:31:26,450 INFO [train.py:842] (1/4) Epoch 8, batch 1650, loss[loss=0.2792, simple_loss=0.3489, pruned_loss=0.1048, over 7286.00 frames.], tot_loss[loss=0.2281, simple_loss=0.304, pruned_loss=0.07609, over 1419656.05 frames.], batch size: 24, lr: 6.65e-04
2022-05-27 03:32:05,124 INFO [train.py:842] (1/4) Epoch 8, batch 1700, loss[loss=0.2404, simple_loss=0.3174, pruned_loss=0.08175, over 7323.00 frames.], tot_loss[loss=0.2281, simple_loss=0.3046, pruned_loss=0.0758, over 1419678.66 frames.], batch size: 21, lr: 6.65e-04
2022-05-27 03:32:43,707 INFO [train.py:842] (1/4) Epoch 8, batch 1750, loss[loss=0.2067, simple_loss=0.291, pruned_loss=0.06125, over 7331.00 frames.], tot_loss[loss=0.2266, simple_loss=0.3032, pruned_loss=0.07495, over 1420522.47 frames.], batch size: 22, lr: 6.65e-04
2022-05-27 03:33:22,869 INFO [train.py:842] (1/4) Epoch 8, batch 1800, loss[loss=0.2198, simple_loss=0.3158, pruned_loss=0.06194, over 7339.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3012, pruned_loss=0.0735, over 1421578.57 frames.], batch size: 22, lr: 6.64e-04
2022-05-27 03:34:01,572 INFO [train.py:842] (1/4) Epoch 8, batch 1850, loss[loss=0.2468, simple_loss=0.3206, pruned_loss=0.08643, over 7240.00 frames.], tot_loss[loss=0.2253, simple_loss=0.3024, pruned_loss=0.07413, over 1424183.84 frames.], batch size: 20, lr: 6.64e-04
2022-05-27 03:34:40,358 INFO [train.py:842] (1/4) Epoch 8, batch 1900, loss[loss=0.2382, simple_loss=0.3186, pruned_loss=0.07891, over 7319.00 frames.], tot_loss[loss=0.2239, simple_loss=0.3009, pruned_loss=0.0735, over 1422273.48 frames.], batch size: 25, lr: 6.64e-04
2022-05-27 03:35:19,053 INFO [train.py:842] (1/4) Epoch 8, batch 1950, loss[loss=0.2175, simple_loss=0.2859, pruned_loss=0.07456, over 6996.00 frames.], tot_loss[loss=0.2242, simple_loss=0.3011, pruned_loss=0.07358, over 1426441.97 frames.], batch size: 16, lr: 6.64e-04
2022-05-27 03:35:57,683 INFO [train.py:842] (1/4) Epoch 8, batch 2000, loss[loss=0.2407, simple_loss=0.3065, pruned_loss=0.08743, over 7119.00 frames.], tot_loss[loss=0.2238, simple_loss=0.3005, pruned_loss=0.07349, over 1427081.90 frames.], batch size: 21, lr: 6.63e-04
2022-05-27 03:36:36,042 INFO [train.py:842] (1/4) Epoch 8, batch 2050, loss[loss=0.2432, simple_loss=0.3173, pruned_loss=0.08459, over 5307.00 frames.], tot_loss[loss=0.2253, simple_loss=0.3017, pruned_loss=0.0744, over 1421596.33 frames.], batch size: 53, lr: 6.63e-04
2022-05-27 03:37:14,887 INFO [train.py:842] (1/4) Epoch 8, batch 2100, loss[loss=0.2338, simple_loss=0.3194, pruned_loss=0.07411, over 7232.00 frames.], tot_loss[loss=0.2251, simple_loss=0.302, pruned_loss=0.0741, over 1417752.23 frames.], batch size: 20, lr: 6.63e-04
2022-05-27 03:37:53,566 INFO [train.py:842] (1/4) Epoch 8, batch 2150, loss[loss=0.2028, simple_loss=0.2889, pruned_loss=0.05837, over 7201.00 frames.], tot_loss[loss=0.2247, simple_loss=0.3015, pruned_loss=0.0739, over 1418702.17 frames.], batch size: 22, lr: 6.63e-04
2022-05-27 03:38:32,394 INFO [train.py:842] (1/4) Epoch 8, batch 2200, loss[loss=0.1981, simple_loss=0.2824, pruned_loss=0.0569, over 7278.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2992, pruned_loss=0.07227, over 1416428.35 frames.], batch size: 24, lr: 6.62e-04
2022-05-27 03:39:11,118 INFO [train.py:842] (1/4) Epoch 8, batch 2250, loss[loss=0.2048, simple_loss=0.2853, pruned_loss=0.06217, over 7199.00 frames.], tot_loss[loss=0.2226, simple_loss=0.2993, pruned_loss=0.07295, over 1412036.99 frames.], batch size: 23, lr: 6.62e-04
2022-05-27 03:39:49,980 INFO [train.py:842] (1/4) Epoch 8, batch 2300, loss[loss=0.1892, simple_loss=0.2632, pruned_loss=0.05759, over 7414.00 frames.], tot_loss[loss=0.2207, simple_loss=0.2978, pruned_loss=0.07183, over 1412780.06 frames.], batch size: 18, lr: 6.62e-04
2022-05-27 03:40:28,563 INFO [train.py:842] (1/4) Epoch 8, batch 2350, loss[loss=0.3103, simple_loss=0.3548, pruned_loss=0.1329, over 7061.00 frames.], tot_loss[loss=0.2224, simple_loss=0.2991, pruned_loss=0.07284, over 1413169.71 frames.], batch size: 18, lr: 6.62e-04
2022-05-27 03:41:07,409 INFO [train.py:842] (1/4) Epoch 8, batch 2400, loss[loss=0.2355, simple_loss=0.3193, pruned_loss=0.07588, over 7258.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2984, pruned_loss=0.0723, over 1417364.36 frames.], batch size: 19, lr: 6.61e-04
2022-05-27 03:41:46,090 INFO [train.py:842] (1/4) Epoch 8, batch 2450, loss[loss=0.2584, simple_loss=0.3306, pruned_loss=0.0931, over 7314.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2986, pruned_loss=0.07219, over 1423496.85 frames.], batch size: 24, lr: 6.61e-04
2022-05-27 03:42:24,992 INFO [train.py:842] (1/4) Epoch 8, batch 2500, loss[loss=0.255, simple_loss=0.3233, pruned_loss=0.09333, over 7324.00 frames.], tot_loss[loss=0.2239, simple_loss=0.3004, pruned_loss=0.07369, over 1421404.39 frames.], batch size: 21, lr: 6.61e-04
2022-05-27 03:43:03,686 INFO [train.py:842] (1/4) Epoch 8, batch 2550, loss[loss=0.2037, simple_loss=0.2801, pruned_loss=0.06372, over 7361.00 frames.], tot_loss[loss=0.2234, simple_loss=0.2994, pruned_loss=0.07371, over 1425749.43 frames.], batch size: 19, lr: 6.61e-04
2022-05-27 03:43:43,040 INFO [train.py:842] (1/4) Epoch 8, batch 2600, loss[loss=0.1868, simple_loss=0.2613, pruned_loss=0.05619, over 7220.00 frames.], tot_loss[loss=0.2226, simple_loss=0.2989, pruned_loss=0.0731, over 1425994.87 frames.], batch size: 16, lr: 6.60e-04
2022-05-27 03:44:21,509 INFO [train.py:842] (1/4) Epoch 8, batch 2650, loss[loss=0.2327, simple_loss=0.3053, pruned_loss=0.08, over 7110.00 frames.], tot_loss[loss=0.2217, simple_loss=0.2986, pruned_loss=0.07238, over 1426957.00 frames.], batch size: 21, lr: 6.60e-04
2022-05-27 03:45:00,449 INFO [train.py:842] (1/4) Epoch 8, batch 2700, loss[loss=0.2187, simple_loss=0.2809, pruned_loss=0.07826, over 7208.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2986, pruned_loss=0.07253, over 1429244.81 frames.], batch size: 16, lr: 6.60e-04
2022-05-27 03:45:38,996 INFO [train.py:842] (1/4) Epoch 8, batch 2750, loss[loss=0.176, simple_loss=0.2536, pruned_loss=0.04915, over 6998.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2984, pruned_loss=0.07215, over 1428290.05 frames.], batch size: 16, lr: 6.60e-04
2022-05-27 03:46:17,905 INFO [train.py:842] (1/4) Epoch 8, batch 2800, loss[loss=0.2196, simple_loss=0.3029, pruned_loss=0.06814, over 7146.00 frames.], tot_loss[loss=0.2222, simple_loss=0.299, pruned_loss=0.07274, over 1428992.70 frames.], batch size: 20, lr: 6.60e-04
2022-05-27 03:46:56,413 INFO [train.py:842] (1/4) Epoch 8, batch 2850, loss[loss=0.2224, simple_loss=0.2976, pruned_loss=0.07358, over 7196.00 frames.], tot_loss[loss=0.2232, simple_loss=0.2997, pruned_loss=0.07328, over 1426922.44 frames.], batch size: 22, lr: 6.59e-04
2022-05-27 03:47:35,361 INFO [train.py:842] (1/4) Epoch 8, batch 2900, loss[loss=0.2046, simple_loss=0.2725, pruned_loss=0.06835, over 7145.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2994, pruned_loss=0.0726, over 1426091.02 frames.], batch size: 17, lr: 6.59e-04
2022-05-27 03:48:13,868 INFO [train.py:842] (1/4) Epoch 8, batch 2950, loss[loss=0.2477, simple_loss=0.3211, pruned_loss=0.08713, over 7066.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2991, pruned_loss=0.07274, over 1424290.21 frames.], batch size: 18, lr: 6.59e-04
2022-05-27 03:48:52,480 INFO [train.py:842] (1/4) Epoch 8, batch 3000, loss[loss=0.2679, simple_loss=0.3415, pruned_loss=0.09719, over 5077.00 frames.], tot_loss[loss=0.2218, simple_loss=0.299, pruned_loss=0.0723, over 1421352.84 frames.], batch size: 53, lr: 6.59e-04
2022-05-27 03:48:52,481 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 03:49:01,707 INFO [train.py:871] (1/4) Epoch 8, validation: loss=0.1787, simple_loss=0.2793, pruned_loss=0.03905, over 868885.00 frames.
2022-05-27 03:49:40,132 INFO [train.py:842] (1/4) Epoch 8, batch 3050, loss[loss=0.2592, simple_loss=0.3188, pruned_loss=0.09986, over 6588.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2989, pruned_loss=0.07284, over 1414799.27 frames.], batch size: 38, lr: 6.58e-04
2022-05-27 03:50:18,981 INFO [train.py:842] (1/4) Epoch 8, batch 3100, loss[loss=0.2176, simple_loss=0.291, pruned_loss=0.07204, over 7259.00 frames.], tot_loss[loss=0.2217, simple_loss=0.2982, pruned_loss=0.07257, over 1419656.46 frames.], batch size: 19, lr: 6.58e-04
2022-05-27 03:50:57,750 INFO [train.py:842] (1/4) Epoch 8, batch 3150, loss[loss=0.2176, simple_loss=0.3114, pruned_loss=0.06193, over 7427.00 frames.], tot_loss[loss=0.2212, simple_loss=0.2975, pruned_loss=0.07251, over 1421775.78 frames.], batch size: 20, lr: 6.58e-04
2022-05-27 03:51:36,895 INFO [train.py:842] (1/4) Epoch 8, batch 3200, loss[loss=0.2003, simple_loss=0.2747, pruned_loss=0.06299, over 7427.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2972, pruned_loss=0.0723, over 1424378.93 frames.], batch size: 20, lr: 6.58e-04
2022-05-27 03:52:15,363 INFO [train.py:842] (1/4) Epoch 8, batch 3250, loss[loss=0.2444, simple_loss=0.3231, pruned_loss=0.08281, over 7064.00 frames.], tot_loss[loss=0.2237, simple_loss=0.2998, pruned_loss=0.07379, over 1422626.97 frames.], batch size: 28, lr: 6.57e-04
2022-05-27 03:52:54,461 INFO [train.py:842] (1/4) Epoch 8, batch 3300, loss[loss=0.2297, simple_loss=0.3071, pruned_loss=0.07612, over 6599.00 frames.], tot_loss[loss=0.223, simple_loss=0.2989, pruned_loss=0.07355, over 1421551.32 frames.], batch size: 31, lr: 6.57e-04
2022-05-27 03:53:33,002 INFO [train.py:842] (1/4) Epoch 8, batch 3350, loss[loss=0.1967, simple_loss=0.2753, pruned_loss=0.05908, over 7426.00 frames.], tot_loss[loss=0.223, simple_loss=0.2991, pruned_loss=0.07346, over 1420353.35 frames.], batch size: 20, lr: 6.57e-04
2022-05-27 03:54:11,883 INFO [train.py:842] (1/4) Epoch 8, batch 3400, loss[loss=0.3349, simple_loss=0.3868, pruned_loss=0.1415, over 6909.00 frames.], tot_loss[loss=0.2231, simple_loss=0.2994, pruned_loss=0.0734, over 1418825.16 frames.], batch size: 31, lr: 6.57e-04
2022-05-27 03:54:50,309 INFO [train.py:842] (1/4) Epoch 8, batch 3450, loss[loss=0.2859, simple_loss=0.3373, pruned_loss=0.1172, over 7416.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3006, pruned_loss=0.07408, over 1421802.82 frames.], batch size: 18, lr: 6.56e-04
2022-05-27 03:55:29,174 INFO [train.py:842] (1/4) Epoch 8, batch 3500, loss[loss=0.2467, simple_loss=0.3151, pruned_loss=0.08914, over 7394.00 frames.], tot_loss[loss=0.2247, simple_loss=0.3012, pruned_loss=0.07411, over 1421104.64 frames.], batch size: 23, lr: 6.56e-04
2022-05-27 03:56:07,716 INFO [train.py:842] (1/4) Epoch 8, batch 3550, loss[loss=0.2874, simple_loss=0.3293, pruned_loss=0.1227, over 7253.00 frames.], tot_loss[loss=0.222, simple_loss=0.2991, pruned_loss=0.07249, over 1421961.17 frames.], batch size: 19, lr: 6.56e-04
2022-05-27 03:56:46,632 INFO [train.py:842] (1/4) Epoch 8, batch 3600, loss[loss=0.178, simple_loss=0.2517, pruned_loss=0.05208, over 7282.00 frames.], tot_loss[loss=0.2231, simple_loss=0.2996, pruned_loss=0.07332, over 1419879.70 frames.], batch size: 17, lr: 6.56e-04
2022-05-27 03:57:25,064 INFO [train.py:842] (1/4) Epoch 8, batch 3650, loss[loss=0.1882, simple_loss=0.2761, pruned_loss=0.05013, over 7416.00 frames.], tot_loss[loss=0.2254, simple_loss=0.3017, pruned_loss=0.07456, over 1413907.98 frames.], batch size: 21, lr: 6.55e-04
2022-05-27 03:58:03,912 INFO [train.py:842] (1/4) Epoch 8, batch 3700, loss[loss=0.2402, simple_loss=0.3238, pruned_loss=0.07832, over 7317.00 frames.], tot_loss[loss=0.223, simple_loss=0.3, pruned_loss=0.07298, over 1417535.80 frames.], batch size: 22, lr: 6.55e-04
2022-05-27 03:58:42,480 INFO [train.py:842] (1/4) Epoch 8, batch 3750, loss[loss=0.306, simple_loss=0.361, pruned_loss=0.1255, over 7381.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2992, pruned_loss=0.07268, over 1416770.89 frames.], batch size: 23, lr: 6.55e-04
2022-05-27 03:59:21,174 INFO [train.py:842] (1/4) Epoch 8, batch 3800, loss[loss=0.251, simple_loss=0.3268, pruned_loss=0.08758, over 7188.00 frames.], tot_loss[loss=0.2236, simple_loss=0.3007, pruned_loss=0.07324, over 1417481.35 frames.], batch size: 22, lr: 6.55e-04
2022-05-27 03:59:59,632 INFO [train.py:842] (1/4) Epoch 8, batch 3850, loss[loss=0.2408, simple_loss=0.3209, pruned_loss=0.08037, over 7317.00 frames.], tot_loss[loss=0.2226, simple_loss=0.3002, pruned_loss=0.07248, over 1416630.21 frames.], batch size: 25, lr: 6.54e-04
2022-05-27 04:00:38,578 INFO [train.py:842] (1/4) Epoch 8, batch 3900, loss[loss=0.1754, simple_loss=0.2577, pruned_loss=0.04659, over 7071.00 frames.], tot_loss[loss=0.222, simple_loss=0.2992, pruned_loss=0.07238, over 1420826.14 frames.], batch size: 18, lr: 6.54e-04
2022-05-27 04:01:17,108 INFO [train.py:842] (1/4) Epoch 8, batch 3950, loss[loss=0.2199, simple_loss=0.294, pruned_loss=0.07286, over 7210.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2995, pruned_loss=0.0721, over 1423705.21 frames.], batch size: 22, lr: 6.54e-04
2022-05-27 04:01:55,864 INFO [train.py:842] (1/4) Epoch 8, batch 4000, loss[loss=0.2418, simple_loss=0.3154, pruned_loss=0.08414, over 7411.00 frames.], tot_loss[loss=0.2234, simple_loss=0.3005, pruned_loss=0.07311, over 1422428.44 frames.], batch size: 21, lr: 6.54e-04
2022-05-27 04:02:34,545 INFO [train.py:842] (1/4) Epoch 8, batch 4050, loss[loss=0.2294, simple_loss=0.2932, pruned_loss=0.08282, over 6991.00 frames.], tot_loss[loss=0.2238, simple_loss=0.301, pruned_loss=0.07329, over 1423328.01 frames.], batch size: 16, lr: 6.53e-04
2022-05-27 04:03:13,368 INFO [train.py:842] (1/4) Epoch 8, batch 4100, loss[loss=0.346, simple_loss=0.3908, pruned_loss=0.1506, over 7346.00 frames.], tot_loss[loss=0.225, simple_loss=0.3019, pruned_loss=0.07402, over 1417686.47 frames.], batch size: 23, lr: 6.53e-04
2022-05-27 04:03:51,853 INFO [train.py:842] (1/4) Epoch 8, batch 4150, loss[loss=0.2256, simple_loss=0.3022, pruned_loss=0.07446, over 6813.00 frames.], tot_loss[loss=0.2242, simple_loss=0.3012, pruned_loss=0.0736, over 1418530.14 frames.], batch size: 31, lr: 6.53e-04
2022-05-27 04:04:30,538 INFO [train.py:842] (1/4) Epoch 8, batch 4200, loss[loss=0.207, simple_loss=0.2874, pruned_loss=0.06325, over 7074.00 frames.], tot_loss[loss=0.225, simple_loss=0.3016, pruned_loss=0.07418, over 1417549.40 frames.], batch size: 18, lr: 6.53e-04
2022-05-27 04:05:09,116 INFO [train.py:842] (1/4) Epoch 8, batch 4250, loss[loss=0.243, simple_loss=0.3204, pruned_loss=0.08277, over 7148.00 frames.], tot_loss[loss=0.2249, simple_loss=0.3019, pruned_loss=0.07393, over 1416265.24 frames.], batch size: 26, lr: 6.53e-04
2022-05-27 04:05:47,927 INFO [train.py:842] (1/4) Epoch 8, batch 4300, loss[loss=0.2287, simple_loss=0.3072, pruned_loss=0.07516, over 7053.00 frames.], tot_loss[loss=0.2236, simple_loss=0.3011, pruned_loss=0.07304, over 1423472.10 frames.], batch size: 28, lr: 6.52e-04
2022-05-27 04:06:26,328 INFO [train.py:842] (1/4) Epoch 8, batch 4350, loss[loss=0.2015, simple_loss=0.2892, pruned_loss=0.05684, over 7206.00 frames.], tot_loss[loss=0.2227, simple_loss=0.3003, pruned_loss=0.07257, over 1422594.90 frames.], batch size: 22, lr: 6.52e-04
2022-05-27 04:07:05,492 INFO [train.py:842] (1/4) Epoch 8, batch 4400, loss[loss=0.2057, simple_loss=0.2924, pruned_loss=0.0595, over 7151.00 frames.], tot_loss[loss=0.2206, simple_loss=0.2981, pruned_loss=0.0715, over 1421220.88 frames.], batch size: 19, lr: 6.52e-04
2022-05-27 04:07:43,984 INFO [train.py:842] (1/4) Epoch 8, batch 4450, loss[loss=0.3025, simple_loss=0.3631, pruned_loss=0.121, over 7321.00 frames.], tot_loss[loss=0.2208, simple_loss=0.2982, pruned_loss=0.07169, over 1420671.16 frames.], batch size: 22, lr: 6.52e-04
2022-05-27 04:08:22,887 INFO [train.py:842] (1/4) Epoch 8, batch 4500, loss[loss=0.2507, simple_loss=0.3074, pruned_loss=0.09702, over 7125.00 frames.], tot_loss[loss=0.2202, simple_loss=0.2976, pruned_loss=0.07144, over 1422364.30 frames.], batch size: 17, lr: 6.51e-04
2022-05-27 04:09:01,470 INFO [train.py:842] (1/4) Epoch 8, batch 4550, loss[loss=0.2126, simple_loss=0.2949, pruned_loss=0.0652, over 7254.00 frames.], tot_loss[loss=0.2214, simple_loss=0.2991, pruned_loss=0.07181, over 1423831.03 frames.], batch size: 19, lr: 6.51e-04
2022-05-27 04:09:40,169 INFO [train.py:842] (1/4) Epoch 8, batch 4600, loss[loss=0.2425, simple_loss=0.3177, pruned_loss=0.08368, over 6768.00 frames.], tot_loss[loss=0.2237, simple_loss=0.3009, pruned_loss=0.07328, over 1421978.60 frames.], batch size: 31, lr: 6.51e-04
2022-05-27 04:10:18,844 INFO [train.py:842] (1/4) Epoch 8, batch 4650, loss[loss=0.185, simple_loss=0.2719, pruned_loss=0.04903, over 7066.00 frames.], tot_loss[loss=0.224, simple_loss=0.3007, pruned_loss=0.0737, over 1419708.75 frames.], batch size: 28, lr: 6.51e-04
2022-05-27 04:10:57,636 INFO [train.py:842] (1/4) Epoch 8, batch 4700, loss[loss=0.228, simple_loss=0.3184, pruned_loss=0.06877, over 7314.00 frames.], tot_loss[loss=0.2243, simple_loss=0.301, pruned_loss=0.07385, over 1421772.78 frames.], batch size: 25, lr: 6.50e-04
2022-05-27 04:11:36,344 INFO [train.py:842] (1/4) Epoch 8, batch 4750, loss[loss=0.2077, simple_loss=0.2898, pruned_loss=0.06276, over 7429.00 frames.], tot_loss[loss=0.2267, simple_loss=0.303, pruned_loss=0.07524, over 1418513.56 frames.], batch size: 20, lr: 6.50e-04
2022-05-27 04:12:15,132 INFO [train.py:842] (1/4) Epoch 8, batch 4800, loss[loss=0.2253, simple_loss=0.3077, pruned_loss=0.07146, over 7154.00 frames.], tot_loss[loss=0.2262, simple_loss=0.3025, pruned_loss=0.07494, over 1421704.40 frames.], batch size: 26, lr: 6.50e-04
2022-05-27 04:12:53,772 INFO [train.py:842] (1/4) Epoch 8, batch 4850, loss[loss=0.2115, simple_loss=0.2813, pruned_loss=0.07083, over 7355.00 frames.], tot_loss[loss=0.2237, simple_loss=0.3007, pruned_loss=0.07339, over 1427274.65 frames.], batch size: 19, lr: 6.50e-04
2022-05-27 04:13:32,390 INFO [train.py:842] (1/4) Epoch 8, batch 4900, loss[loss=0.2763, simple_loss=0.3415, pruned_loss=0.1056, over 6713.00 frames.], tot_loss[loss=0.2225, simple_loss=0.3, pruned_loss=0.07245, over 1424702.51 frames.], batch size: 31, lr: 6.49e-04
2022-05-27 04:14:10,915 INFO [train.py:842] (1/4) Epoch 8, batch 4950, loss[loss=0.2055, simple_loss=0.2817, pruned_loss=0.06464, over 7063.00 frames.], tot_loss[loss=0.2222, simple_loss=0.3, pruned_loss=0.07222, over 1424796.67 frames.], batch size: 18, lr: 6.49e-04
2022-05-27 04:14:50,172 INFO [train.py:842] (1/4) Epoch 8, batch 5000, loss[loss=0.1818, simple_loss=0.2627, pruned_loss=0.05048, over 7260.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2994, pruned_loss=0.07239, over 1420435.46 frames.], batch size: 19, lr: 6.49e-04
2022-05-27 04:15:28,682 INFO [train.py:842] (1/4) Epoch 8, batch 5050, loss[loss=0.1972, simple_loss=0.2907, pruned_loss=0.05181, over 6407.00 frames.], tot_loss[loss=0.2237, simple_loss=0.3012, pruned_loss=0.07314, over 1419772.70 frames.], batch size: 38, lr: 6.49e-04
2022-05-27 04:16:07,696 INFO [train.py:842] (1/4) Epoch 8, batch 5100, loss[loss=0.1954, simple_loss=0.2735, pruned_loss=0.05869, over 7277.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2997, pruned_loss=0.07228, over 1425626.93 frames.], batch size: 17, lr: 6.49e-04
2022-05-27 04:16:46,295 INFO [train.py:842] (1/4) Epoch 8, batch 5150, loss[loss=0.1984, simple_loss=0.2774, pruned_loss=0.05967, over 7357.00 frames.], tot_loss[loss=0.2217, simple_loss=0.2996, pruned_loss=0.07192, over 1428324.36 frames.], batch size: 19, lr: 6.48e-04
2022-05-27 04:17:25,425 INFO [train.py:842] (1/4) Epoch 8, batch 5200, loss[loss=0.2218, simple_loss=0.3221, pruned_loss=0.06077, over 7271.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2985, pruned_loss=0.07083, over 1426055.99 frames.], batch size: 19, lr: 6.48e-04
2022-05-27 04:18:03,679 INFO [train.py:842] (1/4) Epoch 8, batch 5250, loss[loss=0.2006, simple_loss=0.2788, pruned_loss=0.06118, over 7161.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2995, pruned_loss=0.07174, over 1419760.65 frames.], batch size: 19, lr: 6.48e-04
2022-05-27 04:18:42,887 INFO [train.py:842] (1/4) Epoch 8, batch 5300, loss[loss=0.1965, simple_loss=0.2828, pruned_loss=0.05511, over 7158.00 frames.], tot_loss[loss=0.2214, simple_loss=0.299, pruned_loss=0.07188, over 1419741.30 frames.], batch size: 19, lr: 6.48e-04
2022-05-27 04:19:21,457 INFO [train.py:842] (1/4) Epoch 8, batch 5350, loss[loss=0.1901, simple_loss=0.2696, pruned_loss=0.05531, over 7158.00 frames.], tot_loss[loss=0.2217, simple_loss=0.2989, pruned_loss=0.07219, over 1420934.49 frames.], batch size: 19, lr: 6.47e-04
2022-05-27 04:20:00,558 INFO [train.py:842] (1/4) Epoch 8, batch 5400, loss[loss=0.192, simple_loss=0.2805, pruned_loss=0.05177, over 7321.00 frames.], tot_loss[loss=0.2212, simple_loss=0.2984, pruned_loss=0.07198, over 1419093.97 frames.], batch size: 21, lr: 6.47e-04
2022-05-27 04:20:39,243 INFO [train.py:842] (1/4) Epoch 8, batch 5450, loss[loss=0.1802, simple_loss=0.2589, pruned_loss=0.05073, over 7345.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2978, pruned_loss=0.07116, over 1419722.45 frames.], batch size: 19, lr: 6.47e-04
2022-05-27 04:21:18,483 INFO [train.py:842] (1/4) Epoch 8, batch 5500, loss[loss=0.2016, simple_loss=0.2768, pruned_loss=0.06323, over 7345.00 frames.], tot_loss[loss=0.219, simple_loss=0.2967, pruned_loss=0.07068, over 1419809.62 frames.], batch size: 19, lr: 6.47e-04
2022-05-27 04:21:56,813 INFO [train.py:842] (1/4) Epoch 8, batch 5550, loss[loss=0.2367, simple_loss=0.3119, pruned_loss=0.08072, over 7145.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2978, pruned_loss=0.07125, over 1416681.51 frames.], batch size: 20, lr: 6.46e-04
2022-05-27 04:22:35,511 INFO [train.py:842] (1/4) Epoch 8, batch 5600, loss[loss=0.1841, simple_loss=0.2609, pruned_loss=0.05362, over 7289.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2998, pruned_loss=0.07238, over 1416724.67 frames.], batch size: 18, lr: 6.46e-04
2022-05-27 04:23:14,181 INFO [train.py:842] (1/4) Epoch 8, batch 5650, loss[loss=0.2048, simple_loss=0.2894, pruned_loss=0.0601, over 7331.00 frames.], tot_loss[loss=0.221, simple_loss=0.2985, pruned_loss=0.07175, over 1417305.10 frames.], batch size: 22, lr: 6.46e-04
2022-05-27 04:23:53,477 INFO [train.py:842] (1/4) Epoch 8, batch 5700, loss[loss=0.2102, simple_loss=0.3054, pruned_loss=0.05746, over 7234.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2989, pruned_loss=0.07182, over 1423045.22 frames.], batch size: 20, lr: 6.46e-04
2022-05-27 04:24:32,082 INFO [train.py:842] (1/4) Epoch 8, batch 5750, loss[loss=0.1868, simple_loss=0.269, pruned_loss=0.05226, over 7068.00 frames.], tot_loss[loss=0.219, simple_loss=0.2972, pruned_loss=0.07044, over 1426972.84 frames.], batch size: 18, lr: 6.46e-04
2022-05-27 04:25:10,878 INFO [train.py:842] (1/4) Epoch 8, batch 5800, loss[loss=0.1648, simple_loss=0.2448, pruned_loss=0.04244, over 7265.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2974, pruned_loss=0.07007, over 1425672.88 frames.], batch size: 17, lr: 6.45e-04
2022-05-27 04:25:49,400 INFO [train.py:842] (1/4) Epoch 8, batch 5850, loss[loss=0.2665, simple_loss=0.3287, pruned_loss=0.1021, over 7233.00 frames.], tot_loss[loss=0.2212, simple_loss=0.2993, pruned_loss=0.07151, over 1427366.17 frames.], batch size: 20, lr: 6.45e-04
2022-05-27 04:26:28,992 INFO [train.py:842] (1/4) Epoch 8, batch 5900, loss[loss=0.1975, simple_loss=0.2702, pruned_loss=0.06238, over 7281.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2988, pruned_loss=0.07154, over 1427026.33 frames.], batch size: 17, lr: 6.45e-04
2022-05-27 04:27:07,422 INFO [train.py:842] (1/4) Epoch 8, batch 5950, loss[loss=0.1581, simple_loss=0.237, pruned_loss=0.03955, over 7283.00 frames.], tot_loss[loss=0.222, simple_loss=0.2995, pruned_loss=0.07226, over 1425120.03 frames.], batch size: 17, lr: 6.45e-04
2022-05-27 04:27:46,632 INFO [train.py:842] (1/4) Epoch 8, batch 6000, loss[loss=0.2196, simple_loss=0.3189, pruned_loss=0.06011, over 7315.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2993, pruned_loss=0.07188, over 1425695.82 frames.], batch size: 21, lr: 6.44e-04
2022-05-27 04:27:46,633 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 04:27:55,933 INFO [train.py:871] (1/4) Epoch 8, validation: loss=0.1785, simple_loss=0.2788, pruned_loss=0.03907, over 868885.00 frames.
2022-05-27 04:28:34,541 INFO [train.py:842] (1/4) Epoch 8, batch 6050, loss[loss=0.1782, simple_loss=0.2741, pruned_loss=0.04113, over 7358.00 frames.], tot_loss[loss=0.2211, simple_loss=0.2984, pruned_loss=0.07197, over 1426732.28 frames.], batch size: 19, lr: 6.44e-04
2022-05-27 04:29:13,571 INFO [train.py:842] (1/4) Epoch 8, batch 6100, loss[loss=0.2916, simple_loss=0.3424, pruned_loss=0.1204, over 7161.00 frames.], tot_loss[loss=0.2229, simple_loss=0.2995, pruned_loss=0.07316, over 1429177.12 frames.], batch size: 18, lr: 6.44e-04
2022-05-27 04:29:52,012 INFO [train.py:842] (1/4) Epoch 8, batch 6150, loss[loss=0.1911, simple_loss=0.2662, pruned_loss=0.05804, over 7281.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2994, pruned_loss=0.0722, over 1427885.89 frames.], batch size: 18, lr: 6.44e-04
2022-05-27 04:30:30,794 INFO [train.py:842] (1/4) Epoch 8, batch 6200, loss[loss=0.2249, simple_loss=0.3003, pruned_loss=0.07471, over 7320.00 frames.], tot_loss[loss=0.22, simple_loss=0.298, pruned_loss=0.07101, over 1427439.71 frames.], batch size: 25, lr: 6.43e-04
2022-05-27 04:31:09,317 INFO [train.py:842] (1/4) Epoch 8, batch 6250, loss[loss=0.2193, simple_loss=0.3, pruned_loss=0.0693, over 7198.00 frames.], tot_loss[loss=0.221, simple_loss=0.2986, pruned_loss=0.0717, over 1426150.95 frames.], batch size: 22, lr: 6.43e-04
2022-05-27 04:31:47,867 INFO [train.py:842] (1/4) Epoch 8, batch 6300, loss[loss=0.3441, simple_loss=0.3926, pruned_loss=0.1478, over 7175.00 frames.], tot_loss[loss=0.2217, simple_loss=0.2995, pruned_loss=0.07194, over 1426902.03 frames.], batch size: 23, lr: 6.43e-04
2022-05-27 04:32:26,516 INFO [train.py:842] (1/4) Epoch 8, batch 6350, loss[loss=0.1965, simple_loss=0.2719, pruned_loss=0.06059, over 7247.00 frames.], tot_loss[loss=0.2224, simple_loss=0.3, pruned_loss=0.07242, over 1426684.25 frames.], batch size: 16, lr: 6.43e-04
2022-05-27 04:33:05,971 INFO [train.py:842] (1/4) Epoch 8, batch 6400, loss[loss=0.2537, simple_loss=0.3337, pruned_loss=0.08691, over 7116.00 frames.], tot_loss[loss=0.225, simple_loss=0.3015, pruned_loss=0.07426, over 1423280.80 frames.], batch size: 21, lr: 6.43e-04
2022-05-27 04:33:44,699 INFO [train.py:842] (1/4) Epoch 8, batch 6450, loss[loss=0.1873, simple_loss=0.2627, pruned_loss=0.05602, over 7280.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2986, pruned_loss=0.0726, over 1428280.41 frames.], batch size: 18, lr: 6.42e-04
2022-05-27 04:34:23,950 INFO [train.py:842] (1/4) Epoch 8, batch 6500, loss[loss=0.2252, simple_loss=0.2997, pruned_loss=0.07537, over 7041.00 frames.], tot_loss[loss=0.2204, simple_loss=0.2971, pruned_loss=0.07184, over 1427669.07 frames.], batch size: 28, lr: 6.42e-04
2022-05-27 04:35:02,479 INFO [train.py:842] (1/4) Epoch 8, batch 6550, loss[loss=0.1808, simple_loss=0.2565, pruned_loss=0.0526, over 7002.00 frames.], tot_loss[loss=0.2209, simple_loss=0.298, pruned_loss=0.07189, over 1429847.76 frames.], batch size: 16, lr: 6.42e-04
2022-05-27 04:35:41,389 INFO [train.py:842] (1/4) Epoch 8, batch 6600, loss[loss=0.1904, simple_loss=0.2762, pruned_loss=0.05227, over 7156.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2982, pruned_loss=0.07219, over 1429359.15 frames.], batch size: 19, lr: 6.42e-04
2022-05-27 04:36:20,199 INFO [train.py:842] (1/4) Epoch 8, batch 6650, loss[loss=0.1996, simple_loss=0.2927, pruned_loss=0.05325, over 7303.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2987, pruned_loss=0.07255, over 1426867.33 frames.], batch size: 24, lr: 6.41e-04
2022-05-27 04:36:59,114 INFO [train.py:842] (1/4) Epoch 8, batch 6700, loss[loss=0.2197, simple_loss=0.2964, pruned_loss=0.07153, over 6707.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2965, pruned_loss=0.07084, over 1427253.33 frames.], batch size: 38, lr: 6.41e-04
2022-05-27 04:37:37,723 INFO [train.py:842] (1/4) Epoch 8, batch 6750, loss[loss=0.2153, simple_loss=0.3051, pruned_loss=0.0628, over 7344.00 frames.], tot_loss[loss=0.2171, simple_loss=0.2955, pruned_loss=0.06939, over 1430474.69 frames.], batch size: 22, lr: 6.41e-04
2022-05-27 04:38:16,578 INFO [train.py:842] (1/4) Epoch 8, batch 6800, loss[loss=0.2209, simple_loss=0.3025, pruned_loss=0.06966, over 7322.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2962, pruned_loss=0.07013, over 1429784.50 frames.], batch size: 21, lr: 6.41e-04
2022-05-27 04:38:55,340 INFO [train.py:842] (1/4) Epoch 8, batch 6850, loss[loss=0.2346, simple_loss=0.3269, pruned_loss=0.07115, over 7238.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2961, pruned_loss=0.07018, over 1431520.76 frames.], batch size: 20, lr: 6.41e-04
2022-05-27 04:39:34,222 INFO [train.py:842] (1/4) Epoch 8, batch 6900, loss[loss=0.1966, simple_loss=0.2719, pruned_loss=0.06068, over 7272.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2961, pruned_loss=0.0713, over 1430812.63 frames.], batch size: 18, lr: 6.40e-04
2022-05-27 04:40:12,647 INFO [train.py:842] (1/4) Epoch 8, batch 6950, loss[loss=0.253, simple_loss=0.3069, pruned_loss=0.09953, over 7264.00 frames.], tot_loss[loss=0.2196, simple_loss=0.2964, pruned_loss=0.0714, over 1428127.63 frames.], batch size: 19, lr: 6.40e-04
2022-05-27 04:40:51,440 INFO [train.py:842] (1/4) Epoch 8, batch 7000, loss[loss=0.2432, simple_loss=0.3179, pruned_loss=0.0843, over 7376.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2966, pruned_loss=0.07108, over 1428559.59 frames.], batch size: 23, lr: 6.40e-04
2022-05-27 04:41:30,072 INFO [train.py:842] (1/4) Epoch 8, batch 7050, loss[loss=0.1934, simple_loss=0.2759, pruned_loss=0.05538, over 7165.00 frames.], tot_loss[loss=0.2173, simple_loss=0.2949, pruned_loss=0.06981, over 1428047.29 frames.], batch size: 18, lr: 6.40e-04
2022-05-27 04:42:09,250 INFO [train.py:842] (1/4) Epoch 8, batch 7100, loss[loss=0.173, simple_loss=0.2513, pruned_loss=0.04731, over 7403.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2953, pruned_loss=0.06978, over 1424796.53 frames.], batch size: 18, lr: 6.39e-04
2022-05-27 04:42:47,951 INFO [train.py:842] (1/4) Epoch 8, batch 7150, loss[loss=0.1887, simple_loss=0.266, pruned_loss=0.05575, over 7280.00 frames.], tot_loss[loss=0.2198, simple_loss=0.2972, pruned_loss=0.07126, over 1421576.89 frames.], batch size: 18, lr: 6.39e-04
2022-05-27 04:43:26,577 INFO [train.py:842] (1/4) Epoch 8, batch 7200, loss[loss=0.2136, simple_loss=0.2972, pruned_loss=0.06498, over 7144.00 frames.], tot_loss[loss=0.2202, simple_loss=0.2974, pruned_loss=0.07155, over 1422114.06 frames.], batch size: 20, lr: 6.39e-04
2022-05-27 04:44:05,043 INFO [train.py:842] (1/4) Epoch 8, batch 7250, loss[loss=0.1904, simple_loss=0.2582, pruned_loss=0.06133, over 6817.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2969, pruned_loss=0.07162, over 1419147.40 frames.], batch size: 15, lr: 6.39e-04
2022-05-27 04:44:43,934 INFO [train.py:842] (1/4) Epoch 8, batch 7300, loss[loss=0.2715, simple_loss=0.3357, pruned_loss=0.1037, over 7168.00 frames.], tot_loss[loss=0.2202, simple_loss=0.2969, pruned_loss=0.07171, over 1416114.83 frames.], batch size: 19, lr: 6.39e-04
2022-05-27 04:45:22,490 INFO [train.py:842] (1/4) Epoch 8, batch 7350, loss[loss=0.2469, simple_loss=0.3263, pruned_loss=0.08379, over 7374.00 frames.], tot_loss[loss=0.2224, simple_loss=0.2986, pruned_loss=0.0731, over 1416747.07 frames.], batch size: 23, lr: 6.38e-04
2022-05-27 04:46:01,493 INFO [train.py:842] (1/4) Epoch 8, batch 7400, loss[loss=0.1759, simple_loss=0.2558, pruned_loss=0.04803, over 7421.00 frames.], tot_loss[loss=0.2207, simple_loss=0.2971, pruned_loss=0.07216, over 1415355.09 frames.], batch size: 18, lr: 6.38e-04
2022-05-27 04:46:40,154 INFO [train.py:842] (1/4) Epoch 8, batch 7450, loss[loss=0.1609, simple_loss=0.2377, pruned_loss=0.04208, over 7275.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2975, pruned_loss=0.07216, over 1413665.43 frames.], batch size: 18, lr: 6.38e-04
2022-05-27 04:47:19,043 INFO [train.py:842] (1/4) Epoch 8, batch 7500, loss[loss=0.1972, simple_loss=0.2753, pruned_loss=0.05949, over 7070.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2981, pruned_loss=0.07249, over 1414864.37 frames.], batch size: 18, lr: 6.38e-04
2022-05-27 04:47:57,392 INFO [train.py:842] (1/4) Epoch 8, batch 7550, loss[loss=0.2226, simple_loss=0.2916, pruned_loss=0.07682, over 7257.00 frames.], tot_loss[loss=0.2225, simple_loss=0.2992, pruned_loss=0.07293, over 1413926.58 frames.], batch size: 19, lr: 6.37e-04
2022-05-27 04:48:36,321 INFO [train.py:842] (1/4) Epoch 8, batch 7600, loss[loss=0.2707, simple_loss=0.3204, pruned_loss=0.1105, over 7394.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2968, pruned_loss=0.07128, over 1415642.26 frames.], batch size: 18, lr: 6.37e-04
2022-05-27 04:49:15,203 INFO [train.py:842] (1/4) Epoch 8, batch 7650, loss[loss=0.2363, simple_loss=0.3168, pruned_loss=0.07791, over 7304.00 frames.], tot_loss[loss=0.222, simple_loss=0.2988, pruned_loss=0.07261, over 1418252.14 frames.], batch size: 25, lr: 6.37e-04
2022-05-27 04:49:56,804 INFO [train.py:842] (1/4) Epoch 8, batch 7700, loss[loss=0.2289, simple_loss=0.31, pruned_loss=0.07387, over 7309.00 frames.], tot_loss[loss=0.2228, simple_loss=0.3, pruned_loss=0.07285, over 1415501.11 frames.], batch size: 21, lr: 6.37e-04
2022-05-27 04:50:35,228 INFO [train.py:842] (1/4) Epoch 8, batch 7750, loss[loss=0.1774, simple_loss=0.2607, pruned_loss=0.04701, over 7422.00 frames.], tot_loss[loss=0.2227, simple_loss=0.3002, pruned_loss=0.07257, over 1418618.67 frames.], batch size: 18, lr: 6.37e-04
2022-05-27 04:51:14,011 INFO [train.py:842] (1/4) Epoch 8, batch 7800, loss[loss=0.3255, simple_loss=0.3714, pruned_loss=0.1398, over 7362.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2999, pruned_loss=0.07232, over 1419044.75 frames.], batch size: 19, lr: 6.36e-04
2022-05-27 04:51:52,491 INFO [train.py:842] (1/4) Epoch 8, batch 7850, loss[loss=0.2578, simple_loss=0.3315, pruned_loss=0.09198, over 7194.00 frames.], tot_loss[loss=0.2237, simple_loss=0.3011, pruned_loss=0.0732, over 1417604.19 frames.], batch size: 22, lr: 6.36e-04
2022-05-27 04:52:31,256 INFO [train.py:842] (1/4) Epoch 8, batch 7900, loss[loss=0.215, simple_loss=0.3012, pruned_loss=0.06442, over 7291.00 frames.], tot_loss[loss=0.2247, simple_loss=0.3022, pruned_loss=0.07364, over 1419486.02 frames.], batch size: 25, lr: 6.36e-04
2022-05-27 04:53:09,840 INFO [train.py:842] (1/4) Epoch 8, batch 7950, loss[loss=0.2006, simple_loss=0.2849, pruned_loss=0.05815, over 7147.00 frames.], tot_loss[loss=0.224, simple_loss=0.3014, pruned_loss=0.07335, over 1421043.55 frames.], batch size: 20, lr: 6.36e-04
2022-05-27 04:53:49,263 INFO [train.py:842] (1/4) Epoch 8, batch 8000, loss[loss=0.2019, simple_loss=0.2831, pruned_loss=0.06037, over 7289.00 frames.], tot_loss[loss=0.2207, simple_loss=0.2987, pruned_loss=0.07141, over 1421978.88 frames.], batch size: 25, lr: 6.35e-04
2022-05-27 04:54:27,865 INFO [train.py:842] (1/4) Epoch 8, batch 8050, loss[loss=0.2157, simple_loss=0.295, pruned_loss=0.0682, over 7319.00 frames.], tot_loss[loss=0.2203, simple_loss=0.2985, pruned_loss=0.07099, over 1426239.87 frames.], batch size: 21, lr: 6.35e-04
2022-05-27 04:55:06,864 INFO [train.py:842] (1/4) Epoch 8, batch 8100, loss[loss=0.1955, simple_loss=0.2714, pruned_loss=0.05977, over 7274.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2974, pruned_loss=0.07098, over 1426023.96 frames.], batch size: 18, lr: 6.35e-04
2022-05-27 04:55:45,322 INFO [train.py:842] (1/4) Epoch 8, batch 8150, loss[loss=0.2034, simple_loss=0.2823, pruned_loss=0.06222, over 7155.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2997, pruned_loss=0.07207, over 1416452.16 frames.], batch size: 18, lr: 6.35e-04
2022-05-27 04:56:24,244 INFO [train.py:842] (1/4) Epoch 8, batch 8200, loss[loss=0.1736, simple_loss=0.2654, pruned_loss=0.04095, over 7323.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2979, pruned_loss=0.0715, over 1419315.40 frames.], batch size: 21, lr: 6.35e-04
2022-05-27 04:57:02,824 INFO [train.py:842] (1/4) Epoch 8, batch 8250, loss[loss=0.2052, simple_loss=0.2678, pruned_loss=0.07125, over 7165.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2973, pruned_loss=0.07072, over 1418688.66 frames.], batch size: 18, lr: 6.34e-04
2022-05-27 04:57:41,692 INFO [train.py:842] (1/4) Epoch 8, batch 8300, loss[loss=0.2442, simple_loss=0.3097, pruned_loss=0.08938, over 7144.00 frames.], tot_loss[loss=0.2212, simple_loss=0.2985, pruned_loss=0.07188, over 1419596.87 frames.], batch size: 20, lr: 6.34e-04
2022-05-27 04:58:20,167 INFO [train.py:842] (1/4) Epoch 8, batch 8350, loss[loss=0.226, simple_loss=0.3073, pruned_loss=0.07232, over 7190.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2992, pruned_loss=0.07224, over 1421572.91 frames.], batch size: 26, lr: 6.34e-04
2022-05-27 04:58:58,957 INFO [train.py:842] (1/4) Epoch 8, batch 8400, loss[loss=0.1997, simple_loss=0.2768, pruned_loss=0.06132, over 7267.00 frames.], tot_loss[loss=0.2212, simple_loss=0.2988, pruned_loss=0.0718, over 1424223.25 frames.], batch size: 18, lr: 6.34e-04
2022-05-27 04:59:37,684 INFO [train.py:842] (1/4) Epoch 8, batch 8450, loss[loss=0.2004, simple_loss=0.2867, pruned_loss=0.05708, over 7147.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2992, pruned_loss=0.07231, over 1421048.61 frames.], batch size: 20, lr: 6.34e-04
2022-05-27 05:00:16,704 INFO [train.py:842] (1/4) Epoch 8, batch 8500, loss[loss=0.1914, simple_loss=0.2856, pruned_loss=0.04861, over 7213.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2983, pruned_loss=0.07128, over 1420783.05 frames.], batch size: 22, lr: 6.33e-04
2022-05-27 05:00:55,186 INFO [train.py:842] (1/4) Epoch 8, batch 8550, loss[loss=0.2283, simple_loss=0.3201, pruned_loss=0.06821, over 7136.00 frames.], tot_loss[loss=0.2202, simple_loss=0.2982, pruned_loss=0.07112, over 1418924.66 frames.], batch size: 20, lr: 6.33e-04
2022-05-27 05:01:34,185 INFO [train.py:842] (1/4) Epoch 8, batch 8600, loss[loss=0.2341, simple_loss=0.3046, pruned_loss=0.0818, over 7279.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2974, pruned_loss=0.07138, over 1419886.62 frames.], batch size: 17, lr: 6.33e-04
2022-05-27 05:02:12,583 INFO [train.py:842] (1/4) Epoch 8, batch 8650, loss[loss=0.2261, simple_loss=0.3094, pruned_loss=0.07143, over 7112.00 frames.], tot_loss[loss=0.2199, simple_loss=0.2975, pruned_loss=0.07116, over 1414160.49 frames.], batch size: 21, lr: 6.33e-04
2022-05-27 05:02:51,371 INFO [train.py:842] (1/4) Epoch 8, batch 8700, loss[loss=0.1838, simple_loss=0.2646, pruned_loss=0.05155, over 7272.00 frames.], tot_loss[loss=0.2207, simple_loss=0.298, pruned_loss=0.0717, over 1418332.68 frames.], batch size: 18, lr: 6.32e-04
2022-05-27 05:03:30,086 INFO [train.py:842] (1/4) Epoch 8, batch 8750, loss[loss=0.1841, simple_loss=0.2576, pruned_loss=0.05533, over 6766.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2954, pruned_loss=0.07015, over 1422154.23 frames.], batch size: 15, lr: 6.32e-04
2022-05-27 05:04:09,316 INFO [train.py:842] (1/4) Epoch 8, batch 8800, loss[loss=0.2827, simple_loss=0.3593, pruned_loss=0.1031, over 7338.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2974, pruned_loss=0.07222, over 1417718.94 frames.], batch size: 22, lr: 6.32e-04
2022-05-27 05:04:47,683 INFO [train.py:842] (1/4) Epoch 8, batch 8850, loss[loss=0.2239, simple_loss=0.2879, pruned_loss=0.07995, over 7290.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2988, pruned_loss=0.07238, over 1415681.92 frames.], batch size: 17, lr: 6.32e-04
2022-05-27 05:05:27,291 INFO [train.py:842] (1/4) Epoch 8, batch 8900, loss[loss=0.1831, simple_loss=0.27, pruned_loss=0.04813, over 7362.00 frames.], tot_loss[loss=0.2217, simple_loss=0.2985, pruned_loss=0.07243, over 1409225.62 frames.], batch size: 19, lr: 6.32e-04
2022-05-27 05:06:05,790 INFO [train.py:842] (1/4) Epoch 8, batch 8950, loss[loss=0.1966, simple_loss=0.2743, pruned_loss=0.0594, over 6299.00 frames.], tot_loss[loss=0.223, simple_loss=0.2998, pruned_loss=0.07312, over 1407523.94 frames.], batch size: 37, lr: 6.31e-04
2022-05-27 05:06:44,581 INFO [train.py:842] (1/4) Epoch 8, batch 9000, loss[loss=0.2207, simple_loss=0.2985, pruned_loss=0.07141, over 5217.00 frames.], tot_loss[loss=0.223, simple_loss=0.2999, pruned_loss=0.07301, over 1404845.69 frames.], batch size: 53, lr: 6.31e-04
2022-05-27 05:06:44,582 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 05:06:53,927 INFO [train.py:871] (1/4) Epoch 8, validation: loss=0.1786, simple_loss=0.2794, pruned_loss=0.03893, over 868885.00 frames.
2022-05-27 05:07:32,088 INFO [train.py:842] (1/4) Epoch 8, batch 9050, loss[loss=0.264, simple_loss=0.3292, pruned_loss=0.09941, over 4984.00 frames.], tot_loss[loss=0.224, simple_loss=0.3008, pruned_loss=0.07358, over 1398120.07 frames.], batch size: 53, lr: 6.31e-04
2022-05-27 05:08:11,463 INFO [train.py:842] (1/4) Epoch 8, batch 9100, loss[loss=0.235, simple_loss=0.3016, pruned_loss=0.08424, over 5312.00 frames.], tot_loss[loss=0.2246, simple_loss=0.2998, pruned_loss=0.07465, over 1383691.03 frames.], batch size: 54, lr: 6.31e-04
2022-05-27 05:08:49,170 INFO [train.py:842] (1/4) Epoch 8, batch 9150, loss[loss=0.2527, simple_loss=0.3132, pruned_loss=0.09607, over 4900.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3049, pruned_loss=0.0793, over 1309593.90 frames.], batch size: 52, lr: 6.31e-04
2022-05-27 05:09:41,210 INFO [train.py:842] (1/4) Epoch 9, batch 0, loss[loss=0.2614, simple_loss=0.3305, pruned_loss=0.09613, over 7203.00 frames.], tot_loss[loss=0.2614, simple_loss=0.3305, pruned_loss=0.09613, over 7203.00 frames.], batch size: 23, lr: 6.05e-04
2022-05-27 05:10:19,742 INFO [train.py:842] (1/4) Epoch 9, batch 50, loss[loss=0.2122, simple_loss=0.2923, pruned_loss=0.06606, over 7091.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3089, pruned_loss=0.07767, over 319386.62 frames.], batch size: 28, lr: 6.05e-04
2022-05-27 05:10:58,684 INFO [train.py:842] (1/4) Epoch 9, batch 100, loss[loss=0.2259, simple_loss=0.3101, pruned_loss=0.07081, over 7234.00 frames.], tot_loss[loss=0.2207, simple_loss=0.2987, pruned_loss=0.07135, over 566283.68 frames.], batch size: 20, lr: 6.05e-04
2022-05-27 05:11:37,224 INFO [train.py:842] (1/4) Epoch 9, batch 150, loss[loss=0.2231, simple_loss=0.2978, pruned_loss=0.07421, over 5233.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2979, pruned_loss=0.07075, over 753290.43 frames.], batch size: 52, lr: 6.05e-04
2022-05-27 05:12:15,909 INFO [train.py:842] (1/4) Epoch 9, batch 200, loss[loss=0.2488, simple_loss=0.3331, pruned_loss=0.0823, over 7215.00 frames.], tot_loss[loss=0.2234, simple_loss=0.3007, pruned_loss=0.07301, over 902224.40 frames.], batch size: 22, lr: 6.04e-04
2022-05-27 05:13:04,724 INFO [train.py:842] (1/4) Epoch 9, batch 250, loss[loss=0.2077, simple_loss=0.2897, pruned_loss=0.06286, over 7434.00 frames.], tot_loss[loss=0.2227, simple_loss=0.3005, pruned_loss=0.0724, over 1018636.07 frames.], batch size: 20, lr: 6.04e-04
2022-05-27 05:13:43,497 INFO [train.py:842] (1/4) Epoch 9, batch 300, loss[loss=0.3081, simple_loss=0.3904, pruned_loss=0.1129, over 7341.00 frames.], tot_loss[loss=0.2208, simple_loss=0.2993, pruned_loss=0.07112, over 1104249.90 frames.], batch size: 22, lr: 6.04e-04
2022-05-27 05:14:22,311 INFO [train.py:842] (1/4) Epoch 9, batch 350, loss[loss=0.2159, simple_loss=0.2941, pruned_loss=0.06886, over 7163.00 frames.], tot_loss[loss=0.2183, simple_loss=0.2969, pruned_loss=0.06986, over 1177969.90 frames.], batch size: 19, lr: 6.04e-04
2022-05-27 05:15:01,119 INFO [train.py:842] (1/4) Epoch 9, batch 400, loss[loss=0.2183, simple_loss=0.3021, pruned_loss=0.06722, over 7117.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2961, pruned_loss=0.06936, over 1237497.13 frames.], batch size: 17, lr: 6.04e-04
2022-05-27 05:15:39,708 INFO [train.py:842] (1/4) Epoch 9, batch 450, loss[loss=0.1847, simple_loss=0.2631, pruned_loss=0.05312, over 7254.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2953, pruned_loss=0.06913, over 1278543.06 frames.], batch size: 19, lr: 6.03e-04
2022-05-27 05:16:18,383 INFO [train.py:842] (1/4) Epoch 9, batch 500, loss[loss=0.2114, simple_loss=0.2829, pruned_loss=0.06998, over 7405.00 frames.], tot_loss[loss=0.2177, simple_loss=0.2958, pruned_loss=0.06978, over 1310938.20 frames.], batch size: 18, lr: 6.03e-04
2022-05-27 05:16:57,095 INFO [train.py:842] (1/4) Epoch 9, batch 550, loss[loss=0.2553, simple_loss=0.3257, pruned_loss=0.09244, over 7080.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2961, pruned_loss=0.06949, over 1338612.21 frames.], batch size: 18, lr: 6.03e-04
2022-05-27 05:17:36,168 INFO [train.py:842] (1/4) Epoch 9, batch 600, loss[loss=0.2082, simple_loss=0.2879, pruned_loss=0.06429, over 7071.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2976, pruned_loss=0.07059, over 1360236.69 frames.], batch size: 18, lr: 6.03e-04
2022-05-27 05:18:14,772 INFO [train.py:842] (1/4) Epoch 9, batch 650, loss[loss=0.1956, simple_loss=0.2696, pruned_loss=0.06077, over 7348.00 frames.], tot_loss[loss=0.2208, simple_loss=0.2988, pruned_loss=0.07143, over 1374305.10 frames.], batch size: 19, lr: 6.03e-04
2022-05-27 05:18:53,502 INFO [train.py:842] (1/4) Epoch 9, batch 700, loss[loss=0.2295, simple_loss=0.306, pruned_loss=0.07652, over 7431.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2986, pruned_loss=0.07083, over 1386664.19 frames.], batch size: 20, lr: 6.02e-04
2022-05-27 05:19:32,055 INFO [train.py:842] (1/4) Epoch 9, batch 750, loss[loss=0.2072, simple_loss=0.2767, pruned_loss=0.06882, over 7156.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2993, pruned_loss=0.07129, over 1389267.39 frames.], batch size: 18, lr: 6.02e-04
2022-05-27 05:20:11,011 INFO [train.py:842] (1/4) Epoch 9, batch 800, loss[loss=0.2125, simple_loss=0.2995, pruned_loss=0.06274, over 7381.00 frames.], tot_loss[loss=0.2196, simple_loss=0.298, pruned_loss=0.07062, over 1395608.79 frames.], batch size: 23, lr: 6.02e-04
2022-05-27 05:20:49,560 INFO [train.py:842] (1/4) Epoch 9, batch 850, loss[loss=0.2154, simple_loss=0.3015, pruned_loss=0.06465, over 7327.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2971, pruned_loss=0.07018, over 1401872.70 frames.], batch size: 21, lr: 6.02e-04
2022-05-27 05:21:28,818 INFO [train.py:842] (1/4) Epoch 9, batch 900, loss[loss=0.3019, simple_loss=0.3725, pruned_loss=0.1156, over 7230.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2974, pruned_loss=0.07069, over 1410790.82 frames.], batch size: 21, lr: 6.02e-04
2022-05-27 05:22:07,487 INFO [train.py:842] (1/4) Epoch 9, batch 950, loss[loss=0.1995, simple_loss=0.2873, pruned_loss=0.05588, over 7323.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2966, pruned_loss=0.0704, over 1408964.85 frames.], batch size: 20, lr: 6.01e-04
2022-05-27 05:22:46,348 INFO [train.py:842] (1/4) Epoch 9, batch 1000, loss[loss=0.2606, simple_loss=0.3363, pruned_loss=0.09243, over 7435.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2952, pruned_loss=0.06923, over 1413391.88 frames.], batch size: 20, lr: 6.01e-04
2022-05-27 05:23:24,854 INFO [train.py:842] (1/4) Epoch 9, batch 1050, loss[loss=0.1881, simple_loss=0.2697, pruned_loss=0.05329, over 7268.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2947, pruned_loss=0.06893, over 1417992.57 frames.], batch size: 19, lr: 6.01e-04
2022-05-27 05:24:03,723 INFO [train.py:842] (1/4) Epoch 9, batch 1100, loss[loss=0.1823, simple_loss=0.2553, pruned_loss=0.05465, over 7292.00 frames.], tot_loss[loss=0.2171, simple_loss=0.296, pruned_loss=0.0691, over 1421375.05 frames.], batch size: 17, lr: 6.01e-04
2022-05-27 05:24:42,295 INFO [train.py:842] (1/4) Epoch 9, batch 1150, loss[loss=0.2015, simple_loss=0.2951, pruned_loss=0.05401, over 7321.00 frames.], tot_loss[loss=0.2171, simple_loss=0.296, pruned_loss=0.06908, over 1421098.87 frames.], batch size: 25, lr: 6.01e-04
2022-05-27 05:25:21,229 INFO [train.py:842] (1/4) Epoch 9, batch 1200, loss[loss=0.1915, simple_loss=0.2661, pruned_loss=0.05844, over 7435.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2963, pruned_loss=0.06995, over 1420763.60 frames.], batch size: 20, lr: 6.00e-04
2022-05-27 05:25:59,841 INFO [train.py:842] (1/4) Epoch 9, batch 1250, loss[loss=0.1624, simple_loss=0.2427, pruned_loss=0.04107, over 7240.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2966, pruned_loss=0.07056, over 1416856.22 frames.], batch size: 16, lr: 6.00e-04
2022-05-27 05:26:38,544 INFO [train.py:842] (1/4) Epoch 9, batch 1300, loss[loss=0.2582, simple_loss=0.3338, pruned_loss=0.09135, over 7157.00 frames.], tot_loss[loss=0.2202, simple_loss=0.2978, pruned_loss=0.07127, over 1413836.05 frames.], batch size: 19, lr: 6.00e-04
2022-05-27 05:27:17,259 INFO [train.py:842] (1/4) Epoch 9, batch 1350, loss[loss=0.1765, simple_loss=0.2602, pruned_loss=0.04636, over 7432.00 frames.], tot_loss[loss=0.22, simple_loss=0.2974, pruned_loss=0.07133, over 1418044.05 frames.], batch size: 20, lr: 6.00e-04
2022-05-27 05:27:56,067 INFO [train.py:842] (1/4) Epoch 9, batch 1400, loss[loss=0.2299, simple_loss=0.3059, pruned_loss=0.07691, over 7213.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2959, pruned_loss=0.07016, over 1414603.11 frames.], batch size: 21, lr: 6.00e-04
2022-05-27 05:28:34,830 INFO [train.py:842] (1/4) Epoch 9, batch 1450, loss[loss=0.2921, simple_loss=0.3508, pruned_loss=0.1167, over 7330.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2965, pruned_loss=0.07056, over 1419786.59 frames.], batch size: 21, lr: 5.99e-04
2022-05-27 05:29:13,612 INFO [train.py:842] (1/4) Epoch 9, batch 1500, loss[loss=0.211, simple_loss=0.2945, pruned_loss=0.06368, over 7237.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2963, pruned_loss=0.06994, over 1422830.98 frames.], batch size: 20, lr: 5.99e-04
2022-05-27 05:29:52,246 INFO [train.py:842] (1/4) Epoch 9, batch 1550, loss[loss=0.2012, simple_loss=0.2891, pruned_loss=0.05668, over 7219.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2952, pruned_loss=0.06965, over 1421835.00 frames.], batch size: 22, lr: 5.99e-04
2022-05-27 05:30:51,706 INFO [train.py:842] (1/4) Epoch 9, batch 1600, loss[loss=0.2246, simple_loss=0.3016, pruned_loss=0.07383, over 7064.00 frames.], tot_loss[loss=0.2186, simple_loss=0.297, pruned_loss=0.0701, over 1420706.14 frames.], batch size: 18, lr: 5.99e-04
2022-05-27 05:31:40,491 INFO [train.py:842] (1/4) Epoch 9, batch 1650, loss[loss=0.2353, simple_loss=0.3238, pruned_loss=0.07344, over 7114.00 frames.], tot_loss[loss=0.2183, simple_loss=0.297, pruned_loss=0.06985, over 1421836.16 frames.], batch size: 21, lr: 5.99e-04
2022-05-27 05:32:19,080 INFO [train.py:842] (1/4) Epoch 9, batch 1700, loss[loss=0.2279, simple_loss=0.3094, pruned_loss=0.07321, over 7143.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2988, pruned_loss=0.0707, over 1421017.09 frames.], batch size: 20, lr: 5.98e-04
2022-05-27 05:32:57,920 INFO [train.py:842] (1/4) Epoch 9, batch 1750, loss[loss=0.1978, simple_loss=0.2855, pruned_loss=0.05503, over 7316.00 frames.], tot_loss[loss=0.2196, simple_loss=0.2982, pruned_loss=0.0705, over 1422520.30 frames.], batch size: 21, lr: 5.98e-04
2022-05-27 05:33:36,468 INFO [train.py:842] (1/4) Epoch 9, batch 1800, loss[loss=0.2087, simple_loss=0.2955, pruned_loss=0.06095, over 7226.00 frames.], tot_loss[loss=0.2177, simple_loss=0.2966, pruned_loss=0.06939, over 1419665.92 frames.], batch size: 20, lr: 5.98e-04
2022-05-27 05:34:14,975 INFO [train.py:842] (1/4) Epoch 9, batch 1850, loss[loss=0.2442, simple_loss=0.324, pruned_loss=0.08218, over 7239.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2967, pruned_loss=0.06952, over 1422651.86 frames.], batch size: 20, lr: 5.98e-04
2022-05-27 05:34:54,127 INFO [train.py:842] (1/4) Epoch 9, batch 1900, loss[loss=0.2013, simple_loss=0.2834, pruned_loss=0.05963, over 7160.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2981, pruned_loss=0.07046, over 1421067.74 frames.], batch size: 19, lr: 5.98e-04
2022-05-27 05:35:32,745 INFO [train.py:842] (1/4) Epoch 9, batch 1950, loss[loss=0.1797, simple_loss=0.2712, pruned_loss=0.04407, over 7107.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2976, pruned_loss=0.07031, over 1422639.00 frames.], batch size: 21, lr: 5.97e-04
2022-05-27 05:36:11,650 INFO [train.py:842] (1/4) Epoch 9, batch 2000, loss[loss=0.2119, simple_loss=0.2968, pruned_loss=0.06351, over 7302.00 frames.], tot_loss[loss=0.218, simple_loss=0.2967, pruned_loss=0.06971, over 1423377.07 frames.], batch size: 24, lr: 5.97e-04
2022-05-27 05:36:50,201 INFO [train.py:842] (1/4) Epoch 9, batch 2050, loss[loss=0.2165, simple_loss=0.2726, pruned_loss=0.08023, over 7289.00 frames.], tot_loss[loss=0.2172, simple_loss=0.296, pruned_loss=0.06923, over 1423285.78 frames.], batch size: 17, lr: 5.97e-04
2022-05-27 05:37:28,877 INFO [train.py:842] (1/4) Epoch 9, batch 2100, loss[loss=0.2527, simple_loss=0.3148, pruned_loss=0.09527, over 7259.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2963, pruned_loss=0.06938, over 1425090.34 frames.], batch size: 19, lr: 5.97e-04
2022-05-27 05:38:07,473 INFO [train.py:842] (1/4) Epoch 9, batch 2150, loss[loss=0.1907, simple_loss=0.2677, pruned_loss=0.05688, over 7062.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2949, pruned_loss=0.06812, over 1427047.54 frames.], batch size: 18, lr: 5.97e-04
2022-05-27 05:38:46,493 INFO [train.py:842] (1/4) Epoch 9, batch 2200, loss[loss=0.1883, simple_loss=0.2522, pruned_loss=0.06222, over 7265.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2949, pruned_loss=0.06833, over 1425579.16 frames.], batch size: 17, lr: 5.96e-04
2022-05-27 05:39:25,146 INFO [train.py:842] (1/4) Epoch 9, batch 2250, loss[loss=0.1996, simple_loss=0.2799, pruned_loss=0.05962, over 7156.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2955, pruned_loss=0.06879, over 1425894.20 frames.], batch size: 18, lr: 5.96e-04
2022-05-27 05:40:03,860 INFO [train.py:842] (1/4) Epoch 9, batch 2300, loss[loss=0.1782, simple_loss=0.2711, pruned_loss=0.04269, over 7154.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2941, pruned_loss=0.06776, over 1427402.87 frames.], batch size: 20, lr: 5.96e-04
2022-05-27 05:40:42,429 INFO [train.py:842] (1/4) Epoch 9, batch 2350, loss[loss=0.2526, simple_loss=0.3288, pruned_loss=0.0882, over 6745.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2948, pruned_loss=0.06892, over 1425691.25 frames.], batch size: 31, lr: 5.96e-04
2022-05-27 05:41:21,201 INFO [train.py:842] (1/4) Epoch 9, batch 2400, loss[loss=0.2427, simple_loss=0.3098, pruned_loss=0.08779, over 7269.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2945, pruned_loss=0.0685, over 1425449.94 frames.], batch size: 18, lr: 5.96e-04
2022-05-27 05:41:59,654 INFO [train.py:842] (1/4) Epoch 9, batch 2450, loss[loss=0.2265, simple_loss=0.2827, pruned_loss=0.08519, over 7412.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2939, pruned_loss=0.06797, over 1426276.87 frames.], batch size: 18, lr: 5.95e-04
2022-05-27 05:42:38,367 INFO [train.py:842] (1/4) Epoch 9, batch 2500, loss[loss=0.2032, simple_loss=0.2811, pruned_loss=0.06263, over 7198.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2955, pruned_loss=0.0691, over 1424961.21 frames.], batch size: 22, lr: 5.95e-04
2022-05-27 05:43:16,892 INFO [train.py:842] (1/4) Epoch 9, batch 2550, loss[loss=0.1961, simple_loss=0.2678, pruned_loss=0.06216, over 7138.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2949, pruned_loss=0.06891, over 1422693.85 frames.], batch size: 17, lr: 5.95e-04
2022-05-27 05:43:55,666 INFO [train.py:842] (1/4) Epoch 9, batch 2600, loss[loss=0.2491, simple_loss=0.3112, pruned_loss=0.09352, over 7386.00 frames.], tot_loss[loss=0.2184, simple_loss=0.2965, pruned_loss=0.07015, over 1419645.15 frames.], batch size: 23, lr: 5.95e-04
2022-05-27 05:44:34,264 INFO [train.py:842] (1/4) Epoch 9, batch 2650, loss[loss=0.2414, simple_loss=0.3122, pruned_loss=0.08526, over 5204.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2956, pruned_loss=0.07005, over 1417794.63 frames.], batch size: 54, lr: 5.95e-04
2022-05-27 05:45:13,034 INFO [train.py:842] (1/4) Epoch 9, batch 2700, loss[loss=0.2001, simple_loss=0.2826, pruned_loss=0.05882, over 7336.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2961, pruned_loss=0.07003, over 1419372.83 frames.], batch size: 22, lr: 5.94e-04
2022-05-27 05:45:51,573 INFO [train.py:842] (1/4) Epoch 9, batch 2750, loss[loss=0.1736, simple_loss=0.2667, pruned_loss=0.04024, over 7333.00 frames.], tot_loss[loss=0.217, simple_loss=0.2951, pruned_loss=0.0695, over 1423931.43 frames.], batch size: 20, lr: 5.94e-04
2022-05-27 05:46:30,158 INFO [train.py:842] (1/4) Epoch 9, batch 2800, loss[loss=0.232, simple_loss=0.3185, pruned_loss=0.07273, over 7217.00 frames.], tot_loss[loss=0.2171, simple_loss=0.2954, pruned_loss=0.06937, over 1427429.73 frames.], batch size: 22, lr: 5.94e-04
2022-05-27 05:47:08,825 INFO [train.py:842] (1/4) Epoch 9, batch 2850, loss[loss=0.243, simple_loss=0.3265, pruned_loss=0.07975, over 7161.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2949, pruned_loss=0.06847, over 1429541.68 frames.], batch size: 19, lr: 5.94e-04
2022-05-27 05:47:47,792 INFO [train.py:842] (1/4) Epoch 9, batch 2900, loss[loss=0.2844, simple_loss=0.3493, pruned_loss=0.1097, over 7320.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2956, pruned_loss=0.069, over 1427790.71 frames.], batch size: 21, lr: 5.94e-04
2022-05-27 05:48:26,363 INFO [train.py:842] (1/4) Epoch 9, batch 2950, loss[loss=0.1779, simple_loss=0.2578, pruned_loss=0.04904, over 7280.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2955, pruned_loss=0.06946, over 1422977.82 frames.], batch size: 18, lr: 5.94e-04
2022-05-27 05:49:05,573 INFO [train.py:842] (1/4) Epoch 9, batch 3000, loss[loss=0.1831, simple_loss=0.275, pruned_loss=0.04553, over 7276.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2946, pruned_loss=0.06885, over 1422254.34 frames.], batch size: 24, lr: 5.93e-04
2022-05-27 05:49:05,574 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 05:49:14,865 INFO [train.py:871] (1/4) Epoch 9, validation: loss=0.1779, simple_loss=0.2778, pruned_loss=0.039, over 868885.00 frames.
2022-05-27 05:49:53,729 INFO [train.py:842] (1/4) Epoch 9, batch 3050, loss[loss=0.1885, simple_loss=0.2712, pruned_loss=0.05288, over 7332.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2957, pruned_loss=0.06971, over 1419387.56 frames.], batch size: 20, lr: 5.93e-04
2022-05-27 05:50:32,307 INFO [train.py:842] (1/4) Epoch 9, batch 3100, loss[loss=0.2769, simple_loss=0.34, pruned_loss=0.1069, over 6858.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2956, pruned_loss=0.06888, over 1414354.43 frames.], batch size: 31, lr: 5.93e-04
2022-05-27 05:51:11,006 INFO [train.py:842] (1/4) Epoch 9, batch 3150, loss[loss=0.2057, simple_loss=0.2884, pruned_loss=0.06152, over 7164.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2947, pruned_loss=0.06874, over 1418017.23 frames.], batch size: 19, lr: 5.93e-04
2022-05-27 05:51:50,051 INFO [train.py:842] (1/4) Epoch 9, batch 3200, loss[loss=0.2429, simple_loss=0.3259, pruned_loss=0.07995, over 7153.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2955, pruned_loss=0.0689, over 1422421.48 frames.], batch size: 20, lr: 5.93e-04
2022-05-27 05:52:28,400 INFO [train.py:842] (1/4) Epoch 9, batch 3250, loss[loss=0.23, simple_loss=0.3076, pruned_loss=0.0762, over 5000.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2965, pruned_loss=0.06985, over 1420493.30 frames.], batch size: 52, lr: 5.92e-04
2022-05-27 05:53:07,205 INFO [train.py:842] (1/4) Epoch 9, batch 3300, loss[loss=0.2175, simple_loss=0.2949, pruned_loss=0.07002, over 7209.00 frames.], tot_loss[loss=0.2163, simple_loss=0.295, pruned_loss=0.06882, over 1420007.28 frames.], batch size: 22, lr: 5.92e-04
2022-05-27 05:53:45,787 INFO [train.py:842] (1/4) Epoch 9, batch 3350, loss[loss=0.2107, simple_loss=0.3006, pruned_loss=0.0604, over 7267.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2953, pruned_loss=0.06907, over 1423283.45 frames.], batch size: 19, lr: 5.92e-04
2022-05-27 05:54:24,436 INFO [train.py:842] (1/4) Epoch 9, batch 3400, loss[loss=0.2055, simple_loss=0.2878, pruned_loss=0.0616, over 6704.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2966, pruned_loss=0.07043, over 1421266.56 frames.], batch size: 31, lr: 5.92e-04
2022-05-27 05:55:02,956 INFO [train.py:842] (1/4) Epoch 9, batch 3450, loss[loss=0.1816, simple_loss=0.268, pruned_loss=0.04763, over 7407.00 frames.], tot_loss[loss=0.2184, simple_loss=0.2966, pruned_loss=0.07009, over 1423655.54 frames.], batch size: 18, lr: 5.92e-04
2022-05-27 05:55:41,935 INFO [train.py:842] (1/4) Epoch 9, batch 3500, loss[loss=0.2255, simple_loss=0.298, pruned_loss=0.07654, over 7155.00 frames.], tot_loss[loss=0.2191, simple_loss=0.297, pruned_loss=0.07058, over 1423741.36 frames.], batch size: 19, lr: 5.91e-04
2022-05-27 05:56:20,580 INFO [train.py:842] (1/4) Epoch 9, batch 3550, loss[loss=0.1922, simple_loss=0.2733, pruned_loss=0.05549, over 7152.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2961, pruned_loss=0.06959, over 1426570.36 frames.], batch size: 18, lr: 5.91e-04
2022-05-27 05:56:59,457 INFO [train.py:842] (1/4) Epoch 9, batch 3600, loss[loss=0.168, simple_loss=0.2478, pruned_loss=0.0441, over 7281.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2955, pruned_loss=0.06913, over 1424699.45 frames.], batch size: 18, lr: 5.91e-04
2022-05-27 05:57:38,126 INFO [train.py:842] (1/4) Epoch 9, batch 3650, loss[loss=0.1648, simple_loss=0.2455, pruned_loss=0.04203, over 7154.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2948, pruned_loss=0.06921, over 1426618.30 frames.], batch size: 17, lr: 5.91e-04
2022-05-27 05:58:16,874 INFO [train.py:842] (1/4) Epoch 9, batch 3700, loss[loss=0.2256, simple_loss=0.3162, pruned_loss=0.06752, over 7272.00 frames.], tot_loss[loss=0.2173, simple_loss=0.2957, pruned_loss=0.06948, over 1427127.33 frames.], batch size: 25, lr: 5.91e-04
2022-05-27 05:58:55,438 INFO [train.py:842] (1/4) Epoch 9, batch 3750, loss[loss=0.2118, simple_loss=0.2931, pruned_loss=0.0653, over 7431.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2965, pruned_loss=0.06988, over 1425820.40 frames.], batch size: 20, lr: 5.90e-04
2022-05-27 05:59:34,171 INFO [train.py:842] (1/4) Epoch 9, batch 3800, loss[loss=0.2205, simple_loss=0.2972, pruned_loss=0.07186, over 7316.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2961, pruned_loss=0.06944, over 1427879.09 frames.], batch size: 21, lr: 5.90e-04
2022-05-27 06:00:12,810 INFO [train.py:842] (1/4) Epoch 9, batch 3850, loss[loss=0.1929, simple_loss=0.2716, pruned_loss=0.05714, over 7430.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2948, pruned_loss=0.06844, over 1429335.41 frames.], batch size: 20, lr: 5.90e-04
2022-05-27 06:00:51,542 INFO [train.py:842] (1/4) Epoch 9, batch 3900, loss[loss=0.2178, simple_loss=0.2906, pruned_loss=0.07248, over 7256.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2953, pruned_loss=0.06881, over 1428144.32 frames.], batch size: 19, lr: 5.90e-04
2022-05-27 06:01:30,206 INFO [train.py:842] (1/4) Epoch 9, batch 3950, loss[loss=0.1667, simple_loss=0.2506, pruned_loss=0.04145, over 7270.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2948, pruned_loss=0.06867, over 1426558.77 frames.], batch size: 19, lr: 5.90e-04
2022-05-27 06:02:08,913 INFO [train.py:842] (1/4) Epoch 9, batch 4000, loss[loss=0.1794, simple_loss=0.2608, pruned_loss=0.04896, over 7121.00 frames.], tot_loss[loss=0.218, simple_loss=0.2971, pruned_loss=0.06941, over 1424823.59 frames.], batch size: 21, lr: 5.89e-04
2022-05-27 06:02:47,527 INFO [train.py:842] (1/4) Epoch 9, batch 4050, loss[loss=0.1994, simple_loss=0.2745, pruned_loss=0.0622, over 7330.00 frames.], tot_loss[loss=0.2167, simple_loss=0.296, pruned_loss=0.06872, over 1423370.96 frames.], batch size: 20, lr: 5.89e-04
2022-05-27 06:03:26,352 INFO [train.py:842] (1/4) Epoch 9, batch 4100, loss[loss=0.195, simple_loss=0.268, pruned_loss=0.06103, over 7279.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2956, pruned_loss=0.06875, over 1425997.96 frames.], batch size: 17, lr: 5.89e-04
2022-05-27 06:04:05,007 INFO [train.py:842] (1/4) Epoch 9, batch 4150, loss[loss=0.2424, simple_loss=0.3189, pruned_loss=0.08298, over 7198.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2956, pruned_loss=0.0687, over 1429188.76 frames.], batch size: 22, lr: 5.89e-04
2022-05-27 06:04:43,795 INFO [train.py:842] (1/4) Epoch 9, batch 4200, loss[loss=0.1844, simple_loss=0.2603, pruned_loss=0.0542, over 7000.00 frames.], tot_loss[loss=0.2157, simple_loss=0.295, pruned_loss=0.0682, over 1424117.76 frames.], batch size: 16, lr: 5.89e-04
2022-05-27 06:05:22,394 INFO [train.py:842] (1/4) Epoch 9, batch 4250, loss[loss=0.2209, simple_loss=0.3052, pruned_loss=0.06834, over 7059.00 frames.], tot_loss[loss=0.2173, simple_loss=0.2958, pruned_loss=0.06939, over 1423275.73 frames.], batch size: 28, lr: 5.89e-04
2022-05-27 06:06:01,310 INFO [train.py:842] (1/4) Epoch 9, batch 4300, loss[loss=0.2393, simple_loss=0.3312, pruned_loss=0.07367, over 7416.00 frames.], tot_loss[loss=0.2177, simple_loss=0.2962, pruned_loss=0.06967, over 1424501.86 frames.], batch size: 21, lr: 5.88e-04
2022-05-27 06:06:39,825 INFO [train.py:842] (1/4) Epoch 9, batch 4350, loss[loss=0.1761, simple_loss=0.249, pruned_loss=0.05162, over 6991.00 frames.], tot_loss[loss=0.2175, simple_loss=0.296, pruned_loss=0.06947, over 1418832.43 frames.], batch size: 16, lr: 5.88e-04
2022-05-27 06:07:18,613 INFO [train.py:842] (1/4) Epoch 9, batch 4400, loss[loss=0.2554, simple_loss=0.3175, pruned_loss=0.09669, over 6564.00 frames.], tot_loss[loss=0.2173, simple_loss=0.2958, pruned_loss=0.06936, over 1417096.51 frames.], batch size: 38, lr: 5.88e-04
2022-05-27 06:07:57,172 INFO [train.py:842] (1/4) Epoch 9, batch 4450, loss[loss=0.2067, simple_loss=0.2868, pruned_loss=0.06332, over 7377.00 frames.], tot_loss[loss=0.2171, simple_loss=0.2956, pruned_loss=0.06931, over 1420646.11 frames.], batch size: 23, lr: 5.88e-04
2022-05-27 06:08:35,967 INFO [train.py:842] (1/4) Epoch 9, batch 4500, loss[loss=0.2101, simple_loss=0.298, pruned_loss=0.06106, over 7196.00 frames.], tot_loss[loss=0.2183, simple_loss=0.2961, pruned_loss=0.07022, over 1422253.76 frames.], batch size: 23, lr: 5.88e-04
2022-05-27 06:09:14,580 INFO [train.py:842] (1/4) Epoch 9, batch 4550, loss[loss=0.2311, simple_loss=0.3161, pruned_loss=0.0731, over 7185.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2949, pruned_loss=0.06897, over 1423370.90 frames.], batch size: 26, lr: 5.87e-04
2022-05-27 06:09:53,656 INFO [train.py:842] (1/4) Epoch 9, batch 4600, loss[loss=0.195, simple_loss=0.2657, pruned_loss=0.0622, over 7074.00 frames.], tot_loss[loss=0.2168, simple_loss=0.295, pruned_loss=0.06926, over 1422733.19 frames.], batch size: 18, lr: 5.87e-04
2022-05-27 06:10:32,404 INFO [train.py:842] (1/4) Epoch 9, batch 4650, loss[loss=0.2215, simple_loss=0.2943, pruned_loss=0.07429, over 7156.00 frames.], tot_loss[loss=0.2178, simple_loss=0.2956, pruned_loss=0.07003, over 1422017.04 frames.], batch size: 19, lr: 5.87e-04
2022-05-27 06:11:11,222 INFO [train.py:842] (1/4) Epoch 9, batch 4700, loss[loss=0.2374, simple_loss=0.3187, pruned_loss=0.07804, over 7309.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2929, pruned_loss=0.06826, over 1420794.02 frames.], batch size: 24, lr: 5.87e-04
2022-05-27 06:11:49,724 INFO [train.py:842] (1/4) Epoch 9, batch 4750, loss[loss=0.2158, simple_loss=0.3008, pruned_loss=0.06537, over 7215.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2948, pruned_loss=0.06932, over 1418607.83 frames.], batch size: 23, lr: 5.87e-04
2022-05-27 06:12:28,733 INFO [train.py:842] (1/4) Epoch 9, batch 4800, loss[loss=0.2409, simple_loss=0.3046, pruned_loss=0.08862, over 7409.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2936, pruned_loss=0.06906, over 1416846.65 frames.], batch size: 18, lr: 5.86e-04
2022-05-27 06:13:07,356 INFO [train.py:842] (1/4) Epoch 9, batch 4850, loss[loss=0.2001, simple_loss=0.2774, pruned_loss=0.0614, over 7261.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2927, pruned_loss=0.0687, over 1420782.43 frames.], batch size: 17, lr: 5.86e-04
2022-05-27 06:13:46,255 INFO [train.py:842] (1/4) Epoch 9, batch 4900, loss[loss=0.2888, simple_loss=0.342, pruned_loss=0.1178, over 4790.00 frames.], tot_loss[loss=0.217, simple_loss=0.2945, pruned_loss=0.06972, over 1419382.73 frames.], batch size: 53, lr: 5.86e-04
2022-05-27 06:14:24,858 INFO [train.py:842] (1/4) Epoch 9, batch 4950, loss[loss=0.2387, simple_loss=0.3159, pruned_loss=0.08076, over 7291.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2934, pruned_loss=0.06891, over 1420394.18 frames.], batch size: 25, lr: 5.86e-04
2022-05-27 06:15:03,637 INFO [train.py:842] (1/4) Epoch 9, batch 5000, loss[loss=0.2023, simple_loss=0.2885, pruned_loss=0.05804, over 7272.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2942, pruned_loss=0.06941, over 1417857.83 frames.], batch size: 24, lr: 5.86e-04
2022-05-27 06:15:42,261 INFO [train.py:842] (1/4) Epoch 9, batch 5050, loss[loss=0.1693, simple_loss=0.2541, pruned_loss=0.0423, over 7428.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2933, pruned_loss=0.06828, over 1419142.28 frames.], batch size: 20, lr: 5.86e-04
2022-05-27 06:16:21,068 INFO [train.py:842] (1/4) Epoch 9, batch 5100, loss[loss=0.2745, simple_loss=0.3398, pruned_loss=0.1046, over 6213.00 frames.], tot_loss[loss=0.215, simple_loss=0.2935, pruned_loss=0.06826, over 1420857.49 frames.], batch size: 37, lr: 5.85e-04
2022-05-27 06:16:59,712 INFO [train.py:842] (1/4) Epoch 9, batch 5150, loss[loss=0.2277, simple_loss=0.309, pruned_loss=0.07325, over 7142.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2945, pruned_loss=0.06882, over 1423380.70 frames.], batch size: 20, lr: 5.85e-04
2022-05-27 06:17:38,704 INFO [train.py:842] (1/4) Epoch 9, batch 5200, loss[loss=0.1751, simple_loss=0.2576, pruned_loss=0.04631, over 7318.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2955, pruned_loss=0.06984, over 1424622.26 frames.], batch size: 20, lr: 5.85e-04
2022-05-27 06:18:17,383 INFO [train.py:842] (1/4) Epoch 9, batch 5250, loss[loss=0.1973, simple_loss=0.2801, pruned_loss=0.05722, over 7239.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2944, pruned_loss=0.0687, over 1425043.18 frames.], batch size: 20, lr: 5.85e-04
2022-05-27 06:18:56,029 INFO [train.py:842] (1/4) Epoch 9, batch 5300, loss[loss=0.1877, simple_loss=0.2735, pruned_loss=0.05096, over 7432.00 frames.], tot_loss[loss=0.216, simple_loss=0.2945, pruned_loss=0.06878, over 1422248.55 frames.], batch size: 20, lr: 5.85e-04
2022-05-27 06:19:34,528 INFO [train.py:842] (1/4) Epoch 9, batch 5350, loss[loss=0.1902, simple_loss=0.2707, pruned_loss=0.05484, over 7282.00 frames.], tot_loss[loss=0.2173, simple_loss=0.2957, pruned_loss=0.06947, over 1423550.92 frames.], batch size: 24, lr: 5.84e-04
2022-05-27 06:20:13,320 INFO [train.py:842] (1/4) Epoch 9, batch 5400, loss[loss=0.2692, simple_loss=0.3271, pruned_loss=0.1056, over 5217.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2968, pruned_loss=0.07041, over 1418792.74 frames.], batch size: 52, lr: 5.84e-04
2022-05-27 06:20:51,796 INFO [train.py:842] (1/4) Epoch 9, batch 5450, loss[loss=0.1961, simple_loss=0.2847, pruned_loss=0.05376, over 6794.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2958, pruned_loss=0.06974, over 1419868.49 frames.], batch size: 31, lr: 5.84e-04
2022-05-27 06:21:30,677 INFO [train.py:842] (1/4) Epoch 9, batch 5500, loss[loss=0.1924, simple_loss=0.2831, pruned_loss=0.05086, over 7118.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2963, pruned_loss=0.06971, over 1419567.60 frames.], batch size: 21, lr: 5.84e-04
2022-05-27 06:22:09,226 INFO [train.py:842] (1/4) Epoch 9, batch 5550, loss[loss=0.2991, simple_loss=0.3406, pruned_loss=0.1289, over 5156.00 frames.], tot_loss[loss=0.2183, simple_loss=0.2962, pruned_loss=0.07017, over 1418682.78 frames.], batch size: 52, lr: 5.84e-04
2022-05-27 06:22:47,831 INFO [train.py:842] (1/4) Epoch 9, batch 5600, loss[loss=0.2324, simple_loss=0.3132, pruned_loss=0.07576, over 6889.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2959, pruned_loss=0.06966, over 1418508.46 frames.], batch size: 31, lr: 5.84e-04
2022-05-27 06:23:26,375 INFO [train.py:842] (1/4) Epoch 9, batch 5650, loss[loss=0.1915, simple_loss=0.2863, pruned_loss=0.04837, over 7117.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2954, pruned_loss=0.06921, over 1419788.01 frames.], batch size: 21, lr: 5.83e-04
2022-05-27 06:24:05,343 INFO [train.py:842] (1/4) Epoch 9, batch 5700, loss[loss=0.2554, simple_loss=0.3378, pruned_loss=0.08656, over 7233.00 frames.], tot_loss[loss=0.216, simple_loss=0.2942, pruned_loss=0.06895, over 1418219.88 frames.], batch size: 20, lr: 5.83e-04
2022-05-27 06:24:44,092 INFO [train.py:842] (1/4) Epoch 9, batch 5750, loss[loss=0.2676, simple_loss=0.3363, pruned_loss=0.09939, over 7108.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2948, pruned_loss=0.06893, over 1422585.88 frames.], batch size: 21, lr: 5.83e-04
2022-05-27 06:25:23,067 INFO [train.py:842] (1/4) Epoch 9, batch 5800, loss[loss=0.2265, simple_loss=0.3074, pruned_loss=0.07276, over 7317.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2954, pruned_loss=0.06895, over 1421844.34 frames.], batch size: 21, lr: 5.83e-04
2022-05-27 06:26:01,753 INFO [train.py:842] (1/4) Epoch 9, batch 5850, loss[loss=0.2075, simple_loss=0.2782, pruned_loss=0.06842, over 7160.00 frames.], tot_loss[loss=0.218, simple_loss=0.2961, pruned_loss=0.06992, over 1419498.19 frames.], batch size: 19, lr: 5.83e-04
2022-05-27 06:26:41,219 INFO [train.py:842] (1/4) Epoch 9, batch 5900, loss[loss=0.1996, simple_loss=0.2651, pruned_loss=0.06707, over 7424.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2942, pruned_loss=0.06864, over 1422524.13 frames.], batch size: 18, lr: 5.82e-04
2022-05-27 06:27:19,689 INFO [train.py:842] (1/4) Epoch 9, batch 5950, loss[loss=0.2197, simple_loss=0.3124, pruned_loss=0.06346, over 7280.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2941, pruned_loss=0.06827, over 1422043.39 frames.], batch size: 24, lr: 5.82e-04
2022-05-27 06:27:58,513 INFO [train.py:842] (1/4) Epoch 9, batch 6000, loss[loss=0.1604, simple_loss=0.2397, pruned_loss=0.04056, over 7006.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2942, pruned_loss=0.0685, over 1420969.53 frames.], batch size: 16, lr: 5.82e-04
2022-05-27 06:27:58,514 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 06:28:07,748 INFO [train.py:871] (1/4) Epoch 9, validation: loss=0.1769, simple_loss=0.2771, pruned_loss=0.03838, over 868885.00 frames.
2022-05-27 06:28:46,382 INFO [train.py:842] (1/4) Epoch 9, batch 6050, loss[loss=0.187, simple_loss=0.2659, pruned_loss=0.05401, over 6832.00 frames.], tot_loss[loss=0.215, simple_loss=0.2938, pruned_loss=0.06812, over 1420558.82 frames.], batch size: 15, lr: 5.82e-04
2022-05-27 06:29:25,564 INFO [train.py:842] (1/4) Epoch 9, batch 6100, loss[loss=0.2009, simple_loss=0.2659, pruned_loss=0.068, over 7258.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2941, pruned_loss=0.06779, over 1419597.87 frames.], batch size: 17, lr: 5.82e-04
2022-05-27 06:30:04,190 INFO [train.py:842] (1/4) Epoch 9, batch 6150, loss[loss=0.1912, simple_loss=0.2676, pruned_loss=0.05737, over 7270.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2939, pruned_loss=0.06787, over 1416332.61 frames.], batch size: 18, lr: 5.82e-04
2022-05-27 06:30:42,935 INFO [train.py:842] (1/4) Epoch 9, batch 6200, loss[loss=0.1813, simple_loss=0.2479, pruned_loss=0.05739, over 7140.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2929, pruned_loss=0.06724, over 1419428.41 frames.], batch size: 17, lr: 5.81e-04
2022-05-27 06:31:21,483 INFO [train.py:842] (1/4) Epoch 9, batch 6250, loss[loss=0.227, simple_loss=0.3089, pruned_loss=0.07255, over 7429.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2928, pruned_loss=0.06728, over 1419314.54 frames.], batch size: 20, lr: 5.81e-04
2022-05-27 06:32:00,445 INFO [train.py:842] (1/4) Epoch 9, batch 6300, loss[loss=0.2711, simple_loss=0.3335, pruned_loss=0.1044, over 5289.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2927, pruned_loss=0.06748, over 1419069.08 frames.], batch size: 52, lr: 5.81e-04
2022-05-27 06:32:38,973 INFO [train.py:842] (1/4) Epoch 9, batch 6350, loss[loss=0.1898, simple_loss=0.2796, pruned_loss=0.04998, over 7414.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2925, pruned_loss=0.06753, over 1419923.16 frames.], batch size: 21, lr: 5.81e-04
2022-05-27 06:33:17,819 INFO [train.py:842] (1/4) Epoch 9, batch 6400, loss[loss=0.2873, simple_loss=0.3489, pruned_loss=0.1128, over 7123.00 frames.], tot_loss[loss=0.214, simple_loss=0.2921, pruned_loss=0.06798, over 1420843.21 frames.], batch size: 21, lr: 5.81e-04
2022-05-27 06:33:56,425 INFO [train.py:842] (1/4) Epoch 9, batch 6450, loss[loss=0.1729, simple_loss=0.257, pruned_loss=0.04443, over 7365.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2923, pruned_loss=0.06828, over 1421443.90 frames.], batch size: 19, lr: 5.80e-04
2022-05-27 06:34:38,051 INFO [train.py:842] (1/4) Epoch 9, batch 6500, loss[loss=0.2274, simple_loss=0.2997, pruned_loss=0.07758, over 7409.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2922, pruned_loss=0.06811, over 1423172.30 frames.], batch size: 21, lr: 5.80e-04
2022-05-27 06:35:16,524 INFO [train.py:842] (1/4) Epoch 9, batch 6550, loss[loss=0.2209, simple_loss=0.2964, pruned_loss=0.07271, over 7145.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2927, pruned_loss=0.06826, over 1423297.50 frames.], batch size: 20, lr: 5.80e-04
2022-05-27 06:35:55,376 INFO [train.py:842] (1/4) Epoch 9, batch 6600, loss[loss=0.2314, simple_loss=0.2976, pruned_loss=0.08262, over 7063.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2944, pruned_loss=0.06969, over 1424215.53 frames.], batch size: 18, lr: 5.80e-04
2022-05-27 06:36:34,074 INFO [train.py:842] (1/4) Epoch 9, batch 6650, loss[loss=0.1881, simple_loss=0.2708, pruned_loss=0.05269, over 7449.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2942, pruned_loss=0.06953, over 1425492.09 frames.], batch size: 19, lr: 5.80e-04
2022-05-27 06:37:12,639 INFO [train.py:842] (1/4) Epoch 9, batch 6700, loss[loss=0.1726, simple_loss=0.2506, pruned_loss=0.04731, over 7263.00 frames.], tot_loss[loss=0.2158, simple_loss=0.294, pruned_loss=0.06878, over 1423282.88 frames.], batch size: 17, lr: 5.80e-04
2022-05-27 06:37:51,233 INFO [train.py:842] (1/4) Epoch 9, batch 6750, loss[loss=0.1961, simple_loss=0.2803, pruned_loss=0.05593, over 7259.00 frames.], tot_loss[loss=0.2164, simple_loss=0.295, pruned_loss=0.06893, over 1422988.40 frames.], batch size: 19, lr: 5.79e-04
2022-05-27 06:38:30,289 INFO [train.py:842] (1/4) Epoch 9, batch 6800, loss[loss=0.2753, simple_loss=0.3413, pruned_loss=0.1046, over 7349.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2947, pruned_loss=0.06872, over 1426549.53 frames.], batch size: 22, lr: 5.79e-04
2022-05-27 06:39:08,684 INFO [train.py:842] (1/4) Epoch 9, batch 6850, loss[loss=0.2602, simple_loss=0.3317, pruned_loss=0.0944, over 7114.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2959, pruned_loss=0.06884, over 1426930.03 frames.], batch size: 21, lr: 5.79e-04
2022-05-27 06:39:47,804 INFO [train.py:842] (1/4) Epoch 9, batch 6900, loss[loss=0.2391, simple_loss=0.3043, pruned_loss=0.08694, over 7334.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2959, pruned_loss=0.06868, over 1423147.44 frames.], batch size: 20, lr: 5.79e-04
2022-05-27 06:40:26,404 INFO [train.py:842] (1/4) Epoch 9, batch 6950, loss[loss=0.1876, simple_loss=0.264, pruned_loss=0.05558, over 7368.00 frames.], tot_loss[loss=0.2184, simple_loss=0.2969, pruned_loss=0.06995, over 1420089.86 frames.], batch size: 19, lr: 5.79e-04
2022-05-27 06:41:05,238 INFO [train.py:842] (1/4) Epoch 9, batch 7000, loss[loss=0.2523, simple_loss=0.3345, pruned_loss=0.08505, over 6779.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2952, pruned_loss=0.06872, over 1421592.83 frames.], batch size: 31, lr: 5.78e-04
2022-05-27 06:41:43,610 INFO [train.py:842] (1/4) Epoch 9, batch 7050, loss[loss=0.2201, simple_loss=0.3007, pruned_loss=0.06973, over 7127.00 frames.], tot_loss[loss=0.2157, simple_loss=0.295, pruned_loss=0.06824, over 1420184.10 frames.], batch size: 21, lr: 5.78e-04
2022-05-27 06:42:22,507 INFO [train.py:842] (1/4) Epoch 9, batch 7100, loss[loss=0.1827, simple_loss=0.2657, pruned_loss=0.04981, over 7063.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2949, pruned_loss=0.06823, over 1423534.67 frames.], batch size: 18, lr: 5.78e-04
2022-05-27 06:43:01,079 INFO [train.py:842] (1/4) Epoch 9, batch 7150, loss[loss=0.2174, simple_loss=0.3014, pruned_loss=0.06674, over 7184.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2949, pruned_loss=0.06844, over 1418863.50 frames.], batch size: 26, lr: 5.78e-04
2022-05-27 06:43:40,014 INFO [train.py:842] (1/4) Epoch 9, batch 7200, loss[loss=0.1873, simple_loss=0.259, pruned_loss=0.05783, over 7359.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2948, pruned_loss=0.06837, over 1422668.36 frames.], batch size: 19, lr: 5.78e-04
2022-05-27 06:44:18,604 INFO [train.py:842] (1/4) Epoch 9, batch 7250, loss[loss=0.2428, simple_loss=0.3115, pruned_loss=0.08707, over 6398.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2934, pruned_loss=0.06766, over 1424631.64 frames.], batch size: 37, lr: 5.78e-04
2022-05-27 06:44:57,348 INFO [train.py:842] (1/4) Epoch 9, batch 7300, loss[loss=0.1577, simple_loss=0.2456, pruned_loss=0.03491, over 7068.00 frames.], tot_loss[loss=0.215, simple_loss=0.2941, pruned_loss=0.06796, over 1426274.03 frames.], batch size: 18, lr: 5.77e-04
2022-05-27 06:45:35,776 INFO [train.py:842] (1/4) Epoch 9, batch 7350, loss[loss=0.2086, simple_loss=0.3005, pruned_loss=0.05838, over 7203.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2958, pruned_loss=0.06898, over 1425675.38 frames.], batch size: 23, lr: 5.77e-04
2022-05-27 06:46:15,107 INFO [train.py:842] (1/4) Epoch 9, batch 7400, loss[loss=0.1808, simple_loss=0.2571, pruned_loss=0.05226, over 7425.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2945, pruned_loss=0.06834, over 1427520.62 frames.], batch size: 18, lr: 5.77e-04
2022-05-27 06:46:53,833 INFO [train.py:842] (1/4) Epoch 9, batch 7450, loss[loss=0.2007, simple_loss=0.2922, pruned_loss=0.05456, over 7280.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2938, pruned_loss=0.06753, over 1429058.84 frames.], batch size: 25, lr: 5.77e-04
2022-05-27 06:47:32,692 INFO [train.py:842] (1/4) Epoch 9, batch 7500, loss[loss=0.2971, simple_loss=0.3644, pruned_loss=0.1149, over 5148.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2951, pruned_loss=0.0681, over 1419408.45 frames.], batch size: 52, lr: 5.77e-04
2022-05-27 06:48:11,233 INFO [train.py:842] (1/4) Epoch 9, batch 7550, loss[loss=0.1796, simple_loss=0.2702, pruned_loss=0.04449, over 7203.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2946, pruned_loss=0.06775, over 1417780.97 frames.], batch size: 23, lr: 5.76e-04
2022-05-27 06:48:50,195 INFO [train.py:842] (1/4) Epoch 9, batch 7600, loss[loss=0.2222, simple_loss=0.2886, pruned_loss=0.07784, over 7118.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2956, pruned_loss=0.06886, over 1417241.52 frames.], batch size: 17, lr: 5.76e-04
2022-05-27 06:49:28,771 INFO [train.py:842] (1/4) Epoch 9, batch 7650, loss[loss=0.2298, simple_loss=0.3051, pruned_loss=0.07729, over 7150.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2942, pruned_loss=0.06771, over 1419911.15 frames.], batch size: 20, lr: 5.76e-04
2022-05-27 06:50:07,684 INFO [train.py:842] (1/4) Epoch 9, batch 7700, loss[loss=0.1681, simple_loss=0.2543, pruned_loss=0.04099, over 7395.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2948, pruned_loss=0.06808, over 1419866.23 frames.], batch size: 18, lr: 5.76e-04
2022-05-27 06:50:46,195 INFO [train.py:842] (1/4) Epoch 9, batch 7750, loss[loss=0.2079, simple_loss=0.2984, pruned_loss=0.05871, over 6573.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2944, pruned_loss=0.06787, over 1415215.89 frames.], batch size: 38, lr: 5.76e-04
2022-05-27 06:51:25,034 INFO [train.py:842] (1/4) Epoch 9, batch 7800, loss[loss=0.1968, simple_loss=0.2745, pruned_loss=0.05953, over 7358.00 frames.], tot_loss[loss=0.216, simple_loss=0.2949, pruned_loss=0.06855, over 1422176.24 frames.], batch size: 19, lr: 5.76e-04
2022-05-27 06:52:03,445 INFO [train.py:842] (1/4) Epoch 9, batch 7850, loss[loss=0.2246, simple_loss=0.3023, pruned_loss=0.07351, over 7278.00 frames.], tot_loss[loss=0.217, simple_loss=0.2963, pruned_loss=0.0689, over 1426783.87 frames.], batch size: 24, lr: 5.75e-04
2022-05-27 06:52:42,325 INFO [train.py:842] (1/4) Epoch 9, batch 7900, loss[loss=0.1808, simple_loss=0.2644, pruned_loss=0.0486, over 7365.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2954, pruned_loss=0.06799, over 1430132.56 frames.], batch size: 19, lr: 5.75e-04
2022-05-27 06:53:21,007 INFO [train.py:842] (1/4) Epoch 9, batch 7950, loss[loss=0.2071, simple_loss=0.2967, pruned_loss=0.05877, over 7139.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2958, pruned_loss=0.06845, over 1428924.33 frames.], batch size: 20, lr: 5.75e-04
2022-05-27 06:53:59,864 INFO [train.py:842] (1/4) Epoch 9, batch 8000, loss[loss=0.191, simple_loss=0.2812, pruned_loss=0.05044, over 7155.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2938, pruned_loss=0.06781, over 1425763.03 frames.], batch size: 18, lr: 5.75e-04
2022-05-27 06:54:38,296 INFO [train.py:842] (1/4) Epoch 9, batch 8050, loss[loss=0.2015, simple_loss=0.2834, pruned_loss=0.05983, over 7252.00 frames.], tot_loss[loss=0.2149, simple_loss=0.294, pruned_loss=0.06785, over 1425218.32 frames.], batch size: 19, lr: 5.75e-04
2022-05-27 06:55:17,194 INFO [train.py:842] (1/4) Epoch 9, batch 8100, loss[loss=0.197, simple_loss=0.2721, pruned_loss=0.06098, over 7071.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2932, pruned_loss=0.06754, over 1425345.65 frames.], batch size: 18, lr: 5.75e-04
2022-05-27 06:55:55,754 INFO [train.py:842] (1/4) Epoch 9, batch 8150, loss[loss=0.1984, simple_loss=0.2869, pruned_loss=0.05492, over 6702.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2925, pruned_loss=0.0673, over 1423395.13 frames.], batch size: 31, lr: 5.74e-04
2022-05-27 06:56:34,512 INFO [train.py:842] (1/4) Epoch 9, batch 8200, loss[loss=0.1989, simple_loss=0.2935, pruned_loss=0.05214, over 7108.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2943, pruned_loss=0.06849, over 1415989.06 frames.], batch size: 21, lr: 5.74e-04
2022-05-27 06:57:13,127 INFO [train.py:842] (1/4) Epoch 9, batch 8250, loss[loss=0.199, simple_loss=0.2717, pruned_loss=0.0632, over 7260.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2945, pruned_loss=0.06882, over 1420895.93 frames.], batch size: 17, lr: 5.74e-04
2022-05-27 06:57:51,715 INFO [train.py:842] (1/4) Epoch 9, batch 8300, loss[loss=0.2114, simple_loss=0.2963, pruned_loss=0.06319, over 7321.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2949, pruned_loss=0.06936, over 1410990.90 frames.], batch size: 21, lr: 5.74e-04
2022-05-27 06:58:30,499 INFO [train.py:842] (1/4) Epoch 9, batch 8350, loss[loss=0.1989, simple_loss=0.2846, pruned_loss=0.05661, over 7318.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2933, pruned_loss=0.06809, over 1416360.17 frames.], batch size: 21, lr: 5.74e-04
2022-05-27 06:59:09,824 INFO [train.py:842] (1/4) Epoch 9, batch 8400, loss[loss=0.212, simple_loss=0.2912, pruned_loss=0.0664, over 7057.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2924, pruned_loss=0.06794, over 1423745.31 frames.], batch size: 28, lr: 5.74e-04
2022-05-27 06:59:48,376 INFO [train.py:842] (1/4) Epoch 9, batch 8450, loss[loss=0.255, simple_loss=0.3321, pruned_loss=0.08894, over 7121.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2918, pruned_loss=0.06739, over 1425355.54 frames.], batch size: 21, lr: 5.73e-04
2022-05-27 07:00:27,404 INFO [train.py:842] (1/4) Epoch 9, batch 8500, loss[loss=0.2344, simple_loss=0.3123, pruned_loss=0.07826, over 7141.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2918, pruned_loss=0.06737, over 1425545.84 frames.], batch size: 19, lr: 5.73e-04
2022-05-27 07:01:05,929 INFO [train.py:842] (1/4) Epoch 9, batch 8550, loss[loss=0.2295, simple_loss=0.3016, pruned_loss=0.07869, over 6375.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2921, pruned_loss=0.06716, over 1421988.21 frames.], batch size: 38, lr: 5.73e-04
2022-05-27 07:01:44,692 INFO [train.py:842] (1/4) Epoch 9, batch 8600, loss[loss=0.2737, simple_loss=0.3444, pruned_loss=0.1015, over 5109.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2927, pruned_loss=0.06711, over 1418028.84 frames.], batch size: 53, lr: 5.73e-04
2022-05-27 07:02:23,124 INFO [train.py:842] (1/4) Epoch 9, batch 8650, loss[loss=0.3076, simple_loss=0.3556, pruned_loss=0.1298, over 7319.00 frames.], tot_loss[loss=0.2133, simple_loss=0.293, pruned_loss=0.0668, over 1421472.05 frames.], batch size: 21, lr: 5.73e-04
2022-05-27 07:03:02,370 INFO [train.py:842] (1/4) Epoch 9, batch 8700, loss[loss=0.1801, simple_loss=0.274, pruned_loss=0.04307, over 7353.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2906, pruned_loss=0.06577, over 1422471.09 frames.], batch size: 19, lr: 5.72e-04
2022-05-27 07:03:40,720 INFO [train.py:842] (1/4) Epoch 9, batch 8750, loss[loss=0.2097, simple_loss=0.2823, pruned_loss=0.06857, over 7161.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2925, pruned_loss=0.06665, over 1418563.34 frames.], batch size: 18, lr: 5.72e-04
2022-05-27 07:04:19,472 INFO [train.py:842] (1/4) Epoch 9, batch 8800, loss[loss=0.2476, simple_loss=0.3338, pruned_loss=0.08068, over 7206.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2935, pruned_loss=0.06678, over 1419714.66 frames.], batch size: 23, lr: 5.72e-04
2022-05-27 07:04:57,938 INFO [train.py:842] (1/4) Epoch 9, batch 8850, loss[loss=0.2124, simple_loss=0.2942, pruned_loss=0.06536, over 7275.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2924, pruned_loss=0.06671, over 1411307.62 frames.], batch size: 24, lr: 5.72e-04
2022-05-27 07:05:37,301 INFO [train.py:842] (1/4) Epoch 9, batch 8900, loss[loss=0.2594, simple_loss=0.3322, pruned_loss=0.09333, over 7371.00 frames.], tot_loss[loss=0.213, simple_loss=0.2922, pruned_loss=0.06689, over 1406669.38 frames.], batch size: 23, lr: 5.72e-04
2022-05-27 07:06:15,877 INFO [train.py:842] (1/4) Epoch 9, batch 8950, loss[loss=0.247, simple_loss=0.3337, pruned_loss=0.08017, over 7340.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2929, pruned_loss=0.06741, over 1400686.84 frames.], batch size: 19, lr: 5.72e-04
2022-05-27 07:06:55,093 INFO [train.py:842] (1/4) Epoch 9, batch 9000, loss[loss=0.2167, simple_loss=0.302, pruned_loss=0.06576, over 6230.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2924, pruned_loss=0.06788, over 1392667.69 frames.], batch size: 37, lr: 5.71e-04
2022-05-27 07:06:55,094 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 07:07:04,566 INFO [train.py:871] (1/4) Epoch 9, validation: loss=0.1768, simple_loss=0.2774, pruned_loss=0.03806, over 868885.00 frames.
2022-05-27 07:07:43,576 INFO [train.py:842] (1/4) Epoch 9, batch 9050, loss[loss=0.1725, simple_loss=0.2539, pruned_loss=0.04549, over 7263.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2934, pruned_loss=0.06871, over 1386606.07 frames.], batch size: 18, lr: 5.71e-04
2022-05-27 07:08:21,632 INFO [train.py:842] (1/4) Epoch 9, batch 9100, loss[loss=0.2759, simple_loss=0.3365, pruned_loss=0.1076, over 4959.00 frames.], tot_loss[loss=0.221, simple_loss=0.2985, pruned_loss=0.07175, over 1351034.49 frames.], batch size: 52, lr: 5.71e-04
2022-05-27 07:08:59,049 INFO [train.py:842] (1/4) Epoch 9, batch 9150, loss[loss=0.2185, simple_loss=0.2881, pruned_loss=0.0745, over 5017.00 frames.], tot_loss[loss=0.2275, simple_loss=0.3037, pruned_loss=0.07562, over 1297399.85 frames.], batch size: 52, lr: 5.71e-04
2022-05-27 07:09:51,891 INFO [train.py:842] (1/4) Epoch 10, batch 0, loss[loss=0.1881, simple_loss=0.2733, pruned_loss=0.05149, over 7408.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2733, pruned_loss=0.05149, over 7408.00 frames.], batch size: 21, lr: 5.49e-04
2022-05-27 07:10:30,752 INFO [train.py:842] (1/4) Epoch 10, batch 50, loss[loss=0.2217, simple_loss=0.3183, pruned_loss=0.0625, over 7202.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2944, pruned_loss=0.0684, over 321456.14 frames.], batch size: 23, lr: 5.49e-04
2022-05-27 07:11:09,438 INFO [train.py:842] (1/4) Epoch 10, batch 100, loss[loss=0.2366, simple_loss=0.3008, pruned_loss=0.08618, over 5252.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2913, pruned_loss=0.06705, over 557298.85 frames.], batch size: 53, lr: 5.48e-04
2022-05-27 07:11:48,009 INFO [train.py:842] (1/4) Epoch 10, batch 150, loss[loss=0.1991, simple_loss=0.2878, pruned_loss=0.05522, over 7430.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2904, pruned_loss=0.06513, over 750688.22 frames.], batch size: 20, lr: 5.48e-04
2022-05-27 07:12:26,893 INFO [train.py:842] (1/4) Epoch 10, batch 200, loss[loss=0.1783, simple_loss=0.2648, pruned_loss=0.0459, over 7430.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2924, pruned_loss=0.06672, over 897580.18 frames.], batch size: 20, lr: 5.48e-04
2022-05-27 07:13:05,275 INFO [train.py:842] (1/4) Epoch 10, batch 250, loss[loss=0.162, simple_loss=0.2448, pruned_loss=0.03959, over 7162.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2934, pruned_loss=0.06653, over 1010231.33 frames.], batch size: 18, lr: 5.48e-04
2022-05-27 07:13:44,129 INFO [train.py:842] (1/4) Epoch 10, batch 300, loss[loss=0.2105, simple_loss=0.293, pruned_loss=0.06397, over 7318.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2921, pruned_loss=0.06605, over 1103912.21 frames.], batch size: 20, lr: 5.48e-04
2022-05-27 07:14:22,634 INFO [train.py:842] (1/4) Epoch 10, batch 350, loss[loss=0.2122, simple_loss=0.3066, pruned_loss=0.05886, over 7195.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2916, pruned_loss=0.0656, over 1172388.21 frames.], batch size: 23, lr: 5.48e-04
2022-05-27 07:15:01,362 INFO [train.py:842] (1/4) Epoch 10, batch 400, loss[loss=0.3221, simple_loss=0.3865, pruned_loss=0.1288, over 7174.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2934, pruned_loss=0.06645, over 1222330.77 frames.], batch size: 26, lr: 5.47e-04
2022-05-27 07:15:39,898 INFO [train.py:842] (1/4) Epoch 10, batch 450, loss[loss=0.2082, simple_loss=0.2995, pruned_loss=0.05846, over 6300.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2942, pruned_loss=0.0668, over 1261062.85 frames.], batch size: 37, lr: 5.47e-04
2022-05-27 07:16:18,859 INFO [train.py:842] (1/4) Epoch 10, batch 500, loss[loss=0.1748, simple_loss=0.264, pruned_loss=0.04281, over 7166.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2937, pruned_loss=0.06678, over 1296172.79 frames.], batch size: 19, lr: 5.47e-04
2022-05-27 07:16:57,395 INFO [train.py:842] (1/4) Epoch 10, batch 550, loss[loss=0.1642, simple_loss=0.2506, pruned_loss=0.03889, over 7150.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2944, pruned_loss=0.06735, over 1324298.29 frames.], batch size: 17, lr: 5.47e-04
2022-05-27 07:17:36,420 INFO [train.py:842] (1/4) Epoch 10, batch 600, loss[loss=0.2383, simple_loss=0.3095, pruned_loss=0.08352, over 7287.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2942, pruned_loss=0.06729, over 1347212.31 frames.], batch size: 18, lr: 5.47e-04
2022-05-27 07:18:15,053 INFO [train.py:842] (1/4) Epoch 10, batch 650, loss[loss=0.2372, simple_loss=0.3081, pruned_loss=0.08316, over 7213.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2932, pruned_loss=0.06698, over 1363268.47 frames.], batch size: 26, lr: 5.47e-04
2022-05-27 07:18:53,916 INFO [train.py:842] (1/4) Epoch 10, batch 700, loss[loss=0.2357, simple_loss=0.3168, pruned_loss=0.07729, over 7279.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2923, pruned_loss=0.06643, over 1378135.89 frames.], batch size: 25, lr: 5.46e-04
2022-05-27 07:19:32,406 INFO [train.py:842] (1/4) Epoch 10, batch 750, loss[loss=0.2002, simple_loss=0.2819, pruned_loss=0.05921, over 7430.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2917, pruned_loss=0.06628, over 1388048.55 frames.], batch size: 20, lr: 5.46e-04
2022-05-27 07:20:11,290 INFO [train.py:842] (1/4) Epoch 10, batch 800, loss[loss=0.3045, simple_loss=0.384, pruned_loss=0.1125, over 7274.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2922, pruned_loss=0.06637, over 1394398.12 frames.], batch size: 24, lr: 5.46e-04
2022-05-27 07:20:50,010 INFO [train.py:842] (1/4) Epoch 10, batch 850, loss[loss=0.2339, simple_loss=0.3156, pruned_loss=0.07608, over 6365.00 frames.], tot_loss[loss=0.2121, simple_loss=0.292, pruned_loss=0.06605, over 1397768.04 frames.], batch size: 37, lr: 5.46e-04
2022-05-27 07:21:29,304 INFO [train.py:842] (1/4) Epoch 10, batch 900, loss[loss=0.2258, simple_loss=0.3028, pruned_loss=0.07441, over 7329.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2923, pruned_loss=0.06615, over 1407769.41 frames.], batch size: 21, lr: 5.46e-04
2022-05-27 07:22:07,887 INFO [train.py:842] (1/4) Epoch 10, batch 950, loss[loss=0.2059, simple_loss=0.2953, pruned_loss=0.05822, over 7171.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2937, pruned_loss=0.06691, over 1407680.55 frames.], batch size: 26, lr: 5.46e-04
2022-05-27 07:22:46,799 INFO [train.py:842] (1/4) Epoch 10, batch 1000, loss[loss=0.2041, simple_loss=0.2932, pruned_loss=0.05753, over 7321.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2925, pruned_loss=0.06607, over 1414726.19 frames.], batch size: 20, lr: 5.46e-04
2022-05-27 07:23:25,634 INFO [train.py:842] (1/4) Epoch 10, batch 1050, loss[loss=0.1942, simple_loss=0.2787, pruned_loss=0.05487, over 7039.00 frames.], tot_loss[loss=0.212, simple_loss=0.2921, pruned_loss=0.06592, over 1416876.50 frames.], batch size: 28, lr: 5.45e-04
2022-05-27 07:24:04,249 INFO [train.py:842] (1/4) Epoch 10, batch 1100, loss[loss=0.2347, simple_loss=0.3183, pruned_loss=0.0755, over 7069.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2938, pruned_loss=0.06726, over 1416752.87 frames.], batch size: 28, lr: 5.45e-04
2022-05-27 07:24:42,895 INFO [train.py:842] (1/4) Epoch 10, batch 1150, loss[loss=0.1998, simple_loss=0.2807, pruned_loss=0.05944, over 7325.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2941, pruned_loss=0.06686, over 1421712.05 frames.], batch size: 20, lr: 5.45e-04
2022-05-27 07:25:21,682 INFO [train.py:842] (1/4) Epoch 10, batch 1200, loss[loss=0.2981, simple_loss=0.3567, pruned_loss=0.1197, over 7204.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2935, pruned_loss=0.06634, over 1420906.48 frames.], batch size: 23, lr: 5.45e-04
2022-05-27 07:26:00,223 INFO [train.py:842] (1/4) Epoch 10, batch 1250, loss[loss=0.2017, simple_loss=0.2788, pruned_loss=0.06229, over 7287.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2945, pruned_loss=0.06735, over 1418895.58 frames.], batch size: 17, lr: 5.45e-04
2022-05-27 07:26:39,234 INFO [train.py:842] (1/4) Epoch 10, batch 1300, loss[loss=0.1655, simple_loss=0.2444, pruned_loss=0.04332, over 7415.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2934, pruned_loss=0.06716, over 1417471.94 frames.], batch size: 17, lr: 5.45e-04
2022-05-27 07:27:17,674 INFO [train.py:842] (1/4) Epoch 10, batch 1350, loss[loss=0.2076, simple_loss=0.3014, pruned_loss=0.05687, over 7320.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2926, pruned_loss=0.06661, over 1416124.54 frames.], batch size: 21, lr: 5.44e-04
2022-05-27 07:27:56,262 INFO [train.py:842] (1/4) Epoch 10, batch 1400, loss[loss=0.2317, simple_loss=0.3158, pruned_loss=0.07383, over 7119.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2942, pruned_loss=0.06728, over 1419414.68 frames.], batch size: 21, lr: 5.44e-04
2022-05-27 07:28:35,047 INFO [train.py:842] (1/4) Epoch 10, batch 1450, loss[loss=0.2447, simple_loss=0.3265, pruned_loss=0.08144, over 7269.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2934, pruned_loss=0.06718, over 1420677.59 frames.], batch size: 25, lr: 5.44e-04
2022-05-27 07:29:13,796 INFO [train.py:842] (1/4) Epoch 10, batch 1500, loss[loss=0.2895, simple_loss=0.3487, pruned_loss=0.1152, over 4827.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2946, pruned_loss=0.06814, over 1415364.65 frames.], batch size: 52, lr: 5.44e-04
2022-05-27 07:29:52,341 INFO [train.py:842] (1/4) Epoch 10, batch 1550, loss[loss=0.207, simple_loss=0.2803, pruned_loss=0.06683, over 7359.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2942, pruned_loss=0.06745, over 1418592.28 frames.], batch size: 19, lr: 5.44e-04
2022-05-27 07:30:31,305 INFO [train.py:842] (1/4) Epoch 10, batch 1600, loss[loss=0.2114, simple_loss=0.2955, pruned_loss=0.06361, over 7263.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2943, pruned_loss=0.06772, over 1417244.57 frames.], batch size: 19, lr: 5.44e-04
2022-05-27 07:31:09,817 INFO [train.py:842] (1/4) Epoch 10, batch 1650, loss[loss=0.2259, simple_loss=0.3073, pruned_loss=0.07226, over 7413.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2945, pruned_loss=0.06821, over 1415735.22 frames.], batch size: 21, lr: 5.43e-04
2022-05-27 07:31:48,619 INFO [train.py:842] (1/4) Epoch 10, batch 1700, loss[loss=0.2254, simple_loss=0.3027, pruned_loss=0.07404, over 7259.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2938, pruned_loss=0.06744, over 1413676.50 frames.], batch size: 24, lr: 5.43e-04
2022-05-27 07:32:27,165 INFO [train.py:842] (1/4) Epoch 10, batch 1750, loss[loss=0.2254, simple_loss=0.292, pruned_loss=0.07944, over 7209.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2941, pruned_loss=0.06733, over 1405956.74 frames.], batch size: 16, lr: 5.43e-04
2022-05-27 07:33:05,924 INFO [train.py:842] (1/4) Epoch 10, batch 1800, loss[loss=0.2193, simple_loss=0.2975, pruned_loss=0.07056, over 7369.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2946, pruned_loss=0.06743, over 1410177.81 frames.], batch size: 19, lr: 5.43e-04
2022-05-27 07:33:44,429 INFO [train.py:842] (1/4) Epoch 10, batch 1850, loss[loss=0.2439, simple_loss=0.3113, pruned_loss=0.08823, over 7359.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2946, pruned_loss=0.06759, over 1410993.01 frames.], batch size: 19, lr: 5.43e-04
2022-05-27 07:34:23,391 INFO [train.py:842] (1/4) Epoch 10, batch 1900, loss[loss=0.2021, simple_loss=0.2851, pruned_loss=0.05951, over 7289.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2931, pruned_loss=0.06678, over 1414611.78 frames.], batch size: 18, lr: 5.43e-04
2022-05-27 07:35:02,038 INFO [train.py:842] (1/4) Epoch 10, batch 1950, loss[loss=0.2251, simple_loss=0.3068, pruned_loss=0.07174, over 7188.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2928, pruned_loss=0.06676, over 1414055.98 frames.], batch size: 23, lr: 5.42e-04
2022-05-27 07:35:40,863 INFO [train.py:842] (1/4) Epoch 10, batch 2000, loss[loss=0.1979, simple_loss=0.2835, pruned_loss=0.05617, over 7229.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2919, pruned_loss=0.06663, over 1417470.69 frames.], batch size: 20, lr: 5.42e-04
2022-05-27 07:36:19,536 INFO [train.py:842] (1/4) Epoch 10, batch 2050, loss[loss=0.2091, simple_loss=0.2874, pruned_loss=0.06536, over 7221.00 frames.], tot_loss[loss=0.212, simple_loss=0.2918, pruned_loss=0.06612, over 1419949.57 frames.], batch size: 23, lr: 5.42e-04
2022-05-27 07:36:58,483 INFO [train.py:842] (1/4) Epoch 10, batch 2100, loss[loss=0.1812, simple_loss=0.265, pruned_loss=0.04874, over 7137.00 frames.], tot_loss[loss=0.211, simple_loss=0.291, pruned_loss=0.06548, over 1424645.96 frames.], batch size: 20, lr: 5.42e-04
2022-05-27 07:37:37,344 INFO [train.py:842] (1/4) Epoch 10, batch 2150, loss[loss=0.1655, simple_loss=0.2382, pruned_loss=0.04641, over 7398.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2893, pruned_loss=0.06469, over 1426173.49 frames.], batch size: 18, lr: 5.42e-04
2022-05-27 07:38:16,090 INFO [train.py:842] (1/4) Epoch 10, batch 2200, loss[loss=0.212, simple_loss=0.3019, pruned_loss=0.06105, over 6538.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2911, pruned_loss=0.06538, over 1426887.21 frames.], batch size: 38, lr: 5.42e-04
2022-05-27 07:38:54,676 INFO [train.py:842] (1/4) Epoch 10, batch 2250, loss[loss=0.2109, simple_loss=0.3039, pruned_loss=0.05894, over 7316.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2907, pruned_loss=0.06544, over 1428530.31 frames.], batch size: 21, lr: 5.42e-04
2022-05-27 07:39:33,341 INFO [train.py:842] (1/4) Epoch 10, batch 2300, loss[loss=0.1888, simple_loss=0.2844, pruned_loss=0.04662, over 7135.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2926, pruned_loss=0.06636, over 1427334.98 frames.], batch size: 20, lr: 5.41e-04
2022-05-27 07:40:11,934 INFO [train.py:842] (1/4) Epoch 10, batch 2350, loss[loss=0.2246, simple_loss=0.3079, pruned_loss=0.07066, over 7220.00 frames.], tot_loss[loss=0.213, simple_loss=0.2925, pruned_loss=0.06672, over 1425101.56 frames.], batch size: 22, lr: 5.41e-04
2022-05-27 07:40:50,785 INFO [train.py:842] (1/4) Epoch 10, batch 2400, loss[loss=0.1896, simple_loss=0.2688, pruned_loss=0.0552, over 7280.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2918, pruned_loss=0.06628, over 1426747.23 frames.], batch size: 18, lr: 5.41e-04
2022-05-27 07:41:29,385 INFO [train.py:842] (1/4) Epoch 10, batch 2450, loss[loss=0.2002, simple_loss=0.2786, pruned_loss=0.06092, over 7077.00 frames.], tot_loss[loss=0.2115, simple_loss=0.2912, pruned_loss=0.06585, over 1429642.40 frames.], batch size: 18, lr: 5.41e-04
2022-05-27 07:42:08,599 INFO [train.py:842] (1/4) Epoch 10, batch 2500, loss[loss=0.2141, simple_loss=0.2987, pruned_loss=0.06476, over 7319.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2914, pruned_loss=0.0657, over 1428346.02 frames.], batch size: 21, lr: 5.41e-04
2022-05-27 07:42:47,183 INFO [train.py:842] (1/4) Epoch 10, batch 2550, loss[loss=0.3242, simple_loss=0.3771, pruned_loss=0.1357, over 7219.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2918, pruned_loss=0.06628, over 1426998.28 frames.], batch size: 21, lr: 5.41e-04
2022-05-27 07:43:26,340 INFO [train.py:842] (1/4) Epoch 10, batch 2600, loss[loss=0.2427, simple_loss=0.3203, pruned_loss=0.08261, over 7208.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2929, pruned_loss=0.06698, over 1429526.94 frames.], batch size: 26, lr: 5.40e-04
2022-05-27 07:44:04,692 INFO [train.py:842] (1/4) Epoch 10, batch 2650, loss[loss=0.198, simple_loss=0.2916, pruned_loss=0.05217, over 7336.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2935, pruned_loss=0.06689, over 1425587.94 frames.], batch size: 22, lr: 5.40e-04
2022-05-27 07:44:43,526 INFO [train.py:842] (1/4) Epoch 10, batch 2700, loss[loss=0.2983, simple_loss=0.3659, pruned_loss=0.1154, over 6826.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2928, pruned_loss=0.06681, over 1425478.41 frames.], batch size: 31, lr: 5.40e-04
2022-05-27 07:45:22,170 INFO [train.py:842] (1/4) Epoch 10, batch 2750, loss[loss=0.2088, simple_loss=0.2949, pruned_loss=0.06138, over 6733.00 frames.], tot_loss[loss=0.2127, simple_loss=0.292, pruned_loss=0.06669, over 1423529.09 frames.], batch size: 31, lr: 5.40e-04
2022-05-27 07:46:01,464 INFO [train.py:842] (1/4) Epoch 10, batch 2800, loss[loss=0.2151, simple_loss=0.307, pruned_loss=0.06164, over 7390.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2925, pruned_loss=0.06666, over 1428821.18 frames.], batch size: 23, lr: 5.40e-04
2022-05-27 07:46:50,638 INFO [train.py:842] (1/4) Epoch 10, batch 2850, loss[loss=0.2236, simple_loss=0.3082, pruned_loss=0.06944, over 7333.00 frames.], tot_loss[loss=0.212, simple_loss=0.292, pruned_loss=0.066, over 1426891.39 frames.], batch size: 22, lr: 5.40e-04
2022-05-27 07:47:29,737 INFO [train.py:842] (1/4) Epoch 10, batch 2900, loss[loss=0.1604, simple_loss=0.2434, pruned_loss=0.03863, over 7112.00 frames.], tot_loss[loss=0.2107, simple_loss=0.2908, pruned_loss=0.06525, over 1425907.48 frames.], batch size: 21, lr: 5.39e-04
2022-05-27 07:48:08,434 INFO [train.py:842] (1/4) Epoch 10, batch 2950, loss[loss=0.1859, simple_loss=0.2665, pruned_loss=0.05262, over 7280.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2895, pruned_loss=0.06493, over 1426732.92 frames.], batch size: 18, lr: 5.39e-04
2022-05-27 07:48:47,535 INFO [train.py:842] (1/4) Epoch 10, batch 3000, loss[loss=0.1878, simple_loss=0.2456, pruned_loss=0.06501, over 7302.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2899, pruned_loss=0.06533, over 1426842.53 frames.], batch size: 17, lr: 5.39e-04
2022-05-27 07:48:47,535 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 07:48:56,971 INFO [train.py:871] (1/4) Epoch 10, validation: loss=0.1722, simple_loss=0.2729, pruned_loss=0.03575, over 868885.00 frames.
2022-05-27 07:49:35,678 INFO [train.py:842] (1/4) Epoch 10, batch 3050, loss[loss=0.203, simple_loss=0.2779, pruned_loss=0.06412, over 7170.00 frames.], tot_loss[loss=0.211, simple_loss=0.2903, pruned_loss=0.06589, over 1426364.18 frames.], batch size: 19, lr: 5.39e-04
2022-05-27 07:50:14,624 INFO [train.py:842] (1/4) Epoch 10, batch 3100, loss[loss=0.2165, simple_loss=0.3014, pruned_loss=0.06582, over 7112.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2918, pruned_loss=0.06667, over 1429027.81 frames.], batch size: 21, lr: 5.39e-04
2022-05-27 07:50:53,116 INFO [train.py:842] (1/4) Epoch 10, batch 3150, loss[loss=0.2128, simple_loss=0.2929, pruned_loss=0.0663, over 7321.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2914, pruned_loss=0.06672, over 1425489.03 frames.], batch size: 21, lr: 5.39e-04
2022-05-27 07:51:32,425 INFO [train.py:842] (1/4) Epoch 10, batch 3200, loss[loss=0.1853, simple_loss=0.28, pruned_loss=0.04526, over 7235.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2912, pruned_loss=0.06651, over 1425695.13 frames.], batch size: 20, lr: 5.39e-04
2022-05-27 07:52:11,117 INFO [train.py:842] (1/4) Epoch 10, batch 3250, loss[loss=0.2017, simple_loss=0.2915, pruned_loss=0.05599, over 7416.00 frames.], tot_loss[loss=0.2114, simple_loss=0.291, pruned_loss=0.06594, over 1426410.34 frames.], batch size: 21, lr: 5.38e-04
2022-05-27 07:52:49,919 INFO [train.py:842] (1/4) Epoch 10, batch 3300, loss[loss=0.2296, simple_loss=0.3105, pruned_loss=0.07436, over 7218.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2912, pruned_loss=0.06568, over 1427957.92 frames.], batch size: 22, lr: 5.38e-04
2022-05-27 07:53:28,329 INFO [train.py:842] (1/4) Epoch 10, batch 3350, loss[loss=0.2125, simple_loss=0.2996, pruned_loss=0.06269, over 7206.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2913, pruned_loss=0.06568, over 1429198.62 frames.], batch size: 23, lr: 5.38e-04
2022-05-27 07:54:07,097 INFO [train.py:842] (1/4) Epoch 10, batch 3400, loss[loss=0.1849, simple_loss=0.2681, pruned_loss=0.05087, over 7271.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2923, pruned_loss=0.06665, over 1425394.95 frames.], batch size: 17, lr: 5.38e-04
2022-05-27 07:54:45,620 INFO [train.py:842] (1/4) Epoch 10, batch 3450, loss[loss=0.2332, simple_loss=0.3198, pruned_loss=0.07328, over 7265.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2916, pruned_loss=0.06639, over 1424551.05 frames.], batch size: 24, lr: 5.38e-04
2022-05-27 07:55:24,450 INFO [train.py:842] (1/4) Epoch 10, batch 3500, loss[loss=0.199, simple_loss=0.2849, pruned_loss=0.05656, over 7415.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2912, pruned_loss=0.06605, over 1425128.83 frames.], batch size: 21, lr: 5.38e-04
2022-05-27 07:56:03,132 INFO [train.py:842] (1/4) Epoch 10, batch 3550, loss[loss=0.2316, simple_loss=0.3209, pruned_loss=0.07116, over 7076.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2897, pruned_loss=0.06499, over 1427688.65 frames.], batch size: 28, lr: 5.37e-04
2022-05-27 07:56:42,290 INFO [train.py:842] (1/4) Epoch 10, batch 3600, loss[loss=0.2111, simple_loss=0.2842, pruned_loss=0.06895, over 7044.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2894, pruned_loss=0.06548, over 1428635.43 frames.], batch size: 28, lr: 5.37e-04
2022-05-27 07:57:21,007 INFO [train.py:842] (1/4) Epoch 10, batch 3650, loss[loss=0.2008, simple_loss=0.2832, pruned_loss=0.05924, over 7446.00 frames.], tot_loss[loss=0.211, simple_loss=0.2904, pruned_loss=0.06578, over 1425151.65 frames.], batch size: 19, lr: 5.37e-04
2022-05-27 07:57:59,628 INFO [train.py:842] (1/4) Epoch 10, batch 3700, loss[loss=0.1747, simple_loss=0.2397, pruned_loss=0.0548, over 7284.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2909, pruned_loss=0.06564, over 1427218.34 frames.], batch size: 17, lr: 5.37e-04
2022-05-27 07:58:38,217 INFO [train.py:842] (1/4) Epoch 10, batch 3750, loss[loss=0.2006, simple_loss=0.2867, pruned_loss=0.05731, over 7163.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2914, pruned_loss=0.06563, over 1429478.44 frames.], batch size: 19, lr: 5.37e-04
2022-05-27 07:59:17,019 INFO [train.py:842] (1/4) Epoch 10, batch 3800, loss[loss=0.1931, simple_loss=0.2724, pruned_loss=0.05686, over 7433.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2906, pruned_loss=0.065, over 1427703.15 frames.], batch size: 20, lr: 5.37e-04
2022-05-27 07:59:55,561 INFO [train.py:842] (1/4) Epoch 10, batch 3850, loss[loss=0.1944, simple_loss=0.2718, pruned_loss=0.05847, over 7061.00 frames.], tot_loss[loss=0.2116, simple_loss=0.2923, pruned_loss=0.06544, over 1426331.68 frames.], batch size: 18, lr: 5.36e-04
2022-05-27 08:00:34,411 INFO [train.py:842] (1/4) Epoch 10, batch 3900, loss[loss=0.276, simple_loss=0.3456, pruned_loss=0.1031, over 7141.00 frames.], tot_loss[loss=0.2118, simple_loss=0.2919, pruned_loss=0.06587, over 1427628.39 frames.], batch size: 20, lr: 5.36e-04
2022-05-27 08:01:13,250 INFO [train.py:842] (1/4) Epoch 10, batch 3950, loss[loss=0.2099, simple_loss=0.2931, pruned_loss=0.06335, over 7070.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2902, pruned_loss=0.06542, over 1424813.40 frames.], batch size: 18, lr: 5.36e-04
2022-05-27 08:01:51,969 INFO [train.py:842] (1/4) Epoch 10, batch 4000, loss[loss=0.1857, simple_loss=0.2597, pruned_loss=0.05582, over 7282.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2916, pruned_loss=0.06658, over 1422942.03 frames.], batch size: 17, lr: 5.36e-04
2022-05-27 08:02:30,697 INFO [train.py:842] (1/4) Epoch 10, batch 4050, loss[loss=0.2153, simple_loss=0.2982, pruned_loss=0.06617, over 7232.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2917, pruned_loss=0.06688, over 1423933.64 frames.], batch size: 20, lr: 5.36e-04
2022-05-27 08:03:09,542 INFO [train.py:842] (1/4) Epoch 10, batch 4100, loss[loss=0.1921, simple_loss=0.2758, pruned_loss=0.05419, over 7336.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2921, pruned_loss=0.06746, over 1424862.61 frames.], batch size: 20, lr: 5.36e-04
2022-05-27 08:03:48,069 INFO [train.py:842] (1/4) Epoch 10, batch 4150, loss[loss=0.2792, simple_loss=0.345, pruned_loss=0.1067, over 7380.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2932, pruned_loss=0.06772, over 1427344.01 frames.], batch size: 23, lr: 5.36e-04
2022-05-27 08:04:27,007 INFO [train.py:842] (1/4) Epoch 10, batch 4200, loss[loss=0.1996, simple_loss=0.2854, pruned_loss=0.05694, over 7314.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2928, pruned_loss=0.06712, over 1426243.64 frames.], batch size: 24, lr: 5.35e-04
2022-05-27 08:05:05,680 INFO [train.py:842] (1/4) Epoch 10, batch 4250, loss[loss=0.2312, simple_loss=0.3109, pruned_loss=0.07571, over 6866.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2925, pruned_loss=0.06628, over 1428262.82 frames.], batch size: 32, lr: 5.35e-04
2022-05-27 08:05:44,542 INFO [train.py:842] (1/4) Epoch 10, batch 4300, loss[loss=0.1727, simple_loss=0.2561, pruned_loss=0.04465, over 7277.00 frames.], tot_loss[loss=0.213, simple_loss=0.293, pruned_loss=0.06652, over 1427894.23 frames.], batch size: 17, lr: 5.35e-04
2022-05-27 08:06:22,937 INFO [train.py:842] (1/4) Epoch 10, batch 4350, loss[loss=0.2512, simple_loss=0.3206, pruned_loss=0.09087, over 7227.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2946, pruned_loss=0.06728, over 1420357.74 frames.], batch size: 26, lr: 5.35e-04
2022-05-27 08:07:01,871 INFO [train.py:842] (1/4) Epoch 10, batch 4400, loss[loss=0.2056, simple_loss=0.2968, pruned_loss=0.05721, over 7154.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2936, pruned_loss=0.06668, over 1420454.18 frames.], batch size: 20, lr: 5.35e-04
2022-05-27 08:08:01,214 INFO [train.py:842] (1/4) Epoch 10, batch 4450, loss[loss=0.2287, simple_loss=0.3118, pruned_loss=0.07285, over 7346.00 frames.], tot_loss[loss=0.214, simple_loss=0.2938, pruned_loss=0.0671, over 1419372.57 frames.], batch size: 22, lr: 5.35e-04
2022-05-27 08:08:50,714 INFO [train.py:842] (1/4) Epoch 10, batch 4500, loss[loss=0.1919, simple_loss=0.2771, pruned_loss=0.05336, over 7121.00 frames.], tot_loss[loss=0.213, simple_loss=0.2927, pruned_loss=0.06667, over 1421102.56 frames.], batch size: 21, lr: 5.35e-04
2022-05-27 08:09:29,231 INFO [train.py:842] (1/4) Epoch 10, batch 4550, loss[loss=0.1792, simple_loss=0.265, pruned_loss=0.0467, over 7431.00 frames.], tot_loss[loss=0.214, simple_loss=0.2935, pruned_loss=0.0672, over 1417748.53 frames.], batch size: 20, lr: 5.34e-04
2022-05-27 08:10:07,934 INFO [train.py:842] (1/4) Epoch 10, batch 4600, loss[loss=0.1752, simple_loss=0.2734, pruned_loss=0.03847, over 7230.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2942, pruned_loss=0.06718, over 1422187.58 frames.], batch size: 21, lr: 5.34e-04
2022-05-27 08:10:46,404 INFO [train.py:842] (1/4) Epoch 10, batch 4650, loss[loss=0.1883, simple_loss=0.2827, pruned_loss=0.04689, over 7207.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2938, pruned_loss=0.06704, over 1419423.83 frames.], batch size: 23, lr: 5.34e-04
2022-05-27 08:11:25,348 INFO [train.py:842] (1/4) Epoch 10, batch 4700, loss[loss=0.2323, simple_loss=0.3003, pruned_loss=0.08214, over 7223.00 frames.], tot_loss[loss=0.214, simple_loss=0.2938, pruned_loss=0.06708, over 1413365.85 frames.], batch size: 21, lr: 5.34e-04
2022-05-27 08:12:03,838 INFO [train.py:842] (1/4) Epoch 10, batch 4750, loss[loss=0.1812, simple_loss=0.2533, pruned_loss=0.05458, over 6987.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2933, pruned_loss=0.06626, over 1416627.45 frames.], batch size: 16, lr: 5.34e-04
2022-05-27 08:12:42,494 INFO [train.py:842] (1/4) Epoch 10, batch 4800, loss[loss=0.1749, simple_loss=0.2686, pruned_loss=0.04055, over 7285.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2934, pruned_loss=0.06676, over 1417673.73 frames.], batch size: 25, lr: 5.34e-04
2022-05-27 08:13:21,010 INFO [train.py:842] (1/4) Epoch 10, batch 4850, loss[loss=0.2102, simple_loss=0.3002, pruned_loss=0.06013, over 7110.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2912, pruned_loss=0.06547, over 1419956.68 frames.], batch size: 21, lr: 5.33e-04
2022-05-27 08:14:00,193 INFO [train.py:842] (1/4) Epoch 10, batch 4900, loss[loss=0.1939, simple_loss=0.2714, pruned_loss=0.05817, over 7423.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2903, pruned_loss=0.06464, over 1423493.88 frames.], batch size: 18, lr: 5.33e-04
2022-05-27 08:14:38,901 INFO [train.py:842] (1/4) Epoch 10, batch 4950, loss[loss=0.1917, simple_loss=0.261, pruned_loss=0.06119, over 7208.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2882, pruned_loss=0.06332, over 1423204.66 frames.], batch size: 16, lr: 5.33e-04
2022-05-27 08:15:17,741 INFO [train.py:842] (1/4) Epoch 10, batch 5000, loss[loss=0.1797, simple_loss=0.2605, pruned_loss=0.0495, over 7162.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2889, pruned_loss=0.06346, over 1424738.55 frames.], batch size: 18, lr: 5.33e-04
2022-05-27 08:15:56,165 INFO [train.py:842] (1/4) Epoch 10, batch 5050, loss[loss=0.177, simple_loss=0.2565, pruned_loss=0.04872, over 7262.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2903, pruned_loss=0.06432, over 1421811.07 frames.], batch size: 17, lr: 5.33e-04
2022-05-27 08:16:34,940 INFO [train.py:842] (1/4) Epoch 10, batch 5100, loss[loss=0.2052, simple_loss=0.2876, pruned_loss=0.06146, over 7059.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2901, pruned_loss=0.06469, over 1420783.17 frames.], batch size: 28, lr: 5.33e-04
2022-05-27 08:17:13,476 INFO [train.py:842] (1/4) Epoch 10, batch 5150, loss[loss=0.204, simple_loss=0.2964, pruned_loss=0.05579, over 7341.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2911, pruned_loss=0.06556, over 1417403.38 frames.], batch size: 22, lr: 5.33e-04
2022-05-27 08:17:52,720 INFO [train.py:842] (1/4) Epoch 10, batch 5200, loss[loss=0.1984, simple_loss=0.2863, pruned_loss=0.0553, over 7165.00 frames.], tot_loss[loss=0.2115, simple_loss=0.2918, pruned_loss=0.06566, over 1423641.08 frames.], batch size: 19, lr: 5.32e-04
2022-05-27 08:18:31,287 INFO [train.py:842] (1/4) Epoch 10, batch 5250, loss[loss=0.2136, simple_loss=0.3041, pruned_loss=0.06156, over 7306.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2927, pruned_loss=0.06652, over 1425602.67 frames.], batch size: 24, lr: 5.32e-04
2022-05-27 08:19:12,934 INFO [train.py:842] (1/4) Epoch 10, batch 5300, loss[loss=0.1832, simple_loss=0.255, pruned_loss=0.0557, over 7410.00 frames.], tot_loss[loss=0.21, simple_loss=0.2903, pruned_loss=0.06488, over 1426498.76 frames.], batch size: 18, lr: 5.32e-04
2022-05-27 08:19:51,544 INFO [train.py:842] (1/4) Epoch 10, batch 5350, loss[loss=0.2202, simple_loss=0.2939, pruned_loss=0.07324, over 7398.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2922, pruned_loss=0.06651, over 1428174.12 frames.], batch size: 23, lr: 5.32e-04
2022-05-27 08:20:30,721 INFO [train.py:842] (1/4) Epoch 10, batch 5400, loss[loss=0.1974, simple_loss=0.2723, pruned_loss=0.06122, over 7265.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2906, pruned_loss=0.0656, over 1432647.30 frames.], batch size: 18, lr: 5.32e-04
2022-05-27 08:21:09,310 INFO [train.py:842] (1/4) Epoch 10, batch 5450, loss[loss=0.2229, simple_loss=0.307, pruned_loss=0.06942, over 7414.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2914, pruned_loss=0.06623, over 1429912.83 frames.], batch size: 21, lr: 5.32e-04
2022-05-27 08:21:47,998 INFO [train.py:842] (1/4) Epoch 10, batch 5500, loss[loss=0.1831, simple_loss=0.2603, pruned_loss=0.05293, over 7016.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2926, pruned_loss=0.0671, over 1424873.08 frames.], batch size: 16, lr: 5.31e-04
2022-05-27 08:22:26,529 INFO [train.py:842] (1/4) Epoch 10, batch 5550, loss[loss=0.2118, simple_loss=0.3051, pruned_loss=0.05927, over 7303.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2919, pruned_loss=0.06618, over 1424804.48 frames.], batch size: 24, lr: 5.31e-04
2022-05-27 08:23:05,406 INFO [train.py:842] (1/4) Epoch 10, batch 5600, loss[loss=0.2479, simple_loss=0.3298, pruned_loss=0.08301, over 7142.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2925, pruned_loss=0.06601, over 1422691.20 frames.], batch size: 20, lr: 5.31e-04
2022-05-27 08:23:44,290 INFO [train.py:842] (1/4) Epoch 10, batch 5650, loss[loss=0.169, simple_loss=0.2557, pruned_loss=0.04117, over 7060.00 frames.], tot_loss[loss=0.2115, simple_loss=0.2914, pruned_loss=0.06582, over 1428593.76 frames.], batch size: 18, lr: 5.31e-04
2022-05-27 08:24:23,129 INFO [train.py:842] (1/4) Epoch 10, batch 5700, loss[loss=0.2014, simple_loss=0.2725, pruned_loss=0.06516, over 7061.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2921, pruned_loss=0.06621, over 1430552.88 frames.], batch size: 18, lr: 5.31e-04
2022-05-27 08:25:01,724 INFO [train.py:842] (1/4) Epoch 10, batch 5750, loss[loss=0.1798, simple_loss=0.268, pruned_loss=0.04577, over 6658.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2927, pruned_loss=0.06633, over 1424906.56 frames.], batch size: 31, lr: 5.31e-04
2022-05-27 08:25:40,659 INFO [train.py:842] (1/4) Epoch 10, batch 5800, loss[loss=0.2027, simple_loss=0.2683, pruned_loss=0.06859, over 7283.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2921, pruned_loss=0.06635, over 1425963.71 frames.], batch size: 17, lr: 5.31e-04
2022-05-27 08:26:19,922 INFO [train.py:842] (1/4) Epoch 10, batch 5850, loss[loss=0.2524, simple_loss=0.3298, pruned_loss=0.08747, over 7331.00 frames.], tot_loss[loss=0.213, simple_loss=0.2924, pruned_loss=0.06674, over 1427003.89 frames.], batch size: 22, lr: 5.30e-04
2022-05-27 08:26:58,600 INFO [train.py:842] (1/4) Epoch 10, batch 5900, loss[loss=0.3276, simple_loss=0.3751, pruned_loss=0.14, over 7229.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2933, pruned_loss=0.0675, over 1425243.19 frames.], batch size: 23, lr: 5.30e-04
2022-05-27 08:27:37,133 INFO [train.py:842] (1/4) Epoch 10, batch 5950, loss[loss=0.1625, simple_loss=0.2375, pruned_loss=0.04371, over 7267.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2932, pruned_loss=0.06747, over 1421380.00 frames.], batch size: 18, lr: 5.30e-04
2022-05-27 08:28:15,989 INFO [train.py:842] (1/4) Epoch 10, batch 6000, loss[loss=0.2086, simple_loss=0.2929, pruned_loss=0.06215, over 7146.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2942, pruned_loss=0.06768, over 1423900.45 frames.], batch size: 20, lr: 5.30e-04
2022-05-27 08:28:15,990 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 08:28:25,356 INFO [train.py:871] (1/4) Epoch 10, validation: loss=0.173, simple_loss=0.2732, pruned_loss=0.0364, over 868885.00 frames.
2022-05-27 08:29:04,361 INFO [train.py:842] (1/4) Epoch 10, batch 6050, loss[loss=0.1721, simple_loss=0.2495, pruned_loss=0.04732, over 7429.00 frames.], tot_loss[loss=0.214, simple_loss=0.2935, pruned_loss=0.06725, over 1425845.52 frames.], batch size: 20, lr: 5.30e-04
2022-05-27 08:29:43,211 INFO [train.py:842] (1/4) Epoch 10, batch 6100, loss[loss=0.2747, simple_loss=0.3529, pruned_loss=0.09824, over 7370.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2933, pruned_loss=0.06681, over 1424161.68 frames.], batch size: 23, lr: 5.30e-04
2022-05-27 08:30:22,056 INFO [train.py:842] (1/4) Epoch 10, batch 6150, loss[loss=0.1505, simple_loss=0.2341, pruned_loss=0.03346, over 6792.00 frames.], tot_loss[loss=0.214, simple_loss=0.2934, pruned_loss=0.06728, over 1423394.29 frames.], batch size: 15, lr: 5.30e-04
2022-05-27 08:31:00,966 INFO [train.py:842] (1/4) Epoch 10, batch 6200, loss[loss=0.2702, simple_loss=0.3338, pruned_loss=0.1033, over 7286.00 frames.], tot_loss[loss=0.213, simple_loss=0.2924, pruned_loss=0.06676, over 1427958.38 frames.], batch size: 24, lr: 5.29e-04
2022-05-27 08:31:39,421 INFO [train.py:842] (1/4) Epoch 10, batch 6250, loss[loss=0.2202, simple_loss=0.3053, pruned_loss=0.06758, over 7159.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2925, pruned_loss=0.06661, over 1418910.27 frames.], batch size: 19, lr: 5.29e-04
2022-05-27 08:32:18,066 INFO [train.py:842] (1/4) Epoch 10, batch 6300, loss[loss=0.1986, simple_loss=0.2918, pruned_loss=0.05267, over 7147.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2927, pruned_loss=0.06591, over 1422941.40 frames.], batch size: 20, lr: 5.29e-04
2022-05-27 08:32:56,578 INFO [train.py:842] (1/4) Epoch 10, batch 6350, loss[loss=0.2248, simple_loss=0.2995, pruned_loss=0.07506, over 7155.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2916, pruned_loss=0.06556, over 1422151.50 frames.], batch size: 20, lr: 5.29e-04
2022-05-27 08:33:35,412 INFO [train.py:842] (1/4) Epoch 10, batch 6400, loss[loss=0.2059, simple_loss=0.2904, pruned_loss=0.06072, over 7413.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2906, pruned_loss=0.06487, over 1423746.18 frames.], batch size: 21, lr: 5.29e-04
2022-05-27 08:34:14,100 INFO [train.py:842] (1/4) Epoch 10, batch 6450, loss[loss=0.1962, simple_loss=0.2571, pruned_loss=0.06769, over 7277.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2908, pruned_loss=0.06551, over 1422356.53 frames.], batch size: 17, lr: 5.29e-04
2022-05-27 08:34:52,934 INFO [train.py:842] (1/4) Epoch 10, batch 6500, loss[loss=0.1966, simple_loss=0.2809, pruned_loss=0.05611, over 6797.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2901, pruned_loss=0.06509, over 1420661.86 frames.], batch size: 31, lr: 5.28e-04
2022-05-27 08:35:31,468 INFO [train.py:842] (1/4) Epoch 10, batch 6550, loss[loss=0.3092, simple_loss=0.3765, pruned_loss=0.121, over 7282.00 frames.], tot_loss[loss=0.211, simple_loss=0.2908, pruned_loss=0.06559, over 1422609.87 frames.], batch size: 24, lr: 5.28e-04
2022-05-27 08:36:10,163 INFO [train.py:842] (1/4) Epoch 10, batch 6600, loss[loss=0.2571, simple_loss=0.3397, pruned_loss=0.08725, over 7353.00 frames.], tot_loss[loss=0.213, simple_loss=0.2931, pruned_loss=0.06648, over 1424876.93 frames.], batch size: 25, lr: 5.28e-04
2022-05-27 08:36:48,768 INFO [train.py:842] (1/4) Epoch 10, batch 6650, loss[loss=0.1752, simple_loss=0.2623, pruned_loss=0.04403, over 7413.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2932, pruned_loss=0.06646, over 1426454.67 frames.], batch size: 18, lr: 5.28e-04
2022-05-27 08:37:27,503 INFO [train.py:842] (1/4) Epoch 10, batch 6700, loss[loss=0.2227, simple_loss=0.2979, pruned_loss=0.07371, over 7066.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2947, pruned_loss=0.06742, over 1422779.60 frames.], batch size: 18, lr: 5.28e-04
2022-05-27 08:38:06,134 INFO [train.py:842] (1/4) Epoch 10, batch 6750, loss[loss=0.1934, simple_loss=0.2679, pruned_loss=0.05949, over 7404.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2955, pruned_loss=0.0675, over 1425821.93 frames.], batch size: 18, lr: 5.28e-04
2022-05-27 08:38:45,064 INFO [train.py:842] (1/4) Epoch 10, batch 6800, loss[loss=0.2061, simple_loss=0.293, pruned_loss=0.05963, over 7087.00 frames.], tot_loss[loss=0.2145, simple_loss=0.2939, pruned_loss=0.06756, over 1423375.47 frames.], batch size: 28, lr: 5.28e-04
2022-05-27 08:39:23,812 INFO [train.py:842] (1/4) Epoch 10, batch 6850, loss[loss=0.2019, simple_loss=0.2839, pruned_loss=0.05994, over 6782.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2923, pruned_loss=0.06635, over 1427988.26 frames.], batch size: 31, lr: 5.27e-04
2022-05-27 08:40:02,433 INFO [train.py:842] (1/4) Epoch 10, batch 6900, loss[loss=0.2632, simple_loss=0.3234, pruned_loss=0.1015, over 7194.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2942, pruned_loss=0.06716, over 1424401.51 frames.], batch size: 23, lr: 5.27e-04
2022-05-27 08:40:40,975 INFO [train.py:842] (1/4) Epoch 10, batch 6950, loss[loss=0.2272, simple_loss=0.3037, pruned_loss=0.07533, over 7362.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2939, pruned_loss=0.06687, over 1420345.18 frames.], batch size: 23, lr: 5.27e-04
2022-05-27 08:41:19,723 INFO [train.py:842] (1/4) Epoch 10, batch 7000, loss[loss=0.1926, simple_loss=0.2881, pruned_loss=0.04851, over 7148.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2928, pruned_loss=0.06651, over 1422423.98 frames.], batch size: 20, lr: 5.27e-04
2022-05-27 08:41:58,249 INFO [train.py:842] (1/4) Epoch 10, batch 7050, loss[loss=0.2219, simple_loss=0.3065, pruned_loss=0.06868, over 7238.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2927, pruned_loss=0.06645, over 1420670.19 frames.], batch size: 20, lr: 5.27e-04
2022-05-27 08:42:37,042 INFO [train.py:842] (1/4) Epoch 10, batch 7100, loss[loss=0.2167, simple_loss=0.2993, pruned_loss=0.06706, over 7326.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2925, pruned_loss=0.06623, over 1422189.91 frames.], batch size: 22, lr: 5.27e-04
2022-05-27 08:43:15,635 INFO [train.py:842] (1/4) Epoch 10, batch 7150, loss[loss=0.2, simple_loss=0.2803, pruned_loss=0.05982, over 7374.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2905, pruned_loss=0.06513, over 1424119.31 frames.], batch size: 23, lr: 5.27e-04
2022-05-27 08:43:54,142 INFO [train.py:842] (1/4) Epoch 10, batch 7200, loss[loss=0.3351, simple_loss=0.3837, pruned_loss=0.1433, over 5115.00 frames.], tot_loss[loss=0.212, simple_loss=0.2921, pruned_loss=0.06592, over 1417390.15 frames.], batch size: 53, lr: 5.26e-04
2022-05-27 08:44:32,606 INFO [train.py:842] (1/4) Epoch 10, batch 7250, loss[loss=0.2683, simple_loss=0.3297, pruned_loss=0.1035, over 7313.00 frames.], tot_loss[loss=0.213, simple_loss=0.2935, pruned_loss=0.06628, over 1412588.50 frames.], batch size: 25, lr: 5.26e-04
2022-05-27 08:45:11,719 INFO [train.py:842] (1/4) Epoch 10, batch 7300, loss[loss=0.1725, simple_loss=0.2662, pruned_loss=0.03937, over 7427.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2941, pruned_loss=0.06668, over 1416824.00 frames.], batch size: 20, lr: 5.26e-04
2022-05-27 08:45:50,391 INFO [train.py:842] (1/4) Epoch 10, batch 7350, loss[loss=0.1863, simple_loss=0.2679, pruned_loss=0.05235, over 7133.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2933, pruned_loss=0.06617, over 1420868.86 frames.], batch size: 17, lr: 5.26e-04
2022-05-27 08:46:29,224 INFO [train.py:842] (1/4) Epoch 10, batch 7400, loss[loss=0.2524, simple_loss=0.3291, pruned_loss=0.08782, over 7426.00 frames.], tot_loss[loss=0.213, simple_loss=0.2932, pruned_loss=0.06643, over 1421646.34 frames.], batch size: 21, lr: 5.26e-04
2022-05-27 08:47:07,760 INFO [train.py:842] (1/4) Epoch 10, batch 7450, loss[loss=0.1574, simple_loss=0.2282, pruned_loss=0.04333, over 7242.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2928, pruned_loss=0.06603, over 1418454.52 frames.], batch size: 16, lr: 5.26e-04
2022-05-27 08:47:46,317 INFO [train.py:842] (1/4) Epoch 10, batch 7500, loss[loss=0.2139, simple_loss=0.2964, pruned_loss=0.06573, over 7223.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2925, pruned_loss=0.06596, over 1419036.16 frames.], batch size: 21, lr: 5.26e-04
2022-05-27 08:48:24,794 INFO [train.py:842] (1/4) Epoch 10, batch 7550, loss[loss=0.1808, simple_loss=0.2766, pruned_loss=0.04254, over 7145.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2925, pruned_loss=0.06611, over 1421103.16 frames.], batch size: 20, lr: 5.25e-04
2022-05-27 08:49:03,937 INFO [train.py:842] (1/4) Epoch 10, batch 7600, loss[loss=0.1655, simple_loss=0.2494, pruned_loss=0.04075, over 7272.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2911, pruned_loss=0.06526, over 1422582.82 frames.], batch size: 18, lr: 5.25e-04
2022-05-27 08:49:42,450 INFO [train.py:842] (1/4) Epoch 10, batch 7650, loss[loss=0.2035, simple_loss=0.2809, pruned_loss=0.06303, over 7018.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2908, pruned_loss=0.0652, over 1421974.65 frames.], batch size: 16, lr: 5.25e-04
2022-05-27 08:50:21,193 INFO [train.py:842] (1/4) Epoch 10, batch 7700, loss[loss=0.1926, simple_loss=0.2887, pruned_loss=0.04829, over 7343.00 frames.], tot_loss[loss=0.2106, simple_loss=0.291, pruned_loss=0.06504, over 1420141.17 frames.], batch size: 22, lr: 5.25e-04
2022-05-27 08:50:59,820 INFO [train.py:842] (1/4) Epoch 10, batch 7750, loss[loss=0.1845, simple_loss=0.2775, pruned_loss=0.04573, over 6776.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2912, pruned_loss=0.06516, over 1424150.07 frames.], batch size: 31, lr: 5.25e-04
2022-05-27 08:51:38,620 INFO [train.py:842] (1/4) Epoch 10, batch 7800, loss[loss=0.211, simple_loss=0.2915, pruned_loss=0.06523, over 7170.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2917, pruned_loss=0.06584, over 1426809.48 frames.], batch size: 18, lr: 5.25e-04
2022-05-27 08:52:17,197 INFO [train.py:842] (1/4) Epoch 10, batch 7850, loss[loss=0.1984, simple_loss=0.2858, pruned_loss=0.05544, over 7329.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2901, pruned_loss=0.06486, over 1422384.42 frames.], batch size: 21, lr: 5.25e-04
2022-05-27 08:52:56,097 INFO [train.py:842] (1/4) Epoch 10, batch 7900, loss[loss=0.1673, simple_loss=0.2578, pruned_loss=0.03839, over 7159.00 frames.], tot_loss[loss=0.2113, simple_loss=0.291, pruned_loss=0.06585, over 1424258.90 frames.], batch size: 19, lr: 5.24e-04
2022-05-27 08:53:34,561 INFO [train.py:842] (1/4) Epoch 10, batch 7950, loss[loss=0.1847, simple_loss=0.2586, pruned_loss=0.05533, over 7277.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2923, pruned_loss=0.06622, over 1425709.84 frames.], batch size: 18, lr: 5.24e-04
2022-05-27 08:54:13,262 INFO [train.py:842] (1/4) Epoch 10, batch 8000, loss[loss=0.168, simple_loss=0.2579, pruned_loss=0.03904, over 7075.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2929, pruned_loss=0.06674, over 1425766.49 frames.], batch size: 18, lr: 5.24e-04
2022-05-27 08:54:51,834 INFO [train.py:842] (1/4) Epoch 10, batch 8050, loss[loss=0.2859, simple_loss=0.3516, pruned_loss=0.1101, over 5452.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2945, pruned_loss=0.0683, over 1421896.44 frames.], batch size: 53, lr: 5.24e-04
2022-05-27 08:55:30,391 INFO [train.py:842] (1/4) Epoch 10, batch 8100, loss[loss=0.2134, simple_loss=0.2908, pruned_loss=0.06804, over 7278.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2934, pruned_loss=0.06756, over 1418233.33 frames.], batch size: 17, lr: 5.24e-04
2022-05-27 08:56:08,974 INFO [train.py:842] (1/4) Epoch 10, batch 8150, loss[loss=0.2261, simple_loss=0.2956, pruned_loss=0.07833, over 7283.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2931, pruned_loss=0.06734, over 1419547.56 frames.], batch size: 25, lr: 5.24e-04
2022-05-27 08:56:47,759 INFO [train.py:842] (1/4) Epoch 10, batch 8200, loss[loss=0.217, simple_loss=0.3084, pruned_loss=0.06276, over 6814.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2916, pruned_loss=0.06659, over 1419377.86 frames.], batch size: 31, lr: 5.24e-04
2022-05-27 08:57:26,300 INFO [train.py:842] (1/4) Epoch 10, batch 8250, loss[loss=0.2162, simple_loss=0.2874, pruned_loss=0.07248, over 7354.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2922, pruned_loss=0.06621, over 1419124.84 frames.], batch size: 19, lr: 5.23e-04
2022-05-27 08:58:04,882 INFO [train.py:842] (1/4) Epoch 10, batch 8300, loss[loss=0.2129, simple_loss=0.2811, pruned_loss=0.07235, over 7147.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2922, pruned_loss=0.06624, over 1414840.24 frames.], batch size: 17, lr: 5.23e-04
2022-05-27 08:58:43,218 INFO [train.py:842] (1/4) Epoch 10, batch 8350, loss[loss=0.2722, simple_loss=0.3318, pruned_loss=0.1063, over 5106.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2919, pruned_loss=0.06546, over 1416909.92 frames.], batch size: 53, lr: 5.23e-04
2022-05-27 08:59:21,882 INFO [train.py:842] (1/4) Epoch 10, batch 8400, loss[loss=0.1842, simple_loss=0.2842, pruned_loss=0.04204, over 7317.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2922, pruned_loss=0.0658, over 1415752.91 frames.], batch size: 24, lr: 5.23e-04
2022-05-27 09:00:00,367 INFO [train.py:842] (1/4) Epoch 10, batch 8450, loss[loss=0.2033, simple_loss=0.2927, pruned_loss=0.05694, over 6756.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2925, pruned_loss=0.06567, over 1415611.11 frames.], batch size: 31, lr: 5.23e-04
2022-05-27 09:00:39,094 INFO [train.py:842] (1/4) Epoch 10, batch 8500, loss[loss=0.1832, simple_loss=0.2632, pruned_loss=0.05161, over 7170.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2934, pruned_loss=0.06677, over 1415973.97 frames.], batch size: 18, lr: 5.23e-04
2022-05-27 09:01:17,519 INFO [train.py:842] (1/4) Epoch 10, batch 8550, loss[loss=0.278, simple_loss=0.3478, pruned_loss=0.1042, over 7294.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2928, pruned_loss=0.06665, over 1414577.08 frames.], batch size: 24, lr: 5.23e-04
2022-05-27 09:01:56,480 INFO [train.py:842] (1/4) Epoch 10, batch 8600, loss[loss=0.1963, simple_loss=0.2875, pruned_loss=0.05258, over 7103.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2933, pruned_loss=0.06738, over 1417917.60 frames.], batch size: 21, lr: 5.22e-04
2022-05-27 09:02:35,162 INFO [train.py:842] (1/4) Epoch 10, batch 8650, loss[loss=0.1738, simple_loss=0.2462, pruned_loss=0.05066, over 7151.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2922, pruned_loss=0.06612, over 1423163.82 frames.], batch size: 17, lr: 5.22e-04
2022-05-27 09:03:14,154 INFO [train.py:842] (1/4) Epoch 10, batch 8700, loss[loss=0.1884, simple_loss=0.2672, pruned_loss=0.05481, over 7165.00 frames.], tot_loss[loss=0.211, simple_loss=0.2913, pruned_loss=0.06531, over 1420263.14 frames.], batch size: 18, lr: 5.22e-04
2022-05-27 09:03:52,574 INFO [train.py:842] (1/4) Epoch 10, batch 8750, loss[loss=0.2567, simple_loss=0.3302, pruned_loss=0.09165, over 7186.00 frames.], tot_loss[loss=0.2115, simple_loss=0.2915, pruned_loss=0.0658, over 1415802.93 frames.], batch size: 23, lr: 5.22e-04
2022-05-27 09:04:31,535 INFO [train.py:842] (1/4) Epoch 10, batch 8800, loss[loss=0.2242, simple_loss=0.3155, pruned_loss=0.06645, over 7187.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2899, pruned_loss=0.06458, over 1416629.02 frames.], batch size: 23, lr: 5.22e-04
2022-05-27 09:05:10,727 INFO [train.py:842] (1/4) Epoch 10, batch 8850, loss[loss=0.2216, simple_loss=0.2998, pruned_loss=0.07172, over 7071.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2882, pruned_loss=0.06427, over 1420929.15 frames.], batch size: 28, lr: 5.22e-04
2022-05-27 09:05:49,802 INFO [train.py:842] (1/4) Epoch 10, batch 8900, loss[loss=0.2161, simple_loss=0.2755, pruned_loss=0.07833, over 7125.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2878, pruned_loss=0.06369, over 1421688.29 frames.], batch size: 17, lr: 5.22e-04
2022-05-27 09:06:28,603 INFO [train.py:842] (1/4) Epoch 10, batch 8950, loss[loss=0.1767, simple_loss=0.2487, pruned_loss=0.05239, over 7132.00 frames.], tot_loss[loss=0.208, simple_loss=0.2878, pruned_loss=0.06411, over 1423045.47 frames.], batch size: 17, lr: 5.21e-04
2022-05-27 09:07:07,859 INFO [train.py:842] (1/4) Epoch 10, batch 9000, loss[loss=0.174, simple_loss=0.2529, pruned_loss=0.04761, over 7259.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2879, pruned_loss=0.0643, over 1419896.61 frames.], batch size: 16, lr: 5.21e-04
2022-05-27 09:07:07,860 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 09:07:17,179 INFO [train.py:871] (1/4) Epoch 10, validation: loss=0.1756, simple_loss=0.2759, pruned_loss=0.03767, over 868885.00 frames.
2022-05-27 09:07:56,082 INFO [train.py:842] (1/4) Epoch 10, batch 9050, loss[loss=0.1967, simple_loss=0.2811, pruned_loss=0.05617, over 6338.00 frames.], tot_loss[loss=0.209, simple_loss=0.2883, pruned_loss=0.06484, over 1405427.28 frames.], batch size: 37, lr: 5.21e-04
2022-05-27 09:08:34,933 INFO [train.py:842] (1/4) Epoch 10, batch 9100, loss[loss=0.2108, simple_loss=0.2794, pruned_loss=0.07106, over 7113.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2909, pruned_loss=0.06724, over 1363722.46 frames.], batch size: 17, lr: 5.21e-04
2022-05-27 09:09:12,741 INFO [train.py:842] (1/4) Epoch 10, batch 9150, loss[loss=0.2386, simple_loss=0.3112, pruned_loss=0.08297, over 5160.00 frames.], tot_loss[loss=0.2192, simple_loss=0.2955, pruned_loss=0.07144, over 1292042.68 frames.], batch size: 53, lr: 5.21e-04
2022-05-27 09:10:05,865 INFO [train.py:842] (1/4) Epoch 11, batch 0, loss[loss=0.2694, simple_loss=0.3449, pruned_loss=0.09691, over 7437.00 frames.], tot_loss[loss=0.2694, simple_loss=0.3449, pruned_loss=0.09691, over 7437.00 frames.], batch size: 20, lr: 5.01e-04
2022-05-27 09:10:44,597 INFO [train.py:842] (1/4) Epoch 11, batch 50, loss[loss=0.1938, simple_loss=0.2756, pruned_loss=0.05596, over 7415.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2966, pruned_loss=0.06779, over 322445.86 frames.], batch size: 20, lr: 5.01e-04
2022-05-27 09:11:23,554 INFO [train.py:842] (1/4) Epoch 11, batch 100, loss[loss=0.2356, simple_loss=0.3049, pruned_loss=0.08319, over 7283.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2935, pruned_loss=0.06535, over 566385.68 frames.], batch size: 18, lr: 5.01e-04
2022-05-27 09:12:02,325 INFO [train.py:842] (1/4) Epoch 11, batch 150, loss[loss=0.202, simple_loss=0.2737, pruned_loss=0.06521, over 6794.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2934, pruned_loss=0.06542, over 759662.31 frames.], batch size: 15, lr: 5.01e-04
2022-05-27 09:12:41,233 INFO [train.py:842] (1/4) Epoch 11, batch 200, loss[loss=0.1837, simple_loss=0.2525, pruned_loss=0.0575, over 7410.00 frames.], tot_loss[loss=0.2113, simple_loss=0.292, pruned_loss=0.06533, over 907001.76 frames.], batch size: 18, lr: 5.01e-04
2022-05-27 09:13:19,992 INFO [train.py:842] (1/4) Epoch 11, batch 250, loss[loss=0.2533, simple_loss=0.3291, pruned_loss=0.08874, over 6303.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2899, pruned_loss=0.06385, over 1022642.41 frames.], batch size: 37, lr: 5.01e-04
2022-05-27 09:13:59,432 INFO [train.py:842] (1/4) Epoch 11, batch 300, loss[loss=0.2027, simple_loss=0.2791, pruned_loss=0.06314, over 5193.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2883, pruned_loss=0.06326, over 1113884.94 frames.], batch size: 52, lr: 5.01e-04
2022-05-27 09:14:38,185 INFO [train.py:842] (1/4) Epoch 11, batch 350, loss[loss=0.2533, simple_loss=0.3324, pruned_loss=0.08713, over 6837.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2894, pruned_loss=0.06358, over 1187366.02 frames.], batch size: 31, lr: 5.01e-04
2022-05-27 09:15:17,126 INFO [train.py:842] (1/4) Epoch 11, batch 400, loss[loss=0.1841, simple_loss=0.2703, pruned_loss=0.049, over 7423.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2884, pruned_loss=0.06272, over 1241697.97 frames.], batch size: 20, lr: 5.00e-04
2022-05-27 09:15:56,240 INFO [train.py:842] (1/4) Epoch 11, batch 450, loss[loss=0.2203, simple_loss=0.2948, pruned_loss=0.07292, over 7234.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2869, pruned_loss=0.06278, over 1281900.21 frames.], batch size: 20, lr: 5.00e-04
2022-05-27 09:16:35,462 INFO [train.py:842] (1/4) Epoch 11, batch 500, loss[loss=0.1736, simple_loss=0.2649, pruned_loss=0.04115, over 7337.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2873, pruned_loss=0.06274, over 1317113.62 frames.], batch size: 20, lr: 5.00e-04
2022-05-27 09:17:14,285 INFO [train.py:842] (1/4) Epoch 11, batch 550, loss[loss=0.1825, simple_loss=0.2657, pruned_loss=0.04965, over 7070.00 frames.], tot_loss[loss=0.2079, simple_loss=0.289, pruned_loss=0.06342, over 1342310.09 frames.], batch size: 18, lr: 5.00e-04
2022-05-27 09:17:53,336 INFO [train.py:842] (1/4) Epoch 11, batch 600, loss[loss=0.1986, simple_loss=0.2657, pruned_loss=0.06569, over 6995.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2892, pruned_loss=0.06388, over 1360952.84 frames.], batch size: 16, lr: 5.00e-04
2022-05-27 09:18:32,207 INFO [train.py:842] (1/4) Epoch 11, batch 650, loss[loss=0.1726, simple_loss=0.2476, pruned_loss=0.04877, over 7112.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2896, pruned_loss=0.06467, over 1365640.93 frames.], batch size: 17, lr: 5.00e-04
2022-05-27 09:19:11,363 INFO [train.py:842] (1/4) Epoch 11, batch 700, loss[loss=0.2106, simple_loss=0.272, pruned_loss=0.0746, over 7203.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2903, pruned_loss=0.06491, over 1376093.66 frames.], batch size: 16, lr: 5.00e-04
2022-05-27 09:19:50,249 INFO [train.py:842] (1/4) Epoch 11, batch 750, loss[loss=0.2647, simple_loss=0.3393, pruned_loss=0.09505, over 7136.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2891, pruned_loss=0.06454, over 1383519.07 frames.], batch size: 20, lr: 4.99e-04
2022-05-27 09:20:29,322 INFO [train.py:842] (1/4) Epoch 11, batch 800, loss[loss=0.2508, simple_loss=0.3369, pruned_loss=0.08238, over 7134.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2895, pruned_loss=0.06491, over 1395111.16 frames.], batch size: 26, lr: 4.99e-04
2022-05-27 09:21:08,047 INFO [train.py:842] (1/4) Epoch 11, batch 850, loss[loss=0.2164, simple_loss=0.3052, pruned_loss=0.0638, over 7330.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2882, pruned_loss=0.06401, over 1399215.84 frames.], batch size: 20, lr: 4.99e-04
2022-05-27 09:21:46,917 INFO [train.py:842] (1/4) Epoch 11, batch 900, loss[loss=0.2183, simple_loss=0.3097, pruned_loss=0.06348, over 7420.00 frames.], tot_loss[loss=0.208, simple_loss=0.2885, pruned_loss=0.06377, over 1408003.80 frames.], batch size: 20, lr: 4.99e-04
2022-05-27 09:22:25,671 INFO [train.py:842] (1/4) Epoch 11, batch 950, loss[loss=0.2208, simple_loss=0.2902, pruned_loss=0.0757, over 7005.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2901, pruned_loss=0.06483, over 1410040.60 frames.], batch size: 16, lr: 4.99e-04
2022-05-27 09:23:05,077 INFO [train.py:842] (1/4) Epoch 11, batch 1000, loss[loss=0.236, simple_loss=0.3078, pruned_loss=0.08214, over 7286.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2898, pruned_loss=0.06443, over 1414724.33 frames.], batch size: 25, lr: 4.99e-04
2022-05-27 09:23:43,646 INFO [train.py:842] (1/4) Epoch 11, batch 1050, loss[loss=0.2194, simple_loss=0.3021, pruned_loss=0.06836, over 7261.00 frames.], tot_loss[loss=0.2112, simple_loss=0.2917, pruned_loss=0.06531, over 1410169.89 frames.], batch size: 19, lr: 4.99e-04
2022-05-27 09:24:22,806 INFO [train.py:842] (1/4) Epoch 11, batch 1100, loss[loss=0.2004, simple_loss=0.2628, pruned_loss=0.069, over 7170.00 frames.], tot_loss[loss=0.2091, simple_loss=0.29, pruned_loss=0.06412, over 1414636.18 frames.], batch size: 18, lr: 4.99e-04
2022-05-27 09:25:01,966 INFO [train.py:842] (1/4) Epoch 11, batch 1150, loss[loss=0.1903, simple_loss=0.2781, pruned_loss=0.05128, over 7060.00 frames.], tot_loss[loss=0.208, simple_loss=0.2888, pruned_loss=0.06353, over 1418638.76 frames.], batch size: 18, lr: 4.98e-04
2022-05-27 09:25:40,993 INFO [train.py:842] (1/4) Epoch 11, batch 1200, loss[loss=0.203, simple_loss=0.2711, pruned_loss=0.06749, over 7221.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2869, pruned_loss=0.06283, over 1421099.33 frames.], batch size: 16, lr: 4.98e-04
2022-05-27 09:26:19,777 INFO [train.py:842] (1/4) Epoch 11, batch 1250, loss[loss=0.1682, simple_loss=0.2452, pruned_loss=0.0456, over 7126.00 frames.], tot_loss[loss=0.206, simple_loss=0.2868, pruned_loss=0.06257, over 1424693.08 frames.], batch size: 17, lr: 4.98e-04
2022-05-27 09:26:58,843 INFO [train.py:842] (1/4) Epoch 11, batch 1300, loss[loss=0.229, simple_loss=0.3028, pruned_loss=0.07767, over 7320.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2867, pruned_loss=0.06253, over 1420594.57 frames.], batch size: 21, lr: 4.98e-04
2022-05-27 09:27:37,519 INFO [train.py:842] (1/4) Epoch 11, batch 1350, loss[loss=0.1973, simple_loss=0.2831, pruned_loss=0.05572, over 7321.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2868, pruned_loss=0.06278, over 1424008.84 frames.], batch size: 21, lr: 4.98e-04
2022-05-27 09:28:16,456 INFO [train.py:842] (1/4) Epoch 11, batch 1400, loss[loss=0.1906, simple_loss=0.2688, pruned_loss=0.05622, over 7165.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2868, pruned_loss=0.06286, over 1427886.97 frames.], batch size: 19, lr: 4.98e-04
2022-05-27 09:28:55,138 INFO [train.py:842] (1/4) Epoch 11, batch 1450, loss[loss=0.1619, simple_loss=0.2384, pruned_loss=0.04267, over 7275.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2884, pruned_loss=0.06408, over 1428444.12 frames.], batch size: 17, lr: 4.98e-04
2022-05-27 09:29:34,193 INFO [train.py:842] (1/4) Epoch 11, batch 1500, loss[loss=0.2228, simple_loss=0.3007, pruned_loss=0.07247, over 7040.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2881, pruned_loss=0.06333, over 1426285.41 frames.], batch size: 28, lr: 4.97e-04
2022-05-27 09:30:12,898 INFO [train.py:842] (1/4) Epoch 11, batch 1550, loss[loss=0.1936, simple_loss=0.275, pruned_loss=0.05612, over 7426.00 frames.], tot_loss[loss=0.2084, simple_loss=0.289, pruned_loss=0.06391, over 1425206.21 frames.], batch size: 20, lr: 4.97e-04
2022-05-27 09:30:51,668 INFO [train.py:842] (1/4) Epoch 11, batch 1600, loss[loss=0.2693, simple_loss=0.3465, pruned_loss=0.09607, over 6883.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2891, pruned_loss=0.06383, over 1420396.09 frames.], batch size: 32, lr: 4.97e-04
2022-05-27 09:31:30,332 INFO [train.py:842] (1/4) Epoch 11, batch 1650, loss[loss=0.2173, simple_loss=0.2774, pruned_loss=0.07856, over 7186.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2889, pruned_loss=0.0638, over 1420108.14 frames.], batch size: 16, lr: 4.97e-04
2022-05-27 09:32:09,188 INFO [train.py:842] (1/4) Epoch 11, batch 1700, loss[loss=0.1312, simple_loss=0.2063, pruned_loss=0.02807, over 6788.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2893, pruned_loss=0.06415, over 1418842.38 frames.], batch size: 15, lr: 4.97e-04
2022-05-27 09:32:47,719 INFO [train.py:842] (1/4) Epoch 11, batch 1750, loss[loss=0.1924, simple_loss=0.2766, pruned_loss=0.05408, over 7109.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2875, pruned_loss=0.06341, over 1415137.87 frames.], batch size: 21, lr: 4.97e-04
2022-05-27 09:33:26,554 INFO [train.py:842] (1/4) Epoch 11, batch 1800, loss[loss=0.2472, simple_loss=0.3275, pruned_loss=0.08343, over 5085.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2881, pruned_loss=0.06342, over 1414664.31 frames.], batch size: 52, lr: 4.97e-04
2022-05-27 09:34:05,162 INFO [train.py:842] (1/4) Epoch 11, batch 1850, loss[loss=0.255, simple_loss=0.3282, pruned_loss=0.09092, over 6418.00 frames.], tot_loss[loss=0.207, simple_loss=0.2879, pruned_loss=0.06303, over 1418090.39 frames.], batch size: 37, lr: 4.97e-04
2022-05-27 09:34:44,019 INFO [train.py:842] (1/4) Epoch 11, batch 1900, loss[loss=0.1726, simple_loss=0.2674, pruned_loss=0.0389, over 7307.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2889, pruned_loss=0.06418, over 1422851.44 frames.], batch size: 21, lr: 4.96e-04
2022-05-27 09:35:22,633 INFO [train.py:842] (1/4) Epoch 11, batch 1950, loss[loss=0.2364, simple_loss=0.3146, pruned_loss=0.07912, over 7363.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2888, pruned_loss=0.06412, over 1421928.23 frames.], batch size: 19, lr: 4.96e-04
2022-05-27 09:36:01,473 INFO [train.py:842] (1/4) Epoch 11, batch 2000, loss[loss=0.1745, simple_loss=0.2551, pruned_loss=0.04695, over 7162.00 frames.], tot_loss[loss=0.208, simple_loss=0.2884, pruned_loss=0.06378, over 1423765.85 frames.], batch size: 18, lr: 4.96e-04
2022-05-27 09:36:40,061 INFO [train.py:842] (1/4) Epoch 11, batch 2050, loss[loss=0.1703, simple_loss=0.2451, pruned_loss=0.04778, over 7287.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2886, pruned_loss=0.06411, over 1425273.12 frames.], batch size: 17, lr: 4.96e-04
2022-05-27 09:37:19,135 INFO [train.py:842] (1/4) Epoch 11, batch 2100, loss[loss=0.2243, simple_loss=0.2986, pruned_loss=0.07506, over 7374.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2876, pruned_loss=0.06313, over 1426212.43 frames.], batch size: 23, lr: 4.96e-04
2022-05-27 09:37:57,714 INFO [train.py:842] (1/4) Epoch 11, batch 2150, loss[loss=0.1866, simple_loss=0.2633, pruned_loss=0.05496, over 7168.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2871, pruned_loss=0.06267, over 1426569.70 frames.], batch size: 18, lr: 4.96e-04
2022-05-27 09:38:36,761 INFO [train.py:842] (1/4) Epoch 11, batch 2200, loss[loss=0.2133, simple_loss=0.2995, pruned_loss=0.06352, over 7224.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2867, pruned_loss=0.06227, over 1424540.51 frames.], batch size: 20, lr: 4.96e-04
2022-05-27 09:39:15,379 INFO [train.py:842] (1/4) Epoch 11, batch 2250, loss[loss=0.223, simple_loss=0.3016, pruned_loss=0.07225, over 7350.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2879, pruned_loss=0.06284, over 1427653.69 frames.], batch size: 22, lr: 4.95e-04
2022-05-27 09:39:54,342 INFO [train.py:842] (1/4) Epoch 11, batch 2300, loss[loss=0.1947, simple_loss=0.3015, pruned_loss=0.04395, over 7198.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2889, pruned_loss=0.06363, over 1427414.77 frames.], batch size: 26, lr: 4.95e-04
2022-05-27 09:40:33,041 INFO [train.py:842] (1/4) Epoch 11, batch 2350, loss[loss=0.2684, simple_loss=0.3403, pruned_loss=0.09821, over 6824.00 frames.], tot_loss[loss=0.207, simple_loss=0.2882, pruned_loss=0.06291, over 1429637.88 frames.], batch size: 31, lr: 4.95e-04
2022-05-27 09:41:11,942 INFO [train.py:842] (1/4) Epoch 11, batch 2400, loss[loss=0.2074, simple_loss=0.2856, pruned_loss=0.0646, over 7321.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2892, pruned_loss=0.06351, over 1423606.22 frames.], batch size: 21, lr: 4.95e-04
2022-05-27 09:41:50,640 INFO [train.py:842] (1/4) Epoch 11, batch 2450, loss[loss=0.1586, simple_loss=0.2438, pruned_loss=0.03677, over 6984.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2889, pruned_loss=0.06335, over 1423291.85 frames.], batch size: 16, lr: 4.95e-04
2022-05-27 09:42:29,493 INFO [train.py:842] (1/4) Epoch 11, batch 2500, loss[loss=0.2047, simple_loss=0.2899, pruned_loss=0.05969, over 7146.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2889, pruned_loss=0.06361, over 1422225.11 frames.], batch size: 19, lr: 4.95e-04
2022-05-27 09:43:08,223 INFO [train.py:842] (1/4) Epoch 11, batch 2550, loss[loss=0.1755, simple_loss=0.2637, pruned_loss=0.04367, over 7225.00 frames.], tot_loss[loss=0.2066, simple_loss=0.288, pruned_loss=0.06255, over 1426490.15 frames.], batch size: 16, lr: 4.95e-04
2022-05-27 09:43:47,157 INFO [train.py:842] (1/4) Epoch 11, batch 2600, loss[loss=0.2204, simple_loss=0.3002, pruned_loss=0.07032, over 7388.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2875, pruned_loss=0.06278, over 1428726.10 frames.], batch size: 23, lr: 4.95e-04
2022-05-27 09:44:25,675 INFO [train.py:842] (1/4) Epoch 11, batch 2650, loss[loss=0.1713, simple_loss=0.247, pruned_loss=0.0478, over 7001.00 frames.], tot_loss[loss=0.2069, simple_loss=0.288, pruned_loss=0.06286, over 1424391.94 frames.], batch size: 16, lr: 4.94e-04
2022-05-27 09:45:04,551 INFO [train.py:842] (1/4) Epoch 11, batch 2700, loss[loss=0.2181, simple_loss=0.2951, pruned_loss=0.07058, over 7408.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2899, pruned_loss=0.0642, over 1427283.79 frames.], batch size: 21, lr: 4.94e-04
2022-05-27 09:45:43,289 INFO [train.py:842] (1/4) Epoch 11, batch 2750, loss[loss=0.2008, simple_loss=0.2786, pruned_loss=0.06149, over 7271.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2899, pruned_loss=0.06449, over 1425921.81 frames.], batch size: 18, lr: 4.94e-04
2022-05-27 09:46:22,107 INFO [train.py:842] (1/4) Epoch 11, batch 2800, loss[loss=0.2081, simple_loss=0.2902, pruned_loss=0.06295, over 7163.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2898, pruned_loss=0.06456, over 1424680.50 frames.], batch size: 19, lr: 4.94e-04
2022-05-27 09:47:01,422 INFO [train.py:842] (1/4) Epoch 11, batch 2850, loss[loss=0.2041, simple_loss=0.2936, pruned_loss=0.0573, over 7307.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2881, pruned_loss=0.06343, over 1424466.88 frames.], batch size: 21, lr: 4.94e-04
2022-05-27 09:47:40,644 INFO [train.py:842] (1/4) Epoch 11, batch 2900, loss[loss=0.2048, simple_loss=0.2954, pruned_loss=0.05712, over 7214.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2877, pruned_loss=0.06307, over 1427183.24 frames.], batch size: 23, lr: 4.94e-04
2022-05-27 09:48:19,165 INFO [train.py:842] (1/4) Epoch 11, batch 2950, loss[loss=0.2308, simple_loss=0.3078, pruned_loss=0.07691, over 7211.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2879, pruned_loss=0.06281, over 1424174.40 frames.], batch size: 22, lr: 4.94e-04
2022-05-27 09:48:58,071 INFO [train.py:842] (1/4) Epoch 11, batch 3000, loss[loss=0.2323, simple_loss=0.3174, pruned_loss=0.07362, over 7160.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2886, pruned_loss=0.06252, over 1422379.54 frames.], batch size: 18, lr: 4.94e-04
2022-05-27 09:48:58,071 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 09:49:07,580 INFO [train.py:871] (1/4) Epoch 11, validation: loss=0.1731, simple_loss=0.2734, pruned_loss=0.03642, over 868885.00 frames.
2022-05-27 09:49:46,403 INFO [train.py:842] (1/4) Epoch 11, batch 3050, loss[loss=0.2229, simple_loss=0.3068, pruned_loss=0.06949, over 7148.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2878, pruned_loss=0.0619, over 1426506.37 frames.], batch size: 26, lr: 4.93e-04
2022-05-27 09:50:25,571 INFO [train.py:842] (1/4) Epoch 11, batch 3100, loss[loss=0.2002, simple_loss=0.2802, pruned_loss=0.06015, over 7409.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2883, pruned_loss=0.06238, over 1424405.98 frames.], batch size: 18, lr: 4.93e-04
2022-05-27 09:51:04,345 INFO [train.py:842] (1/4) Epoch 11, batch 3150, loss[loss=0.2269, simple_loss=0.3012, pruned_loss=0.07627, over 7276.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2883, pruned_loss=0.06271, over 1426883.24 frames.], batch size: 18, lr: 4.93e-04
2022-05-27 09:51:43,761 INFO [train.py:842] (1/4) Epoch 11, batch 3200, loss[loss=0.2166, simple_loss=0.2852, pruned_loss=0.074, over 7152.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2861, pruned_loss=0.06188, over 1428490.60 frames.], batch size: 18, lr: 4.93e-04
2022-05-27 09:52:22,388 INFO [train.py:842] (1/4) Epoch 11, batch 3250, loss[loss=0.2411, simple_loss=0.3172, pruned_loss=0.08248, over 7071.00 frames.], tot_loss[loss=0.205, simple_loss=0.2859, pruned_loss=0.06208, over 1430327.13 frames.], batch size: 18, lr: 4.93e-04
2022-05-27 09:53:01,430 INFO [train.py:842] (1/4) Epoch 11, batch 3300, loss[loss=0.2399, simple_loss=0.3114, pruned_loss=0.08414, over 6226.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2867, pruned_loss=0.06222, over 1430224.52 frames.], batch size: 37, lr: 4.93e-04
2022-05-27 09:53:39,890 INFO [train.py:842] (1/4) Epoch 11, batch 3350, loss[loss=0.2041, simple_loss=0.2856, pruned_loss=0.06127, over 7122.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2879, pruned_loss=0.06295, over 1423816.26 frames.], batch size: 21, lr: 4.93e-04
2022-05-27 09:54:18,841 INFO [train.py:842] (1/4) Epoch 11, batch 3400, loss[loss=0.2527, simple_loss=0.304, pruned_loss=0.1007, over 6994.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2889, pruned_loss=0.06364, over 1420488.14 frames.], batch size: 16, lr: 4.92e-04
2022-05-27 09:54:57,568 INFO [train.py:842] (1/4) Epoch 11, batch 3450, loss[loss=0.1944, simple_loss=0.2874, pruned_loss=0.05068, over 7440.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2889, pruned_loss=0.06341, over 1423872.26 frames.], batch size: 22, lr: 4.92e-04
2022-05-27 09:55:36,307 INFO [train.py:842] (1/4) Epoch 11, batch 3500, loss[loss=0.1719, simple_loss=0.2523, pruned_loss=0.04575, over 7414.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2888, pruned_loss=0.06326, over 1424537.23 frames.], batch size: 18, lr: 4.92e-04
2022-05-27 09:56:14,767 INFO [train.py:842] (1/4) Epoch 11, batch 3550, loss[loss=0.1864, simple_loss=0.269, pruned_loss=0.05189, over 6250.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2876, pruned_loss=0.06273, over 1422907.58 frames.], batch size: 37, lr: 4.92e-04
2022-05-27 09:56:53,643 INFO [train.py:842] (1/4) Epoch 11, batch 3600, loss[loss=0.2045, simple_loss=0.2927, pruned_loss=0.05814, over 6181.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2887, pruned_loss=0.06344, over 1418356.01 frames.], batch size: 37, lr: 4.92e-04
2022-05-27 09:57:32,187 INFO [train.py:842] (1/4) Epoch 11, batch 3650, loss[loss=0.1886, simple_loss=0.2842, pruned_loss=0.04645, over 7110.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2872, pruned_loss=0.06217, over 1422022.89 frames.], batch size: 21, lr: 4.92e-04
2022-05-27 09:58:10,882 INFO [train.py:842] (1/4) Epoch 11, batch 3700, loss[loss=0.2101, simple_loss=0.2909, pruned_loss=0.06467, over 7116.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2876, pruned_loss=0.06243, over 1418053.34 frames.], batch size: 21, lr: 4.92e-04
2022-05-27 09:58:49,439 INFO [train.py:842] (1/4) Epoch 11, batch 3750, loss[loss=0.1814, simple_loss=0.2688, pruned_loss=0.04694, over 7432.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2893, pruned_loss=0.06318, over 1423829.93 frames.], batch size: 20, lr: 4.92e-04
2022-05-27 09:59:28,308 INFO [train.py:842] (1/4) Epoch 11, batch 3800, loss[loss=0.2288, simple_loss=0.3186, pruned_loss=0.06952, over 7288.00 frames.], tot_loss[loss=0.2076, simple_loss=0.289, pruned_loss=0.06309, over 1421957.65 frames.], batch size: 24, lr: 4.91e-04
2022-05-27 10:00:06,878 INFO [train.py:842] (1/4) Epoch 11, batch 3850, loss[loss=0.2237, simple_loss=0.3049, pruned_loss=0.07121, over 7037.00 frames.], tot_loss[loss=0.2062, simple_loss=0.288, pruned_loss=0.0622, over 1425459.45 frames.], batch size: 28, lr: 4.91e-04
2022-05-27 10:00:45,731 INFO [train.py:842] (1/4) Epoch 11, batch 3900, loss[loss=0.2404, simple_loss=0.3125, pruned_loss=0.08416, over 7330.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2871, pruned_loss=0.06205, over 1426465.43 frames.], batch size: 22, lr: 4.91e-04
2022-05-27 10:01:24,359 INFO [train.py:842] (1/4) Epoch 11, batch 3950, loss[loss=0.2035, simple_loss=0.2874, pruned_loss=0.05977, over 7420.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2877, pruned_loss=0.06253, over 1427762.10 frames.], batch size: 21, lr: 4.91e-04
2022-05-27 10:02:03,412 INFO [train.py:842] (1/4) Epoch 11, batch 4000, loss[loss=0.214, simple_loss=0.3013, pruned_loss=0.06339, over 7282.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2881, pruned_loss=0.06262, over 1424551.09 frames.], batch size: 25, lr: 4.91e-04
2022-05-27 10:02:42,055 INFO [train.py:842] (1/4) Epoch 11, batch 4050, loss[loss=0.2298, simple_loss=0.3153, pruned_loss=0.07216, over 7184.00 frames.], tot_loss[loss=0.2058, simple_loss=0.287, pruned_loss=0.06226, over 1423729.97 frames.], batch size: 26, lr: 4.91e-04
2022-05-27 10:03:23,528 INFO [train.py:842] (1/4) Epoch 11, batch 4100, loss[loss=0.1862, simple_loss=0.2606, pruned_loss=0.05587, over 7140.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2872, pruned_loss=0.06225, over 1421580.56 frames.], batch size: 17, lr: 4.91e-04
2022-05-27 10:04:02,272 INFO [train.py:842] (1/4) Epoch 11, batch 4150, loss[loss=0.1842, simple_loss=0.2783, pruned_loss=0.04502, over 7111.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2863, pruned_loss=0.06158, over 1422501.11 frames.], batch size: 21, lr: 4.91e-04
2022-05-27 10:04:40,830 INFO [train.py:842] (1/4) Epoch 11, batch 4200, loss[loss=0.2144, simple_loss=0.3006, pruned_loss=0.06416, over 7187.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2871, pruned_loss=0.0616, over 1419941.86 frames.], batch size: 23, lr: 4.90e-04
2022-05-27 10:05:19,320 INFO [train.py:842] (1/4) Epoch 11, batch 4250, loss[loss=0.2587, simple_loss=0.3143, pruned_loss=0.1016, over 7066.00 frames.], tot_loss[loss=0.2063, simple_loss=0.288, pruned_loss=0.06232, over 1420776.85 frames.], batch size: 18, lr: 4.90e-04
2022-05-27 10:05:58,287 INFO [train.py:842] (1/4) Epoch 11, batch 4300, loss[loss=0.1888, simple_loss=0.2646, pruned_loss=0.05646, over 7068.00 frames.], tot_loss[loss=0.206, simple_loss=0.2872, pruned_loss=0.0624, over 1426202.80 frames.], batch size: 18, lr: 4.90e-04
2022-05-27 10:06:36,938 INFO [train.py:842] (1/4) Epoch 11, batch 4350, loss[loss=0.232, simple_loss=0.3016, pruned_loss=0.08119, over 7356.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2888, pruned_loss=0.06374, over 1426419.66 frames.], batch size: 19, lr: 4.90e-04
2022-05-27 10:07:16,071 INFO [train.py:842] (1/4) Epoch 11, batch 4400, loss[loss=0.2359, simple_loss=0.3, pruned_loss=0.08594, over 6799.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2882, pruned_loss=0.06363, over 1426355.43 frames.], batch size: 15, lr: 4.90e-04
2022-05-27 10:07:55,072 INFO [train.py:842] (1/4) Epoch 11, batch 4450, loss[loss=0.241, simple_loss=0.3033, pruned_loss=0.08942, over 7159.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2882, pruned_loss=0.06372, over 1422120.40 frames.], batch size: 18, lr: 4.90e-04
2022-05-27 10:08:33,900 INFO [train.py:842] (1/4) Epoch 11, batch 4500, loss[loss=0.2218, simple_loss=0.2985, pruned_loss=0.07252, over 7355.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2898, pruned_loss=0.06499, over 1421456.24 frames.], batch size: 19, lr: 4.90e-04
2022-05-27 10:09:12,426 INFO [train.py:842] (1/4) Epoch 11, batch 4550, loss[loss=0.2214, simple_loss=0.3018, pruned_loss=0.07049, over 7358.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2892, pruned_loss=0.06417, over 1423877.77 frames.], batch size: 19, lr: 4.90e-04
2022-05-27 10:09:51,214 INFO [train.py:842] (1/4) Epoch 11, batch 4600, loss[loss=0.2441, simple_loss=0.3269, pruned_loss=0.0807, over 7168.00 frames.], tot_loss[loss=0.209, simple_loss=0.2899, pruned_loss=0.06405, over 1427852.34 frames.], batch size: 18, lr: 4.89e-04
2022-05-27 10:10:29,818 INFO [train.py:842] (1/4) Epoch 11, batch 4650, loss[loss=0.1966, simple_loss=0.2702, pruned_loss=0.06146, over 7287.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2888, pruned_loss=0.06364, over 1426769.29 frames.], batch size: 17, lr: 4.89e-04
2022-05-27 10:11:08,718 INFO [train.py:842] (1/4) Epoch 11, batch 4700, loss[loss=0.1884, simple_loss=0.2761, pruned_loss=0.05036, over 7150.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2893, pruned_loss=0.06369, over 1428817.59 frames.], batch size: 19, lr: 4.89e-04
2022-05-27 10:11:47,145 INFO [train.py:842] (1/4) Epoch 11, batch 4750, loss[loss=0.2147, simple_loss=0.3013, pruned_loss=0.06406, over 7317.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2897, pruned_loss=0.06349, over 1428218.77 frames.], batch size: 21, lr: 4.89e-04
2022-05-27 10:12:26,000 INFO [train.py:842] (1/4) Epoch 11, batch 4800, loss[loss=0.1946, simple_loss=0.2681, pruned_loss=0.06054, over 7354.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2893, pruned_loss=0.06385, over 1428110.80 frames.], batch size: 19, lr: 4.89e-04
2022-05-27 10:13:04,720 INFO [train.py:842] (1/4) Epoch 11, batch 4850, loss[loss=0.1748, simple_loss=0.2533, pruned_loss=0.04816, over 7274.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2904, pruned_loss=0.06428, over 1425527.98 frames.], batch size: 18, lr: 4.89e-04
2022-05-27 10:13:44,138 INFO [train.py:842] (1/4) Epoch 11, batch 4900, loss[loss=0.1901, simple_loss=0.2674, pruned_loss=0.05645, over 6765.00 frames.], tot_loss[loss=0.2089, simple_loss=0.2899, pruned_loss=0.06397, over 1428774.07 frames.], batch size: 15, lr: 4.89e-04
2022-05-27 10:14:22,589 INFO [train.py:842] (1/4) Epoch 11, batch 4950, loss[loss=0.197, simple_loss=0.2835, pruned_loss=0.05519, over 7318.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2895, pruned_loss=0.06399, over 1427849.11 frames.], batch size: 21, lr: 4.89e-04
2022-05-27 10:15:01,230 INFO [train.py:842] (1/4) Epoch 11, batch 5000, loss[loss=0.2418, simple_loss=0.3067, pruned_loss=0.0885, over 7331.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2906, pruned_loss=0.06494, over 1423336.74 frames.], batch size: 20, lr: 4.88e-04
2022-05-27 10:15:39,774 INFO [train.py:842] (1/4) Epoch 11, batch 5050, loss[loss=0.1852, simple_loss=0.281, pruned_loss=0.04471, over 7289.00 frames.], tot_loss[loss=0.2107, simple_loss=0.2911, pruned_loss=0.06512, over 1425002.08 frames.], batch size: 24, lr: 4.88e-04
2022-05-27 10:16:18,518 INFO [train.py:842] (1/4) Epoch 11, batch 5100, loss[loss=0.1849, simple_loss=0.2746, pruned_loss=0.04757, over 7153.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2905, pruned_loss=0.06435, over 1427577.83 frames.], batch size: 18, lr: 4.88e-04
2022-05-27 10:16:57,146 INFO [train.py:842] (1/4) Epoch 11, batch 5150, loss[loss=0.1909, simple_loss=0.2778, pruned_loss=0.05196, over 7146.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2902, pruned_loss=0.06411, over 1420282.00 frames.], batch size: 20, lr: 4.88e-04
2022-05-27 10:17:36,109 INFO [train.py:842] (1/4) Epoch 11, batch 5200, loss[loss=0.2045, simple_loss=0.2952, pruned_loss=0.05692, over 7225.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2909, pruned_loss=0.06477, over 1419062.32 frames.], batch size: 23, lr: 4.88e-04
2022-05-27 10:18:14,673 INFO [train.py:842] (1/4) Epoch 11, batch 5250, loss[loss=0.2038, simple_loss=0.2786, pruned_loss=0.06452, over 7261.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2909, pruned_loss=0.06499, over 1422612.31 frames.], batch size: 19, lr: 4.88e-04
2022-05-27 10:18:53,614 INFO [train.py:842] (1/4) Epoch 11, batch 5300, loss[loss=0.2403, simple_loss=0.3115, pruned_loss=0.08457, over 7372.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2909, pruned_loss=0.06473, over 1424235.25 frames.], batch size: 23, lr: 4.88e-04
2022-05-27 10:19:32,054 INFO [train.py:842] (1/4) Epoch 11, batch 5350, loss[loss=0.189, simple_loss=0.2735, pruned_loss=0.05229, over 7237.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2894, pruned_loss=0.06365, over 1425947.10 frames.], batch size: 20, lr: 4.88e-04
2022-05-27 10:20:10,510 INFO [train.py:842] (1/4) Epoch 11, batch 5400, loss[loss=0.2584, simple_loss=0.3462, pruned_loss=0.08535, over 7203.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2913, pruned_loss=0.06455, over 1426787.88 frames.], batch size: 23, lr: 4.87e-04
2022-05-27 10:20:49,098 INFO [train.py:842] (1/4) Epoch 11, batch 5450, loss[loss=0.186, simple_loss=0.2717, pruned_loss=0.05009, over 7286.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2891, pruned_loss=0.06331, over 1427574.04 frames.], batch size: 24, lr: 4.87e-04
2022-05-27 10:21:28,148 INFO [train.py:842] (1/4) Epoch 11, batch 5500, loss[loss=0.2009, simple_loss=0.2745, pruned_loss=0.06365, over 7271.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2889, pruned_loss=0.06302, over 1425734.53 frames.], batch size: 18, lr: 4.87e-04
2022-05-27 10:22:16,776 INFO [train.py:842] (1/4) Epoch 11, batch 5550, loss[loss=0.1994, simple_loss=0.2833, pruned_loss=0.05778, over 7149.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2891, pruned_loss=0.06327, over 1425980.46 frames.], batch size: 19, lr: 4.87e-04
2022-05-27 10:22:55,815 INFO [train.py:842] (1/4) Epoch 11, batch 5600, loss[loss=0.2089, simple_loss=0.2911, pruned_loss=0.06337, over 5517.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2884, pruned_loss=0.06321, over 1423727.21 frames.], batch size: 53, lr: 4.87e-04
2022-05-27 10:23:34,193 INFO [train.py:842] (1/4) Epoch 11, batch 5650, loss[loss=0.2162, simple_loss=0.3131, pruned_loss=0.05962, over 7412.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2898, pruned_loss=0.06355, over 1425211.92 frames.], batch size: 21, lr: 4.87e-04
2022-05-27 10:24:13,257 INFO [train.py:842] (1/4) Epoch 11, batch 5700, loss[loss=0.2051, simple_loss=0.2955, pruned_loss=0.05732, over 7377.00 frames.], tot_loss[loss=0.207, simple_loss=0.2882, pruned_loss=0.06292, over 1424660.95 frames.], batch size: 23, lr: 4.87e-04
2022-05-27 10:24:51,713 INFO [train.py:842] (1/4) Epoch 11, batch 5750, loss[loss=0.198, simple_loss=0.292, pruned_loss=0.05201, over 7228.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2893, pruned_loss=0.06347, over 1419197.42 frames.], batch size: 20, lr: 4.87e-04
2022-05-27 10:25:30,835 INFO [train.py:842] (1/4) Epoch 11, batch 5800, loss[loss=0.228, simple_loss=0.3011, pruned_loss=0.07747, over 5141.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2884, pruned_loss=0.06241, over 1423086.33 frames.], batch size: 53, lr: 4.86e-04
2022-05-27 10:26:09,473 INFO [train.py:842] (1/4) Epoch 11, batch 5850, loss[loss=0.2227, simple_loss=0.2865, pruned_loss=0.07947, over 7110.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2896, pruned_loss=0.06335, over 1422959.02 frames.], batch size: 28, lr: 4.86e-04
2022-05-27 10:26:48,396 INFO [train.py:842] (1/4) Epoch 11, batch 5900, loss[loss=0.1728, simple_loss=0.2624, pruned_loss=0.04161, over 7435.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2883, pruned_loss=0.06251, over 1424740.91 frames.], batch size: 20, lr: 4.86e-04
2022-05-27 10:27:26,964 INFO [train.py:842] (1/4) Epoch 11, batch 5950, loss[loss=0.2883, simple_loss=0.3536, pruned_loss=0.1115, over 7185.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2868, pruned_loss=0.06167, over 1427674.07 frames.], batch size: 26, lr: 4.86e-04
2022-05-27 10:28:06,086 INFO [train.py:842] (1/4) Epoch 11, batch 6000, loss[loss=0.2091, simple_loss=0.2911, pruned_loss=0.06351, over 7140.00 frames.], tot_loss[loss=0.206, simple_loss=0.2873, pruned_loss=0.0624, over 1431566.04 frames.], batch size: 20, lr: 4.86e-04
2022-05-27 10:28:06,087 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 10:28:15,409 INFO [train.py:871] (1/4) Epoch 11, validation: loss=0.1722, simple_loss=0.2724, pruned_loss=0.03599, over 868885.00 frames.
2022-05-27 10:28:54,266 INFO [train.py:842] (1/4) Epoch 11, batch 6050, loss[loss=0.2055, simple_loss=0.2903, pruned_loss=0.06036, over 7294.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2868, pruned_loss=0.06226, over 1428151.83 frames.], batch size: 24, lr: 4.86e-04
2022-05-27 10:29:33,424 INFO [train.py:842] (1/4) Epoch 11, batch 6100, loss[loss=0.1942, simple_loss=0.2852, pruned_loss=0.0516, over 7269.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2859, pruned_loss=0.06231, over 1427867.00 frames.], batch size: 24, lr: 4.86e-04
2022-05-27 10:30:11,959 INFO [train.py:842] (1/4) Epoch 11, batch 6150, loss[loss=0.2119, simple_loss=0.2893, pruned_loss=0.06723, over 7160.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2876, pruned_loss=0.06353, over 1431355.27 frames.], batch size: 19, lr: 4.86e-04
2022-05-27 10:30:50,895 INFO [train.py:842] (1/4) Epoch 11, batch 6200, loss[loss=0.2098, simple_loss=0.3077, pruned_loss=0.05596, over 7288.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2873, pruned_loss=0.06322, over 1431280.82 frames.], batch size: 24, lr: 4.85e-04
2022-05-27 10:31:29,391 INFO [train.py:842] (1/4) Epoch 11, batch 6250, loss[loss=0.186, simple_loss=0.2756, pruned_loss=0.04824, over 6823.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2874, pruned_loss=0.06311, over 1434021.22 frames.], batch size: 31, lr: 4.85e-04
2022-05-27 10:32:08,627 INFO [train.py:842] (1/4) Epoch 11, batch 6300, loss[loss=0.1932, simple_loss=0.2597, pruned_loss=0.06337, over 6994.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2868, pruned_loss=0.06268, over 1431088.70 frames.], batch size: 16, lr: 4.85e-04
2022-05-27 10:32:47,161 INFO [train.py:842] (1/4) Epoch 11, batch 6350, loss[loss=0.2719, simple_loss=0.3456, pruned_loss=0.09917, over 7193.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2877, pruned_loss=0.06296, over 1427481.86 frames.], batch size: 26, lr: 4.85e-04
2022-05-27 10:33:25,792 INFO [train.py:842] (1/4) Epoch 11, batch 6400, loss[loss=0.1917, simple_loss=0.2691, pruned_loss=0.05719, over 7170.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2888, pruned_loss=0.06371, over 1423215.34 frames.], batch size: 18, lr: 4.85e-04
2022-05-27 10:34:04,318 INFO [train.py:842] (1/4) Epoch 11, batch 6450, loss[loss=0.1698, simple_loss=0.2577, pruned_loss=0.04094, over 7343.00 frames.], tot_loss[loss=0.2084, simple_loss=0.289, pruned_loss=0.06394, over 1414601.66 frames.], batch size: 22, lr: 4.85e-04
2022-05-27 10:34:42,919 INFO [train.py:842] (1/4) Epoch 11, batch 6500, loss[loss=0.2273, simple_loss=0.3045, pruned_loss=0.07503, over 7236.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2895, pruned_loss=0.06394, over 1415188.84 frames.], batch size: 20, lr: 4.85e-04
2022-05-27 10:35:21,594 INFO [train.py:842] (1/4) Epoch 11, batch 6550, loss[loss=0.2227, simple_loss=0.2886, pruned_loss=0.07837, over 7253.00 frames.], tot_loss[loss=0.2075, simple_loss=0.288, pruned_loss=0.06355, over 1415706.74 frames.], batch size: 19, lr: 4.85e-04
2022-05-27 10:36:00,474 INFO [train.py:842] (1/4) Epoch 11, batch 6600, loss[loss=0.1733, simple_loss=0.2562, pruned_loss=0.04522, over 7059.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2875, pruned_loss=0.06314, over 1414558.49 frames.], batch size: 28, lr: 4.84e-04
2022-05-27 10:36:39,102 INFO [train.py:842] (1/4) Epoch 11, batch 6650, loss[loss=0.2208, simple_loss=0.3059, pruned_loss=0.06786, over 7325.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2889, pruned_loss=0.06369, over 1412770.84 frames.], batch size: 24, lr: 4.84e-04
2022-05-27 10:37:18,336 INFO [train.py:842] (1/4) Epoch 11, batch 6700, loss[loss=0.2412, simple_loss=0.3112, pruned_loss=0.08557, over 7385.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2884, pruned_loss=0.06342, over 1416013.63 frames.], batch size: 23, lr: 4.84e-04
2022-05-27 10:37:56,917 INFO [train.py:842] (1/4) Epoch 11, batch 6750, loss[loss=0.1749, simple_loss=0.272, pruned_loss=0.03888, over 7328.00 frames.], tot_loss[loss=0.2069, simple_loss=0.288, pruned_loss=0.06286, over 1416447.06 frames.], batch size: 22, lr: 4.84e-04
2022-05-27 10:38:35,769 INFO [train.py:842] (1/4) Epoch 11, batch 6800, loss[loss=0.1661, simple_loss=0.2463, pruned_loss=0.04293, over 6799.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2873, pruned_loss=0.0619, over 1420236.22 frames.], batch size: 15, lr: 4.84e-04
2022-05-27 10:39:14,377 INFO [train.py:842] (1/4) Epoch 11, batch 6850, loss[loss=0.1697, simple_loss=0.2496, pruned_loss=0.04487, over 6998.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2863, pruned_loss=0.06146, over 1422175.04 frames.], batch size: 16, lr: 4.84e-04
2022-05-27 10:39:53,316 INFO [train.py:842] (1/4) Epoch 11, batch 6900, loss[loss=0.1835, simple_loss=0.2778, pruned_loss=0.04465, over 7224.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2862, pruned_loss=0.06143, over 1425954.72 frames.], batch size: 21, lr: 4.84e-04
2022-05-27 10:40:31,865 INFO [train.py:842] (1/4) Epoch 11, batch 6950, loss[loss=0.2234, simple_loss=0.3099, pruned_loss=0.06843, over 7137.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2869, pruned_loss=0.0621, over 1429270.63 frames.], batch size: 20, lr: 4.84e-04
2022-05-27 10:41:10,694 INFO [train.py:842] (1/4) Epoch 11, batch 7000, loss[loss=0.2038, simple_loss=0.2844, pruned_loss=0.06157, over 6863.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2872, pruned_loss=0.06215, over 1426155.82 frames.], batch size: 31, lr: 4.83e-04
2022-05-27 10:41:49,519 INFO [train.py:842] (1/4) Epoch 11, batch 7050, loss[loss=0.1997, simple_loss=0.274, pruned_loss=0.06272, over 7157.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2877, pruned_loss=0.06258, over 1423739.81 frames.], batch size: 18, lr: 4.83e-04
2022-05-27 10:42:28,471 INFO [train.py:842] (1/4) Epoch 11, batch 7100, loss[loss=0.1969, simple_loss=0.2662, pruned_loss=0.0638, over 7251.00 frames.], tot_loss[loss=0.207, simple_loss=0.2879, pruned_loss=0.06306, over 1423665.73 frames.], batch size: 16, lr: 4.83e-04
2022-05-27 10:43:06,954 INFO [train.py:842] (1/4) Epoch 11, batch 7150, loss[loss=0.1793, simple_loss=0.2666, pruned_loss=0.04595, over 7429.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2896, pruned_loss=0.0639, over 1424557.48 frames.], batch size: 20, lr: 4.83e-04
2022-05-27 10:43:45,964 INFO [train.py:842] (1/4) Epoch 11, batch 7200, loss[loss=0.176, simple_loss=0.2707, pruned_loss=0.04072, over 7231.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2877, pruned_loss=0.06286, over 1424375.56 frames.], batch size: 20, lr: 4.83e-04
2022-05-27 10:44:24,354 INFO [train.py:842] (1/4) Epoch 11, batch 7250, loss[loss=0.2343, simple_loss=0.3149, pruned_loss=0.07679, over 7145.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2894, pruned_loss=0.0634, over 1424685.63 frames.], batch size: 20, lr: 4.83e-04
2022-05-27 10:45:23,835 INFO [train.py:842] (1/4) Epoch 11, batch 7300, loss[loss=0.2521, simple_loss=0.3389, pruned_loss=0.08267, over 6843.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2903, pruned_loss=0.06351, over 1423745.77 frames.], batch size: 31, lr: 4.83e-04
2022-05-27 10:46:12,723 INFO [train.py:842] (1/4) Epoch 11, batch 7350, loss[loss=0.1934, simple_loss=0.2859, pruned_loss=0.05051, over 7057.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2879, pruned_loss=0.06251, over 1426447.63 frames.], batch size: 28, lr: 4.83e-04
2022-05-27 10:46:51,450 INFO [train.py:842] (1/4) Epoch 11, batch 7400, loss[loss=0.3242, simple_loss=0.3767, pruned_loss=0.1358, over 7236.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2881, pruned_loss=0.06271, over 1425193.93 frames.], batch size: 20, lr: 4.83e-04
2022-05-27 10:47:30,209 INFO [train.py:842] (1/4) Epoch 11, batch 7450, loss[loss=0.2279, simple_loss=0.3032, pruned_loss=0.0763, over 7246.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2878, pruned_loss=0.06233, over 1425578.88 frames.], batch size: 25, lr: 4.82e-04
2022-05-27 10:48:09,498 INFO [train.py:842] (1/4) Epoch 11, batch 7500, loss[loss=0.2008, simple_loss=0.2889, pruned_loss=0.05631, over 7331.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2876, pruned_loss=0.06265, over 1427553.28 frames.], batch size: 20, lr: 4.82e-04
2022-05-27 10:48:48,113 INFO [train.py:842] (1/4) Epoch 11, batch 7550, loss[loss=0.2175, simple_loss=0.3097, pruned_loss=0.06259, over 7330.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2878, pruned_loss=0.06262, over 1424597.81 frames.], batch size: 22, lr: 4.82e-04
2022-05-27 10:49:26,803 INFO [train.py:842] (1/4) Epoch 11, batch 7600, loss[loss=0.2178, simple_loss=0.2919, pruned_loss=0.0719, over 7257.00 frames.], tot_loss[loss=0.2058, simple_loss=0.287, pruned_loss=0.06229, over 1423023.44 frames.], batch size: 19, lr: 4.82e-04
2022-05-27 10:50:05,342 INFO [train.py:842] (1/4) Epoch 11, batch 7650, loss[loss=0.2168, simple_loss=0.2848, pruned_loss=0.07438, over 6816.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2871, pruned_loss=0.06254, over 1417703.01 frames.], batch size: 15, lr: 4.82e-04
2022-05-27 10:50:44,220 INFO [train.py:842] (1/4) Epoch 11, batch 7700, loss[loss=0.1947, simple_loss=0.2881, pruned_loss=0.05065, over 7200.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2879, pruned_loss=0.06273, over 1422135.80 frames.], batch size: 22, lr: 4.82e-04
2022-05-27 10:51:22,648 INFO [train.py:842] (1/4) Epoch 11, batch 7750, loss[loss=0.2203, simple_loss=0.3069, pruned_loss=0.06685, over 7228.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2887, pruned_loss=0.06312, over 1421023.07 frames.], batch size: 21, lr: 4.82e-04
2022-05-27 10:52:01,629 INFO [train.py:842] (1/4) Epoch 11, batch 7800, loss[loss=0.205, simple_loss=0.2937, pruned_loss=0.05818, over 7065.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2888, pruned_loss=0.06309, over 1420988.36 frames.], batch size: 18, lr: 4.82e-04
2022-05-27 10:52:40,118 INFO [train.py:842] (1/4) Epoch 11, batch 7850, loss[loss=0.2184, simple_loss=0.3012, pruned_loss=0.06775, over 7223.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2879, pruned_loss=0.06245, over 1419946.86 frames.], batch size: 21, lr: 4.81e-04
2022-05-27 10:53:18,854 INFO [train.py:842] (1/4) Epoch 11, batch 7900, loss[loss=0.1877, simple_loss=0.2687, pruned_loss=0.05338, over 7163.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2887, pruned_loss=0.06277, over 1422095.97 frames.], batch size: 18, lr: 4.81e-04
2022-05-27 10:53:57,414 INFO [train.py:842] (1/4) Epoch 11, batch 7950, loss[loss=0.2328, simple_loss=0.3048, pruned_loss=0.0804, over 7413.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2878, pruned_loss=0.06277, over 1424928.05 frames.], batch size: 21, lr: 4.81e-04
2022-05-27 10:54:36,323 INFO [train.py:842] (1/4) Epoch 11, batch 8000, loss[loss=0.2009, simple_loss=0.2832, pruned_loss=0.05936, over 7424.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2877, pruned_loss=0.06296, over 1425165.95 frames.], batch size: 21, lr: 4.81e-04
2022-05-27 10:55:14,873 INFO [train.py:842] (1/4) Epoch 11, batch 8050, loss[loss=0.2298, simple_loss=0.3109, pruned_loss=0.07435, over 7289.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2874, pruned_loss=0.06245, over 1429608.05 frames.], batch size: 25, lr: 4.81e-04
2022-05-27 10:55:53,870 INFO [train.py:842] (1/4) Epoch 11, batch 8100, loss[loss=0.2249, simple_loss=0.3001, pruned_loss=0.07486, over 7287.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2863, pruned_loss=0.06246, over 1430018.69 frames.], batch size: 24, lr: 4.81e-04
2022-05-27 10:56:32,352 INFO [train.py:842] (1/4) Epoch 11, batch 8150, loss[loss=0.22, simple_loss=0.2946, pruned_loss=0.07275, over 7386.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2869, pruned_loss=0.06279, over 1430012.89 frames.], batch size: 23, lr: 4.81e-04
2022-05-27 10:57:11,043 INFO [train.py:842] (1/4) Epoch 11, batch 8200, loss[loss=0.2184, simple_loss=0.2963, pruned_loss=0.07027, over 7290.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2876, pruned_loss=0.06294, over 1424489.87 frames.], batch size: 24, lr: 4.81e-04
2022-05-27 10:57:49,625 INFO [train.py:842] (1/4) Epoch 11, batch 8250, loss[loss=0.1878, simple_loss=0.2783, pruned_loss=0.04869, over 7422.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2871, pruned_loss=0.06254, over 1427098.53 frames.], batch size: 20, lr: 4.80e-04
2022-05-27 10:58:28,730 INFO [train.py:842] (1/4) Epoch 11, batch 8300, loss[loss=0.2106, simple_loss=0.2965, pruned_loss=0.06238, over 7211.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2875, pruned_loss=0.06271, over 1429628.60 frames.], batch size: 22, lr: 4.80e-04
2022-05-27 10:59:07,272 INFO [train.py:842] (1/4) Epoch 11, batch 8350, loss[loss=0.1684, simple_loss=0.2338, pruned_loss=0.05152, over 7430.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2864, pruned_loss=0.06201, over 1427789.13 frames.], batch size: 18, lr: 4.80e-04
2022-05-27 10:59:46,103 INFO [train.py:842] (1/4) Epoch 11, batch 8400, loss[loss=0.1904, simple_loss=0.2769, pruned_loss=0.05193, over 7288.00 frames.], tot_loss[loss=0.2044, simple_loss=0.286, pruned_loss=0.06145, over 1429420.24 frames.], batch size: 25, lr: 4.80e-04
2022-05-27 11:00:24,516 INFO [train.py:842] (1/4) Epoch 11, batch 8450, loss[loss=0.1942, simple_loss=0.2771, pruned_loss=0.05564, over 7306.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2859, pruned_loss=0.06181, over 1424235.93 frames.], batch size: 24, lr: 4.80e-04
2022-05-27 11:01:03,521 INFO [train.py:842] (1/4) Epoch 11, batch 8500, loss[loss=0.2159, simple_loss=0.2955, pruned_loss=0.06818, over 7203.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2852, pruned_loss=0.06166, over 1422706.95 frames.], batch size: 22, lr: 4.80e-04
2022-05-27 11:01:42,108 INFO [train.py:842] (1/4) Epoch 11, batch 8550, loss[loss=0.1639, simple_loss=0.2447, pruned_loss=0.04153, over 7359.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2845, pruned_loss=0.06135, over 1423205.48 frames.], batch size: 19, lr: 4.80e-04
2022-05-27 11:02:21,310 INFO [train.py:842] (1/4) Epoch 11, batch 8600, loss[loss=0.1625, simple_loss=0.2325, pruned_loss=0.04626, over 7138.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2837, pruned_loss=0.06142, over 1420673.88 frames.], batch size: 17, lr: 4.80e-04
2022-05-27 11:02:59,931 INFO [train.py:842] (1/4) Epoch 11, batch 8650, loss[loss=0.1905, simple_loss=0.2641, pruned_loss=0.05847, over 7405.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2842, pruned_loss=0.06141, over 1424188.73 frames.], batch size: 18, lr: 4.80e-04
2022-05-27 11:03:38,558 INFO [train.py:842] (1/4) Epoch 11, batch 8700, loss[loss=0.203, simple_loss=0.2761, pruned_loss=0.06495, over 7424.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2842, pruned_loss=0.06135, over 1419206.60 frames.], batch size: 18, lr: 4.79e-04
2022-05-27 11:04:16,858 INFO [train.py:842] (1/4) Epoch 11, batch 8750, loss[loss=0.1973, simple_loss=0.3003, pruned_loss=0.04713, over 7171.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2845, pruned_loss=0.06152, over 1418677.43 frames.], batch size: 26, lr: 4.79e-04
2022-05-27 11:04:55,658 INFO [train.py:842] (1/4) Epoch 11, batch 8800, loss[loss=0.1673, simple_loss=0.246, pruned_loss=0.04428, over 7351.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2851, pruned_loss=0.06189, over 1417581.16 frames.], batch size: 19, lr: 4.79e-04
2022-05-27 11:05:34,679 INFO [train.py:842] (1/4) Epoch 11, batch 8850, loss[loss=0.204, simple_loss=0.2669, pruned_loss=0.07059, over 7422.00 frames.], tot_loss[loss=0.206, simple_loss=0.2864, pruned_loss=0.06287, over 1412184.54 frames.], batch size: 18, lr: 4.79e-04
2022-05-27 11:06:13,194 INFO [train.py:842] (1/4) Epoch 11, batch 8900, loss[loss=0.1992, simple_loss=0.2666, pruned_loss=0.06591, over 7227.00 frames.], tot_loss[loss=0.2065, simple_loss=0.287, pruned_loss=0.06304, over 1413066.93 frames.], batch size: 21, lr: 4.79e-04
2022-05-27 11:06:51,390 INFO [train.py:842] (1/4) Epoch 11, batch 8950, loss[loss=0.2052, simple_loss=0.2879, pruned_loss=0.06131, over 7138.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2886, pruned_loss=0.06399, over 1399787.18 frames.], batch size: 26, lr: 4.79e-04
2022-05-27 11:07:29,534 INFO [train.py:842] (1/4) Epoch 11, batch 9000, loss[loss=0.1597, simple_loss=0.2534, pruned_loss=0.033, over 6858.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2903, pruned_loss=0.065, over 1382557.36 frames.], batch size: 31, lr: 4.79e-04
2022-05-27 11:07:29,535 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 11:07:39,052 INFO [train.py:871] (1/4) Epoch 11, validation: loss=0.1709, simple_loss=0.2721, pruned_loss=0.03482, over 868885.00 frames.
2022-05-27 11:08:17,112 INFO [train.py:842] (1/4) Epoch 11, batch 9050, loss[loss=0.2429, simple_loss=0.3235, pruned_loss=0.08112, over 5141.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2928, pruned_loss=0.0664, over 1370495.61 frames.], batch size: 55, lr: 4.79e-04
2022-05-27 11:08:55,120 INFO [train.py:842] (1/4) Epoch 11, batch 9100, loss[loss=0.2311, simple_loss=0.3103, pruned_loss=0.07595, over 5174.00 frames.], tot_loss[loss=0.219, simple_loss=0.2971, pruned_loss=0.07044, over 1295313.43 frames.], batch size: 52, lr: 4.78e-04
2022-05-27 11:09:32,710 INFO [train.py:842] (1/4) Epoch 11, batch 9150, loss[loss=0.233, simple_loss=0.3005, pruned_loss=0.08272, over 4941.00 frames.], tot_loss[loss=0.2257, simple_loss=0.3022, pruned_loss=0.0746, over 1230857.37 frames.], batch size: 52, lr: 4.78e-04
2022-05-27 11:10:24,882 INFO [train.py:842] (1/4) Epoch 12, batch 0, loss[loss=0.2309, simple_loss=0.3137, pruned_loss=0.07408, over 7408.00 frames.], tot_loss[loss=0.2309, simple_loss=0.3137, pruned_loss=0.07408, over 7408.00 frames.], batch size: 21, lr: 4.61e-04
2022-05-27 11:11:03,721 INFO [train.py:842] (1/4) Epoch 12, batch 50, loss[loss=0.2114, simple_loss=0.2833, pruned_loss=0.06973, over 4545.00 frames.], tot_loss[loss=0.207, simple_loss=0.2883, pruned_loss=0.06285, over 318489.07 frames.], batch size: 53, lr: 4.61e-04
2022-05-27 11:11:42,656 INFO [train.py:842] (1/4) Epoch 12, batch 100, loss[loss=0.2204, simple_loss=0.2952, pruned_loss=0.07282, over 6378.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2891, pruned_loss=0.06236, over 557724.41 frames.], batch size: 38, lr: 4.61e-04
2022-05-27 11:12:21,233 INFO [train.py:842] (1/4) Epoch 12, batch 150, loss[loss=0.1921, simple_loss=0.2759, pruned_loss=0.05413, over 7268.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2904, pruned_loss=0.06257, over 747596.17 frames.], batch size: 17, lr: 4.61e-04
2022-05-27 11:12:59,976 INFO [train.py:842] (1/4) Epoch 12, batch 200, loss[loss=0.2055, simple_loss=0.2937, pruned_loss=0.05868, over 7192.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2913, pruned_loss=0.0625, over 895810.40 frames.], batch size: 22, lr: 4.61e-04
2022-05-27 11:13:38,459 INFO [train.py:842] (1/4) Epoch 12, batch 250, loss[loss=0.2453, simple_loss=0.326, pruned_loss=0.08234, over 6912.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2905, pruned_loss=0.06244, over 1013454.96 frames.], batch size: 31, lr: 4.61e-04
2022-05-27 11:14:17,110 INFO [train.py:842] (1/4) Epoch 12, batch 300, loss[loss=0.2107, simple_loss=0.2982, pruned_loss=0.06154, over 7202.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2906, pruned_loss=0.06263, over 1098137.71 frames.], batch size: 22, lr: 4.61e-04
2022-05-27 11:14:55,711 INFO [train.py:842] (1/4) Epoch 12, batch 350, loss[loss=0.2051, simple_loss=0.298, pruned_loss=0.05612, over 7335.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2882, pruned_loss=0.06142, over 1165161.70 frames.], batch size: 22, lr: 4.61e-04
2022-05-27 11:15:34,519 INFO [train.py:842] (1/4) Epoch 12, batch 400, loss[loss=0.185, simple_loss=0.2742, pruned_loss=0.04793, over 7331.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2866, pruned_loss=0.06043, over 1220161.62 frames.], batch size: 22, lr: 4.60e-04
2022-05-27 11:16:13,174 INFO [train.py:842] (1/4) Epoch 12, batch 450, loss[loss=0.1726, simple_loss=0.2661, pruned_loss=0.03962, over 7157.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2841, pruned_loss=0.05901, over 1268764.84 frames.], batch size: 19, lr: 4.60e-04
2022-05-27 11:16:52,031 INFO [train.py:842] (1/4) Epoch 12, batch 500, loss[loss=0.243, simple_loss=0.3241, pruned_loss=0.08098, over 7396.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2854, pruned_loss=0.06036, over 1303270.42 frames.], batch size: 23, lr: 4.60e-04
2022-05-27 11:17:30,956 INFO [train.py:842] (1/4) Epoch 12, batch 550, loss[loss=0.2014, simple_loss=0.2966, pruned_loss=0.0531, over 7402.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2848, pruned_loss=0.06, over 1329593.86 frames.], batch size: 21, lr: 4.60e-04
2022-05-27 11:18:10,124 INFO [train.py:842] (1/4) Epoch 12, batch 600, loss[loss=0.1765, simple_loss=0.2639, pruned_loss=0.04453, over 7344.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2858, pruned_loss=0.06099, over 1349303.43 frames.], batch size: 22, lr: 4.60e-04
2022-05-27 11:18:48,933 INFO [train.py:842] (1/4) Epoch 12, batch 650, loss[loss=0.2038, simple_loss=0.2969, pruned_loss=0.05534, over 7394.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2855, pruned_loss=0.0611, over 1369937.14 frames.], batch size: 23, lr: 4.60e-04
2022-05-27 11:19:27,773 INFO [train.py:842] (1/4) Epoch 12, batch 700, loss[loss=0.2355, simple_loss=0.3139, pruned_loss=0.07855, over 7304.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2859, pruned_loss=0.06078, over 1380752.60 frames.], batch size: 24, lr: 4.60e-04
2022-05-27 11:20:06,288 INFO [train.py:842] (1/4) Epoch 12, batch 750, loss[loss=0.1826, simple_loss=0.2791, pruned_loss=0.04306, over 7327.00 frames.], tot_loss[loss=0.207, simple_loss=0.2886, pruned_loss=0.06272, over 1386425.40 frames.], batch size: 20, lr: 4.60e-04
2022-05-27 11:20:45,310 INFO [train.py:842] (1/4) Epoch 12, batch 800, loss[loss=0.1759, simple_loss=0.2552, pruned_loss=0.04827, over 7417.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2881, pruned_loss=0.06265, over 1399117.36 frames.], batch size: 18, lr: 4.60e-04
2022-05-27 11:21:23,925 INFO [train.py:842] (1/4) Epoch 12, batch 850, loss[loss=0.2183, simple_loss=0.2945, pruned_loss=0.07103, over 6727.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2872, pruned_loss=0.06212, over 1403027.91 frames.], batch size: 31, lr: 4.59e-04
2022-05-27 11:22:02,958 INFO [train.py:842] (1/4) Epoch 12, batch 900, loss[loss=0.2593, simple_loss=0.3215, pruned_loss=0.09859, over 7332.00 frames.], tot_loss[loss=0.2052, simple_loss=0.287, pruned_loss=0.06174, over 1407632.18 frames.], batch size: 22, lr: 4.59e-04
2022-05-27 11:22:41,567 INFO [train.py:842] (1/4) Epoch 12, batch 950, loss[loss=0.1836, simple_loss=0.262, pruned_loss=0.05256, over 7437.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2857, pruned_loss=0.06103, over 1413088.20 frames.], batch size: 20, lr: 4.59e-04
2022-05-27 11:23:20,318 INFO [train.py:842] (1/4) Epoch 12, batch 1000, loss[loss=0.2104, simple_loss=0.2902, pruned_loss=0.06537, over 7159.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2861, pruned_loss=0.061, over 1416491.66 frames.], batch size: 19, lr: 4.59e-04
2022-05-27 11:23:58,899 INFO [train.py:842] (1/4) Epoch 12, batch 1050, loss[loss=0.1583, simple_loss=0.2345, pruned_loss=0.04103, over 6988.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2869, pruned_loss=0.06129, over 1415231.81 frames.], batch size: 16, lr: 4.59e-04
2022-05-27 11:24:37,604 INFO [train.py:842] (1/4) Epoch 12, batch 1100, loss[loss=0.2017, simple_loss=0.2685, pruned_loss=0.06747, over 7156.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2878, pruned_loss=0.06173, over 1417331.31 frames.], batch size: 19, lr: 4.59e-04
2022-05-27 11:25:16,149 INFO [train.py:842] (1/4) Epoch 12, batch 1150, loss[loss=0.2906, simple_loss=0.3446, pruned_loss=0.1183, over 5178.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2877, pruned_loss=0.06148, over 1420072.35 frames.], batch size: 52, lr: 4.59e-04
2022-05-27 11:25:55,248 INFO [train.py:842] (1/4) Epoch 12, batch 1200, loss[loss=0.2059, simple_loss=0.2959, pruned_loss=0.05796, over 7441.00 frames.], tot_loss[loss=0.2055, simple_loss=0.288, pruned_loss=0.06149, over 1422997.66 frames.], batch size: 22, lr: 4.59e-04
2022-05-27 11:26:33,779 INFO [train.py:842] (1/4) Epoch 12, batch 1250, loss[loss=0.1712, simple_loss=0.2533, pruned_loss=0.04456, over 6993.00 frames.], tot_loss[loss=0.2059, simple_loss=0.288, pruned_loss=0.06186, over 1424230.32 frames.], batch size: 16, lr: 4.59e-04
2022-05-27 11:27:12,552 INFO [train.py:842] (1/4) Epoch 12, batch 1300, loss[loss=0.1739, simple_loss=0.2573, pruned_loss=0.04522, over 7331.00 frames.], tot_loss[loss=0.204, simple_loss=0.2863, pruned_loss=0.06083, over 1426739.47 frames.], batch size: 20, lr: 4.58e-04
2022-05-27 11:27:51,036 INFO [train.py:842] (1/4) Epoch 12, batch 1350, loss[loss=0.1958, simple_loss=0.2815, pruned_loss=0.05504, over 7320.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2865, pruned_loss=0.06111, over 1422757.71 frames.], batch size: 21, lr: 4.58e-04
2022-05-27 11:28:30,047 INFO [train.py:842] (1/4) Epoch 12, batch 1400, loss[loss=0.2442, simple_loss=0.3308, pruned_loss=0.07883, over 7320.00 frames.], tot_loss[loss=0.205, simple_loss=0.2868, pruned_loss=0.0616, over 1420226.29 frames.], batch size: 21, lr: 4.58e-04
2022-05-27 11:29:08,597 INFO [train.py:842] (1/4) Epoch 12, batch 1450, loss[loss=0.1922, simple_loss=0.2804, pruned_loss=0.05198, over 7049.00 frames.], tot_loss[loss=0.2051, simple_loss=0.287, pruned_loss=0.06158, over 1421029.32 frames.], batch size: 18, lr: 4.58e-04
2022-05-27 11:29:47,667 INFO [train.py:842] (1/4) Epoch 12, batch 1500, loss[loss=0.2302, simple_loss=0.3157, pruned_loss=0.07241, over 7194.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2861, pruned_loss=0.0612, over 1425118.99 frames.], batch size: 23, lr: 4.58e-04
2022-05-27 11:30:26,275 INFO [train.py:842] (1/4) Epoch 12, batch 1550, loss[loss=0.1762, simple_loss=0.2731, pruned_loss=0.03967, over 7229.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2846, pruned_loss=0.06045, over 1424671.19 frames.], batch size: 20, lr: 4.58e-04
2022-05-27 11:31:04,964 INFO [train.py:842] (1/4) Epoch 12, batch 1600, loss[loss=0.2208, simple_loss=0.2876, pruned_loss=0.07695, over 7365.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2864, pruned_loss=0.06134, over 1425420.94 frames.], batch size: 19, lr: 4.58e-04
2022-05-27 11:31:43,510 INFO [train.py:842] (1/4) Epoch 12, batch 1650, loss[loss=0.2047, simple_loss=0.2945, pruned_loss=0.05751, over 7390.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2876, pruned_loss=0.06242, over 1426630.31 frames.], batch size: 23, lr: 4.58e-04
2022-05-27 11:32:22,290 INFO [train.py:842] (1/4) Epoch 12, batch 1700, loss[loss=0.1678, simple_loss=0.2662, pruned_loss=0.03469, over 7208.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2869, pruned_loss=0.06136, over 1428294.25 frames.], batch size: 21, lr: 4.58e-04
2022-05-27 11:33:00,963 INFO [train.py:842] (1/4) Epoch 12, batch 1750, loss[loss=0.2909, simple_loss=0.3607, pruned_loss=0.1106, over 7094.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2882, pruned_loss=0.06219, over 1428777.93 frames.], batch size: 26, lr: 4.57e-04
2022-05-27 11:33:40,072 INFO [train.py:842] (1/4) Epoch 12, batch 1800, loss[loss=0.2063, simple_loss=0.2778, pruned_loss=0.06734, over 7010.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2873, pruned_loss=0.06168, over 1428645.11 frames.], batch size: 16, lr: 4.57e-04
2022-05-27 11:34:18,692 INFO [train.py:842] (1/4) Epoch 12, batch 1850, loss[loss=0.1836, simple_loss=0.2753, pruned_loss=0.04588, over 7208.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2872, pruned_loss=0.06164, over 1427555.51 frames.], batch size: 26, lr: 4.57e-04
2022-05-27 11:34:57,836 INFO [train.py:842] (1/4) Epoch 12, batch 1900, loss[loss=0.1813, simple_loss=0.2703, pruned_loss=0.04618, over 7428.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2869, pruned_loss=0.06195, over 1429908.87 frames.], batch size: 20, lr: 4.57e-04
2022-05-27 11:35:36,399 INFO [train.py:842] (1/4) Epoch 12, batch 1950, loss[loss=0.1757, simple_loss=0.2632, pruned_loss=0.04405, over 6999.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2866, pruned_loss=0.06198, over 1428917.68 frames.], batch size: 16, lr: 4.57e-04
2022-05-27 11:36:15,183 INFO [train.py:842] (1/4) Epoch 12, batch 2000, loss[loss=0.2556, simple_loss=0.3327, pruned_loss=0.08929, over 6348.00 frames.], tot_loss[loss=0.2068, simple_loss=0.288, pruned_loss=0.06283, over 1426912.41 frames.], batch size: 38, lr: 4.57e-04
2022-05-27 11:36:53,633 INFO [train.py:842] (1/4) Epoch 12, batch 2050, loss[loss=0.2463, simple_loss=0.318, pruned_loss=0.08726, over 7374.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2877, pruned_loss=0.0628, over 1424379.59 frames.], batch size: 23, lr: 4.57e-04
2022-05-27 11:37:32,475 INFO [train.py:842] (1/4) Epoch 12, batch 2100, loss[loss=0.2242, simple_loss=0.3096, pruned_loss=0.06934, over 6813.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2877, pruned_loss=0.0624, over 1428370.32 frames.], batch size: 31, lr: 4.57e-04
2022-05-27 11:38:11,269 INFO [train.py:842] (1/4) Epoch 12, batch 2150, loss[loss=0.1578, simple_loss=0.242, pruned_loss=0.03684, over 6853.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2872, pruned_loss=0.06249, over 1423108.89 frames.], batch size: 15, lr: 4.57e-04
2022-05-27 11:38:50,346 INFO [train.py:842] (1/4) Epoch 12, batch 2200, loss[loss=0.1827, simple_loss=0.26, pruned_loss=0.05272, over 7415.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2868, pruned_loss=0.06207, over 1427019.68 frames.], batch size: 20, lr: 4.56e-04
2022-05-27 11:39:29,072 INFO [train.py:842] (1/4) Epoch 12, batch 2250, loss[loss=0.2049, simple_loss=0.2783, pruned_loss=0.06581, over 7128.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2867, pruned_loss=0.06173, over 1425919.65 frames.], batch size: 17, lr: 4.56e-04
2022-05-27 11:40:07,789 INFO [train.py:842] (1/4) Epoch 12, batch 2300, loss[loss=0.1972, simple_loss=0.2703, pruned_loss=0.06201, over 7370.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2869, pruned_loss=0.06125, over 1424000.75 frames.], batch size: 19, lr: 4.56e-04
2022-05-27 11:40:46,484 INFO [train.py:842] (1/4) Epoch 12, batch 2350, loss[loss=0.2027, simple_loss=0.2915, pruned_loss=0.05689, over 7290.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2865, pruned_loss=0.06147, over 1426367.86 frames.], batch size: 24, lr: 4.56e-04
2022-05-27 11:41:25,318 INFO [train.py:842] (1/4) Epoch 12, batch 2400, loss[loss=0.2108, simple_loss=0.3007, pruned_loss=0.06048, over 7104.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2868, pruned_loss=0.0617, over 1428459.37 frames.], batch size: 21, lr: 4.56e-04
2022-05-27 11:42:03,660 INFO [train.py:842] (1/4) Epoch 12, batch 2450, loss[loss=0.181, simple_loss=0.275, pruned_loss=0.04347, over 7238.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2867, pruned_loss=0.06131, over 1426299.97 frames.], batch size: 20, lr: 4.56e-04
2022-05-27 11:42:42,645 INFO [train.py:842] (1/4) Epoch 12, batch 2500, loss[loss=0.1989, simple_loss=0.2703, pruned_loss=0.0637, over 7065.00 frames.], tot_loss[loss=0.203, simple_loss=0.2852, pruned_loss=0.06038, over 1424991.18 frames.], batch size: 18, lr: 4.56e-04
2022-05-27 11:43:21,060 INFO [train.py:842] (1/4) Epoch 12, batch 2550, loss[loss=0.1808, simple_loss=0.2558, pruned_loss=0.05288, over 7271.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2858, pruned_loss=0.06055, over 1427332.10 frames.], batch size: 17, lr: 4.56e-04
2022-05-27 11:43:59,769 INFO [train.py:842] (1/4) Epoch 12, batch 2600, loss[loss=0.1958, simple_loss=0.2808, pruned_loss=0.05545, over 7302.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2851, pruned_loss=0.06021, over 1421416.46 frames.], batch size: 24, lr: 4.56e-04
2022-05-27 11:44:38,256 INFO [train.py:842] (1/4) Epoch 12, batch 2650, loss[loss=0.2054, simple_loss=0.2838, pruned_loss=0.06348, over 7262.00 frames.], tot_loss[loss=0.203, simple_loss=0.2853, pruned_loss=0.0603, over 1419459.99 frames.], batch size: 19, lr: 4.55e-04
2022-05-27 11:45:17,038 INFO [train.py:842] (1/4) Epoch 12, batch 2700, loss[loss=0.2531, simple_loss=0.3361, pruned_loss=0.08508, over 7299.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2853, pruned_loss=0.0604, over 1423109.29 frames.], batch size: 25, lr: 4.55e-04
2022-05-27 11:45:55,573 INFO [train.py:842] (1/4) Epoch 12, batch 2750, loss[loss=0.2141, simple_loss=0.2923, pruned_loss=0.06799, over 7434.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2866, pruned_loss=0.06139, over 1425783.99 frames.], batch size: 20, lr: 4.55e-04
2022-05-27 11:46:34,709 INFO [train.py:842] (1/4) Epoch 12, batch 2800, loss[loss=0.2073, simple_loss=0.2899, pruned_loss=0.06234, over 7117.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2871, pruned_loss=0.06185, over 1426374.90 frames.], batch size: 21, lr: 4.55e-04
2022-05-27 11:47:13,252 INFO [train.py:842] (1/4) Epoch 12, batch 2850, loss[loss=0.1986, simple_loss=0.2816, pruned_loss=0.05784, over 7317.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2858, pruned_loss=0.06116, over 1428673.34 frames.], batch size: 21, lr: 4.55e-04
2022-05-27 11:47:54,701 INFO [train.py:842] (1/4) Epoch 12, batch 2900, loss[loss=0.235, simple_loss=0.3113, pruned_loss=0.07937, over 7274.00 frames.], tot_loss[loss=0.2052, simple_loss=0.287, pruned_loss=0.06174, over 1424762.77 frames.], batch size: 24, lr: 4.55e-04
2022-05-27 11:48:33,270 INFO [train.py:842] (1/4) Epoch 12, batch 2950, loss[loss=0.303, simple_loss=0.3505, pruned_loss=0.1277, over 7219.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2868, pruned_loss=0.0619, over 1420709.11 frames.], batch size: 21, lr: 4.55e-04
2022-05-27 11:49:12,203 INFO [train.py:842] (1/4) Epoch 12, batch 3000, loss[loss=0.2318, simple_loss=0.3204, pruned_loss=0.07153, over 7322.00 frames.], tot_loss[loss=0.205, simple_loss=0.2868, pruned_loss=0.06163, over 1421983.54 frames.], batch size: 25, lr: 4.55e-04
2022-05-27 11:49:12,204 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 11:49:21,566 INFO [train.py:871] (1/4) Epoch 12, validation: loss=0.172, simple_loss=0.2724, pruned_loss=0.03584, over 868885.00 frames.
2022-05-27 11:49:59,984 INFO [train.py:842] (1/4) Epoch 12, batch 3050, loss[loss=0.2444, simple_loss=0.3246, pruned_loss=0.08215, over 7367.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2873, pruned_loss=0.06108, over 1420156.41 frames.], batch size: 23, lr: 4.55e-04
2022-05-27 11:50:39,208 INFO [train.py:842] (1/4) Epoch 12, batch 3100, loss[loss=0.19, simple_loss=0.2755, pruned_loss=0.05225, over 7331.00 frames.], tot_loss[loss=0.2047, simple_loss=0.287, pruned_loss=0.06119, over 1422277.30 frames.], batch size: 20, lr: 4.54e-04
2022-05-27 11:51:17,938 INFO [train.py:842] (1/4) Epoch 12, batch 3150, loss[loss=0.2184, simple_loss=0.3128, pruned_loss=0.06202, over 7361.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2867, pruned_loss=0.06092, over 1424318.64 frames.], batch size: 23, lr: 4.54e-04
2022-05-27 11:51:56,828 INFO [train.py:842] (1/4) Epoch 12, batch 3200, loss[loss=0.2072, simple_loss=0.2966, pruned_loss=0.0589, over 7114.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2861, pruned_loss=0.061, over 1423800.05 frames.], batch size: 21, lr: 4.54e-04
2022-05-27 11:52:35,720 INFO [train.py:842] (1/4) Epoch 12, batch 3250, loss[loss=0.1691, simple_loss=0.2615, pruned_loss=0.03841, over 7410.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2856, pruned_loss=0.0609, over 1425233.45 frames.], batch size: 21, lr: 4.54e-04
2022-05-27 11:53:14,317 INFO [train.py:842] (1/4) Epoch 12, batch 3300, loss[loss=0.183, simple_loss=0.2576, pruned_loss=0.05421, over 6992.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2869, pruned_loss=0.06141, over 1425821.72 frames.], batch size: 16, lr: 4.54e-04
2022-05-27 11:53:52,891 INFO [train.py:842] (1/4) Epoch 12, batch 3350, loss[loss=0.1641, simple_loss=0.2476, pruned_loss=0.04034, over 7273.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2866, pruned_loss=0.06139, over 1426753.37 frames.], batch size: 18, lr: 4.54e-04
2022-05-27 11:54:31,804 INFO [train.py:842] (1/4) Epoch 12, batch 3400, loss[loss=0.2372, simple_loss=0.3259, pruned_loss=0.07422, over 6420.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2863, pruned_loss=0.06126, over 1421260.01 frames.], batch size: 38, lr: 4.54e-04
2022-05-27 11:55:10,427 INFO [train.py:842] (1/4) Epoch 12, batch 3450, loss[loss=0.2356, simple_loss=0.3115, pruned_loss=0.0798, over 7114.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2854, pruned_loss=0.06116, over 1418957.15 frames.], batch size: 21, lr: 4.54e-04
2022-05-27 11:55:49,192 INFO [train.py:842] (1/4) Epoch 12, batch 3500, loss[loss=0.2042, simple_loss=0.2918, pruned_loss=0.0583, over 7317.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2872, pruned_loss=0.0618, over 1424823.31 frames.], batch size: 21, lr: 4.54e-04
2022-05-27 11:56:27,756 INFO [train.py:842] (1/4) Epoch 12, batch 3550, loss[loss=0.1697, simple_loss=0.2482, pruned_loss=0.04559, over 7005.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2866, pruned_loss=0.0612, over 1423916.19 frames.], batch size: 16, lr: 4.53e-04
2022-05-27 11:57:06,220 INFO [train.py:842] (1/4) Epoch 12, batch 3600, loss[loss=0.2069, simple_loss=0.3002, pruned_loss=0.0568, over 7238.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2874, pruned_loss=0.06103, over 1426118.52 frames.], batch size: 20, lr: 4.53e-04
2022-05-27 11:57:44,690 INFO [train.py:842] (1/4) Epoch 12, batch 3650, loss[loss=0.1802, simple_loss=0.2632, pruned_loss=0.04862, over 7430.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2862, pruned_loss=0.06036, over 1424962.50 frames.], batch size: 20, lr: 4.53e-04
2022-05-27 11:58:23,630 INFO [train.py:842] (1/4) Epoch 12, batch 3700, loss[loss=0.186, simple_loss=0.2762, pruned_loss=0.04791, over 6848.00 frames.], tot_loss[loss=0.2038, simple_loss=0.286, pruned_loss=0.06084, over 1421556.60 frames.], batch size: 31, lr: 4.53e-04
2022-05-27 11:59:02,231 INFO [train.py:842] (1/4) Epoch 12, batch 3750, loss[loss=0.2327, simple_loss=0.3054, pruned_loss=0.07994, over 7384.00 frames.], tot_loss[loss=0.204, simple_loss=0.286, pruned_loss=0.06094, over 1425406.42 frames.], batch size: 23, lr: 4.53e-04
2022-05-27 11:59:41,088 INFO [train.py:842] (1/4) Epoch 12, batch 3800, loss[loss=0.2427, simple_loss=0.3077, pruned_loss=0.08885, over 7166.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2873, pruned_loss=0.06183, over 1428007.05 frames.], batch size: 26, lr: 4.53e-04
2022-05-27 12:00:19,479 INFO [train.py:842] (1/4) Epoch 12, batch 3850, loss[loss=0.1716, simple_loss=0.2613, pruned_loss=0.0409, over 7059.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2867, pruned_loss=0.06076, over 1428310.76 frames.], batch size: 18, lr: 4.53e-04
2022-05-27 12:00:58,242 INFO [train.py:842] (1/4) Epoch 12, batch 3900, loss[loss=0.2194, simple_loss=0.3013, pruned_loss=0.06874, over 5068.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2863, pruned_loss=0.06056, over 1429569.61 frames.], batch size: 52, lr: 4.53e-04
2022-05-27 12:01:36,970 INFO [train.py:842] (1/4) Epoch 12, batch 3950, loss[loss=0.2003, simple_loss=0.2859, pruned_loss=0.05739, over 7254.00 frames.], tot_loss[loss=0.2049, simple_loss=0.287, pruned_loss=0.06138, over 1430426.36 frames.], batch size: 19, lr: 4.53e-04
2022-05-27 12:02:15,755 INFO [train.py:842] (1/4) Epoch 12, batch 4000, loss[loss=0.2028, simple_loss=0.2705, pruned_loss=0.06754, over 7353.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2866, pruned_loss=0.0612, over 1427105.27 frames.], batch size: 19, lr: 4.53e-04
2022-05-27 12:02:54,558 INFO [train.py:842] (1/4) Epoch 12, batch 4050, loss[loss=0.2822, simple_loss=0.3311, pruned_loss=0.1167, over 7407.00 frames.], tot_loss[loss=0.206, simple_loss=0.2875, pruned_loss=0.06221, over 1426980.75 frames.], batch size: 18, lr: 4.52e-04
2022-05-27 12:03:33,381 INFO [train.py:842] (1/4) Epoch 12, batch 4100, loss[loss=0.2133, simple_loss=0.3014, pruned_loss=0.06259, over 7111.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2876, pruned_loss=0.06269, over 1422863.79 frames.], batch size: 21, lr: 4.52e-04
2022-05-27 12:04:12,129 INFO [train.py:842] (1/4) Epoch 12, batch 4150, loss[loss=0.2033, simple_loss=0.2816, pruned_loss=0.06247, over 7206.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2882, pruned_loss=0.06281, over 1424205.31 frames.], batch size: 22, lr: 4.52e-04
2022-05-27 12:04:50,971 INFO [train.py:842] (1/4) Epoch 12, batch 4200, loss[loss=0.2136, simple_loss=0.3062, pruned_loss=0.06051, over 7141.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2883, pruned_loss=0.06253, over 1426126.80 frames.], batch size: 20, lr: 4.52e-04
2022-05-27 12:05:29,425 INFO [train.py:842] (1/4) Epoch 12, batch 4250, loss[loss=0.2066, simple_loss=0.3025, pruned_loss=0.05536, over 6771.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2881, pruned_loss=0.0623, over 1423892.17 frames.], batch size: 31, lr: 4.52e-04
2022-05-27 12:06:08,270 INFO [train.py:842] (1/4) Epoch 12, batch 4300, loss[loss=0.2646, simple_loss=0.314, pruned_loss=0.1076, over 7280.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2885, pruned_loss=0.06248, over 1425916.33 frames.], batch size: 17, lr: 4.52e-04
2022-05-27 12:06:46,749 INFO [train.py:842] (1/4) Epoch 12, batch 4350, loss[loss=0.1768, simple_loss=0.2527, pruned_loss=0.05042, over 7157.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2895, pruned_loss=0.06268, over 1419110.32 frames.], batch size: 18, lr: 4.52e-04
2022-05-27 12:07:25,736 INFO [train.py:842] (1/4) Epoch 12, batch 4400, loss[loss=0.2101, simple_loss=0.2819, pruned_loss=0.06913, over 7118.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2892, pruned_loss=0.06271, over 1422383.22 frames.], batch size: 21, lr: 4.52e-04
2022-05-27 12:08:04,079 INFO [train.py:842] (1/4) Epoch 12, batch 4450, loss[loss=0.1763, simple_loss=0.2624, pruned_loss=0.04506, over 7262.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2885, pruned_loss=0.0626, over 1420276.12 frames.], batch size: 19, lr: 4.52e-04
2022-05-27 12:08:43,194 INFO [train.py:842] (1/4) Epoch 12, batch 4500, loss[loss=0.1913, simple_loss=0.2707, pruned_loss=0.05593, over 7408.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2872, pruned_loss=0.06213, over 1424200.08 frames.], batch size: 18, lr: 4.51e-04
2022-05-27 12:09:22,014 INFO [train.py:842] (1/4) Epoch 12, batch 4550, loss[loss=0.2025, simple_loss=0.2846, pruned_loss=0.0602, over 7151.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2873, pruned_loss=0.06247, over 1425093.81 frames.], batch size: 20, lr: 4.51e-04
2022-05-27 12:10:00,792 INFO [train.py:842] (1/4) Epoch 12, batch 4600, loss[loss=0.2057, simple_loss=0.2936, pruned_loss=0.05888, over 7064.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2868, pruned_loss=0.06167, over 1420976.44 frames.], batch size: 28, lr: 4.51e-04
2022-05-27 12:10:39,214 INFO [train.py:842] (1/4) Epoch 12, batch 4650, loss[loss=0.1919, simple_loss=0.2843, pruned_loss=0.04972, over 7331.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2867, pruned_loss=0.06124, over 1423407.42 frames.], batch size: 21, lr: 4.51e-04
2022-05-27 12:11:18,047 INFO [train.py:842] (1/4) Epoch 12, batch 4700, loss[loss=0.2239, simple_loss=0.3007, pruned_loss=0.07352, over 5032.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2869, pruned_loss=0.06145, over 1421117.65 frames.], batch size: 52, lr: 4.51e-04
2022-05-27 12:11:56,429 INFO [train.py:842] (1/4) Epoch 12, batch 4750, loss[loss=0.2033, simple_loss=0.2768, pruned_loss=0.06495, over 7268.00 frames.], tot_loss[loss=0.2049, simple_loss=0.287, pruned_loss=0.0614, over 1422593.33 frames.], batch size: 19, lr: 4.51e-04
2022-05-27 12:12:35,430 INFO [train.py:842] (1/4) Epoch 12, batch 4800, loss[loss=0.1891, simple_loss=0.2736, pruned_loss=0.05232, over 7356.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2875, pruned_loss=0.06156, over 1423943.35 frames.], batch size: 19, lr: 4.51e-04
2022-05-27 12:13:13,831 INFO [train.py:842] (1/4) Epoch 12, batch 4850, loss[loss=0.2236, simple_loss=0.2895, pruned_loss=0.07882, over 7158.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2866, pruned_loss=0.06135, over 1425880.19 frames.], batch size: 18, lr: 4.51e-04
2022-05-27 12:13:52,885 INFO [train.py:842] (1/4) Epoch 12, batch 4900, loss[loss=0.2294, simple_loss=0.3055, pruned_loss=0.07661, over 7401.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2856, pruned_loss=0.06046, over 1426142.09 frames.], batch size: 18, lr: 4.51e-04
2022-05-27 12:14:31,295 INFO [train.py:842] (1/4) Epoch 12, batch 4950, loss[loss=0.2129, simple_loss=0.2977, pruned_loss=0.06405, over 7199.00 frames.], tot_loss[loss=0.204, simple_loss=0.286, pruned_loss=0.06098, over 1423933.39 frames.], batch size: 26, lr: 4.50e-04
2022-05-27 12:15:10,343 INFO [train.py:842] (1/4) Epoch 12, batch 5000, loss[loss=0.1501, simple_loss=0.2295, pruned_loss=0.03537, over 7423.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2845, pruned_loss=0.06022, over 1418016.73 frames.], batch size: 18, lr: 4.50e-04
2022-05-27 12:15:48,943 INFO [train.py:842] (1/4) Epoch 12, batch 5050, loss[loss=0.186, simple_loss=0.2805, pruned_loss=0.0457, over 7061.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2841, pruned_loss=0.06026, over 1422522.57 frames.], batch size: 18, lr: 4.50e-04
2022-05-27 12:16:27,511 INFO [train.py:842] (1/4) Epoch 12, batch 5100, loss[loss=0.1962, simple_loss=0.2822, pruned_loss=0.05511, over 7215.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2847, pruned_loss=0.06041, over 1417428.30 frames.], batch size: 22, lr: 4.50e-04
2022-05-27 12:17:06,090 INFO [train.py:842] (1/4) Epoch 12, batch 5150, loss[loss=0.2022, simple_loss=0.2942, pruned_loss=0.0551, over 7207.00 frames.], tot_loss[loss=0.2032, simple_loss=0.285, pruned_loss=0.06074, over 1421465.85 frames.], batch size: 22, lr: 4.50e-04
2022-05-27 12:17:44,834 INFO [train.py:842] (1/4) Epoch 12, batch 5200, loss[loss=0.206, simple_loss=0.3025, pruned_loss=0.05479, over 7233.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2854, pruned_loss=0.06085, over 1422534.00 frames.], batch size: 20, lr: 4.50e-04
2022-05-27 12:18:23,384 INFO [train.py:842] (1/4) Epoch 12, batch 5250, loss[loss=0.2601, simple_loss=0.3226, pruned_loss=0.09875, over 7298.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2853, pruned_loss=0.0612, over 1421201.78 frames.], batch size: 24, lr: 4.50e-04
2022-05-27 12:19:02,159 INFO [train.py:842] (1/4) Epoch 12, batch 5300, loss[loss=0.1596, simple_loss=0.2406, pruned_loss=0.03932, over 7233.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2851, pruned_loss=0.06101, over 1420802.77 frames.], batch size: 16, lr: 4.50e-04
2022-05-27 12:19:41,025 INFO [train.py:842] (1/4) Epoch 12, batch 5350, loss[loss=0.1817, simple_loss=0.2723, pruned_loss=0.04556, over 6496.00 frames.], tot_loss[loss=0.204, simple_loss=0.2854, pruned_loss=0.0613, over 1421265.09 frames.], batch size: 38, lr: 4.50e-04
2022-05-27 12:20:19,736 INFO [train.py:842] (1/4) Epoch 12, batch 5400, loss[loss=0.1896, simple_loss=0.2833, pruned_loss=0.04796, over 7222.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2854, pruned_loss=0.06066, over 1420606.47 frames.], batch size: 21, lr: 4.50e-04
2022-05-27 12:20:58,140 INFO [train.py:842] (1/4) Epoch 12, batch 5450, loss[loss=0.2655, simple_loss=0.3237, pruned_loss=0.1036, over 7322.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2859, pruned_loss=0.06096, over 1419843.83 frames.], batch size: 20, lr: 4.49e-04
2022-05-27 12:21:37,207 INFO [train.py:842] (1/4) Epoch 12, batch 5500, loss[loss=0.1649, simple_loss=0.2405, pruned_loss=0.04461, over 7272.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2863, pruned_loss=0.06131, over 1418007.54 frames.], batch size: 18, lr: 4.49e-04
2022-05-27 12:22:15,894 INFO [train.py:842] (1/4) Epoch 12, batch 5550, loss[loss=0.1759, simple_loss=0.277, pruned_loss=0.03743, over 7318.00 frames.], tot_loss[loss=0.203, simple_loss=0.2847, pruned_loss=0.06062, over 1423637.82 frames.], batch size: 21, lr: 4.49e-04
2022-05-27 12:22:54,575 INFO [train.py:842] (1/4) Epoch 12, batch 5600, loss[loss=0.1887, simple_loss=0.2827, pruned_loss=0.04731, over 7137.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2857, pruned_loss=0.06124, over 1419402.22 frames.], batch size: 20, lr: 4.49e-04
2022-05-27 12:23:33,076 INFO [train.py:842] (1/4) Epoch 12, batch 5650, loss[loss=0.2259, simple_loss=0.3109, pruned_loss=0.07046, over 7178.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2848, pruned_loss=0.06083, over 1423549.82 frames.], batch size: 26, lr: 4.49e-04
2022-05-27 12:24:12,164 INFO [train.py:842] (1/4) Epoch 12, batch 5700, loss[loss=0.1664, simple_loss=0.2599, pruned_loss=0.03646, over 7363.00 frames.], tot_loss[loss=0.2024, simple_loss=0.284, pruned_loss=0.06039, over 1422832.59 frames.], batch size: 19, lr: 4.49e-04
2022-05-27 12:24:50,780 INFO [train.py:842] (1/4) Epoch 12, batch 5750, loss[loss=0.2163, simple_loss=0.2992, pruned_loss=0.06673, over 7108.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2856, pruned_loss=0.06142, over 1425453.51 frames.], batch size: 21, lr: 4.49e-04
2022-05-27 12:25:29,879 INFO [train.py:842] (1/4) Epoch 12, batch 5800, loss[loss=0.178, simple_loss=0.2702, pruned_loss=0.04295, over 7276.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2857, pruned_loss=0.06142, over 1423609.97 frames.], batch size: 25, lr: 4.49e-04
2022-05-27 12:26:08,564 INFO [train.py:842] (1/4) Epoch 12, batch 5850, loss[loss=0.2041, simple_loss=0.2741, pruned_loss=0.06708, over 6765.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2856, pruned_loss=0.06152, over 1422416.24 frames.], batch size: 15, lr: 4.49e-04
2022-05-27 12:26:47,346 INFO [train.py:842] (1/4) Epoch 12, batch 5900, loss[loss=0.2989, simple_loss=0.3613, pruned_loss=0.1182, over 6797.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2867, pruned_loss=0.06209, over 1425929.72 frames.], batch size: 31, lr: 4.48e-04
2022-05-27 12:27:25,930 INFO [train.py:842] (1/4) Epoch 12, batch 5950, loss[loss=0.1793, simple_loss=0.2686, pruned_loss=0.04502, over 7420.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2855, pruned_loss=0.06106, over 1429796.59 frames.], batch size: 20, lr: 4.48e-04
2022-05-27 12:28:04,952 INFO [train.py:842] (1/4) Epoch 12, batch 6000, loss[loss=0.1852, simple_loss=0.2786, pruned_loss=0.04593, over 7095.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2852, pruned_loss=0.0608, over 1427532.95 frames.], batch size: 28, lr: 4.48e-04
2022-05-27 12:28:04,953 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 12:28:14,266 INFO [train.py:871] (1/4) Epoch 12, validation: loss=0.1696, simple_loss=0.2704, pruned_loss=0.03442, over 868885.00 frames.
2022-05-27 12:28:52,729 INFO [train.py:842] (1/4) Epoch 12, batch 6050, loss[loss=0.1819, simple_loss=0.2603, pruned_loss=0.05177, over 6996.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2846, pruned_loss=0.0605, over 1428292.44 frames.], batch size: 16, lr: 4.48e-04
2022-05-27 12:29:32,021 INFO [train.py:842] (1/4) Epoch 12, batch 6100, loss[loss=0.2157, simple_loss=0.2963, pruned_loss=0.06753, over 7143.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2835, pruned_loss=0.05997, over 1430834.57 frames.], batch size: 19, lr: 4.48e-04
2022-05-27 12:30:10,615 INFO [train.py:842] (1/4) Epoch 12, batch 6150, loss[loss=0.17, simple_loss=0.2588, pruned_loss=0.04061, over 7253.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2852, pruned_loss=0.06074, over 1430187.08 frames.], batch size: 19, lr: 4.48e-04
2022-05-27 12:30:49,461 INFO [train.py:842] (1/4) Epoch 12, batch 6200, loss[loss=0.2014, simple_loss=0.2889, pruned_loss=0.057, over 7236.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2852, pruned_loss=0.06077, over 1428915.58 frames.], batch size: 20, lr: 4.48e-04
2022-05-27 12:31:27,893 INFO [train.py:842] (1/4) Epoch 12, batch 6250, loss[loss=0.2266, simple_loss=0.2875, pruned_loss=0.08286, over 7159.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2852, pruned_loss=0.06046, over 1425575.42 frames.], batch size: 18, lr: 4.48e-04
2022-05-27 12:32:06,965 INFO [train.py:842] (1/4) Epoch 12, batch 6300, loss[loss=0.2174, simple_loss=0.31, pruned_loss=0.06243, over 6632.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2839, pruned_loss=0.05986, over 1427986.05 frames.], batch size: 31, lr: 4.48e-04
2022-05-27 12:32:45,630 INFO [train.py:842] (1/4) Epoch 12, batch 6350, loss[loss=0.194, simple_loss=0.2877, pruned_loss=0.05019, over 7139.00 frames.], tot_loss[loss=0.2028, simple_loss=0.285, pruned_loss=0.06024, over 1426392.41 frames.], batch size: 20, lr: 4.48e-04
2022-05-27 12:33:24,540 INFO [train.py:842] (1/4) Epoch 12, batch 6400, loss[loss=0.2128, simple_loss=0.2994, pruned_loss=0.06308, over 7174.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2858, pruned_loss=0.06072, over 1418728.16 frames.], batch size: 26, lr: 4.47e-04
2022-05-27 12:34:03,183 INFO [train.py:842] (1/4) Epoch 12, batch 6450, loss[loss=0.1553, simple_loss=0.2364, pruned_loss=0.03703, over 7155.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2856, pruned_loss=0.06152, over 1418478.04 frames.], batch size: 18, lr: 4.47e-04
2022-05-27 12:34:41,838 INFO [train.py:842] (1/4) Epoch 12, batch 6500, loss[loss=0.2395, simple_loss=0.3108, pruned_loss=0.08409, over 7208.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2855, pruned_loss=0.06091, over 1421823.61 frames.], batch size: 22, lr: 4.47e-04
2022-05-27 12:35:20,541 INFO [train.py:842] (1/4) Epoch 12, batch 6550, loss[loss=0.2108, simple_loss=0.2867, pruned_loss=0.0675, over 7147.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2853, pruned_loss=0.06112, over 1420773.52 frames.], batch size: 18, lr: 4.47e-04
2022-05-27 12:35:59,555 INFO [train.py:842] (1/4) Epoch 12, batch 6600, loss[loss=0.1713, simple_loss=0.2582, pruned_loss=0.04217, over 7432.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2828, pruned_loss=0.05953, over 1420236.70 frames.], batch size: 20, lr: 4.47e-04
2022-05-27 12:36:38,270 INFO [train.py:842] (1/4) Epoch 12, batch 6650, loss[loss=0.1852, simple_loss=0.2669, pruned_loss=0.0518, over 7286.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2843, pruned_loss=0.06043, over 1421067.70 frames.], batch size: 17, lr: 4.47e-04
2022-05-27 12:37:17,217 INFO [train.py:842] (1/4) Epoch 12, batch 6700, loss[loss=0.205, simple_loss=0.2878, pruned_loss=0.06108, over 7145.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2848, pruned_loss=0.06089, over 1418204.01 frames.], batch size: 20, lr: 4.47e-04
2022-05-27 12:37:56,139 INFO [train.py:842] (1/4) Epoch 12, batch 6750, loss[loss=0.2083, simple_loss=0.301, pruned_loss=0.05776, over 7339.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2849, pruned_loss=0.0603, over 1419794.39 frames.], batch size: 22, lr: 4.47e-04
2022-05-27 12:38:35,155 INFO [train.py:842] (1/4) Epoch 12, batch 6800, loss[loss=0.1987, simple_loss=0.2826, pruned_loss=0.05742, over 7152.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2862, pruned_loss=0.06145, over 1421585.21 frames.], batch size: 19, lr: 4.47e-04
2022-05-27 12:39:13,723 INFO [train.py:842] (1/4) Epoch 12, batch 6850, loss[loss=0.1653, simple_loss=0.2613, pruned_loss=0.0346, over 7231.00 frames.], tot_loss[loss=0.204, simple_loss=0.2858, pruned_loss=0.06104, over 1423028.95 frames.], batch size: 20, lr: 4.47e-04
2022-05-27 12:39:52,705 INFO [train.py:842] (1/4) Epoch 12, batch 6900, loss[loss=0.2074, simple_loss=0.2923, pruned_loss=0.06129, over 6747.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2859, pruned_loss=0.06182, over 1420418.36 frames.], batch size: 31, lr: 4.46e-04
2022-05-27 12:40:31,031 INFO [train.py:842] (1/4) Epoch 12, batch 6950, loss[loss=0.19, simple_loss=0.2726, pruned_loss=0.05365, over 7123.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2863, pruned_loss=0.06172, over 1417158.10 frames.], batch size: 21, lr: 4.46e-04
2022-05-27 12:41:09,839 INFO [train.py:842] (1/4) Epoch 12, batch 7000, loss[loss=0.2322, simple_loss=0.3097, pruned_loss=0.07731, over 7211.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2863, pruned_loss=0.06179, over 1415009.29 frames.], batch size: 23, lr: 4.46e-04
2022-05-27 12:41:48,362 INFO [train.py:842] (1/4) Epoch 12, batch 7050, loss[loss=0.1915, simple_loss=0.2792, pruned_loss=0.05185, over 6229.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2869, pruned_loss=0.06176, over 1417624.01 frames.], batch size: 37, lr: 4.46e-04
2022-05-27 12:42:27,201 INFO [train.py:842] (1/4) Epoch 12, batch 7100, loss[loss=0.1885, simple_loss=0.2733, pruned_loss=0.05184, over 7137.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2869, pruned_loss=0.06187, over 1415533.54 frames.], batch size: 20, lr: 4.46e-04
2022-05-27 12:43:05,921 INFO [train.py:842] (1/4) Epoch 12, batch 7150, loss[loss=0.1854, simple_loss=0.2735, pruned_loss=0.04865, over 7436.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2861, pruned_loss=0.06187, over 1420005.66 frames.], batch size: 20, lr: 4.46e-04
2022-05-27 12:43:44,467 INFO [train.py:842] (1/4) Epoch 12, batch 7200, loss[loss=0.2276, simple_loss=0.3121, pruned_loss=0.07157, over 7412.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2859, pruned_loss=0.0609, over 1422605.02 frames.], batch size: 21, lr: 4.46e-04
2022-05-27 12:44:23,096 INFO [train.py:842] (1/4) Epoch 12, batch 7250, loss[loss=0.1999, simple_loss=0.2915, pruned_loss=0.05416, over 7121.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2861, pruned_loss=0.06088, over 1418190.16 frames.], batch size: 21, lr: 4.46e-04
2022-05-27 12:45:01,909 INFO [train.py:842] (1/4) Epoch 12, batch 7300, loss[loss=0.1824, simple_loss=0.2583, pruned_loss=0.05331, over 7011.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2854, pruned_loss=0.06052, over 1420984.13 frames.], batch size: 16, lr: 4.46e-04
2022-05-27 12:45:40,535 INFO [train.py:842] (1/4) Epoch 12, batch 7350, loss[loss=0.1817, simple_loss=0.2683, pruned_loss=0.04756, over 7136.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2861, pruned_loss=0.06081, over 1422768.04 frames.], batch size: 17, lr: 4.45e-04
2022-05-27 12:46:19,330 INFO [train.py:842] (1/4) Epoch 12, batch 7400, loss[loss=0.1787, simple_loss=0.2589, pruned_loss=0.04927, over 7416.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2858, pruned_loss=0.06044, over 1423487.76 frames.], batch size: 18, lr: 4.45e-04
2022-05-27 12:46:57,756 INFO [train.py:842] (1/4) Epoch 12, batch 7450, loss[loss=0.1634, simple_loss=0.2487, pruned_loss=0.03901, over 7285.00 frames.], tot_loss[loss=0.203, simple_loss=0.2862, pruned_loss=0.05992, over 1426167.57 frames.], batch size: 18, lr: 4.45e-04
2022-05-27 12:47:36,479 INFO [train.py:842] (1/4) Epoch 12, batch 7500, loss[loss=0.1919, simple_loss=0.2777, pruned_loss=0.05307, over 6745.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2856, pruned_loss=0.05973, over 1426763.75 frames.], batch size: 31, lr: 4.45e-04
2022-05-27 12:48:15,135 INFO [train.py:842] (1/4) Epoch 12, batch 7550, loss[loss=0.1852, simple_loss=0.2685, pruned_loss=0.05096, over 7143.00 frames.], tot_loss[loss=0.2009, simple_loss=0.284, pruned_loss=0.05892, over 1426472.11 frames.], batch size: 20, lr: 4.45e-04
2022-05-27 12:48:54,050 INFO [train.py:842] (1/4) Epoch 12, batch 7600, loss[loss=0.2573, simple_loss=0.3319, pruned_loss=0.09132, over 7321.00 frames.], tot_loss[loss=0.201, simple_loss=0.2842, pruned_loss=0.05894, over 1431920.71 frames.], batch size: 21, lr: 4.45e-04
2022-05-27 12:49:32,614 INFO [train.py:842] (1/4) Epoch 12, batch 7650, loss[loss=0.2731, simple_loss=0.3458, pruned_loss=0.1002, over 7142.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2855, pruned_loss=0.06001, over 1423814.98 frames.], batch size: 20, lr: 4.45e-04
2022-05-27 12:50:11,574 INFO [train.py:842] (1/4) Epoch 12, batch 7700, loss[loss=0.1664, simple_loss=0.244, pruned_loss=0.0444, over 7280.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2854, pruned_loss=0.05965, over 1424297.67 frames.], batch size: 17, lr: 4.45e-04
2022-05-27 12:50:50,206 INFO [train.py:842] (1/4) Epoch 12, batch 7750, loss[loss=0.182, simple_loss=0.2642, pruned_loss=0.04987, over 6803.00 frames.], tot_loss[loss=0.202, simple_loss=0.285, pruned_loss=0.05952, over 1420960.12 frames.], batch size: 15, lr: 4.45e-04
2022-05-27 12:51:28,915 INFO [train.py:842] (1/4) Epoch 12, batch 7800, loss[loss=0.2112, simple_loss=0.2965, pruned_loss=0.06296, over 7325.00 frames.], tot_loss[loss=0.202, simple_loss=0.285, pruned_loss=0.05953, over 1420141.15 frames.], batch size: 20, lr: 4.45e-04
2022-05-27 12:52:07,451 INFO [train.py:842] (1/4) Epoch 12, batch 7850, loss[loss=0.2951, simple_loss=0.3493, pruned_loss=0.1205, over 4594.00 frames.], tot_loss[loss=0.202, simple_loss=0.285, pruned_loss=0.05953, over 1417782.18 frames.], batch size: 52, lr: 4.44e-04
2022-05-27 12:52:46,490 INFO [train.py:842] (1/4) Epoch 12, batch 7900, loss[loss=0.252, simple_loss=0.325, pruned_loss=0.08946, over 7211.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2848, pruned_loss=0.05984, over 1422268.30 frames.], batch size: 23, lr: 4.44e-04
2022-05-27 12:53:25,079 INFO [train.py:842] (1/4) Epoch 12, batch 7950, loss[loss=0.2286, simple_loss=0.311, pruned_loss=0.07306, over 7296.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2863, pruned_loss=0.0612, over 1424459.91 frames.], batch size: 24, lr: 4.44e-04
2022-05-27 12:54:03,975 INFO [train.py:842] (1/4) Epoch 12, batch 8000, loss[loss=0.1669, simple_loss=0.2567, pruned_loss=0.03855, over 7163.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2855, pruned_loss=0.06064, over 1424492.14 frames.], batch size: 18, lr: 4.44e-04
2022-05-27 12:54:42,599 INFO [train.py:842] (1/4) Epoch 12, batch 8050, loss[loss=0.2236, simple_loss=0.3046, pruned_loss=0.07132, over 7374.00 frames.], tot_loss[loss=0.2042, simple_loss=0.286, pruned_loss=0.06117, over 1428825.35 frames.], batch size: 23, lr: 4.44e-04
2022-05-27 12:55:21,330 INFO [train.py:842] (1/4) Epoch 12, batch 8100, loss[loss=0.2073, simple_loss=0.2944, pruned_loss=0.0601, over 7220.00 frames.], tot_loss[loss=0.2053, simple_loss=0.287, pruned_loss=0.06183, over 1429317.09 frames.], batch size: 21, lr: 4.44e-04
2022-05-27 12:55:59,851 INFO [train.py:842] (1/4) Epoch 12, batch 8150, loss[loss=0.1957, simple_loss=0.2922, pruned_loss=0.04957, over 7146.00 frames.], tot_loss[loss=0.2041, simple_loss=0.286, pruned_loss=0.06109, over 1426351.11 frames.], batch size: 20, lr: 4.44e-04
2022-05-27 12:56:49,270 INFO [train.py:842] (1/4) Epoch 12, batch 8200, loss[loss=0.1952, simple_loss=0.282, pruned_loss=0.05421, over 7232.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2864, pruned_loss=0.06117, over 1426768.28 frames.], batch size: 21, lr: 4.44e-04
2022-05-27 12:57:27,634 INFO [train.py:842] (1/4) Epoch 12, batch 8250, loss[loss=0.234, simple_loss=0.3202, pruned_loss=0.0739, over 7211.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2862, pruned_loss=0.06125, over 1425417.66 frames.], batch size: 26, lr: 4.44e-04
2022-05-27 12:58:06,341 INFO [train.py:842] (1/4) Epoch 12, batch 8300, loss[loss=0.1854, simple_loss=0.2672, pruned_loss=0.0518, over 7211.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2866, pruned_loss=0.06134, over 1423971.96 frames.], batch size: 22, lr: 4.44e-04
2022-05-27 12:58:44,891 INFO [train.py:842] (1/4) Epoch 12, batch 8350, loss[loss=0.2305, simple_loss=0.3033, pruned_loss=0.07879, over 4997.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2848, pruned_loss=0.0602, over 1427274.30 frames.], batch size: 53, lr: 4.43e-04
2022-05-27 12:59:23,804 INFO [train.py:842] (1/4) Epoch 12, batch 8400, loss[loss=0.2239, simple_loss=0.301, pruned_loss=0.07343, over 7309.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2851, pruned_loss=0.06058, over 1428512.82 frames.], batch size: 24, lr: 4.43e-04
2022-05-27 13:00:02,281 INFO [train.py:842] (1/4) Epoch 12, batch 8450, loss[loss=0.2093, simple_loss=0.2901, pruned_loss=0.06427, over 6752.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2849, pruned_loss=0.06026, over 1428102.14 frames.], batch size: 31, lr: 4.43e-04
2022-05-27 13:00:41,083 INFO [train.py:842] (1/4) Epoch 12, batch 8500, loss[loss=0.218, simple_loss=0.2973, pruned_loss=0.06934, over 7154.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2852, pruned_loss=0.06104, over 1427711.90 frames.], batch size: 19, lr: 4.43e-04
2022-05-27 13:01:19,602 INFO [train.py:842] (1/4) Epoch 12, batch 8550, loss[loss=0.1526, simple_loss=0.2439, pruned_loss=0.03059, over 7119.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2844, pruned_loss=0.06043, over 1426452.75 frames.], batch size: 17, lr: 4.43e-04
2022-05-27 13:01:58,555 INFO [train.py:842] (1/4) Epoch 12, batch 8600, loss[loss=0.2072, simple_loss=0.2816, pruned_loss=0.06644, over 7284.00 frames.], tot_loss[loss=0.204, simple_loss=0.2852, pruned_loss=0.06137, over 1424935.61 frames.], batch size: 18, lr: 4.43e-04
2022-05-27 13:02:36,980 INFO [train.py:842] (1/4) Epoch 12, batch 8650, loss[loss=0.2047, simple_loss=0.2748, pruned_loss=0.06728, over 7135.00 frames.], tot_loss[loss=0.205, simple_loss=0.2862, pruned_loss=0.06189, over 1421432.51 frames.], batch size: 17, lr: 4.43e-04
2022-05-27 13:03:15,870 INFO [train.py:842] (1/4) Epoch 12, batch 8700, loss[loss=0.2213, simple_loss=0.3058, pruned_loss=0.06839, over 7069.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2861, pruned_loss=0.06169, over 1421249.23 frames.], batch size: 28, lr: 4.43e-04
2022-05-27 13:03:54,263 INFO [train.py:842] (1/4) Epoch 12, batch 8750, loss[loss=0.2115, simple_loss=0.2835, pruned_loss=0.06972, over 5274.00 frames.], tot_loss[loss=0.204, simple_loss=0.2854, pruned_loss=0.06132, over 1418567.55 frames.], batch size: 52, lr: 4.43e-04
2022-05-27 13:04:33,563 INFO [train.py:842] (1/4) Epoch 12, batch 8800, loss[loss=0.2108, simple_loss=0.2977, pruned_loss=0.06196, over 7183.00 frames.], tot_loss[loss=0.204, simple_loss=0.2853, pruned_loss=0.06132, over 1415462.66 frames.], batch size: 26, lr: 4.43e-04
2022-05-27 13:05:12,095 INFO [train.py:842] (1/4) Epoch 12, batch 8850, loss[loss=0.2018, simple_loss=0.295, pruned_loss=0.05433, over 7114.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2838, pruned_loss=0.06101, over 1413530.56 frames.], batch size: 21, lr: 4.42e-04
2022-05-27 13:05:50,823 INFO [train.py:842] (1/4) Epoch 12, batch 8900, loss[loss=0.2124, simple_loss=0.2929, pruned_loss=0.06601, over 7300.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2834, pruned_loss=0.061, over 1410292.46 frames.], batch size: 24, lr: 4.42e-04
2022-05-27 13:06:29,082 INFO [train.py:842] (1/4) Epoch 12, batch 8950, loss[loss=0.284, simple_loss=0.3454, pruned_loss=0.1113, over 6338.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2822, pruned_loss=0.061, over 1396324.77 frames.], batch size: 38, lr: 4.42e-04
2022-05-27 13:07:07,597 INFO [train.py:842] (1/4) Epoch 12, batch 9000, loss[loss=0.2581, simple_loss=0.324, pruned_loss=0.0961, over 5286.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2825, pruned_loss=0.06135, over 1387640.50 frames.], batch size: 52, lr: 4.42e-04
2022-05-27 13:07:07,597 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 13:07:16,708 INFO [train.py:871] (1/4) Epoch 12, validation: loss=0.1688, simple_loss=0.2696, pruned_loss=0.03404, over 868885.00 frames.
2022-05-27 13:07:54,042 INFO [train.py:842] (1/4) Epoch 12, batch 9050, loss[loss=0.1862, simple_loss=0.2685, pruned_loss=0.05193, over 7069.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2825, pruned_loss=0.06163, over 1363530.16 frames.], batch size: 18, lr: 4.42e-04
2022-05-27 13:08:31,793 INFO [train.py:842] (1/4) Epoch 12, batch 9100, loss[loss=0.2891, simple_loss=0.3642, pruned_loss=0.107, over 4991.00 frames.], tot_loss[loss=0.206, simple_loss=0.2845, pruned_loss=0.06373, over 1335235.85 frames.], batch size: 52, lr: 4.42e-04
2022-05-27 13:09:08,598 INFO [train.py:842] (1/4) Epoch 12, batch 9150, loss[loss=0.2434, simple_loss=0.3122, pruned_loss=0.08728, over 4895.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2905, pruned_loss=0.06807, over 1264542.34 frames.], batch size: 52, lr: 4.42e-04
2022-05-27 13:09:59,354 INFO [train.py:842] (1/4) Epoch 13, batch 0, loss[loss=0.1777, simple_loss=0.267, pruned_loss=0.04419, over 7150.00 frames.], tot_loss[loss=0.1777, simple_loss=0.267, pruned_loss=0.04419, over 7150.00 frames.], batch size: 20, lr: 4.27e-04
2022-05-27 13:10:37,599 INFO [train.py:842] (1/4) Epoch 13, batch 50, loss[loss=0.2074, simple_loss=0.3035, pruned_loss=0.05569, over 7231.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2887, pruned_loss=0.06276, over 318355.64 frames.], batch size: 20, lr: 4.27e-04
2022-05-27 13:11:15,858 INFO [train.py:842] (1/4) Epoch 13, batch 100, loss[loss=0.2289, simple_loss=0.3123, pruned_loss=0.07277, over 7202.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2877, pruned_loss=0.06082, over 564177.56 frames.], batch size: 23, lr: 4.27e-04
2022-05-27 13:11:53,599 INFO [train.py:842] (1/4) Epoch 13, batch 150, loss[loss=0.1855, simple_loss=0.2789, pruned_loss=0.04604, over 7145.00 frames.], tot_loss[loss=0.204, simple_loss=0.2879, pruned_loss=0.06007, over 753445.97 frames.], batch size: 20, lr: 4.27e-04
2022-05-27 13:12:31,960 INFO [train.py:842] (1/4) Epoch 13, batch 200, loss[loss=0.1963, simple_loss=0.2822, pruned_loss=0.05518, over 7142.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2861, pruned_loss=0.05963, over 899943.55 frames.], batch size: 20, lr: 4.27e-04
2022-05-27 13:13:09,995 INFO [train.py:842] (1/4) Epoch 13, batch 250, loss[loss=0.2286, simple_loss=0.2901, pruned_loss=0.0835, over 7265.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2868, pruned_loss=0.06093, over 1013606.35 frames.], batch size: 16, lr: 4.26e-04
2022-05-27 13:13:48,337 INFO [train.py:842] (1/4) Epoch 13, batch 300, loss[loss=0.2146, simple_loss=0.2867, pruned_loss=0.07122, over 7152.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2865, pruned_loss=0.06064, over 1103368.00 frames.], batch size: 20, lr: 4.26e-04
2022-05-27 13:14:26,159 INFO [train.py:842] (1/4) Epoch 13, batch 350, loss[loss=0.2586, simple_loss=0.3288, pruned_loss=0.09413, over 7050.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2874, pruned_loss=0.06077, over 1176247.00 frames.], batch size: 28, lr: 4.26e-04
2022-05-27 13:15:04,461 INFO [train.py:842] (1/4) Epoch 13, batch 400, loss[loss=0.187, simple_loss=0.2715, pruned_loss=0.05126, over 7356.00 frames.], tot_loss[loss=0.203, simple_loss=0.2859, pruned_loss=0.06006, over 1233217.29 frames.], batch size: 19, lr: 4.26e-04
2022-05-27 13:15:42,483 INFO [train.py:842] (1/4) Epoch 13, batch 450, loss[loss=0.1782, simple_loss=0.2685, pruned_loss=0.044, over 7320.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2854, pruned_loss=0.05962, over 1277047.28 frames.], batch size: 21, lr: 4.26e-04
2022-05-27 13:16:21,019 INFO [train.py:842] (1/4) Epoch 13, batch 500, loss[loss=0.22, simple_loss=0.3166, pruned_loss=0.06164, over 6373.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2842, pruned_loss=0.05981, over 1311037.72 frames.], batch size: 37, lr: 4.26e-04
2022-05-27 13:16:59,157 INFO [train.py:842] (1/4) Epoch 13, batch 550, loss[loss=0.1884, simple_loss=0.2652, pruned_loss=0.05575, over 7374.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2842, pruned_loss=0.06035, over 1333038.28 frames.], batch size: 23, lr: 4.26e-04
2022-05-27 13:17:37,523 INFO [train.py:842] (1/4) Epoch 13, batch 600, loss[loss=0.261, simple_loss=0.3071, pruned_loss=0.1075, over 7214.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2837, pruned_loss=0.0607, over 1347847.52 frames.], batch size: 16, lr: 4.26e-04
2022-05-27 13:18:15,527 INFO [train.py:842] (1/4) Epoch 13, batch 650, loss[loss=0.1522, simple_loss=0.2371, pruned_loss=0.03372, over 7284.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2831, pruned_loss=0.05976, over 1366648.04 frames.], batch size: 18, lr: 4.26e-04
2022-05-27 13:19:03,256 INFO [train.py:842] (1/4) Epoch 13, batch 700, loss[loss=0.1859, simple_loss=0.2673, pruned_loss=0.05227, over 7216.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2834, pruned_loss=0.05973, over 1384191.73 frames.], batch size: 16, lr: 4.26e-04
2022-05-27 13:19:50,637 INFO [train.py:842] (1/4) Epoch 13, batch 750, loss[loss=0.1989, simple_loss=0.2789, pruned_loss=0.05945, over 7189.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2826, pruned_loss=0.05876, over 1396272.22 frames.], batch size: 23, lr: 4.25e-04
2022-05-27 13:20:38,509 INFO [train.py:842] (1/4) Epoch 13, batch 800, loss[loss=0.2049, simple_loss=0.285, pruned_loss=0.06245, over 7199.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2829, pruned_loss=0.05844, over 1405222.85 frames.], batch size: 22, lr: 4.25e-04
2022-05-27 13:21:16,372 INFO [train.py:842] (1/4) Epoch 13, batch 850, loss[loss=0.2061, simple_loss=0.2704, pruned_loss=0.07089, over 7138.00 frames.], tot_loss[loss=0.1998, simple_loss=0.283, pruned_loss=0.05826, over 1411325.69 frames.], batch size: 17, lr: 4.25e-04
2022-05-27 13:21:54,759 INFO [train.py:842] (1/4) Epoch 13, batch 900, loss[loss=0.2, simple_loss=0.2786, pruned_loss=0.06077, over 7327.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2816, pruned_loss=0.05772, over 1415127.73 frames.], batch size: 20, lr: 4.25e-04
2022-05-27 13:22:32,530 INFO [train.py:842] (1/4) Epoch 13, batch 950, loss[loss=0.1979, simple_loss=0.2952, pruned_loss=0.05032, over 7172.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2844, pruned_loss=0.06001, over 1415769.83 frames.], batch size: 26, lr: 4.25e-04
2022-05-27 13:23:10,780 INFO [train.py:842] (1/4) Epoch 13, batch 1000, loss[loss=0.1911, simple_loss=0.2758, pruned_loss=0.05325, over 6295.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2845, pruned_loss=0.0598, over 1415305.37 frames.], batch size: 37, lr: 4.25e-04
2022-05-27 13:23:48,825 INFO [train.py:842] (1/4) Epoch 13, batch 1050, loss[loss=0.182, simple_loss=0.264, pruned_loss=0.05002, over 7259.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2841, pruned_loss=0.05919, over 1416969.12 frames.], batch size: 19, lr: 4.25e-04
2022-05-27 13:24:27,312 INFO [train.py:842] (1/4) Epoch 13, batch 1100, loss[loss=0.1899, simple_loss=0.2726, pruned_loss=0.05359, over 7372.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2835, pruned_loss=0.05868, over 1423303.94 frames.], batch size: 23, lr: 4.25e-04
2022-05-27 13:25:05,358 INFO [train.py:842] (1/4) Epoch 13, batch 1150, loss[loss=0.2243, simple_loss=0.3097, pruned_loss=0.06946, over 7329.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2837, pruned_loss=0.05883, over 1426200.05 frames.], batch size: 20, lr: 4.25e-04
2022-05-27 13:25:43,695 INFO [train.py:842] (1/4) Epoch 13, batch 1200, loss[loss=0.2939, simple_loss=0.3674, pruned_loss=0.1102, over 5125.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2844, pruned_loss=0.05917, over 1423411.70 frames.], batch size: 52, lr: 4.25e-04
2022-05-27 13:26:21,649 INFO [train.py:842] (1/4) Epoch 13, batch 1250, loss[loss=0.184, simple_loss=0.2819, pruned_loss=0.04301, over 7153.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2842, pruned_loss=0.05908, over 1419827.22 frames.], batch size: 19, lr: 4.25e-04
2022-05-27 13:27:00,088 INFO [train.py:842] (1/4) Epoch 13, batch 1300, loss[loss=0.1737, simple_loss=0.2501, pruned_loss=0.04864, over 7072.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2824, pruned_loss=0.05863, over 1420043.62 frames.], batch size: 18, lr: 4.24e-04
2022-05-27 13:27:37,828 INFO [train.py:842] (1/4) Epoch 13, batch 1350, loss[loss=0.2595, simple_loss=0.3462, pruned_loss=0.08637, over 5075.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2839, pruned_loss=0.05924, over 1417570.42 frames.], batch size: 52, lr: 4.24e-04
2022-05-27 13:28:15,968 INFO [train.py:842] (1/4) Epoch 13, batch 1400, loss[loss=0.1899, simple_loss=0.2801, pruned_loss=0.04982, over 7283.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2835, pruned_loss=0.05917, over 1416237.98 frames.], batch size: 25, lr: 4.24e-04
2022-05-27 13:28:53,717 INFO [train.py:842] (1/4) Epoch 13, batch 1450, loss[loss=0.1589, simple_loss=0.253, pruned_loss=0.03236, over 7315.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2828, pruned_loss=0.05872, over 1414457.40 frames.], batch size: 21, lr: 4.24e-04
2022-05-27 13:29:31,937 INFO [train.py:842] (1/4) Epoch 13, batch 1500, loss[loss=0.2417, simple_loss=0.3221, pruned_loss=0.08065, over 7198.00 frames.], tot_loss[loss=0.199, simple_loss=0.2819, pruned_loss=0.05804, over 1418270.97 frames.], batch size: 23, lr: 4.24e-04
2022-05-27 13:30:10,032 INFO [train.py:842] (1/4) Epoch 13, batch 1550, loss[loss=0.1685, simple_loss=0.262, pruned_loss=0.03753, over 7039.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2834, pruned_loss=0.05871, over 1419719.05 frames.], batch size: 28, lr: 4.24e-04
2022-05-27 13:30:48,259 INFO [train.py:842] (1/4) Epoch 13, batch 1600, loss[loss=0.1952, simple_loss=0.2774, pruned_loss=0.05651, over 7299.00 frames.], tot_loss[loss=0.201, simple_loss=0.2841, pruned_loss=0.05896, over 1419611.00 frames.], batch size: 25, lr: 4.24e-04
2022-05-27 13:31:26,204 INFO [train.py:842] (1/4) Epoch 13, batch 1650, loss[loss=0.2265, simple_loss=0.3106, pruned_loss=0.07122, over 7295.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2844, pruned_loss=0.05915, over 1421699.99 frames.], batch size: 24, lr: 4.24e-04
2022-05-27 13:32:07,092 INFO [train.py:842] (1/4) Epoch 13, batch 1700, loss[loss=0.172, simple_loss=0.2472, pruned_loss=0.04839, over 7135.00 frames.], tot_loss[loss=0.201, simple_loss=0.2841, pruned_loss=0.05896, over 1417479.13 frames.], batch size: 17, lr: 4.24e-04
2022-05-27 13:32:45,289 INFO [train.py:842] (1/4) Epoch 13, batch 1750, loss[loss=0.1735, simple_loss=0.2649, pruned_loss=0.04106, over 7243.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2828, pruned_loss=0.05851, over 1421842.12 frames.], batch size: 26, lr: 4.24e-04
2022-05-27 13:33:23,697 INFO [train.py:842] (1/4) Epoch 13, batch 1800, loss[loss=0.257, simple_loss=0.305, pruned_loss=0.1045, over 7003.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2825, pruned_loss=0.05842, over 1427476.86 frames.], batch size: 16, lr: 4.23e-04
2022-05-27 13:34:01,774 INFO [train.py:842] (1/4) Epoch 13, batch 1850, loss[loss=0.2079, simple_loss=0.2937, pruned_loss=0.06107, over 7334.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2832, pruned_loss=0.0592, over 1428367.66 frames.], batch size: 22, lr: 4.23e-04
2022-05-27 13:34:40,136 INFO [train.py:842] (1/4) Epoch 13, batch 1900, loss[loss=0.2201, simple_loss=0.301, pruned_loss=0.06958, over 7230.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2838, pruned_loss=0.05969, over 1428947.38 frames.], batch size: 20, lr: 4.23e-04
2022-05-27 13:35:17,979 INFO [train.py:842] (1/4) Epoch 13, batch 1950, loss[loss=0.2362, simple_loss=0.2943, pruned_loss=0.08909, over 7290.00 frames.], tot_loss[loss=0.202, simple_loss=0.2841, pruned_loss=0.05993, over 1429275.86 frames.], batch size: 17, lr: 4.23e-04
2022-05-27 13:35:56,458 INFO [train.py:842] (1/4) Epoch 13, batch 2000, loss[loss=0.1854, simple_loss=0.2657, pruned_loss=0.05255, over 7005.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2829, pruned_loss=0.0595, over 1428957.40 frames.], batch size: 16, lr: 4.23e-04
2022-05-27 13:36:34,503 INFO [train.py:842] (1/4) Epoch 13, batch 2050, loss[loss=0.1656, simple_loss=0.2466, pruned_loss=0.04233, over 7161.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2817, pruned_loss=0.059, over 1421940.64 frames.], batch size: 19, lr: 4.23e-04
2022-05-27 13:37:12,810 INFO [train.py:842] (1/4) Epoch 13, batch 2100, loss[loss=0.1846, simple_loss=0.2725, pruned_loss=0.04835, over 7154.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2821, pruned_loss=0.0592, over 1422479.39 frames.], batch size: 19, lr: 4.23e-04
2022-05-27 13:37:50,701 INFO [train.py:842] (1/4) Epoch 13, batch 2150, loss[loss=0.1798, simple_loss=0.2589, pruned_loss=0.0504, over 7269.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2832, pruned_loss=0.05946, over 1422315.33 frames.], batch size: 18, lr: 4.23e-04
2022-05-27 13:38:28,986 INFO [train.py:842] (1/4) Epoch 13, batch 2200, loss[loss=0.1904, simple_loss=0.2822, pruned_loss=0.04929, over 7325.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2819, pruned_loss=0.05896, over 1423003.88 frames.], batch size: 20, lr: 4.23e-04
2022-05-27 13:39:06,921 INFO [train.py:842] (1/4) Epoch 13, batch 2250, loss[loss=0.1879, simple_loss=0.2749, pruned_loss=0.0505, over 7109.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2826, pruned_loss=0.05934, over 1421129.87 frames.], batch size: 28, lr: 4.23e-04
2022-05-27 13:39:45,194 INFO [train.py:842] (1/4) Epoch 13, batch 2300, loss[loss=0.1838, simple_loss=0.2696, pruned_loss=0.04902, over 7117.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2829, pruned_loss=0.05907, over 1423992.42 frames.], batch size: 21, lr: 4.23e-04
2022-05-27 13:40:23,247 INFO [train.py:842] (1/4) Epoch 13, batch 2350, loss[loss=0.1789, simple_loss=0.2662, pruned_loss=0.04581, over 7172.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2828, pruned_loss=0.05892, over 1425825.54 frames.], batch size: 19, lr: 4.22e-04
2022-05-27 13:41:01,578 INFO [train.py:842] (1/4) Epoch 13, batch 2400, loss[loss=0.1764, simple_loss=0.2605, pruned_loss=0.0462, over 7141.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2825, pruned_loss=0.05836, over 1426378.66 frames.], batch size: 17, lr: 4.22e-04
2022-05-27 13:41:39,477 INFO [train.py:842] (1/4) Epoch 13, batch 2450, loss[loss=0.2219, simple_loss=0.2994, pruned_loss=0.07221, over 7209.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2826, pruned_loss=0.0583, over 1425521.97 frames.], batch size: 21, lr: 4.22e-04
2022-05-27 13:42:17,597 INFO [train.py:842] (1/4) Epoch 13, batch 2500, loss[loss=0.178, simple_loss=0.2566, pruned_loss=0.04973, over 7277.00 frames.], tot_loss[loss=0.2005, simple_loss=0.284, pruned_loss=0.0585, over 1426768.51 frames.], batch size: 18, lr: 4.22e-04
2022-05-27 13:42:55,546 INFO [train.py:842] (1/4) Epoch 13, batch 2550, loss[loss=0.1667, simple_loss=0.2426, pruned_loss=0.04544, over 6804.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2836, pruned_loss=0.0581, over 1427732.02 frames.], batch size: 15, lr: 4.22e-04
2022-05-27 13:43:33,885 INFO [train.py:842] (1/4) Epoch 13, batch 2600, loss[loss=0.1649, simple_loss=0.2469, pruned_loss=0.04147, over 6807.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2833, pruned_loss=0.05853, over 1423626.68 frames.], batch size: 15, lr: 4.22e-04
2022-05-27 13:44:11,771 INFO [train.py:842] (1/4) Epoch 13, batch 2650, loss[loss=0.1848, simple_loss=0.2629, pruned_loss=0.05333, over 7007.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2845, pruned_loss=0.05917, over 1421961.05 frames.], batch size: 16, lr: 4.22e-04
2022-05-27 13:44:50,113 INFO [train.py:842] (1/4) Epoch 13, batch 2700, loss[loss=0.1468, simple_loss=0.2258, pruned_loss=0.03389, over 6992.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2836, pruned_loss=0.05895, over 1424069.11 frames.], batch size: 16, lr: 4.22e-04
2022-05-27 13:45:27,942 INFO [train.py:842] (1/4) Epoch 13, batch 2750, loss[loss=0.2028, simple_loss=0.3068, pruned_loss=0.04939, over 7124.00 frames.], tot_loss[loss=0.2014, simple_loss=0.284, pruned_loss=0.05937, over 1421725.24 frames.], batch size: 21, lr: 4.22e-04
2022-05-27 13:46:05,889 INFO [train.py:842] (1/4) Epoch 13, batch 2800, loss[loss=0.1652, simple_loss=0.2378, pruned_loss=0.04633, over 7137.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2841, pruned_loss=0.05911, over 1421445.35 frames.], batch size: 17, lr: 4.22e-04
2022-05-27 13:46:44,101 INFO [train.py:842] (1/4) Epoch 13, batch 2850, loss[loss=0.2051, simple_loss=0.2793, pruned_loss=0.06542, over 7386.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2838, pruned_loss=0.05881, over 1427919.23 frames.], batch size: 23, lr: 4.22e-04
2022-05-27 13:47:22,066 INFO [train.py:842] (1/4) Epoch 13, batch 2900, loss[loss=0.1715, simple_loss=0.2541, pruned_loss=0.04443, over 7360.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2844, pruned_loss=0.05874, over 1425440.14 frames.], batch size: 19, lr: 4.21e-04
2022-05-27 13:48:00,127 INFO [train.py:842] (1/4) Epoch 13, batch 2950, loss[loss=0.1882, simple_loss=0.2824, pruned_loss=0.047, over 7117.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2829, pruned_loss=0.05794, over 1427108.15 frames.], batch size: 21, lr: 4.21e-04
2022-05-27 13:48:38,460 INFO [train.py:842] (1/4) Epoch 13, batch 3000, loss[loss=0.179, simple_loss=0.2473, pruned_loss=0.0553, over 7298.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2846, pruned_loss=0.05932, over 1427968.41 frames.], batch size: 17, lr: 4.21e-04
2022-05-27 13:48:38,461 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 13:48:47,488 INFO [train.py:871] (1/4) Epoch 13, validation: loss=0.1706, simple_loss=0.2707, pruned_loss=0.03524, over 868885.00 frames.
2022-05-27 13:49:25,621 INFO [train.py:842] (1/4) Epoch 13, batch 3050, loss[loss=0.1498, simple_loss=0.2384, pruned_loss=0.03062, over 7137.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2832, pruned_loss=0.05858, over 1428638.72 frames.], batch size: 17, lr: 4.21e-04
2022-05-27 13:50:04,001 INFO [train.py:842] (1/4) Epoch 13, batch 3100, loss[loss=0.2116, simple_loss=0.3042, pruned_loss=0.05954, over 7107.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2831, pruned_loss=0.05873, over 1427539.92 frames.], batch size: 21, lr: 4.21e-04
2022-05-27 13:50:41,765 INFO [train.py:842] (1/4) Epoch 13, batch 3150, loss[loss=0.2261, simple_loss=0.3035, pruned_loss=0.07434, over 7320.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2846, pruned_loss=0.05944, over 1425363.99 frames.], batch size: 25, lr: 4.21e-04
2022-05-27 13:51:19,885 INFO [train.py:842] (1/4) Epoch 13, batch 3200, loss[loss=0.2115, simple_loss=0.297, pruned_loss=0.06302, over 4874.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2844, pruned_loss=0.0595, over 1426360.82 frames.], batch size: 54, lr: 4.21e-04
2022-05-27 13:51:58,062 INFO [train.py:842] (1/4) Epoch 13, batch 3250, loss[loss=0.1598, simple_loss=0.2406, pruned_loss=0.03947, over 7270.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2844, pruned_loss=0.05964, over 1428448.78 frames.], batch size: 17, lr: 4.21e-04
2022-05-27 13:52:36,363 INFO [train.py:842] (1/4) Epoch 13, batch 3300, loss[loss=0.222, simple_loss=0.3162, pruned_loss=0.06396, over 7322.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2839, pruned_loss=0.05944, over 1427747.61 frames.], batch size: 20, lr: 4.21e-04
2022-05-27 13:53:14,264 INFO [train.py:842] (1/4) Epoch 13, batch 3350, loss[loss=0.1691, simple_loss=0.2451, pruned_loss=0.04656, over 6994.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2841, pruned_loss=0.05979, over 1420729.91 frames.], batch size: 16, lr: 4.21e-04
2022-05-27 13:53:52,554 INFO [train.py:842] (1/4) Epoch 13, batch 3400, loss[loss=0.1945, simple_loss=0.2915, pruned_loss=0.04874, over 7353.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2849, pruned_loss=0.06007, over 1424576.98 frames.], batch size: 23, lr: 4.20e-04
2022-05-27 13:54:30,245 INFO [train.py:842] (1/4) Epoch 13, batch 3450, loss[loss=0.2059, simple_loss=0.2736, pruned_loss=0.0691, over 7404.00 frames.], tot_loss[loss=0.203, simple_loss=0.2852, pruned_loss=0.06035, over 1413757.64 frames.], batch size: 18, lr: 4.20e-04
2022-05-27 13:55:08,638 INFO [train.py:842] (1/4) Epoch 13, batch 3500, loss[loss=0.1969, simple_loss=0.2864, pruned_loss=0.05369, over 6810.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2847, pruned_loss=0.06004, over 1415952.75 frames.], batch size: 31, lr: 4.20e-04
2022-05-27 13:55:47,225 INFO [train.py:842] (1/4) Epoch 13, batch 3550, loss[loss=0.1674, simple_loss=0.2463, pruned_loss=0.04422, over 6990.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2839, pruned_loss=0.05972, over 1421421.59 frames.], batch size: 16, lr: 4.20e-04
2022-05-27 13:56:25,870 INFO [train.py:842] (1/4) Epoch 13, batch 3600, loss[loss=0.1531, simple_loss=0.2337, pruned_loss=0.03626, over 7269.00 frames.], tot_loss[loss=0.2018, simple_loss=0.284, pruned_loss=0.05985, over 1421855.65 frames.], batch size: 18, lr: 4.20e-04
2022-05-27 13:57:04,080 INFO [train.py:842] (1/4) Epoch 13, batch 3650, loss[loss=0.1737, simple_loss=0.2586, pruned_loss=0.04439, over 7417.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2836, pruned_loss=0.05949, over 1424480.92 frames.], batch size: 21, lr: 4.20e-04
2022-05-27 13:57:43,037 INFO [train.py:842] (1/4) Epoch 13, batch 3700, loss[loss=0.1486, simple_loss=0.2342, pruned_loss=0.03153, over 7267.00 frames.], tot_loss[loss=0.2013, simple_loss=0.283, pruned_loss=0.05976, over 1424833.80 frames.], batch size: 19, lr: 4.20e-04
2022-05-27 13:58:21,608 INFO [train.py:842] (1/4) Epoch 13, batch 3750, loss[loss=0.1949, simple_loss=0.28, pruned_loss=0.05495, over 7419.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2825, pruned_loss=0.05869, over 1425733.11 frames.], batch size: 21, lr: 4.20e-04
2022-05-27 13:59:00,727 INFO [train.py:842] (1/4) Epoch 13, batch 3800, loss[loss=0.2738, simple_loss=0.3467, pruned_loss=0.1004, over 7036.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2833, pruned_loss=0.05921, over 1428972.43 frames.], batch size: 28, lr: 4.20e-04
2022-05-27 13:59:39,265 INFO [train.py:842] (1/4) Epoch 13, batch 3850, loss[loss=0.2014, simple_loss=0.2929, pruned_loss=0.05497, over 7208.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2848, pruned_loss=0.05984, over 1425973.43 frames.], batch size: 22, lr: 4.20e-04
2022-05-27 14:00:18,602 INFO [train.py:842] (1/4) Epoch 13, batch 3900, loss[loss=0.2166, simple_loss=0.3157, pruned_loss=0.05873, over 7076.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2832, pruned_loss=0.05905, over 1425430.27 frames.], batch size: 28, lr: 4.20e-04
2022-05-27 14:00:57,298 INFO [train.py:842] (1/4) Epoch 13, batch 3950, loss[loss=0.1646, simple_loss=0.2468, pruned_loss=0.04122, over 7210.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2845, pruned_loss=0.05966, over 1425393.62 frames.], batch size: 16, lr: 4.19e-04
2022-05-27 14:01:36,256 INFO [train.py:842] (1/4) Epoch 13, batch 4000, loss[loss=0.2036, simple_loss=0.2823, pruned_loss=0.06241, over 7102.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2829, pruned_loss=0.05885, over 1424294.01 frames.], batch size: 28, lr: 4.19e-04
2022-05-27 14:02:15,238 INFO [train.py:842] (1/4) Epoch 13, batch 4050, loss[loss=0.1979, simple_loss=0.287, pruned_loss=0.0544, over 7197.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2823, pruned_loss=0.05843, over 1429074.12 frames.], batch size: 22, lr: 4.19e-04
2022-05-27 14:02:54,492 INFO [train.py:842] (1/4) Epoch 13, batch 4100, loss[loss=0.1737, simple_loss=0.263, pruned_loss=0.04221, over 7172.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2814, pruned_loss=0.0581, over 1430453.90 frames.], batch size: 19, lr: 4.19e-04
2022-05-27 14:03:33,678 INFO [train.py:842] (1/4) Epoch 13, batch 4150, loss[loss=0.1697, simple_loss=0.2424, pruned_loss=0.04851, over 6978.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2819, pruned_loss=0.05855, over 1430568.47 frames.], batch size: 16, lr: 4.19e-04
2022-05-27 14:04:12,683 INFO [train.py:842] (1/4) Epoch 13, batch 4200, loss[loss=0.1833, simple_loss=0.2693, pruned_loss=0.04864, over 6229.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2819, pruned_loss=0.0585, over 1425094.82 frames.], batch size: 37, lr: 4.19e-04
2022-05-27 14:04:51,220 INFO [train.py:842] (1/4) Epoch 13, batch 4250, loss[loss=0.2502, simple_loss=0.3225, pruned_loss=0.08895, over 7433.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2829, pruned_loss=0.05898, over 1427747.17 frames.], batch size: 20, lr: 4.19e-04
2022-05-27 14:05:30,468 INFO [train.py:842] (1/4) Epoch 13, batch 4300, loss[loss=0.1811, simple_loss=0.2421, pruned_loss=0.06003, over 7224.00 frames.], tot_loss[loss=0.201, simple_loss=0.2831, pruned_loss=0.05941, over 1424648.52 frames.], batch size: 16, lr: 4.19e-04
2022-05-27 14:06:09,106 INFO [train.py:842] (1/4) Epoch 13, batch 4350, loss[loss=0.2386, simple_loss=0.3116, pruned_loss=0.0828, over 5021.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2821, pruned_loss=0.05854, over 1425485.10 frames.], batch size: 54, lr: 4.19e-04
2022-05-27 14:06:48,126 INFO [train.py:842] (1/4) Epoch 13, batch 4400, loss[loss=0.1524, simple_loss=0.2354, pruned_loss=0.03468, over 7155.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2818, pruned_loss=0.05836, over 1424950.33 frames.], batch size: 17, lr: 4.19e-04
2022-05-27 14:07:27,295 INFO [train.py:842] (1/4) Epoch 13, batch 4450, loss[loss=0.2022, simple_loss=0.2742, pruned_loss=0.06504, over 7264.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2813, pruned_loss=0.05826, over 1428699.34 frames.], batch size: 17, lr: 4.19e-04
2022-05-27 14:08:06,605 INFO [train.py:842] (1/4) Epoch 13, batch 4500, loss[loss=0.1692, simple_loss=0.2618, pruned_loss=0.03834, over 7243.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2819, pruned_loss=0.05849, over 1427899.29 frames.], batch size: 20, lr: 4.18e-04
2022-05-27 14:08:45,556 INFO [train.py:842] (1/4) Epoch 13, batch 4550, loss[loss=0.2503, simple_loss=0.3123, pruned_loss=0.09416, over 7121.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2819, pruned_loss=0.05818, over 1426953.41 frames.], batch size: 28, lr: 4.18e-04
2022-05-27 14:09:25,126 INFO [train.py:842] (1/4) Epoch 13, batch 4600, loss[loss=0.2438, simple_loss=0.3116, pruned_loss=0.08804, over 7133.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2823, pruned_loss=0.0587, over 1424761.61 frames.], batch size: 20, lr: 4.18e-04
2022-05-27 14:10:04,092 INFO [train.py:842] (1/4) Epoch 13, batch 4650, loss[loss=0.3051, simple_loss=0.3487, pruned_loss=0.1308, over 7066.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2828, pruned_loss=0.05949, over 1421984.79 frames.], batch size: 18, lr: 4.18e-04
2022-05-27 14:10:43,265 INFO [train.py:842] (1/4) Epoch 13, batch 4700, loss[loss=0.2186, simple_loss=0.2989, pruned_loss=0.0692, over 6972.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2823, pruned_loss=0.05847, over 1426620.93 frames.], batch size: 32, lr: 4.18e-04
2022-05-27 14:11:22,086 INFO [train.py:842] (1/4) Epoch 13, batch 4750, loss[loss=0.2089, simple_loss=0.2832, pruned_loss=0.06732, over 7205.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2845, pruned_loss=0.05958, over 1423657.67 frames.], batch size: 22, lr: 4.18e-04
2022-05-27 14:12:01,225 INFO [train.py:842] (1/4) Epoch 13, batch 4800, loss[loss=0.1872, simple_loss=0.2829, pruned_loss=0.04578, over 7147.00 frames.], tot_loss[loss=0.202, simple_loss=0.2848, pruned_loss=0.0596, over 1419376.62 frames.], batch size: 26, lr: 4.18e-04
2022-05-27 14:12:40,078 INFO [train.py:842] (1/4) Epoch 13, batch 4850, loss[loss=0.2232, simple_loss=0.3118, pruned_loss=0.06731, over 7150.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2855, pruned_loss=0.06061, over 1413327.84 frames.], batch size: 20, lr: 4.18e-04
2022-05-27 14:13:19,448 INFO [train.py:842] (1/4) Epoch 13, batch 4900, loss[loss=0.1851, simple_loss=0.2623, pruned_loss=0.05393, over 7273.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2855, pruned_loss=0.06069, over 1411315.21 frames.], batch size: 18, lr: 4.18e-04
2022-05-27 14:13:58,398 INFO [train.py:842] (1/4) Epoch 13, batch 4950, loss[loss=0.1664, simple_loss=0.2622, pruned_loss=0.0353, over 7237.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2844, pruned_loss=0.06009, over 1408820.87 frames.], batch size: 20, lr: 4.18e-04
2022-05-27 14:14:37,844 INFO [train.py:842] (1/4) Epoch 13, batch 5000, loss[loss=0.2022, simple_loss=0.2781, pruned_loss=0.06313, over 7212.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2843, pruned_loss=0.06009, over 1410711.06 frames.], batch size: 23, lr: 4.18e-04
2022-05-27 14:15:16,587 INFO [train.py:842] (1/4) Epoch 13, batch 5050, loss[loss=0.1482, simple_loss=0.2377, pruned_loss=0.0294, over 7251.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2844, pruned_loss=0.05964, over 1411248.66 frames.], batch size: 17, lr: 4.17e-04
2022-05-27 14:15:55,769 INFO [train.py:842] (1/4) Epoch 13, batch 5100, loss[loss=0.2114, simple_loss=0.2894, pruned_loss=0.06673, over 7264.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2852, pruned_loss=0.06, over 1414179.82 frames.], batch size: 19, lr: 4.17e-04
2022-05-27 14:16:34,509 INFO [train.py:842] (1/4) Epoch 13, batch 5150, loss[loss=0.2181, simple_loss=0.3153, pruned_loss=0.06044, over 7315.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2865, pruned_loss=0.06034, over 1415376.95 frames.], batch size: 24, lr: 4.17e-04
2022-05-27 14:17:13,815 INFO [train.py:842] (1/4) Epoch 13, batch 5200, loss[loss=0.1881, simple_loss=0.2721, pruned_loss=0.052, over 7076.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2862, pruned_loss=0.06063, over 1416837.29 frames.], batch size: 28, lr: 4.17e-04
2022-05-27 14:17:52,706 INFO [train.py:842] (1/4) Epoch 13, batch 5250, loss[loss=0.2021, simple_loss=0.2835, pruned_loss=0.06034, over 7240.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2868, pruned_loss=0.06139, over 1420288.99 frames.], batch size: 23, lr: 4.17e-04
2022-05-27 14:18:31,890 INFO [train.py:842] (1/4) Epoch 13, batch 5300, loss[loss=0.1917, simple_loss=0.2745, pruned_loss=0.05439, over 7232.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2867, pruned_loss=0.06107, over 1424887.52 frames.], batch size: 20, lr: 4.17e-04
2022-05-27 14:19:11,036 INFO [train.py:842] (1/4) Epoch 13, batch 5350, loss[loss=0.1796, simple_loss=0.2601, pruned_loss=0.04957, over 7146.00 frames.], tot_loss[loss=0.2026, simple_loss=0.285, pruned_loss=0.06017, over 1429363.96 frames.], batch size: 17, lr: 4.17e-04
2022-05-27 14:19:50,005 INFO [train.py:842] (1/4) Epoch 13, batch 5400, loss[loss=0.1789, simple_loss=0.2594, pruned_loss=0.04919, over 7134.00 frames.], tot_loss[loss=0.202, simple_loss=0.2843, pruned_loss=0.05985, over 1427356.12 frames.], batch size: 17, lr: 4.17e-04
2022-05-27 14:20:28,968 INFO [train.py:842] (1/4) Epoch 13, batch 5450, loss[loss=0.2014, simple_loss=0.2809, pruned_loss=0.0609, over 6789.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2846, pruned_loss=0.05988, over 1424216.71 frames.], batch size: 15, lr: 4.17e-04
2022-05-27 14:21:08,154 INFO [train.py:842] (1/4) Epoch 13, batch 5500, loss[loss=0.1749, simple_loss=0.2477, pruned_loss=0.05103, over 7277.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2836, pruned_loss=0.05945, over 1422653.42 frames.], batch size: 17, lr: 4.17e-04
2022-05-27 14:21:46,679 INFO [train.py:842] (1/4) Epoch 13, batch 5550, loss[loss=0.2085, simple_loss=0.2859, pruned_loss=0.06559, over 7354.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2842, pruned_loss=0.05929, over 1424933.08 frames.], batch size: 19, lr: 4.17e-04
2022-05-27 14:22:26,139 INFO [train.py:842] (1/4) Epoch 13, batch 5600, loss[loss=0.1659, simple_loss=0.2436, pruned_loss=0.04409, over 7327.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2832, pruned_loss=0.0588, over 1424735.71 frames.], batch size: 20, lr: 4.16e-04
2022-05-27 14:23:05,043 INFO [train.py:842] (1/4) Epoch 13, batch 5650, loss[loss=0.1757, simple_loss=0.2639, pruned_loss=0.04368, over 7330.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2833, pruned_loss=0.05891, over 1424212.37 frames.], batch size: 20, lr: 4.16e-04
2022-05-27 14:23:43,965 INFO [train.py:842] (1/4) Epoch 13, batch 5700, loss[loss=0.1594, simple_loss=0.2429, pruned_loss=0.03798, over 7362.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2837, pruned_loss=0.059, over 1422067.98 frames.], batch size: 19, lr: 4.16e-04
2022-05-27 14:24:22,945 INFO [train.py:842] (1/4) Epoch 13, batch 5750, loss[loss=0.2148, simple_loss=0.3084, pruned_loss=0.06062, over 7233.00 frames.], tot_loss[loss=0.202, simple_loss=0.2846, pruned_loss=0.05965, over 1424920.37 frames.], batch size: 21, lr: 4.16e-04
2022-05-27 14:25:02,247 INFO [train.py:842] (1/4) Epoch 13, batch 5800, loss[loss=0.1803, simple_loss=0.2667, pruned_loss=0.04697, over 7222.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2839, pruned_loss=0.05963, over 1423104.40 frames.], batch size: 20, lr: 4.16e-04
2022-05-27 14:25:41,232 INFO [train.py:842] (1/4) Epoch 13, batch 5850, loss[loss=0.1749, simple_loss=0.2637, pruned_loss=0.04301, over 7353.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2829, pruned_loss=0.05891, over 1426576.84 frames.], batch size: 19, lr: 4.16e-04
2022-05-27 14:26:20,600 INFO [train.py:842] (1/4) Epoch 13, batch 5900, loss[loss=0.174, simple_loss=0.2653, pruned_loss=0.04136, over 7137.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2828, pruned_loss=0.05905, over 1427204.18 frames.], batch size: 20, lr: 4.16e-04
2022-05-27 14:26:59,203 INFO [train.py:842] (1/4) Epoch 13, batch 5950, loss[loss=0.2146, simple_loss=0.3093, pruned_loss=0.05998, over 7212.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2843, pruned_loss=0.05998, over 1426927.03 frames.], batch size: 23, lr: 4.16e-04
2022-05-27 14:27:38,228 INFO [train.py:842] (1/4) Epoch 13, batch 6000, loss[loss=0.165, simple_loss=0.2423, pruned_loss=0.04383, over 7366.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2836, pruned_loss=0.0591, over 1424798.50 frames.], batch size: 19, lr: 4.16e-04
2022-05-27 14:27:38,228 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 14:27:48,003 INFO [train.py:871] (1/4) Epoch 13, validation: loss=0.1712, simple_loss=0.2713, pruned_loss=0.03553, over 868885.00 frames.
2022-05-27 14:28:27,130 INFO [train.py:842] (1/4) Epoch 13, batch 6050, loss[loss=0.1661, simple_loss=0.2547, pruned_loss=0.03873, over 7065.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2822, pruned_loss=0.05861, over 1422659.07 frames.], batch size: 18, lr: 4.16e-04
2022-05-27 14:29:06,290 INFO [train.py:842] (1/4) Epoch 13, batch 6100, loss[loss=0.1944, simple_loss=0.2767, pruned_loss=0.05608, over 7123.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2812, pruned_loss=0.05795, over 1426659.50 frames.], batch size: 21, lr: 4.16e-04
2022-05-27 14:29:45,155 INFO [train.py:842] (1/4) Epoch 13, batch 6150, loss[loss=0.1969, simple_loss=0.2847, pruned_loss=0.05458, over 7216.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2814, pruned_loss=0.0579, over 1422185.70 frames.], batch size: 20, lr: 4.16e-04
2022-05-27 14:30:23,985 INFO [train.py:842] (1/4) Epoch 13, batch 6200, loss[loss=0.2119, simple_loss=0.2939, pruned_loss=0.06501, over 7213.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2827, pruned_loss=0.05819, over 1425567.04 frames.], batch size: 23, lr: 4.15e-04
2022-05-27 14:31:03,016 INFO [train.py:842] (1/4) Epoch 13, batch 6250, loss[loss=0.1992, simple_loss=0.2656, pruned_loss=0.06635, over 6752.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2829, pruned_loss=0.05809, over 1424314.94 frames.], batch size: 15, lr: 4.15e-04
2022-05-27 14:31:41,858 INFO [train.py:842] (1/4) Epoch 13, batch 6300, loss[loss=0.2347, simple_loss=0.3133, pruned_loss=0.07801, over 6390.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2829, pruned_loss=0.05805, over 1423287.33 frames.], batch size: 37, lr: 4.15e-04
2022-05-27 14:32:20,547 INFO [train.py:842] (1/4) Epoch 13, batch 6350, loss[loss=0.2298, simple_loss=0.3082, pruned_loss=0.07569, over 4611.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2823, pruned_loss=0.05767, over 1418451.33 frames.], batch size: 52, lr: 4.15e-04
2022-05-27 14:32:59,614 INFO [train.py:842] (1/4) Epoch 13, batch 6400, loss[loss=0.1689, simple_loss=0.2474, pruned_loss=0.04523, over 7066.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2831, pruned_loss=0.05836, over 1417343.07 frames.], batch size: 18, lr: 4.15e-04
2022-05-27 14:33:38,355 INFO [train.py:842] (1/4) Epoch 13, batch 6450, loss[loss=0.2678, simple_loss=0.3392, pruned_loss=0.09824, over 7148.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2833, pruned_loss=0.05875, over 1418353.80 frames.], batch size: 26, lr: 4.15e-04
2022-05-27 14:34:17,567 INFO [train.py:842] (1/4) Epoch 13, batch 6500, loss[loss=0.1722, simple_loss=0.2511, pruned_loss=0.04663, over 7130.00 frames.], tot_loss[loss=0.201, simple_loss=0.2834, pruned_loss=0.05933, over 1416680.44 frames.], batch size: 17, lr: 4.15e-04
2022-05-27 14:34:56,666 INFO [train.py:842] (1/4) Epoch 13, batch 6550, loss[loss=0.1857, simple_loss=0.2653, pruned_loss=0.05309, over 7279.00 frames.], tot_loss[loss=0.201, simple_loss=0.2833, pruned_loss=0.05942, over 1418165.33 frames.], batch size: 18, lr: 4.15e-04
2022-05-27 14:35:36,268 INFO [train.py:842] (1/4) Epoch 13, batch 6600, loss[loss=0.2185, simple_loss=0.3085, pruned_loss=0.0643, over 7165.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2824, pruned_loss=0.05853, over 1421547.50 frames.], batch size: 26, lr: 4.15e-04
2022-05-27 14:36:15,609 INFO [train.py:842] (1/4) Epoch 13, batch 6650, loss[loss=0.2605, simple_loss=0.3344, pruned_loss=0.09331, over 7025.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2826, pruned_loss=0.05874, over 1424391.51 frames.], batch size: 28, lr: 4.15e-04
2022-05-27 14:36:54,855 INFO [train.py:842] (1/4) Epoch 13, batch 6700, loss[loss=0.1775, simple_loss=0.2689, pruned_loss=0.04307, over 7236.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2836, pruned_loss=0.0591, over 1423036.20 frames.], batch size: 20, lr: 4.15e-04
2022-05-27 14:37:33,728 INFO [train.py:842] (1/4) Epoch 13, batch 6750, loss[loss=0.1845, simple_loss=0.2798, pruned_loss=0.04459, over 7403.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2823, pruned_loss=0.05878, over 1423765.76 frames.], batch size: 21, lr: 4.14e-04
2022-05-27 14:38:12,759 INFO [train.py:842] (1/4) Epoch 13, batch 6800, loss[loss=0.1695, simple_loss=0.2578, pruned_loss=0.04062, over 7424.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2828, pruned_loss=0.05893, over 1424907.67 frames.], batch size: 18, lr: 4.14e-04
2022-05-27 14:38:51,494 INFO [train.py:842] (1/4) Epoch 13, batch 6850, loss[loss=0.2276, simple_loss=0.3105, pruned_loss=0.07236, over 7382.00 frames.], tot_loss[loss=0.199, simple_loss=0.2815, pruned_loss=0.05822, over 1423338.20 frames.], batch size: 23, lr: 4.14e-04
2022-05-27 14:39:30,671 INFO [train.py:842] (1/4) Epoch 13, batch 6900, loss[loss=0.1567, simple_loss=0.2482, pruned_loss=0.03262, over 7436.00 frames.], tot_loss[loss=0.199, simple_loss=0.2818, pruned_loss=0.05815, over 1423345.30 frames.], batch size: 20, lr: 4.14e-04
2022-05-27 14:40:09,778 INFO [train.py:842] (1/4) Epoch 13, batch 6950, loss[loss=0.2188, simple_loss=0.307, pruned_loss=0.06533, over 7152.00 frames.], tot_loss[loss=0.2005, simple_loss=0.283, pruned_loss=0.05904, over 1423113.37 frames.], batch size: 20, lr: 4.14e-04
2022-05-27 14:40:48,925 INFO [train.py:842] (1/4) Epoch 13, batch 7000, loss[loss=0.1701, simple_loss=0.2577, pruned_loss=0.04126, over 7354.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2829, pruned_loss=0.05924, over 1421898.95 frames.], batch size: 19, lr: 4.14e-04
2022-05-27 14:41:27,967 INFO [train.py:842] (1/4) Epoch 13, batch 7050, loss[loss=0.1447, simple_loss=0.2314, pruned_loss=0.02898, over 7159.00 frames.], tot_loss[loss=0.2005, simple_loss=0.283, pruned_loss=0.05906, over 1425410.90 frames.], batch size: 18, lr: 4.14e-04
2022-05-27 14:42:07,617 INFO [train.py:842] (1/4) Epoch 13, batch 7100, loss[loss=0.2289, simple_loss=0.3135, pruned_loss=0.07216, over 7324.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2826, pruned_loss=0.05895, over 1426554.15 frames.], batch size: 22, lr: 4.14e-04
2022-05-27 14:42:46,583 INFO [train.py:842] (1/4) Epoch 13, batch 7150, loss[loss=0.2586, simple_loss=0.3354, pruned_loss=0.09083, over 7215.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2815, pruned_loss=0.05815, over 1425419.91 frames.], batch size: 22, lr: 4.14e-04
2022-05-27 14:43:25,706 INFO [train.py:842] (1/4) Epoch 13, batch 7200, loss[loss=0.1752, simple_loss=0.2641, pruned_loss=0.0432, over 7121.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2825, pruned_loss=0.05897, over 1424966.59 frames.], batch size: 17, lr: 4.14e-04
2022-05-27 14:44:04,769 INFO [train.py:842] (1/4) Epoch 13, batch 7250, loss[loss=0.1867, simple_loss=0.2761, pruned_loss=0.04863, over 6459.00 frames.], tot_loss[loss=0.2007, simple_loss=0.283, pruned_loss=0.05916, over 1418675.37 frames.], batch size: 38, lr: 4.14e-04
2022-05-27 14:44:43,676 INFO [train.py:842] (1/4) Epoch 13, batch 7300, loss[loss=0.1592, simple_loss=0.2388, pruned_loss=0.03984, over 7069.00 frames.], tot_loss[loss=0.201, simple_loss=0.2835, pruned_loss=0.05926, over 1422290.36 frames.], batch size: 18, lr: 4.13e-04
2022-05-27 14:45:22,111 INFO [train.py:842] (1/4) Epoch 13, batch 7350, loss[loss=0.2007, simple_loss=0.2749, pruned_loss=0.0632, over 7223.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2845, pruned_loss=0.05954, over 1421670.91 frames.], batch size: 20, lr: 4.13e-04
2022-05-27 14:46:01,414 INFO [train.py:842] (1/4) Epoch 13, batch 7400, loss[loss=0.2019, simple_loss=0.2865, pruned_loss=0.05869, over 7110.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2857, pruned_loss=0.0608, over 1418165.38 frames.], batch size: 21, lr: 4.13e-04
2022-05-27 14:46:40,454 INFO [train.py:842] (1/4) Epoch 13, batch 7450, loss[loss=0.2958, simple_loss=0.346, pruned_loss=0.1228, over 6734.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2845, pruned_loss=0.06034, over 1421915.72 frames.], batch size: 31, lr: 4.13e-04
2022-05-27 14:47:19,676 INFO [train.py:842] (1/4) Epoch 13, batch 7500, loss[loss=0.2181, simple_loss=0.2989, pruned_loss=0.06861, over 7081.00 frames.], tot_loss[loss=0.2018, simple_loss=0.284, pruned_loss=0.05973, over 1422049.67 frames.], batch size: 28, lr: 4.13e-04
2022-05-27 14:47:58,782 INFO [train.py:842] (1/4) Epoch 13, batch 7550, loss[loss=0.1675, simple_loss=0.2568, pruned_loss=0.03908, over 7425.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2836, pruned_loss=0.05945, over 1423189.04 frames.], batch size: 20, lr: 4.13e-04
2022-05-27 14:48:37,992 INFO [train.py:842] (1/4) Epoch 13, batch 7600, loss[loss=0.1563, simple_loss=0.2431, pruned_loss=0.03474, over 7322.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2835, pruned_loss=0.05965, over 1422234.18 frames.], batch size: 20, lr: 4.13e-04
2022-05-27 14:49:16,809 INFO [train.py:842] (1/4) Epoch 13, batch 7650, loss[loss=0.1962, simple_loss=0.2756, pruned_loss=0.05836, over 7076.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2836, pruned_loss=0.05949, over 1422466.26 frames.], batch size: 18, lr: 4.13e-04
2022-05-27 14:49:56,157 INFO [train.py:842] (1/4) Epoch 13, batch 7700, loss[loss=0.1771, simple_loss=0.2685, pruned_loss=0.04287, over 7197.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2826, pruned_loss=0.05893, over 1424019.72 frames.], batch size: 22, lr: 4.13e-04
2022-05-27 14:50:35,219 INFO [train.py:842] (1/4) Epoch 13, batch 7750, loss[loss=0.223, simple_loss=0.3085, pruned_loss=0.06881, over 7411.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2825, pruned_loss=0.05853, over 1419244.42 frames.], batch size: 21, lr: 4.13e-04
2022-05-27 14:51:14,286 INFO [train.py:842] (1/4) Epoch 13, batch 7800, loss[loss=0.1933, simple_loss=0.2763, pruned_loss=0.05515, over 7006.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2794, pruned_loss=0.0572, over 1421216.81 frames.], batch size: 28, lr: 4.13e-04
2022-05-27 14:51:53,607 INFO [train.py:842] (1/4) Epoch 13, batch 7850, loss[loss=0.2126, simple_loss=0.2897, pruned_loss=0.06771, over 6575.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2793, pruned_loss=0.05662, over 1425287.15 frames.], batch size: 38, lr: 4.13e-04
2022-05-27 14:52:33,071 INFO [train.py:842] (1/4) Epoch 13, batch 7900, loss[loss=0.2119, simple_loss=0.2821, pruned_loss=0.07081, over 7416.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2804, pruned_loss=0.05768, over 1425857.15 frames.], batch size: 18, lr: 4.12e-04
2022-05-27 14:53:11,962 INFO [train.py:842] (1/4) Epoch 13, batch 7950, loss[loss=0.1857, simple_loss=0.2828, pruned_loss=0.04424, over 7108.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2801, pruned_loss=0.05728, over 1425961.61 frames.], batch size: 21, lr: 4.12e-04
2022-05-27 14:53:51,107 INFO [train.py:842] (1/4) Epoch 13, batch 8000, loss[loss=0.1769, simple_loss=0.271, pruned_loss=0.04146, over 7219.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2809, pruned_loss=0.0577, over 1427952.45 frames.], batch size: 16, lr: 4.12e-04
2022-05-27 14:54:29,901 INFO [train.py:842] (1/4) Epoch 13, batch 8050, loss[loss=0.1949, simple_loss=0.266, pruned_loss=0.0619, over 7282.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2817, pruned_loss=0.0583, over 1426825.89 frames.], batch size: 18, lr: 4.12e-04
2022-05-27 14:55:09,278 INFO [train.py:842] (1/4) Epoch 13, batch 8100, loss[loss=0.226, simple_loss=0.2913, pruned_loss=0.08035, over 7163.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2822, pruned_loss=0.05896, over 1425175.15 frames.], batch size: 19, lr: 4.12e-04
2022-05-27 14:55:48,011 INFO [train.py:842] (1/4) Epoch 13, batch 8150, loss[loss=0.2287, simple_loss=0.3028, pruned_loss=0.07732, over 7288.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2828, pruned_loss=0.05955, over 1426868.98 frames.], batch size: 25, lr: 4.12e-04
2022-05-27 14:56:27,306 INFO [train.py:842] (1/4) Epoch 13, batch 8200, loss[loss=0.1708, simple_loss=0.2696, pruned_loss=0.03602, over 7220.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2821, pruned_loss=0.05901, over 1429080.55 frames.], batch size: 22, lr: 4.12e-04
2022-05-27 14:57:06,480 INFO [train.py:842] (1/4) Epoch 13, batch 8250, loss[loss=0.1902, simple_loss=0.2777, pruned_loss=0.05138, over 7070.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2827, pruned_loss=0.05915, over 1432512.74 frames.], batch size: 18, lr: 4.12e-04
2022-05-27 14:57:45,599 INFO [train.py:842] (1/4) Epoch 13, batch 8300, loss[loss=0.2446, simple_loss=0.3166, pruned_loss=0.08628, over 6811.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2828, pruned_loss=0.05948, over 1434465.40 frames.], batch size: 31, lr: 4.12e-04
2022-05-27 14:58:24,376 INFO [train.py:842] (1/4) Epoch 13, batch 8350, loss[loss=0.1748, simple_loss=0.2473, pruned_loss=0.05112, over 7295.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2826, pruned_loss=0.05927, over 1434155.78 frames.], batch size: 17, lr: 4.12e-04
2022-05-27 14:59:03,767 INFO [train.py:842] (1/4) Epoch 13, batch 8400, loss[loss=0.1838, simple_loss=0.2698, pruned_loss=0.0489, over 7145.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2823, pruned_loss=0.05916, over 1435508.43 frames.], batch size: 18, lr: 4.12e-04
2022-05-27 14:59:42,404 INFO [train.py:842] (1/4) Epoch 13, batch 8450, loss[loss=0.2022, simple_loss=0.2846, pruned_loss=0.05992, over 7171.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2816, pruned_loss=0.05871, over 1428927.23 frames.], batch size: 26, lr: 4.11e-04
2022-05-27 15:00:21,272 INFO [train.py:842] (1/4) Epoch 13, batch 8500, loss[loss=0.2048, simple_loss=0.2807, pruned_loss=0.06447, over 7285.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2815, pruned_loss=0.05844, over 1428502.72 frames.], batch size: 17, lr: 4.11e-04
2022-05-27 15:00:59,974 INFO [train.py:842] (1/4) Epoch 13, batch 8550, loss[loss=0.1926, simple_loss=0.2891, pruned_loss=0.04809, over 7166.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2817, pruned_loss=0.05847, over 1424904.80 frames.], batch size: 26, lr: 4.11e-04
2022-05-27 15:01:38,584 INFO [train.py:842] (1/4) Epoch 13, batch 8600, loss[loss=0.1946, simple_loss=0.2779, pruned_loss=0.05567, over 6676.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2833, pruned_loss=0.05872, over 1426531.84 frames.], batch size: 38, lr: 4.11e-04
2022-05-27 15:02:17,372 INFO [train.py:842] (1/4) Epoch 13, batch 8650, loss[loss=0.2413, simple_loss=0.3099, pruned_loss=0.08637, over 7438.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2828, pruned_loss=0.05845, over 1429218.62 frames.], batch size: 20, lr: 4.11e-04
2022-05-27 15:02:56,155 INFO [train.py:842] (1/4) Epoch 13, batch 8700, loss[loss=0.1679, simple_loss=0.2519, pruned_loss=0.04193, over 7159.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2828, pruned_loss=0.05815, over 1426260.78 frames.], batch size: 18, lr: 4.11e-04
2022-05-27 15:03:34,668 INFO [train.py:842] (1/4) Epoch 13, batch 8750, loss[loss=0.1977, simple_loss=0.2977, pruned_loss=0.04881, over 7216.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2841, pruned_loss=0.05867, over 1423479.25 frames.], batch size: 21, lr: 4.11e-04
2022-05-27 15:04:13,456 INFO [train.py:842] (1/4) Epoch 13, batch 8800, loss[loss=0.2043, simple_loss=0.2879, pruned_loss=0.06033, over 7104.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2845, pruned_loss=0.05918, over 1419848.99 frames.], batch size: 21, lr: 4.11e-04
2022-05-27 15:04:52,101 INFO [train.py:842] (1/4) Epoch 13, batch 8850, loss[loss=0.2527, simple_loss=0.33, pruned_loss=0.08771, over 4921.00 frames.], tot_loss[loss=0.202, simple_loss=0.2846, pruned_loss=0.05976, over 1415710.54 frames.], batch size: 53, lr: 4.11e-04
2022-05-27 15:05:30,850 INFO [train.py:842] (1/4) Epoch 13, batch 8900, loss[loss=0.2059, simple_loss=0.2904, pruned_loss=0.06072, over 7149.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2843, pruned_loss=0.05944, over 1410696.62 frames.], batch size: 19, lr: 4.11e-04
2022-05-27 15:06:09,677 INFO [train.py:842] (1/4) Epoch 13, batch 8950, loss[loss=0.2146, simple_loss=0.2927, pruned_loss=0.06821, over 7145.00 frames.], tot_loss[loss=0.201, simple_loss=0.2838, pruned_loss=0.05913, over 1404241.59 frames.], batch size: 26, lr: 4.11e-04
2022-05-27 15:06:48,214 INFO [train.py:842] (1/4) Epoch 13, batch 9000, loss[loss=0.1944, simple_loss=0.2854, pruned_loss=0.05168, over 6346.00 frames.], tot_loss[loss=0.203, simple_loss=0.2854, pruned_loss=0.06027, over 1388931.61 frames.], batch size: 38, lr: 4.11e-04
2022-05-27 15:06:48,215 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 15:06:57,716 INFO [train.py:871] (1/4) Epoch 13, validation: loss=0.1693, simple_loss=0.2699, pruned_loss=0.03436, over 868885.00 frames.
2022-05-27 15:07:34,928 INFO [train.py:842] (1/4) Epoch 13, batch 9050, loss[loss=0.2025, simple_loss=0.2892, pruned_loss=0.05785, over 6224.00 frames.], tot_loss[loss=0.2073, simple_loss=0.289, pruned_loss=0.06283, over 1351620.34 frames.], batch size: 37, lr: 4.10e-04
2022-05-27 15:08:12,258 INFO [train.py:842] (1/4) Epoch 13, batch 9100, loss[loss=0.1963, simple_loss=0.2849, pruned_loss=0.05388, over 6433.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2917, pruned_loss=0.06465, over 1306857.50 frames.], batch size: 38, lr: 4.10e-04
2022-05-27 15:08:49,684 INFO [train.py:842] (1/4) Epoch 13, batch 9150, loss[loss=0.2567, simple_loss=0.3254, pruned_loss=0.09397, over 4933.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2938, pruned_loss=0.06661, over 1255559.30 frames.], batch size: 53, lr: 4.10e-04
2022-05-27 15:09:36,420 INFO [train.py:842] (1/4) Epoch 14, batch 0, loss[loss=0.1698, simple_loss=0.2612, pruned_loss=0.03921, over 7391.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2612, pruned_loss=0.03921, over 7391.00 frames.], batch size: 23, lr: 3.97e-04
2022-05-27 15:10:16,188 INFO [train.py:842] (1/4) Epoch 14, batch 50, loss[loss=0.2437, simple_loss=0.3244, pruned_loss=0.08152, over 7118.00 frames.], tot_loss[loss=0.197, simple_loss=0.279, pruned_loss=0.05751, over 322781.77 frames.], batch size: 21, lr: 3.97e-04
2022-05-27 15:10:55,351 INFO [train.py:842] (1/4) Epoch 14, batch 100, loss[loss=0.1679, simple_loss=0.2632, pruned_loss=0.03633, over 7146.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2792, pruned_loss=0.05671, over 572895.10 frames.], batch size: 20, lr: 3.97e-04
2022-05-27 15:11:34,739 INFO [train.py:842] (1/4) Epoch 14, batch 150, loss[loss=0.1589, simple_loss=0.2321, pruned_loss=0.0428, over 6989.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2778, pruned_loss=0.05578, over 762820.69 frames.], batch size: 16, lr: 3.97e-04
2022-05-27 15:12:13,389 INFO [train.py:842] (1/4) Epoch 14, batch 200, loss[loss=0.2069, simple_loss=0.2867, pruned_loss=0.06358, over 7190.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2797, pruned_loss=0.05656, over 910332.59 frames.], batch size: 22, lr: 3.97e-04
2022-05-27 15:12:52,365 INFO [train.py:842] (1/4) Epoch 14, batch 250, loss[loss=0.2206, simple_loss=0.3045, pruned_loss=0.06833, over 7207.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2789, pruned_loss=0.05572, over 1026116.49 frames.], batch size: 22, lr: 3.97e-04
2022-05-27 15:13:30,900 INFO [train.py:842] (1/4) Epoch 14, batch 300, loss[loss=0.1667, simple_loss=0.2562, pruned_loss=0.03858, over 7419.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2801, pruned_loss=0.05587, over 1112634.08 frames.], batch size: 21, lr: 3.97e-04
2022-05-27 15:14:09,852 INFO [train.py:842] (1/4) Epoch 14, batch 350, loss[loss=0.1597, simple_loss=0.2433, pruned_loss=0.03803, over 7430.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2803, pruned_loss=0.05648, over 1180346.74 frames.], batch size: 20, lr: 3.96e-04
2022-05-27 15:14:48,806 INFO [train.py:842] (1/4) Epoch 14, batch 400, loss[loss=0.1797, simple_loss=0.265, pruned_loss=0.04724, over 7036.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2792, pruned_loss=0.05631, over 1230943.73 frames.], batch size: 28, lr: 3.96e-04
2022-05-27 15:15:28,276 INFO [train.py:842] (1/4) Epoch 14, batch 450, loss[loss=0.207, simple_loss=0.2889, pruned_loss=0.06251, over 6342.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2793, pruned_loss=0.05604, over 1272610.91 frames.], batch size: 38, lr: 3.96e-04
2022-05-27 15:16:07,089 INFO [train.py:842] (1/4) Epoch 14, batch 500, loss[loss=0.2262, simple_loss=0.3105, pruned_loss=0.07096, over 7106.00 frames.], tot_loss[loss=0.196, simple_loss=0.2794, pruned_loss=0.05624, over 1300409.42 frames.], batch size: 28, lr: 3.96e-04
2022-05-27 15:16:48,835 INFO [train.py:842] (1/4) Epoch 14, batch 550, loss[loss=0.1767, simple_loss=0.2772, pruned_loss=0.03805, over 6404.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2799, pruned_loss=0.05618, over 1325231.30 frames.], batch size: 38, lr: 3.96e-04
2022-05-27 15:17:27,828 INFO [train.py:842] (1/4) Epoch 14, batch 600, loss[loss=0.215, simple_loss=0.3102, pruned_loss=0.05987, over 7328.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2806, pruned_loss=0.0566, over 1347620.07 frames.], batch size: 21, lr: 3.96e-04
2022-05-27 15:18:06,497 INFO [train.py:842] (1/4) Epoch 14, batch 650, loss[loss=0.1644, simple_loss=0.2397, pruned_loss=0.04453, over 7063.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2812, pruned_loss=0.05732, over 1360930.52 frames.], batch size: 18, lr: 3.96e-04
2022-05-27 15:18:45,279 INFO [train.py:842] (1/4) Epoch 14, batch 700, loss[loss=0.1564, simple_loss=0.2355, pruned_loss=0.03866, over 7293.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2805, pruned_loss=0.05728, over 1375992.94 frames.], batch size: 18, lr: 3.96e-04
2022-05-27 15:19:24,152 INFO [train.py:842] (1/4) Epoch 14, batch 750, loss[loss=0.2321, simple_loss=0.3072, pruned_loss=0.07845, over 7181.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2814, pruned_loss=0.05812, over 1382429.07 frames.], batch size: 23, lr: 3.96e-04
2022-05-27 15:20:03,030 INFO [train.py:842] (1/4) Epoch 14, batch 800, loss[loss=0.2246, simple_loss=0.3035, pruned_loss=0.07284, over 7304.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2824, pruned_loss=0.0583, over 1392415.28 frames.], batch size: 25, lr: 3.96e-04
2022-05-27 15:20:42,356 INFO [train.py:842] (1/4) Epoch 14, batch 850, loss[loss=0.1992, simple_loss=0.2757, pruned_loss=0.06138, over 7220.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2815, pruned_loss=0.05755, over 1400197.10 frames.], batch size: 21, lr: 3.96e-04
2022-05-27 15:21:21,186 INFO [train.py:842] (1/4) Epoch 14, batch 900, loss[loss=0.2338, simple_loss=0.3003, pruned_loss=0.0836, over 7173.00 frames.], tot_loss[loss=0.1989, simple_loss=0.282, pruned_loss=0.05788, over 1403390.80 frames.], batch size: 18, lr: 3.96e-04
2022-05-27 15:21:59,906 INFO [train.py:842] (1/4) Epoch 14, batch 950, loss[loss=0.2498, simple_loss=0.3207, pruned_loss=0.08948, over 7233.00 frames.], tot_loss[loss=0.2, simple_loss=0.2832, pruned_loss=0.05845, over 1403722.53 frames.], batch size: 21, lr: 3.96e-04
2022-05-27 15:22:39,001 INFO [train.py:842] (1/4) Epoch 14, batch 1000, loss[loss=0.1859, simple_loss=0.2713, pruned_loss=0.05028, over 7200.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2814, pruned_loss=0.05761, over 1410383.92 frames.], batch size: 22, lr: 3.95e-04
2022-05-27 15:23:18,292 INFO [train.py:842] (1/4) Epoch 14, batch 1050, loss[loss=0.1637, simple_loss=0.2562, pruned_loss=0.03562, over 7413.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2824, pruned_loss=0.05824, over 1411457.61 frames.], batch size: 21, lr: 3.95e-04
2022-05-27 15:23:57,022 INFO [train.py:842] (1/4) Epoch 14, batch 1100, loss[loss=0.2205, simple_loss=0.3046, pruned_loss=0.06824, over 6712.00 frames.], tot_loss[loss=0.1994, simple_loss=0.282, pruned_loss=0.05839, over 1410943.88 frames.], batch size: 31, lr: 3.95e-04
2022-05-27 15:24:35,946 INFO [train.py:842] (1/4) Epoch 14, batch 1150, loss[loss=0.2203, simple_loss=0.3054, pruned_loss=0.06758, over 7329.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2844, pruned_loss=0.05927, over 1410721.91 frames.], batch size: 22, lr: 3.95e-04
2022-05-27 15:25:14,703 INFO [train.py:842] (1/4) Epoch 14, batch 1200, loss[loss=0.2526, simple_loss=0.3199, pruned_loss=0.09268, over 5296.00 frames.], tot_loss[loss=0.201, simple_loss=0.2839, pruned_loss=0.05912, over 1410444.97 frames.], batch size: 52, lr: 3.95e-04
2022-05-27 15:25:53,907 INFO [train.py:842] (1/4) Epoch 14, batch 1250, loss[loss=0.1793, simple_loss=0.2621, pruned_loss=0.04826, over 7444.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2834, pruned_loss=0.05883, over 1414560.79 frames.], batch size: 20, lr: 3.95e-04
2022-05-27 15:26:32,826 INFO [train.py:842] (1/4) Epoch 14, batch 1300, loss[loss=0.2332, simple_loss=0.3105, pruned_loss=0.07794, over 7261.00 frames.], tot_loss[loss=0.2, simple_loss=0.2835, pruned_loss=0.05826, over 1417919.64 frames.], batch size: 19, lr: 3.95e-04
2022-05-27 15:27:22,197 INFO [train.py:842] (1/4) Epoch 14, batch 1350, loss[loss=0.1554, simple_loss=0.2465, pruned_loss=0.03212, over 7288.00 frames.], tot_loss[loss=0.198, simple_loss=0.2815, pruned_loss=0.05722, over 1422095.24 frames.], batch size: 18, lr: 3.95e-04
2022-05-27 15:28:01,182 INFO [train.py:842] (1/4) Epoch 14, batch 1400, loss[loss=0.1703, simple_loss=0.2562, pruned_loss=0.04219, over 7159.00 frames.], tot_loss[loss=0.1991, simple_loss=0.282, pruned_loss=0.05808, over 1417639.04 frames.], batch size: 18, lr: 3.95e-04
2022-05-27 15:28:40,388 INFO [train.py:842] (1/4) Epoch 14, batch 1450, loss[loss=0.2437, simple_loss=0.2982, pruned_loss=0.0946, over 7281.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2812, pruned_loss=0.05778, over 1421527.28 frames.], batch size: 17, lr: 3.95e-04
2022-05-27 15:29:19,119 INFO [train.py:842] (1/4) Epoch 14, batch 1500, loss[loss=0.1947, simple_loss=0.2734, pruned_loss=0.05797, over 7270.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2812, pruned_loss=0.05806, over 1422995.29 frames.], batch size: 17, lr: 3.95e-04
2022-05-27 15:29:58,056 INFO [train.py:842] (1/4) Epoch 14, batch 1550, loss[loss=0.2143, simple_loss=0.2961, pruned_loss=0.06624, over 6480.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2821, pruned_loss=0.05871, over 1419178.82 frames.], batch size: 38, lr: 3.95e-04
2022-05-27 15:30:37,035 INFO [train.py:842] (1/4) Epoch 14, batch 1600, loss[loss=0.1845, simple_loss=0.2796, pruned_loss=0.04471, over 7420.00 frames.], tot_loss[loss=0.2016, simple_loss=0.284, pruned_loss=0.05961, over 1418286.70 frames.], batch size: 21, lr: 3.94e-04
2022-05-27 15:31:16,019 INFO [train.py:842] (1/4) Epoch 14, batch 1650, loss[loss=0.2252, simple_loss=0.3113, pruned_loss=0.06949, over 7237.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2837, pruned_loss=0.0593, over 1420062.23 frames.], batch size: 20, lr: 3.94e-04
2022-05-27 15:31:54,468 INFO [train.py:842] (1/4) Epoch 14, batch 1700, loss[loss=0.1884, simple_loss=0.2728, pruned_loss=0.05203, over 6168.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2847, pruned_loss=0.05992, over 1418881.93 frames.], batch size: 37, lr: 3.94e-04
2022-05-27 15:32:33,874 INFO [train.py:842] (1/4) Epoch 14, batch 1750, loss[loss=0.1549, simple_loss=0.2377, pruned_loss=0.03609, over 7273.00 frames.], tot_loss[loss=0.201, simple_loss=0.2834, pruned_loss=0.05935, over 1421366.68 frames.], batch size: 17, lr: 3.94e-04
2022-05-27 15:33:12,934 INFO [train.py:842] (1/4) Epoch 14, batch 1800, loss[loss=0.1861, simple_loss=0.269, pruned_loss=0.05157, over 7154.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2819, pruned_loss=0.0585, over 1425862.60 frames.], batch size: 20, lr: 3.94e-04
2022-05-27 15:33:51,929 INFO [train.py:842] (1/4) Epoch 14, batch 1850, loss[loss=0.2089, simple_loss=0.2976, pruned_loss=0.0601, over 7306.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2823, pruned_loss=0.05856, over 1426140.74 frames.], batch size: 25, lr: 3.94e-04
2022-05-27 15:34:30,698 INFO [train.py:842] (1/4) Epoch 14, batch 1900, loss[loss=0.2214, simple_loss=0.3049, pruned_loss=0.06896, over 6608.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2827, pruned_loss=0.05883, over 1422206.68 frames.], batch size: 38, lr: 3.94e-04
2022-05-27 15:35:09,523 INFO [train.py:842] (1/4) Epoch 14, batch 1950, loss[loss=0.1665, simple_loss=0.2565, pruned_loss=0.03823, over 7258.00 frames.], tot_loss[loss=0.2023, simple_loss=0.285, pruned_loss=0.05985, over 1422798.22 frames.], batch size: 19, lr: 3.94e-04
2022-05-27 15:35:48,260 INFO [train.py:842] (1/4) Epoch 14, batch 2000, loss[loss=0.1972, simple_loss=0.2886, pruned_loss=0.05289, over 7334.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2841, pruned_loss=0.05921, over 1423750.37 frames.], batch size: 22, lr: 3.94e-04
2022-05-27 15:36:27,730 INFO [train.py:842] (1/4) Epoch 14, batch 2050, loss[loss=0.1786, simple_loss=0.2784, pruned_loss=0.03941, over 7379.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2833, pruned_loss=0.05853, over 1424772.64 frames.], batch size: 23, lr: 3.94e-04
2022-05-27 15:37:06,318 INFO [train.py:842] (1/4) Epoch 14, batch 2100, loss[loss=0.1947, simple_loss=0.2709, pruned_loss=0.05925, over 7227.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2835, pruned_loss=0.05808, over 1425121.22 frames.], batch size: 20, lr: 3.94e-04
2022-05-27 15:37:45,644 INFO [train.py:842] (1/4) Epoch 14, batch 2150, loss[loss=0.1816, simple_loss=0.2755, pruned_loss=0.0439, over 7198.00 frames.], tot_loss[loss=0.199, simple_loss=0.283, pruned_loss=0.05748, over 1427777.74 frames.], batch size: 26, lr: 3.94e-04
2022-05-27 15:38:24,818 INFO [train.py:842] (1/4) Epoch 14, batch 2200, loss[loss=0.1965, simple_loss=0.2845, pruned_loss=0.05425, over 7428.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2827, pruned_loss=0.05727, over 1426258.70 frames.], batch size: 20, lr: 3.93e-04
2022-05-27 15:39:04,016 INFO [train.py:842] (1/4) Epoch 14, batch 2250, loss[loss=0.1737, simple_loss=0.2706, pruned_loss=0.03844, over 7231.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2822, pruned_loss=0.05712, over 1427216.00 frames.], batch size: 20, lr: 3.93e-04
2022-05-27 15:39:42,948 INFO [train.py:842] (1/4) Epoch 14, batch 2300, loss[loss=0.2039, simple_loss=0.2875, pruned_loss=0.06021, over 7044.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2805, pruned_loss=0.05648, over 1427892.86 frames.], batch size: 28, lr: 3.93e-04
2022-05-27 15:40:22,154 INFO [train.py:842] (1/4) Epoch 14, batch 2350, loss[loss=0.2532, simple_loss=0.326, pruned_loss=0.09019, over 4945.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2803, pruned_loss=0.05677, over 1427025.57 frames.], batch size: 53, lr: 3.93e-04
2022-05-27 15:41:00,860 INFO [train.py:842] (1/4) Epoch 14, batch 2400, loss[loss=0.1405, simple_loss=0.221, pruned_loss=0.02999, over 7282.00 frames.], tot_loss[loss=0.197, simple_loss=0.2805, pruned_loss=0.05676, over 1428259.90 frames.], batch size: 17, lr: 3.93e-04
2022-05-27 15:41:39,988 INFO [train.py:842] (1/4) Epoch 14, batch 2450, loss[loss=0.1812, simple_loss=0.2724, pruned_loss=0.04499, over 6853.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2812, pruned_loss=0.05669, over 1430707.23 frames.], batch size: 31, lr: 3.93e-04
2022-05-27 15:42:19,136 INFO [train.py:842] (1/4) Epoch 14, batch 2500, loss[loss=0.1981, simple_loss=0.2685, pruned_loss=0.06387, over 7290.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2826, pruned_loss=0.05755, over 1427013.04 frames.], batch size: 17, lr: 3.93e-04
2022-05-27 15:42:58,095 INFO [train.py:842] (1/4) Epoch 14, batch 2550, loss[loss=0.1991, simple_loss=0.284, pruned_loss=0.05717, over 7324.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2836, pruned_loss=0.0579, over 1422739.27 frames.], batch size: 25, lr: 3.93e-04
2022-05-27 15:43:37,314 INFO [train.py:842] (1/4) Epoch 14, batch 2600, loss[loss=0.213, simple_loss=0.2892, pruned_loss=0.06841, over 7409.00 frames.], tot_loss[loss=0.2003, simple_loss=0.284, pruned_loss=0.05831, over 1419437.87 frames.], batch size: 21, lr: 3.93e-04
2022-05-27 15:44:16,190 INFO [train.py:842] (1/4) Epoch 14, batch 2650, loss[loss=0.1988, simple_loss=0.2855, pruned_loss=0.05611, over 7115.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2842, pruned_loss=0.05861, over 1417624.57 frames.], batch size: 21, lr: 3.93e-04
2022-05-27 15:44:55,675 INFO [train.py:842] (1/4) Epoch 14, batch 2700, loss[loss=0.1401, simple_loss=0.2284, pruned_loss=0.02594, over 6976.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2825, pruned_loss=0.05755, over 1422741.84 frames.], batch size: 16, lr: 3.93e-04
2022-05-27 15:45:35,124 INFO [train.py:842] (1/4) Epoch 14, batch 2750, loss[loss=0.2302, simple_loss=0.3126, pruned_loss=0.07391, over 7291.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2817, pruned_loss=0.05674, over 1427317.67 frames.], batch size: 24, lr: 3.93e-04
2022-05-27 15:46:14,000 INFO [train.py:842] (1/4) Epoch 14, batch 2800, loss[loss=0.1881, simple_loss=0.2634, pruned_loss=0.05638, over 7142.00 frames.], tot_loss[loss=0.197, simple_loss=0.2809, pruned_loss=0.0565, over 1425532.60 frames.], batch size: 17, lr: 3.93e-04
2022-05-27 15:46:53,104 INFO [train.py:842] (1/4) Epoch 14, batch 2850, loss[loss=0.1865, simple_loss=0.2787, pruned_loss=0.04713, over 7406.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2793, pruned_loss=0.05601, over 1425742.35 frames.], batch size: 21, lr: 3.92e-04
2022-05-27 15:47:31,766 INFO [train.py:842] (1/4) Epoch 14, batch 2900, loss[loss=0.194, simple_loss=0.2813, pruned_loss=0.05334, over 7108.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2802, pruned_loss=0.05639, over 1426885.36 frames.], batch size: 21, lr: 3.92e-04
2022-05-27 15:48:10,952 INFO [train.py:842] (1/4) Epoch 14, batch 2950, loss[loss=0.2004, simple_loss=0.2961, pruned_loss=0.05238, over 7184.00 frames.], tot_loss[loss=0.1968, simple_loss=0.281, pruned_loss=0.05626, over 1428109.98 frames.], batch size: 23, lr: 3.92e-04
2022-05-27 15:48:50,370 INFO [train.py:842] (1/4) Epoch 14, batch 3000, loss[loss=0.2064, simple_loss=0.2895, pruned_loss=0.06159, over 7281.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2802, pruned_loss=0.05653, over 1429618.69 frames.], batch size: 24, lr: 3.92e-04
2022-05-27 15:48:50,371 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 15:48:59,799 INFO [train.py:871] (1/4) Epoch 14, validation: loss=0.17, simple_loss=0.2697, pruned_loss=0.03515, over 868885.00 frames.
2022-05-27 15:49:39,104 INFO [train.py:842] (1/4) Epoch 14, batch 3050, loss[loss=0.212, simple_loss=0.2888, pruned_loss=0.06763, over 7277.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2806, pruned_loss=0.05695, over 1430082.06 frames.], batch size: 17, lr: 3.92e-04
2022-05-27 15:50:17,920 INFO [train.py:842] (1/4) Epoch 14, batch 3100, loss[loss=0.1896, simple_loss=0.2769, pruned_loss=0.05116, over 7229.00 frames.], tot_loss[loss=0.1987, simple_loss=0.282, pruned_loss=0.05775, over 1431128.76 frames.], batch size: 23, lr: 3.92e-04
2022-05-27 15:50:57,001 INFO [train.py:842] (1/4) Epoch 14, batch 3150, loss[loss=0.2661, simple_loss=0.3295, pruned_loss=0.1013, over 4832.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2802, pruned_loss=0.05716, over 1430587.10 frames.], batch size: 52, lr: 3.92e-04
2022-05-27 15:51:35,896 INFO [train.py:842] (1/4) Epoch 14, batch 3200, loss[loss=0.2142, simple_loss=0.2992, pruned_loss=0.06465, over 7338.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2808, pruned_loss=0.05779, over 1430725.97 frames.], batch size: 22, lr: 3.92e-04
2022-05-27 15:52:14,751 INFO [train.py:842] (1/4) Epoch 14, batch 3250, loss[loss=0.2573, simple_loss=0.3358, pruned_loss=0.0894, over 7228.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2815, pruned_loss=0.05843, over 1428240.57 frames.], batch size: 26, lr: 3.92e-04
2022-05-27 15:52:53,673 INFO [train.py:842] (1/4) Epoch 14, batch 3300, loss[loss=0.1803, simple_loss=0.2581, pruned_loss=0.05119, over 7151.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2806, pruned_loss=0.05788, over 1424267.73 frames.], batch size: 18, lr: 3.92e-04
2022-05-27 15:53:32,475 INFO [train.py:842] (1/4) Epoch 14, batch 3350, loss[loss=0.1984, simple_loss=0.2697, pruned_loss=0.06357, over 7394.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2801, pruned_loss=0.05686, over 1425948.79 frames.], batch size: 18, lr: 3.92e-04
2022-05-27 15:54:11,133 INFO [train.py:842] (1/4) Epoch 14, batch 3400, loss[loss=0.1765, simple_loss=0.257, pruned_loss=0.048, over 7157.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2804, pruned_loss=0.05689, over 1427384.74 frames.], batch size: 18, lr: 3.92e-04
2022-05-27 15:55:00,323 INFO [train.py:842] (1/4) Epoch 14, batch 3450, loss[loss=0.1984, simple_loss=0.2888, pruned_loss=0.05397, over 7110.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2809, pruned_loss=0.05684, over 1426376.66 frames.], batch size: 21, lr: 3.91e-04
2022-05-27 15:55:39,226 INFO [train.py:842] (1/4) Epoch 14, batch 3500, loss[loss=0.2759, simple_loss=0.3719, pruned_loss=0.08997, over 7345.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2804, pruned_loss=0.05694, over 1427469.47 frames.], batch size: 22, lr: 3.91e-04
2022-05-27 15:56:28,956 INFO [train.py:842] (1/4) Epoch 14, batch 3550, loss[loss=0.1746, simple_loss=0.2732, pruned_loss=0.038, over 7308.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2804, pruned_loss=0.05638, over 1427177.35 frames.], batch size: 21, lr: 3.91e-04
2022-05-27 15:57:08,502 INFO [train.py:842] (1/4) Epoch 14, batch 3600, loss[loss=0.1737, simple_loss=0.2578, pruned_loss=0.04476, over 7358.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2799, pruned_loss=0.05638, over 1430688.45 frames.], batch size: 19, lr: 3.91e-04
2022-05-27 15:57:57,901 INFO [train.py:842] (1/4) Epoch 14, batch 3650, loss[loss=0.234, simple_loss=0.3097, pruned_loss=0.07919, over 7226.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2795, pruned_loss=0.05632, over 1430021.14 frames.], batch size: 20, lr: 3.91e-04
2022-05-27 15:58:36,735 INFO [train.py:842] (1/4) Epoch 14, batch 3700, loss[loss=0.2108, simple_loss=0.3026, pruned_loss=0.05955, over 7281.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2815, pruned_loss=0.05792, over 1421605.05 frames.], batch size: 24, lr: 3.91e-04
2022-05-27 15:59:15,397 INFO [train.py:842] (1/4) Epoch 14, batch 3750, loss[loss=0.2674, simple_loss=0.3231, pruned_loss=0.1059, over 5013.00 frames.], tot_loss[loss=0.2004, simple_loss=0.283, pruned_loss=0.05894, over 1420127.50 frames.], batch size: 52, lr: 3.91e-04
2022-05-27 15:59:54,265 INFO [train.py:842] (1/4) Epoch 14, batch 3800, loss[loss=0.2281, simple_loss=0.3029, pruned_loss=0.07663, over 7257.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2828, pruned_loss=0.05853, over 1418499.90 frames.], batch size: 19, lr: 3.91e-04
2022-05-27 16:00:33,478 INFO [train.py:842] (1/4) Epoch 14, batch 3850, loss[loss=0.1737, simple_loss=0.2668, pruned_loss=0.04032, over 6398.00 frames.], tot_loss[loss=0.199, simple_loss=0.2822, pruned_loss=0.05788, over 1419795.58 frames.], batch size: 38, lr: 3.91e-04
2022-05-27 16:01:12,305 INFO [train.py:842] (1/4) Epoch 14, batch 3900, loss[loss=0.2037, simple_loss=0.2834, pruned_loss=0.06201, over 7112.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2826, pruned_loss=0.05802, over 1420637.74 frames.], batch size: 21, lr: 3.91e-04
2022-05-27 16:01:51,359 INFO [train.py:842] (1/4) Epoch 14, batch 3950, loss[loss=0.2016, simple_loss=0.2807, pruned_loss=0.06126, over 4964.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2821, pruned_loss=0.05742, over 1420680.72 frames.], batch size: 54, lr: 3.91e-04
2022-05-27 16:02:30,325 INFO [train.py:842] (1/4) Epoch 14, batch 4000, loss[loss=0.2045, simple_loss=0.2767, pruned_loss=0.06618, over 7165.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2818, pruned_loss=0.05683, over 1421366.40 frames.], batch size: 18, lr: 3.91e-04
2022-05-27 16:03:09,209 INFO [train.py:842] (1/4) Epoch 14, batch 4050, loss[loss=0.1844, simple_loss=0.278, pruned_loss=0.04538, over 7196.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2818, pruned_loss=0.05676, over 1423820.99 frames.], batch size: 22, lr: 3.91e-04
2022-05-27 16:03:48,346 INFO [train.py:842] (1/4) Epoch 14, batch 4100, loss[loss=0.2024, simple_loss=0.2905, pruned_loss=0.05714, over 7211.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2816, pruned_loss=0.0571, over 1426215.18 frames.], batch size: 22, lr: 3.90e-04
2022-05-27 16:04:27,751 INFO [train.py:842] (1/4) Epoch 14, batch 4150, loss[loss=0.187, simple_loss=0.2786, pruned_loss=0.04768, over 7326.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2825, pruned_loss=0.05797, over 1421927.40 frames.], batch size: 21, lr: 3.90e-04
2022-05-27 16:05:06,397 INFO [train.py:842] (1/4) Epoch 14, batch 4200, loss[loss=0.2001, simple_loss=0.2731, pruned_loss=0.06356, over 7144.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2812, pruned_loss=0.05748, over 1424090.59 frames.], batch size: 17, lr: 3.90e-04
2022-05-27 16:05:45,521 INFO [train.py:842] (1/4) Epoch 14, batch 4250, loss[loss=0.1709, simple_loss=0.2558, pruned_loss=0.04303, over 7418.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2803, pruned_loss=0.05736, over 1420386.25 frames.], batch size: 20, lr: 3.90e-04
2022-05-27 16:06:24,410 INFO [train.py:842] (1/4) Epoch 14, batch 4300, loss[loss=0.2451, simple_loss=0.3186, pruned_loss=0.0858, over 7412.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2825, pruned_loss=0.05866, over 1416077.19 frames.], batch size: 21, lr: 3.90e-04
2022-05-27 16:07:03,438 INFO [train.py:842] (1/4) Epoch 14, batch 4350, loss[loss=0.1634, simple_loss=0.2527, pruned_loss=0.03706, over 7441.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2828, pruned_loss=0.0588, over 1417849.92 frames.], batch size: 20, lr: 3.90e-04
2022-05-27 16:07:42,430 INFO [train.py:842] (1/4) Epoch 14, batch 4400, loss[loss=0.2194, simple_loss=0.2915, pruned_loss=0.07362, over 6939.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2833, pruned_loss=0.05905, over 1418876.55 frames.], batch size: 32, lr: 3.90e-04
2022-05-27 16:08:21,617 INFO [train.py:842] (1/4) Epoch 14, batch 4450, loss[loss=0.1718, simple_loss=0.2649, pruned_loss=0.03938, over 7425.00 frames.], tot_loss[loss=0.199, simple_loss=0.2814, pruned_loss=0.05831, over 1418575.81 frames.], batch size: 21, lr: 3.90e-04
2022-05-27 16:09:00,493 INFO [train.py:842] (1/4) Epoch 14, batch 4500, loss[loss=0.1925, simple_loss=0.2928, pruned_loss=0.04615, over 7236.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2809, pruned_loss=0.05793, over 1419807.64 frames.], batch size: 21, lr: 3.90e-04
2022-05-27 16:09:39,602 INFO [train.py:842] (1/4) Epoch 14, batch 4550, loss[loss=0.2417, simple_loss=0.3149, pruned_loss=0.08428, over 7341.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2819, pruned_loss=0.05815, over 1414959.49 frames.], batch size: 22, lr: 3.90e-04
2022-05-27 16:10:18,146 INFO [train.py:842] (1/4) Epoch 14, batch 4600, loss[loss=0.187, simple_loss=0.2755, pruned_loss=0.04923, over 6474.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2831, pruned_loss=0.05882, over 1415119.97 frames.], batch size: 38, lr: 3.90e-04
2022-05-27 16:10:57,480 INFO [train.py:842] (1/4) Epoch 14, batch 4650, loss[loss=0.1893, simple_loss=0.2803, pruned_loss=0.04915, over 7352.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2833, pruned_loss=0.05894, over 1415247.37 frames.], batch size: 19, lr: 3.90e-04
2022-05-27 16:11:35,872 INFO [train.py:842] (1/4) Epoch 14, batch 4700, loss[loss=0.205, simple_loss=0.3001, pruned_loss=0.05494, over 7182.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2819, pruned_loss=0.05793, over 1412792.52 frames.], batch size: 26, lr: 3.90e-04
2022-05-27 16:12:14,905 INFO [train.py:842] (1/4) Epoch 14, batch 4750, loss[loss=0.2469, simple_loss=0.3099, pruned_loss=0.09196, over 7274.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2826, pruned_loss=0.05862, over 1415093.95 frames.], batch size: 19, lr: 3.89e-04
2022-05-27 16:12:53,686 INFO [train.py:842] (1/4) Epoch 14, batch 4800, loss[loss=0.2001, simple_loss=0.2929, pruned_loss=0.05365, over 7410.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2822, pruned_loss=0.05772, over 1417983.85 frames.], batch size: 21, lr: 3.89e-04
2022-05-27 16:13:32,514 INFO [train.py:842] (1/4) Epoch 14, batch 4850, loss[loss=0.2244, simple_loss=0.3073, pruned_loss=0.07074, over 7211.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2823, pruned_loss=0.0572, over 1419669.62 frames.], batch size: 22, lr: 3.89e-04
2022-05-27 16:14:11,446 INFO [train.py:842] (1/4) Epoch 14, batch 4900, loss[loss=0.2021, simple_loss=0.2804, pruned_loss=0.06192, over 6887.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2821, pruned_loss=0.0571, over 1420714.81 frames.], batch size: 32, lr: 3.89e-04
2022-05-27 16:14:50,468 INFO [train.py:842] (1/4) Epoch 14, batch 4950, loss[loss=0.2029, simple_loss=0.2865, pruned_loss=0.05963, over 7215.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2812, pruned_loss=0.0569, over 1420391.64 frames.], batch size: 22, lr: 3.89e-04
2022-05-27 16:15:29,232 INFO [train.py:842] (1/4) Epoch 14, batch 5000, loss[loss=0.1811, simple_loss=0.2635, pruned_loss=0.0493, over 7160.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2812, pruned_loss=0.05683, over 1423478.01 frames.], batch size: 18, lr: 3.89e-04
2022-05-27 16:16:08,280 INFO [train.py:842] (1/4) Epoch 14, batch 5050, loss[loss=0.178, simple_loss=0.2519, pruned_loss=0.05208, over 7010.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2809, pruned_loss=0.05715, over 1420699.27 frames.], batch size: 16, lr: 3.89e-04
2022-05-27 16:16:47,138 INFO [train.py:842] (1/4) Epoch 14, batch 5100, loss[loss=0.1659, simple_loss=0.2563, pruned_loss=0.03776, over 7256.00 frames.], tot_loss[loss=0.1977, simple_loss=0.281, pruned_loss=0.05716, over 1420510.28 frames.], batch size: 19, lr: 3.89e-04
2022-05-27 16:17:26,325 INFO [train.py:842] (1/4) Epoch 14, batch 5150, loss[loss=0.2075, simple_loss=0.2965, pruned_loss=0.05924, over 7306.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2811, pruned_loss=0.05739, over 1423694.95 frames.], batch size: 24, lr: 3.89e-04
2022-05-27 16:18:04,979 INFO [train.py:842] (1/4) Epoch 14, batch 5200, loss[loss=0.2423, simple_loss=0.331, pruned_loss=0.07683, over 7404.00 frames.], tot_loss[loss=0.199, simple_loss=0.2822, pruned_loss=0.05787, over 1426594.63 frames.], batch size: 21, lr: 3.89e-04
2022-05-27 16:18:44,061 INFO [train.py:842] (1/4) Epoch 14, batch 5250, loss[loss=0.2155, simple_loss=0.2956, pruned_loss=0.06769, over 7367.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2822, pruned_loss=0.05767, over 1428737.92 frames.], batch size: 23, lr: 3.89e-04
2022-05-27 16:19:22,719 INFO [train.py:842] (1/4) Epoch 14, batch 5300, loss[loss=0.266, simple_loss=0.3264, pruned_loss=0.1028, over 5080.00 frames.], tot_loss[loss=0.1995, simple_loss=0.283, pruned_loss=0.05804, over 1422021.32 frames.], batch size: 52, lr: 3.89e-04
2022-05-27 16:20:01,793 INFO [train.py:842] (1/4) Epoch 14, batch 5350, loss[loss=0.2215, simple_loss=0.3088, pruned_loss=0.06706, over 7289.00 frames.], tot_loss[loss=0.1998, simple_loss=0.283, pruned_loss=0.05828, over 1424087.17 frames.], batch size: 24, lr: 3.88e-04
2022-05-27 16:20:40,856 INFO [train.py:842] (1/4) Epoch 14, batch 5400, loss[loss=0.2072, simple_loss=0.3028, pruned_loss=0.05578, over 7341.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2835, pruned_loss=0.05849, over 1427723.87 frames.], batch size: 22, lr: 3.88e-04
2022-05-27 16:21:20,189 INFO [train.py:842] (1/4) Epoch 14, batch 5450, loss[loss=0.2075, simple_loss=0.289, pruned_loss=0.06299, over 6802.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2832, pruned_loss=0.05857, over 1427734.51 frames.], batch size: 31, lr: 3.88e-04
2022-05-27 16:21:59,184 INFO [train.py:842] (1/4) Epoch 14, batch 5500, loss[loss=0.2164, simple_loss=0.3079, pruned_loss=0.06245, over 7188.00 frames.], tot_loss[loss=0.199, simple_loss=0.2823, pruned_loss=0.05782, over 1430532.85 frames.], batch size: 23, lr: 3.88e-04
2022-05-27 16:22:38,737 INFO [train.py:842] (1/4) Epoch 14, batch 5550, loss[loss=0.1726, simple_loss=0.2583, pruned_loss=0.04344, over 7327.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2817, pruned_loss=0.05758, over 1433292.66 frames.], batch size: 20, lr: 3.88e-04
2022-05-27 16:23:17,931 INFO [train.py:842] (1/4) Epoch 14, batch 5600, loss[loss=0.1571, simple_loss=0.2365, pruned_loss=0.03888, over 7275.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2799, pruned_loss=0.05683, over 1431142.32 frames.], batch size: 17, lr: 3.88e-04
2022-05-27 16:23:57,207 INFO [train.py:842] (1/4) Epoch 14, batch 5650, loss[loss=0.2084, simple_loss=0.2928, pruned_loss=0.06202, over 7271.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2805, pruned_loss=0.05718, over 1429721.54 frames.], batch size: 24, lr: 3.88e-04
2022-05-27 16:24:36,053 INFO [train.py:842] (1/4) Epoch 14, batch 5700, loss[loss=0.1689, simple_loss=0.2559, pruned_loss=0.04095, over 7438.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2804, pruned_loss=0.05735, over 1432334.67 frames.], batch size: 20, lr: 3.88e-04
2022-05-27 16:25:15,125 INFO [train.py:842] (1/4) Epoch 14, batch 5750, loss[loss=0.2325, simple_loss=0.3246, pruned_loss=0.0702, over 7296.00 frames.], tot_loss[loss=0.1967, simple_loss=0.28, pruned_loss=0.0567, over 1430140.60 frames.], batch size: 24, lr: 3.88e-04
2022-05-27 16:25:54,358 INFO [train.py:842] (1/4) Epoch 14, batch 5800, loss[loss=0.2604, simple_loss=0.3265, pruned_loss=0.09719, over 7326.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2801, pruned_loss=0.05708, over 1428609.21 frames.], batch size: 21, lr: 3.88e-04
2022-05-27 16:26:33,809 INFO [train.py:842] (1/4) Epoch 14, batch 5850, loss[loss=0.1624, simple_loss=0.246, pruned_loss=0.03936, over 7354.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2804, pruned_loss=0.0574, over 1426154.44 frames.], batch size: 19, lr: 3.88e-04
2022-05-27 16:27:12,634 INFO [train.py:842] (1/4) Epoch 14, batch 5900, loss[loss=0.1852, simple_loss=0.2817, pruned_loss=0.04429, over 6360.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2809, pruned_loss=0.05741, over 1421035.56 frames.], batch size: 38, lr: 3.88e-04
2022-05-27 16:27:52,041 INFO [train.py:842] (1/4) Epoch 14, batch 5950, loss[loss=0.2446, simple_loss=0.3004, pruned_loss=0.09443, over 7291.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2817, pruned_loss=0.05833, over 1422717.79 frames.], batch size: 17, lr: 3.88e-04
2022-05-27 16:28:31,063 INFO [train.py:842] (1/4) Epoch 14, batch 6000, loss[loss=0.2159, simple_loss=0.2917, pruned_loss=0.07002, over 6447.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2822, pruned_loss=0.05861, over 1417930.37 frames.], batch size: 38, lr: 3.87e-04
2022-05-27 16:28:31,064 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 16:28:40,676 INFO [train.py:871] (1/4) Epoch 14, validation: loss=0.1701, simple_loss=0.2708, pruned_loss=0.03472, over 868885.00 frames.
2022-05-27 16:29:19,886 INFO [train.py:842] (1/4) Epoch 14, batch 6050, loss[loss=0.1991, simple_loss=0.2866, pruned_loss=0.05584, over 7234.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2802, pruned_loss=0.05735, over 1418736.27 frames.], batch size: 20, lr: 3.87e-04
2022-05-27 16:29:58,720 INFO [train.py:842] (1/4) Epoch 14, batch 6100, loss[loss=0.1546, simple_loss=0.245, pruned_loss=0.03213, over 7063.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2808, pruned_loss=0.05743, over 1421527.87 frames.], batch size: 18, lr: 3.87e-04
2022-05-27 16:30:38,173 INFO [train.py:842] (1/4) Epoch 14, batch 6150, loss[loss=0.172, simple_loss=0.2488, pruned_loss=0.04759, over 6807.00 frames.], tot_loss[loss=0.1982, simple_loss=0.281, pruned_loss=0.0577, over 1421589.40 frames.], batch size: 15, lr: 3.87e-04
2022-05-27 16:31:17,315 INFO [train.py:842] (1/4) Epoch 14, batch 6200, loss[loss=0.2057, simple_loss=0.2802, pruned_loss=0.06561, over 7274.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2824, pruned_loss=0.05855, over 1415472.38 frames.], batch size: 17, lr: 3.87e-04
2022-05-27 16:31:56,492 INFO [train.py:842] (1/4) Epoch 14, batch 6250, loss[loss=0.1925, simple_loss=0.2762, pruned_loss=0.05434, over 7207.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2825, pruned_loss=0.05842, over 1416136.38 frames.], batch size: 22, lr: 3.87e-04
2022-05-27 16:32:35,090 INFO [train.py:842] (1/4) Epoch 14, batch 6300, loss[loss=0.1877, simple_loss=0.2675, pruned_loss=0.05391, over 7273.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2827, pruned_loss=0.05784, over 1417495.36 frames.], batch size: 17, lr: 3.87e-04
2022-05-27 16:33:14,425 INFO [train.py:842] (1/4) Epoch 14, batch 6350, loss[loss=0.2464, simple_loss=0.3392, pruned_loss=0.07685, over 6430.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2823, pruned_loss=0.05749, over 1416898.06 frames.], batch size: 38, lr: 3.87e-04
2022-05-27 16:33:53,379 INFO [train.py:842] (1/4) Epoch 14, batch 6400, loss[loss=0.1931, simple_loss=0.2813, pruned_loss=0.05249, over 7109.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2815, pruned_loss=0.05705, over 1419320.49 frames.], batch size: 21, lr: 3.87e-04
2022-05-27 16:34:32,584 INFO [train.py:842] (1/4) Epoch 14, batch 6450, loss[loss=0.1954, simple_loss=0.2562, pruned_loss=0.06731, over 7264.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2803, pruned_loss=0.05633, over 1421629.59 frames.], batch size: 17, lr: 3.87e-04
2022-05-27 16:35:11,312 INFO [train.py:842] (1/4) Epoch 14, batch 6500, loss[loss=0.215, simple_loss=0.2908, pruned_loss=0.06961, over 5466.00 frames.], tot_loss[loss=0.1984, simple_loss=0.282, pruned_loss=0.05743, over 1417544.98 frames.], batch size: 52, lr: 3.87e-04
2022-05-27 16:35:50,518 INFO [train.py:842] (1/4) Epoch 14, batch 6550, loss[loss=0.1811, simple_loss=0.2627, pruned_loss=0.04981, over 7219.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2821, pruned_loss=0.05749, over 1418371.50 frames.], batch size: 21, lr: 3.87e-04
2022-05-27 16:36:29,225 INFO [train.py:842] (1/4) Epoch 14, batch 6600, loss[loss=0.1858, simple_loss=0.2701, pruned_loss=0.05072, over 7332.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2828, pruned_loss=0.05816, over 1415658.14 frames.], batch size: 20, lr: 3.87e-04
2022-05-27 16:37:08,478 INFO [train.py:842] (1/4) Epoch 14, batch 6650, loss[loss=0.2002, simple_loss=0.285, pruned_loss=0.0577, over 7143.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2817, pruned_loss=0.05731, over 1415105.62 frames.], batch size: 20, lr: 3.86e-04
2022-05-27 16:37:47,806 INFO [train.py:842] (1/4) Epoch 14, batch 6700, loss[loss=0.1464, simple_loss=0.2265, pruned_loss=0.03309, over 7325.00 frames.], tot_loss[loss=0.198, simple_loss=0.281, pruned_loss=0.05751, over 1418864.30 frames.], batch size: 20, lr: 3.86e-04
2022-05-27 16:38:27,393 INFO [train.py:842] (1/4) Epoch 14, batch 6750, loss[loss=0.1934, simple_loss=0.2796, pruned_loss=0.05357, over 7261.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2813, pruned_loss=0.05758, over 1418600.23 frames.], batch size: 19, lr: 3.86e-04
2022-05-27 16:39:06,468 INFO [train.py:842] (1/4) Epoch 14, batch 6800, loss[loss=0.1674, simple_loss=0.257, pruned_loss=0.03892, over 7146.00 frames.], tot_loss[loss=0.1975, simple_loss=0.281, pruned_loss=0.05702, over 1413259.18 frames.], batch size: 19, lr: 3.86e-04
2022-05-27 16:39:45,776 INFO [train.py:842] (1/4) Epoch 14, batch 6850, loss[loss=0.2518, simple_loss=0.3246, pruned_loss=0.08951, over 7206.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2809, pruned_loss=0.05668, over 1413170.66 frames.], batch size: 22, lr: 3.86e-04
2022-05-27 16:40:24,721 INFO [train.py:842] (1/4) Epoch 14, batch 6900, loss[loss=0.2221, simple_loss=0.2881, pruned_loss=0.07805, over 7147.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2811, pruned_loss=0.0572, over 1415340.55 frames.], batch size: 17, lr: 3.86e-04
2022-05-27 16:41:03,789 INFO [train.py:842] (1/4) Epoch 14, batch 6950, loss[loss=0.1995, simple_loss=0.2825, pruned_loss=0.05827, over 6297.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2815, pruned_loss=0.05736, over 1417553.98 frames.], batch size: 38, lr: 3.86e-04
2022-05-27 16:41:42,267 INFO [train.py:842] (1/4) Epoch 14, batch 7000, loss[loss=0.181, simple_loss=0.2629, pruned_loss=0.04954, over 7275.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2817, pruned_loss=0.05729, over 1418737.69 frames.], batch size: 18, lr: 3.86e-04
2022-05-27 16:42:21,230 INFO [train.py:842] (1/4) Epoch 14, batch 7050, loss[loss=0.2188, simple_loss=0.2929, pruned_loss=0.07241, over 7079.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2825, pruned_loss=0.05802, over 1418092.15 frames.], batch size: 18, lr: 3.86e-04
2022-05-27 16:43:00,166 INFO [train.py:842] (1/4) Epoch 14, batch 7100, loss[loss=0.2164, simple_loss=0.2927, pruned_loss=0.07006, over 7414.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2823, pruned_loss=0.05821, over 1419722.38 frames.], batch size: 18, lr: 3.86e-04
2022-05-27 16:43:39,115 INFO [train.py:842] (1/4) Epoch 14, batch 7150, loss[loss=0.1799, simple_loss=0.2692, pruned_loss=0.04535, over 7070.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2834, pruned_loss=0.05855, over 1420276.10 frames.], batch size: 18, lr: 3.86e-04
2022-05-27 16:44:18,101 INFO [train.py:842] (1/4) Epoch 14, batch 7200, loss[loss=0.2037, simple_loss=0.3024, pruned_loss=0.05251, over 7331.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2826, pruned_loss=0.05828, over 1422149.08 frames.], batch size: 21, lr: 3.86e-04
2022-05-27 16:44:57,231 INFO [train.py:842] (1/4) Epoch 14, batch 7250, loss[loss=0.215, simple_loss=0.3004, pruned_loss=0.06485, over 7312.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2824, pruned_loss=0.05802, over 1422470.61 frames.], batch size: 25, lr: 3.86e-04
2022-05-27 16:45:35,821 INFO [train.py:842] (1/4) Epoch 14, batch 7300, loss[loss=0.175, simple_loss=0.2596, pruned_loss=0.04522, over 7068.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2823, pruned_loss=0.05774, over 1426056.97 frames.], batch size: 18, lr: 3.85e-04
2022-05-27 16:46:14,803 INFO [train.py:842] (1/4) Epoch 14, batch 7350, loss[loss=0.2337, simple_loss=0.3217, pruned_loss=0.07286, over 7064.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2824, pruned_loss=0.05734, over 1428935.98 frames.], batch size: 28, lr: 3.85e-04
2022-05-27 16:46:53,870 INFO [train.py:842] (1/4) Epoch 14, batch 7400, loss[loss=0.1442, simple_loss=0.2284, pruned_loss=0.03002, over 7285.00 frames.], tot_loss[loss=0.198, simple_loss=0.2818, pruned_loss=0.05706, over 1430094.60 frames.], batch size: 17, lr: 3.85e-04
2022-05-27 16:47:33,389 INFO [train.py:842] (1/4) Epoch 14, batch 7450, loss[loss=0.2146, simple_loss=0.2963, pruned_loss=0.0665, over 7393.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2825, pruned_loss=0.05824, over 1424923.97 frames.], batch size: 23, lr: 3.85e-04
2022-05-27 16:48:12,176 INFO [train.py:842] (1/4) Epoch 14, batch 7500, loss[loss=0.2244, simple_loss=0.2926, pruned_loss=0.07808, over 7154.00 frames.], tot_loss[loss=0.2002, simple_loss=0.283, pruned_loss=0.0587, over 1421993.14 frames.], batch size: 18, lr: 3.85e-04
2022-05-27 16:48:51,395 INFO [train.py:842] (1/4) Epoch 14, batch 7550, loss[loss=0.1816, simple_loss=0.2519, pruned_loss=0.05569, over 7298.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2829, pruned_loss=0.05848, over 1422921.81 frames.], batch size: 17, lr: 3.85e-04
2022-05-27 16:49:30,463 INFO [train.py:842] (1/4) Epoch 14, batch 7600, loss[loss=0.2019, simple_loss=0.2746, pruned_loss=0.0646, over 7195.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2826, pruned_loss=0.05825, over 1424473.65 frames.], batch size: 16, lr: 3.85e-04
2022-05-27 16:50:09,791 INFO [train.py:842] (1/4) Epoch 14, batch 7650, loss[loss=0.1893, simple_loss=0.282, pruned_loss=0.04828, over 7142.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2829, pruned_loss=0.05821, over 1426433.66 frames.], batch size: 20, lr: 3.85e-04
2022-05-27 16:50:49,111 INFO [train.py:842] (1/4) Epoch 14, batch 7700, loss[loss=0.1966, simple_loss=0.2723, pruned_loss=0.06041, over 7236.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2821, pruned_loss=0.05764, over 1424577.00 frames.], batch size: 16, lr: 3.85e-04
2022-05-27 16:51:28,090 INFO [train.py:842] (1/4) Epoch 14, batch 7750, loss[loss=0.1745, simple_loss=0.2483, pruned_loss=0.05039, over 7269.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2832, pruned_loss=0.05844, over 1420650.11 frames.], batch size: 18, lr: 3.85e-04
2022-05-27 16:52:06,988 INFO [train.py:842] (1/4) Epoch 14, batch 7800, loss[loss=0.1773, simple_loss=0.2585, pruned_loss=0.04808, over 7274.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2839, pruned_loss=0.05822, over 1418081.57 frames.], batch size: 18, lr: 3.85e-04
2022-05-27 16:52:45,714 INFO [train.py:842] (1/4) Epoch 14, batch 7850, loss[loss=0.1602, simple_loss=0.2412, pruned_loss=0.03954, over 7294.00 frames.], tot_loss[loss=0.199, simple_loss=0.2828, pruned_loss=0.05763, over 1415093.96 frames.], batch size: 18, lr: 3.85e-04
2022-05-27 16:53:24,467 INFO [train.py:842] (1/4) Epoch 14, batch 7900, loss[loss=0.2363, simple_loss=0.316, pruned_loss=0.07827, over 7194.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2822, pruned_loss=0.05731, over 1416037.06 frames.], batch size: 26, lr: 3.85e-04
2022-05-27 16:54:03,644 INFO [train.py:842] (1/4) Epoch 14, batch 7950, loss[loss=0.1997, simple_loss=0.2876, pruned_loss=0.0559, over 7231.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2818, pruned_loss=0.05723, over 1414473.35 frames.], batch size: 21, lr: 3.85e-04
2022-05-27 16:54:42,308 INFO [train.py:842] (1/4) Epoch 14, batch 8000, loss[loss=0.1714, simple_loss=0.2618, pruned_loss=0.04052, over 7140.00 frames.], tot_loss[loss=0.199, simple_loss=0.2823, pruned_loss=0.05781, over 1410448.10 frames.], batch size: 20, lr: 3.84e-04
2022-05-27 16:55:20,896 INFO [train.py:842] (1/4) Epoch 14, batch 8050, loss[loss=0.2015, simple_loss=0.2898, pruned_loss=0.05663, over 7409.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2829, pruned_loss=0.05802, over 1408402.53 frames.], batch size: 21, lr: 3.84e-04
2022-05-27 16:55:59,553 INFO [train.py:842] (1/4) Epoch 14, batch 8100, loss[loss=0.186, simple_loss=0.2656, pruned_loss=0.05323, over 7431.00 frames.], tot_loss[loss=0.1994, simple_loss=0.283, pruned_loss=0.05787, over 1413904.02 frames.], batch size: 20, lr: 3.84e-04
2022-05-27 16:56:38,989 INFO [train.py:842] (1/4) Epoch 14, batch 8150, loss[loss=0.239, simple_loss=0.3175, pruned_loss=0.0803, over 7337.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2823, pruned_loss=0.05815, over 1411596.09 frames.], batch size: 22, lr: 3.84e-04
2022-05-27 16:57:17,977 INFO [train.py:842] (1/4) Epoch 14, batch 8200, loss[loss=0.1681, simple_loss=0.2401, pruned_loss=0.04811, over 7268.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2823, pruned_loss=0.05796, over 1416201.15 frames.], batch size: 17, lr: 3.84e-04
2022-05-27 16:57:56,782 INFO [train.py:842] (1/4) Epoch 14, batch 8250, loss[loss=0.1689, simple_loss=0.2633, pruned_loss=0.03726, over 7227.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2824, pruned_loss=0.05797, over 1417416.86 frames.], batch size: 21, lr: 3.84e-04
2022-05-27 16:58:35,694 INFO [train.py:842] (1/4) Epoch 14, batch 8300, loss[loss=0.16, simple_loss=0.2562, pruned_loss=0.03192, over 7219.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2804, pruned_loss=0.05698, over 1422302.18 frames.], batch size: 21, lr: 3.84e-04
2022-05-27 16:59:14,916 INFO [train.py:842] (1/4) Epoch 14, batch 8350, loss[loss=0.187, simple_loss=0.2812, pruned_loss=0.04643, over 7297.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2802, pruned_loss=0.05702, over 1423144.19 frames.], batch size: 25, lr: 3.84e-04
2022-05-27 16:59:53,722 INFO [train.py:842] (1/4) Epoch 14, batch 8400, loss[loss=0.2157, simple_loss=0.3067, pruned_loss=0.06238, over 7302.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2793, pruned_loss=0.05709, over 1419329.40 frames.], batch size: 24, lr: 3.84e-04
2022-05-27 17:00:32,578 INFO [train.py:842] (1/4) Epoch 14, batch 8450, loss[loss=0.1907, simple_loss=0.2789, pruned_loss=0.05123, over 7139.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2802, pruned_loss=0.05732, over 1421642.54 frames.], batch size: 20, lr: 3.84e-04
2022-05-27 17:01:11,194 INFO [train.py:842] (1/4) Epoch 14, batch 8500, loss[loss=0.181, simple_loss=0.276, pruned_loss=0.04303, over 6748.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2812, pruned_loss=0.0579, over 1422109.13 frames.], batch size: 31, lr: 3.84e-04
2022-05-27 17:01:53,166 INFO [train.py:842] (1/4) Epoch 14, batch 8550, loss[loss=0.2341, simple_loss=0.312, pruned_loss=0.07807, over 6395.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2802, pruned_loss=0.0574, over 1416425.15 frames.], batch size: 38, lr: 3.84e-04
2022-05-27 17:02:32,120 INFO [train.py:842] (1/4) Epoch 14, batch 8600, loss[loss=0.1965, simple_loss=0.2594, pruned_loss=0.06678, over 7406.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2784, pruned_loss=0.05623, over 1418954.23 frames.], batch size: 18, lr: 3.84e-04
2022-05-27 17:03:11,235 INFO [train.py:842] (1/4) Epoch 14, batch 8650, loss[loss=0.1734, simple_loss=0.2451, pruned_loss=0.05088, over 6780.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2781, pruned_loss=0.05578, over 1421615.34 frames.], batch size: 15, lr: 3.83e-04
2022-05-27 17:03:49,934 INFO [train.py:842] (1/4) Epoch 14, batch 8700, loss[loss=0.1786, simple_loss=0.2623, pruned_loss=0.04743, over 7160.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2782, pruned_loss=0.05544, over 1419060.16 frames.], batch size: 19, lr: 3.83e-04
2022-05-27 17:04:29,258 INFO [train.py:842] (1/4) Epoch 14, batch 8750, loss[loss=0.2369, simple_loss=0.3167, pruned_loss=0.07857, over 7227.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2789, pruned_loss=0.05597, over 1417070.71 frames.], batch size: 21, lr: 3.83e-04
2022-05-27 17:05:08,103 INFO [train.py:842] (1/4) Epoch 14, batch 8800, loss[loss=0.2126, simple_loss=0.3042, pruned_loss=0.06047, over 7219.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2805, pruned_loss=0.05703, over 1413131.30 frames.], batch size: 21, lr: 3.83e-04
2022-05-27 17:05:47,079 INFO [train.py:842] (1/4) Epoch 14, batch 8850, loss[loss=0.1364, simple_loss=0.2279, pruned_loss=0.02242, over 7059.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2802, pruned_loss=0.05728, over 1404565.93 frames.], batch size: 18, lr: 3.83e-04
2022-05-27 17:06:26,050 INFO [train.py:842] (1/4) Epoch 14, batch 8900, loss[loss=0.2016, simple_loss=0.2978, pruned_loss=0.05272, over 6735.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2787, pruned_loss=0.05646, over 1403614.82 frames.], batch size: 31, lr: 3.83e-04
2022-05-27 17:07:05,149 INFO [train.py:842] (1/4) Epoch 14, batch 8950, loss[loss=0.1929, simple_loss=0.2832, pruned_loss=0.05126, over 7262.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2793, pruned_loss=0.05675, over 1405539.93 frames.], batch size: 19, lr: 3.83e-04
2022-05-27 17:07:44,234 INFO [train.py:842] (1/4) Epoch 14, batch 9000, loss[loss=0.2033, simple_loss=0.2879, pruned_loss=0.05935, over 7023.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2784, pruned_loss=0.05631, over 1406914.75 frames.], batch size: 28, lr: 3.83e-04
2022-05-27 17:07:44,235 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 17:07:53,833 INFO [train.py:871] (1/4) Epoch 14, validation: loss=0.1693, simple_loss=0.2692, pruned_loss=0.03464, over 868885.00 frames.
2022-05-27 17:08:33,153 INFO [train.py:842] (1/4) Epoch 14, batch 9050, loss[loss=0.239, simple_loss=0.3138, pruned_loss=0.08205, over 5087.00 frames.], tot_loss[loss=0.194, simple_loss=0.2767, pruned_loss=0.05564, over 1398485.87 frames.], batch size: 53, lr: 3.83e-04
2022-05-27 17:09:11,604 INFO [train.py:842] (1/4) Epoch 14, batch 9100, loss[loss=0.231, simple_loss=0.3031, pruned_loss=0.07946, over 4798.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2792, pruned_loss=0.05707, over 1377491.50 frames.], batch size: 53, lr: 3.83e-04
2022-05-27 17:09:49,533 INFO [train.py:842] (1/4) Epoch 14, batch 9150, loss[loss=0.2428, simple_loss=0.3158, pruned_loss=0.08494, over 5175.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2849, pruned_loss=0.06142, over 1315280.05 frames.], batch size: 53, lr: 3.83e-04
2022-05-27 17:10:39,680 INFO [train.py:842] (1/4) Epoch 15, batch 0, loss[loss=0.2225, simple_loss=0.3029, pruned_loss=0.07099, over 7098.00 frames.], tot_loss[loss=0.2225, simple_loss=0.3029, pruned_loss=0.07099, over 7098.00 frames.], batch size: 28, lr: 3.71e-04
2022-05-27 17:11:18,904 INFO [train.py:842] (1/4) Epoch 15, batch 50, loss[loss=0.1968, simple_loss=0.2774, pruned_loss=0.05807, over 5332.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2766, pruned_loss=0.05408, over 321744.97 frames.], batch size: 54, lr: 3.71e-04
2022-05-27 17:11:57,671 INFO [train.py:842] (1/4) Epoch 15, batch 100, loss[loss=0.1591, simple_loss=0.2413, pruned_loss=0.03842, over 7156.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2802, pruned_loss=0.05545, over 569142.16 frames.], batch size: 18, lr: 3.71e-04
2022-05-27 17:12:36,394 INFO [train.py:842] (1/4) Epoch 15, batch 150, loss[loss=0.1859, simple_loss=0.279, pruned_loss=0.04641, over 7123.00 frames.], tot_loss[loss=0.1966, simple_loss=0.282, pruned_loss=0.05561, over 759301.22 frames.], batch size: 21, lr: 3.71e-04
2022-05-27 17:13:15,161 INFO [train.py:842] (1/4) Epoch 15, batch 200, loss[loss=0.1711, simple_loss=0.2606, pruned_loss=0.0408, over 7328.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2816, pruned_loss=0.05592, over 903856.34 frames.], batch size: 20, lr: 3.71e-04
2022-05-27 17:13:54,170 INFO [train.py:842] (1/4) Epoch 15, batch 250, loss[loss=0.2226, simple_loss=0.301, pruned_loss=0.07209, over 6506.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2801, pruned_loss=0.05469, over 1020364.94 frames.], batch size: 38, lr: 3.71e-04
2022-05-27 17:14:33,338 INFO [train.py:842] (1/4) Epoch 15, batch 300, loss[loss=0.2102, simple_loss=0.2737, pruned_loss=0.07334, over 7132.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2798, pruned_loss=0.05502, over 1110620.42 frames.], batch size: 17, lr: 3.71e-04
2022-05-27 17:15:12,263 INFO [train.py:842] (1/4) Epoch 15, batch 350, loss[loss=0.1549, simple_loss=0.2397, pruned_loss=0.03507, over 6832.00 frames.], tot_loss[loss=0.1955, simple_loss=0.28, pruned_loss=0.05552, over 1172442.16 frames.], batch size: 15, lr: 3.70e-04
2022-05-27 17:15:51,604 INFO [train.py:842] (1/4) Epoch 15, batch 400, loss[loss=0.1921, simple_loss=0.273, pruned_loss=0.05561, over 7154.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2793, pruned_loss=0.0554, over 1227180.31 frames.], batch size: 20, lr: 3.70e-04
2022-05-27 17:16:30,736 INFO [train.py:842] (1/4) Epoch 15, batch 450, loss[loss=0.1801, simple_loss=0.258, pruned_loss=0.05105, over 7157.00 frames.], tot_loss[loss=0.195, simple_loss=0.2791, pruned_loss=0.05549, over 1271168.27 frames.], batch size: 19, lr: 3.70e-04
2022-05-27 17:17:09,307 INFO [train.py:842] (1/4) Epoch 15, batch 500, loss[loss=0.1878, simple_loss=0.2715, pruned_loss=0.05207, over 7436.00 frames.], tot_loss[loss=0.1958, simple_loss=0.28, pruned_loss=0.05577, over 1302856.42 frames.], batch size: 20, lr: 3.70e-04
2022-05-27 17:17:48,666 INFO [train.py:842] (1/4) Epoch 15, batch 550, loss[loss=0.1449, simple_loss=0.2329, pruned_loss=0.02842, over 7282.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2798, pruned_loss=0.05619, over 1332202.85 frames.], batch size: 18, lr: 3.70e-04
2022-05-27 17:18:27,590 INFO [train.py:842] (1/4) Epoch 15, batch 600, loss[loss=0.2497, simple_loss=0.3267, pruned_loss=0.08638, over 7227.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2797, pruned_loss=0.05646, over 1355137.10 frames.], batch size: 20, lr: 3.70e-04
2022-05-27 17:19:06,360 INFO [train.py:842] (1/4) Epoch 15, batch 650, loss[loss=0.1591, simple_loss=0.2525, pruned_loss=0.03283, over 7337.00 frames.], tot_loss[loss=0.196, simple_loss=0.2796, pruned_loss=0.05619, over 1369991.29 frames.], batch size: 22, lr: 3.70e-04
2022-05-27 17:19:45,096 INFO [train.py:842] (1/4) Epoch 15, batch 700, loss[loss=0.1937, simple_loss=0.2866, pruned_loss=0.05044, over 7329.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2804, pruned_loss=0.05627, over 1383412.17 frames.], batch size: 20, lr: 3.70e-04
2022-05-27 17:20:24,135 INFO [train.py:842] (1/4) Epoch 15, batch 750, loss[loss=0.2732, simple_loss=0.3496, pruned_loss=0.09846, over 7329.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2796, pruned_loss=0.05586, over 1390938.68 frames.], batch size: 22, lr: 3.70e-04
2022-05-27 17:21:03,142 INFO [train.py:842] (1/4) Epoch 15, batch 800, loss[loss=0.1906, simple_loss=0.2841, pruned_loss=0.04855, over 7327.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2794, pruned_loss=0.05571, over 1398749.66 frames.], batch size: 22, lr: 3.70e-04
2022-05-27 17:21:42,298 INFO [train.py:842] (1/4) Epoch 15, batch 850, loss[loss=0.1835, simple_loss=0.2574, pruned_loss=0.05482, over 7136.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2788, pruned_loss=0.05509, over 1401938.00 frames.], batch size: 17, lr: 3.70e-04
2022-05-27 17:22:21,047 INFO [train.py:842] (1/4) Epoch 15, batch 900, loss[loss=0.1692, simple_loss=0.249, pruned_loss=0.04468, over 7259.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2789, pruned_loss=0.05546, over 1397086.63 frames.], batch size: 19, lr: 3.70e-04
2022-05-27 17:22:59,786 INFO [train.py:842] (1/4) Epoch 15, batch 950, loss[loss=0.1847, simple_loss=0.2773, pruned_loss=0.04603, over 7332.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2803, pruned_loss=0.05597, over 1405793.50 frames.], batch size: 22, lr: 3.70e-04
2022-05-27 17:23:38,678 INFO [train.py:842] (1/4) Epoch 15, batch 1000, loss[loss=0.2432, simple_loss=0.3195, pruned_loss=0.0835, over 7051.00 frames.], tot_loss[loss=0.1961, simple_loss=0.28, pruned_loss=0.0561, over 1406974.22 frames.], batch size: 28, lr: 3.70e-04
2022-05-27 17:24:17,877 INFO [train.py:842] (1/4) Epoch 15, batch 1050, loss[loss=0.1802, simple_loss=0.2619, pruned_loss=0.04925, over 7273.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2791, pruned_loss=0.05575, over 1413028.24 frames.], batch size: 18, lr: 3.70e-04
2022-05-27 17:24:57,076 INFO [train.py:842] (1/4) Epoch 15, batch 1100, loss[loss=0.1514, simple_loss=0.2331, pruned_loss=0.03491, over 7301.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2793, pruned_loss=0.05613, over 1416273.54 frames.], batch size: 17, lr: 3.69e-04
2022-05-27 17:25:36,301 INFO [train.py:842] (1/4) Epoch 15, batch 1150, loss[loss=0.1906, simple_loss=0.2768, pruned_loss=0.05221, over 7419.00 frames.], tot_loss[loss=0.196, simple_loss=0.2795, pruned_loss=0.05626, over 1420814.54 frames.], batch size: 21, lr: 3.69e-04
2022-05-27 17:26:15,261 INFO [train.py:842] (1/4) Epoch 15, batch 1200, loss[loss=0.1982, simple_loss=0.2819, pruned_loss=0.05727, over 7424.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2779, pruned_loss=0.0553, over 1422095.35 frames.], batch size: 20, lr: 3.69e-04
2022-05-27 17:26:54,523 INFO [train.py:842] (1/4) Epoch 15, batch 1250, loss[loss=0.1844, simple_loss=0.266, pruned_loss=0.05139, over 7360.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2784, pruned_loss=0.05545, over 1425103.91 frames.], batch size: 19, lr: 3.69e-04
2022-05-27 17:27:33,152 INFO [train.py:842] (1/4) Epoch 15, batch 1300, loss[loss=0.2571, simple_loss=0.3336, pruned_loss=0.09032, over 6425.00 frames.], tot_loss[loss=0.1953, simple_loss=0.279, pruned_loss=0.05575, over 1418744.80 frames.], batch size: 38, lr: 3.69e-04
2022-05-27 17:28:12,450 INFO [train.py:842] (1/4) Epoch 15, batch 1350, loss[loss=0.1592, simple_loss=0.2368, pruned_loss=0.04079, over 6995.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2804, pruned_loss=0.05642, over 1421370.57 frames.], batch size: 16, lr: 3.69e-04
2022-05-27 17:28:51,241 INFO [train.py:842] (1/4) Epoch 15, batch 1400, loss[loss=0.2253, simple_loss=0.3058, pruned_loss=0.07244, over 7281.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2801, pruned_loss=0.0561, over 1421255.63 frames.], batch size: 24, lr: 3.69e-04
2022-05-27 17:29:30,186 INFO [train.py:842] (1/4) Epoch 15, batch 1450, loss[loss=0.1878, simple_loss=0.2915, pruned_loss=0.0421, over 7385.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2808, pruned_loss=0.05619, over 1418412.54 frames.], batch size: 23, lr: 3.69e-04
2022-05-27 17:30:08,791 INFO [train.py:842] (1/4) Epoch 15, batch 1500, loss[loss=0.1921, simple_loss=0.2867, pruned_loss=0.04877, over 7151.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2813, pruned_loss=0.05692, over 1413025.13 frames.], batch size: 20, lr: 3.69e-04
2022-05-27 17:30:48,098 INFO [train.py:842] (1/4) Epoch 15, batch 1550, loss[loss=0.1903, simple_loss=0.274, pruned_loss=0.05331, over 7110.00 frames.], tot_loss[loss=0.197, simple_loss=0.2805, pruned_loss=0.05674, over 1418289.26 frames.], batch size: 21, lr: 3.69e-04
2022-05-27 17:31:26,958 INFO [train.py:842] (1/4) Epoch 15, batch 1600, loss[loss=0.1507, simple_loss=0.2414, pruned_loss=0.03003, over 7410.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2783, pruned_loss=0.05568, over 1419507.65 frames.], batch size: 21, lr: 3.69e-04
2022-05-27 17:32:05,919 INFO [train.py:842] (1/4) Epoch 15, batch 1650, loss[loss=0.1923, simple_loss=0.2851, pruned_loss=0.04978, over 7206.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2774, pruned_loss=0.05474, over 1424542.20 frames.], batch size: 23, lr: 3.69e-04
2022-05-27 17:32:44,965 INFO [train.py:842] (1/4) Epoch 15, batch 1700, loss[loss=0.1824, simple_loss=0.2671, pruned_loss=0.04885, over 7269.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2755, pruned_loss=0.05336, over 1428560.74 frames.], batch size: 25, lr: 3.69e-04
2022-05-27 17:33:24,369 INFO [train.py:842] (1/4) Epoch 15, batch 1750, loss[loss=0.2505, simple_loss=0.3298, pruned_loss=0.08565, over 7090.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2774, pruned_loss=0.05472, over 1431669.22 frames.], batch size: 28, lr: 3.69e-04
2022-05-27 17:34:03,108 INFO [train.py:842] (1/4) Epoch 15, batch 1800, loss[loss=0.1391, simple_loss=0.2251, pruned_loss=0.02658, over 7277.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2778, pruned_loss=0.05474, over 1428526.00 frames.], batch size: 17, lr: 3.68e-04
2022-05-27 17:34:42,337 INFO [train.py:842] (1/4) Epoch 15, batch 1850, loss[loss=0.1891, simple_loss=0.2652, pruned_loss=0.05655, over 7173.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2794, pruned_loss=0.0557, over 1432980.64 frames.], batch size: 18, lr: 3.68e-04
2022-05-27 17:35:21,430 INFO [train.py:842] (1/4) Epoch 15, batch 1900, loss[loss=0.1809, simple_loss=0.2798, pruned_loss=0.04099, over 7107.00 frames.], tot_loss[loss=0.1948, simple_loss=0.279, pruned_loss=0.05527, over 1431863.35 frames.], batch size: 21, lr: 3.68e-04
2022-05-27 17:36:00,616 INFO [train.py:842] (1/4) Epoch 15, batch 1950, loss[loss=0.1684, simple_loss=0.2434, pruned_loss=0.0467, over 7293.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2785, pruned_loss=0.0556, over 1431898.33 frames.], batch size: 18, lr: 3.68e-04
2022-05-27 17:36:39,351 INFO [train.py:842] (1/4) Epoch 15, batch 2000, loss[loss=0.2379, simple_loss=0.3184, pruned_loss=0.07865, over 6405.00 frames.], tot_loss[loss=0.1957, simple_loss=0.279, pruned_loss=0.05615, over 1427982.76 frames.], batch size: 37, lr: 3.68e-04
2022-05-27 17:37:18,697 INFO [train.py:842] (1/4) Epoch 15, batch 2050, loss[loss=0.1849, simple_loss=0.2702, pruned_loss=0.0498, over 7319.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2794, pruned_loss=0.05602, over 1429490.48 frames.], batch size: 25, lr: 3.68e-04
2022-05-27 17:37:57,584 INFO [train.py:842] (1/4) Epoch 15, batch 2100, loss[loss=0.2129, simple_loss=0.2844, pruned_loss=0.07066, over 7421.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2781, pruned_loss=0.05533, over 1422918.92 frames.], batch size: 18, lr: 3.68e-04
2022-05-27 17:38:36,689 INFO [train.py:842] (1/4) Epoch 15, batch 2150, loss[loss=0.2511, simple_loss=0.3394, pruned_loss=0.08142, over 7211.00 frames.], tot_loss[loss=0.1944, simple_loss=0.278, pruned_loss=0.05538, over 1420265.46 frames.], batch size: 22, lr: 3.68e-04
2022-05-27 17:39:15,641 INFO [train.py:842] (1/4) Epoch 15, batch 2200, loss[loss=0.2054, simple_loss=0.2832, pruned_loss=0.06382, over 7428.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2778, pruned_loss=0.05487, over 1419246.82 frames.], batch size: 20, lr: 3.68e-04
2022-05-27 17:39:54,617 INFO [train.py:842] (1/4) Epoch 15, batch 2250, loss[loss=0.1996, simple_loss=0.2847, pruned_loss=0.0573, over 7063.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2776, pruned_loss=0.05442, over 1420256.86 frames.], batch size: 28, lr: 3.68e-04
2022-05-27 17:40:33,451 INFO [train.py:842] (1/4) Epoch 15, batch 2300, loss[loss=0.1384, simple_loss=0.2238, pruned_loss=0.02647, over 7265.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2777, pruned_loss=0.05476, over 1419914.24 frames.], batch size: 16, lr: 3.68e-04
2022-05-27 17:41:12,440 INFO [train.py:842] (1/4) Epoch 15, batch 2350, loss[loss=0.1781, simple_loss=0.2595, pruned_loss=0.04832, over 7415.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2768, pruned_loss=0.05428, over 1422881.67 frames.], batch size: 18, lr: 3.68e-04
2022-05-27 17:41:51,250 INFO [train.py:842] (1/4) Epoch 15, batch 2400, loss[loss=0.1606, simple_loss=0.2456, pruned_loss=0.03775, over 7408.00 frames.], tot_loss[loss=0.1938, simple_loss=0.278, pruned_loss=0.05477, over 1421362.02 frames.], batch size: 18, lr: 3.68e-04
2022-05-27 17:42:30,538 INFO [train.py:842] (1/4) Epoch 15, batch 2450, loss[loss=0.2181, simple_loss=0.2951, pruned_loss=0.07055, over 7417.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2784, pruned_loss=0.05512, over 1423108.10 frames.], batch size: 21, lr: 3.68e-04
2022-05-27 17:43:09,564 INFO [train.py:842] (1/4) Epoch 15, batch 2500, loss[loss=0.1888, simple_loss=0.2834, pruned_loss=0.04706, over 7316.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2795, pruned_loss=0.05542, over 1424471.81 frames.], batch size: 21, lr: 3.67e-04
2022-05-27 17:43:48,434 INFO [train.py:842] (1/4) Epoch 15, batch 2550, loss[loss=0.1644, simple_loss=0.2473, pruned_loss=0.04074, over 7168.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2794, pruned_loss=0.05559, over 1427288.90 frames.], batch size: 18, lr: 3.67e-04
2022-05-27 17:44:27,025 INFO [train.py:842] (1/4) Epoch 15, batch 2600, loss[loss=0.2477, simple_loss=0.323, pruned_loss=0.08617, over 7198.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2795, pruned_loss=0.05558, over 1421953.45 frames.], batch size: 23, lr: 3.67e-04
2022-05-27 17:45:06,242 INFO [train.py:842] (1/4) Epoch 15, batch 2650, loss[loss=0.1608, simple_loss=0.2652, pruned_loss=0.0282, over 7300.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2797, pruned_loss=0.0558, over 1421548.11 frames.], batch size: 25, lr: 3.67e-04
2022-05-27 17:45:45,129 INFO [train.py:842] (1/4) Epoch 15, batch 2700, loss[loss=0.1975, simple_loss=0.2831, pruned_loss=0.056, over 7318.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2801, pruned_loss=0.05575, over 1423356.10 frames.], batch size: 21, lr: 3.67e-04
2022-05-27 17:46:24,224 INFO [train.py:842] (1/4) Epoch 15, batch 2750, loss[loss=0.2216, simple_loss=0.3082, pruned_loss=0.06752, over 7275.00 frames.], tot_loss[loss=0.1961, simple_loss=0.28, pruned_loss=0.05606, over 1424049.28 frames.], batch size: 24, lr: 3.67e-04
2022-05-27 17:47:03,183 INFO [train.py:842] (1/4) Epoch 15, batch 2800, loss[loss=0.1758, simple_loss=0.2684, pruned_loss=0.0416, over 7134.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2786, pruned_loss=0.05499, over 1427360.08 frames.], batch size: 20, lr: 3.67e-04
2022-05-27 17:47:42,020 INFO [train.py:842] (1/4) Epoch 15, batch 2850, loss[loss=0.183, simple_loss=0.2541, pruned_loss=0.05592, over 6738.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2792, pruned_loss=0.0553, over 1427279.61 frames.], batch size: 15, lr: 3.67e-04
2022-05-27 17:48:20,927 INFO [train.py:842] (1/4) Epoch 15, batch 2900, loss[loss=0.1857, simple_loss=0.2754, pruned_loss=0.04799, over 7385.00 frames.], tot_loss[loss=0.194, simple_loss=0.2782, pruned_loss=0.05487, over 1423095.73 frames.], batch size: 23, lr: 3.67e-04
2022-05-27 17:49:00,335 INFO [train.py:842] (1/4) Epoch 15, batch 2950, loss[loss=0.2004, simple_loss=0.2863, pruned_loss=0.05723, over 7425.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2787, pruned_loss=0.05516, over 1424924.05 frames.], batch size: 20, lr: 3.67e-04
2022-05-27 17:49:39,744 INFO [train.py:842] (1/4) Epoch 15, batch 3000, loss[loss=0.2345, simple_loss=0.3074, pruned_loss=0.0808, over 7162.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2787, pruned_loss=0.05556, over 1422504.12 frames.], batch size: 19, lr: 3.67e-04
2022-05-27 17:49:39,745 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 17:49:49,311 INFO [train.py:871] (1/4) Epoch 15, validation: loss=0.1691, simple_loss=0.2695, pruned_loss=0.03437, over 868885.00 frames.
2022-05-27 17:50:28,466 INFO [train.py:842] (1/4) Epoch 15, batch 3050, loss[loss=0.2012, simple_loss=0.2685, pruned_loss=0.06696, over 6790.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2786, pruned_loss=0.05504, over 1424971.21 frames.], batch size: 15, lr: 3.67e-04
2022-05-27 17:51:07,343 INFO [train.py:842] (1/4) Epoch 15, batch 3100, loss[loss=0.1511, simple_loss=0.2567, pruned_loss=0.02279, over 7319.00 frames.], tot_loss[loss=0.1946, simple_loss=0.279, pruned_loss=0.05509, over 1421385.26 frames.], batch size: 20, lr: 3.67e-04
2022-05-27 17:51:46,476 INFO [train.py:842] (1/4) Epoch 15, batch 3150, loss[loss=0.2125, simple_loss=0.281, pruned_loss=0.07202, over 7288.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2788, pruned_loss=0.05537, over 1426259.24 frames.], batch size: 17, lr: 3.67e-04
2022-05-27 17:52:25,454 INFO [train.py:842] (1/4) Epoch 15, batch 3200, loss[loss=0.2041, simple_loss=0.2864, pruned_loss=0.06092, over 7120.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2793, pruned_loss=0.05621, over 1426743.46 frames.], batch size: 28, lr: 3.66e-04
2022-05-27 17:53:04,612 INFO [train.py:842] (1/4) Epoch 15, batch 3250, loss[loss=0.19, simple_loss=0.2679, pruned_loss=0.05609, over 7057.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2786, pruned_loss=0.05611, over 1427326.69 frames.], batch size: 18, lr: 3.66e-04
2022-05-27 17:53:43,719 INFO [train.py:842] (1/4) Epoch 15, batch 3300, loss[loss=0.1806, simple_loss=0.2538, pruned_loss=0.0537, over 7267.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2778, pruned_loss=0.056, over 1427038.49 frames.], batch size: 17, lr: 3.66e-04
2022-05-27 17:54:22,801 INFO [train.py:842] (1/4) Epoch 15, batch 3350, loss[loss=0.1786, simple_loss=0.2806, pruned_loss=0.03828, over 7205.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2789, pruned_loss=0.05635, over 1426894.45 frames.], batch size: 23, lr: 3.66e-04
2022-05-27 17:55:01,356 INFO [train.py:842] (1/4) Epoch 15, batch 3400, loss[loss=0.2056, simple_loss=0.2986, pruned_loss=0.05627, over 7221.00 frames.], tot_loss[loss=0.1968, simple_loss=0.28, pruned_loss=0.05677, over 1423753.27 frames.], batch size: 21, lr: 3.66e-04
2022-05-27 17:55:40,262 INFO [train.py:842] (1/4) Epoch 15, batch 3450, loss[loss=0.2, simple_loss=0.2862, pruned_loss=0.05693, over 7084.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2817, pruned_loss=0.05727, over 1421181.17 frames.], batch size: 28, lr: 3.66e-04
2022-05-27 17:56:19,157 INFO [train.py:842] (1/4) Epoch 15, batch 3500, loss[loss=0.177, simple_loss=0.2688, pruned_loss=0.04262, over 7175.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2807, pruned_loss=0.05623, over 1426376.97 frames.], batch size: 26, lr: 3.66e-04
2022-05-27 17:56:58,062 INFO [train.py:842] (1/4) Epoch 15, batch 3550, loss[loss=0.1859, simple_loss=0.2879, pruned_loss=0.04191, over 7235.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2807, pruned_loss=0.05585, over 1427788.23 frames.], batch size: 20, lr: 3.66e-04
2022-05-27 17:57:36,716 INFO [train.py:842] (1/4) Epoch 15, batch 3600, loss[loss=0.2206, simple_loss=0.3044, pruned_loss=0.0684, over 7315.00 frames.], tot_loss[loss=0.197, simple_loss=0.281, pruned_loss=0.05653, over 1424285.60 frames.], batch size: 21, lr: 3.66e-04
2022-05-27 17:58:15,834 INFO [train.py:842] (1/4) Epoch 15, batch 3650, loss[loss=0.1813, simple_loss=0.2642, pruned_loss=0.04918, over 7252.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2818, pruned_loss=0.05699, over 1424948.00 frames.], batch size: 19, lr: 3.66e-04
2022-05-27 17:58:54,658 INFO [train.py:842] (1/4) Epoch 15, batch 3700, loss[loss=0.2457, simple_loss=0.3172, pruned_loss=0.08708, over 7425.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2809, pruned_loss=0.05634, over 1421397.21 frames.], batch size: 20, lr: 3.66e-04
2022-05-27 17:59:34,185 INFO [train.py:842] (1/4) Epoch 15, batch 3750, loss[loss=0.2263, simple_loss=0.3009, pruned_loss=0.07584, over 5018.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2794, pruned_loss=0.05588, over 1423969.76 frames.], batch size: 52, lr: 3.66e-04
2022-05-27 18:00:12,934 INFO [train.py:842] (1/4) Epoch 15, batch 3800, loss[loss=0.201, simple_loss=0.2896, pruned_loss=0.05621, over 7068.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2815, pruned_loss=0.05673, over 1425891.08 frames.], batch size: 18, lr: 3.66e-04
2022-05-27 18:00:51,702 INFO [train.py:842] (1/4) Epoch 15, batch 3850, loss[loss=0.2149, simple_loss=0.307, pruned_loss=0.06136, over 7237.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2815, pruned_loss=0.05617, over 1428622.40 frames.], batch size: 20, lr: 3.66e-04
2022-05-27 18:01:30,660 INFO [train.py:842] (1/4) Epoch 15, batch 3900, loss[loss=0.1734, simple_loss=0.2609, pruned_loss=0.04296, over 7257.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2808, pruned_loss=0.05592, over 1425680.42 frames.], batch size: 19, lr: 3.66e-04
2022-05-27 18:02:19,755 INFO [train.py:842] (1/4) Epoch 15, batch 3950, loss[loss=0.2143, simple_loss=0.3052, pruned_loss=0.0617, over 7142.00 frames.], tot_loss[loss=0.196, simple_loss=0.2801, pruned_loss=0.05599, over 1421924.04 frames.], batch size: 20, lr: 3.65e-04
2022-05-27 18:02:58,502 INFO [train.py:842] (1/4) Epoch 15, batch 4000, loss[loss=0.2146, simple_loss=0.2754, pruned_loss=0.07688, over 7124.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2811, pruned_loss=0.05685, over 1421405.69 frames.], batch size: 17, lr: 3.65e-04
2022-05-27 18:03:37,562 INFO [train.py:842] (1/4) Epoch 15, batch 4050, loss[loss=0.1762, simple_loss=0.2689, pruned_loss=0.04179, over 6513.00 frames.], tot_loss[loss=0.1981, simple_loss=0.282, pruned_loss=0.05704, over 1425328.22 frames.], batch size: 37, lr: 3.65e-04
2022-05-27 18:04:16,177 INFO [train.py:842] (1/4) Epoch 15, batch 4100, loss[loss=0.213, simple_loss=0.2936, pruned_loss=0.06621, over 7421.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2824, pruned_loss=0.05709, over 1419927.41 frames.], batch size: 21, lr: 3.65e-04
2022-05-27 18:04:55,258 INFO [train.py:842] (1/4) Epoch 15, batch 4150, loss[loss=0.1593, simple_loss=0.2379, pruned_loss=0.04031, over 7419.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2821, pruned_loss=0.05686, over 1422127.07 frames.], batch size: 18, lr: 3.65e-04
2022-05-27 18:05:33,911 INFO [train.py:842] (1/4) Epoch 15, batch 4200, loss[loss=0.1881, simple_loss=0.2793, pruned_loss=0.04843, over 7366.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2824, pruned_loss=0.05765, over 1416516.09 frames.], batch size: 23, lr: 3.65e-04
2022-05-27 18:06:13,486 INFO [train.py:842] (1/4) Epoch 15, batch 4250, loss[loss=0.1815, simple_loss=0.2708, pruned_loss=0.04606, over 7300.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2808, pruned_loss=0.05701, over 1417485.51 frames.], batch size: 24, lr: 3.65e-04
2022-05-27 18:06:52,354 INFO [train.py:842] (1/4) Epoch 15, batch 4300, loss[loss=0.1896, simple_loss=0.2755, pruned_loss=0.0518, over 7257.00 frames.], tot_loss[loss=0.196, simple_loss=0.2795, pruned_loss=0.05628, over 1415260.53 frames.], batch size: 25, lr: 3.65e-04
2022-05-27 18:07:31,746 INFO [train.py:842] (1/4) Epoch 15, batch 4350, loss[loss=0.1544, simple_loss=0.2389, pruned_loss=0.03495, over 7153.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2805, pruned_loss=0.05638, over 1419229.27 frames.], batch size: 18, lr: 3.65e-04
2022-05-27 18:08:10,566 INFO [train.py:842] (1/4) Epoch 15, batch 4400, loss[loss=0.2162, simple_loss=0.2861, pruned_loss=0.07309, over 7285.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2811, pruned_loss=0.05662, over 1417854.47 frames.], batch size: 18, lr: 3.65e-04
2022-05-27 18:08:49,765 INFO [train.py:842] (1/4) Epoch 15, batch 4450, loss[loss=0.1956, simple_loss=0.2971, pruned_loss=0.04704, over 7420.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2811, pruned_loss=0.05689, over 1418675.23 frames.], batch size: 21, lr: 3.65e-04
2022-05-27 18:09:28,920 INFO [train.py:842] (1/4) Epoch 15, batch 4500, loss[loss=0.1996, simple_loss=0.284, pruned_loss=0.05755, over 7298.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2815, pruned_loss=0.05718, over 1422701.42 frames.], batch size: 25, lr: 3.65e-04
2022-05-27 18:10:07,924 INFO [train.py:842] (1/4) Epoch 15, batch 4550, loss[loss=0.1681, simple_loss=0.2626, pruned_loss=0.03683, over 7322.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2816, pruned_loss=0.05688, over 1425367.97 frames.], batch size: 20, lr: 3.65e-04
2022-05-27 18:10:46,894 INFO [train.py:842] (1/4) Epoch 15, batch 4600, loss[loss=0.2377, simple_loss=0.3104, pruned_loss=0.08246, over 7220.00 frames.], tot_loss[loss=0.198, simple_loss=0.2813, pruned_loss=0.05734, over 1427197.56 frames.], batch size: 21, lr: 3.65e-04
2022-05-27 18:11:26,082 INFO [train.py:842] (1/4) Epoch 15, batch 4650, loss[loss=0.2459, simple_loss=0.3167, pruned_loss=0.08759, over 6770.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2811, pruned_loss=0.05683, over 1426275.01 frames.], batch size: 31, lr: 3.64e-04
2022-05-27 18:12:04,856 INFO [train.py:842] (1/4) Epoch 15, batch 4700, loss[loss=0.2537, simple_loss=0.3263, pruned_loss=0.09052, over 7138.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2802, pruned_loss=0.05657, over 1429713.51 frames.], batch size: 20, lr: 3.64e-04
2022-05-27 18:12:44,049 INFO [train.py:842] (1/4) Epoch 15, batch 4750, loss[loss=0.2189, simple_loss=0.2873, pruned_loss=0.07522, over 7258.00 frames.], tot_loss[loss=0.1971, simple_loss=0.281, pruned_loss=0.05663, over 1429849.11 frames.], batch size: 17, lr: 3.64e-04
2022-05-27 18:13:22,834 INFO [train.py:842] (1/4) Epoch 15, batch 4800, loss[loss=0.1988, simple_loss=0.2784, pruned_loss=0.05959, over 6656.00 frames.], tot_loss[loss=0.196, simple_loss=0.2798, pruned_loss=0.05615, over 1428383.59 frames.], batch size: 31, lr: 3.64e-04
2022-05-27 18:14:02,055 INFO [train.py:842] (1/4) Epoch 15, batch 4850, loss[loss=0.1836, simple_loss=0.2718, pruned_loss=0.04775, over 7064.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2786, pruned_loss=0.05531, over 1427383.52 frames.], batch size: 28, lr: 3.64e-04
2022-05-27 18:14:40,906 INFO [train.py:842] (1/4) Epoch 15, batch 4900, loss[loss=0.1654, simple_loss=0.2618, pruned_loss=0.03448, over 7150.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2784, pruned_loss=0.05529, over 1429687.92 frames.], batch size: 19, lr: 3.64e-04
2022-05-27 18:15:20,253 INFO [train.py:842] (1/4) Epoch 15, batch 4950, loss[loss=0.1865, simple_loss=0.2705, pruned_loss=0.05126, over 7152.00 frames.], tot_loss[loss=0.1945, simple_loss=0.278, pruned_loss=0.05547, over 1430836.10 frames.], batch size: 19, lr: 3.64e-04
2022-05-27 18:15:58,921 INFO [train.py:842] (1/4) Epoch 15, batch 5000, loss[loss=0.2216, simple_loss=0.3035, pruned_loss=0.06978, over 7147.00 frames.], tot_loss[loss=0.1942, simple_loss=0.278, pruned_loss=0.05518, over 1429366.27 frames.], batch size: 26, lr: 3.64e-04
2022-05-27 18:16:38,092 INFO [train.py:842] (1/4) Epoch 15, batch 5050, loss[loss=0.1742, simple_loss=0.2623, pruned_loss=0.04307, over 7239.00 frames.], tot_loss[loss=0.193, simple_loss=0.2773, pruned_loss=0.05441, over 1431187.47 frames.], batch size: 20, lr: 3.64e-04
2022-05-27 18:17:17,306 INFO [train.py:842] (1/4) Epoch 15, batch 5100, loss[loss=0.1743, simple_loss=0.2567, pruned_loss=0.04597, over 7421.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2767, pruned_loss=0.05434, over 1429841.55 frames.], batch size: 20, lr: 3.64e-04
2022-05-27 18:17:56,532 INFO [train.py:842] (1/4) Epoch 15, batch 5150, loss[loss=0.2083, simple_loss=0.2861, pruned_loss=0.06528, over 7364.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2769, pruned_loss=0.05411, over 1426242.92 frames.], batch size: 23, lr: 3.64e-04
2022-05-27 18:18:35,554 INFO [train.py:842] (1/4) Epoch 15, batch 5200, loss[loss=0.2229, simple_loss=0.3018, pruned_loss=0.07199, over 6821.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2767, pruned_loss=0.05401, over 1426740.08 frames.], batch size: 31, lr: 3.64e-04
2022-05-27 18:19:14,549 INFO [train.py:842] (1/4) Epoch 15, batch 5250, loss[loss=0.2213, simple_loss=0.3005, pruned_loss=0.07106, over 7149.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2776, pruned_loss=0.05446, over 1425121.07 frames.], batch size: 19, lr: 3.64e-04
2022-05-27 18:19:53,406 INFO [train.py:842] (1/4) Epoch 15, batch 5300, loss[loss=0.2276, simple_loss=0.3033, pruned_loss=0.07594, over 7319.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2781, pruned_loss=0.05522, over 1419007.85 frames.], batch size: 20, lr: 3.64e-04
2022-05-27 18:20:32,637 INFO [train.py:842] (1/4) Epoch 15, batch 5350, loss[loss=0.1849, simple_loss=0.2654, pruned_loss=0.05227, over 7260.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2776, pruned_loss=0.05493, over 1420748.67 frames.], batch size: 19, lr: 3.64e-04
2022-05-27 18:21:11,496 INFO [train.py:842] (1/4) Epoch 15, batch 5400, loss[loss=0.1888, simple_loss=0.2635, pruned_loss=0.05707, over 7356.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2775, pruned_loss=0.055, over 1423255.58 frames.], batch size: 19, lr: 3.63e-04
2022-05-27 18:21:50,471 INFO [train.py:842] (1/4) Epoch 15, batch 5450, loss[loss=0.1911, simple_loss=0.2763, pruned_loss=0.05298, over 7342.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2773, pruned_loss=0.0549, over 1427846.46 frames.], batch size: 22, lr: 3.63e-04
2022-05-27 18:22:29,611 INFO [train.py:842] (1/4) Epoch 15, batch 5500, loss[loss=0.2005, simple_loss=0.2793, pruned_loss=0.06089, over 7201.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2773, pruned_loss=0.05474, over 1426773.27 frames.], batch size: 22, lr: 3.63e-04
2022-05-27 18:23:08,903 INFO [train.py:842] (1/4) Epoch 15, batch 5550, loss[loss=0.1656, simple_loss=0.25, pruned_loss=0.04059, over 7260.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2764, pruned_loss=0.05466, over 1429221.05 frames.], batch size: 19, lr: 3.63e-04
2022-05-27 18:23:47,683 INFO [train.py:842] (1/4) Epoch 15, batch 5600, loss[loss=0.2302, simple_loss=0.314, pruned_loss=0.07314, over 7198.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2789, pruned_loss=0.05614, over 1426055.36 frames.], batch size: 22, lr: 3.63e-04
2022-05-27 18:24:26,886 INFO [train.py:842] (1/4) Epoch 15, batch 5650, loss[loss=0.2332, simple_loss=0.3026, pruned_loss=0.08191, over 7155.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2778, pruned_loss=0.05557, over 1423900.77 frames.], batch size: 18, lr: 3.63e-04
2022-05-27 18:25:05,615 INFO [train.py:842] (1/4) Epoch 15, batch 5700, loss[loss=0.1886, simple_loss=0.2739, pruned_loss=0.05165, over 7432.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2781, pruned_loss=0.05562, over 1422302.75 frames.], batch size: 20, lr: 3.63e-04
2022-05-27 18:25:44,763 INFO [train.py:842] (1/4) Epoch 15, batch 5750, loss[loss=0.1932, simple_loss=0.275, pruned_loss=0.05567, over 7043.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2791, pruned_loss=0.05618, over 1425645.37 frames.], batch size: 28, lr: 3.63e-04
2022-05-27 18:26:23,896 INFO [train.py:842] (1/4) Epoch 15, batch 5800, loss[loss=0.2052, simple_loss=0.2884, pruned_loss=0.06098, over 7331.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2793, pruned_loss=0.05607, over 1428200.97 frames.], batch size: 20, lr: 3.63e-04
2022-05-27 18:27:03,390 INFO [train.py:842] (1/4) Epoch 15, batch 5850, loss[loss=0.1859, simple_loss=0.2782, pruned_loss=0.04677, over 7362.00 frames.], tot_loss[loss=0.1945, simple_loss=0.278, pruned_loss=0.05545, over 1427261.06 frames.], batch size: 19, lr: 3.63e-04
2022-05-27 18:27:42,361 INFO [train.py:842] (1/4) Epoch 15, batch 5900, loss[loss=0.1951, simple_loss=0.2823, pruned_loss=0.05394, over 7152.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2773, pruned_loss=0.05499, over 1425992.17 frames.], batch size: 20, lr: 3.63e-04
2022-05-27 18:28:21,538 INFO [train.py:842] (1/4) Epoch 15, batch 5950, loss[loss=0.1583, simple_loss=0.2508, pruned_loss=0.03289, over 7311.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2777, pruned_loss=0.05547, over 1423690.08 frames.], batch size: 20, lr: 3.63e-04
2022-05-27 18:29:00,328 INFO [train.py:842] (1/4) Epoch 15, batch 6000, loss[loss=0.2743, simple_loss=0.327, pruned_loss=0.1108, over 7296.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2793, pruned_loss=0.05604, over 1421141.19 frames.], batch size: 18, lr: 3.63e-04
2022-05-27 18:29:00,329 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 18:29:10,459 INFO [train.py:871] (1/4) Epoch 15, validation: loss=0.1678, simple_loss=0.2677, pruned_loss=0.03392, over 868885.00 frames.
2022-05-27 18:29:49,733 INFO [train.py:842] (1/4) Epoch 15, batch 6050, loss[loss=0.1708, simple_loss=0.2633, pruned_loss=0.03918, over 7091.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2777, pruned_loss=0.05494, over 1424999.02 frames.], batch size: 28, lr: 3.63e-04
2022-05-27 18:30:28,770 INFO [train.py:842] (1/4) Epoch 15, batch 6100, loss[loss=0.1655, simple_loss=0.2642, pruned_loss=0.03342, over 7219.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2773, pruned_loss=0.05454, over 1426642.58 frames.], batch size: 21, lr: 3.63e-04
2022-05-27 18:31:08,035 INFO [train.py:842] (1/4) Epoch 15, batch 6150, loss[loss=0.24, simple_loss=0.3087, pruned_loss=0.08565, over 4735.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2788, pruned_loss=0.05572, over 1426734.89 frames.], batch size: 53, lr: 3.62e-04
2022-05-27 18:31:47,168 INFO [train.py:842] (1/4) Epoch 15, batch 6200, loss[loss=0.1882, simple_loss=0.29, pruned_loss=0.04317, over 7196.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2786, pruned_loss=0.05533, over 1423675.91 frames.], batch size: 23, lr: 3.62e-04
2022-05-27 18:32:26,337 INFO [train.py:842] (1/4) Epoch 15, batch 6250, loss[loss=0.1806, simple_loss=0.2719, pruned_loss=0.0446, over 7204.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2783, pruned_loss=0.05524, over 1419488.21 frames.], batch size: 22, lr: 3.62e-04
2022-05-27 18:33:04,924 INFO [train.py:842] (1/4) Epoch 15, batch 6300, loss[loss=0.2212, simple_loss=0.2972, pruned_loss=0.07258, over 7145.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2791, pruned_loss=0.05576, over 1419001.12 frames.], batch size: 26, lr: 3.62e-04
2022-05-27 18:33:54,409 INFO [train.py:842] (1/4) Epoch 15, batch 6350, loss[loss=0.1568, simple_loss=0.2329, pruned_loss=0.04037, over 7225.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2801, pruned_loss=0.0568, over 1421524.68 frames.], batch size: 16, lr: 3.62e-04
2022-05-27 18:34:43,506 INFO [train.py:842] (1/4) Epoch 15, batch 6400, loss[loss=0.2103, simple_loss=0.29, pruned_loss=0.06524, over 7059.00 frames.], tot_loss[loss=0.1966, simple_loss=0.28, pruned_loss=0.05659, over 1419235.01 frames.], batch size: 18, lr: 3.62e-04
2022-05-27 18:35:22,910 INFO [train.py:842] (1/4) Epoch 15, batch 6450, loss[loss=0.156, simple_loss=0.2532, pruned_loss=0.02947, over 7111.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2787, pruned_loss=0.05555, over 1422477.98 frames.], batch size: 21, lr: 3.62e-04
2022-05-27 18:36:11,921 INFO [train.py:842] (1/4) Epoch 15, batch 6500, loss[loss=0.2158, simple_loss=0.2938, pruned_loss=0.06886, over 6791.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2785, pruned_loss=0.05519, over 1417481.82 frames.], batch size: 31, lr: 3.62e-04
2022-05-27 18:36:50,710 INFO [train.py:842] (1/4) Epoch 15, batch 6550, loss[loss=0.1612, simple_loss=0.259, pruned_loss=0.03171, over 6776.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2803, pruned_loss=0.05603, over 1420419.77 frames.], batch size: 31, lr: 3.62e-04
2022-05-27 18:37:29,218 INFO [train.py:842] (1/4) Epoch 15, batch 6600, loss[loss=0.2312, simple_loss=0.3247, pruned_loss=0.06886, over 7227.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2801, pruned_loss=0.05603, over 1417321.98 frames.], batch size: 20, lr: 3.62e-04
2022-05-27 18:38:08,158 INFO [train.py:842] (1/4) Epoch 15, batch 6650, loss[loss=0.1892, simple_loss=0.2865, pruned_loss=0.04598, over 7306.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2789, pruned_loss=0.05545, over 1419763.14 frames.], batch size: 24, lr: 3.62e-04
2022-05-27 18:38:47,091 INFO [train.py:842] (1/4) Epoch 15, batch 6700, loss[loss=0.1972, simple_loss=0.2794, pruned_loss=0.05747, over 7159.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2782, pruned_loss=0.05523, over 1421780.98 frames.], batch size: 18, lr: 3.62e-04
2022-05-27 18:39:26,023 INFO [train.py:842] (1/4) Epoch 15, batch 6750, loss[loss=0.2268, simple_loss=0.2882, pruned_loss=0.08271, over 7277.00 frames.], tot_loss[loss=0.1947, simple_loss=0.279, pruned_loss=0.05521, over 1424223.57 frames.], batch size: 18, lr: 3.62e-04
2022-05-27 18:40:05,049 INFO [train.py:842] (1/4) Epoch 15, batch 6800, loss[loss=0.1621, simple_loss=0.2383, pruned_loss=0.04291, over 7276.00 frames.], tot_loss[loss=0.195, simple_loss=0.2797, pruned_loss=0.0551, over 1427020.49 frames.], batch size: 18, lr: 3.62e-04
2022-05-27 18:40:44,154 INFO [train.py:842] (1/4) Epoch 15, batch 6850, loss[loss=0.2516, simple_loss=0.3397, pruned_loss=0.08176, over 7377.00 frames.], tot_loss[loss=0.194, simple_loss=0.2788, pruned_loss=0.05461, over 1425951.34 frames.], batch size: 23, lr: 3.62e-04
2022-05-27 18:41:23,571 INFO [train.py:842] (1/4) Epoch 15, batch 6900, loss[loss=0.1806, simple_loss=0.2562, pruned_loss=0.05253, over 7143.00 frames.], tot_loss[loss=0.1933, simple_loss=0.278, pruned_loss=0.05433, over 1428970.50 frames.], batch size: 17, lr: 3.61e-04
2022-05-27 18:42:02,770 INFO [train.py:842] (1/4) Epoch 15, batch 6950, loss[loss=0.2257, simple_loss=0.3154, pruned_loss=0.06806, over 6853.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2791, pruned_loss=0.05508, over 1427287.58 frames.], batch size: 31, lr: 3.61e-04
2022-05-27 18:42:41,960 INFO [train.py:842] (1/4) Epoch 15, batch 7000, loss[loss=0.1632, simple_loss=0.2578, pruned_loss=0.0343, over 7228.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2798, pruned_loss=0.0552, over 1428868.00 frames.], batch size: 20, lr: 3.61e-04
2022-05-27 18:43:21,065 INFO [train.py:842] (1/4) Epoch 15, batch 7050, loss[loss=0.2373, simple_loss=0.3157, pruned_loss=0.07946, over 7216.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2806, pruned_loss=0.05563, over 1427769.11 frames.], batch size: 22, lr: 3.61e-04
2022-05-27 18:43:59,633 INFO [train.py:842] (1/4) Epoch 15, batch 7100, loss[loss=0.2236, simple_loss=0.3045, pruned_loss=0.07134, over 7373.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2809, pruned_loss=0.05612, over 1430192.62 frames.], batch size: 23, lr: 3.61e-04
2022-05-27 18:44:38,600 INFO [train.py:842] (1/4) Epoch 15, batch 7150, loss[loss=0.1984, simple_loss=0.2885, pruned_loss=0.05416, over 7242.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2807, pruned_loss=0.05601, over 1426497.63 frames.], batch size: 20, lr: 3.61e-04
2022-05-27 18:45:17,387 INFO [train.py:842] (1/4) Epoch 15, batch 7200, loss[loss=0.1676, simple_loss=0.2544, pruned_loss=0.0404, over 7353.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2794, pruned_loss=0.05542, over 1426088.12 frames.], batch size: 19, lr: 3.61e-04
2022-05-27 18:45:56,674 INFO [train.py:842] (1/4) Epoch 15, batch 7250, loss[loss=0.2426, simple_loss=0.3199, pruned_loss=0.0826, over 7207.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2787, pruned_loss=0.05512, over 1424509.03 frames.], batch size: 22, lr: 3.61e-04
2022-05-27 18:46:35,254 INFO [train.py:842] (1/4) Epoch 15, batch 7300, loss[loss=0.205, simple_loss=0.3042, pruned_loss=0.05293, over 7303.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2774, pruned_loss=0.05408, over 1423915.44 frames.], batch size: 24, lr: 3.61e-04
2022-05-27 18:47:16,970 INFO [train.py:842] (1/4) Epoch 15, batch 7350, loss[loss=0.2464, simple_loss=0.3188, pruned_loss=0.08699, over 7373.00 frames.], tot_loss[loss=0.193, simple_loss=0.2774, pruned_loss=0.05429, over 1425935.50 frames.], batch size: 23, lr: 3.61e-04
2022-05-27 18:47:55,654 INFO [train.py:842] (1/4) Epoch 15, batch 7400, loss[loss=0.2114, simple_loss=0.2953, pruned_loss=0.06375, over 6373.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2792, pruned_loss=0.05552, over 1427151.56 frames.], batch size: 38, lr: 3.61e-04
2022-05-27 18:48:34,721 INFO [train.py:842] (1/4) Epoch 15, batch 7450, loss[loss=0.1709, simple_loss=0.2556, pruned_loss=0.0431, over 7328.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2799, pruned_loss=0.0559, over 1427394.58 frames.], batch size: 20, lr: 3.61e-04
2022-05-27 18:49:13,763 INFO [train.py:842] (1/4) Epoch 15, batch 7500, loss[loss=0.1897, simple_loss=0.2721, pruned_loss=0.05368, over 7080.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2793, pruned_loss=0.05496, over 1427962.66 frames.], batch size: 18, lr: 3.61e-04
2022-05-27 18:49:52,819 INFO [train.py:842] (1/4) Epoch 15, batch 7550, loss[loss=0.1933, simple_loss=0.2769, pruned_loss=0.05484, over 6860.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2788, pruned_loss=0.05511, over 1423432.95 frames.], batch size: 31, lr: 3.61e-04
2022-05-27 18:50:31,967 INFO [train.py:842] (1/4) Epoch 15, batch 7600, loss[loss=0.1637, simple_loss=0.2546, pruned_loss=0.0364, over 7347.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2779, pruned_loss=0.05461, over 1422340.20 frames.], batch size: 19, lr: 3.61e-04
2022-05-27 18:51:10,944 INFO [train.py:842] (1/4) Epoch 15, batch 7650, loss[loss=0.2029, simple_loss=0.2825, pruned_loss=0.06167, over 7061.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2797, pruned_loss=0.05543, over 1420762.59 frames.], batch size: 28, lr: 3.60e-04
2022-05-27 18:51:49,849 INFO [train.py:842] (1/4) Epoch 15, batch 7700, loss[loss=0.1964, simple_loss=0.2787, pruned_loss=0.05706, over 7110.00 frames.], tot_loss[loss=0.195, simple_loss=0.2793, pruned_loss=0.05532, over 1420215.45 frames.], batch size: 28, lr: 3.60e-04
2022-05-27 18:52:29,100 INFO [train.py:842] (1/4) Epoch 15, batch 7750, loss[loss=0.2082, simple_loss=0.2794, pruned_loss=0.06852, over 6789.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2799, pruned_loss=0.05563, over 1424356.02 frames.], batch size: 31, lr: 3.60e-04
2022-05-27 18:53:07,844 INFO [train.py:842] (1/4) Epoch 15, batch 7800, loss[loss=0.1387, simple_loss=0.2127, pruned_loss=0.03231, over 7296.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2788, pruned_loss=0.05519, over 1417210.63 frames.], batch size: 17, lr: 3.60e-04
2022-05-27 18:53:47,096 INFO [train.py:842] (1/4) Epoch 15, batch 7850, loss[loss=0.1469, simple_loss=0.2341, pruned_loss=0.02984, over 7015.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2798, pruned_loss=0.05578, over 1419106.40 frames.], batch size: 16, lr: 3.60e-04
2022-05-27 18:54:25,877 INFO [train.py:842] (1/4) Epoch 15, batch 7900, loss[loss=0.1885, simple_loss=0.2769, pruned_loss=0.05005, over 7376.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2786, pruned_loss=0.05513, over 1421837.43 frames.], batch size: 23, lr: 3.60e-04
2022-05-27 18:55:04,783 INFO [train.py:842] (1/4) Epoch 15, batch 7950, loss[loss=0.2023, simple_loss=0.292, pruned_loss=0.05635, over 7190.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2793, pruned_loss=0.05572, over 1420580.70 frames.], batch size: 23, lr: 3.60e-04
2022-05-27 18:55:44,032 INFO [train.py:842] (1/4) Epoch 15, batch 8000, loss[loss=0.1655, simple_loss=0.2445, pruned_loss=0.04322, over 7229.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2793, pruned_loss=0.05641, over 1420688.07 frames.], batch size: 16, lr: 3.60e-04
2022-05-27 18:56:22,907 INFO [train.py:842] (1/4) Epoch 15, batch 8050, loss[loss=0.1935, simple_loss=0.2895, pruned_loss=0.0488, over 7313.00 frames.], tot_loss[loss=0.1955, simple_loss=0.279, pruned_loss=0.05596, over 1414999.89 frames.], batch size: 25, lr: 3.60e-04
2022-05-27 18:57:02,122 INFO [train.py:842] (1/4) Epoch 15, batch 8100, loss[loss=0.1754, simple_loss=0.265, pruned_loss=0.04293, over 7238.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2794, pruned_loss=0.05612, over 1421977.92 frames.], batch size: 20, lr: 3.60e-04
2022-05-27 18:57:41,268 INFO [train.py:842] (1/4) Epoch 15, batch 8150, loss[loss=0.1778, simple_loss=0.27, pruned_loss=0.04276, over 7335.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2796, pruned_loss=0.05683, over 1420088.83 frames.], batch size: 20, lr: 3.60e-04
2022-05-27 18:58:20,240 INFO [train.py:842] (1/4) Epoch 15, batch 8200, loss[loss=0.189, simple_loss=0.2812, pruned_loss=0.04836, over 7256.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2782, pruned_loss=0.05564, over 1421885.26 frames.], batch size: 19, lr: 3.60e-04
2022-05-27 18:58:59,238 INFO [train.py:842] (1/4) Epoch 15, batch 8250, loss[loss=0.1923, simple_loss=0.2769, pruned_loss=0.05381, over 7264.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2793, pruned_loss=0.05648, over 1420926.48 frames.], batch size: 19, lr: 3.60e-04
2022-05-27 18:59:37,869 INFO [train.py:842] (1/4) Epoch 15, batch 8300, loss[loss=0.167, simple_loss=0.2558, pruned_loss=0.03906, over 7328.00 frames.], tot_loss[loss=0.194, simple_loss=0.2776, pruned_loss=0.05515, over 1422500.29 frames.], batch size: 20, lr: 3.60e-04
2022-05-27 19:00:17,024 INFO [train.py:842] (1/4) Epoch 15, batch 8350, loss[loss=0.1669, simple_loss=0.2562, pruned_loss=0.03881, over 7360.00 frames.], tot_loss[loss=0.1934, simple_loss=0.277, pruned_loss=0.05486, over 1423397.52 frames.], batch size: 19, lr: 3.60e-04
2022-05-27 19:00:56,001 INFO [train.py:842] (1/4) Epoch 15, batch 8400, loss[loss=0.2292, simple_loss=0.3126, pruned_loss=0.07289, over 7173.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2775, pruned_loss=0.05489, over 1423710.99 frames.], batch size: 26, lr: 3.59e-04
2022-05-27 19:01:34,940 INFO [train.py:842] (1/4) Epoch 15, batch 8450, loss[loss=0.194, simple_loss=0.2774, pruned_loss=0.05533, over 7148.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2801, pruned_loss=0.0564, over 1423005.06 frames.], batch size: 20, lr: 3.59e-04
2022-05-27 19:02:13,490 INFO [train.py:842] (1/4) Epoch 15, batch 8500, loss[loss=0.2005, simple_loss=0.2776, pruned_loss=0.06169, over 7166.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2795, pruned_loss=0.05578, over 1420586.03 frames.], batch size: 18, lr: 3.59e-04
2022-05-27 19:02:52,546 INFO [train.py:842] (1/4) Epoch 15, batch 8550, loss[loss=0.2171, simple_loss=0.2966, pruned_loss=0.06884, over 7126.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2801, pruned_loss=0.05626, over 1422600.63 frames.], batch size: 21, lr: 3.59e-04
2022-05-27 19:03:31,274 INFO [train.py:842] (1/4) Epoch 15, batch 8600, loss[loss=0.1921, simple_loss=0.2773, pruned_loss=0.0535, over 7322.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2809, pruned_loss=0.05668, over 1418812.40 frames.], batch size: 21, lr: 3.59e-04
2022-05-27 19:04:10,118 INFO [train.py:842] (1/4) Epoch 15, batch 8650, loss[loss=0.198, simple_loss=0.3001, pruned_loss=0.04795, over 7318.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2818, pruned_loss=0.05668, over 1415956.65 frames.], batch size: 21, lr: 3.59e-04
2022-05-27 19:04:49,113 INFO [train.py:842] (1/4) Epoch 15, batch 8700, loss[loss=0.1842, simple_loss=0.277, pruned_loss=0.0457, over 7217.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2803, pruned_loss=0.05555, over 1421185.61 frames.], batch size: 22, lr: 3.59e-04
2022-05-27 19:05:28,367 INFO [train.py:842] (1/4) Epoch 15, batch 8750, loss[loss=0.2434, simple_loss=0.3203, pruned_loss=0.08323, over 6754.00 frames.], tot_loss[loss=0.194, simple_loss=0.2782, pruned_loss=0.0549, over 1421153.00 frames.], batch size: 31, lr: 3.59e-04
2022-05-27 19:06:07,388 INFO [train.py:842] (1/4) Epoch 15, batch 8800, loss[loss=0.2674, simple_loss=0.3337, pruned_loss=0.1006, over 7420.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2777, pruned_loss=0.05478, over 1419918.35 frames.], batch size: 21, lr: 3.59e-04
2022-05-27 19:06:46,141 INFO [train.py:842] (1/4) Epoch 15, batch 8850, loss[loss=0.229, simple_loss=0.313, pruned_loss=0.07249, over 6709.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2779, pruned_loss=0.05461, over 1417639.43 frames.], batch size: 31, lr: 3.59e-04
2022-05-27 19:07:24,575 INFO [train.py:842] (1/4) Epoch 15, batch 8900, loss[loss=0.2249, simple_loss=0.3072, pruned_loss=0.07132, over 7325.00 frames.], tot_loss[loss=0.195, simple_loss=0.2788, pruned_loss=0.05557, over 1407660.59 frames.], batch size: 22, lr: 3.59e-04
2022-05-27 19:08:03,447 INFO [train.py:842] (1/4) Epoch 15, batch 8950, loss[loss=0.1596, simple_loss=0.2446, pruned_loss=0.03728, over 7229.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2785, pruned_loss=0.05563, over 1391068.96 frames.], batch size: 16, lr: 3.59e-04
2022-05-27 19:08:41,906 INFO [train.py:842] (1/4) Epoch 15, batch 9000, loss[loss=0.1999, simple_loss=0.2888, pruned_loss=0.05548, over 7198.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2782, pruned_loss=0.0556, over 1386110.71 frames.], batch size: 23, lr: 3.59e-04
2022-05-27 19:08:41,907 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 19:08:52,122 INFO [train.py:871] (1/4) Epoch 15, validation: loss=0.1676, simple_loss=0.2672, pruned_loss=0.03401, over 868885.00 frames.
2022-05-27 19:09:30,776 INFO [train.py:842] (1/4) Epoch 15, batch 9050, loss[loss=0.2191, simple_loss=0.2951, pruned_loss=0.07153, over 7178.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2802, pruned_loss=0.05656, over 1369357.33 frames.], batch size: 23, lr: 3.59e-04
2022-05-27 19:10:09,054 INFO [train.py:842] (1/4) Epoch 15, batch 9100, loss[loss=0.2167, simple_loss=0.3035, pruned_loss=0.06498, over 5289.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2807, pruned_loss=0.0574, over 1328341.21 frames.], batch size: 52, lr: 3.59e-04
2022-05-27 19:10:46,733 INFO [train.py:842] (1/4) Epoch 15, batch 9150, loss[loss=0.1986, simple_loss=0.2731, pruned_loss=0.06207, over 4774.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2849, pruned_loss=0.06116, over 1258421.89 frames.], batch size: 52, lr: 3.58e-04
2022-05-27 19:11:37,240 INFO [train.py:842] (1/4) Epoch 16, batch 0, loss[loss=0.1913, simple_loss=0.2803, pruned_loss=0.05116, over 7291.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2803, pruned_loss=0.05116, over 7291.00 frames.], batch size: 24, lr: 3.48e-04
2022-05-27 19:12:16,183 INFO [train.py:842] (1/4) Epoch 16, batch 50, loss[loss=0.1519, simple_loss=0.2371, pruned_loss=0.03335, over 7404.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2788, pruned_loss=0.05528, over 321260.04 frames.], batch size: 18, lr: 3.48e-04
2022-05-27 19:12:55,628 INFO [train.py:842] (1/4) Epoch 16, batch 100, loss[loss=0.1831, simple_loss=0.2735, pruned_loss=0.04641, over 7321.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2775, pruned_loss=0.05376, over 565950.96 frames.], batch size: 20, lr: 3.48e-04
2022-05-27 19:13:38,126 INFO [train.py:842] (1/4) Epoch 16, batch 150, loss[loss=0.1867, simple_loss=0.2802, pruned_loss=0.04658, over 7140.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2776, pruned_loss=0.05336, over 756237.70 frames.], batch size: 20, lr: 3.48e-04
2022-05-27 19:14:16,663 INFO [train.py:842] (1/4) Epoch 16, batch 200, loss[loss=0.179, simple_loss=0.2678, pruned_loss=0.04509, over 7111.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2761, pruned_loss=0.05373, over 899097.05 frames.], batch size: 21, lr: 3.48e-04
2022-05-27 19:14:56,295 INFO [train.py:842] (1/4) Epoch 16, batch 250, loss[loss=0.2025, simple_loss=0.2893, pruned_loss=0.05782, over 7159.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2744, pruned_loss=0.05255, over 1016221.23 frames.], batch size: 19, lr: 3.48e-04
2022-05-27 19:15:36,400 INFO [train.py:842] (1/4) Epoch 16, batch 300, loss[loss=0.1715, simple_loss=0.2632, pruned_loss=0.03993, over 7157.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2758, pruned_loss=0.05338, over 1110730.36 frames.], batch size: 19, lr: 3.48e-04
2022-05-27 19:16:18,281 INFO [train.py:842] (1/4) Epoch 16, batch 350, loss[loss=0.1705, simple_loss=0.2548, pruned_loss=0.0431, over 7284.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2757, pruned_loss=0.05326, over 1181623.67 frames.], batch size: 18, lr: 3.48e-04
2022-05-27 19:16:56,987 INFO [train.py:842] (1/4) Epoch 16, batch 400, loss[loss=0.1832, simple_loss=0.2598, pruned_loss=0.05332, over 7249.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2768, pruned_loss=0.05396, over 1234968.76 frames.], batch size: 19, lr: 3.48e-04
2022-05-27 19:17:36,529 INFO [train.py:842] (1/4) Epoch 16, batch 450, loss[loss=0.174, simple_loss=0.2614, pruned_loss=0.04332, over 7434.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2765, pruned_loss=0.05385, over 1281886.86 frames.], batch size: 20, lr: 3.47e-04
2022-05-27 19:18:15,376 INFO [train.py:842] (1/4) Epoch 16, batch 500, loss[loss=0.1935, simple_loss=0.2844, pruned_loss=0.05131, over 7200.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2777, pruned_loss=0.05438, over 1318506.84 frames.], batch size: 23, lr: 3.47e-04
2022-05-27 19:18:54,580 INFO [train.py:842] (1/4) Epoch 16, batch 550, loss[loss=0.1489, simple_loss=0.2371, pruned_loss=0.03032, over 7287.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2751, pruned_loss=0.05331, over 1345270.27 frames.], batch size: 18, lr: 3.47e-04
2022-05-27 19:19:33,244 INFO [train.py:842] (1/4) Epoch 16, batch 600, loss[loss=0.1913, simple_loss=0.2769, pruned_loss=0.05284, over 7154.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2765, pruned_loss=0.05386, over 1361692.11 frames.], batch size: 19, lr: 3.47e-04
2022-05-27 19:20:12,220 INFO [train.py:842] (1/4) Epoch 16, batch 650, loss[loss=0.1756, simple_loss=0.2663, pruned_loss=0.04239, over 6424.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2769, pruned_loss=0.05413, over 1374382.78 frames.], batch size: 38, lr: 3.47e-04
2022-05-27 19:20:51,106 INFO [train.py:842] (1/4) Epoch 16, batch 700, loss[loss=0.2478, simple_loss=0.3239, pruned_loss=0.08587, over 7077.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2772, pruned_loss=0.05458, over 1385571.00 frames.], batch size: 28, lr: 3.47e-04
2022-05-27 19:21:33,889 INFO [train.py:842] (1/4) Epoch 16, batch 750, loss[loss=0.1894, simple_loss=0.2658, pruned_loss=0.05653, over 7152.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2767, pruned_loss=0.0541, over 1394425.44 frames.], batch size: 19, lr: 3.47e-04
2022-05-27 19:22:12,446 INFO [train.py:842] (1/4) Epoch 16, batch 800, loss[loss=0.1843, simple_loss=0.2654, pruned_loss=0.05156, over 7251.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2774, pruned_loss=0.05438, over 1402120.62 frames.], batch size: 19, lr: 3.47e-04
2022-05-27 19:22:51,515 INFO [train.py:842] (1/4) Epoch 16, batch 850, loss[loss=0.1699, simple_loss=0.2627, pruned_loss=0.03858, over 7146.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2786, pruned_loss=0.05487, over 1404328.61 frames.], batch size: 20, lr: 3.47e-04
2022-05-27 19:23:30,177 INFO [train.py:842] (1/4) Epoch 16, batch 900, loss[loss=0.1496, simple_loss=0.2362, pruned_loss=0.03154, over 7364.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2789, pruned_loss=0.05514, over 1403214.68 frames.], batch size: 19, lr: 3.47e-04
2022-05-27 19:24:09,377 INFO [train.py:842] (1/4) Epoch 16, batch 950, loss[loss=0.2409, simple_loss=0.3112, pruned_loss=0.08529, over 7427.00 frames.], tot_loss[loss=0.1939, simple_loss=0.278, pruned_loss=0.05492, over 1405969.69 frames.], batch size: 20, lr: 3.47e-04
2022-05-27 19:24:48,132 INFO [train.py:842] (1/4) Epoch 16, batch 1000, loss[loss=0.2024, simple_loss=0.2955, pruned_loss=0.05469, over 7271.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2772, pruned_loss=0.05436, over 1411964.38 frames.], batch size: 25, lr: 3.47e-04
2022-05-27 19:25:27,128 INFO [train.py:842] (1/4) Epoch 16, batch 1050, loss[loss=0.1959, simple_loss=0.2748, pruned_loss=0.05854, over 7333.00 frames.], tot_loss[loss=0.194, simple_loss=0.2781, pruned_loss=0.05498, over 1417302.44 frames.], batch size: 20, lr: 3.47e-04
2022-05-27 19:26:05,993 INFO [train.py:842] (1/4) Epoch 16, batch 1100, loss[loss=0.2119, simple_loss=0.2814, pruned_loss=0.07122, over 7364.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2772, pruned_loss=0.0546, over 1420268.20 frames.], batch size: 19, lr: 3.47e-04
2022-05-27 19:26:45,427 INFO [train.py:842] (1/4) Epoch 16, batch 1150, loss[loss=0.1942, simple_loss=0.276, pruned_loss=0.05614, over 5075.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2776, pruned_loss=0.05486, over 1421219.25 frames.], batch size: 52, lr: 3.47e-04
2022-05-27 19:27:24,206 INFO [train.py:842] (1/4) Epoch 16, batch 1200, loss[loss=0.167, simple_loss=0.2601, pruned_loss=0.03694, over 7109.00 frames.], tot_loss[loss=0.195, simple_loss=0.2785, pruned_loss=0.05569, over 1418541.03 frames.], batch size: 21, lr: 3.47e-04
2022-05-27 19:28:03,612 INFO [train.py:842] (1/4) Epoch 16, batch 1250, loss[loss=0.1841, simple_loss=0.2611, pruned_loss=0.05352, over 7251.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2787, pruned_loss=0.05601, over 1419582.33 frames.], batch size: 16, lr: 3.46e-04
2022-05-27 19:28:42,344 INFO [train.py:842] (1/4) Epoch 16, batch 1300, loss[loss=0.1998, simple_loss=0.2809, pruned_loss=0.05933, over 7184.00 frames.], tot_loss[loss=0.1952, simple_loss=0.279, pruned_loss=0.05571, over 1425572.99 frames.], batch size: 22, lr: 3.46e-04
2022-05-27 19:29:21,397 INFO [train.py:842] (1/4) Epoch 16, batch 1350, loss[loss=0.1848, simple_loss=0.2803, pruned_loss=0.0447, over 7150.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2775, pruned_loss=0.05469, over 1418767.63 frames.], batch size: 19, lr: 3.46e-04
2022-05-27 19:30:00,119 INFO [train.py:842] (1/4) Epoch 16, batch 1400, loss[loss=0.2123, simple_loss=0.2942, pruned_loss=0.06515, over 7338.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2777, pruned_loss=0.05473, over 1416034.27 frames.], batch size: 22, lr: 3.46e-04
2022-05-27 19:30:39,309 INFO [train.py:842] (1/4) Epoch 16, batch 1450, loss[loss=0.1802, simple_loss=0.2753, pruned_loss=0.04252, over 7418.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2781, pruned_loss=0.05449, over 1421861.53 frames.], batch size: 21, lr: 3.46e-04
2022-05-27 19:31:18,227 INFO [train.py:842] (1/4) Epoch 16, batch 1500, loss[loss=0.193, simple_loss=0.2826, pruned_loss=0.05174, over 7204.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2779, pruned_loss=0.05445, over 1421722.73 frames.], batch size: 23, lr: 3.46e-04
2022-05-27 19:31:57,538 INFO [train.py:842] (1/4) Epoch 16, batch 1550, loss[loss=0.1729, simple_loss=0.2534, pruned_loss=0.04619, over 7212.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2775, pruned_loss=0.05416, over 1421220.75 frames.], batch size: 16, lr: 3.46e-04
2022-05-27 19:32:36,431 INFO [train.py:842] (1/4) Epoch 16, batch 1600, loss[loss=0.1635, simple_loss=0.2448, pruned_loss=0.04111, over 7196.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2776, pruned_loss=0.05384, over 1423414.23 frames.], batch size: 16, lr: 3.46e-04
2022-05-27 19:33:15,591 INFO [train.py:842] (1/4) Epoch 16, batch 1650, loss[loss=0.2117, simple_loss=0.3078, pruned_loss=0.05776, over 7149.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2769, pruned_loss=0.05347, over 1424837.61 frames.], batch size: 20, lr: 3.46e-04
2022-05-27 19:33:54,732 INFO [train.py:842] (1/4) Epoch 16, batch 1700, loss[loss=0.1632, simple_loss=0.2406, pruned_loss=0.04289, over 7406.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2758, pruned_loss=0.05321, over 1424186.27 frames.], batch size: 18, lr: 3.46e-04
2022-05-27 19:34:34,152 INFO [train.py:842] (1/4) Epoch 16, batch 1750, loss[loss=0.2302, simple_loss=0.2976, pruned_loss=0.08135, over 7373.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2772, pruned_loss=0.05395, over 1423312.35 frames.], batch size: 23, lr: 3.46e-04
2022-05-27 19:35:13,055 INFO [train.py:842] (1/4) Epoch 16, batch 1800, loss[loss=0.2331, simple_loss=0.299, pruned_loss=0.08364, over 7365.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2779, pruned_loss=0.05472, over 1421794.44 frames.], batch size: 19, lr: 3.46e-04
2022-05-27 19:35:52,250 INFO [train.py:842] (1/4) Epoch 16, batch 1850, loss[loss=0.1892, simple_loss=0.2776, pruned_loss=0.05037, over 7145.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2771, pruned_loss=0.05464, over 1424220.11 frames.], batch size: 20, lr: 3.46e-04
2022-05-27 19:36:31,033 INFO [train.py:842] (1/4) Epoch 16, batch 1900, loss[loss=0.1735, simple_loss=0.2656, pruned_loss=0.0407, over 7293.00 frames.], tot_loss[loss=0.194, simple_loss=0.2777, pruned_loss=0.05519, over 1428531.02 frames.], batch size: 25, lr: 3.46e-04
2022-05-27 19:37:10,355 INFO [train.py:842] (1/4) Epoch 16, batch 1950, loss[loss=0.2133, simple_loss=0.2915, pruned_loss=0.06755, over 7178.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2777, pruned_loss=0.05453, over 1430342.41 frames.], batch size: 23, lr: 3.46e-04
2022-05-27 19:37:48,993 INFO [train.py:842] (1/4) Epoch 16, batch 2000, loss[loss=0.2358, simple_loss=0.3081, pruned_loss=0.08174, over 4897.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2778, pruned_loss=0.05479, over 1424201.83 frames.], batch size: 52, lr: 3.46e-04
2022-05-27 19:38:28,292 INFO [train.py:842] (1/4) Epoch 16, batch 2050, loss[loss=0.2369, simple_loss=0.3169, pruned_loss=0.07842, over 6447.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2785, pruned_loss=0.05581, over 1423424.51 frames.], batch size: 38, lr: 3.45e-04
2022-05-27 19:39:07,369 INFO [train.py:842] (1/4) Epoch 16, batch 2100, loss[loss=0.2006, simple_loss=0.2891, pruned_loss=0.05601, over 7116.00 frames.], tot_loss[loss=0.194, simple_loss=0.2778, pruned_loss=0.05506, over 1424459.69 frames.], batch size: 21, lr: 3.45e-04
2022-05-27 19:39:46,921 INFO [train.py:842] (1/4) Epoch 16, batch 2150, loss[loss=0.1478, simple_loss=0.2372, pruned_loss=0.02924, over 7260.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2782, pruned_loss=0.0551, over 1419567.20 frames.], batch size: 19, lr: 3.45e-04
2022-05-27 19:40:25,540 INFO [train.py:842] (1/4) Epoch 16, batch 2200, loss[loss=0.2862, simple_loss=0.3539, pruned_loss=0.1092, over 7217.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2783, pruned_loss=0.05511, over 1416696.76 frames.], batch size: 22, lr: 3.45e-04
2022-05-27 19:41:05,233 INFO [train.py:842] (1/4) Epoch 16, batch 2250, loss[loss=0.2463, simple_loss=0.3223, pruned_loss=0.0852, over 7410.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2779, pruned_loss=0.05493, over 1419076.85 frames.], batch size: 21, lr: 3.45e-04
2022-05-27 19:41:43,756 INFO [train.py:842] (1/4) Epoch 16, batch 2300, loss[loss=0.2165, simple_loss=0.3036, pruned_loss=0.06471, over 7192.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2782, pruned_loss=0.05522, over 1420061.42 frames.], batch size: 23, lr: 3.45e-04
2022-05-27 19:42:23,145 INFO [train.py:842] (1/4) Epoch 16, batch 2350, loss[loss=0.2093, simple_loss=0.2943, pruned_loss=0.0622, over 7294.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2776, pruned_loss=0.05472, over 1422302.86 frames.], batch size: 25, lr: 3.45e-04
2022-05-27 19:43:02,178 INFO [train.py:842] (1/4) Epoch 16, batch 2400, loss[loss=0.1919, simple_loss=0.2793, pruned_loss=0.05227, over 7227.00 frames.], tot_loss[loss=0.1925, simple_loss=0.277, pruned_loss=0.05398, over 1425939.48 frames.], batch size: 25, lr: 3.45e-04
2022-05-27 19:43:41,267 INFO [train.py:842] (1/4) Epoch 16, batch 2450, loss[loss=0.1838, simple_loss=0.2738, pruned_loss=0.04684, over 6731.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2785, pruned_loss=0.05516, over 1424159.14 frames.], batch size: 31, lr: 3.45e-04
2022-05-27 19:44:20,556 INFO [train.py:842] (1/4) Epoch 16, batch 2500, loss[loss=0.1694, simple_loss=0.2619, pruned_loss=0.03849, over 7222.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2776, pruned_loss=0.05461, over 1426447.73 frames.], batch size: 21, lr: 3.45e-04
2022-05-27 19:44:59,652 INFO [train.py:842] (1/4) Epoch 16, batch 2550, loss[loss=0.1982, simple_loss=0.2933, pruned_loss=0.05153, over 7147.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2774, pruned_loss=0.05465, over 1423619.85 frames.], batch size: 20, lr: 3.45e-04
2022-05-27 19:45:38,462 INFO [train.py:842] (1/4) Epoch 16, batch 2600, loss[loss=0.1674, simple_loss=0.2482, pruned_loss=0.0433, over 7362.00 frames.], tot_loss[loss=0.195, simple_loss=0.2787, pruned_loss=0.05567, over 1422411.75 frames.], batch size: 19, lr: 3.45e-04
2022-05-27 19:46:17,830 INFO [train.py:842] (1/4) Epoch 16, batch 2650, loss[loss=0.2578, simple_loss=0.334, pruned_loss=0.09078, over 7359.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2783, pruned_loss=0.05555, over 1422377.44 frames.], batch size: 23, lr: 3.45e-04
2022-05-27 19:46:56,746 INFO [train.py:842] (1/4) Epoch 16, batch 2700, loss[loss=0.1868, simple_loss=0.2792, pruned_loss=0.04723, over 7119.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2778, pruned_loss=0.05535, over 1420329.77 frames.], batch size: 26, lr: 3.45e-04
2022-05-27 19:47:35,940 INFO [train.py:842] (1/4) Epoch 16, batch 2750, loss[loss=0.1971, simple_loss=0.2685, pruned_loss=0.06283, over 7289.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2781, pruned_loss=0.05527, over 1424181.86 frames.], batch size: 18, lr: 3.45e-04
2022-05-27 19:48:14,903 INFO [train.py:842] (1/4) Epoch 16, batch 2800, loss[loss=0.2091, simple_loss=0.3019, pruned_loss=0.05817, over 7223.00 frames.], tot_loss[loss=0.1938, simple_loss=0.278, pruned_loss=0.05485, over 1425851.22 frames.], batch size: 21, lr: 3.45e-04
2022-05-27 19:48:54,090 INFO [train.py:842] (1/4) Epoch 16, batch 2850, loss[loss=0.2133, simple_loss=0.2916, pruned_loss=0.06755, over 7154.00 frames.], tot_loss[loss=0.1947, simple_loss=0.279, pruned_loss=0.05524, over 1425518.59 frames.], batch size: 18, lr: 3.45e-04
2022-05-27 19:49:32,833 INFO [train.py:842] (1/4) Epoch 16, batch 2900, loss[loss=0.1466, simple_loss=0.2339, pruned_loss=0.02962, over 7157.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2774, pruned_loss=0.05446, over 1428128.79 frames.], batch size: 18, lr: 3.44e-04
2022-05-27 19:50:12,017 INFO [train.py:842] (1/4) Epoch 16, batch 2950, loss[loss=0.1823, simple_loss=0.2759, pruned_loss=0.0443, over 7334.00 frames.], tot_loss[loss=0.193, simple_loss=0.2773, pruned_loss=0.05438, over 1424991.66 frames.], batch size: 22, lr: 3.44e-04
2022-05-27 19:50:50,702 INFO [train.py:842] (1/4) Epoch 16, batch 3000, loss[loss=0.2231, simple_loss=0.3058, pruned_loss=0.07018, over 7415.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2793, pruned_loss=0.05519, over 1429179.46 frames.], batch size: 21, lr: 3.44e-04
2022-05-27 19:50:50,702 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 19:51:00,435 INFO [train.py:871] (1/4) Epoch 16, validation: loss=0.1694, simple_loss=0.2694, pruned_loss=0.03473, over 868885.00 frames.
2022-05-27 19:51:39,578 INFO [train.py:842] (1/4) Epoch 16, batch 3050, loss[loss=0.1629, simple_loss=0.238, pruned_loss=0.04386, over 7409.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2786, pruned_loss=0.05495, over 1427188.75 frames.], batch size: 18, lr: 3.44e-04
2022-05-27 19:52:18,313 INFO [train.py:842] (1/4) Epoch 16, batch 3100, loss[loss=0.2291, simple_loss=0.3092, pruned_loss=0.07451, over 7191.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2793, pruned_loss=0.0552, over 1427547.89 frames.], batch size: 23, lr: 3.44e-04
2022-05-27 19:52:57,434 INFO [train.py:842] (1/4) Epoch 16, batch 3150, loss[loss=0.1658, simple_loss=0.2548, pruned_loss=0.03834, over 7166.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2792, pruned_loss=0.0555, over 1424585.19 frames.], batch size: 18, lr: 3.44e-04
2022-05-27 19:53:36,213 INFO [train.py:842] (1/4) Epoch 16, batch 3200, loss[loss=0.2194, simple_loss=0.3041, pruned_loss=0.06737, over 7298.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2798, pruned_loss=0.05604, over 1424397.61 frames.], batch size: 24, lr: 3.44e-04
2022-05-27 19:54:15,777 INFO [train.py:842] (1/4) Epoch 16, batch 3250, loss[loss=0.1996, simple_loss=0.2894, pruned_loss=0.05484, over 7316.00 frames.], tot_loss[loss=0.1944, simple_loss=0.278, pruned_loss=0.05544, over 1425672.35 frames.], batch size: 21, lr: 3.44e-04
2022-05-27 19:54:54,921 INFO [train.py:842] (1/4) Epoch 16, batch 3300, loss[loss=0.2403, simple_loss=0.3289, pruned_loss=0.07586, over 7324.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2786, pruned_loss=0.05513, over 1429506.89 frames.], batch size: 25, lr: 3.44e-04
2022-05-27 19:55:34,112 INFO [train.py:842] (1/4) Epoch 16, batch 3350, loss[loss=0.1733, simple_loss=0.2639, pruned_loss=0.04134, over 7241.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2782, pruned_loss=0.05484, over 1431757.38 frames.], batch size: 20, lr: 3.44e-04
2022-05-27 19:56:12,907 INFO [train.py:842] (1/4) Epoch 16, batch 3400, loss[loss=0.1961, simple_loss=0.2889, pruned_loss=0.05159, over 7116.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2764, pruned_loss=0.05391, over 1429409.19 frames.], batch size: 28, lr: 3.44e-04
2022-05-27 19:56:52,462 INFO [train.py:842] (1/4) Epoch 16, batch 3450, loss[loss=0.1673, simple_loss=0.2553, pruned_loss=0.03959, over 7356.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2772, pruned_loss=0.05411, over 1431328.75 frames.], batch size: 19, lr: 3.44e-04
2022-05-27 19:57:31,233 INFO [train.py:842] (1/4) Epoch 16, batch 3500, loss[loss=0.1748, simple_loss=0.2652, pruned_loss=0.04222, over 7328.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2778, pruned_loss=0.05459, over 1430011.67 frames.], batch size: 21, lr: 3.44e-04
2022-05-27 19:58:10,270 INFO [train.py:842] (1/4) Epoch 16, batch 3550, loss[loss=0.1803, simple_loss=0.2747, pruned_loss=0.04295, over 7154.00 frames.], tot_loss[loss=0.195, simple_loss=0.2792, pruned_loss=0.05536, over 1425763.13 frames.], batch size: 26, lr: 3.44e-04
2022-05-27 19:58:48,880 INFO [train.py:842] (1/4) Epoch 16, batch 3600, loss[loss=0.1986, simple_loss=0.2864, pruned_loss=0.05539, over 7322.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2786, pruned_loss=0.05501, over 1427005.47 frames.], batch size: 21, lr: 3.44e-04
2022-05-27 19:59:28,037 INFO [train.py:842] (1/4) Epoch 16, batch 3650, loss[loss=0.1698, simple_loss=0.2554, pruned_loss=0.04207, over 7283.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2771, pruned_loss=0.0541, over 1426870.33 frames.], batch size: 18, lr: 3.44e-04
2022-05-27 20:00:07,147 INFO [train.py:842] (1/4) Epoch 16, batch 3700, loss[loss=0.1899, simple_loss=0.2794, pruned_loss=0.05024, over 7259.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2772, pruned_loss=0.05415, over 1423891.33 frames.], batch size: 16, lr: 3.43e-04
2022-05-27 20:00:46,330 INFO [train.py:842] (1/4) Epoch 16, batch 3750, loss[loss=0.2229, simple_loss=0.3174, pruned_loss=0.06418, over 7301.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2771, pruned_loss=0.05425, over 1422157.90 frames.], batch size: 25, lr: 3.43e-04
2022-05-27 20:01:24,924 INFO [train.py:842] (1/4) Epoch 16, batch 3800, loss[loss=0.2066, simple_loss=0.2952, pruned_loss=0.05902, over 7207.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2776, pruned_loss=0.05461, over 1424932.11 frames.], batch size: 22, lr: 3.43e-04
2022-05-27 20:02:03,875 INFO [train.py:842] (1/4) Epoch 16, batch 3850, loss[loss=0.2645, simple_loss=0.3317, pruned_loss=0.09871, over 7152.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2786, pruned_loss=0.05484, over 1420662.85 frames.], batch size: 20, lr: 3.43e-04
2022-05-27 20:02:42,889 INFO [train.py:842] (1/4) Epoch 16, batch 3900, loss[loss=0.18, simple_loss=0.2731, pruned_loss=0.04345, over 7282.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2788, pruned_loss=0.05489, over 1422752.19 frames.], batch size: 24, lr: 3.43e-04
2022-05-27 20:03:21,515 INFO [train.py:842] (1/4) Epoch 16, batch 3950, loss[loss=0.1755, simple_loss=0.2661, pruned_loss=0.04242, over 7220.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2798, pruned_loss=0.05545, over 1420792.12 frames.], batch size: 21, lr: 3.43e-04
2022-05-27 20:04:00,261 INFO [train.py:842] (1/4) Epoch 16, batch 4000, loss[loss=0.216, simple_loss=0.3019, pruned_loss=0.06511, over 7227.00 frames.], tot_loss[loss=0.1942, simple_loss=0.279, pruned_loss=0.05473, over 1420878.37 frames.], batch size: 21, lr: 3.43e-04
2022-05-27 20:04:39,442 INFO [train.py:842] (1/4) Epoch 16, batch 4050, loss[loss=0.2811, simple_loss=0.3394, pruned_loss=0.1114, over 7206.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2789, pruned_loss=0.05497, over 1422204.70 frames.], batch size: 22, lr: 3.43e-04
2022-05-27 20:05:18,199 INFO [train.py:842] (1/4) Epoch 16, batch 4100, loss[loss=0.2351, simple_loss=0.3089, pruned_loss=0.08071, over 7161.00 frames.], tot_loss[loss=0.195, simple_loss=0.2794, pruned_loss=0.05532, over 1423929.95 frames.], batch size: 18, lr: 3.43e-04
2022-05-27 20:05:57,580 INFO [train.py:842] (1/4) Epoch 16, batch 4150, loss[loss=0.1472, simple_loss=0.2216, pruned_loss=0.03641, over 7003.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2784, pruned_loss=0.05485, over 1424931.18 frames.], batch size: 16, lr: 3.43e-04
2022-05-27 20:06:36,486 INFO [train.py:842] (1/4) Epoch 16, batch 4200, loss[loss=0.1693, simple_loss=0.2611, pruned_loss=0.03879, over 7409.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2782, pruned_loss=0.05454, over 1423167.36 frames.], batch size: 21, lr: 3.43e-04
2022-05-27 20:07:15,426 INFO [train.py:842] (1/4) Epoch 16, batch 4250, loss[loss=0.1832, simple_loss=0.272, pruned_loss=0.04714, over 7326.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2781, pruned_loss=0.05443, over 1422868.00 frames.], batch size: 25, lr: 3.43e-04
2022-05-27 20:07:54,448 INFO [train.py:842] (1/4) Epoch 16, batch 4300, loss[loss=0.1953, simple_loss=0.2827, pruned_loss=0.05396, over 7237.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2783, pruned_loss=0.05447, over 1422425.77 frames.], batch size: 20, lr: 3.43e-04
2022-05-27 20:08:33,202 INFO [train.py:842] (1/4) Epoch 16, batch 4350, loss[loss=0.1897, simple_loss=0.2806, pruned_loss=0.04946, over 7210.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2789, pruned_loss=0.05427, over 1423795.38 frames.], batch size: 22, lr: 3.43e-04
2022-05-27 20:09:12,169 INFO [train.py:842] (1/4) Epoch 16, batch 4400, loss[loss=0.1729, simple_loss=0.2694, pruned_loss=0.03819, over 7318.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2776, pruned_loss=0.05363, over 1422316.62 frames.], batch size: 21, lr: 3.43e-04
2022-05-27 20:09:51,436 INFO [train.py:842] (1/4) Epoch 16, batch 4450, loss[loss=0.1601, simple_loss=0.2465, pruned_loss=0.0369, over 7156.00 frames.], tot_loss[loss=0.194, simple_loss=0.2788, pruned_loss=0.05456, over 1424914.28 frames.], batch size: 18, lr: 3.43e-04
2022-05-27 20:10:30,366 INFO [train.py:842] (1/4) Epoch 16, batch 4500, loss[loss=0.2649, simple_loss=0.3372, pruned_loss=0.09631, over 7342.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2789, pruned_loss=0.05424, over 1428432.65 frames.], batch size: 22, lr: 3.43e-04
2022-05-27 20:11:09,725 INFO [train.py:842] (1/4) Epoch 16, batch 4550, loss[loss=0.2327, simple_loss=0.3024, pruned_loss=0.08146, over 7209.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2772, pruned_loss=0.05394, over 1429511.29 frames.], batch size: 22, lr: 3.42e-04
2022-05-27 20:11:48,704 INFO [train.py:842] (1/4) Epoch 16, batch 4600, loss[loss=0.1582, simple_loss=0.2485, pruned_loss=0.03393, over 7270.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2759, pruned_loss=0.05321, over 1431416.29 frames.], batch size: 18, lr: 3.42e-04
2022-05-27 20:12:27,777 INFO [train.py:842] (1/4) Epoch 16, batch 4650, loss[loss=0.2165, simple_loss=0.3077, pruned_loss=0.06259, over 7246.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2762, pruned_loss=0.05321, over 1431098.88 frames.], batch size: 20, lr: 3.42e-04
2022-05-27 20:13:06,633 INFO [train.py:842] (1/4) Epoch 16, batch 4700, loss[loss=0.2186, simple_loss=0.301, pruned_loss=0.06815, over 7108.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2771, pruned_loss=0.05382, over 1432793.78 frames.], batch size: 21, lr: 3.42e-04
2022-05-27 20:13:45,909 INFO [train.py:842] (1/4) Epoch 16, batch 4750, loss[loss=0.1495, simple_loss=0.2345, pruned_loss=0.03224, over 6839.00 frames.], tot_loss[loss=0.192, simple_loss=0.2764, pruned_loss=0.05378, over 1429949.56 frames.], batch size: 15, lr: 3.42e-04
2022-05-27 20:14:24,742 INFO [train.py:842] (1/4) Epoch 16, batch 4800, loss[loss=0.1689, simple_loss=0.2639, pruned_loss=0.03692, over 7412.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2771, pruned_loss=0.05422, over 1432982.63 frames.], batch size: 20, lr: 3.42e-04
2022-05-27 20:15:03,927 INFO [train.py:842] (1/4) Epoch 16, batch 4850, loss[loss=0.2072, simple_loss=0.2931, pruned_loss=0.0607, over 7143.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2769, pruned_loss=0.05401, over 1426950.53 frames.], batch size: 20, lr: 3.42e-04
2022-05-27 20:15:43,043 INFO [train.py:842] (1/4) Epoch 16, batch 4900, loss[loss=0.2175, simple_loss=0.2983, pruned_loss=0.06836, over 7320.00 frames.], tot_loss[loss=0.1916, simple_loss=0.276, pruned_loss=0.05364, over 1426341.47 frames.], batch size: 20, lr: 3.42e-04
2022-05-27 20:16:22,175 INFO [train.py:842] (1/4) Epoch 16, batch 4950, loss[loss=0.1718, simple_loss=0.2676, pruned_loss=0.03802, over 6977.00 frames.], tot_loss[loss=0.1915, simple_loss=0.276, pruned_loss=0.05348, over 1426343.19 frames.], batch size: 28, lr: 3.42e-04
2022-05-27 20:17:00,888 INFO [train.py:842] (1/4) Epoch 16, batch 5000, loss[loss=0.1809, simple_loss=0.2728, pruned_loss=0.04447, over 7187.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2763, pruned_loss=0.05364, over 1422689.36 frames.], batch size: 23, lr: 3.42e-04
2022-05-27 20:17:40,079 INFO [train.py:842] (1/4) Epoch 16, batch 5050, loss[loss=0.1551, simple_loss=0.2365, pruned_loss=0.03686, over 7132.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2766, pruned_loss=0.05391, over 1420421.39 frames.], batch size: 17, lr: 3.42e-04
2022-05-27 20:18:18,511 INFO [train.py:842] (1/4) Epoch 16, batch 5100, loss[loss=0.1989, simple_loss=0.2911, pruned_loss=0.05338, over 7199.00 frames.], tot_loss[loss=0.1931, simple_loss=0.278, pruned_loss=0.05411, over 1417849.38 frames.], batch size: 26, lr: 3.42e-04
2022-05-27 20:18:57,623 INFO [train.py:842] (1/4) Epoch 16, batch 5150, loss[loss=0.2117, simple_loss=0.2974, pruned_loss=0.06298, over 6261.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2772, pruned_loss=0.05362, over 1421963.73 frames.], batch size: 37, lr: 3.42e-04
2022-05-27 20:19:36,717 INFO [train.py:842] (1/4) Epoch 16, batch 5200, loss[loss=0.165, simple_loss=0.2603, pruned_loss=0.03489, over 7442.00 frames.], tot_loss[loss=0.1911, simple_loss=0.276, pruned_loss=0.05303, over 1427328.09 frames.], batch size: 19, lr: 3.42e-04
2022-05-27 20:20:15,558 INFO [train.py:842] (1/4) Epoch 16, batch 5250, loss[loss=0.1817, simple_loss=0.2686, pruned_loss=0.0474, over 7267.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2778, pruned_loss=0.0537, over 1429291.05 frames.], batch size: 19, lr: 3.42e-04
2022-05-27 20:20:54,284 INFO [train.py:842] (1/4) Epoch 16, batch 5300, loss[loss=0.1889, simple_loss=0.28, pruned_loss=0.04888, over 7327.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2776, pruned_loss=0.05435, over 1428841.55 frames.], batch size: 21, lr: 3.42e-04
2022-05-27 20:21:33,695 INFO [train.py:842] (1/4) Epoch 16, batch 5350, loss[loss=0.1908, simple_loss=0.2618, pruned_loss=0.05988, over 7287.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2785, pruned_loss=0.05467, over 1430774.76 frames.], batch size: 17, lr: 3.41e-04
2022-05-27 20:22:12,413 INFO [train.py:842] (1/4) Epoch 16, batch 5400, loss[loss=0.1838, simple_loss=0.2621, pruned_loss=0.05269, over 6998.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2781, pruned_loss=0.05467, over 1430200.42 frames.], batch size: 16, lr: 3.41e-04
2022-05-27 20:22:51,671 INFO [train.py:842] (1/4) Epoch 16, batch 5450, loss[loss=0.1961, simple_loss=0.2802, pruned_loss=0.05596, over 7270.00 frames.], tot_loss[loss=0.194, simple_loss=0.2787, pruned_loss=0.05467, over 1430089.74 frames.], batch size: 19, lr: 3.41e-04
2022-05-27 20:23:30,919 INFO [train.py:842] (1/4) Epoch 16, batch 5500, loss[loss=0.2081, simple_loss=0.2918, pruned_loss=0.06217, over 7382.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2784, pruned_loss=0.05427, over 1430699.77 frames.], batch size: 23, lr: 3.41e-04
2022-05-27 20:24:09,998 INFO [train.py:842] (1/4) Epoch 16, batch 5550, loss[loss=0.2589, simple_loss=0.3384, pruned_loss=0.08971, over 7201.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2794, pruned_loss=0.05518, over 1429058.28 frames.], batch size: 22, lr: 3.41e-04
2022-05-27 20:24:48,776 INFO [train.py:842] (1/4) Epoch 16, batch 5600, loss[loss=0.1968, simple_loss=0.2836, pruned_loss=0.05495, over 6735.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2802, pruned_loss=0.05567, over 1422140.84 frames.], batch size: 31, lr: 3.41e-04
2022-05-27 20:25:28,076 INFO [train.py:842] (1/4) Epoch 16, batch 5650, loss[loss=0.1719, simple_loss=0.2506, pruned_loss=0.04659, over 7279.00 frames.], tot_loss[loss=0.1968, simple_loss=0.281, pruned_loss=0.05631, over 1417594.75 frames.], batch size: 17, lr: 3.41e-04
2022-05-27 20:26:06,923 INFO [train.py:842] (1/4) Epoch 16, batch 5700, loss[loss=0.1893, simple_loss=0.2741, pruned_loss=0.05222, over 6726.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2801, pruned_loss=0.05566, over 1417111.24 frames.], batch size: 31, lr: 3.41e-04
2022-05-27 20:26:45,779 INFO [train.py:842] (1/4) Epoch 16, batch 5750, loss[loss=0.1942, simple_loss=0.267, pruned_loss=0.06073, over 7366.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2795, pruned_loss=0.05509, over 1415136.59 frames.], batch size: 19, lr: 3.41e-04
2022-05-27 20:27:25,116 INFO [train.py:842] (1/4) Epoch 16, batch 5800, loss[loss=0.1699, simple_loss=0.266, pruned_loss=0.03692, over 7281.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2782, pruned_loss=0.05476, over 1417227.75 frames.], batch size: 25, lr: 3.41e-04
2022-05-27 20:28:04,515 INFO [train.py:842] (1/4) Epoch 16, batch 5850, loss[loss=0.2068, simple_loss=0.2919, pruned_loss=0.06081, over 7326.00 frames.], tot_loss[loss=0.194, simple_loss=0.2781, pruned_loss=0.05498, over 1419270.03 frames.], batch size: 21, lr: 3.41e-04
2022-05-27 20:28:43,325 INFO [train.py:842] (1/4) Epoch 16, batch 5900, loss[loss=0.1949, simple_loss=0.2745, pruned_loss=0.05763, over 6769.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2782, pruned_loss=0.05421, over 1422631.31 frames.], batch size: 15, lr: 3.41e-04
2022-05-27 20:29:22,146 INFO [train.py:842] (1/4) Epoch 16, batch 5950, loss[loss=0.1916, simple_loss=0.2678, pruned_loss=0.05766, over 7426.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2783, pruned_loss=0.05437, over 1419342.36 frames.], batch size: 18, lr: 3.41e-04
2022-05-27 20:30:00,985 INFO [train.py:842] (1/4) Epoch 16, batch 6000, loss[loss=0.1807, simple_loss=0.2715, pruned_loss=0.04492, over 7226.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2781, pruned_loss=0.0546, over 1419053.20 frames.], batch size: 20, lr: 3.41e-04
2022-05-27 20:30:00,985 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 20:30:10,685 INFO [train.py:871] (1/4) Epoch 16, validation: loss=0.1676, simple_loss=0.2671, pruned_loss=0.03399, over 868885.00 frames.
2022-05-27 20:30:49,752 INFO [train.py:842] (1/4) Epoch 16, batch 6050, loss[loss=0.1624, simple_loss=0.2474, pruned_loss=0.03876, over 7141.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2782, pruned_loss=0.05448, over 1422127.82 frames.], batch size: 17, lr: 3.41e-04
2022-05-27 20:31:28,704 INFO [train.py:842] (1/4) Epoch 16, batch 6100, loss[loss=0.1792, simple_loss=0.2565, pruned_loss=0.051, over 7435.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2786, pruned_loss=0.05451, over 1422523.36 frames.], batch size: 20, lr: 3.41e-04
2022-05-27 20:32:11,229 INFO [train.py:842] (1/4) Epoch 16, batch 6150, loss[loss=0.201, simple_loss=0.2849, pruned_loss=0.05856, over 7431.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2774, pruned_loss=0.0538, over 1421140.09 frames.], batch size: 20, lr: 3.41e-04
2022-05-27 20:32:50,490 INFO [train.py:842] (1/4) Epoch 16, batch 6200, loss[loss=0.1954, simple_loss=0.2848, pruned_loss=0.05303, over 7314.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2772, pruned_loss=0.05414, over 1426317.01 frames.], batch size: 21, lr: 3.40e-04
2022-05-27 20:33:29,295 INFO [train.py:842] (1/4) Epoch 16, batch 6250, loss[loss=0.1773, simple_loss=0.2705, pruned_loss=0.04202, over 7335.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2776, pruned_loss=0.05393, over 1425434.44 frames.], batch size: 20, lr: 3.40e-04
2022-05-27 20:34:08,324 INFO [train.py:842] (1/4) Epoch 16, batch 6300, loss[loss=0.1874, simple_loss=0.2825, pruned_loss=0.04617, over 7341.00 frames.], tot_loss[loss=0.1923, simple_loss=0.277, pruned_loss=0.05387, over 1428584.40 frames.], batch size: 22, lr: 3.40e-04
2022-05-27 20:34:47,588 INFO [train.py:842] (1/4) Epoch 16, batch 6350, loss[loss=0.2438, simple_loss=0.3233, pruned_loss=0.08217, over 6407.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2775, pruned_loss=0.05452, over 1423711.30 frames.], batch size: 38, lr: 3.40e-04
2022-05-27 20:35:26,589 INFO [train.py:842] (1/4) Epoch 16, batch 6400, loss[loss=0.1759, simple_loss=0.2743, pruned_loss=0.03876, over 7217.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2777, pruned_loss=0.05483, over 1425518.77 frames.], batch size: 21, lr: 3.40e-04
2022-05-27 20:36:05,666 INFO [train.py:842] (1/4) Epoch 16, batch 6450, loss[loss=0.1573, simple_loss=0.2571, pruned_loss=0.02874, over 7317.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2785, pruned_loss=0.05545, over 1423609.92 frames.], batch size: 21, lr: 3.40e-04
2022-05-27 20:36:44,217 INFO [train.py:842] (1/4) Epoch 16, batch 6500, loss[loss=0.1709, simple_loss=0.2598, pruned_loss=0.04095, over 7220.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2792, pruned_loss=0.05496, over 1420293.24 frames.], batch size: 21, lr: 3.40e-04
2022-05-27 20:37:23,366 INFO [train.py:842] (1/4) Epoch 16, batch 6550, loss[loss=0.1859, simple_loss=0.2776, pruned_loss=0.04714, over 7207.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2782, pruned_loss=0.0545, over 1421195.19 frames.], batch size: 22, lr: 3.40e-04
2022-05-27 20:38:12,085 INFO [train.py:842] (1/4) Epoch 16, batch 6600, loss[loss=0.1577, simple_loss=0.244, pruned_loss=0.03572, over 7061.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2771, pruned_loss=0.05332, over 1424024.05 frames.], batch size: 18, lr: 3.40e-04
2022-05-27 20:38:51,377 INFO [train.py:842] (1/4) Epoch 16, batch 6650, loss[loss=0.2087, simple_loss=0.2931, pruned_loss=0.06216, over 7082.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2781, pruned_loss=0.05416, over 1422951.32 frames.], batch size: 28, lr: 3.40e-04
2022-05-27 20:39:30,073 INFO [train.py:842] (1/4) Epoch 16, batch 6700, loss[loss=0.1776, simple_loss=0.2617, pruned_loss=0.04672, over 7319.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2786, pruned_loss=0.0545, over 1424031.86 frames.], batch size: 20, lr: 3.40e-04
2022-05-27 20:40:09,398 INFO [train.py:842] (1/4) Epoch 16, batch 6750, loss[loss=0.19, simple_loss=0.293, pruned_loss=0.04357, over 7327.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2778, pruned_loss=0.054, over 1425293.42 frames.], batch size: 20, lr: 3.40e-04
2022-05-27 20:40:48,525 INFO [train.py:842] (1/4) Epoch 16, batch 6800, loss[loss=0.1662, simple_loss=0.2585, pruned_loss=0.03693, over 7422.00 frames.], tot_loss[loss=0.1934, simple_loss=0.278, pruned_loss=0.0544, over 1428731.53 frames.], batch size: 20, lr: 3.40e-04
2022-05-27 20:41:27,657 INFO [train.py:842] (1/4) Epoch 16, batch 6850, loss[loss=0.1466, simple_loss=0.238, pruned_loss=0.02757, over 7260.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2774, pruned_loss=0.05417, over 1428669.67 frames.], batch size: 19, lr: 3.40e-04
2022-05-27 20:42:06,261 INFO [train.py:842] (1/4) Epoch 16, batch 6900, loss[loss=0.2422, simple_loss=0.3279, pruned_loss=0.0782, over 7140.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2773, pruned_loss=0.05402, over 1425800.35 frames.], batch size: 28, lr: 3.40e-04
2022-05-27 20:42:45,938 INFO [train.py:842] (1/4) Epoch 16, batch 6950, loss[loss=0.1961, simple_loss=0.2911, pruned_loss=0.05058, over 7294.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2763, pruned_loss=0.05357, over 1426106.64 frames.], batch size: 24, lr: 3.40e-04
2022-05-27 20:43:25,278 INFO [train.py:842] (1/4) Epoch 16, batch 7000, loss[loss=0.1762, simple_loss=0.2415, pruned_loss=0.05547, over 7233.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2754, pruned_loss=0.05347, over 1424491.94 frames.], batch size: 16, lr: 3.40e-04
2022-05-27 20:44:04,315 INFO [train.py:842] (1/4) Epoch 16, batch 7050, loss[loss=0.2083, simple_loss=0.2819, pruned_loss=0.06734, over 7230.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2757, pruned_loss=0.05378, over 1426053.81 frames.], batch size: 26, lr: 3.39e-04
2022-05-27 20:44:43,346 INFO [train.py:842] (1/4) Epoch 16, batch 7100, loss[loss=0.1983, simple_loss=0.2942, pruned_loss=0.05116, over 7200.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2773, pruned_loss=0.05426, over 1429167.53 frames.], batch size: 26, lr: 3.39e-04
2022-05-27 20:45:22,612 INFO [train.py:842] (1/4) Epoch 16, batch 7150, loss[loss=0.2217, simple_loss=0.3042, pruned_loss=0.06958, over 4994.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2769, pruned_loss=0.05384, over 1425768.51 frames.], batch size: 52, lr: 3.39e-04
2022-05-27 20:46:01,392 INFO [train.py:842] (1/4) Epoch 16, batch 7200, loss[loss=0.2002, simple_loss=0.2925, pruned_loss=0.05398, over 7369.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2756, pruned_loss=0.05308, over 1428511.28 frames.], batch size: 23, lr: 3.39e-04
2022-05-27 20:46:40,327 INFO [train.py:842] (1/4) Epoch 16, batch 7250, loss[loss=0.2875, simple_loss=0.3337, pruned_loss=0.1206, over 7168.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2776, pruned_loss=0.05431, over 1425877.86 frames.], batch size: 19, lr: 3.39e-04
2022-05-27 20:47:19,343 INFO [train.py:842] (1/4) Epoch 16, batch 7300, loss[loss=0.1418, simple_loss=0.2261, pruned_loss=0.02872, over 7071.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2775, pruned_loss=0.05453, over 1421048.32 frames.], batch size: 18, lr: 3.39e-04
2022-05-27 20:47:58,162 INFO [train.py:842] (1/4) Epoch 16, batch 7350, loss[loss=0.1606, simple_loss=0.2378, pruned_loss=0.0417, over 6991.00 frames.], tot_loss[loss=0.193, simple_loss=0.2775, pruned_loss=0.0543, over 1422568.67 frames.], batch size: 16, lr: 3.39e-04
2022-05-27 20:48:36,987 INFO [train.py:842] (1/4) Epoch 16, batch 7400, loss[loss=0.1598, simple_loss=0.2348, pruned_loss=0.04245, over 6761.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2786, pruned_loss=0.05505, over 1424829.22 frames.], batch size: 15, lr: 3.39e-04
2022-05-27 20:49:15,883 INFO [train.py:842] (1/4) Epoch 16, batch 7450, loss[loss=0.1583, simple_loss=0.2319, pruned_loss=0.04233, over 7209.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2782, pruned_loss=0.05471, over 1421252.19 frames.], batch size: 16, lr: 3.39e-04
2022-05-27 20:49:54,826 INFO [train.py:842] (1/4) Epoch 16, batch 7500, loss[loss=0.155, simple_loss=0.2448, pruned_loss=0.03259, over 7171.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2776, pruned_loss=0.0541, over 1421148.60 frames.], batch size: 18, lr: 3.39e-04
2022-05-27 20:50:33,695 INFO [train.py:842] (1/4) Epoch 16, batch 7550, loss[loss=0.1959, simple_loss=0.2769, pruned_loss=0.05744, over 7420.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2767, pruned_loss=0.05341, over 1422688.37 frames.], batch size: 18, lr: 3.39e-04
2022-05-27 20:51:12,618 INFO [train.py:842] (1/4) Epoch 16, batch 7600, loss[loss=0.2474, simple_loss=0.3346, pruned_loss=0.08008, over 6725.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2772, pruned_loss=0.05424, over 1421046.53 frames.], batch size: 31, lr: 3.39e-04
2022-05-27 20:51:51,604 INFO [train.py:842] (1/4) Epoch 16, batch 7650, loss[loss=0.1876, simple_loss=0.2626, pruned_loss=0.05631, over 7000.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2769, pruned_loss=0.05419, over 1422217.47 frames.], batch size: 16, lr: 3.39e-04
2022-05-27 20:52:30,505 INFO [train.py:842] (1/4) Epoch 16, batch 7700, loss[loss=0.194, simple_loss=0.2809, pruned_loss=0.05351, over 7409.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2772, pruned_loss=0.05423, over 1422215.09 frames.], batch size: 21, lr: 3.39e-04
2022-05-27 20:53:09,727 INFO [train.py:842] (1/4) Epoch 16, batch 7750, loss[loss=0.1754, simple_loss=0.2547, pruned_loss=0.04805, over 7168.00 frames.], tot_loss[loss=0.193, simple_loss=0.2774, pruned_loss=0.05428, over 1425220.23 frames.], batch size: 18, lr: 3.39e-04
2022-05-27 20:53:48,644 INFO [train.py:842] (1/4) Epoch 16, batch 7800, loss[loss=0.203, simple_loss=0.2873, pruned_loss=0.05936, over 6806.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2773, pruned_loss=0.05441, over 1428179.65 frames.], batch size: 31, lr: 3.39e-04
2022-05-27 20:54:27,524 INFO [train.py:842] (1/4) Epoch 16, batch 7850, loss[loss=0.1819, simple_loss=0.2648, pruned_loss=0.04949, over 6791.00 frames.], tot_loss[loss=0.1925, simple_loss=0.277, pruned_loss=0.05402, over 1429182.02 frames.], batch size: 15, lr: 3.39e-04
2022-05-27 20:55:06,441 INFO [train.py:842] (1/4) Epoch 16, batch 7900, loss[loss=0.1595, simple_loss=0.2495, pruned_loss=0.03478, over 7363.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2765, pruned_loss=0.05423, over 1425961.32 frames.], batch size: 19, lr: 3.38e-04
2022-05-27 20:55:45,930 INFO [train.py:842] (1/4) Epoch 16, batch 7950, loss[loss=0.1592, simple_loss=0.2478, pruned_loss=0.03531, over 7140.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2771, pruned_loss=0.05457, over 1427148.73 frames.], batch size: 17, lr: 3.38e-04
2022-05-27 20:56:24,801 INFO [train.py:842] (1/4) Epoch 16, batch 8000, loss[loss=0.2091, simple_loss=0.2852, pruned_loss=0.0665, over 7265.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2762, pruned_loss=0.05382, over 1428627.21 frames.], batch size: 18, lr: 3.38e-04
2022-05-27 20:57:04,137 INFO [train.py:842] (1/4) Epoch 16, batch 8050, loss[loss=0.1411, simple_loss=0.2208, pruned_loss=0.03073, over 6800.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2754, pruned_loss=0.05388, over 1427316.03 frames.], batch size: 15, lr: 3.38e-04
2022-05-27 20:57:42,710 INFO [train.py:842] (1/4) Epoch 16, batch 8100, loss[loss=0.1544, simple_loss=0.2509, pruned_loss=0.02899, over 7345.00 frames.], tot_loss[loss=0.1916, simple_loss=0.276, pruned_loss=0.05363, over 1429617.70 frames.], batch size: 19, lr: 3.38e-04
2022-05-27 20:58:21,838 INFO [train.py:842] (1/4) Epoch 16, batch 8150, loss[loss=0.2049, simple_loss=0.2923, pruned_loss=0.05878, over 7208.00 frames.], tot_loss[loss=0.1922, simple_loss=0.276, pruned_loss=0.05419, over 1430409.06 frames.], batch size: 22, lr: 3.38e-04
2022-05-27 20:59:00,929 INFO [train.py:842] (1/4) Epoch 16, batch 8200, loss[loss=0.1718, simple_loss=0.2478, pruned_loss=0.04787, over 7268.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2765, pruned_loss=0.05467, over 1428034.86 frames.], batch size: 16, lr: 3.38e-04
2022-05-27 20:59:39,541 INFO [train.py:842] (1/4) Epoch 16, batch 8250, loss[loss=0.1889, simple_loss=0.2798, pruned_loss=0.04894, over 7272.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2772, pruned_loss=0.05485, over 1424456.43 frames.], batch size: 25, lr: 3.38e-04
2022-05-27 21:00:18,895 INFO [train.py:842] (1/4) Epoch 16, batch 8300, loss[loss=0.1595, simple_loss=0.2561, pruned_loss=0.03143, over 7332.00 frames.], tot_loss[loss=0.193, simple_loss=0.2766, pruned_loss=0.05467, over 1424148.07 frames.], batch size: 20, lr: 3.38e-04
2022-05-27 21:00:57,556 INFO [train.py:842] (1/4) Epoch 16, batch 8350, loss[loss=0.1987, simple_loss=0.2883, pruned_loss=0.05456, over 7198.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2781, pruned_loss=0.05521, over 1421236.89 frames.], batch size: 26, lr: 3.38e-04
2022-05-27 21:01:36,493 INFO [train.py:842] (1/4) Epoch 16, batch 8400, loss[loss=0.2491, simple_loss=0.3071, pruned_loss=0.09558, over 6795.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2773, pruned_loss=0.05477, over 1418367.57 frames.], batch size: 15, lr: 3.38e-04
2022-05-27 21:02:15,831 INFO [train.py:842] (1/4) Epoch 16, batch 8450, loss[loss=0.2081, simple_loss=0.2969, pruned_loss=0.05966, over 7422.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2776, pruned_loss=0.05471, over 1421866.73 frames.], batch size: 20, lr: 3.38e-04
2022-05-27 21:02:54,711 INFO [train.py:842] (1/4) Epoch 16, batch 8500, loss[loss=0.1979, simple_loss=0.2782, pruned_loss=0.05882, over 7167.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2769, pruned_loss=0.05432, over 1422228.49 frames.], batch size: 19, lr: 3.38e-04
2022-05-27 21:03:33,738 INFO [train.py:842] (1/4) Epoch 16, batch 8550, loss[loss=0.2025, simple_loss=0.2886, pruned_loss=0.0582, over 7424.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2768, pruned_loss=0.05475, over 1421541.16 frames.], batch size: 20, lr: 3.38e-04
2022-05-27 21:04:12,818 INFO [train.py:842] (1/4) Epoch 16, batch 8600, loss[loss=0.1636, simple_loss=0.2484, pruned_loss=0.03943, over 7255.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2773, pruned_loss=0.05489, over 1419608.85 frames.], batch size: 18, lr: 3.38e-04
2022-05-27 21:04:52,304 INFO [train.py:842] (1/4) Epoch 16, batch 8650, loss[loss=0.236, simple_loss=0.3037, pruned_loss=0.08419, over 4894.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2774, pruned_loss=0.05492, over 1415323.84 frames.], batch size: 52, lr: 3.38e-04
2022-05-27 21:05:31,129 INFO [train.py:842] (1/4) Epoch 16, batch 8700, loss[loss=0.1922, simple_loss=0.2818, pruned_loss=0.05131, over 7143.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2751, pruned_loss=0.05356, over 1412270.95 frames.], batch size: 20, lr: 3.38e-04
2022-05-27 21:06:10,234 INFO [train.py:842] (1/4) Epoch 16, batch 8750, loss[loss=0.2101, simple_loss=0.2905, pruned_loss=0.06488, over 7063.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2752, pruned_loss=0.05313, over 1413483.06 frames.], batch size: 18, lr: 3.38e-04
2022-05-27 21:06:48,792 INFO [train.py:842] (1/4) Epoch 16, batch 8800, loss[loss=0.1706, simple_loss=0.2623, pruned_loss=0.0395, over 7194.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2756, pruned_loss=0.05383, over 1411372.71 frames.], batch size: 22, lr: 3.37e-04
2022-05-27 21:07:27,875 INFO [train.py:842] (1/4) Epoch 16, batch 8850, loss[loss=0.1478, simple_loss=0.2338, pruned_loss=0.03088, over 7081.00 frames.], tot_loss[loss=0.1933, simple_loss=0.277, pruned_loss=0.05484, over 1409870.13 frames.], batch size: 18, lr: 3.37e-04
2022-05-27 21:08:06,058 INFO [train.py:842] (1/4) Epoch 16, batch 8900, loss[loss=0.2213, simple_loss=0.3005, pruned_loss=0.07105, over 4916.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2776, pruned_loss=0.05529, over 1399352.78 frames.], batch size: 52, lr: 3.37e-04
2022-05-27 21:08:44,683 INFO [train.py:842] (1/4) Epoch 16, batch 8950, loss[loss=0.1682, simple_loss=0.2541, pruned_loss=0.04112, over 7257.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2774, pruned_loss=0.05474, over 1395142.17 frames.], batch size: 19, lr: 3.37e-04
2022-05-27 21:09:22,812 INFO [train.py:842] (1/4) Epoch 16, batch 9000, loss[loss=0.2365, simple_loss=0.318, pruned_loss=0.07749, over 7095.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2794, pruned_loss=0.05581, over 1380259.05 frames.], batch size: 28, lr: 3.37e-04
2022-05-27 21:09:22,813 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 21:09:32,341 INFO [train.py:871] (1/4) Epoch 16, validation: loss=0.167, simple_loss=0.2669, pruned_loss=0.03357, over 868885.00 frames.
2022-05-27 21:10:10,542 INFO [train.py:842] (1/4) Epoch 16, batch 9050, loss[loss=0.1719, simple_loss=0.2543, pruned_loss=0.04474, over 7260.00 frames.], tot_loss[loss=0.197, simple_loss=0.281, pruned_loss=0.05647, over 1364024.45 frames.], batch size: 19, lr: 3.37e-04
2022-05-27 21:10:58,385 INFO [train.py:842] (1/4) Epoch 16, batch 9100, loss[loss=0.2561, simple_loss=0.3307, pruned_loss=0.09071, over 5032.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2851, pruned_loss=0.06, over 1310198.21 frames.], batch size: 52, lr: 3.37e-04
2022-05-27 21:11:46,304 INFO [train.py:842] (1/4) Epoch 16, batch 9150, loss[loss=0.2363, simple_loss=0.3191, pruned_loss=0.07677, over 4793.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2894, pruned_loss=0.06354, over 1241933.45 frames.], batch size: 52, lr: 3.37e-04
2022-05-27 21:12:47,121 INFO [train.py:842] (1/4) Epoch 17, batch 0, loss[loss=0.2033, simple_loss=0.2987, pruned_loss=0.0539, over 7118.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2987, pruned_loss=0.0539, over 7118.00 frames.], batch size: 21, lr: 3.28e-04
2022-05-27 21:13:26,275 INFO [train.py:842] (1/4) Epoch 17, batch 50, loss[loss=0.2013, simple_loss=0.294, pruned_loss=0.05425, over 7328.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2781, pruned_loss=0.05358, over 316903.56 frames.], batch size: 21, lr: 3.28e-04
2022-05-27 21:14:04,946 INFO [train.py:842] (1/4) Epoch 17, batch 100, loss[loss=0.1732, simple_loss=0.262, pruned_loss=0.04223, over 7145.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2761, pruned_loss=0.05262, over 558130.24 frames.], batch size: 20, lr: 3.28e-04
2022-05-27 21:14:43,827 INFO [train.py:842] (1/4) Epoch 17, batch 150, loss[loss=0.1753, simple_loss=0.2525, pruned_loss=0.04902, over 6986.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2768, pruned_loss=0.05309, over 746259.30 frames.], batch size: 16, lr: 3.28e-04
2022-05-27 21:15:22,351 INFO [train.py:842] (1/4) Epoch 17, batch 200, loss[loss=0.1488, simple_loss=0.2256, pruned_loss=0.03594, over 7144.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2787, pruned_loss=0.05375, over 896009.76 frames.], batch size: 17, lr: 3.27e-04
2022-05-27 21:16:01,200 INFO [train.py:842] (1/4) Epoch 17, batch 250, loss[loss=0.1692, simple_loss=0.2609, pruned_loss=0.03876, over 7263.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2776, pruned_loss=0.05337, over 1015673.55 frames.], batch size: 19, lr: 3.27e-04
2022-05-27 21:16:39,729 INFO [train.py:842] (1/4) Epoch 17, batch 300, loss[loss=0.2034, simple_loss=0.2828, pruned_loss=0.06197, over 7066.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2791, pruned_loss=0.05379, over 1101790.01 frames.], batch size: 18, lr: 3.27e-04
2022-05-27 21:17:18,905 INFO [train.py:842] (1/4) Epoch 17, batch 350, loss[loss=0.1935, simple_loss=0.2833, pruned_loss=0.05191, over 6845.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2779, pruned_loss=0.05352, over 1171847.20 frames.], batch size: 15, lr: 3.27e-04
2022-05-27 21:17:57,650 INFO [train.py:842] (1/4) Epoch 17, batch 400, loss[loss=0.2137, simple_loss=0.2909, pruned_loss=0.0682, over 4717.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2778, pruned_loss=0.05359, over 1226910.10 frames.], batch size: 53, lr: 3.27e-04
2022-05-27 21:18:36,644 INFO [train.py:842] (1/4) Epoch 17, batch 450, loss[loss=0.1547, simple_loss=0.2397, pruned_loss=0.03489, over 7359.00 frames.], tot_loss[loss=0.191, simple_loss=0.2764, pruned_loss=0.0528, over 1268173.62 frames.], batch size: 19, lr: 3.27e-04
2022-05-27 21:19:15,517 INFO [train.py:842] (1/4) Epoch 17, batch 500, loss[loss=0.1649, simple_loss=0.251, pruned_loss=0.03937, over 7158.00 frames.], tot_loss[loss=0.189, simple_loss=0.2746, pruned_loss=0.05174, over 1301406.27 frames.], batch size: 18, lr: 3.27e-04
2022-05-27 21:19:55,022 INFO [train.py:842] (1/4) Epoch 17, batch 550, loss[loss=0.1699, simple_loss=0.2425, pruned_loss=0.04859, over 7139.00 frames.], tot_loss[loss=0.19, simple_loss=0.2751, pruned_loss=0.05243, over 1327004.10 frames.], batch size: 17, lr: 3.27e-04
2022-05-27 21:20:33,627 INFO [train.py:842] (1/4) Epoch 17, batch 600, loss[loss=0.1794, simple_loss=0.2731, pruned_loss=0.04285, over 7029.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2745, pruned_loss=0.05227, over 1342657.04 frames.], batch size: 28, lr: 3.27e-04
2022-05-27 21:21:12,978 INFO [train.py:842] (1/4) Epoch 17, batch 650, loss[loss=0.2307, simple_loss=0.3104, pruned_loss=0.07554, over 7336.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2761, pruned_loss=0.05309, over 1360958.31 frames.], batch size: 20, lr: 3.27e-04
2022-05-27 21:21:51,612 INFO [train.py:842] (1/4) Epoch 17, batch 700, loss[loss=0.1902, simple_loss=0.2875, pruned_loss=0.04649, over 7267.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2771, pruned_loss=0.05326, over 1367584.53 frames.], batch size: 19, lr: 3.27e-04
2022-05-27 21:22:30,807 INFO [train.py:842] (1/4) Epoch 17, batch 750, loss[loss=0.1761, simple_loss=0.2693, pruned_loss=0.04139, over 7151.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2772, pruned_loss=0.05319, over 1376286.83 frames.], batch size: 20, lr: 3.27e-04
2022-05-27 21:23:09,454 INFO [train.py:842] (1/4) Epoch 17, batch 800, loss[loss=0.245, simple_loss=0.3241, pruned_loss=0.0829, over 7169.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2773, pruned_loss=0.05344, over 1387237.28 frames.], batch size: 19, lr: 3.27e-04
2022-05-27 21:23:48,550 INFO [train.py:842] (1/4) Epoch 17, batch 850, loss[loss=0.1957, simple_loss=0.2857, pruned_loss=0.05282, over 6504.00 frames.], tot_loss[loss=0.1918, simple_loss=0.277, pruned_loss=0.0533, over 1395482.55 frames.], batch size: 38, lr: 3.27e-04
2022-05-27 21:24:27,498 INFO [train.py:842] (1/4) Epoch 17, batch 900, loss[loss=0.1959, simple_loss=0.2856, pruned_loss=0.05311, over 7330.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2776, pruned_loss=0.05333, over 1406887.00 frames.], batch size: 20, lr: 3.27e-04
2022-05-27 21:25:06,290 INFO [train.py:842] (1/4) Epoch 17, batch 950, loss[loss=0.1523, simple_loss=0.2374, pruned_loss=0.03362, over 7145.00 frames.], tot_loss[loss=0.192, simple_loss=0.2776, pruned_loss=0.05321, over 1411570.15 frames.], batch size: 17, lr: 3.27e-04
2022-05-27 21:25:44,928 INFO [train.py:842] (1/4) Epoch 17, batch 1000, loss[loss=0.1728, simple_loss=0.2685, pruned_loss=0.03857, over 7127.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2775, pruned_loss=0.05317, over 1416071.78 frames.], batch size: 21, lr: 3.27e-04
2022-05-27 21:26:24,306 INFO [train.py:842] (1/4) Epoch 17, batch 1050, loss[loss=0.1837, simple_loss=0.2739, pruned_loss=0.04674, over 7338.00 frames.], tot_loss[loss=0.192, simple_loss=0.2773, pruned_loss=0.05334, over 1420863.60 frames.], batch size: 22, lr: 3.27e-04
2022-05-27 21:27:03,137 INFO [train.py:842] (1/4) Epoch 17, batch 1100, loss[loss=0.1985, simple_loss=0.2883, pruned_loss=0.05433, over 7292.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2778, pruned_loss=0.05396, over 1421708.11 frames.], batch size: 24, lr: 3.26e-04
2022-05-27 21:27:42,212 INFO [train.py:842] (1/4) Epoch 17, batch 1150, loss[loss=0.1941, simple_loss=0.2767, pruned_loss=0.05574, over 7295.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2788, pruned_loss=0.05423, over 1422792.93 frames.], batch size: 24, lr: 3.26e-04
2022-05-27 21:28:20,866 INFO [train.py:842] (1/4) Epoch 17, batch 1200, loss[loss=0.2118, simple_loss=0.2924, pruned_loss=0.06558, over 7320.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2788, pruned_loss=0.05481, over 1419797.99 frames.], batch size: 25, lr: 3.26e-04
2022-05-27 21:28:59,938 INFO [train.py:842] (1/4) Epoch 17, batch 1250, loss[loss=0.168, simple_loss=0.2492, pruned_loss=0.04335, over 7263.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2791, pruned_loss=0.05506, over 1416120.93 frames.], batch size: 18, lr: 3.26e-04
2022-05-27 21:29:38,949 INFO [train.py:842] (1/4) Epoch 17, batch 1300, loss[loss=0.1834, simple_loss=0.2746, pruned_loss=0.04615, over 7337.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2777, pruned_loss=0.05397, over 1414820.39 frames.], batch size: 22, lr: 3.26e-04
2022-05-27 21:30:18,030 INFO [train.py:842] (1/4) Epoch 17, batch 1350, loss[loss=0.147, simple_loss=0.2349, pruned_loss=0.02957, over 6985.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2779, pruned_loss=0.05386, over 1419878.84 frames.], batch size: 16, lr: 3.26e-04
2022-05-27 21:30:56,916 INFO [train.py:842] (1/4) Epoch 17, batch 1400, loss[loss=0.2349, simple_loss=0.3129, pruned_loss=0.07847, over 7141.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2774, pruned_loss=0.05419, over 1420409.23 frames.], batch size: 20, lr: 3.26e-04
2022-05-27 21:31:36,145 INFO [train.py:842] (1/4) Epoch 17, batch 1450, loss[loss=0.1979, simple_loss=0.2838, pruned_loss=0.05595, over 7351.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2775, pruned_loss=0.05384, over 1419967.64 frames.], batch size: 22, lr: 3.26e-04
2022-05-27 21:32:15,400 INFO [train.py:842] (1/4) Epoch 17, batch 1500, loss[loss=0.1546, simple_loss=0.2475, pruned_loss=0.0309, over 7248.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2769, pruned_loss=0.05372, over 1425686.46 frames.], batch size: 19, lr: 3.26e-04
2022-05-27 21:32:54,706 INFO [train.py:842] (1/4) Epoch 17, batch 1550, loss[loss=0.1836, simple_loss=0.2796, pruned_loss=0.0438, over 7218.00 frames.], tot_loss[loss=0.191, simple_loss=0.276, pruned_loss=0.05302, over 1422957.43 frames.], batch size: 21, lr: 3.26e-04
2022-05-27 21:33:33,694 INFO [train.py:842] (1/4) Epoch 17, batch 1600, loss[loss=0.2034, simple_loss=0.2922, pruned_loss=0.05729, over 7433.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2749, pruned_loss=0.0521, over 1427165.62 frames.], batch size: 20, lr: 3.26e-04
2022-05-27 21:34:12,955 INFO [train.py:842] (1/4) Epoch 17, batch 1650, loss[loss=0.1886, simple_loss=0.2815, pruned_loss=0.04784, over 7427.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2757, pruned_loss=0.05207, over 1429483.38 frames.], batch size: 21, lr: 3.26e-04
2022-05-27 21:34:51,641 INFO [train.py:842] (1/4) Epoch 17, batch 1700, loss[loss=0.1889, simple_loss=0.2805, pruned_loss=0.04869, over 4948.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2768, pruned_loss=0.05326, over 1423541.64 frames.], batch size: 52, lr: 3.26e-04
2022-05-27 21:35:30,467 INFO [train.py:842] (1/4) Epoch 17, batch 1750, loss[loss=0.1788, simple_loss=0.2597, pruned_loss=0.04897, over 7387.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2767, pruned_loss=0.05256, over 1415879.97 frames.], batch size: 23, lr: 3.26e-04
2022-05-27 21:36:08,991 INFO [train.py:842] (1/4) Epoch 17, batch 1800, loss[loss=0.1948, simple_loss=0.2877, pruned_loss=0.051, over 7192.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2781, pruned_loss=0.05356, over 1416943.51 frames.], batch size: 23, lr: 3.26e-04
2022-05-27 21:36:48,049 INFO [train.py:842] (1/4) Epoch 17, batch 1850, loss[loss=0.1928, simple_loss=0.2736, pruned_loss=0.05605, over 6419.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2777, pruned_loss=0.05356, over 1417911.96 frames.], batch size: 38, lr: 3.26e-04
2022-05-27 21:37:26,825 INFO [train.py:842] (1/4) Epoch 17, batch 1900, loss[loss=0.1903, simple_loss=0.2797, pruned_loss=0.05049, over 7426.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2775, pruned_loss=0.05381, over 1421516.96 frames.], batch size: 20, lr: 3.26e-04
2022-05-27 21:38:05,894 INFO [train.py:842] (1/4) Epoch 17, batch 1950, loss[loss=0.1867, simple_loss=0.2777, pruned_loss=0.04787, over 7319.00 frames.], tot_loss[loss=0.191, simple_loss=0.2758, pruned_loss=0.05306, over 1424021.28 frames.], batch size: 21, lr: 3.26e-04
2022-05-27 21:38:44,425 INFO [train.py:842] (1/4) Epoch 17, batch 2000, loss[loss=0.1597, simple_loss=0.2462, pruned_loss=0.03656, over 7262.00 frames.], tot_loss[loss=0.19, simple_loss=0.2752, pruned_loss=0.05238, over 1426004.37 frames.], batch size: 19, lr: 3.25e-04
2022-05-27 21:39:23,674 INFO [train.py:842] (1/4) Epoch 17, batch 2050, loss[loss=0.1783, simple_loss=0.2581, pruned_loss=0.0493, over 7411.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2745, pruned_loss=0.05223, over 1429057.67 frames.], batch size: 18, lr: 3.25e-04
2022-05-27 21:40:02,246 INFO [train.py:842] (1/4) Epoch 17, batch 2100, loss[loss=0.1922, simple_loss=0.2875, pruned_loss=0.0484, over 7416.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2746, pruned_loss=0.05199, over 1429350.05 frames.], batch size: 21, lr: 3.25e-04
2022-05-27 21:40:41,274 INFO [train.py:842] (1/4) Epoch 17, batch 2150, loss[loss=0.1808, simple_loss=0.2587, pruned_loss=0.05143, over 7362.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2744, pruned_loss=0.05216, over 1424975.17 frames.], batch size: 19, lr: 3.25e-04
2022-05-27 21:41:20,100 INFO [train.py:842] (1/4) Epoch 17, batch 2200, loss[loss=0.212, simple_loss=0.3109, pruned_loss=0.05657, over 7329.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2753, pruned_loss=0.05217, over 1422316.85 frames.], batch size: 22, lr: 3.25e-04
2022-05-27 21:41:59,517 INFO [train.py:842] (1/4) Epoch 17, batch 2250, loss[loss=0.1946, simple_loss=0.2818, pruned_loss=0.05365, over 7412.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2766, pruned_loss=0.05306, over 1424869.55 frames.], batch size: 21, lr: 3.25e-04
2022-05-27 21:42:38,170 INFO [train.py:842] (1/4) Epoch 17, batch 2300, loss[loss=0.1917, simple_loss=0.2774, pruned_loss=0.05297, over 7290.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2762, pruned_loss=0.05275, over 1423492.23 frames.], batch size: 24, lr: 3.25e-04
2022-05-27 21:43:17,581 INFO [train.py:842] (1/4) Epoch 17, batch 2350, loss[loss=0.1697, simple_loss=0.2648, pruned_loss=0.0373, over 7386.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2741, pruned_loss=0.05159, over 1426453.50 frames.], batch size: 23, lr: 3.25e-04
2022-05-27 21:43:56,276 INFO [train.py:842] (1/4) Epoch 17, batch 2400, loss[loss=0.1634, simple_loss=0.2457, pruned_loss=0.04057, over 6996.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2736, pruned_loss=0.05134, over 1424899.53 frames.], batch size: 16, lr: 3.25e-04
2022-05-27 21:44:35,704 INFO [train.py:842] (1/4) Epoch 17, batch 2450, loss[loss=0.1876, simple_loss=0.2855, pruned_loss=0.04479, over 7349.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2727, pruned_loss=0.05103, over 1424025.83 frames.], batch size: 22, lr: 3.25e-04
2022-05-27 21:45:14,360 INFO [train.py:842] (1/4) Epoch 17, batch 2500, loss[loss=0.1876, simple_loss=0.2766, pruned_loss=0.0493, over 7225.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2716, pruned_loss=0.05037, over 1423586.38 frames.], batch size: 21, lr: 3.25e-04
2022-05-27 21:45:53,535 INFO [train.py:842] (1/4) Epoch 17, batch 2550, loss[loss=0.1927, simple_loss=0.2826, pruned_loss=0.05145, over 7214.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2714, pruned_loss=0.05034, over 1419162.24 frames.], batch size: 21, lr: 3.25e-04
2022-05-27 21:46:32,096 INFO [train.py:842] (1/4) Epoch 17, batch 2600, loss[loss=0.1732, simple_loss=0.2634, pruned_loss=0.04151, over 7060.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2724, pruned_loss=0.05067, over 1421202.08 frames.], batch size: 28, lr: 3.25e-04
2022-05-27 21:47:11,551 INFO [train.py:842] (1/4) Epoch 17, batch 2650, loss[loss=0.2078, simple_loss=0.2867, pruned_loss=0.06443, over 7345.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2744, pruned_loss=0.05168, over 1419786.39 frames.], batch size: 19, lr: 3.25e-04
2022-05-27 21:47:50,581 INFO [train.py:842] (1/4) Epoch 17, batch 2700, loss[loss=0.1715, simple_loss=0.269, pruned_loss=0.03706, over 7336.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2746, pruned_loss=0.05237, over 1423149.59 frames.], batch size: 22, lr: 3.25e-04
2022-05-27 21:48:29,804 INFO [train.py:842] (1/4) Epoch 17, batch 2750, loss[loss=0.1803, simple_loss=0.2685, pruned_loss=0.04609, over 7172.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2747, pruned_loss=0.05277, over 1422564.24 frames.], batch size: 19, lr: 3.25e-04
2022-05-27 21:49:09,030 INFO [train.py:842] (1/4) Epoch 17, batch 2800, loss[loss=0.2346, simple_loss=0.3054, pruned_loss=0.08189, over 4865.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2749, pruned_loss=0.05308, over 1421947.77 frames.], batch size: 52, lr: 3.25e-04
2022-05-27 21:49:47,938 INFO [train.py:842] (1/4) Epoch 17, batch 2850, loss[loss=0.2399, simple_loss=0.3195, pruned_loss=0.0802, over 7316.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2751, pruned_loss=0.05306, over 1422378.97 frames.], batch size: 21, lr: 3.25e-04
2022-05-27 21:50:27,036 INFO [train.py:842] (1/4) Epoch 17, batch 2900, loss[loss=0.204, simple_loss=0.3015, pruned_loss=0.05324, over 7239.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2756, pruned_loss=0.05315, over 1418174.41 frames.], batch size: 20, lr: 3.24e-04
2022-05-27 21:51:06,317 INFO [train.py:842] (1/4) Epoch 17, batch 2950, loss[loss=0.1609, simple_loss=0.2356, pruned_loss=0.04303, over 7272.00 frames.], tot_loss[loss=0.191, simple_loss=0.2757, pruned_loss=0.05313, over 1418833.42 frames.], batch size: 18, lr: 3.24e-04
2022-05-27 21:51:45,533 INFO [train.py:842] (1/4) Epoch 17, batch 3000, loss[loss=0.161, simple_loss=0.2655, pruned_loss=0.02822, over 7149.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2763, pruned_loss=0.05301, over 1424222.09 frames.], batch size: 20, lr: 3.24e-04
2022-05-27 21:51:45,534 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 21:51:55,147 INFO [train.py:871] (1/4) Epoch 17, validation: loss=0.1666, simple_loss=0.2663, pruned_loss=0.03343, over 868885.00 frames.
2022-05-27 21:52:34,196 INFO [train.py:842] (1/4) Epoch 17, batch 3050, loss[loss=0.1945, simple_loss=0.2853, pruned_loss=0.05185, over 6396.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2758, pruned_loss=0.05254, over 1423495.06 frames.], batch size: 37, lr: 3.24e-04
2022-05-27 21:53:12,906 INFO [train.py:842] (1/4) Epoch 17, batch 3100, loss[loss=0.296, simple_loss=0.3652, pruned_loss=0.1134, over 7333.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2765, pruned_loss=0.05317, over 1419809.28 frames.], batch size: 25, lr: 3.24e-04
2022-05-27 21:53:52,111 INFO [train.py:842] (1/4) Epoch 17, batch 3150, loss[loss=0.1658, simple_loss=0.2517, pruned_loss=0.03996, over 7322.00 frames.], tot_loss[loss=0.1911, simple_loss=0.276, pruned_loss=0.05307, over 1418504.36 frames.], batch size: 20, lr: 3.24e-04
2022-05-27 21:54:30,942 INFO [train.py:842] (1/4) Epoch 17, batch 3200, loss[loss=0.1977, simple_loss=0.276, pruned_loss=0.05968, over 7354.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2764, pruned_loss=0.05358, over 1417769.89 frames.], batch size: 19, lr: 3.24e-04
2022-05-27 21:55:10,323 INFO [train.py:842] (1/4) Epoch 17, batch 3250, loss[loss=0.1861, simple_loss=0.2718, pruned_loss=0.0502, over 7072.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2767, pruned_loss=0.05351, over 1423125.56 frames.], batch size: 18, lr: 3.24e-04
2022-05-27 21:55:49,295 INFO [train.py:842] (1/4) Epoch 17, batch 3300, loss[loss=0.1467, simple_loss=0.2309, pruned_loss=0.0313, over 7159.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2775, pruned_loss=0.05368, over 1423700.59 frames.], batch size: 19, lr: 3.24e-04
2022-05-27 21:56:28,699 INFO [train.py:842] (1/4) Epoch 17, batch 3350, loss[loss=0.2077, simple_loss=0.2917, pruned_loss=0.0618, over 7327.00 frames.], tot_loss[loss=0.1908, simple_loss=0.276, pruned_loss=0.05279, over 1425138.35 frames.], batch size: 22, lr: 3.24e-04
2022-05-27 21:57:07,332 INFO [train.py:842] (1/4) Epoch 17, batch 3400, loss[loss=0.191, simple_loss=0.2935, pruned_loss=0.04424, over 7161.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2762, pruned_loss=0.05294, over 1422691.85 frames.], batch size: 20, lr: 3.24e-04
2022-05-27 21:57:46,426 INFO [train.py:842] (1/4) Epoch 17, batch 3450, loss[loss=0.1738, simple_loss=0.2566, pruned_loss=0.04547, over 7321.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2751, pruned_loss=0.05287, over 1425203.29 frames.], batch size: 20, lr: 3.24e-04
2022-05-27 21:58:25,549 INFO [train.py:842] (1/4) Epoch 17, batch 3500, loss[loss=0.2142, simple_loss=0.2991, pruned_loss=0.06462, over 7204.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2749, pruned_loss=0.0528, over 1424581.42 frames.], batch size: 22, lr: 3.24e-04
2022-05-27 21:59:04,670 INFO [train.py:842] (1/4) Epoch 17, batch 3550, loss[loss=0.1793, simple_loss=0.2741, pruned_loss=0.04225, over 7112.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2766, pruned_loss=0.05343, over 1427280.60 frames.], batch size: 21, lr: 3.24e-04
2022-05-27 21:59:43,841 INFO [train.py:842] (1/4) Epoch 17, batch 3600, loss[loss=0.1438, simple_loss=0.2341, pruned_loss=0.02673, over 7270.00 frames.], tot_loss[loss=0.1921, simple_loss=0.277, pruned_loss=0.05359, over 1428174.08 frames.], batch size: 18, lr: 3.24e-04
2022-05-27 22:00:23,007 INFO [train.py:842] (1/4) Epoch 17, batch 3650, loss[loss=0.1815, simple_loss=0.2708, pruned_loss=0.04617, over 7319.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2764, pruned_loss=0.0531, over 1432493.41 frames.], batch size: 21, lr: 3.24e-04
2022-05-27 22:01:02,001 INFO [train.py:842] (1/4) Epoch 17, batch 3700, loss[loss=0.1775, simple_loss=0.2712, pruned_loss=0.04189, over 7139.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2759, pruned_loss=0.05342, over 1431591.55 frames.], batch size: 20, lr: 3.24e-04
2022-05-27 22:01:41,185 INFO [train.py:842] (1/4) Epoch 17, batch 3750, loss[loss=0.2193, simple_loss=0.307, pruned_loss=0.06584, over 6272.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2756, pruned_loss=0.05289, over 1428595.83 frames.], batch size: 37, lr: 3.24e-04
2022-05-27 22:02:19,682 INFO [train.py:842] (1/4) Epoch 17, batch 3800, loss[loss=0.1967, simple_loss=0.2887, pruned_loss=0.05235, over 6464.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2763, pruned_loss=0.05311, over 1427740.75 frames.], batch size: 37, lr: 3.24e-04
2022-05-27 22:02:58,460 INFO [train.py:842] (1/4) Epoch 17, batch 3850, loss[loss=0.1706, simple_loss=0.2555, pruned_loss=0.04284, over 7017.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2765, pruned_loss=0.05287, over 1427142.32 frames.], batch size: 16, lr: 3.23e-04
2022-05-27 22:03:37,694 INFO [train.py:842] (1/4) Epoch 17, batch 3900, loss[loss=0.2304, simple_loss=0.3175, pruned_loss=0.07161, over 7215.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2762, pruned_loss=0.05341, over 1429889.72 frames.], batch size: 22, lr: 3.23e-04
2022-05-27 22:04:16,897 INFO [train.py:842] (1/4) Epoch 17, batch 3950, loss[loss=0.1839, simple_loss=0.2778, pruned_loss=0.04499, over 7208.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2768, pruned_loss=0.0534, over 1429358.81 frames.], batch size: 23, lr: 3.23e-04
2022-05-27 22:04:55,789 INFO [train.py:842] (1/4) Epoch 17, batch 4000, loss[loss=0.165, simple_loss=0.2477, pruned_loss=0.04114, over 7283.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2766, pruned_loss=0.0532, over 1429671.53 frames.], batch size: 18, lr: 3.23e-04
2022-05-27 22:05:35,106 INFO [train.py:842] (1/4) Epoch 17, batch 4050, loss[loss=0.1795, simple_loss=0.2699, pruned_loss=0.04453, over 6788.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2754, pruned_loss=0.05316, over 1425572.05 frames.], batch size: 31, lr: 3.23e-04
2022-05-27 22:06:13,936 INFO [train.py:842] (1/4) Epoch 17, batch 4100, loss[loss=0.2033, simple_loss=0.2912, pruned_loss=0.05769, over 6483.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2766, pruned_loss=0.05362, over 1424788.41 frames.], batch size: 38, lr: 3.23e-04
2022-05-27 22:06:52,833 INFO [train.py:842] (1/4) Epoch 17, batch 4150, loss[loss=0.1969, simple_loss=0.2858, pruned_loss=0.05397, over 7323.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2754, pruned_loss=0.05292, over 1423058.26 frames.], batch size: 22, lr: 3.23e-04
2022-05-27 22:07:31,532 INFO [train.py:842] (1/4) Epoch 17, batch 4200, loss[loss=0.1526, simple_loss=0.2446, pruned_loss=0.03026, over 7157.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2753, pruned_loss=0.05267, over 1422761.11 frames.], batch size: 19, lr: 3.23e-04
2022-05-27 22:08:10,857 INFO [train.py:842] (1/4) Epoch 17, batch 4250, loss[loss=0.1831, simple_loss=0.2509, pruned_loss=0.05769, over 7146.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2756, pruned_loss=0.05272, over 1425091.87 frames.], batch size: 17, lr: 3.23e-04
2022-05-27 22:08:49,652 INFO [train.py:842] (1/4) Epoch 17, batch 4300, loss[loss=0.2077, simple_loss=0.2972, pruned_loss=0.0591, over 7314.00 frames.], tot_loss[loss=0.1927, simple_loss=0.277, pruned_loss=0.05422, over 1421928.11 frames.], batch size: 21, lr: 3.23e-04
2022-05-27 22:09:29,065 INFO [train.py:842] (1/4) Epoch 17, batch 4350, loss[loss=0.2219, simple_loss=0.3205, pruned_loss=0.06162, over 6716.00 frames.], tot_loss[loss=0.193, simple_loss=0.2772, pruned_loss=0.05443, over 1420357.36 frames.], batch size: 31, lr: 3.23e-04
2022-05-27 22:10:07,987 INFO [train.py:842] (1/4) Epoch 17, batch 4400, loss[loss=0.1694, simple_loss=0.2606, pruned_loss=0.03917, over 7258.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2763, pruned_loss=0.05416, over 1422244.28 frames.], batch size: 19, lr: 3.23e-04
2022-05-27 22:10:47,305 INFO [train.py:842] (1/4) Epoch 17, batch 4450, loss[loss=0.1828, simple_loss=0.2649, pruned_loss=0.05031, over 7067.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2752, pruned_loss=0.05372, over 1426261.62 frames.], batch size: 18, lr: 3.23e-04
2022-05-27 22:11:26,052 INFO [train.py:842] (1/4) Epoch 17, batch 4500, loss[loss=0.2022, simple_loss=0.284, pruned_loss=0.06022, over 6639.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2756, pruned_loss=0.0535, over 1423604.31 frames.], batch size: 38, lr: 3.23e-04
2022-05-27 22:12:04,917 INFO [train.py:842] (1/4) Epoch 17, batch 4550, loss[loss=0.1957, simple_loss=0.2968, pruned_loss=0.04727, over 7340.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2768, pruned_loss=0.0538, over 1422793.69 frames.], batch size: 22, lr: 3.23e-04
2022-05-27 22:12:43,477 INFO [train.py:842] (1/4) Epoch 17, batch 4600, loss[loss=0.2077, simple_loss=0.2993, pruned_loss=0.0581, over 7200.00 frames.], tot_loss[loss=0.1926, simple_loss=0.277, pruned_loss=0.05407, over 1424680.47 frames.], batch size: 22, lr: 3.23e-04
2022-05-27 22:13:22,510 INFO [train.py:842] (1/4) Epoch 17, batch 4650, loss[loss=0.2348, simple_loss=0.3301, pruned_loss=0.06971, over 7334.00 frames.], tot_loss[loss=0.1924, simple_loss=0.277, pruned_loss=0.05385, over 1427257.46 frames.], batch size: 22, lr: 3.23e-04
2022-05-27 22:14:01,631 INFO [train.py:842] (1/4) Epoch 17, batch 4700, loss[loss=0.2305, simple_loss=0.3047, pruned_loss=0.07813, over 7237.00 frames.], tot_loss[loss=0.1926, simple_loss=0.277, pruned_loss=0.05412, over 1422640.24 frames.], batch size: 21, lr: 3.23e-04
2022-05-27 22:14:40,366 INFO [train.py:842] (1/4) Epoch 17, batch 4750, loss[loss=0.1859, simple_loss=0.2737, pruned_loss=0.04899, over 7071.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2766, pruned_loss=0.05359, over 1423326.74 frames.], batch size: 18, lr: 3.23e-04
2022-05-27 22:15:19,290 INFO [train.py:842] (1/4) Epoch 17, batch 4800, loss[loss=0.165, simple_loss=0.2422, pruned_loss=0.04386, over 7273.00 frames.], tot_loss[loss=0.1901, simple_loss=0.275, pruned_loss=0.05264, over 1423232.84 frames.], batch size: 17, lr: 3.22e-04
2022-05-27 22:15:58,780 INFO [train.py:842] (1/4) Epoch 17, batch 4850, loss[loss=0.2036, simple_loss=0.2776, pruned_loss=0.06478, over 7070.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2748, pruned_loss=0.05245, over 1422407.62 frames.], batch size: 18, lr: 3.22e-04
2022-05-27 22:16:37,940 INFO [train.py:842] (1/4) Epoch 17, batch 4900, loss[loss=0.1672, simple_loss=0.263, pruned_loss=0.03569, over 7176.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2759, pruned_loss=0.05338, over 1423226.70 frames.], batch size: 26, lr: 3.22e-04
2022-05-27 22:17:20,307 INFO [train.py:842] (1/4) Epoch 17, batch 4950, loss[loss=0.1619, simple_loss=0.2302, pruned_loss=0.04682, over 6821.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2734, pruned_loss=0.05246, over 1426048.33 frames.], batch size: 15, lr: 3.22e-04
2022-05-27 22:17:59,061 INFO [train.py:842] (1/4) Epoch 17, batch 5000, loss[loss=0.1774, simple_loss=0.247, pruned_loss=0.05389, over 7009.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2741, pruned_loss=0.0525, over 1422833.78 frames.], batch size: 16, lr: 3.22e-04
2022-05-27 22:18:38,076 INFO [train.py:842] (1/4) Epoch 17, batch 5050, loss[loss=0.2004, simple_loss=0.2857, pruned_loss=0.05754, over 7292.00 frames.], tot_loss[loss=0.19, simple_loss=0.2748, pruned_loss=0.05261, over 1423490.19 frames.], batch size: 18, lr: 3.22e-04
2022-05-27 22:19:17,093 INFO [train.py:842] (1/4) Epoch 17, batch 5100, loss[loss=0.161, simple_loss=0.2441, pruned_loss=0.03892, over 7018.00 frames.], tot_loss[loss=0.1897, simple_loss=0.275, pruned_loss=0.05221, over 1421539.68 frames.], batch size: 16, lr: 3.22e-04
2022-05-27 22:19:56,263 INFO [train.py:842] (1/4) Epoch 17, batch 5150, loss[loss=0.1822, simple_loss=0.2814, pruned_loss=0.04153, over 7233.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2752, pruned_loss=0.05264, over 1420143.06 frames.], batch size: 20, lr: 3.22e-04
2022-05-27 22:20:35,400 INFO [train.py:842] (1/4) Epoch 17, batch 5200, loss[loss=0.2081, simple_loss=0.2865, pruned_loss=0.06486, over 7133.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2735, pruned_loss=0.05182, over 1424836.07 frames.], batch size: 20, lr: 3.22e-04
2022-05-27 22:21:14,378 INFO [train.py:842] (1/4) Epoch 17, batch 5250, loss[loss=0.2167, simple_loss=0.3077, pruned_loss=0.06278, over 7114.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2742, pruned_loss=0.05206, over 1423776.67 frames.], batch size: 21, lr: 3.22e-04
2022-05-27 22:21:53,446 INFO [train.py:842] (1/4) Epoch 17, batch 5300, loss[loss=0.1637, simple_loss=0.2485, pruned_loss=0.03942, over 6873.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2744, pruned_loss=0.05228, over 1422460.85 frames.], batch size: 15, lr: 3.22e-04
2022-05-27 22:22:32,641 INFO [train.py:842] (1/4) Epoch 17, batch 5350, loss[loss=0.1893, simple_loss=0.2691, pruned_loss=0.05478, over 6822.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2762, pruned_loss=0.05336, over 1423286.97 frames.], batch size: 15, lr: 3.22e-04
2022-05-27 22:23:11,624 INFO [train.py:842] (1/4) Epoch 17, batch 5400, loss[loss=0.1697, simple_loss=0.2663, pruned_loss=0.03657, over 7247.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2764, pruned_loss=0.05337, over 1424573.06 frames.], batch size: 20, lr: 3.22e-04
2022-05-27 22:23:51,032 INFO [train.py:842] (1/4) Epoch 17, batch 5450, loss[loss=0.1756, simple_loss=0.259, pruned_loss=0.04611, over 7155.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2774, pruned_loss=0.05362, over 1428063.44 frames.], batch size: 19, lr: 3.22e-04
2022-05-27 22:24:29,917 INFO [train.py:842] (1/4) Epoch 17, batch 5500, loss[loss=0.1745, simple_loss=0.2637, pruned_loss=0.04262, over 7302.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2769, pruned_loss=0.0533, over 1428952.41 frames.], batch size: 24, lr: 3.22e-04
2022-05-27 22:25:09,377 INFO [train.py:842] (1/4) Epoch 17, batch 5550, loss[loss=0.1795, simple_loss=0.2649, pruned_loss=0.04702, over 7237.00 frames.], tot_loss[loss=0.1919, simple_loss=0.277, pruned_loss=0.05343, over 1430140.49 frames.], batch size: 20, lr: 3.22e-04
2022-05-27 22:25:48,404 INFO [train.py:842] (1/4) Epoch 17, batch 5600, loss[loss=0.2037, simple_loss=0.3021, pruned_loss=0.05265, over 7332.00 frames.], tot_loss[loss=0.19, simple_loss=0.275, pruned_loss=0.05255, over 1432535.73 frames.], batch size: 20, lr: 3.22e-04
2022-05-27 22:26:27,333 INFO [train.py:842] (1/4) Epoch 17, batch 5650, loss[loss=0.2185, simple_loss=0.3033, pruned_loss=0.06683, over 7304.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2773, pruned_loss=0.05377, over 1431194.41 frames.], batch size: 24, lr: 3.22e-04
2022-05-27 22:27:05,745 INFO [train.py:842] (1/4) Epoch 17, batch 5700, loss[loss=0.1449, simple_loss=0.228, pruned_loss=0.03095, over 7390.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2783, pruned_loss=0.05373, over 1428987.73 frames.], batch size: 18, lr: 3.22e-04
2022-05-27 22:27:45,014 INFO [train.py:842] (1/4) Epoch 17, batch 5750, loss[loss=0.2052, simple_loss=0.2866, pruned_loss=0.06193, over 6156.00 frames.], tot_loss[loss=0.1931, simple_loss=0.278, pruned_loss=0.05408, over 1422197.73 frames.], batch size: 37, lr: 3.21e-04
2022-05-27 22:28:23,771 INFO [train.py:842] (1/4) Epoch 17, batch 5800, loss[loss=0.1855, simple_loss=0.2753, pruned_loss=0.04789, over 7328.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2771, pruned_loss=0.05371, over 1421929.04 frames.], batch size: 20, lr: 3.21e-04
2022-05-27 22:29:02,962 INFO [train.py:842] (1/4) Epoch 17, batch 5850, loss[loss=0.1967, simple_loss=0.2763, pruned_loss=0.05852, over 7294.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2772, pruned_loss=0.05352, over 1424726.97 frames.], batch size: 18, lr: 3.21e-04
2022-05-27 22:29:41,709 INFO [train.py:842] (1/4) Epoch 17, batch 5900, loss[loss=0.1985, simple_loss=0.282, pruned_loss=0.05755, over 7395.00 frames.], tot_loss[loss=0.1913, simple_loss=0.276, pruned_loss=0.05324, over 1424522.32 frames.], batch size: 23, lr: 3.21e-04
2022-05-27 22:30:20,770 INFO [train.py:842] (1/4) Epoch 17, batch 5950, loss[loss=0.2294, simple_loss=0.3123, pruned_loss=0.07322, over 7153.00 frames.], tot_loss[loss=0.1927, simple_loss=0.277, pruned_loss=0.05423, over 1418820.17 frames.], batch size: 26, lr: 3.21e-04
2022-05-27 22:30:59,314 INFO [train.py:842] (1/4) Epoch 17, batch 6000, loss[loss=0.2126, simple_loss=0.2956, pruned_loss=0.06479, over 7332.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2778, pruned_loss=0.05429, over 1418411.45 frames.], batch size: 20, lr: 3.21e-04
2022-05-27 22:30:59,315 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 22:31:09,111 INFO [train.py:871] (1/4) Epoch 17, validation: loss=0.1659, simple_loss=0.2656, pruned_loss=0.03306, over 868885.00 frames.
2022-05-27 22:31:48,632 INFO [train.py:842] (1/4) Epoch 17, batch 6050, loss[loss=0.1747, simple_loss=0.2729, pruned_loss=0.03823, over 7320.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2781, pruned_loss=0.05432, over 1422625.27 frames.], batch size: 21, lr: 3.21e-04
2022-05-27 22:32:27,137 INFO [train.py:842] (1/4) Epoch 17, batch 6100, loss[loss=0.2509, simple_loss=0.3226, pruned_loss=0.08958, over 7192.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2792, pruned_loss=0.05465, over 1424401.26 frames.], batch size: 23, lr: 3.21e-04
2022-05-27 22:33:05,953 INFO [train.py:842] (1/4) Epoch 17, batch 6150, loss[loss=0.1762, simple_loss=0.2549, pruned_loss=0.04879, over 7174.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2785, pruned_loss=0.05438, over 1420572.96 frames.], batch size: 16, lr: 3.21e-04
2022-05-27 22:33:44,755 INFO [train.py:842] (1/4) Epoch 17, batch 6200, loss[loss=0.1901, simple_loss=0.2838, pruned_loss=0.04821, over 7413.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2772, pruned_loss=0.05356, over 1422313.97 frames.], batch size: 21, lr: 3.21e-04
2022-05-27 22:34:23,868 INFO [train.py:842] (1/4) Epoch 17, batch 6250, loss[loss=0.1925, simple_loss=0.278, pruned_loss=0.05348, over 6810.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2779, pruned_loss=0.05422, over 1420935.24 frames.], batch size: 31, lr: 3.21e-04
2022-05-27 22:35:02,516 INFO [train.py:842] (1/4) Epoch 17, batch 6300, loss[loss=0.2033, simple_loss=0.2912, pruned_loss=0.05767, over 7412.00 frames.], tot_loss[loss=0.192, simple_loss=0.2773, pruned_loss=0.05339, over 1419728.42 frames.], batch size: 21, lr: 3.21e-04
2022-05-27 22:35:41,902 INFO [train.py:842] (1/4) Epoch 17, batch 6350, loss[loss=0.1665, simple_loss=0.2368, pruned_loss=0.04807, over 7288.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2769, pruned_loss=0.05342, over 1423726.31 frames.], batch size: 17, lr: 3.21e-04
2022-05-27 22:36:20,836 INFO [train.py:842] (1/4) Epoch 17, batch 6400, loss[loss=0.1606, simple_loss=0.2532, pruned_loss=0.034, over 7238.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2767, pruned_loss=0.05336, over 1429301.03 frames.], batch size: 20, lr: 3.21e-04
2022-05-27 22:36:59,899 INFO [train.py:842] (1/4) Epoch 17, batch 6450, loss[loss=0.1715, simple_loss=0.2519, pruned_loss=0.04557, over 7362.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2774, pruned_loss=0.05386, over 1427896.14 frames.], batch size: 19, lr: 3.21e-04
2022-05-27 22:37:38,898 INFO [train.py:842] (1/4) Epoch 17, batch 6500, loss[loss=0.1524, simple_loss=0.2426, pruned_loss=0.03111, over 7426.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2758, pruned_loss=0.05235, over 1428987.94 frames.], batch size: 18, lr: 3.21e-04
2022-05-27 22:38:18,080 INFO [train.py:842] (1/4) Epoch 17, batch 6550, loss[loss=0.187, simple_loss=0.2742, pruned_loss=0.04991, over 7195.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2761, pruned_loss=0.05258, over 1427140.06 frames.], batch size: 23, lr: 3.21e-04
2022-05-27 22:38:57,331 INFO [train.py:842] (1/4) Epoch 17, batch 6600, loss[loss=0.2563, simple_loss=0.3321, pruned_loss=0.09027, over 4833.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2773, pruned_loss=0.05257, over 1426458.47 frames.], batch size: 52, lr: 3.21e-04
2022-05-27 22:39:36,189 INFO [train.py:842] (1/4) Epoch 17, batch 6650, loss[loss=0.1245, simple_loss=0.2127, pruned_loss=0.01813, over 6983.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2786, pruned_loss=0.05405, over 1424113.49 frames.], batch size: 16, lr: 3.21e-04
2022-05-27 22:40:14,935 INFO [train.py:842] (1/4) Epoch 17, batch 6700, loss[loss=0.2087, simple_loss=0.2871, pruned_loss=0.06512, over 7196.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2786, pruned_loss=0.05417, over 1419840.94 frames.], batch size: 22, lr: 3.20e-04
2022-05-27 22:40:54,202 INFO [train.py:842] (1/4) Epoch 17, batch 6750, loss[loss=0.2008, simple_loss=0.2919, pruned_loss=0.05479, over 7196.00 frames.], tot_loss[loss=0.193, simple_loss=0.2778, pruned_loss=0.05411, over 1415239.45 frames.], batch size: 22, lr: 3.20e-04
2022-05-27 22:41:33,198 INFO [train.py:842] (1/4) Epoch 17, batch 6800, loss[loss=0.2208, simple_loss=0.2903, pruned_loss=0.07564, over 7417.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2764, pruned_loss=0.05301, over 1418750.68 frames.], batch size: 18, lr: 3.20e-04
2022-05-27 22:42:12,492 INFO [train.py:842] (1/4) Epoch 17, batch 6850, loss[loss=0.184, simple_loss=0.2604, pruned_loss=0.05387, over 7066.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2769, pruned_loss=0.05367, over 1420638.98 frames.], batch size: 18, lr: 3.20e-04
2022-05-27 22:42:51,356 INFO [train.py:842] (1/4) Epoch 17, batch 6900, loss[loss=0.1799, simple_loss=0.2695, pruned_loss=0.04512, over 7222.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2766, pruned_loss=0.0535, over 1422213.16 frames.], batch size: 21, lr: 3.20e-04
2022-05-27 22:43:30,337 INFO [train.py:842] (1/4) Epoch 17, batch 6950, loss[loss=0.2218, simple_loss=0.3114, pruned_loss=0.06604, over 7422.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2771, pruned_loss=0.05407, over 1423025.57 frames.], batch size: 21, lr: 3.20e-04
2022-05-27 22:44:09,726 INFO [train.py:842] (1/4) Epoch 17, batch 7000, loss[loss=0.1786, simple_loss=0.273, pruned_loss=0.04205, over 7370.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2772, pruned_loss=0.0545, over 1424484.59 frames.], batch size: 23, lr: 3.20e-04
2022-05-27 22:44:49,107 INFO [train.py:842] (1/4) Epoch 17, batch 7050, loss[loss=0.1958, simple_loss=0.2915, pruned_loss=0.05001, over 7216.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2767, pruned_loss=0.0542, over 1421981.12 frames.], batch size: 23, lr: 3.20e-04
2022-05-27 22:45:28,482 INFO [train.py:842] (1/4) Epoch 17, batch 7100, loss[loss=0.1641, simple_loss=0.2634, pruned_loss=0.03233, over 7325.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2768, pruned_loss=0.05406, over 1424342.78 frames.], batch size: 21, lr: 3.20e-04
2022-05-27 22:46:07,819 INFO [train.py:842] (1/4) Epoch 17, batch 7150, loss[loss=0.3125, simple_loss=0.3702, pruned_loss=0.1274, over 7277.00 frames.], tot_loss[loss=0.1921, simple_loss=0.276, pruned_loss=0.05407, over 1426843.10 frames.], batch size: 24, lr: 3.20e-04
2022-05-27 22:46:46,844 INFO [train.py:842] (1/4) Epoch 17, batch 7200, loss[loss=0.1957, simple_loss=0.2831, pruned_loss=0.05413, over 7197.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2752, pruned_loss=0.05382, over 1426283.50 frames.], batch size: 23, lr: 3.20e-04
2022-05-27 22:47:26,416 INFO [train.py:842] (1/4) Epoch 17, batch 7250, loss[loss=0.2175, simple_loss=0.3149, pruned_loss=0.06002, over 7317.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2765, pruned_loss=0.05424, over 1427974.14 frames.], batch size: 21, lr: 3.20e-04
2022-05-27 22:48:05,625 INFO [train.py:842] (1/4) Epoch 17, batch 7300, loss[loss=0.1306, simple_loss=0.217, pruned_loss=0.0221, over 7272.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2751, pruned_loss=0.05285, over 1430167.37 frames.], batch size: 17, lr: 3.20e-04
2022-05-27 22:48:44,789 INFO [train.py:842] (1/4) Epoch 17, batch 7350, loss[loss=0.1723, simple_loss=0.2596, pruned_loss=0.04248, over 7322.00 frames.], tot_loss[loss=0.1915, simple_loss=0.276, pruned_loss=0.05353, over 1430889.47 frames.], batch size: 21, lr: 3.20e-04
2022-05-27 22:49:23,633 INFO [train.py:842] (1/4) Epoch 17, batch 7400, loss[loss=0.217, simple_loss=0.3068, pruned_loss=0.06363, over 5166.00 frames.], tot_loss[loss=0.191, simple_loss=0.2756, pruned_loss=0.05314, over 1422826.40 frames.], batch size: 54, lr: 3.20e-04
2022-05-27 22:50:02,536 INFO [train.py:842] (1/4) Epoch 17, batch 7450, loss[loss=0.1519, simple_loss=0.232, pruned_loss=0.03589, over 7287.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2774, pruned_loss=0.05356, over 1426948.32 frames.], batch size: 17, lr: 3.20e-04
2022-05-27 22:50:41,675 INFO [train.py:842] (1/4) Epoch 17, batch 7500, loss[loss=0.1949, simple_loss=0.2879, pruned_loss=0.05092, over 7068.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2762, pruned_loss=0.05353, over 1428448.52 frames.], batch size: 18, lr: 3.20e-04
2022-05-27 22:51:20,639 INFO [train.py:842] (1/4) Epoch 17, batch 7550, loss[loss=0.2207, simple_loss=0.3123, pruned_loss=0.06452, over 7201.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2763, pruned_loss=0.05345, over 1427321.65 frames.], batch size: 23, lr: 3.20e-04
2022-05-27 22:51:59,833 INFO [train.py:842] (1/4) Epoch 17, batch 7600, loss[loss=0.1954, simple_loss=0.29, pruned_loss=0.0504, over 7285.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2758, pruned_loss=0.05317, over 1429844.97 frames.], batch size: 18, lr: 3.20e-04
2022-05-27 22:52:38,891 INFO [train.py:842] (1/4) Epoch 17, batch 7650, loss[loss=0.1419, simple_loss=0.2247, pruned_loss=0.02958, over 7167.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2754, pruned_loss=0.05322, over 1428400.71 frames.], batch size: 16, lr: 3.19e-04
2022-05-27 22:53:17,429 INFO [train.py:842] (1/4) Epoch 17, batch 7700, loss[loss=0.1827, simple_loss=0.2772, pruned_loss=0.04413, over 7330.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2763, pruned_loss=0.05303, over 1428158.09 frames.], batch size: 22, lr: 3.19e-04
2022-05-27 22:53:56,291 INFO [train.py:842] (1/4) Epoch 17, batch 7750, loss[loss=0.2008, simple_loss=0.2885, pruned_loss=0.05661, over 7203.00 frames.], tot_loss[loss=0.1899, simple_loss=0.275, pruned_loss=0.05242, over 1428373.42 frames.], batch size: 22, lr: 3.19e-04
2022-05-27 22:54:34,923 INFO [train.py:842] (1/4) Epoch 17, batch 7800, loss[loss=0.1766, simple_loss=0.2555, pruned_loss=0.04884, over 7001.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2773, pruned_loss=0.05354, over 1424577.20 frames.], batch size: 16, lr: 3.19e-04
2022-05-27 22:55:13,932 INFO [train.py:842] (1/4) Epoch 17, batch 7850, loss[loss=0.1537, simple_loss=0.2321, pruned_loss=0.03764, over 7134.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2782, pruned_loss=0.05404, over 1424955.89 frames.], batch size: 17, lr: 3.19e-04
2022-05-27 22:55:52,894 INFO [train.py:842] (1/4) Epoch 17, batch 7900, loss[loss=0.1791, simple_loss=0.2638, pruned_loss=0.04721, over 7270.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2773, pruned_loss=0.05312, over 1426024.29 frames.], batch size: 19, lr: 3.19e-04
2022-05-27 22:56:32,271 INFO [train.py:842] (1/4) Epoch 17, batch 7950, loss[loss=0.2272, simple_loss=0.3042, pruned_loss=0.07509, over 7053.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2769, pruned_loss=0.05314, over 1424824.11 frames.], batch size: 18, lr: 3.19e-04
2022-05-27 22:57:10,823 INFO [train.py:842] (1/4) Epoch 17, batch 8000, loss[loss=0.2005, simple_loss=0.2934, pruned_loss=0.05382, over 7334.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2765, pruned_loss=0.05286, over 1419611.16 frames.], batch size: 20, lr: 3.19e-04
2022-05-27 22:57:50,142 INFO [train.py:842] (1/4) Epoch 17, batch 8050, loss[loss=0.1776, simple_loss=0.2649, pruned_loss=0.0451, over 7153.00 frames.], tot_loss[loss=0.191, simple_loss=0.2758, pruned_loss=0.05317, over 1415133.67 frames.], batch size: 19, lr: 3.19e-04
2022-05-27 22:58:28,860 INFO [train.py:842] (1/4) Epoch 17, batch 8100, loss[loss=0.1776, simple_loss=0.2635, pruned_loss=0.04584, over 6433.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2753, pruned_loss=0.05318, over 1415430.01 frames.], batch size: 38, lr: 3.19e-04
2022-05-27 22:59:08,275 INFO [train.py:842] (1/4) Epoch 17, batch 8150, loss[loss=0.1867, simple_loss=0.2692, pruned_loss=0.05209, over 7211.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2752, pruned_loss=0.05295, over 1414359.04 frames.], batch size: 22, lr: 3.19e-04
2022-05-27 22:59:46,909 INFO [train.py:842] (1/4) Epoch 17, batch 8200, loss[loss=0.1902, simple_loss=0.2733, pruned_loss=0.05361, over 7430.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2759, pruned_loss=0.05293, over 1411635.93 frames.], batch size: 20, lr: 3.19e-04
2022-05-27 23:00:26,385 INFO [train.py:842] (1/4) Epoch 17, batch 8250, loss[loss=0.2042, simple_loss=0.2994, pruned_loss=0.0545, over 7316.00 frames.], tot_loss[loss=0.19, simple_loss=0.2754, pruned_loss=0.05229, over 1417528.64 frames.], batch size: 21, lr: 3.19e-04
2022-05-27 23:01:04,943 INFO [train.py:842] (1/4) Epoch 17, batch 8300, loss[loss=0.1613, simple_loss=0.2599, pruned_loss=0.03141, over 7121.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2767, pruned_loss=0.05277, over 1417464.88 frames.], batch size: 21, lr: 3.19e-04
2022-05-27 23:01:43,878 INFO [train.py:842] (1/4) Epoch 17, batch 8350, loss[loss=0.2128, simple_loss=0.2993, pruned_loss=0.06319, over 7298.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2761, pruned_loss=0.05268, over 1421155.05 frames.], batch size: 25, lr: 3.19e-04
2022-05-27 23:02:22,669 INFO [train.py:842] (1/4) Epoch 17, batch 8400, loss[loss=0.1783, simple_loss=0.2711, pruned_loss=0.0428, over 7139.00 frames.], tot_loss[loss=0.1893, simple_loss=0.275, pruned_loss=0.05179, over 1423895.78 frames.], batch size: 28, lr: 3.19e-04
2022-05-27 23:03:01,604 INFO [train.py:842] (1/4) Epoch 17, batch 8450, loss[loss=0.2788, simple_loss=0.3516, pruned_loss=0.103, over 6630.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2758, pruned_loss=0.05258, over 1421434.74 frames.], batch size: 38, lr: 3.19e-04
2022-05-27 23:03:40,258 INFO [train.py:842] (1/4) Epoch 17, batch 8500, loss[loss=0.2058, simple_loss=0.2918, pruned_loss=0.05994, over 7175.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2757, pruned_loss=0.05264, over 1414517.31 frames.], batch size: 26, lr: 3.19e-04
2022-05-27 23:04:19,048 INFO [train.py:842] (1/4) Epoch 17, batch 8550, loss[loss=0.1748, simple_loss=0.2616, pruned_loss=0.044, over 6193.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2762, pruned_loss=0.0526, over 1411734.76 frames.], batch size: 37, lr: 3.19e-04
2022-05-27 23:04:57,769 INFO [train.py:842] (1/4) Epoch 17, batch 8600, loss[loss=0.1682, simple_loss=0.2621, pruned_loss=0.0372, over 7334.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2767, pruned_loss=0.05294, over 1416883.05 frames.], batch size: 22, lr: 3.19e-04
2022-05-27 23:05:36,634 INFO [train.py:842] (1/4) Epoch 17, batch 8650, loss[loss=0.1819, simple_loss=0.2614, pruned_loss=0.05115, over 7284.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2766, pruned_loss=0.05281, over 1419863.17 frames.], batch size: 18, lr: 3.18e-04
2022-05-27 23:06:15,459 INFO [train.py:842] (1/4) Epoch 17, batch 8700, loss[loss=0.1977, simple_loss=0.2882, pruned_loss=0.05361, over 7311.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2774, pruned_loss=0.05311, over 1423760.16 frames.], batch size: 25, lr: 3.18e-04
2022-05-27 23:06:54,776 INFO [train.py:842] (1/4) Epoch 17, batch 8750, loss[loss=0.18, simple_loss=0.2613, pruned_loss=0.04938, over 7067.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2771, pruned_loss=0.05301, over 1423845.39 frames.], batch size: 18, lr: 3.18e-04
2022-05-27 23:07:33,414 INFO [train.py:842] (1/4) Epoch 17, batch 8800, loss[loss=0.176, simple_loss=0.2666, pruned_loss=0.04271, over 7074.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2782, pruned_loss=0.05363, over 1420283.96 frames.], batch size: 18, lr: 3.18e-04
2022-05-27 23:08:12,274 INFO [train.py:842] (1/4) Epoch 17, batch 8850, loss[loss=0.24, simple_loss=0.305, pruned_loss=0.08747, over 5159.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2781, pruned_loss=0.05384, over 1417920.54 frames.], batch size: 52, lr: 3.18e-04
2022-05-27 23:08:51,302 INFO [train.py:842] (1/4) Epoch 17, batch 8900, loss[loss=0.1685, simple_loss=0.2705, pruned_loss=0.03322, over 7153.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2762, pruned_loss=0.05308, over 1418711.70 frames.], batch size: 20, lr: 3.18e-04
2022-05-27 23:09:30,571 INFO [train.py:842] (1/4) Epoch 17, batch 8950, loss[loss=0.1491, simple_loss=0.231, pruned_loss=0.03362, over 7282.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2774, pruned_loss=0.05441, over 1410417.70 frames.], batch size: 18, lr: 3.18e-04
2022-05-27 23:10:09,454 INFO [train.py:842] (1/4) Epoch 17, batch 9000, loss[loss=0.2052, simple_loss=0.2941, pruned_loss=0.05812, over 7144.00 frames.], tot_loss[loss=0.1928, simple_loss=0.277, pruned_loss=0.05429, over 1400819.57 frames.], batch size: 20, lr: 3.18e-04
2022-05-27 23:10:09,455 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 23:10:18,901 INFO [train.py:871] (1/4) Epoch 17, validation: loss=0.1657, simple_loss=0.2652, pruned_loss=0.03308, over 868885.00 frames.
2022-05-27 23:10:57,856 INFO [train.py:842] (1/4) Epoch 17, batch 9050, loss[loss=0.1893, simple_loss=0.3041, pruned_loss=0.03718, over 7315.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2782, pruned_loss=0.05477, over 1395887.10 frames.], batch size: 20, lr: 3.18e-04
2022-05-27 23:11:46,171 INFO [train.py:842] (1/4) Epoch 17, batch 9100, loss[loss=0.2638, simple_loss=0.3285, pruned_loss=0.09952, over 5399.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2812, pruned_loss=0.05679, over 1350118.07 frames.], batch size: 52, lr: 3.18e-04
2022-05-27 23:12:24,144 INFO [train.py:842] (1/4) Epoch 17, batch 9150, loss[loss=0.2004, simple_loss=0.2853, pruned_loss=0.05776, over 4847.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2846, pruned_loss=0.05998, over 1274435.33 frames.], batch size: 52, lr: 3.18e-04
2022-05-27 23:13:16,677 INFO [train.py:842] (1/4) Epoch 18, batch 0, loss[loss=0.2093, simple_loss=0.2911, pruned_loss=0.06374, over 7234.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2911, pruned_loss=0.06374, over 7234.00 frames.], batch size: 20, lr: 3.10e-04
2022-05-27 23:13:56,056 INFO [train.py:842] (1/4) Epoch 18, batch 50, loss[loss=0.1703, simple_loss=0.2446, pruned_loss=0.04796, over 6995.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2728, pruned_loss=0.05179, over 323474.74 frames.], batch size: 16, lr: 3.09e-04
2022-05-27 23:14:34,730 INFO [train.py:842] (1/4) Epoch 18, batch 100, loss[loss=0.1612, simple_loss=0.2471, pruned_loss=0.03768, over 7163.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2749, pruned_loss=0.05161, over 564411.24 frames.], batch size: 18, lr: 3.09e-04
2022-05-27 23:15:14,082 INFO [train.py:842] (1/4) Epoch 18, batch 150, loss[loss=0.195, simple_loss=0.2941, pruned_loss=0.04793, over 7148.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2768, pruned_loss=0.05289, over 751998.44 frames.], batch size: 20, lr: 3.09e-04
2022-05-27 23:15:53,009 INFO [train.py:842] (1/4) Epoch 18, batch 200, loss[loss=0.214, simple_loss=0.2912, pruned_loss=0.06844, over 7164.00 frames.], tot_loss[loss=0.19, simple_loss=0.2757, pruned_loss=0.05217, over 902636.35 frames.], batch size: 18, lr: 3.09e-04
2022-05-27 23:16:31,860 INFO [train.py:842] (1/4) Epoch 18, batch 250, loss[loss=0.2181, simple_loss=0.3101, pruned_loss=0.06302, over 6725.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2767, pruned_loss=0.05253, over 1020438.32 frames.], batch size: 31, lr: 3.09e-04
2022-05-27 23:17:10,691 INFO [train.py:842] (1/4) Epoch 18, batch 300, loss[loss=0.1721, simple_loss=0.2664, pruned_loss=0.03893, over 7073.00 frames.], tot_loss[loss=0.192, simple_loss=0.2776, pruned_loss=0.05318, over 1104243.81 frames.], batch size: 28, lr: 3.09e-04
2022-05-27 23:17:49,747 INFO [train.py:842] (1/4) Epoch 18, batch 350, loss[loss=0.1674, simple_loss=0.2564, pruned_loss=0.03921, over 7328.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2761, pruned_loss=0.05322, over 1171757.54 frames.], batch size: 22, lr: 3.09e-04
2022-05-27 23:18:28,738 INFO [train.py:842] (1/4) Epoch 18, batch 400, loss[loss=0.1574, simple_loss=0.2402, pruned_loss=0.03726, over 7246.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2769, pruned_loss=0.0537, over 1232239.48 frames.], batch size: 16, lr: 3.09e-04
2022-05-27 23:19:07,686 INFO [train.py:842] (1/4) Epoch 18, batch 450, loss[loss=0.2049, simple_loss=0.2893, pruned_loss=0.06027, over 7210.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2773, pruned_loss=0.05344, over 1275023.76 frames.], batch size: 22, lr: 3.09e-04
2022-05-27 23:19:46,700 INFO [train.py:842] (1/4) Epoch 18, batch 500, loss[loss=0.1829, simple_loss=0.2713, pruned_loss=0.0472, over 7333.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2757, pruned_loss=0.05264, over 1311447.72 frames.], batch size: 22, lr: 3.09e-04
2022-05-27 23:20:26,022 INFO [train.py:842] (1/4) Epoch 18, batch 550, loss[loss=0.1603, simple_loss=0.2396, pruned_loss=0.04054, over 7145.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2747, pruned_loss=0.05209, over 1338210.30 frames.], batch size: 17, lr: 3.09e-04
2022-05-27 23:21:04,750 INFO [train.py:842] (1/4) Epoch 18, batch 600, loss[loss=0.1744, simple_loss=0.2687, pruned_loss=0.0401, over 6688.00 frames.], tot_loss[loss=0.1904, simple_loss=0.276, pruned_loss=0.05239, over 1356437.51 frames.], batch size: 38, lr: 3.09e-04
2022-05-27 23:21:43,501 INFO [train.py:842] (1/4) Epoch 18, batch 650, loss[loss=0.2145, simple_loss=0.2948, pruned_loss=0.06715, over 4873.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2761, pruned_loss=0.05252, over 1369137.66 frames.], batch size: 52, lr: 3.09e-04
2022-05-27 23:22:22,378 INFO [train.py:842] (1/4) Epoch 18, batch 700, loss[loss=0.229, simple_loss=0.2999, pruned_loss=0.07903, over 7312.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2771, pruned_loss=0.05286, over 1381048.51 frames.], batch size: 21, lr: 3.09e-04
2022-05-27 23:23:01,858 INFO [train.py:842] (1/4) Epoch 18, batch 750, loss[loss=0.1836, simple_loss=0.2608, pruned_loss=0.05324, over 7403.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2753, pruned_loss=0.05239, over 1391484.29 frames.], batch size: 18, lr: 3.09e-04
2022-05-27 23:23:41,021 INFO [train.py:842] (1/4) Epoch 18, batch 800, loss[loss=0.1855, simple_loss=0.2692, pruned_loss=0.0509, over 7333.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2743, pruned_loss=0.05146, over 1403718.44 frames.], batch size: 21, lr: 3.09e-04
2022-05-27 23:24:20,295 INFO [train.py:842] (1/4) Epoch 18, batch 850, loss[loss=0.1796, simple_loss=0.2636, pruned_loss=0.04778, over 7419.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2741, pruned_loss=0.05176, over 1406497.67 frames.], batch size: 21, lr: 3.09e-04
2022-05-27 23:24:58,887 INFO [train.py:842] (1/4) Epoch 18, batch 900, loss[loss=0.1919, simple_loss=0.2846, pruned_loss=0.04957, over 7220.00 frames.], tot_loss[loss=0.189, simple_loss=0.2748, pruned_loss=0.05164, over 1406398.74 frames.], batch size: 22, lr: 3.09e-04
2022-05-27 23:25:37,806 INFO [train.py:842] (1/4) Epoch 18, batch 950, loss[loss=0.1958, simple_loss=0.2821, pruned_loss=0.05473, over 7255.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2742, pruned_loss=0.05128, over 1409361.16 frames.], batch size: 19, lr: 3.09e-04
2022-05-27 23:26:16,530 INFO [train.py:842] (1/4) Epoch 18, batch 1000, loss[loss=0.2074, simple_loss=0.2977, pruned_loss=0.05854, over 7286.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2743, pruned_loss=0.05141, over 1414687.13 frames.], batch size: 24, lr: 3.09e-04
2022-05-27 23:26:55,808 INFO [train.py:842] (1/4) Epoch 18, batch 1050, loss[loss=0.1854, simple_loss=0.263, pruned_loss=0.05394, over 7285.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2749, pruned_loss=0.05222, over 1417153.94 frames.], batch size: 17, lr: 3.08e-04
2022-05-27 23:27:34,984 INFO [train.py:842] (1/4) Epoch 18, batch 1100, loss[loss=0.2352, simple_loss=0.3095, pruned_loss=0.08047, over 7289.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2767, pruned_loss=0.05337, over 1420874.74 frames.], batch size: 25, lr: 3.08e-04
2022-05-27 23:28:14,147 INFO [train.py:842] (1/4) Epoch 18, batch 1150, loss[loss=0.1907, simple_loss=0.2665, pruned_loss=0.05747, over 7392.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2766, pruned_loss=0.05356, over 1419115.76 frames.], batch size: 23, lr: 3.08e-04
2022-05-27 23:28:53,134 INFO [train.py:842] (1/4) Epoch 18, batch 1200, loss[loss=0.1709, simple_loss=0.2628, pruned_loss=0.03949, over 7281.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2757, pruned_loss=0.0528, over 1417178.09 frames.], batch size: 18, lr: 3.08e-04
2022-05-27 23:29:32,562 INFO [train.py:842] (1/4) Epoch 18, batch 1250, loss[loss=0.1923, simple_loss=0.2849, pruned_loss=0.04982, over 7408.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2748, pruned_loss=0.05225, over 1419009.24 frames.], batch size: 21, lr: 3.08e-04
2022-05-27 23:30:11,874 INFO [train.py:842] (1/4) Epoch 18, batch 1300, loss[loss=0.1714, simple_loss=0.2558, pruned_loss=0.04352, over 7137.00 frames.], tot_loss[loss=0.189, simple_loss=0.2738, pruned_loss=0.05209, over 1419296.54 frames.], batch size: 26, lr: 3.08e-04
2022-05-27 23:30:51,060 INFO [train.py:842] (1/4) Epoch 18, batch 1350, loss[loss=0.1642, simple_loss=0.2392, pruned_loss=0.0446, over 6990.00 frames.], tot_loss[loss=0.189, simple_loss=0.2741, pruned_loss=0.05198, over 1421929.76 frames.], batch size: 16, lr: 3.08e-04
2022-05-27 23:31:29,677 INFO [train.py:842] (1/4) Epoch 18, batch 1400, loss[loss=0.194, simple_loss=0.2787, pruned_loss=0.05465, over 7115.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2751, pruned_loss=0.05215, over 1423840.81 frames.], batch size: 21, lr: 3.08e-04
2022-05-27 23:32:08,753 INFO [train.py:842] (1/4) Epoch 18, batch 1450, loss[loss=0.1797, simple_loss=0.2688, pruned_loss=0.04533, over 7139.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2756, pruned_loss=0.05227, over 1421810.15 frames.], batch size: 20, lr: 3.08e-04
2022-05-27 23:32:47,813 INFO [train.py:842] (1/4) Epoch 18, batch 1500, loss[loss=0.1871, simple_loss=0.2742, pruned_loss=0.04999, over 7303.00 frames.], tot_loss[loss=0.1908, simple_loss=0.276, pruned_loss=0.05279, over 1414263.61 frames.], batch size: 25, lr: 3.08e-04
2022-05-27 23:33:26,870 INFO [train.py:842] (1/4) Epoch 18, batch 1550, loss[loss=0.1552, simple_loss=0.2441, pruned_loss=0.03312, over 7163.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2757, pruned_loss=0.05288, over 1421481.09 frames.], batch size: 19, lr: 3.08e-04
2022-05-27 23:34:05,596 INFO [train.py:842] (1/4) Epoch 18, batch 1600, loss[loss=0.2088, simple_loss=0.2875, pruned_loss=0.06503, over 7430.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2769, pruned_loss=0.05316, over 1422267.98 frames.], batch size: 20, lr: 3.08e-04
2022-05-27 23:34:44,649 INFO [train.py:842] (1/4) Epoch 18, batch 1650, loss[loss=0.1882, simple_loss=0.2663, pruned_loss=0.05506, over 7263.00 frames.], tot_loss[loss=0.192, simple_loss=0.2776, pruned_loss=0.05319, over 1421120.86 frames.], batch size: 17, lr: 3.08e-04
2022-05-27 23:35:23,695 INFO [train.py:842] (1/4) Epoch 18, batch 1700, loss[loss=0.1642, simple_loss=0.2504, pruned_loss=0.03904, over 7343.00 frames.], tot_loss[loss=0.1911, simple_loss=0.277, pruned_loss=0.05266, over 1423728.61 frames.], batch size: 19, lr: 3.08e-04
2022-05-27 23:36:03,266 INFO [train.py:842] (1/4) Epoch 18, batch 1750, loss[loss=0.1762, simple_loss=0.2642, pruned_loss=0.04407, over 7323.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2772, pruned_loss=0.0531, over 1424247.81 frames.], batch size: 21, lr: 3.08e-04
2022-05-27 23:36:42,471 INFO [train.py:842] (1/4) Epoch 18, batch 1800, loss[loss=0.1832, simple_loss=0.2722, pruned_loss=0.0471, over 7223.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2768, pruned_loss=0.0527, over 1427866.75 frames.], batch size: 20, lr: 3.08e-04
2022-05-27 23:37:22,119 INFO [train.py:842] (1/4) Epoch 18, batch 1850, loss[loss=0.2377, simple_loss=0.3161, pruned_loss=0.07971, over 4980.00 frames.], tot_loss[loss=0.19, simple_loss=0.2751, pruned_loss=0.05241, over 1426858.69 frames.], batch size: 52, lr: 3.08e-04
2022-05-27 23:38:00,788 INFO [train.py:842] (1/4) Epoch 18, batch 1900, loss[loss=0.1978, simple_loss=0.2878, pruned_loss=0.05391, over 7335.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2763, pruned_loss=0.05292, over 1427189.47 frames.], batch size: 21, lr: 3.08e-04
2022-05-27 23:38:39,663 INFO [train.py:842] (1/4) Epoch 18, batch 1950, loss[loss=0.2363, simple_loss=0.3182, pruned_loss=0.07721, over 7321.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2765, pruned_loss=0.05283, over 1423859.29 frames.], batch size: 21, lr: 3.08e-04
2022-05-27 23:39:18,753 INFO [train.py:842] (1/4) Epoch 18, batch 2000, loss[loss=0.2422, simple_loss=0.3186, pruned_loss=0.0829, over 5152.00 frames.], tot_loss[loss=0.191, simple_loss=0.2764, pruned_loss=0.05282, over 1424973.99 frames.], batch size: 53, lr: 3.08e-04
2022-05-27 23:39:57,973 INFO [train.py:842] (1/4) Epoch 18, batch 2050, loss[loss=0.1811, simple_loss=0.2747, pruned_loss=0.04369, over 7113.00 frames.], tot_loss[loss=0.192, simple_loss=0.2772, pruned_loss=0.05345, over 1421726.79 frames.], batch size: 21, lr: 3.07e-04
2022-05-27 23:40:36,531 INFO [train.py:842] (1/4) Epoch 18, batch 2100, loss[loss=0.218, simple_loss=0.2978, pruned_loss=0.06912, over 6832.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2758, pruned_loss=0.05271, over 1418097.99 frames.], batch size: 31, lr: 3.07e-04
2022-05-27 23:41:15,747 INFO [train.py:842] (1/4) Epoch 18, batch 2150, loss[loss=0.2055, simple_loss=0.2857, pruned_loss=0.0627, over 7221.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2756, pruned_loss=0.05248, over 1419230.88 frames.], batch size: 21, lr: 3.07e-04
2022-05-27 23:41:54,564 INFO [train.py:842] (1/4) Epoch 18, batch 2200, loss[loss=0.1623, simple_loss=0.241, pruned_loss=0.0418, over 6818.00 frames.], tot_loss[loss=0.19, simple_loss=0.2754, pruned_loss=0.05231, over 1421814.23 frames.], batch size: 15, lr: 3.07e-04
2022-05-27 23:42:33,991 INFO [train.py:842] (1/4) Epoch 18, batch 2250, loss[loss=0.1989, simple_loss=0.2762, pruned_loss=0.06082, over 7441.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2749, pruned_loss=0.05216, over 1425111.07 frames.], batch size: 17, lr: 3.07e-04
2022-05-27 23:43:12,897 INFO [train.py:842] (1/4) Epoch 18, batch 2300, loss[loss=0.1834, simple_loss=0.2703, pruned_loss=0.04824, over 7138.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2748, pruned_loss=0.05168, over 1427479.46 frames.], batch size: 20, lr: 3.07e-04
2022-05-27 23:43:52,050 INFO [train.py:842] (1/4) Epoch 18, batch 2350, loss[loss=0.1892, simple_loss=0.2827, pruned_loss=0.04781, over 7158.00 frames.], tot_loss[loss=0.189, simple_loss=0.2745, pruned_loss=0.05179, over 1427255.90 frames.], batch size: 26, lr: 3.07e-04
2022-05-27 23:44:31,173 INFO [train.py:842] (1/4) Epoch 18, batch 2400, loss[loss=0.2181, simple_loss=0.3033, pruned_loss=0.06648, over 6273.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2754, pruned_loss=0.05214, over 1426177.75 frames.], batch size: 38, lr: 3.07e-04
2022-05-27 23:45:10,099 INFO [train.py:842] (1/4) Epoch 18, batch 2450, loss[loss=0.1804, simple_loss=0.2658, pruned_loss=0.04752, over 7160.00 frames.], tot_loss[loss=0.1885, simple_loss=0.274, pruned_loss=0.05144, over 1427298.57 frames.], batch size: 19, lr: 3.07e-04
2022-05-27 23:45:58,778 INFO [train.py:842] (1/4) Epoch 18, batch 2500, loss[loss=0.2073, simple_loss=0.2997, pruned_loss=0.05744, over 7120.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2753, pruned_loss=0.05181, over 1419152.74 frames.], batch size: 21, lr: 3.07e-04
2022-05-27 23:46:37,744 INFO [train.py:842] (1/4) Epoch 18, batch 2550, loss[loss=0.19, simple_loss=0.2785, pruned_loss=0.0507, over 7327.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2754, pruned_loss=0.05193, over 1419367.11 frames.], batch size: 21, lr: 3.07e-04
2022-05-27 23:47:16,432 INFO [train.py:842] (1/4) Epoch 18, batch 2600, loss[loss=0.1995, simple_loss=0.2624, pruned_loss=0.06824, over 6794.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2755, pruned_loss=0.05169, over 1418583.02 frames.], batch size: 15, lr: 3.07e-04
2022-05-27 23:48:05,815 INFO [train.py:842] (1/4) Epoch 18, batch 2650, loss[loss=0.198, simple_loss=0.2757, pruned_loss=0.06021, over 7353.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2758, pruned_loss=0.05219, over 1420203.96 frames.], batch size: 19, lr: 3.07e-04
2022-05-27 23:48:44,815 INFO [train.py:842] (1/4) Epoch 18, batch 2700, loss[loss=0.1729, simple_loss=0.2516, pruned_loss=0.04714, over 7249.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2754, pruned_loss=0.05215, over 1419889.58 frames.], batch size: 18, lr: 3.07e-04
2022-05-27 23:49:23,619 INFO [train.py:842] (1/4) Epoch 18, batch 2750, loss[loss=0.2004, simple_loss=0.2779, pruned_loss=0.06145, over 7150.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2756, pruned_loss=0.05212, over 1417683.81 frames.], batch size: 20, lr: 3.07e-04
2022-05-27 23:50:12,202 INFO [train.py:842] (1/4) Epoch 18, batch 2800, loss[loss=0.2119, simple_loss=0.3151, pruned_loss=0.05434, over 7319.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2754, pruned_loss=0.05219, over 1417489.13 frames.], batch size: 21, lr: 3.07e-04
2022-05-27 23:50:50,919 INFO [train.py:842] (1/4) Epoch 18, batch 2850, loss[loss=0.19, simple_loss=0.2838, pruned_loss=0.0481, over 7341.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2753, pruned_loss=0.05143, over 1419995.42 frames.], batch size: 25, lr: 3.07e-04
2022-05-27 23:51:29,798 INFO [train.py:842] (1/4) Epoch 18, batch 2900, loss[loss=0.1751, simple_loss=0.2694, pruned_loss=0.0404, over 7209.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2765, pruned_loss=0.05242, over 1423211.73 frames.], batch size: 22, lr: 3.07e-04
2022-05-27 23:52:08,754 INFO [train.py:842] (1/4) Epoch 18, batch 2950, loss[loss=0.1992, simple_loss=0.2873, pruned_loss=0.05555, over 6326.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2765, pruned_loss=0.05257, over 1420978.55 frames.], batch size: 37, lr: 3.07e-04
2022-05-27 23:52:47,314 INFO [train.py:842] (1/4) Epoch 18, batch 3000, loss[loss=0.3005, simple_loss=0.3621, pruned_loss=0.1195, over 7279.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2778, pruned_loss=0.0532, over 1420241.26 frames.], batch size: 25, lr: 3.07e-04
2022-05-27 23:52:47,316 INFO [train.py:862] (1/4) Computing validation loss
2022-05-27 23:52:57,054 INFO [train.py:871] (1/4) Epoch 18, validation: loss=0.1661, simple_loss=0.2662, pruned_loss=0.03302, over 868885.00 frames.
2022-05-27 23:53:36,019 INFO [train.py:842] (1/4) Epoch 18, batch 3050, loss[loss=0.2132, simple_loss=0.3007, pruned_loss=0.06285, over 7116.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2778, pruned_loss=0.05367, over 1419245.19 frames.], batch size: 21, lr: 3.07e-04
2022-05-27 23:54:14,581 INFO [train.py:842] (1/4) Epoch 18, batch 3100, loss[loss=0.1908, simple_loss=0.2898, pruned_loss=0.0459, over 7231.00 frames.], tot_loss[loss=0.192, simple_loss=0.2771, pruned_loss=0.05346, over 1419816.11 frames.], batch size: 20, lr: 3.06e-04
2022-05-27 23:54:53,778 INFO [train.py:842] (1/4) Epoch 18, batch 3150, loss[loss=0.1713, simple_loss=0.2596, pruned_loss=0.04146, over 7255.00 frames.], tot_loss[loss=0.1923, simple_loss=0.277, pruned_loss=0.05379, over 1422879.01 frames.], batch size: 19, lr: 3.06e-04
2022-05-27 23:55:32,544 INFO [train.py:842] (1/4) Epoch 18, batch 3200, loss[loss=0.1923, simple_loss=0.2878, pruned_loss=0.04844, over 6703.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2765, pruned_loss=0.05325, over 1420586.82 frames.], batch size: 31, lr: 3.06e-04
2022-05-27 23:56:11,872 INFO [train.py:842] (1/4) Epoch 18, batch 3250, loss[loss=0.2027, simple_loss=0.2755, pruned_loss=0.06493, over 7374.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2756, pruned_loss=0.05275, over 1422822.25 frames.], batch size: 23, lr: 3.06e-04
2022-05-27 23:56:51,356 INFO [train.py:842] (1/4) Epoch 18, batch 3300, loss[loss=0.1531, simple_loss=0.242, pruned_loss=0.03214, over 7159.00 frames.], tot_loss[loss=0.19, simple_loss=0.2751, pruned_loss=0.05247, over 1427637.82 frames.], batch size: 18, lr: 3.06e-04
2022-05-27 23:57:30,307 INFO [train.py:842] (1/4) Epoch 18, batch 3350, loss[loss=0.1889, simple_loss=0.2684, pruned_loss=0.05468, over 7418.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2759, pruned_loss=0.05255, over 1427240.03 frames.], batch size: 18, lr: 3.06e-04
2022-05-27 23:58:09,190 INFO [train.py:842] (1/4) Epoch 18, batch 3400, loss[loss=0.2191, simple_loss=0.3011, pruned_loss=0.06857, over 7362.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2753, pruned_loss=0.05198, over 1430001.55 frames.], batch size: 23, lr: 3.06e-04
2022-05-27 23:58:48,412 INFO [train.py:842] (1/4) Epoch 18, batch 3450, loss[loss=0.178, simple_loss=0.2522, pruned_loss=0.05188, over 7414.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2755, pruned_loss=0.05193, over 1430505.94 frames.], batch size: 18, lr: 3.06e-04
2022-05-27 23:59:27,882 INFO [train.py:842] (1/4) Epoch 18, batch 3500, loss[loss=0.1779, simple_loss=0.2791, pruned_loss=0.03838, over 6390.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2759, pruned_loss=0.05243, over 1433124.60 frames.], batch size: 37, lr: 3.06e-04
2022-05-28 00:00:06,996 INFO [train.py:842] (1/4) Epoch 18, batch 3550, loss[loss=0.1776, simple_loss=0.2598, pruned_loss=0.04765, over 7195.00 frames.], tot_loss[loss=0.1915, simple_loss=0.277, pruned_loss=0.053, over 1431030.27 frames.], batch size: 23, lr: 3.06e-04
2022-05-28 00:00:45,906 INFO [train.py:842] (1/4) Epoch 18, batch 3600, loss[loss=0.1631, simple_loss=0.2673, pruned_loss=0.02939, over 7219.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2762, pruned_loss=0.0524, over 1431938.99 frames.], batch size: 21, lr: 3.06e-04
2022-05-28 00:01:24,905 INFO [train.py:842] (1/4) Epoch 18, batch 3650, loss[loss=0.2179, simple_loss=0.3068, pruned_loss=0.06447, over 7342.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2757, pruned_loss=0.05233, over 1423477.35 frames.], batch size: 22, lr: 3.06e-04
2022-05-28 00:02:03,651 INFO [train.py:842] (1/4) Epoch 18, batch 3700, loss[loss=0.1752, simple_loss=0.2589, pruned_loss=0.0458, over 7006.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2746, pruned_loss=0.05155, over 1424083.56 frames.], batch size: 16, lr: 3.06e-04
2022-05-28 00:02:45,145 INFO [train.py:842] (1/4) Epoch 18, batch 3750, loss[loss=0.2115, simple_loss=0.2966, pruned_loss=0.06324, over 7319.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2765, pruned_loss=0.05245, over 1426511.71 frames.], batch size: 25, lr: 3.06e-04
2022-05-28 00:03:23,940 INFO [train.py:842] (1/4) Epoch 18, batch 3800, loss[loss=0.2209, simple_loss=0.2901, pruned_loss=0.07583, over 7358.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2762, pruned_loss=0.05252, over 1426449.91 frames.], batch size: 19, lr: 3.06e-04
2022-05-28 00:04:03,237 INFO [train.py:842] (1/4) Epoch 18, batch 3850, loss[loss=0.1484, simple_loss=0.23, pruned_loss=0.03342, over 7413.00 frames.], tot_loss[loss=0.1893, simple_loss=0.275, pruned_loss=0.05183, over 1424621.95 frames.], batch size: 18, lr: 3.06e-04
2022-05-28 00:04:42,031 INFO [train.py:842] (1/4) Epoch 18, batch 3900, loss[loss=0.1766, simple_loss=0.2674, pruned_loss=0.04292, over 7116.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2751, pruned_loss=0.05186, over 1421200.96 frames.], batch size: 21, lr: 3.06e-04
2022-05-28 00:05:21,338 INFO [train.py:842] (1/4) Epoch 18, batch 3950, loss[loss=0.1691, simple_loss=0.2538, pruned_loss=0.04216, over 7331.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2741, pruned_loss=0.05136, over 1423090.64 frames.], batch size: 20, lr: 3.06e-04
2022-05-28 00:06:00,210 INFO [train.py:842] (1/4) Epoch 18, batch 4000, loss[loss=0.2168, simple_loss=0.2968, pruned_loss=0.06844, over 7378.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2753, pruned_loss=0.05224, over 1425044.93 frames.], batch size: 23, lr: 3.06e-04
2022-05-28 00:06:39,315 INFO [train.py:842] (1/4) Epoch 18, batch 4050, loss[loss=0.1553, simple_loss=0.2392, pruned_loss=0.03572, over 7426.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2748, pruned_loss=0.05197, over 1429346.19 frames.], batch size: 18, lr: 3.06e-04
2022-05-28 00:07:18,093 INFO [train.py:842] (1/4) Epoch 18, batch 4100, loss[loss=0.2239, simple_loss=0.2959, pruned_loss=0.07594, over 7066.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2755, pruned_loss=0.05232, over 1428602.06 frames.], batch size: 18, lr: 3.06e-04
2022-05-28 00:07:57,193 INFO [train.py:842] (1/4) Epoch 18, batch 4150, loss[loss=0.1735, simple_loss=0.2666, pruned_loss=0.04021, over 7203.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2748, pruned_loss=0.05191, over 1425552.68 frames.], batch size: 23, lr: 3.05e-04
2022-05-28 00:08:36,167 INFO [train.py:842] (1/4) Epoch 18, batch 4200, loss[loss=0.212, simple_loss=0.302, pruned_loss=0.06105, over 7344.00 frames.], tot_loss[loss=0.189, simple_loss=0.2738, pruned_loss=0.05207, over 1425027.19 frames.], batch size: 22, lr: 3.05e-04
2022-05-28 00:09:15,383 INFO [train.py:842] (1/4) Epoch 18, batch 4250, loss[loss=0.2105, simple_loss=0.3061, pruned_loss=0.05747, over 7296.00 frames.], tot_loss[loss=0.1903, simple_loss=0.275, pruned_loss=0.0528, over 1423616.13 frames.], batch size: 24, lr: 3.05e-04
2022-05-28 00:09:54,189 INFO [train.py:842] (1/4) Epoch 18, batch 4300, loss[loss=0.2182, simple_loss=0.2989, pruned_loss=0.06879, over 6293.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2757, pruned_loss=0.05305, over 1424821.07 frames.], batch size: 37, lr: 3.05e-04
2022-05-28 00:10:33,486 INFO [train.py:842] (1/4) Epoch 18, batch 4350, loss[loss=0.1872, simple_loss=0.2658, pruned_loss=0.0543, over 7402.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2748, pruned_loss=0.05269, over 1426044.03 frames.], batch size: 18, lr: 3.05e-04
2022-05-28 00:11:12,564 INFO [train.py:842] (1/4) Epoch 18, batch 4400, loss[loss=0.1843, simple_loss=0.2779, pruned_loss=0.04531, over 7177.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2732, pruned_loss=0.0522, over 1425255.10 frames.], batch size: 28, lr: 3.05e-04
2022-05-28 00:11:51,920 INFO [train.py:842] (1/4) Epoch 18, batch 4450, loss[loss=0.1657, simple_loss=0.2553, pruned_loss=0.03808, over 7303.00 frames.], tot_loss[loss=0.1894, simple_loss=0.274, pruned_loss=0.05237, over 1424507.43 frames.], batch size: 24, lr: 3.05e-04
2022-05-28 00:12:30,697 INFO [train.py:842] (1/4) Epoch 18, batch 4500, loss[loss=0.1839, simple_loss=0.2639, pruned_loss=0.0519, over 7260.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2738, pruned_loss=0.05217, over 1424914.84 frames.], batch size: 19, lr: 3.05e-04
2022-05-28 00:13:09,704 INFO [train.py:842] (1/4) Epoch 18, batch 4550, loss[loss=0.1942, simple_loss=0.2749, pruned_loss=0.05675, over 7011.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2745, pruned_loss=0.05222, over 1423552.84 frames.], batch size: 28, lr: 3.05e-04
2022-05-28 00:13:48,453 INFO [train.py:842] (1/4) Epoch 18, batch 4600, loss[loss=0.1645, simple_loss=0.2629, pruned_loss=0.03305, over 7226.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2748, pruned_loss=0.05239, over 1421879.14 frames.], batch size: 21, lr: 3.05e-04
2022-05-28 00:14:27,508 INFO [train.py:842] (1/4) Epoch 18, batch 4650, loss[loss=0.2096, simple_loss=0.3031, pruned_loss=0.05803, over 7184.00 frames.], tot_loss[loss=0.1896, simple_loss=0.275, pruned_loss=0.05209, over 1416936.91 frames.], batch size: 22, lr: 3.05e-04
2022-05-28 00:15:06,714 INFO [train.py:842] (1/4) Epoch 18, batch 4700, loss[loss=0.219, simple_loss=0.2945, pruned_loss=0.07179, over 7082.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2748, pruned_loss=0.05186, over 1420120.38 frames.], batch size: 18, lr: 3.05e-04
2022-05-28 00:15:46,180 INFO [train.py:842] (1/4) Epoch 18, batch 4750, loss[loss=0.162, simple_loss=0.2506, pruned_loss=0.0367, over 7351.00 frames.], tot_loss[loss=0.1887, simple_loss=0.274, pruned_loss=0.05164, over 1420818.24 frames.], batch size: 19, lr: 3.05e-04
2022-05-28 00:16:25,209 INFO [train.py:842] (1/4) Epoch 18, batch 4800, loss[loss=0.1501, simple_loss=0.2342, pruned_loss=0.03299, over 7265.00 frames.], tot_loss[loss=0.1897, simple_loss=0.275, pruned_loss=0.0522, over 1422261.59 frames.], batch size: 19, lr: 3.05e-04
2022-05-28 00:17:04,349 INFO [train.py:842] (1/4) Epoch 18, batch 4850, loss[loss=0.2009, simple_loss=0.2857, pruned_loss=0.05812, over 7092.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2743, pruned_loss=0.05166, over 1426168.03 frames.], batch size: 28, lr: 3.05e-04
2022-05-28 00:17:43,320 INFO [train.py:842] (1/4) Epoch 18, batch 4900, loss[loss=0.1806, simple_loss=0.2506, pruned_loss=0.05532, over 7150.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2743, pruned_loss=0.05154, over 1429355.80 frames.], batch size: 20, lr: 3.05e-04
2022-05-28 00:18:22,524 INFO [train.py:842] (1/4) Epoch 18, batch 4950, loss[loss=0.1787, simple_loss=0.2574, pruned_loss=0.04997, over 7268.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2742, pruned_loss=0.05206, over 1428138.81 frames.], batch size: 19, lr: 3.05e-04
2022-05-28 00:19:01,599 INFO [train.py:842] (1/4) Epoch 18, batch 5000, loss[loss=0.181, simple_loss=0.2672, pruned_loss=0.04745, over 7205.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2733, pruned_loss=0.05142, over 1428458.45 frames.], batch size: 23, lr: 3.05e-04
2022-05-28 00:19:40,952 INFO [train.py:842] (1/4) Epoch 18, batch 5050, loss[loss=0.2061, simple_loss=0.2864, pruned_loss=0.06289, over 7168.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2744, pruned_loss=0.05174, over 1431350.53 frames.], batch size: 18, lr: 3.05e-04
2022-05-28 00:20:19,752 INFO [train.py:842] (1/4) Epoch 18, batch 5100, loss[loss=0.1968, simple_loss=0.2818, pruned_loss=0.05591, over 7205.00 frames.], tot_loss[loss=0.189, simple_loss=0.2742, pruned_loss=0.05188, over 1428618.71 frames.], batch size: 23, lr: 3.05e-04
2022-05-28 00:20:58,728 INFO [train.py:842] (1/4) Epoch 18, batch 5150, loss[loss=0.1657, simple_loss=0.2428, pruned_loss=0.04428, over 7415.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2733, pruned_loss=0.05147, over 1430653.56 frames.], batch size: 18, lr: 3.05e-04
2022-05-28 00:21:37,359 INFO [train.py:842] (1/4) Epoch 18, batch 5200, loss[loss=0.2126, simple_loss=0.3025, pruned_loss=0.06137, over 7079.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2737, pruned_loss=0.05174, over 1431780.05 frames.], batch size: 28, lr: 3.04e-04
2022-05-28 00:22:17,188 INFO [train.py:842] (1/4) Epoch 18, batch 5250, loss[loss=0.2016, simple_loss=0.2787, pruned_loss=0.0622, over 6747.00 frames.], tot_loss[loss=0.1875, simple_loss=0.273, pruned_loss=0.051, over 1434010.70 frames.], batch size: 31, lr: 3.04e-04
2022-05-28 00:22:55,883 INFO [train.py:842] (1/4) Epoch 18, batch 5300, loss[loss=0.1729, simple_loss=0.2611, pruned_loss=0.04234, over 7306.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2743, pruned_loss=0.05156, over 1432372.31 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:23:35,028 INFO [train.py:842] (1/4) Epoch 18, batch 5350, loss[loss=0.1675, simple_loss=0.255, pruned_loss=0.04001, over 7413.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2745, pruned_loss=0.05151, over 1433228.06 frames.], batch size: 18, lr: 3.04e-04
2022-05-28 00:24:13,963 INFO [train.py:842] (1/4) Epoch 18, batch 5400, loss[loss=0.1726, simple_loss=0.2676, pruned_loss=0.0388, over 7113.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2747, pruned_loss=0.05177, over 1430413.06 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:24:53,092 INFO [train.py:842] (1/4) Epoch 18, batch 5450, loss[loss=0.1882, simple_loss=0.2781, pruned_loss=0.04917, over 7359.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2742, pruned_loss=0.05156, over 1431867.18 frames.], batch size: 19, lr: 3.04e-04
2022-05-28 00:25:32,188 INFO [train.py:842] (1/4) Epoch 18, batch 5500, loss[loss=0.1922, simple_loss=0.2836, pruned_loss=0.0504, over 7196.00 frames.], tot_loss[loss=0.1886, simple_loss=0.274, pruned_loss=0.0516, over 1433828.41 frames.], batch size: 26, lr: 3.04e-04
2022-05-28 00:26:11,395 INFO [train.py:842] (1/4) Epoch 18, batch 5550, loss[loss=0.2112, simple_loss=0.3042, pruned_loss=0.05907, over 7204.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2745, pruned_loss=0.0518, over 1434323.98 frames.], batch size: 22, lr: 3.04e-04
2022-05-28 00:26:50,011 INFO [train.py:842] (1/4) Epoch 18, batch 5600, loss[loss=0.1894, simple_loss=0.271, pruned_loss=0.05392, over 7294.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2746, pruned_loss=0.05181, over 1433493.36 frames.], batch size: 24, lr: 3.04e-04
2022-05-28 00:27:28,967 INFO [train.py:842] (1/4) Epoch 18, batch 5650, loss[loss=0.1758, simple_loss=0.2585, pruned_loss=0.04655, over 7066.00 frames.], tot_loss[loss=0.189, simple_loss=0.2748, pruned_loss=0.05162, over 1429739.81 frames.], batch size: 18, lr: 3.04e-04
2022-05-28 00:28:08,013 INFO [train.py:842] (1/4) Epoch 18, batch 5700, loss[loss=0.2142, simple_loss=0.3085, pruned_loss=0.05996, over 7387.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2765, pruned_loss=0.05253, over 1429566.53 frames.], batch size: 23, lr: 3.04e-04
2022-05-28 00:28:47,274 INFO [train.py:842] (1/4) Epoch 18, batch 5750, loss[loss=0.219, simple_loss=0.2968, pruned_loss=0.07057, over 7128.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2771, pruned_loss=0.05276, over 1428477.53 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:29:26,193 INFO [train.py:842] (1/4) Epoch 18, batch 5800, loss[loss=0.2086, simple_loss=0.2962, pruned_loss=0.06049, over 7323.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2758, pruned_loss=0.05244, over 1429375.84 frames.], batch size: 25, lr: 3.04e-04
2022-05-28 00:30:05,277 INFO [train.py:842] (1/4) Epoch 18, batch 5850, loss[loss=0.2147, simple_loss=0.3089, pruned_loss=0.06026, over 7209.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2753, pruned_loss=0.05208, over 1424949.96 frames.], batch size: 23, lr: 3.04e-04
2022-05-28 00:30:44,179 INFO [train.py:842] (1/4) Epoch 18, batch 5900, loss[loss=0.1404, simple_loss=0.2244, pruned_loss=0.0282, over 7076.00 frames.], tot_loss[loss=0.19, simple_loss=0.2757, pruned_loss=0.05216, over 1424878.87 frames.], batch size: 18, lr: 3.04e-04
2022-05-28 00:31:22,984 INFO [train.py:842] (1/4) Epoch 18, batch 5950, loss[loss=0.1699, simple_loss=0.2627, pruned_loss=0.03853, over 7251.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2756, pruned_loss=0.05251, over 1425064.18 frames.], batch size: 19, lr: 3.04e-04
2022-05-28 00:32:01,742 INFO [train.py:842] (1/4) Epoch 18, batch 6000, loss[loss=0.228, simple_loss=0.309, pruned_loss=0.07353, over 7296.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2763, pruned_loss=0.0525, over 1429199.76 frames.], batch size: 25, lr: 3.04e-04
2022-05-28 00:32:01,743 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 00:32:11,165 INFO [train.py:871] (1/4) Epoch 18, validation: loss=0.1679, simple_loss=0.2675, pruned_loss=0.03415, over 868885.00 frames.
2022-05-28 00:32:50,443 INFO [train.py:842] (1/4) Epoch 18, batch 6050, loss[loss=0.1658, simple_loss=0.2606, pruned_loss=0.03552, over 7410.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2752, pruned_loss=0.05191, over 1427840.06 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:33:29,265 INFO [train.py:842] (1/4) Epoch 18, batch 6100, loss[loss=0.1681, simple_loss=0.2477, pruned_loss=0.04428, over 7424.00 frames.], tot_loss[loss=0.19, simple_loss=0.2757, pruned_loss=0.05211, over 1429481.76 frames.], batch size: 20, lr: 3.04e-04
2022-05-28 00:34:08,671 INFO [train.py:842] (1/4) Epoch 18, batch 6150, loss[loss=0.2048, simple_loss=0.2942, pruned_loss=0.05772, over 7120.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2745, pruned_loss=0.05164, over 1431793.13 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:34:47,565 INFO [train.py:842] (1/4) Epoch 18, batch 6200, loss[loss=0.176, simple_loss=0.2748, pruned_loss=0.03857, over 7107.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2733, pruned_loss=0.05112, over 1426923.14 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:35:26,840 INFO [train.py:842] (1/4) Epoch 18, batch 6250, loss[loss=0.2068, simple_loss=0.2947, pruned_loss=0.05939, over 7218.00 frames.], tot_loss[loss=0.1875, simple_loss=0.273, pruned_loss=0.05098, over 1425111.67 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:36:05,689 INFO [train.py:842] (1/4) Epoch 18, batch 6300, loss[loss=0.2151, simple_loss=0.3154, pruned_loss=0.0574, over 7152.00 frames.], tot_loss[loss=0.188, simple_loss=0.2737, pruned_loss=0.05119, over 1422348.88 frames.], batch size: 20, lr: 3.03e-04
2022-05-28 00:36:45,068 INFO [train.py:842] (1/4) Epoch 18, batch 6350, loss[loss=0.2058, simple_loss=0.2843, pruned_loss=0.0636, over 7216.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2733, pruned_loss=0.05094, over 1425834.48 frames.], batch size: 21, lr: 3.03e-04
2022-05-28 00:37:23,917 INFO [train.py:842] (1/4) Epoch 18, batch 6400, loss[loss=0.1941, simple_loss=0.263, pruned_loss=0.06258, over 7412.00 frames.], tot_loss[loss=0.189, simple_loss=0.2747, pruned_loss=0.05162, over 1424531.00 frames.], batch size: 18, lr: 3.03e-04
2022-05-28 00:38:03,174 INFO [train.py:842] (1/4) Epoch 18, batch 6450, loss[loss=0.1687, simple_loss=0.2613, pruned_loss=0.03805, over 7348.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2743, pruned_loss=0.05129, over 1426011.59 frames.], batch size: 19, lr: 3.03e-04
2022-05-28 00:38:41,960 INFO [train.py:842] (1/4) Epoch 18, batch 6500, loss[loss=0.1749, simple_loss=0.2617, pruned_loss=0.04408, over 7140.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2752, pruned_loss=0.05224, over 1423737.07 frames.], batch size: 17, lr: 3.03e-04
2022-05-28 00:39:21,190 INFO [train.py:842] (1/4) Epoch 18, batch 6550, loss[loss=0.2271, simple_loss=0.3021, pruned_loss=0.07609, over 7326.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2745, pruned_loss=0.05239, over 1425964.25 frames.], batch size: 20, lr: 3.03e-04
2022-05-28 00:40:00,112 INFO [train.py:842] (1/4) Epoch 18, batch 6600, loss[loss=0.1945, simple_loss=0.2871, pruned_loss=0.05097, over 7203.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2732, pruned_loss=0.05146, over 1425542.98 frames.], batch size: 22, lr: 3.03e-04
2022-05-28 00:40:38,928 INFO [train.py:842] (1/4) Epoch 18, batch 6650, loss[loss=0.1763, simple_loss=0.2795, pruned_loss=0.03659, over 7341.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2746, pruned_loss=0.05232, over 1419397.47 frames.], batch size: 22, lr: 3.03e-04
2022-05-28 00:41:17,422 INFO [train.py:842] (1/4) Epoch 18, batch 6700, loss[loss=0.2366, simple_loss=0.3185, pruned_loss=0.0774, over 7341.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2758, pruned_loss=0.05299, over 1416712.59 frames.], batch size: 25, lr: 3.03e-04
2022-05-28 00:41:56,424 INFO [train.py:842] (1/4) Epoch 18, batch 6750, loss[loss=0.188, simple_loss=0.2783, pruned_loss=0.04889, over 7209.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2747, pruned_loss=0.05244, over 1416282.77 frames.], batch size: 22, lr: 3.03e-04
2022-05-28 00:42:35,493 INFO [train.py:842] (1/4) Epoch 18, batch 6800, loss[loss=0.1987, simple_loss=0.2775, pruned_loss=0.05998, over 7281.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2737, pruned_loss=0.05192, over 1417554.64 frames.], batch size: 18, lr: 3.03e-04
2022-05-28 00:43:14,484 INFO [train.py:842] (1/4) Epoch 18, batch 6850, loss[loss=0.2034, simple_loss=0.2818, pruned_loss=0.06247, over 7377.00 frames.], tot_loss[loss=0.19, simple_loss=0.2752, pruned_loss=0.05244, over 1420334.19 frames.], batch size: 23, lr: 3.03e-04
2022-05-28 00:43:53,157 INFO [train.py:842] (1/4) Epoch 18, batch 6900, loss[loss=0.1805, simple_loss=0.272, pruned_loss=0.04451, over 7140.00 frames.], tot_loss[loss=0.1895, simple_loss=0.275, pruned_loss=0.05196, over 1421139.72 frames.], batch size: 20, lr: 3.03e-04
2022-05-28 00:44:31,886 INFO [train.py:842] (1/4) Epoch 18, batch 6950, loss[loss=0.1985, simple_loss=0.2986, pruned_loss=0.04926, over 7270.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2748, pruned_loss=0.05174, over 1421505.90 frames.], batch size: 24, lr: 3.03e-04
2022-05-28 00:45:09,746 INFO [train.py:842] (1/4) Epoch 18, batch 7000, loss[loss=0.2058, simple_loss=0.2909, pruned_loss=0.06032, over 4962.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2746, pruned_loss=0.05134, over 1421851.26 frames.], batch size: 52, lr: 3.03e-04
2022-05-28 00:45:48,072 INFO [train.py:842] (1/4) Epoch 18, batch 7050, loss[loss=0.1796, simple_loss=0.256, pruned_loss=0.05161, over 7165.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2733, pruned_loss=0.05122, over 1423359.94 frames.], batch size: 19, lr: 3.03e-04
2022-05-28 00:46:26,161 INFO [train.py:842] (1/4) Epoch 18, batch 7100, loss[loss=0.173, simple_loss=0.2686, pruned_loss=0.03873, over 7226.00 frames.], tot_loss[loss=0.189, simple_loss=0.2738, pruned_loss=0.05207, over 1423284.71 frames.], batch size: 21, lr: 3.03e-04
2022-05-28 00:47:04,374 INFO [train.py:842] (1/4) Epoch 18, batch 7150, loss[loss=0.2458, simple_loss=0.3242, pruned_loss=0.08367, over 7254.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2733, pruned_loss=0.05144, over 1426231.12 frames.], batch size: 19, lr: 3.03e-04
2022-05-28 00:47:42,505 INFO [train.py:842] (1/4) Epoch 18, batch 7200, loss[loss=0.2924, simple_loss=0.3529, pruned_loss=0.1159, over 7162.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2744, pruned_loss=0.05205, over 1428654.79 frames.], batch size: 19, lr: 3.03e-04
2022-05-28 00:48:20,744 INFO [train.py:842] (1/4) Epoch 18, batch 7250, loss[loss=0.2458, simple_loss=0.3255, pruned_loss=0.08301, over 7197.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2752, pruned_loss=0.05256, over 1427985.92 frames.], batch size: 23, lr: 3.03e-04
2022-05-28 00:48:58,653 INFO [train.py:842] (1/4) Epoch 18, batch 7300, loss[loss=0.1947, simple_loss=0.2818, pruned_loss=0.05384, over 7228.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2753, pruned_loss=0.05252, over 1424718.28 frames.], batch size: 20, lr: 3.03e-04
2022-05-28 00:49:37,078 INFO [train.py:842] (1/4) Epoch 18, batch 7350, loss[loss=0.1833, simple_loss=0.2603, pruned_loss=0.05315, over 7141.00 frames.], tot_loss[loss=0.1901, simple_loss=0.275, pruned_loss=0.05263, over 1427691.06 frames.], batch size: 17, lr: 3.02e-04
2022-05-28 00:50:15,157 INFO [train.py:842] (1/4) Epoch 18, batch 7400, loss[loss=0.1889, simple_loss=0.2731, pruned_loss=0.05236, over 7326.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2744, pruned_loss=0.05236, over 1424752.02 frames.], batch size: 21, lr: 3.02e-04
2022-05-28 00:50:53,593 INFO [train.py:842] (1/4) Epoch 18, batch 7450, loss[loss=0.1954, simple_loss=0.2825, pruned_loss=0.05419, over 7406.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2745, pruned_loss=0.05259, over 1422975.40 frames.], batch size: 21, lr: 3.02e-04
2022-05-28 00:51:31,509 INFO [train.py:842] (1/4) Epoch 18, batch 7500, loss[loss=0.1853, simple_loss=0.2745, pruned_loss=0.04804, over 7235.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2737, pruned_loss=0.05167, over 1422057.85 frames.], batch size: 20, lr: 3.02e-04
2022-05-28 00:52:09,623 INFO [train.py:842] (1/4) Epoch 18, batch 7550, loss[loss=0.2248, simple_loss=0.2956, pruned_loss=0.07698, over 5045.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2745, pruned_loss=0.0522, over 1421574.06 frames.], batch size: 52, lr: 3.02e-04
2022-05-28 00:52:47,700 INFO [train.py:842] (1/4) Epoch 18, batch 7600, loss[loss=0.2247, simple_loss=0.309, pruned_loss=0.07021, over 7167.00 frames.], tot_loss[loss=0.1887, simple_loss=0.274, pruned_loss=0.05165, over 1425603.27 frames.], batch size: 26, lr: 3.02e-04
2022-05-28 00:53:25,832 INFO [train.py:842] (1/4) Epoch 18, batch 7650, loss[loss=0.1818, simple_loss=0.2503, pruned_loss=0.05667, over 7415.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2731, pruned_loss=0.0511, over 1424081.03 frames.], batch size: 18, lr: 3.02e-04
2022-05-28 00:54:03,764 INFO [train.py:842] (1/4) Epoch 18, batch 7700, loss[loss=0.1825, simple_loss=0.2726, pruned_loss=0.04623, over 6797.00 frames.], tot_loss[loss=0.1884, simple_loss=0.274, pruned_loss=0.05143, over 1424422.85 frames.], batch size: 31, lr: 3.02e-04
2022-05-28 00:54:42,121 INFO [train.py:842] (1/4) Epoch 18, batch 7750, loss[loss=0.1601, simple_loss=0.2393, pruned_loss=0.04042, over 7270.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2729, pruned_loss=0.05111, over 1424750.43 frames.], batch size: 17, lr: 3.02e-04
2022-05-28 00:55:20,295 INFO [train.py:842] (1/4) Epoch 18, batch 7800, loss[loss=0.1676, simple_loss=0.2555, pruned_loss=0.03988, over 7168.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2729, pruned_loss=0.05141, over 1427634.99 frames.], batch size: 18, lr: 3.02e-04
2022-05-28 00:55:58,691 INFO [train.py:842] (1/4) Epoch 18, batch 7850, loss[loss=0.18, simple_loss=0.2618, pruned_loss=0.0491, over 7356.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2725, pruned_loss=0.05085, over 1428904.36 frames.], batch size: 19, lr: 3.02e-04
2022-05-28 00:56:36,704 INFO [train.py:842] (1/4) Epoch 18, batch 7900, loss[loss=0.1788, simple_loss=0.2655, pruned_loss=0.04604, over 7273.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2726, pruned_loss=0.05042, over 1431762.72 frames.], batch size: 24, lr: 3.02e-04
2022-05-28 00:57:14,990 INFO [train.py:842] (1/4) Epoch 18, batch 7950, loss[loss=0.2205, simple_loss=0.2986, pruned_loss=0.07118, over 4936.00 frames.], tot_loss[loss=0.1871, simple_loss=0.273, pruned_loss=0.05057, over 1431519.80 frames.], batch size: 54, lr: 3.02e-04
2022-05-28 00:57:52,946 INFO [train.py:842] (1/4) Epoch 18, batch 8000, loss[loss=0.1538, simple_loss=0.2479, pruned_loss=0.02991, over 7221.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2732, pruned_loss=0.05062, over 1432778.05 frames.], batch size: 21, lr: 3.02e-04
2022-05-28 00:58:31,216 INFO [train.py:842] (1/4) Epoch 18, batch 8050, loss[loss=0.2174, simple_loss=0.3072, pruned_loss=0.06385, over 7042.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2731, pruned_loss=0.05086, over 1426760.28 frames.], batch size: 28, lr: 3.02e-04
2022-05-28 00:59:09,185 INFO [train.py:842] (1/4) Epoch 18, batch 8100, loss[loss=0.1645, simple_loss=0.2427, pruned_loss=0.04319, over 7226.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2741, pruned_loss=0.05124, over 1423926.06 frames.], batch size: 16, lr: 3.02e-04
2022-05-28 00:59:47,552 INFO [train.py:842] (1/4) Epoch 18, batch 8150, loss[loss=0.191, simple_loss=0.2898, pruned_loss=0.04614, over 7060.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2721, pruned_loss=0.05022, over 1426938.73 frames.], batch size: 28, lr: 3.02e-04
2022-05-28 01:00:25,314 INFO [train.py:842] (1/4) Epoch 18, batch 8200, loss[loss=0.1637, simple_loss=0.2558, pruned_loss=0.03582, over 7125.00 frames.], tot_loss[loss=0.187, simple_loss=0.2731, pruned_loss=0.05048, over 1425180.80 frames.], batch size: 17, lr: 3.02e-04
2022-05-28 01:01:03,704 INFO [train.py:842] (1/4) Epoch 18, batch 8250, loss[loss=0.2258, simple_loss=0.3172, pruned_loss=0.0672, over 7204.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2729, pruned_loss=0.05076, over 1425288.30 frames.], batch size: 22, lr: 3.02e-04
2022-05-28 01:01:41,869 INFO [train.py:842] (1/4) Epoch 18, batch 8300, loss[loss=0.1612, simple_loss=0.2412, pruned_loss=0.04058, over 7124.00 frames.], tot_loss[loss=0.188, simple_loss=0.2734, pruned_loss=0.05129, over 1426040.87 frames.], batch size: 17, lr: 3.02e-04
2022-05-28 01:02:20,123 INFO [train.py:842] (1/4) Epoch 18, batch 8350, loss[loss=0.1609, simple_loss=0.2593, pruned_loss=0.03131, over 7124.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2732, pruned_loss=0.05123, over 1425483.76 frames.], batch size: 21, lr: 3.02e-04
2022-05-28 01:02:58,017 INFO [train.py:842] (1/4) Epoch 18, batch 8400, loss[loss=0.2022, simple_loss=0.2909, pruned_loss=0.05678, over 7413.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2727, pruned_loss=0.05115, over 1421485.91 frames.], batch size: 21, lr: 3.02e-04
2022-05-28 01:03:36,358 INFO [train.py:842] (1/4) Epoch 18, batch 8450, loss[loss=0.2287, simple_loss=0.3176, pruned_loss=0.06995, over 7141.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2728, pruned_loss=0.05089, over 1423439.33 frames.], batch size: 26, lr: 3.01e-04
2022-05-28 01:04:14,383 INFO [train.py:842] (1/4) Epoch 18, batch 8500, loss[loss=0.1768, simple_loss=0.2639, pruned_loss=0.04487, over 7451.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2718, pruned_loss=0.05037, over 1425449.03 frames.], batch size: 19, lr: 3.01e-04
2022-05-28 01:04:52,545 INFO [train.py:842] (1/4) Epoch 18, batch 8550, loss[loss=0.2097, simple_loss=0.2961, pruned_loss=0.06163, over 7413.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2736, pruned_loss=0.05157, over 1427367.14 frames.], batch size: 21, lr: 3.01e-04
2022-05-28 01:05:30,285 INFO [train.py:842] (1/4) Epoch 18, batch 8600, loss[loss=0.1517, simple_loss=0.2276, pruned_loss=0.03787, over 7298.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2746, pruned_loss=0.05207, over 1425272.10 frames.], batch size: 17, lr: 3.01e-04
2022-05-28 01:06:08,305 INFO [train.py:842] (1/4) Epoch 18, batch 8650, loss[loss=0.189, simple_loss=0.2741, pruned_loss=0.05195, over 7258.00 frames.], tot_loss[loss=0.1904, simple_loss=0.276, pruned_loss=0.05241, over 1419965.97 frames.], batch size: 19, lr: 3.01e-04
2022-05-28 01:06:46,194 INFO [train.py:842] (1/4) Epoch 18, batch 8700, loss[loss=0.1992, simple_loss=0.2803, pruned_loss=0.05911, over 7293.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2742, pruned_loss=0.05136, over 1420236.53 frames.], batch size: 25, lr: 3.01e-04
2022-05-28 01:07:24,555 INFO [train.py:842] (1/4) Epoch 18, batch 8750, loss[loss=0.1834, simple_loss=0.2743, pruned_loss=0.04622, over 7208.00 frames.], tot_loss[loss=0.188, simple_loss=0.2735, pruned_loss=0.0512, over 1420235.11 frames.], batch size: 23, lr: 3.01e-04
2022-05-28 01:08:02,253 INFO [train.py:842] (1/4) Epoch 18, batch 8800, loss[loss=0.1539, simple_loss=0.2371, pruned_loss=0.03536, over 7129.00 frames.], tot_loss[loss=0.19, simple_loss=0.2751, pruned_loss=0.05245, over 1410130.80 frames.], batch size: 17, lr: 3.01e-04
2022-05-28 01:08:40,269 INFO [train.py:842] (1/4) Epoch 18, batch 8850, loss[loss=0.1713, simple_loss=0.2617, pruned_loss=0.04044, over 6457.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2754, pruned_loss=0.05302, over 1403574.00 frames.], batch size: 38, lr: 3.01e-04
2022-05-28 01:09:17,565 INFO [train.py:842] (1/4) Epoch 18, batch 8900, loss[loss=0.2505, simple_loss=0.3143, pruned_loss=0.09331, over 6981.00 frames.], tot_loss[loss=0.1915, simple_loss=0.276, pruned_loss=0.05349, over 1397196.49 frames.], batch size: 16, lr: 3.01e-04
2022-05-28 01:09:55,311 INFO [train.py:842] (1/4) Epoch 18, batch 8950, loss[loss=0.2443, simple_loss=0.3094, pruned_loss=0.08957, over 4827.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2756, pruned_loss=0.05345, over 1386685.97 frames.], batch size: 52, lr: 3.01e-04
2022-05-28 01:10:32,669 INFO [train.py:842] (1/4) Epoch 18, batch 9000, loss[loss=0.1735, simple_loss=0.2712, pruned_loss=0.03791, over 6750.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2764, pruned_loss=0.05359, over 1383038.31 frames.], batch size: 31, lr: 3.01e-04
2022-05-28 01:10:32,670 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 01:10:41,756 INFO [train.py:871] (1/4) Epoch 18, validation: loss=0.1664, simple_loss=0.2661, pruned_loss=0.03336, over 868885.00 frames.
2022-05-28 01:11:19,008 INFO [train.py:842] (1/4) Epoch 18, batch 9050, loss[loss=0.2001, simple_loss=0.2833, pruned_loss=0.0585, over 6768.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2767, pruned_loss=0.05417, over 1367527.40 frames.], batch size: 31, lr: 3.01e-04
2022-05-28 01:11:55,782 INFO [train.py:842] (1/4) Epoch 18, batch 9100, loss[loss=0.223, simple_loss=0.3047, pruned_loss=0.0706, over 5181.00 frames.], tot_loss[loss=0.1992, simple_loss=0.282, pruned_loss=0.0582, over 1293362.45 frames.], batch size: 53, lr: 3.01e-04
2022-05-28 01:12:32,908 INFO [train.py:842] (1/4) Epoch 18, batch 9150, loss[loss=0.1965, simple_loss=0.2706, pruned_loss=0.06124, over 5026.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2861, pruned_loss=0.06123, over 1229154.74 frames.], batch size: 54, lr: 3.01e-04
2022-05-28 01:13:18,590 INFO [train.py:842] (1/4) Epoch 19, batch 0, loss[loss=0.2215, simple_loss=0.3068, pruned_loss=0.06809, over 7299.00 frames.], tot_loss[loss=0.2215, simple_loss=0.3068, pruned_loss=0.06809, over 7299.00 frames.], batch size: 25, lr: 2.93e-04
2022-05-28 01:13:57,228 INFO [train.py:842] (1/4) Epoch 19, batch 50, loss[loss=0.1827, simple_loss=0.2792, pruned_loss=0.0431, over 7331.00 frames.], tot_loss[loss=0.1881, simple_loss=0.275, pruned_loss=0.05062, over 325106.32 frames.], batch size: 22, lr: 2.93e-04
2022-05-28 01:14:35,410 INFO [train.py:842] (1/4) Epoch 19, batch 100, loss[loss=0.1928, simple_loss=0.2838, pruned_loss=0.05086, over 7328.00 frames.], tot_loss[loss=0.186, simple_loss=0.2732, pruned_loss=0.04939, over 575247.19 frames.], batch size: 22, lr: 2.93e-04
2022-05-28 01:15:13,708 INFO [train.py:842] (1/4) Epoch 19, batch 150, loss[loss=0.1849, simple_loss=0.2787, pruned_loss=0.04551, over 7216.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2709, pruned_loss=0.04861, over 764659.63 frames.], batch size: 21, lr: 2.93e-04
2022-05-28 01:15:51,849 INFO [train.py:842] (1/4) Epoch 19, batch 200, loss[loss=0.204, simple_loss=0.289, pruned_loss=0.05953, over 7272.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2697, pruned_loss=0.04827, over 910415.81 frames.], batch size: 17, lr: 2.93e-04
2022-05-28 01:16:30,156 INFO [train.py:842] (1/4) Epoch 19, batch 250, loss[loss=0.2038, simple_loss=0.2883, pruned_loss=0.05967, over 6795.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2704, pruned_loss=0.04884, over 1026026.78 frames.], batch size: 31, lr: 2.93e-04
2022-05-28 01:17:08,200 INFO [train.py:842] (1/4) Epoch 19, batch 300, loss[loss=0.1733, simple_loss=0.2699, pruned_loss=0.03837, over 7228.00 frames.], tot_loss[loss=0.1845, simple_loss=0.271, pruned_loss=0.04905, over 1116136.44 frames.], batch size: 20, lr: 2.93e-04
2022-05-28 01:17:46,569 INFO [train.py:842] (1/4) Epoch 19, batch 350, loss[loss=0.2151, simple_loss=0.2952, pruned_loss=0.06744, over 6759.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2714, pruned_loss=0.04982, over 1183134.66 frames.], batch size: 31, lr: 2.93e-04
2022-05-28 01:18:24,401 INFO [train.py:842] (1/4) Epoch 19, batch 400, loss[loss=0.1858, simple_loss=0.2612, pruned_loss=0.05524, over 7062.00 frames.], tot_loss[loss=0.1862, simple_loss=0.272, pruned_loss=0.05023, over 1234067.31 frames.], batch size: 18, lr: 2.93e-04
2022-05-28 01:19:02,591 INFO [train.py:842] (1/4) Epoch 19, batch 450, loss[loss=0.193, simple_loss=0.2803, pruned_loss=0.05284, over 7331.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2735, pruned_loss=0.05118, over 1275837.37 frames.], batch size: 22, lr: 2.93e-04
2022-05-28 01:19:40,373 INFO [train.py:842] (1/4) Epoch 19, batch 500, loss[loss=0.1513, simple_loss=0.2339, pruned_loss=0.03441, over 7114.00 frames.], tot_loss[loss=0.1889, simple_loss=0.274, pruned_loss=0.0519, over 1306531.81 frames.], batch size: 17, lr: 2.93e-04
2022-05-28 01:20:18,785 INFO [train.py:842] (1/4) Epoch 19, batch 550, loss[loss=0.1863, simple_loss=0.2604, pruned_loss=0.05606, over 7273.00 frames.], tot_loss[loss=0.1874, simple_loss=0.273, pruned_loss=0.05089, over 1336602.87 frames.], batch size: 17, lr: 2.93e-04
2022-05-28 01:20:56,846 INFO [train.py:842] (1/4) Epoch 19, batch 600, loss[loss=0.1909, simple_loss=0.2657, pruned_loss=0.05808, over 7274.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2737, pruned_loss=0.05143, over 1357329.89 frames.], batch size: 18, lr: 2.93e-04
2022-05-28 01:21:35,377 INFO [train.py:842] (1/4) Epoch 19, batch 650, loss[loss=0.1845, simple_loss=0.2844, pruned_loss=0.04232, over 7112.00 frames.], tot_loss[loss=0.1877, simple_loss=0.273, pruned_loss=0.05118, over 1376710.97 frames.], batch size: 21, lr: 2.93e-04
2022-05-28 01:22:13,254 INFO [train.py:842] (1/4) Epoch 19, batch 700, loss[loss=0.1916, simple_loss=0.2704, pruned_loss=0.05643, over 4774.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2742, pruned_loss=0.0514, over 1387029.48 frames.], batch size: 54, lr: 2.93e-04
2022-05-28 01:22:51,653 INFO [train.py:842] (1/4) Epoch 19, batch 750, loss[loss=0.1663, simple_loss=0.2617, pruned_loss=0.03545, over 7158.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2729, pruned_loss=0.05008, over 1394773.95 frames.], batch size: 19, lr: 2.93e-04
2022-05-28 01:23:29,312 INFO [train.py:842] (1/4) Epoch 19, batch 800, loss[loss=0.1937, simple_loss=0.2787, pruned_loss=0.05435, over 6728.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2727, pruned_loss=0.04998, over 1397209.05 frames.], batch size: 31, lr: 2.92e-04
2022-05-28 01:24:07,575 INFO [train.py:842] (1/4) Epoch 19, batch 850, loss[loss=0.1979, simple_loss=0.2912, pruned_loss=0.05229, over 7067.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2731, pruned_loss=0.04951, over 1404766.27 frames.], batch size: 18, lr: 2.92e-04
2022-05-28 01:24:45,426 INFO [train.py:842] (1/4) Epoch 19, batch 900, loss[loss=0.1611, simple_loss=0.2393, pruned_loss=0.04143, over 6823.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2745, pruned_loss=0.05029, over 1410152.40 frames.], batch size: 15, lr: 2.92e-04
2022-05-28 01:25:23,694 INFO [train.py:842] (1/4) Epoch 19, batch 950, loss[loss=0.219, simple_loss=0.3061, pruned_loss=0.06593, over 7362.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2747, pruned_loss=0.05098, over 1413389.13 frames.], batch size: 23, lr: 2.92e-04
2022-05-28 01:26:01,659 INFO [train.py:842] (1/4) Epoch 19, batch 1000, loss[loss=0.1555, simple_loss=0.2459, pruned_loss=0.03255, over 7144.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2748, pruned_loss=0.05112, over 1419917.89 frames.], batch size: 20, lr: 2.92e-04
2022-05-28 01:26:39,857 INFO [train.py:842] (1/4) Epoch 19, batch 1050, loss[loss=0.2058, simple_loss=0.2789, pruned_loss=0.06634, over 7265.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2742, pruned_loss=0.05121, over 1417859.35 frames.], batch size: 25, lr: 2.92e-04
2022-05-28 01:27:17,943 INFO [train.py:842] (1/4) Epoch 19, batch 1100, loss[loss=0.1857, simple_loss=0.269, pruned_loss=0.05117, over 7328.00 frames.], tot_loss[loss=0.1883, simple_loss=0.274, pruned_loss=0.05127, over 1419523.53 frames.], batch size: 20, lr: 2.92e-04
2022-05-28 01:27:56,173 INFO [train.py:842] (1/4) Epoch 19, batch 1150, loss[loss=0.2057, simple_loss=0.2955, pruned_loss=0.05796, over 7320.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2737, pruned_loss=0.05104, over 1420107.79 frames.], batch size: 24, lr: 2.92e-04
2022-05-28 01:28:34,213 INFO [train.py:842] (1/4) Epoch 19, batch 1200, loss[loss=0.2022, simple_loss=0.278, pruned_loss=0.06319, over 4862.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2744, pruned_loss=0.05155, over 1414039.55 frames.], batch size: 53, lr: 2.92e-04
2022-05-28 01:29:12,410 INFO [train.py:842] (1/4) Epoch 19, batch 1250, loss[loss=0.1767, simple_loss=0.2754, pruned_loss=0.03899, over 7125.00 frames.], tot_loss[loss=0.1891, simple_loss=0.275, pruned_loss=0.0516, over 1414640.72 frames.], batch size: 21, lr: 2.92e-04
2022-05-28 01:29:50,149 INFO [train.py:842] (1/4) Epoch 19, batch 1300, loss[loss=0.1495, simple_loss=0.243, pruned_loss=0.02805, over 7157.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2746, pruned_loss=0.05111, over 1413677.97 frames.], batch size: 19, lr: 2.92e-04
2022-05-28 01:30:28,065 INFO [train.py:842] (1/4) Epoch 19, batch 1350, loss[loss=0.1671, simple_loss=0.2575, pruned_loss=0.03834, over 7095.00 frames.], tot_loss[loss=0.1903, simple_loss=0.276, pruned_loss=0.05232, over 1411645.02 frames.], batch size: 28, lr: 2.92e-04
2022-05-28 01:31:06,128 INFO [train.py:842] (1/4) Epoch 19, batch 1400, loss[loss=0.2016, simple_loss=0.2831, pruned_loss=0.06001, over 7058.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2753, pruned_loss=0.05251, over 1409656.50 frames.], batch size: 18, lr: 2.92e-04
2022-05-28 01:31:44,478 INFO [train.py:842] (1/4) Epoch 19, batch 1450, loss[loss=0.1879, simple_loss=0.2735, pruned_loss=0.05114, over 7320.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2763, pruned_loss=0.05329, over 1417109.92 frames.], batch size: 21, lr: 2.92e-04
2022-05-28 01:32:22,418 INFO [train.py:842] (1/4) Epoch 19, batch 1500, loss[loss=0.1555, simple_loss=0.2405, pruned_loss=0.03524, over 7247.00 frames.], tot_loss[loss=0.19, simple_loss=0.2754, pruned_loss=0.05229, over 1420992.27 frames.], batch size: 19, lr: 2.92e-04
2022-05-28 01:33:00,826 INFO [train.py:842] (1/4) Epoch 19, batch 1550, loss[loss=0.1905, simple_loss=0.2834, pruned_loss=0.0488, over 7407.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2743, pruned_loss=0.05161, over 1424225.47 frames.], batch size: 21, lr: 2.92e-04
2022-05-28 01:33:38,790 INFO [train.py:842] (1/4) Epoch 19, batch 1600, loss[loss=0.2005, simple_loss=0.2921, pruned_loss=0.0544, over 7206.00 frames.], tot_loss[loss=0.188, simple_loss=0.2735, pruned_loss=0.05129, over 1423031.59 frames.], batch size: 22, lr: 2.92e-04
2022-05-28 01:34:17,154 INFO [train.py:842] (1/4) Epoch 19, batch 1650, loss[loss=0.1614, simple_loss=0.2469, pruned_loss=0.03793, over 7163.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2729, pruned_loss=0.05083, over 1422292.85 frames.], batch size: 18, lr: 2.92e-04
2022-05-28 01:34:55,112 INFO [train.py:842] (1/4) Epoch 19, batch 1700, loss[loss=0.1529, simple_loss=0.2353, pruned_loss=0.03529, over 7150.00 frames.], tot_loss[loss=0.187, simple_loss=0.2727, pruned_loss=0.05064, over 1422593.29 frames.], batch size: 18, lr: 2.92e-04
2022-05-28 01:35:32,925 INFO [train.py:842] (1/4) Epoch 19, batch 1750, loss[loss=0.2079, simple_loss=0.2962, pruned_loss=0.05979, over 7148.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2741, pruned_loss=0.05089, over 1415512.42 frames.], batch size: 20, lr: 2.92e-04
2022-05-28 01:36:10,617 INFO [train.py:842] (1/4) Epoch 19, batch 1800, loss[loss=0.2088, simple_loss=0.291, pruned_loss=0.06328, over 7257.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2752, pruned_loss=0.05111, over 1416281.32 frames.], batch size: 19, lr: 2.92e-04
2022-05-28 01:36:48,924 INFO [train.py:842] (1/4) Epoch 19, batch 1850, loss[loss=0.1832, simple_loss=0.2838, pruned_loss=0.04133, over 7278.00 frames.], tot_loss[loss=0.188, simple_loss=0.2743, pruned_loss=0.05087, over 1422106.93 frames.], batch size: 24, lr: 2.92e-04
2022-05-28 01:37:26,738 INFO [train.py:842] (1/4) Epoch 19, batch 1900, loss[loss=0.1763, simple_loss=0.2675, pruned_loss=0.04255, over 7095.00 frames.], tot_loss[loss=0.1878, simple_loss=0.274, pruned_loss=0.0508, over 1418741.42 frames.], batch size: 28, lr: 2.92e-04
2022-05-28 01:38:04,998 INFO [train.py:842] (1/4) Epoch 19, batch 1950, loss[loss=0.1432, simple_loss=0.2238, pruned_loss=0.0313, over 7001.00 frames.], tot_loss[loss=0.187, simple_loss=0.273, pruned_loss=0.05043, over 1419710.16 frames.], batch size: 16, lr: 2.91e-04
2022-05-28 01:38:43,029 INFO [train.py:842] (1/4) Epoch 19, batch 2000, loss[loss=0.1702, simple_loss=0.2639, pruned_loss=0.03825, over 7152.00 frames.], tot_loss[loss=0.1861, simple_loss=0.272, pruned_loss=0.05014, over 1422963.65 frames.], batch size: 20, lr: 2.91e-04
2022-05-28 01:39:21,320 INFO [train.py:842] (1/4) Epoch 19, batch 2050, loss[loss=0.1768, simple_loss=0.262, pruned_loss=0.04585, over 7313.00 frames.], tot_loss[loss=0.1863, simple_loss=0.272, pruned_loss=0.05032, over 1422439.02 frames.], batch size: 25, lr: 2.91e-04
2022-05-28 01:39:59,179 INFO [train.py:842] (1/4) Epoch 19, batch 2100, loss[loss=0.1536, simple_loss=0.2479, pruned_loss=0.02963, over 7151.00 frames.], tot_loss[loss=0.1861, simple_loss=0.272, pruned_loss=0.05014, over 1423445.09 frames.], batch size: 19, lr: 2.91e-04
2022-05-28 01:40:37,576 INFO [train.py:842] (1/4) Epoch 19, batch 2150, loss[loss=0.193, simple_loss=0.2772, pruned_loss=0.0544, over 7215.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2719, pruned_loss=0.05012, over 1420596.24 frames.], batch size: 21, lr: 2.91e-04
2022-05-28 01:41:15,629 INFO [train.py:842] (1/4) Epoch 19, batch 2200, loss[loss=0.1974, simple_loss=0.2765, pruned_loss=0.05917, over 7123.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2717, pruned_loss=0.04982, over 1424528.42 frames.], batch size: 21, lr: 2.91e-04
2022-05-28 01:41:53,807 INFO [train.py:842] (1/4) Epoch 19, batch 2250, loss[loss=0.2225, simple_loss=0.2965, pruned_loss=0.0742, over 6574.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2722, pruned_loss=0.05054, over 1423862.48 frames.], batch size: 38, lr: 2.91e-04
2022-05-28 01:42:31,841 INFO [train.py:842] (1/4) Epoch 19, batch 2300, loss[loss=0.2552, simple_loss=0.318, pruned_loss=0.09615, over 7384.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2727, pruned_loss=0.05086, over 1426693.62 frames.], batch size: 23, lr: 2.91e-04
2022-05-28 01:43:09,945 INFO [train.py:842] (1/4) Epoch 19, batch 2350, loss[loss=0.1595, simple_loss=0.2435, pruned_loss=0.03777, over 7315.00 frames.], tot_loss[loss=0.1883, simple_loss=0.274, pruned_loss=0.05127, over 1423360.56 frames.], batch size: 17, lr: 2.91e-04
2022-05-28 01:43:57,328 INFO [train.py:842] (1/4) Epoch 19, batch 2400, loss[loss=0.1505, simple_loss=0.2393, pruned_loss=0.03081, over 7150.00 frames.], tot_loss[loss=0.188, simple_loss=0.2738, pruned_loss=0.05107, over 1419196.21 frames.], batch size: 20, lr: 2.91e-04
2022-05-28 01:44:35,690 INFO [train.py:842] (1/4) Epoch 19, batch 2450, loss[loss=0.1838, simple_loss=0.2752, pruned_loss=0.04624, over 7140.00 frames.], tot_loss[loss=0.1881, simple_loss=0.274, pruned_loss=0.05115, over 1420616.87 frames.], batch size: 20, lr: 2.91e-04
2022-05-28 01:45:13,670 INFO [train.py:842] (1/4) Epoch 19, batch 2500, loss[loss=0.2207, simple_loss=0.3026, pruned_loss=0.06941, over 7186.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2738, pruned_loss=0.05125, over 1419971.68 frames.], batch size: 26, lr: 2.91e-04
2022-05-28 01:45:54,639 INFO [train.py:842] (1/4) Epoch 19, batch 2550, loss[loss=0.2699, simple_loss=0.3314, pruned_loss=0.1042, over 7306.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2736, pruned_loss=0.05136, over 1419539.18 frames.], batch size: 24, lr: 2.91e-04
2022-05-28 01:46:32,542 INFO [train.py:842] (1/4) Epoch 19, batch 2600, loss[loss=0.1705, simple_loss=0.2531, pruned_loss=0.04395, over 7016.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2734, pruned_loss=0.05066, over 1423416.73 frames.], batch size: 16, lr: 2.91e-04
2022-05-28 01:47:10,856 INFO [train.py:842] (1/4) Epoch 19, batch 2650, loss[loss=0.212, simple_loss=0.3043, pruned_loss=0.05988, over 7295.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2733, pruned_loss=0.05065, over 1426807.95 frames.], batch size: 24, lr: 2.91e-04
2022-05-28 01:47:49,149 INFO [train.py:842] (1/4) Epoch 19, batch 2700, loss[loss=0.1783, simple_loss=0.2707, pruned_loss=0.04295, over 7283.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2723, pruned_loss=0.0501, over 1430596.26 frames.], batch size: 25, lr: 2.91e-04
2022-05-28 01:48:27,304 INFO [train.py:842] (1/4) Epoch 19, batch 2750, loss[loss=0.2329, simple_loss=0.3047, pruned_loss=0.08061, over 7415.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2748, pruned_loss=0.05172, over 1429512.27 frames.], batch size: 21, lr: 2.91e-04
2022-05-28 01:49:05,393 INFO [train.py:842] (1/4) Epoch 19, batch 2800, loss[loss=0.1396, simple_loss=0.2267, pruned_loss=0.0262, over 7071.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2748, pruned_loss=0.05224, over 1430626.14 frames.], batch size: 18, lr: 2.91e-04
2022-05-28 01:49:43,508 INFO [train.py:842] (1/4) Epoch 19, batch 2850, loss[loss=0.1677, simple_loss=0.2569, pruned_loss=0.03922, over 7155.00 frames.], tot_loss[loss=0.1885, simple_loss=0.274, pruned_loss=0.0515, over 1427682.16 frames.], batch size: 19, lr: 2.91e-04
2022-05-28 01:50:21,406 INFO [train.py:842] (1/4) Epoch 19, batch 2900, loss[loss=0.1669, simple_loss=0.2585, pruned_loss=0.03768, over 7156.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2721, pruned_loss=0.0502, over 1425222.75 frames.], batch size: 26, lr: 2.91e-04
2022-05-28 01:50:59,788 INFO [train.py:842] (1/4) Epoch 19, batch 2950, loss[loss=0.1711, simple_loss=0.2447, pruned_loss=0.0488, over 7268.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2724, pruned_loss=0.05042, over 1430626.49 frames.], batch size: 17, lr: 2.91e-04
2022-05-28 01:51:37,811 INFO [train.py:842] (1/4) Epoch 19, batch 3000, loss[loss=0.2864, simple_loss=0.3528, pruned_loss=0.11, over 4931.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2725, pruned_loss=0.05062, over 1430149.20 frames.], batch size: 53, lr: 2.91e-04
2022-05-28 01:51:37,811 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 01:51:46,796 INFO [train.py:871] (1/4) Epoch 19, validation: loss=0.1654, simple_loss=0.2649, pruned_loss=0.03297, over 868885.00 frames.
2022-05-28 01:52:25,075 INFO [train.py:842] (1/4) Epoch 19, batch 3050, loss[loss=0.2625, simple_loss=0.3586, pruned_loss=0.08316, over 7195.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2737, pruned_loss=0.05087, over 1431282.48 frames.], batch size: 23, lr: 2.91e-04
2022-05-28 01:53:03,101 INFO [train.py:842] (1/4) Epoch 19, batch 3100, loss[loss=0.1922, simple_loss=0.2705, pruned_loss=0.05695, over 6431.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2739, pruned_loss=0.0512, over 1432290.37 frames.], batch size: 37, lr: 2.90e-04
2022-05-28 01:53:41,174 INFO [train.py:842] (1/4) Epoch 19, batch 3150, loss[loss=0.1666, simple_loss=0.2512, pruned_loss=0.04105, over 7276.00 frames.], tot_loss[loss=0.189, simple_loss=0.2746, pruned_loss=0.05172, over 1429583.54 frames.], batch size: 18, lr: 2.90e-04
2022-05-28 01:54:19,121 INFO [train.py:842] (1/4) Epoch 19, batch 3200, loss[loss=0.2026, simple_loss=0.292, pruned_loss=0.05662, over 7152.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2752, pruned_loss=0.052, over 1427954.44 frames.], batch size: 19, lr: 2.90e-04
2022-05-28 01:54:57,219 INFO [train.py:842] (1/4) Epoch 19, batch 3250, loss[loss=0.1805, simple_loss=0.2608, pruned_loss=0.05009, over 7357.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2753, pruned_loss=0.05184, over 1425478.08 frames.], batch size: 19, lr: 2.90e-04
2022-05-28 01:55:35,142 INFO [train.py:842] (1/4) Epoch 19, batch 3300, loss[loss=0.1826, simple_loss=0.2796, pruned_loss=0.04281, over 6062.00 frames.], tot_loss[loss=0.19, simple_loss=0.2759, pruned_loss=0.05202, over 1425779.19 frames.], batch size: 37, lr: 2.90e-04
2022-05-28 01:56:13,606 INFO [train.py:842] (1/4) Epoch 19, batch 3350, loss[loss=0.1878, simple_loss=0.2743, pruned_loss=0.05065, over 7111.00 frames.], tot_loss[loss=0.189, simple_loss=0.2745, pruned_loss=0.05171, over 1424588.30 frames.], batch size: 21, lr: 2.90e-04
2022-05-28 01:56:51,533 INFO [train.py:842] (1/4) Epoch 19, batch 3400, loss[loss=0.2222, simple_loss=0.2909, pruned_loss=0.0767, over 7267.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2751, pruned_loss=0.05204, over 1424814.04 frames.], batch size: 18, lr: 2.90e-04
2022-05-28 01:57:29,946 INFO [train.py:842] (1/4) Epoch 19, batch 3450, loss[loss=0.1641, simple_loss=0.2438, pruned_loss=0.04222, over 7367.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2744, pruned_loss=0.05225, over 1421241.02 frames.], batch size: 19, lr: 2.90e-04
2022-05-28 01:58:07,911 INFO [train.py:842] (1/4) Epoch 19, batch 3500, loss[loss=0.2364, simple_loss=0.2979, pruned_loss=0.08744, over 7286.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2745, pruned_loss=0.0521, over 1423443.84 frames.], batch size: 18, lr: 2.90e-04
2022-05-28 01:58:46,252 INFO [train.py:842] (1/4) Epoch 19, batch 3550, loss[loss=0.1754, simple_loss=0.2568, pruned_loss=0.04693, over 7154.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2747, pruned_loss=0.05217, over 1423956.00 frames.], batch size: 17, lr: 2.90e-04
2022-05-28 01:59:24,116 INFO [train.py:842] (1/4) Epoch 19, batch 3600, loss[loss=0.2152, simple_loss=0.301, pruned_loss=0.06472, over 7198.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2755, pruned_loss=0.05262, over 1420855.43 frames.], batch size: 23, lr: 2.90e-04
2022-05-28 02:00:02,224 INFO [train.py:842] (1/4) Epoch 19, batch 3650, loss[loss=0.1989, simple_loss=0.2785, pruned_loss=0.05965, over 7326.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2761, pruned_loss=0.05275, over 1414976.34 frames.], batch size: 20, lr: 2.90e-04
2022-05-28 02:00:40,211 INFO [train.py:842] (1/4) Epoch 19, batch 3700, loss[loss=0.1476, simple_loss=0.2233, pruned_loss=0.0359, over 7284.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2753, pruned_loss=0.05226, over 1416955.53 frames.], batch size: 17, lr: 2.90e-04
2022-05-28 02:01:18,452 INFO [train.py:842] (1/4) Epoch 19, batch 3750, loss[loss=0.2141, simple_loss=0.3049, pruned_loss=0.06168, over 7333.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2759, pruned_loss=0.05259, over 1412645.83 frames.], batch size: 22, lr: 2.90e-04
2022-05-28 02:01:56,351 INFO [train.py:842] (1/4) Epoch 19, batch 3800, loss[loss=0.1463, simple_loss=0.2301, pruned_loss=0.0313, over 6975.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2758, pruned_loss=0.05229, over 1417132.08 frames.], batch size: 16, lr: 2.90e-04
2022-05-28 02:02:34,511 INFO [train.py:842] (1/4) Epoch 19, batch 3850, loss[loss=0.2508, simple_loss=0.3249, pruned_loss=0.08838, over 5238.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2758, pruned_loss=0.05221, over 1414779.32 frames.], batch size: 52, lr: 2.90e-04
2022-05-28 02:03:12,472 INFO [train.py:842] (1/4) Epoch 19, batch 3900, loss[loss=0.1992, simple_loss=0.2837, pruned_loss=0.05734, over 7153.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2753, pruned_loss=0.0521, over 1415494.91 frames.], batch size: 26, lr: 2.90e-04
2022-05-28 02:03:50,752 INFO [train.py:842] (1/4) Epoch 19, batch 3950, loss[loss=0.1833, simple_loss=0.2774, pruned_loss=0.04465, over 7230.00 frames.], tot_loss[loss=0.1893, simple_loss=0.275, pruned_loss=0.05181, over 1418260.21 frames.], batch size: 20, lr: 2.90e-04
2022-05-28 02:04:28,771 INFO [train.py:842] (1/4) Epoch 19, batch 4000, loss[loss=0.1795, simple_loss=0.2682, pruned_loss=0.04544, over 7125.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2737, pruned_loss=0.05107, over 1420719.38 frames.], batch size: 21, lr: 2.90e-04
2022-05-28 02:05:07,113 INFO [train.py:842] (1/4) Epoch 19, batch 4050, loss[loss=0.1515, simple_loss=0.2404, pruned_loss=0.03131, over 7360.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2723, pruned_loss=0.05048, over 1420534.17 frames.], batch size: 19, lr: 2.90e-04
2022-05-28 02:05:45,234 INFO [train.py:842] (1/4) Epoch 19, batch 4100, loss[loss=0.1826, simple_loss=0.2705, pruned_loss=0.04733, over 7144.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2717, pruned_loss=0.04996, over 1418117.44 frames.], batch size: 20, lr: 2.90e-04
2022-05-28 02:06:23,176 INFO [train.py:842] (1/4) Epoch 19, batch 4150, loss[loss=0.2245, simple_loss=0.314, pruned_loss=0.06749, over 7214.00 frames.], tot_loss[loss=0.1882, simple_loss=0.274, pruned_loss=0.05118, over 1416688.51 frames.], batch size: 22, lr: 2.90e-04
2022-05-28 02:07:01,161 INFO [train.py:842] (1/4) Epoch 19, batch 4200, loss[loss=0.2046, simple_loss=0.2962, pruned_loss=0.05645, over 7327.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2742, pruned_loss=0.05083, over 1423507.07 frames.], batch size: 22, lr: 2.90e-04
2022-05-28 02:07:39,415 INFO [train.py:842] (1/4) Epoch 19, batch 4250, loss[loss=0.1935, simple_loss=0.2869, pruned_loss=0.05001, over 7323.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2738, pruned_loss=0.05074, over 1422440.76 frames.], batch size: 20, lr: 2.90e-04
2022-05-28 02:08:17,283 INFO [train.py:842] (1/4) Epoch 19, batch 4300, loss[loss=0.1841, simple_loss=0.2717, pruned_loss=0.04822, over 7199.00 frames.], tot_loss[loss=0.189, simple_loss=0.2748, pruned_loss=0.05154, over 1421178.06 frames.], batch size: 23, lr: 2.89e-04
2022-05-28 02:08:55,440 INFO [train.py:842] (1/4) Epoch 19, batch 4350, loss[loss=0.2228, simple_loss=0.3145, pruned_loss=0.06556, over 6740.00 frames.], tot_loss[loss=0.189, simple_loss=0.2753, pruned_loss=0.05133, over 1420967.10 frames.], batch size: 31, lr: 2.89e-04
2022-05-28 02:09:33,408 INFO [train.py:842] (1/4) Epoch 19, batch 4400, loss[loss=0.167, simple_loss=0.2633, pruned_loss=0.0353, over 7234.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2752, pruned_loss=0.05101, over 1422789.61 frames.], batch size: 20, lr: 2.89e-04
2022-05-28 02:10:11,532 INFO [train.py:842] (1/4) Epoch 19, batch 4450, loss[loss=0.1621, simple_loss=0.253, pruned_loss=0.03559, over 7129.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2755, pruned_loss=0.05133, over 1421006.93 frames.], batch size: 21, lr: 2.89e-04
2022-05-28 02:10:49,582 INFO [train.py:842] (1/4) Epoch 19, batch 4500, loss[loss=0.2172, simple_loss=0.2989, pruned_loss=0.06771, over 7304.00 frames.], tot_loss[loss=0.1887, simple_loss=0.275, pruned_loss=0.05118, over 1420641.71 frames.], batch size: 24, lr: 2.89e-04
2022-05-28 02:11:28,105 INFO [train.py:842] (1/4) Epoch 19, batch 4550, loss[loss=0.1895, simple_loss=0.2803, pruned_loss=0.04931, over 7383.00 frames.], tot_loss[loss=0.188, simple_loss=0.2739, pruned_loss=0.05105, over 1425509.84 frames.], batch size: 23, lr: 2.89e-04
2022-05-28 02:12:06,113 INFO [train.py:842] (1/4) Epoch 19, batch 4600, loss[loss=0.2095, simple_loss=0.2955, pruned_loss=0.06174, over 7419.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2745, pruned_loss=0.05168, over 1423811.78 frames.], batch size: 21, lr: 2.89e-04
2022-05-28 02:12:44,402 INFO [train.py:842] (1/4) Epoch 19, batch 4650, loss[loss=0.1708, simple_loss=0.253, pruned_loss=0.04428, over 7356.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2736, pruned_loss=0.05185, over 1421047.24 frames.], batch size: 19, lr: 2.89e-04
2022-05-28 02:13:22,317 INFO [train.py:842] (1/4) Epoch 19, batch 4700, loss[loss=0.1573, simple_loss=0.2412, pruned_loss=0.03669, over 7281.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2736, pruned_loss=0.05142, over 1422018.77 frames.], batch size: 17, lr: 2.89e-04
2022-05-28 02:14:00,471 INFO [train.py:842] (1/4) Epoch 19, batch 4750, loss[loss=0.1444, simple_loss=0.2187, pruned_loss=0.03501, over 7283.00 frames.], tot_loss[loss=0.187, simple_loss=0.2732, pruned_loss=0.05046, over 1424715.06 frames.], batch size: 17, lr: 2.89e-04
2022-05-28 02:14:38,599 INFO [train.py:842] (1/4) Epoch 19, batch 4800, loss[loss=0.1643, simple_loss=0.249, pruned_loss=0.03982, over 7261.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2734, pruned_loss=0.05084, over 1419730.10 frames.], batch size: 19, lr: 2.89e-04
2022-05-28 02:15:16,767 INFO [train.py:842] (1/4) Epoch 19, batch 4850, loss[loss=0.1658, simple_loss=0.2439, pruned_loss=0.04383, over 6741.00 frames.], tot_loss[loss=0.1884, simple_loss=0.274, pruned_loss=0.0514, over 1419378.55 frames.], batch size: 15, lr: 2.89e-04
2022-05-28 02:15:54,797 INFO [train.py:842] (1/4) Epoch 19, batch 4900, loss[loss=0.208, simple_loss=0.3008, pruned_loss=0.0576, over 7224.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2727, pruned_loss=0.0504, over 1420381.10 frames.], batch size: 21, lr: 2.89e-04
2022-05-28 02:16:32,987 INFO [train.py:842] (1/4) Epoch 19, batch 4950, loss[loss=0.1921, simple_loss=0.2628, pruned_loss=0.06075, over 5031.00 frames.], tot_loss[loss=0.187, simple_loss=0.2728, pruned_loss=0.05063, over 1419201.44 frames.], batch size: 52, lr: 2.89e-04
2022-05-28 02:17:10,629 INFO [train.py:842] (1/4) Epoch 19, batch 5000, loss[loss=0.1776, simple_loss=0.2672, pruned_loss=0.04397, over 6824.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2743, pruned_loss=0.05129, over 1422255.10 frames.], batch size: 31, lr: 2.89e-04
2022-05-28 02:17:48,867 INFO [train.py:842] (1/4) Epoch 19, batch 5050, loss[loss=0.1432, simple_loss=0.2326, pruned_loss=0.02686, over 7011.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2745, pruned_loss=0.05101, over 1422698.51 frames.], batch size: 16, lr: 2.89e-04
2022-05-28 02:18:26,632 INFO [train.py:842] (1/4) Epoch 19, batch 5100, loss[loss=0.2607, simple_loss=0.3318, pruned_loss=0.09479, over 4965.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2742, pruned_loss=0.05063, over 1420786.74 frames.], batch size: 53, lr: 2.89e-04
2022-05-28 02:19:05,000 INFO [train.py:842] (1/4) Epoch 19, batch 5150, loss[loss=0.1855, simple_loss=0.279, pruned_loss=0.04595, over 7150.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2734, pruned_loss=0.05054, over 1422890.19 frames.], batch size: 19, lr: 2.89e-04
2022-05-28 02:19:43,068 INFO [train.py:842] (1/4) Epoch 19, batch 5200, loss[loss=0.173, simple_loss=0.2632, pruned_loss=0.04136, over 6788.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2735, pruned_loss=0.0508, over 1424947.29 frames.], batch size: 31, lr: 2.89e-04
2022-05-28 02:20:21,226 INFO [train.py:842] (1/4) Epoch 19, batch 5250, loss[loss=0.2136, simple_loss=0.2766, pruned_loss=0.07532, over 7290.00 frames.], tot_loss[loss=0.189, simple_loss=0.2744, pruned_loss=0.05182, over 1418525.82 frames.], batch size: 17, lr: 2.89e-04
2022-05-28 02:21:08,635 INFO [train.py:842] (1/4) Epoch 19, batch 5300, loss[loss=0.1496, simple_loss=0.2367, pruned_loss=0.03124, over 7296.00 frames.], tot_loss[loss=0.188, simple_loss=0.2734, pruned_loss=0.05128, over 1421963.57 frames.], batch size: 18, lr: 2.89e-04
2022-05-28 02:21:47,032 INFO [train.py:842] (1/4) Epoch 19, batch 5350, loss[loss=0.1973, simple_loss=0.3014, pruned_loss=0.04656, over 7227.00 frames.], tot_loss[loss=0.187, simple_loss=0.2725, pruned_loss=0.05077, over 1421560.91 frames.], batch size: 21, lr: 2.89e-04
2022-05-28 02:22:24,939 INFO [train.py:842] (1/4) Epoch 19, batch 5400, loss[loss=0.2032, simple_loss=0.2866, pruned_loss=0.05989, over 7430.00 frames.], tot_loss[loss=0.1877, simple_loss=0.273, pruned_loss=0.05126, over 1419893.66 frames.], batch size: 20, lr: 2.89e-04
2022-05-28 02:23:12,406 INFO [train.py:842] (1/4) Epoch 19, batch 5450, loss[loss=0.1729, simple_loss=0.2665, pruned_loss=0.03959, over 6679.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2725, pruned_loss=0.05056, over 1419238.50 frames.], batch size: 31, lr: 2.88e-04
2022-05-28 02:23:50,556 INFO [train.py:842] (1/4) Epoch 19, batch 5500, loss[loss=0.1872, simple_loss=0.2639, pruned_loss=0.05524, over 6993.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2718, pruned_loss=0.05034, over 1419287.85 frames.], batch size: 16, lr: 2.88e-04
2022-05-28 02:24:28,422 INFO [train.py:842] (1/4) Epoch 19, batch 5550, loss[loss=0.3492, simple_loss=0.4024, pruned_loss=0.148, over 5490.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2747, pruned_loss=0.05154, over 1419377.74 frames.], batch size: 54, lr: 2.88e-04
2022-05-28 02:25:06,228 INFO [train.py:842] (1/4) Epoch 19, batch 5600, loss[loss=0.1495, simple_loss=0.2384, pruned_loss=0.03032, over 7172.00 frames.], tot_loss[loss=0.189, simple_loss=0.275, pruned_loss=0.05146, over 1421624.74 frames.], batch size: 18, lr: 2.88e-04
2022-05-28 02:25:44,623 INFO [train.py:842] (1/4) Epoch 19, batch 5650, loss[loss=0.2031, simple_loss=0.285, pruned_loss=0.06062, over 7338.00 frames.], tot_loss[loss=0.1883, simple_loss=0.274, pruned_loss=0.05132, over 1423918.52 frames.], batch size: 22, lr: 2.88e-04
2022-05-28 02:26:31,925 INFO [train.py:842] (1/4) Epoch 19, batch 5700, loss[loss=0.2525, simple_loss=0.326, pruned_loss=0.08945, over 7049.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2744, pruned_loss=0.05158, over 1424794.82 frames.], batch size: 28, lr: 2.88e-04
2022-05-28 02:27:10,283 INFO [train.py:842] (1/4) Epoch 19, batch 5750, loss[loss=0.1775, simple_loss=0.2578, pruned_loss=0.04855, over 7129.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2754, pruned_loss=0.05168, over 1430584.83 frames.], batch size: 17, lr: 2.88e-04
2022-05-28 02:27:48,265 INFO [train.py:842] (1/4) Epoch 19, batch 5800, loss[loss=0.229, simple_loss=0.3016, pruned_loss=0.07822, over 7320.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2743, pruned_loss=0.05167, over 1428112.16 frames.], batch size: 20, lr: 2.88e-04
2022-05-28 02:28:26,601 INFO [train.py:842] (1/4) Epoch 19, batch 5850, loss[loss=0.2227, simple_loss=0.2953, pruned_loss=0.075, over 5195.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2735, pruned_loss=0.0514, over 1426589.82 frames.], batch size: 52, lr: 2.88e-04
2022-05-28 02:29:04,691 INFO [train.py:842] (1/4) Epoch 19, batch 5900, loss[loss=0.1674, simple_loss=0.2588, pruned_loss=0.03805, over 7334.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2716, pruned_loss=0.05036, over 1422843.34 frames.], batch size: 20, lr: 2.88e-04
2022-05-28 02:29:43,073 INFO [train.py:842] (1/4) Epoch 19, batch 5950, loss[loss=0.1747, simple_loss=0.2668, pruned_loss=0.04132, over 7307.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2717, pruned_loss=0.05057, over 1426492.69 frames.], batch size: 21, lr: 2.88e-04
2022-05-28 02:30:20,901 INFO [train.py:842] (1/4) Epoch 19, batch 6000, loss[loss=0.2614, simple_loss=0.3456, pruned_loss=0.08862, over 6810.00 frames.], tot_loss[loss=0.186, simple_loss=0.2716, pruned_loss=0.05026, over 1423586.00 frames.], batch size: 31, lr: 2.88e-04
2022-05-28 02:30:20,901 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 02:30:29,955 INFO [train.py:871] (1/4) Epoch 19, validation: loss=0.1648, simple_loss=0.2646, pruned_loss=0.03256, over 868885.00 frames.
2022-05-28 02:31:08,292 INFO [train.py:842] (1/4) Epoch 19, batch 6050, loss[loss=0.2039, simple_loss=0.3088, pruned_loss=0.04951, over 7414.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2712, pruned_loss=0.04951, over 1425395.48 frames.], batch size: 21, lr: 2.88e-04
2022-05-28 02:31:45,886 INFO [train.py:842] (1/4) Epoch 19, batch 6100, loss[loss=0.174, simple_loss=0.269, pruned_loss=0.03955, over 6789.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2731, pruned_loss=0.05025, over 1423284.31 frames.], batch size: 31, lr: 2.88e-04
2022-05-28 02:32:24,070 INFO [train.py:842] (1/4) Epoch 19, batch 6150, loss[loss=0.2077, simple_loss=0.3012, pruned_loss=0.05708, over 7153.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2729, pruned_loss=0.04952, over 1427465.46 frames.], batch size: 20, lr: 2.88e-04
2022-05-28 02:33:02,032 INFO [train.py:842] (1/4) Epoch 19, batch 6200, loss[loss=0.1912, simple_loss=0.2737, pruned_loss=0.05431, over 7262.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2747, pruned_loss=0.05125, over 1424363.45 frames.], batch size: 19, lr: 2.88e-04
2022-05-28 02:33:40,515 INFO [train.py:842] (1/4) Epoch 19, batch 6250, loss[loss=0.1563, simple_loss=0.2405, pruned_loss=0.03604, over 7409.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2739, pruned_loss=0.05143, over 1429104.29 frames.], batch size: 18, lr: 2.88e-04
2022-05-28 02:34:18,349 INFO [train.py:842] (1/4) Epoch 19, batch 6300, loss[loss=0.1956, simple_loss=0.2817, pruned_loss=0.05478, over 7303.00 frames.], tot_loss[loss=0.188, simple_loss=0.2738, pruned_loss=0.05112, over 1425765.49 frames.], batch size: 25, lr: 2.88e-04
2022-05-28 02:34:56,849 INFO [train.py:842] (1/4) Epoch 19, batch 6350, loss[loss=0.1879, simple_loss=0.2715, pruned_loss=0.05212, over 7149.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2735, pruned_loss=0.05144, over 1427583.65 frames.], batch size: 18, lr: 2.88e-04
2022-05-28 02:35:34,604 INFO [train.py:842] (1/4) Epoch 19, batch 6400, loss[loss=0.2142, simple_loss=0.295, pruned_loss=0.06671, over 7072.00 frames.], tot_loss[loss=0.1889, simple_loss=0.274, pruned_loss=0.05186, over 1426076.12 frames.], batch size: 28, lr: 2.88e-04
2022-05-28 02:36:12,800 INFO [train.py:842] (1/4) Epoch 19, batch 6450, loss[loss=0.1564, simple_loss=0.2353, pruned_loss=0.03875, over 7062.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2745, pruned_loss=0.05257, over 1424324.22 frames.], batch size: 18, lr: 2.88e-04
2022-05-28 02:36:50,726 INFO [train.py:842] (1/4) Epoch 19, batch 6500, loss[loss=0.1882, simple_loss=0.2818, pruned_loss=0.04732, over 6303.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2736, pruned_loss=0.05145, over 1423517.46 frames.], batch size: 37, lr: 2.88e-04
2022-05-28 02:37:28,653 INFO [train.py:842] (1/4) Epoch 19, batch 6550, loss[loss=0.1701, simple_loss=0.2734, pruned_loss=0.03338, over 7121.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2745, pruned_loss=0.05152, over 1421078.07 frames.], batch size: 21, lr: 2.88e-04
2022-05-28 02:38:06,627 INFO [train.py:842] (1/4) Epoch 19, batch 6600, loss[loss=0.1699, simple_loss=0.2585, pruned_loss=0.04061, over 7238.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2742, pruned_loss=0.05197, over 1423958.22 frames.], batch size: 20, lr: 2.88e-04
2022-05-28 02:38:44,826 INFO [train.py:842] (1/4) Epoch 19, batch 6650, loss[loss=0.1485, simple_loss=0.2429, pruned_loss=0.02703, over 7333.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2732, pruned_loss=0.05155, over 1418942.11 frames.], batch size: 20, lr: 2.87e-04
2022-05-28 02:39:22,757 INFO [train.py:842] (1/4) Epoch 19, batch 6700, loss[loss=0.1888, simple_loss=0.2813, pruned_loss=0.04816, over 7352.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2736, pruned_loss=0.05107, over 1420116.95 frames.], batch size: 22, lr: 2.87e-04
2022-05-28 02:40:00,839 INFO [train.py:842] (1/4) Epoch 19, batch 6750, loss[loss=0.1808, simple_loss=0.2712, pruned_loss=0.0452, over 7106.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2743, pruned_loss=0.05155, over 1419673.79 frames.], batch size: 21, lr: 2.87e-04
2022-05-28 02:40:39,055 INFO [train.py:842] (1/4) Epoch 19, batch 6800, loss[loss=0.1865, simple_loss=0.2761, pruned_loss=0.04848, over 7332.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2727, pruned_loss=0.05053, over 1425648.26 frames.], batch size: 25, lr: 2.87e-04
2022-05-28 02:41:17,209 INFO [train.py:842] (1/4) Epoch 19, batch 6850, loss[loss=0.1763, simple_loss=0.2572, pruned_loss=0.04771, over 7210.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2723, pruned_loss=0.05052, over 1426961.96 frames.], batch size: 23, lr: 2.87e-04
2022-05-28 02:41:55,495 INFO [train.py:842] (1/4) Epoch 19, batch 6900, loss[loss=0.2086, simple_loss=0.2939, pruned_loss=0.06162, over 7412.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2716, pruned_loss=0.05071, over 1428795.25 frames.], batch size: 21, lr: 2.87e-04
2022-05-28 02:42:33,704 INFO [train.py:842] (1/4) Epoch 19, batch 6950, loss[loss=0.2345, simple_loss=0.3099, pruned_loss=0.07951, over 7156.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2731, pruned_loss=0.05155, over 1426391.80 frames.], batch size: 20, lr: 2.87e-04
2022-05-28 02:43:11,786 INFO [train.py:842] (1/4) Epoch 19, batch 7000, loss[loss=0.1659, simple_loss=0.2446, pruned_loss=0.04362, over 7123.00 frames.], tot_loss[loss=0.1865, simple_loss=0.272, pruned_loss=0.05049, over 1424015.21 frames.], batch size: 17, lr: 2.87e-04
2022-05-28 02:43:50,067 INFO [train.py:842] (1/4) Epoch 19, batch 7050, loss[loss=0.2618, simple_loss=0.3333, pruned_loss=0.0952, over 6771.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2711, pruned_loss=0.04992, over 1423281.82 frames.], batch size: 31, lr: 2.87e-04
2022-05-28 02:44:27,962 INFO [train.py:842] (1/4) Epoch 19, batch 7100, loss[loss=0.2277, simple_loss=0.3017, pruned_loss=0.07688, over 7206.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2724, pruned_loss=0.05067, over 1424712.90 frames.], batch size: 22, lr: 2.87e-04
2022-05-28 02:45:06,038 INFO [train.py:842] (1/4) Epoch 19, batch 7150, loss[loss=0.173, simple_loss=0.2587, pruned_loss=0.0436, over 7271.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2732, pruned_loss=0.05102, over 1424473.22 frames.], batch size: 17, lr: 2.87e-04
2022-05-28 02:45:44,155 INFO [train.py:842] (1/4) Epoch 19, batch 7200, loss[loss=0.1651, simple_loss=0.2546, pruned_loss=0.03775, over 7283.00 frames.], tot_loss[loss=0.188, simple_loss=0.2733, pruned_loss=0.05139, over 1425201.95 frames.], batch size: 18, lr: 2.87e-04
2022-05-28 02:46:22,503 INFO [train.py:842] (1/4) Epoch 19, batch 7250, loss[loss=0.1839, simple_loss=0.2738, pruned_loss=0.04705, over 7198.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2735, pruned_loss=0.05156, over 1424487.21 frames.], batch size: 23, lr: 2.87e-04
2022-05-28 02:47:00,627 INFO [train.py:842] (1/4) Epoch 19, batch 7300, loss[loss=0.2173, simple_loss=0.3042, pruned_loss=0.0652, over 7326.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2719, pruned_loss=0.0509, over 1424218.17 frames.], batch size: 20, lr: 2.87e-04
2022-05-28 02:47:38,836 INFO [train.py:842] (1/4) Epoch 19, batch 7350, loss[loss=0.1315, simple_loss=0.2063, pruned_loss=0.02833, over 7134.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2723, pruned_loss=0.05074, over 1423182.35 frames.], batch size: 17, lr: 2.87e-04
2022-05-28 02:48:16,554 INFO [train.py:842] (1/4) Epoch 19, batch 7400, loss[loss=0.2392, simple_loss=0.311, pruned_loss=0.08373, over 7353.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2736, pruned_loss=0.05087, over 1419677.34 frames.], batch size: 19, lr: 2.87e-04
2022-05-28 02:48:54,937 INFO [train.py:842] (1/4) Epoch 19, batch 7450, loss[loss=0.1972, simple_loss=0.2799, pruned_loss=0.0572, over 7152.00 frames.], tot_loss[loss=0.1872, simple_loss=0.273, pruned_loss=0.05073, over 1419863.93 frames.], batch size: 19, lr: 2.87e-04
2022-05-28 02:49:33,069 INFO [train.py:842] (1/4) Epoch 19, batch 7500, loss[loss=0.1615, simple_loss=0.2471, pruned_loss=0.03797, over 7283.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2723, pruned_loss=0.05039, over 1423911.17 frames.], batch size: 17, lr: 2.87e-04
2022-05-28 02:50:11,372 INFO [train.py:842] (1/4) Epoch 19, batch 7550, loss[loss=0.183, simple_loss=0.2776, pruned_loss=0.04415, over 7424.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2731, pruned_loss=0.051, over 1426386.28 frames.], batch size: 21, lr: 2.87e-04
2022-05-28 02:50:49,175 INFO [train.py:842] (1/4) Epoch 19, batch 7600, loss[loss=0.1914, simple_loss=0.2716, pruned_loss=0.05561, over 7026.00 frames.], tot_loss[loss=0.1893, simple_loss=0.275, pruned_loss=0.0518, over 1425235.52 frames.], batch size: 28, lr: 2.87e-04
2022-05-28 02:51:27,606 INFO [train.py:842] (1/4) Epoch 19, batch 7650, loss[loss=0.2009, simple_loss=0.2915, pruned_loss=0.05514, over 7008.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2738, pruned_loss=0.05089, over 1426234.38 frames.], batch size: 28, lr: 2.87e-04
2022-05-28 02:52:05,420 INFO [train.py:842] (1/4) Epoch 19, batch 7700, loss[loss=0.2064, simple_loss=0.2967, pruned_loss=0.05807, over 7304.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2747, pruned_loss=0.05134, over 1422981.80 frames.], batch size: 24, lr: 2.87e-04
2022-05-28 02:52:43,760 INFO [train.py:842] (1/4) Epoch 19, batch 7750, loss[loss=0.1716, simple_loss=0.2601, pruned_loss=0.04155, over 7161.00 frames.], tot_loss[loss=0.188, simple_loss=0.2739, pruned_loss=0.05104, over 1423705.88 frames.], batch size: 18, lr: 2.87e-04
2022-05-28 02:53:21,940 INFO [train.py:842] (1/4) Epoch 19, batch 7800, loss[loss=0.1695, simple_loss=0.2614, pruned_loss=0.03877, over 7427.00 frames.], tot_loss[loss=0.1882, simple_loss=0.274, pruned_loss=0.05125, over 1424825.41 frames.], batch size: 20, lr: 2.87e-04
2022-05-28 02:53:59,973 INFO [train.py:842] (1/4) Epoch 19, batch 7850, loss[loss=0.1689, simple_loss=0.2611, pruned_loss=0.03835, over 7323.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2747, pruned_loss=0.05186, over 1418565.92 frames.], batch size: 25, lr: 2.86e-04
2022-05-28 02:54:37,951 INFO [train.py:842] (1/4) Epoch 19, batch 7900, loss[loss=0.1816, simple_loss=0.2803, pruned_loss=0.04147, over 7332.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2756, pruned_loss=0.05234, over 1420361.75 frames.], batch size: 22, lr: 2.86e-04
2022-05-28 02:55:16,102 INFO [train.py:842] (1/4) Epoch 19, batch 7950, loss[loss=0.1465, simple_loss=0.2337, pruned_loss=0.02963, over 7357.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2756, pruned_loss=0.05251, over 1422449.92 frames.], batch size: 19, lr: 2.86e-04
2022-05-28 02:55:54,124 INFO [train.py:842] (1/4) Epoch 19, batch 8000, loss[loss=0.1798, simple_loss=0.2539, pruned_loss=0.05287, over 7397.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2731, pruned_loss=0.05114, over 1421563.02 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 02:56:32,336 INFO [train.py:842] (1/4) Epoch 19, batch 8050, loss[loss=0.1815, simple_loss=0.2614, pruned_loss=0.05076, over 7334.00 frames.], tot_loss[loss=0.188, simple_loss=0.2737, pruned_loss=0.05112, over 1425257.82 frames.], batch size: 21, lr: 2.86e-04
2022-05-28 02:57:10,367 INFO [train.py:842] (1/4) Epoch 19, batch 8100, loss[loss=0.2057, simple_loss=0.2886, pruned_loss=0.06138, over 7230.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2727, pruned_loss=0.05101, over 1425856.80 frames.], batch size: 21, lr: 2.86e-04
2022-05-28 02:57:48,671 INFO [train.py:842] (1/4) Epoch 19, batch 8150, loss[loss=0.1675, simple_loss=0.2636, pruned_loss=0.03571, over 7422.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2714, pruned_loss=0.05073, over 1422141.96 frames.], batch size: 20, lr: 2.86e-04
2022-05-28 02:58:26,740 INFO [train.py:842] (1/4) Epoch 19, batch 8200, loss[loss=0.1703, simple_loss=0.2489, pruned_loss=0.0458, over 7358.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2712, pruned_loss=0.05008, over 1424782.85 frames.], batch size: 19, lr: 2.86e-04
2022-05-28 02:59:05,082 INFO [train.py:842] (1/4) Epoch 19, batch 8250, loss[loss=0.1688, simple_loss=0.2478, pruned_loss=0.04492, over 7172.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2711, pruned_loss=0.0499, over 1427963.87 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 02:59:42,814 INFO [train.py:842] (1/4) Epoch 19, batch 8300, loss[loss=0.206, simple_loss=0.2864, pruned_loss=0.06283, over 7066.00 frames.], tot_loss[loss=0.185, simple_loss=0.2714, pruned_loss=0.04935, over 1427100.72 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 03:00:21,233 INFO [train.py:842] (1/4) Epoch 19, batch 8350, loss[loss=0.1805, simple_loss=0.2611, pruned_loss=0.04995, over 6998.00 frames.], tot_loss[loss=0.186, simple_loss=0.272, pruned_loss=0.05003, over 1428176.99 frames.], batch size: 16, lr: 2.86e-04
2022-05-28 03:00:59,126 INFO [train.py:842] (1/4) Epoch 19, batch 8400, loss[loss=0.2791, simple_loss=0.3427, pruned_loss=0.1078, over 7318.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2737, pruned_loss=0.0507, over 1421828.31 frames.], batch size: 21, lr: 2.86e-04
2022-05-28 03:01:37,405 INFO [train.py:842] (1/4) Epoch 19, batch 8450, loss[loss=0.164, simple_loss=0.256, pruned_loss=0.03605, over 7278.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2722, pruned_loss=0.05035, over 1420454.30 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 03:02:15,563 INFO [train.py:842] (1/4) Epoch 19, batch 8500, loss[loss=0.1786, simple_loss=0.274, pruned_loss=0.04163, over 7438.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2709, pruned_loss=0.04933, over 1420501.25 frames.], batch size: 20, lr: 2.86e-04
2022-05-28 03:02:53,776 INFO [train.py:842] (1/4) Epoch 19, batch 8550, loss[loss=0.2093, simple_loss=0.2937, pruned_loss=0.06244, over 7287.00 frames.], tot_loss[loss=0.185, simple_loss=0.271, pruned_loss=0.04946, over 1423026.86 frames.], batch size: 24, lr: 2.86e-04
2022-05-28 03:03:31,642 INFO [train.py:842] (1/4) Epoch 19, batch 8600, loss[loss=0.2208, simple_loss=0.2957, pruned_loss=0.07295, over 4852.00 frames.], tot_loss[loss=0.185, simple_loss=0.2711, pruned_loss=0.04943, over 1419427.73 frames.], batch size: 52, lr: 2.86e-04
2022-05-28 03:04:09,686 INFO [train.py:842] (1/4) Epoch 19, batch 8650, loss[loss=0.1786, simple_loss=0.2623, pruned_loss=0.04743, over 7170.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2714, pruned_loss=0.04938, over 1418819.18 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 03:04:47,378 INFO [train.py:842] (1/4) Epoch 19, batch 8700, loss[loss=0.2195, simple_loss=0.3044, pruned_loss=0.06736, over 7352.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2712, pruned_loss=0.0495, over 1415967.06 frames.], batch size: 19, lr: 2.86e-04
2022-05-28 03:05:25,585 INFO [train.py:842] (1/4) Epoch 19, batch 8750, loss[loss=0.1976, simple_loss=0.2713, pruned_loss=0.06197, over 7221.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2722, pruned_loss=0.05008, over 1416899.60 frames.], batch size: 16, lr: 2.86e-04
2022-05-28 03:06:03,563 INFO [train.py:842] (1/4) Epoch 19, batch 8800, loss[loss=0.18, simple_loss=0.2581, pruned_loss=0.05096, over 7284.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2725, pruned_loss=0.05058, over 1417539.77 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 03:06:41,772 INFO [train.py:842] (1/4) Epoch 19, batch 8850, loss[loss=0.2049, simple_loss=0.2866, pruned_loss=0.06161, over 7166.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2731, pruned_loss=0.05132, over 1415208.59 frames.], batch size: 23, lr: 2.86e-04
2022-05-28 03:07:19,682 INFO [train.py:842] (1/4) Epoch 19, batch 8900, loss[loss=0.1643, simple_loss=0.2506, pruned_loss=0.039, over 7258.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2729, pruned_loss=0.05124, over 1410318.42 frames.], batch size: 19, lr: 2.86e-04
2022-05-28 03:07:57,656 INFO [train.py:842] (1/4) Epoch 19, batch 8950, loss[loss=0.1474, simple_loss=0.2374, pruned_loss=0.02865, over 7273.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2736, pruned_loss=0.05205, over 1403209.92 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 03:08:35,127 INFO [train.py:842] (1/4) Epoch 19, batch 9000, loss[loss=0.2197, simple_loss=0.3118, pruned_loss=0.06379, over 7199.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2754, pruned_loss=0.05304, over 1400690.70 frames.], batch size: 23, lr: 2.86e-04
2022-05-28 03:08:35,127 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 03:08:44,267 INFO [train.py:871] (1/4) Epoch 19, validation: loss=0.1647, simple_loss=0.2641, pruned_loss=0.03266, over 868885.00 frames.
2022-05-28 03:09:22,102 INFO [train.py:842] (1/4) Epoch 19, batch 9050, loss[loss=0.2078, simple_loss=0.2937, pruned_loss=0.06097, over 4993.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2742, pruned_loss=0.05248, over 1381448.11 frames.], batch size: 53, lr: 2.86e-04
2022-05-28 03:09:59,164 INFO [train.py:842] (1/4) Epoch 19, batch 9100, loss[loss=0.2807, simple_loss=0.3462, pruned_loss=0.1076, over 5280.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2777, pruned_loss=0.05487, over 1333299.28 frames.], batch size: 52, lr: 2.85e-04
2022-05-28 03:10:36,170 INFO [train.py:842] (1/4) Epoch 19, batch 9150, loss[loss=0.2766, simple_loss=0.3352, pruned_loss=0.109, over 4942.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2816, pruned_loss=0.05794, over 1262884.21 frames.], batch size: 52, lr: 2.85e-04
2022-05-28 03:11:25,257 INFO [train.py:842] (1/4) Epoch 20, batch 0, loss[loss=0.163, simple_loss=0.2564, pruned_loss=0.03484, over 7359.00 frames.], tot_loss[loss=0.163, simple_loss=0.2564, pruned_loss=0.03484, over 7359.00 frames.], batch size: 19, lr: 2.78e-04
2022-05-28 03:12:03,127 INFO [train.py:842] (1/4) Epoch 20, batch 50, loss[loss=0.2122, simple_loss=0.2822, pruned_loss=0.07109, over 7284.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2748, pruned_loss=0.05027, over 320926.22 frames.], batch size: 18, lr: 2.78e-04
2022-05-28 03:12:41,510 INFO [train.py:842] (1/4) Epoch 20, batch 100, loss[loss=0.2001, simple_loss=0.2841, pruned_loss=0.05802, over 5112.00 frames.], tot_loss[loss=0.186, simple_loss=0.2727, pruned_loss=0.04958, over 566738.56 frames.], batch size: 53, lr: 2.78e-04
2022-05-28 03:13:19,280 INFO [train.py:842] (1/4) Epoch 20, batch 150, loss[loss=0.1766, simple_loss=0.2702, pruned_loss=0.04153, over 7316.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2757, pruned_loss=0.05053, over 756670.17 frames.], batch size: 21, lr: 2.78e-04
2022-05-28 03:13:57,458 INFO [train.py:842] (1/4) Epoch 20, batch 200, loss[loss=0.2121, simple_loss=0.2901, pruned_loss=0.06703, over 7352.00 frames.], tot_loss[loss=0.1892, simple_loss=0.276, pruned_loss=0.05115, over 904140.65 frames.], batch size: 22, lr: 2.78e-04
2022-05-28 03:14:35,701 INFO [train.py:842] (1/4) Epoch 20, batch 250, loss[loss=0.2125, simple_loss=0.3105, pruned_loss=0.05727, over 7342.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2731, pruned_loss=0.04917, over 1023518.27 frames.], batch size: 22, lr: 2.78e-04
2022-05-28 03:15:13,682 INFO [train.py:842] (1/4) Epoch 20, batch 300, loss[loss=0.2097, simple_loss=0.2877, pruned_loss=0.06588, over 7211.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2729, pruned_loss=0.04887, over 1112727.88 frames.], batch size: 23, lr: 2.78e-04
2022-05-28 03:15:51,621 INFO [train.py:842] (1/4) Epoch 20, batch 350, loss[loss=0.1655, simple_loss=0.2596, pruned_loss=0.0357, over 7150.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2732, pruned_loss=0.04916, over 1185193.47 frames.], batch size: 20, lr: 2.78e-04
2022-05-28 03:16:29,691 INFO [train.py:842] (1/4) Epoch 20, batch 400, loss[loss=0.1712, simple_loss=0.2646, pruned_loss=0.03895, over 7136.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2742, pruned_loss=0.04983, over 1238484.05 frames.], batch size: 20, lr: 2.78e-04
2022-05-28 03:17:07,420 INFO [train.py:842] (1/4) Epoch 20, batch 450, loss[loss=0.171, simple_loss=0.2674, pruned_loss=0.03735, over 7377.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2744, pruned_loss=0.05016, over 1276096.81 frames.], batch size: 23, lr: 2.78e-04
2022-05-28 03:17:45,574 INFO [train.py:842] (1/4) Epoch 20, batch 500, loss[loss=0.1607, simple_loss=0.248, pruned_loss=0.03675, over 7208.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2755, pruned_loss=0.05099, over 1308024.58 frames.], batch size: 21, lr: 2.78e-04
2022-05-28 03:18:23,486 INFO [train.py:842] (1/4) Epoch 20, batch 550, loss[loss=0.1851, simple_loss=0.2747, pruned_loss=0.04778, over 6839.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2767, pruned_loss=0.05205, over 1334146.92 frames.], batch size: 31, lr: 2.78e-04
2022-05-28 03:19:02,113 INFO [train.py:842] (1/4) Epoch 20, batch 600, loss[loss=0.1995, simple_loss=0.2749, pruned_loss=0.06202, over 7160.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2744, pruned_loss=0.05112, over 1357149.70 frames.], batch size: 18, lr: 2.78e-04
2022-05-28 03:19:40,165 INFO [train.py:842] (1/4) Epoch 20, batch 650, loss[loss=0.165, simple_loss=0.2501, pruned_loss=0.03998, over 7165.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2739, pruned_loss=0.05075, over 1371529.69 frames.], batch size: 18, lr: 2.78e-04
2022-05-28 03:20:18,369 INFO [train.py:842] (1/4) Epoch 20, batch 700, loss[loss=0.1571, simple_loss=0.2517, pruned_loss=0.03123, over 7225.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2741, pruned_loss=0.05007, over 1384605.58 frames.], batch size: 20, lr: 2.78e-04
2022-05-28 03:20:56,451 INFO [train.py:842] (1/4) Epoch 20, batch 750, loss[loss=0.2057, simple_loss=0.2924, pruned_loss=0.05948, over 7298.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2729, pruned_loss=0.04987, over 1395093.21 frames.], batch size: 25, lr: 2.78e-04
2022-05-28 03:21:34,816 INFO [train.py:842] (1/4) Epoch 20, batch 800, loss[loss=0.1866, simple_loss=0.2612, pruned_loss=0.05603, over 7401.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2725, pruned_loss=0.05006, over 1403866.98 frames.], batch size: 18, lr: 2.78e-04
2022-05-28 03:22:12,810 INFO [train.py:842] (1/4) Epoch 20, batch 850, loss[loss=0.1818, simple_loss=0.2778, pruned_loss=0.04284, over 7027.00 frames.], tot_loss[loss=0.187, simple_loss=0.2732, pruned_loss=0.05046, over 1411125.95 frames.], batch size: 28, lr: 2.78e-04
2022-05-28 03:22:51,273 INFO [train.py:842] (1/4) Epoch 20, batch 900, loss[loss=0.2252, simple_loss=0.2851, pruned_loss=0.08268, over 7347.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2716, pruned_loss=0.04974, over 1416465.04 frames.], batch size: 19, lr: 2.78e-04
2022-05-28 03:23:29,307 INFO [train.py:842] (1/4) Epoch 20, batch 950, loss[loss=0.2113, simple_loss=0.2968, pruned_loss=0.06286, over 7240.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2719, pruned_loss=0.0502, over 1419597.35 frames.], batch size: 20, lr: 2.78e-04
2022-05-28 03:24:07,520 INFO [train.py:842] (1/4) Epoch 20, batch 1000, loss[loss=0.2426, simple_loss=0.3151, pruned_loss=0.08504, over 7295.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2724, pruned_loss=0.05044, over 1420259.47 frames.], batch size: 24, lr: 2.78e-04
2022-05-28 03:24:45,373 INFO [train.py:842] (1/4) Epoch 20, batch 1050, loss[loss=0.1859, simple_loss=0.2814, pruned_loss=0.04517, over 7207.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2723, pruned_loss=0.05003, over 1420264.37 frames.], batch size: 22, lr: 2.78e-04
2022-05-28 03:25:23,604 INFO [train.py:842] (1/4) Epoch 20, batch 1100, loss[loss=0.1966, simple_loss=0.2845, pruned_loss=0.0543, over 7200.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2715, pruned_loss=0.04966, over 1416345.19 frames.], batch size: 22, lr: 2.78e-04
2022-05-28 03:26:01,385 INFO [train.py:842] (1/4) Epoch 20, batch 1150, loss[loss=0.2074, simple_loss=0.2891, pruned_loss=0.06289, over 7296.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2723, pruned_loss=0.04972, over 1420492.87 frames.], batch size: 24, lr: 2.78e-04
2022-05-28 03:26:39,929 INFO [train.py:842] (1/4) Epoch 20, batch 1200, loss[loss=0.1952, simple_loss=0.2871, pruned_loss=0.05163, over 7323.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2717, pruned_loss=0.04976, over 1425533.05 frames.], batch size: 22, lr: 2.78e-04
2022-05-28 03:27:17,888 INFO [train.py:842] (1/4) Epoch 20, batch 1250, loss[loss=0.1697, simple_loss=0.2508, pruned_loss=0.04431, over 7136.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2714, pruned_loss=0.04976, over 1425259.30 frames.], batch size: 17, lr: 2.78e-04
2022-05-28 03:27:56,123 INFO [train.py:842] (1/4) Epoch 20, batch 1300, loss[loss=0.1846, simple_loss=0.2834, pruned_loss=0.04295, over 7110.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2711, pruned_loss=0.04937, over 1426752.01 frames.], batch size: 21, lr: 2.77e-04
2022-05-28 03:28:34,016 INFO [train.py:842] (1/4) Epoch 20, batch 1350, loss[loss=0.2461, simple_loss=0.3125, pruned_loss=0.0898, over 7191.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2721, pruned_loss=0.04986, over 1428268.78 frames.], batch size: 22, lr: 2.77e-04
2022-05-28 03:29:15,151 INFO [train.py:842] (1/4) Epoch 20, batch 1400, loss[loss=0.1686, simple_loss=0.2539, pruned_loss=0.04167, over 7162.00 frames.], tot_loss[loss=0.1846, simple_loss=0.271, pruned_loss=0.04909, over 1429976.08 frames.], batch size: 26, lr: 2.77e-04
2022-05-28 03:29:53,004 INFO [train.py:842] (1/4) Epoch 20, batch 1450, loss[loss=0.1973, simple_loss=0.2767, pruned_loss=0.05898, over 7191.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2723, pruned_loss=0.04951, over 1428374.21 frames.], batch size: 26, lr: 2.77e-04
2022-05-28 03:30:31,245 INFO [train.py:842] (1/4) Epoch 20, batch 1500, loss[loss=0.1835, simple_loss=0.2776, pruned_loss=0.04469, over 7368.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2731, pruned_loss=0.05007, over 1426703.89 frames.], batch size: 23, lr: 2.77e-04
2022-05-28 03:31:09,385 INFO [train.py:842] (1/4) Epoch 20, batch 1550, loss[loss=0.1874, simple_loss=0.277, pruned_loss=0.04888, over 7434.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2721, pruned_loss=0.04971, over 1429052.94 frames.], batch size: 20, lr: 2.77e-04
2022-05-28 03:31:47,532 INFO [train.py:842] (1/4) Epoch 20, batch 1600, loss[loss=0.1894, simple_loss=0.2824, pruned_loss=0.04816, over 7338.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2733, pruned_loss=0.05019, over 1424390.56 frames.], batch size: 22, lr: 2.77e-04
2022-05-28 03:32:25,382 INFO [train.py:842] (1/4) Epoch 20, batch 1650, loss[loss=0.2403, simple_loss=0.3285, pruned_loss=0.07607, over 7201.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2738, pruned_loss=0.05004, over 1421996.23 frames.], batch size: 23, lr: 2.77e-04
2022-05-28 03:33:03,579 INFO [train.py:842] (1/4) Epoch 20, batch 1700, loss[loss=0.2089, simple_loss=0.2983, pruned_loss=0.05971, over 7162.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2726, pruned_loss=0.04952, over 1420444.31 frames.], batch size: 19, lr: 2.77e-04
2022-05-28 03:33:41,621 INFO [train.py:842] (1/4) Epoch 20, batch 1750, loss[loss=0.1988, simple_loss=0.2939, pruned_loss=0.05189, over 7326.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2737, pruned_loss=0.05036, over 1426079.23 frames.], batch size: 22, lr: 2.77e-04
2022-05-28 03:34:19,917 INFO [train.py:842] (1/4) Epoch 20, batch 1800, loss[loss=0.2115, simple_loss=0.2884, pruned_loss=0.06725, over 7300.00 frames.], tot_loss[loss=0.187, simple_loss=0.2736, pruned_loss=0.05022, over 1425010.12 frames.], batch size: 25, lr: 2.77e-04
2022-05-28 03:34:58,001 INFO [train.py:842] (1/4) Epoch 20, batch 1850, loss[loss=0.1701, simple_loss=0.2541, pruned_loss=0.04301, over 7076.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2726, pruned_loss=0.04964, over 1428473.38 frames.], batch size: 18, lr: 2.77e-04
2022-05-28 03:35:36,125 INFO [train.py:842] (1/4) Epoch 20, batch 1900, loss[loss=0.1509, simple_loss=0.2448, pruned_loss=0.02848, over 7237.00 frames.], tot_loss[loss=0.186, simple_loss=0.2728, pruned_loss=0.04966, over 1430053.79 frames.], batch size: 20, lr: 2.77e-04
2022-05-28 03:36:14,213 INFO [train.py:842] (1/4) Epoch 20, batch 1950, loss[loss=0.1794, simple_loss=0.2734, pruned_loss=0.04275, over 6584.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2726, pruned_loss=0.04962, over 1430805.02 frames.], batch size: 37, lr: 2.77e-04
2022-05-28 03:36:52,617 INFO [train.py:842] (1/4) Epoch 20, batch 2000, loss[loss=0.1661, simple_loss=0.2668, pruned_loss=0.03275, over 7236.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2721, pruned_loss=0.0495, over 1431065.97 frames.], batch size: 20, lr: 2.77e-04
2022-05-28 03:37:30,760 INFO [train.py:842] (1/4) Epoch 20, batch 2050, loss[loss=0.2279, simple_loss=0.3019, pruned_loss=0.0769, over 7232.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2717, pruned_loss=0.04958, over 1430299.34 frames.], batch size: 21, lr: 2.77e-04
2022-05-28 03:38:09,219 INFO [train.py:842] (1/4) Epoch 20, batch 2100, loss[loss=0.1635, simple_loss=0.2395, pruned_loss=0.0438, over 7423.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2705, pruned_loss=0.04851, over 1432385.88 frames.], batch size: 20, lr: 2.77e-04
2022-05-28 03:38:47,112 INFO [train.py:842] (1/4) Epoch 20, batch 2150, loss[loss=0.1827, simple_loss=0.2739, pruned_loss=0.04578, over 7205.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2709, pruned_loss=0.04913, over 1426446.99 frames.], batch size: 22, lr: 2.77e-04
2022-05-28 03:39:25,458 INFO [train.py:842] (1/4) Epoch 20, batch 2200, loss[loss=0.18, simple_loss=0.2546, pruned_loss=0.05274, over 7216.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2706, pruned_loss=0.04909, over 1421832.90 frames.], batch size: 16, lr: 2.77e-04
2022-05-28 03:40:03,674 INFO [train.py:842] (1/4) Epoch 20, batch 2250, loss[loss=0.2175, simple_loss=0.308, pruned_loss=0.06353, over 7150.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2707, pruned_loss=0.04938, over 1424878.60 frames.], batch size: 20, lr: 2.77e-04
2022-05-28 03:40:41,875 INFO [train.py:842] (1/4) Epoch 20, batch 2300, loss[loss=0.2571, simple_loss=0.3374, pruned_loss=0.08841, over 7379.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2709, pruned_loss=0.04971, over 1424701.88 frames.], batch size: 23, lr: 2.77e-04
2022-05-28 03:41:19,752 INFO [train.py:842] (1/4) Epoch 20, batch 2350, loss[loss=0.1743, simple_loss=0.2554, pruned_loss=0.0466, over 7319.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2712, pruned_loss=0.04964, over 1423302.94 frames.], batch size: 21, lr: 2.77e-04
2022-05-28 03:41:58,028 INFO [train.py:842] (1/4) Epoch 20, batch 2400, loss[loss=0.1835, simple_loss=0.2685, pruned_loss=0.04922, over 7436.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2714, pruned_loss=0.04978, over 1424550.94 frames.], batch size: 20, lr: 2.77e-04
2022-05-28 03:42:36,045 INFO [train.py:842] (1/4) Epoch 20, batch 2450, loss[loss=0.1607, simple_loss=0.2571, pruned_loss=0.0322, over 7001.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2698, pruned_loss=0.04867, over 1427218.30 frames.], batch size: 28, lr: 2.77e-04
2022-05-28 03:43:14,459 INFO [train.py:842] (1/4) Epoch 20, batch 2500, loss[loss=0.1954, simple_loss=0.2901, pruned_loss=0.05036, over 7107.00 frames.], tot_loss[loss=0.1835, simple_loss=0.27, pruned_loss=0.04853, over 1426051.20 frames.], batch size: 26, lr: 2.77e-04
2022-05-28 03:43:52,364 INFO [train.py:842] (1/4) Epoch 20, batch 2550, loss[loss=0.1863, simple_loss=0.279, pruned_loss=0.04683, over 7320.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2702, pruned_loss=0.04878, over 1424423.36 frames.], batch size: 20, lr: 2.76e-04
2022-05-28 03:44:30,393 INFO [train.py:842] (1/4) Epoch 20, batch 2600, loss[loss=0.2166, simple_loss=0.3049, pruned_loss=0.06416, over 6840.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2713, pruned_loss=0.04951, over 1425919.28 frames.], batch size: 32, lr: 2.76e-04
2022-05-28 03:45:08,532 INFO [train.py:842] (1/4) Epoch 20, batch 2650, loss[loss=0.1388, simple_loss=0.2289, pruned_loss=0.02437, over 7003.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2722, pruned_loss=0.05061, over 1426760.58 frames.], batch size: 16, lr: 2.76e-04
2022-05-28 03:45:46,857 INFO [train.py:842] (1/4) Epoch 20, batch 2700, loss[loss=0.1954, simple_loss=0.2863, pruned_loss=0.0522, over 7376.00 frames.], tot_loss[loss=0.185, simple_loss=0.2707, pruned_loss=0.04963, over 1428310.45 frames.], batch size: 23, lr: 2.76e-04
2022-05-28 03:46:24,694 INFO [train.py:842] (1/4) Epoch 20, batch 2750, loss[loss=0.2204, simple_loss=0.3041, pruned_loss=0.06841, over 7188.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2715, pruned_loss=0.04943, over 1426870.56 frames.], batch size: 23, lr: 2.76e-04
2022-05-28 03:47:03,032 INFO [train.py:842] (1/4) Epoch 20, batch 2800, loss[loss=0.1758, simple_loss=0.2608, pruned_loss=0.04542, over 7157.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2713, pruned_loss=0.04914, over 1430837.43 frames.], batch size: 18, lr: 2.76e-04
2022-05-28 03:47:41,134 INFO [train.py:842] (1/4) Epoch 20, batch 2850, loss[loss=0.1897, simple_loss=0.2802, pruned_loss=0.04957, over 7421.00 frames.], tot_loss[loss=0.185, simple_loss=0.2714, pruned_loss=0.04931, over 1433061.58 frames.], batch size: 21, lr: 2.76e-04
2022-05-28 03:48:19,359 INFO [train.py:842] (1/4) Epoch 20, batch 2900, loss[loss=0.2127, simple_loss=0.2956, pruned_loss=0.06495, over 7223.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2704, pruned_loss=0.04854, over 1429320.39 frames.], batch size: 26, lr: 2.76e-04
2022-05-28 03:48:57,423 INFO [train.py:842] (1/4) Epoch 20, batch 2950, loss[loss=0.1757, simple_loss=0.2663, pruned_loss=0.04253, over 7226.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2712, pruned_loss=0.04903, over 1432900.57 frames.], batch size: 20, lr: 2.76e-04
2022-05-28 03:49:35,440 INFO [train.py:842] (1/4) Epoch 20, batch 3000, loss[loss=0.1928, simple_loss=0.2715, pruned_loss=0.05701, over 7379.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2723, pruned_loss=0.04956, over 1432094.39 frames.], batch size: 23, lr: 2.76e-04
2022-05-28 03:49:35,440 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 03:49:44,759 INFO [train.py:871] (1/4) Epoch 20, validation: loss=0.1666, simple_loss=0.2655, pruned_loss=0.0338, over 868885.00 frames.
2022-05-28 03:50:22,735 INFO [train.py:842] (1/4) Epoch 20, batch 3050, loss[loss=0.174, simple_loss=0.2632, pruned_loss=0.04233, over 7143.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2725, pruned_loss=0.0494, over 1433329.66 frames.], batch size: 19, lr: 2.76e-04
2022-05-28 03:51:00,929 INFO [train.py:842] (1/4) Epoch 20, batch 3100, loss[loss=0.1774, simple_loss=0.2752, pruned_loss=0.03976, over 7126.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2727, pruned_loss=0.04927, over 1432562.55 frames.], batch size: 21, lr: 2.76e-04
2022-05-28 03:51:38,897 INFO [train.py:842] (1/4) Epoch 20, batch 3150, loss[loss=0.2097, simple_loss=0.2835, pruned_loss=0.06796, over 7269.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2726, pruned_loss=0.04937, over 1432936.41 frames.], batch size: 18, lr: 2.76e-04
2022-05-28 03:52:17,377 INFO [train.py:842] (1/4) Epoch 20, batch 3200, loss[loss=0.2104, simple_loss=0.312, pruned_loss=0.05437, over 6776.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2717, pruned_loss=0.04911, over 1432518.74 frames.], batch size: 31, lr: 2.76e-04
2022-05-28 03:52:55,237 INFO [train.py:842] (1/4) Epoch 20, batch 3250, loss[loss=0.1577, simple_loss=0.2463, pruned_loss=0.03454, over 7081.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2722, pruned_loss=0.04937, over 1429676.63 frames.], batch size: 18, lr: 2.76e-04
2022-05-28 03:53:33,557 INFO [train.py:842] (1/4) Epoch 20, batch 3300, loss[loss=0.1471, simple_loss=0.2256, pruned_loss=0.03433, over 7131.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2714, pruned_loss=0.04906, over 1426710.88 frames.], batch size: 17, lr: 2.76e-04
2022-05-28 03:54:11,569 INFO [train.py:842] (1/4) Epoch 20, batch 3350, loss[loss=0.1827, simple_loss=0.2762, pruned_loss=0.04455, over 7142.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2707, pruned_loss=0.04893, over 1427391.06 frames.], batch size: 20, lr: 2.76e-04
2022-05-28 03:54:49,728 INFO [train.py:842] (1/4) Epoch 20, batch 3400, loss[loss=0.1531, simple_loss=0.2372, pruned_loss=0.03453, over 7296.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2707, pruned_loss=0.04878, over 1426477.02 frames.], batch size: 17, lr: 2.76e-04
2022-05-28 03:55:27,650 INFO [train.py:842] (1/4) Epoch 20, batch 3450, loss[loss=0.1698, simple_loss=0.2623, pruned_loss=0.03871, over 7231.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2707, pruned_loss=0.04842, over 1425100.69 frames.], batch size: 20, lr: 2.76e-04
2022-05-28 03:56:05,950 INFO [train.py:842] (1/4) Epoch 20, batch 3500, loss[loss=0.1954, simple_loss=0.2699, pruned_loss=0.06048, over 7254.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2702, pruned_loss=0.04825, over 1424025.53 frames.], batch size: 19, lr: 2.76e-04
2022-05-28 03:56:43,799 INFO [train.py:842] (1/4) Epoch 20, batch 3550, loss[loss=0.1654, simple_loss=0.2694, pruned_loss=0.03069, over 7112.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2716, pruned_loss=0.04893, over 1426890.42 frames.], batch size: 21, lr: 2.76e-04
2022-05-28 03:57:22,220 INFO [train.py:842] (1/4) Epoch 20, batch 3600, loss[loss=0.1751, simple_loss=0.2711, pruned_loss=0.03952, over 7423.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2723, pruned_loss=0.04954, over 1431173.20 frames.], batch size: 20, lr: 2.76e-04
2022-05-28 03:58:00,179 INFO [train.py:842] (1/4) Epoch 20, batch 3650, loss[loss=0.1958, simple_loss=0.2908, pruned_loss=0.0504, over 7413.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2714, pruned_loss=0.04896, over 1431553.05 frames.], batch size: 21, lr: 2.76e-04
2022-05-28 03:58:38,388 INFO [train.py:842] (1/4) Epoch 20, batch 3700, loss[loss=0.1729, simple_loss=0.2646, pruned_loss=0.04064, over 7214.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2723, pruned_loss=0.04967, over 1432760.66 frames.], batch size: 21, lr: 2.76e-04
2022-05-28 03:59:16,259 INFO [train.py:842] (1/4) Epoch 20, batch 3750, loss[loss=0.1626, simple_loss=0.2592, pruned_loss=0.03302, over 7320.00 frames.], tot_loss[loss=0.1865, simple_loss=0.273, pruned_loss=0.04999, over 1428483.99 frames.], batch size: 21, lr: 2.76e-04
2022-05-28 03:59:54,687 INFO [train.py:842] (1/4) Epoch 20, batch 3800, loss[loss=0.2007, simple_loss=0.275, pruned_loss=0.06316, over 7259.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2741, pruned_loss=0.05133, over 1428337.10 frames.], batch size: 17, lr: 2.76e-04
2022-05-28 04:00:32,647 INFO [train.py:842] (1/4) Epoch 20, batch 3850, loss[loss=0.1675, simple_loss=0.257, pruned_loss=0.03899, over 7353.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2731, pruned_loss=0.05082, over 1424373.25 frames.], batch size: 19, lr: 2.75e-04
2022-05-28 04:01:10,454 INFO [train.py:842] (1/4) Epoch 20, batch 3900, loss[loss=0.2164, simple_loss=0.3039, pruned_loss=0.06445, over 7317.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2741, pruned_loss=0.0508, over 1421024.63 frames.], batch size: 25, lr: 2.75e-04
2022-05-28 04:01:48,147 INFO [train.py:842] (1/4) Epoch 20, batch 3950, loss[loss=0.1364, simple_loss=0.2358, pruned_loss=0.01852, over 7415.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2735, pruned_loss=0.05047, over 1419229.52 frames.], batch size: 21, lr: 2.75e-04
2022-05-28 04:02:26,110 INFO [train.py:842] (1/4) Epoch 20, batch 4000, loss[loss=0.1577, simple_loss=0.2525, pruned_loss=0.03146, over 7223.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2746, pruned_loss=0.05108, over 1410699.93 frames.], batch size: 21, lr: 2.75e-04
2022-05-28 04:03:04,022 INFO [train.py:842] (1/4) Epoch 20, batch 4050, loss[loss=0.1838, simple_loss=0.268, pruned_loss=0.04978, over 7218.00 frames.], tot_loss[loss=0.188, simple_loss=0.2742, pruned_loss=0.05085, over 1411728.43 frames.], batch size: 21, lr: 2.75e-04
2022-05-28 04:03:42,438 INFO [train.py:842] (1/4) Epoch 20, batch 4100, loss[loss=0.195, simple_loss=0.2741, pruned_loss=0.05797, over 7199.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2741, pruned_loss=0.05075, over 1410970.38 frames.], batch size: 26, lr: 2.75e-04
2022-05-28 04:04:20,457 INFO [train.py:842] (1/4) Epoch 20, batch 4150, loss[loss=0.2026, simple_loss=0.2979, pruned_loss=0.05368, over 7327.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2724, pruned_loss=0.05009, over 1415554.35 frames.], batch size: 20, lr: 2.75e-04
2022-05-28 04:04:58,990 INFO [train.py:842] (1/4) Epoch 20, batch 4200, loss[loss=0.1634, simple_loss=0.2419, pruned_loss=0.04239, over 7346.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2701, pruned_loss=0.04958, over 1418380.95 frames.], batch size: 19, lr: 2.75e-04
2022-05-28 04:05:36,677 INFO [train.py:842] (1/4) Epoch 20, batch 4250, loss[loss=0.1533, simple_loss=0.2535, pruned_loss=0.02653, over 7145.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2721, pruned_loss=0.05069, over 1415210.02 frames.], batch size: 20, lr: 2.75e-04
2022-05-28 04:06:14,975 INFO [train.py:842] (1/4) Epoch 20, batch 4300, loss[loss=0.1683, simple_loss=0.264, pruned_loss=0.03631, over 7178.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2714, pruned_loss=0.05013, over 1414909.80 frames.], batch size: 26, lr: 2.75e-04
2022-05-28 04:06:53,172 INFO [train.py:842] (1/4) Epoch 20, batch 4350, loss[loss=0.1696, simple_loss=0.2492, pruned_loss=0.04497, over 7412.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2708, pruned_loss=0.04979, over 1416921.26 frames.], batch size: 18, lr: 2.75e-04
2022-05-28 04:07:31,384 INFO [train.py:842] (1/4) Epoch 20, batch 4400, loss[loss=0.1936, simple_loss=0.2774, pruned_loss=0.05487, over 7304.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2721, pruned_loss=0.05036, over 1420393.25 frames.], batch size: 25, lr: 2.75e-04
2022-05-28 04:08:09,371 INFO [train.py:842] (1/4) Epoch 20, batch 4450, loss[loss=0.166, simple_loss=0.2658, pruned_loss=0.03308, over 7406.00 frames.], tot_loss[loss=0.1854, simple_loss=0.271, pruned_loss=0.04986, over 1416496.83 frames.], batch size: 21, lr: 2.75e-04
2022-05-28 04:08:47,640 INFO [train.py:842] (1/4) Epoch 20, batch 4500, loss[loss=0.1839, simple_loss=0.2672, pruned_loss=0.05029, over 7185.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2706, pruned_loss=0.04953, over 1421757.33 frames.], batch size: 26, lr: 2.75e-04
2022-05-28 04:09:25,675 INFO [train.py:842] (1/4) Epoch 20, batch 4550, loss[loss=0.2078, simple_loss=0.2842, pruned_loss=0.06566, over 7360.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2702, pruned_loss=0.04901, over 1426804.92 frames.], batch size: 19, lr: 2.75e-04
2022-05-28 04:10:04,094 INFO [train.py:842] (1/4) Epoch 20, batch 4600, loss[loss=0.1941, simple_loss=0.285, pruned_loss=0.05159, over 7320.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2704, pruned_loss=0.04968, over 1424177.44 frames.], batch size: 20, lr: 2.75e-04
2022-05-28 04:10:42,087 INFO [train.py:842] (1/4) Epoch 20, batch 4650, loss[loss=0.216, simple_loss=0.3065, pruned_loss=0.0628, over 7149.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2716, pruned_loss=0.0503, over 1426301.29 frames.], batch size: 20, lr: 2.75e-04
2022-05-28 04:11:20,092 INFO [train.py:842] (1/4) Epoch 20, batch 4700, loss[loss=0.1963, simple_loss=0.2964, pruned_loss=0.04815, over 7205.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2713, pruned_loss=0.05003, over 1422907.64 frames.], batch size: 21, lr: 2.75e-04
2022-05-28 04:11:57,873 INFO [train.py:842] (1/4) Epoch 20, batch 4750, loss[loss=0.249, simple_loss=0.3339, pruned_loss=0.08205, over 6126.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2722, pruned_loss=0.05024, over 1421891.93 frames.], batch size: 37, lr: 2.75e-04
2022-05-28 04:12:36,327 INFO [train.py:842] (1/4) Epoch 20, batch 4800, loss[loss=0.1881, simple_loss=0.2806, pruned_loss=0.04778, over 5225.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2721, pruned_loss=0.05051, over 1423529.45 frames.], batch size: 52, lr: 2.75e-04
2022-05-28 04:13:14,132 INFO [train.py:842] (1/4) Epoch 20, batch 4850, loss[loss=0.2038, simple_loss=0.3067, pruned_loss=0.05051, over 7141.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2719, pruned_loss=0.04995, over 1420005.81 frames.], batch size: 20, lr: 2.75e-04
2022-05-28 04:13:52,314 INFO [train.py:842] (1/4) Epoch 20, batch 4900, loss[loss=0.2308, simple_loss=0.3085, pruned_loss=0.07657, over 5027.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2718, pruned_loss=0.0498, over 1421514.96 frames.], batch size: 52, lr: 2.75e-04
2022-05-28 04:14:30,423 INFO [train.py:842] (1/4) Epoch 20, batch 4950, loss[loss=0.2399, simple_loss=0.3253, pruned_loss=0.07721, over 7145.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2717, pruned_loss=0.04985, over 1423276.50 frames.], batch size: 20, lr: 2.75e-04
2022-05-28 04:15:18,007 INFO [train.py:842] (1/4) Epoch 20, batch 5000, loss[loss=0.1878, simple_loss=0.2881, pruned_loss=0.04378, over 7175.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2728, pruned_loss=0.05017, over 1428415.56 frames.], batch size: 26, lr: 2.75e-04
2022-05-28 04:15:55,717 INFO [train.py:842] (1/4) Epoch 20, batch 5050, loss[loss=0.1918, simple_loss=0.2677, pruned_loss=0.05796, over 6772.00 frames.], tot_loss[loss=0.1873, simple_loss=0.273, pruned_loss=0.05077, over 1418960.21 frames.], batch size: 15, lr: 2.75e-04
2022-05-28 04:16:33,965 INFO [train.py:842] (1/4) Epoch 20, batch 5100, loss[loss=0.1447, simple_loss=0.2324, pruned_loss=0.02854, over 7366.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2724, pruned_loss=0.05003, over 1424161.51 frames.], batch size: 19, lr: 2.75e-04
2022-05-28 04:17:11,881 INFO [train.py:842] (1/4) Epoch 20, batch 5150, loss[loss=0.1355, simple_loss=0.2129, pruned_loss=0.02905, over 7294.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2726, pruned_loss=0.05047, over 1425082.74 frames.], batch size: 17, lr: 2.74e-04
2022-05-28 04:17:50,059 INFO [train.py:842] (1/4) Epoch 20, batch 5200, loss[loss=0.1635, simple_loss=0.2659, pruned_loss=0.03058, over 7240.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2724, pruned_loss=0.04954, over 1427894.15 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:18:27,920 INFO [train.py:842] (1/4) Epoch 20, batch 5250, loss[loss=0.2095, simple_loss=0.3004, pruned_loss=0.05931, over 7349.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2727, pruned_loss=0.05014, over 1421893.94 frames.], batch size: 22, lr: 2.74e-04
2022-05-28 04:19:06,160 INFO [train.py:842] (1/4) Epoch 20, batch 5300, loss[loss=0.1731, simple_loss=0.2658, pruned_loss=0.0402, over 7376.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2728, pruned_loss=0.05014, over 1419814.11 frames.], batch size: 23, lr: 2.74e-04
2022-05-28 04:19:43,980 INFO [train.py:842] (1/4) Epoch 20, batch 5350, loss[loss=0.2569, simple_loss=0.346, pruned_loss=0.0839, over 7290.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2723, pruned_loss=0.04964, over 1421677.75 frames.], batch size: 24, lr: 2.74e-04
2022-05-28 04:20:22,321 INFO [train.py:842] (1/4) Epoch 20, batch 5400, loss[loss=0.1742, simple_loss=0.2677, pruned_loss=0.04039, over 7233.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2722, pruned_loss=0.04973, over 1421294.25 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:21:00,425 INFO [train.py:842] (1/4) Epoch 20, batch 5450, loss[loss=0.1853, simple_loss=0.2726, pruned_loss=0.04904, over 7440.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2732, pruned_loss=0.05052, over 1421806.92 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:21:38,513 INFO [train.py:842] (1/4) Epoch 20, batch 5500, loss[loss=0.2108, simple_loss=0.2995, pruned_loss=0.0611, over 7336.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2713, pruned_loss=0.04913, over 1420588.27 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:22:16,449 INFO [train.py:842] (1/4) Epoch 20, batch 5550, loss[loss=0.1938, simple_loss=0.2868, pruned_loss=0.05039, over 7419.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2724, pruned_loss=0.04989, over 1423574.50 frames.], batch size: 21, lr: 2.74e-04
2022-05-28 04:22:54,450 INFO [train.py:842] (1/4) Epoch 20, batch 5600, loss[loss=0.1883, simple_loss=0.2875, pruned_loss=0.04455, over 7305.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2729, pruned_loss=0.04987, over 1423769.52 frames.], batch size: 25, lr: 2.74e-04
2022-05-28 04:23:32,209 INFO [train.py:842] (1/4) Epoch 20, batch 5650, loss[loss=0.2057, simple_loss=0.2883, pruned_loss=0.06157, over 7210.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2735, pruned_loss=0.05042, over 1420151.61 frames.], batch size: 22, lr: 2.74e-04
2022-05-28 04:24:10,420 INFO [train.py:842] (1/4) Epoch 20, batch 5700, loss[loss=0.1575, simple_loss=0.2433, pruned_loss=0.03584, over 7228.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2732, pruned_loss=0.04986, over 1420527.90 frames.], batch size: 16, lr: 2.74e-04
2022-05-28 04:24:48,366 INFO [train.py:842] (1/4) Epoch 20, batch 5750, loss[loss=0.1911, simple_loss=0.2837, pruned_loss=0.04922, over 7208.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2729, pruned_loss=0.04991, over 1419025.00 frames.], batch size: 23, lr: 2.74e-04
2022-05-28 04:25:26,845 INFO [train.py:842] (1/4) Epoch 20, batch 5800, loss[loss=0.177, simple_loss=0.2631, pruned_loss=0.04546, over 7143.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2724, pruned_loss=0.04999, over 1422987.03 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:26:04,822 INFO [train.py:842] (1/4) Epoch 20, batch 5850, loss[loss=0.1957, simple_loss=0.2852, pruned_loss=0.05309, over 6782.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2723, pruned_loss=0.04988, over 1427327.97 frames.], batch size: 31, lr: 2.74e-04
2022-05-28 04:26:42,986 INFO [train.py:842] (1/4) Epoch 20, batch 5900, loss[loss=0.1998, simple_loss=0.2866, pruned_loss=0.05656, over 7317.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2722, pruned_loss=0.04982, over 1420842.52 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:27:20,877 INFO [train.py:842] (1/4) Epoch 20, batch 5950, loss[loss=0.1855, simple_loss=0.2658, pruned_loss=0.05261, over 7329.00 frames.], tot_loss[loss=0.187, simple_loss=0.2727, pruned_loss=0.05064, over 1417624.22 frames.], batch size: 22, lr: 2.74e-04
2022-05-28 04:27:59,348 INFO [train.py:842] (1/4) Epoch 20, batch 6000, loss[loss=0.21, simple_loss=0.3016, pruned_loss=0.05919, over 7344.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2707, pruned_loss=0.04927, over 1421854.71 frames.], batch size: 22, lr: 2.74e-04
2022-05-28 04:27:59,349 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 04:28:08,359 INFO [train.py:871] (1/4) Epoch 20, validation: loss=0.1665, simple_loss=0.2662, pruned_loss=0.03341, over 868885.00 frames.
2022-05-28 04:28:46,203 INFO [train.py:842] (1/4) Epoch 20, batch 6050, loss[loss=0.1949, simple_loss=0.2737, pruned_loss=0.05808, over 7065.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2709, pruned_loss=0.04931, over 1420855.69 frames.], batch size: 18, lr: 2.74e-04
2022-05-28 04:29:24,665 INFO [train.py:842] (1/4) Epoch 20, batch 6100, loss[loss=0.1923, simple_loss=0.2808, pruned_loss=0.0519, over 7428.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2706, pruned_loss=0.04919, over 1420519.51 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:30:02,609 INFO [train.py:842] (1/4) Epoch 20, batch 6150, loss[loss=0.2276, simple_loss=0.3078, pruned_loss=0.07369, over 7459.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2718, pruned_loss=0.04952, over 1423369.17 frames.], batch size: 19, lr: 2.74e-04
2022-05-28 04:30:41,026 INFO [train.py:842] (1/4) Epoch 20, batch 6200, loss[loss=0.1789, simple_loss=0.2551, pruned_loss=0.05136, over 7433.00 frames.], tot_loss[loss=0.1858, simple_loss=0.272, pruned_loss=0.04982, over 1424719.86 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:31:18,807 INFO [train.py:842] (1/4) Epoch 20, batch 6250, loss[loss=0.1381, simple_loss=0.223, pruned_loss=0.0266, over 7364.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2721, pruned_loss=0.04958, over 1422503.50 frames.], batch size: 19, lr: 2.74e-04
2022-05-28 04:31:56,956 INFO [train.py:842] (1/4) Epoch 20, batch 6300, loss[loss=0.2245, simple_loss=0.312, pruned_loss=0.0685, over 7295.00 frames.], tot_loss[loss=0.188, simple_loss=0.2744, pruned_loss=0.05082, over 1421081.24 frames.], batch size: 25, lr: 2.74e-04
2022-05-28 04:32:34,969 INFO [train.py:842] (1/4) Epoch 20, batch 6350, loss[loss=0.1622, simple_loss=0.2656, pruned_loss=0.02944, over 7311.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2738, pruned_loss=0.05053, over 1424832.99 frames.], batch size: 21, lr: 2.74e-04
2022-05-28 04:33:13,295 INFO [train.py:842] (1/4) Epoch 20, batch 6400, loss[loss=0.1732, simple_loss=0.2661, pruned_loss=0.04016, over 7321.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2739, pruned_loss=0.05094, over 1423810.38 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:33:50,983 INFO [train.py:842] (1/4) Epoch 20, batch 6450, loss[loss=0.16, simple_loss=0.2525, pruned_loss=0.03373, over 7326.00 frames.], tot_loss[loss=0.1871, simple_loss=0.273, pruned_loss=0.0506, over 1420755.51 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:34:29,314 INFO [train.py:842] (1/4) Epoch 20, batch 6500, loss[loss=0.1595, simple_loss=0.2449, pruned_loss=0.03698, over 7459.00 frames.], tot_loss[loss=0.186, simple_loss=0.2719, pruned_loss=0.0501, over 1424670.79 frames.], batch size: 19, lr: 2.73e-04
2022-05-28 04:35:07,339 INFO [train.py:842] (1/4) Epoch 20, batch 6550, loss[loss=0.1745, simple_loss=0.262, pruned_loss=0.04347, over 7153.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2708, pruned_loss=0.04985, over 1424261.74 frames.], batch size: 19, lr: 2.73e-04
2022-05-28 04:35:45,536 INFO [train.py:842] (1/4) Epoch 20, batch 6600, loss[loss=0.197, simple_loss=0.2751, pruned_loss=0.05949, over 6793.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2713, pruned_loss=0.0499, over 1426339.62 frames.], batch size: 15, lr: 2.73e-04
2022-05-28 04:36:23,634 INFO [train.py:842] (1/4) Epoch 20, batch 6650, loss[loss=0.1691, simple_loss=0.2514, pruned_loss=0.04343, over 7243.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2717, pruned_loss=0.05027, over 1425788.77 frames.], batch size: 20, lr: 2.73e-04
2022-05-28 04:37:02,177 INFO [train.py:842] (1/4) Epoch 20, batch 6700, loss[loss=0.1565, simple_loss=0.2331, pruned_loss=0.03996, over 7131.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2703, pruned_loss=0.04964, over 1425343.54 frames.], batch size: 17, lr: 2.73e-04
2022-05-28 04:37:40,089 INFO [train.py:842] (1/4) Epoch 20, batch 6750, loss[loss=0.2495, simple_loss=0.3176, pruned_loss=0.09075, over 7168.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2704, pruned_loss=0.04958, over 1429976.67 frames.], batch size: 18, lr: 2.73e-04
2022-05-28 04:38:18,255 INFO [train.py:842] (1/4) Epoch 20, batch 6800, loss[loss=0.1769, simple_loss=0.2465, pruned_loss=0.05363, over 7276.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2706, pruned_loss=0.04954, over 1428630.31 frames.], batch size: 17, lr: 2.73e-04
2022-05-28 04:38:56,161 INFO [train.py:842] (1/4) Epoch 20, batch 6850, loss[loss=0.1443, simple_loss=0.2224, pruned_loss=0.03314, over 7000.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2704, pruned_loss=0.04917, over 1428598.20 frames.], batch size: 16, lr: 2.73e-04
2022-05-28 04:39:34,138 INFO [train.py:842] (1/4) Epoch 20, batch 6900, loss[loss=0.2094, simple_loss=0.2953, pruned_loss=0.06178, over 7311.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2708, pruned_loss=0.04976, over 1425050.63 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:40:11,938 INFO [train.py:842] (1/4) Epoch 20, batch 6950, loss[loss=0.2023, simple_loss=0.2937, pruned_loss=0.05547, over 7208.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2711, pruned_loss=0.04953, over 1426122.96 frames.], batch size: 22, lr: 2.73e-04
2022-05-28 04:40:50,223 INFO [train.py:842] (1/4) Epoch 20, batch 7000, loss[loss=0.205, simple_loss=0.2752, pruned_loss=0.06742, over 7172.00 frames.], tot_loss[loss=0.187, simple_loss=0.2725, pruned_loss=0.05074, over 1427375.58 frames.], batch size: 16, lr: 2.73e-04
2022-05-28 04:41:28,299 INFO [train.py:842] (1/4) Epoch 20, batch 7050, loss[loss=0.1864, simple_loss=0.2626, pruned_loss=0.05513, over 7010.00 frames.], tot_loss[loss=0.186, simple_loss=0.2718, pruned_loss=0.05007, over 1430564.60 frames.], batch size: 28, lr: 2.73e-04
2022-05-28 04:42:06,605 INFO [train.py:842] (1/4) Epoch 20, batch 7100, loss[loss=0.1446, simple_loss=0.2278, pruned_loss=0.03074, over 7161.00 frames.], tot_loss[loss=0.186, simple_loss=0.272, pruned_loss=0.04999, over 1431298.49 frames.], batch size: 19, lr: 2.73e-04
2022-05-28 04:42:44,640 INFO [train.py:842] (1/4) Epoch 20, batch 7150, loss[loss=0.1763, simple_loss=0.2772, pruned_loss=0.03764, over 7226.00 frames.], tot_loss[loss=0.1858, simple_loss=0.272, pruned_loss=0.04976, over 1432650.04 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:43:22,720 INFO [train.py:842] (1/4) Epoch 20, batch 7200, loss[loss=0.1877, simple_loss=0.2752, pruned_loss=0.05011, over 7111.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2723, pruned_loss=0.0497, over 1426389.92 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:44:00,674 INFO [train.py:842] (1/4) Epoch 20, batch 7250, loss[loss=0.2079, simple_loss=0.2965, pruned_loss=0.05966, over 7341.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2729, pruned_loss=0.05014, over 1425067.44 frames.], batch size: 22, lr: 2.73e-04
2022-05-28 04:44:38,830 INFO [train.py:842] (1/4) Epoch 20, batch 7300, loss[loss=0.2409, simple_loss=0.3, pruned_loss=0.09095, over 4856.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2723, pruned_loss=0.04959, over 1421443.39 frames.], batch size: 53, lr: 2.73e-04
2022-05-28 04:45:16,991 INFO [train.py:842] (1/4) Epoch 20, batch 7350, loss[loss=0.1685, simple_loss=0.2511, pruned_loss=0.04291, over 7174.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2721, pruned_loss=0.04947, over 1423506.07 frames.], batch size: 18, lr: 2.73e-04
2022-05-28 04:45:55,203 INFO [train.py:842] (1/4) Epoch 20, batch 7400, loss[loss=0.1493, simple_loss=0.2305, pruned_loss=0.034, over 7144.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2711, pruned_loss=0.0489, over 1425994.30 frames.], batch size: 17, lr: 2.73e-04
2022-05-28 04:46:32,951 INFO [train.py:842] (1/4) Epoch 20, batch 7450, loss[loss=0.1777, simple_loss=0.2689, pruned_loss=0.04328, over 7324.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2722, pruned_loss=0.04981, over 1427033.43 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:47:11,137 INFO [train.py:842] (1/4) Epoch 20, batch 7500, loss[loss=0.2073, simple_loss=0.2944, pruned_loss=0.06004, over 7215.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2726, pruned_loss=0.04999, over 1429470.62 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:47:48,922 INFO [train.py:842] (1/4) Epoch 20, batch 7550, loss[loss=0.1865, simple_loss=0.2592, pruned_loss=0.05687, over 7283.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2737, pruned_loss=0.05091, over 1424995.22 frames.], batch size: 18, lr: 2.73e-04
2022-05-28 04:48:27,067 INFO [train.py:842] (1/4) Epoch 20, batch 7600, loss[loss=0.1898, simple_loss=0.2731, pruned_loss=0.05321, over 7078.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2737, pruned_loss=0.05103, over 1424588.18 frames.], batch size: 18, lr: 2.73e-04
2022-05-28 04:49:05,141 INFO [train.py:842] (1/4) Epoch 20, batch 7650, loss[loss=0.1895, simple_loss=0.2857, pruned_loss=0.04669, over 7218.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2723, pruned_loss=0.05018, over 1425007.84 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:49:43,395 INFO [train.py:842] (1/4) Epoch 20, batch 7700, loss[loss=0.1549, simple_loss=0.2373, pruned_loss=0.03621, over 7263.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2718, pruned_loss=0.05025, over 1426075.46 frames.], batch size: 19, lr: 2.73e-04
2022-05-28 04:50:21,410 INFO [train.py:842] (1/4) Epoch 20, batch 7750, loss[loss=0.1957, simple_loss=0.2844, pruned_loss=0.05354, over 7143.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2717, pruned_loss=0.05032, over 1426257.97 frames.], batch size: 20, lr: 2.73e-04
2022-05-28 04:50:59,585 INFO [train.py:842] (1/4) Epoch 20, batch 7800, loss[loss=0.1441, simple_loss=0.2307, pruned_loss=0.02877, over 7169.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2719, pruned_loss=0.05061, over 1426044.04 frames.], batch size: 18, lr: 2.72e-04
2022-05-28 04:51:37,534 INFO [train.py:842] (1/4) Epoch 20, batch 7850, loss[loss=0.1535, simple_loss=0.2459, pruned_loss=0.03053, over 7143.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2717, pruned_loss=0.05024, over 1425228.02 frames.], batch size: 20, lr: 2.72e-04
2022-05-28 04:52:15,873 INFO [train.py:842] (1/4) Epoch 20, batch 7900, loss[loss=0.2003, simple_loss=0.2968, pruned_loss=0.05194, over 7108.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2711, pruned_loss=0.04983, over 1426632.84 frames.], batch size: 21, lr: 2.72e-04
2022-05-28 04:52:53,898 INFO [train.py:842] (1/4) Epoch 20, batch 7950, loss[loss=0.2013, simple_loss=0.2892, pruned_loss=0.05672, over 7306.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2703, pruned_loss=0.0493, over 1429307.92 frames.], batch size: 25, lr: 2.72e-04
2022-05-28 04:53:32,239 INFO [train.py:842] (1/4) Epoch 20, batch 8000, loss[loss=0.1507, simple_loss=0.2386, pruned_loss=0.03146, over 7356.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2696, pruned_loss=0.04896, over 1430282.35 frames.], batch size: 19, lr: 2.72e-04
2022-05-28 04:54:10,489 INFO [train.py:842] (1/4) Epoch 20, batch 8050, loss[loss=0.1695, simple_loss=0.2615, pruned_loss=0.03873, over 7223.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2685, pruned_loss=0.04855, over 1427845.47 frames.], batch size: 21, lr: 2.72e-04
2022-05-28 04:54:48,888 INFO [train.py:842] (1/4) Epoch 20, batch 8100, loss[loss=0.1905, simple_loss=0.2714, pruned_loss=0.05475, over 7437.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2687, pruned_loss=0.04904, over 1431203.83 frames.], batch size: 20, lr: 2.72e-04
2022-05-28 04:55:36,033 INFO [train.py:842] (1/4) Epoch 20, batch 8150, loss[loss=0.1689, simple_loss=0.2467, pruned_loss=0.04561, over 7149.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2699, pruned_loss=0.04972, over 1423954.98 frames.], batch size: 17, lr: 2.72e-04
2022-05-28 04:56:14,319 INFO [train.py:842] (1/4) Epoch 20, batch 8200, loss[loss=0.1451, simple_loss=0.22, pruned_loss=0.03508, over 7416.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2702, pruned_loss=0.04917, over 1425798.87 frames.], batch size: 18, lr: 2.72e-04
2022-05-28 04:56:52,108 INFO [train.py:842] (1/4) Epoch 20, batch 8250, loss[loss=0.2001, simple_loss=0.2811, pruned_loss=0.05954, over 7276.00 frames.], tot_loss[loss=0.1849, simple_loss=0.271, pruned_loss=0.04934, over 1425192.24 frames.], batch size: 18, lr: 2.72e-04
2022-05-28 04:57:30,457 INFO [train.py:842] (1/4) Epoch 20, batch 8300, loss[loss=0.1931, simple_loss=0.2881, pruned_loss=0.04908, over 7329.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2715, pruned_loss=0.04977, over 1425895.17 frames.], batch size: 21, lr: 2.72e-04
2022-05-28 04:58:17,707 INFO [train.py:842] (1/4) Epoch 20, batch 8350, loss[loss=0.1885, simple_loss=0.2784, pruned_loss=0.04933, over 7206.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2727, pruned_loss=0.05016, over 1421087.41 frames.], batch size: 23, lr: 2.72e-04
2022-05-28 04:58:55,799 INFO [train.py:842] (1/4) Epoch 20, batch 8400, loss[loss=0.1632, simple_loss=0.2506, pruned_loss=0.03792, over 7326.00 frames.], tot_loss[loss=0.1856, simple_loss=0.272, pruned_loss=0.04955, over 1420990.65 frames.], batch size: 20, lr: 2.72e-04
2022-05-28 04:59:33,820 INFO [train.py:842] (1/4) Epoch 20, batch 8450, loss[loss=0.1522, simple_loss=0.2404, pruned_loss=0.03203, over 7405.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2719, pruned_loss=0.04939, over 1423487.80 frames.], batch size: 18, lr: 2.72e-04
2022-05-28 05:00:21,176 INFO [train.py:842] (1/4) Epoch 20, batch 8500, loss[loss=0.1855, simple_loss=0.2871, pruned_loss=0.04191, over 7314.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2718, pruned_loss=0.04934, over 1417876.13 frames.], batch size: 24, lr: 2.72e-04
2022-05-28 05:00:59,185 INFO [train.py:842] (1/4) Epoch 20, batch 8550, loss[loss=0.235, simple_loss=0.2962, pruned_loss=0.08693, over 7271.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2723, pruned_loss=0.04962, over 1420735.01 frames.], batch size: 19, lr: 2.72e-04
2022-05-28 05:01:37,333 INFO [train.py:842] (1/4) Epoch 20, batch 8600, loss[loss=0.1466, simple_loss=0.2433, pruned_loss=0.02491, over 7310.00 frames.], tot_loss[loss=0.1845, simple_loss=0.271, pruned_loss=0.04904, over 1423444.03 frames.], batch size: 21, lr: 2.72e-04
2022-05-28 05:02:15,322 INFO [train.py:842] (1/4) Epoch 20, batch 8650, loss[loss=0.2057, simple_loss=0.2819, pruned_loss=0.06474, over 7240.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2712, pruned_loss=0.04968, over 1421206.80 frames.], batch size: 20, lr: 2.72e-04
2022-05-28 05:02:53,355 INFO [train.py:842] (1/4) Epoch 20, batch 8700, loss[loss=0.1635, simple_loss=0.2446, pruned_loss=0.04116, over 7286.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2702, pruned_loss=0.04919, over 1413239.64 frames.], batch size: 18, lr: 2.72e-04
2022-05-28 05:03:31,387 INFO [train.py:842] (1/4) Epoch 20, batch 8750, loss[loss=0.2188, simple_loss=0.3072, pruned_loss=0.06518, over 7211.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2696, pruned_loss=0.04863, over 1415253.22 frames.], batch size: 23, lr: 2.72e-04
2022-05-28 05:04:09,703 INFO [train.py:842] (1/4) Epoch 20, batch 8800, loss[loss=0.1661, simple_loss=0.2623, pruned_loss=0.03496, over 7155.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2695, pruned_loss=0.04844, over 1415901.28 frames.], batch size: 19, lr: 2.72e-04
2022-05-28 05:04:47,742 INFO [train.py:842] (1/4) Epoch 20, batch 8850, loss[loss=0.1561, simple_loss=0.2338, pruned_loss=0.0392, over 6988.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2693, pruned_loss=0.0488, over 1410287.50 frames.], batch size: 16, lr: 2.72e-04
2022-05-28 05:05:25,747 INFO [train.py:842] (1/4) Epoch 20, batch 8900, loss[loss=0.1777, simple_loss=0.2626, pruned_loss=0.04638, over 7256.00 frames.], tot_loss[loss=0.183, simple_loss=0.2686, pruned_loss=0.0487, over 1401010.60 frames.], batch size: 19, lr: 2.72e-04
2022-05-28 05:06:03,646 INFO [train.py:842] (1/4) Epoch 20, batch 8950, loss[loss=0.1908, simple_loss=0.2947, pruned_loss=0.04343, over 7219.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2678, pruned_loss=0.0482, over 1391712.34 frames.], batch size: 21, lr: 2.72e-04
2022-05-28 05:06:42,109 INFO [train.py:842] (1/4) Epoch 20, batch 9000, loss[loss=0.2913, simple_loss=0.3606, pruned_loss=0.1109, over 6373.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2672, pruned_loss=0.04876, over 1369004.38 frames.], batch size: 38, lr: 2.72e-04
2022-05-28 05:06:42,110 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 05:06:51,176 INFO [train.py:871] (1/4) Epoch 20, validation: loss=0.164, simple_loss=0.2628, pruned_loss=0.03265, over 868885.00 frames.
2022-05-28 05:07:28,673 INFO [train.py:842] (1/4) Epoch 20, batch 9050, loss[loss=0.2379, simple_loss=0.3272, pruned_loss=0.07432, over 5514.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2697, pruned_loss=0.05075, over 1335342.40 frames.], batch size: 52, lr: 2.72e-04
2022-05-28 05:08:05,305 INFO [train.py:842] (1/4) Epoch 20, batch 9100, loss[loss=0.2179, simple_loss=0.3146, pruned_loss=0.06056, over 6588.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2735, pruned_loss=0.05257, over 1290545.97 frames.], batch size: 38, lr: 2.72e-04
2022-05-28 05:08:41,902 INFO [train.py:842] (1/4) Epoch 20, batch 9150, loss[loss=0.2658, simple_loss=0.3224, pruned_loss=0.1045, over 4679.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2775, pruned_loss=0.05569, over 1237409.56 frames.], batch size: 52, lr: 2.71e-04
2022-05-28 05:09:33,861 INFO [train.py:842] (1/4) Epoch 21, batch 0, loss[loss=0.1644, simple_loss=0.2485, pruned_loss=0.04018, over 7006.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2485, pruned_loss=0.04018, over 7006.00 frames.], batch size: 16, lr: 2.65e-04
2022-05-28 05:10:12,045 INFO [train.py:842] (1/4) Epoch 21, batch 50, loss[loss=0.179, simple_loss=0.2619, pruned_loss=0.04806, over 6394.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2748, pruned_loss=0.05151, over 323591.92 frames.], batch size: 38, lr: 2.65e-04
2022-05-28 05:10:50,331 INFO [train.py:842] (1/4) Epoch 21, batch 100, loss[loss=0.2232, simple_loss=0.3042, pruned_loss=0.07113, over 6850.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2722, pruned_loss=0.05072, over 566654.15 frames.], batch size: 15, lr: 2.65e-04
2022-05-28 05:11:28,270 INFO [train.py:842] (1/4) Epoch 21, batch 150, loss[loss=0.1895, simple_loss=0.2658, pruned_loss=0.05662, over 7180.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2725, pruned_loss=0.04989, over 755787.06 frames.], batch size: 18, lr: 2.65e-04
2022-05-28 05:12:09,314 INFO [train.py:842] (1/4) Epoch 21, batch 200, loss[loss=0.2068, simple_loss=0.2911, pruned_loss=0.06132, over 6800.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2732, pruned_loss=0.05061, over 900592.54 frames.], batch size: 31, lr: 2.65e-04
2022-05-28 05:12:47,243 INFO [train.py:842] (1/4) Epoch 21, batch 250, loss[loss=0.2138, simple_loss=0.2922, pruned_loss=0.06768, over 7171.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2732, pruned_loss=0.05097, over 1013051.60 frames.], batch size: 19, lr: 2.65e-04
2022-05-28 05:13:25,537 INFO [train.py:842] (1/4) Epoch 21, batch 300, loss[loss=0.2003, simple_loss=0.2776, pruned_loss=0.06148, over 7300.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2724, pruned_loss=0.05037, over 1101866.30 frames.], batch size: 18, lr: 2.65e-04
2022-05-28 05:14:03,308 INFO [train.py:842] (1/4) Epoch 21, batch 350, loss[loss=0.1969, simple_loss=0.2742, pruned_loss=0.0598, over 7269.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2732, pruned_loss=0.04992, over 1169686.96 frames.], batch size: 19, lr: 2.65e-04
2022-05-28 05:14:41,727 INFO [train.py:842] (1/4) Epoch 21, batch 400, loss[loss=0.1749, simple_loss=0.2555, pruned_loss=0.04712, over 7057.00 frames.], tot_loss[loss=0.1867, simple_loss=0.273, pruned_loss=0.05015, over 1229453.84 frames.], batch size: 18, lr: 2.65e-04
2022-05-28 05:15:19,621 INFO [train.py:842] (1/4) Epoch 21, batch 450, loss[loss=0.158, simple_loss=0.2431, pruned_loss=0.03638, over 7064.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2722, pruned_loss=0.04966, over 1273498.70 frames.], batch size: 18, lr: 2.65e-04
2022-05-28 05:15:57,952 INFO [train.py:842] (1/4) Epoch 21, batch 500, loss[loss=0.1691, simple_loss=0.2679, pruned_loss=0.03518, over 7022.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2715, pruned_loss=0.04914, over 1311475.23 frames.], batch size: 28, lr: 2.65e-04
2022-05-28 05:16:36,038 INFO [train.py:842] (1/4) Epoch 21, batch 550, loss[loss=0.1607, simple_loss=0.2354, pruned_loss=0.04303, over 6818.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2709, pruned_loss=0.04904, over 1337050.74 frames.], batch size: 15, lr: 2.65e-04
2022-05-28 05:17:14,245 INFO [train.py:842] (1/4) Epoch 21, batch 600, loss[loss=0.1814, simple_loss=0.2709, pruned_loss=0.046, over 7216.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2715, pruned_loss=0.04955, over 1355216.03 frames.], batch size: 22, lr: 2.65e-04
2022-05-28 05:17:52,435 INFO [train.py:842] (1/4) Epoch 21, batch 650, loss[loss=0.1319, simple_loss=0.2152, pruned_loss=0.02432, over 7142.00 frames.], tot_loss[loss=0.184, simple_loss=0.2697, pruned_loss=0.0491, over 1368688.34 frames.], batch size: 17, lr: 2.65e-04
2022-05-28 05:18:30,492 INFO [train.py:842] (1/4) Epoch 21, batch 700, loss[loss=0.1828, simple_loss=0.279, pruned_loss=0.04326, over 7224.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2697, pruned_loss=0.04865, over 1378726.98 frames.], batch size: 20, lr: 2.65e-04
2022-05-28 05:19:08,400 INFO [train.py:842] (1/4) Epoch 21, batch 750, loss[loss=0.2121, simple_loss=0.2827, pruned_loss=0.07074, over 7414.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2709, pruned_loss=0.04927, over 1384867.83 frames.], batch size: 18, lr: 2.65e-04
2022-05-28 05:19:46,510 INFO [train.py:842] (1/4) Epoch 21, batch 800, loss[loss=0.2598, simple_loss=0.3318, pruned_loss=0.09387, over 7236.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2709, pruned_loss=0.04979, over 1383311.20 frames.], batch size: 20, lr: 2.65e-04
2022-05-28 05:20:24,485 INFO [train.py:842] (1/4) Epoch 21, batch 850, loss[loss=0.2047, simple_loss=0.2982, pruned_loss=0.05564, over 7259.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2703, pruned_loss=0.0497, over 1390149.20 frames.], batch size: 25, lr: 2.65e-04
2022-05-28 05:21:02,926 INFO [train.py:842] (1/4) Epoch 21, batch 900, loss[loss=0.1624, simple_loss=0.2557, pruned_loss=0.03453, over 7228.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2687, pruned_loss=0.04852, over 1399234.56 frames.], batch size: 20, lr: 2.65e-04
2022-05-28 05:21:40,799 INFO [train.py:842] (1/4) Epoch 21, batch 950, loss[loss=0.2179, simple_loss=0.313, pruned_loss=0.06141, over 7349.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2699, pruned_loss=0.04882, over 1405181.85 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:22:18,913 INFO [train.py:842] (1/4) Epoch 21, batch 1000, loss[loss=0.2187, simple_loss=0.3095, pruned_loss=0.06397, over 7211.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2709, pruned_loss=0.0487, over 1404391.22 frames.], batch size: 23, lr: 2.64e-04
2022-05-28 05:22:56,518 INFO [train.py:842] (1/4) Epoch 21, batch 1050, loss[loss=0.2266, simple_loss=0.3021, pruned_loss=0.07553, over 7410.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2722, pruned_loss=0.04911, over 1405942.64 frames.], batch size: 21, lr: 2.64e-04
2022-05-28 05:23:34,907 INFO [train.py:842] (1/4) Epoch 21, batch 1100, loss[loss=0.1678, simple_loss=0.2352, pruned_loss=0.05018, over 6801.00 frames.], tot_loss[loss=0.184, simple_loss=0.2707, pruned_loss=0.04861, over 1408152.64 frames.], batch size: 15, lr: 2.64e-04
2022-05-28 05:24:12,843 INFO [train.py:842] (1/4) Epoch 21, batch 1150, loss[loss=0.1776, simple_loss=0.2703, pruned_loss=0.04248, over 7287.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2716, pruned_loss=0.0489, over 1413430.30 frames.], batch size: 24, lr: 2.64e-04
2022-05-28 05:24:50,888 INFO [train.py:842] (1/4) Epoch 21, batch 1200, loss[loss=0.1373, simple_loss=0.2195, pruned_loss=0.02756, over 7285.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2715, pruned_loss=0.04869, over 1415871.16 frames.], batch size: 18, lr: 2.64e-04
2022-05-28 05:25:28,911 INFO [train.py:842] (1/4) Epoch 21, batch 1250, loss[loss=0.1688, simple_loss=0.2556, pruned_loss=0.04095, over 7310.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2704, pruned_loss=0.04847, over 1417441.44 frames.], batch size: 24, lr: 2.64e-04
2022-05-28 05:26:07,272 INFO [train.py:842] (1/4) Epoch 21, batch 1300, loss[loss=0.1759, simple_loss=0.2664, pruned_loss=0.04273, over 7062.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2705, pruned_loss=0.0489, over 1416741.18 frames.], batch size: 18, lr: 2.64e-04
2022-05-28 05:26:45,555 INFO [train.py:842] (1/4) Epoch 21, batch 1350, loss[loss=0.1752, simple_loss=0.2639, pruned_loss=0.04326, over 7340.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2692, pruned_loss=0.04822, over 1424018.67 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:27:23,883 INFO [train.py:842] (1/4) Epoch 21, batch 1400, loss[loss=0.2354, simple_loss=0.3282, pruned_loss=0.07132, over 7388.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2699, pruned_loss=0.04864, over 1426618.83 frames.], batch size: 23, lr: 2.64e-04
2022-05-28 05:28:01,810 INFO [train.py:842] (1/4) Epoch 21, batch 1450, loss[loss=0.224, simple_loss=0.3018, pruned_loss=0.07306, over 4980.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2695, pruned_loss=0.04849, over 1421216.40 frames.], batch size: 52, lr: 2.64e-04
2022-05-28 05:28:39,860 INFO [train.py:842] (1/4) Epoch 21, batch 1500, loss[loss=0.2062, simple_loss=0.3023, pruned_loss=0.05505, over 7334.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2702, pruned_loss=0.04894, over 1418989.50 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:29:17,831 INFO [train.py:842] (1/4) Epoch 21, batch 1550, loss[loss=0.2343, simple_loss=0.3127, pruned_loss=0.078, over 6788.00 frames.], tot_loss[loss=0.184, simple_loss=0.2705, pruned_loss=0.04882, over 1421272.10 frames.], batch size: 31, lr: 2.64e-04
2022-05-28 05:29:56,071 INFO [train.py:842] (1/4) Epoch 21, batch 1600, loss[loss=0.1766, simple_loss=0.2725, pruned_loss=0.04038, over 7324.00 frames.], tot_loss[loss=0.1841, simple_loss=0.271, pruned_loss=0.04857, over 1422492.86 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:30:33,975 INFO [train.py:842] (1/4) Epoch 21, batch 1650, loss[loss=0.1825, simple_loss=0.2697, pruned_loss=0.04766, over 7329.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2705, pruned_loss=0.04823, over 1422840.39 frames.], batch size: 20, lr: 2.64e-04
2022-05-28 05:31:12,225 INFO [train.py:842] (1/4) Epoch 21, batch 1700, loss[loss=0.28, simple_loss=0.3413, pruned_loss=0.1094, over 7328.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2702, pruned_loss=0.04819, over 1422696.61 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:31:50,201 INFO [train.py:842] (1/4) Epoch 21, batch 1750, loss[loss=0.1778, simple_loss=0.2627, pruned_loss=0.04648, over 7420.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2695, pruned_loss=0.04776, over 1423287.49 frames.], batch size: 18, lr: 2.64e-04
2022-05-28 05:32:28,370 INFO [train.py:842] (1/4) Epoch 21, batch 1800, loss[loss=0.1807, simple_loss=0.2678, pruned_loss=0.04681, over 7211.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2694, pruned_loss=0.04805, over 1424061.13 frames.], batch size: 23, lr: 2.64e-04
2022-05-28 05:33:06,415 INFO [train.py:842] (1/4) Epoch 21, batch 1850, loss[loss=0.134, simple_loss=0.2203, pruned_loss=0.02381, over 7419.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2689, pruned_loss=0.04769, over 1423443.61 frames.], batch size: 18, lr: 2.64e-04
2022-05-28 05:33:44,675 INFO [train.py:842] (1/4) Epoch 21, batch 1900, loss[loss=0.1937, simple_loss=0.284, pruned_loss=0.05174, over 7153.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2692, pruned_loss=0.04768, over 1425073.44 frames.], batch size: 19, lr: 2.64e-04
2022-05-28 05:34:22,663 INFO [train.py:842] (1/4) Epoch 21, batch 1950, loss[loss=0.1614, simple_loss=0.2555, pruned_loss=0.03366, over 7261.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2686, pruned_loss=0.04728, over 1428307.70 frames.], batch size: 19, lr: 2.64e-04
2022-05-28 05:35:00,911 INFO [train.py:842] (1/4) Epoch 21, batch 2000, loss[loss=0.1767, simple_loss=0.2627, pruned_loss=0.04533, over 6771.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2692, pruned_loss=0.04781, over 1424112.69 frames.], batch size: 31, lr: 2.64e-04
2022-05-28 05:35:38,844 INFO [train.py:842] (1/4) Epoch 21, batch 2050, loss[loss=0.1827, simple_loss=0.2788, pruned_loss=0.04331, over 7221.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2705, pruned_loss=0.04865, over 1424524.77 frames.], batch size: 21, lr: 2.64e-04
2022-05-28 05:36:17,024 INFO [train.py:842] (1/4) Epoch 21, batch 2100, loss[loss=0.297, simple_loss=0.3355, pruned_loss=0.1293, over 7072.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2709, pruned_loss=0.04867, over 1423228.20 frames.], batch size: 18, lr: 2.64e-04
2022-05-28 05:36:54,838 INFO [train.py:842] (1/4) Epoch 21, batch 2150, loss[loss=0.1658, simple_loss=0.2425, pruned_loss=0.04462, over 6865.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2706, pruned_loss=0.04858, over 1422288.79 frames.], batch size: 15, lr: 2.64e-04
2022-05-28 05:37:33,252 INFO [train.py:842] (1/4) Epoch 21, batch 2200, loss[loss=0.1937, simple_loss=0.2789, pruned_loss=0.05425, over 7199.00 frames.], tot_loss[loss=0.1833, simple_loss=0.27, pruned_loss=0.04825, over 1423772.46 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:38:11,124 INFO [train.py:842] (1/4) Epoch 21, batch 2250, loss[loss=0.2137, simple_loss=0.3038, pruned_loss=0.06185, over 7208.00 frames.], tot_loss[loss=0.1842, simple_loss=0.271, pruned_loss=0.04869, over 1424583.65 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:38:49,550 INFO [train.py:842] (1/4) Epoch 21, batch 2300, loss[loss=0.1966, simple_loss=0.2874, pruned_loss=0.0529, over 5046.00 frames.], tot_loss[loss=0.1834, simple_loss=0.27, pruned_loss=0.04837, over 1421801.59 frames.], batch size: 52, lr: 2.64e-04
2022-05-28 05:39:27,157 INFO [train.py:842] (1/4) Epoch 21, batch 2350, loss[loss=0.1927, simple_loss=0.2778, pruned_loss=0.05378, over 7293.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2715, pruned_loss=0.04892, over 1416224.20 frames.], batch size: 24, lr: 2.63e-04
2022-05-28 05:40:05,575 INFO [train.py:842] (1/4) Epoch 21, batch 2400, loss[loss=0.1644, simple_loss=0.2545, pruned_loss=0.03715, over 7207.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2703, pruned_loss=0.04848, over 1419822.08 frames.], batch size: 23, lr: 2.63e-04
2022-05-28 05:40:43,640 INFO [train.py:842] (1/4) Epoch 21, batch 2450, loss[loss=0.1702, simple_loss=0.2622, pruned_loss=0.0391, over 7174.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2693, pruned_loss=0.04775, over 1420696.53 frames.], batch size: 19, lr: 2.63e-04
2022-05-28 05:41:21,973 INFO [train.py:842] (1/4) Epoch 21, batch 2500, loss[loss=0.1947, simple_loss=0.2901, pruned_loss=0.04962, over 7417.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2704, pruned_loss=0.04796, over 1422378.93 frames.], batch size: 21, lr: 2.63e-04
2022-05-28 05:41:59,714 INFO [train.py:842] (1/4) Epoch 21, batch 2550, loss[loss=0.2087, simple_loss=0.2892, pruned_loss=0.06407, over 4867.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2713, pruned_loss=0.04828, over 1420225.13 frames.], batch size: 52, lr: 2.63e-04
2022-05-28 05:42:37,950 INFO [train.py:842] (1/4) Epoch 21, batch 2600, loss[loss=0.1532, simple_loss=0.2446, pruned_loss=0.03088, over 7071.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2715, pruned_loss=0.04829, over 1420991.38 frames.], batch size: 18, lr: 2.63e-04
2022-05-28 05:43:15,791 INFO [train.py:842] (1/4) Epoch 21, batch 2650, loss[loss=0.1728, simple_loss=0.2616, pruned_loss=0.04198, over 7334.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2718, pruned_loss=0.04871, over 1416236.70 frames.], batch size: 20, lr: 2.63e-04
2022-05-28 05:43:53,878 INFO [train.py:842] (1/4) Epoch 21, batch 2700, loss[loss=0.1735, simple_loss=0.2455, pruned_loss=0.05077, over 7398.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2717, pruned_loss=0.04902, over 1419743.43 frames.], batch size: 18, lr: 2.63e-04
2022-05-28 05:44:31,853 INFO [train.py:842] (1/4) Epoch 21, batch 2750, loss[loss=0.1784, simple_loss=0.2666, pruned_loss=0.04511, over 7156.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2721, pruned_loss=0.04963, over 1420992.84 frames.], batch size: 18, lr: 2.63e-04
2022-05-28 05:45:10,231 INFO [train.py:842] (1/4) Epoch 21, batch 2800, loss[loss=0.1796, simple_loss=0.2763, pruned_loss=0.04146, over 7382.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2731, pruned_loss=0.05023, over 1424629.53 frames.], batch size: 23, lr: 2.63e-04
2022-05-28 05:45:48,223 INFO [train.py:842] (1/4) Epoch 21, batch 2850, loss[loss=0.2165, simple_loss=0.296, pruned_loss=0.06845, over 7199.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2728, pruned_loss=0.05021, over 1420306.16 frames.], batch size: 23, lr: 2.63e-04
2022-05-28 05:46:26,344 INFO [train.py:842] (1/4) Epoch 21, batch 2900, loss[loss=0.1703, simple_loss=0.2622, pruned_loss=0.03916, over 7085.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2727, pruned_loss=0.04993, over 1416234.31 frames.], batch size: 28, lr: 2.63e-04
2022-05-28 05:47:04,345 INFO [train.py:842] (1/4) Epoch 21, batch 2950, loss[loss=0.2016, simple_loss=0.2765, pruned_loss=0.0634, over 7345.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2726, pruned_loss=0.04982, over 1415677.90 frames.], batch size: 19, lr: 2.63e-04
2022-05-28 05:47:42,621 INFO [train.py:842] (1/4) Epoch 21, batch 3000, loss[loss=0.2001, simple_loss=0.2862, pruned_loss=0.05703, over 6859.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2728, pruned_loss=0.04986, over 1415244.50 frames.], batch size: 31, lr: 2.63e-04
2022-05-28 05:47:42,622 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 05:47:51,726 INFO [train.py:871] (1/4) Epoch 21, validation: loss=0.1652, simple_loss=0.2649, pruned_loss=0.0328, over 868885.00 frames.
2022-05-28 05:48:29,681 INFO [train.py:842] (1/4) Epoch 21, batch 3050, loss[loss=0.1658, simple_loss=0.2462, pruned_loss=0.04275, over 7275.00 frames.], tot_loss[loss=0.186, simple_loss=0.2725, pruned_loss=0.0497, over 1416154.75 frames.], batch size: 18, lr: 2.63e-04
2022-05-28 05:49:07,872 INFO [train.py:842] (1/4) Epoch 21, batch 3100, loss[loss=0.2847, simple_loss=0.3689, pruned_loss=0.1002, over 7367.00 frames.], tot_loss[loss=0.1865, simple_loss=0.273, pruned_loss=0.04995, over 1414406.59 frames.], batch size: 23, lr: 2.63e-04
2022-05-28 05:49:46,058 INFO [train.py:842] (1/4) Epoch 21, batch 3150, loss[loss=0.2307, simple_loss=0.3107, pruned_loss=0.0753, over 7307.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2719, pruned_loss=0.04925, over 1418828.63 frames.], batch size: 24, lr: 2.63e-04
2022-05-28 05:50:24,122 INFO [train.py:842] (1/4) Epoch 21, batch 3200, loss[loss=0.2023, simple_loss=0.2953, pruned_loss=0.05465, over 7315.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2718, pruned_loss=0.04861, over 1422972.18 frames.], batch size: 21, lr: 2.63e-04
2022-05-28 05:51:01,971 INFO [train.py:842] (1/4) Epoch 21, batch 3250, loss[loss=0.1471, simple_loss=0.2328, pruned_loss=0.03069, over 7063.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2717, pruned_loss=0.0484, over 1421777.91 frames.], batch size: 18, lr: 2.63e-04
2022-05-28 05:51:40,402 INFO [train.py:842] (1/4) Epoch 21, batch 3300, loss[loss=0.164, simple_loss=0.2492, pruned_loss=0.03943, over 7138.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2716, pruned_loss=0.0486, over 1423530.40 frames.], batch size: 17, lr: 2.63e-04
2022-05-28 05:52:18,327 INFO [train.py:842] (1/4) Epoch 21, batch 3350, loss[loss=0.2308, simple_loss=0.3007, pruned_loss=0.08043, over 7237.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2724, pruned_loss=0.04913, over 1419715.36 frames.], batch size: 20, lr: 2.63e-04
2022-05-28 05:52:56,397 INFO [train.py:842] (1/4) Epoch 21, batch 3400, loss[loss=0.181, simple_loss=0.2701, pruned_loss=0.04599, over 6310.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2721, pruned_loss=0.04878, over 1416220.11 frames.], batch size: 38, lr: 2.63e-04
2022-05-28 05:53:34,251 INFO [train.py:842] (1/4) Epoch 21, batch 3450, loss[loss=0.1971, simple_loss=0.2918, pruned_loss=0.05118, over 7307.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2725, pruned_loss=0.04925, over 1414525.18 frames.], batch size: 21, lr: 2.63e-04
2022-05-28 05:54:12,331 INFO [train.py:842] (1/4) Epoch 21, batch 3500, loss[loss=0.19, simple_loss=0.281, pruned_loss=0.04952, over 7061.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2735, pruned_loss=0.04973, over 1410406.63 frames.], batch size: 28, lr: 2.63e-04
2022-05-28 05:54:50,557 INFO [train.py:842] (1/4) Epoch 21, batch 3550, loss[loss=0.1865, simple_loss=0.2679, pruned_loss=0.05253, over 7265.00 frames.], tot_loss[loss=0.1863, simple_loss=0.273, pruned_loss=0.04982, over 1414285.56 frames.], batch size: 17, lr: 2.63e-04
2022-05-28 05:55:28,700 INFO [train.py:842] (1/4) Epoch 21, batch 3600, loss[loss=0.2216, simple_loss=0.3069, pruned_loss=0.06812, over 7383.00 frames.], tot_loss[loss=0.1865, simple_loss=0.273, pruned_loss=0.05, over 1411977.66 frames.], batch size: 23, lr: 2.63e-04
2022-05-28 05:56:06,583 INFO [train.py:842] (1/4) Epoch 21, batch 3650, loss[loss=0.2006, simple_loss=0.2913, pruned_loss=0.05494, over 7186.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2722, pruned_loss=0.04973, over 1413949.63 frames.], batch size: 26, lr: 2.63e-04
2022-05-28 05:56:44,825 INFO [train.py:842] (1/4) Epoch 21, batch 3700, loss[loss=0.2048, simple_loss=0.3074, pruned_loss=0.0511, over 7314.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2727, pruned_loss=0.04974, over 1415169.41 frames.], batch size: 21, lr: 2.63e-04
2022-05-28 05:57:22,924 INFO [train.py:842] (1/4) Epoch 21, batch 3750, loss[loss=0.2032, simple_loss=0.2957, pruned_loss=0.05537, over 7283.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2726, pruned_loss=0.05014, over 1418435.68 frames.], batch size: 25, lr: 2.62e-04
2022-05-28 05:58:01,033 INFO [train.py:842] (1/4) Epoch 21, batch 3800, loss[loss=0.2014, simple_loss=0.2959, pruned_loss=0.05342, over 7167.00 frames.], tot_loss[loss=0.186, simple_loss=0.2719, pruned_loss=0.04999, over 1418357.52 frames.], batch size: 26, lr: 2.62e-04
2022-05-28 05:58:38,983 INFO [train.py:842] (1/4) Epoch 21, batch 3850, loss[loss=0.1737, simple_loss=0.2681, pruned_loss=0.03969, over 7329.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2727, pruned_loss=0.04994, over 1419397.33 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 05:59:17,311 INFO [train.py:842] (1/4) Epoch 21, batch 3900, loss[loss=0.1929, simple_loss=0.2757, pruned_loss=0.05505, over 7268.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2727, pruned_loss=0.04986, over 1423220.65 frames.], batch size: 19, lr: 2.62e-04
2022-05-28 05:59:54,988 INFO [train.py:842] (1/4) Epoch 21, batch 3950, loss[loss=0.1615, simple_loss=0.2482, pruned_loss=0.03738, over 7417.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2737, pruned_loss=0.05028, over 1419101.21 frames.], batch size: 18, lr: 2.62e-04
2022-05-28 06:00:33,126 INFO [train.py:842] (1/4) Epoch 21, batch 4000, loss[loss=0.1627, simple_loss=0.2614, pruned_loss=0.03203, over 7354.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2724, pruned_loss=0.0494, over 1422734.16 frames.], batch size: 19, lr: 2.62e-04
2022-05-28 06:01:11,316 INFO [train.py:842] (1/4) Epoch 21, batch 4050, loss[loss=0.1743, simple_loss=0.268, pruned_loss=0.04025, over 7428.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2706, pruned_loss=0.04906, over 1422234.28 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:01:49,089 INFO [train.py:842] (1/4) Epoch 21, batch 4100, loss[loss=0.1454, simple_loss=0.225, pruned_loss=0.03286, over 7154.00 frames.], tot_loss[loss=0.1855, simple_loss=0.272, pruned_loss=0.04951, over 1413631.55 frames.], batch size: 17, lr: 2.62e-04
2022-05-28 06:02:26,898 INFO [train.py:842] (1/4) Epoch 21, batch 4150, loss[loss=0.1924, simple_loss=0.2829, pruned_loss=0.05097, over 7182.00 frames.], tot_loss[loss=0.1859, simple_loss=0.272, pruned_loss=0.0499, over 1411456.34 frames.], batch size: 23, lr: 2.62e-04
2022-05-28 06:03:05,081 INFO [train.py:842] (1/4) Epoch 21, batch 4200, loss[loss=0.203, simple_loss=0.2894, pruned_loss=0.05829, over 5022.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2725, pruned_loss=0.04999, over 1415834.68 frames.], batch size: 52, lr: 2.62e-04
2022-05-28 06:03:42,987 INFO [train.py:842] (1/4) Epoch 21, batch 4250, loss[loss=0.179, simple_loss=0.2707, pruned_loss=0.04363, over 7218.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2715, pruned_loss=0.0492, over 1416299.41 frames.], batch size: 21, lr: 2.62e-04
2022-05-28 06:04:21,417 INFO [train.py:842] (1/4) Epoch 21, batch 4300, loss[loss=0.2071, simple_loss=0.2785, pruned_loss=0.06786, over 7016.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2718, pruned_loss=0.04994, over 1419682.73 frames.], batch size: 16, lr: 2.62e-04
2022-05-28 06:04:59,215 INFO [train.py:842] (1/4) Epoch 21, batch 4350, loss[loss=0.1807, simple_loss=0.2732, pruned_loss=0.04411, over 7295.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2724, pruned_loss=0.05025, over 1419460.96 frames.], batch size: 24, lr: 2.62e-04
2022-05-28 06:05:37,554 INFO [train.py:842] (1/4) Epoch 21, batch 4400, loss[loss=0.1682, simple_loss=0.2643, pruned_loss=0.03609, over 6422.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2717, pruned_loss=0.04992, over 1421216.64 frames.], batch size: 37, lr: 2.62e-04
2022-05-28 06:06:15,538 INFO [train.py:842] (1/4) Epoch 21, batch 4450, loss[loss=0.2165, simple_loss=0.3006, pruned_loss=0.06623, over 7211.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2712, pruned_loss=0.04973, over 1423426.47 frames.], batch size: 21, lr: 2.62e-04
2022-05-28 06:06:53,885 INFO [train.py:842] (1/4) Epoch 21, batch 4500, loss[loss=0.194, simple_loss=0.2857, pruned_loss=0.0511, over 7240.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2712, pruned_loss=0.04991, over 1425904.73 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:07:31,875 INFO [train.py:842] (1/4) Epoch 21, batch 4550, loss[loss=0.2316, simple_loss=0.319, pruned_loss=0.07208, over 7066.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2699, pruned_loss=0.0494, over 1427078.98 frames.], batch size: 28, lr: 2.62e-04
2022-05-28 06:08:10,249 INFO [train.py:842] (1/4) Epoch 21, batch 4600, loss[loss=0.155, simple_loss=0.2434, pruned_loss=0.0333, over 7155.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2705, pruned_loss=0.04923, over 1424852.02 frames.], batch size: 18, lr: 2.62e-04
2022-05-28 06:08:48,132 INFO [train.py:842] (1/4) Epoch 21, batch 4650, loss[loss=0.1841, simple_loss=0.2719, pruned_loss=0.04816, over 7225.00 frames.], tot_loss[loss=0.1849, simple_loss=0.271, pruned_loss=0.0494, over 1424806.87 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:09:26,161 INFO [train.py:842] (1/4) Epoch 21, batch 4700, loss[loss=0.1621, simple_loss=0.258, pruned_loss=0.03305, over 7162.00 frames.], tot_loss[loss=0.1856, simple_loss=0.272, pruned_loss=0.04964, over 1426218.15 frames.], batch size: 19, lr: 2.62e-04
2022-05-28 06:10:04,161 INFO [train.py:842] (1/4) Epoch 21, batch 4750, loss[loss=0.2207, simple_loss=0.3105, pruned_loss=0.0654, over 7051.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2724, pruned_loss=0.04998, over 1424878.64 frames.], batch size: 28, lr: 2.62e-04
2022-05-28 06:10:42,360 INFO [train.py:842] (1/4) Epoch 21, batch 4800, loss[loss=0.2366, simple_loss=0.3242, pruned_loss=0.07448, over 7273.00 frames.], tot_loss[loss=0.1878, simple_loss=0.274, pruned_loss=0.0508, over 1421981.78 frames.], batch size: 24, lr: 2.62e-04
2022-05-28 06:11:20,369 INFO [train.py:842] (1/4) Epoch 21, batch 4850, loss[loss=0.1819, simple_loss=0.2719, pruned_loss=0.04592, over 7327.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2745, pruned_loss=0.05122, over 1418706.97 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:11:58,842 INFO [train.py:842] (1/4) Epoch 21, batch 4900, loss[loss=0.1707, simple_loss=0.2666, pruned_loss=0.03736, over 7278.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2727, pruned_loss=0.04981, over 1422157.40 frames.], batch size: 24, lr: 2.62e-04
2022-05-28 06:12:36,421 INFO [train.py:842] (1/4) Epoch 21, batch 4950, loss[loss=0.1677, simple_loss=0.2533, pruned_loss=0.04102, over 7138.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2727, pruned_loss=0.04945, over 1414863.92 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:13:14,605 INFO [train.py:842] (1/4) Epoch 21, batch 5000, loss[loss=0.1637, simple_loss=0.2483, pruned_loss=0.03956, over 7438.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2721, pruned_loss=0.04923, over 1418470.69 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:13:52,561 INFO [train.py:842] (1/4) Epoch 21, batch 5050, loss[loss=0.1782, simple_loss=0.2703, pruned_loss=0.04306, over 7437.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2731, pruned_loss=0.05027, over 1419887.85 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:14:30,643 INFO [train.py:842] (1/4) Epoch 21, batch 5100, loss[loss=0.1595, simple_loss=0.24, pruned_loss=0.03943, over 7160.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2727, pruned_loss=0.05029, over 1421255.29 frames.], batch size: 18, lr: 2.62e-04
2022-05-28 06:15:08,571 INFO [train.py:842] (1/4) Epoch 21, batch 5150, loss[loss=0.2394, simple_loss=0.3089, pruned_loss=0.08499, over 5025.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2733, pruned_loss=0.05084, over 1416039.83 frames.], batch size: 52, lr: 2.62e-04
2022-05-28 06:15:46,846 INFO [train.py:842] (1/4) Epoch 21, batch 5200, loss[loss=0.1913, simple_loss=0.2879, pruned_loss=0.04735, over 6703.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2725, pruned_loss=0.05022, over 1420457.79 frames.], batch size: 31, lr: 2.61e-04
2022-05-28 06:16:24,696 INFO [train.py:842] (1/4) Epoch 21, batch 5250, loss[loss=0.1833, simple_loss=0.2735, pruned_loss=0.04657, over 6498.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2722, pruned_loss=0.05015, over 1421595.81 frames.], batch size: 37, lr: 2.61e-04
2022-05-28 06:17:02,976 INFO [train.py:842] (1/4) Epoch 21, batch 5300, loss[loss=0.1523, simple_loss=0.2297, pruned_loss=0.03738, over 7163.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2711, pruned_loss=0.04887, over 1424547.15 frames.], batch size: 18, lr: 2.61e-04
2022-05-28 06:17:40,971 INFO [train.py:842] (1/4) Epoch 21, batch 5350, loss[loss=0.1629, simple_loss=0.2574, pruned_loss=0.03421, over 7330.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2717, pruned_loss=0.04922, over 1424956.10 frames.], batch size: 25, lr: 2.61e-04
2022-05-28 06:18:19,348 INFO [train.py:842] (1/4) Epoch 21, batch 5400, loss[loss=0.1674, simple_loss=0.2504, pruned_loss=0.04218, over 7286.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2706, pruned_loss=0.04898, over 1420929.01 frames.], batch size: 18, lr: 2.61e-04
2022-05-28 06:18:57,187 INFO [train.py:842] (1/4) Epoch 21, batch 5450, loss[loss=0.1723, simple_loss=0.2621, pruned_loss=0.04122, over 7178.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2712, pruned_loss=0.04914, over 1422947.77 frames.], batch size: 23, lr: 2.61e-04
2022-05-28 06:19:35,460 INFO [train.py:842] (1/4) Epoch 21, batch 5500, loss[loss=0.201, simple_loss=0.2899, pruned_loss=0.0561, over 7354.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2712, pruned_loss=0.04899, over 1422018.13 frames.], batch size: 23, lr: 2.61e-04
2022-05-28 06:20:13,582 INFO [train.py:842] (1/4) Epoch 21, batch 5550, loss[loss=0.164, simple_loss=0.2552, pruned_loss=0.03645, over 7338.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2712, pruned_loss=0.04889, over 1417931.36 frames.], batch size: 22, lr: 2.61e-04
2022-05-28 06:20:51,846 INFO [train.py:842] (1/4) Epoch 21, batch 5600, loss[loss=0.1421, simple_loss=0.2226, pruned_loss=0.03081, over 7016.00 frames.], tot_loss[loss=0.1843, simple_loss=0.271, pruned_loss=0.04885, over 1415273.30 frames.], batch size: 16, lr: 2.61e-04
2022-05-28 06:21:29,933 INFO [train.py:842] (1/4) Epoch 21, batch 5650, loss[loss=0.1856, simple_loss=0.2859, pruned_loss=0.04265, over 7310.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2706, pruned_loss=0.04897, over 1418386.91 frames.], batch size: 21, lr: 2.61e-04
2022-05-28 06:22:08,299 INFO [train.py:842] (1/4) Epoch 21, batch 5700, loss[loss=0.1919, simple_loss=0.28, pruned_loss=0.05195, over 7079.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2711, pruned_loss=0.04928, over 1421227.93 frames.], batch size: 28, lr: 2.61e-04
2022-05-28 06:22:46,373 INFO [train.py:842] (1/4) Epoch 21, batch 5750, loss[loss=0.1886, simple_loss=0.2808, pruned_loss=0.04825, over 7340.00 frames.], tot_loss[loss=0.185, simple_loss=0.2709, pruned_loss=0.04958, over 1425249.84 frames.], batch size: 22, lr: 2.61e-04
2022-05-28 06:23:24,676 INFO [train.py:842] (1/4) Epoch 21, batch 5800, loss[loss=0.1824, simple_loss=0.2666, pruned_loss=0.04915, over 7290.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2707, pruned_loss=0.04915, over 1428061.92 frames.], batch size: 25, lr: 2.61e-04
2022-05-28 06:24:02,710 INFO [train.py:842] (1/4) Epoch 21, batch 5850, loss[loss=0.1674, simple_loss=0.2668, pruned_loss=0.03401, over 7432.00 frames.], tot_loss[loss=0.184, simple_loss=0.2699, pruned_loss=0.04899, over 1422532.09 frames.], batch size: 20, lr: 2.61e-04
2022-05-28 06:24:40,938 INFO [train.py:842] (1/4) Epoch 21, batch 5900, loss[loss=0.1663, simple_loss=0.2545, pruned_loss=0.03902, over 7306.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2704, pruned_loss=0.04901, over 1422424.14 frames.], batch size: 24, lr: 2.61e-04
2022-05-28 06:25:18,661 INFO [train.py:842] (1/4) Epoch 21, batch 5950, loss[loss=0.1717, simple_loss=0.264, pruned_loss=0.03969, over 6899.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2717, pruned_loss=0.04952, over 1416702.26 frames.], batch size: 32, lr: 2.61e-04
2022-05-28 06:25:56,953 INFO [train.py:842] (1/4) Epoch 21, batch 6000, loss[loss=0.1534, simple_loss=0.2376, pruned_loss=0.03463, over 6771.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2709, pruned_loss=0.0491, over 1418623.22 frames.], batch size: 15, lr: 2.61e-04
2022-05-28 06:25:56,953 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 06:26:05,956 INFO [train.py:871] (1/4) Epoch 21, validation: loss=0.1654, simple_loss=0.2646, pruned_loss=0.03309, over 868885.00 frames.
2022-05-28 06:26:43,749 INFO [train.py:842] (1/4) Epoch 21, batch 6050, loss[loss=0.2054, simple_loss=0.2982, pruned_loss=0.0563, over 6501.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2727, pruned_loss=0.05004, over 1415354.80 frames.], batch size: 38, lr: 2.61e-04
2022-05-28 06:27:22,090 INFO [train.py:842] (1/4) Epoch 21, batch 6100, loss[loss=0.1809, simple_loss=0.2616, pruned_loss=0.05011, over 7134.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2718, pruned_loss=0.0497, over 1418245.36 frames.], batch size: 17, lr: 2.61e-04
2022-05-28 06:27:59,905 INFO [train.py:842] (1/4) Epoch 21, batch 6150, loss[loss=0.215, simple_loss=0.2976, pruned_loss=0.06622, over 7339.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2722, pruned_loss=0.04963, over 1418187.58 frames.], batch size: 22, lr: 2.61e-04
2022-05-28 06:28:38,155 INFO [train.py:842] (1/4) Epoch 21, batch 6200, loss[loss=0.1873, simple_loss=0.2797, pruned_loss=0.04743, over 7172.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2714, pruned_loss=0.04884, over 1422761.96 frames.], batch size: 26, lr: 2.61e-04
2022-05-28 06:29:16,116 INFO [train.py:842] (1/4) Epoch 21, batch 6250, loss[loss=0.2149, simple_loss=0.3054, pruned_loss=0.06218, over 7272.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2707, pruned_loss=0.04819, over 1421171.60 frames.], batch size: 24, lr: 2.61e-04
2022-05-28 06:29:54,357 INFO [train.py:842] (1/4) Epoch 21, batch 6300, loss[loss=0.2287, simple_loss=0.3148, pruned_loss=0.0713, over 7332.00 frames.], tot_loss[loss=0.1841, simple_loss=0.271, pruned_loss=0.04864, over 1424925.15 frames.], batch size: 22, lr: 2.61e-04
2022-05-28 06:30:32,476 INFO [train.py:842] (1/4) Epoch 21, batch 6350, loss[loss=0.1845, simple_loss=0.2764, pruned_loss=0.04631, over 7329.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2705, pruned_loss=0.04868, over 1427933.56 frames.], batch size: 20, lr: 2.61e-04
2022-05-28 06:31:10,714 INFO [train.py:842] (1/4) Epoch 21, batch 6400, loss[loss=0.23, simple_loss=0.2928, pruned_loss=0.08359, over 5443.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2697, pruned_loss=0.04835, over 1425914.65 frames.], batch size: 55, lr: 2.61e-04
2022-05-28 06:31:48,573 INFO [train.py:842] (1/4) Epoch 21, batch 6450, loss[loss=0.1728, simple_loss=0.2785, pruned_loss=0.03352, over 7426.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2709, pruned_loss=0.04888, over 1424773.08 frames.], batch size: 20, lr: 2.61e-04
2022-05-28 06:32:26,778 INFO [train.py:842] (1/4) Epoch 21, batch 6500, loss[loss=0.1413, simple_loss=0.2255, pruned_loss=0.02859, over 7455.00 frames.], tot_loss[loss=0.185, simple_loss=0.2716, pruned_loss=0.04914, over 1427680.28 frames.], batch size: 19, lr: 2.61e-04
2022-05-28 06:33:04,412 INFO [train.py:842] (1/4) Epoch 21, batch 6550, loss[loss=0.1626, simple_loss=0.2572, pruned_loss=0.03407, over 7421.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2716, pruned_loss=0.04873, over 1424688.42 frames.], batch size: 20, lr: 2.61e-04
2022-05-28 06:33:42,756 INFO [train.py:842] (1/4) Epoch 21, batch 6600, loss[loss=0.1932, simple_loss=0.2966, pruned_loss=0.04493, over 7333.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2716, pruned_loss=0.04882, over 1423639.41 frames.], batch size: 22, lr: 2.61e-04
2022-05-28 06:34:20,559 INFO [train.py:842] (1/4) Epoch 21, batch 6650, loss[loss=0.1794, simple_loss=0.2604, pruned_loss=0.04922, over 7418.00 frames.], tot_loss[loss=0.1853, simple_loss=0.272, pruned_loss=0.04927, over 1418271.79 frames.], batch size: 18, lr: 2.60e-04
2022-05-28 06:34:59,051 INFO [train.py:842] (1/4) Epoch 21, batch 6700, loss[loss=0.1878, simple_loss=0.2728, pruned_loss=0.05139, over 7374.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2714, pruned_loss=0.049, over 1423701.24 frames.], batch size: 23, lr: 2.60e-04
2022-05-28 06:35:36,977 INFO [train.py:842] (1/4) Epoch 21, batch 6750, loss[loss=0.1676, simple_loss=0.2498, pruned_loss=0.0427, over 7416.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2711, pruned_loss=0.04883, over 1426294.36 frames.], batch size: 17, lr: 2.60e-04
2022-05-28 06:36:14,939 INFO [train.py:842] (1/4) Epoch 21, batch 6800, loss[loss=0.1945, simple_loss=0.2776, pruned_loss=0.05567, over 7408.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2722, pruned_loss=0.0492, over 1424256.15 frames.], batch size: 21, lr: 2.60e-04
2022-05-28 06:36:52,969 INFO [train.py:842] (1/4) Epoch 21, batch 6850, loss[loss=0.1469, simple_loss=0.2366, pruned_loss=0.0286, over 7066.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2721, pruned_loss=0.04917, over 1427054.44 frames.], batch size: 18, lr: 2.60e-04
2022-05-28 06:37:31,257 INFO [train.py:842] (1/4) Epoch 21, batch 6900, loss[loss=0.1931, simple_loss=0.2798, pruned_loss=0.05322, over 7148.00 frames.], tot_loss[loss=0.185, simple_loss=0.2717, pruned_loss=0.04914, over 1427511.52 frames.], batch size: 19, lr: 2.60e-04
2022-05-28 06:38:09,063 INFO [train.py:842] (1/4) Epoch 21, batch 6950, loss[loss=0.1854, simple_loss=0.2711, pruned_loss=0.04986, over 7210.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2718, pruned_loss=0.04945, over 1427146.94 frames.], batch size: 23, lr: 2.60e-04
2022-05-28 06:38:47,238 INFO [train.py:842] (1/4) Epoch 21, batch 7000, loss[loss=0.2138, simple_loss=0.2919, pruned_loss=0.06784, over 7064.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2713, pruned_loss=0.04858, over 1427096.11 frames.], batch size: 18, lr: 2.60e-04
2022-05-28 06:39:25,166 INFO [train.py:842] (1/4) Epoch 21, batch 7050, loss[loss=0.1897, simple_loss=0.2759, pruned_loss=0.05173, over 7211.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2708, pruned_loss=0.04847, over 1427501.95 frames.], batch size: 22, lr: 2.60e-04
2022-05-28 06:40:03,305 INFO [train.py:842] (1/4) Epoch 21, batch 7100, loss[loss=0.1478, simple_loss=0.2425, pruned_loss=0.0266, over 7062.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2707, pruned_loss=0.04828, over 1425111.08 frames.], batch size: 18, lr: 2.60e-04
2022-05-28 06:40:41,324 INFO [train.py:842] (1/4) Epoch 21, batch 7150, loss[loss=0.1575, simple_loss=0.2487, pruned_loss=0.03312, over 7156.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2705, pruned_loss=0.0482, over 1428350.46 frames.], batch size: 19, lr: 2.60e-04
2022-05-28 06:41:19,790 INFO [train.py:842] (1/4) Epoch 21, batch 7200, loss[loss=0.1736, simple_loss=0.2685, pruned_loss=0.03936, over 7328.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2696, pruned_loss=0.04783, over 1430477.52 frames.], batch size: 20, lr: 2.60e-04
2022-05-28 06:41:57,867 INFO [train.py:842] (1/4) Epoch 21, batch 7250, loss[loss=0.1584, simple_loss=0.2528, pruned_loss=0.03198, over 7424.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2687, pruned_loss=0.04772, over 1428041.97 frames.], batch size: 20, lr: 2.60e-04
2022-05-28 06:42:35,996 INFO [train.py:842] (1/4) Epoch 21, batch 7300, loss[loss=0.1891, simple_loss=0.2666, pruned_loss=0.05578, over 7133.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2692, pruned_loss=0.04801, over 1427951.07 frames.], batch size: 17, lr: 2.60e-04
2022-05-28 06:43:13,739 INFO [train.py:842] (1/4) Epoch 21, batch 7350, loss[loss=0.2051, simple_loss=0.2939, pruned_loss=0.05817, over 7298.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2703, pruned_loss=0.04842, over 1425610.80 frames.], batch size: 24, lr: 2.60e-04
2022-05-28 06:43:51,810 INFO [train.py:842] (1/4) Epoch 21, batch 7400, loss[loss=0.1989, simple_loss=0.2973, pruned_loss=0.05028, over 7326.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2718, pruned_loss=0.04932, over 1424967.43 frames.], batch size: 20, lr: 2.60e-04
2022-05-28 06:44:29,674 INFO [train.py:842] (1/4) Epoch 21, batch 7450, loss[loss=0.1859, simple_loss=0.268, pruned_loss=0.05196, over 7256.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2722, pruned_loss=0.04956, over 1424328.79 frames.], batch size: 19, lr: 2.60e-04
2022-05-28 06:45:08,003 INFO [train.py:842] (1/4) Epoch 21, batch 7500, loss[loss=0.1944, simple_loss=0.2861, pruned_loss=0.05133, over 7246.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2728, pruned_loss=0.04999, over 1422937.92 frames.], batch size: 19, lr: 2.60e-04
2022-05-28 06:45:46,001 INFO [train.py:842] (1/4) Epoch 21, batch 7550, loss[loss=0.2249, simple_loss=0.3169, pruned_loss=0.06644, over 7044.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2726, pruned_loss=0.04983, over 1423646.78 frames.], batch size: 28, lr: 2.60e-04
2022-05-28 06:46:33,473 INFO [train.py:842] (1/4) Epoch 21, batch 7600, loss[loss=0.145, simple_loss=0.2504, pruned_loss=0.01981, over 7201.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2732, pruned_loss=0.0506, over 1417139.15 frames.], batch size: 22, lr: 2.60e-04
2022-05-28 06:47:11,536 INFO [train.py:842] (1/4) Epoch 21, batch 7650, loss[loss=0.2102, simple_loss=0.2919, pruned_loss=0.06419, over 7275.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2723, pruned_loss=0.05009, over 1417592.70 frames.], batch size: 17, lr: 2.60e-04
2022-05-28 06:47:49,876 INFO [train.py:842] (1/4) Epoch 21, batch 7700, loss[loss=0.168, simple_loss=0.2686, pruned_loss=0.03368, over 7324.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2725, pruned_loss=0.04981, over 1418072.91 frames.], batch size: 22, lr: 2.60e-04
2022-05-28 06:48:27,632 INFO [train.py:842] (1/4) Epoch 21, batch 7750, loss[loss=0.1476, simple_loss=0.2353, pruned_loss=0.02993, over 7152.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2727, pruned_loss=0.04996, over 1416826.20 frames.], batch size: 18, lr: 2.60e-04
2022-05-28 06:49:05,971 INFO [train.py:842] (1/4) Epoch 21, batch 7800, loss[loss=0.1412, simple_loss=0.2259, pruned_loss=0.02824, over 7407.00 frames.], tot_loss[loss=0.185, simple_loss=0.2714, pruned_loss=0.04931, over 1421655.76 frames.], batch size: 18, lr: 2.60e-04
2022-05-28 06:49:44,032 INFO [train.py:842] (1/4) Epoch 21, batch 7850, loss[loss=0.1588, simple_loss=0.2537, pruned_loss=0.03198, over 7223.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2702, pruned_loss=0.04907, over 1422109.65 frames.], batch size: 21, lr: 2.60e-04
2022-05-28 06:50:22,200 INFO [train.py:842] (1/4) Epoch 21, batch 7900, loss[loss=0.1943, simple_loss=0.2852, pruned_loss=0.05164, over 7317.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2701, pruned_loss=0.04887, over 1423837.92 frames.], batch size: 21, lr: 2.60e-04
2022-05-28 06:51:00,114 INFO [train.py:842] (1/4) Epoch 21, batch 7950, loss[loss=0.1542, simple_loss=0.2356, pruned_loss=0.03634, over 6982.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2705, pruned_loss=0.04911, over 1425552.32 frames.], batch size: 16, lr: 2.60e-04
2022-05-28 06:51:38,304 INFO [train.py:842] (1/4) Epoch 21, batch 8000, loss[loss=0.1961, simple_loss=0.2857, pruned_loss=0.05327, over 7264.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2714, pruned_loss=0.04961, over 1424857.57 frames.], batch size: 25, lr: 2.60e-04
2022-05-28 06:52:16,325 INFO [train.py:842] (1/4) Epoch 21, batch 8050, loss[loss=0.2589, simple_loss=0.3205, pruned_loss=0.09868, over 7352.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2713, pruned_loss=0.04939, over 1427410.01 frames.], batch size: 23, lr: 2.60e-04
2022-05-28 06:52:54,476 INFO [train.py:842] (1/4) Epoch 21, batch 8100, loss[loss=0.2072, simple_loss=0.2934, pruned_loss=0.06045, over 7271.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2715, pruned_loss=0.04963, over 1426064.76 frames.], batch size: 24, lr: 2.60e-04
2022-05-28 06:53:32,529 INFO [train.py:842] (1/4) Epoch 21, batch 8150, loss[loss=0.1604, simple_loss=0.2584, pruned_loss=0.03116, over 7329.00 frames.], tot_loss[loss=0.185, simple_loss=0.2717, pruned_loss=0.04916, over 1426251.78 frames.], batch size: 20, lr: 2.59e-04
2022-05-28 06:54:13,538 INFO [train.py:842] (1/4) Epoch 21, batch 8200, loss[loss=0.153, simple_loss=0.2389, pruned_loss=0.03351, over 7065.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2717, pruned_loss=0.04937, over 1428346.69 frames.], batch size: 18, lr: 2.59e-04
2022-05-28 06:54:51,650 INFO [train.py:842] (1/4) Epoch 21, batch 8250, loss[loss=0.2256, simple_loss=0.2918, pruned_loss=0.0797, over 5166.00 frames.], tot_loss[loss=0.1848, simple_loss=0.271, pruned_loss=0.04929, over 1428546.47 frames.], batch size: 52, lr: 2.59e-04
2022-05-28 06:55:30,097 INFO [train.py:842] (1/4) Epoch 21, batch 8300, loss[loss=0.196, simple_loss=0.2789, pruned_loss=0.05656, over 7316.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2706, pruned_loss=0.04884, over 1428283.92 frames.], batch size: 25, lr: 2.59e-04
2022-05-28 06:56:07,849 INFO [train.py:842] (1/4) Epoch 21, batch 8350, loss[loss=0.2249, simple_loss=0.3276, pruned_loss=0.0611, over 7406.00 frames.], tot_loss[loss=0.1854, simple_loss=0.272, pruned_loss=0.0494, over 1427194.13 frames.], batch size: 21, lr: 2.59e-04
2022-05-28 06:56:46,227 INFO [train.py:842] (1/4) Epoch 21, batch 8400, loss[loss=0.1704, simple_loss=0.2492, pruned_loss=0.0458, over 7158.00 frames.], tot_loss[loss=0.186, simple_loss=0.2721, pruned_loss=0.04998, over 1430738.40 frames.], batch size: 26, lr: 2.59e-04
2022-05-28 06:57:24,200 INFO [train.py:842] (1/4) Epoch 21, batch 8450, loss[loss=0.2452, simple_loss=0.3218, pruned_loss=0.08435, over 7150.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2729, pruned_loss=0.04975, over 1426914.59 frames.], batch size: 20, lr: 2.59e-04
2022-05-28 06:58:02,579 INFO [train.py:842] (1/4) Epoch 21, batch 8500, loss[loss=0.187, simple_loss=0.268, pruned_loss=0.05296, over 7427.00 frames.], tot_loss[loss=0.1866, simple_loss=0.273, pruned_loss=0.05008, over 1425602.33 frames.], batch size: 20, lr: 2.59e-04
2022-05-28 06:58:40,510 INFO [train.py:842] (1/4) Epoch 21, batch 8550, loss[loss=0.149, simple_loss=0.2227, pruned_loss=0.03766, over 7273.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2719, pruned_loss=0.04925, over 1425769.15 frames.], batch size: 17, lr: 2.59e-04
2022-05-28 06:59:18,644 INFO [train.py:842] (1/4) Epoch 21, batch 8600, loss[loss=0.1836, simple_loss=0.2799, pruned_loss=0.04369, over 7315.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2714, pruned_loss=0.04881, over 1422130.66 frames.], batch size: 25, lr: 2.59e-04
2022-05-28 06:59:56,666 INFO [train.py:842] (1/4) Epoch 21, batch 8650, loss[loss=0.2278, simple_loss=0.3083, pruned_loss=0.07367, over 7154.00 frames.], tot_loss[loss=0.185, simple_loss=0.2717, pruned_loss=0.0491, over 1418922.58 frames.], batch size: 18, lr: 2.59e-04
2022-05-28 07:00:35,009 INFO [train.py:842] (1/4) Epoch 21, batch 8700, loss[loss=0.1823, simple_loss=0.2728, pruned_loss=0.04592, over 7112.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2705, pruned_loss=0.04862, over 1415329.55 frames.], batch size: 21, lr: 2.59e-04
2022-05-28 07:01:12,973 INFO [train.py:842] (1/4) Epoch 21, batch 8750, loss[loss=0.1602, simple_loss=0.2533, pruned_loss=0.03354, over 6718.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2695, pruned_loss=0.04801, over 1417119.87 frames.], batch size: 31, lr: 2.59e-04
2022-05-28 07:01:51,337 INFO [train.py:842] (1/4) Epoch 21, batch 8800, loss[loss=0.1407, simple_loss=0.2145, pruned_loss=0.03348, over 7274.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2677, pruned_loss=0.04726, over 1421080.32 frames.], batch size: 17, lr: 2.59e-04
2022-05-28 07:02:29,284 INFO [train.py:842] (1/4) Epoch 21, batch 8850, loss[loss=0.1973, simple_loss=0.2834, pruned_loss=0.05561, over 6208.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2692, pruned_loss=0.04799, over 1418486.91 frames.], batch size: 37, lr: 2.59e-04
2022-05-28 07:03:07,726 INFO [train.py:842] (1/4) Epoch 21, batch 8900, loss[loss=0.2049, simple_loss=0.3004, pruned_loss=0.05474, over 7112.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2701, pruned_loss=0.04884, over 1419880.77 frames.], batch size: 21, lr: 2.59e-04
2022-05-28 07:03:45,610 INFO [train.py:842] (1/4) Epoch 21, batch 8950, loss[loss=0.1747, simple_loss=0.2669, pruned_loss=0.04121, over 7149.00 frames.], tot_loss[loss=0.184, simple_loss=0.2703, pruned_loss=0.04879, over 1410622.25 frames.], batch size: 20, lr: 2.59e-04
2022-05-28 07:04:23,624 INFO [train.py:842] (1/4) Epoch 21, batch 9000, loss[loss=0.1776, simple_loss=0.2629, pruned_loss=0.04616, over 6416.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2716, pruned_loss=0.04936, over 1398296.64 frames.], batch size: 38, lr: 2.59e-04
2022-05-28 07:04:23,624 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 07:04:32,661 INFO [train.py:871] (1/4) Epoch 21, validation: loss=0.1634, simple_loss=0.2627, pruned_loss=0.03203, over 868885.00 frames.
2022-05-28 07:05:10,710 INFO [train.py:842] (1/4) Epoch 21, batch 9050, loss[loss=0.1798, simple_loss=0.2679, pruned_loss=0.04587, over 6800.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2711, pruned_loss=0.04955, over 1385415.20 frames.], batch size: 15, lr: 2.59e-04
2022-05-28 07:05:48,353 INFO [train.py:842] (1/4) Epoch 21, batch 9100, loss[loss=0.2197, simple_loss=0.296, pruned_loss=0.07167, over 5059.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2731, pruned_loss=0.05087, over 1356384.23 frames.], batch size: 52, lr: 2.59e-04
2022-05-28 07:06:25,194 INFO [train.py:842] (1/4) Epoch 21, batch 9150, loss[loss=0.2642, simple_loss=0.3516, pruned_loss=0.08838, over 7113.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2765, pruned_loss=0.05295, over 1321089.37 frames.], batch size: 21, lr: 2.59e-04
2022-05-28 07:07:15,926 INFO [train.py:842] (1/4) Epoch 22, batch 0, loss[loss=0.1836, simple_loss=0.2779, pruned_loss=0.04465, over 7299.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2779, pruned_loss=0.04465, over 7299.00 frames.], batch size: 25, lr: 2.53e-04
2022-05-28 07:07:53,997 INFO [train.py:842] (1/4) Epoch 22, batch 50, loss[loss=0.1618, simple_loss=0.2542, pruned_loss=0.0347, over 7155.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2742, pruned_loss=0.05028, over 318020.28 frames.], batch size: 18, lr: 2.53e-04
2022-05-28 07:08:32,416 INFO [train.py:842] (1/4) Epoch 22, batch 100, loss[loss=0.1718, simple_loss=0.2634, pruned_loss=0.04011, over 7111.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2706, pruned_loss=0.0491, over 564924.46 frames.], batch size: 21, lr: 2.53e-04
2022-05-28 07:09:10,278 INFO [train.py:842] (1/4) Epoch 22, batch 150, loss[loss=0.1951, simple_loss=0.2855, pruned_loss=0.05236, over 7326.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2717, pruned_loss=0.04946, over 754733.41 frames.], batch size: 21, lr: 2.53e-04
2022-05-28 07:09:48,505 INFO [train.py:842] (1/4) Epoch 22, batch 200, loss[loss=0.2076, simple_loss=0.2991, pruned_loss=0.05807, over 7335.00 frames.], tot_loss[loss=0.1845, simple_loss=0.271, pruned_loss=0.049, over 902702.55 frames.], batch size: 22, lr: 2.53e-04
2022-05-28 07:10:26,527 INFO [train.py:842] (1/4) Epoch 22, batch 250, loss[loss=0.1648, simple_loss=0.2524, pruned_loss=0.0386, over 7246.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2703, pruned_loss=0.04903, over 1016600.09 frames.], batch size: 19, lr: 2.53e-04
2022-05-28 07:11:04,685 INFO [train.py:842] (1/4) Epoch 22, batch 300, loss[loss=0.2069, simple_loss=0.2939, pruned_loss=0.05995, over 7234.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2706, pruned_loss=0.04935, over 1108764.88 frames.], batch size: 20, lr: 2.53e-04
2022-05-28 07:11:42,636 INFO [train.py:842] (1/4) Epoch 22, batch 350, loss[loss=0.1452, simple_loss=0.2403, pruned_loss=0.0251, over 7163.00 frames.], tot_loss[loss=0.1837, simple_loss=0.27, pruned_loss=0.04877, over 1179271.41 frames.], batch size: 19, lr: 2.53e-04
2022-05-28 07:12:20,796 INFO [train.py:842] (1/4) Epoch 22, batch 400, loss[loss=0.1884, simple_loss=0.2897, pruned_loss=0.04351, over 7228.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2701, pruned_loss=0.04841, over 1231399.91 frames.], batch size: 21, lr: 2.53e-04
2022-05-28 07:12:58,845 INFO [train.py:842] (1/4) Epoch 22, batch 450, loss[loss=0.1836, simple_loss=0.2632, pruned_loss=0.05195, over 5070.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2692, pruned_loss=0.04832, over 1274795.31 frames.], batch size: 53, lr: 2.53e-04
2022-05-28 07:13:36,978 INFO [train.py:842] (1/4) Epoch 22, batch 500, loss[loss=0.1815, simple_loss=0.2648, pruned_loss=0.04912, over 7290.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2699, pruned_loss=0.04773, over 1309825.65 frames.], batch size: 25, lr: 2.53e-04
2022-05-28 07:14:14,810 INFO [train.py:842] (1/4) Epoch 22, batch 550, loss[loss=0.1501, simple_loss=0.2366, pruned_loss=0.03175, over 7426.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2708, pruned_loss=0.04802, over 1333385.95 frames.], batch size: 20, lr: 2.53e-04
2022-05-28 07:14:53,172 INFO [train.py:842] (1/4) Epoch 22, batch 600, loss[loss=0.2196, simple_loss=0.3033, pruned_loss=0.0679, over 7338.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2692, pruned_loss=0.04757, over 1354969.02 frames.], batch size: 22, lr: 2.53e-04
2022-05-28 07:15:30,883 INFO [train.py:842] (1/4) Epoch 22, batch 650, loss[loss=0.1807, simple_loss=0.2743, pruned_loss=0.04357, over 7327.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2695, pruned_loss=0.04744, over 1370511.76 frames.], batch size: 22, lr: 2.53e-04
2022-05-28 07:16:09,258 INFO [train.py:842] (1/4) Epoch 22, batch 700, loss[loss=0.1879, simple_loss=0.2834, pruned_loss=0.04623, over 7297.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2704, pruned_loss=0.04836, over 1379953.30 frames.], batch size: 25, lr: 2.53e-04
2022-05-28 07:16:47,346 INFO [train.py:842] (1/4) Epoch 22, batch 750, loss[loss=0.1655, simple_loss=0.2544, pruned_loss=0.03829, over 7160.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2694, pruned_loss=0.04788, over 1388312.05 frames.], batch size: 18, lr: 2.53e-04
2022-05-28 07:17:25,596 INFO [train.py:842] (1/4) Epoch 22, batch 800, loss[loss=0.1914, simple_loss=0.285, pruned_loss=0.04889, over 7300.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2697, pruned_loss=0.04771, over 1400623.85 frames.], batch size: 25, lr: 2.53e-04
2022-05-28 07:18:03,630 INFO [train.py:842] (1/4) Epoch 22, batch 850, loss[loss=0.1551, simple_loss=0.2333, pruned_loss=0.0384, over 7415.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2689, pruned_loss=0.04747, over 1406479.87 frames.], batch size: 18, lr: 2.52e-04
2022-05-28 07:18:41,833 INFO [train.py:842] (1/4) Epoch 22, batch 900, loss[loss=0.1671, simple_loss=0.2598, pruned_loss=0.03722, over 6522.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2687, pruned_loss=0.04726, over 1410183.81 frames.], batch size: 38, lr: 2.52e-04
2022-05-28 07:19:19,850 INFO [train.py:842] (1/4) Epoch 22, batch 950, loss[loss=0.1968, simple_loss=0.2788, pruned_loss=0.05746, over 7264.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2686, pruned_loss=0.04708, over 1411668.04 frames.], batch size: 18, lr: 2.52e-04
2022-05-28 07:19:57,843 INFO [train.py:842] (1/4) Epoch 22, batch 1000, loss[loss=0.1859, simple_loss=0.2674, pruned_loss=0.05221, over 7155.00 frames.], tot_loss[loss=0.183, simple_loss=0.2705, pruned_loss=0.04777, over 1412153.46 frames.], batch size: 19, lr: 2.52e-04
2022-05-28 07:20:36,024 INFO [train.py:842] (1/4) Epoch 22, batch 1050, loss[loss=0.2035, simple_loss=0.2993, pruned_loss=0.05379, over 7331.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2694, pruned_loss=0.0478, over 1416154.42 frames.], batch size: 22, lr: 2.52e-04
2022-05-28 07:21:14,300 INFO [train.py:842] (1/4) Epoch 22, batch 1100, loss[loss=0.2066, simple_loss=0.2933, pruned_loss=0.05994, over 6332.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2709, pruned_loss=0.04888, over 1419800.80 frames.], batch size: 37, lr: 2.52e-04
2022-05-28 07:21:52,335 INFO [train.py:842] (1/4) Epoch 22, batch 1150, loss[loss=0.1673, simple_loss=0.2596, pruned_loss=0.03753, over 7264.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2699, pruned_loss=0.04817, over 1421039.80 frames.], batch size: 19, lr: 2.52e-04
2022-05-28 07:22:30,717 INFO [train.py:842] (1/4) Epoch 22, batch 1200, loss[loss=0.2604, simple_loss=0.335, pruned_loss=0.09291, over 7301.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2704, pruned_loss=0.04922, over 1421425.09 frames.], batch size: 25, lr: 2.52e-04
2022-05-28 07:23:08,665 INFO [train.py:842] (1/4) Epoch 22, batch 1250, loss[loss=0.1665, simple_loss=0.2442, pruned_loss=0.04441, over 7003.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2709, pruned_loss=0.04905, over 1420295.81 frames.], batch size: 16, lr: 2.52e-04
2022-05-28 07:23:46,947 INFO [train.py:842] (1/4) Epoch 22, batch 1300, loss[loss=0.1698, simple_loss=0.2493, pruned_loss=0.04515, over 7158.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2703, pruned_loss=0.04865, over 1419009.36 frames.], batch size: 19, lr: 2.52e-04
2022-05-28 07:24:25,115 INFO [train.py:842] (1/4) Epoch 22, batch 1350, loss[loss=0.1703, simple_loss=0.2649, pruned_loss=0.03789, over 7415.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2704, pruned_loss=0.04883, over 1422911.44 frames.], batch size: 21, lr: 2.52e-04
2022-05-28 07:25:03,519 INFO [train.py:842] (1/4) Epoch 22, batch 1400, loss[loss=0.1889, simple_loss=0.2897, pruned_loss=0.04405, over 7195.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2696, pruned_loss=0.04841, over 1419076.64 frames.], batch size: 22, lr: 2.52e-04
2022-05-28 07:25:41,557 INFO [train.py:842] (1/4) Epoch 22, batch 1450, loss[loss=0.1655, simple_loss=0.2529, pruned_loss=0.03909, over 7432.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2715, pruned_loss=0.04948, over 1424501.55 frames.], batch size: 20, lr: 2.52e-04
2022-05-28 07:26:19,929 INFO [train.py:842] (1/4) Epoch 22, batch 1500, loss[loss=0.1727, simple_loss=0.2719, pruned_loss=0.03674, over 7236.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2718, pruned_loss=0.04986, over 1426342.50 frames.], batch size: 20, lr: 2.52e-04
2022-05-28 07:26:58,145 INFO [train.py:842] (1/4) Epoch 22, batch 1550, loss[loss=0.1645, simple_loss=0.2527, pruned_loss=0.03813, over 7234.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2701, pruned_loss=0.04902, over 1429232.63 frames.], batch size: 20, lr: 2.52e-04
2022-05-28 07:27:45,576 INFO [train.py:842] (1/4) Epoch 22, batch 1600, loss[loss=0.1757, simple_loss=0.2422, pruned_loss=0.05459, over 6774.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2692, pruned_loss=0.04819, over 1430303.15 frames.], batch size: 15, lr: 2.52e-04
2022-05-28 07:28:23,579 INFO [train.py:842] (1/4) Epoch 22, batch 1650, loss[loss=0.1788, simple_loss=0.2639, pruned_loss=0.04691, over 6807.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2696, pruned_loss=0.04804, over 1432850.20 frames.], batch size: 31, lr: 2.52e-04
2022-05-28 07:29:02,074 INFO [train.py:842] (1/4) Epoch 22, batch 1700, loss[loss=0.1817, simple_loss=0.266, pruned_loss=0.04874, over 7341.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2688, pruned_loss=0.04803, over 1434839.23 frames.], batch size: 22, lr: 2.52e-04
2022-05-28 07:29:40,021 INFO [train.py:842] (1/4) Epoch 22, batch 1750, loss[loss=0.1796, simple_loss=0.2767, pruned_loss=0.0413, over 7227.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2698, pruned_loss=0.04832, over 1433799.18 frames.], batch size: 20, lr: 2.52e-04
2022-05-28 07:30:27,696 INFO [train.py:842] (1/4) Epoch 22, batch 1800, loss[loss=0.173, simple_loss=0.2516, pruned_loss=0.04722, over 7267.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2692, pruned_loss=0.04887, over 1431480.70 frames.], batch size: 17, lr: 2.52e-04
2022-05-28 07:31:05,566 INFO [train.py:842] (1/4) Epoch 22, batch 1850, loss[loss=0.2006, simple_loss=0.2863, pruned_loss=0.05751, over 6588.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2699, pruned_loss=0.04919, over 1427077.17 frames.], batch size: 38, lr: 2.52e-04
2022-05-28 07:31:53,091 INFO [train.py:842] (1/4) Epoch 22, batch 1900, loss[loss=0.3054, simple_loss=0.3906, pruned_loss=0.1102, over 5170.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2711, pruned_loss=0.04988, over 1425468.03 frames.], batch size: 52, lr: 2.52e-04
2022-05-28 07:32:31,069 INFO [train.py:842] (1/4) Epoch 22, batch 1950, loss[loss=0.1725, simple_loss=0.2436, pruned_loss=0.05065, over 7278.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2704, pruned_loss=0.04923, over 1426121.92 frames.], batch size: 17, lr: 2.52e-04
2022-05-28 07:33:09,371 INFO [train.py:842] (1/4) Epoch 22, batch 2000, loss[loss=0.1864, simple_loss=0.2778, pruned_loss=0.04746, over 7337.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2706, pruned_loss=0.0493, over 1428344.87 frames.], batch size: 20, lr: 2.52e-04
2022-05-28 07:33:47,298 INFO [train.py:842] (1/4) Epoch 22, batch 2050, loss[loss=0.1966, simple_loss=0.2738, pruned_loss=0.05966, over 7284.00 frames.], tot_loss[loss=0.185, simple_loss=0.2714, pruned_loss=0.04927, over 1429063.03 frames.], batch size: 17, lr: 2.52e-04
2022-05-28 07:34:25,512 INFO [train.py:842] (1/4) Epoch 22, batch 2100, loss[loss=0.1468, simple_loss=0.2349, pruned_loss=0.02929, over 7428.00 frames.], tot_loss[loss=0.1842, simple_loss=0.271, pruned_loss=0.04866, over 1427950.12 frames.], batch size: 18, lr: 2.52e-04
2022-05-28 07:35:03,404 INFO [train.py:842] (1/4) Epoch 22, batch 2150, loss[loss=0.1403, simple_loss=0.2222, pruned_loss=0.02917, over 7170.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2704, pruned_loss=0.04868, over 1424273.57 frames.], batch size: 18, lr: 2.52e-04
2022-05-28 07:35:41,675 INFO [train.py:842] (1/4) Epoch 22, batch 2200, loss[loss=0.1861, simple_loss=0.275, pruned_loss=0.04865, over 7107.00 frames.], tot_loss[loss=0.183, simple_loss=0.2699, pruned_loss=0.04804, over 1426857.79 frames.], batch size: 21, lr: 2.52e-04
2022-05-28 07:36:19,629 INFO [train.py:842] (1/4) Epoch 22, batch 2250, loss[loss=0.1854, simple_loss=0.2642, pruned_loss=0.05329, over 6807.00 frames.], tot_loss[loss=0.183, simple_loss=0.2702, pruned_loss=0.04792, over 1424568.36 frames.], batch size: 15, lr: 2.52e-04
2022-05-28 07:36:57,809 INFO [train.py:842] (1/4) Epoch 22, batch 2300, loss[loss=0.2306, simple_loss=0.3221, pruned_loss=0.06956, over 5002.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2707, pruned_loss=0.04838, over 1425454.29 frames.], batch size: 52, lr: 2.52e-04
2022-05-28 07:37:35,873 INFO [train.py:842] (1/4) Epoch 22, batch 2350, loss[loss=0.2694, simple_loss=0.3327, pruned_loss=0.1031, over 6357.00 frames.], tot_loss[loss=0.1832, simple_loss=0.27, pruned_loss=0.04825, over 1427239.78 frames.], batch size: 38, lr: 2.52e-04
2022-05-28 07:38:14,396 INFO [train.py:842] (1/4) Epoch 22, batch 2400, loss[loss=0.1713, simple_loss=0.2468, pruned_loss=0.04786, over 7151.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2686, pruned_loss=0.04809, over 1426904.98 frames.], batch size: 17, lr: 2.51e-04
2022-05-28 07:38:52,303 INFO [train.py:842] (1/4) Epoch 22, batch 2450, loss[loss=0.1439, simple_loss=0.2272, pruned_loss=0.03032, over 7289.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2688, pruned_loss=0.04772, over 1425375.99 frames.], batch size: 17, lr: 2.51e-04
2022-05-28 07:39:30,629 INFO [train.py:842] (1/4) Epoch 22, batch 2500, loss[loss=0.1628, simple_loss=0.2565, pruned_loss=0.03459, over 7408.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2681, pruned_loss=0.04733, over 1422191.21 frames.], batch size: 21, lr: 2.51e-04
2022-05-28 07:40:08,486 INFO [train.py:842] (1/4) Epoch 22, batch 2550, loss[loss=0.1568, simple_loss=0.2529, pruned_loss=0.03039, over 7054.00 frames.], tot_loss[loss=0.182, simple_loss=0.2686, pruned_loss=0.04774, over 1420784.90 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:40:46,516 INFO [train.py:842] (1/4) Epoch 22, batch 2600, loss[loss=0.1887, simple_loss=0.2728, pruned_loss=0.05227, over 7157.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2711, pruned_loss=0.04923, over 1418030.00 frames.], batch size: 19, lr: 2.51e-04
2022-05-28 07:41:24,754 INFO [train.py:842] (1/4) Epoch 22, batch 2650, loss[loss=0.1759, simple_loss=0.263, pruned_loss=0.0444, over 7263.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2698, pruned_loss=0.0485, over 1421243.75 frames.], batch size: 19, lr: 2.51e-04
2022-05-28 07:42:02,994 INFO [train.py:842] (1/4) Epoch 22, batch 2700, loss[loss=0.2416, simple_loss=0.3231, pruned_loss=0.08002, over 7160.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2703, pruned_loss=0.04865, over 1419922.93 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:42:40,719 INFO [train.py:842] (1/4) Epoch 22, batch 2750, loss[loss=0.1708, simple_loss=0.2519, pruned_loss=0.04486, over 7067.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2701, pruned_loss=0.04857, over 1419683.70 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:43:18,873 INFO [train.py:842] (1/4) Epoch 22, batch 2800, loss[loss=0.1549, simple_loss=0.2313, pruned_loss=0.03928, over 7263.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2689, pruned_loss=0.04727, over 1420633.76 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:43:56,994 INFO [train.py:842] (1/4) Epoch 22, batch 2850, loss[loss=0.162, simple_loss=0.2546, pruned_loss=0.03471, over 7165.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2679, pruned_loss=0.04697, over 1419359.27 frames.], batch size: 19, lr: 2.51e-04
2022-05-28 07:44:35,181 INFO [train.py:842] (1/4) Epoch 22, batch 2900, loss[loss=0.1741, simple_loss=0.2637, pruned_loss=0.04223, over 7164.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2678, pruned_loss=0.04676, over 1421575.82 frames.], batch size: 19, lr: 2.51e-04
2022-05-28 07:45:13,316 INFO [train.py:842] (1/4) Epoch 22, batch 2950, loss[loss=0.2083, simple_loss=0.2882, pruned_loss=0.06418, over 7418.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2685, pruned_loss=0.04727, over 1421531.51 frames.], batch size: 21, lr: 2.51e-04
2022-05-28 07:45:51,548 INFO [train.py:842] (1/4) Epoch 22, batch 3000, loss[loss=0.15, simple_loss=0.2411, pruned_loss=0.02942, over 7176.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2679, pruned_loss=0.04718, over 1425284.62 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:45:51,548 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 07:46:00,591 INFO [train.py:871] (1/4) Epoch 22, validation: loss=0.1649, simple_loss=0.2645, pruned_loss=0.03269, over 868885.00 frames.
2022-05-28 07:46:38,622 INFO [train.py:842] (1/4) Epoch 22, batch 3050, loss[loss=0.1867, simple_loss=0.2762, pruned_loss=0.04856, over 7081.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2677, pruned_loss=0.04703, over 1427003.37 frames.], batch size: 28, lr: 2.51e-04
2022-05-28 07:47:17,288 INFO [train.py:842] (1/4) Epoch 22, batch 3100, loss[loss=0.1701, simple_loss=0.2634, pruned_loss=0.03841, over 5170.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2681, pruned_loss=0.04739, over 1428068.95 frames.], batch size: 57, lr: 2.51e-04
2022-05-28 07:47:55,461 INFO [train.py:842] (1/4) Epoch 22, batch 3150, loss[loss=0.1835, simple_loss=0.2691, pruned_loss=0.04897, over 7412.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2697, pruned_loss=0.04834, over 1426039.11 frames.], batch size: 21, lr: 2.51e-04
2022-05-28 07:48:33,859 INFO [train.py:842] (1/4) Epoch 22, batch 3200, loss[loss=0.1826, simple_loss=0.2649, pruned_loss=0.05015, over 7074.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2702, pruned_loss=0.04853, over 1427196.01 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:49:11,741 INFO [train.py:842] (1/4) Epoch 22, batch 3250, loss[loss=0.219, simple_loss=0.2844, pruned_loss=0.07678, over 7014.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2716, pruned_loss=0.04924, over 1428454.09 frames.], batch size: 16, lr: 2.51e-04
2022-05-28 07:49:49,917 INFO [train.py:842] (1/4) Epoch 22, batch 3300, loss[loss=0.1935, simple_loss=0.28, pruned_loss=0.05348, over 7437.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2718, pruned_loss=0.04942, over 1430086.21 frames.], batch size: 20, lr: 2.51e-04
2022-05-28 07:50:27,862 INFO [train.py:842] (1/4) Epoch 22, batch 3350, loss[loss=0.1641, simple_loss=0.2474, pruned_loss=0.04038, over 7366.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2709, pruned_loss=0.0484, over 1428474.50 frames.], batch size: 19, lr: 2.51e-04
2022-05-28 07:51:05,908 INFO [train.py:842] (1/4) Epoch 22, batch 3400, loss[loss=0.1616, simple_loss=0.2331, pruned_loss=0.04501, over 7128.00 frames.], tot_loss[loss=0.1817, simple_loss=0.269, pruned_loss=0.04724, over 1424830.83 frames.], batch size: 17, lr: 2.51e-04
2022-05-28 07:51:43,854 INFO [train.py:842] (1/4) Epoch 22, batch 3450, loss[loss=0.1973, simple_loss=0.2868, pruned_loss=0.05384, over 7338.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2692, pruned_loss=0.04732, over 1426206.39 frames.], batch size: 22, lr: 2.51e-04
2022-05-28 07:52:22,330 INFO [train.py:842] (1/4) Epoch 22, batch 3500, loss[loss=0.1599, simple_loss=0.2441, pruned_loss=0.03784, over 7349.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2692, pruned_loss=0.04731, over 1429633.42 frames.], batch size: 22, lr: 2.51e-04
2022-05-28 07:53:00,189 INFO [train.py:842] (1/4) Epoch 22, batch 3550, loss[loss=0.1971, simple_loss=0.2912, pruned_loss=0.05154, over 6723.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2691, pruned_loss=0.04687, over 1427623.83 frames.], batch size: 31, lr: 2.51e-04
2022-05-28 07:53:38,516 INFO [train.py:842] (1/4) Epoch 22, batch 3600, loss[loss=0.1497, simple_loss=0.2379, pruned_loss=0.03072, over 7298.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2688, pruned_loss=0.04682, over 1423609.25 frames.], batch size: 17, lr: 2.51e-04
2022-05-28 07:54:16,513 INFO [train.py:842] (1/4) Epoch 22, batch 3650, loss[loss=0.1707, simple_loss=0.2472, pruned_loss=0.04711, over 7253.00 frames.], tot_loss[loss=0.1813, simple_loss=0.269, pruned_loss=0.04681, over 1425979.96 frames.], batch size: 19, lr: 2.51e-04
2022-05-28 07:54:54,676 INFO [train.py:842] (1/4) Epoch 22, batch 3700, loss[loss=0.1811, simple_loss=0.2723, pruned_loss=0.04497, over 7139.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2682, pruned_loss=0.04647, over 1426977.56 frames.], batch size: 20, lr: 2.51e-04
2022-05-28 07:55:32,499 INFO [train.py:842] (1/4) Epoch 22, batch 3750, loss[loss=0.1716, simple_loss=0.2713, pruned_loss=0.03595, over 7277.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2682, pruned_loss=0.04631, over 1428843.55 frames.], batch size: 24, lr: 2.51e-04
2022-05-28 07:56:11,024 INFO [train.py:842] (1/4) Epoch 22, batch 3800, loss[loss=0.2557, simple_loss=0.3383, pruned_loss=0.08651, over 4986.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2683, pruned_loss=0.04652, over 1425148.38 frames.], batch size: 52, lr: 2.51e-04
2022-05-28 07:56:49,079 INFO [train.py:842] (1/4) Epoch 22, batch 3850, loss[loss=0.2057, simple_loss=0.2792, pruned_loss=0.06613, over 7274.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2686, pruned_loss=0.04694, over 1425843.64 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:57:27,544 INFO [train.py:842] (1/4) Epoch 22, batch 3900, loss[loss=0.2195, simple_loss=0.3063, pruned_loss=0.0664, over 7340.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2687, pruned_loss=0.04714, over 1428096.19 frames.], batch size: 20, lr: 2.51e-04
2022-05-28 07:58:05,560 INFO [train.py:842] (1/4) Epoch 22, batch 3950, loss[loss=0.1909, simple_loss=0.2736, pruned_loss=0.05416, over 7419.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2693, pruned_loss=0.04759, over 1427465.26 frames.], batch size: 21, lr: 2.50e-04
2022-05-28 07:58:43,890 INFO [train.py:842] (1/4) Epoch 22, batch 4000, loss[loss=0.1506, simple_loss=0.2465, pruned_loss=0.02733, over 6745.00 frames.], tot_loss[loss=0.181, simple_loss=0.268, pruned_loss=0.04697, over 1428240.60 frames.], batch size: 31, lr: 2.50e-04
2022-05-28 07:59:21,796 INFO [train.py:842] (1/4) Epoch 22, batch 4050, loss[loss=0.1948, simple_loss=0.2868, pruned_loss=0.05138, over 7407.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2695, pruned_loss=0.04759, over 1425918.71 frames.], batch size: 21, lr: 2.50e-04
2022-05-28 08:00:00,257 INFO [train.py:842] (1/4) Epoch 22, batch 4100, loss[loss=0.1723, simple_loss=0.2623, pruned_loss=0.04121, over 7338.00 frames.], tot_loss[loss=0.1823, simple_loss=0.269, pruned_loss=0.04777, over 1425134.66 frames.], batch size: 22, lr: 2.50e-04
2022-05-28 08:00:38,142 INFO [train.py:842] (1/4) Epoch 22, batch 4150, loss[loss=0.2024, simple_loss=0.2849, pruned_loss=0.05993, over 7337.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2685, pruned_loss=0.04763, over 1427306.90 frames.], batch size: 22, lr: 2.50e-04
2022-05-28 08:01:16,334 INFO [train.py:842] (1/4) Epoch 22, batch 4200, loss[loss=0.2989, simple_loss=0.3659, pruned_loss=0.1159, over 4983.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2681, pruned_loss=0.04767, over 1419193.35 frames.], batch size: 53, lr: 2.50e-04
2022-05-28 08:01:54,108 INFO [train.py:842] (1/4) Epoch 22, batch 4250, loss[loss=0.1821, simple_loss=0.2688, pruned_loss=0.04772, over 4965.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2684, pruned_loss=0.04758, over 1414444.55 frames.], batch size: 52, lr: 2.50e-04
2022-05-28 08:02:32,400 INFO [train.py:842] (1/4) Epoch 22, batch 4300, loss[loss=0.1637, simple_loss=0.2471, pruned_loss=0.04017, over 7414.00 frames.], tot_loss[loss=0.1812, simple_loss=0.268, pruned_loss=0.04726, over 1418065.20 frames.], batch size: 18, lr: 2.50e-04
2022-05-28 08:03:10,574 INFO [train.py:842] (1/4) Epoch 22, batch 4350, loss[loss=0.1704, simple_loss=0.2504, pruned_loss=0.04517, over 7273.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2668, pruned_loss=0.04643, over 1419074.01 frames.], batch size: 17, lr: 2.50e-04
2022-05-28 08:03:48,874 INFO [train.py:842] (1/4) Epoch 22, batch 4400, loss[loss=0.1844, simple_loss=0.2715, pruned_loss=0.04865, over 7320.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2675, pruned_loss=0.0464, over 1420386.32 frames.], batch size: 21, lr: 2.50e-04
2022-05-28 08:04:26,819 INFO [train.py:842] (1/4) Epoch 22, batch 4450, loss[loss=0.1895, simple_loss=0.2789, pruned_loss=0.05011, over 7309.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2674, pruned_loss=0.04668, over 1418033.83 frames.], batch size: 24, lr: 2.50e-04
2022-05-28 08:05:05,058 INFO [train.py:842] (1/4) Epoch 22, batch 4500, loss[loss=0.2113, simple_loss=0.2834, pruned_loss=0.0696, over 7382.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2693, pruned_loss=0.04796, over 1420651.05 frames.], batch size: 23, lr: 2.50e-04
2022-05-28 08:05:43,122 INFO [train.py:842] (1/4) Epoch 22, batch 4550, loss[loss=0.1669, simple_loss=0.2465, pruned_loss=0.04363, over 7169.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2692, pruned_loss=0.04803, over 1421012.41 frames.], batch size: 18, lr: 2.50e-04
2022-05-28 08:06:21,014 INFO [train.py:842] (1/4) Epoch 22, batch 4600, loss[loss=0.1659, simple_loss=0.2576, pruned_loss=0.03708, over 7225.00 frames.], tot_loss[loss=0.183, simple_loss=0.2697, pruned_loss=0.04814, over 1420273.13 frames.], batch size: 20, lr: 2.50e-04
2022-05-28 08:06:58,740 INFO [train.py:842] (1/4) Epoch 22, batch 4650, loss[loss=0.1735, simple_loss=0.2549, pruned_loss=0.04607, over 7057.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2699, pruned_loss=0.04811, over 1416741.47 frames.], batch size: 18, lr: 2.50e-04
2022-05-28 08:07:37,056 INFO [train.py:842] (1/4) Epoch 22, batch 4700, loss[loss=0.1717, simple_loss=0.264, pruned_loss=0.03975, over 7356.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2708, pruned_loss=0.04848, over 1417356.02 frames.], batch size: 19, lr: 2.50e-04
2022-05-28 08:08:15,473 INFO [train.py:842] (1/4) Epoch 22, batch 4750, loss[loss=0.1568, simple_loss=0.2347, pruned_loss=0.03948, over 7278.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2685, pruned_loss=0.04828, over 1423677.92 frames.], batch size: 18, lr: 2.50e-04
2022-05-28 08:08:53,586 INFO [train.py:842] (1/4) Epoch 22, batch 4800, loss[loss=0.261, simple_loss=0.3311, pruned_loss=0.09544, over 5198.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2709, pruned_loss=0.049, over 1419145.93 frames.], batch size: 52, lr: 2.50e-04
2022-05-28 08:09:31,583 INFO [train.py:842] (1/4) Epoch 22, batch 4850, loss[loss=0.2079, simple_loss=0.2913, pruned_loss=0.0622, over 7106.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2715, pruned_loss=0.04906, over 1421281.45 frames.], batch size: 21, lr: 2.50e-04
2022-05-28 08:10:09,775 INFO [train.py:842] (1/4) Epoch 22, batch 4900, loss[loss=0.2315, simple_loss=0.315, pruned_loss=0.07403, over 7192.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2702, pruned_loss=0.04865, over 1420342.52 frames.], batch size: 23, lr: 2.50e-04
2022-05-28 08:10:47,867 INFO [train.py:842] (1/4) Epoch 22, batch 4950, loss[loss=0.2267, simple_loss=0.2919, pruned_loss=0.08075, over 7250.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2699, pruned_loss=0.04853, over 1416401.59 frames.], batch size: 19, lr: 2.50e-04
2022-05-28 08:11:25,834 INFO [train.py:842] (1/4) Epoch 22, batch 5000, loss[loss=0.2618, simple_loss=0.3389, pruned_loss=0.09231, over 6212.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2707, pruned_loss=0.04889, over 1414333.94 frames.], batch size: 37, lr: 2.50e-04
2022-05-28 08:12:03,915 INFO [train.py:842] (1/4) Epoch 22, batch 5050, loss[loss=0.169, simple_loss=0.2497, pruned_loss=0.04409, over 7405.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2696, pruned_loss=0.04829, over 1417929.53 frames.], batch size: 18, lr: 2.50e-04
2022-05-28 08:12:42,391 INFO [train.py:842] (1/4) Epoch 22, batch 5100, loss[loss=0.1786, simple_loss=0.271, pruned_loss=0.0431, over 7315.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2689, pruned_loss=0.04813, over 1422360.15 frames.], batch size: 21, lr: 2.50e-04
2022-05-28 08:13:20,356 INFO [train.py:842] (1/4) Epoch 22, batch 5150, loss[loss=0.1678, simple_loss=0.2538, pruned_loss=0.04094, over 7335.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2693, pruned_loss=0.04747, over 1428397.56 frames.], batch size: 22, lr: 2.50e-04
2022-05-28 08:13:58,903 INFO [train.py:842] (1/4) Epoch 22, batch 5200, loss[loss=0.1608, simple_loss=0.255, pruned_loss=0.03329, over 7332.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2685, pruned_loss=0.04728, over 1426314.98 frames.], batch size: 20, lr: 2.50e-04
2022-05-28 08:14:36,954 INFO [train.py:842] (1/4) Epoch 22, batch 5250, loss[loss=0.1916, simple_loss=0.2816, pruned_loss=0.05082, over 7147.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2684, pruned_loss=0.04738, over 1422868.50 frames.], batch size: 28, lr: 2.50e-04
2022-05-28 08:15:14,911 INFO [train.py:842] (1/4) Epoch 22, batch 5300, loss[loss=0.1972, simple_loss=0.2867, pruned_loss=0.05386, over 7334.00 frames.], tot_loss[loss=0.182, simple_loss=0.2692, pruned_loss=0.04743, over 1423384.30 frames.], batch size: 22, lr: 2.50e-04
2022-05-28 08:15:52,744 INFO [train.py:842] (1/4) Epoch 22, batch 5350, loss[loss=0.2111, simple_loss=0.2971, pruned_loss=0.06248, over 6696.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2701, pruned_loss=0.04773, over 1424260.00 frames.], batch size: 31, lr: 2.50e-04
2022-05-28 08:16:30,989 INFO [train.py:842] (1/4) Epoch 22, batch 5400, loss[loss=0.1698, simple_loss=0.2497, pruned_loss=0.04499, over 7061.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2704, pruned_loss=0.0481, over 1424987.32 frames.], batch size: 18, lr: 2.50e-04
2022-05-28 08:17:09,162 INFO [train.py:842] (1/4) Epoch 22, batch 5450, loss[loss=0.1815, simple_loss=0.2773, pruned_loss=0.04285, over 7428.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2705, pruned_loss=0.04858, over 1424937.27 frames.], batch size: 20, lr: 2.50e-04
2022-05-28 08:17:47,417 INFO [train.py:842] (1/4) Epoch 22, batch 5500, loss[loss=0.1842, simple_loss=0.275, pruned_loss=0.04669, over 7413.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2702, pruned_loss=0.04827, over 1422702.07 frames.], batch size: 21, lr: 2.49e-04
2022-05-28 08:18:25,338 INFO [train.py:842] (1/4) Epoch 22, batch 5550, loss[loss=0.1907, simple_loss=0.2661, pruned_loss=0.05769, over 7160.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2696, pruned_loss=0.04795, over 1420269.08 frames.], batch size: 18, lr: 2.49e-04
2022-05-28 08:19:03,349 INFO [train.py:842] (1/4) Epoch 22, batch 5600, loss[loss=0.1717, simple_loss=0.2619, pruned_loss=0.0407, over 7153.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2698, pruned_loss=0.04778, over 1421135.00 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:19:41,112 INFO [train.py:842] (1/4) Epoch 22, batch 5650, loss[loss=0.1931, simple_loss=0.2848, pruned_loss=0.05071, over 7358.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2707, pruned_loss=0.0483, over 1419189.79 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:20:19,520 INFO [train.py:842] (1/4) Epoch 22, batch 5700, loss[loss=0.1734, simple_loss=0.2732, pruned_loss=0.0368, over 7331.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2702, pruned_loss=0.04839, over 1424938.03 frames.], batch size: 22, lr: 2.49e-04
2022-05-28 08:20:57,467 INFO [train.py:842] (1/4) Epoch 22, batch 5750, loss[loss=0.1661, simple_loss=0.2481, pruned_loss=0.04211, over 7392.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2703, pruned_loss=0.04811, over 1427083.94 frames.], batch size: 18, lr: 2.49e-04
2022-05-28 08:21:35,777 INFO [train.py:842] (1/4) Epoch 22, batch 5800, loss[loss=0.1489, simple_loss=0.2287, pruned_loss=0.0346, over 7129.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2692, pruned_loss=0.04796, over 1427842.02 frames.], batch size: 17, lr: 2.49e-04
2022-05-28 08:22:13,665 INFO [train.py:842] (1/4) Epoch 22, batch 5850, loss[loss=0.2214, simple_loss=0.3037, pruned_loss=0.06953, over 7278.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2698, pruned_loss=0.04822, over 1429481.94 frames.], batch size: 24, lr: 2.49e-04
2022-05-28 08:22:52,080 INFO [train.py:842] (1/4) Epoch 22, batch 5900, loss[loss=0.1832, simple_loss=0.2776, pruned_loss=0.04438, over 7204.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2695, pruned_loss=0.04818, over 1431939.97 frames.], batch size: 23, lr: 2.49e-04
2022-05-28 08:23:29,855 INFO [train.py:842] (1/4) Epoch 22, batch 5950, loss[loss=0.1525, simple_loss=0.2496, pruned_loss=0.02764, over 7359.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2706, pruned_loss=0.04897, over 1425641.46 frames.], batch size: 22, lr: 2.49e-04
2022-05-28 08:24:08,104 INFO [train.py:842] (1/4) Epoch 22, batch 6000, loss[loss=0.1595, simple_loss=0.2436, pruned_loss=0.03765, over 7406.00 frames.], tot_loss[loss=0.183, simple_loss=0.2695, pruned_loss=0.04822, over 1427409.86 frames.], batch size: 18, lr: 2.49e-04
2022-05-28 08:24:08,105 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 08:24:17,058 INFO [train.py:871] (1/4) Epoch 22, validation: loss=0.1664, simple_loss=0.2658, pruned_loss=0.03347, over 868885.00 frames.
2022-05-28 08:24:55,056 INFO [train.py:842] (1/4) Epoch 22, batch 6050, loss[loss=0.169, simple_loss=0.2462, pruned_loss=0.0459, over 7284.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2691, pruned_loss=0.04814, over 1425010.70 frames.], batch size: 17, lr: 2.49e-04
2022-05-28 08:25:33,542 INFO [train.py:842] (1/4) Epoch 22, batch 6100, loss[loss=0.1492, simple_loss=0.232, pruned_loss=0.03314, over 7165.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2682, pruned_loss=0.04772, over 1425726.20 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:26:11,604 INFO [train.py:842] (1/4) Epoch 22, batch 6150, loss[loss=0.1732, simple_loss=0.2589, pruned_loss=0.04373, over 7053.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2691, pruned_loss=0.04781, over 1420952.23 frames.], batch size: 18, lr: 2.49e-04
2022-05-28 08:26:49,985 INFO [train.py:842] (1/4) Epoch 22, batch 6200, loss[loss=0.1727, simple_loss=0.2631, pruned_loss=0.04118, over 7414.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2695, pruned_loss=0.04769, over 1423926.78 frames.], batch size: 21, lr: 2.49e-04
2022-05-28 08:27:27,701 INFO [train.py:842] (1/4) Epoch 22, batch 6250, loss[loss=0.1957, simple_loss=0.287, pruned_loss=0.05226, over 6779.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2705, pruned_loss=0.04809, over 1420020.66 frames.], batch size: 31, lr: 2.49e-04
2022-05-28 08:28:05,776 INFO [train.py:842] (1/4) Epoch 22, batch 6300, loss[loss=0.1708, simple_loss=0.2693, pruned_loss=0.03613, over 7336.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2698, pruned_loss=0.04756, over 1420502.01 frames.], batch size: 22, lr: 2.49e-04
2022-05-28 08:28:43,773 INFO [train.py:842] (1/4) Epoch 22, batch 6350, loss[loss=0.2042, simple_loss=0.2855, pruned_loss=0.06143, over 5020.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2709, pruned_loss=0.04823, over 1422274.16 frames.], batch size: 52, lr: 2.49e-04
2022-05-28 08:29:22,118 INFO [train.py:842] (1/4) Epoch 22, batch 6400, loss[loss=0.1689, simple_loss=0.2552, pruned_loss=0.04131, over 7158.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2717, pruned_loss=0.04853, over 1422862.56 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:30:00,142 INFO [train.py:842] (1/4) Epoch 22, batch 6450, loss[loss=0.1779, simple_loss=0.2679, pruned_loss=0.04393, over 7269.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2714, pruned_loss=0.04863, over 1422832.59 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:30:38,599 INFO [train.py:842] (1/4) Epoch 22, batch 6500, loss[loss=0.1581, simple_loss=0.2381, pruned_loss=0.03908, over 7005.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2707, pruned_loss=0.04852, over 1424508.51 frames.], batch size: 16, lr: 2.49e-04
2022-05-28 08:31:16,551 INFO [train.py:842] (1/4) Epoch 22, batch 6550, loss[loss=0.1614, simple_loss=0.2454, pruned_loss=0.0387, over 7448.00 frames.], tot_loss[loss=0.1844, simple_loss=0.271, pruned_loss=0.04896, over 1420123.29 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:31:54,820 INFO [train.py:842] (1/4) Epoch 22, batch 6600, loss[loss=0.1734, simple_loss=0.2709, pruned_loss=0.03793, over 7265.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2712, pruned_loss=0.04907, over 1417613.26 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:32:32,835 INFO [train.py:842] (1/4) Epoch 22, batch 6650, loss[loss=0.1718, simple_loss=0.2601, pruned_loss=0.04176, over 7347.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2701, pruned_loss=0.04859, over 1420324.95 frames.], batch size: 22, lr: 2.49e-04
2022-05-28 08:33:11,320 INFO [train.py:842] (1/4) Epoch 22, batch 6700, loss[loss=0.153, simple_loss=0.2455, pruned_loss=0.03021, over 7259.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2693, pruned_loss=0.04812, over 1424877.77 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:33:49,375 INFO [train.py:842] (1/4) Epoch 22, batch 6750, loss[loss=0.1768, simple_loss=0.268, pruned_loss=0.04283, over 7239.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2696, pruned_loss=0.04852, over 1423060.80 frames.], batch size: 20, lr: 2.49e-04
2022-05-28 08:34:27,411 INFO [train.py:842] (1/4) Epoch 22, batch 6800, loss[loss=0.1877, simple_loss=0.2783, pruned_loss=0.04855, over 6332.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2712, pruned_loss=0.04919, over 1421266.50 frames.], batch size: 37, lr: 2.49e-04
2022-05-28 08:35:05,256 INFO [train.py:842] (1/4) Epoch 22, batch 6850, loss[loss=0.1785, simple_loss=0.2789, pruned_loss=0.039, over 7291.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2705, pruned_loss=0.04833, over 1420056.90 frames.], batch size: 24, lr: 2.49e-04
2022-05-28 08:35:43,339 INFO [train.py:842] (1/4) Epoch 22, batch 6900, loss[loss=0.1974, simple_loss=0.2962, pruned_loss=0.04927, over 7219.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2711, pruned_loss=0.04862, over 1416370.67 frames.], batch size: 21, lr: 2.49e-04
2022-05-28 08:36:21,223 INFO [train.py:842] (1/4) Epoch 22, batch 6950, loss[loss=0.1645, simple_loss=0.2547, pruned_loss=0.03709, over 7433.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2703, pruned_loss=0.04835, over 1413045.00 frames.], batch size: 20, lr: 2.49e-04
2022-05-28 08:37:02,019 INFO [train.py:842] (1/4) Epoch 22, batch 7000, loss[loss=0.1699, simple_loss=0.266, pruned_loss=0.03694, over 7317.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2705, pruned_loss=0.0487, over 1415033.85 frames.], batch size: 21, lr: 2.49e-04
2022-05-28 08:37:39,804 INFO [train.py:842] (1/4) Epoch 22, batch 7050, loss[loss=0.2791, simple_loss=0.3435, pruned_loss=0.1074, over 7315.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2709, pruned_loss=0.04889, over 1416408.93 frames.], batch size: 24, lr: 2.49e-04
2022-05-28 08:38:17,976 INFO [train.py:842] (1/4) Epoch 22, batch 7100, loss[loss=0.1514, simple_loss=0.238, pruned_loss=0.03236, over 6975.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2711, pruned_loss=0.04875, over 1418472.19 frames.], batch size: 16, lr: 2.49e-04
2022-05-28 08:38:55,913 INFO [train.py:842] (1/4) Epoch 22, batch 7150, loss[loss=0.1663, simple_loss=0.2592, pruned_loss=0.03667, over 7423.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2707, pruned_loss=0.04832, over 1417845.27 frames.], batch size: 18, lr: 2.48e-04
2022-05-28 08:39:34,204 INFO [train.py:842] (1/4) Epoch 22, batch 7200, loss[loss=0.1751, simple_loss=0.2731, pruned_loss=0.03858, over 7232.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2704, pruned_loss=0.04858, over 1411286.01 frames.], batch size: 20, lr: 2.48e-04
2022-05-28 08:40:12,372 INFO [train.py:842] (1/4) Epoch 22, batch 7250, loss[loss=0.2094, simple_loss=0.2838, pruned_loss=0.06749, over 7175.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2703, pruned_loss=0.04858, over 1414844.24 frames.], batch size: 23, lr: 2.48e-04
2022-05-28 08:40:50,459 INFO [train.py:842] (1/4) Epoch 22, batch 7300, loss[loss=0.1692, simple_loss=0.2468, pruned_loss=0.04582, over 7276.00 frames.], tot_loss[loss=0.184, simple_loss=0.2705, pruned_loss=0.04879, over 1413565.39 frames.], batch size: 17, lr: 2.48e-04
2022-05-28 08:41:28,172 INFO [train.py:842] (1/4) Epoch 22, batch 7350, loss[loss=0.1948, simple_loss=0.2842, pruned_loss=0.05272, over 7141.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2722, pruned_loss=0.04937, over 1416386.27 frames.], batch size: 20, lr: 2.48e-04
2022-05-28 08:42:06,484 INFO [train.py:842] (1/4) Epoch 22, batch 7400, loss[loss=0.2229, simple_loss=0.3148, pruned_loss=0.06546, over 7246.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2731, pruned_loss=0.05037, over 1418951.58 frames.], batch size: 25, lr: 2.48e-04
2022-05-28 08:42:44,752 INFO [train.py:842] (1/4) Epoch 22, batch 7450, loss[loss=0.1935, simple_loss=0.2784, pruned_loss=0.05424, over 7157.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2726, pruned_loss=0.05063, over 1418610.65 frames.], batch size: 18, lr: 2.48e-04
2022-05-28 08:43:22,940 INFO [train.py:842] (1/4) Epoch 22, batch 7500, loss[loss=0.1492, simple_loss=0.2292, pruned_loss=0.03454, over 7404.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2728, pruned_loss=0.05039, over 1418507.75 frames.], batch size: 18, lr: 2.48e-04
2022-05-28 08:44:01,048 INFO [train.py:842] (1/4) Epoch 22, batch 7550, loss[loss=0.232, simple_loss=0.3079, pruned_loss=0.07811, over 7210.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2708, pruned_loss=0.04922, over 1418160.67 frames.], batch size: 23, lr: 2.48e-04
2022-05-28 08:44:39,278 INFO [train.py:842] (1/4) Epoch 22, batch 7600, loss[loss=0.193, simple_loss=0.2747, pruned_loss=0.05568, over 7182.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2703, pruned_loss=0.04916, over 1418951.04 frames.], batch size: 22, lr: 2.48e-04
2022-05-28 08:45:17,060 INFO [train.py:842] (1/4) Epoch 22, batch 7650, loss[loss=0.2216, simple_loss=0.2946, pruned_loss=0.0743, over 7324.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2707, pruned_loss=0.04889, over 1420236.47 frames.], batch size: 20, lr: 2.48e-04
2022-05-28 08:45:55,432 INFO [train.py:842] (1/4) Epoch 22, batch 7700, loss[loss=0.1915, simple_loss=0.2943, pruned_loss=0.04431, over 7222.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2697, pruned_loss=0.04866, over 1420341.54 frames.], batch size: 21, lr: 2.48e-04
2022-05-28 08:46:33,282 INFO [train.py:842] (1/4) Epoch 22, batch 7750, loss[loss=0.1918, simple_loss=0.2947, pruned_loss=0.04443, over 7145.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2707, pruned_loss=0.04879, over 1423396.07 frames.], batch size: 20, lr: 2.48e-04
2022-05-28 08:47:11,741 INFO [train.py:842] (1/4) Epoch 22, batch 7800, loss[loss=0.1998, simple_loss=0.3041, pruned_loss=0.04773, over 7278.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2697, pruned_loss=0.04799, over 1420131.43 frames.], batch size: 25, lr: 2.48e-04
2022-05-28 08:47:49,718 INFO [train.py:842] (1/4) Epoch 22, batch 7850, loss[loss=0.1942, simple_loss=0.2772, pruned_loss=0.05556, over 7336.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2695, pruned_loss=0.04804, over 1420586.06 frames.], batch size: 25, lr: 2.48e-04
2022-05-28 08:48:27,744 INFO [train.py:842] (1/4) Epoch 22, batch 7900, loss[loss=0.1788, simple_loss=0.2602, pruned_loss=0.04872, over 7072.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2705, pruned_loss=0.04899, over 1414609.70 frames.], batch size: 18, lr: 2.48e-04
2022-05-28 08:49:05,727 INFO [train.py:842] (1/4) Epoch 22, batch 7950, loss[loss=0.1921, simple_loss=0.295, pruned_loss=0.04466, over 7289.00 frames.], tot_loss[loss=0.184, simple_loss=0.2704, pruned_loss=0.04879, over 1412676.26 frames.], batch size: 25, lr: 2.48e-04
2022-05-28 08:49:43,864 INFO [train.py:842] (1/4) Epoch 22, batch 8000, loss[loss=0.2079, simple_loss=0.3015, pruned_loss=0.05718, over 7141.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2716, pruned_loss=0.04954, over 1414401.58 frames.], batch size: 28, lr: 2.48e-04
2022-05-28 08:50:21,585 INFO [train.py:842] (1/4) Epoch 22, batch 8050, loss[loss=0.1305, simple_loss=0.2107, pruned_loss=0.02519, over 7001.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2713, pruned_loss=0.04888, over 1411149.76 frames.], batch size: 16, lr: 2.48e-04
2022-05-28 08:50:59,774 INFO [train.py:842] (1/4) Epoch 22, batch 8100, loss[loss=0.2496, simple_loss=0.319, pruned_loss=0.09008, over 7073.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2719, pruned_loss=0.04963, over 1407782.40 frames.], batch size: 18, lr: 2.48e-04
2022-05-28 08:51:37,737 INFO [train.py:842] (1/4) Epoch 22, batch 8150, loss[loss=0.1509, simple_loss=0.2313, pruned_loss=0.03528, over 7262.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2715, pruned_loss=0.04933, over 1412417.86 frames.], batch size: 17, lr: 2.48e-04
2022-05-28 08:52:15,698 INFO [train.py:842] (1/4) Epoch 22, batch 8200, loss[loss=0.1839, simple_loss=0.2837, pruned_loss=0.04207, over 6354.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2726, pruned_loss=0.04959, over 1416832.49 frames.], batch size: 38, lr: 2.48e-04
2022-05-28 08:52:53,592 INFO [train.py:842] (1/4) Epoch 22, batch 8250, loss[loss=0.1631, simple_loss=0.268, pruned_loss=0.02908, over 7114.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2735, pruned_loss=0.05018, over 1416829.87 frames.], batch size: 28, lr: 2.48e-04
2022-05-28 08:53:31,677 INFO [train.py:842] (1/4) Epoch 22, batch 8300, loss[loss=0.2261, simple_loss=0.3035, pruned_loss=0.07431, over 7292.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2731, pruned_loss=0.04976, over 1418305.29 frames.], batch size: 24, lr: 2.48e-04
2022-05-28 08:54:09,571 INFO [train.py:842] (1/4) Epoch 22, batch 8350, loss[loss=0.1775, simple_loss=0.2687, pruned_loss=0.04309, over 7215.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2726, pruned_loss=0.04953, over 1419878.20 frames.], batch size: 21, lr: 2.48e-04
2022-05-28 08:54:47,764 INFO [train.py:842] (1/4) Epoch 22, batch 8400, loss[loss=0.1867, simple_loss=0.2791, pruned_loss=0.0471, over 7216.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2719, pruned_loss=0.04918, over 1421606.31 frames.], batch size: 21, lr: 2.48e-04
2022-05-28 08:55:25,643 INFO [train.py:842] (1/4) Epoch 22, batch 8450, loss[loss=0.2192, simple_loss=0.3034, pruned_loss=0.06754, over 7329.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2719, pruned_loss=0.04971, over 1417712.31 frames.], batch size: 20, lr: 2.48e-04
2022-05-28 08:56:03,901 INFO [train.py:842] (1/4) Epoch 22, batch 8500, loss[loss=0.143, simple_loss=0.2274, pruned_loss=0.02931, over 6983.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2709, pruned_loss=0.04911, over 1419297.07 frames.], batch size: 16, lr: 2.48e-04
2022-05-28 08:56:41,569 INFO [train.py:842] (1/4) Epoch 22, batch 8550, loss[loss=0.2687, simple_loss=0.3484, pruned_loss=0.09448, over 7292.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2712, pruned_loss=0.04873, over 1415727.58 frames.], batch size: 25, lr: 2.48e-04
2022-05-28 08:57:19,538 INFO [train.py:842] (1/4) Epoch 22, batch 8600, loss[loss=0.1676, simple_loss=0.2649, pruned_loss=0.03509, over 7283.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2708, pruned_loss=0.04809, over 1418480.54 frames.], batch size: 24, lr: 2.48e-04
2022-05-28 08:57:57,116 INFO [train.py:842] (1/4) Epoch 22, batch 8650, loss[loss=0.1573, simple_loss=0.237, pruned_loss=0.03881, over 7167.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2712, pruned_loss=0.04887, over 1411373.17 frames.], batch size: 18, lr: 2.48e-04
2022-05-28 08:58:35,191 INFO [train.py:842] (1/4) Epoch 22, batch 8700, loss[loss=0.2113, simple_loss=0.2873, pruned_loss=0.06765, over 7357.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2723, pruned_loss=0.04919, over 1411932.27 frames.], batch size: 19, lr: 2.48e-04
2022-05-28 08:59:13,000 INFO [train.py:842] (1/4) Epoch 22, batch 8750, loss[loss=0.2156, simple_loss=0.3092, pruned_loss=0.061, over 7340.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2722, pruned_loss=0.04924, over 1414003.85 frames.], batch size: 22, lr: 2.47e-04
2022-05-28 08:59:50,871 INFO [train.py:842] (1/4) Epoch 22, batch 8800, loss[loss=0.1452, simple_loss=0.2415, pruned_loss=0.02441, over 7169.00 frames.], tot_loss[loss=0.1852, simple_loss=0.272, pruned_loss=0.04916, over 1411401.33 frames.], batch size: 18, lr: 2.47e-04
2022-05-28 09:00:28,601 INFO [train.py:842] (1/4) Epoch 22, batch 8850, loss[loss=0.1618, simple_loss=0.2581, pruned_loss=0.03274, over 7432.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2733, pruned_loss=0.05005, over 1409122.85 frames.], batch size: 20, lr: 2.47e-04
2022-05-28 09:01:06,630 INFO [train.py:842] (1/4) Epoch 22, batch 8900, loss[loss=0.1578, simple_loss=0.2509, pruned_loss=0.03231, over 7232.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2718, pruned_loss=0.04925, over 1411785.42 frames.], batch size: 20, lr: 2.47e-04
2022-05-28 09:01:44,251 INFO [train.py:842] (1/4) Epoch 22, batch 8950, loss[loss=0.2163, simple_loss=0.2995, pruned_loss=0.06659, over 7305.00 frames.], tot_loss[loss=0.1865, simple_loss=0.273, pruned_loss=0.05002, over 1405991.46 frames.], batch size: 25, lr: 2.47e-04
2022-05-28 09:02:22,235 INFO [train.py:842] (1/4) Epoch 22, batch 9000, loss[loss=0.1709, simple_loss=0.2589, pruned_loss=0.04149, over 6999.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2739, pruned_loss=0.05033, over 1400656.51 frames.], batch size: 16, lr: 2.47e-04
2022-05-28 09:02:22,236 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 09:02:31,315 INFO [train.py:871] (1/4) Epoch 22, validation: loss=0.1647, simple_loss=0.2634, pruned_loss=0.03302, over 868885.00 frames.
2022-05-28 09:03:08,926 INFO [train.py:842] (1/4) Epoch 22, batch 9050, loss[loss=0.2449, simple_loss=0.3216, pruned_loss=0.08414, over 4858.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2742, pruned_loss=0.05058, over 1393678.87 frames.], batch size: 52, lr: 2.47e-04
2022-05-28 09:03:46,321 INFO [train.py:842] (1/4) Epoch 22, batch 9100, loss[loss=0.2088, simple_loss=0.2982, pruned_loss=0.05975, over 5284.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2759, pruned_loss=0.05173, over 1367487.48 frames.], batch size: 53, lr: 2.47e-04
2022-05-28 09:04:23,079 INFO [train.py:842] (1/4) Epoch 22, batch 9150, loss[loss=0.2387, simple_loss=0.3086, pruned_loss=0.08441, over 5282.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2803, pruned_loss=0.05533, over 1291536.50 frames.], batch size: 52, lr: 2.47e-04
2022-05-28 09:05:08,930 INFO [train.py:842] (1/4) Epoch 23, batch 0, loss[loss=0.1498, simple_loss=0.2323, pruned_loss=0.03366, over 7189.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2323, pruned_loss=0.03366, over 7189.00 frames.], batch size: 16, lr: 2.42e-04
2022-05-28 09:05:47,160 INFO [train.py:842] (1/4) Epoch 23, batch 50, loss[loss=0.2107, simple_loss=0.301, pruned_loss=0.0602, over 7150.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2681, pruned_loss=0.0467, over 319285.54 frames.], batch size: 19, lr: 2.42e-04
2022-05-28 09:06:25,606 INFO [train.py:842] (1/4) Epoch 23, batch 100, loss[loss=0.1616, simple_loss=0.2488, pruned_loss=0.03715, over 7265.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2666, pruned_loss=0.04638, over 566632.17 frames.], batch size: 18, lr: 2.42e-04
2022-05-28 09:07:03,398 INFO [train.py:842] (1/4) Epoch 23, batch 150, loss[loss=0.1987, simple_loss=0.2943, pruned_loss=0.0516, over 7299.00 frames.], tot_loss[loss=0.183, simple_loss=0.2706, pruned_loss=0.04771, over 754574.93 frames.], batch size: 24, lr: 2.42e-04
2022-05-28 09:07:41,555 INFO [train.py:842] (1/4) Epoch 23, batch 200, loss[loss=0.1901, simple_loss=0.283, pruned_loss=0.04866, over 6394.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2701, pruned_loss=0.04656, over 902587.63 frames.], batch size: 37, lr: 2.42e-04
2022-05-28 09:08:19,370 INFO [train.py:842] (1/4) Epoch 23, batch 250, loss[loss=0.2033, simple_loss=0.2966, pruned_loss=0.05504, over 7206.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2715, pruned_loss=0.04774, over 1017776.56 frames.], batch size: 23, lr: 2.42e-04
2022-05-28 09:08:57,669 INFO [train.py:842] (1/4) Epoch 23, batch 300, loss[loss=0.1635, simple_loss=0.256, pruned_loss=0.0355, over 7156.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2708, pruned_loss=0.04738, over 1103417.95 frames.], batch size: 19, lr: 2.42e-04
2022-05-28 09:09:35,673 INFO [train.py:842] (1/4) Epoch 23, batch 350, loss[loss=0.1644, simple_loss=0.2631, pruned_loss=0.03281, over 7336.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2682, pruned_loss=0.04594, over 1177600.44 frames.], batch size: 22, lr: 2.42e-04
2022-05-28 09:10:13,898 INFO [train.py:842] (1/4) Epoch 23, batch 400, loss[loss=0.1836, simple_loss=0.2754, pruned_loss=0.04592, over 7193.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2675, pruned_loss=0.04557, over 1229659.17 frames.], batch size: 23, lr: 2.42e-04
2022-05-28 09:10:51,790 INFO [train.py:842] (1/4) Epoch 23, batch 450, loss[loss=0.1835, simple_loss=0.2744, pruned_loss=0.04632, over 7273.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2686, pruned_loss=0.04618, over 1270299.24 frames.], batch size: 24, lr: 2.42e-04
2022-05-28 09:11:30,079 INFO [train.py:842] (1/4) Epoch 23, batch 500, loss[loss=0.1596, simple_loss=0.2436, pruned_loss=0.03775, over 7206.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2687, pruned_loss=0.04642, over 1305573.56 frames.], batch size: 16, lr: 2.42e-04
2022-05-28 09:12:08,359 INFO [train.py:842] (1/4) Epoch 23, batch 550, loss[loss=0.1944, simple_loss=0.2841, pruned_loss=0.05236, over 7298.00 frames.], tot_loss[loss=0.1816, simple_loss=0.269, pruned_loss=0.04709, over 1335162.74 frames.], batch size: 24, lr: 2.42e-04
2022-05-28 09:12:46,593 INFO [train.py:842] (1/4) Epoch 23, batch 600, loss[loss=0.1768, simple_loss=0.2707, pruned_loss=0.04151, over 7115.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2692, pruned_loss=0.0473, over 1357678.25 frames.], batch size: 21, lr: 2.42e-04
2022-05-28 09:13:24,490 INFO [train.py:842] (1/4) Epoch 23, batch 650, loss[loss=0.1727, simple_loss=0.2575, pruned_loss=0.044, over 6785.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2698, pruned_loss=0.04767, over 1372877.28 frames.], batch size: 31, lr: 2.42e-04
2022-05-28 09:14:02,709 INFO [train.py:842] (1/4) Epoch 23, batch 700, loss[loss=0.1992, simple_loss=0.2789, pruned_loss=0.0598, over 5019.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2698, pruned_loss=0.04763, over 1379034.91 frames.], batch size: 52, lr: 2.42e-04
2022-05-28 09:14:40,549 INFO [train.py:842] (1/4) Epoch 23, batch 750, loss[loss=0.1915, simple_loss=0.2778, pruned_loss=0.05258, over 7204.00 frames.], tot_loss[loss=0.184, simple_loss=0.2711, pruned_loss=0.04848, over 1390703.27 frames.], batch size: 23, lr: 2.41e-04
2022-05-28 09:15:18,803 INFO [train.py:842] (1/4) Epoch 23, batch 800, loss[loss=0.1664, simple_loss=0.2494, pruned_loss=0.04172, over 7362.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2702, pruned_loss=0.04799, over 1395589.41 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:16:05,927 INFO [train.py:842] (1/4) Epoch 23, batch 850, loss[loss=0.1871, simple_loss=0.2804, pruned_loss=0.04694, over 7434.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2702, pruned_loss=0.04762, over 1403976.38 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:16:44,112 INFO [train.py:842] (1/4) Epoch 23, batch 900, loss[loss=0.1868, simple_loss=0.2631, pruned_loss=0.0552, over 7152.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2704, pruned_loss=0.04758, over 1408059.41 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:17:21,900 INFO [train.py:842] (1/4) Epoch 23, batch 950, loss[loss=0.1665, simple_loss=0.2637, pruned_loss=0.03467, over 7054.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2716, pruned_loss=0.0483, over 1410201.01 frames.], batch size: 28, lr: 2.41e-04
2022-05-28 09:18:00,185 INFO [train.py:842] (1/4) Epoch 23, batch 1000, loss[loss=0.2078, simple_loss=0.2897, pruned_loss=0.06294, over 7361.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2719, pruned_loss=0.04826, over 1417148.67 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:18:38,353 INFO [train.py:842] (1/4) Epoch 23, batch 1050, loss[loss=0.2064, simple_loss=0.2909, pruned_loss=0.0609, over 5250.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2709, pruned_loss=0.04824, over 1417974.72 frames.], batch size: 52, lr: 2.41e-04
2022-05-28 09:19:16,283 INFO [train.py:842] (1/4) Epoch 23, batch 1100, loss[loss=0.1548, simple_loss=0.2338, pruned_loss=0.03787, over 7260.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2716, pruned_loss=0.04847, over 1417810.85 frames.], batch size: 17, lr: 2.41e-04
2022-05-28 09:19:54,203 INFO [train.py:842] (1/4) Epoch 23, batch 1150, loss[loss=0.1808, simple_loss=0.2655, pruned_loss=0.04811, over 7427.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2719, pruned_loss=0.04861, over 1421923.62 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:20:32,494 INFO [train.py:842] (1/4) Epoch 23, batch 1200, loss[loss=0.1847, simple_loss=0.2632, pruned_loss=0.05314, over 7280.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2729, pruned_loss=0.04927, over 1421442.49 frames.], batch size: 18, lr: 2.41e-04
2022-05-28 09:21:10,637 INFO [train.py:842] (1/4) Epoch 23, batch 1250, loss[loss=0.157, simple_loss=0.243, pruned_loss=0.03548, over 6777.00 frames.], tot_loss[loss=0.1843, simple_loss=0.271, pruned_loss=0.04874, over 1424416.56 frames.], batch size: 15, lr: 2.41e-04
2022-05-28 09:21:48,974 INFO [train.py:842] (1/4) Epoch 23, batch 1300, loss[loss=0.1814, simple_loss=0.2717, pruned_loss=0.04554, over 7194.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2704, pruned_loss=0.04806, over 1427318.45 frames.], batch size: 23, lr: 2.41e-04
2022-05-28 09:22:27,064 INFO [train.py:842] (1/4) Epoch 23, batch 1350, loss[loss=0.1449, simple_loss=0.2319, pruned_loss=0.0289, over 7284.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2693, pruned_loss=0.04767, over 1427717.83 frames.], batch size: 18, lr: 2.41e-04
2022-05-28 09:23:05,262 INFO [train.py:842] (1/4) Epoch 23, batch 1400, loss[loss=0.2504, simple_loss=0.3309, pruned_loss=0.0849, over 7446.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2707, pruned_loss=0.04802, over 1427548.66 frames.], batch size: 22, lr: 2.41e-04
2022-05-28 09:23:43,278 INFO [train.py:842] (1/4) Epoch 23, batch 1450, loss[loss=0.1617, simple_loss=0.2437, pruned_loss=0.03983, over 7414.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2715, pruned_loss=0.0488, over 1421212.00 frames.], batch size: 18, lr: 2.41e-04
2022-05-28 09:24:21,883 INFO [train.py:842] (1/4) Epoch 23, batch 1500, loss[loss=0.1783, simple_loss=0.2747, pruned_loss=0.04091, over 7056.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2691, pruned_loss=0.04781, over 1422610.25 frames.], batch size: 28, lr: 2.41e-04
2022-05-28 09:24:59,674 INFO [train.py:842] (1/4) Epoch 23, batch 1550, loss[loss=0.1451, simple_loss=0.2426, pruned_loss=0.02382, over 7365.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2694, pruned_loss=0.04778, over 1413155.18 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:25:37,906 INFO [train.py:842] (1/4) Epoch 23, batch 1600, loss[loss=0.1663, simple_loss=0.2567, pruned_loss=0.0379, over 7236.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2692, pruned_loss=0.04806, over 1411907.57 frames.], batch size: 21, lr: 2.41e-04
2022-05-28 09:26:16,018 INFO [train.py:842] (1/4) Epoch 23, batch 1650, loss[loss=0.1752, simple_loss=0.2767, pruned_loss=0.03683, over 7374.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2695, pruned_loss=0.04809, over 1414853.20 frames.], batch size: 23, lr: 2.41e-04
2022-05-28 09:26:54,166 INFO [train.py:842] (1/4) Epoch 23, batch 1700, loss[loss=0.1422, simple_loss=0.2206, pruned_loss=0.03186, over 7428.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2693, pruned_loss=0.04795, over 1416107.11 frames.], batch size: 18, lr: 2.41e-04
2022-05-28 09:27:31,886 INFO [train.py:842] (1/4) Epoch 23, batch 1750, loss[loss=0.1547, simple_loss=0.2478, pruned_loss=0.03081, over 7199.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2696, pruned_loss=0.04758, over 1414508.23 frames.], batch size: 26, lr: 2.41e-04
2022-05-28 09:28:10,171 INFO [train.py:842] (1/4) Epoch 23, batch 1800, loss[loss=0.2279, simple_loss=0.2932, pruned_loss=0.08127, over 4930.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2695, pruned_loss=0.04776, over 1413075.85 frames.], batch size: 52, lr: 2.41e-04
2022-05-28 09:28:48,254 INFO [train.py:842] (1/4) Epoch 23, batch 1850, loss[loss=0.1617, simple_loss=0.2492, pruned_loss=0.03711, over 7424.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2691, pruned_loss=0.04756, over 1417706.08 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:29:26,613 INFO [train.py:842] (1/4) Epoch 23, batch 1900, loss[loss=0.1697, simple_loss=0.2666, pruned_loss=0.03641, over 7150.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2685, pruned_loss=0.04715, over 1420960.25 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:30:04,583 INFO [train.py:842] (1/4) Epoch 23, batch 1950, loss[loss=0.1634, simple_loss=0.2611, pruned_loss=0.03289, over 7162.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2693, pruned_loss=0.04812, over 1418121.73 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:30:42,753 INFO [train.py:842] (1/4) Epoch 23, batch 2000, loss[loss=0.1344, simple_loss=0.229, pruned_loss=0.01993, over 7261.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2697, pruned_loss=0.04774, over 1421241.61 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:31:20,832 INFO [train.py:842] (1/4) Epoch 23, batch 2050, loss[loss=0.1667, simple_loss=0.2526, pruned_loss=0.04046, over 7237.00 frames.], tot_loss[loss=0.183, simple_loss=0.2702, pruned_loss=0.04794, over 1425403.15 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:31:59,068 INFO [train.py:842] (1/4) Epoch 23, batch 2100, loss[loss=0.1674, simple_loss=0.2552, pruned_loss=0.03977, over 7178.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2694, pruned_loss=0.04713, over 1419946.62 frames.], batch size: 23, lr: 2.41e-04
2022-05-28 09:32:37,115 INFO [train.py:842] (1/4) Epoch 23, batch 2150, loss[loss=0.1738, simple_loss=0.2643, pruned_loss=0.04161, over 7166.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2693, pruned_loss=0.04728, over 1420778.94 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:33:15,365 INFO [train.py:842] (1/4) Epoch 23, batch 2200, loss[loss=0.1592, simple_loss=0.2538, pruned_loss=0.03226, over 7150.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2694, pruned_loss=0.04747, over 1416506.41 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:33:53,115 INFO [train.py:842] (1/4) Epoch 23, batch 2250, loss[loss=0.162, simple_loss=0.2557, pruned_loss=0.0342, over 7151.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2707, pruned_loss=0.04854, over 1412271.70 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:34:31,496 INFO [train.py:842] (1/4) Epoch 23, batch 2300, loss[loss=0.1758, simple_loss=0.2611, pruned_loss=0.04519, over 7321.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2691, pruned_loss=0.04814, over 1413212.59 frames.], batch size: 21, lr: 2.41e-04
2022-05-28 09:35:09,541 INFO [train.py:842] (1/4) Epoch 23, batch 2350, loss[loss=0.1682, simple_loss=0.2508, pruned_loss=0.04274, over 7326.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2692, pruned_loss=0.04789, over 1415499.05 frames.], batch size: 22, lr: 2.41e-04
2022-05-28 09:35:47,651 INFO [train.py:842] (1/4) Epoch 23, batch 2400, loss[loss=0.1803, simple_loss=0.2704, pruned_loss=0.04508, over 7306.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2699, pruned_loss=0.04819, over 1418399.69 frames.], batch size: 24, lr: 2.41e-04
2022-05-28 09:36:25,395 INFO [train.py:842] (1/4) Epoch 23, batch 2450, loss[loss=0.1701, simple_loss=0.2633, pruned_loss=0.0384, over 7201.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2717, pruned_loss=0.04878, over 1422349.95 frames.], batch size: 22, lr: 2.40e-04
2022-05-28 09:37:03,844 INFO [train.py:842] (1/4) Epoch 23, batch 2500, loss[loss=0.2025, simple_loss=0.2928, pruned_loss=0.05608, over 6258.00 frames.], tot_loss[loss=0.1831, simple_loss=0.27, pruned_loss=0.04811, over 1420417.71 frames.], batch size: 37, lr: 2.40e-04
2022-05-28 09:37:41,690 INFO [train.py:842] (1/4) Epoch 23, batch 2550, loss[loss=0.1519, simple_loss=0.2496, pruned_loss=0.02707, over 7377.00 frames.], tot_loss[loss=0.1829, simple_loss=0.27, pruned_loss=0.04794, over 1421652.86 frames.], batch size: 23, lr: 2.40e-04
2022-05-28 09:38:20,089 INFO [train.py:842] (1/4) Epoch 23, batch 2600, loss[loss=0.166, simple_loss=0.2615, pruned_loss=0.03524, over 7331.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2697, pruned_loss=0.04785, over 1426184.64 frames.], batch size: 22, lr: 2.40e-04
2022-05-28 09:38:58,229 INFO [train.py:842] (1/4) Epoch 23, batch 2650, loss[loss=0.2163, simple_loss=0.3091, pruned_loss=0.06176, over 7283.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2686, pruned_loss=0.04752, over 1423018.51 frames.], batch size: 25, lr: 2.40e-04
2022-05-28 09:39:36,478 INFO [train.py:842] (1/4) Epoch 23, batch 2700, loss[loss=0.1462, simple_loss=0.2325, pruned_loss=0.02992, over 7159.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2693, pruned_loss=0.04789, over 1422412.16 frames.], batch size: 19, lr: 2.40e-04
2022-05-28 09:40:14,500 INFO [train.py:842] (1/4) Epoch 23, batch 2750, loss[loss=0.1576, simple_loss=0.2504, pruned_loss=0.03244, over 7167.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2686, pruned_loss=0.0478, over 1420112.33 frames.], batch size: 18, lr: 2.40e-04
2022-05-28 09:40:52,721 INFO [train.py:842] (1/4) Epoch 23, batch 2800, loss[loss=0.1539, simple_loss=0.2465, pruned_loss=0.03063, over 7171.00 frames.], tot_loss[loss=0.182, simple_loss=0.2684, pruned_loss=0.04776, over 1419278.00 frames.], batch size: 18, lr: 2.40e-04
2022-05-28 09:41:30,792 INFO [train.py:842] (1/4) Epoch 23, batch 2850, loss[loss=0.2366, simple_loss=0.3186, pruned_loss=0.07728, over 7048.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2681, pruned_loss=0.04758, over 1421091.33 frames.], batch size: 28, lr: 2.40e-04
2022-05-28 09:42:08,988 INFO [train.py:842] (1/4) Epoch 23, batch 2900, loss[loss=0.2319, simple_loss=0.3171, pruned_loss=0.07333, over 7309.00 frames.], tot_loss[loss=0.182, simple_loss=0.2685, pruned_loss=0.04772, over 1423297.37 frames.], batch size: 25, lr: 2.40e-04
2022-05-28 09:42:46,956 INFO [train.py:842] (1/4) Epoch 23, batch 2950, loss[loss=0.2281, simple_loss=0.318, pruned_loss=0.06916, over 7197.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2695, pruned_loss=0.04835, over 1423464.26 frames.], batch size: 22, lr: 2.40e-04
2022-05-28 09:43:25,060 INFO [train.py:842] (1/4) Epoch 23, batch 3000, loss[loss=0.1469, simple_loss=0.2304, pruned_loss=0.03169, over 7001.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2698, pruned_loss=0.04799, over 1424015.68 frames.], batch size: 16, lr: 2.40e-04
2022-05-28 09:43:25,062 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 09:43:34,050 INFO [train.py:871] (1/4) Epoch 23, validation: loss=0.1673, simple_loss=0.2658, pruned_loss=0.03441, over 868885.00 frames.
2022-05-28 09:44:12,030 INFO [train.py:842] (1/4) Epoch 23, batch 3050, loss[loss=0.1615, simple_loss=0.2513, pruned_loss=0.03592, over 7163.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2698, pruned_loss=0.04795, over 1426776.37 frames.], batch size: 19, lr: 2.40e-04
2022-05-28 09:44:50,430 INFO [train.py:842] (1/4) Epoch 23, batch 3100, loss[loss=0.2175, simple_loss=0.3135, pruned_loss=0.0608, over 7234.00 frames.], tot_loss[loss=0.183, simple_loss=0.2694, pruned_loss=0.04825, over 1425516.44 frames.], batch size: 20, lr: 2.40e-04
2022-05-28 09:45:28,468 INFO [train.py:842] (1/4) Epoch 23, batch 3150, loss[loss=0.162, simple_loss=0.2474, pruned_loss=0.03832, over 7326.00 frames.], tot_loss[loss=0.1814, simple_loss=0.268, pruned_loss=0.04744, over 1426776.83 frames.], batch size: 20, lr: 2.40e-04
2022-05-28 09:46:06,789 INFO [train.py:842] (1/4) Epoch 23, batch 3200, loss[loss=0.1894, simple_loss=0.2778, pruned_loss=0.05053, over 7119.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2675, pruned_loss=0.04694, over 1428237.55 frames.], batch size: 21, lr: 2.40e-04
2022-05-28 09:46:44,585 INFO [train.py:842] (1/4) Epoch 23, batch 3250, loss[loss=0.183, simple_loss=0.2754, pruned_loss=0.04532, over 6464.00 frames.], tot_loss[loss=0.1833, simple_loss=0.27, pruned_loss=0.04829, over 1422804.57 frames.], batch size: 37, lr: 2.40e-04
2022-05-28 09:47:22,768 INFO [train.py:842] (1/4) Epoch 23, batch 3300, loss[loss=0.1872, simple_loss=0.2691, pruned_loss=0.05266, over 7286.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2697, pruned_loss=0.04786, over 1422967.57 frames.], batch size: 24, lr: 2.40e-04
2022-05-28 09:48:00,971 INFO [train.py:842] (1/4) Epoch 23, batch 3350, loss[loss=0.1949, simple_loss=0.2782, pruned_loss=0.05578, over 7188.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2695, pruned_loss=0.04803, over 1427227.92 frames.], batch size: 26, lr: 2.40e-04
2022-05-28 09:48:39,184 INFO [train.py:842] (1/4) Epoch 23, batch 3400, loss[loss=0.2056, simple_loss=0.2988, pruned_loss=0.05621, over 7176.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2706, pruned_loss=0.04882, over 1428143.16 frames.], batch size: 19, lr: 2.40e-04
2022-05-28 09:49:17,346 INFO [train.py:842] (1/4) Epoch 23, batch 3450, loss[loss=0.1589, simple_loss=0.2298, pruned_loss=0.04397, over 7237.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2698, pruned_loss=0.04852, over 1430301.28 frames.], batch size: 16, lr: 2.40e-04
2022-05-28 09:49:55,690 INFO [train.py:842] (1/4) Epoch 23, batch 3500, loss[loss=0.1618, simple_loss=0.2486, pruned_loss=0.03751, over 6782.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2689, pruned_loss=0.04785, over 1431192.88 frames.], batch size: 15, lr: 2.40e-04
2022-05-28 09:50:33,625 INFO [train.py:842] (1/4) Epoch 23, batch 3550, loss[loss=0.1633, simple_loss=0.2457, pruned_loss=0.0404, over 7393.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2683, pruned_loss=0.04767, over 1430416.28 frames.], batch size: 18, lr: 2.40e-04
2022-05-28 09:51:11,857 INFO [train.py:842] (1/4) Epoch 23, batch 3600, loss[loss=0.158, simple_loss=0.2395, pruned_loss=0.03826, over 7278.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2696, pruned_loss=0.0479, over 1431652.86 frames.], batch size: 17, lr: 2.40e-04
2022-05-28 09:51:49,914 INFO [train.py:842] (1/4) Epoch 23, batch 3650, loss[loss=0.1749, simple_loss=0.2822, pruned_loss=0.03373, over 6435.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2695, pruned_loss=0.04784, over 1431781.60 frames.], batch size: 37, lr: 2.40e-04
2022-05-28 09:52:28,071 INFO [train.py:842] (1/4) Epoch 23, batch 3700, loss[loss=0.1611, simple_loss=0.25, pruned_loss=0.03612, over 7156.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2692, pruned_loss=0.04774, over 1430714.84 frames.], batch size: 19, lr: 2.40e-04
2022-05-28 09:53:05,872 INFO [train.py:842] (1/4) Epoch 23, batch 3750, loss[loss=0.1643, simple_loss=0.2508, pruned_loss=0.03889, over 7264.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2702, pruned_loss=0.04915, over 1428426.57 frames.], batch size: 17, lr: 2.40e-04
2022-05-28 09:53:44,128 INFO [train.py:842] (1/4) Epoch 23, batch 3800, loss[loss=0.1801, simple_loss=0.2831, pruned_loss=0.03857, over 7375.00 frames.], tot_loss[loss=0.184, simple_loss=0.2703, pruned_loss=0.04887, over 1429478.10 frames.], batch size: 23, lr: 2.40e-04
2022-05-28 09:54:22,217 INFO [train.py:842] (1/4) Epoch 23, batch 3850, loss[loss=0.189, simple_loss=0.281, pruned_loss=0.04853, over 7103.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2699, pruned_loss=0.04847, over 1430390.28 frames.], batch size: 28, lr: 2.40e-04
2022-05-28 09:55:00,590 INFO [train.py:842] (1/4) Epoch 23, batch 3900, loss[loss=0.1728, simple_loss=0.2636, pruned_loss=0.04098, over 7099.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2696, pruned_loss=0.04884, over 1429933.86 frames.], batch size: 21, lr: 2.40e-04
2022-05-28 09:55:38,445 INFO [train.py:842] (1/4) Epoch 23, batch 3950, loss[loss=0.1648, simple_loss=0.2672, pruned_loss=0.03115, over 7155.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2699, pruned_loss=0.04853, over 1429406.40 frames.], batch size: 19, lr: 2.40e-04
2022-05-28 09:56:16,613 INFO [train.py:842] (1/4) Epoch 23, batch 4000, loss[loss=0.1486, simple_loss=0.2432, pruned_loss=0.02703, over 7276.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2705, pruned_loss=0.04818, over 1426277.24 frames.], batch size: 17, lr: 2.40e-04
2022-05-28 09:56:54,388 INFO [train.py:842] (1/4) Epoch 23, batch 4050, loss[loss=0.1552, simple_loss=0.2419, pruned_loss=0.03431, over 6809.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2702, pruned_loss=0.04757, over 1419912.80 frames.], batch size: 15, lr: 2.40e-04
2022-05-28 09:57:32,629 INFO [train.py:842] (1/4) Epoch 23, batch 4100, loss[loss=0.1818, simple_loss=0.2656, pruned_loss=0.04903, over 7150.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2705, pruned_loss=0.04785, over 1417507.33 frames.], batch size: 20, lr: 2.40e-04
2022-05-28 09:58:10,726 INFO [train.py:842] (1/4) Epoch 23, batch 4150, loss[loss=0.1647, simple_loss=0.2474, pruned_loss=0.04096, over 7074.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2691, pruned_loss=0.04759, over 1417258.25 frames.], batch size: 18, lr: 2.39e-04
2022-05-28 09:58:48,824 INFO [train.py:842] (1/4) Epoch 23, batch 4200, loss[loss=0.1715, simple_loss=0.2606, pruned_loss=0.04116, over 7442.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2707, pruned_loss=0.04811, over 1421060.53 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 09:59:26,828 INFO [train.py:842] (1/4) Epoch 23, batch 4250, loss[loss=0.1473, simple_loss=0.2322, pruned_loss=0.03115, over 7284.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2706, pruned_loss=0.04812, over 1425835.60 frames.], batch size: 17, lr: 2.39e-04
2022-05-28 10:00:04,969 INFO [train.py:842] (1/4) Epoch 23, batch 4300, loss[loss=0.2086, simple_loss=0.2979, pruned_loss=0.05964, over 6811.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2705, pruned_loss=0.04801, over 1427238.23 frames.], batch size: 31, lr: 2.39e-04
2022-05-28 10:00:42,796 INFO [train.py:842] (1/4) Epoch 23, batch 4350, loss[loss=0.1868, simple_loss=0.2858, pruned_loss=0.04392, over 7415.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2712, pruned_loss=0.04809, over 1426062.05 frames.], batch size: 21, lr: 2.39e-04
2022-05-28 10:01:30,391 INFO [train.py:842] (1/4) Epoch 23, batch 4400, loss[loss=0.1914, simple_loss=0.2779, pruned_loss=0.05249, over 6895.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2703, pruned_loss=0.04768, over 1428115.21 frames.], batch size: 32, lr: 2.39e-04
2022-05-28 10:02:08,606 INFO [train.py:842] (1/4) Epoch 23, batch 4450, loss[loss=0.1732, simple_loss=0.2564, pruned_loss=0.04501, over 7121.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2699, pruned_loss=0.04773, over 1429348.08 frames.], batch size: 17, lr: 2.39e-04
2022-05-28 10:02:46,917 INFO [train.py:842] (1/4) Epoch 23, batch 4500, loss[loss=0.1642, simple_loss=0.2425, pruned_loss=0.04292, over 7412.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2705, pruned_loss=0.04804, over 1427984.27 frames.], batch size: 18, lr: 2.39e-04
2022-05-28 10:03:24,946 INFO [train.py:842] (1/4) Epoch 23, batch 4550, loss[loss=0.2461, simple_loss=0.3265, pruned_loss=0.08286, over 7210.00 frames.], tot_loss[loss=0.183, simple_loss=0.2706, pruned_loss=0.04769, over 1430523.22 frames.], batch size: 22, lr: 2.39e-04
2022-05-28 10:04:12,778 INFO [train.py:842] (1/4) Epoch 23, batch 4600, loss[loss=0.165, simple_loss=0.2637, pruned_loss=0.03316, over 7372.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2707, pruned_loss=0.0481, over 1425424.89 frames.], batch size: 23, lr: 2.39e-04
2022-05-28 10:04:50,912 INFO [train.py:842] (1/4) Epoch 23, batch 4650, loss[loss=0.1905, simple_loss=0.2748, pruned_loss=0.05313, over 7428.00 frames.], tot_loss[loss=0.1828, simple_loss=0.27, pruned_loss=0.04781, over 1428509.02 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 10:05:38,563 INFO [train.py:842] (1/4) Epoch 23, batch 4700, loss[loss=0.1739, simple_loss=0.2786, pruned_loss=0.03464, over 7425.00 frames.], tot_loss[loss=0.182, simple_loss=0.2693, pruned_loss=0.04733, over 1429727.57 frames.], batch size: 21, lr: 2.39e-04
2022-05-28 10:06:16,325 INFO [train.py:842] (1/4) Epoch 23, batch 4750, loss[loss=0.1916, simple_loss=0.2764, pruned_loss=0.05338, over 7148.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2707, pruned_loss=0.04825, over 1423612.73 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 10:06:54,363 INFO [train.py:842] (1/4) Epoch 23, batch 4800, loss[loss=0.174, simple_loss=0.2553, pruned_loss=0.0464, over 7447.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2717, pruned_loss=0.04883, over 1420096.15 frames.], batch size: 19, lr: 2.39e-04
2022-05-28 10:07:32,379 INFO [train.py:842] (1/4) Epoch 23, batch 4850, loss[loss=0.1572, simple_loss=0.2416, pruned_loss=0.0364, over 7416.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2696, pruned_loss=0.04757, over 1418932.15 frames.], batch size: 18, lr: 2.39e-04
2022-05-28 10:08:10,712 INFO [train.py:842] (1/4) Epoch 23, batch 4900, loss[loss=0.2338, simple_loss=0.3069, pruned_loss=0.08036, over 7192.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2712, pruned_loss=0.04875, over 1423067.76 frames.], batch size: 22, lr: 2.39e-04
2022-05-28 10:08:48,738 INFO [train.py:842] (1/4) Epoch 23, batch 4950, loss[loss=0.1912, simple_loss=0.2817, pruned_loss=0.05034, over 7413.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2703, pruned_loss=0.04827, over 1423267.81 frames.], batch size: 21, lr: 2.39e-04
2022-05-28 10:09:27,014 INFO [train.py:842] (1/4) Epoch 23, batch 5000, loss[loss=0.1862, simple_loss=0.2811, pruned_loss=0.04566, over 7435.00 frames.], tot_loss[loss=0.1844, simple_loss=0.271, pruned_loss=0.0489, over 1421712.58 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 10:10:05,045 INFO [train.py:842] (1/4) Epoch 23, batch 5050, loss[loss=0.1687, simple_loss=0.2551, pruned_loss=0.04112, over 7156.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2693, pruned_loss=0.04814, over 1420506.36 frames.], batch size: 19, lr: 2.39e-04
2022-05-28 10:10:43,287 INFO [train.py:842] (1/4) Epoch 23, batch 5100, loss[loss=0.1819, simple_loss=0.2659, pruned_loss=0.04892, over 7286.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2694, pruned_loss=0.04839, over 1421697.64 frames.], batch size: 24, lr: 2.39e-04
2022-05-28 10:11:21,306 INFO [train.py:842] (1/4) Epoch 23, batch 5150, loss[loss=0.1723, simple_loss=0.2619, pruned_loss=0.04129, over 7414.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2694, pruned_loss=0.04803, over 1425889.32 frames.], batch size: 21, lr: 2.39e-04
2022-05-28 10:11:59,865 INFO [train.py:842] (1/4) Epoch 23, batch 5200, loss[loss=0.1747, simple_loss=0.2735, pruned_loss=0.03797, over 7389.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2685, pruned_loss=0.04753, over 1427919.72 frames.], batch size: 23, lr: 2.39e-04
2022-05-28 10:12:37,839 INFO [train.py:842] (1/4) Epoch 23, batch 5250, loss[loss=0.1875, simple_loss=0.2764, pruned_loss=0.04933, over 7331.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2692, pruned_loss=0.04827, over 1429774.59 frames.], batch size: 22, lr: 2.39e-04
2022-05-28 10:13:16,068 INFO [train.py:842] (1/4) Epoch 23, batch 5300, loss[loss=0.2083, simple_loss=0.2942, pruned_loss=0.06122, over 6392.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2691, pruned_loss=0.04775, over 1428845.10 frames.], batch size: 38, lr: 2.39e-04
2022-05-28 10:13:54,136 INFO [train.py:842] (1/4) Epoch 23, batch 5350, loss[loss=0.2338, simple_loss=0.3194, pruned_loss=0.07412, over 7108.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2685, pruned_loss=0.04785, over 1426396.17 frames.], batch size: 21, lr: 2.39e-04
2022-05-28 10:14:32,596 INFO [train.py:842] (1/4) Epoch 23, batch 5400, loss[loss=0.1811, simple_loss=0.2792, pruned_loss=0.04146, over 7326.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2687, pruned_loss=0.04782, over 1430168.64 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 10:15:10,492 INFO [train.py:842] (1/4) Epoch 23, batch 5450, loss[loss=0.1695, simple_loss=0.26, pruned_loss=0.03953, over 7090.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2695, pruned_loss=0.0478, over 1431721.41 frames.], batch size: 28, lr: 2.39e-04
2022-05-28 10:15:48,467 INFO [train.py:842] (1/4) Epoch 23, batch 5500, loss[loss=0.1666, simple_loss=0.269, pruned_loss=0.03204, over 7198.00 frames.], tot_loss[loss=0.184, simple_loss=0.2711, pruned_loss=0.04849, over 1425545.47 frames.], batch size: 26, lr: 2.39e-04
2022-05-28 10:16:26,514 INFO [train.py:842] (1/4) Epoch 23, batch 5550, loss[loss=0.1858, simple_loss=0.2731, pruned_loss=0.0493, over 7203.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2703, pruned_loss=0.04809, over 1427266.25 frames.], batch size: 22, lr: 2.39e-04
2022-05-28 10:17:04,857 INFO [train.py:842] (1/4) Epoch 23, batch 5600, loss[loss=0.1564, simple_loss=0.2391, pruned_loss=0.03684, over 7224.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2707, pruned_loss=0.04831, over 1427919.12 frames.], batch size: 16, lr: 2.39e-04
2022-05-28 10:17:43,161 INFO [train.py:842] (1/4) Epoch 23, batch 5650, loss[loss=0.163, simple_loss=0.2554, pruned_loss=0.03532, over 7433.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2688, pruned_loss=0.04747, over 1430285.77 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 10:18:21,423 INFO [train.py:842] (1/4) Epoch 23, batch 5700, loss[loss=0.2186, simple_loss=0.3056, pruned_loss=0.06573, over 7145.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2697, pruned_loss=0.04821, over 1425322.52 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 10:18:59,409 INFO [train.py:842] (1/4) Epoch 23, batch 5750, loss[loss=0.1794, simple_loss=0.249, pruned_loss=0.05492, over 7134.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2689, pruned_loss=0.04775, over 1423756.56 frames.], batch size: 17, lr: 2.39e-04
2022-05-28 10:19:40,409 INFO [train.py:842] (1/4) Epoch 23, batch 5800, loss[loss=0.1529, simple_loss=0.2447, pruned_loss=0.03053, over 7060.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2675, pruned_loss=0.04738, over 1425918.38 frames.], batch size: 18, lr: 2.39e-04
2022-05-28 10:20:18,497 INFO [train.py:842] (1/4) Epoch 23, batch 5850, loss[loss=0.1677, simple_loss=0.263, pruned_loss=0.03617, over 7323.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2665, pruned_loss=0.04695, over 1428420.32 frames.], batch size: 21, lr: 2.39e-04
2022-05-28 10:20:56,748 INFO [train.py:842] (1/4) Epoch 23, batch 5900, loss[loss=0.1637, simple_loss=0.258, pruned_loss=0.03467, over 7435.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2679, pruned_loss=0.04761, over 1424170.18 frames.], batch size: 20, lr: 2.38e-04
2022-05-28 10:21:34,795 INFO [train.py:842] (1/4) Epoch 23, batch 5950, loss[loss=0.1497, simple_loss=0.2295, pruned_loss=0.03499, over 7010.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2683, pruned_loss=0.04776, over 1418795.33 frames.], batch size: 16, lr: 2.38e-04
2022-05-28 10:22:13,043 INFO [train.py:842] (1/4) Epoch 23, batch 6000, loss[loss=0.1846, simple_loss=0.2759, pruned_loss=0.04661, over 6785.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2685, pruned_loss=0.04751, over 1418601.31 frames.], batch size: 31, lr: 2.38e-04
2022-05-28 10:22:13,044 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 10:22:22,049 INFO [train.py:871] (1/4) Epoch 23, validation: loss=0.1637, simple_loss=0.2625, pruned_loss=0.03241, over 868885.00 frames.
2022-05-28 10:22:59,810 INFO [train.py:842] (1/4) Epoch 23, batch 6050, loss[loss=0.1608, simple_loss=0.237, pruned_loss=0.04237, over 7397.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2693, pruned_loss=0.04799, over 1418895.85 frames.], batch size: 18, lr: 2.38e-04
2022-05-28 10:23:37,967 INFO [train.py:842] (1/4) Epoch 23, batch 6100, loss[loss=0.1662, simple_loss=0.2638, pruned_loss=0.03426, over 6649.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2698, pruned_loss=0.04772, over 1420735.89 frames.], batch size: 31, lr: 2.38e-04
2022-05-28 10:24:16,057 INFO [train.py:842] (1/4) Epoch 23, batch 6150, loss[loss=0.1877, simple_loss=0.2922, pruned_loss=0.04155, over 7338.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2702, pruned_loss=0.04779, over 1421125.08 frames.], batch size: 24, lr: 2.38e-04
2022-05-28 10:24:54,365 INFO [train.py:842] (1/4) Epoch 23, batch 6200, loss[loss=0.1701, simple_loss=0.2636, pruned_loss=0.03832, over 7139.00 frames.], tot_loss[loss=0.183, simple_loss=0.2703, pruned_loss=0.04785, over 1423292.14 frames.], batch size: 26, lr: 2.38e-04
2022-05-28 10:25:32,196 INFO [train.py:842] (1/4) Epoch 23, batch 6250, loss[loss=0.1872, simple_loss=0.2724, pruned_loss=0.05095, over 6708.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2703, pruned_loss=0.04795, over 1421220.91 frames.], batch size: 31, lr: 2.38e-04
2022-05-28 10:26:10,650 INFO [train.py:842] (1/4) Epoch 23, batch 6300, loss[loss=0.1917, simple_loss=0.2836, pruned_loss=0.04989, over 7304.00 frames.], tot_loss[loss=0.1829, simple_loss=0.27, pruned_loss=0.0479, over 1423471.50 frames.], batch size: 25, lr: 2.38e-04
2022-05-28 10:26:48,656 INFO [train.py:842] (1/4) Epoch 23, batch 6350, loss[loss=0.1822, simple_loss=0.2791, pruned_loss=0.04265, over 7172.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2692, pruned_loss=0.04731, over 1422016.30 frames.], batch size: 26, lr: 2.38e-04
2022-05-28 10:27:27,047 INFO [train.py:842] (1/4) Epoch 23, batch 6400, loss[loss=0.1725, simple_loss=0.2755, pruned_loss=0.03472, over 7081.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2675, pruned_loss=0.04649, over 1424927.44 frames.], batch size: 28, lr: 2.38e-04
2022-05-28 10:28:05,063 INFO [train.py:842] (1/4) Epoch 23, batch 6450, loss[loss=0.2485, simple_loss=0.3249, pruned_loss=0.08606, over 7333.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2677, pruned_loss=0.047, over 1421172.84 frames.], batch size: 20, lr: 2.38e-04
2022-05-28 10:28:43,396 INFO [train.py:842] (1/4) Epoch 23, batch 6500, loss[loss=0.1959, simple_loss=0.277, pruned_loss=0.05744, over 7171.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2672, pruned_loss=0.04685, over 1422300.61 frames.], batch size: 18, lr: 2.38e-04
2022-05-28 10:29:21,293 INFO [train.py:842] (1/4) Epoch 23, batch 6550, loss[loss=0.1672, simple_loss=0.2546, pruned_loss=0.03986, over 7266.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2678, pruned_loss=0.0469, over 1422698.98 frames.], batch size: 19, lr: 2.38e-04
2022-05-28 10:29:59,632 INFO [train.py:842] (1/4) Epoch 23, batch 6600, loss[loss=0.2049, simple_loss=0.2849, pruned_loss=0.06245, over 7007.00 frames.], tot_loss[loss=0.181, simple_loss=0.2682, pruned_loss=0.04695, over 1426911.73 frames.], batch size: 32, lr: 2.38e-04
2022-05-28 10:30:37,535 INFO [train.py:842] (1/4) Epoch 23, batch 6650, loss[loss=0.1849, simple_loss=0.2682, pruned_loss=0.05075, over 7311.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2683, pruned_loss=0.04718, over 1428989.66 frames.], batch size: 21, lr: 2.38e-04
2022-05-28 10:31:15,834 INFO [train.py:842] (1/4) Epoch 23, batch 6700, loss[loss=0.1786, simple_loss=0.2543, pruned_loss=0.05145, over 7372.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2683, pruned_loss=0.04745, over 1428527.92 frames.], batch size: 19, lr: 2.38e-04
2022-05-28 10:31:54,042 INFO [train.py:842] (1/4) Epoch 23, batch 6750, loss[loss=0.1894, simple_loss=0.2732, pruned_loss=0.05287, over 7413.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2672, pruned_loss=0.04717, over 1429832.88 frames.], batch size: 21, lr: 2.38e-04
2022-05-28 10:32:32,269 INFO [train.py:842] (1/4) Epoch 23, batch 6800, loss[loss=0.1497, simple_loss=0.2308, pruned_loss=0.03435, over 7357.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2685, pruned_loss=0.04765, over 1431833.45 frames.], batch size: 19, lr: 2.38e-04
2022-05-28 10:33:10,270 INFO [train.py:842] (1/4) Epoch 23, batch 6850, loss[loss=0.1447, simple_loss=0.2288, pruned_loss=0.03035, over 7266.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2681, pruned_loss=0.04787, over 1426152.32 frames.], batch size: 18, lr: 2.38e-04
2022-05-28 10:33:48,538 INFO [train.py:842] (1/4) Epoch 23, batch 6900, loss[loss=0.187, simple_loss=0.2767, pruned_loss=0.04866, over 7404.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2695, pruned_loss=0.04847, over 1425981.57 frames.], batch size: 21, lr: 2.38e-04
2022-05-28 10:34:26,509 INFO [train.py:842] (1/4) Epoch 23, batch 6950, loss[loss=0.1603, simple_loss=0.2353, pruned_loss=0.04264, over 7005.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2701, pruned_loss=0.04858, over 1428656.64 frames.], batch size: 16, lr: 2.38e-04
2022-05-28 10:35:04,770 INFO [train.py:842] (1/4) Epoch 23, batch 7000, loss[loss=0.2055, simple_loss=0.2841, pruned_loss=0.06344, over 4926.00 frames.], tot_loss[loss=0.1834, simple_loss=0.27, pruned_loss=0.0484, over 1427523.40 frames.], batch size: 52, lr: 2.38e-04
2022-05-28 10:35:42,813 INFO [train.py:842] (1/4) Epoch 23, batch 7050, loss[loss=0.1537, simple_loss=0.2361, pruned_loss=0.03567, over 7234.00 frames.], tot_loss[loss=0.182, simple_loss=0.2686, pruned_loss=0.04775, over 1426835.11 frames.], batch size: 20, lr: 2.38e-04
2022-05-28 10:36:21,015 INFO [train.py:842] (1/4) Epoch 23, batch 7100, loss[loss=0.1939, simple_loss=0.2875, pruned_loss=0.05015, over 7295.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2688, pruned_loss=0.0475, over 1423594.51 frames.], batch size: 24, lr: 2.38e-04
2022-05-28 10:36:58,903 INFO [train.py:842] (1/4) Epoch 23, batch 7150, loss[loss=0.1914, simple_loss=0.2892, pruned_loss=0.04674, over 7305.00 frames.], tot_loss[loss=0.182, simple_loss=0.2692, pruned_loss=0.04743, over 1426296.41 frames.], batch size: 25, lr: 2.38e-04
2022-05-28 10:37:37,090 INFO [train.py:842] (1/4) Epoch 23, batch 7200, loss[loss=0.2264, simple_loss=0.3128, pruned_loss=0.07001, over 7319.00 frames.], tot_loss[loss=0.182, simple_loss=0.2692, pruned_loss=0.04743, over 1420345.96 frames.], batch size: 20, lr: 2.38e-04
2022-05-28 10:38:15,062 INFO [train.py:842] (1/4) Epoch 23, batch 7250, loss[loss=0.1566, simple_loss=0.2536, pruned_loss=0.02979, over 7159.00 frames.], tot_loss[loss=0.182, simple_loss=0.2689, pruned_loss=0.04749, over 1417861.28 frames.], batch size: 19, lr: 2.38e-04
2022-05-28 10:38:53,296 INFO [train.py:842] (1/4) Epoch 23, batch 7300, loss[loss=0.2176, simple_loss=0.3077, pruned_loss=0.06376, over 7164.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2683, pruned_loss=0.04735, over 1417445.41 frames.], batch size: 26, lr: 2.38e-04
2022-05-28 10:39:31,507 INFO [train.py:842] (1/4) Epoch 23, batch 7350, loss[loss=0.2068, simple_loss=0.2927, pruned_loss=0.06044, over 5177.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2688, pruned_loss=0.04822, over 1420813.14 frames.], batch size: 52, lr: 2.38e-04
2022-05-28 10:40:09,511 INFO [train.py:842] (1/4) Epoch 23, batch 7400, loss[loss=0.1943, simple_loss=0.283, pruned_loss=0.05284, over 7150.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2691, pruned_loss=0.04809, over 1421784.78 frames.], batch size: 20, lr: 2.38e-04
2022-05-28 10:40:47,425 INFO [train.py:842] (1/4) Epoch 23, batch 7450, loss[loss=0.2119, simple_loss=0.3056, pruned_loss=0.05914, over 7167.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2696, pruned_loss=0.04791, over 1424120.74 frames.], batch size: 19, lr: 2.38e-04
2022-05-28 10:41:25,545 INFO [train.py:842] (1/4) Epoch 23, batch 7500, loss[loss=0.267, simple_loss=0.357, pruned_loss=0.08854, over 7207.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2703, pruned_loss=0.04853, over 1417262.49 frames.], batch size: 22, lr: 2.38e-04
2022-05-28 10:42:03,563 INFO [train.py:842] (1/4) Epoch 23, batch 7550, loss[loss=0.2125, simple_loss=0.2966, pruned_loss=0.06414, over 7397.00 frames.], tot_loss[loss=0.183, simple_loss=0.2698, pruned_loss=0.04807, over 1421115.11 frames.], batch size: 21, lr: 2.38e-04
2022-05-28 10:42:41,665 INFO [train.py:842] (1/4) Epoch 23, batch 7600, loss[loss=0.1842, simple_loss=0.2734, pruned_loss=0.04753, over 4977.00 frames.], tot_loss[loss=0.183, simple_loss=0.2699, pruned_loss=0.04805, over 1417903.17 frames.], batch size: 52, lr: 2.38e-04
2022-05-28 10:43:19,664 INFO [train.py:842] (1/4) Epoch 23, batch 7650, loss[loss=0.1897, simple_loss=0.2713, pruned_loss=0.0541, over 6931.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2688, pruned_loss=0.04771, over 1415464.67 frames.], batch size: 31, lr: 2.37e-04
2022-05-28 10:43:57,924 INFO [train.py:842] (1/4) Epoch 23, batch 7700, loss[loss=0.1546, simple_loss=0.2442, pruned_loss=0.03249, over 7156.00 frames.], tot_loss[loss=0.1821, simple_loss=0.269, pruned_loss=0.04763, over 1415124.71 frames.], batch size: 20, lr: 2.37e-04
2022-05-28 10:44:35,746 INFO [train.py:842] (1/4) Epoch 23, batch 7750, loss[loss=0.2213, simple_loss=0.304, pruned_loss=0.06925, over 7416.00 frames.], tot_loss[loss=0.1823, simple_loss=0.269, pruned_loss=0.04777, over 1418112.17 frames.], batch size: 21, lr: 2.37e-04
2022-05-28 10:45:14,277 INFO [train.py:842] (1/4) Epoch 23, batch 7800, loss[loss=0.2274, simple_loss=0.3216, pruned_loss=0.0666, over 7132.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2684, pruned_loss=0.04758, over 1422347.75 frames.], batch size: 26, lr: 2.37e-04
2022-05-28 10:45:52,170 INFO [train.py:842] (1/4) Epoch 23, batch 7850, loss[loss=0.1903, simple_loss=0.2845, pruned_loss=0.04804, over 5125.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2679, pruned_loss=0.04718, over 1417446.88 frames.], batch size: 52, lr: 2.37e-04
2022-05-28 10:46:30,467 INFO [train.py:842] (1/4) Epoch 23, batch 7900, loss[loss=0.2001, simple_loss=0.2797, pruned_loss=0.06028, over 4878.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2679, pruned_loss=0.04712, over 1418763.24 frames.], batch size: 53, lr: 2.37e-04
2022-05-28 10:47:08,310 INFO [train.py:842] (1/4) Epoch 23, batch 7950, loss[loss=0.1495, simple_loss=0.2304, pruned_loss=0.0343, over 7126.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2682, pruned_loss=0.04699, over 1419630.77 frames.], batch size: 17, lr: 2.37e-04
2022-05-28 10:47:46,449 INFO [train.py:842] (1/4) Epoch 23, batch 8000, loss[loss=0.1755, simple_loss=0.2635, pruned_loss=0.04377, over 7124.00 frames.], tot_loss[loss=0.181, simple_loss=0.2681, pruned_loss=0.04698, over 1423548.14 frames.], batch size: 26, lr: 2.37e-04
2022-05-28 10:48:24,476 INFO [train.py:842] (1/4) Epoch 23, batch 8050, loss[loss=0.1762, simple_loss=0.2494, pruned_loss=0.05148, over 7167.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2673, pruned_loss=0.04643, over 1423163.92 frames.], batch size: 18, lr: 2.37e-04
2022-05-28 10:49:02,721 INFO [train.py:842] (1/4) Epoch 23, batch 8100, loss[loss=0.1538, simple_loss=0.2287, pruned_loss=0.03941, over 7282.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2668, pruned_loss=0.04653, over 1424079.36 frames.], batch size: 17, lr: 2.37e-04
2022-05-28 10:49:40,301 INFO [train.py:842] (1/4) Epoch 23, batch 8150, loss[loss=0.1779, simple_loss=0.269, pruned_loss=0.04335, over 7234.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2688, pruned_loss=0.04704, over 1420046.81 frames.], batch size: 21, lr: 2.37e-04
2022-05-28 10:50:18,856 INFO [train.py:842] (1/4) Epoch 23, batch 8200, loss[loss=0.1877, simple_loss=0.2791, pruned_loss=0.04818, over 7289.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2677, pruned_loss=0.0467, over 1421240.39 frames.], batch size: 25, lr: 2.37e-04
2022-05-28 10:50:56,566 INFO [train.py:842] (1/4) Epoch 23, batch 8250, loss[loss=0.1776, simple_loss=0.2685, pruned_loss=0.04334, over 7207.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2679, pruned_loss=0.04648, over 1420526.76 frames.], batch size: 22, lr: 2.37e-04
2022-05-28 10:51:34,719 INFO [train.py:842] (1/4) Epoch 23, batch 8300, loss[loss=0.2037, simple_loss=0.2701, pruned_loss=0.06871, over 7447.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2687, pruned_loss=0.04731, over 1415834.02 frames.], batch size: 19, lr: 2.37e-04
2022-05-28 10:52:12,730 INFO [train.py:842] (1/4) Epoch 23, batch 8350, loss[loss=0.2014, simple_loss=0.3004, pruned_loss=0.05119, over 6333.00 frames.], tot_loss[loss=0.1808, simple_loss=0.268, pruned_loss=0.04677, over 1414104.19 frames.], batch size: 37, lr: 2.37e-04
2022-05-28 10:52:50,728 INFO [train.py:842] (1/4) Epoch 23, batch 8400, loss[loss=0.1967, simple_loss=0.2834, pruned_loss=0.055, over 7077.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2689, pruned_loss=0.04735, over 1409226.03 frames.], batch size: 18, lr: 2.37e-04
2022-05-28 10:53:28,673 INFO [train.py:842] (1/4) Epoch 23, batch 8450, loss[loss=0.1972, simple_loss=0.2741, pruned_loss=0.0602, over 6989.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2685, pruned_loss=0.04745, over 1408857.71 frames.], batch size: 16, lr: 2.37e-04
2022-05-28 10:54:06,873 INFO [train.py:842] (1/4) Epoch 23, batch 8500, loss[loss=0.2018, simple_loss=0.2773, pruned_loss=0.06315, over 7203.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2692, pruned_loss=0.0475, over 1409648.37 frames.], batch size: 16, lr: 2.37e-04
2022-05-28 10:54:44,746 INFO [train.py:842] (1/4) Epoch 23, batch 8550, loss[loss=0.1693, simple_loss=0.2602, pruned_loss=0.03924, over 7225.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2703, pruned_loss=0.04866, over 1411629.44 frames.], batch size: 21, lr: 2.37e-04
2022-05-28 10:55:22,743 INFO [train.py:842] (1/4) Epoch 23, batch 8600, loss[loss=0.171, simple_loss=0.2562, pruned_loss=0.0429, over 7389.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2714, pruned_loss=0.04895, over 1411083.70 frames.], batch size: 23, lr: 2.37e-04
2022-05-28 10:56:00,902 INFO [train.py:842] (1/4) Epoch 23, batch 8650, loss[loss=0.1889, simple_loss=0.262, pruned_loss=0.05793, over 7268.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2693, pruned_loss=0.04788, over 1415643.26 frames.], batch size: 17, lr: 2.37e-04
2022-05-28 10:56:39,175 INFO [train.py:842] (1/4) Epoch 23, batch 8700, loss[loss=0.1444, simple_loss=0.2244, pruned_loss=0.03216, over 7003.00 frames.], tot_loss[loss=0.183, simple_loss=0.2695, pruned_loss=0.04826, over 1413654.87 frames.], batch size: 16, lr: 2.37e-04
2022-05-28 10:57:17,036 INFO [train.py:842] (1/4) Epoch 23, batch 8750, loss[loss=0.2141, simple_loss=0.279, pruned_loss=0.07459, over 7146.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2688, pruned_loss=0.04798, over 1412028.38 frames.], batch size: 17, lr: 2.37e-04
2022-05-28 10:57:55,224 INFO [train.py:842] (1/4) Epoch 23, batch 8800, loss[loss=0.1619, simple_loss=0.2585, pruned_loss=0.0327, over 7311.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2698, pruned_loss=0.04873, over 1407283.08 frames.], batch size: 24, lr: 2.37e-04
2022-05-28 10:58:33,253 INFO [train.py:842] (1/4) Epoch 23, batch 8850, loss[loss=0.1823, simple_loss=0.2796, pruned_loss=0.04255, over 7112.00 frames.], tot_loss[loss=0.182, simple_loss=0.2688, pruned_loss=0.04762, over 1409706.22 frames.], batch size: 21, lr: 2.37e-04
2022-05-28 10:59:11,045 INFO [train.py:842] (1/4) Epoch 23, batch 8900, loss[loss=0.1849, simple_loss=0.2869, pruned_loss=0.04144, over 7143.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2696, pruned_loss=0.04777, over 1401213.29 frames.], batch size: 26, lr: 2.37e-04
2022-05-28 10:59:48,797 INFO [train.py:842] (1/4) Epoch 23, batch 8950, loss[loss=0.2077, simple_loss=0.2976, pruned_loss=0.05892, over 6238.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2705, pruned_loss=0.04832, over 1395080.46 frames.], batch size: 37, lr: 2.37e-04
2022-05-28 11:00:26,428 INFO [train.py:842] (1/4) Epoch 23, batch 9000, loss[loss=0.2367, simple_loss=0.3047, pruned_loss=0.08436, over 4887.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2718, pruned_loss=0.04836, over 1391635.76 frames.], batch size: 52, lr: 2.37e-04
2022-05-28 11:00:26,429 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 11:00:35,435 INFO [train.py:871] (1/4) Epoch 23, validation: loss=0.1641, simple_loss=0.2627, pruned_loss=0.03273, over 868885.00 frames.
2022-05-28 11:01:12,466 INFO [train.py:842] (1/4) Epoch 23, batch 9050, loss[loss=0.1885, simple_loss=0.2806, pruned_loss=0.04817, over 6740.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2739, pruned_loss=0.04916, over 1379796.34 frames.], batch size: 31, lr: 2.37e-04
2022-05-28 11:01:49,744 INFO [train.py:842] (1/4) Epoch 23, batch 9100, loss[loss=0.1982, simple_loss=0.2842, pruned_loss=0.05607, over 7152.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2776, pruned_loss=0.05161, over 1345662.09 frames.], batch size: 26, lr: 2.37e-04
2022-05-28 11:02:26,559 INFO [train.py:842] (1/4) Epoch 23, batch 9150, loss[loss=0.2066, simple_loss=0.2904, pruned_loss=0.06145, over 4985.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2809, pruned_loss=0.05418, over 1280159.40 frames.], batch size: 52, lr: 2.37e-04
2022-05-28 11:03:11,876 INFO [train.py:842] (1/4) Epoch 24, batch 0, loss[loss=0.1566, simple_loss=0.2356, pruned_loss=0.03887, over 6836.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2356, pruned_loss=0.03887, over 6836.00 frames.], batch size: 15, lr: 2.32e-04
2022-05-28 11:03:49,769 INFO [train.py:842] (1/4) Epoch 24, batch 50, loss[loss=0.1424, simple_loss=0.2307, pruned_loss=0.02702, over 7274.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2667, pruned_loss=0.04513, over 316103.54 frames.], batch size: 17, lr: 2.32e-04
2022-05-28 11:04:28,174 INFO [train.py:842] (1/4) Epoch 24, batch 100, loss[loss=0.205, simple_loss=0.2908, pruned_loss=0.05955, over 7336.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2694, pruned_loss=0.04698, over 567005.80 frames.], batch size: 20, lr: 2.32e-04
2022-05-28 11:05:05,931 INFO [train.py:842] (1/4) Epoch 24, batch 150, loss[loss=0.1834, simple_loss=0.2702, pruned_loss=0.04833, over 7394.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2706, pruned_loss=0.04824, over 752363.92 frames.], batch size: 23, lr: 2.32e-04
2022-05-28 11:05:44,210 INFO [train.py:842] (1/4) Epoch 24, batch 200, loss[loss=0.1985, simple_loss=0.2961, pruned_loss=0.05049, over 7203.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2687, pruned_loss=0.04719, over 903352.52 frames.], batch size: 22, lr: 2.32e-04
2022-05-28 11:06:22,173 INFO [train.py:842] (1/4) Epoch 24, batch 250, loss[loss=0.1598, simple_loss=0.2481, pruned_loss=0.03576, over 7419.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2691, pruned_loss=0.04703, over 1016342.26 frames.], batch size: 21, lr: 2.32e-04
2022-05-28 11:07:00,469 INFO [train.py:842] (1/4) Epoch 24, batch 300, loss[loss=0.1495, simple_loss=0.2435, pruned_loss=0.02774, over 7146.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2688, pruned_loss=0.04705, over 1107735.65 frames.], batch size: 20, lr: 2.32e-04
2022-05-28 11:07:38,466 INFO [train.py:842] (1/4) Epoch 24, batch 350, loss[loss=0.2065, simple_loss=0.2955, pruned_loss=0.05872, over 7282.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2682, pruned_loss=0.04634, over 1179423.84 frames.], batch size: 25, lr: 2.32e-04
2022-05-28 11:08:16,629 INFO [train.py:842] (1/4) Epoch 24, batch 400, loss[loss=0.2024, simple_loss=0.2915, pruned_loss=0.05662, over 7278.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2681, pruned_loss=0.04637, over 1230221.42 frames.], batch size: 24, lr: 2.32e-04
2022-05-28 11:08:54,685 INFO [train.py:842] (1/4) Epoch 24, batch 450, loss[loss=0.2037, simple_loss=0.297, pruned_loss=0.0552, over 7153.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2686, pruned_loss=0.04637, over 1276276.79 frames.], batch size: 20, lr: 2.32e-04
2022-05-28 11:09:32,938 INFO [train.py:842] (1/4) Epoch 24, batch 500, loss[loss=0.1831, simple_loss=0.2692, pruned_loss=0.04848, over 7369.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2684, pruned_loss=0.04692, over 1308418.45 frames.], batch size: 19, lr: 2.31e-04
2022-05-28 11:10:11,039 INFO [train.py:842] (1/4) Epoch 24, batch 550, loss[loss=0.1909, simple_loss=0.2835, pruned_loss=0.04911, over 7211.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2672, pruned_loss=0.04658, over 1336813.14 frames.], batch size: 22, lr: 2.31e-04
2022-05-28 11:10:49,413 INFO [train.py:842] (1/4) Epoch 24, batch 600, loss[loss=0.1394, simple_loss=0.2241, pruned_loss=0.02734, over 7358.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2653, pruned_loss=0.04563, over 1353521.12 frames.], batch size: 19, lr: 2.31e-04
2022-05-28 11:11:27,464 INFO [train.py:842] (1/4) Epoch 24, batch 650, loss[loss=0.1653, simple_loss=0.2547, pruned_loss=0.03795, over 7355.00 frames.], tot_loss[loss=0.179, simple_loss=0.266, pruned_loss=0.04598, over 1364133.41 frames.], batch size: 19, lr: 2.31e-04
2022-05-28 11:12:06,143 INFO [train.py:842] (1/4) Epoch 24, batch 700, loss[loss=0.1935, simple_loss=0.284, pruned_loss=0.05153, over 7165.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2646, pruned_loss=0.04552, over 1381702.22 frames.], batch size: 26, lr: 2.31e-04
2022-05-28 11:12:44,042 INFO [train.py:842] (1/4) Epoch 24, batch 750, loss[loss=0.1811, simple_loss=0.2519, pruned_loss=0.05515, over 6999.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2658, pruned_loss=0.04588, over 1392507.84 frames.], batch size: 16, lr: 2.31e-04
2022-05-28 11:13:22,548 INFO [train.py:842] (1/4) Epoch 24, batch 800, loss[loss=0.1277, simple_loss=0.2181, pruned_loss=0.01867, over 7255.00 frames.], tot_loss[loss=0.179, simple_loss=0.2658, pruned_loss=0.04606, over 1399361.42 frames.], batch size: 19, lr: 2.31e-04
2022-05-28 11:14:00,597 INFO [train.py:842] (1/4) Epoch 24, batch 850, loss[loss=0.1906, simple_loss=0.2785, pruned_loss=0.05137, over 6693.00 frames.], tot_loss[loss=0.179, simple_loss=0.2658, pruned_loss=0.04608, over 1405520.21 frames.], batch size: 31, lr: 2.31e-04
2022-05-28 11:14:38,793 INFO [train.py:842] (1/4) Epoch 24, batch 900, loss[loss=0.1682, simple_loss=0.2541, pruned_loss=0.04115, over 7419.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2672, pruned_loss=0.04666, over 1411209.84 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:15:16,791 INFO [train.py:842] (1/4) Epoch 24, batch 950, loss[loss=0.1771, simple_loss=0.2681, pruned_loss=0.04304, over 6423.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2665, pruned_loss=0.04635, over 1416335.40 frames.], batch size: 38, lr: 2.31e-04
2022-05-28 11:15:55,341 INFO [train.py:842] (1/4) Epoch 24, batch 1000, loss[loss=0.1798, simple_loss=0.2717, pruned_loss=0.04393, over 7332.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2657, pruned_loss=0.0458, over 1419150.87 frames.], batch size: 21, lr: 2.31e-04
2022-05-28 11:16:33,151 INFO [train.py:842] (1/4) Epoch 24, batch 1050, loss[loss=0.1622, simple_loss=0.2631, pruned_loss=0.0307, over 7229.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2671, pruned_loss=0.04662, over 1412448.59 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:17:11,285 INFO [train.py:842] (1/4) Epoch 24, batch 1100, loss[loss=0.1736, simple_loss=0.26, pruned_loss=0.04356, over 7146.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2674, pruned_loss=0.04677, over 1411602.19 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:17:49,520 INFO [train.py:842] (1/4) Epoch 24, batch 1150, loss[loss=0.1783, simple_loss=0.2655, pruned_loss=0.04562, over 6539.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2677, pruned_loss=0.04743, over 1414907.55 frames.], batch size: 38, lr: 2.31e-04
2022-05-28 11:18:27,584 INFO [train.py:842] (1/4) Epoch 24, batch 1200, loss[loss=0.1719, simple_loss=0.2581, pruned_loss=0.0429, over 7153.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2681, pruned_loss=0.04754, over 1417785.08 frames.], batch size: 18, lr: 2.31e-04
2022-05-28 11:19:05,766 INFO [train.py:842] (1/4) Epoch 24, batch 1250, loss[loss=0.1973, simple_loss=0.2752, pruned_loss=0.05972, over 7323.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2677, pruned_loss=0.04724, over 1418805.41 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:19:44,013 INFO [train.py:842] (1/4) Epoch 24, batch 1300, loss[loss=0.184, simple_loss=0.2711, pruned_loss=0.04845, over 6721.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2678, pruned_loss=0.04728, over 1419947.20 frames.], batch size: 31, lr: 2.31e-04
2022-05-28 11:20:21,957 INFO [train.py:842] (1/4) Epoch 24, batch 1350, loss[loss=0.1362, simple_loss=0.2171, pruned_loss=0.02762, over 7415.00 frames.], tot_loss[loss=0.182, simple_loss=0.2688, pruned_loss=0.04759, over 1425709.64 frames.], batch size: 18, lr: 2.31e-04
2022-05-28 11:21:00,270 INFO [train.py:842] (1/4) Epoch 24, batch 1400, loss[loss=0.1806, simple_loss=0.2717, pruned_loss=0.04476, over 7171.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2695, pruned_loss=0.04802, over 1423632.86 frames.], batch size: 26, lr: 2.31e-04
2022-05-28 11:21:38,254 INFO [train.py:842] (1/4) Epoch 24, batch 1450, loss[loss=0.1968, simple_loss=0.2778, pruned_loss=0.0579, over 7135.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2695, pruned_loss=0.04786, over 1421350.77 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:22:16,542 INFO [train.py:842] (1/4) Epoch 24, batch 1500, loss[loss=0.1736, simple_loss=0.2673, pruned_loss=0.03995, over 7139.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2688, pruned_loss=0.04781, over 1419818.47 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:22:54,647 INFO [train.py:842] (1/4) Epoch 24, batch 1550, loss[loss=0.2155, simple_loss=0.301, pruned_loss=0.06505, over 6775.00 frames.], tot_loss[loss=0.183, simple_loss=0.2699, pruned_loss=0.04809, over 1420777.28 frames.], batch size: 31, lr: 2.31e-04
2022-05-28 11:23:32,823 INFO [train.py:842] (1/4) Epoch 24, batch 1600, loss[loss=0.1781, simple_loss=0.2692, pruned_loss=0.04348, over 7328.00 frames.], tot_loss[loss=0.183, simple_loss=0.2702, pruned_loss=0.04791, over 1422300.05 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:24:10,609 INFO [train.py:842] (1/4) Epoch 24, batch 1650, loss[loss=0.153, simple_loss=0.2315, pruned_loss=0.03726, over 6788.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2705, pruned_loss=0.04799, over 1414486.22 frames.], batch size: 15, lr: 2.31e-04
2022-05-28 11:24:48,819 INFO [train.py:842] (1/4) Epoch 24, batch 1700, loss[loss=0.1745, simple_loss=0.2672, pruned_loss=0.04091, over 7321.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2695, pruned_loss=0.04717, over 1418529.56 frames.], batch size: 21, lr: 2.31e-04
2022-05-28 11:25:26,715 INFO [train.py:842] (1/4) Epoch 24, batch 1750, loss[loss=0.1841, simple_loss=0.2642, pruned_loss=0.05199, over 7068.00 frames.], tot_loss[loss=0.182, simple_loss=0.2693, pruned_loss=0.04733, over 1419896.58 frames.], batch size: 18, lr: 2.31e-04
2022-05-28 11:26:05,003 INFO [train.py:842] (1/4) Epoch 24, batch 1800, loss[loss=0.2061, simple_loss=0.3018, pruned_loss=0.05524, over 7336.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2687, pruned_loss=0.04706, over 1420695.04 frames.], batch size: 22, lr: 2.31e-04
2022-05-28 11:26:42,922 INFO [train.py:842] (1/4) Epoch 24, batch 1850, loss[loss=0.2411, simple_loss=0.3175, pruned_loss=0.0824, over 7292.00 frames.], tot_loss[loss=0.1817, simple_loss=0.269, pruned_loss=0.04722, over 1424212.64 frames.], batch size: 24, lr: 2.31e-04
2022-05-28 11:27:21,190 INFO [train.py:842] (1/4) Epoch 24, batch 1900, loss[loss=0.1768, simple_loss=0.2665, pruned_loss=0.04349, over 7086.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2695, pruned_loss=0.04762, over 1421892.35 frames.], batch size: 28, lr: 2.31e-04
2022-05-28 11:27:59,202 INFO [train.py:842] (1/4) Epoch 24, batch 1950, loss[loss=0.1668, simple_loss=0.2645, pruned_loss=0.03458, over 7118.00 frames.], tot_loss[loss=0.1806, simple_loss=0.268, pruned_loss=0.04664, over 1423499.89 frames.], batch size: 21, lr: 2.31e-04
2022-05-28 11:28:37,370 INFO [train.py:842] (1/4) Epoch 24, batch 2000, loss[loss=0.1996, simple_loss=0.2781, pruned_loss=0.06057, over 4913.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2695, pruned_loss=0.04752, over 1421663.95 frames.], batch size: 53, lr: 2.31e-04
2022-05-28 11:29:15,376 INFO [train.py:842] (1/4) Epoch 24, batch 2050, loss[loss=0.1912, simple_loss=0.2701, pruned_loss=0.05615, over 7428.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2694, pruned_loss=0.04739, over 1421688.35 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:29:53,648 INFO [train.py:842] (1/4) Epoch 24, batch 2100, loss[loss=0.1555, simple_loss=0.2368, pruned_loss=0.03709, over 7007.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2685, pruned_loss=0.04702, over 1423195.02 frames.], batch size: 16, lr: 2.31e-04
2022-05-28 11:30:31,675 INFO [train.py:842] (1/4) Epoch 24, batch 2150, loss[loss=0.2282, simple_loss=0.3122, pruned_loss=0.07207, over 4873.00 frames.], tot_loss[loss=0.181, simple_loss=0.268, pruned_loss=0.04701, over 1420856.78 frames.], batch size: 52, lr: 2.31e-04
2022-05-28 11:31:10,137 INFO [train.py:842] (1/4) Epoch 24, batch 2200, loss[loss=0.1487, simple_loss=0.2263, pruned_loss=0.03553, over 7129.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2681, pruned_loss=0.04724, over 1419913.92 frames.], batch size: 17, lr: 2.31e-04
2022-05-28 11:31:47,814 INFO [train.py:842] (1/4) Epoch 24, batch 2250, loss[loss=0.1767, simple_loss=0.272, pruned_loss=0.04073, over 7265.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2696, pruned_loss=0.04779, over 1408890.76 frames.], batch size: 25, lr: 2.31e-04
2022-05-28 11:32:26,197 INFO [train.py:842] (1/4) Epoch 24, batch 2300, loss[loss=0.1694, simple_loss=0.2433, pruned_loss=0.04781, over 7287.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2684, pruned_loss=0.04693, over 1415922.75 frames.], batch size: 17, lr: 2.31e-04
2022-05-28 11:33:04,227 INFO [train.py:842] (1/4) Epoch 24, batch 2350, loss[loss=0.1918, simple_loss=0.2901, pruned_loss=0.0467, over 7337.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2687, pruned_loss=0.0469, over 1417613.34 frames.], batch size: 22, lr: 2.30e-04
2022-05-28 11:33:42,618 INFO [train.py:842] (1/4) Epoch 24, batch 2400, loss[loss=0.1681, simple_loss=0.2427, pruned_loss=0.04682, over 7266.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2685, pruned_loss=0.04647, over 1421798.48 frames.], batch size: 16, lr: 2.30e-04
2022-05-28 11:34:20,481 INFO [train.py:842] (1/4) Epoch 24, batch 2450, loss[loss=0.1554, simple_loss=0.2521, pruned_loss=0.02938, over 7230.00 frames.], tot_loss[loss=0.1804, simple_loss=0.268, pruned_loss=0.04638, over 1418382.24 frames.], batch size: 20, lr: 2.30e-04
2022-05-28 11:34:58,778 INFO [train.py:842] (1/4) Epoch 24, batch 2500, loss[loss=0.1759, simple_loss=0.2707, pruned_loss=0.04057, over 7318.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2681, pruned_loss=0.04658, over 1418520.79 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:35:36,689 INFO [train.py:842] (1/4) Epoch 24, batch 2550, loss[loss=0.189, simple_loss=0.2652, pruned_loss=0.05636, over 5285.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2682, pruned_loss=0.04703, over 1413717.20 frames.], batch size: 52, lr: 2.30e-04
2022-05-28 11:36:14,959 INFO [train.py:842] (1/4) Epoch 24, batch 2600, loss[loss=0.1888, simple_loss=0.2676, pruned_loss=0.05502, over 7266.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2704, pruned_loss=0.04803, over 1417698.57 frames.], batch size: 18, lr: 2.30e-04
2022-05-28 11:36:52,933 INFO [train.py:842] (1/4) Epoch 24, batch 2650, loss[loss=0.1706, simple_loss=0.2726, pruned_loss=0.03432, over 7320.00 frames.], tot_loss[loss=0.1829, simple_loss=0.27, pruned_loss=0.04784, over 1416667.21 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:37:31,150 INFO [train.py:842] (1/4) Epoch 24, batch 2700, loss[loss=0.1822, simple_loss=0.2706, pruned_loss=0.04688, over 7325.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2689, pruned_loss=0.04697, over 1421126.74 frames.], batch size: 22, lr: 2.30e-04
2022-05-28 11:38:09,222 INFO [train.py:842] (1/4) Epoch 24, batch 2750, loss[loss=0.1671, simple_loss=0.2547, pruned_loss=0.03976, over 7412.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2674, pruned_loss=0.0462, over 1424422.76 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:38:47,215 INFO [train.py:842] (1/4) Epoch 24, batch 2800, loss[loss=0.1751, simple_loss=0.2674, pruned_loss=0.04141, over 7234.00 frames.], tot_loss[loss=0.1813, simple_loss=0.269, pruned_loss=0.0468, over 1421036.08 frames.], batch size: 20, lr: 2.30e-04
2022-05-28 11:39:25,095 INFO [train.py:842] (1/4) Epoch 24, batch 2850, loss[loss=0.1905, simple_loss=0.2752, pruned_loss=0.05287, over 7350.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2686, pruned_loss=0.04658, over 1421562.42 frames.], batch size: 19, lr: 2.30e-04
2022-05-28 11:40:03,389 INFO [train.py:842] (1/4) Epoch 24, batch 2900, loss[loss=0.1955, simple_loss=0.2802, pruned_loss=0.05543, over 7278.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2686, pruned_loss=0.04654, over 1421975.72 frames.], batch size: 25, lr: 2.30e-04
2022-05-28 11:40:41,511 INFO [train.py:842] (1/4) Epoch 24, batch 2950, loss[loss=0.1582, simple_loss=0.227, pruned_loss=0.04466, over 7268.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2682, pruned_loss=0.04637, over 1425655.96 frames.], batch size: 17, lr: 2.30e-04
2022-05-28 11:41:19,738 INFO [train.py:842] (1/4) Epoch 24, batch 3000, loss[loss=0.1847, simple_loss=0.2808, pruned_loss=0.04429, over 7099.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2691, pruned_loss=0.04702, over 1422057.83 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:41:19,738 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 11:41:28,713 INFO [train.py:871] (1/4) Epoch 24, validation: loss=0.1662, simple_loss=0.2647, pruned_loss=0.03391, over 868885.00 frames.
2022-05-28 11:42:06,679 INFO [train.py:842] (1/4) Epoch 24, batch 3050, loss[loss=0.1666, simple_loss=0.2484, pruned_loss=0.04243, over 7278.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2691, pruned_loss=0.04719, over 1417124.34 frames.], batch size: 18, lr: 2.30e-04
2022-05-28 11:42:45,179 INFO [train.py:842] (1/4) Epoch 24, batch 3100, loss[loss=0.2069, simple_loss=0.3029, pruned_loss=0.05546, over 6756.00 frames.], tot_loss[loss=0.1806, simple_loss=0.268, pruned_loss=0.04663, over 1419956.61 frames.], batch size: 31, lr: 2.30e-04
2022-05-28 11:43:23,172 INFO [train.py:842] (1/4) Epoch 24, batch 3150, loss[loss=0.1769, simple_loss=0.2562, pruned_loss=0.04884, over 6993.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2678, pruned_loss=0.04682, over 1421008.48 frames.], batch size: 16, lr: 2.30e-04
2022-05-28 11:44:01,733 INFO [train.py:842] (1/4) Epoch 24, batch 3200, loss[loss=0.2005, simple_loss=0.2904, pruned_loss=0.05525, over 7333.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2671, pruned_loss=0.04606, over 1425280.34 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:44:39,930 INFO [train.py:842] (1/4) Epoch 24, batch 3250, loss[loss=0.1565, simple_loss=0.2434, pruned_loss=0.03485, over 7150.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2684, pruned_loss=0.04703, over 1426827.03 frames.], batch size: 18, lr: 2.30e-04
2022-05-28 11:45:18,233 INFO [train.py:842] (1/4) Epoch 24, batch 3300, loss[loss=0.1805, simple_loss=0.2711, pruned_loss=0.04496, over 7284.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2677, pruned_loss=0.04633, over 1427212.67 frames.], batch size: 24, lr: 2.30e-04
2022-05-28 11:45:56,721 INFO [train.py:842] (1/4) Epoch 24, batch 3350, loss[loss=0.2102, simple_loss=0.2978, pruned_loss=0.06127, over 7279.00 frames.], tot_loss[loss=0.181, simple_loss=0.2686, pruned_loss=0.04671, over 1423966.00 frames.], batch size: 24, lr: 2.30e-04
2022-05-28 11:46:35,162 INFO [train.py:842] (1/4) Epoch 24, batch 3400, loss[loss=0.1777, simple_loss=0.2625, pruned_loss=0.04642, over 7367.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2685, pruned_loss=0.04681, over 1428769.81 frames.], batch size: 19, lr: 2.30e-04
2022-05-28 11:47:13,090 INFO [train.py:842] (1/4) Epoch 24, batch 3450, loss[loss=0.1732, simple_loss=0.2705, pruned_loss=0.03793, over 7339.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2687, pruned_loss=0.04694, over 1424003.45 frames.], batch size: 22, lr: 2.30e-04
2022-05-28 11:47:51,622 INFO [train.py:842] (1/4) Epoch 24, batch 3500, loss[loss=0.1794, simple_loss=0.2562, pruned_loss=0.05131, over 6781.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2661, pruned_loss=0.04589, over 1421044.14 frames.], batch size: 15, lr: 2.30e-04
2022-05-28 11:48:29,734 INFO [train.py:842] (1/4) Epoch 24, batch 3550, loss[loss=0.2079, simple_loss=0.2847, pruned_loss=0.06559, over 7122.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2656, pruned_loss=0.04587, over 1422657.11 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:49:17,340 INFO [train.py:842] (1/4) Epoch 24, batch 3600, loss[loss=0.149, simple_loss=0.2342, pruned_loss=0.03188, over 7072.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2655, pruned_loss=0.04556, over 1422757.50 frames.], batch size: 18, lr: 2.30e-04
2022-05-28 11:49:55,163 INFO [train.py:842] (1/4) Epoch 24, batch 3650, loss[loss=0.1513, simple_loss=0.2315, pruned_loss=0.03555, over 7351.00 frames.], tot_loss[loss=0.1796, simple_loss=0.267, pruned_loss=0.04612, over 1423825.66 frames.], batch size: 19, lr: 2.30e-04
2022-05-28 11:50:33,269 INFO [train.py:842] (1/4) Epoch 24, batch 3700, loss[loss=0.1663, simple_loss=0.2625, pruned_loss=0.03507, over 6302.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2679, pruned_loss=0.04633, over 1421108.85 frames.], batch size: 37, lr: 2.30e-04
2022-05-28 11:51:11,250 INFO [train.py:842] (1/4) Epoch 24, batch 3750, loss[loss=0.1374, simple_loss=0.2249, pruned_loss=0.02492, over 7284.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2696, pruned_loss=0.04741, over 1422369.95 frames.], batch size: 18, lr: 2.30e-04
2022-05-28 11:51:49,641 INFO [train.py:842] (1/4) Epoch 24, batch 3800, loss[loss=0.1576, simple_loss=0.2492, pruned_loss=0.03297, over 7436.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2699, pruned_loss=0.04764, over 1424067.32 frames.], batch size: 20, lr: 2.30e-04
2022-05-28 11:52:27,527 INFO [train.py:842] (1/4) Epoch 24, batch 3850, loss[loss=0.1746, simple_loss=0.2593, pruned_loss=0.045, over 5118.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2694, pruned_loss=0.0474, over 1420803.78 frames.], batch size: 54, lr: 2.30e-04
2022-05-28 11:53:05,679 INFO [train.py:842] (1/4) Epoch 24, batch 3900, loss[loss=0.1813, simple_loss=0.2721, pruned_loss=0.04524, over 6760.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2696, pruned_loss=0.04735, over 1418196.85 frames.], batch size: 31, lr: 2.30e-04
2022-05-28 11:53:43,324 INFO [train.py:842] (1/4) Epoch 24, batch 3950, loss[loss=0.2297, simple_loss=0.3121, pruned_loss=0.07367, over 7311.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2708, pruned_loss=0.04741, over 1417655.06 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:54:21,411 INFO [train.py:842] (1/4) Epoch 24, batch 4000, loss[loss=0.1577, simple_loss=0.2462, pruned_loss=0.03465, over 7165.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2702, pruned_loss=0.04717, over 1417982.30 frames.], batch size: 19, lr: 2.30e-04
2022-05-28 11:54:59,369 INFO [train.py:842] (1/4) Epoch 24, batch 4050, loss[loss=0.1611, simple_loss=0.2573, pruned_loss=0.03244, over 7420.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2695, pruned_loss=0.04697, over 1421558.50 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:55:37,618 INFO [train.py:842] (1/4) Epoch 24, batch 4100, loss[loss=0.2098, simple_loss=0.2928, pruned_loss=0.06339, over 7266.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2694, pruned_loss=0.04695, over 1421651.67 frames.], batch size: 24, lr: 2.30e-04
2022-05-28 11:56:15,698 INFO [train.py:842] (1/4) Epoch 24, batch 4150, loss[loss=0.1775, simple_loss=0.2616, pruned_loss=0.0467, over 7249.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2711, pruned_loss=0.04811, over 1425872.15 frames.], batch size: 19, lr: 2.30e-04
2022-05-28 11:56:54,004 INFO [train.py:842] (1/4) Epoch 24, batch 4200, loss[loss=0.242, simple_loss=0.3201, pruned_loss=0.08199, over 5073.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2697, pruned_loss=0.04755, over 1423661.62 frames.], batch size: 52, lr: 2.29e-04
2022-05-28 11:57:31,913 INFO [train.py:842] (1/4) Epoch 24, batch 4250, loss[loss=0.1882, simple_loss=0.2709, pruned_loss=0.05276, over 7138.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2703, pruned_loss=0.04811, over 1427449.45 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 11:58:10,162 INFO [train.py:842] (1/4) Epoch 24, batch 4300, loss[loss=0.2228, simple_loss=0.3049, pruned_loss=0.07038, over 7224.00 frames.], tot_loss[loss=0.1832, simple_loss=0.27, pruned_loss=0.04815, over 1423225.81 frames.], batch size: 21, lr: 2.29e-04
2022-05-28 11:58:48,032 INFO [train.py:842] (1/4) Epoch 24, batch 4350, loss[loss=0.1874, simple_loss=0.2705, pruned_loss=0.05212, over 7320.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2698, pruned_loss=0.04776, over 1421205.14 frames.], batch size: 20, lr: 2.29e-04
2022-05-28 11:59:26,292 INFO [train.py:842] (1/4) Epoch 24, batch 4400, loss[loss=0.1793, simple_loss=0.2675, pruned_loss=0.04548, over 7416.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2689, pruned_loss=0.04691, over 1422386.08 frames.], batch size: 21, lr: 2.29e-04
2022-05-28 12:00:04,055 INFO [train.py:842] (1/4) Epoch 24, batch 4450, loss[loss=0.1494, simple_loss=0.225, pruned_loss=0.03694, over 7128.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2701, pruned_loss=0.04771, over 1420366.44 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 12:00:42,348 INFO [train.py:842] (1/4) Epoch 24, batch 4500, loss[loss=0.1692, simple_loss=0.2474, pruned_loss=0.04546, over 7138.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2696, pruned_loss=0.04739, over 1421983.21 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 12:01:20,176 INFO [train.py:842] (1/4) Epoch 24, batch 4550, loss[loss=0.1454, simple_loss=0.2352, pruned_loss=0.02784, over 7358.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2685, pruned_loss=0.04642, over 1420744.47 frames.], batch size: 19, lr: 2.29e-04
2022-05-28 12:02:01,222 INFO [train.py:842] (1/4) Epoch 24, batch 4600, loss[loss=0.1508, simple_loss=0.2309, pruned_loss=0.03537, over 7292.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2683, pruned_loss=0.04637, over 1426384.43 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 12:02:38,971 INFO [train.py:842] (1/4) Epoch 24, batch 4650, loss[loss=0.191, simple_loss=0.2778, pruned_loss=0.05211, over 6785.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2687, pruned_loss=0.04689, over 1425759.78 frames.], batch size: 31, lr: 2.29e-04
2022-05-28 12:03:17,221 INFO [train.py:842] (1/4) Epoch 24, batch 4700, loss[loss=0.1426, simple_loss=0.2193, pruned_loss=0.03298, over 7128.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2692, pruned_loss=0.04697, over 1423262.11 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 12:03:55,139 INFO [train.py:842] (1/4) Epoch 24, batch 4750, loss[loss=0.1481, simple_loss=0.2299, pruned_loss=0.03317, over 7156.00 frames.], tot_loss[loss=0.181, simple_loss=0.2685, pruned_loss=0.04679, over 1423093.70 frames.], batch size: 18, lr: 2.29e-04
2022-05-28 12:04:33,321 INFO [train.py:842] (1/4) Epoch 24, batch 4800, loss[loss=0.2055, simple_loss=0.2931, pruned_loss=0.05895, over 7086.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2691, pruned_loss=0.04683, over 1424883.03 frames.], batch size: 28, lr: 2.29e-04
2022-05-28 12:05:10,963 INFO [train.py:842] (1/4) Epoch 24, batch 4850, loss[loss=0.1709, simple_loss=0.2558, pruned_loss=0.04296, over 6250.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2691, pruned_loss=0.04659, over 1419076.57 frames.], batch size: 37, lr: 2.29e-04
2022-05-28 12:05:49,023 INFO [train.py:842] (1/4) Epoch 24, batch 4900, loss[loss=0.1844, simple_loss=0.256, pruned_loss=0.05642, over 7430.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2686, pruned_loss=0.0469, over 1419061.83 frames.], batch size: 18, lr: 2.29e-04
2022-05-28 12:06:27,199 INFO [train.py:842] (1/4) Epoch 24, batch 4950, loss[loss=0.1581, simple_loss=0.2392, pruned_loss=0.03853, over 7281.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2679, pruned_loss=0.04656, over 1418507.76 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 12:07:05,413 INFO [train.py:842] (1/4) Epoch 24, batch 5000, loss[loss=0.2151, simple_loss=0.2949, pruned_loss=0.06761, over 7194.00 frames.], tot_loss[loss=0.1817, simple_loss=0.269, pruned_loss=0.04718, over 1417405.95 frames.], batch size: 22, lr: 2.29e-04
2022-05-28 12:07:43,339 INFO [train.py:842] (1/4) Epoch 24, batch 5050, loss[loss=0.1865, simple_loss=0.2669, pruned_loss=0.05307, over 7218.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2688, pruned_loss=0.04672, over 1418825.97 frames.], batch size: 16, lr: 2.29e-04
2022-05-28 12:08:21,581 INFO [train.py:842] (1/4) Epoch 24, batch 5100, loss[loss=0.1975, simple_loss=0.2818, pruned_loss=0.05661, over 5328.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2686, pruned_loss=0.04678, over 1418915.69 frames.], batch size: 52, lr: 2.29e-04
2022-05-28 12:08:59,669 INFO [train.py:842] (1/4) Epoch 24, batch 5150, loss[loss=0.2416, simple_loss=0.3025, pruned_loss=0.09038, over 7356.00 frames.], tot_loss[loss=0.1818, simple_loss=0.269, pruned_loss=0.04729, over 1423871.96 frames.], batch size: 19, lr: 2.29e-04
2022-05-28 12:09:37,926 INFO [train.py:842] (1/4) Epoch 24, batch 5200, loss[loss=0.1766, simple_loss=0.2524, pruned_loss=0.05037, over 7354.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2685, pruned_loss=0.04705, over 1427316.05 frames.], batch size: 19, lr: 2.29e-04
2022-05-28 12:10:16,002 INFO [train.py:842] (1/4) Epoch 24, batch 5250, loss[loss=0.1761, simple_loss=0.2714, pruned_loss=0.04038, over 7355.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2683, pruned_loss=0.04732, over 1428879.91 frames.], batch size: 23, lr: 2.29e-04
2022-05-28 12:10:54,305 INFO [train.py:842] (1/4) Epoch 24, batch 5300, loss[loss=0.1803, simple_loss=0.275, pruned_loss=0.04275, over 7174.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2675, pruned_loss=0.04657, over 1431008.57 frames.], batch size: 26, lr: 2.29e-04
2022-05-28 12:11:32,300 INFO [train.py:842] (1/4) Epoch 24, batch 5350, loss[loss=0.2081, simple_loss=0.286, pruned_loss=0.06515, over 7414.00 frames.], tot_loss[loss=0.1796, simple_loss=0.267, pruned_loss=0.04614, over 1428301.94 frames.], batch size: 21, lr: 2.29e-04
2022-05-28 12:12:10,623 INFO [train.py:842] (1/4) Epoch 24, batch 5400, loss[loss=0.2339, simple_loss=0.3143, pruned_loss=0.07682, over 4939.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2679, pruned_loss=0.04649, over 1429599.94 frames.], batch size: 52, lr: 2.29e-04
2022-05-28 12:12:48,586 INFO [train.py:842] (1/4) Epoch 24, batch 5450, loss[loss=0.1848, simple_loss=0.2741, pruned_loss=0.04773, over 7219.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2677, pruned_loss=0.0465, over 1432681.16 frames.], batch size: 23, lr: 2.29e-04
2022-05-28 12:13:26,959 INFO [train.py:842] (1/4) Epoch 24, batch 5500, loss[loss=0.191, simple_loss=0.2793, pruned_loss=0.05136, over 7181.00 frames.], tot_loss[loss=0.1806, simple_loss=0.268, pruned_loss=0.04657, over 1428646.69 frames.], batch size: 26, lr: 2.29e-04
2022-05-28 12:14:04,958 INFO [train.py:842] (1/4) Epoch 24, batch 5550, loss[loss=0.1942, simple_loss=0.2845, pruned_loss=0.05194, over 7308.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2686, pruned_loss=0.04678, over 1427448.48 frames.], batch size: 25, lr: 2.29e-04
2022-05-28 12:14:43,504 INFO [train.py:842] (1/4) Epoch 24, batch 5600, loss[loss=0.1987, simple_loss=0.2681, pruned_loss=0.06461, over 7009.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2678, pruned_loss=0.04655, over 1425349.52 frames.], batch size: 16, lr: 2.29e-04
2022-05-28 12:15:21,855 INFO [train.py:842] (1/4) Epoch 24, batch 5650, loss[loss=0.1641, simple_loss=0.2532, pruned_loss=0.03753, over 7144.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2685, pruned_loss=0.04696, over 1423833.97 frames.], batch size: 20, lr: 2.29e-04
2022-05-28 12:16:00,294 INFO [train.py:842] (1/4) Epoch 24, batch 5700, loss[loss=0.1837, simple_loss=0.2776, pruned_loss=0.0449, over 7304.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2694, pruned_loss=0.04699, over 1424359.41 frames.], batch size: 25, lr: 2.29e-04
2022-05-28 12:16:38,565 INFO [train.py:842] (1/4) Epoch 24, batch 5750, loss[loss=0.2307, simple_loss=0.3047, pruned_loss=0.07835, over 7284.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2704, pruned_loss=0.04789, over 1421865.48 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 12:17:17,298 INFO [train.py:842] (1/4) Epoch 24, batch 5800, loss[loss=0.1987, simple_loss=0.28, pruned_loss=0.05864, over 7205.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2703, pruned_loss=0.04814, over 1421671.64 frames.], batch size: 22, lr: 2.29e-04
2022-05-28 12:17:55,622 INFO [train.py:842] (1/4) Epoch 24, batch 5850, loss[loss=0.1849, simple_loss=0.285, pruned_loss=0.04241, over 7209.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2705, pruned_loss=0.0491, over 1419871.45 frames.], batch size: 23, lr: 2.29e-04
2022-05-28 12:18:34,453 INFO [train.py:842] (1/4) Epoch 24, batch 5900, loss[loss=0.1641, simple_loss=0.2415, pruned_loss=0.04338, over 6803.00 frames.], tot_loss[loss=0.182, simple_loss=0.2681, pruned_loss=0.04796, over 1419481.14 frames.], batch size: 15, lr: 2.29e-04
2022-05-28 12:19:12,844 INFO [train.py:842] (1/4) Epoch 24, batch 5950, loss[loss=0.1589, simple_loss=0.2604, pruned_loss=0.02872, over 7219.00 frames.], tot_loss[loss=0.181, simple_loss=0.2673, pruned_loss=0.04731, over 1419010.67 frames.], batch size: 26, lr: 2.29e-04
2022-05-28 12:19:51,435 INFO [train.py:842] (1/4) Epoch 24, batch 6000, loss[loss=0.2123, simple_loss=0.2832, pruned_loss=0.07071, over 4879.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2673, pruned_loss=0.04713, over 1417379.28 frames.], batch size: 52, lr: 2.29e-04
2022-05-28 12:19:51,437 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 12:20:00,718 INFO [train.py:871] (1/4) Epoch 24, validation: loss=0.166, simple_loss=0.2648, pruned_loss=0.03361, over 868885.00 frames.
2022-05-28 12:20:38,961 INFO [train.py:842] (1/4) Epoch 24, batch 6050, loss[loss=0.2025, simple_loss=0.2926, pruned_loss=0.0562, over 6778.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2673, pruned_loss=0.04668, over 1419420.40 frames.], batch size: 31, lr: 2.29e-04
2022-05-28 12:21:17,787 INFO [train.py:842] (1/4) Epoch 24, batch 6100, loss[loss=0.1697, simple_loss=0.2591, pruned_loss=0.04018, over 7388.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2663, pruned_loss=0.04634, over 1422156.63 frames.], batch size: 23, lr: 2.28e-04
2022-05-28 12:21:56,412 INFO [train.py:842] (1/4) Epoch 24, batch 6150, loss[loss=0.1667, simple_loss=0.2536, pruned_loss=0.03991, over 7429.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2658, pruned_loss=0.04623, over 1423358.85 frames.], batch size: 20, lr: 2.28e-04
2022-05-28 12:22:35,166 INFO [train.py:842] (1/4) Epoch 24, batch 6200, loss[loss=0.187, simple_loss=0.2723, pruned_loss=0.0508, over 7275.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2666, pruned_loss=0.04646, over 1423507.89 frames.], batch size: 25, lr: 2.28e-04
2022-05-28 12:23:13,723 INFO [train.py:842] (1/4) Epoch 24, batch 6250, loss[loss=0.1739, simple_loss=0.2689, pruned_loss=0.03945, over 7103.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2662, pruned_loss=0.04643, over 1425440.42 frames.], batch size: 28, lr: 2.28e-04
2022-05-28 12:23:52,508 INFO [train.py:842] (1/4) Epoch 24, batch 6300, loss[loss=0.1704, simple_loss=0.2614, pruned_loss=0.03974, over 7414.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2661, pruned_loss=0.04635, over 1423971.38 frames.], batch size: 21, lr: 2.28e-04
2022-05-28 12:24:30,647 INFO [train.py:842] (1/4) Epoch 24, batch 6350, loss[loss=0.1869, simple_loss=0.2832, pruned_loss=0.04534, over 6786.00 frames.], tot_loss[loss=0.18, simple_loss=0.2667, pruned_loss=0.04667, over 1420216.05 frames.], batch size: 31, lr: 2.28e-04
2022-05-28 12:25:09,321 INFO [train.py:842] (1/4) Epoch 24, batch 6400, loss[loss=0.1668, simple_loss=0.2398, pruned_loss=0.04692, over 7409.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2667, pruned_loss=0.04644, over 1421999.64 frames.], batch size: 17, lr: 2.28e-04
2022-05-28 12:25:47,745 INFO [train.py:842] (1/4) Epoch 24, batch 6450, loss[loss=0.1836, simple_loss=0.2795, pruned_loss=0.04387, over 6697.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2669, pruned_loss=0.04613, over 1421735.40 frames.], batch size: 31, lr: 2.28e-04
2022-05-28 12:26:26,356 INFO [train.py:842] (1/4) Epoch 24, batch 6500, loss[loss=0.1581, simple_loss=0.2346, pruned_loss=0.04081, over 7001.00 frames.], tot_loss[loss=0.1794, simple_loss=0.267, pruned_loss=0.0459, over 1422035.64 frames.], batch size: 16, lr: 2.28e-04
2022-05-28 12:27:04,749 INFO [train.py:842] (1/4) Epoch 24, batch 6550, loss[loss=0.1594, simple_loss=0.2553, pruned_loss=0.03173, over 7295.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2664, pruned_loss=0.0457, over 1424730.56 frames.], batch size: 24, lr: 2.28e-04
2022-05-28 12:27:43,591 INFO [train.py:842] (1/4) Epoch 24, batch 6600, loss[loss=0.1836, simple_loss=0.2713, pruned_loss=0.04793, over 7001.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2665, pruned_loss=0.04614, over 1425632.43 frames.], batch size: 28, lr: 2.28e-04
2022-05-28 12:28:22,106 INFO [train.py:842] (1/4) Epoch 24, batch 6650, loss[loss=0.185, simple_loss=0.2695, pruned_loss=0.05024, over 7323.00 frames.], tot_loss[loss=0.179, simple_loss=0.2664, pruned_loss=0.04582, over 1427700.23 frames.], batch size: 24, lr: 2.28e-04
2022-05-28 12:29:00,918 INFO [train.py:842] (1/4) Epoch 24, batch 6700, loss[loss=0.1724, simple_loss=0.2507, pruned_loss=0.04706, over 7281.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2664, pruned_loss=0.04595, over 1430175.48 frames.], batch size: 17, lr: 2.28e-04
2022-05-28 12:29:39,682 INFO [train.py:842] (1/4) Epoch 24, batch 6750, loss[loss=0.1618, simple_loss=0.2459, pruned_loss=0.03882, over 7146.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2667, pruned_loss=0.04652, over 1429330.51 frames.], batch size: 19, lr: 2.28e-04
2022-05-28 12:30:18,209 INFO [train.py:842] (1/4) Epoch 24, batch 6800, loss[loss=0.208, simple_loss=0.2999, pruned_loss=0.05802, over 7144.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2679, pruned_loss=0.04695, over 1426436.29 frames.], batch size: 20, lr: 2.28e-04
2022-05-28 12:30:56,689 INFO [train.py:842] (1/4) Epoch 24, batch 6850, loss[loss=0.1769, simple_loss=0.268, pruned_loss=0.04296, over 6601.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2668, pruned_loss=0.04635, over 1427428.22 frames.], batch size: 38, lr: 2.28e-04
2022-05-28 12:31:35,012 INFO [train.py:842] (1/4) Epoch 24, batch 6900, loss[loss=0.2166, simple_loss=0.3051, pruned_loss=0.06402, over 7214.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2685, pruned_loss=0.04697, over 1428659.29 frames.], batch size: 23, lr: 2.28e-04
2022-05-28 12:32:13,548 INFO [train.py:842] (1/4) Epoch 24, batch 6950, loss[loss=0.1913, simple_loss=0.2667, pruned_loss=0.05799, over 7206.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2688, pruned_loss=0.0473, over 1430108.66 frames.], batch size: 16, lr: 2.28e-04
2022-05-28 12:32:52,263 INFO [train.py:842] (1/4) Epoch 24, batch 7000, loss[loss=0.15, simple_loss=0.2325, pruned_loss=0.03378, over 7199.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2686, pruned_loss=0.04723, over 1428508.98 frames.], batch size: 16, lr: 2.28e-04
2022-05-28 12:33:30,587 INFO [train.py:842] (1/4) Epoch 24, batch 7050, loss[loss=0.2038, simple_loss=0.2928, pruned_loss=0.05742, over 7190.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2687, pruned_loss=0.047, over 1429165.46 frames.], batch size: 26, lr: 2.28e-04
2022-05-28 12:34:09,120 INFO [train.py:842] (1/4) Epoch 24, batch 7100, loss[loss=0.1797, simple_loss=0.2709, pruned_loss=0.04429, over 7072.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2685, pruned_loss=0.04689, over 1427468.20 frames.], batch size: 28, lr: 2.28e-04
2022-05-28 12:34:47,733 INFO [train.py:842] (1/4) Epoch 24, batch 7150, loss[loss=0.1808, simple_loss=0.278, pruned_loss=0.04176, over 7164.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2686, pruned_loss=0.04693, over 1426484.91 frames.], batch size: 19, lr: 2.28e-04
2022-05-28 12:35:36,398 INFO [train.py:842] (1/4) Epoch 24, batch 7200, loss[loss=0.2186, simple_loss=0.3054, pruned_loss=0.06584, over 7118.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2693, pruned_loss=0.04673, over 1427212.25 frames.], batch size: 21, lr: 2.28e-04
2022-05-28 12:36:14,998 INFO [train.py:842] (1/4) Epoch 24, batch 7250, loss[loss=0.1714, simple_loss=0.2677, pruned_loss=0.0375, over 7348.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2698, pruned_loss=0.04685, over 1431743.70 frames.], batch size: 22, lr: 2.28e-04
2022-05-28 12:36:53,721 INFO [train.py:842] (1/4) Epoch 24, batch 7300, loss[loss=0.1703, simple_loss=0.251, pruned_loss=0.04479, over 7061.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2688, pruned_loss=0.0468, over 1431183.49 frames.], batch size: 18, lr: 2.28e-04
2022-05-28 12:37:32,028 INFO [train.py:842] (1/4) Epoch 24, batch 7350, loss[loss=0.1764, simple_loss=0.2442, pruned_loss=0.05434, over 7020.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2694, pruned_loss=0.04741, over 1432573.72 frames.], batch size: 16, lr: 2.28e-04
2022-05-28 12:38:10,661 INFO [train.py:842] (1/4) Epoch 24, batch 7400, loss[loss=0.2016, simple_loss=0.2906, pruned_loss=0.05631, over 7232.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2695, pruned_loss=0.04731, over 1432321.87 frames.], batch size: 20, lr: 2.28e-04
2022-05-28 12:38:59,114 INFO [train.py:842] (1/4) Epoch 24, batch 7450, loss[loss=0.1883, simple_loss=0.2718, pruned_loss=0.05245, over 7432.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2688, pruned_loss=0.04767, over 1430495.09 frames.], batch size: 20, lr: 2.28e-04
2022-05-28 12:39:37,698 INFO [train.py:842] (1/4) Epoch 24, batch 7500, loss[loss=0.1739, simple_loss=0.2638, pruned_loss=0.04198, over 7265.00 frames.], tot_loss[loss=0.1818, simple_loss=0.269, pruned_loss=0.04733, over 1428340.72 frames.], batch size: 19, lr: 2.28e-04
2022-05-28 12:40:26,191 INFO [train.py:842] (1/4) Epoch 24, batch 7550, loss[loss=0.157, simple_loss=0.2409, pruned_loss=0.03658, over 7351.00 frames.], tot_loss[loss=0.1828, simple_loss=0.27, pruned_loss=0.04786, over 1427753.29 frames.], batch size: 19, lr: 2.28e-04
2022-05-28 12:41:04,834 INFO [train.py:842] (1/4) Epoch 24, batch 7600, loss[loss=0.1919, simple_loss=0.2867, pruned_loss=0.04857, over 7158.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2707, pruned_loss=0.04785, over 1428718.88 frames.], batch size: 26, lr: 2.28e-04
2022-05-28 12:41:43,341 INFO [train.py:842] (1/4) Epoch 24, batch 7650, loss[loss=0.1977, simple_loss=0.2918, pruned_loss=0.05181, over 7320.00 frames.], tot_loss[loss=0.183, simple_loss=0.2706, pruned_loss=0.04769, over 1432230.16 frames.], batch size: 25, lr: 2.28e-04
2022-05-28 12:42:22,070 INFO [train.py:842] (1/4) Epoch 24, batch 7700, loss[loss=0.1884, simple_loss=0.281, pruned_loss=0.04789, over 7059.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2698, pruned_loss=0.04762, over 1430592.08 frames.], batch size: 28, lr: 2.28e-04
2022-05-28 12:43:00,405 INFO [train.py:842] (1/4) Epoch 24, batch 7750, loss[loss=0.1547, simple_loss=0.2452, pruned_loss=0.03205, over 7361.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2705, pruned_loss=0.04795, over 1431354.51 frames.], batch size: 19, lr: 2.28e-04
2022-05-28 12:43:39,170 INFO [train.py:842] (1/4) Epoch 24, batch 7800, loss[loss=0.2264, simple_loss=0.3223, pruned_loss=0.06524, over 7397.00 frames.], tot_loss[loss=0.184, simple_loss=0.2712, pruned_loss=0.04835, over 1431742.31 frames.], batch size: 23, lr: 2.28e-04
2022-05-28 12:44:17,732 INFO [train.py:842] (1/4) Epoch 24, batch 7850, loss[loss=0.2582, simple_loss=0.3268, pruned_loss=0.09474, over 5396.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2706, pruned_loss=0.04817, over 1427634.32 frames.], batch size: 52, lr: 2.28e-04
2022-05-28 12:44:56,194 INFO [train.py:842] (1/4) Epoch 24, batch 7900, loss[loss=0.1914, simple_loss=0.2828, pruned_loss=0.04999, over 7394.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2721, pruned_loss=0.04868, over 1423389.65 frames.], batch size: 18, lr: 2.28e-04
2022-05-28 12:45:34,546 INFO [train.py:842] (1/4) Epoch 24, batch 7950, loss[loss=0.248, simple_loss=0.3364, pruned_loss=0.07982, over 6431.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2723, pruned_loss=0.04853, over 1425707.43 frames.], batch size: 37, lr: 2.28e-04
2022-05-28 12:46:13,254 INFO [train.py:842] (1/4) Epoch 24, batch 8000, loss[loss=0.1959, simple_loss=0.2754, pruned_loss=0.05823, over 7297.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2711, pruned_loss=0.04804, over 1418314.44 frames.], batch size: 24, lr: 2.27e-04
2022-05-28 12:46:51,895 INFO [train.py:842] (1/4) Epoch 24, batch 8050, loss[loss=0.2041, simple_loss=0.3002, pruned_loss=0.05403, over 6790.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2698, pruned_loss=0.04738, over 1416732.74 frames.], batch size: 31, lr: 2.27e-04
2022-05-28 12:47:30,412 INFO [train.py:842] (1/4) Epoch 24, batch 8100, loss[loss=0.1872, simple_loss=0.2852, pruned_loss=0.04462, over 7297.00 frames.], tot_loss[loss=0.1826, simple_loss=0.27, pruned_loss=0.04763, over 1416096.54 frames.], batch size: 24, lr: 2.27e-04
2022-05-28 12:48:08,866 INFO [train.py:842] (1/4) Epoch 24, batch 8150, loss[loss=0.1792, simple_loss=0.2607, pruned_loss=0.04879, over 7069.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2691, pruned_loss=0.04726, over 1414458.57 frames.], batch size: 18, lr: 2.27e-04
2022-05-28 12:48:47,292 INFO [train.py:842] (1/4) Epoch 24, batch 8200, loss[loss=0.1962, simple_loss=0.2901, pruned_loss=0.05116, over 7163.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2703, pruned_loss=0.04811, over 1418242.38 frames.], batch size: 19, lr: 2.27e-04
2022-05-28 12:49:25,441 INFO [train.py:842] (1/4) Epoch 24, batch 8250, loss[loss=0.1797, simple_loss=0.2649, pruned_loss=0.04723, over 6539.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2697, pruned_loss=0.04791, over 1419532.01 frames.], batch size: 38, lr: 2.27e-04
2022-05-28 12:50:04,139 INFO [train.py:842] (1/4) Epoch 24, batch 8300, loss[loss=0.1832, simple_loss=0.2796, pruned_loss=0.04339, over 7325.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2689, pruned_loss=0.04727, over 1422663.52 frames.], batch size: 21, lr: 2.27e-04
2022-05-28 12:50:42,587 INFO [train.py:842] (1/4) Epoch 24, batch 8350, loss[loss=0.1571, simple_loss=0.2587, pruned_loss=0.0278, over 7099.00 frames.], tot_loss[loss=0.1818, simple_loss=0.269, pruned_loss=0.04729, over 1421022.45 frames.], batch size: 28, lr: 2.27e-04
2022-05-28 12:51:21,493 INFO [train.py:842] (1/4) Epoch 24, batch 8400, loss[loss=0.1695, simple_loss=0.2561, pruned_loss=0.04145, over 7412.00 frames.], tot_loss[loss=0.181, simple_loss=0.2681, pruned_loss=0.04689, over 1425590.91 frames.], batch size: 18, lr: 2.27e-04
2022-05-28 12:51:59,871 INFO [train.py:842] (1/4) Epoch 24, batch 8450, loss[loss=0.1904, simple_loss=0.2865, pruned_loss=0.0471, over 6576.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2676, pruned_loss=0.04672, over 1425411.73 frames.], batch size: 37, lr: 2.27e-04
2022-05-28 12:52:39,118 INFO [train.py:842] (1/4) Epoch 24, batch 8500, loss[loss=0.1948, simple_loss=0.2915, pruned_loss=0.04906, over 7214.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2686, pruned_loss=0.04753, over 1427513.33 frames.], batch size: 22, lr: 2.27e-04
2022-05-28 12:53:18,141 INFO [train.py:842] (1/4) Epoch 24, batch 8550, loss[loss=0.217, simple_loss=0.2997, pruned_loss=0.06715, over 7151.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2669, pruned_loss=0.04663, over 1427737.99 frames.], batch size: 26, lr: 2.27e-04
2022-05-28 12:53:57,159 INFO [train.py:842] (1/4) Epoch 24, batch 8600, loss[loss=0.1469, simple_loss=0.2388, pruned_loss=0.02755, over 7164.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2669, pruned_loss=0.0465, over 1430427.32 frames.], batch size: 18, lr: 2.27e-04
2022-05-28 12:54:35,700 INFO [train.py:842] (1/4) Epoch 24, batch 8650, loss[loss=0.1587, simple_loss=0.2433, pruned_loss=0.037, over 7230.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2689, pruned_loss=0.04761, over 1424247.03 frames.], batch size: 20, lr: 2.27e-04
2022-05-28 12:55:14,295 INFO [train.py:842] (1/4) Epoch 24, batch 8700, loss[loss=0.2127, simple_loss=0.295, pruned_loss=0.06523, over 7163.00 frames.], tot_loss[loss=0.1817, simple_loss=0.269, pruned_loss=0.04716, over 1418832.27 frames.], batch size: 18, lr: 2.27e-04
2022-05-28 12:55:52,909 INFO [train.py:842] (1/4) Epoch 24, batch 8750, loss[loss=0.2338, simple_loss=0.3091, pruned_loss=0.07919, over 7188.00 frames.], tot_loss[loss=0.18, simple_loss=0.2672, pruned_loss=0.04644, over 1420971.81 frames.], batch size: 23, lr: 2.27e-04
2022-05-28 12:56:31,819 INFO [train.py:842] (1/4) Epoch 24, batch 8800, loss[loss=0.1627, simple_loss=0.2548, pruned_loss=0.03535, over 7213.00 frames.], tot_loss[loss=0.18, simple_loss=0.2674, pruned_loss=0.04629, over 1421195.26 frames.], batch size: 21, lr: 2.27e-04
2022-05-28 12:57:10,116 INFO [train.py:842] (1/4) Epoch 24, batch 8850, loss[loss=0.1711, simple_loss=0.2608, pruned_loss=0.04064, over 6418.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2676, pruned_loss=0.04647, over 1412342.33 frames.], batch size: 37, lr: 2.27e-04
2022-05-28 12:57:48,729 INFO [train.py:842] (1/4) Epoch 24, batch 8900, loss[loss=0.1979, simple_loss=0.2831, pruned_loss=0.05635, over 7197.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2683, pruned_loss=0.04734, over 1403152.09 frames.], batch size: 22, lr: 2.27e-04
2022-05-28 12:58:27,163 INFO [train.py:842] (1/4) Epoch 24, batch 8950, loss[loss=0.2325, simple_loss=0.3121, pruned_loss=0.07641, over 5318.00 frames.], tot_loss[loss=0.181, simple_loss=0.2678, pruned_loss=0.04713, over 1399442.64 frames.], batch size: 52, lr: 2.27e-04
2022-05-28 12:59:05,934 INFO [train.py:842] (1/4) Epoch 24, batch 9000, loss[loss=0.1496, simple_loss=0.2327, pruned_loss=0.0333, over 6761.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2677, pruned_loss=0.04768, over 1392872.59 frames.], batch size: 15, lr: 2.27e-04
2022-05-28 12:59:05,935 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 12:59:15,344 INFO [train.py:871] (1/4) Epoch 24, validation: loss=0.1628, simple_loss=0.2615, pruned_loss=0.03209, over 868885.00 frames.
2022-05-28 12:59:53,659 INFO [train.py:842] (1/4) Epoch 24, batch 9050, loss[loss=0.168, simple_loss=0.2612, pruned_loss=0.03742, over 7360.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2693, pruned_loss=0.04818, over 1380566.64 frames.], batch size: 23, lr: 2.27e-04
2022-05-28 13:00:31,890 INFO [train.py:842] (1/4) Epoch 24, batch 9100, loss[loss=0.2368, simple_loss=0.3138, pruned_loss=0.07991, over 5195.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2723, pruned_loss=0.05026, over 1332722.30 frames.], batch size: 52, lr: 2.27e-04
2022-05-28 13:01:09,339 INFO [train.py:842] (1/4) Epoch 24, batch 9150, loss[loss=0.2175, simple_loss=0.2922, pruned_loss=0.07138, over 5227.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2772, pruned_loss=0.05367, over 1261375.64 frames.], batch size: 53, lr: 2.27e-04
2022-05-28 13:02:00,926 INFO [train.py:842] (1/4) Epoch 25, batch 0, loss[loss=0.2178, simple_loss=0.296, pruned_loss=0.06985, over 7069.00 frames.], tot_loss[loss=0.2178, simple_loss=0.296, pruned_loss=0.06985, over 7069.00 frames.], batch size: 18, lr: 2.22e-04
2022-05-28 13:02:39,747 INFO [train.py:842] (1/4) Epoch 25, batch 50, loss[loss=0.1674, simple_loss=0.2587, pruned_loss=0.03801, over 7253.00 frames.], tot_loss[loss=0.18, simple_loss=0.2674, pruned_loss=0.04631, over 321983.52 frames.], batch size: 19, lr: 2.22e-04
2022-05-28 13:03:18,844 INFO [train.py:842] (1/4) Epoch 25, batch 100, loss[loss=0.1899, simple_loss=0.2767, pruned_loss=0.05152, over 7324.00 frames.], tot_loss[loss=0.1825, simple_loss=0.269, pruned_loss=0.04802, over 569903.32 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:03:57,401 INFO [train.py:842] (1/4) Epoch 25, batch 150, loss[loss=0.1929, simple_loss=0.2892, pruned_loss=0.04829, over 7303.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2696, pruned_loss=0.04787, over 761797.48 frames.], batch size: 21, lr: 2.22e-04
2022-05-28 13:04:36,559 INFO [train.py:842] (1/4) Epoch 25, batch 200, loss[loss=0.1763, simple_loss=0.2513, pruned_loss=0.05061, over 7206.00 frames.], tot_loss[loss=0.1809, simple_loss=0.268, pruned_loss=0.04692, over 907723.65 frames.], batch size: 16, lr: 2.22e-04
2022-05-28 13:05:14,869 INFO [train.py:842] (1/4) Epoch 25, batch 250, loss[loss=0.1795, simple_loss=0.2808, pruned_loss=0.03905, over 7225.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2685, pruned_loss=0.0474, over 1019288.80 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:05:53,574 INFO [train.py:842] (1/4) Epoch 25, batch 300, loss[loss=0.1603, simple_loss=0.2484, pruned_loss=0.03612, over 7155.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2686, pruned_loss=0.04764, over 1112454.50 frames.], batch size: 19, lr: 2.22e-04
2022-05-28 13:06:31,933 INFO [train.py:842] (1/4) Epoch 25, batch 350, loss[loss=0.1692, simple_loss=0.2596, pruned_loss=0.03938, over 7228.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2678, pruned_loss=0.04726, over 1181142.68 frames.], batch size: 23, lr: 2.22e-04
2022-05-28 13:07:10,743 INFO [train.py:842] (1/4) Epoch 25, batch 400, loss[loss=0.1934, simple_loss=0.2853, pruned_loss=0.0508, over 7236.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2674, pruned_loss=0.04675, over 1236117.09 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:07:49,144 INFO [train.py:842] (1/4) Epoch 25, batch 450, loss[loss=0.185, simple_loss=0.2752, pruned_loss=0.04743, over 6981.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2659, pruned_loss=0.04582, over 1276651.95 frames.], batch size: 28, lr: 2.22e-04
2022-05-28 13:08:27,845 INFO [train.py:842] (1/4) Epoch 25, batch 500, loss[loss=0.1376, simple_loss=0.2283, pruned_loss=0.02351, over 7169.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2658, pruned_loss=0.04565, over 1311754.92 frames.], batch size: 18, lr: 2.22e-04
2022-05-28 13:09:06,203 INFO [train.py:842] (1/4) Epoch 25, batch 550, loss[loss=0.1528, simple_loss=0.2348, pruned_loss=0.03536, over 7164.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2651, pruned_loss=0.04535, over 1339211.30 frames.], batch size: 18, lr: 2.22e-04
2022-05-28 13:09:45,032 INFO [train.py:842] (1/4) Epoch 25, batch 600, loss[loss=0.1714, simple_loss=0.253, pruned_loss=0.04488, over 7177.00 frames.], tot_loss[loss=0.179, simple_loss=0.2663, pruned_loss=0.04584, over 1358334.34 frames.], batch size: 23, lr: 2.22e-04
2022-05-28 13:10:23,507 INFO [train.py:842] (1/4) Epoch 25, batch 650, loss[loss=0.1577, simple_loss=0.2326, pruned_loss=0.04144, over 7285.00 frames.], tot_loss[loss=0.178, simple_loss=0.2649, pruned_loss=0.04553, over 1370590.27 frames.], batch size: 17, lr: 2.22e-04
2022-05-28 13:11:02,295 INFO [train.py:842] (1/4) Epoch 25, batch 700, loss[loss=0.1467, simple_loss=0.226, pruned_loss=0.03369, over 6787.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2649, pruned_loss=0.04514, over 1386434.86 frames.], batch size: 15, lr: 2.22e-04
2022-05-28 13:11:40,746 INFO [train.py:842] (1/4) Epoch 25, batch 750, loss[loss=0.1602, simple_loss=0.25, pruned_loss=0.03524, over 7240.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2654, pruned_loss=0.0454, over 1397236.57 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:12:19,435 INFO [train.py:842] (1/4) Epoch 25, batch 800, loss[loss=0.1729, simple_loss=0.2626, pruned_loss=0.04158, over 7414.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2658, pruned_loss=0.04547, over 1405006.06 frames.], batch size: 21, lr: 2.22e-04
2022-05-28 13:12:57,617 INFO [train.py:842] (1/4) Epoch 25, batch 850, loss[loss=0.1689, simple_loss=0.2659, pruned_loss=0.03593, over 7318.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2664, pruned_loss=0.04556, over 1407745.28 frames.], batch size: 21, lr: 2.22e-04
2022-05-28 13:13:36,189 INFO [train.py:842] (1/4) Epoch 25, batch 900, loss[loss=0.1624, simple_loss=0.2465, pruned_loss=0.03917, over 7290.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2679, pruned_loss=0.04635, over 1410386.40 frames.], batch size: 25, lr: 2.22e-04
2022-05-28 13:14:14,461 INFO [train.py:842] (1/4) Epoch 25, batch 950, loss[loss=0.2371, simple_loss=0.3134, pruned_loss=0.08042, over 4911.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2693, pruned_loss=0.04726, over 1404771.63 frames.], batch size: 52, lr: 2.22e-04
2022-05-28 13:14:53,230 INFO [train.py:842] (1/4) Epoch 25, batch 1000, loss[loss=0.1921, simple_loss=0.2795, pruned_loss=0.05236, over 7416.00 frames.], tot_loss[loss=0.182, simple_loss=0.2695, pruned_loss=0.04731, over 1411198.80 frames.], batch size: 21, lr: 2.22e-04
2022-05-28 13:15:31,609 INFO [train.py:842] (1/4) Epoch 25, batch 1050, loss[loss=0.1676, simple_loss=0.2771, pruned_loss=0.02905, over 7328.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2711, pruned_loss=0.04807, over 1418305.46 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:16:10,349 INFO [train.py:842] (1/4) Epoch 25, batch 1100, loss[loss=0.1838, simple_loss=0.2765, pruned_loss=0.04557, over 7336.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2708, pruned_loss=0.04811, over 1420746.62 frames.], batch size: 22, lr: 2.22e-04
2022-05-28 13:16:48,919 INFO [train.py:842] (1/4) Epoch 25, batch 1150, loss[loss=0.21, simple_loss=0.3067, pruned_loss=0.05663, over 7203.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2709, pruned_loss=0.04795, over 1423778.06 frames.], batch size: 23, lr: 2.22e-04
2022-05-28 13:17:27,841 INFO [train.py:842] (1/4) Epoch 25, batch 1200, loss[loss=0.1933, simple_loss=0.2778, pruned_loss=0.05438, over 7367.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2707, pruned_loss=0.04822, over 1423070.17 frames.], batch size: 23, lr: 2.22e-04
2022-05-28 13:18:06,182 INFO [train.py:842] (1/4) Epoch 25, batch 1250, loss[loss=0.172, simple_loss=0.2688, pruned_loss=0.03758, over 7141.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2694, pruned_loss=0.04721, over 1421409.87 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:18:44,990 INFO [train.py:842] (1/4) Epoch 25, batch 1300, loss[loss=0.1739, simple_loss=0.2499, pruned_loss=0.04894, over 6758.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2688, pruned_loss=0.04712, over 1421101.76 frames.], batch size: 15, lr: 2.22e-04
2022-05-28 13:19:23,297 INFO [train.py:842] (1/4) Epoch 25, batch 1350, loss[loss=0.156, simple_loss=0.246, pruned_loss=0.03302, over 6313.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2687, pruned_loss=0.04677, over 1420872.86 frames.], batch size: 38, lr: 2.22e-04
2022-05-28 13:20:01,959 INFO [train.py:842] (1/4) Epoch 25, batch 1400, loss[loss=0.1758, simple_loss=0.2565, pruned_loss=0.04748, over 7289.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2693, pruned_loss=0.04672, over 1425831.68 frames.], batch size: 17, lr: 2.22e-04
2022-05-28 13:20:40,267 INFO [train.py:842] (1/4) Epoch 25, batch 1450, loss[loss=0.1782, simple_loss=0.2729, pruned_loss=0.04176, over 7146.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2686, pruned_loss=0.0463, over 1422394.16 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:21:18,925 INFO [train.py:842] (1/4) Epoch 25, batch 1500, loss[loss=0.1842, simple_loss=0.2677, pruned_loss=0.0503, over 6723.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2682, pruned_loss=0.04628, over 1421948.67 frames.], batch size: 31, lr: 2.22e-04
2022-05-28 13:21:57,243 INFO [train.py:842] (1/4) Epoch 25, batch 1550, loss[loss=0.1686, simple_loss=0.2579, pruned_loss=0.03967, over 7271.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2691, pruned_loss=0.04621, over 1423037.92 frames.], batch size: 18, lr: 2.22e-04
2022-05-28 13:22:36,322 INFO [train.py:842] (1/4) Epoch 25, batch 1600, loss[loss=0.1746, simple_loss=0.2604, pruned_loss=0.04438, over 6814.00 frames.], tot_loss[loss=0.1799, simple_loss=0.268, pruned_loss=0.0459, over 1421372.32 frames.], batch size: 15, lr: 2.22e-04
2022-05-28 13:23:14,834 INFO [train.py:842] (1/4) Epoch 25, batch 1650, loss[loss=0.1969, simple_loss=0.2832, pruned_loss=0.05535, over 7215.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2668, pruned_loss=0.04547, over 1421953.49 frames.], batch size: 21, lr: 2.22e-04
2022-05-28 13:23:53,570 INFO [train.py:842] (1/4) Epoch 25, batch 1700, loss[loss=0.1666, simple_loss=0.2604, pruned_loss=0.0364, over 7391.00 frames.], tot_loss[loss=0.179, simple_loss=0.2671, pruned_loss=0.04543, over 1421094.13 frames.], batch size: 23, lr: 2.22e-04
2022-05-28 13:24:31,713 INFO [train.py:842] (1/4) Epoch 25, batch 1750, loss[loss=0.1396, simple_loss=0.2274, pruned_loss=0.02594, over 7132.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2676, pruned_loss=0.04591, over 1424258.89 frames.], batch size: 17, lr: 2.22e-04
2022-05-28 13:25:10,185 INFO [train.py:842] (1/4) Epoch 25, batch 1800, loss[loss=0.1617, simple_loss=0.2332, pruned_loss=0.04508, over 7430.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2693, pruned_loss=0.04705, over 1425004.82 frames.], batch size: 17, lr: 2.21e-04
2022-05-28 13:25:48,945 INFO [train.py:842] (1/4) Epoch 25, batch 1850, loss[loss=0.1487, simple_loss=0.2338, pruned_loss=0.03181, over 6764.00 frames.], tot_loss[loss=0.181, simple_loss=0.268, pruned_loss=0.04702, over 1420725.12 frames.], batch size: 15, lr: 2.21e-04
2022-05-28 13:26:27,668 INFO [train.py:842] (1/4) Epoch 25, batch 1900, loss[loss=0.1964, simple_loss=0.2846, pruned_loss=0.05413, over 7299.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2678, pruned_loss=0.04654, over 1422635.93 frames.], batch size: 25, lr: 2.21e-04
2022-05-28 13:27:06,011 INFO [train.py:842] (1/4) Epoch 25, batch 1950, loss[loss=0.1829, simple_loss=0.2706, pruned_loss=0.04759, over 7268.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2684, pruned_loss=0.0469, over 1424466.85 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:27:44,812 INFO [train.py:842] (1/4) Epoch 25, batch 2000, loss[loss=0.156, simple_loss=0.2422, pruned_loss=0.03488, over 7161.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2677, pruned_loss=0.04651, over 1424493.43 frames.], batch size: 18, lr: 2.21e-04
2022-05-28 13:28:23,560 INFO [train.py:842] (1/4) Epoch 25, batch 2050, loss[loss=0.1793, simple_loss=0.277, pruned_loss=0.04076, over 7320.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2671, pruned_loss=0.0466, over 1428366.93 frames.], batch size: 21, lr: 2.21e-04
2022-05-28 13:29:02,058 INFO [train.py:842] (1/4) Epoch 25, batch 2100, loss[loss=0.1772, simple_loss=0.2652, pruned_loss=0.04462, over 7247.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2685, pruned_loss=0.0471, over 1424388.42 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:29:40,348 INFO [train.py:842] (1/4) Epoch 25, batch 2150, loss[loss=0.1521, simple_loss=0.2412, pruned_loss=0.03145, over 7431.00 frames.], tot_loss[loss=0.181, simple_loss=0.2685, pruned_loss=0.04678, over 1423399.79 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:30:19,187 INFO [train.py:842] (1/4) Epoch 25, batch 2200, loss[loss=0.1644, simple_loss=0.2514, pruned_loss=0.03874, over 6863.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2674, pruned_loss=0.04659, over 1421887.38 frames.], batch size: 15, lr: 2.21e-04
2022-05-28 13:30:57,857 INFO [train.py:842] (1/4) Epoch 25, batch 2250, loss[loss=0.1601, simple_loss=0.2571, pruned_loss=0.03158, over 7058.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2687, pruned_loss=0.04769, over 1417756.91 frames.], batch size: 18, lr: 2.21e-04
2022-05-28 13:31:36,600 INFO [train.py:842] (1/4) Epoch 25, batch 2300, loss[loss=0.1463, simple_loss=0.2319, pruned_loss=0.03036, over 7228.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2669, pruned_loss=0.04662, over 1418856.15 frames.], batch size: 16, lr: 2.21e-04
2022-05-28 13:32:14,960 INFO [train.py:842] (1/4) Epoch 25, batch 2350, loss[loss=0.2188, simple_loss=0.3079, pruned_loss=0.06483, over 7314.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2674, pruned_loss=0.04702, over 1419093.92 frames.], batch size: 21, lr: 2.21e-04
2022-05-28 13:32:53,607 INFO [train.py:842] (1/4) Epoch 25, batch 2400, loss[loss=0.1801, simple_loss=0.2631, pruned_loss=0.04859, over 7360.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2681, pruned_loss=0.04707, over 1424350.90 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:33:32,095 INFO [train.py:842] (1/4) Epoch 25, batch 2450, loss[loss=0.1519, simple_loss=0.2421, pruned_loss=0.0308, over 7129.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2682, pruned_loss=0.04719, over 1423567.85 frames.], batch size: 17, lr: 2.21e-04
2022-05-28 13:34:10,892 INFO [train.py:842] (1/4) Epoch 25, batch 2500, loss[loss=0.2337, simple_loss=0.315, pruned_loss=0.07619, over 7413.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2675, pruned_loss=0.04641, over 1423821.51 frames.], batch size: 21, lr: 2.21e-04
2022-05-28 13:34:49,184 INFO [train.py:842] (1/4) Epoch 25, batch 2550, loss[loss=0.1496, simple_loss=0.2415, pruned_loss=0.02891, over 7422.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2674, pruned_loss=0.04619, over 1424250.84 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:35:27,912 INFO [train.py:842] (1/4) Epoch 25, batch 2600, loss[loss=0.1324, simple_loss=0.2166, pruned_loss=0.02414, over 7136.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2676, pruned_loss=0.04652, over 1421407.66 frames.], batch size: 17, lr: 2.21e-04
2022-05-28 13:36:06,382 INFO [train.py:842] (1/4) Epoch 25, batch 2650, loss[loss=0.1955, simple_loss=0.2869, pruned_loss=0.05207, over 7208.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2678, pruned_loss=0.04584, over 1423571.94 frames.], batch size: 22, lr: 2.21e-04
2022-05-28 13:36:45,205 INFO [train.py:842] (1/4) Epoch 25, batch 2700, loss[loss=0.1668, simple_loss=0.2547, pruned_loss=0.03943, over 7469.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2672, pruned_loss=0.04554, over 1425600.34 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:37:23,659 INFO [train.py:842] (1/4) Epoch 25, batch 2750, loss[loss=0.1604, simple_loss=0.2598, pruned_loss=0.03053, over 7151.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2661, pruned_loss=0.04542, over 1421718.53 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:38:02,439 INFO [train.py:842] (1/4) Epoch 25, batch 2800, loss[loss=0.1532, simple_loss=0.2474, pruned_loss=0.02949, over 7256.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2661, pruned_loss=0.04551, over 1422334.21 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:38:40,765 INFO [train.py:842] (1/4) Epoch 25, batch 2850, loss[loss=0.1725, simple_loss=0.2601, pruned_loss=0.04247, over 7441.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2654, pruned_loss=0.0449, over 1421117.03 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:39:19,308 INFO [train.py:842] (1/4) Epoch 25, batch 2900, loss[loss=0.1725, simple_loss=0.2597, pruned_loss=0.0427, over 7190.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2662, pruned_loss=0.045, over 1421332.68 frames.], batch size: 23, lr: 2.21e-04
2022-05-28 13:39:57,707 INFO [train.py:842] (1/4) Epoch 25, batch 2950, loss[loss=0.197, simple_loss=0.2857, pruned_loss=0.05413, over 7449.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2661, pruned_loss=0.04486, over 1426494.49 frames.], batch size: 22, lr: 2.21e-04
2022-05-28 13:40:36,713 INFO [train.py:842] (1/4) Epoch 25, batch 3000, loss[loss=0.1852, simple_loss=0.2832, pruned_loss=0.04357, over 6784.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2653, pruned_loss=0.04489, over 1428603.49 frames.], batch size: 31, lr: 2.21e-04
2022-05-28 13:40:36,714 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 13:40:46,045 INFO [train.py:871] (1/4) Epoch 25, validation: loss=0.1651, simple_loss=0.2634, pruned_loss=0.03341, over 868885.00 frames.
2022-05-28 13:41:24,603 INFO [train.py:842] (1/4) Epoch 25, batch 3050, loss[loss=0.1662, simple_loss=0.2579, pruned_loss=0.03728, over 7118.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2661, pruned_loss=0.04566, over 1429206.02 frames.], batch size: 21, lr: 2.21e-04
2022-05-28 13:42:03,495 INFO [train.py:842] (1/4) Epoch 25, batch 3100, loss[loss=0.1785, simple_loss=0.2675, pruned_loss=0.04482, over 6789.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2655, pruned_loss=0.04551, over 1429449.42 frames.], batch size: 15, lr: 2.21e-04
2022-05-28 13:42:41,846 INFO [train.py:842] (1/4) Epoch 25, batch 3150, loss[loss=0.1693, simple_loss=0.2532, pruned_loss=0.04276, over 7268.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2658, pruned_loss=0.04562, over 1430457.24 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:43:20,554 INFO [train.py:842] (1/4) Epoch 25, batch 3200, loss[loss=0.2106, simple_loss=0.2996, pruned_loss=0.06079, over 5118.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2655, pruned_loss=0.04564, over 1429280.99 frames.], batch size: 52, lr: 2.21e-04
2022-05-28 13:43:59,043 INFO [train.py:842] (1/4) Epoch 25, batch 3250, loss[loss=0.2591, simple_loss=0.3449, pruned_loss=0.08664, over 7231.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2663, pruned_loss=0.04601, over 1426610.94 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:44:37,657 INFO [train.py:842] (1/4) Epoch 25, batch 3300, loss[loss=0.1604, simple_loss=0.2456, pruned_loss=0.03764, over 7142.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2673, pruned_loss=0.04653, over 1425617.67 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:45:16,119 INFO [train.py:842] (1/4) Epoch 25, batch 3350, loss[loss=0.1776, simple_loss=0.2621, pruned_loss=0.04657, over 7263.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2671, pruned_loss=0.04625, over 1421793.48 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:45:57,679 INFO [train.py:842] (1/4) Epoch 25, batch 3400, loss[loss=0.1289, simple_loss=0.2158, pruned_loss=0.02101, over 7267.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2673, pruned_loss=0.04643, over 1423251.12 frames.], batch size: 17, lr: 2.21e-04
2022-05-28 13:46:36,037 INFO [train.py:842] (1/4) Epoch 25, batch 3450, loss[loss=0.2007, simple_loss=0.2926, pruned_loss=0.05445, over 7207.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2677, pruned_loss=0.04646, over 1420558.25 frames.], batch size: 21, lr: 2.21e-04
2022-05-28 13:47:14,714 INFO [train.py:842] (1/4) Epoch 25, batch 3500, loss[loss=0.1657, simple_loss=0.2511, pruned_loss=0.04019, over 7131.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2678, pruned_loss=0.04635, over 1421773.73 frames.], batch size: 17, lr: 2.21e-04
2022-05-28 13:47:52,986 INFO [train.py:842] (1/4) Epoch 25, batch 3550, loss[loss=0.1563, simple_loss=0.2481, pruned_loss=0.03224, over 7319.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2684, pruned_loss=0.04656, over 1423515.36 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:48:31,600 INFO [train.py:842] (1/4) Epoch 25, batch 3600, loss[loss=0.2181, simple_loss=0.3023, pruned_loss=0.0669, over 7205.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2684, pruned_loss=0.04667, over 1421961.79 frames.], batch size: 23, lr: 2.21e-04
2022-05-28 13:49:09,896 INFO [train.py:842] (1/4) Epoch 25, batch 3650, loss[loss=0.1981, simple_loss=0.2871, pruned_loss=0.05457, over 6371.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2686, pruned_loss=0.04676, over 1417646.20 frames.], batch size: 38, lr: 2.21e-04
2022-05-28 13:49:48,745 INFO [train.py:842] (1/4) Epoch 25, batch 3700, loss[loss=0.1772, simple_loss=0.2627, pruned_loss=0.04587, over 7417.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2676, pruned_loss=0.04675, over 1420931.78 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:50:27,339 INFO [train.py:842] (1/4) Epoch 25, batch 3750, loss[loss=0.1911, simple_loss=0.2795, pruned_loss=0.0513, over 7358.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2673, pruned_loss=0.04613, over 1423725.18 frames.], batch size: 23, lr: 2.21e-04
2022-05-28 13:51:06,207 INFO [train.py:842] (1/4) Epoch 25, batch 3800, loss[loss=0.2125, simple_loss=0.2976, pruned_loss=0.06372, over 5220.00 frames.], tot_loss[loss=0.181, simple_loss=0.2679, pruned_loss=0.04706, over 1422524.35 frames.], batch size: 52, lr: 2.21e-04
2022-05-28 13:51:44,469 INFO [train.py:842] (1/4) Epoch 25, batch 3850, loss[loss=0.1637, simple_loss=0.2471, pruned_loss=0.04016, over 7270.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2679, pruned_loss=0.0465, over 1422135.69 frames.], batch size: 18, lr: 2.20e-04
2022-05-28 13:52:23,078 INFO [train.py:842] (1/4) Epoch 25, batch 3900, loss[loss=0.1727, simple_loss=0.2571, pruned_loss=0.04412, over 7259.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2681, pruned_loss=0.04622, over 1421757.79 frames.], batch size: 19, lr: 2.20e-04
2022-05-28 13:53:01,448 INFO [train.py:842] (1/4) Epoch 25, batch 3950, loss[loss=0.1602, simple_loss=0.2435, pruned_loss=0.0385, over 7391.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2669, pruned_loss=0.04529, over 1423502.17 frames.], batch size: 18, lr: 2.20e-04
2022-05-28 13:53:40,183 INFO [train.py:842] (1/4) Epoch 25, batch 4000, loss[loss=0.1507, simple_loss=0.2414, pruned_loss=0.02997, over 7412.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2662, pruned_loss=0.04513, over 1425777.28 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 13:54:18,689 INFO [train.py:842] (1/4) Epoch 25, batch 4050, loss[loss=0.1446, simple_loss=0.2295, pruned_loss=0.02987, over 7120.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2652, pruned_loss=0.04474, over 1423485.70 frames.], batch size: 17, lr: 2.20e-04
2022-05-28 13:54:57,374 INFO [train.py:842] (1/4) Epoch 25, batch 4100, loss[loss=0.1741, simple_loss=0.2731, pruned_loss=0.0375, over 7318.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2657, pruned_loss=0.04469, over 1426716.40 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 13:55:35,871 INFO [train.py:842] (1/4) Epoch 25, batch 4150, loss[loss=0.163, simple_loss=0.2562, pruned_loss=0.03491, over 7402.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2661, pruned_loss=0.0456, over 1423093.05 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 13:56:14,588 INFO [train.py:842] (1/4) Epoch 25, batch 4200, loss[loss=0.1703, simple_loss=0.2636, pruned_loss=0.03856, over 7236.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2671, pruned_loss=0.04605, over 1420256.57 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 13:56:52,842 INFO [train.py:842] (1/4) Epoch 25, batch 4250, loss[loss=0.203, simple_loss=0.2975, pruned_loss=0.0542, over 7377.00 frames.], tot_loss[loss=0.1815, simple_loss=0.269, pruned_loss=0.04703, over 1422966.70 frames.], batch size: 23, lr: 2.20e-04
2022-05-28 13:57:31,643 INFO [train.py:842] (1/4) Epoch 25, batch 4300, loss[loss=0.2483, simple_loss=0.3237, pruned_loss=0.08644, over 6975.00 frames.], tot_loss[loss=0.182, simple_loss=0.2691, pruned_loss=0.04747, over 1425220.71 frames.], batch size: 28, lr: 2.20e-04
2022-05-28 13:58:10,172 INFO [train.py:842] (1/4) Epoch 25, batch 4350, loss[loss=0.1636, simple_loss=0.2504, pruned_loss=0.03845, over 7421.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2691, pruned_loss=0.04782, over 1424923.87 frames.], batch size: 18, lr: 2.20e-04
2022-05-28 13:58:48,807 INFO [train.py:842] (1/4) Epoch 25, batch 4400, loss[loss=0.2345, simple_loss=0.3245, pruned_loss=0.07229, over 7234.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2686, pruned_loss=0.04727, over 1423746.64 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 13:59:27,180 INFO [train.py:842] (1/4) Epoch 25, batch 4450, loss[loss=0.2394, simple_loss=0.3189, pruned_loss=0.07997, over 7282.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2675, pruned_loss=0.04637, over 1422627.57 frames.], batch size: 24, lr: 2.20e-04
2022-05-28 14:00:05,961 INFO [train.py:842] (1/4) Epoch 25, batch 4500, loss[loss=0.1797, simple_loss=0.2812, pruned_loss=0.03909, over 7147.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2672, pruned_loss=0.0458, over 1424104.90 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 14:00:44,418 INFO [train.py:842] (1/4) Epoch 25, batch 4550, loss[loss=0.1639, simple_loss=0.2554, pruned_loss=0.03619, over 6387.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2668, pruned_loss=0.04541, over 1424432.87 frames.], batch size: 38, lr: 2.20e-04
2022-05-28 14:01:23,182 INFO [train.py:842] (1/4) Epoch 25, batch 4600, loss[loss=0.19, simple_loss=0.2792, pruned_loss=0.05038, over 6774.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2659, pruned_loss=0.0451, over 1423301.79 frames.], batch size: 31, lr: 2.20e-04
2022-05-28 14:02:01,602 INFO [train.py:842] (1/4) Epoch 25, batch 4650, loss[loss=0.2005, simple_loss=0.2894, pruned_loss=0.05582, over 7163.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2657, pruned_loss=0.045, over 1423274.84 frames.], batch size: 26, lr: 2.20e-04
2022-05-28 14:02:40,413 INFO [train.py:842] (1/4) Epoch 25, batch 4700, loss[loss=0.1725, simple_loss=0.2532, pruned_loss=0.04594, over 6998.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2663, pruned_loss=0.04512, over 1418982.22 frames.], batch size: 16, lr: 2.20e-04
2022-05-28 14:03:18,774 INFO [train.py:842] (1/4) Epoch 25, batch 4750, loss[loss=0.2125, simple_loss=0.3063, pruned_loss=0.05933, over 7275.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2659, pruned_loss=0.04523, over 1419720.07 frames.], batch size: 24, lr: 2.20e-04
2022-05-28 14:03:57,593 INFO [train.py:842] (1/4) Epoch 25, batch 4800, loss[loss=0.1733, simple_loss=0.2583, pruned_loss=0.04411, over 7209.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2658, pruned_loss=0.04544, over 1423556.13 frames.], batch size: 22, lr: 2.20e-04
2022-05-28 14:04:36,177 INFO [train.py:842] (1/4) Epoch 25, batch 4850, loss[loss=0.1663, simple_loss=0.2689, pruned_loss=0.03181, over 7323.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2666, pruned_loss=0.04563, over 1428964.85 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 14:05:14,804 INFO [train.py:842] (1/4) Epoch 25, batch 4900, loss[loss=0.1822, simple_loss=0.2739, pruned_loss=0.04527, over 7371.00 frames.], tot_loss[loss=0.179, simple_loss=0.2664, pruned_loss=0.04585, over 1421272.28 frames.], batch size: 23, lr: 2.20e-04
2022-05-28 14:05:53,357 INFO [train.py:842] (1/4) Epoch 25, batch 4950, loss[loss=0.2205, simple_loss=0.3038, pruned_loss=0.06861, over 5238.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2662, pruned_loss=0.0456, over 1419520.99 frames.], batch size: 52, lr: 2.20e-04
2022-05-28 14:06:31,864 INFO [train.py:842] (1/4) Epoch 25, batch 5000, loss[loss=0.1771, simple_loss=0.2651, pruned_loss=0.04456, over 7441.00 frames.], tot_loss[loss=0.179, simple_loss=0.2667, pruned_loss=0.0457, over 1416631.20 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 14:07:10,242 INFO [train.py:842] (1/4) Epoch 25, batch 5050, loss[loss=0.1705, simple_loss=0.2734, pruned_loss=0.03381, over 7318.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2668, pruned_loss=0.04545, over 1423297.96 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 14:07:49,143 INFO [train.py:842] (1/4) Epoch 25, batch 5100, loss[loss=0.1924, simple_loss=0.2859, pruned_loss=0.04942, over 7153.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2659, pruned_loss=0.04531, over 1425269.81 frames.], batch size: 19, lr: 2.20e-04
2022-05-28 14:08:27,763 INFO [train.py:842] (1/4) Epoch 25, batch 5150, loss[loss=0.2001, simple_loss=0.294, pruned_loss=0.0531, over 7112.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2663, pruned_loss=0.04575, over 1424988.27 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 14:09:06,422 INFO [train.py:842] (1/4) Epoch 25, batch 5200, loss[loss=0.1919, simple_loss=0.2839, pruned_loss=0.04996, over 7125.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2681, pruned_loss=0.04649, over 1425790.48 frames.], batch size: 26, lr: 2.20e-04
2022-05-28 14:09:44,932 INFO [train.py:842] (1/4) Epoch 25, batch 5250, loss[loss=0.2173, simple_loss=0.303, pruned_loss=0.06578, over 7354.00 frames.], tot_loss[loss=0.18, simple_loss=0.2671, pruned_loss=0.04647, over 1423667.99 frames.], batch size: 19, lr: 2.20e-04
2022-05-28 14:10:23,858 INFO [train.py:842] (1/4) Epoch 25, batch 5300, loss[loss=0.1874, simple_loss=0.279, pruned_loss=0.04794, over 7323.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2674, pruned_loss=0.04637, over 1420308.73 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 14:11:02,331 INFO [train.py:842] (1/4) Epoch 25, batch 5350, loss[loss=0.161, simple_loss=0.2566, pruned_loss=0.03274, over 7346.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2678, pruned_loss=0.0466, over 1414916.00 frames.], batch size: 19, lr: 2.20e-04
2022-05-28 14:11:41,154 INFO [train.py:842] (1/4) Epoch 25, batch 5400, loss[loss=0.1521, simple_loss=0.2411, pruned_loss=0.03158, over 7059.00 frames.], tot_loss[loss=0.1784, simple_loss=0.266, pruned_loss=0.0454, over 1421206.55 frames.], batch size: 18, lr: 2.20e-04
2022-05-28 14:12:19,669 INFO [train.py:842] (1/4) Epoch 25, batch 5450, loss[loss=0.1518, simple_loss=0.239, pruned_loss=0.03231, over 7430.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2659, pruned_loss=0.04542, over 1419475.41 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 14:12:58,310 INFO [train.py:842] (1/4) Epoch 25, batch 5500, loss[loss=0.1646, simple_loss=0.2495, pruned_loss=0.0399, over 6513.00 frames.], tot_loss[loss=0.1782, simple_loss=0.266, pruned_loss=0.04518, over 1420273.08 frames.], batch size: 38, lr: 2.20e-04
2022-05-28 14:13:36,706 INFO [train.py:842] (1/4) Epoch 25, batch 5550, loss[loss=0.1768, simple_loss=0.2758, pruned_loss=0.0389, over 7419.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2651, pruned_loss=0.04465, over 1424271.71 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 14:14:15,453 INFO [train.py:842] (1/4) Epoch 25, batch 5600, loss[loss=0.2126, simple_loss=0.2982, pruned_loss=0.06352, over 7219.00 frames.], tot_loss[loss=0.177, simple_loss=0.2648, pruned_loss=0.04457, over 1427895.24 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 14:14:53,929 INFO [train.py:842] (1/4) Epoch 25, batch 5650, loss[loss=0.2011, simple_loss=0.2905, pruned_loss=0.05581, over 7035.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2655, pruned_loss=0.04495, over 1431232.20 frames.], batch size: 28, lr: 2.20e-04
2022-05-28 14:15:32,483 INFO [train.py:842] (1/4) Epoch 25, batch 5700, loss[loss=0.1755, simple_loss=0.2723, pruned_loss=0.03932, over 7328.00 frames.], tot_loss[loss=0.177, simple_loss=0.265, pruned_loss=0.04454, over 1427069.12 frames.], batch size: 22, lr: 2.20e-04
2022-05-28 14:16:11,072 INFO [train.py:842] (1/4) Epoch 25, batch 5750, loss[loss=0.1428, simple_loss=0.2292, pruned_loss=0.02816, over 7126.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2647, pruned_loss=0.04435, over 1428360.98 frames.], batch size: 17, lr: 2.20e-04
2022-05-28 14:16:49,550 INFO [train.py:842] (1/4) Epoch 25, batch 5800, loss[loss=0.158, simple_loss=0.2502, pruned_loss=0.03289, over 7143.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2652, pruned_loss=0.04464, over 1430201.96 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 14:17:27,568 INFO [train.py:842] (1/4) Epoch 25, batch 5850, loss[loss=0.1745, simple_loss=0.2688, pruned_loss=0.04016, over 6434.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2659, pruned_loss=0.04477, over 1424005.03 frames.], batch size: 38, lr: 2.20e-04
2022-05-28 14:18:06,224 INFO [train.py:842] (1/4) Epoch 25, batch 5900, loss[loss=0.1426, simple_loss=0.2399, pruned_loss=0.02261, over 7333.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2653, pruned_loss=0.04445, over 1424036.05 frames.], batch size: 22, lr: 2.19e-04
2022-05-28 14:18:44,330 INFO [train.py:842] (1/4) Epoch 25, batch 5950, loss[loss=0.1626, simple_loss=0.2507, pruned_loss=0.03727, over 7436.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2678, pruned_loss=0.04591, over 1422050.77 frames.], batch size: 20, lr: 2.19e-04
2022-05-28 14:19:23,249 INFO [train.py:842] (1/4) Epoch 25, batch 6000, loss[loss=0.1785, simple_loss=0.2695, pruned_loss=0.04374, over 7338.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2666, pruned_loss=0.04552, over 1422921.19 frames.], batch size: 22, lr: 2.19e-04
2022-05-28 14:19:23,250 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 14:19:32,870 INFO [train.py:871] (1/4) Epoch 25, validation: loss=0.1658, simple_loss=0.2641, pruned_loss=0.03372, over 868885.00 frames.
2022-05-28 14:20:11,403 INFO [train.py:842] (1/4) Epoch 25, batch 6050, loss[loss=0.1976, simple_loss=0.2799, pruned_loss=0.05768, over 7197.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2663, pruned_loss=0.04544, over 1424266.62 frames.], batch size: 23, lr: 2.19e-04
2022-05-28 14:20:50,174 INFO [train.py:842] (1/4) Epoch 25, batch 6100, loss[loss=0.1611, simple_loss=0.2447, pruned_loss=0.0388, over 7007.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2662, pruned_loss=0.04541, over 1426374.03 frames.], batch size: 16, lr: 2.19e-04
2022-05-28 14:21:28,815 INFO [train.py:842] (1/4) Epoch 25, batch 6150, loss[loss=0.1793, simple_loss=0.2762, pruned_loss=0.04115, over 7110.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2665, pruned_loss=0.04585, over 1424692.09 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:22:07,508 INFO [train.py:842] (1/4) Epoch 25, batch 6200, loss[loss=0.183, simple_loss=0.2725, pruned_loss=0.04676, over 7331.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2665, pruned_loss=0.04654, over 1421583.23 frames.], batch size: 20, lr: 2.19e-04
2022-05-28 14:22:45,916 INFO [train.py:842] (1/4) Epoch 25, batch 6250, loss[loss=0.1745, simple_loss=0.2766, pruned_loss=0.03621, over 7222.00 frames.], tot_loss[loss=0.1801, simple_loss=0.267, pruned_loss=0.04655, over 1418062.49 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:23:34,550 INFO [train.py:842] (1/4) Epoch 25, batch 6300, loss[loss=0.1592, simple_loss=0.2538, pruned_loss=0.03237, over 7344.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2686, pruned_loss=0.04716, over 1418873.68 frames.], batch size: 22, lr: 2.19e-04
2022-05-28 14:24:12,966 INFO [train.py:842] (1/4) Epoch 25, batch 6350, loss[loss=0.1773, simple_loss=0.2682, pruned_loss=0.04317, over 7408.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2684, pruned_loss=0.04766, over 1418604.90 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:24:51,925 INFO [train.py:842] (1/4) Epoch 25, batch 6400, loss[loss=0.159, simple_loss=0.2499, pruned_loss=0.03404, over 7255.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2682, pruned_loss=0.04714, over 1420610.65 frames.], batch size: 19, lr: 2.19e-04
2022-05-28 14:25:30,521 INFO [train.py:842] (1/4) Epoch 25, batch 6450, loss[loss=0.1761, simple_loss=0.2521, pruned_loss=0.05006, over 7427.00 frames.], tot_loss[loss=0.181, simple_loss=0.2681, pruned_loss=0.04695, over 1424662.98 frames.], batch size: 17, lr: 2.19e-04
2022-05-28 14:26:09,239 INFO [train.py:842] (1/4) Epoch 25, batch 6500, loss[loss=0.1838, simple_loss=0.2753, pruned_loss=0.04621, over 6228.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2694, pruned_loss=0.04767, over 1423239.89 frames.], batch size: 37, lr: 2.19e-04
2022-05-28 14:26:47,615 INFO [train.py:842] (1/4) Epoch 25, batch 6550, loss[loss=0.1456, simple_loss=0.2454, pruned_loss=0.0229, over 7161.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2683, pruned_loss=0.04723, over 1420978.24 frames.], batch size: 19, lr: 2.19e-04
2022-05-28 14:27:26,215 INFO [train.py:842] (1/4) Epoch 25, batch 6600, loss[loss=0.2194, simple_loss=0.2986, pruned_loss=0.07007, over 7225.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2682, pruned_loss=0.04686, over 1422813.12 frames.], batch size: 20, lr: 2.19e-04
2022-05-28 14:28:04,731 INFO [train.py:842] (1/4) Epoch 25, batch 6650, loss[loss=0.2096, simple_loss=0.3018, pruned_loss=0.05866, over 6801.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2684, pruned_loss=0.04671, over 1424305.98 frames.], batch size: 31, lr: 2.19e-04
2022-05-28 14:28:43,453 INFO [train.py:842] (1/4) Epoch 25, batch 6700, loss[loss=0.1739, simple_loss=0.2557, pruned_loss=0.04604, over 7164.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2686, pruned_loss=0.04656, over 1425798.94 frames.], batch size: 19, lr: 2.19e-04
2022-05-28 14:29:22,082 INFO [train.py:842] (1/4) Epoch 25, batch 6750, loss[loss=0.1721, simple_loss=0.2643, pruned_loss=0.03995, over 7155.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2683, pruned_loss=0.04661, over 1426662.81 frames.], batch size: 20, lr: 2.19e-04
2022-05-28 14:30:00,926 INFO [train.py:842] (1/4) Epoch 25, batch 6800, loss[loss=0.2452, simple_loss=0.3086, pruned_loss=0.09088, over 5140.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2673, pruned_loss=0.04617, over 1424147.75 frames.], batch size: 53, lr: 2.19e-04
2022-05-28 14:30:39,301 INFO [train.py:842] (1/4) Epoch 25, batch 6850, loss[loss=0.1662, simple_loss=0.2449, pruned_loss=0.04373, over 7440.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2688, pruned_loss=0.04687, over 1427386.32 frames.], batch size: 20, lr: 2.19e-04
2022-05-28 14:31:18,200 INFO [train.py:842] (1/4) Epoch 25, batch 6900, loss[loss=0.1752, simple_loss=0.2548, pruned_loss=0.04781, over 7132.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2673, pruned_loss=0.046, over 1430179.16 frames.], batch size: 17, lr: 2.19e-04
2022-05-28 14:31:56,685 INFO [train.py:842] (1/4) Epoch 25, batch 6950, loss[loss=0.1716, simple_loss=0.2711, pruned_loss=0.03609, over 7315.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2676, pruned_loss=0.04648, over 1431288.87 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:32:35,591 INFO [train.py:842] (1/4) Epoch 25, batch 7000, loss[loss=0.1444, simple_loss=0.2245, pruned_loss=0.03218, over 7275.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2668, pruned_loss=0.04582, over 1434114.89 frames.], batch size: 18, lr: 2.19e-04
2022-05-28 14:33:13,749 INFO [train.py:842] (1/4) Epoch 25, batch 7050, loss[loss=0.1498, simple_loss=0.2508, pruned_loss=0.02439, over 7241.00 frames.], tot_loss[loss=0.18, simple_loss=0.2676, pruned_loss=0.04625, over 1428821.80 frames.], batch size: 19, lr: 2.19e-04
2022-05-28 14:33:52,527 INFO [train.py:842] (1/4) Epoch 25, batch 7100, loss[loss=0.1727, simple_loss=0.2757, pruned_loss=0.03491, over 7330.00 frames.], tot_loss[loss=0.1797, simple_loss=0.267, pruned_loss=0.04619, over 1429223.66 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:34:31,052 INFO [train.py:842] (1/4) Epoch 25, batch 7150, loss[loss=0.1472, simple_loss=0.2321, pruned_loss=0.03118, over 7289.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2658, pruned_loss=0.0455, over 1427822.46 frames.], batch size: 17, lr: 2.19e-04
2022-05-28 14:35:09,892 INFO [train.py:842] (1/4) Epoch 25, batch 7200, loss[loss=0.1995, simple_loss=0.2938, pruned_loss=0.05257, over 7319.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2667, pruned_loss=0.04609, over 1428439.45 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:35:48,496 INFO [train.py:842] (1/4) Epoch 25, batch 7250, loss[loss=0.174, simple_loss=0.2694, pruned_loss=0.0393, over 7162.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2646, pruned_loss=0.04496, over 1428757.43 frames.], batch size: 26, lr: 2.19e-04
2022-05-28 14:36:26,927 INFO [train.py:842] (1/4) Epoch 25, batch 7300, loss[loss=0.1836, simple_loss=0.2732, pruned_loss=0.047, over 7321.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2646, pruned_loss=0.04426, over 1424947.96 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:37:05,549 INFO [train.py:842] (1/4) Epoch 25, batch 7350, loss[loss=0.1827, simple_loss=0.2696, pruned_loss=0.04792, over 7204.00 frames.], tot_loss[loss=0.177, simple_loss=0.2647, pruned_loss=0.04464, over 1427304.29 frames.], batch size: 22, lr: 2.19e-04
2022-05-28 14:37:44,471 INFO [train.py:842] (1/4) Epoch 25, batch 7400, loss[loss=0.1998, simple_loss=0.2674, pruned_loss=0.06609, over 7456.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2654, pruned_loss=0.04558, over 1426143.88 frames.], batch size: 19, lr: 2.19e-04
2022-05-28 14:38:22,853 INFO [train.py:842] (1/4) Epoch 25, batch 7450, loss[loss=0.1731, simple_loss=0.2666, pruned_loss=0.03978, over 7294.00 frames.], tot_loss[loss=0.1789, simple_loss=0.266, pruned_loss=0.04586, over 1427022.19 frames.], batch size: 24, lr: 2.19e-04
2022-05-28 14:39:01,272 INFO [train.py:842] (1/4) Epoch 25, batch 7500, loss[loss=0.1697, simple_loss=0.2623, pruned_loss=0.0386, over 7058.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2674, pruned_loss=0.04678, over 1425575.80 frames.], batch size: 18, lr: 2.19e-04
2022-05-28 14:39:39,761 INFO [train.py:842] (1/4) Epoch 25, batch 7550, loss[loss=0.2607, simple_loss=0.3342, pruned_loss=0.09356, over 7297.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2673, pruned_loss=0.04617, over 1429484.55 frames.], batch size: 25, lr: 2.19e-04
2022-05-28 14:40:18,794 INFO [train.py:842] (1/4) Epoch 25, batch 7600, loss[loss=0.1905, simple_loss=0.2779, pruned_loss=0.05155, over 7389.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2662, pruned_loss=0.04609, over 1434025.02 frames.], batch size: 23, lr: 2.19e-04
2022-05-28 14:40:57,021 INFO [train.py:842] (1/4) Epoch 25, batch 7650, loss[loss=0.1916, simple_loss=0.2835, pruned_loss=0.04986, over 7108.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2673, pruned_loss=0.04582, over 1433625.08 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:41:35,621 INFO [train.py:842] (1/4) Epoch 25, batch 7700, loss[loss=0.157, simple_loss=0.2535, pruned_loss=0.0303, over 7060.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2667, pruned_loss=0.04545, over 1432226.97 frames.], batch size: 18, lr: 2.19e-04
2022-05-28 14:42:13,974 INFO [train.py:842] (1/4) Epoch 25, batch 7750, loss[loss=0.1857, simple_loss=0.2778, pruned_loss=0.04676, over 4868.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2671, pruned_loss=0.04593, over 1431130.77 frames.], batch size: 52, lr: 2.19e-04
2022-05-28 14:42:52,794 INFO [train.py:842] (1/4) Epoch 25, batch 7800, loss[loss=0.1813, simple_loss=0.2659, pruned_loss=0.04832, over 6687.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2669, pruned_loss=0.04622, over 1426935.02 frames.], batch size: 31, lr: 2.19e-04
2022-05-28 14:43:31,082 INFO [train.py:842] (1/4) Epoch 25, batch 7850, loss[loss=0.1613, simple_loss=0.2561, pruned_loss=0.03326, over 7315.00 frames.], tot_loss[loss=0.179, simple_loss=0.2662, pruned_loss=0.04585, over 1426129.93 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:44:10,152 INFO [train.py:842] (1/4) Epoch 25, batch 7900, loss[loss=0.1368, simple_loss=0.2278, pruned_loss=0.02286, over 7281.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2664, pruned_loss=0.04613, over 1426441.42 frames.], batch size: 18, lr: 2.19e-04
2022-05-28 14:44:48,425 INFO [train.py:842] (1/4) Epoch 25, batch 7950, loss[loss=0.1981, simple_loss=0.2723, pruned_loss=0.06194, over 7010.00 frames.], tot_loss[loss=0.1798, simple_loss=0.267, pruned_loss=0.04634, over 1425401.60 frames.], batch size: 16, lr: 2.18e-04
2022-05-28 14:45:27,118 INFO [train.py:842] (1/4) Epoch 25, batch 8000, loss[loss=0.2497, simple_loss=0.3299, pruned_loss=0.08478, over 7318.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2683, pruned_loss=0.04734, over 1421019.14 frames.], batch size: 20, lr: 2.18e-04
2022-05-28 14:46:05,221 INFO [train.py:842] (1/4) Epoch 25, batch 8050, loss[loss=0.1764, simple_loss=0.2629, pruned_loss=0.0449, over 7276.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2687, pruned_loss=0.04721, over 1418054.48 frames.], batch size: 18, lr: 2.18e-04
2022-05-28 14:46:43,913 INFO [train.py:842] (1/4) Epoch 25, batch 8100, loss[loss=0.1564, simple_loss=0.2401, pruned_loss=0.03635, over 7203.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2686, pruned_loss=0.04718, over 1421075.80 frames.], batch size: 22, lr: 2.18e-04
2022-05-28 14:47:22,093 INFO [train.py:842] (1/4) Epoch 25, batch 8150, loss[loss=0.1647, simple_loss=0.2548, pruned_loss=0.03724, over 7234.00 frames.], tot_loss[loss=0.1822, simple_loss=0.269, pruned_loss=0.04771, over 1416027.66 frames.], batch size: 20, lr: 2.18e-04
2022-05-28 14:48:00,760 INFO [train.py:842] (1/4) Epoch 25, batch 8200, loss[loss=0.1742, simple_loss=0.2567, pruned_loss=0.04585, over 7270.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2694, pruned_loss=0.04805, over 1412970.70 frames.], batch size: 17, lr: 2.18e-04
2022-05-28 14:48:39,308 INFO [train.py:842] (1/4) Epoch 25, batch 8250, loss[loss=0.1635, simple_loss=0.2524, pruned_loss=0.0373, over 7162.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2679, pruned_loss=0.04693, over 1417780.57 frames.], batch size: 19, lr: 2.18e-04
2022-05-28 14:49:18,040 INFO [train.py:842] (1/4) Epoch 25, batch 8300, loss[loss=0.1931, simple_loss=0.275, pruned_loss=0.05562, over 7070.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2682, pruned_loss=0.04673, over 1420297.01 frames.], batch size: 18, lr: 2.18e-04
2022-05-28 14:49:56,277 INFO [train.py:842] (1/4) Epoch 25, batch 8350, loss[loss=0.149, simple_loss=0.2286, pruned_loss=0.03471, over 7289.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2678, pruned_loss=0.04628, over 1416139.42 frames.], batch size: 17, lr: 2.18e-04
2022-05-28 14:50:35,119 INFO [train.py:842] (1/4) Epoch 25, batch 8400, loss[loss=0.1593, simple_loss=0.2641, pruned_loss=0.02724, over 7231.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2672, pruned_loss=0.04618, over 1415658.52 frames.], batch size: 21, lr: 2.18e-04
2022-05-28 14:51:13,341 INFO [train.py:842] (1/4) Epoch 25, batch 8450, loss[loss=0.1856, simple_loss=0.2854, pruned_loss=0.04292, over 7143.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2684, pruned_loss=0.04667, over 1416994.18 frames.], batch size: 26, lr: 2.18e-04
2022-05-28 14:51:51,811 INFO [train.py:842] (1/4) Epoch 25, batch 8500, loss[loss=0.1971, simple_loss=0.2741, pruned_loss=0.06002, over 7077.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2696, pruned_loss=0.04737, over 1415658.09 frames.], batch size: 18, lr: 2.18e-04
2022-05-28 14:52:29,828 INFO [train.py:842] (1/4) Epoch 25, batch 8550, loss[loss=0.1823, simple_loss=0.2578, pruned_loss=0.05344, over 7404.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2694, pruned_loss=0.04698, over 1413117.40 frames.], batch size: 18, lr: 2.18e-04
2022-05-28 14:53:08,343 INFO [train.py:842] (1/4) Epoch 25, batch 8600, loss[loss=0.1769, simple_loss=0.2759, pruned_loss=0.03895, over 7120.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2687, pruned_loss=0.04629, over 1414708.42 frames.], batch size: 21, lr: 2.18e-04
2022-05-28 14:53:46,543 INFO [train.py:842] (1/4) Epoch 25, batch 8650, loss[loss=0.2126, simple_loss=0.2919, pruned_loss=0.06662, over 7273.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2688, pruned_loss=0.04612, over 1418706.47 frames.], batch size: 24, lr: 2.18e-04
2022-05-28 14:54:25,092 INFO [train.py:842] (1/4) Epoch 25, batch 8700, loss[loss=0.1702, simple_loss=0.2606, pruned_loss=0.03993, over 7279.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2675, pruned_loss=0.04513, over 1420584.75 frames.], batch size: 18, lr: 2.18e-04
2022-05-28 14:55:03,400 INFO [train.py:842] (1/4) Epoch 25, batch 8750, loss[loss=0.196, simple_loss=0.286, pruned_loss=0.05303, over 7169.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2674, pruned_loss=0.04504, over 1423757.19 frames.], batch size: 23, lr: 2.18e-04
2022-05-28 14:55:42,059 INFO [train.py:842] (1/4) Epoch 25, batch 8800, loss[loss=0.1825, simple_loss=0.2728, pruned_loss=0.04611, over 7074.00 frames.], tot_loss[loss=0.1798, simple_loss=0.268, pruned_loss=0.04577, over 1422212.08 frames.], batch size: 18, lr: 2.18e-04
2022-05-28 14:56:20,239 INFO [train.py:842] (1/4) Epoch 25, batch 8850, loss[loss=0.1504, simple_loss=0.2501, pruned_loss=0.02534, over 7213.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2683, pruned_loss=0.04579, over 1423441.62 frames.], batch size: 21, lr: 2.18e-04
2022-05-28 14:56:58,668 INFO [train.py:842] (1/4) Epoch 25, batch 8900, loss[loss=0.2133, simple_loss=0.2948, pruned_loss=0.06585, over 7060.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2689, pruned_loss=0.04619, over 1409651.46 frames.], batch size: 28, lr: 2.18e-04
2022-05-28 14:57:36,572 INFO [train.py:842] (1/4) Epoch 25, batch 8950, loss[loss=0.1921, simple_loss=0.2712, pruned_loss=0.05651, over 5286.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2699, pruned_loss=0.04622, over 1402510.90 frames.], batch size: 52, lr: 2.18e-04
2022-05-28 14:58:14,367 INFO [train.py:842] (1/4) Epoch 25, batch 9000, loss[loss=0.1918, simple_loss=0.2839, pruned_loss=0.04982, over 6393.00 frames.], tot_loss[loss=0.1822, simple_loss=0.271, pruned_loss=0.04668, over 1387066.88 frames.], batch size: 38, lr: 2.18e-04
2022-05-28 14:58:14,368 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 14:58:23,547 INFO [train.py:871] (1/4) Epoch 25, validation: loss=0.1658, simple_loss=0.265, pruned_loss=0.03327, over 868885.00 frames.
2022-05-28 14:59:00,508 INFO [train.py:842] (1/4) Epoch 25, batch 9050, loss[loss=0.1574, simple_loss=0.2586, pruned_loss=0.02805, over 6339.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2736, pruned_loss=0.04815, over 1349198.72 frames.], batch size: 38, lr: 2.18e-04
2022-05-28 14:59:37,683 INFO [train.py:842] (1/4) Epoch 25, batch 9100, loss[loss=0.1868, simple_loss=0.2629, pruned_loss=0.05537, over 5069.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2761, pruned_loss=0.04981, over 1301003.56 frames.], batch size: 53, lr: 2.18e-04
2022-05-28 15:00:14,852 INFO [train.py:842] (1/4) Epoch 25, batch 9150, loss[loss=0.2145, simple_loss=0.2851, pruned_loss=0.07192, over 4804.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2784, pruned_loss=0.05245, over 1244564.92 frames.], batch size: 53, lr: 2.18e-04
2022-05-28 15:01:00,949 INFO [train.py:842] (1/4) Epoch 26, batch 0, loss[loss=0.2052, simple_loss=0.2919, pruned_loss=0.05927, over 7216.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2919, pruned_loss=0.05927, over 7216.00 frames.], batch size: 21, lr: 2.14e-04
2022-05-28 15:01:39,903 INFO [train.py:842] (1/4) Epoch 26, batch 50, loss[loss=0.1788, simple_loss=0.2746, pruned_loss=0.04146, over 7323.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2647, pruned_loss=0.04456, over 322935.45 frames.], batch size: 21, lr: 2.14e-04
2022-05-28 15:02:18,128 INFO [train.py:842] (1/4) Epoch 26, batch 100, loss[loss=0.2751, simple_loss=0.3406, pruned_loss=0.1048, over 5160.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2706, pruned_loss=0.04708, over 566522.89 frames.], batch size: 52, lr: 2.14e-04
2022-05-28 15:02:56,776 INFO [train.py:842] (1/4) Epoch 26, batch 150, loss[loss=0.1626, simple_loss=0.2511, pruned_loss=0.037, over 7273.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2696, pruned_loss=0.04644, over 760363.09 frames.], batch size: 17, lr: 2.14e-04
2022-05-28 15:03:35,196 INFO [train.py:842] (1/4) Epoch 26, batch 200, loss[loss=0.1854, simple_loss=0.2764, pruned_loss=0.04725, over 7387.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2683, pruned_loss=0.04565, over 908188.32 frames.], batch size: 23, lr: 2.14e-04
2022-05-28 15:04:13,803 INFO [train.py:842] (1/4) Epoch 26, batch 250, loss[loss=0.1832, simple_loss=0.2783, pruned_loss=0.04409, over 7200.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2689, pruned_loss=0.04607, over 1020892.79 frames.], batch size: 22, lr: 2.14e-04
2022-05-28 15:04:51,894 INFO [train.py:842] (1/4) Epoch 26, batch 300, loss[loss=0.1617, simple_loss=0.2546, pruned_loss=0.03436, over 7329.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2696, pruned_loss=0.0466, over 1106641.06 frames.], batch size: 20, lr: 2.14e-04
2022-05-28 15:05:30,451 INFO [train.py:842] (1/4) Epoch 26, batch 350, loss[loss=0.1547, simple_loss=0.2401, pruned_loss=0.03471, over 7170.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2694, pruned_loss=0.04721, over 1175932.86 frames.], batch size: 18, lr: 2.14e-04
2022-05-28 15:06:08,845 INFO [train.py:842] (1/4) Epoch 26, batch 400, loss[loss=0.1512, simple_loss=0.2305, pruned_loss=0.03594, over 7412.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2683, pruned_loss=0.04653, over 1233220.48 frames.], batch size: 18, lr: 2.14e-04
2022-05-28 15:06:47,642 INFO [train.py:842] (1/4) Epoch 26, batch 450, loss[loss=0.1639, simple_loss=0.2638, pruned_loss=0.032, over 7412.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2675, pruned_loss=0.04595, over 1274848.76 frames.], batch size: 21, lr: 2.14e-04
2022-05-28 15:07:25,851 INFO [train.py:842] (1/4) Epoch 26, batch 500, loss[loss=0.196, simple_loss=0.2852, pruned_loss=0.05346, over 7363.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2676, pruned_loss=0.04596, over 1302793.90 frames.], batch size: 23, lr: 2.14e-04
2022-05-28 15:08:04,616 INFO [train.py:842] (1/4) Epoch 26, batch 550, loss[loss=0.1928, simple_loss=0.2809, pruned_loss=0.05238, over 7233.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2664, pruned_loss=0.0457, over 1328923.81 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:08:43,124 INFO [train.py:842] (1/4) Epoch 26, batch 600, loss[loss=0.1755, simple_loss=0.2744, pruned_loss=0.03834, over 7054.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2664, pruned_loss=0.04598, over 1346971.41 frames.], batch size: 28, lr: 2.13e-04
2022-05-28 15:09:22,095 INFO [train.py:842] (1/4) Epoch 26, batch 650, loss[loss=0.1576, simple_loss=0.2588, pruned_loss=0.02824, over 7336.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2649, pruned_loss=0.04504, over 1360916.41 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:10:10,858 INFO [train.py:842] (1/4) Epoch 26, batch 700, loss[loss=0.1762, simple_loss=0.2631, pruned_loss=0.04463, over 7144.00 frames.], tot_loss[loss=0.176, simple_loss=0.2637, pruned_loss=0.04415, over 1374088.34 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:10:49,689 INFO [train.py:842] (1/4) Epoch 26, batch 750, loss[loss=0.1452, simple_loss=0.2424, pruned_loss=0.02401, over 7439.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2634, pruned_loss=0.0436, over 1389788.36 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:11:27,878 INFO [train.py:842] (1/4) Epoch 26, batch 800, loss[loss=0.1879, simple_loss=0.2728, pruned_loss=0.05147, over 6759.00 frames.], tot_loss[loss=0.177, simple_loss=0.265, pruned_loss=0.04449, over 1395101.65 frames.], batch size: 31, lr: 2.13e-04
2022-05-28 15:12:06,491 INFO [train.py:842] (1/4) Epoch 26, batch 850, loss[loss=0.206, simple_loss=0.2829, pruned_loss=0.06457, over 7110.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2658, pruned_loss=0.04481, over 1405847.51 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:12:54,992 INFO [train.py:842] (1/4) Epoch 26, batch 900, loss[loss=0.1695, simple_loss=0.2525, pruned_loss=0.04325, over 7214.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2668, pruned_loss=0.04543, over 1405948.29 frames.], batch size: 16, lr: 2.13e-04
2022-05-28 15:13:43,632 INFO [train.py:842] (1/4) Epoch 26, batch 950, loss[loss=0.1511, simple_loss=0.2321, pruned_loss=0.03502, over 7276.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2649, pruned_loss=0.04431, over 1412185.62 frames.], batch size: 17, lr: 2.13e-04
2022-05-28 15:14:21,997 INFO [train.py:842] (1/4) Epoch 26, batch 1000, loss[loss=0.1606, simple_loss=0.2507, pruned_loss=0.03526, over 7112.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2658, pruned_loss=0.04504, over 1411933.97 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:15:00,539 INFO [train.py:842] (1/4) Epoch 26, batch 1050, loss[loss=0.1845, simple_loss=0.265, pruned_loss=0.05203, over 5378.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2666, pruned_loss=0.04521, over 1413067.33 frames.], batch size: 52, lr: 2.13e-04
2022-05-28 15:15:38,915 INFO [train.py:842] (1/4) Epoch 26, batch 1100, loss[loss=0.1682, simple_loss=0.2613, pruned_loss=0.03754, over 7110.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2666, pruned_loss=0.04539, over 1414306.09 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:16:17,612 INFO [train.py:842] (1/4) Epoch 26, batch 1150, loss[loss=0.1754, simple_loss=0.2618, pruned_loss=0.04451, over 7353.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2666, pruned_loss=0.04524, over 1417551.95 frames.], batch size: 23, lr: 2.13e-04
2022-05-28 15:16:56,069 INFO [train.py:842] (1/4) Epoch 26, batch 1200, loss[loss=0.163, simple_loss=0.2359, pruned_loss=0.04508, over 7115.00 frames.], tot_loss[loss=0.178, simple_loss=0.2661, pruned_loss=0.04492, over 1421117.86 frames.], batch size: 17, lr: 2.13e-04
2022-05-28 15:17:34,814 INFO [train.py:842] (1/4) Epoch 26, batch 1250, loss[loss=0.2114, simple_loss=0.2963, pruned_loss=0.06329, over 7319.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2666, pruned_loss=0.04547, over 1423847.09 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:18:13,314 INFO [train.py:842] (1/4) Epoch 26, batch 1300, loss[loss=0.1888, simple_loss=0.2772, pruned_loss=0.05027, over 7424.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2665, pruned_loss=0.04523, over 1427083.59 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:18:51,820 INFO [train.py:842] (1/4) Epoch 26, batch 1350, loss[loss=0.1834, simple_loss=0.2774, pruned_loss=0.04473, over 7319.00 frames.], tot_loss[loss=0.1789, simple_loss=0.267, pruned_loss=0.04543, over 1427060.18 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:19:30,191 INFO [train.py:842] (1/4) Epoch 26, batch 1400, loss[loss=0.1769, simple_loss=0.2624, pruned_loss=0.04566, over 7321.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2672, pruned_loss=0.0457, over 1427099.19 frames.], batch size: 22, lr: 2.13e-04
2022-05-28 15:20:08,708 INFO [train.py:842] (1/4) Epoch 26, batch 1450, loss[loss=0.1308, simple_loss=0.214, pruned_loss=0.02379, over 6993.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2669, pruned_loss=0.0457, over 1428590.63 frames.], batch size: 16, lr: 2.13e-04
2022-05-28 15:20:47,211 INFO [train.py:842] (1/4) Epoch 26, batch 1500, loss[loss=0.172, simple_loss=0.2659, pruned_loss=0.03909, over 7230.00 frames.], tot_loss[loss=0.178, simple_loss=0.2658, pruned_loss=0.04517, over 1427492.58 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:21:25,992 INFO [train.py:842] (1/4) Epoch 26, batch 1550, loss[loss=0.1388, simple_loss=0.2216, pruned_loss=0.02797, over 7127.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2663, pruned_loss=0.04546, over 1426291.86 frames.], batch size: 17, lr: 2.13e-04
2022-05-28 15:22:03,981 INFO [train.py:842] (1/4) Epoch 26, batch 1600, loss[loss=0.1807, simple_loss=0.2712, pruned_loss=0.04515, over 7142.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2677, pruned_loss=0.04571, over 1424054.03 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:22:43,201 INFO [train.py:842] (1/4) Epoch 26, batch 1650, loss[loss=0.1697, simple_loss=0.2599, pruned_loss=0.03968, over 7163.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2658, pruned_loss=0.04489, over 1425374.22 frames.], batch size: 28, lr: 2.13e-04
2022-05-28 15:23:21,401 INFO [train.py:842] (1/4) Epoch 26, batch 1700, loss[loss=0.2067, simple_loss=0.3092, pruned_loss=0.05206, over 7316.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2669, pruned_loss=0.04498, over 1425212.73 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:24:00,056 INFO [train.py:842] (1/4) Epoch 26, batch 1750, loss[loss=0.1524, simple_loss=0.2385, pruned_loss=0.0332, over 7148.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2659, pruned_loss=0.04441, over 1425344.79 frames.], batch size: 17, lr: 2.13e-04
2022-05-28 15:24:38,389 INFO [train.py:842] (1/4) Epoch 26, batch 1800, loss[loss=0.1538, simple_loss=0.2485, pruned_loss=0.0295, over 7135.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2648, pruned_loss=0.04396, over 1421926.08 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:25:17,106 INFO [train.py:842] (1/4) Epoch 26, batch 1850, loss[loss=0.1535, simple_loss=0.2457, pruned_loss=0.03067, over 7430.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2654, pruned_loss=0.04409, over 1422920.01 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:25:55,282 INFO [train.py:842] (1/4) Epoch 26, batch 1900, loss[loss=0.1562, simple_loss=0.2402, pruned_loss=0.03607, over 7142.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2655, pruned_loss=0.04412, over 1423450.51 frames.], batch size: 17, lr: 2.13e-04
2022-05-28 15:26:33,991 INFO [train.py:842] (1/4) Epoch 26, batch 1950, loss[loss=0.2395, simple_loss=0.3044, pruned_loss=0.08733, over 5052.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2665, pruned_loss=0.04482, over 1421533.65 frames.], batch size: 52, lr: 2.13e-04
2022-05-28 15:27:12,151 INFO [train.py:842] (1/4) Epoch 26, batch 2000, loss[loss=0.1686, simple_loss=0.2424, pruned_loss=0.04742, over 7171.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2655, pruned_loss=0.04449, over 1417820.56 frames.], batch size: 19, lr: 2.13e-04
2022-05-28 15:27:51,028 INFO [train.py:842] (1/4) Epoch 26, batch 2050, loss[loss=0.1911, simple_loss=0.2834, pruned_loss=0.04942, over 7328.00 frames.], tot_loss[loss=0.177, simple_loss=0.2648, pruned_loss=0.0446, over 1418550.98 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:28:29,360 INFO [train.py:842] (1/4) Epoch 26, batch 2100, loss[loss=0.1871, simple_loss=0.2732, pruned_loss=0.05047, over 7198.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2662, pruned_loss=0.04518, over 1418260.93 frames.], batch size: 22, lr: 2.13e-04
2022-05-28 15:29:07,769 INFO [train.py:842] (1/4) Epoch 26, batch 2150, loss[loss=0.1607, simple_loss=0.2518, pruned_loss=0.03481, over 7155.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2664, pruned_loss=0.04501, over 1420008.02 frames.], batch size: 18, lr: 2.13e-04
2022-05-28 15:29:46,152 INFO [train.py:842] (1/4) Epoch 26, batch 2200, loss[loss=0.2054, simple_loss=0.2996, pruned_loss=0.05563, over 7101.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2658, pruned_loss=0.04465, over 1422184.61 frames.], batch size: 28, lr: 2.13e-04
2022-05-28 15:30:27,788 INFO [train.py:842] (1/4) Epoch 26, batch 2250, loss[loss=0.1651, simple_loss=0.2563, pruned_loss=0.03697, over 7393.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2656, pruned_loss=0.04475, over 1423635.81 frames.], batch size: 23, lr: 2.13e-04
2022-05-28 15:31:06,155 INFO [train.py:842] (1/4) Epoch 26, batch 2300, loss[loss=0.1419, simple_loss=0.2273, pruned_loss=0.02824, over 7460.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2661, pruned_loss=0.04469, over 1424789.64 frames.], batch size: 19, lr: 2.13e-04
2022-05-28 15:31:45,073 INFO [train.py:842] (1/4) Epoch 26, batch 2350, loss[loss=0.1661, simple_loss=0.2619, pruned_loss=0.03511, over 7266.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2666, pruned_loss=0.0453, over 1425350.51 frames.], batch size: 19, lr: 2.13e-04
2022-05-28 15:32:23,724 INFO [train.py:842] (1/4) Epoch 26, batch 2400, loss[loss=0.2124, simple_loss=0.2946, pruned_loss=0.06511, over 7372.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2669, pruned_loss=0.04585, over 1424558.46 frames.], batch size: 23, lr: 2.13e-04
2022-05-28 15:33:02,378 INFO [train.py:842] (1/4) Epoch 26, batch 2450, loss[loss=0.2388, simple_loss=0.3179, pruned_loss=0.07983, over 6688.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2666, pruned_loss=0.04557, over 1422560.42 frames.], batch size: 31, lr: 2.13e-04
2022-05-28 15:33:40,956 INFO [train.py:842] (1/4) Epoch 26, batch 2500, loss[loss=0.1453, simple_loss=0.2284, pruned_loss=0.03111, over 7361.00 frames.], tot_loss[loss=0.178, simple_loss=0.2659, pruned_loss=0.04502, over 1423354.57 frames.], batch size: 19, lr: 2.13e-04
2022-05-28 15:34:19,820 INFO [train.py:842] (1/4) Epoch 26, batch 2550, loss[loss=0.1629, simple_loss=0.2444, pruned_loss=0.04065, over 7421.00 frames.], tot_loss[loss=0.179, simple_loss=0.2671, pruned_loss=0.04544, over 1426515.42 frames.], batch size: 18, lr: 2.13e-04
2022-05-28 15:34:58,272 INFO [train.py:842] (1/4) Epoch 26, batch 2600, loss[loss=0.1529, simple_loss=0.2419, pruned_loss=0.03199, over 7152.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2676, pruned_loss=0.04593, over 1423818.79 frames.], batch size: 19, lr: 2.13e-04
2022-05-28 15:35:36,578 INFO [train.py:842] (1/4) Epoch 26, batch 2650, loss[loss=0.2616, simple_loss=0.3291, pruned_loss=0.0971, over 7090.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2677, pruned_loss=0.04562, over 1419801.96 frames.], batch size: 28, lr: 2.13e-04
2022-05-28 15:36:14,919 INFO [train.py:842] (1/4) Epoch 26, batch 2700, loss[loss=0.1964, simple_loss=0.2776, pruned_loss=0.05758, over 7272.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2679, pruned_loss=0.0457, over 1420992.72 frames.], batch size: 19, lr: 2.13e-04
2022-05-28 15:36:53,431 INFO [train.py:842] (1/4) Epoch 26, batch 2750, loss[loss=0.1748, simple_loss=0.2768, pruned_loss=0.0364, over 7298.00 frames.], tot_loss[loss=0.1797, simple_loss=0.268, pruned_loss=0.04569, over 1414405.71 frames.], batch size: 25, lr: 2.12e-04
2022-05-28 15:37:32,271 INFO [train.py:842] (1/4) Epoch 26, batch 2800, loss[loss=0.1468, simple_loss=0.2373, pruned_loss=0.02813, over 7287.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2669, pruned_loss=0.04511, over 1416661.31 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 15:38:11,413 INFO [train.py:842] (1/4) Epoch 26, batch 2850, loss[loss=0.1768, simple_loss=0.2772, pruned_loss=0.0382, over 7408.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2667, pruned_loss=0.04545, over 1411728.78 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 15:38:50,434 INFO [train.py:842] (1/4) Epoch 26, batch 2900, loss[loss=0.1841, simple_loss=0.2777, pruned_loss=0.04525, over 7143.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2678, pruned_loss=0.04596, over 1417479.80 frames.], batch size: 20, lr: 2.12e-04
2022-05-28 15:39:29,978 INFO [train.py:842] (1/4) Epoch 26, batch 2950, loss[loss=0.1695, simple_loss=0.2615, pruned_loss=0.03873, over 7332.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2685, pruned_loss=0.04628, over 1418286.14 frames.], batch size: 20, lr: 2.12e-04
2022-05-28 15:40:09,198 INFO [train.py:842] (1/4) Epoch 26, batch 3000, loss[loss=0.1749, simple_loss=0.2557, pruned_loss=0.04705, over 6362.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2682, pruned_loss=0.04609, over 1422280.86 frames.], batch size: 37, lr: 2.12e-04
2022-05-28 15:40:09,199 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 15:40:19,618 INFO [train.py:871] (1/4) Epoch 26, validation: loss=0.166, simple_loss=0.2639, pruned_loss=0.0341, over 868885.00 frames.
2022-05-28 15:40:59,109 INFO [train.py:842] (1/4) Epoch 26, batch 3050, loss[loss=0.1678, simple_loss=0.2621, pruned_loss=0.03672, over 7349.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2688, pruned_loss=0.04626, over 1421610.21 frames.], batch size: 22, lr: 2.12e-04
2022-05-28 15:41:38,586 INFO [train.py:842] (1/4) Epoch 26, batch 3100, loss[loss=0.148, simple_loss=0.2411, pruned_loss=0.0275, over 7254.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2679, pruned_loss=0.04566, over 1419057.76 frames.], batch size: 19, lr: 2.12e-04
2022-05-28 15:42:18,096 INFO [train.py:842] (1/4) Epoch 26, batch 3150, loss[loss=0.1488, simple_loss=0.2325, pruned_loss=0.03257, over 7136.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2677, pruned_loss=0.04566, over 1418570.32 frames.], batch size: 17, lr: 2.12e-04
2022-05-28 15:42:57,598 INFO [train.py:842] (1/4) Epoch 26, batch 3200, loss[loss=0.1667, simple_loss=0.2493, pruned_loss=0.04204, over 7166.00 frames.], tot_loss[loss=0.1777, simple_loss=0.266, pruned_loss=0.04467, over 1420707.41 frames.], batch size: 19, lr: 2.12e-04
2022-05-28 15:43:37,417 INFO [train.py:842] (1/4) Epoch 26, batch 3250, loss[loss=0.1767, simple_loss=0.2607, pruned_loss=0.04636, over 7289.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2649, pruned_loss=0.0447, over 1423469.59 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 15:44:16,466 INFO [train.py:842] (1/4) Epoch 26, batch 3300, loss[loss=0.2153, simple_loss=0.301, pruned_loss=0.06478, over 7144.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2667, pruned_loss=0.04571, over 1416635.36 frames.], batch size: 26, lr: 2.12e-04
2022-05-28 15:44:55,918 INFO [train.py:842] (1/4) Epoch 26, batch 3350, loss[loss=0.1736, simple_loss=0.2647, pruned_loss=0.04127, over 7318.00 frames.], tot_loss[loss=0.1782, simple_loss=0.266, pruned_loss=0.04526, over 1414370.01 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 15:45:35,665 INFO [train.py:842] (1/4) Epoch 26, batch 3400, loss[loss=0.1669, simple_loss=0.2547, pruned_loss=0.03952, over 6255.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2661, pruned_loss=0.04588, over 1419400.55 frames.], batch size: 37, lr: 2.12e-04
2022-05-28 15:46:15,357 INFO [train.py:842] (1/4) Epoch 26, batch 3450, loss[loss=0.1458, simple_loss=0.2334, pruned_loss=0.02909, over 7167.00 frames.], tot_loss[loss=0.1797, simple_loss=0.267, pruned_loss=0.04624, over 1418988.15 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 15:46:54,504 INFO [train.py:842] (1/4) Epoch 26, batch 3500, loss[loss=0.1814, simple_loss=0.2808, pruned_loss=0.041, over 7372.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2686, pruned_loss=0.04652, over 1418320.24 frames.], batch size: 23, lr: 2.12e-04
2022-05-28 15:47:33,963 INFO [train.py:842] (1/4) Epoch 26, batch 3550, loss[loss=0.207, simple_loss=0.3021, pruned_loss=0.05592, over 7417.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2671, pruned_loss=0.04555, over 1420668.18 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 15:48:13,488 INFO [train.py:842] (1/4) Epoch 26, batch 3600, loss[loss=0.1723, simple_loss=0.2562, pruned_loss=0.04417, over 7189.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2655, pruned_loss=0.04479, over 1425643.22 frames.], batch size: 23, lr: 2.12e-04
2022-05-28 15:48:53,116 INFO [train.py:842] (1/4) Epoch 26, batch 3650, loss[loss=0.1639, simple_loss=0.2524, pruned_loss=0.03771, over 7260.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2652, pruned_loss=0.04472, over 1426857.70 frames.], batch size: 19, lr: 2.12e-04
2022-05-28 15:49:32,414 INFO [train.py:842] (1/4) Epoch 26, batch 3700, loss[loss=0.1844, simple_loss=0.2646, pruned_loss=0.05207, over 7450.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2649, pruned_loss=0.045, over 1424321.42 frames.], batch size: 19, lr: 2.12e-04
2022-05-28 15:50:12,025 INFO [train.py:842] (1/4) Epoch 26, batch 3750, loss[loss=0.1727, simple_loss=0.2648, pruned_loss=0.04031, over 7160.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2658, pruned_loss=0.04501, over 1422275.93 frames.], batch size: 19, lr: 2.12e-04
2022-05-28 15:50:51,085 INFO [train.py:842] (1/4) Epoch 26, batch 3800, loss[loss=0.168, simple_loss=0.2542, pruned_loss=0.04085, over 6192.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2655, pruned_loss=0.04478, over 1419891.76 frames.], batch size: 37, lr: 2.12e-04
2022-05-28 15:51:30,418 INFO [train.py:842] (1/4) Epoch 26, batch 3850, loss[loss=0.1656, simple_loss=0.2577, pruned_loss=0.03676, over 7136.00 frames.], tot_loss[loss=0.177, simple_loss=0.2651, pruned_loss=0.04447, over 1418124.34 frames.], batch size: 20, lr: 2.12e-04
2022-05-28 15:52:09,596 INFO [train.py:842] (1/4) Epoch 26, batch 3900, loss[loss=0.2193, simple_loss=0.3011, pruned_loss=0.06877, over 7237.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2665, pruned_loss=0.04501, over 1419592.89 frames.], batch size: 20, lr: 2.12e-04
2022-05-28 15:52:49,322 INFO [train.py:842] (1/4) Epoch 26, batch 3950, loss[loss=0.1816, simple_loss=0.2751, pruned_loss=0.04407, over 6906.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2662, pruned_loss=0.04532, over 1425243.73 frames.], batch size: 31, lr: 2.12e-04
2022-05-28 15:53:28,758 INFO [train.py:842] (1/4) Epoch 26, batch 4000, loss[loss=0.1764, simple_loss=0.2609, pruned_loss=0.0459, over 7102.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2658, pruned_loss=0.04562, over 1417572.09 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 15:54:08,330 INFO [train.py:842] (1/4) Epoch 26, batch 4050, loss[loss=0.1528, simple_loss=0.2517, pruned_loss=0.02693, over 6395.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2653, pruned_loss=0.04484, over 1419880.39 frames.], batch size: 38, lr: 2.12e-04
2022-05-28 15:54:48,035 INFO [train.py:842] (1/4) Epoch 26, batch 4100, loss[loss=0.1718, simple_loss=0.2533, pruned_loss=0.04513, over 7288.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2653, pruned_loss=0.04502, over 1418215.34 frames.], batch size: 17, lr: 2.12e-04
2022-05-28 15:55:28,375 INFO [train.py:842] (1/4) Epoch 26, batch 4150, loss[loss=0.1982, simple_loss=0.2938, pruned_loss=0.05134, over 7112.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2655, pruned_loss=0.04497, over 1422156.43 frames.], batch size: 28, lr: 2.12e-04
2022-05-28 15:56:07,756 INFO [train.py:842] (1/4) Epoch 26, batch 4200, loss[loss=0.1368, simple_loss=0.2157, pruned_loss=0.02901, over 7275.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2655, pruned_loss=0.04461, over 1421881.19 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 15:56:47,354 INFO [train.py:842] (1/4) Epoch 26, batch 4250, loss[loss=0.2115, simple_loss=0.2933, pruned_loss=0.0649, over 7209.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2647, pruned_loss=0.04498, over 1424240.69 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 15:57:26,574 INFO [train.py:842] (1/4) Epoch 26, batch 4300, loss[loss=0.1984, simple_loss=0.2773, pruned_loss=0.05977, over 7432.00 frames.], tot_loss[loss=0.1787, simple_loss=0.266, pruned_loss=0.04565, over 1422913.59 frames.], batch size: 20, lr: 2.12e-04
2022-05-28 15:58:06,183 INFO [train.py:842] (1/4) Epoch 26, batch 4350, loss[loss=0.1917, simple_loss=0.2924, pruned_loss=0.04552, over 7382.00 frames.], tot_loss[loss=0.178, simple_loss=0.2654, pruned_loss=0.04534, over 1423984.72 frames.], batch size: 23, lr: 2.12e-04
2022-05-28 15:58:45,519 INFO [train.py:842] (1/4) Epoch 26, batch 4400, loss[loss=0.2062, simple_loss=0.2939, pruned_loss=0.05929, over 7219.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2652, pruned_loss=0.04477, over 1424442.10 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 15:59:24,717 INFO [train.py:842] (1/4) Epoch 26, batch 4450, loss[loss=0.1839, simple_loss=0.2641, pruned_loss=0.05185, over 7290.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2652, pruned_loss=0.04491, over 1417944.86 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 16:00:03,657 INFO [train.py:842] (1/4) Epoch 26, batch 4500, loss[loss=0.1757, simple_loss=0.2711, pruned_loss=0.04016, over 6485.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2662, pruned_loss=0.04521, over 1417499.96 frames.], batch size: 37, lr: 2.12e-04
2022-05-28 16:00:44,552 INFO [train.py:842] (1/4) Epoch 26, batch 4550, loss[loss=0.1905, simple_loss=0.2733, pruned_loss=0.05384, over 7128.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2668, pruned_loss=0.0458, over 1415535.33 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 16:01:23,777 INFO [train.py:842] (1/4) Epoch 26, batch 4600, loss[loss=0.1485, simple_loss=0.2369, pruned_loss=0.03006, over 7068.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2655, pruned_loss=0.04538, over 1419036.66 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 16:02:03,267 INFO [train.py:842] (1/4) Epoch 26, batch 4650, loss[loss=0.2679, simple_loss=0.3409, pruned_loss=0.09743, over 6196.00 frames.], tot_loss[loss=0.1809, simple_loss=0.268, pruned_loss=0.04695, over 1413177.82 frames.], batch size: 37, lr: 2.12e-04
2022-05-28 16:02:42,712 INFO [train.py:842] (1/4) Epoch 26, batch 4700, loss[loss=0.1894, simple_loss=0.2868, pruned_loss=0.046, over 7190.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2678, pruned_loss=0.04678, over 1417144.43 frames.], batch size: 22, lr: 2.12e-04
2022-05-28 16:03:22,503 INFO [train.py:842] (1/4) Epoch 26, batch 4750, loss[loss=0.1878, simple_loss=0.2757, pruned_loss=0.04991, over 7361.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2665, pruned_loss=0.0463, over 1417931.17 frames.], batch size: 23, lr: 2.12e-04
2022-05-28 16:04:03,201 INFO [train.py:842] (1/4) Epoch 26, batch 4800, loss[loss=0.1806, simple_loss=0.2676, pruned_loss=0.04681, over 7329.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2673, pruned_loss=0.04624, over 1421670.08 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 16:04:43,139 INFO [train.py:842] (1/4) Epoch 26, batch 4850, loss[loss=0.214, simple_loss=0.3004, pruned_loss=0.06383, over 7331.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2665, pruned_loss=0.04606, over 1424166.27 frames.], batch size: 22, lr: 2.12e-04
2022-05-28 16:05:22,312 INFO [train.py:842] (1/4) Epoch 26, batch 4900, loss[loss=0.1798, simple_loss=0.2673, pruned_loss=0.04612, over 7158.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2679, pruned_loss=0.04679, over 1420962.71 frames.], batch size: 19, lr: 2.12e-04
2022-05-28 16:06:02,509 INFO [train.py:842] (1/4) Epoch 26, batch 4950, loss[loss=0.1932, simple_loss=0.2632, pruned_loss=0.06154, over 7062.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2667, pruned_loss=0.04619, over 1418236.68 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:06:42,115 INFO [train.py:842] (1/4) Epoch 26, batch 5000, loss[loss=0.1524, simple_loss=0.2472, pruned_loss=0.02883, over 7124.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2671, pruned_loss=0.046, over 1419960.87 frames.], batch size: 21, lr: 2.11e-04
2022-05-28 16:07:21,650 INFO [train.py:842] (1/4) Epoch 26, batch 5050, loss[loss=0.1669, simple_loss=0.2488, pruned_loss=0.04254, over 6759.00 frames.], tot_loss[loss=0.1793, simple_loss=0.267, pruned_loss=0.0458, over 1418739.50 frames.], batch size: 15, lr: 2.11e-04
2022-05-28 16:08:00,714 INFO [train.py:842] (1/4) Epoch 26, batch 5100, loss[loss=0.2637, simple_loss=0.3194, pruned_loss=0.104, over 5141.00 frames.], tot_loss[loss=0.179, simple_loss=0.2664, pruned_loss=0.04581, over 1418417.05 frames.], batch size: 53, lr: 2.11e-04
2022-05-28 16:08:40,307 INFO [train.py:842] (1/4) Epoch 26, batch 5150, loss[loss=0.1712, simple_loss=0.2624, pruned_loss=0.03999, over 7342.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2669, pruned_loss=0.04607, over 1420646.30 frames.], batch size: 22, lr: 2.11e-04
2022-05-28 16:09:19,797 INFO [train.py:842] (1/4) Epoch 26, batch 5200, loss[loss=0.1715, simple_loss=0.2601, pruned_loss=0.04144, over 6586.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2667, pruned_loss=0.04618, over 1420817.63 frames.], batch size: 38, lr: 2.11e-04
2022-05-28 16:09:59,260 INFO [train.py:842] (1/4) Epoch 26, batch 5250, loss[loss=0.1779, simple_loss=0.264, pruned_loss=0.04588, over 7239.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2662, pruned_loss=0.04578, over 1421516.39 frames.], batch size: 20, lr: 2.11e-04
2022-05-28 16:10:38,546 INFO [train.py:842] (1/4) Epoch 26, batch 5300, loss[loss=0.1447, simple_loss=0.2297, pruned_loss=0.02979, over 7271.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2663, pruned_loss=0.04561, over 1421675.28 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:11:17,701 INFO [train.py:842] (1/4) Epoch 26, batch 5350, loss[loss=0.1487, simple_loss=0.2397, pruned_loss=0.02885, over 7149.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2676, pruned_loss=0.04582, over 1419294.45 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:11:56,901 INFO [train.py:842] (1/4) Epoch 26, batch 5400, loss[loss=0.1566, simple_loss=0.2423, pruned_loss=0.03541, over 7161.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2675, pruned_loss=0.04569, over 1417373.78 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:12:36,477 INFO [train.py:842] (1/4) Epoch 26, batch 5450, loss[loss=0.1463, simple_loss=0.2293, pruned_loss=0.03165, over 7157.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2669, pruned_loss=0.04523, over 1418085.10 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:13:15,647 INFO [train.py:842] (1/4) Epoch 26, batch 5500, loss[loss=0.1368, simple_loss=0.2174, pruned_loss=0.02808, over 6995.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2662, pruned_loss=0.04515, over 1411944.24 frames.], batch size: 16, lr: 2.11e-04
2022-05-28 16:13:54,897 INFO [train.py:842] (1/4) Epoch 26, batch 5550, loss[loss=0.1775, simple_loss=0.2675, pruned_loss=0.04371, over 7230.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2661, pruned_loss=0.04525, over 1414558.95 frames.], batch size: 21, lr: 2.11e-04
2022-05-28 16:14:34,114 INFO [train.py:842] (1/4) Epoch 26, batch 5600, loss[loss=0.2271, simple_loss=0.314, pruned_loss=0.07014, over 7204.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2673, pruned_loss=0.04566, over 1416989.97 frames.], batch size: 22, lr: 2.11e-04
2022-05-28 16:15:15,720 INFO [train.py:842] (1/4) Epoch 26, batch 5650, loss[loss=0.2104, simple_loss=0.2911, pruned_loss=0.0648, over 7198.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2672, pruned_loss=0.0453, over 1417884.68 frames.], batch size: 23, lr: 2.11e-04
2022-05-28 16:15:55,758 INFO [train.py:842] (1/4) Epoch 26, batch 5700, loss[loss=0.1687, simple_loss=0.257, pruned_loss=0.04017, over 7183.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2676, pruned_loss=0.04582, over 1419911.44 frames.], batch size: 26, lr: 2.11e-04
2022-05-28 16:16:35,385 INFO [train.py:842] (1/4) Epoch 26, batch 5750, loss[loss=0.1512, simple_loss=0.2532, pruned_loss=0.02455, over 7167.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2664, pruned_loss=0.04535, over 1418847.89 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:17:14,689 INFO [train.py:842] (1/4) Epoch 26, batch 5800, loss[loss=0.211, simple_loss=0.2921, pruned_loss=0.06497, over 7229.00 frames.], tot_loss[loss=0.179, simple_loss=0.267, pruned_loss=0.04553, over 1420432.22 frames.], batch size: 20, lr: 2.11e-04
2022-05-28 16:17:54,289 INFO [train.py:842] (1/4) Epoch 26, batch 5850, loss[loss=0.1589, simple_loss=0.2317, pruned_loss=0.04309, over 7277.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2681, pruned_loss=0.04668, over 1420836.97 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:18:34,375 INFO [train.py:842] (1/4) Epoch 26, batch 5900, loss[loss=0.2012, simple_loss=0.2756, pruned_loss=0.06344, over 7283.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2675, pruned_loss=0.04613, over 1423085.33 frames.], batch size: 17, lr: 2.11e-04
2022-05-28 16:19:16,506 INFO [train.py:842] (1/4) Epoch 26, batch 5950, loss[loss=0.174, simple_loss=0.2678, pruned_loss=0.04016, over 7210.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2671, pruned_loss=0.04587, over 1424151.09 frames.], batch size: 21, lr: 2.11e-04
2022-05-28 16:19:56,748 INFO [train.py:842] (1/4) Epoch 26, batch 6000, loss[loss=0.1545, simple_loss=0.2449, pruned_loss=0.03201, over 6794.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2678, pruned_loss=0.04598, over 1420875.03 frames.], batch size: 31, lr: 2.11e-04
2022-05-28 16:19:56,749 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 16:20:06,474 INFO [train.py:871] (1/4) Epoch 26, validation: loss=0.164, simple_loss=0.2621, pruned_loss=0.03292, over 868885.00 frames.
2022-05-28 16:20:46,099 INFO [train.py:842] (1/4) Epoch 26, batch 6050, loss[loss=0.1839, simple_loss=0.2772, pruned_loss=0.04533, over 7143.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2684, pruned_loss=0.0464, over 1420906.47 frames.], batch size: 20, lr: 2.11e-04
2022-05-28 16:21:25,134 INFO [train.py:842] (1/4) Epoch 26, batch 6100, loss[loss=0.1564, simple_loss=0.2542, pruned_loss=0.02928, over 7151.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2676, pruned_loss=0.04578, over 1421316.77 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:22:05,103 INFO [train.py:842] (1/4) Epoch 26, batch 6150, loss[loss=0.1789, simple_loss=0.2641, pruned_loss=0.04684, over 7272.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2666, pruned_loss=0.04527, over 1425611.12 frames.], batch size: 24, lr: 2.11e-04
2022-05-28 16:22:44,673 INFO [train.py:842] (1/4) Epoch 26, batch 6200, loss[loss=0.173, simple_loss=0.2709, pruned_loss=0.03758, over 7216.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2654, pruned_loss=0.04487, over 1426619.63 frames.], batch size: 26, lr: 2.11e-04
2022-05-28 16:23:24,235 INFO [train.py:842] (1/4) Epoch 26, batch 6250, loss[loss=0.1967, simple_loss=0.285, pruned_loss=0.05424, over 7370.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2665, pruned_loss=0.0451, over 1429398.08 frames.], batch size: 23, lr: 2.11e-04
2022-05-28 16:24:03,360 INFO [train.py:842] (1/4) Epoch 26, batch 6300, loss[loss=0.1993, simple_loss=0.2973, pruned_loss=0.05062, over 7307.00 frames.], tot_loss[loss=0.1784, simple_loss=0.266, pruned_loss=0.04543, over 1425891.39 frames.], batch size: 25, lr: 2.11e-04
2022-05-28 16:24:42,824 INFO [train.py:842] (1/4) Epoch 26, batch 6350, loss[loss=0.1565, simple_loss=0.2356, pruned_loss=0.03868, over 7121.00 frames.], tot_loss[loss=0.178, simple_loss=0.2653, pruned_loss=0.0453, over 1422874.80 frames.], batch size: 17, lr: 2.11e-04
2022-05-28 16:25:22,042 INFO [train.py:842] (1/4) Epoch 26, batch 6400, loss[loss=0.1982, simple_loss=0.279, pruned_loss=0.0587, over 7448.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2662, pruned_loss=0.04541, over 1425498.38 frames.], batch size: 20, lr: 2.11e-04
2022-05-28 16:26:01,611 INFO [train.py:842] (1/4) Epoch 26, batch 6450, loss[loss=0.1819, simple_loss=0.2726, pruned_loss=0.04554, over 7267.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2665, pruned_loss=0.0458, over 1421198.61 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:26:41,174 INFO [train.py:842] (1/4) Epoch 26, batch 6500, loss[loss=0.1758, simple_loss=0.2689, pruned_loss=0.04135, over 7062.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2672, pruned_loss=0.0458, over 1424331.39 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:27:20,706 INFO [train.py:842] (1/4) Epoch 26, batch 6550, loss[loss=0.1744, simple_loss=0.2618, pruned_loss=0.04355, over 7424.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2672, pruned_loss=0.04577, over 1420057.43 frames.], batch size: 20, lr: 2.11e-04
2022-05-28 16:28:00,084 INFO [train.py:842] (1/4) Epoch 26, batch 6600, loss[loss=0.1714, simple_loss=0.2577, pruned_loss=0.04258, over 7196.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2665, pruned_loss=0.04513, over 1422979.54 frames.], batch size: 22, lr: 2.11e-04
2022-05-28 16:28:39,882 INFO [train.py:842] (1/4) Epoch 26, batch 6650, loss[loss=0.1945, simple_loss=0.29, pruned_loss=0.04949, over 7373.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2662, pruned_loss=0.04532, over 1426175.65 frames.], batch size: 23, lr: 2.11e-04
2022-05-28 16:29:19,217 INFO [train.py:842] (1/4) Epoch 26, batch 6700, loss[loss=0.2037, simple_loss=0.2766, pruned_loss=0.06542, over 7275.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2668, pruned_loss=0.04568, over 1427772.37 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:29:59,187 INFO [train.py:842] (1/4) Epoch 26, batch 6750, loss[loss=0.1368, simple_loss=0.2242, pruned_loss=0.02474, over 7074.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2652, pruned_loss=0.04512, over 1429429.87 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:30:38,554 INFO [train.py:842] (1/4) Epoch 26, batch 6800, loss[loss=0.1455, simple_loss=0.2339, pruned_loss=0.02855, over 7330.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2657, pruned_loss=0.04522, over 1431430.78 frames.], batch size: 20, lr: 2.11e-04
2022-05-28 16:31:18,197 INFO [train.py:842] (1/4) Epoch 26, batch 6850, loss[loss=0.1848, simple_loss=0.2711, pruned_loss=0.04925, over 7344.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2653, pruned_loss=0.04506, over 1431489.77 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:31:57,852 INFO [train.py:842] (1/4) Epoch 26, batch 6900, loss[loss=0.161, simple_loss=0.2517, pruned_loss=0.03512, over 7241.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2643, pruned_loss=0.04478, over 1431294.51 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:32:37,597 INFO [train.py:842] (1/4) Epoch 26, batch 6950, loss[loss=0.1477, simple_loss=0.2326, pruned_loss=0.03136, over 7133.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2638, pruned_loss=0.04468, over 1426742.32 frames.], batch size: 17, lr: 2.11e-04
2022-05-28 16:33:16,724 INFO [train.py:842] (1/4) Epoch 26, batch 7000, loss[loss=0.1358, simple_loss=0.2195, pruned_loss=0.02603, over 7260.00 frames.], tot_loss[loss=0.1774, simple_loss=0.265, pruned_loss=0.04487, over 1427761.93 frames.], batch size: 17, lr: 2.11e-04
2022-05-28 16:33:56,338 INFO [train.py:842] (1/4) Epoch 26, batch 7050, loss[loss=0.1998, simple_loss=0.2846, pruned_loss=0.05751, over 5006.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2646, pruned_loss=0.0445, over 1425395.66 frames.], batch size: 53, lr: 2.11e-04
2022-05-28 16:34:35,505 INFO [train.py:842] (1/4) Epoch 26, batch 7100, loss[loss=0.1952, simple_loss=0.2846, pruned_loss=0.0529, over 7064.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2662, pruned_loss=0.0457, over 1420274.76 frames.], batch size: 28, lr: 2.11e-04
2022-05-28 16:35:15,069 INFO [train.py:842] (1/4) Epoch 26, batch 7150, loss[loss=0.1766, simple_loss=0.2659, pruned_loss=0.04362, over 7304.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2666, pruned_loss=0.04605, over 1422636.00 frames.], batch size: 25, lr: 2.11e-04
2022-05-28 16:35:54,035 INFO [train.py:842] (1/4) Epoch 26, batch 7200, loss[loss=0.2096, simple_loss=0.2883, pruned_loss=0.06543, over 7418.00 frames.], tot_loss[loss=0.1795, simple_loss=0.267, pruned_loss=0.04604, over 1422802.45 frames.], batch size: 21, lr: 2.10e-04
2022-05-28 16:36:33,677 INFO [train.py:842] (1/4) Epoch 26, batch 7250, loss[loss=0.2051, simple_loss=0.2958, pruned_loss=0.05715, over 7292.00 frames.], tot_loss[loss=0.1786, simple_loss=0.266, pruned_loss=0.04558, over 1425295.08 frames.], batch size: 24, lr: 2.10e-04
2022-05-28 16:37:12,950 INFO [train.py:842] (1/4) Epoch 26, batch 7300, loss[loss=0.1562, simple_loss=0.2343, pruned_loss=0.03906, over 7281.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2667, pruned_loss=0.04611, over 1424490.51 frames.], batch size: 17, lr: 2.10e-04
2022-05-28 16:37:52,156 INFO [train.py:842] (1/4) Epoch 26, batch 7350, loss[loss=0.1462, simple_loss=0.2354, pruned_loss=0.02847, over 7074.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2671, pruned_loss=0.0461, over 1424647.17 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:38:31,883 INFO [train.py:842] (1/4) Epoch 26, batch 7400, loss[loss=0.1423, simple_loss=0.2198, pruned_loss=0.03243, over 7272.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2667, pruned_loss=0.04588, over 1427920.72 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:39:11,461 INFO [train.py:842] (1/4) Epoch 26, batch 7450, loss[loss=0.1567, simple_loss=0.2487, pruned_loss=0.03232, over 7354.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2663, pruned_loss=0.04505, over 1426149.33 frames.], batch size: 19, lr: 2.10e-04
2022-05-28 16:39:50,853 INFO [train.py:842] (1/4) Epoch 26, batch 7500, loss[loss=0.1702, simple_loss=0.2671, pruned_loss=0.0366, over 7131.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2663, pruned_loss=0.04502, over 1430122.20 frames.], batch size: 26, lr: 2.10e-04
2022-05-28 16:40:30,597 INFO [train.py:842] (1/4) Epoch 26, batch 7550, loss[loss=0.1405, simple_loss=0.2224, pruned_loss=0.02933, over 7167.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2665, pruned_loss=0.04585, over 1423338.47 frames.], batch size: 19, lr: 2.10e-04
2022-05-28 16:41:09,952 INFO [train.py:842] (1/4) Epoch 26, batch 7600, loss[loss=0.1599, simple_loss=0.2547, pruned_loss=0.03258, over 7149.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2663, pruned_loss=0.0455, over 1424691.61 frames.], batch size: 20, lr: 2.10e-04
2022-05-28 16:41:49,254 INFO [train.py:842] (1/4) Epoch 26, batch 7650, loss[loss=0.1732, simple_loss=0.2675, pruned_loss=0.03947, over 7210.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2673, pruned_loss=0.04558, over 1425267.70 frames.], batch size: 23, lr: 2.10e-04
2022-05-28 16:42:28,499 INFO [train.py:842] (1/4) Epoch 26, batch 7700, loss[loss=0.1669, simple_loss=0.258, pruned_loss=0.03786, over 7435.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2673, pruned_loss=0.04587, over 1426429.60 frames.], batch size: 20, lr: 2.10e-04
2022-05-28 16:43:08,337 INFO [train.py:842] (1/4) Epoch 26, batch 7750, loss[loss=0.1583, simple_loss=0.2485, pruned_loss=0.03402, over 7167.00 frames.], tot_loss[loss=0.1794, simple_loss=0.267, pruned_loss=0.0459, over 1429447.93 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:43:47,821 INFO [train.py:842] (1/4) Epoch 26, batch 7800, loss[loss=0.1619, simple_loss=0.2403, pruned_loss=0.04179, over 6983.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2663, pruned_loss=0.04544, over 1427247.19 frames.], batch size: 16, lr: 2.10e-04
2022-05-28 16:44:27,459 INFO [train.py:842] (1/4) Epoch 26, batch 7850, loss[loss=0.179, simple_loss=0.2681, pruned_loss=0.04492, over 6338.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2667, pruned_loss=0.04588, over 1423772.53 frames.], batch size: 37, lr: 2.10e-04
2022-05-28 16:45:06,410 INFO [train.py:842] (1/4) Epoch 26, batch 7900, loss[loss=0.1443, simple_loss=0.2239, pruned_loss=0.03241, over 6834.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2657, pruned_loss=0.04569, over 1421765.66 frames.], batch size: 15, lr: 2.10e-04
2022-05-28 16:45:46,027 INFO [train.py:842] (1/4) Epoch 26, batch 7950, loss[loss=0.1687, simple_loss=0.2499, pruned_loss=0.04377, over 7160.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2658, pruned_loss=0.04596, over 1419862.47 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:46:25,450 INFO [train.py:842] (1/4) Epoch 26, batch 8000, loss[loss=0.1906, simple_loss=0.2736, pruned_loss=0.05376, over 7167.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2644, pruned_loss=0.0453, over 1425107.13 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:47:04,793 INFO [train.py:842] (1/4) Epoch 26, batch 8050, loss[loss=0.1992, simple_loss=0.2706, pruned_loss=0.06386, over 6787.00 frames.], tot_loss[loss=0.1779, simple_loss=0.265, pruned_loss=0.04545, over 1427166.04 frames.], batch size: 15, lr: 2.10e-04
2022-05-28 16:47:43,877 INFO [train.py:842] (1/4) Epoch 26, batch 8100, loss[loss=0.1614, simple_loss=0.2543, pruned_loss=0.03425, over 7436.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2649, pruned_loss=0.04493, over 1429197.61 frames.], batch size: 20, lr: 2.10e-04
2022-05-28 16:48:23,585 INFO [train.py:842] (1/4) Epoch 26, batch 8150, loss[loss=0.1837, simple_loss=0.2855, pruned_loss=0.04095, over 7325.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2645, pruned_loss=0.04469, over 1431619.86 frames.], batch size: 21, lr: 2.10e-04
2022-05-28 16:49:02,892 INFO [train.py:842] (1/4) Epoch 26, batch 8200, loss[loss=0.1473, simple_loss=0.2319, pruned_loss=0.03131, over 7251.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2657, pruned_loss=0.04535, over 1430338.94 frames.], batch size: 19, lr: 2.10e-04
2022-05-28 16:49:42,445 INFO [train.py:842] (1/4) Epoch 26, batch 8250, loss[loss=0.1556, simple_loss=0.2427, pruned_loss=0.03423, over 7408.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2648, pruned_loss=0.04497, over 1430352.52 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:50:21,835 INFO [train.py:842] (1/4) Epoch 26, batch 8300, loss[loss=0.1987, simple_loss=0.2906, pruned_loss=0.05337, over 7265.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2643, pruned_loss=0.04453, over 1432224.95 frames.], batch size: 25, lr: 2.10e-04
2022-05-28 16:51:01,199 INFO [train.py:842] (1/4) Epoch 26, batch 8350, loss[loss=0.1497, simple_loss=0.2387, pruned_loss=0.03038, over 7351.00 frames.], tot_loss[loss=0.178, simple_loss=0.2651, pruned_loss=0.04545, over 1423488.43 frames.], batch size: 19, lr: 2.10e-04
2022-05-28 16:51:40,138 INFO [train.py:842] (1/4) Epoch 26, batch 8400, loss[loss=0.1811, simple_loss=0.2571, pruned_loss=0.05255, over 7164.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2659, pruned_loss=0.04597, over 1420629.49 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:52:19,773 INFO [train.py:842] (1/4) Epoch 26, batch 8450, loss[loss=0.198, simple_loss=0.2828, pruned_loss=0.05666, over 5052.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2656, pruned_loss=0.04595, over 1420808.05 frames.], batch size: 52, lr: 2.10e-04
2022-05-28 16:52:58,992 INFO [train.py:842] (1/4) Epoch 26, batch 8500, loss[loss=0.1665, simple_loss=0.2534, pruned_loss=0.03977, over 7258.00 frames.], tot_loss[loss=0.18, simple_loss=0.2666, pruned_loss=0.04675, over 1419847.19 frames.], batch size: 19, lr: 2.10e-04
2022-05-28 16:53:38,743 INFO [train.py:842] (1/4) Epoch 26, batch 8550, loss[loss=0.193, simple_loss=0.2821, pruned_loss=0.0519, over 7072.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2676, pruned_loss=0.04692, over 1421591.76 frames.], batch size: 28, lr: 2.10e-04
2022-05-28 16:54:18,372 INFO [train.py:842] (1/4) Epoch 26, batch 8600, loss[loss=0.1368, simple_loss=0.2199, pruned_loss=0.02687, over 7143.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2664, pruned_loss=0.0461, over 1425996.26 frames.], batch size: 17, lr: 2.10e-04
2022-05-28 16:54:58,061 INFO [train.py:842] (1/4) Epoch 26, batch 8650, loss[loss=0.1459, simple_loss=0.234, pruned_loss=0.0289, over 7136.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2659, pruned_loss=0.04589, over 1419608.15 frames.], batch size: 17, lr: 2.10e-04
2022-05-28 16:55:37,236 INFO [train.py:842] (1/4) Epoch 26, batch 8700, loss[loss=0.1646, simple_loss=0.2652, pruned_loss=0.03198, over 7323.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2661, pruned_loss=0.04579, over 1415299.71 frames.], batch size: 20, lr: 2.10e-04
2022-05-28 16:56:16,927 INFO [train.py:842] (1/4) Epoch 26, batch 8750, loss[loss=0.1716, simple_loss=0.2584, pruned_loss=0.04244, over 7140.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2666, pruned_loss=0.04576, over 1419772.05 frames.], batch size: 26, lr: 2.10e-04
2022-05-28 16:56:56,113 INFO [train.py:842] (1/4) Epoch 26, batch 8800, loss[loss=0.2234, simple_loss=0.3047, pruned_loss=0.07102, over 7302.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2679, pruned_loss=0.04634, over 1421752.67 frames.], batch size: 24, lr: 2.10e-04
2022-05-28 16:57:35,791 INFO [train.py:842] (1/4) Epoch 26, batch 8850, loss[loss=0.1759, simple_loss=0.2653, pruned_loss=0.04321, over 7063.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2691, pruned_loss=0.04661, over 1419394.36 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:58:14,968 INFO [train.py:842] (1/4) Epoch 26, batch 8900, loss[loss=0.2117, simple_loss=0.2945, pruned_loss=0.06449, over 7140.00 frames.], tot_loss[loss=0.1802, simple_loss=0.268, pruned_loss=0.04621, over 1420463.72 frames.], batch size: 20, lr: 2.10e-04
2022-05-28 16:59:04,921 INFO [train.py:842] (1/4) Epoch 26, batch 8950, loss[loss=0.2291, simple_loss=0.2998, pruned_loss=0.07917, over 7186.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2688, pruned_loss=0.04627, over 1418532.98 frames.], batch size: 26, lr: 2.10e-04
2022-05-28 16:59:43,861 INFO [train.py:842] (1/4) Epoch 26, batch 9000, loss[loss=0.1915, simple_loss=0.2825, pruned_loss=0.05027, over 4929.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2707, pruned_loss=0.0472, over 1414270.19 frames.], batch size: 53, lr: 2.10e-04
2022-05-28 16:59:43,862 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 16:59:53,411 INFO [train.py:871] (1/4) Epoch 26, validation: loss=0.1634, simple_loss=0.2612, pruned_loss=0.03281, over 868885.00 frames.
2022-05-28 17:00:32,385 INFO [train.py:842] (1/4) Epoch 26, batch 9050, loss[loss=0.1947, simple_loss=0.2777, pruned_loss=0.05581, over 4973.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2704, pruned_loss=0.04724, over 1389686.67 frames.], batch size: 52, lr: 2.10e-04
2022-05-28 17:01:10,197 INFO [train.py:842] (1/4) Epoch 26, batch 9100, loss[loss=0.232, simple_loss=0.3112, pruned_loss=0.07641, over 5046.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2731, pruned_loss=0.04883, over 1343654.96 frames.], batch size: 52, lr: 2.10e-04
2022-05-28 17:01:48,418 INFO [train.py:842] (1/4) Epoch 26, batch 9150, loss[loss=0.2336, simple_loss=0.3173, pruned_loss=0.07489, over 5466.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2777, pruned_loss=0.05223, over 1277725.88 frames.], batch size: 52, lr: 2.10e-04
2022-05-28 17:02:39,085 INFO [train.py:842] (1/4) Epoch 27, batch 0, loss[loss=0.1427, simple_loss=0.2284, pruned_loss=0.02856, over 7176.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2284, pruned_loss=0.02856, over 7176.00 frames.], batch size: 18, lr: 2.06e-04
2022-05-28 17:03:18,971 INFO [train.py:842] (1/4) Epoch 27, batch 50, loss[loss=0.1376, simple_loss=0.2165, pruned_loss=0.0294, over 7284.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2623, pruned_loss=0.04328, over 318795.54 frames.], batch size: 17, lr: 2.06e-04
2022-05-28 17:03:58,046 INFO [train.py:842] (1/4) Epoch 27, batch 100, loss[loss=0.1623, simple_loss=0.2435, pruned_loss=0.04054, over 7285.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2634, pruned_loss=0.04371, over 562861.95 frames.], batch size: 17, lr: 2.06e-04
2022-05-28 17:04:37,604 INFO [train.py:842] (1/4) Epoch 27, batch 150, loss[loss=0.1551, simple_loss=0.2522, pruned_loss=0.02899, over 6683.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2622, pruned_loss=0.04258, over 751512.09 frames.], batch size: 38, lr: 2.06e-04
2022-05-28 17:05:16,834 INFO [train.py:842] (1/4) Epoch 27, batch 200, loss[loss=0.1579, simple_loss=0.2449, pruned_loss=0.03551, over 7161.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2634, pruned_loss=0.04419, over 894451.95 frames.], batch size: 26, lr: 2.06e-04
2022-05-28 17:05:56,193 INFO [train.py:842] (1/4) Epoch 27, batch 250, loss[loss=0.1734, simple_loss=0.264, pruned_loss=0.04141, over 6149.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2645, pruned_loss=0.04433, over 1006186.07 frames.], batch size: 37, lr: 2.06e-04
2022-05-28 17:06:35,433 INFO [train.py:842] (1/4) Epoch 27, batch 300, loss[loss=0.1616, simple_loss=0.2507, pruned_loss=0.0362, over 6356.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2635, pruned_loss=0.04333, over 1100324.48 frames.], batch size: 37, lr: 2.06e-04
2022-05-28 17:07:15,029 INFO [train.py:842] (1/4) Epoch 27, batch 350, loss[loss=0.1884, simple_loss=0.2753, pruned_loss=0.05073, over 6723.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2632, pruned_loss=0.0429, over 1167360.03 frames.], batch size: 31, lr: 2.06e-04
2022-05-28 17:07:54,294 INFO [train.py:842] (1/4) Epoch 27, batch 400, loss[loss=0.1562, simple_loss=0.2556, pruned_loss=0.02837, over 7140.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2637, pruned_loss=0.04344, over 1227994.28 frames.], batch size: 20, lr: 2.06e-04
2022-05-28 17:08:33,797 INFO [train.py:842] (1/4) Epoch 27, batch 450, loss[loss=0.214, simple_loss=0.2949, pruned_loss=0.06651, over 7232.00 frames.], tot_loss[loss=0.176, simple_loss=0.2645, pruned_loss=0.04376, over 1275777.77 frames.], batch size: 20, lr: 2.06e-04
2022-05-28 17:09:13,077 INFO [train.py:842] (1/4) Epoch 27, batch 500, loss[loss=0.2408, simple_loss=0.3145, pruned_loss=0.08351, over 4978.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2645, pruned_loss=0.04366, over 1307200.17 frames.], batch size: 53, lr: 2.06e-04
2022-05-28 17:09:52,615 INFO [train.py:842] (1/4) Epoch 27, batch 550, loss[loss=0.2263, simple_loss=0.3014, pruned_loss=0.07564, over 7213.00 frames.], tot_loss[loss=0.177, simple_loss=0.2654, pruned_loss=0.04425, over 1332169.73 frames.], batch size: 22, lr: 2.06e-04
2022-05-28 17:10:31,931 INFO [train.py:842] (1/4) Epoch 27, batch 600, loss[loss=0.1809, simple_loss=0.2719, pruned_loss=0.04494, over 7253.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2665, pruned_loss=0.04508, over 1355519.48 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:11:11,825 INFO [train.py:842] (1/4) Epoch 27, batch 650, loss[loss=0.1351, simple_loss=0.2223, pruned_loss=0.02395, over 7283.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2661, pruned_loss=0.04506, over 1372089.86 frames.], batch size: 18, lr: 2.05e-04
2022-05-28 17:11:50,943 INFO [train.py:842] (1/4) Epoch 27, batch 700, loss[loss=0.1662, simple_loss=0.258, pruned_loss=0.03716, over 7108.00 frames.], tot_loss[loss=0.178, simple_loss=0.2662, pruned_loss=0.04495, over 1381202.80 frames.], batch size: 21, lr: 2.05e-04
2022-05-28 17:12:30,578 INFO [train.py:842] (1/4) Epoch 27, batch 750, loss[loss=0.1634, simple_loss=0.2657, pruned_loss=0.03058, over 7147.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2657, pruned_loss=0.04465, over 1389965.00 frames.], batch size: 20, lr: 2.05e-04
2022-05-28 17:13:09,877 INFO [train.py:842] (1/4) Epoch 27, batch 800, loss[loss=0.1798, simple_loss=0.2685, pruned_loss=0.04557, over 7243.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2658, pruned_loss=0.04428, over 1395537.40 frames.], batch size: 20, lr: 2.05e-04
2022-05-28 17:13:49,273 INFO [train.py:842] (1/4) Epoch 27, batch 850, loss[loss=0.1869, simple_loss=0.269, pruned_loss=0.05241, over 4655.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2671, pruned_loss=0.04489, over 1397942.24 frames.], batch size: 52, lr: 2.05e-04
2022-05-28 17:14:28,642 INFO [train.py:842] (1/4) Epoch 27, batch 900, loss[loss=0.177, simple_loss=0.2552, pruned_loss=0.04942, over 7431.00 frames.], tot_loss[loss=0.1763, simple_loss=0.265, pruned_loss=0.04387, over 1407198.35 frames.], batch size: 18, lr: 2.05e-04
2022-05-28 17:15:08,240 INFO [train.py:842] (1/4) Epoch 27, batch 950, loss[loss=0.1836, simple_loss=0.2647, pruned_loss=0.0513, over 7208.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2661, pruned_loss=0.0442, over 1408316.03 frames.], batch size: 16, lr: 2.05e-04
2022-05-28 17:15:47,446 INFO [train.py:842] (1/4) Epoch 27, batch 1000, loss[loss=0.1879, simple_loss=0.2793, pruned_loss=0.0482, over 7281.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2666, pruned_loss=0.04493, over 1411366.22 frames.], batch size: 24, lr: 2.05e-04
2022-05-28 17:16:29,708 INFO [train.py:842] (1/4) Epoch 27, batch 1050, loss[loss=0.1625, simple_loss=0.2496, pruned_loss=0.03776, over 7206.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2663, pruned_loss=0.04463, over 1417339.33 frames.], batch size: 23, lr: 2.05e-04
2022-05-28 17:17:09,235 INFO [train.py:842] (1/4) Epoch 27, batch 1100, loss[loss=0.1861, simple_loss=0.2764, pruned_loss=0.04786, over 7194.00 frames.], tot_loss[loss=0.177, simple_loss=0.2653, pruned_loss=0.04433, over 1421040.85 frames.], batch size: 22, lr: 2.05e-04
2022-05-28 17:17:48,463 INFO [train.py:842] (1/4) Epoch 27, batch 1150, loss[loss=0.1649, simple_loss=0.2521, pruned_loss=0.0388, over 7164.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2648, pruned_loss=0.04371, over 1422142.15 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:18:27,748 INFO [train.py:842] (1/4) Epoch 27, batch 1200, loss[loss=0.1718, simple_loss=0.2676, pruned_loss=0.03803, over 7274.00 frames.], tot_loss[loss=0.1761, simple_loss=0.265, pruned_loss=0.0436, over 1426014.77 frames.], batch size: 24, lr: 2.05e-04
2022-05-28 17:19:07,426 INFO [train.py:842] (1/4) Epoch 27, batch 1250, loss[loss=0.1723, simple_loss=0.2691, pruned_loss=0.03775, over 6688.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2645, pruned_loss=0.04351, over 1426032.06 frames.], batch size: 38, lr: 2.05e-04
2022-05-28 17:19:46,501 INFO [train.py:842] (1/4) Epoch 27, batch 1300, loss[loss=0.1437, simple_loss=0.234, pruned_loss=0.02673, over 7276.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2652, pruned_loss=0.04411, over 1422065.10 frames.], batch size: 18, lr: 2.05e-04
2022-05-28 17:20:26,340 INFO [train.py:842] (1/4) Epoch 27, batch 1350, loss[loss=0.1589, simple_loss=0.2482, pruned_loss=0.03476, over 7421.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2639, pruned_loss=0.04371, over 1425847.41 frames.], batch size: 18, lr: 2.05e-04
2022-05-28 17:21:05,304 INFO [train.py:842] (1/4) Epoch 27, batch 1400, loss[loss=0.1733, simple_loss=0.2601, pruned_loss=0.04325, over 7178.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2645, pruned_loss=0.04466, over 1419271.70 frames.], batch size: 23, lr: 2.05e-04
2022-05-28 17:21:44,756 INFO [train.py:842] (1/4) Epoch 27, batch 1450, loss[loss=0.1638, simple_loss=0.2476, pruned_loss=0.04004, over 7269.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2647, pruned_loss=0.04476, over 1422156.07 frames.], batch size: 18, lr: 2.05e-04
2022-05-28 17:22:23,912 INFO [train.py:842] (1/4) Epoch 27, batch 1500, loss[loss=0.1788, simple_loss=0.2661, pruned_loss=0.04577, over 5108.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2654, pruned_loss=0.04474, over 1417661.42 frames.], batch size: 52, lr: 2.05e-04
2022-05-28 17:23:03,659 INFO [train.py:842] (1/4) Epoch 27, batch 1550, loss[loss=0.1575, simple_loss=0.2431, pruned_loss=0.03599, over 7123.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2643, pruned_loss=0.04393, over 1421350.55 frames.], batch size: 21, lr: 2.05e-04
2022-05-28 17:23:43,257 INFO [train.py:842] (1/4) Epoch 27, batch 1600, loss[loss=0.1766, simple_loss=0.259, pruned_loss=0.04713, over 7257.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2639, pruned_loss=0.04413, over 1424889.04 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:24:22,815 INFO [train.py:842] (1/4) Epoch 27, batch 1650, loss[loss=0.1547, simple_loss=0.2449, pruned_loss=0.03222, over 7161.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2642, pruned_loss=0.04428, over 1428581.25 frames.], batch size: 26, lr: 2.05e-04
2022-05-28 17:25:02,023 INFO [train.py:842] (1/4) Epoch 27, batch 1700, loss[loss=0.1654, simple_loss=0.2624, pruned_loss=0.03424, over 7353.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2646, pruned_loss=0.04434, over 1430176.25 frames.], batch size: 22, lr: 2.05e-04
2022-05-28 17:25:41,539 INFO [train.py:842] (1/4) Epoch 27, batch 1750, loss[loss=0.1811, simple_loss=0.2708, pruned_loss=0.04569, over 7138.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2648, pruned_loss=0.04405, over 1431189.00 frames.], batch size: 26, lr: 2.05e-04
2022-05-28 17:26:20,754 INFO [train.py:842] (1/4) Epoch 27, batch 1800, loss[loss=0.1577, simple_loss=0.2546, pruned_loss=0.03036, over 7122.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2643, pruned_loss=0.04408, over 1428413.96 frames.], batch size: 21, lr: 2.05e-04
2022-05-28 17:27:00,488 INFO [train.py:842] (1/4) Epoch 27, batch 1850, loss[loss=0.3061, simple_loss=0.362, pruned_loss=0.1251, over 5103.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2642, pruned_loss=0.04414, over 1429008.94 frames.], batch size: 53, lr: 2.05e-04
2022-05-28 17:27:40,059 INFO [train.py:842] (1/4) Epoch 27, batch 1900, loss[loss=0.1722, simple_loss=0.2668, pruned_loss=0.03883, over 7365.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2637, pruned_loss=0.04393, over 1428359.34 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:28:19,443 INFO [train.py:842] (1/4) Epoch 27, batch 1950, loss[loss=0.2034, simple_loss=0.286, pruned_loss=0.0604, over 6393.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2646, pruned_loss=0.04413, over 1424831.02 frames.], batch size: 37, lr: 2.05e-04
2022-05-28 17:28:58,964 INFO [train.py:842] (1/4) Epoch 27, batch 2000, loss[loss=0.1919, simple_loss=0.2878, pruned_loss=0.04797, over 6814.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2649, pruned_loss=0.04483, over 1422204.95 frames.], batch size: 31, lr: 2.05e-04
2022-05-28 17:29:38,290 INFO [train.py:842] (1/4) Epoch 27, batch 2050, loss[loss=0.1903, simple_loss=0.2856, pruned_loss=0.04753, over 7183.00 frames.], tot_loss[loss=0.177, simple_loss=0.265, pruned_loss=0.04454, over 1425675.99 frames.], batch size: 26, lr: 2.05e-04
2022-05-28 17:30:17,471 INFO [train.py:842] (1/4) Epoch 27, batch 2100, loss[loss=0.2421, simple_loss=0.3264, pruned_loss=0.07886, over 7196.00 frames.], tot_loss[loss=0.177, simple_loss=0.265, pruned_loss=0.04456, over 1424396.81 frames.], batch size: 22, lr: 2.05e-04
2022-05-28 17:30:56,963 INFO [train.py:842] (1/4) Epoch 27, batch 2150, loss[loss=0.1639, simple_loss=0.2535, pruned_loss=0.03715, over 7345.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2655, pruned_loss=0.04472, over 1427723.15 frames.], batch size: 25, lr: 2.05e-04
2022-05-28 17:31:36,239 INFO [train.py:842] (1/4) Epoch 27, batch 2200, loss[loss=0.1546, simple_loss=0.2418, pruned_loss=0.03367, over 7233.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2672, pruned_loss=0.04572, over 1426598.65 frames.], batch size: 20, lr: 2.05e-04
2022-05-28 17:32:15,916 INFO [train.py:842] (1/4) Epoch 27, batch 2250, loss[loss=0.1475, simple_loss=0.2283, pruned_loss=0.03331, over 7013.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2669, pruned_loss=0.04542, over 1431525.38 frames.], batch size: 16, lr: 2.05e-04
2022-05-28 17:32:55,026 INFO [train.py:842] (1/4) Epoch 27, batch 2300, loss[loss=0.1649, simple_loss=0.2482, pruned_loss=0.04081, over 7137.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2664, pruned_loss=0.04508, over 1433172.46 frames.], batch size: 17, lr: 2.05e-04
2022-05-28 17:33:34,536 INFO [train.py:842] (1/4) Epoch 27, batch 2350, loss[loss=0.1643, simple_loss=0.2543, pruned_loss=0.03711, over 7132.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2671, pruned_loss=0.04521, over 1431232.93 frames.], batch size: 20, lr: 2.05e-04
2022-05-28 17:34:13,862 INFO [train.py:842] (1/4) Epoch 27, batch 2400, loss[loss=0.1844, simple_loss=0.2751, pruned_loss=0.04689, over 7308.00 frames.], tot_loss[loss=0.1786, simple_loss=0.267, pruned_loss=0.04512, over 1433014.91 frames.], batch size: 24, lr: 2.05e-04
2022-05-28 17:34:53,602 INFO [train.py:842] (1/4) Epoch 27, batch 2450, loss[loss=0.2101, simple_loss=0.3038, pruned_loss=0.0582, over 7232.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2674, pruned_loss=0.04518, over 1435931.95 frames.], batch size: 20, lr: 2.05e-04
2022-05-28 17:35:32,776 INFO [train.py:842] (1/4) Epoch 27, batch 2500, loss[loss=0.176, simple_loss=0.2776, pruned_loss=0.03716, over 7222.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2674, pruned_loss=0.04508, over 1437359.59 frames.], batch size: 21, lr: 2.05e-04
2022-05-28 17:36:12,397 INFO [train.py:842] (1/4) Epoch 27, batch 2550, loss[loss=0.204, simple_loss=0.29, pruned_loss=0.05897, over 6768.00 frames.], tot_loss[loss=0.179, simple_loss=0.2678, pruned_loss=0.0451, over 1434985.73 frames.], batch size: 31, lr: 2.05e-04
2022-05-28 17:36:51,698 INFO [train.py:842] (1/4) Epoch 27, batch 2600, loss[loss=0.1876, simple_loss=0.2619, pruned_loss=0.05662, over 7262.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2673, pruned_loss=0.04541, over 1435537.49 frames.], batch size: 16, lr: 2.05e-04
2022-05-28 17:37:31,348 INFO [train.py:842] (1/4) Epoch 27, batch 2650, loss[loss=0.1973, simple_loss=0.2857, pruned_loss=0.0545, over 7288.00 frames.], tot_loss[loss=0.1787, simple_loss=0.267, pruned_loss=0.04518, over 1431253.45 frames.], batch size: 24, lr: 2.05e-04
2022-05-28 17:38:10,521 INFO [train.py:842] (1/4) Epoch 27, batch 2700, loss[loss=0.1711, simple_loss=0.267, pruned_loss=0.03764, over 7336.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2666, pruned_loss=0.0449, over 1428550.70 frames.], batch size: 22, lr: 2.05e-04
2022-05-28 17:38:50,270 INFO [train.py:842] (1/4) Epoch 27, batch 2750, loss[loss=0.1581, simple_loss=0.2559, pruned_loss=0.03011, over 7148.00 frames.], tot_loss[loss=0.178, simple_loss=0.2666, pruned_loss=0.04469, over 1428158.84 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:39:29,464 INFO [train.py:842] (1/4) Epoch 27, batch 2800, loss[loss=0.1658, simple_loss=0.2625, pruned_loss=0.03454, over 7289.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2664, pruned_loss=0.04472, over 1427882.26 frames.], batch size: 25, lr: 2.05e-04
2022-05-28 17:40:08,856 INFO [train.py:842] (1/4) Epoch 27, batch 2850, loss[loss=0.1516, simple_loss=0.2373, pruned_loss=0.03292, over 7269.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2666, pruned_loss=0.04455, over 1427495.23 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:40:48,056 INFO [train.py:842] (1/4) Epoch 27, batch 2900, loss[loss=0.1686, simple_loss=0.2574, pruned_loss=0.03988, over 7171.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2664, pruned_loss=0.0446, over 1426176.86 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:41:27,482 INFO [train.py:842] (1/4) Epoch 27, batch 2950, loss[loss=0.1722, simple_loss=0.272, pruned_loss=0.03625, over 7105.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2667, pruned_loss=0.04469, over 1419560.13 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 17:42:06,541 INFO [train.py:842] (1/4) Epoch 27, batch 3000, loss[loss=0.1995, simple_loss=0.2909, pruned_loss=0.05407, over 7425.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2668, pruned_loss=0.04456, over 1418636.91 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 17:42:06,541 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 17:42:16,304 INFO [train.py:871] (1/4) Epoch 27, validation: loss=0.1636, simple_loss=0.2622, pruned_loss=0.03251, over 868885.00 frames.
2022-05-28 17:42:56,070 INFO [train.py:842] (1/4) Epoch 27, batch 3050, loss[loss=0.1892, simple_loss=0.2782, pruned_loss=0.05015, over 7130.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2652, pruned_loss=0.04448, over 1411367.12 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 17:43:35,176 INFO [train.py:842] (1/4) Epoch 27, batch 3100, loss[loss=0.1656, simple_loss=0.2526, pruned_loss=0.03936, over 7327.00 frames.], tot_loss[loss=0.177, simple_loss=0.2656, pruned_loss=0.04424, over 1416703.85 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 17:44:14,965 INFO [train.py:842] (1/4) Epoch 27, batch 3150, loss[loss=0.2346, simple_loss=0.3013, pruned_loss=0.08396, over 7189.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2658, pruned_loss=0.04444, over 1417471.33 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:44:54,119 INFO [train.py:842] (1/4) Epoch 27, batch 3200, loss[loss=0.2191, simple_loss=0.3049, pruned_loss=0.06666, over 7207.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2661, pruned_loss=0.04466, over 1419244.48 frames.], batch size: 23, lr: 2.04e-04
2022-05-28 17:45:33,940 INFO [train.py:842] (1/4) Epoch 27, batch 3250, loss[loss=0.1746, simple_loss=0.2662, pruned_loss=0.04146, over 6309.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2654, pruned_loss=0.04447, over 1420551.43 frames.], batch size: 37, lr: 2.04e-04
2022-05-28 17:46:13,051 INFO [train.py:842] (1/4) Epoch 27, batch 3300, loss[loss=0.1725, simple_loss=0.2684, pruned_loss=0.0383, over 6913.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2646, pruned_loss=0.04395, over 1420020.11 frames.], batch size: 32, lr: 2.04e-04
2022-05-28 17:46:52,299 INFO [train.py:842] (1/4) Epoch 27, batch 3350, loss[loss=0.1651, simple_loss=0.2652, pruned_loss=0.03252, over 7336.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2662, pruned_loss=0.04428, over 1420653.35 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:47:31,378 INFO [train.py:842] (1/4) Epoch 27, batch 3400, loss[loss=0.2055, simple_loss=0.2954, pruned_loss=0.05782, over 7155.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2665, pruned_loss=0.04481, over 1417625.57 frames.], batch size: 20, lr: 2.04e-04
2022-05-28 17:48:10,896 INFO [train.py:842] (1/4) Epoch 27, batch 3450, loss[loss=0.1433, simple_loss=0.2353, pruned_loss=0.02565, over 7353.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2662, pruned_loss=0.04456, over 1420983.26 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:49:02,128 INFO [train.py:842] (1/4) Epoch 27, batch 3500, loss[loss=0.1377, simple_loss=0.2215, pruned_loss=0.02695, over 7238.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2643, pruned_loss=0.04392, over 1423891.79 frames.], batch size: 16, lr: 2.04e-04
2022-05-28 17:49:41,578 INFO [train.py:842] (1/4) Epoch 27, batch 3550, loss[loss=0.268, simple_loss=0.346, pruned_loss=0.09504, over 4954.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2643, pruned_loss=0.04434, over 1417121.54 frames.], batch size: 52, lr: 2.04e-04
2022-05-28 17:50:20,545 INFO [train.py:842] (1/4) Epoch 27, batch 3600, loss[loss=0.2071, simple_loss=0.297, pruned_loss=0.05858, over 7164.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2647, pruned_loss=0.04427, over 1414393.83 frames.], batch size: 19, lr: 2.04e-04
2022-05-28 17:51:00,312 INFO [train.py:842] (1/4) Epoch 27, batch 3650, loss[loss=0.1548, simple_loss=0.2402, pruned_loss=0.03468, over 7066.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2644, pruned_loss=0.04408, over 1414052.40 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 17:51:50,553 INFO [train.py:842] (1/4) Epoch 27, batch 3700, loss[loss=0.1731, simple_loss=0.2709, pruned_loss=0.03765, over 7191.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2653, pruned_loss=0.04494, over 1413248.20 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:52:41,173 INFO [train.py:842] (1/4) Epoch 27, batch 3750, loss[loss=0.1468, simple_loss=0.2346, pruned_loss=0.02951, over 7161.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2641, pruned_loss=0.04478, over 1416287.22 frames.], batch size: 19, lr: 2.04e-04
2022-05-28 17:53:20,684 INFO [train.py:842] (1/4) Epoch 27, batch 3800, loss[loss=0.1392, simple_loss=0.2159, pruned_loss=0.03128, over 7403.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2636, pruned_loss=0.04489, over 1419443.71 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 17:54:00,199 INFO [train.py:842] (1/4) Epoch 27, batch 3850, loss[loss=0.1973, simple_loss=0.2824, pruned_loss=0.05606, over 7185.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2636, pruned_loss=0.04456, over 1413221.74 frames.], batch size: 23, lr: 2.04e-04
2022-05-28 17:54:39,432 INFO [train.py:842] (1/4) Epoch 27, batch 3900, loss[loss=0.1675, simple_loss=0.2642, pruned_loss=0.03537, over 7196.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2641, pruned_loss=0.04485, over 1411922.90 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:55:18,920 INFO [train.py:842] (1/4) Epoch 27, batch 3950, loss[loss=0.1595, simple_loss=0.255, pruned_loss=0.03201, over 7333.00 frames.], tot_loss[loss=0.1756, simple_loss=0.263, pruned_loss=0.04414, over 1416901.00 frames.], batch size: 20, lr: 2.04e-04
2022-05-28 17:55:58,119 INFO [train.py:842] (1/4) Epoch 27, batch 4000, loss[loss=0.1506, simple_loss=0.2376, pruned_loss=0.03176, over 7407.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2639, pruned_loss=0.04432, over 1424014.11 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 17:56:37,760 INFO [train.py:842] (1/4) Epoch 27, batch 4050, loss[loss=0.225, simple_loss=0.2925, pruned_loss=0.07876, over 7194.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2648, pruned_loss=0.04453, over 1427359.16 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:57:17,354 INFO [train.py:842] (1/4) Epoch 27, batch 4100, loss[loss=0.1405, simple_loss=0.2306, pruned_loss=0.02525, over 7260.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2637, pruned_loss=0.04426, over 1429675.55 frames.], batch size: 19, lr: 2.04e-04
2022-05-28 17:57:56,847 INFO [train.py:842] (1/4) Epoch 27, batch 4150, loss[loss=0.1858, simple_loss=0.2918, pruned_loss=0.03984, over 7330.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2639, pruned_loss=0.04426, over 1423612.16 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:58:36,173 INFO [train.py:842] (1/4) Epoch 27, batch 4200, loss[loss=0.1613, simple_loss=0.2421, pruned_loss=0.04021, over 7069.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2652, pruned_loss=0.04529, over 1424656.73 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 17:59:15,961 INFO [train.py:842] (1/4) Epoch 27, batch 4250, loss[loss=0.1663, simple_loss=0.2632, pruned_loss=0.03469, over 7322.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2667, pruned_loss=0.04644, over 1425825.77 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 17:59:55,136 INFO [train.py:842] (1/4) Epoch 27, batch 4300, loss[loss=0.1662, simple_loss=0.2504, pruned_loss=0.04099, over 7122.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2678, pruned_loss=0.04703, over 1423515.60 frames.], batch size: 17, lr: 2.04e-04
2022-05-28 18:00:34,771 INFO [train.py:842] (1/4) Epoch 27, batch 4350, loss[loss=0.2111, simple_loss=0.3048, pruned_loss=0.05874, over 7305.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2675, pruned_loss=0.04692, over 1421359.43 frames.], batch size: 24, lr: 2.04e-04
2022-05-28 18:01:13,888 INFO [train.py:842] (1/4) Epoch 27, batch 4400, loss[loss=0.1938, simple_loss=0.2814, pruned_loss=0.05311, over 7210.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2687, pruned_loss=0.04751, over 1420727.56 frames.], batch size: 23, lr: 2.04e-04
2022-05-28 18:01:53,433 INFO [train.py:842] (1/4) Epoch 27, batch 4450, loss[loss=0.1975, simple_loss=0.2899, pruned_loss=0.05256, over 7060.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2679, pruned_loss=0.04722, over 1419440.36 frames.], batch size: 28, lr: 2.04e-04
2022-05-28 18:02:32,865 INFO [train.py:842] (1/4) Epoch 27, batch 4500, loss[loss=0.2327, simple_loss=0.3211, pruned_loss=0.07217, over 7410.00 frames.], tot_loss[loss=0.18, simple_loss=0.2671, pruned_loss=0.04642, over 1425086.27 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 18:03:12,538 INFO [train.py:842] (1/4) Epoch 27, batch 4550, loss[loss=0.1967, simple_loss=0.2874, pruned_loss=0.05304, over 7422.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2674, pruned_loss=0.04659, over 1417505.12 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 18:03:51,724 INFO [train.py:842] (1/4) Epoch 27, batch 4600, loss[loss=0.1736, simple_loss=0.2626, pruned_loss=0.04235, over 7287.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2667, pruned_loss=0.04582, over 1417987.37 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 18:04:31,170 INFO [train.py:842] (1/4) Epoch 27, batch 4650, loss[loss=0.1522, simple_loss=0.233, pruned_loss=0.03574, over 7004.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2658, pruned_loss=0.04537, over 1421154.65 frames.], batch size: 16, lr: 2.04e-04
2022-05-28 18:05:10,284 INFO [train.py:842] (1/4) Epoch 27, batch 4700, loss[loss=0.1565, simple_loss=0.2466, pruned_loss=0.03315, over 7258.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2652, pruned_loss=0.04437, over 1421518.52 frames.], batch size: 19, lr: 2.04e-04
2022-05-28 18:05:49,590 INFO [train.py:842] (1/4) Epoch 27, batch 4750, loss[loss=0.1646, simple_loss=0.2586, pruned_loss=0.0353, over 7225.00 frames.], tot_loss[loss=0.1786, simple_loss=0.267, pruned_loss=0.04509, over 1424696.78 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 18:06:28,952 INFO [train.py:842] (1/4) Epoch 27, batch 4800, loss[loss=0.1758, simple_loss=0.2603, pruned_loss=0.04561, over 7278.00 frames.], tot_loss[loss=0.178, simple_loss=0.2665, pruned_loss=0.04473, over 1424209.64 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 18:07:08,570 INFO [train.py:842] (1/4) Epoch 27, batch 4850, loss[loss=0.1892, simple_loss=0.2814, pruned_loss=0.04857, over 7321.00 frames.], tot_loss[loss=0.177, simple_loss=0.2654, pruned_loss=0.04431, over 1425936.88 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 18:07:47,945 INFO [train.py:842] (1/4) Epoch 27, batch 4900, loss[loss=0.2446, simple_loss=0.3214, pruned_loss=0.08392, over 7197.00 frames.], tot_loss[loss=0.178, simple_loss=0.2664, pruned_loss=0.0448, over 1426789.58 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 18:08:27,462 INFO [train.py:842] (1/4) Epoch 27, batch 4950, loss[loss=0.1378, simple_loss=0.2192, pruned_loss=0.02816, over 7289.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2647, pruned_loss=0.04413, over 1421432.42 frames.], batch size: 17, lr: 2.04e-04
2022-05-28 18:09:06,640 INFO [train.py:842] (1/4) Epoch 27, batch 5000, loss[loss=0.2129, simple_loss=0.3004, pruned_loss=0.06266, over 7310.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2653, pruned_loss=0.04466, over 1420787.85 frames.], batch size: 25, lr: 2.04e-04
2022-05-28 18:09:46,192 INFO [train.py:842] (1/4) Epoch 27, batch 5050, loss[loss=0.1654, simple_loss=0.2687, pruned_loss=0.03103, over 7226.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2659, pruned_loss=0.04482, over 1424521.99 frames.], batch size: 20, lr: 2.04e-04
2022-05-28 18:10:25,444 INFO [train.py:842] (1/4) Epoch 27, batch 5100, loss[loss=0.1891, simple_loss=0.2588, pruned_loss=0.05965, over 7275.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2651, pruned_loss=0.04431, over 1423453.99 frames.], batch size: 17, lr: 2.04e-04
2022-05-28 18:11:05,135 INFO [train.py:842] (1/4) Epoch 27, batch 5150, loss[loss=0.1906, simple_loss=0.2854, pruned_loss=0.04794, over 7200.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2645, pruned_loss=0.04391, over 1426050.62 frames.], batch size: 23, lr: 2.04e-04
2022-05-28 18:11:44,277 INFO [train.py:842] (1/4) Epoch 27, batch 5200, loss[loss=0.1957, simple_loss=0.2944, pruned_loss=0.04847, over 6319.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2651, pruned_loss=0.04428, over 1425862.59 frames.], batch size: 37, lr: 2.04e-04
2022-05-28 18:12:24,097 INFO [train.py:842] (1/4) Epoch 27, batch 5250, loss[loss=0.1674, simple_loss=0.253, pruned_loss=0.0409, over 7167.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2649, pruned_loss=0.04465, over 1429033.94 frames.], batch size: 19, lr: 2.04e-04
2022-05-28 18:13:03,664 INFO [train.py:842] (1/4) Epoch 27, batch 5300, loss[loss=0.1733, simple_loss=0.2584, pruned_loss=0.04412, over 7075.00 frames.], tot_loss[loss=0.1773, simple_loss=0.265, pruned_loss=0.04475, over 1432399.45 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 18:13:43,390 INFO [train.py:842] (1/4) Epoch 27, batch 5350, loss[loss=0.1716, simple_loss=0.2608, pruned_loss=0.04116, over 7113.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2653, pruned_loss=0.04491, over 1429608.71 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:14:22,783 INFO [train.py:842] (1/4) Epoch 27, batch 5400, loss[loss=0.1493, simple_loss=0.2362, pruned_loss=0.03119, over 7434.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2653, pruned_loss=0.04482, over 1431348.67 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:15:02,198 INFO [train.py:842] (1/4) Epoch 27, batch 5450, loss[loss=0.1865, simple_loss=0.2734, pruned_loss=0.04978, over 7070.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2646, pruned_loss=0.04435, over 1428905.83 frames.], batch size: 18, lr: 2.03e-04
2022-05-28 18:15:41,469 INFO [train.py:842] (1/4) Epoch 27, batch 5500, loss[loss=0.1809, simple_loss=0.2752, pruned_loss=0.04334, over 7188.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2643, pruned_loss=0.04402, over 1427482.59 frames.], batch size: 26, lr: 2.03e-04
2022-05-28 18:16:21,184 INFO [train.py:842] (1/4) Epoch 27, batch 5550, loss[loss=0.1424, simple_loss=0.2386, pruned_loss=0.02317, over 7408.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2647, pruned_loss=0.04495, over 1426077.06 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:17:00,336 INFO [train.py:842] (1/4) Epoch 27, batch 5600, loss[loss=0.1418, simple_loss=0.2263, pruned_loss=0.02858, over 7150.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2646, pruned_loss=0.04461, over 1426506.05 frames.], batch size: 17, lr: 2.03e-04
2022-05-28 18:17:39,893 INFO [train.py:842] (1/4) Epoch 27, batch 5650, loss[loss=0.1674, simple_loss=0.2645, pruned_loss=0.03512, over 7235.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2651, pruned_loss=0.04428, over 1426378.97 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:18:19,121 INFO [train.py:842] (1/4) Epoch 27, batch 5700, loss[loss=0.173, simple_loss=0.2638, pruned_loss=0.04113, over 7409.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2657, pruned_loss=0.04472, over 1420132.05 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:18:58,812 INFO [train.py:842] (1/4) Epoch 27, batch 5750, loss[loss=0.1611, simple_loss=0.2463, pruned_loss=0.03798, over 7438.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2668, pruned_loss=0.04504, over 1422744.15 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:19:38,093 INFO [train.py:842] (1/4) Epoch 27, batch 5800, loss[loss=0.1392, simple_loss=0.2173, pruned_loss=0.03057, over 7002.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2672, pruned_loss=0.04528, over 1418802.18 frames.], batch size: 16, lr: 2.03e-04
2022-05-28 18:20:17,607 INFO [train.py:842] (1/4) Epoch 27, batch 5850, loss[loss=0.1823, simple_loss=0.2784, pruned_loss=0.04309, over 7166.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2671, pruned_loss=0.04528, over 1418150.18 frames.], batch size: 26, lr: 2.03e-04
2022-05-28 18:20:56,989 INFO [train.py:842] (1/4) Epoch 27, batch 5900, loss[loss=0.1549, simple_loss=0.2359, pruned_loss=0.03693, over 7431.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2674, pruned_loss=0.0457, over 1422677.88 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:21:36,801 INFO [train.py:842] (1/4) Epoch 27, batch 5950, loss[loss=0.1979, simple_loss=0.287, pruned_loss=0.05442, over 7238.00 frames.], tot_loss[loss=0.1781, simple_loss=0.266, pruned_loss=0.04512, over 1421220.94 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:22:15,967 INFO [train.py:842] (1/4) Epoch 27, batch 6000, loss[loss=0.1886, simple_loss=0.2774, pruned_loss=0.04992, over 7331.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2659, pruned_loss=0.04515, over 1420167.62 frames.], batch size: 22, lr: 2.03e-04
2022-05-28 18:22:15,968 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 18:22:25,576 INFO [train.py:871] (1/4) Epoch 27, validation: loss=0.1638, simple_loss=0.2621, pruned_loss=0.03281, over 868885.00 frames.
2022-05-28 18:23:04,737 INFO [train.py:842] (1/4) Epoch 27, batch 6050, loss[loss=0.1744, simple_loss=0.2564, pruned_loss=0.04622, over 7364.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2671, pruned_loss=0.04587, over 1414822.19 frames.], batch size: 19, lr: 2.03e-04
2022-05-28 18:23:44,262 INFO [train.py:842] (1/4) Epoch 27, batch 6100, loss[loss=0.2011, simple_loss=0.2842, pruned_loss=0.05904, over 7129.00 frames.], tot_loss[loss=0.178, simple_loss=0.2657, pruned_loss=0.04513, over 1415449.39 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:24:23,924 INFO [train.py:842] (1/4) Epoch 27, batch 6150, loss[loss=0.1507, simple_loss=0.2292, pruned_loss=0.03612, over 7124.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2665, pruned_loss=0.04558, over 1421093.58 frames.], batch size: 17, lr: 2.03e-04
2022-05-28 18:25:03,235 INFO [train.py:842] (1/4) Epoch 27, batch 6200, loss[loss=0.1646, simple_loss=0.2488, pruned_loss=0.04026, over 7285.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2663, pruned_loss=0.04512, over 1424162.86 frames.], batch size: 18, lr: 2.03e-04
2022-05-28 18:25:42,831 INFO [train.py:842] (1/4) Epoch 27, batch 6250, loss[loss=0.1731, simple_loss=0.2605, pruned_loss=0.04288, over 7330.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2658, pruned_loss=0.04479, over 1422560.97 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:26:22,151 INFO [train.py:842] (1/4) Epoch 27, batch 6300, loss[loss=0.1983, simple_loss=0.2943, pruned_loss=0.05117, over 7342.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2665, pruned_loss=0.04547, over 1419462.11 frames.], batch size: 22, lr: 2.03e-04
2022-05-28 18:27:01,742 INFO [train.py:842] (1/4) Epoch 27, batch 6350, loss[loss=0.1615, simple_loss=0.255, pruned_loss=0.03405, over 7326.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2652, pruned_loss=0.04479, over 1420421.61 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:27:40,931 INFO [train.py:842] (1/4) Epoch 27, batch 6400, loss[loss=0.1855, simple_loss=0.2813, pruned_loss=0.04483, over 7336.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2651, pruned_loss=0.04456, over 1421453.71 frames.], batch size: 22, lr: 2.03e-04
2022-05-28 18:28:20,597 INFO [train.py:842] (1/4) Epoch 27, batch 6450, loss[loss=0.1865, simple_loss=0.2768, pruned_loss=0.04809, over 7020.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2663, pruned_loss=0.04525, over 1417171.68 frames.], batch size: 28, lr: 2.03e-04
2022-05-28 18:28:59,835 INFO [train.py:842] (1/4) Epoch 27, batch 6500, loss[loss=0.2165, simple_loss=0.31, pruned_loss=0.06143, over 7205.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2665, pruned_loss=0.04553, over 1421402.22 frames.], batch size: 22, lr: 2.03e-04
2022-05-28 18:29:39,295 INFO [train.py:842] (1/4) Epoch 27, batch 6550, loss[loss=0.2042, simple_loss=0.2919, pruned_loss=0.05819, over 7209.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2659, pruned_loss=0.04535, over 1423590.89 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:30:18,673 INFO [train.py:842] (1/4) Epoch 27, batch 6600, loss[loss=0.1715, simple_loss=0.2596, pruned_loss=0.04174, over 7443.00 frames.], tot_loss[loss=0.1783, simple_loss=0.266, pruned_loss=0.04526, over 1424717.49 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:30:58,060 INFO [train.py:842] (1/4) Epoch 27, batch 6650, loss[loss=0.1707, simple_loss=0.2691, pruned_loss=0.03614, over 7178.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2667, pruned_loss=0.04522, over 1421807.44 frames.], batch size: 26, lr: 2.03e-04
2022-05-28 18:31:37,219 INFO [train.py:842] (1/4) Epoch 27, batch 6700, loss[loss=0.1895, simple_loss=0.2835, pruned_loss=0.04773, over 7195.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2675, pruned_loss=0.04554, over 1424196.48 frames.], batch size: 26, lr: 2.03e-04
2022-05-28 18:32:16,894 INFO [train.py:842] (1/4) Epoch 27, batch 6750, loss[loss=0.1525, simple_loss=0.2344, pruned_loss=0.03528, over 7243.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2676, pruned_loss=0.04565, over 1423437.65 frames.], batch size: 16, lr: 2.03e-04
2022-05-28 18:32:56,070 INFO [train.py:842] (1/4) Epoch 27, batch 6800, loss[loss=0.1477, simple_loss=0.2339, pruned_loss=0.03074, over 7267.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2672, pruned_loss=0.04522, over 1424368.58 frames.], batch size: 17, lr: 2.03e-04
2022-05-28 18:33:35,646 INFO [train.py:842] (1/4) Epoch 27, batch 6850, loss[loss=0.1859, simple_loss=0.2679, pruned_loss=0.05194, over 7161.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2671, pruned_loss=0.04553, over 1422699.03 frames.], batch size: 18, lr: 2.03e-04
2022-05-28 18:34:14,919 INFO [train.py:842] (1/4) Epoch 27, batch 6900, loss[loss=0.1872, simple_loss=0.2731, pruned_loss=0.05061, over 7155.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2673, pruned_loss=0.04551, over 1425759.91 frames.], batch size: 19, lr: 2.03e-04
2022-05-28 18:34:54,549 INFO [train.py:842] (1/4) Epoch 27, batch 6950, loss[loss=0.1863, simple_loss=0.2765, pruned_loss=0.04802, over 6632.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2672, pruned_loss=0.04581, over 1426790.92 frames.], batch size: 31, lr: 2.03e-04
2022-05-28 18:35:33,885 INFO [train.py:842] (1/4) Epoch 27, batch 7000, loss[loss=0.1582, simple_loss=0.2369, pruned_loss=0.03978, over 7404.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2661, pruned_loss=0.04513, over 1427868.51 frames.], batch size: 18, lr: 2.03e-04
2022-05-28 18:36:13,578 INFO [train.py:842] (1/4) Epoch 27, batch 7050, loss[loss=0.1924, simple_loss=0.2783, pruned_loss=0.05326, over 7336.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2665, pruned_loss=0.04532, over 1427957.45 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:36:52,890 INFO [train.py:842] (1/4) Epoch 27, batch 7100, loss[loss=0.1742, simple_loss=0.2642, pruned_loss=0.04206, over 7254.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2657, pruned_loss=0.04485, over 1422556.24 frames.], batch size: 19, lr: 2.03e-04
2022-05-28 18:37:32,340 INFO [train.py:842] (1/4) Epoch 27, batch 7150, loss[loss=0.184, simple_loss=0.2666, pruned_loss=0.05072, over 7290.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2663, pruned_loss=0.04561, over 1419426.19 frames.], batch size: 24, lr: 2.03e-04
2022-05-28 18:38:11,624 INFO [train.py:842] (1/4) Epoch 27, batch 7200, loss[loss=0.1375, simple_loss=0.2243, pruned_loss=0.02529, over 7281.00 frames.], tot_loss[loss=0.178, simple_loss=0.2662, pruned_loss=0.04488, over 1422146.86 frames.], batch size: 17, lr: 2.03e-04
2022-05-28 18:38:51,123 INFO [train.py:842] (1/4) Epoch 27, batch 7250, loss[loss=0.1419, simple_loss=0.2331, pruned_loss=0.02538, over 7408.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2667, pruned_loss=0.04499, over 1423954.11 frames.], batch size: 18, lr: 2.03e-04
2022-05-28 18:39:30,082 INFO [train.py:842] (1/4) Epoch 27, batch 7300, loss[loss=0.1957, simple_loss=0.2883, pruned_loss=0.05151, over 7414.00 frames.], tot_loss[loss=0.178, simple_loss=0.2663, pruned_loss=0.04488, over 1425264.33 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:40:09,830 INFO [train.py:842] (1/4) Epoch 27, batch 7350, loss[loss=0.1658, simple_loss=0.2525, pruned_loss=0.03961, over 7143.00 frames.], tot_loss[loss=0.177, simple_loss=0.2652, pruned_loss=0.04439, over 1425727.69 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:40:49,109 INFO [train.py:842] (1/4) Epoch 27, batch 7400, loss[loss=0.1808, simple_loss=0.2739, pruned_loss=0.04383, over 7199.00 frames.], tot_loss[loss=0.176, simple_loss=0.2643, pruned_loss=0.0438, over 1423116.96 frames.], batch size: 23, lr: 2.03e-04
2022-05-28 18:41:28,899 INFO [train.py:842] (1/4) Epoch 27, batch 7450, loss[loss=0.1412, simple_loss=0.2267, pruned_loss=0.02786, over 7279.00 frames.], tot_loss[loss=0.176, simple_loss=0.2643, pruned_loss=0.0439, over 1427289.90 frames.], batch size: 17, lr: 2.03e-04
2022-05-28 18:42:08,154 INFO [train.py:842] (1/4) Epoch 27, batch 7500, loss[loss=0.2515, simple_loss=0.337, pruned_loss=0.08295, over 5132.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2645, pruned_loss=0.04429, over 1424217.19 frames.], batch size: 52, lr: 2.03e-04
2022-05-28 18:42:47,768 INFO [train.py:842] (1/4) Epoch 27, batch 7550, loss[loss=0.1761, simple_loss=0.2769, pruned_loss=0.03768, over 7328.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2649, pruned_loss=0.04425, over 1426049.16 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:43:27,153 INFO [train.py:842] (1/4) Epoch 27, batch 7600, loss[loss=0.1715, simple_loss=0.2657, pruned_loss=0.03862, over 7336.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2644, pruned_loss=0.04387, over 1427143.72 frames.], batch size: 22, lr: 2.03e-04
2022-05-28 18:44:07,172 INFO [train.py:842] (1/4) Epoch 27, batch 7650, loss[loss=0.1965, simple_loss=0.2851, pruned_loss=0.05393, over 7272.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2645, pruned_loss=0.04402, over 1431582.10 frames.], batch size: 24, lr: 2.03e-04
2022-05-28 18:44:46,343 INFO [train.py:842] (1/4) Epoch 27, batch 7700, loss[loss=0.1761, simple_loss=0.2651, pruned_loss=0.04353, over 7204.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2634, pruned_loss=0.04364, over 1426610.59 frames.], batch size: 22, lr: 2.03e-04
2022-05-28 18:45:26,182 INFO [train.py:842] (1/4) Epoch 27, batch 7750, loss[loss=0.1793, simple_loss=0.2769, pruned_loss=0.04088, over 7206.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2624, pruned_loss=0.04342, over 1426248.91 frames.], batch size: 22, lr: 2.02e-04
2022-05-28 18:46:05,421 INFO [train.py:842] (1/4) Epoch 27, batch 7800, loss[loss=0.1986, simple_loss=0.2826, pruned_loss=0.0573, over 7303.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2627, pruned_loss=0.04373, over 1429334.68 frames.], batch size: 25, lr: 2.02e-04
2022-05-28 18:46:45,078 INFO [train.py:842] (1/4) Epoch 27, batch 7850, loss[loss=0.1563, simple_loss=0.2337, pruned_loss=0.03939, over 6786.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2623, pruned_loss=0.04371, over 1427706.35 frames.], batch size: 15, lr: 2.02e-04
2022-05-28 18:47:24,275 INFO [train.py:842] (1/4) Epoch 27, batch 7900, loss[loss=0.1622, simple_loss=0.2554, pruned_loss=0.03454, over 7314.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2622, pruned_loss=0.04342, over 1426020.22 frames.], batch size: 21, lr: 2.02e-04
2022-05-28 18:48:03,877 INFO [train.py:842] (1/4) Epoch 27, batch 7950, loss[loss=0.158, simple_loss=0.2403, pruned_loss=0.03781, over 7303.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2634, pruned_loss=0.0441, over 1424404.90 frames.], batch size: 17, lr: 2.02e-04
2022-05-28 18:48:42,923 INFO [train.py:842] (1/4) Epoch 27, batch 8000, loss[loss=0.1711, simple_loss=0.2473, pruned_loss=0.04743, over 7150.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2641, pruned_loss=0.04452, over 1418094.68 frames.], batch size: 17, lr: 2.02e-04
2022-05-28 18:49:22,365 INFO [train.py:842] (1/4) Epoch 27, batch 8050, loss[loss=0.177, simple_loss=0.2729, pruned_loss=0.0406, over 7224.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2642, pruned_loss=0.04461, over 1416949.35 frames.], batch size: 26, lr: 2.02e-04
2022-05-28 18:50:01,716 INFO [train.py:842] (1/4) Epoch 27, batch 8100, loss[loss=0.1917, simple_loss=0.2826, pruned_loss=0.05034, over 7248.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2651, pruned_loss=0.04522, over 1419106.83 frames.], batch size: 20, lr: 2.02e-04
2022-05-28 18:50:41,129 INFO [train.py:842] (1/4) Epoch 27, batch 8150, loss[loss=0.1562, simple_loss=0.2391, pruned_loss=0.03669, over 7363.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2658, pruned_loss=0.04552, over 1417270.19 frames.], batch size: 19, lr: 2.02e-04
2022-05-28 18:51:20,449 INFO [train.py:842] (1/4) Epoch 27, batch 8200, loss[loss=0.1608, simple_loss=0.2431, pruned_loss=0.03924, over 6767.00 frames.], tot_loss[loss=0.1785, simple_loss=0.266, pruned_loss=0.0455, over 1421469.29 frames.], batch size: 15, lr: 2.02e-04
2022-05-28 18:51:59,949 INFO [train.py:842] (1/4) Epoch 27, batch 8250, loss[loss=0.1842, simple_loss=0.2807, pruned_loss=0.04381, over 6728.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2657, pruned_loss=0.04523, over 1419431.48 frames.], batch size: 31, lr: 2.02e-04
2022-05-28 18:52:39,230 INFO [train.py:842] (1/4) Epoch 27, batch 8300, loss[loss=0.1522, simple_loss=0.2572, pruned_loss=0.02356, over 7114.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2668, pruned_loss=0.04531, over 1422333.78 frames.], batch size: 21, lr: 2.02e-04
2022-05-28 18:53:18,876 INFO [train.py:842] (1/4) Epoch 27, batch 8350, loss[loss=0.1781, simple_loss=0.256, pruned_loss=0.05006, over 7149.00 frames.], tot_loss[loss=0.1779, simple_loss=0.266, pruned_loss=0.04487, over 1421019.56 frames.], batch size: 17, lr: 2.02e-04
2022-05-28 18:53:58,071 INFO [train.py:842] (1/4) Epoch 27, batch 8400, loss[loss=0.1651, simple_loss=0.254, pruned_loss=0.03804, over 7413.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2668, pruned_loss=0.04434, over 1424134.39 frames.], batch size: 21, lr: 2.02e-04
2022-05-28 18:54:37,716 INFO [train.py:842] (1/4) Epoch 27, batch 8450, loss[loss=0.1563, simple_loss=0.2444, pruned_loss=0.03407, over 7170.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2674, pruned_loss=0.04491, over 1423358.26 frames.], batch size: 18, lr: 2.02e-04
2022-05-28 18:55:16,714 INFO [train.py:842] (1/4) Epoch 27, batch 8500, loss[loss=0.1978, simple_loss=0.2835, pruned_loss=0.05601, over 7209.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2671, pruned_loss=0.0448, over 1425726.64 frames.], batch size: 22, lr: 2.02e-04
2022-05-28 18:55:56,062 INFO [train.py:842] (1/4) Epoch 27, batch 8550, loss[loss=0.1683, simple_loss=0.2587, pruned_loss=0.03894, over 6709.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2676, pruned_loss=0.04511, over 1424517.67 frames.], batch size: 31, lr: 2.02e-04
2022-05-28 18:56:35,121 INFO [train.py:842] (1/4) Epoch 27, batch 8600, loss[loss=0.1803, simple_loss=0.2709, pruned_loss=0.04487, over 7126.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2679, pruned_loss=0.04541, over 1422965.38 frames.], batch size: 21, lr: 2.02e-04
2022-05-28 18:57:14,397 INFO [train.py:842] (1/4) Epoch 27, batch 8650, loss[loss=0.1824, simple_loss=0.2784, pruned_loss=0.04322, over 7219.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2671, pruned_loss=0.04485, over 1415970.39 frames.], batch size: 21, lr: 2.02e-04
2022-05-28 18:57:53,612 INFO [train.py:842] (1/4) Epoch 27, batch 8700, loss[loss=0.182, simple_loss=0.2733, pruned_loss=0.04532, over 7203.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2676, pruned_loss=0.04551, over 1416835.24 frames.], batch size: 22, lr: 2.02e-04
2022-05-28 18:58:33,170 INFO [train.py:842] (1/4) Epoch 27, batch 8750, loss[loss=0.2037, simple_loss=0.294, pruned_loss=0.05671, over 7188.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2672, pruned_loss=0.045, over 1413760.32 frames.], batch size: 22, lr: 2.02e-04
2022-05-28 18:59:12,374 INFO [train.py:842] (1/4) Epoch 27, batch 8800, loss[loss=0.1279, simple_loss=0.2077, pruned_loss=0.02408, over 6812.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2671, pruned_loss=0.04495, over 1410783.62 frames.], batch size: 15, lr: 2.02e-04
2022-05-28 18:59:51,554 INFO [train.py:842] (1/4) Epoch 27, batch 8850, loss[loss=0.1618, simple_loss=0.2497, pruned_loss=0.03692, over 7072.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2666, pruned_loss=0.04439, over 1412555.23 frames.], batch size: 18, lr: 2.02e-04
2022-05-28 19:00:30,271 INFO [train.py:842] (1/4) Epoch 27, batch 8900, loss[loss=0.2338, simple_loss=0.3131, pruned_loss=0.07718, over 7190.00 frames.], tot_loss[loss=0.1794, simple_loss=0.268, pruned_loss=0.04538, over 1407045.14 frames.], batch size: 22, lr: 2.02e-04
2022-05-28 19:01:09,136 INFO [train.py:842] (1/4) Epoch 27, batch 8950, loss[loss=0.1588, simple_loss=0.2351, pruned_loss=0.04123, over 7019.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2679, pruned_loss=0.04544, over 1403043.90 frames.], batch size: 16, lr: 2.02e-04
2022-05-28 19:01:47,930 INFO [train.py:842] (1/4) Epoch 27, batch 9000, loss[loss=0.1887, simple_loss=0.281, pruned_loss=0.04822, over 7094.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2677, pruned_loss=0.04565, over 1396235.40 frames.], batch size: 28, lr: 2.02e-04
2022-05-28 19:01:47,930 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 19:01:58,049 INFO [train.py:871] (1/4) Epoch 27, validation: loss=0.1653, simple_loss=0.2632, pruned_loss=0.03366, over 868885.00 frames.
2022-05-28 19:02:39,563 INFO [train.py:842] (1/4) Epoch 27, batch 9050, loss[loss=0.1662, simple_loss=0.2564, pruned_loss=0.03804, over 6507.00 frames.], tot_loss[loss=0.1809, simple_loss=0.269, pruned_loss=0.04635, over 1371124.68 frames.], batch size: 39, lr: 2.02e-04
2022-05-28 19:03:17,109 INFO [train.py:842] (1/4) Epoch 27, batch 9100, loss[loss=0.1594, simple_loss=0.2544, pruned_loss=0.03223, over 6834.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2715, pruned_loss=0.04752, over 1334390.31 frames.], batch size: 31, lr: 2.02e-04
2022-05-28 19:03:55,319 INFO [train.py:842] (1/4) Epoch 27, batch 9150, loss[loss=0.2113, simple_loss=0.304, pruned_loss=0.05932, over 4783.00 frames.], tot_loss[loss=0.187, simple_loss=0.2743, pruned_loss=0.04984, over 1268098.73 frames.], batch size: 52, lr: 2.02e-04
2022-05-28 19:04:48,249 INFO [train.py:842] (1/4) Epoch 28, batch 0, loss[loss=0.154, simple_loss=0.248, pruned_loss=0.03001, over 7264.00 frames.], tot_loss[loss=0.154, simple_loss=0.248, pruned_loss=0.03001, over 7264.00 frames.], batch size: 19, lr: 1.98e-04
2022-05-28 19:05:27,976 INFO [train.py:842] (1/4) Epoch 28, batch 50, loss[loss=0.1722, simple_loss=0.2552, pruned_loss=0.04459, over 7262.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2656, pruned_loss=0.04531, over 321714.38 frames.], batch size: 19, lr: 1.98e-04
2022-05-28 19:06:07,173 INFO [train.py:842] (1/4) Epoch 28, batch 100, loss[loss=0.167, simple_loss=0.2564, pruned_loss=0.03887, over 7150.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2643, pruned_loss=0.04392, over 564294.74 frames.], batch size: 20, lr: 1.98e-04
2022-05-28 19:06:46,680 INFO [train.py:842] (1/4) Epoch 28, batch 150, loss[loss=0.1518, simple_loss=0.2481, pruned_loss=0.02773, over 6273.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2647, pruned_loss=0.0441, over 752332.50 frames.], batch size: 38, lr: 1.98e-04
2022-05-28 19:07:25,767 INFO [train.py:842] (1/4) Epoch 28, batch 200, loss[loss=0.2171, simple_loss=0.3019, pruned_loss=0.06614, over 7203.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2648, pruned_loss=0.04447, over 898430.37 frames.], batch size: 23, lr: 1.98e-04
2022-05-28 19:08:05,235 INFO [train.py:842] (1/4) Epoch 28, batch 250, loss[loss=0.1899, simple_loss=0.2848, pruned_loss=0.04753, over 7265.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2658, pruned_loss=0.04472, over 1015854.05 frames.], batch size: 24, lr: 1.98e-04
2022-05-28 19:08:44,424 INFO [train.py:842] (1/4) Epoch 28, batch 300, loss[loss=0.1912, simple_loss=0.2795, pruned_loss=0.05146, over 6762.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2666, pruned_loss=0.04533, over 1105607.63 frames.], batch size: 31, lr: 1.98e-04
2022-05-28 19:09:23,904 INFO [train.py:842] (1/4) Epoch 28, batch 350, loss[loss=0.1805, simple_loss=0.2689, pruned_loss=0.04608, over 7167.00 frames.], tot_loss[loss=0.177, simple_loss=0.2652, pruned_loss=0.04444, over 1177219.65 frames.], batch size: 19, lr: 1.98e-04
2022-05-28 19:10:03,125 INFO [train.py:842] (1/4) Epoch 28, batch 400, loss[loss=0.1653, simple_loss=0.2536, pruned_loss=0.03852, over 7133.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2652, pruned_loss=0.04462, over 1233134.57 frames.], batch size: 17, lr: 1.98e-04
2022-05-28 19:10:42,659 INFO [train.py:842] (1/4) Epoch 28, batch 450, loss[loss=0.1871, simple_loss=0.288, pruned_loss=0.04311, over 7305.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2665, pruned_loss=0.04545, over 1270050.49 frames.], batch size: 25, lr: 1.98e-04
2022-05-28 19:11:21,973 INFO [train.py:842] (1/4) Epoch 28, batch 500, loss[loss=0.1667, simple_loss=0.2778, pruned_loss=0.02784, over 7322.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2664, pruned_loss=0.0445, over 1307385.76 frames.], batch size: 21, lr: 1.98e-04
2022-05-28 19:12:01,645 INFO [train.py:842] (1/4) Epoch 28, batch 550, loss[loss=0.1898, simple_loss=0.2727, pruned_loss=0.05349, over 7065.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2661, pruned_loss=0.04477, over 1329674.51 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:12:41,062 INFO [train.py:842] (1/4) Epoch 28, batch 600, loss[loss=0.1859, simple_loss=0.2739, pruned_loss=0.04894, over 7329.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2666, pruned_loss=0.04562, over 1347960.59 frames.], batch size: 20, lr: 1.98e-04
2022-05-28 19:13:20,387 INFO [train.py:842] (1/4) Epoch 28, batch 650, loss[loss=0.1517, simple_loss=0.2521, pruned_loss=0.0257, over 7078.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2661, pruned_loss=0.04506, over 1365243.87 frames.], batch size: 28, lr: 1.98e-04
2022-05-28 19:13:59,785 INFO [train.py:842] (1/4) Epoch 28, batch 700, loss[loss=0.1721, simple_loss=0.2662, pruned_loss=0.03903, over 7070.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2653, pruned_loss=0.04442, over 1379686.94 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:14:39,594 INFO [train.py:842] (1/4) Epoch 28, batch 750, loss[loss=0.1599, simple_loss=0.246, pruned_loss=0.03696, over 7212.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2637, pruned_loss=0.04376, over 1391107.91 frames.], batch size: 21, lr: 1.98e-04
2022-05-28 19:15:18,781 INFO [train.py:842] (1/4) Epoch 28, batch 800, loss[loss=0.1739, simple_loss=0.2681, pruned_loss=0.03988, over 7050.00 frames.], tot_loss[loss=0.1755, simple_loss=0.264, pruned_loss=0.04352, over 1397966.63 frames.], batch size: 28, lr: 1.98e-04
2022-05-28 19:15:58,572 INFO [train.py:842] (1/4) Epoch 28, batch 850, loss[loss=0.1966, simple_loss=0.2992, pruned_loss=0.04698, over 7258.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2645, pruned_loss=0.04324, over 1405554.35 frames.], batch size: 25, lr: 1.98e-04
2022-05-28 19:16:37,595 INFO [train.py:842] (1/4) Epoch 28, batch 900, loss[loss=0.1778, simple_loss=0.2584, pruned_loss=0.04861, over 6995.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2651, pruned_loss=0.04362, over 1408435.62 frames.], batch size: 16, lr: 1.98e-04
2022-05-28 19:17:17,018 INFO [train.py:842] (1/4) Epoch 28, batch 950, loss[loss=0.1687, simple_loss=0.25, pruned_loss=0.04373, over 7161.00 frames.], tot_loss[loss=0.176, simple_loss=0.2649, pruned_loss=0.04361, over 1410406.05 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:17:56,530 INFO [train.py:842] (1/4) Epoch 28, batch 1000, loss[loss=0.1859, simple_loss=0.27, pruned_loss=0.05094, over 7426.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2651, pruned_loss=0.0439, over 1415865.84 frames.], batch size: 20, lr: 1.98e-04
2022-05-28 19:18:36,070 INFO [train.py:842] (1/4) Epoch 28, batch 1050, loss[loss=0.1782, simple_loss=0.2643, pruned_loss=0.04608, over 7415.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2656, pruned_loss=0.0441, over 1416139.92 frames.], batch size: 21, lr: 1.98e-04
2022-05-28 19:19:15,232 INFO [train.py:842] (1/4) Epoch 28, batch 1100, loss[loss=0.1589, simple_loss=0.2501, pruned_loss=0.03383, over 7071.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2655, pruned_loss=0.04449, over 1414333.80 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:19:55,046 INFO [train.py:842] (1/4) Epoch 28, batch 1150, loss[loss=0.1943, simple_loss=0.29, pruned_loss=0.04932, over 7199.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2656, pruned_loss=0.04488, over 1419628.85 frames.], batch size: 23, lr: 1.98e-04
2022-05-28 19:20:34,393 INFO [train.py:842] (1/4) Epoch 28, batch 1200, loss[loss=0.2281, simple_loss=0.2981, pruned_loss=0.079, over 7137.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2655, pruned_loss=0.04461, over 1424156.60 frames.], batch size: 17, lr: 1.98e-04
2022-05-28 19:21:13,747 INFO [train.py:842] (1/4) Epoch 28, batch 1250, loss[loss=0.1528, simple_loss=0.2355, pruned_loss=0.03506, over 7122.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2657, pruned_loss=0.04505, over 1422294.94 frames.], batch size: 17, lr: 1.98e-04
2022-05-28 19:21:53,006 INFO [train.py:842] (1/4) Epoch 28, batch 1300, loss[loss=0.1622, simple_loss=0.2498, pruned_loss=0.03732, over 7275.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2669, pruned_loss=0.04608, over 1418691.37 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:22:32,610 INFO [train.py:842] (1/4) Epoch 28, batch 1350, loss[loss=0.1644, simple_loss=0.2456, pruned_loss=0.04161, over 7348.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2661, pruned_loss=0.04535, over 1418800.01 frames.], batch size: 19, lr: 1.98e-04
2022-05-28 19:23:11,739 INFO [train.py:842] (1/4) Epoch 28, batch 1400, loss[loss=0.2206, simple_loss=0.298, pruned_loss=0.07165, over 7074.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2678, pruned_loss=0.04645, over 1419542.38 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:23:51,640 INFO [train.py:842] (1/4) Epoch 28, batch 1450, loss[loss=0.167, simple_loss=0.2507, pruned_loss=0.04164, over 7337.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2659, pruned_loss=0.04546, over 1421783.43 frames.], batch size: 20, lr: 1.98e-04
2022-05-28 19:24:30,671 INFO [train.py:842] (1/4) Epoch 28, batch 1500, loss[loss=0.1798, simple_loss=0.2722, pruned_loss=0.04367, over 7108.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2664, pruned_loss=0.04512, over 1423491.90 frames.], batch size: 21, lr: 1.98e-04
2022-05-28 19:25:10,191 INFO [train.py:842] (1/4) Epoch 28, batch 1550, loss[loss=0.1374, simple_loss=0.2176, pruned_loss=0.02855, over 6765.00 frames.], tot_loss[loss=0.178, simple_loss=0.2661, pruned_loss=0.04498, over 1420384.94 frames.], batch size: 15, lr: 1.98e-04
2022-05-28 19:25:49,471 INFO [train.py:842] (1/4) Epoch 28, batch 1600, loss[loss=0.1887, simple_loss=0.273, pruned_loss=0.05223, over 7419.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2661, pruned_loss=0.04508, over 1423979.30 frames.], batch size: 21, lr: 1.98e-04
2022-05-28 19:26:29,070 INFO [train.py:842] (1/4) Epoch 28, batch 1650, loss[loss=0.1742, simple_loss=0.2573, pruned_loss=0.04556, over 7062.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2656, pruned_loss=0.04476, over 1424839.21 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:27:08,103 INFO [train.py:842] (1/4) Epoch 28, batch 1700, loss[loss=0.1938, simple_loss=0.26, pruned_loss=0.0638, over 7353.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2665, pruned_loss=0.04519, over 1427067.86 frames.], batch size: 19, lr: 1.98e-04
2022-05-28 19:27:48,075 INFO [train.py:842] (1/4) Epoch 28, batch 1750, loss[loss=0.2735, simple_loss=0.3462, pruned_loss=0.1004, over 6718.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2654, pruned_loss=0.04465, over 1428358.07 frames.], batch size: 31, lr: 1.98e-04
2022-05-28 19:28:27,182 INFO [train.py:842] (1/4) Epoch 28, batch 1800, loss[loss=0.1681, simple_loss=0.2606, pruned_loss=0.0378, over 7245.00 frames.], tot_loss[loss=0.1767, simple_loss=0.265, pruned_loss=0.04419, over 1427486.38 frames.], batch size: 20, lr: 1.98e-04
2022-05-28 19:29:06,916 INFO [train.py:842] (1/4) Epoch 28, batch 1850, loss[loss=0.1849, simple_loss=0.2684, pruned_loss=0.0507, over 7160.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2656, pruned_loss=0.04464, over 1430273.19 frames.], batch size: 19, lr: 1.98e-04
2022-05-28 19:29:46,184 INFO [train.py:842] (1/4) Epoch 28, batch 1900, loss[loss=0.1508, simple_loss=0.2285, pruned_loss=0.03652, over 7265.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2662, pruned_loss=0.04436, over 1430111.51 frames.], batch size: 17, lr: 1.98e-04
2022-05-28 19:30:25,836 INFO [train.py:842] (1/4) Epoch 28, batch 1950, loss[loss=0.1795, simple_loss=0.269, pruned_loss=0.04498, over 6507.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2659, pruned_loss=0.04476, over 1425681.37 frames.], batch size: 38, lr: 1.98e-04
2022-05-28 19:31:05,123 INFO [train.py:842] (1/4) Epoch 28, batch 2000, loss[loss=0.1816, simple_loss=0.2771, pruned_loss=0.04301, over 7218.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2654, pruned_loss=0.04493, over 1424824.45 frames.], batch size: 21, lr: 1.98e-04
2022-05-28 19:31:44,623 INFO [train.py:842] (1/4) Epoch 28, batch 2050, loss[loss=0.1942, simple_loss=0.2848, pruned_loss=0.05178, over 7196.00 frames.], tot_loss[loss=0.1779, simple_loss=0.266, pruned_loss=0.04494, over 1423301.71 frames.], batch size: 23, lr: 1.97e-04
2022-05-28 19:32:34,443 INFO [train.py:842] (1/4) Epoch 28, batch 2100, loss[loss=0.1956, simple_loss=0.2716, pruned_loss=0.05983, over 7278.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2649, pruned_loss=0.04468, over 1423236.32 frames.], batch size: 25, lr: 1.97e-04
2022-05-28 19:33:13,901 INFO [train.py:842] (1/4) Epoch 28, batch 2150, loss[loss=0.1864, simple_loss=0.2684, pruned_loss=0.05214, over 7135.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2659, pruned_loss=0.0452, over 1422128.78 frames.], batch size: 17, lr: 1.97e-04
2022-05-28 19:33:53,236 INFO [train.py:842] (1/4) Epoch 28, batch 2200, loss[loss=0.2101, simple_loss=0.3007, pruned_loss=0.05973, over 7285.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2653, pruned_loss=0.04507, over 1420626.95 frames.], batch size: 24, lr: 1.97e-04
2022-05-28 19:34:32,666 INFO [train.py:842] (1/4) Epoch 28, batch 2250, loss[loss=0.1866, simple_loss=0.2767, pruned_loss=0.04823, over 7335.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2655, pruned_loss=0.04484, over 1423247.55 frames.], batch size: 22, lr: 1.97e-04
2022-05-28 19:35:11,815 INFO [train.py:842] (1/4) Epoch 28, batch 2300, loss[loss=0.1765, simple_loss=0.269, pruned_loss=0.042, over 7143.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2656, pruned_loss=0.04475, over 1421217.07 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:35:51,234 INFO [train.py:842] (1/4) Epoch 28, batch 2350, loss[loss=0.1531, simple_loss=0.249, pruned_loss=0.02858, over 7157.00 frames.], tot_loss[loss=0.178, simple_loss=0.2661, pruned_loss=0.04491, over 1419375.29 frames.], batch size: 19, lr: 1.97e-04
2022-05-28 19:36:30,507 INFO [train.py:842] (1/4) Epoch 28, batch 2400, loss[loss=0.2087, simple_loss=0.2873, pruned_loss=0.06507, over 7190.00 frames.], tot_loss[loss=0.1787, simple_loss=0.267, pruned_loss=0.04524, over 1422417.99 frames.], batch size: 23, lr: 1.97e-04
2022-05-28 19:37:10,248 INFO [train.py:842] (1/4) Epoch 28, batch 2450, loss[loss=0.1579, simple_loss=0.2636, pruned_loss=0.0261, over 6230.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2658, pruned_loss=0.04443, over 1423269.63 frames.], batch size: 37, lr: 1.97e-04
2022-05-28 19:37:49,530 INFO [train.py:842] (1/4) Epoch 28, batch 2500, loss[loss=0.1692, simple_loss=0.2412, pruned_loss=0.04863, over 7257.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2653, pruned_loss=0.04431, over 1420522.02 frames.], batch size: 16, lr: 1.97e-04
2022-05-28 19:38:29,251 INFO [train.py:842] (1/4) Epoch 28, batch 2550, loss[loss=0.1787, simple_loss=0.2708, pruned_loss=0.04332, over 7264.00 frames.], tot_loss[loss=0.1768, simple_loss=0.265, pruned_loss=0.04429, over 1421446.79 frames.], batch size: 19, lr: 1.97e-04
2022-05-28 19:39:08,410 INFO [train.py:842] (1/4) Epoch 28, batch 2600, loss[loss=0.2168, simple_loss=0.3127, pruned_loss=0.06049, over 7236.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2649, pruned_loss=0.04425, over 1421745.53 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:39:48,075 INFO [train.py:842] (1/4) Epoch 28, batch 2650, loss[loss=0.1528, simple_loss=0.2392, pruned_loss=0.03319, over 7005.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2652, pruned_loss=0.04422, over 1420198.02 frames.], batch size: 16, lr: 1.97e-04
2022-05-28 19:40:27,365 INFO [train.py:842] (1/4) Epoch 28, batch 2700, loss[loss=0.1662, simple_loss=0.2558, pruned_loss=0.03827, over 7322.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2651, pruned_loss=0.04403, over 1422034.96 frames.], batch size: 21, lr: 1.97e-04
2022-05-28 19:41:06,933 INFO [train.py:842] (1/4) Epoch 28, batch 2750, loss[loss=0.1861, simple_loss=0.2683, pruned_loss=0.05193, over 7264.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2656, pruned_loss=0.04444, over 1419532.95 frames.], batch size: 19, lr: 1.97e-04
2022-05-28 19:41:45,945 INFO [train.py:842] (1/4) Epoch 28, batch 2800, loss[loss=0.2025, simple_loss=0.2965, pruned_loss=0.05422, over 7238.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2659, pruned_loss=0.0453, over 1415389.04 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:42:25,440 INFO [train.py:842] (1/4) Epoch 28, batch 2850, loss[loss=0.1224, simple_loss=0.2127, pruned_loss=0.01601, over 7131.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2659, pruned_loss=0.0453, over 1420209.50 frames.], batch size: 17, lr: 1.97e-04
2022-05-28 19:43:04,593 INFO [train.py:842] (1/4) Epoch 28, batch 2900, loss[loss=0.193, simple_loss=0.2827, pruned_loss=0.05163, over 7298.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2674, pruned_loss=0.04582, over 1419375.57 frames.], batch size: 25, lr: 1.97e-04
2022-05-28 19:43:43,926 INFO [train.py:842] (1/4) Epoch 28, batch 2950, loss[loss=0.2094, simple_loss=0.2826, pruned_loss=0.06814, over 7218.00 frames.], tot_loss[loss=0.18, simple_loss=0.2678, pruned_loss=0.04611, over 1422468.85 frames.], batch size: 23, lr: 1.97e-04
2022-05-28 19:44:22,906 INFO [train.py:842] (1/4) Epoch 28, batch 3000, loss[loss=0.1954, simple_loss=0.2848, pruned_loss=0.05306, over 7018.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2664, pruned_loss=0.04523, over 1424911.42 frames.], batch size: 28, lr: 1.97e-04
2022-05-28 19:44:22,907 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 19:44:32,547 INFO [train.py:871] (1/4) Epoch 28, validation: loss=0.165, simple_loss=0.263, pruned_loss=0.03349, over 868885.00 frames.
2022-05-28 19:45:12,187 INFO [train.py:842] (1/4) Epoch 28, batch 3050, loss[loss=0.151, simple_loss=0.2255, pruned_loss=0.03829, over 7124.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2653, pruned_loss=0.04477, over 1426553.36 frames.], batch size: 17, lr: 1.97e-04
2022-05-28 19:45:51,489 INFO [train.py:842] (1/4) Epoch 28, batch 3100, loss[loss=0.2081, simple_loss=0.309, pruned_loss=0.05366, over 7372.00 frames.], tot_loss[loss=0.1787, simple_loss=0.266, pruned_loss=0.04565, over 1425340.09 frames.], batch size: 23, lr: 1.97e-04
2022-05-28 19:46:31,131 INFO [train.py:842] (1/4) Epoch 28, batch 3150, loss[loss=0.1545, simple_loss=0.2298, pruned_loss=0.03966, over 7408.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2652, pruned_loss=0.04582, over 1423630.48 frames.], batch size: 18, lr: 1.97e-04
2022-05-28 19:47:10,211 INFO [train.py:842] (1/4) Epoch 28, batch 3200, loss[loss=0.1866, simple_loss=0.2791, pruned_loss=0.04703, over 7330.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2658, pruned_loss=0.04548, over 1424938.83 frames.], batch size: 21, lr: 1.97e-04
2022-05-28 19:47:50,112 INFO [train.py:842] (1/4) Epoch 28, batch 3250, loss[loss=0.1697, simple_loss=0.257, pruned_loss=0.04121, over 7170.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2645, pruned_loss=0.04452, over 1423840.25 frames.], batch size: 18, lr: 1.97e-04
2022-05-28 19:48:29,260 INFO [train.py:842] (1/4) Epoch 28, batch 3300, loss[loss=0.1479, simple_loss=0.2236, pruned_loss=0.03608, over 7005.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2652, pruned_loss=0.04494, over 1423113.51 frames.], batch size: 16, lr: 1.97e-04
2022-05-28 19:49:08,812 INFO [train.py:842] (1/4) Epoch 28, batch 3350, loss[loss=0.1685, simple_loss=0.2639, pruned_loss=0.03649, over 7373.00 frames.], tot_loss[loss=0.1773, simple_loss=0.265, pruned_loss=0.04478, over 1420278.28 frames.], batch size: 23, lr: 1.97e-04
2022-05-28 19:49:48,111 INFO [train.py:842] (1/4) Epoch 28, batch 3400, loss[loss=0.1736, simple_loss=0.2684, pruned_loss=0.03943, over 7334.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2643, pruned_loss=0.04454, over 1422935.70 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:50:27,832 INFO [train.py:842] (1/4) Epoch 28, batch 3450, loss[loss=0.2396, simple_loss=0.3081, pruned_loss=0.08557, over 7202.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2656, pruned_loss=0.04535, over 1423818.46 frames.], batch size: 22, lr: 1.97e-04
2022-05-28 19:51:07,232 INFO [train.py:842] (1/4) Epoch 28, batch 3500, loss[loss=0.1749, simple_loss=0.2516, pruned_loss=0.04913, over 7066.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2657, pruned_loss=0.04494, over 1423053.47 frames.], batch size: 18, lr: 1.97e-04
2022-05-28 19:51:46,793 INFO [train.py:842] (1/4) Epoch 28, batch 3550, loss[loss=0.1873, simple_loss=0.2829, pruned_loss=0.0459, over 7322.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2659, pruned_loss=0.04471, over 1423805.29 frames.], batch size: 22, lr: 1.97e-04
2022-05-28 19:52:25,773 INFO [train.py:842] (1/4) Epoch 28, batch 3600, loss[loss=0.1487, simple_loss=0.2445, pruned_loss=0.0265, over 7073.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2666, pruned_loss=0.04461, over 1423192.42 frames.], batch size: 18, lr: 1.97e-04
2022-05-28 19:53:05,435 INFO [train.py:842] (1/4) Epoch 28, batch 3650, loss[loss=0.1655, simple_loss=0.25, pruned_loss=0.04053, over 7148.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2648, pruned_loss=0.04416, over 1423309.28 frames.], batch size: 17, lr: 1.97e-04
2022-05-28 19:53:44,351 INFO [train.py:842] (1/4) Epoch 28, batch 3700, loss[loss=0.1784, simple_loss=0.2584, pruned_loss=0.04917, over 7441.00 frames.], tot_loss[loss=0.176, simple_loss=0.2643, pruned_loss=0.04388, over 1422998.22 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:54:23,825 INFO [train.py:842] (1/4) Epoch 28, batch 3750, loss[loss=0.1865, simple_loss=0.2637, pruned_loss=0.05465, over 7330.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2648, pruned_loss=0.04404, over 1424069.70 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:55:02,959 INFO [train.py:842] (1/4) Epoch 28, batch 3800, loss[loss=0.1929, simple_loss=0.279, pruned_loss=0.05336, over 7229.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2661, pruned_loss=0.04504, over 1422010.18 frames.], batch size: 22, lr: 1.97e-04
2022-05-28 19:55:42,611 INFO [train.py:842] (1/4) Epoch 28, batch 3850, loss[loss=0.2045, simple_loss=0.2882, pruned_loss=0.06037, over 7139.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2649, pruned_loss=0.04402, over 1426817.95 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:56:21,898 INFO [train.py:842] (1/4) Epoch 28, batch 3900, loss[loss=0.1392, simple_loss=0.2208, pruned_loss=0.0288, over 7003.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2639, pruned_loss=0.04349, over 1427387.26 frames.], batch size: 16, lr: 1.97e-04
2022-05-28 19:57:01,249 INFO [train.py:842] (1/4) Epoch 28, batch 3950, loss[loss=0.1371, simple_loss=0.218, pruned_loss=0.02811, over 6999.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2637, pruned_loss=0.04325, over 1427100.90 frames.], batch size: 16, lr: 1.97e-04
2022-05-28 19:57:40,568 INFO [train.py:842] (1/4) Epoch 28, batch 4000, loss[loss=0.1449, simple_loss=0.2383, pruned_loss=0.02574, over 7331.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2635, pruned_loss=0.04293, over 1428938.04 frames.], batch size: 21, lr: 1.97e-04
2022-05-28 19:58:20,469 INFO [train.py:842] (1/4) Epoch 28, batch 4050, loss[loss=0.1559, simple_loss=0.244, pruned_loss=0.03388, over 7304.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2626, pruned_loss=0.04287, over 1429690.35 frames.], batch size: 24, lr: 1.97e-04
2022-05-28 19:58:59,911 INFO [train.py:842] (1/4) Epoch 28, batch 4100, loss[loss=0.1414, simple_loss=0.2255, pruned_loss=0.02865, over 7222.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2632, pruned_loss=0.04356, over 1426022.84 frames.], batch size: 16, lr: 1.97e-04
2022-05-28 19:59:39,538 INFO [train.py:842] (1/4) Epoch 28, batch 4150, loss[loss=0.178, simple_loss=0.2726, pruned_loss=0.04165, over 7404.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2634, pruned_loss=0.04342, over 1426504.69 frames.], batch size: 21, lr: 1.97e-04
2022-05-28 20:00:18,984 INFO [train.py:842] (1/4) Epoch 28, batch 4200, loss[loss=0.1635, simple_loss=0.2467, pruned_loss=0.04018, over 7127.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2632, pruned_loss=0.04346, over 1428493.81 frames.], batch size: 17, lr: 1.97e-04
2022-05-28 20:00:58,743 INFO [train.py:842] (1/4) Epoch 28, batch 4250, loss[loss=0.2048, simple_loss=0.2908, pruned_loss=0.05937, over 7230.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2635, pruned_loss=0.04316, over 1430180.10 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 20:01:38,129 INFO [train.py:842] (1/4) Epoch 28, batch 4300, loss[loss=0.1953, simple_loss=0.2803, pruned_loss=0.05519, over 7161.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2637, pruned_loss=0.04355, over 1428643.88 frames.], batch size: 19, lr: 1.97e-04
2022-05-28 20:02:17,313 INFO [train.py:842] (1/4) Epoch 28, batch 4350, loss[loss=0.1487, simple_loss=0.2415, pruned_loss=0.02794, over 7226.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2659, pruned_loss=0.04477, over 1422092.52 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 20:02:56,676 INFO [train.py:842] (1/4) Epoch 28, batch 4400, loss[loss=0.1472, simple_loss=0.2264, pruned_loss=0.03397, over 6997.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2665, pruned_loss=0.04488, over 1423213.69 frames.], batch size: 16, lr: 1.97e-04
2022-05-28 20:03:36,343 INFO [train.py:842] (1/4) Epoch 28, batch 4450, loss[loss=0.1357, simple_loss=0.2204, pruned_loss=0.02544, over 7269.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2663, pruned_loss=0.04539, over 1426887.41 frames.], batch size: 18, lr: 1.97e-04
2022-05-28 20:04:15,403 INFO [train.py:842] (1/4) Epoch 28, batch 4500, loss[loss=0.1685, simple_loss=0.2691, pruned_loss=0.03399, over 7352.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2666, pruned_loss=0.04556, over 1426293.75 frames.], batch size: 22, lr: 1.97e-04
2022-05-28 20:04:54,893 INFO [train.py:842] (1/4) Epoch 28, batch 4550, loss[loss=0.1645, simple_loss=0.2589, pruned_loss=0.03503, over 7166.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2671, pruned_loss=0.0454, over 1421255.99 frames.], batch size: 18, lr: 1.97e-04
2022-05-28 20:05:33,884 INFO [train.py:842] (1/4) Epoch 28, batch 4600, loss[loss=0.1652, simple_loss=0.2582, pruned_loss=0.03614, over 7159.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2685, pruned_loss=0.04602, over 1423768.74 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:06:13,397 INFO [train.py:842] (1/4) Epoch 28, batch 4650, loss[loss=0.1652, simple_loss=0.2663, pruned_loss=0.03203, over 6837.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2689, pruned_loss=0.0461, over 1424304.87 frames.], batch size: 31, lr: 1.96e-04
2022-05-28 20:06:52,743 INFO [train.py:842] (1/4) Epoch 28, batch 4700, loss[loss=0.1603, simple_loss=0.2431, pruned_loss=0.03879, over 7280.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2682, pruned_loss=0.04575, over 1423091.36 frames.], batch size: 18, lr: 1.96e-04
2022-05-28 20:07:32,432 INFO [train.py:842] (1/4) Epoch 28, batch 4750, loss[loss=0.2124, simple_loss=0.2891, pruned_loss=0.06791, over 7218.00 frames.], tot_loss[loss=0.179, simple_loss=0.2672, pruned_loss=0.04541, over 1422971.09 frames.], batch size: 22, lr: 1.96e-04
2022-05-28 20:08:11,805 INFO [train.py:842] (1/4) Epoch 28, batch 4800, loss[loss=0.1995, simple_loss=0.2893, pruned_loss=0.05483, over 6844.00 frames.], tot_loss[loss=0.178, simple_loss=0.2657, pruned_loss=0.04509, over 1417149.89 frames.], batch size: 31, lr: 1.96e-04
2022-05-28 20:08:51,422 INFO [train.py:842] (1/4) Epoch 28, batch 4850, loss[loss=0.1933, simple_loss=0.2809, pruned_loss=0.05285, over 7317.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2646, pruned_loss=0.04459, over 1420045.49 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:09:30,521 INFO [train.py:842] (1/4) Epoch 28, batch 4900, loss[loss=0.1712, simple_loss=0.2559, pruned_loss=0.04331, over 7335.00 frames.], tot_loss[loss=0.178, simple_loss=0.2657, pruned_loss=0.04514, over 1421582.88 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:10:10,236 INFO [train.py:842] (1/4) Epoch 28, batch 4950, loss[loss=0.2095, simple_loss=0.2878, pruned_loss=0.0656, over 7332.00 frames.], tot_loss[loss=0.1777, simple_loss=0.266, pruned_loss=0.04474, over 1422167.46 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:10:49,490 INFO [train.py:842] (1/4) Epoch 28, batch 5000, loss[loss=0.2016, simple_loss=0.2866, pruned_loss=0.05828, over 7309.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2656, pruned_loss=0.04472, over 1426750.18 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:11:28,936 INFO [train.py:842] (1/4) Epoch 28, batch 5050, loss[loss=0.1441, simple_loss=0.224, pruned_loss=0.03216, over 7230.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2648, pruned_loss=0.04392, over 1419369.19 frames.], batch size: 16, lr: 1.96e-04
2022-05-28 20:12:08,122 INFO [train.py:842] (1/4) Epoch 28, batch 5100, loss[loss=0.1676, simple_loss=0.2625, pruned_loss=0.03638, over 7229.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2651, pruned_loss=0.044, over 1418160.11 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:12:47,611 INFO [train.py:842] (1/4) Epoch 28, batch 5150, loss[loss=0.1453, simple_loss=0.2283, pruned_loss=0.03113, over 7268.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2636, pruned_loss=0.04338, over 1417644.87 frames.], batch size: 18, lr: 1.96e-04
2022-05-28 20:13:26,656 INFO [train.py:842] (1/4) Epoch 28, batch 5200, loss[loss=0.1758, simple_loss=0.2686, pruned_loss=0.04152, over 7326.00 frames.], tot_loss[loss=0.177, simple_loss=0.2655, pruned_loss=0.0442, over 1418685.89 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:14:06,391 INFO [train.py:842] (1/4) Epoch 28, batch 5250, loss[loss=0.1522, simple_loss=0.2352, pruned_loss=0.03457, over 7350.00 frames.], tot_loss[loss=0.176, simple_loss=0.2648, pruned_loss=0.04359, over 1420361.01 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:14:45,260 INFO [train.py:842] (1/4) Epoch 28, batch 5300, loss[loss=0.2074, simple_loss=0.3023, pruned_loss=0.0562, over 7361.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2652, pruned_loss=0.04366, over 1413930.64 frames.], batch size: 23, lr: 1.96e-04
2022-05-28 20:15:25,174 INFO [train.py:842] (1/4) Epoch 28, batch 5350, loss[loss=0.1995, simple_loss=0.2856, pruned_loss=0.05673, over 7382.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2645, pruned_loss=0.04353, over 1416670.00 frames.], batch size: 23, lr: 1.96e-04
2022-05-28 20:16:04,671 INFO [train.py:842] (1/4) Epoch 28, batch 5400, loss[loss=0.1773, simple_loss=0.2604, pruned_loss=0.04706, over 6737.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2643, pruned_loss=0.04374, over 1420157.97 frames.], batch size: 31, lr: 1.96e-04
2022-05-28 20:16:44,123 INFO [train.py:842] (1/4) Epoch 28, batch 5450, loss[loss=0.1541, simple_loss=0.2429, pruned_loss=0.03262, over 7260.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2642, pruned_loss=0.04356, over 1415715.67 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:17:23,311 INFO [train.py:842] (1/4) Epoch 28, batch 5500, loss[loss=0.2118, simple_loss=0.2993, pruned_loss=0.06218, over 7291.00 frames.], tot_loss[loss=0.177, simple_loss=0.2655, pruned_loss=0.0442, over 1416208.78 frames.], batch size: 24, lr: 1.96e-04
2022-05-28 20:18:03,000 INFO [train.py:842] (1/4) Epoch 28, batch 5550, loss[loss=0.1749, simple_loss=0.2534, pruned_loss=0.04817, over 7266.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2661, pruned_loss=0.04477, over 1420767.79 frames.], batch size: 18, lr: 1.96e-04
2022-05-28 20:18:42,374 INFO [train.py:842] (1/4) Epoch 28, batch 5600, loss[loss=0.2515, simple_loss=0.3367, pruned_loss=0.08314, over 7335.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2657, pruned_loss=0.04426, over 1420033.82 frames.], batch size: 22, lr: 1.96e-04
2022-05-28 20:19:22,311 INFO [train.py:842] (1/4) Epoch 28, batch 5650, loss[loss=0.2064, simple_loss=0.299, pruned_loss=0.05684, over 7332.00 frames.], tot_loss[loss=0.1765, simple_loss=0.265, pruned_loss=0.04399, over 1425885.86 frames.], batch size: 22, lr: 1.96e-04
2022-05-28 20:20:01,631 INFO [train.py:842] (1/4) Epoch 28, batch 5700, loss[loss=0.1736, simple_loss=0.2699, pruned_loss=0.03863, over 7138.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2645, pruned_loss=0.04411, over 1427854.07 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:20:41,242 INFO [train.py:842] (1/4) Epoch 28, batch 5750, loss[loss=0.1747, simple_loss=0.2661, pruned_loss=0.04161, over 7319.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2647, pruned_loss=0.04403, over 1425962.55 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:21:20,853 INFO [train.py:842] (1/4) Epoch 28, batch 5800, loss[loss=0.1677, simple_loss=0.2581, pruned_loss=0.03862, over 7153.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2653, pruned_loss=0.04416, over 1430665.86 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:22:00,593 INFO [train.py:842] (1/4) Epoch 28, batch 5850, loss[loss=0.1696, simple_loss=0.2631, pruned_loss=0.03801, over 7160.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2645, pruned_loss=0.04401, over 1433016.89 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:22:39,830 INFO [train.py:842] (1/4) Epoch 28, batch 5900, loss[loss=0.163, simple_loss=0.2437, pruned_loss=0.04115, over 7428.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2641, pruned_loss=0.0435, over 1436262.47 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:23:19,384 INFO [train.py:842] (1/4) Epoch 28, batch 5950, loss[loss=0.2183, simple_loss=0.3012, pruned_loss=0.06772, over 7313.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2645, pruned_loss=0.04333, over 1436617.60 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:23:58,877 INFO [train.py:842] (1/4) Epoch 28, batch 6000, loss[loss=0.2134, simple_loss=0.3011, pruned_loss=0.06289, over 7228.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2635, pruned_loss=0.0431, over 1436410.91 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:23:58,877 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 20:24:08,607 INFO [train.py:871] (1/4) Epoch 28, validation: loss=0.1653, simple_loss=0.2626, pruned_loss=0.03405, over 868885.00 frames.
2022-05-28 20:24:48,321 INFO [train.py:842] (1/4) Epoch 28, batch 6050, loss[loss=0.1803, simple_loss=0.2762, pruned_loss=0.04222, over 7057.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2645, pruned_loss=0.04421, over 1431541.84 frames.], batch size: 28, lr: 1.96e-04
2022-05-28 20:25:27,633 INFO [train.py:842] (1/4) Epoch 28, batch 6100, loss[loss=0.1478, simple_loss=0.227, pruned_loss=0.03432, over 7074.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2632, pruned_loss=0.0438, over 1430578.59 frames.], batch size: 18, lr: 1.96e-04
2022-05-28 20:26:07,230 INFO [train.py:842] (1/4) Epoch 28, batch 6150, loss[loss=0.2074, simple_loss=0.2804, pruned_loss=0.06725, over 7315.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2631, pruned_loss=0.04366, over 1430051.35 frames.], batch size: 25, lr: 1.96e-04
2022-05-28 20:26:46,774 INFO [train.py:842] (1/4) Epoch 28, batch 6200, loss[loss=0.2369, simple_loss=0.3103, pruned_loss=0.08171, over 7194.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2641, pruned_loss=0.04419, over 1428090.22 frames.], batch size: 22, lr: 1.96e-04
2022-05-28 20:27:26,684 INFO [train.py:842] (1/4) Epoch 28, batch 6250, loss[loss=0.1755, simple_loss=0.2672, pruned_loss=0.04187, over 7251.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2648, pruned_loss=0.04447, over 1426575.80 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:28:17,004 INFO [train.py:842] (1/4) Epoch 28, batch 6300, loss[loss=0.1761, simple_loss=0.2727, pruned_loss=0.03972, over 7226.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2652, pruned_loss=0.04473, over 1425126.82 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:28:56,518 INFO [train.py:842] (1/4) Epoch 28, batch 6350, loss[loss=0.1793, simple_loss=0.2755, pruned_loss=0.04151, over 7146.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2661, pruned_loss=0.04529, over 1421779.11 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:29:35,525 INFO [train.py:842] (1/4) Epoch 28, batch 6400, loss[loss=0.1986, simple_loss=0.2889, pruned_loss=0.05413, over 7137.00 frames.], tot_loss[loss=0.1779, simple_loss=0.266, pruned_loss=0.04489, over 1418883.03 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:30:15,276 INFO [train.py:842] (1/4) Epoch 28, batch 6450, loss[loss=0.1632, simple_loss=0.2552, pruned_loss=0.03554, over 7365.00 frames.], tot_loss[loss=0.1769, simple_loss=0.265, pruned_loss=0.04446, over 1414729.53 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:30:54,476 INFO [train.py:842] (1/4) Epoch 28, batch 6500, loss[loss=0.2026, simple_loss=0.2911, pruned_loss=0.05708, over 7142.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2653, pruned_loss=0.045, over 1415081.84 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:31:33,939 INFO [train.py:842] (1/4) Epoch 28, batch 6550, loss[loss=0.2296, simple_loss=0.3093, pruned_loss=0.07493, over 5078.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2666, pruned_loss=0.04561, over 1413860.13 frames.], batch size: 52, lr: 1.96e-04
2022-05-28 20:32:34,873 INFO [train.py:842] (1/4) Epoch 28, batch 6600, loss[loss=0.1315, simple_loss=0.2153, pruned_loss=0.02389, over 7138.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2669, pruned_loss=0.046, over 1410603.89 frames.], batch size: 17, lr: 1.96e-04
2022-05-28 20:33:14,413 INFO [train.py:842] (1/4) Epoch 28, batch 6650, loss[loss=0.2261, simple_loss=0.3104, pruned_loss=0.07091, over 7205.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2667, pruned_loss=0.04537, over 1414581.98 frames.], batch size: 23, lr: 1.96e-04
2022-05-28 20:33:53,568 INFO [train.py:842] (1/4) Epoch 28, batch 6700, loss[loss=0.2477, simple_loss=0.3252, pruned_loss=0.08514, over 7201.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2672, pruned_loss=0.04574, over 1420497.34 frames.], batch size: 26, lr: 1.96e-04
2022-05-28 20:34:33,410 INFO [train.py:842] (1/4) Epoch 28, batch 6750, loss[loss=0.1627, simple_loss=0.2655, pruned_loss=0.02992, over 7377.00 frames.], tot_loss[loss=0.1782, simple_loss=0.266, pruned_loss=0.04518, over 1422215.42 frames.], batch size: 23, lr: 1.96e-04
2022-05-28 20:35:12,647 INFO [train.py:842] (1/4) Epoch 28, batch 6800, loss[loss=0.1606, simple_loss=0.2419, pruned_loss=0.03965, over 7280.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2664, pruned_loss=0.04536, over 1423171.36 frames.], batch size: 18, lr: 1.96e-04
2022-05-28 20:35:52,284 INFO [train.py:842] (1/4) Epoch 28, batch 6850, loss[loss=0.1874, simple_loss=0.2852, pruned_loss=0.0448, over 7088.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2668, pruned_loss=0.04533, over 1419181.45 frames.], batch size: 28, lr: 1.96e-04
2022-05-28 20:36:31,713 INFO [train.py:842] (1/4) Epoch 28, batch 6900, loss[loss=0.1553, simple_loss=0.2481, pruned_loss=0.03123, over 7110.00 frames.], tot_loss[loss=0.1781, simple_loss=0.266, pruned_loss=0.04515, over 1419659.00 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:37:11,500 INFO [train.py:842] (1/4) Epoch 28, batch 6950, loss[loss=0.1959, simple_loss=0.2824, pruned_loss=0.05466, over 7303.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2656, pruned_loss=0.04491, over 1422133.38 frames.], batch size: 25, lr: 1.96e-04
2022-05-28 20:37:50,769 INFO [train.py:842] (1/4) Epoch 28, batch 7000, loss[loss=0.1818, simple_loss=0.2697, pruned_loss=0.04698, over 7148.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2652, pruned_loss=0.04448, over 1422352.63 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:38:30,276 INFO [train.py:842] (1/4) Epoch 28, batch 7050, loss[loss=0.149, simple_loss=0.2338, pruned_loss=0.03208, over 7362.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2642, pruned_loss=0.04405, over 1422145.03 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:39:09,601 INFO [train.py:842] (1/4) Epoch 28, batch 7100, loss[loss=0.1616, simple_loss=0.2532, pruned_loss=0.03502, over 7431.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2642, pruned_loss=0.0443, over 1425010.77 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:39:49,134 INFO [train.py:842] (1/4) Epoch 28, batch 7150, loss[loss=0.1708, simple_loss=0.2687, pruned_loss=0.03652, over 7111.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2663, pruned_loss=0.04539, over 1424535.17 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:40:28,354 INFO [train.py:842] (1/4) Epoch 28, batch 7200, loss[loss=0.1679, simple_loss=0.252, pruned_loss=0.04195, over 7007.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2673, pruned_loss=0.04623, over 1420909.44 frames.], batch size: 16, lr: 1.95e-04
2022-05-28 20:41:08,029 INFO [train.py:842] (1/4) Epoch 28, batch 7250, loss[loss=0.1659, simple_loss=0.2557, pruned_loss=0.03801, over 7203.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2664, pruned_loss=0.04548, over 1418675.72 frames.], batch size: 16, lr: 1.95e-04
2022-05-28 20:41:46,988 INFO [train.py:842] (1/4) Epoch 28, batch 7300, loss[loss=0.1775, simple_loss=0.265, pruned_loss=0.04499, over 7227.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2648, pruned_loss=0.04418, over 1419041.26 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 20:42:26,417 INFO [train.py:842] (1/4) Epoch 28, batch 7350, loss[loss=0.2069, simple_loss=0.2769, pruned_loss=0.06842, over 7172.00 frames.], tot_loss[loss=0.1769, simple_loss=0.265, pruned_loss=0.04439, over 1420148.61 frames.], batch size: 18, lr: 1.95e-04
2022-05-28 20:43:05,474 INFO [train.py:842] (1/4) Epoch 28, batch 7400, loss[loss=0.1851, simple_loss=0.2705, pruned_loss=0.04982, over 7307.00 frames.], tot_loss[loss=0.177, simple_loss=0.265, pruned_loss=0.04448, over 1416916.60 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 20:43:45,197 INFO [train.py:842] (1/4) Epoch 28, batch 7450, loss[loss=0.2334, simple_loss=0.3215, pruned_loss=0.07268, over 7421.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2646, pruned_loss=0.04456, over 1422389.55 frames.], batch size: 20, lr: 1.95e-04
2022-05-28 20:44:24,566 INFO [train.py:842] (1/4) Epoch 28, batch 7500, loss[loss=0.1895, simple_loss=0.2686, pruned_loss=0.05516, over 7183.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2648, pruned_loss=0.04455, over 1424735.98 frames.], batch size: 26, lr: 1.95e-04
2022-05-28 20:45:04,414 INFO [train.py:842] (1/4) Epoch 28, batch 7550, loss[loss=0.1857, simple_loss=0.2774, pruned_loss=0.04694, over 7326.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2649, pruned_loss=0.04486, over 1426245.18 frames.], batch size: 22, lr: 1.95e-04
2022-05-28 20:45:43,609 INFO [train.py:842] (1/4) Epoch 28, batch 7600, loss[loss=0.171, simple_loss=0.2462, pruned_loss=0.04788, over 6757.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2635, pruned_loss=0.0441, over 1426711.53 frames.], batch size: 15, lr: 1.95e-04
2022-05-28 20:46:23,194 INFO [train.py:842] (1/4) Epoch 28, batch 7650, loss[loss=0.1467, simple_loss=0.2244, pruned_loss=0.03451, over 7232.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2643, pruned_loss=0.04435, over 1427009.23 frames.], batch size: 16, lr: 1.95e-04
2022-05-28 20:47:02,320 INFO [train.py:842] (1/4) Epoch 28, batch 7700, loss[loss=0.194, simple_loss=0.282, pruned_loss=0.05304, over 7195.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2656, pruned_loss=0.04493, over 1426389.74 frames.], batch size: 22, lr: 1.95e-04
2022-05-28 20:47:41,998 INFO [train.py:842] (1/4) Epoch 28, batch 7750, loss[loss=0.1606, simple_loss=0.2435, pruned_loss=0.03887, over 7174.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2646, pruned_loss=0.04482, over 1420603.35 frames.], batch size: 18, lr: 1.95e-04
2022-05-28 20:48:21,321 INFO [train.py:842] (1/4) Epoch 28, batch 7800, loss[loss=0.184, simple_loss=0.2739, pruned_loss=0.04704, over 7329.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2648, pruned_loss=0.04448, over 1424445.61 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 20:49:04,094 INFO [train.py:842] (1/4) Epoch 28, batch 7850, loss[loss=0.2214, simple_loss=0.3059, pruned_loss=0.06847, over 6844.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2655, pruned_loss=0.04535, over 1423493.33 frames.], batch size: 31, lr: 1.95e-04
2022-05-28 20:49:43,315 INFO [train.py:842] (1/4) Epoch 28, batch 7900, loss[loss=0.1604, simple_loss=0.2515, pruned_loss=0.03465, over 7438.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2657, pruned_loss=0.04549, over 1426016.23 frames.], batch size: 20, lr: 1.95e-04
2022-05-28 20:50:22,485 INFO [train.py:842] (1/4) Epoch 28, batch 7950, loss[loss=0.1474, simple_loss=0.2385, pruned_loss=0.02814, over 7330.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2654, pruned_loss=0.0451, over 1421197.18 frames.], batch size: 20, lr: 1.95e-04
2022-05-28 20:51:01,513 INFO [train.py:842] (1/4) Epoch 28, batch 8000, loss[loss=0.1543, simple_loss=0.2553, pruned_loss=0.02667, over 7121.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2646, pruned_loss=0.04398, over 1420010.54 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 20:51:40,841 INFO [train.py:842] (1/4) Epoch 28, batch 8050, loss[loss=0.1561, simple_loss=0.2529, pruned_loss=0.02958, over 7225.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2646, pruned_loss=0.04428, over 1419627.08 frames.], batch size: 20, lr: 1.95e-04
2022-05-28 20:52:19,887 INFO [train.py:842] (1/4) Epoch 28, batch 8100, loss[loss=0.1991, simple_loss=0.2901, pruned_loss=0.05404, over 7292.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2653, pruned_loss=0.04466, over 1421141.57 frames.], batch size: 24, lr: 1.95e-04
2022-05-28 20:52:59,296 INFO [train.py:842] (1/4) Epoch 28, batch 8150, loss[loss=0.1828, simple_loss=0.2598, pruned_loss=0.05285, over 7424.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2654, pruned_loss=0.04453, over 1415475.61 frames.], batch size: 18, lr: 1.95e-04
2022-05-28 20:53:38,452 INFO [train.py:842] (1/4) Epoch 28, batch 8200, loss[loss=0.1967, simple_loss=0.2816, pruned_loss=0.05588, over 7287.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2659, pruned_loss=0.04517, over 1418214.02 frames.], batch size: 24, lr: 1.95e-04
2022-05-28 20:54:18,211 INFO [train.py:842] (1/4) Epoch 28, batch 8250, loss[loss=0.155, simple_loss=0.248, pruned_loss=0.03096, over 7354.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2644, pruned_loss=0.04401, over 1419684.44 frames.], batch size: 22, lr: 1.95e-04
2022-05-28 20:54:57,215 INFO [train.py:842] (1/4) Epoch 28, batch 8300, loss[loss=0.1965, simple_loss=0.301, pruned_loss=0.04598, over 7228.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2642, pruned_loss=0.04359, over 1421824.08 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 20:55:36,884 INFO [train.py:842] (1/4) Epoch 28, batch 8350, loss[loss=0.1977, simple_loss=0.2827, pruned_loss=0.05636, over 6828.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2652, pruned_loss=0.04392, over 1423812.47 frames.], batch size: 15, lr: 1.95e-04
2022-05-28 20:56:16,226 INFO [train.py:842] (1/4) Epoch 28, batch 8400, loss[loss=0.1453, simple_loss=0.23, pruned_loss=0.03029, over 7124.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2658, pruned_loss=0.04428, over 1423850.30 frames.], batch size: 17, lr: 1.95e-04
2022-05-28 20:56:55,869 INFO [train.py:842] (1/4) Epoch 28, batch 8450, loss[loss=0.1711, simple_loss=0.259, pruned_loss=0.04158, over 7133.00 frames.], tot_loss[loss=0.1777, simple_loss=0.266, pruned_loss=0.04466, over 1419138.04 frames.], batch size: 17, lr: 1.95e-04
2022-05-28 20:57:34,966 INFO [train.py:842] (1/4) Epoch 28, batch 8500, loss[loss=0.1684, simple_loss=0.2542, pruned_loss=0.04127, over 7367.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2666, pruned_loss=0.04526, over 1417420.85 frames.], batch size: 23, lr: 1.95e-04
2022-05-28 20:58:14,422 INFO [train.py:842] (1/4) Epoch 28, batch 8550, loss[loss=0.1599, simple_loss=0.2555, pruned_loss=0.03213, over 7371.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2671, pruned_loss=0.04574, over 1413349.22 frames.], batch size: 23, lr: 1.95e-04
2022-05-28 20:58:53,548 INFO [train.py:842] (1/4) Epoch 28, batch 8600, loss[loss=0.1887, simple_loss=0.2724, pruned_loss=0.05248, over 7385.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2671, pruned_loss=0.04591, over 1408978.13 frames.], batch size: 23, lr: 1.95e-04
2022-05-28 20:59:32,595 INFO [train.py:842] (1/4) Epoch 28, batch 8650, loss[loss=0.1863, simple_loss=0.2732, pruned_loss=0.04968, over 7440.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2687, pruned_loss=0.04639, over 1408392.55 frames.], batch size: 20, lr: 1.95e-04
2022-05-28 21:00:11,774 INFO [train.py:842] (1/4) Epoch 28, batch 8700, loss[loss=0.1783, simple_loss=0.2683, pruned_loss=0.04417, over 6343.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2675, pruned_loss=0.04578, over 1410351.33 frames.], batch size: 37, lr: 1.95e-04
2022-05-28 21:00:51,048 INFO [train.py:842] (1/4) Epoch 28, batch 8750, loss[loss=0.1764, simple_loss=0.2765, pruned_loss=0.03816, over 7120.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2675, pruned_loss=0.04558, over 1407829.96 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 21:01:30,141 INFO [train.py:842] (1/4) Epoch 28, batch 8800, loss[loss=0.181, simple_loss=0.2712, pruned_loss=0.04535, over 7369.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2681, pruned_loss=0.04632, over 1405679.32 frames.], batch size: 23, lr: 1.95e-04
2022-05-28 21:02:09,476 INFO [train.py:842] (1/4) Epoch 28, batch 8850, loss[loss=0.1718, simple_loss=0.2591, pruned_loss=0.04229, over 7227.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2684, pruned_loss=0.04622, over 1405461.68 frames.], batch size: 26, lr: 1.95e-04
2022-05-28 21:02:48,617 INFO [train.py:842] (1/4) Epoch 28, batch 8900, loss[loss=0.1922, simple_loss=0.2703, pruned_loss=0.05698, over 4900.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2684, pruned_loss=0.04611, over 1393766.41 frames.], batch size: 52, lr: 1.95e-04
2022-05-28 21:03:28,079 INFO [train.py:842] (1/4) Epoch 28, batch 8950, loss[loss=0.1692, simple_loss=0.2643, pruned_loss=0.03706, over 7101.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2672, pruned_loss=0.04587, over 1390069.72 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 21:04:07,008 INFO [train.py:842] (1/4) Epoch 28, batch 9000, loss[loss=0.1394, simple_loss=0.2321, pruned_loss=0.02335, over 7149.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2667, pruned_loss=0.04555, over 1381376.50 frames.], batch size: 19, lr: 1.95e-04
2022-05-28 21:04:07,009 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 21:04:16,596 INFO [train.py:871] (1/4) Epoch 28, validation: loss=0.1632, simple_loss=0.2611, pruned_loss=0.03268, over 868885.00 frames.
2022-05-28 21:04:55,661 INFO [train.py:842] (1/4) Epoch 28, batch 9050, loss[loss=0.1843, simple_loss=0.2763, pruned_loss=0.04618, over 6379.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2665, pruned_loss=0.04581, over 1358630.61 frames.], batch size: 37, lr: 1.95e-04
2022-05-28 21:05:34,545 INFO [train.py:842] (1/4) Epoch 28, batch 9100, loss[loss=0.1813, simple_loss=0.2759, pruned_loss=0.04338, over 5052.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2661, pruned_loss=0.04651, over 1332904.26 frames.], batch size: 52, lr: 1.95e-04
2022-05-28 21:06:12,624 INFO [train.py:842] (1/4) Epoch 28, batch 9150, loss[loss=0.1693, simple_loss=0.2635, pruned_loss=0.03756, over 6640.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2682, pruned_loss=0.04758, over 1299536.02 frames.], batch size: 38, lr: 1.95e-04
2022-05-28 21:07:05,018 INFO [train.py:842] (1/4) Epoch 29, batch 0, loss[loss=0.1689, simple_loss=0.2533, pruned_loss=0.04229, over 7097.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2533, pruned_loss=0.04229, over 7097.00 frames.], batch size: 28, lr: 1.91e-04
2022-05-28 21:07:44,651 INFO [train.py:842] (1/4) Epoch 29, batch 50, loss[loss=0.19, simple_loss=0.2833, pruned_loss=0.04841, over 7283.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2657, pruned_loss=0.04343, over 323900.52 frames.], batch size: 24, lr: 1.91e-04
2022-05-28 21:08:24,033 INFO [train.py:842] (1/4) Epoch 29, batch 100, loss[loss=0.1719, simple_loss=0.2672, pruned_loss=0.03835, over 7323.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2651, pruned_loss=0.04352, over 570145.59 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:09:03,634 INFO [train.py:842] (1/4) Epoch 29, batch 150, loss[loss=0.2345, simple_loss=0.3177, pruned_loss=0.07567, over 7231.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2689, pruned_loss=0.04573, over 759870.42 frames.], batch size: 20, lr: 1.91e-04
2022-05-28 21:09:43,191 INFO [train.py:842] (1/4) Epoch 29, batch 200, loss[loss=0.1503, simple_loss=0.2337, pruned_loss=0.03342, over 7068.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2667, pruned_loss=0.04473, over 909413.15 frames.], batch size: 18, lr: 1.91e-04
2022-05-28 21:10:22,696 INFO [train.py:842] (1/4) Epoch 29, batch 250, loss[loss=0.2351, simple_loss=0.3146, pruned_loss=0.0778, over 4971.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2661, pruned_loss=0.04442, over 1019815.36 frames.], batch size: 52, lr: 1.91e-04
2022-05-28 21:11:01,713 INFO [train.py:842] (1/4) Epoch 29, batch 300, loss[loss=0.1601, simple_loss=0.249, pruned_loss=0.03561, over 7177.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2652, pruned_loss=0.04358, over 1109375.46 frames.], batch size: 18, lr: 1.91e-04
2022-05-28 21:11:41,207 INFO [train.py:842] (1/4) Epoch 29, batch 350, loss[loss=0.1555, simple_loss=0.253, pruned_loss=0.02903, over 7073.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2654, pruned_loss=0.04312, over 1181605.06 frames.], batch size: 18, lr: 1.91e-04
2022-05-28 21:12:20,451 INFO [train.py:842] (1/4) Epoch 29, batch 400, loss[loss=0.2258, simple_loss=0.3006, pruned_loss=0.07552, over 7140.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2648, pruned_loss=0.04311, over 1236886.49 frames.], batch size: 20, lr: 1.91e-04
2022-05-28 21:13:00,118 INFO [train.py:842] (1/4) Epoch 29, batch 450, loss[loss=0.1826, simple_loss=0.2776, pruned_loss=0.04379, over 7114.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2645, pruned_loss=0.04298, over 1282692.28 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:13:39,151 INFO [train.py:842] (1/4) Epoch 29, batch 500, loss[loss=0.2285, simple_loss=0.299, pruned_loss=0.07899, over 4830.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2642, pruned_loss=0.04318, over 1310964.45 frames.], batch size: 52, lr: 1.91e-04
2022-05-28 21:14:18,733 INFO [train.py:842] (1/4) Epoch 29, batch 550, loss[loss=0.1946, simple_loss=0.2875, pruned_loss=0.05081, over 7228.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2646, pruned_loss=0.04325, over 1333257.79 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:14:58,129 INFO [train.py:842] (1/4) Epoch 29, batch 600, loss[loss=0.1508, simple_loss=0.2331, pruned_loss=0.03419, over 7252.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2646, pruned_loss=0.04418, over 1349533.14 frames.], batch size: 19, lr: 1.91e-04
2022-05-28 21:15:37,660 INFO [train.py:842] (1/4) Epoch 29, batch 650, loss[loss=0.1715, simple_loss=0.2542, pruned_loss=0.04445, over 7065.00 frames.], tot_loss[loss=0.176, simple_loss=0.2639, pruned_loss=0.04402, over 1367557.64 frames.], batch size: 18, lr: 1.91e-04
2022-05-28 21:16:17,150 INFO [train.py:842] (1/4) Epoch 29, batch 700, loss[loss=0.1949, simple_loss=0.2831, pruned_loss=0.05333, over 5285.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2655, pruned_loss=0.04484, over 1376102.48 frames.], batch size: 53, lr: 1.91e-04
2022-05-28 21:16:56,514 INFO [train.py:842] (1/4) Epoch 29, batch 750, loss[loss=0.1604, simple_loss=0.2489, pruned_loss=0.03597, over 7430.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2653, pruned_loss=0.04472, over 1383804.00 frames.], batch size: 20, lr: 1.91e-04
2022-05-28 21:17:35,598 INFO [train.py:842] (1/4) Epoch 29, batch 800, loss[loss=0.1862, simple_loss=0.2835, pruned_loss=0.04439, over 7112.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2656, pruned_loss=0.04475, over 1389218.91 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:18:15,121 INFO [train.py:842] (1/4) Epoch 29, batch 850, loss[loss=0.1885, simple_loss=0.2838, pruned_loss=0.04658, over 6268.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2655, pruned_loss=0.04472, over 1394020.95 frames.], batch size: 37, lr: 1.91e-04
2022-05-28 21:18:54,083 INFO [train.py:842] (1/4) Epoch 29, batch 900, loss[loss=0.2059, simple_loss=0.2962, pruned_loss=0.05781, over 6729.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2653, pruned_loss=0.04462, over 1400220.62 frames.], batch size: 31, lr: 1.91e-04
2022-05-28 21:19:33,689 INFO [train.py:842] (1/4) Epoch 29, batch 950, loss[loss=0.2006, simple_loss=0.2941, pruned_loss=0.05358, over 7199.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2656, pruned_loss=0.04437, over 1409098.65 frames.], batch size: 22, lr: 1.91e-04
2022-05-28 21:20:13,153 INFO [train.py:842] (1/4) Epoch 29, batch 1000, loss[loss=0.1873, simple_loss=0.267, pruned_loss=0.0538, over 6763.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2655, pruned_loss=0.04465, over 1415216.59 frames.], batch size: 15, lr: 1.91e-04
2022-05-28 21:20:52,874 INFO [train.py:842] (1/4) Epoch 29, batch 1050, loss[loss=0.1822, simple_loss=0.2804, pruned_loss=0.04193, over 7407.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2657, pruned_loss=0.04433, over 1421038.99 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:21:32,448 INFO [train.py:842] (1/4) Epoch 29, batch 1100, loss[loss=0.1666, simple_loss=0.2437, pruned_loss=0.04472, over 7272.00 frames.], tot_loss[loss=0.1789, simple_loss=0.267, pruned_loss=0.04539, over 1423741.59 frames.], batch size: 17, lr: 1.91e-04
2022-05-28 21:22:11,771 INFO [train.py:842] (1/4) Epoch 29, batch 1150, loss[loss=0.1704, simple_loss=0.2664, pruned_loss=0.03725, over 7063.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2674, pruned_loss=0.04581, over 1422011.51 frames.], batch size: 28, lr: 1.91e-04
2022-05-28 21:22:50,760 INFO [train.py:842] (1/4) Epoch 29, batch 1200, loss[loss=0.1766, simple_loss=0.2662, pruned_loss=0.04349, over 7068.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2679, pruned_loss=0.04526, over 1424263.95 frames.], batch size: 28, lr: 1.91e-04
2022-05-28 21:23:30,569 INFO [train.py:842] (1/4) Epoch 29, batch 1250, loss[loss=0.1717, simple_loss=0.2632, pruned_loss=0.04012, over 7211.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2666, pruned_loss=0.04501, over 1418752.71 frames.], batch size: 22, lr: 1.91e-04
2022-05-28 21:24:09,912 INFO [train.py:842] (1/4) Epoch 29, batch 1300, loss[loss=0.1582, simple_loss=0.255, pruned_loss=0.03066, over 7152.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2662, pruned_loss=0.04487, over 1420749.94 frames.], batch size: 20, lr: 1.91e-04
2022-05-28 21:24:49,615 INFO [train.py:842] (1/4) Epoch 29, batch 1350, loss[loss=0.1916, simple_loss=0.2795, pruned_loss=0.05191, over 7107.00 frames.], tot_loss[loss=0.177, simple_loss=0.2653, pruned_loss=0.0443, over 1426198.64 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:25:29,113 INFO [train.py:842] (1/4) Epoch 29, batch 1400, loss[loss=0.159, simple_loss=0.2384, pruned_loss=0.03975, over 7266.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2654, pruned_loss=0.04446, over 1427734.64 frames.], batch size: 17, lr: 1.91e-04
2022-05-28 21:26:08,862 INFO [train.py:842] (1/4) Epoch 29, batch 1450, loss[loss=0.1639, simple_loss=0.257, pruned_loss=0.03537, over 7299.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2648, pruned_loss=0.04353, over 1431646.30 frames.], batch size: 24, lr: 1.91e-04
2022-05-28 21:26:47,897 INFO [train.py:842] (1/4) Epoch 29, batch 1500, loss[loss=0.1584, simple_loss=0.2512, pruned_loss=0.03279, over 7332.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2651, pruned_loss=0.04308, over 1428428.50 frames.], batch size: 20, lr: 1.91e-04
2022-05-28 21:27:27,465 INFO [train.py:842] (1/4) Epoch 29, batch 1550, loss[loss=0.183, simple_loss=0.2689, pruned_loss=0.04852, over 7210.00 frames.], tot_loss[loss=0.1772, simple_loss=0.266, pruned_loss=0.04421, over 1429950.76 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:28:06,658 INFO [train.py:842] (1/4) Epoch 29, batch 1600, loss[loss=0.1958, simple_loss=0.2715, pruned_loss=0.06002, over 6808.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2668, pruned_loss=0.04471, over 1427143.93 frames.], batch size: 15, lr: 1.91e-04
2022-05-28 21:28:46,408 INFO [train.py:842] (1/4) Epoch 29, batch 1650, loss[loss=0.1799, simple_loss=0.2539, pruned_loss=0.05295, over 6768.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2659, pruned_loss=0.04469, over 1428484.47 frames.], batch size: 15, lr: 1.91e-04
2022-05-28 21:29:25,924 INFO [train.py:842] (1/4) Epoch 29, batch 1700, loss[loss=0.1374, simple_loss=0.2273, pruned_loss=0.02378, over 7267.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2651, pruned_loss=0.04455, over 1430887.38 frames.], batch size: 19, lr: 1.91e-04
2022-05-28 21:30:05,636 INFO [train.py:842] (1/4) Epoch 29, batch 1750, loss[loss=0.1971, simple_loss=0.2905, pruned_loss=0.05181, over 7125.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2653, pruned_loss=0.04493, over 1433070.72 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:30:44,922 INFO [train.py:842] (1/4) Epoch 29, batch 1800, loss[loss=0.147, simple_loss=0.2331, pruned_loss=0.03046, over 7004.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2655, pruned_loss=0.04531, over 1423627.82 frames.], batch size: 16, lr: 1.91e-04
2022-05-28 21:31:24,552 INFO [train.py:842] (1/4) Epoch 29, batch 1850, loss[loss=0.1876, simple_loss=0.2646, pruned_loss=0.05528, over 7423.00 frames.], tot_loss[loss=0.1785, simple_loss=0.266, pruned_loss=0.04552, over 1425840.79 frames.], batch size: 18, lr: 1.91e-04
2022-05-28 21:32:03,694 INFO [train.py:842] (1/4) Epoch 29, batch 1900, loss[loss=0.1833, simple_loss=0.2654, pruned_loss=0.05063, over 7173.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2654, pruned_loss=0.04518, over 1425963.06 frames.], batch size: 26, lr: 1.91e-04
2022-05-28 21:32:43,289 INFO [train.py:842] (1/4) Epoch 29, batch 1950, loss[loss=0.2061, simple_loss=0.284, pruned_loss=0.0641, over 7287.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2656, pruned_loss=0.04528, over 1428515.97 frames.], batch size: 25, lr: 1.91e-04
2022-05-28 21:33:22,660 INFO [train.py:842] (1/4) Epoch 29, batch 2000, loss[loss=0.1846, simple_loss=0.276, pruned_loss=0.04659, over 7195.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2663, pruned_loss=0.04579, over 1431122.98 frames.], batch size: 23, lr: 1.91e-04
2022-05-28 21:34:02,034 INFO [train.py:842] (1/4) Epoch 29, batch 2050, loss[loss=0.1658, simple_loss=0.2488, pruned_loss=0.04143, over 7328.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2667, pruned_loss=0.0457, over 1424254.96 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:34:41,439 INFO [train.py:842] (1/4) Epoch 29, batch 2100, loss[loss=0.2073, simple_loss=0.3055, pruned_loss=0.05449, over 7294.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2654, pruned_loss=0.04486, over 1426038.34 frames.], batch size: 25, lr: 1.91e-04
2022-05-28 21:35:21,001 INFO [train.py:842] (1/4) Epoch 29, batch 2150, loss[loss=0.1736, simple_loss=0.2668, pruned_loss=0.04017, over 7230.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2663, pruned_loss=0.04541, over 1427245.19 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:35:59,981 INFO [train.py:842] (1/4) Epoch 29, batch 2200, loss[loss=0.1879, simple_loss=0.2803, pruned_loss=0.04776, over 7318.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2649, pruned_loss=0.04461, over 1422704.31 frames.], batch size: 25, lr: 1.91e-04
2022-05-28 21:36:39,492 INFO [train.py:842] (1/4) Epoch 29, batch 2250, loss[loss=0.1854, simple_loss=0.2792, pruned_loss=0.04577, over 7118.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2645, pruned_loss=0.04421, over 1426770.99 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:37:18,701 INFO [train.py:842] (1/4) Epoch 29, batch 2300, loss[loss=0.1575, simple_loss=0.2428, pruned_loss=0.03614, over 7291.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2644, pruned_loss=0.04431, over 1428645.62 frames.], batch size: 24, lr: 1.91e-04
2022-05-28 21:37:58,221 INFO [train.py:842] (1/4) Epoch 29, batch 2350, loss[loss=0.2156, simple_loss=0.2928, pruned_loss=0.06915, over 7076.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2641, pruned_loss=0.04417, over 1425540.82 frames.], batch size: 18, lr: 1.91e-04
2022-05-28 21:38:37,552 INFO [train.py:842] (1/4) Epoch 29, batch 2400, loss[loss=0.1317, simple_loss=0.2161, pruned_loss=0.0237, over 7363.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2629, pruned_loss=0.04337, over 1426949.05 frames.], batch size: 19, lr: 1.90e-04
2022-05-28 21:39:16,867 INFO [train.py:842] (1/4) Epoch 29, batch 2450, loss[loss=0.1567, simple_loss=0.2463, pruned_loss=0.03356, over 7116.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2649, pruned_loss=0.04461, over 1417202.38 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 21:39:56,308 INFO [train.py:842] (1/4) Epoch 29, batch 2500, loss[loss=0.1273, simple_loss=0.2075, pruned_loss=0.0236, over 7420.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2639, pruned_loss=0.04438, over 1420731.64 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:40:35,905 INFO [train.py:842] (1/4) Epoch 29, batch 2550, loss[loss=0.1508, simple_loss=0.2391, pruned_loss=0.03119, over 7166.00 frames.], tot_loss[loss=0.177, simple_loss=0.2644, pruned_loss=0.04479, over 1417901.26 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:41:14,980 INFO [train.py:842] (1/4) Epoch 29, batch 2600, loss[loss=0.1945, simple_loss=0.2933, pruned_loss=0.04781, over 7187.00 frames.], tot_loss[loss=0.177, simple_loss=0.2645, pruned_loss=0.04476, over 1415406.49 frames.], batch size: 23, lr: 1.90e-04
2022-05-28 21:41:54,634 INFO [train.py:842] (1/4) Epoch 29, batch 2650, loss[loss=0.1733, simple_loss=0.2525, pruned_loss=0.04709, over 7414.00 frames.], tot_loss[loss=0.1765, simple_loss=0.264, pruned_loss=0.04451, over 1418296.02 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:42:33,907 INFO [train.py:842] (1/4) Epoch 29, batch 2700, loss[loss=0.2053, simple_loss=0.3005, pruned_loss=0.055, over 5177.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2631, pruned_loss=0.04374, over 1418219.80 frames.], batch size: 52, lr: 1.90e-04
2022-05-28 21:43:13,577 INFO [train.py:842] (1/4) Epoch 29, batch 2750, loss[loss=0.257, simple_loss=0.3365, pruned_loss=0.08873, over 7314.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2651, pruned_loss=0.04505, over 1414931.52 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 21:43:52,884 INFO [train.py:842] (1/4) Epoch 29, batch 2800, loss[loss=0.1488, simple_loss=0.2434, pruned_loss=0.02706, over 7353.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2644, pruned_loss=0.04403, over 1417834.64 frames.], batch size: 22, lr: 1.90e-04
2022-05-28 21:44:32,579 INFO [train.py:842] (1/4) Epoch 29, batch 2850, loss[loss=0.1631, simple_loss=0.2488, pruned_loss=0.03869, over 7271.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2634, pruned_loss=0.04362, over 1419264.64 frames.], batch size: 19, lr: 1.90e-04
2022-05-28 21:45:11,782 INFO [train.py:842] (1/4) Epoch 29, batch 2900, loss[loss=0.1916, simple_loss=0.2574, pruned_loss=0.06287, over 7300.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2635, pruned_loss=0.04399, over 1418408.46 frames.], batch size: 17, lr: 1.90e-04
2022-05-28 21:45:51,688 INFO [train.py:842] (1/4) Epoch 29, batch 2950, loss[loss=0.135, simple_loss=0.2178, pruned_loss=0.02611, over 7141.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2616, pruned_loss=0.04354, over 1418667.57 frames.], batch size: 17, lr: 1.90e-04
2022-05-28 21:46:30,712 INFO [train.py:842] (1/4) Epoch 29, batch 3000, loss[loss=0.1673, simple_loss=0.26, pruned_loss=0.03726, over 7234.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2618, pruned_loss=0.04292, over 1419949.19 frames.], batch size: 20, lr: 1.90e-04
2022-05-28 21:46:30,712 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 21:46:40,406 INFO [train.py:871] (1/4) Epoch 29, validation: loss=0.1638, simple_loss=0.2614, pruned_loss=0.03305, over 868885.00 frames.
2022-05-28 21:47:20,128 INFO [train.py:842] (1/4) Epoch 29, batch 3050, loss[loss=0.1507, simple_loss=0.2404, pruned_loss=0.03051, over 7155.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2617, pruned_loss=0.043, over 1421992.03 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:47:59,298 INFO [train.py:842] (1/4) Epoch 29, batch 3100, loss[loss=0.1891, simple_loss=0.2666, pruned_loss=0.05578, over 7269.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2623, pruned_loss=0.04326, over 1419099.70 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:48:38,804 INFO [train.py:842] (1/4) Epoch 29, batch 3150, loss[loss=0.1813, simple_loss=0.2747, pruned_loss=0.04388, over 7224.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2641, pruned_loss=0.0446, over 1422727.08 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 21:49:18,085 INFO [train.py:842] (1/4) Epoch 29, batch 3200, loss[loss=0.1792, simple_loss=0.2723, pruned_loss=0.04306, over 7109.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2641, pruned_loss=0.04431, over 1421919.00 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 21:49:57,931 INFO [train.py:842] (1/4) Epoch 29, batch 3250, loss[loss=0.1592, simple_loss=0.2361, pruned_loss=0.04116, over 6789.00 frames.], tot_loss[loss=0.1764, simple_loss=0.264, pruned_loss=0.04434, over 1421747.18 frames.], batch size: 15, lr: 1.90e-04
2022-05-28 21:50:37,008 INFO [train.py:842] (1/4) Epoch 29, batch 3300, loss[loss=0.157, simple_loss=0.2623, pruned_loss=0.02584, over 7221.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2647, pruned_loss=0.04439, over 1421660.33 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 21:51:16,454 INFO [train.py:842] (1/4) Epoch 29, batch 3350, loss[loss=0.1778, simple_loss=0.2665, pruned_loss=0.04456, over 7081.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2643, pruned_loss=0.04429, over 1419438.58 frames.], batch size: 28, lr: 1.90e-04
2022-05-28 21:51:55,683 INFO [train.py:842] (1/4) Epoch 29, batch 3400, loss[loss=0.1789, simple_loss=0.2522, pruned_loss=0.05284, over 7064.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2654, pruned_loss=0.04501, over 1417462.78 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:52:35,508 INFO [train.py:842] (1/4) Epoch 29, batch 3450, loss[loss=0.2054, simple_loss=0.2763, pruned_loss=0.06722, over 7272.00 frames.], tot_loss[loss=0.177, simple_loss=0.2642, pruned_loss=0.04488, over 1419266.31 frames.], batch size: 17, lr: 1.90e-04
2022-05-28 21:53:14,701 INFO [train.py:842] (1/4) Epoch 29, batch 3500, loss[loss=0.2012, simple_loss=0.2907, pruned_loss=0.05589, over 6759.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2646, pruned_loss=0.04439, over 1418751.89 frames.], batch size: 31, lr: 1.90e-04
2022-05-28 21:53:54,287 INFO [train.py:842] (1/4) Epoch 29, batch 3550, loss[loss=0.1764, simple_loss=0.2518, pruned_loss=0.05048, over 7287.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2638, pruned_loss=0.04388, over 1422099.47 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:54:33,677 INFO [train.py:842] (1/4) Epoch 29, batch 3600, loss[loss=0.1516, simple_loss=0.2325, pruned_loss=0.03532, over 7169.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2643, pruned_loss=0.04434, over 1422492.85 frames.], batch size: 16, lr: 1.90e-04
2022-05-28 21:55:13,261 INFO [train.py:842] (1/4) Epoch 29, batch 3650, loss[loss=0.1877, simple_loss=0.2769, pruned_loss=0.04925, over 7351.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2643, pruned_loss=0.04415, over 1426122.49 frames.], batch size: 22, lr: 1.90e-04
2022-05-28 21:55:52,638 INFO [train.py:842] (1/4) Epoch 29, batch 3700, loss[loss=0.2586, simple_loss=0.3434, pruned_loss=0.08689, over 7200.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2657, pruned_loss=0.04494, over 1426428.20 frames.], batch size: 23, lr: 1.90e-04
2022-05-28 21:56:32,178 INFO [train.py:842] (1/4) Epoch 29, batch 3750, loss[loss=0.1962, simple_loss=0.2814, pruned_loss=0.05546, over 5126.00 frames.], tot_loss[loss=0.1781, simple_loss=0.266, pruned_loss=0.04508, over 1426173.86 frames.], batch size: 52, lr: 1.90e-04
2022-05-28 21:57:11,445 INFO [train.py:842] (1/4) Epoch 29, batch 3800, loss[loss=0.1495, simple_loss=0.2346, pruned_loss=0.03215, over 7069.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2653, pruned_loss=0.04446, over 1428742.59 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:57:51,288 INFO [train.py:842] (1/4) Epoch 29, batch 3850, loss[loss=0.1294, simple_loss=0.2091, pruned_loss=0.02489, over 7253.00 frames.], tot_loss[loss=0.1758, simple_loss=0.264, pruned_loss=0.0438, over 1428390.58 frames.], batch size: 16, lr: 1.90e-04
2022-05-28 21:58:30,619 INFO [train.py:842] (1/4) Epoch 29, batch 3900, loss[loss=0.1614, simple_loss=0.2315, pruned_loss=0.04568, over 7430.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2644, pruned_loss=0.04402, over 1430986.32 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:59:10,095 INFO [train.py:842] (1/4) Epoch 29, batch 3950, loss[loss=0.1718, simple_loss=0.2801, pruned_loss=0.03174, over 7126.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2642, pruned_loss=0.04335, over 1431180.56 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 21:59:49,041 INFO [train.py:842] (1/4) Epoch 29, batch 4000, loss[loss=0.185, simple_loss=0.2757, pruned_loss=0.04714, over 7118.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2643, pruned_loss=0.0434, over 1430436.82 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 22:00:28,484 INFO [train.py:842] (1/4) Epoch 29, batch 4050, loss[loss=0.1853, simple_loss=0.2762, pruned_loss=0.04723, over 7061.00 frames.], tot_loss[loss=0.176, simple_loss=0.2649, pruned_loss=0.04355, over 1429229.26 frames.], batch size: 28, lr: 1.90e-04
2022-05-28 22:01:07,527 INFO [train.py:842] (1/4) Epoch 29, batch 4100, loss[loss=0.1477, simple_loss=0.2406, pruned_loss=0.02737, over 7006.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2645, pruned_loss=0.04358, over 1429797.45 frames.], batch size: 28, lr: 1.90e-04
2022-05-28 22:01:47,165 INFO [train.py:842] (1/4) Epoch 29, batch 4150, loss[loss=0.2053, simple_loss=0.2963, pruned_loss=0.05722, over 7221.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2638, pruned_loss=0.04357, over 1432690.41 frames.], batch size: 20, lr: 1.90e-04
2022-05-28 22:02:26,325 INFO [train.py:842] (1/4) Epoch 29, batch 4200, loss[loss=0.1688, simple_loss=0.266, pruned_loss=0.03574, over 7354.00 frames.], tot_loss[loss=0.176, simple_loss=0.2644, pruned_loss=0.04385, over 1427777.60 frames.], batch size: 22, lr: 1.90e-04
2022-05-28 22:03:05,994 INFO [train.py:842] (1/4) Epoch 29, batch 4250, loss[loss=0.1521, simple_loss=0.2367, pruned_loss=0.03375, over 7260.00 frames.], tot_loss[loss=0.176, simple_loss=0.2642, pruned_loss=0.04388, over 1427208.37 frames.], batch size: 19, lr: 1.90e-04
2022-05-28 22:03:45,563 INFO [train.py:842] (1/4) Epoch 29, batch 4300, loss[loss=0.137, simple_loss=0.2154, pruned_loss=0.02927, over 6824.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2644, pruned_loss=0.04394, over 1428692.73 frames.], batch size: 15, lr: 1.90e-04
2022-05-28 22:04:25,119 INFO [train.py:842] (1/4) Epoch 29, batch 4350, loss[loss=0.2226, simple_loss=0.3068, pruned_loss=0.06918, over 7179.00 frames.], tot_loss[loss=0.177, simple_loss=0.2654, pruned_loss=0.04431, over 1430856.68 frames.], batch size: 26, lr: 1.90e-04
2022-05-28 22:05:04,060 INFO [train.py:842] (1/4) Epoch 29, batch 4400, loss[loss=0.249, simple_loss=0.3169, pruned_loss=0.0905, over 5113.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2656, pruned_loss=0.04449, over 1426807.28 frames.], batch size: 53, lr: 1.90e-04
2022-05-28 22:05:43,317 INFO [train.py:842] (1/4) Epoch 29, batch 4450, loss[loss=0.1601, simple_loss=0.2513, pruned_loss=0.03442, over 6779.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2648, pruned_loss=0.044, over 1426518.28 frames.], batch size: 31, lr: 1.90e-04
2022-05-28 22:06:22,612 INFO [train.py:842] (1/4) Epoch 29, batch 4500, loss[loss=0.1685, simple_loss=0.2766, pruned_loss=0.03017, over 7334.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2637, pruned_loss=0.04338, over 1428860.38 frames.], batch size: 22, lr: 1.90e-04
2022-05-28 22:07:02,126 INFO [train.py:842] (1/4) Epoch 29, batch 4550, loss[loss=0.156, simple_loss=0.2406, pruned_loss=0.03571, over 6833.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2634, pruned_loss=0.0435, over 1427201.02 frames.], batch size: 15, lr: 1.90e-04
2022-05-28 22:07:41,327 INFO [train.py:842] (1/4) Epoch 29, batch 4600, loss[loss=0.2072, simple_loss=0.2901, pruned_loss=0.06214, over 7439.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2645, pruned_loss=0.04419, over 1428613.92 frames.], batch size: 20, lr: 1.90e-04
2022-05-28 22:08:21,037 INFO [train.py:842] (1/4) Epoch 29, batch 4650, loss[loss=0.1898, simple_loss=0.2711, pruned_loss=0.0543, over 7200.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2658, pruned_loss=0.04603, over 1428433.86 frames.], batch size: 22, lr: 1.90e-04
2022-05-28 22:09:00,207 INFO [train.py:842] (1/4) Epoch 29, batch 4700, loss[loss=0.1717, simple_loss=0.2644, pruned_loss=0.03955, over 7169.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2657, pruned_loss=0.04574, over 1424602.47 frames.], batch size: 19, lr: 1.90e-04
2022-05-28 22:09:50,590 INFO [train.py:842] (1/4) Epoch 29, batch 4750, loss[loss=0.1773, simple_loss=0.2625, pruned_loss=0.0461, over 7286.00 frames.], tot_loss[loss=0.1784, simple_loss=0.266, pruned_loss=0.0454, over 1423993.00 frames.], batch size: 24, lr: 1.90e-04
2022-05-28 22:10:29,955 INFO [train.py:842] (1/4) Epoch 29, batch 4800, loss[loss=0.1587, simple_loss=0.2452, pruned_loss=0.03609, over 7259.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2641, pruned_loss=0.04459, over 1427324.04 frames.], batch size: 19, lr: 1.90e-04
2022-05-28 22:11:09,586 INFO [train.py:842] (1/4) Epoch 29, batch 4850, loss[loss=0.1472, simple_loss=0.2441, pruned_loss=0.02521, over 7230.00 frames.], tot_loss[loss=0.1763, simple_loss=0.264, pruned_loss=0.04426, over 1428038.99 frames.], batch size: 20, lr: 1.90e-04
2022-05-28 22:11:48,947 INFO [train.py:842] (1/4) Epoch 29, batch 4900, loss[loss=0.1678, simple_loss=0.2556, pruned_loss=0.03999, over 7057.00 frames.], tot_loss[loss=0.176, simple_loss=0.2644, pruned_loss=0.04383, over 1428304.94 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 22:12:28,570 INFO [train.py:842] (1/4) Epoch 29, batch 4950, loss[loss=0.1919, simple_loss=0.2748, pruned_loss=0.0545, over 6402.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2644, pruned_loss=0.04367, over 1427947.98 frames.], batch size: 37, lr: 1.90e-04
2022-05-28 22:13:07,886 INFO [train.py:842] (1/4) Epoch 29, batch 5000, loss[loss=0.1739, simple_loss=0.274, pruned_loss=0.03687, over 7372.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2652, pruned_loss=0.04469, over 1422829.61 frames.], batch size: 23, lr: 1.90e-04
2022-05-28 22:13:47,752 INFO [train.py:842] (1/4) Epoch 29, batch 5050, loss[loss=0.1431, simple_loss=0.2254, pruned_loss=0.03037, over 7295.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2639, pruned_loss=0.04392, over 1429487.24 frames.], batch size: 17, lr: 1.90e-04
2022-05-28 22:14:27,123 INFO [train.py:842] (1/4) Epoch 29, batch 5100, loss[loss=0.1674, simple_loss=0.265, pruned_loss=0.03486, over 7149.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2633, pruned_loss=0.04368, over 1430118.46 frames.], batch size: 20, lr: 1.90e-04
2022-05-28 22:15:06,664 INFO [train.py:842] (1/4) Epoch 29, batch 5150, loss[loss=0.1661, simple_loss=0.2658, pruned_loss=0.03316, over 6468.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2639, pruned_loss=0.04371, over 1431427.99 frames.], batch size: 37, lr: 1.89e-04
2022-05-28 22:15:45,874 INFO [train.py:842] (1/4) Epoch 29, batch 5200, loss[loss=0.1817, simple_loss=0.2759, pruned_loss=0.0437, over 7055.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2631, pruned_loss=0.04334, over 1430289.41 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:16:25,563 INFO [train.py:842] (1/4) Epoch 29, batch 5250, loss[loss=0.1798, simple_loss=0.2608, pruned_loss=0.04939, over 7160.00 frames.], tot_loss[loss=0.1752, simple_loss=0.263, pruned_loss=0.04372, over 1431870.60 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:17:04,973 INFO [train.py:842] (1/4) Epoch 29, batch 5300, loss[loss=0.1919, simple_loss=0.2802, pruned_loss=0.05176, over 7121.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2637, pruned_loss=0.04363, over 1433262.79 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:17:44,510 INFO [train.py:842] (1/4) Epoch 29, batch 5350, loss[loss=0.166, simple_loss=0.2506, pruned_loss=0.04067, over 7277.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2634, pruned_loss=0.04369, over 1429929.00 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:18:23,823 INFO [train.py:842] (1/4) Epoch 29, batch 5400, loss[loss=0.1566, simple_loss=0.2503, pruned_loss=0.03144, over 7359.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2632, pruned_loss=0.04326, over 1428848.88 frames.], batch size: 23, lr: 1.89e-04
2022-05-28 22:19:03,661 INFO [train.py:842] (1/4) Epoch 29, batch 5450, loss[loss=0.1921, simple_loss=0.2739, pruned_loss=0.05516, over 7323.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2644, pruned_loss=0.04369, over 1431076.83 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:19:42,609 INFO [train.py:842] (1/4) Epoch 29, batch 5500, loss[loss=0.1705, simple_loss=0.2608, pruned_loss=0.04008, over 7208.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2658, pruned_loss=0.04438, over 1430754.35 frames.], batch size: 22, lr: 1.89e-04
2022-05-28 22:20:22,053 INFO [train.py:842] (1/4) Epoch 29, batch 5550, loss[loss=0.1928, simple_loss=0.2798, pruned_loss=0.05296, over 4949.00 frames.], tot_loss[loss=0.1778, simple_loss=0.266, pruned_loss=0.04482, over 1426230.87 frames.], batch size: 53, lr: 1.89e-04
2022-05-28 22:21:01,168 INFO [train.py:842] (1/4) Epoch 29, batch 5600, loss[loss=0.1625, simple_loss=0.241, pruned_loss=0.042, over 7286.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2659, pruned_loss=0.04455, over 1427626.48 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:21:40,641 INFO [train.py:842] (1/4) Epoch 29, batch 5650, loss[loss=0.1564, simple_loss=0.2408, pruned_loss=0.03598, over 7277.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2648, pruned_loss=0.04388, over 1428368.00 frames.], batch size: 17, lr: 1.89e-04
2022-05-28 22:22:19,816 INFO [train.py:842] (1/4) Epoch 29, batch 5700, loss[loss=0.1999, simple_loss=0.29, pruned_loss=0.05488, over 6820.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2648, pruned_loss=0.04406, over 1429113.30 frames.], batch size: 31, lr: 1.89e-04
2022-05-28 22:22:59,407 INFO [train.py:842] (1/4) Epoch 29, batch 5750, loss[loss=0.1572, simple_loss=0.2404, pruned_loss=0.03702, over 7278.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2649, pruned_loss=0.04399, over 1428371.12 frames.], batch size: 17, lr: 1.89e-04
2022-05-28 22:23:38,539 INFO [train.py:842] (1/4) Epoch 29, batch 5800, loss[loss=0.2087, simple_loss=0.2856, pruned_loss=0.06588, over 7147.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2657, pruned_loss=0.04452, over 1423704.81 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:24:18,026 INFO [train.py:842] (1/4) Epoch 29, batch 5850, loss[loss=0.1548, simple_loss=0.2579, pruned_loss=0.02581, over 7418.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2652, pruned_loss=0.0442, over 1420352.40 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:24:57,152 INFO [train.py:842] (1/4) Epoch 29, batch 5900, loss[loss=0.177, simple_loss=0.2734, pruned_loss=0.04029, over 7141.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2646, pruned_loss=0.04379, over 1422988.96 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:25:36,571 INFO [train.py:842] (1/4) Epoch 29, batch 5950, loss[loss=0.1782, simple_loss=0.2678, pruned_loss=0.0443, over 7236.00 frames.], tot_loss[loss=0.176, simple_loss=0.2645, pruned_loss=0.04375, over 1418604.15 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:26:15,716 INFO [train.py:842] (1/4) Epoch 29, batch 6000, loss[loss=0.1662, simple_loss=0.2532, pruned_loss=0.03963, over 7139.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2654, pruned_loss=0.04418, over 1418362.66 frames.], batch size: 17, lr: 1.89e-04
2022-05-28 22:26:15,717 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 22:26:26,013 INFO [train.py:871] (1/4) Epoch 29, validation: loss=0.1668, simple_loss=0.2645, pruned_loss=0.03459, over 868885.00 frames.
2022-05-28 22:27:05,782 INFO [train.py:842] (1/4) Epoch 29, batch 6050, loss[loss=0.1813, simple_loss=0.2783, pruned_loss=0.04214, over 7220.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2648, pruned_loss=0.04408, over 1421629.26 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:27:44,942 INFO [train.py:842] (1/4) Epoch 29, batch 6100, loss[loss=0.2393, simple_loss=0.311, pruned_loss=0.08383, over 7222.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2646, pruned_loss=0.04382, over 1421615.45 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:28:24,707 INFO [train.py:842] (1/4) Epoch 29, batch 6150, loss[loss=0.1388, simple_loss=0.2221, pruned_loss=0.02775, over 7266.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2635, pruned_loss=0.04315, over 1422501.22 frames.], batch size: 17, lr: 1.89e-04
2022-05-28 22:29:03,807 INFO [train.py:842] (1/4) Epoch 29, batch 6200, loss[loss=0.1512, simple_loss=0.2445, pruned_loss=0.02889, over 7429.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2635, pruned_loss=0.04345, over 1419484.29 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:29:43,509 INFO [train.py:842] (1/4) Epoch 29, batch 6250, loss[loss=0.1939, simple_loss=0.2822, pruned_loss=0.05275, over 7204.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2639, pruned_loss=0.04417, over 1423239.80 frames.], batch size: 22, lr: 1.89e-04
2022-05-28 22:30:22,909 INFO [train.py:842] (1/4) Epoch 29, batch 6300, loss[loss=0.1672, simple_loss=0.2618, pruned_loss=0.03629, over 7316.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2636, pruned_loss=0.04389, over 1426062.43 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:31:02,416 INFO [train.py:842] (1/4) Epoch 29, batch 6350, loss[loss=0.1821, simple_loss=0.2646, pruned_loss=0.04984, over 7064.00 frames.], tot_loss[loss=0.1751, simple_loss=0.263, pruned_loss=0.04356, over 1425174.53 frames.], batch size: 28, lr: 1.89e-04
2022-05-28 22:31:41,474 INFO [train.py:842] (1/4) Epoch 29, batch 6400, loss[loss=0.1828, simple_loss=0.2703, pruned_loss=0.04772, over 7441.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2639, pruned_loss=0.04373, over 1421900.18 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:32:20,942 INFO [train.py:842] (1/4) Epoch 29, batch 6450, loss[loss=0.1799, simple_loss=0.2614, pruned_loss=0.04914, over 7161.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2637, pruned_loss=0.04384, over 1423501.83 frames.], batch size: 19, lr: 1.89e-04
2022-05-28 22:33:00,142 INFO [train.py:842] (1/4) Epoch 29, batch 6500, loss[loss=0.1566, simple_loss=0.2516, pruned_loss=0.03084, over 7220.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2647, pruned_loss=0.04371, over 1422251.17 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:33:39,851 INFO [train.py:842] (1/4) Epoch 29, batch 6550, loss[loss=0.1548, simple_loss=0.2465, pruned_loss=0.03158, over 7324.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2635, pruned_loss=0.04288, over 1421569.25 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:34:18,913 INFO [train.py:842] (1/4) Epoch 29, batch 6600, loss[loss=0.1825, simple_loss=0.2767, pruned_loss=0.04418, over 7157.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2647, pruned_loss=0.04381, over 1420523.93 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:35:01,109 INFO [train.py:842] (1/4) Epoch 29, batch 6650, loss[loss=0.2013, simple_loss=0.2972, pruned_loss=0.05266, over 7364.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2651, pruned_loss=0.04402, over 1422730.18 frames.], batch size: 23, lr: 1.89e-04
2022-05-28 22:35:40,383 INFO [train.py:842] (1/4) Epoch 29, batch 6700, loss[loss=0.1355, simple_loss=0.2226, pruned_loss=0.02415, over 7281.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2644, pruned_loss=0.04388, over 1416369.62 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:36:19,874 INFO [train.py:842] (1/4) Epoch 29, batch 6750, loss[loss=0.1799, simple_loss=0.2624, pruned_loss=0.04871, over 7205.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2649, pruned_loss=0.04426, over 1414131.72 frames.], batch size: 23, lr: 1.89e-04
2022-05-28 22:36:58,895 INFO [train.py:842] (1/4) Epoch 29, batch 6800, loss[loss=0.1898, simple_loss=0.2825, pruned_loss=0.04853, over 6784.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2661, pruned_loss=0.04476, over 1417401.55 frames.], batch size: 31, lr: 1.89e-04
2022-05-28 22:37:38,499 INFO [train.py:842] (1/4) Epoch 29, batch 6850, loss[loss=0.1804, simple_loss=0.2649, pruned_loss=0.04792, over 5009.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2657, pruned_loss=0.04419, over 1414156.95 frames.], batch size: 52, lr: 1.89e-04
2022-05-28 22:38:17,540 INFO [train.py:842] (1/4) Epoch 29, batch 6900, loss[loss=0.1745, simple_loss=0.2705, pruned_loss=0.03923, over 6708.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2653, pruned_loss=0.04411, over 1417061.25 frames.], batch size: 31, lr: 1.89e-04
2022-05-28 22:38:57,128 INFO [train.py:842] (1/4) Epoch 29, batch 6950, loss[loss=0.1484, simple_loss=0.2305, pruned_loss=0.03317, over 7149.00 frames.], tot_loss[loss=0.177, simple_loss=0.2656, pruned_loss=0.04421, over 1416637.83 frames.], batch size: 17, lr: 1.89e-04
2022-05-28 22:39:36,333 INFO [train.py:842] (1/4) Epoch 29, batch 7000, loss[loss=0.1477, simple_loss=0.2361, pruned_loss=0.02969, over 7171.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2647, pruned_loss=0.04386, over 1417477.24 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:40:15,842 INFO [train.py:842] (1/4) Epoch 29, batch 7050, loss[loss=0.1658, simple_loss=0.251, pruned_loss=0.04027, over 7074.00 frames.], tot_loss[loss=0.176, simple_loss=0.2647, pruned_loss=0.04361, over 1420428.33 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:40:54,875 INFO [train.py:842] (1/4) Epoch 29, batch 7100, loss[loss=0.1899, simple_loss=0.2838, pruned_loss=0.04801, over 7221.00 frames.], tot_loss[loss=0.1763, simple_loss=0.265, pruned_loss=0.0438, over 1416299.71 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:41:34,448 INFO [train.py:842] (1/4) Epoch 29, batch 7150, loss[loss=0.1479, simple_loss=0.2347, pruned_loss=0.03057, over 7148.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2657, pruned_loss=0.04437, over 1416853.69 frames.], batch size: 19, lr: 1.89e-04
2022-05-28 22:42:14,017 INFO [train.py:842] (1/4) Epoch 29, batch 7200, loss[loss=0.1609, simple_loss=0.2516, pruned_loss=0.03514, over 7329.00 frames.], tot_loss[loss=0.178, simple_loss=0.2661, pruned_loss=0.04494, over 1420981.44 frames.], batch size: 24, lr: 1.89e-04
2022-05-28 22:42:53,939 INFO [train.py:842] (1/4) Epoch 29, batch 7250, loss[loss=0.1754, simple_loss=0.2701, pruned_loss=0.04036, over 7222.00 frames.], tot_loss[loss=0.177, simple_loss=0.2651, pruned_loss=0.04452, over 1427325.88 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:43:33,120 INFO [train.py:842] (1/4) Epoch 29, batch 7300, loss[loss=0.1894, simple_loss=0.2799, pruned_loss=0.04943, over 6326.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2659, pruned_loss=0.04483, over 1429513.29 frames.], batch size: 38, lr: 1.89e-04
2022-05-28 22:44:12,678 INFO [train.py:842] (1/4) Epoch 29, batch 7350, loss[loss=0.1636, simple_loss=0.2536, pruned_loss=0.03675, over 7412.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2648, pruned_loss=0.04386, over 1428924.03 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:44:51,881 INFO [train.py:842] (1/4) Epoch 29, batch 7400, loss[loss=0.227, simple_loss=0.3099, pruned_loss=0.07203, over 6877.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2638, pruned_loss=0.04387, over 1426468.29 frames.], batch size: 31, lr: 1.89e-04
2022-05-28 22:45:31,639 INFO [train.py:842] (1/4) Epoch 29, batch 7450, loss[loss=0.1705, simple_loss=0.2593, pruned_loss=0.04086, over 7164.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2651, pruned_loss=0.04485, over 1424469.24 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:46:10,867 INFO [train.py:842] (1/4) Epoch 29, batch 7500, loss[loss=0.1613, simple_loss=0.2325, pruned_loss=0.04501, over 7140.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2648, pruned_loss=0.04483, over 1423182.42 frames.], batch size: 17, lr: 1.89e-04
2022-05-28 22:46:50,624 INFO [train.py:842] (1/4) Epoch 29, batch 7550, loss[loss=0.1327, simple_loss=0.2122, pruned_loss=0.02662, over 7070.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2645, pruned_loss=0.04437, over 1425879.32 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:47:29,679 INFO [train.py:842] (1/4) Epoch 29, batch 7600, loss[loss=0.1533, simple_loss=0.2561, pruned_loss=0.0253, over 7320.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2643, pruned_loss=0.04424, over 1423385.03 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:48:09,243 INFO [train.py:842] (1/4) Epoch 29, batch 7650, loss[loss=0.1824, simple_loss=0.2654, pruned_loss=0.04964, over 7331.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2643, pruned_loss=0.04441, over 1423118.30 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:48:48,346 INFO [train.py:842] (1/4) Epoch 29, batch 7700, loss[loss=0.1682, simple_loss=0.265, pruned_loss=0.03571, over 7141.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2657, pruned_loss=0.04471, over 1424450.71 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:49:27,980 INFO [train.py:842] (1/4) Epoch 29, batch 7750, loss[loss=0.1563, simple_loss=0.2541, pruned_loss=0.02925, over 7233.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2661, pruned_loss=0.04464, over 1421444.03 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:50:07,203 INFO [train.py:842] (1/4) Epoch 29, batch 7800, loss[loss=0.1646, simple_loss=0.2581, pruned_loss=0.03551, over 7148.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2665, pruned_loss=0.04538, over 1420019.13 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:50:46,758 INFO [train.py:842] (1/4) Epoch 29, batch 7850, loss[loss=0.1579, simple_loss=0.2584, pruned_loss=0.02876, over 6450.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2662, pruned_loss=0.04527, over 1420662.64 frames.], batch size: 38, lr: 1.89e-04
2022-05-28 22:51:26,061 INFO [train.py:842] (1/4) Epoch 29, batch 7900, loss[loss=0.1919, simple_loss=0.2736, pruned_loss=0.05512, over 7331.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2649, pruned_loss=0.0447, over 1421685.84 frames.], batch size: 22, lr: 1.89e-04
2022-05-28 22:52:05,538 INFO [train.py:842] (1/4) Epoch 29, batch 7950, loss[loss=0.1472, simple_loss=0.2297, pruned_loss=0.03237, over 7275.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2653, pruned_loss=0.04517, over 1421630.43 frames.], batch size: 17, lr: 1.88e-04
2022-05-28 22:52:44,468 INFO [train.py:842] (1/4) Epoch 29, batch 8000, loss[loss=0.202, simple_loss=0.2887, pruned_loss=0.05761, over 7328.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2658, pruned_loss=0.04524, over 1421780.75 frames.], batch size: 22, lr: 1.88e-04
2022-05-28 22:53:24,174 INFO [train.py:842] (1/4) Epoch 29, batch 8050, loss[loss=0.1551, simple_loss=0.243, pruned_loss=0.03357, over 7434.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2647, pruned_loss=0.04427, over 1427401.28 frames.], batch size: 20, lr: 1.88e-04
2022-05-28 22:54:03,207 INFO [train.py:842] (1/4) Epoch 29, batch 8100, loss[loss=0.169, simple_loss=0.2633, pruned_loss=0.03738, over 7294.00 frames.], tot_loss[loss=0.1778, simple_loss=0.266, pruned_loss=0.04484, over 1427383.71 frames.], batch size: 25, lr: 1.88e-04
2022-05-28 22:54:43,012 INFO [train.py:842] (1/4) Epoch 29, batch 8150, loss[loss=0.1493, simple_loss=0.2324, pruned_loss=0.03317, over 7280.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2648, pruned_loss=0.04451, over 1426289.32 frames.], batch size: 17, lr: 1.88e-04
2022-05-28 22:55:22,236 INFO [train.py:842] (1/4) Epoch 29, batch 8200, loss[loss=0.1748, simple_loss=0.2691, pruned_loss=0.04024, over 7237.00 frames.], tot_loss[loss=0.1761, simple_loss=0.264, pruned_loss=0.04415, over 1423423.90 frames.], batch size: 20, lr: 1.88e-04
2022-05-28 22:56:01,699 INFO [train.py:842] (1/4) Epoch 29, batch 8250, loss[loss=0.2007, simple_loss=0.289, pruned_loss=0.05625, over 7178.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2636, pruned_loss=0.04335, over 1426281.82 frames.], batch size: 19, lr: 1.88e-04
2022-05-28 22:56:40,908 INFO [train.py:842] (1/4) Epoch 29, batch 8300, loss[loss=0.1728, simple_loss=0.2678, pruned_loss=0.03887, over 7324.00 frames.], tot_loss[loss=0.1755, simple_loss=0.264, pruned_loss=0.0435, over 1425663.01 frames.], batch size: 20, lr: 1.88e-04
2022-05-28 22:57:20,360 INFO [train.py:842] (1/4) Epoch 29, batch 8350, loss[loss=0.1484, simple_loss=0.2321, pruned_loss=0.03231, over 6995.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2627, pruned_loss=0.04241, over 1422380.99 frames.], batch size: 16, lr: 1.88e-04
2022-05-28 22:57:59,577 INFO [train.py:842] (1/4) Epoch 29, batch 8400, loss[loss=0.2201, simple_loss=0.2964, pruned_loss=0.07189, over 7434.00 frames.], tot_loss[loss=0.1737, simple_loss=0.263, pruned_loss=0.04223, over 1420918.42 frames.], batch size: 20, lr: 1.88e-04
2022-05-28 22:58:38,725 INFO [train.py:842] (1/4) Epoch 29, batch 8450, loss[loss=0.1856, simple_loss=0.2763, pruned_loss=0.04746, over 7171.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2627, pruned_loss=0.04222, over 1414806.33 frames.], batch size: 23, lr: 1.88e-04
2022-05-28 22:59:17,799 INFO [train.py:842] (1/4) Epoch 29, batch 8500, loss[loss=0.1652, simple_loss=0.2649, pruned_loss=0.03277, over 7325.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2637, pruned_loss=0.04252, over 1418598.90 frames.], batch size: 20, lr: 1.88e-04
2022-05-28 22:59:57,402 INFO [train.py:842] (1/4) Epoch 29, batch 8550, loss[loss=0.1519, simple_loss=0.2379, pruned_loss=0.03292, over 7254.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2645, pruned_loss=0.043, over 1419417.10 frames.], batch size: 19, lr: 1.88e-04
2022-05-28 23:00:36,821 INFO [train.py:842] (1/4) Epoch 29, batch 8600, loss[loss=0.1977, simple_loss=0.2854, pruned_loss=0.05498, over 7337.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2638, pruned_loss=0.04269, over 1422818.66 frames.], batch size: 21, lr: 1.88e-04
2022-05-28 23:01:16,288 INFO [train.py:842] (1/4) Epoch 29, batch 8650, loss[loss=0.1335, simple_loss=0.2174, pruned_loss=0.0248, over 6771.00 frames.], tot_loss[loss=0.175, simple_loss=0.2647, pruned_loss=0.04269, over 1422588.47 frames.], batch size: 15, lr: 1.88e-04
2022-05-28 23:01:55,581 INFO [train.py:842] (1/4) Epoch 29, batch 8700, loss[loss=0.1373, simple_loss=0.2333, pruned_loss=0.02063, over 7357.00 frames.], tot_loss[loss=0.1744, simple_loss=0.264, pruned_loss=0.0424, over 1419729.14 frames.], batch size: 19, lr: 1.88e-04
2022-05-28 23:02:35,313 INFO [train.py:842] (1/4) Epoch 29, batch 8750, loss[loss=0.1991, simple_loss=0.2876, pruned_loss=0.05525, over 7267.00 frames.], tot_loss[loss=0.176, simple_loss=0.265, pruned_loss=0.04349, over 1423272.49 frames.], batch size: 25, lr: 1.88e-04
2022-05-28 23:03:14,613 INFO [train.py:842] (1/4) Epoch 29, batch 8800, loss[loss=0.199, simple_loss=0.2677, pruned_loss=0.06516, over 7006.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2646, pruned_loss=0.04309, over 1424911.14 frames.], batch size: 16, lr: 1.88e-04
2022-05-28 23:03:54,168 INFO [train.py:842] (1/4) Epoch 29, batch 8850, loss[loss=0.1844, simple_loss=0.2763, pruned_loss=0.04628, over 7143.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2649, pruned_loss=0.04375, over 1416173.34 frames.], batch size: 19, lr: 1.88e-04
2022-05-28 23:04:33,312 INFO [train.py:842] (1/4) Epoch 29, batch 8900, loss[loss=0.2074, simple_loss=0.2955, pruned_loss=0.05961, over 6746.00 frames.], tot_loss[loss=0.1754, simple_loss=0.264, pruned_loss=0.0434, over 1413841.21 frames.], batch size: 31, lr: 1.88e-04
2022-05-28 23:05:12,289 INFO [train.py:842] (1/4) Epoch 29, batch 8950, loss[loss=0.2485, simple_loss=0.331, pruned_loss=0.08306, over 7200.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2654, pruned_loss=0.04411, over 1401148.03 frames.], batch size: 22, lr: 1.88e-04
2022-05-28 23:05:50,678 INFO [train.py:842] (1/4) Epoch 29, batch 9000, loss[loss=0.1747, simple_loss=0.2653, pruned_loss=0.04206, over 6246.00 frames.], tot_loss[loss=0.1786, simple_loss=0.267, pruned_loss=0.0451, over 1382074.64 frames.], batch size: 37, lr: 1.88e-04
2022-05-28 23:05:50,679 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 23:06:00,408 INFO [train.py:871] (1/4) Epoch 29, validation: loss=0.164, simple_loss=0.2612, pruned_loss=0.03344, over 868885.00 frames.
2022-05-28 23:06:39,643 INFO [train.py:842] (1/4) Epoch 29, batch 9050, loss[loss=0.1694, simple_loss=0.2605, pruned_loss=0.03913, over 7151.00 frames.], tot_loss[loss=0.1797, simple_loss=0.268, pruned_loss=0.04573, over 1368112.04 frames.], batch size: 28, lr: 1.88e-04
2022-05-28 23:07:27,929 INFO [train.py:842] (1/4) Epoch 29, batch 9100, loss[loss=0.1694, simple_loss=0.2459, pruned_loss=0.04642, over 4775.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2696, pruned_loss=0.0466, over 1312585.44 frames.], batch size: 52, lr: 1.88e-04
2022-05-28 23:08:06,154 INFO [train.py:842] (1/4) Epoch 29, batch 9150, loss[loss=0.1961, simple_loss=0.2825, pruned_loss=0.05486, over 5340.00 frames.], tot_loss[loss=0.187, simple_loss=0.2739, pruned_loss=0.05001, over 1246965.27 frames.], batch size: 52, lr: 1.88e-04
2022-05-28 23:08:53,866 INFO [train.py:842] (1/4) Epoch 30, batch 0, loss[loss=0.1631, simple_loss=0.2547, pruned_loss=0.03572, over 7333.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2547, pruned_loss=0.03572, over 7333.00 frames.], batch size: 20, lr: 1.85e-04
2022-05-28 23:09:44,400 INFO [train.py:842] (1/4) Epoch 30, batch 50, loss[loss=0.1283, simple_loss=0.2089, pruned_loss=0.0238, over 7278.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2578, pruned_loss=0.03848, over 324187.96 frames.], batch size: 18, lr: 1.85e-04
2022-05-28 23:10:34,862 INFO [train.py:842] (1/4) Epoch 30, batch 100, loss[loss=0.2153, simple_loss=0.2842, pruned_loss=0.0732, over 7288.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2619, pruned_loss=0.04154, over 572644.84 frames.], batch size: 17, lr: 1.85e-04
2022-05-28 23:11:14,506 INFO [train.py:842] (1/4) Epoch 30, batch 150, loss[loss=0.1781, simple_loss=0.2726, pruned_loss=0.04182, over 7293.00 frames.], tot_loss[loss=0.1752, simple_loss=0.264, pruned_loss=0.04315, over 750362.65 frames.], batch size: 24, lr: 1.85e-04
2022-05-28 23:11:53,901 INFO [train.py:842] (1/4) Epoch 30, batch 200, loss[loss=0.1543, simple_loss=0.238, pruned_loss=0.0353, over 7346.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2625, pruned_loss=0.04287, over 900053.87 frames.], batch size: 19, lr: 1.85e-04
2022-05-28 23:12:33,261 INFO [train.py:842] (1/4) Epoch 30, batch 250, loss[loss=0.1412, simple_loss=0.224, pruned_loss=0.0292, over 7212.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2649, pruned_loss=0.04371, over 1015636.21 frames.], batch size: 16, lr: 1.85e-04
2022-05-28 23:13:12,508 INFO [train.py:842] (1/4) Epoch 30, batch 300, loss[loss=0.1505, simple_loss=0.2284, pruned_loss=0.03635, over 7291.00 frames.], tot_loss[loss=0.1785, simple_loss=0.267, pruned_loss=0.04504, over 1108373.77 frames.], batch size: 18, lr: 1.85e-04
2022-05-28 23:13:52,336 INFO [train.py:842] (1/4) Epoch 30, batch 350, loss[loss=0.1343, simple_loss=0.2243, pruned_loss=0.02212, over 7329.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2651, pruned_loss=0.04467, over 1180841.75 frames.], batch size: 20, lr: 1.85e-04
2022-05-28 23:14:31,589 INFO [train.py:842] (1/4) Epoch 30, batch 400, loss[loss=0.1634, simple_loss=0.2546, pruned_loss=0.0361, over 7290.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2653, pruned_loss=0.04455, over 1237845.46 frames.], batch size: 24, lr: 1.85e-04
2022-05-28 23:15:11,127 INFO [train.py:842] (1/4) Epoch 30, batch 450, loss[loss=0.1816, simple_loss=0.2691, pruned_loss=0.04702, over 7422.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2644, pruned_loss=0.04399, over 1280548.61 frames.], batch size: 21, lr: 1.85e-04
2022-05-28 23:15:50,251 INFO [train.py:842] (1/4) Epoch 30, batch 500, loss[loss=0.162, simple_loss=0.252, pruned_loss=0.03598, over 7331.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2661, pruned_loss=0.04468, over 1308533.84 frames.], batch size: 20, lr: 1.85e-04
2022-05-28 23:16:29,809 INFO [train.py:842] (1/4) Epoch 30, batch 550, loss[loss=0.1926, simple_loss=0.2801, pruned_loss=0.05249, over 7287.00 frames.], tot_loss[loss=0.177, simple_loss=0.2657, pruned_loss=0.04417, over 1336774.13 frames.], batch size: 24, lr: 1.85e-04
2022-05-28 23:17:08,962 INFO [train.py:842] (1/4) Epoch 30, batch 600, loss[loss=0.1787, simple_loss=0.2657, pruned_loss=0.0458, over 7201.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2642, pruned_loss=0.04321, over 1352200.53 frames.], batch size: 22, lr: 1.85e-04
2022-05-28 23:17:48,542 INFO [train.py:842] (1/4) Epoch 30, batch 650, loss[loss=0.1831, simple_loss=0.2667, pruned_loss=0.0497, over 7071.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2632, pruned_loss=0.04277, over 1367448.21 frames.], batch size: 18, lr: 1.85e-04
2022-05-28 23:18:27,658 INFO [train.py:842] (1/4) Epoch 30, batch 700, loss[loss=0.2138, simple_loss=0.305, pruned_loss=0.06131, over 7335.00 frames.], tot_loss[loss=0.174, simple_loss=0.2631, pruned_loss=0.04248, over 1376044.97 frames.], batch size: 20, lr: 1.85e-04
2022-05-28 23:19:07,247 INFO [train.py:842] (1/4) Epoch 30, batch 750, loss[loss=0.1611, simple_loss=0.2553, pruned_loss=0.03346, over 7230.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2629, pruned_loss=0.04241, over 1381684.12 frames.], batch size: 20, lr: 1.85e-04
2022-05-28 23:19:46,353 INFO [train.py:842] (1/4) Epoch 30, batch 800, loss[loss=0.1984, simple_loss=0.2868, pruned_loss=0.055, over 7330.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2631, pruned_loss=0.04307, over 1387378.71 frames.], batch size: 22, lr: 1.85e-04
2022-05-28 23:20:25,925 INFO [train.py:842] (1/4) Epoch 30, batch 850, loss[loss=0.2247, simple_loss=0.2994, pruned_loss=0.07496, over 7067.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2614, pruned_loss=0.04294, over 1396317.87 frames.], batch size: 18, lr: 1.85e-04
2022-05-28 23:21:05,186 INFO [train.py:842] (1/4) Epoch 30, batch 900, loss[loss=0.1557, simple_loss=0.2536, pruned_loss=0.02891, over 7219.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2622, pruned_loss=0.04308, over 1400506.74 frames.], batch size: 21, lr: 1.85e-04
2022-05-28 23:21:44,650 INFO [train.py:842] (1/4) Epoch 30, batch 950, loss[loss=0.2139, simple_loss=0.308, pruned_loss=0.05994, over 7111.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2633, pruned_loss=0.04388, over 1407003.60 frames.], batch size: 21, lr: 1.85e-04
2022-05-28 23:22:23,778 INFO [train.py:842] (1/4) Epoch 30, batch 1000, loss[loss=0.1776, simple_loss=0.2657, pruned_loss=0.04474, over 7143.00 frames.], tot_loss[loss=0.1757, simple_loss=0.264, pruned_loss=0.04366, over 1410638.12 frames.], batch size: 20, lr: 1.85e-04
2022-05-28 23:23:03,144 INFO [train.py:842] (1/4) Epoch 30, batch 1050, loss[loss=0.1554, simple_loss=0.2361, pruned_loss=0.03734, over 7268.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2645, pruned_loss=0.04389, over 1407677.28 frames.], batch size: 18, lr: 1.85e-04
2022-05-28 23:23:42,411 INFO [train.py:842] (1/4) Epoch 30, batch 1100, loss[loss=0.1716, simple_loss=0.2553, pruned_loss=0.04394, over 7316.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2657, pruned_loss=0.04431, over 1416971.84 frames.], batch size: 21, lr: 1.85e-04
2022-05-28 23:24:21,897 INFO [train.py:842] (1/4) Epoch 30, batch 1150, loss[loss=0.1552, simple_loss=0.2331, pruned_loss=0.03866, over 6986.00 frames.], tot_loss[loss=0.1772, simple_loss=0.266, pruned_loss=0.0442, over 1418111.83 frames.], batch size: 16, lr: 1.85e-04
2022-05-28 23:25:01,235 INFO [train.py:842] (1/4) Epoch 30, batch 1200, loss[loss=0.1663, simple_loss=0.2531, pruned_loss=0.03977, over 7155.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2654, pruned_loss=0.04386, over 1422473.08 frames.], batch size: 19, lr: 1.85e-04
2022-05-28 23:25:40,937 INFO [train.py:842] (1/4) Epoch 30, batch 1250, loss[loss=0.2008, simple_loss=0.2842, pruned_loss=0.05869, over 5072.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2656, pruned_loss=0.0444, over 1417807.43 frames.], batch size: 52, lr: 1.84e-04
2022-05-28 23:26:20,218 INFO [train.py:842] (1/4) Epoch 30, batch 1300, loss[loss=0.16, simple_loss=0.2581, pruned_loss=0.03097, over 7339.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2651, pruned_loss=0.0441, over 1419077.16 frames.], batch size: 22, lr: 1.84e-04
2022-05-28 23:26:59,744 INFO [train.py:842] (1/4) Epoch 30, batch 1350, loss[loss=0.1707, simple_loss=0.2674, pruned_loss=0.03697, over 6321.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2653, pruned_loss=0.04419, over 1419333.61 frames.], batch size: 38, lr: 1.84e-04
2022-05-28 23:27:39,222 INFO [train.py:842] (1/4) Epoch 30, batch 1400, loss[loss=0.1501, simple_loss=0.2355, pruned_loss=0.03234, over 6766.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2645, pruned_loss=0.04413, over 1419631.71 frames.], batch size: 15, lr: 1.84e-04
2022-05-28 23:28:18,760 INFO [train.py:842] (1/4) Epoch 30, batch 1450, loss[loss=0.1832, simple_loss=0.2717, pruned_loss=0.04733, over 7117.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2648, pruned_loss=0.04426, over 1417779.80 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:28:58,172 INFO [train.py:842] (1/4) Epoch 30, batch 1500, loss[loss=0.1525, simple_loss=0.2477, pruned_loss=0.02871, over 7271.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2645, pruned_loss=0.04435, over 1417427.79 frames.], batch size: 19, lr: 1.84e-04
2022-05-28 23:29:37,428 INFO [train.py:842] (1/4) Epoch 30, batch 1550, loss[loss=0.2128, simple_loss=0.299, pruned_loss=0.06327, over 7218.00 frames.], tot_loss[loss=0.1762, simple_loss=0.264, pruned_loss=0.04421, over 1418371.90 frames.], batch size: 23, lr: 1.84e-04
2022-05-28 23:30:16,523 INFO [train.py:842] (1/4) Epoch 30, batch 1600, loss[loss=0.1602, simple_loss=0.2577, pruned_loss=0.03137, over 7312.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2646, pruned_loss=0.04442, over 1419996.26 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:30:56,196 INFO [train.py:842] (1/4) Epoch 30, batch 1650, loss[loss=0.1842, simple_loss=0.2742, pruned_loss=0.0471, over 7198.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2648, pruned_loss=0.04464, over 1423827.79 frames.], batch size: 26, lr: 1.84e-04
2022-05-28 23:31:35,529 INFO [train.py:842] (1/4) Epoch 30, batch 1700, loss[loss=0.1435, simple_loss=0.2327, pruned_loss=0.02719, over 7138.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2653, pruned_loss=0.04498, over 1426745.57 frames.], batch size: 17, lr: 1.84e-04
2022-05-28 23:32:15,275 INFO [train.py:842] (1/4) Epoch 30, batch 1750, loss[loss=0.1718, simple_loss=0.2628, pruned_loss=0.04041, over 7147.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2641, pruned_loss=0.04426, over 1423230.53 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:32:54,419 INFO [train.py:842] (1/4) Epoch 30, batch 1800, loss[loss=0.2076, simple_loss=0.2761, pruned_loss=0.06948, over 5129.00 frames.], tot_loss[loss=0.1768, simple_loss=0.265, pruned_loss=0.04433, over 1420649.19 frames.], batch size: 52, lr: 1.84e-04
2022-05-28 23:33:33,984 INFO [train.py:842] (1/4) Epoch 30, batch 1850, loss[loss=0.1752, simple_loss=0.2672, pruned_loss=0.04165, over 7109.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2645, pruned_loss=0.0437, over 1424791.39 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:34:13,221 INFO [train.py:842] (1/4) Epoch 30, batch 1900, loss[loss=0.1649, simple_loss=0.2458, pruned_loss=0.04202, over 6888.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2644, pruned_loss=0.04405, over 1427448.82 frames.], batch size: 15, lr: 1.84e-04
2022-05-28 23:34:52,921 INFO [train.py:842] (1/4) Epoch 30, batch 1950, loss[loss=0.1411, simple_loss=0.2225, pruned_loss=0.02984, over 7266.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2645, pruned_loss=0.04433, over 1429153.39 frames.], batch size: 17, lr: 1.84e-04
2022-05-28 23:35:32,335 INFO [train.py:842] (1/4) Epoch 30, batch 2000, loss[loss=0.173, simple_loss=0.2706, pruned_loss=0.03765, over 7339.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2636, pruned_loss=0.04347, over 1431292.22 frames.], batch size: 22, lr: 1.84e-04
2022-05-28 23:36:11,883 INFO [train.py:842] (1/4) Epoch 30, batch 2050, loss[loss=0.1827, simple_loss=0.2768, pruned_loss=0.04435, over 7195.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2637, pruned_loss=0.04326, over 1431178.63 frames.], batch size: 23, lr: 1.84e-04
2022-05-28 23:36:50,953 INFO [train.py:842] (1/4) Epoch 30, batch 2100, loss[loss=0.163, simple_loss=0.2586, pruned_loss=0.03371, over 7143.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2636, pruned_loss=0.04268, over 1429630.89 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:37:30,438 INFO [train.py:842] (1/4) Epoch 30, batch 2150, loss[loss=0.1869, simple_loss=0.2606, pruned_loss=0.05659, over 7128.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2643, pruned_loss=0.0434, over 1428314.02 frames.], batch size: 17, lr: 1.84e-04
2022-05-28 23:38:09,553 INFO [train.py:842] (1/4) Epoch 30, batch 2200, loss[loss=0.1841, simple_loss=0.2825, pruned_loss=0.04284, over 7284.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2639, pruned_loss=0.04362, over 1423678.89 frames.], batch size: 24, lr: 1.84e-04
2022-05-28 23:38:49,011 INFO [train.py:842] (1/4) Epoch 30, batch 2250, loss[loss=0.163, simple_loss=0.2608, pruned_loss=0.03257, over 7106.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2649, pruned_loss=0.04433, over 1422534.44 frames.], batch size: 26, lr: 1.84e-04
2022-05-28 23:39:28,066 INFO [train.py:842] (1/4) Epoch 30, batch 2300, loss[loss=0.1727, simple_loss=0.2646, pruned_loss=0.04041, over 7321.00 frames.], tot_loss[loss=0.1768, simple_loss=0.265, pruned_loss=0.0443, over 1419044.16 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:40:07,922 INFO [train.py:842] (1/4) Epoch 30, batch 2350, loss[loss=0.1678, simple_loss=0.2634, pruned_loss=0.0361, over 7349.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2656, pruned_loss=0.04438, over 1421207.62 frames.], batch size: 22, lr: 1.84e-04
2022-05-28 23:40:47,276 INFO [train.py:842] (1/4) Epoch 30, batch 2400, loss[loss=0.2058, simple_loss=0.2932, pruned_loss=0.05921, over 7315.00 frames.], tot_loss[loss=0.176, simple_loss=0.2647, pruned_loss=0.04363, over 1423005.15 frames.], batch size: 25, lr: 1.84e-04
2022-05-28 23:41:27,095 INFO [train.py:842] (1/4) Epoch 30, batch 2450, loss[loss=0.1772, simple_loss=0.2631, pruned_loss=0.04562, over 7141.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2634, pruned_loss=0.04301, over 1427449.55 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:42:06,591 INFO [train.py:842] (1/4) Epoch 30, batch 2500, loss[loss=0.1897, simple_loss=0.2652, pruned_loss=0.05715, over 6829.00 frames.], tot_loss[loss=0.1747, simple_loss=0.263, pruned_loss=0.04321, over 1430989.97 frames.], batch size: 15, lr: 1.84e-04
2022-05-28 23:42:46,101 INFO [train.py:842] (1/4) Epoch 30, batch 2550, loss[loss=0.1468, simple_loss=0.23, pruned_loss=0.03183, over 7416.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2624, pruned_loss=0.04297, over 1428402.29 frames.], batch size: 18, lr: 1.84e-04
2022-05-28 23:43:25,265 INFO [train.py:842] (1/4) Epoch 30, batch 2600, loss[loss=0.1458, simple_loss=0.2322, pruned_loss=0.02974, over 7109.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2621, pruned_loss=0.04279, over 1427453.33 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:44:04,904 INFO [train.py:842] (1/4) Epoch 30, batch 2650, loss[loss=0.1509, simple_loss=0.2288, pruned_loss=0.03647, over 7145.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2629, pruned_loss=0.04341, over 1429146.38 frames.], batch size: 17, lr: 1.84e-04
2022-05-28 23:44:44,000 INFO [train.py:842] (1/4) Epoch 30, batch 2700, loss[loss=0.2251, simple_loss=0.3038, pruned_loss=0.07323, over 7118.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2646, pruned_loss=0.04449, over 1429637.29 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:45:23,606 INFO [train.py:842] (1/4) Epoch 30, batch 2750, loss[loss=0.1441, simple_loss=0.2346, pruned_loss=0.02677, over 7236.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2649, pruned_loss=0.04429, over 1426027.38 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:46:02,902 INFO [train.py:842] (1/4) Epoch 30, batch 2800, loss[loss=0.1688, simple_loss=0.2675, pruned_loss=0.0351, over 7318.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2641, pruned_loss=0.04347, over 1423860.01 frames.], batch size: 22, lr: 1.84e-04
2022-05-28 23:46:42,631 INFO [train.py:842] (1/4) Epoch 30, batch 2850, loss[loss=0.1744, simple_loss=0.2661, pruned_loss=0.04131, over 7243.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2626, pruned_loss=0.04276, over 1417927.62 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:47:21,849 INFO [train.py:842] (1/4) Epoch 30, batch 2900, loss[loss=0.1372, simple_loss=0.2248, pruned_loss=0.02476, over 7020.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2625, pruned_loss=0.04313, over 1420714.67 frames.], batch size: 16, lr: 1.84e-04
2022-05-28 23:48:01,442 INFO [train.py:842] (1/4) Epoch 30, batch 2950, loss[loss=0.195, simple_loss=0.2837, pruned_loss=0.05313, over 6402.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2621, pruned_loss=0.04301, over 1422065.38 frames.], batch size: 37, lr: 1.84e-04
2022-05-28 23:48:40,744 INFO [train.py:842] (1/4) Epoch 30, batch 3000, loss[loss=0.1743, simple_loss=0.2678, pruned_loss=0.04045, over 7122.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2627, pruned_loss=0.0437, over 1425018.43 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:48:40,745 INFO [train.py:862] (1/4) Computing validation loss
2022-05-28 23:48:50,533 INFO [train.py:871] (1/4) Epoch 30, validation: loss=0.1622, simple_loss=0.26, pruned_loss=0.03222, over 868885.00 frames.
2022-05-28 23:49:30,112 INFO [train.py:842] (1/4) Epoch 30, batch 3050, loss[loss=0.1619, simple_loss=0.2543, pruned_loss=0.03473, over 7114.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2643, pruned_loss=0.04375, over 1427137.88 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:50:09,335 INFO [train.py:842] (1/4) Epoch 30, batch 3100, loss[loss=0.1617, simple_loss=0.2545, pruned_loss=0.03445, over 7425.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2641, pruned_loss=0.04356, over 1427870.28 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:50:48,884 INFO [train.py:842] (1/4) Epoch 30, batch 3150, loss[loss=0.1517, simple_loss=0.2452, pruned_loss=0.02905, over 7163.00 frames.], tot_loss[loss=0.1747, simple_loss=0.263, pruned_loss=0.04324, over 1423050.33 frames.], batch size: 18, lr: 1.84e-04
2022-05-28 23:51:28,446 INFO [train.py:842] (1/4) Epoch 30, batch 3200, loss[loss=0.1558, simple_loss=0.2386, pruned_loss=0.03656, over 7266.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2618, pruned_loss=0.04302, over 1426173.01 frames.], batch size: 19, lr: 1.84e-04
2022-05-28 23:52:07,973 INFO [train.py:842] (1/4) Epoch 30, batch 3250, loss[loss=0.1952, simple_loss=0.2805, pruned_loss=0.05499, over 7058.00 frames.], tot_loss[loss=0.175, simple_loss=0.263, pruned_loss=0.04348, over 1420957.20 frames.], batch size: 28, lr: 1.84e-04
2022-05-28 23:52:47,282 INFO [train.py:842] (1/4) Epoch 30, batch 3300, loss[loss=0.1762, simple_loss=0.278, pruned_loss=0.03722, over 7334.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2646, pruned_loss=0.04418, over 1423475.48 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:53:26,878 INFO [train.py:842] (1/4) Epoch 30, batch 3350, loss[loss=0.16, simple_loss=0.2425, pruned_loss=0.03882, over 7255.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2637, pruned_loss=0.04374, over 1427651.11 frames.], batch size: 17, lr: 1.84e-04
2022-05-28 23:54:06,105 INFO [train.py:842] (1/4) Epoch 30, batch 3400, loss[loss=0.2177, simple_loss=0.295, pruned_loss=0.0702, over 5268.00 frames.], tot_loss[loss=0.1748, simple_loss=0.263, pruned_loss=0.04334, over 1424242.90 frames.], batch size: 54, lr: 1.84e-04
2022-05-28 23:54:45,887 INFO [train.py:842] (1/4) Epoch 30, batch 3450, loss[loss=0.1762, simple_loss=0.2595, pruned_loss=0.04645, over 7295.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2614, pruned_loss=0.04263, over 1421490.36 frames.], batch size: 24, lr: 1.84e-04
2022-05-28 23:55:25,173 INFO [train.py:842] (1/4) Epoch 30, batch 3500, loss[loss=0.1796, simple_loss=0.2719, pruned_loss=0.04369, over 7221.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2618, pruned_loss=0.04249, over 1423529.16 frames.], batch size: 26, lr: 1.84e-04
2022-05-28 23:56:04,834 INFO [train.py:842] (1/4) Epoch 30, batch 3550, loss[loss=0.1679, simple_loss=0.2457, pruned_loss=0.04505, over 7169.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2614, pruned_loss=0.04237, over 1422515.19 frames.], batch size: 18, lr: 1.84e-04
2022-05-28 23:56:44,252 INFO [train.py:842] (1/4) Epoch 30, batch 3600, loss[loss=0.1628, simple_loss=0.2466, pruned_loss=0.03952, over 7266.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2616, pruned_loss=0.04242, over 1427200.72 frames.], batch size: 19, lr: 1.84e-04
2022-05-28 23:57:23,881 INFO [train.py:842] (1/4) Epoch 30, batch 3650, loss[loss=0.1683, simple_loss=0.2608, pruned_loss=0.03794, over 6790.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2614, pruned_loss=0.04197, over 1428967.88 frames.], batch size: 31, lr: 1.84e-04
2022-05-28 23:58:03,113 INFO [train.py:842] (1/4) Epoch 30, batch 3700, loss[loss=0.1648, simple_loss=0.246, pruned_loss=0.04185, over 7267.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2616, pruned_loss=0.04216, over 1429691.62 frames.], batch size: 17, lr: 1.84e-04
2022-05-28 23:58:42,919 INFO [train.py:842] (1/4) Epoch 30, batch 3750, loss[loss=0.2106, simple_loss=0.2916, pruned_loss=0.06481, over 6997.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.04232, over 1432282.22 frames.], batch size: 28, lr: 1.84e-04
2022-05-28 23:59:21,901 INFO [train.py:842] (1/4) Epoch 30, batch 3800, loss[loss=0.1884, simple_loss=0.2757, pruned_loss=0.05054, over 7197.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2643, pruned_loss=0.04336, over 1424121.49 frames.], batch size: 22, lr: 1.84e-04
2022-05-29 00:00:01,429 INFO [train.py:842] (1/4) Epoch 30, batch 3850, loss[loss=0.1695, simple_loss=0.2679, pruned_loss=0.03552, over 7202.00 frames.], tot_loss[loss=0.175, simple_loss=0.2639, pruned_loss=0.04304, over 1425363.09 frames.], batch size: 22, lr: 1.84e-04
2022-05-29 00:00:40,343 INFO [train.py:842] (1/4) Epoch 30, batch 3900, loss[loss=0.2, simple_loss=0.2956, pruned_loss=0.05221, over 7213.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2656, pruned_loss=0.04398, over 1425984.74 frames.], batch size: 21, lr: 1.84e-04
2022-05-29 00:01:19,904 INFO [train.py:842] (1/4) Epoch 30, batch 3950, loss[loss=0.1594, simple_loss=0.2456, pruned_loss=0.03659, over 7353.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2651, pruned_loss=0.04371, over 1423188.51 frames.], batch size: 19, lr: 1.84e-04
2022-05-29 00:01:59,027 INFO [train.py:842] (1/4) Epoch 30, batch 4000, loss[loss=0.1468, simple_loss=0.2395, pruned_loss=0.02707, over 7159.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2655, pruned_loss=0.0437, over 1420537.59 frames.], batch size: 18, lr: 1.84e-04
2022-05-29 00:02:38,725 INFO [train.py:842] (1/4) Epoch 30, batch 4050, loss[loss=0.1682, simple_loss=0.2647, pruned_loss=0.03585, over 7278.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2646, pruned_loss=0.04341, over 1421332.39 frames.], batch size: 24, lr: 1.84e-04
2022-05-29 00:03:17,907 INFO [train.py:842] (1/4) Epoch 30, batch 4100, loss[loss=0.157, simple_loss=0.2475, pruned_loss=0.0332, over 7213.00 frames.], tot_loss[loss=0.176, simple_loss=0.2646, pruned_loss=0.04372, over 1421108.93 frames.], batch size: 21, lr: 1.84e-04
2022-05-29 00:03:57,489 INFO [train.py:842] (1/4) Epoch 30, batch 4150, loss[loss=0.169, simple_loss=0.256, pruned_loss=0.04094, over 7277.00 frames.], tot_loss[loss=0.1768, simple_loss=0.265, pruned_loss=0.04425, over 1425693.76 frames.], batch size: 18, lr: 1.84e-04
2022-05-29 00:04:36,804 INFO [train.py:842] (1/4) Epoch 30, batch 4200, loss[loss=0.1747, simple_loss=0.271, pruned_loss=0.03926, over 7372.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2639, pruned_loss=0.04369, over 1427494.43 frames.], batch size: 23, lr: 1.83e-04
2022-05-29 00:05:16,538 INFO [train.py:842] (1/4) Epoch 30, batch 4250, loss[loss=0.1368, simple_loss=0.2313, pruned_loss=0.02119, over 7139.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2631, pruned_loss=0.04329, over 1426763.08 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:05:55,525 INFO [train.py:842] (1/4) Epoch 30, batch 4300, loss[loss=0.205, simple_loss=0.2872, pruned_loss=0.06144, over 7284.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2655, pruned_loss=0.04398, over 1426927.74 frames.], batch size: 25, lr: 1.83e-04
2022-05-29 00:06:35,249 INFO [train.py:842] (1/4) Epoch 30, batch 4350, loss[loss=0.1566, simple_loss=0.2399, pruned_loss=0.03667, over 7012.00 frames.], tot_loss[loss=0.1763, simple_loss=0.265, pruned_loss=0.04383, over 1425789.68 frames.], batch size: 16, lr: 1.83e-04
2022-05-29 00:07:14,668 INFO [train.py:842] (1/4) Epoch 30, batch 4400, loss[loss=0.1595, simple_loss=0.2454, pruned_loss=0.03677, over 7437.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2647, pruned_loss=0.04337, over 1428321.49 frames.], batch size: 20, lr: 1.83e-04
2022-05-29 00:07:54,190 INFO [train.py:842] (1/4) Epoch 30, batch 4450, loss[loss=0.1598, simple_loss=0.264, pruned_loss=0.02782, over 7148.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2644, pruned_loss=0.04332, over 1427858.65 frames.], batch size: 26, lr: 1.83e-04
2022-05-29 00:08:33,374 INFO [train.py:842] (1/4) Epoch 30, batch 4500, loss[loss=0.1992, simple_loss=0.2888, pruned_loss=0.05485, over 7325.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2651, pruned_loss=0.04363, over 1425326.63 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:09:13,169 INFO [train.py:842] (1/4) Epoch 30, batch 4550, loss[loss=0.1777, simple_loss=0.2676, pruned_loss=0.04385, over 7416.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2644, pruned_loss=0.04353, over 1428158.57 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:09:52,434 INFO [train.py:842] (1/4) Epoch 30, batch 4600, loss[loss=0.1643, simple_loss=0.2553, pruned_loss=0.03667, over 7203.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2639, pruned_loss=0.0437, over 1425630.92 frames.], batch size: 22, lr: 1.83e-04
2022-05-29 00:10:32,216 INFO [train.py:842] (1/4) Epoch 30, batch 4650, loss[loss=0.193, simple_loss=0.2804, pruned_loss=0.05278, over 6927.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2635, pruned_loss=0.04341, over 1426641.25 frames.], batch size: 32, lr: 1.83e-04
2022-05-29 00:11:11,533 INFO [train.py:842] (1/4) Epoch 30, batch 4700, loss[loss=0.1779, simple_loss=0.2754, pruned_loss=0.0402, over 7317.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2632, pruned_loss=0.04283, over 1428602.69 frames.], batch size: 24, lr: 1.83e-04
2022-05-29 00:11:51,329 INFO [train.py:842] (1/4) Epoch 30, batch 4750, loss[loss=0.1975, simple_loss=0.2817, pruned_loss=0.05669, over 7156.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2643, pruned_loss=0.04393, over 1429778.39 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:12:30,801 INFO [train.py:842] (1/4) Epoch 30, batch 4800, loss[loss=0.1906, simple_loss=0.2813, pruned_loss=0.04996, over 7353.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2648, pruned_loss=0.04369, over 1430327.70 frames.], batch size: 23, lr: 1.83e-04
2022-05-29 00:13:10,339 INFO [train.py:842] (1/4) Epoch 30, batch 4850, loss[loss=0.1645, simple_loss=0.2576, pruned_loss=0.03573, over 7219.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2649, pruned_loss=0.04388, over 1428681.59 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:13:49,476 INFO [train.py:842] (1/4) Epoch 30, batch 4900, loss[loss=0.1577, simple_loss=0.2427, pruned_loss=0.03632, over 7423.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2662, pruned_loss=0.04458, over 1426788.37 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:14:29,065 INFO [train.py:842] (1/4) Epoch 30, batch 4950, loss[loss=0.1872, simple_loss=0.2734, pruned_loss=0.05052, over 7271.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2668, pruned_loss=0.04478, over 1422006.22 frames.], batch size: 24, lr: 1.83e-04
2022-05-29 00:15:08,295 INFO [train.py:842] (1/4) Epoch 30, batch 5000, loss[loss=0.1589, simple_loss=0.2402, pruned_loss=0.03887, over 6798.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2658, pruned_loss=0.04423, over 1422591.28 frames.], batch size: 15, lr: 1.83e-04
2022-05-29 00:15:47,586 INFO [train.py:842] (1/4) Epoch 30, batch 5050, loss[loss=0.1757, simple_loss=0.2658, pruned_loss=0.04285, over 7077.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2665, pruned_loss=0.0444, over 1418392.78 frames.], batch size: 28, lr: 1.83e-04
2022-05-29 00:16:27,072 INFO [train.py:842] (1/4) Epoch 30, batch 5100, loss[loss=0.1795, simple_loss=0.2637, pruned_loss=0.04769, over 7227.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2647, pruned_loss=0.04394, over 1416413.58 frames.], batch size: 16, lr: 1.83e-04
2022-05-29 00:17:06,579 INFO [train.py:842] (1/4) Epoch 30, batch 5150, loss[loss=0.2644, simple_loss=0.3254, pruned_loss=0.1017, over 7276.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2655, pruned_loss=0.04465, over 1413224.01 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:17:45,825 INFO [train.py:842] (1/4) Epoch 30, batch 5200, loss[loss=0.1824, simple_loss=0.2804, pruned_loss=0.04224, over 7382.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2648, pruned_loss=0.04433, over 1416856.81 frames.], batch size: 23, lr: 1.83e-04
2022-05-29 00:18:25,593 INFO [train.py:842] (1/4) Epoch 30, batch 5250, loss[loss=0.1634, simple_loss=0.2597, pruned_loss=0.03353, over 7323.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2643, pruned_loss=0.04394, over 1419617.09 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:19:04,589 INFO [train.py:842] (1/4) Epoch 30, batch 5300, loss[loss=0.1465, simple_loss=0.2213, pruned_loss=0.03589, over 7134.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2657, pruned_loss=0.04437, over 1421569.65 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:19:44,227 INFO [train.py:842] (1/4) Epoch 30, batch 5350, loss[loss=0.1414, simple_loss=0.222, pruned_loss=0.0304, over 7151.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2652, pruned_loss=0.04413, over 1423237.79 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:20:23,334 INFO [train.py:842] (1/4) Epoch 30, batch 5400, loss[loss=0.177, simple_loss=0.2557, pruned_loss=0.04912, over 7143.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2649, pruned_loss=0.04395, over 1423812.69 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:21:05,455 INFO [train.py:842] (1/4) Epoch 30, batch 5450, loss[loss=0.163, simple_loss=0.2502, pruned_loss=0.03789, over 7255.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2655, pruned_loss=0.04401, over 1423444.69 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:21:44,380 INFO [train.py:842] (1/4) Epoch 30, batch 5500, loss[loss=0.1786, simple_loss=0.265, pruned_loss=0.04606, over 7411.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2651, pruned_loss=0.04406, over 1421897.56 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:22:23,771 INFO [train.py:842] (1/4) Epoch 30, batch 5550, loss[loss=0.2036, simple_loss=0.2797, pruned_loss=0.06378, over 7331.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2656, pruned_loss=0.0444, over 1420384.70 frames.], batch size: 20, lr: 1.83e-04
2022-05-29 00:23:02,849 INFO [train.py:842] (1/4) Epoch 30, batch 5600, loss[loss=0.1418, simple_loss=0.2267, pruned_loss=0.02846, over 7353.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2653, pruned_loss=0.04426, over 1409027.58 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:23:42,331 INFO [train.py:842] (1/4) Epoch 30, batch 5650, loss[loss=0.1682, simple_loss=0.2523, pruned_loss=0.04203, over 7350.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2655, pruned_loss=0.0444, over 1409387.14 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:24:21,733 INFO [train.py:842] (1/4) Epoch 30, batch 5700, loss[loss=0.1522, simple_loss=0.2328, pruned_loss=0.03578, over 7002.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2656, pruned_loss=0.04428, over 1417627.11 frames.], batch size: 16, lr: 1.83e-04
2022-05-29 00:25:01,309 INFO [train.py:842] (1/4) Epoch 30, batch 5750, loss[loss=0.2442, simple_loss=0.3419, pruned_loss=0.0733, over 7301.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2651, pruned_loss=0.04397, over 1420597.62 frames.], batch size: 24, lr: 1.83e-04
2022-05-29 00:25:40,659 INFO [train.py:842] (1/4) Epoch 30, batch 5800, loss[loss=0.1584, simple_loss=0.2473, pruned_loss=0.03479, over 7423.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2649, pruned_loss=0.04391, over 1421076.10 frames.], batch size: 20, lr: 1.83e-04
2022-05-29 00:26:19,848 INFO [train.py:842] (1/4) Epoch 30, batch 5850, loss[loss=0.1509, simple_loss=0.2281, pruned_loss=0.03682, over 7064.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2663, pruned_loss=0.04452, over 1420870.43 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:26:59,118 INFO [train.py:842] (1/4) Epoch 30, batch 5900, loss[loss=0.2251, simple_loss=0.3052, pruned_loss=0.07251, over 7136.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2655, pruned_loss=0.04437, over 1422399.32 frames.], batch size: 20, lr: 1.83e-04
2022-05-29 00:27:38,790 INFO [train.py:842] (1/4) Epoch 30, batch 5950, loss[loss=0.1915, simple_loss=0.2892, pruned_loss=0.04697, over 7121.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2651, pruned_loss=0.04438, over 1425947.89 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:28:18,031 INFO [train.py:842] (1/4) Epoch 30, batch 6000, loss[loss=0.1759, simple_loss=0.2747, pruned_loss=0.03856, over 7424.00 frames.], tot_loss[loss=0.176, simple_loss=0.2646, pruned_loss=0.04377, over 1425190.84 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:28:18,031 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 00:28:27,765 INFO [train.py:871] (1/4) Epoch 30, validation: loss=0.1626, simple_loss=0.2605, pruned_loss=0.03232, over 868885.00 frames.
2022-05-29 00:29:07,527 INFO [train.py:842] (1/4) Epoch 30, batch 6050, loss[loss=0.1589, simple_loss=0.2432, pruned_loss=0.03735, over 7160.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2652, pruned_loss=0.04367, over 1425035.90 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:29:46,801 INFO [train.py:842] (1/4) Epoch 30, batch 6100, loss[loss=0.1654, simple_loss=0.2519, pruned_loss=0.03946, over 7067.00 frames.], tot_loss[loss=0.176, simple_loss=0.2648, pruned_loss=0.04358, over 1422987.11 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:30:26,351 INFO [train.py:842] (1/4) Epoch 30, batch 6150, loss[loss=0.1746, simple_loss=0.2632, pruned_loss=0.04302, over 7358.00 frames.], tot_loss[loss=0.175, simple_loss=0.2639, pruned_loss=0.04306, over 1420152.20 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:31:05,516 INFO [train.py:842] (1/4) Epoch 30, batch 6200, loss[loss=0.2094, simple_loss=0.284, pruned_loss=0.06736, over 7282.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2639, pruned_loss=0.04337, over 1420530.80 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:31:45,369 INFO [train.py:842] (1/4) Epoch 30, batch 6250, loss[loss=0.1714, simple_loss=0.2602, pruned_loss=0.04129, over 7157.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2635, pruned_loss=0.04282, over 1423127.98 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:32:24,518 INFO [train.py:842] (1/4) Epoch 30, batch 6300, loss[loss=0.183, simple_loss=0.2767, pruned_loss=0.04469, over 6852.00 frames.], tot_loss[loss=0.1747, simple_loss=0.264, pruned_loss=0.04273, over 1427591.71 frames.], batch size: 31, lr: 1.83e-04
2022-05-29 00:33:03,718 INFO [train.py:842] (1/4) Epoch 30, batch 6350, loss[loss=0.1559, simple_loss=0.2361, pruned_loss=0.03789, over 7280.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2649, pruned_loss=0.0433, over 1427427.39 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:33:42,871 INFO [train.py:842] (1/4) Epoch 30, batch 6400, loss[loss=0.1447, simple_loss=0.227, pruned_loss=0.03123, over 7128.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2656, pruned_loss=0.04361, over 1423864.30 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:34:22,378 INFO [train.py:842] (1/4) Epoch 30, batch 6450, loss[loss=0.1694, simple_loss=0.2681, pruned_loss=0.03535, over 7299.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2659, pruned_loss=0.04431, over 1426479.46 frames.], batch size: 24, lr: 1.83e-04
2022-05-29 00:35:01,387 INFO [train.py:842] (1/4) Epoch 30, batch 6500, loss[loss=0.2093, simple_loss=0.2938, pruned_loss=0.06243, over 7284.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2671, pruned_loss=0.0445, over 1427725.89 frames.], batch size: 24, lr: 1.83e-04
2022-05-29 00:35:41,143 INFO [train.py:842] (1/4) Epoch 30, batch 6550, loss[loss=0.1925, simple_loss=0.2806, pruned_loss=0.05226, over 7417.00 frames.], tot_loss[loss=0.1772, simple_loss=0.266, pruned_loss=0.04419, over 1425983.63 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:36:20,396 INFO [train.py:842] (1/4) Epoch 30, batch 6600, loss[loss=0.1707, simple_loss=0.2547, pruned_loss=0.04338, over 7405.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2662, pruned_loss=0.0444, over 1427562.39 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:37:00,149 INFO [train.py:842] (1/4) Epoch 30, batch 6650, loss[loss=0.1933, simple_loss=0.2717, pruned_loss=0.05744, over 7156.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2649, pruned_loss=0.04385, over 1426262.09 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:37:39,120 INFO [train.py:842] (1/4) Epoch 30, batch 6700, loss[loss=0.1609, simple_loss=0.2365, pruned_loss=0.04269, over 7265.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2638, pruned_loss=0.04364, over 1423221.78 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:38:18,787 INFO [train.py:842] (1/4) Epoch 30, batch 6750, loss[loss=0.1419, simple_loss=0.2264, pruned_loss=0.02867, over 7407.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2623, pruned_loss=0.0426, over 1426061.95 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:38:57,942 INFO [train.py:842] (1/4) Epoch 30, batch 6800, loss[loss=0.2115, simple_loss=0.2926, pruned_loss=0.06523, over 7293.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2635, pruned_loss=0.04301, over 1425514.39 frames.], batch size: 25, lr: 1.83e-04
2022-05-29 00:39:37,559 INFO [train.py:842] (1/4) Epoch 30, batch 6850, loss[loss=0.212, simple_loss=0.3026, pruned_loss=0.06068, over 7198.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2644, pruned_loss=0.0436, over 1425789.44 frames.], batch size: 22, lr: 1.83e-04
2022-05-29 00:40:17,010 INFO [train.py:842] (1/4) Epoch 30, batch 6900, loss[loss=0.1528, simple_loss=0.2341, pruned_loss=0.03576, over 7302.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2633, pruned_loss=0.04286, over 1424338.66 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:40:56,511 INFO [train.py:842] (1/4) Epoch 30, batch 6950, loss[loss=0.1721, simple_loss=0.2642, pruned_loss=0.03997, over 7065.00 frames.], tot_loss[loss=0.1766, simple_loss=0.265, pruned_loss=0.04406, over 1420943.80 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:41:35,805 INFO [train.py:842] (1/4) Epoch 30, batch 7000, loss[loss=0.1782, simple_loss=0.2695, pruned_loss=0.04348, over 7250.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2646, pruned_loss=0.04388, over 1420723.43 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:42:15,294 INFO [train.py:842] (1/4) Epoch 30, batch 7050, loss[loss=0.1802, simple_loss=0.2763, pruned_loss=0.04209, over 7316.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2658, pruned_loss=0.04425, over 1420194.73 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:42:54,717 INFO [train.py:842] (1/4) Epoch 30, batch 7100, loss[loss=0.146, simple_loss=0.2284, pruned_loss=0.03182, over 7402.00 frames.], tot_loss[loss=0.177, simple_loss=0.2657, pruned_loss=0.04417, over 1417044.73 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:43:34,108 INFO [train.py:842] (1/4) Epoch 30, batch 7150, loss[loss=0.1976, simple_loss=0.2833, pruned_loss=0.05588, over 7207.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2651, pruned_loss=0.04417, over 1417517.61 frames.], batch size: 22, lr: 1.82e-04
2022-05-29 00:44:13,147 INFO [train.py:842] (1/4) Epoch 30, batch 7200, loss[loss=0.1657, simple_loss=0.2731, pruned_loss=0.02911, over 7123.00 frames.], tot_loss[loss=0.1767, simple_loss=0.265, pruned_loss=0.04417, over 1416097.24 frames.], batch size: 21, lr: 1.82e-04
2022-05-29 00:44:52,846 INFO [train.py:842] (1/4) Epoch 30, batch 7250, loss[loss=0.1982, simple_loss=0.2914, pruned_loss=0.05256, over 7349.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2647, pruned_loss=0.04379, over 1417070.19 frames.], batch size: 22, lr: 1.82e-04
2022-05-29 00:45:32,231 INFO [train.py:842] (1/4) Epoch 30, batch 7300, loss[loss=0.139, simple_loss=0.2294, pruned_loss=0.02435, over 7063.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2629, pruned_loss=0.04274, over 1420132.04 frames.], batch size: 18, lr: 1.82e-04
2022-05-29 00:46:12,040 INFO [train.py:842] (1/4) Epoch 30, batch 7350, loss[loss=0.2094, simple_loss=0.2829, pruned_loss=0.06793, over 7020.00 frames.], tot_loss[loss=0.173, simple_loss=0.2614, pruned_loss=0.0423, over 1422377.06 frames.], batch size: 28, lr: 1.82e-04
2022-05-29 00:47:02,165 INFO [train.py:842] (1/4) Epoch 30, batch 7400, loss[loss=0.1534, simple_loss=0.2412, pruned_loss=0.0328, over 6919.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2629, pruned_loss=0.04314, over 1420965.36 frames.], batch size: 31, lr: 1.82e-04
2022-05-29 00:47:41,647 INFO [train.py:842] (1/4) Epoch 30, batch 7450, loss[loss=0.2577, simple_loss=0.3422, pruned_loss=0.08656, over 7324.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2647, pruned_loss=0.04402, over 1426653.25 frames.], batch size: 21, lr: 1.82e-04
2022-05-29 00:48:20,885 INFO [train.py:842] (1/4) Epoch 30, batch 7500, loss[loss=0.1481, simple_loss=0.2376, pruned_loss=0.02935, over 7062.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2654, pruned_loss=0.04412, over 1426258.96 frames.], batch size: 18, lr: 1.82e-04
2022-05-29 00:49:00,491 INFO [train.py:842] (1/4) Epoch 30, batch 7550, loss[loss=0.192, simple_loss=0.2711, pruned_loss=0.05645, over 7161.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2653, pruned_loss=0.04427, over 1423782.13 frames.], batch size: 19, lr: 1.82e-04
2022-05-29 00:49:39,617 INFO [train.py:842] (1/4) Epoch 30, batch 7600, loss[loss=0.1575, simple_loss=0.2534, pruned_loss=0.03081, over 7318.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2656, pruned_loss=0.04396, over 1423399.28 frames.], batch size: 20, lr: 1.82e-04
2022-05-29 00:50:19,187 INFO [train.py:842] (1/4) Epoch 30, batch 7650, loss[loss=0.1707, simple_loss=0.2635, pruned_loss=0.03896, over 7235.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2659, pruned_loss=0.04455, over 1421941.49 frames.], batch size: 20, lr: 1.82e-04
2022-05-29 00:50:58,444 INFO [train.py:842] (1/4) Epoch 30, batch 7700, loss[loss=0.2538, simple_loss=0.3425, pruned_loss=0.08255, over 7326.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2653, pruned_loss=0.04408, over 1418983.03 frames.], batch size: 25, lr: 1.82e-04
2022-05-29 00:51:38,122 INFO [train.py:842] (1/4) Epoch 30, batch 7750, loss[loss=0.1701, simple_loss=0.2582, pruned_loss=0.041, over 7356.00 frames.], tot_loss[loss=0.176, simple_loss=0.2649, pruned_loss=0.04353, over 1419994.43 frames.], batch size: 19, lr: 1.82e-04
2022-05-29 00:52:17,460 INFO [train.py:842] (1/4) Epoch 30, batch 7800, loss[loss=0.1645, simple_loss=0.2418, pruned_loss=0.04361, over 7062.00 frames.], tot_loss[loss=0.175, simple_loss=0.2637, pruned_loss=0.04317, over 1421477.53 frames.], batch size: 18, lr: 1.82e-04
2022-05-29 00:52:57,223 INFO [train.py:842] (1/4) Epoch 30, batch 7850, loss[loss=0.1811, simple_loss=0.2576, pruned_loss=0.05232, over 7157.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2625, pruned_loss=0.04286, over 1427330.70 frames.], batch size: 16, lr: 1.82e-04
2022-05-29 00:53:36,711 INFO [train.py:842] (1/4) Epoch 30, batch 7900, loss[loss=0.2026, simple_loss=0.2915, pruned_loss=0.05682, over 7333.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2616, pruned_loss=0.0426, over 1425839.47 frames.], batch size: 20, lr: 1.82e-04
2022-05-29 00:54:16,417 INFO [train.py:842] (1/4) Epoch 30, batch 7950, loss[loss=0.1538, simple_loss=0.2463, pruned_loss=0.0307, over 7159.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2611, pruned_loss=0.04256, over 1424335.07 frames.], batch size: 19, lr: 1.82e-04
2022-05-29 00:54:55,573 INFO [train.py:842] (1/4) Epoch 30, batch 8000, loss[loss=0.1695, simple_loss=0.256, pruned_loss=0.0415, over 7195.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2616, pruned_loss=0.04311, over 1426262.14 frames.], batch size: 22, lr: 1.82e-04
2022-05-29 00:55:35,061 INFO [train.py:842] (1/4) Epoch 30, batch 8050, loss[loss=0.1769, simple_loss=0.2645, pruned_loss=0.04467, over 7238.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2604, pruned_loss=0.04236, over 1428945.82 frames.], batch size: 20, lr: 1.82e-04
2022-05-29 00:56:14,427 INFO [train.py:842] (1/4) Epoch 30, batch 8100, loss[loss=0.1944, simple_loss=0.2909, pruned_loss=0.04897, over 7134.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2632, pruned_loss=0.04397, over 1431930.31 frames.], batch size: 20, lr: 1.82e-04
2022-05-29 00:56:53,807 INFO [train.py:842] (1/4) Epoch 30, batch 8150, loss[loss=0.1749, simple_loss=0.2818, pruned_loss=0.03394, over 7338.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2637, pruned_loss=0.04382, over 1423523.25 frames.], batch size: 22, lr: 1.82e-04
2022-05-29 00:57:33,248 INFO [train.py:842] (1/4) Epoch 30, batch 8200, loss[loss=0.1721, simple_loss=0.2688, pruned_loss=0.03769, over 7202.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2632, pruned_loss=0.04373, over 1425480.08 frames.], batch size: 26, lr: 1.82e-04
2022-05-29 00:58:12,717 INFO [train.py:842] (1/4) Epoch 30, batch 8250, loss[loss=0.1399, simple_loss=0.23, pruned_loss=0.02492, over 7413.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2625, pruned_loss=0.04312, over 1424221.64 frames.], batch size: 18, lr: 1.82e-04
2022-05-29 00:58:51,851 INFO [train.py:842] (1/4) Epoch 30, batch 8300, loss[loss=0.144, simple_loss=0.2449, pruned_loss=0.02156, over 7214.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2627, pruned_loss=0.04311, over 1425903.45 frames.], batch size: 21, lr: 1.82e-04
2022-05-29 00:59:31,524 INFO [train.py:842] (1/4) Epoch 30, batch 8350, loss[loss=0.1993, simple_loss=0.2911, pruned_loss=0.05374, over 7061.00 frames.], tot_loss[loss=0.174, simple_loss=0.2625, pruned_loss=0.04277, over 1430298.36 frames.], batch size: 28, lr: 1.82e-04
2022-05-29 01:00:10,554 INFO [train.py:842] (1/4) Epoch 30, batch 8400, loss[loss=0.1439, simple_loss=0.2243, pruned_loss=0.0318, over 7002.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2617, pruned_loss=0.04224, over 1426979.08 frames.], batch size: 16, lr: 1.82e-04
2022-05-29 01:00:49,932 INFO [train.py:842] (1/4) Epoch 30, batch 8450, loss[loss=0.1795, simple_loss=0.2745, pruned_loss=0.0423, over 7205.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2632, pruned_loss=0.04344, over 1424590.54 frames.], batch size: 22, lr: 1.82e-04
2022-05-29 01:01:28,989 INFO [train.py:842] (1/4) Epoch 30, batch 8500, loss[loss=0.185, simple_loss=0.276, pruned_loss=0.047, over 7235.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2633, pruned_loss=0.04347, over 1423726.59 frames.], batch size: 20, lr: 1.82e-04
2022-05-29 01:02:08,198 INFO [train.py:842] (1/4) Epoch 30, batch 8550, loss[loss=0.1329, simple_loss=0.2157, pruned_loss=0.02504, over 7003.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2631, pruned_loss=0.04287, over 1422232.66 frames.], batch size: 16, lr: 1.82e-04
2022-05-29 01:02:47,577 INFO [train.py:842] (1/4) Epoch 30, batch 8600, loss[loss=0.1541, simple_loss=0.2468, pruned_loss=0.03076, over 7218.00 frames.], tot_loss[loss=0.1746, simple_loss=0.263, pruned_loss=0.04307, over 1419695.37 frames.], batch size: 21, lr: 1.82e-04
2022-05-29 01:03:27,024 INFO [train.py:842] (1/4) Epoch 30, batch 8650, loss[loss=0.1772, simple_loss=0.2703, pruned_loss=0.04205, over 7191.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2624, pruned_loss=0.04274, over 1420106.18 frames.], batch size: 23, lr: 1.82e-04
2022-05-29 01:04:06,189 INFO [train.py:842] (1/4) Epoch 30, batch 8700, loss[loss=0.1661, simple_loss=0.2454, pruned_loss=0.04336, over 7236.00 frames.], tot_loss[loss=0.175, simple_loss=0.2632, pruned_loss=0.04341, over 1416795.78 frames.], batch size: 16, lr: 1.82e-04
2022-05-29 01:04:45,520 INFO [train.py:842] (1/4) Epoch 30, batch 8750, loss[loss=0.2149, simple_loss=0.2968, pruned_loss=0.06649, over 5450.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2644, pruned_loss=0.04395, over 1414467.19 frames.], batch size: 53, lr: 1.82e-04
2022-05-29 01:05:24,824 INFO [train.py:842] (1/4) Epoch 30, batch 8800, loss[loss=0.2302, simple_loss=0.3092, pruned_loss=0.07561, over 7109.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2639, pruned_loss=0.04394, over 1417603.32 frames.], batch size: 21, lr: 1.82e-04
2022-05-29 01:06:04,541 INFO [train.py:842] (1/4) Epoch 30, batch 8850, loss[loss=0.2099, simple_loss=0.2887, pruned_loss=0.06548, over 7204.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2625, pruned_loss=0.04332, over 1419823.31 frames.], batch size: 22, lr: 1.82e-04
2022-05-29 01:06:43,651 INFO [train.py:842] (1/4) Epoch 30, batch 8900, loss[loss=0.1789, simple_loss=0.2583, pruned_loss=0.04975, over 7159.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2621, pruned_loss=0.04343, over 1414394.48 frames.], batch size: 18, lr: 1.82e-04
2022-05-29 01:07:22,754 INFO [train.py:842] (1/4) Epoch 30, batch 8950, loss[loss=0.1703, simple_loss=0.2478, pruned_loss=0.04637, over 7404.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2627, pruned_loss=0.04374, over 1404862.56 frames.], batch size: 18, lr: 1.82e-04
2022-05-29 01:08:01,602 INFO [train.py:842] (1/4) Epoch 30, batch 9000, loss[loss=0.1674, simple_loss=0.2611, pruned_loss=0.03685, over 6669.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2635, pruned_loss=0.04413, over 1391389.65 frames.], batch size: 31, lr: 1.82e-04
2022-05-29 01:08:01,604 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 01:08:11,201 INFO [train.py:871] (1/4) Epoch 30, validation: loss=0.1653, simple_loss=0.2631, pruned_loss=0.03376, over 868885.00 frames.
2022-05-29 01:08:49,650 INFO [train.py:842] (1/4) Epoch 30, batch 9050, loss[loss=0.164, simple_loss=0.2502, pruned_loss=0.03892, over 4778.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2652, pruned_loss=0.04447, over 1372950.61 frames.], batch size: 53, lr: 1.82e-04
2022-05-29 01:09:27,649 INFO [train.py:842] (1/4) Epoch 30, batch 9100, loss[loss=0.1681, simple_loss=0.2608, pruned_loss=0.03766, over 6367.00 frames.], tot_loss[loss=0.1788, simple_loss=0.267, pruned_loss=0.04525, over 1329762.43 frames.], batch size: 38, lr: 1.82e-04
2022-05-29 01:10:06,017 INFO [train.py:842] (1/4) Epoch 30, batch 9150, loss[loss=0.1758, simple_loss=0.2616, pruned_loss=0.04506, over 5519.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2715, pruned_loss=0.04836, over 1261707.62 frames.], batch size: 53, lr: 1.82e-04
2022-05-29 01:10:56,763 INFO [train.py:842] (1/4) Epoch 31, batch 0, loss[loss=0.1635, simple_loss=0.2618, pruned_loss=0.0326, over 7333.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2618, pruned_loss=0.0326, over 7333.00 frames.], batch size: 20, lr: 1.79e-04
2022-05-29 01:11:36,533 INFO [train.py:842] (1/4) Epoch 31, batch 50, loss[loss=0.1629, simple_loss=0.2599, pruned_loss=0.03294, over 7253.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2651, pruned_loss=0.04369, over 317198.87 frames.], batch size: 19, lr: 1.79e-04
2022-05-29 01:12:15,802 INFO [train.py:842] (1/4) Epoch 31, batch 100, loss[loss=0.2234, simple_loss=0.3059, pruned_loss=0.07043, over 7375.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2668, pruned_loss=0.04436, over 561435.77 frames.], batch size: 23, lr: 1.79e-04
2022-05-29 01:12:55,628 INFO [train.py:842] (1/4) Epoch 31, batch 150, loss[loss=0.2416, simple_loss=0.3273, pruned_loss=0.07794, over 7203.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2653, pruned_loss=0.04458, over 756107.19 frames.], batch size: 22, lr: 1.79e-04
2022-05-29 01:13:34,914 INFO [train.py:842] (1/4) Epoch 31, batch 200, loss[loss=0.2255, simple_loss=0.302, pruned_loss=0.07451, over 5310.00 frames.], tot_loss[loss=0.177, simple_loss=0.265, pruned_loss=0.04451, over 901207.03 frames.], batch size: 53, lr: 1.79e-04
2022-05-29 01:14:14,379 INFO [train.py:842] (1/4) Epoch 31, batch 250, loss[loss=0.2019, simple_loss=0.2791, pruned_loss=0.0623, over 7275.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2677, pruned_loss=0.04596, over 1015446.55 frames.], batch size: 25, lr: 1.79e-04
2022-05-29 01:14:53,598 INFO [train.py:842] (1/4) Epoch 31, batch 300, loss[loss=0.1668, simple_loss=0.2566, pruned_loss=0.03849, over 7324.00 frames.], tot_loss[loss=0.177, simple_loss=0.2655, pruned_loss=0.04422, over 1107420.50 frames.], batch size: 21, lr: 1.79e-04
2022-05-29 01:15:33,030 INFO [train.py:842] (1/4) Epoch 31, batch 350, loss[loss=0.159, simple_loss=0.2461, pruned_loss=0.03591, over 7166.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2651, pruned_loss=0.04407, over 1175098.12 frames.], batch size: 18, lr: 1.79e-04
2022-05-29 01:16:12,221 INFO [train.py:842] (1/4) Epoch 31, batch 400, loss[loss=0.2226, simple_loss=0.3136, pruned_loss=0.0658, over 7215.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2651, pruned_loss=0.04406, over 1225317.88 frames.], batch size: 21, lr: 1.79e-04
2022-05-29 01:16:51,529 INFO [train.py:842] (1/4) Epoch 31, batch 450, loss[loss=0.2021, simple_loss=0.2972, pruned_loss=0.05352, over 7115.00 frames.], tot_loss[loss=0.176, simple_loss=0.2651, pruned_loss=0.04345, over 1265872.57 frames.], batch size: 26, lr: 1.79e-04
2022-05-29 01:17:30,773 INFO [train.py:842] (1/4) Epoch 31, batch 500, loss[loss=0.142, simple_loss=0.2263, pruned_loss=0.02883, over 7274.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2647, pruned_loss=0.04351, over 1301794.72 frames.], batch size: 17, lr: 1.79e-04
2022-05-29 01:18:10,314 INFO [train.py:842] (1/4) Epoch 31, batch 550, loss[loss=0.1707, simple_loss=0.2596, pruned_loss=0.04088, over 7415.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2655, pruned_loss=0.04433, over 1329092.80 frames.], batch size: 21, lr: 1.79e-04
2022-05-29 01:18:49,302 INFO [train.py:842] (1/4) Epoch 31, batch 600, loss[loss=0.1753, simple_loss=0.2687, pruned_loss=0.04098, over 7062.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2659, pruned_loss=0.04416, over 1348563.95 frames.], batch size: 18, lr: 1.79e-04
2022-05-29 01:19:29,065 INFO [train.py:842] (1/4) Epoch 31, batch 650, loss[loss=0.2169, simple_loss=0.2944, pruned_loss=0.06972, over 7149.00 frames.], tot_loss[loss=0.1765, simple_loss=0.265, pruned_loss=0.04395, over 1369967.40 frames.], batch size: 20, lr: 1.79e-04
2022-05-29 01:20:08,321 INFO [train.py:842] (1/4) Epoch 31, batch 700, loss[loss=0.1469, simple_loss=0.2283, pruned_loss=0.03273, over 6787.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2646, pruned_loss=0.04389, over 1378831.90 frames.], batch size: 15, lr: 1.79e-04
2022-05-29 01:20:47,931 INFO [train.py:842] (1/4) Epoch 31, batch 750, loss[loss=0.1477, simple_loss=0.2433, pruned_loss=0.0261, over 7241.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2646, pruned_loss=0.0439, over 1386566.19 frames.], batch size: 20, lr: 1.79e-04
2022-05-29 01:21:27,105 INFO [train.py:842] (1/4) Epoch 31, batch 800, loss[loss=0.1913, simple_loss=0.2972, pruned_loss=0.04274, over 7325.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2639, pruned_loss=0.04326, over 1394892.95 frames.], batch size: 20, lr: 1.79e-04
2022-05-29 01:22:06,646 INFO [train.py:842] (1/4) Epoch 31, batch 850, loss[loss=0.1269, simple_loss=0.2137, pruned_loss=0.02004, over 7437.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2622, pruned_loss=0.04257, over 1398335.58 frames.], batch size: 20, lr: 1.79e-04
2022-05-29 01:22:46,069 INFO [train.py:842] (1/4) Epoch 31, batch 900, loss[loss=0.1606, simple_loss=0.2392, pruned_loss=0.04101, over 6796.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2622, pruned_loss=0.04254, over 1403508.66 frames.], batch size: 15, lr: 1.79e-04
2022-05-29 01:23:25,748 INFO [train.py:842] (1/4) Epoch 31, batch 950, loss[loss=0.2, simple_loss=0.287, pruned_loss=0.05654, over 7146.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2616, pruned_loss=0.04264, over 1404994.41 frames.], batch size: 28, lr: 1.79e-04
2022-05-29 01:24:04,928 INFO [train.py:842] (1/4) Epoch 31, batch 1000, loss[loss=0.1749, simple_loss=0.2684, pruned_loss=0.04064, over 7341.00 frames.], tot_loss[loss=0.1748, simple_loss=0.263, pruned_loss=0.04332, over 1408036.97 frames.], batch size: 22, lr: 1.79e-04
2022-05-29 01:24:44,401 INFO [train.py:842] (1/4) Epoch 31, batch 1050, loss[loss=0.1876, simple_loss=0.2751, pruned_loss=0.05008, over 7103.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2628, pruned_loss=0.04294, over 1410318.68 frames.], batch size: 28, lr: 1.79e-04
2022-05-29 01:25:23,509 INFO [train.py:842] (1/4) Epoch 31, batch 1100, loss[loss=0.152, simple_loss=0.2425, pruned_loss=0.03074, over 7070.00 frames.], tot_loss[loss=0.1734, simple_loss=0.262, pruned_loss=0.04239, over 1414986.67 frames.], batch size: 18, lr: 1.79e-04
2022-05-29 01:26:03,315 INFO [train.py:842] (1/4) Epoch 31, batch 1150, loss[loss=0.2079, simple_loss=0.2909, pruned_loss=0.06249, over 7071.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2618, pruned_loss=0.04235, over 1417015.91 frames.], batch size: 18, lr: 1.79e-04
2022-05-29 01:26:42,684 INFO [train.py:842] (1/4) Epoch 31, batch 1200, loss[loss=0.1717, simple_loss=0.2652, pruned_loss=0.03915, over 7225.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2614, pruned_loss=0.04199, over 1418823.45 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 01:27:22,217 INFO [train.py:842] (1/4) Epoch 31, batch 1250, loss[loss=0.1644, simple_loss=0.2503, pruned_loss=0.0393, over 7416.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2625, pruned_loss=0.04235, over 1418521.40 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:28:01,508 INFO [train.py:842] (1/4) Epoch 31, batch 1300, loss[loss=0.1925, simple_loss=0.29, pruned_loss=0.04748, over 7142.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2623, pruned_loss=0.04245, over 1418134.89 frames.], batch size: 26, lr: 1.78e-04
2022-05-29 01:28:40,910 INFO [train.py:842] (1/4) Epoch 31, batch 1350, loss[loss=0.1789, simple_loss=0.2637, pruned_loss=0.0471, over 7145.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2641, pruned_loss=0.04311, over 1415656.10 frames.], batch size: 17, lr: 1.78e-04
2022-05-29 01:29:20,141 INFO [train.py:842] (1/4) Epoch 31, batch 1400, loss[loss=0.1689, simple_loss=0.2651, pruned_loss=0.03638, over 7321.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2639, pruned_loss=0.04273, over 1420174.35 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 01:29:59,809 INFO [train.py:842] (1/4) Epoch 31, batch 1450, loss[loss=0.1442, simple_loss=0.2406, pruned_loss=0.02393, over 7149.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2636, pruned_loss=0.04254, over 1421276.07 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:30:38,801 INFO [train.py:842] (1/4) Epoch 31, batch 1500, loss[loss=0.1689, simple_loss=0.2635, pruned_loss=0.03713, over 7294.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2649, pruned_loss=0.04288, over 1426736.37 frames.], batch size: 25, lr: 1.78e-04
2022-05-29 01:31:18,564 INFO [train.py:842] (1/4) Epoch 31, batch 1550, loss[loss=0.2146, simple_loss=0.3106, pruned_loss=0.05934, over 7306.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2636, pruned_loss=0.04281, over 1427988.76 frames.], batch size: 25, lr: 1.78e-04
2022-05-29 01:31:57,807 INFO [train.py:842] (1/4) Epoch 31, batch 1600, loss[loss=0.1759, simple_loss=0.2587, pruned_loss=0.04661, over 7246.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2638, pruned_loss=0.04338, over 1428710.10 frames.], batch size: 19, lr: 1.78e-04
2022-05-29 01:32:37,048 INFO [train.py:842] (1/4) Epoch 31, batch 1650, loss[loss=0.1601, simple_loss=0.2577, pruned_loss=0.03123, over 7120.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2652, pruned_loss=0.04375, over 1428975.44 frames.], batch size: 21, lr: 1.78e-04
2022-05-29 01:33:16,434 INFO [train.py:842] (1/4) Epoch 31, batch 1700, loss[loss=0.1721, simple_loss=0.2649, pruned_loss=0.03967, over 7285.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2635, pruned_loss=0.04299, over 1425793.64 frames.], batch size: 24, lr: 1.78e-04
2022-05-29 01:33:55,905 INFO [train.py:842] (1/4) Epoch 31, batch 1750, loss[loss=0.1906, simple_loss=0.2677, pruned_loss=0.05675, over 7388.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2634, pruned_loss=0.04298, over 1427740.95 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 01:34:34,925 INFO [train.py:842] (1/4) Epoch 31, batch 1800, loss[loss=0.1597, simple_loss=0.2525, pruned_loss=0.03348, over 7432.00 frames.], tot_loss[loss=0.1745, simple_loss=0.263, pruned_loss=0.04294, over 1423885.55 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:35:14,411 INFO [train.py:842] (1/4) Epoch 31, batch 1850, loss[loss=0.1717, simple_loss=0.2521, pruned_loss=0.04567, over 7123.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2619, pruned_loss=0.04226, over 1421724.25 frames.], batch size: 17, lr: 1.78e-04
2022-05-29 01:35:53,695 INFO [train.py:842] (1/4) Epoch 31, batch 1900, loss[loss=0.1741, simple_loss=0.2573, pruned_loss=0.0454, over 7327.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2622, pruned_loss=0.04256, over 1425043.10 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:36:33,291 INFO [train.py:842] (1/4) Epoch 31, batch 1950, loss[loss=0.2104, simple_loss=0.2839, pruned_loss=0.06843, over 7379.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2631, pruned_loss=0.0427, over 1424407.77 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 01:37:12,649 INFO [train.py:842] (1/4) Epoch 31, batch 2000, loss[loss=0.1574, simple_loss=0.2468, pruned_loss=0.03401, over 7167.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2628, pruned_loss=0.04277, over 1425934.12 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:37:52,324 INFO [train.py:842] (1/4) Epoch 31, batch 2050, loss[loss=0.1947, simple_loss=0.2761, pruned_loss=0.05667, over 7201.00 frames.], tot_loss[loss=0.174, simple_loss=0.2623, pruned_loss=0.04286, over 1423340.09 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 01:38:31,272 INFO [train.py:842] (1/4) Epoch 31, batch 2100, loss[loss=0.1674, simple_loss=0.2604, pruned_loss=0.03723, over 7150.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2623, pruned_loss=0.04269, over 1422179.72 frames.], batch size: 19, lr: 1.78e-04
2022-05-29 01:39:10,887 INFO [train.py:842] (1/4) Epoch 31, batch 2150, loss[loss=0.1425, simple_loss=0.2359, pruned_loss=0.02457, over 7164.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2617, pruned_loss=0.04225, over 1425626.48 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:39:50,073 INFO [train.py:842] (1/4) Epoch 31, batch 2200, loss[loss=0.1936, simple_loss=0.2785, pruned_loss=0.05436, over 7074.00 frames.], tot_loss[loss=0.173, simple_loss=0.262, pruned_loss=0.04203, over 1428463.14 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:40:29,734 INFO [train.py:842] (1/4) Epoch 31, batch 2250, loss[loss=0.1777, simple_loss=0.2666, pruned_loss=0.04445, over 7226.00 frames.], tot_loss[loss=0.1728, simple_loss=0.262, pruned_loss=0.04178, over 1428134.85 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 01:41:09,304 INFO [train.py:842] (1/4) Epoch 31, batch 2300, loss[loss=0.174, simple_loss=0.2526, pruned_loss=0.04772, over 7251.00 frames.], tot_loss[loss=0.1723, simple_loss=0.261, pruned_loss=0.04181, over 1430880.58 frames.], batch size: 19, lr: 1.78e-04
2022-05-29 01:41:48,961 INFO [train.py:842] (1/4) Epoch 31, batch 2350, loss[loss=0.1846, simple_loss=0.2675, pruned_loss=0.05088, over 7076.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2612, pruned_loss=0.04195, over 1430043.78 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:42:27,932 INFO [train.py:842] (1/4) Epoch 31, batch 2400, loss[loss=0.1575, simple_loss=0.2491, pruned_loss=0.03294, over 7222.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2621, pruned_loss=0.04238, over 1428617.76 frames.], batch size: 21, lr: 1.78e-04
2022-05-29 01:43:07,275 INFO [train.py:842] (1/4) Epoch 31, batch 2450, loss[loss=0.1805, simple_loss=0.2742, pruned_loss=0.04342, over 7229.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2628, pruned_loss=0.04298, over 1424200.32 frames.], batch size: 21, lr: 1.78e-04
2022-05-29 01:43:46,472 INFO [train.py:842] (1/4) Epoch 31, batch 2500, loss[loss=0.1669, simple_loss=0.2626, pruned_loss=0.03563, over 7343.00 frames.], tot_loss[loss=0.173, simple_loss=0.2616, pruned_loss=0.04221, over 1426753.09 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 01:44:37,189 INFO [train.py:842] (1/4) Epoch 31, batch 2550, loss[loss=0.209, simple_loss=0.2965, pruned_loss=0.06077, over 7202.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2616, pruned_loss=0.04216, over 1428396.24 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 01:45:16,383 INFO [train.py:842] (1/4) Epoch 31, batch 2600, loss[loss=0.1442, simple_loss=0.2273, pruned_loss=0.03057, over 7428.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2611, pruned_loss=0.04217, over 1428241.10 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:45:55,625 INFO [train.py:842] (1/4) Epoch 31, batch 2650, loss[loss=0.1641, simple_loss=0.2575, pruned_loss=0.03531, over 7421.00 frames.], tot_loss[loss=0.173, simple_loss=0.2616, pruned_loss=0.0422, over 1424690.72 frames.], batch size: 21, lr: 1.78e-04
2022-05-29 01:46:34,525 INFO [train.py:842] (1/4) Epoch 31, batch 2700, loss[loss=0.1934, simple_loss=0.2818, pruned_loss=0.05256, over 7306.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2629, pruned_loss=0.04274, over 1418871.25 frames.], batch size: 25, lr: 1.78e-04
2022-05-29 01:47:14,159 INFO [train.py:842] (1/4) Epoch 31, batch 2750, loss[loss=0.2061, simple_loss=0.2932, pruned_loss=0.05945, over 7145.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2634, pruned_loss=0.04294, over 1420106.79 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:47:53,460 INFO [train.py:842] (1/4) Epoch 31, batch 2800, loss[loss=0.1829, simple_loss=0.2725, pruned_loss=0.04666, over 7163.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2627, pruned_loss=0.04229, over 1422113.25 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:48:43,781 INFO [train.py:842] (1/4) Epoch 31, batch 2850, loss[loss=0.2001, simple_loss=0.2856, pruned_loss=0.0573, over 7196.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2632, pruned_loss=0.04259, over 1420187.22 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 01:49:23,035 INFO [train.py:842] (1/4) Epoch 31, batch 2900, loss[loss=0.2224, simple_loss=0.299, pruned_loss=0.07296, over 7111.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2641, pruned_loss=0.04343, over 1423678.21 frames.], batch size: 21, lr: 1.78e-04
2022-05-29 01:50:02,446 INFO [train.py:842] (1/4) Epoch 31, batch 2950, loss[loss=0.204, simple_loss=0.2926, pruned_loss=0.05771, over 7250.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2639, pruned_loss=0.04331, over 1422993.83 frames.], batch size: 19, lr: 1.78e-04
2022-05-29 01:50:52,115 INFO [train.py:842] (1/4) Epoch 31, batch 3000, loss[loss=0.163, simple_loss=0.2504, pruned_loss=0.03781, over 7333.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2639, pruned_loss=0.04325, over 1422922.41 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:50:52,116 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 01:51:02,035 INFO [train.py:871] (1/4) Epoch 31, validation: loss=0.165, simple_loss=0.262, pruned_loss=0.03402, over 868885.00 frames.
2022-05-29 01:51:41,767 INFO [train.py:842] (1/4) Epoch 31, batch 3050, loss[loss=0.1533, simple_loss=0.2462, pruned_loss=0.03018, over 7000.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2643, pruned_loss=0.04355, over 1422526.51 frames.], batch size: 16, lr: 1.78e-04
2022-05-29 01:52:21,231 INFO [train.py:842] (1/4) Epoch 31, batch 3100, loss[loss=0.195, simple_loss=0.2852, pruned_loss=0.05241, over 7279.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2643, pruned_loss=0.04371, over 1426060.99 frames.], batch size: 25, lr: 1.78e-04
2022-05-29 01:53:00,858 INFO [train.py:842] (1/4) Epoch 31, batch 3150, loss[loss=0.156, simple_loss=0.2347, pruned_loss=0.03868, over 6997.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2638, pruned_loss=0.04355, over 1425094.42 frames.], batch size: 16, lr: 1.78e-04
2022-05-29 01:53:39,925 INFO [train.py:842] (1/4) Epoch 31, batch 3200, loss[loss=0.2095, simple_loss=0.2922, pruned_loss=0.06345, over 7210.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2641, pruned_loss=0.04389, over 1417812.42 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 01:54:19,547 INFO [train.py:842] (1/4) Epoch 31, batch 3250, loss[loss=0.183, simple_loss=0.2745, pruned_loss=0.04574, over 7136.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2645, pruned_loss=0.04387, over 1416479.78 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:54:58,951 INFO [train.py:842] (1/4) Epoch 31, batch 3300, loss[loss=0.1588, simple_loss=0.2444, pruned_loss=0.03659, over 7283.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2643, pruned_loss=0.04343, over 1423203.57 frames.], batch size: 17, lr: 1.78e-04
2022-05-29 01:55:38,536 INFO [train.py:842] (1/4) Epoch 31, batch 3350, loss[loss=0.1734, simple_loss=0.2749, pruned_loss=0.03598, over 7217.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2635, pruned_loss=0.04337, over 1421493.59 frames.], batch size: 21, lr: 1.78e-04
2022-05-29 01:56:17,501 INFO [train.py:842] (1/4) Epoch 31, batch 3400, loss[loss=0.1822, simple_loss=0.2734, pruned_loss=0.04553, over 7337.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2622, pruned_loss=0.04241, over 1421252.02 frames.], batch size: 25, lr: 1.78e-04
2022-05-29 01:56:57,104 INFO [train.py:842] (1/4) Epoch 31, batch 3450, loss[loss=0.1717, simple_loss=0.2625, pruned_loss=0.04049, over 6427.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2634, pruned_loss=0.04311, over 1425207.20 frames.], batch size: 38, lr: 1.78e-04
2022-05-29 01:57:36,439 INFO [train.py:842] (1/4) Epoch 31, batch 3500, loss[loss=0.2111, simple_loss=0.309, pruned_loss=0.05655, over 7366.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2629, pruned_loss=0.04294, over 1426759.02 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 01:58:15,843 INFO [train.py:842] (1/4) Epoch 31, batch 3550, loss[loss=0.1733, simple_loss=0.2545, pruned_loss=0.04609, over 7432.00 frames.], tot_loss[loss=0.174, simple_loss=0.2626, pruned_loss=0.04268, over 1428318.93 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:58:54,940 INFO [train.py:842] (1/4) Epoch 31, batch 3600, loss[loss=0.1697, simple_loss=0.2606, pruned_loss=0.0394, over 7293.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2628, pruned_loss=0.04227, over 1423393.72 frames.], batch size: 24, lr: 1.78e-04
2022-05-29 01:59:34,569 INFO [train.py:842] (1/4) Epoch 31, batch 3650, loss[loss=0.1632, simple_loss=0.2416, pruned_loss=0.04242, over 7147.00 frames.], tot_loss[loss=0.1752, simple_loss=0.264, pruned_loss=0.04322, over 1422207.02 frames.], batch size: 17, lr: 1.78e-04
2022-05-29 02:00:14,115 INFO [train.py:842] (1/4) Epoch 31, batch 3700, loss[loss=0.1469, simple_loss=0.2214, pruned_loss=0.03616, over 7275.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2624, pruned_loss=0.04234, over 1424973.39 frames.], batch size: 17, lr: 1.78e-04
2022-05-29 02:00:53,392 INFO [train.py:842] (1/4) Epoch 31, batch 3750, loss[loss=0.1522, simple_loss=0.2419, pruned_loss=0.0312, over 7269.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2619, pruned_loss=0.04233, over 1423547.07 frames.], batch size: 19, lr: 1.78e-04
2022-05-29 02:01:33,004 INFO [train.py:842] (1/4) Epoch 31, batch 3800, loss[loss=0.167, simple_loss=0.2627, pruned_loss=0.03565, over 7381.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2629, pruned_loss=0.04304, over 1426823.86 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 02:02:12,572 INFO [train.py:842] (1/4) Epoch 31, batch 3850, loss[loss=0.1831, simple_loss=0.2523, pruned_loss=0.05695, over 7008.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2641, pruned_loss=0.04354, over 1426325.33 frames.], batch size: 16, lr: 1.78e-04
2022-05-29 02:02:51,833 INFO [train.py:842] (1/4) Epoch 31, batch 3900, loss[loss=0.1769, simple_loss=0.278, pruned_loss=0.03794, over 7340.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2636, pruned_loss=0.043, over 1430093.38 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 02:03:31,397 INFO [train.py:842] (1/4) Epoch 31, batch 3950, loss[loss=0.1735, simple_loss=0.2616, pruned_loss=0.04271, over 7265.00 frames.], tot_loss[loss=0.174, simple_loss=0.2629, pruned_loss=0.04261, over 1430938.56 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 02:04:10,620 INFO [train.py:842] (1/4) Epoch 31, batch 4000, loss[loss=0.1552, simple_loss=0.2429, pruned_loss=0.03378, over 7433.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2629, pruned_loss=0.04245, over 1432672.97 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 02:04:50,059 INFO [train.py:842] (1/4) Epoch 31, batch 4050, loss[loss=0.1338, simple_loss=0.2155, pruned_loss=0.02604, over 7421.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2637, pruned_loss=0.04272, over 1428856.85 frames.], batch size: 17, lr: 1.78e-04
2022-05-29 02:05:29,198 INFO [train.py:842] (1/4) Epoch 31, batch 4100, loss[loss=0.1571, simple_loss=0.2431, pruned_loss=0.03553, over 7411.00 frames.], tot_loss[loss=0.175, simple_loss=0.2639, pruned_loss=0.043, over 1428258.36 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 02:06:08,839 INFO [train.py:842] (1/4) Epoch 31, batch 4150, loss[loss=0.1724, simple_loss=0.2717, pruned_loss=0.0366, over 7140.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2633, pruned_loss=0.04256, over 1430540.98 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 02:06:47,959 INFO [train.py:842] (1/4) Epoch 31, batch 4200, loss[loss=0.1415, simple_loss=0.2378, pruned_loss=0.02257, over 7442.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2631, pruned_loss=0.04214, over 1431208.36 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 02:07:30,392 INFO [train.py:842] (1/4) Epoch 31, batch 4250, loss[loss=0.173, simple_loss=0.2665, pruned_loss=0.03975, over 7347.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2621, pruned_loss=0.04188, over 1432055.34 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 02:08:09,300 INFO [train.py:842] (1/4) Epoch 31, batch 4300, loss[loss=0.2059, simple_loss=0.3002, pruned_loss=0.05578, over 7328.00 frames.], tot_loss[loss=0.1738, simple_loss=0.263, pruned_loss=0.04228, over 1431424.12 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 02:08:48,999 INFO [train.py:842] (1/4) Epoch 31, batch 4350, loss[loss=0.1654, simple_loss=0.2517, pruned_loss=0.03958, over 7070.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2616, pruned_loss=0.04189, over 1432212.06 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:09:28,383 INFO [train.py:842] (1/4) Epoch 31, batch 4400, loss[loss=0.1594, simple_loss=0.2575, pruned_loss=0.03059, over 7092.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2612, pruned_loss=0.04184, over 1434164.94 frames.], batch size: 28, lr: 1.77e-04
2022-05-29 02:10:08,094 INFO [train.py:842] (1/4) Epoch 31, batch 4450, loss[loss=0.1652, simple_loss=0.2572, pruned_loss=0.03661, over 7162.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2608, pruned_loss=0.04143, over 1433670.99 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:10:47,323 INFO [train.py:842] (1/4) Epoch 31, batch 4500, loss[loss=0.18, simple_loss=0.2722, pruned_loss=0.04387, over 7212.00 frames.], tot_loss[loss=0.173, simple_loss=0.2619, pruned_loss=0.04206, over 1429167.91 frames.], batch size: 22, lr: 1.77e-04
2022-05-29 02:11:26,838 INFO [train.py:842] (1/4) Epoch 31, batch 4550, loss[loss=0.1674, simple_loss=0.2613, pruned_loss=0.0368, over 5048.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2631, pruned_loss=0.043, over 1420257.05 frames.], batch size: 52, lr: 1.77e-04
2022-05-29 02:12:06,252 INFO [train.py:842] (1/4) Epoch 31, batch 4600, loss[loss=0.1728, simple_loss=0.2758, pruned_loss=0.03489, over 7145.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2632, pruned_loss=0.0428, over 1423241.93 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:12:45,683 INFO [train.py:842] (1/4) Epoch 31, batch 4650, loss[loss=0.2143, simple_loss=0.2978, pruned_loss=0.06543, over 7435.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2638, pruned_loss=0.04339, over 1423417.75 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:13:24,761 INFO [train.py:842] (1/4) Epoch 31, batch 4700, loss[loss=0.1994, simple_loss=0.2763, pruned_loss=0.06121, over 7247.00 frames.], tot_loss[loss=0.176, simple_loss=0.2643, pruned_loss=0.04383, over 1422012.31 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:14:04,488 INFO [train.py:842] (1/4) Epoch 31, batch 4750, loss[loss=0.1467, simple_loss=0.2296, pruned_loss=0.03188, over 7149.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2641, pruned_loss=0.04337, over 1424704.81 frames.], batch size: 17, lr: 1.77e-04
2022-05-29 02:14:43,783 INFO [train.py:842] (1/4) Epoch 31, batch 4800, loss[loss=0.1845, simple_loss=0.2742, pruned_loss=0.04739, over 7159.00 frames.], tot_loss[loss=0.1753, simple_loss=0.264, pruned_loss=0.04333, over 1424760.89 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:15:23,357 INFO [train.py:842] (1/4) Epoch 31, batch 4850, loss[loss=0.1605, simple_loss=0.2562, pruned_loss=0.03244, over 6430.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2642, pruned_loss=0.04348, over 1418480.47 frames.], batch size: 37, lr: 1.77e-04
2022-05-29 02:16:02,464 INFO [train.py:842] (1/4) Epoch 31, batch 4900, loss[loss=0.1379, simple_loss=0.2326, pruned_loss=0.02158, over 7433.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2644, pruned_loss=0.04342, over 1420159.52 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:16:42,079 INFO [train.py:842] (1/4) Epoch 31, batch 4950, loss[loss=0.1649, simple_loss=0.2561, pruned_loss=0.03688, over 7069.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2641, pruned_loss=0.04359, over 1416947.81 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:17:21,109 INFO [train.py:842] (1/4) Epoch 31, batch 5000, loss[loss=0.1519, simple_loss=0.2359, pruned_loss=0.03396, over 7452.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2647, pruned_loss=0.04384, over 1416253.34 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:18:00,721 INFO [train.py:842] (1/4) Epoch 31, batch 5050, loss[loss=0.1679, simple_loss=0.2594, pruned_loss=0.03822, over 7153.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2638, pruned_loss=0.0433, over 1417282.18 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:18:39,763 INFO [train.py:842] (1/4) Epoch 31, batch 5100, loss[loss=0.2069, simple_loss=0.2837, pruned_loss=0.06503, over 7402.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2635, pruned_loss=0.04307, over 1422049.04 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:19:19,607 INFO [train.py:842] (1/4) Epoch 31, batch 5150, loss[loss=0.1817, simple_loss=0.2668, pruned_loss=0.04829, over 7165.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2647, pruned_loss=0.04371, over 1425086.13 frames.], batch size: 17, lr: 1.77e-04
2022-05-29 02:19:59,040 INFO [train.py:842] (1/4) Epoch 31, batch 5200, loss[loss=0.2227, simple_loss=0.2963, pruned_loss=0.07461, over 7253.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2642, pruned_loss=0.04355, over 1424738.43 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:20:38,695 INFO [train.py:842] (1/4) Epoch 31, batch 5250, loss[loss=0.1757, simple_loss=0.264, pruned_loss=0.04368, over 7037.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2624, pruned_loss=0.0427, over 1423865.98 frames.], batch size: 28, lr: 1.77e-04
2022-05-29 02:21:18,168 INFO [train.py:842] (1/4) Epoch 31, batch 5300, loss[loss=0.1815, simple_loss=0.2783, pruned_loss=0.04236, over 7371.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2611, pruned_loss=0.04213, over 1424210.33 frames.], batch size: 23, lr: 1.77e-04
2022-05-29 02:21:57,665 INFO [train.py:842] (1/4) Epoch 31, batch 5350, loss[loss=0.2922, simple_loss=0.3476, pruned_loss=0.1184, over 5153.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2619, pruned_loss=0.04246, over 1420122.16 frames.], batch size: 52, lr: 1.77e-04
2022-05-29 02:22:36,653 INFO [train.py:842] (1/4) Epoch 31, batch 5400, loss[loss=0.1766, simple_loss=0.2683, pruned_loss=0.04245, over 7319.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2625, pruned_loss=0.0429, over 1422265.89 frames.], batch size: 21, lr: 1.77e-04
2022-05-29 02:23:16,007 INFO [train.py:842] (1/4) Epoch 31, batch 5450, loss[loss=0.1569, simple_loss=0.2383, pruned_loss=0.0377, over 6992.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2625, pruned_loss=0.04313, over 1418013.40 frames.], batch size: 16, lr: 1.77e-04
2022-05-29 02:23:55,280 INFO [train.py:842] (1/4) Epoch 31, batch 5500, loss[loss=0.1843, simple_loss=0.2667, pruned_loss=0.05098, over 7333.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2639, pruned_loss=0.04342, over 1419791.98 frames.], batch size: 22, lr: 1.77e-04
2022-05-29 02:24:34,760 INFO [train.py:842] (1/4) Epoch 31, batch 5550, loss[loss=0.1373, simple_loss=0.2341, pruned_loss=0.02029, over 7410.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2638, pruned_loss=0.04326, over 1420630.36 frames.], batch size: 21, lr: 1.77e-04
2022-05-29 02:25:13,934 INFO [train.py:842] (1/4) Epoch 31, batch 5600, loss[loss=0.1514, simple_loss=0.2386, pruned_loss=0.03213, over 7010.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2636, pruned_loss=0.04296, over 1420781.23 frames.], batch size: 16, lr: 1.77e-04
2022-05-29 02:25:53,443 INFO [train.py:842] (1/4) Epoch 31, batch 5650, loss[loss=0.1484, simple_loss=0.234, pruned_loss=0.03139, over 7406.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2632, pruned_loss=0.0427, over 1420366.42 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:26:32,508 INFO [train.py:842] (1/4) Epoch 31, batch 5700, loss[loss=0.1569, simple_loss=0.2468, pruned_loss=0.03346, over 7329.00 frames.], tot_loss[loss=0.175, simple_loss=0.2637, pruned_loss=0.04315, over 1413507.93 frames.], batch size: 22, lr: 1.77e-04
2022-05-29 02:27:12,176 INFO [train.py:842] (1/4) Epoch 31, batch 5750, loss[loss=0.1971, simple_loss=0.2967, pruned_loss=0.04879, over 7113.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2644, pruned_loss=0.04299, over 1418346.15 frames.], batch size: 21, lr: 1.77e-04
2022-05-29 02:27:51,407 INFO [train.py:842] (1/4) Epoch 31, batch 5800, loss[loss=0.1905, simple_loss=0.2797, pruned_loss=0.05065, over 7259.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2645, pruned_loss=0.04257, over 1418054.32 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:28:31,130 INFO [train.py:842] (1/4) Epoch 31, batch 5850, loss[loss=0.1656, simple_loss=0.2478, pruned_loss=0.04169, over 7385.00 frames.], tot_loss[loss=0.1743, simple_loss=0.264, pruned_loss=0.04229, over 1422015.17 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:29:10,380 INFO [train.py:842] (1/4) Epoch 31, batch 5900, loss[loss=0.1658, simple_loss=0.2602, pruned_loss=0.03573, over 7321.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2637, pruned_loss=0.04257, over 1424169.58 frames.], batch size: 22, lr: 1.77e-04
2022-05-29 02:29:50,355 INFO [train.py:842] (1/4) Epoch 31, batch 5950, loss[loss=0.1649, simple_loss=0.2496, pruned_loss=0.04014, over 7153.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2615, pruned_loss=0.04211, over 1429245.80 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:30:29,429 INFO [train.py:842] (1/4) Epoch 31, batch 6000, loss[loss=0.1871, simple_loss=0.2764, pruned_loss=0.04897, over 7362.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2622, pruned_loss=0.0425, over 1426582.33 frames.], batch size: 23, lr: 1.77e-04
2022-05-29 02:30:29,430 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 02:30:38,842 INFO [train.py:871] (1/4) Epoch 31, validation: loss=0.1644, simple_loss=0.2618, pruned_loss=0.0335, over 868885.00 frames.
2022-05-29 02:31:18,337 INFO [train.py:842] (1/4) Epoch 31, batch 6050, loss[loss=0.15, simple_loss=0.241, pruned_loss=0.02945, over 7446.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2629, pruned_loss=0.04325, over 1426067.84 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:31:57,798 INFO [train.py:842] (1/4) Epoch 31, batch 6100, loss[loss=0.143, simple_loss=0.2259, pruned_loss=0.03008, over 7366.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2624, pruned_loss=0.04265, over 1430321.60 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:32:37,360 INFO [train.py:842] (1/4) Epoch 31, batch 6150, loss[loss=0.1691, simple_loss=0.2618, pruned_loss=0.0382, over 7166.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2628, pruned_loss=0.04272, over 1429401.21 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:33:16,226 INFO [train.py:842] (1/4) Epoch 31, batch 6200, loss[loss=0.1761, simple_loss=0.2688, pruned_loss=0.0417, over 7144.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2632, pruned_loss=0.04294, over 1422636.86 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:33:55,498 INFO [train.py:842] (1/4) Epoch 31, batch 6250, loss[loss=0.2036, simple_loss=0.2852, pruned_loss=0.06099, over 6752.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2643, pruned_loss=0.04339, over 1423833.10 frames.], batch size: 31, lr: 1.77e-04
2022-05-29 02:34:34,693 INFO [train.py:842] (1/4) Epoch 31, batch 6300, loss[loss=0.1588, simple_loss=0.2531, pruned_loss=0.03231, over 7334.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2645, pruned_loss=0.04393, over 1421691.08 frames.], batch size: 22, lr: 1.77e-04
2022-05-29 02:35:14,270 INFO [train.py:842] (1/4) Epoch 31, batch 6350, loss[loss=0.1567, simple_loss=0.2441, pruned_loss=0.03465, over 7152.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2645, pruned_loss=0.04404, over 1426502.33 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:35:53,437 INFO [train.py:842] (1/4) Epoch 31, batch 6400, loss[loss=0.1981, simple_loss=0.2931, pruned_loss=0.05159, over 6558.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2657, pruned_loss=0.04432, over 1424496.22 frames.], batch size: 38, lr: 1.77e-04
2022-05-29 02:36:33,055 INFO [train.py:842] (1/4) Epoch 31, batch 6450, loss[loss=0.2117, simple_loss=0.298, pruned_loss=0.06275, over 7428.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2644, pruned_loss=0.0436, over 1420532.36 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:37:12,495 INFO [train.py:842] (1/4) Epoch 31, batch 6500, loss[loss=0.1611, simple_loss=0.2457, pruned_loss=0.03826, over 7257.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2631, pruned_loss=0.04289, over 1426787.06 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:37:52,305 INFO [train.py:842] (1/4) Epoch 31, batch 6550, loss[loss=0.1339, simple_loss=0.2148, pruned_loss=0.02652, over 7413.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2618, pruned_loss=0.04231, over 1423883.58 frames.], batch size: 17, lr: 1.77e-04
2022-05-29 02:38:31,468 INFO [train.py:842] (1/4) Epoch 31, batch 6600, loss[loss=0.2105, simple_loss=0.3005, pruned_loss=0.06025, over 7194.00 frames.], tot_loss[loss=0.175, simple_loss=0.2635, pruned_loss=0.04325, over 1423064.44 frames.], batch size: 23, lr: 1.77e-04
2022-05-29 02:39:11,131 INFO [train.py:842] (1/4) Epoch 31, batch 6650, loss[loss=0.1633, simple_loss=0.2549, pruned_loss=0.03584, over 7423.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2637, pruned_loss=0.04328, over 1427569.65 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:39:50,717 INFO [train.py:842] (1/4) Epoch 31, batch 6700, loss[loss=0.1843, simple_loss=0.2717, pruned_loss=0.04841, over 7215.00 frames.], tot_loss[loss=0.1738, simple_loss=0.262, pruned_loss=0.04279, over 1431713.84 frames.], batch size: 21, lr: 1.77e-04
2022-05-29 02:40:30,506 INFO [train.py:842] (1/4) Epoch 31, batch 6750, loss[loss=0.1649, simple_loss=0.2576, pruned_loss=0.03611, over 7296.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2612, pruned_loss=0.04188, over 1430473.78 frames.], batch size: 24, lr: 1.77e-04
2022-05-29 02:41:09,483 INFO [train.py:842] (1/4) Epoch 31, batch 6800, loss[loss=0.1877, simple_loss=0.2845, pruned_loss=0.04543, over 6361.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2621, pruned_loss=0.04207, over 1430254.54 frames.], batch size: 37, lr: 1.77e-04
2022-05-29 02:41:49,171 INFO [train.py:842] (1/4) Epoch 31, batch 6850, loss[loss=0.1409, simple_loss=0.2333, pruned_loss=0.02424, over 7280.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2624, pruned_loss=0.04208, over 1426024.63 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:42:28,726 INFO [train.py:842] (1/4) Epoch 31, batch 6900, loss[loss=0.1928, simple_loss=0.2815, pruned_loss=0.05206, over 7105.00 frames.], tot_loss[loss=0.174, simple_loss=0.2628, pruned_loss=0.04264, over 1424938.04 frames.], batch size: 21, lr: 1.77e-04
2022-05-29 02:43:08,193 INFO [train.py:842] (1/4) Epoch 31, batch 6950, loss[loss=0.1537, simple_loss=0.2506, pruned_loss=0.0284, over 7314.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2641, pruned_loss=0.0432, over 1421989.77 frames.], batch size: 21, lr: 1.77e-04
2022-05-29 02:43:47,667 INFO [train.py:842] (1/4) Epoch 31, batch 7000, loss[loss=0.1604, simple_loss=0.2473, pruned_loss=0.03672, over 7324.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2642, pruned_loss=0.04317, over 1418503.58 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:44:27,086 INFO [train.py:842] (1/4) Epoch 31, batch 7050, loss[loss=0.1586, simple_loss=0.2458, pruned_loss=0.03567, over 7270.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2644, pruned_loss=0.0435, over 1407404.41 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:45:06,180 INFO [train.py:842] (1/4) Epoch 31, batch 7100, loss[loss=0.1815, simple_loss=0.2607, pruned_loss=0.05112, over 7433.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2657, pruned_loss=0.04407, over 1399144.68 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:45:46,112 INFO [train.py:842] (1/4) Epoch 31, batch 7150, loss[loss=0.1682, simple_loss=0.2453, pruned_loss=0.04548, over 7268.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2648, pruned_loss=0.04348, over 1401628.85 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:46:25,440 INFO [train.py:842] (1/4) Epoch 31, batch 7200, loss[loss=0.1421, simple_loss=0.2239, pruned_loss=0.03017, over 7218.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2653, pruned_loss=0.04351, over 1399913.88 frames.], batch size: 16, lr: 1.77e-04
2022-05-29 02:47:05,282 INFO [train.py:842] (1/4) Epoch 31, batch 7250, loss[loss=0.1452, simple_loss=0.222, pruned_loss=0.03427, over 6786.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2651, pruned_loss=0.04337, over 1405755.73 frames.], batch size: 15, lr: 1.77e-04
2022-05-29 02:47:44,470 INFO [train.py:842] (1/4) Epoch 31, batch 7300, loss[loss=0.1442, simple_loss=0.2252, pruned_loss=0.03158, over 7280.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2646, pruned_loss=0.04318, over 1407504.01 frames.], batch size: 17, lr: 1.77e-04
2022-05-29 02:48:24,354 INFO [train.py:842] (1/4) Epoch 31, batch 7350, loss[loss=0.1334, simple_loss=0.2243, pruned_loss=0.02128, over 7287.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2635, pruned_loss=0.04318, over 1411186.87 frames.], batch size: 17, lr: 1.77e-04
2022-05-29 02:49:03,698 INFO [train.py:842] (1/4) Epoch 31, batch 7400, loss[loss=0.1578, simple_loss=0.2581, pruned_loss=0.02872, over 6306.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2631, pruned_loss=0.04309, over 1415634.42 frames.], batch size: 37, lr: 1.77e-04
2022-05-29 02:49:43,370 INFO [train.py:842] (1/4) Epoch 31, batch 7450, loss[loss=0.1985, simple_loss=0.274, pruned_loss=0.06148, over 6767.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2636, pruned_loss=0.04382, over 1417041.81 frames.], batch size: 15, lr: 1.77e-04
2022-05-29 02:50:22,747 INFO [train.py:842] (1/4) Epoch 31, batch 7500, loss[loss=0.1689, simple_loss=0.2526, pruned_loss=0.04262, over 7246.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2628, pruned_loss=0.04366, over 1414893.07 frames.], batch size: 19, lr: 1.76e-04
2022-05-29 02:51:02,364 INFO [train.py:842] (1/4) Epoch 31, batch 7550, loss[loss=0.1595, simple_loss=0.2495, pruned_loss=0.03476, over 7142.00 frames.], tot_loss[loss=0.1758, simple_loss=0.264, pruned_loss=0.04383, over 1415440.41 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 02:51:41,400 INFO [train.py:842] (1/4) Epoch 31, batch 7600, loss[loss=0.1636, simple_loss=0.2475, pruned_loss=0.03984, over 7423.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2647, pruned_loss=0.04448, over 1414470.36 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 02:52:20,783 INFO [train.py:842] (1/4) Epoch 31, batch 7650, loss[loss=0.1567, simple_loss=0.2416, pruned_loss=0.03596, over 7257.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2653, pruned_loss=0.04472, over 1414380.03 frames.], batch size: 19, lr: 1.76e-04
2022-05-29 02:53:00,145 INFO [train.py:842] (1/4) Epoch 31, batch 7700, loss[loss=0.1779, simple_loss=0.2605, pruned_loss=0.0477, over 4940.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2647, pruned_loss=0.04381, over 1416650.24 frames.], batch size: 52, lr: 1.76e-04
2022-05-29 02:53:39,805 INFO [train.py:842] (1/4) Epoch 31, batch 7750, loss[loss=0.1796, simple_loss=0.2633, pruned_loss=0.04792, over 7329.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2646, pruned_loss=0.04387, over 1417614.81 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 02:54:19,346 INFO [train.py:842] (1/4) Epoch 31, batch 7800, loss[loss=0.1461, simple_loss=0.2358, pruned_loss=0.02823, over 7324.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2618, pruned_loss=0.04261, over 1418058.85 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 02:54:58,959 INFO [train.py:842] (1/4) Epoch 31, batch 7850, loss[loss=0.1594, simple_loss=0.2513, pruned_loss=0.03373, over 7270.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2632, pruned_loss=0.04328, over 1417952.26 frames.], batch size: 25, lr: 1.76e-04
2022-05-29 02:55:38,155 INFO [train.py:842] (1/4) Epoch 31, batch 7900, loss[loss=0.1545, simple_loss=0.2314, pruned_loss=0.03878, over 7418.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2638, pruned_loss=0.04336, over 1418508.54 frames.], batch size: 18, lr: 1.76e-04
2022-05-29 02:56:17,847 INFO [train.py:842] (1/4) Epoch 31, batch 7950, loss[loss=0.1368, simple_loss=0.2192, pruned_loss=0.02726, over 7410.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2634, pruned_loss=0.04298, over 1419172.01 frames.], batch size: 18, lr: 1.76e-04
2022-05-29 02:56:57,331 INFO [train.py:842] (1/4) Epoch 31, batch 8000, loss[loss=0.2034, simple_loss=0.2886, pruned_loss=0.05915, over 7425.00 frames.], tot_loss[loss=0.176, simple_loss=0.2646, pruned_loss=0.04369, over 1419505.16 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 02:57:36,916 INFO [train.py:842] (1/4) Epoch 31, batch 8050, loss[loss=0.1819, simple_loss=0.2805, pruned_loss=0.04171, over 7212.00 frames.], tot_loss[loss=0.1758, simple_loss=0.264, pruned_loss=0.04374, over 1415387.21 frames.], batch size: 21, lr: 1.76e-04
2022-05-29 02:58:16,069 INFO [train.py:842] (1/4) Epoch 31, batch 8100, loss[loss=0.1701, simple_loss=0.2706, pruned_loss=0.03476, over 7328.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2647, pruned_loss=0.04411, over 1415101.97 frames.], batch size: 22, lr: 1.76e-04
2022-05-29 02:58:55,593 INFO [train.py:842] (1/4) Epoch 31, batch 8150, loss[loss=0.1321, simple_loss=0.2111, pruned_loss=0.02655, over 7262.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2642, pruned_loss=0.04359, over 1418313.10 frames.], batch size: 17, lr: 1.76e-04
2022-05-29 02:59:34,723 INFO [train.py:842] (1/4) Epoch 31, batch 8200, loss[loss=0.1576, simple_loss=0.2405, pruned_loss=0.03729, over 7147.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2629, pruned_loss=0.04314, over 1418399.45 frames.], batch size: 17, lr: 1.76e-04
2022-05-29 03:00:14,414 INFO [train.py:842] (1/4) Epoch 31, batch 8250, loss[loss=0.1452, simple_loss=0.2243, pruned_loss=0.03311, over 7257.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2628, pruned_loss=0.04324, over 1414342.84 frames.], batch size: 17, lr: 1.76e-04
2022-05-29 03:00:53,614 INFO [train.py:842] (1/4) Epoch 31, batch 8300, loss[loss=0.1552, simple_loss=0.2378, pruned_loss=0.03632, over 7275.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2629, pruned_loss=0.04317, over 1412672.68 frames.], batch size: 18, lr: 1.76e-04
2022-05-29 03:01:33,298 INFO [train.py:842] (1/4) Epoch 31, batch 8350, loss[loss=0.1671, simple_loss=0.2651, pruned_loss=0.03459, over 7109.00 frames.], tot_loss[loss=0.174, simple_loss=0.2624, pruned_loss=0.04286, over 1413828.66 frames.], batch size: 21, lr: 1.76e-04
2022-05-29 03:02:12,654 INFO [train.py:842] (1/4) Epoch 31, batch 8400, loss[loss=0.198, simple_loss=0.288, pruned_loss=0.05401, over 7233.00 frames.], tot_loss[loss=0.174, simple_loss=0.2622, pruned_loss=0.04292, over 1413866.63 frames.], batch size: 21, lr: 1.76e-04
2022-05-29 03:02:52,207 INFO [train.py:842] (1/4) Epoch 31, batch 8450, loss[loss=0.1835, simple_loss=0.2698, pruned_loss=0.0486, over 7317.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2631, pruned_loss=0.04293, over 1415384.85 frames.], batch size: 21, lr: 1.76e-04
2022-05-29 03:03:31,499 INFO [train.py:842] (1/4) Epoch 31, batch 8500, loss[loss=0.2167, simple_loss=0.3034, pruned_loss=0.065, over 7150.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2647, pruned_loss=0.04457, over 1414043.03 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 03:04:11,181 INFO [train.py:842] (1/4) Epoch 31, batch 8550, loss[loss=0.2067, simple_loss=0.2871, pruned_loss=0.06311, over 7409.00 frames.], tot_loss[loss=0.1764, simple_loss=0.264, pruned_loss=0.04446, over 1412830.50 frames.], batch size: 21, lr: 1.76e-04
2022-05-29 03:04:50,573 INFO [train.py:842] (1/4) Epoch 31, batch 8600, loss[loss=0.183, simple_loss=0.2819, pruned_loss=0.04208, over 7316.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2633, pruned_loss=0.04376, over 1416414.72 frames.], batch size: 21, lr: 1.76e-04
2022-05-29 03:05:30,248 INFO [train.py:842] (1/4) Epoch 31, batch 8650, loss[loss=0.2019, simple_loss=0.2948, pruned_loss=0.05444, over 6410.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2633, pruned_loss=0.04341, over 1419029.37 frames.], batch size: 38, lr: 1.76e-04
2022-05-29 03:06:09,458 INFO [train.py:842] (1/4) Epoch 31, batch 8700, loss[loss=0.1339, simple_loss=0.2142, pruned_loss=0.02682, over 7006.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2629, pruned_loss=0.04293, over 1418659.44 frames.], batch size: 16, lr: 1.76e-04
2022-05-29 03:06:48,533 INFO [train.py:842] (1/4) Epoch 31, batch 8750, loss[loss=0.1737, simple_loss=0.266, pruned_loss=0.04069, over 6855.00 frames.], tot_loss[loss=0.175, simple_loss=0.2635, pruned_loss=0.04323, over 1407956.66 frames.], batch size: 31, lr: 1.76e-04
2022-05-29 03:07:28,052 INFO [train.py:842] (1/4) Epoch 31, batch 8800, loss[loss=0.1453, simple_loss=0.2271, pruned_loss=0.03178, over 6812.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2623, pruned_loss=0.04297, over 1407700.42 frames.], batch size: 15, lr: 1.76e-04
2022-05-29 03:08:07,328 INFO [train.py:842] (1/4) Epoch 31, batch 8850, loss[loss=0.1385, simple_loss=0.2205, pruned_loss=0.02825, over 7400.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2635, pruned_loss=0.04352, over 1404073.09 frames.], batch size: 18, lr: 1.76e-04
2022-05-29 03:08:46,460 INFO [train.py:842] (1/4) Epoch 31, batch 8900, loss[loss=0.1829, simple_loss=0.2699, pruned_loss=0.04797, over 5358.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2661, pruned_loss=0.04535, over 1395539.09 frames.], batch size: 52, lr: 1.76e-04
2022-05-29 03:09:25,597 INFO [train.py:842] (1/4) Epoch 31, batch 8950, loss[loss=0.1591, simple_loss=0.2536, pruned_loss=0.03228, over 7331.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2665, pruned_loss=0.04529, over 1392806.94 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 03:10:04,391 INFO [train.py:842] (1/4) Epoch 31, batch 9000, loss[loss=0.1763, simple_loss=0.2689, pruned_loss=0.04181, over 7228.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2661, pruned_loss=0.04479, over 1391258.51 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 03:10:04,392 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 03:10:13,977 INFO [train.py:871] (1/4) Epoch 31, validation: loss=0.1628, simple_loss=0.2602, pruned_loss=0.03268, over 868885.00 frames.
2022-05-29 03:10:53,025 INFO [train.py:842] (1/4) Epoch 31, batch 9050, loss[loss=0.1649, simple_loss=0.2669, pruned_loss=0.03147, over 6414.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2663, pruned_loss=0.04454, over 1373976.13 frames.], batch size: 38, lr: 1.76e-04
2022-05-29 03:11:31,591 INFO [train.py:842] (1/4) Epoch 31, batch 9100, loss[loss=0.1702, simple_loss=0.2591, pruned_loss=0.04062, over 7321.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2663, pruned_loss=0.0445, over 1360517.57 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 03:12:10,290 INFO [train.py:842] (1/4) Epoch 31, batch 9150, loss[loss=0.208, simple_loss=0.2957, pruned_loss=0.06018, over 6263.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2687, pruned_loss=0.04575, over 1328115.23 frames.], batch size: 37, lr: 1.76e-04
2022-05-29 03:13:02,499 INFO [train.py:842] (1/4) Epoch 32, batch 0, loss[loss=0.1648, simple_loss=0.257, pruned_loss=0.0363, over 5146.00 frames.], tot_loss[loss=0.1648, simple_loss=0.257, pruned_loss=0.0363, over 5146.00 frames.], batch size: 52, lr: 1.73e-04
2022-05-29 03:13:41,544 INFO [train.py:842] (1/4) Epoch 32, batch 50, loss[loss=0.172, simple_loss=0.2613, pruned_loss=0.0414, over 6535.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2652, pruned_loss=0.04173, over 320190.09 frames.], batch size: 37, lr: 1.73e-04
2022-05-29 03:14:21,210 INFO [train.py:842] (1/4) Epoch 32, batch 100, loss[loss=0.2547, simple_loss=0.3175, pruned_loss=0.09601, over 7281.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2648, pruned_loss=0.04271, over 567558.79 frames.], batch size: 25, lr: 1.73e-04
2022-05-29 03:15:00,453 INFO [train.py:842] (1/4) Epoch 32, batch 150, loss[loss=0.1774, simple_loss=0.2763, pruned_loss=0.03921, over 7167.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2642, pruned_loss=0.04297, over 758470.07 frames.], batch size: 26, lr: 1.73e-04
2022-05-29 03:15:39,791 INFO [train.py:842] (1/4) Epoch 32, batch 200, loss[loss=0.1251, simple_loss=0.2035, pruned_loss=0.02334, over 7010.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2646, pruned_loss=0.0436, over 903421.84 frames.], batch size: 16, lr: 1.73e-04
2022-05-29 03:16:19,191 INFO [train.py:842] (1/4) Epoch 32, batch 250, loss[loss=0.1984, simple_loss=0.2899, pruned_loss=0.05351, over 7291.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2628, pruned_loss=0.04247, over 1023197.28 frames.], batch size: 24, lr: 1.73e-04
2022-05-29 03:16:58,600 INFO [train.py:842] (1/4) Epoch 32, batch 300, loss[loss=0.1679, simple_loss=0.2606, pruned_loss=0.03755, over 7291.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2617, pruned_loss=0.04167, over 1114787.11 frames.], batch size: 24, lr: 1.73e-04
2022-05-29 03:17:37,870 INFO [train.py:842] (1/4) Epoch 32, batch 350, loss[loss=0.2035, simple_loss=0.3018, pruned_loss=0.05263, over 7080.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2617, pruned_loss=0.04195, over 1182030.37 frames.], batch size: 28, lr: 1.73e-04
2022-05-29 03:18:17,619 INFO [train.py:842] (1/4) Epoch 32, batch 400, loss[loss=0.1789, simple_loss=0.2655, pruned_loss=0.04615, over 7177.00 frames.], tot_loss[loss=0.174, simple_loss=0.2624, pruned_loss=0.04281, over 1237106.40 frames.], batch size: 26, lr: 1.73e-04
2022-05-29 03:18:56,930 INFO [train.py:842] (1/4) Epoch 32, batch 450, loss[loss=0.1595, simple_loss=0.2635, pruned_loss=0.02771, over 7328.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2619, pruned_loss=0.04249, over 1277434.82 frames.], batch size: 21, lr: 1.73e-04
2022-05-29 03:19:36,533 INFO [train.py:842] (1/4) Epoch 32, batch 500, loss[loss=0.1628, simple_loss=0.252, pruned_loss=0.03679, over 7338.00 frames.], tot_loss[loss=0.1715, simple_loss=0.26, pruned_loss=0.04154, over 1313659.48 frames.], batch size: 22, lr: 1.73e-04
2022-05-29 03:20:15,859 INFO [train.py:842] (1/4) Epoch 32, batch 550, loss[loss=0.1675, simple_loss=0.2559, pruned_loss=0.03949, over 7345.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2618, pruned_loss=0.04241, over 1341483.58 frames.], batch size: 22, lr: 1.73e-04
2022-05-29 03:20:55,446 INFO [train.py:842] (1/4) Epoch 32, batch 600, loss[loss=0.1638, simple_loss=0.242, pruned_loss=0.04279, over 7147.00 frames.], tot_loss[loss=0.172, simple_loss=0.2602, pruned_loss=0.04185, over 1363920.31 frames.], batch size: 17, lr: 1.73e-04
2022-05-29 03:21:45,352 INFO [train.py:842] (1/4) Epoch 32, batch 650, loss[loss=0.1317, simple_loss=0.2169, pruned_loss=0.02329, over 6971.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2606, pruned_loss=0.04183, over 1379370.94 frames.], batch size: 16, lr: 1.73e-04
2022-05-29 03:22:24,821 INFO [train.py:842] (1/4) Epoch 32, batch 700, loss[loss=0.2007, simple_loss=0.2963, pruned_loss=0.05255, over 7190.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2628, pruned_loss=0.04254, over 1387648.18 frames.], batch size: 23, lr: 1.73e-04
2022-05-29 03:23:04,319 INFO [train.py:842] (1/4) Epoch 32, batch 750, loss[loss=0.1684, simple_loss=0.2627, pruned_loss=0.03708, over 7115.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2634, pruned_loss=0.04292, over 1395864.64 frames.], batch size: 21, lr: 1.73e-04
2022-05-29 03:23:43,765 INFO [train.py:842] (1/4) Epoch 32, batch 800, loss[loss=0.1547, simple_loss=0.2379, pruned_loss=0.03573, over 7283.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2633, pruned_loss=0.04287, over 1400716.39 frames.], batch size: 18, lr: 1.73e-04
2022-05-29 03:24:22,944 INFO [train.py:842] (1/4) Epoch 32, batch 850, loss[loss=0.1711, simple_loss=0.2668, pruned_loss=0.03767, over 7313.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2637, pruned_loss=0.04249, over 1408294.32 frames.], batch size: 25, lr: 1.73e-04
2022-05-29 03:25:02,022 INFO [train.py:842] (1/4) Epoch 32, batch 900, loss[loss=0.152, simple_loss=0.2571, pruned_loss=0.02349, over 7342.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2649, pruned_loss=0.04251, over 1410240.56 frames.], batch size: 22, lr: 1.73e-04
2022-05-29 03:25:41,189 INFO [train.py:842] (1/4) Epoch 32, batch 950, loss[loss=0.171, simple_loss=0.2486, pruned_loss=0.04668, over 6772.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2634, pruned_loss=0.04199, over 1412942.26 frames.], batch size: 15, lr: 1.73e-04
2022-05-29 03:26:20,897 INFO [train.py:842] (1/4) Epoch 32, batch 1000, loss[loss=0.1792, simple_loss=0.2594, pruned_loss=0.04953, over 7420.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2629, pruned_loss=0.04189, over 1417281.30 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:27:00,306 INFO [train.py:842] (1/4) Epoch 32, batch 1050, loss[loss=0.1668, simple_loss=0.2651, pruned_loss=0.03432, over 7240.00 frames.], tot_loss[loss=0.173, simple_loss=0.2622, pruned_loss=0.04191, over 1420912.84 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:27:39,943 INFO [train.py:842] (1/4) Epoch 32, batch 1100, loss[loss=0.2007, simple_loss=0.2966, pruned_loss=0.05237, over 7201.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2628, pruned_loss=0.04234, over 1418759.29 frames.], batch size: 22, lr: 1.73e-04
2022-05-29 03:28:19,239 INFO [train.py:842] (1/4) Epoch 32, batch 1150, loss[loss=0.164, simple_loss=0.2399, pruned_loss=0.04406, over 7146.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2628, pruned_loss=0.04224, over 1422304.81 frames.], batch size: 17, lr: 1.73e-04
2022-05-29 03:28:59,059 INFO [train.py:842] (1/4) Epoch 32, batch 1200, loss[loss=0.169, simple_loss=0.2665, pruned_loss=0.03575, over 7404.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2625, pruned_loss=0.04189, over 1424948.35 frames.], batch size: 21, lr: 1.73e-04
2022-05-29 03:29:38,256 INFO [train.py:842] (1/4) Epoch 32, batch 1250, loss[loss=0.1998, simple_loss=0.2999, pruned_loss=0.04988, over 7195.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2627, pruned_loss=0.04204, over 1417918.92 frames.], batch size: 23, lr: 1.73e-04
2022-05-29 03:30:17,915 INFO [train.py:842] (1/4) Epoch 32, batch 1300, loss[loss=0.1826, simple_loss=0.2749, pruned_loss=0.04517, over 7151.00 frames.], tot_loss[loss=0.1739, simple_loss=0.263, pruned_loss=0.04243, over 1423817.23 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:30:57,306 INFO [train.py:842] (1/4) Epoch 32, batch 1350, loss[loss=0.2091, simple_loss=0.2944, pruned_loss=0.0619, over 7333.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2633, pruned_loss=0.04269, over 1422669.26 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:31:37,059 INFO [train.py:842] (1/4) Epoch 32, batch 1400, loss[loss=0.1938, simple_loss=0.2876, pruned_loss=0.05003, over 7231.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2632, pruned_loss=0.04269, over 1422436.05 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:32:16,187 INFO [train.py:842] (1/4) Epoch 32, batch 1450, loss[loss=0.1182, simple_loss=0.2115, pruned_loss=0.01245, over 7325.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2645, pruned_loss=0.04356, over 1424110.09 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:32:55,646 INFO [train.py:842] (1/4) Epoch 32, batch 1500, loss[loss=0.2774, simple_loss=0.3448, pruned_loss=0.105, over 4844.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2635, pruned_loss=0.043, over 1422427.20 frames.], batch size: 53, lr: 1.73e-04
2022-05-29 03:33:35,099 INFO [train.py:842] (1/4) Epoch 32, batch 1550, loss[loss=0.222, simple_loss=0.2987, pruned_loss=0.07267, over 7403.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2642, pruned_loss=0.04385, over 1421636.35 frames.], batch size: 18, lr: 1.73e-04
2022-05-29 03:34:14,472 INFO [train.py:842] (1/4) Epoch 32, batch 1600, loss[loss=0.1674, simple_loss=0.2604, pruned_loss=0.03722, over 7201.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2629, pruned_loss=0.04332, over 1418407.08 frames.], batch size: 23, lr: 1.73e-04
2022-05-29 03:34:53,762 INFO [train.py:842] (1/4) Epoch 32, batch 1650, loss[loss=0.1898, simple_loss=0.2807, pruned_loss=0.04946, over 7418.00 frames.], tot_loss[loss=0.1746, simple_loss=0.263, pruned_loss=0.04307, over 1417932.94 frames.], batch size: 21, lr: 1.73e-04
2022-05-29 03:35:33,285 INFO [train.py:842] (1/4) Epoch 32, batch 1700, loss[loss=0.1484, simple_loss=0.236, pruned_loss=0.03038, over 7109.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2626, pruned_loss=0.04284, over 1412930.43 frames.], batch size: 21, lr: 1.73e-04
2022-05-29 03:36:12,400 INFO [train.py:842] (1/4) Epoch 32, batch 1750, loss[loss=0.1947, simple_loss=0.2837, pruned_loss=0.05287, over 5301.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2632, pruned_loss=0.0429, over 1411352.02 frames.], batch size: 52, lr: 1.73e-04
2022-05-29 03:36:51,780 INFO [train.py:842] (1/4) Epoch 32, batch 1800, loss[loss=0.1984, simple_loss=0.2852, pruned_loss=0.05579, over 7246.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2656, pruned_loss=0.04397, over 1412353.68 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:37:30,781 INFO [train.py:842] (1/4) Epoch 32, batch 1850, loss[loss=0.1656, simple_loss=0.2538, pruned_loss=0.03866, over 7435.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2654, pruned_loss=0.04383, over 1406310.54 frames.], batch size: 17, lr: 1.73e-04
2022-05-29 03:38:10,511 INFO [train.py:842] (1/4) Epoch 32, batch 1900, loss[loss=0.1531, simple_loss=0.2388, pruned_loss=0.03376, over 7348.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2635, pruned_loss=0.0431, over 1413177.11 frames.], batch size: 19, lr: 1.73e-04
2022-05-29 03:38:49,845 INFO [train.py:842] (1/4) Epoch 32, batch 1950, loss[loss=0.1539, simple_loss=0.2468, pruned_loss=0.0305, over 7352.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2629, pruned_loss=0.04289, over 1419189.94 frames.], batch size: 19, lr: 1.73e-04
2022-05-29 03:39:29,631 INFO [train.py:842] (1/4) Epoch 32, batch 2000, loss[loss=0.1677, simple_loss=0.2491, pruned_loss=0.04313, over 7283.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2628, pruned_loss=0.04273, over 1419939.72 frames.], batch size: 18, lr: 1.73e-04
2022-05-29 03:40:08,868 INFO [train.py:842] (1/4) Epoch 32, batch 2050, loss[loss=0.1466, simple_loss=0.2451, pruned_loss=0.02405, over 7140.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2626, pruned_loss=0.04305, over 1417134.65 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:40:48,278 INFO [train.py:842] (1/4) Epoch 32, batch 2100, loss[loss=0.1369, simple_loss=0.2282, pruned_loss=0.02281, over 6825.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2639, pruned_loss=0.04346, over 1417056.90 frames.], batch size: 15, lr: 1.73e-04
2022-05-29 03:41:27,448 INFO [train.py:842] (1/4) Epoch 32, batch 2150, loss[loss=0.1561, simple_loss=0.2572, pruned_loss=0.02745, over 7224.00 frames.], tot_loss[loss=0.175, simple_loss=0.2637, pruned_loss=0.04315, over 1421115.30 frames.], batch size: 21, lr: 1.73e-04
2022-05-29 03:42:07,274 INFO [train.py:842] (1/4) Epoch 32, batch 2200, loss[loss=0.209, simple_loss=0.2945, pruned_loss=0.06175, over 7162.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2626, pruned_loss=0.0428, over 1423520.38 frames.], batch size: 26, lr: 1.73e-04
2022-05-29 03:42:46,577 INFO [train.py:842] (1/4) Epoch 32, batch 2250, loss[loss=0.2512, simple_loss=0.3202, pruned_loss=0.09114, over 7061.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2631, pruned_loss=0.04275, over 1425089.41 frames.], batch size: 18, lr: 1.73e-04
2022-05-29 03:43:25,963 INFO [train.py:842] (1/4) Epoch 32, batch 2300, loss[loss=0.1562, simple_loss=0.2479, pruned_loss=0.03229, over 7333.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2623, pruned_loss=0.04264, over 1421563.12 frames.], batch size: 22, lr: 1.73e-04
2022-05-29 03:44:05,272 INFO [train.py:842] (1/4) Epoch 32, batch 2350, loss[loss=0.145, simple_loss=0.2244, pruned_loss=0.0328, over 7296.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2623, pruned_loss=0.0424, over 1425594.24 frames.], batch size: 17, lr: 1.73e-04
2022-05-29 03:44:44,568 INFO [train.py:842] (1/4) Epoch 32, batch 2400, loss[loss=0.1615, simple_loss=0.2476, pruned_loss=0.03776, over 7337.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2623, pruned_loss=0.04233, over 1420411.77 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:45:24,047 INFO [train.py:842] (1/4) Epoch 32, batch 2450, loss[loss=0.1805, simple_loss=0.2714, pruned_loss=0.04479, over 7218.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2621, pruned_loss=0.04249, over 1421259.91 frames.], batch size: 26, lr: 1.72e-04
2022-05-29 03:46:03,776 INFO [train.py:842] (1/4) Epoch 32, batch 2500, loss[loss=0.1416, simple_loss=0.2293, pruned_loss=0.02691, over 7276.00 frames.], tot_loss[loss=0.173, simple_loss=0.2617, pruned_loss=0.04209, over 1423374.29 frames.], batch size: 17, lr: 1.72e-04
2022-05-29 03:46:43,186 INFO [train.py:842] (1/4) Epoch 32, batch 2550, loss[loss=0.1524, simple_loss=0.2458, pruned_loss=0.02954, over 7342.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2623, pruned_loss=0.04247, over 1422382.95 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:47:22,982 INFO [train.py:842] (1/4) Epoch 32, batch 2600, loss[loss=0.1483, simple_loss=0.2355, pruned_loss=0.03053, over 7129.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2612, pruned_loss=0.04208, over 1421176.20 frames.], batch size: 17, lr: 1.72e-04
2022-05-29 03:48:02,149 INFO [train.py:842] (1/4) Epoch 32, batch 2650, loss[loss=0.1929, simple_loss=0.2757, pruned_loss=0.05501, over 7221.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2609, pruned_loss=0.04178, over 1423868.58 frames.], batch size: 26, lr: 1.72e-04
2022-05-29 03:48:41,738 INFO [train.py:842] (1/4) Epoch 32, batch 2700, loss[loss=0.1568, simple_loss=0.254, pruned_loss=0.02976, over 7325.00 frames.], tot_loss[loss=0.173, simple_loss=0.2617, pruned_loss=0.04217, over 1423388.47 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:49:21,045 INFO [train.py:842] (1/4) Epoch 32, batch 2750, loss[loss=0.1734, simple_loss=0.2647, pruned_loss=0.04103, over 7097.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2617, pruned_loss=0.04183, over 1424994.98 frames.], batch size: 28, lr: 1.72e-04
2022-05-29 03:50:00,720 INFO [train.py:842] (1/4) Epoch 32, batch 2800, loss[loss=0.1651, simple_loss=0.2445, pruned_loss=0.04287, over 7416.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2605, pruned_loss=0.04145, over 1424277.06 frames.], batch size: 18, lr: 1.72e-04
2022-05-29 03:50:40,063 INFO [train.py:842] (1/4) Epoch 32, batch 2850, loss[loss=0.1805, simple_loss=0.2801, pruned_loss=0.04045, over 6429.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2609, pruned_loss=0.04184, over 1421275.70 frames.], batch size: 37, lr: 1.72e-04
2022-05-29 03:51:19,928 INFO [train.py:842] (1/4) Epoch 32, batch 2900, loss[loss=0.1853, simple_loss=0.2751, pruned_loss=0.0477, over 7228.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2623, pruned_loss=0.04251, over 1425400.43 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:51:58,839 INFO [train.py:842] (1/4) Epoch 32, batch 2950, loss[loss=0.1933, simple_loss=0.2773, pruned_loss=0.05467, over 7213.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2631, pruned_loss=0.0428, over 1417291.89 frames.], batch size: 23, lr: 1.72e-04
2022-05-29 03:52:38,224 INFO [train.py:842] (1/4) Epoch 32, batch 3000, loss[loss=0.2302, simple_loss=0.3108, pruned_loss=0.07483, over 7425.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2647, pruned_loss=0.04344, over 1418237.73 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:52:38,225 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 03:52:48,106 INFO [train.py:871] (1/4) Epoch 32, validation: loss=0.1619, simple_loss=0.2592, pruned_loss=0.03236, over 868885.00 frames.
2022-05-29 03:53:27,727 INFO [train.py:842] (1/4) Epoch 32, batch 3050, loss[loss=0.1906, simple_loss=0.2861, pruned_loss=0.04757, over 7281.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2644, pruned_loss=0.04319, over 1422643.98 frames.], batch size: 25, lr: 1.72e-04
2022-05-29 03:54:10,058 INFO [train.py:842] (1/4) Epoch 32, batch 3100, loss[loss=0.1615, simple_loss=0.2431, pruned_loss=0.0399, over 7092.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2642, pruned_loss=0.04302, over 1426415.26 frames.], batch size: 28, lr: 1.72e-04
2022-05-29 03:54:49,461 INFO [train.py:842] (1/4) Epoch 32, batch 3150, loss[loss=0.1525, simple_loss=0.2344, pruned_loss=0.03527, over 7273.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2632, pruned_loss=0.0427, over 1424425.89 frames.], batch size: 17, lr: 1.72e-04
2022-05-29 03:55:28,987 INFO [train.py:842] (1/4) Epoch 32, batch 3200, loss[loss=0.1736, simple_loss=0.2733, pruned_loss=0.03695, over 7115.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2625, pruned_loss=0.0421, over 1426552.54 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 03:56:08,329 INFO [train.py:842] (1/4) Epoch 32, batch 3250, loss[loss=0.1765, simple_loss=0.2677, pruned_loss=0.04263, over 7324.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2631, pruned_loss=0.04234, over 1427801.86 frames.], batch size: 22, lr: 1.72e-04
2022-05-29 03:56:47,744 INFO [train.py:842] (1/4) Epoch 32, batch 3300, loss[loss=0.186, simple_loss=0.2788, pruned_loss=0.04663, over 7428.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2626, pruned_loss=0.0421, over 1423985.71 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:57:27,361 INFO [train.py:842] (1/4) Epoch 32, batch 3350, loss[loss=0.1731, simple_loss=0.2723, pruned_loss=0.03697, over 7335.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2616, pruned_loss=0.04203, over 1425791.16 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 03:58:06,801 INFO [train.py:842] (1/4) Epoch 32, batch 3400, loss[loss=0.1562, simple_loss=0.2482, pruned_loss=0.03213, over 7322.00 frames.], tot_loss[loss=0.1738, simple_loss=0.263, pruned_loss=0.04233, over 1422771.38 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:58:45,713 INFO [train.py:842] (1/4) Epoch 32, batch 3450, loss[loss=0.2015, simple_loss=0.2906, pruned_loss=0.05627, over 7202.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2638, pruned_loss=0.04248, over 1425354.50 frames.], batch size: 22, lr: 1.72e-04
2022-05-29 03:59:25,409 INFO [train.py:842] (1/4) Epoch 32, batch 3500, loss[loss=0.1657, simple_loss=0.2619, pruned_loss=0.03472, over 7278.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2649, pruned_loss=0.04346, over 1429037.84 frames.], batch size: 24, lr: 1.72e-04
2022-05-29 04:00:04,732 INFO [train.py:842] (1/4) Epoch 32, batch 3550, loss[loss=0.1825, simple_loss=0.2789, pruned_loss=0.04306, over 7371.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2645, pruned_loss=0.04355, over 1431439.59 frames.], batch size: 23, lr: 1.72e-04
2022-05-29 04:00:44,307 INFO [train.py:842] (1/4) Epoch 32, batch 3600, loss[loss=0.1776, simple_loss=0.2636, pruned_loss=0.04582, over 6319.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2625, pruned_loss=0.04241, over 1428059.01 frames.], batch size: 38, lr: 1.72e-04
2022-05-29 04:01:23,423 INFO [train.py:842] (1/4) Epoch 32, batch 3650, loss[loss=0.1785, simple_loss=0.2593, pruned_loss=0.04888, over 7232.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2637, pruned_loss=0.04269, over 1428119.59 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:02:03,201 INFO [train.py:842] (1/4) Epoch 32, batch 3700, loss[loss=0.1496, simple_loss=0.236, pruned_loss=0.0316, over 7139.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2624, pruned_loss=0.04243, over 1430205.01 frames.], batch size: 17, lr: 1.72e-04
2022-05-29 04:02:41,995 INFO [train.py:842] (1/4) Epoch 32, batch 3750, loss[loss=0.2114, simple_loss=0.2847, pruned_loss=0.06907, over 7204.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2625, pruned_loss=0.04241, over 1424646.47 frames.], batch size: 23, lr: 1.72e-04
2022-05-29 04:03:21,673 INFO [train.py:842] (1/4) Epoch 32, batch 3800, loss[loss=0.2116, simple_loss=0.2975, pruned_loss=0.06282, over 7373.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2625, pruned_loss=0.0421, over 1426187.71 frames.], batch size: 23, lr: 1.72e-04
2022-05-29 04:04:01,025 INFO [train.py:842] (1/4) Epoch 32, batch 3850, loss[loss=0.1705, simple_loss=0.2535, pruned_loss=0.04375, over 7434.00 frames.], tot_loss[loss=0.1733, simple_loss=0.262, pruned_loss=0.04232, over 1428372.95 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:04:40,463 INFO [train.py:842] (1/4) Epoch 32, batch 3900, loss[loss=0.1831, simple_loss=0.2598, pruned_loss=0.05317, over 7157.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2619, pruned_loss=0.0423, over 1429665.33 frames.], batch size: 18, lr: 1.72e-04
2022-05-29 04:05:19,793 INFO [train.py:842] (1/4) Epoch 32, batch 3950, loss[loss=0.1637, simple_loss=0.2588, pruned_loss=0.03434, over 7225.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2631, pruned_loss=0.04299, over 1425431.09 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 04:05:59,346 INFO [train.py:842] (1/4) Epoch 32, batch 4000, loss[loss=0.1529, simple_loss=0.2378, pruned_loss=0.03405, over 7401.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2622, pruned_loss=0.04268, over 1421150.34 frames.], batch size: 18, lr: 1.72e-04
2022-05-29 04:06:38,590 INFO [train.py:842] (1/4) Epoch 32, batch 4050, loss[loss=0.1534, simple_loss=0.2472, pruned_loss=0.02973, over 7363.00 frames.], tot_loss[loss=0.174, simple_loss=0.2625, pruned_loss=0.04274, over 1418806.65 frames.], batch size: 23, lr: 1.72e-04
2022-05-29 04:07:17,980 INFO [train.py:842] (1/4) Epoch 32, batch 4100, loss[loss=0.1837, simple_loss=0.2758, pruned_loss=0.04581, over 7140.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2641, pruned_loss=0.04338, over 1419417.98 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:07:57,046 INFO [train.py:842] (1/4) Epoch 32, batch 4150, loss[loss=0.2143, simple_loss=0.298, pruned_loss=0.06533, over 6797.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2648, pruned_loss=0.04348, over 1422350.77 frames.], batch size: 31, lr: 1.72e-04
2022-05-29 04:08:36,788 INFO [train.py:842] (1/4) Epoch 32, batch 4200, loss[loss=0.1652, simple_loss=0.2498, pruned_loss=0.04029, over 7259.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2629, pruned_loss=0.04244, over 1424996.73 frames.], batch size: 24, lr: 1.72e-04
2022-05-29 04:09:15,977 INFO [train.py:842] (1/4) Epoch 32, batch 4250, loss[loss=0.2113, simple_loss=0.2947, pruned_loss=0.06392, over 7226.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2646, pruned_loss=0.04355, over 1420905.05 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:09:55,676 INFO [train.py:842] (1/4) Epoch 32, batch 4300, loss[loss=0.1668, simple_loss=0.2655, pruned_loss=0.03405, over 7147.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2635, pruned_loss=0.04281, over 1423522.85 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:10:34,807 INFO [train.py:842] (1/4) Epoch 32, batch 4350, loss[loss=0.2354, simple_loss=0.3181, pruned_loss=0.07637, over 6383.00 frames.], tot_loss[loss=0.175, simple_loss=0.2637, pruned_loss=0.04316, over 1425016.92 frames.], batch size: 37, lr: 1.72e-04
2022-05-29 04:11:14,396 INFO [train.py:842] (1/4) Epoch 32, batch 4400, loss[loss=0.1529, simple_loss=0.2488, pruned_loss=0.02851, over 7344.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2634, pruned_loss=0.04286, over 1425488.12 frames.], batch size: 22, lr: 1.72e-04
2022-05-29 04:11:53,984 INFO [train.py:842] (1/4) Epoch 32, batch 4450, loss[loss=0.1718, simple_loss=0.2646, pruned_loss=0.03955, over 7263.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2626, pruned_loss=0.04233, over 1429672.12 frames.], batch size: 19, lr: 1.72e-04
2022-05-29 04:12:33,486 INFO [train.py:842] (1/4) Epoch 32, batch 4500, loss[loss=0.2135, simple_loss=0.3137, pruned_loss=0.05668, over 7115.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2631, pruned_loss=0.04275, over 1424277.47 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 04:13:12,652 INFO [train.py:842] (1/4) Epoch 32, batch 4550, loss[loss=0.1993, simple_loss=0.2886, pruned_loss=0.05498, over 7321.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2627, pruned_loss=0.04251, over 1415689.79 frames.], batch size: 22, lr: 1.72e-04
2022-05-29 04:13:52,353 INFO [train.py:842] (1/4) Epoch 32, batch 4600, loss[loss=0.1482, simple_loss=0.2302, pruned_loss=0.03308, over 7000.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2624, pruned_loss=0.04239, over 1419214.68 frames.], batch size: 16, lr: 1.72e-04
2022-05-29 04:14:31,880 INFO [train.py:842] (1/4) Epoch 32, batch 4650, loss[loss=0.1514, simple_loss=0.2428, pruned_loss=0.03002, over 7219.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2629, pruned_loss=0.04233, over 1424250.39 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 04:15:11,520 INFO [train.py:842] (1/4) Epoch 32, batch 4700, loss[loss=0.1849, simple_loss=0.2694, pruned_loss=0.05016, over 7240.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2632, pruned_loss=0.04268, over 1424877.97 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:15:51,124 INFO [train.py:842] (1/4) Epoch 32, batch 4750, loss[loss=0.1798, simple_loss=0.2689, pruned_loss=0.0454, over 7204.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2622, pruned_loss=0.0421, over 1423056.54 frames.], batch size: 22, lr: 1.72e-04
2022-05-29 04:16:30,630 INFO [train.py:842] (1/4) Epoch 32, batch 4800, loss[loss=0.1655, simple_loss=0.2546, pruned_loss=0.03818, over 7324.00 frames.], tot_loss[loss=0.173, simple_loss=0.2619, pruned_loss=0.04201, over 1419493.98 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 04:17:09,850 INFO [train.py:842] (1/4) Epoch 32, batch 4850, loss[loss=0.1959, simple_loss=0.29, pruned_loss=0.05096, over 7231.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2616, pruned_loss=0.04167, over 1419736.29 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:17:49,585 INFO [train.py:842] (1/4) Epoch 32, batch 4900, loss[loss=0.1783, simple_loss=0.2775, pruned_loss=0.03951, over 7284.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2625, pruned_loss=0.04245, over 1421683.58 frames.], batch size: 25, lr: 1.72e-04
2022-05-29 04:18:28,876 INFO [train.py:842] (1/4) Epoch 32, batch 4950, loss[loss=0.1425, simple_loss=0.2334, pruned_loss=0.02582, over 7424.00 frames.], tot_loss[loss=0.174, simple_loss=0.263, pruned_loss=0.04256, over 1425430.18 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:19:08,238 INFO [train.py:842] (1/4) Epoch 32, batch 5000, loss[loss=0.1934, simple_loss=0.2898, pruned_loss=0.04849, over 6641.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2646, pruned_loss=0.04306, over 1423174.71 frames.], batch size: 31, lr: 1.72e-04
2022-05-29 04:19:47,596 INFO [train.py:842] (1/4) Epoch 32, batch 5050, loss[loss=0.1311, simple_loss=0.2167, pruned_loss=0.02272, over 7262.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2636, pruned_loss=0.04282, over 1423153.50 frames.], batch size: 18, lr: 1.72e-04
2022-05-29 04:20:27,283 INFO [train.py:842] (1/4) Epoch 32, batch 5100, loss[loss=0.1736, simple_loss=0.2679, pruned_loss=0.03968, over 7318.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2628, pruned_loss=0.04223, over 1420762.34 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 04:21:06,330 INFO [train.py:842] (1/4) Epoch 32, batch 5150, loss[loss=0.1692, simple_loss=0.2646, pruned_loss=0.03687, over 7071.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2629, pruned_loss=0.042, over 1417298.86 frames.], batch size: 18, lr: 1.72e-04
2022-05-29 04:21:45,898 INFO [train.py:842] (1/4) Epoch 32, batch 5200, loss[loss=0.1568, simple_loss=0.2352, pruned_loss=0.03916, over 7266.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2625, pruned_loss=0.04211, over 1419400.31 frames.], batch size: 17, lr: 1.72e-04
2022-05-29 04:22:25,263 INFO [train.py:842] (1/4) Epoch 32, batch 5250, loss[loss=0.1991, simple_loss=0.2672, pruned_loss=0.06552, over 6993.00 frames.], tot_loss[loss=0.1731, simple_loss=0.262, pruned_loss=0.04212, over 1419852.41 frames.], batch size: 16, lr: 1.72e-04
2022-05-29 04:23:04,885 INFO [train.py:842] (1/4) Epoch 32, batch 5300, loss[loss=0.1476, simple_loss=0.2441, pruned_loss=0.02561, over 7239.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2623, pruned_loss=0.04292, over 1422200.86 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:23:43,916 INFO [train.py:842] (1/4) Epoch 32, batch 5350, loss[loss=0.1882, simple_loss=0.2834, pruned_loss=0.04649, over 7380.00 frames.], tot_loss[loss=0.1748, simple_loss=0.263, pruned_loss=0.04331, over 1425130.02 frames.], batch size: 23, lr: 1.72e-04
2022-05-29 04:24:23,435 INFO [train.py:842] (1/4) Epoch 32, batch 5400, loss[loss=0.1661, simple_loss=0.261, pruned_loss=0.03564, over 7148.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2637, pruned_loss=0.04354, over 1426615.65 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:25:13,708 INFO [train.py:842] (1/4) Epoch 32, batch 5450, loss[loss=0.1716, simple_loss=0.2417, pruned_loss=0.05075, over 7234.00 frames.], tot_loss[loss=0.1748, simple_loss=0.263, pruned_loss=0.04334, over 1429260.30 frames.], batch size: 16, lr: 1.72e-04
2022-05-29 04:25:53,346 INFO [train.py:842] (1/4) Epoch 32, batch 5500, loss[loss=0.1404, simple_loss=0.2276, pruned_loss=0.0266, over 7202.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2639, pruned_loss=0.04347, over 1426613.26 frames.], batch size: 22, lr: 1.72e-04
2022-05-29 04:26:32,573 INFO [train.py:842] (1/4) Epoch 32, batch 5550, loss[loss=0.1814, simple_loss=0.2775, pruned_loss=0.04258, over 7420.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2628, pruned_loss=0.04273, over 1426873.28 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 04:27:12,132 INFO [train.py:842] (1/4) Epoch 32, batch 5600, loss[loss=0.2533, simple_loss=0.3261, pruned_loss=0.0903, over 5233.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2622, pruned_loss=0.04208, over 1426842.46 frames.], batch size: 52, lr: 1.72e-04
2022-05-29 04:27:51,464 INFO [train.py:842] (1/4) Epoch 32, batch 5650, loss[loss=0.1901, simple_loss=0.2744, pruned_loss=0.0529, over 7321.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2634, pruned_loss=0.043, over 1427288.40 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:28:41,643 INFO [train.py:842] (1/4) Epoch 32, batch 5700, loss[loss=0.189, simple_loss=0.2675, pruned_loss=0.05523, over 7009.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2634, pruned_loss=0.0429, over 1421770.92 frames.], batch size: 16, lr: 1.72e-04
2022-05-29 04:29:21,081 INFO [train.py:842] (1/4) Epoch 32, batch 5750, loss[loss=0.1991, simple_loss=0.2735, pruned_loss=0.06237, over 7072.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2636, pruned_loss=0.04327, over 1423422.12 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 04:30:11,322 INFO [train.py:842] (1/4) Epoch 32, batch 5800, loss[loss=0.1546, simple_loss=0.2387, pruned_loss=0.03524, over 7325.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2639, pruned_loss=0.04333, over 1419868.22 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:30:50,462 INFO [train.py:842] (1/4) Epoch 32, batch 5850, loss[loss=0.177, simple_loss=0.2557, pruned_loss=0.0491, over 7360.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2643, pruned_loss=0.0435, over 1418753.17 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:31:30,317 INFO [train.py:842] (1/4) Epoch 32, batch 5900, loss[loss=0.1857, simple_loss=0.2733, pruned_loss=0.04908, over 7286.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2629, pruned_loss=0.04269, over 1424288.40 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 04:32:09,712 INFO [train.py:842] (1/4) Epoch 32, batch 5950, loss[loss=0.1832, simple_loss=0.2748, pruned_loss=0.04578, over 7225.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2612, pruned_loss=0.04207, over 1421811.98 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:32:49,017 INFO [train.py:842] (1/4) Epoch 32, batch 6000, loss[loss=0.145, simple_loss=0.2262, pruned_loss=0.03191, over 6987.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2612, pruned_loss=0.04248, over 1416606.77 frames.], batch size: 16, lr: 1.71e-04
2022-05-29 04:32:49,019 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 04:32:58,750 INFO [train.py:871] (1/4) Epoch 32, validation: loss=0.1638, simple_loss=0.2607, pruned_loss=0.0334, over 868885.00 frames.
2022-05-29 04:33:38,119 INFO [train.py:842] (1/4) Epoch 32, batch 6050, loss[loss=0.1821, simple_loss=0.2579, pruned_loss=0.05321, over 7293.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2619, pruned_loss=0.04241, over 1419859.45 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 04:34:17,901 INFO [train.py:842] (1/4) Epoch 32, batch 6100, loss[loss=0.163, simple_loss=0.2494, pruned_loss=0.03829, over 7431.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2623, pruned_loss=0.04256, over 1421079.77 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:34:57,145 INFO [train.py:842] (1/4) Epoch 32, batch 6150, loss[loss=0.203, simple_loss=0.3032, pruned_loss=0.05137, over 7300.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2615, pruned_loss=0.04186, over 1419438.24 frames.], batch size: 25, lr: 1.71e-04
2022-05-29 04:35:36,532 INFO [train.py:842] (1/4) Epoch 32, batch 6200, loss[loss=0.1892, simple_loss=0.2963, pruned_loss=0.04101, over 7418.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2621, pruned_loss=0.04171, over 1422152.98 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 04:36:16,136 INFO [train.py:842] (1/4) Epoch 32, batch 6250, loss[loss=0.1606, simple_loss=0.2474, pruned_loss=0.0369, over 7403.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2613, pruned_loss=0.04119, over 1425213.17 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 04:36:55,657 INFO [train.py:842] (1/4) Epoch 32, batch 6300, loss[loss=0.1787, simple_loss=0.2612, pruned_loss=0.04808, over 7066.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2622, pruned_loss=0.04175, over 1424024.58 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 04:37:34,965 INFO [train.py:842] (1/4) Epoch 32, batch 6350, loss[loss=0.1382, simple_loss=0.2275, pruned_loss=0.02449, over 7354.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2621, pruned_loss=0.04178, over 1423920.40 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:38:14,618 INFO [train.py:842] (1/4) Epoch 32, batch 6400, loss[loss=0.1786, simple_loss=0.2606, pruned_loss=0.04833, over 7309.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2619, pruned_loss=0.04162, over 1423163.29 frames.], batch size: 25, lr: 1.71e-04
2022-05-29 04:38:53,829 INFO [train.py:842] (1/4) Epoch 32, batch 6450, loss[loss=0.152, simple_loss=0.2428, pruned_loss=0.03059, over 7442.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2623, pruned_loss=0.0421, over 1424925.84 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:39:33,322 INFO [train.py:842] (1/4) Epoch 32, batch 6500, loss[loss=0.1738, simple_loss=0.266, pruned_loss=0.04082, over 7294.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2629, pruned_loss=0.04218, over 1423867.64 frames.], batch size: 24, lr: 1.71e-04
2022-05-29 04:40:12,624 INFO [train.py:842] (1/4) Epoch 32, batch 6550, loss[loss=0.1747, simple_loss=0.2471, pruned_loss=0.05112, over 7426.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2627, pruned_loss=0.04256, over 1420688.74 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 04:40:52,219 INFO [train.py:842] (1/4) Epoch 32, batch 6600, loss[loss=0.1651, simple_loss=0.2539, pruned_loss=0.03817, over 6713.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2625, pruned_loss=0.04255, over 1422100.44 frames.], batch size: 31, lr: 1.71e-04
2022-05-29 04:41:31,522 INFO [train.py:842] (1/4) Epoch 32, batch 6650, loss[loss=0.1576, simple_loss=0.2496, pruned_loss=0.03279, over 7296.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2616, pruned_loss=0.04228, over 1420160.51 frames.], batch size: 25, lr: 1.71e-04
2022-05-29 04:42:11,107 INFO [train.py:842] (1/4) Epoch 32, batch 6700, loss[loss=0.184, simple_loss=0.2619, pruned_loss=0.05302, over 7155.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2621, pruned_loss=0.04249, over 1419753.53 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:42:50,478 INFO [train.py:842] (1/4) Epoch 32, batch 6750, loss[loss=0.204, simple_loss=0.2884, pruned_loss=0.05983, over 7163.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2626, pruned_loss=0.04295, over 1419604.98 frames.], batch size: 26, lr: 1.71e-04
2022-05-29 04:43:30,256 INFO [train.py:842] (1/4) Epoch 32, batch 6800, loss[loss=0.1882, simple_loss=0.2856, pruned_loss=0.0454, over 7226.00 frames.], tot_loss[loss=0.1732, simple_loss=0.262, pruned_loss=0.04221, over 1425139.99 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:44:09,498 INFO [train.py:842] (1/4) Epoch 32, batch 6850, loss[loss=0.1563, simple_loss=0.2437, pruned_loss=0.03443, over 7268.00 frames.], tot_loss[loss=0.173, simple_loss=0.2617, pruned_loss=0.0422, over 1424792.37 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:44:48,816 INFO [train.py:842] (1/4) Epoch 32, batch 6900, loss[loss=0.1499, simple_loss=0.2403, pruned_loss=0.02972, over 7232.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2614, pruned_loss=0.04219, over 1425656.21 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:45:28,012 INFO [train.py:842] (1/4) Epoch 32, batch 6950, loss[loss=0.1755, simple_loss=0.2793, pruned_loss=0.03583, over 7145.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2634, pruned_loss=0.04298, over 1427901.12 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:46:07,549 INFO [train.py:842] (1/4) Epoch 32, batch 7000, loss[loss=0.1559, simple_loss=0.2476, pruned_loss=0.03213, over 7150.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2642, pruned_loss=0.04341, over 1426375.62 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:46:46,442 INFO [train.py:842] (1/4) Epoch 32, batch 7050, loss[loss=0.1629, simple_loss=0.2594, pruned_loss=0.03321, over 7200.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2642, pruned_loss=0.04313, over 1426718.09 frames.], batch size: 23, lr: 1.71e-04
2022-05-29 04:47:25,845 INFO [train.py:842] (1/4) Epoch 32, batch 7100, loss[loss=0.1691, simple_loss=0.2626, pruned_loss=0.03778, over 7215.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2638, pruned_loss=0.04295, over 1426070.19 frames.], batch size: 22, lr: 1.71e-04
2022-05-29 04:48:04,913 INFO [train.py:842] (1/4) Epoch 32, batch 7150, loss[loss=0.1839, simple_loss=0.2733, pruned_loss=0.04725, over 6636.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2639, pruned_loss=0.04299, over 1422731.11 frames.], batch size: 31, lr: 1.71e-04
2022-05-29 04:48:44,502 INFO [train.py:842] (1/4) Epoch 32, batch 7200, loss[loss=0.1996, simple_loss=0.289, pruned_loss=0.05512, over 7234.00 frames.], tot_loss[loss=0.175, simple_loss=0.2641, pruned_loss=0.04296, over 1425207.83 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 04:49:23,960 INFO [train.py:842] (1/4) Epoch 32, batch 7250, loss[loss=0.1563, simple_loss=0.2544, pruned_loss=0.02907, over 7415.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2637, pruned_loss=0.04256, over 1429725.96 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 04:50:03,694 INFO [train.py:842] (1/4) Epoch 32, batch 7300, loss[loss=0.1676, simple_loss=0.2611, pruned_loss=0.03703, over 7419.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2626, pruned_loss=0.04207, over 1432056.19 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 04:50:43,023 INFO [train.py:842] (1/4) Epoch 32, batch 7350, loss[loss=0.1748, simple_loss=0.2561, pruned_loss=0.04679, over 7361.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2621, pruned_loss=0.04179, over 1430676.05 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:51:22,688 INFO [train.py:842] (1/4) Epoch 32, batch 7400, loss[loss=0.205, simple_loss=0.2944, pruned_loss=0.05781, over 4871.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2627, pruned_loss=0.0421, over 1426511.44 frames.], batch size: 52, lr: 1.71e-04
2022-05-29 04:52:02,058 INFO [train.py:842] (1/4) Epoch 32, batch 7450, loss[loss=0.185, simple_loss=0.274, pruned_loss=0.04796, over 7291.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2629, pruned_loss=0.04219, over 1428617.67 frames.], batch size: 24, lr: 1.71e-04
2022-05-29 04:52:41,538 INFO [train.py:842] (1/4) Epoch 32, batch 7500, loss[loss=0.2847, simple_loss=0.3504, pruned_loss=0.1095, over 7333.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2633, pruned_loss=0.04243, over 1427379.85 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:53:20,806 INFO [train.py:842] (1/4) Epoch 32, batch 7550, loss[loss=0.1759, simple_loss=0.275, pruned_loss=0.03843, over 7311.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2631, pruned_loss=0.0426, over 1426327.39 frames.], batch size: 24, lr: 1.71e-04
2022-05-29 04:54:00,349 INFO [train.py:842] (1/4) Epoch 32, batch 7600, loss[loss=0.1526, simple_loss=0.2441, pruned_loss=0.03051, over 7353.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2635, pruned_loss=0.04263, over 1424244.44 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:54:39,797 INFO [train.py:842] (1/4) Epoch 32, batch 7650, loss[loss=0.1681, simple_loss=0.2636, pruned_loss=0.03628, over 7238.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2628, pruned_loss=0.0424, over 1426122.25 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:55:19,475 INFO [train.py:842] (1/4) Epoch 32, batch 7700, loss[loss=0.1877, simple_loss=0.2708, pruned_loss=0.05229, over 7298.00 frames.], tot_loss[loss=0.1743, simple_loss=0.263, pruned_loss=0.04279, over 1427691.72 frames.], batch size: 24, lr: 1.71e-04
2022-05-29 04:55:58,606 INFO [train.py:842] (1/4) Epoch 32, batch 7750, loss[loss=0.1463, simple_loss=0.2428, pruned_loss=0.02489, over 7038.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2633, pruned_loss=0.04288, over 1422373.14 frames.], batch size: 28, lr: 1.71e-04
2022-05-29 04:56:38,200 INFO [train.py:842] (1/4) Epoch 32, batch 7800, loss[loss=0.2813, simple_loss=0.3542, pruned_loss=0.1042, over 6359.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2634, pruned_loss=0.04326, over 1421931.41 frames.], batch size: 37, lr: 1.71e-04
2022-05-29 04:57:17,412 INFO [train.py:842] (1/4) Epoch 32, batch 7850, loss[loss=0.1433, simple_loss=0.2239, pruned_loss=0.0314, over 7154.00 frames.], tot_loss[loss=0.174, simple_loss=0.2623, pruned_loss=0.04282, over 1421651.24 frames.], batch size: 17, lr: 1.71e-04
2022-05-29 04:57:56,956 INFO [train.py:842] (1/4) Epoch 32, batch 7900, loss[loss=0.1904, simple_loss=0.2793, pruned_loss=0.05071, over 7236.00 frames.], tot_loss[loss=0.175, simple_loss=0.2632, pruned_loss=0.0434, over 1421889.57 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:58:36,028 INFO [train.py:842] (1/4) Epoch 32, batch 7950, loss[loss=0.1693, simple_loss=0.2579, pruned_loss=0.0404, over 6682.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2634, pruned_loss=0.04325, over 1424343.13 frames.], batch size: 31, lr: 1.71e-04
2022-05-29 04:59:15,413 INFO [train.py:842] (1/4) Epoch 32, batch 8000, loss[loss=0.1752, simple_loss=0.2669, pruned_loss=0.04178, over 7328.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2629, pruned_loss=0.04323, over 1424789.38 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:59:54,729 INFO [train.py:842] (1/4) Epoch 32, batch 8050, loss[loss=0.1666, simple_loss=0.2489, pruned_loss=0.04214, over 7415.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2636, pruned_loss=0.04366, over 1422285.39 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 05:00:34,442 INFO [train.py:842] (1/4) Epoch 32, batch 8100, loss[loss=0.1727, simple_loss=0.2449, pruned_loss=0.05027, over 6753.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2631, pruned_loss=0.04334, over 1421258.25 frames.], batch size: 15, lr: 1.71e-04
2022-05-29 05:01:13,689 INFO [train.py:842] (1/4) Epoch 32, batch 8150, loss[loss=0.1805, simple_loss=0.2782, pruned_loss=0.04142, over 7171.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2643, pruned_loss=0.04376, over 1423302.58 frames.], batch size: 26, lr: 1.71e-04
2022-05-29 05:01:53,209 INFO [train.py:842] (1/4) Epoch 32, batch 8200, loss[loss=0.204, simple_loss=0.297, pruned_loss=0.05547, over 7218.00 frames.], tot_loss[loss=0.1755, simple_loss=0.264, pruned_loss=0.04356, over 1422154.81 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 05:02:32,246 INFO [train.py:842] (1/4) Epoch 32, batch 8250, loss[loss=0.1944, simple_loss=0.2966, pruned_loss=0.0461, over 6271.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2635, pruned_loss=0.0431, over 1420768.01 frames.], batch size: 37, lr: 1.71e-04
2022-05-29 05:03:11,962 INFO [train.py:842] (1/4) Epoch 32, batch 8300, loss[loss=0.1359, simple_loss=0.2271, pruned_loss=0.0223, over 7056.00 frames.], tot_loss[loss=0.1746, simple_loss=0.263, pruned_loss=0.04304, over 1424292.88 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 05:03:51,010 INFO [train.py:842] (1/4) Epoch 32, batch 8350, loss[loss=0.1781, simple_loss=0.2738, pruned_loss=0.04123, over 7110.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2642, pruned_loss=0.04327, over 1426177.99 frames.], batch size: 28, lr: 1.71e-04
2022-05-29 05:04:30,237 INFO [train.py:842] (1/4) Epoch 32, batch 8400, loss[loss=0.1446, simple_loss=0.2273, pruned_loss=0.03097, over 7160.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2642, pruned_loss=0.04328, over 1422597.68 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 05:05:09,521 INFO [train.py:842] (1/4) Epoch 32, batch 8450, loss[loss=0.1572, simple_loss=0.2445, pruned_loss=0.03497, over 7288.00 frames.], tot_loss[loss=0.175, simple_loss=0.2634, pruned_loss=0.04334, over 1419659.56 frames.], batch size: 17, lr: 1.71e-04
2022-05-29 05:05:49,195 INFO [train.py:842] (1/4) Epoch 32, batch 8500, loss[loss=0.1645, simple_loss=0.2503, pruned_loss=0.03934, over 7068.00 frames.], tot_loss[loss=0.1748, simple_loss=0.263, pruned_loss=0.04332, over 1419702.11 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 05:06:28,121 INFO [train.py:842] (1/4) Epoch 32, batch 8550, loss[loss=0.1815, simple_loss=0.2753, pruned_loss=0.04387, over 7096.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2628, pruned_loss=0.04335, over 1419320.81 frames.], batch size: 28, lr: 1.71e-04
2022-05-29 05:07:07,472 INFO [train.py:842] (1/4) Epoch 32, batch 8600, loss[loss=0.1792, simple_loss=0.2669, pruned_loss=0.04578, over 7218.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2621, pruned_loss=0.04233, over 1416016.36 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 05:07:46,516 INFO [train.py:842] (1/4) Epoch 32, batch 8650, loss[loss=0.1821, simple_loss=0.2685, pruned_loss=0.04788, over 7237.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2623, pruned_loss=0.04204, over 1420148.08 frames.], batch size: 16, lr: 1.71e-04
2022-05-29 05:08:26,010 INFO [train.py:842] (1/4) Epoch 32, batch 8700, loss[loss=0.146, simple_loss=0.2347, pruned_loss=0.0287, over 7424.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2629, pruned_loss=0.04226, over 1423966.28 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 05:09:05,101 INFO [train.py:842] (1/4) Epoch 32, batch 8750, loss[loss=0.1951, simple_loss=0.2826, pruned_loss=0.05378, over 5249.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2632, pruned_loss=0.04293, over 1417838.26 frames.], batch size: 53, lr: 1.71e-04
2022-05-29 05:09:44,404 INFO [train.py:842] (1/4) Epoch 32, batch 8800, loss[loss=0.2077, simple_loss=0.307, pruned_loss=0.0542, over 7291.00 frames.], tot_loss[loss=0.1746, simple_loss=0.263, pruned_loss=0.04307, over 1411302.10 frames.], batch size: 24, lr: 1.71e-04
2022-05-29 05:10:23,478 INFO [train.py:842] (1/4) Epoch 32, batch 8850, loss[loss=0.1719, simple_loss=0.2531, pruned_loss=0.04533, over 7063.00 frames.], tot_loss[loss=0.175, simple_loss=0.2637, pruned_loss=0.0432, over 1410259.80 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 05:11:02,866 INFO [train.py:842] (1/4) Epoch 32, batch 8900, loss[loss=0.2008, simple_loss=0.2841, pruned_loss=0.05877, over 7324.00 frames.], tot_loss[loss=0.176, simple_loss=0.2648, pruned_loss=0.0436, over 1400436.32 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 05:11:41,574 INFO [train.py:842] (1/4) Epoch 32, batch 8950, loss[loss=0.1755, simple_loss=0.2699, pruned_loss=0.04056, over 4908.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2654, pruned_loss=0.04381, over 1395248.03 frames.], batch size: 52, lr: 1.71e-04
2022-05-29 05:12:20,905 INFO [train.py:842] (1/4) Epoch 32, batch 9000, loss[loss=0.1576, simple_loss=0.2377, pruned_loss=0.03879, over 6851.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2654, pruned_loss=0.04396, over 1390247.36 frames.], batch size: 15, lr: 1.71e-04
2022-05-29 05:12:20,906 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 05:12:30,815 INFO [train.py:871] (1/4) Epoch 32, validation: loss=0.1637, simple_loss=0.261, pruned_loss=0.03323, over 868885.00 frames.
2022-05-29 05:13:09,673 INFO [train.py:842] (1/4) Epoch 32, batch 9050, loss[loss=0.2631, simple_loss=0.3403, pruned_loss=0.09301, over 7074.00 frames.], tot_loss[loss=0.1771, simple_loss=0.266, pruned_loss=0.04406, over 1379407.14 frames.], batch size: 28, lr: 1.71e-04
2022-05-29 05:13:48,018 INFO [train.py:842] (1/4) Epoch 32, batch 9100, loss[loss=0.2083, simple_loss=0.2938, pruned_loss=0.06136, over 5278.00 frames.], tot_loss[loss=0.182, simple_loss=0.2705, pruned_loss=0.04668, over 1328905.17 frames.], batch size: 52, lr: 1.71e-04
2022-05-29 05:14:25,947 INFO [train.py:842] (1/4) Epoch 32, batch 9150, loss[loss=0.1752, simple_loss=0.2666, pruned_loss=0.04191, over 5427.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2741, pruned_loss=0.04965, over 1257100.69 frames.], batch size: 53, lr: 1.71e-04
2022-05-29 05:15:18,071 INFO [train.py:842] (1/4) Epoch 33, batch 0, loss[loss=0.2482, simple_loss=0.336, pruned_loss=0.08025, over 6857.00 frames.], tot_loss[loss=0.2482, simple_loss=0.336, pruned_loss=0.08025, over 6857.00 frames.], batch size: 31, lr: 1.68e-04
2022-05-29 05:15:57,459 INFO [train.py:842] (1/4) Epoch 33, batch 50, loss[loss=0.1772, simple_loss=0.2609, pruned_loss=0.04677, over 4798.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2671, pruned_loss=0.04514, over 313662.71 frames.], batch size: 52, lr: 1.68e-04
2022-05-29 05:16:36,942 INFO [train.py:842] (1/4) Epoch 33, batch 100, loss[loss=0.1722, simple_loss=0.2692, pruned_loss=0.03762, over 6282.00 frames.], tot_loss[loss=0.1756, simple_loss=0.265, pruned_loss=0.04314, over 558138.06 frames.], batch size: 37, lr: 1.68e-04
2022-05-29 05:17:16,117 INFO [train.py:842] (1/4) Epoch 33, batch 150, loss[loss=0.1703, simple_loss=0.2654, pruned_loss=0.03764, over 7206.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2669, pruned_loss=0.04336, over 751183.70 frames.], batch size: 23, lr: 1.68e-04
2022-05-29 05:17:55,491 INFO [train.py:842] (1/4) Epoch 33, batch 200, loss[loss=0.1586, simple_loss=0.2442, pruned_loss=0.03647, over 6992.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2646, pruned_loss=0.04254, over 894860.91 frames.], batch size: 16, lr: 1.68e-04
2022-05-29 05:18:34,578 INFO [train.py:842] (1/4) Epoch 33, batch 250, loss[loss=0.1661, simple_loss=0.2535, pruned_loss=0.03936, over 7235.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2649, pruned_loss=0.04279, over 1009857.79 frames.], batch size: 20, lr: 1.68e-04
2022-05-29 05:19:13,818 INFO [train.py:842] (1/4) Epoch 33, batch 300, loss[loss=0.1772, simple_loss=0.2771, pruned_loss=0.0387, over 6740.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2641, pruned_loss=0.0422, over 1092988.80 frames.], batch size: 31, lr: 1.68e-04
2022-05-29 05:19:52,987 INFO [train.py:842] (1/4) Epoch 33, batch 350, loss[loss=0.1542, simple_loss=0.2441, pruned_loss=0.03215, over 7403.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2637, pruned_loss=0.04199, over 1164049.12 frames.], batch size: 18, lr: 1.68e-04
2022-05-29 05:20:32,702 INFO [train.py:842] (1/4) Epoch 33, batch 400, loss[loss=0.1565, simple_loss=0.2449, pruned_loss=0.03404, over 7429.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2627, pruned_loss=0.04183, over 1220558.41 frames.], batch size: 20, lr: 1.68e-04
2022-05-29 05:21:12,042 INFO [train.py:842] (1/4) Epoch 33, batch 450, loss[loss=0.1832, simple_loss=0.2843, pruned_loss=0.04107, over 6873.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2623, pruned_loss=0.04225, over 1263308.66 frames.], batch size: 31, lr: 1.68e-04
2022-05-29 05:21:51,595 INFO [train.py:842] (1/4) Epoch 33, batch 500, loss[loss=0.1977, simple_loss=0.2821, pruned_loss=0.05667, over 7210.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2628, pruned_loss=0.04234, over 1300931.95 frames.], batch size: 23, lr: 1.68e-04
2022-05-29 05:22:30,641 INFO [train.py:842] (1/4) Epoch 33, batch 550, loss[loss=0.1861, simple_loss=0.2756, pruned_loss=0.04828, over 7314.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2641, pruned_loss=0.04279, over 1329372.79 frames.], batch size: 21, lr: 1.68e-04
2022-05-29 05:23:09,925 INFO [train.py:842] (1/4) Epoch 33, batch 600, loss[loss=0.1983, simple_loss=0.289, pruned_loss=0.05384, over 7285.00 frames.], tot_loss[loss=0.176, simple_loss=0.2652, pruned_loss=0.04337, over 1347013.55 frames.], batch size: 24, lr: 1.68e-04
2022-05-29 05:23:49,136 INFO [train.py:842] (1/4) Epoch 33, batch 650, loss[loss=0.2031, simple_loss=0.2921, pruned_loss=0.05706, over 7133.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2653, pruned_loss=0.04346, over 1364305.73 frames.], batch size: 26, lr: 1.68e-04
2022-05-29 05:24:28,766 INFO [train.py:842] (1/4) Epoch 33, batch 700, loss[loss=0.1484, simple_loss=0.2264, pruned_loss=0.03523, over 7117.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2647, pruned_loss=0.04351, over 1374654.17 frames.], batch size: 17, lr: 1.68e-04
2022-05-29 05:25:07,929 INFO [train.py:842] (1/4) Epoch 33, batch 750, loss[loss=0.156, simple_loss=0.2492, pruned_loss=0.03139, over 7223.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2651, pruned_loss=0.04377, over 1380636.98 frames.], batch size: 21, lr: 1.68e-04
2022-05-29 05:25:47,798 INFO [train.py:842] (1/4) Epoch 33, batch 800, loss[loss=0.1782, simple_loss=0.2662, pruned_loss=0.04506, over 7414.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2634, pruned_loss=0.04295, over 1392197.36 frames.], batch size: 20, lr: 1.68e-04
2022-05-29 05:26:26,920 INFO [train.py:842] (1/4) Epoch 33, batch 850, loss[loss=0.1696, simple_loss=0.2672, pruned_loss=0.03599, over 7378.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2635, pruned_loss=0.0427, over 1399787.17 frames.], batch size: 23, lr: 1.68e-04
2022-05-29 05:27:06,490 INFO [train.py:842] (1/4) Epoch 33, batch 900, loss[loss=0.1574, simple_loss=0.2487, pruned_loss=0.03303, over 7205.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2614, pruned_loss=0.04193, over 1409595.26 frames.], batch size: 23, lr: 1.68e-04
2022-05-29 05:27:45,835 INFO [train.py:842] (1/4) Epoch 33, batch 950, loss[loss=0.1726, simple_loss=0.2632, pruned_loss=0.04104, over 7432.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2619, pruned_loss=0.04229, over 1413917.35 frames.], batch size: 20, lr: 1.68e-04
2022-05-29 05:28:25,616 INFO [train.py:842] (1/4) Epoch 33, batch 1000, loss[loss=0.1714, simple_loss=0.2702, pruned_loss=0.03627, over 7199.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2612, pruned_loss=0.04197, over 1413260.50 frames.], batch size: 23, lr: 1.68e-04
2022-05-29 05:29:04,496 INFO [train.py:842] (1/4) Epoch 33, batch 1050, loss[loss=0.1813, simple_loss=0.2822, pruned_loss=0.04018, over 7018.00 frames.], tot_loss[loss=0.1731, simple_loss=0.262, pruned_loss=0.04211, over 1412311.14 frames.], batch size: 28, lr: 1.68e-04
2022-05-29 05:29:43,851 INFO [train.py:842] (1/4) Epoch 33, batch 1100, loss[loss=0.1601, simple_loss=0.2567, pruned_loss=0.03175, over 7289.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2617, pruned_loss=0.04189, over 1418000.52 frames.], batch size: 24, lr: 1.68e-04
2022-05-29 05:30:22,966 INFO [train.py:842] (1/4) Epoch 33, batch 1150, loss[loss=0.1363, simple_loss=0.2349, pruned_loss=0.01888, over 7194.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2622, pruned_loss=0.04166, over 1419147.87 frames.], batch size: 23, lr: 1.68e-04
2022-05-29 05:31:02,277 INFO [train.py:842] (1/4) Epoch 33, batch 1200, loss[loss=0.1657, simple_loss=0.263, pruned_loss=0.03421, over 7170.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2632, pruned_loss=0.04203, over 1421780.03 frames.], batch size: 26, lr: 1.68e-04
2022-05-29 05:31:41,538 INFO [train.py:842] (1/4) Epoch 33, batch 1250, loss[loss=0.1627, simple_loss=0.2633, pruned_loss=0.03107, over 6083.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2637, pruned_loss=0.04232, over 1420629.91 frames.], batch size: 37, lr: 1.68e-04
2022-05-29 05:32:21,184 INFO [train.py:842] (1/4) Epoch 33, batch 1300, loss[loss=0.1562, simple_loss=0.2542, pruned_loss=0.02905, over 7229.00 frames.], tot_loss[loss=0.173, simple_loss=0.2625, pruned_loss=0.04178, over 1420768.19 frames.], batch size: 21, lr: 1.68e-04
2022-05-29 05:33:00,621 INFO [train.py:842] (1/4) Epoch 33, batch 1350, loss[loss=0.1426, simple_loss=0.2211, pruned_loss=0.03198, over 7281.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2618, pruned_loss=0.04176, over 1420046.52 frames.], batch size: 17, lr: 1.68e-04
2022-05-29 05:33:40,411 INFO [train.py:842] (1/4) Epoch 33, batch 1400, loss[loss=0.2207, simple_loss=0.3094, pruned_loss=0.06603, over 7140.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2611, pruned_loss=0.04136, over 1421630.40 frames.], batch size: 20, lr: 1.68e-04
2022-05-29 05:34:19,592 INFO [train.py:842] (1/4) Epoch 33, batch 1450, loss[loss=0.1868, simple_loss=0.2841, pruned_loss=0.04472, over 6778.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2607, pruned_loss=0.04106, over 1424716.56 frames.], batch size: 31, lr: 1.67e-04
2022-05-29 05:34:59,093 INFO [train.py:842] (1/4) Epoch 33, batch 1500, loss[loss=0.2045, simple_loss=0.286, pruned_loss=0.06147, over 5247.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2606, pruned_loss=0.04114, over 1422648.79 frames.], batch size: 52, lr: 1.67e-04
2022-05-29 05:35:38,246 INFO [train.py:842] (1/4) Epoch 33, batch 1550, loss[loss=0.1817, simple_loss=0.2721, pruned_loss=0.04559, over 7216.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2624, pruned_loss=0.04223, over 1418050.60 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 05:36:17,793 INFO [train.py:842] (1/4) Epoch 33, batch 1600, loss[loss=0.2026, simple_loss=0.2974, pruned_loss=0.05394, over 7414.00 frames.], tot_loss[loss=0.1734, simple_loss=0.262, pruned_loss=0.04239, over 1419503.51 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 05:36:57,045 INFO [train.py:842] (1/4) Epoch 33, batch 1650, loss[loss=0.2123, simple_loss=0.3008, pruned_loss=0.06192, over 7221.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2622, pruned_loss=0.04265, over 1419941.13 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 05:37:36,359 INFO [train.py:842] (1/4) Epoch 33, batch 1700, loss[loss=0.2017, simple_loss=0.3056, pruned_loss=0.04888, over 7307.00 frames.], tot_loss[loss=0.1742, simple_loss=0.263, pruned_loss=0.04268, over 1421940.98 frames.], batch size: 24, lr: 1.67e-04
2022-05-29 05:38:15,205 INFO [train.py:842] (1/4) Epoch 33, batch 1750, loss[loss=0.2095, simple_loss=0.2981, pruned_loss=0.0605, over 7092.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2637, pruned_loss=0.04288, over 1415913.45 frames.], batch size: 28, lr: 1.67e-04
2022-05-29 05:38:54,945 INFO [train.py:842] (1/4) Epoch 33, batch 1800, loss[loss=0.1688, simple_loss=0.256, pruned_loss=0.04086, over 7256.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2632, pruned_loss=0.04294, over 1419609.11 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 05:39:34,277 INFO [train.py:842] (1/4) Epoch 33, batch 1850, loss[loss=0.1869, simple_loss=0.2819, pruned_loss=0.04599, over 7311.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2635, pruned_loss=0.04332, over 1422465.66 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 05:40:16,637 INFO [train.py:842] (1/4) Epoch 33, batch 1900, loss[loss=0.1978, simple_loss=0.2852, pruned_loss=0.05521, over 7383.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2624, pruned_loss=0.04251, over 1424603.92 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 05:40:55,636 INFO [train.py:842] (1/4) Epoch 33, batch 1950, loss[loss=0.1897, simple_loss=0.2754, pruned_loss=0.05202, over 7278.00 frames.], tot_loss[loss=0.1734, simple_loss=0.262, pruned_loss=0.04237, over 1422975.21 frames.], batch size: 24, lr: 1.67e-04
2022-05-29 05:41:35,580 INFO [train.py:842] (1/4) Epoch 33, batch 2000, loss[loss=0.1925, simple_loss=0.282, pruned_loss=0.05146, over 6358.00 frames.], tot_loss[loss=0.1732, simple_loss=0.262, pruned_loss=0.04219, over 1424137.50 frames.], batch size: 38, lr: 1.67e-04
2022-05-29 05:42:14,864 INFO [train.py:842] (1/4) Epoch 33, batch 2050, loss[loss=0.1495, simple_loss=0.2314, pruned_loss=0.03382, over 7157.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2617, pruned_loss=0.04189, over 1424928.78 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 05:42:54,692 INFO [train.py:842] (1/4) Epoch 33, batch 2100, loss[loss=0.1546, simple_loss=0.2352, pruned_loss=0.03699, over 7156.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2611, pruned_loss=0.0415, over 1425830.92 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 05:43:33,991 INFO [train.py:842] (1/4) Epoch 33, batch 2150, loss[loss=0.1659, simple_loss=0.2454, pruned_loss=0.04323, over 7424.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2613, pruned_loss=0.04121, over 1427386.74 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 05:44:13,562 INFO [train.py:842] (1/4) Epoch 33, batch 2200, loss[loss=0.1887, simple_loss=0.2813, pruned_loss=0.04807, over 5184.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2609, pruned_loss=0.0409, over 1422115.22 frames.], batch size: 52, lr: 1.67e-04
2022-05-29 05:44:52,699 INFO [train.py:842] (1/4) Epoch 33, batch 2250, loss[loss=0.1766, simple_loss=0.2683, pruned_loss=0.04244, over 7141.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2609, pruned_loss=0.04121, over 1420258.38 frames.], batch size: 26, lr: 1.67e-04
2022-05-29 05:45:32,555 INFO [train.py:842] (1/4) Epoch 33, batch 2300, loss[loss=0.1589, simple_loss=0.2502, pruned_loss=0.03379, over 7217.00 frames.], tot_loss[loss=0.171, simple_loss=0.2597, pruned_loss=0.04113, over 1419532.57 frames.], batch size: 22, lr: 1.67e-04
2022-05-29 05:46:11,704 INFO [train.py:842] (1/4) Epoch 33, batch 2350, loss[loss=0.1615, simple_loss=0.2345, pruned_loss=0.04424, over 6797.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2603, pruned_loss=0.04123, over 1421895.71 frames.], batch size: 15, lr: 1.67e-04
2022-05-29 05:46:51,695 INFO [train.py:842] (1/4) Epoch 33, batch 2400, loss[loss=0.148, simple_loss=0.2374, pruned_loss=0.02925, over 7438.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2596, pruned_loss=0.04107, over 1424099.41 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 05:47:31,139 INFO [train.py:842] (1/4) Epoch 33, batch 2450, loss[loss=0.1593, simple_loss=0.2587, pruned_loss=0.02998, over 7261.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2595, pruned_loss=0.04098, over 1426091.00 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 05:48:10,869 INFO [train.py:842] (1/4) Epoch 33, batch 2500, loss[loss=0.1804, simple_loss=0.2741, pruned_loss=0.04339, over 7320.00 frames.], tot_loss[loss=0.171, simple_loss=0.2599, pruned_loss=0.04105, over 1427520.27 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 05:48:50,214 INFO [train.py:842] (1/4) Epoch 33, batch 2550, loss[loss=0.1727, simple_loss=0.2613, pruned_loss=0.04201, over 7379.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2604, pruned_loss=0.04184, over 1427553.28 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 05:49:30,006 INFO [train.py:842] (1/4) Epoch 33, batch 2600, loss[loss=0.2092, simple_loss=0.2997, pruned_loss=0.05935, over 7208.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2608, pruned_loss=0.04218, over 1428149.00 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 05:50:08,969 INFO [train.py:842] (1/4) Epoch 33, batch 2650, loss[loss=0.1447, simple_loss=0.2278, pruned_loss=0.03077, over 6825.00 frames.], tot_loss[loss=0.172, simple_loss=0.2607, pruned_loss=0.04161, over 1423255.23 frames.], batch size: 15, lr: 1.67e-04
2022-05-29 05:50:48,452 INFO [train.py:842] (1/4) Epoch 33, batch 2700, loss[loss=0.1408, simple_loss=0.2237, pruned_loss=0.0289, over 7420.00 frames.], tot_loss[loss=0.173, simple_loss=0.2615, pruned_loss=0.04229, over 1424494.92 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 05:51:27,567 INFO [train.py:842] (1/4) Epoch 33, batch 2750, loss[loss=0.1591, simple_loss=0.2291, pruned_loss=0.04459, over 7278.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2612, pruned_loss=0.04206, over 1425652.97 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 05:52:07,117 INFO [train.py:842] (1/4) Epoch 33, batch 2800, loss[loss=0.1958, simple_loss=0.278, pruned_loss=0.05681, over 7219.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2612, pruned_loss=0.04188, over 1424098.75 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 05:52:46,428 INFO [train.py:842] (1/4) Epoch 33, batch 2850, loss[loss=0.1886, simple_loss=0.2822, pruned_loss=0.04753, over 7320.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2608, pruned_loss=0.04174, over 1425214.76 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 05:53:25,961 INFO [train.py:842] (1/4) Epoch 33, batch 2900, loss[loss=0.2031, simple_loss=0.3053, pruned_loss=0.05046, over 7283.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2617, pruned_loss=0.04224, over 1424549.58 frames.], batch size: 25, lr: 1.67e-04
2022-05-29 05:54:05,075 INFO [train.py:842] (1/4) Epoch 33, batch 2950, loss[loss=0.2061, simple_loss=0.2967, pruned_loss=0.05773, over 7422.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.0422, over 1427099.89 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 05:54:44,594 INFO [train.py:842] (1/4) Epoch 33, batch 3000, loss[loss=0.2008, simple_loss=0.2672, pruned_loss=0.06719, over 7066.00 frames.], tot_loss[loss=0.174, simple_loss=0.2627, pruned_loss=0.04263, over 1426384.19 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 05:54:44,595 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 05:54:54,324 INFO [train.py:871] (1/4) Epoch 33, validation: loss=0.1647, simple_loss=0.2614, pruned_loss=0.03398, over 868885.00 frames.
2022-05-29 05:55:33,719 INFO [train.py:842] (1/4) Epoch 33, batch 3050, loss[loss=0.1661, simple_loss=0.2505, pruned_loss=0.04079, over 6471.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2617, pruned_loss=0.04247, over 1423491.29 frames.], batch size: 38, lr: 1.67e-04
2022-05-29 05:56:13,293 INFO [train.py:842] (1/4) Epoch 33, batch 3100, loss[loss=0.2007, simple_loss=0.2939, pruned_loss=0.05378, over 7398.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2613, pruned_loss=0.042, over 1424066.35 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 05:56:52,626 INFO [train.py:842] (1/4) Epoch 33, batch 3150, loss[loss=0.1826, simple_loss=0.2653, pruned_loss=0.04996, over 7049.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2608, pruned_loss=0.04226, over 1421162.38 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 05:57:42,962 INFO [train.py:842] (1/4) Epoch 33, batch 3200, loss[loss=0.1525, simple_loss=0.2315, pruned_loss=0.03674, over 6799.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2609, pruned_loss=0.04216, over 1421195.52 frames.], batch size: 15, lr: 1.67e-04
2022-05-29 05:58:22,105 INFO [train.py:842] (1/4) Epoch 33, batch 3250, loss[loss=0.1401, simple_loss=0.2301, pruned_loss=0.02505, over 7297.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2603, pruned_loss=0.04152, over 1419071.82 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 05:59:02,055 INFO [train.py:842] (1/4) Epoch 33, batch 3300, loss[loss=0.1908, simple_loss=0.2848, pruned_loss=0.04843, over 7238.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2602, pruned_loss=0.04141, over 1424407.70 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 05:59:41,363 INFO [train.py:842] (1/4) Epoch 33, batch 3350, loss[loss=0.1781, simple_loss=0.2609, pruned_loss=0.04764, over 7312.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2614, pruned_loss=0.04212, over 1428014.64 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 06:00:20,973 INFO [train.py:842] (1/4) Epoch 33, batch 3400, loss[loss=0.1439, simple_loss=0.2306, pruned_loss=0.02855, over 7281.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2616, pruned_loss=0.04231, over 1428451.70 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 06:01:00,225 INFO [train.py:842] (1/4) Epoch 33, batch 3450, loss[loss=0.1672, simple_loss=0.2628, pruned_loss=0.03582, over 7323.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2623, pruned_loss=0.04267, over 1431931.14 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 06:01:39,796 INFO [train.py:842] (1/4) Epoch 33, batch 3500, loss[loss=0.1533, simple_loss=0.2492, pruned_loss=0.02873, over 7384.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2625, pruned_loss=0.04242, over 1428745.41 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 06:02:19,036 INFO [train.py:842] (1/4) Epoch 33, batch 3550, loss[loss=0.2489, simple_loss=0.3057, pruned_loss=0.09605, over 7414.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2622, pruned_loss=0.04206, over 1427170.43 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 06:02:58,528 INFO [train.py:842] (1/4) Epoch 33, batch 3600, loss[loss=0.162, simple_loss=0.256, pruned_loss=0.03402, over 7313.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.04222, over 1424731.48 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 06:03:38,012 INFO [train.py:842] (1/4) Epoch 33, batch 3650, loss[loss=0.1861, simple_loss=0.2696, pruned_loss=0.05132, over 7339.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2625, pruned_loss=0.04244, over 1423853.49 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 06:04:17,604 INFO [train.py:842] (1/4) Epoch 33, batch 3700, loss[loss=0.193, simple_loss=0.2725, pruned_loss=0.05674, over 7292.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2634, pruned_loss=0.04275, over 1427450.12 frames.], batch size: 17, lr: 1.67e-04
2022-05-29 06:04:56,737 INFO [train.py:842] (1/4) Epoch 33, batch 3750, loss[loss=0.2094, simple_loss=0.2961, pruned_loss=0.06134, over 7226.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2638, pruned_loss=0.04302, over 1427782.95 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 06:05:36,541 INFO [train.py:842] (1/4) Epoch 33, batch 3800, loss[loss=0.1899, simple_loss=0.2903, pruned_loss=0.04474, over 7219.00 frames.], tot_loss[loss=0.175, simple_loss=0.2636, pruned_loss=0.04325, over 1428232.04 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 06:06:15,660 INFO [train.py:842] (1/4) Epoch 33, batch 3850, loss[loss=0.1647, simple_loss=0.2669, pruned_loss=0.03124, over 7324.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2628, pruned_loss=0.04244, over 1428992.37 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 06:06:55,042 INFO [train.py:842] (1/4) Epoch 33, batch 3900, loss[loss=0.1659, simple_loss=0.2381, pruned_loss=0.04689, over 7284.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2637, pruned_loss=0.04253, over 1429477.60 frames.], batch size: 16, lr: 1.67e-04
2022-05-29 06:07:34,357 INFO [train.py:842] (1/4) Epoch 33, batch 3950, loss[loss=0.1646, simple_loss=0.2401, pruned_loss=0.04452, over 6815.00 frames.], tot_loss[loss=0.1745, simple_loss=0.264, pruned_loss=0.04255, over 1429426.30 frames.], batch size: 15, lr: 1.67e-04
2022-05-29 06:08:14,162 INFO [train.py:842] (1/4) Epoch 33, batch 4000, loss[loss=0.2104, simple_loss=0.2919, pruned_loss=0.06439, over 4768.00 frames.], tot_loss[loss=0.1737, simple_loss=0.263, pruned_loss=0.04221, over 1430074.89 frames.], batch size: 53, lr: 1.67e-04
2022-05-29 06:08:53,346 INFO [train.py:842] (1/4) Epoch 33, batch 4050, loss[loss=0.1491, simple_loss=0.2381, pruned_loss=0.03004, over 7272.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2614, pruned_loss=0.04168, over 1425637.71 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 06:09:33,003 INFO [train.py:842] (1/4) Epoch 33, batch 4100, loss[loss=0.2106, simple_loss=0.3138, pruned_loss=0.05365, over 7304.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2617, pruned_loss=0.04183, over 1424328.50 frames.], batch size: 25, lr: 1.67e-04
2022-05-29 06:10:12,269 INFO [train.py:842] (1/4) Epoch 33, batch 4150, loss[loss=0.1551, simple_loss=0.2505, pruned_loss=0.02979, over 7151.00 frames.], tot_loss[loss=0.1727, simple_loss=0.262, pruned_loss=0.04168, over 1420571.85 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 06:10:52,066 INFO [train.py:842] (1/4) Epoch 33, batch 4200, loss[loss=0.1947, simple_loss=0.277, pruned_loss=0.05619, over 7404.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2615, pruned_loss=0.04165, over 1425940.90 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 06:11:31,148 INFO [train.py:842] (1/4) Epoch 33, batch 4250, loss[loss=0.1803, simple_loss=0.2694, pruned_loss=0.04565, over 7348.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2625, pruned_loss=0.04213, over 1424455.31 frames.], batch size: 22, lr: 1.67e-04
2022-05-29 06:12:10,728 INFO [train.py:842] (1/4) Epoch 33, batch 4300, loss[loss=0.1753, simple_loss=0.2602, pruned_loss=0.04521, over 7163.00 frames.], tot_loss[loss=0.174, simple_loss=0.2632, pruned_loss=0.0424, over 1423525.74 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 06:12:50,159 INFO [train.py:842] (1/4) Epoch 33, batch 4350, loss[loss=0.1528, simple_loss=0.2484, pruned_loss=0.02862, over 7242.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2634, pruned_loss=0.04257, over 1425343.51 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 06:13:29,840 INFO [train.py:842] (1/4) Epoch 33, batch 4400, loss[loss=0.285, simple_loss=0.3521, pruned_loss=0.109, over 6331.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2644, pruned_loss=0.04297, over 1423933.86 frames.], batch size: 38, lr: 1.67e-04
2022-05-29 06:14:09,308 INFO [train.py:842] (1/4) Epoch 33, batch 4450, loss[loss=0.1806, simple_loss=0.2585, pruned_loss=0.05138, over 7438.00 frames.], tot_loss[loss=0.1761, simple_loss=0.265, pruned_loss=0.04358, over 1423656.19 frames.], batch size: 17, lr: 1.67e-04
2022-05-29 06:14:48,774 INFO [train.py:842] (1/4) Epoch 33, batch 4500, loss[loss=0.1735, simple_loss=0.2701, pruned_loss=0.03849, over 7321.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2642, pruned_loss=0.04294, over 1425326.94 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 06:15:28,041 INFO [train.py:842] (1/4) Epoch 33, batch 4550, loss[loss=0.163, simple_loss=0.257, pruned_loss=0.03447, over 7300.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2638, pruned_loss=0.04201, over 1425315.81 frames.], batch size: 25, lr: 1.67e-04
2022-05-29 06:16:07,564 INFO [train.py:842] (1/4) Epoch 33, batch 4600, loss[loss=0.2039, simple_loss=0.2885, pruned_loss=0.05958, over 6900.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2631, pruned_loss=0.04197, over 1424317.91 frames.], batch size: 31, lr: 1.67e-04
2022-05-29 06:16:46,704 INFO [train.py:842] (1/4) Epoch 33, batch 4650, loss[loss=0.1737, simple_loss=0.2539, pruned_loss=0.04678, over 7413.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2622, pruned_loss=0.04155, over 1421921.76 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 06:17:26,302 INFO [train.py:842] (1/4) Epoch 33, batch 4700, loss[loss=0.1371, simple_loss=0.2413, pruned_loss=0.01646, over 6482.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2638, pruned_loss=0.0428, over 1423534.34 frames.], batch size: 38, lr: 1.67e-04
2022-05-29 06:18:05,553 INFO [train.py:842] (1/4) Epoch 33, batch 4750, loss[loss=0.1519, simple_loss=0.2399, pruned_loss=0.03195, over 7292.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2631, pruned_loss=0.04262, over 1423346.68 frames.], batch size: 17, lr: 1.67e-04
2022-05-29 06:18:45,235 INFO [train.py:842] (1/4) Epoch 33, batch 4800, loss[loss=0.1489, simple_loss=0.2525, pruned_loss=0.02264, over 7128.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2627, pruned_loss=0.04245, over 1423816.28 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 06:19:24,421 INFO [train.py:842] (1/4) Epoch 33, batch 4850, loss[loss=0.2052, simple_loss=0.2903, pruned_loss=0.06005, over 6289.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2623, pruned_loss=0.04266, over 1419966.74 frames.], batch size: 37, lr: 1.67e-04
2022-05-29 06:20:03,996 INFO [train.py:842] (1/4) Epoch 33, batch 4900, loss[loss=0.2009, simple_loss=0.2921, pruned_loss=0.0549, over 7252.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2621, pruned_loss=0.04251, over 1419661.27 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 06:20:43,309 INFO [train.py:842] (1/4) Epoch 33, batch 4950, loss[loss=0.1452, simple_loss=0.2263, pruned_loss=0.03204, over 7057.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2625, pruned_loss=0.04269, over 1418020.35 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 06:21:22,686 INFO [train.py:842] (1/4) Epoch 33, batch 5000, loss[loss=0.163, simple_loss=0.2608, pruned_loss=0.03257, over 6663.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2627, pruned_loss=0.04216, over 1414695.90 frames.], batch size: 31, lr: 1.66e-04
2022-05-29 06:22:02,145 INFO [train.py:842] (1/4) Epoch 33, batch 5050, loss[loss=0.1745, simple_loss=0.2719, pruned_loss=0.03853, over 7116.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2627, pruned_loss=0.04205, over 1414995.66 frames.], batch size: 26, lr: 1.66e-04
2022-05-29 06:22:41,607 INFO [train.py:842] (1/4) Epoch 33, batch 5100, loss[loss=0.1232, simple_loss=0.2088, pruned_loss=0.01885, over 7298.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2632, pruned_loss=0.04209, over 1412780.79 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:23:20,500 INFO [train.py:842] (1/4) Epoch 33, batch 5150, loss[loss=0.1774, simple_loss=0.2714, pruned_loss=0.04167, over 7221.00 frames.], tot_loss[loss=0.1747, simple_loss=0.264, pruned_loss=0.04273, over 1406734.06 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 06:24:00,151 INFO [train.py:842] (1/4) Epoch 33, batch 5200, loss[loss=0.1595, simple_loss=0.2439, pruned_loss=0.03753, over 7004.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2635, pruned_loss=0.04276, over 1413443.05 frames.], batch size: 16, lr: 1.66e-04
2022-05-29 06:24:38,958 INFO [train.py:842] (1/4) Epoch 33, batch 5250, loss[loss=0.1883, simple_loss=0.2866, pruned_loss=0.04498, over 7154.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2643, pruned_loss=0.04294, over 1415295.69 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:25:18,426 INFO [train.py:842] (1/4) Epoch 33, batch 5300, loss[loss=0.1884, simple_loss=0.2765, pruned_loss=0.05015, over 7056.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2639, pruned_loss=0.04287, over 1416423.29 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:25:57,786 INFO [train.py:842] (1/4) Epoch 33, batch 5350, loss[loss=0.151, simple_loss=0.2455, pruned_loss=0.02821, over 7216.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2632, pruned_loss=0.04279, over 1419105.95 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 06:26:37,411 INFO [train.py:842] (1/4) Epoch 33, batch 5400, loss[loss=0.1975, simple_loss=0.2797, pruned_loss=0.05768, over 6668.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2629, pruned_loss=0.04267, over 1420156.27 frames.], batch size: 31, lr: 1.66e-04
2022-05-29 06:27:16,745 INFO [train.py:842] (1/4) Epoch 33, batch 5450, loss[loss=0.1423, simple_loss=0.2456, pruned_loss=0.01955, over 7332.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2616, pruned_loss=0.04201, over 1422333.96 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 06:27:56,412 INFO [train.py:842] (1/4) Epoch 33, batch 5500, loss[loss=0.1828, simple_loss=0.2631, pruned_loss=0.05124, over 7284.00 frames.], tot_loss[loss=0.172, simple_loss=0.2609, pruned_loss=0.04152, over 1424966.00 frames.], batch size: 17, lr: 1.66e-04
2022-05-29 06:28:35,863 INFO [train.py:842] (1/4) Epoch 33, batch 5550, loss[loss=0.1779, simple_loss=0.2691, pruned_loss=0.0434, over 7431.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2594, pruned_loss=0.04095, over 1426492.15 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:29:15,500 INFO [train.py:842] (1/4) Epoch 33, batch 5600, loss[loss=0.2037, simple_loss=0.2855, pruned_loss=0.06098, over 5241.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2589, pruned_loss=0.04093, over 1421269.33 frames.], batch size: 52, lr: 1.66e-04
2022-05-29 06:29:54,955 INFO [train.py:842] (1/4) Epoch 33, batch 5650, loss[loss=0.2051, simple_loss=0.2931, pruned_loss=0.05858, over 7384.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2592, pruned_loss=0.04125, over 1424976.51 frames.], batch size: 23, lr: 1.66e-04
2022-05-29 06:30:34,464 INFO [train.py:842] (1/4) Epoch 33, batch 5700, loss[loss=0.1947, simple_loss=0.2837, pruned_loss=0.05289, over 6436.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2608, pruned_loss=0.04207, over 1418794.12 frames.], batch size: 38, lr: 1.66e-04
2022-05-29 06:31:13,666 INFO [train.py:842] (1/4) Epoch 33, batch 5750, loss[loss=0.1937, simple_loss=0.273, pruned_loss=0.05719, over 7065.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2621, pruned_loss=0.04273, over 1421843.47 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:31:53,553 INFO [train.py:842] (1/4) Epoch 33, batch 5800, loss[loss=0.2013, simple_loss=0.2895, pruned_loss=0.05655, over 7338.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2618, pruned_loss=0.04301, over 1422514.04 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 06:32:32,845 INFO [train.py:842] (1/4) Epoch 33, batch 5850, loss[loss=0.156, simple_loss=0.2313, pruned_loss=0.04031, over 7414.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2625, pruned_loss=0.04326, over 1420354.18 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:33:12,583 INFO [train.py:842] (1/4) Epoch 33, batch 5900, loss[loss=0.1782, simple_loss=0.2778, pruned_loss=0.03926, over 7425.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2619, pruned_loss=0.04254, over 1425459.32 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:33:51,708 INFO [train.py:842] (1/4) Epoch 33, batch 5950, loss[loss=0.1603, simple_loss=0.2563, pruned_loss=0.03214, over 6538.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2631, pruned_loss=0.0429, over 1428490.89 frames.], batch size: 38, lr: 1.66e-04
2022-05-29 06:34:31,357 INFO [train.py:842] (1/4) Epoch 33, batch 6000, loss[loss=0.1608, simple_loss=0.2588, pruned_loss=0.03142, over 7138.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2634, pruned_loss=0.04311, over 1427811.09 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:34:31,358 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 06:34:41,081 INFO [train.py:871] (1/4) Epoch 33, validation: loss=0.1642, simple_loss=0.2614, pruned_loss=0.03347, over 868885.00 frames.
2022-05-29 06:35:20,440 INFO [train.py:842] (1/4) Epoch 33, batch 6050, loss[loss=0.1863, simple_loss=0.2648, pruned_loss=0.05386, over 7214.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2637, pruned_loss=0.04363, over 1427584.38 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 06:35:59,990 INFO [train.py:842] (1/4) Epoch 33, batch 6100, loss[loss=0.1831, simple_loss=0.2737, pruned_loss=0.04622, over 7341.00 frames.], tot_loss[loss=0.1744, simple_loss=0.263, pruned_loss=0.04292, over 1425496.77 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 06:36:39,102 INFO [train.py:842] (1/4) Epoch 33, batch 6150, loss[loss=0.1252, simple_loss=0.2115, pruned_loss=0.0194, over 6819.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2632, pruned_loss=0.04323, over 1425259.83 frames.], batch size: 15, lr: 1.66e-04
2022-05-29 06:37:18,642 INFO [train.py:842] (1/4) Epoch 33, batch 6200, loss[loss=0.1541, simple_loss=0.2509, pruned_loss=0.02868, over 7404.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2624, pruned_loss=0.04296, over 1424662.80 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 06:37:57,938 INFO [train.py:842] (1/4) Epoch 33, batch 6250, loss[loss=0.1483, simple_loss=0.227, pruned_loss=0.0348, over 7148.00 frames.], tot_loss[loss=0.173, simple_loss=0.2611, pruned_loss=0.04245, over 1425324.57 frames.], batch size: 17, lr: 1.66e-04
2022-05-29 06:38:37,871 INFO [train.py:842] (1/4) Epoch 33, batch 6300, loss[loss=0.228, simple_loss=0.307, pruned_loss=0.07451, over 7290.00 frames.], tot_loss[loss=0.1732, simple_loss=0.261, pruned_loss=0.0427, over 1424979.68 frames.], batch size: 24, lr: 1.66e-04
2022-05-29 06:39:17,426 INFO [train.py:842] (1/4) Epoch 33, batch 6350, loss[loss=0.1626, simple_loss=0.2453, pruned_loss=0.03997, over 7167.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2611, pruned_loss=0.04282, over 1426770.02 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:39:57,019 INFO [train.py:842] (1/4) Epoch 33, batch 6400, loss[loss=0.1832, simple_loss=0.2794, pruned_loss=0.04355, over 7233.00 frames.], tot_loss[loss=0.173, simple_loss=0.2608, pruned_loss=0.04265, over 1425574.55 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:40:36,393 INFO [train.py:842] (1/4) Epoch 33, batch 6450, loss[loss=0.1662, simple_loss=0.2591, pruned_loss=0.03659, over 7381.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2603, pruned_loss=0.04196, over 1426286.75 frames.], batch size: 23, lr: 1.66e-04
2022-05-29 06:41:16,118 INFO [train.py:842] (1/4) Epoch 33, batch 6500, loss[loss=0.1954, simple_loss=0.2912, pruned_loss=0.04983, over 7409.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2624, pruned_loss=0.04288, over 1428784.69 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 06:41:55,393 INFO [train.py:842] (1/4) Epoch 33, batch 6550, loss[loss=0.1699, simple_loss=0.2428, pruned_loss=0.04854, over 6797.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2632, pruned_loss=0.04351, over 1428045.69 frames.], batch size: 15, lr: 1.66e-04
2022-05-29 06:42:34,833 INFO [train.py:842] (1/4) Epoch 33, batch 6600, loss[loss=0.1711, simple_loss=0.2504, pruned_loss=0.0459, over 6983.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2629, pruned_loss=0.04301, over 1426752.46 frames.], batch size: 16, lr: 1.66e-04
2022-05-29 06:43:14,040 INFO [train.py:842] (1/4) Epoch 33, batch 6650, loss[loss=0.1758, simple_loss=0.2699, pruned_loss=0.04091, over 6797.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2628, pruned_loss=0.04326, over 1423868.68 frames.], batch size: 31, lr: 1.66e-04
2022-05-29 06:43:53,731 INFO [train.py:842] (1/4) Epoch 33, batch 6700, loss[loss=0.1552, simple_loss=0.2482, pruned_loss=0.03113, over 7061.00 frames.], tot_loss[loss=0.1742, simple_loss=0.262, pruned_loss=0.04317, over 1420530.13 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:44:32,928 INFO [train.py:842] (1/4) Epoch 33, batch 6750, loss[loss=0.134, simple_loss=0.2276, pruned_loss=0.02023, over 7169.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2627, pruned_loss=0.04342, over 1421053.42 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:45:12,711 INFO [train.py:842] (1/4) Epoch 33, batch 6800, loss[loss=0.1886, simple_loss=0.2792, pruned_loss=0.04902, over 7180.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2616, pruned_loss=0.04262, over 1417489.75 frames.], batch size: 23, lr: 1.66e-04
2022-05-29 06:45:51,925 INFO [train.py:842] (1/4) Epoch 33, batch 6850, loss[loss=0.1834, simple_loss=0.2706, pruned_loss=0.04812, over 7167.00 frames.], tot_loss[loss=0.1746, simple_loss=0.263, pruned_loss=0.0431, over 1422419.30 frames.], batch size: 26, lr: 1.66e-04
2022-05-29 06:46:31,082 INFO [train.py:842] (1/4) Epoch 33, batch 6900, loss[loss=0.1726, simple_loss=0.2606, pruned_loss=0.04227, over 6864.00 frames.], tot_loss[loss=0.175, simple_loss=0.2636, pruned_loss=0.04315, over 1417624.36 frames.], batch size: 31, lr: 1.66e-04
2022-05-29 06:47:10,488 INFO [train.py:842] (1/4) Epoch 33, batch 6950, loss[loss=0.2039, simple_loss=0.2924, pruned_loss=0.0577, over 7430.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2634, pruned_loss=0.04306, over 1424384.33 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:47:50,187 INFO [train.py:842] (1/4) Epoch 33, batch 7000, loss[loss=0.1792, simple_loss=0.27, pruned_loss=0.04419, over 7066.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.04229, over 1426714.62 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:48:29,631 INFO [train.py:842] (1/4) Epoch 33, batch 7050, loss[loss=0.1305, simple_loss=0.2282, pruned_loss=0.0164, over 7164.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2624, pruned_loss=0.04256, over 1423738.44 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:49:09,091 INFO [train.py:842] (1/4) Epoch 33, batch 7100, loss[loss=0.1688, simple_loss=0.2653, pruned_loss=0.03615, over 7171.00 frames.], tot_loss[loss=0.173, simple_loss=0.262, pruned_loss=0.04202, over 1426279.62 frames.], batch size: 26, lr: 1.66e-04
2022-05-29 06:49:48,451 INFO [train.py:842] (1/4) Epoch 33, batch 7150, loss[loss=0.1882, simple_loss=0.2786, pruned_loss=0.04887, over 7145.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2628, pruned_loss=0.04267, over 1429928.54 frames.], batch size: 26, lr: 1.66e-04
2022-05-29 06:50:28,341 INFO [train.py:842] (1/4) Epoch 33, batch 7200, loss[loss=0.1745, simple_loss=0.2687, pruned_loss=0.04014, over 7279.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2622, pruned_loss=0.04235, over 1429889.66 frames.], batch size: 24, lr: 1.66e-04
2022-05-29 06:51:07,730 INFO [train.py:842] (1/4) Epoch 33, batch 7250, loss[loss=0.1723, simple_loss=0.2638, pruned_loss=0.0404, over 7276.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2615, pruned_loss=0.0416, over 1426792.79 frames.], batch size: 19, lr: 1.66e-04
2022-05-29 06:51:46,993 INFO [train.py:842] (1/4) Epoch 33, batch 7300, loss[loss=0.2091, simple_loss=0.3022, pruned_loss=0.05802, over 7176.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2616, pruned_loss=0.04176, over 1427406.03 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:52:26,336 INFO [train.py:842] (1/4) Epoch 33, batch 7350, loss[loss=0.1753, simple_loss=0.2694, pruned_loss=0.04063, over 7222.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2616, pruned_loss=0.04164, over 1426592.62 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 06:53:05,853 INFO [train.py:842] (1/4) Epoch 33, batch 7400, loss[loss=0.1587, simple_loss=0.2497, pruned_loss=0.03387, over 5267.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2617, pruned_loss=0.04177, over 1422175.51 frames.], batch size: 53, lr: 1.66e-04
2022-05-29 06:53:45,077 INFO [train.py:842] (1/4) Epoch 33, batch 7450, loss[loss=0.1564, simple_loss=0.2435, pruned_loss=0.03471, over 7276.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2612, pruned_loss=0.04161, over 1415097.03 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:54:24,410 INFO [train.py:842] (1/4) Epoch 33, batch 7500, loss[loss=0.18, simple_loss=0.27, pruned_loss=0.04501, over 6356.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2614, pruned_loss=0.04157, over 1418872.07 frames.], batch size: 37, lr: 1.66e-04
2022-05-29 06:55:03,721 INFO [train.py:842] (1/4) Epoch 33, batch 7550, loss[loss=0.1459, simple_loss=0.2259, pruned_loss=0.033, over 7412.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2595, pruned_loss=0.04102, over 1421557.10 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:55:43,339 INFO [train.py:842] (1/4) Epoch 33, batch 7600, loss[loss=0.2007, simple_loss=0.2958, pruned_loss=0.05277, over 7105.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2601, pruned_loss=0.04079, over 1426616.72 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 06:56:22,528 INFO [train.py:842] (1/4) Epoch 33, batch 7650, loss[loss=0.1431, simple_loss=0.2205, pruned_loss=0.03285, over 7276.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2608, pruned_loss=0.04093, over 1425264.89 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:57:02,116 INFO [train.py:842] (1/4) Epoch 33, batch 7700, loss[loss=0.1432, simple_loss=0.2237, pruned_loss=0.03136, over 6827.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2605, pruned_loss=0.04125, over 1423932.36 frames.], batch size: 15, lr: 1.66e-04
2022-05-29 06:57:41,096 INFO [train.py:842] (1/4) Epoch 33, batch 7750, loss[loss=0.203, simple_loss=0.2855, pruned_loss=0.06022, over 7437.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2626, pruned_loss=0.04216, over 1422971.29 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:58:20,691 INFO [train.py:842] (1/4) Epoch 33, batch 7800, loss[loss=0.1754, simple_loss=0.2695, pruned_loss=0.04067, over 6751.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2618, pruned_loss=0.04204, over 1423552.97 frames.], batch size: 31, lr: 1.66e-04
2022-05-29 06:58:59,886 INFO [train.py:842] (1/4) Epoch 33, batch 7850, loss[loss=0.1786, simple_loss=0.272, pruned_loss=0.04263, over 7327.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2623, pruned_loss=0.0423, over 1426095.67 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:59:39,475 INFO [train.py:842] (1/4) Epoch 33, batch 7900, loss[loss=0.1676, simple_loss=0.2675, pruned_loss=0.03382, over 7331.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2627, pruned_loss=0.04272, over 1429724.34 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 07:00:18,955 INFO [train.py:842] (1/4) Epoch 33, batch 7950, loss[loss=0.2001, simple_loss=0.2903, pruned_loss=0.05499, over 6388.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2621, pruned_loss=0.04241, over 1427051.56 frames.], batch size: 38, lr: 1.66e-04
2022-05-29 07:00:58,321 INFO [train.py:842] (1/4) Epoch 33, batch 8000, loss[loss=0.1884, simple_loss=0.2803, pruned_loss=0.04829, over 7142.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2634, pruned_loss=0.04255, over 1425651.80 frames.], batch size: 28, lr: 1.66e-04
2022-05-29 07:01:37,421 INFO [train.py:842] (1/4) Epoch 33, batch 8050, loss[loss=0.2135, simple_loss=0.299, pruned_loss=0.06396, over 7102.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2647, pruned_loss=0.04325, over 1425649.52 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 07:02:17,300 INFO [train.py:842] (1/4) Epoch 33, batch 8100, loss[loss=0.168, simple_loss=0.2634, pruned_loss=0.03628, over 7215.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2636, pruned_loss=0.04255, over 1425333.56 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 07:02:56,280 INFO [train.py:842] (1/4) Epoch 33, batch 8150, loss[loss=0.2078, simple_loss=0.2983, pruned_loss=0.05867, over 7343.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2647, pruned_loss=0.04313, over 1422916.26 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 07:03:35,664 INFO [train.py:842] (1/4) Epoch 33, batch 8200, loss[loss=0.1782, simple_loss=0.2628, pruned_loss=0.04679, over 4876.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2639, pruned_loss=0.04264, over 1420493.68 frames.], batch size: 52, lr: 1.66e-04
2022-05-29 07:04:25,765 INFO [train.py:842] (1/4) Epoch 33, batch 8250, loss[loss=0.1329, simple_loss=0.2216, pruned_loss=0.02213, over 7005.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2633, pruned_loss=0.04242, over 1426576.13 frames.], batch size: 16, lr: 1.66e-04
2022-05-29 07:05:05,271 INFO [train.py:842] (1/4) Epoch 33, batch 8300, loss[loss=0.1378, simple_loss=0.2167, pruned_loss=0.02948, over 6993.00 frames.], tot_loss[loss=0.1741, simple_loss=0.263, pruned_loss=0.04261, over 1423543.46 frames.], batch size: 16, lr: 1.66e-04
2022-05-29 07:05:44,139 INFO [train.py:842] (1/4) Epoch 33, batch 8350, loss[loss=0.1597, simple_loss=0.2542, pruned_loss=0.03264, over 7225.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2637, pruned_loss=0.04284, over 1422696.98 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 07:06:23,451 INFO [train.py:842] (1/4) Epoch 33, batch 8400, loss[loss=0.1556, simple_loss=0.2472, pruned_loss=0.03206, over 7329.00 frames.], tot_loss[loss=0.174, simple_loss=0.2631, pruned_loss=0.04245, over 1417333.91 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 07:07:02,630 INFO [train.py:842] (1/4) Epoch 33, batch 8450, loss[loss=0.2106, simple_loss=0.2865, pruned_loss=0.06739, over 7064.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2626, pruned_loss=0.04216, over 1421018.27 frames.], batch size: 28, lr: 1.66e-04
2022-05-29 07:07:54,012 INFO [train.py:842] (1/4) Epoch 33, batch 8500, loss[loss=0.2363, simple_loss=0.324, pruned_loss=0.07436, over 7312.00 frames.], tot_loss[loss=0.173, simple_loss=0.2621, pruned_loss=0.04198, over 1423919.12 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 07:08:33,111 INFO [train.py:842] (1/4) Epoch 33, batch 8550, loss[loss=0.2055, simple_loss=0.2867, pruned_loss=0.06217, over 6876.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2616, pruned_loss=0.04172, over 1423754.25 frames.], batch size: 31, lr: 1.66e-04
2022-05-29 07:09:23,043 INFO [train.py:842] (1/4) Epoch 33, batch 8600, loss[loss=0.246, simple_loss=0.3331, pruned_loss=0.07941, over 5152.00 frames.], tot_loss[loss=0.173, simple_loss=0.2619, pruned_loss=0.04209, over 1416852.50 frames.], batch size: 52, lr: 1.65e-04
2022-05-29 07:10:01,969 INFO [train.py:842] (1/4) Epoch 33, batch 8650, loss[loss=0.2294, simple_loss=0.3137, pruned_loss=0.07252, over 7158.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2635, pruned_loss=0.04283, over 1407735.04 frames.], batch size: 26, lr: 1.65e-04
2022-05-29 07:10:41,461 INFO [train.py:842] (1/4) Epoch 33, batch 8700, loss[loss=0.233, simple_loss=0.3048, pruned_loss=0.08062, over 4841.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2643, pruned_loss=0.04313, over 1403860.91 frames.], batch size: 52, lr: 1.65e-04
2022-05-29 07:11:20,629 INFO [train.py:842] (1/4) Epoch 33, batch 8750, loss[loss=0.2122, simple_loss=0.3062, pruned_loss=0.05913, over 6413.00 frames.], tot_loss[loss=0.176, simple_loss=0.2657, pruned_loss=0.0432, over 1407328.80 frames.], batch size: 37, lr: 1.65e-04
2022-05-29 07:11:59,656 INFO [train.py:842] (1/4) Epoch 33, batch 8800, loss[loss=0.1701, simple_loss=0.2633, pruned_loss=0.03846, over 7148.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2655, pruned_loss=0.04274, over 1403027.06 frames.], batch size: 20, lr: 1.65e-04
2022-05-29 07:12:38,535 INFO [train.py:842] (1/4) Epoch 33, batch 8850, loss[loss=0.1576, simple_loss=0.2517, pruned_loss=0.03178, over 7217.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2672, pruned_loss=0.04398, over 1390715.54 frames.], batch size: 21, lr: 1.65e-04
2022-05-29 07:13:17,800 INFO [train.py:842] (1/4) Epoch 33, batch 8900, loss[loss=0.1815, simple_loss=0.2877, pruned_loss=0.03763, over 7136.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2669, pruned_loss=0.04403, over 1390873.95 frames.], batch size: 26, lr: 1.65e-04
2022-05-29 07:13:56,506 INFO [train.py:842] (1/4) Epoch 33, batch 8950, loss[loss=0.1739, simple_loss=0.2554, pruned_loss=0.04618, over 7266.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2678, pruned_loss=0.04466, over 1383783.09 frames.], batch size: 19, lr: 1.65e-04
2022-05-29 07:14:35,698 INFO [train.py:842] (1/4) Epoch 33, batch 9000, loss[loss=0.161, simple_loss=0.2619, pruned_loss=0.03001, over 7212.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2668, pruned_loss=0.04441, over 1378623.38 frames.], batch size: 23, lr: 1.65e-04
2022-05-29 07:14:35,699 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 07:14:45,199 INFO [train.py:871] (1/4) Epoch 33, validation: loss=0.1641, simple_loss=0.2616, pruned_loss=0.03332, over 868885.00 frames.
2022-05-29 07:15:24,278 INFO [train.py:842] (1/4) Epoch 33, batch 9050, loss[loss=0.1833, simple_loss=0.2695, pruned_loss=0.04852, over 7048.00 frames.], tot_loss[loss=0.178, simple_loss=0.2667, pruned_loss=0.04461, over 1373229.34 frames.], batch size: 28, lr: 1.65e-04
2022-05-29 07:16:03,491 INFO [train.py:842] (1/4) Epoch 33, batch 9100, loss[loss=0.1701, simple_loss=0.2544, pruned_loss=0.04284, over 5061.00 frames.], tot_loss[loss=0.1785, simple_loss=0.267, pruned_loss=0.04498, over 1353970.26 frames.], batch size: 52, lr: 1.65e-04
2022-05-29 07:16:41,693 INFO [train.py:842] (1/4) Epoch 33, batch 9150, loss[loss=0.2377, simple_loss=0.3021, pruned_loss=0.0867, over 4836.00 frames.], tot_loss[loss=0.182, simple_loss=0.2701, pruned_loss=0.04698, over 1307369.71 frames.], batch size: 52, lr: 1.65e-04
2022-05-29 07:17:29,984 INFO [train.py:842] (1/4) Epoch 34, batch 0, loss[loss=0.1831, simple_loss=0.273, pruned_loss=0.04659, over 7438.00 frames.], tot_loss[loss=0.1831, simple_loss=0.273, pruned_loss=0.04659, over 7438.00 frames.], batch size: 20, lr: 1.63e-04
2022-05-29 07:18:09,758 INFO [train.py:842] (1/4) Epoch 34, batch 50, loss[loss=0.1486, simple_loss=0.245, pruned_loss=0.02616, over 7059.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2574, pruned_loss=0.03991, over 324537.32 frames.], batch size: 28, lr: 1.63e-04
2022-05-29 07:18:49,378 INFO [train.py:842] (1/4) Epoch 34, batch 100, loss[loss=0.1791, simple_loss=0.2692, pruned_loss=0.04448, over 7109.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2616, pruned_loss=0.04265, over 565500.08 frames.], batch size: 21, lr: 1.63e-04
2022-05-29 07:19:28,876 INFO [train.py:842] (1/4) Epoch 34, batch 150, loss[loss=0.15, simple_loss=0.2361, pruned_loss=0.03195, over 7447.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2606, pruned_loss=0.04278, over 755781.87 frames.], batch size: 19, lr: 1.63e-04
2022-05-29 07:20:08,757 INFO [train.py:842] (1/4) Epoch 34, batch 200, loss[loss=0.1533, simple_loss=0.2302, pruned_loss=0.03816, over 7288.00 frames.], tot_loss[loss=0.173, simple_loss=0.2603, pruned_loss=0.04288, over 905638.95 frames.], batch size: 17, lr: 1.63e-04
2022-05-29 07:20:48,026 INFO [train.py:842] (1/4) Epoch 34, batch 250, loss[loss=0.2217, simple_loss=0.3116, pruned_loss=0.06588, over 5060.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2607, pruned_loss=0.04292, over 1012214.19 frames.], batch size: 52, lr: 1.63e-04
2022-05-29 07:21:27,682 INFO [train.py:842] (1/4) Epoch 34, batch 300, loss[loss=0.1789, simple_loss=0.2629, pruned_loss=0.04745, over 7385.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2608, pruned_loss=0.04211, over 1102994.83 frames.], batch size: 23, lr: 1.63e-04
2022-05-29 07:22:06,479 INFO [train.py:842] (1/4) Epoch 34, batch 350, loss[loss=0.1307, simple_loss=0.2167, pruned_loss=0.02241, over 7148.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2629, pruned_loss=0.04259, over 1168386.70 frames.], batch size: 17, lr: 1.63e-04
2022-05-29 07:22:46,337 INFO [train.py:842] (1/4) Epoch 34, batch 400, loss[loss=0.1737, simple_loss=0.2608, pruned_loss=0.04326, over 7410.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2602, pruned_loss=0.04134, over 1229667.27 frames.], batch size: 21, lr: 1.63e-04
2022-05-29 07:23:25,549 INFO [train.py:842] (1/4) Epoch 34, batch 450, loss[loss=0.1771, simple_loss=0.2518, pruned_loss=0.05122, over 7423.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2623, pruned_loss=0.04222, over 1274897.84 frames.], batch size: 18, lr: 1.63e-04
2022-05-29 07:24:05,250 INFO [train.py:842] (1/4) Epoch 34, batch 500, loss[loss=0.1719, simple_loss=0.2605, pruned_loss=0.04165, over 7290.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2621, pruned_loss=0.04217, over 1307173.49 frames.], batch size: 24, lr: 1.63e-04
2022-05-29 07:24:44,471 INFO [train.py:842] (1/4) Epoch 34, batch 550, loss[loss=0.1354, simple_loss=0.2315, pruned_loss=0.01972, over 6439.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2626, pruned_loss=0.04196, over 1330386.68 frames.], batch size: 37, lr: 1.63e-04
2022-05-29 07:25:24,070 INFO [train.py:842] (1/4) Epoch 34, batch 600, loss[loss=0.1896, simple_loss=0.2885, pruned_loss=0.04534, over 7275.00 frames.], tot_loss[loss=0.174, simple_loss=0.2638, pruned_loss=0.04215, over 1352701.84 frames.], batch size: 25, lr: 1.63e-04
2022-05-29 07:26:03,433 INFO [train.py:842] (1/4) Epoch 34, batch 650, loss[loss=0.1565, simple_loss=0.2409, pruned_loss=0.03605, over 7163.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2641, pruned_loss=0.0425, over 1370823.33 frames.], batch size: 18, lr: 1.63e-04
2022-05-29 07:26:46,161 INFO [train.py:842] (1/4) Epoch 34, batch 700, loss[loss=0.1602, simple_loss=0.2329, pruned_loss=0.04378, over 7126.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2624, pruned_loss=0.04175, over 1377616.51 frames.], batch size: 17, lr: 1.63e-04
2022-05-29 07:27:25,324 INFO [train.py:842] (1/4) Epoch 34, batch 750, loss[loss=0.2155, simple_loss=0.3023, pruned_loss=0.06433, over 7191.00 frames.], tot_loss[loss=0.1723, simple_loss=0.262, pruned_loss=0.04127, over 1388923.19 frames.], batch size: 23, lr: 1.63e-04
2022-05-29 07:28:04,927 INFO [train.py:842] (1/4) Epoch 34, batch 800, loss[loss=0.1588, simple_loss=0.244, pruned_loss=0.03686, over 7281.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2615, pruned_loss=0.04084, over 1394956.21 frames.], batch size: 18, lr: 1.63e-04
2022-05-29 07:28:44,202 INFO [train.py:842] (1/4) Epoch 34, batch 850, loss[loss=0.1644, simple_loss=0.2564, pruned_loss=0.03619, over 6294.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2608, pruned_loss=0.04071, over 1404469.33 frames.], batch size: 37, lr: 1.63e-04
2022-05-29 07:29:23,937 INFO [train.py:842] (1/4) Epoch 34, batch 900, loss[loss=0.1655, simple_loss=0.2677, pruned_loss=0.0316, over 5122.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2598, pruned_loss=0.0406, over 1409543.59 frames.], batch size: 52, lr: 1.63e-04
2022-05-29 07:30:03,380 INFO [train.py:842] (1/4) Epoch 34, batch 950, loss[loss=0.1765, simple_loss=0.2546, pruned_loss=0.04916, over 7274.00 frames.], tot_loss[loss=0.1709, simple_loss=0.26, pruned_loss=0.04087, over 1407578.08 frames.], batch size: 18, lr: 1.63e-04
2022-05-29 07:30:43,029 INFO [train.py:842] (1/4) Epoch 34, batch 1000, loss[loss=0.1781, simple_loss=0.2634, pruned_loss=0.04636, over 7417.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2604, pruned_loss=0.04114, over 1408449.06 frames.], batch size: 20, lr: 1.63e-04
2022-05-29 07:31:22,371 INFO [train.py:842] (1/4) Epoch 34, batch 1050, loss[loss=0.131, simple_loss=0.2189, pruned_loss=0.02152, over 7168.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2605, pruned_loss=0.04086, over 1414500.46 frames.], batch size: 19, lr: 1.63e-04
2022-05-29 07:32:01,629 INFO [train.py:842] (1/4) Epoch 34, batch 1100, loss[loss=0.2224, simple_loss=0.3051, pruned_loss=0.06985, over 6415.00 frames.], tot_loss[loss=0.171, simple_loss=0.2606, pruned_loss=0.04067, over 1413213.85 frames.], batch size: 38, lr: 1.63e-04
2022-05-29 07:32:40,970 INFO [train.py:842] (1/4) Epoch 34, batch 1150, loss[loss=0.1663, simple_loss=0.2473, pruned_loss=0.04265, over 7429.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2611, pruned_loss=0.04101, over 1415781.66 frames.], batch size: 20, lr: 1.63e-04
2022-05-29 07:33:20,625 INFO [train.py:842] (1/4) Epoch 34, batch 1200, loss[loss=0.1814, simple_loss=0.2712, pruned_loss=0.0458, over 7194.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2617, pruned_loss=0.04132, over 1419841.48 frames.], batch size: 23, lr: 1.63e-04
2022-05-29 07:33:59,726 INFO [train.py:842] (1/4) Epoch 34, batch 1250, loss[loss=0.1819, simple_loss=0.2819, pruned_loss=0.04098, over 7338.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2619, pruned_loss=0.0419, over 1418165.02 frames.], batch size: 22, lr: 1.63e-04
2022-05-29 07:34:39,371 INFO [train.py:842] (1/4) Epoch 34, batch 1300, loss[loss=0.2654, simple_loss=0.3437, pruned_loss=0.09351, over 7162.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2609, pruned_loss=0.04198, over 1416730.67 frames.], batch size: 26, lr: 1.63e-04
2022-05-29 07:35:18,845 INFO [train.py:842] (1/4) Epoch 34, batch 1350, loss[loss=0.1849, simple_loss=0.2803, pruned_loss=0.04471, over 7224.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2607, pruned_loss=0.04189, over 1417939.89 frames.], batch size: 21, lr: 1.63e-04
2022-05-29 07:35:58,711 INFO [train.py:842] (1/4) Epoch 34, batch 1400, loss[loss=0.1661, simple_loss=0.2524, pruned_loss=0.03989, over 7255.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2608, pruned_loss=0.04199, over 1421409.03 frames.], batch size: 19, lr: 1.63e-04
2022-05-29 07:36:38,129 INFO [train.py:842] (1/4) Epoch 34, batch 1450, loss[loss=0.1835, simple_loss=0.2736, pruned_loss=0.04667, over 7415.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2608, pruned_loss=0.04164, over 1425678.79 frames.], batch size: 21, lr: 1.63e-04
2022-05-29 07:37:17,594 INFO [train.py:842] (1/4) Epoch 34, batch 1500, loss[loss=0.1779, simple_loss=0.2704, pruned_loss=0.04276, over 7372.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2616, pruned_loss=0.04149, over 1424306.32 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 07:37:56,875 INFO [train.py:842] (1/4) Epoch 34, batch 1550, loss[loss=0.1597, simple_loss=0.251, pruned_loss=0.03416, over 7295.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2618, pruned_loss=0.04162, over 1422236.14 frames.], batch size: 24, lr: 1.62e-04
2022-05-29 07:38:36,526 INFO [train.py:842] (1/4) Epoch 34, batch 1600, loss[loss=0.1513, simple_loss=0.2401, pruned_loss=0.03123, over 7332.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2616, pruned_loss=0.0414, over 1423804.78 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 07:39:15,575 INFO [train.py:842] (1/4) Epoch 34, batch 1650, loss[loss=0.1537, simple_loss=0.2408, pruned_loss=0.03331, over 7205.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2628, pruned_loss=0.04188, over 1423829.58 frames.], batch size: 22, lr: 1.62e-04
2022-05-29 07:39:54,971 INFO [train.py:842] (1/4) Epoch 34, batch 1700, loss[loss=0.1931, simple_loss=0.2787, pruned_loss=0.05375, over 7386.00 frames.], tot_loss[loss=0.172, simple_loss=0.2619, pruned_loss=0.04107, over 1427538.72 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 07:40:34,181 INFO [train.py:842] (1/4) Epoch 34, batch 1750, loss[loss=0.1789, simple_loss=0.2791, pruned_loss=0.03933, over 7019.00 frames.], tot_loss[loss=0.1724, simple_loss=0.262, pruned_loss=0.04139, over 1421656.46 frames.], batch size: 28, lr: 1.62e-04
2022-05-29 07:41:13,638 INFO [train.py:842] (1/4) Epoch 34, batch 1800, loss[loss=0.1375, simple_loss=0.2159, pruned_loss=0.02956, over 7265.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2618, pruned_loss=0.04141, over 1422960.94 frames.], batch size: 17, lr: 1.62e-04
2022-05-29 07:41:52,940 INFO [train.py:842] (1/4) Epoch 34, batch 1850, loss[loss=0.1595, simple_loss=0.2494, pruned_loss=0.03477, over 7329.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2627, pruned_loss=0.04199, over 1414087.73 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 07:42:32,349 INFO [train.py:842] (1/4) Epoch 34, batch 1900, loss[loss=0.1817, simple_loss=0.2709, pruned_loss=0.04622, over 6795.00 frames.], tot_loss[loss=0.1734, simple_loss=0.263, pruned_loss=0.04194, over 1409754.24 frames.], batch size: 31, lr: 1.62e-04
2022-05-29 07:43:11,708 INFO [train.py:842] (1/4) Epoch 34, batch 1950, loss[loss=0.1514, simple_loss=0.2385, pruned_loss=0.03213, over 7002.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2631, pruned_loss=0.04206, over 1416099.61 frames.], batch size: 16, lr: 1.62e-04
2022-05-29 07:43:51,632 INFO [train.py:842] (1/4) Epoch 34, batch 2000, loss[loss=0.1567, simple_loss=0.2423, pruned_loss=0.03554, over 7418.00 frames.], tot_loss[loss=0.173, simple_loss=0.2628, pruned_loss=0.04157, over 1420768.33 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 07:44:31,028 INFO [train.py:842] (1/4) Epoch 34, batch 2050, loss[loss=0.1594, simple_loss=0.2553, pruned_loss=0.03173, over 7168.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2615, pruned_loss=0.04135, over 1420092.30 frames.], batch size: 26, lr: 1.62e-04
2022-05-29 07:45:10,487 INFO [train.py:842] (1/4) Epoch 34, batch 2100, loss[loss=0.2116, simple_loss=0.3042, pruned_loss=0.05953, over 7222.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2632, pruned_loss=0.04207, over 1423533.24 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 07:45:49,747 INFO [train.py:842] (1/4) Epoch 34, batch 2150, loss[loss=0.1958, simple_loss=0.2821, pruned_loss=0.05477, over 7305.00 frames.], tot_loss[loss=0.1725, simple_loss=0.262, pruned_loss=0.04153, over 1422708.30 frames.], batch size: 24, lr: 1.62e-04
2022-05-29 07:46:29,278 INFO [train.py:842] (1/4) Epoch 34, batch 2200, loss[loss=0.188, simple_loss=0.2893, pruned_loss=0.04337, over 7318.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2624, pruned_loss=0.04159, over 1426238.32 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 07:47:08,714 INFO [train.py:842] (1/4) Epoch 34, batch 2250, loss[loss=0.155, simple_loss=0.2354, pruned_loss=0.03736, over 7269.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2624, pruned_loss=0.04221, over 1422590.84 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 07:47:47,851 INFO [train.py:842] (1/4) Epoch 34, batch 2300, loss[loss=0.1775, simple_loss=0.266, pruned_loss=0.04448, over 7166.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2637, pruned_loss=0.04244, over 1423783.95 frames.], batch size: 19, lr: 1.62e-04
2022-05-29 07:48:27,167 INFO [train.py:842] (1/4) Epoch 34, batch 2350, loss[loss=0.1982, simple_loss=0.2966, pruned_loss=0.04991, over 7168.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2627, pruned_loss=0.0421, over 1424318.68 frames.], batch size: 19, lr: 1.62e-04
2022-05-29 07:49:06,892 INFO [train.py:842] (1/4) Epoch 34, batch 2400, loss[loss=0.1771, simple_loss=0.2742, pruned_loss=0.03995, over 7386.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2617, pruned_loss=0.04175, over 1425559.63 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 07:49:45,784 INFO [train.py:842] (1/4) Epoch 34, batch 2450, loss[loss=0.1662, simple_loss=0.2582, pruned_loss=0.03707, over 7207.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2624, pruned_loss=0.04222, over 1419672.65 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 07:50:25,155 INFO [train.py:842] (1/4) Epoch 34, batch 2500, loss[loss=0.1821, simple_loss=0.2596, pruned_loss=0.0523, over 6986.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2632, pruned_loss=0.04259, over 1417701.39 frames.], batch size: 16, lr: 1.62e-04
2022-05-29 07:51:04,343 INFO [train.py:842] (1/4) Epoch 34, batch 2550, loss[loss=0.2056, simple_loss=0.3016, pruned_loss=0.05483, over 7344.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2623, pruned_loss=0.04191, over 1419686.51 frames.], batch size: 22, lr: 1.62e-04
2022-05-29 07:51:44,011 INFO [train.py:842] (1/4) Epoch 34, batch 2600, loss[loss=0.2025, simple_loss=0.2933, pruned_loss=0.05584, over 7070.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2621, pruned_loss=0.04208, over 1419025.75 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 07:52:23,462 INFO [train.py:842] (1/4) Epoch 34, batch 2650, loss[loss=0.1959, simple_loss=0.2795, pruned_loss=0.05611, over 7329.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2614, pruned_loss=0.04202, over 1421219.61 frames.], batch size: 22, lr: 1.62e-04
2022-05-29 07:53:03,164 INFO [train.py:842] (1/4) Epoch 34, batch 2700, loss[loss=0.1546, simple_loss=0.2372, pruned_loss=0.03598, over 7280.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2613, pruned_loss=0.04167, over 1426260.89 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 07:53:42,424 INFO [train.py:842] (1/4) Epoch 34, batch 2750, loss[loss=0.152, simple_loss=0.2352, pruned_loss=0.03435, over 7325.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2609, pruned_loss=0.04141, over 1424611.97 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 07:54:21,981 INFO [train.py:842] (1/4) Epoch 34, batch 2800, loss[loss=0.1614, simple_loss=0.2447, pruned_loss=0.03906, over 7401.00 frames.], tot_loss[loss=0.1718, simple_loss=0.261, pruned_loss=0.04128, over 1430222.56 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 07:55:01,296 INFO [train.py:842] (1/4) Epoch 34, batch 2850, loss[loss=0.2055, simple_loss=0.2944, pruned_loss=0.05833, over 7198.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2614, pruned_loss=0.04157, over 1431031.56 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 07:55:40,894 INFO [train.py:842] (1/4) Epoch 34, batch 2900, loss[loss=0.1846, simple_loss=0.2739, pruned_loss=0.04765, over 7144.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2622, pruned_loss=0.04228, over 1427508.32 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 07:56:20,245 INFO [train.py:842] (1/4) Epoch 34, batch 2950, loss[loss=0.1662, simple_loss=0.2606, pruned_loss=0.03588, over 7142.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2611, pruned_loss=0.04189, over 1427639.65 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 07:56:59,642 INFO [train.py:842] (1/4) Epoch 34, batch 3000, loss[loss=0.1326, simple_loss=0.226, pruned_loss=0.01957, over 7353.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2608, pruned_loss=0.04172, over 1427687.42 frames.], batch size: 19, lr: 1.62e-04
2022-05-29 07:56:59,643 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 07:57:09,215 INFO [train.py:871] (1/4) Epoch 34, validation: loss=0.165, simple_loss=0.2618, pruned_loss=0.03414, over 868885.00 frames.
2022-05-29 07:57:48,364 INFO [train.py:842] (1/4) Epoch 34, batch 3050, loss[loss=0.1187, simple_loss=0.2106, pruned_loss=0.01343, over 7354.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2604, pruned_loss=0.04088, over 1428913.52 frames.], batch size: 19, lr: 1.62e-04
2022-05-29 07:58:28,176 INFO [train.py:842] (1/4) Epoch 34, batch 3100, loss[loss=0.1659, simple_loss=0.2515, pruned_loss=0.04015, over 7231.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2609, pruned_loss=0.04093, over 1430153.83 frames.], batch size: 16, lr: 1.62e-04
2022-05-29 07:59:07,349 INFO [train.py:842] (1/4) Epoch 34, batch 3150, loss[loss=0.1408, simple_loss=0.2251, pruned_loss=0.02825, over 7275.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2603, pruned_loss=0.04076, over 1429847.52 frames.], batch size: 17, lr: 1.62e-04
2022-05-29 07:59:46,844 INFO [train.py:842] (1/4) Epoch 34, batch 3200, loss[loss=0.1908, simple_loss=0.2723, pruned_loss=0.05471, over 5183.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2596, pruned_loss=0.04059, over 1426230.67 frames.], batch size: 52, lr: 1.62e-04
2022-05-29 08:00:26,129 INFO [train.py:842] (1/4) Epoch 34, batch 3250, loss[loss=0.1606, simple_loss=0.238, pruned_loss=0.0416, over 7145.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2596, pruned_loss=0.04084, over 1423345.21 frames.], batch size: 17, lr: 1.62e-04
2022-05-29 08:01:05,719 INFO [train.py:842] (1/4) Epoch 34, batch 3300, loss[loss=0.2008, simple_loss=0.2914, pruned_loss=0.05508, over 7080.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2598, pruned_loss=0.04097, over 1419481.04 frames.], batch size: 28, lr: 1.62e-04
2022-05-29 08:01:45,247 INFO [train.py:842] (1/4) Epoch 34, batch 3350, loss[loss=0.1438, simple_loss=0.2418, pruned_loss=0.02289, over 7152.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2592, pruned_loss=0.0407, over 1422017.43 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:02:24,792 INFO [train.py:842] (1/4) Epoch 34, batch 3400, loss[loss=0.1861, simple_loss=0.2858, pruned_loss=0.04315, over 7223.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2602, pruned_loss=0.04106, over 1422177.31 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 08:03:04,116 INFO [train.py:842] (1/4) Epoch 34, batch 3450, loss[loss=0.132, simple_loss=0.2217, pruned_loss=0.02112, over 6990.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2602, pruned_loss=0.04119, over 1427668.84 frames.], batch size: 16, lr: 1.62e-04
2022-05-29 08:03:43,424 INFO [train.py:842] (1/4) Epoch 34, batch 3500, loss[loss=0.1732, simple_loss=0.2716, pruned_loss=0.03741, over 7192.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2628, pruned_loss=0.04215, over 1429086.55 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 08:04:22,876 INFO [train.py:842] (1/4) Epoch 34, batch 3550, loss[loss=0.1475, simple_loss=0.2281, pruned_loss=0.03345, over 7289.00 frames.], tot_loss[loss=0.173, simple_loss=0.2622, pruned_loss=0.04197, over 1431370.69 frames.], batch size: 17, lr: 1.62e-04
2022-05-29 08:05:02,738 INFO [train.py:842] (1/4) Epoch 34, batch 3600, loss[loss=0.1371, simple_loss=0.2318, pruned_loss=0.02115, over 7321.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2622, pruned_loss=0.04204, over 1432945.85 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 08:05:41,978 INFO [train.py:842] (1/4) Epoch 34, batch 3650, loss[loss=0.1666, simple_loss=0.2458, pruned_loss=0.04373, over 7424.00 frames.], tot_loss[loss=0.1731, simple_loss=0.262, pruned_loss=0.04212, over 1430574.82 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:06:21,246 INFO [train.py:842] (1/4) Epoch 34, batch 3700, loss[loss=0.2143, simple_loss=0.2961, pruned_loss=0.06621, over 4992.00 frames.], tot_loss[loss=0.173, simple_loss=0.2619, pruned_loss=0.04206, over 1422799.71 frames.], batch size: 52, lr: 1.62e-04
2022-05-29 08:07:00,406 INFO [train.py:842] (1/4) Epoch 34, batch 3750, loss[loss=0.1852, simple_loss=0.265, pruned_loss=0.0527, over 7140.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2608, pruned_loss=0.04136, over 1420931.77 frames.], batch size: 17, lr: 1.62e-04
2022-05-29 08:07:40,296 INFO [train.py:842] (1/4) Epoch 34, batch 3800, loss[loss=0.2597, simple_loss=0.3405, pruned_loss=0.08946, over 7229.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2602, pruned_loss=0.04114, over 1423699.53 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:08:19,541 INFO [train.py:842] (1/4) Epoch 34, batch 3850, loss[loss=0.1573, simple_loss=0.2486, pruned_loss=0.03299, over 7086.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2615, pruned_loss=0.04166, over 1424997.86 frames.], batch size: 28, lr: 1.62e-04
2022-05-29 08:08:59,246 INFO [train.py:842] (1/4) Epoch 34, batch 3900, loss[loss=0.1653, simple_loss=0.2578, pruned_loss=0.03642, over 7357.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2605, pruned_loss=0.04094, over 1427942.18 frames.], batch size: 19, lr: 1.62e-04
2022-05-29 08:09:38,320 INFO [train.py:842] (1/4) Epoch 34, batch 3950, loss[loss=0.1696, simple_loss=0.2641, pruned_loss=0.0375, over 7331.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2602, pruned_loss=0.04095, over 1422587.57 frames.], batch size: 22, lr: 1.62e-04
2022-05-29 08:10:18,064 INFO [train.py:842] (1/4) Epoch 34, batch 4000, loss[loss=0.1611, simple_loss=0.2599, pruned_loss=0.03116, over 7248.00 frames.], tot_loss[loss=0.1706, simple_loss=0.26, pruned_loss=0.04061, over 1426178.40 frames.], batch size: 26, lr: 1.62e-04
2022-05-29 08:10:57,410 INFO [train.py:842] (1/4) Epoch 34, batch 4050, loss[loss=0.1398, simple_loss=0.2454, pruned_loss=0.01713, over 7432.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2599, pruned_loss=0.04081, over 1424901.93 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:11:36,847 INFO [train.py:842] (1/4) Epoch 34, batch 4100, loss[loss=0.167, simple_loss=0.256, pruned_loss=0.03902, over 7323.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2614, pruned_loss=0.04136, over 1422037.61 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:12:16,161 INFO [train.py:842] (1/4) Epoch 34, batch 4150, loss[loss=0.1548, simple_loss=0.2494, pruned_loss=0.03006, over 7434.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2612, pruned_loss=0.04113, over 1423931.06 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:12:55,706 INFO [train.py:842] (1/4) Epoch 34, batch 4200, loss[loss=0.2042, simple_loss=0.3051, pruned_loss=0.05161, over 6832.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2618, pruned_loss=0.04155, over 1421807.82 frames.], batch size: 31, lr: 1.62e-04
2022-05-29 08:13:35,023 INFO [train.py:842] (1/4) Epoch 34, batch 4250, loss[loss=0.1511, simple_loss=0.246, pruned_loss=0.02808, over 7315.00 frames.], tot_loss[loss=0.1724, simple_loss=0.262, pruned_loss=0.04139, over 1424268.20 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:14:14,630 INFO [train.py:842] (1/4) Epoch 34, batch 4300, loss[loss=0.1836, simple_loss=0.2801, pruned_loss=0.04353, over 6847.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2616, pruned_loss=0.04148, over 1425034.66 frames.], batch size: 31, lr: 1.62e-04
2022-05-29 08:14:53,545 INFO [train.py:842] (1/4) Epoch 34, batch 4350, loss[loss=0.1838, simple_loss=0.2868, pruned_loss=0.04036, over 7318.00 frames.], tot_loss[loss=0.173, simple_loss=0.2628, pruned_loss=0.04156, over 1424326.01 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 08:15:33,266 INFO [train.py:842] (1/4) Epoch 34, batch 4400, loss[loss=0.1606, simple_loss=0.2552, pruned_loss=0.03295, over 7228.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2626, pruned_loss=0.04166, over 1427491.36 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:16:12,429 INFO [train.py:842] (1/4) Epoch 34, batch 4450, loss[loss=0.2327, simple_loss=0.3149, pruned_loss=0.07523, over 7207.00 frames.], tot_loss[loss=0.174, simple_loss=0.2634, pruned_loss=0.04234, over 1426200.92 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 08:16:52,078 INFO [train.py:842] (1/4) Epoch 34, batch 4500, loss[loss=0.1398, simple_loss=0.2244, pruned_loss=0.02762, over 6775.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2631, pruned_loss=0.04255, over 1425699.10 frames.], batch size: 15, lr: 1.62e-04
2022-05-29 08:17:31,646 INFO [train.py:842] (1/4) Epoch 34, batch 4550, loss[loss=0.142, simple_loss=0.2294, pruned_loss=0.02731, over 7280.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2624, pruned_loss=0.04197, over 1426709.52 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 08:18:11,497 INFO [train.py:842] (1/4) Epoch 34, batch 4600, loss[loss=0.1917, simple_loss=0.2914, pruned_loss=0.04605, over 6437.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2618, pruned_loss=0.04222, over 1420857.27 frames.], batch size: 38, lr: 1.62e-04
2022-05-29 08:18:50,817 INFO [train.py:842] (1/4) Epoch 34, batch 4650, loss[loss=0.1537, simple_loss=0.2466, pruned_loss=0.03039, over 7408.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2614, pruned_loss=0.04196, over 1421310.96 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 08:19:30,490 INFO [train.py:842] (1/4) Epoch 34, batch 4700, loss[loss=0.1538, simple_loss=0.2332, pruned_loss=0.0372, over 7286.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2601, pruned_loss=0.04155, over 1421053.69 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 08:20:09,813 INFO [train.py:842] (1/4) Epoch 34, batch 4750, loss[loss=0.1678, simple_loss=0.2577, pruned_loss=0.0389, over 7146.00 frames.], tot_loss[loss=0.1725, simple_loss=0.261, pruned_loss=0.04203, over 1421355.57 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:20:49,487 INFO [train.py:842] (1/4) Epoch 34, batch 4800, loss[loss=0.2496, simple_loss=0.3219, pruned_loss=0.08865, over 7416.00 frames.], tot_loss[loss=0.173, simple_loss=0.2613, pruned_loss=0.04232, over 1424138.81 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 08:21:28,552 INFO [train.py:842] (1/4) Epoch 34, batch 4850, loss[loss=0.2062, simple_loss=0.2909, pruned_loss=0.06072, over 7408.00 frames.], tot_loss[loss=0.174, simple_loss=0.2628, pruned_loss=0.04263, over 1423917.69 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 08:22:08,235 INFO [train.py:842] (1/4) Epoch 34, batch 4900, loss[loss=0.1638, simple_loss=0.2636, pruned_loss=0.03194, over 7332.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2626, pruned_loss=0.0423, over 1423245.83 frames.], batch size: 22, lr: 1.62e-04
2022-05-29 08:22:47,446 INFO [train.py:842] (1/4) Epoch 34, batch 4950, loss[loss=0.1577, simple_loss=0.2441, pruned_loss=0.03562, over 6977.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2617, pruned_loss=0.04181, over 1421834.97 frames.], batch size: 16, lr: 1.62e-04
2022-05-29 08:23:27,063 INFO [train.py:842] (1/4) Epoch 34, batch 5000, loss[loss=0.1531, simple_loss=0.2524, pruned_loss=0.02691, over 7148.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2609, pruned_loss=0.04134, over 1419237.84 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:24:06,279 INFO [train.py:842] (1/4) Epoch 34, batch 5050, loss[loss=0.1931, simple_loss=0.2662, pruned_loss=0.06004, over 7278.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2606, pruned_loss=0.0412, over 1422952.41 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 08:24:46,030 INFO [train.py:842] (1/4) Epoch 34, batch 5100, loss[loss=0.1642, simple_loss=0.2691, pruned_loss=0.02965, over 7286.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2611, pruned_loss=0.04132, over 1426555.85 frames.], batch size: 25, lr: 1.62e-04
2022-05-29 08:25:25,127 INFO [train.py:842] (1/4) Epoch 34, batch 5150, loss[loss=0.194, simple_loss=0.2776, pruned_loss=0.05523, over 6653.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2612, pruned_loss=0.04185, over 1423672.99 frames.], batch size: 31, lr: 1.62e-04
2022-05-29 08:26:04,897 INFO [train.py:842] (1/4) Epoch 34, batch 5200, loss[loss=0.1455, simple_loss=0.2406, pruned_loss=0.02522, over 7425.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2601, pruned_loss=0.04133, over 1425340.56 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:26:44,181 INFO [train.py:842] (1/4) Epoch 34, batch 5250, loss[loss=0.2272, simple_loss=0.3023, pruned_loss=0.07604, over 7365.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2603, pruned_loss=0.04111, over 1426203.57 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 08:27:23,818 INFO [train.py:842] (1/4) Epoch 34, batch 5300, loss[loss=0.1499, simple_loss=0.2348, pruned_loss=0.03251, over 7299.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2601, pruned_loss=0.04105, over 1426195.47 frames.], batch size: 17, lr: 1.61e-04
2022-05-29 08:28:03,193 INFO [train.py:842] (1/4) Epoch 34, batch 5350, loss[loss=0.1308, simple_loss=0.2097, pruned_loss=0.02598, over 7142.00 frames.], tot_loss[loss=0.1727, simple_loss=0.261, pruned_loss=0.04219, over 1418340.00 frames.], batch size: 17, lr: 1.61e-04
2022-05-29 08:28:42,831 INFO [train.py:842] (1/4) Epoch 34, batch 5400, loss[loss=0.1963, simple_loss=0.2887, pruned_loss=0.05195, over 7295.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2626, pruned_loss=0.04279, over 1420917.89 frames.], batch size: 25, lr: 1.61e-04
2022-05-29 08:29:21,700 INFO [train.py:842] (1/4) Epoch 34, batch 5450, loss[loss=0.1715, simple_loss=0.2692, pruned_loss=0.03684, over 6526.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2627, pruned_loss=0.04323, over 1418945.62 frames.], batch size: 38, lr: 1.61e-04
2022-05-29 08:30:01,349 INFO [train.py:842] (1/4) Epoch 34, batch 5500, loss[loss=0.1783, simple_loss=0.2679, pruned_loss=0.04434, over 7209.00 frames.], tot_loss[loss=0.1744, simple_loss=0.263, pruned_loss=0.04292, over 1421575.37 frames.], batch size: 22, lr: 1.61e-04
2022-05-29 08:30:40,412 INFO [train.py:842] (1/4) Epoch 34, batch 5550, loss[loss=0.1777, simple_loss=0.2627, pruned_loss=0.04641, over 7232.00 frames.], tot_loss[loss=0.1753, simple_loss=0.264, pruned_loss=0.0433, over 1419908.89 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:31:19,922 INFO [train.py:842] (1/4) Epoch 34, batch 5600, loss[loss=0.1685, simple_loss=0.2674, pruned_loss=0.0348, over 7327.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2642, pruned_loss=0.04318, over 1419883.92 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:31:59,178 INFO [train.py:842] (1/4) Epoch 34, batch 5650, loss[loss=0.2072, simple_loss=0.2907, pruned_loss=0.0619, over 7183.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2624, pruned_loss=0.04224, over 1418889.37 frames.], batch size: 23, lr: 1.61e-04
2022-05-29 08:32:38,754 INFO [train.py:842] (1/4) Epoch 34, batch 5700, loss[loss=0.1742, simple_loss=0.2639, pruned_loss=0.04221, over 7319.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2622, pruned_loss=0.04206, over 1420786.51 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:33:17,970 INFO [train.py:842] (1/4) Epoch 34, batch 5750, loss[loss=0.1647, simple_loss=0.2522, pruned_loss=0.03857, over 7353.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2618, pruned_loss=0.04168, over 1423312.79 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 08:33:57,860 INFO [train.py:842] (1/4) Epoch 34, batch 5800, loss[loss=0.147, simple_loss=0.2446, pruned_loss=0.02473, over 7313.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2627, pruned_loss=0.04223, over 1423411.92 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:34:37,243 INFO [train.py:842] (1/4) Epoch 34, batch 5850, loss[loss=0.2138, simple_loss=0.3066, pruned_loss=0.06044, over 6300.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.04228, over 1425120.70 frames.], batch size: 37, lr: 1.61e-04
2022-05-29 08:35:27,461 INFO [train.py:842] (1/4) Epoch 34, batch 5900, loss[loss=0.1582, simple_loss=0.2539, pruned_loss=0.03126, over 7225.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2619, pruned_loss=0.04189, over 1420617.36 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:36:06,829 INFO [train.py:842] (1/4) Epoch 34, batch 5950, loss[loss=0.1913, simple_loss=0.2849, pruned_loss=0.04883, over 6470.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2612, pruned_loss=0.04184, over 1421710.68 frames.], batch size: 37, lr: 1.61e-04
2022-05-29 08:36:46,433 INFO [train.py:842] (1/4) Epoch 34, batch 6000, loss[loss=0.168, simple_loss=0.2484, pruned_loss=0.0438, over 7023.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2629, pruned_loss=0.04282, over 1422095.42 frames.], batch size: 16, lr: 1.61e-04
2022-05-29 08:36:46,434 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 08:36:56,770 INFO [train.py:871] (1/4) Epoch 34, validation: loss=0.1642, simple_loss=0.2613, pruned_loss=0.03356, over 868885.00 frames.
2022-05-29 08:37:36,214 INFO [train.py:842] (1/4) Epoch 34, batch 6050, loss[loss=0.156, simple_loss=0.2465, pruned_loss=0.03268, over 5339.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2634, pruned_loss=0.04306, over 1425571.08 frames.], batch size: 52, lr: 1.61e-04
2022-05-29 08:38:15,769 INFO [train.py:842] (1/4) Epoch 34, batch 6100, loss[loss=0.1872, simple_loss=0.2853, pruned_loss=0.04452, over 7237.00 frames.], tot_loss[loss=0.175, simple_loss=0.2639, pruned_loss=0.04309, over 1424526.63 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:38:55,085 INFO [train.py:842] (1/4) Epoch 34, batch 6150, loss[loss=0.1718, simple_loss=0.2709, pruned_loss=0.03638, over 7201.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2638, pruned_loss=0.0432, over 1426148.28 frames.], batch size: 23, lr: 1.61e-04
2022-05-29 08:39:34,535 INFO [train.py:842] (1/4) Epoch 34, batch 6200, loss[loss=0.1882, simple_loss=0.2604, pruned_loss=0.05802, over 7273.00 frames.], tot_loss[loss=0.174, simple_loss=0.263, pruned_loss=0.04252, over 1425002.66 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 08:40:13,593 INFO [train.py:842] (1/4) Epoch 34, batch 6250, loss[loss=0.1481, simple_loss=0.244, pruned_loss=0.02614, over 7217.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2629, pruned_loss=0.0423, over 1428686.69 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:40:53,095 INFO [train.py:842] (1/4) Epoch 34, batch 6300, loss[loss=0.1615, simple_loss=0.2547, pruned_loss=0.03416, over 7159.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2642, pruned_loss=0.04273, over 1431974.63 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 08:41:32,266 INFO [train.py:842] (1/4) Epoch 34, batch 6350, loss[loss=0.1754, simple_loss=0.2641, pruned_loss=0.04336, over 7194.00 frames.], tot_loss[loss=0.176, simple_loss=0.2651, pruned_loss=0.04349, over 1428130.44 frames.], batch size: 26, lr: 1.61e-04
2022-05-29 08:42:12,045 INFO [train.py:842] (1/4) Epoch 34, batch 6400, loss[loss=0.1769, simple_loss=0.2658, pruned_loss=0.04397, over 7325.00 frames.], tot_loss[loss=0.175, simple_loss=0.2641, pruned_loss=0.04294, over 1431984.15 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:42:51,296 INFO [train.py:842] (1/4) Epoch 34, batch 6450, loss[loss=0.1641, simple_loss=0.2658, pruned_loss=0.03118, over 7231.00 frames.], tot_loss[loss=0.1739, simple_loss=0.263, pruned_loss=0.04241, over 1427020.03 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:43:31,017 INFO [train.py:842] (1/4) Epoch 34, batch 6500, loss[loss=0.1511, simple_loss=0.231, pruned_loss=0.03561, over 7404.00 frames.], tot_loss[loss=0.173, simple_loss=0.2625, pruned_loss=0.04172, over 1429252.09 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 08:44:10,198 INFO [train.py:842] (1/4) Epoch 34, batch 6550, loss[loss=0.1613, simple_loss=0.2532, pruned_loss=0.03475, over 7111.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2622, pruned_loss=0.04148, over 1430231.71 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:44:50,111 INFO [train.py:842] (1/4) Epoch 34, batch 6600, loss[loss=0.1484, simple_loss=0.2331, pruned_loss=0.03184, over 6807.00 frames.], tot_loss[loss=0.1717, simple_loss=0.261, pruned_loss=0.04122, over 1432122.27 frames.], batch size: 15, lr: 1.61e-04
2022-05-29 08:45:29,269 INFO [train.py:842] (1/4) Epoch 34, batch 6650, loss[loss=0.1409, simple_loss=0.2238, pruned_loss=0.02896, over 7170.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2605, pruned_loss=0.04115, over 1427286.41 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 08:46:08,636 INFO [train.py:842] (1/4) Epoch 34, batch 6700, loss[loss=0.1718, simple_loss=0.2711, pruned_loss=0.03625, over 7226.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2625, pruned_loss=0.04202, over 1425596.50 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:46:47,741 INFO [train.py:842] (1/4) Epoch 34, batch 6750, loss[loss=0.1644, simple_loss=0.2621, pruned_loss=0.03333, over 7232.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2643, pruned_loss=0.04296, over 1422847.78 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:47:27,229 INFO [train.py:842] (1/4) Epoch 34, batch 6800, loss[loss=0.1619, simple_loss=0.2492, pruned_loss=0.03729, over 7140.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2636, pruned_loss=0.04282, over 1415112.39 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:48:06,412 INFO [train.py:842] (1/4) Epoch 34, batch 6850, loss[loss=0.1448, simple_loss=0.2364, pruned_loss=0.02662, over 6712.00 frames.], tot_loss[loss=0.1742, simple_loss=0.263, pruned_loss=0.04268, over 1416125.26 frames.], batch size: 31, lr: 1.61e-04
2022-05-29 08:48:45,886 INFO [train.py:842] (1/4) Epoch 34, batch 6900, loss[loss=0.1764, simple_loss=0.2773, pruned_loss=0.03772, over 6637.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2632, pruned_loss=0.04247, over 1415093.65 frames.], batch size: 31, lr: 1.61e-04
2022-05-29 08:49:25,590 INFO [train.py:842] (1/4) Epoch 34, batch 6950, loss[loss=0.2064, simple_loss=0.3037, pruned_loss=0.05457, over 7147.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2634, pruned_loss=0.04239, over 1420321.32 frames.], batch size: 26, lr: 1.61e-04
2022-05-29 08:50:05,179 INFO [train.py:842] (1/4) Epoch 34, batch 7000, loss[loss=0.1836, simple_loss=0.272, pruned_loss=0.04762, over 7122.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2618, pruned_loss=0.04188, over 1419242.47 frames.], batch size: 26, lr: 1.61e-04
2022-05-29 08:50:44,460 INFO [train.py:842] (1/4) Epoch 34, batch 7050, loss[loss=0.1782, simple_loss=0.2618, pruned_loss=0.04728, over 7450.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2624, pruned_loss=0.04207, over 1419037.42 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 08:51:24,155 INFO [train.py:842] (1/4) Epoch 34, batch 7100, loss[loss=0.1717, simple_loss=0.2639, pruned_loss=0.03976, over 7420.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2633, pruned_loss=0.04277, over 1423287.88 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:52:03,416 INFO [train.py:842] (1/4) Epoch 34, batch 7150, loss[loss=0.1423, simple_loss=0.2331, pruned_loss=0.02574, over 7441.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2638, pruned_loss=0.04277, over 1423736.79 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:52:43,321 INFO [train.py:842] (1/4) Epoch 34, batch 7200, loss[loss=0.1638, simple_loss=0.2446, pruned_loss=0.04152, over 7154.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2628, pruned_loss=0.04249, over 1423370.05 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 08:53:22,458 INFO [train.py:842] (1/4) Epoch 34, batch 7250, loss[loss=0.1444, simple_loss=0.2265, pruned_loss=0.03118, over 6776.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2626, pruned_loss=0.04263, over 1422133.39 frames.], batch size: 15, lr: 1.61e-04
2022-05-29 08:54:02,120 INFO [train.py:842] (1/4) Epoch 34, batch 7300, loss[loss=0.1603, simple_loss=0.2482, pruned_loss=0.03617, over 7159.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2614, pruned_loss=0.04246, over 1425210.32 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 08:54:41,409 INFO [train.py:842] (1/4) Epoch 34, batch 7350, loss[loss=0.1968, simple_loss=0.298, pruned_loss=0.04785, over 7075.00 frames.], tot_loss[loss=0.1728, simple_loss=0.261, pruned_loss=0.04228, over 1421695.57 frames.], batch size: 28, lr: 1.61e-04
2022-05-29 08:55:20,711 INFO [train.py:842] (1/4) Epoch 34, batch 7400, loss[loss=0.1815, simple_loss=0.2706, pruned_loss=0.04621, over 7049.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2623, pruned_loss=0.04253, over 1419142.97 frames.], batch size: 28, lr: 1.61e-04
2022-05-29 08:55:59,982 INFO [train.py:842] (1/4) Epoch 34, batch 7450, loss[loss=0.191, simple_loss=0.2831, pruned_loss=0.04947, over 7124.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2626, pruned_loss=0.04254, over 1422005.34 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:56:39,506 INFO [train.py:842] (1/4) Epoch 34, batch 7500, loss[loss=0.1645, simple_loss=0.2682, pruned_loss=0.03043, over 7281.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2616, pruned_loss=0.04213, over 1423546.55 frames.], batch size: 25, lr: 1.61e-04
2022-05-29 08:57:18,947 INFO [train.py:842] (1/4) Epoch 34, batch 7550, loss[loss=0.1302, simple_loss=0.2175, pruned_loss=0.02148, over 7245.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2601, pruned_loss=0.04107, over 1423655.06 frames.], batch size: 16, lr: 1.61e-04
2022-05-29 08:57:58,715 INFO [train.py:842] (1/4) Epoch 34, batch 7600, loss[loss=0.1355, simple_loss=0.2168, pruned_loss=0.02705, over 7276.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2617, pruned_loss=0.04205, over 1429385.97 frames.], batch size: 16, lr: 1.61e-04
2022-05-29 08:58:37,758 INFO [train.py:842] (1/4) Epoch 34, batch 7650, loss[loss=0.1681, simple_loss=0.2611, pruned_loss=0.03757, over 7119.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2607, pruned_loss=0.04103, over 1428934.73 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:59:17,465 INFO [train.py:842] (1/4) Epoch 34, batch 7700, loss[loss=0.1822, simple_loss=0.2756, pruned_loss=0.04443, over 7193.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2605, pruned_loss=0.04137, over 1427782.81 frames.], batch size: 26, lr: 1.61e-04
2022-05-29 08:59:56,651 INFO [train.py:842] (1/4) Epoch 34, batch 7750, loss[loss=0.1827, simple_loss=0.2653, pruned_loss=0.05006, over 7360.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2593, pruned_loss=0.04086, over 1428272.31 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 09:00:36,172 INFO [train.py:842] (1/4) Epoch 34, batch 7800, loss[loss=0.145, simple_loss=0.2337, pruned_loss=0.02813, over 7288.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2605, pruned_loss=0.04126, over 1425356.75 frames.], batch size: 17, lr: 1.61e-04
2022-05-29 09:01:15,309 INFO [train.py:842] (1/4) Epoch 34, batch 7850, loss[loss=0.1831, simple_loss=0.2695, pruned_loss=0.04837, over 5111.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2608, pruned_loss=0.04134, over 1424162.07 frames.], batch size: 52, lr: 1.61e-04
2022-05-29 09:01:54,564 INFO [train.py:842] (1/4) Epoch 34, batch 7900, loss[loss=0.2277, simple_loss=0.3007, pruned_loss=0.07733, over 4889.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2621, pruned_loss=0.04163, over 1417999.68 frames.], batch size: 52, lr: 1.61e-04
2022-05-29 09:02:33,837 INFO [train.py:842] (1/4) Epoch 34, batch 7950, loss[loss=0.1745, simple_loss=0.2734, pruned_loss=0.03782, over 7299.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2623, pruned_loss=0.04207, over 1420596.46 frames.], batch size: 24, lr: 1.61e-04
2022-05-29 09:03:13,374 INFO [train.py:842] (1/4) Epoch 34, batch 8000, loss[loss=0.1784, simple_loss=0.2703, pruned_loss=0.04328, over 7202.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2615, pruned_loss=0.04157, over 1418860.07 frames.], batch size: 23, lr: 1.61e-04
2022-05-29 09:03:52,531 INFO [train.py:842] (1/4) Epoch 34, batch 8050, loss[loss=0.168, simple_loss=0.26, pruned_loss=0.03801, over 7155.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2622, pruned_loss=0.04201, over 1415301.64 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 09:04:32,350 INFO [train.py:842] (1/4) Epoch 34, batch 8100, loss[loss=0.1813, simple_loss=0.271, pruned_loss=0.04583, over 7256.00 frames.], tot_loss[loss=0.172, simple_loss=0.2614, pruned_loss=0.04132, over 1421583.13 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 09:05:11,678 INFO [train.py:842] (1/4) Epoch 34, batch 8150, loss[loss=0.1952, simple_loss=0.2877, pruned_loss=0.05136, over 7229.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2617, pruned_loss=0.04186, over 1422983.63 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 09:05:51,194 INFO [train.py:842] (1/4) Epoch 34, batch 8200, loss[loss=0.1762, simple_loss=0.2743, pruned_loss=0.03908, over 7156.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2626, pruned_loss=0.04222, over 1423657.43 frames.], batch size: 28, lr: 1.61e-04
2022-05-29 09:06:30,365 INFO [train.py:842] (1/4) Epoch 34, batch 8250, loss[loss=0.1664, simple_loss=0.2639, pruned_loss=0.03448, over 7303.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2626, pruned_loss=0.04216, over 1419512.35 frames.], batch size: 25, lr: 1.61e-04
2022-05-29 09:07:09,892 INFO [train.py:842] (1/4) Epoch 34, batch 8300, loss[loss=0.2123, simple_loss=0.2922, pruned_loss=0.06616, over 4970.00 frames.], tot_loss[loss=0.173, simple_loss=0.2621, pruned_loss=0.04195, over 1420249.46 frames.], batch size: 53, lr: 1.61e-04
2022-05-29 09:07:49,131 INFO [train.py:842] (1/4) Epoch 34, batch 8350, loss[loss=0.1931, simple_loss=0.2731, pruned_loss=0.05657, over 7154.00 frames.], tot_loss[loss=0.1732, simple_loss=0.262, pruned_loss=0.04218, over 1419308.23 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 09:08:28,618 INFO [train.py:842] (1/4) Epoch 34, batch 8400, loss[loss=0.1402, simple_loss=0.2366, pruned_loss=0.02193, over 7247.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2619, pruned_loss=0.04251, over 1419117.62 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 09:09:08,005 INFO [train.py:842] (1/4) Epoch 34, batch 8450, loss[loss=0.15, simple_loss=0.2277, pruned_loss=0.03612, over 7127.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2623, pruned_loss=0.04264, over 1420321.47 frames.], batch size: 17, lr: 1.61e-04
2022-05-29 09:09:47,746 INFO [train.py:842] (1/4) Epoch 34, batch 8500, loss[loss=0.1656, simple_loss=0.2642, pruned_loss=0.03351, over 7134.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2624, pruned_loss=0.04251, over 1419054.30 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 09:10:26,702 INFO [train.py:842] (1/4) Epoch 34, batch 8550, loss[loss=0.1738, simple_loss=0.274, pruned_loss=0.03684, over 7198.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2621, pruned_loss=0.04233, over 1417237.29 frames.], batch size: 23, lr: 1.61e-04
2022-05-29 09:11:06,047 INFO [train.py:842] (1/4) Epoch 34, batch 8600, loss[loss=0.1341, simple_loss=0.2114, pruned_loss=0.02836, over 6802.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2625, pruned_loss=0.04229, over 1420069.06 frames.], batch size: 15, lr: 1.61e-04
2022-05-29 09:11:45,289 INFO [train.py:842] (1/4) Epoch 34, batch 8650, loss[loss=0.1411, simple_loss=0.2281, pruned_loss=0.02708, over 7264.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2622, pruned_loss=0.04215, over 1417851.05 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 09:12:27,547 INFO [train.py:842] (1/4) Epoch 34, batch 8700, loss[loss=0.1499, simple_loss=0.2449, pruned_loss=0.02743, over 7161.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2626, pruned_loss=0.04196, over 1415549.25 frames.], batch size: 26, lr: 1.61e-04
2022-05-29 09:13:06,631 INFO [train.py:842] (1/4) Epoch 34, batch 8750, loss[loss=0.1701, simple_loss=0.2521, pruned_loss=0.04404, over 7326.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2637, pruned_loss=0.04282, over 1414924.47 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 09:13:45,942 INFO [train.py:842] (1/4) Epoch 34, batch 8800, loss[loss=0.205, simple_loss=0.2989, pruned_loss=0.05553, over 7327.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2646, pruned_loss=0.04333, over 1407912.72 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 09:14:24,658 INFO [train.py:842] (1/4) Epoch 34, batch 8850, loss[loss=0.1536, simple_loss=0.2413, pruned_loss=0.03294, over 7402.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2662, pruned_loss=0.04407, over 1407043.84 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 09:15:04,329 INFO [train.py:842] (1/4) Epoch 34, batch 8900, loss[loss=0.1993, simple_loss=0.2927, pruned_loss=0.05297, over 6846.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2665, pruned_loss=0.04442, over 1405852.30 frames.], batch size: 31, lr: 1.61e-04
2022-05-29 09:15:43,401 INFO [train.py:842] (1/4) Epoch 34, batch 8950, loss[loss=0.1494, simple_loss=0.2446, pruned_loss=0.02707, over 7163.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2664, pruned_loss=0.04434, over 1406357.66 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 09:16:22,280 INFO [train.py:842] (1/4) Epoch 34, batch 9000, loss[loss=0.1963, simple_loss=0.2835, pruned_loss=0.05454, over 7215.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2678, pruned_loss=0.0447, over 1395419.12 frames.], batch size: 22, lr: 1.61e-04
2022-05-29 09:16:22,281 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 09:16:31,958 INFO [train.py:871] (1/4) Epoch 34, validation: loss=0.1642, simple_loss=0.2613, pruned_loss=0.03353, over 868885.00 frames.
2022-05-29 09:17:10,312 INFO [train.py:842] (1/4) Epoch 34, batch 9050, loss[loss=0.1583, simple_loss=0.248, pruned_loss=0.03436, over 6401.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2677, pruned_loss=0.04486, over 1376350.30 frames.], batch size: 38, lr: 1.61e-04
2022-05-29 09:17:48,547 INFO [train.py:842] (1/4) Epoch 34, batch 9100, loss[loss=0.1927, simple_loss=0.2924, pruned_loss=0.04654, over 6514.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2687, pruned_loss=0.0451, over 1339944.75 frames.], batch size: 38, lr: 1.61e-04
2022-05-29 09:18:26,629 INFO [train.py:842] (1/4) Epoch 34, batch 9150, loss[loss=0.207, simple_loss=0.2948, pruned_loss=0.05962, over 5229.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2718, pruned_loss=0.04729, over 1275646.91 frames.], batch size: 52, lr: 1.60e-04
2022-05-29 09:19:15,535 INFO [train.py:842] (1/4) Epoch 35, batch 0, loss[loss=0.1965, simple_loss=0.2855, pruned_loss=0.05377, over 7240.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2855, pruned_loss=0.05377, over 7240.00 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:19:54,686 INFO [train.py:842] (1/4) Epoch 35, batch 50, loss[loss=0.204, simple_loss=0.3002, pruned_loss=0.05393, over 7297.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2664, pruned_loss=0.04471, over 318414.78 frames.], batch size: 24, lr: 1.58e-04
2022-05-29 09:20:34,697 INFO [train.py:842] (1/4) Epoch 35, batch 100, loss[loss=0.1692, simple_loss=0.2623, pruned_loss=0.03805, over 7162.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2642, pruned_loss=0.0437, over 567586.48 frames.], batch size: 26, lr: 1.58e-04
2022-05-29 09:21:14,003 INFO [train.py:842] (1/4) Epoch 35, batch 150, loss[loss=0.2145, simple_loss=0.2958, pruned_loss=0.06655, over 7378.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2647, pruned_loss=0.04402, over 760153.54 frames.], batch size: 23, lr: 1.58e-04
2022-05-29 09:21:53,695 INFO [train.py:842] (1/4) Epoch 35, batch 200, loss[loss=0.1573, simple_loss=0.2415, pruned_loss=0.03657, over 7063.00 frames.], tot_loss[loss=0.1755, simple_loss=0.264, pruned_loss=0.04353, over 909325.75 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:22:33,034 INFO [train.py:842] (1/4) Epoch 35, batch 250, loss[loss=0.1456, simple_loss=0.2422, pruned_loss=0.02447, over 7226.00 frames.], tot_loss[loss=0.174, simple_loss=0.2628, pruned_loss=0.04258, over 1026751.39 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:23:12,662 INFO [train.py:842] (1/4) Epoch 35, batch 300, loss[loss=0.1769, simple_loss=0.2682, pruned_loss=0.04276, over 7161.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2615, pruned_loss=0.04182, over 1112991.61 frames.], batch size: 19, lr: 1.58e-04
2022-05-29 09:23:51,975 INFO [train.py:842] (1/4) Epoch 35, batch 350, loss[loss=0.2049, simple_loss=0.2989, pruned_loss=0.05545, over 7197.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2601, pruned_loss=0.04124, over 1185416.17 frames.], batch size: 23, lr: 1.58e-04
2022-05-29 09:24:31,414 INFO [train.py:842] (1/4) Epoch 35, batch 400, loss[loss=0.1453, simple_loss=0.2402, pruned_loss=0.02521, over 7330.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2606, pruned_loss=0.04152, over 1239625.99 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:25:10,965 INFO [train.py:842] (1/4) Epoch 35, batch 450, loss[loss=0.191, simple_loss=0.2804, pruned_loss=0.05082, over 6762.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2601, pruned_loss=0.04139, over 1284541.82 frames.], batch size: 31, lr: 1.58e-04
2022-05-29 09:25:50,502 INFO [train.py:842] (1/4) Epoch 35, batch 500, loss[loss=0.2236, simple_loss=0.3106, pruned_loss=0.06826, over 7334.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2605, pruned_loss=0.04149, over 1314185.40 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:26:29,920 INFO [train.py:842] (1/4) Epoch 35, batch 550, loss[loss=0.15, simple_loss=0.2406, pruned_loss=0.02974, over 7060.00 frames.], tot_loss[loss=0.172, simple_loss=0.2605, pruned_loss=0.04175, over 1335340.81 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:27:09,514 INFO [train.py:842] (1/4) Epoch 35, batch 600, loss[loss=0.1614, simple_loss=0.2652, pruned_loss=0.02887, over 7337.00 frames.], tot_loss[loss=0.172, simple_loss=0.2608, pruned_loss=0.04156, over 1354404.77 frames.], batch size: 22, lr: 1.58e-04
2022-05-29 09:27:48,643 INFO [train.py:842] (1/4) Epoch 35, batch 650, loss[loss=0.1513, simple_loss=0.2385, pruned_loss=0.03205, over 7157.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2611, pruned_loss=0.04112, over 1373376.02 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:28:28,647 INFO [train.py:842] (1/4) Epoch 35, batch 700, loss[loss=0.1683, simple_loss=0.2523, pruned_loss=0.04215, over 7292.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2599, pruned_loss=0.04047, over 1387388.45 frames.], batch size: 17, lr: 1.58e-04
2022-05-29 09:29:08,042 INFO [train.py:842] (1/4) Epoch 35, batch 750, loss[loss=0.1439, simple_loss=0.2288, pruned_loss=0.02954, over 7261.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2595, pruned_loss=0.04011, over 1394920.94 frames.], batch size: 19, lr: 1.58e-04
2022-05-29 09:29:47,595 INFO [train.py:842] (1/4) Epoch 35, batch 800, loss[loss=0.1727, simple_loss=0.2724, pruned_loss=0.03653, over 7213.00 frames.], tot_loss[loss=0.1711, simple_loss=0.261, pruned_loss=0.04054, over 1403537.98 frames.], batch size: 21, lr: 1.58e-04
2022-05-29 09:30:26,774 INFO [train.py:842] (1/4) Epoch 35, batch 850, loss[loss=0.1747, simple_loss=0.2659, pruned_loss=0.04171, over 7288.00 frames.], tot_loss[loss=0.171, simple_loss=0.2612, pruned_loss=0.04041, over 1403150.47 frames.], batch size: 24, lr: 1.58e-04
2022-05-29 09:31:06,396 INFO [train.py:842] (1/4) Epoch 35, batch 900, loss[loss=0.1849, simple_loss=0.2739, pruned_loss=0.04793, over 5281.00 frames.], tot_loss[loss=0.171, simple_loss=0.2614, pruned_loss=0.04032, over 1407028.16 frames.], batch size: 52, lr: 1.58e-04
2022-05-29 09:31:45,739 INFO [train.py:842] (1/4) Epoch 35, batch 950, loss[loss=0.1688, simple_loss=0.2647, pruned_loss=0.03641, over 7259.00 frames.], tot_loss[loss=0.171, simple_loss=0.261, pruned_loss=0.04054, over 1410607.76 frames.], batch size: 19, lr: 1.58e-04
2022-05-29 09:32:25,425 INFO [train.py:842] (1/4) Epoch 35, batch 1000, loss[loss=0.1845, simple_loss=0.2777, pruned_loss=0.04563, over 6916.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2612, pruned_loss=0.04057, over 1412572.61 frames.], batch size: 32, lr: 1.58e-04
2022-05-29 09:33:04,774 INFO [train.py:842] (1/4) Epoch 35, batch 1050, loss[loss=0.1718, simple_loss=0.2514, pruned_loss=0.0461, over 7413.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2592, pruned_loss=0.03973, over 1416564.83 frames.], batch size: 21, lr: 1.58e-04
2022-05-29 09:33:44,557 INFO [train.py:842] (1/4) Epoch 35, batch 1100, loss[loss=0.1754, simple_loss=0.2619, pruned_loss=0.04445, over 7353.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2582, pruned_loss=0.03911, over 1420442.30 frames.], batch size: 19, lr: 1.58e-04
2022-05-29 09:34:23,663 INFO [train.py:842] (1/4) Epoch 35, batch 1150, loss[loss=0.1911, simple_loss=0.2877, pruned_loss=0.04726, over 7209.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2601, pruned_loss=0.04012, over 1422598.07 frames.], batch size: 23, lr: 1.58e-04
2022-05-29 09:35:03,461 INFO [train.py:842] (1/4) Epoch 35, batch 1200, loss[loss=0.1525, simple_loss=0.23, pruned_loss=0.03751, over 7279.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2614, pruned_loss=0.04091, over 1426164.42 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:35:42,611 INFO [train.py:842] (1/4) Epoch 35, batch 1250, loss[loss=0.1724, simple_loss=0.2693, pruned_loss=0.03777, over 7332.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2619, pruned_loss=0.04114, over 1424564.25 frames.], batch size: 22, lr: 1.58e-04
2022-05-29 09:36:21,902 INFO [train.py:842] (1/4) Epoch 35, batch 1300, loss[loss=0.1828, simple_loss=0.2709, pruned_loss=0.04734, over 7070.00 frames.], tot_loss[loss=0.173, simple_loss=0.2626, pruned_loss=0.04165, over 1420635.32 frames.], batch size: 28, lr: 1.58e-04
2022-05-29 09:37:00,998 INFO [train.py:842] (1/4) Epoch 35, batch 1350, loss[loss=0.186, simple_loss=0.276, pruned_loss=0.04804, over 7097.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2627, pruned_loss=0.04172, over 1423212.18 frames.], batch size: 28, lr: 1.58e-04
2022-05-29 09:37:40,236 INFO [train.py:842] (1/4) Epoch 35, batch 1400, loss[loss=0.1649, simple_loss=0.2593, pruned_loss=0.03522, over 7335.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2631, pruned_loss=0.04168, over 1420320.29 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:38:19,787 INFO [train.py:842] (1/4) Epoch 35, batch 1450, loss[loss=0.1558, simple_loss=0.2478, pruned_loss=0.03192, over 7254.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2619, pruned_loss=0.04158, over 1418615.70 frames.], batch size: 19, lr: 1.58e-04
2022-05-29 09:38:59,592 INFO [train.py:842] (1/4) Epoch 35, batch 1500, loss[loss=0.1478, simple_loss=0.239, pruned_loss=0.0283, over 7132.00 frames.], tot_loss[loss=0.1739, simple_loss=0.263, pruned_loss=0.0424, over 1419463.11 frames.], batch size: 17, lr: 1.58e-04
2022-05-29 09:39:38,699 INFO [train.py:842] (1/4) Epoch 35, batch 1550, loss[loss=0.1764, simple_loss=0.2749, pruned_loss=0.03892, over 7221.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2632, pruned_loss=0.04234, over 1419419.74 frames.], batch size: 21, lr: 1.58e-04
2022-05-29 09:40:18,419 INFO [train.py:842] (1/4) Epoch 35, batch 1600, loss[loss=0.1648, simple_loss=0.2629, pruned_loss=0.03337, over 7088.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2625, pruned_loss=0.0419, over 1421166.99 frames.], batch size: 28, lr: 1.58e-04
2022-05-29 09:40:57,765 INFO [train.py:842] (1/4) Epoch 35, batch 1650, loss[loss=0.2026, simple_loss=0.2825, pruned_loss=0.06132, over 7420.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2619, pruned_loss=0.0417, over 1425673.07 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:41:48,188 INFO [train.py:842] (1/4) Epoch 35, batch 1700, loss[loss=0.2363, simple_loss=0.3145, pruned_loss=0.07908, over 4918.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2631, pruned_loss=0.04204, over 1424605.29 frames.], batch size: 52, lr: 1.58e-04
2022-05-29 09:42:27,680 INFO [train.py:842] (1/4) Epoch 35, batch 1750, loss[loss=0.1342, simple_loss=0.2252, pruned_loss=0.02163, over 7168.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2622, pruned_loss=0.04199, over 1424761.71 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:43:07,500 INFO [train.py:842] (1/4) Epoch 35, batch 1800, loss[loss=0.1501, simple_loss=0.2542, pruned_loss=0.02296, over 7322.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2609, pruned_loss=0.04118, over 1428632.32 frames.], batch size: 25, lr: 1.58e-04
2022-05-29 09:43:46,633 INFO [train.py:842] (1/4) Epoch 35, batch 1850, loss[loss=0.2231, simple_loss=0.2983, pruned_loss=0.07399, over 7077.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2613, pruned_loss=0.04182, over 1424961.93 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:44:26,182 INFO [train.py:842] (1/4) Epoch 35, batch 1900, loss[loss=0.1967, simple_loss=0.2879, pruned_loss=0.05273, over 7372.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2614, pruned_loss=0.04176, over 1424722.25 frames.], batch size: 23, lr: 1.58e-04
2022-05-29 09:45:05,540 INFO [train.py:842] (1/4) Epoch 35, batch 1950, loss[loss=0.1475, simple_loss=0.235, pruned_loss=0.03, over 7165.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2616, pruned_loss=0.04191, over 1422855.07 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:45:55,982 INFO [train.py:842] (1/4) Epoch 35, batch 2000, loss[loss=0.2081, simple_loss=0.2953, pruned_loss=0.06044, over 6430.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2617, pruned_loss=0.04209, over 1419273.85 frames.], batch size: 38, lr: 1.58e-04
2022-05-29 09:46:35,117 INFO [train.py:842] (1/4) Epoch 35, batch 2050, loss[loss=0.2532, simple_loss=0.3193, pruned_loss=0.09356, over 7107.00 frames.], tot_loss[loss=0.174, simple_loss=0.2626, pruned_loss=0.04272, over 1420794.14 frames.], batch size: 21, lr: 1.58e-04
2022-05-29 09:47:25,673 INFO [train.py:842] (1/4) Epoch 35, batch 2100, loss[loss=0.2084, simple_loss=0.2971, pruned_loss=0.05991, over 7406.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2631, pruned_loss=0.0426, over 1423773.87 frames.], batch size: 21, lr: 1.58e-04
2022-05-29 09:48:05,108 INFO [train.py:842] (1/4) Epoch 35, batch 2150, loss[loss=0.1548, simple_loss=0.2568, pruned_loss=0.02638, over 6532.00 frames.], tot_loss[loss=0.174, simple_loss=0.2631, pruned_loss=0.04243, over 1427371.42 frames.], batch size: 38, lr: 1.58e-04
2022-05-29 09:48:44,613 INFO [train.py:842] (1/4) Epoch 35, batch 2200, loss[loss=0.1657, simple_loss=0.2583, pruned_loss=0.03652, over 7431.00 frames.], tot_loss[loss=0.173, simple_loss=0.2622, pruned_loss=0.04188, over 1423047.19 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:49:23,726 INFO [train.py:842] (1/4) Epoch 35, batch 2250, loss[loss=0.1618, simple_loss=0.2477, pruned_loss=0.03798, over 7285.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2617, pruned_loss=0.042, over 1421630.33 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:50:03,227 INFO [train.py:842] (1/4) Epoch 35, batch 2300, loss[loss=0.1967, simple_loss=0.2826, pruned_loss=0.05539, over 7177.00 frames.], tot_loss[loss=0.1733, simple_loss=0.262, pruned_loss=0.04228, over 1418217.48 frames.], batch size: 26, lr: 1.58e-04
2022-05-29 09:50:42,239 INFO [train.py:842] (1/4) Epoch 35, batch 2350, loss[loss=0.1699, simple_loss=0.2676, pruned_loss=0.03614, over 7024.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2616, pruned_loss=0.04205, over 1415859.32 frames.], batch size: 28, lr: 1.58e-04
2022-05-29 09:51:21,908 INFO [train.py:842] (1/4) Epoch 35, batch 2400, loss[loss=0.1389, simple_loss=0.2181, pruned_loss=0.0299, over 7000.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2614, pruned_loss=0.04202, over 1421370.57 frames.], batch size: 16, lr: 1.58e-04
2022-05-29 09:52:01,229 INFO [train.py:842] (1/4) Epoch 35, batch 2450, loss[loss=0.1507, simple_loss=0.2501, pruned_loss=0.02567, over 7429.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2606, pruned_loss=0.0415, over 1422223.81 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:52:41,072 INFO [train.py:842] (1/4) Epoch 35, batch 2500, loss[loss=0.1707, simple_loss=0.2665, pruned_loss=0.03742, over 6496.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2592, pruned_loss=0.04113, over 1424212.84 frames.], batch size: 38, lr: 1.58e-04
2022-05-29 09:53:20,272 INFO [train.py:842] (1/4) Epoch 35, batch 2550, loss[loss=0.1662, simple_loss=0.2616, pruned_loss=0.03539, over 7111.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2594, pruned_loss=0.04113, over 1423722.59 frames.], batch size: 21, lr: 1.58e-04
2022-05-29 09:53:59,847 INFO [train.py:842] (1/4) Epoch 35, batch 2600, loss[loss=0.2131, simple_loss=0.2898, pruned_loss=0.06817, over 7204.00 frames.], tot_loss[loss=0.172, simple_loss=0.2602, pruned_loss=0.04186, over 1422908.69 frames.], batch size: 22, lr: 1.58e-04
2022-05-29 09:54:38,857 INFO [train.py:842] (1/4) Epoch 35, batch 2650, loss[loss=0.1699, simple_loss=0.2613, pruned_loss=0.0393, over 7195.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2604, pruned_loss=0.04185, over 1421301.62 frames.], batch size: 23, lr: 1.58e-04
2022-05-29 09:55:18,676 INFO [train.py:842] (1/4) Epoch 35, batch 2700, loss[loss=0.1663, simple_loss=0.2555, pruned_loss=0.03853, over 7112.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2605, pruned_loss=0.04148, over 1423132.69 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 09:55:57,864 INFO [train.py:842] (1/4) Epoch 35, batch 2750, loss[loss=0.2095, simple_loss=0.3027, pruned_loss=0.05813, over 7315.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2608, pruned_loss=0.04103, over 1423461.02 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 09:56:37,370 INFO [train.py:842] (1/4) Epoch 35, batch 2800, loss[loss=0.1773, simple_loss=0.2814, pruned_loss=0.0366, over 7327.00 frames.], tot_loss[loss=0.1716, simple_loss=0.261, pruned_loss=0.04108, over 1425004.55 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 09:57:16,510 INFO [train.py:842] (1/4) Epoch 35, batch 2850, loss[loss=0.1714, simple_loss=0.2627, pruned_loss=0.04003, over 7153.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2619, pruned_loss=0.04156, over 1423350.57 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 09:57:56,056 INFO [train.py:842] (1/4) Epoch 35, batch 2900, loss[loss=0.2543, simple_loss=0.3389, pruned_loss=0.08485, over 6419.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2619, pruned_loss=0.04169, over 1422855.86 frames.], batch size: 38, lr: 1.57e-04
2022-05-29 09:58:34,876 INFO [train.py:842] (1/4) Epoch 35, batch 2950, loss[loss=0.1534, simple_loss=0.2302, pruned_loss=0.03831, over 6778.00 frames.], tot_loss[loss=0.173, simple_loss=0.2623, pruned_loss=0.04178, over 1416078.53 frames.], batch size: 15, lr: 1.57e-04
2022-05-29 09:59:14,511 INFO [train.py:842] (1/4) Epoch 35, batch 3000, loss[loss=0.1625, simple_loss=0.2463, pruned_loss=0.03932, over 7400.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2615, pruned_loss=0.041, over 1420141.81 frames.], batch size: 23, lr: 1.57e-04
2022-05-29 09:59:14,513 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 09:59:24,201 INFO [train.py:871] (1/4) Epoch 35, validation: loss=0.1643, simple_loss=0.2608, pruned_loss=0.03387, over 868885.00 frames.
2022-05-29 10:00:03,549 INFO [train.py:842] (1/4) Epoch 35, batch 3050, loss[loss=0.1575, simple_loss=0.2469, pruned_loss=0.034, over 7235.00 frames.], tot_loss[loss=0.173, simple_loss=0.2626, pruned_loss=0.0417, over 1422817.36 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:00:43,000 INFO [train.py:842] (1/4) Epoch 35, batch 3100, loss[loss=0.1959, simple_loss=0.2861, pruned_loss=0.05286, over 7372.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2635, pruned_loss=0.04247, over 1419814.65 frames.], batch size: 23, lr: 1.57e-04
2022-05-29 10:01:22,494 INFO [train.py:842] (1/4) Epoch 35, batch 3150, loss[loss=0.1805, simple_loss=0.2668, pruned_loss=0.04707, over 7207.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2616, pruned_loss=0.04169, over 1421879.04 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:02:01,994 INFO [train.py:842] (1/4) Epoch 35, batch 3200, loss[loss=0.2146, simple_loss=0.3064, pruned_loss=0.06144, over 7204.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2627, pruned_loss=0.04197, over 1426777.72 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:02:41,285 INFO [train.py:842] (1/4) Epoch 35, batch 3250, loss[loss=0.1634, simple_loss=0.2502, pruned_loss=0.0383, over 7431.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2619, pruned_loss=0.04165, over 1424982.08 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:03:20,929 INFO [train.py:842] (1/4) Epoch 35, batch 3300, loss[loss=0.1719, simple_loss=0.2626, pruned_loss=0.0406, over 7427.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2614, pruned_loss=0.04124, over 1425802.08 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:04:00,231 INFO [train.py:842] (1/4) Epoch 35, batch 3350, loss[loss=0.1788, simple_loss=0.2718, pruned_loss=0.04288, over 7425.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2603, pruned_loss=0.04057, over 1429646.45 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:04:39,812 INFO [train.py:842] (1/4) Epoch 35, batch 3400, loss[loss=0.1804, simple_loss=0.2725, pruned_loss=0.04422, over 7287.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2601, pruned_loss=0.04067, over 1425604.30 frames.], batch size: 18, lr: 1.57e-04
2022-05-29 10:05:18,868 INFO [train.py:842] (1/4) Epoch 35, batch 3450, loss[loss=0.1726, simple_loss=0.2603, pruned_loss=0.04247, over 7004.00 frames.], tot_loss[loss=0.1721, simple_loss=0.261, pruned_loss=0.04159, over 1428623.91 frames.], batch size: 16, lr: 1.57e-04
2022-05-29 10:05:58,332 INFO [train.py:842] (1/4) Epoch 35, batch 3500, loss[loss=0.1716, simple_loss=0.2717, pruned_loss=0.03577, over 7333.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2606, pruned_loss=0.04111, over 1427678.37 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:06:37,458 INFO [train.py:842] (1/4) Epoch 35, batch 3550, loss[loss=0.166, simple_loss=0.2689, pruned_loss=0.03156, over 6829.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2611, pruned_loss=0.04138, over 1421803.44 frames.], batch size: 31, lr: 1.57e-04
2022-05-29 10:07:17,145 INFO [train.py:842] (1/4) Epoch 35, batch 3600, loss[loss=0.1857, simple_loss=0.262, pruned_loss=0.05472, over 7192.00 frames.], tot_loss[loss=0.172, simple_loss=0.261, pruned_loss=0.04149, over 1421043.00 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:07:56,201 INFO [train.py:842] (1/4) Epoch 35, batch 3650, loss[loss=0.1838, simple_loss=0.2746, pruned_loss=0.04655, over 7328.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2599, pruned_loss=0.0407, over 1422033.44 frames.], batch size: 25, lr: 1.57e-04
2022-05-29 10:08:35,604 INFO [train.py:842] (1/4) Epoch 35, batch 3700, loss[loss=0.1522, simple_loss=0.2459, pruned_loss=0.02922, over 6382.00 frames.], tot_loss[loss=0.171, simple_loss=0.2604, pruned_loss=0.04082, over 1421409.93 frames.], batch size: 37, lr: 1.57e-04
2022-05-29 10:09:14,959 INFO [train.py:842] (1/4) Epoch 35, batch 3750, loss[loss=0.1806, simple_loss=0.2641, pruned_loss=0.04853, over 4918.00 frames.], tot_loss[loss=0.172, simple_loss=0.2614, pruned_loss=0.04134, over 1418857.23 frames.], batch size: 53, lr: 1.57e-04
2022-05-29 10:09:54,424 INFO [train.py:842] (1/4) Epoch 35, batch 3800, loss[loss=0.1811, simple_loss=0.2643, pruned_loss=0.04894, over 5213.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2617, pruned_loss=0.04124, over 1419490.24 frames.], batch size: 52, lr: 1.57e-04
2022-05-29 10:10:33,450 INFO [train.py:842] (1/4) Epoch 35, batch 3850, loss[loss=0.2255, simple_loss=0.2953, pruned_loss=0.07787, over 7003.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2624, pruned_loss=0.04201, over 1420218.04 frames.], batch size: 16, lr: 1.57e-04
2022-05-29 10:11:12,962 INFO [train.py:842] (1/4) Epoch 35, batch 3900, loss[loss=0.1784, simple_loss=0.2618, pruned_loss=0.04752, over 7306.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2632, pruned_loss=0.04228, over 1417063.64 frames.], batch size: 18, lr: 1.57e-04
2022-05-29 10:11:52,261 INFO [train.py:842] (1/4) Epoch 35, batch 3950, loss[loss=0.1536, simple_loss=0.241, pruned_loss=0.03311, over 7162.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2619, pruned_loss=0.04172, over 1417427.98 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 10:12:31,934 INFO [train.py:842] (1/4) Epoch 35, batch 4000, loss[loss=0.2443, simple_loss=0.331, pruned_loss=0.07881, over 7244.00 frames.], tot_loss[loss=0.173, simple_loss=0.2623, pruned_loss=0.04186, over 1418319.65 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:13:11,427 INFO [train.py:842] (1/4) Epoch 35, batch 4050, loss[loss=0.1618, simple_loss=0.26, pruned_loss=0.0318, over 7204.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2626, pruned_loss=0.04213, over 1420843.63 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:13:50,834 INFO [train.py:842] (1/4) Epoch 35, batch 4100, loss[loss=0.1548, simple_loss=0.2512, pruned_loss=0.02924, over 6760.00 frames.], tot_loss[loss=0.1737, simple_loss=0.263, pruned_loss=0.04222, over 1423936.20 frames.], batch size: 31, lr: 1.57e-04
2022-05-29 10:14:30,212 INFO [train.py:842] (1/4) Epoch 35, batch 4150, loss[loss=0.1339, simple_loss=0.2306, pruned_loss=0.01864, over 7276.00 frames.], tot_loss[loss=0.1725, simple_loss=0.262, pruned_loss=0.0415, over 1427689.89 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:15:09,609 INFO [train.py:842] (1/4) Epoch 35, batch 4200, loss[loss=0.1733, simple_loss=0.2665, pruned_loss=0.04002, over 7426.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2611, pruned_loss=0.04118, over 1424517.74 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:15:48,839 INFO [train.py:842] (1/4) Epoch 35, batch 4250, loss[loss=0.2041, simple_loss=0.2853, pruned_loss=0.06148, over 7169.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2611, pruned_loss=0.04116, over 1426199.85 frames.], batch size: 18, lr: 1.57e-04
2022-05-29 10:16:28,507 INFO [train.py:842] (1/4) Epoch 35, batch 4300, loss[loss=0.1352, simple_loss=0.2208, pruned_loss=0.02476, over 7142.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2613, pruned_loss=0.04167, over 1426844.67 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:17:07,788 INFO [train.py:842] (1/4) Epoch 35, batch 4350, loss[loss=0.1453, simple_loss=0.2437, pruned_loss=0.02343, over 7333.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2605, pruned_loss=0.04103, over 1431103.53 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 10:17:47,265 INFO [train.py:842] (1/4) Epoch 35, batch 4400, loss[loss=0.1673, simple_loss=0.2667, pruned_loss=0.03393, over 7204.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2603, pruned_loss=0.04121, over 1423332.25 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:18:26,585 INFO [train.py:842] (1/4) Epoch 35, batch 4450, loss[loss=0.2039, simple_loss=0.2923, pruned_loss=0.05776, over 7322.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2603, pruned_loss=0.04144, over 1420375.53 frames.], batch size: 24, lr: 1.57e-04
2022-05-29 10:19:06,146 INFO [train.py:842] (1/4) Epoch 35, batch 4500, loss[loss=0.1238, simple_loss=0.2111, pruned_loss=0.01824, over 6766.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2608, pruned_loss=0.04168, over 1420352.96 frames.], batch size: 15, lr: 1.57e-04
2022-05-29 10:19:45,371 INFO [train.py:842] (1/4) Epoch 35, batch 4550, loss[loss=0.1774, simple_loss=0.2666, pruned_loss=0.04411, over 7181.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2618, pruned_loss=0.04224, over 1419102.25 frames.], batch size: 23, lr: 1.57e-04
2022-05-29 10:20:25,297 INFO [train.py:842] (1/4) Epoch 35, batch 4600, loss[loss=0.1595, simple_loss=0.2598, pruned_loss=0.02958, over 7148.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2602, pruned_loss=0.04131, over 1420100.86 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:21:04,515 INFO [train.py:842] (1/4) Epoch 35, batch 4650, loss[loss=0.2039, simple_loss=0.2798, pruned_loss=0.06403, over 7233.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2603, pruned_loss=0.04175, over 1416951.04 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:21:44,103 INFO [train.py:842] (1/4) Epoch 35, batch 4700, loss[loss=0.1514, simple_loss=0.252, pruned_loss=0.02541, over 7220.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2595, pruned_loss=0.04088, over 1418364.09 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 10:22:23,127 INFO [train.py:842] (1/4) Epoch 35, batch 4750, loss[loss=0.198, simple_loss=0.3009, pruned_loss=0.04754, over 7197.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2601, pruned_loss=0.04087, over 1419659.70 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:23:02,767 INFO [train.py:842] (1/4) Epoch 35, batch 4800, loss[loss=0.1448, simple_loss=0.2259, pruned_loss=0.03187, over 7267.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2602, pruned_loss=0.041, over 1425702.16 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 10:23:42,030 INFO [train.py:842] (1/4) Epoch 35, batch 4850, loss[loss=0.221, simple_loss=0.3086, pruned_loss=0.06676, over 4998.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2611, pruned_loss=0.04113, over 1423622.51 frames.], batch size: 52, lr: 1.57e-04
2022-05-29 10:24:21,718 INFO [train.py:842] (1/4) Epoch 35, batch 4900, loss[loss=0.1609, simple_loss=0.2464, pruned_loss=0.03765, over 7294.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2609, pruned_loss=0.04095, over 1424800.03 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:25:01,008 INFO [train.py:842] (1/4) Epoch 35, batch 4950, loss[loss=0.1835, simple_loss=0.2645, pruned_loss=0.05129, over 7432.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2605, pruned_loss=0.04066, over 1428533.60 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:25:40,570 INFO [train.py:842] (1/4) Epoch 35, batch 5000, loss[loss=0.1767, simple_loss=0.2669, pruned_loss=0.0432, over 7225.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2612, pruned_loss=0.04121, over 1427667.03 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 10:26:19,934 INFO [train.py:842] (1/4) Epoch 35, batch 5050, loss[loss=0.157, simple_loss=0.246, pruned_loss=0.03402, over 7156.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2606, pruned_loss=0.04124, over 1430807.02 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 10:26:59,257 INFO [train.py:842] (1/4) Epoch 35, batch 5100, loss[loss=0.135, simple_loss=0.2189, pruned_loss=0.02557, over 7282.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2617, pruned_loss=0.04134, over 1426536.26 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:27:38,568 INFO [train.py:842] (1/4) Epoch 35, batch 5150, loss[loss=0.1427, simple_loss=0.2388, pruned_loss=0.02327, over 7313.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2627, pruned_loss=0.04192, over 1426276.47 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 10:28:18,437 INFO [train.py:842] (1/4) Epoch 35, batch 5200, loss[loss=0.1384, simple_loss=0.2229, pruned_loss=0.02699, over 7243.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2621, pruned_loss=0.0418, over 1427854.92 frames.], batch size: 16, lr: 1.57e-04
2022-05-29 10:28:57,712 INFO [train.py:842] (1/4) Epoch 35, batch 5250, loss[loss=0.1789, simple_loss=0.2651, pruned_loss=0.04637, over 7358.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2626, pruned_loss=0.04206, over 1428989.78 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 10:29:37,114 INFO [train.py:842] (1/4) Epoch 35, batch 5300, loss[loss=0.1617, simple_loss=0.2517, pruned_loss=0.03581, over 7314.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2624, pruned_loss=0.04147, over 1429009.35 frames.], batch size: 24, lr: 1.57e-04
2022-05-29 10:30:16,474 INFO [train.py:842] (1/4) Epoch 35, batch 5350, loss[loss=0.1778, simple_loss=0.2648, pruned_loss=0.04538, over 7341.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2622, pruned_loss=0.04146, over 1427780.95 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:30:56,266 INFO [train.py:842] (1/4) Epoch 35, batch 5400, loss[loss=0.1579, simple_loss=0.2487, pruned_loss=0.03349, over 7333.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2627, pruned_loss=0.0413, over 1432000.23 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:31:35,706 INFO [train.py:842] (1/4) Epoch 35, batch 5450, loss[loss=0.1569, simple_loss=0.2409, pruned_loss=0.03649, over 7170.00 frames.], tot_loss[loss=0.1723, simple_loss=0.262, pruned_loss=0.04134, over 1433981.48 frames.], batch size: 18, lr: 1.57e-04
2022-05-29 10:32:14,943 INFO [train.py:842] (1/4) Epoch 35, batch 5500, loss[loss=0.1765, simple_loss=0.2725, pruned_loss=0.04024, over 7141.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2625, pruned_loss=0.04116, over 1429155.74 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:32:54,354 INFO [train.py:842] (1/4) Epoch 35, batch 5550, loss[loss=0.1553, simple_loss=0.2469, pruned_loss=0.03188, over 7277.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2627, pruned_loss=0.04129, over 1427340.78 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:33:33,908 INFO [train.py:842] (1/4) Epoch 35, batch 5600, loss[loss=0.1929, simple_loss=0.2836, pruned_loss=0.05115, over 7228.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2621, pruned_loss=0.04105, over 1426609.98 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:34:13,266 INFO [train.py:842] (1/4) Epoch 35, batch 5650, loss[loss=0.1413, simple_loss=0.2291, pruned_loss=0.0267, over 7164.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2623, pruned_loss=0.04139, over 1428295.98 frames.], batch size: 18, lr: 1.57e-04
2022-05-29 10:34:52,888 INFO [train.py:842] (1/4) Epoch 35, batch 5700, loss[loss=0.1614, simple_loss=0.2641, pruned_loss=0.02941, over 7310.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2629, pruned_loss=0.04201, over 1430006.04 frames.], batch size: 24, lr: 1.57e-04
2022-05-29 10:35:32,089 INFO [train.py:842] (1/4) Epoch 35, batch 5750, loss[loss=0.1872, simple_loss=0.2748, pruned_loss=0.04975, over 6725.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2624, pruned_loss=0.04193, over 1432811.37 frames.], batch size: 31, lr: 1.57e-04
2022-05-29 10:36:11,751 INFO [train.py:842] (1/4) Epoch 35, batch 5800, loss[loss=0.145, simple_loss=0.2435, pruned_loss=0.02329, over 7232.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2616, pruned_loss=0.0414, over 1430830.77 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:36:51,133 INFO [train.py:842] (1/4) Epoch 35, batch 5850, loss[loss=0.229, simple_loss=0.3325, pruned_loss=0.06276, over 7101.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2611, pruned_loss=0.04119, over 1429445.64 frames.], batch size: 28, lr: 1.57e-04
2022-05-29 10:37:30,767 INFO [train.py:842] (1/4) Epoch 35, batch 5900, loss[loss=0.1865, simple_loss=0.2723, pruned_loss=0.05038, over 7205.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2607, pruned_loss=0.04102, over 1429108.79 frames.], batch size: 23, lr: 1.57e-04
2022-05-29 10:38:10,005 INFO [train.py:842] (1/4) Epoch 35, batch 5950, loss[loss=0.1408, simple_loss=0.2371, pruned_loss=0.02226, over 7258.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2602, pruned_loss=0.04105, over 1429219.31 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 10:38:49,690 INFO [train.py:842] (1/4) Epoch 35, batch 6000, loss[loss=0.1865, simple_loss=0.2887, pruned_loss=0.04212, over 7325.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2603, pruned_loss=0.0411, over 1431594.50 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:38:49,691 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 10:38:59,233 INFO [train.py:871] (1/4) Epoch 35, validation: loss=0.1626, simple_loss=0.2599, pruned_loss=0.03269, over 868885.00 frames.
2022-05-29 10:39:38,650 INFO [train.py:842] (1/4) Epoch 35, batch 6050, loss[loss=0.1609, simple_loss=0.243, pruned_loss=0.03934, over 6997.00 frames.], tot_loss[loss=0.172, simple_loss=0.2608, pruned_loss=0.04166, over 1431009.37 frames.], batch size: 16, lr: 1.57e-04
2022-05-29 10:40:18,095 INFO [train.py:842] (1/4) Epoch 35, batch 6100, loss[loss=0.1776, simple_loss=0.2506, pruned_loss=0.05236, over 6989.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2618, pruned_loss=0.04201, over 1424251.84 frames.], batch size: 16, lr: 1.57e-04
2022-05-29 10:40:57,398 INFO [train.py:842] (1/4) Epoch 35, batch 6150, loss[loss=0.1686, simple_loss=0.2584, pruned_loss=0.03944, over 7117.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2627, pruned_loss=0.04232, over 1422476.18 frames.], batch size: 28, lr: 1.57e-04
2022-05-29 10:41:36,736 INFO [train.py:842] (1/4) Epoch 35, batch 6200, loss[loss=0.1658, simple_loss=0.2604, pruned_loss=0.03563, over 7236.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2642, pruned_loss=0.0425, over 1425906.49 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:42:16,072 INFO [train.py:842] (1/4) Epoch 35, batch 6250, loss[loss=0.1529, simple_loss=0.2514, pruned_loss=0.02719, over 7420.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2642, pruned_loss=0.04256, over 1429455.42 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 10:42:55,589 INFO [train.py:842] (1/4) Epoch 35, batch 6300, loss[loss=0.1677, simple_loss=0.2599, pruned_loss=0.0378, over 7277.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2633, pruned_loss=0.04209, over 1426960.10 frames.], batch size: 18, lr: 1.57e-04
2022-05-29 10:43:34,637 INFO [train.py:842] (1/4) Epoch 35, batch 6350, loss[loss=0.2367, simple_loss=0.3251, pruned_loss=0.07415, over 4894.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2616, pruned_loss=0.04129, over 1427036.44 frames.], batch size: 53, lr: 1.57e-04
2022-05-29 10:44:14,156 INFO [train.py:842] (1/4) Epoch 35, batch 6400, loss[loss=0.1563, simple_loss=0.2613, pruned_loss=0.02566, over 7293.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2619, pruned_loss=0.04129, over 1427165.34 frames.], batch size: 24, lr: 1.57e-04
2022-05-29 10:44:53,492 INFO [train.py:842] (1/4) Epoch 35, batch 6450, loss[loss=0.2532, simple_loss=0.3307, pruned_loss=0.08781, over 7156.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2624, pruned_loss=0.042, over 1427848.47 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:45:32,956 INFO [train.py:842] (1/4) Epoch 35, batch 6500, loss[loss=0.1731, simple_loss=0.2678, pruned_loss=0.03916, over 6725.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2626, pruned_loss=0.04195, over 1429096.14 frames.], batch size: 31, lr: 1.57e-04
2022-05-29 10:46:12,207 INFO [train.py:842] (1/4) Epoch 35, batch 6550, loss[loss=0.1797, simple_loss=0.2672, pruned_loss=0.04612, over 7357.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2618, pruned_loss=0.04182, over 1426913.30 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 10:46:51,913 INFO [train.py:842] (1/4) Epoch 35, batch 6600, loss[loss=0.2275, simple_loss=0.3044, pruned_loss=0.07525, over 7080.00 frames.], tot_loss[loss=0.174, simple_loss=0.2626, pruned_loss=0.04267, over 1419195.38 frames.], batch size: 28, lr: 1.57e-04
2022-05-29 10:47:31,082 INFO [train.py:842] (1/4) Epoch 35, batch 6650, loss[loss=0.1437, simple_loss=0.2324, pruned_loss=0.02745, over 7117.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2634, pruned_loss=0.043, over 1419174.64 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:48:10,550 INFO [train.py:842] (1/4) Epoch 35, batch 6700, loss[loss=0.1884, simple_loss=0.25, pruned_loss=0.06341, over 7289.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2625, pruned_loss=0.04269, over 1416834.06 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:48:49,702 INFO [train.py:842] (1/4) Epoch 35, batch 6750, loss[loss=0.1315, simple_loss=0.2187, pruned_loss=0.02217, over 7125.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2631, pruned_loss=0.04314, over 1413263.86 frames.], batch size: 17, lr: 1.56e-04
2022-05-29 10:49:29,350 INFO [train.py:842] (1/4) Epoch 35, batch 6800, loss[loss=0.1962, simple_loss=0.2738, pruned_loss=0.05931, over 6901.00 frames.], tot_loss[loss=0.174, simple_loss=0.2626, pruned_loss=0.04269, over 1417129.16 frames.], batch size: 32, lr: 1.56e-04
2022-05-29 10:50:08,374 INFO [train.py:842] (1/4) Epoch 35, batch 6850, loss[loss=0.1399, simple_loss=0.2201, pruned_loss=0.02982, over 7199.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2621, pruned_loss=0.04225, over 1419254.15 frames.], batch size: 16, lr: 1.56e-04
2022-05-29 10:50:47,921 INFO [train.py:842] (1/4) Epoch 35, batch 6900, loss[loss=0.1501, simple_loss=0.2467, pruned_loss=0.02677, over 7234.00 frames.], tot_loss[loss=0.173, simple_loss=0.2617, pruned_loss=0.04218, over 1420422.32 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 10:51:27,321 INFO [train.py:842] (1/4) Epoch 35, batch 6950, loss[loss=0.1659, simple_loss=0.2643, pruned_loss=0.0338, over 7384.00 frames.], tot_loss[loss=0.1723, simple_loss=0.261, pruned_loss=0.04182, over 1421532.86 frames.], batch size: 23, lr: 1.56e-04
2022-05-29 10:52:06,978 INFO [train.py:842] (1/4) Epoch 35, batch 7000, loss[loss=0.2043, simple_loss=0.2807, pruned_loss=0.06394, over 7209.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2602, pruned_loss=0.04126, over 1426954.14 frames.], batch size: 22, lr: 1.56e-04
2022-05-29 10:52:46,349 INFO [train.py:842] (1/4) Epoch 35, batch 7050, loss[loss=0.1676, simple_loss=0.2724, pruned_loss=0.03143, over 7314.00 frames.], tot_loss[loss=0.172, simple_loss=0.2607, pruned_loss=0.04167, over 1427579.91 frames.], batch size: 21, lr: 1.56e-04
2022-05-29 10:53:25,719 INFO [train.py:842] (1/4) Epoch 35, batch 7100, loss[loss=0.1481, simple_loss=0.241, pruned_loss=0.02759, over 7163.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2601, pruned_loss=0.04089, over 1428109.44 frames.], batch size: 19, lr: 1.56e-04
2022-05-29 10:54:05,020 INFO [train.py:842] (1/4) Epoch 35, batch 7150, loss[loss=0.1561, simple_loss=0.2395, pruned_loss=0.03635, over 7359.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2603, pruned_loss=0.0412, over 1425773.82 frames.], batch size: 19, lr: 1.56e-04
2022-05-29 10:54:44,606 INFO [train.py:842] (1/4) Epoch 35, batch 7200, loss[loss=0.1483, simple_loss=0.2508, pruned_loss=0.02293, over 7318.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2611, pruned_loss=0.04108, over 1428387.10 frames.], batch size: 21, lr: 1.56e-04
2022-05-29 10:55:23,868 INFO [train.py:842] (1/4) Epoch 35, batch 7250, loss[loss=0.2023, simple_loss=0.2919, pruned_loss=0.05632, over 7292.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2603, pruned_loss=0.04071, over 1428183.41 frames.], batch size: 25, lr: 1.56e-04
2022-05-29 10:56:03,402 INFO [train.py:842] (1/4) Epoch 35, batch 7300, loss[loss=0.1664, simple_loss=0.2607, pruned_loss=0.03605, over 7330.00 frames.], tot_loss[loss=0.171, simple_loss=0.2606, pruned_loss=0.04066, over 1427455.27 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 10:56:42,500 INFO [train.py:842] (1/4) Epoch 35, batch 7350, loss[loss=0.1318, simple_loss=0.2121, pruned_loss=0.02573, over 6855.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2601, pruned_loss=0.04063, over 1427839.15 frames.], batch size: 15, lr: 1.56e-04
2022-05-29 10:57:22,061 INFO [train.py:842] (1/4) Epoch 35, batch 7400, loss[loss=0.1975, simple_loss=0.2897, pruned_loss=0.05261, over 7322.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2609, pruned_loss=0.04092, over 1429988.75 frames.], batch size: 25, lr: 1.56e-04
2022-05-29 10:58:01,179 INFO [train.py:842] (1/4) Epoch 35, batch 7450, loss[loss=0.1945, simple_loss=0.2884, pruned_loss=0.05035, over 6761.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2613, pruned_loss=0.04154, over 1426059.87 frames.], batch size: 31, lr: 1.56e-04
2022-05-29 10:58:43,688 INFO [train.py:842] (1/4) Epoch 35, batch 7500, loss[loss=0.1366, simple_loss=0.2307, pruned_loss=0.02127, over 7430.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2601, pruned_loss=0.04069, over 1428028.68 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 10:59:23,002 INFO [train.py:842] (1/4) Epoch 35, batch 7550, loss[loss=0.1544, simple_loss=0.2418, pruned_loss=0.03355, over 7355.00 frames.], tot_loss[loss=0.1715, simple_loss=0.261, pruned_loss=0.04103, over 1429625.15 frames.], batch size: 19, lr: 1.56e-04
2022-05-29 11:00:02,450 INFO [train.py:842] (1/4) Epoch 35, batch 7600, loss[loss=0.1748, simple_loss=0.2636, pruned_loss=0.04302, over 7229.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2611, pruned_loss=0.0411, over 1421734.54 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:00:41,966 INFO [train.py:842] (1/4) Epoch 35, batch 7650, loss[loss=0.1607, simple_loss=0.2506, pruned_loss=0.03541, over 6865.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2606, pruned_loss=0.04084, over 1424771.06 frames.], batch size: 15, lr: 1.56e-04
2022-05-29 11:01:21,749 INFO [train.py:842] (1/4) Epoch 35, batch 7700, loss[loss=0.2107, simple_loss=0.2934, pruned_loss=0.06398, over 7029.00 frames.], tot_loss[loss=0.173, simple_loss=0.2618, pruned_loss=0.04212, over 1424894.35 frames.], batch size: 28, lr: 1.56e-04
2022-05-29 11:02:00,878 INFO [train.py:842] (1/4) Epoch 35, batch 7750, loss[loss=0.1848, simple_loss=0.2779, pruned_loss=0.04584, over 7326.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.04227, over 1429981.27 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:02:40,409 INFO [train.py:842] (1/4) Epoch 35, batch 7800, loss[loss=0.2084, simple_loss=0.2994, pruned_loss=0.05872, over 7200.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2637, pruned_loss=0.04289, over 1428803.69 frames.], batch size: 22, lr: 1.56e-04
2022-05-29 11:03:19,940 INFO [train.py:842] (1/4) Epoch 35, batch 7850, loss[loss=0.171, simple_loss=0.2653, pruned_loss=0.03831, over 7345.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2632, pruned_loss=0.04265, over 1431733.73 frames.], batch size: 22, lr: 1.56e-04
2022-05-29 11:03:59,689 INFO [train.py:842] (1/4) Epoch 35, batch 7900, loss[loss=0.1837, simple_loss=0.2634, pruned_loss=0.052, over 7206.00 frames.], tot_loss[loss=0.174, simple_loss=0.2624, pruned_loss=0.04284, over 1429950.03 frames.], batch size: 22, lr: 1.56e-04
2022-05-29 11:04:38,845 INFO [train.py:842] (1/4) Epoch 35, batch 7950, loss[loss=0.1686, simple_loss=0.2599, pruned_loss=0.0387, over 7297.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2625, pruned_loss=0.04266, over 1429263.63 frames.], batch size: 25, lr: 1.56e-04
2022-05-29 11:05:18,278 INFO [train.py:842] (1/4) Epoch 35, batch 8000, loss[loss=0.1627, simple_loss=0.2613, pruned_loss=0.03205, over 7296.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2633, pruned_loss=0.04253, over 1429690.03 frames.], batch size: 24, lr: 1.56e-04
2022-05-29 11:05:57,341 INFO [train.py:842] (1/4) Epoch 35, batch 8050, loss[loss=0.1777, simple_loss=0.2772, pruned_loss=0.03912, over 7040.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2629, pruned_loss=0.04223, over 1432073.95 frames.], batch size: 28, lr: 1.56e-04
2022-05-29 11:06:36,987 INFO [train.py:842] (1/4) Epoch 35, batch 8100, loss[loss=0.1883, simple_loss=0.2839, pruned_loss=0.0464, over 7317.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2626, pruned_loss=0.04193, over 1432795.74 frames.], batch size: 24, lr: 1.56e-04
2022-05-29 11:07:15,985 INFO [train.py:842] (1/4) Epoch 35, batch 8150, loss[loss=0.1657, simple_loss=0.244, pruned_loss=0.04367, over 7359.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2632, pruned_loss=0.04231, over 1428592.01 frames.], batch size: 19, lr: 1.56e-04
2022-05-29 11:07:55,468 INFO [train.py:842] (1/4) Epoch 35, batch 8200, loss[loss=0.1916, simple_loss=0.2802, pruned_loss=0.05154, over 6877.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2632, pruned_loss=0.04247, over 1428814.06 frames.], batch size: 31, lr: 1.56e-04
2022-05-29 11:08:34,630 INFO [train.py:842] (1/4) Epoch 35, batch 8250, loss[loss=0.2005, simple_loss=0.2943, pruned_loss=0.0533, over 7304.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2631, pruned_loss=0.04262, over 1423892.59 frames.], batch size: 25, lr: 1.56e-04
2022-05-29 11:09:14,299 INFO [train.py:842] (1/4) Epoch 35, batch 8300, loss[loss=0.17, simple_loss=0.258, pruned_loss=0.04103, over 7261.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2622, pruned_loss=0.04225, over 1421924.93 frames.], batch size: 19, lr: 1.56e-04
2022-05-29 11:09:53,323 INFO [train.py:842] (1/4) Epoch 35, batch 8350, loss[loss=0.1603, simple_loss=0.2491, pruned_loss=0.03573, over 7426.00 frames.], tot_loss[loss=0.1734, simple_loss=0.263, pruned_loss=0.04184, over 1425568.41 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:10:32,673 INFO [train.py:842] (1/4) Epoch 35, batch 8400, loss[loss=0.1866, simple_loss=0.2837, pruned_loss=0.04472, over 7188.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2625, pruned_loss=0.04216, over 1416637.75 frames.], batch size: 26, lr: 1.56e-04
2022-05-29 11:11:11,780 INFO [train.py:842] (1/4) Epoch 35, batch 8450, loss[loss=0.1656, simple_loss=0.2604, pruned_loss=0.03542, over 7143.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2624, pruned_loss=0.04249, over 1414624.68 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:11:51,480 INFO [train.py:842] (1/4) Epoch 35, batch 8500, loss[loss=0.1295, simple_loss=0.2176, pruned_loss=0.02072, over 7065.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2608, pruned_loss=0.04195, over 1418148.94 frames.], batch size: 18, lr: 1.56e-04
2022-05-29 11:12:41,680 INFO [train.py:842] (1/4) Epoch 35, batch 8550, loss[loss=0.1723, simple_loss=0.2715, pruned_loss=0.03653, over 6641.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2606, pruned_loss=0.04204, over 1419169.62 frames.], batch size: 31, lr: 1.56e-04
2022-05-29 11:13:21,182 INFO [train.py:842] (1/4) Epoch 35, batch 8600, loss[loss=0.209, simple_loss=0.2877, pruned_loss=0.06518, over 5062.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2613, pruned_loss=0.04225, over 1411754.75 frames.], batch size: 53, lr: 1.56e-04
2022-05-29 11:14:00,831 INFO [train.py:842] (1/4) Epoch 35, batch 8650, loss[loss=0.1631, simple_loss=0.2587, pruned_loss=0.0338, over 7224.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2603, pruned_loss=0.04173, over 1419133.92 frames.], batch size: 21, lr: 1.56e-04
2022-05-29 11:14:40,493 INFO [train.py:842] (1/4) Epoch 35, batch 8700, loss[loss=0.1725, simple_loss=0.2593, pruned_loss=0.04291, over 7359.00 frames.], tot_loss[loss=0.1726, simple_loss=0.261, pruned_loss=0.04209, over 1415012.45 frames.], batch size: 19, lr: 1.56e-04
2022-05-29 11:15:19,849 INFO [train.py:842] (1/4) Epoch 35, batch 8750, loss[loss=0.1558, simple_loss=0.2438, pruned_loss=0.03394, over 7441.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2609, pruned_loss=0.04203, over 1414966.10 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:15:59,160 INFO [train.py:842] (1/4) Epoch 35, batch 8800, loss[loss=0.1598, simple_loss=0.256, pruned_loss=0.03175, over 7322.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2616, pruned_loss=0.04195, over 1411427.46 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:16:38,461 INFO [train.py:842] (1/4) Epoch 35, batch 8850, loss[loss=0.1809, simple_loss=0.2722, pruned_loss=0.04487, over 7221.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2595, pruned_loss=0.0409, over 1409336.44 frames.], batch size: 21, lr: 1.56e-04
2022-05-29 11:17:17,815 INFO [train.py:842] (1/4) Epoch 35, batch 8900, loss[loss=0.1649, simple_loss=0.2561, pruned_loss=0.03689, over 7323.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2586, pruned_loss=0.03991, over 1411555.06 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:17:56,607 INFO [train.py:842] (1/4) Epoch 35, batch 8950, loss[loss=0.2152, simple_loss=0.3048, pruned_loss=0.06282, over 5158.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2592, pruned_loss=0.04051, over 1401972.41 frames.], batch size: 52, lr: 1.56e-04
2022-05-29 11:18:35,210 INFO [train.py:842] (1/4) Epoch 35, batch 9000, loss[loss=0.2087, simple_loss=0.2896, pruned_loss=0.06395, over 6395.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2638, pruned_loss=0.0432, over 1379768.02 frames.], batch size: 38, lr: 1.56e-04
2022-05-29 11:18:35,211 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 11:18:44,739 INFO [train.py:871] (1/4) Epoch 35, validation: loss=0.1633, simple_loss=0.2602, pruned_loss=0.03324, over 868885.00 frames.
2022-05-29 11:19:22,594 INFO [train.py:842] (1/4) Epoch 35, batch 9050, loss[loss=0.1944, simple_loss=0.2895, pruned_loss=0.04968, over 6466.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2667, pruned_loss=0.04442, over 1348092.74 frames.], batch size: 37, lr: 1.56e-04
2022-05-29 11:20:00,767 INFO [train.py:842] (1/4) Epoch 35, batch 9100, loss[loss=0.2355, simple_loss=0.3294, pruned_loss=0.0708, over 5265.00 frames.], tot_loss[loss=0.1816, simple_loss=0.27, pruned_loss=0.04666, over 1288099.05 frames.], batch size: 52, lr: 1.56e-04
2022-05-29 11:20:38,917 INFO [train.py:842] (1/4) Epoch 35, batch 9150, loss[loss=0.1846, simple_loss=0.2725, pruned_loss=0.04837, over 4919.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2725, pruned_loss=0.0489, over 1227195.95 frames.], batch size: 53, lr: 1.56e-04
2022-05-29 11:21:27,208 INFO [train.py:842] (1/4) Epoch 36, batch 0, loss[loss=0.1607, simple_loss=0.2498, pruned_loss=0.03585, over 7336.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2498, pruned_loss=0.03585, over 7336.00 frames.], batch size: 20, lr: 1.54e-04
2022-05-29 11:22:06,543 INFO [train.py:842] (1/4) Epoch 36, batch 50, loss[loss=0.1703, simple_loss=0.2589, pruned_loss=0.04079, over 7425.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2628, pruned_loss=0.04006, over 316394.37 frames.], batch size: 20, lr: 1.54e-04
2022-05-29 11:22:46,049 INFO [train.py:842] (1/4) Epoch 36, batch 100, loss[loss=0.1633, simple_loss=0.2563, pruned_loss=0.03514, over 5033.00 frames.], tot_loss[loss=0.174, simple_loss=0.2638, pruned_loss=0.04207, over 562078.55 frames.], batch size: 52, lr: 1.54e-04
2022-05-29 11:23:25,173 INFO [train.py:842] (1/4) Epoch 36, batch 150, loss[loss=0.1726, simple_loss=0.2762, pruned_loss=0.03454, over 7232.00 frames.], tot_loss[loss=0.1738, simple_loss=0.263, pruned_loss=0.04223, over 751495.72 frames.], batch size: 20, lr: 1.54e-04
2022-05-29 11:24:04,875 INFO [train.py:842] (1/4) Epoch 36, batch 200, loss[loss=0.1614, simple_loss=0.2657, pruned_loss=0.02853, over 7325.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2624, pruned_loss=0.0421, over 901594.20 frames.], batch size: 21, lr: 1.54e-04
2022-05-29 11:24:44,248 INFO [train.py:842] (1/4) Epoch 36, batch 250, loss[loss=0.1487, simple_loss=0.2423, pruned_loss=0.02759, over 7163.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2617, pruned_loss=0.04165, over 1020890.20 frames.], batch size: 19, lr: 1.54e-04
2022-05-29 11:25:23,532 INFO [train.py:842] (1/4) Epoch 36, batch 300, loss[loss=0.194, simple_loss=0.2949, pruned_loss=0.04651, over 7167.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2615, pruned_loss=0.04179, over 1105618.63 frames.], batch size: 26, lr: 1.54e-04
2022-05-29 11:26:02,720 INFO [train.py:842] (1/4) Epoch 36, batch 350, loss[loss=0.1566, simple_loss=0.2559, pruned_loss=0.02869, over 6789.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2611, pruned_loss=0.04123, over 1175411.46 frames.], batch size: 31, lr: 1.54e-04
2022-05-29 11:26:41,942 INFO [train.py:842] (1/4) Epoch 36, batch 400, loss[loss=0.1815, simple_loss=0.2743, pruned_loss=0.04434, over 7227.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2624, pruned_loss=0.0415, over 1231588.71 frames.], batch size: 22, lr: 1.54e-04
2022-05-29 11:27:21,345 INFO [train.py:842] (1/4) Epoch 36, batch 450, loss[loss=0.1755, simple_loss=0.2655, pruned_loss=0.04279, over 7149.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2621, pruned_loss=0.04147, over 1278827.02 frames.], batch size: 26, lr: 1.54e-04
2022-05-29 11:28:00,713 INFO [train.py:842] (1/4) Epoch 36, batch 500, loss[loss=0.1575, simple_loss=0.2529, pruned_loss=0.03108, over 7187.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2626, pruned_loss=0.04161, over 1310848.69 frames.], batch size: 23, lr: 1.54e-04
2022-05-29 11:28:39,881 INFO [train.py:842] (1/4) Epoch 36, batch 550, loss[loss=0.1803, simple_loss=0.2754, pruned_loss=0.04266, over 7427.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2633, pruned_loss=0.04163, over 1337756.31 frames.], batch size: 20, lr: 1.54e-04
2022-05-29 11:29:19,668 INFO [train.py:842] (1/4) Epoch 36, batch 600, loss[loss=0.2129, simple_loss=0.3096, pruned_loss=0.05813, over 7204.00 frames.], tot_loss[loss=0.174, simple_loss=0.2634, pruned_loss=0.04234, over 1359949.96 frames.], batch size: 23, lr: 1.54e-04
2022-05-29 11:29:59,103 INFO [train.py:842] (1/4) Epoch 36, batch 650, loss[loss=0.1414, simple_loss=0.2332, pruned_loss=0.02477, over 7162.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2622, pruned_loss=0.04168, over 1374511.33 frames.], batch size: 19, lr: 1.54e-04
2022-05-29 11:30:38,746 INFO [train.py:842] (1/4) Epoch 36, batch 700, loss[loss=0.1506, simple_loss=0.2478, pruned_loss=0.02673, over 7267.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2615, pruned_loss=0.04117, over 1386151.28 frames.], batch size: 19, lr: 1.54e-04
2022-05-29 11:31:17,838 INFO [train.py:842] (1/4) Epoch 36, batch 750, loss[loss=0.182, simple_loss=0.2701, pruned_loss=0.04694, over 7330.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2621, pruned_loss=0.04163, over 1386115.70 frames.], batch size: 20, lr: 1.54e-04
2022-05-29 11:31:57,481 INFO [train.py:842] (1/4) Epoch 36, batch 800, loss[loss=0.203, simple_loss=0.2901, pruned_loss=0.05795, over 7418.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2621, pruned_loss=0.04158, over 1393863.82 frames.], batch size: 21, lr: 1.54e-04
2022-05-29 11:32:36,699 INFO [train.py:842] (1/4) Epoch 36, batch 850, loss[loss=0.1755, simple_loss=0.2747, pruned_loss=0.03808, over 7224.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2618, pruned_loss=0.04136, over 1395078.94 frames.], batch size: 21, lr: 1.54e-04
2022-05-29 11:33:16,368 INFO [train.py:842] (1/4) Epoch 36, batch 900, loss[loss=0.1633, simple_loss=0.256, pruned_loss=0.03526, over 6815.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2614, pruned_loss=0.04121, over 1403390.06 frames.], batch size: 31, lr: 1.54e-04
2022-05-29 11:33:55,573 INFO [train.py:842] (1/4) Epoch 36, batch 950, loss[loss=0.1499, simple_loss=0.2254, pruned_loss=0.03715, over 6996.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2616, pruned_loss=0.04129, over 1406614.28 frames.], batch size: 16, lr: 1.53e-04
2022-05-29 11:34:35,016 INFO [train.py:842] (1/4) Epoch 36, batch 1000, loss[loss=0.1436, simple_loss=0.2197, pruned_loss=0.03376, over 7276.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2624, pruned_loss=0.04176, over 1408319.87 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 11:35:14,291 INFO [train.py:842] (1/4) Epoch 36, batch 1050, loss[loss=0.1446, simple_loss=0.2334, pruned_loss=0.02786, over 7358.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2627, pruned_loss=0.0421, over 1408442.89 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 11:35:53,843 INFO [train.py:842] (1/4) Epoch 36, batch 1100, loss[loss=0.2642, simple_loss=0.3361, pruned_loss=0.09618, over 7204.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2623, pruned_loss=0.04201, over 1408970.32 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 11:36:33,129 INFO [train.py:842] (1/4) Epoch 36, batch 1150, loss[loss=0.2445, simple_loss=0.3093, pruned_loss=0.08981, over 7285.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2615, pruned_loss=0.04187, over 1414190.82 frames.], batch size: 24, lr: 1.53e-04
2022-05-29 11:37:12,433 INFO [train.py:842] (1/4) Epoch 36, batch 1200, loss[loss=0.147, simple_loss=0.225, pruned_loss=0.03449, over 7268.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2628, pruned_loss=0.04263, over 1409240.71 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 11:37:51,890 INFO [train.py:842] (1/4) Epoch 36, batch 1250, loss[loss=0.1379, simple_loss=0.2239, pruned_loss=0.02592, over 7000.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2624, pruned_loss=0.04257, over 1411208.40 frames.], batch size: 16, lr: 1.53e-04
2022-05-29 11:38:31,196 INFO [train.py:842] (1/4) Epoch 36, batch 1300, loss[loss=0.1639, simple_loss=0.24, pruned_loss=0.04393, over 7135.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2635, pruned_loss=0.04299, over 1415478.16 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 11:39:10,487 INFO [train.py:842] (1/4) Epoch 36, batch 1350, loss[loss=0.1608, simple_loss=0.258, pruned_loss=0.03182, over 7269.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2616, pruned_loss=0.04193, over 1421243.76 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 11:39:49,831 INFO [train.py:842] (1/4) Epoch 36, batch 1400, loss[loss=0.1385, simple_loss=0.2322, pruned_loss=0.02238, over 6979.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2625, pruned_loss=0.04198, over 1419242.92 frames.], batch size: 16, lr: 1.53e-04
2022-05-29 11:40:29,046 INFO [train.py:842] (1/4) Epoch 36, batch 1450, loss[loss=0.1412, simple_loss=0.2157, pruned_loss=0.03335, over 6828.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2626, pruned_loss=0.04222, over 1416617.87 frames.], batch size: 15, lr: 1.53e-04
2022-05-29 11:41:08,699 INFO [train.py:842] (1/4) Epoch 36, batch 1500, loss[loss=0.1577, simple_loss=0.2602, pruned_loss=0.02763, over 7316.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2635, pruned_loss=0.04237, over 1420133.76 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 11:41:47,984 INFO [train.py:842] (1/4) Epoch 36, batch 1550, loss[loss=0.1739, simple_loss=0.2702, pruned_loss=0.03886, over 7237.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2633, pruned_loss=0.04246, over 1421853.19 frames.], batch size: 20, lr: 1.53e-04
2022-05-29 11:42:27,499 INFO [train.py:842] (1/4) Epoch 36, batch 1600, loss[loss=0.1753, simple_loss=0.2736, pruned_loss=0.03846, over 7377.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2628, pruned_loss=0.04255, over 1421506.31 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 11:43:06,837 INFO [train.py:842] (1/4) Epoch 36, batch 1650, loss[loss=0.1682, simple_loss=0.2518, pruned_loss=0.04226, over 7155.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2633, pruned_loss=0.04287, over 1422702.21 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 11:43:46,258 INFO [train.py:842] (1/4) Epoch 36, batch 1700, loss[loss=0.1804, simple_loss=0.2626, pruned_loss=0.04913, over 7283.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2621, pruned_loss=0.04177, over 1424164.96 frames.], batch size: 25, lr: 1.53e-04
2022-05-29 11:44:25,408 INFO [train.py:842] (1/4) Epoch 36, batch 1750, loss[loss=0.2132, simple_loss=0.2956, pruned_loss=0.06543, over 7285.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2618, pruned_loss=0.04132, over 1420460.57 frames.], batch size: 18, lr: 1.53e-04
2022-05-29 11:45:04,888 INFO [train.py:842] (1/4) Epoch 36, batch 1800, loss[loss=0.1622, simple_loss=0.2563, pruned_loss=0.03409, over 7201.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2621, pruned_loss=0.04167, over 1422531.39 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 11:45:44,139 INFO [train.py:842] (1/4) Epoch 36, batch 1850, loss[loss=0.1501, simple_loss=0.2414, pruned_loss=0.02941, over 7112.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2621, pruned_loss=0.04185, over 1424801.93 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 11:46:23,712 INFO [train.py:842] (1/4) Epoch 36, batch 1900, loss[loss=0.2023, simple_loss=0.2873, pruned_loss=0.05861, over 6782.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2611, pruned_loss=0.04123, over 1425399.26 frames.], batch size: 31, lr: 1.53e-04
2022-05-29 11:47:02,849 INFO [train.py:842] (1/4) Epoch 36, batch 1950, loss[loss=0.1591, simple_loss=0.2596, pruned_loss=0.02935, over 7229.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2613, pruned_loss=0.04096, over 1422815.23 frames.], batch size: 20, lr: 1.53e-04
2022-05-29 11:47:42,074 INFO [train.py:842] (1/4) Epoch 36, batch 2000, loss[loss=0.1354, simple_loss=0.2205, pruned_loss=0.02517, over 6999.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2606, pruned_loss=0.04046, over 1420172.74 frames.], batch size: 16, lr: 1.53e-04
2022-05-29 11:48:21,461 INFO [train.py:842] (1/4) Epoch 36, batch 2050, loss[loss=0.1532, simple_loss=0.2578, pruned_loss=0.02426, over 7332.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2602, pruned_loss=0.04025, over 1425219.74 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 11:49:01,228 INFO [train.py:842] (1/4) Epoch 36, batch 2100, loss[loss=0.1505, simple_loss=0.2471, pruned_loss=0.02694, over 7411.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2596, pruned_loss=0.04011, over 1423555.80 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 11:49:40,706 INFO [train.py:842] (1/4) Epoch 36, batch 2150, loss[loss=0.143, simple_loss=0.2302, pruned_loss=0.02785, over 7258.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2583, pruned_loss=0.03958, over 1425965.50 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 11:50:20,100 INFO [train.py:842] (1/4) Epoch 36, batch 2200, loss[loss=0.1876, simple_loss=0.2695, pruned_loss=0.05286, over 7415.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2598, pruned_loss=0.04029, over 1425544.64 frames.], batch size: 18, lr: 1.53e-04
2022-05-29 11:50:58,853 INFO [train.py:842] (1/4) Epoch 36, batch 2250, loss[loss=0.1881, simple_loss=0.2828, pruned_loss=0.04667, over 7327.00 frames.], tot_loss[loss=0.172, simple_loss=0.2619, pruned_loss=0.04101, over 1422399.07 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 11:51:38,539 INFO [train.py:842] (1/4) Epoch 36, batch 2300, loss[loss=0.1241, simple_loss=0.2057, pruned_loss=0.02127, over 7145.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2605, pruned_loss=0.04048, over 1426185.10 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 11:52:17,583 INFO [train.py:842] (1/4) Epoch 36, batch 2350, loss[loss=0.2293, simple_loss=0.3021, pruned_loss=0.07828, over 4809.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2603, pruned_loss=0.04045, over 1424171.56 frames.], batch size: 52, lr: 1.53e-04
2022-05-29 11:52:57,224 INFO [train.py:842] (1/4) Epoch 36, batch 2400, loss[loss=0.1438, simple_loss=0.2275, pruned_loss=0.03001, over 7417.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2607, pruned_loss=0.04071, over 1427429.71 frames.], batch size: 18, lr: 1.53e-04
2022-05-29 11:53:36,466 INFO [train.py:842] (1/4) Epoch 36, batch 2450, loss[loss=0.1344, simple_loss=0.2265, pruned_loss=0.02119, over 7166.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2604, pruned_loss=0.04064, over 1423542.77 frames.], batch size: 18, lr: 1.53e-04
2022-05-29 11:54:16,220 INFO [train.py:842] (1/4) Epoch 36, batch 2500, loss[loss=0.1883, simple_loss=0.2815, pruned_loss=0.04756, over 7145.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2608, pruned_loss=0.04109, over 1426890.83 frames.], batch size: 20, lr: 1.53e-04
2022-05-29 11:54:55,474 INFO [train.py:842] (1/4) Epoch 36, batch 2550, loss[loss=0.1666, simple_loss=0.2445, pruned_loss=0.04437, over 7368.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2608, pruned_loss=0.04091, over 1423620.16 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 11:55:34,940 INFO [train.py:842] (1/4) Epoch 36, batch 2600, loss[loss=0.1696, simple_loss=0.2613, pruned_loss=0.03893, over 7163.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2614, pruned_loss=0.04117, over 1424094.49 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 11:56:14,150 INFO [train.py:842] (1/4) Epoch 36, batch 2650, loss[loss=0.1988, simple_loss=0.2751, pruned_loss=0.06127, over 4636.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2613, pruned_loss=0.04075, over 1421465.92 frames.], batch size: 52, lr: 1.53e-04
2022-05-29 11:56:54,050 INFO [train.py:842] (1/4) Epoch 36, batch 2700, loss[loss=0.1613, simple_loss=0.2586, pruned_loss=0.03196, over 7320.00 frames.], tot_loss[loss=0.171, simple_loss=0.2606, pruned_loss=0.04067, over 1422786.51 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 11:57:33,286 INFO [train.py:842] (1/4) Epoch 36, batch 2750, loss[loss=0.1688, simple_loss=0.2564, pruned_loss=0.04062, over 7117.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2602, pruned_loss=0.04062, over 1425411.58 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 11:58:12,922 INFO [train.py:842] (1/4) Epoch 36, batch 2800, loss[loss=0.2276, simple_loss=0.3192, pruned_loss=0.06794, over 7208.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2595, pruned_loss=0.04033, over 1426958.43 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 11:58:52,378 INFO [train.py:842] (1/4) Epoch 36, batch 2850, loss[loss=0.1301, simple_loss=0.2125, pruned_loss=0.02382, over 7263.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2597, pruned_loss=0.04075, over 1427845.94 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 11:59:32,202 INFO [train.py:842] (1/4) Epoch 36, batch 2900, loss[loss=0.1546, simple_loss=0.2365, pruned_loss=0.03639, over 7255.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2584, pruned_loss=0.04068, over 1426775.61 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 12:00:11,110 INFO [train.py:842] (1/4) Epoch 36, batch 2950, loss[loss=0.1431, simple_loss=0.2288, pruned_loss=0.02871, over 7169.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2592, pruned_loss=0.04074, over 1425061.71 frames.], batch size: 18, lr: 1.53e-04
2022-05-29 12:00:50,582 INFO [train.py:842] (1/4) Epoch 36, batch 3000, loss[loss=0.1836, simple_loss=0.265, pruned_loss=0.05111, over 7163.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2603, pruned_loss=0.0409, over 1421078.49 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 12:00:50,583 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 12:01:00,560 INFO [train.py:871] (1/4) Epoch 36, validation: loss=0.1657, simple_loss=0.263, pruned_loss=0.03426, over 868885.00 frames.
2022-05-29 12:01:39,788 INFO [train.py:842] (1/4) Epoch 36, batch 3050, loss[loss=0.175, simple_loss=0.2666, pruned_loss=0.04174, over 7293.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2609, pruned_loss=0.04108, over 1423545.52 frames.], batch size: 24, lr: 1.53e-04
2022-05-29 12:02:19,601 INFO [train.py:842] (1/4) Epoch 36, batch 3100, loss[loss=0.19, simple_loss=0.2849, pruned_loss=0.04761, over 7325.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2609, pruned_loss=0.04081, over 1427921.65 frames.], batch size: 25, lr: 1.53e-04
2022-05-29 12:02:58,614 INFO [train.py:842] (1/4) Epoch 36, batch 3150, loss[loss=0.1852, simple_loss=0.283, pruned_loss=0.04366, over 7367.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2619, pruned_loss=0.04145, over 1426124.02 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:03:38,069 INFO [train.py:842] (1/4) Epoch 36, batch 3200, loss[loss=0.1378, simple_loss=0.2272, pruned_loss=0.02425, over 7145.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2629, pruned_loss=0.04222, over 1419644.07 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 12:04:17,312 INFO [train.py:842] (1/4) Epoch 36, batch 3250, loss[loss=0.2316, simple_loss=0.2983, pruned_loss=0.08241, over 4667.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2623, pruned_loss=0.04208, over 1416811.55 frames.], batch size: 52, lr: 1.53e-04
2022-05-29 12:04:56,916 INFO [train.py:842] (1/4) Epoch 36, batch 3300, loss[loss=0.2041, simple_loss=0.2817, pruned_loss=0.0633, over 7222.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2617, pruned_loss=0.04151, over 1420474.31 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:05:36,399 INFO [train.py:842] (1/4) Epoch 36, batch 3350, loss[loss=0.1702, simple_loss=0.2636, pruned_loss=0.03841, over 7216.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2626, pruned_loss=0.04176, over 1424698.58 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:06:16,129 INFO [train.py:842] (1/4) Epoch 36, batch 3400, loss[loss=0.1765, simple_loss=0.249, pruned_loss=0.05198, over 7264.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2623, pruned_loss=0.04205, over 1423156.74 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 12:06:55,248 INFO [train.py:842] (1/4) Epoch 36, batch 3450, loss[loss=0.1627, simple_loss=0.244, pruned_loss=0.04073, over 7307.00 frames.], tot_loss[loss=0.1725, simple_loss=0.262, pruned_loss=0.04153, over 1421139.50 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 12:07:34,930 INFO [train.py:842] (1/4) Epoch 36, batch 3500, loss[loss=0.1992, simple_loss=0.2985, pruned_loss=0.04996, over 7407.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2617, pruned_loss=0.0417, over 1417975.64 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 12:08:13,973 INFO [train.py:842] (1/4) Epoch 36, batch 3550, loss[loss=0.1648, simple_loss=0.2655, pruned_loss=0.03201, over 7115.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2617, pruned_loss=0.04129, over 1421695.08 frames.], batch size: 28, lr: 1.53e-04
2022-05-29 12:08:53,410 INFO [train.py:842] (1/4) Epoch 36, batch 3600, loss[loss=0.1761, simple_loss=0.2756, pruned_loss=0.03827, over 7282.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2621, pruned_loss=0.0411, over 1421219.07 frames.], batch size: 25, lr: 1.53e-04
2022-05-29 12:09:32,778 INFO [train.py:842] (1/4) Epoch 36, batch 3650, loss[loss=0.2055, simple_loss=0.302, pruned_loss=0.05455, over 7305.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2614, pruned_loss=0.0408, over 1423520.43 frames.], batch size: 24, lr: 1.53e-04
2022-05-29 12:10:12,408 INFO [train.py:842] (1/4) Epoch 36, batch 3700, loss[loss=0.1923, simple_loss=0.2809, pruned_loss=0.0518, over 7123.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2612, pruned_loss=0.04043, over 1426655.02 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 12:10:51,842 INFO [train.py:842] (1/4) Epoch 36, batch 3750, loss[loss=0.1678, simple_loss=0.2757, pruned_loss=0.02994, over 7331.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2603, pruned_loss=0.03993, over 1426241.88 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 12:11:31,340 INFO [train.py:842] (1/4) Epoch 36, batch 3800, loss[loss=0.1482, simple_loss=0.2407, pruned_loss=0.02784, over 7345.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2609, pruned_loss=0.04031, over 1427681.82 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 12:12:10,353 INFO [train.py:842] (1/4) Epoch 36, batch 3850, loss[loss=0.1366, simple_loss=0.218, pruned_loss=0.02761, over 7004.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2615, pruned_loss=0.0408, over 1424510.31 frames.], batch size: 16, lr: 1.53e-04
2022-05-29 12:12:50,355 INFO [train.py:842] (1/4) Epoch 36, batch 3900, loss[loss=0.181, simple_loss=0.2778, pruned_loss=0.04207, over 7188.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2622, pruned_loss=0.04144, over 1426712.84 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:13:29,186 INFO [train.py:842] (1/4) Epoch 36, batch 3950, loss[loss=0.2028, simple_loss=0.2889, pruned_loss=0.05839, over 6806.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2623, pruned_loss=0.04131, over 1424300.77 frames.], batch size: 31, lr: 1.53e-04
2022-05-29 12:14:08,389 INFO [train.py:842] (1/4) Epoch 36, batch 4000, loss[loss=0.1792, simple_loss=0.2684, pruned_loss=0.04497, over 7113.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2631, pruned_loss=0.04205, over 1424352.88 frames.], batch size: 28, lr: 1.53e-04
2022-05-29 12:14:47,625 INFO [train.py:842] (1/4) Epoch 36, batch 4050, loss[loss=0.1488, simple_loss=0.2503, pruned_loss=0.02365, over 6540.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2629, pruned_loss=0.04197, over 1425609.98 frames.], batch size: 37, lr: 1.53e-04
2022-05-29 12:15:27,241 INFO [train.py:842] (1/4) Epoch 36, batch 4100, loss[loss=0.1937, simple_loss=0.2694, pruned_loss=0.05897, over 7238.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2629, pruned_loss=0.04212, over 1425916.97 frames.], batch size: 20, lr: 1.53e-04
2022-05-29 12:16:06,485 INFO [train.py:842] (1/4) Epoch 36, batch 4150, loss[loss=0.1924, simple_loss=0.2824, pruned_loss=0.05121, over 7345.00 frames.], tot_loss[loss=0.173, simple_loss=0.2624, pruned_loss=0.04182, over 1423317.36 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 12:16:45,806 INFO [train.py:842] (1/4) Epoch 36, batch 4200, loss[loss=0.1452, simple_loss=0.2433, pruned_loss=0.02352, over 7326.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2623, pruned_loss=0.04198, over 1419087.71 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 12:17:25,112 INFO [train.py:842] (1/4) Epoch 36, batch 4250, loss[loss=0.1953, simple_loss=0.2854, pruned_loss=0.0526, over 7199.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2622, pruned_loss=0.04224, over 1418483.95 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 12:18:04,938 INFO [train.py:842] (1/4) Epoch 36, batch 4300, loss[loss=0.1767, simple_loss=0.2768, pruned_loss=0.03828, over 7214.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2625, pruned_loss=0.0425, over 1419961.78 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:18:44,049 INFO [train.py:842] (1/4) Epoch 36, batch 4350, loss[loss=0.1705, simple_loss=0.2646, pruned_loss=0.03815, over 7362.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2633, pruned_loss=0.04288, over 1415038.38 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 12:19:23,734 INFO [train.py:842] (1/4) Epoch 36, batch 4400, loss[loss=0.1742, simple_loss=0.2608, pruned_loss=0.04383, over 6609.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2631, pruned_loss=0.04293, over 1417919.74 frames.], batch size: 31, lr: 1.53e-04
2022-05-29 12:20:13,625 INFO [train.py:842] (1/4) Epoch 36, batch 4450, loss[loss=0.1633, simple_loss=0.2564, pruned_loss=0.03509, over 7419.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2635, pruned_loss=0.04262, over 1417706.72 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 12:20:53,260 INFO [train.py:842] (1/4) Epoch 36, batch 4500, loss[loss=0.1516, simple_loss=0.2423, pruned_loss=0.03046, over 7159.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2632, pruned_loss=0.0422, over 1421887.56 frames.], batch size: 18, lr: 1.53e-04
2022-05-29 12:21:32,470 INFO [train.py:842] (1/4) Epoch 36, batch 4550, loss[loss=0.1788, simple_loss=0.2698, pruned_loss=0.04391, over 7383.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2623, pruned_loss=0.04148, over 1422790.98 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:22:11,935 INFO [train.py:842] (1/4) Epoch 36, batch 4600, loss[loss=0.2281, simple_loss=0.3084, pruned_loss=0.07395, over 5049.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2635, pruned_loss=0.04237, over 1420218.80 frames.], batch size: 52, lr: 1.53e-04
2022-05-29 12:22:50,833 INFO [train.py:842] (1/4) Epoch 36, batch 4650, loss[loss=0.154, simple_loss=0.2336, pruned_loss=0.03721, over 7276.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2629, pruned_loss=0.042, over 1416199.35 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 12:23:30,486 INFO [train.py:842] (1/4) Epoch 36, batch 4700, loss[loss=0.1942, simple_loss=0.2959, pruned_loss=0.04623, over 6550.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2632, pruned_loss=0.042, over 1419215.88 frames.], batch size: 38, lr: 1.53e-04
2022-05-29 12:24:09,575 INFO [train.py:842] (1/4) Epoch 36, batch 4750, loss[loss=0.1713, simple_loss=0.2688, pruned_loss=0.03691, over 7099.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2636, pruned_loss=0.04232, over 1414042.04 frames.], batch size: 28, lr: 1.53e-04
2022-05-29 12:24:49,160 INFO [train.py:842] (1/4) Epoch 36, batch 4800, loss[loss=0.1595, simple_loss=0.2543, pruned_loss=0.03229, over 7199.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2619, pruned_loss=0.04191, over 1414783.21 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:25:49,890 INFO [train.py:842] (1/4) Epoch 36, batch 4850, loss[loss=0.1873, simple_loss=0.2779, pruned_loss=0.04835, over 7115.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2616, pruned_loss=0.04148, over 1415984.67 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 12:26:29,634 INFO [train.py:842] (1/4) Epoch 36, batch 4900, loss[loss=0.1455, simple_loss=0.228, pruned_loss=0.03146, over 7291.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2603, pruned_loss=0.04124, over 1420217.03 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 12:27:08,904 INFO [train.py:842] (1/4) Epoch 36, batch 4950, loss[loss=0.1773, simple_loss=0.274, pruned_loss=0.04028, over 7315.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2616, pruned_loss=0.04182, over 1420475.98 frames.], batch size: 25, lr: 1.53e-04
2022-05-29 12:27:48,581 INFO [train.py:842] (1/4) Epoch 36, batch 5000, loss[loss=0.1933, simple_loss=0.2812, pruned_loss=0.05265, over 7374.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2608, pruned_loss=0.04115, over 1424786.12 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:28:27,749 INFO [train.py:842] (1/4) Epoch 36, batch 5050, loss[loss=0.1767, simple_loss=0.2652, pruned_loss=0.04408, over 5335.00 frames.], tot_loss[loss=0.1716, simple_loss=0.261, pruned_loss=0.04114, over 1420170.03 frames.], batch size: 52, lr: 1.53e-04
2022-05-29 12:29:07,306 INFO [train.py:842] (1/4) Epoch 36, batch 5100, loss[loss=0.1649, simple_loss=0.2546, pruned_loss=0.03765, over 7093.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2611, pruned_loss=0.04121, over 1421959.00 frames.], batch size: 28, lr: 1.53e-04
2022-05-29 12:29:46,365 INFO [train.py:842] (1/4) Epoch 36, batch 5150, loss[loss=0.147, simple_loss=0.2439, pruned_loss=0.025, over 7320.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2615, pruned_loss=0.04145, over 1422396.50 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 12:30:26,072 INFO [train.py:842] (1/4) Epoch 36, batch 5200, loss[loss=0.1604, simple_loss=0.2485, pruned_loss=0.03609, over 7351.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2614, pruned_loss=0.04145, over 1420340.49 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 12:31:05,243 INFO [train.py:842] (1/4) Epoch 36, batch 5250, loss[loss=0.19, simple_loss=0.2826, pruned_loss=0.04866, over 7109.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2614, pruned_loss=0.04123, over 1424095.43 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 12:31:44,675 INFO [train.py:842] (1/4) Epoch 36, batch 5300, loss[loss=0.2226, simple_loss=0.2987, pruned_loss=0.07321, over 7210.00 frames.], tot_loss[loss=0.1725, simple_loss=0.262, pruned_loss=0.04146, over 1427805.63 frames.], batch size: 23, lr: 1.52e-04
2022-05-29 12:32:23,848 INFO [train.py:842] (1/4) Epoch 36, batch 5350, loss[loss=0.1866, simple_loss=0.2834, pruned_loss=0.04491, over 7302.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2626, pruned_loss=0.04207, over 1424532.20 frames.], batch size: 24, lr: 1.52e-04
2022-05-29 12:33:03,189 INFO [train.py:842] (1/4) Epoch 36, batch 5400, loss[loss=0.1614, simple_loss=0.254, pruned_loss=0.03445, over 7063.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2627, pruned_loss=0.04201, over 1419974.41 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:33:42,357 INFO [train.py:842] (1/4) Epoch 36, batch 5450, loss[loss=0.1492, simple_loss=0.231, pruned_loss=0.03372, over 7149.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2621, pruned_loss=0.04178, over 1418561.24 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:34:21,828 INFO [train.py:842] (1/4) Epoch 36, batch 5500, loss[loss=0.1764, simple_loss=0.2746, pruned_loss=0.03906, over 7221.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2617, pruned_loss=0.04145, over 1419611.88 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 12:35:00,640 INFO [train.py:842] (1/4) Epoch 36, batch 5550, loss[loss=0.155, simple_loss=0.2525, pruned_loss=0.02872, over 7336.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2623, pruned_loss=0.0416, over 1416135.11 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:35:40,066 INFO [train.py:842] (1/4) Epoch 36, batch 5600, loss[loss=0.1919, simple_loss=0.2792, pruned_loss=0.05226, over 7331.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2629, pruned_loss=0.04163, over 1417532.32 frames.], batch size: 22, lr: 1.52e-04
2022-05-29 12:36:19,248 INFO [train.py:842] (1/4) Epoch 36, batch 5650, loss[loss=0.2112, simple_loss=0.2936, pruned_loss=0.06444, over 7370.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2623, pruned_loss=0.04165, over 1417789.77 frames.], batch size: 23, lr: 1.52e-04
2022-05-29 12:36:58,840 INFO [train.py:842] (1/4) Epoch 36, batch 5700, loss[loss=0.1535, simple_loss=0.2369, pruned_loss=0.03509, over 7409.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2617, pruned_loss=0.04131, over 1414697.26 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:37:37,871 INFO [train.py:842] (1/4) Epoch 36, batch 5750, loss[loss=0.17, simple_loss=0.2646, pruned_loss=0.03767, over 7292.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2615, pruned_loss=0.04097, over 1411330.24 frames.], batch size: 25, lr: 1.52e-04
2022-05-29 12:38:17,292 INFO [train.py:842] (1/4) Epoch 36, batch 5800, loss[loss=0.1828, simple_loss=0.2717, pruned_loss=0.04701, over 7107.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2611, pruned_loss=0.04054, over 1415494.62 frames.], batch size: 28, lr: 1.52e-04
2022-05-29 12:38:56,579 INFO [train.py:842] (1/4) Epoch 36, batch 5850, loss[loss=0.1356, simple_loss=0.2231, pruned_loss=0.024, over 7164.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2618, pruned_loss=0.04132, over 1419467.65 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:39:36,368 INFO [train.py:842] (1/4) Epoch 36, batch 5900, loss[loss=0.1576, simple_loss=0.2336, pruned_loss=0.04076, over 7408.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2616, pruned_loss=0.04168, over 1419779.48 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:40:15,755 INFO [train.py:842] (1/4) Epoch 36, batch 5950, loss[loss=0.1646, simple_loss=0.2517, pruned_loss=0.03875, over 7155.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2604, pruned_loss=0.04141, over 1419219.60 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 12:40:55,444 INFO [train.py:842] (1/4) Epoch 36, batch 6000, loss[loss=0.1753, simple_loss=0.26, pruned_loss=0.04532, over 7201.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2609, pruned_loss=0.04126, over 1420463.34 frames.], batch size: 22, lr: 1.52e-04
2022-05-29 12:40:55,445 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 12:41:05,037 INFO [train.py:871] (1/4) Epoch 36, validation: loss=0.1636, simple_loss=0.2603, pruned_loss=0.03352, over 868885.00 frames.
2022-05-29 12:41:44,538 INFO [train.py:842] (1/4) Epoch 36, batch 6050, loss[loss=0.1706, simple_loss=0.2516, pruned_loss=0.04481, over 7125.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2604, pruned_loss=0.04147, over 1420565.52 frames.], batch size: 17, lr: 1.52e-04
2022-05-29 12:42:24,243 INFO [train.py:842] (1/4) Epoch 36, batch 6100, loss[loss=0.1439, simple_loss=0.2357, pruned_loss=0.02608, over 7163.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2611, pruned_loss=0.04199, over 1422650.32 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 12:43:03,494 INFO [train.py:842] (1/4) Epoch 36, batch 6150, loss[loss=0.1908, simple_loss=0.284, pruned_loss=0.04876, over 7340.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2606, pruned_loss=0.04152, over 1423161.81 frames.], batch size: 22, lr: 1.52e-04
2022-05-29 12:43:43,145 INFO [train.py:842] (1/4) Epoch 36, batch 6200, loss[loss=0.1531, simple_loss=0.2496, pruned_loss=0.02831, over 7084.00 frames.], tot_loss[loss=0.172, simple_loss=0.261, pruned_loss=0.04148, over 1420495.34 frames.], batch size: 28, lr: 1.52e-04
2022-05-29 12:44:22,484 INFO [train.py:842] (1/4) Epoch 36, batch 6250, loss[loss=0.1895, simple_loss=0.2795, pruned_loss=0.04973, over 7280.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2616, pruned_loss=0.04167, over 1421516.90 frames.], batch size: 25, lr: 1.52e-04
2022-05-29 12:45:04,921 INFO [train.py:842] (1/4) Epoch 36, batch 6300, loss[loss=0.1433, simple_loss=0.2218, pruned_loss=0.0324, over 7151.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2622, pruned_loss=0.04223, over 1419838.05 frames.], batch size: 17, lr: 1.52e-04
2022-05-29 12:45:44,255 INFO [train.py:842] (1/4) Epoch 36, batch 6350, loss[loss=0.2073, simple_loss=0.2997, pruned_loss=0.05749, over 7332.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2606, pruned_loss=0.04115, over 1419689.23 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 12:46:23,723 INFO [train.py:842] (1/4) Epoch 36, batch 6400, loss[loss=0.1655, simple_loss=0.2618, pruned_loss=0.03464, over 7349.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2599, pruned_loss=0.04069, over 1422187.12 frames.], batch size: 22, lr: 1.52e-04
2022-05-29 12:47:02,951 INFO [train.py:842] (1/4) Epoch 36, batch 6450, loss[loss=0.1691, simple_loss=0.259, pruned_loss=0.03958, over 7257.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2606, pruned_loss=0.04115, over 1423437.57 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 12:47:42,452 INFO [train.py:842] (1/4) Epoch 36, batch 6500, loss[loss=0.1534, simple_loss=0.2417, pruned_loss=0.03259, over 7164.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2599, pruned_loss=0.04096, over 1424212.15 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:48:21,608 INFO [train.py:842] (1/4) Epoch 36, batch 6550, loss[loss=0.1621, simple_loss=0.2531, pruned_loss=0.03558, over 7142.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2613, pruned_loss=0.04197, over 1422651.65 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:49:01,321 INFO [train.py:842] (1/4) Epoch 36, batch 6600, loss[loss=0.1727, simple_loss=0.2636, pruned_loss=0.04086, over 7171.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2612, pruned_loss=0.04181, over 1423888.47 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:49:40,644 INFO [train.py:842] (1/4) Epoch 36, batch 6650, loss[loss=0.1772, simple_loss=0.2746, pruned_loss=0.03996, over 6741.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2626, pruned_loss=0.04275, over 1424636.31 frames.], batch size: 31, lr: 1.52e-04
2022-05-29 12:50:20,363 INFO [train.py:842] (1/4) Epoch 36, batch 6700, loss[loss=0.1664, simple_loss=0.2649, pruned_loss=0.03393, over 7240.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2625, pruned_loss=0.0424, over 1426734.51 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:50:59,337 INFO [train.py:842] (1/4) Epoch 36, batch 6750, loss[loss=0.1542, simple_loss=0.2455, pruned_loss=0.03141, over 7343.00 frames.], tot_loss[loss=0.1747, simple_loss=0.264, pruned_loss=0.04272, over 1420725.64 frames.], batch size: 22, lr: 1.52e-04
2022-05-29 12:51:38,813 INFO [train.py:842] (1/4) Epoch 36, batch 6800, loss[loss=0.166, simple_loss=0.2513, pruned_loss=0.04034, over 7358.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2633, pruned_loss=0.04172, over 1424837.47 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 12:52:18,262 INFO [train.py:842] (1/4) Epoch 36, batch 6850, loss[loss=0.1811, simple_loss=0.2722, pruned_loss=0.04507, over 7089.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2627, pruned_loss=0.04144, over 1425150.51 frames.], batch size: 28, lr: 1.52e-04
2022-05-29 12:52:58,002 INFO [train.py:842] (1/4) Epoch 36, batch 6900, loss[loss=0.1914, simple_loss=0.283, pruned_loss=0.04985, over 7433.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2616, pruned_loss=0.04063, over 1426976.56 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:53:37,194 INFO [train.py:842] (1/4) Epoch 36, batch 6950, loss[loss=0.14, simple_loss=0.2353, pruned_loss=0.02234, over 7243.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2612, pruned_loss=0.04075, over 1429116.06 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:54:16,944 INFO [train.py:842] (1/4) Epoch 36, batch 7000, loss[loss=0.1816, simple_loss=0.2676, pruned_loss=0.0478, over 7427.00 frames.], tot_loss[loss=0.171, simple_loss=0.261, pruned_loss=0.04043, over 1429441.53 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:54:56,277 INFO [train.py:842] (1/4) Epoch 36, batch 7050, loss[loss=0.1844, simple_loss=0.2771, pruned_loss=0.0458, over 7430.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2613, pruned_loss=0.0405, over 1425516.48 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:55:36,088 INFO [train.py:842] (1/4) Epoch 36, batch 7100, loss[loss=0.1808, simple_loss=0.2761, pruned_loss=0.04278, over 7250.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2609, pruned_loss=0.0411, over 1426640.90 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 12:56:15,258 INFO [train.py:842] (1/4) Epoch 36, batch 7150, loss[loss=0.1812, simple_loss=0.2746, pruned_loss=0.04388, over 7302.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2624, pruned_loss=0.04164, over 1430387.91 frames.], batch size: 24, lr: 1.52e-04
2022-05-29 12:56:54,973 INFO [train.py:842] (1/4) Epoch 36, batch 7200, loss[loss=0.1187, simple_loss=0.207, pruned_loss=0.01518, over 7292.00 frames.], tot_loss[loss=0.1713, simple_loss=0.261, pruned_loss=0.04085, over 1428906.36 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:57:34,378 INFO [train.py:842] (1/4) Epoch 36, batch 7250, loss[loss=0.1731, simple_loss=0.2709, pruned_loss=0.03764, over 7136.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2623, pruned_loss=0.04155, over 1427696.87 frames.], batch size: 26, lr: 1.52e-04
2022-05-29 12:58:13,746 INFO [train.py:842] (1/4) Epoch 36, batch 7300, loss[loss=0.172, simple_loss=0.2654, pruned_loss=0.03929, over 7084.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2625, pruned_loss=0.04136, over 1425759.94 frames.], batch size: 28, lr: 1.52e-04
2022-05-29 12:58:52,894 INFO [train.py:842] (1/4) Epoch 36, batch 7350, loss[loss=0.1621, simple_loss=0.2489, pruned_loss=0.03763, over 6799.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2624, pruned_loss=0.04123, over 1425982.61 frames.], batch size: 15, lr: 1.52e-04
2022-05-29 12:59:32,497 INFO [train.py:842] (1/4) Epoch 36, batch 7400, loss[loss=0.1785, simple_loss=0.2573, pruned_loss=0.04983, over 7442.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2608, pruned_loss=0.04051, over 1424261.38 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 13:00:11,797 INFO [train.py:842] (1/4) Epoch 36, batch 7450, loss[loss=0.1601, simple_loss=0.2392, pruned_loss=0.04047, over 7414.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2611, pruned_loss=0.04091, over 1420882.93 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 13:00:51,491 INFO [train.py:842] (1/4) Epoch 36, batch 7500, loss[loss=0.1631, simple_loss=0.2464, pruned_loss=0.03984, over 7157.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2618, pruned_loss=0.04116, over 1423843.75 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 13:01:30,813 INFO [train.py:842] (1/4) Epoch 36, batch 7550, loss[loss=0.2397, simple_loss=0.3354, pruned_loss=0.07203, over 7222.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2625, pruned_loss=0.04139, over 1424542.43 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:02:10,478 INFO [train.py:842] (1/4) Epoch 36, batch 7600, loss[loss=0.153, simple_loss=0.237, pruned_loss=0.03449, over 7274.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2633, pruned_loss=0.04197, over 1421644.16 frames.], batch size: 17, lr: 1.52e-04
2022-05-29 13:02:49,593 INFO [train.py:842] (1/4) Epoch 36, batch 7650, loss[loss=0.1909, simple_loss=0.2783, pruned_loss=0.05171, over 7372.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2633, pruned_loss=0.0419, over 1420146.26 frames.], batch size: 23, lr: 1.52e-04
2022-05-29 13:03:29,283 INFO [train.py:842] (1/4) Epoch 36, batch 7700, loss[loss=0.1786, simple_loss=0.2705, pruned_loss=0.04335, over 7224.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2612, pruned_loss=0.04107, over 1425129.52 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:04:08,594 INFO [train.py:842] (1/4) Epoch 36, batch 7750, loss[loss=0.1611, simple_loss=0.2403, pruned_loss=0.04089, over 7167.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2612, pruned_loss=0.04115, over 1424666.70 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 13:04:48,279 INFO [train.py:842] (1/4) Epoch 36, batch 7800, loss[loss=0.1638, simple_loss=0.2619, pruned_loss=0.03291, over 7441.00 frames.], tot_loss[loss=0.1706, simple_loss=0.26, pruned_loss=0.04062, over 1425178.08 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 13:05:27,398 INFO [train.py:842] (1/4) Epoch 36, batch 7850, loss[loss=0.1682, simple_loss=0.2575, pruned_loss=0.0394, over 7409.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2602, pruned_loss=0.04059, over 1427579.20 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:06:07,002 INFO [train.py:842] (1/4) Epoch 36, batch 7900, loss[loss=0.1458, simple_loss=0.2351, pruned_loss=0.02826, over 7072.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2596, pruned_loss=0.0405, over 1425374.61 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 13:06:46,360 INFO [train.py:842] (1/4) Epoch 36, batch 7950, loss[loss=0.1738, simple_loss=0.2736, pruned_loss=0.03707, over 7031.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2612, pruned_loss=0.04166, over 1424442.41 frames.], batch size: 28, lr: 1.52e-04
2022-05-29 13:07:25,920 INFO [train.py:842] (1/4) Epoch 36, batch 8000, loss[loss=0.1523, simple_loss=0.2506, pruned_loss=0.02704, over 7297.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2607, pruned_loss=0.04132, over 1424887.22 frames.], batch size: 24, lr: 1.52e-04
2022-05-29 13:08:05,184 INFO [train.py:842] (1/4) Epoch 36, batch 8050, loss[loss=0.1941, simple_loss=0.2883, pruned_loss=0.04992, over 6648.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2608, pruned_loss=0.0413, over 1422990.76 frames.], batch size: 31, lr: 1.52e-04
2022-05-29 13:08:44,875 INFO [train.py:842] (1/4) Epoch 36, batch 8100, loss[loss=0.1583, simple_loss=0.2495, pruned_loss=0.03358, over 7352.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2607, pruned_loss=0.04087, over 1422862.54 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 13:09:24,149 INFO [train.py:842] (1/4) Epoch 36, batch 8150, loss[loss=0.205, simple_loss=0.2942, pruned_loss=0.05786, over 7275.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2612, pruned_loss=0.04113, over 1423365.72 frames.], batch size: 25, lr: 1.52e-04
2022-05-29 13:10:03,990 INFO [train.py:842] (1/4) Epoch 36, batch 8200, loss[loss=0.1871, simple_loss=0.2786, pruned_loss=0.04776, over 7151.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2603, pruned_loss=0.04154, over 1427814.44 frames.], batch size: 26, lr: 1.52e-04
2022-05-29 13:10:43,106 INFO [train.py:842] (1/4) Epoch 36, batch 8250, loss[loss=0.1407, simple_loss=0.2349, pruned_loss=0.0232, over 7239.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2609, pruned_loss=0.04171, over 1424675.93 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 13:11:22,809 INFO [train.py:842] (1/4) Epoch 36, batch 8300, loss[loss=0.1917, simple_loss=0.2852, pruned_loss=0.04908, over 7145.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2607, pruned_loss=0.04132, over 1418956.28 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 13:12:02,045 INFO [train.py:842] (1/4) Epoch 36, batch 8350, loss[loss=0.1598, simple_loss=0.2549, pruned_loss=0.03235, over 7317.00 frames.], tot_loss[loss=0.173, simple_loss=0.2619, pruned_loss=0.04208, over 1419112.31 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:12:41,459 INFO [train.py:842] (1/4) Epoch 36, batch 8400, loss[loss=0.163, simple_loss=0.2403, pruned_loss=0.04285, over 6998.00 frames.], tot_loss[loss=0.172, simple_loss=0.2608, pruned_loss=0.04154, over 1418857.97 frames.], batch size: 16, lr: 1.52e-04
2022-05-29 13:13:20,650 INFO [train.py:842] (1/4) Epoch 36, batch 8450, loss[loss=0.1812, simple_loss=0.2622, pruned_loss=0.05008, over 5443.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2598, pruned_loss=0.0414, over 1418663.74 frames.], batch size: 52, lr: 1.52e-04
2022-05-29 13:14:00,559 INFO [train.py:842] (1/4) Epoch 36, batch 8500, loss[loss=0.168, simple_loss=0.2468, pruned_loss=0.04456, over 7155.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2601, pruned_loss=0.04199, over 1417845.11 frames.], batch size: 17, lr: 1.52e-04
2022-05-29 13:14:39,937 INFO [train.py:842] (1/4) Epoch 36, batch 8550, loss[loss=0.1527, simple_loss=0.2474, pruned_loss=0.02894, over 7227.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2601, pruned_loss=0.0415, over 1415986.52 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 13:15:19,722 INFO [train.py:842] (1/4) Epoch 36, batch 8600, loss[loss=0.2082, simple_loss=0.3008, pruned_loss=0.05778, over 7217.00 frames.], tot_loss[loss=0.1714, simple_loss=0.26, pruned_loss=0.04141, over 1413008.33 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:15:59,157 INFO [train.py:842] (1/4) Epoch 36, batch 8650, loss[loss=0.2112, simple_loss=0.2867, pruned_loss=0.06784, over 4957.00 frames.], tot_loss[loss=0.1705, simple_loss=0.259, pruned_loss=0.04096, over 1413109.99 frames.], batch size: 52, lr: 1.52e-04
2022-05-29 13:16:38,695 INFO [train.py:842] (1/4) Epoch 36, batch 8700, loss[loss=0.1624, simple_loss=0.2505, pruned_loss=0.03718, over 6282.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2589, pruned_loss=0.04088, over 1411559.00 frames.], batch size: 37, lr: 1.52e-04
2022-05-29 13:17:17,956 INFO [train.py:842] (1/4) Epoch 36, batch 8750, loss[loss=0.1574, simple_loss=0.2588, pruned_loss=0.028, over 7231.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2603, pruned_loss=0.04151, over 1412903.22 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 13:17:57,600 INFO [train.py:842] (1/4) Epoch 36, batch 8800, loss[loss=0.1643, simple_loss=0.2608, pruned_loss=0.03386, over 7223.00 frames.], tot_loss[loss=0.171, simple_loss=0.2598, pruned_loss=0.04113, over 1411966.74 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:18:36,651 INFO [train.py:842] (1/4) Epoch 36, batch 8850, loss[loss=0.2037, simple_loss=0.2936, pruned_loss=0.05694, over 5176.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2594, pruned_loss=0.04181, over 1398729.08 frames.], batch size: 53, lr: 1.52e-04
2022-05-29 13:19:16,407 INFO [train.py:842] (1/4) Epoch 36, batch 8900, loss[loss=0.3102, simple_loss=0.3649, pruned_loss=0.1278, over 5059.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2605, pruned_loss=0.0423, over 1399416.04 frames.], batch size: 52, lr: 1.52e-04
2022-05-29 13:19:55,424 INFO [train.py:842] (1/4) Epoch 36, batch 8950, loss[loss=0.1688, simple_loss=0.2633, pruned_loss=0.03717, over 7317.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2597, pruned_loss=0.04161, over 1392526.47 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:20:34,544 INFO [train.py:842] (1/4) Epoch 36, batch 9000, loss[loss=0.186, simple_loss=0.2716, pruned_loss=0.05022, over 7265.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2599, pruned_loss=0.04153, over 1383709.64 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 13:20:34,544 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 13:20:44,416 INFO [train.py:871] (1/4) Epoch 36, validation: loss=0.1634, simple_loss=0.2603, pruned_loss=0.03321, over 868885.00 frames.
2022-05-29 13:21:23,721 INFO [train.py:842] (1/4) Epoch 36, batch 9050, loss[loss=0.1388, simple_loss=0.2216, pruned_loss=0.02801, over 6997.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2596, pruned_loss=0.0415, over 1383330.63 frames.], batch size: 16, lr: 1.52e-04
2022-05-29 13:22:03,050 INFO [train.py:842] (1/4) Epoch 36, batch 9100, loss[loss=0.2156, simple_loss=0.2956, pruned_loss=0.06779, over 4616.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2595, pruned_loss=0.0417, over 1369079.40 frames.], batch size: 52, lr: 1.52e-04
2022-05-29 13:22:41,485 INFO [train.py:842] (1/4) Epoch 36, batch 9150, loss[loss=0.1911, simple_loss=0.2783, pruned_loss=0.05193, over 4648.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2617, pruned_loss=0.04321, over 1319975.97 frames.], batch size: 53, lr: 1.52e-04
2022-05-29 13:23:33,555 INFO [train.py:842] (1/4) Epoch 37, batch 0, loss[loss=0.2282, simple_loss=0.3015, pruned_loss=0.07744, over 7346.00 frames.], tot_loss[loss=0.2282, simple_loss=0.3015, pruned_loss=0.07744, over 7346.00 frames.], batch size: 22, lr: 1.50e-04
2022-05-29 13:24:13,167 INFO [train.py:842] (1/4) Epoch 37, batch 50, loss[loss=0.1486, simple_loss=0.2385, pruned_loss=0.02932, over 7063.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2596, pruned_loss=0.04114, over 321144.56 frames.], batch size: 18, lr: 1.50e-04
2022-05-29 13:24:52,837 INFO [train.py:842] (1/4) Epoch 37, batch 100, loss[loss=0.1774, simple_loss=0.2722, pruned_loss=0.04126, over 7321.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2608, pruned_loss=0.04068, over 566535.98 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:25:32,059 INFO [train.py:842] (1/4) Epoch 37, batch 150, loss[loss=0.1676, simple_loss=0.2454, pruned_loss=0.04488, over 7160.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2616, pruned_loss=0.04103, over 754145.59 frames.], batch size: 28, lr: 1.49e-04
2022-05-29 13:26:11,482 INFO [train.py:842] (1/4) Epoch 37, batch 200, loss[loss=0.1746, simple_loss=0.2694, pruned_loss=0.03989, over 7323.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2632, pruned_loss=0.04162, over 905837.92 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 13:26:50,732 INFO [train.py:842] (1/4) Epoch 37, batch 250, loss[loss=0.147, simple_loss=0.2381, pruned_loss=0.02799, over 7256.00 frames.], tot_loss[loss=0.172, simple_loss=0.2624, pruned_loss=0.04079, over 1017835.76 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:27:30,277 INFO [train.py:842] (1/4) Epoch 37, batch 300, loss[loss=0.1487, simple_loss=0.2396, pruned_loss=0.02892, over 7341.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2611, pruned_loss=0.04031, over 1104607.06 frames.], batch size: 22, lr: 1.49e-04
2022-05-29 13:28:09,421 INFO [train.py:842] (1/4) Epoch 37, batch 350, loss[loss=0.1596, simple_loss=0.2494, pruned_loss=0.03494, over 7152.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2616, pruned_loss=0.0405, over 1173819.32 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 13:28:49,094 INFO [train.py:842] (1/4) Epoch 37, batch 400, loss[loss=0.2264, simple_loss=0.3304, pruned_loss=0.06117, over 7232.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2617, pruned_loss=0.04043, over 1232674.42 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:29:28,314 INFO [train.py:842] (1/4) Epoch 37, batch 450, loss[loss=0.1811, simple_loss=0.2734, pruned_loss=0.04443, over 7140.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2619, pruned_loss=0.04088, over 1276541.55 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:30:07,662 INFO [train.py:842] (1/4) Epoch 37, batch 500, loss[loss=0.1497, simple_loss=0.2518, pruned_loss=0.02374, over 7233.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2618, pruned_loss=0.04134, over 1306508.13 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:30:46,583 INFO [train.py:842] (1/4) Epoch 37, batch 550, loss[loss=0.1443, simple_loss=0.2374, pruned_loss=0.02557, over 7061.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2619, pruned_loss=0.04119, over 1323497.84 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 13:31:26,497 INFO [train.py:842] (1/4) Epoch 37, batch 600, loss[loss=0.1898, simple_loss=0.2816, pruned_loss=0.04902, over 7429.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2607, pruned_loss=0.04054, over 1348763.12 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:32:06,334 INFO [train.py:842] (1/4) Epoch 37, batch 650, loss[loss=0.1553, simple_loss=0.2393, pruned_loss=0.03565, over 7122.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2603, pruned_loss=0.04064, over 1367876.24 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 13:32:45,865 INFO [train.py:842] (1/4) Epoch 37, batch 700, loss[loss=0.1474, simple_loss=0.2471, pruned_loss=0.02382, over 7236.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2603, pruned_loss=0.04039, over 1381386.48 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:33:25,166 INFO [train.py:842] (1/4) Epoch 37, batch 750, loss[loss=0.1516, simple_loss=0.2366, pruned_loss=0.0333, over 7169.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2599, pruned_loss=0.04061, over 1389841.40 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:34:04,837 INFO [train.py:842] (1/4) Epoch 37, batch 800, loss[loss=0.1321, simple_loss=0.2205, pruned_loss=0.02183, over 7422.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2595, pruned_loss=0.0406, over 1399029.06 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 13:34:43,819 INFO [train.py:842] (1/4) Epoch 37, batch 850, loss[loss=0.1586, simple_loss=0.2496, pruned_loss=0.03383, over 7259.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2596, pruned_loss=0.04051, over 1398431.80 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:35:23,652 INFO [train.py:842] (1/4) Epoch 37, batch 900, loss[loss=0.1589, simple_loss=0.2408, pruned_loss=0.03852, over 7067.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2594, pruned_loss=0.04061, over 1406152.24 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 13:36:03,015 INFO [train.py:842] (1/4) Epoch 37, batch 950, loss[loss=0.1552, simple_loss=0.2434, pruned_loss=0.03347, over 7287.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2595, pruned_loss=0.04099, over 1409805.13 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 13:36:42,580 INFO [train.py:842] (1/4) Epoch 37, batch 1000, loss[loss=0.1508, simple_loss=0.2467, pruned_loss=0.0274, over 6764.00 frames.], tot_loss[loss=0.1699, simple_loss=0.259, pruned_loss=0.04044, over 1412523.23 frames.], batch size: 31, lr: 1.49e-04
2022-05-29 13:37:22,115 INFO [train.py:842] (1/4) Epoch 37, batch 1050, loss[loss=0.1541, simple_loss=0.2537, pruned_loss=0.02721, over 7389.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2585, pruned_loss=0.0404, over 1417537.74 frames.], batch size: 23, lr: 1.49e-04
2022-05-29 13:38:01,724 INFO [train.py:842] (1/4) Epoch 37, batch 1100, loss[loss=0.162, simple_loss=0.2623, pruned_loss=0.03087, over 7207.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2583, pruned_loss=0.04026, over 1419750.54 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 13:38:41,075 INFO [train.py:842] (1/4) Epoch 37, batch 1150, loss[loss=0.2582, simple_loss=0.3344, pruned_loss=0.09097, over 5295.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2582, pruned_loss=0.04018, over 1418406.17 frames.], batch size: 52, lr: 1.49e-04
2022-05-29 13:39:20,631 INFO [train.py:842] (1/4) Epoch 37, batch 1200, loss[loss=0.2399, simple_loss=0.3055, pruned_loss=0.08711, over 7152.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2595, pruned_loss=0.0406, over 1420120.57 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:39:59,649 INFO [train.py:842] (1/4) Epoch 37, batch 1250, loss[loss=0.1803, simple_loss=0.2709, pruned_loss=0.04486, over 7190.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2596, pruned_loss=0.04031, over 1420444.31 frames.], batch size: 22, lr: 1.49e-04
2022-05-29 13:40:39,204 INFO [train.py:842] (1/4) Epoch 37, batch 1300, loss[loss=0.1323, simple_loss=0.2146, pruned_loss=0.025, over 7131.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2605, pruned_loss=0.04069, over 1422315.02 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 13:41:18,371 INFO [train.py:842] (1/4) Epoch 37, batch 1350, loss[loss=0.1445, simple_loss=0.2367, pruned_loss=0.02608, over 7059.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2597, pruned_loss=0.04033, over 1418302.25 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 13:41:58,013 INFO [train.py:842] (1/4) Epoch 37, batch 1400, loss[loss=0.1714, simple_loss=0.246, pruned_loss=0.04845, over 7006.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2599, pruned_loss=0.0408, over 1418245.63 frames.], batch size: 16, lr: 1.49e-04
2022-05-29 13:42:37,217 INFO [train.py:842] (1/4) Epoch 37, batch 1450, loss[loss=0.185, simple_loss=0.2821, pruned_loss=0.044, over 7281.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2604, pruned_loss=0.04061, over 1420015.93 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 13:43:16,536 INFO [train.py:842] (1/4) Epoch 37, batch 1500, loss[loss=0.1936, simple_loss=0.2832, pruned_loss=0.05197, over 7287.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2616, pruned_loss=0.0411, over 1416118.72 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 13:43:55,702 INFO [train.py:842] (1/4) Epoch 37, batch 1550, loss[loss=0.1468, simple_loss=0.2411, pruned_loss=0.02629, over 6959.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2616, pruned_loss=0.04153, over 1411146.63 frames.], batch size: 32, lr: 1.49e-04
2022-05-29 13:44:35,495 INFO [train.py:842] (1/4) Epoch 37, batch 1600, loss[loss=0.1861, simple_loss=0.2812, pruned_loss=0.04551, over 7389.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2611, pruned_loss=0.04137, over 1411540.41 frames.], batch size: 23, lr: 1.49e-04
2022-05-29 13:45:14,788 INFO [train.py:842] (1/4) Epoch 37, batch 1650, loss[loss=0.1961, simple_loss=0.2875, pruned_loss=0.05233, over 7210.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2613, pruned_loss=0.04159, over 1414789.56 frames.], batch size: 22, lr: 1.49e-04
2022-05-29 13:45:53,926 INFO [train.py:842] (1/4) Epoch 37, batch 1700, loss[loss=0.1704, simple_loss=0.2577, pruned_loss=0.04158, over 7177.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2611, pruned_loss=0.0416, over 1413895.21 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:46:43,576 INFO [train.py:842] (1/4) Epoch 37, batch 1750, loss[loss=0.1572, simple_loss=0.2515, pruned_loss=0.03142, over 7350.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2603, pruned_loss=0.04131, over 1408425.76 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:47:22,947 INFO [train.py:842] (1/4) Epoch 37, batch 1800, loss[loss=0.1756, simple_loss=0.2641, pruned_loss=0.04353, over 7274.00 frames.], tot_loss[loss=0.171, simple_loss=0.2599, pruned_loss=0.04107, over 1410193.83 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 13:48:02,150 INFO [train.py:842] (1/4) Epoch 37, batch 1850, loss[loss=0.1935, simple_loss=0.2717, pruned_loss=0.05759, over 7264.00 frames.], tot_loss[loss=0.1701, simple_loss=0.259, pruned_loss=0.04062, over 1410573.98 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:48:41,599 INFO [train.py:842] (1/4) Epoch 37, batch 1900, loss[loss=0.2009, simple_loss=0.2987, pruned_loss=0.05155, over 6817.00 frames.], tot_loss[loss=0.1707, simple_loss=0.26, pruned_loss=0.0407, over 1417071.77 frames.], batch size: 31, lr: 1.49e-04
2022-05-29 13:49:20,957 INFO [train.py:842] (1/4) Epoch 37, batch 1950, loss[loss=0.1795, simple_loss=0.268, pruned_loss=0.0455, over 7226.00 frames.], tot_loss[loss=0.1697, simple_loss=0.259, pruned_loss=0.04021, over 1420428.60 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 13:50:00,393 INFO [train.py:842] (1/4) Epoch 37, batch 2000, loss[loss=0.1623, simple_loss=0.2658, pruned_loss=0.02937, over 7413.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2599, pruned_loss=0.04091, over 1417438.25 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 13:50:39,857 INFO [train.py:842] (1/4) Epoch 37, batch 2050, loss[loss=0.1845, simple_loss=0.277, pruned_loss=0.04603, over 7234.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2592, pruned_loss=0.04076, over 1420171.93 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:51:19,329 INFO [train.py:842] (1/4) Epoch 37, batch 2100, loss[loss=0.1919, simple_loss=0.2756, pruned_loss=0.05405, over 7138.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2599, pruned_loss=0.04143, over 1419735.11 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:51:58,322 INFO [train.py:842] (1/4) Epoch 37, batch 2150, loss[loss=0.1578, simple_loss=0.2552, pruned_loss=0.03019, over 7408.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2598, pruned_loss=0.04104, over 1416991.09 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 13:52:37,793 INFO [train.py:842] (1/4) Epoch 37, batch 2200, loss[loss=0.1722, simple_loss=0.2576, pruned_loss=0.04337, over 7254.00 frames.], tot_loss[loss=0.1713, simple_loss=0.26, pruned_loss=0.04127, over 1418372.56 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:53:16,121 INFO [train.py:842] (1/4) Epoch 37, batch 2250, loss[loss=0.2025, simple_loss=0.2884, pruned_loss=0.05827, over 7145.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2615, pruned_loss=0.04184, over 1419205.14 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:53:54,630 INFO [train.py:842] (1/4) Epoch 37, batch 2300, loss[loss=0.1825, simple_loss=0.2677, pruned_loss=0.04862, over 7202.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2602, pruned_loss=0.04104, over 1419361.82 frames.], batch size: 23, lr: 1.49e-04
2022-05-29 13:54:32,877 INFO [train.py:842] (1/4) Epoch 37, batch 2350, loss[loss=0.1493, simple_loss=0.2192, pruned_loss=0.03972, over 7301.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2608, pruned_loss=0.04114, over 1412966.65 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 13:55:11,697 INFO [train.py:842] (1/4) Epoch 37, batch 2400, loss[loss=0.1826, simple_loss=0.2853, pruned_loss=0.03993, over 7293.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2605, pruned_loss=0.04079, over 1419231.88 frames.], batch size: 25, lr: 1.49e-04
2022-05-29 13:55:50,272 INFO [train.py:842] (1/4) Epoch 37, batch 2450, loss[loss=0.1735, simple_loss=0.2662, pruned_loss=0.04043, over 7143.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2603, pruned_loss=0.0407, over 1424949.58 frames.], batch size: 26, lr: 1.49e-04
2022-05-29 13:56:28,827 INFO [train.py:842] (1/4) Epoch 37, batch 2500, loss[loss=0.1532, simple_loss=0.2412, pruned_loss=0.03254, over 7163.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2595, pruned_loss=0.04014, over 1427528.33 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:57:07,140 INFO [train.py:842] (1/4) Epoch 37, batch 2550, loss[loss=0.1739, simple_loss=0.27, pruned_loss=0.03893, over 7270.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2598, pruned_loss=0.04017, over 1427755.50 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 13:57:45,694 INFO [train.py:842] (1/4) Epoch 37, batch 2600, loss[loss=0.1537, simple_loss=0.2312, pruned_loss=0.03813, over 7202.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2601, pruned_loss=0.04044, over 1423921.29 frames.], batch size: 16, lr: 1.49e-04
2022-05-29 13:58:24,132 INFO [train.py:842] (1/4) Epoch 37, batch 2650, loss[loss=0.1615, simple_loss=0.2458, pruned_loss=0.03865, over 7208.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2602, pruned_loss=0.04067, over 1427406.73 frames.], batch size: 22, lr: 1.49e-04
2022-05-29 13:59:02,656 INFO [train.py:842] (1/4) Epoch 37, batch 2700, loss[loss=0.1705, simple_loss=0.2624, pruned_loss=0.03929, over 6460.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2604, pruned_loss=0.04095, over 1423616.23 frames.], batch size: 38, lr: 1.49e-04
2022-05-29 13:59:40,797 INFO [train.py:842] (1/4) Epoch 37, batch 2750, loss[loss=0.2234, simple_loss=0.2894, pruned_loss=0.07872, over 5225.00 frames.], tot_loss[loss=0.1715, simple_loss=0.261, pruned_loss=0.04096, over 1424627.48 frames.], batch size: 52, lr: 1.49e-04
2022-05-29 14:00:19,747 INFO [train.py:842] (1/4) Epoch 37, batch 2800, loss[loss=0.1479, simple_loss=0.2271, pruned_loss=0.03434, over 7278.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2607, pruned_loss=0.04098, over 1429467.22 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 14:00:58,251 INFO [train.py:842] (1/4) Epoch 37, batch 2850, loss[loss=0.1715, simple_loss=0.2607, pruned_loss=0.04121, over 6241.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2614, pruned_loss=0.04137, over 1428474.89 frames.], batch size: 37, lr: 1.49e-04
2022-05-29 14:01:37,123 INFO [train.py:842] (1/4) Epoch 37, batch 2900, loss[loss=0.1284, simple_loss=0.2073, pruned_loss=0.02481, over 6999.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2607, pruned_loss=0.04115, over 1429024.63 frames.], batch size: 16, lr: 1.49e-04
2022-05-29 14:02:15,624 INFO [train.py:842] (1/4) Epoch 37, batch 2950, loss[loss=0.1476, simple_loss=0.2339, pruned_loss=0.0306, over 7426.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2606, pruned_loss=0.04119, over 1424827.68 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 14:02:54,132 INFO [train.py:842] (1/4) Epoch 37, batch 3000, loss[loss=0.1712, simple_loss=0.2639, pruned_loss=0.03923, over 7220.00 frames.], tot_loss[loss=0.171, simple_loss=0.2602, pruned_loss=0.04085, over 1421657.78 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 14:02:54,133 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 14:03:03,497 INFO [train.py:871] (1/4) Epoch 37, validation: loss=0.1648, simple_loss=0.2616, pruned_loss=0.03398, over 868885.00 frames.
2022-05-29 14:03:41,913 INFO [train.py:842] (1/4) Epoch 37, batch 3050, loss[loss=0.1462, simple_loss=0.2241, pruned_loss=0.0342, over 6846.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2596, pruned_loss=0.04051, over 1419928.96 frames.], batch size: 15, lr: 1.49e-04
2022-05-29 14:04:20,747 INFO [train.py:842] (1/4) Epoch 37, batch 3100, loss[loss=0.1664, simple_loss=0.2552, pruned_loss=0.03878, over 7068.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2602, pruned_loss=0.04097, over 1418246.74 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 14:04:59,167 INFO [train.py:842] (1/4) Epoch 37, batch 3150, loss[loss=0.1758, simple_loss=0.2475, pruned_loss=0.05201, over 6993.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2608, pruned_loss=0.04106, over 1417161.35 frames.], batch size: 16, lr: 1.49e-04
2022-05-29 14:05:37,847 INFO [train.py:842] (1/4) Epoch 37, batch 3200, loss[loss=0.2281, simple_loss=0.3087, pruned_loss=0.07372, over 5231.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2607, pruned_loss=0.04087, over 1417825.24 frames.], batch size: 53, lr: 1.49e-04
2022-05-29 14:06:16,111 INFO [train.py:842] (1/4) Epoch 37, batch 3250, loss[loss=0.184, simple_loss=0.273, pruned_loss=0.04749, over 7208.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2607, pruned_loss=0.04114, over 1417781.52 frames.], batch size: 22, lr: 1.49e-04
2022-05-29 14:06:54,710 INFO [train.py:842] (1/4) Epoch 37, batch 3300, loss[loss=0.1694, simple_loss=0.2786, pruned_loss=0.03014, over 7421.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2608, pruned_loss=0.04128, over 1415480.20 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 14:07:32,801 INFO [train.py:842] (1/4) Epoch 37, batch 3350, loss[loss=0.1906, simple_loss=0.2916, pruned_loss=0.04483, over 7369.00 frames.], tot_loss[loss=0.172, simple_loss=0.2616, pruned_loss=0.0412, over 1411188.05 frames.], batch size: 23, lr: 1.49e-04
2022-05-29 14:08:11,574 INFO [train.py:842] (1/4) Epoch 37, batch 3400, loss[loss=0.1946, simple_loss=0.2699, pruned_loss=0.05966, over 7147.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2625, pruned_loss=0.04195, over 1415955.64 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 14:08:50,151 INFO [train.py:842] (1/4) Epoch 37, batch 3450, loss[loss=0.1573, simple_loss=0.2464, pruned_loss=0.03412, over 7289.00 frames.], tot_loss[loss=0.172, simple_loss=0.2614, pruned_loss=0.04136, over 1419286.49 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 14:09:28,737 INFO [train.py:842] (1/4) Epoch 37, batch 3500, loss[loss=0.135, simple_loss=0.2246, pruned_loss=0.02273, over 7357.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2615, pruned_loss=0.04138, over 1417096.18 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 14:10:06,959 INFO [train.py:842] (1/4) Epoch 37, batch 3550, loss[loss=0.1657, simple_loss=0.2449, pruned_loss=0.04324, over 6873.00 frames.], tot_loss[loss=0.1717, simple_loss=0.261, pruned_loss=0.04118, over 1414376.40 frames.], batch size: 15, lr: 1.49e-04
2022-05-29 14:10:45,909 INFO [train.py:842] (1/4) Epoch 37, batch 3600, loss[loss=0.144, simple_loss=0.2296, pruned_loss=0.02921, over 7004.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2585, pruned_loss=0.04023, over 1421670.16 frames.], batch size: 16, lr: 1.49e-04
2022-05-29 14:11:24,121 INFO [train.py:842] (1/4) Epoch 37, batch 3650, loss[loss=0.1975, simple_loss=0.289, pruned_loss=0.05298, over 7155.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2588, pruned_loss=0.04037, over 1423673.25 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 14:12:02,918 INFO [train.py:842] (1/4) Epoch 37, batch 3700, loss[loss=0.1656, simple_loss=0.2593, pruned_loss=0.03591, over 7238.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2585, pruned_loss=0.04008, over 1426777.64 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 14:12:41,212 INFO [train.py:842] (1/4) Epoch 37, batch 3750, loss[loss=0.1784, simple_loss=0.2663, pruned_loss=0.04527, over 7313.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2588, pruned_loss=0.04012, over 1424374.50 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 14:13:19,980 INFO [train.py:842] (1/4) Epoch 37, batch 3800, loss[loss=0.1518, simple_loss=0.2387, pruned_loss=0.03244, over 7266.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2585, pruned_loss=0.04008, over 1425686.77 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 14:13:58,266 INFO [train.py:842] (1/4) Epoch 37, batch 3850, loss[loss=0.2174, simple_loss=0.3027, pruned_loss=0.06607, over 5281.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2601, pruned_loss=0.04112, over 1424120.79 frames.], batch size: 52, lr: 1.49e-04
2022-05-29 14:14:37,038 INFO [train.py:842] (1/4) Epoch 37, batch 3900, loss[loss=0.1619, simple_loss=0.2544, pruned_loss=0.03469, over 7329.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2595, pruned_loss=0.04089, over 1425817.84 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 14:15:15,726 INFO [train.py:842] (1/4) Epoch 37, batch 3950, loss[loss=0.1565, simple_loss=0.2418, pruned_loss=0.03563, over 7278.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2595, pruned_loss=0.04063, over 1426594.72 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 14:15:54,292 INFO [train.py:842] (1/4) Epoch 37, batch 4000, loss[loss=0.1451, simple_loss=0.2377, pruned_loss=0.0262, over 7056.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2604, pruned_loss=0.04116, over 1426409.10 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 14:16:32,614 INFO [train.py:842] (1/4) Epoch 37, batch 4050, loss[loss=0.1322, simple_loss=0.2205, pruned_loss=0.02194, over 7299.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2607, pruned_loss=0.04087, over 1428139.70 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 14:17:11,131 INFO [train.py:842] (1/4) Epoch 37, batch 4100, loss[loss=0.1593, simple_loss=0.2566, pruned_loss=0.03102, over 7113.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2605, pruned_loss=0.04044, over 1424408.84 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 14:17:49,514 INFO [train.py:842] (1/4) Epoch 37, batch 4150, loss[loss=0.1823, simple_loss=0.2761, pruned_loss=0.04427, over 7263.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2601, pruned_loss=0.04053, over 1425686.59 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 14:18:28,587 INFO [train.py:842] (1/4) Epoch 37, batch 4200, loss[loss=0.1572, simple_loss=0.2395, pruned_loss=0.03743, over 7267.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2587, pruned_loss=0.04015, over 1429151.33 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 14:19:06,942 INFO [train.py:842] (1/4) Epoch 37, batch 4250, loss[loss=0.1363, simple_loss=0.2309, pruned_loss=0.02086, over 7239.00 frames.], tot_loss[loss=0.1689, simple_loss=0.258, pruned_loss=0.03986, over 1428469.18 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 14:19:45,860 INFO [train.py:842] (1/4) Epoch 37, batch 4300, loss[loss=0.1462, simple_loss=0.2377, pruned_loss=0.0273, over 7408.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2593, pruned_loss=0.04083, over 1430335.27 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 14:20:24,421 INFO [train.py:842] (1/4) Epoch 37, batch 4350, loss[loss=0.1657, simple_loss=0.2587, pruned_loss=0.03635, over 7079.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2585, pruned_loss=0.04001, over 1427117.56 frames.], batch size: 28, lr: 1.49e-04
2022-05-29 14:21:03,034 INFO [train.py:842] (1/4) Epoch 37, batch 4400, loss[loss=0.1717, simple_loss=0.2631, pruned_loss=0.04015, over 7344.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2603, pruned_loss=0.04097, over 1425428.33 frames.], batch size: 22, lr: 1.49e-04
2022-05-29 14:21:41,576 INFO [train.py:842] (1/4) Epoch 37, batch 4450, loss[loss=0.1573, simple_loss=0.2473, pruned_loss=0.03363, over 7065.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2599, pruned_loss=0.04091, over 1428587.68 frames.], batch size: 28, lr: 1.49e-04
2022-05-29 14:22:20,385 INFO [train.py:842] (1/4) Epoch 37, batch 4500, loss[loss=0.1923, simple_loss=0.2792, pruned_loss=0.05273, over 7273.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2607, pruned_loss=0.04155, over 1424875.18 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 14:22:58,727 INFO [train.py:842] (1/4) Epoch 37, batch 4550, loss[loss=0.2015, simple_loss=0.2858, pruned_loss=0.0586, over 7311.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2613, pruned_loss=0.04197, over 1424483.78 frames.], batch size: 25, lr: 1.48e-04
2022-05-29 14:23:37,641 INFO [train.py:842] (1/4) Epoch 37, batch 4600, loss[loss=0.1357, simple_loss=0.2252, pruned_loss=0.02307, over 7169.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2622, pruned_loss=0.04198, over 1423916.93 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:24:16,120 INFO [train.py:842] (1/4) Epoch 37, batch 4650, loss[loss=0.1815, simple_loss=0.2738, pruned_loss=0.04466, over 7140.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2606, pruned_loss=0.04114, over 1425773.86 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:24:54,742 INFO [train.py:842] (1/4) Epoch 37, batch 4700, loss[loss=0.1643, simple_loss=0.255, pruned_loss=0.0368, over 6833.00 frames.], tot_loss[loss=0.1717, simple_loss=0.261, pruned_loss=0.04121, over 1425062.22 frames.], batch size: 31, lr: 1.48e-04
2022-05-29 14:25:33,361 INFO [train.py:842] (1/4) Epoch 37, batch 4750, loss[loss=0.1715, simple_loss=0.2602, pruned_loss=0.04137, over 7231.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2603, pruned_loss=0.0413, over 1428279.16 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:26:11,910 INFO [train.py:842] (1/4) Epoch 37, batch 4800, loss[loss=0.216, simple_loss=0.3018, pruned_loss=0.06505, over 7212.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2612, pruned_loss=0.04161, over 1426860.12 frames.], batch size: 26, lr: 1.48e-04
2022-05-29 14:26:50,415 INFO [train.py:842] (1/4) Epoch 37, batch 4850, loss[loss=0.2289, simple_loss=0.3051, pruned_loss=0.07633, over 7114.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2611, pruned_loss=0.04137, over 1430884.34 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:27:29,128 INFO [train.py:842] (1/4) Epoch 37, batch 4900, loss[loss=0.1406, simple_loss=0.2116, pruned_loss=0.0348, over 7262.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2617, pruned_loss=0.0415, over 1433650.85 frames.], batch size: 16, lr: 1.48e-04
2022-05-29 14:28:07,583 INFO [train.py:842] (1/4) Epoch 37, batch 4950, loss[loss=0.1701, simple_loss=0.2568, pruned_loss=0.0417, over 7417.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2604, pruned_loss=0.0409, over 1435383.81 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:28:46,394 INFO [train.py:842] (1/4) Epoch 37, batch 5000, loss[loss=0.1443, simple_loss=0.2366, pruned_loss=0.02604, over 7264.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2597, pruned_loss=0.04106, over 1431241.73 frames.], batch size: 17, lr: 1.48e-04
2022-05-29 14:29:24,649 INFO [train.py:842] (1/4) Epoch 37, batch 5050, loss[loss=0.212, simple_loss=0.3102, pruned_loss=0.05692, over 7074.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2602, pruned_loss=0.04096, over 1427486.43 frames.], batch size: 28, lr: 1.48e-04
2022-05-29 14:30:06,275 INFO [train.py:842] (1/4) Epoch 37, batch 5100, loss[loss=0.1797, simple_loss=0.2735, pruned_loss=0.04298, over 7225.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2598, pruned_loss=0.04097, over 1421058.94 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:30:44,610 INFO [train.py:842] (1/4) Epoch 37, batch 5150, loss[loss=0.2, simple_loss=0.293, pruned_loss=0.05348, over 7109.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2601, pruned_loss=0.04085, over 1420381.20 frames.], batch size: 28, lr: 1.48e-04
2022-05-29 14:31:23,407 INFO [train.py:842] (1/4) Epoch 37, batch 5200, loss[loss=0.1594, simple_loss=0.2391, pruned_loss=0.03984, over 7286.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2602, pruned_loss=0.04118, over 1420786.01 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:32:01,740 INFO [train.py:842] (1/4) Epoch 37, batch 5250, loss[loss=0.1765, simple_loss=0.2472, pruned_loss=0.05286, over 6844.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2612, pruned_loss=0.04131, over 1421831.63 frames.], batch size: 15, lr: 1.48e-04
2022-05-29 14:32:40,355 INFO [train.py:842] (1/4) Epoch 37, batch 5300, loss[loss=0.1855, simple_loss=0.2755, pruned_loss=0.04776, over 7328.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2611, pruned_loss=0.04126, over 1425492.99 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:33:18,729 INFO [train.py:842] (1/4) Epoch 37, batch 5350, loss[loss=0.1703, simple_loss=0.2667, pruned_loss=0.03699, over 7338.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2604, pruned_loss=0.04093, over 1425648.63 frames.], batch size: 22, lr: 1.48e-04
2022-05-29 14:33:57,370 INFO [train.py:842] (1/4) Epoch 37, batch 5400, loss[loss=0.1735, simple_loss=0.2606, pruned_loss=0.04321, over 7309.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2607, pruned_loss=0.04112, over 1422796.93 frames.], batch size: 25, lr: 1.48e-04
2022-05-29 14:34:35,837 INFO [train.py:842] (1/4) Epoch 37, batch 5450, loss[loss=0.1594, simple_loss=0.2482, pruned_loss=0.0353, over 7371.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2601, pruned_loss=0.04063, over 1424953.00 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 14:35:14,223 INFO [train.py:842] (1/4) Epoch 37, batch 5500, loss[loss=0.2142, simple_loss=0.3022, pruned_loss=0.06309, over 7257.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2604, pruned_loss=0.04073, over 1420374.46 frames.], batch size: 19, lr: 1.48e-04
2022-05-29 14:35:52,480 INFO [train.py:842] (1/4) Epoch 37, batch 5550, loss[loss=0.1892, simple_loss=0.2749, pruned_loss=0.05175, over 7214.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2614, pruned_loss=0.04116, over 1417949.86 frames.], batch size: 22, lr: 1.48e-04
2022-05-29 14:36:31,227 INFO [train.py:842] (1/4) Epoch 37, batch 5600, loss[loss=0.211, simple_loss=0.2907, pruned_loss=0.0657, over 7059.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2609, pruned_loss=0.04097, over 1418830.41 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:37:09,316 INFO [train.py:842] (1/4) Epoch 37, batch 5650, loss[loss=0.1666, simple_loss=0.2524, pruned_loss=0.0404, over 7332.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2608, pruned_loss=0.04086, over 1416647.86 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:37:47,963 INFO [train.py:842] (1/4) Epoch 37, batch 5700, loss[loss=0.1502, simple_loss=0.24, pruned_loss=0.03024, over 7167.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2608, pruned_loss=0.0412, over 1418087.14 frames.], batch size: 19, lr: 1.48e-04
2022-05-29 14:38:26,357 INFO [train.py:842] (1/4) Epoch 37, batch 5750, loss[loss=0.1509, simple_loss=0.2393, pruned_loss=0.03129, over 7257.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2602, pruned_loss=0.04096, over 1419495.69 frames.], batch size: 19, lr: 1.48e-04
2022-05-29 14:39:04,944 INFO [train.py:842] (1/4) Epoch 37, batch 5800, loss[loss=0.1627, simple_loss=0.2453, pruned_loss=0.04005, over 7419.00 frames.], tot_loss[loss=0.1712, simple_loss=0.261, pruned_loss=0.04071, over 1420921.61 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:39:43,218 INFO [train.py:842] (1/4) Epoch 37, batch 5850, loss[loss=0.1649, simple_loss=0.2691, pruned_loss=0.03039, over 7074.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2615, pruned_loss=0.0409, over 1420680.84 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:40:22,001 INFO [train.py:842] (1/4) Epoch 37, batch 5900, loss[loss=0.2371, simple_loss=0.3282, pruned_loss=0.07298, over 7175.00 frames.], tot_loss[loss=0.172, simple_loss=0.2618, pruned_loss=0.04107, over 1422575.60 frames.], batch size: 26, lr: 1.48e-04
2022-05-29 14:41:00,168 INFO [train.py:842] (1/4) Epoch 37, batch 5950, loss[loss=0.1698, simple_loss=0.2597, pruned_loss=0.03993, over 7055.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2624, pruned_loss=0.0411, over 1421665.01 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:41:39,110 INFO [train.py:842] (1/4) Epoch 37, batch 6000, loss[loss=0.1499, simple_loss=0.2368, pruned_loss=0.03153, over 7425.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2619, pruned_loss=0.04109, over 1425002.06 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:41:39,112 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 14:41:48,826 INFO [train.py:871] (1/4) Epoch 37, validation: loss=0.1649, simple_loss=0.2613, pruned_loss=0.03429, over 868885.00 frames.
2022-05-29 14:42:27,126 INFO [train.py:842] (1/4) Epoch 37, batch 6050, loss[loss=0.1769, simple_loss=0.2697, pruned_loss=0.04202, over 7311.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2626, pruned_loss=0.04153, over 1421732.05 frames.], batch size: 25, lr: 1.48e-04
2022-05-29 14:43:05,750 INFO [train.py:842] (1/4) Epoch 37, batch 6100, loss[loss=0.1491, simple_loss=0.2419, pruned_loss=0.02816, over 7327.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2626, pruned_loss=0.04103, over 1422857.08 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:43:43,963 INFO [train.py:842] (1/4) Epoch 37, batch 6150, loss[loss=0.1493, simple_loss=0.2248, pruned_loss=0.03688, over 6999.00 frames.], tot_loss[loss=0.1725, simple_loss=0.263, pruned_loss=0.04097, over 1418788.57 frames.], batch size: 16, lr: 1.48e-04
2022-05-29 14:44:22,777 INFO [train.py:842] (1/4) Epoch 37, batch 6200, loss[loss=0.1765, simple_loss=0.2663, pruned_loss=0.04342, over 7030.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2616, pruned_loss=0.04043, over 1416896.53 frames.], batch size: 28, lr: 1.48e-04
2022-05-29 14:45:01,306 INFO [train.py:842] (1/4) Epoch 37, batch 6250, loss[loss=0.1957, simple_loss=0.2703, pruned_loss=0.0606, over 7130.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2615, pruned_loss=0.04097, over 1421113.48 frames.], batch size: 17, lr: 1.48e-04
2022-05-29 14:45:40,238 INFO [train.py:842] (1/4) Epoch 37, batch 6300, loss[loss=0.1655, simple_loss=0.2458, pruned_loss=0.04258, over 7012.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2596, pruned_loss=0.04058, over 1421329.79 frames.], batch size: 16, lr: 1.48e-04
2022-05-29 14:46:18,706 INFO [train.py:842] (1/4) Epoch 37, batch 6350, loss[loss=0.1967, simple_loss=0.2856, pruned_loss=0.05392, over 7192.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2595, pruned_loss=0.04029, over 1423564.78 frames.], batch size: 26, lr: 1.48e-04
2022-05-29 14:46:57,206 INFO [train.py:842] (1/4) Epoch 37, batch 6400, loss[loss=0.1732, simple_loss=0.2696, pruned_loss=0.03835, over 7325.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2593, pruned_loss=0.03995, over 1421554.30 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:47:35,892 INFO [train.py:842] (1/4) Epoch 37, batch 6450, loss[loss=0.1873, simple_loss=0.2792, pruned_loss=0.04772, over 6407.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2599, pruned_loss=0.04067, over 1423418.03 frames.], batch size: 38, lr: 1.48e-04
2022-05-29 14:48:14,933 INFO [train.py:842] (1/4) Epoch 37, batch 6500, loss[loss=0.1512, simple_loss=0.2467, pruned_loss=0.02779, over 7144.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2595, pruned_loss=0.04057, over 1427209.05 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:48:53,201 INFO [train.py:842] (1/4) Epoch 37, batch 6550, loss[loss=0.1618, simple_loss=0.2594, pruned_loss=0.03213, over 7280.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2608, pruned_loss=0.04096, over 1423484.89 frames.], batch size: 24, lr: 1.48e-04
2022-05-29 14:49:31,825 INFO [train.py:842] (1/4) Epoch 37, batch 6600, loss[loss=0.1909, simple_loss=0.2714, pruned_loss=0.05519, over 7369.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2597, pruned_loss=0.04081, over 1419846.55 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 14:50:10,220 INFO [train.py:842] (1/4) Epoch 37, batch 6650, loss[loss=0.1797, simple_loss=0.2752, pruned_loss=0.04214, over 7108.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2595, pruned_loss=0.04051, over 1416055.06 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:50:48,912 INFO [train.py:842] (1/4) Epoch 37, batch 6700, loss[loss=0.2677, simple_loss=0.3454, pruned_loss=0.09497, over 7148.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2603, pruned_loss=0.04097, over 1414684.18 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:51:27,569 INFO [train.py:842] (1/4) Epoch 37, batch 6750, loss[loss=0.1907, simple_loss=0.2812, pruned_loss=0.05013, over 7401.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2594, pruned_loss=0.04082, over 1418595.43 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:52:06,471 INFO [train.py:842] (1/4) Epoch 37, batch 6800, loss[loss=0.1809, simple_loss=0.2834, pruned_loss=0.03922, over 7228.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2589, pruned_loss=0.04023, over 1422117.57 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:52:44,814 INFO [train.py:842] (1/4) Epoch 37, batch 6850, loss[loss=0.1862, simple_loss=0.275, pruned_loss=0.04865, over 7203.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2592, pruned_loss=0.04032, over 1415664.27 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 14:53:23,529 INFO [train.py:842] (1/4) Epoch 37, batch 6900, loss[loss=0.1611, simple_loss=0.2415, pruned_loss=0.04036, over 7236.00 frames.], tot_loss[loss=0.169, simple_loss=0.2582, pruned_loss=0.0399, over 1419449.60 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:54:01,941 INFO [train.py:842] (1/4) Epoch 37, batch 6950, loss[loss=0.1626, simple_loss=0.2473, pruned_loss=0.03894, over 7354.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2582, pruned_loss=0.03975, over 1420076.58 frames.], batch size: 19, lr: 1.48e-04
2022-05-29 14:54:40,638 INFO [train.py:842] (1/4) Epoch 37, batch 7000, loss[loss=0.1696, simple_loss=0.2748, pruned_loss=0.0322, over 7382.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2591, pruned_loss=0.04007, over 1418931.26 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 14:55:19,154 INFO [train.py:842] (1/4) Epoch 37, batch 7050, loss[loss=0.1952, simple_loss=0.2996, pruned_loss=0.04537, over 7206.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2588, pruned_loss=0.03975, over 1419032.58 frames.], batch size: 22, lr: 1.48e-04
2022-05-29 14:55:57,881 INFO [train.py:842] (1/4) Epoch 37, batch 7100, loss[loss=0.1813, simple_loss=0.2685, pruned_loss=0.04703, over 7379.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2587, pruned_loss=0.04013, over 1415505.83 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 14:56:36,305 INFO [train.py:842] (1/4) Epoch 37, batch 7150, loss[loss=0.1615, simple_loss=0.2563, pruned_loss=0.0334, over 6550.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2581, pruned_loss=0.03979, over 1417725.72 frames.], batch size: 38, lr: 1.48e-04
2022-05-29 14:57:14,672 INFO [train.py:842] (1/4) Epoch 37, batch 7200, loss[loss=0.1899, simple_loss=0.2663, pruned_loss=0.05681, over 7276.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2585, pruned_loss=0.03968, over 1415533.75 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:58:03,035 INFO [train.py:842] (1/4) Epoch 37, batch 7250, loss[loss=0.179, simple_loss=0.2723, pruned_loss=0.04287, over 6432.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2588, pruned_loss=0.04009, over 1411824.75 frames.], batch size: 38, lr: 1.48e-04
2022-05-29 14:58:41,379 INFO [train.py:842] (1/4) Epoch 37, batch 7300, loss[loss=0.1809, simple_loss=0.2752, pruned_loss=0.04326, over 7145.00 frames.], tot_loss[loss=0.1713, simple_loss=0.261, pruned_loss=0.04084, over 1407348.01 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:59:20,066 INFO [train.py:842] (1/4) Epoch 37, batch 7350, loss[loss=0.1835, simple_loss=0.2769, pruned_loss=0.04499, over 7430.00 frames.], tot_loss[loss=0.17, simple_loss=0.2597, pruned_loss=0.04016, over 1416238.18 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:59:58,581 INFO [train.py:842] (1/4) Epoch 37, batch 7400, loss[loss=0.2182, simple_loss=0.2919, pruned_loss=0.07231, over 7198.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2601, pruned_loss=0.04086, over 1412260.99 frames.], batch size: 22, lr: 1.48e-04
2022-05-29 15:00:37,088 INFO [train.py:842] (1/4) Epoch 37, batch 7450, loss[loss=0.2305, simple_loss=0.321, pruned_loss=0.06999, over 7193.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2612, pruned_loss=0.04125, over 1415826.39 frames.], batch size: 26, lr: 1.48e-04
2022-05-29 15:01:15,778 INFO [train.py:842] (1/4) Epoch 37, batch 7500, loss[loss=0.1706, simple_loss=0.2594, pruned_loss=0.04091, over 7416.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2613, pruned_loss=0.04144, over 1416778.51 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:01:54,109 INFO [train.py:842] (1/4) Epoch 37, batch 7550, loss[loss=0.1864, simple_loss=0.2794, pruned_loss=0.0467, over 6906.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2612, pruned_loss=0.04128, over 1420321.29 frames.], batch size: 31, lr: 1.48e-04
2022-05-29 15:02:32,828 INFO [train.py:842] (1/4) Epoch 37, batch 7600, loss[loss=0.2105, simple_loss=0.285, pruned_loss=0.06801, over 4855.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2609, pruned_loss=0.04169, over 1416617.14 frames.], batch size: 52, lr: 1.48e-04
2022-05-29 15:03:20,988 INFO [train.py:842] (1/4) Epoch 37, batch 7650, loss[loss=0.1742, simple_loss=0.2614, pruned_loss=0.04351, over 7425.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2617, pruned_loss=0.04204, over 1422222.10 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:04:09,694 INFO [train.py:842] (1/4) Epoch 37, batch 7700, loss[loss=0.1448, simple_loss=0.2356, pruned_loss=0.02698, over 7213.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2605, pruned_loss=0.0413, over 1422777.94 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 15:04:48,022 INFO [train.py:842] (1/4) Epoch 37, batch 7750, loss[loss=0.1437, simple_loss=0.2293, pruned_loss=0.02906, over 6788.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2604, pruned_loss=0.041, over 1422653.60 frames.], batch size: 15, lr: 1.48e-04
2022-05-29 15:05:26,792 INFO [train.py:842] (1/4) Epoch 37, batch 7800, loss[loss=0.1756, simple_loss=0.2638, pruned_loss=0.0437, over 7335.00 frames.], tot_loss[loss=0.172, simple_loss=0.2613, pruned_loss=0.04132, over 1423356.58 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 15:06:05,351 INFO [train.py:842] (1/4) Epoch 37, batch 7850, loss[loss=0.156, simple_loss=0.2547, pruned_loss=0.02867, over 6341.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2597, pruned_loss=0.04081, over 1428054.64 frames.], batch size: 37, lr: 1.48e-04
2022-05-29 15:06:44,257 INFO [train.py:842] (1/4) Epoch 37, batch 7900, loss[loss=0.1766, simple_loss=0.2578, pruned_loss=0.04773, over 7352.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2595, pruned_loss=0.04044, over 1431444.09 frames.], batch size: 19, lr: 1.48e-04
2022-05-29 15:07:22,858 INFO [train.py:842] (1/4) Epoch 37, batch 7950, loss[loss=0.208, simple_loss=0.2901, pruned_loss=0.06292, over 7317.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2587, pruned_loss=0.03992, over 1433126.09 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:08:01,508 INFO [train.py:842] (1/4) Epoch 37, batch 8000, loss[loss=0.1373, simple_loss=0.2233, pruned_loss=0.02564, over 6989.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2591, pruned_loss=0.04038, over 1425476.77 frames.], batch size: 16, lr: 1.48e-04
2022-05-29 15:08:39,687 INFO [train.py:842] (1/4) Epoch 37, batch 8050, loss[loss=0.1579, simple_loss=0.2521, pruned_loss=0.0318, over 7144.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2599, pruned_loss=0.04113, over 1423568.11 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 15:09:18,481 INFO [train.py:842] (1/4) Epoch 37, batch 8100, loss[loss=0.1507, simple_loss=0.25, pruned_loss=0.02569, over 7320.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2602, pruned_loss=0.04124, over 1424784.74 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:09:56,851 INFO [train.py:842] (1/4) Epoch 37, batch 8150, loss[loss=0.1823, simple_loss=0.2837, pruned_loss=0.04043, over 7317.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2609, pruned_loss=0.04148, over 1418337.88 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:10:35,184 INFO [train.py:842] (1/4) Epoch 37, batch 8200, loss[loss=0.1711, simple_loss=0.269, pruned_loss=0.03662, over 7151.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2611, pruned_loss=0.04117, over 1419371.30 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 15:11:13,511 INFO [train.py:842] (1/4) Epoch 37, batch 8250, loss[loss=0.2164, simple_loss=0.2999, pruned_loss=0.06649, over 7282.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2605, pruned_loss=0.04092, over 1419968.10 frames.], batch size: 24, lr: 1.48e-04
2022-05-29 15:11:51,981 INFO [train.py:842] (1/4) Epoch 37, batch 8300, loss[loss=0.1837, simple_loss=0.2737, pruned_loss=0.04685, over 7200.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2627, pruned_loss=0.04199, over 1418889.44 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 15:12:30,169 INFO [train.py:842] (1/4) Epoch 37, batch 8350, loss[loss=0.204, simple_loss=0.2965, pruned_loss=0.0558, over 7325.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2643, pruned_loss=0.04249, over 1421630.74 frames.], batch size: 22, lr: 1.48e-04
2022-05-29 15:13:09,203 INFO [train.py:842] (1/4) Epoch 37, batch 8400, loss[loss=0.1459, simple_loss=0.2254, pruned_loss=0.03322, over 7263.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2627, pruned_loss=0.04212, over 1424143.61 frames.], batch size: 16, lr: 1.48e-04
2022-05-29 15:13:47,730 INFO [train.py:842] (1/4) Epoch 37, batch 8450, loss[loss=0.162, simple_loss=0.255, pruned_loss=0.03453, over 7061.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2619, pruned_loss=0.04161, over 1423780.26 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 15:14:26,395 INFO [train.py:842] (1/4) Epoch 37, batch 8500, loss[loss=0.1792, simple_loss=0.2721, pruned_loss=0.0431, over 7279.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2616, pruned_loss=0.04146, over 1423756.99 frames.], batch size: 17, lr: 1.48e-04
2022-05-29 15:15:04,515 INFO [train.py:842] (1/4) Epoch 37, batch 8550, loss[loss=0.1684, simple_loss=0.2553, pruned_loss=0.04075, over 7115.00 frames.], tot_loss[loss=0.1732, simple_loss=0.263, pruned_loss=0.04174, over 1423207.45 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:15:43,227 INFO [train.py:842] (1/4) Epoch 37, batch 8600, loss[loss=0.2007, simple_loss=0.293, pruned_loss=0.05421, over 7059.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2632, pruned_loss=0.04203, over 1419031.62 frames.], batch size: 28, lr: 1.48e-04
2022-05-29 15:16:21,383 INFO [train.py:842] (1/4) Epoch 37, batch 8650, loss[loss=0.1353, simple_loss=0.2343, pruned_loss=0.01811, over 7425.00 frames.], tot_loss[loss=0.172, simple_loss=0.2616, pruned_loss=0.04116, over 1418538.56 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 15:16:59,580 INFO [train.py:842] (1/4) Epoch 37, batch 8700, loss[loss=0.1574, simple_loss=0.2539, pruned_loss=0.03038, over 7426.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2615, pruned_loss=0.04114, over 1412103.89 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 15:17:37,723 INFO [train.py:842] (1/4) Epoch 37, batch 8750, loss[loss=0.1583, simple_loss=0.2446, pruned_loss=0.03601, over 7154.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2625, pruned_loss=0.04162, over 1411509.36 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 15:18:16,302 INFO [train.py:842] (1/4) Epoch 37, batch 8800, loss[loss=0.1596, simple_loss=0.2516, pruned_loss=0.0338, over 7149.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2615, pruned_loss=0.04091, over 1412516.86 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 15:18:54,788 INFO [train.py:842] (1/4) Epoch 37, batch 8850, loss[loss=0.1718, simple_loss=0.2642, pruned_loss=0.03972, over 7284.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2615, pruned_loss=0.04082, over 1412716.14 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 15:19:33,457 INFO [train.py:842] (1/4) Epoch 37, batch 8900, loss[loss=0.1673, simple_loss=0.2631, pruned_loss=0.03578, over 6343.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2617, pruned_loss=0.04084, over 1414137.02 frames.], batch size: 38, lr: 1.48e-04
2022-05-29 15:20:11,866 INFO [train.py:842] (1/4) Epoch 37, batch 8950, loss[loss=0.1678, simple_loss=0.2438, pruned_loss=0.04588, over 7146.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2617, pruned_loss=0.04085, over 1412373.23 frames.], batch size: 17, lr: 1.48e-04
2022-05-29 15:20:50,456 INFO [train.py:842] (1/4) Epoch 37, batch 9000, loss[loss=0.1638, simple_loss=0.2556, pruned_loss=0.03598, over 7445.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2616, pruned_loss=0.04042, over 1409034.76 frames.], batch size: 22, lr: 1.48e-04
2022-05-29 15:20:50,456 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 15:20:59,818 INFO [train.py:871] (1/4) Epoch 37, validation: loss=0.1648, simple_loss=0.2612, pruned_loss=0.03416, over 868885.00 frames.
2022-05-29 15:21:38,653 INFO [train.py:842] (1/4) Epoch 37, batch 9050, loss[loss=0.1647, simple_loss=0.2464, pruned_loss=0.04154, over 7136.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2593, pruned_loss=0.04028, over 1403926.41 frames.], batch size: 17, lr: 1.48e-04
2022-05-29 15:22:16,587 INFO [train.py:842] (1/4) Epoch 37, batch 9100, loss[loss=0.1782, simple_loss=0.2611, pruned_loss=0.04769, over 6413.00 frames.], tot_loss[loss=0.172, simple_loss=0.2611, pruned_loss=0.04147, over 1371287.70 frames.], batch size: 37, lr: 1.47e-04
2022-05-29 15:22:53,405 INFO [train.py:842] (1/4) Epoch 37, batch 9150, loss[loss=0.2144, simple_loss=0.2991, pruned_loss=0.06487, over 5140.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2649, pruned_loss=0.04364, over 1315750.87 frames.], batch size: 52, lr: 1.47e-04
2022-05-29 15:23:42,224 INFO [train.py:842] (1/4) Epoch 38, batch 0, loss[loss=0.1431, simple_loss=0.2344, pruned_loss=0.02584, over 7365.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2344, pruned_loss=0.02584, over 7365.00 frames.], batch size: 19, lr: 1.46e-04
2022-05-29 15:24:21,102 INFO [train.py:842] (1/4) Epoch 38, batch 50, loss[loss=0.1765, simple_loss=0.2718, pruned_loss=0.04065, over 6440.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2566, pruned_loss=0.03762, over 322524.15 frames.], batch size: 38, lr: 1.46e-04
2022-05-29 15:24:59,396 INFO [train.py:842] (1/4) Epoch 38, batch 100, loss[loss=0.1726, simple_loss=0.264, pruned_loss=0.04066, over 7263.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2609, pruned_loss=0.03983, over 559687.42 frames.], batch size: 19, lr: 1.46e-04
2022-05-29 15:25:37,984 INFO [train.py:842] (1/4) Epoch 38, batch 150, loss[loss=0.1676, simple_loss=0.2527, pruned_loss=0.04125, over 7383.00 frames.], tot_loss[loss=0.1706, simple_loss=0.261, pruned_loss=0.04007, over 747653.24 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 15:26:16,311 INFO [train.py:842] (1/4) Epoch 38, batch 200, loss[loss=0.1799, simple_loss=0.2716, pruned_loss=0.04406, over 7417.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2592, pruned_loss=0.0399, over 895981.11 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 15:26:54,849 INFO [train.py:842] (1/4) Epoch 38, batch 250, loss[loss=0.1667, simple_loss=0.255, pruned_loss=0.03918, over 7363.00 frames.], tot_loss[loss=0.1681, simple_loss=0.258, pruned_loss=0.03909, over 1014122.44 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:27:33,115 INFO [train.py:842] (1/4) Epoch 38, batch 300, loss[loss=0.1472, simple_loss=0.246, pruned_loss=0.02422, over 7245.00 frames.], tot_loss[loss=0.169, simple_loss=0.2586, pruned_loss=0.03969, over 1104083.98 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:28:11,832 INFO [train.py:842] (1/4) Epoch 38, batch 350, loss[loss=0.1828, simple_loss=0.258, pruned_loss=0.05382, over 7247.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2597, pruned_loss=0.0403, over 1171474.34 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:28:50,327 INFO [train.py:842] (1/4) Epoch 38, batch 400, loss[loss=0.2232, simple_loss=0.2767, pruned_loss=0.08488, over 7276.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2605, pruned_loss=0.04103, over 1231525.14 frames.], batch size: 17, lr: 1.45e-04
2022-05-29 15:29:28,791 INFO [train.py:842] (1/4) Epoch 38, batch 450, loss[loss=0.1649, simple_loss=0.2616, pruned_loss=0.03413, over 7126.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2597, pruned_loss=0.04073, over 1274695.78 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 15:30:07,452 INFO [train.py:842] (1/4) Epoch 38, batch 500, loss[loss=0.1758, simple_loss=0.2568, pruned_loss=0.0474, over 7273.00 frames.], tot_loss[loss=0.169, simple_loss=0.2579, pruned_loss=0.04, over 1310732.77 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:30:46,034 INFO [train.py:842] (1/4) Epoch 38, batch 550, loss[loss=0.1555, simple_loss=0.256, pruned_loss=0.02745, over 7329.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2595, pruned_loss=0.04033, over 1335298.59 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:31:24,326 INFO [train.py:842] (1/4) Epoch 38, batch 600, loss[loss=0.1698, simple_loss=0.2627, pruned_loss=0.03847, over 7361.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2598, pruned_loss=0.04033, over 1357049.55 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 15:32:03,011 INFO [train.py:842] (1/4) Epoch 38, batch 650, loss[loss=0.1815, simple_loss=0.2714, pruned_loss=0.04584, over 7344.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2611, pruned_loss=0.04096, over 1373285.36 frames.], batch size: 22, lr: 1.45e-04
2022-05-29 15:32:41,269 INFO [train.py:842] (1/4) Epoch 38, batch 700, loss[loss=0.1785, simple_loss=0.2625, pruned_loss=0.0472, over 7156.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2604, pruned_loss=0.04068, over 1385778.12 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:33:19,932 INFO [train.py:842] (1/4) Epoch 38, batch 750, loss[loss=0.1698, simple_loss=0.2632, pruned_loss=0.03824, over 7379.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2595, pruned_loss=0.03996, over 1400225.58 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 15:33:58,069 INFO [train.py:842] (1/4) Epoch 38, batch 800, loss[loss=0.1603, simple_loss=0.2394, pruned_loss=0.04059, over 7402.00 frames.], tot_loss[loss=0.1693, simple_loss=0.259, pruned_loss=0.03974, over 1408003.83 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:34:36,750 INFO [train.py:842] (1/4) Epoch 38, batch 850, loss[loss=0.1563, simple_loss=0.2403, pruned_loss=0.03616, over 7359.00 frames.], tot_loss[loss=0.1689, simple_loss=0.259, pruned_loss=0.0394, over 1410883.67 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:35:15,000 INFO [train.py:842] (1/4) Epoch 38, batch 900, loss[loss=0.2038, simple_loss=0.3029, pruned_loss=0.0523, over 7291.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2587, pruned_loss=0.03889, over 1412721.68 frames.], batch size: 24, lr: 1.45e-04
2022-05-29 15:35:53,554 INFO [train.py:842] (1/4) Epoch 38, batch 950, loss[loss=0.2141, simple_loss=0.2981, pruned_loss=0.06509, over 7263.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2591, pruned_loss=0.03927, over 1418095.89 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:36:31,940 INFO [train.py:842] (1/4) Epoch 38, batch 1000, loss[loss=0.1777, simple_loss=0.2705, pruned_loss=0.04238, over 7197.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2588, pruned_loss=0.03902, over 1421100.13 frames.], batch size: 22, lr: 1.45e-04
2022-05-29 15:37:10,452 INFO [train.py:842] (1/4) Epoch 38, batch 1050, loss[loss=0.1531, simple_loss=0.2522, pruned_loss=0.02695, over 7339.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2602, pruned_loss=0.03983, over 1422068.49 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:37:48,986 INFO [train.py:842] (1/4) Epoch 38, batch 1100, loss[loss=0.1616, simple_loss=0.2417, pruned_loss=0.04081, over 6797.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2609, pruned_loss=0.04063, over 1424947.85 frames.], batch size: 15, lr: 1.45e-04
2022-05-29 15:38:27,633 INFO [train.py:842] (1/4) Epoch 38, batch 1150, loss[loss=0.1487, simple_loss=0.2319, pruned_loss=0.03281, over 7274.00 frames.], tot_loss[loss=0.1709, simple_loss=0.261, pruned_loss=0.04045, over 1422287.32 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:39:05,900 INFO [train.py:842] (1/4) Epoch 38, batch 1200, loss[loss=0.2049, simple_loss=0.2947, pruned_loss=0.05759, over 7156.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2628, pruned_loss=0.04138, over 1424064.60 frames.], batch size: 26, lr: 1.45e-04
2022-05-29 15:39:44,563 INFO [train.py:842] (1/4) Epoch 38, batch 1250, loss[loss=0.1734, simple_loss=0.2791, pruned_loss=0.03384, over 6456.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2629, pruned_loss=0.04164, over 1427374.59 frames.], batch size: 38, lr: 1.45e-04
2022-05-29 15:40:22,866 INFO [train.py:842] (1/4) Epoch 38, batch 1300, loss[loss=0.1378, simple_loss=0.2307, pruned_loss=0.02246, over 7296.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2624, pruned_loss=0.04141, over 1426536.56 frames.], batch size: 17, lr: 1.45e-04
2022-05-29 15:41:01,504 INFO [train.py:842] (1/4) Epoch 38, batch 1350, loss[loss=0.1877, simple_loss=0.2853, pruned_loss=0.04509, over 7118.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2619, pruned_loss=0.04136, over 1420328.70 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 15:41:40,073 INFO [train.py:842] (1/4) Epoch 38, batch 1400, loss[loss=0.1698, simple_loss=0.2603, pruned_loss=0.03962, over 7298.00 frames.], tot_loss[loss=0.1718, simple_loss=0.261, pruned_loss=0.04126, over 1419770.15 frames.], batch size: 24, lr: 1.45e-04
2022-05-29 15:42:18,697 INFO [train.py:842] (1/4) Epoch 38, batch 1450, loss[loss=0.1584, simple_loss=0.2568, pruned_loss=0.03002, over 7201.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2607, pruned_loss=0.04131, over 1424466.33 frames.], batch size: 22, lr: 1.45e-04
2022-05-29 15:42:57,036 INFO [train.py:842] (1/4) Epoch 38, batch 1500, loss[loss=0.2074, simple_loss=0.2826, pruned_loss=0.06612, over 7311.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2602, pruned_loss=0.04106, over 1424645.34 frames.], batch size: 25, lr: 1.45e-04
2022-05-29 15:43:35,784 INFO [train.py:842] (1/4) Epoch 38, batch 1550, loss[loss=0.184, simple_loss=0.2724, pruned_loss=0.04775, over 7231.00 frames.], tot_loss[loss=0.1707, simple_loss=0.26, pruned_loss=0.04075, over 1421157.22 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:44:14,287 INFO [train.py:842] (1/4) Epoch 38, batch 1600, loss[loss=0.1487, simple_loss=0.2413, pruned_loss=0.028, over 7265.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2599, pruned_loss=0.04054, over 1423941.05 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:44:53,036 INFO [train.py:842] (1/4) Epoch 38, batch 1650, loss[loss=0.1629, simple_loss=0.269, pruned_loss=0.02839, over 7038.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2607, pruned_loss=0.04111, over 1423969.93 frames.], batch size: 28, lr: 1.45e-04
2022-05-29 15:45:31,677 INFO [train.py:842] (1/4) Epoch 38, batch 1700, loss[loss=0.1446, simple_loss=0.2378, pruned_loss=0.02573, over 7174.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2601, pruned_loss=0.04114, over 1422836.61 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:46:10,388 INFO [train.py:842] (1/4) Epoch 38, batch 1750, loss[loss=0.2104, simple_loss=0.3014, pruned_loss=0.05965, over 4589.00 frames.], tot_loss[loss=0.1706, simple_loss=0.26, pruned_loss=0.04066, over 1420795.61 frames.], batch size: 52, lr: 1.45e-04
2022-05-29 15:46:48,914 INFO [train.py:842] (1/4) Epoch 38, batch 1800, loss[loss=0.1951, simple_loss=0.2776, pruned_loss=0.05631, over 7321.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2601, pruned_loss=0.0408, over 1418774.39 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:47:27,693 INFO [train.py:842] (1/4) Epoch 38, batch 1850, loss[loss=0.1645, simple_loss=0.2465, pruned_loss=0.04125, over 7287.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2607, pruned_loss=0.04107, over 1420300.70 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:48:05,992 INFO [train.py:842] (1/4) Epoch 38, batch 1900, loss[loss=0.161, simple_loss=0.2443, pruned_loss=0.03884, over 6807.00 frames.], tot_loss[loss=0.1716, simple_loss=0.261, pruned_loss=0.04107, over 1422744.91 frames.], batch size: 15, lr: 1.45e-04
2022-05-29 15:48:44,643 INFO [train.py:842] (1/4) Epoch 38, batch 1950, loss[loss=0.141, simple_loss=0.2265, pruned_loss=0.02775, over 7254.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2613, pruned_loss=0.04126, over 1425572.84 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:49:23,251 INFO [train.py:842] (1/4) Epoch 38, batch 2000, loss[loss=0.1397, simple_loss=0.2313, pruned_loss=0.02407, over 7395.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2606, pruned_loss=0.04113, over 1425036.31 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:50:01,711 INFO [train.py:842] (1/4) Epoch 38, batch 2050, loss[loss=0.1663, simple_loss=0.247, pruned_loss=0.04279, over 7255.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2598, pruned_loss=0.04077, over 1422582.25 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:50:39,903 INFO [train.py:842] (1/4) Epoch 38, batch 2100, loss[loss=0.17, simple_loss=0.256, pruned_loss=0.04196, over 7135.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2604, pruned_loss=0.04103, over 1417421.83 frames.], batch size: 26, lr: 1.45e-04
2022-05-29 15:51:18,623 INFO [train.py:842] (1/4) Epoch 38, batch 2150, loss[loss=0.1467, simple_loss=0.2321, pruned_loss=0.03064, over 7073.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2604, pruned_loss=0.04106, over 1417159.42 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:51:56,336 INFO [train.py:842] (1/4) Epoch 38, batch 2200, loss[loss=0.1675, simple_loss=0.2485, pruned_loss=0.0433, over 7058.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2618, pruned_loss=0.04137, over 1417962.09 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:52:34,389 INFO [train.py:842] (1/4) Epoch 38, batch 2250, loss[loss=0.1885, simple_loss=0.2789, pruned_loss=0.049, over 6298.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2614, pruned_loss=0.04102, over 1416604.73 frames.], batch size: 37, lr: 1.45e-04
2022-05-29 15:53:12,412 INFO [train.py:842] (1/4) Epoch 38, batch 2300, loss[loss=0.1432, simple_loss=0.2352, pruned_loss=0.02555, over 7069.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2615, pruned_loss=0.04111, over 1421072.88 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:53:50,465 INFO [train.py:842] (1/4) Epoch 38, batch 2350, loss[loss=0.1425, simple_loss=0.2401, pruned_loss=0.02247, over 7330.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2615, pruned_loss=0.041, over 1418668.76 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:54:28,596 INFO [train.py:842] (1/4) Epoch 38, batch 2400, loss[loss=0.2139, simple_loss=0.2814, pruned_loss=0.07318, over 7404.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2608, pruned_loss=0.04087, over 1423614.98 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:55:06,818 INFO [train.py:842] (1/4) Epoch 38, batch 2450, loss[loss=0.2229, simple_loss=0.3074, pruned_loss=0.06923, over 7334.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2617, pruned_loss=0.04144, over 1425999.75 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:55:44,797 INFO [train.py:842] (1/4) Epoch 38, batch 2500, loss[loss=0.1716, simple_loss=0.2636, pruned_loss=0.03983, over 7158.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2614, pruned_loss=0.04103, over 1426304.33 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:56:22,957 INFO [train.py:842] (1/4) Epoch 38, batch 2550, loss[loss=0.1565, simple_loss=0.2494, pruned_loss=0.03178, over 7163.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2616, pruned_loss=0.04127, over 1423218.38 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:57:00,960 INFO [train.py:842] (1/4) Epoch 38, batch 2600, loss[loss=0.1489, simple_loss=0.2471, pruned_loss=0.02537, over 7424.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2614, pruned_loss=0.0412, over 1422822.90 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:57:39,138 INFO [train.py:842] (1/4) Epoch 38, batch 2650, loss[loss=0.1677, simple_loss=0.2721, pruned_loss=0.0316, over 7194.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2633, pruned_loss=0.04199, over 1424022.39 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 15:58:17,264 INFO [train.py:842] (1/4) Epoch 38, batch 2700, loss[loss=0.1844, simple_loss=0.2681, pruned_loss=0.05031, over 7229.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2619, pruned_loss=0.04168, over 1423798.32 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:58:55,522 INFO [train.py:842] (1/4) Epoch 38, batch 2750, loss[loss=0.1751, simple_loss=0.2754, pruned_loss=0.03739, over 7360.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2619, pruned_loss=0.04126, over 1424392.90 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:59:33,532 INFO [train.py:842] (1/4) Epoch 38, batch 2800, loss[loss=0.1696, simple_loss=0.2591, pruned_loss=0.04004, over 7295.00 frames.], tot_loss[loss=0.1727, simple_loss=0.262, pruned_loss=0.04169, over 1423111.43 frames.], batch size: 24, lr: 1.45e-04
2022-05-29 16:00:11,773 INFO [train.py:842] (1/4) Epoch 38, batch 2850, loss[loss=0.1579, simple_loss=0.254, pruned_loss=0.03085, over 7407.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2619, pruned_loss=0.04144, over 1423675.73 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 16:00:49,756 INFO [train.py:842] (1/4) Epoch 38, batch 2900, loss[loss=0.1779, simple_loss=0.2598, pruned_loss=0.04801, over 7148.00 frames.], tot_loss[loss=0.171, simple_loss=0.2606, pruned_loss=0.04073, over 1424378.41 frames.], batch size: 17, lr: 1.45e-04
2022-05-29 16:01:28,078 INFO [train.py:842] (1/4) Epoch 38, batch 2950, loss[loss=0.1744, simple_loss=0.2567, pruned_loss=0.04607, over 7417.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2599, pruned_loss=0.04037, over 1428743.16 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 16:02:05,958 INFO [train.py:842] (1/4) Epoch 38, batch 3000, loss[loss=0.1879, simple_loss=0.2884, pruned_loss=0.04376, over 7191.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2604, pruned_loss=0.04039, over 1428216.85 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 16:02:05,959 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 16:02:15,341 INFO [train.py:871] (1/4) Epoch 38, validation: loss=0.1634, simple_loss=0.2602, pruned_loss=0.03326, over 868885.00 frames.
2022-05-29 16:02:53,596 INFO [train.py:842] (1/4) Epoch 38, batch 3050, loss[loss=0.1431, simple_loss=0.2265, pruned_loss=0.02985, over 7164.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2601, pruned_loss=0.04014, over 1428689.63 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 16:03:31,421 INFO [train.py:842] (1/4) Epoch 38, batch 3100, loss[loss=0.1899, simple_loss=0.2708, pruned_loss=0.05456, over 7198.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2602, pruned_loss=0.04009, over 1421651.17 frames.], batch size: 22, lr: 1.45e-04
2022-05-29 16:04:09,676 INFO [train.py:842] (1/4) Epoch 38, batch 3150, loss[loss=0.2088, simple_loss=0.2983, pruned_loss=0.05966, over 7370.00 frames.], tot_loss[loss=0.1713, simple_loss=0.261, pruned_loss=0.04084, over 1420673.55 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 16:04:47,693 INFO [train.py:842] (1/4) Epoch 38, batch 3200, loss[loss=0.159, simple_loss=0.2523, pruned_loss=0.03283, over 7105.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2601, pruned_loss=0.04012, over 1424895.59 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 16:05:26,137 INFO [train.py:842] (1/4) Epoch 38, batch 3250, loss[loss=0.1439, simple_loss=0.224, pruned_loss=0.03193, over 7263.00 frames.], tot_loss[loss=0.17, simple_loss=0.2598, pruned_loss=0.04011, over 1425807.97 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 16:06:04,016 INFO [train.py:842] (1/4) Epoch 38, batch 3300, loss[loss=0.1713, simple_loss=0.2604, pruned_loss=0.04116, over 7224.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2593, pruned_loss=0.03988, over 1425501.50 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:06:41,996 INFO [train.py:842] (1/4) Epoch 38, batch 3350, loss[loss=0.2091, simple_loss=0.2871, pruned_loss=0.06552, over 7215.00 frames.], tot_loss[loss=0.171, simple_loss=0.261, pruned_loss=0.04048, over 1426827.03 frames.], batch size: 22, lr: 1.45e-04
2022-05-29 16:07:19,989 INFO [train.py:842] (1/4) Epoch 38, batch 3400, loss[loss=0.1764, simple_loss=0.2729, pruned_loss=0.03996, over 6654.00 frames.], tot_loss[loss=0.171, simple_loss=0.2611, pruned_loss=0.04046, over 1430256.93 frames.], batch size: 31, lr: 1.45e-04
2022-05-29 16:07:58,236 INFO [train.py:842] (1/4) Epoch 38, batch 3450, loss[loss=0.1639, simple_loss=0.2596, pruned_loss=0.03413, over 7419.00 frames.], tot_loss[loss=0.1712, simple_loss=0.261, pruned_loss=0.04071, over 1432041.03 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:08:36,212 INFO [train.py:842] (1/4) Epoch 38, batch 3500, loss[loss=0.1667, simple_loss=0.2649, pruned_loss=0.03425, over 7237.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2603, pruned_loss=0.04034, over 1430425.10 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:09:14,390 INFO [train.py:842] (1/4) Epoch 38, batch 3550, loss[loss=0.2497, simple_loss=0.3293, pruned_loss=0.08502, over 7149.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2606, pruned_loss=0.04064, over 1430556.19 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:09:52,130 INFO [train.py:842] (1/4) Epoch 38, batch 3600, loss[loss=0.1637, simple_loss=0.2614, pruned_loss=0.03297, over 6815.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2605, pruned_loss=0.04038, over 1428896.87 frames.], batch size: 31, lr: 1.45e-04
2022-05-29 16:10:30,407 INFO [train.py:842] (1/4) Epoch 38, batch 3650, loss[loss=0.2076, simple_loss=0.3063, pruned_loss=0.05446, over 7087.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2599, pruned_loss=0.04015, over 1431577.94 frames.], batch size: 28, lr: 1.45e-04
2022-05-29 16:11:08,251 INFO [train.py:842] (1/4) Epoch 38, batch 3700, loss[loss=0.1693, simple_loss=0.2715, pruned_loss=0.03352, over 7294.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2604, pruned_loss=0.04071, over 1422776.04 frames.], batch size: 24, lr: 1.45e-04
2022-05-29 16:11:46,336 INFO [train.py:842] (1/4) Epoch 38, batch 3750, loss[loss=0.1917, simple_loss=0.2726, pruned_loss=0.0554, over 7155.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2614, pruned_loss=0.04126, over 1419088.88 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 16:12:24,512 INFO [train.py:842] (1/4) Epoch 38, batch 3800, loss[loss=0.1583, simple_loss=0.2557, pruned_loss=0.03047, over 7384.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2609, pruned_loss=0.04112, over 1419541.79 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 16:13:02,701 INFO [train.py:842] (1/4) Epoch 38, batch 3850, loss[loss=0.1702, simple_loss=0.2741, pruned_loss=0.03315, over 7125.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2601, pruned_loss=0.04009, over 1421346.84 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 16:13:40,941 INFO [train.py:842] (1/4) Epoch 38, batch 3900, loss[loss=0.1726, simple_loss=0.2616, pruned_loss=0.04186, over 7324.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2594, pruned_loss=0.04004, over 1422913.70 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:14:21,833 INFO [train.py:842] (1/4) Epoch 38, batch 3950, loss[loss=0.1607, simple_loss=0.2505, pruned_loss=0.03549, over 6701.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2591, pruned_loss=0.03992, over 1418452.26 frames.], batch size: 31, lr: 1.45e-04
2022-05-29 16:14:59,662 INFO [train.py:842] (1/4) Epoch 38, batch 4000, loss[loss=0.1796, simple_loss=0.2585, pruned_loss=0.05039, over 7138.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2596, pruned_loss=0.04029, over 1418829.76 frames.], batch size: 17, lr: 1.45e-04
2022-05-29 16:15:37,912 INFO [train.py:842] (1/4) Epoch 38, batch 4050, loss[loss=0.1433, simple_loss=0.2217, pruned_loss=0.03241, over 7002.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2591, pruned_loss=0.04009, over 1417503.26 frames.], batch size: 16, lr: 1.45e-04
2022-05-29 16:16:15,907 INFO [train.py:842] (1/4) Epoch 38, batch 4100, loss[loss=0.1743, simple_loss=0.2654, pruned_loss=0.04164, over 7145.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2591, pruned_loss=0.03994, over 1417423.41 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:16:54,255 INFO [train.py:842] (1/4) Epoch 38, batch 4150, loss[loss=0.1546, simple_loss=0.2408, pruned_loss=0.03416, over 7221.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2582, pruned_loss=0.03926, over 1419888.96 frames.], batch size: 16, lr: 1.45e-04
2022-05-29 16:17:32,188 INFO [train.py:842] (1/4) Epoch 38, batch 4200, loss[loss=0.1798, simple_loss=0.2668, pruned_loss=0.04638, over 7357.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2581, pruned_loss=0.03938, over 1418302.34 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 16:18:10,305 INFO [train.py:842] (1/4) Epoch 38, batch 4250, loss[loss=0.2051, simple_loss=0.2889, pruned_loss=0.06065, over 7289.00 frames.], tot_loss[loss=0.169, simple_loss=0.2589, pruned_loss=0.0396, over 1418691.59 frames.], batch size: 24, lr: 1.45e-04
2022-05-29 16:18:57,692 INFO [train.py:842] (1/4) Epoch 38, batch 4300, loss[loss=0.1581, simple_loss=0.2533, pruned_loss=0.03142, over 7335.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2585, pruned_loss=0.03953, over 1422345.55 frames.], batch size: 22, lr: 1.45e-04
2022-05-29 16:19:35,763 INFO [train.py:842] (1/4) Epoch 38, batch 4350, loss[loss=0.1381, simple_loss=0.2184, pruned_loss=0.02893, over 7298.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2593, pruned_loss=0.03948, over 1421787.83 frames.], batch size: 17, lr: 1.45e-04
2022-05-29 16:20:13,804 INFO [train.py:842] (1/4) Epoch 38, batch 4400, loss[loss=0.1868, simple_loss=0.2736, pruned_loss=0.05001, over 6988.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2586, pruned_loss=0.03922, over 1422261.14 frames.], batch size: 28, lr: 1.45e-04
2022-05-29 16:20:52,137 INFO [train.py:842] (1/4) Epoch 38, batch 4450, loss[loss=0.161, simple_loss=0.2555, pruned_loss=0.03321, over 7322.00 frames.], tot_loss[loss=0.169, simple_loss=0.2591, pruned_loss=0.03951, over 1424715.42 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:21:30,249 INFO [train.py:842] (1/4) Epoch 38, batch 4500, loss[loss=0.1719, simple_loss=0.269, pruned_loss=0.03736, over 7112.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2588, pruned_loss=0.0399, over 1428146.87 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 16:22:08,241 INFO [train.py:842] (1/4) Epoch 38, batch 4550, loss[loss=0.158, simple_loss=0.2545, pruned_loss=0.03072, over 7259.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2597, pruned_loss=0.03978, over 1429848.51 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 16:22:46,091 INFO [train.py:842] (1/4) Epoch 38, batch 4600, loss[loss=0.1787, simple_loss=0.2655, pruned_loss=0.04599, over 7396.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2595, pruned_loss=0.03999, over 1427345.03 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 16:23:24,377 INFO [train.py:842] (1/4) Epoch 38, batch 4650, loss[loss=0.1771, simple_loss=0.2631, pruned_loss=0.0456, over 7374.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2592, pruned_loss=0.03989, over 1428290.78 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 16:24:02,472 INFO [train.py:842] (1/4) Epoch 38, batch 4700, loss[loss=0.168, simple_loss=0.2593, pruned_loss=0.03841, over 7189.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2581, pruned_loss=0.03984, over 1424371.37 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 16:24:40,735 INFO [train.py:842] (1/4) Epoch 38, batch 4750, loss[loss=0.1584, simple_loss=0.2466, pruned_loss=0.03509, over 7147.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2577, pruned_loss=0.03968, over 1422376.71 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 16:25:18,456 INFO [train.py:842] (1/4) Epoch 38, batch 4800, loss[loss=0.1484, simple_loss=0.2522, pruned_loss=0.02227, over 7151.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2588, pruned_loss=0.03991, over 1423134.28 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:25:56,656 INFO [train.py:842] (1/4) Epoch 38, batch 4850, loss[loss=0.185, simple_loss=0.2819, pruned_loss=0.04407, over 7065.00 frames.], tot_loss[loss=0.17, simple_loss=0.2589, pruned_loss=0.04051, over 1421217.73 frames.], batch size: 28, lr: 1.44e-04
2022-05-29 16:26:34,237 INFO [train.py:842] (1/4) Epoch 38, batch 4900, loss[loss=0.154, simple_loss=0.2541, pruned_loss=0.02692, over 7215.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2593, pruned_loss=0.0402, over 1414521.79 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 16:27:12,489 INFO [train.py:842] (1/4) Epoch 38, batch 4950, loss[loss=0.1872, simple_loss=0.2726, pruned_loss=0.05094, over 7061.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2592, pruned_loss=0.03997, over 1417782.67 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:27:50,532 INFO [train.py:842] (1/4) Epoch 38, batch 5000, loss[loss=0.1541, simple_loss=0.2487, pruned_loss=0.02974, over 7123.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2586, pruned_loss=0.04019, over 1421135.62 frames.], batch size: 26, lr: 1.44e-04
2022-05-29 16:28:28,901 INFO [train.py:842] (1/4) Epoch 38, batch 5050, loss[loss=0.2061, simple_loss=0.2937, pruned_loss=0.05925, over 6340.00 frames.], tot_loss[loss=0.1701, simple_loss=0.259, pruned_loss=0.04059, over 1424896.71 frames.], batch size: 37, lr: 1.44e-04
2022-05-29 16:29:07,183 INFO [train.py:842] (1/4) Epoch 38, batch 5100, loss[loss=0.1859, simple_loss=0.2756, pruned_loss=0.0481, over 7275.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2583, pruned_loss=0.04036, over 1426464.66 frames.], batch size: 24, lr: 1.44e-04
2022-05-29 16:29:45,466 INFO [train.py:842] (1/4) Epoch 38, batch 5150, loss[loss=0.2083, simple_loss=0.292, pruned_loss=0.06228, over 7431.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2579, pruned_loss=0.04018, over 1428813.79 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:30:23,533 INFO [train.py:842] (1/4) Epoch 38, batch 5200, loss[loss=0.157, simple_loss=0.2652, pruned_loss=0.02435, over 7215.00 frames.], tot_loss[loss=0.1692, simple_loss=0.258, pruned_loss=0.04023, over 1425806.25 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 16:31:01,712 INFO [train.py:842] (1/4) Epoch 38, batch 5250, loss[loss=0.1702, simple_loss=0.2583, pruned_loss=0.04108, over 7317.00 frames.], tot_loss[loss=0.17, simple_loss=0.2587, pruned_loss=0.04067, over 1421973.12 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:31:39,569 INFO [train.py:842] (1/4) Epoch 38, batch 5300, loss[loss=0.289, simple_loss=0.357, pruned_loss=0.1105, over 4842.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2592, pruned_loss=0.04071, over 1416456.95 frames.], batch size: 52, lr: 1.44e-04
2022-05-29 16:32:17,625 INFO [train.py:842] (1/4) Epoch 38, batch 5350, loss[loss=0.1917, simple_loss=0.2748, pruned_loss=0.05436, over 7265.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2599, pruned_loss=0.041, over 1411881.65 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:32:55,664 INFO [train.py:842] (1/4) Epoch 38, batch 5400, loss[loss=0.1595, simple_loss=0.2574, pruned_loss=0.03082, over 7325.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2614, pruned_loss=0.04202, over 1415621.82 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 16:33:34,073 INFO [train.py:842] (1/4) Epoch 38, batch 5450, loss[loss=0.1679, simple_loss=0.2674, pruned_loss=0.03418, over 7215.00 frames.], tot_loss[loss=0.1721, simple_loss=0.261, pruned_loss=0.04164, over 1417353.47 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 16:34:11,847 INFO [train.py:842] (1/4) Epoch 38, batch 5500, loss[loss=0.1548, simple_loss=0.2426, pruned_loss=0.03348, over 7425.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2613, pruned_loss=0.04178, over 1419560.11 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:34:50,170 INFO [train.py:842] (1/4) Epoch 38, batch 5550, loss[loss=0.1645, simple_loss=0.2626, pruned_loss=0.03325, over 7327.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2605, pruned_loss=0.04124, over 1419405.00 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:35:27,888 INFO [train.py:842] (1/4) Epoch 38, batch 5600, loss[loss=0.1845, simple_loss=0.2858, pruned_loss=0.04167, over 7322.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2606, pruned_loss=0.04108, over 1417259.53 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 16:36:06,394 INFO [train.py:842] (1/4) Epoch 38, batch 5650, loss[loss=0.1454, simple_loss=0.2279, pruned_loss=0.03147, over 7401.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2606, pruned_loss=0.04132, over 1422886.98 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:36:44,331 INFO [train.py:842] (1/4) Epoch 38, batch 5700, loss[loss=0.1807, simple_loss=0.2744, pruned_loss=0.04346, over 7269.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2608, pruned_loss=0.04151, over 1422914.92 frames.], batch size: 24, lr: 1.44e-04
2022-05-29 16:37:22,672 INFO [train.py:842] (1/4) Epoch 38, batch 5750, loss[loss=0.1852, simple_loss=0.2729, pruned_loss=0.04875, over 7077.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2608, pruned_loss=0.04115, over 1425921.69 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:38:00,884 INFO [train.py:842] (1/4) Epoch 38, batch 5800, loss[loss=0.1372, simple_loss=0.2296, pruned_loss=0.0224, over 7271.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2591, pruned_loss=0.04032, over 1429870.47 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:38:39,000 INFO [train.py:842] (1/4) Epoch 38, batch 5850, loss[loss=0.1607, simple_loss=0.2516, pruned_loss=0.03488, over 6808.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2589, pruned_loss=0.04017, over 1423341.19 frames.], batch size: 31, lr: 1.44e-04
2022-05-29 16:39:16,842 INFO [train.py:842] (1/4) Epoch 38, batch 5900, loss[loss=0.1647, simple_loss=0.2446, pruned_loss=0.04243, over 7217.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2604, pruned_loss=0.04116, over 1421997.56 frames.], batch size: 16, lr: 1.44e-04
2022-05-29 16:39:55,212 INFO [train.py:842] (1/4) Epoch 38, batch 5950, loss[loss=0.1556, simple_loss=0.2522, pruned_loss=0.02953, over 7298.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2594, pruned_loss=0.04059, over 1421937.20 frames.], batch size: 25, lr: 1.44e-04
2022-05-29 16:40:33,183 INFO [train.py:842] (1/4) Epoch 38, batch 6000, loss[loss=0.2004, simple_loss=0.2858, pruned_loss=0.05746, over 7153.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2606, pruned_loss=0.04103, over 1419804.60 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:40:33,183 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 16:40:42,179 INFO [train.py:871] (1/4) Epoch 38, validation: loss=0.1638, simple_loss=0.2604, pruned_loss=0.03358, over 868885.00 frames.
2022-05-29 16:41:20,631 INFO [train.py:842] (1/4) Epoch 38, batch 6050, loss[loss=0.1472, simple_loss=0.2379, pruned_loss=0.02825, over 7249.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2598, pruned_loss=0.04059, over 1416034.09 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:41:58,592 INFO [train.py:842] (1/4) Epoch 38, batch 6100, loss[loss=0.1702, simple_loss=0.2711, pruned_loss=0.03466, over 7329.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2606, pruned_loss=0.04101, over 1416420.69 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 16:42:36,997 INFO [train.py:842] (1/4) Epoch 38, batch 6150, loss[loss=0.1709, simple_loss=0.2643, pruned_loss=0.03871, over 6674.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2599, pruned_loss=0.04079, over 1418579.81 frames.], batch size: 31, lr: 1.44e-04
2022-05-29 16:43:15,006 INFO [train.py:842] (1/4) Epoch 38, batch 6200, loss[loss=0.2451, simple_loss=0.323, pruned_loss=0.08358, over 7342.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2604, pruned_loss=0.04099, over 1421326.62 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 16:43:53,227 INFO [train.py:842] (1/4) Epoch 38, batch 6250, loss[loss=0.1475, simple_loss=0.2357, pruned_loss=0.02969, over 7164.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2599, pruned_loss=0.04073, over 1422905.53 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:44:31,157 INFO [train.py:842] (1/4) Epoch 38, batch 6300, loss[loss=0.192, simple_loss=0.2682, pruned_loss=0.05791, over 7351.00 frames.], tot_loss[loss=0.171, simple_loss=0.2601, pruned_loss=0.04094, over 1423625.95 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:45:09,392 INFO [train.py:842] (1/4) Epoch 38, batch 6350, loss[loss=0.1886, simple_loss=0.2833, pruned_loss=0.047, over 7389.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2599, pruned_loss=0.04046, over 1425635.78 frames.], batch size: 23, lr: 1.44e-04
2022-05-29 16:45:47,313 INFO [train.py:842] (1/4) Epoch 38, batch 6400, loss[loss=0.1753, simple_loss=0.2553, pruned_loss=0.04763, over 7261.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2593, pruned_loss=0.04022, over 1424224.08 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:46:25,810 INFO [train.py:842] (1/4) Epoch 38, batch 6450, loss[loss=0.1796, simple_loss=0.2818, pruned_loss=0.03873, over 7241.00 frames.], tot_loss[loss=0.168, simple_loss=0.2571, pruned_loss=0.03944, over 1424553.81 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:47:03,931 INFO [train.py:842] (1/4) Epoch 38, batch 6500, loss[loss=0.1689, simple_loss=0.2753, pruned_loss=0.03128, over 7141.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2573, pruned_loss=0.03949, over 1427174.42 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:47:42,058 INFO [train.py:842] (1/4) Epoch 38, batch 6550, loss[loss=0.1686, simple_loss=0.2646, pruned_loss=0.03627, over 7138.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2583, pruned_loss=0.03958, over 1427368.48 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:48:20,072 INFO [train.py:842] (1/4) Epoch 38, batch 6600, loss[loss=0.1531, simple_loss=0.2465, pruned_loss=0.02987, over 7162.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2589, pruned_loss=0.04026, over 1423149.48 frames.], batch size: 26, lr: 1.44e-04
2022-05-29 16:48:58,336 INFO [train.py:842] (1/4) Epoch 38, batch 6650, loss[loss=0.1659, simple_loss=0.2587, pruned_loss=0.03656, over 7360.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2595, pruned_loss=0.04055, over 1421341.14 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:49:36,290 INFO [train.py:842] (1/4) Epoch 38, batch 6700, loss[loss=0.1429, simple_loss=0.2301, pruned_loss=0.02789, over 6765.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2593, pruned_loss=0.04017, over 1423280.94 frames.], batch size: 15, lr: 1.44e-04
2022-05-29 16:50:14,384 INFO [train.py:842] (1/4) Epoch 38, batch 6750, loss[loss=0.1649, simple_loss=0.2586, pruned_loss=0.03557, over 7190.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2603, pruned_loss=0.04054, over 1418383.88 frames.], batch size: 23, lr: 1.44e-04
2022-05-29 16:50:52,132 INFO [train.py:842] (1/4) Epoch 38, batch 6800, loss[loss=0.1666, simple_loss=0.2551, pruned_loss=0.03906, over 7323.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2608, pruned_loss=0.04068, over 1417425.82 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:51:30,582 INFO [train.py:842] (1/4) Epoch 38, batch 6850, loss[loss=0.147, simple_loss=0.2309, pruned_loss=0.03157, over 7275.00 frames.], tot_loss[loss=0.17, simple_loss=0.2596, pruned_loss=0.04024, over 1421420.49 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:52:08,721 INFO [train.py:842] (1/4) Epoch 38, batch 6900, loss[loss=0.1708, simple_loss=0.2694, pruned_loss=0.0361, over 7287.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2593, pruned_loss=0.04017, over 1426441.58 frames.], batch size: 24, lr: 1.44e-04
2022-05-29 16:52:46,981 INFO [train.py:842] (1/4) Epoch 38, batch 6950, loss[loss=0.125, simple_loss=0.202, pruned_loss=0.024, over 7409.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2591, pruned_loss=0.04, over 1426647.68 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:53:24,873 INFO [train.py:842] (1/4) Epoch 38, batch 7000, loss[loss=0.1578, simple_loss=0.2491, pruned_loss=0.03325, over 7069.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2608, pruned_loss=0.0411, over 1427417.84 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:54:03,213 INFO [train.py:842] (1/4) Epoch 38, batch 7050, loss[loss=0.1572, simple_loss=0.2562, pruned_loss=0.02908, over 7358.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2597, pruned_loss=0.04094, over 1427612.78 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:54:41,060 INFO [train.py:842] (1/4) Epoch 38, batch 7100, loss[loss=0.1783, simple_loss=0.2645, pruned_loss=0.04607, over 7118.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2599, pruned_loss=0.04093, over 1424282.61 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 16:55:19,344 INFO [train.py:842] (1/4) Epoch 38, batch 7150, loss[loss=0.1818, simple_loss=0.272, pruned_loss=0.04579, over 6453.00 frames.], tot_loss[loss=0.1713, simple_loss=0.26, pruned_loss=0.04137, over 1422531.16 frames.], batch size: 38, lr: 1.44e-04
2022-05-29 16:55:57,119 INFO [train.py:842] (1/4) Epoch 38, batch 7200, loss[loss=0.1528, simple_loss=0.2444, pruned_loss=0.03065, over 7435.00 frames.], tot_loss[loss=0.1701, simple_loss=0.259, pruned_loss=0.04055, over 1422315.66 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:56:35,185 INFO [train.py:842] (1/4) Epoch 38, batch 7250, loss[loss=0.1726, simple_loss=0.2687, pruned_loss=0.03829, over 7379.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2597, pruned_loss=0.04053, over 1422325.49 frames.], batch size: 23, lr: 1.44e-04
2022-05-29 16:57:13,243 INFO [train.py:842] (1/4) Epoch 38, batch 7300, loss[loss=0.1486, simple_loss=0.2348, pruned_loss=0.0312, over 7418.00 frames.], tot_loss[loss=0.171, simple_loss=0.2602, pruned_loss=0.04091, over 1426890.65 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:57:51,510 INFO [train.py:842] (1/4) Epoch 38, batch 7350, loss[loss=0.1895, simple_loss=0.2681, pruned_loss=0.05541, over 7009.00 frames.], tot_loss[loss=0.171, simple_loss=0.26, pruned_loss=0.041, over 1428775.39 frames.], batch size: 16, lr: 1.44e-04
2022-05-29 16:58:29,505 INFO [train.py:842] (1/4) Epoch 38, batch 7400, loss[loss=0.2055, simple_loss=0.2995, pruned_loss=0.05576, over 7418.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2605, pruned_loss=0.04092, over 1430425.52 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 16:59:07,991 INFO [train.py:842] (1/4) Epoch 38, batch 7450, loss[loss=0.1818, simple_loss=0.27, pruned_loss=0.04674, over 7126.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2596, pruned_loss=0.04083, over 1434351.73 frames.], batch size: 28, lr: 1.44e-04
2022-05-29 16:59:45,799 INFO [train.py:842] (1/4) Epoch 38, batch 7500, loss[loss=0.1846, simple_loss=0.2781, pruned_loss=0.04554, over 7153.00 frames.], tot_loss[loss=0.171, simple_loss=0.2599, pruned_loss=0.04108, over 1431979.08 frames.], batch size: 26, lr: 1.44e-04
2022-05-29 17:00:23,997 INFO [train.py:842] (1/4) Epoch 38, batch 7550, loss[loss=0.1787, simple_loss=0.2756, pruned_loss=0.04091, over 6791.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2609, pruned_loss=0.04098, over 1431834.69 frames.], batch size: 31, lr: 1.44e-04
2022-05-29 17:01:01,991 INFO [train.py:842] (1/4) Epoch 38, batch 7600, loss[loss=0.1666, simple_loss=0.2625, pruned_loss=0.03531, over 7119.00 frames.], tot_loss[loss=0.1703, simple_loss=0.26, pruned_loss=0.04032, over 1431178.22 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 17:01:40,328 INFO [train.py:842] (1/4) Epoch 38, batch 7650, loss[loss=0.2049, simple_loss=0.2859, pruned_loss=0.06199, over 7232.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2597, pruned_loss=0.04029, over 1431289.59 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 17:02:18,330 INFO [train.py:842] (1/4) Epoch 38, batch 7700, loss[loss=0.1768, simple_loss=0.266, pruned_loss=0.04381, over 7288.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2593, pruned_loss=0.04025, over 1430255.49 frames.], batch size: 24, lr: 1.44e-04
2022-05-29 17:02:56,566 INFO [train.py:842] (1/4) Epoch 38, batch 7750, loss[loss=0.1945, simple_loss=0.2803, pruned_loss=0.05434, over 7200.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2599, pruned_loss=0.0407, over 1430126.17 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 17:03:34,680 INFO [train.py:842] (1/4) Epoch 38, batch 7800, loss[loss=0.166, simple_loss=0.2627, pruned_loss=0.03461, over 7197.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2592, pruned_loss=0.04025, over 1429064.05 frames.], batch size: 23, lr: 1.44e-04
2022-05-29 17:04:13,042 INFO [train.py:842] (1/4) Epoch 38, batch 7850, loss[loss=0.1978, simple_loss=0.2844, pruned_loss=0.05557, over 6815.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2589, pruned_loss=0.03988, over 1430330.27 frames.], batch size: 31, lr: 1.44e-04
2022-05-29 17:04:51,067 INFO [train.py:842] (1/4) Epoch 38, batch 7900, loss[loss=0.2813, simple_loss=0.3653, pruned_loss=0.09863, over 7191.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2607, pruned_loss=0.04083, over 1428255.86 frames.], batch size: 23, lr: 1.44e-04
2022-05-29 17:05:29,269 INFO [train.py:842] (1/4) Epoch 38, batch 7950, loss[loss=0.1664, simple_loss=0.2614, pruned_loss=0.0357, over 6295.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2595, pruned_loss=0.04046, over 1425637.28 frames.], batch size: 37, lr: 1.44e-04
2022-05-29 17:06:07,455 INFO [train.py:842] (1/4) Epoch 38, batch 8000, loss[loss=0.1891, simple_loss=0.2766, pruned_loss=0.05076, over 7360.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2592, pruned_loss=0.04032, over 1429028.55 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 17:06:45,756 INFO [train.py:842] (1/4) Epoch 38, batch 8050, loss[loss=0.1597, simple_loss=0.259, pruned_loss=0.0302, over 7310.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2595, pruned_loss=0.04037, over 1430141.94 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 17:07:23,707 INFO [train.py:842] (1/4) Epoch 38, batch 8100, loss[loss=0.1818, simple_loss=0.2672, pruned_loss=0.04818, over 7209.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2598, pruned_loss=0.04055, over 1428100.69 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 17:08:01,985 INFO [train.py:842] (1/4) Epoch 38, batch 8150, loss[loss=0.1879, simple_loss=0.2755, pruned_loss=0.05021, over 7194.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2617, pruned_loss=0.04134, over 1430584.69 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 17:08:39,921 INFO [train.py:842] (1/4) Epoch 38, batch 8200, loss[loss=0.1589, simple_loss=0.2407, pruned_loss=0.03851, over 7016.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2619, pruned_loss=0.04132, over 1426092.37 frames.], batch size: 16, lr: 1.44e-04
2022-05-29 17:09:18,220 INFO [train.py:842] (1/4) Epoch 38, batch 8250, loss[loss=0.1301, simple_loss=0.2177, pruned_loss=0.02124, over 6996.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2616, pruned_loss=0.04084, over 1425012.54 frames.], batch size: 16, lr: 1.44e-04
2022-05-29 17:09:56,313 INFO [train.py:842] (1/4) Epoch 38, batch 8300, loss[loss=0.3001, simple_loss=0.3647, pruned_loss=0.1178, over 7302.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2616, pruned_loss=0.04098, over 1426382.05 frames.], batch size: 25, lr: 1.44e-04
2022-05-29 17:10:34,659 INFO [train.py:842] (1/4) Epoch 38, batch 8350, loss[loss=0.1881, simple_loss=0.2772, pruned_loss=0.04948, over 7215.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2619, pruned_loss=0.0415, over 1426877.79 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 17:11:12,609 INFO [train.py:842] (1/4) Epoch 38, batch 8400, loss[loss=0.194, simple_loss=0.283, pruned_loss=0.05251, over 6815.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2606, pruned_loss=0.04061, over 1425829.41 frames.], batch size: 31, lr: 1.44e-04
2022-05-29 17:11:50,864 INFO [train.py:842] (1/4) Epoch 38, batch 8450, loss[loss=0.1805, simple_loss=0.2749, pruned_loss=0.04311, over 6735.00 frames.], tot_loss[loss=0.1717, simple_loss=0.261, pruned_loss=0.04121, over 1425409.22 frames.], batch size: 31, lr: 1.44e-04
2022-05-29 17:12:28,628 INFO [train.py:842] (1/4) Epoch 38, batch 8500, loss[loss=0.1703, simple_loss=0.2709, pruned_loss=0.03483, over 7329.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2617, pruned_loss=0.0416, over 1418326.17 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 17:13:06,637 INFO [train.py:842] (1/4) Epoch 38, batch 8550, loss[loss=0.152, simple_loss=0.2349, pruned_loss=0.03457, over 7123.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2628, pruned_loss=0.04214, over 1417899.47 frames.], batch size: 17, lr: 1.44e-04
2022-05-29 17:13:44,594 INFO [train.py:842] (1/4) Epoch 38, batch 8600, loss[loss=0.1337, simple_loss=0.2277, pruned_loss=0.01981, over 7159.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2618, pruned_loss=0.04138, over 1416565.39 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 17:14:22,659 INFO [train.py:842] (1/4) Epoch 38, batch 8650, loss[loss=0.1294, simple_loss=0.2102, pruned_loss=0.0243, over 7124.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2614, pruned_loss=0.04104, over 1415527.28 frames.], batch size: 17, lr: 1.44e-04
2022-05-29 17:15:00,649 INFO [train.py:842] (1/4) Epoch 38, batch 8700, loss[loss=0.1514, simple_loss=0.243, pruned_loss=0.0299, over 7326.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2595, pruned_loss=0.04045, over 1418165.75 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 17:15:38,993 INFO [train.py:842] (1/4) Epoch 38, batch 8750, loss[loss=0.1386, simple_loss=0.2146, pruned_loss=0.03132, over 7189.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2583, pruned_loss=0.03971, over 1414259.61 frames.], batch size: 16, lr: 1.44e-04
2022-05-29 17:16:17,262 INFO [train.py:842] (1/4) Epoch 38, batch 8800, loss[loss=0.1582, simple_loss=0.2488, pruned_loss=0.03377, over 7364.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2577, pruned_loss=0.03976, over 1412368.35 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 17:16:55,770 INFO [train.py:842] (1/4) Epoch 38, batch 8850, loss[loss=0.1764, simple_loss=0.2569, pruned_loss=0.04797, over 7423.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2578, pruned_loss=0.04003, over 1413121.49 frames.], batch size: 17, lr: 1.44e-04
2022-05-29 17:17:33,405 INFO [train.py:842] (1/4) Epoch 38, batch 8900, loss[loss=0.1694, simple_loss=0.2665, pruned_loss=0.03613, over 7418.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2592, pruned_loss=0.04004, over 1405373.77 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 17:18:11,734 INFO [train.py:842] (1/4) Epoch 38, batch 8950, loss[loss=0.1609, simple_loss=0.2452, pruned_loss=0.03831, over 7267.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2586, pruned_loss=0.0399, over 1405432.39 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 17:18:49,632 INFO [train.py:842] (1/4) Epoch 38, batch 9000, loss[loss=0.1658, simple_loss=0.2652, pruned_loss=0.03317, over 6397.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2574, pruned_loss=0.0396, over 1392737.10 frames.], batch size: 37, lr: 1.44e-04
2022-05-29 17:18:49,633 INFO [train.py:862] (1/4) Computing validation loss
2022-05-29 17:18:58,750 INFO [train.py:871] (1/4) Epoch 38, validation: loss=0.165, simple_loss=0.2618, pruned_loss=0.03413, over 868885.00 frames.
2022-05-29 17:19:36,246 INFO [train.py:842] (1/4) Epoch 38, batch 9050, loss[loss=0.1964, simple_loss=0.2781, pruned_loss=0.05731, over 5162.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2603, pruned_loss=0.04139, over 1356911.62 frames.], batch size: 52, lr: 1.44e-04
2022-05-29 17:20:12,625 INFO [train.py:842] (1/4) Epoch 38, batch 9100, loss[loss=0.2186, simple_loss=0.3043, pruned_loss=0.06648, over 5031.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2635, pruned_loss=0.0427, over 1305038.96 frames.], batch size: 52, lr: 1.44e-04
2022-05-29 17:20:49,734 INFO [train.py:842] (1/4) Epoch 38, batch 9150, loss[loss=0.2119, simple_loss=0.2922, pruned_loss=0.06582, over 5081.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2672, pruned_loss=0.04534, over 1240445.57 frames.], batch size: 52, lr: 1.44e-04
2022-05-29 17:21:35,364 INFO [train.py:842] (1/4) Epoch 39, batch 0, loss[loss=0.1731, simple_loss=0.2679, pruned_loss=0.03917, over 7250.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2679, pruned_loss=0.03917, over 7250.00 frames.], batch size: 19, lr: 1.42e-04
2022-05-29 17:22:13,622 INFO [train.py:842] (1/4) Epoch 39, batch 50, loss[loss=0.193, simple_loss=0.2795, pruned_loss=0.0532, over 7143.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2623, pruned_loss=0.04118, over 319882.97 frames.], batch size: 20, lr: 1.42e-04
2022-05-29 17:22:51,516 INFO [train.py:842] (1/4) Epoch 39, batch 100, loss[loss=0.1759, simple_loss=0.2666, pruned_loss=0.04263, over 6897.00 frames.], tot_loss[loss=0.1721, simple_loss=0.262, pruned_loss=0.04112, over 565450.66 frames.], batch size: 31, lr: 1.42e-04
2022-05-29 17:23:29,995 INFO [train.py:842] (1/4) Epoch 39, batch 150, loss[loss=0.1412, simple_loss=0.2264, pruned_loss=0.02801, over 7155.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2586, pruned_loss=0.04059, over 755148.16 frames.], batch size: 18, lr: 1.42e-04
2022-05-29 17:24:07,934 INFO [train.py:842] (1/4) Epoch 39, batch 200, loss[loss=0.1859, simple_loss=0.2807, pruned_loss=0.04553, over 7437.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2619, pruned_loss=0.04163, over 902196.52 frames.], batch size: 20, lr: 1.42e-04
2022-05-29 17:24:45,986 INFO [train.py:842] (1/4) Epoch 39, batch 250, loss[loss=0.1563, simple_loss=0.2534, pruned_loss=0.02961, over 6472.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2618, pruned_loss=0.04152, over 1017720.48 frames.], batch size: 38, lr: 1.42e-04
2022-05-29 17:25:24,155 INFO [train.py:842] (1/4) Epoch 39, batch 300, loss[loss=0.1976, simple_loss=0.2753, pruned_loss=0.06, over 7425.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2608, pruned_loss=0.04066, over 1113325.30 frames.], batch size: 20, lr: 1.42e-04
2022-05-29 17:26:02,388 INFO [train.py:842] (1/4) Epoch 39, batch 350, loss[loss=0.1719, simple_loss=0.265, pruned_loss=0.03938, over 7305.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2611, pruned_loss=0.04075, over 1179781.80 frames.], batch size: 24, lr: 1.42e-04
2022-05-29 17:26:40,283 INFO [train.py:842] (1/4) Epoch 39, batch 400, loss[loss=0.1889, simple_loss=0.2861, pruned_loss=0.04589, over 7219.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2612, pruned_loss=0.04087, over 1229057.92 frames.], batch size: 21, lr: 1.42e-04
2022-05-29 17:27:18,767 INFO [train.py:842] (1/4) Epoch 39, batch 450, loss[loss=0.1771, simple_loss=0.2661, pruned_loss=0.04405, over 7207.00 frames.], tot_loss[loss=0.171, simple_loss=0.2605, pruned_loss=0.04081, over 1274495.59 frames.], batch size: 23, lr: 1.42e-04
2022-05-29 17:27:56,685 INFO [train.py:842] (1/4) Epoch 39, batch 500, loss[loss=0.176, simple_loss=0.2753, pruned_loss=0.03836, over 7155.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2598, pruned_loss=0.04048, over 1301874.79 frames.], batch size: 20, lr: 1.42e-04
2022-05-29 17:28:35,071 INFO [train.py:842] (1/4) Epoch 39, batch 550, loss[loss=0.1908, simple_loss=0.2818, pruned_loss=0.0499, over 7424.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2597, pruned_loss=0.04096, over 1326993.26 frames.], batch size: 20, lr: 1.42e-04
2022-05-29 17:29:12,930 INFO [train.py:842] (1/4) Epoch 39, batch 600, loss[loss=0.2092, simple_loss=0.2974, pruned_loss=0.06046, over 7164.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2602, pruned_loss=0.04093, over 1345979.61 frames.], batch size: 18, lr: 1.42e-04
2022-05-29 17:29:51,404 INFO [train.py:842] (1/4) Epoch 39, batch 650, loss[loss=0.1297, simple_loss=0.2154, pruned_loss=0.02204, over 7270.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2587, pruned_loss=0.04015, over 1365005.22 frames.], batch size: 17, lr: 1.42e-04
2022-05-29 17:30:39,083 INFO [train.py:842] (1/4) Epoch 39, batch 700, loss[loss=0.172, simple_loss=0.2557, pruned_loss=0.04415, over 7227.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2573, pruned_loss=0.03965, over 1377859.32 frames.], batch size: 16, lr: 1.42e-04
2022-05-29 17:31:17,547 INFO [train.py:842] (1/4) Epoch 39, batch 750, loss[loss=0.1852, simple_loss=0.2748, pruned_loss=0.04778, over 6496.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2578, pruned_loss=0.04049, over 1386689.79 frames.], batch size: 37, lr: 1.42e-04
2022-05-29 17:31:55,666 INFO [train.py:842] (1/4) Epoch 39, batch 800, loss[loss=0.1795, simple_loss=0.2614, pruned_loss=0.0488, over 7234.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2586, pruned_loss=0.04042, over 1399510.98 frames.], batch size: 20, lr: 1.42e-04
2022-05-29 17:32:33,965 INFO [train.py:842] (1/4) Epoch 39, batch 850, loss[loss=0.1794, simple_loss=0.2765, pruned_loss=0.04111, over 7047.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2589, pruned_loss=0.04036, over 1405841.71 frames.], batch size: 28, lr: 1.42e-04
2022-05-29 17:33:11,861 INFO [train.py:842] (1/4) Epoch 39, batch 900, loss[loss=0.1784, simple_loss=0.2735, pruned_loss=0.04168, over 7411.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2588, pruned_loss=0.04018, over 1404519.40 frames.], batch size: 21, lr: 1.42e-04
2022-05-29 17:33:49,932 INFO [train.py:842] (1/4) Epoch 39, batch 950, loss[loss=0.1328, simple_loss=0.2184, pruned_loss=0.02363, over 7130.00 frames.], tot_loss[loss=0.1708, simple_loss=0.26, pruned_loss=0.04078, over 1405581.26 frames.], batch size: 17, lr: 1.42e-04
2022-05-29 17:34:28,091 INFO [train.py:842] (1/4) Epoch 39, batch 1000, loss[loss=0.2056, simple_loss=0.2767, pruned_loss=0.06722, over 7359.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2599, pruned_loss=0.04069, over 1408843.95 frames.], batch size: 19, lr: 1.42e-04
2022-05-29 17:35:06,299 INFO [train.py:842] (1/4) Epoch 39, batch 1050, loss[loss=0.205, simple_loss=0.3003, pruned_loss=0.05491, over 6821.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2594, pruned_loss=0.03996, over 1411074.75 frames.], batch size: 31, lr: 1.42e-04
2022-05-29 17:35:53,925 INFO [train.py:842] (1/4) Epoch 39, batch 1100, loss[loss=0.1839, simple_loss=0.2692, pruned_loss=0.04924, over 7377.00 frames.], tot_loss[loss=0.1694, simple_loss=0.259, pruned_loss=0.03987, over 1415985.14 frames.], batch size: 23, lr: 1.42e-04
2022-05-29 17:36:32,295 INFO [train.py:842] (1/4) Epoch 39, batch 1150, loss[loss=0.1571, simple_loss=0.2498, pruned_loss=0.03219, over 7271.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2579, pruned_loss=0.03977, over 1419474.28 frames.], batch size: 18, lr: 1.42e-04
2022-05-29 17:37:19,673 INFO [train.py:842] (1/4) Epoch 39, batch 1200, loss[loss=0.1908, simple_loss=0.2744, pruned_loss=0.0536, over 6824.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2596, pruned_loss=0.04128, over 1421124.97 frames.], batch size: 31, lr: 1.42e-04
2022-05-29 17:37:57,975 INFO [train.py:842] (1/4) Epoch 39, batch 1250, loss[loss=0.1549, simple_loss=0.2494, pruned_loss=0.03016, over 7417.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2599, pruned_loss=0.04091, over 1422504.86 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:38:36,169 INFO [train.py:842] (1/4) Epoch 39, batch 1300, loss[loss=0.1448, simple_loss=0.2268, pruned_loss=0.03142, over 7277.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2588, pruned_loss=0.04033, over 1425723.44 frames.], batch size: 17, lr: 1.41e-04
2022-05-29 17:39:14,322 INFO [train.py:842] (1/4) Epoch 39, batch 1350, loss[loss=0.1545, simple_loss=0.2494, pruned_loss=0.02979, over 7328.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2587, pruned_loss=0.03973, over 1425959.26 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:39:52,326 INFO [train.py:842] (1/4) Epoch 39, batch 1400, loss[loss=0.1623, simple_loss=0.2504, pruned_loss=0.03708, over 7167.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2589, pruned_loss=0.04009, over 1424634.43 frames.], batch size: 19, lr: 1.41e-04
2022-05-29 17:40:30,523 INFO [train.py:842] (1/4) Epoch 39, batch 1450, loss[loss=0.1609, simple_loss=0.2532, pruned_loss=0.03432, over 7313.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2604, pruned_loss=0.04064, over 1425339.83 frames.], batch size: 25, lr: 1.41e-04
2022-05-29 17:41:08,507 INFO [train.py:842] (1/4) Epoch 39, batch 1500, loss[loss=0.1432, simple_loss=0.2381, pruned_loss=0.02414, over 7117.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2614, pruned_loss=0.04101, over 1423564.35 frames.], batch size: 21, lr: 1.41e-04
2022-05-29 17:41:46,919 INFO [train.py:842] (1/4) Epoch 39, batch 1550, loss[loss=0.1914, simple_loss=0.285, pruned_loss=0.04888, over 7207.00 frames.], tot_loss[loss=0.1717, simple_loss=0.261, pruned_loss=0.04116, over 1423563.69 frames.], batch size: 22, lr: 1.41e-04
2022-05-29 17:42:25,011 INFO [train.py:842] (1/4) Epoch 39, batch 1600, loss[loss=0.186, simple_loss=0.272, pruned_loss=0.04999, over 6740.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2594, pruned_loss=0.04018, over 1425383.78 frames.], batch size: 31, lr: 1.41e-04
2022-05-29 17:43:03,329 INFO [train.py:842] (1/4) Epoch 39, batch 1650, loss[loss=0.1816, simple_loss=0.2705, pruned_loss=0.04637, over 7222.00 frames.], tot_loss[loss=0.1696, simple_loss=0.259, pruned_loss=0.04008, over 1424352.81 frames.], batch size: 21, lr: 1.41e-04
2022-05-29 17:43:41,228 INFO [train.py:842] (1/4) Epoch 39, batch 1700, loss[loss=0.1774, simple_loss=0.2713, pruned_loss=0.04178, over 6988.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2591, pruned_loss=0.04004, over 1425736.01 frames.], batch size: 28, lr: 1.41e-04
2022-05-29 17:44:19,444 INFO [train.py:842] (1/4) Epoch 39, batch 1750, loss[loss=0.159, simple_loss=0.2542, pruned_loss=0.03193, over 7426.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2596, pruned_loss=0.04006, over 1425192.20 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:44:57,270 INFO [train.py:842] (1/4) Epoch 39, batch 1800, loss[loss=0.172, simple_loss=0.2637, pruned_loss=0.04021, over 7210.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2594, pruned_loss=0.03977, over 1422915.75 frames.], batch size: 23, lr: 1.41e-04
2022-05-29 17:45:35,466 INFO [train.py:842] (1/4) Epoch 39, batch 1850, loss[loss=0.1517, simple_loss=0.2324, pruned_loss=0.0355, over 7162.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2588, pruned_loss=0.03938, over 1421153.44 frames.], batch size: 19, lr: 1.41e-04
2022-05-29 17:46:13,562 INFO [train.py:842] (1/4) Epoch 39, batch 1900, loss[loss=0.1663, simple_loss=0.2568, pruned_loss=0.03791, over 7276.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2592, pruned_loss=0.04, over 1424609.90 frames.], batch size: 18, lr: 1.41e-04
2022-05-29 17:46:51,819 INFO [train.py:842] (1/4) Epoch 39, batch 1950, loss[loss=0.1588, simple_loss=0.2529, pruned_loss=0.0323, over 7321.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2593, pruned_loss=0.03977, over 1424422.21 frames.], batch size: 21, lr: 1.41e-04
2022-05-29 17:47:29,743 INFO [train.py:842] (1/4) Epoch 39, batch 2000, loss[loss=0.1461, simple_loss=0.243, pruned_loss=0.02456, over 7262.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2598, pruned_loss=0.03983, over 1423343.44 frames.], batch size: 19, lr: 1.41e-04
2022-05-29 17:48:07,766 INFO [train.py:842] (1/4) Epoch 39, batch 2050, loss[loss=0.179, simple_loss=0.2755, pruned_loss=0.0412, over 7327.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2608, pruned_loss=0.04, over 1421471.51 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:48:45,822 INFO [train.py:842] (1/4) Epoch 39, batch 2100, loss[loss=0.1357, simple_loss=0.2189, pruned_loss=0.02626, over 6799.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2599, pruned_loss=0.03971, over 1421904.64 frames.], batch size: 15, lr: 1.41e-04
2022-05-29 17:49:24,064 INFO [train.py:842] (1/4) Epoch 39, batch 2150, loss[loss=0.134, simple_loss=0.2237, pruned_loss=0.02218, over 7259.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2596, pruned_loss=0.03965, over 1419584.44 frames.], batch size: 19, lr: 1.41e-04
2022-05-29 17:50:01,948 INFO [train.py:842] (1/4) Epoch 39, batch 2200, loss[loss=0.1583, simple_loss=0.2529, pruned_loss=0.03187, over 7189.00 frames.], tot_loss[loss=0.17, simple_loss=0.2603, pruned_loss=0.03989, over 1420414.77 frames.], batch size: 22, lr: 1.41e-04
2022-05-29 17:50:40,598 INFO [train.py:842] (1/4) Epoch 39, batch 2250, loss[loss=0.1681, simple_loss=0.2623, pruned_loss=0.03694, over 7143.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2589, pruned_loss=0.03962, over 1423113.68 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:51:18,402 INFO [train.py:842] (1/4) Epoch 39, batch 2300, loss[loss=0.2328, simple_loss=0.3178, pruned_loss=0.07391, over 7157.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2601, pruned_loss=0.04011, over 1423177.94 frames.], batch size: 19, lr: 1.41e-04
2022-05-29 17:51:56,797 INFO [train.py:842] (1/4) Epoch 39, batch 2350, loss[loss=0.1681, simple_loss=0.2595, pruned_loss=0.03833, over 7238.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2593, pruned_loss=0.03987, over 1424991.83 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:52:34,836 INFO [train.py:842] (1/4) Epoch 39, batch 2400, loss[loss=0.1918, simple_loss=0.2817, pruned_loss=0.05094, over 7139.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2589, pruned_loss=0.03993, over 1427827.75 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:53:13,352 INFO [train.py:842] (1/4) Epoch 39, batch 2450, loss[loss=0.152, simple_loss=0.2347, pruned_loss=0.03472, over 7401.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2577, pruned_loss=0.03951, over 1428568.11 frames.], batch size: 18, lr: 1.41e-04
2022-05-29 17:53:51,364 INFO [train.py:842] (1/4) Epoch 39, batch 2500, loss[loss=0.1446, simple_loss=0.2316, pruned_loss=0.02883, over 7424.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2575, pruned_loss=0.03961, over 1427328.95 frames.], batch size: 18, lr: 1.41e-04
2022-05-29 17:54:29,852 INFO [train.py:842] (1/4) Epoch 39, batch 2550, loss[loss=0.1544, simple_loss=0.2503, pruned_loss=0.02926, over 7434.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2583, pruned_loss=0.03992, over 1431729.77 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:55:07,764 INFO [train.py:842] (1/4) Epoch 39, batch 2600, loss[loss=0.162, simple_loss=0.2537, pruned_loss=0.03516, over 7195.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2588, pruned_loss=0.04011, over 1429501.32 frames.], batch size: 26, lr: 1.41e-04
2022-05-29 17:55:46,324 INFO [train.py:842] (1/4) Epoch 39, batch 2650, loss[loss=0.1669, simple_loss=0.2587, pruned_loss=0.03751, over 7142.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2585, pruned_loss=0.03986, over 1431593.38 frames.], batch size: 28, lr: 1.41e-04
2022-05-29 17:56:24,502 INFO [train.py:842] (1/4) Epoch 39, batch 2700, loss[loss=0.1413, simple_loss=0.2375, pruned_loss=0.02248, over 7290.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2585, pruned_loss=0.04002, over 1429382.99 frames.], batch size: 25, lr: 1.41e-04
2022-05-29 17:57:06,510 INFO [train.py:842] (1/4) Epoch 39, batch 2750, loss[loss=0.1712, simple_loss=0.2514, pruned_loss=0.04549, over 7164.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2582, pruned_loss=0.03974, over 1429098.14 frames.], batch size: 19, lr: 1.41e-04
2022-05-29 17:57:44,935 INFO [train.py:842] (1/4) Epoch 39, batch 2800, loss[loss=0.166, simple_loss=0.2719, pruned_loss=0.03004, over 7331.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2598, pruned_loss=0.04019, over 1427298.61 frames.], batch size: 22, lr: 1.41e-04
2022-05-29 17:58:23,863 INFO [train.py:842] (1/4) Epoch 39, batch 2850, loss[loss=0.1819, simple_loss=0.2815, pruned_loss=0.04115, over 6226.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2607, pruned_loss=0.04058, over 1426966.59 frames.], batch size: 37, lr: 1.41e-04