2022-05-26 15:07:31,889 INFO [train.py:906] (2/4) Training started
2022-05-26 15:07:31,889 INFO [train.py:916] (2/4) Device: cuda:2
2022-05-26 15:07:31,893 INFO [train.py:934] (2/4) {'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] (2/4) About to create model
2022-05-26 15:07:32,326 INFO [train.py:940] (2/4) Number of model parameters: 78648040
2022-05-26 15:07:32,326 INFO [checkpoint.py:112] (2/4) Loading checkpoint from streaming_pruned_transducer_stateless4/exp/epoch-1.pt
2022-05-26 15:07:44,568 INFO [train.py:955] (2/4) Using DDP
2022-05-26 15:07:44,729 INFO [train.py:963] (2/4) Loading optimizer state dict
2022-05-26 15:07:45,495 INFO [train.py:971] (2/4) Loading scheduler state dict
2022-05-26 15:07:45,495 INFO [asr_datamodule.py:391] (2/4) About to get train-clean-100 cuts
2022-05-26 15:07:51,861 INFO [asr_datamodule.py:398] (2/4) About to get train-clean-360 cuts
2022-05-26 15:08:18,864 INFO [asr_datamodule.py:405] (2/4) About to get train-other-500 cuts
2022-05-26 15:09:04,354 INFO [asr_datamodule.py:209] (2/4) Enable MUSAN
2022-05-26 15:09:04,354 INFO [asr_datamodule.py:210] (2/4) About to get Musan cuts
2022-05-26 15:09:05,788 INFO [asr_datamodule.py:238] (2/4) Enable SpecAugment
2022-05-26 15:09:05,788 INFO [asr_datamodule.py:239] (2/4) Time warp factor: 80
2022-05-26 15:09:05,788 INFO [asr_datamodule.py:251] (2/4) Num frame mask: 10
2022-05-26 15:09:05,789 INFO [asr_datamodule.py:264] (2/4) About to create train dataset
2022-05-26 15:09:05,789 INFO [asr_datamodule.py:292] (2/4) Using BucketingSampler.
2022-05-26 15:09:10,981 INFO [asr_datamodule.py:308] (2/4) About to create train dataloader
2022-05-26 15:09:10,982 INFO [asr_datamodule.py:412] (2/4) About to get dev-clean cuts
2022-05-26 15:09:11,273 INFO [asr_datamodule.py:417] (2/4) About to get dev-other cuts
2022-05-26 15:09:11,422 INFO [asr_datamodule.py:339] (2/4) About to create dev dataset
2022-05-26 15:09:11,435 INFO [asr_datamodule.py:358] (2/4) About to create dev dataloader
2022-05-26 15:09:11,436 INFO [train.py:1082] (2/4) 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] (2/4) Reducer buckets have been rebuilt in this iteration.
2022-05-26 15:09:26,258 INFO [train.py:1023] (2/4) Loading grad scaler state dict
2022-05-26 15:09:38,896 INFO [train.py:842] (2/4) Epoch 2, batch 0, loss[loss=0.4601, simple_loss=0.4367, pruned_loss=0.2417, over 7186.00 frames.], tot_loss[loss=0.4601, simple_loss=0.4367, pruned_loss=0.2417, over 7186.00 frames.], batch size: 26, lr: 2.06e-03
2022-05-26 15:10:18,169 INFO [train.py:842] (2/4) Epoch 2, batch 50, loss[loss=0.3384, simple_loss=0.3769, pruned_loss=0.15, over 7234.00 frames.], tot_loss[loss=0.3604, simple_loss=0.3906, pruned_loss=0.1651, over 312324.82 frames.], batch size: 20, lr: 2.06e-03
2022-05-26 15:10:57,099 INFO [train.py:842] (2/4) Epoch 2, batch 100, loss[loss=0.3225, simple_loss=0.3649, pruned_loss=0.14, over 7427.00 frames.], tot_loss[loss=0.355, simple_loss=0.3866, pruned_loss=0.1617, over 560210.94 frames.], batch size: 20, lr: 2.05e-03
2022-05-26 15:11:35,985 INFO [train.py:842] (2/4) Epoch 2, batch 150, loss[loss=0.2762, simple_loss=0.3476, pruned_loss=0.1024, over 7327.00 frames.], tot_loss[loss=0.3476, simple_loss=0.3819, pruned_loss=0.1566, over 751075.15 frames.], batch size: 20, lr: 2.05e-03
2022-05-26 15:12:14,710 INFO [train.py:842] (2/4) Epoch 2, batch 200, loss[loss=0.4148, simple_loss=0.4073, pruned_loss=0.2112, over 7166.00 frames.], tot_loss[loss=0.3433, simple_loss=0.3791, pruned_loss=0.1538, over 901016.37 frames.], batch size: 19, lr: 2.04e-03
2022-05-26 15:12:53,665 INFO [train.py:842] (2/4) Epoch 2, batch 250, loss[loss=0.2871, simple_loss=0.348, pruned_loss=0.1131, over 7387.00 frames.], tot_loss[loss=0.3391, simple_loss=0.3763, pruned_loss=0.151, over 1015787.87 frames.], batch size: 23, lr: 2.04e-03
2022-05-26 15:13:32,390 INFO [train.py:842] (2/4) Epoch 2, batch 300, loss[loss=0.3118, simple_loss=0.3652, pruned_loss=0.1291, over 7270.00 frames.], tot_loss[loss=0.3399, simple_loss=0.3774, pruned_loss=0.1512, over 1104881.58 frames.], batch size: 19, lr: 2.03e-03
2022-05-26 15:14:11,456 INFO [train.py:842] (2/4) Epoch 2, batch 350, loss[loss=0.3663, simple_loss=0.396, pruned_loss=0.1683, over 7218.00 frames.], tot_loss[loss=0.3374, simple_loss=0.3756, pruned_loss=0.1496, over 1174002.75 frames.], batch size: 21, lr: 2.03e-03
2022-05-26 15:14:50,190 INFO [train.py:842] (2/4) Epoch 2, batch 400, loss[loss=0.3918, simple_loss=0.4053, pruned_loss=0.1892, over 7142.00 frames.], tot_loss[loss=0.3394, simple_loss=0.3769, pruned_loss=0.151, over 1230889.42 frames.], batch size: 20, lr: 2.03e-03
2022-05-26 15:15:28,837 INFO [train.py:842] (2/4) Epoch 2, batch 450, loss[loss=0.3, simple_loss=0.3483, pruned_loss=0.1259, over 7159.00 frames.], tot_loss[loss=0.3391, simple_loss=0.3772, pruned_loss=0.1506, over 1276071.47 frames.], batch size: 19, lr: 2.02e-03
2022-05-26 15:16:07,189 INFO [train.py:842] (2/4) Epoch 2, batch 500, loss[loss=0.2909, simple_loss=0.3436, pruned_loss=0.1191, over 7183.00 frames.], tot_loss[loss=0.3383, simple_loss=0.377, pruned_loss=0.1498, over 1307951.72 frames.], batch size: 18, lr: 2.02e-03
2022-05-26 15:16:46,576 INFO [train.py:842] (2/4) Epoch 2, batch 550, loss[loss=0.2952, simple_loss=0.364, pruned_loss=0.1132, over 7347.00 frames.], tot_loss[loss=0.3374, simple_loss=0.3764, pruned_loss=0.1492, over 1332808.02 frames.], batch size: 19, lr: 2.01e-03
2022-05-26 15:17:25,157 INFO [train.py:842] (2/4) Epoch 2, batch 600, loss[loss=0.332, simple_loss=0.3828, pruned_loss=0.1405, over 7398.00 frames.], tot_loss[loss=0.3393, simple_loss=0.378, pruned_loss=0.1503, over 1354032.62 frames.], batch size: 23, lr: 2.01e-03
2022-05-26 15:18:04,044 INFO [train.py:842] (2/4) Epoch 2, batch 650, loss[loss=0.2787, simple_loss=0.3344, pruned_loss=0.1115, over 7271.00 frames.], tot_loss[loss=0.3366, simple_loss=0.3758, pruned_loss=0.1487, over 1368213.90 frames.], batch size: 18, lr: 2.01e-03
2022-05-26 15:18:42,824 INFO [train.py:842] (2/4) Epoch 2, batch 700, loss[loss=0.4193, simple_loss=0.419, pruned_loss=0.2098, over 5203.00 frames.], tot_loss[loss=0.3346, simple_loss=0.3745, pruned_loss=0.1473, over 1380117.64 frames.], batch size: 53, lr: 2.00e-03
2022-05-26 15:19:21,647 INFO [train.py:842] (2/4) Epoch 2, batch 750, loss[loss=0.2777, simple_loss=0.3339, pruned_loss=0.1108, over 7255.00 frames.], tot_loss[loss=0.3348, simple_loss=0.3745, pruned_loss=0.1475, over 1391635.39 frames.], batch size: 19, lr: 2.00e-03
2022-05-26 15:20:00,318 INFO [train.py:842] (2/4) Epoch 2, batch 800, loss[loss=0.2865, simple_loss=0.3403, pruned_loss=0.1164, over 7060.00 frames.], tot_loss[loss=0.3344, simple_loss=0.3742, pruned_loss=0.1473, over 1401356.14 frames.], batch size: 18, lr: 1.99e-03
2022-05-26 15:20:39,193 INFO [train.py:842] (2/4) Epoch 2, batch 850, loss[loss=0.4028, simple_loss=0.4187, pruned_loss=0.1935, over 7329.00 frames.], tot_loss[loss=0.3307, simple_loss=0.3717, pruned_loss=0.1448, over 1408430.79 frames.], batch size: 20, lr: 1.99e-03
2022-05-26 15:21:17,873 INFO [train.py:842] (2/4) Epoch 2, batch 900, loss[loss=0.3181, simple_loss=0.3662, pruned_loss=0.135, over 7428.00 frames.], tot_loss[loss=0.3324, simple_loss=0.3728, pruned_loss=0.146, over 1412458.51 frames.], batch size: 20, lr: 1.99e-03
2022-05-26 15:21:56,750 INFO [train.py:842] (2/4) Epoch 2, batch 950, loss[loss=0.2709, simple_loss=0.3382, pruned_loss=0.1018, over 7251.00 frames.], tot_loss[loss=0.3323, simple_loss=0.373, pruned_loss=0.1458, over 1415187.68 frames.], batch size: 19, lr: 1.98e-03
2022-05-26 15:22:35,346 INFO [train.py:842] (2/4) Epoch 2, batch 1000, loss[loss=0.3714, simple_loss=0.4127, pruned_loss=0.1651, over 6851.00 frames.], tot_loss[loss=0.3323, simple_loss=0.3735, pruned_loss=0.1455, over 1417102.04 frames.], batch size: 31, lr: 1.98e-03
2022-05-26 15:23:14,144 INFO [train.py:842] (2/4) Epoch 2, batch 1050, loss[loss=0.3109, simple_loss=0.3641, pruned_loss=0.1289, over 7428.00 frames.], tot_loss[loss=0.3329, simple_loss=0.374, pruned_loss=0.1459, over 1419696.01 frames.], batch size: 20, lr: 1.97e-03
2022-05-26 15:23:52,746 INFO [train.py:842] (2/4) Epoch 2, batch 1100, loss[loss=0.2895, simple_loss=0.3401, pruned_loss=0.1195, over 7171.00 frames.], tot_loss[loss=0.3354, simple_loss=0.376, pruned_loss=0.1475, over 1421020.59 frames.], batch size: 18, lr: 1.97e-03
2022-05-26 15:24:32,108 INFO [train.py:842] (2/4) Epoch 2, batch 1150, loss[loss=0.3581, simple_loss=0.3927, pruned_loss=0.1618, over 7234.00 frames.], tot_loss[loss=0.3331, simple_loss=0.3739, pruned_loss=0.1461, over 1424918.57 frames.], batch size: 20, lr: 1.97e-03
2022-05-26 15:25:10,577 INFO [train.py:842] (2/4) Epoch 2, batch 1200, loss[loss=0.3335, simple_loss=0.3619, pruned_loss=0.1526, over 7012.00 frames.], tot_loss[loss=0.3324, simple_loss=0.3738, pruned_loss=0.1455, over 1423602.27 frames.], batch size: 28, lr: 1.96e-03
2022-05-26 15:25:49,492 INFO [train.py:842] (2/4) Epoch 2, batch 1250, loss[loss=0.2465, simple_loss=0.3045, pruned_loss=0.09423, over 7284.00 frames.], tot_loss[loss=0.3319, simple_loss=0.3735, pruned_loss=0.1452, over 1423002.53 frames.], batch size: 18, lr: 1.96e-03
2022-05-26 15:26:28,034 INFO [train.py:842] (2/4) Epoch 2, batch 1300, loss[loss=0.3053, simple_loss=0.3654, pruned_loss=0.1226, over 7212.00 frames.], tot_loss[loss=0.3325, simple_loss=0.3736, pruned_loss=0.1457, over 1416893.03 frames.], batch size: 21, lr: 1.95e-03
2022-05-26 15:27:06,801 INFO [train.py:842] (2/4) Epoch 2, batch 1350, loss[loss=0.3823, simple_loss=0.3844, pruned_loss=0.1902, over 7277.00 frames.], tot_loss[loss=0.3325, simple_loss=0.3735, pruned_loss=0.1458, over 1419980.20 frames.], batch size: 17, lr: 1.95e-03
2022-05-26 15:27:45,172 INFO [train.py:842] (2/4) Epoch 2, batch 1400, loss[loss=0.3413, simple_loss=0.3693, pruned_loss=0.1567, over 7209.00 frames.], tot_loss[loss=0.3333, simple_loss=0.374, pruned_loss=0.1463, over 1417966.83 frames.], batch size: 21, lr: 1.95e-03
2022-05-26 15:28:24,275 INFO [train.py:842] (2/4) Epoch 2, batch 1450, loss[loss=0.3869, simple_loss=0.4243, pruned_loss=0.1748, over 7187.00 frames.], tot_loss[loss=0.3317, simple_loss=0.3727, pruned_loss=0.1454, over 1421816.97 frames.], batch size: 26, lr: 1.94e-03
2022-05-26 15:29:02,868 INFO [train.py:842] (2/4) Epoch 2, batch 1500, loss[loss=0.3975, simple_loss=0.4147, pruned_loss=0.1901, over 6554.00 frames.], tot_loss[loss=0.3317, simple_loss=0.3726, pruned_loss=0.1454, over 1421318.16 frames.], batch size: 38, lr: 1.94e-03
2022-05-26 15:29:41,824 INFO [train.py:842] (2/4) Epoch 2, batch 1550, loss[loss=0.3319, simple_loss=0.3843, pruned_loss=0.1397, over 7422.00 frames.], tot_loss[loss=0.3295, simple_loss=0.3714, pruned_loss=0.1438, over 1424804.86 frames.], batch size: 20, lr: 1.94e-03
2022-05-26 15:30:20,570 INFO [train.py:842] (2/4) Epoch 2, batch 1600, loss[loss=0.2971, simple_loss=0.351, pruned_loss=0.1216, over 7178.00 frames.], tot_loss[loss=0.3268, simple_loss=0.3693, pruned_loss=0.1421, over 1424087.19 frames.], batch size: 18, lr: 1.93e-03
2022-05-26 15:30:59,403 INFO [train.py:842] (2/4) Epoch 2, batch 1650, loss[loss=0.3183, simple_loss=0.3669, pruned_loss=0.1349, over 7427.00 frames.], tot_loss[loss=0.3269, simple_loss=0.3699, pruned_loss=0.1419, over 1423667.95 frames.], batch size: 20, lr: 1.93e-03
2022-05-26 15:31:38,107 INFO [train.py:842] (2/4) Epoch 2, batch 1700, loss[loss=0.3579, simple_loss=0.3969, pruned_loss=0.1594, over 7416.00 frames.], tot_loss[loss=0.327, simple_loss=0.3702, pruned_loss=0.1419, over 1422450.54 frames.], batch size: 21, lr: 1.92e-03
2022-05-26 15:32:16,707 INFO [train.py:842] (2/4) Epoch 2, batch 1750, loss[loss=0.2957, simple_loss=0.3469, pruned_loss=0.1223, over 7278.00 frames.], tot_loss[loss=0.3275, simple_loss=0.371, pruned_loss=0.142, over 1422469.04 frames.], batch size: 18, lr: 1.92e-03
2022-05-26 15:32:55,310 INFO [train.py:842] (2/4) Epoch 2, batch 1800, loss[loss=0.3592, simple_loss=0.3844, pruned_loss=0.167, over 7349.00 frames.], tot_loss[loss=0.3278, simple_loss=0.3713, pruned_loss=0.1422, over 1424032.29 frames.], batch size: 19, lr: 1.92e-03
2022-05-26 15:33:34,109 INFO [train.py:842] (2/4) Epoch 2, batch 1850, loss[loss=0.3127, simple_loss=0.3718, pruned_loss=0.1268, over 7309.00 frames.], tot_loss[loss=0.324, simple_loss=0.3682, pruned_loss=0.1399, over 1424356.17 frames.], batch size: 20, lr: 1.91e-03
2022-05-26 15:34:12,726 INFO [train.py:842] (2/4) Epoch 2, batch 1900, loss[loss=0.283, simple_loss=0.3208, pruned_loss=0.1226, over 6992.00 frames.], tot_loss[loss=0.3249, simple_loss=0.3692, pruned_loss=0.1403, over 1428315.37 frames.], batch size: 16, lr: 1.91e-03
2022-05-26 15:34:51,938 INFO [train.py:842] (2/4) Epoch 2, batch 1950, loss[loss=0.4175, simple_loss=0.4204, pruned_loss=0.2073, over 7296.00 frames.], tot_loss[loss=0.3269, simple_loss=0.3709, pruned_loss=0.1415, over 1428509.27 frames.], batch size: 18, lr: 1.91e-03
2022-05-26 15:35:30,331 INFO [train.py:842] (2/4) Epoch 2, batch 2000, loss[loss=0.3454, simple_loss=0.3763, pruned_loss=0.1573, over 7107.00 frames.], tot_loss[loss=0.3318, simple_loss=0.3744, pruned_loss=0.1446, over 1423005.87 frames.], batch size: 21, lr: 1.90e-03
2022-05-26 15:36:09,248 INFO [train.py:842] (2/4) Epoch 2, batch 2050, loss[loss=0.3529, simple_loss=0.3884, pruned_loss=0.1587, over 7097.00 frames.], tot_loss[loss=0.3294, simple_loss=0.3725, pruned_loss=0.1432, over 1423932.53 frames.], batch size: 28, lr: 1.90e-03
2022-05-26 15:36:48,111 INFO [train.py:842] (2/4) Epoch 2, batch 2100, loss[loss=0.3105, simple_loss=0.354, pruned_loss=0.1335, over 7410.00 frames.], tot_loss[loss=0.3301, simple_loss=0.3726, pruned_loss=0.1438, over 1424634.48 frames.], batch size: 18, lr: 1.90e-03
2022-05-26 15:37:27,086 INFO [train.py:842] (2/4) Epoch 2, batch 2150, loss[loss=0.3377, simple_loss=0.3875, pruned_loss=0.144, over 7406.00 frames.], tot_loss[loss=0.3298, simple_loss=0.372, pruned_loss=0.1438, over 1423971.09 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 15:38:05,651 INFO [train.py:842] (2/4) Epoch 2, batch 2200, loss[loss=0.3162, simple_loss=0.3682, pruned_loss=0.1321, over 7109.00 frames.], tot_loss[loss=0.3271, simple_loss=0.3699, pruned_loss=0.1421, over 1423301.29 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 15:38:44,472 INFO [train.py:842] (2/4) Epoch 2, batch 2250, loss[loss=0.3055, simple_loss=0.3602, pruned_loss=0.1254, over 7215.00 frames.], tot_loss[loss=0.3247, simple_loss=0.3681, pruned_loss=0.1406, over 1424822.51 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 15:39:23,293 INFO [train.py:842] (2/4) Epoch 2, batch 2300, loss[loss=0.3135, simple_loss=0.3645, pruned_loss=0.1312, over 7200.00 frames.], tot_loss[loss=0.3208, simple_loss=0.366, pruned_loss=0.1379, over 1424963.61 frames.], batch size: 22, lr: 1.88e-03
2022-05-26 15:40:02,249 INFO [train.py:842] (2/4) Epoch 2, batch 2350, loss[loss=0.3688, simple_loss=0.402, pruned_loss=0.1678, over 7229.00 frames.], tot_loss[loss=0.3215, simple_loss=0.3661, pruned_loss=0.1384, over 1422108.61 frames.], batch size: 20, lr: 1.88e-03
2022-05-26 15:40:40,746 INFO [train.py:842] (2/4) Epoch 2, batch 2400, loss[loss=0.3044, simple_loss=0.3608, pruned_loss=0.124, over 7319.00 frames.], tot_loss[loss=0.3215, simple_loss=0.3661, pruned_loss=0.1385, over 1421868.25 frames.], batch size: 21, lr: 1.87e-03
2022-05-26 15:41:19,576 INFO [train.py:842] (2/4) Epoch 2, batch 2450, loss[loss=0.3092, simple_loss=0.355, pruned_loss=0.1317, over 7312.00 frames.], tot_loss[loss=0.3232, simple_loss=0.3678, pruned_loss=0.1393, over 1425775.44 frames.], batch size: 21, lr: 1.87e-03
2022-05-26 15:41:58,101 INFO [train.py:842] (2/4) Epoch 2, batch 2500, loss[loss=0.3778, simple_loss=0.4041, pruned_loss=0.1758, over 7141.00 frames.], tot_loss[loss=0.3224, simple_loss=0.3676, pruned_loss=0.1386, over 1426143.21 frames.], batch size: 26, lr: 1.87e-03
2022-05-26 15:42:36,842 INFO [train.py:842] (2/4) Epoch 2, batch 2550, loss[loss=0.2938, simple_loss=0.3295, pruned_loss=0.129, over 6989.00 frames.], tot_loss[loss=0.3221, simple_loss=0.3669, pruned_loss=0.1387, over 1426357.67 frames.], batch size: 16, lr: 1.86e-03
2022-05-26 15:43:15,430 INFO [train.py:842] (2/4) Epoch 2, batch 2600, loss[loss=0.3533, simple_loss=0.397, pruned_loss=0.1548, over 7134.00 frames.], tot_loss[loss=0.3215, simple_loss=0.3662, pruned_loss=0.1384, over 1428748.18 frames.], batch size: 26, lr: 1.86e-03
2022-05-26 15:43:54,089 INFO [train.py:842] (2/4) Epoch 2, batch 2650, loss[loss=0.3395, simple_loss=0.3842, pruned_loss=0.1474, over 6666.00 frames.], tot_loss[loss=0.3206, simple_loss=0.3655, pruned_loss=0.1378, over 1427821.12 frames.], batch size: 38, lr: 1.86e-03
2022-05-26 15:44:32,731 INFO [train.py:842] (2/4) Epoch 2, batch 2700, loss[loss=0.3399, simple_loss=0.3844, pruned_loss=0.1477, over 6721.00 frames.], tot_loss[loss=0.3198, simple_loss=0.3652, pruned_loss=0.1372, over 1427350.30 frames.], batch size: 31, lr: 1.85e-03
2022-05-26 15:45:11,858 INFO [train.py:842] (2/4) Epoch 2, batch 2750, loss[loss=0.4782, simple_loss=0.483, pruned_loss=0.2367, over 7299.00 frames.], tot_loss[loss=0.3215, simple_loss=0.3662, pruned_loss=0.1384, over 1424020.04 frames.], batch size: 24, lr: 1.85e-03
2022-05-26 15:45:50,240 INFO [train.py:842] (2/4) Epoch 2, batch 2800, loss[loss=0.2976, simple_loss=0.3541, pruned_loss=0.1205, over 7211.00 frames.], tot_loss[loss=0.3215, simple_loss=0.3662, pruned_loss=0.1384, over 1427283.47 frames.], batch size: 23, lr: 1.85e-03
2022-05-26 15:46:29,150 INFO [train.py:842] (2/4) Epoch 2, batch 2850, loss[loss=0.3155, simple_loss=0.3714, pruned_loss=0.1298, over 7275.00 frames.], tot_loss[loss=0.3222, simple_loss=0.367, pruned_loss=0.1387, over 1426634.68 frames.], batch size: 24, lr: 1.84e-03
2022-05-26 15:47:07,589 INFO [train.py:842] (2/4) Epoch 2, batch 2900, loss[loss=0.3787, simple_loss=0.4105, pruned_loss=0.1735, over 7229.00 frames.], tot_loss[loss=0.3233, simple_loss=0.368, pruned_loss=0.1393, over 1422140.75 frames.], batch size: 20, lr: 1.84e-03
2022-05-26 15:47:46,390 INFO [train.py:842] (2/4) Epoch 2, batch 2950, loss[loss=0.2852, simple_loss=0.3515, pruned_loss=0.1095, over 7230.00 frames.], tot_loss[loss=0.3241, simple_loss=0.3685, pruned_loss=0.1399, over 1423672.06 frames.], batch size: 20, lr: 1.84e-03
2022-05-26 15:48:24,992 INFO [train.py:842] (2/4) Epoch 2, batch 3000, loss[loss=0.2512, simple_loss=0.3107, pruned_loss=0.09587, over 7275.00 frames.], tot_loss[loss=0.32, simple_loss=0.3657, pruned_loss=0.1372, over 1426973.15 frames.], batch size: 17, lr: 1.84e-03
2022-05-26 15:48:24,993 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 15:48:34,574 INFO [train.py:871] (2/4) Epoch 2, validation: loss=0.2365, simple_loss=0.3276, pruned_loss=0.07266, over 868885.00 frames.
2022-05-26 15:49:14,141 INFO [train.py:842] (2/4) Epoch 2, batch 3050, loss[loss=0.2692, simple_loss=0.3266, pruned_loss=0.1059, over 7281.00 frames.], tot_loss[loss=0.3217, simple_loss=0.3669, pruned_loss=0.1383, over 1421864.65 frames.], batch size: 18, lr: 1.83e-03
2022-05-26 15:49:52,553 INFO [train.py:842] (2/4) Epoch 2, batch 3100, loss[loss=0.4172, simple_loss=0.4295, pruned_loss=0.2025, over 5245.00 frames.], tot_loss[loss=0.3221, simple_loss=0.3671, pruned_loss=0.1385, over 1422048.19 frames.], batch size: 52, lr: 1.83e-03
2022-05-26 15:50:31,849 INFO [train.py:842] (2/4) Epoch 2, batch 3150, loss[loss=0.2451, simple_loss=0.2942, pruned_loss=0.098, over 6783.00 frames.], tot_loss[loss=0.3199, simple_loss=0.366, pruned_loss=0.1369, over 1424231.86 frames.], batch size: 15, lr: 1.83e-03
2022-05-26 15:51:10,292 INFO [train.py:842] (2/4) Epoch 2, batch 3200, loss[loss=0.3646, simple_loss=0.3976, pruned_loss=0.1658, over 5110.00 frames.], tot_loss[loss=0.3217, simple_loss=0.3678, pruned_loss=0.1378, over 1414187.62 frames.], batch size: 52, lr: 1.82e-03
2022-05-26 15:51:49,106 INFO [train.py:842] (2/4) Epoch 2, batch 3250, loss[loss=0.2911, simple_loss=0.3548, pruned_loss=0.1138, over 7189.00 frames.], tot_loss[loss=0.3232, simple_loss=0.3686, pruned_loss=0.1389, over 1416487.06 frames.], batch size: 23, lr: 1.82e-03
2022-05-26 15:52:27,661 INFO [train.py:842] (2/4) Epoch 2, batch 3300, loss[loss=0.2809, simple_loss=0.3376, pruned_loss=0.1121, over 7208.00 frames.], tot_loss[loss=0.3202, simple_loss=0.3664, pruned_loss=0.137, over 1421203.77 frames.], batch size: 22, lr: 1.82e-03
2022-05-26 15:53:06,356 INFO [train.py:842] (2/4) Epoch 2, batch 3350, loss[loss=0.3821, simple_loss=0.4245, pruned_loss=0.1698, over 7211.00 frames.], tot_loss[loss=0.3203, simple_loss=0.3665, pruned_loss=0.137, over 1423942.00 frames.], batch size: 26, lr: 1.81e-03
2022-05-26 15:53:45,046 INFO [train.py:842] (2/4) Epoch 2, batch 3400, loss[loss=0.2684, simple_loss=0.3206, pruned_loss=0.1081, over 7146.00 frames.], tot_loss[loss=0.3194, simple_loss=0.3655, pruned_loss=0.1366, over 1425532.83 frames.], batch size: 17, lr: 1.81e-03
2022-05-26 15:54:23,675 INFO [train.py:842] (2/4) Epoch 2, batch 3450, loss[loss=0.3537, simple_loss=0.3791, pruned_loss=0.1642, over 7299.00 frames.], tot_loss[loss=0.3193, simple_loss=0.3654, pruned_loss=0.1367, over 1427515.14 frames.], batch size: 24, lr: 1.81e-03
2022-05-26 15:55:02,063 INFO [train.py:842] (2/4) Epoch 2, batch 3500, loss[loss=0.3275, simple_loss=0.3686, pruned_loss=0.1432, over 6264.00 frames.], tot_loss[loss=0.3205, simple_loss=0.3666, pruned_loss=0.1372, over 1424072.96 frames.], batch size: 37, lr: 1.80e-03
2022-05-26 15:55:40,849 INFO [train.py:842] (2/4) Epoch 2, batch 3550, loss[loss=0.3912, simple_loss=0.4131, pruned_loss=0.1847, over 7292.00 frames.], tot_loss[loss=0.321, simple_loss=0.3669, pruned_loss=0.1376, over 1424506.85 frames.], batch size: 25, lr: 1.80e-03
2022-05-26 15:56:19,286 INFO [train.py:842] (2/4) Epoch 2, batch 3600, loss[loss=0.4252, simple_loss=0.4375, pruned_loss=0.2065, over 7245.00 frames.], tot_loss[loss=0.319, simple_loss=0.366, pruned_loss=0.136, over 1426266.67 frames.], batch size: 20, lr: 1.80e-03
2022-05-26 15:56:58,175 INFO [train.py:842] (2/4) Epoch 2, batch 3650, loss[loss=0.2939, simple_loss=0.3257, pruned_loss=0.131, over 6828.00 frames.], tot_loss[loss=0.3172, simple_loss=0.3651, pruned_loss=0.1346, over 1428402.48 frames.], batch size: 15, lr: 1.79e-03
2022-05-26 15:57:36,613 INFO [train.py:842] (2/4) Epoch 2, batch 3700, loss[loss=0.3375, simple_loss=0.3737, pruned_loss=0.1507, over 7159.00 frames.], tot_loss[loss=0.3147, simple_loss=0.3639, pruned_loss=0.1328, over 1429659.19 frames.], batch size: 19, lr: 1.79e-03
2022-05-26 15:58:15,399 INFO [train.py:842] (2/4) Epoch 2, batch 3750, loss[loss=0.3379, simple_loss=0.3815, pruned_loss=0.1471, over 7303.00 frames.], tot_loss[loss=0.3177, simple_loss=0.3659, pruned_loss=0.1348, over 1430593.11 frames.], batch size: 24, lr: 1.79e-03
2022-05-26 15:58:54,100 INFO [train.py:842] (2/4) Epoch 2, batch 3800, loss[loss=0.2614, simple_loss=0.3205, pruned_loss=0.1011, over 6988.00 frames.], tot_loss[loss=0.3183, simple_loss=0.366, pruned_loss=0.1353, over 1431154.57 frames.], batch size: 16, lr: 1.79e-03
2022-05-26 15:59:32,920 INFO [train.py:842] (2/4) Epoch 2, batch 3850, loss[loss=0.254, simple_loss=0.3189, pruned_loss=0.09457, over 7199.00 frames.], tot_loss[loss=0.3169, simple_loss=0.3648, pruned_loss=0.1345, over 1431244.83 frames.], batch size: 22, lr: 1.78e-03
2022-05-26 16:00:11,542 INFO [train.py:842] (2/4) Epoch 2, batch 3900, loss[loss=0.3712, simple_loss=0.4069, pruned_loss=0.1678, over 6605.00 frames.], tot_loss[loss=0.3169, simple_loss=0.3652, pruned_loss=0.1343, over 1433181.24 frames.], batch size: 38, lr: 1.78e-03
2022-05-26 16:00:50,504 INFO [train.py:842] (2/4) Epoch 2, batch 3950, loss[loss=0.3299, simple_loss=0.3801, pruned_loss=0.1398, over 7324.00 frames.], tot_loss[loss=0.3139, simple_loss=0.3625, pruned_loss=0.1327, over 1431069.80 frames.], batch size: 21, lr: 1.78e-03
2022-05-26 16:01:29,037 INFO [train.py:842] (2/4) Epoch 2, batch 4000, loss[loss=0.4483, simple_loss=0.4494, pruned_loss=0.2237, over 4483.00 frames.], tot_loss[loss=0.3151, simple_loss=0.3634, pruned_loss=0.1333, over 1431323.36 frames.], batch size: 52, lr: 1.77e-03
2022-05-26 16:02:07,563 INFO [train.py:842] (2/4) Epoch 2, batch 4050, loss[loss=0.3678, simple_loss=0.4157, pruned_loss=0.16, over 6689.00 frames.], tot_loss[loss=0.3178, simple_loss=0.3655, pruned_loss=0.1351, over 1426258.26 frames.], batch size: 31, lr: 1.77e-03
2022-05-26 16:02:46,186 INFO [train.py:842] (2/4) Epoch 2, batch 4100, loss[loss=0.3147, simple_loss=0.3773, pruned_loss=0.1261, over 7061.00 frames.], tot_loss[loss=0.3188, simple_loss=0.3662, pruned_loss=0.1357, over 1429102.37 frames.], batch size: 28, lr: 1.77e-03
2022-05-26 16:03:25,024 INFO [train.py:842] (2/4) Epoch 2, batch 4150, loss[loss=0.3078, simple_loss=0.3688, pruned_loss=0.1234, over 7188.00 frames.], tot_loss[loss=0.3169, simple_loss=0.3647, pruned_loss=0.1345, over 1426610.38 frames.], batch size: 26, lr: 1.76e-03
2022-05-26 16:04:03,576 INFO [train.py:842] (2/4) Epoch 2, batch 4200, loss[loss=0.2556, simple_loss=0.3157, pruned_loss=0.09773, over 7000.00 frames.], tot_loss[loss=0.3159, simple_loss=0.3642, pruned_loss=0.1338, over 1425715.03 frames.], batch size: 16, lr: 1.76e-03
2022-05-26 16:04:42,389 INFO [train.py:842] (2/4) Epoch 2, batch 4250, loss[loss=0.2934, simple_loss=0.3451, pruned_loss=0.1209, over 7196.00 frames.], tot_loss[loss=0.3158, simple_loss=0.3642, pruned_loss=0.1336, over 1423976.80 frames.], batch size: 22, lr: 1.76e-03
2022-05-26 16:05:21,025 INFO [train.py:842] (2/4) Epoch 2, batch 4300, loss[loss=0.3261, simple_loss=0.3738, pruned_loss=0.1392, over 7338.00 frames.], tot_loss[loss=0.3161, simple_loss=0.3642, pruned_loss=0.134, over 1425634.27 frames.], batch size: 22, lr: 1.76e-03
2022-05-26 16:05:59,680 INFO [train.py:842] (2/4) Epoch 2, batch 4350, loss[loss=0.3244, simple_loss=0.3798, pruned_loss=0.1345, over 7152.00 frames.], tot_loss[loss=0.3142, simple_loss=0.3632, pruned_loss=0.1326, over 1422192.96 frames.], batch size: 19, lr: 1.75e-03
2022-05-26 16:06:38,215 INFO [train.py:842] (2/4) Epoch 2, batch 4400, loss[loss=0.3071, simple_loss=0.367, pruned_loss=0.1236, over 7284.00 frames.], tot_loss[loss=0.3158, simple_loss=0.3643, pruned_loss=0.1336, over 1423255.17 frames.], batch size: 24, lr: 1.75e-03
2022-05-26 16:07:17,566 INFO [train.py:842] (2/4) Epoch 2, batch 4450, loss[loss=0.3007, simple_loss=0.3469, pruned_loss=0.1272, over 7395.00 frames.], tot_loss[loss=0.3127, simple_loss=0.362, pruned_loss=0.1317, over 1423558.94 frames.], batch size: 18, lr: 1.75e-03
2022-05-26 16:07:56,065 INFO [train.py:842] (2/4) Epoch 2, batch 4500, loss[loss=0.288, simple_loss=0.3577, pruned_loss=0.1092, over 7315.00 frames.], tot_loss[loss=0.3154, simple_loss=0.3638, pruned_loss=0.1335, over 1425798.40 frames.], batch size: 20, lr: 1.74e-03
2022-05-26 16:08:34,946 INFO [train.py:842] (2/4) Epoch 2, batch 4550, loss[loss=0.4238, simple_loss=0.4448, pruned_loss=0.2015, over 7276.00 frames.], tot_loss[loss=0.3143, simple_loss=0.3632, pruned_loss=0.1327, over 1426419.07 frames.], batch size: 18, lr: 1.74e-03
2022-05-26 16:09:13,351 INFO [train.py:842] (2/4) Epoch 2, batch 4600, loss[loss=0.2969, simple_loss=0.3642, pruned_loss=0.1148, over 7202.00 frames.], tot_loss[loss=0.3132, simple_loss=0.3626, pruned_loss=0.1319, over 1420816.07 frames.], batch size: 22, lr: 1.74e-03
2022-05-26 16:09:52,087 INFO [train.py:842] (2/4) Epoch 2, batch 4650, loss[loss=0.2841, simple_loss=0.3424, pruned_loss=0.1129, over 7297.00 frames.], tot_loss[loss=0.3101, simple_loss=0.3604, pruned_loss=0.1299, over 1424332.80 frames.], batch size: 25, lr: 1.74e-03
2022-05-26 16:10:30,648 INFO [train.py:842] (2/4) Epoch 2, batch 4700, loss[loss=0.2735, simple_loss=0.3451, pruned_loss=0.1009, over 7322.00 frames.], tot_loss[loss=0.3098, simple_loss=0.3602, pruned_loss=0.1297, over 1424232.14 frames.], batch size: 21, lr: 1.73e-03
2022-05-26 16:11:09,332 INFO [train.py:842] (2/4) Epoch 2, batch 4750, loss[loss=0.2907, simple_loss=0.3595, pruned_loss=0.1109, over 7418.00 frames.], tot_loss[loss=0.3119, simple_loss=0.3615, pruned_loss=0.1312, over 1417413.12 frames.], batch size: 21, lr: 1.73e-03
2022-05-26 16:11:47,769 INFO [train.py:842] (2/4) Epoch 2, batch 4800, loss[loss=0.2303, simple_loss=0.315, pruned_loss=0.07281, over 7303.00 frames.], tot_loss[loss=0.3123, simple_loss=0.362, pruned_loss=0.1313, over 1415715.40 frames.], batch size: 24, lr: 1.73e-03
2022-05-26 16:12:26,436 INFO [train.py:842] (2/4) Epoch 2, batch 4850, loss[loss=0.3061, simple_loss=0.35, pruned_loss=0.1311, over 7159.00 frames.], tot_loss[loss=0.3122, simple_loss=0.362, pruned_loss=0.1312, over 1415803.40 frames.], batch size: 18, lr: 1.73e-03
2022-05-26 16:13:04,911 INFO [train.py:842] (2/4) Epoch 2, batch 4900, loss[loss=0.2454, simple_loss=0.3063, pruned_loss=0.09231, over 7265.00 frames.], tot_loss[loss=0.3082, simple_loss=0.3595, pruned_loss=0.1285, over 1418069.27 frames.], batch size: 17, lr: 1.72e-03
2022-05-26 16:13:43,581 INFO [train.py:842] (2/4) Epoch 2, batch 4950, loss[loss=0.2635, simple_loss=0.3401, pruned_loss=0.09338, over 7241.00 frames.], tot_loss[loss=0.3059, simple_loss=0.3577, pruned_loss=0.127, over 1420298.96 frames.], batch size: 20, lr: 1.72e-03
2022-05-26 16:14:22,292 INFO [train.py:842] (2/4) Epoch 2, batch 5000, loss[loss=0.3708, simple_loss=0.3891, pruned_loss=0.1763, over 7273.00 frames.], tot_loss[loss=0.31, simple_loss=0.3604, pruned_loss=0.1298, over 1422893.24 frames.], batch size: 17, lr: 1.72e-03
2022-05-26 16:15:00,738 INFO [train.py:842] (2/4) Epoch 2, batch 5050, loss[loss=0.3191, simple_loss=0.3695, pruned_loss=0.1343, over 7413.00 frames.], tot_loss[loss=0.3107, simple_loss=0.3611, pruned_loss=0.1301, over 1416402.75 frames.], batch size: 21, lr: 1.71e-03
2022-05-26 16:15:39,316 INFO [train.py:842] (2/4) Epoch 2, batch 5100, loss[loss=0.3084, simple_loss=0.3713, pruned_loss=0.1228, over 7156.00 frames.], tot_loss[loss=0.3084, simple_loss=0.3594, pruned_loss=0.1287, over 1419992.62 frames.], batch size: 19, lr: 1.71e-03
2022-05-26 16:16:18,365 INFO [train.py:842] (2/4) Epoch 2, batch 5150, loss[loss=0.3679, simple_loss=0.4158, pruned_loss=0.16, over 7225.00 frames.], tot_loss[loss=0.3106, simple_loss=0.3609, pruned_loss=0.1301, over 1421088.66 frames.], batch size: 21, lr: 1.71e-03
2022-05-26 16:16:56,912 INFO [train.py:842] (2/4) Epoch 2, batch 5200, loss[loss=0.4046, simple_loss=0.4228, pruned_loss=0.1933, over 7297.00 frames.], tot_loss[loss=0.3113, simple_loss=0.3614, pruned_loss=0.1306, over 1422255.38 frames.], batch size: 25, lr: 1.71e-03
2022-05-26 16:17:35,687 INFO [train.py:842] (2/4) Epoch 2, batch 5250, loss[loss=0.2759, simple_loss=0.3371, pruned_loss=0.1074, over 6781.00 frames.], tot_loss[loss=0.3106, simple_loss=0.3608, pruned_loss=0.1302, over 1425003.87 frames.], batch size: 31, lr: 1.70e-03
2022-05-26 16:18:14,229 INFO [train.py:842] (2/4) Epoch 2, batch 5300, loss[loss=0.3312, simple_loss=0.3794, pruned_loss=0.1416, over 7379.00 frames.], tot_loss[loss=0.3089, simple_loss=0.3595, pruned_loss=0.1292, over 1421722.82 frames.], batch size: 23, lr: 1.70e-03
2022-05-26 16:18:53,029 INFO [train.py:842] (2/4) Epoch 2, batch 5350, loss[loss=0.2698, simple_loss=0.3348, pruned_loss=0.1024, over 7365.00 frames.], tot_loss[loss=0.3066, simple_loss=0.3573, pruned_loss=0.128, over 1419352.01 frames.], batch size: 19, lr: 1.70e-03
2022-05-26 16:19:31,683 INFO [train.py:842] (2/4) Epoch 2, batch 5400, loss[loss=0.3125, simple_loss=0.3618, pruned_loss=0.1316, over 6525.00 frames.], tot_loss[loss=0.3053, simple_loss=0.3565, pruned_loss=0.127, over 1420373.41 frames.], batch size: 38, lr: 1.70e-03
2022-05-26 16:20:10,826 INFO [train.py:842] (2/4) Epoch 2, batch 5450, loss[loss=0.2895, simple_loss=0.335, pruned_loss=0.122, over 6821.00 frames.], tot_loss[loss=0.3055, simple_loss=0.3566, pruned_loss=0.1272, over 1421473.64 frames.], batch size: 15, lr: 1.69e-03
2022-05-26 16:20:49,321 INFO [train.py:842] (2/4) Epoch 2, batch 5500, loss[loss=0.2322, simple_loss=0.3088, pruned_loss=0.07784, over 7134.00 frames.], tot_loss[loss=0.3038, simple_loss=0.3552, pruned_loss=0.1262, over 1423660.42 frames.], batch size: 17, lr: 1.69e-03
2022-05-26 16:21:28,406 INFO [train.py:842] (2/4) Epoch 2, batch 5550, loss[loss=0.2573, simple_loss=0.3127, pruned_loss=0.1009, over 6984.00 frames.], tot_loss[loss=0.3032, simple_loss=0.3548, pruned_loss=0.1258, over 1424205.91 frames.], batch size: 16, lr: 1.69e-03
2022-05-26 16:22:06,834 INFO [train.py:842] (2/4) Epoch 2, batch 5600, loss[loss=0.3522, simple_loss=0.4072, pruned_loss=0.1486, over 7309.00 frames.], tot_loss[loss=0.3048, simple_loss=0.3564, pruned_loss=0.1266, over 1424723.58 frames.], batch size: 24, lr: 1.69e-03
2022-05-26 16:22:45,455 INFO [train.py:842] (2/4) Epoch 2, batch 5650, loss[loss=0.2935, simple_loss=0.363, pruned_loss=0.112, over 7205.00 frames.], tot_loss[loss=0.305, simple_loss=0.357, pruned_loss=0.1265, over 1425579.90 frames.], batch size: 23, lr: 1.68e-03
2022-05-26 16:23:24,159 INFO [train.py:842] (2/4) Epoch 2, batch 5700, loss[loss=0.1947, simple_loss=0.2715, pruned_loss=0.05895, over 7296.00 frames.], tot_loss[loss=0.304, simple_loss=0.3558, pruned_loss=0.1261, over 1424592.99 frames.], batch size: 18, lr: 1.68e-03
2022-05-26 16:24:03,263 INFO [train.py:842] (2/4) Epoch 2, batch 5750, loss[loss=0.3628, simple_loss=0.4049, pruned_loss=0.1604, over 7312.00 frames.], tot_loss[loss=0.3066, simple_loss=0.3575, pruned_loss=0.1279, over 1422557.67 frames.], batch size: 21, lr: 1.68e-03
2022-05-26 16:24:41,860 INFO [train.py:842] (2/4) Epoch 2, batch 5800, loss[loss=0.2959, simple_loss=0.3527, pruned_loss=0.1196, over 7159.00 frames.], tot_loss[loss=0.3061, simple_loss=0.3573, pruned_loss=0.1274, over 1426718.11 frames.], batch size: 26, lr: 1.68e-03
2022-05-26 16:25:20,720 INFO [train.py:842] (2/4) Epoch 2, batch 5850, loss[loss=0.3153, simple_loss=0.3539, pruned_loss=0.1383, over 7416.00 frames.], tot_loss[loss=0.3066, simple_loss=0.3572, pruned_loss=0.1281, over 1422592.24 frames.], batch size: 21, lr: 1.67e-03
2022-05-26 16:26:08,847 INFO [train.py:842] (2/4) Epoch 2, batch 5900, loss[loss=0.3725, simple_loss=0.3805, pruned_loss=0.1823, over 7294.00 frames.], tot_loss[loss=0.3049, simple_loss=0.3562, pruned_loss=0.1268, over 1425126.25 frames.], batch size: 17, lr: 1.67e-03
2022-05-26 16:26:47,917 INFO [train.py:842] (2/4) Epoch 2, batch 5950, loss[loss=0.3076, simple_loss=0.3641, pruned_loss=0.1255, over 7207.00 frames.], tot_loss[loss=0.3048, simple_loss=0.3566, pruned_loss=0.1266, over 1423968.37 frames.], batch size: 22, lr: 1.67e-03
2022-05-26 16:27:26,495 INFO [train.py:842] (2/4) Epoch 2, batch 6000, loss[loss=0.2982, simple_loss=0.3444, pruned_loss=0.126, over 7412.00 frames.], tot_loss[loss=0.3067, simple_loss=0.3579, pruned_loss=0.1278, over 1420396.97 frames.], batch size: 21, lr: 1.67e-03
2022-05-26 16:27:26,497 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 16:27:35,906 INFO [train.py:871] (2/4) 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] (2/4) Epoch 2, batch 6050, loss[loss=0.2746, simple_loss=0.342, pruned_loss=0.1036, over 7206.00 frames.], tot_loss[loss=0.3069, simple_loss=0.3583, pruned_loss=0.1277, over 1424709.34 frames.], batch size: 23, lr: 1.66e-03
2022-05-26 16:28:53,640 INFO [train.py:842] (2/4) Epoch 2, batch 6100, loss[loss=0.2718, simple_loss=0.3383, pruned_loss=0.1026, over 7369.00 frames.], tot_loss[loss=0.3059, simple_loss=0.3576, pruned_loss=0.1271, over 1426325.20 frames.], batch size: 23, lr: 1.66e-03
2022-05-26 16:29:42,212 INFO [train.py:842] (2/4) Epoch 2, batch 6150, loss[loss=0.3487, simple_loss=0.3964, pruned_loss=0.1505, over 7003.00 frames.], tot_loss[loss=0.3055, simple_loss=0.3574, pruned_loss=0.1267, over 1426082.95 frames.], batch size: 28, lr: 1.66e-03
2022-05-26 16:30:39,576 INFO [train.py:842] (2/4) Epoch 2, batch 6200, loss[loss=0.3459, simple_loss=0.3883, pruned_loss=0.1518, over 6753.00 frames.], tot_loss[loss=0.3057, simple_loss=0.3576, pruned_loss=0.1269, over 1424811.47 frames.], batch size: 31, lr: 1.66e-03
2022-05-26 16:31:18,943 INFO [train.py:842] (2/4) Epoch 2, batch 6250, loss[loss=0.3054, simple_loss=0.3675, pruned_loss=0.1216, over 7106.00 frames.], tot_loss[loss=0.305, simple_loss=0.3572, pruned_loss=0.1265, over 1427485.41 frames.], batch size: 21, lr: 1.65e-03
2022-05-26 16:31:57,545 INFO [train.py:842] (2/4) Epoch 2, batch 6300, loss[loss=0.3292, simple_loss=0.3743, pruned_loss=0.142, over 7173.00 frames.], tot_loss[loss=0.3033, simple_loss=0.3558, pruned_loss=0.1254, over 1431298.10 frames.], batch size: 26, lr: 1.65e-03
2022-05-26 16:32:36,115 INFO [train.py:842] (2/4) Epoch 2, batch 6350, loss[loss=0.3392, simple_loss=0.3801, pruned_loss=0.1491, over 6294.00 frames.], tot_loss[loss=0.3068, simple_loss=0.3585, pruned_loss=0.1275, over 1430170.82 frames.], batch size: 37, lr: 1.65e-03
2022-05-26 16:33:14,716 INFO [train.py:842] (2/4) Epoch 2, batch 6400, loss[loss=0.3655, simple_loss=0.397, pruned_loss=0.167, over 6795.00 frames.], tot_loss[loss=0.3063, simple_loss=0.3577, pruned_loss=0.1274, over 1424750.03 frames.], batch size: 31, lr: 1.65e-03
2022-05-26 16:33:53,450 INFO [train.py:842] (2/4) Epoch 2, batch 6450, loss[loss=0.2372, simple_loss=0.2968, pruned_loss=0.08885, over 7408.00 frames.], tot_loss[loss=0.3048, simple_loss=0.3566, pruned_loss=0.1265, over 1424349.42 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 16:34:31,951 INFO [train.py:842] (2/4) Epoch 2, batch 6500, loss[loss=0.2778, simple_loss=0.3381, pruned_loss=0.1088, over 7195.00 frames.], tot_loss[loss=0.3031, simple_loss=0.3553, pruned_loss=0.1254, over 1423286.17 frames.], batch size: 22, lr: 1.64e-03
2022-05-26 16:35:10,752 INFO [train.py:842] (2/4) Epoch 2, batch 6550, loss[loss=0.2502, simple_loss=0.311, pruned_loss=0.09475, over 7072.00 frames.], tot_loss[loss=0.3032, simple_loss=0.3555, pruned_loss=0.1254, over 1420096.36 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 16:35:49,285 INFO [train.py:842] (2/4) Epoch 2, batch 6600, loss[loss=0.2927, simple_loss=0.3463, pruned_loss=0.1195, over 7298.00 frames.], tot_loss[loss=0.3017, simple_loss=0.3545, pruned_loss=0.1245, over 1420307.27 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 16:36:27,747 INFO [train.py:842] (2/4) Epoch 2, batch 6650, loss[loss=0.3562, simple_loss=0.3946, pruned_loss=0.1588, over 7193.00 frames.], tot_loss[loss=0.3029, simple_loss=0.3552, pruned_loss=0.1252, over 1413181.74 frames.], batch size: 23, lr: 1.63e-03
2022-05-26 16:37:06,262 INFO [train.py:842] (2/4) Epoch 2, batch 6700, loss[loss=0.2905, simple_loss=0.3361, pruned_loss=0.1224, over 7274.00 frames.], tot_loss[loss=0.3019, simple_loss=0.3549, pruned_loss=0.1245, over 1419667.22 frames.], batch size: 17, lr: 1.63e-03
2022-05-26 16:37:45,036 INFO [train.py:842] (2/4) Epoch 2, batch 6750, loss[loss=0.3522, simple_loss=0.3848, pruned_loss=0.1598, over 7222.00 frames.], tot_loss[loss=0.3025, simple_loss=0.3558, pruned_loss=0.1246, over 1422429.12 frames.], batch size: 20, lr: 1.63e-03
2022-05-26 16:38:23,578 INFO [train.py:842] (2/4) Epoch 2, batch 6800, loss[loss=0.3383, simple_loss=0.3984, pruned_loss=0.1391, over 7130.00 frames.], tot_loss[loss=0.3006, simple_loss=0.3543, pruned_loss=0.1234, over 1424640.23 frames.], batch size: 21, lr: 1.63e-03
2022-05-26 16:39:05,224 INFO [train.py:842] (2/4) Epoch 2, batch 6850, loss[loss=0.3406, simple_loss=0.3675, pruned_loss=0.1568, over 7329.00 frames.], tot_loss[loss=0.301, simple_loss=0.3548, pruned_loss=0.1236, over 1421308.87 frames.], batch size: 20, lr: 1.63e-03
2022-05-26 16:39:43,768 INFO [train.py:842] (2/4) Epoch 2, batch 6900, loss[loss=0.3401, simple_loss=0.38, pruned_loss=0.1502, over 7438.00 frames.], tot_loss[loss=0.3018, simple_loss=0.3554, pruned_loss=0.1241, over 1421414.44 frames.], batch size: 20, lr: 1.62e-03
2022-05-26 16:40:22,591 INFO [train.py:842] (2/4) Epoch 2, batch 6950, loss[loss=0.2107, simple_loss=0.2779, pruned_loss=0.07178, over 7284.00 frames.], tot_loss[loss=0.3003, simple_loss=0.354, pruned_loss=0.1233, over 1421262.56 frames.], batch size: 18, lr: 1.62e-03
2022-05-26 16:41:01,157 INFO [train.py:842] (2/4) Epoch 2, batch 7000, loss[loss=0.3417, simple_loss=0.3948, pruned_loss=0.1443, over 7316.00 frames.], tot_loss[loss=0.301, simple_loss=0.3543, pruned_loss=0.1238, over 1423409.55 frames.], batch size: 21, lr: 1.62e-03
2022-05-26 16:41:40,648 INFO [train.py:842] (2/4) Epoch 2, batch 7050, loss[loss=0.5081, simple_loss=0.4826, pruned_loss=0.2668, over 5305.00 frames.], tot_loss[loss=0.3005, simple_loss=0.3534, pruned_loss=0.1238, over 1425588.80 frames.], batch size: 53, lr: 1.62e-03
2022-05-26 16:42:19,188 INFO [train.py:842] (2/4) Epoch 2, batch 7100, loss[loss=0.2724, simple_loss=0.3394, pruned_loss=0.1027, over 7102.00 frames.], tot_loss[loss=0.3004, simple_loss=0.3537, pruned_loss=0.1236, over 1425292.31 frames.], batch size: 21, lr: 1.61e-03
2022-05-26 16:42:57,891 INFO [train.py:842] (2/4) Epoch 2, batch 7150, loss[loss=0.26, simple_loss=0.3319, pruned_loss=0.09404, over 7416.00 frames.], tot_loss[loss=0.3022, simple_loss=0.3553, pruned_loss=0.1246, over 1422247.85 frames.], batch size: 21, lr: 1.61e-03
2022-05-26 16:43:36,579 INFO [train.py:842] (2/4) Epoch 2, batch 7200, loss[loss=0.2592, simple_loss=0.3098, pruned_loss=0.1043, over 6984.00 frames.], tot_loss[loss=0.303, simple_loss=0.3558, pruned_loss=0.1251, over 1420616.02 frames.], batch size: 16, lr: 1.61e-03
2022-05-26 16:44:15,836 INFO [train.py:842] (2/4) Epoch 2, batch 7250, loss[loss=0.2461, simple_loss=0.3229, pruned_loss=0.08467, over 7227.00 frames.], tot_loss[loss=0.301, simple_loss=0.3541, pruned_loss=0.124, over 1425739.97 frames.], batch size: 20, lr: 1.61e-03
2022-05-26 16:44:54,471 INFO [train.py:842] (2/4) Epoch 2, batch 7300, loss[loss=0.3004, simple_loss=0.3602, pruned_loss=0.1202, over 7222.00 frames.], tot_loss[loss=0.2998, simple_loss=0.3536, pruned_loss=0.123, over 1427990.37 frames.], batch size: 21, lr: 1.60e-03
2022-05-26 16:45:33,707 INFO [train.py:842] (2/4) Epoch 2, batch 7350, loss[loss=0.4004, simple_loss=0.4197, pruned_loss=0.1905, over 5367.00 frames.], tot_loss[loss=0.2985, simple_loss=0.3517, pruned_loss=0.1226, over 1424508.21 frames.], batch size: 52, lr: 1.60e-03
2022-05-26 16:46:12,356 INFO [train.py:842] (2/4) Epoch 2, batch 7400, loss[loss=0.313, simple_loss=0.3389, pruned_loss=0.1436, over 7015.00 frames.], tot_loss[loss=0.2967, simple_loss=0.3506, pruned_loss=0.1214, over 1424109.13 frames.], batch size: 16, lr: 1.60e-03
2022-05-26 16:46:51,029 INFO [train.py:842] (2/4) Epoch 2, batch 7450, loss[loss=0.2447, simple_loss=0.3105, pruned_loss=0.08949, over 7364.00 frames.], tot_loss[loss=0.2975, simple_loss=0.3509, pruned_loss=0.1221, over 1419080.47 frames.], batch size: 19, lr: 1.60e-03
2022-05-26 16:47:29,540 INFO [train.py:842] (2/4) Epoch 2, batch 7500, loss[loss=0.2759, simple_loss=0.3477, pruned_loss=0.102, over 7219.00 frames.], tot_loss[loss=0.2995, simple_loss=0.3528, pruned_loss=0.123, over 1419567.37 frames.], batch size: 21, lr: 1.60e-03
2022-05-26 16:48:08,282 INFO [train.py:842] (2/4) Epoch 2, batch 7550, loss[loss=0.3238, simple_loss=0.3797, pruned_loss=0.134, over 7405.00 frames.], tot_loss[loss=0.2998, simple_loss=0.3532, pruned_loss=0.1232, over 1421156.60 frames.], batch size: 21, lr: 1.59e-03
2022-05-26 16:48:46,895 INFO [train.py:842] (2/4) Epoch 2, batch 7600, loss[loss=0.3848, simple_loss=0.4027, pruned_loss=0.1835, over 4913.00 frames.], tot_loss[loss=0.2983, simple_loss=0.3521, pruned_loss=0.1223, over 1420924.38 frames.], batch size: 52, lr: 1.59e-03
2022-05-26 16:49:26,107 INFO [train.py:842] (2/4) Epoch 2, batch 7650, loss[loss=0.2906, simple_loss=0.3494, pruned_loss=0.1159, over 7409.00 frames.], tot_loss[loss=0.2971, simple_loss=0.3515, pruned_loss=0.1214, over 1421842.91 frames.], batch size: 21, lr: 1.59e-03
2022-05-26 16:50:04,705 INFO [train.py:842] (2/4) Epoch 2, batch 7700, loss[loss=0.3267, simple_loss=0.3895, pruned_loss=0.132, over 7339.00 frames.], tot_loss[loss=0.2984, simple_loss=0.352, pruned_loss=0.1224, over 1423144.39 frames.], batch size: 22, lr: 1.59e-03
2022-05-26 16:50:43,502 INFO [train.py:842] (2/4) Epoch 2, batch 7750, loss[loss=0.3765, simple_loss=0.4076, pruned_loss=0.1727, over 7039.00 frames.], tot_loss[loss=0.2988, simple_loss=0.3527, pruned_loss=0.1225, over 1424122.10 frames.], batch size: 28, lr: 1.59e-03
2022-05-26 16:51:21,994 INFO [train.py:842] (2/4) Epoch 2, batch 7800, loss[loss=0.3406, simple_loss=0.3725, pruned_loss=0.1544, over 7139.00 frames.], tot_loss[loss=0.2995, simple_loss=0.3533, pruned_loss=0.1228, over 1423708.02 frames.], batch size: 20, lr: 1.58e-03
2022-05-26 16:52:00,786 INFO [train.py:842] (2/4) Epoch 2, batch 7850, loss[loss=0.2885, simple_loss=0.3489, pruned_loss=0.114, over 7316.00 frames.], tot_loss[loss=0.3002, simple_loss=0.3536, pruned_loss=0.1234, over 1423636.68 frames.], batch size: 21, lr: 1.58e-03
2022-05-26 16:52:39,329 INFO [train.py:842] (2/4) Epoch 2, batch 7900, loss[loss=0.3808, simple_loss=0.4087, pruned_loss=0.1765, over 5012.00 frames.], tot_loss[loss=0.3003, simple_loss=0.3537, pruned_loss=0.1234, over 1425539.29 frames.], batch size: 52, lr: 1.58e-03
2022-05-26 16:53:18,138 INFO [train.py:842] (2/4) Epoch 2, batch 7950, loss[loss=0.368, simple_loss=0.402, pruned_loss=0.167, over 7164.00 frames.], tot_loss[loss=0.3011, simple_loss=0.3546, pruned_loss=0.1238, over 1427748.92 frames.], batch size: 18, lr: 1.58e-03
2022-05-26 16:53:56,766 INFO [train.py:842] (2/4) Epoch 2, batch 8000, loss[loss=0.3059, simple_loss=0.3601, pruned_loss=0.1259, over 7232.00 frames.], tot_loss[loss=0.2986, simple_loss=0.3533, pruned_loss=0.122, over 1426965.04 frames.], batch size: 21, lr: 1.57e-03
2022-05-26 16:54:35,616 INFO [train.py:842] (2/4) Epoch 2, batch 8050, loss[loss=0.3389, simple_loss=0.3959, pruned_loss=0.141, over 6388.00 frames.], tot_loss[loss=0.2995, simple_loss=0.3536, pruned_loss=0.1227, over 1424652.37 frames.], batch size: 37, lr: 1.57e-03
2022-05-26 16:55:14,281 INFO [train.py:842] (2/4) Epoch 2, batch 8100, loss[loss=0.26, simple_loss=0.3351, pruned_loss=0.09245, over 7163.00 frames.], tot_loss[loss=0.2993, simple_loss=0.3533, pruned_loss=0.1227, over 1427027.64 frames.], batch size: 26, lr: 1.57e-03
2022-05-26 16:55:53,019 INFO [train.py:842] (2/4) Epoch 2, batch 8150, loss[loss=0.3382, simple_loss=0.3791, pruned_loss=0.1487, over 7063.00 frames.], tot_loss[loss=0.2988, simple_loss=0.3531, pruned_loss=0.1223, over 1428197.20 frames.], batch size: 18, lr: 1.57e-03
2022-05-26 16:56:31,569 INFO [train.py:842] (2/4) Epoch 2, batch 8200, loss[loss=0.2701, simple_loss=0.3257, pruned_loss=0.1072, over 7289.00 frames.], tot_loss[loss=0.301, simple_loss=0.3545, pruned_loss=0.1238, over 1423434.32 frames.], batch size: 18, lr: 1.57e-03
2022-05-26 16:57:10,902 INFO [train.py:842] (2/4) Epoch 2, batch 8250, loss[loss=0.2852, simple_loss=0.3582, pruned_loss=0.1061, over 7058.00 frames.], tot_loss[loss=0.2982, simple_loss=0.3517, pruned_loss=0.1223, over 1420623.98 frames.], batch size: 28, lr: 1.56e-03
2022-05-26 16:57:49,493 INFO [train.py:842] (2/4) Epoch 2, batch 8300, loss[loss=0.3423, simple_loss=0.3859, pruned_loss=0.1493, over 7145.00 frames.], tot_loss[loss=0.2982, simple_loss=0.3519, pruned_loss=0.1223, over 1418915.58 frames.], batch size: 20, lr: 1.56e-03
2022-05-26 16:58:28,235 INFO [train.py:842] (2/4) Epoch 2, batch 8350, loss[loss=0.4636, simple_loss=0.4581, pruned_loss=0.2346, over 5062.00 frames.], tot_loss[loss=0.2973, simple_loss=0.3517, pruned_loss=0.1214, over 1417418.48 frames.], batch size: 53, lr: 1.56e-03
2022-05-26 16:59:06,606 INFO [train.py:842] (2/4) Epoch 2, batch 8400, loss[loss=0.2742, simple_loss=0.3141, pruned_loss=0.1172, over 7117.00 frames.], tot_loss[loss=0.2977, simple_loss=0.352, pruned_loss=0.1217, over 1418021.72 frames.], batch size: 17, lr: 1.56e-03
2022-05-26 16:59:45,119 INFO [train.py:842] (2/4) Epoch 2, batch 8450, loss[loss=0.2779, simple_loss=0.3528, pruned_loss=0.1015, over 7189.00 frames.], tot_loss[loss=0.2978, simple_loss=0.3524, pruned_loss=0.1216, over 1413648.58 frames.], batch size: 22, lr: 1.56e-03
2022-05-26 17:00:23,698 INFO [train.py:842] (2/4) Epoch 2, batch 8500, loss[loss=0.3287, simple_loss=0.3584, pruned_loss=0.1495, over 7125.00 frames.], tot_loss[loss=0.3004, simple_loss=0.3545, pruned_loss=0.1232, over 1417615.95 frames.], batch size: 17, lr: 1.55e-03
2022-05-26 17:01:02,430 INFO [train.py:842] (2/4) Epoch 2, batch 8550, loss[loss=0.2848, simple_loss=0.3366, pruned_loss=0.1165, over 7354.00 frames.], tot_loss[loss=0.2994, simple_loss=0.3539, pruned_loss=0.1224, over 1423287.35 frames.], batch size: 19, lr: 1.55e-03
2022-05-26 17:01:41,159 INFO [train.py:842] (2/4) Epoch 2, batch 8600, loss[loss=0.3709, simple_loss=0.4109, pruned_loss=0.1655, over 6384.00 frames.], tot_loss[loss=0.2988, simple_loss=0.3532, pruned_loss=0.1223, over 1420856.84 frames.], batch size: 38, lr: 1.55e-03
2022-05-26 17:02:19,993 INFO [train.py:842] (2/4) Epoch 2, batch 8650, loss[loss=0.3272, simple_loss=0.3874, pruned_loss=0.1335, over 7145.00 frames.], tot_loss[loss=0.2979, simple_loss=0.3522, pruned_loss=0.1218, over 1422843.69 frames.], batch size: 20, lr: 1.55e-03
2022-05-26 17:02:58,639 INFO [train.py:842] (2/4) Epoch 2, batch 8700, loss[loss=0.2618, simple_loss=0.3251, pruned_loss=0.09927, over 7069.00 frames.], tot_loss[loss=0.2948, simple_loss=0.3497, pruned_loss=0.12, over 1421339.00 frames.], batch size: 18, lr: 1.55e-03
2022-05-26 17:03:37,055 INFO [train.py:842] (2/4) Epoch 2, batch 8750, loss[loss=0.2706, simple_loss=0.3323, pruned_loss=0.1044, over 7156.00 frames.], tot_loss[loss=0.2951, simple_loss=0.3497, pruned_loss=0.1202, over 1420259.03 frames.], batch size: 18, lr: 1.54e-03
2022-05-26 17:04:15,669 INFO [train.py:842] (2/4) Epoch 2, batch 8800, loss[loss=0.3528, simple_loss=0.4037, pruned_loss=0.151, over 7331.00 frames.], tot_loss[loss=0.2963, simple_loss=0.3501, pruned_loss=0.1212, over 1412729.38 frames.], batch size: 22, lr: 1.54e-03
2022-05-26 17:04:54,416 INFO [train.py:842] (2/4) Epoch 2, batch 8850, loss[loss=0.3972, simple_loss=0.4317, pruned_loss=0.1813, over 7296.00 frames.], tot_loss[loss=0.2968, simple_loss=0.3511, pruned_loss=0.1213, over 1410443.92 frames.], batch size: 24, lr: 1.54e-03
2022-05-26 17:05:32,685 INFO [train.py:842] (2/4) Epoch 2, batch 8900, loss[loss=0.2947, simple_loss=0.3589, pruned_loss=0.1153, over 6772.00 frames.], tot_loss[loss=0.2964, simple_loss=0.3509, pruned_loss=0.121, over 1400972.25 frames.], batch size: 31, lr: 1.54e-03
2022-05-26 17:06:11,175 INFO [train.py:842] (2/4) Epoch 2, batch 8950, loss[loss=0.2768, simple_loss=0.3506, pruned_loss=0.1015, over 7106.00 frames.], tot_loss[loss=0.2958, simple_loss=0.351, pruned_loss=0.1203, over 1400664.00 frames.], batch size: 21, lr: 1.54e-03
2022-05-26 17:06:49,708 INFO [train.py:842] (2/4) Epoch 2, batch 9000, loss[loss=0.3023, simple_loss=0.3508, pruned_loss=0.1269, over 7275.00 frames.], tot_loss[loss=0.2981, simple_loss=0.3524, pruned_loss=0.1219, over 1396835.82 frames.], batch size: 18, lr: 1.53e-03
2022-05-26 17:06:49,710 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 17:06:59,111 INFO [train.py:871] (2/4) Epoch 2, validation: loss=0.2204, simple_loss=0.3177, pruned_loss=0.06159, over 868885.00 frames.
2022-05-26 17:07:37,635 INFO [train.py:842] (2/4) Epoch 2, batch 9050, loss[loss=0.2791, simple_loss=0.346, pruned_loss=0.1061, over 7266.00 frames.], tot_loss[loss=0.299, simple_loss=0.3528, pruned_loss=0.1226, over 1381754.41 frames.], batch size: 18, lr: 1.53e-03
2022-05-26 17:08:15,243 INFO [train.py:842] (2/4) Epoch 2, batch 9100, loss[loss=0.3088, simple_loss=0.3639, pruned_loss=0.1269, over 4915.00 frames.], tot_loss[loss=0.3051, simple_loss=0.3571, pruned_loss=0.1265, over 1327044.20 frames.], batch size: 53, lr: 1.53e-03
2022-05-26 17:08:52,760 INFO [train.py:842] (2/4) Epoch 2, batch 9150, loss[loss=0.3075, simple_loss=0.3492, pruned_loss=0.1329, over 4816.00 frames.], tot_loss[loss=0.3142, simple_loss=0.3633, pruned_loss=0.1326, over 1257783.86 frames.], batch size: 52, lr: 1.53e-03
2022-05-26 17:09:46,532 INFO [train.py:842] (2/4) Epoch 3, batch 0, loss[loss=0.2391, simple_loss=0.3, pruned_loss=0.08908, over 7269.00 frames.], tot_loss[loss=0.2391, simple_loss=0.3, pruned_loss=0.08908, over 7269.00 frames.], batch size: 17, lr: 1.50e-03
2022-05-26 17:10:25,876 INFO [train.py:842] (2/4) Epoch 3, batch 50, loss[loss=0.4863, simple_loss=0.4738, pruned_loss=0.2494, over 7281.00 frames.], tot_loss[loss=0.3051, simple_loss=0.3569, pruned_loss=0.1266, over 321074.29 frames.], batch size: 25, lr: 1.49e-03
2022-05-26 17:11:04,575 INFO [train.py:842] (2/4) Epoch 3, batch 100, loss[loss=0.2921, simple_loss=0.3355, pruned_loss=0.1243, over 7004.00 frames.], tot_loss[loss=0.2996, simple_loss=0.3535, pruned_loss=0.1229, over 568439.71 frames.], batch size: 16, lr: 1.49e-03
2022-05-26 17:11:43,656 INFO [train.py:842] (2/4) Epoch 3, batch 150, loss[loss=0.2804, simple_loss=0.3363, pruned_loss=0.1122, over 6756.00 frames.], tot_loss[loss=0.2929, simple_loss=0.3486, pruned_loss=0.1186, over 761231.45 frames.], batch size: 31, lr: 1.49e-03
2022-05-26 17:12:22,301 INFO [train.py:842] (2/4) Epoch 3, batch 200, loss[loss=0.2869, simple_loss=0.3202, pruned_loss=0.1267, over 6831.00 frames.], tot_loss[loss=0.2903, simple_loss=0.3463, pruned_loss=0.1172, over 900304.47 frames.], batch size: 15, lr: 1.49e-03
2022-05-26 17:13:00,908 INFO [train.py:842] (2/4) Epoch 3, batch 250, loss[loss=0.2959, simple_loss=0.3406, pruned_loss=0.1256, over 7362.00 frames.], tot_loss[loss=0.2896, simple_loss=0.3458, pruned_loss=0.1167, over 1010904.47 frames.], batch size: 19, lr: 1.49e-03
2022-05-26 17:13:39,433 INFO [train.py:842] (2/4) Epoch 3, batch 300, loss[loss=0.2469, simple_loss=0.3175, pruned_loss=0.08814, over 6727.00 frames.], tot_loss[loss=0.2912, simple_loss=0.3474, pruned_loss=0.1175, over 1101096.07 frames.], batch size: 31, lr: 1.49e-03
2022-05-26 17:14:18,345 INFO [train.py:842] (2/4) Epoch 3, batch 350, loss[loss=0.3037, simple_loss=0.3628, pruned_loss=0.1223, over 7316.00 frames.], tot_loss[loss=0.2937, simple_loss=0.3498, pruned_loss=0.1188, over 1171788.89 frames.], batch size: 21, lr: 1.48e-03
2022-05-26 17:14:56,914 INFO [train.py:842] (2/4) Epoch 3, batch 400, loss[loss=0.3333, simple_loss=0.3958, pruned_loss=0.1354, over 7278.00 frames.], tot_loss[loss=0.2926, simple_loss=0.3489, pruned_loss=0.1181, over 1223149.46 frames.], batch size: 24, lr: 1.48e-03
2022-05-26 17:15:35,611 INFO [train.py:842] (2/4) Epoch 3, batch 450, loss[loss=0.2854, simple_loss=0.3434, pruned_loss=0.1137, over 7217.00 frames.], tot_loss[loss=0.2931, simple_loss=0.3492, pruned_loss=0.1185, over 1263320.28 frames.], batch size: 22, lr: 1.48e-03
2022-05-26 17:16:14,211 INFO [train.py:842] (2/4) Epoch 3, batch 500, loss[loss=0.2656, simple_loss=0.3178, pruned_loss=0.1067, over 7012.00 frames.], tot_loss[loss=0.2904, simple_loss=0.3473, pruned_loss=0.1167, over 1301370.28 frames.], batch size: 16, lr: 1.48e-03
2022-05-26 17:16:53,096 INFO [train.py:842] (2/4) Epoch 3, batch 550, loss[loss=0.3402, simple_loss=0.3845, pruned_loss=0.148, over 7214.00 frames.], tot_loss[loss=0.2884, simple_loss=0.3463, pruned_loss=0.1153, over 1331774.72 frames.], batch size: 21, lr: 1.48e-03
2022-05-26 17:17:31,916 INFO [train.py:842] (2/4) Epoch 3, batch 600, loss[loss=0.2911, simple_loss=0.3538, pruned_loss=0.1142, over 7293.00 frames.], tot_loss[loss=0.2865, simple_loss=0.3441, pruned_loss=0.1145, over 1352098.16 frames.], batch size: 25, lr: 1.47e-03
2022-05-26 17:18:10,661 INFO [train.py:842] (2/4) Epoch 3, batch 650, loss[loss=0.2758, simple_loss=0.3485, pruned_loss=0.1016, over 7368.00 frames.], tot_loss[loss=0.286, simple_loss=0.3436, pruned_loss=0.1142, over 1366645.67 frames.], batch size: 19, lr: 1.47e-03
2022-05-26 17:18:49,279 INFO [train.py:842] (2/4) Epoch 3, batch 700, loss[loss=0.2991, simple_loss=0.367, pruned_loss=0.1156, over 7222.00 frames.], tot_loss[loss=0.2859, simple_loss=0.3439, pruned_loss=0.114, over 1377451.19 frames.], batch size: 21, lr: 1.47e-03
2022-05-26 17:19:28,527 INFO [train.py:842] (2/4) Epoch 3, batch 750, loss[loss=0.3418, simple_loss=0.3956, pruned_loss=0.144, over 7218.00 frames.], tot_loss[loss=0.2861, simple_loss=0.3441, pruned_loss=0.114, over 1390891.66 frames.], batch size: 23, lr: 1.47e-03
2022-05-26 17:20:07,124 INFO [train.py:842] (2/4) Epoch 3, batch 800, loss[loss=0.2884, simple_loss=0.3512, pruned_loss=0.1128, over 7198.00 frames.], tot_loss[loss=0.287, simple_loss=0.345, pruned_loss=0.1145, over 1401662.41 frames.], batch size: 23, lr: 1.47e-03
2022-05-26 17:20:46,497 INFO [train.py:842] (2/4) Epoch 3, batch 850, loss[loss=0.3978, simple_loss=0.4267, pruned_loss=0.1845, over 7332.00 frames.], tot_loss[loss=0.286, simple_loss=0.3437, pruned_loss=0.1141, over 1409750.47 frames.], batch size: 25, lr: 1.47e-03
2022-05-26 17:21:24,955 INFO [train.py:842] (2/4) Epoch 3, batch 900, loss[loss=0.2524, simple_loss=0.3129, pruned_loss=0.09598, over 7076.00 frames.], tot_loss[loss=0.2879, simple_loss=0.3454, pruned_loss=0.1152, over 1412082.19 frames.], batch size: 18, lr: 1.46e-03
2022-05-26 17:22:03,733 INFO [train.py:842] (2/4) Epoch 3, batch 950, loss[loss=0.3479, simple_loss=0.3982, pruned_loss=0.1488, over 7145.00 frames.], tot_loss[loss=0.29, simple_loss=0.3472, pruned_loss=0.1164, over 1417375.57 frames.], batch size: 20, lr: 1.46e-03
2022-05-26 17:22:42,289 INFO [train.py:842] (2/4) Epoch 3, batch 1000, loss[loss=0.2918, simple_loss=0.3591, pruned_loss=0.1122, over 6681.00 frames.], tot_loss[loss=0.2908, simple_loss=0.3485, pruned_loss=0.1166, over 1416700.15 frames.], batch size: 31, lr: 1.46e-03
2022-05-26 17:23:20,998 INFO [train.py:842] (2/4) Epoch 3, batch 1050, loss[loss=0.2529, simple_loss=0.3143, pruned_loss=0.09571, over 7289.00 frames.], tot_loss[loss=0.2922, simple_loss=0.3495, pruned_loss=0.1175, over 1414427.80 frames.], batch size: 18, lr: 1.46e-03
2022-05-26 17:23:59,521 INFO [train.py:842] (2/4) Epoch 3, batch 1100, loss[loss=0.3176, simple_loss=0.3736, pruned_loss=0.1308, over 7224.00 frames.], tot_loss[loss=0.2903, simple_loss=0.3485, pruned_loss=0.1161, over 1419282.15 frames.], batch size: 21, lr: 1.46e-03
2022-05-26 17:24:38,370 INFO [train.py:842] (2/4) Epoch 3, batch 1150, loss[loss=0.2781, simple_loss=0.3517, pruned_loss=0.1022, over 7229.00 frames.], tot_loss[loss=0.2882, simple_loss=0.3467, pruned_loss=0.1149, over 1420589.78 frames.], batch size: 20, lr: 1.45e-03
2022-05-26 17:25:16,894 INFO [train.py:842] (2/4) Epoch 3, batch 1200, loss[loss=0.2576, simple_loss=0.3206, pruned_loss=0.0973, over 7444.00 frames.], tot_loss[loss=0.2893, simple_loss=0.3474, pruned_loss=0.1156, over 1424188.01 frames.], batch size: 20, lr: 1.45e-03
2022-05-26 17:25:55,754 INFO [train.py:842] (2/4) Epoch 3, batch 1250, loss[loss=0.263, simple_loss=0.3344, pruned_loss=0.09585, over 7416.00 frames.], tot_loss[loss=0.2898, simple_loss=0.3473, pruned_loss=0.1162, over 1424681.57 frames.], batch size: 21, lr: 1.45e-03
2022-05-26 17:26:34,357 INFO [train.py:842] (2/4) Epoch 3, batch 1300, loss[loss=0.2568, simple_loss=0.335, pruned_loss=0.08927, over 7319.00 frames.], tot_loss[loss=0.288, simple_loss=0.3461, pruned_loss=0.1149, over 1426288.07 frames.], batch size: 21, lr: 1.45e-03
2022-05-26 17:27:13,050 INFO [train.py:842] (2/4) Epoch 3, batch 1350, loss[loss=0.3909, simple_loss=0.4011, pruned_loss=0.1904, over 7438.00 frames.], tot_loss[loss=0.2902, simple_loss=0.3478, pruned_loss=0.1163, over 1426120.48 frames.], batch size: 20, lr: 1.45e-03
2022-05-26 17:27:51,778 INFO [train.py:842] (2/4) Epoch 3, batch 1400, loss[loss=0.275, simple_loss=0.3557, pruned_loss=0.0971, over 7156.00 frames.], tot_loss[loss=0.2908, simple_loss=0.3487, pruned_loss=0.1165, over 1422986.68 frames.], batch size: 19, lr: 1.45e-03
2022-05-26 17:28:30,419 INFO [train.py:842] (2/4) Epoch 3, batch 1450, loss[loss=0.3152, simple_loss=0.3407, pruned_loss=0.1448, over 7145.00 frames.], tot_loss[loss=0.2896, simple_loss=0.3474, pruned_loss=0.1159, over 1420411.91 frames.], batch size: 17, lr: 1.44e-03
2022-05-26 17:29:08,821 INFO [train.py:842] (2/4) Epoch 3, batch 1500, loss[loss=0.3907, simple_loss=0.4199, pruned_loss=0.1808, over 7320.00 frames.], tot_loss[loss=0.2908, simple_loss=0.348, pruned_loss=0.1168, over 1417788.21 frames.], batch size: 21, lr: 1.44e-03
2022-05-26 17:29:47,538 INFO [train.py:842] (2/4) Epoch 3, batch 1550, loss[loss=0.2721, simple_loss=0.3398, pruned_loss=0.1022, over 7153.00 frames.], tot_loss[loss=0.2933, simple_loss=0.3499, pruned_loss=0.1183, over 1421652.55 frames.], batch size: 19, lr: 1.44e-03
2022-05-26 17:30:26,309 INFO [train.py:842] (2/4) Epoch 3, batch 1600, loss[loss=0.3043, simple_loss=0.3498, pruned_loss=0.1294, over 7161.00 frames.], tot_loss[loss=0.2925, simple_loss=0.349, pruned_loss=0.118, over 1423483.85 frames.], batch size: 19, lr: 1.44e-03
2022-05-26 17:31:05,606 INFO [train.py:842] (2/4) Epoch 3, batch 1650, loss[loss=0.273, simple_loss=0.3341, pruned_loss=0.106, over 7436.00 frames.], tot_loss[loss=0.289, simple_loss=0.3465, pruned_loss=0.1157, over 1425412.59 frames.], batch size: 20, lr: 1.44e-03
2022-05-26 17:31:43,970 INFO [train.py:842] (2/4) Epoch 3, batch 1700, loss[loss=0.2569, simple_loss=0.3388, pruned_loss=0.08754, over 7139.00 frames.], tot_loss[loss=0.2887, simple_loss=0.3464, pruned_loss=0.1155, over 1416145.47 frames.], batch size: 20, lr: 1.44e-03
2022-05-26 17:32:23,192 INFO [train.py:842] (2/4) Epoch 3, batch 1750, loss[loss=0.3045, simple_loss=0.3696, pruned_loss=0.1197, over 7223.00 frames.], tot_loss[loss=0.2903, simple_loss=0.3475, pruned_loss=0.1165, over 1423381.80 frames.], batch size: 20, lr: 1.43e-03
2022-05-26 17:33:01,598 INFO [train.py:842] (2/4) Epoch 3, batch 1800, loss[loss=0.2707, simple_loss=0.3462, pruned_loss=0.09753, over 7119.00 frames.], tot_loss[loss=0.2907, simple_loss=0.3478, pruned_loss=0.1168, over 1416987.91 frames.], batch size: 21, lr: 1.43e-03
2022-05-26 17:33:40,964 INFO [train.py:842] (2/4) Epoch 3, batch 1850, loss[loss=0.3125, simple_loss=0.3697, pruned_loss=0.1276, over 7413.00 frames.], tot_loss[loss=0.289, simple_loss=0.3463, pruned_loss=0.1158, over 1418344.55 frames.], batch size: 21, lr: 1.43e-03
2022-05-26 17:34:19,482 INFO [train.py:842] (2/4) Epoch 3, batch 1900, loss[loss=0.2668, simple_loss=0.3232, pruned_loss=0.1052, over 7174.00 frames.], tot_loss[loss=0.2885, simple_loss=0.3463, pruned_loss=0.1153, over 1416693.96 frames.], batch size: 18, lr: 1.43e-03
2022-05-26 17:34:58,244 INFO [train.py:842] (2/4) Epoch 3, batch 1950, loss[loss=0.3238, simple_loss=0.3862, pruned_loss=0.1308, over 6700.00 frames.], tot_loss[loss=0.2857, simple_loss=0.3439, pruned_loss=0.1137, over 1417317.47 frames.], batch size: 31, lr: 1.43e-03
2022-05-26 17:35:36,885 INFO [train.py:842] (2/4) Epoch 3, batch 2000, loss[loss=0.3089, simple_loss=0.358, pruned_loss=0.1298, over 7152.00 frames.], tot_loss[loss=0.2846, simple_loss=0.3435, pruned_loss=0.1128, over 1421843.99 frames.], batch size: 19, lr: 1.43e-03
2022-05-26 17:36:15,533 INFO [train.py:842] (2/4) Epoch 3, batch 2050, loss[loss=0.3562, simple_loss=0.3812, pruned_loss=0.1656, over 5079.00 frames.], tot_loss[loss=0.2878, simple_loss=0.3462, pruned_loss=0.1147, over 1421621.24 frames.], batch size: 53, lr: 1.42e-03
2022-05-26 17:36:54,239 INFO [train.py:842] (2/4) Epoch 3, batch 2100, loss[loss=0.3272, simple_loss=0.3766, pruned_loss=0.1389, over 7311.00 frames.], tot_loss[loss=0.2878, simple_loss=0.3463, pruned_loss=0.1147, over 1424503.16 frames.], batch size: 21, lr: 1.42e-03
2022-05-26 17:37:33,244 INFO [train.py:842] (2/4) Epoch 3, batch 2150, loss[loss=0.2393, simple_loss=0.3229, pruned_loss=0.07784, over 7246.00 frames.], tot_loss[loss=0.286, simple_loss=0.3448, pruned_loss=0.1136, over 1425637.89 frames.], batch size: 20, lr: 1.42e-03
2022-05-26 17:38:11,809 INFO [train.py:842] (2/4) Epoch 3, batch 2200, loss[loss=0.2649, simple_loss=0.3229, pruned_loss=0.1035, over 7139.00 frames.], tot_loss[loss=0.2847, simple_loss=0.3435, pruned_loss=0.113, over 1424535.39 frames.], batch size: 20, lr: 1.42e-03
2022-05-26 17:38:50,513 INFO [train.py:842] (2/4) Epoch 3, batch 2250, loss[loss=0.2724, simple_loss=0.3269, pruned_loss=0.1089, over 7336.00 frames.], tot_loss[loss=0.2852, simple_loss=0.344, pruned_loss=0.1132, over 1424517.31 frames.], batch size: 20, lr: 1.42e-03
2022-05-26 17:39:29,084 INFO [train.py:842] (2/4) Epoch 3, batch 2300, loss[loss=0.2467, simple_loss=0.3113, pruned_loss=0.09098, over 7356.00 frames.], tot_loss[loss=0.2873, simple_loss=0.3455, pruned_loss=0.1146, over 1412927.35 frames.], batch size: 19, lr: 1.42e-03
2022-05-26 17:40:07,873 INFO [train.py:842] (2/4) Epoch 3, batch 2350, loss[loss=0.2444, simple_loss=0.3103, pruned_loss=0.08924, over 7268.00 frames.], tot_loss[loss=0.2852, simple_loss=0.3439, pruned_loss=0.1133, over 1414853.00 frames.], batch size: 19, lr: 1.41e-03
2022-05-26 17:40:46,289 INFO [train.py:842] (2/4) Epoch 3, batch 2400, loss[loss=0.2014, simple_loss=0.2732, pruned_loss=0.06477, over 7261.00 frames.], tot_loss[loss=0.2823, simple_loss=0.3419, pruned_loss=0.1114, over 1417575.35 frames.], batch size: 19, lr: 1.41e-03
2022-05-26 17:41:25,125 INFO [train.py:842] (2/4) Epoch 3, batch 2450, loss[loss=0.3019, simple_loss=0.3545, pruned_loss=0.1247, over 7228.00 frames.], tot_loss[loss=0.2855, simple_loss=0.3443, pruned_loss=0.1134, over 1415531.42 frames.], batch size: 20, lr: 1.41e-03
2022-05-26 17:42:03,917 INFO [train.py:842] (2/4) Epoch 3, batch 2500, loss[loss=0.2703, simple_loss=0.3405, pruned_loss=0.1001, over 7162.00 frames.], tot_loss[loss=0.283, simple_loss=0.3424, pruned_loss=0.1118, over 1414079.92 frames.], batch size: 19, lr: 1.41e-03
2022-05-26 17:42:42,665 INFO [train.py:842] (2/4) Epoch 3, batch 2550, loss[loss=0.3159, simple_loss=0.3713, pruned_loss=0.1302, over 7219.00 frames.], tot_loss[loss=0.285, simple_loss=0.3435, pruned_loss=0.1133, over 1413377.93 frames.], batch size: 21, lr: 1.41e-03
2022-05-26 17:43:21,458 INFO [train.py:842] (2/4) Epoch 3, batch 2600, loss[loss=0.2863, simple_loss=0.3358, pruned_loss=0.1184, over 7297.00 frames.], tot_loss[loss=0.286, simple_loss=0.3441, pruned_loss=0.114, over 1419420.30 frames.], batch size: 18, lr: 1.41e-03
2022-05-26 17:44:00,640 INFO [train.py:842] (2/4) Epoch 3, batch 2650, loss[loss=0.2485, simple_loss=0.3158, pruned_loss=0.09064, over 7338.00 frames.], tot_loss[loss=0.284, simple_loss=0.3424, pruned_loss=0.1128, over 1419870.41 frames.], batch size: 20, lr: 1.41e-03
2022-05-26 17:44:39,137 INFO [train.py:842] (2/4) Epoch 3, batch 2700, loss[loss=0.2764, simple_loss=0.3316, pruned_loss=0.1106, over 7071.00 frames.], tot_loss[loss=0.2847, simple_loss=0.3428, pruned_loss=0.1133, over 1420043.21 frames.], batch size: 18, lr: 1.40e-03
2022-05-26 17:45:17,846 INFO [train.py:842] (2/4) Epoch 3, batch 2750, loss[loss=0.3395, simple_loss=0.3833, pruned_loss=0.1478, over 7190.00 frames.], tot_loss[loss=0.2827, simple_loss=0.3414, pruned_loss=0.112, over 1419524.92 frames.], batch size: 26, lr: 1.40e-03
2022-05-26 17:45:56,561 INFO [train.py:842] (2/4) Epoch 3, batch 2800, loss[loss=0.3351, simple_loss=0.3766, pruned_loss=0.1468, over 5061.00 frames.], tot_loss[loss=0.2822, simple_loss=0.3414, pruned_loss=0.1115, over 1418937.69 frames.], batch size: 52, lr: 1.40e-03
2022-05-26 17:46:35,461 INFO [train.py:842] (2/4) Epoch 3, batch 2850, loss[loss=0.3219, simple_loss=0.3764, pruned_loss=0.1337, over 7222.00 frames.], tot_loss[loss=0.2829, simple_loss=0.3419, pruned_loss=0.1119, over 1420384.53 frames.], batch size: 21, lr: 1.40e-03
2022-05-26 17:47:13,965 INFO [train.py:842] (2/4) Epoch 3, batch 2900, loss[loss=0.261, simple_loss=0.3311, pruned_loss=0.09545, over 6638.00 frames.], tot_loss[loss=0.2832, simple_loss=0.3421, pruned_loss=0.1122, over 1417878.82 frames.], batch size: 38, lr: 1.40e-03
2022-05-26 17:47:52,716 INFO [train.py:842] (2/4) Epoch 3, batch 2950, loss[loss=0.4103, simple_loss=0.4413, pruned_loss=0.1896, over 7209.00 frames.], tot_loss[loss=0.2867, simple_loss=0.3447, pruned_loss=0.1143, over 1416399.01 frames.], batch size: 26, lr: 1.40e-03
2022-05-26 17:48:31,449 INFO [train.py:842] (2/4) Epoch 3, batch 3000, loss[loss=0.3642, simple_loss=0.4097, pruned_loss=0.1594, over 7344.00 frames.], tot_loss[loss=0.2864, simple_loss=0.3448, pruned_loss=0.114, over 1420220.64 frames.], batch size: 22, lr: 1.39e-03
2022-05-26 17:48:31,450 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 17:48:40,684 INFO [train.py:871] (2/4) Epoch 3, validation: loss=0.2137, simple_loss=0.3102, pruned_loss=0.05856, over 868885.00 frames.
2022-05-26 17:49:19,908 INFO [train.py:842] (2/4) Epoch 3, batch 3050, loss[loss=0.2277, simple_loss=0.3005, pruned_loss=0.07751, over 7410.00 frames.], tot_loss[loss=0.2856, simple_loss=0.3452, pruned_loss=0.113, over 1425293.33 frames.], batch size: 21, lr: 1.39e-03
2022-05-26 17:49:58,624 INFO [train.py:842] (2/4) Epoch 3, batch 3100, loss[loss=0.2574, simple_loss=0.3194, pruned_loss=0.09769, over 7300.00 frames.], tot_loss[loss=0.284, simple_loss=0.3438, pruned_loss=0.1121, over 1428475.97 frames.], batch size: 18, lr: 1.39e-03
2022-05-26 17:50:37,604 INFO [train.py:842] (2/4) Epoch 3, batch 3150, loss[loss=0.2607, simple_loss=0.3285, pruned_loss=0.09648, over 7211.00 frames.], tot_loss[loss=0.2857, simple_loss=0.3445, pruned_loss=0.1135, over 1422891.55 frames.], batch size: 21, lr: 1.39e-03
2022-05-26 17:51:16,012 INFO [train.py:842] (2/4) Epoch 3, batch 3200, loss[loss=0.3075, simple_loss=0.3664, pruned_loss=0.1243, over 7384.00 frames.], tot_loss[loss=0.2854, simple_loss=0.3448, pruned_loss=0.113, over 1425584.73 frames.], batch size: 23, lr: 1.39e-03
2022-05-26 17:51:54,666 INFO [train.py:842] (2/4) Epoch 3, batch 3250, loss[loss=0.2541, simple_loss=0.332, pruned_loss=0.08807, over 7158.00 frames.], tot_loss[loss=0.2853, simple_loss=0.3445, pruned_loss=0.113, over 1426301.02 frames.], batch size: 19, lr: 1.39e-03
2022-05-26 17:52:33,226 INFO [train.py:842] (2/4) Epoch 3, batch 3300, loss[loss=0.2643, simple_loss=0.331, pruned_loss=0.09882, over 7127.00 frames.], tot_loss[loss=0.2847, simple_loss=0.3439, pruned_loss=0.1127, over 1428843.91 frames.], batch size: 26, lr: 1.39e-03
2022-05-26 17:53:11,848 INFO [train.py:842] (2/4) Epoch 3, batch 3350, loss[loss=0.2721, simple_loss=0.3272, pruned_loss=0.1085, over 7293.00 frames.], tot_loss[loss=0.287, simple_loss=0.3459, pruned_loss=0.1141, over 1425526.98 frames.], batch size: 18, lr: 1.38e-03
2022-05-26 17:53:50,293 INFO [train.py:842] (2/4) Epoch 3, batch 3400, loss[loss=0.2406, simple_loss=0.3086, pruned_loss=0.08631, over 7417.00 frames.], tot_loss[loss=0.2861, simple_loss=0.3454, pruned_loss=0.1134, over 1422749.21 frames.], batch size: 18, lr: 1.38e-03
2022-05-26 17:54:29,385 INFO [train.py:842] (2/4) Epoch 3, batch 3450, loss[loss=0.2658, simple_loss=0.3258, pruned_loss=0.1029, over 7259.00 frames.], tot_loss[loss=0.2844, simple_loss=0.3433, pruned_loss=0.1127, over 1419224.03 frames.], batch size: 19, lr: 1.38e-03
2022-05-26 17:55:07,993 INFO [train.py:842] (2/4) Epoch 3, batch 3500, loss[loss=0.3566, simple_loss=0.4193, pruned_loss=0.147, over 7306.00 frames.], tot_loss[loss=0.2827, simple_loss=0.3421, pruned_loss=0.1117, over 1420507.85 frames.], batch size: 25, lr: 1.38e-03
2022-05-26 17:55:46,692 INFO [train.py:842] (2/4) Epoch 3, batch 3550, loss[loss=0.2542, simple_loss=0.3271, pruned_loss=0.09064, over 7214.00 frames.], tot_loss[loss=0.2846, simple_loss=0.3434, pruned_loss=0.1129, over 1419464.95 frames.], batch size: 21, lr: 1.38e-03
2022-05-26 17:56:25,356 INFO [train.py:842] (2/4) Epoch 3, batch 3600, loss[loss=0.2495, simple_loss=0.3209, pruned_loss=0.08903, over 7283.00 frames.], tot_loss[loss=0.2835, simple_loss=0.3423, pruned_loss=0.1123, over 1420396.82 frames.], batch size: 24, lr: 1.38e-03
2022-05-26 17:57:04,456 INFO [train.py:842] (2/4) Epoch 3, batch 3650, loss[loss=0.3162, simple_loss=0.3708, pruned_loss=0.1309, over 7375.00 frames.], tot_loss[loss=0.2842, simple_loss=0.3427, pruned_loss=0.1129, over 1420585.41 frames.], batch size: 23, lr: 1.37e-03
2022-05-26 17:57:43,092 INFO [train.py:842] (2/4) Epoch 3, batch 3700, loss[loss=0.247, simple_loss=0.3041, pruned_loss=0.09491, over 7410.00 frames.], tot_loss[loss=0.2859, simple_loss=0.3439, pruned_loss=0.114, over 1416150.82 frames.], batch size: 18, lr: 1.37e-03
2022-05-26 17:58:22,042 INFO [train.py:842] (2/4) Epoch 3, batch 3750, loss[loss=0.2559, simple_loss=0.3026, pruned_loss=0.1046, over 7285.00 frames.], tot_loss[loss=0.2855, simple_loss=0.3437, pruned_loss=0.1136, over 1422423.36 frames.], batch size: 18, lr: 1.37e-03
2022-05-26 17:59:00,546 INFO [train.py:842] (2/4) Epoch 3, batch 3800, loss[loss=0.2527, simple_loss=0.3335, pruned_loss=0.08592, over 7158.00 frames.], tot_loss[loss=0.2833, simple_loss=0.3424, pruned_loss=0.1121, over 1423201.60 frames.], batch size: 18, lr: 1.37e-03
2022-05-26 17:59:39,271 INFO [train.py:842] (2/4) Epoch 3, batch 3850, loss[loss=0.2542, simple_loss=0.3391, pruned_loss=0.08469, over 7338.00 frames.], tot_loss[loss=0.2824, simple_loss=0.3416, pruned_loss=0.1116, over 1422385.81 frames.], batch size: 22, lr: 1.37e-03
2022-05-26 18:00:17,857 INFO [train.py:842] (2/4) Epoch 3, batch 3900, loss[loss=0.2473, simple_loss=0.3203, pruned_loss=0.08714, over 7333.00 frames.], tot_loss[loss=0.2826, simple_loss=0.3417, pruned_loss=0.1117, over 1424322.55 frames.], batch size: 20, lr: 1.37e-03
2022-05-26 18:00:57,115 INFO [train.py:842] (2/4) Epoch 3, batch 3950, loss[loss=0.2785, simple_loss=0.3531, pruned_loss=0.1019, over 7315.00 frames.], tot_loss[loss=0.2835, simple_loss=0.3424, pruned_loss=0.1123, over 1421059.61 frames.], batch size: 21, lr: 1.37e-03
2022-05-26 18:01:35,675 INFO [train.py:842] (2/4) Epoch 3, batch 4000, loss[loss=0.353, simple_loss=0.3945, pruned_loss=0.1558, over 7326.00 frames.], tot_loss[loss=0.2844, simple_loss=0.3431, pruned_loss=0.1128, over 1426210.31 frames.], batch size: 22, lr: 1.36e-03
2022-05-26 18:02:15,091 INFO [train.py:842] (2/4) Epoch 3, batch 4050, loss[loss=0.2602, simple_loss=0.319, pruned_loss=0.1007, over 7440.00 frames.], tot_loss[loss=0.2833, simple_loss=0.3422, pruned_loss=0.1122, over 1426827.29 frames.], batch size: 20, lr: 1.36e-03
2022-05-26 18:02:53,421 INFO [train.py:842] (2/4) Epoch 3, batch 4100, loss[loss=0.2275, simple_loss=0.3002, pruned_loss=0.07747, over 7446.00 frames.], tot_loss[loss=0.282, simple_loss=0.3414, pruned_loss=0.1113, over 1418660.61 frames.], batch size: 19, lr: 1.36e-03
2022-05-26 18:03:32,194 INFO [train.py:842] (2/4) Epoch 3, batch 4150, loss[loss=0.2612, simple_loss=0.3274, pruned_loss=0.09748, over 7303.00 frames.], tot_loss[loss=0.2797, simple_loss=0.34, pruned_loss=0.1097, over 1423834.31 frames.], batch size: 25, lr: 1.36e-03
2022-05-26 18:04:10,673 INFO [train.py:842] (2/4) Epoch 3, batch 4200, loss[loss=0.3641, simple_loss=0.3987, pruned_loss=0.1647, over 7226.00 frames.], tot_loss[loss=0.2805, simple_loss=0.3403, pruned_loss=0.1103, over 1422345.44 frames.], batch size: 22, lr: 1.36e-03
2022-05-26 18:04:49,676 INFO [train.py:842] (2/4) Epoch 3, batch 4250, loss[loss=0.2309, simple_loss=0.3088, pruned_loss=0.07652, over 7269.00 frames.], tot_loss[loss=0.2785, simple_loss=0.3393, pruned_loss=0.1088, over 1426751.80 frames.], batch size: 19, lr: 1.36e-03
2022-05-26 18:05:28,274 INFO [train.py:842] (2/4) Epoch 3, batch 4300, loss[loss=0.3273, simple_loss=0.3574, pruned_loss=0.1486, over 7251.00 frames.], tot_loss[loss=0.2787, simple_loss=0.3394, pruned_loss=0.109, over 1425862.93 frames.], batch size: 16, lr: 1.36e-03
2022-05-26 18:06:07,396 INFO [train.py:842] (2/4) Epoch 3, batch 4350, loss[loss=0.2304, simple_loss=0.2898, pruned_loss=0.08556, over 7359.00 frames.], tot_loss[loss=0.2764, simple_loss=0.3376, pruned_loss=0.1076, over 1429580.03 frames.], batch size: 19, lr: 1.35e-03
2022-05-26 18:06:45,881 INFO [train.py:842] (2/4) Epoch 3, batch 4400, loss[loss=0.4135, simple_loss=0.4286, pruned_loss=0.1992, over 7446.00 frames.], tot_loss[loss=0.2785, simple_loss=0.3396, pruned_loss=0.1087, over 1429503.21 frames.], batch size: 20, lr: 1.35e-03
2022-05-26 18:07:24,813 INFO [train.py:842] (2/4) Epoch 3, batch 4450, loss[loss=0.2363, simple_loss=0.2969, pruned_loss=0.08783, over 6992.00 frames.], tot_loss[loss=0.2766, simple_loss=0.338, pruned_loss=0.1076, over 1433440.21 frames.], batch size: 16, lr: 1.35e-03
2022-05-26 18:08:03,442 INFO [train.py:842] (2/4) Epoch 3, batch 4500, loss[loss=0.3186, simple_loss=0.3731, pruned_loss=0.132, over 7188.00 frames.], tot_loss[loss=0.2785, simple_loss=0.3389, pruned_loss=0.109, over 1428790.44 frames.], batch size: 23, lr: 1.35e-03
2022-05-26 18:08:42,205 INFO [train.py:842] (2/4) Epoch 3, batch 4550, loss[loss=0.2802, simple_loss=0.3503, pruned_loss=0.105, over 7313.00 frames.], tot_loss[loss=0.2781, simple_loss=0.3384, pruned_loss=0.1089, over 1427981.20 frames.], batch size: 20, lr: 1.35e-03
2022-05-26 18:09:20,902 INFO [train.py:842] (2/4) Epoch 3, batch 4600, loss[loss=0.3561, simple_loss=0.3821, pruned_loss=0.165, over 7401.00 frames.], tot_loss[loss=0.2787, simple_loss=0.3394, pruned_loss=0.109, over 1428931.68 frames.], batch size: 18, lr: 1.35e-03
2022-05-26 18:10:00,060 INFO [train.py:842] (2/4) Epoch 3, batch 4650, loss[loss=0.2321, simple_loss=0.3012, pruned_loss=0.0815, over 6994.00 frames.], tot_loss[loss=0.2795, simple_loss=0.3402, pruned_loss=0.1094, over 1429408.70 frames.], batch size: 16, lr: 1.35e-03
2022-05-26 18:10:38,690 INFO [train.py:842] (2/4) Epoch 3, batch 4700, loss[loss=0.2438, simple_loss=0.3094, pruned_loss=0.08914, over 7251.00 frames.], tot_loss[loss=0.2803, simple_loss=0.3403, pruned_loss=0.1101, over 1433016.22 frames.], batch size: 19, lr: 1.34e-03
2022-05-26 18:11:17,515 INFO [train.py:842] (2/4) Epoch 3, batch 4750, loss[loss=0.2802, simple_loss=0.3476, pruned_loss=0.1064, over 7003.00 frames.], tot_loss[loss=0.2818, simple_loss=0.3415, pruned_loss=0.1111, over 1432810.76 frames.], batch size: 28, lr: 1.34e-03
2022-05-26 18:11:55,936 INFO [train.py:842] (2/4) Epoch 3, batch 4800, loss[loss=0.2856, simple_loss=0.3294, pruned_loss=0.1209, over 7291.00 frames.], tot_loss[loss=0.2824, simple_loss=0.3418, pruned_loss=0.1115, over 1432685.68 frames.], batch size: 17, lr: 1.34e-03
2022-05-26 18:12:34,712 INFO [train.py:842] (2/4) Epoch 3, batch 4850, loss[loss=0.2868, simple_loss=0.3544, pruned_loss=0.1097, over 7320.00 frames.], tot_loss[loss=0.2813, simple_loss=0.3415, pruned_loss=0.1105, over 1429834.54 frames.], batch size: 21, lr: 1.34e-03
2022-05-26 18:13:13,093 INFO [train.py:842] (2/4) Epoch 3, batch 4900, loss[loss=0.2372, simple_loss=0.3134, pruned_loss=0.08047, over 7226.00 frames.], tot_loss[loss=0.2808, simple_loss=0.3413, pruned_loss=0.1101, over 1426991.74 frames.], batch size: 20, lr: 1.34e-03
2022-05-26 18:13:51,835 INFO [train.py:842] (2/4) Epoch 3, batch 4950, loss[loss=0.3157, simple_loss=0.3714, pruned_loss=0.1299, over 7104.00 frames.], tot_loss[loss=0.2814, simple_loss=0.3415, pruned_loss=0.1106, over 1426256.09 frames.], batch size: 21, lr: 1.34e-03
2022-05-26 18:14:30,275 INFO [train.py:842] (2/4) Epoch 3, batch 5000, loss[loss=0.2852, simple_loss=0.3392, pruned_loss=0.1156, over 7154.00 frames.], tot_loss[loss=0.2797, simple_loss=0.3399, pruned_loss=0.1097, over 1420453.53 frames.], batch size: 19, lr: 1.34e-03
2022-05-26 18:15:09,120 INFO [train.py:842] (2/4) Epoch 3, batch 5050, loss[loss=0.3085, simple_loss=0.3588, pruned_loss=0.1291, over 7316.00 frames.], tot_loss[loss=0.278, simple_loss=0.3388, pruned_loss=0.1086, over 1420623.91 frames.], batch size: 21, lr: 1.33e-03
2022-05-26 18:15:47,521 INFO [train.py:842] (2/4) Epoch 3, batch 5100, loss[loss=0.2774, simple_loss=0.3271, pruned_loss=0.1138, over 7361.00 frames.], tot_loss[loss=0.2776, simple_loss=0.339, pruned_loss=0.1081, over 1421981.09 frames.], batch size: 19, lr: 1.33e-03
2022-05-26 18:16:26,335 INFO [train.py:842] (2/4) Epoch 3, batch 5150, loss[loss=0.2483, simple_loss=0.3106, pruned_loss=0.09303, over 7155.00 frames.], tot_loss[loss=0.2756, simple_loss=0.3369, pruned_loss=0.1072, over 1422804.46 frames.], batch size: 18, lr: 1.33e-03
2022-05-26 18:17:04,921 INFO [train.py:842] (2/4) Epoch 3, batch 5200, loss[loss=0.3539, simple_loss=0.4037, pruned_loss=0.152, over 7211.00 frames.], tot_loss[loss=0.2763, simple_loss=0.3374, pruned_loss=0.1076, over 1423067.60 frames.], batch size: 22, lr: 1.33e-03
2022-05-26 18:17:43,696 INFO [train.py:842] (2/4) Epoch 3, batch 5250, loss[loss=0.2806, simple_loss=0.3458, pruned_loss=0.1077, over 7330.00 frames.], tot_loss[loss=0.2779, simple_loss=0.3385, pruned_loss=0.1087, over 1423785.52 frames.], batch size: 20, lr: 1.33e-03
2022-05-26 18:18:22,090 INFO [train.py:842] (2/4) Epoch 3, batch 5300, loss[loss=0.3067, simple_loss=0.3662, pruned_loss=0.1236, over 7292.00 frames.], tot_loss[loss=0.2798, simple_loss=0.3405, pruned_loss=0.1095, over 1421711.67 frames.], batch size: 25, lr: 1.33e-03
2022-05-26 18:19:00,847 INFO [train.py:842] (2/4) Epoch 3, batch 5350, loss[loss=0.2757, simple_loss=0.3475, pruned_loss=0.102, over 7405.00 frames.], tot_loss[loss=0.2813, simple_loss=0.3421, pruned_loss=0.1102, over 1418361.62 frames.], batch size: 21, lr: 1.33e-03
2022-05-26 18:19:39,499 INFO [train.py:842] (2/4) Epoch 3, batch 5400, loss[loss=0.3111, simple_loss=0.3772, pruned_loss=0.1225, over 7080.00 frames.], tot_loss[loss=0.2817, simple_loss=0.3424, pruned_loss=0.1105, over 1417763.44 frames.], batch size: 18, lr: 1.33e-03
2022-05-26 18:20:18,486 INFO [train.py:842] (2/4) Epoch 3, batch 5450, loss[loss=0.2899, simple_loss=0.3482, pruned_loss=0.1158, over 7162.00 frames.], tot_loss[loss=0.2815, simple_loss=0.3416, pruned_loss=0.1107, over 1418695.40 frames.], batch size: 18, lr: 1.32e-03
2022-05-26 18:20:57,208 INFO [train.py:842] (2/4) Epoch 3, batch 5500, loss[loss=0.3363, simple_loss=0.3702, pruned_loss=0.1512, over 7216.00 frames.], tot_loss[loss=0.2804, simple_loss=0.3404, pruned_loss=0.1102, over 1421019.52 frames.], batch size: 21, lr: 1.32e-03
2022-05-26 18:21:36,013 INFO [train.py:842] (2/4) Epoch 3, batch 5550, loss[loss=0.242, simple_loss=0.3005, pruned_loss=0.09174, over 7200.00 frames.], tot_loss[loss=0.2785, simple_loss=0.3391, pruned_loss=0.109, over 1420003.93 frames.], batch size: 16, lr: 1.32e-03
2022-05-26 18:22:14,556 INFO [train.py:842] (2/4) Epoch 3, batch 5600, loss[loss=0.3287, simple_loss=0.3871, pruned_loss=0.1352, over 7168.00 frames.], tot_loss[loss=0.2777, simple_loss=0.3387, pruned_loss=0.1084, over 1424233.72 frames.], batch size: 26, lr: 1.32e-03
2022-05-26 18:22:55,842 INFO [train.py:842] (2/4) Epoch 3, batch 5650, loss[loss=0.2473, simple_loss=0.3098, pruned_loss=0.09241, over 7273.00 frames.], tot_loss[loss=0.2769, simple_loss=0.338, pruned_loss=0.1079, over 1425628.38 frames.], batch size: 17, lr: 1.32e-03
2022-05-26 18:23:34,208 INFO [train.py:842] (2/4) Epoch 3, batch 5700, loss[loss=0.2923, simple_loss=0.3525, pruned_loss=0.1161, over 6628.00 frames.], tot_loss[loss=0.2761, simple_loss=0.338, pruned_loss=0.1072, over 1424111.31 frames.], batch size: 38, lr: 1.32e-03
2022-05-26 18:24:13,058 INFO [train.py:842] (2/4) Epoch 3, batch 5750, loss[loss=0.3195, simple_loss=0.3648, pruned_loss=0.1371, over 5138.00 frames.], tot_loss[loss=0.2755, simple_loss=0.3372, pruned_loss=0.1069, over 1420624.81 frames.], batch size: 52, lr: 1.32e-03
2022-05-26 18:24:51,657 INFO [train.py:842] (2/4) Epoch 3, batch 5800, loss[loss=0.3004, simple_loss=0.3497, pruned_loss=0.1255, over 7138.00 frames.], tot_loss[loss=0.2749, simple_loss=0.337, pruned_loss=0.1064, over 1425775.98 frames.], batch size: 17, lr: 1.31e-03
2022-05-26 18:25:30,393 INFO [train.py:842] (2/4) Epoch 3, batch 5850, loss[loss=0.3305, simple_loss=0.3842, pruned_loss=0.1384, over 4914.00 frames.], tot_loss[loss=0.2742, simple_loss=0.3366, pruned_loss=0.1059, over 1424875.00 frames.], batch size: 52, lr: 1.31e-03
2022-05-26 18:26:08,878 INFO [train.py:842] (2/4) Epoch 3, batch 5900, loss[loss=0.2416, simple_loss=0.3158, pruned_loss=0.0837, over 7223.00 frames.], tot_loss[loss=0.2748, simple_loss=0.3374, pruned_loss=0.1061, over 1427007.54 frames.], batch size: 21, lr: 1.31e-03
2022-05-26 18:26:47,674 INFO [train.py:842] (2/4) Epoch 3, batch 5950, loss[loss=0.2504, simple_loss=0.3206, pruned_loss=0.09011, over 7376.00 frames.], tot_loss[loss=0.2743, simple_loss=0.3371, pruned_loss=0.1057, over 1423418.67 frames.], batch size: 23, lr: 1.31e-03
2022-05-26 18:27:26,257 INFO [train.py:842] (2/4) Epoch 3, batch 6000, loss[loss=0.2065, simple_loss=0.2763, pruned_loss=0.06832, over 7277.00 frames.], tot_loss[loss=0.2753, simple_loss=0.3375, pruned_loss=0.1065, over 1421283.07 frames.], batch size: 17, lr: 1.31e-03
2022-05-26 18:27:26,258 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 18:27:36,052 INFO [train.py:871] (2/4) Epoch 3, validation: loss=0.2084, simple_loss=0.3064, pruned_loss=0.05526, over 868885.00 frames.
2022-05-26 18:28:14,702 INFO [train.py:842] (2/4) Epoch 3, batch 6050, loss[loss=0.2959, simple_loss=0.3542, pruned_loss=0.1187, over 7142.00 frames.], tot_loss[loss=0.275, simple_loss=0.3377, pruned_loss=0.1061, over 1421037.48 frames.], batch size: 20, lr: 1.31e-03
2022-05-26 18:28:53,181 INFO [train.py:842] (2/4) Epoch 3, batch 6100, loss[loss=0.2539, simple_loss=0.3253, pruned_loss=0.09122, over 7177.00 frames.], tot_loss[loss=0.2746, simple_loss=0.3366, pruned_loss=0.1063, over 1420050.84 frames.], batch size: 26, lr: 1.31e-03
2022-05-26 18:29:31,924 INFO [train.py:842] (2/4) Epoch 3, batch 6150, loss[loss=0.2402, simple_loss=0.3132, pruned_loss=0.08358, over 7410.00 frames.], tot_loss[loss=0.2747, simple_loss=0.3365, pruned_loss=0.1064, over 1419408.66 frames.], batch size: 21, lr: 1.31e-03
2022-05-26 18:30:10,405 INFO [train.py:842] (2/4) Epoch 3, batch 6200, loss[loss=0.3296, simple_loss=0.3796, pruned_loss=0.1398, over 5223.00 frames.], tot_loss[loss=0.2744, simple_loss=0.3362, pruned_loss=0.1063, over 1417240.31 frames.], batch size: 52, lr: 1.30e-03
2022-05-26 18:30:49,612 INFO [train.py:842] (2/4) Epoch 3, batch 6250, loss[loss=0.2674, simple_loss=0.3331, pruned_loss=0.1008, over 6857.00 frames.], tot_loss[loss=0.2726, simple_loss=0.3345, pruned_loss=0.1053, over 1419167.38 frames.], batch size: 31, lr: 1.30e-03
2022-05-26 18:31:28,263 INFO [train.py:842] (2/4) Epoch 3, batch 6300, loss[loss=0.2318, simple_loss=0.2987, pruned_loss=0.08243, over 7145.00 frames.], tot_loss[loss=0.2742, simple_loss=0.3356, pruned_loss=0.1064, over 1413369.11 frames.], batch size: 18, lr: 1.30e-03
2022-05-26 18:32:07,488 INFO [train.py:842] (2/4) Epoch 3, batch 6350, loss[loss=0.2367, simple_loss=0.3027, pruned_loss=0.08536, over 7288.00 frames.], tot_loss[loss=0.2766, simple_loss=0.3368, pruned_loss=0.1082, over 1417998.91 frames.], batch size: 17, lr: 1.30e-03
2022-05-26 18:32:46,019 INFO [train.py:842] (2/4) Epoch 3, batch 6400, loss[loss=0.2982, simple_loss=0.3633, pruned_loss=0.1166, over 7292.00 frames.], tot_loss[loss=0.278, simple_loss=0.3382, pruned_loss=0.109, over 1418454.08 frames.], batch size: 25, lr: 1.30e-03
2022-05-26 18:33:24,827 INFO [train.py:842] (2/4) Epoch 3, batch 6450, loss[loss=0.3408, simple_loss=0.3794, pruned_loss=0.1511, over 7103.00 frames.], tot_loss[loss=0.2785, simple_loss=0.3387, pruned_loss=0.1091, over 1418265.94 frames.], batch size: 21, lr: 1.30e-03
2022-05-26 18:34:03,177 INFO [train.py:842] (2/4) Epoch 3, batch 6500, loss[loss=0.2502, simple_loss=0.3172, pruned_loss=0.09157, over 7281.00 frames.], tot_loss[loss=0.278, simple_loss=0.3393, pruned_loss=0.1084, over 1417170.91 frames.], batch size: 18, lr: 1.30e-03
2022-05-26 18:34:42,299 INFO [train.py:842] (2/4) Epoch 3, batch 6550, loss[loss=0.2476, simple_loss=0.3232, pruned_loss=0.08601, over 7154.00 frames.], tot_loss[loss=0.2776, simple_loss=0.3388, pruned_loss=0.1082, over 1421536.18 frames.], batch size: 20, lr: 1.30e-03
2022-05-26 18:35:21,030 INFO [train.py:842] (2/4) Epoch 3, batch 6600, loss[loss=0.417, simple_loss=0.4305, pruned_loss=0.2017, over 7107.00 frames.], tot_loss[loss=0.2765, simple_loss=0.338, pruned_loss=0.1075, over 1422053.44 frames.], batch size: 21, lr: 1.29e-03
2022-05-26 18:36:00,217 INFO [train.py:842] (2/4) Epoch 3, batch 6650, loss[loss=0.2628, simple_loss=0.3385, pruned_loss=0.09362, over 7121.00 frames.], tot_loss[loss=0.2761, simple_loss=0.3377, pruned_loss=0.1072, over 1422359.58 frames.], batch size: 21, lr: 1.29e-03
2022-05-26 18:36:38,882 INFO [train.py:842] (2/4) Epoch 3, batch 6700, loss[loss=0.3287, simple_loss=0.3781, pruned_loss=0.1397, over 7217.00 frames.], tot_loss[loss=0.2775, simple_loss=0.3388, pruned_loss=0.1081, over 1420190.08 frames.], batch size: 21, lr: 1.29e-03
2022-05-26 18:37:17,622 INFO [train.py:842] (2/4) Epoch 3, batch 6750, loss[loss=0.2273, simple_loss=0.2949, pruned_loss=0.07983, over 6797.00 frames.], tot_loss[loss=0.2788, simple_loss=0.3402, pruned_loss=0.1086, over 1421685.84 frames.], batch size: 15, lr: 1.29e-03
2022-05-26 18:37:56,017 INFO [train.py:842] (2/4) Epoch 3, batch 6800, loss[loss=0.3338, simple_loss=0.3956, pruned_loss=0.136, over 7160.00 frames.], tot_loss[loss=0.2767, simple_loss=0.3388, pruned_loss=0.1073, over 1422565.90 frames.], batch size: 26, lr: 1.29e-03
2022-05-26 18:38:34,911 INFO [train.py:842] (2/4) Epoch 3, batch 6850, loss[loss=0.2545, simple_loss=0.3166, pruned_loss=0.09623, over 7391.00 frames.], tot_loss[loss=0.2763, simple_loss=0.3381, pruned_loss=0.1072, over 1423977.43 frames.], batch size: 23, lr: 1.29e-03
2022-05-26 18:39:13,430 INFO [train.py:842] (2/4) Epoch 3, batch 6900, loss[loss=0.2921, simple_loss=0.3347, pruned_loss=0.1248, over 7133.00 frames.], tot_loss[loss=0.2752, simple_loss=0.3377, pruned_loss=0.1063, over 1427385.75 frames.], batch size: 17, lr: 1.29e-03
2022-05-26 18:39:52,125 INFO [train.py:842] (2/4) Epoch 3, batch 6950, loss[loss=0.2551, simple_loss=0.3376, pruned_loss=0.08631, over 7227.00 frames.], tot_loss[loss=0.2753, simple_loss=0.3381, pruned_loss=0.1063, over 1429190.24 frames.], batch size: 21, lr: 1.29e-03
2022-05-26 18:40:30,491 INFO [train.py:842] (2/4) Epoch 3, batch 7000, loss[loss=0.217, simple_loss=0.3067, pruned_loss=0.06368, over 7318.00 frames.], tot_loss[loss=0.2741, simple_loss=0.3373, pruned_loss=0.1055, over 1427759.75 frames.], batch size: 21, lr: 1.28e-03
2022-05-26 18:41:09,291 INFO [train.py:842] (2/4) Epoch 3, batch 7050, loss[loss=0.2798, simple_loss=0.3421, pruned_loss=0.1088, over 7210.00 frames.], tot_loss[loss=0.274, simple_loss=0.337, pruned_loss=0.1055, over 1426053.25 frames.], batch size: 22, lr: 1.28e-03
2022-05-26 18:41:47,871 INFO [train.py:842] (2/4) Epoch 3, batch 7100, loss[loss=0.2372, simple_loss=0.3125, pruned_loss=0.08091, over 7241.00 frames.], tot_loss[loss=0.2737, simple_loss=0.337, pruned_loss=0.1052, over 1424291.09 frames.], batch size: 20, lr: 1.28e-03
2022-05-26 18:42:26,627 INFO [train.py:842] (2/4) Epoch 3, batch 7150, loss[loss=0.3204, simple_loss=0.3558, pruned_loss=0.1425, over 7178.00 frames.], tot_loss[loss=0.2731, simple_loss=0.3363, pruned_loss=0.1049, over 1423826.01 frames.], batch size: 19, lr: 1.28e-03
2022-05-26 18:43:05,175 INFO [train.py:842] (2/4) Epoch 3, batch 7200, loss[loss=0.3616, simple_loss=0.3973, pruned_loss=0.163, over 4829.00 frames.], tot_loss[loss=0.2738, simple_loss=0.3369, pruned_loss=0.1054, over 1416709.11 frames.], batch size: 52, lr: 1.28e-03
2022-05-26 18:43:43,951 INFO [train.py:842] (2/4) Epoch 3, batch 7250, loss[loss=0.206, simple_loss=0.276, pruned_loss=0.06796, over 7286.00 frames.], tot_loss[loss=0.2754, simple_loss=0.3378, pruned_loss=0.1065, over 1418969.42 frames.], batch size: 17, lr: 1.28e-03
2022-05-26 18:44:22,268 INFO [train.py:842] (2/4) Epoch 3, batch 7300, loss[loss=0.3339, simple_loss=0.3894, pruned_loss=0.1392, over 7326.00 frames.], tot_loss[loss=0.2747, simple_loss=0.3377, pruned_loss=0.1059, over 1421644.81 frames.], batch size: 21, lr: 1.28e-03
2022-05-26 18:45:01,027 INFO [train.py:842] (2/4) Epoch 3, batch 7350, loss[loss=0.302, simple_loss=0.3611, pruned_loss=0.1214, over 7289.00 frames.], tot_loss[loss=0.2738, simple_loss=0.3369, pruned_loss=0.1054, over 1423282.58 frames.], batch size: 24, lr: 1.28e-03
2022-05-26 18:45:39,600 INFO [train.py:842] (2/4) Epoch 3, batch 7400, loss[loss=0.2847, simple_loss=0.3367, pruned_loss=0.1164, over 7259.00 frames.], tot_loss[loss=0.2741, simple_loss=0.337, pruned_loss=0.1056, over 1426106.36 frames.], batch size: 19, lr: 1.27e-03
2022-05-26 18:46:18,835 INFO [train.py:842] (2/4) Epoch 3, batch 7450, loss[loss=0.2442, simple_loss=0.3172, pruned_loss=0.08555, over 7402.00 frames.], tot_loss[loss=0.2725, simple_loss=0.3355, pruned_loss=0.1048, over 1425662.36 frames.], batch size: 21, lr: 1.27e-03
2022-05-26 18:46:57,503 INFO [train.py:842] (2/4) Epoch 3, batch 7500, loss[loss=0.25, simple_loss=0.3204, pruned_loss=0.08982, over 7146.00 frames.], tot_loss[loss=0.271, simple_loss=0.3342, pruned_loss=0.1039, over 1428659.86 frames.], batch size: 17, lr: 1.27e-03
2022-05-26 18:47:36,159 INFO [train.py:842] (2/4) Epoch 3, batch 7550, loss[loss=0.2389, simple_loss=0.3136, pruned_loss=0.0821, over 7309.00 frames.], tot_loss[loss=0.2726, simple_loss=0.3353, pruned_loss=0.1049, over 1427498.90 frames.], batch size: 24, lr: 1.27e-03
2022-05-26 18:48:14,671 INFO [train.py:842] (2/4) Epoch 3, batch 7600, loss[loss=0.2439, simple_loss=0.331, pruned_loss=0.07838, over 7333.00 frames.], tot_loss[loss=0.2744, simple_loss=0.3369, pruned_loss=0.1059, over 1424310.38 frames.], batch size: 22, lr: 1.27e-03
2022-05-26 18:48:53,763 INFO [train.py:842] (2/4) Epoch 3, batch 7650, loss[loss=0.2073, simple_loss=0.2635, pruned_loss=0.07553, over 7005.00 frames.], tot_loss[loss=0.2727, simple_loss=0.335, pruned_loss=0.1052, over 1417811.43 frames.], batch size: 16, lr: 1.27e-03
2022-05-26 18:49:32,389 INFO [train.py:842] (2/4) Epoch 3, batch 7700, loss[loss=0.3076, simple_loss=0.3615, pruned_loss=0.1268, over 6991.00 frames.], tot_loss[loss=0.2726, simple_loss=0.3349, pruned_loss=0.1052, over 1418867.30 frames.], batch size: 28, lr: 1.27e-03
2022-05-26 18:50:11,094 INFO [train.py:842] (2/4) Epoch 3, batch 7750, loss[loss=0.3072, simple_loss=0.3419, pruned_loss=0.1363, over 7250.00 frames.], tot_loss[loss=0.2731, simple_loss=0.3355, pruned_loss=0.1054, over 1424434.75 frames.], batch size: 16, lr: 1.27e-03
2022-05-26 18:50:49,654 INFO [train.py:842] (2/4) Epoch 3, batch 7800, loss[loss=0.2808, simple_loss=0.3446, pruned_loss=0.1085, over 7391.00 frames.], tot_loss[loss=0.273, simple_loss=0.3352, pruned_loss=0.1054, over 1424875.38 frames.], batch size: 23, lr: 1.27e-03
2022-05-26 18:51:28,447 INFO [train.py:842] (2/4) Epoch 3, batch 7850, loss[loss=0.289, simple_loss=0.3484, pruned_loss=0.1148, over 7181.00 frames.], tot_loss[loss=0.2709, simple_loss=0.3338, pruned_loss=0.104, over 1425324.79 frames.], batch size: 26, lr: 1.26e-03
2022-05-26 18:52:07,136 INFO [train.py:842] (2/4) Epoch 3, batch 7900, loss[loss=0.3337, simple_loss=0.3896, pruned_loss=0.1389, over 7330.00 frames.], tot_loss[loss=0.2704, simple_loss=0.3331, pruned_loss=0.1038, over 1425555.76 frames.], batch size: 20, lr: 1.26e-03
2022-05-26 18:52:45,863 INFO [train.py:842] (2/4) Epoch 3, batch 7950, loss[loss=0.2749, simple_loss=0.3484, pruned_loss=0.1007, over 7231.00 frames.], tot_loss[loss=0.2708, simple_loss=0.3338, pruned_loss=0.1039, over 1423980.45 frames.], batch size: 20, lr: 1.26e-03
2022-05-26 18:53:24,307 INFO [train.py:842] (2/4) Epoch 3, batch 8000, loss[loss=0.2243, simple_loss=0.3013, pruned_loss=0.07365, over 7156.00 frames.], tot_loss[loss=0.2729, simple_loss=0.3353, pruned_loss=0.1053, over 1420338.38 frames.], batch size: 26, lr: 1.26e-03
2022-05-26 18:54:03,062 INFO [train.py:842] (2/4) Epoch 3, batch 8050, loss[loss=0.2887, simple_loss=0.3473, pruned_loss=0.115, over 7142.00 frames.], tot_loss[loss=0.2723, simple_loss=0.3347, pruned_loss=0.105, over 1418526.97 frames.], batch size: 26, lr: 1.26e-03
2022-05-26 18:54:41,744 INFO [train.py:842] (2/4) Epoch 3, batch 8100, loss[loss=0.2873, simple_loss=0.3328, pruned_loss=0.1209, over 7208.00 frames.], tot_loss[loss=0.2716, simple_loss=0.3339, pruned_loss=0.1047, over 1419124.65 frames.], batch size: 22, lr: 1.26e-03
2022-05-26 18:55:20,571 INFO [train.py:842] (2/4) Epoch 3, batch 8150, loss[loss=0.2415, simple_loss=0.3153, pruned_loss=0.08384, over 7250.00 frames.], tot_loss[loss=0.2712, simple_loss=0.3339, pruned_loss=0.1042, over 1413055.81 frames.], batch size: 19, lr: 1.26e-03
2022-05-26 18:55:59,059 INFO [train.py:842] (2/4) Epoch 3, batch 8200, loss[loss=0.2973, simple_loss=0.3598, pruned_loss=0.1174, over 7328.00 frames.], tot_loss[loss=0.2706, simple_loss=0.3335, pruned_loss=0.1038, over 1414796.55 frames.], batch size: 20, lr: 1.26e-03
2022-05-26 18:56:37,962 INFO [train.py:842] (2/4) Epoch 3, batch 8250, loss[loss=0.238, simple_loss=0.315, pruned_loss=0.08045, over 7265.00 frames.], tot_loss[loss=0.269, simple_loss=0.3323, pruned_loss=0.1029, over 1418216.39 frames.], batch size: 19, lr: 1.26e-03
2022-05-26 18:57:16,529 INFO [train.py:842] (2/4) Epoch 3, batch 8300, loss[loss=0.2567, simple_loss=0.3267, pruned_loss=0.09334, over 7108.00 frames.], tot_loss[loss=0.2696, simple_loss=0.3333, pruned_loss=0.1029, over 1420049.60 frames.], batch size: 21, lr: 1.25e-03
2022-05-26 18:57:55,085 INFO [train.py:842] (2/4) Epoch 3, batch 8350, loss[loss=0.4072, simple_loss=0.4283, pruned_loss=0.193, over 5189.00 frames.], tot_loss[loss=0.2727, simple_loss=0.3354, pruned_loss=0.105, over 1416959.01 frames.], batch size: 52, lr: 1.25e-03
2022-05-26 18:58:33,821 INFO [train.py:842] (2/4) Epoch 3, batch 8400, loss[loss=0.2411, simple_loss=0.3209, pruned_loss=0.08061, over 7272.00 frames.], tot_loss[loss=0.2724, simple_loss=0.3353, pruned_loss=0.1047, over 1417369.02 frames.], batch size: 19, lr: 1.25e-03
2022-05-26 18:59:12,590 INFO [train.py:842] (2/4) Epoch 3, batch 8450, loss[loss=0.2451, simple_loss=0.3266, pruned_loss=0.08178, over 7066.00 frames.], tot_loss[loss=0.2744, simple_loss=0.3369, pruned_loss=0.1059, over 1418178.16 frames.], batch size: 28, lr: 1.25e-03
2022-05-26 19:00:01,252 INFO [train.py:842] (2/4) Epoch 3, batch 8500, loss[loss=0.2532, simple_loss=0.3183, pruned_loss=0.09399, over 7168.00 frames.], tot_loss[loss=0.2747, simple_loss=0.3368, pruned_loss=0.1063, over 1420456.27 frames.], batch size: 19, lr: 1.25e-03
2022-05-26 19:00:39,773 INFO [train.py:842] (2/4) Epoch 3, batch 8550, loss[loss=0.293, simple_loss=0.3575, pruned_loss=0.1143, over 6431.00 frames.], tot_loss[loss=0.2748, simple_loss=0.3372, pruned_loss=0.1062, over 1417996.81 frames.], batch size: 38, lr: 1.25e-03
2022-05-26 19:01:18,435 INFO [train.py:842] (2/4) Epoch 3, batch 8600, loss[loss=0.2503, simple_loss=0.2985, pruned_loss=0.101, over 7270.00 frames.], tot_loss[loss=0.2734, simple_loss=0.3359, pruned_loss=0.1055, over 1419402.52 frames.], batch size: 17, lr: 1.25e-03
2022-05-26 19:01:57,288 INFO [train.py:842] (2/4) Epoch 3, batch 8650, loss[loss=0.2239, simple_loss=0.2915, pruned_loss=0.07818, over 7274.00 frames.], tot_loss[loss=0.2736, simple_loss=0.3361, pruned_loss=0.1055, over 1417342.49 frames.], batch size: 18, lr: 1.25e-03
2022-05-26 19:02:35,799 INFO [train.py:842] (2/4) Epoch 3, batch 8700, loss[loss=0.2553, simple_loss=0.3236, pruned_loss=0.09352, over 7095.00 frames.], tot_loss[loss=0.2738, simple_loss=0.3362, pruned_loss=0.1057, over 1417009.21 frames.], batch size: 28, lr: 1.24e-03
2022-05-26 19:03:14,506 INFO [train.py:842] (2/4) Epoch 3, batch 8750, loss[loss=0.2916, simple_loss=0.3618, pruned_loss=0.1107, over 7089.00 frames.], tot_loss[loss=0.2728, simple_loss=0.3354, pruned_loss=0.1051, over 1418927.26 frames.], batch size: 28, lr: 1.24e-03
2022-05-26 19:03:53,176 INFO [train.py:842] (2/4) Epoch 3, batch 8800, loss[loss=0.2233, simple_loss=0.2811, pruned_loss=0.08278, over 7291.00 frames.], tot_loss[loss=0.2736, simple_loss=0.336, pruned_loss=0.1056, over 1418778.03 frames.], batch size: 18, lr: 1.24e-03
2022-05-26 19:04:31,958 INFO [train.py:842] (2/4) Epoch 3, batch 8850, loss[loss=0.2528, simple_loss=0.3187, pruned_loss=0.09351, over 7334.00 frames.], tot_loss[loss=0.2732, simple_loss=0.3359, pruned_loss=0.1052, over 1420221.29 frames.], batch size: 22, lr: 1.24e-03
2022-05-26 19:05:10,557 INFO [train.py:842] (2/4) Epoch 3, batch 8900, loss[loss=0.3458, simple_loss=0.3839, pruned_loss=0.1539, over 7029.00 frames.], tot_loss[loss=0.2728, simple_loss=0.3354, pruned_loss=0.1051, over 1420055.52 frames.], batch size: 28, lr: 1.24e-03
2022-05-26 19:05:59,430 INFO [train.py:842] (2/4) Epoch 3, batch 8950, loss[loss=0.2946, simple_loss=0.3513, pruned_loss=0.1189, over 7275.00 frames.], tot_loss[loss=0.274, simple_loss=0.3367, pruned_loss=0.1056, over 1414395.61 frames.], batch size: 17, lr: 1.24e-03
2022-05-26 19:06:48,323 INFO [train.py:842] (2/4) Epoch 3, batch 9000, loss[loss=0.3171, simple_loss=0.3672, pruned_loss=0.1335, over 5075.00 frames.], tot_loss[loss=0.2753, simple_loss=0.3376, pruned_loss=0.1065, over 1404603.93 frames.], batch size: 52, lr: 1.24e-03
2022-05-26 19:06:48,325 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 19:07:08,221 INFO [train.py:871] (2/4) Epoch 3, validation: loss=0.2052, simple_loss=0.3029, pruned_loss=0.0537, over 868885.00 frames.
2022-05-26 19:07:46,536 INFO [train.py:842] (2/4) Epoch 3, batch 9050, loss[loss=0.2783, simple_loss=0.3531, pruned_loss=0.1018, over 7276.00 frames.], tot_loss[loss=0.2769, simple_loss=0.3396, pruned_loss=0.107, over 1388958.98 frames.], batch size: 25, lr: 1.24e-03
2022-05-26 19:08:24,015 INFO [train.py:842] (2/4) Epoch 3, batch 9100, loss[loss=0.277, simple_loss=0.335, pruned_loss=0.1095, over 4864.00 frames.], tot_loss[loss=0.2823, simple_loss=0.3437, pruned_loss=0.1104, over 1358479.10 frames.], batch size: 52, lr: 1.24e-03
2022-05-26 19:09:01,536 INFO [train.py:842] (2/4) Epoch 3, batch 9150, loss[loss=0.3305, simple_loss=0.3684, pruned_loss=0.1462, over 5258.00 frames.], tot_loss[loss=0.2889, simple_loss=0.348, pruned_loss=0.115, over 1296657.64 frames.], batch size: 53, lr: 1.24e-03
2022-05-26 19:09:53,253 INFO [train.py:842] (2/4) Epoch 4, batch 0, loss[loss=0.2373, simple_loss=0.3196, pruned_loss=0.07753, over 7196.00 frames.], tot_loss[loss=0.2373, simple_loss=0.3196, pruned_loss=0.07753, over 7196.00 frames.], batch size: 23, lr: 1.20e-03
2022-05-26 19:10:32,529 INFO [train.py:842] (2/4) Epoch 4, batch 50, loss[loss=0.224, simple_loss=0.2853, pruned_loss=0.08134, over 7262.00 frames.], tot_loss[loss=0.2617, simple_loss=0.3258, pruned_loss=0.09879, over 318010.53 frames.], batch size: 17, lr: 1.20e-03
2022-05-26 19:11:11,264 INFO [train.py:842] (2/4) Epoch 4, batch 100, loss[loss=0.2571, simple_loss=0.3208, pruned_loss=0.09668, over 7276.00 frames.], tot_loss[loss=0.265, simple_loss=0.3277, pruned_loss=0.1011, over 564204.36 frames.], batch size: 17, lr: 1.20e-03
2022-05-26 19:11:50,051 INFO [train.py:842] (2/4) Epoch 4, batch 150, loss[loss=0.2594, simple_loss=0.3314, pruned_loss=0.09363, over 7329.00 frames.], tot_loss[loss=0.2644, simple_loss=0.3279, pruned_loss=0.1005, over 754744.61 frames.], batch size: 22, lr: 1.20e-03
2022-05-26 19:12:28,800 INFO [train.py:842] (2/4) Epoch 4, batch 200, loss[loss=0.3075, simple_loss=0.3792, pruned_loss=0.1179, over 7210.00 frames.], tot_loss[loss=0.2681, simple_loss=0.331, pruned_loss=0.1026, over 903224.34 frames.], batch size: 23, lr: 1.19e-03
2022-05-26 19:13:07,630 INFO [train.py:842] (2/4) Epoch 4, batch 250, loss[loss=0.2814, simple_loss=0.3478, pruned_loss=0.1075, over 7340.00 frames.], tot_loss[loss=0.2708, simple_loss=0.3339, pruned_loss=0.1039, over 1015640.84 frames.], batch size: 22, lr: 1.19e-03
2022-05-26 19:13:46,331 INFO [train.py:842] (2/4) Epoch 4, batch 300, loss[loss=0.2884, simple_loss=0.3479, pruned_loss=0.1145, over 7373.00 frames.], tot_loss[loss=0.269, simple_loss=0.3328, pruned_loss=0.1026, over 1110220.67 frames.], batch size: 23, lr: 1.19e-03
2022-05-26 19:14:25,613 INFO [train.py:842] (2/4) Epoch 4, batch 350, loss[loss=0.2479, simple_loss=0.3315, pruned_loss=0.08213, over 7325.00 frames.], tot_loss[loss=0.2663, simple_loss=0.331, pruned_loss=0.1008, over 1181573.04 frames.], batch size: 21, lr: 1.19e-03
2022-05-26 19:15:04,167 INFO [train.py:842] (2/4) Epoch 4, batch 400, loss[loss=0.2615, simple_loss=0.3385, pruned_loss=0.09229, over 7239.00 frames.], tot_loss[loss=0.2641, simple_loss=0.3293, pruned_loss=0.09942, over 1232695.25 frames.], batch size: 20, lr: 1.19e-03
2022-05-26 19:15:42,984 INFO [train.py:842] (2/4) Epoch 4, batch 450, loss[loss=0.2402, simple_loss=0.3102, pruned_loss=0.08505, over 7145.00 frames.], tot_loss[loss=0.2642, simple_loss=0.3292, pruned_loss=0.09961, over 1275376.69 frames.], batch size: 20, lr: 1.19e-03
2022-05-26 19:16:21,303 INFO [train.py:842] (2/4) Epoch 4, batch 500, loss[loss=0.2448, simple_loss=0.3269, pruned_loss=0.08134, over 7165.00 frames.], tot_loss[loss=0.2635, simple_loss=0.3295, pruned_loss=0.09877, over 1304525.21 frames.], batch size: 19, lr: 1.19e-03
2022-05-26 19:17:00,278 INFO [train.py:842] (2/4) Epoch 4, batch 550, loss[loss=0.2579, simple_loss=0.3193, pruned_loss=0.09825, over 7159.00 frames.], tot_loss[loss=0.2662, simple_loss=0.3312, pruned_loss=0.1006, over 1330700.98 frames.], batch size: 18, lr: 1.19e-03
2022-05-26 19:17:38,802 INFO [train.py:842] (2/4) Epoch 4, batch 600, loss[loss=0.3448, simple_loss=0.386, pruned_loss=0.1518, over 6312.00 frames.], tot_loss[loss=0.2662, simple_loss=0.3309, pruned_loss=0.1008, over 1348130.90 frames.], batch size: 37, lr: 1.19e-03
2022-05-26 19:18:17,917 INFO [train.py:842] (2/4) Epoch 4, batch 650, loss[loss=0.258, simple_loss=0.3276, pruned_loss=0.09415, over 7431.00 frames.], tot_loss[loss=0.2655, simple_loss=0.3307, pruned_loss=0.1002, over 1368530.40 frames.], batch size: 20, lr: 1.18e-03
2022-05-26 19:18:56,786 INFO [train.py:842] (2/4) Epoch 4, batch 700, loss[loss=0.3717, simple_loss=0.4086, pruned_loss=0.1674, over 7291.00 frames.], tot_loss[loss=0.2634, simple_loss=0.329, pruned_loss=0.09892, over 1385616.52 frames.], batch size: 24, lr: 1.18e-03
2022-05-26 19:19:35,622 INFO [train.py:842] (2/4) Epoch 4, batch 750, loss[loss=0.2748, simple_loss=0.3478, pruned_loss=0.1009, over 7279.00 frames.], tot_loss[loss=0.2647, simple_loss=0.3296, pruned_loss=0.09987, over 1393059.31 frames.], batch size: 24, lr: 1.18e-03
2022-05-26 19:20:14,179 INFO [train.py:842] (2/4) Epoch 4, batch 800, loss[loss=0.3096, simple_loss=0.3588, pruned_loss=0.1302, over 7262.00 frames.], tot_loss[loss=0.2654, simple_loss=0.3304, pruned_loss=0.1002, over 1397520.48 frames.], batch size: 19, lr: 1.18e-03
2022-05-26 19:20:53,466 INFO [train.py:842] (2/4) Epoch 4, batch 850, loss[loss=0.2751, simple_loss=0.3291, pruned_loss=0.1106, over 7072.00 frames.], tot_loss[loss=0.2644, simple_loss=0.3296, pruned_loss=0.09957, over 1407291.17 frames.], batch size: 18, lr: 1.18e-03
2022-05-26 19:21:32,125 INFO [train.py:842] (2/4) Epoch 4, batch 900, loss[loss=0.2562, simple_loss=0.3369, pruned_loss=0.08774, over 7113.00 frames.], tot_loss[loss=0.2623, simple_loss=0.328, pruned_loss=0.09829, over 1414560.57 frames.], batch size: 21, lr: 1.18e-03
2022-05-26 19:22:10,974 INFO [train.py:842] (2/4) Epoch 4, batch 950, loss[loss=0.242, simple_loss=0.3131, pruned_loss=0.08544, over 7164.00 frames.], tot_loss[loss=0.264, simple_loss=0.3292, pruned_loss=0.09936, over 1419303.93 frames.], batch size: 26, lr: 1.18e-03
2022-05-26 19:22:49,568 INFO [train.py:842] (2/4) Epoch 4, batch 1000, loss[loss=0.2713, simple_loss=0.3188, pruned_loss=0.1119, over 7293.00 frames.], tot_loss[loss=0.2639, simple_loss=0.3287, pruned_loss=0.09955, over 1420198.30 frames.], batch size: 18, lr: 1.18e-03
2022-05-26 19:23:28,174 INFO [train.py:842] (2/4) Epoch 4, batch 1050, loss[loss=0.3032, simple_loss=0.3666, pruned_loss=0.1199, over 6766.00 frames.], tot_loss[loss=0.2656, simple_loss=0.33, pruned_loss=0.1005, over 1417848.44 frames.], batch size: 31, lr: 1.18e-03
2022-05-26 19:24:06,791 INFO [train.py:842] (2/4) Epoch 4, batch 1100, loss[loss=0.2533, simple_loss=0.3326, pruned_loss=0.08697, over 7408.00 frames.], tot_loss[loss=0.2657, simple_loss=0.3302, pruned_loss=0.1007, over 1419108.80 frames.], batch size: 21, lr: 1.18e-03
2022-05-26 19:24:45,510 INFO [train.py:842] (2/4) Epoch 4, batch 1150, loss[loss=0.2698, simple_loss=0.3452, pruned_loss=0.09717, over 7322.00 frames.], tot_loss[loss=0.2677, simple_loss=0.3323, pruned_loss=0.1015, over 1416690.93 frames.], batch size: 21, lr: 1.17e-03
2022-05-26 19:25:24,167 INFO [train.py:842] (2/4) Epoch 4, batch 1200, loss[loss=0.296, simple_loss=0.3469, pruned_loss=0.1226, over 7308.00 frames.], tot_loss[loss=0.2677, simple_loss=0.3322, pruned_loss=0.1016, over 1414017.28 frames.], batch size: 21, lr: 1.17e-03
2022-05-26 19:26:03,106 INFO [train.py:842] (2/4) Epoch 4, batch 1250, loss[loss=0.2417, simple_loss=0.2977, pruned_loss=0.09287, over 6768.00 frames.], tot_loss[loss=0.2669, simple_loss=0.3319, pruned_loss=0.1009, over 1412212.34 frames.], batch size: 15, lr: 1.17e-03
2022-05-26 19:26:45,096 INFO [train.py:842] (2/4) Epoch 4, batch 1300, loss[loss=0.3067, simple_loss=0.3658, pruned_loss=0.1238, over 7209.00 frames.], tot_loss[loss=0.2665, simple_loss=0.3313, pruned_loss=0.1009, over 1415929.66 frames.], batch size: 23, lr: 1.17e-03
2022-05-26 19:27:23,918 INFO [train.py:842] (2/4) Epoch 4, batch 1350, loss[loss=0.2408, simple_loss=0.3194, pruned_loss=0.08105, over 7243.00 frames.], tot_loss[loss=0.2659, simple_loss=0.331, pruned_loss=0.1004, over 1414760.41 frames.], batch size: 20, lr: 1.17e-03
2022-05-26 19:28:02,850 INFO [train.py:842] (2/4) Epoch 4, batch 1400, loss[loss=0.2953, simple_loss=0.3546, pruned_loss=0.118, over 7206.00 frames.], tot_loss[loss=0.2675, simple_loss=0.3314, pruned_loss=0.1018, over 1418911.23 frames.], batch size: 22, lr: 1.17e-03
2022-05-26 19:28:42,081 INFO [train.py:842] (2/4) Epoch 4, batch 1450, loss[loss=0.295, simple_loss=0.3556, pruned_loss=0.1172, over 7310.00 frames.], tot_loss[loss=0.2675, simple_loss=0.3318, pruned_loss=0.1016, over 1420817.49 frames.], batch size: 24, lr: 1.17e-03
2022-05-26 19:29:23,395 INFO [train.py:842] (2/4) Epoch 4, batch 1500, loss[loss=0.2745, simple_loss=0.3384, pruned_loss=0.1054, over 7286.00 frames.], tot_loss[loss=0.2669, simple_loss=0.3313, pruned_loss=0.1013, over 1417579.38 frames.], batch size: 24, lr: 1.17e-03
2022-05-26 19:30:02,830 INFO [train.py:842] (2/4) Epoch 4, batch 1550, loss[loss=0.3535, simple_loss=0.3878, pruned_loss=0.1596, over 4760.00 frames.], tot_loss[loss=0.2655, simple_loss=0.3305, pruned_loss=0.1003, over 1416470.59 frames.], batch size: 53, lr: 1.17e-03
2022-05-26 19:30:42,130 INFO [train.py:842] (2/4) Epoch 4, batch 1600, loss[loss=0.2479, simple_loss=0.3346, pruned_loss=0.08056, over 7263.00 frames.], tot_loss[loss=0.2652, simple_loss=0.3307, pruned_loss=0.09988, over 1412908.94 frames.], batch size: 25, lr: 1.17e-03
2022-05-26 19:31:21,039 INFO [train.py:842] (2/4) Epoch 4, batch 1650, loss[loss=0.2492, simple_loss=0.3203, pruned_loss=0.08907, over 7332.00 frames.], tot_loss[loss=0.2651, simple_loss=0.3302, pruned_loss=0.1001, over 1414401.71 frames.], batch size: 20, lr: 1.17e-03
2022-05-26 19:31:59,634 INFO [train.py:842] (2/4) Epoch 4, batch 1700, loss[loss=0.3739, simple_loss=0.4095, pruned_loss=0.1692, over 7141.00 frames.], tot_loss[loss=0.2653, simple_loss=0.3307, pruned_loss=0.09993, over 1418690.50 frames.], batch size: 20, lr: 1.16e-03
2022-05-26 19:32:38,143 INFO [train.py:842] (2/4) Epoch 4, batch 1750, loss[loss=0.2463, simple_loss=0.3247, pruned_loss=0.08393, over 7204.00 frames.], tot_loss[loss=0.2647, simple_loss=0.3306, pruned_loss=0.09942, over 1418545.72 frames.], batch size: 22, lr: 1.16e-03
2022-05-26 19:33:16,535 INFO [train.py:842] (2/4) Epoch 4, batch 1800, loss[loss=0.2728, simple_loss=0.3304, pruned_loss=0.1076, over 7238.00 frames.], tot_loss[loss=0.2663, simple_loss=0.3323, pruned_loss=0.1001, over 1420645.01 frames.], batch size: 21, lr: 1.16e-03
2022-05-26 19:33:55,178 INFO [train.py:842] (2/4) Epoch 4, batch 1850, loss[loss=0.2213, simple_loss=0.2939, pruned_loss=0.07437, over 7145.00 frames.], tot_loss[loss=0.267, simple_loss=0.3327, pruned_loss=0.1007, over 1419107.25 frames.], batch size: 17, lr: 1.16e-03
2022-05-26 19:34:33,652 INFO [train.py:842] (2/4) Epoch 4, batch 1900, loss[loss=0.2323, simple_loss=0.3098, pruned_loss=0.07739, over 7155.00 frames.], tot_loss[loss=0.2663, simple_loss=0.3321, pruned_loss=0.1003, over 1422483.35 frames.], batch size: 19, lr: 1.16e-03
2022-05-26 19:35:12,540 INFO [train.py:842] (2/4) Epoch 4, batch 1950, loss[loss=0.3046, simple_loss=0.3542, pruned_loss=0.1275, over 6481.00 frames.], tot_loss[loss=0.2661, simple_loss=0.3317, pruned_loss=0.1002, over 1427779.75 frames.], batch size: 38, lr: 1.16e-03
2022-05-26 19:35:51,031 INFO [train.py:842] (2/4) Epoch 4, batch 2000, loss[loss=0.2294, simple_loss=0.3155, pruned_loss=0.07168, over 7113.00 frames.], tot_loss[loss=0.2662, simple_loss=0.3317, pruned_loss=0.1003, over 1425671.68 frames.], batch size: 21, lr: 1.16e-03
2022-05-26 19:36:29,968 INFO [train.py:842] (2/4) Epoch 4, batch 2050, loss[loss=0.3247, simple_loss=0.3823, pruned_loss=0.1336, over 6712.00 frames.], tot_loss[loss=0.2692, simple_loss=0.3339, pruned_loss=0.1023, over 1422042.17 frames.], batch size: 31, lr: 1.16e-03
2022-05-26 19:37:08,654 INFO [train.py:842] (2/4) Epoch 4, batch 2100, loss[loss=0.2276, simple_loss=0.3202, pruned_loss=0.06748, over 7319.00 frames.], tot_loss[loss=0.269, simple_loss=0.3336, pruned_loss=0.1022, over 1421132.01 frames.], batch size: 21, lr: 1.16e-03
2022-05-26 19:37:47,572 INFO [train.py:842] (2/4) Epoch 4, batch 2150, loss[loss=0.2464, simple_loss=0.3112, pruned_loss=0.09083, over 7334.00 frames.], tot_loss[loss=0.2689, simple_loss=0.3336, pruned_loss=0.1021, over 1423225.80 frames.], batch size: 22, lr: 1.16e-03
2022-05-26 19:38:26,054 INFO [train.py:842] (2/4) Epoch 4, batch 2200, loss[loss=0.2334, simple_loss=0.3139, pruned_loss=0.07647, over 7217.00 frames.], tot_loss[loss=0.2683, simple_loss=0.3333, pruned_loss=0.1017, over 1424844.70 frames.], batch size: 21, lr: 1.15e-03
2022-05-26 19:39:04,827 INFO [train.py:842] (2/4) Epoch 4, batch 2250, loss[loss=0.3362, simple_loss=0.3864, pruned_loss=0.143, over 4914.00 frames.], tot_loss[loss=0.2676, simple_loss=0.3331, pruned_loss=0.101, over 1426492.69 frames.], batch size: 52, lr: 1.15e-03
2022-05-26 19:39:43,433 INFO [train.py:842] (2/4) Epoch 4, batch 2300, loss[loss=0.3065, simple_loss=0.346, pruned_loss=0.1335, over 7156.00 frames.], tot_loss[loss=0.2654, simple_loss=0.3317, pruned_loss=0.09954, over 1429983.16 frames.], batch size: 19, lr: 1.15e-03
2022-05-26 19:40:22,286 INFO [train.py:842] (2/4) Epoch 4, batch 2350, loss[loss=0.2676, simple_loss=0.3314, pruned_loss=0.1019, over 7328.00 frames.], tot_loss[loss=0.2641, simple_loss=0.3304, pruned_loss=0.09886, over 1431355.08 frames.], batch size: 20, lr: 1.15e-03
2022-05-26 19:41:00,755 INFO [train.py:842] (2/4) Epoch 4, batch 2400, loss[loss=0.2256, simple_loss=0.3028, pruned_loss=0.07422, over 7279.00 frames.], tot_loss[loss=0.2658, simple_loss=0.3319, pruned_loss=0.09983, over 1433964.84 frames.], batch size: 25, lr: 1.15e-03
2022-05-26 19:41:39,551 INFO [train.py:842] (2/4) Epoch 4, batch 2450, loss[loss=0.2501, simple_loss=0.3175, pruned_loss=0.09131, over 7372.00 frames.], tot_loss[loss=0.2647, simple_loss=0.3311, pruned_loss=0.09916, over 1436515.93 frames.], batch size: 23, lr: 1.15e-03
2022-05-26 19:42:18,056 INFO [train.py:842] (2/4) Epoch 4, batch 2500, loss[loss=0.2194, simple_loss=0.2942, pruned_loss=0.0723, over 7164.00 frames.], tot_loss[loss=0.2646, simple_loss=0.3307, pruned_loss=0.09923, over 1434310.09 frames.], batch size: 19, lr: 1.15e-03
2022-05-26 19:42:56,652 INFO [train.py:842] (2/4) Epoch 4, batch 2550, loss[loss=0.2184, simple_loss=0.2883, pruned_loss=0.07427, over 7419.00 frames.], tot_loss[loss=0.2685, simple_loss=0.3332, pruned_loss=0.1019, over 1425934.55 frames.], batch size: 18, lr: 1.15e-03
2022-05-26 19:43:35,176 INFO [train.py:842] (2/4) Epoch 4, batch 2600, loss[loss=0.2227, simple_loss=0.296, pruned_loss=0.07469, over 7231.00 frames.], tot_loss[loss=0.2671, simple_loss=0.3325, pruned_loss=0.1008, over 1426159.79 frames.], batch size: 20, lr: 1.15e-03
2022-05-26 19:44:13,877 INFO [train.py:842] (2/4) Epoch 4, batch 2650, loss[loss=0.2123, simple_loss=0.2806, pruned_loss=0.07199, over 7405.00 frames.], tot_loss[loss=0.2676, simple_loss=0.3331, pruned_loss=0.1011, over 1420229.33 frames.], batch size: 17, lr: 1.15e-03
2022-05-26 19:44:52,280 INFO [train.py:842] (2/4) Epoch 4, batch 2700, loss[loss=0.2006, simple_loss=0.2659, pruned_loss=0.06768, over 7248.00 frames.], tot_loss[loss=0.2671, simple_loss=0.3326, pruned_loss=0.1008, over 1419308.98 frames.], batch size: 16, lr: 1.15e-03
2022-05-26 19:45:31,376 INFO [train.py:842] (2/4) Epoch 4, batch 2750, loss[loss=0.2411, simple_loss=0.3208, pruned_loss=0.08068, over 7258.00 frames.], tot_loss[loss=0.2657, simple_loss=0.3318, pruned_loss=0.09978, over 1422022.52 frames.], batch size: 19, lr: 1.14e-03
2022-05-26 19:46:09,933 INFO [train.py:842] (2/4) Epoch 4, batch 2800, loss[loss=0.3287, simple_loss=0.3643, pruned_loss=0.1465, over 7158.00 frames.], tot_loss[loss=0.2644, simple_loss=0.3307, pruned_loss=0.0991, over 1423899.08 frames.], batch size: 19, lr: 1.14e-03
2022-05-26 19:46:48,826 INFO [train.py:842] (2/4) Epoch 4, batch 2850, loss[loss=0.3089, simple_loss=0.3637, pruned_loss=0.127, over 5001.00 frames.], tot_loss[loss=0.2628, simple_loss=0.3294, pruned_loss=0.09807, over 1422851.61 frames.], batch size: 52, lr: 1.14e-03
2022-05-26 19:47:27,283 INFO [train.py:842] (2/4) Epoch 4, batch 2900, loss[loss=0.2687, simple_loss=0.3297, pruned_loss=0.1038, over 6810.00 frames.], tot_loss[loss=0.2606, simple_loss=0.3277, pruned_loss=0.0968, over 1423554.92 frames.], batch size: 31, lr: 1.14e-03
2022-05-26 19:48:06,163 INFO [train.py:842] (2/4) Epoch 4, batch 2950, loss[loss=0.2556, simple_loss=0.331, pruned_loss=0.09009, over 7116.00 frames.], tot_loss[loss=0.2618, simple_loss=0.3286, pruned_loss=0.09752, over 1427676.95 frames.], batch size: 28, lr: 1.14e-03
2022-05-26 19:48:45,205 INFO [train.py:842] (2/4) Epoch 4, batch 3000, loss[loss=0.3551, simple_loss=0.4013, pruned_loss=0.1545, over 7145.00 frames.], tot_loss[loss=0.2633, simple_loss=0.33, pruned_loss=0.09835, over 1425406.06 frames.], batch size: 20, lr: 1.14e-03
2022-05-26 19:48:45,207 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 19:48:54,321 INFO [train.py:871] (2/4) Epoch 4, validation: loss=0.2021, simple_loss=0.3, pruned_loss=0.05209, over 868885.00 frames.
2022-05-26 19:49:33,153 INFO [train.py:842] (2/4) Epoch 4, batch 3050, loss[loss=0.2363, simple_loss=0.3135, pruned_loss=0.07953, over 7119.00 frames.], tot_loss[loss=0.2648, simple_loss=0.331, pruned_loss=0.09926, over 1420557.16 frames.], batch size: 21, lr: 1.14e-03
2022-05-26 19:50:11,902 INFO [train.py:842] (2/4) Epoch 4, batch 3100, loss[loss=0.3542, simple_loss=0.4128, pruned_loss=0.1478, over 7301.00 frames.], tot_loss[loss=0.2651, simple_loss=0.3309, pruned_loss=0.09962, over 1416785.71 frames.], batch size: 24, lr: 1.14e-03
2022-05-26 19:50:51,323 INFO [train.py:842] (2/4) Epoch 4, batch 3150, loss[loss=0.2769, simple_loss=0.3451, pruned_loss=0.1044, over 7287.00 frames.], tot_loss[loss=0.2632, simple_loss=0.3294, pruned_loss=0.0985, over 1421362.62 frames.], batch size: 25, lr: 1.14e-03
2022-05-26 19:51:30,088 INFO [train.py:842] (2/4) Epoch 4, batch 3200, loss[loss=0.2283, simple_loss=0.2994, pruned_loss=0.07856, over 7081.00 frames.], tot_loss[loss=0.2615, simple_loss=0.3278, pruned_loss=0.09764, over 1422396.65 frames.], batch size: 18, lr: 1.14e-03
2022-05-26 19:52:09,194 INFO [train.py:842] (2/4) Epoch 4, batch 3250, loss[loss=0.3192, simple_loss=0.369, pruned_loss=0.1347, over 7252.00 frames.], tot_loss[loss=0.263, simple_loss=0.3287, pruned_loss=0.09867, over 1423614.58 frames.], batch size: 19, lr: 1.14e-03
2022-05-26 19:52:47,447 INFO [train.py:842] (2/4) Epoch 4, batch 3300, loss[loss=0.279, simple_loss=0.3435, pruned_loss=0.1073, over 7220.00 frames.], tot_loss[loss=0.2644, simple_loss=0.3303, pruned_loss=0.09924, over 1422484.03 frames.], batch size: 23, lr: 1.13e-03
2022-05-26 19:53:26,306 INFO [train.py:842] (2/4) Epoch 4, batch 3350, loss[loss=0.2817, simple_loss=0.3546, pruned_loss=0.1044, over 6212.00 frames.], tot_loss[loss=0.2636, simple_loss=0.3293, pruned_loss=0.09894, over 1419619.24 frames.], batch size: 37, lr: 1.13e-03
2022-05-26 19:54:04,824 INFO [train.py:842] (2/4) Epoch 4, batch 3400, loss[loss=0.2747, simple_loss=0.3244, pruned_loss=0.1125, over 7003.00 frames.], tot_loss[loss=0.2629, simple_loss=0.3289, pruned_loss=0.09843, over 1420868.54 frames.], batch size: 16, lr: 1.13e-03
2022-05-26 19:54:43,746 INFO [train.py:842] (2/4) Epoch 4, batch 3450, loss[loss=0.2366, simple_loss=0.2986, pruned_loss=0.08729, over 7177.00 frames.], tot_loss[loss=0.2598, simple_loss=0.3266, pruned_loss=0.09647, over 1425962.82 frames.], batch size: 18, lr: 1.13e-03
2022-05-26 19:55:22,250 INFO [train.py:842] (2/4) Epoch 4, batch 3500, loss[loss=0.2715, simple_loss=0.343, pruned_loss=0.09996, over 7375.00 frames.], tot_loss[loss=0.2592, simple_loss=0.3261, pruned_loss=0.0962, over 1427755.59 frames.], batch size: 23, lr: 1.13e-03
2022-05-26 19:56:01,283 INFO [train.py:842] (2/4) Epoch 4, batch 3550, loss[loss=0.2755, simple_loss=0.3425, pruned_loss=0.1042, over 7286.00 frames.], tot_loss[loss=0.2603, simple_loss=0.3271, pruned_loss=0.09677, over 1428546.85 frames.], batch size: 24, lr: 1.13e-03
2022-05-26 19:56:39,769 INFO [train.py:842] (2/4) Epoch 4, batch 3600, loss[loss=0.1963, simple_loss=0.2754, pruned_loss=0.0586, over 6982.00 frames.], tot_loss[loss=0.2625, simple_loss=0.329, pruned_loss=0.09793, over 1427189.80 frames.], batch size: 16, lr: 1.13e-03
2022-05-26 19:57:18,391 INFO [train.py:842] (2/4) Epoch 4, batch 3650, loss[loss=0.263, simple_loss=0.3238, pruned_loss=0.1011, over 7145.00 frames.], tot_loss[loss=0.262, simple_loss=0.3288, pruned_loss=0.09764, over 1427476.53 frames.], batch size: 17, lr: 1.13e-03
2022-05-26 19:57:56,886 INFO [train.py:842] (2/4) Epoch 4, batch 3700, loss[loss=0.1983, simple_loss=0.2655, pruned_loss=0.06549, over 7001.00 frames.], tot_loss[loss=0.2616, simple_loss=0.3281, pruned_loss=0.09759, over 1426729.67 frames.], batch size: 16, lr: 1.13e-03
2022-05-26 19:58:35,901 INFO [train.py:842] (2/4) Epoch 4, batch 3750, loss[loss=0.3129, simple_loss=0.3628, pruned_loss=0.1315, over 7425.00 frames.], tot_loss[loss=0.2608, simple_loss=0.327, pruned_loss=0.09733, over 1424759.37 frames.], batch size: 20, lr: 1.13e-03
2022-05-26 19:59:14,460 INFO [train.py:842] (2/4) Epoch 4, batch 3800, loss[loss=0.2322, simple_loss=0.3123, pruned_loss=0.07608, over 7065.00 frames.], tot_loss[loss=0.261, simple_loss=0.3272, pruned_loss=0.09739, over 1421112.65 frames.], batch size: 18, lr: 1.13e-03
2022-05-26 19:59:53,434 INFO [train.py:842] (2/4) Epoch 4, batch 3850, loss[loss=0.204, simple_loss=0.2685, pruned_loss=0.0698, over 7424.00 frames.], tot_loss[loss=0.261, simple_loss=0.3269, pruned_loss=0.09751, over 1424804.48 frames.], batch size: 18, lr: 1.12e-03
2022-05-26 20:00:32,056 INFO [train.py:842] (2/4) Epoch 4, batch 3900, loss[loss=0.3874, simple_loss=0.4223, pruned_loss=0.1763, over 5304.00 frames.], tot_loss[loss=0.2611, simple_loss=0.3271, pruned_loss=0.09758, over 1426005.68 frames.], batch size: 52, lr: 1.12e-03
2022-05-26 20:01:10,958 INFO [train.py:842] (2/4) Epoch 4, batch 3950, loss[loss=0.2163, simple_loss=0.2943, pruned_loss=0.06917, over 7233.00 frames.], tot_loss[loss=0.2589, simple_loss=0.3255, pruned_loss=0.09616, over 1425140.68 frames.], batch size: 16, lr: 1.12e-03
2022-05-26 20:01:49,423 INFO [train.py:842] (2/4) Epoch 4, batch 4000, loss[loss=0.3033, simple_loss=0.3667, pruned_loss=0.12, over 7210.00 frames.], tot_loss[loss=0.2592, simple_loss=0.3258, pruned_loss=0.09625, over 1417685.30 frames.], batch size: 21, lr: 1.12e-03
2022-05-26 20:02:28,189 INFO [train.py:842] (2/4) Epoch 4, batch 4050, loss[loss=0.2754, simple_loss=0.3489, pruned_loss=0.1009, over 7411.00 frames.], tot_loss[loss=0.2591, simple_loss=0.326, pruned_loss=0.0961, over 1419257.27 frames.], batch size: 21, lr: 1.12e-03
2022-05-26 20:03:06,973 INFO [train.py:842] (2/4) Epoch 4, batch 4100, loss[loss=0.2235, simple_loss=0.2894, pruned_loss=0.07879, over 7408.00 frames.], tot_loss[loss=0.2611, simple_loss=0.3274, pruned_loss=0.09744, over 1424267.46 frames.], batch size: 18, lr: 1.12e-03
2022-05-26 20:03:45,838 INFO [train.py:842] (2/4) Epoch 4, batch 4150, loss[loss=0.1917, simple_loss=0.2712, pruned_loss=0.05613, over 7229.00 frames.], tot_loss[loss=0.2624, simple_loss=0.3286, pruned_loss=0.09814, over 1427055.89 frames.], batch size: 16, lr: 1.12e-03
2022-05-26 20:04:24,276 INFO [train.py:842] (2/4) Epoch 4, batch 4200, loss[loss=0.1909, simple_loss=0.2693, pruned_loss=0.05627, over 7269.00 frames.], tot_loss[loss=0.262, simple_loss=0.3281, pruned_loss=0.09796, over 1427368.11 frames.], batch size: 17, lr: 1.12e-03
2022-05-26 20:05:03,049 INFO [train.py:842] (2/4) Epoch 4, batch 4250, loss[loss=0.2466, simple_loss=0.3248, pruned_loss=0.08424, over 7230.00 frames.], tot_loss[loss=0.2617, simple_loss=0.3279, pruned_loss=0.09781, over 1424657.89 frames.], batch size: 20, lr: 1.12e-03
2022-05-26 20:05:41,572 INFO [train.py:842] (2/4) Epoch 4, batch 4300, loss[loss=0.2199, simple_loss=0.3031, pruned_loss=0.06833, over 7253.00 frames.], tot_loss[loss=0.2596, simple_loss=0.3266, pruned_loss=0.09631, over 1422930.83 frames.], batch size: 19, lr: 1.12e-03
2022-05-26 20:06:20,306 INFO [train.py:842] (2/4) Epoch 4, batch 4350, loss[loss=0.2942, simple_loss=0.3464, pruned_loss=0.121, over 7193.00 frames.], tot_loss[loss=0.2584, simple_loss=0.3259, pruned_loss=0.09545, over 1421863.83 frames.], batch size: 23, lr: 1.12e-03
2022-05-26 20:06:58,790 INFO [train.py:842] (2/4) Epoch 4, batch 4400, loss[loss=0.2653, simple_loss=0.3428, pruned_loss=0.0939, over 7247.00 frames.], tot_loss[loss=0.2589, simple_loss=0.327, pruned_loss=0.09546, over 1421719.20 frames.], batch size: 20, lr: 1.12e-03
2022-05-26 20:07:40,548 INFO [train.py:842] (2/4) Epoch 4, batch 4450, loss[loss=0.1892, simple_loss=0.2697, pruned_loss=0.0544, over 7363.00 frames.], tot_loss[loss=0.2587, simple_loss=0.3264, pruned_loss=0.09549, over 1423211.01 frames.], batch size: 19, lr: 1.11e-03
2022-05-26 20:08:19,163 INFO [train.py:842] (2/4) Epoch 4, batch 4500, loss[loss=0.2625, simple_loss=0.3385, pruned_loss=0.09326, over 7107.00 frames.], tot_loss[loss=0.2568, simple_loss=0.3245, pruned_loss=0.09456, over 1425905.98 frames.], batch size: 21, lr: 1.11e-03
2022-05-26 20:08:58,302 INFO [train.py:842] (2/4) Epoch 4, batch 4550, loss[loss=0.2402, simple_loss=0.3092, pruned_loss=0.0856, over 7415.00 frames.], tot_loss[loss=0.2547, simple_loss=0.3229, pruned_loss=0.09328, over 1424490.11 frames.], batch size: 18, lr: 1.11e-03
2022-05-26 20:09:36,942 INFO [train.py:842] (2/4) Epoch 4, batch 4600, loss[loss=0.2278, simple_loss=0.3104, pruned_loss=0.07255, over 7414.00 frames.], tot_loss[loss=0.2563, simple_loss=0.324, pruned_loss=0.09433, over 1424333.99 frames.], batch size: 21, lr: 1.11e-03
2022-05-26 20:10:15,675 INFO [train.py:842] (2/4) Epoch 4, batch 4650, loss[loss=0.2764, simple_loss=0.3423, pruned_loss=0.1052, over 7412.00 frames.], tot_loss[loss=0.2571, simple_loss=0.3247, pruned_loss=0.09477, over 1423723.15 frames.], batch size: 21, lr: 1.11e-03
2022-05-26 20:10:54,166 INFO [train.py:842] (2/4) Epoch 4, batch 4700, loss[loss=0.2986, simple_loss=0.3563, pruned_loss=0.1205, over 6818.00 frames.], tot_loss[loss=0.2602, simple_loss=0.3269, pruned_loss=0.09678, over 1423653.29 frames.], batch size: 31, lr: 1.11e-03
2022-05-26 20:11:33,092 INFO [train.py:842] (2/4) Epoch 4, batch 4750, loss[loss=0.2516, simple_loss=0.3307, pruned_loss=0.08625, over 7106.00 frames.], tot_loss[loss=0.2584, simple_loss=0.3252, pruned_loss=0.09575, over 1424362.01 frames.], batch size: 21, lr: 1.11e-03
2022-05-26 20:12:11,647 INFO [train.py:842] (2/4) Epoch 4, batch 4800, loss[loss=0.2812, simple_loss=0.3256, pruned_loss=0.1184, over 7143.00 frames.], tot_loss[loss=0.2587, simple_loss=0.3257, pruned_loss=0.09585, over 1425352.39 frames.], batch size: 17, lr: 1.11e-03
2022-05-26 20:12:51,097 INFO [train.py:842] (2/4) Epoch 4, batch 4850, loss[loss=0.2864, simple_loss=0.3437, pruned_loss=0.1146, over 6827.00 frames.], tot_loss[loss=0.2574, simple_loss=0.3243, pruned_loss=0.09524, over 1426377.26 frames.], batch size: 15, lr: 1.11e-03
2022-05-26 20:13:29,713 INFO [train.py:842] (2/4) Epoch 4, batch 4900, loss[loss=0.2659, simple_loss=0.3475, pruned_loss=0.09213, over 7314.00 frames.], tot_loss[loss=0.2582, simple_loss=0.3247, pruned_loss=0.09587, over 1425109.04 frames.], batch size: 24, lr: 1.11e-03
2022-05-26 20:14:08,461 INFO [train.py:842] (2/4) Epoch 4, batch 4950, loss[loss=0.2208, simple_loss=0.2991, pruned_loss=0.07127, over 7123.00 frames.], tot_loss[loss=0.2575, simple_loss=0.3243, pruned_loss=0.09539, over 1425390.31 frames.], batch size: 21, lr: 1.11e-03
2022-05-26 20:14:46,924 INFO [train.py:842] (2/4) Epoch 4, batch 5000, loss[loss=0.2485, simple_loss=0.3306, pruned_loss=0.08322, over 7329.00 frames.], tot_loss[loss=0.2572, simple_loss=0.3239, pruned_loss=0.09526, over 1424255.94 frames.], batch size: 20, lr: 1.11e-03
2022-05-26 20:15:25,475 INFO [train.py:842] (2/4) Epoch 4, batch 5050, loss[loss=0.2618, simple_loss=0.3414, pruned_loss=0.09107, over 7137.00 frames.], tot_loss[loss=0.2595, simple_loss=0.326, pruned_loss=0.09644, over 1424714.46 frames.], batch size: 26, lr: 1.10e-03
2022-05-26 20:16:04,051 INFO [train.py:842] (2/4) Epoch 4, batch 5100, loss[loss=0.2883, simple_loss=0.3491, pruned_loss=0.1138, over 7064.00 frames.], tot_loss[loss=0.2599, simple_loss=0.3266, pruned_loss=0.09664, over 1422838.36 frames.], batch size: 28, lr: 1.10e-03
2022-05-26 20:16:43,105 INFO [train.py:842] (2/4) Epoch 4, batch 5150, loss[loss=0.1904, simple_loss=0.2724, pruned_loss=0.05416, over 7277.00 frames.], tot_loss[loss=0.2571, simple_loss=0.3245, pruned_loss=0.09485, over 1427756.89 frames.], batch size: 17, lr: 1.10e-03
2022-05-26 20:17:21,941 INFO [train.py:842] (2/4) Epoch 4, batch 5200, loss[loss=0.2612, simple_loss=0.3215, pruned_loss=0.1004, over 7364.00 frames.], tot_loss[loss=0.2581, simple_loss=0.3257, pruned_loss=0.0952, over 1428362.21 frames.], batch size: 19, lr: 1.10e-03
2022-05-26 20:18:00,741 INFO [train.py:842] (2/4) Epoch 4, batch 5250, loss[loss=0.267, simple_loss=0.3285, pruned_loss=0.1028, over 7120.00 frames.], tot_loss[loss=0.2582, simple_loss=0.3254, pruned_loss=0.09551, over 1426648.76 frames.], batch size: 21, lr: 1.10e-03
2022-05-26 20:18:39,348 INFO [train.py:842] (2/4) Epoch 4, batch 5300, loss[loss=0.2721, simple_loss=0.3317, pruned_loss=0.1062, over 7075.00 frames.], tot_loss[loss=0.2547, simple_loss=0.3222, pruned_loss=0.09361, over 1430412.53 frames.], batch size: 18, lr: 1.10e-03
2022-05-26 20:19:18,344 INFO [train.py:842] (2/4) Epoch 4, batch 5350, loss[loss=0.2257, simple_loss=0.2754, pruned_loss=0.08804, over 7275.00 frames.], tot_loss[loss=0.2559, simple_loss=0.3228, pruned_loss=0.09452, over 1431858.14 frames.], batch size: 17, lr: 1.10e-03
2022-05-26 20:19:56,805 INFO [train.py:842] (2/4) Epoch 4, batch 5400, loss[loss=0.228, simple_loss=0.2799, pruned_loss=0.08806, over 7263.00 frames.], tot_loss[loss=0.2577, simple_loss=0.324, pruned_loss=0.09575, over 1431773.96 frames.], batch size: 17, lr: 1.10e-03
2022-05-26 20:20:35,580 INFO [train.py:842] (2/4) Epoch 4, batch 5450, loss[loss=0.2501, simple_loss=0.3265, pruned_loss=0.08681, over 7188.00 frames.], tot_loss[loss=0.2579, simple_loss=0.3244, pruned_loss=0.09574, over 1430530.65 frames.], batch size: 23, lr: 1.10e-03
2022-05-26 20:21:14,028 INFO [train.py:842] (2/4) Epoch 4, batch 5500, loss[loss=0.2367, simple_loss=0.3116, pruned_loss=0.08089, over 7187.00 frames.], tot_loss[loss=0.2595, simple_loss=0.3259, pruned_loss=0.09655, over 1428218.93 frames.], batch size: 26, lr: 1.10e-03
2022-05-26 20:21:52,996 INFO [train.py:842] (2/4) Epoch 4, batch 5550, loss[loss=0.3058, simple_loss=0.3637, pruned_loss=0.124, over 6763.00 frames.], tot_loss[loss=0.2589, simple_loss=0.3253, pruned_loss=0.09621, over 1421733.86 frames.], batch size: 31, lr: 1.10e-03
2022-05-26 20:22:31,504 INFO [train.py:842] (2/4) Epoch 4, batch 5600, loss[loss=0.1991, simple_loss=0.2847, pruned_loss=0.05677, over 7276.00 frames.], tot_loss[loss=0.2596, simple_loss=0.3262, pruned_loss=0.09651, over 1425534.21 frames.], batch size: 18, lr: 1.10e-03
2022-05-26 20:23:10,157 INFO [train.py:842] (2/4) Epoch 4, batch 5650, loss[loss=0.2934, simple_loss=0.3639, pruned_loss=0.1114, over 7217.00 frames.], tot_loss[loss=0.2609, simple_loss=0.3272, pruned_loss=0.0973, over 1421986.38 frames.], batch size: 23, lr: 1.09e-03
2022-05-26 20:23:48,740 INFO [train.py:842] (2/4) Epoch 4, batch 5700, loss[loss=0.2855, simple_loss=0.3527, pruned_loss=0.1092, over 7234.00 frames.], tot_loss[loss=0.2619, simple_loss=0.3284, pruned_loss=0.09773, over 1423150.60 frames.], batch size: 20, lr: 1.09e-03
2022-05-26 20:24:27,441 INFO [train.py:842] (2/4) Epoch 4, batch 5750, loss[loss=0.3141, simple_loss=0.3657, pruned_loss=0.1312, over 7315.00 frames.], tot_loss[loss=0.2634, simple_loss=0.3292, pruned_loss=0.09876, over 1422464.16 frames.], batch size: 25, lr: 1.09e-03
2022-05-26 20:25:06,083 INFO [train.py:842] (2/4) Epoch 4, batch 5800, loss[loss=0.2741, simple_loss=0.3457, pruned_loss=0.1012, over 7326.00 frames.], tot_loss[loss=0.2638, simple_loss=0.3298, pruned_loss=0.09895, over 1422562.40 frames.], batch size: 21, lr: 1.09e-03
2022-05-26 20:25:44,933 INFO [train.py:842] (2/4) Epoch 4, batch 5850, loss[loss=0.2881, simple_loss=0.3588, pruned_loss=0.1087, over 6300.00 frames.], tot_loss[loss=0.2644, simple_loss=0.3301, pruned_loss=0.09942, over 1418565.13 frames.], batch size: 37, lr: 1.09e-03
2022-05-26 20:26:23,775 INFO [train.py:842] (2/4) Epoch 4, batch 5900, loss[loss=0.2503, simple_loss=0.3184, pruned_loss=0.09109, over 7311.00 frames.], tot_loss[loss=0.2625, simple_loss=0.3284, pruned_loss=0.09833, over 1424263.41 frames.], batch size: 21, lr: 1.09e-03
2022-05-26 20:27:02,266 INFO [train.py:842] (2/4) Epoch 4, batch 5950, loss[loss=0.2716, simple_loss=0.3412, pruned_loss=0.101, over 7157.00 frames.], tot_loss[loss=0.265, simple_loss=0.3303, pruned_loss=0.09988, over 1423809.87 frames.], batch size: 19, lr: 1.09e-03
2022-05-26 20:27:41,333 INFO [train.py:842] (2/4) Epoch 4, batch 6000, loss[loss=0.2523, simple_loss=0.331, pruned_loss=0.0868, over 7210.00 frames.], tot_loss[loss=0.2619, simple_loss=0.3279, pruned_loss=0.09799, over 1423039.53 frames.], batch size: 23, lr: 1.09e-03
2022-05-26 20:27:41,334 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 20:27:50,628 INFO [train.py:871] (2/4) Epoch 4, validation: loss=0.1997, simple_loss=0.2983, pruned_loss=0.05051, over 868885.00 frames.
2022-05-26 20:28:29,533 INFO [train.py:842] (2/4) Epoch 4, batch 6050, loss[loss=0.4291, simple_loss=0.4467, pruned_loss=0.2057, over 7241.00 frames.], tot_loss[loss=0.2622, simple_loss=0.3285, pruned_loss=0.09799, over 1419683.38 frames.], batch size: 20, lr: 1.09e-03
2022-05-26 20:29:08,215 INFO [train.py:842] (2/4) Epoch 4, batch 6100, loss[loss=0.2619, simple_loss=0.3344, pruned_loss=0.09468, over 7112.00 frames.], tot_loss[loss=0.2612, simple_loss=0.3278, pruned_loss=0.09725, over 1415179.50 frames.], batch size: 21, lr: 1.09e-03
2022-05-26 20:29:47,421 INFO [train.py:842] (2/4) Epoch 4, batch 6150, loss[loss=0.2738, simple_loss=0.3432, pruned_loss=0.1022, over 7314.00 frames.], tot_loss[loss=0.2617, simple_loss=0.328, pruned_loss=0.09771, over 1419701.03 frames.], batch size: 25, lr: 1.09e-03
2022-05-26 20:30:26,273 INFO [train.py:842] (2/4) Epoch 4, batch 6200, loss[loss=0.2031, simple_loss=0.2798, pruned_loss=0.06322, over 7130.00 frames.], tot_loss[loss=0.2591, simple_loss=0.3253, pruned_loss=0.09646, over 1421686.43 frames.], batch size: 17, lr: 1.09e-03
2022-05-26 20:31:05,437 INFO [train.py:842] (2/4) Epoch 4, batch 6250, loss[loss=0.2524, simple_loss=0.3235, pruned_loss=0.09062, over 7438.00 frames.], tot_loss[loss=0.2574, simple_loss=0.324, pruned_loss=0.09539, over 1420117.74 frames.], batch size: 20, lr: 1.08e-03
2022-05-26 20:31:44,039 INFO [train.py:842] (2/4) Epoch 4, batch 6300, loss[loss=0.2317, simple_loss=0.3127, pruned_loss=0.07541, over 7317.00 frames.], tot_loss[loss=0.2572, simple_loss=0.3241, pruned_loss=0.09517, over 1423409.72 frames.], batch size: 21, lr: 1.08e-03
2022-05-26 20:32:22,627 INFO [train.py:842] (2/4) Epoch 4, batch 6350, loss[loss=0.273, simple_loss=0.3364, pruned_loss=0.1048, over 7434.00 frames.], tot_loss[loss=0.2578, simple_loss=0.325, pruned_loss=0.09535, over 1419086.05 frames.], batch size: 20, lr: 1.08e-03
2022-05-26 20:33:01,287 INFO [train.py:842] (2/4) Epoch 4, batch 6400, loss[loss=0.2966, simple_loss=0.3517, pruned_loss=0.1207, over 7380.00 frames.], tot_loss[loss=0.2573, simple_loss=0.3248, pruned_loss=0.09494, over 1419557.56 frames.], batch size: 23, lr: 1.08e-03
2022-05-26 20:33:40,213 INFO [train.py:842] (2/4) Epoch 4, batch 6450, loss[loss=0.2907, simple_loss=0.3564, pruned_loss=0.1125, over 7328.00 frames.], tot_loss[loss=0.2592, simple_loss=0.3261, pruned_loss=0.09611, over 1421857.08 frames.], batch size: 21, lr: 1.08e-03
2022-05-26 20:34:18,857 INFO [train.py:842] (2/4) Epoch 4, batch 6500, loss[loss=0.2922, simple_loss=0.3397, pruned_loss=0.1223, over 6775.00 frames.], tot_loss[loss=0.2592, simple_loss=0.3263, pruned_loss=0.09605, over 1421897.61 frames.], batch size: 15, lr: 1.08e-03
2022-05-26 20:34:57,615 INFO [train.py:842] (2/4) Epoch 4, batch 6550, loss[loss=0.2657, simple_loss=0.3241, pruned_loss=0.1037, over 7352.00 frames.], tot_loss[loss=0.2587, simple_loss=0.3258, pruned_loss=0.09575, over 1425793.13 frames.], batch size: 19, lr: 1.08e-03
2022-05-26 20:35:36,109 INFO [train.py:842] (2/4) Epoch 4, batch 6600, loss[loss=0.2361, simple_loss=0.3142, pruned_loss=0.07902, over 7206.00 frames.], tot_loss[loss=0.2581, simple_loss=0.3251, pruned_loss=0.09553, over 1420519.99 frames.], batch size: 22, lr: 1.08e-03
2022-05-26 20:36:14,968 INFO [train.py:842] (2/4) Epoch 4, batch 6650, loss[loss=0.2622, simple_loss=0.3334, pruned_loss=0.0955, over 7338.00 frames.], tot_loss[loss=0.2595, simple_loss=0.3262, pruned_loss=0.09641, over 1423451.70 frames.], batch size: 22, lr: 1.08e-03
2022-05-26 20:36:53,539 INFO [train.py:842] (2/4) Epoch 4, batch 6700, loss[loss=0.2246, simple_loss=0.2924, pruned_loss=0.07839, over 7146.00 frames.], tot_loss[loss=0.2591, simple_loss=0.3264, pruned_loss=0.09588, over 1421638.73 frames.], batch size: 17, lr: 1.08e-03
2022-05-26 20:37:32,303 INFO [train.py:842] (2/4) Epoch 4, batch 6750, loss[loss=0.2531, simple_loss=0.3223, pruned_loss=0.09189, over 7191.00 frames.], tot_loss[loss=0.2581, simple_loss=0.3257, pruned_loss=0.09522, over 1422029.67 frames.], batch size: 23, lr: 1.08e-03
2022-05-26 20:38:11,165 INFO [train.py:842] (2/4) Epoch 4, batch 6800, loss[loss=0.2907, simple_loss=0.356, pruned_loss=0.1127, over 7420.00 frames.], tot_loss[loss=0.2568, simple_loss=0.3246, pruned_loss=0.09455, over 1424069.72 frames.], batch size: 21, lr: 1.08e-03
2022-05-26 20:38:50,355 INFO [train.py:842] (2/4) Epoch 4, batch 6850, loss[loss=0.2701, simple_loss=0.3483, pruned_loss=0.09599, over 7316.00 frames.], tot_loss[loss=0.258, simple_loss=0.3251, pruned_loss=0.09541, over 1423009.07 frames.], batch size: 25, lr: 1.08e-03
2022-05-26 20:39:29,147 INFO [train.py:842] (2/4) Epoch 4, batch 6900, loss[loss=0.2883, simple_loss=0.3508, pruned_loss=0.1129, over 7198.00 frames.], tot_loss[loss=0.2564, simple_loss=0.3241, pruned_loss=0.09438, over 1424084.33 frames.], batch size: 22, lr: 1.07e-03
2022-05-26 20:40:08,045 INFO [train.py:842] (2/4) Epoch 4, batch 6950, loss[loss=0.2239, simple_loss=0.302, pruned_loss=0.07292, over 7257.00 frames.], tot_loss[loss=0.2582, simple_loss=0.3247, pruned_loss=0.09588, over 1422352.72 frames.], batch size: 19, lr: 1.07e-03
2022-05-26 20:40:46,540 INFO [train.py:842] (2/4) Epoch 4, batch 7000, loss[loss=0.2128, simple_loss=0.2853, pruned_loss=0.07011, over 7154.00 frames.], tot_loss[loss=0.2586, simple_loss=0.3251, pruned_loss=0.09608, over 1419628.07 frames.], batch size: 19, lr: 1.07e-03
2022-05-26 20:41:25,584 INFO [train.py:842] (2/4) Epoch 4, batch 7050, loss[loss=0.2416, simple_loss=0.3255, pruned_loss=0.07879, over 7320.00 frames.], tot_loss[loss=0.2597, simple_loss=0.3263, pruned_loss=0.0966, over 1417257.39 frames.], batch size: 21, lr: 1.07e-03
2022-05-26 20:42:04,320 INFO [train.py:842] (2/4) Epoch 4, batch 7100, loss[loss=0.2518, simple_loss=0.3184, pruned_loss=0.09261, over 7327.00 frames.], tot_loss[loss=0.2584, simple_loss=0.3252, pruned_loss=0.09577, over 1420394.27 frames.], batch size: 21, lr: 1.07e-03
2022-05-26 20:42:43,534 INFO [train.py:842] (2/4) Epoch 4, batch 7150, loss[loss=0.226, simple_loss=0.3123, pruned_loss=0.06981, over 7149.00 frames.], tot_loss[loss=0.2588, simple_loss=0.3259, pruned_loss=0.09589, over 1419632.54 frames.], batch size: 20, lr: 1.07e-03
2022-05-26 20:43:22,308 INFO [train.py:842] (2/4) Epoch 4, batch 7200, loss[loss=0.2471, simple_loss=0.3353, pruned_loss=0.07947, over 7221.00 frames.], tot_loss[loss=0.2584, simple_loss=0.3254, pruned_loss=0.09575, over 1418430.87 frames.], batch size: 21, lr: 1.07e-03
2022-05-26 20:44:01,283 INFO [train.py:842] (2/4) Epoch 4, batch 7250, loss[loss=0.2618, simple_loss=0.332, pruned_loss=0.09582, over 7408.00 frames.], tot_loss[loss=0.2573, simple_loss=0.3244, pruned_loss=0.09509, over 1419368.83 frames.], batch size: 21, lr: 1.07e-03
2022-05-26 20:44:39,863 INFO [train.py:842] (2/4) Epoch 4, batch 7300, loss[loss=0.264, simple_loss=0.3308, pruned_loss=0.09857, over 7331.00 frames.], tot_loss[loss=0.2579, simple_loss=0.3251, pruned_loss=0.09537, over 1420309.79 frames.], batch size: 22, lr: 1.07e-03
2022-05-26 20:45:18,998 INFO [train.py:842] (2/4) Epoch 4, batch 7350, loss[loss=0.2728, simple_loss=0.3467, pruned_loss=0.09944, over 7321.00 frames.], tot_loss[loss=0.2575, simple_loss=0.3248, pruned_loss=0.09511, over 1420380.17 frames.], batch size: 21, lr: 1.07e-03
2022-05-26 20:45:57,676 INFO [train.py:842] (2/4) Epoch 4, batch 7400, loss[loss=0.227, simple_loss=0.3102, pruned_loss=0.07191, over 7326.00 frames.], tot_loss[loss=0.2573, simple_loss=0.3249, pruned_loss=0.0949, over 1421487.60 frames.], batch size: 20, lr: 1.07e-03
2022-05-26 20:46:36,595 INFO [train.py:842] (2/4) Epoch 4, batch 7450, loss[loss=0.2465, simple_loss=0.3213, pruned_loss=0.08584, over 7300.00 frames.], tot_loss[loss=0.2561, simple_loss=0.3237, pruned_loss=0.09421, over 1418594.35 frames.], batch size: 25, lr: 1.07e-03
2022-05-26 20:47:15,372 INFO [train.py:842] (2/4) Epoch 4, batch 7500, loss[loss=0.2369, simple_loss=0.3034, pruned_loss=0.08521, over 7366.00 frames.], tot_loss[loss=0.2567, simple_loss=0.3241, pruned_loss=0.09468, over 1422730.73 frames.], batch size: 19, lr: 1.07e-03
2022-05-26 20:47:54,188 INFO [train.py:842] (2/4) Epoch 4, batch 7550, loss[loss=0.2365, simple_loss=0.3062, pruned_loss=0.08343, over 6380.00 frames.], tot_loss[loss=0.2557, simple_loss=0.3235, pruned_loss=0.09388, over 1425162.67 frames.], batch size: 37, lr: 1.07e-03
2022-05-26 20:48:32,773 INFO [train.py:842] (2/4) Epoch 4, batch 7600, loss[loss=0.2444, simple_loss=0.295, pruned_loss=0.0969, over 7145.00 frames.], tot_loss[loss=0.2552, simple_loss=0.3227, pruned_loss=0.09387, over 1425102.47 frames.], batch size: 17, lr: 1.06e-03
2022-05-26 20:49:11,712 INFO [train.py:842] (2/4) Epoch 4, batch 7650, loss[loss=0.2299, simple_loss=0.3002, pruned_loss=0.07984, over 7254.00 frames.], tot_loss[loss=0.2559, simple_loss=0.3233, pruned_loss=0.09419, over 1427736.57 frames.], batch size: 19, lr: 1.06e-03
2022-05-26 20:49:50,462 INFO [train.py:842] (2/4) Epoch 4, batch 7700, loss[loss=0.2422, simple_loss=0.3052, pruned_loss=0.08962, over 7163.00 frames.], tot_loss[loss=0.2564, simple_loss=0.3235, pruned_loss=0.09464, over 1427723.16 frames.], batch size: 19, lr: 1.06e-03
2022-05-26 20:50:29,409 INFO [train.py:842] (2/4) Epoch 4, batch 7750, loss[loss=0.2378, simple_loss=0.3192, pruned_loss=0.07822, over 6324.00 frames.], tot_loss[loss=0.2561, simple_loss=0.3234, pruned_loss=0.09438, over 1429444.26 frames.], batch size: 37, lr: 1.06e-03
2022-05-26 20:51:08,058 INFO [train.py:842] (2/4) Epoch 4, batch 7800, loss[loss=0.2667, simple_loss=0.3254, pruned_loss=0.104, over 7342.00 frames.], tot_loss[loss=0.254, simple_loss=0.3218, pruned_loss=0.09306, over 1427566.45 frames.], batch size: 20, lr: 1.06e-03
2022-05-26 20:51:46,861 INFO [train.py:842] (2/4) Epoch 4, batch 7850, loss[loss=0.2622, simple_loss=0.3336, pruned_loss=0.09539, over 6232.00 frames.], tot_loss[loss=0.2535, simple_loss=0.3217, pruned_loss=0.09271, over 1423634.26 frames.], batch size: 37, lr: 1.06e-03
2022-05-26 20:52:25,501 INFO [train.py:842] (2/4) Epoch 4, batch 7900, loss[loss=0.2428, simple_loss=0.3067, pruned_loss=0.08946, over 7393.00 frames.], tot_loss[loss=0.2537, simple_loss=0.3215, pruned_loss=0.09297, over 1425357.29 frames.], batch size: 18, lr: 1.06e-03
2022-05-26 20:53:04,275 INFO [train.py:842] (2/4) Epoch 4, batch 7950, loss[loss=0.2313, simple_loss=0.3027, pruned_loss=0.07998, over 7161.00 frames.], tot_loss[loss=0.2542, simple_loss=0.3218, pruned_loss=0.09325, over 1424665.81 frames.], batch size: 18, lr: 1.06e-03
2022-05-26 20:53:42,672 INFO [train.py:842] (2/4) Epoch 4, batch 8000, loss[loss=0.2545, simple_loss=0.3243, pruned_loss=0.09238, over 6320.00 frames.], tot_loss[loss=0.2564, simple_loss=0.3239, pruned_loss=0.09446, over 1422993.94 frames.], batch size: 37, lr: 1.06e-03
2022-05-26 20:54:21,447 INFO [train.py:842] (2/4) Epoch 4, batch 8050, loss[loss=0.2556, simple_loss=0.3338, pruned_loss=0.08865, over 7318.00 frames.], tot_loss[loss=0.2562, simple_loss=0.3239, pruned_loss=0.09426, over 1422998.81 frames.], batch size: 21, lr: 1.06e-03
2022-05-26 20:54:59,918 INFO [train.py:842] (2/4) Epoch 4, batch 8100, loss[loss=0.3144, simple_loss=0.3612, pruned_loss=0.1338, over 7129.00 frames.], tot_loss[loss=0.2549, simple_loss=0.3232, pruned_loss=0.0933, over 1425346.13 frames.], batch size: 28, lr: 1.06e-03
2022-05-26 20:55:38,685 INFO [train.py:842] (2/4) Epoch 4, batch 8150, loss[loss=0.2625, simple_loss=0.3267, pruned_loss=0.09918, over 7426.00 frames.], tot_loss[loss=0.2561, simple_loss=0.3245, pruned_loss=0.0938, over 1427720.98 frames.], batch size: 20, lr: 1.06e-03
2022-05-26 20:56:17,360 INFO [train.py:842] (2/4) Epoch 4, batch 8200, loss[loss=0.2722, simple_loss=0.3518, pruned_loss=0.09629, over 7195.00 frames.], tot_loss[loss=0.255, simple_loss=0.3237, pruned_loss=0.09313, over 1430333.91 frames.], batch size: 23, lr: 1.06e-03
2022-05-26 20:56:56,002 INFO [train.py:842] (2/4) Epoch 4, batch 8250, loss[loss=0.2589, simple_loss=0.3354, pruned_loss=0.0912, over 7282.00 frames.], tot_loss[loss=0.2572, simple_loss=0.3252, pruned_loss=0.09463, over 1420873.73 frames.], batch size: 25, lr: 1.05e-03
2022-05-26 20:57:34,496 INFO [train.py:842] (2/4) Epoch 4, batch 8300, loss[loss=0.3714, simple_loss=0.4181, pruned_loss=0.1623, over 7129.00 frames.], tot_loss[loss=0.259, simple_loss=0.3265, pruned_loss=0.09576, over 1421743.45 frames.], batch size: 26, lr: 1.05e-03
2022-05-26 20:58:13,602 INFO [train.py:842] (2/4) Epoch 4, batch 8350, loss[loss=0.2623, simple_loss=0.3232, pruned_loss=0.1007, over 7201.00 frames.], tot_loss[loss=0.257, simple_loss=0.3245, pruned_loss=0.09478, over 1419332.13 frames.], batch size: 26, lr: 1.05e-03
2022-05-26 20:58:52,457 INFO [train.py:842] (2/4) Epoch 4, batch 8400, loss[loss=0.187, simple_loss=0.2687, pruned_loss=0.0527, over 7060.00 frames.], tot_loss[loss=0.2565, simple_loss=0.3237, pruned_loss=0.09468, over 1420210.35 frames.], batch size: 18, lr: 1.05e-03
2022-05-26 20:59:31,363 INFO [train.py:842] (2/4) Epoch 4, batch 8450, loss[loss=0.2077, simple_loss=0.2884, pruned_loss=0.0635, over 7351.00 frames.], tot_loss[loss=0.2565, simple_loss=0.3241, pruned_loss=0.09449, over 1421471.52 frames.], batch size: 19, lr: 1.05e-03
2022-05-26 21:00:09,902 INFO [train.py:842] (2/4) Epoch 4, batch 8500, loss[loss=0.274, simple_loss=0.344, pruned_loss=0.102, over 7255.00 frames.], tot_loss[loss=0.2565, simple_loss=0.3242, pruned_loss=0.09441, over 1421420.91 frames.], batch size: 19, lr: 1.05e-03
2022-05-26 21:00:48,715 INFO [train.py:842] (2/4) Epoch 4, batch 8550, loss[loss=0.2062, simple_loss=0.2701, pruned_loss=0.07114, over 7429.00 frames.], tot_loss[loss=0.2558, simple_loss=0.3234, pruned_loss=0.09412, over 1416087.72 frames.], batch size: 18, lr: 1.05e-03
2022-05-26 21:01:27,363 INFO [train.py:842] (2/4) Epoch 4, batch 8600, loss[loss=0.2439, simple_loss=0.3178, pruned_loss=0.08503, over 7210.00 frames.], tot_loss[loss=0.2543, simple_loss=0.322, pruned_loss=0.0933, over 1417965.34 frames.], batch size: 26, lr: 1.05e-03
2022-05-26 21:02:06,569 INFO [train.py:842] (2/4) Epoch 4, batch 8650, loss[loss=0.2802, simple_loss=0.3474, pruned_loss=0.1065, over 7384.00 frames.], tot_loss[loss=0.2542, simple_loss=0.3219, pruned_loss=0.09322, over 1417279.48 frames.], batch size: 23, lr: 1.05e-03
2022-05-26 21:02:45,052 INFO [train.py:842] (2/4) Epoch 4, batch 8700, loss[loss=0.2601, simple_loss=0.3293, pruned_loss=0.09548, over 7215.00 frames.], tot_loss[loss=0.2521, simple_loss=0.3203, pruned_loss=0.09193, over 1413257.46 frames.], batch size: 26, lr: 1.05e-03
2022-05-26 21:03:23,728 INFO [train.py:842] (2/4) Epoch 4, batch 8750, loss[loss=0.3325, simple_loss=0.3738, pruned_loss=0.1456, over 5369.00 frames.], tot_loss[loss=0.252, simple_loss=0.3198, pruned_loss=0.09203, over 1402586.17 frames.], batch size: 54, lr: 1.05e-03
2022-05-26 21:04:02,231 INFO [train.py:842] (2/4) Epoch 4, batch 8800, loss[loss=0.3181, simple_loss=0.3799, pruned_loss=0.1281, over 6862.00 frames.], tot_loss[loss=0.2514, simple_loss=0.3188, pruned_loss=0.09199, over 1401357.72 frames.], batch size: 31, lr: 1.05e-03
2022-05-26 21:04:40,825 INFO [train.py:842] (2/4) Epoch 4, batch 8850, loss[loss=0.2069, simple_loss=0.2904, pruned_loss=0.0617, over 7157.00 frames.], tot_loss[loss=0.2505, simple_loss=0.3178, pruned_loss=0.09158, over 1395607.88 frames.], batch size: 18, lr: 1.05e-03
2022-05-26 21:05:19,459 INFO [train.py:842] (2/4) Epoch 4, batch 8900, loss[loss=0.2214, simple_loss=0.2899, pruned_loss=0.07646, over 7157.00 frames.], tot_loss[loss=0.2499, simple_loss=0.317, pruned_loss=0.09142, over 1392645.56 frames.], batch size: 18, lr: 1.05e-03
2022-05-26 21:05:58,249 INFO [train.py:842] (2/4) Epoch 4, batch 8950, loss[loss=0.2384, simple_loss=0.3077, pruned_loss=0.08461, over 7363.00 frames.], tot_loss[loss=0.2515, simple_loss=0.3178, pruned_loss=0.09261, over 1391335.87 frames.], batch size: 19, lr: 1.04e-03
2022-05-26 21:06:36,765 INFO [train.py:842] (2/4) Epoch 4, batch 9000, loss[loss=0.2462, simple_loss=0.3068, pruned_loss=0.09282, over 7159.00 frames.], tot_loss[loss=0.2536, simple_loss=0.3194, pruned_loss=0.09395, over 1378103.54 frames.], batch size: 19, lr: 1.04e-03
2022-05-26 21:06:36,766 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 21:06:46,020 INFO [train.py:871] (2/4) Epoch 4, validation: loss=0.1922, simple_loss=0.292, pruned_loss=0.04621, over 868885.00 frames.
2022-05-26 21:07:24,191 INFO [train.py:842] (2/4) Epoch 4, batch 9050, loss[loss=0.2603, simple_loss=0.3222, pruned_loss=0.09921, over 4938.00 frames.], tot_loss[loss=0.2561, simple_loss=0.3216, pruned_loss=0.09527, over 1361922.88 frames.], batch size: 52, lr: 1.04e-03
2022-05-26 21:08:01,817 INFO [train.py:842] (2/4) Epoch 4, batch 9100, loss[loss=0.28, simple_loss=0.3455, pruned_loss=0.1072, over 6550.00 frames.], tot_loss[loss=0.2609, simple_loss=0.3256, pruned_loss=0.0981, over 1340811.50 frames.], batch size: 38, lr: 1.04e-03
2022-05-26 21:08:39,495 INFO [train.py:842] (2/4) Epoch 4, batch 9150, loss[loss=0.3085, simple_loss=0.364, pruned_loss=0.1265, over 5019.00 frames.], tot_loss[loss=0.2678, simple_loss=0.3306, pruned_loss=0.1025, over 1284387.98 frames.], batch size: 52, lr: 1.04e-03
2022-05-26 21:09:32,024 INFO [train.py:842] (2/4) Epoch 5, batch 0, loss[loss=0.2778, simple_loss=0.348, pruned_loss=0.1038, over 7179.00 frames.], tot_loss[loss=0.2778, simple_loss=0.348, pruned_loss=0.1038, over 7179.00 frames.], batch size: 23, lr: 1.00e-03
2022-05-26 21:10:11,442 INFO [train.py:842] (2/4) Epoch 5, batch 50, loss[loss=0.2492, simple_loss=0.3379, pruned_loss=0.08022, over 7339.00 frames.], tot_loss[loss=0.2507, simple_loss=0.3213, pruned_loss=0.09007, over 320899.73 frames.], batch size: 22, lr: 1.00e-03
2022-05-26 21:10:50,253 INFO [train.py:842] (2/4) Epoch 5, batch 100, loss[loss=0.2918, simple_loss=0.3661, pruned_loss=0.1087, over 7337.00 frames.], tot_loss[loss=0.2531, simple_loss=0.3228, pruned_loss=0.09164, over 566713.49 frames.], batch size: 22, lr: 1.00e-03
2022-05-26 21:11:29,051 INFO [train.py:842] (2/4) Epoch 5, batch 150, loss[loss=0.3025, simple_loss=0.3659, pruned_loss=0.1196, over 5088.00 frames.], tot_loss[loss=0.2587, simple_loss=0.3262, pruned_loss=0.09567, over 755364.08 frames.], batch size: 52, lr: 1.00e-03
2022-05-26 21:12:07,518 INFO [train.py:842] (2/4) Epoch 5, batch 200, loss[loss=0.2329, simple_loss=0.2888, pruned_loss=0.08848, over 7157.00 frames.], tot_loss[loss=0.2564, simple_loss=0.3249, pruned_loss=0.09398, over 903955.06 frames.], batch size: 19, lr: 1.00e-03
2022-05-26 21:12:46,168 INFO [train.py:842] (2/4) Epoch 5, batch 250, loss[loss=0.3151, simple_loss=0.3734, pruned_loss=0.1284, over 7331.00 frames.], tot_loss[loss=0.2582, simple_loss=0.327, pruned_loss=0.09469, over 1021796.34 frames.], batch size: 22, lr: 1.00e-03
2022-05-26 21:13:24,985 INFO [train.py:842] (2/4) Epoch 5, batch 300, loss[loss=0.2522, simple_loss=0.2927, pruned_loss=0.1059, over 7261.00 frames.], tot_loss[loss=0.2561, simple_loss=0.3247, pruned_loss=0.09377, over 1115183.27 frames.], batch size: 17, lr: 1.00e-03
2022-05-26 21:14:03,869 INFO [train.py:842] (2/4) Epoch 5, batch 350, loss[loss=0.2231, simple_loss=0.2953, pruned_loss=0.07542, over 7148.00 frames.], tot_loss[loss=0.254, simple_loss=0.3223, pruned_loss=0.09286, over 1182861.91 frames.], batch size: 19, lr: 1.00e-03
2022-05-26 21:14:42,418 INFO [train.py:842] (2/4) Epoch 5, batch 400, loss[loss=0.2819, simple_loss=0.3455, pruned_loss=0.1092, over 7107.00 frames.], tot_loss[loss=0.2534, simple_loss=0.3217, pruned_loss=0.09253, over 1233667.85 frames.], batch size: 28, lr: 9.99e-04
2022-05-26 21:15:21,483 INFO [train.py:842] (2/4) Epoch 5, batch 450, loss[loss=0.2477, simple_loss=0.3275, pruned_loss=0.08391, over 7072.00 frames.], tot_loss[loss=0.2529, simple_loss=0.3216, pruned_loss=0.09207, over 1274687.46 frames.], batch size: 28, lr: 9.99e-04
2022-05-26 21:16:00,452 INFO [train.py:842] (2/4) Epoch 5, batch 500, loss[loss=0.2132, simple_loss=0.3001, pruned_loss=0.06311, over 7315.00 frames.], tot_loss[loss=0.2505, simple_loss=0.32, pruned_loss=0.09052, over 1310062.91 frames.], batch size: 21, lr: 9.98e-04
2022-05-26 21:16:39,269 INFO [train.py:842] (2/4) Epoch 5, batch 550, loss[loss=0.2331, simple_loss=0.3089, pruned_loss=0.07867, over 6749.00 frames.], tot_loss[loss=0.2482, simple_loss=0.3186, pruned_loss=0.08895, over 1333933.17 frames.], batch size: 31, lr: 9.97e-04
2022-05-26 21:17:17,948 INFO [train.py:842] (2/4) Epoch 5, batch 600, loss[loss=0.2519, simple_loss=0.3058, pruned_loss=0.09906, over 6988.00 frames.], tot_loss[loss=0.2476, simple_loss=0.3177, pruned_loss=0.08879, over 1356007.92 frames.], batch size: 16, lr: 9.97e-04
2022-05-26 21:17:56,775 INFO [train.py:842] (2/4) Epoch 5, batch 650, loss[loss=0.2219, simple_loss=0.3017, pruned_loss=0.071, over 7328.00 frames.], tot_loss[loss=0.2495, simple_loss=0.3192, pruned_loss=0.0899, over 1370937.58 frames.], batch size: 20, lr: 9.96e-04
2022-05-26 21:18:35,150 INFO [train.py:842] (2/4) Epoch 5, batch 700, loss[loss=0.295, simple_loss=0.3594, pruned_loss=0.1153, over 7292.00 frames.], tot_loss[loss=0.2507, simple_loss=0.3203, pruned_loss=0.09056, over 1380652.07 frames.], batch size: 25, lr: 9.95e-04
2022-05-26 21:19:14,036 INFO [train.py:842] (2/4) Epoch 5, batch 750, loss[loss=0.2012, simple_loss=0.2748, pruned_loss=0.06379, over 7065.00 frames.], tot_loss[loss=0.2495, simple_loss=0.319, pruned_loss=0.08995, over 1384921.02 frames.], batch size: 18, lr: 9.95e-04
2022-05-26 21:19:52,739 INFO [train.py:842] (2/4) Epoch 5, batch 800, loss[loss=0.1811, simple_loss=0.2685, pruned_loss=0.04683, over 7066.00 frames.], tot_loss[loss=0.2494, simple_loss=0.3185, pruned_loss=0.09013, over 1397052.05 frames.], batch size: 18, lr: 9.94e-04
2022-05-26 21:20:31,417 INFO [train.py:842] (2/4) Epoch 5, batch 850, loss[loss=0.2848, simple_loss=0.3463, pruned_loss=0.1117, over 7066.00 frames.], tot_loss[loss=0.2481, simple_loss=0.3177, pruned_loss=0.08927, over 1395846.26 frames.], batch size: 18, lr: 9.93e-04
2022-05-26 21:21:09,958 INFO [train.py:842] (2/4) Epoch 5, batch 900, loss[loss=0.2068, simple_loss=0.2859, pruned_loss=0.06385, over 7330.00 frames.], tot_loss[loss=0.2471, simple_loss=0.317, pruned_loss=0.08865, over 1402768.86 frames.], batch size: 21, lr: 9.93e-04
2022-05-26 21:21:48,885 INFO [train.py:842] (2/4) Epoch 5, batch 950, loss[loss=0.3106, simple_loss=0.3556, pruned_loss=0.1328, over 7047.00 frames.], tot_loss[loss=0.2497, simple_loss=0.3192, pruned_loss=0.09008, over 1406952.22 frames.], batch size: 28, lr: 9.92e-04
2022-05-26 21:22:27,571 INFO [train.py:842] (2/4) Epoch 5, batch 1000, loss[loss=0.2422, simple_loss=0.3116, pruned_loss=0.08643, over 7060.00 frames.], tot_loss[loss=0.2485, simple_loss=0.3184, pruned_loss=0.0893, over 1410997.62 frames.], batch size: 18, lr: 9.91e-04
2022-05-26 21:23:06,447 INFO [train.py:842] (2/4) Epoch 5, batch 1050, loss[loss=0.2413, simple_loss=0.3188, pruned_loss=0.08191, over 7304.00 frames.], tot_loss[loss=0.2497, simple_loss=0.3197, pruned_loss=0.08987, over 1416471.50 frames.], batch size: 24, lr: 9.91e-04
2022-05-26 21:23:44,730 INFO [train.py:842] (2/4) Epoch 5, batch 1100, loss[loss=0.2539, simple_loss=0.322, pruned_loss=0.09292, over 6430.00 frames.], tot_loss[loss=0.2509, simple_loss=0.3206, pruned_loss=0.09053, over 1413157.12 frames.], batch size: 37, lr: 9.90e-04
2022-05-26 21:24:23,623 INFO [train.py:842] (2/4) Epoch 5, batch 1150, loss[loss=0.2425, simple_loss=0.3143, pruned_loss=0.08529, over 7428.00 frames.], tot_loss[loss=0.2507, simple_loss=0.3203, pruned_loss=0.09052, over 1416105.61 frames.], batch size: 20, lr: 9.89e-04
2022-05-26 21:25:02,149 INFO [train.py:842] (2/4) Epoch 5, batch 1200, loss[loss=0.2691, simple_loss=0.3445, pruned_loss=0.09689, over 6514.00 frames.], tot_loss[loss=0.2511, simple_loss=0.3208, pruned_loss=0.09075, over 1418400.78 frames.], batch size: 38, lr: 9.89e-04
2022-05-26 21:25:40,991 INFO [train.py:842] (2/4) Epoch 5, batch 1250, loss[loss=0.2415, simple_loss=0.3177, pruned_loss=0.08269, over 7267.00 frames.], tot_loss[loss=0.2516, simple_loss=0.3209, pruned_loss=0.09112, over 1413110.19 frames.], batch size: 19, lr: 9.88e-04
2022-05-26 21:26:19,437 INFO [train.py:842] (2/4) Epoch 5, batch 1300, loss[loss=0.2541, simple_loss=0.3237, pruned_loss=0.0922, over 7333.00 frames.], tot_loss[loss=0.252, simple_loss=0.3216, pruned_loss=0.09119, over 1416341.75 frames.], batch size: 20, lr: 9.87e-04
2022-05-26 21:26:58,319 INFO [train.py:842] (2/4) Epoch 5, batch 1350, loss[loss=0.2235, simple_loss=0.2955, pruned_loss=0.07573, over 7135.00 frames.], tot_loss[loss=0.2513, simple_loss=0.3209, pruned_loss=0.0909, over 1423544.51 frames.], batch size: 17, lr: 9.87e-04
2022-05-26 21:27:36,964 INFO [train.py:842] (2/4) Epoch 5, batch 1400, loss[loss=0.2278, simple_loss=0.2986, pruned_loss=0.0785, over 7245.00 frames.], tot_loss[loss=0.2524, simple_loss=0.3218, pruned_loss=0.09148, over 1418906.86 frames.], batch size: 20, lr: 9.86e-04
2022-05-26 21:28:15,794 INFO [train.py:842] (2/4) Epoch 5, batch 1450, loss[loss=0.1954, simple_loss=0.2601, pruned_loss=0.06538, over 6999.00 frames.], tot_loss[loss=0.2519, simple_loss=0.3215, pruned_loss=0.09119, over 1420072.79 frames.], batch size: 16, lr: 9.86e-04
2022-05-26 21:28:54,593 INFO [train.py:842] (2/4) Epoch 5, batch 1500, loss[loss=0.2687, simple_loss=0.3345, pruned_loss=0.1014, over 7327.00 frames.], tot_loss[loss=0.2506, simple_loss=0.3202, pruned_loss=0.09048, over 1423284.86 frames.], batch size: 20, lr: 9.85e-04
2022-05-26 21:29:33,893 INFO [train.py:842] (2/4) Epoch 5, batch 1550, loss[loss=0.3226, simple_loss=0.3809, pruned_loss=0.1321, over 7364.00 frames.], tot_loss[loss=0.2502, simple_loss=0.3196, pruned_loss=0.09046, over 1426072.87 frames.], batch size: 23, lr: 9.84e-04
2022-05-26 21:30:12,532 INFO [train.py:842] (2/4) Epoch 5, batch 1600, loss[loss=0.3636, simple_loss=0.4127, pruned_loss=0.1573, over 7287.00 frames.], tot_loss[loss=0.2506, simple_loss=0.3196, pruned_loss=0.09074, over 1424844.61 frames.], batch size: 25, lr: 9.84e-04
2022-05-26 21:31:01,987 INFO [train.py:842] (2/4) Epoch 5, batch 1650, loss[loss=0.258, simple_loss=0.3222, pruned_loss=0.09685, over 7123.00 frames.], tot_loss[loss=0.2502, simple_loss=0.3195, pruned_loss=0.09042, over 1422182.29 frames.], batch size: 21, lr: 9.83e-04
2022-05-26 21:31:40,731 INFO [train.py:842] (2/4) Epoch 5, batch 1700, loss[loss=0.2967, simple_loss=0.3547, pruned_loss=0.1193, over 7341.00 frames.], tot_loss[loss=0.2494, simple_loss=0.3188, pruned_loss=0.09004, over 1423991.00 frames.], batch size: 22, lr: 9.82e-04
2022-05-26 21:32:19,655 INFO [train.py:842] (2/4) Epoch 5, batch 1750, loss[loss=0.2257, simple_loss=0.2977, pruned_loss=0.07688, over 7307.00 frames.], tot_loss[loss=0.2485, simple_loss=0.3181, pruned_loss=0.08943, over 1422824.40 frames.], batch size: 24, lr: 9.82e-04
2022-05-26 21:32:58,277 INFO [train.py:842] (2/4) Epoch 5, batch 1800, loss[loss=0.2515, simple_loss=0.3366, pruned_loss=0.08316, over 7310.00 frames.], tot_loss[loss=0.2488, simple_loss=0.319, pruned_loss=0.08932, over 1425013.84 frames.], batch size: 21, lr: 9.81e-04
2022-05-26 21:33:37,264 INFO [train.py:842] (2/4) Epoch 5, batch 1850, loss[loss=0.3066, simple_loss=0.3606, pruned_loss=0.1263, over 6359.00 frames.], tot_loss[loss=0.2497, simple_loss=0.3193, pruned_loss=0.09004, over 1425038.73 frames.], batch size: 37, lr: 9.81e-04
2022-05-26 21:34:15,859 INFO [train.py:842] (2/4) Epoch 5, batch 1900, loss[loss=0.2835, simple_loss=0.3408, pruned_loss=0.1131, over 7109.00 frames.], tot_loss[loss=0.2487, simple_loss=0.3189, pruned_loss=0.08923, over 1426515.34 frames.], batch size: 21, lr: 9.80e-04
2022-05-26 21:34:55,059 INFO [train.py:842] (2/4) Epoch 5, batch 1950, loss[loss=0.2037, simple_loss=0.2846, pruned_loss=0.06144, over 7160.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3184, pruned_loss=0.08943, over 1428154.40 frames.], batch size: 18, lr: 9.79e-04
2022-05-26 21:35:33,834 INFO [train.py:842] (2/4) Epoch 5, batch 2000, loss[loss=0.249, simple_loss=0.3211, pruned_loss=0.08846, over 7337.00 frames.], tot_loss[loss=0.2485, simple_loss=0.318, pruned_loss=0.08949, over 1425086.45 frames.], batch size: 25, lr: 9.79e-04
2022-05-26 21:36:12,762 INFO [train.py:842] (2/4) Epoch 5, batch 2050, loss[loss=0.2333, simple_loss=0.3145, pruned_loss=0.07603, over 7300.00 frames.], tot_loss[loss=0.2483, simple_loss=0.3179, pruned_loss=0.08933, over 1430268.77 frames.], batch size: 24, lr: 9.78e-04
2022-05-26 21:36:51,526 INFO [train.py:842] (2/4) Epoch 5, batch 2100, loss[loss=0.2037, simple_loss=0.281, pruned_loss=0.06318, over 7427.00 frames.], tot_loss[loss=0.2492, simple_loss=0.319, pruned_loss=0.08972, over 1433324.97 frames.], batch size: 18, lr: 9.77e-04
2022-05-26 21:37:30,128 INFO [train.py:842] (2/4) Epoch 5, batch 2150, loss[loss=0.2135, simple_loss=0.2934, pruned_loss=0.06683, over 7055.00 frames.], tot_loss[loss=0.2498, simple_loss=0.3202, pruned_loss=0.08972, over 1431725.94 frames.], batch size: 18, lr: 9.77e-04
2022-05-26 21:38:08,835 INFO [train.py:842] (2/4) Epoch 5, batch 2200, loss[loss=0.2237, simple_loss=0.3006, pruned_loss=0.07341, over 7313.00 frames.], tot_loss[loss=0.2481, simple_loss=0.3186, pruned_loss=0.08884, over 1433526.09 frames.], batch size: 22, lr: 9.76e-04
2022-05-26 21:38:47,513 INFO [train.py:842] (2/4) Epoch 5, batch 2250, loss[loss=0.2766, simple_loss=0.347, pruned_loss=0.1031, over 7360.00 frames.], tot_loss[loss=0.2481, simple_loss=0.3185, pruned_loss=0.08882, over 1430685.71 frames.], batch size: 23, lr: 9.76e-04
2022-05-26 21:39:26,231 INFO [train.py:842] (2/4) Epoch 5, batch 2300, loss[loss=0.1949, simple_loss=0.2642, pruned_loss=0.06276, over 7287.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3188, pruned_loss=0.08917, over 1429016.79 frames.], batch size: 17, lr: 9.75e-04
2022-05-26 21:40:05,046 INFO [train.py:842] (2/4) Epoch 5, batch 2350, loss[loss=0.2005, simple_loss=0.2781, pruned_loss=0.06148, over 7415.00 frames.], tot_loss[loss=0.2469, simple_loss=0.3174, pruned_loss=0.08814, over 1432121.87 frames.], batch size: 18, lr: 9.74e-04
2022-05-26 21:40:53,737 INFO [train.py:842] (2/4) Epoch 5, batch 2400, loss[loss=0.2263, simple_loss=0.3074, pruned_loss=0.07254, over 7224.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3175, pruned_loss=0.08834, over 1433391.88 frames.], batch size: 21, lr: 9.74e-04
2022-05-26 21:41:32,642 INFO [train.py:842] (2/4) Epoch 5, batch 2450, loss[loss=0.2411, simple_loss=0.302, pruned_loss=0.09011, over 7274.00 frames.], tot_loss[loss=0.2477, simple_loss=0.318, pruned_loss=0.0887, over 1433893.04 frames.], batch size: 18, lr: 9.73e-04
2022-05-26 21:42:21,534 INFO [train.py:842] (2/4) Epoch 5, batch 2500, loss[loss=0.2893, simple_loss=0.351, pruned_loss=0.1138, over 7215.00 frames.], tot_loss[loss=0.247, simple_loss=0.3175, pruned_loss=0.08823, over 1431780.55 frames.], batch size: 22, lr: 9.73e-04
2022-05-26 21:43:11,046 INFO [train.py:842] (2/4) Epoch 5, batch 2550, loss[loss=0.2636, simple_loss=0.3314, pruned_loss=0.09794, over 7140.00 frames.], tot_loss[loss=0.2466, simple_loss=0.3173, pruned_loss=0.08792, over 1432552.98 frames.], batch size: 20, lr: 9.72e-04
2022-05-26 21:43:49,608 INFO [train.py:842] (2/4) Epoch 5, batch 2600, loss[loss=0.2361, simple_loss=0.3096, pruned_loss=0.08134, over 7306.00 frames.], tot_loss[loss=0.2483, simple_loss=0.3189, pruned_loss=0.08882, over 1431448.77 frames.], batch size: 21, lr: 9.71e-04
2022-05-26 21:44:28,562 INFO [train.py:842] (2/4) Epoch 5, batch 2650, loss[loss=0.2392, simple_loss=0.2933, pruned_loss=0.09249, over 6982.00 frames.], tot_loss[loss=0.248, simple_loss=0.3183, pruned_loss=0.08888, over 1430258.23 frames.], batch size: 16, lr: 9.71e-04
2022-05-26 21:45:07,104 INFO [train.py:842] (2/4) Epoch 5, batch 2700, loss[loss=0.2248, simple_loss=0.2822, pruned_loss=0.08375, over 7267.00 frames.], tot_loss[loss=0.2459, simple_loss=0.3166, pruned_loss=0.08764, over 1432438.43 frames.], batch size: 18, lr: 9.70e-04
2022-05-26 21:45:46,201 INFO [train.py:842] (2/4) Epoch 5, batch 2750, loss[loss=0.2845, simple_loss=0.3398, pruned_loss=0.1146, over 7355.00 frames.], tot_loss[loss=0.2462, simple_loss=0.3169, pruned_loss=0.08773, over 1433905.67 frames.], batch size: 19, lr: 9.70e-04
2022-05-26 21:46:24,821 INFO [train.py:842] (2/4) Epoch 5, batch 2800, loss[loss=0.2138, simple_loss=0.2801, pruned_loss=0.07376, over 7135.00 frames.], tot_loss[loss=0.2454, simple_loss=0.3162, pruned_loss=0.08728, over 1434111.47 frames.], batch size: 17, lr: 9.69e-04
2022-05-26 21:47:03,559 INFO [train.py:842] (2/4) Epoch 5, batch 2850, loss[loss=0.2714, simple_loss=0.3385, pruned_loss=0.1021, over 6817.00 frames.], tot_loss[loss=0.246, simple_loss=0.3168, pruned_loss=0.08755, over 1431367.35 frames.], batch size: 31, lr: 9.68e-04
2022-05-26 21:47:41,771 INFO [train.py:842] (2/4) Epoch 5, batch 2900, loss[loss=0.2111, simple_loss=0.2896, pruned_loss=0.06631, over 7301.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3168, pruned_loss=0.08741, over 1429181.24 frames.], batch size: 24, lr: 9.68e-04
2022-05-26 21:48:20,710 INFO [train.py:842] (2/4) Epoch 5, batch 2950, loss[loss=0.2703, simple_loss=0.3487, pruned_loss=0.09591, over 7338.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3163, pruned_loss=0.08767, over 1428808.73 frames.], batch size: 22, lr: 9.67e-04
2022-05-26 21:48:59,372 INFO [train.py:842] (2/4) Epoch 5, batch 3000, loss[loss=0.2298, simple_loss=0.3092, pruned_loss=0.07522, over 7187.00 frames.], tot_loss[loss=0.2455, simple_loss=0.3161, pruned_loss=0.08745, over 1424674.36 frames.], batch size: 26, lr: 9.66e-04
2022-05-26 21:48:59,373 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 21:49:08,664 INFO [train.py:871] (2/4) Epoch 5, validation: loss=0.1927, simple_loss=0.2922, pruned_loss=0.04663, over 868885.00 frames.
2022-05-26 21:49:47,990 INFO [train.py:842] (2/4) Epoch 5, batch 3050, loss[loss=0.232, simple_loss=0.3109, pruned_loss=0.07652, over 7194.00 frames.], tot_loss[loss=0.2464, simple_loss=0.3166, pruned_loss=0.08807, over 1428319.61 frames.], batch size: 22, lr: 9.66e-04
2022-05-26 21:50:26,486 INFO [train.py:842] (2/4) Epoch 5, batch 3100, loss[loss=0.2473, simple_loss=0.3296, pruned_loss=0.08245, over 7229.00 frames.], tot_loss[loss=0.2479, simple_loss=0.3181, pruned_loss=0.08889, over 1426929.09 frames.], batch size: 20, lr: 9.65e-04
2022-05-26 21:51:05,262 INFO [train.py:842] (2/4) Epoch 5, batch 3150, loss[loss=0.2454, simple_loss=0.3155, pruned_loss=0.08764, over 7325.00 frames.], tot_loss[loss=0.2462, simple_loss=0.3167, pruned_loss=0.08785, over 1427316.04 frames.], batch size: 25, lr: 9.65e-04
2022-05-26 21:51:43,967 INFO [train.py:842] (2/4) Epoch 5, batch 3200, loss[loss=0.2096, simple_loss=0.2799, pruned_loss=0.0697, over 7359.00 frames.], tot_loss[loss=0.2474, simple_loss=0.3174, pruned_loss=0.08872, over 1428709.30 frames.], batch size: 19, lr: 9.64e-04
2022-05-26 21:52:25,508 INFO [train.py:842] (2/4) Epoch 5, batch 3250, loss[loss=0.2321, simple_loss=0.3022, pruned_loss=0.08096, over 7171.00 frames.], tot_loss[loss=0.2465, simple_loss=0.3166, pruned_loss=0.08821, over 1427247.30 frames.], batch size: 18, lr: 9.64e-04
2022-05-26 21:53:04,084 INFO [train.py:842] (2/4) Epoch 5, batch 3300, loss[loss=0.3216, simple_loss=0.3844, pruned_loss=0.1294, over 7192.00 frames.], tot_loss[loss=0.2492, simple_loss=0.3188, pruned_loss=0.08978, over 1422369.36 frames.], batch size: 26, lr: 9.63e-04
2022-05-26 21:53:43,083 INFO [train.py:842] (2/4) Epoch 5, batch 3350, loss[loss=0.237, simple_loss=0.317, pruned_loss=0.0785, over 7118.00 frames.], tot_loss[loss=0.2497, simple_loss=0.3191, pruned_loss=0.09015, over 1425313.21 frames.], batch size: 21, lr: 9.62e-04
2022-05-26 21:54:21,687 INFO [train.py:842] (2/4) Epoch 5, batch 3400, loss[loss=0.2451, simple_loss=0.3208, pruned_loss=0.08475, over 7227.00 frames.], tot_loss[loss=0.2499, simple_loss=0.3194, pruned_loss=0.09022, over 1426587.11 frames.], batch size: 20, lr: 9.62e-04
2022-05-26 21:55:00,655 INFO [train.py:842] (2/4) Epoch 5, batch 3450, loss[loss=0.2582, simple_loss=0.3297, pruned_loss=0.09339, over 7176.00 frames.], tot_loss[loss=0.2481, simple_loss=0.3179, pruned_loss=0.08919, over 1426430.69 frames.], batch size: 23, lr: 9.61e-04
2022-05-26 21:55:39,262 INFO [train.py:842] (2/4) Epoch 5, batch 3500, loss[loss=0.2648, simple_loss=0.3455, pruned_loss=0.09203, over 7314.00 frames.], tot_loss[loss=0.2481, simple_loss=0.3181, pruned_loss=0.08904, over 1428418.43 frames.], batch size: 21, lr: 9.61e-04
2022-05-26 21:56:18,052 INFO [train.py:842] (2/4) Epoch 5, batch 3550, loss[loss=0.2386, simple_loss=0.3169, pruned_loss=0.0802, over 7333.00 frames.], tot_loss[loss=0.2479, simple_loss=0.318, pruned_loss=0.08884, over 1425038.67 frames.], batch size: 20, lr: 9.60e-04
2022-05-26 21:56:56,515 INFO [train.py:842] (2/4) Epoch 5, batch 3600, loss[loss=0.2398, simple_loss=0.3024, pruned_loss=0.0886, over 7316.00 frames.], tot_loss[loss=0.2512, simple_loss=0.3206, pruned_loss=0.09095, over 1420994.29 frames.], batch size: 20, lr: 9.59e-04
2022-05-26 21:57:35,258 INFO [train.py:842] (2/4) Epoch 5, batch 3650, loss[loss=0.2086, simple_loss=0.286, pruned_loss=0.0656, over 7061.00 frames.], tot_loss[loss=0.2518, simple_loss=0.3209, pruned_loss=0.09133, over 1413242.54 frames.], batch size: 18, lr: 9.59e-04
2022-05-26 21:58:13,856 INFO [train.py:842] (2/4) Epoch 5, batch 3700, loss[loss=0.2723, simple_loss=0.3405, pruned_loss=0.1021, over 7213.00 frames.], tot_loss[loss=0.2499, simple_loss=0.3189, pruned_loss=0.09044, over 1419246.60 frames.], batch size: 21, lr: 9.58e-04
2022-05-26 21:58:52,969 INFO [train.py:842] (2/4) Epoch 5, batch 3750, loss[loss=0.2707, simple_loss=0.3296, pruned_loss=0.1059, over 5269.00 frames.], tot_loss[loss=0.2485, simple_loss=0.3181, pruned_loss=0.08939, over 1418974.37 frames.], batch size: 53, lr: 9.58e-04
2022-05-26 21:59:31,656 INFO [train.py:842] (2/4) Epoch 5, batch 3800, loss[loss=0.1816, simple_loss=0.2587, pruned_loss=0.05228, over 6787.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3173, pruned_loss=0.08869, over 1419126.15 frames.], batch size: 15, lr: 9.57e-04
2022-05-26 22:00:10,486 INFO [train.py:842] (2/4) Epoch 5, batch 3850, loss[loss=0.2058, simple_loss=0.2837, pruned_loss=0.06396, over 7414.00 frames.], tot_loss[loss=0.2466, simple_loss=0.317, pruned_loss=0.08804, over 1419648.86 frames.], batch size: 18, lr: 9.56e-04
2022-05-26 22:00:49,036 INFO [train.py:842] (2/4) Epoch 5, batch 3900, loss[loss=0.2205, simple_loss=0.2918, pruned_loss=0.07458, over 7365.00 frames.], tot_loss[loss=0.2466, simple_loss=0.3168, pruned_loss=0.08819, over 1417491.31 frames.], batch size: 19, lr: 9.56e-04
2022-05-26 22:01:28,106 INFO [train.py:842] (2/4) Epoch 5, batch 3950, loss[loss=0.2305, simple_loss=0.3059, pruned_loss=0.07757, over 7257.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3175, pruned_loss=0.08861, over 1415298.96 frames.], batch size: 19, lr: 9.55e-04
2022-05-26 22:02:06,777 INFO [train.py:842] (2/4) Epoch 5, batch 4000, loss[loss=0.2748, simple_loss=0.3311, pruned_loss=0.1093, over 7339.00 frames.], tot_loss[loss=0.247, simple_loss=0.3171, pruned_loss=0.08845, over 1418330.90 frames.], batch size: 22, lr: 9.55e-04
2022-05-26 22:02:46,091 INFO [train.py:842] (2/4) Epoch 5, batch 4050, loss[loss=0.2216, simple_loss=0.2947, pruned_loss=0.07419, over 7288.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3172, pruned_loss=0.0885, over 1420938.67 frames.], batch size: 18, lr: 9.54e-04
2022-05-26 22:03:24,839 INFO [train.py:842] (2/4) Epoch 5, batch 4100, loss[loss=0.2138, simple_loss=0.297, pruned_loss=0.06528, over 7128.00 frames.], tot_loss[loss=0.2462, simple_loss=0.3168, pruned_loss=0.08785, over 1421350.34 frames.], batch size: 26, lr: 9.54e-04
2022-05-26 22:04:03,568 INFO [train.py:842] (2/4) Epoch 5, batch 4150, loss[loss=0.2485, simple_loss=0.3351, pruned_loss=0.08094, over 7216.00 frames.], tot_loss[loss=0.248, simple_loss=0.3186, pruned_loss=0.08871, over 1416019.85 frames.], batch size: 26, lr: 9.53e-04
2022-05-26 22:04:42,302 INFO [train.py:842] (2/4) Epoch 5, batch 4200, loss[loss=0.2587, simple_loss=0.3204, pruned_loss=0.09849, over 7287.00 frames.], tot_loss[loss=0.2483, simple_loss=0.3188, pruned_loss=0.08887, over 1420187.34 frames.], batch size: 18, lr: 9.52e-04
2022-05-26 22:05:21,085 INFO [train.py:842] (2/4) Epoch 5, batch 4250, loss[loss=0.3115, simple_loss=0.3584, pruned_loss=0.1323, over 7217.00 frames.], tot_loss[loss=0.25, simple_loss=0.3195, pruned_loss=0.09027, over 1420233.60 frames.], batch size: 22, lr: 9.52e-04
2022-05-26 22:05:59,640 INFO [train.py:842] (2/4) Epoch 5, batch 4300, loss[loss=0.2723, simple_loss=0.3252, pruned_loss=0.1097, over 7167.00 frames.], tot_loss[loss=0.2485, simple_loss=0.318, pruned_loss=0.08951, over 1421047.06 frames.], batch size: 18, lr: 9.51e-04
2022-05-26 22:06:38,337 INFO [train.py:842] (2/4) Epoch 5, batch 4350, loss[loss=0.2831, simple_loss=0.3508, pruned_loss=0.1076, over 7162.00 frames.], tot_loss[loss=0.2474, simple_loss=0.3169, pruned_loss=0.08896, over 1422209.47 frames.], batch size: 26, lr: 9.51e-04
2022-05-26 22:07:17,015 INFO [train.py:842] (2/4) Epoch 5, batch 4400, loss[loss=0.2859, simple_loss=0.3669, pruned_loss=0.1025, over 7146.00 frames.], tot_loss[loss=0.2464, simple_loss=0.3161, pruned_loss=0.08832, over 1424007.97 frames.], batch size: 20, lr: 9.50e-04
2022-05-26 22:07:55,964 INFO [train.py:842] (2/4) Epoch 5, batch 4450, loss[loss=0.2301, simple_loss=0.3171, pruned_loss=0.07157, over 7213.00 frames.], tot_loss[loss=0.2472, simple_loss=0.3175, pruned_loss=0.08848, over 1426723.44 frames.], batch size: 26, lr: 9.50e-04
2022-05-26 22:08:34,455 INFO [train.py:842] (2/4) Epoch 5, batch 4500, loss[loss=0.2584, simple_loss=0.3294, pruned_loss=0.09372, over 7384.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3178, pruned_loss=0.0884, over 1425960.52 frames.], batch size: 23, lr: 9.49e-04
2022-05-26 22:09:13,378 INFO [train.py:842] (2/4) Epoch 5, batch 4550, loss[loss=0.2144, simple_loss=0.2938, pruned_loss=0.06752, over 7083.00 frames.], tot_loss[loss=0.2482, simple_loss=0.3182, pruned_loss=0.0891, over 1427352.47 frames.], batch size: 28, lr: 9.48e-04
2022-05-26 22:09:51,898 INFO [train.py:842] (2/4) Epoch 5, batch 4600, loss[loss=0.2933, simple_loss=0.3483, pruned_loss=0.1191, over 7207.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3179, pruned_loss=0.08842, over 1424221.38 frames.], batch size: 22, lr: 9.48e-04
2022-05-26 22:10:31,323 INFO [train.py:842] (2/4) Epoch 5, batch 4650, loss[loss=0.2386, simple_loss=0.3152, pruned_loss=0.08102, over 7323.00 frames.], tot_loss[loss=0.2478, simple_loss=0.3177, pruned_loss=0.08893, over 1425769.43 frames.], batch size: 21, lr: 9.47e-04
2022-05-26 22:11:09,885 INFO [train.py:842] (2/4) Epoch 5, batch 4700, loss[loss=0.2173, simple_loss=0.2881, pruned_loss=0.07328, over 7322.00 frames.], tot_loss[loss=0.2484, simple_loss=0.3184, pruned_loss=0.08924, over 1424809.95 frames.], batch size: 20, lr: 9.47e-04
2022-05-26 22:11:48,504 INFO [train.py:842] (2/4) Epoch 5, batch 4750, loss[loss=0.2001, simple_loss=0.2768, pruned_loss=0.06171, over 7337.00 frames.], tot_loss[loss=0.247, simple_loss=0.3177, pruned_loss=0.08818, over 1423644.39 frames.], batch size: 20, lr: 9.46e-04
2022-05-26 22:12:26,971 INFO [train.py:842] (2/4) Epoch 5, batch 4800, loss[loss=0.2484, simple_loss=0.3364, pruned_loss=0.08023, over 7327.00 frames.], tot_loss[loss=0.247, simple_loss=0.3181, pruned_loss=0.08796, over 1422522.08 frames.], batch size: 22, lr: 9.46e-04
2022-05-26 22:13:05,801 INFO [train.py:842] (2/4) Epoch 5, batch 4850, loss[loss=0.2062, simple_loss=0.2846, pruned_loss=0.0639, over 7417.00 frames.], tot_loss[loss=0.2478, simple_loss=0.3188, pruned_loss=0.08837, over 1424892.87 frames.], batch size: 18, lr: 9.45e-04
2022-05-26 22:13:44,304 INFO [train.py:842] (2/4) Epoch 5, batch 4900, loss[loss=0.2528, simple_loss=0.3301, pruned_loss=0.08773, over 7193.00 frames.], tot_loss[loss=0.2473, simple_loss=0.318, pruned_loss=0.08826, over 1425505.42 frames.], batch size: 23, lr: 9.45e-04
2022-05-26 22:14:23,579 INFO [train.py:842] (2/4) Epoch 5, batch 4950, loss[loss=0.2444, simple_loss=0.3084, pruned_loss=0.09022, over 7377.00 frames.], tot_loss[loss=0.2472, simple_loss=0.3175, pruned_loss=0.0885, over 1427458.17 frames.], batch size: 23, lr: 9.44e-04
2022-05-26 22:15:02,124 INFO [train.py:842] (2/4) Epoch 5, batch 5000, loss[loss=0.2563, simple_loss=0.3342, pruned_loss=0.08919, over 7033.00 frames.], tot_loss[loss=0.2482, simple_loss=0.3183, pruned_loss=0.08901, over 1424759.06 frames.], batch size: 28, lr: 9.43e-04
2022-05-26 22:15:41,328 INFO [train.py:842] (2/4) Epoch 5, batch 5050, loss[loss=0.2691, simple_loss=0.3453, pruned_loss=0.09648, over 7407.00 frames.], tot_loss[loss=0.2457, simple_loss=0.3163, pruned_loss=0.0875, over 1425619.36 frames.], batch size: 21, lr: 9.43e-04
2022-05-26 22:16:20,157 INFO [train.py:842] (2/4) Epoch 5, batch 5100, loss[loss=0.2676, simple_loss=0.35, pruned_loss=0.09262, over 7342.00 frames.], tot_loss[loss=0.2476, simple_loss=0.3179, pruned_loss=0.08864, over 1421194.72 frames.], batch size: 22, lr: 9.42e-04
2022-05-26 22:16:59,090 INFO [train.py:842] (2/4) Epoch 5, batch 5150, loss[loss=0.2879, simple_loss=0.3373, pruned_loss=0.1193, over 7328.00 frames.], tot_loss[loss=0.2457, simple_loss=0.316, pruned_loss=0.08766, over 1423398.25 frames.], batch size: 20, lr: 9.42e-04
2022-05-26 22:17:37,806 INFO [train.py:842] (2/4) Epoch 5, batch 5200, loss[loss=0.2476, simple_loss=0.3148, pruned_loss=0.09017, over 7430.00 frames.], tot_loss[loss=0.2474, simple_loss=0.3174, pruned_loss=0.08875, over 1423647.42 frames.], batch size: 20, lr: 9.41e-04
2022-05-26 22:18:16,856 INFO [train.py:842] (2/4) Epoch 5, batch 5250, loss[loss=0.2153, simple_loss=0.2933, pruned_loss=0.06865, over 7227.00 frames.], tot_loss[loss=0.2478, simple_loss=0.3176, pruned_loss=0.08904, over 1424568.72 frames.], batch size: 21, lr: 9.41e-04
2022-05-26 22:18:55,413 INFO [train.py:842] (2/4) Epoch 5, batch 5300, loss[loss=0.2441, simple_loss=0.3128, pruned_loss=0.0877, over 6751.00 frames.], tot_loss[loss=0.2502, simple_loss=0.3197, pruned_loss=0.09032, over 1419337.57 frames.], batch size: 15, lr: 9.40e-04
2022-05-26 22:19:34,410 INFO [train.py:842] (2/4) Epoch 5, batch 5350, loss[loss=0.2951, simple_loss=0.3524, pruned_loss=0.119, over 7429.00 frames.], tot_loss[loss=0.2519, simple_loss=0.3211, pruned_loss=0.09139, over 1422952.36 frames.], batch size: 20, lr: 9.40e-04
2022-05-26 22:20:12,918 INFO [train.py:842] (2/4) Epoch 5, batch 5400, loss[loss=0.2366, simple_loss=0.2983, pruned_loss=0.08744, over 7273.00 frames.], tot_loss[loss=0.251, simple_loss=0.3202, pruned_loss=0.09094, over 1421008.66 frames.], batch size: 18, lr: 9.39e-04
2022-05-26 22:20:51,934 INFO [train.py:842] (2/4) Epoch 5, batch 5450, loss[loss=0.2739, simple_loss=0.3338, pruned_loss=0.107, over 7333.00 frames.], tot_loss[loss=0.2482, simple_loss=0.3181, pruned_loss=0.08913, over 1424667.74 frames.], batch size: 22, lr: 9.38e-04
2022-05-26 22:21:30,508 INFO [train.py:842] (2/4) Epoch 5, batch 5500, loss[loss=0.2332, simple_loss=0.3099, pruned_loss=0.07822, over 7235.00 frames.], tot_loss[loss=0.2496, simple_loss=0.3192, pruned_loss=0.08999, over 1417149.94 frames.], batch size: 20, lr: 9.38e-04
2022-05-26 22:22:09,557 INFO [train.py:842] (2/4) Epoch 5, batch 5550, loss[loss=0.2511, simple_loss=0.3293, pruned_loss=0.08644, over 7304.00 frames.], tot_loss[loss=0.2494, simple_loss=0.3189, pruned_loss=0.08998, over 1420687.09 frames.], batch size: 25, lr: 9.37e-04
2022-05-26 22:22:48,036 INFO [train.py:842] (2/4) Epoch 5, batch 5600, loss[loss=0.2426, simple_loss=0.3228, pruned_loss=0.08126, over 7190.00 frames.], tot_loss[loss=0.2494, simple_loss=0.3196, pruned_loss=0.08958, over 1418054.14 frames.], batch size: 22, lr: 9.37e-04
2022-05-26 22:23:26,840 INFO [train.py:842] (2/4) Epoch 5, batch 5650, loss[loss=0.2405, simple_loss=0.3046, pruned_loss=0.08815, over 7427.00 frames.], tot_loss[loss=0.2481, simple_loss=0.3189, pruned_loss=0.08871, over 1416605.43 frames.], batch size: 18, lr: 9.36e-04
2022-05-26 22:24:05,334 INFO [train.py:842] (2/4) Epoch 5, batch 5700, loss[loss=0.2493, simple_loss=0.3249, pruned_loss=0.0869, over 7193.00 frames.], tot_loss[loss=0.2479, simple_loss=0.3182, pruned_loss=0.08879, over 1419575.87 frames.], batch size: 26, lr: 9.36e-04
2022-05-26 22:24:44,529 INFO [train.py:842] (2/4) Epoch 5, batch 5750, loss[loss=0.1939, simple_loss=0.2754, pruned_loss=0.05623, over 7159.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3176, pruned_loss=0.08845, over 1424363.53 frames.], batch size: 18, lr: 9.35e-04
2022-05-26 22:25:23,067 INFO [train.py:842] (2/4) Epoch 5, batch 5800, loss[loss=0.3007, simple_loss=0.353, pruned_loss=0.1242, over 5189.00 frames.], tot_loss[loss=0.2468, simple_loss=0.3173, pruned_loss=0.08814, over 1422270.13 frames.], batch size: 52, lr: 9.35e-04
2022-05-26 22:26:01,722 INFO [train.py:842] (2/4) Epoch 5, batch 5850, loss[loss=0.2411, simple_loss=0.3171, pruned_loss=0.0826, over 7140.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3173, pruned_loss=0.08842, over 1417819.28 frames.], batch size: 20, lr: 9.34e-04
2022-05-26 22:26:40,296 INFO [train.py:842] (2/4) Epoch 5, batch 5900, loss[loss=0.2839, simple_loss=0.3499, pruned_loss=0.109, over 6739.00 frames.], tot_loss[loss=0.2475, simple_loss=0.3176, pruned_loss=0.08871, over 1419723.18 frames.], batch size: 31, lr: 9.34e-04
2022-05-26 22:27:19,114 INFO [train.py:842] (2/4) Epoch 5, batch 5950, loss[loss=0.205, simple_loss=0.2847, pruned_loss=0.06262, over 7158.00 frames.], tot_loss[loss=0.2475, simple_loss=0.3177, pruned_loss=0.0887, over 1421430.24 frames.], batch size: 19, lr: 9.33e-04
2022-05-26 22:27:58,406 INFO [train.py:842] (2/4) Epoch 5, batch 6000, loss[loss=0.3182, simple_loss=0.3693, pruned_loss=0.1335, over 7241.00 frames.], tot_loss[loss=0.2465, simple_loss=0.3168, pruned_loss=0.08805, over 1425333.59 frames.], batch size: 20, lr: 9.32e-04
2022-05-26 22:27:58,407 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 22:28:07,775 INFO [train.py:871] (2/4) Epoch 5, validation: loss=0.1919, simple_loss=0.2921, pruned_loss=0.04582, over 868885.00 frames.
2022-05-26 22:28:46,653 INFO [train.py:842] (2/4) Epoch 5, batch 6050, loss[loss=0.216, simple_loss=0.2823, pruned_loss=0.07488, over 7171.00 frames.], tot_loss[loss=0.2461, simple_loss=0.3171, pruned_loss=0.08756, over 1424915.71 frames.], batch size: 18, lr: 9.32e-04
2022-05-26 22:29:25,228 INFO [train.py:842] (2/4) Epoch 5, batch 6100, loss[loss=0.2284, simple_loss=0.2989, pruned_loss=0.07902, over 5220.00 frames.], tot_loss[loss=0.2459, simple_loss=0.317, pruned_loss=0.0874, over 1421483.55 frames.], batch size: 54, lr: 9.31e-04
2022-05-26 22:30:04,151 INFO [train.py:842] (2/4) Epoch 5, batch 6150, loss[loss=0.1754, simple_loss=0.2523, pruned_loss=0.04923, over 7174.00 frames.], tot_loss[loss=0.2456, simple_loss=0.3167, pruned_loss=0.0872, over 1424290.07 frames.], batch size: 18, lr: 9.31e-04
2022-05-26 22:30:42,837 INFO [train.py:842] (2/4) Epoch 5, batch 6200, loss[loss=0.2035, simple_loss=0.264, pruned_loss=0.07152, over 7391.00 frames.], tot_loss[loss=0.2441, simple_loss=0.3155, pruned_loss=0.0864, over 1427229.58 frames.], batch size: 18, lr: 9.30e-04
2022-05-26 22:31:21,500 INFO [train.py:842] (2/4) Epoch 5, batch 6250, loss[loss=0.2192, simple_loss=0.3054, pruned_loss=0.06649, over 7119.00 frames.], tot_loss[loss=0.2466, simple_loss=0.3175, pruned_loss=0.08788, over 1427272.11 frames.], batch size: 21, lr: 9.30e-04
2022-05-26 22:32:00,100 INFO [train.py:842] (2/4) Epoch 5, batch 6300, loss[loss=0.2161, simple_loss=0.3059, pruned_loss=0.06316, over 7386.00 frames.], tot_loss[loss=0.2452, simple_loss=0.3165, pruned_loss=0.08698, over 1428703.27 frames.], batch size: 23, lr: 9.29e-04
2022-05-26 22:32:39,015 INFO [train.py:842] (2/4) Epoch 5, batch 6350, loss[loss=0.2295, simple_loss=0.3013, pruned_loss=0.07886, over 7155.00 frames.], tot_loss[loss=0.2456, simple_loss=0.317, pruned_loss=0.08714, over 1429316.57 frames.], batch size: 18, lr: 9.29e-04
2022-05-26 22:33:17,675 INFO [train.py:842] (2/4) Epoch 5, batch 6400, loss[loss=0.2371, simple_loss=0.3003, pruned_loss=0.08699, over 7323.00 frames.], tot_loss[loss=0.2452, simple_loss=0.3161, pruned_loss=0.08716, over 1429399.72 frames.], batch size: 20, lr: 9.28e-04
2022-05-26 22:33:56,745 INFO [train.py:842] (2/4) Epoch 5, batch 6450, loss[loss=0.2013, simple_loss=0.2696, pruned_loss=0.06654, over 7203.00 frames.], tot_loss[loss=0.2454, simple_loss=0.3163, pruned_loss=0.08729, over 1431519.23 frames.], batch size: 16, lr: 9.28e-04
2022-05-26 22:34:35,282 INFO [train.py:842] (2/4) Epoch 5, batch 6500, loss[loss=0.2197, simple_loss=0.2817, pruned_loss=0.07882, over 7164.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3177, pruned_loss=0.08843, over 1430711.30 frames.], batch size: 18, lr: 9.27e-04
2022-05-26 22:35:13,828 INFO [train.py:842] (2/4) Epoch 5, batch 6550, loss[loss=0.2287, simple_loss=0.3131, pruned_loss=0.07216, over 7316.00 frames.], tot_loss[loss=0.2465, simple_loss=0.317, pruned_loss=0.08802, over 1424508.18 frames.], batch size: 21, lr: 9.27e-04
2022-05-26 22:35:52,496 INFO [train.py:842] (2/4) Epoch 5, batch 6600, loss[loss=0.2431, simple_loss=0.3106, pruned_loss=0.08783, over 6464.00 frames.], tot_loss[loss=0.2477, simple_loss=0.3179, pruned_loss=0.08872, over 1425765.60 frames.], batch size: 38, lr: 9.26e-04
2022-05-26 22:36:31,324 INFO [train.py:842] (2/4) Epoch 5, batch 6650, loss[loss=0.225, simple_loss=0.2963, pruned_loss=0.07682, over 7141.00 frames.], tot_loss[loss=0.248, simple_loss=0.3177, pruned_loss=0.08909, over 1423981.67 frames.], batch size: 20, lr: 9.26e-04
2022-05-26 22:37:09,818 INFO [train.py:842] (2/4) Epoch 5, batch 6700, loss[loss=0.29, simple_loss=0.3533, pruned_loss=0.1133, over 6693.00 frames.], tot_loss[loss=0.249, simple_loss=0.3185, pruned_loss=0.08976, over 1423554.19 frames.], batch size: 31, lr: 9.25e-04
2022-05-26 22:37:49,190 INFO [train.py:842] (2/4) Epoch 5, batch 6750, loss[loss=0.247, simple_loss=0.3302, pruned_loss=0.08185, over 7323.00 frames.], tot_loss[loss=0.2469, simple_loss=0.3168, pruned_loss=0.08854, over 1426512.78 frames.], batch size: 21, lr: 9.25e-04
2022-05-26 22:38:27,825 INFO [train.py:842] (2/4) Epoch 5, batch 6800, loss[loss=0.2237, simple_loss=0.3045, pruned_loss=0.07148, over 7267.00 frames.], tot_loss[loss=0.2456, simple_loss=0.316, pruned_loss=0.08764, over 1424818.59 frames.], batch size: 24, lr: 9.24e-04
2022-05-26 22:39:06,768 INFO [train.py:842] (2/4) Epoch 5, batch 6850, loss[loss=0.24, simple_loss=0.3118, pruned_loss=0.08405, over 7329.00 frames.], tot_loss[loss=0.2443, simple_loss=0.3153, pruned_loss=0.08662, over 1426228.57 frames.], batch size: 20, lr: 9.23e-04
2022-05-26 22:39:45,098 INFO [train.py:842] (2/4) Epoch 5, batch 6900, loss[loss=0.2196, simple_loss=0.3005, pruned_loss=0.06936, over 7197.00 frames.], tot_loss[loss=0.2457, simple_loss=0.3166, pruned_loss=0.08744, over 1426865.51 frames.], batch size: 23, lr: 9.23e-04
2022-05-26 22:40:24,008 INFO [train.py:842] (2/4) Epoch 5, batch 6950, loss[loss=0.227, simple_loss=0.3082, pruned_loss=0.07289, over 7116.00 frames.], tot_loss[loss=0.2466, simple_loss=0.3175, pruned_loss=0.08786, over 1428981.41 frames.], batch size: 21, lr: 9.22e-04
2022-05-26 22:41:02,651 INFO [train.py:842] (2/4) Epoch 5, batch 7000, loss[loss=0.2336, simple_loss=0.3089, pruned_loss=0.07912, over 7069.00 frames.], tot_loss[loss=0.2467, simple_loss=0.3175, pruned_loss=0.08795, over 1432589.83 frames.], batch size: 18, lr: 9.22e-04
2022-05-26 22:41:41,557 INFO [train.py:842] (2/4) Epoch 5, batch 7050, loss[loss=0.2393, simple_loss=0.3124, pruned_loss=0.08312, over 7156.00 frames.], tot_loss[loss=0.246, simple_loss=0.3162, pruned_loss=0.08793, over 1424739.86 frames.], batch size: 20, lr: 9.21e-04
2022-05-26 22:42:20,187 INFO [train.py:842] (2/4) Epoch 5, batch 7100, loss[loss=0.2755, simple_loss=0.3388, pruned_loss=0.1061, over 7225.00 frames.], tot_loss[loss=0.2453, simple_loss=0.3154, pruned_loss=0.08759, over 1428689.42 frames.], batch size: 20, lr: 9.21e-04
2022-05-26 22:42:59,039 INFO [train.py:842] (2/4) Epoch 5, batch 7150, loss[loss=0.2036, simple_loss=0.2783, pruned_loss=0.06447, over 7284.00 frames.], tot_loss[loss=0.2459, simple_loss=0.3164, pruned_loss=0.08769, over 1429118.36 frames.], batch size: 17, lr: 9.20e-04
2022-05-26 22:43:37,761 INFO [train.py:842] (2/4) Epoch 5, batch 7200, loss[loss=0.2236, simple_loss=0.2952, pruned_loss=0.07594, over 6800.00 frames.], tot_loss[loss=0.2465, simple_loss=0.317, pruned_loss=0.08803, over 1428314.73 frames.], batch size: 15, lr: 9.20e-04
2022-05-26 22:44:16,571 INFO [train.py:842] (2/4) Epoch 5, batch 7250, loss[loss=0.2176, simple_loss=0.2906, pruned_loss=0.07226, over 6978.00 frames.], tot_loss[loss=0.2459, simple_loss=0.3164, pruned_loss=0.08773, over 1424086.44 frames.], batch size: 16, lr: 9.19e-04
2022-05-26 22:44:55,167 INFO [train.py:842] (2/4) Epoch 5, batch 7300, loss[loss=0.2345, simple_loss=0.3172, pruned_loss=0.07589, over 6824.00 frames.], tot_loss[loss=0.2444, simple_loss=0.3153, pruned_loss=0.08671, over 1422743.24 frames.], batch size: 31, lr: 9.19e-04
2022-05-26 22:45:33,959 INFO [train.py:842] (2/4) Epoch 5, batch 7350, loss[loss=0.2523, simple_loss=0.3218, pruned_loss=0.09142, over 7065.00 frames.], tot_loss[loss=0.2434, simple_loss=0.3146, pruned_loss=0.08607, over 1423143.32 frames.], batch size: 28, lr: 9.18e-04
2022-05-26 22:46:12,464 INFO [train.py:842] (2/4) Epoch 5, batch 7400, loss[loss=0.2418, simple_loss=0.3108, pruned_loss=0.08634, over 7249.00 frames.], tot_loss[loss=0.2434, simple_loss=0.3141, pruned_loss=0.08636, over 1417006.67 frames.], batch size: 19, lr: 9.18e-04
2022-05-26 22:46:51,146 INFO [train.py:842] (2/4) Epoch 5, batch 7450, loss[loss=0.2066, simple_loss=0.2722, pruned_loss=0.07055, over 7423.00 frames.], tot_loss[loss=0.2442, simple_loss=0.315, pruned_loss=0.08665, over 1419045.45 frames.], batch size: 18, lr: 9.17e-04
2022-05-26 22:47:29,608 INFO [train.py:842] (2/4) Epoch 5, batch 7500, loss[loss=0.2241, simple_loss=0.2876, pruned_loss=0.08026, over 7273.00 frames.], tot_loss[loss=0.244, simple_loss=0.3149, pruned_loss=0.08656, over 1421528.28 frames.], batch size: 18, lr: 9.17e-04
2022-05-26 22:48:08,438 INFO [train.py:842] (2/4) Epoch 5, batch 7550, loss[loss=0.2434, simple_loss=0.3215, pruned_loss=0.08266, over 7339.00 frames.], tot_loss[loss=0.2439, simple_loss=0.3147, pruned_loss=0.08653, over 1420414.41 frames.], batch size: 22, lr: 9.16e-04
2022-05-26 22:48:46,889 INFO [train.py:842] (2/4) Epoch 5, batch 7600, loss[loss=0.26, simple_loss=0.3323, pruned_loss=0.09382, over 7207.00 frames.], tot_loss[loss=0.2434, simple_loss=0.3146, pruned_loss=0.08613, over 1419792.82 frames.], batch size: 22, lr: 9.16e-04
2022-05-26 22:49:25,697 INFO [train.py:842] (2/4) Epoch 5, batch 7650, loss[loss=0.2058, simple_loss=0.2828, pruned_loss=0.06443, over 7439.00 frames.], tot_loss[loss=0.243, simple_loss=0.3139, pruned_loss=0.08607, over 1419752.88 frames.], batch size: 20, lr: 9.15e-04
2022-05-26 22:50:04,137 INFO [train.py:842] (2/4) Epoch 5, batch 7700, loss[loss=0.2506, simple_loss=0.3328, pruned_loss=0.08423, over 7136.00 frames.], tot_loss[loss=0.2455, simple_loss=0.316, pruned_loss=0.08752, over 1421669.03 frames.], batch size: 20, lr: 9.15e-04
2022-05-26 22:50:43,135 INFO [train.py:842] (2/4) Epoch 5, batch 7750, loss[loss=0.1983, simple_loss=0.2795, pruned_loss=0.05853, over 7404.00 frames.], tot_loss[loss=0.2453, simple_loss=0.3156, pruned_loss=0.08745, over 1423303.47 frames.], batch size: 18, lr: 9.14e-04
2022-05-26 22:51:21,833 INFO [train.py:842] (2/4) Epoch 5, batch 7800, loss[loss=0.2646, simple_loss=0.3412, pruned_loss=0.09403, over 7329.00 frames.], tot_loss[loss=0.2445, simple_loss=0.315, pruned_loss=0.08702, over 1425925.88 frames.], batch size: 20, lr: 9.14e-04
2022-05-26 22:52:00,602 INFO [train.py:842] (2/4) Epoch 5, batch 7850, loss[loss=0.1984, simple_loss=0.2889, pruned_loss=0.054, over 7250.00 frames.], tot_loss[loss=0.2455, simple_loss=0.3164, pruned_loss=0.08732, over 1427738.39 frames.], batch size: 19, lr: 9.13e-04
2022-05-26 22:52:39,123 INFO [train.py:842] (2/4) Epoch 5, batch 7900, loss[loss=0.1858, simple_loss=0.2542, pruned_loss=0.05869, over 7277.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3175, pruned_loss=0.08833, over 1428707.85 frames.], batch size: 17, lr: 9.13e-04
2022-05-26 22:53:18,010 INFO [train.py:842] (2/4) Epoch 5, batch 7950, loss[loss=0.2094, simple_loss=0.2866, pruned_loss=0.06615, over 7066.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3164, pruned_loss=0.08764, over 1427918.56 frames.], batch size: 28, lr: 9.12e-04
2022-05-26 22:53:56,517 INFO [train.py:842] (2/4) Epoch 5, batch 8000, loss[loss=0.2163, simple_loss=0.2729, pruned_loss=0.07984, over 7143.00 frames.], tot_loss[loss=0.2457, simple_loss=0.3161, pruned_loss=0.08762, over 1428364.32 frames.], batch size: 17, lr: 9.12e-04
2022-05-26 22:54:35,446 INFO [train.py:842] (2/4) Epoch 5, batch 8050, loss[loss=0.1958, simple_loss=0.2692, pruned_loss=0.0612, over 7352.00 frames.], tot_loss[loss=0.2456, simple_loss=0.3164, pruned_loss=0.0874, over 1428570.72 frames.], batch size: 19, lr: 9.11e-04
2022-05-26 22:55:14,022 INFO [train.py:842] (2/4) Epoch 5, batch 8100, loss[loss=0.2504, simple_loss=0.3365, pruned_loss=0.08211, over 7060.00 frames.], tot_loss[loss=0.2465, simple_loss=0.317, pruned_loss=0.08796, over 1427873.48 frames.], batch size: 28, lr: 9.11e-04
2022-05-26 22:55:52,776 INFO [train.py:842] (2/4) Epoch 5, batch 8150, loss[loss=0.2373, simple_loss=0.3132, pruned_loss=0.08066, over 7161.00 frames.], tot_loss[loss=0.2463, simple_loss=0.3171, pruned_loss=0.08781, over 1421775.16 frames.], batch size: 26, lr: 9.10e-04
2022-05-26 22:56:31,194 INFO [train.py:842] (2/4) Epoch 5, batch 8200, loss[loss=0.2598, simple_loss=0.3356, pruned_loss=0.09195, over 7237.00 frames.], tot_loss[loss=0.2474, simple_loss=0.3178, pruned_loss=0.08847, over 1419310.55 frames.], batch size: 20, lr: 9.10e-04
2022-05-26 22:57:10,101 INFO [train.py:842] (2/4) Epoch 5, batch 8250, loss[loss=0.223, simple_loss=0.2887, pruned_loss=0.07868, over 7271.00 frames.], tot_loss[loss=0.2466, simple_loss=0.317, pruned_loss=0.08807, over 1421096.30 frames.], batch size: 18, lr: 9.09e-04
2022-05-26 22:57:48,649 INFO [train.py:842] (2/4) Epoch 5, batch 8300, loss[loss=0.2701, simple_loss=0.3468, pruned_loss=0.09666, over 7047.00 frames.], tot_loss[loss=0.2462, simple_loss=0.3165, pruned_loss=0.08796, over 1424424.01 frames.], batch size: 28, lr: 9.09e-04
2022-05-26 22:58:27,291 INFO [train.py:842] (2/4) Epoch 5, batch 8350, loss[loss=0.2288, simple_loss=0.3132, pruned_loss=0.07226, over 7415.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3173, pruned_loss=0.08841, over 1421506.12 frames.], batch size: 21, lr: 9.08e-04
2022-05-26 22:59:05,780 INFO [train.py:842] (2/4) Epoch 5, batch 8400, loss[loss=0.2238, simple_loss=0.307, pruned_loss=0.07029, over 7220.00 frames.], tot_loss[loss=0.2442, simple_loss=0.3152, pruned_loss=0.08658, over 1421864.17 frames.], batch size: 20, lr: 9.08e-04
2022-05-26 22:59:44,478 INFO [train.py:842] (2/4) Epoch 5, batch 8450, loss[loss=0.2928, simple_loss=0.3321, pruned_loss=0.1268, over 7134.00 frames.], tot_loss[loss=0.2453, simple_loss=0.3156, pruned_loss=0.08752, over 1416109.00 frames.], batch size: 17, lr: 9.07e-04
2022-05-26 23:00:23,193 INFO [train.py:842] (2/4) Epoch 5, batch 8500, loss[loss=0.1778, simple_loss=0.2551, pruned_loss=0.05031, over 7263.00 frames.], tot_loss[loss=0.2442, simple_loss=0.3148, pruned_loss=0.08675, over 1419446.66 frames.], batch size: 17, lr: 9.07e-04
2022-05-26 23:01:02,284 INFO [train.py:842] (2/4) Epoch 5, batch 8550, loss[loss=0.2165, simple_loss=0.2924, pruned_loss=0.07034, over 7261.00 frames.], tot_loss[loss=0.2445, simple_loss=0.3152, pruned_loss=0.08688, over 1421805.09 frames.], batch size: 19, lr: 9.06e-04
2022-05-26 23:01:41,070 INFO [train.py:842] (2/4) Epoch 5, batch 8600, loss[loss=0.2335, simple_loss=0.3097, pruned_loss=0.07871, over 7361.00 frames.], tot_loss[loss=0.245, simple_loss=0.3162, pruned_loss=0.08695, over 1423929.61 frames.], batch size: 19, lr: 9.06e-04
2022-05-26 23:02:19,899 INFO [train.py:842] (2/4) Epoch 5, batch 8650, loss[loss=0.2109, simple_loss=0.2986, pruned_loss=0.06159, over 7227.00 frames.], tot_loss[loss=0.2449, simple_loss=0.3162, pruned_loss=0.08685, over 1417715.65 frames.], batch size: 21, lr: 9.05e-04
2022-05-26 23:02:58,454 INFO [train.py:842] (2/4) Epoch 5, batch 8700, loss[loss=0.2392, simple_loss=0.3186, pruned_loss=0.07985, over 7235.00 frames.], tot_loss[loss=0.2443, simple_loss=0.3155, pruned_loss=0.0865, over 1414862.79 frames.], batch size: 20, lr: 9.05e-04
2022-05-26 23:03:37,421 INFO [train.py:842] (2/4) Epoch 5, batch 8750, loss[loss=0.276, simple_loss=0.3535, pruned_loss=0.09922, over 7237.00 frames.], tot_loss[loss=0.2425, simple_loss=0.3145, pruned_loss=0.0853, over 1415823.91 frames.], batch size: 26, lr: 9.04e-04
2022-05-26 23:04:16,099 INFO [train.py:842] (2/4) Epoch 5, batch 8800, loss[loss=0.2679, simple_loss=0.3348, pruned_loss=0.1004, over 7286.00 frames.], tot_loss[loss=0.2462, simple_loss=0.3169, pruned_loss=0.08775, over 1415403.40 frames.], batch size: 24, lr: 9.04e-04
2022-05-26 23:04:55,035 INFO [train.py:842] (2/4) Epoch 5, batch 8850, loss[loss=0.2951, simple_loss=0.3575, pruned_loss=0.1163, over 4858.00 frames.], tot_loss[loss=0.2443, simple_loss=0.315, pruned_loss=0.08676, over 1411242.52 frames.], batch size: 52, lr: 9.03e-04
2022-05-26 23:05:33,493 INFO [train.py:842] (2/4) Epoch 5, batch 8900, loss[loss=0.2716, simple_loss=0.3386, pruned_loss=0.1023, over 6542.00 frames.], tot_loss[loss=0.246, simple_loss=0.3165, pruned_loss=0.08782, over 1410949.93 frames.], batch size: 38, lr: 9.03e-04
2022-05-26 23:06:11,780 INFO [train.py:842] (2/4) Epoch 5, batch 8950, loss[loss=0.1985, simple_loss=0.2839, pruned_loss=0.05657, over 7207.00 frames.], tot_loss[loss=0.2466, simple_loss=0.3174, pruned_loss=0.08788, over 1401324.78 frames.], batch size: 23, lr: 9.02e-04
2022-05-26 23:06:49,944 INFO [train.py:842] (2/4) Epoch 5, batch 9000, loss[loss=0.2312, simple_loss=0.2986, pruned_loss=0.0819, over 6465.00 frames.], tot_loss[loss=0.2466, simple_loss=0.3184, pruned_loss=0.08744, over 1394777.66 frames.], batch size: 37, lr: 9.02e-04
2022-05-26 23:06:49,945 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 23:06:59,287 INFO [train.py:871] (2/4) Epoch 5, validation: loss=0.191, simple_loss=0.2904, pruned_loss=0.04585, over 868885.00 frames.
2022-05-26 23:07:37,130 INFO [train.py:842] (2/4) Epoch 5, batch 9050, loss[loss=0.26, simple_loss=0.3299, pruned_loss=0.09505, over 5247.00 frames.], tot_loss[loss=0.2509, simple_loss=0.322, pruned_loss=0.08988, over 1363262.45 frames.], batch size: 52, lr: 9.01e-04
2022-05-26 23:08:14,685 INFO [train.py:842] (2/4) Epoch 5, batch 9100, loss[loss=0.2424, simple_loss=0.3212, pruned_loss=0.08183, over 5072.00 frames.], tot_loss[loss=0.2568, simple_loss=0.3259, pruned_loss=0.09378, over 1298633.03 frames.], batch size: 52, lr: 9.01e-04
2022-05-26 23:08:52,437 INFO [train.py:842] (2/4) Epoch 5, batch 9150, loss[loss=0.3489, simple_loss=0.3853, pruned_loss=0.1562, over 5222.00 frames.], tot_loss[loss=0.2635, simple_loss=0.33, pruned_loss=0.09844, over 1236214.21 frames.], batch size: 54, lr: 9.00e-04
2022-05-26 23:09:43,994 INFO [train.py:842] (2/4) Epoch 6, batch 0, loss[loss=0.2327, simple_loss=0.3091, pruned_loss=0.07816, over 7155.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3091, pruned_loss=0.07816, over 7155.00 frames.], batch size: 19, lr: 8.65e-04
2022-05-26 23:10:23,196 INFO [train.py:842] (2/4) Epoch 6, batch 50, loss[loss=0.3589, simple_loss=0.3999, pruned_loss=0.159, over 4956.00 frames.], tot_loss[loss=0.2463, simple_loss=0.3166, pruned_loss=0.08798, over 318478.11 frames.], batch size: 52, lr: 8.64e-04
2022-05-26 23:11:01,578 INFO [train.py:842] (2/4) Epoch 6, batch 100, loss[loss=0.3342, simple_loss=0.3818, pruned_loss=0.1433, over 7129.00 frames.], tot_loss[loss=0.2487, simple_loss=0.3194, pruned_loss=0.08899, over 561557.35 frames.], batch size: 20, lr: 8.64e-04
2022-05-26 23:11:40,517 INFO [train.py:842] (2/4) Epoch 6, batch 150, loss[loss=0.266, simple_loss=0.3382, pruned_loss=0.09689, over 6694.00 frames.], tot_loss[loss=0.2423, simple_loss=0.315, pruned_loss=0.08478, over 748464.31 frames.], batch size: 31, lr: 8.63e-04
2022-05-26 23:12:19,091 INFO [train.py:842] (2/4) Epoch 6, batch 200, loss[loss=0.2701, simple_loss=0.3239, pruned_loss=0.1081, over 7397.00 frames.], tot_loss[loss=0.2418, simple_loss=0.3143, pruned_loss=0.08462, over 898892.12 frames.], batch size: 18, lr: 8.63e-04
2022-05-26 23:12:57,963 INFO [train.py:842] (2/4) Epoch 6, batch 250, loss[loss=0.2182, simple_loss=0.2979, pruned_loss=0.06923, over 7334.00 frames.], tot_loss[loss=0.242, simple_loss=0.3147, pruned_loss=0.08469, over 1018416.90 frames.], batch size: 22, lr: 8.62e-04
2022-05-26 23:13:36,480 INFO [train.py:842] (2/4) Epoch 6, batch 300, loss[loss=0.3022, simple_loss=0.3512, pruned_loss=0.1266, over 7233.00 frames.], tot_loss[loss=0.2401, simple_loss=0.3127, pruned_loss=0.08381, over 1110929.49 frames.], batch size: 20, lr: 8.62e-04
2022-05-26 23:14:15,771 INFO [train.py:842] (2/4) Epoch 6, batch 350, loss[loss=0.2157, simple_loss=0.2881, pruned_loss=0.07163, over 7334.00 frames.], tot_loss[loss=0.2379, simple_loss=0.3108, pruned_loss=0.08249, over 1184080.49 frames.], batch size: 20, lr: 8.61e-04
2022-05-26 23:14:54,201 INFO [train.py:842] (2/4) Epoch 6, batch 400, loss[loss=0.2746, simple_loss=0.3453, pruned_loss=0.102, over 7374.00 frames.], tot_loss[loss=0.2385, simple_loss=0.3121, pruned_loss=0.08252, over 1236069.20 frames.], batch size: 23, lr: 8.61e-04
2022-05-26 23:15:33,162 INFO [train.py:842] (2/4) Epoch 6, batch 450, loss[loss=0.2121, simple_loss=0.2776, pruned_loss=0.07325, over 7206.00 frames.], tot_loss[loss=0.2401, simple_loss=0.3133, pruned_loss=0.08343, over 1280053.77 frames.], batch size: 16, lr: 8.61e-04
2022-05-26 23:16:11,610 INFO [train.py:842] (2/4) Epoch 6, batch 500, loss[loss=0.2344, simple_loss=0.3103, pruned_loss=0.07931, over 5237.00 frames.], tot_loss[loss=0.2419, simple_loss=0.315, pruned_loss=0.08437, over 1309250.21 frames.], batch size: 53, lr: 8.60e-04
2022-05-26 23:16:50,471 INFO [train.py:842] (2/4) Epoch 6, batch 550, loss[loss=0.235, simple_loss=0.3243, pruned_loss=0.07288, over 6290.00 frames.], tot_loss[loss=0.24, simple_loss=0.3134, pruned_loss=0.08327, over 1333058.14 frames.], batch size: 37, lr: 8.60e-04
2022-05-26 23:17:29,181 INFO [train.py:842] (2/4) Epoch 6, batch 600, loss[loss=0.2048, simple_loss=0.2826, pruned_loss=0.06356, over 7143.00 frames.], tot_loss[loss=0.2383, simple_loss=0.3115, pruned_loss=0.08254, over 1352220.31 frames.], batch size: 20, lr: 8.59e-04
2022-05-26 23:18:08,046 INFO [train.py:842] (2/4) Epoch 6, batch 650, loss[loss=0.2339, simple_loss=0.3098, pruned_loss=0.07899, over 7402.00 frames.], tot_loss[loss=0.2395, simple_loss=0.3122, pruned_loss=0.08334, over 1366807.93 frames.], batch size: 21, lr: 8.59e-04
2022-05-26 23:18:46,480 INFO [train.py:842] (2/4) Epoch 6, batch 700, loss[loss=0.1949, simple_loss=0.2707, pruned_loss=0.05959, over 7227.00 frames.], tot_loss[loss=0.239, simple_loss=0.3121, pruned_loss=0.08299, over 1378934.81 frames.], batch size: 16, lr: 8.58e-04
2022-05-26 23:19:25,313 INFO [train.py:842] (2/4) Epoch 6, batch 750, loss[loss=0.2601, simple_loss=0.3345, pruned_loss=0.09286, over 7224.00 frames.], tot_loss[loss=0.2406, simple_loss=0.3134, pruned_loss=0.08385, over 1388557.90 frames.], batch size: 21, lr: 8.58e-04
2022-05-26 23:20:03,924 INFO [train.py:842] (2/4) Epoch 6, batch 800, loss[loss=0.2346, simple_loss=0.3188, pruned_loss=0.07518, over 7212.00 frames.], tot_loss[loss=0.2391, simple_loss=0.3124, pruned_loss=0.08287, over 1399462.56 frames.], batch size: 21, lr: 8.57e-04
2022-05-26 23:20:42,687 INFO [train.py:842] (2/4) Epoch 6, batch 850, loss[loss=0.2375, simple_loss=0.3262, pruned_loss=0.07434, over 7196.00 frames.], tot_loss[loss=0.2381, simple_loss=0.3121, pruned_loss=0.08207, over 1405008.69 frames.], batch size: 23, lr: 8.57e-04
2022-05-26 23:21:21,252 INFO [train.py:842] (2/4) Epoch 6, batch 900, loss[loss=0.2806, simple_loss=0.3476, pruned_loss=0.1068, over 7408.00 frames.], tot_loss[loss=0.239, simple_loss=0.3127, pruned_loss=0.08262, over 1406456.80 frames.], batch size: 21, lr: 8.56e-04
2022-05-26 23:21:59,936 INFO [train.py:842] (2/4) Epoch 6, batch 950, loss[loss=0.2329, simple_loss=0.293, pruned_loss=0.08643, over 7136.00 frames.], tot_loss[loss=0.2391, simple_loss=0.3127, pruned_loss=0.08271, over 1406889.75 frames.], batch size: 17, lr: 8.56e-04
2022-05-26 23:22:38,498 INFO [train.py:842] (2/4) Epoch 6, batch 1000, loss[loss=0.2093, simple_loss=0.2949, pruned_loss=0.06183, over 7422.00 frames.], tot_loss[loss=0.2405, simple_loss=0.3132, pruned_loss=0.08388, over 1409515.86 frames.], batch size: 21, lr: 8.56e-04
2022-05-26 23:23:17,220 INFO [train.py:842] (2/4) Epoch 6, batch 1050, loss[loss=0.1936, simple_loss=0.2852, pruned_loss=0.05098, over 7325.00 frames.], tot_loss[loss=0.2405, simple_loss=0.3131, pruned_loss=0.08392, over 1413982.04 frames.], batch size: 20, lr: 8.55e-04
2022-05-26 23:23:55,809 INFO [train.py:842] (2/4) Epoch 6, batch 1100, loss[loss=0.2408, simple_loss=0.3243, pruned_loss=0.07861, over 7317.00 frames.], tot_loss[loss=0.2398, simple_loss=0.3128, pruned_loss=0.08346, over 1409148.24 frames.], batch size: 21, lr: 8.55e-04
2022-05-26 23:24:34,774 INFO [train.py:842] (2/4) Epoch 6, batch 1150, loss[loss=0.2709, simple_loss=0.3389, pruned_loss=0.1014, over 7147.00 frames.], tot_loss[loss=0.2409, simple_loss=0.3135, pruned_loss=0.08418, over 1413979.00 frames.], batch size: 20, lr: 8.54e-04
2022-05-26 23:25:13,299 INFO [train.py:842] (2/4) Epoch 6, batch 1200, loss[loss=0.2287, simple_loss=0.3187, pruned_loss=0.06932, over 7167.00 frames.], tot_loss[loss=0.2405, simple_loss=0.3131, pruned_loss=0.0839, over 1414582.45 frames.], batch size: 26, lr: 8.54e-04
2022-05-26 23:25:52,097 INFO [train.py:842] (2/4) Epoch 6, batch 1250, loss[loss=0.2544, simple_loss=0.326, pruned_loss=0.09137, over 7142.00 frames.], tot_loss[loss=0.2413, simple_loss=0.3139, pruned_loss=0.0843, over 1413771.14 frames.], batch size: 20, lr: 8.53e-04
2022-05-26 23:26:30,727 INFO [train.py:842] (2/4) Epoch 6, batch 1300, loss[loss=0.2094, simple_loss=0.2911, pruned_loss=0.06386, over 7355.00 frames.], tot_loss[loss=0.2418, simple_loss=0.3142, pruned_loss=0.08471, over 1411651.40 frames.], batch size: 19, lr: 8.53e-04
2022-05-26 23:27:09,693 INFO [train.py:842] (2/4) Epoch 6, batch 1350, loss[loss=0.2398, simple_loss=0.3176, pruned_loss=0.08099, over 7062.00 frames.], tot_loss[loss=0.2393, simple_loss=0.3122, pruned_loss=0.08319, over 1415285.05 frames.], batch size: 28, lr: 8.52e-04
2022-05-26 23:27:48,109 INFO [train.py:842] (2/4) Epoch 6, batch 1400, loss[loss=0.1954, simple_loss=0.2641, pruned_loss=0.06332, over 7327.00 frames.], tot_loss[loss=0.239, simple_loss=0.3119, pruned_loss=0.08304, over 1419049.17 frames.], batch size: 20, lr: 8.52e-04
2022-05-26 23:28:26,818 INFO [train.py:842] (2/4) Epoch 6, batch 1450, loss[loss=0.2916, simple_loss=0.3474, pruned_loss=0.1179, over 7416.00 frames.], tot_loss[loss=0.242, simple_loss=0.3136, pruned_loss=0.08516, over 1420426.21 frames.], batch size: 20, lr: 8.52e-04
2022-05-26 23:29:05,414 INFO [train.py:842] (2/4) Epoch 6, batch 1500, loss[loss=0.19, simple_loss=0.283, pruned_loss=0.04847, over 7153.00 frames.], tot_loss[loss=0.2421, simple_loss=0.3139, pruned_loss=0.08513, over 1420644.79 frames.], batch size: 20, lr: 8.51e-04
2022-05-26 23:29:44,026 INFO [train.py:842] (2/4) Epoch 6, batch 1550, loss[loss=0.2195, simple_loss=0.2985, pruned_loss=0.07023, over 7275.00 frames.], tot_loss[loss=0.2418, simple_loss=0.3141, pruned_loss=0.08476, over 1423511.91 frames.], batch size: 17, lr: 8.51e-04
2022-05-26 23:30:22,451 INFO [train.py:842] (2/4) Epoch 6, batch 1600, loss[loss=0.2213, simple_loss=0.2924, pruned_loss=0.07512, over 7429.00 frames.], tot_loss[loss=0.2431, simple_loss=0.3151, pruned_loss=0.08557, over 1416630.81 frames.], batch size: 20, lr: 8.50e-04
2022-05-26 23:31:01,141 INFO [train.py:842] (2/4) Epoch 6, batch 1650, loss[loss=0.2834, simple_loss=0.3583, pruned_loss=0.1042, over 7293.00 frames.], tot_loss[loss=0.2428, simple_loss=0.3143, pruned_loss=0.08559, over 1416004.38 frames.], batch size: 25, lr: 8.50e-04
2022-05-26 23:31:39,580 INFO [train.py:842] (2/4) Epoch 6, batch 1700, loss[loss=0.2474, simple_loss=0.3294, pruned_loss=0.08265, over 7206.00 frames.], tot_loss[loss=0.2437, simple_loss=0.3149, pruned_loss=0.08626, over 1414038.52 frames.], batch size: 22, lr: 8.49e-04
2022-05-26 23:32:18,317 INFO [train.py:842] (2/4) Epoch 6, batch 1750, loss[loss=0.2173, simple_loss=0.2758, pruned_loss=0.07934, over 7271.00 frames.], tot_loss[loss=0.2423, simple_loss=0.3141, pruned_loss=0.08526, over 1411109.75 frames.], batch size: 18, lr: 8.49e-04
2022-05-26 23:32:56,822 INFO [train.py:842] (2/4) Epoch 6, batch 1800, loss[loss=0.2809, simple_loss=0.3403, pruned_loss=0.1108, over 5454.00 frames.], tot_loss[loss=0.2427, simple_loss=0.3144, pruned_loss=0.08545, over 1413422.18 frames.], batch size: 53, lr: 8.48e-04
2022-05-26 23:33:35,703 INFO [train.py:842] (2/4) Epoch 6, batch 1850, loss[loss=0.2297, simple_loss=0.3037, pruned_loss=0.07784, over 7149.00 frames.], tot_loss[loss=0.2414, simple_loss=0.3134, pruned_loss=0.08465, over 1416822.95 frames.], batch size: 18, lr: 8.48e-04
2022-05-26 23:34:14,179 INFO [train.py:842] (2/4) Epoch 6, batch 1900, loss[loss=0.2349, simple_loss=0.3022, pruned_loss=0.08383, over 7118.00 frames.], tot_loss[loss=0.2407, simple_loss=0.3131, pruned_loss=0.08415, over 1415455.27 frames.], batch size: 17, lr: 8.48e-04
2022-05-26 23:34:53,001 INFO [train.py:842] (2/4) Epoch 6, batch 1950, loss[loss=0.2678, simple_loss=0.3444, pruned_loss=0.09562, over 7111.00 frames.], tot_loss[loss=0.2404, simple_loss=0.3132, pruned_loss=0.08379, over 1419841.48 frames.], batch size: 21, lr: 8.47e-04
2022-05-26 23:35:31,651 INFO [train.py:842] (2/4) Epoch 6, batch 2000, loss[loss=0.2823, simple_loss=0.3473, pruned_loss=0.1087, over 7276.00 frames.], tot_loss[loss=0.2392, simple_loss=0.3124, pruned_loss=0.08305, over 1424516.43 frames.], batch size: 18, lr: 8.47e-04
2022-05-26 23:36:13,253 INFO [train.py:842] (2/4) Epoch 6, batch 2050, loss[loss=0.2642, simple_loss=0.3371, pruned_loss=0.09561, over 6988.00 frames.], tot_loss[loss=0.2407, simple_loss=0.3138, pruned_loss=0.08386, over 1423667.85 frames.], batch size: 28, lr: 8.46e-04
2022-05-26 23:36:51,796 INFO [train.py:842] (2/4) Epoch 6, batch 2100, loss[loss=0.2637, simple_loss=0.3313, pruned_loss=0.09802, over 6252.00 frames.], tot_loss[loss=0.2409, simple_loss=0.3141, pruned_loss=0.08389, over 1425145.85 frames.], batch size: 37, lr: 8.46e-04
2022-05-26 23:37:30,938 INFO [train.py:842] (2/4) Epoch 6, batch 2150, loss[loss=0.2754, simple_loss=0.3435, pruned_loss=0.1037, over 7144.00 frames.], tot_loss[loss=0.2404, simple_loss=0.3136, pruned_loss=0.08357, over 1430223.28 frames.], batch size: 20, lr: 8.45e-04
2022-05-26 23:38:09,301 INFO [train.py:842] (2/4) Epoch 6, batch 2200, loss[loss=0.2061, simple_loss=0.2959, pruned_loss=0.05812, over 7137.00 frames.], tot_loss[loss=0.2396, simple_loss=0.3129, pruned_loss=0.08319, over 1426986.44 frames.], batch size: 20, lr: 8.45e-04
2022-05-26 23:38:48,182 INFO [train.py:842] (2/4) Epoch 6, batch 2250, loss[loss=0.2342, simple_loss=0.299, pruned_loss=0.08475, over 7352.00 frames.], tot_loss[loss=0.2384, simple_loss=0.3119, pruned_loss=0.08243, over 1425123.54 frames.], batch size: 19, lr: 8.45e-04
2022-05-26 23:39:26,675 INFO [train.py:842] (2/4) Epoch 6, batch 2300, loss[loss=0.2534, simple_loss=0.3303, pruned_loss=0.08827, over 7279.00 frames.], tot_loss[loss=0.2391, simple_loss=0.3123, pruned_loss=0.08297, over 1422692.24 frames.], batch size: 24, lr: 8.44e-04
2022-05-26 23:40:05,770 INFO [train.py:842] (2/4) Epoch 6, batch 2350, loss[loss=0.216, simple_loss=0.3029, pruned_loss=0.06455, over 7220.00 frames.], tot_loss[loss=0.2395, simple_loss=0.3124, pruned_loss=0.08327, over 1421908.51 frames.], batch size: 21, lr: 8.44e-04
2022-05-26 23:40:44,302 INFO [train.py:842] (2/4) Epoch 6, batch 2400, loss[loss=0.2638, simple_loss=0.3286, pruned_loss=0.09956, over 7339.00 frames.], tot_loss[loss=0.2382, simple_loss=0.311, pruned_loss=0.08272, over 1422106.18 frames.], batch size: 20, lr: 8.43e-04
2022-05-26 23:41:23,362 INFO [train.py:842] (2/4) Epoch 6, batch 2450, loss[loss=0.2036, simple_loss=0.2656, pruned_loss=0.0708, over 7179.00 frames.], tot_loss[loss=0.2378, simple_loss=0.3105, pruned_loss=0.08254, over 1422296.79 frames.], batch size: 16, lr: 8.43e-04
2022-05-26 23:42:01,766 INFO [train.py:842] (2/4) Epoch 6, batch 2500, loss[loss=0.2069, simple_loss=0.289, pruned_loss=0.0624, over 7335.00 frames.], tot_loss[loss=0.2382, simple_loss=0.3108, pruned_loss=0.08282, over 1421719.49 frames.], batch size: 22, lr: 8.42e-04
2022-05-26 23:42:40,571 INFO [train.py:842] (2/4) Epoch 6, batch 2550, loss[loss=0.2162, simple_loss=0.2743, pruned_loss=0.07907, over 6778.00 frames.], tot_loss[loss=0.2387, simple_loss=0.3112, pruned_loss=0.08315, over 1423546.48 frames.], batch size: 15, lr: 8.42e-04
2022-05-26 23:43:19,149 INFO [train.py:842] (2/4) Epoch 6, batch 2600, loss[loss=0.2314, simple_loss=0.3115, pruned_loss=0.07565, over 7318.00 frames.], tot_loss[loss=0.2392, simple_loss=0.3116, pruned_loss=0.08335, over 1426234.19 frames.], batch size: 21, lr: 8.42e-04
2022-05-26 23:43:57,912 INFO [train.py:842] (2/4) Epoch 6, batch 2650, loss[loss=0.2448, simple_loss=0.3179, pruned_loss=0.08587, over 7279.00 frames.], tot_loss[loss=0.2407, simple_loss=0.3127, pruned_loss=0.08436, over 1423731.00 frames.], batch size: 25, lr: 8.41e-04
2022-05-26 23:44:36,527 INFO [train.py:842] (2/4) Epoch 6, batch 2700, loss[loss=0.2113, simple_loss=0.2816, pruned_loss=0.07044, over 6809.00 frames.], tot_loss[loss=0.2401, simple_loss=0.3122, pruned_loss=0.08397, over 1425289.89 frames.], batch size: 15, lr: 8.41e-04
2022-05-26 23:45:15,204 INFO [train.py:842] (2/4) Epoch 6, batch 2750, loss[loss=0.2228, simple_loss=0.3034, pruned_loss=0.0711, over 7236.00 frames.], tot_loss[loss=0.2383, simple_loss=0.3112, pruned_loss=0.08273, over 1423090.70 frames.], batch size: 20, lr: 8.40e-04
2022-05-26 23:45:53,698 INFO [train.py:842] (2/4) Epoch 6, batch 2800, loss[loss=0.2114, simple_loss=0.2883, pruned_loss=0.06727, over 7282.00 frames.], tot_loss[loss=0.2365, simple_loss=0.31, pruned_loss=0.08145, over 1420813.61 frames.], batch size: 18, lr: 8.40e-04
2022-05-26 23:46:32,464 INFO [train.py:842] (2/4) Epoch 6, batch 2850, loss[loss=0.233, simple_loss=0.296, pruned_loss=0.085, over 7284.00 frames.], tot_loss[loss=0.237, simple_loss=0.3103, pruned_loss=0.0818, over 1418444.56 frames.], batch size: 17, lr: 8.39e-04
2022-05-26 23:47:11,007 INFO [train.py:842] (2/4) Epoch 6, batch 2900, loss[loss=0.2698, simple_loss=0.3425, pruned_loss=0.09855, over 6845.00 frames.], tot_loss[loss=0.2359, simple_loss=0.3095, pruned_loss=0.08117, over 1420857.25 frames.], batch size: 31, lr: 8.39e-04
2022-05-26 23:47:50,193 INFO [train.py:842] (2/4) Epoch 6, batch 2950, loss[loss=0.2779, simple_loss=0.3562, pruned_loss=0.09982, over 7146.00 frames.], tot_loss[loss=0.2364, simple_loss=0.3097, pruned_loss=0.08158, over 1419887.84 frames.], batch size: 20, lr: 8.39e-04
2022-05-26 23:48:28,894 INFO [train.py:842] (2/4) Epoch 6, batch 3000, loss[loss=0.2113, simple_loss=0.2952, pruned_loss=0.06368, over 7236.00 frames.], tot_loss[loss=0.2376, simple_loss=0.3107, pruned_loss=0.08228, over 1419386.93 frames.], batch size: 20, lr: 8.38e-04
2022-05-26 23:48:28,895 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 23:48:38,153 INFO [train.py:871] (2/4) Epoch 6, validation: loss=0.1895, simple_loss=0.2892, pruned_loss=0.04494, over 868885.00 frames.
2022-05-26 23:49:17,573 INFO [train.py:842] (2/4) Epoch 6, batch 3050, loss[loss=0.2483, simple_loss=0.3197, pruned_loss=0.08852, over 7207.00 frames.], tot_loss[loss=0.2375, simple_loss=0.3108, pruned_loss=0.08214, over 1425322.97 frames.], batch size: 23, lr: 8.38e-04
2022-05-26 23:49:56,216 INFO [train.py:842] (2/4) Epoch 6, batch 3100, loss[loss=0.2407, simple_loss=0.3144, pruned_loss=0.08353, over 7342.00 frames.], tot_loss[loss=0.2364, simple_loss=0.3094, pruned_loss=0.08174, over 1423094.41 frames.], batch size: 22, lr: 8.37e-04
2022-05-26 23:50:35,005 INFO [train.py:842] (2/4) Epoch 6, batch 3150, loss[loss=0.2122, simple_loss=0.2923, pruned_loss=0.06606, over 7204.00 frames.], tot_loss[loss=0.2376, simple_loss=0.3108, pruned_loss=0.08225, over 1423689.23 frames.], batch size: 23, lr: 8.37e-04
2022-05-26 23:51:13,519 INFO [train.py:842] (2/4) Epoch 6, batch 3200, loss[loss=0.2639, simple_loss=0.3427, pruned_loss=0.09259, over 7226.00 frames.], tot_loss[loss=0.2406, simple_loss=0.3131, pruned_loss=0.08404, over 1425297.36 frames.], batch size: 21, lr: 8.36e-04
2022-05-26 23:51:52,212 INFO [train.py:842] (2/4) Epoch 6, batch 3250, loss[loss=0.1975, simple_loss=0.275, pruned_loss=0.05995, over 7343.00 frames.], tot_loss[loss=0.2399, simple_loss=0.3128, pruned_loss=0.08346, over 1425052.46 frames.], batch size: 19, lr: 8.36e-04
2022-05-26 23:52:30,645 INFO [train.py:842] (2/4) Epoch 6, batch 3300, loss[loss=0.2416, simple_loss=0.3237, pruned_loss=0.07975, over 7180.00 frames.], tot_loss[loss=0.2393, simple_loss=0.3124, pruned_loss=0.08314, over 1421373.44 frames.], batch size: 23, lr: 8.36e-04
2022-05-26 23:53:09,689 INFO [train.py:842] (2/4) Epoch 6, batch 3350, loss[loss=0.215, simple_loss=0.2812, pruned_loss=0.0744, over 7256.00 frames.], tot_loss[loss=0.2385, simple_loss=0.3116, pruned_loss=0.08271, over 1425719.19 frames.], batch size: 19, lr: 8.35e-04
2022-05-26 23:53:48,172 INFO [train.py:842] (2/4) Epoch 6, batch 3400, loss[loss=0.2833, simple_loss=0.3478, pruned_loss=0.1094, over 7286.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3109, pruned_loss=0.08174, over 1425747.20 frames.], batch size: 24, lr: 8.35e-04
2022-05-26 23:54:26,999 INFO [train.py:842] (2/4) Epoch 6, batch 3450, loss[loss=0.1931, simple_loss=0.2926, pruned_loss=0.04677, over 7408.00 frames.], tot_loss[loss=0.2391, simple_loss=0.3129, pruned_loss=0.08265, over 1427631.47 frames.], batch size: 21, lr: 8.34e-04
2022-05-26 23:55:05,847 INFO [train.py:842] (2/4) Epoch 6, batch 3500, loss[loss=0.2669, simple_loss=0.343, pruned_loss=0.09542, over 7216.00 frames.], tot_loss[loss=0.2374, simple_loss=0.3109, pruned_loss=0.08191, over 1425434.52 frames.], batch size: 22, lr: 8.34e-04
2022-05-26 23:55:44,588 INFO [train.py:842] (2/4) Epoch 6, batch 3550, loss[loss=0.2098, simple_loss=0.2954, pruned_loss=0.06212, over 7333.00 frames.], tot_loss[loss=0.2354, simple_loss=0.3095, pruned_loss=0.08064, over 1427734.03 frames.], batch size: 21, lr: 8.33e-04
2022-05-26 23:56:22,947 INFO [train.py:842] (2/4) Epoch 6, batch 3600, loss[loss=0.217, simple_loss=0.2905, pruned_loss=0.07173, over 7166.00 frames.], tot_loss[loss=0.2337, simple_loss=0.3083, pruned_loss=0.07955, over 1428791.17 frames.], batch size: 18, lr: 8.33e-04
2022-05-26 23:57:01,910 INFO [train.py:842] (2/4) Epoch 6, batch 3650, loss[loss=0.2113, simple_loss=0.2984, pruned_loss=0.06207, over 7403.00 frames.], tot_loss[loss=0.235, simple_loss=0.3094, pruned_loss=0.08027, over 1427850.41 frames.], batch size: 21, lr: 8.33e-04
2022-05-26 23:57:40,536 INFO [train.py:842] (2/4) Epoch 6, batch 3700, loss[loss=0.2489, simple_loss=0.3203, pruned_loss=0.08879, over 7236.00 frames.], tot_loss[loss=0.2341, simple_loss=0.3086, pruned_loss=0.07979, over 1427238.89 frames.], batch size: 20, lr: 8.32e-04
2022-05-26 23:58:19,401 INFO [train.py:842] (2/4) Epoch 6, batch 3750, loss[loss=0.2627, simple_loss=0.3382, pruned_loss=0.09357, over 7377.00 frames.], tot_loss[loss=0.2365, simple_loss=0.3103, pruned_loss=0.08132, over 1425549.83 frames.], batch size: 23, lr: 8.32e-04
2022-05-26 23:58:57,941 INFO [train.py:842] (2/4) Epoch 6, batch 3800, loss[loss=0.2353, simple_loss=0.331, pruned_loss=0.0698, over 7290.00 frames.], tot_loss[loss=0.236, simple_loss=0.3098, pruned_loss=0.0811, over 1421209.29 frames.], batch size: 24, lr: 8.31e-04
2022-05-26 23:59:36,777 INFO [train.py:842] (2/4) Epoch 6, batch 3850, loss[loss=0.2512, simple_loss=0.3207, pruned_loss=0.09089, over 7332.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3097, pruned_loss=0.08064, over 1419320.13 frames.], batch size: 22, lr: 8.31e-04
2022-05-27 00:00:15,369 INFO [train.py:842] (2/4) Epoch 6, batch 3900, loss[loss=0.201, simple_loss=0.2735, pruned_loss=0.06428, over 7270.00 frames.], tot_loss[loss=0.2344, simple_loss=0.3085, pruned_loss=0.08018, over 1423295.84 frames.], batch size: 18, lr: 8.31e-04
2022-05-27 00:00:54,255 INFO [train.py:842] (2/4) Epoch 6, batch 3950, loss[loss=0.2629, simple_loss=0.3322, pruned_loss=0.09676, over 6891.00 frames.], tot_loss[loss=0.2338, simple_loss=0.3078, pruned_loss=0.0799, over 1424451.24 frames.], batch size: 31, lr: 8.30e-04
2022-05-27 00:01:33,017 INFO [train.py:842] (2/4) Epoch 6, batch 4000, loss[loss=0.3279, simple_loss=0.3794, pruned_loss=0.1382, over 7394.00 frames.], tot_loss[loss=0.2336, simple_loss=0.3076, pruned_loss=0.07978, over 1426894.84 frames.], batch size: 23, lr: 8.30e-04
2022-05-27 00:02:11,874 INFO [train.py:842] (2/4) Epoch 6, batch 4050, loss[loss=0.2317, simple_loss=0.3092, pruned_loss=0.07709, over 7148.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3085, pruned_loss=0.08032, over 1429557.21 frames.], batch size: 19, lr: 8.29e-04
2022-05-27 00:02:50,474 INFO [train.py:842] (2/4) Epoch 6, batch 4100, loss[loss=0.3339, simple_loss=0.3783, pruned_loss=0.1447, over 7375.00 frames.], tot_loss[loss=0.2371, simple_loss=0.3104, pruned_loss=0.08186, over 1427064.82 frames.], batch size: 23, lr: 8.29e-04
2022-05-27 00:03:29,596 INFO [train.py:842] (2/4) Epoch 6, batch 4150, loss[loss=0.234, simple_loss=0.2946, pruned_loss=0.08676, over 7120.00 frames.], tot_loss[loss=0.2345, simple_loss=0.3081, pruned_loss=0.08045, over 1425956.97 frames.], batch size: 17, lr: 8.29e-04
2022-05-27 00:04:18,768 INFO [train.py:842] (2/4) Epoch 6, batch 4200, loss[loss=0.2428, simple_loss=0.296, pruned_loss=0.09476, over 7414.00 frames.], tot_loss[loss=0.235, simple_loss=0.3082, pruned_loss=0.08085, over 1427783.49 frames.], batch size: 18, lr: 8.28e-04
2022-05-27 00:04:57,595 INFO [train.py:842] (2/4) Epoch 6, batch 4250, loss[loss=0.2275, simple_loss=0.3132, pruned_loss=0.0709, over 7294.00 frames.], tot_loss[loss=0.2347, simple_loss=0.3079, pruned_loss=0.08071, over 1427466.03 frames.], batch size: 24, lr: 8.28e-04
2022-05-27 00:05:36,221 INFO [train.py:842] (2/4) Epoch 6, batch 4300, loss[loss=0.3153, simple_loss=0.3815, pruned_loss=0.1245, over 7322.00 frames.], tot_loss[loss=0.2352, simple_loss=0.3081, pruned_loss=0.08115, over 1428767.86 frames.], batch size: 22, lr: 8.27e-04
2022-05-27 00:06:15,201 INFO [train.py:842] (2/4) Epoch 6, batch 4350, loss[loss=0.2375, simple_loss=0.2981, pruned_loss=0.08848, over 7075.00 frames.], tot_loss[loss=0.2367, simple_loss=0.3092, pruned_loss=0.08205, over 1429547.11 frames.], batch size: 18, lr: 8.27e-04
2022-05-27 00:06:53,750 INFO [train.py:842] (2/4) Epoch 6, batch 4400, loss[loss=0.2434, simple_loss=0.3264, pruned_loss=0.08019, over 7228.00 frames.], tot_loss[loss=0.2374, simple_loss=0.3101, pruned_loss=0.08232, over 1427207.99 frames.], batch size: 20, lr: 8.26e-04
2022-05-27 00:07:32,806 INFO [train.py:842] (2/4) Epoch 6, batch 4450, loss[loss=0.236, simple_loss=0.3126, pruned_loss=0.07972, over 7244.00 frames.], tot_loss[loss=0.2378, simple_loss=0.3104, pruned_loss=0.08255, over 1428538.55 frames.], batch size: 20, lr: 8.26e-04
2022-05-27 00:08:11,512 INFO [train.py:842] (2/4) Epoch 6, batch 4500, loss[loss=0.2573, simple_loss=0.3189, pruned_loss=0.09786, over 5101.00 frames.], tot_loss[loss=0.2394, simple_loss=0.3114, pruned_loss=0.08374, over 1428154.98 frames.], batch size: 52, lr: 8.26e-04
2022-05-27 00:08:50,458 INFO [train.py:842] (2/4) Epoch 6, batch 4550, loss[loss=0.2178, simple_loss=0.2967, pruned_loss=0.06944, over 7068.00 frames.], tot_loss[loss=0.239, simple_loss=0.3111, pruned_loss=0.08349, over 1428614.90 frames.], batch size: 18, lr: 8.25e-04
2022-05-27 00:09:28,910 INFO [train.py:842] (2/4) Epoch 6, batch 4600, loss[loss=0.3, simple_loss=0.3675, pruned_loss=0.1162, over 7025.00 frames.], tot_loss[loss=0.2373, simple_loss=0.3099, pruned_loss=0.08234, over 1429342.20 frames.], batch size: 28, lr: 8.25e-04
2022-05-27 00:10:07,620 INFO [train.py:842] (2/4) Epoch 6, batch 4650, loss[loss=0.2112, simple_loss=0.2983, pruned_loss=0.06206, over 7437.00 frames.], tot_loss[loss=0.2397, simple_loss=0.3119, pruned_loss=0.08374, over 1419472.95 frames.], batch size: 22, lr: 8.24e-04
2022-05-27 00:10:46,044 INFO [train.py:842] (2/4) Epoch 6, batch 4700, loss[loss=0.2516, simple_loss=0.3022, pruned_loss=0.1005, over 6976.00 frames.], tot_loss[loss=0.2407, simple_loss=0.3128, pruned_loss=0.08424, over 1425705.25 frames.], batch size: 16, lr: 8.24e-04
2022-05-27 00:11:25,098 INFO [train.py:842] (2/4) Epoch 6, batch 4750, loss[loss=0.1932, simple_loss=0.2694, pruned_loss=0.05848, over 7419.00 frames.], tot_loss[loss=0.2393, simple_loss=0.312, pruned_loss=0.08333, over 1428174.28 frames.], batch size: 18, lr: 8.24e-04
2022-05-27 00:12:03,899 INFO [train.py:842] (2/4) Epoch 6, batch 4800, loss[loss=0.228, simple_loss=0.3029, pruned_loss=0.07651, over 7262.00 frames.], tot_loss[loss=0.2378, simple_loss=0.3107, pruned_loss=0.08249, over 1426451.66 frames.], batch size: 19, lr: 8.23e-04
2022-05-27 00:12:42,813 INFO [train.py:842] (2/4) Epoch 6, batch 4850, loss[loss=0.1969, simple_loss=0.285, pruned_loss=0.05443, over 7409.00 frames.], tot_loss[loss=0.2364, simple_loss=0.3094, pruned_loss=0.08175, over 1427714.90 frames.], batch size: 21, lr: 8.23e-04
2022-05-27 00:13:21,324 INFO [train.py:842] (2/4) Epoch 6, batch 4900, loss[loss=0.2439, simple_loss=0.3329, pruned_loss=0.07739, over 7329.00 frames.], tot_loss[loss=0.2379, simple_loss=0.311, pruned_loss=0.08239, over 1431096.13 frames.], batch size: 22, lr: 8.22e-04
2022-05-27 00:14:00,201 INFO [train.py:842] (2/4) Epoch 6, batch 4950, loss[loss=0.2391, simple_loss=0.3251, pruned_loss=0.07655, over 7052.00 frames.], tot_loss[loss=0.2374, simple_loss=0.3106, pruned_loss=0.08213, over 1427593.94 frames.], batch size: 28, lr: 8.22e-04
2022-05-27 00:14:38,739 INFO [train.py:842] (2/4) Epoch 6, batch 5000, loss[loss=0.2317, simple_loss=0.311, pruned_loss=0.07618, over 6782.00 frames.], tot_loss[loss=0.2369, simple_loss=0.3098, pruned_loss=0.08201, over 1425908.91 frames.], batch size: 31, lr: 8.22e-04
2022-05-27 00:15:17,428 INFO [train.py:842] (2/4) Epoch 6, batch 5050, loss[loss=0.2221, simple_loss=0.294, pruned_loss=0.07507, over 7132.00 frames.], tot_loss[loss=0.2361, simple_loss=0.3093, pruned_loss=0.08144, over 1423149.09 frames.], batch size: 17, lr: 8.21e-04
2022-05-27 00:15:55,969 INFO [train.py:842] (2/4) Epoch 6, batch 5100, loss[loss=0.2269, simple_loss=0.3002, pruned_loss=0.07685, over 7062.00 frames.], tot_loss[loss=0.2365, simple_loss=0.3096, pruned_loss=0.08165, over 1423935.58 frames.], batch size: 18, lr: 8.21e-04
2022-05-27 00:16:34,567 INFO [train.py:842] (2/4) Epoch 6, batch 5150, loss[loss=0.2056, simple_loss=0.2691, pruned_loss=0.07106, over 7292.00 frames.], tot_loss[loss=0.2375, simple_loss=0.3107, pruned_loss=0.08221, over 1423158.38 frames.], batch size: 17, lr: 8.20e-04
2022-05-27 00:17:13,246 INFO [train.py:842] (2/4) Epoch 6, batch 5200, loss[loss=0.2185, simple_loss=0.3043, pruned_loss=0.06637, over 7380.00 frames.], tot_loss[loss=0.2361, simple_loss=0.3094, pruned_loss=0.08141, over 1427854.97 frames.], batch size: 23, lr: 8.20e-04
2022-05-27 00:18:02,456 INFO [train.py:842] (2/4) Epoch 6, batch 5250, loss[loss=0.2881, simple_loss=0.3601, pruned_loss=0.1081, over 7292.00 frames.], tot_loss[loss=0.2347, simple_loss=0.3081, pruned_loss=0.08063, over 1428184.29 frames.], batch size: 25, lr: 8.20e-04
2022-05-27 00:19:01,433 INFO [train.py:842] (2/4) Epoch 6, batch 5300, loss[loss=0.2283, simple_loss=0.2999, pruned_loss=0.0784, over 7114.00 frames.], tot_loss[loss=0.2367, simple_loss=0.3098, pruned_loss=0.08181, over 1418214.17 frames.], batch size: 21, lr: 8.19e-04
2022-05-27 00:19:40,593 INFO [train.py:842] (2/4) Epoch 6, batch 5350, loss[loss=0.2529, simple_loss=0.3312, pruned_loss=0.08727, over 7405.00 frames.], tot_loss[loss=0.2356, simple_loss=0.3096, pruned_loss=0.08086, over 1423290.97 frames.], batch size: 21, lr: 8.19e-04
2022-05-27 00:20:19,201 INFO [train.py:842] (2/4) Epoch 6, batch 5400, loss[loss=0.2377, simple_loss=0.303, pruned_loss=0.08619, over 7274.00 frames.], tot_loss[loss=0.2356, simple_loss=0.3091, pruned_loss=0.08111, over 1420111.71 frames.], batch size: 18, lr: 8.18e-04
2022-05-27 00:20:58,435 INFO [train.py:842] (2/4) Epoch 6, batch 5450, loss[loss=0.2224, simple_loss=0.3121, pruned_loss=0.06637, over 7342.00 frames.], tot_loss[loss=0.2358, simple_loss=0.3093, pruned_loss=0.08117, over 1424301.95 frames.], batch size: 22, lr: 8.18e-04
2022-05-27 00:21:37,365 INFO [train.py:842] (2/4) Epoch 6, batch 5500, loss[loss=0.1833, simple_loss=0.2626, pruned_loss=0.05196, over 7164.00 frames.], tot_loss[loss=0.2365, simple_loss=0.3098, pruned_loss=0.08162, over 1420583.46 frames.], batch size: 18, lr: 8.18e-04
2022-05-27 00:22:16,099 INFO [train.py:842] (2/4) Epoch 6, batch 5550, loss[loss=0.2195, simple_loss=0.2946, pruned_loss=0.0722, over 7209.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3109, pruned_loss=0.08313, over 1417822.68 frames.], batch size: 22, lr: 8.17e-04
2022-05-27 00:22:54,569 INFO [train.py:842] (2/4) Epoch 6, batch 5600, loss[loss=0.2504, simple_loss=0.3213, pruned_loss=0.08972, over 7080.00 frames.], tot_loss[loss=0.238, simple_loss=0.3104, pruned_loss=0.08281, over 1420029.67 frames.], batch size: 28, lr: 8.17e-04
2022-05-27 00:23:33,351 INFO [train.py:842] (2/4) Epoch 6, batch 5650, loss[loss=0.276, simple_loss=0.3496, pruned_loss=0.1012, over 7203.00 frames.], tot_loss[loss=0.2361, simple_loss=0.309, pruned_loss=0.08167, over 1417434.65 frames.], batch size: 22, lr: 8.17e-04
2022-05-27 00:24:12,118 INFO [train.py:842] (2/4) Epoch 6, batch 5700, loss[loss=0.1915, simple_loss=0.2833, pruned_loss=0.04986, over 7116.00 frames.], tot_loss[loss=0.2349, simple_loss=0.3074, pruned_loss=0.08123, over 1419215.11 frames.], batch size: 21, lr: 8.16e-04
2022-05-27 00:24:50,734 INFO [train.py:842] (2/4) Epoch 6, batch 5750, loss[loss=0.3085, simple_loss=0.3541, pruned_loss=0.1314, over 7147.00 frames.], tot_loss[loss=0.2367, simple_loss=0.3093, pruned_loss=0.08205, over 1420018.43 frames.], batch size: 19, lr: 8.16e-04
2022-05-27 00:25:29,329 INFO [train.py:842] (2/4) Epoch 6, batch 5800, loss[loss=0.2326, simple_loss=0.3225, pruned_loss=0.07132, over 7220.00 frames.], tot_loss[loss=0.2374, simple_loss=0.3099, pruned_loss=0.08243, over 1419884.10 frames.], batch size: 21, lr: 8.15e-04
2022-05-27 00:26:08,344 INFO [train.py:842] (2/4) Epoch 6, batch 5850, loss[loss=0.2065, simple_loss=0.2773, pruned_loss=0.06789, over 7418.00 frames.], tot_loss[loss=0.2366, simple_loss=0.3095, pruned_loss=0.08189, over 1425135.21 frames.], batch size: 18, lr: 8.15e-04
2022-05-27 00:26:46,769 INFO [train.py:842] (2/4) Epoch 6, batch 5900, loss[loss=0.3297, simple_loss=0.3782, pruned_loss=0.1406, over 7416.00 frames.], tot_loss[loss=0.2371, simple_loss=0.3102, pruned_loss=0.08201, over 1425354.01 frames.], batch size: 21, lr: 8.15e-04
2022-05-27 00:27:25,965 INFO [train.py:842] (2/4) Epoch 6, batch 5950, loss[loss=0.2704, simple_loss=0.3398, pruned_loss=0.1005, over 7362.00 frames.], tot_loss[loss=0.2374, simple_loss=0.3105, pruned_loss=0.08215, over 1425383.05 frames.], batch size: 19, lr: 8.14e-04
2022-05-27 00:28:04,705 INFO [train.py:842] (2/4) Epoch 6, batch 6000, loss[loss=0.2032, simple_loss=0.2935, pruned_loss=0.05649, over 7348.00 frames.], tot_loss[loss=0.2383, simple_loss=0.3113, pruned_loss=0.08269, over 1424779.28 frames.], batch size: 22, lr: 8.14e-04
2022-05-27 00:28:04,706 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 00:28:13,973 INFO [train.py:871] (2/4) Epoch 6, validation: loss=0.1847, simple_loss=0.2853, pruned_loss=0.04201, over 868885.00 frames.
2022-05-27 00:28:52,838 INFO [train.py:842] (2/4) Epoch 6, batch 6050, loss[loss=0.1941, simple_loss=0.2547, pruned_loss=0.06681, over 7273.00 frames.], tot_loss[loss=0.2345, simple_loss=0.3083, pruned_loss=0.08035, over 1428517.82 frames.], batch size: 18, lr: 8.13e-04
2022-05-27 00:29:31,595 INFO [train.py:842] (2/4) Epoch 6, batch 6100, loss[loss=0.2067, simple_loss=0.2793, pruned_loss=0.06705, over 6811.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3076, pruned_loss=0.08009, over 1426275.85 frames.], batch size: 15, lr: 8.13e-04
2022-05-27 00:30:10,442 INFO [train.py:842] (2/4) Epoch 6, batch 6150, loss[loss=0.2721, simple_loss=0.3533, pruned_loss=0.09545, over 7110.00 frames.], tot_loss[loss=0.2349, simple_loss=0.308, pruned_loss=0.08089, over 1417872.92 frames.], batch size: 21, lr: 8.13e-04
2022-05-27 00:30:48,827 INFO [train.py:842] (2/4) Epoch 6, batch 6200, loss[loss=0.2682, simple_loss=0.3454, pruned_loss=0.09555, over 6578.00 frames.], tot_loss[loss=0.2364, simple_loss=0.3095, pruned_loss=0.08169, over 1413478.87 frames.], batch size: 38, lr: 8.12e-04
2022-05-27 00:31:27,494 INFO [train.py:842] (2/4) Epoch 6, batch 6250, loss[loss=0.2431, simple_loss=0.3206, pruned_loss=0.08284, over 6498.00 frames.], tot_loss[loss=0.2362, simple_loss=0.31, pruned_loss=0.08125, over 1415865.02 frames.], batch size: 38, lr: 8.12e-04
2022-05-27 00:32:06,048 INFO [train.py:842] (2/4) Epoch 6, batch 6300, loss[loss=0.2315, simple_loss=0.3061, pruned_loss=0.07843, over 7327.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3095, pruned_loss=0.08078, over 1419002.47 frames.], batch size: 20, lr: 8.11e-04
2022-05-27 00:32:44,918 INFO [train.py:842] (2/4) Epoch 6, batch 6350, loss[loss=0.1946, simple_loss=0.2642, pruned_loss=0.06253, over 7397.00 frames.], tot_loss[loss=0.235, simple_loss=0.3088, pruned_loss=0.08058, over 1420779.65 frames.], batch size: 18, lr: 8.11e-04
2022-05-27 00:33:23,417 INFO [train.py:842] (2/4) Epoch 6, batch 6400, loss[loss=0.2698, simple_loss=0.3348, pruned_loss=0.1024, over 7031.00 frames.], tot_loss[loss=0.2354, simple_loss=0.3091, pruned_loss=0.08086, over 1418631.03 frames.], batch size: 28, lr: 8.11e-04
2022-05-27 00:34:02,221 INFO [train.py:842] (2/4) Epoch 6, batch 6450, loss[loss=0.1574, simple_loss=0.2356, pruned_loss=0.03961, over 7286.00 frames.], tot_loss[loss=0.2345, simple_loss=0.3082, pruned_loss=0.08042, over 1418480.33 frames.], batch size: 17, lr: 8.10e-04
2022-05-27 00:34:40,760 INFO [train.py:842] (2/4) Epoch 6, batch 6500, loss[loss=0.2455, simple_loss=0.323, pruned_loss=0.084, over 7269.00 frames.], tot_loss[loss=0.2342, simple_loss=0.3077, pruned_loss=0.08032, over 1419877.36 frames.], batch size: 25, lr: 8.10e-04
2022-05-27 00:35:19,801 INFO [train.py:842] (2/4) Epoch 6, batch 6550, loss[loss=0.2343, simple_loss=0.317, pruned_loss=0.07574, over 7109.00 frames.], tot_loss[loss=0.2352, simple_loss=0.3086, pruned_loss=0.08089, over 1418080.57 frames.], batch size: 21, lr: 8.10e-04
2022-05-27 00:35:58,637 INFO [train.py:842] (2/4) Epoch 6, batch 6600, loss[loss=0.2337, simple_loss=0.3116, pruned_loss=0.07791, over 7261.00 frames.], tot_loss[loss=0.2363, simple_loss=0.309, pruned_loss=0.08184, over 1420707.18 frames.], batch size: 24, lr: 8.09e-04
2022-05-27 00:36:37,417 INFO [train.py:842] (2/4) Epoch 6, batch 6650, loss[loss=0.222, simple_loss=0.3023, pruned_loss=0.07087, over 7146.00 frames.], tot_loss[loss=0.2361, simple_loss=0.3089, pruned_loss=0.08167, over 1421988.11 frames.], batch size: 20, lr: 8.09e-04
2022-05-27 00:37:16,124 INFO [train.py:842] (2/4) Epoch 6, batch 6700, loss[loss=0.2375, simple_loss=0.3116, pruned_loss=0.08167, over 7184.00 frames.], tot_loss[loss=0.2362, simple_loss=0.3091, pruned_loss=0.0817, over 1420591.50 frames.], batch size: 23, lr: 8.08e-04
2022-05-27 00:37:54,961 INFO [train.py:842] (2/4) Epoch 6, batch 6750, loss[loss=0.2744, simple_loss=0.3573, pruned_loss=0.09569, over 7383.00 frames.], tot_loss[loss=0.2358, simple_loss=0.3092, pruned_loss=0.08122, over 1419541.18 frames.], batch size: 23, lr: 8.08e-04
2022-05-27 00:38:33,403 INFO [train.py:842] (2/4) Epoch 6, batch 6800, loss[loss=0.2011, simple_loss=0.2827, pruned_loss=0.05976, over 7338.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3101, pruned_loss=0.08218, over 1419623.00 frames.], batch size: 20, lr: 8.08e-04
2022-05-27 00:39:12,300 INFO [train.py:842] (2/4) Epoch 6, batch 6850, loss[loss=0.1902, simple_loss=0.2664, pruned_loss=0.05695, over 7075.00 frames.], tot_loss[loss=0.2374, simple_loss=0.3106, pruned_loss=0.08207, over 1420925.18 frames.], batch size: 18, lr: 8.07e-04
2022-05-27 00:39:50,890 INFO [train.py:842] (2/4) Epoch 6, batch 6900, loss[loss=0.2629, simple_loss=0.3395, pruned_loss=0.09312, over 7139.00 frames.], tot_loss[loss=0.238, simple_loss=0.3109, pruned_loss=0.08259, over 1422406.71 frames.], batch size: 20, lr: 8.07e-04
2022-05-27 00:40:29,629 INFO [train.py:842] (2/4) Epoch 6, batch 6950, loss[loss=0.237, simple_loss=0.3151, pruned_loss=0.07948, over 7327.00 frames.], tot_loss[loss=0.2407, simple_loss=0.3134, pruned_loss=0.084, over 1426521.06 frames.], batch size: 20, lr: 8.07e-04
2022-05-27 00:41:08,365 INFO [train.py:842] (2/4) Epoch 6, batch 7000, loss[loss=0.2391, simple_loss=0.3275, pruned_loss=0.07537, over 7233.00 frames.], tot_loss[loss=0.2394, simple_loss=0.3124, pruned_loss=0.08318, over 1428213.09 frames.], batch size: 20, lr: 8.06e-04
2022-05-27 00:41:47,476 INFO [train.py:842] (2/4) Epoch 6, batch 7050, loss[loss=0.2073, simple_loss=0.2946, pruned_loss=0.06004, over 7332.00 frames.], tot_loss[loss=0.237, simple_loss=0.3101, pruned_loss=0.08194, over 1424994.26 frames.], batch size: 22, lr: 8.06e-04
2022-05-27 00:42:26,213 INFO [train.py:842] (2/4) Epoch 6, batch 7100, loss[loss=0.2682, simple_loss=0.324, pruned_loss=0.1063, over 7269.00 frames.], tot_loss[loss=0.2371, simple_loss=0.3099, pruned_loss=0.08212, over 1423382.37 frames.], batch size: 17, lr: 8.05e-04
2022-05-27 00:43:05,028 INFO [train.py:842] (2/4) Epoch 6, batch 7150, loss[loss=0.2234, simple_loss=0.3117, pruned_loss=0.06749, over 7412.00 frames.], tot_loss[loss=0.2354, simple_loss=0.3089, pruned_loss=0.08095, over 1425604.28 frames.], batch size: 21, lr: 8.05e-04
2022-05-27 00:43:43,852 INFO [train.py:842] (2/4) Epoch 6, batch 7200, loss[loss=0.2177, simple_loss=0.3008, pruned_loss=0.06729, over 7315.00 frames.], tot_loss[loss=0.2349, simple_loss=0.3083, pruned_loss=0.0807, over 1427409.16 frames.], batch size: 24, lr: 8.05e-04
2022-05-27 00:44:22,616 INFO [train.py:842] (2/4) Epoch 6, batch 7250, loss[loss=0.2657, simple_loss=0.3539, pruned_loss=0.08877, over 7325.00 frames.], tot_loss[loss=0.2367, simple_loss=0.3101, pruned_loss=0.08159, over 1423640.68 frames.], batch size: 20, lr: 8.04e-04
2022-05-27 00:45:01,195 INFO [train.py:842] (2/4) Epoch 6, batch 7300, loss[loss=0.2349, simple_loss=0.3265, pruned_loss=0.07168, over 7149.00 frames.], tot_loss[loss=0.235, simple_loss=0.3087, pruned_loss=0.0806, over 1423811.51 frames.], batch size: 20, lr: 8.04e-04
2022-05-27 00:45:40,186 INFO [train.py:842] (2/4) Epoch 6, batch 7350, loss[loss=0.2626, simple_loss=0.305, pruned_loss=0.1101, over 7199.00 frames.], tot_loss[loss=0.234, simple_loss=0.3074, pruned_loss=0.08033, over 1425296.49 frames.], batch size: 16, lr: 8.04e-04
2022-05-27 00:46:18,804 INFO [train.py:842] (2/4) Epoch 6, batch 7400, loss[loss=0.2148, simple_loss=0.2845, pruned_loss=0.07257, over 7428.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3073, pruned_loss=0.08029, over 1430978.13 frames.], batch size: 20, lr: 8.03e-04
2022-05-27 00:46:57,724 INFO [train.py:842] (2/4) Epoch 6, batch 7450, loss[loss=0.1641, simple_loss=0.237, pruned_loss=0.04563, over 7298.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3086, pruned_loss=0.08119, over 1425650.50 frames.], batch size: 17, lr: 8.03e-04
2022-05-27 00:47:36,438 INFO [train.py:842] (2/4) Epoch 6, batch 7500, loss[loss=0.2271, simple_loss=0.3224, pruned_loss=0.06587, over 7217.00 frames.], tot_loss[loss=0.2347, simple_loss=0.308, pruned_loss=0.08067, over 1421076.17 frames.], batch size: 22, lr: 8.02e-04
2022-05-27 00:48:15,420 INFO [train.py:842] (2/4) Epoch 6, batch 7550, loss[loss=0.2157, simple_loss=0.3038, pruned_loss=0.06382, over 7331.00 frames.], tot_loss[loss=0.2352, simple_loss=0.3083, pruned_loss=0.08104, over 1420272.22 frames.], batch size: 20, lr: 8.02e-04
2022-05-27 00:48:53,866 INFO [train.py:842] (2/4) Epoch 6, batch 7600, loss[loss=0.2731, simple_loss=0.3517, pruned_loss=0.09726, over 7413.00 frames.], tot_loss[loss=0.2371, simple_loss=0.3104, pruned_loss=0.08195, over 1417521.72 frames.], batch size: 21, lr: 8.02e-04
2022-05-27 00:49:32,680 INFO [train.py:842] (2/4) Epoch 6, batch 7650, loss[loss=0.2994, simple_loss=0.36, pruned_loss=0.1194, over 7300.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3102, pruned_loss=0.0821, over 1414800.80 frames.], batch size: 25, lr: 8.01e-04
2022-05-27 00:50:11,216 INFO [train.py:842] (2/4) Epoch 6, batch 7700, loss[loss=0.2214, simple_loss=0.2912, pruned_loss=0.0758, over 7071.00 frames.], tot_loss[loss=0.2366, simple_loss=0.3095, pruned_loss=0.08187, over 1417661.22 frames.], batch size: 18, lr: 8.01e-04
2022-05-27 00:50:49,903 INFO [train.py:842] (2/4) Epoch 6, batch 7750, loss[loss=0.2594, simple_loss=0.3329, pruned_loss=0.09297, over 7170.00 frames.], tot_loss[loss=0.2359, simple_loss=0.3089, pruned_loss=0.08141, over 1412396.58 frames.], batch size: 26, lr: 8.01e-04
2022-05-27 00:51:28,322 INFO [train.py:842] (2/4) Epoch 6, batch 7800, loss[loss=0.2042, simple_loss=0.2779, pruned_loss=0.06528, over 7124.00 frames.], tot_loss[loss=0.237, simple_loss=0.3102, pruned_loss=0.08188, over 1412523.54 frames.], batch size: 17, lr: 8.00e-04
2022-05-27 00:52:07,421 INFO [train.py:842] (2/4) Epoch 6, batch 7850, loss[loss=0.2245, simple_loss=0.3078, pruned_loss=0.07057, over 7409.00 frames.], tot_loss[loss=0.2368, simple_loss=0.3097, pruned_loss=0.08195, over 1411240.84 frames.], batch size: 21, lr: 8.00e-04
2022-05-27 00:52:46,286 INFO [train.py:842] (2/4) Epoch 6, batch 7900, loss[loss=0.2198, simple_loss=0.2792, pruned_loss=0.08017, over 6792.00 frames.], tot_loss[loss=0.2364, simple_loss=0.309, pruned_loss=0.08187, over 1412966.35 frames.], batch size: 15, lr: 7.99e-04
2022-05-27 00:53:25,060 INFO [train.py:842] (2/4) Epoch 6, batch 7950, loss[loss=0.2449, simple_loss=0.3308, pruned_loss=0.07951, over 7320.00 frames.], tot_loss[loss=0.2357, simple_loss=0.3086, pruned_loss=0.08144, over 1418168.89 frames.], batch size: 25, lr: 7.99e-04
2022-05-27 00:54:03,685 INFO [train.py:842] (2/4) Epoch 6, batch 8000, loss[loss=0.2107, simple_loss=0.2902, pruned_loss=0.06555, over 7324.00 frames.], tot_loss[loss=0.2352, simple_loss=0.3087, pruned_loss=0.08089, over 1420573.58 frames.], batch size: 21, lr: 7.99e-04
2022-05-27 00:54:42,717 INFO [train.py:842] (2/4) Epoch 6, batch 8050, loss[loss=0.3503, simple_loss=0.3826, pruned_loss=0.159, over 4838.00 frames.], tot_loss[loss=0.2351, simple_loss=0.3092, pruned_loss=0.08046, over 1417563.66 frames.], batch size: 52, lr: 7.98e-04
2022-05-27 00:55:21,171 INFO [train.py:842] (2/4) Epoch 6, batch 8100, loss[loss=0.2136, simple_loss=0.3048, pruned_loss=0.06122, over 7330.00 frames.], tot_loss[loss=0.2356, simple_loss=0.3097, pruned_loss=0.08071, over 1421706.10 frames.], batch size: 20, lr: 7.98e-04
2022-05-27 00:55:59,928 INFO [train.py:842] (2/4) Epoch 6, batch 8150, loss[loss=0.3441, simple_loss=0.4009, pruned_loss=0.1436, over 7182.00 frames.], tot_loss[loss=0.2366, simple_loss=0.3104, pruned_loss=0.08142, over 1418075.09 frames.], batch size: 26, lr: 7.98e-04
2022-05-27 00:56:38,268 INFO [train.py:842] (2/4) Epoch 6, batch 8200, loss[loss=0.2942, simple_loss=0.3506, pruned_loss=0.1189, over 7348.00 frames.], tot_loss[loss=0.2363, simple_loss=0.3101, pruned_loss=0.0812, over 1419868.81 frames.], batch size: 22, lr: 7.97e-04
2022-05-27 00:57:17,219 INFO [train.py:842] (2/4) Epoch 6, batch 8250, loss[loss=0.2477, simple_loss=0.3158, pruned_loss=0.08977, over 6481.00 frames.], tot_loss[loss=0.2354, simple_loss=0.3093, pruned_loss=0.08076, over 1420000.33 frames.], batch size: 38, lr: 7.97e-04
2022-05-27 00:57:55,643 INFO [train.py:842] (2/4) Epoch 6, batch 8300, loss[loss=0.269, simple_loss=0.336, pruned_loss=0.101, over 7376.00 frames.], tot_loss[loss=0.2346, simple_loss=0.309, pruned_loss=0.0801, over 1423744.40 frames.], batch size: 23, lr: 7.97e-04
2022-05-27 00:58:34,557 INFO [train.py:842] (2/4) Epoch 6, batch 8350, loss[loss=0.2164, simple_loss=0.3121, pruned_loss=0.0604, over 7337.00 frames.], tot_loss[loss=0.2351, simple_loss=0.3097, pruned_loss=0.0802, over 1425321.23 frames.], batch size: 22, lr: 7.96e-04
2022-05-27 00:59:13,024 INFO [train.py:842] (2/4) Epoch 6, batch 8400, loss[loss=0.1965, simple_loss=0.2627, pruned_loss=0.06515, over 7259.00 frames.], tot_loss[loss=0.2371, simple_loss=0.3111, pruned_loss=0.08155, over 1422383.56 frames.], batch size: 17, lr: 7.96e-04
2022-05-27 00:59:52,144 INFO [train.py:842] (2/4) Epoch 6, batch 8450, loss[loss=0.251, simple_loss=0.3031, pruned_loss=0.09951, over 7276.00 frames.], tot_loss[loss=0.2363, simple_loss=0.3102, pruned_loss=0.08123, over 1423934.55 frames.], batch size: 17, lr: 7.95e-04
2022-05-27 01:00:31,032 INFO [train.py:842] (2/4) Epoch 6, batch 8500, loss[loss=0.2356, simple_loss=0.3208, pruned_loss=0.07518, over 7117.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3089, pruned_loss=0.08108, over 1423796.57 frames.], batch size: 21, lr: 7.95e-04
2022-05-27 01:01:10,178 INFO [train.py:842] (2/4) Epoch 6, batch 8550, loss[loss=0.2625, simple_loss=0.3281, pruned_loss=0.09848, over 7378.00 frames.], tot_loss[loss=0.2371, simple_loss=0.3099, pruned_loss=0.08215, over 1426763.69 frames.], batch size: 23, lr: 7.95e-04
2022-05-27 01:01:48,815 INFO [train.py:842] (2/4) Epoch 6, batch 8600, loss[loss=0.2597, simple_loss=0.3291, pruned_loss=0.09512, over 7225.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3113, pruned_loss=0.08297, over 1427458.20 frames.], batch size: 21, lr: 7.94e-04
2022-05-27 01:02:27,485 INFO [train.py:842] (2/4) Epoch 6, batch 8650, loss[loss=0.2434, simple_loss=0.318, pruned_loss=0.08443, over 7323.00 frames.], tot_loss[loss=0.2375, simple_loss=0.3103, pruned_loss=0.08236, over 1425375.02 frames.], batch size: 20, lr: 7.94e-04
2022-05-27 01:03:06,053 INFO [train.py:842] (2/4) Epoch 6, batch 8700, loss[loss=0.1989, simple_loss=0.2824, pruned_loss=0.05776, over 7135.00 frames.], tot_loss[loss=0.235, simple_loss=0.3082, pruned_loss=0.08089, over 1420094.02 frames.], batch size: 17, lr: 7.94e-04
2022-05-27 01:03:44,890 INFO [train.py:842] (2/4) Epoch 6, batch 8750, loss[loss=0.1771, simple_loss=0.255, pruned_loss=0.04959, over 7141.00 frames.], tot_loss[loss=0.2356, simple_loss=0.3086, pruned_loss=0.08132, over 1417735.28 frames.], batch size: 17, lr: 7.93e-04
2022-05-27 01:04:23,730 INFO [train.py:842] (2/4) Epoch 6, batch 8800, loss[loss=0.215, simple_loss=0.2867, pruned_loss=0.0716, over 7132.00 frames.], tot_loss[loss=0.2357, simple_loss=0.3085, pruned_loss=0.08141, over 1417157.82 frames.], batch size: 17, lr: 7.93e-04
2022-05-27 01:05:02,661 INFO [train.py:842] (2/4) Epoch 6, batch 8850, loss[loss=0.2002, simple_loss=0.2629, pruned_loss=0.06872, over 7284.00 frames.], tot_loss[loss=0.2348, simple_loss=0.3075, pruned_loss=0.08107, over 1417517.32 frames.], batch size: 17, lr: 7.93e-04
2022-05-27 01:05:41,177 INFO [train.py:842] (2/4) Epoch 6, batch 8900, loss[loss=0.2173, simple_loss=0.2975, pruned_loss=0.06853, over 7170.00 frames.], tot_loss[loss=0.233, simple_loss=0.3059, pruned_loss=0.08008, over 1412152.55 frames.], batch size: 26, lr: 7.92e-04
2022-05-27 01:06:20,553 INFO [train.py:842] (2/4) Epoch 6, batch 8950, loss[loss=0.2007, simple_loss=0.2829, pruned_loss=0.05923, over 7360.00 frames.], tot_loss[loss=0.2333, simple_loss=0.3057, pruned_loss=0.08043, over 1405993.73 frames.], batch size: 19, lr: 7.92e-04
2022-05-27 01:06:58,921 INFO [train.py:842] (2/4) Epoch 6, batch 9000, loss[loss=0.1802, simple_loss=0.2544, pruned_loss=0.05296, over 7271.00 frames.], tot_loss[loss=0.2343, simple_loss=0.3069, pruned_loss=0.08086, over 1398618.16 frames.], batch size: 17, lr: 7.91e-04
2022-05-27 01:06:58,921 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 01:07:08,313 INFO [train.py:871] (2/4) Epoch 6, validation: loss=0.186, simple_loss=0.2866, pruned_loss=0.04271, over 868885.00 frames.
2022-05-27 01:07:46,568 INFO [train.py:842] (2/4) Epoch 6, batch 9050, loss[loss=0.1788, simple_loss=0.2447, pruned_loss=0.0565, over 7293.00 frames.], tot_loss[loss=0.2374, simple_loss=0.3091, pruned_loss=0.08279, over 1366854.85 frames.], batch size: 17, lr: 7.91e-04
2022-05-27 01:08:24,116 INFO [train.py:842] (2/4) Epoch 6, batch 9100, loss[loss=0.2112, simple_loss=0.3013, pruned_loss=0.06054, over 7229.00 frames.], tot_loss[loss=0.2408, simple_loss=0.3122, pruned_loss=0.08466, over 1338524.48 frames.], batch size: 21, lr: 7.91e-04
2022-05-27 01:09:01,928 INFO [train.py:842] (2/4) Epoch 6, batch 9150, loss[loss=0.2768, simple_loss=0.3258, pruned_loss=0.1139, over 4760.00 frames.], tot_loss[loss=0.2482, simple_loss=0.3182, pruned_loss=0.08906, over 1289489.82 frames.], batch size: 53, lr: 7.90e-04
2022-05-27 01:09:54,904 INFO [train.py:842] (2/4) Epoch 7, batch 0, loss[loss=0.1928, simple_loss=0.2682, pruned_loss=0.05868, over 7416.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2682, pruned_loss=0.05868, over 7416.00 frames.], batch size: 18, lr: 7.58e-04
2022-05-27 01:10:33,936 INFO [train.py:842] (2/4) Epoch 7, batch 50, loss[loss=0.1905, simple_loss=0.27, pruned_loss=0.05553, over 7424.00 frames.], tot_loss[loss=0.2283, simple_loss=0.3029, pruned_loss=0.07687, over 322637.48 frames.], batch size: 18, lr: 7.58e-04
2022-05-27 01:11:12,607 INFO [train.py:842] (2/4) Epoch 7, batch 100, loss[loss=0.1948, simple_loss=0.2832, pruned_loss=0.05322, over 7154.00 frames.], tot_loss[loss=0.2266, simple_loss=0.3021, pruned_loss=0.07554, over 567145.62 frames.], batch size: 19, lr: 7.57e-04
2022-05-27 01:11:51,438 INFO [train.py:842] (2/4) Epoch 7, batch 150, loss[loss=0.2633, simple_loss=0.3327, pruned_loss=0.09693, over 7173.00 frames.], tot_loss[loss=0.2333, simple_loss=0.3073, pruned_loss=0.07962, over 757160.60 frames.], batch size: 19, lr: 7.57e-04
2022-05-27 01:12:30,099 INFO [train.py:842] (2/4) Epoch 7, batch 200, loss[loss=0.3094, simple_loss=0.3854, pruned_loss=0.1167, over 7367.00 frames.], tot_loss[loss=0.2329, simple_loss=0.3071, pruned_loss=0.07937, over 906014.75 frames.], batch size: 23, lr: 7.57e-04
2022-05-27 01:13:09,010 INFO [train.py:842] (2/4) Epoch 7, batch 250, loss[loss=0.2668, simple_loss=0.347, pruned_loss=0.09335, over 7142.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3068, pruned_loss=0.07839, over 1020642.48 frames.], batch size: 20, lr: 7.56e-04
2022-05-27 01:13:47,469 INFO [train.py:842] (2/4) Epoch 7, batch 300, loss[loss=0.171, simple_loss=0.2479, pruned_loss=0.04708, over 7260.00 frames.], tot_loss[loss=0.2305, simple_loss=0.3059, pruned_loss=0.07758, over 1107676.39 frames.], batch size: 16, lr: 7.56e-04
2022-05-27 01:14:26,503 INFO [train.py:842] (2/4) Epoch 7, batch 350, loss[loss=0.2568, simple_loss=0.3317, pruned_loss=0.09094, over 7123.00 frames.], tot_loss[loss=0.232, simple_loss=0.307, pruned_loss=0.07848, over 1178517.21 frames.], batch size: 21, lr: 7.56e-04
2022-05-27 01:15:04,811 INFO [train.py:842] (2/4) Epoch 7, batch 400, loss[loss=0.2172, simple_loss=0.2845, pruned_loss=0.07495, over 7159.00 frames.], tot_loss[loss=0.2315, simple_loss=0.3067, pruned_loss=0.07813, over 1231258.02 frames.], batch size: 18, lr: 7.55e-04
2022-05-27 01:15:43,927 INFO [train.py:842] (2/4) Epoch 7, batch 450, loss[loss=0.2311, simple_loss=0.3088, pruned_loss=0.07667, over 7365.00 frames.], tot_loss[loss=0.2326, simple_loss=0.3074, pruned_loss=0.07891, over 1276878.05 frames.], batch size: 19, lr: 7.55e-04
2022-05-27 01:16:22,214 INFO [train.py:842] (2/4) Epoch 7, batch 500, loss[loss=0.333, simple_loss=0.3739, pruned_loss=0.146, over 6436.00 frames.], tot_loss[loss=0.2333, simple_loss=0.3079, pruned_loss=0.07941, over 1305339.40 frames.], batch size: 37, lr: 7.55e-04
2022-05-27 01:17:01,068 INFO [train.py:842] (2/4) Epoch 7, batch 550, loss[loss=0.2035, simple_loss=0.2885, pruned_loss=0.05922, over 7122.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3066, pruned_loss=0.07886, over 1330622.23 frames.], batch size: 21, lr: 7.54e-04
2022-05-27 01:17:39,717 INFO [train.py:842] (2/4) Epoch 7, batch 600, loss[loss=0.3184, simple_loss=0.3755, pruned_loss=0.1307, over 7015.00 frames.], tot_loss[loss=0.234, simple_loss=0.3086, pruned_loss=0.07971, over 1348954.30 frames.], batch size: 28, lr: 7.54e-04
2022-05-27 01:18:18,869 INFO [train.py:842] (2/4) Epoch 7, batch 650, loss[loss=0.2435, simple_loss=0.3188, pruned_loss=0.08409, over 5292.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3067, pruned_loss=0.07849, over 1364254.55 frames.], batch size: 53, lr: 7.54e-04
2022-05-27 01:18:57,442 INFO [train.py:842] (2/4) Epoch 7, batch 700, loss[loss=0.2045, simple_loss=0.2852, pruned_loss=0.06193, over 7168.00 frames.], tot_loss[loss=0.2313, simple_loss=0.3062, pruned_loss=0.07823, over 1378453.63 frames.], batch size: 18, lr: 7.53e-04
2022-05-27 01:19:36,357 INFO [train.py:842] (2/4) Epoch 7, batch 750, loss[loss=0.2367, simple_loss=0.3083, pruned_loss=0.08249, over 6682.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3053, pruned_loss=0.0775, over 1390697.64 frames.], batch size: 31, lr: 7.53e-04
2022-05-27 01:20:15,031 INFO [train.py:842] (2/4) Epoch 7, batch 800, loss[loss=0.1912, simple_loss=0.288, pruned_loss=0.04716, over 7330.00 frames.], tot_loss[loss=0.2311, simple_loss=0.3056, pruned_loss=0.07834, over 1390660.14 frames.], batch size: 20, lr: 7.53e-04
2022-05-27 01:20:56,566 INFO [train.py:842] (2/4) Epoch 7, batch 850, loss[loss=0.2367, simple_loss=0.3211, pruned_loss=0.0762, over 7299.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3048, pruned_loss=0.07772, over 1397755.03 frames.], batch size: 24, lr: 7.52e-04
2022-05-27 01:21:35,043 INFO [train.py:842] (2/4) Epoch 7, batch 900, loss[loss=0.2159, simple_loss=0.3048, pruned_loss=0.06352, over 7381.00 frames.], tot_loss[loss=0.229, simple_loss=0.3042, pruned_loss=0.07688, over 1403563.16 frames.], batch size: 23, lr: 7.52e-04
2022-05-27 01:22:13,827 INFO [train.py:842] (2/4) Epoch 7, batch 950, loss[loss=0.2324, simple_loss=0.3081, pruned_loss=0.07838, over 7364.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3059, pruned_loss=0.07722, over 1407283.49 frames.], batch size: 23, lr: 7.52e-04
2022-05-27 01:22:52,357 INFO [train.py:842] (2/4) Epoch 7, batch 1000, loss[loss=0.2111, simple_loss=0.2923, pruned_loss=0.06491, over 7396.00 frames.], tot_loss[loss=0.2293, simple_loss=0.305, pruned_loss=0.07684, over 1408576.24 frames.], batch size: 23, lr: 7.51e-04
2022-05-27 01:23:31,593 INFO [train.py:842] (2/4) Epoch 7, batch 1050, loss[loss=0.2283, simple_loss=0.2998, pruned_loss=0.07842, over 7159.00 frames.], tot_loss[loss=0.2294, simple_loss=0.3049, pruned_loss=0.07691, over 1415780.13 frames.], batch size: 19, lr: 7.51e-04
2022-05-27 01:24:10,619 INFO [train.py:842] (2/4) Epoch 7, batch 1100, loss[loss=0.2986, simple_loss=0.3622, pruned_loss=0.1175, over 7315.00 frames.], tot_loss[loss=0.2294, simple_loss=0.3054, pruned_loss=0.07671, over 1419952.71 frames.], batch size: 25, lr: 7.51e-04
2022-05-27 01:24:49,510 INFO [train.py:842] (2/4) Epoch 7, batch 1150, loss[loss=0.1791, simple_loss=0.2527, pruned_loss=0.05271, over 7150.00 frames.], tot_loss[loss=0.2296, simple_loss=0.3055, pruned_loss=0.07688, over 1418144.11 frames.], batch size: 17, lr: 7.50e-04
2022-05-27 01:25:28,094 INFO [train.py:842] (2/4) Epoch 7, batch 1200, loss[loss=0.2057, simple_loss=0.2755, pruned_loss=0.06798, over 7238.00 frames.], tot_loss[loss=0.2306, simple_loss=0.3063, pruned_loss=0.07747, over 1414013.33 frames.], batch size: 16, lr: 7.50e-04
2022-05-27 01:26:07,113 INFO [train.py:842] (2/4) Epoch 7, batch 1250, loss[loss=0.2197, simple_loss=0.3133, pruned_loss=0.06303, over 7234.00 frames.], tot_loss[loss=0.2297, simple_loss=0.3055, pruned_loss=0.07698, over 1415608.44 frames.], batch size: 20, lr: 7.50e-04
2022-05-27 01:26:45,646 INFO [train.py:842] (2/4) Epoch 7, batch 1300, loss[loss=0.313, simple_loss=0.3506, pruned_loss=0.1377, over 7270.00 frames.], tot_loss[loss=0.2306, simple_loss=0.3063, pruned_loss=0.07749, over 1416894.71 frames.], batch size: 17, lr: 7.49e-04
2022-05-27 01:27:24,560 INFO [train.py:842] (2/4) Epoch 7, batch 1350, loss[loss=0.2342, simple_loss=0.3132, pruned_loss=0.07758, over 7416.00 frames.], tot_loss[loss=0.2313, simple_loss=0.3069, pruned_loss=0.07783, over 1421747.92 frames.], batch size: 21, lr: 7.49e-04
2022-05-27 01:28:02,937 INFO [train.py:842] (2/4) Epoch 7, batch 1400, loss[loss=0.2567, simple_loss=0.3289, pruned_loss=0.09222, over 7172.00 frames.], tot_loss[loss=0.2335, simple_loss=0.3087, pruned_loss=0.07914, over 1420193.04 frames.], batch size: 19, lr: 7.49e-04
2022-05-27 01:28:41,868 INFO [train.py:842] (2/4) Epoch 7, batch 1450, loss[loss=0.2413, simple_loss=0.3147, pruned_loss=0.08394, over 6765.00 frames.], tot_loss[loss=0.2333, simple_loss=0.3083, pruned_loss=0.07915, over 1419905.18 frames.], batch size: 31, lr: 7.48e-04
2022-05-27 01:29:20,369 INFO [train.py:842] (2/4) Epoch 7, batch 1500, loss[loss=0.226, simple_loss=0.3104, pruned_loss=0.07079, over 7411.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3071, pruned_loss=0.07827, over 1423856.25 frames.], batch size: 21, lr: 7.48e-04
2022-05-27 01:29:59,203 INFO [train.py:842] (2/4) Epoch 7, batch 1550, loss[loss=0.204, simple_loss=0.284, pruned_loss=0.06196, over 7206.00 frames.], tot_loss[loss=0.231, simple_loss=0.3065, pruned_loss=0.0778, over 1418939.99 frames.], batch size: 26, lr: 7.48e-04
2022-05-27 01:30:37,822 INFO [train.py:842] (2/4) Epoch 7, batch 1600, loss[loss=0.2548, simple_loss=0.3275, pruned_loss=0.09104, over 7124.00 frames.], tot_loss[loss=0.2308, simple_loss=0.306, pruned_loss=0.07779, over 1424875.20 frames.], batch size: 21, lr: 7.47e-04
2022-05-27 01:31:16,725 INFO [train.py:842] (2/4) Epoch 7, batch 1650, loss[loss=0.2485, simple_loss=0.3213, pruned_loss=0.08786, over 7071.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3057, pruned_loss=0.07794, over 1418634.42 frames.], batch size: 18, lr: 7.47e-04
2022-05-27 01:31:55,643 INFO [train.py:842] (2/4) Epoch 7, batch 1700, loss[loss=0.2109, simple_loss=0.2992, pruned_loss=0.06124, over 7208.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3054, pruned_loss=0.0775, over 1417458.82 frames.], batch size: 22, lr: 7.47e-04
2022-05-27 01:32:34,542 INFO [train.py:842] (2/4) Epoch 7, batch 1750, loss[loss=0.2176, simple_loss=0.3044, pruned_loss=0.06534, over 7345.00 frames.], tot_loss[loss=0.2299, simple_loss=0.305, pruned_loss=0.07736, over 1413085.03 frames.], batch size: 22, lr: 7.46e-04
2022-05-27 01:33:12,933 INFO [train.py:842] (2/4) Epoch 7, batch 1800, loss[loss=0.2347, simple_loss=0.3118, pruned_loss=0.07882, over 7284.00 frames.], tot_loss[loss=0.2316, simple_loss=0.3068, pruned_loss=0.07817, over 1415796.13 frames.], batch size: 25, lr: 7.46e-04
2022-05-27 01:33:51,777 INFO [train.py:842] (2/4) Epoch 7, batch 1850, loss[loss=0.1786, simple_loss=0.2605, pruned_loss=0.04829, over 6995.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3061, pruned_loss=0.07776, over 1417676.94 frames.], batch size: 16, lr: 7.46e-04
2022-05-27 01:34:30,342 INFO [train.py:842] (2/4) Epoch 7, batch 1900, loss[loss=0.2141, simple_loss=0.286, pruned_loss=0.07105, over 7069.00 frames.], tot_loss[loss=0.2319, simple_loss=0.3068, pruned_loss=0.07854, over 1414849.85 frames.], batch size: 18, lr: 7.45e-04
2022-05-27 01:35:09,633 INFO [train.py:842] (2/4) Epoch 7, batch 1950, loss[loss=0.181, simple_loss=0.259, pruned_loss=0.05148, over 7277.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3055, pruned_loss=0.07789, over 1418790.73 frames.], batch size: 18, lr: 7.45e-04
2022-05-27 01:35:48,202 INFO [train.py:842] (2/4) Epoch 7, batch 2000, loss[loss=0.2357, simple_loss=0.3127, pruned_loss=0.07931, over 7306.00 frames.], tot_loss[loss=0.2297, simple_loss=0.3048, pruned_loss=0.07736, over 1419657.21 frames.], batch size: 25, lr: 7.45e-04
2022-05-27 01:36:27,078 INFO [train.py:842] (2/4) Epoch 7, batch 2050, loss[loss=0.2416, simple_loss=0.3251, pruned_loss=0.07911, over 7317.00 frames.], tot_loss[loss=0.2296, simple_loss=0.3049, pruned_loss=0.07716, over 1417144.35 frames.], batch size: 24, lr: 7.44e-04
2022-05-27 01:37:05,439 INFO [train.py:842] (2/4) Epoch 7, batch 2100, loss[loss=0.1905, simple_loss=0.2571, pruned_loss=0.06195, over 6996.00 frames.], tot_loss[loss=0.2298, simple_loss=0.3047, pruned_loss=0.07747, over 1419542.07 frames.], batch size: 16, lr: 7.44e-04
2022-05-27 01:37:44,442 INFO [train.py:842] (2/4) Epoch 7, batch 2150, loss[loss=0.2446, simple_loss=0.3203, pruned_loss=0.08448, over 7407.00 frames.], tot_loss[loss=0.2305, simple_loss=0.3053, pruned_loss=0.07781, over 1424602.64 frames.], batch size: 21, lr: 7.44e-04
2022-05-27 01:38:22,864 INFO [train.py:842] (2/4) Epoch 7, batch 2200, loss[loss=0.1826, simple_loss=0.2648, pruned_loss=0.0502, over 7144.00 frames.], tot_loss[loss=0.2303, simple_loss=0.3052, pruned_loss=0.07773, over 1422980.79 frames.], batch size: 17, lr: 7.43e-04
2022-05-27 01:39:01,908 INFO [train.py:842] (2/4) Epoch 7, batch 2250, loss[loss=0.1745, simple_loss=0.2492, pruned_loss=0.04985, over 7282.00 frames.], tot_loss[loss=0.2309, simple_loss=0.3056, pruned_loss=0.07807, over 1416347.43 frames.], batch size: 17, lr: 7.43e-04
2022-05-27 01:39:40,339 INFO [train.py:842] (2/4) Epoch 7, batch 2300, loss[loss=0.2256, simple_loss=0.2993, pruned_loss=0.07595, over 7213.00 frames.], tot_loss[loss=0.231, simple_loss=0.3056, pruned_loss=0.07816, over 1419721.14 frames.], batch size: 23, lr: 7.43e-04
2022-05-27 01:40:19,213 INFO [train.py:842] (2/4) Epoch 7, batch 2350, loss[loss=0.2042, simple_loss=0.2975, pruned_loss=0.05546, over 7410.00 frames.], tot_loss[loss=0.2291, simple_loss=0.3043, pruned_loss=0.07691, over 1418114.84 frames.], batch size: 21, lr: 7.42e-04
2022-05-27 01:40:57,831 INFO [train.py:842] (2/4) Epoch 7, batch 2400, loss[loss=0.2079, simple_loss=0.2756, pruned_loss=0.07011, over 7271.00 frames.], tot_loss[loss=0.2274, simple_loss=0.3033, pruned_loss=0.07577, over 1421956.81 frames.], batch size: 18, lr: 7.42e-04
2022-05-27 01:41:36,506 INFO [train.py:842] (2/4) Epoch 7, batch 2450, loss[loss=0.2185, simple_loss=0.2976, pruned_loss=0.06966, over 7418.00 frames.], tot_loss[loss=0.2278, simple_loss=0.3038, pruned_loss=0.07586, over 1417074.29 frames.], batch size: 21, lr: 7.42e-04
2022-05-27 01:42:14,974 INFO [train.py:842] (2/4) Epoch 7, batch 2500, loss[loss=0.2575, simple_loss=0.3373, pruned_loss=0.08886, over 7322.00 frames.], tot_loss[loss=0.2291, simple_loss=0.3052, pruned_loss=0.07653, over 1417687.52 frames.], batch size: 21, lr: 7.42e-04
2022-05-27 01:42:53,975 INFO [train.py:842] (2/4) Epoch 7, batch 2550, loss[loss=0.2145, simple_loss=0.2915, pruned_loss=0.06873, over 7426.00 frames.], tot_loss[loss=0.2296, simple_loss=0.3056, pruned_loss=0.07683, over 1423943.07 frames.], batch size: 20, lr: 7.41e-04
2022-05-27 01:43:32,349 INFO [train.py:842] (2/4) Epoch 7, batch 2600, loss[loss=0.2049, simple_loss=0.2806, pruned_loss=0.06464, over 7160.00 frames.], tot_loss[loss=0.2287, simple_loss=0.3046, pruned_loss=0.07642, over 1417122.43 frames.], batch size: 18, lr: 7.41e-04
2022-05-27 01:44:11,312 INFO [train.py:842] (2/4) Epoch 7, batch 2650, loss[loss=0.2363, simple_loss=0.3073, pruned_loss=0.08269, over 7166.00 frames.], tot_loss[loss=0.228, simple_loss=0.3041, pruned_loss=0.07588, over 1416943.55 frames.], batch size: 18, lr: 7.41e-04
2022-05-27 01:44:49,753 INFO [train.py:842] (2/4) Epoch 7, batch 2700, loss[loss=0.2364, simple_loss=0.3026, pruned_loss=0.08507, over 6786.00 frames.], tot_loss[loss=0.2279, simple_loss=0.3044, pruned_loss=0.07567, over 1418552.50 frames.], batch size: 15, lr: 7.40e-04
2022-05-27 01:45:28,482 INFO [train.py:842] (2/4) Epoch 7, batch 2750, loss[loss=0.1573, simple_loss=0.2426, pruned_loss=0.03607, over 7412.00 frames.], tot_loss[loss=0.2296, simple_loss=0.3061, pruned_loss=0.07658, over 1418418.15 frames.], batch size: 18, lr: 7.40e-04
2022-05-27 01:46:06,995 INFO [train.py:842] (2/4) Epoch 7, batch 2800, loss[loss=0.2203, simple_loss=0.287, pruned_loss=0.07676, over 7010.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3064, pruned_loss=0.07705, over 1417455.05 frames.], batch size: 16, lr: 7.40e-04
2022-05-27 01:46:46,091 INFO [train.py:842] (2/4) Epoch 7, batch 2850, loss[loss=0.2202, simple_loss=0.305, pruned_loss=0.06772, over 7316.00 frames.], tot_loss[loss=0.2289, simple_loss=0.3042, pruned_loss=0.07683, over 1422151.95 frames.], batch size: 21, lr: 7.39e-04
2022-05-27 01:47:24,748 INFO [train.py:842] (2/4) Epoch 7, batch 2900, loss[loss=0.1955, simple_loss=0.2865, pruned_loss=0.05225, over 5277.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3049, pruned_loss=0.07759, over 1424339.90 frames.], batch size: 52, lr: 7.39e-04
2022-05-27 01:48:03,869 INFO [train.py:842] (2/4) Epoch 7, batch 2950, loss[loss=0.3286, simple_loss=0.383, pruned_loss=0.1371, over 7309.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3056, pruned_loss=0.07789, over 1424437.65 frames.], batch size: 25, lr: 7.39e-04
2022-05-27 01:48:42,432 INFO [train.py:842] (2/4) Epoch 7, batch 3000, loss[loss=0.2822, simple_loss=0.3549, pruned_loss=0.1047, over 7144.00 frames.], tot_loss[loss=0.2317, simple_loss=0.3065, pruned_loss=0.07845, over 1425901.14 frames.], batch size: 26, lr: 7.38e-04
2022-05-27 01:48:42,433 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 01:48:51,662 INFO [train.py:871] (2/4) Epoch 7, validation: loss=0.1805, simple_loss=0.2812, pruned_loss=0.03987, over 868885.00 frames.
2022-05-27 01:49:30,590 INFO [train.py:842] (2/4) Epoch 7, batch 3050, loss[loss=0.2418, simple_loss=0.3172, pruned_loss=0.0832, over 7133.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3068, pruned_loss=0.07869, over 1425999.51 frames.], batch size: 26, lr: 7.38e-04
2022-05-27 01:50:09,079 INFO [train.py:842] (2/4) Epoch 7, batch 3100, loss[loss=0.271, simple_loss=0.3378, pruned_loss=0.1021, over 7168.00 frames.], tot_loss[loss=0.232, simple_loss=0.3066, pruned_loss=0.07872, over 1423586.34 frames.], batch size: 26, lr: 7.38e-04
2022-05-27 01:50:47,932 INFO [train.py:842] (2/4) Epoch 7, batch 3150, loss[loss=0.242, simple_loss=0.324, pruned_loss=0.08001, over 6984.00 frames.], tot_loss[loss=0.232, simple_loss=0.3067, pruned_loss=0.0787, over 1427429.31 frames.], batch size: 28, lr: 7.37e-04
2022-05-27 01:51:26,351 INFO [train.py:842] (2/4) Epoch 7, batch 3200, loss[loss=0.2018, simple_loss=0.292, pruned_loss=0.0558, over 7345.00 frames.], tot_loss[loss=0.2323, simple_loss=0.307, pruned_loss=0.07883, over 1423680.07 frames.], batch size: 22, lr: 7.37e-04
2022-05-27 01:52:05,326 INFO [train.py:842] (2/4) Epoch 7, batch 3250, loss[loss=0.2492, simple_loss=0.3343, pruned_loss=0.08205, over 7144.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3062, pruned_loss=0.07773, over 1422362.45 frames.], batch size: 28, lr: 7.37e-04
2022-05-27 01:52:43,646 INFO [train.py:842] (2/4) Epoch 7, batch 3300, loss[loss=0.3193, simple_loss=0.3725, pruned_loss=0.1331, over 7145.00 frames.], tot_loss[loss=0.2332, simple_loss=0.3079, pruned_loss=0.07924, over 1417842.85 frames.], batch size: 20, lr: 7.36e-04
2022-05-27 01:53:22,485 INFO [train.py:842] (2/4) Epoch 7, batch 3350, loss[loss=0.2292, simple_loss=0.3046, pruned_loss=0.07691, over 7150.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3071, pruned_loss=0.07857, over 1418639.22 frames.], batch size: 19, lr: 7.36e-04
2022-05-27 01:54:01,074 INFO [train.py:842] (2/4) Epoch 7, batch 3400, loss[loss=0.2149, simple_loss=0.2937, pruned_loss=0.06808, over 7123.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3059, pruned_loss=0.07777, over 1421978.45 frames.], batch size: 21, lr: 7.36e-04
2022-05-27 01:54:39,837 INFO [train.py:842] (2/4) Epoch 7, batch 3450, loss[loss=0.2531, simple_loss=0.3255, pruned_loss=0.09033, over 7312.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3069, pruned_loss=0.07842, over 1419734.31 frames.], batch size: 24, lr: 7.36e-04
2022-05-27 01:55:18,270 INFO [train.py:842] (2/4) Epoch 7, batch 3500, loss[loss=0.2492, simple_loss=0.3221, pruned_loss=0.08816, over 7219.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3067, pruned_loss=0.07847, over 1421508.12 frames.], batch size: 21, lr: 7.35e-04
2022-05-27 01:55:57,330 INFO [train.py:842] (2/4) Epoch 7, batch 3550, loss[loss=0.2895, simple_loss=0.3514, pruned_loss=0.1137, over 7376.00 frames.], tot_loss[loss=0.2313, simple_loss=0.306, pruned_loss=0.07827, over 1423208.23 frames.], batch size: 23, lr: 7.35e-04
2022-05-27 01:56:36,220 INFO [train.py:842] (2/4) Epoch 7, batch 3600, loss[loss=0.2594, simple_loss=0.3132, pruned_loss=0.1028, over 7225.00 frames.], tot_loss[loss=0.231, simple_loss=0.3056, pruned_loss=0.07823, over 1424753.83 frames.], batch size: 21, lr: 7.35e-04
2022-05-27 01:57:15,152 INFO [train.py:842] (2/4) Epoch 7, batch 3650, loss[loss=0.219, simple_loss=0.3078, pruned_loss=0.06509, over 7115.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3068, pruned_loss=0.07883, over 1420682.93 frames.], batch size: 28, lr: 7.34e-04
2022-05-27 01:57:53,780 INFO [train.py:842] (2/4) Epoch 7, batch 3700, loss[loss=0.2338, simple_loss=0.306, pruned_loss=0.08077, over 7415.00 frames.], tot_loss[loss=0.2303, simple_loss=0.305, pruned_loss=0.07782, over 1422198.98 frames.], batch size: 20, lr: 7.34e-04
2022-05-27 01:58:32,562 INFO [train.py:842] (2/4) Epoch 7, batch 3750, loss[loss=0.2813, simple_loss=0.3423, pruned_loss=0.1101, over 5094.00 frames.], tot_loss[loss=0.2297, simple_loss=0.3045, pruned_loss=0.0775, over 1423118.22 frames.], batch size: 52, lr: 7.34e-04
2022-05-27 01:59:10,983 INFO [train.py:842] (2/4) Epoch 7, batch 3800, loss[loss=0.2038, simple_loss=0.2889, pruned_loss=0.05937, over 7353.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3047, pruned_loss=0.07757, over 1420678.52 frames.], batch size: 19, lr: 7.33e-04
2022-05-27 01:59:50,091 INFO [train.py:842] (2/4) Epoch 7, batch 3850, loss[loss=0.1903, simple_loss=0.2673, pruned_loss=0.05665, over 7147.00 frames.], tot_loss[loss=0.228, simple_loss=0.303, pruned_loss=0.07647, over 1423566.63 frames.], batch size: 17, lr: 7.33e-04
2022-05-27 02:00:28,941 INFO [train.py:842] (2/4) Epoch 7, batch 3900, loss[loss=0.2097, simple_loss=0.2841, pruned_loss=0.06764, over 7432.00 frames.], tot_loss[loss=0.2267, simple_loss=0.3021, pruned_loss=0.07566, over 1424830.48 frames.], batch size: 20, lr: 7.33e-04
2022-05-27 02:01:07,923 INFO [train.py:842] (2/4) Epoch 7, batch 3950, loss[loss=0.1929, simple_loss=0.2753, pruned_loss=0.05525, over 7290.00 frames.], tot_loss[loss=0.228, simple_loss=0.3027, pruned_loss=0.07668, over 1424094.55 frames.], batch size: 18, lr: 7.32e-04
2022-05-27 02:01:46,436 INFO [train.py:842] (2/4) Epoch 7, batch 4000, loss[loss=0.2848, simple_loss=0.3379, pruned_loss=0.1159, over 7351.00 frames.], tot_loss[loss=0.2287, simple_loss=0.3037, pruned_loss=0.07684, over 1430393.02 frames.], batch size: 22, lr: 7.32e-04
2022-05-27 02:02:25,554 INFO [train.py:842] (2/4) Epoch 7, batch 4050, loss[loss=0.2515, simple_loss=0.3201, pruned_loss=0.09144, over 7324.00 frames.], tot_loss[loss=0.2283, simple_loss=0.3029, pruned_loss=0.07686, over 1432769.58 frames.], batch size: 22, lr: 7.32e-04
2022-05-27 02:03:04,003 INFO [train.py:842] (2/4) Epoch 7, batch 4100, loss[loss=0.2065, simple_loss=0.2884, pruned_loss=0.06234, over 6749.00 frames.], tot_loss[loss=0.2287, simple_loss=0.3033, pruned_loss=0.07701, over 1427590.46 frames.], batch size: 31, lr: 7.32e-04
2022-05-27 02:03:42,703 INFO [train.py:842] (2/4) Epoch 7, batch 4150, loss[loss=0.2064, simple_loss=0.2837, pruned_loss=0.06458, over 7254.00 frames.], tot_loss[loss=0.2278, simple_loss=0.3031, pruned_loss=0.07629, over 1427544.05 frames.], batch size: 19, lr: 7.31e-04
2022-05-27 02:04:21,259 INFO [train.py:842] (2/4) Epoch 7, batch 4200, loss[loss=0.26, simple_loss=0.3291, pruned_loss=0.09545, over 6473.00 frames.], tot_loss[loss=0.2281, simple_loss=0.3031, pruned_loss=0.07652, over 1429150.19 frames.], batch size: 38, lr: 7.31e-04
2022-05-27 02:05:00,146 INFO [train.py:842] (2/4) Epoch 7, batch 4250, loss[loss=0.3148, simple_loss=0.3668, pruned_loss=0.1314, over 7112.00 frames.], tot_loss[loss=0.2271, simple_loss=0.3025, pruned_loss=0.07583, over 1431464.17 frames.], batch size: 21, lr: 7.31e-04
2022-05-27 02:05:38,644 INFO [train.py:842] (2/4) Epoch 7, batch 4300, loss[loss=0.2544, simple_loss=0.3359, pruned_loss=0.08642, over 6700.00 frames.], tot_loss[loss=0.2274, simple_loss=0.3021, pruned_loss=0.07632, over 1425934.53 frames.], batch size: 31, lr: 7.30e-04
2022-05-27 02:06:17,401 INFO [train.py:842] (2/4) Epoch 7, batch 4350, loss[loss=0.2161, simple_loss=0.2849, pruned_loss=0.07366, over 7438.00 frames.], tot_loss[loss=0.2275, simple_loss=0.3027, pruned_loss=0.07616, over 1421682.78 frames.], batch size: 20, lr: 7.30e-04
2022-05-27 02:06:55,825 INFO [train.py:842] (2/4) Epoch 7, batch 4400, loss[loss=0.2047, simple_loss=0.286, pruned_loss=0.06171, over 7407.00 frames.], tot_loss[loss=0.2278, simple_loss=0.3028, pruned_loss=0.0764, over 1415481.50 frames.], batch size: 18, lr: 7.30e-04
2022-05-27 02:07:34,685 INFO [train.py:842] (2/4) Epoch 7, batch 4450, loss[loss=0.2044, simple_loss=0.2801, pruned_loss=0.06436, over 7143.00 frames.], tot_loss[loss=0.2282, simple_loss=0.3029, pruned_loss=0.07672, over 1417400.11 frames.], batch size: 20, lr: 7.29e-04
2022-05-27 02:08:13,173 INFO [train.py:842] (2/4) Epoch 7, batch 4500, loss[loss=0.2213, simple_loss=0.289, pruned_loss=0.07681, over 7281.00 frames.], tot_loss[loss=0.2279, simple_loss=0.303, pruned_loss=0.07645, over 1420283.47 frames.], batch size: 17, lr: 7.29e-04
2022-05-27 02:08:52,139 INFO [train.py:842] (2/4) Epoch 7, batch 4550, loss[loss=0.1833, simple_loss=0.2649, pruned_loss=0.05086, over 6811.00 frames.], tot_loss[loss=0.2284, simple_loss=0.3033, pruned_loss=0.07672, over 1419667.89 frames.], batch size: 15, lr: 7.29e-04
2022-05-27 02:09:30,770 INFO [train.py:842] (2/4) Epoch 7, batch 4600, loss[loss=0.2043, simple_loss=0.2837, pruned_loss=0.06246, over 7416.00 frames.], tot_loss[loss=0.2288, simple_loss=0.3033, pruned_loss=0.07713, over 1415973.07 frames.], batch size: 21, lr: 7.28e-04
2022-05-27 02:10:09,713 INFO [train.py:842] (2/4) Epoch 7, batch 4650, loss[loss=0.2185, simple_loss=0.2872, pruned_loss=0.07486, over 7177.00 frames.], tot_loss[loss=0.2286, simple_loss=0.3036, pruned_loss=0.07683, over 1420997.99 frames.], batch size: 18, lr: 7.28e-04
2022-05-27 02:10:48,244 INFO [train.py:842] (2/4) Epoch 7, batch 4700, loss[loss=0.2675, simple_loss=0.3407, pruned_loss=0.09719, over 7291.00 frames.], tot_loss[loss=0.2282, simple_loss=0.3035, pruned_loss=0.07648, over 1422558.97 frames.], batch size: 24, lr: 7.28e-04
2022-05-27 02:11:27,367 INFO [train.py:842] (2/4) Epoch 7, batch 4750, loss[loss=0.2431, simple_loss=0.3147, pruned_loss=0.08569, over 7358.00 frames.], tot_loss[loss=0.2277, simple_loss=0.303, pruned_loss=0.07623, over 1423361.31 frames.], batch size: 19, lr: 7.28e-04
2022-05-27 02:12:05,924 INFO [train.py:842] (2/4) Epoch 7, batch 4800, loss[loss=0.2116, simple_loss=0.2882, pruned_loss=0.06746, over 7271.00 frames.], tot_loss[loss=0.227, simple_loss=0.3025, pruned_loss=0.07569, over 1421745.99 frames.], batch size: 18, lr: 7.27e-04
2022-05-27 02:12:44,815 INFO [train.py:842] (2/4) Epoch 7, batch 4850, loss[loss=0.2163, simple_loss=0.2939, pruned_loss=0.06938, over 7403.00 frames.], tot_loss[loss=0.2262, simple_loss=0.3018, pruned_loss=0.07528, over 1419298.29 frames.], batch size: 21, lr: 7.27e-04
2022-05-27 02:13:23,583 INFO [train.py:842] (2/4) Epoch 7, batch 4900, loss[loss=0.2114, simple_loss=0.2925, pruned_loss=0.06521, over 7205.00 frames.], tot_loss[loss=0.227, simple_loss=0.3026, pruned_loss=0.07568, over 1419589.68 frames.], batch size: 23, lr: 7.27e-04
2022-05-27 02:14:02,742 INFO [train.py:842] (2/4) Epoch 7, batch 4950, loss[loss=0.2412, simple_loss=0.3255, pruned_loss=0.07852, over 7322.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3013, pruned_loss=0.07502, over 1422388.60 frames.], batch size: 21, lr: 7.26e-04
2022-05-27 02:14:41,330 INFO [train.py:842] (2/4) Epoch 7, batch 5000, loss[loss=0.2333, simple_loss=0.3207, pruned_loss=0.07295, over 7207.00 frames.], tot_loss[loss=0.2272, simple_loss=0.3025, pruned_loss=0.07598, over 1423505.60 frames.], batch size: 23, lr: 7.26e-04
2022-05-27 02:15:20,277 INFO [train.py:842] (2/4) Epoch 7, batch 5050, loss[loss=0.2193, simple_loss=0.3055, pruned_loss=0.06655, over 7301.00 frames.], tot_loss[loss=0.2276, simple_loss=0.3029, pruned_loss=0.0762, over 1413994.46 frames.], batch size: 25, lr: 7.26e-04
2022-05-27 02:15:58,679 INFO [train.py:842] (2/4) Epoch 7, batch 5100, loss[loss=0.1923, simple_loss=0.275, pruned_loss=0.05474, over 7163.00 frames.], tot_loss[loss=0.2276, simple_loss=0.3031, pruned_loss=0.07605, over 1416773.47 frames.], batch size: 18, lr: 7.25e-04
2022-05-27 02:16:37,764 INFO [train.py:842] (2/4) Epoch 7, batch 5150, loss[loss=0.1946, simple_loss=0.2558, pruned_loss=0.06672, over 7399.00 frames.], tot_loss[loss=0.2282, simple_loss=0.3029, pruned_loss=0.07678, over 1418630.96 frames.], batch size: 18, lr: 7.25e-04
2022-05-27 02:17:16,399 INFO [train.py:842] (2/4) Epoch 7, batch 5200, loss[loss=0.1919, simple_loss=0.2758, pruned_loss=0.05402, over 7329.00 frames.], tot_loss[loss=0.2282, simple_loss=0.3031, pruned_loss=0.0767, over 1420592.53 frames.], batch size: 20, lr: 7.25e-04
2022-05-27 02:17:55,308 INFO [train.py:842] (2/4) Epoch 7, batch 5250, loss[loss=0.2408, simple_loss=0.3228, pruned_loss=0.07934, over 7328.00 frames.], tot_loss[loss=0.2277, simple_loss=0.3024, pruned_loss=0.07657, over 1416590.26 frames.], batch size: 20, lr: 7.25e-04
2022-05-27 02:18:33,906 INFO [train.py:842] (2/4) Epoch 7, batch 5300, loss[loss=0.1676, simple_loss=0.2441, pruned_loss=0.04561, over 7007.00 frames.], tot_loss[loss=0.2275, simple_loss=0.3021, pruned_loss=0.07646, over 1420492.82 frames.], batch size: 16, lr: 7.24e-04
2022-05-27 02:19:12,857 INFO [train.py:842] (2/4) Epoch 7, batch 5350, loss[loss=0.2485, simple_loss=0.3087, pruned_loss=0.0942, over 7224.00 frames.], tot_loss[loss=0.2277, simple_loss=0.3025, pruned_loss=0.07641, over 1422753.92 frames.], batch size: 20, lr: 7.24e-04
2022-05-27 02:19:51,743 INFO [train.py:842] (2/4) Epoch 7, batch 5400, loss[loss=0.2434, simple_loss=0.306, pruned_loss=0.09038, over 5368.00 frames.], tot_loss[loss=0.2282, simple_loss=0.3028, pruned_loss=0.07679, over 1414442.08 frames.], batch size: 52, lr: 7.24e-04
2022-05-27 02:20:30,643 INFO [train.py:842] (2/4) Epoch 7, batch 5450, loss[loss=0.2272, simple_loss=0.315, pruned_loss=0.06971, over 7280.00 frames.], tot_loss[loss=0.2291, simple_loss=0.3034, pruned_loss=0.07737, over 1415790.90 frames.], batch size: 24, lr: 7.23e-04
2022-05-27 02:21:09,278 INFO [train.py:842] (2/4) Epoch 7, batch 5500, loss[loss=0.1988, simple_loss=0.2773, pruned_loss=0.06015, over 7157.00 frames.], tot_loss[loss=0.2284, simple_loss=0.303, pruned_loss=0.07694, over 1417247.16 frames.], batch size: 19, lr: 7.23e-04
2022-05-27 02:21:48,183 INFO [train.py:842] (2/4) Epoch 7, batch 5550, loss[loss=0.2482, simple_loss=0.3252, pruned_loss=0.0856, over 7270.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3047, pruned_loss=0.07772, over 1417842.51 frames.], batch size: 24, lr: 7.23e-04
2022-05-27 02:22:26,584 INFO [train.py:842] (2/4) Epoch 7, batch 5600, loss[loss=0.2659, simple_loss=0.3234, pruned_loss=0.1042, over 7216.00 frames.], tot_loss[loss=0.2292, simple_loss=0.3037, pruned_loss=0.07737, over 1418063.30 frames.], batch size: 23, lr: 7.22e-04
2022-05-27 02:23:05,529 INFO [train.py:842] (2/4) Epoch 7, batch 5650, loss[loss=0.1782, simple_loss=0.2543, pruned_loss=0.05103, over 7278.00 frames.], tot_loss[loss=0.2289, simple_loss=0.3036, pruned_loss=0.07715, over 1417688.89 frames.], batch size: 17, lr: 7.22e-04
2022-05-27 02:23:44,727 INFO [train.py:842] (2/4) Epoch 7, batch 5700, loss[loss=0.2065, simple_loss=0.296, pruned_loss=0.05847, over 7152.00 frames.], tot_loss[loss=0.2295, simple_loss=0.3045, pruned_loss=0.07725, over 1421922.59 frames.], batch size: 20, lr: 7.22e-04
2022-05-27 02:24:23,472 INFO [train.py:842] (2/4) Epoch 7, batch 5750, loss[loss=0.2089, simple_loss=0.2978, pruned_loss=0.05995, over 7308.00 frames.], tot_loss[loss=0.2292, simple_loss=0.304, pruned_loss=0.07715, over 1421077.18 frames.], batch size: 25, lr: 7.22e-04
2022-05-27 02:25:01,794 INFO [train.py:842] (2/4) Epoch 7, batch 5800, loss[loss=0.3163, simple_loss=0.3631, pruned_loss=0.1348, over 6277.00 frames.], tot_loss[loss=0.2278, simple_loss=0.3028, pruned_loss=0.07641, over 1418883.13 frames.], batch size: 37, lr: 7.21e-04
2022-05-27 02:25:40,546 INFO [train.py:842] (2/4) Epoch 7, batch 5850, loss[loss=0.2109, simple_loss=0.2831, pruned_loss=0.06939, over 7404.00 frames.], tot_loss[loss=0.2319, simple_loss=0.3062, pruned_loss=0.07878, over 1416017.55 frames.], batch size: 18, lr: 7.21e-04
2022-05-27 02:26:19,073 INFO [train.py:842] (2/4) Epoch 7, batch 5900, loss[loss=0.1958, simple_loss=0.2663, pruned_loss=0.06263, over 7254.00 frames.], tot_loss[loss=0.2315, simple_loss=0.3055, pruned_loss=0.07875, over 1415959.21 frames.], batch size: 17, lr: 7.21e-04
2022-05-27 02:26:58,427 INFO [train.py:842] (2/4) Epoch 7, batch 5950, loss[loss=0.2579, simple_loss=0.3259, pruned_loss=0.09496, over 7428.00 frames.], tot_loss[loss=0.2332, simple_loss=0.3077, pruned_loss=0.07933, over 1420492.32 frames.], batch size: 20, lr: 7.20e-04
2022-05-27 02:27:37,185 INFO [train.py:842] (2/4) Epoch 7, batch 6000, loss[loss=0.1944, simple_loss=0.283, pruned_loss=0.05292, over 7165.00 frames.], tot_loss[loss=0.2297, simple_loss=0.3047, pruned_loss=0.07735, over 1418880.93 frames.], batch size: 18, lr: 7.20e-04
2022-05-27 02:27:37,186 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 02:27:46,475 INFO [train.py:871] (2/4) Epoch 7, validation: loss=0.1828, simple_loss=0.2835, pruned_loss=0.04102, over 868885.00 frames.
2022-05-27 02:28:25,432 INFO [train.py:842] (2/4) Epoch 7, batch 6050, loss[loss=0.2172, simple_loss=0.3023, pruned_loss=0.06601, over 7346.00 frames.], tot_loss[loss=0.2286, simple_loss=0.304, pruned_loss=0.07664, over 1422134.68 frames.], batch size: 20, lr: 7.20e-04
2022-05-27 02:29:04,025 INFO [train.py:842] (2/4) Epoch 7, batch 6100, loss[loss=0.174, simple_loss=0.2517, pruned_loss=0.04818, over 7219.00 frames.], tot_loss[loss=0.2284, simple_loss=0.3038, pruned_loss=0.07651, over 1422759.25 frames.], batch size: 16, lr: 7.20e-04
2022-05-27 02:29:42,980 INFO [train.py:842] (2/4) Epoch 7, batch 6150, loss[loss=0.2007, simple_loss=0.2828, pruned_loss=0.05935, over 7423.00 frames.], tot_loss[loss=0.2268, simple_loss=0.3026, pruned_loss=0.07553, over 1423835.73 frames.], batch size: 18, lr: 7.19e-04
2022-05-27 02:30:21,588 INFO [train.py:842] (2/4) Epoch 7, batch 6200, loss[loss=0.2141, simple_loss=0.2952, pruned_loss=0.06655, over 7269.00 frames.], tot_loss[loss=0.2274, simple_loss=0.3033, pruned_loss=0.07573, over 1422833.12 frames.], batch size: 18, lr: 7.19e-04
2022-05-27 02:31:00,700 INFO [train.py:842] (2/4) Epoch 7, batch 6250, loss[loss=0.1986, simple_loss=0.2749, pruned_loss=0.0612, over 7259.00 frames.], tot_loss[loss=0.2267, simple_loss=0.3023, pruned_loss=0.07552, over 1423742.18 frames.], batch size: 16, lr: 7.19e-04
2022-05-27 02:31:39,567 INFO [train.py:842] (2/4) Epoch 7, batch 6300, loss[loss=0.1794, simple_loss=0.2632, pruned_loss=0.04779, over 7355.00 frames.], tot_loss[loss=0.2263, simple_loss=0.3019, pruned_loss=0.07535, over 1415502.86 frames.], batch size: 19, lr: 7.18e-04
2022-05-27 02:32:18,502 INFO [train.py:842] (2/4) Epoch 7, batch 6350, loss[loss=0.1813, simple_loss=0.2669, pruned_loss=0.04786, over 7295.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3001, pruned_loss=0.07406, over 1419218.75 frames.], batch size: 18, lr: 7.18e-04
2022-05-27 02:32:57,028 INFO [train.py:842] (2/4) Epoch 7, batch 6400, loss[loss=0.2292, simple_loss=0.3017, pruned_loss=0.07837, over 5040.00 frames.], tot_loss[loss=0.228, simple_loss=0.3038, pruned_loss=0.07612, over 1421905.28 frames.], batch size: 52, lr: 7.18e-04
2022-05-27 02:33:36,089 INFO [train.py:842] (2/4) Epoch 7, batch 6450, loss[loss=0.1946, simple_loss=0.2799, pruned_loss=0.05461, over 7221.00 frames.], tot_loss[loss=0.2271, simple_loss=0.3026, pruned_loss=0.07575, over 1425986.24 frames.], batch size: 23, lr: 7.18e-04
2022-05-27 02:34:14,474 INFO [train.py:842] (2/4) Epoch 7, batch 6500, loss[loss=0.2413, simple_loss=0.3164, pruned_loss=0.08314, over 7025.00 frames.], tot_loss[loss=0.2266, simple_loss=0.3021, pruned_loss=0.07551, over 1426705.73 frames.], batch size: 28, lr: 7.17e-04
2022-05-27 02:34:53,300 INFO [train.py:842] (2/4) Epoch 7, batch 6550, loss[loss=0.2482, simple_loss=0.3284, pruned_loss=0.08402, over 7290.00 frames.], tot_loss[loss=0.228, simple_loss=0.3034, pruned_loss=0.07626, over 1422257.98 frames.], batch size: 25, lr: 7.17e-04
2022-05-27 02:35:31,818 INFO [train.py:842] (2/4) Epoch 7, batch 6600, loss[loss=0.218, simple_loss=0.3055, pruned_loss=0.06521, over 7409.00 frames.], tot_loss[loss=0.2287, simple_loss=0.3039, pruned_loss=0.07676, over 1420914.70 frames.], batch size: 21, lr: 7.17e-04
2022-05-27 02:36:10,719 INFO [train.py:842] (2/4) Epoch 7, batch 6650, loss[loss=0.2261, simple_loss=0.2934, pruned_loss=0.07939, over 7068.00 frames.], tot_loss[loss=0.2282, simple_loss=0.3035, pruned_loss=0.07649, over 1420920.69 frames.], batch size: 18, lr: 7.16e-04
2022-05-27 02:36:49,519 INFO [train.py:842] (2/4) Epoch 7, batch 6700, loss[loss=0.2239, simple_loss=0.2972, pruned_loss=0.07534, over 7060.00 frames.], tot_loss[loss=0.2295, simple_loss=0.3052, pruned_loss=0.07692, over 1423983.43 frames.], batch size: 18, lr: 7.16e-04
2022-05-27 02:37:28,478 INFO [train.py:842] (2/4) Epoch 7, batch 6750, loss[loss=0.2407, simple_loss=0.3166, pruned_loss=0.08243, over 7163.00 frames.], tot_loss[loss=0.2293, simple_loss=0.3052, pruned_loss=0.07669, over 1426209.48 frames.], batch size: 19, lr: 7.16e-04
2022-05-27 02:38:06,891 INFO [train.py:842] (2/4) Epoch 7, batch 6800, loss[loss=0.2026, simple_loss=0.2941, pruned_loss=0.05551, over 7325.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3066, pruned_loss=0.07738, over 1423659.55 frames.], batch size: 25, lr: 7.16e-04
2022-05-27 02:38:45,852 INFO [train.py:842] (2/4) Epoch 7, batch 6850, loss[loss=0.173, simple_loss=0.2516, pruned_loss=0.04724, over 7259.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3062, pruned_loss=0.07684, over 1420919.31 frames.], batch size: 16, lr: 7.15e-04
2022-05-27 02:39:35,206 INFO [train.py:842] (2/4) Epoch 7, batch 6900, loss[loss=0.2305, simple_loss=0.3139, pruned_loss=0.07353, over 7330.00 frames.], tot_loss[loss=0.2306, simple_loss=0.306, pruned_loss=0.0776, over 1422712.74 frames.], batch size: 22, lr: 7.15e-04
2022-05-27 02:40:14,046 INFO [train.py:842] (2/4) Epoch 7, batch 6950, loss[loss=0.2376, simple_loss=0.3155, pruned_loss=0.07982, over 7414.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3052, pruned_loss=0.07732, over 1419054.00 frames.], batch size: 21, lr: 7.15e-04
2022-05-27 02:40:52,472 INFO [train.py:842] (2/4) Epoch 7, batch 7000, loss[loss=0.2429, simple_loss=0.3247, pruned_loss=0.08058, over 6228.00 frames.], tot_loss[loss=0.2297, simple_loss=0.3055, pruned_loss=0.077, over 1420497.89 frames.], batch size: 37, lr: 7.14e-04
2022-05-27 02:41:31,355 INFO [train.py:842] (2/4) Epoch 7, batch 7050, loss[loss=0.2092, simple_loss=0.3003, pruned_loss=0.05905, over 7281.00 frames.], tot_loss[loss=0.2279, simple_loss=0.3044, pruned_loss=0.07574, over 1424752.91 frames.], batch size: 25, lr: 7.14e-04
2022-05-27 02:42:09,939 INFO [train.py:842] (2/4) Epoch 7, batch 7100, loss[loss=0.2046, simple_loss=0.297, pruned_loss=0.05612, over 7185.00 frames.], tot_loss[loss=0.2278, simple_loss=0.304, pruned_loss=0.07579, over 1425970.06 frames.], batch size: 23, lr: 7.14e-04
2022-05-27 02:42:49,039 INFO [train.py:842] (2/4) Epoch 7, batch 7150, loss[loss=0.1532, simple_loss=0.2311, pruned_loss=0.03769, over 7269.00 frames.], tot_loss[loss=0.2286, simple_loss=0.3043, pruned_loss=0.07645, over 1423710.43 frames.], batch size: 17, lr: 7.14e-04
2022-05-27 02:43:27,661 INFO [train.py:842] (2/4) Epoch 7, batch 7200, loss[loss=0.2119, simple_loss=0.2827, pruned_loss=0.07057, over 7385.00 frames.], tot_loss[loss=0.2289, simple_loss=0.3043, pruned_loss=0.07677, over 1414983.96 frames.], batch size: 19, lr: 7.13e-04
2022-05-27 02:44:06,728 INFO [train.py:842] (2/4) Epoch 7, batch 7250, loss[loss=0.3074, simple_loss=0.3718, pruned_loss=0.1215, over 7045.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3052, pruned_loss=0.07752, over 1416481.26 frames.], batch size: 28, lr: 7.13e-04
2022-05-27 02:44:45,281 INFO [train.py:842] (2/4) Epoch 7, batch 7300, loss[loss=0.3343, simple_loss=0.3883, pruned_loss=0.1401, over 7213.00 frames.], tot_loss[loss=0.2297, simple_loss=0.3049, pruned_loss=0.0772, over 1419449.29 frames.], batch size: 26, lr: 7.13e-04
2022-05-27 02:45:24,104 INFO [train.py:842] (2/4) Epoch 7, batch 7350, loss[loss=0.2075, simple_loss=0.2827, pruned_loss=0.06611, over 7001.00 frames.], tot_loss[loss=0.2292, simple_loss=0.3044, pruned_loss=0.07703, over 1420859.02 frames.], batch size: 16, lr: 7.12e-04
2022-05-27 02:46:02,466 INFO [train.py:842] (2/4) Epoch 7, batch 7400, loss[loss=0.2162, simple_loss=0.2954, pruned_loss=0.06852, over 7061.00 frames.], tot_loss[loss=0.2295, simple_loss=0.3045, pruned_loss=0.07722, over 1416622.42 frames.], batch size: 18, lr: 7.12e-04
2022-05-27 02:46:41,240 INFO [train.py:842] (2/4) Epoch 7, batch 7450, loss[loss=0.2141, simple_loss=0.2846, pruned_loss=0.07176, over 7301.00 frames.], tot_loss[loss=0.2325, simple_loss=0.3071, pruned_loss=0.07892, over 1415968.93 frames.], batch size: 17, lr: 7.12e-04
2022-05-27 02:47:19,891 INFO [train.py:842] (2/4) Epoch 7, batch 7500, loss[loss=0.2163, simple_loss=0.3035, pruned_loss=0.06451, over 7145.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3052, pruned_loss=0.07748, over 1418098.87 frames.], batch size: 20, lr: 7.12e-04
2022-05-27 02:47:58,783 INFO [train.py:842] (2/4) Epoch 7, batch 7550, loss[loss=0.2468, simple_loss=0.3255, pruned_loss=0.08405, over 7305.00 frames.], tot_loss[loss=0.2303, simple_loss=0.305, pruned_loss=0.07776, over 1416271.48 frames.], batch size: 21, lr: 7.11e-04
2022-05-27 02:48:37,251 INFO [train.py:842] (2/4) Epoch 7, batch 7600, loss[loss=0.198, simple_loss=0.2857, pruned_loss=0.05518, over 7338.00 frames.], tot_loss[loss=0.2295, simple_loss=0.3047, pruned_loss=0.07716, over 1420384.40 frames.], batch size: 22, lr: 7.11e-04
2022-05-27 02:49:16,243 INFO [train.py:842] (2/4) Epoch 7, batch 7650, loss[loss=0.3319, simple_loss=0.3869, pruned_loss=0.1384, over 4997.00 frames.], tot_loss[loss=0.2273, simple_loss=0.3032, pruned_loss=0.07564, over 1423057.21 frames.], batch size: 52, lr: 7.11e-04
2022-05-27 02:49:54,714 INFO [train.py:842] (2/4) Epoch 7, batch 7700, loss[loss=0.1956, simple_loss=0.2768, pruned_loss=0.05722, over 7108.00 frames.], tot_loss[loss=0.228, simple_loss=0.3035, pruned_loss=0.07624, over 1419837.93 frames.], batch size: 21, lr: 7.10e-04
2022-05-27 02:50:33,579 INFO [train.py:842] (2/4) Epoch 7, batch 7750, loss[loss=0.2798, simple_loss=0.3469, pruned_loss=0.1064, over 7242.00 frames.], tot_loss[loss=0.2294, simple_loss=0.3045, pruned_loss=0.07711, over 1424223.85 frames.], batch size: 20, lr: 7.10e-04
2022-05-27 02:51:12,168 INFO [train.py:842] (2/4) Epoch 7, batch 7800, loss[loss=0.1891, simple_loss=0.2761, pruned_loss=0.05103, over 7430.00 frames.], tot_loss[loss=0.2293, simple_loss=0.3046, pruned_loss=0.07698, over 1425246.99 frames.], batch size: 20, lr: 7.10e-04
2022-05-27 02:51:51,056 INFO [train.py:842] (2/4) Epoch 7, batch 7850, loss[loss=0.2106, simple_loss=0.2735, pruned_loss=0.0738, over 6830.00 frames.], tot_loss[loss=0.229, simple_loss=0.3045, pruned_loss=0.07677, over 1424480.49 frames.], batch size: 15, lr: 7.10e-04
2022-05-27 02:52:29,409 INFO [train.py:842] (2/4) Epoch 7, batch 7900, loss[loss=0.1774, simple_loss=0.258, pruned_loss=0.04837, over 7254.00 frames.], tot_loss[loss=0.2283, simple_loss=0.304, pruned_loss=0.07632, over 1422528.39 frames.], batch size: 19, lr: 7.09e-04
2022-05-27 02:53:07,964 INFO [train.py:842] (2/4) Epoch 7, batch 7950, loss[loss=0.2398, simple_loss=0.3223, pruned_loss=0.07865, over 7204.00 frames.], tot_loss[loss=0.2277, simple_loss=0.3033, pruned_loss=0.07603, over 1416615.93 frames.], batch size: 23, lr: 7.09e-04
2022-05-27 02:53:46,558 INFO [train.py:842] (2/4) Epoch 7, batch 8000, loss[loss=0.3443, simple_loss=0.383, pruned_loss=0.1528, over 5261.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3051, pruned_loss=0.07768, over 1415281.46 frames.], batch size: 53, lr: 7.09e-04
2022-05-27 02:54:25,423 INFO [train.py:842] (2/4) Epoch 7, batch 8050, loss[loss=0.2078, simple_loss=0.2969, pruned_loss=0.05934, over 7412.00 frames.], tot_loss[loss=0.2278, simple_loss=0.3034, pruned_loss=0.07604, over 1415547.19 frames.], batch size: 21, lr: 7.08e-04
2022-05-27 02:55:24,508 INFO [train.py:842] (2/4) Epoch 7, batch 8100, loss[loss=0.2082, simple_loss=0.2915, pruned_loss=0.0624, over 6799.00 frames.], tot_loss[loss=0.2273, simple_loss=0.3037, pruned_loss=0.07552, over 1415317.41 frames.], batch size: 31, lr: 7.08e-04
2022-05-27 02:56:13,872 INFO [train.py:842] (2/4) Epoch 7, batch 8150, loss[loss=0.2144, simple_loss=0.2958, pruned_loss=0.06647, over 7320.00 frames.], tot_loss[loss=0.2255, simple_loss=0.302, pruned_loss=0.07449, over 1419202.40 frames.], batch size: 21, lr: 7.08e-04
2022-05-27 02:56:52,270 INFO [train.py:842] (2/4) Epoch 7, batch 8200, loss[loss=0.1974, simple_loss=0.2825, pruned_loss=0.05613, over 7359.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3022, pruned_loss=0.0745, over 1420030.55 frames.], batch size: 19, lr: 7.08e-04
2022-05-27 02:57:31,160 INFO [train.py:842] (2/4) Epoch 7, batch 8250, loss[loss=0.2514, simple_loss=0.3157, pruned_loss=0.09351, over 6839.00 frames.], tot_loss[loss=0.2259, simple_loss=0.3023, pruned_loss=0.07479, over 1420781.88 frames.], batch size: 31, lr: 7.07e-04
2022-05-27 02:58:09,605 INFO [train.py:842] (2/4) Epoch 7, batch 8300, loss[loss=0.2218, simple_loss=0.2977, pruned_loss=0.07297, over 6993.00 frames.], tot_loss[loss=0.2254, simple_loss=0.3015, pruned_loss=0.07465, over 1415915.48 frames.], batch size: 16, lr: 7.07e-04
2022-05-27 02:58:48,415 INFO [train.py:842] (2/4) Epoch 7, batch 8350, loss[loss=0.2234, simple_loss=0.2966, pruned_loss=0.07508, over 7218.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3016, pruned_loss=0.07471, over 1420370.21 frames.], batch size: 21, lr: 7.07e-04
2022-05-27 02:59:26,861 INFO [train.py:842] (2/4) Epoch 7, batch 8400, loss[loss=0.2117, simple_loss=0.2916, pruned_loss=0.06589, over 7234.00 frames.], tot_loss[loss=0.2246, simple_loss=0.3009, pruned_loss=0.0741, over 1422602.59 frames.], batch size: 20, lr: 7.06e-04
2022-05-27 03:00:05,682 INFO [train.py:842] (2/4) Epoch 7, batch 8450, loss[loss=0.2317, simple_loss=0.3123, pruned_loss=0.07556, over 7417.00 frames.], tot_loss[loss=0.2262, simple_loss=0.3022, pruned_loss=0.0751, over 1423306.39 frames.], batch size: 21, lr: 7.06e-04
2022-05-27 03:00:44,402 INFO [train.py:842] (2/4) Epoch 7, batch 8500, loss[loss=0.2255, simple_loss=0.3078, pruned_loss=0.07157, over 7344.00 frames.], tot_loss[loss=0.2274, simple_loss=0.303, pruned_loss=0.07586, over 1422723.23 frames.], batch size: 22, lr: 7.06e-04
2022-05-27 03:01:22,936 INFO [train.py:842] (2/4) Epoch 7, batch 8550, loss[loss=0.1964, simple_loss=0.2645, pruned_loss=0.06415, over 7151.00 frames.], tot_loss[loss=0.2276, simple_loss=0.3031, pruned_loss=0.07603, over 1417033.57 frames.], batch size: 19, lr: 7.06e-04
2022-05-27 03:02:01,345 INFO [train.py:842] (2/4) Epoch 7, batch 8600, loss[loss=0.1991, simple_loss=0.2821, pruned_loss=0.05806, over 7156.00 frames.], tot_loss[loss=0.2272, simple_loss=0.3031, pruned_loss=0.07565, over 1418121.93 frames.], batch size: 18, lr: 7.05e-04
2022-05-27 03:02:40,395 INFO [train.py:842] (2/4) Epoch 7, batch 8650, loss[loss=0.2328, simple_loss=0.3184, pruned_loss=0.07357, over 7337.00 frames.], tot_loss[loss=0.2263, simple_loss=0.3022, pruned_loss=0.07515, over 1418127.18 frames.], batch size: 22, lr: 7.05e-04
2022-05-27 03:03:18,979 INFO [train.py:842] (2/4) Epoch 7, batch 8700, loss[loss=0.1851, simple_loss=0.2661, pruned_loss=0.05206, over 7401.00 frames.], tot_loss[loss=0.226, simple_loss=0.3022, pruned_loss=0.07488, over 1421000.61 frames.], batch size: 18, lr: 7.05e-04
2022-05-27 03:03:57,708 INFO [train.py:842] (2/4) Epoch 7, batch 8750, loss[loss=0.2163, simple_loss=0.3064, pruned_loss=0.06315, over 7223.00 frames.], tot_loss[loss=0.2279, simple_loss=0.3041, pruned_loss=0.0759, over 1420508.41 frames.], batch size: 21, lr: 7.05e-04
2022-05-27 03:04:35,969 INFO [train.py:842] (2/4) Epoch 7, batch 8800, loss[loss=0.2341, simple_loss=0.3085, pruned_loss=0.07989, over 5240.00 frames.], tot_loss[loss=0.2274, simple_loss=0.3036, pruned_loss=0.07566, over 1416110.24 frames.], batch size: 53, lr: 7.04e-04
2022-05-27 03:05:17,313 INFO [train.py:842] (2/4) Epoch 7, batch 8850, loss[loss=0.188, simple_loss=0.2645, pruned_loss=0.0557, over 7427.00 frames.], tot_loss[loss=0.2265, simple_loss=0.303, pruned_loss=0.07502, over 1418979.38 frames.], batch size: 18, lr: 7.04e-04
2022-05-27 03:05:55,608 INFO [train.py:842] (2/4) Epoch 7, batch 8900, loss[loss=0.2245, simple_loss=0.2959, pruned_loss=0.0766, over 7158.00 frames.], tot_loss[loss=0.2274, simple_loss=0.3033, pruned_loss=0.07577, over 1414314.70 frames.], batch size: 18, lr: 7.04e-04
2022-05-27 03:06:34,517 INFO [train.py:842] (2/4) Epoch 7, batch 8950, loss[loss=0.2044, simple_loss=0.2963, pruned_loss=0.05624, over 7333.00 frames.], tot_loss[loss=0.228, simple_loss=0.3042, pruned_loss=0.07596, over 1408891.88 frames.], batch size: 22, lr: 7.03e-04
2022-05-27 03:07:12,745 INFO [train.py:842] (2/4) Epoch 7, batch 9000, loss[loss=0.3188, simple_loss=0.379, pruned_loss=0.1293, over 7231.00 frames.], tot_loss[loss=0.2297, simple_loss=0.3057, pruned_loss=0.07684, over 1404230.34 frames.], batch size: 21, lr: 7.03e-04
2022-05-27 03:07:12,745 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 03:07:22,108 INFO [train.py:871] (2/4) Epoch 7, validation: loss=0.1806, simple_loss=0.2819, pruned_loss=0.03969, over 868885.00 frames.
2022-05-27 03:08:00,672 INFO [train.py:842] (2/4) Epoch 7, batch 9050, loss[loss=0.2041, simple_loss=0.2881, pruned_loss=0.06005, over 5015.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3065, pruned_loss=0.07745, over 1397950.91 frames.], batch size: 52, lr: 7.03e-04
2022-05-27 03:08:38,271 INFO [train.py:842] (2/4) Epoch 7, batch 9100, loss[loss=0.2261, simple_loss=0.3026, pruned_loss=0.07483, over 7266.00 frames.], tot_loss[loss=0.2317, simple_loss=0.3077, pruned_loss=0.07791, over 1379229.09 frames.], batch size: 25, lr: 7.03e-04
2022-05-27 03:09:15,873 INFO [train.py:842] (2/4) Epoch 7, batch 9150, loss[loss=0.2283, simple_loss=0.3162, pruned_loss=0.07014, over 6323.00 frames.], tot_loss[loss=0.2357, simple_loss=0.3108, pruned_loss=0.08032, over 1338762.62 frames.], batch size: 37, lr: 7.02e-04
2022-05-27 03:10:09,042 INFO [train.py:842] (2/4) Epoch 8, batch 0, loss[loss=0.2787, simple_loss=0.3502, pruned_loss=0.1036, over 7333.00 frames.], tot_loss[loss=0.2787, simple_loss=0.3502, pruned_loss=0.1036, over 7333.00 frames.], batch size: 22, lr: 6.74e-04
2022-05-27 03:10:47,711 INFO [train.py:842] (2/4) Epoch 8, batch 50, loss[loss=0.2201, simple_loss=0.2893, pruned_loss=0.07548, over 7129.00 frames.], tot_loss[loss=0.2354, simple_loss=0.312, pruned_loss=0.07935, over 320387.81 frames.], batch size: 17, lr: 6.73e-04
2022-05-27 03:11:26,528 INFO [train.py:842] (2/4) Epoch 8, batch 100, loss[loss=0.2545, simple_loss=0.338, pruned_loss=0.08549, over 7277.00 frames.], tot_loss[loss=0.2304, simple_loss=0.3084, pruned_loss=0.07615, over 568921.42 frames.], batch size: 25, lr: 6.73e-04
2022-05-27 03:12:05,186 INFO [train.py:842] (2/4) Epoch 8, batch 150, loss[loss=0.214, simple_loss=0.3001, pruned_loss=0.06399, over 7098.00 frames.], tot_loss[loss=0.2259, simple_loss=0.3034, pruned_loss=0.07417, over 758089.09 frames.], batch size: 21, lr: 6.73e-04
2022-05-27 03:12:43,917 INFO [train.py:842] (2/4) Epoch 8, batch 200, loss[loss=0.2193, simple_loss=0.2947, pruned_loss=0.07196, over 7200.00 frames.], tot_loss[loss=0.2249, simple_loss=0.302, pruned_loss=0.07391, over 906666.07 frames.], batch size: 22, lr: 6.73e-04
2022-05-27 03:13:22,329 INFO [train.py:842] (2/4) Epoch 8, batch 250, loss[loss=0.2197, simple_loss=0.2911, pruned_loss=0.07417, over 7110.00 frames.], tot_loss[loss=0.2257, simple_loss=0.3023, pruned_loss=0.07455, over 1019945.31 frames.], batch size: 21, lr: 6.72e-04
2022-05-27 03:14:01,063 INFO [train.py:842] (2/4) Epoch 8, batch 300, loss[loss=0.2638, simple_loss=0.3347, pruned_loss=0.09645, over 7073.00 frames.], tot_loss[loss=0.2272, simple_loss=0.3033, pruned_loss=0.07552, over 1105429.82 frames.], batch size: 18, lr: 6.72e-04
2022-05-27 03:14:39,755 INFO [train.py:842] (2/4) Epoch 8, batch 350, loss[loss=0.2027, simple_loss=0.2885, pruned_loss=0.05848, over 7122.00 frames.], tot_loss[loss=0.2262, simple_loss=0.3023, pruned_loss=0.07503, over 1177081.90 frames.], batch size: 21, lr: 6.72e-04
2022-05-27 03:15:18,896 INFO [train.py:842] (2/4) Epoch 8, batch 400, loss[loss=0.255, simple_loss=0.3181, pruned_loss=0.09593, over 4898.00 frames.], tot_loss[loss=0.2247, simple_loss=0.3015, pruned_loss=0.07401, over 1230543.80 frames.], batch size: 52, lr: 6.72e-04
2022-05-27 03:15:57,566 INFO [train.py:842] (2/4) Epoch 8, batch 450, loss[loss=0.2177, simple_loss=0.2926, pruned_loss=0.07146, over 7243.00 frames.], tot_loss[loss=0.2246, simple_loss=0.3011, pruned_loss=0.074, over 1271936.58 frames.], batch size: 16, lr: 6.71e-04
2022-05-27 03:16:36,524 INFO [train.py:842] (2/4) Epoch 8, batch 500, loss[loss=0.1928, simple_loss=0.2762, pruned_loss=0.05473, over 7203.00 frames.], tot_loss[loss=0.2225, simple_loss=0.2995, pruned_loss=0.07272, over 1305223.05 frames.], batch size: 23, lr: 6.71e-04
2022-05-27 03:17:15,278 INFO [train.py:842] (2/4) Epoch 8, batch 550, loss[loss=0.2815, simple_loss=0.3305, pruned_loss=0.1162, over 7198.00 frames.], tot_loss[loss=0.224, simple_loss=0.3007, pruned_loss=0.07364, over 1333085.06 frames.], batch size: 23, lr: 6.71e-04
2022-05-27 03:17:54,029 INFO [train.py:842] (2/4) Epoch 8, batch 600, loss[loss=0.222, simple_loss=0.2965, pruned_loss=0.07373, over 7210.00 frames.], tot_loss[loss=0.2246, simple_loss=0.3014, pruned_loss=0.07388, over 1353167.89 frames.], batch size: 21, lr: 6.71e-04
2022-05-27 03:18:32,600 INFO [train.py:842] (2/4) Epoch 8, batch 650, loss[loss=0.2278, simple_loss=0.2986, pruned_loss=0.07851, over 7246.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3005, pruned_loss=0.07329, over 1368548.09 frames.], batch size: 19, lr: 6.70e-04
2022-05-27 03:19:11,321 INFO [train.py:842] (2/4) Epoch 8, batch 700, loss[loss=0.2616, simple_loss=0.3216, pruned_loss=0.1009, over 5078.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3019, pruned_loss=0.07458, over 1377354.34 frames.], batch size: 53, lr: 6.70e-04
2022-05-27 03:19:49,829 INFO [train.py:842] (2/4) Epoch 8, batch 750, loss[loss=0.1883, simple_loss=0.2761, pruned_loss=0.05031, over 7369.00 frames.], tot_loss[loss=0.2249, simple_loss=0.3019, pruned_loss=0.07392, over 1385151.16 frames.], batch size: 19, lr: 6.70e-04
2022-05-27 03:20:28,423 INFO [train.py:842] (2/4) Epoch 8, batch 800, loss[loss=0.2189, simple_loss=0.3003, pruned_loss=0.06874, over 6358.00 frames.], tot_loss[loss=0.2266, simple_loss=0.3039, pruned_loss=0.07467, over 1389827.97 frames.], batch size: 37, lr: 6.69e-04
2022-05-27 03:21:07,287 INFO [train.py:842] (2/4) Epoch 8, batch 850, loss[loss=0.191, simple_loss=0.2757, pruned_loss=0.05315, over 7415.00 frames.], tot_loss[loss=0.2239, simple_loss=0.3012, pruned_loss=0.0733, over 1398875.54 frames.], batch size: 18, lr: 6.69e-04
2022-05-27 03:21:46,344 INFO [train.py:842] (2/4) Epoch 8, batch 900, loss[loss=0.2543, simple_loss=0.3308, pruned_loss=0.0889, over 6759.00 frames.], tot_loss[loss=0.2249, simple_loss=0.3018, pruned_loss=0.07395, over 1398981.90 frames.], batch size: 31, lr: 6.69e-04
2022-05-27 03:22:24,854 INFO [train.py:842] (2/4) Epoch 8, batch 950, loss[loss=0.206, simple_loss=0.2997, pruned_loss=0.05619, over 7231.00 frames.], tot_loss[loss=0.2245, simple_loss=0.3015, pruned_loss=0.07373, over 1404345.53 frames.], batch size: 20, lr: 6.69e-04
2022-05-27 03:23:03,643 INFO [train.py:842] (2/4) Epoch 8, batch 1000, loss[loss=0.2598, simple_loss=0.3332, pruned_loss=0.09319, over 7216.00 frames.], tot_loss[loss=0.2231, simple_loss=0.3002, pruned_loss=0.07303, over 1408908.19 frames.], batch size: 21, lr: 6.68e-04
2022-05-27 03:23:42,254 INFO [train.py:842] (2/4) Epoch 8, batch 1050, loss[loss=0.1953, simple_loss=0.2736, pruned_loss=0.05849, over 7146.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3014, pruned_loss=0.0737, over 1407224.75 frames.], batch size: 17, lr: 6.68e-04
2022-05-27 03:24:21,161 INFO [train.py:842] (2/4) Epoch 8, batch 1100, loss[loss=0.2238, simple_loss=0.3058, pruned_loss=0.07093, over 7215.00 frames.], tot_loss[loss=0.2236, simple_loss=0.3006, pruned_loss=0.07332, over 1411723.92 frames.], batch size: 22, lr: 6.68e-04
2022-05-27 03:24:59,655 INFO [train.py:842] (2/4) Epoch 8, batch 1150, loss[loss=0.2855, simple_loss=0.3503, pruned_loss=0.1103, over 4894.00 frames.], tot_loss[loss=0.2252, simple_loss=0.3025, pruned_loss=0.07397, over 1416748.18 frames.], batch size: 52, lr: 6.68e-04
2022-05-27 03:25:38,516 INFO [train.py:842] (2/4) Epoch 8, batch 1200, loss[loss=0.2502, simple_loss=0.3306, pruned_loss=0.08489, over 7137.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3013, pruned_loss=0.07346, over 1419751.62 frames.], batch size: 20, lr: 6.67e-04
2022-05-27 03:26:16,963 INFO [train.py:842] (2/4) Epoch 8, batch 1250, loss[loss=0.2241, simple_loss=0.2969, pruned_loss=0.07568, over 7274.00 frames.], tot_loss[loss=0.2246, simple_loss=0.3012, pruned_loss=0.07396, over 1419368.13 frames.], batch size: 18, lr: 6.67e-04
2022-05-27 03:26:55,517 INFO [train.py:842] (2/4) Epoch 8, batch 1300, loss[loss=0.2175, simple_loss=0.3043, pruned_loss=0.06534, over 7143.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3024, pruned_loss=0.07433, over 1415953.86 frames.], batch size: 20, lr: 6.67e-04
2022-05-27 03:27:34,030 INFO [train.py:842] (2/4) Epoch 8, batch 1350, loss[loss=0.1887, simple_loss=0.2729, pruned_loss=0.0522, over 7153.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3014, pruned_loss=0.07364, over 1415080.59 frames.], batch size: 19, lr: 6.67e-04
2022-05-27 03:28:12,706 INFO [train.py:842] (2/4) Epoch 8, batch 1400, loss[loss=0.1912, simple_loss=0.2641, pruned_loss=0.05918, over 7277.00 frames.], tot_loss[loss=0.2239, simple_loss=0.3011, pruned_loss=0.07333, over 1416629.56 frames.], batch size: 18, lr: 6.66e-04
2022-05-27 03:28:51,222 INFO [train.py:842] (2/4) Epoch 8, batch 1450, loss[loss=0.1733, simple_loss=0.2573, pruned_loss=0.04461, over 7163.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3014, pruned_loss=0.07341, over 1416742.36 frames.], batch size: 18, lr: 6.66e-04
2022-05-27 03:29:30,491 INFO [train.py:842] (2/4) Epoch 8, batch 1500, loss[loss=0.1785, simple_loss=0.2506, pruned_loss=0.05325, over 7415.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3008, pruned_loss=0.07398, over 1416409.04 frames.], batch size: 18, lr: 6.66e-04
2022-05-27 03:30:08,990 INFO [train.py:842] (2/4) Epoch 8, batch 1550, loss[loss=0.2061, simple_loss=0.2918, pruned_loss=0.06022, over 7206.00 frames.], tot_loss[loss=0.2258, simple_loss=0.3022, pruned_loss=0.07468, over 1421111.35 frames.], batch size: 22, lr: 6.66e-04
2022-05-27 03:30:47,989 INFO [train.py:842] (2/4) Epoch 8, batch 1600, loss[loss=0.2428, simple_loss=0.3211, pruned_loss=0.08225, over 6516.00 frames.], tot_loss[loss=0.2286, simple_loss=0.3045, pruned_loss=0.07633, over 1421121.99 frames.], batch size: 38, lr: 6.65e-04
2022-05-27 03:31:26,459 INFO [train.py:842] (2/4) Epoch 8, batch 1650, loss[loss=0.2272, simple_loss=0.3098, pruned_loss=0.07223, over 7306.00 frames.], tot_loss[loss=0.2294, simple_loss=0.305, pruned_loss=0.07686, over 1419757.15 frames.], batch size: 24, lr: 6.65e-04
2022-05-27 03:32:05,103 INFO [train.py:842] (2/4) Epoch 8, batch 1700, loss[loss=0.227, simple_loss=0.3082, pruned_loss=0.07291, over 7328.00 frames.], tot_loss[loss=0.2279, simple_loss=0.3043, pruned_loss=0.07571, over 1420141.08 frames.], batch size: 21, lr: 6.65e-04
2022-05-27 03:32:43,703 INFO [train.py:842] (2/4) Epoch 8, batch 1750, loss[loss=0.2055, simple_loss=0.2899, pruned_loss=0.06051, over 7338.00 frames.], tot_loss[loss=0.227, simple_loss=0.3035, pruned_loss=0.07526, over 1419587.01 frames.], batch size: 22, lr: 6.65e-04
2022-05-27 03:33:22,873 INFO [train.py:842] (2/4) Epoch 8, batch 1800, loss[loss=0.2242, simple_loss=0.3076, pruned_loss=0.07044, over 7344.00 frames.], tot_loss[loss=0.2263, simple_loss=0.3026, pruned_loss=0.075, over 1420449.15 frames.], batch size: 22, lr: 6.64e-04
2022-05-27 03:34:01,568 INFO [train.py:842] (2/4) Epoch 8, batch 1850, loss[loss=0.2036, simple_loss=0.29, pruned_loss=0.05858, over 7230.00 frames.], tot_loss[loss=0.2266, simple_loss=0.3031, pruned_loss=0.07508, over 1422240.27 frames.], batch size: 20, lr: 6.64e-04
2022-05-27 03:34:40,353 INFO [train.py:842] (2/4) Epoch 8, batch 1900, loss[loss=0.2566, simple_loss=0.3277, pruned_loss=0.09272, over 7284.00 frames.], tot_loss[loss=0.2261, simple_loss=0.3021, pruned_loss=0.07506, over 1421818.87 frames.], batch size: 25, lr: 6.64e-04
2022-05-27 03:35:19,061 INFO [train.py:842] (2/4) Epoch 8, batch 1950, loss[loss=0.2108, simple_loss=0.2839, pruned_loss=0.06888, over 7000.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3018, pruned_loss=0.07465, over 1426064.43 frames.], batch size: 16, lr: 6.64e-04
2022-05-27 03:35:57,666 INFO [train.py:842] (2/4) Epoch 8, batch 2000, loss[loss=0.2032, simple_loss=0.2944, pruned_loss=0.05604, over 7120.00 frames.], tot_loss[loss=0.2247, simple_loss=0.3013, pruned_loss=0.07403, over 1426622.69 frames.], batch size: 21, lr: 6.63e-04
2022-05-27 03:36:36,043 INFO [train.py:842] (2/4) Epoch 8, batch 2050, loss[loss=0.2393, simple_loss=0.3125, pruned_loss=0.08306, over 5109.00 frames.], tot_loss[loss=0.2264, simple_loss=0.3025, pruned_loss=0.07509, over 1421069.68 frames.], batch size: 52, lr: 6.63e-04
2022-05-27 03:37:14,882 INFO [train.py:842] (2/4) Epoch 8, batch 2100, loss[loss=0.2613, simple_loss=0.331, pruned_loss=0.09579, over 7237.00 frames.], tot_loss[loss=0.2256, simple_loss=0.302, pruned_loss=0.07457, over 1417128.73 frames.], batch size: 20, lr: 6.63e-04
2022-05-27 03:37:53,572 INFO [train.py:842] (2/4) Epoch 8, batch 2150, loss[loss=0.2401, simple_loss=0.3189, pruned_loss=0.08064, over 7203.00 frames.], tot_loss[loss=0.2252, simple_loss=0.3016, pruned_loss=0.0744, over 1418092.50 frames.], batch size: 22, lr: 6.63e-04
2022-05-27 03:38:32,391 INFO [train.py:842] (2/4) Epoch 8, batch 2200, loss[loss=0.2146, simple_loss=0.2971, pruned_loss=0.06605, over 7306.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2988, pruned_loss=0.07289, over 1416476.99 frames.], batch size: 24, lr: 6.62e-04
2022-05-27 03:39:11,113 INFO [train.py:842] (2/4) Epoch 8, batch 2250, loss[loss=0.207, simple_loss=0.3014, pruned_loss=0.0563, over 7214.00 frames.], tot_loss[loss=0.2229, simple_loss=0.299, pruned_loss=0.07342, over 1411791.05 frames.], batch size: 23, lr: 6.62e-04
2022-05-27 03:39:49,976 INFO [train.py:842] (2/4) Epoch 8, batch 2300, loss[loss=0.1986, simple_loss=0.2747, pruned_loss=0.06121, over 7406.00 frames.], tot_loss[loss=0.221, simple_loss=0.2976, pruned_loss=0.07218, over 1412324.72 frames.], batch size: 18, lr: 6.62e-04
2022-05-27 03:40:28,564 INFO [train.py:842] (2/4) Epoch 8, batch 2350, loss[loss=0.2312, simple_loss=0.2985, pruned_loss=0.08196, over 7069.00 frames.], tot_loss[loss=0.2224, simple_loss=0.2989, pruned_loss=0.07292, over 1412177.83 frames.], batch size: 18, lr: 6.62e-04
2022-05-27 03:41:07,410 INFO [train.py:842] (2/4) Epoch 8, batch 2400, loss[loss=0.1771, simple_loss=0.2565, pruned_loss=0.04883, over 7265.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2978, pruned_loss=0.07198, over 1415587.77 frames.], batch size: 19, lr: 6.61e-04
2022-05-27 03:41:46,100 INFO [train.py:842] (2/4) Epoch 8, batch 2450, loss[loss=0.2354, simple_loss=0.31, pruned_loss=0.08042, over 7278.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2992, pruned_loss=0.07216, over 1422112.12 frames.], batch size: 24, lr: 6.61e-04
2022-05-27 03:42:24,990 INFO [train.py:842] (2/4) Epoch 8, batch 2500, loss[loss=0.2134, simple_loss=0.2983, pruned_loss=0.06425, over 7313.00 frames.], tot_loss[loss=0.2243, simple_loss=0.3013, pruned_loss=0.0737, over 1420406.08 frames.], batch size: 21, lr: 6.61e-04
2022-05-27 03:43:03,682 INFO [train.py:842] (2/4) Epoch 8, batch 2550, loss[loss=0.2301, simple_loss=0.3032, pruned_loss=0.07854, over 7361.00 frames.], tot_loss[loss=0.2239, simple_loss=0.3004, pruned_loss=0.07367, over 1425209.22 frames.], batch size: 19, lr: 6.61e-04
2022-05-27 03:43:43,041 INFO [train.py:842] (2/4) Epoch 8, batch 2600, loss[loss=0.1966, simple_loss=0.2686, pruned_loss=0.06226, over 7212.00 frames.], tot_loss[loss=0.2236, simple_loss=0.3004, pruned_loss=0.07341, over 1425699.43 frames.], batch size: 16, lr: 6.60e-04
2022-05-27 03:44:21,525 INFO [train.py:842] (2/4) Epoch 8, batch 2650, loss[loss=0.2087, simple_loss=0.2917, pruned_loss=0.06285, over 7120.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3006, pruned_loss=0.07321, over 1426946.69 frames.], batch size: 21, lr: 6.60e-04
2022-05-27 03:45:00,463 INFO [train.py:842] (2/4) Epoch 8, batch 2700, loss[loss=0.1898, simple_loss=0.2606, pruned_loss=0.05947, over 6811.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2992, pruned_loss=0.07232, over 1429007.50 frames.], batch size: 15, lr: 6.60e-04
2022-05-27 03:45:39,006 INFO [train.py:842] (2/4) Epoch 8, batch 2750, loss[loss=0.1958, simple_loss=0.2709, pruned_loss=0.06031, over 7011.00 frames.], tot_loss[loss=0.2203, simple_loss=0.2976, pruned_loss=0.07151, over 1427777.85 frames.], batch size: 16, lr: 6.60e-04
2022-05-27 03:46:17,917 INFO [train.py:842] (2/4) Epoch 8, batch 2800, loss[loss=0.3164, simple_loss=0.3618, pruned_loss=0.1354, over 7151.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2985, pruned_loss=0.07232, over 1428043.79 frames.], batch size: 20, lr: 6.60e-04
2022-05-27 03:46:56,410 INFO [train.py:842] (2/4) Epoch 8, batch 2850, loss[loss=0.2224, simple_loss=0.3123, pruned_loss=0.06627, over 7200.00 frames.], tot_loss[loss=0.2206, simple_loss=0.298, pruned_loss=0.07159, over 1426234.96 frames.], batch size: 22, lr: 6.59e-04
2022-05-27 03:47:35,363 INFO [train.py:842] (2/4) Epoch 8, batch 2900, loss[loss=0.2167, simple_loss=0.2864, pruned_loss=0.07354, over 7132.00 frames.], tot_loss[loss=0.2207, simple_loss=0.2984, pruned_loss=0.07151, over 1425395.81 frames.], batch size: 17, lr: 6.59e-04
2022-05-27 03:48:13,869 INFO [train.py:842] (2/4) Epoch 8, batch 2950, loss[loss=0.1806, simple_loss=0.2712, pruned_loss=0.04506, over 7068.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2968, pruned_loss=0.07075, over 1423823.45 frames.], batch size: 18, lr: 6.59e-04
2022-05-27 03:48:52,476 INFO [train.py:842] (2/4) Epoch 8, batch 3000, loss[loss=0.2631, simple_loss=0.3361, pruned_loss=0.09502, over 5032.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2979, pruned_loss=0.07122, over 1420201.15 frames.], batch size: 52, lr: 6.59e-04
2022-05-27 03:48:52,477 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 03:49:01,707 INFO [train.py:871] (2/4) Epoch 8, validation: loss=0.1787, simple_loss=0.2793, pruned_loss=0.03905, over 868885.00 frames.
2022-05-27 03:49:40,159 INFO [train.py:842] (2/4) Epoch 8, batch 3050, loss[loss=0.2096, simple_loss=0.2984, pruned_loss=0.06039, over 6386.00 frames.], tot_loss[loss=0.2217, simple_loss=0.2986, pruned_loss=0.07239, over 1414454.48 frames.], batch size: 38, lr: 6.58e-04
2022-05-27 03:50:18,977 INFO [train.py:842] (2/4) Epoch 8, batch 3100, loss[loss=0.2306, simple_loss=0.3009, pruned_loss=0.08016, over 7255.00 frames.], tot_loss[loss=0.2208, simple_loss=0.2979, pruned_loss=0.07187, over 1419038.88 frames.], batch size: 19, lr: 6.58e-04
2022-05-27 03:50:57,750 INFO [train.py:842] (2/4) Epoch 8, batch 3150, loss[loss=0.2224, simple_loss=0.2987, pruned_loss=0.07301, over 7426.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2958, pruned_loss=0.07102, over 1420629.32 frames.], batch size: 20, lr: 6.58e-04
2022-05-27 03:51:36,892 INFO [train.py:842] (2/4) Epoch 8, batch 3200, loss[loss=0.1948, simple_loss=0.2769, pruned_loss=0.05636, over 7431.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2962, pruned_loss=0.07123, over 1423989.60 frames.], batch size: 20, lr: 6.58e-04
2022-05-27 03:52:15,379 INFO [train.py:842] (2/4) Epoch 8, batch 3250, loss[loss=0.2233, simple_loss=0.3007, pruned_loss=0.07297, over 7073.00 frames.], tot_loss[loss=0.2213, simple_loss=0.298, pruned_loss=0.07231, over 1423421.32 frames.], batch size: 28, lr: 6.57e-04
2022-05-27 03:52:54,429 INFO [train.py:842] (2/4) Epoch 8, batch 3300, loss[loss=0.2424, simple_loss=0.3131, pruned_loss=0.08587, over 6830.00 frames.], tot_loss[loss=0.2212, simple_loss=0.2979, pruned_loss=0.07226, over 1422628.75 frames.], batch size: 31, lr: 6.57e-04
2022-05-27 03:53:33,003 INFO [train.py:842] (2/4) Epoch 8, batch 3350, loss[loss=0.1622, simple_loss=0.2533, pruned_loss=0.03556, over 7437.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2982, pruned_loss=0.07249, over 1420312.51 frames.], batch size: 20, lr: 6.57e-04
2022-05-27 03:54:11,883 INFO [train.py:842] (2/4) Epoch 8, batch 3400, loss[loss=0.3177, simple_loss=0.3616, pruned_loss=0.1369, over 6899.00 frames.], tot_loss[loss=0.2203, simple_loss=0.297, pruned_loss=0.07177, over 1419464.25 frames.], batch size: 31, lr: 6.57e-04
2022-05-27 03:54:50,299 INFO [train.py:842] (2/4) Epoch 8, batch 3450, loss[loss=0.2218, simple_loss=0.3009, pruned_loss=0.07136, over 7416.00 frames.], tot_loss[loss=0.2217, simple_loss=0.2988, pruned_loss=0.07233, over 1422424.60 frames.], batch size: 18, lr: 6.56e-04
2022-05-27 03:55:29,171 INFO [train.py:842] (2/4) Epoch 8, batch 3500, loss[loss=0.2296, simple_loss=0.3127, pruned_loss=0.07331, over 7395.00 frames.], tot_loss[loss=0.2222, simple_loss=0.2995, pruned_loss=0.07244, over 1421792.21 frames.], batch size: 23, lr: 6.56e-04
2022-05-27 03:56:07,712 INFO [train.py:842] (2/4) Epoch 8, batch 3550, loss[loss=0.2381, simple_loss=0.3095, pruned_loss=0.08332, over 7264.00 frames.], tot_loss[loss=0.2214, simple_loss=0.2988, pruned_loss=0.07201, over 1422845.56 frames.], batch size: 19, lr: 6.56e-04
2022-05-27 03:56:46,632 INFO [train.py:842] (2/4) Epoch 8, batch 3600, loss[loss=0.1677, simple_loss=0.239, pruned_loss=0.04817, over 7280.00 frames.], tot_loss[loss=0.2207, simple_loss=0.2977, pruned_loss=0.07181, over 1421299.70 frames.], batch size: 17, lr: 6.56e-04
2022-05-27 03:57:25,084 INFO [train.py:842] (2/4) Epoch 8, batch 3650, loss[loss=0.2081, simple_loss=0.3083, pruned_loss=0.05396, over 7419.00 frames.], tot_loss[loss=0.2228, simple_loss=0.2997, pruned_loss=0.07296, over 1415629.41 frames.], batch size: 21, lr: 6.55e-04
2022-05-27 03:58:03,908 INFO [train.py:842] (2/4) Epoch 8, batch 3700, loss[loss=0.2334, simple_loss=0.3131, pruned_loss=0.07681, over 7329.00 frames.], tot_loss[loss=0.2226, simple_loss=0.3001, pruned_loss=0.07259, over 1418764.37 frames.], batch size: 22, lr: 6.55e-04
2022-05-27 03:58:42,481 INFO [train.py:842] (2/4) Epoch 8, batch 3750, loss[loss=0.2976, simple_loss=0.3472, pruned_loss=0.124, over 7385.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2994, pruned_loss=0.07224, over 1418002.03 frames.], batch size: 23, lr: 6.55e-04
2022-05-27 03:59:21,190 INFO [train.py:842] (2/4) Epoch 8, batch 3800, loss[loss=0.3194, simple_loss=0.3741, pruned_loss=0.1324, over 7200.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3012, pruned_loss=0.07291, over 1419098.38 frames.], batch size: 22, lr: 6.55e-04
2022-05-27 03:59:59,657 INFO [train.py:842] (2/4) Epoch 8, batch 3850, loss[loss=0.2717, simple_loss=0.3367, pruned_loss=0.1034, over 7304.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3017, pruned_loss=0.07321, over 1418515.34 frames.], batch size: 25, lr: 6.54e-04
2022-05-27 04:00:38,573 INFO [train.py:842] (2/4) Epoch 8, batch 3900, loss[loss=0.2081, simple_loss=0.2847, pruned_loss=0.06581, over 7059.00 frames.], tot_loss[loss=0.2217, simple_loss=0.2991, pruned_loss=0.07217, over 1423302.29 frames.], batch size: 18, lr: 6.54e-04
2022-05-27 04:01:17,109 INFO [train.py:842] (2/4) Epoch 8, batch 3950, loss[loss=0.2356, simple_loss=0.3053, pruned_loss=0.08293, over 7196.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2989, pruned_loss=0.07188, over 1425776.24 frames.], batch size: 22, lr: 6.54e-04
2022-05-27 04:01:55,859 INFO [train.py:842] (2/4) Epoch 8, batch 4000, loss[loss=0.2837, simple_loss=0.3373, pruned_loss=0.115, over 7425.00 frames.], tot_loss[loss=0.2232, simple_loss=0.3, pruned_loss=0.07315, over 1425171.82 frames.], batch size: 21, lr: 6.54e-04
2022-05-27 04:02:34,545 INFO [train.py:842] (2/4) Epoch 8, batch 4050, loss[loss=0.2165, simple_loss=0.279, pruned_loss=0.07701, over 6986.00 frames.], tot_loss[loss=0.2242, simple_loss=0.3008, pruned_loss=0.07385, over 1425365.73 frames.], batch size: 16, lr: 6.53e-04
2022-05-27 04:03:13,364 INFO [train.py:842] (2/4) Epoch 8, batch 4100, loss[loss=0.3325, simple_loss=0.3911, pruned_loss=0.1369, over 7386.00 frames.], tot_loss[loss=0.2254, simple_loss=0.3016, pruned_loss=0.0746, over 1417965.44 frames.], batch size: 23, lr: 6.53e-04
2022-05-27 04:03:51,850 INFO [train.py:842] (2/4) Epoch 8, batch 4150, loss[loss=0.2294, simple_loss=0.3116, pruned_loss=0.07362, over 6740.00 frames.], tot_loss[loss=0.2241, simple_loss=0.301, pruned_loss=0.07359, over 1418345.35 frames.], batch size: 31, lr: 6.53e-04
2022-05-27 04:04:30,524 INFO [train.py:842] (2/4) Epoch 8, batch 4200, loss[loss=0.2245, simple_loss=0.3087, pruned_loss=0.07017, over 7069.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3014, pruned_loss=0.07373, over 1417877.97 frames.], batch size: 18, lr: 6.53e-04
2022-05-27 04:05:09,117 INFO [train.py:842] (2/4) Epoch 8, batch 4250, loss[loss=0.2594, simple_loss=0.3332, pruned_loss=0.09281, over 7188.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3027, pruned_loss=0.07425, over 1416709.92 frames.], batch size: 26, lr: 6.53e-04
2022-05-27 04:05:47,926 INFO [train.py:842] (2/4) Epoch 8, batch 4300, loss[loss=0.2093, simple_loss=0.2947, pruned_loss=0.06195, over 7053.00 frames.], tot_loss[loss=0.2227, simple_loss=0.3002, pruned_loss=0.0726, over 1424142.34 frames.], batch size: 28, lr: 6.52e-04
2022-05-27 04:06:26,323 INFO [train.py:842] (2/4) Epoch 8, batch 4350, loss[loss=0.2199, simple_loss=0.2966, pruned_loss=0.07165, over 7209.00 frames.], tot_loss[loss=0.2228, simple_loss=0.3004, pruned_loss=0.07255, over 1423761.72 frames.], batch size: 22, lr: 6.52e-04
2022-05-27 04:07:05,504 INFO [train.py:842] (2/4) Epoch 8, batch 4400, loss[loss=0.2246, simple_loss=0.3051, pruned_loss=0.07206, over 7155.00 frames.], tot_loss[loss=0.222, simple_loss=0.2995, pruned_loss=0.07222, over 1422493.45 frames.], batch size: 19, lr: 6.52e-04
2022-05-27 04:07:43,997 INFO [train.py:842] (2/4) Epoch 8, batch 4450, loss[loss=0.2317, simple_loss=0.3152, pruned_loss=0.07411, over 7340.00 frames.], tot_loss[loss=0.2212, simple_loss=0.2984, pruned_loss=0.07196, over 1423372.83 frames.], batch size: 22, lr: 6.52e-04
2022-05-27 04:08:22,873 INFO [train.py:842] (2/4) Epoch 8, batch 4500, loss[loss=0.1891, simple_loss=0.2614, pruned_loss=0.05843, over 7141.00 frames.], tot_loss[loss=0.2206, simple_loss=0.2979, pruned_loss=0.07171, over 1424440.30 frames.], batch size: 17, lr: 6.51e-04
2022-05-27 04:09:01,447 INFO [train.py:842] (2/4) Epoch 8, batch 4550, loss[loss=0.2424, simple_loss=0.3128, pruned_loss=0.08606, over 7248.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2991, pruned_loss=0.07178, over 1426514.66 frames.], batch size: 19, lr: 6.51e-04
2022-05-27 04:09:40,174 INFO [train.py:842] (2/4) Epoch 8, batch 4600, loss[loss=0.2259, simple_loss=0.3143, pruned_loss=0.06871, over 6945.00 frames.], tot_loss[loss=0.2236, simple_loss=0.301, pruned_loss=0.07308, over 1424179.11 frames.], batch size: 31, lr: 6.51e-04
2022-05-27 04:10:18,851 INFO [train.py:842] (2/4) Epoch 8, batch 4650, loss[loss=0.2547, simple_loss=0.32, pruned_loss=0.09472, over 7016.00 frames.], tot_loss[loss=0.2236, simple_loss=0.3006, pruned_loss=0.07329, over 1421797.55 frames.], batch size: 28, lr: 6.51e-04
2022-05-27 04:10:57,599 INFO [train.py:842] (2/4) Epoch 8, batch 4700, loss[loss=0.2485, simple_loss=0.3198, pruned_loss=0.08864, over 7263.00 frames.], tot_loss[loss=0.223, simple_loss=0.3001, pruned_loss=0.07295, over 1423432.20 frames.], batch size: 25, lr: 6.50e-04
2022-05-27 04:11:36,316 INFO [train.py:842] (2/4) Epoch 8, batch 4750, loss[loss=0.1985, simple_loss=0.2716, pruned_loss=0.06266, over 7422.00 frames.], tot_loss[loss=0.2229, simple_loss=0.3001, pruned_loss=0.07285, over 1420438.76 frames.], batch size: 20, lr: 6.50e-04
2022-05-27 04:12:15,130 INFO [train.py:842] (2/4) Epoch 8, batch 4800, loss[loss=0.2278, simple_loss=0.312, pruned_loss=0.0718, over 7148.00 frames.], tot_loss[loss=0.2233, simple_loss=0.3004, pruned_loss=0.07308, over 1423480.67 frames.], batch size: 26, lr: 6.50e-04
2022-05-27 04:12:53,770 INFO [train.py:842] (2/4) Epoch 8, batch 4850, loss[loss=0.2131, simple_loss=0.282, pruned_loss=0.07206, over 7359.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2994, pruned_loss=0.07188, over 1428915.71 frames.], batch size: 19, lr: 6.50e-04
2022-05-27 04:13:32,375 INFO [train.py:842] (2/4) Epoch 8, batch 4900, loss[loss=0.2201, simple_loss=0.2987, pruned_loss=0.07074, over 6726.00 frames.], tot_loss[loss=0.2213, simple_loss=0.299, pruned_loss=0.07182, over 1426771.66 frames.], batch size: 31, lr: 6.49e-04
2022-05-27 04:14:10,925 INFO [train.py:842] (2/4) Epoch 8, batch 4950, loss[loss=0.2154, simple_loss=0.2869, pruned_loss=0.07197, over 7071.00 frames.], tot_loss[loss=0.2206, simple_loss=0.2982, pruned_loss=0.07152, over 1426348.84 frames.], batch size: 18, lr: 6.49e-04
2022-05-27 04:14:50,205 INFO [train.py:842] (2/4) Epoch 8, batch 5000, loss[loss=0.2217, simple_loss=0.2972, pruned_loss=0.07312, over 7257.00 frames.], tot_loss[loss=0.2206, simple_loss=0.2976, pruned_loss=0.07181, over 1422375.73 frames.], batch size: 19, lr: 6.49e-04
2022-05-27 04:15:28,689 INFO [train.py:842] (2/4) Epoch 8, batch 5050, loss[loss=0.1975, simple_loss=0.2801, pruned_loss=0.0574, over 6619.00 frames.], tot_loss[loss=0.2227, simple_loss=0.2994, pruned_loss=0.07294, over 1422266.92 frames.], batch size: 38, lr: 6.49e-04
2022-05-27 04:16:07,693 INFO [train.py:842] (2/4) Epoch 8, batch 5100, loss[loss=0.1908, simple_loss=0.2683, pruned_loss=0.05671, over 7274.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2986, pruned_loss=0.07233, over 1427219.52 frames.], batch size: 17, lr: 6.49e-04
2022-05-27 04:16:46,296 INFO [train.py:842] (2/4) Epoch 8, batch 5150, loss[loss=0.1951, simple_loss=0.266, pruned_loss=0.06211, over 7361.00 frames.], tot_loss[loss=0.2206, simple_loss=0.298, pruned_loss=0.07158, over 1428962.13 frames.], batch size: 19, lr: 6.48e-04
2022-05-27 04:17:25,420 INFO [train.py:842] (2/4) Epoch 8, batch 5200, loss[loss=0.2439, simple_loss=0.3177, pruned_loss=0.08506, over 7263.00 frames.], tot_loss[loss=0.2202, simple_loss=0.2974, pruned_loss=0.07146, over 1426562.15 frames.], batch size: 19, lr: 6.48e-04
2022-05-27 04:18:03,674 INFO [train.py:842] (2/4) Epoch 8, batch 5250, loss[loss=0.1917, simple_loss=0.2709, pruned_loss=0.05629, over 7159.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2985, pruned_loss=0.07234, over 1419598.57 frames.], batch size: 19, lr: 6.48e-04
2022-05-27 04:18:42,889 INFO [train.py:842] (2/4) Epoch 8, batch 5300, loss[loss=0.2069, simple_loss=0.2863, pruned_loss=0.06371, over 7158.00 frames.], tot_loss[loss=0.2226, simple_loss=0.2993, pruned_loss=0.07292, over 1419318.35 frames.], batch size: 19, lr: 6.48e-04
2022-05-27 04:19:21,454 INFO [train.py:842] (2/4) Epoch 8, batch 5350, loss[loss=0.1907, simple_loss=0.2723, pruned_loss=0.05458, over 7148.00 frames.], tot_loss[loss=0.2214, simple_loss=0.2986, pruned_loss=0.07212, over 1420257.97 frames.], batch size: 19, lr: 6.47e-04
2022-05-27 04:20:00,555 INFO [train.py:842] (2/4) Epoch 8, batch 5400, loss[loss=0.1997, simple_loss=0.2896, pruned_loss=0.05487, over 7314.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2986, pruned_loss=0.07254, over 1418360.95 frames.], batch size: 21, lr: 6.47e-04
2022-05-27 04:20:39,241 INFO [train.py:842] (2/4) Epoch 8, batch 5450, loss[loss=0.1992, simple_loss=0.2833, pruned_loss=0.05752, over 7357.00 frames.], tot_loss[loss=0.2232, simple_loss=0.2998, pruned_loss=0.07329, over 1418519.65 frames.], batch size: 19, lr: 6.47e-04
2022-05-27 04:21:18,484 INFO [train.py:842] (2/4) Epoch 8, batch 5500, loss[loss=0.1738, simple_loss=0.2605, pruned_loss=0.04355, over 7367.00 frames.], tot_loss[loss=0.223, simple_loss=0.2996, pruned_loss=0.07326, over 1417991.38 frames.], batch size: 19, lr: 6.47e-04
2022-05-27 04:21:56,803 INFO [train.py:842] (2/4) Epoch 8, batch 5550, loss[loss=0.2274, simple_loss=0.3049, pruned_loss=0.07489, over 7150.00 frames.], tot_loss[loss=0.2226, simple_loss=0.2996, pruned_loss=0.07284, over 1414585.90 frames.], batch size: 20, lr: 6.46e-04
2022-05-27 04:22:35,513 INFO [train.py:842] (2/4) Epoch 8, batch 5600, loss[loss=0.1715, simple_loss=0.2489, pruned_loss=0.04701, over 7254.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2997, pruned_loss=0.07247, over 1415111.52 frames.], batch size: 18, lr: 6.46e-04
2022-05-27 04:23:14,203 INFO [train.py:842] (2/4) Epoch 8, batch 5650, loss[loss=0.2483, simple_loss=0.3292, pruned_loss=0.08368, over 7339.00 frames.], tot_loss[loss=0.2207, simple_loss=0.2983, pruned_loss=0.07153, over 1416900.05 frames.], batch size: 22, lr: 6.46e-04
2022-05-27 04:23:53,469 INFO [train.py:842] (2/4) Epoch 8, batch 5700, loss[loss=0.2054, simple_loss=0.2846, pruned_loss=0.06315, over 7230.00 frames.], tot_loss[loss=0.2204, simple_loss=0.2979, pruned_loss=0.0715, over 1421554.96 frames.], batch size: 20, lr: 6.46e-04
2022-05-27 04:24:32,077 INFO [train.py:842] (2/4) Epoch 8, batch 5750, loss[loss=0.1709, simple_loss=0.2504, pruned_loss=0.04573, over 7056.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2969, pruned_loss=0.0704, over 1425886.08 frames.], batch size: 18, lr: 6.46e-04
2022-05-27 04:25:10,893 INFO [train.py:842] (2/4) Epoch 8, batch 5800, loss[loss=0.1653, simple_loss=0.2405, pruned_loss=0.04501, over 7290.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2977, pruned_loss=0.07065, over 1424904.82 frames.], batch size: 17, lr: 6.45e-04
2022-05-27 04:25:49,413 INFO [train.py:842] (2/4) Epoch 8, batch 5850, loss[loss=0.1891, simple_loss=0.2683, pruned_loss=0.05488, over 7241.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2979, pruned_loss=0.07074, over 1427133.48 frames.], batch size: 20, lr: 6.45e-04
2022-05-27 04:26:28,994 INFO [train.py:842] (2/4) Epoch 8, batch 5900, loss[loss=0.1673, simple_loss=0.2438, pruned_loss=0.0454, over 7271.00 frames.], tot_loss[loss=0.2212, simple_loss=0.299, pruned_loss=0.07172, over 1426007.94 frames.], batch size: 17, lr: 6.45e-04
2022-05-27 04:27:07,422 INFO [train.py:842] (2/4) Epoch 8, batch 5950, loss[loss=0.1424, simple_loss=0.2259, pruned_loss=0.02947, over 7268.00 frames.], tot_loss[loss=0.2227, simple_loss=0.3001, pruned_loss=0.07267, over 1424876.33 frames.], batch size: 17, lr: 6.45e-04
2022-05-27 04:27:46,629 INFO [train.py:842] (2/4) Epoch 8, batch 6000, loss[loss=0.2252, simple_loss=0.3228, pruned_loss=0.06379, over 7324.00 frames.], tot_loss[loss=0.2222, simple_loss=0.2998, pruned_loss=0.0723, over 1425297.60 frames.], batch size: 21, lr: 6.44e-04
2022-05-27 04:27:46,629 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 04:27:55,933 INFO [train.py:871] (2/4) Epoch 8, validation: loss=0.1785, simple_loss=0.2788, pruned_loss=0.03907, over 868885.00 frames.
2022-05-27 04:28:34,547 INFO [train.py:842] (2/4) Epoch 8, batch 6050, loss[loss=0.2258, simple_loss=0.3062, pruned_loss=0.07273, over 7365.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2989, pruned_loss=0.07204, over 1425448.95 frames.], batch size: 19, lr: 6.44e-04
2022-05-27 04:29:13,569 INFO [train.py:842] (2/4) Epoch 8, batch 6100, loss[loss=0.2064, simple_loss=0.2817, pruned_loss=0.06557, over 7155.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2992, pruned_loss=0.07247, over 1427868.86 frames.], batch size: 18, lr: 6.44e-04
2022-05-27 04:29:52,011 INFO [train.py:842] (2/4) Epoch 8, batch 6150, loss[loss=0.1803, simple_loss=0.2564, pruned_loss=0.0521, over 7277.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2994, pruned_loss=0.07184, over 1427173.27 frames.], batch size: 18, lr: 6.44e-04
2022-05-27 04:30:30,792 INFO [train.py:842] (2/4) Epoch 8, batch 6200, loss[loss=0.2292, simple_loss=0.3148, pruned_loss=0.07175, over 7280.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2995, pruned_loss=0.07187, over 1426730.64 frames.], batch size: 25, lr: 6.43e-04
2022-05-27 04:31:09,335 INFO [train.py:842] (2/4) Epoch 8, batch 6250, loss[loss=0.2102, simple_loss=0.3018, pruned_loss=0.05927, over 7212.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2997, pruned_loss=0.07177, over 1425923.31 frames.], batch size: 22, lr: 6.43e-04
2022-05-27 04:31:47,865 INFO [train.py:842] (2/4) Epoch 8, batch 6300, loss[loss=0.2849, simple_loss=0.3497, pruned_loss=0.1101, over 7178.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2994, pruned_loss=0.0712, over 1426208.47 frames.], batch size: 23, lr: 6.43e-04
2022-05-27 04:32:26,540 INFO [train.py:842] (2/4) Epoch 8, batch 6350, loss[loss=0.1688, simple_loss=0.2534, pruned_loss=0.04213, over 6863.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2991, pruned_loss=0.07135, over 1425734.29 frames.], batch size: 15, lr: 6.43e-04
2022-05-27 04:33:05,972 INFO [train.py:842] (2/4) Epoch 8, batch 6400, loss[loss=0.2271, simple_loss=0.3049, pruned_loss=0.07466, over 7108.00 frames.], tot_loss[loss=0.2222, simple_loss=0.2998, pruned_loss=0.0723, over 1423128.38 frames.], batch size: 21, lr: 6.43e-04
2022-05-27 04:33:44,695 INFO [train.py:842] (2/4) Epoch 8, batch 6450, loss[loss=0.2128, simple_loss=0.2919, pruned_loss=0.06681, over 7302.00 frames.], tot_loss[loss=0.2196, simple_loss=0.297, pruned_loss=0.07113, over 1427787.34 frames.], batch size: 18, lr: 6.42e-04
2022-05-27 04:34:23,913 INFO [train.py:842] (2/4) Epoch 8, batch 6500, loss[loss=0.2342, simple_loss=0.3211, pruned_loss=0.07361, over 7059.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2959, pruned_loss=0.07076, over 1426142.56 frames.], batch size: 28, lr: 6.42e-04
2022-05-27 04:35:02,476 INFO [train.py:842] (2/4) Epoch 8, batch 6550, loss[loss=0.2066, simple_loss=0.2724, pruned_loss=0.07034, over 7001.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2967, pruned_loss=0.07077, over 1428900.17 frames.], batch size: 16, lr: 6.42e-04
2022-05-27 04:35:41,388 INFO [train.py:842] (2/4) Epoch 8, batch 6600, loss[loss=0.2302, simple_loss=0.3095, pruned_loss=0.07542, over 7160.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2965, pruned_loss=0.07062, over 1429258.32 frames.], batch size: 19, lr: 6.42e-04
2022-05-27 04:36:20,198 INFO [train.py:842] (2/4) Epoch 8, batch 6650, loss[loss=0.2406, simple_loss=0.309, pruned_loss=0.08612, over 7304.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2973, pruned_loss=0.07089, over 1425829.17 frames.], batch size: 24, lr: 6.41e-04
2022-05-27 04:36:59,109 INFO [train.py:842] (2/4) Epoch 8, batch 6700, loss[loss=0.214, simple_loss=0.2963, pruned_loss=0.06588, over 6318.00 frames.], tot_loss[loss=0.2173, simple_loss=0.2954, pruned_loss=0.06957, over 1426164.09 frames.], batch size: 38, lr: 6.41e-04
2022-05-27 04:37:37,720 INFO [train.py:842] (2/4) Epoch 8, batch 6750, loss[loss=0.2086, simple_loss=0.2973, pruned_loss=0.05992, over 7343.00 frames.], tot_loss[loss=0.217, simple_loss=0.2956, pruned_loss=0.06919, over 1429825.76 frames.], batch size: 22, lr: 6.41e-04
2022-05-27 04:38:16,596 INFO [train.py:842] (2/4) Epoch 8, batch 6800, loss[loss=0.2347, simple_loss=0.3165, pruned_loss=0.07642, over 7325.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2951, pruned_loss=0.06863, over 1429212.88 frames.], batch size: 21, lr: 6.41e-04
2022-05-27 04:38:55,341 INFO [train.py:842] (2/4) Epoch 8, batch 6850, loss[loss=0.2435, simple_loss=0.3223, pruned_loss=0.08232, over 7243.00 frames.], tot_loss[loss=0.2162, simple_loss=0.295, pruned_loss=0.06865, over 1431604.39 frames.], batch size: 20, lr: 6.41e-04
2022-05-27 04:39:34,222 INFO [train.py:842] (2/4) Epoch 8, batch 6900, loss[loss=0.2151, simple_loss=0.301, pruned_loss=0.06465, over 7265.00 frames.], tot_loss[loss=0.2192, simple_loss=0.2968, pruned_loss=0.07079, over 1430713.44 frames.], batch size: 18, lr: 6.40e-04
2022-05-27 04:40:12,644 INFO [train.py:842] (2/4) Epoch 8, batch 6950, loss[loss=0.2697, simple_loss=0.3247, pruned_loss=0.1074, over 7265.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2973, pruned_loss=0.07085, over 1427608.88 frames.], batch size: 19, lr: 6.40e-04
2022-05-27 04:40:51,441 INFO [train.py:842] (2/4) Epoch 8, batch 7000, loss[loss=0.2246, simple_loss=0.3028, pruned_loss=0.07326, over 7374.00 frames.], tot_loss[loss=0.2198, simple_loss=0.2975, pruned_loss=0.07102, over 1427470.62 frames.], batch size: 23, lr: 6.40e-04
2022-05-27 04:41:30,072 INFO [train.py:842] (2/4) Epoch 8, batch 7050, loss[loss=0.1899, simple_loss=0.27, pruned_loss=0.05488, over 7161.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2969, pruned_loss=0.07084, over 1426551.62 frames.], batch size: 18, lr: 6.40e-04
2022-05-27 04:42:09,251 INFO [train.py:842] (2/4) Epoch 8, batch 7100, loss[loss=0.2144, simple_loss=0.2861, pruned_loss=0.07131, over 7417.00 frames.], tot_loss[loss=0.2198, simple_loss=0.2973, pruned_loss=0.07118, over 1422739.02 frames.], batch size: 18, lr: 6.39e-04
2022-05-27 04:42:47,952 INFO [train.py:842] (2/4) Epoch 8, batch 7150, loss[loss=0.2074, simple_loss=0.2908, pruned_loss=0.06196, over 7291.00 frames.], tot_loss[loss=0.2214, simple_loss=0.2986, pruned_loss=0.07208, over 1419459.78 frames.], batch size: 18, lr: 6.39e-04
2022-05-27 04:43:26,612 INFO [train.py:842] (2/4) Epoch 8, batch 7200, loss[loss=0.244, simple_loss=0.3235, pruned_loss=0.08223, over 7152.00 frames.], tot_loss[loss=0.2217, simple_loss=0.2987, pruned_loss=0.07237, over 1420396.75 frames.], batch size: 20, lr: 6.39e-04
2022-05-27 04:44:05,029 INFO [train.py:842] (2/4) Epoch 8, batch 7250, loss[loss=0.2486, simple_loss=0.3083, pruned_loss=0.09443, over 6798.00 frames.], tot_loss[loss=0.2211, simple_loss=0.2982, pruned_loss=0.07204, over 1417724.75 frames.], batch size: 15, lr: 6.39e-04
2022-05-27 04:44:43,929 INFO [train.py:842] (2/4) Epoch 8, batch 7300, loss[loss=0.2347, simple_loss=0.2984, pruned_loss=0.0855, over 7163.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2974, pruned_loss=0.07175, over 1414996.13 frames.], batch size: 19, lr: 6.39e-04
2022-05-27 04:45:22,506 INFO [train.py:842] (2/4) Epoch 8, batch 7350, loss[loss=0.2319, simple_loss=0.3151, pruned_loss=0.07434, over 7359.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2984, pruned_loss=0.07262, over 1415743.31 frames.], batch size: 23, lr: 6.38e-04
2022-05-27 04:46:01,508 INFO [train.py:842] (2/4) Epoch 8, batch 7400, loss[loss=0.1508, simple_loss=0.2304, pruned_loss=0.03557, over 7415.00 frames.], tot_loss[loss=0.2211, simple_loss=0.2976, pruned_loss=0.07234, over 1413160.76 frames.], batch size: 18, lr: 6.38e-04
2022-05-27 04:46:40,139 INFO [train.py:842] (2/4) Epoch 8, batch 7450, loss[loss=0.2236, simple_loss=0.3108, pruned_loss=0.06822, over 7274.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2967, pruned_loss=0.07137, over 1412649.65 frames.], batch size: 18, lr: 6.38e-04
2022-05-27 04:47:19,059 INFO [train.py:842] (2/4) Epoch 8, batch 7500, loss[loss=0.1887, simple_loss=0.2669, pruned_loss=0.05521, over 7452.00 frames.], tot_loss[loss=0.2207, simple_loss=0.2974, pruned_loss=0.07196, over 1415732.89 frames.], batch size: 19, lr: 6.38e-04
2022-05-27 04:47:57,405 INFO [train.py:842] (2/4) Epoch 8, batch 7550, loss[loss=0.2213, simple_loss=0.287, pruned_loss=0.07779, over 7254.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2986, pruned_loss=0.07204, over 1414985.32 frames.], batch size: 19, lr: 6.37e-04
2022-05-27 04:48:36,315 INFO [train.py:842] (2/4) Epoch 8, batch 7600, loss[loss=0.1954, simple_loss=0.2794, pruned_loss=0.05572, over 7411.00 frames.], tot_loss[loss=0.2196, simple_loss=0.2969, pruned_loss=0.07117, over 1415971.41 frames.], batch size: 18, lr: 6.37e-04
2022-05-27 04:49:15,202 INFO [train.py:842] (2/4) Epoch 8, batch 7650, loss[loss=0.295, simple_loss=0.3629, pruned_loss=0.1136, over 7301.00 frames.], tot_loss[loss=0.2232, simple_loss=0.2997, pruned_loss=0.07337, over 1417917.75 frames.], batch size: 25, lr: 6.37e-04
2022-05-27 04:49:56,834 INFO [train.py:842] (2/4) Epoch 8, batch 7700, loss[loss=0.226, simple_loss=0.3161, pruned_loss=0.06793, over 7327.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3007, pruned_loss=0.07371, over 1416126.74 frames.], batch size: 21, lr: 6.37e-04
2022-05-27 04:50:35,223 INFO [train.py:842] (2/4) Epoch 8, batch 7750, loss[loss=0.2108, simple_loss=0.2817, pruned_loss=0.06989, over 7404.00 frames.], tot_loss[loss=0.223, simple_loss=0.3003, pruned_loss=0.07283, over 1418714.88 frames.], batch size: 18, lr: 6.37e-04
2022-05-27 04:51:14,007 INFO [train.py:842] (2/4) Epoch 8, batch 7800, loss[loss=0.2765, simple_loss=0.3348, pruned_loss=0.1091, over 7351.00 frames.], tot_loss[loss=0.223, simple_loss=0.2999, pruned_loss=0.07307, over 1419386.48 frames.], batch size: 19, lr: 6.36e-04
2022-05-27 04:51:52,493 INFO [train.py:842] (2/4) Epoch 8, batch 7850, loss[loss=0.2449, simple_loss=0.3185, pruned_loss=0.08562, over 7198.00 frames.], tot_loss[loss=0.2238, simple_loss=0.3004, pruned_loss=0.07355, over 1417210.46 frames.], batch size: 22, lr: 6.36e-04
2022-05-27 04:52:31,257 INFO [train.py:842] (2/4) Epoch 8, batch 7900, loss[loss=0.225, simple_loss=0.3079, pruned_loss=0.07102, over 7354.00 frames.], tot_loss[loss=0.2238, simple_loss=0.3005, pruned_loss=0.07355, over 1419963.85 frames.], batch size: 25, lr: 6.36e-04
2022-05-27 04:53:09,837 INFO [train.py:842] (2/4) Epoch 8, batch 7950, loss[loss=0.1973, simple_loss=0.2848, pruned_loss=0.05485, over 7148.00 frames.], tot_loss[loss=0.2225, simple_loss=0.2998, pruned_loss=0.0726, over 1421714.51 frames.], batch size: 20, lr: 6.36e-04
2022-05-27 04:53:49,267 INFO [train.py:842] (2/4) Epoch 8, batch 8000, loss[loss=0.2565, simple_loss=0.3225, pruned_loss=0.09524, over 7282.00 frames.], tot_loss[loss=0.22, simple_loss=0.2972, pruned_loss=0.07139, over 1422951.45 frames.], batch size: 25, lr: 6.35e-04
2022-05-27 04:54:27,866 INFO [train.py:842] (2/4) Epoch 8, batch 8050, loss[loss=0.222, simple_loss=0.303, pruned_loss=0.07052, over 7316.00 frames.], tot_loss[loss=0.2209, simple_loss=0.298, pruned_loss=0.0719, over 1426095.44 frames.], batch size: 21, lr: 6.35e-04
2022-05-27 04:55:06,862 INFO [train.py:842] (2/4) Epoch 8, batch 8100, loss[loss=0.2286, simple_loss=0.2889, pruned_loss=0.08413, over 7264.00 frames.], tot_loss[loss=0.221, simple_loss=0.2977, pruned_loss=0.07213, over 1426199.34 frames.], batch size: 18, lr: 6.35e-04
2022-05-27 04:55:45,298 INFO [train.py:842] (2/4) Epoch 8, batch 8150, loss[loss=0.1943, simple_loss=0.2794, pruned_loss=0.05463, over 7166.00 frames.], tot_loss[loss=0.2224, simple_loss=0.2995, pruned_loss=0.07271, over 1416461.10 frames.], batch size: 18, lr: 6.35e-04
2022-05-27 04:56:24,244 INFO [train.py:842] (2/4) Epoch 8, batch 8200, loss[loss=0.2663, simple_loss=0.3277, pruned_loss=0.1024, over 7315.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2988, pruned_loss=0.07243, over 1418766.68 frames.], batch size: 21, lr: 6.35e-04
2022-05-27 04:57:02,825 INFO [train.py:842] (2/4) Epoch 8, batch 8250, loss[loss=0.1896, simple_loss=0.2723, pruned_loss=0.05345, over 7159.00 frames.], tot_loss[loss=0.223, simple_loss=0.3, pruned_loss=0.07302, over 1419091.90 frames.], batch size: 18, lr: 6.34e-04
2022-05-27 04:57:41,674 INFO [train.py:842] (2/4) Epoch 8, batch 8300, loss[loss=0.2266, simple_loss=0.3147, pruned_loss=0.06921, over 7143.00 frames.], tot_loss[loss=0.222, simple_loss=0.2997, pruned_loss=0.07211, over 1419268.20 frames.], batch size: 20, lr: 6.34e-04
2022-05-27 04:58:20,174 INFO [train.py:842] (2/4) Epoch 8, batch 8350, loss[loss=0.1943, simple_loss=0.2713, pruned_loss=0.05867, over 7183.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2993, pruned_loss=0.07224, over 1421574.40 frames.], batch size: 26, lr: 6.34e-04
2022-05-27 04:58:58,958 INFO [train.py:842] (2/4) Epoch 8, batch 8400, loss[loss=0.184, simple_loss=0.26, pruned_loss=0.05401, over 7270.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2991, pruned_loss=0.07198, over 1424332.78 frames.], batch size: 18, lr: 6.34e-04
2022-05-27 04:59:37,685 INFO [train.py:842] (2/4) Epoch 8, batch 8450, loss[loss=0.2125, simple_loss=0.2977, pruned_loss=0.06365, over 7151.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2993, pruned_loss=0.07224, over 1420459.70 frames.], batch size: 20, lr: 6.34e-04
2022-05-27 05:00:16,705 INFO [train.py:842] (2/4) Epoch 8, batch 8500, loss[loss=0.2239, simple_loss=0.3055, pruned_loss=0.0711, over 7205.00 frames.], tot_loss[loss=0.2223, simple_loss=0.299, pruned_loss=0.07275, over 1420763.39 frames.], batch size: 22, lr: 6.33e-04
2022-05-27 05:00:55,183 INFO [train.py:842] (2/4) Epoch 8, batch 8550, loss[loss=0.1814, simple_loss=0.2603, pruned_loss=0.05123, over 7143.00 frames.], tot_loss[loss=0.2227, simple_loss=0.2993, pruned_loss=0.07309, over 1418797.84 frames.], batch size: 20, lr: 6.33e-04
2022-05-27 05:01:34,186 INFO [train.py:842] (2/4) Epoch 8, batch 8600, loss[loss=0.2616, simple_loss=0.3218, pruned_loss=0.1007, over 7284.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2974, pruned_loss=0.0714, over 1418234.78 frames.], batch size: 17, lr: 6.33e-04
2022-05-27 05:02:12,598 INFO [train.py:842] (2/4) Epoch 8, batch 8650, loss[loss=0.2073, simple_loss=0.2987, pruned_loss=0.05798, over 7111.00 frames.], tot_loss[loss=0.2203, simple_loss=0.2975, pruned_loss=0.07153, over 1412931.42 frames.], batch size: 21, lr: 6.33e-04
2022-05-27 05:02:51,371 INFO [train.py:842] (2/4) Epoch 8, batch 8700, loss[loss=0.1945, simple_loss=0.2568, pruned_loss=0.0661, over 7282.00 frames.], tot_loss[loss=0.2204, simple_loss=0.2978, pruned_loss=0.07156, over 1416712.15 frames.], batch size: 18, lr: 6.32e-04
2022-05-27 05:03:30,087 INFO [train.py:842] (2/4) Epoch 8, batch 8750, loss[loss=0.1842, simple_loss=0.2569, pruned_loss=0.05577, over 6809.00 frames.], tot_loss[loss=0.2183, simple_loss=0.2959, pruned_loss=0.07038, over 1420163.38 frames.], batch size: 15, lr: 6.32e-04
2022-05-27 05:04:09,297 INFO [train.py:842] (2/4) Epoch 8, batch 8800, loss[loss=0.2653, simple_loss=0.3527, pruned_loss=0.08894, over 7326.00 frames.], tot_loss[loss=0.2199, simple_loss=0.2972, pruned_loss=0.07136, over 1416871.33 frames.], batch size: 22, lr: 6.32e-04
2022-05-27 05:04:47,684 INFO [train.py:842] (2/4) Epoch 8, batch 8850, loss[loss=0.1836, simple_loss=0.2556, pruned_loss=0.05585, over 7285.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2982, pruned_loss=0.07138, over 1415497.53 frames.], batch size: 17, lr: 6.32e-04
2022-05-27 05:05:27,277 INFO [train.py:842] (2/4) Epoch 8, batch 8900, loss[loss=0.1992, simple_loss=0.2791, pruned_loss=0.05961, over 7360.00 frames.], tot_loss[loss=0.2208, simple_loss=0.2981, pruned_loss=0.07178, over 1409622.72 frames.], batch size: 19, lr: 6.32e-04
2022-05-27 05:06:05,780 INFO [train.py:842] (2/4) Epoch 8, batch 8950, loss[loss=0.2558, simple_loss=0.3199, pruned_loss=0.09584, over 6217.00 frames.], tot_loss[loss=0.2212, simple_loss=0.2983, pruned_loss=0.07205, over 1408444.77 frames.], batch size: 37, lr: 6.31e-04
2022-05-27 05:06:44,582 INFO [train.py:842] (2/4) Epoch 8, batch 9000, loss[loss=0.234, simple_loss=0.3155, pruned_loss=0.07621, over 5089.00 frames.], tot_loss[loss=0.2229, simple_loss=0.2997, pruned_loss=0.07308, over 1404858.91 frames.], batch size: 53, lr: 6.31e-04
2022-05-27 05:06:44,583 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 05:06:53,926 INFO [train.py:871] (2/4) Epoch 8, validation: loss=0.1786, simple_loss=0.2794, pruned_loss=0.03893, over 868885.00 frames.
2022-05-27 05:07:32,087 INFO [train.py:842] (2/4) Epoch 8, batch 9050, loss[loss=0.312, simple_loss=0.3609, pruned_loss=0.1316, over 5202.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3003, pruned_loss=0.0733, over 1397739.68 frames.], batch size: 56, lr: 6.31e-04
2022-05-27 05:08:11,471 INFO [train.py:842] (2/4) Epoch 8, batch 9100, loss[loss=0.217, simple_loss=0.2903, pruned_loss=0.07181, over 5150.00 frames.], tot_loss[loss=0.2233, simple_loss=0.2993, pruned_loss=0.07364, over 1382771.33 frames.], batch size: 52, lr: 6.31e-04
2022-05-27 05:08:49,172 INFO [train.py:842] (2/4) Epoch 8, batch 9150, loss[loss=0.2501, simple_loss=0.3259, pruned_loss=0.08714, over 5290.00 frames.], tot_loss[loss=0.2297, simple_loss=0.3039, pruned_loss=0.07777, over 1305087.68 frames.], batch size: 53, lr: 6.31e-04
2022-05-27 05:09:41,243 INFO [train.py:842] (2/4) Epoch 9, batch 0, loss[loss=0.2356, simple_loss=0.3116, pruned_loss=0.07979, over 7179.00 frames.], tot_loss[loss=0.2356, simple_loss=0.3116, pruned_loss=0.07979, over 7179.00 frames.], batch size: 23, lr: 6.05e-04
2022-05-27 05:10:19,730 INFO [train.py:842] (2/4) Epoch 9, batch 50, loss[loss=0.2715, simple_loss=0.328, pruned_loss=0.1075, over 7123.00 frames.], tot_loss[loss=0.2241, simple_loss=0.302, pruned_loss=0.07305, over 319299.00 frames.], batch size: 28, lr: 6.05e-04
2022-05-27 05:10:58,685 INFO [train.py:842] (2/4) Epoch 9, batch 100, loss[loss=0.189, simple_loss=0.2837, pruned_loss=0.04713, over 7240.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2964, pruned_loss=0.06991, over 566312.02 frames.], batch size: 20, lr: 6.05e-04
2022-05-27 05:11:37,203 INFO [train.py:842] (2/4) Epoch 9, batch 150, loss[loss=0.2786, simple_loss=0.333, pruned_loss=0.112, over 5104.00 frames.], tot_loss[loss=0.2167, simple_loss=0.296, pruned_loss=0.06872, over 753564.59 frames.], batch size: 54, lr: 6.05e-04
2022-05-27 05:12:15,908 INFO [train.py:842] (2/4) Epoch 9, batch 200, loss[loss=0.2372, simple_loss=0.322, pruned_loss=0.07615, over 7205.00 frames.], tot_loss[loss=0.2198, simple_loss=0.2986, pruned_loss=0.07053, over 902563.49 frames.], batch size: 22, lr: 6.04e-04
2022-05-27 05:13:04,719 INFO [train.py:842] (2/4) Epoch 9, batch 250, loss[loss=0.2178, simple_loss=0.2992, pruned_loss=0.0682, over 7435.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2974, pruned_loss=0.06954, over 1018767.85 frames.], batch size: 20, lr: 6.04e-04
2022-05-27 05:13:43,498 INFO [train.py:842] (2/4) Epoch 9, batch 300, loss[loss=0.2625, simple_loss=0.3411, pruned_loss=0.09195, over 7341.00 frames.], tot_loss[loss=0.2177, simple_loss=0.2968, pruned_loss=0.06925, over 1104748.50 frames.], batch size: 22, lr: 6.04e-04
2022-05-27 05:14:22,313 INFO [train.py:842] (2/4) Epoch 9, batch 350, loss[loss=0.1659, simple_loss=0.2476, pruned_loss=0.04212, over 7160.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2943, pruned_loss=0.06822, over 1178569.49 frames.], batch size: 19, lr: 6.04e-04
2022-05-27 05:15:01,119 INFO [train.py:842] (2/4) Epoch 9, batch 400, loss[loss=0.2654, simple_loss=0.331, pruned_loss=0.09992, over 7151.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2946, pruned_loss=0.0684, over 1237568.56 frames.], batch size: 17, lr: 6.04e-04
2022-05-27 05:15:39,703 INFO [train.py:842] (2/4) Epoch 9, batch 450, loss[loss=0.1844, simple_loss=0.2767, pruned_loss=0.04605, over 7260.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2948, pruned_loss=0.06864, over 1278089.08 frames.], batch size: 19, lr: 6.03e-04
2022-05-27 05:16:18,378 INFO [train.py:842] (2/4) Epoch 9, batch 500, loss[loss=0.1825, simple_loss=0.2585, pruned_loss=0.0533, over 7393.00 frames.], tot_loss[loss=0.217, simple_loss=0.2956, pruned_loss=0.06919, over 1311067.10 frames.], batch size: 18, lr: 6.03e-04
2022-05-27 05:16:57,101 INFO [train.py:842] (2/4) Epoch 9, batch 550, loss[loss=0.232, simple_loss=0.3086, pruned_loss=0.07766, over 7446.00 frames.], tot_loss[loss=0.2171, simple_loss=0.296, pruned_loss=0.06905, over 1339228.13 frames.], batch size: 19, lr: 6.03e-04
2022-05-27 05:17:36,147 INFO [train.py:842] (2/4) Epoch 9, batch 600, loss[loss=0.2013, simple_loss=0.2766, pruned_loss=0.06303, over 7079.00 frames.], tot_loss[loss=0.2178, simple_loss=0.2965, pruned_loss=0.06959, over 1361322.16 frames.], batch size: 18, lr: 6.03e-04
2022-05-27 05:18:14,768 INFO [train.py:842] (2/4) Epoch 9, batch 650, loss[loss=0.2182, simple_loss=0.2863, pruned_loss=0.0751, over 7357.00 frames.], tot_loss[loss=0.2182, simple_loss=0.297, pruned_loss=0.06975, over 1374330.52 frames.], batch size: 19, lr: 6.03e-04
2022-05-27 05:18:53,497 INFO [train.py:842] (2/4) Epoch 9, batch 700, loss[loss=0.2142, simple_loss=0.29, pruned_loss=0.06921, over 7442.00 frames.], tot_loss[loss=0.2202, simple_loss=0.2986, pruned_loss=0.0709, over 1386909.86 frames.], batch size: 20, lr: 6.02e-04
2022-05-27 05:19:32,069 INFO [train.py:842] (2/4) Epoch 9, batch 750, loss[loss=0.1835, simple_loss=0.2619, pruned_loss=0.05256, over 7165.00 frames.], tot_loss[loss=0.2206, simple_loss=0.2983, pruned_loss=0.07139, over 1390608.19 frames.], batch size: 18, lr: 6.02e-04
2022-05-27 05:20:11,006 INFO [train.py:842] (2/4) Epoch 9, batch 800, loss[loss=0.2087, simple_loss=0.298, pruned_loss=0.05966, over 7396.00 frames.], tot_loss[loss=0.2196, simple_loss=0.2978, pruned_loss=0.07071, over 1396369.14 frames.], batch size: 23, lr: 6.02e-04
2022-05-27 05:20:49,557 INFO [train.py:842] (2/4) Epoch 9, batch 850, loss[loss=0.2228, simple_loss=0.3061, pruned_loss=0.06971, over 7321.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2975, pruned_loss=0.07066, over 1401605.39 frames.], batch size: 21, lr: 6.02e-04
2022-05-27 05:21:28,782 INFO [train.py:842] (2/4) Epoch 9, batch 900, loss[loss=0.251, simple_loss=0.3186, pruned_loss=0.09173, over 7229.00 frames.], tot_loss[loss=0.219, simple_loss=0.297, pruned_loss=0.07056, over 1410892.81 frames.], batch size: 21, lr: 6.02e-04
2022-05-27 05:22:07,454 INFO [train.py:842] (2/4) Epoch 9, batch 950, loss[loss=0.1932, simple_loss=0.278, pruned_loss=0.05416, over 7318.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2961, pruned_loss=0.07053, over 1408227.03 frames.], batch size: 20, lr: 6.01e-04
2022-05-27 05:22:46,360 INFO [train.py:842] (2/4) Epoch 9, batch 1000, loss[loss=0.2446, simple_loss=0.3257, pruned_loss=0.08175, over 7429.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2952, pruned_loss=0.06983, over 1412585.20 frames.], batch size: 20, lr: 6.01e-04
2022-05-27 05:23:24,852 INFO [train.py:842] (2/4) Epoch 9, batch 1050, loss[loss=0.2169, simple_loss=0.2883, pruned_loss=0.07277, over 7255.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2955, pruned_loss=0.06976, over 1417263.85 frames.], batch size: 19, lr: 6.01e-04
2022-05-27 05:24:03,718 INFO [train.py:842] (2/4) Epoch 9, batch 1100, loss[loss=0.1901, simple_loss=0.2584, pruned_loss=0.0609, over 7279.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2958, pruned_loss=0.06889, over 1420163.45 frames.], batch size: 17, lr: 6.01e-04
2022-05-27 05:24:42,288 INFO [train.py:842] (2/4) Epoch 9, batch 1150, loss[loss=0.3245, simple_loss=0.3698, pruned_loss=0.1396, over 7313.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2949, pruned_loss=0.06911, over 1419724.24 frames.], batch size: 25, lr: 6.01e-04
2022-05-27 05:25:21,214 INFO [train.py:842] (2/4) Epoch 9, batch 1200, loss[loss=0.1834, simple_loss=0.2691, pruned_loss=0.04886, over 7450.00 frames.], tot_loss[loss=0.2171, simple_loss=0.2954, pruned_loss=0.06938, over 1420662.64 frames.], batch size: 20, lr: 6.00e-04
2022-05-27 05:25:59,859 INFO [train.py:842] (2/4) Epoch 9, batch 1250, loss[loss=0.1824, simple_loss=0.2558, pruned_loss=0.05455, over 7247.00 frames.], tot_loss[loss=0.217, simple_loss=0.2948, pruned_loss=0.06959, over 1417729.44 frames.], batch size: 16, lr: 6.00e-04
2022-05-27 05:26:38,571 INFO [train.py:842] (2/4) Epoch 9, batch 1300, loss[loss=0.2302, simple_loss=0.3092, pruned_loss=0.07558, over 7156.00 frames.], tot_loss[loss=0.217, simple_loss=0.2953, pruned_loss=0.06934, over 1414428.17 frames.], batch size: 19, lr: 6.00e-04
2022-05-27 05:27:17,257 INFO [train.py:842] (2/4) Epoch 9, batch 1350, loss[loss=0.1744, simple_loss=0.2638, pruned_loss=0.04247, over 7425.00 frames.], tot_loss[loss=0.2171, simple_loss=0.2954, pruned_loss=0.06939, over 1419208.36 frames.], batch size: 20, lr: 6.00e-04
2022-05-27 05:27:56,069 INFO [train.py:842] (2/4) Epoch 9, batch 1400, loss[loss=0.2009, simple_loss=0.2811, pruned_loss=0.06031, over 7220.00 frames.], tot_loss[loss=0.2178, simple_loss=0.2957, pruned_loss=0.06997, over 1416001.40 frames.], batch size: 21, lr: 6.00e-04
2022-05-27 05:28:34,848 INFO [train.py:842] (2/4) Epoch 9, batch 1450, loss[loss=0.2977, simple_loss=0.3667, pruned_loss=0.1143, over 7315.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2955, pruned_loss=0.06987, over 1421442.80 frames.], batch size: 21, lr: 5.99e-04
2022-05-27 05:29:13,624 INFO [train.py:842] (2/4) Epoch 9, batch 1500, loss[loss=0.1841, simple_loss=0.2675, pruned_loss=0.05034, over 7223.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2953, pruned_loss=0.06952, over 1423933.25 frames.], batch size: 20, lr: 5.99e-04
2022-05-27 05:29:52,242 INFO [train.py:842] (2/4) Epoch 9, batch 1550, loss[loss=0.2188, simple_loss=0.2996, pruned_loss=0.06899, over 7213.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2963, pruned_loss=0.07043, over 1422954.43 frames.], batch size: 22, lr: 5.99e-04
2022-05-27 05:30:51,691 INFO [train.py:842] (2/4) Epoch 9, batch 1600, loss[loss=0.2258, simple_loss=0.2985, pruned_loss=0.07655, over 7065.00 frames.], tot_loss[loss=0.2202, simple_loss=0.298, pruned_loss=0.07125, over 1421175.06 frames.], batch size: 18, lr: 5.99e-04
2022-05-27 05:31:40,489 INFO [train.py:842] (2/4) Epoch 9, batch 1650, loss[loss=0.2147, simple_loss=0.3023, pruned_loss=0.06356, over 7118.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2969, pruned_loss=0.07016, over 1422431.59 frames.], batch size: 21, lr: 5.99e-04
2022-05-27 05:32:19,069 INFO [train.py:842] (2/4) Epoch 9, batch 1700, loss[loss=0.1972, simple_loss=0.2891, pruned_loss=0.05264, over 7143.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2976, pruned_loss=0.07026, over 1420518.47 frames.], batch size: 20, lr: 5.98e-04
2022-05-27 05:32:57,921 INFO [train.py:842] (2/4) Epoch 9, batch 1750, loss[loss=0.2228, simple_loss=0.3004, pruned_loss=0.07261, over 7325.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2974, pruned_loss=0.0702, over 1421622.25 frames.], batch size: 21, lr: 5.98e-04
2022-05-27 05:33:36,469 INFO [train.py:842] (2/4) Epoch 9, batch 1800, loss[loss=0.211, simple_loss=0.2938, pruned_loss=0.06415, over 7242.00 frames.], tot_loss[loss=0.218, simple_loss=0.2967, pruned_loss=0.06961, over 1418498.41 frames.], batch size: 20, lr: 5.98e-04
2022-05-27 05:34:14,969 INFO [train.py:842] (2/4) Epoch 9, batch 1850, loss[loss=0.234, simple_loss=0.313, pruned_loss=0.07746, over 7234.00 frames.], tot_loss[loss=0.219, simple_loss=0.2979, pruned_loss=0.07006, over 1420929.31 frames.], batch size: 20, lr: 5.98e-04
2022-05-27 05:34:54,162 INFO [train.py:842] (2/4) Epoch 9, batch 1900, loss[loss=0.2181, simple_loss=0.2977, pruned_loss=0.06928, over 7158.00 frames.], tot_loss[loss=0.2205, simple_loss=0.299, pruned_loss=0.07103, over 1419420.67 frames.], batch size: 19, lr: 5.98e-04
2022-05-27 05:35:32,743 INFO [train.py:842] (2/4) Epoch 9, batch 1950, loss[loss=0.1907, simple_loss=0.2826, pruned_loss=0.04941, over 7118.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2982, pruned_loss=0.07067, over 1420954.15 frames.], batch size: 21, lr: 5.97e-04
2022-05-27 05:36:11,674 INFO [train.py:842] (2/4) Epoch 9, batch 2000, loss[loss=0.2151, simple_loss=0.2918, pruned_loss=0.06918, over 7294.00 frames.], tot_loss[loss=0.2177, simple_loss=0.2966, pruned_loss=0.06947, over 1422728.83 frames.], batch size: 24, lr: 5.97e-04
2022-05-27 05:36:50,196 INFO [train.py:842] (2/4) Epoch 9, batch 2050, loss[loss=0.1491, simple_loss=0.2242, pruned_loss=0.03695, over 7271.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2968, pruned_loss=0.07022, over 1422172.91 frames.], batch size: 17, lr: 5.97e-04
2022-05-27 05:37:28,864 INFO [train.py:842] (2/4) Epoch 9, batch 2100, loss[loss=0.2001, simple_loss=0.2772, pruned_loss=0.0615, over 7263.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2971, pruned_loss=0.07052, over 1423508.36 frames.], batch size: 19, lr: 5.97e-04
2022-05-27 05:38:07,465 INFO [train.py:842] (2/4) Epoch 9, batch 2150, loss[loss=0.1852, simple_loss=0.2733, pruned_loss=0.04851, over 7059.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2961, pruned_loss=0.06988, over 1425851.36 frames.], batch size: 18, lr: 5.97e-04
2022-05-27 05:38:46,494 INFO [train.py:842] (2/4) Epoch 9, batch 2200, loss[loss=0.2454, simple_loss=0.3004, pruned_loss=0.09524, over 7262.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2956, pruned_loss=0.06937, over 1423960.70 frames.], batch size: 17, lr: 5.96e-04
2022-05-27 05:39:25,141 INFO [train.py:842] (2/4) Epoch 9, batch 2250, loss[loss=0.1917, simple_loss=0.2715, pruned_loss=0.05595, over 7174.00 frames.], tot_loss[loss=0.2163, simple_loss=0.295, pruned_loss=0.06883, over 1424528.79 frames.], batch size: 18, lr: 5.96e-04
2022-05-27 05:40:03,861 INFO [train.py:842] (2/4) Epoch 9, batch 2300, loss[loss=0.2042, simple_loss=0.2825, pruned_loss=0.06294, over 7149.00 frames.], tot_loss[loss=0.2148, simple_loss=0.294, pruned_loss=0.06781, over 1425317.61 frames.], batch size: 20, lr: 5.96e-04
2022-05-27 05:40:42,429 INFO [train.py:842] (2/4) Epoch 9, batch 2350, loss[loss=0.2456, simple_loss=0.3122, pruned_loss=0.08947, over 6795.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2939, pruned_loss=0.06791, over 1423373.89 frames.], batch size: 31, lr: 5.96e-04
2022-05-27 05:41:21,196 INFO [train.py:842] (2/4) Epoch 9, batch 2400, loss[loss=0.2071, simple_loss=0.2884, pruned_loss=0.06288, over 7263.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2945, pruned_loss=0.06868, over 1423918.36 frames.], batch size: 18, lr: 5.96e-04
2022-05-27 05:41:59,638 INFO [train.py:842] (2/4) Epoch 9, batch 2450, loss[loss=0.257, simple_loss=0.3151, pruned_loss=0.09951, over 7401.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2941, pruned_loss=0.06851, over 1425563.16 frames.], batch size: 18, lr: 5.95e-04
2022-05-27 05:42:38,357 INFO [train.py:842] (2/4) Epoch 9, batch 2500, loss[loss=0.2497, simple_loss=0.3288, pruned_loss=0.08528, over 7201.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2941, pruned_loss=0.06857, over 1424845.75 frames.], batch size: 22, lr: 5.95e-04
2022-05-27 05:43:16,889 INFO [train.py:842] (2/4) Epoch 9, batch 2550, loss[loss=0.1727, simple_loss=0.2512, pruned_loss=0.04713, over 7130.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2937, pruned_loss=0.06847, over 1423117.70 frames.], batch size: 17, lr: 5.95e-04
2022-05-27 05:43:55,664 INFO [train.py:842] (2/4) Epoch 9, batch 2600, loss[loss=0.2949, simple_loss=0.3609, pruned_loss=0.1144, over 7357.00 frames.], tot_loss[loss=0.2178, simple_loss=0.2959, pruned_loss=0.06985, over 1419710.84 frames.], batch size: 23, lr: 5.95e-04
2022-05-27 05:44:34,265 INFO [train.py:842] (2/4) Epoch 9, batch 2650, loss[loss=0.2389, simple_loss=0.3091, pruned_loss=0.08431, over 5004.00 frames.], tot_loss[loss=0.2173, simple_loss=0.2954, pruned_loss=0.0696, over 1417780.63 frames.], batch size: 52, lr: 5.95e-04
2022-05-27 05:45:13,035 INFO [train.py:842] (2/4) Epoch 9, batch 2700, loss[loss=0.2758, simple_loss=0.3386, pruned_loss=0.1064, over 7347.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2969, pruned_loss=0.07024, over 1419505.30 frames.], batch size: 22, lr: 5.94e-04
2022-05-27 05:45:51,574 INFO [train.py:842] (2/4) Epoch 9, batch 2750, loss[loss=0.1907, simple_loss=0.2682, pruned_loss=0.05663, over 7331.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2954, pruned_loss=0.06905, over 1424368.35 frames.], batch size: 20, lr: 5.94e-04
2022-05-27 05:46:30,153 INFO [train.py:842] (2/4) Epoch 9, batch 2800, loss[loss=0.1949, simple_loss=0.2748, pruned_loss=0.05746, over 7200.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2957, pruned_loss=0.06904, over 1426824.83 frames.], batch size: 22, lr: 5.94e-04
2022-05-27 05:47:08,797 INFO [train.py:842] (2/4) Epoch 9, batch 2850, loss[loss=0.2114, simple_loss=0.2784, pruned_loss=0.07216, over 7160.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2954, pruned_loss=0.06901, over 1429433.14 frames.], batch size: 19, lr: 5.94e-04
2022-05-27 05:47:47,787 INFO [train.py:842] (2/4) Epoch 9, batch 2900, loss[loss=0.2146, simple_loss=0.2971, pruned_loss=0.06601, over 7313.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2948, pruned_loss=0.06829, over 1428291.48 frames.], batch size: 21, lr: 5.94e-04
2022-05-27 05:48:26,353 INFO [train.py:842] (2/4) Epoch 9, batch 2950, loss[loss=0.1948, simple_loss=0.2783, pruned_loss=0.05565, over 7273.00 frames.], tot_loss[loss=0.216, simple_loss=0.295, pruned_loss=0.0685, over 1424688.11 frames.], batch size: 18, lr: 5.94e-04
2022-05-27 05:49:05,571 INFO [train.py:842] (2/4) Epoch 9, batch 3000, loss[loss=0.2191, simple_loss=0.2966, pruned_loss=0.07081, over 7272.00 frames.], tot_loss[loss=0.216, simple_loss=0.2947, pruned_loss=0.06862, over 1422716.73 frames.], batch size: 24, lr: 5.93e-04
2022-05-27 05:49:05,572 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 05:49:14,865 INFO [train.py:871] (2/4) Epoch 9, validation: loss=0.1779, simple_loss=0.2778, pruned_loss=0.039, over 868885.00 frames.
2022-05-27 05:49:53,753 INFO [train.py:842] (2/4) Epoch 9, batch 3050, loss[loss=0.2177, simple_loss=0.2996, pruned_loss=0.06791, over 7333.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2949, pruned_loss=0.06878, over 1418857.35 frames.], batch size: 20, lr: 5.93e-04
2022-05-27 05:50:32,332 INFO [train.py:842] (2/4) Epoch 9, batch 3100, loss[loss=0.2635, simple_loss=0.3353, pruned_loss=0.09591, over 6744.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2942, pruned_loss=0.06798, over 1414119.87 frames.], batch size: 31, lr: 5.93e-04
2022-05-27 05:51:10,968 INFO [train.py:842] (2/4) Epoch 9, batch 3150, loss[loss=0.1746, simple_loss=0.261, pruned_loss=0.04412, over 7164.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2927, pruned_loss=0.06697, over 1418252.00 frames.], batch size: 19, lr: 5.93e-04
2022-05-27 05:51:50,048 INFO [train.py:842] (2/4) Epoch 9, batch 3200, loss[loss=0.2108, simple_loss=0.3013, pruned_loss=0.06016, over 7145.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2934, pruned_loss=0.06764, over 1421902.67 frames.], batch size: 20, lr: 5.93e-04
2022-05-27 05:52:28,397 INFO [train.py:842] (2/4) Epoch 9, batch 3250, loss[loss=0.2669, simple_loss=0.3305, pruned_loss=0.1017, over 4856.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2949, pruned_loss=0.0687, over 1420936.44 frames.], batch size: 52, lr: 5.92e-04
2022-05-27 05:53:07,201 INFO [train.py:842] (2/4) Epoch 9, batch 3300, loss[loss=0.2043, simple_loss=0.2946, pruned_loss=0.05698, over 7202.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2945, pruned_loss=0.06857, over 1420935.33 frames.], batch size: 22, lr: 5.92e-04
2022-05-27 05:53:45,791 INFO [train.py:842] (2/4) Epoch 9, batch 3350, loss[loss=0.2033, simple_loss=0.2833, pruned_loss=0.06165, over 7261.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2947, pruned_loss=0.0689, over 1424181.93 frames.], batch size: 19, lr: 5.92e-04
2022-05-27 05:54:24,437 INFO [train.py:842] (2/4) Epoch 9, batch 3400, loss[loss=0.2605, simple_loss=0.3387, pruned_loss=0.0911, over 6734.00 frames.], tot_loss[loss=0.217, simple_loss=0.2953, pruned_loss=0.06935, over 1422119.80 frames.], batch size: 31, lr: 5.92e-04
2022-05-27 05:55:02,979 INFO [train.py:842] (2/4) Epoch 9, batch 3450, loss[loss=0.1898, simple_loss=0.2603, pruned_loss=0.05961, over 7413.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2957, pruned_loss=0.0697, over 1423699.69 frames.], batch size: 18, lr: 5.92e-04
2022-05-27 05:55:41,913 INFO [train.py:842] (2/4) Epoch 9, batch 3500, loss[loss=0.241, simple_loss=0.3109, pruned_loss=0.08558, over 7172.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2968, pruned_loss=0.07053, over 1424428.29 frames.], batch size: 19, lr: 5.91e-04
2022-05-27 05:56:20,577 INFO [train.py:842] (2/4) Epoch 9, batch 3550, loss[loss=0.2078, simple_loss=0.2845, pruned_loss=0.06554, over 7162.00 frames.], tot_loss[loss=0.2183, simple_loss=0.2961, pruned_loss=0.07021, over 1426064.19 frames.], batch size: 18, lr: 5.91e-04
2022-05-27 05:56:59,461 INFO [train.py:842] (2/4) Epoch 9, batch 3600, loss[loss=0.2297, simple_loss=0.2938, pruned_loss=0.08282, over 7275.00 frames.], tot_loss[loss=0.218, simple_loss=0.2958, pruned_loss=0.07013, over 1423550.92 frames.], batch size: 18, lr: 5.91e-04
2022-05-27 05:57:38,129 INFO [train.py:842] (2/4) Epoch 9, batch 3650, loss[loss=0.2099, simple_loss=0.2735, pruned_loss=0.07316, over 7128.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2954, pruned_loss=0.06995, over 1425019.57 frames.], batch size: 17, lr: 5.91e-04
2022-05-27 05:58:16,860 INFO [train.py:842] (2/4) Epoch 9, batch 3700, loss[loss=0.2347, simple_loss=0.3137, pruned_loss=0.07786, over 7298.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2955, pruned_loss=0.06959, over 1425420.08 frames.], batch size: 25, lr: 5.91e-04
2022-05-27 05:58:55,400 INFO [train.py:842] (2/4) Epoch 9, batch 3750, loss[loss=0.1868, simple_loss=0.2714, pruned_loss=0.05109, over 7421.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2955, pruned_loss=0.06896, over 1425308.29 frames.], batch size: 20, lr: 5.90e-04
2022-05-27 05:59:34,182 INFO [train.py:842] (2/4) Epoch 9, batch 3800, loss[loss=0.1833, simple_loss=0.274, pruned_loss=0.04632, over 7324.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2951, pruned_loss=0.06855, over 1427237.53 frames.], batch size: 21, lr: 5.90e-04
2022-05-27 06:00:12,776 INFO [train.py:842] (2/4) Epoch 9, batch 3850, loss[loss=0.2348, simple_loss=0.3118, pruned_loss=0.07888, over 7440.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2939, pruned_loss=0.06795, over 1429040.20 frames.], batch size: 20, lr: 5.90e-04
2022-05-27 06:00:51,517 INFO [train.py:842] (2/4) Epoch 9, batch 3900, loss[loss=0.2453, simple_loss=0.315, pruned_loss=0.0878, over 7261.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2945, pruned_loss=0.06804, over 1428180.47 frames.], batch size: 19, lr: 5.90e-04
2022-05-27 06:01:30,201 INFO [train.py:842] (2/4) Epoch 9, batch 3950, loss[loss=0.2491, simple_loss=0.3288, pruned_loss=0.08471, over 7259.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2949, pruned_loss=0.06868, over 1425809.37 frames.], batch size: 19, lr: 5.90e-04
2022-05-27 06:02:08,915 INFO [train.py:842] (2/4) Epoch 9, batch 4000, loss[loss=0.2031, simple_loss=0.2891, pruned_loss=0.05855, over 7113.00 frames.], tot_loss[loss=0.2169, simple_loss=0.296, pruned_loss=0.06888, over 1424519.37 frames.], batch size: 21, lr: 5.89e-04
2022-05-27 06:02:47,512 INFO [train.py:842] (2/4) Epoch 9, batch 4050, loss[loss=0.1978, simple_loss=0.288, pruned_loss=0.0538, over 7326.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2957, pruned_loss=0.06891, over 1422629.59 frames.], batch size: 20, lr: 5.89e-04
2022-05-27 06:03:26,336 INFO [train.py:842] (2/4) Epoch 9, batch 4100, loss[loss=0.1758, simple_loss=0.242, pruned_loss=0.0548, over 7276.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2953, pruned_loss=0.06881, over 1425474.29 frames.], batch size: 17, lr: 5.89e-04
2022-05-27 06:04:05,008 INFO [train.py:842] (2/4) Epoch 9, batch 4150, loss[loss=0.2399, simple_loss=0.3201, pruned_loss=0.0799, over 7209.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2956, pruned_loss=0.06908, over 1428623.00 frames.], batch size: 22, lr: 5.89e-04
2022-05-27 06:04:43,804 INFO [train.py:842] (2/4) Epoch 9, batch 4200, loss[loss=0.163, simple_loss=0.2321, pruned_loss=0.04696, over 7423.00 frames.], tot_loss[loss=0.2171, simple_loss=0.2956, pruned_loss=0.0693, over 1423926.10 frames.], batch size: 17, lr: 5.89e-04
2022-05-27 06:05:22,357 INFO [train.py:842] (2/4) Epoch 9, batch 4250, loss[loss=0.2038, simple_loss=0.2935, pruned_loss=0.057, over 7080.00 frames.], tot_loss[loss=0.2177, simple_loss=0.2957, pruned_loss=0.06985, over 1422678.79 frames.], batch size: 28, lr: 5.89e-04
2022-05-27 06:06:01,305 INFO [train.py:842] (2/4) Epoch 9, batch 4300, loss[loss=0.2368, simple_loss=0.3194, pruned_loss=0.07717, over 7405.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2959, pruned_loss=0.06998, over 1423680.06 frames.], batch size: 21, lr: 5.88e-04
2022-05-27 06:06:39,797 INFO [train.py:842] (2/4) Epoch 9, batch 4350, loss[loss=0.2286, simple_loss=0.2866, pruned_loss=0.08534, over 7017.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2956, pruned_loss=0.06975, over 1418225.83 frames.], batch size: 16, lr: 5.88e-04
2022-05-27 06:07:18,602 INFO [train.py:842] (2/4) Epoch 9, batch 4400, loss[loss=0.2543, simple_loss=0.3294, pruned_loss=0.08965, over 6196.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2959, pruned_loss=0.07023, over 1416335.34 frames.], batch size: 37, lr: 5.88e-04
2022-05-27 06:07:57,172 INFO [train.py:842] (2/4) Epoch 9, batch 4450, loss[loss=0.2424, simple_loss=0.3265, pruned_loss=0.0792, over 7378.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2969, pruned_loss=0.07035, over 1420098.24 frames.], batch size: 23, lr: 5.88e-04
2022-05-27 06:08:35,958 INFO [train.py:842] (2/4) Epoch 9, batch 4500, loss[loss=0.2426, simple_loss=0.3263, pruned_loss=0.07944, over 7201.00 frames.], tot_loss[loss=0.2183, simple_loss=0.2966, pruned_loss=0.07001, over 1422134.10 frames.], batch size: 23, lr: 5.88e-04
2022-05-27 06:09:14,576 INFO [train.py:842] (2/4) Epoch 9, batch 4550, loss[loss=0.2007, simple_loss=0.2759, pruned_loss=0.0628, over 7214.00 frames.], tot_loss[loss=0.2164, simple_loss=0.295, pruned_loss=0.06884, over 1423468.74 frames.], batch size: 26, lr: 5.87e-04
2022-05-27 06:09:53,677 INFO [train.py:842] (2/4) Epoch 9, batch 4600, loss[loss=0.1982, simple_loss=0.2702, pruned_loss=0.0631, over 7071.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2946, pruned_loss=0.06907, over 1423585.88 frames.], batch size: 18, lr: 5.87e-04
2022-05-27 06:10:32,399 INFO [train.py:842] (2/4) Epoch 9, batch 4650, loss[loss=0.1771, simple_loss=0.2596, pruned_loss=0.0473, over 7152.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2937, pruned_loss=0.06852, over 1422400.48 frames.], batch size: 19, lr: 5.87e-04
2022-05-27 06:11:11,205 INFO [train.py:842] (2/4) Epoch 9, batch 4700, loss[loss=0.2225, simple_loss=0.3181, pruned_loss=0.06339, over 7297.00 frames.], tot_loss[loss=0.2145, simple_loss=0.2929, pruned_loss=0.06804, over 1421133.82 frames.], batch size: 24, lr: 5.87e-04
2022-05-27 06:11:49,745 INFO [train.py:842] (2/4) Epoch 9, batch 4750, loss[loss=0.2028, simple_loss=0.2907, pruned_loss=0.0574, over 7205.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2945, pruned_loss=0.06914, over 1418608.13 frames.], batch size: 23, lr: 5.87e-04
2022-05-27 06:12:28,763 INFO [train.py:842] (2/4) Epoch 9, batch 4800, loss[loss=0.2006, simple_loss=0.2668, pruned_loss=0.06717, over 7403.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2927, pruned_loss=0.06835, over 1417954.12 frames.], batch size: 18, lr: 5.86e-04
2022-05-27 06:13:07,353 INFO [train.py:842] (2/4) Epoch 9, batch 4850, loss[loss=0.2254, simple_loss=0.2845, pruned_loss=0.08318, over 7288.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2913, pruned_loss=0.06754, over 1421502.76 frames.], batch size: 17, lr: 5.86e-04
2022-05-27 06:13:46,258 INFO [train.py:842] (2/4) Epoch 9, batch 4900, loss[loss=0.225, simple_loss=0.2966, pruned_loss=0.07666, over 5118.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2923, pruned_loss=0.0678, over 1419521.73 frames.], batch size: 52, lr: 5.86e-04
2022-05-27 06:14:24,869 INFO [train.py:842] (2/4) Epoch 9, batch 4950, loss[loss=0.2424, simple_loss=0.3168, pruned_loss=0.08401, over 7323.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2921, pruned_loss=0.06768, over 1420895.33 frames.], batch size: 25, lr: 5.86e-04
2022-05-27 06:15:03,652 INFO [train.py:842] (2/4) Epoch 9, batch 5000, loss[loss=0.1975, simple_loss=0.2822, pruned_loss=0.05644, over 7276.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2925, pruned_loss=0.06748, over 1418380.34 frames.], batch size: 24, lr: 5.86e-04
2022-05-27 06:15:42,258 INFO [train.py:842] (2/4) Epoch 9, batch 5050, loss[loss=0.1853, simple_loss=0.2694, pruned_loss=0.05062, over 7431.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2923, pruned_loss=0.06705, over 1419837.69 frames.], batch size: 20, lr: 5.86e-04
2022-05-27 06:16:21,069 INFO [train.py:842] (2/4) Epoch 9, batch 5100, loss[loss=0.2142, simple_loss=0.2981, pruned_loss=0.06517, over 6390.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2932, pruned_loss=0.06727, over 1421015.55 frames.], batch size: 37, lr: 5.85e-04
2022-05-27 06:16:59,713 INFO [train.py:842] (2/4) Epoch 9, batch 5150, loss[loss=0.2003, simple_loss=0.2952, pruned_loss=0.05269, over 7141.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2942, pruned_loss=0.06831, over 1422765.90 frames.], batch size: 20, lr: 5.85e-04
2022-05-27 06:17:38,692 INFO [train.py:842] (2/4) Epoch 9, batch 5200, loss[loss=0.2098, simple_loss=0.2937, pruned_loss=0.06299, over 7326.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2954, pruned_loss=0.06913, over 1424193.46 frames.], batch size: 20, lr: 5.85e-04
2022-05-27 06:18:17,378 INFO [train.py:842] (2/4) Epoch 9, batch 5250, loss[loss=0.2095, simple_loss=0.2818, pruned_loss=0.06854, over 7222.00 frames.], tot_loss[loss=0.2178, simple_loss=0.2963, pruned_loss=0.06969, over 1425060.73 frames.], batch size: 20, lr: 5.85e-04
2022-05-27 06:18:56,024 INFO [train.py:842] (2/4) Epoch 9, batch 5300, loss[loss=0.2059, simple_loss=0.2929, pruned_loss=0.05945, over 7427.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2959, pruned_loss=0.0696, over 1422227.60 frames.], batch size: 20, lr: 5.85e-04
2022-05-27 06:19:34,525 INFO [train.py:842] (2/4) Epoch 9, batch 5350, loss[loss=0.21, simple_loss=0.3025, pruned_loss=0.05876, over 7289.00 frames.], tot_loss[loss=0.2197, simple_loss=0.298, pruned_loss=0.07065, over 1423966.60 frames.], batch size: 24, lr: 5.84e-04
2022-05-27 06:20:13,321 INFO [train.py:842] (2/4) Epoch 9, batch 5400, loss[loss=0.3143, simple_loss=0.3697, pruned_loss=0.1294, over 5134.00 frames.], tot_loss[loss=0.2203, simple_loss=0.2985, pruned_loss=0.07103, over 1418196.51 frames.], batch size: 52, lr: 5.84e-04
2022-05-27 06:20:51,799 INFO [train.py:842] (2/4) Epoch 9, batch 5450, loss[loss=0.2111, simple_loss=0.2898, pruned_loss=0.06616, over 6778.00 frames.], tot_loss[loss=0.2199, simple_loss=0.2979, pruned_loss=0.07099, over 1419059.80 frames.], batch size: 31, lr: 5.84e-04
2022-05-27 06:21:30,672 INFO [train.py:842] (2/4) Epoch 9, batch 5500, loss[loss=0.1951, simple_loss=0.2858, pruned_loss=0.05217, over 7124.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2981, pruned_loss=0.07063, over 1419650.50 frames.], batch size: 21, lr: 5.84e-04
2022-05-27 06:22:09,227 INFO [train.py:842] (2/4) Epoch 9, batch 5550, loss[loss=0.3914, simple_loss=0.4193, pruned_loss=0.1818, over 5092.00 frames.], tot_loss[loss=0.2199, simple_loss=0.2982, pruned_loss=0.0708, over 1419299.98 frames.], batch size: 52, lr: 5.84e-04
2022-05-27 06:22:47,827 INFO [train.py:842] (2/4) Epoch 9, batch 5600, loss[loss=0.2067, simple_loss=0.2867, pruned_loss=0.06337, over 6819.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2959, pruned_loss=0.06927, over 1419378.79 frames.], batch size: 31, lr: 5.84e-04
2022-05-27 06:23:26,371 INFO [train.py:842] (2/4) Epoch 9, batch 5650, loss[loss=0.2138, simple_loss=0.302, pruned_loss=0.06277, over 7116.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2957, pruned_loss=0.06892, over 1420702.23 frames.], batch size: 21, lr: 5.83e-04
2022-05-27 06:24:05,339 INFO [train.py:842] (2/4) Epoch 9, batch 5700, loss[loss=0.195, simple_loss=0.2768, pruned_loss=0.05666, over 7233.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2955, pruned_loss=0.06968, over 1418880.52 frames.], batch size: 20, lr: 5.83e-04
2022-05-27 06:24:44,081 INFO [train.py:842] (2/4) Epoch 9, batch 5750, loss[loss=0.1941, simple_loss=0.2869, pruned_loss=0.05063, over 7122.00 frames.], tot_loss[loss=0.217, simple_loss=0.2956, pruned_loss=0.06921, over 1422866.23 frames.], batch size: 21, lr: 5.83e-04
2022-05-27 06:25:23,068 INFO [train.py:842] (2/4) Epoch 9, batch 5800, loss[loss=0.2169, simple_loss=0.2966, pruned_loss=0.06856, over 7309.00 frames.], tot_loss[loss=0.217, simple_loss=0.2961, pruned_loss=0.06893, over 1421040.56 frames.], batch size: 21, lr: 5.83e-04
2022-05-27 06:26:01,766 INFO [train.py:842] (2/4) Epoch 9, batch 5850, loss[loss=0.2119, simple_loss=0.2911, pruned_loss=0.06637, over 7152.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2971, pruned_loss=0.07014, over 1417645.97 frames.], batch size: 19, lr: 5.83e-04
2022-05-27 06:26:41,214 INFO [train.py:842] (2/4) Epoch 9, batch 5900, loss[loss=0.1851, simple_loss=0.261, pruned_loss=0.05463, over 7411.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2954, pruned_loss=0.06919, over 1421178.64 frames.], batch size: 18, lr: 5.82e-04
2022-05-27 06:27:19,684 INFO [train.py:842] (2/4) Epoch 9, batch 5950, loss[loss=0.2557, simple_loss=0.3283, pruned_loss=0.09159, over 7297.00 frames.], tot_loss[loss=0.2167, simple_loss=0.295, pruned_loss=0.06918, over 1422023.68 frames.], batch size: 24, lr: 5.82e-04
2022-05-27 06:27:58,479 INFO [train.py:842] (2/4) Epoch 9, batch 6000, loss[loss=0.1868, simple_loss=0.2597, pruned_loss=0.05698, over 7000.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2938, pruned_loss=0.06856, over 1421097.49 frames.], batch size: 16, lr: 5.82e-04
2022-05-27 06:27:58,479 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 06:28:07,748 INFO [train.py:871] (2/4) Epoch 9, validation: loss=0.1769, simple_loss=0.2771, pruned_loss=0.03838, over 868885.00 frames.
2022-05-27 06:28:46,384 INFO [train.py:842] (2/4) Epoch 9, batch 6050, loss[loss=0.186, simple_loss=0.2532, pruned_loss=0.0594, over 7201.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2932, pruned_loss=0.06797, over 1421807.16 frames.], batch size: 16, lr: 5.82e-04
2022-05-27 06:29:25,559 INFO [train.py:842] (2/4) Epoch 9, batch 6100, loss[loss=0.1957, simple_loss=0.2629, pruned_loss=0.0643, over 7281.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2939, pruned_loss=0.0684, over 1420589.94 frames.], batch size: 17, lr: 5.82e-04
2022-05-27 06:30:04,191 INFO [train.py:842] (2/4) Epoch 9, batch 6150, loss[loss=0.1923, simple_loss=0.2668, pruned_loss=0.0589, over 7286.00 frames.], tot_loss[loss=0.2183, simple_loss=0.2963, pruned_loss=0.07018, over 1418213.50 frames.], batch size: 18, lr: 5.82e-04
2022-05-27 06:30:42,918 INFO [train.py:842] (2/4) Epoch 9, batch 6200, loss[loss=0.167, simple_loss=0.2441, pruned_loss=0.045, over 7128.00 frames.], tot_loss[loss=0.2178, simple_loss=0.296, pruned_loss=0.06981, over 1420411.11 frames.], batch size: 17, lr: 5.81e-04
2022-05-27 06:31:21,483 INFO [train.py:842] (2/4) Epoch 9, batch 6250, loss[loss=0.197, simple_loss=0.2751, pruned_loss=0.0595, over 7429.00 frames.], tot_loss[loss=0.2178, simple_loss=0.2957, pruned_loss=0.06989, over 1420273.46 frames.], batch size: 20, lr: 5.81e-04
2022-05-27 06:32:00,443 INFO [train.py:842] (2/4) Epoch 9, batch 6300, loss[loss=0.263, simple_loss=0.3208, pruned_loss=0.1026, over 5179.00 frames.], tot_loss[loss=0.2181, simple_loss=0.296, pruned_loss=0.07008, over 1419888.42 frames.], batch size: 52, lr: 5.81e-04
2022-05-27 06:32:38,974 INFO [train.py:842] (2/4) Epoch 9, batch 6350, loss[loss=0.2337, simple_loss=0.3327, pruned_loss=0.06733, over 7419.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2969, pruned_loss=0.07039, over 1420772.70 frames.], batch size: 21, lr: 5.81e-04
2022-05-27 06:33:17,798 INFO [train.py:842] (2/4) Epoch 9, batch 6400, loss[loss=0.2442, simple_loss=0.3194, pruned_loss=0.08449, over 7119.00 frames.], tot_loss[loss=0.219, simple_loss=0.2965, pruned_loss=0.07075, over 1421744.48 frames.], batch size: 21, lr: 5.81e-04
2022-05-27 06:33:56,397 INFO [train.py:842] (2/4) Epoch 9, batch 6450, loss[loss=0.2366, simple_loss=0.3125, pruned_loss=0.08033, over 7357.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2969, pruned_loss=0.07103, over 1421810.53 frames.], batch size: 19, lr: 5.80e-04
2022-05-27 06:34:38,047 INFO [train.py:842] (2/4) Epoch 9, batch 6500, loss[loss=0.2259, simple_loss=0.3053, pruned_loss=0.07328, over 7412.00 frames.], tot_loss[loss=0.218, simple_loss=0.2958, pruned_loss=0.07012, over 1422926.55 frames.], batch size: 21, lr: 5.80e-04
2022-05-27 06:35:16,522 INFO [train.py:842] (2/4) Epoch 9, batch 6550, loss[loss=0.2703, simple_loss=0.3375, pruned_loss=0.1015, over 7161.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2957, pruned_loss=0.07003, over 1423303.38 frames.], batch size: 20, lr: 5.80e-04
2022-05-27 06:35:55,356 INFO [train.py:842] (2/4) Epoch 9, batch 6600, loss[loss=0.2213, simple_loss=0.2941, pruned_loss=0.07426, over 7062.00 frames.], tot_loss[loss=0.2184, simple_loss=0.2963, pruned_loss=0.0702, over 1424785.82 frames.], batch size: 18, lr: 5.80e-04
2022-05-27 06:36:34,069 INFO [train.py:842] (2/4) Epoch 9, batch 6650, loss[loss=0.1993, simple_loss=0.2724, pruned_loss=0.0631, over 7068.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2959, pruned_loss=0.06992, over 1425413.00 frames.], batch size: 18, lr: 5.80e-04
2022-05-27 06:37:12,640 INFO [train.py:842] (2/4) Epoch 9, batch 6700, loss[loss=0.1865, simple_loss=0.261, pruned_loss=0.05597, over 7260.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2961, pruned_loss=0.06946, over 1422556.92 frames.], batch size: 17, lr: 5.80e-04
2022-05-27 06:37:51,234 INFO [train.py:842] (2/4) Epoch 9, batch 6750, loss[loss=0.1839, simple_loss=0.2712, pruned_loss=0.04834, over 7257.00 frames.], tot_loss[loss=0.2177, simple_loss=0.2966, pruned_loss=0.06938, over 1423191.21 frames.], batch size: 19, lr: 5.79e-04
2022-05-27 06:38:30,277 INFO [train.py:842] (2/4) Epoch 9, batch 6800, loss[loss=0.2742, simple_loss=0.3397, pruned_loss=0.1044, over 7336.00 frames.], tot_loss[loss=0.2162, simple_loss=0.295, pruned_loss=0.06868, over 1427261.78 frames.], batch size: 22, lr: 5.79e-04
2022-05-27 06:39:08,684 INFO [train.py:842] (2/4) Epoch 9, batch 6850, loss[loss=0.2719, simple_loss=0.3325, pruned_loss=0.1057, over 7106.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2951, pruned_loss=0.06804, over 1427830.28 frames.], batch size: 21, lr: 5.79e-04
2022-05-27 06:39:47,803 INFO [train.py:842] (2/4) Epoch 9, batch 6900, loss[loss=0.1859, simple_loss=0.2775, pruned_loss=0.04719, over 7326.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2952, pruned_loss=0.06799, over 1423214.44 frames.], batch size: 20, lr: 5.79e-04
2022-05-27 06:40:26,410 INFO [train.py:842] (2/4) Epoch 9, batch 6950, loss[loss=0.2238, simple_loss=0.2976, pruned_loss=0.07506, over 7351.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2957, pruned_loss=0.06897, over 1420233.64 frames.], batch size: 19, lr: 5.79e-04
2022-05-27 06:41:05,234 INFO [train.py:842] (2/4) Epoch 9, batch 7000, loss[loss=0.2256, simple_loss=0.3082, pruned_loss=0.07147, over 6758.00 frames.], tot_loss[loss=0.2154, simple_loss=0.295, pruned_loss=0.06789, over 1421399.76 frames.], batch size: 31, lr: 5.78e-04
2022-05-27 06:41:43,602 INFO [train.py:842] (2/4) Epoch 9, batch 7050, loss[loss=0.2145, simple_loss=0.3009, pruned_loss=0.06401, over 7107.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2955, pruned_loss=0.06811, over 1419490.50 frames.], batch size: 21, lr: 5.78e-04
2022-05-27 06:42:22,508 INFO [train.py:842] (2/4) Epoch 9, batch 7100, loss[loss=0.1815, simple_loss=0.2691, pruned_loss=0.04692, over 7060.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2939, pruned_loss=0.06762, over 1423361.36 frames.], batch size: 18, lr: 5.78e-04
2022-05-27 06:43:01,104 INFO [train.py:842] (2/4) Epoch 9, batch 7150, loss[loss=0.2891, simple_loss=0.3657, pruned_loss=0.1063, over 7178.00 frames.], tot_loss[loss=0.216, simple_loss=0.2951, pruned_loss=0.06846, over 1418648.06 frames.], batch size: 26, lr: 5.78e-04
2022-05-27 06:43:40,009 INFO [train.py:842] (2/4) Epoch 9, batch 7200, loss[loss=0.1895, simple_loss=0.2734, pruned_loss=0.05276, over 7349.00 frames.], tot_loss[loss=0.2158, simple_loss=0.295, pruned_loss=0.0683, over 1421977.84 frames.], batch size: 19, lr: 5.78e-04
2022-05-27 06:44:18,620 INFO [train.py:842] (2/4) Epoch 9, batch 7250, loss[loss=0.2239, simple_loss=0.3033, pruned_loss=0.07229, over 6446.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2944, pruned_loss=0.06839, over 1423707.24 frames.], batch size: 38, lr: 5.78e-04
2022-05-27 06:44:57,344 INFO [train.py:842] (2/4) Epoch 9, batch 7300, loss[loss=0.1764, simple_loss=0.2671, pruned_loss=0.04282, over 7059.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2947, pruned_loss=0.06844, over 1426411.69 frames.], batch size: 18, lr: 5.77e-04
2022-05-27 06:45:35,790 INFO [train.py:842] (2/4) Epoch 9, batch 7350, loss[loss=0.188, simple_loss=0.2803, pruned_loss=0.0478, over 7178.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2951, pruned_loss=0.06875, over 1425976.72 frames.], batch size: 23, lr: 5.77e-04
2022-05-27 06:46:15,088 INFO [train.py:842] (2/4) Epoch 9, batch 7400, loss[loss=0.2084, simple_loss=0.2829, pruned_loss=0.06691, over 7414.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2935, pruned_loss=0.06788, over 1427474.09 frames.], batch size: 18, lr: 5.77e-04
2022-05-27 06:46:53,829 INFO [train.py:842] (2/4) Epoch 9, batch 7450, loss[loss=0.2442, simple_loss=0.3179, pruned_loss=0.08529, over 7272.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2935, pruned_loss=0.0676, over 1429155.33 frames.], batch size: 25, lr: 5.77e-04
2022-05-27 06:47:32,683 INFO [train.py:842] (2/4) Epoch 9, batch 7500, loss[loss=0.296, simple_loss=0.3585, pruned_loss=0.1167, over 4921.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2932, pruned_loss=0.0673, over 1419206.95 frames.], batch size: 52, lr: 5.77e-04
2022-05-27 06:48:11,234 INFO [train.py:842] (2/4) Epoch 9, batch 7550, loss[loss=0.2395, simple_loss=0.3123, pruned_loss=0.08336, over 7204.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2937, pruned_loss=0.06779, over 1417569.75 frames.], batch size: 23, lr: 5.76e-04
2022-05-27 06:48:50,205 INFO [train.py:842] (2/4) Epoch 9, batch 7600, loss[loss=0.2261, simple_loss=0.2767, pruned_loss=0.08779, over 7136.00 frames.], tot_loss[loss=0.2165, simple_loss=0.295, pruned_loss=0.069, over 1417298.18 frames.], batch size: 17, lr: 5.76e-04
2022-05-27 06:49:28,750 INFO [train.py:842] (2/4) Epoch 9, batch 7650, loss[loss=0.2455, simple_loss=0.3145, pruned_loss=0.08824, over 7146.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2951, pruned_loss=0.0691, over 1418537.18 frames.], batch size: 20, lr: 5.76e-04
2022-05-27 06:50:07,686 INFO [train.py:842] (2/4) Epoch 9, batch 7700, loss[loss=0.1803, simple_loss=0.2519, pruned_loss=0.05432, over 7414.00 frames.], tot_loss[loss=0.2152, simple_loss=0.294, pruned_loss=0.06821, over 1420559.95 frames.], batch size: 18, lr: 5.76e-04
2022-05-27 06:50:46,192 INFO [train.py:842] (2/4) Epoch 9, batch 7750, loss[loss=0.2588, simple_loss=0.3327, pruned_loss=0.09245, over 6438.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2937, pruned_loss=0.06859, over 1416011.32 frames.], batch size: 38, lr: 5.76e-04
2022-05-27 06:51:25,029 INFO [train.py:842] (2/4) Epoch 9, batch 7800, loss[loss=0.1727, simple_loss=0.2521, pruned_loss=0.04662, over 7344.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2944, pruned_loss=0.06869, over 1422661.43 frames.], batch size: 19, lr: 5.76e-04
2022-05-27 06:52:03,422 INFO [train.py:842] (2/4) Epoch 9, batch 7850, loss[loss=0.2101, simple_loss=0.2993, pruned_loss=0.06041, over 7281.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2947, pruned_loss=0.06813, over 1427293.19 frames.], batch size: 24, lr: 5.75e-04
2022-05-27 06:52:42,325 INFO [train.py:842] (2/4) Epoch 9, batch 7900, loss[loss=0.2285, simple_loss=0.3104, pruned_loss=0.07328, over 7361.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2943, pruned_loss=0.0676, over 1430454.52 frames.], batch size: 19, lr: 5.75e-04
2022-05-27 06:53:21,009 INFO [train.py:842] (2/4) Epoch 9, batch 7950, loss[loss=0.2074, simple_loss=0.2998, pruned_loss=0.05753, over 7145.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2952, pruned_loss=0.0686, over 1428783.22 frames.], batch size: 20, lr: 5.75e-04
2022-05-27 06:53:59,875 INFO [train.py:842] (2/4) Epoch 9, batch 8000, loss[loss=0.1841, simple_loss=0.2624, pruned_loss=0.05291, over 7162.00 frames.], tot_loss[loss=0.216, simple_loss=0.2948, pruned_loss=0.06863, over 1425097.05 frames.], batch size: 18, lr: 5.75e-04
2022-05-27 06:54:38,302 INFO [train.py:842] (2/4) Epoch 9, batch 8050, loss[loss=0.1806, simple_loss=0.2629, pruned_loss=0.04915, over 7269.00 frames.], tot_loss[loss=0.216, simple_loss=0.2946, pruned_loss=0.0687, over 1425302.77 frames.], batch size: 19, lr: 5.75e-04
2022-05-27 06:55:17,190 INFO [train.py:842] (2/4) Epoch 9, batch 8100, loss[loss=0.2004, simple_loss=0.2818, pruned_loss=0.05952, over 7072.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2938, pruned_loss=0.06825, over 1424935.87 frames.], batch size: 18, lr: 5.75e-04
2022-05-27 06:55:55,749 INFO [train.py:842] (2/4) Epoch 9, batch 8150, loss[loss=0.2341, simple_loss=0.3126, pruned_loss=0.07783, over 6716.00 frames.], tot_loss[loss=0.215, simple_loss=0.2937, pruned_loss=0.06811, over 1423754.80 frames.], batch size: 31, lr: 5.74e-04
2022-05-27 06:56:34,504 INFO [train.py:842] (2/4) Epoch 9, batch 8200, loss[loss=0.1802, simple_loss=0.2692, pruned_loss=0.04563, over 7119.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2945, pruned_loss=0.06864, over 1417055.25 frames.], batch size: 21, lr: 5.74e-04
2022-05-27 06:57:13,125 INFO [train.py:842] (2/4) Epoch 9, batch 8250, loss[loss=0.1532, simple_loss=0.2357, pruned_loss=0.03533, over 7263.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2951, pruned_loss=0.06932, over 1420556.99 frames.], batch size: 17, lr: 5.74e-04
2022-05-27 06:57:51,714 INFO [train.py:842] (2/4) Epoch 9, batch 8300, loss[loss=0.1966, simple_loss=0.2868, pruned_loss=0.05317, over 7322.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2961, pruned_loss=0.07012, over 1410849.91 frames.], batch size: 21, lr: 5.74e-04
2022-05-27 06:58:30,495 INFO [train.py:842] (2/4) Epoch 9, batch 8350, loss[loss=0.2059, simple_loss=0.2824, pruned_loss=0.0647, over 7327.00 frames.], tot_loss[loss=0.217, simple_loss=0.2947, pruned_loss=0.06965, over 1415311.88 frames.], batch size: 21, lr: 5.74e-04
2022-05-27 06:59:09,819 INFO [train.py:842] (2/4) Epoch 9, batch 8400, loss[loss=0.2158, simple_loss=0.3038, pruned_loss=0.06392, over 7080.00 frames.], tot_loss[loss=0.215, simple_loss=0.2931, pruned_loss=0.06849, over 1422047.19 frames.], batch size: 28, lr: 5.74e-04
2022-05-27 06:59:48,406 INFO [train.py:842] (2/4) Epoch 9, batch 8450, loss[loss=0.208, simple_loss=0.304, pruned_loss=0.05598, over 7105.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2925, pruned_loss=0.06813, over 1424322.23 frames.], batch size: 21, lr: 5.73e-04
2022-05-27 07:00:27,422 INFO [train.py:842] (2/4) Epoch 9, batch 8500, loss[loss=0.2388, simple_loss=0.2987, pruned_loss=0.08947, over 7165.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2932, pruned_loss=0.06855, over 1423388.04 frames.], batch size: 19, lr: 5.73e-04
2022-05-27 07:01:05,923 INFO [train.py:842] (2/4) Epoch 9, batch 8550, loss[loss=0.209, simple_loss=0.2934, pruned_loss=0.06233, over 6350.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2938, pruned_loss=0.06859, over 1419860.61 frames.], batch size: 37, lr: 5.73e-04
2022-05-27 07:01:44,694 INFO [train.py:842] (2/4) Epoch 9, batch 8600, loss[loss=0.3089, simple_loss=0.3641, pruned_loss=0.1269, over 5180.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2955, pruned_loss=0.06962, over 1416307.82 frames.], batch size: 53, lr: 5.73e-04
2022-05-27 07:02:23,104 INFO [train.py:842] (2/4) Epoch 9, batch 8650, loss[loss=0.2735, simple_loss=0.3363, pruned_loss=0.1053, over 7317.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2976, pruned_loss=0.07086, over 1420490.01 frames.], batch size: 21, lr: 5.73e-04
2022-05-27 07:03:02,364 INFO [train.py:842] (2/4) Epoch 9, batch 8700, loss[loss=0.1609, simple_loss=0.245, pruned_loss=0.03835, over 7356.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2945, pruned_loss=0.06932, over 1421810.30 frames.], batch size: 19, lr: 5.72e-04
2022-05-27 07:03:40,722 INFO [train.py:842] (2/4) Epoch 9, batch 8750, loss[loss=0.2362, simple_loss=0.3048, pruned_loss=0.08375, over 7182.00 frames.], tot_loss[loss=0.2178, simple_loss=0.2957, pruned_loss=0.06993, over 1418578.25 frames.], batch size: 18, lr: 5.72e-04
2022-05-27 07:04:19,472 INFO [train.py:842] (2/4) Epoch 9, batch 8800, loss[loss=0.2655, simple_loss=0.354, pruned_loss=0.08855, over 7225.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2964, pruned_loss=0.06917, over 1419464.58 frames.], batch size: 23, lr: 5.72e-04
2022-05-27 07:04:57,956 INFO [train.py:842] (2/4) Epoch 9, batch 8850, loss[loss=0.2369, simple_loss=0.3142, pruned_loss=0.07982, over 7272.00 frames.], tot_loss[loss=0.217, simple_loss=0.2953, pruned_loss=0.06936, over 1411669.30 frames.], batch size: 24, lr: 5.72e-04
2022-05-27 07:05:37,297 INFO [train.py:842] (2/4) Epoch 9, batch 8900, loss[loss=0.2783, simple_loss=0.3714, pruned_loss=0.09259, over 7379.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2948, pruned_loss=0.06934, over 1406484.42 frames.], batch size: 23, lr: 5.72e-04
2022-05-27 07:06:15,871 INFO [train.py:842] (2/4) Epoch 9, batch 8950, loss[loss=0.2573, simple_loss=0.3249, pruned_loss=0.09486, over 7365.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2951, pruned_loss=0.06984, over 1399939.93 frames.], batch size: 19, lr: 5.72e-04
2022-05-27 07:06:55,094 INFO [train.py:842] (2/4) Epoch 9, batch 9000, loss[loss=0.2487, simple_loss=0.3218, pruned_loss=0.08782, over 6440.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2943, pruned_loss=0.0701, over 1393127.32 frames.], batch size: 38, lr: 5.71e-04
2022-05-27 07:06:55,095 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 07:07:04,565 INFO [train.py:871] (2/4) Epoch 9, validation: loss=0.1768, simple_loss=0.2774, pruned_loss=0.03806, over 868885.00 frames.
2022-05-27 07:07:43,591 INFO [train.py:842] (2/4) Epoch 9, batch 9050, loss[loss=0.2265, simple_loss=0.2921, pruned_loss=0.08046, over 7285.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2953, pruned_loss=0.07093, over 1387641.57 frames.], batch size: 18, lr: 5.71e-04
2022-05-27 07:08:21,633 INFO [train.py:842] (2/4) Epoch 9, batch 9100, loss[loss=0.2995, simple_loss=0.364, pruned_loss=0.1175, over 5232.00 frames.], tot_loss[loss=0.2246, simple_loss=0.3007, pruned_loss=0.07422, over 1351369.80 frames.], batch size: 52, lr: 5.71e-04
2022-05-27 07:08:59,050 INFO [train.py:842] (2/4) Epoch 9, batch 9150, loss[loss=0.215, simple_loss=0.2981, pruned_loss=0.06598, over 5335.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3052, pruned_loss=0.0773, over 1297215.37 frames.], batch size: 52, lr: 5.71e-04
2022-05-27 07:09:51,888 INFO [train.py:842] (2/4) Epoch 10, batch 0, loss[loss=0.2161, simple_loss=0.2994, pruned_loss=0.06637, over 7409.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2994, pruned_loss=0.06637, over 7409.00 frames.], batch size: 21, lr: 5.49e-04
2022-05-27 07:10:30,757 INFO [train.py:842] (2/4) Epoch 10, batch 50, loss[loss=0.2379, simple_loss=0.3191, pruned_loss=0.07833, over 7181.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2926, pruned_loss=0.06722, over 321243.50 frames.], batch size: 23, lr: 5.49e-04
2022-05-27 07:11:09,458 INFO [train.py:842] (2/4) Epoch 10, batch 100, loss[loss=0.2955, simple_loss=0.3534, pruned_loss=0.1188, over 5210.00 frames.], tot_loss[loss=0.2143, simple_loss=0.293, pruned_loss=0.06782, over 558258.27 frames.], batch size: 53, lr: 5.48e-04
2022-05-27 07:11:48,008 INFO [train.py:842] (2/4) Epoch 10, batch 150, loss[loss=0.2327, simple_loss=0.3118, pruned_loss=0.07683, over 7424.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2918, pruned_loss=0.06681, over 750972.23 frames.], batch size: 20, lr: 5.48e-04
2022-05-27 07:12:26,887 INFO [train.py:842] (2/4) Epoch 10, batch 200, loss[loss=0.2118, simple_loss=0.2971, pruned_loss=0.06327, over 7429.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2923, pruned_loss=0.06678, over 898961.91 frames.], batch size: 20, lr: 5.48e-04
2022-05-27 07:13:05,247 INFO [train.py:842] (2/4) Epoch 10, batch 250, loss[loss=0.1912, simple_loss=0.2769, pruned_loss=0.05278, over 7165.00 frames.], tot_loss[loss=0.2144, simple_loss=0.294, pruned_loss=0.06745, over 1010946.27 frames.], batch size: 18, lr: 5.48e-04
2022-05-27 07:13:44,130 INFO [train.py:842] (2/4) Epoch 10, batch 300, loss[loss=0.2255, simple_loss=0.3064, pruned_loss=0.07232, over 7320.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2927, pruned_loss=0.06724, over 1104222.54 frames.], batch size: 20, lr: 5.48e-04
2022-05-27 07:14:22,634 INFO [train.py:842] (2/4) Epoch 10, batch 350, loss[loss=0.2316, simple_loss=0.3114, pruned_loss=0.07594, over 7185.00 frames.], tot_loss[loss=0.213, simple_loss=0.2927, pruned_loss=0.06665, over 1173044.96 frames.], batch size: 23, lr: 5.48e-04
2022-05-27 07:15:01,358 INFO [train.py:842] (2/4) Epoch 10, batch 400, loss[loss=0.2048, simple_loss=0.2859, pruned_loss=0.06185, over 7165.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2938, pruned_loss=0.06726, over 1223226.34 frames.], batch size: 26, lr: 5.47e-04
2022-05-27 07:15:39,889 INFO [train.py:842] (2/4) Epoch 10, batch 450, loss[loss=0.2038, simple_loss=0.2857, pruned_loss=0.06099, over 6363.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2936, pruned_loss=0.0667, over 1261887.42 frames.], batch size: 37, lr: 5.47e-04
2022-05-27 07:16:18,859 INFO [train.py:842] (2/4) Epoch 10, batch 500, loss[loss=0.2122, simple_loss=0.2803, pruned_loss=0.07203, over 7164.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2942, pruned_loss=0.06758, over 1297744.77 frames.], batch size: 19, lr: 5.47e-04
2022-05-27 07:16:57,408 INFO [train.py:842] (2/4) Epoch 10, batch 550, loss[loss=0.1815, simple_loss=0.2494, pruned_loss=0.05676, over 7130.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2949, pruned_loss=0.06827, over 1324999.68 frames.], batch size: 17, lr: 5.47e-04
2022-05-27 07:17:36,397 INFO [train.py:842] (2/4) Epoch 10, batch 600, loss[loss=0.1772, simple_loss=0.2553, pruned_loss=0.04954, over 7276.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2953, pruned_loss=0.06851, over 1346360.90 frames.], batch size: 18, lr: 5.47e-04
2022-05-27 07:18:15,049 INFO [train.py:842] (2/4) Epoch 10, batch 650, loss[loss=0.2674, simple_loss=0.3454, pruned_loss=0.09467, over 7148.00 frames.], tot_loss[loss=0.2169, simple_loss=0.296, pruned_loss=0.06889, over 1362805.36 frames.], batch size: 26, lr: 5.47e-04
2022-05-27 07:18:53,917 INFO [train.py:842] (2/4) Epoch 10, batch 700, loss[loss=0.1999, simple_loss=0.2907, pruned_loss=0.05456, over 7289.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2956, pruned_loss=0.0684, over 1377472.45 frames.], batch size: 25, lr: 5.46e-04
2022-05-27 07:19:32,416 INFO [train.py:842] (2/4) Epoch 10, batch 750, loss[loss=0.2128, simple_loss=0.2842, pruned_loss=0.07064, over 7425.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2943, pruned_loss=0.06753, over 1387554.63 frames.], batch size: 20, lr: 5.46e-04
2022-05-27 07:20:11,289 INFO [train.py:842] (2/4) Epoch 10, batch 800, loss[loss=0.2724, simple_loss=0.3579, pruned_loss=0.09349, over 7317.00 frames.], tot_loss[loss=0.2146, simple_loss=0.294, pruned_loss=0.06758, over 1394628.92 frames.], batch size: 24, lr: 5.46e-04
2022-05-27 07:20:50,015 INFO [train.py:842] (2/4) Epoch 10, batch 850, loss[loss=0.2107, simple_loss=0.2993, pruned_loss=0.06102, over 6480.00 frames.], tot_loss[loss=0.2131, simple_loss=0.293, pruned_loss=0.06663, over 1397570.49 frames.], batch size: 38, lr: 5.46e-04
2022-05-27 07:21:29,279 INFO [train.py:842] (2/4) Epoch 10, batch 900, loss[loss=0.2173, simple_loss=0.304, pruned_loss=0.06533, over 7318.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2928, pruned_loss=0.06643, over 1406871.75 frames.], batch size: 21, lr: 5.46e-04
2022-05-27 07:22:07,876 INFO [train.py:842] (2/4) Epoch 10, batch 950, loss[loss=0.1838, simple_loss=0.2715, pruned_loss=0.04804, over 7130.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2935, pruned_loss=0.06662, over 1407589.28 frames.], batch size: 26, lr: 5.46e-04
2022-05-27 07:22:46,819 INFO [train.py:842] (2/4) Epoch 10, batch 1000, loss[loss=0.2249, simple_loss=0.3088, pruned_loss=0.07048, over 7318.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2933, pruned_loss=0.06654, over 1414988.25 frames.], batch size: 20, lr: 5.46e-04
2022-05-27 07:23:25,633 INFO [train.py:842] (2/4) Epoch 10, batch 1050, loss[loss=0.2092, simple_loss=0.2888, pruned_loss=0.06475, over 7107.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2929, pruned_loss=0.06637, over 1417512.51 frames.], batch size: 28, lr: 5.45e-04
2022-05-27 07:24:04,249 INFO [train.py:842] (2/4) Epoch 10, batch 1100, loss[loss=0.2051, simple_loss=0.2895, pruned_loss=0.06039, over 7034.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2941, pruned_loss=0.06738, over 1417255.71 frames.], batch size: 28, lr: 5.45e-04
2022-05-27 07:24:42,896 INFO [train.py:842] (2/4) Epoch 10, batch 1150, loss[loss=0.1947, simple_loss=0.2747, pruned_loss=0.05734, over 7314.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2954, pruned_loss=0.06817, over 1421434.20 frames.], batch size: 20, lr: 5.45e-04
2022-05-27 07:25:21,662 INFO [train.py:842] (2/4) Epoch 10, batch 1200, loss[loss=0.2966, simple_loss=0.3666, pruned_loss=0.1133, over 7197.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2956, pruned_loss=0.06855, over 1420685.28 frames.], batch size: 23, lr: 5.45e-04
2022-05-27 07:26:00,231 INFO [train.py:842] (2/4) Epoch 10, batch 1250, loss[loss=0.3355, simple_loss=0.3721, pruned_loss=0.1495, over 7284.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2963, pruned_loss=0.06905, over 1418955.51 frames.], batch size: 17, lr: 5.45e-04
2022-05-27 07:26:39,246 INFO [train.py:842] (2/4) Epoch 10, batch 1300, loss[loss=0.1531, simple_loss=0.2322, pruned_loss=0.03702, over 6990.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2949, pruned_loss=0.06866, over 1416749.62 frames.], batch size: 16, lr: 5.45e-04
2022-05-27 07:27:17,669 INFO [train.py:842] (2/4) Epoch 10, batch 1350, loss[loss=0.1807, simple_loss=0.2628, pruned_loss=0.04931, over 7310.00 frames.], tot_loss[loss=0.2145, simple_loss=0.2935, pruned_loss=0.06776, over 1415992.69 frames.], batch size: 21, lr: 5.44e-04
2022-05-27 07:27:56,259 INFO [train.py:842] (2/4) Epoch 10, batch 1400, loss[loss=0.2078, simple_loss=0.3003, pruned_loss=0.05772, over 7112.00 frames.], tot_loss[loss=0.2169, simple_loss=0.296, pruned_loss=0.06895, over 1419175.74 frames.], batch size: 21, lr: 5.44e-04
2022-05-27 07:28:35,050 INFO [train.py:842] (2/4) Epoch 10, batch 1450, loss[loss=0.2206, simple_loss=0.3133, pruned_loss=0.06399, over 7286.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2952, pruned_loss=0.06869, over 1420748.79 frames.], batch size: 25, lr: 5.44e-04
2022-05-27 07:29:13,806 INFO [train.py:842] (2/4) Epoch 10, batch 1500, loss[loss=0.2705, simple_loss=0.3402, pruned_loss=0.1004, over 4957.00 frames.], tot_loss[loss=0.2171, simple_loss=0.2962, pruned_loss=0.06901, over 1415824.59 frames.], batch size: 52, lr: 5.44e-04
2022-05-27 07:29:52,332 INFO [train.py:842] (2/4) Epoch 10, batch 1550, loss[loss=0.1944, simple_loss=0.2805, pruned_loss=0.05414, over 7356.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2951, pruned_loss=0.06753, over 1418962.12 frames.], batch size: 19, lr: 5.44e-04
2022-05-27 07:30:31,268 INFO [train.py:842] (2/4) Epoch 10, batch 1600, loss[loss=0.2077, simple_loss=0.291, pruned_loss=0.06218, over 7251.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2939, pruned_loss=0.06748, over 1417970.38 frames.], batch size: 19, lr: 5.44e-04
2022-05-27 07:31:09,813 INFO [train.py:842] (2/4) Epoch 10, batch 1650, loss[loss=0.2233, simple_loss=0.3086, pruned_loss=0.06901, over 7414.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2932, pruned_loss=0.067, over 1416054.63 frames.], batch size: 21, lr: 5.43e-04
2022-05-27 07:31:48,606 INFO [train.py:842] (2/4) Epoch 10, batch 1700, loss[loss=0.1966, simple_loss=0.2836, pruned_loss=0.05478, over 7290.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2935, pruned_loss=0.06715, over 1414197.36 frames.], batch size: 24, lr: 5.43e-04
2022-05-27 07:32:27,189 INFO [train.py:842] (2/4) Epoch 10, batch 1750, loss[loss=0.1855, simple_loss=0.26, pruned_loss=0.05549, over 7204.00 frames.], tot_loss[loss=0.215, simple_loss=0.2944, pruned_loss=0.06782, over 1406315.48 frames.], batch size: 16, lr: 5.43e-04
2022-05-27 07:33:05,907 INFO [train.py:842] (2/4) Epoch 10, batch 1800, loss[loss=0.1921, simple_loss=0.2732, pruned_loss=0.05555, over 7357.00 frames.], tot_loss[loss=0.215, simple_loss=0.2944, pruned_loss=0.06783, over 1410817.39 frames.], batch size: 19, lr: 5.43e-04
2022-05-27 07:33:44,423 INFO [train.py:842] (2/4) Epoch 10, batch 1850, loss[loss=0.2257, simple_loss=0.3056, pruned_loss=0.07288, over 7355.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2941, pruned_loss=0.06778, over 1411215.19 frames.], batch size: 19, lr: 5.43e-04
2022-05-27 07:34:23,387 INFO [train.py:842] (2/4) Epoch 10, batch 1900, loss[loss=0.249, simple_loss=0.3231, pruned_loss=0.08744, over 7279.00 frames.], tot_loss[loss=0.2131, simple_loss=0.293, pruned_loss=0.06664, over 1414655.07 frames.], batch size: 18, lr: 5.43e-04
2022-05-27 07:35:02,021 INFO [train.py:842] (2/4) Epoch 10, batch 1950, loss[loss=0.2271, simple_loss=0.3149, pruned_loss=0.0696, over 7197.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2933, pruned_loss=0.0667, over 1414219.01 frames.], batch size: 23, lr: 5.42e-04
2022-05-27 07:35:40,859 INFO [train.py:842] (2/4) Epoch 10, batch 2000, loss[loss=0.2119, simple_loss=0.2876, pruned_loss=0.06807, over 7233.00 frames.], tot_loss[loss=0.212, simple_loss=0.2915, pruned_loss=0.0663, over 1417076.59 frames.], batch size: 20, lr: 5.42e-04
2022-05-27 07:36:19,532 INFO [train.py:842] (2/4) Epoch 10, batch 2050, loss[loss=0.2197, simple_loss=0.3006, pruned_loss=0.06936, over 7199.00 frames.], tot_loss[loss=0.211, simple_loss=0.2908, pruned_loss=0.06563, over 1419108.32 frames.], batch size: 23, lr: 5.42e-04
2022-05-27 07:36:58,484 INFO [train.py:842] (2/4) Epoch 10, batch 2100, loss[loss=0.1874, simple_loss=0.2811, pruned_loss=0.04689, over 7157.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2898, pruned_loss=0.0646, over 1423779.91 frames.], batch size: 20, lr: 5.42e-04
2022-05-27 07:37:37,345 INFO [train.py:842] (2/4) Epoch 10, batch 2150, loss[loss=0.2179, simple_loss=0.2987, pruned_loss=0.06852, over 7412.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2886, pruned_loss=0.06394, over 1425179.37 frames.], batch size: 18, lr: 5.42e-04
2022-05-27 07:38:16,082 INFO [train.py:842] (2/4) Epoch 10, batch 2200, loss[loss=0.2145, simple_loss=0.3015, pruned_loss=0.06375, over 6370.00 frames.], tot_loss[loss=0.21, simple_loss=0.2906, pruned_loss=0.06465, over 1425392.74 frames.], batch size: 37, lr: 5.42e-04
2022-05-27 07:38:54,671 INFO [train.py:842] (2/4) Epoch 10, batch 2250, loss[loss=0.206, simple_loss=0.2916, pruned_loss=0.06026, over 7325.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2893, pruned_loss=0.06409, over 1427601.41 frames.], batch size: 21, lr: 5.42e-04
2022-05-27 07:39:33,342 INFO [train.py:842] (2/4) Epoch 10, batch 2300, loss[loss=0.1818, simple_loss=0.2736, pruned_loss=0.04495, over 7140.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2904, pruned_loss=0.06496, over 1425511.83 frames.], batch size: 20, lr: 5.41e-04
2022-05-27 07:40:11,935 INFO [train.py:842] (2/4) Epoch 10, batch 2350, loss[loss=0.2516, simple_loss=0.3165, pruned_loss=0.09329, over 7190.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2892, pruned_loss=0.06503, over 1423778.12 frames.], batch size: 22, lr: 5.41e-04
2022-05-27 07:40:50,794 INFO [train.py:842] (2/4) Epoch 10, batch 2400, loss[loss=0.1988, simple_loss=0.2716, pruned_loss=0.06306, over 7285.00 frames.], tot_loss[loss=0.2107, simple_loss=0.2902, pruned_loss=0.06565, over 1426201.48 frames.], batch size: 18, lr: 5.41e-04
2022-05-27 07:41:29,386 INFO [train.py:842] (2/4) Epoch 10, batch 2450, loss[loss=0.2101, simple_loss=0.282, pruned_loss=0.06913, over 7058.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2892, pruned_loss=0.06513, over 1429615.52 frames.], batch size: 18, lr: 5.41e-04
2022-05-27 07:42:08,594 INFO [train.py:842] (2/4) Epoch 10, batch 2500, loss[loss=0.2191, simple_loss=0.3064, pruned_loss=0.06587, over 7309.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2892, pruned_loss=0.06478, over 1428152.77 frames.], batch size: 21, lr: 5.41e-04
2022-05-27 07:42:47,191 INFO [train.py:842] (2/4) Epoch 10, batch 2550, loss[loss=0.307, simple_loss=0.3594, pruned_loss=0.1273, over 7222.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2897, pruned_loss=0.06489, over 1426439.85 frames.], batch size: 21, lr: 5.41e-04
2022-05-27 07:43:26,341 INFO [train.py:842] (2/4) Epoch 10, batch 2600, loss[loss=0.2486, simple_loss=0.3188, pruned_loss=0.08918, over 7145.00 frames.], tot_loss[loss=0.2114, simple_loss=0.291, pruned_loss=0.06589, over 1429757.69 frames.], batch size: 26, lr: 5.40e-04
2022-05-27 07:44:04,693 INFO [train.py:842] (2/4) Epoch 10, batch 2650, loss[loss=0.224, simple_loss=0.3119, pruned_loss=0.06802, over 7349.00 frames.], tot_loss[loss=0.2116, simple_loss=0.2915, pruned_loss=0.06584, over 1425651.03 frames.], batch size: 22, lr: 5.40e-04
2022-05-27 07:44:43,514 INFO [train.py:842] (2/4) Epoch 10, batch 2700, loss[loss=0.2096, simple_loss=0.3001, pruned_loss=0.05951, over 6769.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2905, pruned_loss=0.06539, over 1425856.89 frames.], batch size: 31, lr: 5.40e-04
2022-05-27 07:45:22,171 INFO [train.py:842] (2/4) Epoch 10, batch 2750, loss[loss=0.2416, simple_loss=0.3167, pruned_loss=0.08327, over 6801.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2898, pruned_loss=0.06505, over 1423919.71 frames.], batch size: 31, lr: 5.40e-04
2022-05-27 07:46:01,454 INFO [train.py:842] (2/4) Epoch 10, batch 2800, loss[loss=0.2415, simple_loss=0.3105, pruned_loss=0.08623, over 7400.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2913, pruned_loss=0.06604, over 1428858.12 frames.], batch size: 23, lr: 5.40e-04
2022-05-27 07:46:50,663 INFO [train.py:842] (2/4) Epoch 10, batch 2850, loss[loss=0.2232, simple_loss=0.3048, pruned_loss=0.07074, over 7341.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2898, pruned_loss=0.06478, over 1426044.61 frames.], batch size: 22, lr: 5.40e-04
2022-05-27 07:47:29,738 INFO [train.py:842] (2/4) Epoch 10, batch 2900, loss[loss=0.1966, simple_loss=0.2917, pruned_loss=0.05081, over 7118.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2891, pruned_loss=0.06449, over 1424948.51 frames.], batch size: 21, lr: 5.39e-04
2022-05-27 07:48:08,433 INFO [train.py:842] (2/4) Epoch 10, batch 2950, loss[loss=0.2271, simple_loss=0.2936, pruned_loss=0.08032, over 7270.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2884, pruned_loss=0.06427, over 1425736.85 frames.], batch size: 18, lr: 5.39e-04
2022-05-27 07:48:47,534 INFO [train.py:842] (2/4) Epoch 10, batch 3000, loss[loss=0.2005, simple_loss=0.2666, pruned_loss=0.06717, over 7268.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2891, pruned_loss=0.06469, over 1425793.29 frames.], batch size: 17, lr: 5.39e-04
2022-05-27 07:48:47,535 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 07:48:56,971 INFO [train.py:871] (2/4) Epoch 10, validation: loss=0.1722, simple_loss=0.2729, pruned_loss=0.03575, over 868885.00 frames.
2022-05-27 07:49:35,665 INFO [train.py:842] (2/4) Epoch 10, batch 3050, loss[loss=0.2606, simple_loss=0.3285, pruned_loss=0.09638, over 7158.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2899, pruned_loss=0.06486, over 1425294.17 frames.], batch size: 19, lr: 5.39e-04
2022-05-27 07:50:14,625 INFO [train.py:842] (2/4) Epoch 10, batch 3100, loss[loss=0.1925, simple_loss=0.2771, pruned_loss=0.05392, over 7110.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2905, pruned_loss=0.06503, over 1428257.67 frames.], batch size: 21, lr: 5.39e-04
2022-05-27 07:50:53,117 INFO [train.py:842] (2/4) Epoch 10, batch 3150, loss[loss=0.2148, simple_loss=0.2871, pruned_loss=0.07128, over 7325.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2901, pruned_loss=0.0652, over 1425388.56 frames.], batch size: 21, lr: 5.39e-04
2022-05-27 07:51:32,423 INFO [train.py:842] (2/4) Epoch 10, batch 3200, loss[loss=0.1964, simple_loss=0.2892, pruned_loss=0.05178, over 7231.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2894, pruned_loss=0.06489, over 1426392.12 frames.], batch size: 20, lr: 5.39e-04
2022-05-27 07:52:11,113 INFO [train.py:842] (2/4) Epoch 10, batch 3250, loss[loss=0.1834, simple_loss=0.2852, pruned_loss=0.04084, over 7406.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2898, pruned_loss=0.06486, over 1427310.77 frames.], batch size: 21, lr: 5.38e-04
2022-05-27 07:52:49,907 INFO [train.py:842] (2/4) Epoch 10, batch 3300, loss[loss=0.2295, simple_loss=0.316, pruned_loss=0.07149, over 7200.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2902, pruned_loss=0.06579, over 1428526.85 frames.], batch size: 22, lr: 5.38e-04
2022-05-27 07:53:28,334 INFO [train.py:842] (2/4) Epoch 10, batch 3350, loss[loss=0.1969, simple_loss=0.2873, pruned_loss=0.05327, over 7196.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2908, pruned_loss=0.06586, over 1429517.98 frames.], batch size: 23, lr: 5.38e-04
2022-05-27 07:54:07,098 INFO [train.py:842] (2/4) Epoch 10, batch 3400, loss[loss=0.1598, simple_loss=0.2417, pruned_loss=0.03891, over 7287.00 frames.], tot_loss[loss=0.2112, simple_loss=0.2906, pruned_loss=0.06593, over 1425832.99 frames.], batch size: 17, lr: 5.38e-04
2022-05-27 07:54:45,632 INFO [train.py:842] (2/4) Epoch 10, batch 3450, loss[loss=0.19, simple_loss=0.2755, pruned_loss=0.05232, over 7286.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2909, pruned_loss=0.06622, over 1425129.12 frames.], batch size: 24, lr: 5.38e-04
2022-05-27 07:55:24,447 INFO [train.py:842] (2/4) Epoch 10, batch 3500, loss[loss=0.1917, simple_loss=0.2719, pruned_loss=0.05572, over 7408.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2905, pruned_loss=0.0658, over 1424937.87 frames.], batch size: 21, lr: 5.38e-04
2022-05-27 07:56:03,137 INFO [train.py:842] (2/4) Epoch 10, batch 3550, loss[loss=0.2366, simple_loss=0.3218, pruned_loss=0.07573, over 7120.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2895, pruned_loss=0.06516, over 1428675.80 frames.], batch size: 28, lr: 5.37e-04
2022-05-27 07:56:42,264 INFO [train.py:842] (2/4) Epoch 10, batch 3600, loss[loss=0.2263, simple_loss=0.315, pruned_loss=0.06881, over 7127.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2906, pruned_loss=0.06639, over 1429540.95 frames.], batch size: 28, lr: 5.37e-04
2022-05-27 07:57:21,001 INFO [train.py:842] (2/4) Epoch 10, batch 3650, loss[loss=0.1808, simple_loss=0.2703, pruned_loss=0.04567, over 7059.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2909, pruned_loss=0.06628, over 1424615.07 frames.], batch size: 18, lr: 5.37e-04
2022-05-27 07:57:59,623 INFO [train.py:842] (2/4) Epoch 10, batch 3700, loss[loss=0.1692, simple_loss=0.2429, pruned_loss=0.04771, over 7284.00 frames.], tot_loss[loss=0.2116, simple_loss=0.2912, pruned_loss=0.06595, over 1427006.89 frames.], batch size: 17, lr: 5.37e-04
2022-05-27 07:58:38,196 INFO [train.py:842] (2/4) Epoch 10, batch 3750, loss[loss=0.2334, simple_loss=0.3132, pruned_loss=0.07676, over 7161.00 frames.], tot_loss[loss=0.212, simple_loss=0.2918, pruned_loss=0.06615, over 1429143.99 frames.], batch size: 19, lr: 5.37e-04
2022-05-27 07:59:17,015 INFO [train.py:842] (2/4) Epoch 10, batch 3800, loss[loss=0.1655, simple_loss=0.2498, pruned_loss=0.04057, over 7430.00 frames.], tot_loss[loss=0.2116, simple_loss=0.2912, pruned_loss=0.066, over 1426665.98 frames.], batch size: 20, lr: 5.37e-04
2022-05-27 07:59:55,555 INFO [train.py:842] (2/4) Epoch 10, batch 3850, loss[loss=0.1657, simple_loss=0.2446, pruned_loss=0.04341, over 7067.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2925, pruned_loss=0.06633, over 1425330.47 frames.], batch size: 18, lr: 5.36e-04
2022-05-27 08:00:34,374 INFO [train.py:842] (2/4) Epoch 10, batch 3900, loss[loss=0.2278, simple_loss=0.3205, pruned_loss=0.06756, over 7152.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2921, pruned_loss=0.0662, over 1426595.61 frames.], batch size: 20, lr: 5.36e-04
2022-05-27 08:01:13,250 INFO [train.py:842] (2/4) Epoch 10, batch 3950, loss[loss=0.1831, simple_loss=0.2696, pruned_loss=0.04834, over 7070.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2904, pruned_loss=0.06563, over 1425546.32 frames.], batch size: 18, lr: 5.36e-04
2022-05-27 08:01:51,962 INFO [train.py:842] (2/4) Epoch 10, batch 4000, loss[loss=0.1891, simple_loss=0.2723, pruned_loss=0.05297, over 7289.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2919, pruned_loss=0.06623, over 1424264.25 frames.], batch size: 17, lr: 5.36e-04
2022-05-27 08:02:30,694 INFO [train.py:842] (2/4) Epoch 10, batch 4050, loss[loss=0.1826, simple_loss=0.2689, pruned_loss=0.04812, over 7235.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2917, pruned_loss=0.06636, over 1423696.95 frames.], batch size: 20, lr: 5.36e-04
2022-05-27 08:03:09,556 INFO [train.py:842] (2/4) Epoch 10, batch 4100, loss[loss=0.2005, simple_loss=0.2786, pruned_loss=0.06127, over 7324.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2921, pruned_loss=0.06672, over 1424134.69 frames.], batch size: 20, lr: 5.36e-04
2022-05-27 08:03:48,066 INFO [train.py:842] (2/4) Epoch 10, batch 4150, loss[loss=0.3401, simple_loss=0.3757, pruned_loss=0.1522, over 7395.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2927, pruned_loss=0.06706, over 1426560.87 frames.], batch size: 23, lr: 5.36e-04
2022-05-27 08:04:27,000 INFO [train.py:842] (2/4) Epoch 10, batch 4200, loss[loss=0.2233, simple_loss=0.3087, pruned_loss=0.06897, over 7276.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2936, pruned_loss=0.06733, over 1425115.72 frames.], batch size: 24, lr: 5.35e-04
2022-05-27 08:05:05,675 INFO [train.py:842] (2/4) Epoch 10, batch 4250, loss[loss=0.1846, simple_loss=0.2678, pruned_loss=0.05066, over 6811.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2925, pruned_loss=0.06599, over 1428008.81 frames.], batch size: 31, lr: 5.35e-04
2022-05-27 08:05:44,568 INFO [train.py:842] (2/4) Epoch 10, batch 4300, loss[loss=0.2416, simple_loss=0.306, pruned_loss=0.08859, over 7277.00 frames.], tot_loss[loss=0.2131, simple_loss=0.293, pruned_loss=0.06667, over 1427353.56 frames.], batch size: 17, lr: 5.35e-04
2022-05-27 08:06:22,934 INFO [train.py:842] (2/4) Epoch 10, batch 4350, loss[loss=0.2162, simple_loss=0.2943, pruned_loss=0.0691, over 7194.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2938, pruned_loss=0.0667, over 1420274.22 frames.], batch size: 26, lr: 5.35e-04
2022-05-27 08:07:01,887 INFO [train.py:842] (2/4) Epoch 10, batch 4400, loss[loss=0.207, simple_loss=0.298, pruned_loss=0.05798, over 7144.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2924, pruned_loss=0.06602, over 1420682.58 frames.], batch size: 20, lr: 5.35e-04
2022-05-27 08:08:01,219 INFO [train.py:842] (2/4) Epoch 10, batch 4450, loss[loss=0.2326, simple_loss=0.3258, pruned_loss=0.06972, over 7337.00 frames.], tot_loss[loss=0.213, simple_loss=0.2929, pruned_loss=0.06652, over 1419309.07 frames.], batch size: 22, lr: 5.35e-04
2022-05-27 08:08:50,709 INFO [train.py:842] (2/4) Epoch 10, batch 4500, loss[loss=0.2195, simple_loss=0.3031, pruned_loss=0.06797, over 7113.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2925, pruned_loss=0.06656, over 1420682.72 frames.], batch size: 21, lr: 5.35e-04
2022-05-27 08:09:29,232 INFO [train.py:842] (2/4) Epoch 10, batch 4550, loss[loss=0.1846, simple_loss=0.2717, pruned_loss=0.04879, over 7432.00 frames.], tot_loss[loss=0.2136, simple_loss=0.293, pruned_loss=0.06712, over 1417023.90 frames.], batch size: 20, lr: 5.34e-04
2022-05-27 08:10:07,931 INFO [train.py:842] (2/4) Epoch 10, batch 4600, loss[loss=0.2063, simple_loss=0.2824, pruned_loss=0.0651, over 7221.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2928, pruned_loss=0.06684, over 1421149.88 frames.], batch size: 21, lr: 5.34e-04
2022-05-27 08:10:46,415 INFO [train.py:842] (2/4) Epoch 10, batch 4650, loss[loss=0.2422, simple_loss=0.3302, pruned_loss=0.07706, over 7202.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2928, pruned_loss=0.06688, over 1418433.80 frames.], batch size: 23, lr: 5.34e-04
2022-05-27 08:11:25,355 INFO [train.py:842] (2/4) Epoch 10, batch 4700, loss[loss=0.2412, simple_loss=0.3199, pruned_loss=0.08126, over 7221.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2931, pruned_loss=0.06757, over 1411595.45 frames.], batch size: 21, lr: 5.34e-04
2022-05-27 08:12:03,857 INFO [train.py:842] (2/4) Epoch 10, batch 4750, loss[loss=0.1677, simple_loss=0.2512, pruned_loss=0.04211, over 6989.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2938, pruned_loss=0.06765, over 1414776.13 frames.], batch size: 16, lr: 5.34e-04
2022-05-27 08:12:42,492 INFO [train.py:842] (2/4) Epoch 10, batch 4800, loss[loss=0.1952, simple_loss=0.2838, pruned_loss=0.05327, over 7306.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2943, pruned_loss=0.06765, over 1415971.07 frames.], batch size: 25, lr: 5.34e-04
2022-05-27 08:13:21,006 INFO [train.py:842] (2/4) Epoch 10, batch 4850, loss[loss=0.1953, simple_loss=0.2812, pruned_loss=0.05473, over 7121.00 frames.], tot_loss[loss=0.213, simple_loss=0.2928, pruned_loss=0.06658, over 1418385.50 frames.], batch size: 21, lr: 5.33e-04
2022-05-27 08:14:00,165 INFO [train.py:842] (2/4) Epoch 10, batch 4900, loss[loss=0.1861, simple_loss=0.2704, pruned_loss=0.0509, over 7415.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2918, pruned_loss=0.06554, over 1422534.72 frames.], batch size: 18, lr: 5.33e-04
2022-05-27 08:14:38,901 INFO [train.py:842] (2/4) Epoch 10, batch 4950, loss[loss=0.1959, simple_loss=0.2786, pruned_loss=0.05663, over 7229.00 frames.], tot_loss[loss=0.2107, simple_loss=0.2909, pruned_loss=0.06528, over 1424439.36 frames.], batch size: 16, lr: 5.33e-04
2022-05-27 08:15:17,722 INFO [train.py:842] (2/4) Epoch 10, batch 5000, loss[loss=0.2014, simple_loss=0.2765, pruned_loss=0.06317, over 7161.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2904, pruned_loss=0.06501, over 1426136.99 frames.], batch size: 18, lr: 5.33e-04
2022-05-27 08:15:56,152 INFO [train.py:842] (2/4) Epoch 10, batch 5050, loss[loss=0.1903, simple_loss=0.2691, pruned_loss=0.05569, over 7279.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2909, pruned_loss=0.06537, over 1423656.65 frames.], batch size: 17, lr: 5.33e-04
2022-05-27 08:16:34,941 INFO [train.py:842] (2/4) Epoch 10, batch 5100, loss[loss=0.2226, simple_loss=0.3144, pruned_loss=0.06538, over 7099.00 frames.], tot_loss[loss=0.2112, simple_loss=0.2914, pruned_loss=0.06554, over 1421878.82 frames.], batch size: 28, lr: 5.33e-04
2022-05-27 08:17:13,471 INFO [train.py:842] (2/4) Epoch 10, batch 5150, loss[loss=0.2031, simple_loss=0.2982, pruned_loss=0.05403, over 7338.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2907, pruned_loss=0.0653, over 1418220.71 frames.], batch size: 22, lr: 5.33e-04
2022-05-27 08:17:52,715 INFO [train.py:842] (2/4) Epoch 10, batch 5200, loss[loss=0.1905, simple_loss=0.2831, pruned_loss=0.04893, over 7159.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2915, pruned_loss=0.06559, over 1423981.67 frames.], batch size: 19, lr: 5.32e-04
2022-05-27 08:18:31,267 INFO [train.py:842] (2/4) Epoch 10, batch 5250, loss[loss=0.2379, simple_loss=0.3256, pruned_loss=0.07506, over 7291.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2925, pruned_loss=0.06642, over 1426517.83 frames.], batch size: 24, lr: 5.32e-04
2022-05-27 08:19:12,949 INFO [train.py:842] (2/4) Epoch 10, batch 5300, loss[loss=0.2366, simple_loss=0.3001, pruned_loss=0.08656, over 7415.00 frames.], tot_loss[loss=0.2108, simple_loss=0.291, pruned_loss=0.0653, over 1426843.60 frames.], batch size: 18, lr: 5.32e-04
2022-05-27 08:19:51,541 INFO [train.py:842] (2/4) Epoch 10, batch 5350, loss[loss=0.2106, simple_loss=0.2876, pruned_loss=0.06677, over 7382.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2927, pruned_loss=0.06676, over 1428704.17 frames.], batch size: 23, lr: 5.32e-04
2022-05-27 08:20:30,698 INFO [train.py:842] (2/4) Epoch 10, batch 5400, loss[loss=0.2025, simple_loss=0.2685, pruned_loss=0.06828, over 7278.00 frames.], tot_loss[loss=0.212, simple_loss=0.2916, pruned_loss=0.06618, over 1432960.60 frames.], batch size: 18, lr: 5.32e-04
2022-05-27 08:21:09,305 INFO [train.py:842] (2/4) Epoch 10, batch 5450, loss[loss=0.2071, simple_loss=0.291, pruned_loss=0.06162, over 7419.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2913, pruned_loss=0.06601, over 1429921.16 frames.], batch size: 21, lr: 5.32e-04
2022-05-27 08:21:47,999 INFO [train.py:842] (2/4) Epoch 10, batch 5500, loss[loss=0.1715, simple_loss=0.2415, pruned_loss=0.05079, over 6994.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2921, pruned_loss=0.06662, over 1425519.81 frames.], batch size: 16, lr: 5.31e-04
2022-05-27 08:22:26,511 INFO [train.py:842] (2/4) Epoch 10, batch 5550, loss[loss=0.231, simple_loss=0.3251, pruned_loss=0.06845, over 7272.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2924, pruned_loss=0.06648, over 1425327.59 frames.], batch size: 24, lr: 5.31e-04
2022-05-27 08:23:05,386 INFO [train.py:842] (2/4) Epoch 10, batch 5600, loss[loss=0.2292, simple_loss=0.3198, pruned_loss=0.06933, over 7153.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2927, pruned_loss=0.06628, over 1422759.81 frames.], batch size: 20, lr: 5.31e-04
2022-05-27 08:23:44,288 INFO [train.py:842] (2/4) Epoch 10, batch 5650, loss[loss=0.228, simple_loss=0.2943, pruned_loss=0.08084, over 7065.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2922, pruned_loss=0.066, over 1428160.59 frames.], batch size: 18, lr: 5.31e-04
2022-05-27 08:24:23,130 INFO [train.py:842] (2/4) Epoch 10, batch 5700, loss[loss=0.2067, simple_loss=0.2859, pruned_loss=0.06376, over 7067.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2923, pruned_loss=0.06572, over 1430386.20 frames.], batch size: 18, lr: 5.31e-04
2022-05-27 08:25:01,711 INFO [train.py:842] (2/4) Epoch 10, batch 5750, loss[loss=0.2199, simple_loss=0.2931, pruned_loss=0.07332, over 6724.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2923, pruned_loss=0.06602, over 1423487.73 frames.], batch size: 31, lr: 5.31e-04
2022-05-27 08:25:40,650 INFO [train.py:842] (2/4) Epoch 10, batch 5800, loss[loss=0.1981, simple_loss=0.2754, pruned_loss=0.06044, over 7283.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2908, pruned_loss=0.06542, over 1424539.15 frames.], batch size: 17, lr: 5.31e-04
2022-05-27 08:26:19,939 INFO [train.py:842] (2/4) Epoch 10, batch 5850, loss[loss=0.2446, simple_loss=0.3267, pruned_loss=0.08123, over 7332.00 frames.], tot_loss[loss=0.2116, simple_loss=0.2912, pruned_loss=0.06599, over 1426102.05 frames.], batch size: 22, lr: 5.30e-04
2022-05-27 08:26:58,598 INFO [train.py:842] (2/4) Epoch 10, batch 5900, loss[loss=0.2801, simple_loss=0.3459, pruned_loss=0.1071, over 7202.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2929, pruned_loss=0.067, over 1423363.40 frames.], batch size: 23, lr: 5.30e-04
2022-05-27 08:27:37,133 INFO [train.py:842] (2/4) Epoch 10, batch 5950, loss[loss=0.1709, simple_loss=0.2514, pruned_loss=0.04521, over 7276.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2921, pruned_loss=0.06651, over 1420195.83 frames.], batch size: 18, lr: 5.30e-04
2022-05-27 08:28:15,983 INFO [train.py:842] (2/4) Epoch 10, batch 6000, loss[loss=0.2365, simple_loss=0.3205, pruned_loss=0.07627, over 7140.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2928, pruned_loss=0.0668, over 1423206.11 frames.], batch size: 20, lr: 5.30e-04
2022-05-27 08:28:15,984 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 08:28:25,356 INFO [train.py:871] (2/4) Epoch 10, validation: loss=0.173, simple_loss=0.2732, pruned_loss=0.0364, over 868885.00 frames.
2022-05-27 08:29:04,387 INFO [train.py:842] (2/4) Epoch 10, batch 6050, loss[loss=0.177, simple_loss=0.2591, pruned_loss=0.04742, over 7428.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2933, pruned_loss=0.06747, over 1425378.97 frames.], batch size: 20, lr: 5.30e-04
2022-05-27 08:29:43,220 INFO [train.py:842] (2/4) Epoch 10, batch 6100, loss[loss=0.2886, simple_loss=0.3676, pruned_loss=0.1048, over 7365.00 frames.], tot_loss[loss=0.2121, simple_loss=0.292, pruned_loss=0.06615, over 1423120.04 frames.], batch size: 23, lr: 5.30e-04
2022-05-27 08:30:22,057 INFO [train.py:842] (2/4) Epoch 10, batch 6150, loss[loss=0.1481, simple_loss=0.2319, pruned_loss=0.03217, over 6813.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2919, pruned_loss=0.0667, over 1422636.42 frames.], batch size: 15, lr: 5.30e-04
2022-05-27 08:31:00,962 INFO [train.py:842] (2/4) Epoch 10, batch 6200, loss[loss=0.2256, simple_loss=0.3036, pruned_loss=0.07378, over 7300.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2929, pruned_loss=0.06729, over 1427612.77 frames.], batch size: 24, lr: 5.29e-04
2022-05-27 08:31:39,419 INFO [train.py:842] (2/4) Epoch 10, batch 6250, loss[loss=0.2052, simple_loss=0.281, pruned_loss=0.06466, over 7157.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2923, pruned_loss=0.06724, over 1418230.72 frames.], batch size: 19, lr: 5.29e-04
2022-05-27 08:32:18,076 INFO [train.py:842] (2/4) Epoch 10, batch 6300, loss[loss=0.2674, simple_loss=0.3415, pruned_loss=0.09668, over 7142.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2934, pruned_loss=0.06726, over 1422608.43 frames.], batch size: 20, lr: 5.29e-04
2022-05-27 08:32:56,575 INFO [train.py:842] (2/4) Epoch 10, batch 6350, loss[loss=0.2777, simple_loss=0.335, pruned_loss=0.1102, over 7154.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2921, pruned_loss=0.06644, over 1422062.97 frames.], batch size: 20, lr: 5.29e-04
2022-05-27 08:33:35,407 INFO [train.py:842] (2/4) Epoch 10, batch 6400, loss[loss=0.1891, simple_loss=0.2822, pruned_loss=0.04803, over 7426.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2906, pruned_loss=0.06488, over 1423887.35 frames.], batch size: 21, lr: 5.29e-04
2022-05-27 08:34:14,095 INFO [train.py:842] (2/4) Epoch 10, batch 6450, loss[loss=0.1968, simple_loss=0.266, pruned_loss=0.06379, over 7292.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2903, pruned_loss=0.06471, over 1421872.78 frames.], batch size: 17, lr: 5.29e-04
2022-05-27 08:34:52,931 INFO [train.py:842] (2/4) Epoch 10, batch 6500, loss[loss=0.2247, simple_loss=0.3118, pruned_loss=0.06878, over 6882.00 frames.], tot_loss[loss=0.21, simple_loss=0.2902, pruned_loss=0.06491, over 1421454.19 frames.], batch size: 31, lr: 5.28e-04
2022-05-27 08:35:31,477 INFO [train.py:842] (2/4) Epoch 10, batch 6550, loss[loss=0.2686, simple_loss=0.3399, pruned_loss=0.09866, over 7282.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2907, pruned_loss=0.06519, over 1423170.66 frames.], batch size: 24, lr: 5.28e-04
2022-05-27 08:36:10,164 INFO [train.py:842] (2/4) Epoch 10, batch 6600, loss[loss=0.2423, simple_loss=0.3311, pruned_loss=0.07676, over 7286.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2926, pruned_loss=0.0659, over 1425150.49 frames.], batch size: 25, lr: 5.28e-04
2022-05-27 08:36:48,766 INFO [train.py:842] (2/4) Epoch 10, batch 6650, loss[loss=0.1731, simple_loss=0.2551, pruned_loss=0.04558, over 7400.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2916, pruned_loss=0.06484, over 1426165.14 frames.], batch size: 18, lr: 5.28e-04
2022-05-27 08:37:27,504 INFO [train.py:842] (2/4) Epoch 10, batch 6700, loss[loss=0.1926, simple_loss=0.2708, pruned_loss=0.05718, over 7077.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2923, pruned_loss=0.06555, over 1422193.11 frames.], batch size: 18, lr: 5.28e-04
2022-05-27 08:38:06,123 INFO [train.py:842] (2/4) Epoch 10, batch 6750, loss[loss=0.1979, simple_loss=0.2724, pruned_loss=0.06169, over 7403.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2944, pruned_loss=0.06724, over 1425087.07 frames.], batch size: 18, lr: 5.28e-04
2022-05-27 08:38:45,065 INFO [train.py:842] (2/4) Epoch 10, batch 6800, loss[loss=0.1858, simple_loss=0.271, pruned_loss=0.05035, over 7097.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2914, pruned_loss=0.0656, over 1423559.51 frames.], batch size: 28, lr: 5.28e-04
2022-05-27 08:39:23,800 INFO [train.py:842] (2/4) Epoch 10, batch 6850, loss[loss=0.2434, simple_loss=0.3223, pruned_loss=0.08222, over 6809.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2915, pruned_loss=0.06533, over 1427849.73 frames.], batch size: 31, lr: 5.27e-04
2022-05-27 08:40:02,433 INFO [train.py:842] (2/4) Epoch 10, batch 6900, loss[loss=0.2506, simple_loss=0.3278, pruned_loss=0.0867, over 7206.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2929, pruned_loss=0.06617, over 1423943.82 frames.], batch size: 23, lr: 5.27e-04
2022-05-27 08:40:40,967 INFO [train.py:842] (2/4) Epoch 10, batch 6950, loss[loss=0.1748, simple_loss=0.2666, pruned_loss=0.04148, over 7379.00 frames.], tot_loss[loss=0.212, simple_loss=0.2923, pruned_loss=0.06583, over 1419767.69 frames.], batch size: 23, lr: 5.27e-04
2022-05-27 08:41:19,720 INFO [train.py:842] (2/4) Epoch 10, batch 7000, loss[loss=0.1867, simple_loss=0.2837, pruned_loss=0.04479, over 7141.00 frames.], tot_loss[loss=0.2118, simple_loss=0.2919, pruned_loss=0.06585, over 1421320.21 frames.], batch size: 20, lr: 5.27e-04
2022-05-27 08:41:58,250 INFO [train.py:842] (2/4) Epoch 10, batch 7050, loss[loss=0.281, simple_loss=0.3298, pruned_loss=0.1161, over 7223.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2924, pruned_loss=0.06627, over 1419514.84 frames.], batch size: 20, lr: 5.27e-04
2022-05-27 08:42:37,040 INFO [train.py:842] (2/4) Epoch 10, batch 7100, loss[loss=0.1904, simple_loss=0.2781, pruned_loss=0.05138, over 7342.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2924, pruned_loss=0.0662, over 1421437.18 frames.], batch size: 22, lr: 5.27e-04
2022-05-27 08:43:15,633 INFO [train.py:842] (2/4) Epoch 10, batch 7150, loss[loss=0.2216, simple_loss=0.3075, pruned_loss=0.06788, over 7394.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2904, pruned_loss=0.06493, over 1422896.19 frames.], batch size: 23, lr: 5.27e-04
2022-05-27 08:43:54,158 INFO [train.py:842] (2/4) Epoch 10, batch 7200, loss[loss=0.2623, simple_loss=0.3309, pruned_loss=0.09685, over 4570.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2915, pruned_loss=0.06617, over 1415621.33 frames.], batch size: 52, lr: 5.26e-04
2022-05-27 08:44:32,604 INFO [train.py:842] (2/4) Epoch 10, batch 7250, loss[loss=0.2203, simple_loss=0.3057, pruned_loss=0.06751, over 7259.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2919, pruned_loss=0.06598, over 1410905.54 frames.], batch size: 25, lr: 5.26e-04
2022-05-27 08:45:11,718 INFO [train.py:842] (2/4) Epoch 10, batch 7300, loss[loss=0.1891, simple_loss=0.2788, pruned_loss=0.0497, over 7426.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2923, pruned_loss=0.06595, over 1417043.20 frames.], batch size: 20, lr: 5.26e-04
2022-05-27 08:45:50,392 INFO [train.py:842] (2/4) Epoch 10, batch 7350, loss[loss=0.1912, simple_loss=0.2675, pruned_loss=0.05746, over 7136.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2928, pruned_loss=0.06668, over 1420736.41 frames.], batch size: 17, lr: 5.26e-04
2022-05-27 08:46:29,220 INFO [train.py:842] (2/4) Epoch 10, batch 7400, loss[loss=0.1851, simple_loss=0.2765, pruned_loss=0.04687, over 7418.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2928, pruned_loss=0.06645, over 1422038.57 frames.], batch size: 21, lr: 5.26e-04
2022-05-27 08:47:07,762 INFO [train.py:842] (2/4) Epoch 10, batch 7450, loss[loss=0.1719, simple_loss=0.242, pruned_loss=0.05097, over 6840.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2935, pruned_loss=0.06737, over 1418460.82 frames.], batch size: 15, lr: 5.26e-04
2022-05-27 08:47:46,345 INFO [train.py:842] (2/4) Epoch 10, batch 7500, loss[loss=0.2089, simple_loss=0.2885, pruned_loss=0.06469, over 7222.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2929, pruned_loss=0.06726, over 1418986.18 frames.], batch size: 21, lr: 5.26e-04
2022-05-27 08:48:24,795 INFO [train.py:842] (2/4) Epoch 10, batch 7550, loss[loss=0.2035, simple_loss=0.2795, pruned_loss=0.06372, over 7151.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2918, pruned_loss=0.06649, over 1421241.11 frames.], batch size: 20, lr: 5.25e-04
2022-05-27 08:49:03,932 INFO [train.py:842] (2/4) Epoch 10, batch 7600, loss[loss=0.1828, simple_loss=0.2619, pruned_loss=0.05184, over 7307.00 frames.], tot_loss[loss=0.211, simple_loss=0.2907, pruned_loss=0.06567, over 1423247.95 frames.], batch size: 18, lr: 5.25e-04
2022-05-27 08:49:42,451 INFO [train.py:842] (2/4) Epoch 10, batch 7650, loss[loss=0.2217, simple_loss=0.2831, pruned_loss=0.08015, over 6978.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2909, pruned_loss=0.06592, over 1420576.81 frames.], batch size: 16, lr: 5.25e-04
2022-05-27 08:50:21,179 INFO [train.py:842] (2/4) Epoch 10, batch 7700, loss[loss=0.2234, simple_loss=0.3089, pruned_loss=0.06898, over 7323.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2908, pruned_loss=0.0651, over 1419937.13 frames.], batch size: 22, lr: 5.25e-04
2022-05-27 08:50:59,821 INFO [train.py:842] (2/4) Epoch 10, batch 7750, loss[loss=0.2363, simple_loss=0.3131, pruned_loss=0.07978, over 6850.00 frames.], tot_loss[loss=0.21, simple_loss=0.2903, pruned_loss=0.06491, over 1424478.18 frames.], batch size: 31, lr: 5.25e-04
2022-05-27 08:51:38,586 INFO [train.py:842] (2/4) Epoch 10, batch 7800, loss[loss=0.2022, simple_loss=0.2801, pruned_loss=0.06212, over 7158.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2901, pruned_loss=0.06457, over 1426422.95 frames.], batch size: 18, lr: 5.25e-04
2022-05-27 08:52:17,199 INFO [train.py:842] (2/4) Epoch 10, batch 7850, loss[loss=0.2077, simple_loss=0.2988, pruned_loss=0.05832, over 7323.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2885, pruned_loss=0.06403, over 1422340.75 frames.], batch size: 21, lr: 5.25e-04
2022-05-27 08:52:56,099 INFO [train.py:842] (2/4) Epoch 10, batch 7900, loss[loss=0.1919, simple_loss=0.2748, pruned_loss=0.0545, over 7155.00 frames.], tot_loss[loss=0.2088, simple_loss=0.289, pruned_loss=0.06432, over 1424029.94 frames.], batch size: 19, lr: 5.24e-04
2022-05-27 08:53:34,561 INFO [train.py:842] (2/4) Epoch 10, batch 7950, loss[loss=0.1826, simple_loss=0.2657, pruned_loss=0.04974, over 7274.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2906, pruned_loss=0.0649, over 1425625.22 frames.], batch size: 18, lr: 5.24e-04
2022-05-27 08:54:13,264 INFO [train.py:842] (2/4) Epoch 10, batch 8000, loss[loss=0.1814, simple_loss=0.2566, pruned_loss=0.05311, over 7061.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2899, pruned_loss=0.0646, over 1425583.07 frames.], batch size: 18, lr: 5.24e-04
2022-05-27 08:54:51,835 INFO [train.py:842] (2/4) Epoch 10, batch 8050, loss[loss=0.2211, simple_loss=0.2875, pruned_loss=0.07737, over 5003.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2901, pruned_loss=0.06471, over 1421788.06 frames.], batch size: 52, lr: 5.24e-04
2022-05-27 08:55:30,392 INFO [train.py:842] (2/4) Epoch 10, batch 8100, loss[loss=0.2399, simple_loss=0.2995, pruned_loss=0.0901, over 7274.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2907, pruned_loss=0.06526, over 1417323.55 frames.], batch size: 17, lr: 5.24e-04
2022-05-27 08:56:08,975 INFO [train.py:842] (2/4) Epoch 10, batch 8150, loss[loss=0.1855, simple_loss=0.2794, pruned_loss=0.04578, over 7272.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2921, pruned_loss=0.0664, over 1417224.27 frames.], batch size: 25, lr: 5.24e-04
2022-05-27 08:56:47,760 INFO [train.py:842] (2/4) Epoch 10, batch 8200, loss[loss=0.2269, simple_loss=0.3057, pruned_loss=0.07408, over 6670.00 frames.], tot_loss[loss=0.2107, simple_loss=0.2903, pruned_loss=0.06559, over 1417173.67 frames.], batch size: 31, lr: 5.24e-04
2022-05-27 08:57:26,300 INFO [train.py:842] (2/4) Epoch 10, batch 8250, loss[loss=0.192, simple_loss=0.2723, pruned_loss=0.05585, over 7359.00 frames.], tot_loss[loss=0.2101, simple_loss=0.29, pruned_loss=0.06512, over 1417311.93 frames.], batch size: 19, lr: 5.23e-04
2022-05-27 08:58:04,878 INFO [train.py:842] (2/4) Epoch 10, batch 8300, loss[loss=0.1554, simple_loss=0.2373, pruned_loss=0.03678, over 7137.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2901, pruned_loss=0.06504, over 1413995.79 frames.], batch size: 17, lr: 5.23e-04
2022-05-27 08:58:43,208 INFO [train.py:842] (2/4) Epoch 10, batch 8350, loss[loss=0.2797, simple_loss=0.3512, pruned_loss=0.1041, over 5129.00 frames.], tot_loss[loss=0.21, simple_loss=0.2904, pruned_loss=0.06481, over 1415942.67 frames.], batch size: 52, lr: 5.23e-04
2022-05-27 08:59:21,879 INFO [train.py:842] (2/4) Epoch 10, batch 8400, loss[loss=0.2008, simple_loss=0.2854, pruned_loss=0.05814, over 7290.00 frames.], tot_loss[loss=0.2106, simple_loss=0.291, pruned_loss=0.06512, over 1415804.52 frames.], batch size: 24, lr: 5.23e-04
2022-05-27 09:00:00,368 INFO [train.py:842] (2/4) Epoch 10, batch 8450, loss[loss=0.2599, simple_loss=0.3434, pruned_loss=0.08818, over 6832.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2916, pruned_loss=0.06506, over 1414702.74 frames.], batch size: 31, lr: 5.23e-04
2022-05-27 09:00:39,089 INFO [train.py:842] (2/4) Epoch 10, batch 8500, loss[loss=0.154, simple_loss=0.2243, pruned_loss=0.04184, over 7157.00 frames.], tot_loss[loss=0.2116, simple_loss=0.292, pruned_loss=0.06556, over 1414069.77 frames.], batch size: 18, lr: 5.23e-04
2022-05-27 09:01:17,516 INFO [train.py:842] (2/4) Epoch 10, batch 8550, loss[loss=0.2275, simple_loss=0.3069, pruned_loss=0.07405, over 7284.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2908, pruned_loss=0.0651, over 1412508.96 frames.], batch size: 24, lr: 5.23e-04
2022-05-27 09:01:56,480 INFO [train.py:842] (2/4) Epoch 10, batch 8600, loss[loss=0.1986, simple_loss=0.2962, pruned_loss=0.05047, over 7112.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2902, pruned_loss=0.06463, over 1416618.41 frames.], batch size: 21, lr: 5.22e-04
2022-05-27 09:02:35,163 INFO [train.py:842] (2/4) Epoch 10, batch 8650, loss[loss=0.1699, simple_loss=0.2536, pruned_loss=0.0431, over 7120.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2896, pruned_loss=0.06402, over 1422004.25 frames.], batch size: 17, lr: 5.22e-04
2022-05-27 09:03:14,158 INFO [train.py:842] (2/4) Epoch 10, batch 8700, loss[loss=0.1672, simple_loss=0.2554, pruned_loss=0.03956, over 7155.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2894, pruned_loss=0.06349, over 1419195.08 frames.], batch size: 18, lr: 5.22e-04
2022-05-27 09:03:52,577 INFO [train.py:842] (2/4) Epoch 10, batch 8750, loss[loss=0.2305, simple_loss=0.299, pruned_loss=0.08101, over 7192.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2898, pruned_loss=0.06425, over 1414833.78 frames.], batch size: 23, lr: 5.22e-04
2022-05-27 09:04:31,535 INFO [train.py:842] (2/4) Epoch 10, batch 8800, loss[loss=0.2133, simple_loss=0.2958, pruned_loss=0.06542, over 7212.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2896, pruned_loss=0.06467, over 1415262.20 frames.], batch size: 23, lr: 5.22e-04
2022-05-27 09:05:10,756 INFO [train.py:842] (2/4) Epoch 10, batch 8850, loss[loss=0.2386, simple_loss=0.3293, pruned_loss=0.07393, over 7111.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2875, pruned_loss=0.06349, over 1419159.74 frames.], batch size: 28, lr: 5.22e-04
2022-05-27 09:05:49,801 INFO [train.py:842] (2/4) Epoch 10, batch 8900, loss[loss=0.195, simple_loss=0.2552, pruned_loss=0.06733, over 7147.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2876, pruned_loss=0.06369, over 1419947.34 frames.], batch size: 17, lr: 5.22e-04
2022-05-27 09:06:28,595 INFO [train.py:842] (2/4) Epoch 10, batch 8950, loss[loss=0.1783, simple_loss=0.2607, pruned_loss=0.04792, over 7137.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2877, pruned_loss=0.06431, over 1421271.63 frames.], batch size: 17, lr: 5.21e-04
2022-05-27 09:07:07,865 INFO [train.py:842] (2/4) Epoch 10, batch 9000, loss[loss=0.2217, simple_loss=0.2879, pruned_loss=0.07779, over 7162.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2878, pruned_loss=0.06479, over 1419401.65 frames.], batch size: 16, lr: 5.21e-04
2022-05-27 09:07:07,866 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 09:07:17,179 INFO [train.py:871] (2/4) 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] (2/4) Epoch 10, batch 9050, loss[loss=0.1992, simple_loss=0.2855, pruned_loss=0.05649, over 6229.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2879, pruned_loss=0.06525, over 1404029.83 frames.], batch size: 37, lr: 5.21e-04
2022-05-27 09:08:34,928 INFO [train.py:842] (2/4) Epoch 10, batch 9100, loss[loss=0.168, simple_loss=0.2487, pruned_loss=0.04369, over 7141.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2911, pruned_loss=0.0682, over 1362926.44 frames.], batch size: 17, lr: 5.21e-04
2022-05-27 09:09:12,721 INFO [train.py:842] (2/4) Epoch 10, batch 9150, loss[loss=0.2059, simple_loss=0.2755, pruned_loss=0.06818, over 5152.00 frames.], tot_loss[loss=0.2196, simple_loss=0.2953, pruned_loss=0.07193, over 1291350.81 frames.], batch size: 52, lr: 5.21e-04
2022-05-27 09:10:05,829 INFO [train.py:842] (2/4) Epoch 11, batch 0, loss[loss=0.2708, simple_loss=0.3438, pruned_loss=0.09887, over 7424.00 frames.], tot_loss[loss=0.2708, simple_loss=0.3438, pruned_loss=0.09887, over 7424.00 frames.], batch size: 20, lr: 5.01e-04
2022-05-27 09:10:44,597 INFO [train.py:842] (2/4) Epoch 11, batch 50, loss[loss=0.1943, simple_loss=0.2787, pruned_loss=0.055, over 7421.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2922, pruned_loss=0.06434, over 323069.72 frames.], batch size: 20, lr: 5.01e-04
2022-05-27 09:11:23,549 INFO [train.py:842] (2/4) Epoch 11, batch 100, loss[loss=0.2163, simple_loss=0.3013, pruned_loss=0.06568, over 7297.00 frames.], tot_loss[loss=0.2118, simple_loss=0.2931, pruned_loss=0.06524, over 567528.38 frames.], batch size: 18, lr: 5.01e-04
2022-05-27 09:12:02,341 INFO [train.py:842] (2/4) Epoch 11, batch 150, loss[loss=0.195, simple_loss=0.2706, pruned_loss=0.05966, over 7217.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2943, pruned_loss=0.06601, over 760761.19 frames.], batch size: 16, lr: 5.01e-04
2022-05-27 09:12:41,234 INFO [train.py:842] (2/4) Epoch 11, batch 200, loss[loss=0.189, simple_loss=0.2728, pruned_loss=0.05265, over 7407.00 frames.], tot_loss[loss=0.2126, simple_loss=0.294, pruned_loss=0.0656, over 907363.39 frames.], batch size: 18, lr: 5.01e-04
2022-05-27 09:13:19,980 INFO [train.py:842] (2/4) Epoch 11, batch 250, loss[loss=0.2357, simple_loss=0.3057, pruned_loss=0.08281, over 6296.00 frames.], tot_loss[loss=0.21, simple_loss=0.291, pruned_loss=0.06453, over 1022367.50 frames.], batch size: 37, lr: 5.01e-04
2022-05-27 09:13:59,408 INFO [train.py:842] (2/4) Epoch 11, batch 300, loss[loss=0.229, simple_loss=0.3074, pruned_loss=0.07533, over 5111.00 frames.], tot_loss[loss=0.207, simple_loss=0.2882, pruned_loss=0.06296, over 1113875.83 frames.], batch size: 52, lr: 5.01e-04
2022-05-27 09:14:38,186 INFO [train.py:842] (2/4) Epoch 11, batch 350, loss[loss=0.2465, simple_loss=0.3261, pruned_loss=0.08347, over 6797.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2892, pruned_loss=0.06374, over 1186862.48 frames.], batch size: 31, lr: 5.01e-04
2022-05-27 09:15:17,103 INFO [train.py:842] (2/4) Epoch 11, batch 400, loss[loss=0.1828, simple_loss=0.2666, pruned_loss=0.04955, over 7435.00 frames.], tot_loss[loss=0.207, simple_loss=0.2882, pruned_loss=0.06288, over 1240768.62 frames.], batch size: 20, lr: 5.00e-04
2022-05-27 09:15:56,237 INFO [train.py:842] (2/4) Epoch 11, batch 450, loss[loss=0.1919, simple_loss=0.2868, pruned_loss=0.04852, over 7230.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2865, pruned_loss=0.06217, over 1280685.11 frames.], batch size: 20, lr: 5.00e-04
2022-05-27 09:16:35,457 INFO [train.py:842] (2/4) Epoch 11, batch 500, loss[loss=0.1673, simple_loss=0.2597, pruned_loss=0.03748, over 7339.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2876, pruned_loss=0.06282, over 1314873.66 frames.], batch size: 20, lr: 5.00e-04
2022-05-27 09:17:14,277 INFO [train.py:842] (2/4) Epoch 11, batch 550, loss[loss=0.1858, simple_loss=0.2718, pruned_loss=0.04993, over 7068.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2888, pruned_loss=0.0633, over 1339952.13 frames.], batch size: 18, lr: 5.00e-04
2022-05-27 09:17:53,354 INFO [train.py:842] (2/4) Epoch 11, batch 600, loss[loss=0.1781, simple_loss=0.2507, pruned_loss=0.05274, over 6987.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2886, pruned_loss=0.06342, over 1359645.38 frames.], batch size: 16, lr: 5.00e-04
2022-05-27 09:18:32,239 INFO [train.py:842] (2/4) Epoch 11, batch 650, loss[loss=0.1757, simple_loss=0.2593, pruned_loss=0.04608, over 7127.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2898, pruned_loss=0.06451, over 1365995.49 frames.], batch size: 17, lr: 5.00e-04
2022-05-27 09:19:11,358 INFO [train.py:842] (2/4) Epoch 11, batch 700, loss[loss=0.1687, simple_loss=0.2492, pruned_loss=0.04406, over 6829.00 frames.], tot_loss[loss=0.2109, simple_loss=0.291, pruned_loss=0.06535, over 1376249.29 frames.], batch size: 15, lr: 5.00e-04
2022-05-27 09:19:50,233 INFO [train.py:842] (2/4) Epoch 11, batch 750, loss[loss=0.244, simple_loss=0.3309, pruned_loss=0.07854, over 7145.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2895, pruned_loss=0.06438, over 1382849.91 frames.], batch size: 20, lr: 4.99e-04
2022-05-27 09:20:29,323 INFO [train.py:842] (2/4) Epoch 11, batch 800, loss[loss=0.2559, simple_loss=0.3377, pruned_loss=0.08707, over 7175.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2908, pruned_loss=0.06544, over 1394352.41 frames.], batch size: 26, lr: 4.99e-04
2022-05-27 09:21:08,037 INFO [train.py:842] (2/4) Epoch 11, batch 850, loss[loss=0.2105, simple_loss=0.3068, pruned_loss=0.05714, over 7329.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2897, pruned_loss=0.06473, over 1398582.84 frames.], batch size: 20, lr: 4.99e-04
2022-05-27 09:21:46,916 INFO [train.py:842] (2/4) Epoch 11, batch 900, loss[loss=0.2049, simple_loss=0.2843, pruned_loss=0.06278, over 7435.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2899, pruned_loss=0.06497, over 1406548.13 frames.], batch size: 20, lr: 4.99e-04
2022-05-27 09:22:25,672 INFO [train.py:842] (2/4) Epoch 11, batch 950, loss[loss=0.1607, simple_loss=0.2289, pruned_loss=0.04628, over 7000.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2895, pruned_loss=0.06469, over 1409149.83 frames.], batch size: 16, lr: 4.99e-04
2022-05-27 09:23:05,071 INFO [train.py:842] (2/4) Epoch 11, batch 1000, loss[loss=0.2193, simple_loss=0.3001, pruned_loss=0.06926, over 7276.00 frames.], tot_loss[loss=0.2089, simple_loss=0.2894, pruned_loss=0.06416, over 1413231.39 frames.], batch size: 25, lr: 4.99e-04
2022-05-27 09:23:43,644 INFO [train.py:842] (2/4) Epoch 11, batch 1050, loss[loss=0.2035, simple_loss=0.2815, pruned_loss=0.06282, over 7259.00 frames.], tot_loss[loss=0.2109, simple_loss=0.291, pruned_loss=0.06536, over 1407654.73 frames.], batch size: 19, lr: 4.99e-04
2022-05-27 09:24:22,808 INFO [train.py:842] (2/4) Epoch 11, batch 1100, loss[loss=0.2168, simple_loss=0.2889, pruned_loss=0.07236, over 7162.00 frames.], tot_loss[loss=0.21, simple_loss=0.2906, pruned_loss=0.06469, over 1412796.38 frames.], batch size: 18, lr: 4.99e-04
2022-05-27 09:25:01,964 INFO [train.py:842] (2/4) Epoch 11, batch 1150, loss[loss=0.1861, simple_loss=0.2637, pruned_loss=0.05426, over 7062.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2894, pruned_loss=0.06377, over 1417067.23 frames.], batch size: 18, lr: 4.98e-04
2022-05-27 09:25:40,989 INFO [train.py:842] (2/4) Epoch 11, batch 1200, loss[loss=0.1659, simple_loss=0.2472, pruned_loss=0.04226, over 6789.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2862, pruned_loss=0.06219, over 1419270.51 frames.], batch size: 15, lr: 4.98e-04
2022-05-27 09:26:19,779 INFO [train.py:842] (2/4) Epoch 11, batch 1250, loss[loss=0.2159, simple_loss=0.2908, pruned_loss=0.07052, over 7148.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2862, pruned_loss=0.06251, over 1423138.38 frames.], batch size: 17, lr: 4.98e-04
2022-05-27 09:26:58,838 INFO [train.py:842] (2/4) Epoch 11, batch 1300, loss[loss=0.2185, simple_loss=0.3068, pruned_loss=0.06503, over 7315.00 frames.], tot_loss[loss=0.2045, simple_loss=0.285, pruned_loss=0.06204, over 1419939.87 frames.], batch size: 21, lr: 4.98e-04
2022-05-27 09:27:37,515 INFO [train.py:842] (2/4) Epoch 11, batch 1350, loss[loss=0.1862, simple_loss=0.2727, pruned_loss=0.04981, over 7324.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2859, pruned_loss=0.06247, over 1423468.81 frames.], batch size: 21, lr: 4.98e-04
2022-05-27 09:28:16,453 INFO [train.py:842] (2/4) Epoch 11, batch 1400, loss[loss=0.2072, simple_loss=0.2854, pruned_loss=0.06452, over 7160.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2868, pruned_loss=0.06266, over 1426195.02 frames.], batch size: 19, lr: 4.98e-04
2022-05-27 09:28:55,147 INFO [train.py:842] (2/4) Epoch 11, batch 1450, loss[loss=0.1868, simple_loss=0.2663, pruned_loss=0.05366, over 7277.00 frames.], tot_loss[loss=0.2075, simple_loss=0.288, pruned_loss=0.06351, over 1426416.47 frames.], batch size: 17, lr: 4.98e-04
2022-05-27 09:29:34,191 INFO [train.py:842] (2/4) Epoch 11, batch 1500, loss[loss=0.2057, simple_loss=0.2879, pruned_loss=0.06175, over 7044.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2882, pruned_loss=0.06328, over 1424263.70 frames.], batch size: 28, lr: 4.97e-04
2022-05-27 09:30:12,899 INFO [train.py:842] (2/4) Epoch 11, batch 1550, loss[loss=0.1592, simple_loss=0.2482, pruned_loss=0.03514, over 7433.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2885, pruned_loss=0.06365, over 1422978.30 frames.], batch size: 20, lr: 4.97e-04
2022-05-27 09:30:51,664 INFO [train.py:842] (2/4) Epoch 11, batch 1600, loss[loss=0.2721, simple_loss=0.3434, pruned_loss=0.1004, over 6660.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2883, pruned_loss=0.06341, over 1418329.58 frames.], batch size: 31, lr: 4.97e-04
2022-05-27 09:31:30,333 INFO [train.py:842] (2/4) Epoch 11, batch 1650, loss[loss=0.1699, simple_loss=0.2456, pruned_loss=0.04705, over 6796.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2873, pruned_loss=0.06321, over 1418292.31 frames.], batch size: 15, lr: 4.97e-04
2022-05-27 09:32:09,189 INFO [train.py:842] (2/4) Epoch 11, batch 1700, loss[loss=0.1985, simple_loss=0.2645, pruned_loss=0.06629, over 6871.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2871, pruned_loss=0.06283, over 1417398.74 frames.], batch size: 15, lr: 4.97e-04
2022-05-27 09:32:47,735 INFO [train.py:842] (2/4) Epoch 11, batch 1750, loss[loss=0.1656, simple_loss=0.2646, pruned_loss=0.03329, over 7115.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2855, pruned_loss=0.06181, over 1413716.69 frames.], batch size: 21, lr: 4.97e-04
2022-05-27 09:33:26,550 INFO [train.py:842] (2/4) Epoch 11, batch 1800, loss[loss=0.2478, simple_loss=0.3172, pruned_loss=0.08923, over 5055.00 frames.], tot_loss[loss=0.205, simple_loss=0.2858, pruned_loss=0.06207, over 1413504.86 frames.], batch size: 52, lr: 4.97e-04
2022-05-27 09:34:05,163 INFO [train.py:842] (2/4) Epoch 11, batch 1850, loss[loss=0.2809, simple_loss=0.3639, pruned_loss=0.09897, over 6373.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2865, pruned_loss=0.06235, over 1417267.12 frames.], batch size: 37, lr: 4.97e-04
2022-05-27 09:34:44,020 INFO [train.py:842] (2/4) Epoch 11, batch 1900, loss[loss=0.1892, simple_loss=0.2819, pruned_loss=0.04824, over 7318.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2869, pruned_loss=0.06284, over 1421769.51 frames.], batch size: 21, lr: 4.96e-04
2022-05-27 09:35:22,634 INFO [train.py:842] (2/4) Epoch 11, batch 1950, loss[loss=0.2369, simple_loss=0.3157, pruned_loss=0.07907, over 7367.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2885, pruned_loss=0.06425, over 1421024.81 frames.], batch size: 19, lr: 4.96e-04
2022-05-27 09:36:01,469 INFO [train.py:842] (2/4) Epoch 11, batch 2000, loss[loss=0.1669, simple_loss=0.2532, pruned_loss=0.0403, over 7170.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2887, pruned_loss=0.06416, over 1421953.22 frames.], batch size: 18, lr: 4.96e-04
2022-05-27 09:36:40,072 INFO [train.py:842] (2/4) Epoch 11, batch 2050, loss[loss=0.1743, simple_loss=0.2521, pruned_loss=0.04823, over 7284.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2885, pruned_loss=0.06351, over 1423649.91 frames.], batch size: 17, lr: 4.96e-04
2022-05-27 09:37:19,148 INFO [train.py:842] (2/4) Epoch 11, batch 2100, loss[loss=0.2387, simple_loss=0.3238, pruned_loss=0.07678, over 7379.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2891, pruned_loss=0.06361, over 1424756.49 frames.], batch size: 23, lr: 4.96e-04
2022-05-27 09:37:57,697 INFO [train.py:842] (2/4) Epoch 11, batch 2150, loss[loss=0.184, simple_loss=0.2649, pruned_loss=0.05154, over 7153.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2889, pruned_loss=0.06341, over 1425093.33 frames.], batch size: 18, lr: 4.96e-04
2022-05-27 09:38:36,759 INFO [train.py:842] (2/4) Epoch 11, batch 2200, loss[loss=0.2286, simple_loss=0.3056, pruned_loss=0.07576, over 7245.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2883, pruned_loss=0.06308, over 1423336.97 frames.], batch size: 20, lr: 4.96e-04
2022-05-27 09:39:15,379 INFO [train.py:842] (2/4) Epoch 11, batch 2250, loss[loss=0.2406, simple_loss=0.3215, pruned_loss=0.07987, over 7345.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2887, pruned_loss=0.06304, over 1426673.81 frames.], batch size: 22, lr: 4.95e-04
2022-05-27 09:39:54,339 INFO [train.py:842] (2/4) Epoch 11, batch 2300, loss[loss=0.2193, simple_loss=0.3, pruned_loss=0.06925, over 7153.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2883, pruned_loss=0.06306, over 1426640.22 frames.], batch size: 26, lr: 4.95e-04
2022-05-27 09:40:33,042 INFO [train.py:842] (2/4) Epoch 11, batch 2350, loss[loss=0.2267, simple_loss=0.3147, pruned_loss=0.06935, over 6767.00 frames.], tot_loss[loss=0.206, simple_loss=0.2874, pruned_loss=0.0623, over 1428809.81 frames.], batch size: 31, lr: 4.95e-04
2022-05-27 09:41:11,943 INFO [train.py:842] (2/4) Epoch 11, batch 2400, loss[loss=0.1819, simple_loss=0.2722, pruned_loss=0.04584, over 7315.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2882, pruned_loss=0.06267, over 1422843.06 frames.], batch size: 21, lr: 4.95e-04
2022-05-27 09:41:50,638 INFO [train.py:842] (2/4) Epoch 11, batch 2450, loss[loss=0.1719, simple_loss=0.2595, pruned_loss=0.04219, over 7009.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2865, pruned_loss=0.06188, over 1423159.86 frames.], batch size: 16, lr: 4.95e-04
2022-05-27 09:42:29,490 INFO [train.py:842] (2/4) Epoch 11, batch 2500, loss[loss=0.2103, simple_loss=0.294, pruned_loss=0.06328, over 7154.00 frames.], tot_loss[loss=0.207, simple_loss=0.2881, pruned_loss=0.06295, over 1422359.18 frames.], batch size: 19, lr: 4.95e-04
2022-05-27 09:43:08,225 INFO [train.py:842] (2/4) Epoch 11, batch 2550, loss[loss=0.2029, simple_loss=0.2797, pruned_loss=0.06309, over 6882.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2877, pruned_loss=0.06236, over 1426635.98 frames.], batch size: 15, lr: 4.95e-04
2022-05-27 09:43:47,154 INFO [train.py:842] (2/4) Epoch 11, batch 2600, loss[loss=0.2196, simple_loss=0.3087, pruned_loss=0.06525, over 7382.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2874, pruned_loss=0.06305, over 1428405.80 frames.], batch size: 23, lr: 4.95e-04
2022-05-27 09:44:25,698 INFO [train.py:842] (2/4) Epoch 11, batch 2650, loss[loss=0.2153, simple_loss=0.2819, pruned_loss=0.07436, over 7432.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2882, pruned_loss=0.06319, over 1424080.04 frames.], batch size: 17, lr: 4.94e-04
2022-05-27 09:45:04,552 INFO [train.py:842] (2/4) Epoch 11, batch 2700, loss[loss=0.2247, simple_loss=0.3065, pruned_loss=0.07146, over 7426.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2888, pruned_loss=0.06343, over 1427303.41 frames.], batch size: 21, lr: 4.94e-04
2022-05-27 09:45:43,284 INFO [train.py:842] (2/4) Epoch 11, batch 2750, loss[loss=0.1677, simple_loss=0.2511, pruned_loss=0.04215, over 7283.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2877, pruned_loss=0.06298, over 1426672.37 frames.], batch size: 18, lr: 4.94e-04
2022-05-27 09:46:22,108 INFO [train.py:842] (2/4) Epoch 11, batch 2800, loss[loss=0.2241, simple_loss=0.3009, pruned_loss=0.07368, over 7142.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2878, pruned_loss=0.06304, over 1424926.72 frames.], batch size: 19, lr: 4.94e-04
2022-05-27 09:47:01,426 INFO [train.py:842] (2/4) Epoch 11, batch 2850, loss[loss=0.1794, simple_loss=0.2785, pruned_loss=0.04019, over 7326.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2882, pruned_loss=0.06348, over 1424710.06 frames.], batch size: 21, lr: 4.94e-04
2022-05-27 09:47:40,642 INFO [train.py:842] (2/4) Epoch 11, batch 2900, loss[loss=0.1867, simple_loss=0.2814, pruned_loss=0.04601, over 7220.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2879, pruned_loss=0.06328, over 1427377.74 frames.], batch size: 23, lr: 4.94e-04
2022-05-27 09:48:19,180 INFO [train.py:842] (2/4) Epoch 11, batch 2950, loss[loss=0.198, simple_loss=0.2884, pruned_loss=0.05378, over 7202.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2888, pruned_loss=0.06338, over 1425003.79 frames.], batch size: 22, lr: 4.94e-04
2022-05-27 09:48:58,066 INFO [train.py:842] (2/4) Epoch 11, batch 3000, loss[loss=0.213, simple_loss=0.2988, pruned_loss=0.06357, over 7170.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2896, pruned_loss=0.06326, over 1423471.91 frames.], batch size: 18, lr: 4.94e-04
2022-05-27 09:48:58,066 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 09:49:07,581 INFO [train.py:871] (2/4) Epoch 11, validation: loss=0.1731, simple_loss=0.2734, pruned_loss=0.03642, over 868885.00 frames.
2022-05-27 09:49:46,387 INFO [train.py:842] (2/4) Epoch 11, batch 3050, loss[loss=0.2091, simple_loss=0.2883, pruned_loss=0.06491, over 7181.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2898, pruned_loss=0.06355, over 1427804.01 frames.], batch size: 26, lr: 4.93e-04
2022-05-27 09:50:25,550 INFO [train.py:842] (2/4) Epoch 11, batch 3100, loss[loss=0.2023, simple_loss=0.274, pruned_loss=0.06529, over 7408.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2896, pruned_loss=0.06366, over 1425743.92 frames.], batch size: 18, lr: 4.93e-04
2022-05-27 09:51:04,375 INFO [train.py:842] (2/4) Epoch 11, batch 3150, loss[loss=0.2176, simple_loss=0.2947, pruned_loss=0.07019, over 7277.00 frames.], tot_loss[loss=0.209, simple_loss=0.2895, pruned_loss=0.06422, over 1428021.59 frames.], batch size: 18, lr: 4.93e-04
2022-05-27 09:51:43,756 INFO [train.py:842] (2/4) Epoch 11, batch 3200, loss[loss=0.1874, simple_loss=0.2646, pruned_loss=0.05506, over 7161.00 frames.], tot_loss[loss=0.2078, simple_loss=0.288, pruned_loss=0.06382, over 1430052.44 frames.], batch size: 18, lr: 4.93e-04
2022-05-27 09:52:22,401 INFO [train.py:842] (2/4) Epoch 11, batch 3250, loss[loss=0.2343, simple_loss=0.2948, pruned_loss=0.08683, over 7062.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2881, pruned_loss=0.06412, over 1431130.97 frames.], batch size: 18, lr: 4.93e-04
2022-05-27 09:53:01,431 INFO [train.py:842] (2/4) Epoch 11, batch 3300, loss[loss=0.2339, simple_loss=0.3117, pruned_loss=0.07808, over 6427.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2879, pruned_loss=0.06381, over 1430121.32 frames.], batch size: 38, lr: 4.93e-04
2022-05-27 09:53:39,886 INFO [train.py:842] (2/4) Epoch 11, batch 3350, loss[loss=0.2091, simple_loss=0.2872, pruned_loss=0.06543, over 7118.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2892, pruned_loss=0.06484, over 1423905.34 frames.], batch size: 21, lr: 4.93e-04
2022-05-27 09:54:18,841 INFO [train.py:842] (2/4) Epoch 11, batch 3400, loss[loss=0.1969, simple_loss=0.2729, pruned_loss=0.0605, over 7024.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2896, pruned_loss=0.06499, over 1420756.79 frames.], batch size: 16, lr: 4.92e-04
2022-05-27 09:54:57,564 INFO [train.py:842] (2/4) Epoch 11, batch 3450, loss[loss=0.1888, simple_loss=0.2788, pruned_loss=0.04936, over 7125.00 frames.], tot_loss[loss=0.2089, simple_loss=0.2893, pruned_loss=0.0642, over 1424036.52 frames.], batch size: 21, lr: 4.92e-04
2022-05-27 09:55:36,306 INFO [train.py:842] (2/4) Epoch 11, batch 3500, loss[loss=0.1741, simple_loss=0.2533, pruned_loss=0.04746, over 7419.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2884, pruned_loss=0.06371, over 1425360.04 frames.], batch size: 18, lr: 4.92e-04
2022-05-27 09:56:14,773 INFO [train.py:842] (2/4) Epoch 11, batch 3550, loss[loss=0.2294, simple_loss=0.3004, pruned_loss=0.07921, over 6422.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2878, pruned_loss=0.06299, over 1424174.99 frames.], batch size: 38, lr: 4.92e-04
2022-05-27 09:56:53,630 INFO [train.py:842] (2/4) Epoch 11, batch 3600, loss[loss=0.2181, simple_loss=0.2948, pruned_loss=0.07065, over 6285.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2887, pruned_loss=0.06332, over 1418672.20 frames.], batch size: 37, lr: 4.92e-04
2022-05-27 09:57:32,190 INFO [train.py:842] (2/4) Epoch 11, batch 3650, loss[loss=0.1972, simple_loss=0.2815, pruned_loss=0.05639, over 7112.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2878, pruned_loss=0.06231, over 1421463.98 frames.], batch size: 21, lr: 4.92e-04
2022-05-27 09:58:10,879 INFO [train.py:842] (2/4) Epoch 11, batch 3700, loss[loss=0.187, simple_loss=0.2862, pruned_loss=0.04392, over 7107.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2888, pruned_loss=0.06296, over 1418073.57 frames.], batch size: 21, lr: 4.92e-04
2022-05-27 09:58:49,437 INFO [train.py:842] (2/4) Epoch 11, batch 3750, loss[loss=0.1975, simple_loss=0.2817, pruned_loss=0.05664, over 7421.00 frames.], tot_loss[loss=0.2089, simple_loss=0.2904, pruned_loss=0.06367, over 1423843.99 frames.], batch size: 20, lr: 4.92e-04
2022-05-27 09:59:28,305 INFO [train.py:842] (2/4) Epoch 11, batch 3800, loss[loss=0.2072, simple_loss=0.3003, pruned_loss=0.05705, over 7286.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2905, pruned_loss=0.06443, over 1422799.47 frames.], batch size: 24, lr: 4.91e-04
2022-05-27 10:00:06,875 INFO [train.py:842] (2/4) Epoch 11, batch 3850, loss[loss=0.2003, simple_loss=0.287, pruned_loss=0.05685, over 7042.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2885, pruned_loss=0.06314, over 1426196.96 frames.], batch size: 28, lr: 4.91e-04
2022-05-27 10:00:45,732 INFO [train.py:842] (2/4) Epoch 11, batch 3900, loss[loss=0.2546, simple_loss=0.3413, pruned_loss=0.08393, over 7337.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2886, pruned_loss=0.06339, over 1426514.98 frames.], batch size: 22, lr: 4.91e-04
2022-05-27 10:01:24,369 INFO [train.py:842] (2/4) Epoch 11, batch 3950, loss[loss=0.1888, simple_loss=0.2885, pruned_loss=0.04457, over 7415.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2885, pruned_loss=0.06362, over 1427565.08 frames.], batch size: 21, lr: 4.91e-04
2022-05-27 10:02:03,400 INFO [train.py:842] (2/4) Epoch 11, batch 4000, loss[loss=0.2329, simple_loss=0.3017, pruned_loss=0.08207, over 7317.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2883, pruned_loss=0.06332, over 1424095.52 frames.], batch size: 25, lr: 4.91e-04
2022-05-27 10:02:42,050 INFO [train.py:842] (2/4) Epoch 11, batch 4050, loss[loss=0.2049, simple_loss=0.2825, pruned_loss=0.06362, over 7181.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2881, pruned_loss=0.06361, over 1422974.66 frames.], batch size: 26, lr: 4.91e-04
2022-05-27 10:03:23,546 INFO [train.py:842] (2/4) Epoch 11, batch 4100, loss[loss=0.1792, simple_loss=0.2525, pruned_loss=0.05293, over 7161.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2884, pruned_loss=0.06336, over 1420827.68 frames.], batch size: 17, lr: 4.91e-04
2022-05-27 10:04:02,236 INFO [train.py:842] (2/4) Epoch 11, batch 4150, loss[loss=0.1976, simple_loss=0.284, pruned_loss=0.0556, over 7107.00 frames.], tot_loss[loss=0.2069, simple_loss=0.288, pruned_loss=0.06286, over 1421304.02 frames.], batch size: 21, lr: 4.91e-04
2022-05-27 10:04:40,845 INFO [train.py:842] (2/4) Epoch 11, batch 4200, loss[loss=0.2469, simple_loss=0.3219, pruned_loss=0.08596, over 7193.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2886, pruned_loss=0.06281, over 1418571.27 frames.], batch size: 23, lr: 4.90e-04
2022-05-27 10:05:19,319 INFO [train.py:842] (2/4) Epoch 11, batch 4250, loss[loss=0.2383, simple_loss=0.3034, pruned_loss=0.08654, over 7056.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2889, pruned_loss=0.06296, over 1419299.05 frames.], batch size: 18, lr: 4.90e-04
2022-05-27 10:05:58,288 INFO [train.py:842] (2/4) Epoch 11, batch 4300, loss[loss=0.1775, simple_loss=0.2595, pruned_loss=0.04773, over 7061.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2878, pruned_loss=0.06265, over 1425116.97 frames.], batch size: 18, lr: 4.90e-04
2022-05-27 10:06:36,938 INFO [train.py:842] (2/4) Epoch 11, batch 4350, loss[loss=0.2071, simple_loss=0.2884, pruned_loss=0.06289, over 7356.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2893, pruned_loss=0.06369, over 1424831.37 frames.], batch size: 19, lr: 4.90e-04
2022-05-27 10:07:16,044 INFO [train.py:842] (2/4) Epoch 11, batch 4400, loss[loss=0.1742, simple_loss=0.2461, pruned_loss=0.05113, over 7279.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2884, pruned_loss=0.06352, over 1424599.92 frames.], batch size: 16, lr: 4.90e-04
2022-05-27 10:07:55,068 INFO [train.py:842] (2/4) Epoch 11, batch 4450, loss[loss=0.3242, simple_loss=0.3533, pruned_loss=0.1476, over 7162.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2886, pruned_loss=0.06389, over 1420915.37 frames.], batch size: 18, lr: 4.90e-04
2022-05-27 10:08:33,886 INFO [train.py:842] (2/4) Epoch 11, batch 4500, loss[loss=0.1872, simple_loss=0.2604, pruned_loss=0.05701, over 7359.00 frames.], tot_loss[loss=0.2086, simple_loss=0.289, pruned_loss=0.06404, over 1420650.80 frames.], batch size: 19, lr: 4.90e-04
2022-05-27 10:09:12,428 INFO [train.py:842] (2/4) Epoch 11, batch 4550, loss[loss=0.2053, simple_loss=0.2823, pruned_loss=0.06415, over 7364.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2888, pruned_loss=0.06395, over 1423016.05 frames.], batch size: 19, lr: 4.90e-04
2022-05-27 10:09:51,210 INFO [train.py:842] (2/4) Epoch 11, batch 4600, loss[loss=0.1999, simple_loss=0.2871, pruned_loss=0.05637, over 7153.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2889, pruned_loss=0.06362, over 1427558.94 frames.], batch size: 18, lr: 4.89e-04
2022-05-27 10:10:29,814 INFO [train.py:842] (2/4) Epoch 11, batch 4650, loss[loss=0.1737, simple_loss=0.2403, pruned_loss=0.05353, over 7291.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2888, pruned_loss=0.06383, over 1426160.43 frames.], batch size: 17, lr: 4.89e-04
2022-05-27 10:11:08,715 INFO [train.py:842] (2/4) Epoch 11, batch 4700, loss[loss=0.2445, simple_loss=0.3245, pruned_loss=0.08227, over 7157.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2889, pruned_loss=0.06338, over 1427633.80 frames.], batch size: 19, lr: 4.89e-04
2022-05-27 10:11:47,135 INFO [train.py:842] (2/4) Epoch 11, batch 4750, loss[loss=0.2569, simple_loss=0.3315, pruned_loss=0.0911, over 7323.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2896, pruned_loss=0.06342, over 1427407.99 frames.], batch size: 21, lr: 4.89e-04
2022-05-27 10:12:26,001 INFO [train.py:842] (2/4) Epoch 11, batch 4800, loss[loss=0.1869, simple_loss=0.2698, pruned_loss=0.05205, over 7362.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2894, pruned_loss=0.0639, over 1428082.78 frames.], batch size: 19, lr: 4.89e-04
2022-05-27 10:13:04,715 INFO [train.py:842] (2/4) Epoch 11, batch 4850, loss[loss=0.2041, simple_loss=0.2805, pruned_loss=0.06389, over 7276.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2905, pruned_loss=0.06405, over 1426583.98 frames.], batch size: 18, lr: 4.89e-04
2022-05-27 10:13:44,114 INFO [train.py:842] (2/4) Epoch 11, batch 4900, loss[loss=0.1782, simple_loss=0.2422, pruned_loss=0.05704, over 6813.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2898, pruned_loss=0.06392, over 1428876.87 frames.], batch size: 15, lr: 4.89e-04
2022-05-27 10:14:22,588 INFO [train.py:842] (2/4) Epoch 11, batch 4950, loss[loss=0.2556, simple_loss=0.3386, pruned_loss=0.0863, over 7321.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2897, pruned_loss=0.06396, over 1428352.46 frames.], batch size: 21, lr: 4.89e-04
2022-05-27 10:15:01,193 INFO [train.py:842] (2/4) Epoch 11, batch 5000, loss[loss=0.2274, simple_loss=0.2947, pruned_loss=0.0801, over 7318.00 frames.], tot_loss[loss=0.208, simple_loss=0.2888, pruned_loss=0.06359, over 1423681.14 frames.], batch size: 20, lr: 4.88e-04
2022-05-27 10:15:39,737 INFO [train.py:842] (2/4) Epoch 11, batch 5050, loss[loss=0.1935, simple_loss=0.2719, pruned_loss=0.05753, over 7294.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2889, pruned_loss=0.06348, over 1425389.72 frames.], batch size: 24, lr: 4.88e-04
2022-05-27 10:16:18,511 INFO [train.py:842] (2/4) Epoch 11, batch 5100, loss[loss=0.2256, simple_loss=0.295, pruned_loss=0.07812, over 7173.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2901, pruned_loss=0.0647, over 1428068.70 frames.], batch size: 18, lr: 4.88e-04
2022-05-27 10:16:57,128 INFO [train.py:842] (2/4) Epoch 11, batch 5150, loss[loss=0.2153, simple_loss=0.2934, pruned_loss=0.06859, over 7152.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2906, pruned_loss=0.06505, over 1421664.43 frames.], batch size: 20, lr: 4.88e-04
2022-05-27 10:17:36,110 INFO [train.py:842] (2/4) Epoch 11, batch 5200, loss[loss=0.217, simple_loss=0.3012, pruned_loss=0.0664, over 7177.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2907, pruned_loss=0.06547, over 1420776.15 frames.], batch size: 23, lr: 4.88e-04
2022-05-27 10:18:14,669 INFO [train.py:842] (2/4) Epoch 11, batch 5250, loss[loss=0.1772, simple_loss=0.2605, pruned_loss=0.04698, over 7267.00 frames.], tot_loss[loss=0.21, simple_loss=0.2902, pruned_loss=0.06495, over 1423497.74 frames.], batch size: 19, lr: 4.88e-04
2022-05-27 10:18:53,615 INFO [train.py:842] (2/4) Epoch 11, batch 5300, loss[loss=0.2592, simple_loss=0.3227, pruned_loss=0.09787, over 7385.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2895, pruned_loss=0.064, over 1424187.20 frames.], batch size: 23, lr: 4.88e-04
2022-05-27 10:19:32,058 INFO [train.py:842] (2/4) Epoch 11, batch 5350, loss[loss=0.1616, simple_loss=0.2686, pruned_loss=0.02729, over 7236.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2878, pruned_loss=0.06244, over 1426951.00 frames.], batch size: 20, lr: 4.88e-04
2022-05-27 10:20:10,520 INFO [train.py:842] (2/4) Epoch 11, batch 5400, loss[loss=0.2825, simple_loss=0.3511, pruned_loss=0.1069, over 7193.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2897, pruned_loss=0.06332, over 1427202.48 frames.], batch size: 23, lr: 4.87e-04
2022-05-27 10:20:49,107 INFO [train.py:842] (2/4) Epoch 11, batch 5450, loss[loss=0.2499, simple_loss=0.316, pruned_loss=0.09189, over 7296.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2874, pruned_loss=0.06179, over 1427747.41 frames.], batch size: 24, lr: 4.87e-04
2022-05-27 10:21:28,147 INFO [train.py:842] (2/4) Epoch 11, batch 5500, loss[loss=0.1794, simple_loss=0.2585, pruned_loss=0.05016, over 7283.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2887, pruned_loss=0.06279, over 1425819.02 frames.], batch size: 18, lr: 4.87e-04
2022-05-27 10:22:16,781 INFO [train.py:842] (2/4) Epoch 11, batch 5550, loss[loss=0.2514, simple_loss=0.3203, pruned_loss=0.09125, over 7163.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2897, pruned_loss=0.0635, over 1425682.07 frames.], batch size: 19, lr: 4.87e-04
2022-05-27 10:22:55,817 INFO [train.py:842] (2/4) Epoch 11, batch 5600, loss[loss=0.2863, simple_loss=0.3442, pruned_loss=0.1142, over 4994.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2897, pruned_loss=0.06394, over 1422601.81 frames.], batch size: 52, lr: 4.87e-04
2022-05-27 10:23:34,186 INFO [train.py:842] (2/4) Epoch 11, batch 5650, loss[loss=0.1846, simple_loss=0.2816, pruned_loss=0.04384, over 7412.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2902, pruned_loss=0.06343, over 1424394.15 frames.], batch size: 21, lr: 4.87e-04
2022-05-27 10:24:13,243 INFO [train.py:842] (2/4) Epoch 11, batch 5700, loss[loss=0.2158, simple_loss=0.3007, pruned_loss=0.06542, over 7376.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2885, pruned_loss=0.06237, over 1424201.86 frames.], batch size: 23, lr: 4.87e-04
2022-05-27 10:24:51,693 INFO [train.py:842] (2/4) Epoch 11, batch 5750, loss[loss=0.2278, simple_loss=0.3076, pruned_loss=0.07394, over 7230.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2887, pruned_loss=0.06291, over 1417898.11 frames.], batch size: 20, lr: 4.87e-04
2022-05-27 10:25:30,808 INFO [train.py:842] (2/4) Epoch 11, batch 5800, loss[loss=0.2202, simple_loss=0.3038, pruned_loss=0.06827, over 5158.00 frames.], tot_loss[loss=0.2064, simple_loss=0.288, pruned_loss=0.06237, over 1421112.95 frames.], batch size: 53, lr: 4.86e-04
2022-05-27 10:26:09,453 INFO [train.py:842] (2/4) Epoch 11, batch 5850, loss[loss=0.2401, simple_loss=0.3247, pruned_loss=0.07775, over 7051.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2892, pruned_loss=0.06318, over 1421531.30 frames.], batch size: 28, lr: 4.86e-04
2022-05-27 10:26:48,394 INFO [train.py:842] (2/4) Epoch 11, batch 5900, loss[loss=0.1665, simple_loss=0.2543, pruned_loss=0.03935, over 7437.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2884, pruned_loss=0.06226, over 1424002.96 frames.], batch size: 20, lr: 4.86e-04
2022-05-27 10:27:26,965 INFO [train.py:842] (2/4) Epoch 11, batch 5950, loss[loss=0.2639, simple_loss=0.3207, pruned_loss=0.1036, over 7180.00 frames.], tot_loss[loss=0.205, simple_loss=0.287, pruned_loss=0.06146, over 1427041.41 frames.], batch size: 26, lr: 4.86e-04
2022-05-27 10:28:06,054 INFO [train.py:842] (2/4) Epoch 11, batch 6000, loss[loss=0.2565, simple_loss=0.3301, pruned_loss=0.09143, over 7148.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2885, pruned_loss=0.06235, over 1430977.77 frames.], batch size: 20, lr: 4.86e-04
2022-05-27 10:28:06,055 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 10:28:15,409 INFO [train.py:871] (2/4) Epoch 11, validation: loss=0.1722, simple_loss=0.2724, pruned_loss=0.03599, over 868885.00 frames.
2022-05-27 10:28:54,259 INFO [train.py:842] (2/4) Epoch 11, batch 6050, loss[loss=0.2405, simple_loss=0.3152, pruned_loss=0.08289, over 7305.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2878, pruned_loss=0.0622, over 1427066.31 frames.], batch size: 24, lr: 4.86e-04
2022-05-27 10:29:33,424 INFO [train.py:842] (2/4) Epoch 11, batch 6100, loss[loss=0.2007, simple_loss=0.2857, pruned_loss=0.05785, over 7288.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2866, pruned_loss=0.06186, over 1426649.37 frames.], batch size: 24, lr: 4.86e-04
2022-05-27 10:30:11,972 INFO [train.py:842] (2/4) Epoch 11, batch 6150, loss[loss=0.2175, simple_loss=0.3046, pruned_loss=0.06521, over 7156.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2879, pruned_loss=0.06284, over 1430618.43 frames.], batch size: 19, lr: 4.86e-04
2022-05-27 10:30:50,915 INFO [train.py:842] (2/4) Epoch 11, batch 6200, loss[loss=0.2833, simple_loss=0.3655, pruned_loss=0.1005, over 7284.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2874, pruned_loss=0.06271, over 1430744.59 frames.], batch size: 24, lr: 4.85e-04
2022-05-27 10:31:29,390 INFO [train.py:842] (2/4) Epoch 11, batch 6250, loss[loss=0.2158, simple_loss=0.2975, pruned_loss=0.06701, over 6678.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2879, pruned_loss=0.06264, over 1433788.01 frames.], batch size: 31, lr: 4.85e-04
2022-05-27 10:32:08,622 INFO [train.py:842] (2/4) Epoch 11, batch 6300, loss[loss=0.1807, simple_loss=0.2632, pruned_loss=0.04916, over 6999.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2879, pruned_loss=0.0628, over 1430335.90 frames.], batch size: 16, lr: 4.85e-04
2022-05-27 10:32:47,162 INFO [train.py:842] (2/4) Epoch 11, batch 6350, loss[loss=0.189, simple_loss=0.2819, pruned_loss=0.04809, over 7167.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2877, pruned_loss=0.06248, over 1427527.78 frames.], batch size: 26, lr: 4.85e-04
2022-05-27 10:33:25,793 INFO [train.py:842] (2/4) Epoch 11, batch 6400, loss[loss=0.2064, simple_loss=0.2834, pruned_loss=0.06466, over 7172.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2887, pruned_loss=0.06317, over 1422472.52 frames.], batch size: 18, lr: 4.85e-04
2022-05-27 10:34:04,322 INFO [train.py:842] (2/4) Epoch 11, batch 6450, loss[loss=0.1908, simple_loss=0.2777, pruned_loss=0.05195, over 7337.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2889, pruned_loss=0.0635, over 1415346.91 frames.], batch size: 22, lr: 4.85e-04
2022-05-27 10:34:42,916 INFO [train.py:842] (2/4) Epoch 11, batch 6500, loss[loss=0.2143, simple_loss=0.289, pruned_loss=0.06978, over 7235.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2881, pruned_loss=0.0631, over 1415750.92 frames.], batch size: 20, lr: 4.85e-04
2022-05-27 10:35:21,590 INFO [train.py:842] (2/4) Epoch 11, batch 6550, loss[loss=0.2475, simple_loss=0.3062, pruned_loss=0.09437, over 7264.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2872, pruned_loss=0.06344, over 1416097.27 frames.], batch size: 19, lr: 4.85e-04
2022-05-27 10:36:00,475 INFO [train.py:842] (2/4) Epoch 11, batch 6600, loss[loss=0.2458, simple_loss=0.3298, pruned_loss=0.0809, over 7039.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2871, pruned_loss=0.06306, over 1415209.68 frames.], batch size: 28, lr: 4.84e-04
2022-05-27 10:36:39,103 INFO [train.py:842] (2/4) Epoch 11, batch 6650, loss[loss=0.2299, simple_loss=0.3066, pruned_loss=0.07657, over 7295.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2882, pruned_loss=0.06373, over 1412758.12 frames.], batch size: 24, lr: 4.84e-04
2022-05-27 10:37:18,310 INFO [train.py:842] (2/4) Epoch 11, batch 6700, loss[loss=0.2246, simple_loss=0.3078, pruned_loss=0.07066, over 7380.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2879, pruned_loss=0.06331, over 1415975.79 frames.], batch size: 23, lr: 4.84e-04
2022-05-27 10:37:56,923 INFO [train.py:842] (2/4) Epoch 11, batch 6750, loss[loss=0.1754, simple_loss=0.2646, pruned_loss=0.04308, over 7324.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2883, pruned_loss=0.06332, over 1416094.95 frames.], batch size: 22, lr: 4.84e-04
2022-05-27 10:38:35,780 INFO [train.py:842] (2/4) Epoch 11, batch 6800, loss[loss=0.1689, simple_loss=0.2431, pruned_loss=0.04731, over 7210.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2884, pruned_loss=0.06343, over 1420861.24 frames.], batch size: 16, lr: 4.84e-04
2022-05-27 10:39:14,373 INFO [train.py:842] (2/4) Epoch 11, batch 6850, loss[loss=0.1835, simple_loss=0.2681, pruned_loss=0.0495, over 7009.00 frames.], tot_loss[loss=0.206, simple_loss=0.2871, pruned_loss=0.06243, over 1423072.47 frames.], batch size: 16, lr: 4.84e-04
2022-05-27 10:39:53,313 INFO [train.py:842] (2/4) Epoch 11, batch 6900, loss[loss=0.1731, simple_loss=0.2704, pruned_loss=0.03794, over 7226.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2884, pruned_loss=0.06291, over 1427095.51 frames.], batch size: 21, lr: 4.84e-04
2022-05-27 10:40:31,877 INFO [train.py:842] (2/4) Epoch 11, batch 6950, loss[loss=0.2174, simple_loss=0.3134, pruned_loss=0.06072, over 7142.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2891, pruned_loss=0.06328, over 1429593.51 frames.], batch size: 20, lr: 4.84e-04
2022-05-27 10:41:10,666 INFO [train.py:842] (2/4) Epoch 11, batch 7000, loss[loss=0.2089, simple_loss=0.2919, pruned_loss=0.06295, over 6783.00 frames.], tot_loss[loss=0.2075, simple_loss=0.289, pruned_loss=0.06301, over 1426133.55 frames.], batch size: 31, lr: 4.83e-04
2022-05-27 10:41:49,509 INFO [train.py:842] (2/4) Epoch 11, batch 7050, loss[loss=0.1814, simple_loss=0.2666, pruned_loss=0.04811, over 7151.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2895, pruned_loss=0.06357, over 1423302.42 frames.], batch size: 18, lr: 4.83e-04
2022-05-27 10:42:28,464 INFO [train.py:842] (2/4) Epoch 11, batch 7100, loss[loss=0.1862, simple_loss=0.2704, pruned_loss=0.05098, over 6774.00 frames.], tot_loss[loss=0.2079, simple_loss=0.289, pruned_loss=0.06343, over 1422945.20 frames.], batch size: 15, lr: 4.83e-04
2022-05-27 10:43:06,951 INFO [train.py:842] (2/4) Epoch 11, batch 7150, loss[loss=0.2091, simple_loss=0.2945, pruned_loss=0.06188, over 7436.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2901, pruned_loss=0.06417, over 1424337.22 frames.], batch size: 20, lr: 4.83e-04
2022-05-27 10:43:45,956 INFO [train.py:842] (2/4) Epoch 11, batch 7200, loss[loss=0.166, simple_loss=0.2564, pruned_loss=0.03777, over 7230.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2885, pruned_loss=0.06314, over 1424198.59 frames.], batch size: 20, lr: 4.83e-04
2022-05-27 10:44:24,368 INFO [train.py:842] (2/4) Epoch 11, batch 7250, loss[loss=0.197, simple_loss=0.2863, pruned_loss=0.05386, over 7143.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2892, pruned_loss=0.06321, over 1424122.48 frames.], batch size: 20, lr: 4.83e-04
2022-05-27 10:45:23,830 INFO [train.py:842] (2/4) Epoch 11, batch 7300, loss[loss=0.2347, simple_loss=0.3272, pruned_loss=0.07109, over 6886.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2907, pruned_loss=0.06394, over 1423700.02 frames.], batch size: 31, lr: 4.83e-04
2022-05-27 10:46:12,734 INFO [train.py:842] (2/4) Epoch 11, batch 7350, loss[loss=0.2479, simple_loss=0.3225, pruned_loss=0.08667, over 7079.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2881, pruned_loss=0.06277, over 1426443.80 frames.], batch size: 28, lr: 4.83e-04
2022-05-27 10:46:51,446 INFO [train.py:842] (2/4) Epoch 11, batch 7400, loss[loss=0.2585, simple_loss=0.3113, pruned_loss=0.1029, over 7248.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2879, pruned_loss=0.06268, over 1425497.64 frames.], batch size: 20, lr: 4.83e-04
2022-05-27 10:47:30,234 INFO [train.py:842] (2/4) Epoch 11, batch 7450, loss[loss=0.2783, simple_loss=0.354, pruned_loss=0.1013, over 7289.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2868, pruned_loss=0.06192, over 1425935.99 frames.], batch size: 25, lr: 4.82e-04
2022-05-27 10:48:09,507 INFO [train.py:842] (2/4) Epoch 11, batch 7500, loss[loss=0.179, simple_loss=0.2586, pruned_loss=0.04969, over 7323.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2878, pruned_loss=0.06271, over 1427878.01 frames.], batch size: 20, lr: 4.82e-04
2022-05-27 10:48:48,111 INFO [train.py:842] (2/4) Epoch 11, batch 7550, loss[loss=0.217, simple_loss=0.3135, pruned_loss=0.06024, over 7336.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2884, pruned_loss=0.06299, over 1425489.20 frames.], batch size: 22, lr: 4.82e-04
2022-05-27 10:49:26,800 INFO [train.py:842] (2/4) Epoch 11, batch 7600, loss[loss=0.1682, simple_loss=0.2563, pruned_loss=0.04008, over 7257.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2886, pruned_loss=0.06278, over 1424200.60 frames.], batch size: 19, lr: 4.82e-04
2022-05-27 10:50:05,341 INFO [train.py:842] (2/4) Epoch 11, batch 7650, loss[loss=0.1725, simple_loss=0.2457, pruned_loss=0.04959, over 7142.00 frames.], tot_loss[loss=0.208, simple_loss=0.2888, pruned_loss=0.06354, over 1418812.22 frames.], batch size: 16, lr: 4.82e-04
2022-05-27 10:50:44,216 INFO [train.py:842] (2/4) Epoch 11, batch 7700, loss[loss=0.21, simple_loss=0.2932, pruned_loss=0.06337, over 7198.00 frames.], tot_loss[loss=0.2081, simple_loss=0.289, pruned_loss=0.06366, over 1422606.57 frames.], batch size: 22, lr: 4.82e-04
2022-05-27 10:51:22,670 INFO [train.py:842] (2/4) Epoch 11, batch 7750, loss[loss=0.1824, simple_loss=0.2751, pruned_loss=0.04483, over 7206.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2893, pruned_loss=0.06322, over 1421239.88 frames.], batch size: 21, lr: 4.82e-04
2022-05-27 10:52:01,624 INFO [train.py:842] (2/4) Epoch 11, batch 7800, loss[loss=0.1605, simple_loss=0.2477, pruned_loss=0.03662, over 7058.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2889, pruned_loss=0.06274, over 1421537.85 frames.], batch size: 18, lr: 4.82e-04
2022-05-27 10:52:40,113 INFO [train.py:842] (2/4) Epoch 11, batch 7850, loss[loss=0.1843, simple_loss=0.2727, pruned_loss=0.04799, over 7213.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2889, pruned_loss=0.06291, over 1420970.10 frames.], batch size: 21, lr: 4.81e-04
2022-05-27 10:53:18,847 INFO [train.py:842] (2/4) Epoch 11, batch 7900, loss[loss=0.1861, simple_loss=0.2693, pruned_loss=0.05139, over 7165.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2898, pruned_loss=0.06331, over 1422271.26 frames.], batch size: 18, lr: 4.81e-04
2022-05-27 10:53:57,415 INFO [train.py:842] (2/4) Epoch 11, batch 7950, loss[loss=0.2071, simple_loss=0.2923, pruned_loss=0.06089, over 7406.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2889, pruned_loss=0.0631, over 1425381.64 frames.], batch size: 21, lr: 4.81e-04
2022-05-27 10:54:36,319 INFO [train.py:842] (2/4) Epoch 11, batch 8000, loss[loss=0.2121, simple_loss=0.2926, pruned_loss=0.06583, over 7422.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2883, pruned_loss=0.06274, over 1425810.93 frames.], batch size: 21, lr: 4.81e-04
2022-05-27 10:55:14,872 INFO [train.py:842] (2/4) Epoch 11, batch 8050, loss[loss=0.1759, simple_loss=0.2613, pruned_loss=0.04521, over 7291.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2892, pruned_loss=0.0637, over 1430034.86 frames.], batch size: 25, lr: 4.81e-04
2022-05-27 10:55:53,852 INFO [train.py:842] (2/4) Epoch 11, batch 8100, loss[loss=0.2136, simple_loss=0.3006, pruned_loss=0.06331, over 7270.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2865, pruned_loss=0.06208, over 1430093.96 frames.], batch size: 24, lr: 4.81e-04
2022-05-27 10:56:32,347 INFO [train.py:842] (2/4) Epoch 11, batch 8150, loss[loss=0.2198, simple_loss=0.3073, pruned_loss=0.06613, over 7376.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2872, pruned_loss=0.06233, over 1430089.63 frames.], batch size: 23, lr: 4.81e-04
2022-05-27 10:57:11,043 INFO [train.py:842] (2/4) Epoch 11, batch 8200, loss[loss=0.2462, simple_loss=0.3279, pruned_loss=0.08223, over 7272.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2878, pruned_loss=0.06239, over 1424847.27 frames.], batch size: 24, lr: 4.81e-04
2022-05-27 10:57:49,626 INFO [train.py:842] (2/4) Epoch 11, batch 8250, loss[loss=0.1869, simple_loss=0.2684, pruned_loss=0.0527, over 7425.00 frames.], tot_loss[loss=0.205, simple_loss=0.2865, pruned_loss=0.06172, over 1428151.74 frames.], batch size: 20, lr: 4.80e-04
2022-05-27 10:58:28,728 INFO [train.py:842] (2/4) Epoch 11, batch 8300, loss[loss=0.215, simple_loss=0.3003, pruned_loss=0.06483, over 7204.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2874, pruned_loss=0.06241, over 1430662.20 frames.], batch size: 22, lr: 4.80e-04
2022-05-27 10:59:07,273 INFO [train.py:842] (2/4) Epoch 11, batch 8350, loss[loss=0.1688, simple_loss=0.2549, pruned_loss=0.04135, over 7425.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2869, pruned_loss=0.06218, over 1428187.02 frames.], batch size: 18, lr: 4.80e-04
2022-05-27 10:59:46,099 INFO [train.py:842] (2/4) Epoch 11, batch 8400, loss[loss=0.245, simple_loss=0.3321, pruned_loss=0.07898, over 7291.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2867, pruned_loss=0.06195, over 1429840.93 frames.], batch size: 25, lr: 4.80e-04
2022-05-27 11:00:24,517 INFO [train.py:842] (2/4) Epoch 11, batch 8450, loss[loss=0.2143, simple_loss=0.2942, pruned_loss=0.06718, over 7310.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2863, pruned_loss=0.06219, over 1423984.63 frames.], batch size: 24, lr: 4.80e-04
2022-05-27 11:01:03,516 INFO [train.py:842] (2/4) Epoch 11, batch 8500, loss[loss=0.2165, simple_loss=0.3015, pruned_loss=0.06577, over 7208.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2859, pruned_loss=0.06218, over 1422977.46 frames.], batch size: 22, lr: 4.80e-04
2022-05-27 11:01:42,109 INFO [train.py:842] (2/4) Epoch 11, batch 8550, loss[loss=0.1869, simple_loss=0.2706, pruned_loss=0.05161, over 7363.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2857, pruned_loss=0.06197, over 1423167.74 frames.], batch size: 19, lr: 4.80e-04
2022-05-27 11:02:21,308 INFO [train.py:842] (2/4) Epoch 11, batch 8600, loss[loss=0.1864, simple_loss=0.2592, pruned_loss=0.05678, over 7129.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2852, pruned_loss=0.06179, over 1420933.68 frames.], batch size: 17, lr: 4.80e-04
2022-05-27 11:02:59,931 INFO [train.py:842] (2/4) Epoch 11, batch 8650, loss[loss=0.1695, simple_loss=0.2451, pruned_loss=0.047, over 7399.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2853, pruned_loss=0.06181, over 1424719.27 frames.], batch size: 18, lr: 4.80e-04
2022-05-27 11:03:38,554 INFO [train.py:842] (2/4) Epoch 11, batch 8700, loss[loss=0.1915, simple_loss=0.2717, pruned_loss=0.05569, over 7415.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2849, pruned_loss=0.06193, over 1419732.06 frames.], batch size: 18, lr: 4.79e-04
2022-05-27 11:04:16,853 INFO [train.py:842] (2/4) Epoch 11, batch 8750, loss[loss=0.2447, simple_loss=0.3226, pruned_loss=0.08339, over 7186.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2862, pruned_loss=0.0628, over 1418810.14 frames.], batch size: 26, lr: 4.79e-04
2022-05-27 11:04:55,654 INFO [train.py:842] (2/4) Epoch 11, batch 8800, loss[loss=0.1543, simple_loss=0.2459, pruned_loss=0.03132, over 7355.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2868, pruned_loss=0.06285, over 1417814.28 frames.], batch size: 19, lr: 4.79e-04
2022-05-27 11:05:34,676 INFO [train.py:842] (2/4) Epoch 11, batch 8850, loss[loss=0.2175, simple_loss=0.2805, pruned_loss=0.07723, over 7416.00 frames.], tot_loss[loss=0.2077, simple_loss=0.288, pruned_loss=0.06376, over 1411945.63 frames.], batch size: 18, lr: 4.79e-04
2022-05-27 11:06:13,189 INFO [train.py:842] (2/4) Epoch 11, batch 8900, loss[loss=0.1776, simple_loss=0.2638, pruned_loss=0.04571, over 7218.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2871, pruned_loss=0.06318, over 1413251.97 frames.], batch size: 21, lr: 4.79e-04
2022-05-27 11:06:51,387 INFO [train.py:842] (2/4) Epoch 11, batch 8950, loss[loss=0.1969, simple_loss=0.286, pruned_loss=0.05386, over 7188.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2888, pruned_loss=0.06403, over 1399034.61 frames.], batch size: 26, lr: 4.79e-04
2022-05-27 11:07:29,530 INFO [train.py:842] (2/4) Epoch 11, batch 9000, loss[loss=0.2071, simple_loss=0.2941, pruned_loss=0.06005, over 6843.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2912, pruned_loss=0.06575, over 1381729.50 frames.], batch size: 31, lr: 4.79e-04
2022-05-27 11:07:29,530 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 11:07:39,052 INFO [train.py:871] (2/4) Epoch 11, validation: loss=0.1709, simple_loss=0.2721, pruned_loss=0.03482, over 868885.00 frames.
2022-05-27 11:08:17,095 INFO [train.py:842] (2/4) Epoch 11, batch 9050, loss[loss=0.3322, simple_loss=0.3815, pruned_loss=0.1414, over 5187.00 frames.], tot_loss[loss=0.2133, simple_loss=0.293, pruned_loss=0.06683, over 1369655.40 frames.], batch size: 52, lr: 4.79e-04
2022-05-27 11:08:55,112 INFO [train.py:842] (2/4) Epoch 11, batch 9100, loss[loss=0.2371, simple_loss=0.313, pruned_loss=0.08062, over 5049.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2976, pruned_loss=0.07088, over 1295611.60 frames.], batch size: 52, lr: 4.78e-04
2022-05-27 11:09:32,722 INFO [train.py:842] (2/4) Epoch 11, batch 9150, loss[loss=0.2393, simple_loss=0.3081, pruned_loss=0.0852, over 5023.00 frames.], tot_loss[loss=0.2252, simple_loss=0.3016, pruned_loss=0.07439, over 1234040.27 frames.], batch size: 54, lr: 4.78e-04
2022-05-27 11:10:24,890 INFO [train.py:842] (2/4) Epoch 12, batch 0, loss[loss=0.2343, simple_loss=0.3078, pruned_loss=0.08038, over 7407.00 frames.], tot_loss[loss=0.2343, simple_loss=0.3078, pruned_loss=0.08038, over 7407.00 frames.], batch size: 21, lr: 4.61e-04
2022-05-27 11:11:03,740 INFO [train.py:842] (2/4) Epoch 12, batch 50, loss[loss=0.1976, simple_loss=0.2886, pruned_loss=0.05336, over 4958.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2869, pruned_loss=0.06231, over 318910.00 frames.], batch size: 52, lr: 4.61e-04
2022-05-27 11:11:42,665 INFO [train.py:842] (2/4) Epoch 12, batch 100, loss[loss=0.2075, simple_loss=0.2929, pruned_loss=0.06106, over 6290.00 frames.], tot_loss[loss=0.209, simple_loss=0.2898, pruned_loss=0.06412, over 558896.72 frames.], batch size: 37, lr: 4.61e-04
2022-05-27 11:12:21,233 INFO [train.py:842] (2/4) Epoch 12, batch 150, loss[loss=0.1801, simple_loss=0.2579, pruned_loss=0.05115, over 7292.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2898, pruned_loss=0.0633, over 748714.62 frames.], batch size: 17, lr: 4.61e-04
2022-05-27 11:12:59,948 INFO [train.py:842] (2/4) Epoch 12, batch 200, loss[loss=0.2133, simple_loss=0.3046, pruned_loss=0.06107, over 7210.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2894, pruned_loss=0.06289, over 896068.34 frames.], batch size: 22, lr: 4.61e-04
2022-05-27 11:13:38,460 INFO [train.py:842] (2/4) Epoch 12, batch 250, loss[loss=0.2116, simple_loss=0.296, pruned_loss=0.06359, over 6767.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2878, pruned_loss=0.06221, over 1013800.65 frames.], batch size: 31, lr: 4.61e-04
2022-05-27 11:14:17,121 INFO [train.py:842] (2/4) Epoch 12, batch 300, loss[loss=0.223, simple_loss=0.3087, pruned_loss=0.06862, over 7189.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2901, pruned_loss=0.06317, over 1098338.76 frames.], batch size: 22, lr: 4.61e-04
2022-05-27 11:14:55,695 INFO [train.py:842] (2/4) Epoch 12, batch 350, loss[loss=0.2202, simple_loss=0.3108, pruned_loss=0.06485, over 7330.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2883, pruned_loss=0.06212, over 1165736.81 frames.], batch size: 22, lr: 4.61e-04
2022-05-27 11:15:34,537 INFO [train.py:842] (2/4) Epoch 12, batch 400, loss[loss=0.201, simple_loss=0.2909, pruned_loss=0.05553, over 7346.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2862, pruned_loss=0.06043, over 1220765.20 frames.], batch size: 22, lr: 4.60e-04
2022-05-27 11:16:13,174 INFO [train.py:842] (2/4) Epoch 12, batch 450, loss[loss=0.1768, simple_loss=0.2675, pruned_loss=0.04302, over 7153.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2848, pruned_loss=0.05953, over 1269310.82 frames.], batch size: 19, lr: 4.60e-04
2022-05-27 11:16:52,027 INFO [train.py:842] (2/4) Epoch 12, batch 500, loss[loss=0.2127, simple_loss=0.2966, pruned_loss=0.06438, over 7374.00 frames.], tot_loss[loss=0.203, simple_loss=0.2857, pruned_loss=0.06018, over 1303323.03 frames.], batch size: 23, lr: 4.60e-04
2022-05-27 11:17:30,957 INFO [train.py:842] (2/4) Epoch 12, batch 550, loss[loss=0.1903, simple_loss=0.2758, pruned_loss=0.05243, over 7417.00 frames.], tot_loss[loss=0.2029, simple_loss=0.285, pruned_loss=0.06043, over 1329373.41 frames.], batch size: 21, lr: 4.60e-04
2022-05-27 11:18:10,099 INFO [train.py:842] (2/4) Epoch 12, batch 600, loss[loss=0.1859, simple_loss=0.2827, pruned_loss=0.04454, over 7337.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2857, pruned_loss=0.06074, over 1349008.16 frames.], batch size: 22, lr: 4.60e-04
2022-05-27 11:18:48,936 INFO [train.py:842] (2/4) Epoch 12, batch 650, loss[loss=0.2496, simple_loss=0.3337, pruned_loss=0.08275, over 7369.00 frames.], tot_loss[loss=0.2018, simple_loss=0.284, pruned_loss=0.05979, over 1370053.31 frames.], batch size: 23, lr: 4.60e-04
2022-05-27 11:19:27,773 INFO [train.py:842] (2/4) Epoch 12, batch 700, loss[loss=0.2078, simple_loss=0.2966, pruned_loss=0.05947, over 7319.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2849, pruned_loss=0.06093, over 1380259.26 frames.], batch size: 24, lr: 4.60e-04
2022-05-27 11:20:06,287 INFO [train.py:842] (2/4) Epoch 12, batch 750, loss[loss=0.1561, simple_loss=0.2501, pruned_loss=0.03107, over 7332.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2867, pruned_loss=0.0621, over 1385623.76 frames.], batch size: 20, lr: 4.60e-04
2022-05-27 11:20:45,311 INFO [train.py:842] (2/4) Epoch 12, batch 800, loss[loss=0.1618, simple_loss=0.2472, pruned_loss=0.03818, over 7420.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2866, pruned_loss=0.06208, over 1398055.30 frames.], batch size: 18, lr: 4.60e-04
2022-05-27 11:21:23,944 INFO [train.py:842] (2/4) Epoch 12, batch 850, loss[loss=0.2251, simple_loss=0.3028, pruned_loss=0.07367, over 6768.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2866, pruned_loss=0.06215, over 1402329.97 frames.], batch size: 31, lr: 4.59e-04
2022-05-27 11:22:02,921 INFO [train.py:842] (2/4) Epoch 12, batch 900, loss[loss=0.2185, simple_loss=0.3072, pruned_loss=0.06489, over 7344.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2871, pruned_loss=0.06193, over 1407457.16 frames.], batch size: 22, lr: 4.59e-04
2022-05-27 11:22:41,559 INFO [train.py:842] (2/4) Epoch 12, batch 950, loss[loss=0.192, simple_loss=0.2666, pruned_loss=0.0587, over 7437.00 frames.], tot_loss[loss=0.2054, simple_loss=0.287, pruned_loss=0.06191, over 1412455.92 frames.], batch size: 20, lr: 4.59e-04
2022-05-27 11:23:20,318 INFO [train.py:842] (2/4) Epoch 12, batch 1000, loss[loss=0.1843, simple_loss=0.2653, pruned_loss=0.05163, over 7152.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2875, pruned_loss=0.06179, over 1415382.48 frames.], batch size: 19, lr: 4.59e-04
2022-05-27 11:23:58,894 INFO [train.py:842] (2/4) Epoch 12, batch 1050, loss[loss=0.1974, simple_loss=0.2659, pruned_loss=0.06442, over 7010.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2887, pruned_loss=0.0626, over 1414872.34 frames.], batch size: 16, lr: 4.59e-04
2022-05-27 11:24:37,597 INFO [train.py:842] (2/4) Epoch 12, batch 1100, loss[loss=0.1862, simple_loss=0.2679, pruned_loss=0.05218, over 7167.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2893, pruned_loss=0.06257, over 1417935.44 frames.], batch size: 19, lr: 4.59e-04
2022-05-27 11:25:16,159 INFO [train.py:842] (2/4) Epoch 12, batch 1150, loss[loss=0.2109, simple_loss=0.293, pruned_loss=0.06438, over 5339.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2886, pruned_loss=0.06224, over 1420565.20 frames.], batch size: 52, lr: 4.59e-04
2022-05-27 11:25:55,243 INFO [train.py:842] (2/4) Epoch 12, batch 1200, loss[loss=0.2151, simple_loss=0.2959, pruned_loss=0.0671, over 7106.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2876, pruned_loss=0.06165, over 1422989.98 frames.], batch size: 21, lr: 4.59e-04
2022-05-27 11:26:33,779 INFO [train.py:842] (2/4) Epoch 12, batch 1250, loss[loss=0.1503, simple_loss=0.2301, pruned_loss=0.03526, over 7006.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2871, pruned_loss=0.06186, over 1424648.43 frames.], batch size: 16, lr: 4.59e-04
2022-05-27 11:27:12,547 INFO [train.py:842] (2/4) Epoch 12, batch 1300, loss[loss=0.1602, simple_loss=0.252, pruned_loss=0.03415, over 7325.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2858, pruned_loss=0.06098, over 1426379.96 frames.], batch size: 20, lr: 4.58e-04
2022-05-27 11:27:51,033 INFO [train.py:842] (2/4) Epoch 12, batch 1350, loss[loss=0.1968, simple_loss=0.2758, pruned_loss=0.05888, over 7313.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2865, pruned_loss=0.06135, over 1422751.99 frames.], batch size: 21, lr: 4.58e-04
2022-05-27 11:28:30,043 INFO [train.py:842] (2/4) Epoch 12, batch 1400, loss[loss=0.1925, simple_loss=0.2831, pruned_loss=0.05091, over 7319.00 frames.], tot_loss[loss=0.204, simple_loss=0.2858, pruned_loss=0.06115, over 1419899.35 frames.], batch size: 21, lr: 4.58e-04
2022-05-27 11:29:08,574 INFO [train.py:842] (2/4) Epoch 12, batch 1450, loss[loss=0.2191, simple_loss=0.2886, pruned_loss=0.07482, over 7072.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2874, pruned_loss=0.06219, over 1421310.83 frames.], batch size: 18, lr: 4.58e-04
2022-05-27 11:29:47,662 INFO [train.py:842] (2/4) Epoch 12, batch 1500, loss[loss=0.2369, simple_loss=0.3225, pruned_loss=0.07563, over 7181.00 frames.], tot_loss[loss=0.2059, simple_loss=0.287, pruned_loss=0.06245, over 1425314.58 frames.], batch size: 23, lr: 4.58e-04
2022-05-27 11:30:26,254 INFO [train.py:842] (2/4) Epoch 12, batch 1550, loss[loss=0.1796, simple_loss=0.27, pruned_loss=0.0446, over 7231.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2858, pruned_loss=0.0617, over 1424102.41 frames.], batch size: 20, lr: 4.58e-04
2022-05-27 11:31:04,959 INFO [train.py:842] (2/4) Epoch 12, batch 1600, loss[loss=0.2208, simple_loss=0.2997, pruned_loss=0.07092, over 7358.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2861, pruned_loss=0.06154, over 1424683.11 frames.], batch size: 19, lr: 4.58e-04
2022-05-27 11:31:43,502 INFO [train.py:842] (2/4) Epoch 12, batch 1650, loss[loss=0.1993, simple_loss=0.2951, pruned_loss=0.05171, over 7373.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2857, pruned_loss=0.061, over 1426020.07 frames.], batch size: 23, lr: 4.58e-04
2022-05-27 11:32:22,285 INFO [train.py:842] (2/4) Epoch 12, batch 1700, loss[loss=0.1768, simple_loss=0.2765, pruned_loss=0.0385, over 7211.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2855, pruned_loss=0.06073, over 1427105.82 frames.], batch size: 21, lr: 4.58e-04
2022-05-27 11:33:00,964 INFO [train.py:842] (2/4) Epoch 12, batch 1750, loss[loss=0.2582, simple_loss=0.3354, pruned_loss=0.09052, over 7192.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2871, pruned_loss=0.06192, over 1427919.06 frames.], batch size: 26, lr: 4.57e-04
2022-05-27 11:33:40,072 INFO [train.py:842] (2/4) Epoch 12, batch 1800, loss[loss=0.2199, simple_loss=0.2887, pruned_loss=0.07551, over 7005.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2869, pruned_loss=0.06195, over 1428063.34 frames.], batch size: 16, lr: 4.57e-04
2022-05-27 11:34:18,687 INFO [train.py:842] (2/4) Epoch 12, batch 1850, loss[loss=0.1943, simple_loss=0.28, pruned_loss=0.05427, over 7169.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2864, pruned_loss=0.06141, over 1426805.09 frames.], batch size: 26, lr: 4.57e-04
2022-05-27 11:34:57,825 INFO [train.py:842] (2/4) Epoch 12, batch 1900, loss[loss=0.2043, simple_loss=0.289, pruned_loss=0.05974, over 7435.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2864, pruned_loss=0.06156, over 1429537.55 frames.], batch size: 20, lr: 4.57e-04
2022-05-27 11:35:36,369 INFO [train.py:842] (2/4) Epoch 12, batch 1950, loss[loss=0.1566, simple_loss=0.2392, pruned_loss=0.03703, over 7007.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2865, pruned_loss=0.06134, over 1428158.42 frames.], batch size: 16, lr: 4.57e-04
2022-05-27 11:36:15,178 INFO [train.py:842] (2/4) Epoch 12, batch 2000, loss[loss=0.2027, simple_loss=0.2948, pruned_loss=0.05534, over 6375.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2866, pruned_loss=0.06131, over 1426161.69 frames.], batch size: 37, lr: 4.57e-04
2022-05-27 11:36:53,628 INFO [train.py:842] (2/4) Epoch 12, batch 2050, loss[loss=0.2405, simple_loss=0.3216, pruned_loss=0.0797, over 7385.00 frames.], tot_loss[loss=0.2059, simple_loss=0.287, pruned_loss=0.06242, over 1424697.91 frames.], batch size: 23, lr: 4.57e-04
2022-05-27 11:37:32,479 INFO [train.py:842] (2/4) Epoch 12, batch 2100, loss[loss=0.1907, simple_loss=0.2804, pruned_loss=0.05045, over 6831.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2865, pruned_loss=0.06151, over 1429244.21 frames.], batch size: 31, lr: 4.57e-04
2022-05-27 11:38:11,264 INFO [train.py:842] (2/4) Epoch 12, batch 2150, loss[loss=0.1883, simple_loss=0.261, pruned_loss=0.05784, over 7201.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2864, pruned_loss=0.06164, over 1423283.10 frames.], batch size: 16, lr: 4.57e-04
2022-05-27 11:38:50,345 INFO [train.py:842] (2/4) Epoch 12, batch 2200, loss[loss=0.2158, simple_loss=0.3011, pruned_loss=0.0652, over 7436.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2848, pruned_loss=0.06025, over 1427716.67 frames.], batch size: 20, lr: 4.56e-04
2022-05-27 11:39:29,040 INFO [train.py:842] (2/4) Epoch 12, batch 2250, loss[loss=0.1904, simple_loss=0.2551, pruned_loss=0.06278, over 7141.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2847, pruned_loss=0.05997, over 1426093.28 frames.], batch size: 17, lr: 4.56e-04
2022-05-27 11:40:07,784 INFO [train.py:842] (2/4) Epoch 12, batch 2300, loss[loss=0.1969, simple_loss=0.283, pruned_loss=0.05543, over 7352.00 frames.], tot_loss[loss=0.202, simple_loss=0.2851, pruned_loss=0.05952, over 1424472.21 frames.], batch size: 19, lr: 4.56e-04
2022-05-27 11:40:46,492 INFO [train.py:842] (2/4) Epoch 12, batch 2350, loss[loss=0.195, simple_loss=0.2737, pruned_loss=0.05819, over 7298.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2857, pruned_loss=0.06046, over 1426288.25 frames.], batch size: 24, lr: 4.56e-04
2022-05-27 11:41:25,319 INFO [train.py:842] (2/4) Epoch 12, batch 2400, loss[loss=0.2056, simple_loss=0.3, pruned_loss=0.05564, over 7110.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2856, pruned_loss=0.06, over 1427901.41 frames.], batch size: 21, lr: 4.56e-04
2022-05-27 11:42:03,647 INFO [train.py:842] (2/4) Epoch 12, batch 2450, loss[loss=0.1921, simple_loss=0.2833, pruned_loss=0.05048, over 7241.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2869, pruned_loss=0.06065, over 1426009.88 frames.], batch size: 20, lr: 4.56e-04
2022-05-27 11:42:42,643 INFO [train.py:842] (2/4) Epoch 12, batch 2500, loss[loss=0.1715, simple_loss=0.2574, pruned_loss=0.04285, over 7071.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2866, pruned_loss=0.06043, over 1425568.31 frames.], batch size: 18, lr: 4.56e-04
2022-05-27 11:43:21,073 INFO [train.py:842] (2/4) Epoch 12, batch 2550, loss[loss=0.1843, simple_loss=0.2628, pruned_loss=0.05286, over 7273.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2871, pruned_loss=0.06078, over 1428054.42 frames.], batch size: 17, lr: 4.56e-04
2022-05-27 11:43:59,768 INFO [train.py:842] (2/4) Epoch 12, batch 2600, loss[loss=0.2004, simple_loss=0.2952, pruned_loss=0.05275, over 7289.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2869, pruned_loss=0.06103, over 1423558.83 frames.], batch size: 24, lr: 4.56e-04
2022-05-27 11:44:38,256 INFO [train.py:842] (2/4) Epoch 12, batch 2650, loss[loss=0.2069, simple_loss=0.2793, pruned_loss=0.06728, over 7256.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2869, pruned_loss=0.06089, over 1420694.26 frames.], batch size: 19, lr: 4.55e-04
2022-05-27 11:45:17,039 INFO [train.py:842] (2/4) Epoch 12, batch 2700, loss[loss=0.1988, simple_loss=0.289, pruned_loss=0.05433, over 7288.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2871, pruned_loss=0.06154, over 1424165.30 frames.], batch size: 25, lr: 4.55e-04
2022-05-27 11:45:55,568 INFO [train.py:842] (2/4) Epoch 12, batch 2750, loss[loss=0.2324, simple_loss=0.3081, pruned_loss=0.07841, over 7422.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2877, pruned_loss=0.06185, over 1426436.63 frames.], batch size: 20, lr: 4.55e-04
2022-05-27 11:46:34,709 INFO [train.py:842] (2/4) Epoch 12, batch 2800, loss[loss=0.2149, simple_loss=0.2965, pruned_loss=0.06666, over 7106.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2879, pruned_loss=0.06227, over 1427003.91 frames.], batch size: 21, lr: 4.55e-04
2022-05-27 11:47:13,253 INFO [train.py:842] (2/4) Epoch 12, batch 2850, loss[loss=0.1924, simple_loss=0.2715, pruned_loss=0.05668, over 7319.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2872, pruned_loss=0.06206, over 1429335.62 frames.], batch size: 21, lr: 4.55e-04
2022-05-27 11:47:54,674 INFO [train.py:842] (2/4) Epoch 12, batch 2900, loss[loss=0.229, simple_loss=0.3096, pruned_loss=0.07421, over 7294.00 frames.], tot_loss[loss=0.2076, simple_loss=0.289, pruned_loss=0.06305, over 1425435.51 frames.], batch size: 24, lr: 4.55e-04
2022-05-27 11:48:33,250 INFO [train.py:842] (2/4) Epoch 12, batch 2950, loss[loss=0.2496, simple_loss=0.3189, pruned_loss=0.09018, over 7212.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2888, pruned_loss=0.063, over 1421025.30 frames.], batch size: 21, lr: 4.55e-04
2022-05-27 11:49:12,199 INFO [train.py:842] (2/4) Epoch 12, batch 3000, loss[loss=0.2394, simple_loss=0.3175, pruned_loss=0.08068, over 7279.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2881, pruned_loss=0.0621, over 1422907.56 frames.], batch size: 25, lr: 4.55e-04
2022-05-27 11:49:12,200 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 11:49:21,566 INFO [train.py:871] (2/4) Epoch 12, validation: loss=0.172, simple_loss=0.2724, pruned_loss=0.03584, over 868885.00 frames.
2022-05-27 11:50:00,012 INFO [train.py:842] (2/4) Epoch 12, batch 3050, loss[loss=0.2542, simple_loss=0.3312, pruned_loss=0.0886, over 7373.00 frames.], tot_loss[loss=0.2057, simple_loss=0.288, pruned_loss=0.06175, over 1420899.47 frames.], batch size: 23, lr: 4.55e-04
2022-05-27 11:50:39,205 INFO [train.py:842] (2/4) Epoch 12, batch 3100, loss[loss=0.1933, simple_loss=0.2804, pruned_loss=0.05308, over 7322.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2871, pruned_loss=0.06133, over 1422996.33 frames.], batch size: 20, lr: 4.54e-04
2022-05-27 11:51:17,934 INFO [train.py:842] (2/4) Epoch 12, batch 3150, loss[loss=0.2113, simple_loss=0.2919, pruned_loss=0.0654, over 7376.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2863, pruned_loss=0.06102, over 1425376.22 frames.], batch size: 23, lr: 4.54e-04
2022-05-27 11:51:56,829 INFO [train.py:842] (2/4) Epoch 12, batch 3200, loss[loss=0.2063, simple_loss=0.297, pruned_loss=0.05775, over 7116.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2864, pruned_loss=0.06055, over 1424839.24 frames.], batch size: 21, lr: 4.54e-04
2022-05-27 11:52:35,740 INFO [train.py:842] (2/4) Epoch 12, batch 3250, loss[loss=0.1958, simple_loss=0.2711, pruned_loss=0.06021, over 7417.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2855, pruned_loss=0.06018, over 1425600.36 frames.], batch size: 21, lr: 4.54e-04
2022-05-27 11:53:14,319 INFO [train.py:842] (2/4) Epoch 12, batch 3300, loss[loss=0.1941, simple_loss=0.2646, pruned_loss=0.06177, over 7007.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2856, pruned_loss=0.05978, over 1425485.79 frames.], batch size: 16, lr: 4.54e-04
2022-05-27 11:53:52,887 INFO [train.py:842] (2/4) Epoch 12, batch 3350, loss[loss=0.1459, simple_loss=0.2234, pruned_loss=0.03424, over 7281.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2859, pruned_loss=0.05984, over 1426074.48 frames.], batch size: 18, lr: 4.54e-04
2022-05-27 11:54:31,816 INFO [train.py:842] (2/4) Epoch 12, batch 3400, loss[loss=0.2401, simple_loss=0.3262, pruned_loss=0.07698, over 6410.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2866, pruned_loss=0.06043, over 1420512.57 frames.], batch size: 38, lr: 4.54e-04
2022-05-27 11:55:10,419 INFO [train.py:842] (2/4) Epoch 12, batch 3450, loss[loss=0.2555, simple_loss=0.3322, pruned_loss=0.08937, over 7119.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2868, pruned_loss=0.06095, over 1418267.27 frames.], batch size: 21, lr: 4.54e-04
2022-05-27 11:55:49,187 INFO [train.py:842] (2/4) Epoch 12, batch 3500, loss[loss=0.1773, simple_loss=0.273, pruned_loss=0.04075, over 7305.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2867, pruned_loss=0.06071, over 1424404.71 frames.], batch size: 21, lr: 4.54e-04
2022-05-27 11:56:27,757 INFO [train.py:842] (2/4) Epoch 12, batch 3550, loss[loss=0.1415, simple_loss=0.2215, pruned_loss=0.03078, over 7005.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2859, pruned_loss=0.06027, over 1423251.34 frames.], batch size: 16, lr: 4.53e-04
2022-05-27 11:57:06,236 INFO [train.py:842] (2/4) Epoch 12, batch 3600, loss[loss=0.2126, simple_loss=0.2978, pruned_loss=0.06363, over 7229.00 frames.], tot_loss[loss=0.204, simple_loss=0.2867, pruned_loss=0.0607, over 1424971.24 frames.], batch size: 20, lr: 4.53e-04
2022-05-27 11:57:44,685 INFO [train.py:842] (2/4) Epoch 12, batch 3650, loss[loss=0.1776, simple_loss=0.273, pruned_loss=0.04113, over 7425.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2856, pruned_loss=0.06003, over 1424303.29 frames.], batch size: 20, lr: 4.53e-04
2022-05-27 11:58:23,621 INFO [train.py:842] (2/4) Epoch 12, batch 3700, loss[loss=0.2473, simple_loss=0.3312, pruned_loss=0.0817, over 6764.00 frames.], tot_loss[loss=0.203, simple_loss=0.2853, pruned_loss=0.06034, over 1420558.38 frames.], batch size: 31, lr: 4.53e-04
2022-05-27 11:59:02,227 INFO [train.py:842] (2/4) Epoch 12, batch 3750, loss[loss=0.2139, simple_loss=0.2963, pruned_loss=0.06571, over 7377.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2862, pruned_loss=0.06134, over 1425474.20 frames.], batch size: 23, lr: 4.53e-04
2022-05-27 11:59:41,086 INFO [train.py:842] (2/4) Epoch 12, batch 3800, loss[loss=0.2117, simple_loss=0.3007, pruned_loss=0.06134, over 7160.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2858, pruned_loss=0.06099, over 1427792.84 frames.], batch size: 26, lr: 4.53e-04
2022-05-27 12:00:19,474 INFO [train.py:842] (2/4) Epoch 12, batch 3850, loss[loss=0.1596, simple_loss=0.2446, pruned_loss=0.03729, over 7068.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2858, pruned_loss=0.06081, over 1428175.18 frames.], batch size: 18, lr: 4.53e-04
2022-05-27 12:00:58,247 INFO [train.py:842] (2/4) Epoch 12, batch 3900, loss[loss=0.2854, simple_loss=0.3456, pruned_loss=0.1127, over 5111.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2858, pruned_loss=0.0608, over 1429076.11 frames.], batch size: 52, lr: 4.53e-04
2022-05-27 12:01:36,966 INFO [train.py:842] (2/4) Epoch 12, batch 3950, loss[loss=0.1917, simple_loss=0.2699, pruned_loss=0.05668, over 7260.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2859, pruned_loss=0.06118, over 1430088.81 frames.], batch size: 19, lr: 4.53e-04
2022-05-27 12:02:15,767 INFO [train.py:842] (2/4) Epoch 12, batch 4000, loss[loss=0.1793, simple_loss=0.2601, pruned_loss=0.04928, over 7363.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2864, pruned_loss=0.06123, over 1426737.56 frames.], batch size: 19, lr: 4.53e-04
2022-05-27 12:02:54,553 INFO [train.py:842] (2/4) Epoch 12, batch 4050, loss[loss=0.2401, simple_loss=0.3168, pruned_loss=0.08166, over 7424.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2862, pruned_loss=0.06121, over 1426530.36 frames.], batch size: 18, lr: 4.52e-04
2022-05-27 12:03:33,376 INFO [train.py:842] (2/4) Epoch 12, batch 4100, loss[loss=0.1996, simple_loss=0.2884, pruned_loss=0.05537, over 7115.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2867, pruned_loss=0.06195, over 1422776.19 frames.], batch size: 21, lr: 4.52e-04
2022-05-27 12:04:12,129 INFO [train.py:842] (2/4) Epoch 12, batch 4150, loss[loss=0.2155, simple_loss=0.2915, pruned_loss=0.0697, over 7195.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2869, pruned_loss=0.06177, over 1424566.79 frames.], batch size: 22, lr: 4.52e-04
2022-05-27 12:04:50,967 INFO [train.py:842] (2/4) Epoch 12, batch 4200, loss[loss=0.1941, simple_loss=0.2727, pruned_loss=0.0577, over 7139.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2867, pruned_loss=0.06201, over 1426008.01 frames.], batch size: 20, lr: 4.52e-04
2022-05-27 12:05:29,393 INFO [train.py:842] (2/4) Epoch 12, batch 4250, loss[loss=0.2674, simple_loss=0.3466, pruned_loss=0.09407, over 6707.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2868, pruned_loss=0.06233, over 1423821.11 frames.], batch size: 31, lr: 4.52e-04
2022-05-27 12:06:08,259 INFO [train.py:842] (2/4) Epoch 12, batch 4300, loss[loss=0.2306, simple_loss=0.2926, pruned_loss=0.08431, over 7292.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2867, pruned_loss=0.06192, over 1424914.61 frames.], batch size: 17, lr: 4.52e-04
2022-05-27 12:06:46,752 INFO [train.py:842] (2/4) Epoch 12, batch 4350, loss[loss=0.1742, simple_loss=0.2514, pruned_loss=0.04856, over 7174.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2866, pruned_loss=0.06148, over 1417912.62 frames.], batch size: 18, lr: 4.52e-04
2022-05-27 12:07:25,738 INFO [train.py:842] (2/4) Epoch 12, batch 4400, loss[loss=0.2367, simple_loss=0.3104, pruned_loss=0.08152, over 7123.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2868, pruned_loss=0.06166, over 1421938.13 frames.], batch size: 21, lr: 4.52e-04
2022-05-27 12:08:04,059 INFO [train.py:842] (2/4) Epoch 12, batch 4450, loss[loss=0.1595, simple_loss=0.2424, pruned_loss=0.03827, over 7259.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2863, pruned_loss=0.0611, over 1419829.42 frames.], batch size: 19, lr: 4.52e-04
2022-05-27 12:08:43,197 INFO [train.py:842] (2/4) Epoch 12, batch 4500, loss[loss=0.1924, simple_loss=0.2719, pruned_loss=0.05646, over 7427.00 frames.], tot_loss[loss=0.203, simple_loss=0.2852, pruned_loss=0.06042, over 1423727.00 frames.], batch size: 18, lr: 4.51e-04
2022-05-27 12:09:22,010 INFO [train.py:842] (2/4) Epoch 12, batch 4550, loss[loss=0.2065, simple_loss=0.29, pruned_loss=0.06155, over 7141.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2846, pruned_loss=0.06025, over 1424673.39 frames.], batch size: 20, lr: 4.51e-04
2022-05-27 12:10:00,755 INFO [train.py:842] (2/4) Epoch 12, batch 4600, loss[loss=0.2445, simple_loss=0.3211, pruned_loss=0.08396, over 7080.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2869, pruned_loss=0.0617, over 1421319.84 frames.], batch size: 28, lr: 4.51e-04
2022-05-27 12:10:39,215 INFO [train.py:842] (2/4) Epoch 12, batch 4650, loss[loss=0.1859, simple_loss=0.2765, pruned_loss=0.04771, over 7323.00 frames.], tot_loss[loss=0.206, simple_loss=0.2879, pruned_loss=0.06209, over 1423602.76 frames.], batch size: 21, lr: 4.51e-04
2022-05-27 12:11:18,048 INFO [train.py:842] (2/4) Epoch 12, batch 4700, loss[loss=0.2466, simple_loss=0.3062, pruned_loss=0.09352, over 5099.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2868, pruned_loss=0.06125, over 1420759.88 frames.], batch size: 52, lr: 4.51e-04
2022-05-27 12:11:56,426 INFO [train.py:842] (2/4) Epoch 12, batch 4750, loss[loss=0.2029, simple_loss=0.2945, pruned_loss=0.05561, over 7256.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2874, pruned_loss=0.06185, over 1422256.27 frames.], batch size: 19, lr: 4.51e-04
2022-05-27 12:12:35,430 INFO [train.py:842] (2/4) Epoch 12, batch 4800, loss[loss=0.1569, simple_loss=0.2462, pruned_loss=0.0338, over 7363.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2883, pruned_loss=0.06253, over 1423339.91 frames.], batch size: 19, lr: 4.51e-04
2022-05-27 12:13:13,831 INFO [train.py:842] (2/4) Epoch 12, batch 4850, loss[loss=0.2419, simple_loss=0.3191, pruned_loss=0.08237, over 7168.00 frames.], tot_loss[loss=0.2062, simple_loss=0.288, pruned_loss=0.06225, over 1426040.06 frames.], batch size: 18, lr: 4.51e-04
2022-05-27 12:13:52,885 INFO [train.py:842] (2/4) Epoch 12, batch 4900, loss[loss=0.2171, simple_loss=0.2979, pruned_loss=0.06814, over 7413.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2871, pruned_loss=0.06218, over 1426101.37 frames.], batch size: 18, lr: 4.51e-04
2022-05-27 12:14:31,298 INFO [train.py:842] (2/4) Epoch 12, batch 4950, loss[loss=0.1862, simple_loss=0.2807, pruned_loss=0.04586, over 7113.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2875, pruned_loss=0.0623, over 1422867.28 frames.], batch size: 26, lr: 4.50e-04
2022-05-27 12:15:10,325 INFO [train.py:842] (2/4) Epoch 12, batch 5000, loss[loss=0.1593, simple_loss=0.2352, pruned_loss=0.04166, over 7404.00 frames.], tot_loss[loss=0.2048, simple_loss=0.286, pruned_loss=0.06175, over 1416683.25 frames.], batch size: 18, lr: 4.50e-04
2022-05-27 12:15:48,905 INFO [train.py:842] (2/4) Epoch 12, batch 5050, loss[loss=0.1963, simple_loss=0.2759, pruned_loss=0.05831, over 7073.00 frames.], tot_loss[loss=0.203, simple_loss=0.2843, pruned_loss=0.06088, over 1421327.88 frames.], batch size: 18, lr: 4.50e-04
2022-05-27 12:16:27,509 INFO [train.py:842] (2/4) Epoch 12, batch 5100, loss[loss=0.2331, simple_loss=0.2988, pruned_loss=0.08368, over 7186.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2849, pruned_loss=0.06099, over 1415794.65 frames.], batch size: 22, lr: 4.50e-04
2022-05-27 12:17:06,090 INFO [train.py:842] (2/4) Epoch 12, batch 5150, loss[loss=0.212, simple_loss=0.2922, pruned_loss=0.06587, over 7204.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2853, pruned_loss=0.06113, over 1420389.45 frames.], batch size: 22, lr: 4.50e-04
2022-05-27 12:17:44,835 INFO [train.py:842] (2/4) Epoch 12, batch 5200, loss[loss=0.2388, simple_loss=0.3276, pruned_loss=0.07496, over 7239.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2852, pruned_loss=0.06069, over 1421889.64 frames.], batch size: 20, lr: 4.50e-04
2022-05-27 12:18:23,379 INFO [train.py:842] (2/4) Epoch 12, batch 5250, loss[loss=0.2362, simple_loss=0.3017, pruned_loss=0.08538, over 7298.00 frames.], tot_loss[loss=0.2038, simple_loss=0.285, pruned_loss=0.06132, over 1419977.76 frames.], batch size: 24, lr: 4.50e-04
2022-05-27 12:19:02,154 INFO [train.py:842] (2/4) Epoch 12, batch 5300, loss[loss=0.1439, simple_loss=0.2238, pruned_loss=0.03201, over 7208.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2839, pruned_loss=0.06042, over 1419604.99 frames.], batch size: 16, lr: 4.50e-04
2022-05-27 12:19:41,020 INFO [train.py:842] (2/4) Epoch 12, batch 5350, loss[loss=0.2038, simple_loss=0.2897, pruned_loss=0.05893, over 6591.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2839, pruned_loss=0.06036, over 1420853.57 frames.], batch size: 38, lr: 4.50e-04
2022-05-27 12:20:19,736 INFO [train.py:842] (2/4) Epoch 12, batch 5400, loss[loss=0.1831, simple_loss=0.2811, pruned_loss=0.0425, over 7217.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2855, pruned_loss=0.06103, over 1421137.73 frames.], batch size: 21, lr: 4.50e-04
2022-05-27 12:20:58,145 INFO [train.py:842] (2/4) Epoch 12, batch 5450, loss[loss=0.2771, simple_loss=0.3385, pruned_loss=0.1078, over 7331.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2863, pruned_loss=0.06125, over 1419701.40 frames.], batch size: 20, lr: 4.49e-04
2022-05-27 12:21:37,202 INFO [train.py:842] (2/4) Epoch 12, batch 5500, loss[loss=0.184, simple_loss=0.2547, pruned_loss=0.05663, over 7268.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2872, pruned_loss=0.06215, over 1417452.83 frames.], batch size: 18, lr: 4.49e-04
2022-05-27 12:22:15,859 INFO [train.py:842] (2/4) Epoch 12, batch 5550, loss[loss=0.1853, simple_loss=0.2727, pruned_loss=0.04896, over 7322.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2852, pruned_loss=0.06048, over 1423174.25 frames.], batch size: 21, lr: 4.49e-04
2022-05-27 12:22:54,571 INFO [train.py:842] (2/4) Epoch 12, batch 5600, loss[loss=0.1907, simple_loss=0.2901, pruned_loss=0.04561, over 7148.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2862, pruned_loss=0.06095, over 1418324.32 frames.], batch size: 20, lr: 4.49e-04
2022-05-27 12:23:33,106 INFO [train.py:842] (2/4) Epoch 12, batch 5650, loss[loss=0.2251, simple_loss=0.3065, pruned_loss=0.07189, over 7145.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2864, pruned_loss=0.06095, over 1422967.55 frames.], batch size: 26, lr: 4.49e-04
2022-05-27 12:24:12,163 INFO [train.py:842] (2/4) Epoch 12, batch 5700, loss[loss=0.1981, simple_loss=0.2769, pruned_loss=0.05963, over 7362.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2859, pruned_loss=0.06059, over 1422103.47 frames.], batch size: 19, lr: 4.49e-04
2022-05-27 12:24:50,779 INFO [train.py:842] (2/4) Epoch 12, batch 5750, loss[loss=0.1952, simple_loss=0.2818, pruned_loss=0.05432, over 7111.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2865, pruned_loss=0.06127, over 1424630.69 frames.], batch size: 21, lr: 4.49e-04
2022-05-27 12:25:29,893 INFO [train.py:842] (2/4) Epoch 12, batch 5800, loss[loss=0.2275, simple_loss=0.3036, pruned_loss=0.07571, over 7298.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2867, pruned_loss=0.06119, over 1422844.29 frames.], batch size: 25, lr: 4.49e-04
2022-05-27 12:26:08,565 INFO [train.py:842] (2/4) Epoch 12, batch 5850, loss[loss=0.1976, simple_loss=0.2721, pruned_loss=0.06153, over 6793.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2865, pruned_loss=0.06138, over 1421841.33 frames.], batch size: 15, lr: 4.49e-04
2022-05-27 12:26:47,341 INFO [train.py:842] (2/4) Epoch 12, batch 5900, loss[loss=0.241, simple_loss=0.3113, pruned_loss=0.08541, over 6810.00 frames.], tot_loss[loss=0.2041, simple_loss=0.286, pruned_loss=0.06116, over 1425518.56 frames.], batch size: 31, lr: 4.48e-04
2022-05-27 12:27:25,910 INFO [train.py:842] (2/4) Epoch 12, batch 5950, loss[loss=0.1839, simple_loss=0.2758, pruned_loss=0.04601, over 7421.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2854, pruned_loss=0.06083, over 1429666.96 frames.], batch size: 20, lr: 4.48e-04
2022-05-27 12:28:04,952 INFO [train.py:842] (2/4) Epoch 12, batch 6000, loss[loss=0.2479, simple_loss=0.3229, pruned_loss=0.08649, over 7116.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2856, pruned_loss=0.06096, over 1427381.09 frames.], batch size: 28, lr: 4.48e-04
2022-05-27 12:28:04,952 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 12:28:14,266 INFO [train.py:871] (2/4) 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] (2/4) Epoch 12, batch 6050, loss[loss=0.1664, simple_loss=0.2406, pruned_loss=0.0461, over 7002.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2854, pruned_loss=0.06007, over 1428236.12 frames.], batch size: 16, lr: 4.48e-04
2022-05-27 12:29:32,022 INFO [train.py:842] (2/4) Epoch 12, batch 6100, loss[loss=0.1965, simple_loss=0.2788, pruned_loss=0.05705, over 7157.00 frames.], tot_loss[loss=0.202, simple_loss=0.2841, pruned_loss=0.0599, over 1430804.03 frames.], batch size: 19, lr: 4.48e-04
2022-05-27 12:30:10,610 INFO [train.py:842] (2/4) Epoch 12, batch 6150, loss[loss=0.1792, simple_loss=0.2654, pruned_loss=0.04645, over 7260.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2848, pruned_loss=0.06028, over 1429498.92 frames.], batch size: 19, lr: 4.48e-04
2022-05-27 12:30:49,462 INFO [train.py:842] (2/4) Epoch 12, batch 6200, loss[loss=0.2253, simple_loss=0.2987, pruned_loss=0.07599, over 7233.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2843, pruned_loss=0.06015, over 1428500.80 frames.], batch size: 20, lr: 4.48e-04
2022-05-27 12:31:27,894 INFO [train.py:842] (2/4) Epoch 12, batch 6250, loss[loss=0.1954, simple_loss=0.2716, pruned_loss=0.05955, over 7165.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2845, pruned_loss=0.05983, over 1425988.60 frames.], batch size: 18, lr: 4.48e-04
2022-05-27 12:32:06,961 INFO [train.py:842] (2/4) Epoch 12, batch 6300, loss[loss=0.2108, simple_loss=0.3055, pruned_loss=0.05807, over 6588.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2839, pruned_loss=0.05937, over 1427107.76 frames.], batch size: 31, lr: 4.48e-04
2022-05-27 12:32:45,631 INFO [train.py:842] (2/4) Epoch 12, batch 6350, loss[loss=0.1943, simple_loss=0.2836, pruned_loss=0.05244, over 7137.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2854, pruned_loss=0.0602, over 1425950.58 frames.], batch size: 20, lr: 4.48e-04
2022-05-27 12:33:24,538 INFO [train.py:842] (2/4) Epoch 12, batch 6400, loss[loss=0.2355, simple_loss=0.3163, pruned_loss=0.07731, over 7179.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2853, pruned_loss=0.06042, over 1418156.72 frames.], batch size: 26, lr: 4.47e-04
2022-05-27 12:34:03,168 INFO [train.py:842] (2/4) Epoch 12, batch 6450, loss[loss=0.1672, simple_loss=0.239, pruned_loss=0.0477, over 7173.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2843, pruned_loss=0.06024, over 1417601.08 frames.], batch size: 18, lr: 4.47e-04
2022-05-27 12:34:41,818 INFO [train.py:842] (2/4) Epoch 12, batch 6500, loss[loss=0.2032, simple_loss=0.2883, pruned_loss=0.05904, over 7223.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2837, pruned_loss=0.05932, over 1421586.66 frames.], batch size: 22, lr: 4.47e-04
2022-05-27 12:35:20,542 INFO [train.py:842] (2/4) Epoch 12, batch 6550, loss[loss=0.188, simple_loss=0.2588, pruned_loss=0.05862, over 7160.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2837, pruned_loss=0.05956, over 1419633.64 frames.], batch size: 18, lr: 4.47e-04
2022-05-27 12:35:59,556 INFO [train.py:842] (2/4) Epoch 12, batch 6600, loss[loss=0.2093, simple_loss=0.2839, pruned_loss=0.06739, over 7445.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2822, pruned_loss=0.05933, over 1420023.09 frames.], batch size: 20, lr: 4.47e-04
2022-05-27 12:36:38,268 INFO [train.py:842] (2/4) Epoch 12, batch 6650, loss[loss=0.1727, simple_loss=0.2478, pruned_loss=0.04878, over 7277.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2827, pruned_loss=0.05988, over 1421142.91 frames.], batch size: 17, lr: 4.47e-04
2022-05-27 12:37:17,243 INFO [train.py:842] (2/4) Epoch 12, batch 6700, loss[loss=0.2065, simple_loss=0.2816, pruned_loss=0.06571, over 7155.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2826, pruned_loss=0.05986, over 1419116.14 frames.], batch size: 20, lr: 4.47e-04
2022-05-27 12:37:56,156 INFO [train.py:842] (2/4) Epoch 12, batch 6750, loss[loss=0.2116, simple_loss=0.298, pruned_loss=0.06254, over 7343.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2828, pruned_loss=0.05951, over 1420168.44 frames.], batch size: 22, lr: 4.47e-04
2022-05-27 12:38:35,153 INFO [train.py:842] (2/4) Epoch 12, batch 6800, loss[loss=0.1968, simple_loss=0.2756, pruned_loss=0.05905, over 7168.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2828, pruned_loss=0.0598, over 1421790.27 frames.], batch size: 19, lr: 4.47e-04
2022-05-27 12:39:13,719 INFO [train.py:842] (2/4) Epoch 12, batch 6850, loss[loss=0.2078, simple_loss=0.2972, pruned_loss=0.05918, over 7238.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2818, pruned_loss=0.05883, over 1422782.71 frames.], batch size: 20, lr: 4.47e-04
2022-05-27 12:39:52,707 INFO [train.py:842] (2/4) Epoch 12, batch 6900, loss[loss=0.2459, simple_loss=0.3274, pruned_loss=0.08217, over 6710.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2834, pruned_loss=0.05997, over 1421158.79 frames.], batch size: 31, lr: 4.46e-04
2022-05-27 12:40:31,030 INFO [train.py:842] (2/4) Epoch 12, batch 6950, loss[loss=0.1996, simple_loss=0.2954, pruned_loss=0.05192, over 7121.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2856, pruned_loss=0.06056, over 1416326.17 frames.], batch size: 21, lr: 4.46e-04
2022-05-27 12:41:09,838 INFO [train.py:842] (2/4) Epoch 12, batch 7000, loss[loss=0.2326, simple_loss=0.308, pruned_loss=0.07859, over 7200.00 frames.], tot_loss[loss=0.2026, simple_loss=0.285, pruned_loss=0.06006, over 1415296.88 frames.], batch size: 23, lr: 4.46e-04
2022-05-27 12:41:48,364 INFO [train.py:842] (2/4) Epoch 12, batch 7050, loss[loss=0.2057, simple_loss=0.2958, pruned_loss=0.05779, over 6384.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2865, pruned_loss=0.06084, over 1418163.67 frames.], batch size: 38, lr: 4.46e-04
2022-05-27 12:42:27,202 INFO [train.py:842] (2/4) Epoch 12, batch 7100, loss[loss=0.2025, simple_loss=0.2924, pruned_loss=0.05628, over 7139.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2865, pruned_loss=0.06098, over 1416523.02 frames.], batch size: 20, lr: 4.46e-04
2022-05-27 12:43:05,923 INFO [train.py:842] (2/4) Epoch 12, batch 7150, loss[loss=0.2033, simple_loss=0.2887, pruned_loss=0.05894, over 7427.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2875, pruned_loss=0.06217, over 1420471.30 frames.], batch size: 20, lr: 4.46e-04
2022-05-27 12:43:44,462 INFO [train.py:842] (2/4) Epoch 12, batch 7200, loss[loss=0.2314, simple_loss=0.317, pruned_loss=0.07288, over 7418.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2881, pruned_loss=0.06205, over 1423239.80 frames.], batch size: 21, lr: 4.46e-04
2022-05-27 12:44:23,117 INFO [train.py:842] (2/4) Epoch 12, batch 7250, loss[loss=0.3459, simple_loss=0.389, pruned_loss=0.1514, over 7116.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2884, pruned_loss=0.06238, over 1419329.60 frames.], batch size: 21, lr: 4.46e-04
2022-05-27 12:45:01,908 INFO [train.py:842] (2/4) Epoch 12, batch 7300, loss[loss=0.1635, simple_loss=0.2408, pruned_loss=0.04307, over 6992.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2882, pruned_loss=0.06272, over 1421953.33 frames.], batch size: 16, lr: 4.46e-04
2022-05-27 12:45:40,533 INFO [train.py:842] (2/4) Epoch 12, batch 7350, loss[loss=0.1906, simple_loss=0.273, pruned_loss=0.05411, over 7150.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2881, pruned_loss=0.06277, over 1423109.33 frames.], batch size: 17, lr: 4.45e-04
2022-05-27 12:46:19,310 INFO [train.py:842] (2/4) Epoch 12, batch 7400, loss[loss=0.1806, simple_loss=0.2579, pruned_loss=0.05163, over 7422.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2872, pruned_loss=0.06219, over 1424453.55 frames.], batch size: 18, lr: 4.45e-04
2022-05-27 12:46:57,756 INFO [train.py:842] (2/4) Epoch 12, batch 7450, loss[loss=0.201, simple_loss=0.2839, pruned_loss=0.05908, over 7277.00 frames.], tot_loss[loss=0.2048, simple_loss=0.287, pruned_loss=0.06131, over 1427087.72 frames.], batch size: 18, lr: 4.45e-04
2022-05-27 12:47:36,474 INFO [train.py:842] (2/4) Epoch 12, batch 7500, loss[loss=0.1715, simple_loss=0.2756, pruned_loss=0.03372, over 6782.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2862, pruned_loss=0.0611, over 1428287.86 frames.], batch size: 31, lr: 4.45e-04
2022-05-27 12:48:15,132 INFO [train.py:842] (2/4) Epoch 12, batch 7550, loss[loss=0.2002, simple_loss=0.2846, pruned_loss=0.05794, over 7157.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2857, pruned_loss=0.06087, over 1427777.71 frames.], batch size: 20, lr: 4.45e-04
2022-05-27 12:48:54,045 INFO [train.py:842] (2/4) Epoch 12, batch 7600, loss[loss=0.2801, simple_loss=0.3491, pruned_loss=0.1055, over 7321.00 frames.], tot_loss[loss=0.2038, simple_loss=0.286, pruned_loss=0.06074, over 1433303.53 frames.], batch size: 21, lr: 4.45e-04
2022-05-27 12:49:32,594 INFO [train.py:842] (2/4) Epoch 12, batch 7650, loss[loss=0.2176, simple_loss=0.3013, pruned_loss=0.06695, over 7144.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2876, pruned_loss=0.06201, over 1424571.56 frames.], batch size: 20, lr: 4.45e-04
2022-05-27 12:50:11,564 INFO [train.py:842] (2/4) Epoch 12, batch 7700, loss[loss=0.1821, simple_loss=0.2526, pruned_loss=0.0558, over 7285.00 frames.], tot_loss[loss=0.206, simple_loss=0.2878, pruned_loss=0.06209, over 1424088.91 frames.], batch size: 17, lr: 4.45e-04
2022-05-27 12:50:50,207 INFO [train.py:842] (2/4) Epoch 12, batch 7750, loss[loss=0.2173, simple_loss=0.2857, pruned_loss=0.07447, over 7202.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2875, pruned_loss=0.06183, over 1421534.03 frames.], batch size: 16, lr: 4.45e-04
2022-05-27 12:51:28,926 INFO [train.py:842] (2/4) Epoch 12, batch 7800, loss[loss=0.1841, simple_loss=0.2683, pruned_loss=0.04991, over 7319.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2877, pruned_loss=0.06181, over 1421886.23 frames.], batch size: 20, lr: 4.45e-04
2022-05-27 12:52:07,442 INFO [train.py:842] (2/4) Epoch 12, batch 7850, loss[loss=0.2534, simple_loss=0.328, pruned_loss=0.08941, over 4607.00 frames.], tot_loss[loss=0.2057, simple_loss=0.288, pruned_loss=0.06173, over 1418478.69 frames.], batch size: 52, lr: 4.44e-04
2022-05-27 12:52:46,499 INFO [train.py:842] (2/4) Epoch 12, batch 7900, loss[loss=0.3013, simple_loss=0.3568, pruned_loss=0.1229, over 7204.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2867, pruned_loss=0.06127, over 1422663.42 frames.], batch size: 23, lr: 4.44e-04
2022-05-27 12:53:25,063 INFO [train.py:842] (2/4) Epoch 12, batch 7950, loss[loss=0.2349, simple_loss=0.3171, pruned_loss=0.07638, over 7292.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2866, pruned_loss=0.06155, over 1424147.45 frames.], batch size: 24, lr: 4.44e-04
2022-05-27 12:54:03,976 INFO [train.py:842] (2/4) Epoch 12, batch 8000, loss[loss=0.2006, simple_loss=0.2889, pruned_loss=0.05612, over 7173.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2861, pruned_loss=0.06141, over 1424008.00 frames.], batch size: 18, lr: 4.44e-04
2022-05-27 12:54:42,594 INFO [train.py:842] (2/4) Epoch 12, batch 8050, loss[loss=0.2209, simple_loss=0.3086, pruned_loss=0.06658, over 7372.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2852, pruned_loss=0.06111, over 1428345.84 frames.], batch size: 23, lr: 4.44e-04
2022-05-27 12:55:21,335 INFO [train.py:842] (2/4) Epoch 12, batch 8100, loss[loss=0.2091, simple_loss=0.2937, pruned_loss=0.06226, over 7229.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2875, pruned_loss=0.06244, over 1428410.04 frames.], batch size: 21, lr: 4.44e-04
2022-05-27 12:55:59,852 INFO [train.py:842] (2/4) Epoch 12, batch 8150, loss[loss=0.1798, simple_loss=0.2683, pruned_loss=0.04566, over 7141.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2867, pruned_loss=0.0618, over 1424733.02 frames.], batch size: 20, lr: 4.44e-04
2022-05-27 12:56:49,255 INFO [train.py:842] (2/4) Epoch 12, batch 8200, loss[loss=0.1865, simple_loss=0.2853, pruned_loss=0.04385, over 7222.00 frames.], tot_loss[loss=0.204, simple_loss=0.286, pruned_loss=0.06101, over 1425904.74 frames.], batch size: 21, lr: 4.44e-04
2022-05-27 12:57:27,631 INFO [train.py:842] (2/4) Epoch 12, batch 8250, loss[loss=0.2136, simple_loss=0.3036, pruned_loss=0.06174, over 7214.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2867, pruned_loss=0.0615, over 1424278.84 frames.], batch size: 26, lr: 4.44e-04
2022-05-27 12:58:06,337 INFO [train.py:842] (2/4) Epoch 12, batch 8300, loss[loss=0.2431, simple_loss=0.319, pruned_loss=0.08363, over 7201.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2863, pruned_loss=0.06116, over 1423672.94 frames.], batch size: 22, lr: 4.44e-04
2022-05-27 12:58:44,906 INFO [train.py:842] (2/4) Epoch 12, batch 8350, loss[loss=0.2288, simple_loss=0.2973, pruned_loss=0.08018, over 4632.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2846, pruned_loss=0.06039, over 1426449.31 frames.], batch size: 52, lr: 4.43e-04
2022-05-27 12:59:23,801 INFO [train.py:842] (2/4) Epoch 12, batch 8400, loss[loss=0.2052, simple_loss=0.2922, pruned_loss=0.05908, over 7279.00 frames.], tot_loss[loss=0.203, simple_loss=0.2845, pruned_loss=0.06076, over 1427275.17 frames.], batch size: 24, lr: 4.43e-04
2022-05-27 13:00:02,257 INFO [train.py:842] (2/4) Epoch 12, batch 8450, loss[loss=0.2194, simple_loss=0.3035, pruned_loss=0.06764, over 6739.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2833, pruned_loss=0.05993, over 1427921.47 frames.], batch size: 31, lr: 4.43e-04
2022-05-27 13:00:41,056 INFO [train.py:842] (2/4) Epoch 12, batch 8500, loss[loss=0.1894, simple_loss=0.277, pruned_loss=0.05095, over 7165.00 frames.], tot_loss[loss=0.2013, simple_loss=0.283, pruned_loss=0.05983, over 1427919.20 frames.], batch size: 19, lr: 4.43e-04
2022-05-27 13:01:19,620 INFO [train.py:842] (2/4) Epoch 12, batch 8550, loss[loss=0.194, simple_loss=0.2592, pruned_loss=0.0644, over 7144.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2832, pruned_loss=0.06013, over 1426295.53 frames.], batch size: 17, lr: 4.43e-04
2022-05-27 13:01:58,552 INFO [train.py:842] (2/4) Epoch 12, batch 8600, loss[loss=0.2134, simple_loss=0.2845, pruned_loss=0.07113, over 7270.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2854, pruned_loss=0.06158, over 1424628.43 frames.], batch size: 18, lr: 4.43e-04
2022-05-27 13:02:36,975 INFO [train.py:842] (2/4) Epoch 12, batch 8650, loss[loss=0.1632, simple_loss=0.2417, pruned_loss=0.0424, over 7131.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2856, pruned_loss=0.06169, over 1420185.84 frames.], batch size: 17, lr: 4.43e-04
2022-05-27 13:03:15,857 INFO [train.py:842] (2/4) Epoch 12, batch 8700, loss[loss=0.2096, simple_loss=0.2938, pruned_loss=0.06269, over 7086.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2853, pruned_loss=0.06127, over 1419826.84 frames.], batch size: 28, lr: 4.43e-04
2022-05-27 13:03:54,279 INFO [train.py:842] (2/4) Epoch 12, batch 8750, loss[loss=0.2531, simple_loss=0.3304, pruned_loss=0.08792, over 5094.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2852, pruned_loss=0.06103, over 1417588.02 frames.], batch size: 57, lr: 4.43e-04
2022-05-27 13:04:33,561 INFO [train.py:842] (2/4) Epoch 12, batch 8800, loss[loss=0.2065, simple_loss=0.2976, pruned_loss=0.05773, over 7167.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2847, pruned_loss=0.0607, over 1414317.73 frames.], batch size: 26, lr: 4.43e-04
2022-05-27 13:05:12,066 INFO [train.py:842] (2/4) Epoch 12, batch 8850, loss[loss=0.187, simple_loss=0.2779, pruned_loss=0.04809, over 7118.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2828, pruned_loss=0.05987, over 1412304.62 frames.], batch size: 21, lr: 4.42e-04
2022-05-27 13:05:50,834 INFO [train.py:842] (2/4) Epoch 12, batch 8900, loss[loss=0.1785, simple_loss=0.2766, pruned_loss=0.04016, over 7283.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2825, pruned_loss=0.06004, over 1410369.53 frames.], batch size: 24, lr: 4.42e-04
2022-05-27 13:06:29,080 INFO [train.py:842] (2/4) Epoch 12, batch 8950, loss[loss=0.2219, simple_loss=0.3031, pruned_loss=0.0704, over 6279.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2813, pruned_loss=0.05982, over 1396736.59 frames.], batch size: 38, lr: 4.42e-04
2022-05-27 13:07:07,563 INFO [train.py:842] (2/4) Epoch 12, batch 9000, loss[loss=0.2711, simple_loss=0.3345, pruned_loss=0.1038, over 5427.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2815, pruned_loss=0.06072, over 1387419.29 frames.], batch size: 52, lr: 4.42e-04
2022-05-27 13:07:07,564 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 13:07:16,709 INFO [train.py:871] (2/4) Epoch 12, validation: loss=0.1688, simple_loss=0.2696, pruned_loss=0.03404, over 868885.00 frames.
2022-05-27 13:07:54,044 INFO [train.py:842] (2/4) Epoch 12, batch 9050, loss[loss=0.1773, simple_loss=0.2663, pruned_loss=0.04418, over 7076.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2821, pruned_loss=0.06138, over 1360825.57 frames.], batch size: 18, lr: 4.42e-04
2022-05-27 13:08:31,794 INFO [train.py:842] (2/4) Epoch 12, batch 9100, loss[loss=0.2251, simple_loss=0.3075, pruned_loss=0.07137, over 4876.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2834, pruned_loss=0.0625, over 1333554.68 frames.], batch size: 52, lr: 4.42e-04
2022-05-27 13:09:08,592 INFO [train.py:842] (2/4) Epoch 12, batch 9150, loss[loss=0.2138, simple_loss=0.3007, pruned_loss=0.06344, over 5175.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2898, pruned_loss=0.06697, over 1261941.42 frames.], batch size: 52, lr: 4.42e-04
2022-05-27 13:09:59,360 INFO [train.py:842] (2/4) Epoch 13, batch 0, loss[loss=0.1919, simple_loss=0.2832, pruned_loss=0.05026, over 7149.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2832, pruned_loss=0.05026, over 7149.00 frames.], batch size: 20, lr: 4.27e-04
2022-05-27 13:10:37,599 INFO [train.py:842] (2/4) Epoch 13, batch 50, loss[loss=0.1939, simple_loss=0.2855, pruned_loss=0.05115, over 7241.00 frames.], tot_loss[loss=0.204, simple_loss=0.2861, pruned_loss=0.061, over 318888.34 frames.], batch size: 20, lr: 4.27e-04
2022-05-27 13:11:15,856 INFO [train.py:842] (2/4) Epoch 13, batch 100, loss[loss=0.2304, simple_loss=0.3145, pruned_loss=0.07313, over 7198.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2861, pruned_loss=0.05974, over 565354.95 frames.], batch size: 23, lr: 4.27e-04
2022-05-27 13:11:53,596 INFO [train.py:842] (2/4) Epoch 13, batch 150, loss[loss=0.1965, simple_loss=0.278, pruned_loss=0.05752, over 7144.00 frames.], tot_loss[loss=0.2028, simple_loss=0.286, pruned_loss=0.05986, over 754422.24 frames.], batch size: 20, lr: 4.27e-04
2022-05-27 13:12:31,958 INFO [train.py:842] (2/4) Epoch 13, batch 200, loss[loss=0.1913, simple_loss=0.2842, pruned_loss=0.04921, over 7144.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2855, pruned_loss=0.06012, over 900546.85 frames.], batch size: 20, lr: 4.27e-04
2022-05-27 13:13:09,996 INFO [train.py:842] (2/4) Epoch 13, batch 250, loss[loss=0.2746, simple_loss=0.3197, pruned_loss=0.1148, over 6738.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2859, pruned_loss=0.06097, over 1013956.85 frames.], batch size: 15, lr: 4.26e-04
2022-05-27 13:13:48,335 INFO [train.py:842] (2/4) Epoch 13, batch 300, loss[loss=0.2294, simple_loss=0.3032, pruned_loss=0.0778, over 7140.00 frames.], tot_loss[loss=0.204, simple_loss=0.2856, pruned_loss=0.06119, over 1103934.11 frames.], batch size: 20, lr: 4.26e-04
2022-05-27 13:14:26,159 INFO [train.py:842] (2/4) Epoch 13, batch 350, loss[loss=0.2235, simple_loss=0.3143, pruned_loss=0.06634, over 7078.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2866, pruned_loss=0.06079, over 1176551.82 frames.], batch size: 28, lr: 4.26e-04
2022-05-27 13:15:04,461 INFO [train.py:842] (2/4) Epoch 13, batch 400, loss[loss=0.1839, simple_loss=0.2627, pruned_loss=0.05259, over 7360.00 frames.], tot_loss[loss=0.202, simple_loss=0.2846, pruned_loss=0.05967, over 1233774.76 frames.], batch size: 19, lr: 4.26e-04
2022-05-27 13:15:42,480 INFO [train.py:842] (2/4) Epoch 13, batch 450, loss[loss=0.1754, simple_loss=0.2745, pruned_loss=0.03815, over 7319.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2842, pruned_loss=0.0595, over 1277530.32 frames.], batch size: 21, lr: 4.26e-04
2022-05-27 13:16:21,020 INFO [train.py:842] (2/4) Epoch 13, batch 500, loss[loss=0.2551, simple_loss=0.3305, pruned_loss=0.0898, over 6416.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2821, pruned_loss=0.05868, over 1311922.73 frames.], batch size: 37, lr: 4.26e-04
2022-05-27 13:16:59,156 INFO [train.py:842] (2/4) Epoch 13, batch 550, loss[loss=0.1907, simple_loss=0.2822, pruned_loss=0.04962, over 7380.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2831, pruned_loss=0.059, over 1334321.74 frames.], batch size: 23, lr: 4.26e-04
2022-05-27 13:17:37,519 INFO [train.py:842] (2/4) Epoch 13, batch 600, loss[loss=0.2066, simple_loss=0.2798, pruned_loss=0.06666, over 7255.00 frames.], tot_loss[loss=0.2021, simple_loss=0.284, pruned_loss=0.06008, over 1348209.61 frames.], batch size: 16, lr: 4.26e-04
2022-05-27 13:18:15,526 INFO [train.py:842] (2/4) Epoch 13, batch 650, loss[loss=0.1607, simple_loss=0.2439, pruned_loss=0.03872, over 7280.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2837, pruned_loss=0.05946, over 1366646.73 frames.], batch size: 18, lr: 4.26e-04
2022-05-27 13:19:03,251 INFO [train.py:842] (2/4) Epoch 13, batch 700, loss[loss=0.2445, simple_loss=0.3118, pruned_loss=0.0886, over 6840.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2845, pruned_loss=0.05961, over 1383772.76 frames.], batch size: 15, lr: 4.26e-04
2022-05-27 13:19:50,633 INFO [train.py:842] (2/4) Epoch 13, batch 750, loss[loss=0.2075, simple_loss=0.302, pruned_loss=0.05644, over 7214.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2836, pruned_loss=0.05866, over 1395664.75 frames.], batch size: 23, lr: 4.25e-04
2022-05-27 13:20:38,506 INFO [train.py:842] (2/4) Epoch 13, batch 800, loss[loss=0.1907, simple_loss=0.2807, pruned_loss=0.05032, over 7204.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2838, pruned_loss=0.05874, over 1404791.42 frames.], batch size: 22, lr: 4.25e-04
2022-05-27 13:21:16,368 INFO [train.py:842] (2/4) Epoch 13, batch 850, loss[loss=0.1883, simple_loss=0.2618, pruned_loss=0.05739, over 7149.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2837, pruned_loss=0.05859, over 1411381.23 frames.], batch size: 17, lr: 4.25e-04
2022-05-27 13:21:54,755 INFO [train.py:842] (2/4) Epoch 13, batch 900, loss[loss=0.2222, simple_loss=0.2994, pruned_loss=0.07254, over 7338.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2825, pruned_loss=0.0584, over 1413987.90 frames.], batch size: 20, lr: 4.25e-04
2022-05-27 13:22:32,526 INFO [train.py:842] (2/4) Epoch 13, batch 950, loss[loss=0.2147, simple_loss=0.2902, pruned_loss=0.06961, over 7170.00 frames.], tot_loss[loss=0.2017, simple_loss=0.284, pruned_loss=0.05975, over 1414114.55 frames.], batch size: 26, lr: 4.25e-04
2022-05-27 13:23:10,781 INFO [train.py:842] (2/4) Epoch 13, batch 1000, loss[loss=0.2028, simple_loss=0.2892, pruned_loss=0.05816, over 6426.00 frames.], tot_loss[loss=0.203, simple_loss=0.2853, pruned_loss=0.06036, over 1414467.17 frames.], batch size: 38, lr: 4.25e-04
2022-05-27 13:23:48,820 INFO [train.py:842] (2/4) Epoch 13, batch 1050, loss[loss=0.1784, simple_loss=0.2642, pruned_loss=0.04629, over 7262.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2852, pruned_loss=0.0602, over 1415489.18 frames.], batch size: 19, lr: 4.25e-04
2022-05-27 13:24:27,307 INFO [train.py:842] (2/4) Epoch 13, batch 1100, loss[loss=0.2203, simple_loss=0.3054, pruned_loss=0.06754, over 7378.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2844, pruned_loss=0.05941, over 1421698.98 frames.], batch size: 23, lr: 4.25e-04
2022-05-27 13:25:05,354 INFO [train.py:842] (2/4) Epoch 13, batch 1150, loss[loss=0.2052, simple_loss=0.2874, pruned_loss=0.06147, over 7316.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2849, pruned_loss=0.06013, over 1424982.70 frames.], batch size: 20, lr: 4.25e-04
2022-05-27 13:25:43,696 INFO [train.py:842] (2/4) Epoch 13, batch 1200, loss[loss=0.241, simple_loss=0.3283, pruned_loss=0.07684, over 5232.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2843, pruned_loss=0.05972, over 1422153.24 frames.], batch size: 53, lr: 4.25e-04
2022-05-27 13:26:21,646 INFO [train.py:842] (2/4) Epoch 13, batch 1250, loss[loss=0.1969, simple_loss=0.2757, pruned_loss=0.05905, over 7152.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2842, pruned_loss=0.05936, over 1419887.42 frames.], batch size: 19, lr: 4.25e-04
2022-05-27 13:27:00,085 INFO [train.py:842] (2/4) Epoch 13, batch 1300, loss[loss=0.1759, simple_loss=0.258, pruned_loss=0.04687, over 7060.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2828, pruned_loss=0.05914, over 1421225.63 frames.], batch size: 18, lr: 4.24e-04
2022-05-27 13:27:37,825 INFO [train.py:842] (2/4) Epoch 13, batch 1350, loss[loss=0.2874, simple_loss=0.3531, pruned_loss=0.1109, over 4983.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2848, pruned_loss=0.05994, over 1418244.63 frames.], batch size: 52, lr: 4.24e-04
2022-05-27 13:28:15,968 INFO [train.py:842] (2/4) Epoch 13, batch 1400, loss[loss=0.1806, simple_loss=0.2653, pruned_loss=0.04792, over 7304.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2849, pruned_loss=0.06, over 1417404.93 frames.], batch size: 25, lr: 4.24e-04
2022-05-27 13:28:53,714 INFO [train.py:842] (2/4) Epoch 13, batch 1450, loss[loss=0.1864, simple_loss=0.2802, pruned_loss=0.04628, over 7324.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2841, pruned_loss=0.05909, over 1416254.84 frames.], batch size: 21, lr: 4.24e-04
2022-05-27 13:29:31,932 INFO [train.py:842] (2/4) Epoch 13, batch 1500, loss[loss=0.2299, simple_loss=0.3134, pruned_loss=0.07324, over 7193.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2838, pruned_loss=0.05921, over 1419164.77 frames.], batch size: 23, lr: 4.24e-04
2022-05-27 13:30:10,027 INFO [train.py:842] (2/4) Epoch 13, batch 1550, loss[loss=0.1851, simple_loss=0.2781, pruned_loss=0.04609, over 7066.00 frames.], tot_loss[loss=0.202, simple_loss=0.2843, pruned_loss=0.0598, over 1420792.66 frames.], batch size: 28, lr: 4.24e-04
2022-05-27 13:30:48,256 INFO [train.py:842] (2/4) Epoch 13, batch 1600, loss[loss=0.2139, simple_loss=0.2973, pruned_loss=0.06521, over 7311.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2847, pruned_loss=0.05947, over 1420908.48 frames.], batch size: 25, lr: 4.24e-04
2022-05-27 13:31:26,204 INFO [train.py:842] (2/4) Epoch 13, batch 1650, loss[loss=0.2286, simple_loss=0.3149, pruned_loss=0.07116, over 7286.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2843, pruned_loss=0.05959, over 1423656.74 frames.], batch size: 24, lr: 4.24e-04
2022-05-27 13:32:07,091 INFO [train.py:842] (2/4) Epoch 13, batch 1700, loss[loss=0.172, simple_loss=0.2476, pruned_loss=0.04825, over 7131.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2847, pruned_loss=0.06021, over 1418886.85 frames.], batch size: 17, lr: 4.24e-04
2022-05-27 13:32:45,285 INFO [train.py:842] (2/4) Epoch 13, batch 1750, loss[loss=0.2313, simple_loss=0.316, pruned_loss=0.07332, over 7211.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2826, pruned_loss=0.05923, over 1423370.39 frames.], batch size: 26, lr: 4.24e-04
2022-05-27 13:33:23,697 INFO [train.py:842] (2/4) Epoch 13, batch 1800, loss[loss=0.1959, simple_loss=0.2729, pruned_loss=0.05942, over 6988.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2816, pruned_loss=0.05858, over 1428310.97 frames.], batch size: 16, lr: 4.23e-04
2022-05-27 13:34:01,769 INFO [train.py:842] (2/4) Epoch 13, batch 1850, loss[loss=0.1889, simple_loss=0.2782, pruned_loss=0.04984, over 7346.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2824, pruned_loss=0.05901, over 1428644.44 frames.], batch size: 22, lr: 4.23e-04
2022-05-27 13:34:40,131 INFO [train.py:842] (2/4) Epoch 13, batch 1900, loss[loss=0.2801, simple_loss=0.3459, pruned_loss=0.1072, over 7234.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2841, pruned_loss=0.06009, over 1429038.93 frames.], batch size: 20, lr: 4.23e-04
2022-05-27 13:35:17,977 INFO [train.py:842] (2/4) Epoch 13, batch 1950, loss[loss=0.1637, simple_loss=0.2425, pruned_loss=0.04241, over 7278.00 frames.], tot_loss[loss=0.202, simple_loss=0.2841, pruned_loss=0.05991, over 1428639.68 frames.], batch size: 17, lr: 4.23e-04
2022-05-27 13:35:56,454 INFO [train.py:842] (2/4) Epoch 13, batch 2000, loss[loss=0.1494, simple_loss=0.2317, pruned_loss=0.03357, over 7004.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2825, pruned_loss=0.05891, over 1427548.14 frames.], batch size: 16, lr: 4.23e-04
2022-05-27 13:36:34,501 INFO [train.py:842] (2/4) Epoch 13, batch 2050, loss[loss=0.1468, simple_loss=0.2387, pruned_loss=0.02745, over 7159.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2822, pruned_loss=0.05882, over 1420997.78 frames.], batch size: 19, lr: 4.23e-04
2022-05-27 13:37:12,810 INFO [train.py:842] (2/4) Epoch 13, batch 2100, loss[loss=0.1675, simple_loss=0.2595, pruned_loss=0.03776, over 7163.00 frames.], tot_loss[loss=0.1997, simple_loss=0.282, pruned_loss=0.05872, over 1421131.44 frames.], batch size: 19, lr: 4.23e-04
2022-05-27 13:37:50,697 INFO [train.py:842] (2/4) Epoch 13, batch 2150, loss[loss=0.1782, simple_loss=0.2592, pruned_loss=0.04862, over 7282.00 frames.], tot_loss[loss=0.2, simple_loss=0.2826, pruned_loss=0.05872, over 1421651.40 frames.], batch size: 18, lr: 4.23e-04
2022-05-27 13:38:28,987 INFO [train.py:842] (2/4) Epoch 13, batch 2200, loss[loss=0.1641, simple_loss=0.253, pruned_loss=0.03755, over 7329.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2811, pruned_loss=0.05804, over 1422381.24 frames.], batch size: 20, lr: 4.23e-04
2022-05-27 13:39:06,919 INFO [train.py:842] (2/4) Epoch 13, batch 2250, loss[loss=0.2032, simple_loss=0.2911, pruned_loss=0.05766, over 7044.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2826, pruned_loss=0.05918, over 1421076.13 frames.], batch size: 28, lr: 4.23e-04
2022-05-27 13:39:45,191 INFO [train.py:842] (2/4) Epoch 13, batch 2300, loss[loss=0.2141, simple_loss=0.3012, pruned_loss=0.06351, over 7120.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2845, pruned_loss=0.06006, over 1424443.91 frames.], batch size: 21, lr: 4.23e-04
2022-05-27 13:40:23,242 INFO [train.py:842] (2/4) Epoch 13, batch 2350, loss[loss=0.2099, simple_loss=0.298, pruned_loss=0.06092, over 7161.00 frames.], tot_loss[loss=0.201, simple_loss=0.2836, pruned_loss=0.0592, over 1425533.11 frames.], batch size: 19, lr: 4.22e-04
2022-05-27 13:41:01,578 INFO [train.py:842] (2/4) Epoch 13, batch 2400, loss[loss=0.1643, simple_loss=0.2429, pruned_loss=0.04286, over 7119.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2829, pruned_loss=0.05824, over 1426409.73 frames.], batch size: 17, lr: 4.22e-04
2022-05-27 13:41:39,475 INFO [train.py:842] (2/4) Epoch 13, batch 2450, loss[loss=0.2503, simple_loss=0.3306, pruned_loss=0.085, over 7232.00 frames.], tot_loss[loss=0.1997, simple_loss=0.283, pruned_loss=0.05813, over 1425860.48 frames.], batch size: 21, lr: 4.22e-04
2022-05-27 13:42:17,599 INFO [train.py:842] (2/4) Epoch 13, batch 2500, loss[loss=0.1753, simple_loss=0.2547, pruned_loss=0.04794, over 7269.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2837, pruned_loss=0.05849, over 1427019.70 frames.], batch size: 18, lr: 4.22e-04
2022-05-27 13:42:55,547 INFO [train.py:842] (2/4) Epoch 13, batch 2550, loss[loss=0.2232, simple_loss=0.2823, pruned_loss=0.08209, over 6788.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2823, pruned_loss=0.05767, over 1428008.77 frames.], batch size: 15, lr: 4.22e-04
2022-05-27 13:43:33,891 INFO [train.py:842] (2/4) Epoch 13, batch 2600, loss[loss=0.1818, simple_loss=0.2641, pruned_loss=0.04972, over 7260.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2819, pruned_loss=0.05798, over 1424072.45 frames.], batch size: 16, lr: 4.22e-04
2022-05-27 13:44:11,771 INFO [train.py:842] (2/4) Epoch 13, batch 2650, loss[loss=0.2276, simple_loss=0.2955, pruned_loss=0.07979, over 7014.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2833, pruned_loss=0.0589, over 1421929.61 frames.], batch size: 16, lr: 4.22e-04
2022-05-27 13:44:50,113 INFO [train.py:842] (2/4) Epoch 13, batch 2700, loss[loss=0.1888, simple_loss=0.2573, pruned_loss=0.06018, over 6995.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2835, pruned_loss=0.05914, over 1422877.77 frames.], batch size: 16, lr: 4.22e-04
2022-05-27 13:45:27,939 INFO [train.py:842] (2/4) Epoch 13, batch 2750, loss[loss=0.215, simple_loss=0.2972, pruned_loss=0.06642, over 7125.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2837, pruned_loss=0.05979, over 1420680.27 frames.], batch size: 21, lr: 4.22e-04
2022-05-27 13:46:05,889 INFO [train.py:842] (2/4) Epoch 13, batch 2800, loss[loss=0.1691, simple_loss=0.2439, pruned_loss=0.04716, over 7130.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2834, pruned_loss=0.05918, over 1421524.26 frames.], batch size: 17, lr: 4.22e-04
2022-05-27 13:46:44,097 INFO [train.py:842] (2/4) Epoch 13, batch 2850, loss[loss=0.1938, simple_loss=0.272, pruned_loss=0.0578, over 7404.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2833, pruned_loss=0.05862, over 1427381.41 frames.], batch size: 23, lr: 4.22e-04
2022-05-27 13:47:22,064 INFO [train.py:842] (2/4) Epoch 13, batch 2900, loss[loss=0.1778, simple_loss=0.2571, pruned_loss=0.04921, over 7352.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2831, pruned_loss=0.05829, over 1425081.07 frames.], batch size: 19, lr: 4.21e-04
2022-05-27 13:48:00,127 INFO [train.py:842] (2/4) Epoch 13, batch 2950, loss[loss=0.2082, simple_loss=0.295, pruned_loss=0.0607, over 7127.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2818, pruned_loss=0.05771, over 1426685.15 frames.], batch size: 21, lr: 4.21e-04
2022-05-27 13:48:38,457 INFO [train.py:842] (2/4) Epoch 13, batch 3000, loss[loss=0.2261, simple_loss=0.2921, pruned_loss=0.08005, over 7288.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2835, pruned_loss=0.05938, over 1427096.33 frames.], batch size: 17, lr: 4.21e-04
2022-05-27 13:48:38,458 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 13:48:47,488 INFO [train.py:871] (2/4) Epoch 13, validation: loss=0.1706, simple_loss=0.2707, pruned_loss=0.03524, over 868885.00 frames.
2022-05-27 13:49:25,647 INFO [train.py:842] (2/4) Epoch 13, batch 3050, loss[loss=0.1762, simple_loss=0.2479, pruned_loss=0.05226, over 7144.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2825, pruned_loss=0.05895, over 1428216.02 frames.], batch size: 17, lr: 4.21e-04
2022-05-27 13:50:04,000 INFO [train.py:842] (2/4) Epoch 13, batch 3100, loss[loss=0.2088, simple_loss=0.2862, pruned_loss=0.06564, over 7111.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2823, pruned_loss=0.05874, over 1426736.64 frames.], batch size: 21, lr: 4.21e-04
2022-05-27 13:50:41,763 INFO [train.py:842] (2/4) Epoch 13, batch 3150, loss[loss=0.1952, simple_loss=0.2908, pruned_loss=0.04975, over 7311.00 frames.], tot_loss[loss=0.2026, simple_loss=0.285, pruned_loss=0.06016, over 1424059.57 frames.], batch size: 25, lr: 4.21e-04
2022-05-27 13:51:19,883 INFO [train.py:842] (2/4) Epoch 13, batch 3200, loss[loss=0.2411, simple_loss=0.3141, pruned_loss=0.08403, over 5291.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2845, pruned_loss=0.05989, over 1425432.00 frames.], batch size: 52, lr: 4.21e-04
2022-05-27 13:51:58,060 INFO [train.py:842] (2/4) Epoch 13, batch 3250, loss[loss=0.1502, simple_loss=0.2229, pruned_loss=0.0388, over 7280.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2839, pruned_loss=0.0596, over 1427453.98 frames.], batch size: 17, lr: 4.21e-04
2022-05-27 13:52:36,362 INFO [train.py:842] (2/4) Epoch 13, batch 3300, loss[loss=0.2269, simple_loss=0.3009, pruned_loss=0.07647, over 7325.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2836, pruned_loss=0.05943, over 1427146.47 frames.], batch size: 20, lr: 4.21e-04
2022-05-27 13:53:14,261 INFO [train.py:842] (2/4) Epoch 13, batch 3350, loss[loss=0.1536, simple_loss=0.2302, pruned_loss=0.03856, over 7010.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2833, pruned_loss=0.05881, over 1420422.20 frames.], batch size: 16, lr: 4.21e-04
2022-05-27 13:53:52,551 INFO [train.py:842] (2/4) Epoch 13, batch 3400, loss[loss=0.2937, simple_loss=0.35, pruned_loss=0.1187, over 7383.00 frames.], tot_loss[loss=0.202, simple_loss=0.2847, pruned_loss=0.05961, over 1424824.87 frames.], batch size: 23, lr: 4.20e-04
2022-05-27 13:54:30,243 INFO [train.py:842] (2/4) Epoch 13, batch 3450, loss[loss=0.1747, simple_loss=0.249, pruned_loss=0.05017, over 7409.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2851, pruned_loss=0.06024, over 1412718.39 frames.], batch size: 18, lr: 4.20e-04
2022-05-27 13:55:08,614 INFO [train.py:842] (2/4) Epoch 13, batch 3500, loss[loss=0.2047, simple_loss=0.2904, pruned_loss=0.05951, over 6817.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2855, pruned_loss=0.05997, over 1415008.14 frames.], batch size: 31, lr: 4.20e-04
2022-05-27 13:55:47,221 INFO [train.py:842] (2/4) Epoch 13, batch 3550, loss[loss=0.1737, simple_loss=0.2495, pruned_loss=0.04899, over 6984.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2862, pruned_loss=0.06075, over 1420881.16 frames.], batch size: 16, lr: 4.20e-04
2022-05-27 13:56:25,868 INFO [train.py:842] (2/4) Epoch 13, batch 3600, loss[loss=0.1696, simple_loss=0.2516, pruned_loss=0.04379, over 7270.00 frames.], tot_loss[loss=0.204, simple_loss=0.2865, pruned_loss=0.06069, over 1421393.04 frames.], batch size: 18, lr: 4.20e-04
2022-05-27 13:57:04,081 INFO [train.py:842] (2/4) Epoch 13, batch 3650, loss[loss=0.1634, simple_loss=0.2633, pruned_loss=0.03179, over 7420.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2868, pruned_loss=0.06072, over 1423928.39 frames.], batch size: 21, lr: 4.20e-04
2022-05-27 13:57:43,033 INFO [train.py:842] (2/4) Epoch 13, batch 3700, loss[loss=0.1849, simple_loss=0.2672, pruned_loss=0.05128, over 7252.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2844, pruned_loss=0.05967, over 1425203.55 frames.], batch size: 19, lr: 4.20e-04
2022-05-27 13:58:21,603 INFO [train.py:842] (2/4) Epoch 13, batch 3750, loss[loss=0.198, simple_loss=0.2927, pruned_loss=0.0516, over 7406.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2843, pruned_loss=0.0594, over 1424856.17 frames.], batch size: 21, lr: 4.20e-04
2022-05-27 13:59:00,728 INFO [train.py:842] (2/4) Epoch 13, batch 3800, loss[loss=0.2182, simple_loss=0.3081, pruned_loss=0.06415, over 7071.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2846, pruned_loss=0.05976, over 1428751.81 frames.], batch size: 28, lr: 4.20e-04
2022-05-27 13:59:39,267 INFO [train.py:842] (2/4) Epoch 13, batch 3850, loss[loss=0.2125, simple_loss=0.2941, pruned_loss=0.06543, over 7209.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2852, pruned_loss=0.0599, over 1426533.49 frames.], batch size: 22, lr: 4.20e-04
2022-05-27 14:00:18,622 INFO [train.py:842] (2/4) Epoch 13, batch 3900, loss[loss=0.1979, simple_loss=0.2872, pruned_loss=0.0543, over 7122.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2835, pruned_loss=0.05908, over 1426110.45 frames.], batch size: 28, lr: 4.20e-04
2022-05-27 14:00:57,293 INFO [train.py:842] (2/4) Epoch 13, batch 3950, loss[loss=0.1789, simple_loss=0.2538, pruned_loss=0.05204, over 6776.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2848, pruned_loss=0.05974, over 1425345.73 frames.], batch size: 15, lr: 4.19e-04
2022-05-27 14:01:36,257 INFO [train.py:842] (2/4) Epoch 13, batch 4000, loss[loss=0.1838, simple_loss=0.2643, pruned_loss=0.05162, over 7104.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2843, pruned_loss=0.05958, over 1425099.48 frames.], batch size: 28, lr: 4.19e-04
2022-05-27 14:02:15,239 INFO [train.py:842] (2/4) Epoch 13, batch 4050, loss[loss=0.2078, simple_loss=0.2954, pruned_loss=0.06009, over 7194.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2834, pruned_loss=0.05903, over 1429127.75 frames.], batch size: 22, lr: 4.19e-04
2022-05-27 14:02:54,493 INFO [train.py:842] (2/4) Epoch 13, batch 4100, loss[loss=0.1603, simple_loss=0.2527, pruned_loss=0.03395, over 7151.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2821, pruned_loss=0.05841, over 1430279.58 frames.], batch size: 19, lr: 4.19e-04
2022-05-27 14:03:33,664 INFO [train.py:842] (2/4) Epoch 13, batch 4150, loss[loss=0.1577, simple_loss=0.2377, pruned_loss=0.03883, over 6997.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2833, pruned_loss=0.0592, over 1430141.47 frames.], batch size: 16, lr: 4.19e-04
2022-05-27 14:04:12,671 INFO [train.py:842] (2/4) Epoch 13, batch 4200, loss[loss=0.2185, simple_loss=0.3023, pruned_loss=0.06733, over 6466.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2847, pruned_loss=0.06046, over 1424216.88 frames.], batch size: 38, lr: 4.19e-04
2022-05-27 14:04:51,217 INFO [train.py:842] (2/4) Epoch 13, batch 4250, loss[loss=0.2201, simple_loss=0.3011, pruned_loss=0.0695, over 7427.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2841, pruned_loss=0.05962, over 1427212.67 frames.], batch size: 20, lr: 4.19e-04
2022-05-27 14:05:30,464 INFO [train.py:842] (2/4) Epoch 13, batch 4300, loss[loss=0.167, simple_loss=0.2458, pruned_loss=0.04409, over 6814.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2848, pruned_loss=0.06013, over 1424130.53 frames.], batch size: 15, lr: 4.19e-04
2022-05-27 14:06:09,107 INFO [train.py:842] (2/4) Epoch 13, batch 4350, loss[loss=0.2601, simple_loss=0.3292, pruned_loss=0.09556, over 4786.00 frames.], tot_loss[loss=0.201, simple_loss=0.2834, pruned_loss=0.05928, over 1424935.74 frames.], batch size: 52, lr: 4.19e-04
2022-05-27 14:06:48,110 INFO [train.py:842] (2/4) Epoch 13, batch 4400, loss[loss=0.1789, simple_loss=0.2512, pruned_loss=0.05326, over 7142.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2839, pruned_loss=0.0596, over 1424545.36 frames.], batch size: 17, lr: 4.19e-04
2022-05-27 14:07:27,294 INFO [train.py:842] (2/4) Epoch 13, batch 4450, loss[loss=0.1808, simple_loss=0.26, pruned_loss=0.05075, over 7298.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2816, pruned_loss=0.05836, over 1429441.63 frames.], batch size: 17, lr: 4.19e-04
2022-05-27 14:08:06,629 INFO [train.py:842] (2/4) Epoch 13, batch 4500, loss[loss=0.208, simple_loss=0.2856, pruned_loss=0.06517, over 7233.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2819, pruned_loss=0.05852, over 1428310.79 frames.], batch size: 20, lr: 4.18e-04
2022-05-27 14:08:45,554 INFO [train.py:842] (2/4) Epoch 13, batch 4550, loss[loss=0.2366, simple_loss=0.3317, pruned_loss=0.07077, over 7091.00 frames.], tot_loss[loss=0.1995, simple_loss=0.282, pruned_loss=0.05852, over 1426849.37 frames.], batch size: 28, lr: 4.18e-04
2022-05-27 14:09:25,122 INFO [train.py:842] (2/4) Epoch 13, batch 4600, loss[loss=0.2101, simple_loss=0.2968, pruned_loss=0.06169, over 7142.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2817, pruned_loss=0.05837, over 1423830.15 frames.], batch size: 20, lr: 4.18e-04
2022-05-27 14:10:04,089 INFO [train.py:842] (2/4) Epoch 13, batch 4650, loss[loss=0.2688, simple_loss=0.3278, pruned_loss=0.1049, over 7061.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2821, pruned_loss=0.05904, over 1422752.24 frames.], batch size: 18, lr: 4.18e-04
2022-05-27 14:10:43,275 INFO [train.py:842] (2/4) Epoch 13, batch 4700, loss[loss=0.1954, simple_loss=0.2866, pruned_loss=0.05211, over 6795.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2815, pruned_loss=0.05821, over 1426717.31 frames.], batch size: 31, lr: 4.18e-04
2022-05-27 14:11:22,087 INFO [train.py:842] (2/4) Epoch 13, batch 4750, loss[loss=0.195, simple_loss=0.2788, pruned_loss=0.05558, over 7204.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2836, pruned_loss=0.05943, over 1423382.98 frames.], batch size: 22, lr: 4.18e-04
2022-05-27 14:12:01,227 INFO [train.py:842] (2/4) Epoch 13, batch 4800, loss[loss=0.2006, simple_loss=0.2976, pruned_loss=0.05178, over 7174.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2835, pruned_loss=0.05954, over 1418310.81 frames.], batch size: 26, lr: 4.18e-04
2022-05-27 14:12:40,051 INFO [train.py:842] (2/4) Epoch 13, batch 4850, loss[loss=0.2309, simple_loss=0.3183, pruned_loss=0.07172, over 7152.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2838, pruned_loss=0.05998, over 1412809.72 frames.], batch size: 20, lr: 4.18e-04
2022-05-27 14:13:19,443 INFO [train.py:842] (2/4) Epoch 13, batch 4900, loss[loss=0.166, simple_loss=0.2448, pruned_loss=0.04365, over 7266.00 frames.], tot_loss[loss=0.202, simple_loss=0.2837, pruned_loss=0.06011, over 1410957.28 frames.], batch size: 18, lr: 4.18e-04
2022-05-27 14:13:58,359 INFO [train.py:842] (2/4) Epoch 13, batch 4950, loss[loss=0.1771, simple_loss=0.271, pruned_loss=0.04163, over 7233.00 frames.], tot_loss[loss=0.2022, simple_loss=0.284, pruned_loss=0.06023, over 1408062.24 frames.], batch size: 20, lr: 4.18e-04
2022-05-27 14:14:37,845 INFO [train.py:842] (2/4) Epoch 13, batch 5000, loss[loss=0.2042, simple_loss=0.2839, pruned_loss=0.06224, over 7189.00 frames.], tot_loss[loss=0.202, simple_loss=0.2834, pruned_loss=0.0603, over 1409904.87 frames.], batch size: 23, lr: 4.18e-04
2022-05-27 14:15:16,585 INFO [train.py:842] (2/4) Epoch 13, batch 5050, loss[loss=0.1547, simple_loss=0.2311, pruned_loss=0.03914, over 7261.00 frames.], tot_loss[loss=0.2013, simple_loss=0.283, pruned_loss=0.05983, over 1411096.84 frames.], batch size: 17, lr: 4.17e-04
2022-05-27 14:15:55,750 INFO [train.py:842] (2/4) Epoch 13, batch 5100, loss[loss=0.1711, simple_loss=0.2558, pruned_loss=0.04323, over 7261.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2829, pruned_loss=0.05921, over 1414916.57 frames.], batch size: 19, lr: 4.17e-04
2022-05-27 14:16:34,507 INFO [train.py:842] (2/4) Epoch 13, batch 5150, loss[loss=0.1817, simple_loss=0.2843, pruned_loss=0.03959, over 7321.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2854, pruned_loss=0.06007, over 1415311.98 frames.], batch size: 24, lr: 4.17e-04
2022-05-27 14:17:13,828 INFO [train.py:842] (2/4) Epoch 13, batch 5200, loss[loss=0.2164, simple_loss=0.2978, pruned_loss=0.06745, over 7068.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2849, pruned_loss=0.05991, over 1416728.01 frames.], batch size: 28, lr: 4.17e-04
2022-05-27 14:17:52,721 INFO [train.py:842] (2/4) Epoch 13, batch 5250, loss[loss=0.2011, simple_loss=0.2907, pruned_loss=0.0557, over 7195.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2851, pruned_loss=0.05997, over 1419011.61 frames.], batch size: 23, lr: 4.17e-04
2022-05-27 14:18:31,885 INFO [train.py:842] (2/4) Epoch 13, batch 5300, loss[loss=0.1923, simple_loss=0.2778, pruned_loss=0.05339, over 7245.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2857, pruned_loss=0.06035, over 1424606.32 frames.], batch size: 20, lr: 4.17e-04
2022-05-27 14:19:11,034 INFO [train.py:842] (2/4) Epoch 13, batch 5350, loss[loss=0.175, simple_loss=0.2637, pruned_loss=0.04311, over 7145.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2847, pruned_loss=0.05942, over 1428502.55 frames.], batch size: 17, lr: 4.17e-04
2022-05-27 14:19:50,001 INFO [train.py:842] (2/4) Epoch 13, batch 5400, loss[loss=0.2297, simple_loss=0.2916, pruned_loss=0.08389, over 7118.00 frames.], tot_loss[loss=0.203, simple_loss=0.2855, pruned_loss=0.0603, over 1426362.98 frames.], batch size: 17, lr: 4.17e-04
2022-05-27 14:20:28,969 INFO [train.py:842] (2/4) Epoch 13, batch 5450, loss[loss=0.1921, simple_loss=0.2713, pruned_loss=0.05647, over 6782.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2856, pruned_loss=0.06051, over 1424591.35 frames.], batch size: 15, lr: 4.17e-04
2022-05-27 14:21:08,149 INFO [train.py:842] (2/4) Epoch 13, batch 5500, loss[loss=0.2003, simple_loss=0.2743, pruned_loss=0.0632, over 7278.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2855, pruned_loss=0.06094, over 1422764.11 frames.], batch size: 17, lr: 4.17e-04
2022-05-27 14:21:46,675 INFO [train.py:842] (2/4) Epoch 13, batch 5550, loss[loss=0.2232, simple_loss=0.2963, pruned_loss=0.07508, over 7353.00 frames.], tot_loss[loss=0.2025, simple_loss=0.285, pruned_loss=0.06001, over 1425568.28 frames.], batch size: 19, lr: 4.17e-04
2022-05-27 14:22:26,138 INFO [train.py:842] (2/4) Epoch 13, batch 5600, loss[loss=0.2073, simple_loss=0.2834, pruned_loss=0.06562, over 7332.00 frames.], tot_loss[loss=0.2006, simple_loss=0.283, pruned_loss=0.05913, over 1425222.13 frames.], batch size: 20, lr: 4.16e-04
2022-05-27 14:23:05,042 INFO [train.py:842] (2/4) Epoch 13, batch 5650, loss[loss=0.1951, simple_loss=0.2814, pruned_loss=0.05437, over 7342.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2834, pruned_loss=0.05904, over 1424273.67 frames.], batch size: 20, lr: 4.16e-04
2022-05-27 14:23:43,966 INFO [train.py:842] (2/4) Epoch 13, batch 5700, loss[loss=0.1789, simple_loss=0.2692, pruned_loss=0.04431, over 7353.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2835, pruned_loss=0.0588, over 1421239.81 frames.], batch size: 19, lr: 4.16e-04
2022-05-27 14:24:22,946 INFO [train.py:842] (2/4) Epoch 13, batch 5750, loss[loss=0.1981, simple_loss=0.2794, pruned_loss=0.05839, over 7231.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2846, pruned_loss=0.05991, over 1423311.87 frames.], batch size: 21, lr: 4.16e-04
2022-05-27 14:25:02,242 INFO [train.py:842] (2/4) Epoch 13, batch 5800, loss[loss=0.1994, simple_loss=0.2848, pruned_loss=0.05696, over 7233.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2841, pruned_loss=0.05959, over 1421632.65 frames.], batch size: 20, lr: 4.16e-04
2022-05-27 14:25:41,204 INFO [train.py:842] (2/4) Epoch 13, batch 5850, loss[loss=0.1606, simple_loss=0.256, pruned_loss=0.03258, over 7356.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2839, pruned_loss=0.05953, over 1425287.66 frames.], batch size: 19, lr: 4.16e-04
2022-05-27 14:26:20,586 INFO [train.py:842] (2/4) Epoch 13, batch 5900, loss[loss=0.2221, simple_loss=0.2981, pruned_loss=0.0731, over 7141.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2832, pruned_loss=0.05949, over 1425312.60 frames.], batch size: 20, lr: 4.16e-04
2022-05-27 14:26:59,224 INFO [train.py:842] (2/4) Epoch 13, batch 5950, loss[loss=0.2169, simple_loss=0.299, pruned_loss=0.06747, over 7216.00 frames.], tot_loss[loss=0.202, simple_loss=0.2843, pruned_loss=0.05984, over 1425795.74 frames.], batch size: 23, lr: 4.16e-04
2022-05-27 14:27:38,223 INFO [train.py:842] (2/4) Epoch 13, batch 6000, loss[loss=0.1604, simple_loss=0.2416, pruned_loss=0.03958, over 7374.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2832, pruned_loss=0.059, over 1423440.74 frames.], batch size: 19, lr: 4.16e-04
2022-05-27 14:27:38,223 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 14:27:48,003 INFO [train.py:871] (2/4) Epoch 13, validation: loss=0.1712, simple_loss=0.2713, pruned_loss=0.03553, over 868885.00 frames.
2022-05-27 14:28:27,125 INFO [train.py:842] (2/4) Epoch 13, batch 6050, loss[loss=0.1941, simple_loss=0.2721, pruned_loss=0.05808, over 7073.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2838, pruned_loss=0.05973, over 1421490.02 frames.], batch size: 18, lr: 4.16e-04
2022-05-27 14:29:06,291 INFO [train.py:842] (2/4) Epoch 13, batch 6100, loss[loss=0.1971, simple_loss=0.2866, pruned_loss=0.05378, over 7115.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2833, pruned_loss=0.05957, over 1425957.00 frames.], batch size: 21, lr: 4.16e-04
2022-05-27 14:29:45,155 INFO [train.py:842] (2/4) Epoch 13, batch 6150, loss[loss=0.1716, simple_loss=0.267, pruned_loss=0.03813, over 7231.00 frames.], tot_loss[loss=0.2006, simple_loss=0.283, pruned_loss=0.05909, over 1422119.36 frames.], batch size: 20, lr: 4.16e-04
2022-05-27 14:30:23,982 INFO [train.py:842] (2/4) Epoch 13, batch 6200, loss[loss=0.1971, simple_loss=0.2907, pruned_loss=0.0517, over 7195.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2826, pruned_loss=0.05829, over 1425124.86 frames.], batch size: 23, lr: 4.15e-04
2022-05-27 14:31:03,017 INFO [train.py:842] (2/4) Epoch 13, batch 6250, loss[loss=0.1546, simple_loss=0.2424, pruned_loss=0.03335, over 6804.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2817, pruned_loss=0.05764, over 1423593.68 frames.], batch size: 15, lr: 4.15e-04
2022-05-27 14:31:41,859 INFO [train.py:842] (2/4) Epoch 13, batch 6300, loss[loss=0.2066, simple_loss=0.2909, pruned_loss=0.06116, over 6435.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2825, pruned_loss=0.05845, over 1422266.32 frames.], batch size: 38, lr: 4.15e-04
2022-05-27 14:32:20,540 INFO [train.py:842] (2/4) Epoch 13, batch 6350, loss[loss=0.2586, simple_loss=0.3343, pruned_loss=0.09149, over 5245.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2817, pruned_loss=0.05759, over 1419228.45 frames.], batch size: 53, lr: 4.15e-04
2022-05-27 14:32:59,579 INFO [train.py:842] (2/4) Epoch 13, batch 6400, loss[loss=0.1766, simple_loss=0.2622, pruned_loss=0.04553, over 7075.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2818, pruned_loss=0.05792, over 1418810.65 frames.], batch size: 18, lr: 4.15e-04
2022-05-27 14:33:38,320 INFO [train.py:842] (2/4) Epoch 13, batch 6450, loss[loss=0.2763, simple_loss=0.3481, pruned_loss=0.1023, over 7143.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2825, pruned_loss=0.05895, over 1420012.41 frames.], batch size: 26, lr: 4.15e-04
2022-05-27 14:34:17,568 INFO [train.py:842] (2/4) Epoch 13, batch 6500, loss[loss=0.1587, simple_loss=0.2424, pruned_loss=0.03753, over 7136.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2831, pruned_loss=0.05976, over 1418508.69 frames.], batch size: 17, lr: 4.15e-04
2022-05-27 14:34:56,665 INFO [train.py:842] (2/4) Epoch 13, batch 6550, loss[loss=0.2075, simple_loss=0.2755, pruned_loss=0.06974, over 7286.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2822, pruned_loss=0.05962, over 1419622.99 frames.], batch size: 18, lr: 4.15e-04
2022-05-27 14:35:36,246 INFO [train.py:842] (2/4) Epoch 13, batch 6600, loss[loss=0.2346, simple_loss=0.3172, pruned_loss=0.076, over 7151.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2813, pruned_loss=0.05844, over 1422117.27 frames.], batch size: 26, lr: 4.15e-04
2022-05-27 14:36:15,581 INFO [train.py:842] (2/4) Epoch 13, batch 6650, loss[loss=0.2356, simple_loss=0.3122, pruned_loss=0.07945, over 7056.00 frames.], tot_loss[loss=0.199, simple_loss=0.2816, pruned_loss=0.05821, over 1424521.82 frames.], batch size: 28, lr: 4.15e-04
2022-05-27 14:36:54,854 INFO [train.py:842] (2/4) Epoch 13, batch 6700, loss[loss=0.219, simple_loss=0.3094, pruned_loss=0.06426, over 7237.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2831, pruned_loss=0.05953, over 1422196.74 frames.], batch size: 20, lr: 4.15e-04
2022-05-27 14:37:33,725 INFO [train.py:842] (2/4) Epoch 13, batch 6750, loss[loss=0.1883, simple_loss=0.2799, pruned_loss=0.04838, over 7412.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2816, pruned_loss=0.05849, over 1423913.58 frames.], batch size: 21, lr: 4.14e-04
2022-05-27 14:38:12,756 INFO [train.py:842] (2/4) Epoch 13, batch 6800, loss[loss=0.1835, simple_loss=0.2651, pruned_loss=0.05094, over 7411.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2819, pruned_loss=0.05809, over 1425145.63 frames.], batch size: 18, lr: 4.14e-04
2022-05-27 14:38:51,491 INFO [train.py:842] (2/4) Epoch 13, batch 6850, loss[loss=0.2282, simple_loss=0.3121, pruned_loss=0.07214, over 7369.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2808, pruned_loss=0.05783, over 1422929.41 frames.], batch size: 23, lr: 4.14e-04
2022-05-27 14:39:30,662 INFO [train.py:842] (2/4) Epoch 13, batch 6900, loss[loss=0.1833, simple_loss=0.2586, pruned_loss=0.05397, over 7433.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2816, pruned_loss=0.05813, over 1422410.62 frames.], batch size: 20, lr: 4.14e-04
2022-05-27 14:40:09,779 INFO [train.py:842] (2/4) Epoch 13, batch 6950, loss[loss=0.2305, simple_loss=0.3181, pruned_loss=0.07146, over 7152.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2823, pruned_loss=0.05862, over 1422834.41 frames.], batch size: 20, lr: 4.14e-04
2022-05-27 14:40:48,921 INFO [train.py:842] (2/4) Epoch 13, batch 7000, loss[loss=0.1669, simple_loss=0.2477, pruned_loss=0.0431, over 7361.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2832, pruned_loss=0.05907, over 1421120.37 frames.], batch size: 19, lr: 4.14e-04
2022-05-27 14:41:27,944 INFO [train.py:842] (2/4) Epoch 13, batch 7050, loss[loss=0.1645, simple_loss=0.2533, pruned_loss=0.03785, over 7157.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2837, pruned_loss=0.05959, over 1424098.61 frames.], batch size: 18, lr: 4.14e-04
2022-05-27 14:42:07,609 INFO [train.py:842] (2/4) Epoch 13, batch 7100, loss[loss=0.2282, simple_loss=0.3154, pruned_loss=0.0705, over 7333.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2834, pruned_loss=0.0595, over 1424980.82 frames.], batch size: 22, lr: 4.14e-04
2022-05-27 14:42:46,582 INFO [train.py:842] (2/4) Epoch 13, batch 7150, loss[loss=0.2472, simple_loss=0.3312, pruned_loss=0.0816, over 7212.00 frames.], tot_loss[loss=0.201, simple_loss=0.2833, pruned_loss=0.05934, over 1424570.12 frames.], batch size: 22, lr: 4.14e-04
2022-05-27 14:43:25,707 INFO [train.py:842] (2/4) Epoch 13, batch 7200, loss[loss=0.1848, simple_loss=0.2636, pruned_loss=0.05306, over 7129.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2837, pruned_loss=0.05969, over 1423309.52 frames.], batch size: 17, lr: 4.14e-04
2022-05-27 14:44:04,779 INFO [train.py:842] (2/4) Epoch 13, batch 7250, loss[loss=0.242, simple_loss=0.3197, pruned_loss=0.0822, over 6596.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2836, pruned_loss=0.05939, over 1418446.94 frames.], batch size: 38, lr: 4.14e-04
2022-05-27 14:44:43,663 INFO [train.py:842] (2/4) Epoch 13, batch 7300, loss[loss=0.1601, simple_loss=0.2397, pruned_loss=0.04018, over 7072.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2837, pruned_loss=0.05942, over 1421731.28 frames.], batch size: 18, lr: 4.13e-04
2022-05-27 14:45:22,112 INFO [train.py:842] (2/4) Epoch 13, batch 7350, loss[loss=0.22, simple_loss=0.299, pruned_loss=0.07049, over 7230.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2838, pruned_loss=0.05931, over 1421593.11 frames.], batch size: 20, lr: 4.13e-04
2022-05-27 14:46:01,415 INFO [train.py:842] (2/4) Epoch 13, batch 7400, loss[loss=0.237, simple_loss=0.3088, pruned_loss=0.0826, over 7130.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2842, pruned_loss=0.05971, over 1418536.78 frames.], batch size: 21, lr: 4.13e-04
2022-05-27 14:46:40,443 INFO [train.py:842] (2/4) Epoch 13, batch 7450, loss[loss=0.2784, simple_loss=0.3433, pruned_loss=0.1067, over 6776.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2848, pruned_loss=0.06, over 1422248.57 frames.], batch size: 31, lr: 4.13e-04
2022-05-27 14:47:19,656 INFO [train.py:842] (2/4) Epoch 13, batch 7500, loss[loss=0.1961, simple_loss=0.2934, pruned_loss=0.04943, over 7066.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2835, pruned_loss=0.05911, over 1421268.96 frames.], batch size: 28, lr: 4.13e-04
2022-05-27 14:47:58,781 INFO [train.py:842] (2/4) Epoch 13, batch 7550, loss[loss=0.2001, simple_loss=0.2836, pruned_loss=0.05833, over 7428.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2838, pruned_loss=0.0594, over 1423557.89 frames.], batch size: 20, lr: 4.13e-04
2022-05-27 14:48:38,010 INFO [train.py:842] (2/4) Epoch 13, batch 7600, loss[loss=0.1747, simple_loss=0.267, pruned_loss=0.04117, over 7325.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2837, pruned_loss=0.0596, over 1421964.19 frames.], batch size: 20, lr: 4.13e-04
2022-05-27 14:49:16,786 INFO [train.py:842] (2/4) Epoch 13, batch 7650, loss[loss=0.1895, simple_loss=0.2734, pruned_loss=0.05276, over 7066.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2837, pruned_loss=0.05922, over 1422404.79 frames.], batch size: 18, lr: 4.13e-04
2022-05-27 14:49:56,153 INFO [train.py:842] (2/4) Epoch 13, batch 7700, loss[loss=0.2636, simple_loss=0.3413, pruned_loss=0.09298, over 7208.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2838, pruned_loss=0.05959, over 1423965.82 frames.], batch size: 22, lr: 4.13e-04
2022-05-27 14:50:35,214 INFO [train.py:842] (2/4) Epoch 13, batch 7750, loss[loss=0.2328, simple_loss=0.3274, pruned_loss=0.06909, over 7417.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2841, pruned_loss=0.05958, over 1419169.46 frames.], batch size: 21, lr: 4.13e-04
2022-05-27 14:51:14,280 INFO [train.py:842] (2/4) Epoch 13, batch 7800, loss[loss=0.2198, simple_loss=0.3068, pruned_loss=0.06637, over 7019.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2821, pruned_loss=0.05832, over 1420403.03 frames.], batch size: 28, lr: 4.13e-04
2022-05-27 14:51:53,569 INFO [train.py:842] (2/4) Epoch 13, batch 7850, loss[loss=0.1961, simple_loss=0.2788, pruned_loss=0.05673, over 6413.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2812, pruned_loss=0.05806, over 1424870.77 frames.], batch size: 37, lr: 4.13e-04
2022-05-27 14:52:33,067 INFO [train.py:842] (2/4) Epoch 13, batch 7900, loss[loss=0.1827, simple_loss=0.2591, pruned_loss=0.05314, over 7408.00 frames.], tot_loss[loss=0.198, simple_loss=0.2805, pruned_loss=0.05773, over 1425231.47 frames.], batch size: 18, lr: 4.12e-04
2022-05-27 14:53:11,957 INFO [train.py:842] (2/4) Epoch 13, batch 7950, loss[loss=0.1995, simple_loss=0.2965, pruned_loss=0.05123, over 7116.00 frames.], tot_loss[loss=0.198, simple_loss=0.2807, pruned_loss=0.05764, over 1424966.60 frames.], batch size: 21, lr: 4.12e-04
2022-05-27 14:53:51,116 INFO [train.py:842] (2/4) Epoch 13, batch 8000, loss[loss=0.193, simple_loss=0.2768, pruned_loss=0.05456, over 7192.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2815, pruned_loss=0.05799, over 1427354.17 frames.], batch size: 16, lr: 4.12e-04
2022-05-27 14:54:29,902 INFO [train.py:842] (2/4) Epoch 13, batch 8050, loss[loss=0.1987, simple_loss=0.2869, pruned_loss=0.05532, over 7272.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2815, pruned_loss=0.05793, over 1425966.66 frames.], batch size: 18, lr: 4.12e-04
2022-05-27 14:55:09,278 INFO [train.py:842] (2/4) Epoch 13, batch 8100, loss[loss=0.1795, simple_loss=0.262, pruned_loss=0.0485, over 7163.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2823, pruned_loss=0.05865, over 1424620.29 frames.], batch size: 19, lr: 4.12e-04
2022-05-27 14:55:47,973 INFO [train.py:842] (2/4) Epoch 13, batch 8150, loss[loss=0.218, simple_loss=0.2987, pruned_loss=0.06864, over 7273.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2824, pruned_loss=0.0584, over 1427055.62 frames.], batch size: 25, lr: 4.12e-04
2022-05-27 14:56:27,298 INFO [train.py:842] (2/4) Epoch 13, batch 8200, loss[loss=0.2801, simple_loss=0.3458, pruned_loss=0.1072, over 7191.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2828, pruned_loss=0.0593, over 1428707.36 frames.], batch size: 22, lr: 4.12e-04
2022-05-27 14:57:06,463 INFO [train.py:842] (2/4) Epoch 13, batch 8250, loss[loss=0.1849, simple_loss=0.2716, pruned_loss=0.04907, over 7074.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2826, pruned_loss=0.05928, over 1432218.76 frames.], batch size: 18, lr: 4.12e-04
2022-05-27 14:57:45,592 INFO [train.py:842] (2/4) Epoch 13, batch 8300, loss[loss=0.1878, simple_loss=0.2758, pruned_loss=0.04989, over 6745.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2815, pruned_loss=0.05856, over 1434798.08 frames.], batch size: 31, lr: 4.12e-04
2022-05-27 14:58:24,373 INFO [train.py:842] (2/4) Epoch 13, batch 8350, loss[loss=0.1787, simple_loss=0.2588, pruned_loss=0.04935, over 7290.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2815, pruned_loss=0.05855, over 1434589.57 frames.], batch size: 17, lr: 4.12e-04
2022-05-27 14:59:03,765 INFO [train.py:842] (2/4) Epoch 13, batch 8400, loss[loss=0.1965, simple_loss=0.2823, pruned_loss=0.05539, over 7165.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2814, pruned_loss=0.0586, over 1435624.62 frames.], batch size: 18, lr: 4.12e-04
2022-05-27 14:59:42,400 INFO [train.py:842] (2/4) Epoch 13, batch 8450, loss[loss=0.2115, simple_loss=0.2995, pruned_loss=0.06181, over 7177.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2806, pruned_loss=0.05818, over 1428288.48 frames.], batch size: 26, lr: 4.11e-04
2022-05-27 15:00:21,276 INFO [train.py:842] (2/4) Epoch 13, batch 8500, loss[loss=0.1868, simple_loss=0.2661, pruned_loss=0.05376, over 7265.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2804, pruned_loss=0.05743, over 1427405.07 frames.], batch size: 17, lr: 4.11e-04
2022-05-27 15:00:59,971 INFO [train.py:842] (2/4) Epoch 13, batch 8550, loss[loss=0.1842, simple_loss=0.2759, pruned_loss=0.04628, over 7175.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2805, pruned_loss=0.05763, over 1423872.11 frames.], batch size: 26, lr: 4.11e-04
2022-05-27 15:01:38,579 INFO [train.py:842] (2/4) Epoch 13, batch 8600, loss[loss=0.203, simple_loss=0.2971, pruned_loss=0.05447, over 6435.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2813, pruned_loss=0.05753, over 1426153.60 frames.], batch size: 37, lr: 4.11e-04
2022-05-27 15:02:17,368 INFO [train.py:842] (2/4) Epoch 13, batch 8650, loss[loss=0.2241, simple_loss=0.294, pruned_loss=0.07706, over 7445.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2818, pruned_loss=0.05801, over 1428999.44 frames.], batch size: 20, lr: 4.11e-04
2022-05-27 15:02:56,149 INFO [train.py:842] (2/4) Epoch 13, batch 8700, loss[loss=0.181, simple_loss=0.2549, pruned_loss=0.05357, over 7172.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2823, pruned_loss=0.05824, over 1426319.70 frames.], batch size: 18, lr: 4.11e-04
2022-05-27 15:03:34,669 INFO [train.py:842] (2/4) Epoch 13, batch 8750, loss[loss=0.182, simple_loss=0.2743, pruned_loss=0.04488, over 7211.00 frames.], tot_loss[loss=0.2, simple_loss=0.2828, pruned_loss=0.05859, over 1423426.27 frames.], batch size: 21, lr: 4.11e-04
2022-05-27 15:04:13,452 INFO [train.py:842] (2/4) Epoch 13, batch 8800, loss[loss=0.2248, simple_loss=0.3094, pruned_loss=0.0701, over 7113.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2832, pruned_loss=0.05865, over 1420012.86 frames.], batch size: 21, lr: 4.11e-04
2022-05-27 15:04:52,091 INFO [train.py:842] (2/4) Epoch 13, batch 8850, loss[loss=0.2591, simple_loss=0.341, pruned_loss=0.08862, over 4954.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2844, pruned_loss=0.0593, over 1416312.25 frames.], batch size: 53, lr: 4.11e-04
2022-05-27 15:05:30,849 INFO [train.py:842] (2/4) Epoch 13, batch 8900, loss[loss=0.17, simple_loss=0.2519, pruned_loss=0.04402, over 7164.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2838, pruned_loss=0.05874, over 1411228.13 frames.], batch size: 19, lr: 4.11e-04
2022-05-27 15:06:09,667 INFO [train.py:842] (2/4) Epoch 13, batch 8950, loss[loss=0.2173, simple_loss=0.308, pruned_loss=0.06334, over 7176.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2834, pruned_loss=0.05881, over 1405001.89 frames.], batch size: 26, lr: 4.11e-04
2022-05-27 15:06:48,220 INFO [train.py:842] (2/4) Epoch 13, batch 9000, loss[loss=0.2495, simple_loss=0.3288, pruned_loss=0.08507, over 6379.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2845, pruned_loss=0.05966, over 1388693.27 frames.], batch size: 38, lr: 4.11e-04
2022-05-27 15:06:48,222 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 15:06:57,716 INFO [train.py:871] (2/4) Epoch 13, validation: loss=0.1693, simple_loss=0.2699, pruned_loss=0.03436, over 868885.00 frames.
2022-05-27 15:07:34,930 INFO [train.py:842] (2/4) Epoch 13, batch 9050, loss[loss=0.1636, simple_loss=0.2593, pruned_loss=0.03398, over 6449.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2878, pruned_loss=0.06181, over 1351377.61 frames.], batch size: 38, lr: 4.10e-04
2022-05-27 15:08:12,254 INFO [train.py:842] (2/4) Epoch 13, batch 9100, loss[loss=0.2794, simple_loss=0.3485, pruned_loss=0.1051, over 6470.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2909, pruned_loss=0.06424, over 1307760.35 frames.], batch size: 37, lr: 4.10e-04
2022-05-27 15:08:49,667 INFO [train.py:842] (2/4) Epoch 13, batch 9150, loss[loss=0.2067, simple_loss=0.2949, pruned_loss=0.05924, over 4861.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2945, pruned_loss=0.06713, over 1258741.75 frames.], batch size: 52, lr: 4.10e-04
2022-05-27 15:09:36,409 INFO [train.py:842] (2/4) Epoch 14, batch 0, loss[loss=0.1931, simple_loss=0.2757, pruned_loss=0.05523, over 7359.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2757, pruned_loss=0.05523, over 7359.00 frames.], batch size: 23, lr: 3.97e-04
2022-05-27 15:10:16,189 INFO [train.py:842] (2/4) Epoch 14, batch 50, loss[loss=0.1703, simple_loss=0.2503, pruned_loss=0.04515, over 7107.00 frames.], tot_loss[loss=0.1994, simple_loss=0.28, pruned_loss=0.05934, over 321869.35 frames.], batch size: 21, lr: 3.97e-04
2022-05-27 15:10:55,347 INFO [train.py:842] (2/4) Epoch 14, batch 100, loss[loss=0.1967, simple_loss=0.2897, pruned_loss=0.05179, over 7145.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2789, pruned_loss=0.05762, over 571741.68 frames.], batch size: 20, lr: 3.97e-04
2022-05-27 15:11:34,738 INFO [train.py:842] (2/4) Epoch 14, batch 150, loss[loss=0.1385, simple_loss=0.2163, pruned_loss=0.03041, over 7000.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2795, pruned_loss=0.05707, over 762883.33 frames.], batch size: 16, lr: 3.97e-04
2022-05-27 15:12:13,375 INFO [train.py:842] (2/4) Epoch 14, batch 200, loss[loss=0.204, simple_loss=0.2933, pruned_loss=0.05735, over 7197.00 frames.], tot_loss[loss=0.197, simple_loss=0.2809, pruned_loss=0.0566, over 909837.55 frames.], batch size: 22, lr: 3.97e-04
2022-05-27 15:12:52,351 INFO [train.py:842] (2/4) Epoch 14, batch 250, loss[loss=0.2099, simple_loss=0.2919, pruned_loss=0.064, over 7200.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2807, pruned_loss=0.05596, over 1025240.57 frames.], batch size: 22, lr: 3.97e-04
2022-05-27 15:13:30,928 INFO [train.py:842] (2/4) Epoch 14, batch 300, loss[loss=0.1946, simple_loss=0.2868, pruned_loss=0.05118, over 7413.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2827, pruned_loss=0.05693, over 1112346.04 frames.], batch size: 21, lr: 3.97e-04
2022-05-27 15:14:09,853 INFO [train.py:842] (2/4) Epoch 14, batch 350, loss[loss=0.1725, simple_loss=0.2635, pruned_loss=0.04081, over 7422.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2811, pruned_loss=0.0565, over 1180833.33 frames.], batch size: 20, lr: 3.96e-04
2022-05-27 15:14:48,792 INFO [train.py:842] (2/4) Epoch 14, batch 400, loss[loss=0.1714, simple_loss=0.2652, pruned_loss=0.0388, over 7093.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2814, pruned_loss=0.05708, over 1231034.11 frames.], batch size: 28, lr: 3.96e-04
2022-05-27 15:15:28,277 INFO [train.py:842] (2/4) Epoch 14, batch 450, loss[loss=0.1566, simple_loss=0.25, pruned_loss=0.03164, over 6241.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2811, pruned_loss=0.05684, over 1273104.85 frames.], batch size: 37, lr: 3.96e-04
2022-05-27 15:16:07,108 INFO [train.py:842] (2/4) Epoch 14, batch 500, loss[loss=0.2299, simple_loss=0.3099, pruned_loss=0.07494, over 7094.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2827, pruned_loss=0.05881, over 1300811.87 frames.], batch size: 28, lr: 3.96e-04
2022-05-27 15:16:48,800 INFO [train.py:842] (2/4) Epoch 14, batch 550, loss[loss=0.2021, simple_loss=0.2877, pruned_loss=0.05826, over 6472.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2823, pruned_loss=0.05853, over 1325771.08 frames.], batch size: 38, lr: 3.96e-04
2022-05-27 15:17:27,793 INFO [train.py:842] (2/4) Epoch 14, batch 600, loss[loss=0.2162, simple_loss=0.3103, pruned_loss=0.06107, over 7325.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2815, pruned_loss=0.05739, over 1347994.80 frames.], batch size: 21, lr: 3.96e-04
2022-05-27 15:18:06,506 INFO [train.py:842] (2/4) Epoch 14, batch 650, loss[loss=0.1569, simple_loss=0.243, pruned_loss=0.03541, over 7055.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2816, pruned_loss=0.05762, over 1360410.32 frames.], batch size: 18, lr: 3.96e-04
2022-05-27 15:18:45,276 INFO [train.py:842] (2/4) Epoch 14, batch 700, loss[loss=0.1914, simple_loss=0.278, pruned_loss=0.05238, over 7281.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2807, pruned_loss=0.05717, over 1375424.09 frames.], batch size: 18, lr: 3.96e-04
2022-05-27 15:19:24,153 INFO [train.py:842] (2/4) Epoch 14, batch 750, loss[loss=0.2294, simple_loss=0.3121, pruned_loss=0.07337, over 7203.00 frames.], tot_loss[loss=0.198, simple_loss=0.281, pruned_loss=0.05753, over 1382572.82 frames.], batch size: 23, lr: 3.96e-04
2022-05-27 15:20:03,047 INFO [train.py:842] (2/4) Epoch 14, batch 800, loss[loss=0.2037, simple_loss=0.2935, pruned_loss=0.05695, over 7308.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2812, pruned_loss=0.05672, over 1391065.86 frames.], batch size: 25, lr: 3.96e-04
2022-05-27 15:20:42,360 INFO [train.py:842] (2/4) Epoch 14, batch 850, loss[loss=0.1947, simple_loss=0.2997, pruned_loss=0.04485, over 7228.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2818, pruned_loss=0.05682, over 1399656.78 frames.], batch size: 21, lr: 3.96e-04
2022-05-27 15:21:21,187 INFO [train.py:842] (2/4) Epoch 14, batch 900, loss[loss=0.2087, simple_loss=0.2869, pruned_loss=0.06527, over 7165.00 frames.], tot_loss[loss=0.197, simple_loss=0.2813, pruned_loss=0.05636, over 1401822.27 frames.], batch size: 18, lr: 3.96e-04
2022-05-27 15:21:59,896 INFO [train.py:842] (2/4) Epoch 14, batch 950, loss[loss=0.2863, simple_loss=0.3466, pruned_loss=0.113, over 7214.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2834, pruned_loss=0.05826, over 1403094.56 frames.], batch size: 21, lr: 3.96e-04
2022-05-27 15:22:39,021 INFO [train.py:842] (2/4) Epoch 14, batch 1000, loss[loss=0.2018, simple_loss=0.2915, pruned_loss=0.05603, over 7196.00 frames.], tot_loss[loss=0.1988, simple_loss=0.282, pruned_loss=0.05777, over 1410108.83 frames.], batch size: 22, lr: 3.95e-04
2022-05-27 15:23:18,290 INFO [train.py:842] (2/4) Epoch 14, batch 1050, loss[loss=0.1701, simple_loss=0.2621, pruned_loss=0.03905, over 7409.00 frames.], tot_loss[loss=0.1994, simple_loss=0.282, pruned_loss=0.05838, over 1411304.84 frames.], batch size: 21, lr: 3.95e-04
2022-05-27 15:23:57,025 INFO [train.py:842] (2/4) Epoch 14, batch 1100, loss[loss=0.2055, simple_loss=0.2938, pruned_loss=0.05853, over 6810.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2816, pruned_loss=0.05806, over 1410487.91 frames.], batch size: 31, lr: 3.95e-04
2022-05-27 15:24:35,948 INFO [train.py:842] (2/4) Epoch 14, batch 1150, loss[loss=0.2416, simple_loss=0.3222, pruned_loss=0.08049, over 7334.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2833, pruned_loss=0.05871, over 1410876.41 frames.], batch size: 22, lr: 3.95e-04
2022-05-27 15:25:14,703 INFO [train.py:842] (2/4) Epoch 14, batch 1200, loss[loss=0.2459, simple_loss=0.3025, pruned_loss=0.09472, over 5052.00 frames.], tot_loss[loss=0.2011, simple_loss=0.284, pruned_loss=0.05913, over 1409909.20 frames.], batch size: 52, lr: 3.95e-04
2022-05-27 15:25:53,903 INFO [train.py:842] (2/4) Epoch 14, batch 1250, loss[loss=0.2103, simple_loss=0.2903, pruned_loss=0.06516, over 7432.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2834, pruned_loss=0.05845, over 1414528.50 frames.], batch size: 20, lr: 3.95e-04
2022-05-27 15:26:32,823 INFO [train.py:842] (2/4) Epoch 14, batch 1300, loss[loss=0.2133, simple_loss=0.2987, pruned_loss=0.06402, over 7255.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2826, pruned_loss=0.05759, over 1417429.72 frames.], batch size: 19, lr: 3.95e-04
2022-05-27 15:27:22,191 INFO [train.py:842] (2/4) Epoch 14, batch 1350, loss[loss=0.1986, simple_loss=0.2703, pruned_loss=0.06342, over 7278.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2804, pruned_loss=0.05644, over 1421771.41 frames.], batch size: 18, lr: 3.95e-04
2022-05-27 15:28:01,183 INFO [train.py:842] (2/4) Epoch 14, batch 1400, loss[loss=0.1729, simple_loss=0.2504, pruned_loss=0.0477, over 7154.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2818, pruned_loss=0.05743, over 1417749.86 frames.], batch size: 18, lr: 3.95e-04
2022-05-27 15:28:40,385 INFO [train.py:842] (2/4) Epoch 14, batch 1450, loss[loss=0.2563, simple_loss=0.3144, pruned_loss=0.09909, over 7285.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2813, pruned_loss=0.05718, over 1421341.03 frames.], batch size: 17, lr: 3.95e-04
2022-05-27 15:29:19,130 INFO [train.py:842] (2/4) Epoch 14, batch 1500, loss[loss=0.1949, simple_loss=0.2672, pruned_loss=0.06134, over 7299.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2815, pruned_loss=0.05761, over 1422836.04 frames.], batch size: 17, lr: 3.95e-04
2022-05-27 15:29:58,078 INFO [train.py:842] (2/4) Epoch 14, batch 1550, loss[loss=0.1822, simple_loss=0.2778, pruned_loss=0.04332, over 6398.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2823, pruned_loss=0.05806, over 1418142.46 frames.], batch size: 38, lr: 3.95e-04
2022-05-27 15:30:37,052 INFO [train.py:842] (2/4) Epoch 14, batch 1600, loss[loss=0.1926, simple_loss=0.2916, pruned_loss=0.04682, over 7410.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2838, pruned_loss=0.05875, over 1417419.23 frames.], batch size: 21, lr: 3.94e-04
2022-05-27 15:31:16,020 INFO [train.py:842] (2/4) Epoch 14, batch 1650, loss[loss=0.1794, simple_loss=0.2673, pruned_loss=0.04577, over 7229.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2838, pruned_loss=0.05873, over 1419618.37 frames.], batch size: 20, lr: 3.94e-04
2022-05-27 15:31:54,469 INFO [train.py:842] (2/4) Epoch 14, batch 1700, loss[loss=0.1983, simple_loss=0.2826, pruned_loss=0.05697, over 6120.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2845, pruned_loss=0.05929, over 1419249.47 frames.], batch size: 37, lr: 3.94e-04
2022-05-27 15:32:33,843 INFO [train.py:842] (2/4) Epoch 14, batch 1750, loss[loss=0.1689, simple_loss=0.2423, pruned_loss=0.04772, over 7289.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2831, pruned_loss=0.05869, over 1421468.35 frames.], batch size: 17, lr: 3.94e-04
2022-05-27 15:33:12,930 INFO [train.py:842] (2/4) Epoch 14, batch 1800, loss[loss=0.2015, simple_loss=0.2849, pruned_loss=0.05907, over 7147.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2812, pruned_loss=0.05732, over 1426595.40 frames.], batch size: 20, lr: 3.94e-04
2022-05-27 15:33:51,930 INFO [train.py:842] (2/4) Epoch 14, batch 1850, loss[loss=0.207, simple_loss=0.292, pruned_loss=0.06101, over 7327.00 frames.], tot_loss[loss=0.1985, simple_loss=0.282, pruned_loss=0.05753, over 1426482.62 frames.], batch size: 25, lr: 3.94e-04
2022-05-27 15:34:30,694 INFO [train.py:842] (2/4) Epoch 14, batch 1900, loss[loss=0.2343, simple_loss=0.3132, pruned_loss=0.07768, over 6304.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2818, pruned_loss=0.05775, over 1421673.76 frames.], batch size: 37, lr: 3.94e-04
2022-05-27 15:35:09,516 INFO [train.py:842] (2/4) Epoch 14, batch 1950, loss[loss=0.1736, simple_loss=0.2681, pruned_loss=0.03959, over 7258.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2821, pruned_loss=0.05747, over 1422520.63 frames.], batch size: 19, lr: 3.94e-04
2022-05-27 15:35:48,261 INFO [train.py:842] (2/4) Epoch 14, batch 2000, loss[loss=0.1859, simple_loss=0.2715, pruned_loss=0.05016, over 7333.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2824, pruned_loss=0.05744, over 1423911.72 frames.], batch size: 22, lr: 3.94e-04
2022-05-27 15:36:27,729 INFO [train.py:842] (2/4) Epoch 14, batch 2050, loss[loss=0.1955, simple_loss=0.2921, pruned_loss=0.04942, over 7386.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2811, pruned_loss=0.05676, over 1425329.34 frames.], batch size: 23, lr: 3.94e-04
2022-05-27 15:37:06,329 INFO [train.py:842] (2/4) Epoch 14, batch 2100, loss[loss=0.2046, simple_loss=0.2982, pruned_loss=0.05551, over 7235.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2824, pruned_loss=0.05712, over 1425355.15 frames.], batch size: 20, lr: 3.94e-04
2022-05-27 15:37:45,644 INFO [train.py:842] (2/4) Epoch 14, batch 2150, loss[loss=0.1819, simple_loss=0.2774, pruned_loss=0.04314, over 7139.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2822, pruned_loss=0.05701, over 1427927.86 frames.], batch size: 26, lr: 3.94e-04
2022-05-27 15:38:24,813 INFO [train.py:842] (2/4) Epoch 14, batch 2200, loss[loss=0.2175, simple_loss=0.287, pruned_loss=0.07395, over 7431.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2826, pruned_loss=0.05755, over 1426426.90 frames.], batch size: 20, lr: 3.93e-04
2022-05-27 15:39:03,997 INFO [train.py:842] (2/4) Epoch 14, batch 2250, loss[loss=0.1749, simple_loss=0.2589, pruned_loss=0.0454, over 7243.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2821, pruned_loss=0.05747, over 1427493.49 frames.], batch size: 20, lr: 3.93e-04
2022-05-27 15:39:42,975 INFO [train.py:842] (2/4) Epoch 14, batch 2300, loss[loss=0.175, simple_loss=0.2606, pruned_loss=0.04475, over 7060.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2806, pruned_loss=0.05659, over 1428770.91 frames.], batch size: 28, lr: 3.93e-04
2022-05-27 15:40:22,159 INFO [train.py:842] (2/4) Epoch 14, batch 2350, loss[loss=0.241, simple_loss=0.326, pruned_loss=0.07805, over 4685.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2815, pruned_loss=0.05769, over 1427285.43 frames.], batch size: 52, lr: 3.93e-04
2022-05-27 15:41:00,861 INFO [train.py:842] (2/4) Epoch 14, batch 2400, loss[loss=0.1498, simple_loss=0.2328, pruned_loss=0.03337, over 7275.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2809, pruned_loss=0.05768, over 1428483.65 frames.], batch size: 17, lr: 3.93e-04
2022-05-27 15:41:39,986 INFO [train.py:842] (2/4) Epoch 14, batch 2450, loss[loss=0.1866, simple_loss=0.2764, pruned_loss=0.0484, over 6706.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2812, pruned_loss=0.05729, over 1430498.96 frames.], batch size: 31, lr: 3.93e-04
2022-05-27 15:42:19,124 INFO [train.py:842] (2/4) Epoch 14, batch 2500, loss[loss=0.1949, simple_loss=0.2795, pruned_loss=0.0552, over 7272.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2821, pruned_loss=0.05769, over 1427239.79 frames.], batch size: 17, lr: 3.93e-04
2022-05-27 15:42:58,096 INFO [train.py:842] (2/4) Epoch 14, batch 2550, loss[loss=0.2248, simple_loss=0.3069, pruned_loss=0.07135, over 7322.00 frames.], tot_loss[loss=0.1998, simple_loss=0.283, pruned_loss=0.05823, over 1422371.49 frames.], batch size: 25, lr: 3.93e-04
2022-05-27 15:43:37,311 INFO [train.py:842] (2/4) Epoch 14, batch 2600, loss[loss=0.163, simple_loss=0.2604, pruned_loss=0.03283, over 7419.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2829, pruned_loss=0.05825, over 1419526.46 frames.], batch size: 21, lr: 3.93e-04
2022-05-27 15:44:16,189 INFO [train.py:842] (2/4) Epoch 14, batch 2650, loss[loss=0.1772, simple_loss=0.2663, pruned_loss=0.04407, over 7117.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2836, pruned_loss=0.05866, over 1418198.94 frames.], batch size: 21, lr: 3.93e-04
2022-05-27 15:44:55,674 INFO [train.py:842] (2/4) Epoch 14, batch 2700, loss[loss=0.1714, simple_loss=0.2444, pruned_loss=0.04918, over 6970.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2818, pruned_loss=0.05764, over 1421958.01 frames.], batch size: 16, lr: 3.93e-04
2022-05-27 15:45:35,085 INFO [train.py:842] (2/4) Epoch 14, batch 2750, loss[loss=0.221, simple_loss=0.3122, pruned_loss=0.06495, over 7285.00 frames.], tot_loss[loss=0.1964, simple_loss=0.28, pruned_loss=0.05635, over 1427203.30 frames.], batch size: 24, lr: 3.93e-04
2022-05-27 15:46:13,965 INFO [train.py:842] (2/4) Epoch 14, batch 2800, loss[loss=0.1413, simple_loss=0.2278, pruned_loss=0.02737, over 7123.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2795, pruned_loss=0.05618, over 1426088.07 frames.], batch size: 17, lr: 3.93e-04
2022-05-27 15:46:53,098 INFO [train.py:842] (2/4) Epoch 14, batch 2850, loss[loss=0.1945, simple_loss=0.2872, pruned_loss=0.05092, over 7409.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2799, pruned_loss=0.05679, over 1426853.22 frames.], batch size: 21, lr: 3.92e-04
2022-05-27 15:47:31,767 INFO [train.py:842] (2/4) Epoch 14, batch 2900, loss[loss=0.2056, simple_loss=0.3075, pruned_loss=0.05187, over 7119.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2798, pruned_loss=0.05628, over 1428364.04 frames.], batch size: 21, lr: 3.92e-04
2022-05-27 15:48:10,948 INFO [train.py:842] (2/4) Epoch 14, batch 2950, loss[loss=0.1962, simple_loss=0.2877, pruned_loss=0.05236, over 7196.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2805, pruned_loss=0.05659, over 1429575.88 frames.], batch size: 23, lr: 3.92e-04
2022-05-27 15:48:50,334 INFO [train.py:842] (2/4) Epoch 14, batch 3000, loss[loss=0.2332, simple_loss=0.3165, pruned_loss=0.07496, over 7293.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2799, pruned_loss=0.05632, over 1431034.00 frames.], batch size: 24, lr: 3.92e-04
2022-05-27 15:48:50,336 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 15:48:59,799 INFO [train.py:871] (2/4) Epoch 14, validation: loss=0.17, simple_loss=0.2697, pruned_loss=0.03515, over 868885.00 frames.
2022-05-27 15:49:39,108 INFO [train.py:842] (2/4) Epoch 14, batch 3050, loss[loss=0.1812, simple_loss=0.2587, pruned_loss=0.05186, over 7271.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2793, pruned_loss=0.0561, over 1430504.16 frames.], batch size: 17, lr: 3.92e-04
2022-05-27 15:50:17,909 INFO [train.py:842] (2/4) Epoch 14, batch 3100, loss[loss=0.1712, simple_loss=0.2631, pruned_loss=0.03966, over 7193.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2803, pruned_loss=0.05703, over 1431488.42 frames.], batch size: 23, lr: 3.92e-04
2022-05-27 15:50:57,001 INFO [train.py:842] (2/4) Epoch 14, batch 3150, loss[loss=0.2572, simple_loss=0.3208, pruned_loss=0.09683, over 4950.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2789, pruned_loss=0.05661, over 1430163.77 frames.], batch size: 52, lr: 3.92e-04
2022-05-27 15:51:35,891 INFO [train.py:842] (2/4) Epoch 14, batch 3200, loss[loss=0.2169, simple_loss=0.2969, pruned_loss=0.06839, over 7332.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2805, pruned_loss=0.05736, over 1430605.12 frames.], batch size: 22, lr: 3.92e-04
2022-05-27 15:52:14,782 INFO [train.py:842] (2/4) Epoch 14, batch 3250, loss[loss=0.2517, simple_loss=0.3193, pruned_loss=0.09205, over 7126.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2811, pruned_loss=0.05776, over 1427751.37 frames.], batch size: 26, lr: 3.92e-04
2022-05-27 15:52:53,651 INFO [train.py:842] (2/4) Epoch 14, batch 3300, loss[loss=0.1564, simple_loss=0.2451, pruned_loss=0.03382, over 7168.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2804, pruned_loss=0.0574, over 1424424.89 frames.], batch size: 18, lr: 3.92e-04
2022-05-27 15:53:32,473 INFO [train.py:842] (2/4) Epoch 14, batch 3350, loss[loss=0.1578, simple_loss=0.2401, pruned_loss=0.03772, over 7409.00 frames.], tot_loss[loss=0.1968, simple_loss=0.28, pruned_loss=0.05674, over 1426070.38 frames.], batch size: 18, lr: 3.92e-04
2022-05-27 15:54:11,130 INFO [train.py:842] (2/4) Epoch 14, batch 3400, loss[loss=0.1644, simple_loss=0.2514, pruned_loss=0.03877, over 7154.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2807, pruned_loss=0.05707, over 1426735.88 frames.], batch size: 18, lr: 3.92e-04
2022-05-27 15:55:00,323 INFO [train.py:842] (2/4) Epoch 14, batch 3450, loss[loss=0.1805, simple_loss=0.2767, pruned_loss=0.04217, over 7121.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2811, pruned_loss=0.05688, over 1426042.22 frames.], batch size: 21, lr: 3.91e-04
2022-05-27 15:55:39,216 INFO [train.py:842] (2/4) Epoch 14, batch 3500, loss[loss=0.2818, simple_loss=0.3549, pruned_loss=0.1043, over 7342.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2808, pruned_loss=0.05722, over 1426735.46 frames.], batch size: 22, lr: 3.91e-04
2022-05-27 15:56:28,942 INFO [train.py:842] (2/4) Epoch 14, batch 3550, loss[loss=0.194, simple_loss=0.2788, pruned_loss=0.05463, over 7319.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2808, pruned_loss=0.0573, over 1427052.71 frames.], batch size: 21, lr: 3.91e-04
2022-05-27 15:57:08,506 INFO [train.py:842] (2/4) Epoch 14, batch 3600, loss[loss=0.1521, simple_loss=0.2324, pruned_loss=0.03589, over 7355.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2802, pruned_loss=0.05725, over 1430467.11 frames.], batch size: 19, lr: 3.91e-04
2022-05-27 15:57:57,865 INFO [train.py:842] (2/4) Epoch 14, batch 3650, loss[loss=0.2168, simple_loss=0.2976, pruned_loss=0.06804, over 7239.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2789, pruned_loss=0.05645, over 1430042.34 frames.], batch size: 20, lr: 3.91e-04
2022-05-27 15:58:36,738 INFO [train.py:842] (2/4) Epoch 14, batch 3700, loss[loss=0.1744, simple_loss=0.2741, pruned_loss=0.03734, over 7298.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2807, pruned_loss=0.05752, over 1421234.91 frames.], batch size: 24, lr: 3.91e-04
2022-05-27 15:59:15,368 INFO [train.py:842] (2/4) Epoch 14, batch 3750, loss[loss=0.2109, simple_loss=0.2884, pruned_loss=0.06671, over 5025.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2805, pruned_loss=0.05724, over 1420421.66 frames.], batch size: 52, lr: 3.91e-04
2022-05-27 15:59:54,258 INFO [train.py:842] (2/4) Epoch 14, batch 3800, loss[loss=0.1979, simple_loss=0.2902, pruned_loss=0.05283, over 7252.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2805, pruned_loss=0.05731, over 1419132.45 frames.], batch size: 19, lr: 3.91e-04
2022-05-27 16:00:33,490 INFO [train.py:842] (2/4) Epoch 14, batch 3850, loss[loss=0.2448, simple_loss=0.3242, pruned_loss=0.08268, over 6527.00 frames.], tot_loss[loss=0.1958, simple_loss=0.279, pruned_loss=0.05632, over 1420181.93 frames.], batch size: 38, lr: 3.91e-04
2022-05-27 16:01:12,306 INFO [train.py:842] (2/4) Epoch 14, batch 3900, loss[loss=0.2311, simple_loss=0.3092, pruned_loss=0.07649, over 7122.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2806, pruned_loss=0.05736, over 1420526.58 frames.], batch size: 21, lr: 3.91e-04
2022-05-27 16:01:51,354 INFO [train.py:842] (2/4) Epoch 14, batch 3950, loss[loss=0.2232, simple_loss=0.2997, pruned_loss=0.07336, over 5237.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2817, pruned_loss=0.05809, over 1421088.45 frames.], batch size: 52, lr: 3.91e-04
2022-05-27 16:02:30,312 INFO [train.py:842] (2/4) Epoch 14, batch 4000, loss[loss=0.1645, simple_loss=0.2588, pruned_loss=0.03516, over 7162.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2811, pruned_loss=0.05764, over 1421134.33 frames.], batch size: 18, lr: 3.91e-04
2022-05-27 16:03:09,238 INFO [train.py:842] (2/4) Epoch 14, batch 4050, loss[loss=0.2009, simple_loss=0.2865, pruned_loss=0.05765, over 7207.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2814, pruned_loss=0.05769, over 1423476.51 frames.], batch size: 22, lr: 3.91e-04
2022-05-27 16:03:48,317 INFO [train.py:842] (2/4) Epoch 14, batch 4100, loss[loss=0.1886, simple_loss=0.2789, pruned_loss=0.04912, over 7208.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2814, pruned_loss=0.05793, over 1425502.63 frames.], batch size: 22, lr: 3.90e-04
2022-05-27 16:04:27,745 INFO [train.py:842] (2/4) Epoch 14, batch 4150, loss[loss=0.2003, simple_loss=0.2913, pruned_loss=0.05462, over 7313.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2823, pruned_loss=0.05819, over 1421360.64 frames.], batch size: 21, lr: 3.90e-04
2022-05-27 16:05:06,397 INFO [train.py:842] (2/4) Epoch 14, batch 4200, loss[loss=0.2116, simple_loss=0.2854, pruned_loss=0.0689, over 7144.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2826, pruned_loss=0.05863, over 1423562.58 frames.], batch size: 17, lr: 3.90e-04
2022-05-27 16:05:45,521 INFO [train.py:842] (2/4) Epoch 14, batch 4250, loss[loss=0.188, simple_loss=0.2637, pruned_loss=0.05619, over 7432.00 frames.], tot_loss[loss=0.199, simple_loss=0.2818, pruned_loss=0.05809, over 1419291.56 frames.], batch size: 20, lr: 3.90e-04
2022-05-27 16:06:24,396 INFO [train.py:842] (2/4) Epoch 14, batch 4300, loss[loss=0.3372, simple_loss=0.3835, pruned_loss=0.1454, over 7423.00 frames.], tot_loss[loss=0.201, simple_loss=0.2836, pruned_loss=0.0592, over 1414176.53 frames.], batch size: 21, lr: 3.90e-04
2022-05-27 16:07:03,453 INFO [train.py:842] (2/4) Epoch 14, batch 4350, loss[loss=0.1748, simple_loss=0.2619, pruned_loss=0.04383, over 7434.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2833, pruned_loss=0.05907, over 1416530.37 frames.], batch size: 20, lr: 3.90e-04
2022-05-27 16:07:42,425 INFO [train.py:842] (2/4) Epoch 14, batch 4400, loss[loss=0.2119, simple_loss=0.2896, pruned_loss=0.0671, over 6872.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2836, pruned_loss=0.05887, over 1417180.88 frames.], batch size: 31, lr: 3.90e-04
2022-05-27 16:08:21,598 INFO [train.py:842] (2/4) Epoch 14, batch 4450, loss[loss=0.1826, simple_loss=0.273, pruned_loss=0.04606, over 7415.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2826, pruned_loss=0.05864, over 1417541.22 frames.], batch size: 21, lr: 3.90e-04
2022-05-27 16:09:00,506 INFO [train.py:842] (2/4) Epoch 14, batch 4500, loss[loss=0.2023, simple_loss=0.2903, pruned_loss=0.05719, over 7211.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2822, pruned_loss=0.05843, over 1418728.21 frames.], batch size: 21, lr: 3.90e-04
2022-05-27 16:09:39,603 INFO [train.py:842] (2/4) Epoch 14, batch 4550, loss[loss=0.2079, simple_loss=0.3074, pruned_loss=0.05419, over 7337.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2815, pruned_loss=0.05758, over 1414377.09 frames.], batch size: 22, lr: 3.90e-04
2022-05-27 16:10:18,145 INFO [train.py:842] (2/4) Epoch 14, batch 4600, loss[loss=0.1918, simple_loss=0.2807, pruned_loss=0.05141, over 6261.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2817, pruned_loss=0.05756, over 1414420.21 frames.], batch size: 37, lr: 3.90e-04
2022-05-27 16:10:57,478 INFO [train.py:842] (2/4) Epoch 14, batch 4650, loss[loss=0.2124, simple_loss=0.2993, pruned_loss=0.06277, over 7351.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2818, pruned_loss=0.0578, over 1414611.47 frames.], batch size: 19, lr: 3.90e-04
2022-05-27 16:11:35,854 INFO [train.py:842] (2/4) Epoch 14, batch 4700, loss[loss=0.2003, simple_loss=0.2894, pruned_loss=0.05557, over 7177.00 frames.], tot_loss[loss=0.1988, simple_loss=0.282, pruned_loss=0.05777, over 1412374.79 frames.], batch size: 26, lr: 3.90e-04
2022-05-27 16:12:14,906 INFO [train.py:842] (2/4) Epoch 14, batch 4750, loss[loss=0.2313, simple_loss=0.3074, pruned_loss=0.07761, over 7263.00 frames.], tot_loss[loss=0.2, simple_loss=0.2834, pruned_loss=0.05831, over 1414879.32 frames.], batch size: 19, lr: 3.89e-04
2022-05-27 16:12:53,686 INFO [train.py:842] (2/4) Epoch 14, batch 4800, loss[loss=0.2252, simple_loss=0.3034, pruned_loss=0.07351, over 7405.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2829, pruned_loss=0.05842, over 1417157.91 frames.], batch size: 21, lr: 3.89e-04
2022-05-27 16:13:32,495 INFO [train.py:842] (2/4) Epoch 14, batch 4850, loss[loss=0.2031, simple_loss=0.296, pruned_loss=0.05514, over 7212.00 frames.], tot_loss[loss=0.199, simple_loss=0.2825, pruned_loss=0.05777, over 1418473.96 frames.], batch size: 22, lr: 3.89e-04
2022-05-27 16:14:11,441 INFO [train.py:842] (2/4) Epoch 14, batch 4900, loss[loss=0.2361, simple_loss=0.3166, pruned_loss=0.07778, over 6749.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2819, pruned_loss=0.05725, over 1417771.77 frames.], batch size: 31, lr: 3.89e-04
2022-05-27 16:14:50,464 INFO [train.py:842] (2/4) Epoch 14, batch 4950, loss[loss=0.1952, simple_loss=0.2732, pruned_loss=0.05859, over 7211.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2812, pruned_loss=0.0571, over 1418527.97 frames.], batch size: 22, lr: 3.89e-04
2022-05-27 16:15:29,222 INFO [train.py:842] (2/4) Epoch 14, batch 5000, loss[loss=0.2126, simple_loss=0.2814, pruned_loss=0.07185, over 7161.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2812, pruned_loss=0.0572, over 1421880.68 frames.], batch size: 18, lr: 3.89e-04
2022-05-27 16:16:08,261 INFO [train.py:842] (2/4) Epoch 14, batch 5050, loss[loss=0.1637, simple_loss=0.2465, pruned_loss=0.04042, over 7001.00 frames.], tot_loss[loss=0.1982, simple_loss=0.281, pruned_loss=0.05768, over 1418578.75 frames.], batch size: 16, lr: 3.89e-04
2022-05-27 16:16:47,139 INFO [train.py:842] (2/4) Epoch 14, batch 5100, loss[loss=0.1661, simple_loss=0.2535, pruned_loss=0.03936, over 7255.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2809, pruned_loss=0.05739, over 1418068.46 frames.], batch size: 19, lr: 3.89e-04
2022-05-27 16:17:26,322 INFO [train.py:842] (2/4) Epoch 14, batch 5150, loss[loss=0.2269, simple_loss=0.3022, pruned_loss=0.07577, over 7301.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2813, pruned_loss=0.05789, over 1422235.88 frames.], batch size: 24, lr: 3.89e-04
2022-05-27 16:18:04,982 INFO [train.py:842] (2/4) Epoch 14, batch 5200, loss[loss=0.225, simple_loss=0.305, pruned_loss=0.0725, over 7412.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2813, pruned_loss=0.05761, over 1425592.72 frames.], batch size: 21, lr: 3.89e-04
2022-05-27 16:18:44,061 INFO [train.py:842] (2/4) Epoch 14, batch 5250, loss[loss=0.1989, simple_loss=0.2845, pruned_loss=0.05668, over 7368.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2821, pruned_loss=0.05742, over 1427948.18 frames.], batch size: 23, lr: 3.89e-04
2022-05-27 16:19:22,705 INFO [train.py:842] (2/4) Epoch 14, batch 5300, loss[loss=0.2605, simple_loss=0.3226, pruned_loss=0.09916, over 5098.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2838, pruned_loss=0.05856, over 1422750.67 frames.], batch size: 52, lr: 3.89e-04
2022-05-27 16:20:01,788 INFO [train.py:842] (2/4) Epoch 14, batch 5350, loss[loss=0.2132, simple_loss=0.2994, pruned_loss=0.06353, over 7272.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2845, pruned_loss=0.05918, over 1424286.02 frames.], batch size: 24, lr: 3.88e-04
2022-05-27 16:20:40,881 INFO [train.py:842] (2/4) Epoch 14, batch 5400, loss[loss=0.2148, simple_loss=0.3102, pruned_loss=0.05972, over 7340.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2841, pruned_loss=0.05875, over 1427772.64 frames.], batch size: 22, lr: 3.88e-04
2022-05-27 16:21:20,189 INFO [train.py:842] (2/4) Epoch 14, batch 5450, loss[loss=0.2006, simple_loss=0.282, pruned_loss=0.05963, over 6806.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2842, pruned_loss=0.05903, over 1427806.52 frames.], batch size: 31, lr: 3.88e-04
2022-05-27 16:21:59,187 INFO [train.py:842] (2/4) Epoch 14, batch 5500, loss[loss=0.2278, simple_loss=0.3112, pruned_loss=0.07217, over 7195.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2841, pruned_loss=0.0595, over 1429077.42 frames.], batch size: 23, lr: 3.88e-04
2022-05-27 16:22:38,744 INFO [train.py:842] (2/4) Epoch 14, batch 5550, loss[loss=0.1628, simple_loss=0.2633, pruned_loss=0.03116, over 7324.00 frames.], tot_loss[loss=0.201, simple_loss=0.284, pruned_loss=0.05899, over 1431424.17 frames.], batch size: 20, lr: 3.88e-04
2022-05-27 16:23:17,931 INFO [train.py:842] (2/4) Epoch 14, batch 5600, loss[loss=0.1486, simple_loss=0.2183, pruned_loss=0.03939, over 7276.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2816, pruned_loss=0.05786, over 1431356.42 frames.], batch size: 17, lr: 3.88e-04
2022-05-27 16:23:57,204 INFO [train.py:842] (2/4) Epoch 14, batch 5650, loss[loss=0.1697, simple_loss=0.2566, pruned_loss=0.0414, over 7317.00 frames.], tot_loss[loss=0.198, simple_loss=0.2812, pruned_loss=0.05741, over 1431062.93 frames.], batch size: 24, lr: 3.88e-04
2022-05-27 16:24:36,065 INFO [train.py:842] (2/4) Epoch 14, batch 5700, loss[loss=0.2103, simple_loss=0.2976, pruned_loss=0.06154, over 7434.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2807, pruned_loss=0.05687, over 1432775.99 frames.], batch size: 20, lr: 3.88e-04
2022-05-27 16:25:15,131 INFO [train.py:842] (2/4) Epoch 14, batch 5750, loss[loss=0.2061, simple_loss=0.2954, pruned_loss=0.05843, over 7296.00 frames.], tot_loss[loss=0.196, simple_loss=0.2798, pruned_loss=0.05615, over 1431222.49 frames.], batch size: 24, lr: 3.88e-04
2022-05-27 16:25:54,353 INFO [train.py:842] (2/4) Epoch 14, batch 5800, loss[loss=0.2278, simple_loss=0.2999, pruned_loss=0.0779, over 7314.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2796, pruned_loss=0.05596, over 1429183.90 frames.], batch size: 21, lr: 3.88e-04
2022-05-27 16:26:33,788 INFO [train.py:842] (2/4) Epoch 14, batch 5850, loss[loss=0.1664, simple_loss=0.2576, pruned_loss=0.03757, over 7362.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2797, pruned_loss=0.05607, over 1427214.86 frames.], batch size: 19, lr: 3.88e-04
2022-05-27 16:27:12,648 INFO [train.py:842] (2/4) Epoch 14, batch 5900, loss[loss=0.1774, simple_loss=0.2715, pruned_loss=0.04163, over 6515.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2801, pruned_loss=0.05626, over 1421462.20 frames.], batch size: 38, lr: 3.88e-04
2022-05-27 16:27:52,038 INFO [train.py:842] (2/4) Epoch 14, batch 5950, loss[loss=0.2261, simple_loss=0.2917, pruned_loss=0.08025, over 7272.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2812, pruned_loss=0.05724, over 1423402.42 frames.], batch size: 17, lr: 3.88e-04
2022-05-27 16:28:31,059 INFO [train.py:842] (2/4) Epoch 14, batch 6000, loss[loss=0.1847, simple_loss=0.2801, pruned_loss=0.04461, over 6295.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2819, pruned_loss=0.05746, over 1418615.97 frames.], batch size: 37, lr: 3.87e-04
2022-05-27 16:28:31,060 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 16:28:40,676 INFO [train.py:871] (2/4) Epoch 14, validation: loss=0.1701, simple_loss=0.2708, pruned_loss=0.03472, over 868885.00 frames.
2022-05-27 16:29:19,849 INFO [train.py:842] (2/4) Epoch 14, batch 6050, loss[loss=0.19, simple_loss=0.278, pruned_loss=0.05095, over 7231.00 frames.], tot_loss[loss=0.196, simple_loss=0.2797, pruned_loss=0.05615, over 1418532.76 frames.], batch size: 20, lr: 3.87e-04
2022-05-27 16:29:58,714 INFO [train.py:842] (2/4) Epoch 14, batch 6100, loss[loss=0.1855, simple_loss=0.2738, pruned_loss=0.04859, over 7070.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2801, pruned_loss=0.0568, over 1421825.15 frames.], batch size: 18, lr: 3.87e-04
2022-05-27 16:30:38,142 INFO [train.py:842] (2/4) Epoch 14, batch 6150, loss[loss=0.1845, simple_loss=0.2625, pruned_loss=0.05326, over 7209.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2805, pruned_loss=0.05725, over 1422052.58 frames.], batch size: 16, lr: 3.87e-04
2022-05-27 16:31:17,281 INFO [train.py:842] (2/4) Epoch 14, batch 6200, loss[loss=0.1528, simple_loss=0.23, pruned_loss=0.0378, over 7270.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2807, pruned_loss=0.05792, over 1415545.91 frames.], batch size: 17, lr: 3.87e-04
2022-05-27 16:31:56,494 INFO [train.py:842] (2/4) Epoch 14, batch 6250, loss[loss=0.2, simple_loss=0.2904, pruned_loss=0.05476, over 7199.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2813, pruned_loss=0.05824, over 1416785.67 frames.], batch size: 22, lr: 3.87e-04
2022-05-27 16:32:35,091 INFO [train.py:842] (2/4) Epoch 14, batch 6300, loss[loss=0.172, simple_loss=0.2495, pruned_loss=0.04724, over 7284.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2815, pruned_loss=0.05753, over 1417990.15 frames.], batch size: 17, lr: 3.87e-04
2022-05-27 16:33:14,419 INFO [train.py:842] (2/4) Epoch 14, batch 6350, loss[loss=0.2194, simple_loss=0.3105, pruned_loss=0.06416, over 6251.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2813, pruned_loss=0.05704, over 1416600.84 frames.], batch size: 37, lr: 3.87e-04
2022-05-27 16:33:53,374 INFO [train.py:842] (2/4) Epoch 14, batch 6400, loss[loss=0.1765, simple_loss=0.2684, pruned_loss=0.04225, over 7112.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2809, pruned_loss=0.05673, over 1418646.39 frames.], batch size: 21, lr: 3.87e-04
2022-05-27 16:34:32,585 INFO [train.py:842] (2/4) Epoch 14, batch 6450, loss[loss=0.1607, simple_loss=0.2424, pruned_loss=0.0395, over 7264.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2798, pruned_loss=0.05596, over 1421181.81 frames.], batch size: 17, lr: 3.87e-04
2022-05-27 16:35:11,301 INFO [train.py:842] (2/4) Epoch 14, batch 6500, loss[loss=0.2472, simple_loss=0.3113, pruned_loss=0.0916, over 4880.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2814, pruned_loss=0.05671, over 1414950.81 frames.], batch size: 52, lr: 3.87e-04
2022-05-27 16:35:50,515 INFO [train.py:842] (2/4) Epoch 14, batch 6550, loss[loss=0.205, simple_loss=0.2905, pruned_loss=0.05976, over 7214.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2807, pruned_loss=0.05607, over 1416467.98 frames.], batch size: 21, lr: 3.87e-04
2022-05-27 16:36:29,226 INFO [train.py:842] (2/4) Epoch 14, batch 6600, loss[loss=0.1904, simple_loss=0.2851, pruned_loss=0.04788, over 7328.00 frames.], tot_loss[loss=0.198, simple_loss=0.282, pruned_loss=0.05705, over 1413412.13 frames.], batch size: 20, lr: 3.87e-04
2022-05-27 16:37:08,456 INFO [train.py:842] (2/4) Epoch 14, batch 6650, loss[loss=0.1958, simple_loss=0.2853, pruned_loss=0.05315, over 7148.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2829, pruned_loss=0.05745, over 1414310.67 frames.], batch size: 20, lr: 3.86e-04
2022-05-27 16:37:47,798 INFO [train.py:842] (2/4) Epoch 14, batch 6700, loss[loss=0.1784, simple_loss=0.2618, pruned_loss=0.04746, over 7320.00 frames.], tot_loss[loss=0.198, simple_loss=0.2818, pruned_loss=0.0571, over 1417862.46 frames.], batch size: 20, lr: 3.86e-04
2022-05-27 16:38:27,392 INFO [train.py:842] (2/4) Epoch 14, batch 6750, loss[loss=0.1693, simple_loss=0.2603, pruned_loss=0.03919, over 7265.00 frames.], tot_loss[loss=0.199, simple_loss=0.2825, pruned_loss=0.05782, over 1417920.51 frames.], batch size: 19, lr: 3.86e-04
2022-05-27 16:39:06,436 INFO [train.py:842] (2/4) Epoch 14, batch 6800, loss[loss=0.1908, simple_loss=0.2851, pruned_loss=0.04821, over 7157.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2816, pruned_loss=0.0574, over 1411344.15 frames.], batch size: 19, lr: 3.86e-04
2022-05-27 16:39:45,772 INFO [train.py:842] (2/4) Epoch 14, batch 6850, loss[loss=0.2232, simple_loss=0.3072, pruned_loss=0.06965, over 7198.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2813, pruned_loss=0.05688, over 1412928.17 frames.], batch size: 22, lr: 3.86e-04
2022-05-27 16:40:24,693 INFO [train.py:842] (2/4) Epoch 14, batch 6900, loss[loss=0.1741, simple_loss=0.2581, pruned_loss=0.04504, over 7144.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2816, pruned_loss=0.05681, over 1414754.23 frames.], batch size: 17, lr: 3.86e-04
2022-05-27 16:41:03,789 INFO [train.py:842] (2/4) Epoch 14, batch 6950, loss[loss=0.2027, simple_loss=0.2914, pruned_loss=0.05702, over 6341.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2814, pruned_loss=0.05662, over 1417199.19 frames.], batch size: 37, lr: 3.86e-04
2022-05-27 16:41:42,281 INFO [train.py:842] (2/4) Epoch 14, batch 7000, loss[loss=0.1495, simple_loss=0.2319, pruned_loss=0.03359, over 7268.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2819, pruned_loss=0.05689, over 1418338.11 frames.], batch size: 18, lr: 3.86e-04
2022-05-27 16:42:21,258 INFO [train.py:842] (2/4) Epoch 14, batch 7050, loss[loss=0.198, simple_loss=0.2838, pruned_loss=0.05612, over 7073.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2819, pruned_loss=0.05664, over 1417972.65 frames.], batch size: 18, lr: 3.86e-04
2022-05-27 16:43:00,161 INFO [train.py:842] (2/4) Epoch 14, batch 7100, loss[loss=0.1992, simple_loss=0.2818, pruned_loss=0.05827, over 7397.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2812, pruned_loss=0.05671, over 1419795.20 frames.], batch size: 18, lr: 3.86e-04
2022-05-27 16:43:39,115 INFO [train.py:842] (2/4) Epoch 14, batch 7150, loss[loss=0.1947, simple_loss=0.2664, pruned_loss=0.06156, over 7054.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2822, pruned_loss=0.05669, over 1420823.80 frames.], batch size: 18, lr: 3.86e-04
2022-05-27 16:44:18,101 INFO [train.py:842] (2/4) Epoch 14, batch 7200, loss[loss=0.1868, simple_loss=0.2815, pruned_loss=0.04608, over 7314.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2813, pruned_loss=0.05607, over 1423135.46 frames.], batch size: 21, lr: 3.86e-04
2022-05-27 16:44:57,230 INFO [train.py:842] (2/4) Epoch 14, batch 7250, loss[loss=0.1758, simple_loss=0.2635, pruned_loss=0.04409, over 7285.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2811, pruned_loss=0.05638, over 1423539.53 frames.], batch size: 25, lr: 3.86e-04
2022-05-27 16:45:35,825 INFO [train.py:842] (2/4) Epoch 14, batch 7300, loss[loss=0.1999, simple_loss=0.2751, pruned_loss=0.06234, over 7065.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2821, pruned_loss=0.05702, over 1426833.86 frames.], batch size: 18, lr: 3.85e-04
2022-05-27 16:46:14,778 INFO [train.py:842] (2/4) Epoch 14, batch 7350, loss[loss=0.2072, simple_loss=0.3005, pruned_loss=0.05689, over 7086.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2818, pruned_loss=0.05656, over 1429618.30 frames.], batch size: 28, lr: 3.85e-04
2022-05-27 16:46:53,840 INFO [train.py:842] (2/4) Epoch 14, batch 7400, loss[loss=0.1631, simple_loss=0.2441, pruned_loss=0.04104, over 7279.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2814, pruned_loss=0.05663, over 1431316.92 frames.], batch size: 17, lr: 3.85e-04
2022-05-27 16:47:33,351 INFO [train.py:842] (2/4) Epoch 14, batch 7450, loss[loss=0.1889, simple_loss=0.2766, pruned_loss=0.05058, over 7383.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2813, pruned_loss=0.05684, over 1426228.13 frames.], batch size: 23, lr: 3.85e-04
2022-05-27 16:48:12,176 INFO [train.py:842] (2/4) Epoch 14, batch 7500, loss[loss=0.2295, simple_loss=0.2988, pruned_loss=0.08013, over 7167.00 frames.], tot_loss[loss=0.1985, simple_loss=0.282, pruned_loss=0.05752, over 1423076.47 frames.], batch size: 18, lr: 3.85e-04
2022-05-27 16:48:51,392 INFO [train.py:842] (2/4) Epoch 14, batch 7550, loss[loss=0.1731, simple_loss=0.2427, pruned_loss=0.05175, over 7262.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2813, pruned_loss=0.05667, over 1422952.92 frames.], batch size: 17, lr: 3.85e-04
2022-05-27 16:49:30,421 INFO [train.py:842] (2/4) Epoch 14, batch 7600, loss[loss=0.1635, simple_loss=0.2447, pruned_loss=0.04118, over 6846.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2812, pruned_loss=0.05688, over 1424075.20 frames.], batch size: 15, lr: 3.85e-04
2022-05-27 16:50:09,772 INFO [train.py:842] (2/4) Epoch 14, batch 7650, loss[loss=0.1679, simple_loss=0.2633, pruned_loss=0.03619, over 7146.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2802, pruned_loss=0.05626, over 1426809.36 frames.], batch size: 20, lr: 3.85e-04
2022-05-27 16:50:49,105 INFO [train.py:842] (2/4) Epoch 14, batch 7700, loss[loss=0.163, simple_loss=0.2503, pruned_loss=0.03785, over 7200.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2795, pruned_loss=0.05598, over 1425008.65 frames.], batch size: 16, lr: 3.85e-04
2022-05-27 16:51:28,098 INFO [train.py:842] (2/4) Epoch 14, batch 7750, loss[loss=0.2106, simple_loss=0.2758, pruned_loss=0.07272, over 7270.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2808, pruned_loss=0.0567, over 1419410.50 frames.], batch size: 18, lr: 3.85e-04
2022-05-27 16:52:06,967 INFO [train.py:842] (2/4) Epoch 14, batch 7800, loss[loss=0.2079, simple_loss=0.2827, pruned_loss=0.06659, over 7282.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2817, pruned_loss=0.0568, over 1417532.27 frames.], batch size: 18, lr: 3.85e-04
2022-05-27 16:52:45,709 INFO [train.py:842] (2/4) Epoch 14, batch 7850, loss[loss=0.1805, simple_loss=0.2641, pruned_loss=0.04846, over 7277.00 frames.], tot_loss[loss=0.1985, simple_loss=0.282, pruned_loss=0.05749, over 1415250.79 frames.], batch size: 18, lr: 3.85e-04
2022-05-27 16:53:24,463 INFO [train.py:842] (2/4) Epoch 14, batch 7900, loss[loss=0.1951, simple_loss=0.2818, pruned_loss=0.05419, over 7194.00 frames.], tot_loss[loss=0.198, simple_loss=0.2818, pruned_loss=0.05711, over 1415984.41 frames.], batch size: 26, lr: 3.85e-04
2022-05-27 16:54:03,646 INFO [train.py:842] (2/4) Epoch 14, batch 7950, loss[loss=0.1997, simple_loss=0.275, pruned_loss=0.06223, over 7214.00 frames.], tot_loss[loss=0.198, simple_loss=0.2815, pruned_loss=0.05726, over 1414356.71 frames.], batch size: 21, lr: 3.85e-04
2022-05-27 16:54:42,304 INFO [train.py:842] (2/4) Epoch 14, batch 8000, loss[loss=0.1712, simple_loss=0.2662, pruned_loss=0.0381, over 7150.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2815, pruned_loss=0.05696, over 1408982.12 frames.], batch size: 20, lr: 3.84e-04
2022-05-27 16:55:20,880 INFO [train.py:842] (2/4) Epoch 14, batch 8050, loss[loss=0.1857, simple_loss=0.2699, pruned_loss=0.05073, over 7404.00 frames.], tot_loss[loss=0.199, simple_loss=0.283, pruned_loss=0.05749, over 1407918.95 frames.], batch size: 21, lr: 3.84e-04
2022-05-27 16:55:59,553 INFO [train.py:842] (2/4) Epoch 14, batch 8100, loss[loss=0.188, simple_loss=0.2808, pruned_loss=0.04763, over 7441.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2823, pruned_loss=0.05682, over 1413645.72 frames.], batch size: 20, lr: 3.84e-04
2022-05-27 16:56:39,019 INFO [train.py:842] (2/4) Epoch 14, batch 8150, loss[loss=0.1922, simple_loss=0.2794, pruned_loss=0.05253, over 7336.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2822, pruned_loss=0.05743, over 1411623.61 frames.], batch size: 22, lr: 3.84e-04
2022-05-27 16:57:17,974 INFO [train.py:842] (2/4) Epoch 14, batch 8200, loss[loss=0.1854, simple_loss=0.2606, pruned_loss=0.05511, over 7305.00 frames.], tot_loss[loss=0.199, simple_loss=0.2825, pruned_loss=0.05774, over 1416504.99 frames.], batch size: 17, lr: 3.84e-04
2022-05-27 16:57:56,780 INFO [train.py:842] (2/4) Epoch 14, batch 8250, loss[loss=0.2219, simple_loss=0.3027, pruned_loss=0.07053, over 7213.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2814, pruned_loss=0.05677, over 1417496.51 frames.], batch size: 21, lr: 3.84e-04
2022-05-27 16:58:35,703 INFO [train.py:842] (2/4) Epoch 14, batch 8300, loss[loss=0.1912, simple_loss=0.2873, pruned_loss=0.04757, over 7223.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2798, pruned_loss=0.05616, over 1422264.56 frames.], batch size: 21, lr: 3.84e-04
2022-05-27 16:59:14,911 INFO [train.py:842] (2/4) Epoch 14, batch 8350, loss[loss=0.2355, simple_loss=0.3131, pruned_loss=0.07892, over 7304.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2802, pruned_loss=0.05642, over 1420991.37 frames.], batch size: 25, lr: 3.84e-04
2022-05-27 16:59:53,723 INFO [train.py:842] (2/4) Epoch 14, batch 8400, loss[loss=0.2082, simple_loss=0.3002, pruned_loss=0.05812, over 7317.00 frames.], tot_loss[loss=0.195, simple_loss=0.2787, pruned_loss=0.05559, over 1417638.17 frames.], batch size: 24, lr: 3.84e-04
2022-05-27 17:00:32,540 INFO [train.py:842] (2/4) Epoch 14, batch 8450, loss[loss=0.1767, simple_loss=0.2704, pruned_loss=0.0415, over 7152.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2798, pruned_loss=0.05617, over 1419739.27 frames.], batch size: 20, lr: 3.84e-04
2022-05-27 17:01:11,191 INFO [train.py:842] (2/4) Epoch 14, batch 8500, loss[loss=0.2264, simple_loss=0.313, pruned_loss=0.06988, over 6846.00 frames.], tot_loss[loss=0.1975, simple_loss=0.281, pruned_loss=0.05698, over 1421174.44 frames.], batch size: 31, lr: 3.84e-04
2022-05-27 17:01:53,166 INFO [train.py:842] (2/4) Epoch 14, batch 8550, loss[loss=0.1828, simple_loss=0.2765, pruned_loss=0.04455, over 6354.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2809, pruned_loss=0.05739, over 1415847.41 frames.], batch size: 37, lr: 3.84e-04
2022-05-27 17:02:32,120 INFO [train.py:842] (2/4) Epoch 14, batch 8600, loss[loss=0.1603, simple_loss=0.2422, pruned_loss=0.03917, over 7418.00 frames.], tot_loss[loss=0.1968, simple_loss=0.28, pruned_loss=0.05684, over 1418032.21 frames.], batch size: 18, lr: 3.84e-04
2022-05-27 17:03:11,225 INFO [train.py:842] (2/4) Epoch 14, batch 8650, loss[loss=0.1617, simple_loss=0.2458, pruned_loss=0.03882, over 7220.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2788, pruned_loss=0.05619, over 1420759.93 frames.], batch size: 16, lr: 3.83e-04
2022-05-27 17:03:49,929 INFO [train.py:842] (2/4) Epoch 14, batch 8700, loss[loss=0.2024, simple_loss=0.2878, pruned_loss=0.05855, over 7160.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2798, pruned_loss=0.05673, over 1418584.01 frames.], batch size: 19, lr: 3.83e-04
2022-05-27 17:04:29,257 INFO [train.py:842] (2/4) Epoch 14, batch 8750, loss[loss=0.1942, simple_loss=0.2877, pruned_loss=0.05033, over 7222.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2794, pruned_loss=0.05656, over 1416491.01 frames.], batch size: 21, lr: 3.83e-04
2022-05-27 17:05:08,080 INFO [train.py:842] (2/4) Epoch 14, batch 8800, loss[loss=0.2111, simple_loss=0.2978, pruned_loss=0.06218, over 7227.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2796, pruned_loss=0.05675, over 1412017.30 frames.], batch size: 21, lr: 3.83e-04
2022-05-27 17:05:47,078 INFO [train.py:842] (2/4) Epoch 14, batch 8850, loss[loss=0.1519, simple_loss=0.2375, pruned_loss=0.03313, over 7075.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2787, pruned_loss=0.05657, over 1402432.55 frames.], batch size: 18, lr: 3.83e-04
2022-05-27 17:06:26,076 INFO [train.py:842] (2/4) Epoch 14, batch 8900, loss[loss=0.2246, simple_loss=0.3085, pruned_loss=0.07033, over 6750.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2777, pruned_loss=0.05566, over 1401300.85 frames.], batch size: 31, lr: 3.83e-04
2022-05-27 17:07:05,144 INFO [train.py:842] (2/4) Epoch 14, batch 8950, loss[loss=0.2002, simple_loss=0.2853, pruned_loss=0.05757, over 7267.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2786, pruned_loss=0.05589, over 1403049.57 frames.], batch size: 19, lr: 3.83e-04
2022-05-27 17:07:44,225 INFO [train.py:842] (2/4) Epoch 14, batch 9000, loss[loss=0.2087, simple_loss=0.29, pruned_loss=0.06372, over 7023.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2783, pruned_loss=0.0561, over 1404984.08 frames.], batch size: 28, lr: 3.83e-04
2022-05-27 17:07:44,226 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 17:07:53,833 INFO [train.py:871] (2/4) 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] (2/4) Epoch 14, batch 9050, loss[loss=0.2607, simple_loss=0.3329, pruned_loss=0.0943, over 5282.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2768, pruned_loss=0.0558, over 1396405.25 frames.], batch size: 52, lr: 3.83e-04
2022-05-27 17:09:11,599 INFO [train.py:842] (2/4) Epoch 14, batch 9100, loss[loss=0.2478, simple_loss=0.3235, pruned_loss=0.08602, over 4913.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2783, pruned_loss=0.05664, over 1375530.34 frames.], batch size: 52, lr: 3.83e-04
2022-05-27 17:09:49,562 INFO [train.py:842] (2/4) Epoch 14, batch 9150, loss[loss=0.2601, simple_loss=0.3426, pruned_loss=0.08879, over 5230.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2835, pruned_loss=0.06019, over 1312933.58 frames.], batch size: 52, lr: 3.83e-04
2022-05-27 17:10:39,686 INFO [train.py:842] (2/4) Epoch 15, batch 0, loss[loss=0.193, simple_loss=0.2785, pruned_loss=0.05372, over 7087.00 frames.], tot_loss[loss=0.193, simple_loss=0.2785, pruned_loss=0.05372, over 7087.00 frames.], batch size: 28, lr: 3.71e-04
2022-05-27 17:11:18,903 INFO [train.py:842] (2/4) Epoch 15, batch 50, loss[loss=0.287, simple_loss=0.3614, pruned_loss=0.1063, over 4918.00 frames.], tot_loss[loss=0.204, simple_loss=0.2852, pruned_loss=0.06141, over 321680.53 frames.], batch size: 53, lr: 3.71e-04
2022-05-27 17:11:57,671 INFO [train.py:842] (2/4) Epoch 15, batch 100, loss[loss=0.1625, simple_loss=0.2452, pruned_loss=0.03992, over 7162.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2839, pruned_loss=0.05984, over 567970.41 frames.], batch size: 18, lr: 3.71e-04
2022-05-27 17:12:36,383 INFO [train.py:842] (2/4) Epoch 15, batch 150, loss[loss=0.1926, simple_loss=0.293, pruned_loss=0.04613, over 7112.00 frames.], tot_loss[loss=0.2, simple_loss=0.2838, pruned_loss=0.0581, over 758341.83 frames.], batch size: 21, lr: 3.71e-04
2022-05-27 17:13:15,162 INFO [train.py:842] (2/4) Epoch 15, batch 200, loss[loss=0.1681, simple_loss=0.2623, pruned_loss=0.03702, over 7325.00 frames.], tot_loss[loss=0.1997, simple_loss=0.284, pruned_loss=0.05769, over 902808.01 frames.], batch size: 20, lr: 3.71e-04
2022-05-27 17:13:54,165 INFO [train.py:842] (2/4) Epoch 15, batch 250, loss[loss=0.2593, simple_loss=0.3368, pruned_loss=0.09094, over 6506.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2823, pruned_loss=0.05638, over 1020122.42 frames.], batch size: 38, lr: 3.71e-04
2022-05-27 17:14:33,337 INFO [train.py:842] (2/4) Epoch 15, batch 300, loss[loss=0.1855, simple_loss=0.2544, pruned_loss=0.05827, over 7142.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2803, pruned_loss=0.05537, over 1110589.20 frames.], batch size: 17, lr: 3.71e-04
2022-05-27 17:15:12,298 INFO [train.py:842] (2/4) Epoch 15, batch 350, loss[loss=0.1842, simple_loss=0.2606, pruned_loss=0.05392, over 6822.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2802, pruned_loss=0.05558, over 1172110.21 frames.], batch size: 15, lr: 3.70e-04
2022-05-27 17:15:51,604 INFO [train.py:842] (2/4) Epoch 15, batch 400, loss[loss=0.3197, simple_loss=0.3827, pruned_loss=0.1284, over 7155.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2807, pruned_loss=0.05579, over 1226861.51 frames.], batch size: 20, lr: 3.70e-04
2022-05-27 17:16:30,697 INFO [train.py:842] (2/4) Epoch 15, batch 450, loss[loss=0.2218, simple_loss=0.2948, pruned_loss=0.07442, over 7166.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2806, pruned_loss=0.05616, over 1271196.98 frames.], batch size: 19, lr: 3.70e-04
2022-05-27 17:17:09,298 INFO [train.py:842] (2/4) Epoch 15, batch 500, loss[loss=0.2025, simple_loss=0.286, pruned_loss=0.05955, over 7424.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2813, pruned_loss=0.05692, over 1303281.95 frames.], batch size: 20, lr: 3.70e-04
2022-05-27 17:17:48,676 INFO [train.py:842] (2/4) Epoch 15, batch 550, loss[loss=0.1542, simple_loss=0.2343, pruned_loss=0.03701, over 7274.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2809, pruned_loss=0.05681, over 1331766.48 frames.], batch size: 18, lr: 3.70e-04
2022-05-27 17:18:27,557 INFO [train.py:842] (2/4) Epoch 15, batch 600, loss[loss=0.195, simple_loss=0.2842, pruned_loss=0.05287, over 7230.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2803, pruned_loss=0.05657, over 1354454.88 frames.], batch size: 20, lr: 3.70e-04
2022-05-27 17:19:06,381 INFO [train.py:842] (2/4) Epoch 15, batch 650, loss[loss=0.2012, simple_loss=0.288, pruned_loss=0.05724, over 7349.00 frames.], tot_loss[loss=0.1965, simple_loss=0.28, pruned_loss=0.05651, over 1369750.08 frames.], batch size: 22, lr: 3.70e-04
2022-05-27 17:19:45,094 INFO [train.py:842] (2/4) Epoch 15, batch 700, loss[loss=0.1833, simple_loss=0.2775, pruned_loss=0.04456, over 7333.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2805, pruned_loss=0.05638, over 1383080.33 frames.], batch size: 20, lr: 3.70e-04
2022-05-27 17:20:24,130 INFO [train.py:842] (2/4) Epoch 15, batch 750, loss[loss=0.2103, simple_loss=0.2973, pruned_loss=0.0616, over 7332.00 frames.], tot_loss[loss=0.1956, simple_loss=0.28, pruned_loss=0.05565, over 1391605.67 frames.], batch size: 22, lr: 3.70e-04
2022-05-27 17:21:03,144 INFO [train.py:842] (2/4) Epoch 15, batch 800, loss[loss=0.2132, simple_loss=0.3052, pruned_loss=0.06063, over 7319.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2818, pruned_loss=0.05693, over 1400009.06 frames.], batch size: 22, lr: 3.70e-04
2022-05-27 17:21:42,295 INFO [train.py:842] (2/4) Epoch 15, batch 850, loss[loss=0.1523, simple_loss=0.2408, pruned_loss=0.03188, over 7122.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2808, pruned_loss=0.05645, over 1402162.35 frames.], batch size: 17, lr: 3.70e-04
2022-05-27 17:22:21,037 INFO [train.py:842] (2/4) Epoch 15, batch 900, loss[loss=0.1796, simple_loss=0.2635, pruned_loss=0.04784, over 7268.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2793, pruned_loss=0.05546, over 1397247.42 frames.], batch size: 19, lr: 3.70e-04
2022-05-27 17:22:59,782 INFO [train.py:842] (2/4) Epoch 15, batch 950, loss[loss=0.1862, simple_loss=0.274, pruned_loss=0.0492, over 7330.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2797, pruned_loss=0.05549, over 1405936.85 frames.], batch size: 22, lr: 3.70e-04
2022-05-27 17:23:38,674 INFO [train.py:842] (2/4) Epoch 15, batch 1000, loss[loss=0.167, simple_loss=0.2712, pruned_loss=0.03144, over 7091.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2801, pruned_loss=0.05586, over 1407586.18 frames.], batch size: 28, lr: 3.70e-04
2022-05-27 17:24:17,870 INFO [train.py:842] (2/4) Epoch 15, batch 1050, loss[loss=0.2098, simple_loss=0.295, pruned_loss=0.06229, over 7278.00 frames.], tot_loss[loss=0.195, simple_loss=0.2793, pruned_loss=0.05536, over 1413191.66 frames.], batch size: 18, lr: 3.70e-04
2022-05-27 17:24:57,072 INFO [train.py:842] (2/4) Epoch 15, batch 1100, loss[loss=0.1572, simple_loss=0.2382, pruned_loss=0.03814, over 7271.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2801, pruned_loss=0.05629, over 1417287.82 frames.], batch size: 17, lr: 3.69e-04
2022-05-27 17:25:36,295 INFO [train.py:842] (2/4) Epoch 15, batch 1150, loss[loss=0.1549, simple_loss=0.2544, pruned_loss=0.02771, over 7404.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2803, pruned_loss=0.05676, over 1421570.33 frames.], batch size: 21, lr: 3.69e-04
2022-05-27 17:26:15,262 INFO [train.py:842] (2/4) Epoch 15, batch 1200, loss[loss=0.1632, simple_loss=0.2538, pruned_loss=0.03625, over 7427.00 frames.], tot_loss[loss=0.196, simple_loss=0.2794, pruned_loss=0.05636, over 1423335.11 frames.], batch size: 20, lr: 3.69e-04
2022-05-27 17:26:54,528 INFO [train.py:842] (2/4) Epoch 15, batch 1250, loss[loss=0.1491, simple_loss=0.2364, pruned_loss=0.03087, over 7365.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2794, pruned_loss=0.05608, over 1426112.78 frames.], batch size: 19, lr: 3.69e-04
2022-05-27 17:27:33,148 INFO [train.py:842] (2/4) Epoch 15, batch 1300, loss[loss=0.2403, simple_loss=0.3096, pruned_loss=0.08547, over 6217.00 frames.], tot_loss[loss=0.1967, simple_loss=0.28, pruned_loss=0.05666, over 1419403.55 frames.], batch size: 37, lr: 3.69e-04
2022-05-27 17:28:12,410 INFO [train.py:842] (2/4) Epoch 15, batch 1350, loss[loss=0.1591, simple_loss=0.2316, pruned_loss=0.04325, over 6983.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2802, pruned_loss=0.05642, over 1420545.36 frames.], batch size: 16, lr: 3.69e-04
2022-05-27 17:28:51,238 INFO [train.py:842] (2/4) Epoch 15, batch 1400, loss[loss=0.2064, simple_loss=0.2927, pruned_loss=0.06007, over 7295.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2796, pruned_loss=0.05608, over 1420194.68 frames.], batch size: 24, lr: 3.69e-04
2022-05-27 17:29:30,186 INFO [train.py:842] (2/4) Epoch 15, batch 1450, loss[loss=0.2021, simple_loss=0.3009, pruned_loss=0.05164, over 7359.00 frames.], tot_loss[loss=0.196, simple_loss=0.28, pruned_loss=0.05594, over 1417313.26 frames.], batch size: 23, lr: 3.69e-04
2022-05-27 17:30:08,792 INFO [train.py:842] (2/4) Epoch 15, batch 1500, loss[loss=0.1867, simple_loss=0.2776, pruned_loss=0.04795, over 7148.00 frames.], tot_loss[loss=0.196, simple_loss=0.2799, pruned_loss=0.05602, over 1411413.00 frames.], batch size: 20, lr: 3.69e-04
2022-05-27 17:30:48,094 INFO [train.py:842] (2/4) Epoch 15, batch 1550, loss[loss=0.1721, simple_loss=0.2648, pruned_loss=0.03973, over 7118.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2786, pruned_loss=0.05539, over 1416493.22 frames.], batch size: 21, lr: 3.69e-04
2022-05-27 17:31:26,954 INFO [train.py:842] (2/4) Epoch 15, batch 1600, loss[loss=0.1858, simple_loss=0.2827, pruned_loss=0.04442, over 7418.00 frames.], tot_loss[loss=0.1937, simple_loss=0.278, pruned_loss=0.05475, over 1418455.14 frames.], batch size: 21, lr: 3.69e-04
2022-05-27 17:32:05,920 INFO [train.py:842] (2/4) Epoch 15, batch 1650, loss[loss=0.194, simple_loss=0.2873, pruned_loss=0.05034, over 7210.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2769, pruned_loss=0.05394, over 1423461.63 frames.], batch size: 23, lr: 3.69e-04
2022-05-27 17:32:44,946 INFO [train.py:842] (2/4) Epoch 15, batch 1700, loss[loss=0.2527, simple_loss=0.3314, pruned_loss=0.08701, over 7282.00 frames.], tot_loss[loss=0.1915, simple_loss=0.276, pruned_loss=0.05347, over 1427250.83 frames.], batch size: 25, lr: 3.69e-04
2022-05-27 17:33:24,351 INFO [train.py:842] (2/4) Epoch 15, batch 1750, loss[loss=0.227, simple_loss=0.3112, pruned_loss=0.07142, over 7052.00 frames.], tot_loss[loss=0.192, simple_loss=0.2763, pruned_loss=0.0538, over 1430311.78 frames.], batch size: 28, lr: 3.69e-04
2022-05-27 17:34:03,110 INFO [train.py:842] (2/4) Epoch 15, batch 1800, loss[loss=0.1909, simple_loss=0.2721, pruned_loss=0.05484, over 7283.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2767, pruned_loss=0.05391, over 1427367.30 frames.], batch size: 17, lr: 3.68e-04
2022-05-27 17:34:42,338 INFO [train.py:842] (2/4) Epoch 15, batch 1850, loss[loss=0.1755, simple_loss=0.2601, pruned_loss=0.04548, over 7163.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2779, pruned_loss=0.05482, over 1432247.34 frames.], batch size: 18, lr: 3.68e-04
2022-05-27 17:35:21,429 INFO [train.py:842] (2/4) Epoch 15, batch 1900, loss[loss=0.1861, simple_loss=0.2725, pruned_loss=0.04986, over 7121.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2794, pruned_loss=0.0557, over 1432099.69 frames.], batch size: 21, lr: 3.68e-04
2022-05-27 17:36:00,616 INFO [train.py:842] (2/4) Epoch 15, batch 1950, loss[loss=0.1567, simple_loss=0.2425, pruned_loss=0.03542, over 7260.00 frames.], tot_loss[loss=0.1942, simple_loss=0.278, pruned_loss=0.0552, over 1432060.80 frames.], batch size: 18, lr: 3.68e-04
2022-05-27 17:36:39,389 INFO [train.py:842] (2/4) Epoch 15, batch 2000, loss[loss=0.1977, simple_loss=0.2863, pruned_loss=0.05453, over 6551.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2782, pruned_loss=0.05562, over 1428280.68 frames.], batch size: 38, lr: 3.68e-04
2022-05-27 17:37:18,698 INFO [train.py:842] (2/4) Epoch 15, batch 2050, loss[loss=0.2302, simple_loss=0.3194, pruned_loss=0.07051, over 7311.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2797, pruned_loss=0.05651, over 1429959.28 frames.], batch size: 25, lr: 3.68e-04
2022-05-27 17:37:57,584 INFO [train.py:842] (2/4) Epoch 15, batch 2100, loss[loss=0.2016, simple_loss=0.2725, pruned_loss=0.06534, over 7419.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2788, pruned_loss=0.05623, over 1423608.47 frames.], batch size: 18, lr: 3.68e-04
2022-05-27 17:38:36,704 INFO [train.py:842] (2/4) Epoch 15, batch 2150, loss[loss=0.2901, simple_loss=0.3598, pruned_loss=0.1102, over 7202.00 frames.], tot_loss[loss=0.195, simple_loss=0.2785, pruned_loss=0.05575, over 1422074.20 frames.], batch size: 22, lr: 3.68e-04
2022-05-27 17:39:15,645 INFO [train.py:842] (2/4) Epoch 15, batch 2200, loss[loss=0.1765, simple_loss=0.263, pruned_loss=0.04495, over 7447.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2785, pruned_loss=0.05565, over 1421825.03 frames.], batch size: 20, lr: 3.68e-04
2022-05-27 17:39:54,618 INFO [train.py:842] (2/4) Epoch 15, batch 2250, loss[loss=0.1884, simple_loss=0.2775, pruned_loss=0.04966, over 7086.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2784, pruned_loss=0.05494, over 1422376.57 frames.], batch size: 28, lr: 3.68e-04
2022-05-27 17:40:33,424 INFO [train.py:842] (2/4) Epoch 15, batch 2300, loss[loss=0.146, simple_loss=0.2367, pruned_loss=0.02761, over 6804.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2787, pruned_loss=0.0555, over 1420858.21 frames.], batch size: 15, lr: 3.68e-04
2022-05-27 17:41:12,435 INFO [train.py:842] (2/4) Epoch 15, batch 2350, loss[loss=0.1679, simple_loss=0.2387, pruned_loss=0.04859, over 7411.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2783, pruned_loss=0.05548, over 1424236.72 frames.], batch size: 18, lr: 3.68e-04
2022-05-27 17:41:51,214 INFO [train.py:842] (2/4) Epoch 15, batch 2400, loss[loss=0.1716, simple_loss=0.2546, pruned_loss=0.04431, over 7410.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2786, pruned_loss=0.05497, over 1422821.59 frames.], batch size: 18, lr: 3.68e-04
2022-05-27 17:42:30,537 INFO [train.py:842] (2/4) Epoch 15, batch 2450, loss[loss=0.1905, simple_loss=0.2841, pruned_loss=0.04844, over 7401.00 frames.], tot_loss[loss=0.1948, simple_loss=0.279, pruned_loss=0.0553, over 1424238.90 frames.], batch size: 21, lr: 3.68e-04
2022-05-27 17:43:09,562 INFO [train.py:842] (2/4) Epoch 15, batch 2500, loss[loss=0.1773, simple_loss=0.2678, pruned_loss=0.04343, over 7316.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2787, pruned_loss=0.05489, over 1425819.81 frames.], batch size: 21, lr: 3.67e-04
2022-05-27 17:43:48,430 INFO [train.py:842] (2/4) Epoch 15, batch 2550, loss[loss=0.156, simple_loss=0.2376, pruned_loss=0.03724, over 7161.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2794, pruned_loss=0.05562, over 1428011.15 frames.], batch size: 18, lr: 3.67e-04
2022-05-27 17:44:27,021 INFO [train.py:842] (2/4) Epoch 15, batch 2600, loss[loss=0.2162, simple_loss=0.2989, pruned_loss=0.06677, over 7211.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2785, pruned_loss=0.05515, over 1421562.65 frames.], batch size: 23, lr: 3.67e-04
2022-05-27 17:45:06,244 INFO [train.py:842] (2/4) Epoch 15, batch 2650, loss[loss=0.206, simple_loss=0.2951, pruned_loss=0.05849, over 7294.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2792, pruned_loss=0.05508, over 1422410.23 frames.], batch size: 25, lr: 3.67e-04
2022-05-27 17:45:45,119 INFO [train.py:842] (2/4) Epoch 15, batch 2700, loss[loss=0.2112, simple_loss=0.2814, pruned_loss=0.07052, over 7324.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2795, pruned_loss=0.05549, over 1425138.05 frames.], batch size: 21, lr: 3.67e-04
2022-05-27 17:46:24,221 INFO [train.py:842] (2/4) Epoch 15, batch 2750, loss[loss=0.2388, simple_loss=0.3234, pruned_loss=0.07709, over 7286.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2791, pruned_loss=0.05562, over 1425308.02 frames.], batch size: 24, lr: 3.67e-04
2022-05-27 17:47:03,180 INFO [train.py:842] (2/4) Epoch 15, batch 2800, loss[loss=0.199, simple_loss=0.293, pruned_loss=0.05246, over 7146.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2783, pruned_loss=0.05507, over 1427952.53 frames.], batch size: 20, lr: 3.67e-04
2022-05-27 17:47:42,021 INFO [train.py:842] (2/4) Epoch 15, batch 2850, loss[loss=0.2034, simple_loss=0.2647, pruned_loss=0.07107, over 6856.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2784, pruned_loss=0.05469, over 1428942.54 frames.], batch size: 15, lr: 3.67e-04
2022-05-27 17:48:20,940 INFO [train.py:842] (2/4) Epoch 15, batch 2900, loss[loss=0.237, simple_loss=0.3241, pruned_loss=0.07497, over 7369.00 frames.], tot_loss[loss=0.195, simple_loss=0.2789, pruned_loss=0.05551, over 1423808.73 frames.], batch size: 23, lr: 3.67e-04
2022-05-27 17:49:00,331 INFO [train.py:842] (2/4) Epoch 15, batch 2950, loss[loss=0.1864, simple_loss=0.269, pruned_loss=0.05192, over 7410.00 frames.], tot_loss[loss=0.196, simple_loss=0.2797, pruned_loss=0.05615, over 1425081.79 frames.], batch size: 20, lr: 3.67e-04
2022-05-27 17:49:39,755 INFO [train.py:842] (2/4) Epoch 15, batch 3000, loss[loss=0.1577, simple_loss=0.2462, pruned_loss=0.0346, over 7163.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2803, pruned_loss=0.05665, over 1423510.82 frames.], batch size: 19, lr: 3.67e-04
2022-05-27 17:49:39,756 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 17:49:49,311 INFO [train.py:871] (2/4) Epoch 15, validation: loss=0.1691, simple_loss=0.2695, pruned_loss=0.03437, over 868885.00 frames.
2022-05-27 17:50:28,467 INFO [train.py:842] (2/4) Epoch 15, batch 3050, loss[loss=0.2198, simple_loss=0.2916, pruned_loss=0.07398, over 6834.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2807, pruned_loss=0.05729, over 1426038.40 frames.], batch size: 15, lr: 3.67e-04
2022-05-27 17:51:07,338 INFO [train.py:842] (2/4) Epoch 15, batch 3100, loss[loss=0.1935, simple_loss=0.2804, pruned_loss=0.05334, over 7326.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2804, pruned_loss=0.05706, over 1423201.70 frames.], batch size: 20, lr: 3.67e-04
2022-05-27 17:51:46,492 INFO [train.py:842] (2/4) Epoch 15, batch 3150, loss[loss=0.2243, simple_loss=0.2973, pruned_loss=0.07569, over 7280.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2795, pruned_loss=0.05664, over 1427517.85 frames.], batch size: 17, lr: 3.67e-04
2022-05-27 17:52:25,454 INFO [train.py:842] (2/4) Epoch 15, batch 3200, loss[loss=0.2202, simple_loss=0.3038, pruned_loss=0.06826, over 7024.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2786, pruned_loss=0.0563, over 1427163.52 frames.], batch size: 28, lr: 3.66e-04
2022-05-27 17:53:04,613 INFO [train.py:842] (2/4) Epoch 15, batch 3250, loss[loss=0.2046, simple_loss=0.2758, pruned_loss=0.06666, over 7067.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2776, pruned_loss=0.05574, over 1428043.07 frames.], batch size: 18, lr: 3.66e-04
2022-05-27 17:53:43,715 INFO [train.py:842] (2/4) Epoch 15, batch 3300, loss[loss=0.173, simple_loss=0.2584, pruned_loss=0.04382, over 7295.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2774, pruned_loss=0.0554, over 1427158.84 frames.], batch size: 17, lr: 3.66e-04
2022-05-27 17:54:22,782 INFO [train.py:842] (2/4) Epoch 15, batch 3350, loss[loss=0.1821, simple_loss=0.2762, pruned_loss=0.04398, over 7216.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2786, pruned_loss=0.0554, over 1426293.71 frames.], batch size: 23, lr: 3.66e-04
2022-05-27 17:55:01,380 INFO [train.py:842] (2/4) Epoch 15, batch 3400, loss[loss=0.1967, simple_loss=0.2852, pruned_loss=0.05413, over 7223.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2788, pruned_loss=0.05501, over 1423491.09 frames.], batch size: 21, lr: 3.66e-04
2022-05-27 17:55:40,271 INFO [train.py:842] (2/4) Epoch 15, batch 3450, loss[loss=0.2066, simple_loss=0.2823, pruned_loss=0.06545, over 7120.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2799, pruned_loss=0.05591, over 1421245.32 frames.], batch size: 28, lr: 3.66e-04
2022-05-27 17:56:19,152 INFO [train.py:842] (2/4) Epoch 15, batch 3500, loss[loss=0.1817, simple_loss=0.2764, pruned_loss=0.04351, over 7135.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2787, pruned_loss=0.05504, over 1426215.98 frames.], batch size: 26, lr: 3.66e-04
2022-05-27 17:56:58,074 INFO [train.py:842] (2/4) Epoch 15, batch 3550, loss[loss=0.1699, simple_loss=0.2586, pruned_loss=0.04063, over 7232.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2797, pruned_loss=0.0557, over 1427544.27 frames.], batch size: 20, lr: 3.66e-04
2022-05-27 17:57:36,714 INFO [train.py:842] (2/4) Epoch 15, batch 3600, loss[loss=0.2186, simple_loss=0.3058, pruned_loss=0.06575, over 7314.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2803, pruned_loss=0.05611, over 1423519.58 frames.], batch size: 21, lr: 3.66e-04
2022-05-27 17:58:15,798 INFO [train.py:842] (2/4) Epoch 15, batch 3650, loss[loss=0.1877, simple_loss=0.2731, pruned_loss=0.05115, over 7253.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2804, pruned_loss=0.05658, over 1424747.13 frames.], batch size: 19, lr: 3.66e-04
2022-05-27 17:58:54,659 INFO [train.py:842] (2/4) Epoch 15, batch 3700, loss[loss=0.2515, simple_loss=0.325, pruned_loss=0.08901, over 7439.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2806, pruned_loss=0.05691, over 1422001.63 frames.], batch size: 20, lr: 3.66e-04
2022-05-27 17:59:34,170 INFO [train.py:842] (2/4) Epoch 15, batch 3750, loss[loss=0.291, simple_loss=0.3469, pruned_loss=0.1176, over 5128.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2794, pruned_loss=0.05611, over 1424216.09 frames.], batch size: 52, lr: 3.66e-04
2022-05-27 18:00:12,935 INFO [train.py:842] (2/4) Epoch 15, batch 3800, loss[loss=0.1556, simple_loss=0.2357, pruned_loss=0.03777, over 7060.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2804, pruned_loss=0.05639, over 1426246.24 frames.], batch size: 18, lr: 3.66e-04
2022-05-27 18:00:51,698 INFO [train.py:842] (2/4) Epoch 15, batch 3850, loss[loss=0.2355, simple_loss=0.3323, pruned_loss=0.06938, over 7230.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2809, pruned_loss=0.05595, over 1429023.29 frames.], batch size: 20, lr: 3.66e-04
2022-05-27 18:01:30,651 INFO [train.py:842] (2/4) Epoch 15, batch 3900, loss[loss=0.1671, simple_loss=0.2609, pruned_loss=0.03669, over 7265.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2806, pruned_loss=0.05616, over 1426045.25 frames.], batch size: 19, lr: 3.66e-04
2022-05-27 18:02:19,744 INFO [train.py:842] (2/4) Epoch 15, batch 3950, loss[loss=0.2116, simple_loss=0.2903, pruned_loss=0.06646, over 7151.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2799, pruned_loss=0.05595, over 1421395.34 frames.], batch size: 20, lr: 3.65e-04
2022-05-27 18:02:58,495 INFO [train.py:842] (2/4) Epoch 15, batch 4000, loss[loss=0.19, simple_loss=0.2602, pruned_loss=0.05986, over 7130.00 frames.], tot_loss[loss=0.197, simple_loss=0.2807, pruned_loss=0.05665, over 1422142.16 frames.], batch size: 17, lr: 3.65e-04
2022-05-27 18:03:37,569 INFO [train.py:842] (2/4) Epoch 15, batch 4050, loss[loss=0.1925, simple_loss=0.2781, pruned_loss=0.0534, over 6594.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2826, pruned_loss=0.05724, over 1425905.55 frames.], batch size: 38, lr: 3.65e-04
2022-05-27 18:04:16,175 INFO [train.py:842] (2/4) Epoch 15, batch 4100, loss[loss=0.1848, simple_loss=0.2828, pruned_loss=0.04342, over 7409.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2817, pruned_loss=0.05668, over 1420771.39 frames.], batch size: 21, lr: 3.65e-04
2022-05-27 18:04:55,285 INFO [train.py:842] (2/4) Epoch 15, batch 4150, loss[loss=0.1697, simple_loss=0.2447, pruned_loss=0.04733, over 7416.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2815, pruned_loss=0.05687, over 1422792.65 frames.], batch size: 18, lr: 3.65e-04
2022-05-27 18:05:33,906 INFO [train.py:842] (2/4) Epoch 15, batch 4200, loss[loss=0.2033, simple_loss=0.2902, pruned_loss=0.05827, over 7372.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2819, pruned_loss=0.05734, over 1417149.18 frames.], batch size: 23, lr: 3.65e-04
2022-05-27 18:06:13,517 INFO [train.py:842] (2/4) Epoch 15, batch 4250, loss[loss=0.2055, simple_loss=0.2907, pruned_loss=0.06013, over 7297.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2808, pruned_loss=0.0569, over 1417838.99 frames.], batch size: 24, lr: 3.65e-04
2022-05-27 18:06:52,319 INFO [train.py:842] (2/4) Epoch 15, batch 4300, loss[loss=0.1957, simple_loss=0.2875, pruned_loss=0.05199, over 7289.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2805, pruned_loss=0.05682, over 1415423.15 frames.], batch size: 25, lr: 3.65e-04
2022-05-27 18:07:31,748 INFO [train.py:842] (2/4) Epoch 15, batch 4350, loss[loss=0.1415, simple_loss=0.2262, pruned_loss=0.02847, over 7169.00 frames.], tot_loss[loss=0.196, simple_loss=0.2797, pruned_loss=0.05616, over 1419512.00 frames.], batch size: 18, lr: 3.65e-04
2022-05-27 18:08:10,566 INFO [train.py:842] (2/4) Epoch 15, batch 4400, loss[loss=0.2186, simple_loss=0.2838, pruned_loss=0.07672, over 7264.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2803, pruned_loss=0.05599, over 1419425.18 frames.], batch size: 18, lr: 3.65e-04
2022-05-27 18:08:49,766 INFO [train.py:842] (2/4) Epoch 15, batch 4450, loss[loss=0.1884, simple_loss=0.2822, pruned_loss=0.04729, over 7411.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2794, pruned_loss=0.05604, over 1420275.95 frames.], batch size: 21, lr: 3.65e-04
2022-05-27 18:09:28,901 INFO [train.py:842] (2/4) Epoch 15, batch 4500, loss[loss=0.1986, simple_loss=0.294, pruned_loss=0.05163, over 7288.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2798, pruned_loss=0.0557, over 1423679.53 frames.], batch size: 25, lr: 3.65e-04
2022-05-27 18:10:07,903 INFO [train.py:842] (2/4) Epoch 15, batch 4550, loss[loss=0.1807, simple_loss=0.2684, pruned_loss=0.04646, over 7323.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2807, pruned_loss=0.05573, over 1426014.46 frames.], batch size: 20, lr: 3.65e-04
2022-05-27 18:10:46,889 INFO [train.py:842] (2/4) Epoch 15, batch 4600, loss[loss=0.2496, simple_loss=0.329, pruned_loss=0.08511, over 7226.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2793, pruned_loss=0.05539, over 1426633.73 frames.], batch size: 21, lr: 3.65e-04
2022-05-27 18:11:26,084 INFO [train.py:842] (2/4) Epoch 15, batch 4650, loss[loss=0.2224, simple_loss=0.3046, pruned_loss=0.07013, over 6785.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2797, pruned_loss=0.05577, over 1426204.32 frames.], batch size: 31, lr: 3.64e-04
2022-05-27 18:12:04,839 INFO [train.py:842] (2/4) Epoch 15, batch 4700, loss[loss=0.2317, simple_loss=0.3124, pruned_loss=0.07548, over 7137.00 frames.], tot_loss[loss=0.1957, simple_loss=0.28, pruned_loss=0.05569, over 1429517.25 frames.], batch size: 20, lr: 3.64e-04
2022-05-27 18:12:44,012 INFO [train.py:842] (2/4) Epoch 15, batch 4750, loss[loss=0.2516, simple_loss=0.3066, pruned_loss=0.09831, over 7269.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2807, pruned_loss=0.05599, over 1429819.21 frames.], batch size: 17, lr: 3.64e-04
2022-05-27 18:13:22,835 INFO [train.py:842] (2/4) Epoch 15, batch 4800, loss[loss=0.1644, simple_loss=0.2607, pruned_loss=0.03409, over 6838.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2789, pruned_loss=0.05506, over 1428568.24 frames.], batch size: 32, lr: 3.64e-04
2022-05-27 18:14:02,054 INFO [train.py:842] (2/4) Epoch 15, batch 4850, loss[loss=0.2206, simple_loss=0.2992, pruned_loss=0.07096, over 7012.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2787, pruned_loss=0.05535, over 1428541.94 frames.], batch size: 28, lr: 3.64e-04
2022-05-27 18:14:40,906 INFO [train.py:842] (2/4) Epoch 15, batch 4900, loss[loss=0.1726, simple_loss=0.2546, pruned_loss=0.04535, over 7168.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2783, pruned_loss=0.05535, over 1430026.71 frames.], batch size: 19, lr: 3.64e-04
2022-05-27 18:15:20,257 INFO [train.py:842] (2/4) Epoch 15, batch 4950, loss[loss=0.1714, simple_loss=0.2634, pruned_loss=0.03967, over 7154.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2783, pruned_loss=0.05573, over 1431168.61 frames.], batch size: 19, lr: 3.64e-04
2022-05-27 18:15:58,885 INFO [train.py:842] (2/4) Epoch 15, batch 5000, loss[loss=0.2107, simple_loss=0.2931, pruned_loss=0.06417, over 7145.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2783, pruned_loss=0.05541, over 1429857.75 frames.], batch size: 26, lr: 3.64e-04
2022-05-27 18:16:38,101 INFO [train.py:842] (2/4) Epoch 15, batch 5050, loss[loss=0.1813, simple_loss=0.2753, pruned_loss=0.04361, over 7229.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2777, pruned_loss=0.05498, over 1431629.66 frames.], batch size: 20, lr: 3.64e-04
2022-05-27 18:17:17,305 INFO [train.py:842] (2/4) Epoch 15, batch 5100, loss[loss=0.1522, simple_loss=0.2371, pruned_loss=0.03365, over 7432.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2771, pruned_loss=0.05461, over 1430742.59 frames.], batch size: 20, lr: 3.64e-04
2022-05-27 18:17:56,540 INFO [train.py:842] (2/4) Epoch 15, batch 5150, loss[loss=0.1924, simple_loss=0.2775, pruned_loss=0.05365, over 7375.00 frames.], tot_loss[loss=0.1939, simple_loss=0.278, pruned_loss=0.05495, over 1426701.44 frames.], batch size: 23, lr: 3.64e-04
2022-05-27 18:18:35,583 INFO [train.py:842] (2/4) Epoch 15, batch 5200, loss[loss=0.1949, simple_loss=0.2855, pruned_loss=0.05215, over 6757.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2786, pruned_loss=0.05543, over 1425895.32 frames.], batch size: 31, lr: 3.64e-04
2022-05-27 18:19:14,515 INFO [train.py:842] (2/4) Epoch 15, batch 5250, loss[loss=0.1986, simple_loss=0.2835, pruned_loss=0.05692, over 7160.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2798, pruned_loss=0.05648, over 1424398.59 frames.], batch size: 19, lr: 3.64e-04
2022-05-27 18:19:53,399 INFO [train.py:842] (2/4) Epoch 15, batch 5300, loss[loss=0.1935, simple_loss=0.2765, pruned_loss=0.0553, over 7325.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2804, pruned_loss=0.05667, over 1419072.29 frames.], batch size: 20, lr: 3.64e-04
2022-05-27 18:20:32,636 INFO [train.py:842] (2/4) Epoch 15, batch 5350, loss[loss=0.1667, simple_loss=0.2549, pruned_loss=0.03925, over 7257.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2785, pruned_loss=0.05552, over 1420641.20 frames.], batch size: 19, lr: 3.64e-04
2022-05-27 18:21:11,495 INFO [train.py:842] (2/4) Epoch 15, batch 5400, loss[loss=0.1823, simple_loss=0.2713, pruned_loss=0.04667, over 7359.00 frames.], tot_loss[loss=0.194, simple_loss=0.2777, pruned_loss=0.05511, over 1422569.31 frames.], batch size: 19, lr: 3.63e-04
2022-05-27 18:21:50,467 INFO [train.py:842] (2/4) Epoch 15, batch 5450, loss[loss=0.1648, simple_loss=0.2588, pruned_loss=0.03539, over 7328.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2762, pruned_loss=0.05435, over 1426726.00 frames.], batch size: 22, lr: 3.63e-04
2022-05-27 18:22:29,572 INFO [train.py:842] (2/4) Epoch 15, batch 5500, loss[loss=0.1853, simple_loss=0.2859, pruned_loss=0.04235, over 7199.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2756, pruned_loss=0.05397, over 1425686.88 frames.], batch size: 22, lr: 3.63e-04
2022-05-27 18:23:08,901 INFO [train.py:842] (2/4) Epoch 15, batch 5550, loss[loss=0.1784, simple_loss=0.2734, pruned_loss=0.0417, over 7255.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2769, pruned_loss=0.05442, over 1428055.73 frames.], batch size: 19, lr: 3.63e-04
2022-05-27 18:23:47,696 INFO [train.py:842] (2/4) Epoch 15, batch 5600, loss[loss=0.2185, simple_loss=0.3051, pruned_loss=0.0659, over 7215.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2777, pruned_loss=0.055, over 1425824.03 frames.], batch size: 22, lr: 3.63e-04
2022-05-27 18:24:26,882 INFO [train.py:842] (2/4) Epoch 15, batch 5650, loss[loss=0.2432, simple_loss=0.3091, pruned_loss=0.08864, over 7158.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2767, pruned_loss=0.05456, over 1423673.79 frames.], batch size: 18, lr: 3.63e-04
2022-05-27 18:25:05,591 INFO [train.py:842] (2/4) Epoch 15, batch 5700, loss[loss=0.1545, simple_loss=0.2406, pruned_loss=0.0342, over 7427.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2779, pruned_loss=0.05471, over 1421274.21 frames.], batch size: 20, lr: 3.63e-04
2022-05-27 18:25:44,772 INFO [train.py:842] (2/4) Epoch 15, batch 5750, loss[loss=0.1805, simple_loss=0.277, pruned_loss=0.04198, over 7037.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2787, pruned_loss=0.05485, over 1424292.16 frames.], batch size: 28, lr: 3.63e-04
2022-05-27 18:26:23,918 INFO [train.py:842] (2/4) Epoch 15, batch 5800, loss[loss=0.211, simple_loss=0.2912, pruned_loss=0.06543, over 7321.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2791, pruned_loss=0.05501, over 1426639.90 frames.], batch size: 20, lr: 3.63e-04
2022-05-27 18:27:03,387 INFO [train.py:842] (2/4) Epoch 15, batch 5850, loss[loss=0.152, simple_loss=0.2356, pruned_loss=0.03418, over 7364.00 frames.], tot_loss[loss=0.193, simple_loss=0.2773, pruned_loss=0.0543, over 1425676.57 frames.], batch size: 19, lr: 3.63e-04
2022-05-27 18:27:42,357 INFO [train.py:842] (2/4) Epoch 15, batch 5900, loss[loss=0.1918, simple_loss=0.285, pruned_loss=0.04929, over 7133.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2765, pruned_loss=0.05399, over 1424431.20 frames.], batch size: 20, lr: 3.63e-04
2022-05-27 18:28:21,533 INFO [train.py:842] (2/4) Epoch 15, batch 5950, loss[loss=0.167, simple_loss=0.2599, pruned_loss=0.03708, over 7330.00 frames.], tot_loss[loss=0.1944, simple_loss=0.278, pruned_loss=0.05536, over 1422456.41 frames.], batch size: 20, lr: 3.63e-04
2022-05-27 18:29:00,290 INFO [train.py:842] (2/4) Epoch 15, batch 6000, loss[loss=0.1736, simple_loss=0.264, pruned_loss=0.04166, over 7273.00 frames.], tot_loss[loss=0.196, simple_loss=0.2798, pruned_loss=0.05611, over 1419211.37 frames.], batch size: 18, lr: 3.63e-04
2022-05-27 18:29:00,291 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 18:29:10,459 INFO [train.py:871] (2/4) Epoch 15, validation: loss=0.1678, simple_loss=0.2677, pruned_loss=0.03392, over 868885.00 frames.
2022-05-27 18:29:49,729 INFO [train.py:842] (2/4) Epoch 15, batch 6050, loss[loss=0.218, simple_loss=0.306, pruned_loss=0.065, over 7109.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2787, pruned_loss=0.05553, over 1423084.08 frames.], batch size: 28, lr: 3.63e-04
2022-05-27 18:30:28,765 INFO [train.py:842] (2/4) Epoch 15, batch 6100, loss[loss=0.2072, simple_loss=0.2923, pruned_loss=0.06102, over 7227.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2794, pruned_loss=0.05594, over 1425743.97 frames.], batch size: 21, lr: 3.63e-04
2022-05-27 18:31:08,031 INFO [train.py:842] (2/4) Epoch 15, batch 6150, loss[loss=0.1894, simple_loss=0.2733, pruned_loss=0.05278, over 5217.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2797, pruned_loss=0.05574, over 1426682.68 frames.], batch size: 52, lr: 3.62e-04
2022-05-27 18:31:47,163 INFO [train.py:842] (2/4) Epoch 15, batch 6200, loss[loss=0.207, simple_loss=0.2934, pruned_loss=0.06027, over 7199.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2789, pruned_loss=0.05568, over 1424327.04 frames.], batch size: 23, lr: 3.62e-04
2022-05-27 18:32:26,338 INFO [train.py:842] (2/4) Epoch 15, batch 6250, loss[loss=0.1866, simple_loss=0.2792, pruned_loss=0.04704, over 7223.00 frames.], tot_loss[loss=0.1952, simple_loss=0.279, pruned_loss=0.0557, over 1420537.35 frames.], batch size: 22, lr: 3.62e-04
2022-05-27 18:33:04,925 INFO [train.py:842] (2/4) Epoch 15, batch 6300, loss[loss=0.2167, simple_loss=0.3015, pruned_loss=0.06595, over 7174.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2792, pruned_loss=0.05574, over 1419327.84 frames.], batch size: 26, lr: 3.62e-04
2022-05-27 18:33:54,389 INFO [train.py:842] (2/4) Epoch 15, batch 6350, loss[loss=0.1809, simple_loss=0.2627, pruned_loss=0.04953, over 6804.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2795, pruned_loss=0.05593, over 1421456.13 frames.], batch size: 15, lr: 3.62e-04
2022-05-27 18:34:43,527 INFO [train.py:842] (2/4) Epoch 15, batch 6400, loss[loss=0.2397, simple_loss=0.3001, pruned_loss=0.08967, over 7075.00 frames.], tot_loss[loss=0.1954, simple_loss=0.279, pruned_loss=0.05592, over 1419392.19 frames.], batch size: 18, lr: 3.62e-04
2022-05-27 18:35:22,939 INFO [train.py:842] (2/4) Epoch 15, batch 6450, loss[loss=0.1814, simple_loss=0.2818, pruned_loss=0.04046, over 7115.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2793, pruned_loss=0.05568, over 1423208.56 frames.], batch size: 21, lr: 3.62e-04
2022-05-27 18:36:11,916 INFO [train.py:842] (2/4) Epoch 15, batch 6500, loss[loss=0.215, simple_loss=0.3018, pruned_loss=0.06408, over 6798.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2799, pruned_loss=0.05586, over 1418401.97 frames.], batch size: 31, lr: 3.62e-04
2022-05-27 18:36:50,722 INFO [train.py:842] (2/4) Epoch 15, batch 6550, loss[loss=0.1972, simple_loss=0.2768, pruned_loss=0.0588, over 6745.00 frames.], tot_loss[loss=0.198, simple_loss=0.2818, pruned_loss=0.05709, over 1421369.95 frames.], batch size: 31, lr: 3.62e-04
2022-05-27 18:37:29,209 INFO [train.py:842] (2/4) Epoch 15, batch 6600, loss[loss=0.1793, simple_loss=0.2659, pruned_loss=0.04632, over 7243.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2825, pruned_loss=0.05788, over 1417478.62 frames.], batch size: 20, lr: 3.62e-04
2022-05-27 18:38:08,185 INFO [train.py:842] (2/4) Epoch 15, batch 6650, loss[loss=0.2181, simple_loss=0.3067, pruned_loss=0.0647, over 7325.00 frames.], tot_loss[loss=0.198, simple_loss=0.2818, pruned_loss=0.05713, over 1419830.52 frames.], batch size: 24, lr: 3.62e-04
2022-05-27 18:38:47,062 INFO [train.py:842] (2/4) Epoch 15, batch 6700, loss[loss=0.1932, simple_loss=0.2718, pruned_loss=0.05726, over 7160.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2816, pruned_loss=0.0567, over 1422207.02 frames.], batch size: 18, lr: 3.62e-04
2022-05-27 18:39:26,037 INFO [train.py:842] (2/4) Epoch 15, batch 6750, loss[loss=0.2065, simple_loss=0.274, pruned_loss=0.06944, over 7281.00 frames.], tot_loss[loss=0.197, simple_loss=0.2814, pruned_loss=0.05626, over 1424886.39 frames.], batch size: 18, lr: 3.62e-04
2022-05-27 18:40:05,011 INFO [train.py:842] (2/4) Epoch 15, batch 6800, loss[loss=0.1814, simple_loss=0.2684, pruned_loss=0.04721, over 7278.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2804, pruned_loss=0.05537, over 1427023.78 frames.], batch size: 18, lr: 3.62e-04
2022-05-27 18:40:44,155 INFO [train.py:842] (2/4) Epoch 15, batch 6850, loss[loss=0.22, simple_loss=0.3161, pruned_loss=0.06191, over 7387.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2799, pruned_loss=0.05489, over 1425358.93 frames.], batch size: 23, lr: 3.62e-04
2022-05-27 18:41:23,569 INFO [train.py:842] (2/4) Epoch 15, batch 6900, loss[loss=0.1853, simple_loss=0.2681, pruned_loss=0.05122, over 7131.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2791, pruned_loss=0.05433, over 1428231.99 frames.], batch size: 17, lr: 3.61e-04
2022-05-27 18:42:02,758 INFO [train.py:842] (2/4) Epoch 15, batch 6950, loss[loss=0.2626, simple_loss=0.346, pruned_loss=0.08958, over 6728.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2798, pruned_loss=0.05488, over 1426853.62 frames.], batch size: 31, lr: 3.61e-04
2022-05-27 18:42:41,947 INFO [train.py:842] (2/4) Epoch 15, batch 7000, loss[loss=0.1631, simple_loss=0.2548, pruned_loss=0.03567, over 7237.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2803, pruned_loss=0.0553, over 1428425.41 frames.], batch size: 20, lr: 3.61e-04
2022-05-27 18:43:21,062 INFO [train.py:842] (2/4) Epoch 15, batch 7050, loss[loss=0.2473, simple_loss=0.323, pruned_loss=0.08578, over 7199.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2803, pruned_loss=0.0552, over 1427397.02 frames.], batch size: 22, lr: 3.61e-04
2022-05-27 18:43:59,628 INFO [train.py:842] (2/4) Epoch 15, batch 7100, loss[loss=0.1973, simple_loss=0.2951, pruned_loss=0.04981, over 7400.00 frames.], tot_loss[loss=0.1953, simple_loss=0.28, pruned_loss=0.05529, over 1429867.85 frames.], batch size: 23, lr: 3.61e-04
2022-05-27 18:44:38,564 INFO [train.py:842] (2/4) Epoch 15, batch 7150, loss[loss=0.1815, simple_loss=0.2721, pruned_loss=0.04547, over 7227.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2801, pruned_loss=0.05531, over 1426281.44 frames.], batch size: 20, lr: 3.61e-04
2022-05-27 18:45:17,388 INFO [train.py:842] (2/4) Epoch 15, batch 7200, loss[loss=0.1837, simple_loss=0.2634, pruned_loss=0.05203, over 7362.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2804, pruned_loss=0.05545, over 1426459.33 frames.], batch size: 19, lr: 3.61e-04
2022-05-27 18:45:56,702 INFO [train.py:842] (2/4) Epoch 15, batch 7250, loss[loss=0.2218, simple_loss=0.3127, pruned_loss=0.06544, over 7210.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2805, pruned_loss=0.05579, over 1425193.59 frames.], batch size: 22, lr: 3.61e-04
2022-05-27 18:46:35,253 INFO [train.py:842] (2/4) Epoch 15, batch 7300, loss[loss=0.214, simple_loss=0.3046, pruned_loss=0.06175, over 7300.00 frames.], tot_loss[loss=0.1946, simple_loss=0.279, pruned_loss=0.05516, over 1425431.98 frames.], batch size: 24, lr: 3.61e-04
2022-05-27 18:47:16,996 INFO [train.py:842] (2/4) Epoch 15, batch 7350, loss[loss=0.1738, simple_loss=0.2696, pruned_loss=0.03903, over 7384.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2789, pruned_loss=0.05507, over 1425914.91 frames.], batch size: 23, lr: 3.61e-04
2022-05-27 18:47:55,650 INFO [train.py:842] (2/4) Epoch 15, batch 7400, loss[loss=0.1917, simple_loss=0.2794, pruned_loss=0.05198, over 6610.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2797, pruned_loss=0.05562, over 1427609.71 frames.], batch size: 38, lr: 3.61e-04
2022-05-27 18:48:34,722 INFO [train.py:842] (2/4) Epoch 15, batch 7450, loss[loss=0.1616, simple_loss=0.2557, pruned_loss=0.03379, over 7337.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2787, pruned_loss=0.05476, over 1427166.74 frames.], batch size: 20, lr: 3.61e-04
2022-05-27 18:49:13,764 INFO [train.py:842] (2/4) Epoch 15, batch 7500, loss[loss=0.1665, simple_loss=0.2523, pruned_loss=0.04031, over 7059.00 frames.], tot_loss[loss=0.1944, simple_loss=0.279, pruned_loss=0.05485, over 1428309.91 frames.], batch size: 18, lr: 3.61e-04
2022-05-27 18:49:52,799 INFO [train.py:842] (2/4) Epoch 15, batch 7550, loss[loss=0.2277, simple_loss=0.3053, pruned_loss=0.07502, over 6659.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2781, pruned_loss=0.05443, over 1423228.05 frames.], batch size: 31, lr: 3.61e-04
2022-05-27 18:50:31,964 INFO [train.py:842] (2/4) Epoch 15, batch 7600, loss[loss=0.1591, simple_loss=0.2509, pruned_loss=0.03367, over 7355.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2781, pruned_loss=0.05467, over 1421869.24 frames.], batch size: 19, lr: 3.61e-04
2022-05-27 18:51:10,942 INFO [train.py:842] (2/4) Epoch 15, batch 7650, loss[loss=0.1965, simple_loss=0.2921, pruned_loss=0.05042, over 7087.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2811, pruned_loss=0.05634, over 1419879.21 frames.], batch size: 28, lr: 3.60e-04
2022-05-27 18:51:49,844 INFO [train.py:842] (2/4) Epoch 15, batch 7700, loss[loss=0.2069, simple_loss=0.2953, pruned_loss=0.05925, over 7104.00 frames.], tot_loss[loss=0.196, simple_loss=0.28, pruned_loss=0.05599, over 1419614.28 frames.], batch size: 28, lr: 3.60e-04
2022-05-27 18:52:29,122 INFO [train.py:842] (2/4) Epoch 15, batch 7750, loss[loss=0.196, simple_loss=0.29, pruned_loss=0.05102, over 6686.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2799, pruned_loss=0.05576, over 1424098.32 frames.], batch size: 31, lr: 3.60e-04
2022-05-27 18:53:07,845 INFO [train.py:842] (2/4) Epoch 15, batch 7800, loss[loss=0.1782, simple_loss=0.2582, pruned_loss=0.04909, over 7259.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2785, pruned_loss=0.05516, over 1416698.32 frames.], batch size: 17, lr: 3.60e-04
2022-05-27 18:53:47,092 INFO [train.py:842] (2/4) Epoch 15, batch 7850, loss[loss=0.1744, simple_loss=0.2533, pruned_loss=0.04777, over 6996.00 frames.], tot_loss[loss=0.194, simple_loss=0.2785, pruned_loss=0.05481, over 1419948.95 frames.], batch size: 16, lr: 3.60e-04
2022-05-27 18:54:25,871 INFO [train.py:842] (2/4) Epoch 15, batch 7900, loss[loss=0.2015, simple_loss=0.2887, pruned_loss=0.05718, over 7381.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2781, pruned_loss=0.05438, over 1422774.10 frames.], batch size: 23, lr: 3.60e-04
2022-05-27 18:55:04,759 INFO [train.py:842] (2/4) Epoch 15, batch 7950, loss[loss=0.2138, simple_loss=0.3009, pruned_loss=0.06333, over 7186.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2786, pruned_loss=0.05482, over 1422275.52 frames.], batch size: 23, lr: 3.60e-04
2022-05-27 18:55:44,027 INFO [train.py:842] (2/4) Epoch 15, batch 8000, loss[loss=0.1737, simple_loss=0.2471, pruned_loss=0.05017, over 6821.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2777, pruned_loss=0.05476, over 1422531.59 frames.], batch size: 15, lr: 3.60e-04
2022-05-27 18:56:22,905 INFO [train.py:842] (2/4) Epoch 15, batch 8050, loss[loss=0.1905, simple_loss=0.2736, pruned_loss=0.05371, over 7316.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2772, pruned_loss=0.05449, over 1416271.63 frames.], batch size: 25, lr: 3.60e-04
2022-05-27 18:57:02,119 INFO [train.py:842] (2/4) Epoch 15, batch 8100, loss[loss=0.164, simple_loss=0.2592, pruned_loss=0.03439, over 7228.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2792, pruned_loss=0.05547, over 1422842.23 frames.], batch size: 20, lr: 3.60e-04
2022-05-27 18:57:41,260 INFO [train.py:842] (2/4) Epoch 15, batch 8150, loss[loss=0.1714, simple_loss=0.2574, pruned_loss=0.04276, over 7312.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2787, pruned_loss=0.05568, over 1421060.09 frames.], batch size: 20, lr: 3.60e-04
2022-05-27 18:58:20,264 INFO [train.py:842] (2/4) Epoch 15, batch 8200, loss[loss=0.1748, simple_loss=0.2664, pruned_loss=0.04163, over 7274.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2779, pruned_loss=0.05537, over 1423706.56 frames.], batch size: 19, lr: 3.60e-04
2022-05-27 18:58:59,235 INFO [train.py:842] (2/4) Epoch 15, batch 8250, loss[loss=0.2001, simple_loss=0.2716, pruned_loss=0.06429, over 7253.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2785, pruned_loss=0.0559, over 1421557.01 frames.], batch size: 19, lr: 3.60e-04
2022-05-27 18:59:37,864 INFO [train.py:842] (2/4) Epoch 15, batch 8300, loss[loss=0.1996, simple_loss=0.2874, pruned_loss=0.05585, over 7332.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2782, pruned_loss=0.05578, over 1423234.80 frames.], batch size: 20, lr: 3.60e-04
2022-05-27 19:00:17,025 INFO [train.py:842] (2/4) Epoch 15, batch 8350, loss[loss=0.1991, simple_loss=0.2753, pruned_loss=0.06145, over 7362.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2768, pruned_loss=0.05499, over 1423910.78 frames.], batch size: 19, lr: 3.60e-04
2022-05-27 19:00:56,002 INFO [train.py:842] (2/4) Epoch 15, batch 8400, loss[loss=0.1637, simple_loss=0.257, pruned_loss=0.03513, over 7209.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2766, pruned_loss=0.05479, over 1424701.52 frames.], batch size: 26, lr: 3.59e-04
2022-05-27 19:01:34,935 INFO [train.py:842] (2/4) Epoch 15, batch 8450, loss[loss=0.2281, simple_loss=0.3151, pruned_loss=0.07056, over 7140.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2777, pruned_loss=0.05535, over 1423669.78 frames.], batch size: 20, lr: 3.59e-04
2022-05-27 19:02:13,486 INFO [train.py:842] (2/4) Epoch 15, batch 8500, loss[loss=0.1864, simple_loss=0.255, pruned_loss=0.05887, over 7176.00 frames.], tot_loss[loss=0.195, simple_loss=0.2787, pruned_loss=0.05562, over 1420830.69 frames.], batch size: 18, lr: 3.59e-04
2022-05-27 19:02:52,542 INFO [train.py:842] (2/4) Epoch 15, batch 8550, loss[loss=0.2597, simple_loss=0.3336, pruned_loss=0.09288, over 7112.00 frames.], tot_loss[loss=0.1961, simple_loss=0.28, pruned_loss=0.05606, over 1422023.70 frames.], batch size: 21, lr: 3.59e-04
2022-05-27 19:03:31,271 INFO [train.py:842] (2/4) Epoch 15, batch 8600, loss[loss=0.1827, simple_loss=0.2763, pruned_loss=0.04455, over 7327.00 frames.], tot_loss[loss=0.1976, simple_loss=0.281, pruned_loss=0.05709, over 1419000.52 frames.], batch size: 21, lr: 3.59e-04
2022-05-27 19:04:10,119 INFO [train.py:842] (2/4) Epoch 15, batch 8650, loss[loss=0.184, simple_loss=0.2798, pruned_loss=0.04406, over 7319.00 frames.], tot_loss[loss=0.1973, simple_loss=0.281, pruned_loss=0.05686, over 1416622.54 frames.], batch size: 21, lr: 3.59e-04
2022-05-27 19:04:49,102 INFO [train.py:842] (2/4) Epoch 15, batch 8700, loss[loss=0.2128, simple_loss=0.2987, pruned_loss=0.06345, over 7202.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2795, pruned_loss=0.05544, over 1420789.96 frames.], batch size: 22, lr: 3.59e-04
2022-05-27 19:05:28,368 INFO [train.py:842] (2/4) Epoch 15, batch 8750, loss[loss=0.2181, simple_loss=0.2932, pruned_loss=0.07146, over 6730.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2778, pruned_loss=0.05485, over 1420605.15 frames.], batch size: 31, lr: 3.59e-04
2022-05-27 19:06:07,389 INFO [train.py:842] (2/4) Epoch 15, batch 8800, loss[loss=0.2935, simple_loss=0.3523, pruned_loss=0.1174, over 7409.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2764, pruned_loss=0.0545, over 1418963.70 frames.], batch size: 21, lr: 3.59e-04
2022-05-27 19:06:46,147 INFO [train.py:842] (2/4) Epoch 15, batch 8850, loss[loss=0.1948, simple_loss=0.2776, pruned_loss=0.05605, over 6838.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2776, pruned_loss=0.05499, over 1416514.05 frames.], batch size: 31, lr: 3.59e-04
2022-05-27 19:07:24,578 INFO [train.py:842] (2/4) Epoch 15, batch 8900, loss[loss=0.2545, simple_loss=0.3235, pruned_loss=0.09277, over 7338.00 frames.], tot_loss[loss=0.195, simple_loss=0.2787, pruned_loss=0.05569, over 1406868.03 frames.], batch size: 22, lr: 3.59e-04
2022-05-27 19:08:03,434 INFO [train.py:842] (2/4) Epoch 15, batch 8950, loss[loss=0.188, simple_loss=0.254, pruned_loss=0.06095, over 6803.00 frames.], tot_loss[loss=0.195, simple_loss=0.2779, pruned_loss=0.0561, over 1390541.27 frames.], batch size: 15, lr: 3.59e-04
2022-05-27 19:08:41,907 INFO [train.py:842] (2/4) Epoch 15, batch 9000, loss[loss=0.1722, simple_loss=0.2659, pruned_loss=0.03926, over 7196.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2771, pruned_loss=0.05563, over 1385018.71 frames.], batch size: 23, lr: 3.59e-04
2022-05-27 19:08:41,908 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 19:08:52,122 INFO [train.py:871] (2/4) Epoch 15, validation: loss=0.1676, simple_loss=0.2672, pruned_loss=0.03401, over 868885.00 frames.
2022-05-27 19:09:30,773 INFO [train.py:842] (2/4) Epoch 15, batch 9050, loss[loss=0.2133, simple_loss=0.3023, pruned_loss=0.06216, over 7216.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2782, pruned_loss=0.05587, over 1367530.42 frames.], batch size: 23, lr: 3.59e-04
2022-05-27 19:10:09,049 INFO [train.py:842] (2/4) Epoch 15, batch 9100, loss[loss=0.3038, simple_loss=0.3629, pruned_loss=0.1224, over 5144.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2808, pruned_loss=0.05844, over 1326759.44 frames.], batch size: 52, lr: 3.59e-04
2022-05-27 19:10:46,745 INFO [train.py:842] (2/4) Epoch 15, batch 9150, loss[loss=0.2321, simple_loss=0.3112, pruned_loss=0.07647, over 4976.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2863, pruned_loss=0.06281, over 1256475.11 frames.], batch size: 52, lr: 3.58e-04
2022-05-27 19:11:37,230 INFO [train.py:842] (2/4) Epoch 16, batch 0, loss[loss=0.2, simple_loss=0.2868, pruned_loss=0.05664, over 7301.00 frames.], tot_loss[loss=0.2, simple_loss=0.2868, pruned_loss=0.05664, over 7301.00 frames.], batch size: 24, lr: 3.48e-04
2022-05-27 19:12:16,181 INFO [train.py:842] (2/4) Epoch 16, batch 50, loss[loss=0.2017, simple_loss=0.2805, pruned_loss=0.06148, over 7411.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2824, pruned_loss=0.05633, over 320966.01 frames.], batch size: 18, lr: 3.48e-04
2022-05-27 19:12:55,623 INFO [train.py:842] (2/4) Epoch 16, batch 100, loss[loss=0.2227, simple_loss=0.2924, pruned_loss=0.07644, over 7325.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2791, pruned_loss=0.05504, over 563564.86 frames.], batch size: 20, lr: 3.48e-04
2022-05-27 19:13:38,124 INFO [train.py:842] (2/4) Epoch 16, batch 150, loss[loss=0.2087, simple_loss=0.2907, pruned_loss=0.06332, over 7145.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2799, pruned_loss=0.05576, over 753607.54 frames.], batch size: 20, lr: 3.48e-04
2022-05-27 19:14:16,660 INFO [train.py:842] (2/4) Epoch 16, batch 200, loss[loss=0.1865, simple_loss=0.2812, pruned_loss=0.04589, over 7129.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2788, pruned_loss=0.05539, over 896974.07 frames.], batch size: 21, lr: 3.48e-04
2022-05-27 19:14:56,290 INFO [train.py:842] (2/4) Epoch 16, batch 250, loss[loss=0.1544, simple_loss=0.2386, pruned_loss=0.03506, over 7154.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2774, pruned_loss=0.05407, over 1013566.22 frames.], batch size: 19, lr: 3.48e-04
2022-05-27 19:15:36,398 INFO [train.py:842] (2/4) Epoch 16, batch 300, loss[loss=0.1637, simple_loss=0.253, pruned_loss=0.03718, over 7152.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2785, pruned_loss=0.05485, over 1109235.61 frames.], batch size: 19, lr: 3.48e-04
2022-05-27 19:16:18,282 INFO [train.py:842] (2/4) Epoch 16, batch 350, loss[loss=0.1832, simple_loss=0.2649, pruned_loss=0.05075, over 7284.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2782, pruned_loss=0.05509, over 1179801.01 frames.], batch size: 18, lr: 3.48e-04
2022-05-27 19:16:56,985 INFO [train.py:842] (2/4) Epoch 16, batch 400, loss[loss=0.1691, simple_loss=0.251, pruned_loss=0.0436, over 7264.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2781, pruned_loss=0.05439, over 1234239.62 frames.], batch size: 19, lr: 3.48e-04
2022-05-27 19:17:36,531 INFO [train.py:842] (2/4) Epoch 16, batch 450, loss[loss=0.1663, simple_loss=0.2589, pruned_loss=0.03684, over 7438.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2779, pruned_loss=0.05444, over 1281366.14 frames.], batch size: 20, lr: 3.47e-04
2022-05-27 19:18:15,377 INFO [train.py:842] (2/4) Epoch 16, batch 500, loss[loss=0.1916, simple_loss=0.2795, pruned_loss=0.05185, over 7207.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2788, pruned_loss=0.0551, over 1318083.82 frames.], batch size: 23, lr: 3.47e-04
2022-05-27 19:18:54,561 INFO [train.py:842] (2/4) Epoch 16, batch 550, loss[loss=0.171, simple_loss=0.257, pruned_loss=0.04251, over 7291.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2765, pruned_loss=0.05342, over 1346072.10 frames.], batch size: 18, lr: 3.47e-04
2022-05-27 19:19:33,235 INFO [train.py:842] (2/4) Epoch 16, batch 600, loss[loss=0.2478, simple_loss=0.321, pruned_loss=0.08729, over 7167.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2781, pruned_loss=0.05433, over 1361226.51 frames.], batch size: 19, lr: 3.47e-04
2022-05-27 19:20:12,187 INFO [train.py:842] (2/4) Epoch 16, batch 650, loss[loss=0.1963, simple_loss=0.2897, pruned_loss=0.05141, over 6299.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2791, pruned_loss=0.05503, over 1373843.73 frames.], batch size: 37, lr: 3.47e-04
2022-05-27 19:20:51,084 INFO [train.py:842] (2/4) Epoch 16, batch 700, loss[loss=0.2787, simple_loss=0.3573, pruned_loss=0.1001, over 7085.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2792, pruned_loss=0.05546, over 1385848.89 frames.], batch size: 28, lr: 3.47e-04
2022-05-27 19:21:33,889 INFO [train.py:842] (2/4) Epoch 16, batch 750, loss[loss=0.2249, simple_loss=0.3019, pruned_loss=0.07394, over 7162.00 frames.], tot_loss[loss=0.195, simple_loss=0.2793, pruned_loss=0.0553, over 1394616.30 frames.], batch size: 19, lr: 3.47e-04
2022-05-27 19:22:12,442 INFO [train.py:842] (2/4) Epoch 16, batch 800, loss[loss=0.1674, simple_loss=0.2601, pruned_loss=0.0373, over 7259.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2796, pruned_loss=0.0553, over 1402013.31 frames.], batch size: 19, lr: 3.47e-04
2022-05-27 19:22:51,477 INFO [train.py:842] (2/4) Epoch 16, batch 850, loss[loss=0.1797, simple_loss=0.2712, pruned_loss=0.04405, over 7154.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2794, pruned_loss=0.05483, over 1403815.56 frames.], batch size: 20, lr: 3.47e-04
2022-05-27 19:23:30,172 INFO [train.py:842] (2/4) Epoch 16, batch 900, loss[loss=0.1697, simple_loss=0.256, pruned_loss=0.04166, over 7366.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2798, pruned_loss=0.05543, over 1403284.52 frames.], batch size: 19, lr: 3.47e-04
2022-05-27 19:24:09,378 INFO [train.py:842] (2/4) Epoch 16, batch 950, loss[loss=0.2052, simple_loss=0.2836, pruned_loss=0.06335, over 7427.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2789, pruned_loss=0.05538, over 1407240.53 frames.], batch size: 20, lr: 3.47e-04
2022-05-27 19:24:48,130 INFO [train.py:842] (2/4) Epoch 16, batch 1000, loss[loss=0.1943, simple_loss=0.2761, pruned_loss=0.05629, over 7304.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2775, pruned_loss=0.05463, over 1412495.36 frames.], batch size: 25, lr: 3.47e-04
2022-05-27 19:25:27,125 INFO [train.py:842] (2/4) Epoch 16, batch 1050, loss[loss=0.1972, simple_loss=0.2743, pruned_loss=0.06008, over 7331.00 frames.], tot_loss[loss=0.1938, simple_loss=0.278, pruned_loss=0.05479, over 1417219.44 frames.], batch size: 20, lr: 3.47e-04
2022-05-27 19:26:05,961 INFO [train.py:842] (2/4) Epoch 16, batch 1100, loss[loss=0.1669, simple_loss=0.2473, pruned_loss=0.04324, over 7367.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2781, pruned_loss=0.05484, over 1420410.85 frames.], batch size: 19, lr: 3.47e-04
2022-05-27 19:26:45,428 INFO [train.py:842] (2/4) Epoch 16, batch 1150, loss[loss=0.2221, simple_loss=0.2958, pruned_loss=0.07425, over 4946.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2776, pruned_loss=0.05509, over 1420871.02 frames.], batch size: 52, lr: 3.47e-04
2022-05-27 19:27:24,214 INFO [train.py:842] (2/4) Epoch 16, batch 1200, loss[loss=0.1723, simple_loss=0.2648, pruned_loss=0.03993, over 7112.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2789, pruned_loss=0.05579, over 1418732.87 frames.], batch size: 21, lr: 3.47e-04
2022-05-27 19:28:03,606 INFO [train.py:842] (2/4) Epoch 16, batch 1250, loss[loss=0.1894, simple_loss=0.2707, pruned_loss=0.05406, over 6778.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2793, pruned_loss=0.05622, over 1419303.44 frames.], batch size: 15, lr: 3.46e-04
2022-05-27 19:28:42,341 INFO [train.py:842] (2/4) Epoch 16, batch 1300, loss[loss=0.1892, simple_loss=0.2865, pruned_loss=0.0459, over 7208.00 frames.], tot_loss[loss=0.1962, simple_loss=0.28, pruned_loss=0.05623, over 1425639.55 frames.], batch size: 22, lr: 3.46e-04
2022-05-27 19:29:21,394 INFO [train.py:842] (2/4) Epoch 16, batch 1350, loss[loss=0.1961, simple_loss=0.2942, pruned_loss=0.04897, over 7161.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2792, pruned_loss=0.05568, over 1418493.58 frames.], batch size: 19, lr: 3.46e-04
2022-05-27 19:30:00,086 INFO [train.py:842] (2/4) Epoch 16, batch 1400, loss[loss=0.1941, simple_loss=0.2791, pruned_loss=0.05453, over 7345.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2782, pruned_loss=0.05511, over 1416176.69 frames.], batch size: 22, lr: 3.46e-04
2022-05-27 19:30:39,307 INFO [train.py:842] (2/4) Epoch 16, batch 1450, loss[loss=0.19, simple_loss=0.2908, pruned_loss=0.04466, over 7413.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2776, pruned_loss=0.05428, over 1422361.89 frames.], batch size: 21, lr: 3.46e-04
2022-05-27 19:31:18,225 INFO [train.py:842] (2/4) Epoch 16, batch 1500, loss[loss=0.1828, simple_loss=0.2828, pruned_loss=0.04143, over 7183.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2782, pruned_loss=0.05432, over 1422074.98 frames.], batch size: 23, lr: 3.46e-04
2022-05-27 19:31:57,500 INFO [train.py:842] (2/4) Epoch 16, batch 1550, loss[loss=0.1766, simple_loss=0.2484, pruned_loss=0.05236, over 6772.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2776, pruned_loss=0.05408, over 1420420.91 frames.], batch size: 15, lr: 3.46e-04
2022-05-27 19:32:36,411 INFO [train.py:842] (2/4) Epoch 16, batch 1600, loss[loss=0.1589, simple_loss=0.2442, pruned_loss=0.03677, over 6846.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2773, pruned_loss=0.0536, over 1422694.42 frames.], batch size: 15, lr: 3.46e-04
2022-05-27 19:33:15,591 INFO [train.py:842] (2/4) Epoch 16, batch 1650, loss[loss=0.2206, simple_loss=0.2998, pruned_loss=0.07072, over 7138.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2766, pruned_loss=0.05307, over 1424744.52 frames.], batch size: 20, lr: 3.46e-04
2022-05-27 19:33:54,705 INFO [train.py:842] (2/4) Epoch 16, batch 1700, loss[loss=0.1891, simple_loss=0.2509, pruned_loss=0.06369, over 7422.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2767, pruned_loss=0.05354, over 1424971.25 frames.], batch size: 18, lr: 3.46e-04
2022-05-27 19:34:34,153 INFO [train.py:842] (2/4) Epoch 16, batch 1750, loss[loss=0.1745, simple_loss=0.2714, pruned_loss=0.03884, over 7368.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2786, pruned_loss=0.05453, over 1423727.95 frames.], batch size: 23, lr: 3.46e-04
2022-05-27 19:35:13,058 INFO [train.py:842] (2/4) Epoch 16, batch 1800, loss[loss=0.1976, simple_loss=0.2805, pruned_loss=0.05736, over 7362.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2782, pruned_loss=0.05397, over 1422931.72 frames.], batch size: 19, lr: 3.46e-04
2022-05-27 19:35:52,247 INFO [train.py:842] (2/4) Epoch 16, batch 1850, loss[loss=0.3067, simple_loss=0.3632, pruned_loss=0.1251, over 7146.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2771, pruned_loss=0.05371, over 1425734.19 frames.], batch size: 20, lr: 3.46e-04
2022-05-27 19:36:31,030 INFO [train.py:842] (2/4) Epoch 16, batch 1900, loss[loss=0.1815, simple_loss=0.2766, pruned_loss=0.04321, over 7295.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2766, pruned_loss=0.05365, over 1429951.65 frames.], batch size: 25, lr: 3.46e-04
2022-05-27 19:37:10,353 INFO [train.py:842] (2/4) Epoch 16, batch 1950, loss[loss=0.2051, simple_loss=0.2894, pruned_loss=0.06042, over 7211.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2769, pruned_loss=0.05385, over 1430398.13 frames.], batch size: 23, lr: 3.46e-04
2022-05-27 19:37:49,016 INFO [train.py:842] (2/4) Epoch 16, batch 2000, loss[loss=0.2227, simple_loss=0.2963, pruned_loss=0.07457, over 5121.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2782, pruned_loss=0.0547, over 1424303.15 frames.], batch size: 52, lr: 3.46e-04
2022-05-27 19:38:28,288 INFO [train.py:842] (2/4) Epoch 16, batch 2050, loss[loss=0.1834, simple_loss=0.279, pruned_loss=0.0439, over 6379.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2787, pruned_loss=0.05512, over 1422679.53 frames.], batch size: 37, lr: 3.45e-04
2022-05-27 19:39:07,373 INFO [train.py:842] (2/4) Epoch 16, batch 2100, loss[loss=0.1724, simple_loss=0.2639, pruned_loss=0.04042, over 7120.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2782, pruned_loss=0.05466, over 1423261.01 frames.], batch size: 21, lr: 3.45e-04
2022-05-27 19:39:46,902 INFO [train.py:842] (2/4) Epoch 16, batch 2150, loss[loss=0.1642, simple_loss=0.2601, pruned_loss=0.03417, over 7267.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2787, pruned_loss=0.05439, over 1419104.83 frames.], batch size: 19, lr: 3.45e-04
2022-05-27 19:40:25,559 INFO [train.py:842] (2/4) Epoch 16, batch 2200, loss[loss=0.2489, simple_loss=0.3192, pruned_loss=0.08929, over 7215.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2782, pruned_loss=0.05428, over 1415840.52 frames.], batch size: 22, lr: 3.45e-04
2022-05-27 19:41:05,247 INFO [train.py:842] (2/4) Epoch 16, batch 2250, loss[loss=0.2555, simple_loss=0.3258, pruned_loss=0.09259, over 7408.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2793, pruned_loss=0.05506, over 1416489.12 frames.], batch size: 21, lr: 3.45e-04
2022-05-27 19:41:43,727 INFO [train.py:842] (2/4) Epoch 16, batch 2300, loss[loss=0.1898, simple_loss=0.2752, pruned_loss=0.05226, over 7201.00 frames.], tot_loss[loss=0.195, simple_loss=0.2796, pruned_loss=0.05523, over 1419066.62 frames.], batch size: 23, lr: 3.45e-04
2022-05-27 19:42:23,140 INFO [train.py:842] (2/4) Epoch 16, batch 2350, loss[loss=0.1944, simple_loss=0.2768, pruned_loss=0.05606, over 7300.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2778, pruned_loss=0.05427, over 1421712.97 frames.], batch size: 25, lr: 3.45e-04
2022-05-27 19:43:02,170 INFO [train.py:842] (2/4) Epoch 16, batch 2400, loss[loss=0.2004, simple_loss=0.2872, pruned_loss=0.05679, over 7308.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2774, pruned_loss=0.05468, over 1425541.90 frames.], batch size: 25, lr: 3.45e-04
2022-05-27 19:43:41,237 INFO [train.py:842] (2/4) Epoch 16, batch 2450, loss[loss=0.1689, simple_loss=0.2641, pruned_loss=0.03686, over 6690.00 frames.], tot_loss[loss=0.194, simple_loss=0.2782, pruned_loss=0.0549, over 1424006.02 frames.], batch size: 31, lr: 3.45e-04
2022-05-27 19:44:20,557 INFO [train.py:842] (2/4) Epoch 16, batch 2500, loss[loss=0.1542, simple_loss=0.2547, pruned_loss=0.02679, over 7220.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2778, pruned_loss=0.05472, over 1427118.36 frames.], batch size: 21, lr: 3.45e-04
2022-05-27 19:44:59,647 INFO [train.py:842] (2/4) Epoch 16, batch 2550, loss[loss=0.1754, simple_loss=0.2696, pruned_loss=0.04062, over 7144.00 frames.], tot_loss[loss=0.193, simple_loss=0.277, pruned_loss=0.05447, over 1424829.74 frames.], batch size: 20, lr: 3.45e-04
2022-05-27 19:45:38,429 INFO [train.py:842] (2/4) Epoch 16, batch 2600, loss[loss=0.1887, simple_loss=0.275, pruned_loss=0.05117, over 7365.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2773, pruned_loss=0.05464, over 1423732.26 frames.], batch size: 19, lr: 3.45e-04
2022-05-27 19:46:17,857 INFO [train.py:842] (2/4) Epoch 16, batch 2650, loss[loss=0.2433, simple_loss=0.3196, pruned_loss=0.08353, over 7379.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2768, pruned_loss=0.05448, over 1424009.51 frames.], batch size: 23, lr: 3.45e-04
2022-05-27 19:46:56,759 INFO [train.py:842] (2/4) Epoch 16, batch 2700, loss[loss=0.1988, simple_loss=0.2893, pruned_loss=0.05413, over 7151.00 frames.], tot_loss[loss=0.194, simple_loss=0.2775, pruned_loss=0.05523, over 1421475.53 frames.], batch size: 26, lr: 3.45e-04
2022-05-27 19:47:35,941 INFO [train.py:842] (2/4) Epoch 16, batch 2750, loss[loss=0.18, simple_loss=0.2607, pruned_loss=0.04964, over 7277.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2772, pruned_loss=0.05531, over 1425562.65 frames.], batch size: 18, lr: 3.45e-04
2022-05-27 19:48:14,872 INFO [train.py:842] (2/4) Epoch 16, batch 2800, loss[loss=0.2157, simple_loss=0.305, pruned_loss=0.06325, over 7221.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2775, pruned_loss=0.05491, over 1427144.92 frames.], batch size: 21, lr: 3.45e-04
2022-05-27 19:48:54,086 INFO [train.py:842] (2/4) Epoch 16, batch 2850, loss[loss=0.1906, simple_loss=0.265, pruned_loss=0.05813, over 7169.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2779, pruned_loss=0.05498, over 1425687.40 frames.], batch size: 18, lr: 3.45e-04
2022-05-27 19:49:32,830 INFO [train.py:842] (2/4) Epoch 16, batch 2900, loss[loss=0.1688, simple_loss=0.2485, pruned_loss=0.04455, over 7166.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2776, pruned_loss=0.05449, over 1428483.48 frames.], batch size: 18, lr: 3.44e-04
2022-05-27 19:50:12,017 INFO [train.py:842] (2/4) Epoch 16, batch 2950, loss[loss=0.1946, simple_loss=0.2942, pruned_loss=0.0475, over 7347.00 frames.], tot_loss[loss=0.1926, simple_loss=0.277, pruned_loss=0.05405, over 1424717.50 frames.], batch size: 22, lr: 3.44e-04
2022-05-27 19:50:50,671 INFO [train.py:842] (2/4) Epoch 16, batch 3000, loss[loss=0.2313, simple_loss=0.3165, pruned_loss=0.07312, over 7416.00 frames.], tot_loss[loss=0.1925, simple_loss=0.277, pruned_loss=0.05397, over 1428952.40 frames.], batch size: 21, lr: 3.44e-04
2022-05-27 19:50:50,672 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 19:51:00,435 INFO [train.py:871] (2/4) Epoch 16, validation: loss=0.1694, simple_loss=0.2694, pruned_loss=0.03473, over 868885.00 frames.
2022-05-27 19:51:39,563 INFO [train.py:842] (2/4) Epoch 16, batch 3050, loss[loss=0.181, simple_loss=0.2609, pruned_loss=0.05056, over 7423.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2774, pruned_loss=0.05482, over 1427898.74 frames.], batch size: 18, lr: 3.44e-04
2022-05-27 19:52:18,308 INFO [train.py:842] (2/4) Epoch 16, batch 3100, loss[loss=0.2136, simple_loss=0.3025, pruned_loss=0.06234, over 7216.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2785, pruned_loss=0.05541, over 1427437.77 frames.], batch size: 23, lr: 3.44e-04
2022-05-27 19:52:57,465 INFO [train.py:842] (2/4) Epoch 16, batch 3150, loss[loss=0.1718, simple_loss=0.2538, pruned_loss=0.04486, over 7155.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2781, pruned_loss=0.05528, over 1425213.49 frames.], batch size: 18, lr: 3.44e-04
2022-05-27 19:53:36,209 INFO [train.py:842] (2/4) Epoch 16, batch 3200, loss[loss=0.1892, simple_loss=0.269, pruned_loss=0.05468, over 7280.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2789, pruned_loss=0.05561, over 1424913.58 frames.], batch size: 24, lr: 3.44e-04
2022-05-27 19:54:15,776 INFO [train.py:842] (2/4) Epoch 16, batch 3250, loss[loss=0.2454, simple_loss=0.3321, pruned_loss=0.0793, over 7324.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2773, pruned_loss=0.05447, over 1425605.06 frames.], batch size: 21, lr: 3.44e-04
2022-05-27 19:54:54,915 INFO [train.py:842] (2/4) Epoch 16, batch 3300, loss[loss=0.1838, simple_loss=0.2745, pruned_loss=0.04657, over 7295.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2781, pruned_loss=0.0543, over 1429293.45 frames.], batch size: 25, lr: 3.44e-04
2022-05-27 19:55:34,112 INFO [train.py:842] (2/4) Epoch 16, batch 3350, loss[loss=0.1936, simple_loss=0.2751, pruned_loss=0.05607, over 7226.00 frames.], tot_loss[loss=0.193, simple_loss=0.2778, pruned_loss=0.05407, over 1431757.56 frames.], batch size: 20, lr: 3.44e-04
2022-05-27 19:56:12,886 INFO [train.py:842] (2/4) Epoch 16, batch 3400, loss[loss=0.2118, simple_loss=0.2979, pruned_loss=0.06288, over 7094.00 frames.], tot_loss[loss=0.1933, simple_loss=0.278, pruned_loss=0.05423, over 1429685.86 frames.], batch size: 28, lr: 3.44e-04
2022-05-27 19:56:52,464 INFO [train.py:842] (2/4) Epoch 16, batch 3450, loss[loss=0.1715, simple_loss=0.2595, pruned_loss=0.04178, over 7355.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2785, pruned_loss=0.05444, over 1430206.23 frames.], batch size: 19, lr: 3.44e-04
2022-05-27 19:57:31,234 INFO [train.py:842] (2/4) Epoch 16, batch 3500, loss[loss=0.1983, simple_loss=0.2901, pruned_loss=0.05327, over 7325.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2788, pruned_loss=0.0544, over 1428374.88 frames.], batch size: 21, lr: 3.44e-04
2022-05-27 19:58:10,267 INFO [train.py:842] (2/4) Epoch 16, batch 3550, loss[loss=0.1905, simple_loss=0.2811, pruned_loss=0.04995, over 7178.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2782, pruned_loss=0.05404, over 1424247.47 frames.], batch size: 26, lr: 3.44e-04
2022-05-27 19:58:48,878 INFO [train.py:842] (2/4) Epoch 16, batch 3600, loss[loss=0.2176, simple_loss=0.3075, pruned_loss=0.06383, over 7308.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2785, pruned_loss=0.05438, over 1426264.02 frames.], batch size: 21, lr: 3.44e-04
2022-05-27 19:59:28,049 INFO [train.py:842] (2/4) Epoch 16, batch 3650, loss[loss=0.1553, simple_loss=0.2385, pruned_loss=0.03606, over 7282.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2773, pruned_loss=0.05378, over 1425841.42 frames.], batch size: 18, lr: 3.44e-04
2022-05-27 20:00:07,178 INFO [train.py:842] (2/4) Epoch 16, batch 3700, loss[loss=0.1592, simple_loss=0.249, pruned_loss=0.03469, over 7188.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2773, pruned_loss=0.05394, over 1424342.34 frames.], batch size: 16, lr: 3.43e-04
2022-05-27 20:00:46,294 INFO [train.py:842] (2/4) Epoch 16, batch 3750, loss[loss=0.2494, simple_loss=0.3263, pruned_loss=0.08623, over 7285.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2778, pruned_loss=0.05439, over 1421296.90 frames.], batch size: 25, lr: 3.43e-04
2022-05-27 20:01:24,919 INFO [train.py:842] (2/4) Epoch 16, batch 3800, loss[loss=0.2159, simple_loss=0.3065, pruned_loss=0.06262, over 7204.00 frames.], tot_loss[loss=0.1962, simple_loss=0.28, pruned_loss=0.05618, over 1424611.81 frames.], batch size: 22, lr: 3.43e-04
2022-05-27 20:02:03,872 INFO [train.py:842] (2/4) Epoch 16, batch 3850, loss[loss=0.2148, simple_loss=0.2998, pruned_loss=0.06494, over 7143.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2816, pruned_loss=0.05711, over 1420718.24 frames.], batch size: 20, lr: 3.43e-04
2022-05-27 20:02:42,890 INFO [train.py:842] (2/4) Epoch 16, batch 3900, loss[loss=0.1934, simple_loss=0.2937, pruned_loss=0.04661, over 7279.00 frames.], tot_loss[loss=0.1969, simple_loss=0.281, pruned_loss=0.05639, over 1423521.96 frames.], batch size: 24, lr: 3.43e-04
2022-05-27 20:03:21,527 INFO [train.py:842] (2/4) Epoch 16, batch 3950, loss[loss=0.242, simple_loss=0.3276, pruned_loss=0.07821, over 7222.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2814, pruned_loss=0.05694, over 1420723.38 frames.], batch size: 21, lr: 3.43e-04
2022-05-27 20:04:00,255 INFO [train.py:842] (2/4) Epoch 16, batch 4000, loss[loss=0.1663, simple_loss=0.2628, pruned_loss=0.0349, over 7231.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2806, pruned_loss=0.05619, over 1421093.98 frames.], batch size: 21, lr: 3.43e-04
2022-05-27 20:04:39,436 INFO [train.py:842] (2/4) Epoch 16, batch 4050, loss[loss=0.1788, simple_loss=0.2734, pruned_loss=0.04214, over 7193.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2795, pruned_loss=0.05556, over 1422224.16 frames.], batch size: 22, lr: 3.43e-04
2022-05-27 20:05:18,228 INFO [train.py:842] (2/4) Epoch 16, batch 4100, loss[loss=0.2566, simple_loss=0.3189, pruned_loss=0.09721, over 7159.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2805, pruned_loss=0.05612, over 1423956.00 frames.], batch size: 18, lr: 3.43e-04
2022-05-27 20:05:57,590 INFO [train.py:842] (2/4) Epoch 16, batch 4150, loss[loss=0.1689, simple_loss=0.2467, pruned_loss=0.04551, over 6993.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2794, pruned_loss=0.05534, over 1424811.85 frames.], batch size: 16, lr: 3.43e-04
2022-05-27 20:06:36,498 INFO [train.py:842] (2/4) Epoch 16, batch 4200, loss[loss=0.1969, simple_loss=0.2772, pruned_loss=0.05827, over 7407.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2785, pruned_loss=0.05495, over 1422504.80 frames.], batch size: 21, lr: 3.43e-04
2022-05-27 20:07:15,424 INFO [train.py:842] (2/4) Epoch 16, batch 4250, loss[loss=0.1817, simple_loss=0.2611, pruned_loss=0.05109, over 7310.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2774, pruned_loss=0.05424, over 1421984.14 frames.], batch size: 25, lr: 3.43e-04
2022-05-27 20:07:54,420 INFO [train.py:842] (2/4) Epoch 16, batch 4300, loss[loss=0.2099, simple_loss=0.2904, pruned_loss=0.06474, over 7228.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2782, pruned_loss=0.05421, over 1421339.08 frames.], batch size: 20, lr: 3.43e-04
2022-05-27 20:08:33,211 INFO [train.py:842] (2/4) Epoch 16, batch 4350, loss[loss=0.2102, simple_loss=0.2983, pruned_loss=0.06105, over 7206.00 frames.], tot_loss[loss=0.193, simple_loss=0.2783, pruned_loss=0.0539, over 1423219.70 frames.], batch size: 22, lr: 3.43e-04
2022-05-27 20:09:12,131 INFO [train.py:842] (2/4) Epoch 16, batch 4400, loss[loss=0.1881, simple_loss=0.28, pruned_loss=0.04811, over 7324.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2774, pruned_loss=0.0542, over 1420808.13 frames.], batch size: 21, lr: 3.43e-04
2022-05-27 20:09:51,437 INFO [train.py:842] (2/4) Epoch 16, batch 4450, loss[loss=0.1696, simple_loss=0.2553, pruned_loss=0.04198, over 7172.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2796, pruned_loss=0.0551, over 1423535.42 frames.], batch size: 18, lr: 3.43e-04
2022-05-27 20:10:30,367 INFO [train.py:842] (2/4) Epoch 16, batch 4500, loss[loss=0.2186, simple_loss=0.3031, pruned_loss=0.06698, over 7327.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2802, pruned_loss=0.05546, over 1426991.76 frames.], batch size: 22, lr: 3.43e-04
2022-05-27 20:11:09,728 INFO [train.py:842] (2/4) Epoch 16, batch 4550, loss[loss=0.1917, simple_loss=0.2834, pruned_loss=0.05, over 7208.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2787, pruned_loss=0.05506, over 1427542.32 frames.], batch size: 22, lr: 3.42e-04
2022-05-27 20:11:48,700 INFO [train.py:842] (2/4) Epoch 16, batch 4600, loss[loss=0.1567, simple_loss=0.2565, pruned_loss=0.02843, over 7278.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2768, pruned_loss=0.05407, over 1429119.34 frames.], batch size: 18, lr: 3.42e-04
2022-05-27 20:12:27,773 INFO [train.py:842] (2/4) Epoch 16, batch 4650, loss[loss=0.1805, simple_loss=0.2672, pruned_loss=0.04692, over 7234.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2768, pruned_loss=0.05393, over 1428649.41 frames.], batch size: 20, lr: 3.42e-04
2022-05-27 20:13:06,632 INFO [train.py:842] (2/4) Epoch 16, batch 4700, loss[loss=0.1749, simple_loss=0.2591, pruned_loss=0.04541, over 7119.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2777, pruned_loss=0.05422, over 1430284.84 frames.], batch size: 21, lr: 3.42e-04
2022-05-27 20:13:45,916 INFO [train.py:842] (2/4) Epoch 16, batch 4750, loss[loss=0.157, simple_loss=0.2403, pruned_loss=0.0369, over 6795.00 frames.], tot_loss[loss=0.191, simple_loss=0.2758, pruned_loss=0.05307, over 1427886.75 frames.], batch size: 15, lr: 3.42e-04
2022-05-27 20:14:24,743 INFO [train.py:842] (2/4) Epoch 16, batch 4800, loss[loss=0.1856, simple_loss=0.2734, pruned_loss=0.04887, over 7425.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2761, pruned_loss=0.05332, over 1431696.91 frames.], batch size: 20, lr: 3.42e-04
2022-05-27 20:15:03,906 INFO [train.py:842] (2/4) Epoch 16, batch 4850, loss[loss=0.2474, simple_loss=0.3269, pruned_loss=0.08398, over 7145.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2769, pruned_loss=0.05372, over 1426472.09 frames.], batch size: 20, lr: 3.42e-04
2022-05-27 20:15:43,059 INFO [train.py:842] (2/4) Epoch 16, batch 4900, loss[loss=0.1678, simple_loss=0.2595, pruned_loss=0.03809, over 7326.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2767, pruned_loss=0.05372, over 1425025.32 frames.], batch size: 20, lr: 3.42e-04
2022-05-27 20:16:22,176 INFO [train.py:842] (2/4) Epoch 16, batch 4950, loss[loss=0.2106, simple_loss=0.2901, pruned_loss=0.06553, over 7013.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2774, pruned_loss=0.05417, over 1426367.64 frames.], batch size: 28, lr: 3.42e-04
2022-05-27 20:17:00,882 INFO [train.py:842] (2/4) Epoch 16, batch 5000, loss[loss=0.1948, simple_loss=0.2871, pruned_loss=0.05122, over 7199.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2785, pruned_loss=0.05463, over 1423175.43 frames.], batch size: 23, lr: 3.42e-04
2022-05-27 20:17:40,080 INFO [train.py:842] (2/4) Epoch 16, batch 5050, loss[loss=0.156, simple_loss=0.2343, pruned_loss=0.03891, over 7137.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2773, pruned_loss=0.05402, over 1420347.28 frames.], batch size: 17, lr: 3.42e-04
2022-05-27 20:18:18,498 INFO [train.py:842] (2/4) Epoch 16, batch 5100, loss[loss=0.2163, simple_loss=0.3056, pruned_loss=0.06356, over 7202.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2789, pruned_loss=0.05477, over 1417322.93 frames.], batch size: 26, lr: 3.42e-04
2022-05-27 20:18:57,638 INFO [train.py:842] (2/4) Epoch 16, batch 5150, loss[loss=0.1913, simple_loss=0.2741, pruned_loss=0.05422, over 6260.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2784, pruned_loss=0.05413, over 1421237.00 frames.], batch size: 37, lr: 3.42e-04
2022-05-27 20:19:36,690 INFO [train.py:842] (2/4) Epoch 16, batch 5200, loss[loss=0.1805, simple_loss=0.2633, pruned_loss=0.04883, over 7055.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2782, pruned_loss=0.05424, over 1426210.86 frames.], batch size: 18, lr: 3.42e-04
2022-05-27 20:20:15,551 INFO [train.py:842] (2/4) Epoch 16, batch 5250, loss[loss=0.1796, simple_loss=0.2625, pruned_loss=0.04837, over 7256.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2786, pruned_loss=0.05419, over 1428730.08 frames.], batch size: 19, lr: 3.42e-04
2022-05-27 20:20:54,285 INFO [train.py:842] (2/4) Epoch 16, batch 5300, loss[loss=0.1697, simple_loss=0.2626, pruned_loss=0.03839, over 7312.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2794, pruned_loss=0.05487, over 1429166.11 frames.], batch size: 21, lr: 3.42e-04
2022-05-27 20:21:33,671 INFO [train.py:842] (2/4) Epoch 16, batch 5350, loss[loss=0.2221, simple_loss=0.2875, pruned_loss=0.07834, over 7272.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2796, pruned_loss=0.05506, over 1431460.20 frames.], batch size: 17, lr: 3.41e-04
2022-05-27 20:22:12,412 INFO [train.py:842] (2/4) Epoch 16, batch 5400, loss[loss=0.1891, simple_loss=0.2604, pruned_loss=0.05887, over 6991.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2782, pruned_loss=0.0546, over 1431014.60 frames.], batch size: 16, lr: 3.41e-04
2022-05-27 20:22:51,686 INFO [train.py:842] (2/4) Epoch 16, batch 5450, loss[loss=0.1664, simple_loss=0.2494, pruned_loss=0.0417, over 7266.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2773, pruned_loss=0.05406, over 1431927.97 frames.], batch size: 19, lr: 3.41e-04
2022-05-27 20:23:30,921 INFO [train.py:842] (2/4) Epoch 16, batch 5500, loss[loss=0.1972, simple_loss=0.2828, pruned_loss=0.05578, over 7393.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2777, pruned_loss=0.05405, over 1432059.69 frames.], batch size: 23, lr: 3.41e-04
2022-05-27 20:24:09,995 INFO [train.py:842] (2/4) Epoch 16, batch 5550, loss[loss=0.2474, simple_loss=0.3187, pruned_loss=0.08809, over 7196.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2774, pruned_loss=0.05364, over 1430240.09 frames.], batch size: 22, lr: 3.41e-04
2022-05-27 20:24:48,777 INFO [train.py:842] (2/4) Epoch 16, batch 5600, loss[loss=0.2166, simple_loss=0.303, pruned_loss=0.06506, over 6715.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2782, pruned_loss=0.05408, over 1422905.30 frames.], batch size: 31, lr: 3.41e-04
2022-05-27 20:25:28,077 INFO [train.py:842] (2/4) Epoch 16, batch 5650, loss[loss=0.1465, simple_loss=0.2269, pruned_loss=0.0331, over 7306.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2792, pruned_loss=0.0552, over 1418205.96 frames.], batch size: 17, lr: 3.41e-04
2022-05-27 20:26:06,927 INFO [train.py:842] (2/4) Epoch 16, batch 5700, loss[loss=0.2515, simple_loss=0.3247, pruned_loss=0.08912, over 6714.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2793, pruned_loss=0.05548, over 1418644.23 frames.], batch size: 31, lr: 3.41e-04
2022-05-27 20:26:45,772 INFO [train.py:842] (2/4) Epoch 16, batch 5750, loss[loss=0.1912, simple_loss=0.2649, pruned_loss=0.05871, over 7363.00 frames.], tot_loss[loss=0.194, simple_loss=0.2785, pruned_loss=0.05475, over 1414846.92 frames.], batch size: 19, lr: 3.41e-04
2022-05-27 20:27:25,148 INFO [train.py:842] (2/4) Epoch 16, batch 5800, loss[loss=0.1907, simple_loss=0.2809, pruned_loss=0.05022, over 7304.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2774, pruned_loss=0.05491, over 1417370.49 frames.], batch size: 25, lr: 3.41e-04
2022-05-27 20:28:04,477 INFO [train.py:842] (2/4) Epoch 16, batch 5850, loss[loss=0.1989, simple_loss=0.2937, pruned_loss=0.05207, over 7321.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2779, pruned_loss=0.05534, over 1420288.00 frames.], batch size: 21, lr: 3.41e-04
2022-05-27 20:28:43,325 INFO [train.py:842] (2/4) Epoch 16, batch 5900, loss[loss=0.1732, simple_loss=0.2514, pruned_loss=0.04751, over 7199.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2778, pruned_loss=0.05451, over 1423732.83 frames.], batch size: 16, lr: 3.41e-04
2022-05-27 20:29:22,141 INFO [train.py:842] (2/4) Epoch 16, batch 5950, loss[loss=0.1732, simple_loss=0.2521, pruned_loss=0.0472, over 7399.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2774, pruned_loss=0.05423, over 1420124.85 frames.], batch size: 18, lr: 3.41e-04
2022-05-27 20:30:00,984 INFO [train.py:842] (2/4) Epoch 16, batch 6000, loss[loss=0.1889, simple_loss=0.2844, pruned_loss=0.04669, over 7230.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2775, pruned_loss=0.05464, over 1419609.67 frames.], batch size: 20, lr: 3.41e-04
2022-05-27 20:30:00,985 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 20:30:10,685 INFO [train.py:871] (2/4) Epoch 16, validation: loss=0.1676, simple_loss=0.2671, pruned_loss=0.03399, over 868885.00 frames.
2022-05-27 20:30:49,762 INFO [train.py:842] (2/4) Epoch 16, batch 6050, loss[loss=0.1695, simple_loss=0.2354, pruned_loss=0.05178, over 7113.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2783, pruned_loss=0.05519, over 1422230.68 frames.], batch size: 17, lr: 3.41e-04
2022-05-27 20:31:28,702 INFO [train.py:842] (2/4) Epoch 16, batch 6100, loss[loss=0.212, simple_loss=0.3019, pruned_loss=0.06109, over 7447.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2786, pruned_loss=0.05486, over 1422998.21 frames.], batch size: 20, lr: 3.41e-04
2022-05-27 20:32:11,258 INFO [train.py:842] (2/4) Epoch 16, batch 6150, loss[loss=0.1789, simple_loss=0.2614, pruned_loss=0.04817, over 7424.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2781, pruned_loss=0.05474, over 1421086.48 frames.], batch size: 20, lr: 3.41e-04
2022-05-27 20:32:50,486 INFO [train.py:842] (2/4) Epoch 16, batch 6200, loss[loss=0.1763, simple_loss=0.2718, pruned_loss=0.04045, over 7317.00 frames.], tot_loss[loss=0.1923, simple_loss=0.277, pruned_loss=0.05382, over 1426329.04 frames.], batch size: 21, lr: 3.40e-04
2022-05-27 20:33:29,291 INFO [train.py:842] (2/4) Epoch 16, batch 6250, loss[loss=0.1745, simple_loss=0.2571, pruned_loss=0.04595, over 7327.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2767, pruned_loss=0.0534, over 1423888.16 frames.], batch size: 20, lr: 3.40e-04
2022-05-27 20:34:08,319 INFO [train.py:842] (2/4) Epoch 16, batch 6300, loss[loss=0.178, simple_loss=0.2657, pruned_loss=0.04513, over 7334.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2758, pruned_loss=0.05272, over 1427511.28 frames.], batch size: 22, lr: 3.40e-04
2022-05-27 20:34:47,573 INFO [train.py:842] (2/4) Epoch 16, batch 6350, loss[loss=0.2384, simple_loss=0.3282, pruned_loss=0.07431, over 6501.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2756, pruned_loss=0.05309, over 1422918.08 frames.], batch size: 38, lr: 3.40e-04
2022-05-27 20:35:26,589 INFO [train.py:842] (2/4) Epoch 16, batch 6400, loss[loss=0.1821, simple_loss=0.2673, pruned_loss=0.04841, over 7220.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2749, pruned_loss=0.05307, over 1425469.64 frames.], batch size: 21, lr: 3.40e-04
2022-05-27 20:36:05,679 INFO [train.py:842] (2/4) Epoch 16, batch 6450, loss[loss=0.1835, simple_loss=0.2786, pruned_loss=0.04421, over 7320.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2761, pruned_loss=0.05458, over 1423850.58 frames.], batch size: 21, lr: 3.40e-04
2022-05-27 20:36:44,212 INFO [train.py:842] (2/4) Epoch 16, batch 6500, loss[loss=0.1725, simple_loss=0.2583, pruned_loss=0.04338, over 7228.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2773, pruned_loss=0.05445, over 1419509.95 frames.], batch size: 21, lr: 3.40e-04
2022-05-27 20:37:23,362 INFO [train.py:842] (2/4) Epoch 16, batch 6550, loss[loss=0.2067, simple_loss=0.2875, pruned_loss=0.06291, over 7195.00 frames.], tot_loss[loss=0.1927, simple_loss=0.277, pruned_loss=0.0542, over 1420091.27 frames.], batch size: 22, lr: 3.40e-04
2022-05-27 20:38:12,086 INFO [train.py:842] (2/4) Epoch 16, batch 6600, loss[loss=0.1792, simple_loss=0.2617, pruned_loss=0.04829, over 7069.00 frames.], tot_loss[loss=0.192, simple_loss=0.2765, pruned_loss=0.05371, over 1422752.69 frames.], batch size: 18, lr: 3.40e-04
2022-05-27 20:38:51,378 INFO [train.py:842] (2/4) Epoch 16, batch 6650, loss[loss=0.2042, simple_loss=0.2947, pruned_loss=0.05685, over 7031.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2774, pruned_loss=0.0545, over 1421684.37 frames.], batch size: 28, lr: 3.40e-04
2022-05-27 20:39:30,104 INFO [train.py:842] (2/4) Epoch 16, batch 6700, loss[loss=0.1696, simple_loss=0.2551, pruned_loss=0.04212, over 7339.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2781, pruned_loss=0.05443, over 1422632.13 frames.], batch size: 20, lr: 3.40e-04
2022-05-27 20:40:09,398 INFO [train.py:842] (2/4) Epoch 16, batch 6750, loss[loss=0.1625, simple_loss=0.2502, pruned_loss=0.03741, over 7320.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2786, pruned_loss=0.05488, over 1424664.90 frames.], batch size: 20, lr: 3.40e-04
2022-05-27 20:40:48,522 INFO [train.py:842] (2/4) Epoch 16, batch 6800, loss[loss=0.171, simple_loss=0.2586, pruned_loss=0.04164, over 7439.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2789, pruned_loss=0.05504, over 1427507.79 frames.], batch size: 20, lr: 3.40e-04
2022-05-27 20:41:27,658 INFO [train.py:842] (2/4) Epoch 16, batch 6850, loss[loss=0.1706, simple_loss=0.2522, pruned_loss=0.04445, over 7273.00 frames.], tot_loss[loss=0.1938, simple_loss=0.278, pruned_loss=0.05476, over 1428081.99 frames.], batch size: 19, lr: 3.40e-04
2022-05-27 20:42:06,258 INFO [train.py:842] (2/4) Epoch 16, batch 6900, loss[loss=0.2485, simple_loss=0.3417, pruned_loss=0.07766, over 7073.00 frames.], tot_loss[loss=0.194, simple_loss=0.2783, pruned_loss=0.05486, over 1424804.30 frames.], batch size: 28, lr: 3.40e-04
2022-05-27 20:42:45,902 INFO [train.py:842] (2/4) Epoch 16, batch 6950, loss[loss=0.1936, simple_loss=0.2807, pruned_loss=0.05329, over 7310.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2774, pruned_loss=0.0546, over 1424915.16 frames.], batch size: 24, lr: 3.40e-04
2022-05-27 20:43:25,273 INFO [train.py:842] (2/4) Epoch 16, batch 7000, loss[loss=0.1472, simple_loss=0.2233, pruned_loss=0.03555, over 7194.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2761, pruned_loss=0.05421, over 1423002.33 frames.], batch size: 16, lr: 3.40e-04
2022-05-27 20:44:04,325 INFO [train.py:842] (2/4) Epoch 16, batch 7050, loss[loss=0.2511, simple_loss=0.3167, pruned_loss=0.0928, over 7189.00 frames.], tot_loss[loss=0.1923, simple_loss=0.276, pruned_loss=0.05428, over 1425800.29 frames.], batch size: 26, lr: 3.39e-04
2022-05-27 20:44:43,342 INFO [train.py:842] (2/4) Epoch 16, batch 7100, loss[loss=0.178, simple_loss=0.2715, pruned_loss=0.04226, over 7188.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2771, pruned_loss=0.05434, over 1429573.27 frames.], batch size: 26, lr: 3.39e-04
2022-05-27 20:45:22,598 INFO [train.py:842] (2/4) Epoch 16, batch 7150, loss[loss=0.2381, simple_loss=0.3087, pruned_loss=0.0838, over 5305.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2768, pruned_loss=0.05435, over 1425840.36 frames.], batch size: 52, lr: 3.39e-04
2022-05-27 20:46:01,387 INFO [train.py:842] (2/4) Epoch 16, batch 7200, loss[loss=0.2006, simple_loss=0.2873, pruned_loss=0.05698, over 7387.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2757, pruned_loss=0.05359, over 1427219.79 frames.], batch size: 23, lr: 3.39e-04
2022-05-27 20:46:40,322 INFO [train.py:842] (2/4) Epoch 16, batch 7250, loss[loss=0.2013, simple_loss=0.2816, pruned_loss=0.06046, over 7158.00 frames.], tot_loss[loss=0.1926, simple_loss=0.277, pruned_loss=0.05414, over 1424421.69 frames.], batch size: 19, lr: 3.39e-04
2022-05-27 20:47:19,340 INFO [train.py:842] (2/4) Epoch 16, batch 7300, loss[loss=0.1837, simple_loss=0.2643, pruned_loss=0.05152, over 7063.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2789, pruned_loss=0.05523, over 1420235.39 frames.], batch size: 18, lr: 3.39e-04
2022-05-27 20:47:58,163 INFO [train.py:842] (2/4) Epoch 16, batch 7350, loss[loss=0.1695, simple_loss=0.2497, pruned_loss=0.04466, over 6995.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2797, pruned_loss=0.05546, over 1421727.74 frames.], batch size: 16, lr: 3.39e-04
2022-05-27 20:48:36,988 INFO [train.py:842] (2/4) Epoch 16, batch 7400, loss[loss=0.1507, simple_loss=0.226, pruned_loss=0.03768, over 6781.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2802, pruned_loss=0.05556, over 1424017.10 frames.], batch size: 15, lr: 3.39e-04
2022-05-27 20:49:15,893 INFO [train.py:842] (2/4) Epoch 16, batch 7450, loss[loss=0.169, simple_loss=0.2544, pruned_loss=0.04179, over 7213.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2805, pruned_loss=0.05567, over 1420788.55 frames.], batch size: 16, lr: 3.39e-04
2022-05-27 20:49:54,822 INFO [train.py:842] (2/4) Epoch 16, batch 7500, loss[loss=0.1744, simple_loss=0.264, pruned_loss=0.04237, over 7157.00 frames.], tot_loss[loss=0.1943, simple_loss=0.279, pruned_loss=0.05477, over 1420291.74 frames.], batch size: 18, lr: 3.39e-04
2022-05-27 20:50:33,693 INFO [train.py:842] (2/4) Epoch 16, batch 7550, loss[loss=0.1685, simple_loss=0.2623, pruned_loss=0.03729, over 7399.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2789, pruned_loss=0.05476, over 1422521.43 frames.], batch size: 18, lr: 3.39e-04
2022-05-27 20:51:12,601 INFO [train.py:842] (2/4) Epoch 16, batch 7600, loss[loss=0.1853, simple_loss=0.2843, pruned_loss=0.04321, over 6744.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2781, pruned_loss=0.05424, over 1421019.68 frames.], batch size: 31, lr: 3.39e-04
2022-05-27 20:51:51,600 INFO [train.py:842] (2/4) Epoch 16, batch 7650, loss[loss=0.177, simple_loss=0.2502, pruned_loss=0.05187, over 7000.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2786, pruned_loss=0.05479, over 1421842.19 frames.], batch size: 16, lr: 3.39e-04
2022-05-27 20:52:30,503 INFO [train.py:842] (2/4) Epoch 16, batch 7700, loss[loss=0.1809, simple_loss=0.2796, pruned_loss=0.04107, over 7423.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2785, pruned_loss=0.05483, over 1422263.90 frames.], batch size: 21, lr: 3.39e-04
2022-05-27 20:53:09,723 INFO [train.py:842] (2/4) Epoch 16, batch 7750, loss[loss=0.1796, simple_loss=0.2581, pruned_loss=0.05059, over 7164.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2776, pruned_loss=0.05415, over 1425735.56 frames.], batch size: 18, lr: 3.39e-04
2022-05-27 20:53:48,645 INFO [train.py:842] (2/4) Epoch 16, batch 7800, loss[loss=0.1806, simple_loss=0.2666, pruned_loss=0.04727, over 6886.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2772, pruned_loss=0.05429, over 1428922.48 frames.], batch size: 31, lr: 3.39e-04
2022-05-27 20:54:27,531 INFO [train.py:842] (2/4) Epoch 16, batch 7850, loss[loss=0.1691, simple_loss=0.2499, pruned_loss=0.04418, over 7232.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2775, pruned_loss=0.05407, over 1429970.92 frames.], batch size: 16, lr: 3.39e-04
2022-05-27 20:55:06,427 INFO [train.py:842] (2/4) Epoch 16, batch 7900, loss[loss=0.1975, simple_loss=0.2818, pruned_loss=0.05665, over 7354.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2763, pruned_loss=0.05403, over 1426046.02 frames.], batch size: 19, lr: 3.38e-04
2022-05-27 20:55:45,926 INFO [train.py:842] (2/4) Epoch 16, batch 7950, loss[loss=0.1471, simple_loss=0.2293, pruned_loss=0.03248, over 7145.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2759, pruned_loss=0.05391, over 1426539.19 frames.], batch size: 17, lr: 3.38e-04
2022-05-27 20:56:24,796 INFO [train.py:842] (2/4) Epoch 16, batch 8000, loss[loss=0.1996, simple_loss=0.277, pruned_loss=0.0611, over 7279.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2763, pruned_loss=0.054, over 1427467.22 frames.], batch size: 18, lr: 3.38e-04
2022-05-27 20:57:04,138 INFO [train.py:842] (2/4) Epoch 16, batch 8050, loss[loss=0.1906, simple_loss=0.2665, pruned_loss=0.05733, over 7200.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2758, pruned_loss=0.05398, over 1426452.72 frames.], batch size: 16, lr: 3.38e-04
2022-05-27 20:57:42,708 INFO [train.py:842] (2/4) Epoch 16, batch 8100, loss[loss=0.1573, simple_loss=0.2413, pruned_loss=0.03662, over 7360.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2759, pruned_loss=0.05386, over 1429181.37 frames.], batch size: 19, lr: 3.38e-04
2022-05-27 20:58:21,839 INFO [train.py:842] (2/4) Epoch 16, batch 8150, loss[loss=0.1998, simple_loss=0.2809, pruned_loss=0.05931, over 7200.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2758, pruned_loss=0.05369, over 1430013.59 frames.], batch size: 22, lr: 3.38e-04
2022-05-27 20:59:00,890 INFO [train.py:842] (2/4) Epoch 16, batch 8200, loss[loss=0.1463, simple_loss=0.2327, pruned_loss=0.03001, over 7211.00 frames.], tot_loss[loss=0.191, simple_loss=0.275, pruned_loss=0.05348, over 1427820.22 frames.], batch size: 16, lr: 3.38e-04
2022-05-27 20:59:39,503 INFO [train.py:842] (2/4) Epoch 16, batch 8250, loss[loss=0.1936, simple_loss=0.2789, pruned_loss=0.05421, over 7309.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2768, pruned_loss=0.05433, over 1424969.28 frames.], batch size: 25, lr: 3.38e-04
2022-05-27 21:00:18,889 INFO [train.py:842] (2/4) Epoch 16, batch 8300, loss[loss=0.1786, simple_loss=0.2591, pruned_loss=0.04907, over 7330.00 frames.], tot_loss[loss=0.1932, simple_loss=0.277, pruned_loss=0.0547, over 1424179.03 frames.], batch size: 20, lr: 3.38e-04
2022-05-27 21:00:57,557 INFO [train.py:842] (2/4) Epoch 16, batch 8350, loss[loss=0.2141, simple_loss=0.2998, pruned_loss=0.0642, over 7136.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2779, pruned_loss=0.05476, over 1421795.86 frames.], batch size: 26, lr: 3.38e-04
2022-05-27 21:01:36,494 INFO [train.py:842] (2/4) Epoch 16, batch 8400, loss[loss=0.1717, simple_loss=0.2403, pruned_loss=0.05154, over 6780.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2769, pruned_loss=0.05413, over 1418099.04 frames.], batch size: 15, lr: 3.38e-04
2022-05-27 21:02:15,826 INFO [train.py:842] (2/4) Epoch 16, batch 8450, loss[loss=0.1815, simple_loss=0.2682, pruned_loss=0.04741, over 7425.00 frames.], tot_loss[loss=0.1922, simple_loss=0.277, pruned_loss=0.05373, over 1421237.45 frames.], batch size: 20, lr: 3.38e-04
2022-05-27 21:02:54,710 INFO [train.py:842] (2/4) Epoch 16, batch 8500, loss[loss=0.1729, simple_loss=0.2625, pruned_loss=0.04167, over 7166.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2754, pruned_loss=0.05262, over 1421237.36 frames.], batch size: 19, lr: 3.38e-04
2022-05-27 21:03:33,698 INFO [train.py:842] (2/4) Epoch 16, batch 8550, loss[loss=0.2213, simple_loss=0.3117, pruned_loss=0.06543, over 7425.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2757, pruned_loss=0.0527, over 1420944.94 frames.], batch size: 20, lr: 3.38e-04
2022-05-27 21:04:12,796 INFO [train.py:842] (2/4) Epoch 16, batch 8600, loss[loss=0.1435, simple_loss=0.2331, pruned_loss=0.02699, over 7285.00 frames.], tot_loss[loss=0.191, simple_loss=0.2756, pruned_loss=0.05317, over 1419365.81 frames.], batch size: 18, lr: 3.38e-04
2022-05-27 21:04:52,305 INFO [train.py:842] (2/4) Epoch 16, batch 8650, loss[loss=0.2279, simple_loss=0.2965, pruned_loss=0.07961, over 5190.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2754, pruned_loss=0.0532, over 1415185.32 frames.], batch size: 52, lr: 3.38e-04
2022-05-27 21:05:31,125 INFO [train.py:842] (2/4) Epoch 16, batch 8700, loss[loss=0.17, simple_loss=0.2555, pruned_loss=0.04224, over 7146.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2753, pruned_loss=0.05387, over 1411996.28 frames.], batch size: 20, lr: 3.38e-04
2022-05-27 21:06:10,231 INFO [train.py:842] (2/4) Epoch 16, batch 8750, loss[loss=0.2291, simple_loss=0.2935, pruned_loss=0.08235, over 7062.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2757, pruned_loss=0.05369, over 1412415.46 frames.], batch size: 18, lr: 3.38e-04
2022-05-27 21:06:48,805 INFO [train.py:842] (2/4) Epoch 16, batch 8800, loss[loss=0.1773, simple_loss=0.2699, pruned_loss=0.04237, over 7195.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2759, pruned_loss=0.05353, over 1410654.15 frames.], batch size: 22, lr: 3.37e-04
2022-05-27 21:07:27,895 INFO [train.py:842] (2/4) Epoch 16, batch 8850, loss[loss=0.1899, simple_loss=0.2699, pruned_loss=0.05495, over 7070.00 frames.], tot_loss[loss=0.1908, simple_loss=0.275, pruned_loss=0.0533, over 1409706.10 frames.], batch size: 18, lr: 3.37e-04
2022-05-27 21:08:06,061 INFO [train.py:842] (2/4) Epoch 16, batch 8900, loss[loss=0.243, simple_loss=0.3087, pruned_loss=0.08861, over 5204.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2771, pruned_loss=0.05413, over 1399665.82 frames.], batch size: 52, lr: 3.37e-04
2022-05-27 21:08:44,686 INFO [train.py:842] (2/4) Epoch 16, batch 8950, loss[loss=0.1352, simple_loss=0.2331, pruned_loss=0.01864, over 7257.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2768, pruned_loss=0.05387, over 1396505.22 frames.], batch size: 19, lr: 3.37e-04
2022-05-27 21:09:22,814 INFO [train.py:842] (2/4) Epoch 16, batch 9000, loss[loss=0.2145, simple_loss=0.2986, pruned_loss=0.06524, over 7047.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2795, pruned_loss=0.05544, over 1381382.76 frames.], batch size: 28, lr: 3.37e-04
2022-05-27 21:09:22,815 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 21:09:32,341 INFO [train.py:871] (2/4) Epoch 16, validation: loss=0.167, simple_loss=0.2669, pruned_loss=0.03357, over 868885.00 frames.
2022-05-27 21:10:10,579 INFO [train.py:842] (2/4) Epoch 16, batch 9050, loss[loss=0.1514, simple_loss=0.2387, pruned_loss=0.03204, over 7254.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2818, pruned_loss=0.05637, over 1366420.33 frames.], batch size: 19, lr: 3.37e-04
2022-05-27 21:10:58,387 INFO [train.py:842] (2/4) Epoch 16, batch 9100, loss[loss=0.223, simple_loss=0.2995, pruned_loss=0.0732, over 4811.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2842, pruned_loss=0.05856, over 1310469.81 frames.], batch size: 52, lr: 3.37e-04
2022-05-27 21:11:46,303 INFO [train.py:842] (2/4) Epoch 16, batch 9150, loss[loss=0.2727, simple_loss=0.3419, pruned_loss=0.1017, over 5434.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2893, pruned_loss=0.06274, over 1242958.93 frames.], batch size: 52, lr: 3.37e-04
2022-05-27 21:12:47,122 INFO [train.py:842] (2/4) Epoch 17, batch 0, loss[loss=0.1908, simple_loss=0.2791, pruned_loss=0.05121, over 7116.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2791, pruned_loss=0.05121, over 7116.00 frames.], batch size: 21, lr: 3.28e-04
2022-05-27 21:13:26,305 INFO [train.py:842] (2/4) Epoch 17, batch 50, loss[loss=0.196, simple_loss=0.2824, pruned_loss=0.05481, over 7324.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2761, pruned_loss=0.05542, over 317888.29 frames.], batch size: 21, lr: 3.28e-04
2022-05-27 21:14:04,921 INFO [train.py:842] (2/4) Epoch 17, batch 100, loss[loss=0.1939, simple_loss=0.2765, pruned_loss=0.05562, over 7144.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2774, pruned_loss=0.05459, over 559489.49 frames.], batch size: 20, lr: 3.28e-04
2022-05-27 21:14:43,826 INFO [train.py:842] (2/4) Epoch 17, batch 150, loss[loss=0.1632, simple_loss=0.2445, pruned_loss=0.04096, over 7004.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2777, pruned_loss=0.05436, over 748099.45 frames.], batch size: 16, lr: 3.28e-04
2022-05-27 21:15:22,353 INFO [train.py:842] (2/4) Epoch 17, batch 200, loss[loss=0.1732, simple_loss=0.249, pruned_loss=0.04872, over 7131.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2783, pruned_loss=0.05429, over 896739.23 frames.], batch size: 17, lr: 3.27e-04
2022-05-27 21:16:01,197 INFO [train.py:842] (2/4) Epoch 17, batch 250, loss[loss=0.2408, simple_loss=0.3168, pruned_loss=0.08237, over 7253.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2775, pruned_loss=0.05364, over 1015994.02 frames.], batch size: 19, lr: 3.27e-04
2022-05-27 21:16:39,725 INFO [train.py:842] (2/4) Epoch 17, batch 300, loss[loss=0.1878, simple_loss=0.269, pruned_loss=0.0533, over 7067.00 frames.], tot_loss[loss=0.1938, simple_loss=0.279, pruned_loss=0.05431, over 1102345.09 frames.], batch size: 18, lr: 3.27e-04
2022-05-27 21:17:18,934 INFO [train.py:842] (2/4) Epoch 17, batch 350, loss[loss=0.1779, simple_loss=0.2552, pruned_loss=0.05024, over 6792.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2785, pruned_loss=0.05406, over 1171859.97 frames.], batch size: 15, lr: 3.27e-04
2022-05-27 21:17:57,651 INFO [train.py:842] (2/4) Epoch 17, batch 400, loss[loss=0.1853, simple_loss=0.2679, pruned_loss=0.05133, over 5327.00 frames.], tot_loss[loss=0.192, simple_loss=0.2777, pruned_loss=0.05319, over 1227728.73 frames.], batch size: 52, lr: 3.27e-04
2022-05-27 21:18:36,642 INFO [train.py:842] (2/4) Epoch 17, batch 450, loss[loss=0.1568, simple_loss=0.2526, pruned_loss=0.0305, over 7362.00 frames.], tot_loss[loss=0.193, simple_loss=0.2782, pruned_loss=0.05389, over 1268067.63 frames.], batch size: 19, lr: 3.27e-04
2022-05-27 21:19:15,538 INFO [train.py:842] (2/4) Epoch 17, batch 500, loss[loss=0.1902, simple_loss=0.2726, pruned_loss=0.05391, over 7168.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2769, pruned_loss=0.05301, over 1301274.27 frames.], batch size: 18, lr: 3.27e-04
2022-05-27 21:19:55,017 INFO [train.py:842] (2/4) Epoch 17, batch 550, loss[loss=0.1564, simple_loss=0.231, pruned_loss=0.04095, over 7125.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2768, pruned_loss=0.05321, over 1327138.89 frames.], batch size: 17, lr: 3.27e-04
2022-05-27 21:20:33,623 INFO [train.py:842] (2/4) Epoch 17, batch 600, loss[loss=0.1911, simple_loss=0.2806, pruned_loss=0.05082, over 6991.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2785, pruned_loss=0.05447, over 1341874.41 frames.], batch size: 28, lr: 3.27e-04
2022-05-27 21:21:12,978 INFO [train.py:842] (2/4) Epoch 17, batch 650, loss[loss=0.1814, simple_loss=0.2699, pruned_loss=0.04646, over 7324.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2796, pruned_loss=0.055, over 1360602.67 frames.], batch size: 20, lr: 3.27e-04
2022-05-27 21:21:51,622 INFO [train.py:842] (2/4) Epoch 17, batch 700, loss[loss=0.1928, simple_loss=0.2747, pruned_loss=0.05546, over 7271.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2803, pruned_loss=0.05566, over 1367724.96 frames.], batch size: 19, lr: 3.27e-04
2022-05-27 21:22:30,807 INFO [train.py:842] (2/4) Epoch 17, batch 750, loss[loss=0.1582, simple_loss=0.2557, pruned_loss=0.03039, over 7143.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2798, pruned_loss=0.05521, over 1376448.26 frames.], batch size: 20, lr: 3.27e-04
2022-05-27 21:23:09,454 INFO [train.py:842] (2/4) Epoch 17, batch 800, loss[loss=0.1786, simple_loss=0.2695, pruned_loss=0.04387, over 7166.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2796, pruned_loss=0.05508, over 1387444.45 frames.], batch size: 19, lr: 3.27e-04
2022-05-27 21:23:48,539 INFO [train.py:842] (2/4) Epoch 17, batch 850, loss[loss=0.1957, simple_loss=0.2816, pruned_loss=0.0549, over 6558.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2786, pruned_loss=0.0548, over 1395693.25 frames.], batch size: 38, lr: 3.27e-04
2022-05-27 21:24:27,494 INFO [train.py:842] (2/4) Epoch 17, batch 900, loss[loss=0.2314, simple_loss=0.3144, pruned_loss=0.07421, over 7343.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2792, pruned_loss=0.05498, over 1407550.34 frames.], batch size: 20, lr: 3.27e-04
2022-05-27 21:25:06,297 INFO [train.py:842] (2/4) Epoch 17, batch 950, loss[loss=0.1535, simple_loss=0.2371, pruned_loss=0.03491, over 7139.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2779, pruned_loss=0.05408, over 1412515.88 frames.], batch size: 17, lr: 3.27e-04
2022-05-27 21:25:44,930 INFO [train.py:842] (2/4) Epoch 17, batch 1000, loss[loss=0.1864, simple_loss=0.2759, pruned_loss=0.0484, over 7122.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2784, pruned_loss=0.05413, over 1416321.96 frames.], batch size: 21, lr: 3.27e-04
2022-05-27 21:26:24,308 INFO [train.py:842] (2/4) Epoch 17, batch 1050, loss[loss=0.1944, simple_loss=0.2866, pruned_loss=0.05113, over 7337.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2776, pruned_loss=0.05383, over 1420324.88 frames.], batch size: 22, lr: 3.27e-04
2022-05-27 21:27:03,132 INFO [train.py:842] (2/4) Epoch 17, batch 1100, loss[loss=0.2037, simple_loss=0.2915, pruned_loss=0.05793, over 7284.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2775, pruned_loss=0.05368, over 1420181.80 frames.], batch size: 24, lr: 3.26e-04
2022-05-27 21:27:42,206 INFO [train.py:842] (2/4) Epoch 17, batch 1150, loss[loss=0.2127, simple_loss=0.3018, pruned_loss=0.06175, over 7268.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2783, pruned_loss=0.05399, over 1421479.30 frames.], batch size: 24, lr: 3.26e-04
2022-05-27 21:28:20,877 INFO [train.py:842] (2/4) Epoch 17, batch 1200, loss[loss=0.1848, simple_loss=0.2741, pruned_loss=0.04779, over 7281.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2775, pruned_loss=0.05366, over 1418690.49 frames.], batch size: 25, lr: 3.26e-04
2022-05-27 21:28:59,916 INFO [train.py:842] (2/4) Epoch 17, batch 1250, loss[loss=0.1651, simple_loss=0.246, pruned_loss=0.04208, over 7279.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2789, pruned_loss=0.05481, over 1414501.81 frames.], batch size: 18, lr: 3.26e-04
2022-05-27 21:29:38,919 INFO [train.py:842] (2/4) Epoch 17, batch 1300, loss[loss=0.1969, simple_loss=0.2851, pruned_loss=0.05439, over 7320.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2788, pruned_loss=0.05492, over 1412218.47 frames.], batch size: 22, lr: 3.26e-04
2022-05-27 21:30:18,028 INFO [train.py:842] (2/4) Epoch 17, batch 1350, loss[loss=0.145, simple_loss=0.2306, pruned_loss=0.02974, over 6987.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2791, pruned_loss=0.05488, over 1417728.30 frames.], batch size: 16, lr: 3.26e-04
2022-05-27 21:30:56,924 INFO [train.py:842] (2/4) Epoch 17, batch 1400, loss[loss=0.2105, simple_loss=0.3053, pruned_loss=0.05782, over 7149.00 frames.], tot_loss[loss=0.193, simple_loss=0.2775, pruned_loss=0.05429, over 1419178.37 frames.], batch size: 20, lr: 3.26e-04
2022-05-27 21:31:36,141 INFO [train.py:842] (2/4) Epoch 17, batch 1450, loss[loss=0.2079, simple_loss=0.3073, pruned_loss=0.05424, over 7332.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2778, pruned_loss=0.05432, over 1418328.20 frames.], batch size: 22, lr: 3.26e-04
2022-05-27 21:32:15,395 INFO [train.py:842] (2/4) Epoch 17, batch 1500, loss[loss=0.1461, simple_loss=0.2369, pruned_loss=0.02763, over 7257.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2767, pruned_loss=0.05426, over 1424295.59 frames.], batch size: 19, lr: 3.26e-04
2022-05-27 21:32:54,704 INFO [train.py:842] (2/4) Epoch 17, batch 1550, loss[loss=0.2047, simple_loss=0.2914, pruned_loss=0.05901, over 7223.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2766, pruned_loss=0.05404, over 1421808.82 frames.], batch size: 21, lr: 3.26e-04
2022-05-27 21:33:33,695 INFO [train.py:842] (2/4) Epoch 17, batch 1600, loss[loss=0.1922, simple_loss=0.2845, pruned_loss=0.04993, over 7428.00 frames.], tot_loss[loss=0.1902, simple_loss=0.275, pruned_loss=0.05273, over 1426642.84 frames.], batch size: 20, lr: 3.26e-04
2022-05-27 21:34:12,936 INFO [train.py:842] (2/4) Epoch 17, batch 1650, loss[loss=0.177, simple_loss=0.2739, pruned_loss=0.04012, over 7409.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2766, pruned_loss=0.05333, over 1428881.68 frames.], batch size: 21, lr: 3.26e-04
2022-05-27 21:34:51,642 INFO [train.py:842] (2/4) Epoch 17, batch 1700, loss[loss=0.2271, simple_loss=0.2948, pruned_loss=0.0797, over 5018.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2773, pruned_loss=0.05416, over 1422783.85 frames.], batch size: 52, lr: 3.26e-04
2022-05-27 21:35:30,463 INFO [train.py:842] (2/4) Epoch 17, batch 1750, loss[loss=0.2155, simple_loss=0.3064, pruned_loss=0.06227, over 7384.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2777, pruned_loss=0.05406, over 1414467.24 frames.], batch size: 23, lr: 3.26e-04
2022-05-27 21:36:08,991 INFO [train.py:842] (2/4) Epoch 17, batch 1800, loss[loss=0.2229, simple_loss=0.3089, pruned_loss=0.06842, over 7198.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2791, pruned_loss=0.0544, over 1415722.84 frames.], batch size: 23, lr: 3.26e-04
2022-05-27 21:36:48,044 INFO [train.py:842] (2/4) Epoch 17, batch 1850, loss[loss=0.2061, simple_loss=0.2903, pruned_loss=0.06092, over 6377.00 frames.], tot_loss[loss=0.1931, simple_loss=0.278, pruned_loss=0.05411, over 1417109.32 frames.], batch size: 38, lr: 3.26e-04
2022-05-27 21:37:26,808 INFO [train.py:842] (2/4) Epoch 17, batch 1900, loss[loss=0.1641, simple_loss=0.2556, pruned_loss=0.03634, over 7423.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2786, pruned_loss=0.05444, over 1420945.54 frames.], batch size: 20, lr: 3.26e-04
2022-05-27 21:38:05,891 INFO [train.py:842] (2/4) Epoch 17, batch 1950, loss[loss=0.18, simple_loss=0.2736, pruned_loss=0.04316, over 7319.00 frames.], tot_loss[loss=0.194, simple_loss=0.2785, pruned_loss=0.05476, over 1423026.00 frames.], batch size: 21, lr: 3.26e-04
2022-05-27 21:38:44,427 INFO [train.py:842] (2/4) Epoch 17, batch 2000, loss[loss=0.1474, simple_loss=0.2361, pruned_loss=0.02941, over 7262.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2765, pruned_loss=0.05316, over 1424545.42 frames.], batch size: 19, lr: 3.25e-04
2022-05-27 21:39:23,693 INFO [train.py:842] (2/4) Epoch 17, batch 2050, loss[loss=0.1533, simple_loss=0.2388, pruned_loss=0.03389, over 7398.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2763, pruned_loss=0.05369, over 1427329.23 frames.], batch size: 18, lr: 3.25e-04
2022-05-27 21:40:02,239 INFO [train.py:842] (2/4) Epoch 17, batch 2100, loss[loss=0.1996, simple_loss=0.2835, pruned_loss=0.05783, over 7407.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2772, pruned_loss=0.05382, over 1427680.05 frames.], batch size: 21, lr: 3.25e-04
2022-05-27 21:40:41,275 INFO [train.py:842] (2/4) Epoch 17, batch 2150, loss[loss=0.1959, simple_loss=0.2719, pruned_loss=0.06, over 7360.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2778, pruned_loss=0.05403, over 1423679.84 frames.], batch size: 19, lr: 3.25e-04
2022-05-27 21:41:20,110 INFO [train.py:842] (2/4) Epoch 17, batch 2200, loss[loss=0.2005, simple_loss=0.2915, pruned_loss=0.05473, over 7349.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2779, pruned_loss=0.05388, over 1420764.33 frames.], batch size: 22, lr: 3.25e-04
2022-05-27 21:41:59,517 INFO [train.py:842] (2/4) Epoch 17, batch 2250, loss[loss=0.1882, simple_loss=0.2729, pruned_loss=0.05169, over 7423.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2784, pruned_loss=0.05442, over 1423106.24 frames.], batch size: 21, lr: 3.25e-04
2022-05-27 21:42:38,171 INFO [train.py:842] (2/4) Epoch 17, batch 2300, loss[loss=0.1783, simple_loss=0.2759, pruned_loss=0.04038, over 7289.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2772, pruned_loss=0.05347, over 1422238.81 frames.], batch size: 24, lr: 3.25e-04
2022-05-27 21:43:17,576 INFO [train.py:842] (2/4) Epoch 17, batch 2350, loss[loss=0.2073, simple_loss=0.2859, pruned_loss=0.06433, over 7383.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2762, pruned_loss=0.05318, over 1425856.86 frames.], batch size: 23, lr: 3.25e-04
2022-05-27 21:43:56,290 INFO [train.py:842] (2/4) Epoch 17, batch 2400, loss[loss=0.2335, simple_loss=0.3051, pruned_loss=0.08096, over 6988.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2755, pruned_loss=0.05273, over 1424034.20 frames.], batch size: 16, lr: 3.25e-04
2022-05-27 21:44:35,702 INFO [train.py:842] (2/4) Epoch 17, batch 2450, loss[loss=0.1867, simple_loss=0.2859, pruned_loss=0.04377, over 7339.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2741, pruned_loss=0.05204, over 1423549.34 frames.], batch size: 22, lr: 3.25e-04
2022-05-27 21:45:14,363 INFO [train.py:842] (2/4) Epoch 17, batch 2500, loss[loss=0.1978, simple_loss=0.2772, pruned_loss=0.05921, over 7209.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2738, pruned_loss=0.05219, over 1422848.46 frames.], batch size: 21, lr: 3.25e-04
2022-05-27 21:45:53,519 INFO [train.py:842] (2/4) Epoch 17, batch 2550, loss[loss=0.173, simple_loss=0.27, pruned_loss=0.03804, over 7225.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2735, pruned_loss=0.0521, over 1419098.03 frames.], batch size: 21, lr: 3.25e-04
2022-05-27 21:46:32,091 INFO [train.py:842] (2/4) Epoch 17, batch 2600, loss[loss=0.1886, simple_loss=0.2793, pruned_loss=0.04889, over 7002.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2745, pruned_loss=0.0524, over 1421822.07 frames.], batch size: 28, lr: 3.25e-04
2022-05-27 21:47:11,544 INFO [train.py:842] (2/4) Epoch 17, batch 2650, loss[loss=0.2068, simple_loss=0.2916, pruned_loss=0.061, over 7361.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2759, pruned_loss=0.05324, over 1420614.36 frames.], batch size: 19, lr: 3.25e-04
2022-05-27 21:47:50,582 INFO [train.py:842] (2/4) Epoch 17, batch 2700, loss[loss=0.1933, simple_loss=0.2886, pruned_loss=0.04901, over 7335.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2758, pruned_loss=0.05345, over 1423520.74 frames.], batch size: 22, lr: 3.25e-04
2022-05-27 21:48:29,820 INFO [train.py:842] (2/4) Epoch 17, batch 2750, loss[loss=0.1733, simple_loss=0.2506, pruned_loss=0.04802, over 7157.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2737, pruned_loss=0.05253, over 1422457.91 frames.], batch size: 19, lr: 3.25e-04
2022-05-27 21:49:09,003 INFO [train.py:842] (2/4) Epoch 17, batch 2800, loss[loss=0.2489, simple_loss=0.3274, pruned_loss=0.08519, over 5129.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2741, pruned_loss=0.05264, over 1421944.91 frames.], batch size: 53, lr: 3.25e-04
2022-05-27 21:49:47,933 INFO [train.py:842] (2/4) Epoch 17, batch 2850, loss[loss=0.2709, simple_loss=0.3366, pruned_loss=0.1026, over 7319.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2753, pruned_loss=0.05325, over 1422142.49 frames.], batch size: 21, lr: 3.25e-04
2022-05-27 21:50:27,037 INFO [train.py:842] (2/4) Epoch 17, batch 2900, loss[loss=0.2192, simple_loss=0.2987, pruned_loss=0.06987, over 7238.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2753, pruned_loss=0.05317, over 1417822.16 frames.], batch size: 20, lr: 3.24e-04
2022-05-27 21:51:06,321 INFO [train.py:842] (2/4) Epoch 17, batch 2950, loss[loss=0.1578, simple_loss=0.2301, pruned_loss=0.04277, over 7269.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2749, pruned_loss=0.05314, over 1418307.83 frames.], batch size: 18, lr: 3.24e-04
2022-05-27 21:51:45,529 INFO [train.py:842] (2/4) Epoch 17, batch 3000, loss[loss=0.178, simple_loss=0.2667, pruned_loss=0.04464, over 7141.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2743, pruned_loss=0.05223, over 1423481.22 frames.], batch size: 20, lr: 3.24e-04
2022-05-27 21:51:45,530 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 21:51:55,147 INFO [train.py:871] (2/4) Epoch 17, validation: loss=0.1666, simple_loss=0.2663, pruned_loss=0.03343, over 868885.00 frames.
2022-05-27 21:52:34,180 INFO [train.py:842] (2/4) Epoch 17, batch 3050, loss[loss=0.2371, simple_loss=0.3135, pruned_loss=0.0804, over 6327.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2747, pruned_loss=0.0524, over 1423184.47 frames.], batch size: 37, lr: 3.24e-04
2022-05-27 21:53:12,907 INFO [train.py:842] (2/4) Epoch 17, batch 3100, loss[loss=0.31, simple_loss=0.3831, pruned_loss=0.1184, over 7276.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2751, pruned_loss=0.05287, over 1420300.20 frames.], batch size: 25, lr: 3.24e-04
2022-05-27 21:53:52,111 INFO [train.py:842] (2/4) Epoch 17, batch 3150, loss[loss=0.1702, simple_loss=0.2563, pruned_loss=0.04202, over 7327.00 frames.], tot_loss[loss=0.191, simple_loss=0.2756, pruned_loss=0.05321, over 1419388.79 frames.], batch size: 20, lr: 3.24e-04
2022-05-27 21:54:30,950 INFO [train.py:842] (2/4) Epoch 17, batch 3200, loss[loss=0.1781, simple_loss=0.2539, pruned_loss=0.05112, over 7370.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2765, pruned_loss=0.05399, over 1419394.74 frames.], batch size: 19, lr: 3.24e-04
2022-05-27 21:55:10,317 INFO [train.py:842] (2/4) Epoch 17, batch 3250, loss[loss=0.1564, simple_loss=0.2319, pruned_loss=0.04044, over 7056.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2763, pruned_loss=0.05367, over 1424725.68 frames.], batch size: 18, lr: 3.24e-04
2022-05-27 21:55:49,291 INFO [train.py:842] (2/4) Epoch 17, batch 3300, loss[loss=0.1642, simple_loss=0.2559, pruned_loss=0.03623, over 7155.00 frames.], tot_loss[loss=0.1923, simple_loss=0.277, pruned_loss=0.05378, over 1425655.52 frames.], batch size: 19, lr: 3.24e-04
2022-05-27 21:56:28,696 INFO [train.py:842] (2/4) Epoch 17, batch 3350, loss[loss=0.2085, simple_loss=0.301, pruned_loss=0.05796, over 7346.00 frames.], tot_loss[loss=0.192, simple_loss=0.2769, pruned_loss=0.05355, over 1426806.12 frames.], batch size: 22, lr: 3.24e-04
2022-05-27 21:57:07,333 INFO [train.py:842] (2/4) Epoch 17, batch 3400, loss[loss=0.2094, simple_loss=0.302, pruned_loss=0.05839, over 7141.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2768, pruned_loss=0.05313, over 1423372.43 frames.], batch size: 20, lr: 3.24e-04
2022-05-27 21:57:46,421 INFO [train.py:842] (2/4) Epoch 17, batch 3450, loss[loss=0.1545, simple_loss=0.2339, pruned_loss=0.03757, over 7328.00 frames.], tot_loss[loss=0.1904, simple_loss=0.275, pruned_loss=0.05288, over 1425736.54 frames.], batch size: 20, lr: 3.24e-04
2022-05-27 21:58:25,546 INFO [train.py:842] (2/4) Epoch 17, batch 3500, loss[loss=0.2331, simple_loss=0.312, pruned_loss=0.07714, over 7209.00 frames.], tot_loss[loss=0.19, simple_loss=0.2746, pruned_loss=0.05269, over 1424644.36 frames.], batch size: 22, lr: 3.24e-04
2022-05-27 21:59:04,650 INFO [train.py:842] (2/4) Epoch 17, batch 3550, loss[loss=0.1878, simple_loss=0.2745, pruned_loss=0.05054, over 7113.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2756, pruned_loss=0.05296, over 1426960.26 frames.], batch size: 21, lr: 3.24e-04
2022-05-27 21:59:43,837 INFO [train.py:842] (2/4) Epoch 17, batch 3600, loss[loss=0.2272, simple_loss=0.2898, pruned_loss=0.08228, over 7289.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2768, pruned_loss=0.05347, over 1427756.39 frames.], batch size: 18, lr: 3.24e-04
2022-05-27 22:00:23,008 INFO [train.py:842] (2/4) Epoch 17, batch 3650, loss[loss=0.182, simple_loss=0.2783, pruned_loss=0.04286, over 7314.00 frames.], tot_loss[loss=0.191, simple_loss=0.2759, pruned_loss=0.05308, over 1431491.68 frames.], batch size: 21, lr: 3.24e-04
2022-05-27 22:01:01,999 INFO [train.py:842] (2/4) Epoch 17, batch 3700, loss[loss=0.1837, simple_loss=0.2654, pruned_loss=0.05095, over 7154.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2755, pruned_loss=0.05271, over 1431033.49 frames.], batch size: 20, lr: 3.24e-04
2022-05-27 22:01:41,164 INFO [train.py:842] (2/4) Epoch 17, batch 3750, loss[loss=0.1711, simple_loss=0.269, pruned_loss=0.03665, over 6682.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2752, pruned_loss=0.05248, over 1428756.38 frames.], batch size: 38, lr: 3.24e-04
2022-05-27 22:02:19,683 INFO [train.py:842] (2/4) Epoch 17, batch 3800, loss[loss=0.1827, simple_loss=0.2716, pruned_loss=0.0469, over 6385.00 frames.], tot_loss[loss=0.191, simple_loss=0.2762, pruned_loss=0.05291, over 1427846.12 frames.], batch size: 37, lr: 3.24e-04
2022-05-27 22:02:58,460 INFO [train.py:842] (2/4) Epoch 17, batch 3850, loss[loss=0.1998, simple_loss=0.2748, pruned_loss=0.06239, over 6992.00 frames.], tot_loss[loss=0.191, simple_loss=0.2766, pruned_loss=0.05275, over 1427328.43 frames.], batch size: 16, lr: 3.23e-04
2022-05-27 22:03:37,724 INFO [train.py:842] (2/4) Epoch 17, batch 3900, loss[loss=0.2597, simple_loss=0.3352, pruned_loss=0.09215, over 7206.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2756, pruned_loss=0.05269, over 1429954.24 frames.], batch size: 22, lr: 3.23e-04
2022-05-27 22:04:16,895 INFO [train.py:842] (2/4) Epoch 17, batch 3950, loss[loss=0.197, simple_loss=0.2779, pruned_loss=0.05802, over 7185.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2767, pruned_loss=0.05306, over 1429116.22 frames.], batch size: 23, lr: 3.23e-04
2022-05-27 22:04:55,806 INFO [train.py:842] (2/4) Epoch 17, batch 4000, loss[loss=0.1569, simple_loss=0.2429, pruned_loss=0.03551, over 7280.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2759, pruned_loss=0.05284, over 1429667.34 frames.], batch size: 18, lr: 3.23e-04
2022-05-27 22:05:35,082 INFO [train.py:842] (2/4) Epoch 17, batch 4050, loss[loss=0.2053, simple_loss=0.2864, pruned_loss=0.06215, over 6733.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2755, pruned_loss=0.05293, over 1425355.56 frames.], batch size: 31, lr: 3.23e-04
2022-05-27 22:06:13,959 INFO [train.py:842] (2/4) Epoch 17, batch 4100, loss[loss=0.1778, simple_loss=0.2746, pruned_loss=0.04049, over 6677.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2771, pruned_loss=0.05365, over 1425319.62 frames.], batch size: 38, lr: 3.23e-04
2022-05-27 22:06:52,849 INFO [train.py:842] (2/4) Epoch 17, batch 4150, loss[loss=0.2131, simple_loss=0.293, pruned_loss=0.06662, over 7336.00 frames.], tot_loss[loss=0.191, simple_loss=0.2756, pruned_loss=0.05316, over 1423542.10 frames.], batch size: 22, lr: 3.23e-04
2022-05-27 22:07:31,515 INFO [train.py:842] (2/4) Epoch 17, batch 4200, loss[loss=0.1668, simple_loss=0.2591, pruned_loss=0.03724, over 7155.00 frames.], tot_loss[loss=0.191, simple_loss=0.276, pruned_loss=0.05299, over 1423628.52 frames.], batch size: 19, lr: 3.23e-04
2022-05-27 22:08:10,853 INFO [train.py:842] (2/4) Epoch 17, batch 4250, loss[loss=0.1804, simple_loss=0.2541, pruned_loss=0.05332, over 7141.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2763, pruned_loss=0.05268, over 1425469.14 frames.], batch size: 17, lr: 3.23e-04
2022-05-27 22:08:49,651 INFO [train.py:842] (2/4) Epoch 17, batch 4300, loss[loss=0.182, simple_loss=0.2703, pruned_loss=0.04686, over 7327.00 frames.], tot_loss[loss=0.191, simple_loss=0.2762, pruned_loss=0.05289, over 1424111.45 frames.], batch size: 21, lr: 3.23e-04
2022-05-27 22:09:29,066 INFO [train.py:842] (2/4) Epoch 17, batch 4350, loss[loss=0.2129, simple_loss=0.2975, pruned_loss=0.06415, over 6669.00 frames.], tot_loss[loss=0.1912, simple_loss=0.276, pruned_loss=0.05323, over 1423029.84 frames.], batch size: 31, lr: 3.23e-04
2022-05-27 22:10:07,986 INFO [train.py:842] (2/4) Epoch 17, batch 4400, loss[loss=0.1735, simple_loss=0.2551, pruned_loss=0.04598, over 7262.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2749, pruned_loss=0.05278, over 1424265.80 frames.], batch size: 19, lr: 3.23e-04
2022-05-27 22:10:47,303 INFO [train.py:842] (2/4) Epoch 17, batch 4450, loss[loss=0.1636, simple_loss=0.2482, pruned_loss=0.0395, over 7075.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2736, pruned_loss=0.05237, over 1427777.61 frames.], batch size: 18, lr: 3.23e-04
2022-05-27 22:11:26,013 INFO [train.py:842] (2/4) Epoch 17, batch 4500, loss[loss=0.1775, simple_loss=0.271, pruned_loss=0.04205, over 6280.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2742, pruned_loss=0.05252, over 1424253.30 frames.], batch size: 37, lr: 3.23e-04
2022-05-27 22:12:04,903 INFO [train.py:842] (2/4) Epoch 17, batch 4550, loss[loss=0.1635, simple_loss=0.2643, pruned_loss=0.03134, over 7338.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2757, pruned_loss=0.05254, over 1423271.47 frames.], batch size: 22, lr: 3.23e-04
2022-05-27 22:12:43,476 INFO [train.py:842] (2/4) Epoch 17, batch 4600, loss[loss=0.2435, simple_loss=0.3256, pruned_loss=0.08072, over 7205.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2755, pruned_loss=0.05257, over 1425084.85 frames.], batch size: 22, lr: 3.23e-04
2022-05-27 22:13:22,535 INFO [train.py:842] (2/4) Epoch 17, batch 4650, loss[loss=0.2551, simple_loss=0.3447, pruned_loss=0.08276, over 7333.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2755, pruned_loss=0.05257, over 1427689.21 frames.], batch size: 22, lr: 3.23e-04
2022-05-27 22:14:01,627 INFO [train.py:842] (2/4) Epoch 17, batch 4700, loss[loss=0.2359, simple_loss=0.3208, pruned_loss=0.07545, over 7220.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2766, pruned_loss=0.05376, over 1422574.45 frames.], batch size: 21, lr: 3.23e-04
2022-05-27 22:14:40,345 INFO [train.py:842] (2/4) Epoch 17, batch 4750, loss[loss=0.2211, simple_loss=0.2888, pruned_loss=0.07663, over 7067.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2774, pruned_loss=0.05412, over 1423013.76 frames.], batch size: 18, lr: 3.23e-04
2022-05-27 22:15:19,273 INFO [train.py:842] (2/4) Epoch 17, batch 4800, loss[loss=0.1859, simple_loss=0.2659, pruned_loss=0.05294, over 7262.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2762, pruned_loss=0.05315, over 1423177.19 frames.], batch size: 17, lr: 3.22e-04
2022-05-27 22:15:58,775 INFO [train.py:842] (2/4) Epoch 17, batch 4850, loss[loss=0.1639, simple_loss=0.2521, pruned_loss=0.03789, over 7061.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2764, pruned_loss=0.05345, over 1422486.18 frames.], batch size: 18, lr: 3.22e-04
2022-05-27 22:16:37,911 INFO [train.py:842] (2/4) Epoch 17, batch 4900, loss[loss=0.1864, simple_loss=0.272, pruned_loss=0.05039, over 7191.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2765, pruned_loss=0.05384, over 1424097.89 frames.], batch size: 26, lr: 3.22e-04
2022-05-27 22:17:20,299 INFO [train.py:842] (2/4) Epoch 17, batch 4950, loss[loss=0.1431, simple_loss=0.2156, pruned_loss=0.03531, over 6866.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2736, pruned_loss=0.0524, over 1426356.41 frames.], batch size: 15, lr: 3.22e-04
2022-05-27 22:17:59,060 INFO [train.py:842] (2/4) Epoch 17, batch 5000, loss[loss=0.1401, simple_loss=0.2221, pruned_loss=0.02902, over 7408.00 frames.], tot_loss[loss=0.1895, simple_loss=0.274, pruned_loss=0.05252, over 1423857.63 frames.], batch size: 17, lr: 3.22e-04
2022-05-27 22:18:38,076 INFO [train.py:842] (2/4) Epoch 17, batch 5050, loss[loss=0.2068, simple_loss=0.2771, pruned_loss=0.0682, over 7278.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2752, pruned_loss=0.05316, over 1423592.39 frames.], batch size: 18, lr: 3.22e-04
2022-05-27 22:19:17,090 INFO [train.py:842] (2/4) Epoch 17, batch 5100, loss[loss=0.1511, simple_loss=0.2304, pruned_loss=0.03591, over 7014.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2752, pruned_loss=0.05321, over 1421058.34 frames.], batch size: 16, lr: 3.22e-04
2022-05-27 22:19:56,237 INFO [train.py:842] (2/4) Epoch 17, batch 5150, loss[loss=0.1669, simple_loss=0.2557, pruned_loss=0.03904, over 7221.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2749, pruned_loss=0.0529, over 1419776.76 frames.], batch size: 20, lr: 3.22e-04
2022-05-27 22:20:35,396 INFO [train.py:842] (2/4) Epoch 17, batch 5200, loss[loss=0.1852, simple_loss=0.2783, pruned_loss=0.04605, over 7150.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2733, pruned_loss=0.05193, over 1423772.50 frames.], batch size: 20, lr: 3.22e-04
2022-05-27 22:21:14,373 INFO [train.py:842] (2/4) Epoch 17, batch 5250, loss[loss=0.1996, simple_loss=0.2938, pruned_loss=0.05274, over 7121.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2742, pruned_loss=0.05216, over 1423419.57 frames.], batch size: 21, lr: 3.22e-04
2022-05-27 22:21:53,445 INFO [train.py:842] (2/4) Epoch 17, batch 5300, loss[loss=0.1781, simple_loss=0.2646, pruned_loss=0.04576, over 6772.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2748, pruned_loss=0.05278, over 1422726.59 frames.], batch size: 15, lr: 3.22e-04
2022-05-27 22:22:32,641 INFO [train.py:842] (2/4) Epoch 17, batch 5350, loss[loss=0.2451, simple_loss=0.3096, pruned_loss=0.09028, over 6771.00 frames.], tot_loss[loss=0.192, simple_loss=0.2765, pruned_loss=0.05375, over 1423561.38 frames.], batch size: 15, lr: 3.22e-04
2022-05-27 22:23:11,619 INFO [train.py:842] (2/4) Epoch 17, batch 5400, loss[loss=0.2041, simple_loss=0.285, pruned_loss=0.06162, over 7230.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2755, pruned_loss=0.05307, over 1424347.49 frames.], batch size: 20, lr: 3.22e-04
2022-05-27 22:23:51,010 INFO [train.py:842] (2/4) Epoch 17, batch 5450, loss[loss=0.2008, simple_loss=0.2811, pruned_loss=0.06023, over 7161.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2762, pruned_loss=0.05345, over 1427316.23 frames.], batch size: 19, lr: 3.22e-04
2022-05-27 22:24:29,918 INFO [train.py:842] (2/4) Epoch 17, batch 5500, loss[loss=0.1774, simple_loss=0.2691, pruned_loss=0.04287, over 7298.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2762, pruned_loss=0.05363, over 1427768.65 frames.], batch size: 24, lr: 3.22e-04
2022-05-27 22:25:09,375 INFO [train.py:842] (2/4) Epoch 17, batch 5550, loss[loss=0.168, simple_loss=0.2609, pruned_loss=0.03754, over 7231.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2773, pruned_loss=0.05425, over 1429443.65 frames.], batch size: 20, lr: 3.22e-04
2022-05-27 22:25:48,399 INFO [train.py:842] (2/4) Epoch 17, batch 5600, loss[loss=0.2213, simple_loss=0.296, pruned_loss=0.07326, over 7332.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2764, pruned_loss=0.05393, over 1432455.45 frames.], batch size: 20, lr: 3.22e-04
2022-05-27 22:26:27,330 INFO [train.py:842] (2/4) Epoch 17, batch 5650, loss[loss=0.1882, simple_loss=0.2729, pruned_loss=0.05172, over 7271.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2772, pruned_loss=0.05387, over 1430559.01 frames.], batch size: 24, lr: 3.22e-04
2022-05-27 22:27:05,745 INFO [train.py:842] (2/4) Epoch 17, batch 5700, loss[loss=0.1312, simple_loss=0.2078, pruned_loss=0.02728, over 7420.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2772, pruned_loss=0.05347, over 1429337.46 frames.], batch size: 18, lr: 3.22e-04
2022-05-27 22:27:44,995 INFO [train.py:842] (2/4) Epoch 17, batch 5750, loss[loss=0.1845, simple_loss=0.2729, pruned_loss=0.048, over 6308.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2759, pruned_loss=0.05321, over 1422187.48 frames.], batch size: 37, lr: 3.21e-04
2022-05-27 22:28:23,795 INFO [train.py:842] (2/4) Epoch 17, batch 5800, loss[loss=0.1791, simple_loss=0.2728, pruned_loss=0.0427, over 7327.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2767, pruned_loss=0.05372, over 1422051.78 frames.], batch size: 20, lr: 3.21e-04
2022-05-27 22:29:02,961 INFO [train.py:842] (2/4) Epoch 17, batch 5850, loss[loss=0.1871, simple_loss=0.2721, pruned_loss=0.05107, over 7274.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2773, pruned_loss=0.05365, over 1425091.21 frames.], batch size: 18, lr: 3.21e-04
2022-05-27 22:29:41,680 INFO [train.py:842] (2/4) Epoch 17, batch 5900, loss[loss=0.1587, simple_loss=0.2554, pruned_loss=0.03104, over 7370.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2765, pruned_loss=0.05319, over 1424746.19 frames.], batch size: 23, lr: 3.21e-04
2022-05-27 22:30:20,764 INFO [train.py:842] (2/4) Epoch 17, batch 5950, loss[loss=0.2459, simple_loss=0.3142, pruned_loss=0.08877, over 7228.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2767, pruned_loss=0.05355, over 1419665.84 frames.], batch size: 26, lr: 3.21e-04
2022-05-27 22:30:59,313 INFO [train.py:842] (2/4) Epoch 17, batch 6000, loss[loss=0.2121, simple_loss=0.2979, pruned_loss=0.0631, over 7327.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2769, pruned_loss=0.05344, over 1420240.03 frames.], batch size: 20, lr: 3.21e-04
2022-05-27 22:30:59,314 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 22:31:09,110 INFO [train.py:871] (2/4) Epoch 17, validation: loss=0.1659, simple_loss=0.2656, pruned_loss=0.03306, over 868885.00 frames.
2022-05-27 22:31:48,626 INFO [train.py:842] (2/4) Epoch 17, batch 6050, loss[loss=0.1917, simple_loss=0.2728, pruned_loss=0.05529, over 7317.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2772, pruned_loss=0.05358, over 1423874.70 frames.], batch size: 21, lr: 3.21e-04
2022-05-27 22:32:27,182 INFO [train.py:842] (2/4) Epoch 17, batch 6100, loss[loss=0.2027, simple_loss=0.29, pruned_loss=0.05776, over 7189.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2771, pruned_loss=0.05335, over 1424742.68 frames.], batch size: 23, lr: 3.21e-04
2022-05-27 22:33:05,948 INFO [train.py:842] (2/4) Epoch 17, batch 6150, loss[loss=0.1746, simple_loss=0.2571, pruned_loss=0.04605, over 6758.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2764, pruned_loss=0.0526, over 1420437.25 frames.], batch size: 15, lr: 3.21e-04
2022-05-27 22:33:44,750 INFO [train.py:842] (2/4) Epoch 17, batch 6200, loss[loss=0.1844, simple_loss=0.2783, pruned_loss=0.04524, over 7411.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2773, pruned_loss=0.05361, over 1421756.58 frames.], batch size: 21, lr: 3.21e-04
2022-05-27 22:34:23,863 INFO [train.py:842] (2/4) Epoch 17, batch 6250, loss[loss=0.1966, simple_loss=0.289, pruned_loss=0.05208, over 6775.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2781, pruned_loss=0.05411, over 1420512.17 frames.], batch size: 31, lr: 3.21e-04
2022-05-27 22:35:02,513 INFO [train.py:842] (2/4) Epoch 17, batch 6300, loss[loss=0.2018, simple_loss=0.2865, pruned_loss=0.05858, over 7415.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2774, pruned_loss=0.05376, over 1419923.22 frames.], batch size: 21, lr: 3.21e-04
2022-05-27 22:35:41,903 INFO [train.py:842] (2/4) Epoch 17, batch 6350, loss[loss=0.1738, simple_loss=0.2511, pruned_loss=0.04823, over 7273.00 frames.], tot_loss[loss=0.192, simple_loss=0.2768, pruned_loss=0.05356, over 1423693.38 frames.], batch size: 17, lr: 3.21e-04
2022-05-27 22:36:20,858 INFO [train.py:842] (2/4) Epoch 17, batch 6400, loss[loss=0.1765, simple_loss=0.2674, pruned_loss=0.04278, over 7234.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2766, pruned_loss=0.05327, over 1428788.12 frames.], batch size: 20, lr: 3.21e-04
2022-05-27 22:36:59,894 INFO [train.py:842] (2/4) Epoch 17, batch 6450, loss[loss=0.163, simple_loss=0.2493, pruned_loss=0.03831, over 7350.00 frames.], tot_loss[loss=0.1916, simple_loss=0.277, pruned_loss=0.05316, over 1427611.21 frames.], batch size: 19, lr: 3.21e-04
2022-05-27 22:37:38,893 INFO [train.py:842] (2/4) Epoch 17, batch 6500, loss[loss=0.1514, simple_loss=0.2273, pruned_loss=0.03781, over 7422.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2767, pruned_loss=0.05297, over 1428626.42 frames.], batch size: 18, lr: 3.21e-04
2022-05-27 22:38:18,097 INFO [train.py:842] (2/4) Epoch 17, batch 6550, loss[loss=0.2303, simple_loss=0.3092, pruned_loss=0.0757, over 7205.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2769, pruned_loss=0.05292, over 1426815.99 frames.], batch size: 23, lr: 3.21e-04
2022-05-27 22:38:57,333 INFO [train.py:842] (2/4) Epoch 17, batch 6600, loss[loss=0.2015, simple_loss=0.2752, pruned_loss=0.0639, over 5198.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2788, pruned_loss=0.0534, over 1426177.95 frames.], batch size: 52, lr: 3.21e-04
2022-05-27 22:39:36,189 INFO [train.py:842] (2/4) Epoch 17, batch 6650, loss[loss=0.1684, simple_loss=0.2495, pruned_loss=0.04367, over 6998.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2796, pruned_loss=0.05479, over 1422707.49 frames.], batch size: 16, lr: 3.21e-04
2022-05-27 22:40:14,932 INFO [train.py:842] (2/4) Epoch 17, batch 6700, loss[loss=0.2176, simple_loss=0.3118, pruned_loss=0.06175, over 7207.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2792, pruned_loss=0.05434, over 1419718.62 frames.], batch size: 22, lr: 3.20e-04
2022-05-27 22:40:54,198 INFO [train.py:842] (2/4) Epoch 17, batch 6750, loss[loss=0.2324, simple_loss=0.3214, pruned_loss=0.07174, over 7217.00 frames.], tot_loss[loss=0.1937, simple_loss=0.279, pruned_loss=0.05421, over 1415102.07 frames.], batch size: 22, lr: 3.20e-04
2022-05-27 22:41:33,193 INFO [train.py:842] (2/4) Epoch 17, batch 6800, loss[loss=0.1615, simple_loss=0.244, pruned_loss=0.03948, over 7415.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2774, pruned_loss=0.05343, over 1419461.65 frames.], batch size: 18, lr: 3.20e-04
2022-05-27 22:42:12,493 INFO [train.py:842] (2/4) Epoch 17, batch 6850, loss[loss=0.1739, simple_loss=0.2544, pruned_loss=0.04671, over 7068.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2769, pruned_loss=0.05328, over 1421456.49 frames.], batch size: 18, lr: 3.20e-04
2022-05-27 22:42:51,375 INFO [train.py:842] (2/4) Epoch 17, batch 6900, loss[loss=0.1658, simple_loss=0.2735, pruned_loss=0.02903, over 7222.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2759, pruned_loss=0.05287, over 1422940.59 frames.], batch size: 21, lr: 3.20e-04
2022-05-27 22:43:30,332 INFO [train.py:842] (2/4) Epoch 17, batch 6950, loss[loss=0.2781, simple_loss=0.3535, pruned_loss=0.1014, over 7417.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2761, pruned_loss=0.05323, over 1423310.33 frames.], batch size: 21, lr: 3.20e-04
2022-05-27 22:44:09,726 INFO [train.py:842] (2/4) Epoch 17, batch 7000, loss[loss=0.2009, simple_loss=0.291, pruned_loss=0.05542, over 7370.00 frames.], tot_loss[loss=0.1915, simple_loss=0.276, pruned_loss=0.05347, over 1425153.62 frames.], batch size: 23, lr: 3.20e-04
2022-05-27 22:44:49,111 INFO [train.py:842] (2/4) Epoch 17, batch 7050, loss[loss=0.1976, simple_loss=0.2829, pruned_loss=0.05611, over 7195.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2755, pruned_loss=0.05332, over 1422961.65 frames.], batch size: 23, lr: 3.20e-04
2022-05-27 22:45:28,479 INFO [train.py:842] (2/4) Epoch 17, batch 7100, loss[loss=0.157, simple_loss=0.2498, pruned_loss=0.03213, over 7324.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2751, pruned_loss=0.05275, over 1425173.31 frames.], batch size: 21, lr: 3.20e-04
2022-05-27 22:46:07,819 INFO [train.py:842] (2/4) Epoch 17, batch 7150, loss[loss=0.1903, simple_loss=0.2805, pruned_loss=0.05009, over 7286.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2746, pruned_loss=0.05302, over 1427542.62 frames.], batch size: 24, lr: 3.20e-04
2022-05-27 22:46:46,809 INFO [train.py:842] (2/4) Epoch 17, batch 7200, loss[loss=0.1782, simple_loss=0.2691, pruned_loss=0.04368, over 7173.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2748, pruned_loss=0.05301, over 1426114.82 frames.], batch size: 23, lr: 3.20e-04
2022-05-27 22:47:26,411 INFO [train.py:842] (2/4) Epoch 17, batch 7250, loss[loss=0.2004, simple_loss=0.2926, pruned_loss=0.05411, over 7323.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2767, pruned_loss=0.05401, over 1428193.79 frames.], batch size: 21, lr: 3.20e-04
2022-05-27 22:48:05,623 INFO [train.py:842] (2/4) Epoch 17, batch 7300, loss[loss=0.1775, simple_loss=0.2588, pruned_loss=0.04806, over 7272.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2766, pruned_loss=0.05415, over 1430456.70 frames.], batch size: 17, lr: 3.20e-04
2022-05-27 22:48:44,784 INFO [train.py:842] (2/4) Epoch 17, batch 7350, loss[loss=0.1914, simple_loss=0.2796, pruned_loss=0.05158, over 7321.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2775, pruned_loss=0.05469, over 1431874.29 frames.], batch size: 21, lr: 3.20e-04
2022-05-27 22:49:23,650 INFO [train.py:842] (2/4) Epoch 17, batch 7400, loss[loss=0.3164, simple_loss=0.3895, pruned_loss=0.1217, over 4742.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2785, pruned_loss=0.05564, over 1422699.30 frames.], batch size: 53, lr: 3.20e-04
2022-05-27 22:50:02,521 INFO [train.py:842] (2/4) Epoch 17, batch 7450, loss[loss=0.1713, simple_loss=0.2366, pruned_loss=0.05304, over 7295.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2786, pruned_loss=0.05492, over 1426911.75 frames.], batch size: 17, lr: 3.20e-04
2022-05-27 22:50:41,697 INFO [train.py:842] (2/4) Epoch 17, batch 7500, loss[loss=0.1783, simple_loss=0.2755, pruned_loss=0.04053, over 7061.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2779, pruned_loss=0.05447, over 1427993.71 frames.], batch size: 18, lr: 3.20e-04
2022-05-27 22:51:20,601 INFO [train.py:842] (2/4) Epoch 17, batch 7550, loss[loss=0.2021, simple_loss=0.3056, pruned_loss=0.04932, over 7212.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2772, pruned_loss=0.05393, over 1427452.97 frames.], batch size: 23, lr: 3.20e-04
2022-05-27 22:51:59,794 INFO [train.py:842] (2/4) Epoch 17, batch 7600, loss[loss=0.1797, simple_loss=0.2592, pruned_loss=0.05013, over 7278.00 frames.], tot_loss[loss=0.192, simple_loss=0.2767, pruned_loss=0.05365, over 1429953.76 frames.], batch size: 18, lr: 3.20e-04
2022-05-27 22:52:38,864 INFO [train.py:842] (2/4) Epoch 17, batch 7650, loss[loss=0.1562, simple_loss=0.2423, pruned_loss=0.03507, over 6851.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2757, pruned_loss=0.05303, over 1428182.54 frames.], batch size: 15, lr: 3.19e-04
2022-05-27 22:53:17,423 INFO [train.py:842] (2/4) Epoch 17, batch 7700, loss[loss=0.1869, simple_loss=0.2828, pruned_loss=0.04547, over 7324.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2776, pruned_loss=0.05387, over 1428866.87 frames.], batch size: 22, lr: 3.19e-04
2022-05-27 22:53:56,288 INFO [train.py:842] (2/4) Epoch 17, batch 7750, loss[loss=0.2104, simple_loss=0.2942, pruned_loss=0.0633, over 7212.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2767, pruned_loss=0.05351, over 1428444.56 frames.], batch size: 22, lr: 3.19e-04
2022-05-27 22:54:34,885 INFO [train.py:842] (2/4) Epoch 17, batch 7800, loss[loss=0.1632, simple_loss=0.2343, pruned_loss=0.04606, over 7004.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2773, pruned_loss=0.05365, over 1423856.21 frames.], batch size: 16, lr: 3.19e-04
2022-05-27 22:55:13,929 INFO [train.py:842] (2/4) Epoch 17, batch 7850, loss[loss=0.1804, simple_loss=0.2591, pruned_loss=0.05088, over 7145.00 frames.], tot_loss[loss=0.1933, simple_loss=0.278, pruned_loss=0.05429, over 1423516.68 frames.], batch size: 17, lr: 3.19e-04
2022-05-27 22:55:52,889 INFO [train.py:842] (2/4) Epoch 17, batch 7900, loss[loss=0.1471, simple_loss=0.2355, pruned_loss=0.02933, over 7243.00 frames.], tot_loss[loss=0.192, simple_loss=0.277, pruned_loss=0.0535, over 1424942.03 frames.], batch size: 19, lr: 3.19e-04
2022-05-27 22:56:32,273 INFO [train.py:842] (2/4) Epoch 17, batch 7950, loss[loss=0.1995, simple_loss=0.2815, pruned_loss=0.05878, over 7067.00 frames.], tot_loss[loss=0.1919, simple_loss=0.277, pruned_loss=0.05341, over 1423574.28 frames.], batch size: 18, lr: 3.19e-04
2022-05-27 22:57:10,824 INFO [train.py:842] (2/4) Epoch 17, batch 8000, loss[loss=0.185, simple_loss=0.2668, pruned_loss=0.05159, over 7328.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2769, pruned_loss=0.05385, over 1417766.50 frames.], batch size: 20, lr: 3.19e-04
2022-05-27 22:57:50,142 INFO [train.py:842] (2/4) Epoch 17, batch 8050, loss[loss=0.1807, simple_loss=0.2648, pruned_loss=0.04828, over 7145.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2758, pruned_loss=0.05365, over 1413240.46 frames.], batch size: 19, lr: 3.19e-04
2022-05-27 22:58:28,862 INFO [train.py:842] (2/4) Epoch 17, batch 8100, loss[loss=0.1727, simple_loss=0.2581, pruned_loss=0.04362, over 6407.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2759, pruned_loss=0.05358, over 1414005.23 frames.], batch size: 38, lr: 3.19e-04
2022-05-27 22:59:08,273 INFO [train.py:842] (2/4) Epoch 17, batch 8150, loss[loss=0.2596, simple_loss=0.3279, pruned_loss=0.09564, over 7207.00 frames.], tot_loss[loss=0.192, simple_loss=0.2766, pruned_loss=0.05371, over 1413456.00 frames.], batch size: 22, lr: 3.19e-04
2022-05-27 22:59:46,874 INFO [train.py:842] (2/4) Epoch 17, batch 8200, loss[loss=0.1936, simple_loss=0.2824, pruned_loss=0.05235, over 7426.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2776, pruned_loss=0.05456, over 1410191.12 frames.], batch size: 20, lr: 3.19e-04
2022-05-27 23:00:26,347 INFO [train.py:842] (2/4) Epoch 17, batch 8250, loss[loss=0.1786, simple_loss=0.2787, pruned_loss=0.03921, over 7309.00 frames.], tot_loss[loss=0.192, simple_loss=0.2765, pruned_loss=0.05375, over 1416584.54 frames.], batch size: 21, lr: 3.19e-04
2022-05-27 23:01:04,938 INFO [train.py:842] (2/4) Epoch 17, batch 8300, loss[loss=0.1838, simple_loss=0.2776, pruned_loss=0.04503, over 7118.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2784, pruned_loss=0.0542, over 1416239.10 frames.], batch size: 21, lr: 3.19e-04
2022-05-27 23:01:43,880 INFO [train.py:842] (2/4) Epoch 17, batch 8350, loss[loss=0.2216, simple_loss=0.3062, pruned_loss=0.06847, over 7260.00 frames.], tot_loss[loss=0.1916, simple_loss=0.277, pruned_loss=0.05315, over 1420818.66 frames.], batch size: 25, lr: 3.19e-04
2022-05-27 23:02:22,667 INFO [train.py:842] (2/4) Epoch 17, batch 8400, loss[loss=0.1825, simple_loss=0.2666, pruned_loss=0.04921, over 7165.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2765, pruned_loss=0.05297, over 1423280.48 frames.], batch size: 28, lr: 3.19e-04
2022-05-27 23:03:01,599 INFO [train.py:842] (2/4) Epoch 17, batch 8450, loss[loss=0.2866, simple_loss=0.3516, pruned_loss=0.1107, over 6491.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2766, pruned_loss=0.05318, over 1421074.72 frames.], batch size: 38, lr: 3.19e-04
2022-05-27 23:03:40,290 INFO [train.py:842] (2/4) Epoch 17, batch 8500, loss[loss=0.231, simple_loss=0.3066, pruned_loss=0.07769, over 7169.00 frames.], tot_loss[loss=0.191, simple_loss=0.276, pruned_loss=0.05302, over 1414539.03 frames.], batch size: 26, lr: 3.19e-04
2022-05-27 23:04:19,032 INFO [train.py:842] (2/4) Epoch 17, batch 8550, loss[loss=0.2297, simple_loss=0.3056, pruned_loss=0.07688, over 6593.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2765, pruned_loss=0.05308, over 1412414.59 frames.], batch size: 39, lr: 3.19e-04
2022-05-27 23:04:57,763 INFO [train.py:842] (2/4) Epoch 17, batch 8600, loss[loss=0.1989, simple_loss=0.2865, pruned_loss=0.05563, over 7332.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2765, pruned_loss=0.05296, over 1417563.21 frames.], batch size: 22, lr: 3.19e-04
2022-05-27 23:05:36,630 INFO [train.py:842] (2/4) Epoch 17, batch 8650, loss[loss=0.1698, simple_loss=0.2515, pruned_loss=0.04405, over 7282.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2772, pruned_loss=0.0529, over 1419099.82 frames.], batch size: 18, lr: 3.18e-04
2022-05-27 23:06:15,427 INFO [train.py:842] (2/4) Epoch 17, batch 8700, loss[loss=0.2411, simple_loss=0.3263, pruned_loss=0.0779, over 7291.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2776, pruned_loss=0.05312, over 1423152.58 frames.], batch size: 25, lr: 3.18e-04
2022-05-27 23:06:54,772 INFO [train.py:842] (2/4) Epoch 17, batch 8750, loss[loss=0.1903, simple_loss=0.271, pruned_loss=0.05476, over 7068.00 frames.], tot_loss[loss=0.192, simple_loss=0.2775, pruned_loss=0.05324, over 1422183.89 frames.], batch size: 18, lr: 3.18e-04
2022-05-27 23:07:33,415 INFO [train.py:842] (2/4) Epoch 17, batch 8800, loss[loss=0.1375, simple_loss=0.225, pruned_loss=0.025, over 7058.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2781, pruned_loss=0.05367, over 1418560.15 frames.], batch size: 18, lr: 3.18e-04
2022-05-27 23:08:12,275 INFO [train.py:842] (2/4) Epoch 17, batch 8850, loss[loss=0.1999, simple_loss=0.2883, pruned_loss=0.05579, over 5508.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2775, pruned_loss=0.05353, over 1418222.28 frames.], batch size: 52, lr: 3.18e-04
2022-05-27 23:08:51,303 INFO [train.py:842] (2/4) Epoch 17, batch 8900, loss[loss=0.193, simple_loss=0.2856, pruned_loss=0.05014, over 7147.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2767, pruned_loss=0.05381, over 1417550.94 frames.], batch size: 20, lr: 3.18e-04
2022-05-27 23:09:30,541 INFO [train.py:842] (2/4) Epoch 17, batch 8950, loss[loss=0.1559, simple_loss=0.2491, pruned_loss=0.03136, over 7279.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2766, pruned_loss=0.05426, over 1411078.51 frames.], batch size: 18, lr: 3.18e-04
2022-05-27 23:10:09,439 INFO [train.py:842] (2/4) Epoch 17, batch 9000, loss[loss=0.2172, simple_loss=0.3085, pruned_loss=0.06295, over 7142.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2769, pruned_loss=0.05417, over 1402578.44 frames.], batch size: 20, lr: 3.18e-04
2022-05-27 23:10:09,441 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 23:10:18,901 INFO [train.py:871] (2/4) Epoch 17, validation: loss=0.1657, simple_loss=0.2652, pruned_loss=0.03308, over 868885.00 frames.
2022-05-27 23:10:57,858 INFO [train.py:842] (2/4) Epoch 17, batch 9050, loss[loss=0.2011, simple_loss=0.2924, pruned_loss=0.05487, over 7325.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2786, pruned_loss=0.05504, over 1397329.40 frames.], batch size: 20, lr: 3.18e-04
2022-05-27 23:11:46,173 INFO [train.py:842] (2/4) Epoch 17, batch 9100, loss[loss=0.3318, simple_loss=0.397, pruned_loss=0.1333, over 5096.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2817, pruned_loss=0.05744, over 1349558.59 frames.], batch size: 53, lr: 3.18e-04
2022-05-27 23:12:24,143 INFO [train.py:842] (2/4) Epoch 17, batch 9150, loss[loss=0.2185, simple_loss=0.2947, pruned_loss=0.07118, over 4883.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2845, pruned_loss=0.06014, over 1273993.10 frames.], batch size: 54, lr: 3.18e-04
2022-05-27 23:13:16,678 INFO [train.py:842] (2/4) Epoch 18, batch 0, loss[loss=0.2033, simple_loss=0.2867, pruned_loss=0.0599, over 7226.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2867, pruned_loss=0.0599, over 7226.00 frames.], batch size: 20, lr: 3.10e-04
2022-05-27 23:13:56,054 INFO [train.py:842] (2/4) Epoch 18, batch 50, loss[loss=0.1826, simple_loss=0.258, pruned_loss=0.05353, over 6997.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2714, pruned_loss=0.05237, over 323202.59 frames.], batch size: 16, lr: 3.09e-04
2022-05-27 23:14:34,693 INFO [train.py:842] (2/4) Epoch 18, batch 100, loss[loss=0.1804, simple_loss=0.2554, pruned_loss=0.05269, over 7161.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2751, pruned_loss=0.05328, over 564606.88 frames.], batch size: 18, lr: 3.09e-04
2022-05-27 23:15:14,074 INFO [train.py:842] (2/4) Epoch 18, batch 150, loss[loss=0.2042, simple_loss=0.3019, pruned_loss=0.05327, over 7140.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2753, pruned_loss=0.05309, over 753054.05 frames.], batch size: 20, lr: 3.09e-04
2022-05-27 23:15:53,005 INFO [train.py:842] (2/4) Epoch 18, batch 200, loss[loss=0.2243, simple_loss=0.3042, pruned_loss=0.07221, over 7156.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2758, pruned_loss=0.05286, over 903427.59 frames.], batch size: 18, lr: 3.09e-04
2022-05-27 23:16:31,827 INFO [train.py:842] (2/4) Epoch 18, batch 250, loss[loss=0.2124, simple_loss=0.2953, pruned_loss=0.06479, over 6845.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2753, pruned_loss=0.05179, over 1021068.38 frames.], batch size: 31, lr: 3.09e-04
2022-05-27 23:17:10,697 INFO [train.py:842] (2/4) Epoch 18, batch 300, loss[loss=0.1733, simple_loss=0.2712, pruned_loss=0.0377, over 7107.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2752, pruned_loss=0.05184, over 1105129.68 frames.], batch size: 28, lr: 3.09e-04
2022-05-27 23:17:49,739 INFO [train.py:842] (2/4) Epoch 18, batch 350, loss[loss=0.2114, simple_loss=0.2939, pruned_loss=0.06441, over 7332.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2753, pruned_loss=0.05264, over 1173443.53 frames.], batch size: 22, lr: 3.09e-04
2022-05-27 23:18:28,726 INFO [train.py:842] (2/4) Epoch 18, batch 400, loss[loss=0.1781, simple_loss=0.2501, pruned_loss=0.0531, over 7248.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2751, pruned_loss=0.05196, over 1233640.68 frames.], batch size: 16, lr: 3.09e-04
2022-05-27 23:19:07,681 INFO [train.py:842] (2/4) Epoch 18, batch 450, loss[loss=0.2124, simple_loss=0.2941, pruned_loss=0.06539, over 7207.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2755, pruned_loss=0.05191, over 1277275.51 frames.], batch size: 22, lr: 3.09e-04
2022-05-27 23:19:46,701 INFO [train.py:842] (2/4) Epoch 18, batch 500, loss[loss=0.2045, simple_loss=0.2831, pruned_loss=0.06299, over 7319.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2752, pruned_loss=0.05193, over 1313804.20 frames.], batch size: 22, lr: 3.09e-04
2022-05-27 23:20:26,023 INFO [train.py:842] (2/4) Epoch 18, batch 550, loss[loss=0.1701, simple_loss=0.2474, pruned_loss=0.04644, over 7151.00 frames.], tot_loss[loss=0.1894, simple_loss=0.275, pruned_loss=0.05193, over 1340259.47 frames.], batch size: 17, lr: 3.09e-04
2022-05-27 23:21:04,746 INFO [train.py:842] (2/4) Epoch 18, batch 600, loss[loss=0.2024, simple_loss=0.2956, pruned_loss=0.05461, over 6221.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2763, pruned_loss=0.05254, over 1357641.23 frames.], batch size: 37, lr: 3.09e-04
2022-05-27 23:21:43,502 INFO [train.py:842] (2/4) Epoch 18, batch 650, loss[loss=0.2182, simple_loss=0.2954, pruned_loss=0.07048, over 5172.00 frames.], tot_loss[loss=0.1901, simple_loss=0.276, pruned_loss=0.05211, over 1369909.73 frames.], batch size: 52, lr: 3.09e-04
2022-05-27 23:22:22,376 INFO [train.py:842] (2/4) Epoch 18, batch 700, loss[loss=0.223, simple_loss=0.3097, pruned_loss=0.06817, over 7312.00 frames.], tot_loss[loss=0.1915, simple_loss=0.277, pruned_loss=0.05297, over 1381526.70 frames.], batch size: 21, lr: 3.09e-04
2022-05-27 23:23:01,868 INFO [train.py:842] (2/4) Epoch 18, batch 750, loss[loss=0.1324, simple_loss=0.2188, pruned_loss=0.02298, over 7410.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2748, pruned_loss=0.05193, over 1392227.19 frames.], batch size: 18, lr: 3.09e-04
2022-05-27 23:23:41,021 INFO [train.py:842] (2/4) Epoch 18, batch 800, loss[loss=0.2271, simple_loss=0.3108, pruned_loss=0.07168, over 7315.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2747, pruned_loss=0.0521, over 1403878.11 frames.], batch size: 21, lr: 3.09e-04
2022-05-27 23:24:20,294 INFO [train.py:842] (2/4) Epoch 18, batch 850, loss[loss=0.2035, simple_loss=0.2826, pruned_loss=0.06221, over 7415.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2742, pruned_loss=0.05211, over 1406605.60 frames.], batch size: 21, lr: 3.09e-04
2022-05-27 23:24:58,888 INFO [train.py:842] (2/4) Epoch 18, batch 900, loss[loss=0.2142, simple_loss=0.3033, pruned_loss=0.06257, over 7197.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2758, pruned_loss=0.05247, over 1407279.32 frames.], batch size: 22, lr: 3.09e-04
2022-05-27 23:25:37,802 INFO [train.py:842] (2/4) Epoch 18, batch 950, loss[loss=0.1954, simple_loss=0.2746, pruned_loss=0.05809, over 7245.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2757, pruned_loss=0.05233, over 1409737.21 frames.], batch size: 19, lr: 3.09e-04
2022-05-27 23:26:16,531 INFO [train.py:842] (2/4) Epoch 18, batch 1000, loss[loss=0.2051, simple_loss=0.2863, pruned_loss=0.06189, over 7313.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2756, pruned_loss=0.05245, over 1414688.49 frames.], batch size: 24, lr: 3.09e-04
2022-05-27 23:26:55,798 INFO [train.py:842] (2/4) Epoch 18, batch 1050, loss[loss=0.163, simple_loss=0.2408, pruned_loss=0.0426, over 7283.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2755, pruned_loss=0.05206, over 1415971.85 frames.], batch size: 17, lr: 3.08e-04
2022-05-27 23:27:34,977 INFO [train.py:842] (2/4) Epoch 18, batch 1100, loss[loss=0.2014, simple_loss=0.2964, pruned_loss=0.05317, over 7321.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2756, pruned_loss=0.052, over 1419580.25 frames.], batch size: 25, lr: 3.08e-04
2022-05-27 23:28:14,154 INFO [train.py:842] (2/4) Epoch 18, batch 1150, loss[loss=0.206, simple_loss=0.2923, pruned_loss=0.05988, over 7391.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2752, pruned_loss=0.05192, over 1417984.44 frames.], batch size: 23, lr: 3.08e-04
2022-05-27 23:28:53,124 INFO [train.py:842] (2/4) Epoch 18, batch 1200, loss[loss=0.1843, simple_loss=0.2549, pruned_loss=0.05682, over 7275.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2754, pruned_loss=0.05213, over 1416161.50 frames.], batch size: 18, lr: 3.08e-04
2022-05-27 23:29:32,540 INFO [train.py:842] (2/4) Epoch 18, batch 1250, loss[loss=0.2106, simple_loss=0.2949, pruned_loss=0.06317, over 7406.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2747, pruned_loss=0.05173, over 1418151.11 frames.], batch size: 21, lr: 3.08e-04
2022-05-27 23:30:11,876 INFO [train.py:842] (2/4) Epoch 18, batch 1300, loss[loss=0.2007, simple_loss=0.2852, pruned_loss=0.05809, over 7195.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2757, pruned_loss=0.05252, over 1418965.57 frames.], batch size: 26, lr: 3.08e-04
2022-05-27 23:30:51,068 INFO [train.py:842] (2/4) Epoch 18, batch 1350, loss[loss=0.1813, simple_loss=0.251, pruned_loss=0.05575, over 7005.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2746, pruned_loss=0.05183, over 1421819.54 frames.], batch size: 16, lr: 3.08e-04
2022-05-27 23:31:29,675 INFO [train.py:842] (2/4) Epoch 18, batch 1400, loss[loss=0.1761, simple_loss=0.2671, pruned_loss=0.04255, over 7115.00 frames.], tot_loss[loss=0.1899, simple_loss=0.276, pruned_loss=0.05192, over 1423018.74 frames.], batch size: 21, lr: 3.08e-04
2022-05-27 23:32:08,761 INFO [train.py:842] (2/4) Epoch 18, batch 1450, loss[loss=0.1739, simple_loss=0.2702, pruned_loss=0.03884, over 7148.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2759, pruned_loss=0.05218, over 1421008.86 frames.], batch size: 20, lr: 3.08e-04
2022-05-27 23:32:47,816 INFO [train.py:842] (2/4) Epoch 18, batch 1500, loss[loss=0.1525, simple_loss=0.249, pruned_loss=0.02803, over 7326.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2768, pruned_loss=0.05292, over 1414342.31 frames.], batch size: 25, lr: 3.08e-04
2022-05-27 23:33:26,851 INFO [train.py:842] (2/4) Epoch 18, batch 1550, loss[loss=0.1752, simple_loss=0.2582, pruned_loss=0.04612, over 7154.00 frames.], tot_loss[loss=0.191, simple_loss=0.2764, pruned_loss=0.05277, over 1421762.83 frames.], batch size: 19, lr: 3.08e-04
2022-05-27 23:34:05,624 INFO [train.py:842] (2/4) Epoch 18, batch 1600, loss[loss=0.1815, simple_loss=0.2702, pruned_loss=0.04645, over 7436.00 frames.], tot_loss[loss=0.1907, simple_loss=0.276, pruned_loss=0.0527, over 1422875.04 frames.], batch size: 20, lr: 3.08e-04
2022-05-27 23:34:44,623 INFO [train.py:842] (2/4) Epoch 18, batch 1650, loss[loss=0.2212, simple_loss=0.2969, pruned_loss=0.07269, over 7276.00 frames.], tot_loss[loss=0.1907, simple_loss=0.276, pruned_loss=0.05271, over 1421805.27 frames.], batch size: 17, lr: 3.08e-04
2022-05-27 23:35:23,696 INFO [train.py:842] (2/4) Epoch 18, batch 1700, loss[loss=0.183, simple_loss=0.2697, pruned_loss=0.04819, over 7350.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2763, pruned_loss=0.05251, over 1424784.80 frames.], batch size: 19, lr: 3.08e-04
2022-05-27 23:36:03,266 INFO [train.py:842] (2/4) Epoch 18, batch 1750, loss[loss=0.1665, simple_loss=0.2617, pruned_loss=0.03569, over 7325.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2762, pruned_loss=0.05237, over 1425598.15 frames.], batch size: 21, lr: 3.08e-04
2022-05-27 23:36:42,500 INFO [train.py:842] (2/4) Epoch 18, batch 1800, loss[loss=0.2026, simple_loss=0.2927, pruned_loss=0.05619, over 7226.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2751, pruned_loss=0.05173, over 1430499.03 frames.], batch size: 20, lr: 3.08e-04
2022-05-27 23:37:22,122 INFO [train.py:842] (2/4) Epoch 18, batch 1850, loss[loss=0.2484, simple_loss=0.3079, pruned_loss=0.09442, over 5130.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2744, pruned_loss=0.05208, over 1429053.13 frames.], batch size: 52, lr: 3.08e-04
2022-05-27 23:38:00,805 INFO [train.py:842] (2/4) Epoch 18, batch 1900, loss[loss=0.1983, simple_loss=0.2835, pruned_loss=0.05652, over 7315.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2757, pruned_loss=0.05225, over 1429436.99 frames.], batch size: 21, lr: 3.08e-04
2022-05-27 23:38:39,664 INFO [train.py:842] (2/4) Epoch 18, batch 1950, loss[loss=0.1928, simple_loss=0.2931, pruned_loss=0.04625, over 7321.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2775, pruned_loss=0.05333, over 1425430.91 frames.], batch size: 21, lr: 3.08e-04
2022-05-27 23:39:18,760 INFO [train.py:842] (2/4) Epoch 18, batch 2000, loss[loss=0.2364, simple_loss=0.3189, pruned_loss=0.07691, over 5327.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2768, pruned_loss=0.05321, over 1426990.60 frames.], batch size: 52, lr: 3.08e-04
2022-05-27 23:39:57,969 INFO [train.py:842] (2/4) Epoch 18, batch 2050, loss[loss=0.1982, simple_loss=0.2938, pruned_loss=0.05129, over 7114.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2755, pruned_loss=0.05262, over 1421602.22 frames.], batch size: 21, lr: 3.07e-04
2022-05-27 23:40:36,493 INFO [train.py:842] (2/4) Epoch 18, batch 2100, loss[loss=0.1972, simple_loss=0.2876, pruned_loss=0.0534, over 6757.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2745, pruned_loss=0.05228, over 1418093.26 frames.], batch size: 31, lr: 3.07e-04
2022-05-27 23:41:15,743 INFO [train.py:842] (2/4) Epoch 18, batch 2150, loss[loss=0.2155, simple_loss=0.3017, pruned_loss=0.06469, over 7225.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2741, pruned_loss=0.05204, over 1419635.47 frames.], batch size: 21, lr: 3.07e-04
2022-05-27 23:41:54,570 INFO [train.py:842] (2/4) Epoch 18, batch 2200, loss[loss=0.1643, simple_loss=0.2463, pruned_loss=0.04119, over 6788.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2747, pruned_loss=0.05217, over 1421651.65 frames.], batch size: 15, lr: 3.07e-04
2022-05-27 23:42:33,986 INFO [train.py:842] (2/4) Epoch 18, batch 2250, loss[loss=0.1566, simple_loss=0.2371, pruned_loss=0.03806, over 6997.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2747, pruned_loss=0.05272, over 1424382.68 frames.], batch size: 16, lr: 3.07e-04
2022-05-27 23:43:12,898 INFO [train.py:842] (2/4) Epoch 18, batch 2300, loss[loss=0.1668, simple_loss=0.2634, pruned_loss=0.0351, over 7149.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2747, pruned_loss=0.05206, over 1426468.99 frames.], batch size: 20, lr: 3.07e-04
2022-05-27 23:43:52,050 INFO [train.py:842] (2/4) Epoch 18, batch 2350, loss[loss=0.195, simple_loss=0.2708, pruned_loss=0.05961, over 7145.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2751, pruned_loss=0.05298, over 1425969.19 frames.], batch size: 26, lr: 3.07e-04
2022-05-27 23:44:31,169 INFO [train.py:842] (2/4) Epoch 18, batch 2400, loss[loss=0.233, simple_loss=0.3169, pruned_loss=0.07457, over 6453.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2766, pruned_loss=0.05397, over 1425381.94 frames.], batch size: 38, lr: 3.07e-04
2022-05-27 23:45:10,078 INFO [train.py:842] (2/4) Epoch 18, batch 2450, loss[loss=0.1601, simple_loss=0.2545, pruned_loss=0.03281, over 7159.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2754, pruned_loss=0.05311, over 1427376.17 frames.], batch size: 19, lr: 3.07e-04
2022-05-27 23:45:58,787 INFO [train.py:842] (2/4) Epoch 18, batch 2500, loss[loss=0.2337, simple_loss=0.3118, pruned_loss=0.07775, over 7118.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2761, pruned_loss=0.05327, over 1420081.53 frames.], batch size: 21, lr: 3.07e-04
2022-05-27 23:46:37,745 INFO [train.py:842] (2/4) Epoch 18, batch 2550, loss[loss=0.1894, simple_loss=0.2848, pruned_loss=0.04698, over 7316.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2761, pruned_loss=0.05285, over 1419922.63 frames.], batch size: 21, lr: 3.07e-04
2022-05-27 23:47:16,433 INFO [train.py:842] (2/4) Epoch 18, batch 2600, loss[loss=0.1743, simple_loss=0.2542, pruned_loss=0.04725, over 7167.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2763, pruned_loss=0.05278, over 1419105.09 frames.], batch size: 16, lr: 3.07e-04
2022-05-27 23:48:05,831 INFO [train.py:842] (2/4) Epoch 18, batch 2650, loss[loss=0.1801, simple_loss=0.2568, pruned_loss=0.05175, over 7364.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2761, pruned_loss=0.05273, over 1420021.25 frames.], batch size: 19, lr: 3.07e-04
2022-05-27 23:48:44,817 INFO [train.py:842] (2/4) Epoch 18, batch 2700, loss[loss=0.1676, simple_loss=0.2488, pruned_loss=0.04323, over 7280.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2744, pruned_loss=0.05196, over 1419722.58 frames.], batch size: 18, lr: 3.07e-04
2022-05-27 23:49:23,634 INFO [train.py:842] (2/4) Epoch 18, batch 2750, loss[loss=0.1468, simple_loss=0.2449, pruned_loss=0.02431, over 7135.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2735, pruned_loss=0.05118, over 1417307.01 frames.], batch size: 20, lr: 3.07e-04
2022-05-27 23:50:12,203 INFO [train.py:842] (2/4) Epoch 18, batch 2800, loss[loss=0.2191, simple_loss=0.3017, pruned_loss=0.06822, over 7315.00 frames.], tot_loss[loss=0.188, simple_loss=0.2736, pruned_loss=0.05123, over 1417014.27 frames.], batch size: 21, lr: 3.07e-04
2022-05-27 23:50:50,917 INFO [train.py:842] (2/4) Epoch 18, batch 2850, loss[loss=0.208, simple_loss=0.2968, pruned_loss=0.05954, over 7320.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2741, pruned_loss=0.05124, over 1420341.16 frames.], batch size: 25, lr: 3.07e-04
2022-05-27 23:51:29,796 INFO [train.py:842] (2/4) Epoch 18, batch 2900, loss[loss=0.211, simple_loss=0.2982, pruned_loss=0.06186, over 7219.00 frames.], tot_loss[loss=0.191, simple_loss=0.2763, pruned_loss=0.05285, over 1422685.99 frames.], batch size: 22, lr: 3.07e-04
2022-05-27 23:52:08,755 INFO [train.py:842] (2/4) Epoch 18, batch 2950, loss[loss=0.1941, simple_loss=0.2782, pruned_loss=0.05497, over 6293.00 frames.], tot_loss[loss=0.191, simple_loss=0.276, pruned_loss=0.05303, over 1419426.60 frames.], batch size: 37, lr: 3.07e-04
2022-05-27 23:52:47,339 INFO [train.py:842] (2/4) Epoch 18, batch 3000, loss[loss=0.2157, simple_loss=0.2998, pruned_loss=0.06582, over 7301.00 frames.], tot_loss[loss=0.191, simple_loss=0.2763, pruned_loss=0.05283, over 1417817.70 frames.], batch size: 25, lr: 3.07e-04
2022-05-27 23:52:47,339 INFO [train.py:862] (2/4) Computing validation loss
2022-05-27 23:52:57,054 INFO [train.py:871] (2/4) Epoch 18, validation: loss=0.1661, simple_loss=0.2662, pruned_loss=0.03302, over 868885.00 frames.
2022-05-27 23:53:35,986 INFO [train.py:842] (2/4) Epoch 18, batch 3050, loss[loss=0.2105, simple_loss=0.2892, pruned_loss=0.06593, over 7123.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2767, pruned_loss=0.05337, over 1417386.79 frames.], batch size: 21, lr: 3.07e-04
2022-05-27 23:54:14,588 INFO [train.py:842] (2/4) Epoch 18, batch 3100, loss[loss=0.1746, simple_loss=0.2779, pruned_loss=0.03563, over 7224.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2761, pruned_loss=0.05284, over 1418389.57 frames.], batch size: 20, lr: 3.06e-04
2022-05-27 23:54:53,778 INFO [train.py:842] (2/4) Epoch 18, batch 3150, loss[loss=0.1611, simple_loss=0.2494, pruned_loss=0.03644, over 7255.00 frames.], tot_loss[loss=0.191, simple_loss=0.2758, pruned_loss=0.05308, over 1421379.15 frames.], batch size: 19, lr: 3.06e-04
2022-05-27 23:55:32,545 INFO [train.py:842] (2/4) Epoch 18, batch 3200, loss[loss=0.1917, simple_loss=0.2843, pruned_loss=0.04961, over 6768.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2763, pruned_loss=0.05355, over 1418973.07 frames.], batch size: 31, lr: 3.06e-04
2022-05-27 23:56:11,860 INFO [train.py:842] (2/4) Epoch 18, batch 3250, loss[loss=0.2013, simple_loss=0.2909, pruned_loss=0.05586, over 7382.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2762, pruned_loss=0.05362, over 1421893.08 frames.], batch size: 23, lr: 3.06e-04
2022-05-27 23:56:51,391 INFO [train.py:842] (2/4) Epoch 18, batch 3300, loss[loss=0.1369, simple_loss=0.2257, pruned_loss=0.024, over 7172.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2754, pruned_loss=0.05309, over 1426118.19 frames.], batch size: 18, lr: 3.06e-04
2022-05-27 23:57:30,276 INFO [train.py:842] (2/4) Epoch 18, batch 3350, loss[loss=0.1717, simple_loss=0.2499, pruned_loss=0.04677, over 7425.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2762, pruned_loss=0.05338, over 1425863.75 frames.], batch size: 18, lr: 3.06e-04
2022-05-27 23:58:09,188 INFO [train.py:842] (2/4) Epoch 18, batch 3400, loss[loss=0.2192, simple_loss=0.3001, pruned_loss=0.06917, over 7361.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2755, pruned_loss=0.05254, over 1429073.48 frames.], batch size: 23, lr: 3.06e-04
2022-05-27 23:58:48,411 INFO [train.py:842] (2/4) Epoch 18, batch 3450, loss[loss=0.171, simple_loss=0.2478, pruned_loss=0.04706, over 7408.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2765, pruned_loss=0.05285, over 1430091.42 frames.], batch size: 18, lr: 3.06e-04
2022-05-27 23:59:27,914 INFO [train.py:842] (2/4) Epoch 18, batch 3500, loss[loss=0.1857, simple_loss=0.2721, pruned_loss=0.04961, over 6414.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2757, pruned_loss=0.05247, over 1432483.71 frames.], batch size: 38, lr: 3.06e-04
2022-05-28 00:00:06,997 INFO [train.py:842] (2/4) Epoch 18, batch 3550, loss[loss=0.1893, simple_loss=0.2698, pruned_loss=0.05442, over 7189.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2769, pruned_loss=0.05333, over 1430227.22 frames.], batch size: 23, lr: 3.06e-04
2022-05-28 00:00:45,917 INFO [train.py:842] (2/4) Epoch 18, batch 3600, loss[loss=0.1886, simple_loss=0.2751, pruned_loss=0.05103, over 7222.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2747, pruned_loss=0.05241, over 1431680.17 frames.], batch size: 21, lr: 3.06e-04
2022-05-28 00:01:24,913 INFO [train.py:842] (2/4) Epoch 18, batch 3650, loss[loss=0.1717, simple_loss=0.2687, pruned_loss=0.03729, over 7337.00 frames.], tot_loss[loss=0.189, simple_loss=0.274, pruned_loss=0.05198, over 1423098.95 frames.], batch size: 22, lr: 3.06e-04
2022-05-28 00:02:03,661 INFO [train.py:842] (2/4) Epoch 18, batch 3700, loss[loss=0.1658, simple_loss=0.2463, pruned_loss=0.04271, over 7002.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2738, pruned_loss=0.05161, over 1424511.59 frames.], batch size: 16, lr: 3.06e-04
2022-05-28 00:02:45,142 INFO [train.py:842] (2/4) Epoch 18, batch 3750, loss[loss=0.1815, simple_loss=0.2783, pruned_loss=0.04237, over 7344.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2758, pruned_loss=0.05254, over 1426558.63 frames.], batch size: 25, lr: 3.06e-04
2022-05-28 00:03:23,940 INFO [train.py:842] (2/4) Epoch 18, batch 3800, loss[loss=0.1681, simple_loss=0.2554, pruned_loss=0.04044, over 7344.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2745, pruned_loss=0.05166, over 1427143.53 frames.], batch size: 19, lr: 3.06e-04
2022-05-28 00:04:03,227 INFO [train.py:842] (2/4) Epoch 18, batch 3850, loss[loss=0.1614, simple_loss=0.2393, pruned_loss=0.04176, over 7417.00 frames.], tot_loss[loss=0.188, simple_loss=0.2735, pruned_loss=0.05123, over 1425854.13 frames.], batch size: 18, lr: 3.06e-04
2022-05-28 00:04:42,018 INFO [train.py:842] (2/4) Epoch 18, batch 3900, loss[loss=0.2018, simple_loss=0.2912, pruned_loss=0.05621, over 7116.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2751, pruned_loss=0.05202, over 1421503.71 frames.], batch size: 21, lr: 3.06e-04
2022-05-28 00:05:21,342 INFO [train.py:842] (2/4) Epoch 18, batch 3950, loss[loss=0.1686, simple_loss=0.2527, pruned_loss=0.04226, over 7329.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2734, pruned_loss=0.05143, over 1423845.62 frames.], batch size: 20, lr: 3.06e-04
2022-05-28 00:06:00,171 INFO [train.py:842] (2/4) Epoch 18, batch 4000, loss[loss=0.2091, simple_loss=0.288, pruned_loss=0.06512, over 7379.00 frames.], tot_loss[loss=0.1898, simple_loss=0.275, pruned_loss=0.05228, over 1425221.39 frames.], batch size: 23, lr: 3.06e-04
2022-05-28 00:06:39,279 INFO [train.py:842] (2/4) Epoch 18, batch 4050, loss[loss=0.1649, simple_loss=0.2562, pruned_loss=0.03681, over 7407.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2744, pruned_loss=0.05203, over 1429893.98 frames.], batch size: 18, lr: 3.06e-04
2022-05-28 00:07:18,088 INFO [train.py:842] (2/4) Epoch 18, batch 4100, loss[loss=0.1684, simple_loss=0.2579, pruned_loss=0.03944, over 7056.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2752, pruned_loss=0.05264, over 1428960.64 frames.], batch size: 18, lr: 3.06e-04
2022-05-28 00:07:57,194 INFO [train.py:842] (2/4) Epoch 18, batch 4150, loss[loss=0.2307, simple_loss=0.3113, pruned_loss=0.07503, over 7215.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2753, pruned_loss=0.05284, over 1426855.44 frames.], batch size: 23, lr: 3.05e-04
2022-05-28 00:08:36,163 INFO [train.py:842] (2/4) Epoch 18, batch 4200, loss[loss=0.1709, simple_loss=0.264, pruned_loss=0.03892, over 7332.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2745, pruned_loss=0.05281, over 1425644.31 frames.], batch size: 22, lr: 3.05e-04
2022-05-28 00:09:15,380 INFO [train.py:842] (2/4) Epoch 18, batch 4250, loss[loss=0.2112, simple_loss=0.2892, pruned_loss=0.06661, over 7292.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2753, pruned_loss=0.0531, over 1424245.10 frames.], batch size: 24, lr: 3.05e-04
2022-05-28 00:09:54,191 INFO [train.py:842] (2/4) Epoch 18, batch 4300, loss[loss=0.2419, simple_loss=0.3306, pruned_loss=0.07662, over 6434.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2755, pruned_loss=0.05287, over 1425105.78 frames.], batch size: 38, lr: 3.05e-04
2022-05-28 00:10:33,515 INFO [train.py:842] (2/4) Epoch 18, batch 4350, loss[loss=0.1992, simple_loss=0.2784, pruned_loss=0.06, over 7411.00 frames.], tot_loss[loss=0.1891, simple_loss=0.274, pruned_loss=0.05208, over 1426707.96 frames.], batch size: 18, lr: 3.05e-04
2022-05-28 00:11:12,545 INFO [train.py:842] (2/4) Epoch 18, batch 4400, loss[loss=0.1828, simple_loss=0.2722, pruned_loss=0.04672, over 7074.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2721, pruned_loss=0.05138, over 1424779.11 frames.], batch size: 28, lr: 3.05e-04
2022-05-28 00:11:51,918 INFO [train.py:842] (2/4) Epoch 18, batch 4450, loss[loss=0.1919, simple_loss=0.2811, pruned_loss=0.05134, over 7313.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2732, pruned_loss=0.05202, over 1424338.86 frames.], batch size: 24, lr: 3.05e-04
2022-05-28 00:12:30,692 INFO [train.py:842] (2/4) Epoch 18, batch 4500, loss[loss=0.1866, simple_loss=0.2739, pruned_loss=0.04961, over 7249.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2729, pruned_loss=0.05186, over 1424247.41 frames.], batch size: 19, lr: 3.05e-04
2022-05-28 00:13:09,737 INFO [train.py:842] (2/4) Epoch 18, batch 4550, loss[loss=0.1659, simple_loss=0.2605, pruned_loss=0.03562, over 7123.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2732, pruned_loss=0.05177, over 1423979.59 frames.], batch size: 28, lr: 3.05e-04
2022-05-28 00:13:48,423 INFO [train.py:842] (2/4) Epoch 18, batch 4600, loss[loss=0.1899, simple_loss=0.2896, pruned_loss=0.04513, over 7219.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2741, pruned_loss=0.05202, over 1422964.95 frames.], batch size: 21, lr: 3.05e-04
2022-05-28 00:14:27,507 INFO [train.py:842] (2/4) Epoch 18, batch 4650, loss[loss=0.1745, simple_loss=0.2726, pruned_loss=0.03816, over 7208.00 frames.], tot_loss[loss=0.189, simple_loss=0.2744, pruned_loss=0.05184, over 1417429.04 frames.], batch size: 22, lr: 3.05e-04
2022-05-28 00:15:06,709 INFO [train.py:842] (2/4) Epoch 18, batch 4700, loss[loss=0.1897, simple_loss=0.2663, pruned_loss=0.05661, over 7065.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2746, pruned_loss=0.05225, over 1420113.15 frames.], batch size: 18, lr: 3.05e-04
2022-05-28 00:15:46,204 INFO [train.py:842] (2/4) Epoch 18, batch 4750, loss[loss=0.1809, simple_loss=0.2674, pruned_loss=0.04713, over 7360.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2741, pruned_loss=0.05213, over 1421058.64 frames.], batch size: 19, lr: 3.05e-04
2022-05-28 00:16:25,211 INFO [train.py:842] (2/4) Epoch 18, batch 4800, loss[loss=0.1801, simple_loss=0.2741, pruned_loss=0.0431, over 7253.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2745, pruned_loss=0.05219, over 1422408.12 frames.], batch size: 19, lr: 3.05e-04
2022-05-28 00:17:04,350 INFO [train.py:842] (2/4) Epoch 18, batch 4850, loss[loss=0.2072, simple_loss=0.2944, pruned_loss=0.06001, over 7125.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2749, pruned_loss=0.05225, over 1425576.99 frames.], batch size: 28, lr: 3.05e-04
2022-05-28 00:17:43,300 INFO [train.py:842] (2/4) Epoch 18, batch 4900, loss[loss=0.1781, simple_loss=0.2658, pruned_loss=0.04518, over 7140.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2755, pruned_loss=0.05231, over 1430182.56 frames.], batch size: 20, lr: 3.05e-04
2022-05-28 00:18:22,520 INFO [train.py:842] (2/4) Epoch 18, batch 4950, loss[loss=0.1596, simple_loss=0.2436, pruned_loss=0.03783, over 7258.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2745, pruned_loss=0.05196, over 1428141.46 frames.], batch size: 19, lr: 3.05e-04
2022-05-28 00:19:01,626 INFO [train.py:842] (2/4) Epoch 18, batch 5000, loss[loss=0.1854, simple_loss=0.265, pruned_loss=0.05294, over 7210.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2733, pruned_loss=0.05147, over 1428442.88 frames.], batch size: 23, lr: 3.05e-04
2022-05-28 00:19:40,950 INFO [train.py:842] (2/4) Epoch 18, batch 5050, loss[loss=0.1785, simple_loss=0.2536, pruned_loss=0.05171, over 7157.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2749, pruned_loss=0.05192, over 1431006.50 frames.], batch size: 18, lr: 3.05e-04
2022-05-28 00:20:19,753 INFO [train.py:842] (2/4) Epoch 18, batch 5100, loss[loss=0.1786, simple_loss=0.267, pruned_loss=0.04506, over 7222.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2769, pruned_loss=0.05316, over 1429547.89 frames.], batch size: 23, lr: 3.05e-04
2022-05-28 00:20:58,751 INFO [train.py:842] (2/4) Epoch 18, batch 5150, loss[loss=0.1816, simple_loss=0.2606, pruned_loss=0.05128, over 7403.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2755, pruned_loss=0.05185, over 1431507.45 frames.], batch size: 18, lr: 3.05e-04
2022-05-28 00:21:37,358 INFO [train.py:842] (2/4) Epoch 18, batch 5200, loss[loss=0.1861, simple_loss=0.2779, pruned_loss=0.04714, over 7111.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2756, pruned_loss=0.05191, over 1433426.55 frames.], batch size: 28, lr: 3.04e-04
2022-05-28 00:22:17,189 INFO [train.py:842] (2/4) Epoch 18, batch 5250, loss[loss=0.2563, simple_loss=0.3359, pruned_loss=0.08834, over 6691.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2739, pruned_loss=0.05075, over 1435028.03 frames.], batch size: 31, lr: 3.04e-04
2022-05-28 00:22:55,887 INFO [train.py:842] (2/4) Epoch 18, batch 5300, loss[loss=0.2075, simple_loss=0.292, pruned_loss=0.06149, over 7307.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2754, pruned_loss=0.05199, over 1433599.87 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:23:35,026 INFO [train.py:842] (2/4) Epoch 18, batch 5350, loss[loss=0.1748, simple_loss=0.2522, pruned_loss=0.04863, over 7401.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2752, pruned_loss=0.05178, over 1434458.93 frames.], batch size: 18, lr: 3.04e-04
2022-05-28 00:24:13,948 INFO [train.py:842] (2/4) Epoch 18, batch 5400, loss[loss=0.1744, simple_loss=0.2658, pruned_loss=0.04146, over 7117.00 frames.], tot_loss[loss=0.1893, simple_loss=0.275, pruned_loss=0.05179, over 1431625.81 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:24:53,093 INFO [train.py:842] (2/4) Epoch 18, batch 5450, loss[loss=0.2163, simple_loss=0.3011, pruned_loss=0.06574, over 7351.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2749, pruned_loss=0.05184, over 1432642.24 frames.], batch size: 19, lr: 3.04e-04
2022-05-28 00:25:32,210 INFO [train.py:842] (2/4) Epoch 18, batch 5500, loss[loss=0.2115, simple_loss=0.3006, pruned_loss=0.06121, over 7160.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2753, pruned_loss=0.05206, over 1434493.05 frames.], batch size: 26, lr: 3.04e-04
2022-05-28 00:26:11,396 INFO [train.py:842] (2/4) Epoch 18, batch 5550, loss[loss=0.1951, simple_loss=0.282, pruned_loss=0.05409, over 7217.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2748, pruned_loss=0.05155, over 1435935.33 frames.], batch size: 22, lr: 3.04e-04
2022-05-28 00:26:50,012 INFO [train.py:842] (2/4) Epoch 18, batch 5600, loss[loss=0.213, simple_loss=0.2988, pruned_loss=0.06362, over 7290.00 frames.], tot_loss[loss=0.19, simple_loss=0.2755, pruned_loss=0.05228, over 1434644.93 frames.], batch size: 24, lr: 3.04e-04
2022-05-28 00:27:28,951 INFO [train.py:842] (2/4) Epoch 18, batch 5650, loss[loss=0.185, simple_loss=0.2675, pruned_loss=0.05123, over 7060.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2752, pruned_loss=0.05183, over 1430810.13 frames.], batch size: 18, lr: 3.04e-04
2022-05-28 00:28:08,014 INFO [train.py:842] (2/4) Epoch 18, batch 5700, loss[loss=0.2048, simple_loss=0.2906, pruned_loss=0.05952, over 7369.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2757, pruned_loss=0.05196, over 1430766.40 frames.], batch size: 23, lr: 3.04e-04
2022-05-28 00:28:47,258 INFO [train.py:842] (2/4) Epoch 18, batch 5750, loss[loss=0.259, simple_loss=0.3385, pruned_loss=0.08972, over 7123.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2757, pruned_loss=0.05175, over 1429704.03 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:29:26,194 INFO [train.py:842] (2/4) Epoch 18, batch 5800, loss[loss=0.2183, simple_loss=0.3105, pruned_loss=0.06312, over 7272.00 frames.], tot_loss[loss=0.1889, simple_loss=0.275, pruned_loss=0.05145, over 1429939.44 frames.], batch size: 25, lr: 3.04e-04
2022-05-28 00:30:05,303 INFO [train.py:842] (2/4) Epoch 18, batch 5850, loss[loss=0.2068, simple_loss=0.2998, pruned_loss=0.05692, over 7198.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2741, pruned_loss=0.05104, over 1425901.23 frames.], batch size: 23, lr: 3.04e-04
2022-05-28 00:30:44,194 INFO [train.py:842] (2/4) Epoch 18, batch 5900, loss[loss=0.1622, simple_loss=0.244, pruned_loss=0.04017, over 7062.00 frames.], tot_loss[loss=0.188, simple_loss=0.2737, pruned_loss=0.05117, over 1425233.77 frames.], batch size: 18, lr: 3.04e-04
2022-05-28 00:31:22,991 INFO [train.py:842] (2/4) Epoch 18, batch 5950, loss[loss=0.1618, simple_loss=0.2412, pruned_loss=0.04121, over 7259.00 frames.], tot_loss[loss=0.1878, simple_loss=0.273, pruned_loss=0.05133, over 1424938.37 frames.], batch size: 19, lr: 3.04e-04
2022-05-28 00:32:01,718 INFO [train.py:842] (2/4) Epoch 18, batch 6000, loss[loss=0.2134, simple_loss=0.298, pruned_loss=0.06438, over 7276.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2748, pruned_loss=0.05182, over 1428922.95 frames.], batch size: 25, lr: 3.04e-04
2022-05-28 00:32:01,719 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 00:32:11,164 INFO [train.py:871] (2/4) Epoch 18, validation: loss=0.1679, simple_loss=0.2675, pruned_loss=0.03415, over 868885.00 frames.
2022-05-28 00:32:50,444 INFO [train.py:842] (2/4) Epoch 18, batch 6050, loss[loss=0.1791, simple_loss=0.2687, pruned_loss=0.0447, over 7418.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2744, pruned_loss=0.05223, over 1428627.04 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:33:29,269 INFO [train.py:842] (2/4) Epoch 18, batch 6100, loss[loss=0.1785, simple_loss=0.2593, pruned_loss=0.04881, over 7417.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2746, pruned_loss=0.05201, over 1430326.98 frames.], batch size: 20, lr: 3.04e-04
2022-05-28 00:34:08,672 INFO [train.py:842] (2/4) Epoch 18, batch 6150, loss[loss=0.1839, simple_loss=0.2707, pruned_loss=0.04855, over 7126.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2734, pruned_loss=0.05154, over 1432898.67 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:34:47,566 INFO [train.py:842] (2/4) Epoch 18, batch 6200, loss[loss=0.1685, simple_loss=0.2693, pruned_loss=0.03385, over 7125.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2734, pruned_loss=0.05122, over 1427671.50 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:35:26,836 INFO [train.py:842] (2/4) Epoch 18, batch 6250, loss[loss=0.1765, simple_loss=0.2692, pruned_loss=0.04188, over 7233.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2728, pruned_loss=0.05091, over 1425513.00 frames.], batch size: 21, lr: 3.04e-04
2022-05-28 00:36:05,720 INFO [train.py:842] (2/4) Epoch 18, batch 6300, loss[loss=0.2266, simple_loss=0.3074, pruned_loss=0.07287, over 7146.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2735, pruned_loss=0.05116, over 1422794.63 frames.], batch size: 20, lr: 3.03e-04
2022-05-28 00:36:45,092 INFO [train.py:842] (2/4) Epoch 18, batch 6350, loss[loss=0.1998, simple_loss=0.2879, pruned_loss=0.05588, over 7222.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2727, pruned_loss=0.05074, over 1426062.62 frames.], batch size: 21, lr: 3.03e-04
2022-05-28 00:37:23,919 INFO [train.py:842] (2/4) Epoch 18, batch 6400, loss[loss=0.1955, simple_loss=0.2671, pruned_loss=0.062, over 7410.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2733, pruned_loss=0.05077, over 1425764.24 frames.], batch size: 18, lr: 3.03e-04
2022-05-28 00:38:03,169 INFO [train.py:842] (2/4) Epoch 18, batch 6450, loss[loss=0.1643, simple_loss=0.2492, pruned_loss=0.0397, over 7357.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2726, pruned_loss=0.05049, over 1426770.27 frames.], batch size: 19, lr: 3.03e-04
2022-05-28 00:38:41,958 INFO [train.py:842] (2/4) Epoch 18, batch 6500, loss[loss=0.1732, simple_loss=0.2513, pruned_loss=0.04755, over 7144.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2739, pruned_loss=0.05113, over 1424534.99 frames.], batch size: 17, lr: 3.03e-04
2022-05-28 00:39:21,186 INFO [train.py:842] (2/4) Epoch 18, batch 6550, loss[loss=0.2293, simple_loss=0.3079, pruned_loss=0.07536, over 7333.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2739, pruned_loss=0.05134, over 1426010.09 frames.], batch size: 20, lr: 3.03e-04
2022-05-28 00:40:00,111 INFO [train.py:842] (2/4) Epoch 18, batch 6600, loss[loss=0.1754, simple_loss=0.2711, pruned_loss=0.03979, over 7204.00 frames.], tot_loss[loss=0.187, simple_loss=0.2728, pruned_loss=0.05055, over 1425804.33 frames.], batch size: 22, lr: 3.03e-04
2022-05-28 00:40:38,930 INFO [train.py:842] (2/4) Epoch 18, batch 6650, loss[loss=0.1789, simple_loss=0.2684, pruned_loss=0.04471, over 7333.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2751, pruned_loss=0.0514, over 1419516.38 frames.], batch size: 22, lr: 3.03e-04
2022-05-28 00:41:17,445 INFO [train.py:842] (2/4) Epoch 18, batch 6700, loss[loss=0.1987, simple_loss=0.2822, pruned_loss=0.05758, over 7301.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2761, pruned_loss=0.05216, over 1417161.62 frames.], batch size: 25, lr: 3.03e-04
2022-05-28 00:41:56,394 INFO [train.py:842] (2/4) Epoch 18, batch 6750, loss[loss=0.1942, simple_loss=0.2868, pruned_loss=0.05077, over 7205.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2749, pruned_loss=0.05127, over 1416983.46 frames.], batch size: 22, lr: 3.03e-04
2022-05-28 00:42:35,481 INFO [train.py:842] (2/4) Epoch 18, batch 6800, loss[loss=0.216, simple_loss=0.2871, pruned_loss=0.07244, over 7282.00 frames.], tot_loss[loss=0.1884, simple_loss=0.274, pruned_loss=0.05144, over 1418437.10 frames.], batch size: 18, lr: 3.03e-04
2022-05-28 00:43:14,481 INFO [train.py:842] (2/4) Epoch 18, batch 6850, loss[loss=0.216, simple_loss=0.298, pruned_loss=0.06697, over 7374.00 frames.], tot_loss[loss=0.19, simple_loss=0.2755, pruned_loss=0.05224, over 1421355.93 frames.], batch size: 23, lr: 3.03e-04
2022-05-28 00:43:53,153 INFO [train.py:842] (2/4) Epoch 18, batch 6900, loss[loss=0.1727, simple_loss=0.2709, pruned_loss=0.03727, over 7137.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2751, pruned_loss=0.052, over 1421602.98 frames.], batch size: 20, lr: 3.03e-04
2022-05-28 00:44:31,882 INFO [train.py:842] (2/4) Epoch 18, batch 6950, loss[loss=0.2125, simple_loss=0.294, pruned_loss=0.06546, over 7293.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2765, pruned_loss=0.0523, over 1421135.07 frames.], batch size: 24, lr: 3.03e-04
2022-05-28 00:45:09,746 INFO [train.py:842] (2/4) Epoch 18, batch 7000, loss[loss=0.2895, simple_loss=0.3635, pruned_loss=0.1077, over 4985.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2757, pruned_loss=0.05176, over 1421538.78 frames.], batch size: 52, lr: 3.03e-04
2022-05-28 00:45:48,073 INFO [train.py:842] (2/4) Epoch 18, batch 7050, loss[loss=0.1848, simple_loss=0.2695, pruned_loss=0.05, over 7155.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2741, pruned_loss=0.05142, over 1423126.27 frames.], batch size: 19, lr: 3.03e-04
2022-05-28 00:46:26,161 INFO [train.py:842] (2/4) Epoch 18, batch 7100, loss[loss=0.1785, simple_loss=0.2739, pruned_loss=0.04154, over 7216.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2735, pruned_loss=0.05172, over 1422244.06 frames.], batch size: 21, lr: 3.03e-04
2022-05-28 00:47:04,375 INFO [train.py:842] (2/4) Epoch 18, batch 7150, loss[loss=0.1716, simple_loss=0.2549, pruned_loss=0.04416, over 7263.00 frames.], tot_loss[loss=0.1878, simple_loss=0.273, pruned_loss=0.05126, over 1426305.64 frames.], batch size: 19, lr: 3.03e-04
2022-05-28 00:47:42,503 INFO [train.py:842] (2/4) Epoch 18, batch 7200, loss[loss=0.2364, simple_loss=0.3147, pruned_loss=0.07902, over 7150.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2741, pruned_loss=0.05184, over 1427937.76 frames.], batch size: 19, lr: 3.03e-04
2022-05-28 00:48:20,743 INFO [train.py:842] (2/4) Epoch 18, batch 7250, loss[loss=0.2444, simple_loss=0.3223, pruned_loss=0.08331, over 7201.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2747, pruned_loss=0.05206, over 1427305.15 frames.], batch size: 23, lr: 3.03e-04
2022-05-28 00:48:58,651 INFO [train.py:842] (2/4) Epoch 18, batch 7300, loss[loss=0.2187, simple_loss=0.3034, pruned_loss=0.067, over 7236.00 frames.], tot_loss[loss=0.19, simple_loss=0.2749, pruned_loss=0.0525, over 1424977.98 frames.], batch size: 20, lr: 3.03e-04
2022-05-28 00:49:37,076 INFO [train.py:842] (2/4) Epoch 18, batch 7350, loss[loss=0.1791, simple_loss=0.2563, pruned_loss=0.05097, over 7134.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2737, pruned_loss=0.05193, over 1427970.65 frames.], batch size: 17, lr: 3.02e-04
2022-05-28 00:50:15,154 INFO [train.py:842] (2/4) Epoch 18, batch 7400, loss[loss=0.174, simple_loss=0.2716, pruned_loss=0.0382, over 7318.00 frames.], tot_loss[loss=0.188, simple_loss=0.2733, pruned_loss=0.0514, over 1424567.82 frames.], batch size: 21, lr: 3.02e-04
2022-05-28 00:50:53,594 INFO [train.py:842] (2/4) Epoch 18, batch 7450, loss[loss=0.1878, simple_loss=0.2789, pruned_loss=0.0484, over 7408.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2734, pruned_loss=0.0514, over 1422946.06 frames.], batch size: 21, lr: 3.02e-04
2022-05-28 00:51:31,506 INFO [train.py:842] (2/4) Epoch 18, batch 7500, loss[loss=0.1687, simple_loss=0.2655, pruned_loss=0.03593, over 7231.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2737, pruned_loss=0.05126, over 1422098.28 frames.], batch size: 20, lr: 3.02e-04
2022-05-28 00:52:09,621 INFO [train.py:842] (2/4) Epoch 18, batch 7550, loss[loss=0.2694, simple_loss=0.3319, pruned_loss=0.1034, over 5171.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2742, pruned_loss=0.05152, over 1421780.68 frames.], batch size: 52, lr: 3.02e-04
2022-05-28 00:52:47,701 INFO [train.py:842] (2/4) Epoch 18, batch 7600, loss[loss=0.2378, simple_loss=0.3222, pruned_loss=0.07671, over 7189.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2736, pruned_loss=0.05128, over 1425822.31 frames.], batch size: 26, lr: 3.02e-04
2022-05-28 00:53:25,832 INFO [train.py:842] (2/4) Epoch 18, batch 7650, loss[loss=0.1542, simple_loss=0.2391, pruned_loss=0.03459, over 7407.00 frames.], tot_loss[loss=0.1874, simple_loss=0.273, pruned_loss=0.05093, over 1423328.92 frames.], batch size: 18, lr: 3.02e-04
2022-05-28 00:54:03,764 INFO [train.py:842] (2/4) Epoch 18, batch 7700, loss[loss=0.2046, simple_loss=0.2991, pruned_loss=0.05507, over 6848.00 frames.], tot_loss[loss=0.1893, simple_loss=0.275, pruned_loss=0.0518, over 1424491.10 frames.], batch size: 31, lr: 3.02e-04
2022-05-28 00:54:42,121 INFO [train.py:842] (2/4) Epoch 18, batch 7750, loss[loss=0.1611, simple_loss=0.2392, pruned_loss=0.04149, over 7267.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2747, pruned_loss=0.05155, over 1425301.72 frames.], batch size: 17, lr: 3.02e-04
2022-05-28 00:55:20,294 INFO [train.py:842] (2/4) Epoch 18, batch 7800, loss[loss=0.1703, simple_loss=0.2531, pruned_loss=0.04377, over 7169.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2746, pruned_loss=0.05149, over 1428019.68 frames.], batch size: 18, lr: 3.02e-04
2022-05-28 00:55:58,691 INFO [train.py:842] (2/4) Epoch 18, batch 7850, loss[loss=0.2079, simple_loss=0.2815, pruned_loss=0.06713, over 7366.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2733, pruned_loss=0.05088, over 1430081.71 frames.], batch size: 19, lr: 3.02e-04
2022-05-28 00:56:36,705 INFO [train.py:842] (2/4) Epoch 18, batch 7900, loss[loss=0.2267, simple_loss=0.3129, pruned_loss=0.07027, over 7270.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2733, pruned_loss=0.05094, over 1433161.45 frames.], batch size: 24, lr: 3.02e-04
2022-05-28 00:57:14,986 INFO [train.py:842] (2/4) Epoch 18, batch 7950, loss[loss=0.2203, simple_loss=0.3017, pruned_loss=0.06945, over 5067.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2734, pruned_loss=0.05079, over 1432201.88 frames.], batch size: 52, lr: 3.02e-04
2022-05-28 00:57:52,947 INFO [train.py:842] (2/4) Epoch 18, batch 8000, loss[loss=0.1865, simple_loss=0.2795, pruned_loss=0.04673, over 7213.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2736, pruned_loss=0.05093, over 1434020.27 frames.], batch size: 21, lr: 3.02e-04
2022-05-28 00:58:31,216 INFO [train.py:842] (2/4) Epoch 18, batch 8050, loss[loss=0.1766, simple_loss=0.262, pruned_loss=0.04558, over 7097.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2736, pruned_loss=0.05136, over 1428284.96 frames.], batch size: 28, lr: 3.02e-04
2022-05-28 00:59:09,181 INFO [train.py:842] (2/4) Epoch 18, batch 8100, loss[loss=0.1905, simple_loss=0.2717, pruned_loss=0.05464, over 7217.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2744, pruned_loss=0.05165, over 1424477.64 frames.], batch size: 16, lr: 3.02e-04
2022-05-28 00:59:47,550 INFO [train.py:842] (2/4) Epoch 18, batch 8150, loss[loss=0.2315, simple_loss=0.3124, pruned_loss=0.0753, over 7101.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2731, pruned_loss=0.05064, over 1426893.71 frames.], batch size: 28, lr: 3.02e-04
2022-05-28 01:00:25,310 INFO [train.py:842] (2/4) Epoch 18, batch 8200, loss[loss=0.1607, simple_loss=0.2444, pruned_loss=0.03846, over 7137.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2738, pruned_loss=0.05091, over 1425473.98 frames.], batch size: 17, lr: 3.02e-04
2022-05-28 01:01:03,703 INFO [train.py:842] (2/4) Epoch 18, batch 8250, loss[loss=0.185, simple_loss=0.2685, pruned_loss=0.05079, over 7215.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2736, pruned_loss=0.05112, over 1425788.62 frames.], batch size: 22, lr: 3.02e-04
2022-05-28 01:01:41,865 INFO [train.py:842] (2/4) Epoch 18, batch 8300, loss[loss=0.1818, simple_loss=0.2558, pruned_loss=0.0539, over 7126.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2729, pruned_loss=0.05115, over 1425946.33 frames.], batch size: 17, lr: 3.02e-04
2022-05-28 01:02:20,124 INFO [train.py:842] (2/4) Epoch 18, batch 8350, loss[loss=0.1744, simple_loss=0.2757, pruned_loss=0.03656, over 7119.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2733, pruned_loss=0.05163, over 1425158.15 frames.], batch size: 21, lr: 3.02e-04
2022-05-28 01:02:58,015 INFO [train.py:842] (2/4) Epoch 18, batch 8400, loss[loss=0.215, simple_loss=0.3077, pruned_loss=0.06121, over 7407.00 frames.], tot_loss[loss=0.1882, simple_loss=0.273, pruned_loss=0.05172, over 1421983.64 frames.], batch size: 21, lr: 3.02e-04
2022-05-28 01:03:36,353 INFO [train.py:842] (2/4) Epoch 18, batch 8450, loss[loss=0.1964, simple_loss=0.2846, pruned_loss=0.05407, over 7207.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2724, pruned_loss=0.05113, over 1423433.84 frames.], batch size: 26, lr: 3.01e-04
2022-05-28 01:04:14,378 INFO [train.py:842] (2/4) Epoch 18, batch 8500, loss[loss=0.1823, simple_loss=0.2743, pruned_loss=0.04518, over 7062.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2721, pruned_loss=0.05106, over 1424322.18 frames.], batch size: 18, lr: 3.01e-04
2022-05-28 01:04:52,542 INFO [train.py:842] (2/4) Epoch 18, batch 8550, loss[loss=0.1834, simple_loss=0.2735, pruned_loss=0.0466, over 7415.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2742, pruned_loss=0.05199, over 1426701.06 frames.], batch size: 21, lr: 3.01e-04
2022-05-28 01:05:30,280 INFO [train.py:842] (2/4) Epoch 18, batch 8600, loss[loss=0.1655, simple_loss=0.2464, pruned_loss=0.04226, over 7291.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2758, pruned_loss=0.05239, over 1424933.65 frames.], batch size: 17, lr: 3.01e-04
2022-05-28 01:06:08,302 INFO [train.py:842] (2/4) Epoch 18, batch 8650, loss[loss=0.1842, simple_loss=0.269, pruned_loss=0.04977, over 7258.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2768, pruned_loss=0.05281, over 1418978.07 frames.], batch size: 19, lr: 3.01e-04
2022-05-28 01:06:46,193 INFO [train.py:842] (2/4) Epoch 18, batch 8700, loss[loss=0.257, simple_loss=0.3253, pruned_loss=0.09435, over 7293.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2757, pruned_loss=0.05254, over 1418819.48 frames.], batch size: 25, lr: 3.01e-04
2022-05-28 01:07:24,553 INFO [train.py:842] (2/4) Epoch 18, batch 8750, loss[loss=0.2041, simple_loss=0.2881, pruned_loss=0.06002, over 7197.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2752, pruned_loss=0.05205, over 1418824.15 frames.], batch size: 23, lr: 3.01e-04
2022-05-28 01:08:02,249 INFO [train.py:842] (2/4) Epoch 18, batch 8800, loss[loss=0.1701, simple_loss=0.2587, pruned_loss=0.04075, over 7145.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2755, pruned_loss=0.05257, over 1410373.67 frames.], batch size: 17, lr: 3.01e-04
2022-05-28 01:08:40,264 INFO [train.py:842] (2/4) Epoch 18, batch 8850, loss[loss=0.1908, simple_loss=0.2775, pruned_loss=0.05205, over 6283.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2752, pruned_loss=0.05274, over 1404782.80 frames.], batch size: 37, lr: 3.01e-04
2022-05-28 01:09:17,562 INFO [train.py:842] (2/4) Epoch 18, batch 8900, loss[loss=0.1497, simple_loss=0.2307, pruned_loss=0.03432, over 6987.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2753, pruned_loss=0.05243, over 1397439.00 frames.], batch size: 16, lr: 3.01e-04
2022-05-28 01:09:55,310 INFO [train.py:842] (2/4) Epoch 18, batch 8950, loss[loss=0.1998, simple_loss=0.2781, pruned_loss=0.06075, over 5229.00 frames.], tot_loss[loss=0.19, simple_loss=0.2749, pruned_loss=0.05251, over 1387054.99 frames.], batch size: 52, lr: 3.01e-04
2022-05-28 01:10:32,665 INFO [train.py:842] (2/4) Epoch 18, batch 9000, loss[loss=0.1878, simple_loss=0.2778, pruned_loss=0.04897, over 6883.00 frames.], tot_loss[loss=0.192, simple_loss=0.2769, pruned_loss=0.05353, over 1383511.81 frames.], batch size: 31, lr: 3.01e-04
2022-05-28 01:10:32,665 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 01:10:41,756 INFO [train.py:871] (2/4) Epoch 18, validation: loss=0.1664, simple_loss=0.2661, pruned_loss=0.03336, over 868885.00 frames.
2022-05-28 01:11:19,005 INFO [train.py:842] (2/4) Epoch 18, batch 9050, loss[loss=0.2014, simple_loss=0.2897, pruned_loss=0.05651, over 6774.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2773, pruned_loss=0.05393, over 1367770.82 frames.], batch size: 31, lr: 3.01e-04
2022-05-28 01:11:55,783 INFO [train.py:842] (2/4) Epoch 18, batch 9100, loss[loss=0.2713, simple_loss=0.3378, pruned_loss=0.1023, over 4877.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2831, pruned_loss=0.05841, over 1293246.81 frames.], batch size: 52, lr: 3.01e-04
2022-05-28 01:12:32,909 INFO [train.py:842] (2/4) Epoch 18, batch 9150, loss[loss=0.194, simple_loss=0.2735, pruned_loss=0.05725, over 5084.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2871, pruned_loss=0.06167, over 1230639.25 frames.], batch size: 54, lr: 3.01e-04
2022-05-28 01:13:18,589 INFO [train.py:842] (2/4) Epoch 19, batch 0, loss[loss=0.2316, simple_loss=0.3145, pruned_loss=0.0743, over 7342.00 frames.], tot_loss[loss=0.2316, simple_loss=0.3145, pruned_loss=0.0743, over 7342.00 frames.], batch size: 25, lr: 2.93e-04
2022-05-28 01:13:57,225 INFO [train.py:842] (2/4) Epoch 19, batch 50, loss[loss=0.1732, simple_loss=0.2741, pruned_loss=0.03618, over 7342.00 frames.], tot_loss[loss=0.1887, simple_loss=0.274, pruned_loss=0.05175, over 325578.33 frames.], batch size: 22, lr: 2.93e-04
2022-05-28 01:14:35,411 INFO [train.py:842] (2/4) Epoch 19, batch 100, loss[loss=0.1943, simple_loss=0.2774, pruned_loss=0.05561, over 7337.00 frames.], tot_loss[loss=0.188, simple_loss=0.2745, pruned_loss=0.05078, over 575247.21 frames.], batch size: 22, lr: 2.93e-04
2022-05-28 01:15:13,705 INFO [train.py:842] (2/4) Epoch 19, batch 150, loss[loss=0.1793, simple_loss=0.2659, pruned_loss=0.0464, over 7222.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2747, pruned_loss=0.05143, over 763925.39 frames.], batch size: 21, lr: 2.93e-04
2022-05-28 01:15:51,846 INFO [train.py:842] (2/4) Epoch 19, batch 200, loss[loss=0.2042, simple_loss=0.2802, pruned_loss=0.06408, over 7256.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2734, pruned_loss=0.05085, over 909482.26 frames.], batch size: 17, lr: 2.93e-04
2022-05-28 01:16:30,152 INFO [train.py:842] (2/4) Epoch 19, batch 250, loss[loss=0.1777, simple_loss=0.2644, pruned_loss=0.0455, over 6670.00 frames.], tot_loss[loss=0.1885, simple_loss=0.274, pruned_loss=0.05145, over 1025252.58 frames.], batch size: 31, lr: 2.93e-04
2022-05-28 01:17:08,197 INFO [train.py:842] (2/4) Epoch 19, batch 300, loss[loss=0.1867, simple_loss=0.2855, pruned_loss=0.04392, over 7234.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2747, pruned_loss=0.05205, over 1115445.17 frames.], batch size: 20, lr: 2.93e-04
2022-05-28 01:17:46,565 INFO [train.py:842] (2/4) Epoch 19, batch 350, loss[loss=0.1751, simple_loss=0.2615, pruned_loss=0.04439, over 6782.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2747, pruned_loss=0.05181, over 1182307.37 frames.], batch size: 31, lr: 2.93e-04
2022-05-28 01:18:24,401 INFO [train.py:842] (2/4) Epoch 19, batch 400, loss[loss=0.1505, simple_loss=0.2401, pruned_loss=0.03039, over 7060.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2748, pruned_loss=0.05207, over 1233910.76 frames.], batch size: 18, lr: 2.93e-04
2022-05-28 01:19:02,587 INFO [train.py:842] (2/4) Epoch 19, batch 450, loss[loss=0.1656, simple_loss=0.255, pruned_loss=0.03814, over 7335.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2748, pruned_loss=0.052, over 1275260.74 frames.], batch size: 22, lr: 2.93e-04
2022-05-28 01:19:40,374 INFO [train.py:842] (2/4) Epoch 19, batch 500, loss[loss=0.1837, simple_loss=0.2598, pruned_loss=0.05378, over 7153.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2755, pruned_loss=0.05288, over 1306334.28 frames.], batch size: 17, lr: 2.93e-04
2022-05-28 01:20:18,786 INFO [train.py:842] (2/4) Epoch 19, batch 550, loss[loss=0.1486, simple_loss=0.2285, pruned_loss=0.03429, over 7277.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2751, pruned_loss=0.05228, over 1335856.00 frames.], batch size: 17, lr: 2.93e-04
2022-05-28 01:20:56,845 INFO [train.py:842] (2/4) Epoch 19, batch 600, loss[loss=0.186, simple_loss=0.2599, pruned_loss=0.05602, over 7274.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2745, pruned_loss=0.05205, over 1356321.84 frames.], batch size: 18, lr: 2.93e-04
2022-05-28 01:21:35,372 INFO [train.py:842] (2/4) Epoch 19, batch 650, loss[loss=0.1686, simple_loss=0.2557, pruned_loss=0.04075, over 7105.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2724, pruned_loss=0.05102, over 1375504.23 frames.], batch size: 21, lr: 2.93e-04
2022-05-28 01:22:13,256 INFO [train.py:842] (2/4) Epoch 19, batch 700, loss[loss=0.2343, simple_loss=0.3144, pruned_loss=0.07706, over 5159.00 frames.], tot_loss[loss=0.188, simple_loss=0.2735, pruned_loss=0.05127, over 1386215.08 frames.], batch size: 53, lr: 2.93e-04
2022-05-28 01:22:51,649 INFO [train.py:842] (2/4) Epoch 19, batch 750, loss[loss=0.1622, simple_loss=0.252, pruned_loss=0.03625, over 7171.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2734, pruned_loss=0.05135, over 1394482.79 frames.], batch size: 19, lr: 2.93e-04
2022-05-28 01:23:29,307 INFO [train.py:842] (2/4) Epoch 19, batch 800, loss[loss=0.1924, simple_loss=0.2906, pruned_loss=0.0471, over 6715.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2745, pruned_loss=0.05154, over 1397193.44 frames.], batch size: 31, lr: 2.92e-04
2022-05-28 01:24:07,573 INFO [train.py:842] (2/4) Epoch 19, batch 850, loss[loss=0.191, simple_loss=0.2673, pruned_loss=0.05734, over 7059.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2746, pruned_loss=0.05124, over 1404108.42 frames.], batch size: 18, lr: 2.92e-04
2022-05-28 01:24:45,426 INFO [train.py:842] (2/4) Epoch 19, batch 900, loss[loss=0.2494, simple_loss=0.31, pruned_loss=0.09438, over 6762.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2752, pruned_loss=0.05149, over 1409678.41 frames.], batch size: 15, lr: 2.92e-04
2022-05-28 01:25:23,694 INFO [train.py:842] (2/4) Epoch 19, batch 950, loss[loss=0.2058, simple_loss=0.2971, pruned_loss=0.05723, over 7378.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2746, pruned_loss=0.05114, over 1413036.35 frames.], batch size: 23, lr: 2.92e-04
2022-05-28 01:26:01,656 INFO [train.py:842] (2/4) Epoch 19, batch 1000, loss[loss=0.1832, simple_loss=0.2773, pruned_loss=0.04457, over 7153.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2761, pruned_loss=0.05189, over 1419815.66 frames.], batch size: 20, lr: 2.92e-04
2022-05-28 01:26:39,855 INFO [train.py:842] (2/4) Epoch 19, batch 1050, loss[loss=0.1814, simple_loss=0.2802, pruned_loss=0.04134, over 7304.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2748, pruned_loss=0.05143, over 1418514.35 frames.], batch size: 25, lr: 2.92e-04
2022-05-28 01:27:17,940 INFO [train.py:842] (2/4) Epoch 19, batch 1100, loss[loss=0.2113, simple_loss=0.3009, pruned_loss=0.06086, over 7321.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2741, pruned_loss=0.0514, over 1419112.61 frames.], batch size: 20, lr: 2.92e-04
2022-05-28 01:27:56,170 INFO [train.py:842] (2/4) Epoch 19, batch 1150, loss[loss=0.2033, simple_loss=0.2948, pruned_loss=0.05588, over 7263.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2737, pruned_loss=0.05101, over 1419331.04 frames.], batch size: 24, lr: 2.92e-04
2022-05-28 01:28:34,212 INFO [train.py:842] (2/4) Epoch 19, batch 1200, loss[loss=0.2259, simple_loss=0.3034, pruned_loss=0.07422, over 4872.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2752, pruned_loss=0.0521, over 1413614.30 frames.], batch size: 53, lr: 2.92e-04
2022-05-28 01:29:12,404 INFO [train.py:842] (2/4) Epoch 19, batch 1250, loss[loss=0.1873, simple_loss=0.2729, pruned_loss=0.05086, over 7114.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2743, pruned_loss=0.05156, over 1414710.15 frames.], batch size: 21, lr: 2.92e-04
2022-05-28 01:29:50,146 INFO [train.py:842] (2/4) Epoch 19, batch 1300, loss[loss=0.1447, simple_loss=0.2334, pruned_loss=0.02806, over 7160.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2736, pruned_loss=0.05087, over 1414253.39 frames.], batch size: 19, lr: 2.92e-04
2022-05-28 01:30:28,061 INFO [train.py:842] (2/4) Epoch 19, batch 1350, loss[loss=0.2133, simple_loss=0.2974, pruned_loss=0.06464, over 7043.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2745, pruned_loss=0.05107, over 1413042.04 frames.], batch size: 28, lr: 2.92e-04
2022-05-28 01:31:06,124 INFO [train.py:842] (2/4) Epoch 19, batch 1400, loss[loss=0.2097, simple_loss=0.28, pruned_loss=0.06968, over 7066.00 frames.], tot_loss[loss=0.1893, simple_loss=0.275, pruned_loss=0.05184, over 1410783.86 frames.], batch size: 18, lr: 2.92e-04
2022-05-28 01:31:44,478 INFO [train.py:842] (2/4) Epoch 19, batch 1450, loss[loss=0.2074, simple_loss=0.2963, pruned_loss=0.05926, over 7318.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2752, pruned_loss=0.05212, over 1417402.90 frames.], batch size: 21, lr: 2.92e-04
2022-05-28 01:32:22,419 INFO [train.py:842] (2/4) Epoch 19, batch 1500, loss[loss=0.1565, simple_loss=0.2371, pruned_loss=0.03793, over 7260.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2742, pruned_loss=0.05127, over 1421387.78 frames.], batch size: 19, lr: 2.92e-04
2022-05-28 01:33:00,824 INFO [train.py:842] (2/4) Epoch 19, batch 1550, loss[loss=0.172, simple_loss=0.2696, pruned_loss=0.0372, over 7415.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2732, pruned_loss=0.05076, over 1424422.55 frames.], batch size: 21, lr: 2.92e-04
2022-05-28 01:33:38,787 INFO [train.py:842] (2/4) Epoch 19, batch 1600, loss[loss=0.1781, simple_loss=0.2594, pruned_loss=0.04835, over 7219.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2719, pruned_loss=0.05011, over 1423537.76 frames.], batch size: 22, lr: 2.92e-04
2022-05-28 01:34:17,151 INFO [train.py:842] (2/4) Epoch 19, batch 1650, loss[loss=0.1574, simple_loss=0.2482, pruned_loss=0.0333, over 7163.00 frames.], tot_loss[loss=0.186, simple_loss=0.2717, pruned_loss=0.05014, over 1423156.65 frames.], batch size: 18, lr: 2.92e-04
2022-05-28 01:34:55,112 INFO [train.py:842] (2/4) Epoch 19, batch 1700, loss[loss=0.2173, simple_loss=0.2978, pruned_loss=0.06845, over 7169.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2728, pruned_loss=0.05044, over 1424017.82 frames.], batch size: 18, lr: 2.92e-04
2022-05-28 01:35:32,922 INFO [train.py:842] (2/4) Epoch 19, batch 1750, loss[loss=0.1798, simple_loss=0.2756, pruned_loss=0.04196, over 7137.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2731, pruned_loss=0.04997, over 1416385.36 frames.], batch size: 20, lr: 2.92e-04
2022-05-28 01:36:10,614 INFO [train.py:842] (2/4) Epoch 19, batch 1800, loss[loss=0.2018, simple_loss=0.2928, pruned_loss=0.05537, over 7254.00 frames.], tot_loss[loss=0.1869, simple_loss=0.274, pruned_loss=0.04985, over 1416880.12 frames.], batch size: 19, lr: 2.92e-04
2022-05-28 01:36:48,921 INFO [train.py:842] (2/4) Epoch 19, batch 1850, loss[loss=0.2086, simple_loss=0.2862, pruned_loss=0.06548, over 7279.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2736, pruned_loss=0.04954, over 1422360.90 frames.], batch size: 24, lr: 2.92e-04
2022-05-28 01:37:26,735 INFO [train.py:842] (2/4) Epoch 19, batch 1900, loss[loss=0.2032, simple_loss=0.2881, pruned_loss=0.05919, over 7056.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2749, pruned_loss=0.05125, over 1419755.40 frames.], batch size: 28, lr: 2.92e-04
2022-05-28 01:38:04,995 INFO [train.py:842] (2/4) Epoch 19, batch 1950, loss[loss=0.1485, simple_loss=0.2282, pruned_loss=0.03438, over 6993.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2742, pruned_loss=0.05101, over 1421181.43 frames.], batch size: 16, lr: 2.91e-04
2022-05-28 01:38:43,026 INFO [train.py:842] (2/4) Epoch 19, batch 2000, loss[loss=0.1657, simple_loss=0.2675, pruned_loss=0.03194, over 7144.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2734, pruned_loss=0.05088, over 1424044.61 frames.], batch size: 20, lr: 2.91e-04
2022-05-28 01:39:21,316 INFO [train.py:842] (2/4) Epoch 19, batch 2050, loss[loss=0.2057, simple_loss=0.2841, pruned_loss=0.06366, over 7288.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2734, pruned_loss=0.05093, over 1423980.47 frames.], batch size: 25, lr: 2.91e-04
2022-05-28 01:39:59,174 INFO [train.py:842] (2/4) Epoch 19, batch 2100, loss[loss=0.1571, simple_loss=0.2439, pruned_loss=0.03517, over 7152.00 frames.], tot_loss[loss=0.188, simple_loss=0.2741, pruned_loss=0.05089, over 1424404.13 frames.], batch size: 19, lr: 2.91e-04
2022-05-28 01:40:37,556 INFO [train.py:842] (2/4) Epoch 19, batch 2150, loss[loss=0.2041, simple_loss=0.2926, pruned_loss=0.05773, over 7223.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2733, pruned_loss=0.05063, over 1420950.70 frames.], batch size: 21, lr: 2.91e-04
2022-05-28 01:41:15,624 INFO [train.py:842] (2/4) Epoch 19, batch 2200, loss[loss=0.2051, simple_loss=0.2933, pruned_loss=0.05847, over 7122.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2733, pruned_loss=0.05047, over 1425131.06 frames.], batch size: 21, lr: 2.91e-04
2022-05-28 01:41:53,806 INFO [train.py:842] (2/4) Epoch 19, batch 2250, loss[loss=0.2266, simple_loss=0.3117, pruned_loss=0.07076, over 6106.00 frames.], tot_loss[loss=0.1882, simple_loss=0.274, pruned_loss=0.05123, over 1424224.04 frames.], batch size: 37, lr: 2.91e-04
2022-05-28 01:42:31,838 INFO [train.py:842] (2/4) Epoch 19, batch 2300, loss[loss=0.1963, simple_loss=0.2859, pruned_loss=0.0534, over 7383.00 frames.], tot_loss[loss=0.1882, simple_loss=0.274, pruned_loss=0.05124, over 1425402.69 frames.], batch size: 23, lr: 2.91e-04
2022-05-28 01:43:09,941 INFO [train.py:842] (2/4) Epoch 19, batch 2350, loss[loss=0.167, simple_loss=0.2463, pruned_loss=0.0439, over 7291.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2745, pruned_loss=0.05183, over 1422943.69 frames.], batch size: 17, lr: 2.91e-04
2022-05-28 01:43:57,327 INFO [train.py:842] (2/4) Epoch 19, batch 2400, loss[loss=0.1936, simple_loss=0.2834, pruned_loss=0.05193, over 7152.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2744, pruned_loss=0.0517, over 1420290.85 frames.], batch size: 20, lr: 2.91e-04
2022-05-28 01:44:35,686 INFO [train.py:842] (2/4) Epoch 19, batch 2450, loss[loss=0.2393, simple_loss=0.3178, pruned_loss=0.08043, over 7137.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2747, pruned_loss=0.05187, over 1422175.28 frames.], batch size: 20, lr: 2.91e-04
2022-05-28 01:45:13,667 INFO [train.py:842] (2/4) Epoch 19, batch 2500, loss[loss=0.1969, simple_loss=0.2826, pruned_loss=0.05559, over 7156.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2748, pruned_loss=0.0523, over 1421203.37 frames.], batch size: 26, lr: 2.91e-04
2022-05-28 01:45:54,637 INFO [train.py:842] (2/4) Epoch 19, batch 2550, loss[loss=0.2179, simple_loss=0.3053, pruned_loss=0.06522, over 7287.00 frames.], tot_loss[loss=0.189, simple_loss=0.274, pruned_loss=0.05197, over 1421348.24 frames.], batch size: 24, lr: 2.91e-04
2022-05-28 01:46:32,538 INFO [train.py:842] (2/4) Epoch 19, batch 2600, loss[loss=0.167, simple_loss=0.2464, pruned_loss=0.04377, over 7002.00 frames.], tot_loss[loss=0.189, simple_loss=0.2744, pruned_loss=0.05184, over 1424573.48 frames.], batch size: 16, lr: 2.91e-04
2022-05-28 01:47:10,852 INFO [train.py:842] (2/4) Epoch 19, batch 2650, loss[loss=0.2126, simple_loss=0.3127, pruned_loss=0.05626, over 7286.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2753, pruned_loss=0.05224, over 1427538.18 frames.], batch size: 24, lr: 2.91e-04
2022-05-28 01:47:49,146 INFO [train.py:842] (2/4) Epoch 19, batch 2700, loss[loss=0.1943, simple_loss=0.2855, pruned_loss=0.05157, over 7270.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2738, pruned_loss=0.05162, over 1430765.86 frames.], batch size: 25, lr: 2.91e-04
2022-05-28 01:48:27,302 INFO [train.py:842] (2/4) Epoch 19, batch 2750, loss[loss=0.1797, simple_loss=0.2787, pruned_loss=0.0403, over 7414.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2757, pruned_loss=0.05242, over 1430429.61 frames.], batch size: 21, lr: 2.91e-04
2022-05-28 01:49:05,388 INFO [train.py:842] (2/4) Epoch 19, batch 2800, loss[loss=0.1559, simple_loss=0.2521, pruned_loss=0.02988, over 7072.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2747, pruned_loss=0.05213, over 1430854.92 frames.], batch size: 18, lr: 2.91e-04
2022-05-28 01:49:43,507 INFO [train.py:842] (2/4) Epoch 19, batch 2850, loss[loss=0.1556, simple_loss=0.2388, pruned_loss=0.0362, over 7154.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2738, pruned_loss=0.05144, over 1427591.15 frames.], batch size: 19, lr: 2.91e-04
2022-05-28 01:50:21,401 INFO [train.py:842] (2/4) Epoch 19, batch 2900, loss[loss=0.1924, simple_loss=0.2877, pruned_loss=0.04849, over 7182.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2717, pruned_loss=0.05005, over 1425503.61 frames.], batch size: 26, lr: 2.91e-04
2022-05-28 01:50:59,789 INFO [train.py:842] (2/4) Epoch 19, batch 2950, loss[loss=0.1832, simple_loss=0.2495, pruned_loss=0.05846, over 7281.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2716, pruned_loss=0.05005, over 1430981.64 frames.], batch size: 17, lr: 2.91e-04
2022-05-28 01:51:37,810 INFO [train.py:842] (2/4) Epoch 19, batch 3000, loss[loss=0.3045, simple_loss=0.3664, pruned_loss=0.1213, over 5264.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2726, pruned_loss=0.05086, over 1430964.34 frames.], batch size: 52, lr: 2.91e-04
2022-05-28 01:51:37,810 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 01:51:46,796 INFO [train.py:871] (2/4) Epoch 19, validation: loss=0.1654, simple_loss=0.2649, pruned_loss=0.03297, over 868885.00 frames.
2022-05-28 01:52:25,070 INFO [train.py:842] (2/4) Epoch 19, batch 3050, loss[loss=0.2451, simple_loss=0.3381, pruned_loss=0.07606, over 7183.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2743, pruned_loss=0.05131, over 1431803.00 frames.], batch size: 23, lr: 2.91e-04
2022-05-28 01:53:03,104 INFO [train.py:842] (2/4) Epoch 19, batch 3100, loss[loss=0.1988, simple_loss=0.2859, pruned_loss=0.05582, over 6267.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2748, pruned_loss=0.05151, over 1432670.07 frames.], batch size: 38, lr: 2.90e-04
2022-05-28 01:53:41,175 INFO [train.py:842] (2/4) Epoch 19, batch 3150, loss[loss=0.1734, simple_loss=0.2534, pruned_loss=0.04668, over 7273.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2761, pruned_loss=0.05255, over 1429568.85 frames.], batch size: 18, lr: 2.90e-04
2022-05-28 01:54:19,121 INFO [train.py:842] (2/4) Epoch 19, batch 3200, loss[loss=0.1703, simple_loss=0.2683, pruned_loss=0.03617, over 7160.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2767, pruned_loss=0.05273, over 1427438.25 frames.], batch size: 19, lr: 2.90e-04
2022-05-28 01:54:57,214 INFO [train.py:842] (2/4) Epoch 19, batch 3250, loss[loss=0.1761, simple_loss=0.2555, pruned_loss=0.04833, over 7364.00 frames.], tot_loss[loss=0.1904, simple_loss=0.276, pruned_loss=0.05237, over 1424299.80 frames.], batch size: 19, lr: 2.90e-04
2022-05-28 01:55:35,142 INFO [train.py:842] (2/4) Epoch 19, batch 3300, loss[loss=0.2022, simple_loss=0.2965, pruned_loss=0.05396, over 6382.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2775, pruned_loss=0.05346, over 1424815.43 frames.], batch size: 38, lr: 2.90e-04
2022-05-28 01:56:13,607 INFO [train.py:842] (2/4) Epoch 19, batch 3350, loss[loss=0.1632, simple_loss=0.2507, pruned_loss=0.03787, over 7115.00 frames.], tot_loss[loss=0.192, simple_loss=0.2771, pruned_loss=0.05349, over 1423462.11 frames.], batch size: 21, lr: 2.90e-04
2022-05-28 01:56:51,557 INFO [train.py:842] (2/4) Epoch 19, batch 3400, loss[loss=0.1837, simple_loss=0.269, pruned_loss=0.04925, over 7265.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2763, pruned_loss=0.05302, over 1424220.20 frames.], batch size: 18, lr: 2.90e-04
2022-05-28 01:57:29,942 INFO [train.py:842] (2/4) Epoch 19, batch 3450, loss[loss=0.157, simple_loss=0.2455, pruned_loss=0.03424, over 7362.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2749, pruned_loss=0.05209, over 1420887.35 frames.], batch size: 19, lr: 2.90e-04
2022-05-28 01:58:07,907 INFO [train.py:842] (2/4) Epoch 19, batch 3500, loss[loss=0.2254, simple_loss=0.3048, pruned_loss=0.07301, over 7277.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2749, pruned_loss=0.05203, over 1423900.66 frames.], batch size: 18, lr: 2.90e-04
2022-05-28 01:58:46,247 INFO [train.py:842] (2/4) Epoch 19, batch 3550, loss[loss=0.2489, simple_loss=0.3053, pruned_loss=0.09627, over 7137.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2755, pruned_loss=0.05241, over 1423626.22 frames.], batch size: 17, lr: 2.90e-04
2022-05-28 01:59:24,111 INFO [train.py:842] (2/4) Epoch 19, batch 3600, loss[loss=0.2095, simple_loss=0.2905, pruned_loss=0.06422, over 7200.00 frames.], tot_loss[loss=0.19, simple_loss=0.2757, pruned_loss=0.05218, over 1420352.44 frames.], batch size: 23, lr: 2.90e-04
2022-05-28 02:00:02,222 INFO [train.py:842] (2/4) Epoch 19, batch 3650, loss[loss=0.2058, simple_loss=0.2856, pruned_loss=0.06305, over 7333.00 frames.], tot_loss[loss=0.19, simple_loss=0.2759, pruned_loss=0.05204, over 1413892.33 frames.], batch size: 20, lr: 2.90e-04
2022-05-28 02:00:40,208 INFO [train.py:842] (2/4) Epoch 19, batch 3700, loss[loss=0.1533, simple_loss=0.2318, pruned_loss=0.03736, over 7274.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2752, pruned_loss=0.05181, over 1416017.19 frames.], batch size: 17, lr: 2.90e-04
2022-05-28 02:01:18,449 INFO [train.py:842] (2/4) Epoch 19, batch 3750, loss[loss=0.1971, simple_loss=0.2815, pruned_loss=0.05639, over 7347.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2751, pruned_loss=0.05195, over 1412212.16 frames.], batch size: 22, lr: 2.90e-04
2022-05-28 02:01:56,348 INFO [train.py:842] (2/4) Epoch 19, batch 3800, loss[loss=0.1786, simple_loss=0.2509, pruned_loss=0.05316, over 6979.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2755, pruned_loss=0.0523, over 1417558.47 frames.], batch size: 16, lr: 2.90e-04
2022-05-28 02:02:34,510 INFO [train.py:842] (2/4) Epoch 19, batch 3850, loss[loss=0.2642, simple_loss=0.344, pruned_loss=0.09217, over 5469.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2758, pruned_loss=0.05254, over 1416144.05 frames.], batch size: 52, lr: 2.90e-04
2022-05-28 02:03:12,469 INFO [train.py:842] (2/4) Epoch 19, batch 3900, loss[loss=0.2132, simple_loss=0.301, pruned_loss=0.06276, over 7173.00 frames.], tot_loss[loss=0.1906, simple_loss=0.276, pruned_loss=0.05257, over 1416344.63 frames.], batch size: 26, lr: 2.90e-04
2022-05-28 02:03:50,750 INFO [train.py:842] (2/4) Epoch 19, batch 3950, loss[loss=0.1794, simple_loss=0.2764, pruned_loss=0.04121, over 7232.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2749, pruned_loss=0.05186, over 1418434.39 frames.], batch size: 20, lr: 2.90e-04
2022-05-28 02:04:28,769 INFO [train.py:842] (2/4) Epoch 19, batch 4000, loss[loss=0.1737, simple_loss=0.2667, pruned_loss=0.04033, over 7104.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2743, pruned_loss=0.05132, over 1420455.27 frames.], batch size: 21, lr: 2.90e-04
2022-05-28 02:05:07,110 INFO [train.py:842] (2/4) Epoch 19, batch 4050, loss[loss=0.1727, simple_loss=0.2565, pruned_loss=0.04448, over 7354.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2737, pruned_loss=0.05084, over 1420077.63 frames.], batch size: 19, lr: 2.90e-04
2022-05-28 02:05:45,234 INFO [train.py:842] (2/4) Epoch 19, batch 4100, loss[loss=0.1784, simple_loss=0.2727, pruned_loss=0.04201, over 7143.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2733, pruned_loss=0.05067, over 1417470.43 frames.], batch size: 20, lr: 2.90e-04
2022-05-28 02:06:23,172 INFO [train.py:842] (2/4) Epoch 19, batch 4150, loss[loss=0.2085, simple_loss=0.2944, pruned_loss=0.06133, over 7201.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2756, pruned_loss=0.05215, over 1415326.53 frames.], batch size: 22, lr: 2.90e-04
2022-05-28 02:07:01,156 INFO [train.py:842] (2/4) Epoch 19, batch 4200, loss[loss=0.1949, simple_loss=0.277, pruned_loss=0.05637, over 7332.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2754, pruned_loss=0.0522, over 1422665.05 frames.], batch size: 22, lr: 2.90e-04
2022-05-28 02:07:39,411 INFO [train.py:842] (2/4) Epoch 19, batch 4250, loss[loss=0.1793, simple_loss=0.2636, pruned_loss=0.04747, over 7335.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2745, pruned_loss=0.05186, over 1422188.03 frames.], batch size: 20, lr: 2.90e-04
2022-05-28 02:08:17,281 INFO [train.py:842] (2/4) Epoch 19, batch 4300, loss[loss=0.1791, simple_loss=0.2781, pruned_loss=0.04003, over 7194.00 frames.], tot_loss[loss=0.19, simple_loss=0.2752, pruned_loss=0.05236, over 1421169.22 frames.], batch size: 23, lr: 2.89e-04
2022-05-28 02:08:55,440 INFO [train.py:842] (2/4) Epoch 19, batch 4350, loss[loss=0.1843, simple_loss=0.2767, pruned_loss=0.04593, over 6688.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2756, pruned_loss=0.05193, over 1421286.81 frames.], batch size: 31, lr: 2.89e-04
2022-05-28 02:09:33,403 INFO [train.py:842] (2/4) Epoch 19, batch 4400, loss[loss=0.1801, simple_loss=0.2708, pruned_loss=0.04473, over 7239.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2745, pruned_loss=0.05193, over 1422806.91 frames.], batch size: 20, lr: 2.89e-04
2022-05-28 02:10:11,528 INFO [train.py:842] (2/4) Epoch 19, batch 4450, loss[loss=0.1565, simple_loss=0.2456, pruned_loss=0.03364, over 7126.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2753, pruned_loss=0.05248, over 1419963.84 frames.], batch size: 21, lr: 2.89e-04
2022-05-28 02:10:49,580 INFO [train.py:842] (2/4) Epoch 19, batch 4500, loss[loss=0.1903, simple_loss=0.2843, pruned_loss=0.04814, over 7282.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2748, pruned_loss=0.05189, over 1420267.68 frames.], batch size: 24, lr: 2.89e-04
2022-05-28 02:11:28,100 INFO [train.py:842] (2/4) Epoch 19, batch 4550, loss[loss=0.1896, simple_loss=0.2694, pruned_loss=0.05495, over 7384.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2732, pruned_loss=0.05071, over 1424893.72 frames.], batch size: 23, lr: 2.89e-04
2022-05-28 02:12:06,112 INFO [train.py:842] (2/4) Epoch 19, batch 4600, loss[loss=0.2249, simple_loss=0.303, pruned_loss=0.07343, over 7415.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2733, pruned_loss=0.05088, over 1423706.70 frames.], batch size: 21, lr: 2.89e-04
2022-05-28 02:12:44,397 INFO [train.py:842] (2/4) Epoch 19, batch 4650, loss[loss=0.1603, simple_loss=0.2503, pruned_loss=0.0352, over 7355.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2716, pruned_loss=0.05001, over 1420578.31 frames.], batch size: 19, lr: 2.89e-04
2022-05-28 02:13:22,316 INFO [train.py:842] (2/4) Epoch 19, batch 4700, loss[loss=0.1691, simple_loss=0.2516, pruned_loss=0.04325, over 7276.00 frames.], tot_loss[loss=0.1861, simple_loss=0.272, pruned_loss=0.05014, over 1421376.95 frames.], batch size: 17, lr: 2.89e-04
2022-05-28 02:14:00,470 INFO [train.py:842] (2/4) Epoch 19, batch 4750, loss[loss=0.1559, simple_loss=0.2339, pruned_loss=0.03893, over 7260.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2726, pruned_loss=0.05001, over 1424214.55 frames.], batch size: 17, lr: 2.89e-04
2022-05-28 02:14:38,596 INFO [train.py:842] (2/4) Epoch 19, batch 4800, loss[loss=0.1774, simple_loss=0.2647, pruned_loss=0.04508, over 7259.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2729, pruned_loss=0.05022, over 1418853.20 frames.], batch size: 19, lr: 2.89e-04
2022-05-28 02:15:16,767 INFO [train.py:842] (2/4) Epoch 19, batch 4850, loss[loss=0.1633, simple_loss=0.244, pruned_loss=0.04124, over 6766.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2731, pruned_loss=0.05016, over 1418709.36 frames.], batch size: 15, lr: 2.89e-04
2022-05-28 02:15:54,794 INFO [train.py:842] (2/4) Epoch 19, batch 4900, loss[loss=0.2053, simple_loss=0.2959, pruned_loss=0.0574, over 7216.00 frames.], tot_loss[loss=0.187, simple_loss=0.2732, pruned_loss=0.05047, over 1419867.02 frames.], batch size: 21, lr: 2.89e-04
2022-05-28 02:16:32,986 INFO [train.py:842] (2/4) Epoch 19, batch 4950, loss[loss=0.2222, simple_loss=0.2922, pruned_loss=0.07611, over 5156.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2736, pruned_loss=0.0507, over 1418538.10 frames.], batch size: 54, lr: 2.89e-04
2022-05-28 02:17:10,624 INFO [train.py:842] (2/4) Epoch 19, batch 5000, loss[loss=0.1893, simple_loss=0.2752, pruned_loss=0.05172, over 6811.00 frames.], tot_loss[loss=0.188, simple_loss=0.2745, pruned_loss=0.05078, over 1421017.55 frames.], batch size: 31, lr: 2.89e-04
2022-05-28 02:17:48,864 INFO [train.py:842] (2/4) Epoch 19, batch 5050, loss[loss=0.1983, simple_loss=0.2742, pruned_loss=0.06124, over 6989.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2755, pruned_loss=0.05133, over 1421704.59 frames.], batch size: 16, lr: 2.89e-04
2022-05-28 02:18:26,629 INFO [train.py:842] (2/4) Epoch 19, batch 5100, loss[loss=0.2198, simple_loss=0.3011, pruned_loss=0.06924, over 4898.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2761, pruned_loss=0.05164, over 1420358.93 frames.], batch size: 52, lr: 2.89e-04
2022-05-28 02:19:05,001 INFO [train.py:842] (2/4) Epoch 19, batch 5150, loss[loss=0.1762, simple_loss=0.2669, pruned_loss=0.0428, over 7155.00 frames.], tot_loss[loss=0.188, simple_loss=0.2747, pruned_loss=0.05065, over 1422418.42 frames.], batch size: 19, lr: 2.89e-04
2022-05-28 02:19:43,064 INFO [train.py:842] (2/4) Epoch 19, batch 5200, loss[loss=0.1969, simple_loss=0.2871, pruned_loss=0.05333, over 6705.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2746, pruned_loss=0.0509, over 1424925.13 frames.], batch size: 31, lr: 2.89e-04
2022-05-28 02:20:21,226 INFO [train.py:842] (2/4) Epoch 19, batch 5250, loss[loss=0.1827, simple_loss=0.2594, pruned_loss=0.053, over 7291.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2755, pruned_loss=0.0519, over 1418700.68 frames.], batch size: 17, lr: 2.89e-04
2022-05-28 02:21:08,632 INFO [train.py:842] (2/4) Epoch 19, batch 5300, loss[loss=0.1749, simple_loss=0.2556, pruned_loss=0.04709, over 7270.00 frames.], tot_loss[loss=0.1883, simple_loss=0.274, pruned_loss=0.05128, over 1422010.60 frames.], batch size: 18, lr: 2.89e-04
2022-05-28 02:21:47,028 INFO [train.py:842] (2/4) Epoch 19, batch 5350, loss[loss=0.1702, simple_loss=0.2636, pruned_loss=0.03837, over 7217.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2725, pruned_loss=0.0506, over 1421397.96 frames.], batch size: 21, lr: 2.89e-04
2022-05-28 02:22:24,935 INFO [train.py:842] (2/4) Epoch 19, batch 5400, loss[loss=0.1867, simple_loss=0.2692, pruned_loss=0.05214, over 7428.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2731, pruned_loss=0.05116, over 1419757.98 frames.], batch size: 20, lr: 2.89e-04
2022-05-28 02:23:12,401 INFO [train.py:842] (2/4) Epoch 19, batch 5450, loss[loss=0.1777, simple_loss=0.2762, pruned_loss=0.03957, over 6754.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2735, pruned_loss=0.05105, over 1419372.04 frames.], batch size: 31, lr: 2.88e-04
2022-05-28 02:23:50,551 INFO [train.py:842] (2/4) Epoch 19, batch 5500, loss[loss=0.1688, simple_loss=0.2477, pruned_loss=0.04497, over 7001.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2718, pruned_loss=0.05033, over 1418568.59 frames.], batch size: 16, lr: 2.88e-04
2022-05-28 02:24:28,420 INFO [train.py:842] (2/4) Epoch 19, batch 5550, loss[loss=0.2988, simple_loss=0.3681, pruned_loss=0.1148, over 5029.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2728, pruned_loss=0.05019, over 1418805.15 frames.], batch size: 52, lr: 2.88e-04
2022-05-28 02:25:06,224 INFO [train.py:842] (2/4) Epoch 19, batch 5600, loss[loss=0.1732, simple_loss=0.2692, pruned_loss=0.03857, over 7158.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2732, pruned_loss=0.05, over 1420672.07 frames.], batch size: 18, lr: 2.88e-04
2022-05-28 02:25:44,620 INFO [train.py:842] (2/4) Epoch 19, batch 5650, loss[loss=0.18, simple_loss=0.2751, pruned_loss=0.04242, over 7326.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2728, pruned_loss=0.0503, over 1422644.34 frames.], batch size: 22, lr: 2.88e-04
2022-05-28 02:26:31,922 INFO [train.py:842] (2/4) Epoch 19, batch 5700, loss[loss=0.197, simple_loss=0.2992, pruned_loss=0.04738, over 7013.00 frames.], tot_loss[loss=0.1873, simple_loss=0.273, pruned_loss=0.05078, over 1422797.15 frames.], batch size: 28, lr: 2.88e-04
2022-05-28 02:27:10,278 INFO [train.py:842] (2/4) Epoch 19, batch 5750, loss[loss=0.1774, simple_loss=0.2531, pruned_loss=0.0508, over 7129.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2728, pruned_loss=0.05054, over 1428781.59 frames.], batch size: 17, lr: 2.88e-04
2022-05-28 02:27:48,262 INFO [train.py:842] (2/4) Epoch 19, batch 5800, loss[loss=0.2327, simple_loss=0.3143, pruned_loss=0.07554, over 7339.00 frames.], tot_loss[loss=0.1874, simple_loss=0.273, pruned_loss=0.05088, over 1427579.87 frames.], batch size: 20, lr: 2.88e-04
2022-05-28 02:28:26,598 INFO [train.py:842] (2/4) Epoch 19, batch 5850, loss[loss=0.2119, simple_loss=0.2861, pruned_loss=0.06887, over 4984.00 frames.], tot_loss[loss=0.1866, simple_loss=0.272, pruned_loss=0.05063, over 1426702.19 frames.], batch size: 52, lr: 2.88e-04
2022-05-28 02:29:04,691 INFO [train.py:842] (2/4) Epoch 19, batch 5900, loss[loss=0.1581, simple_loss=0.2438, pruned_loss=0.03625, over 7341.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2707, pruned_loss=0.05008, over 1423510.87 frames.], batch size: 20, lr: 2.88e-04
2022-05-28 02:29:43,068 INFO [train.py:842] (2/4) Epoch 19, batch 5950, loss[loss=0.1583, simple_loss=0.2555, pruned_loss=0.03053, over 7311.00 frames.], tot_loss[loss=0.1861, simple_loss=0.271, pruned_loss=0.05062, over 1427669.37 frames.], batch size: 21, lr: 2.88e-04
2022-05-28 02:30:20,898 INFO [train.py:842] (2/4) Epoch 19, batch 6000, loss[loss=0.2178, simple_loss=0.3008, pruned_loss=0.06739, over 6887.00 frames.], tot_loss[loss=0.1867, simple_loss=0.272, pruned_loss=0.05069, over 1425623.09 frames.], batch size: 31, lr: 2.88e-04
2022-05-28 02:30:20,898 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 02:30:29,955 INFO [train.py:871] (2/4) Epoch 19, validation: loss=0.1648, simple_loss=0.2646, pruned_loss=0.03256, over 868885.00 frames.
2022-05-28 02:31:08,289 INFO [train.py:842] (2/4) Epoch 19, batch 6050, loss[loss=0.1963, simple_loss=0.2901, pruned_loss=0.0513, over 7418.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2716, pruned_loss=0.05037, over 1427392.73 frames.], batch size: 21, lr: 2.88e-04
2022-05-28 02:31:45,883 INFO [train.py:842] (2/4) Epoch 19, batch 6100, loss[loss=0.1663, simple_loss=0.2574, pruned_loss=0.03759, over 6763.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2736, pruned_loss=0.05095, over 1425222.90 frames.], batch size: 31, lr: 2.88e-04
2022-05-28 02:32:24,066 INFO [train.py:842] (2/4) Epoch 19, batch 6150, loss[loss=0.1831, simple_loss=0.2848, pruned_loss=0.04065, over 7149.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2739, pruned_loss=0.05062, over 1429700.67 frames.], batch size: 20, lr: 2.88e-04
2022-05-28 02:33:02,029 INFO [train.py:842] (2/4) Epoch 19, batch 6200, loss[loss=0.1731, simple_loss=0.2605, pruned_loss=0.04283, over 7256.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2753, pruned_loss=0.05184, over 1425238.05 frames.], batch size: 19, lr: 2.88e-04
2022-05-28 02:33:40,511 INFO [train.py:842] (2/4) Epoch 19, batch 6250, loss[loss=0.1889, simple_loss=0.2601, pruned_loss=0.0589, over 7409.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2739, pruned_loss=0.05127, over 1429867.71 frames.], batch size: 18, lr: 2.88e-04
2022-05-28 02:34:18,346 INFO [train.py:842] (2/4) Epoch 19, batch 6300, loss[loss=0.2118, simple_loss=0.296, pruned_loss=0.06382, over 7303.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2745, pruned_loss=0.05141, over 1425678.78 frames.], batch size: 25, lr: 2.88e-04
2022-05-28 02:34:56,845 INFO [train.py:842] (2/4) Epoch 19, batch 6350, loss[loss=0.1667, simple_loss=0.2448, pruned_loss=0.04425, over 7181.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2738, pruned_loss=0.05117, over 1427877.77 frames.], batch size: 18, lr: 2.88e-04
2022-05-28 02:35:34,599 INFO [train.py:842] (2/4) Epoch 19, batch 6400, loss[loss=0.1644, simple_loss=0.2609, pruned_loss=0.03396, over 7011.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2734, pruned_loss=0.05063, over 1426587.31 frames.], batch size: 28, lr: 2.88e-04
2022-05-28 02:36:12,798 INFO [train.py:842] (2/4) Epoch 19, batch 6450, loss[loss=0.1442, simple_loss=0.2324, pruned_loss=0.02804, over 7075.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2738, pruned_loss=0.05133, over 1423944.86 frames.], batch size: 18, lr: 2.88e-04
2022-05-28 02:36:50,726 INFO [train.py:842] (2/4) Epoch 19, batch 6500, loss[loss=0.1748, simple_loss=0.2626, pruned_loss=0.04347, over 6446.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2743, pruned_loss=0.05141, over 1424686.49 frames.], batch size: 37, lr: 2.88e-04
2022-05-28 02:37:28,651 INFO [train.py:842] (2/4) Epoch 19, batch 6550, loss[loss=0.1528, simple_loss=0.2471, pruned_loss=0.02928, over 7119.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2741, pruned_loss=0.05127, over 1421564.91 frames.], batch size: 21, lr: 2.88e-04
2022-05-28 02:38:06,626 INFO [train.py:842] (2/4) Epoch 19, batch 6600, loss[loss=0.1728, simple_loss=0.2639, pruned_loss=0.04091, over 7237.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2739, pruned_loss=0.05154, over 1424570.49 frames.], batch size: 20, lr: 2.88e-04
2022-05-28 02:38:44,826 INFO [train.py:842] (2/4) Epoch 19, batch 6650, loss[loss=0.1609, simple_loss=0.2553, pruned_loss=0.03324, over 7323.00 frames.], tot_loss[loss=0.1885, simple_loss=0.274, pruned_loss=0.05144, over 1418999.55 frames.], batch size: 20, lr: 2.87e-04
2022-05-28 02:39:22,753 INFO [train.py:842] (2/4) Epoch 19, batch 6700, loss[loss=0.1951, simple_loss=0.283, pruned_loss=0.05359, over 7344.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2738, pruned_loss=0.05096, over 1420597.61 frames.], batch size: 22, lr: 2.87e-04
2022-05-28 02:40:00,835 INFO [train.py:842] (2/4) Epoch 19, batch 6750, loss[loss=0.1761, simple_loss=0.2683, pruned_loss=0.04197, over 7114.00 frames.], tot_loss[loss=0.1869, simple_loss=0.273, pruned_loss=0.05043, over 1420567.82 frames.], batch size: 21, lr: 2.87e-04
2022-05-28 02:40:39,053 INFO [train.py:842] (2/4) Epoch 19, batch 6800, loss[loss=0.208, simple_loss=0.306, pruned_loss=0.05499, over 7278.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2733, pruned_loss=0.05042, over 1426897.96 frames.], batch size: 25, lr: 2.87e-04
2022-05-28 02:41:17,206 INFO [train.py:842] (2/4) Epoch 19, batch 6850, loss[loss=0.2134, simple_loss=0.2968, pruned_loss=0.06495, over 7191.00 frames.], tot_loss[loss=0.1867, simple_loss=0.273, pruned_loss=0.05018, over 1427195.69 frames.], batch size: 23, lr: 2.87e-04
2022-05-28 02:41:55,496 INFO [train.py:842] (2/4) Epoch 19, batch 6900, loss[loss=0.1796, simple_loss=0.27, pruned_loss=0.04458, over 7414.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2721, pruned_loss=0.05001, over 1428214.68 frames.], batch size: 21, lr: 2.87e-04
2022-05-28 02:42:33,704 INFO [train.py:842] (2/4) Epoch 19, batch 6950, loss[loss=0.191, simple_loss=0.2872, pruned_loss=0.04736, over 7147.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2741, pruned_loss=0.05112, over 1426031.46 frames.], batch size: 20, lr: 2.87e-04
2022-05-28 02:43:11,783 INFO [train.py:842] (2/4) Epoch 19, batch 7000, loss[loss=0.1662, simple_loss=0.2517, pruned_loss=0.04031, over 7149.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2731, pruned_loss=0.05057, over 1424348.43 frames.], batch size: 17, lr: 2.87e-04
2022-05-28 02:43:50,062 INFO [train.py:842] (2/4) Epoch 19, batch 7050, loss[loss=0.1597, simple_loss=0.2527, pruned_loss=0.03338, over 6827.00 frames.], tot_loss[loss=0.188, simple_loss=0.2738, pruned_loss=0.05108, over 1423615.51 frames.], batch size: 31, lr: 2.87e-04
2022-05-28 02:44:27,962 INFO [train.py:842] (2/4) Epoch 19, batch 7100, loss[loss=0.1944, simple_loss=0.2825, pruned_loss=0.0532, over 7201.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2755, pruned_loss=0.05209, over 1425533.81 frames.], batch size: 22, lr: 2.87e-04
2022-05-28 02:45:06,034 INFO [train.py:842] (2/4) Epoch 19, batch 7150, loss[loss=0.1564, simple_loss=0.2445, pruned_loss=0.0341, over 7272.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2762, pruned_loss=0.05251, over 1425458.96 frames.], batch size: 17, lr: 2.87e-04
2022-05-28 02:45:44,151 INFO [train.py:842] (2/4) Epoch 19, batch 7200, loss[loss=0.1902, simple_loss=0.2719, pruned_loss=0.05429, over 7280.00 frames.], tot_loss[loss=0.189, simple_loss=0.2741, pruned_loss=0.05199, over 1426171.13 frames.], batch size: 18, lr: 2.87e-04
2022-05-28 02:46:22,499 INFO [train.py:842] (2/4) Epoch 19, batch 7250, loss[loss=0.1825, simple_loss=0.2783, pruned_loss=0.04333, over 7176.00 frames.], tot_loss[loss=0.189, simple_loss=0.274, pruned_loss=0.05206, over 1424894.61 frames.], batch size: 23, lr: 2.87e-04
2022-05-28 02:47:00,628 INFO [train.py:842] (2/4) Epoch 19, batch 7300, loss[loss=0.2114, simple_loss=0.2949, pruned_loss=0.06392, over 7330.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2728, pruned_loss=0.05148, over 1424808.85 frames.], batch size: 20, lr: 2.87e-04
2022-05-28 02:47:38,833 INFO [train.py:842] (2/4) Epoch 19, batch 7350, loss[loss=0.1358, simple_loss=0.2222, pruned_loss=0.02469, over 7131.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2736, pruned_loss=0.05137, over 1423938.93 frames.], batch size: 17, lr: 2.87e-04
2022-05-28 02:48:16,552 INFO [train.py:842] (2/4) Epoch 19, batch 7400, loss[loss=0.1993, simple_loss=0.2799, pruned_loss=0.05936, over 7350.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2744, pruned_loss=0.05157, over 1420968.49 frames.], batch size: 19, lr: 2.87e-04
2022-05-28 02:48:54,935 INFO [train.py:842] (2/4) Epoch 19, batch 7450, loss[loss=0.1842, simple_loss=0.2846, pruned_loss=0.0419, over 7165.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2749, pruned_loss=0.05206, over 1420723.51 frames.], batch size: 19, lr: 2.87e-04
2022-05-28 02:49:33,064 INFO [train.py:842] (2/4) Epoch 19, batch 7500, loss[loss=0.191, simple_loss=0.264, pruned_loss=0.059, over 7281.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2746, pruned_loss=0.05163, over 1424643.53 frames.], batch size: 17, lr: 2.87e-04
2022-05-28 02:50:11,368 INFO [train.py:842] (2/4) Epoch 19, batch 7550, loss[loss=0.1683, simple_loss=0.2539, pruned_loss=0.04138, over 7417.00 frames.], tot_loss[loss=0.189, simple_loss=0.2747, pruned_loss=0.05169, over 1426748.85 frames.], batch size: 21, lr: 2.87e-04
2022-05-28 02:50:49,177 INFO [train.py:842] (2/4) Epoch 19, batch 7600, loss[loss=0.2094, simple_loss=0.2919, pruned_loss=0.06342, over 7093.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2752, pruned_loss=0.05192, over 1426329.24 frames.], batch size: 28, lr: 2.87e-04
2022-05-28 02:51:27,607 INFO [train.py:842] (2/4) Epoch 19, batch 7650, loss[loss=0.1927, simple_loss=0.2785, pruned_loss=0.05345, over 7125.00 frames.], tot_loss[loss=0.188, simple_loss=0.2741, pruned_loss=0.05099, over 1426461.10 frames.], batch size: 28, lr: 2.87e-04
2022-05-28 02:52:05,422 INFO [train.py:842] (2/4) Epoch 19, batch 7700, loss[loss=0.1868, simple_loss=0.2785, pruned_loss=0.04755, over 7304.00 frames.], tot_loss[loss=0.188, simple_loss=0.2738, pruned_loss=0.0511, over 1423161.21 frames.], batch size: 24, lr: 2.87e-04
2022-05-28 02:52:43,756 INFO [train.py:842] (2/4) Epoch 19, batch 7750, loss[loss=0.1752, simple_loss=0.2598, pruned_loss=0.04534, over 7166.00 frames.], tot_loss[loss=0.188, simple_loss=0.2738, pruned_loss=0.05112, over 1423144.97 frames.], batch size: 18, lr: 2.87e-04
2022-05-28 02:53:21,936 INFO [train.py:842] (2/4) Epoch 19, batch 7800, loss[loss=0.2062, simple_loss=0.2871, pruned_loss=0.06263, over 7416.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2733, pruned_loss=0.0508, over 1424285.78 frames.], batch size: 20, lr: 2.87e-04
2022-05-28 02:53:59,970 INFO [train.py:842] (2/4) Epoch 19, batch 7850, loss[loss=0.2086, simple_loss=0.3011, pruned_loss=0.05804, over 7297.00 frames.], tot_loss[loss=0.187, simple_loss=0.273, pruned_loss=0.05056, over 1418406.09 frames.], batch size: 25, lr: 2.86e-04
2022-05-28 02:54:37,947 INFO [train.py:842] (2/4) Epoch 19, batch 7900, loss[loss=0.1844, simple_loss=0.2678, pruned_loss=0.05048, over 7339.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2734, pruned_loss=0.05077, over 1419420.77 frames.], batch size: 22, lr: 2.86e-04
2022-05-28 02:55:16,096 INFO [train.py:842] (2/4) Epoch 19, batch 7950, loss[loss=0.161, simple_loss=0.2508, pruned_loss=0.03565, over 7368.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2732, pruned_loss=0.05033, over 1422097.14 frames.], batch size: 19, lr: 2.86e-04
2022-05-28 02:55:54,121 INFO [train.py:842] (2/4) Epoch 19, batch 8000, loss[loss=0.1772, simple_loss=0.2621, pruned_loss=0.04619, over 7406.00 frames.], tot_loss[loss=0.186, simple_loss=0.2722, pruned_loss=0.04993, over 1421873.42 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 02:56:32,333 INFO [train.py:842] (2/4) Epoch 19, batch 8050, loss[loss=0.198, simple_loss=0.2789, pruned_loss=0.05857, over 7326.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2738, pruned_loss=0.05051, over 1425375.32 frames.], batch size: 21, lr: 2.86e-04
2022-05-28 02:57:10,364 INFO [train.py:842] (2/4) Epoch 19, batch 8100, loss[loss=0.2306, simple_loss=0.3196, pruned_loss=0.07085, over 7223.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2726, pruned_loss=0.04998, over 1425815.07 frames.], batch size: 21, lr: 2.86e-04
2022-05-28 02:57:48,667 INFO [train.py:842] (2/4) Epoch 19, batch 8150, loss[loss=0.1755, simple_loss=0.2571, pruned_loss=0.04697, over 7442.00 frames.], tot_loss[loss=0.187, simple_loss=0.2729, pruned_loss=0.05057, over 1421184.84 frames.], batch size: 20, lr: 2.86e-04
2022-05-28 02:58:26,739 INFO [train.py:842] (2/4) Epoch 19, batch 8200, loss[loss=0.163, simple_loss=0.2515, pruned_loss=0.03729, over 7351.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2724, pruned_loss=0.05051, over 1423837.47 frames.], batch size: 19, lr: 2.86e-04
2022-05-28 02:59:05,081 INFO [train.py:842] (2/4) Epoch 19, batch 8250, loss[loss=0.1598, simple_loss=0.2401, pruned_loss=0.03971, over 7172.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2711, pruned_loss=0.04986, over 1427073.34 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 02:59:42,815 INFO [train.py:842] (2/4) Epoch 19, batch 8300, loss[loss=0.2159, simple_loss=0.2887, pruned_loss=0.07152, over 7069.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2721, pruned_loss=0.05009, over 1426695.61 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 03:00:21,252 INFO [train.py:842] (2/4) Epoch 19, batch 8350, loss[loss=0.2, simple_loss=0.2717, pruned_loss=0.06413, over 7022.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2726, pruned_loss=0.05038, over 1427775.07 frames.], batch size: 16, lr: 2.86e-04
2022-05-28 03:00:59,125 INFO [train.py:842] (2/4) Epoch 19, batch 8400, loss[loss=0.2899, simple_loss=0.355, pruned_loss=0.1124, over 7323.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2746, pruned_loss=0.05175, over 1422895.18 frames.], batch size: 21, lr: 2.86e-04
2022-05-28 03:01:37,403 INFO [train.py:842] (2/4) Epoch 19, batch 8450, loss[loss=0.1755, simple_loss=0.2616, pruned_loss=0.04464, over 7277.00 frames.], tot_loss[loss=0.188, simple_loss=0.2735, pruned_loss=0.05124, over 1421614.01 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 03:02:15,562 INFO [train.py:842] (2/4) Epoch 19, batch 8500, loss[loss=0.1671, simple_loss=0.2609, pruned_loss=0.03663, over 7450.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2721, pruned_loss=0.05061, over 1421155.66 frames.], batch size: 20, lr: 2.86e-04
2022-05-28 03:02:53,774 INFO [train.py:842] (2/4) Epoch 19, batch 8550, loss[loss=0.1889, simple_loss=0.2739, pruned_loss=0.05195, over 7279.00 frames.], tot_loss[loss=0.188, simple_loss=0.2734, pruned_loss=0.05132, over 1423053.22 frames.], batch size: 24, lr: 2.86e-04
2022-05-28 03:03:31,638 INFO [train.py:842] (2/4) Epoch 19, batch 8600, loss[loss=0.2188, simple_loss=0.3083, pruned_loss=0.06468, over 5023.00 frames.], tot_loss[loss=0.1874, simple_loss=0.273, pruned_loss=0.05089, over 1419770.83 frames.], batch size: 52, lr: 2.86e-04
2022-05-28 03:04:09,682 INFO [train.py:842] (2/4) Epoch 19, batch 8650, loss[loss=0.1848, simple_loss=0.262, pruned_loss=0.05377, over 7154.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2741, pruned_loss=0.05153, over 1419386.92 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 03:04:47,375 INFO [train.py:842] (2/4) Epoch 19, batch 8700, loss[loss=0.2016, simple_loss=0.2842, pruned_loss=0.05952, over 7364.00 frames.], tot_loss[loss=0.1885, simple_loss=0.274, pruned_loss=0.05147, over 1416555.41 frames.], batch size: 19, lr: 2.86e-04
2022-05-28 03:05:25,583 INFO [train.py:842] (2/4) Epoch 19, batch 8750, loss[loss=0.1729, simple_loss=0.2529, pruned_loss=0.04646, over 6790.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2733, pruned_loss=0.05122, over 1416607.13 frames.], batch size: 15, lr: 2.86e-04
2022-05-28 03:06:03,561 INFO [train.py:842] (2/4) Epoch 19, batch 8800, loss[loss=0.2018, simple_loss=0.2772, pruned_loss=0.06317, over 7287.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2722, pruned_loss=0.05037, over 1415734.59 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 03:06:41,768 INFO [train.py:842] (2/4) Epoch 19, batch 8850, loss[loss=0.1657, simple_loss=0.2538, pruned_loss=0.03885, over 7193.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2723, pruned_loss=0.05026, over 1413257.87 frames.], batch size: 23, lr: 2.86e-04
2022-05-28 03:07:19,678 INFO [train.py:842] (2/4) Epoch 19, batch 8900, loss[loss=0.1346, simple_loss=0.2218, pruned_loss=0.02368, over 7263.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2723, pruned_loss=0.05054, over 1408994.95 frames.], batch size: 19, lr: 2.86e-04
2022-05-28 03:07:57,657 INFO [train.py:842] (2/4) Epoch 19, batch 8950, loss[loss=0.1868, simple_loss=0.2703, pruned_loss=0.05161, over 7286.00 frames.], tot_loss[loss=0.1878, simple_loss=0.273, pruned_loss=0.05132, over 1401904.02 frames.], batch size: 18, lr: 2.86e-04
2022-05-28 03:08:35,128 INFO [train.py:842] (2/4) Epoch 19, batch 9000, loss[loss=0.2088, simple_loss=0.2877, pruned_loss=0.06493, over 7203.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2746, pruned_loss=0.05218, over 1400139.39 frames.], batch size: 23, lr: 2.86e-04
2022-05-28 03:08:35,129 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 03:08:44,267 INFO [train.py:871] (2/4) Epoch 19, validation: loss=0.1647, simple_loss=0.2641, pruned_loss=0.03266, over 868885.00 frames.
2022-05-28 03:09:22,098 INFO [train.py:842] (2/4) Epoch 19, batch 9050, loss[loss=0.2601, simple_loss=0.3375, pruned_loss=0.09136, over 5117.00 frames.], tot_loss[loss=0.1892, simple_loss=0.274, pruned_loss=0.05225, over 1381975.96 frames.], batch size: 52, lr: 2.86e-04
2022-05-28 03:09:59,165 INFO [train.py:842] (2/4) Epoch 19, batch 9100, loss[loss=0.2117, simple_loss=0.2952, pruned_loss=0.06412, over 4963.00 frames.], tot_loss[loss=0.1929, simple_loss=0.277, pruned_loss=0.0544, over 1333484.08 frames.], batch size: 52, lr: 2.85e-04
2022-05-28 03:10:36,165 INFO [train.py:842] (2/4) Epoch 19, batch 9150, loss[loss=0.2036, simple_loss=0.2893, pruned_loss=0.05896, over 4996.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2804, pruned_loss=0.05713, over 1263632.28 frames.], batch size: 53, lr: 2.85e-04
2022-05-28 03:11:25,258 INFO [train.py:842] (2/4) Epoch 20, batch 0, loss[loss=0.1612, simple_loss=0.2579, pruned_loss=0.03222, over 7343.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2579, pruned_loss=0.03222, over 7343.00 frames.], batch size: 19, lr: 2.78e-04
2022-05-28 03:12:03,123 INFO [train.py:842] (2/4) Epoch 20, batch 50, loss[loss=0.1964, simple_loss=0.2672, pruned_loss=0.06283, over 7276.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2739, pruned_loss=0.0479, over 321124.56 frames.], batch size: 18, lr: 2.78e-04
2022-05-28 03:12:41,511 INFO [train.py:842] (2/4) Epoch 20, batch 100, loss[loss=0.2707, simple_loss=0.3414, pruned_loss=0.1, over 5426.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2743, pruned_loss=0.04945, over 566656.82 frames.], batch size: 53, lr: 2.78e-04
2022-05-28 03:13:19,279 INFO [train.py:842] (2/4) Epoch 20, batch 150, loss[loss=0.1763, simple_loss=0.2771, pruned_loss=0.03774, over 7319.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2763, pruned_loss=0.05044, over 756199.48 frames.], batch size: 21, lr: 2.78e-04
2022-05-28 03:13:57,453 INFO [train.py:842] (2/4) Epoch 20, batch 200, loss[loss=0.2068, simple_loss=0.2987, pruned_loss=0.05739, over 7330.00 frames.], tot_loss[loss=0.1872, simple_loss=0.275, pruned_loss=0.04969, over 904186.08 frames.], batch size: 22, lr: 2.78e-04
2022-05-28 03:14:35,701 INFO [train.py:842] (2/4) Epoch 20, batch 250, loss[loss=0.2134, simple_loss=0.2973, pruned_loss=0.06479, over 7337.00 frames.], tot_loss[loss=0.1854, simple_loss=0.273, pruned_loss=0.04891, over 1022664.30 frames.], batch size: 22, lr: 2.78e-04
2022-05-28 03:15:13,679 INFO [train.py:842] (2/4) Epoch 20, batch 300, loss[loss=0.1746, simple_loss=0.2629, pruned_loss=0.04321, over 7208.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2747, pruned_loss=0.05001, over 1111850.15 frames.], batch size: 23, lr: 2.78e-04
2022-05-28 03:15:51,618 INFO [train.py:842] (2/4) Epoch 20, batch 350, loss[loss=0.177, simple_loss=0.278, pruned_loss=0.03796, over 7138.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2745, pruned_loss=0.05031, over 1184324.22 frames.], batch size: 20, lr: 2.78e-04
2022-05-28 03:16:29,704 INFO [train.py:842] (2/4) Epoch 20, batch 400, loss[loss=0.1536, simple_loss=0.2473, pruned_loss=0.02991, over 7156.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2747, pruned_loss=0.05005, over 1237622.47 frames.], batch size: 20, lr: 2.78e-04
2022-05-28 03:17:07,417 INFO [train.py:842] (2/4) Epoch 20, batch 450, loss[loss=0.1774, simple_loss=0.2723, pruned_loss=0.04128, over 7390.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2747, pruned_loss=0.05022, over 1274347.26 frames.], batch size: 23, lr: 2.78e-04
2022-05-28 03:17:45,569 INFO [train.py:842] (2/4) Epoch 20, batch 500, loss[loss=0.1547, simple_loss=0.2476, pruned_loss=0.03092, over 7221.00 frames.], tot_loss[loss=0.188, simple_loss=0.275, pruned_loss=0.05044, over 1306121.61 frames.], batch size: 21, lr: 2.78e-04
2022-05-28 03:18:23,483 INFO [train.py:842] (2/4) Epoch 20, batch 550, loss[loss=0.1938, simple_loss=0.2862, pruned_loss=0.05067, over 6781.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2749, pruned_loss=0.05065, over 1332570.43 frames.], batch size: 31, lr: 2.78e-04
2022-05-28 03:19:02,113 INFO [train.py:842] (2/4) Epoch 20, batch 600, loss[loss=0.2156, simple_loss=0.2726, pruned_loss=0.07935, over 7160.00 frames.], tot_loss[loss=0.186, simple_loss=0.2725, pruned_loss=0.04977, over 1354472.34 frames.], batch size: 18, lr: 2.78e-04
2022-05-28 03:19:40,164 INFO [train.py:842] (2/4) Epoch 20, batch 650, loss[loss=0.1688, simple_loss=0.2511, pruned_loss=0.04322, over 7145.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2726, pruned_loss=0.04947, over 1368662.48 frames.], batch size: 18, lr: 2.78e-04
2022-05-28 03:20:18,364 INFO [train.py:842] (2/4) Epoch 20, batch 700, loss[loss=0.1947, simple_loss=0.2899, pruned_loss=0.04974, over 7235.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2737, pruned_loss=0.04982, over 1382813.24 frames.], batch size: 20, lr: 2.78e-04
2022-05-28 03:20:56,446 INFO [train.py:842] (2/4) Epoch 20, batch 750, loss[loss=0.178, simple_loss=0.266, pruned_loss=0.04502, over 7307.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2733, pruned_loss=0.04981, over 1393596.27 frames.], batch size: 25, lr: 2.78e-04
2022-05-28 03:21:34,812 INFO [train.py:842] (2/4) Epoch 20, batch 800, loss[loss=0.1456, simple_loss=0.2265, pruned_loss=0.03229, over 7410.00 frames.], tot_loss[loss=0.1853, simple_loss=0.272, pruned_loss=0.04931, over 1402310.79 frames.], batch size: 18, lr: 2.78e-04
2022-05-28 03:22:12,806 INFO [train.py:842] (2/4) Epoch 20, batch 850, loss[loss=0.24, simple_loss=0.3117, pruned_loss=0.08415, over 7073.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2729, pruned_loss=0.05022, over 1410336.17 frames.], batch size: 28, lr: 2.78e-04
2022-05-28 03:22:51,269 INFO [train.py:842] (2/4) Epoch 20, batch 900, loss[loss=0.1889, simple_loss=0.2728, pruned_loss=0.05253, over 7366.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2715, pruned_loss=0.05002, over 1415405.79 frames.], batch size: 19, lr: 2.78e-04
2022-05-28 03:23:29,307 INFO [train.py:842] (2/4) Epoch 20, batch 950, loss[loss=0.1727, simple_loss=0.269, pruned_loss=0.03815, over 7224.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2717, pruned_loss=0.04999, over 1418611.65 frames.], batch size: 20, lr: 2.78e-04
2022-05-28 03:24:07,516 INFO [train.py:842] (2/4) Epoch 20, batch 1000, loss[loss=0.2587, simple_loss=0.3322, pruned_loss=0.09265, over 7305.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2726, pruned_loss=0.05004, over 1420192.03 frames.], batch size: 24, lr: 2.78e-04
2022-05-28 03:24:45,370 INFO [train.py:842] (2/4) Epoch 20, batch 1050, loss[loss=0.1707, simple_loss=0.2667, pruned_loss=0.03731, over 7205.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2719, pruned_loss=0.04971, over 1419194.77 frames.], batch size: 22, lr: 2.78e-04
2022-05-28 03:25:23,602 INFO [train.py:842] (2/4) Epoch 20, batch 1100, loss[loss=0.2571, simple_loss=0.3247, pruned_loss=0.09469, over 7204.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2724, pruned_loss=0.0502, over 1414939.33 frames.], batch size: 22, lr: 2.78e-04
2022-05-28 03:26:01,382 INFO [train.py:842] (2/4) Epoch 20, batch 1150, loss[loss=0.2099, simple_loss=0.2909, pruned_loss=0.06445, over 7277.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2732, pruned_loss=0.05005, over 1419575.75 frames.], batch size: 24, lr: 2.78e-04
2022-05-28 03:26:39,926 INFO [train.py:842] (2/4) Epoch 20, batch 1200, loss[loss=0.2136, simple_loss=0.2919, pruned_loss=0.06766, over 7331.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2729, pruned_loss=0.05039, over 1424133.75 frames.], batch size: 22, lr: 2.78e-04
2022-05-28 03:27:17,883 INFO [train.py:842] (2/4) Epoch 20, batch 1250, loss[loss=0.1712, simple_loss=0.2513, pruned_loss=0.04548, over 7151.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2724, pruned_loss=0.05005, over 1424921.23 frames.], batch size: 17, lr: 2.78e-04
2022-05-28 03:27:56,123 INFO [train.py:842] (2/4) Epoch 20, batch 1300, loss[loss=0.1986, simple_loss=0.2964, pruned_loss=0.05044, over 7111.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2729, pruned_loss=0.05038, over 1427350.04 frames.], batch size: 21, lr: 2.77e-04
2022-05-28 03:28:34,014 INFO [train.py:842] (2/4) Epoch 20, batch 1350, loss[loss=0.187, simple_loss=0.2873, pruned_loss=0.0434, over 7191.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2732, pruned_loss=0.05025, over 1429602.50 frames.], batch size: 22, lr: 2.77e-04
2022-05-28 03:29:15,148 INFO [train.py:842] (2/4) Epoch 20, batch 1400, loss[loss=0.1827, simple_loss=0.2744, pruned_loss=0.04549, over 7137.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2722, pruned_loss=0.04943, over 1431320.18 frames.], batch size: 26, lr: 2.77e-04
2022-05-28 03:29:53,000 INFO [train.py:842] (2/4) Epoch 20, batch 1450, loss[loss=0.222, simple_loss=0.3091, pruned_loss=0.06746, over 7161.00 frames.], tot_loss[loss=0.186, simple_loss=0.273, pruned_loss=0.04954, over 1430083.04 frames.], batch size: 26, lr: 2.77e-04
2022-05-28 03:30:31,243 INFO [train.py:842] (2/4) Epoch 20, batch 1500, loss[loss=0.1775, simple_loss=0.2687, pruned_loss=0.04314, over 7372.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2733, pruned_loss=0.04947, over 1427662.43 frames.], batch size: 23, lr: 2.77e-04
2022-05-28 03:31:09,381 INFO [train.py:842] (2/4) Epoch 20, batch 1550, loss[loss=0.1821, simple_loss=0.2731, pruned_loss=0.04551, over 7442.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2723, pruned_loss=0.04939, over 1429692.88 frames.], batch size: 20, lr: 2.77e-04
2022-05-28 03:31:47,532 INFO [train.py:842] (2/4) Epoch 20, batch 1600, loss[loss=0.2416, simple_loss=0.323, pruned_loss=0.08012, over 7344.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2725, pruned_loss=0.04942, over 1424256.53 frames.], batch size: 22, lr: 2.77e-04
2022-05-28 03:32:25,378 INFO [train.py:842] (2/4) Epoch 20, batch 1650, loss[loss=0.1786, simple_loss=0.2667, pruned_loss=0.04524, over 7194.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2733, pruned_loss=0.04949, over 1422028.24 frames.], batch size: 23, lr: 2.77e-04
2022-05-28 03:33:03,575 INFO [train.py:842] (2/4) Epoch 20, batch 1700, loss[loss=0.1945, simple_loss=0.2781, pruned_loss=0.05543, over 7163.00 frames.], tot_loss[loss=0.1863, simple_loss=0.273, pruned_loss=0.04981, over 1420481.50 frames.], batch size: 19, lr: 2.77e-04
2022-05-28 03:33:41,617 INFO [train.py:842] (2/4) Epoch 20, batch 1750, loss[loss=0.2468, simple_loss=0.3279, pruned_loss=0.08281, over 7343.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2749, pruned_loss=0.05084, over 1425618.65 frames.], batch size: 22, lr: 2.77e-04
2022-05-28 03:34:19,912 INFO [train.py:842] (2/4) Epoch 20, batch 1800, loss[loss=0.1889, simple_loss=0.2801, pruned_loss=0.04882, over 7301.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2747, pruned_loss=0.05045, over 1425078.74 frames.], batch size: 25, lr: 2.77e-04
2022-05-28 03:34:57,998 INFO [train.py:842] (2/4) Epoch 20, batch 1850, loss[loss=0.1692, simple_loss=0.2572, pruned_loss=0.04062, over 7070.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2728, pruned_loss=0.04943, over 1427819.50 frames.], batch size: 18, lr: 2.77e-04
2022-05-28 03:35:36,123 INFO [train.py:842] (2/4) Epoch 20, batch 1900, loss[loss=0.1606, simple_loss=0.2523, pruned_loss=0.03449, over 7230.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2736, pruned_loss=0.04952, over 1428573.60 frames.], batch size: 20, lr: 2.77e-04
2022-05-28 03:36:14,209 INFO [train.py:842] (2/4) Epoch 20, batch 1950, loss[loss=0.1773, simple_loss=0.2742, pruned_loss=0.04025, over 6454.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2735, pruned_loss=0.04966, over 1429208.58 frames.], batch size: 38, lr: 2.77e-04
2022-05-28 03:36:52,618 INFO [train.py:842] (2/4) Epoch 20, batch 2000, loss[loss=0.192, simple_loss=0.2809, pruned_loss=0.05155, over 7232.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2736, pruned_loss=0.05049, over 1430218.43 frames.], batch size: 20, lr: 2.77e-04
2022-05-28 03:37:30,756 INFO [train.py:842] (2/4) Epoch 20, batch 2050, loss[loss=0.2441, simple_loss=0.3172, pruned_loss=0.08555, over 7218.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2726, pruned_loss=0.05044, over 1429934.53 frames.], batch size: 21, lr: 2.77e-04
2022-05-28 03:38:09,216 INFO [train.py:842] (2/4) Epoch 20, batch 2100, loss[loss=0.1838, simple_loss=0.2561, pruned_loss=0.05569, over 7431.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2715, pruned_loss=0.04979, over 1432239.51 frames.], batch size: 20, lr: 2.77e-04
2022-05-28 03:38:47,114 INFO [train.py:842] (2/4) Epoch 20, batch 2150, loss[loss=0.2105, simple_loss=0.3031, pruned_loss=0.05894, over 7216.00 frames.], tot_loss[loss=0.186, simple_loss=0.272, pruned_loss=0.05001, over 1426452.40 frames.], batch size: 22, lr: 2.77e-04
2022-05-28 03:39:25,457 INFO [train.py:842] (2/4) Epoch 20, batch 2200, loss[loss=0.1499, simple_loss=0.2347, pruned_loss=0.03257, over 7197.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2708, pruned_loss=0.04956, over 1422188.21 frames.], batch size: 16, lr: 2.77e-04
2022-05-28 03:40:03,669 INFO [train.py:842] (2/4) Epoch 20, batch 2250, loss[loss=0.2031, simple_loss=0.2914, pruned_loss=0.05736, over 7141.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2713, pruned_loss=0.05012, over 1424477.58 frames.], batch size: 20, lr: 2.77e-04
2022-05-28 03:40:41,872 INFO [train.py:842] (2/4) Epoch 20, batch 2300, loss[loss=0.221, simple_loss=0.3084, pruned_loss=0.06675, over 7378.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2719, pruned_loss=0.0503, over 1424248.94 frames.], batch size: 23, lr: 2.77e-04
2022-05-28 03:41:19,749 INFO [train.py:842] (2/4) Epoch 20, batch 2350, loss[loss=0.1753, simple_loss=0.2639, pruned_loss=0.04337, over 7308.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2717, pruned_loss=0.04951, over 1422392.48 frames.], batch size: 21, lr: 2.77e-04
2022-05-28 03:41:58,024 INFO [train.py:842] (2/4) Epoch 20, batch 2400, loss[loss=0.1771, simple_loss=0.2721, pruned_loss=0.04109, over 7431.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2709, pruned_loss=0.04915, over 1424046.94 frames.], batch size: 20, lr: 2.77e-04
2022-05-28 03:42:36,045 INFO [train.py:842] (2/4) Epoch 20, batch 2450, loss[loss=0.1864, simple_loss=0.2809, pruned_loss=0.04601, over 7067.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2707, pruned_loss=0.04899, over 1426816.86 frames.], batch size: 28, lr: 2.77e-04
2022-05-28 03:43:14,461 INFO [train.py:842] (2/4) Epoch 20, batch 2500, loss[loss=0.1859, simple_loss=0.2756, pruned_loss=0.04814, over 7143.00 frames.], tot_loss[loss=0.185, simple_loss=0.2711, pruned_loss=0.04947, over 1424770.78 frames.], batch size: 26, lr: 2.77e-04
2022-05-28 03:43:52,361 INFO [train.py:842] (2/4) Epoch 20, batch 2550, loss[loss=0.1474, simple_loss=0.2385, pruned_loss=0.02811, over 7328.00 frames.], tot_loss[loss=0.185, simple_loss=0.2712, pruned_loss=0.04943, over 1424077.27 frames.], batch size: 20, lr: 2.76e-04
2022-05-28 03:44:30,388 INFO [train.py:842] (2/4) Epoch 20, batch 2600, loss[loss=0.2127, simple_loss=0.2908, pruned_loss=0.06729, over 6640.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2719, pruned_loss=0.04938, over 1424287.41 frames.], batch size: 31, lr: 2.76e-04
2022-05-28 03:45:08,531 INFO [train.py:842] (2/4) Epoch 20, batch 2650, loss[loss=0.14, simple_loss=0.2237, pruned_loss=0.02816, over 6991.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2721, pruned_loss=0.04952, over 1426225.02 frames.], batch size: 16, lr: 2.76e-04
2022-05-28 03:45:46,851 INFO [train.py:842] (2/4) Epoch 20, batch 2700, loss[loss=0.1963, simple_loss=0.2837, pruned_loss=0.05443, over 7374.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2702, pruned_loss=0.04871, over 1427541.46 frames.], batch size: 23, lr: 2.76e-04
2022-05-28 03:46:24,691 INFO [train.py:842] (2/4) Epoch 20, batch 2750, loss[loss=0.1889, simple_loss=0.2732, pruned_loss=0.05229, over 7194.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2712, pruned_loss=0.04911, over 1426295.26 frames.], batch size: 23, lr: 2.76e-04
2022-05-28 03:47:03,027 INFO [train.py:842] (2/4) Epoch 20, batch 2800, loss[loss=0.1777, simple_loss=0.2653, pruned_loss=0.04508, over 7150.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2708, pruned_loss=0.0488, over 1429988.51 frames.], batch size: 18, lr: 2.76e-04
2022-05-28 03:47:41,133 INFO [train.py:842] (2/4) Epoch 20, batch 2850, loss[loss=0.2135, simple_loss=0.308, pruned_loss=0.05953, over 7410.00 frames.], tot_loss[loss=0.1835, simple_loss=0.27, pruned_loss=0.04847, over 1432023.00 frames.], batch size: 21, lr: 2.76e-04
2022-05-28 03:48:19,357 INFO [train.py:842] (2/4) Epoch 20, batch 2900, loss[loss=0.2025, simple_loss=0.2914, pruned_loss=0.0568, over 7172.00 frames.], tot_loss[loss=0.183, simple_loss=0.2694, pruned_loss=0.04829, over 1427232.61 frames.], batch size: 26, lr: 2.76e-04
2022-05-28 03:48:57,419 INFO [train.py:842] (2/4) Epoch 20, batch 2950, loss[loss=0.1977, simple_loss=0.2904, pruned_loss=0.05255, over 7242.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2703, pruned_loss=0.04866, over 1431677.37 frames.], batch size: 20, lr: 2.76e-04
2022-05-28 03:49:35,439 INFO [train.py:842] (2/4) Epoch 20, batch 3000, loss[loss=0.2159, simple_loss=0.2863, pruned_loss=0.07276, over 7386.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2721, pruned_loss=0.04939, over 1430657.64 frames.], batch size: 23, lr: 2.76e-04
2022-05-28 03:49:35,440 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 03:49:44,759 INFO [train.py:871] (2/4) 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] (2/4) Epoch 20, batch 3050, loss[loss=0.1941, simple_loss=0.2714, pruned_loss=0.05845, over 7167.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2725, pruned_loss=0.04913, over 1432292.41 frames.], batch size: 19, lr: 2.76e-04
2022-05-28 03:51:00,928 INFO [train.py:842] (2/4) Epoch 20, batch 3100, loss[loss=0.1811, simple_loss=0.2736, pruned_loss=0.04431, over 7120.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2726, pruned_loss=0.04922, over 1431942.72 frames.], batch size: 21, lr: 2.76e-04
2022-05-28 03:51:38,893 INFO [train.py:842] (2/4) Epoch 20, batch 3150, loss[loss=0.1722, simple_loss=0.2644, pruned_loss=0.03996, over 7274.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2721, pruned_loss=0.04889, over 1432708.99 frames.], batch size: 18, lr: 2.76e-04
2022-05-28 03:52:17,377 INFO [train.py:842] (2/4) Epoch 20, batch 3200, loss[loss=0.1982, simple_loss=0.2931, pruned_loss=0.05164, over 6841.00 frames.], tot_loss[loss=0.1852, simple_loss=0.272, pruned_loss=0.04919, over 1431966.29 frames.], batch size: 31, lr: 2.76e-04
2022-05-28 03:52:55,238 INFO [train.py:842] (2/4) Epoch 20, batch 3250, loss[loss=0.1877, simple_loss=0.2619, pruned_loss=0.05674, over 7070.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2725, pruned_loss=0.04985, over 1428275.98 frames.], batch size: 18, lr: 2.76e-04
2022-05-28 03:53:33,556 INFO [train.py:842] (2/4) Epoch 20, batch 3300, loss[loss=0.1455, simple_loss=0.2272, pruned_loss=0.03191, over 7145.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2724, pruned_loss=0.05048, over 1426042.28 frames.], batch size: 17, lr: 2.76e-04
2022-05-28 03:54:11,566 INFO [train.py:842] (2/4) Epoch 20, batch 3350, loss[loss=0.1624, simple_loss=0.2608, pruned_loss=0.03205, over 7146.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2721, pruned_loss=0.05006, over 1426799.16 frames.], batch size: 20, lr: 2.76e-04
2022-05-28 03:54:49,729 INFO [train.py:842] (2/4) Epoch 20, batch 3400, loss[loss=0.161, simple_loss=0.2421, pruned_loss=0.03989, over 7296.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2727, pruned_loss=0.0503, over 1426106.33 frames.], batch size: 17, lr: 2.76e-04
2022-05-28 03:55:27,650 INFO [train.py:842] (2/4) Epoch 20, batch 3450, loss[loss=0.1558, simple_loss=0.2489, pruned_loss=0.03131, over 7229.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2717, pruned_loss=0.04977, over 1424477.51 frames.], batch size: 20, lr: 2.76e-04
2022-05-28 03:56:05,948 INFO [train.py:842] (2/4) Epoch 20, batch 3500, loss[loss=0.1554, simple_loss=0.2431, pruned_loss=0.03382, over 7259.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2713, pruned_loss=0.04955, over 1422985.82 frames.], batch size: 19, lr: 2.76e-04
2022-05-28 03:56:43,794 INFO [train.py:842] (2/4) Epoch 20, batch 3550, loss[loss=0.1739, simple_loss=0.2697, pruned_loss=0.03903, over 7127.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2718, pruned_loss=0.04955, over 1426455.87 frames.], batch size: 21, lr: 2.76e-04
2022-05-28 03:57:22,218 INFO [train.py:842] (2/4) Epoch 20, batch 3600, loss[loss=0.2027, simple_loss=0.2832, pruned_loss=0.06111, over 7434.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2723, pruned_loss=0.04957, over 1430816.78 frames.], batch size: 20, lr: 2.76e-04
2022-05-28 03:58:00,175 INFO [train.py:842] (2/4) Epoch 20, batch 3650, loss[loss=0.1798, simple_loss=0.274, pruned_loss=0.04284, over 7414.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2718, pruned_loss=0.04969, over 1431723.20 frames.], batch size: 21, lr: 2.76e-04
2022-05-28 03:58:38,384 INFO [train.py:842] (2/4) Epoch 20, batch 3700, loss[loss=0.1593, simple_loss=0.2589, pruned_loss=0.02984, over 7226.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2722, pruned_loss=0.04996, over 1433124.43 frames.], batch size: 21, lr: 2.76e-04
2022-05-28 03:59:16,260 INFO [train.py:842] (2/4) Epoch 20, batch 3750, loss[loss=0.1513, simple_loss=0.2479, pruned_loss=0.02735, over 7321.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2733, pruned_loss=0.05069, over 1428461.61 frames.], batch size: 21, lr: 2.76e-04
2022-05-28 03:59:54,689 INFO [train.py:842] (2/4) Epoch 20, batch 3800, loss[loss=0.1436, simple_loss=0.2189, pruned_loss=0.03418, over 7292.00 frames.], tot_loss[loss=0.187, simple_loss=0.2726, pruned_loss=0.0507, over 1428367.32 frames.], batch size: 17, lr: 2.76e-04
2022-05-28 04:00:32,643 INFO [train.py:842] (2/4) Epoch 20, batch 3850, loss[loss=0.1702, simple_loss=0.2609, pruned_loss=0.03975, over 7362.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2723, pruned_loss=0.0506, over 1424377.05 frames.], batch size: 19, lr: 2.75e-04
2022-05-28 04:01:10,453 INFO [train.py:842] (2/4) Epoch 20, batch 3900, loss[loss=0.1891, simple_loss=0.272, pruned_loss=0.05312, over 7281.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2733, pruned_loss=0.05065, over 1420801.77 frames.], batch size: 25, lr: 2.75e-04
2022-05-28 04:01:48,148 INFO [train.py:842] (2/4) Epoch 20, batch 3950, loss[loss=0.1798, simple_loss=0.2707, pruned_loss=0.04439, over 7408.00 frames.], tot_loss[loss=0.187, simple_loss=0.2732, pruned_loss=0.05038, over 1417636.83 frames.], batch size: 21, lr: 2.75e-04
2022-05-28 04:02:26,108 INFO [train.py:842] (2/4) Epoch 20, batch 4000, loss[loss=0.1905, simple_loss=0.2811, pruned_loss=0.04992, over 7228.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2734, pruned_loss=0.05038, over 1409259.29 frames.], batch size: 21, lr: 2.75e-04
2022-05-28 04:03:04,016 INFO [train.py:842] (2/4) Epoch 20, batch 4050, loss[loss=0.1592, simple_loss=0.255, pruned_loss=0.03167, over 7225.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2739, pruned_loss=0.0503, over 1410978.70 frames.], batch size: 21, lr: 2.75e-04
2022-05-28 04:03:42,435 INFO [train.py:842] (2/4) Epoch 20, batch 4100, loss[loss=0.2083, simple_loss=0.2959, pruned_loss=0.06033, over 7208.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2738, pruned_loss=0.05058, over 1410598.12 frames.], batch size: 26, lr: 2.75e-04
2022-05-28 04:04:20,455 INFO [train.py:842] (2/4) Epoch 20, batch 4150, loss[loss=0.2623, simple_loss=0.346, pruned_loss=0.08929, over 7318.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2733, pruned_loss=0.05063, over 1415336.11 frames.], batch size: 20, lr: 2.75e-04
2022-05-28 04:04:58,988 INFO [train.py:842] (2/4) Epoch 20, batch 4200, loss[loss=0.2108, simple_loss=0.2794, pruned_loss=0.07113, over 7359.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2714, pruned_loss=0.04987, over 1418167.81 frames.], batch size: 19, lr: 2.75e-04
2022-05-28 04:05:36,676 INFO [train.py:842] (2/4) Epoch 20, batch 4250, loss[loss=0.1753, simple_loss=0.2715, pruned_loss=0.03952, over 7137.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2732, pruned_loss=0.05112, over 1414734.63 frames.], batch size: 20, lr: 2.75e-04
2022-05-28 04:06:14,970 INFO [train.py:842] (2/4) Epoch 20, batch 4300, loss[loss=0.1688, simple_loss=0.2658, pruned_loss=0.03585, over 7138.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2729, pruned_loss=0.05107, over 1413089.71 frames.], batch size: 26, lr: 2.75e-04
2022-05-28 04:06:53,168 INFO [train.py:842] (2/4) Epoch 20, batch 4350, loss[loss=0.1837, simple_loss=0.2665, pruned_loss=0.05046, over 7386.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2718, pruned_loss=0.05052, over 1415885.04 frames.], batch size: 18, lr: 2.75e-04
2022-05-28 04:07:31,384 INFO [train.py:842] (2/4) Epoch 20, batch 4400, loss[loss=0.1846, simple_loss=0.2714, pruned_loss=0.04895, over 7293.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2724, pruned_loss=0.05052, over 1419777.00 frames.], batch size: 25, lr: 2.75e-04
2022-05-28 04:08:09,369 INFO [train.py:842] (2/4) Epoch 20, batch 4450, loss[loss=0.2025, simple_loss=0.2915, pruned_loss=0.05677, over 7420.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2722, pruned_loss=0.05063, over 1414460.27 frames.], batch size: 21, lr: 2.75e-04
2022-05-28 04:08:47,641 INFO [train.py:842] (2/4) Epoch 20, batch 4500, loss[loss=0.1852, simple_loss=0.2749, pruned_loss=0.04779, over 7143.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2712, pruned_loss=0.05032, over 1419893.66 frames.], batch size: 26, lr: 2.75e-04
2022-05-28 04:09:25,673 INFO [train.py:842] (2/4) Epoch 20, batch 4550, loss[loss=0.2246, simple_loss=0.2943, pruned_loss=0.07742, over 7355.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2717, pruned_loss=0.05004, over 1425678.70 frames.], batch size: 19, lr: 2.75e-04
2022-05-28 04:10:04,096 INFO [train.py:842] (2/4) Epoch 20, batch 4600, loss[loss=0.1844, simple_loss=0.2804, pruned_loss=0.04417, over 7322.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2712, pruned_loss=0.05022, over 1423012.89 frames.], batch size: 20, lr: 2.75e-04
2022-05-28 04:10:42,085 INFO [train.py:842] (2/4) Epoch 20, batch 4650, loss[loss=0.2739, simple_loss=0.3333, pruned_loss=0.1072, over 7143.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2713, pruned_loss=0.05057, over 1425446.36 frames.], batch size: 20, lr: 2.75e-04
2022-05-28 04:11:20,089 INFO [train.py:842] (2/4) Epoch 20, batch 4700, loss[loss=0.1903, simple_loss=0.2809, pruned_loss=0.04988, over 7219.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2719, pruned_loss=0.05057, over 1422339.10 frames.], batch size: 21, lr: 2.75e-04
2022-05-28 04:11:57,874 INFO [train.py:842] (2/4) Epoch 20, batch 4750, loss[loss=0.163, simple_loss=0.2559, pruned_loss=0.03504, over 6353.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2719, pruned_loss=0.05049, over 1420689.09 frames.], batch size: 37, lr: 2.75e-04
2022-05-28 04:12:36,327 INFO [train.py:842] (2/4) Epoch 20, batch 4800, loss[loss=0.2264, simple_loss=0.2994, pruned_loss=0.07665, over 5107.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2717, pruned_loss=0.05057, over 1422228.23 frames.], batch size: 52, lr: 2.75e-04
2022-05-28 04:13:14,129 INFO [train.py:842] (2/4) Epoch 20, batch 4850, loss[loss=0.2181, simple_loss=0.3189, pruned_loss=0.05862, over 7145.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2718, pruned_loss=0.05, over 1418274.72 frames.], batch size: 20, lr: 2.75e-04
2022-05-28 04:13:52,314 INFO [train.py:842] (2/4) Epoch 20, batch 4900, loss[loss=0.1968, simple_loss=0.2874, pruned_loss=0.05307, over 5193.00 frames.], tot_loss[loss=0.187, simple_loss=0.2731, pruned_loss=0.05041, over 1419951.44 frames.], batch size: 52, lr: 2.75e-04
2022-05-28 04:14:30,424 INFO [train.py:842] (2/4) Epoch 20, batch 4950, loss[loss=0.2231, simple_loss=0.3133, pruned_loss=0.06646, over 7145.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2733, pruned_loss=0.05098, over 1422063.40 frames.], batch size: 20, lr: 2.75e-04
2022-05-28 04:15:18,009 INFO [train.py:842] (2/4) Epoch 20, batch 5000, loss[loss=0.196, simple_loss=0.2799, pruned_loss=0.05601, over 7147.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2731, pruned_loss=0.05053, over 1427327.66 frames.], batch size: 26, lr: 2.75e-04
2022-05-28 04:15:55,718 INFO [train.py:842] (2/4) Epoch 20, batch 5050, loss[loss=0.1497, simple_loss=0.2283, pruned_loss=0.03555, over 6809.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2729, pruned_loss=0.05011, over 1418615.74 frames.], batch size: 15, lr: 2.75e-04
2022-05-28 04:16:33,965 INFO [train.py:842] (2/4) Epoch 20, batch 5100, loss[loss=0.1562, simple_loss=0.243, pruned_loss=0.03466, over 7367.00 frames.], tot_loss[loss=0.186, simple_loss=0.2728, pruned_loss=0.04963, over 1423696.87 frames.], batch size: 19, lr: 2.75e-04
2022-05-28 04:17:11,879 INFO [train.py:842] (2/4) Epoch 20, batch 5150, loss[loss=0.1586, simple_loss=0.2399, pruned_loss=0.03866, over 7282.00 frames.], tot_loss[loss=0.186, simple_loss=0.2724, pruned_loss=0.04976, over 1424625.71 frames.], batch size: 17, lr: 2.74e-04
2022-05-28 04:17:50,060 INFO [train.py:842] (2/4) Epoch 20, batch 5200, loss[loss=0.169, simple_loss=0.2629, pruned_loss=0.03756, over 7231.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2727, pruned_loss=0.05007, over 1427186.77 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:18:27,915 INFO [train.py:842] (2/4) Epoch 20, batch 5250, loss[loss=0.2239, simple_loss=0.3087, pruned_loss=0.06956, over 7338.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2722, pruned_loss=0.04973, over 1421708.88 frames.], batch size: 22, lr: 2.74e-04
2022-05-28 04:19:06,159 INFO [train.py:842] (2/4) Epoch 20, batch 5300, loss[loss=0.1754, simple_loss=0.2668, pruned_loss=0.04197, over 7388.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2729, pruned_loss=0.05031, over 1418571.51 frames.], batch size: 23, lr: 2.74e-04
2022-05-28 04:19:43,979 INFO [train.py:842] (2/4) Epoch 20, batch 5350, loss[loss=0.2823, simple_loss=0.3599, pruned_loss=0.1023, over 7300.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2736, pruned_loss=0.05092, over 1420101.13 frames.], batch size: 24, lr: 2.74e-04
2022-05-28 04:20:22,321 INFO [train.py:842] (2/4) Epoch 20, batch 5400, loss[loss=0.1812, simple_loss=0.2703, pruned_loss=0.04603, over 7226.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2735, pruned_loss=0.05069, over 1419380.65 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:21:00,421 INFO [train.py:842] (2/4) Epoch 20, batch 5450, loss[loss=0.1387, simple_loss=0.2307, pruned_loss=0.02338, over 7433.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2733, pruned_loss=0.05093, over 1419787.57 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:21:38,509 INFO [train.py:842] (2/4) Epoch 20, batch 5500, loss[loss=0.1814, simple_loss=0.2641, pruned_loss=0.04932, over 7320.00 frames.], tot_loss[loss=0.1857, simple_loss=0.272, pruned_loss=0.04972, over 1418372.54 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:22:16,445 INFO [train.py:842] (2/4) Epoch 20, batch 5550, loss[loss=0.1649, simple_loss=0.2509, pruned_loss=0.0394, over 7412.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2728, pruned_loss=0.05046, over 1420999.80 frames.], batch size: 21, lr: 2.74e-04
2022-05-28 04:22:54,446 INFO [train.py:842] (2/4) Epoch 20, batch 5600, loss[loss=0.1728, simple_loss=0.2635, pruned_loss=0.04111, over 7263.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2727, pruned_loss=0.05015, over 1422501.67 frames.], batch size: 25, lr: 2.74e-04
2022-05-28 04:23:32,208 INFO [train.py:842] (2/4) Epoch 20, batch 5650, loss[loss=0.2401, simple_loss=0.3162, pruned_loss=0.08197, over 7210.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2732, pruned_loss=0.05023, over 1418976.01 frames.], batch size: 22, lr: 2.74e-04
2022-05-28 04:24:10,415 INFO [train.py:842] (2/4) Epoch 20, batch 5700, loss[loss=0.1703, simple_loss=0.2529, pruned_loss=0.04386, over 6793.00 frames.], tot_loss[loss=0.185, simple_loss=0.2717, pruned_loss=0.04917, over 1418200.55 frames.], batch size: 15, lr: 2.74e-04
2022-05-28 04:24:48,361 INFO [train.py:842] (2/4) Epoch 20, batch 5750, loss[loss=0.1963, simple_loss=0.283, pruned_loss=0.05477, over 7178.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2714, pruned_loss=0.04941, over 1416608.14 frames.], batch size: 23, lr: 2.74e-04
2022-05-28 04:25:26,841 INFO [train.py:842] (2/4) Epoch 20, batch 5800, loss[loss=0.1817, simple_loss=0.2792, pruned_loss=0.04207, over 7150.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2721, pruned_loss=0.05004, over 1421106.76 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:26:04,819 INFO [train.py:842] (2/4) Epoch 20, batch 5850, loss[loss=0.2252, simple_loss=0.3045, pruned_loss=0.07299, over 6711.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2726, pruned_loss=0.05036, over 1425297.70 frames.], batch size: 31, lr: 2.74e-04
2022-05-28 04:26:42,983 INFO [train.py:842] (2/4) Epoch 20, batch 5900, loss[loss=0.1847, simple_loss=0.2745, pruned_loss=0.04738, over 7342.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2726, pruned_loss=0.05033, over 1419310.01 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:27:20,874 INFO [train.py:842] (2/4) Epoch 20, batch 5950, loss[loss=0.199, simple_loss=0.2865, pruned_loss=0.05579, over 7336.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2727, pruned_loss=0.05048, over 1415987.17 frames.], batch size: 22, lr: 2.74e-04
2022-05-28 04:27:59,345 INFO [train.py:842] (2/4) Epoch 20, batch 6000, loss[loss=0.2059, simple_loss=0.2989, pruned_loss=0.05646, over 7341.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2711, pruned_loss=0.04962, over 1419604.79 frames.], batch size: 22, lr: 2.74e-04
2022-05-28 04:27:59,347 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 04:28:08,359 INFO [train.py:871] (2/4) Epoch 20, validation: loss=0.1665, simple_loss=0.2662, pruned_loss=0.03341, over 868885.00 frames.
2022-05-28 04:28:46,223 INFO [train.py:842] (2/4) Epoch 20, batch 6050, loss[loss=0.1533, simple_loss=0.236, pruned_loss=0.03528, over 7068.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2717, pruned_loss=0.04976, over 1420001.58 frames.], batch size: 18, lr: 2.74e-04
2022-05-28 04:29:24,661 INFO [train.py:842] (2/4) Epoch 20, batch 6100, loss[loss=0.1735, simple_loss=0.2624, pruned_loss=0.04227, over 7436.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2724, pruned_loss=0.05033, over 1419927.58 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:30:02,605 INFO [train.py:842] (2/4) Epoch 20, batch 6150, loss[loss=0.2031, simple_loss=0.2874, pruned_loss=0.0594, over 7072.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2731, pruned_loss=0.05057, over 1422905.25 frames.], batch size: 18, lr: 2.74e-04
2022-05-28 04:30:41,019 INFO [train.py:842] (2/4) Epoch 20, batch 6200, loss[loss=0.2306, simple_loss=0.3072, pruned_loss=0.07702, over 7436.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2729, pruned_loss=0.05081, over 1423559.79 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:31:18,803 INFO [train.py:842] (2/4) Epoch 20, batch 6250, loss[loss=0.1721, simple_loss=0.2491, pruned_loss=0.04757, over 7355.00 frames.], tot_loss[loss=0.1867, simple_loss=0.273, pruned_loss=0.05024, over 1422046.62 frames.], batch size: 19, lr: 2.74e-04
2022-05-28 04:31:56,956 INFO [train.py:842] (2/4) Epoch 20, batch 6300, loss[loss=0.2429, simple_loss=0.3195, pruned_loss=0.08314, over 7302.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2747, pruned_loss=0.05135, over 1420876.85 frames.], batch size: 25, lr: 2.74e-04
2022-05-28 04:32:34,970 INFO [train.py:842] (2/4) Epoch 20, batch 6350, loss[loss=0.1719, simple_loss=0.2608, pruned_loss=0.04156, over 7312.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2734, pruned_loss=0.05078, over 1424644.03 frames.], batch size: 21, lr: 2.74e-04
2022-05-28 04:33:13,291 INFO [train.py:842] (2/4) Epoch 20, batch 6400, loss[loss=0.1643, simple_loss=0.2479, pruned_loss=0.0404, over 7319.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2727, pruned_loss=0.05045, over 1423997.20 frames.], batch size: 20, lr: 2.74e-04
2022-05-28 04:33:50,979 INFO [train.py:842] (2/4) Epoch 20, batch 6450, loss[loss=0.1661, simple_loss=0.2678, pruned_loss=0.03222, over 7321.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2722, pruned_loss=0.0498, over 1420621.53 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:34:29,310 INFO [train.py:842] (2/4) Epoch 20, batch 6500, loss[loss=0.174, simple_loss=0.2481, pruned_loss=0.0499, over 7066.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2712, pruned_loss=0.04934, over 1423471.88 frames.], batch size: 18, lr: 2.73e-04
2022-05-28 04:35:07,337 INFO [train.py:842] (2/4) Epoch 20, batch 6550, loss[loss=0.161, simple_loss=0.243, pruned_loss=0.03952, over 7151.00 frames.], tot_loss[loss=0.1849, simple_loss=0.271, pruned_loss=0.04938, over 1424101.68 frames.], batch size: 19, lr: 2.73e-04
2022-05-28 04:35:45,537 INFO [train.py:842] (2/4) Epoch 20, batch 6600, loss[loss=0.1767, simple_loss=0.259, pruned_loss=0.04719, over 6760.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2716, pruned_loss=0.04964, over 1426094.51 frames.], batch size: 15, lr: 2.73e-04
2022-05-28 04:36:23,631 INFO [train.py:842] (2/4) Epoch 20, batch 6650, loss[loss=0.1788, simple_loss=0.265, pruned_loss=0.04632, over 7239.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2716, pruned_loss=0.04958, over 1425655.54 frames.], batch size: 20, lr: 2.73e-04
2022-05-28 04:37:02,174 INFO [train.py:842] (2/4) Epoch 20, batch 6700, loss[loss=0.1642, simple_loss=0.2389, pruned_loss=0.04472, over 7137.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2709, pruned_loss=0.04967, over 1424106.25 frames.], batch size: 17, lr: 2.73e-04
2022-05-28 04:37:40,086 INFO [train.py:842] (2/4) Epoch 20, batch 6750, loss[loss=0.2164, simple_loss=0.2929, pruned_loss=0.06992, over 7174.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2722, pruned_loss=0.05026, over 1428831.39 frames.], batch size: 18, lr: 2.73e-04
2022-05-28 04:38:18,250 INFO [train.py:842] (2/4) Epoch 20, batch 6800, loss[loss=0.1411, simple_loss=0.2254, pruned_loss=0.02842, over 7278.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2713, pruned_loss=0.04948, over 1427881.49 frames.], batch size: 17, lr: 2.73e-04
2022-05-28 04:38:56,157 INFO [train.py:842] (2/4) Epoch 20, batch 6850, loss[loss=0.1414, simple_loss=0.2236, pruned_loss=0.02957, over 6994.00 frames.], tot_loss[loss=0.184, simple_loss=0.2703, pruned_loss=0.04884, over 1428355.54 frames.], batch size: 16, lr: 2.73e-04
2022-05-28 04:39:34,134 INFO [train.py:842] (2/4) Epoch 20, batch 6900, loss[loss=0.1916, simple_loss=0.2778, pruned_loss=0.05272, over 7314.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2716, pruned_loss=0.04954, over 1424898.12 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:40:11,935 INFO [train.py:842] (2/4) Epoch 20, batch 6950, loss[loss=0.1754, simple_loss=0.2695, pruned_loss=0.04069, over 7199.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2708, pruned_loss=0.04887, over 1425938.37 frames.], batch size: 22, lr: 2.73e-04
2022-05-28 04:40:50,224 INFO [train.py:842] (2/4) Epoch 20, batch 7000, loss[loss=0.1842, simple_loss=0.2644, pruned_loss=0.05199, over 7252.00 frames.], tot_loss[loss=0.185, simple_loss=0.2711, pruned_loss=0.04943, over 1426552.97 frames.], batch size: 16, lr: 2.73e-04
2022-05-28 04:41:28,300 INFO [train.py:842] (2/4) Epoch 20, batch 7050, loss[loss=0.183, simple_loss=0.2775, pruned_loss=0.04427, over 7088.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2719, pruned_loss=0.04983, over 1429845.81 frames.], batch size: 28, lr: 2.73e-04
2022-05-28 04:42:06,607 INFO [train.py:842] (2/4) Epoch 20, batch 7100, loss[loss=0.1824, simple_loss=0.2667, pruned_loss=0.04907, over 7161.00 frames.], tot_loss[loss=0.186, simple_loss=0.2719, pruned_loss=0.05, over 1430793.79 frames.], batch size: 19, lr: 2.73e-04
2022-05-28 04:42:44,636 INFO [train.py:842] (2/4) Epoch 20, batch 7150, loss[loss=0.1937, simple_loss=0.278, pruned_loss=0.05476, over 7223.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2729, pruned_loss=0.05061, over 1432587.15 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:43:22,717 INFO [train.py:842] (2/4) Epoch 20, batch 7200, loss[loss=0.1747, simple_loss=0.2631, pruned_loss=0.04316, over 7105.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2723, pruned_loss=0.05034, over 1426212.72 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:44:00,673 INFO [train.py:842] (2/4) Epoch 20, batch 7250, loss[loss=0.1663, simple_loss=0.2572, pruned_loss=0.03763, over 7333.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2719, pruned_loss=0.05026, over 1424566.44 frames.], batch size: 22, lr: 2.73e-04
2022-05-28 04:44:38,829 INFO [train.py:842] (2/4) Epoch 20, batch 7300, loss[loss=0.2022, simple_loss=0.2792, pruned_loss=0.06259, over 5226.00 frames.], tot_loss[loss=0.186, simple_loss=0.2721, pruned_loss=0.04999, over 1420646.54 frames.], batch size: 52, lr: 2.73e-04
2022-05-28 04:45:16,991 INFO [train.py:842] (2/4) Epoch 20, batch 7350, loss[loss=0.1908, simple_loss=0.2728, pruned_loss=0.05439, over 7159.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2706, pruned_loss=0.04953, over 1422842.35 frames.], batch size: 18, lr: 2.73e-04
2022-05-28 04:45:55,200 INFO [train.py:842] (2/4) Epoch 20, batch 7400, loss[loss=0.1529, simple_loss=0.2307, pruned_loss=0.03759, over 7128.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2694, pruned_loss=0.04866, over 1425243.80 frames.], batch size: 17, lr: 2.73e-04
2022-05-28 04:46:32,946 INFO [train.py:842] (2/4) Epoch 20, batch 7450, loss[loss=0.1982, simple_loss=0.2892, pruned_loss=0.05356, over 7317.00 frames.], tot_loss[loss=0.185, simple_loss=0.2713, pruned_loss=0.04937, over 1425561.68 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:47:11,133 INFO [train.py:842] (2/4) Epoch 20, batch 7500, loss[loss=0.2208, simple_loss=0.3115, pruned_loss=0.06504, over 7212.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2723, pruned_loss=0.05002, over 1429355.70 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:47:48,918 INFO [train.py:842] (2/4) Epoch 20, batch 7550, loss[loss=0.1746, simple_loss=0.2482, pruned_loss=0.05056, over 7284.00 frames.], tot_loss[loss=0.1871, simple_loss=0.273, pruned_loss=0.0506, over 1424421.18 frames.], batch size: 18, lr: 2.73e-04
2022-05-28 04:48:27,065 INFO [train.py:842] (2/4) Epoch 20, batch 7600, loss[loss=0.1756, simple_loss=0.2587, pruned_loss=0.04624, over 7072.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2733, pruned_loss=0.05064, over 1424397.01 frames.], batch size: 18, lr: 2.73e-04
2022-05-28 04:49:05,137 INFO [train.py:842] (2/4) Epoch 20, batch 7650, loss[loss=0.2031, simple_loss=0.2869, pruned_loss=0.05961, over 7215.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2716, pruned_loss=0.04957, over 1426398.47 frames.], batch size: 21, lr: 2.73e-04
2022-05-28 04:49:43,396 INFO [train.py:842] (2/4) Epoch 20, batch 7700, loss[loss=0.187, simple_loss=0.2706, pruned_loss=0.05167, over 7250.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2724, pruned_loss=0.05032, over 1426941.52 frames.], batch size: 19, lr: 2.73e-04
2022-05-28 04:50:21,409 INFO [train.py:842] (2/4) Epoch 20, batch 7750, loss[loss=0.1901, simple_loss=0.2853, pruned_loss=0.04744, over 7146.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2716, pruned_loss=0.0496, over 1427644.07 frames.], batch size: 20, lr: 2.73e-04
2022-05-28 04:50:59,581 INFO [train.py:842] (2/4) Epoch 20, batch 7800, loss[loss=0.1586, simple_loss=0.2438, pruned_loss=0.03676, over 7155.00 frames.], tot_loss[loss=0.185, simple_loss=0.2712, pruned_loss=0.04942, over 1426457.41 frames.], batch size: 18, lr: 2.72e-04
2022-05-28 04:51:37,534 INFO [train.py:842] (2/4) Epoch 20, batch 7850, loss[loss=0.1772, simple_loss=0.2664, pruned_loss=0.04402, over 7142.00 frames.], tot_loss[loss=0.1849, simple_loss=0.271, pruned_loss=0.04936, over 1425455.49 frames.], batch size: 20, lr: 2.72e-04
2022-05-28 04:52:15,873 INFO [train.py:842] (2/4) Epoch 20, batch 7900, loss[loss=0.2148, simple_loss=0.2997, pruned_loss=0.06499, over 7103.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2699, pruned_loss=0.04892, over 1427145.93 frames.], batch size: 21, lr: 2.72e-04
2022-05-28 04:52:53,895 INFO [train.py:842] (2/4) Epoch 20, batch 7950, loss[loss=0.1984, simple_loss=0.2882, pruned_loss=0.05426, over 7322.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2709, pruned_loss=0.04936, over 1429621.37 frames.], batch size: 25, lr: 2.72e-04
2022-05-28 04:53:32,237 INFO [train.py:842] (2/4) Epoch 20, batch 8000, loss[loss=0.1625, simple_loss=0.2449, pruned_loss=0.04007, over 7352.00 frames.], tot_loss[loss=0.184, simple_loss=0.2703, pruned_loss=0.04889, over 1431118.58 frames.], batch size: 19, lr: 2.72e-04
2022-05-28 04:54:10,490 INFO [train.py:842] (2/4) Epoch 20, batch 8050, loss[loss=0.1841, simple_loss=0.2769, pruned_loss=0.04564, over 7208.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2706, pruned_loss=0.04929, over 1428429.93 frames.], batch size: 21, lr: 2.72e-04
2022-05-28 04:54:48,887 INFO [train.py:842] (2/4) Epoch 20, batch 8100, loss[loss=0.1983, simple_loss=0.2793, pruned_loss=0.05868, over 7435.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2697, pruned_loss=0.04898, over 1431489.59 frames.], batch size: 20, lr: 2.72e-04
2022-05-28 04:55:36,031 INFO [train.py:842] (2/4) Epoch 20, batch 8150, loss[loss=0.1565, simple_loss=0.2389, pruned_loss=0.03705, over 7133.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2711, pruned_loss=0.04964, over 1425590.62 frames.], batch size: 17, lr: 2.72e-04
2022-05-28 04:56:14,316 INFO [train.py:842] (2/4) Epoch 20, batch 8200, loss[loss=0.1525, simple_loss=0.2296, pruned_loss=0.03773, over 7405.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2711, pruned_loss=0.04955, over 1426842.82 frames.], batch size: 18, lr: 2.72e-04
2022-05-28 04:56:52,100 INFO [train.py:842] (2/4) Epoch 20, batch 8250, loss[loss=0.1654, simple_loss=0.2456, pruned_loss=0.04263, over 7288.00 frames.], tot_loss[loss=0.186, simple_loss=0.2723, pruned_loss=0.04988, over 1425801.88 frames.], batch size: 18, lr: 2.72e-04
2022-05-28 04:57:30,456 INFO [train.py:842] (2/4) Epoch 20, batch 8300, loss[loss=0.1779, simple_loss=0.2677, pruned_loss=0.04404, over 7322.00 frames.], tot_loss[loss=0.187, simple_loss=0.2729, pruned_loss=0.0505, over 1426364.13 frames.], batch size: 21, lr: 2.72e-04
2022-05-28 04:58:17,702 INFO [train.py:842] (2/4) Epoch 20, batch 8350, loss[loss=0.1858, simple_loss=0.2694, pruned_loss=0.05112, over 7202.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2741, pruned_loss=0.05074, over 1422305.53 frames.], batch size: 23, lr: 2.72e-04
2022-05-28 04:58:55,799 INFO [train.py:842] (2/4) Epoch 20, batch 8400, loss[loss=0.1698, simple_loss=0.2614, pruned_loss=0.03904, over 7328.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2737, pruned_loss=0.05009, over 1421145.31 frames.], batch size: 20, lr: 2.72e-04
2022-05-28 04:59:33,815 INFO [train.py:842] (2/4) Epoch 20, batch 8450, loss[loss=0.146, simple_loss=0.2357, pruned_loss=0.02817, over 7422.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2727, pruned_loss=0.04946, over 1423820.11 frames.], batch size: 18, lr: 2.72e-04
2022-05-28 05:00:21,172 INFO [train.py:842] (2/4) Epoch 20, batch 8500, loss[loss=0.2242, simple_loss=0.3053, pruned_loss=0.07159, over 7301.00 frames.], tot_loss[loss=0.188, simple_loss=0.2743, pruned_loss=0.05091, over 1417116.65 frames.], batch size: 24, lr: 2.72e-04
2022-05-28 05:00:59,185 INFO [train.py:842] (2/4) Epoch 20, batch 8550, loss[loss=0.1945, simple_loss=0.272, pruned_loss=0.05855, over 7268.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2737, pruned_loss=0.05059, over 1420521.35 frames.], batch size: 19, lr: 2.72e-04
2022-05-28 05:01:37,330 INFO [train.py:842] (2/4) Epoch 20, batch 8600, loss[loss=0.1683, simple_loss=0.2664, pruned_loss=0.0351, over 7312.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2721, pruned_loss=0.04956, over 1423045.37 frames.], batch size: 21, lr: 2.72e-04
2022-05-28 05:02:15,317 INFO [train.py:842] (2/4) Epoch 20, batch 8650, loss[loss=0.1748, simple_loss=0.2652, pruned_loss=0.04221, over 7243.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2719, pruned_loss=0.04981, over 1420967.57 frames.], batch size: 20, lr: 2.72e-04
2022-05-28 05:02:53,352 INFO [train.py:842] (2/4) Epoch 20, batch 8700, loss[loss=0.1668, simple_loss=0.2538, pruned_loss=0.03993, over 7278.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2714, pruned_loss=0.04969, over 1412913.35 frames.], batch size: 18, lr: 2.72e-04
2022-05-28 05:03:31,385 INFO [train.py:842] (2/4) Epoch 20, batch 8750, loss[loss=0.2157, simple_loss=0.2931, pruned_loss=0.06919, over 7206.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2717, pruned_loss=0.04966, over 1415261.07 frames.], batch size: 23, lr: 2.72e-04
2022-05-28 05:04:09,700 INFO [train.py:842] (2/4) Epoch 20, batch 8800, loss[loss=0.1431, simple_loss=0.2352, pruned_loss=0.02549, over 7153.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2707, pruned_loss=0.04874, over 1415637.42 frames.], batch size: 19, lr: 2.72e-04
2022-05-28 05:04:47,743 INFO [train.py:842] (2/4) Epoch 20, batch 8850, loss[loss=0.1603, simple_loss=0.2306, pruned_loss=0.04504, over 6998.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2699, pruned_loss=0.0487, over 1409531.82 frames.], batch size: 16, lr: 2.72e-04
2022-05-28 05:05:25,742 INFO [train.py:842] (2/4) Epoch 20, batch 8900, loss[loss=0.1596, simple_loss=0.247, pruned_loss=0.03607, over 7261.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2693, pruned_loss=0.04881, over 1401215.74 frames.], batch size: 19, lr: 2.72e-04
2022-05-28 05:06:03,643 INFO [train.py:842] (2/4) Epoch 20, batch 8950, loss[loss=0.1724, simple_loss=0.2662, pruned_loss=0.03928, over 7218.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2691, pruned_loss=0.04902, over 1391026.78 frames.], batch size: 21, lr: 2.72e-04
2022-05-28 05:06:42,107 INFO [train.py:842] (2/4) Epoch 20, batch 9000, loss[loss=0.237, simple_loss=0.3245, pruned_loss=0.07479, over 6530.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2683, pruned_loss=0.04991, over 1369285.13 frames.], batch size: 39, lr: 2.72e-04
2022-05-28 05:06:42,109 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 05:06:51,176 INFO [train.py:871] (2/4) Epoch 20, validation: loss=0.164, simple_loss=0.2628, pruned_loss=0.03265, over 868885.00 frames.
2022-05-28 05:07:28,674 INFO [train.py:842] (2/4) Epoch 20, batch 9050, loss[loss=0.2444, simple_loss=0.312, pruned_loss=0.08843, over 4855.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2704, pruned_loss=0.05164, over 1331551.81 frames.], batch size: 52, lr: 2.72e-04
2022-05-28 05:08:05,301 INFO [train.py:842] (2/4) Epoch 20, batch 9100, loss[loss=0.2211, simple_loss=0.3168, pruned_loss=0.06273, over 6486.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2749, pruned_loss=0.0538, over 1290232.96 frames.], batch size: 38, lr: 2.72e-04
2022-05-28 05:08:41,900 INFO [train.py:842] (2/4) Epoch 20, batch 9150, loss[loss=0.2655, simple_loss=0.338, pruned_loss=0.09653, over 4560.00 frames.], tot_loss[loss=0.1973, simple_loss=0.28, pruned_loss=0.05725, over 1238747.75 frames.], batch size: 52, lr: 2.71e-04
2022-05-28 05:09:33,861 INFO [train.py:842] (2/4) Epoch 21, batch 0, loss[loss=0.1528, simple_loss=0.2325, pruned_loss=0.03653, over 6981.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2325, pruned_loss=0.03653, over 6981.00 frames.], batch size: 16, lr: 2.65e-04
2022-05-28 05:10:12,041 INFO [train.py:842] (2/4) Epoch 21, batch 50, loss[loss=0.2154, simple_loss=0.3102, pruned_loss=0.06037, over 6452.00 frames.], tot_loss[loss=0.189, simple_loss=0.2757, pruned_loss=0.0512, over 323067.58 frames.], batch size: 37, lr: 2.65e-04
2022-05-28 05:10:50,331 INFO [train.py:842] (2/4) Epoch 21, batch 100, loss[loss=0.1606, simple_loss=0.245, pruned_loss=0.03813, over 7239.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2726, pruned_loss=0.04888, over 566588.20 frames.], batch size: 16, lr: 2.65e-04
2022-05-28 05:11:28,266 INFO [train.py:842] (2/4) Epoch 21, batch 150, loss[loss=0.1688, simple_loss=0.2485, pruned_loss=0.0445, over 7151.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2739, pruned_loss=0.04948, over 755953.56 frames.], batch size: 18, lr: 2.65e-04
2022-05-28 05:12:09,310 INFO [train.py:842] (2/4) Epoch 21, batch 200, loss[loss=0.1778, simple_loss=0.2731, pruned_loss=0.04129, over 6825.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2733, pruned_loss=0.04868, over 901358.74 frames.], batch size: 31, lr: 2.65e-04
2022-05-28 05:12:47,239 INFO [train.py:842] (2/4) Epoch 21, batch 250, loss[loss=0.1681, simple_loss=0.2504, pruned_loss=0.04291, over 7161.00 frames.], tot_loss[loss=0.1855, simple_loss=0.273, pruned_loss=0.04902, over 1013166.59 frames.], batch size: 19, lr: 2.65e-04
2022-05-28 05:13:25,534 INFO [train.py:842] (2/4) Epoch 21, batch 300, loss[loss=0.1928, simple_loss=0.2728, pruned_loss=0.05641, over 7279.00 frames.], tot_loss[loss=0.1863, simple_loss=0.273, pruned_loss=0.04977, over 1101954.80 frames.], batch size: 18, lr: 2.65e-04
2022-05-28 05:14:03,304 INFO [train.py:842] (2/4) Epoch 21, batch 350, loss[loss=0.1716, simple_loss=0.2689, pruned_loss=0.03719, over 7257.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2743, pruned_loss=0.05013, over 1170338.83 frames.], batch size: 19, lr: 2.65e-04
2022-05-28 05:14:41,724 INFO [train.py:842] (2/4) Epoch 21, batch 400, loss[loss=0.1642, simple_loss=0.2504, pruned_loss=0.03894, over 7064.00 frames.], tot_loss[loss=0.1878, simple_loss=0.274, pruned_loss=0.05077, over 1229166.43 frames.], batch size: 18, lr: 2.65e-04
2022-05-28 05:15:19,617 INFO [train.py:842] (2/4) Epoch 21, batch 450, loss[loss=0.145, simple_loss=0.2336, pruned_loss=0.02817, over 7060.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2728, pruned_loss=0.05001, over 1271709.53 frames.], batch size: 18, lr: 2.65e-04
2022-05-28 05:15:57,948 INFO [train.py:842] (2/4) Epoch 21, batch 500, loss[loss=0.1855, simple_loss=0.2773, pruned_loss=0.04683, over 7072.00 frames.], tot_loss[loss=0.1856, simple_loss=0.272, pruned_loss=0.04961, over 1310062.48 frames.], batch size: 28, lr: 2.65e-04
2022-05-28 05:16:36,036 INFO [train.py:842] (2/4) Epoch 21, batch 550, loss[loss=0.2033, simple_loss=0.2723, pruned_loss=0.06715, over 6802.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2712, pruned_loss=0.04979, over 1336727.48 frames.], batch size: 15, lr: 2.65e-04
2022-05-28 05:17:14,241 INFO [train.py:842] (2/4) Epoch 21, batch 600, loss[loss=0.1547, simple_loss=0.2456, pruned_loss=0.03192, over 7209.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2713, pruned_loss=0.04963, over 1355032.97 frames.], batch size: 22, lr: 2.65e-04
2022-05-28 05:17:52,431 INFO [train.py:842] (2/4) Epoch 21, batch 650, loss[loss=0.1475, simple_loss=0.2292, pruned_loss=0.03288, over 7137.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2703, pruned_loss=0.04935, over 1369596.85 frames.], batch size: 17, lr: 2.65e-04
2022-05-28 05:18:30,489 INFO [train.py:842] (2/4) Epoch 21, batch 700, loss[loss=0.2045, simple_loss=0.2888, pruned_loss=0.06012, over 7242.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2707, pruned_loss=0.04919, over 1379262.31 frames.], batch size: 20, lr: 2.65e-04
2022-05-28 05:19:08,397 INFO [train.py:842] (2/4) Epoch 21, batch 750, loss[loss=0.1449, simple_loss=0.2296, pruned_loss=0.03015, over 7412.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2705, pruned_loss=0.04886, over 1385622.72 frames.], batch size: 18, lr: 2.65e-04
2022-05-28 05:19:46,506 INFO [train.py:842] (2/4) Epoch 21, batch 800, loss[loss=0.2276, simple_loss=0.3104, pruned_loss=0.07242, over 7233.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2709, pruned_loss=0.04962, over 1384865.22 frames.], batch size: 20, lr: 2.65e-04
2022-05-28 05:20:24,486 INFO [train.py:842] (2/4) Epoch 21, batch 850, loss[loss=0.218, simple_loss=0.3045, pruned_loss=0.06577, over 7311.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2698, pruned_loss=0.04903, over 1392396.36 frames.], batch size: 25, lr: 2.65e-04
2022-05-28 05:21:02,925 INFO [train.py:842] (2/4) Epoch 21, batch 900, loss[loss=0.1628, simple_loss=0.2494, pruned_loss=0.03805, over 7240.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2686, pruned_loss=0.04833, over 1401002.28 frames.], batch size: 20, lr: 2.65e-04
2022-05-28 05:21:40,823 INFO [train.py:842] (2/4) Epoch 21, batch 950, loss[loss=0.2122, simple_loss=0.2863, pruned_loss=0.06909, over 7341.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2687, pruned_loss=0.04836, over 1407208.81 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:22:18,908 INFO [train.py:842] (2/4) Epoch 21, batch 1000, loss[loss=0.2055, simple_loss=0.2917, pruned_loss=0.05963, over 7215.00 frames.], tot_loss[loss=0.1852, simple_loss=0.271, pruned_loss=0.04967, over 1406258.70 frames.], batch size: 23, lr: 2.64e-04
2022-05-28 05:22:56,514 INFO [train.py:842] (2/4) Epoch 21, batch 1050, loss[loss=0.2043, simple_loss=0.2952, pruned_loss=0.05672, over 7409.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2727, pruned_loss=0.05056, over 1407835.74 frames.], batch size: 21, lr: 2.64e-04
2022-05-28 05:23:34,908 INFO [train.py:842] (2/4) Epoch 21, batch 1100, loss[loss=0.1653, simple_loss=0.2409, pruned_loss=0.04479, over 7212.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2704, pruned_loss=0.0495, over 1409938.57 frames.], batch size: 16, lr: 2.64e-04
2022-05-28 05:24:12,840 INFO [train.py:842] (2/4) Epoch 21, batch 1150, loss[loss=0.2145, simple_loss=0.2959, pruned_loss=0.06651, over 7286.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2716, pruned_loss=0.05013, over 1414417.78 frames.], batch size: 24, lr: 2.64e-04
2022-05-28 05:24:50,884 INFO [train.py:842] (2/4) Epoch 21, batch 1200, loss[loss=0.1609, simple_loss=0.2461, pruned_loss=0.03788, over 7282.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2729, pruned_loss=0.05043, over 1416506.02 frames.], batch size: 18, lr: 2.64e-04
2022-05-28 05:25:28,909 INFO [train.py:842] (2/4) Epoch 21, batch 1250, loss[loss=0.1908, simple_loss=0.2859, pruned_loss=0.0478, over 7289.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2719, pruned_loss=0.05012, over 1418451.27 frames.], batch size: 24, lr: 2.64e-04
2022-05-28 05:26:07,273 INFO [train.py:842] (2/4) Epoch 21, batch 1300, loss[loss=0.2077, simple_loss=0.2781, pruned_loss=0.06862, over 7446.00 frames.], tot_loss[loss=0.186, simple_loss=0.2717, pruned_loss=0.05018, over 1418630.93 frames.], batch size: 19, lr: 2.64e-04
2022-05-28 05:26:45,553 INFO [train.py:842] (2/4) Epoch 21, batch 1350, loss[loss=0.1768, simple_loss=0.275, pruned_loss=0.03933, over 7343.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2714, pruned_loss=0.04986, over 1424952.50 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:27:23,883 INFO [train.py:842] (2/4) Epoch 21, batch 1400, loss[loss=0.2462, simple_loss=0.3254, pruned_loss=0.08353, over 7374.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2719, pruned_loss=0.04992, over 1427105.05 frames.], batch size: 23, lr: 2.64e-04
2022-05-28 05:28:01,806 INFO [train.py:842] (2/4) Epoch 21, batch 1450, loss[loss=0.2552, simple_loss=0.3316, pruned_loss=0.08938, over 4992.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2711, pruned_loss=0.04964, over 1421108.73 frames.], batch size: 53, lr: 2.64e-04
2022-05-28 05:28:39,859 INFO [train.py:842] (2/4) Epoch 21, batch 1500, loss[loss=0.2233, simple_loss=0.3105, pruned_loss=0.06801, over 7335.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2713, pruned_loss=0.04952, over 1418605.94 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:29:17,830 INFO [train.py:842] (2/4) Epoch 21, batch 1550, loss[loss=0.182, simple_loss=0.2736, pruned_loss=0.04524, over 6930.00 frames.], tot_loss[loss=0.1849, simple_loss=0.271, pruned_loss=0.04934, over 1420356.17 frames.], batch size: 31, lr: 2.64e-04
2022-05-28 05:29:56,066 INFO [train.py:842] (2/4) Epoch 21, batch 1600, loss[loss=0.1777, simple_loss=0.2725, pruned_loss=0.04141, over 7338.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2714, pruned_loss=0.0491, over 1421883.33 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:30:33,971 INFO [train.py:842] (2/4) Epoch 21, batch 1650, loss[loss=0.1723, simple_loss=0.266, pruned_loss=0.03936, over 7318.00 frames.], tot_loss[loss=0.184, simple_loss=0.2708, pruned_loss=0.04864, over 1422535.22 frames.], batch size: 20, lr: 2.64e-04
2022-05-28 05:31:12,220 INFO [train.py:842] (2/4) Epoch 21, batch 1700, loss[loss=0.2521, simple_loss=0.333, pruned_loss=0.08561, over 7352.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2709, pruned_loss=0.04891, over 1422404.96 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:31:50,196 INFO [train.py:842] (2/4) Epoch 21, batch 1750, loss[loss=0.1681, simple_loss=0.2562, pruned_loss=0.03998, over 7409.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2705, pruned_loss=0.0486, over 1423104.17 frames.], batch size: 18, lr: 2.64e-04
2022-05-28 05:32:28,365 INFO [train.py:842] (2/4) Epoch 21, batch 1800, loss[loss=0.2289, simple_loss=0.3116, pruned_loss=0.07309, over 7200.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2698, pruned_loss=0.04847, over 1424601.08 frames.], batch size: 23, lr: 2.64e-04
2022-05-28 05:33:06,415 INFO [train.py:842] (2/4) Epoch 21, batch 1850, loss[loss=0.1743, simple_loss=0.2543, pruned_loss=0.04716, over 7402.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2703, pruned_loss=0.04899, over 1423425.25 frames.], batch size: 18, lr: 2.64e-04
2022-05-28 05:33:44,676 INFO [train.py:842] (2/4) Epoch 21, batch 1900, loss[loss=0.1854, simple_loss=0.2832, pruned_loss=0.04376, over 7160.00 frames.], tot_loss[loss=0.1848, simple_loss=0.271, pruned_loss=0.04929, over 1425302.05 frames.], batch size: 19, lr: 2.64e-04
2022-05-28 05:34:22,659 INFO [train.py:842] (2/4) Epoch 21, batch 1950, loss[loss=0.1611, simple_loss=0.2502, pruned_loss=0.03597, over 7256.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2701, pruned_loss=0.0483, over 1429663.47 frames.], batch size: 19, lr: 2.64e-04
2022-05-28 05:35:00,907 INFO [train.py:842] (2/4) Epoch 21, batch 2000, loss[loss=0.1615, simple_loss=0.2648, pruned_loss=0.02911, over 6707.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2703, pruned_loss=0.04839, over 1425346.08 frames.], batch size: 31, lr: 2.64e-04
2022-05-28 05:35:38,840 INFO [train.py:842] (2/4) Epoch 21, batch 2050, loss[loss=0.2094, simple_loss=0.2962, pruned_loss=0.0613, over 7228.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2714, pruned_loss=0.04936, over 1424813.93 frames.], batch size: 21, lr: 2.64e-04
2022-05-28 05:36:17,020 INFO [train.py:842] (2/4) Epoch 21, batch 2100, loss[loss=0.2549, simple_loss=0.314, pruned_loss=0.09787, over 7061.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2714, pruned_loss=0.04958, over 1424110.73 frames.], batch size: 18, lr: 2.64e-04
2022-05-28 05:36:54,840 INFO [train.py:842] (2/4) Epoch 21, batch 2150, loss[loss=0.1402, simple_loss=0.2263, pruned_loss=0.0271, over 7220.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2708, pruned_loss=0.04922, over 1422534.42 frames.], batch size: 16, lr: 2.64e-04
2022-05-28 05:37:33,248 INFO [train.py:842] (2/4) Epoch 21, batch 2200, loss[loss=0.2288, simple_loss=0.3136, pruned_loss=0.072, over 7216.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2708, pruned_loss=0.04909, over 1423825.52 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:38:11,123 INFO [train.py:842] (2/4) Epoch 21, batch 2250, loss[loss=0.2113, simple_loss=0.3003, pruned_loss=0.06113, over 7217.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2716, pruned_loss=0.04955, over 1423840.30 frames.], batch size: 22, lr: 2.64e-04
2022-05-28 05:38:49,547 INFO [train.py:842] (2/4) Epoch 21, batch 2300, loss[loss=0.1964, simple_loss=0.2703, pruned_loss=0.06129, over 5283.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2712, pruned_loss=0.0497, over 1421256.68 frames.], batch size: 52, lr: 2.64e-04
2022-05-28 05:39:27,154 INFO [train.py:842] (2/4) Epoch 21, batch 2350, loss[loss=0.2062, simple_loss=0.2917, pruned_loss=0.06034, over 7282.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2726, pruned_loss=0.05005, over 1417054.52 frames.], batch size: 24, lr: 2.63e-04
2022-05-28 05:40:05,573 INFO [train.py:842] (2/4) Epoch 21, batch 2400, loss[loss=0.1771, simple_loss=0.2716, pruned_loss=0.04132, over 7196.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2709, pruned_loss=0.04907, over 1419550.90 frames.], batch size: 23, lr: 2.63e-04
2022-05-28 05:40:43,635 INFO [train.py:842] (2/4) Epoch 21, batch 2450, loss[loss=0.172, simple_loss=0.2505, pruned_loss=0.04673, over 7155.00 frames.], tot_loss[loss=0.1847, simple_loss=0.271, pruned_loss=0.0492, over 1421008.63 frames.], batch size: 19, lr: 2.63e-04
2022-05-28 05:41:21,974 INFO [train.py:842] (2/4) Epoch 21, batch 2500, loss[loss=0.1562, simple_loss=0.2562, pruned_loss=0.02812, over 7418.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2709, pruned_loss=0.04902, over 1422043.58 frames.], batch size: 21, lr: 2.63e-04
2022-05-28 05:41:59,711 INFO [train.py:842] (2/4) Epoch 21, batch 2550, loss[loss=0.2044, simple_loss=0.2848, pruned_loss=0.06206, over 4861.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2712, pruned_loss=0.04893, over 1419912.00 frames.], batch size: 52, lr: 2.63e-04
2022-05-28 05:42:37,951 INFO [train.py:842] (2/4) Epoch 21, batch 2600, loss[loss=0.1707, simple_loss=0.2581, pruned_loss=0.0417, over 7086.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2725, pruned_loss=0.04948, over 1421005.63 frames.], batch size: 18, lr: 2.63e-04
2022-05-28 05:43:15,788 INFO [train.py:842] (2/4) Epoch 21, batch 2650, loss[loss=0.1919, simple_loss=0.2841, pruned_loss=0.04983, over 7316.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2731, pruned_loss=0.04987, over 1416334.90 frames.], batch size: 20, lr: 2.63e-04
2022-05-28 05:43:53,878 INFO [train.py:842] (2/4) Epoch 21, batch 2700, loss[loss=0.1627, simple_loss=0.2434, pruned_loss=0.04103, over 7409.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2711, pruned_loss=0.04885, over 1420160.18 frames.], batch size: 18, lr: 2.63e-04
2022-05-28 05:44:31,848 INFO [train.py:842] (2/4) Epoch 21, batch 2750, loss[loss=0.2273, simple_loss=0.3076, pruned_loss=0.07348, over 7166.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2712, pruned_loss=0.04892, over 1420979.32 frames.], batch size: 18, lr: 2.63e-04
2022-05-28 05:45:10,227 INFO [train.py:842] (2/4) Epoch 21, batch 2800, loss[loss=0.1885, simple_loss=0.2793, pruned_loss=0.04881, over 7398.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2721, pruned_loss=0.04976, over 1424243.59 frames.], batch size: 23, lr: 2.63e-04
2022-05-28 05:45:48,218 INFO [train.py:842] (2/4) Epoch 21, batch 2850, loss[loss=0.1968, simple_loss=0.2876, pruned_loss=0.05302, over 7213.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2717, pruned_loss=0.04948, over 1419392.17 frames.], batch size: 23, lr: 2.63e-04
2022-05-28 05:46:26,349 INFO [train.py:842] (2/4) Epoch 21, batch 2900, loss[loss=0.2103, simple_loss=0.2932, pruned_loss=0.06365, over 7088.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2725, pruned_loss=0.04987, over 1415284.20 frames.], batch size: 28, lr: 2.63e-04
2022-05-28 05:47:04,345 INFO [train.py:842] (2/4) Epoch 21, batch 2950, loss[loss=0.1779, simple_loss=0.2599, pruned_loss=0.04795, over 7362.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2729, pruned_loss=0.05037, over 1414257.50 frames.], batch size: 19, lr: 2.63e-04
2022-05-28 05:47:42,618 INFO [train.py:842] (2/4) Epoch 21, batch 3000, loss[loss=0.1851, simple_loss=0.2862, pruned_loss=0.04205, over 6802.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2736, pruned_loss=0.05068, over 1413078.94 frames.], batch size: 31, lr: 2.63e-04
2022-05-28 05:47:42,619 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 05:47:51,726 INFO [train.py:871] (2/4) Epoch 21, validation: loss=0.1652, simple_loss=0.2649, pruned_loss=0.0328, over 868885.00 frames.
2022-05-28 05:48:29,677 INFO [train.py:842] (2/4) Epoch 21, batch 3050, loss[loss=0.153, simple_loss=0.2393, pruned_loss=0.03337, over 7275.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2736, pruned_loss=0.05056, over 1414263.79 frames.], batch size: 18, lr: 2.63e-04
2022-05-28 05:49:07,873 INFO [train.py:842] (2/4) Epoch 21, batch 3100, loss[loss=0.2665, simple_loss=0.3372, pruned_loss=0.09789, over 7377.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2739, pruned_loss=0.05051, over 1413006.69 frames.], batch size: 23, lr: 2.63e-04
2022-05-28 05:49:46,058 INFO [train.py:842] (2/4) Epoch 21, batch 3150, loss[loss=0.1761, simple_loss=0.2634, pruned_loss=0.04445, over 7289.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2727, pruned_loss=0.04993, over 1417952.24 frames.], batch size: 24, lr: 2.63e-04
2022-05-28 05:50:24,125 INFO [train.py:842] (2/4) Epoch 21, batch 3200, loss[loss=0.2015, simple_loss=0.2965, pruned_loss=0.05325, over 7323.00 frames.], tot_loss[loss=0.187, simple_loss=0.2734, pruned_loss=0.05033, over 1422791.48 frames.], batch size: 21, lr: 2.63e-04
2022-05-28 05:51:01,972 INFO [train.py:842] (2/4) Epoch 21, batch 3250, loss[loss=0.1545, simple_loss=0.2419, pruned_loss=0.03355, over 7066.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2727, pruned_loss=0.04957, over 1422214.54 frames.], batch size: 18, lr: 2.63e-04
2022-05-28 05:51:40,399 INFO [train.py:842] (2/4) Epoch 21, batch 3300, loss[loss=0.1464, simple_loss=0.2327, pruned_loss=0.03003, over 7133.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2711, pruned_loss=0.04907, over 1423381.02 frames.], batch size: 17, lr: 2.63e-04
2022-05-28 05:52:18,323 INFO [train.py:842] (2/4) Epoch 21, batch 3350, loss[loss=0.1917, simple_loss=0.2855, pruned_loss=0.04901, over 7234.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2716, pruned_loss=0.04955, over 1419074.33 frames.], batch size: 20, lr: 2.63e-04
2022-05-28 05:52:56,393 INFO [train.py:842] (2/4) Epoch 21, batch 3400, loss[loss=0.1812, simple_loss=0.2741, pruned_loss=0.04414, over 6264.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2712, pruned_loss=0.0493, over 1416329.27 frames.], batch size: 37, lr: 2.63e-04
2022-05-28 05:53:34,249 INFO [train.py:842] (2/4) Epoch 21, batch 3450, loss[loss=0.1803, simple_loss=0.2863, pruned_loss=0.0372, over 7322.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2721, pruned_loss=0.0495, over 1415034.56 frames.], batch size: 21, lr: 2.63e-04
2022-05-28 05:54:12,331 INFO [train.py:842] (2/4) Epoch 21, batch 3500, loss[loss=0.174, simple_loss=0.2669, pruned_loss=0.04054, over 7063.00 frames.], tot_loss[loss=0.186, simple_loss=0.2726, pruned_loss=0.04968, over 1409892.35 frames.], batch size: 28, lr: 2.63e-04
2022-05-28 05:54:50,557 INFO [train.py:842] (2/4) Epoch 21, batch 3550, loss[loss=0.1953, simple_loss=0.2679, pruned_loss=0.06134, over 7290.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2716, pruned_loss=0.0495, over 1413749.71 frames.], batch size: 17, lr: 2.63e-04
2022-05-28 05:55:28,699 INFO [train.py:842] (2/4) Epoch 21, batch 3600, loss[loss=0.2312, simple_loss=0.3251, pruned_loss=0.06871, over 7380.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2725, pruned_loss=0.04994, over 1411206.06 frames.], batch size: 23, lr: 2.63e-04
2022-05-28 05:56:06,581 INFO [train.py:842] (2/4) Epoch 21, batch 3650, loss[loss=0.2275, simple_loss=0.3134, pruned_loss=0.07074, over 7170.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2726, pruned_loss=0.05038, over 1413525.07 frames.], batch size: 26, lr: 2.63e-04
2022-05-28 05:56:44,823 INFO [train.py:842] (2/4) Epoch 21, batch 3700, loss[loss=0.2318, simple_loss=0.3271, pruned_loss=0.06829, over 7326.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2727, pruned_loss=0.05022, over 1413936.38 frames.], batch size: 21, lr: 2.63e-04
2022-05-28 05:57:22,921 INFO [train.py:842] (2/4) Epoch 21, batch 3750, loss[loss=0.1991, simple_loss=0.285, pruned_loss=0.05654, over 7313.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2722, pruned_loss=0.04975, over 1417670.44 frames.], batch size: 25, lr: 2.62e-04
2022-05-28 05:58:01,031 INFO [train.py:842] (2/4) Epoch 21, batch 3800, loss[loss=0.1873, simple_loss=0.2814, pruned_loss=0.0466, over 7196.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2721, pruned_loss=0.04954, over 1417621.68 frames.], batch size: 26, lr: 2.62e-04
2022-05-28 05:58:38,979 INFO [train.py:842] (2/4) Epoch 21, batch 3850, loss[loss=0.183, simple_loss=0.2759, pruned_loss=0.04509, over 7329.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2724, pruned_loss=0.04947, over 1419449.50 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 05:59:17,310 INFO [train.py:842] (2/4) Epoch 21, batch 3900, loss[loss=0.1595, simple_loss=0.2397, pruned_loss=0.03962, over 7267.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2717, pruned_loss=0.04876, over 1422147.47 frames.], batch size: 19, lr: 2.62e-04
2022-05-28 05:59:54,988 INFO [train.py:842] (2/4) Epoch 21, batch 3950, loss[loss=0.1476, simple_loss=0.2378, pruned_loss=0.02868, over 7415.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2722, pruned_loss=0.04901, over 1417508.89 frames.], batch size: 18, lr: 2.62e-04
2022-05-28 06:00:33,124 INFO [train.py:842] (2/4) Epoch 21, batch 4000, loss[loss=0.1552, simple_loss=0.241, pruned_loss=0.03477, over 7353.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2713, pruned_loss=0.04871, over 1421392.00 frames.], batch size: 19, lr: 2.62e-04
2022-05-28 06:01:11,314 INFO [train.py:842] (2/4) Epoch 21, batch 4050, loss[loss=0.1696, simple_loss=0.2532, pruned_loss=0.043, over 7431.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2691, pruned_loss=0.04781, over 1421672.07 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:01:49,087 INFO [train.py:842] (2/4) Epoch 21, batch 4100, loss[loss=0.1341, simple_loss=0.2197, pruned_loss=0.02424, over 7149.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2702, pruned_loss=0.04836, over 1412812.18 frames.], batch size: 17, lr: 2.62e-04
2022-05-28 06:02:26,895 INFO [train.py:842] (2/4) Epoch 21, batch 4150, loss[loss=0.1993, simple_loss=0.2926, pruned_loss=0.05303, over 7204.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2708, pruned_loss=0.04873, over 1411148.25 frames.], batch size: 23, lr: 2.62e-04
2022-05-28 06:03:05,082 INFO [train.py:842] (2/4) Epoch 21, batch 4200, loss[loss=0.2162, simple_loss=0.2952, pruned_loss=0.06861, over 5086.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2719, pruned_loss=0.04895, over 1415953.06 frames.], batch size: 52, lr: 2.62e-04
2022-05-28 06:03:42,983 INFO [train.py:842] (2/4) Epoch 21, batch 4250, loss[loss=0.1797, simple_loss=0.2713, pruned_loss=0.04407, over 7227.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2719, pruned_loss=0.04933, over 1416615.42 frames.], batch size: 21, lr: 2.62e-04
2022-05-28 06:04:21,416 INFO [train.py:842] (2/4) Epoch 21, batch 4300, loss[loss=0.1899, simple_loss=0.269, pruned_loss=0.05541, over 7002.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2712, pruned_loss=0.04917, over 1419600.92 frames.], batch size: 16, lr: 2.62e-04
2022-05-28 06:04:59,213 INFO [train.py:842] (2/4) Epoch 21, batch 4350, loss[loss=0.1899, simple_loss=0.2791, pruned_loss=0.05036, over 7307.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2718, pruned_loss=0.049, over 1419288.74 frames.], batch size: 24, lr: 2.62e-04
2022-05-28 06:05:37,554 INFO [train.py:842] (2/4) Epoch 21, batch 4400, loss[loss=0.2049, simple_loss=0.2938, pruned_loss=0.058, over 6466.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2721, pruned_loss=0.04931, over 1420802.46 frames.], batch size: 38, lr: 2.62e-04
2022-05-28 06:06:15,533 INFO [train.py:842] (2/4) Epoch 21, batch 4450, loss[loss=0.189, simple_loss=0.2853, pruned_loss=0.04639, over 7227.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2714, pruned_loss=0.049, over 1423417.63 frames.], batch size: 21, lr: 2.62e-04
2022-05-28 06:06:53,882 INFO [train.py:842] (2/4) Epoch 21, batch 4500, loss[loss=0.2115, simple_loss=0.2892, pruned_loss=0.0669, over 7250.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2711, pruned_loss=0.04886, over 1425312.51 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:07:31,870 INFO [train.py:842] (2/4) Epoch 21, batch 4550, loss[loss=0.1677, simple_loss=0.2638, pruned_loss=0.0358, over 7070.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2704, pruned_loss=0.04823, over 1425960.55 frames.], batch size: 28, lr: 2.62e-04
2022-05-28 06:08:10,247 INFO [train.py:842] (2/4) Epoch 21, batch 4600, loss[loss=0.1856, simple_loss=0.2576, pruned_loss=0.05679, over 7159.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2709, pruned_loss=0.04879, over 1424329.71 frames.], batch size: 18, lr: 2.62e-04
2022-05-28 06:08:48,132 INFO [train.py:842] (2/4) Epoch 21, batch 4650, loss[loss=0.2019, simple_loss=0.2821, pruned_loss=0.06085, over 7234.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2702, pruned_loss=0.04849, over 1423819.57 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:09:26,156 INFO [train.py:842] (2/4) Epoch 21, batch 4700, loss[loss=0.1747, simple_loss=0.2665, pruned_loss=0.04142, over 7162.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2714, pruned_loss=0.04891, over 1425131.55 frames.], batch size: 19, lr: 2.62e-04
2022-05-28 06:10:04,163 INFO [train.py:842] (2/4) Epoch 21, batch 4750, loss[loss=0.1947, simple_loss=0.2912, pruned_loss=0.04909, over 7088.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2723, pruned_loss=0.05005, over 1424437.00 frames.], batch size: 28, lr: 2.62e-04
2022-05-28 06:10:42,355 INFO [train.py:842] (2/4) Epoch 21, batch 4800, loss[loss=0.2027, simple_loss=0.2849, pruned_loss=0.06028, over 7315.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2723, pruned_loss=0.0498, over 1422165.40 frames.], batch size: 24, lr: 2.62e-04
2022-05-28 06:11:20,368 INFO [train.py:842] (2/4) Epoch 21, batch 4850, loss[loss=0.2209, simple_loss=0.2949, pruned_loss=0.07342, over 7322.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2718, pruned_loss=0.04954, over 1418680.41 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:11:58,838 INFO [train.py:842] (2/4) Epoch 21, batch 4900, loss[loss=0.1892, simple_loss=0.2758, pruned_loss=0.05128, over 7327.00 frames.], tot_loss[loss=0.1849, simple_loss=0.271, pruned_loss=0.04936, over 1422409.46 frames.], batch size: 24, lr: 2.62e-04
2022-05-28 06:12:36,420 INFO [train.py:842] (2/4) Epoch 21, batch 4950, loss[loss=0.1698, simple_loss=0.2622, pruned_loss=0.03871, over 7152.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2731, pruned_loss=0.0501, over 1415337.62 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:13:14,601 INFO [train.py:842] (2/4) Epoch 21, batch 5000, loss[loss=0.1958, simple_loss=0.274, pruned_loss=0.05883, over 7437.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2732, pruned_loss=0.05006, over 1418793.67 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:13:52,557 INFO [train.py:842] (2/4) Epoch 21, batch 5050, loss[loss=0.1905, simple_loss=0.2671, pruned_loss=0.05697, over 7430.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2735, pruned_loss=0.05089, over 1419520.25 frames.], batch size: 20, lr: 2.62e-04
2022-05-28 06:14:30,640 INFO [train.py:842] (2/4) Epoch 21, batch 5100, loss[loss=0.1949, simple_loss=0.2814, pruned_loss=0.05424, over 7160.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2733, pruned_loss=0.05093, over 1420580.94 frames.], batch size: 18, lr: 2.62e-04
2022-05-28 06:15:08,572 INFO [train.py:842] (2/4) Epoch 21, batch 5150, loss[loss=0.2208, simple_loss=0.3039, pruned_loss=0.06881, over 4991.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2729, pruned_loss=0.05064, over 1413693.20 frames.], batch size: 52, lr: 2.62e-04
2022-05-28 06:15:46,841 INFO [train.py:842] (2/4) Epoch 21, batch 5200, loss[loss=0.1843, simple_loss=0.2753, pruned_loss=0.04662, over 6843.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2731, pruned_loss=0.05055, over 1417879.85 frames.], batch size: 31, lr: 2.61e-04
2022-05-28 06:16:24,691 INFO [train.py:842] (2/4) Epoch 21, batch 5250, loss[loss=0.1826, simple_loss=0.2711, pruned_loss=0.04704, over 6461.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2729, pruned_loss=0.05035, over 1419986.35 frames.], batch size: 39, lr: 2.61e-04
2022-05-28 06:17:02,972 INFO [train.py:842] (2/4) Epoch 21, batch 5300, loss[loss=0.1572, simple_loss=0.2418, pruned_loss=0.03628, over 7159.00 frames.], tot_loss[loss=0.186, simple_loss=0.2725, pruned_loss=0.04975, over 1423802.88 frames.], batch size: 18, lr: 2.61e-04
2022-05-28 06:17:40,967 INFO [train.py:842] (2/4) Epoch 21, batch 5350, loss[loss=0.1888, simple_loss=0.2764, pruned_loss=0.0506, over 7303.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2723, pruned_loss=0.04939, over 1424670.34 frames.], batch size: 25, lr: 2.61e-04
2022-05-28 06:18:19,346 INFO [train.py:842] (2/4) Epoch 21, batch 5400, loss[loss=0.1612, simple_loss=0.2505, pruned_loss=0.03593, over 7282.00 frames.], tot_loss[loss=0.1855, simple_loss=0.272, pruned_loss=0.04949, over 1420605.88 frames.], batch size: 18, lr: 2.61e-04
2022-05-28 06:18:57,183 INFO [train.py:842] (2/4) Epoch 21, batch 5450, loss[loss=0.1864, simple_loss=0.2695, pruned_loss=0.05167, over 7234.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2721, pruned_loss=0.04917, over 1423490.34 frames.], batch size: 23, lr: 2.61e-04
2022-05-28 06:19:35,456 INFO [train.py:842] (2/4) Epoch 21, batch 5500, loss[loss=0.2047, simple_loss=0.284, pruned_loss=0.06276, over 7392.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2725, pruned_loss=0.04926, over 1422595.39 frames.], batch size: 23, lr: 2.61e-04
2022-05-28 06:20:13,583 INFO [train.py:842] (2/4) Epoch 21, batch 5550, loss[loss=0.1726, simple_loss=0.2676, pruned_loss=0.03885, over 7336.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2727, pruned_loss=0.04956, over 1417929.10 frames.], batch size: 22, lr: 2.61e-04
2022-05-28 06:20:51,847 INFO [train.py:842] (2/4) Epoch 21, batch 5600, loss[loss=0.1831, simple_loss=0.2532, pruned_loss=0.05646, over 6996.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2719, pruned_loss=0.04945, over 1415414.31 frames.], batch size: 16, lr: 2.61e-04
2022-05-28 06:21:29,930 INFO [train.py:842] (2/4) Epoch 21, batch 5650, loss[loss=0.1896, simple_loss=0.2755, pruned_loss=0.0518, over 7313.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2702, pruned_loss=0.04884, over 1418377.28 frames.], batch size: 21, lr: 2.61e-04
2022-05-28 06:22:08,295 INFO [train.py:842] (2/4) Epoch 21, batch 5700, loss[loss=0.1809, simple_loss=0.269, pruned_loss=0.04642, over 7076.00 frames.], tot_loss[loss=0.1839, simple_loss=0.27, pruned_loss=0.04888, over 1421207.51 frames.], batch size: 28, lr: 2.61e-04
2022-05-28 06:22:46,369 INFO [train.py:842] (2/4) Epoch 21, batch 5750, loss[loss=0.1979, simple_loss=0.2817, pruned_loss=0.05709, over 7336.00 frames.], tot_loss[loss=0.184, simple_loss=0.27, pruned_loss=0.04905, over 1424498.63 frames.], batch size: 22, lr: 2.61e-04
2022-05-28 06:23:24,676 INFO [train.py:842] (2/4) Epoch 21, batch 5800, loss[loss=0.2116, simple_loss=0.2952, pruned_loss=0.06404, over 7311.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2707, pruned_loss=0.04938, over 1427406.97 frames.], batch size: 25, lr: 2.61e-04
2022-05-28 06:24:02,708 INFO [train.py:842] (2/4) Epoch 21, batch 5850, loss[loss=0.1583, simple_loss=0.2564, pruned_loss=0.03014, over 7421.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2706, pruned_loss=0.04943, over 1422010.56 frames.], batch size: 20, lr: 2.61e-04
2022-05-28 06:24:40,935 INFO [train.py:842] (2/4) Epoch 21, batch 5900, loss[loss=0.2036, simple_loss=0.293, pruned_loss=0.05711, over 7290.00 frames.], tot_loss[loss=0.184, simple_loss=0.2704, pruned_loss=0.04885, over 1421662.38 frames.], batch size: 24, lr: 2.61e-04
2022-05-28 06:25:18,656 INFO [train.py:842] (2/4) Epoch 21, batch 5950, loss[loss=0.1916, simple_loss=0.2708, pruned_loss=0.0562, over 6851.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2707, pruned_loss=0.04885, over 1416193.41 frames.], batch size: 31, lr: 2.61e-04
2022-05-28 06:25:56,954 INFO [train.py:842] (2/4) Epoch 21, batch 6000, loss[loss=0.1649, simple_loss=0.2439, pruned_loss=0.04294, over 6751.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2705, pruned_loss=0.04849, over 1418804.27 frames.], batch size: 15, lr: 2.61e-04
2022-05-28 06:25:56,955 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 06:26:05,956 INFO [train.py:871] (2/4) Epoch 21, validation: loss=0.1654, simple_loss=0.2646, pruned_loss=0.03309, over 868885.00 frames.
2022-05-28 06:26:43,745 INFO [train.py:842] (2/4) Epoch 21, batch 6050, loss[loss=0.1866, simple_loss=0.2816, pruned_loss=0.04573, over 6554.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2719, pruned_loss=0.04938, over 1416345.51 frames.], batch size: 38, lr: 2.61e-04
2022-05-28 06:27:22,087 INFO [train.py:842] (2/4) Epoch 21, batch 6100, loss[loss=0.1863, simple_loss=0.2535, pruned_loss=0.05952, over 7136.00 frames.], tot_loss[loss=0.1845, simple_loss=0.271, pruned_loss=0.04903, over 1418424.99 frames.], batch size: 17, lr: 2.61e-04
2022-05-28 06:27:59,904 INFO [train.py:842] (2/4) Epoch 21, batch 6150, loss[loss=0.1888, simple_loss=0.2826, pruned_loss=0.04748, over 7336.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2708, pruned_loss=0.04869, over 1418758.32 frames.], batch size: 22, lr: 2.61e-04
2022-05-28 06:28:38,153 INFO [train.py:842] (2/4) Epoch 21, batch 6200, loss[loss=0.2237, simple_loss=0.3028, pruned_loss=0.07235, over 7177.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2705, pruned_loss=0.04845, over 1423306.10 frames.], batch size: 26, lr: 2.61e-04
2022-05-28 06:29:16,115 INFO [train.py:842] (2/4) Epoch 21, batch 6250, loss[loss=0.1943, simple_loss=0.2798, pruned_loss=0.05442, over 7290.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2702, pruned_loss=0.04842, over 1422382.73 frames.], batch size: 24, lr: 2.61e-04
2022-05-28 06:29:54,353 INFO [train.py:842] (2/4) Epoch 21, batch 6300, loss[loss=0.2023, simple_loss=0.2945, pruned_loss=0.05503, over 7324.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2707, pruned_loss=0.04849, over 1425009.56 frames.], batch size: 22, lr: 2.61e-04
2022-05-28 06:30:32,471 INFO [train.py:842] (2/4) Epoch 21, batch 6350, loss[loss=0.1787, simple_loss=0.2706, pruned_loss=0.04342, over 7342.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2702, pruned_loss=0.04839, over 1428671.98 frames.], batch size: 20, lr: 2.61e-04
2022-05-28 06:31:10,710 INFO [train.py:842] (2/4) Epoch 21, batch 6400, loss[loss=0.2501, simple_loss=0.3203, pruned_loss=0.08992, over 4963.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2699, pruned_loss=0.04823, over 1426451.68 frames.], batch size: 52, lr: 2.61e-04
2022-05-28 06:31:48,568 INFO [train.py:842] (2/4) Epoch 21, batch 6450, loss[loss=0.173, simple_loss=0.258, pruned_loss=0.04401, over 7417.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2703, pruned_loss=0.04841, over 1425258.28 frames.], batch size: 20, lr: 2.61e-04
2022-05-28 06:32:26,774 INFO [train.py:842] (2/4) Epoch 21, batch 6500, loss[loss=0.1493, simple_loss=0.2419, pruned_loss=0.02841, over 7075.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2713, pruned_loss=0.04889, over 1426872.14 frames.], batch size: 18, lr: 2.61e-04
2022-05-28 06:33:04,410 INFO [train.py:842] (2/4) Epoch 21, batch 6550, loss[loss=0.1657, simple_loss=0.2538, pruned_loss=0.03883, over 7426.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2707, pruned_loss=0.04859, over 1423506.40 frames.], batch size: 20, lr: 2.61e-04
2022-05-28 06:33:42,753 INFO [train.py:842] (2/4) Epoch 21, batch 6600, loss[loss=0.1651, simple_loss=0.2616, pruned_loss=0.03437, over 7337.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2703, pruned_loss=0.04822, over 1421469.06 frames.], batch size: 22, lr: 2.61e-04
2022-05-28 06:34:20,556 INFO [train.py:842] (2/4) Epoch 21, batch 6650, loss[loss=0.1779, simple_loss=0.2611, pruned_loss=0.04734, over 7414.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2712, pruned_loss=0.04919, over 1416826.70 frames.], batch size: 18, lr: 2.60e-04
2022-05-28 06:34:59,047 INFO [train.py:842] (2/4) Epoch 21, batch 6700, loss[loss=0.1898, simple_loss=0.2789, pruned_loss=0.05029, over 7380.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2709, pruned_loss=0.04902, over 1422049.67 frames.], batch size: 23, lr: 2.60e-04
2022-05-28 06:35:36,974 INFO [train.py:842] (2/4) Epoch 21, batch 6750, loss[loss=0.1469, simple_loss=0.2319, pruned_loss=0.03095, over 7015.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2715, pruned_loss=0.04932, over 1424462.28 frames.], batch size: 16, lr: 2.60e-04
2022-05-28 06:36:14,935 INFO [train.py:842] (2/4) Epoch 21, batch 6800, loss[loss=0.224, simple_loss=0.3186, pruned_loss=0.06474, over 7418.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2722, pruned_loss=0.04967, over 1422954.48 frames.], batch size: 21, lr: 2.60e-04
2022-05-28 06:36:52,969 INFO [train.py:842] (2/4) Epoch 21, batch 6850, loss[loss=0.1669, simple_loss=0.2567, pruned_loss=0.0385, over 7055.00 frames.], tot_loss[loss=0.1844, simple_loss=0.271, pruned_loss=0.04889, over 1425647.22 frames.], batch size: 18, lr: 2.60e-04
2022-05-28 06:37:31,255 INFO [train.py:842] (2/4) Epoch 21, batch 6900, loss[loss=0.1788, simple_loss=0.2722, pruned_loss=0.04273, over 7151.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2708, pruned_loss=0.04852, over 1427363.80 frames.], batch size: 19, lr: 2.60e-04
2022-05-28 06:38:09,062 INFO [train.py:842] (2/4) Epoch 21, batch 6950, loss[loss=0.1985, simple_loss=0.2966, pruned_loss=0.05017, over 7190.00 frames.], tot_loss[loss=0.1855, simple_loss=0.272, pruned_loss=0.04951, over 1426741.42 frames.], batch size: 23, lr: 2.60e-04
2022-05-28 06:38:47,239 INFO [train.py:842] (2/4) Epoch 21, batch 7000, loss[loss=0.1865, simple_loss=0.2749, pruned_loss=0.04906, over 7454.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2732, pruned_loss=0.04969, over 1427247.67 frames.], batch size: 19, lr: 2.60e-04
2022-05-28 06:39:25,163 INFO [train.py:842] (2/4) Epoch 21, batch 7050, loss[loss=0.1854, simple_loss=0.2771, pruned_loss=0.04684, over 7206.00 frames.], tot_loss[loss=0.1861, simple_loss=0.273, pruned_loss=0.04956, over 1428479.89 frames.], batch size: 22, lr: 2.60e-04
2022-05-28 06:40:03,301 INFO [train.py:842] (2/4) Epoch 21, batch 7100, loss[loss=0.1575, simple_loss=0.244, pruned_loss=0.03555, over 7051.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2728, pruned_loss=0.04948, over 1425743.45 frames.], batch size: 18, lr: 2.60e-04
2022-05-28 06:40:41,324 INFO [train.py:842] (2/4) Epoch 21, batch 7150, loss[loss=0.1974, simple_loss=0.2851, pruned_loss=0.05482, over 7165.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2715, pruned_loss=0.04881, over 1428705.79 frames.], batch size: 19, lr: 2.60e-04
2022-05-28 06:41:19,791 INFO [train.py:842] (2/4) Epoch 21, batch 7200, loss[loss=0.2203, simple_loss=0.31, pruned_loss=0.06533, over 7330.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2713, pruned_loss=0.04917, over 1431023.98 frames.], batch size: 20, lr: 2.60e-04
2022-05-28 06:41:57,863 INFO [train.py:842] (2/4) Epoch 21, batch 7250, loss[loss=0.1736, simple_loss=0.269, pruned_loss=0.03911, over 7423.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2714, pruned_loss=0.04943, over 1427528.41 frames.], batch size: 20, lr: 2.60e-04
2022-05-28 06:42:35,997 INFO [train.py:842] (2/4) Epoch 21, batch 7300, loss[loss=0.1615, simple_loss=0.2438, pruned_loss=0.03962, over 7128.00 frames.], tot_loss[loss=0.1858, simple_loss=0.272, pruned_loss=0.04983, over 1428435.33 frames.], batch size: 17, lr: 2.60e-04
2022-05-28 06:43:13,735 INFO [train.py:842] (2/4) Epoch 21, batch 7350, loss[loss=0.199, simple_loss=0.2822, pruned_loss=0.05788, over 7307.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2721, pruned_loss=0.04947, over 1426020.28 frames.], batch size: 24, lr: 2.60e-04
2022-05-28 06:43:51,811 INFO [train.py:842] (2/4) Epoch 21, batch 7400, loss[loss=0.1652, simple_loss=0.2629, pruned_loss=0.03378, over 7330.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2733, pruned_loss=0.04977, over 1425651.92 frames.], batch size: 20, lr: 2.60e-04
2022-05-28 06:44:29,672 INFO [train.py:842] (2/4) Epoch 21, batch 7450, loss[loss=0.1822, simple_loss=0.2539, pruned_loss=0.05522, over 7258.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2728, pruned_loss=0.04973, over 1425030.46 frames.], batch size: 19, lr: 2.60e-04
2022-05-28 06:45:08,002 INFO [train.py:842] (2/4) Epoch 21, batch 7500, loss[loss=0.2115, simple_loss=0.3103, pruned_loss=0.05638, over 7256.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2733, pruned_loss=0.05041, over 1423593.42 frames.], batch size: 19, lr: 2.60e-04
2022-05-28 06:45:45,997 INFO [train.py:842] (2/4) Epoch 21, batch 7550, loss[loss=0.2009, simple_loss=0.2945, pruned_loss=0.0536, over 6989.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2719, pruned_loss=0.04993, over 1422435.54 frames.], batch size: 28, lr: 2.60e-04
2022-05-28 06:46:33,468 INFO [train.py:842] (2/4) Epoch 21, batch 7600, loss[loss=0.2133, simple_loss=0.2883, pruned_loss=0.06909, over 7211.00 frames.], tot_loss[loss=0.186, simple_loss=0.2718, pruned_loss=0.05007, over 1416276.48 frames.], batch size: 22, lr: 2.60e-04
2022-05-28 06:47:11,535 INFO [train.py:842] (2/4) Epoch 21, batch 7650, loss[loss=0.1726, simple_loss=0.2618, pruned_loss=0.04166, over 7292.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2707, pruned_loss=0.04924, over 1417287.84 frames.], batch size: 17, lr: 2.60e-04
2022-05-28 06:47:49,872 INFO [train.py:842] (2/4) Epoch 21, batch 7700, loss[loss=0.1731, simple_loss=0.2733, pruned_loss=0.03647, over 7340.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2704, pruned_loss=0.0491, over 1417949.07 frames.], batch size: 22, lr: 2.60e-04
2022-05-28 06:48:27,629 INFO [train.py:842] (2/4) Epoch 21, batch 7750, loss[loss=0.1774, simple_loss=0.2601, pruned_loss=0.04738, over 7170.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2705, pruned_loss=0.04905, over 1417352.17 frames.], batch size: 18, lr: 2.60e-04
2022-05-28 06:49:05,967 INFO [train.py:842] (2/4) Epoch 21, batch 7800, loss[loss=0.1515, simple_loss=0.2395, pruned_loss=0.03178, over 7393.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2707, pruned_loss=0.04926, over 1421899.16 frames.], batch size: 18, lr: 2.60e-04
2022-05-28 06:49:44,028 INFO [train.py:842] (2/4) Epoch 21, batch 7850, loss[loss=0.1639, simple_loss=0.2583, pruned_loss=0.03477, over 7216.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2706, pruned_loss=0.04935, over 1422040.97 frames.], batch size: 21, lr: 2.60e-04
2022-05-28 06:50:22,197 INFO [train.py:842] (2/4) Epoch 21, batch 7900, loss[loss=0.2149, simple_loss=0.313, pruned_loss=0.05839, over 7320.00 frames.], tot_loss[loss=0.1852, simple_loss=0.271, pruned_loss=0.04971, over 1423814.66 frames.], batch size: 21, lr: 2.60e-04
2022-05-28 06:51:00,110 INFO [train.py:842] (2/4) Epoch 21, batch 7950, loss[loss=0.156, simple_loss=0.2376, pruned_loss=0.0372, over 7007.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2713, pruned_loss=0.04995, over 1425381.54 frames.], batch size: 16, lr: 2.60e-04
2022-05-28 06:51:38,305 INFO [train.py:842] (2/4) Epoch 21, batch 8000, loss[loss=0.2075, simple_loss=0.2891, pruned_loss=0.06291, over 7297.00 frames.], tot_loss[loss=0.185, simple_loss=0.271, pruned_loss=0.04947, over 1425317.81 frames.], batch size: 25, lr: 2.60e-04
2022-05-28 06:52:16,320 INFO [train.py:842] (2/4) Epoch 21, batch 8050, loss[loss=0.2372, simple_loss=0.2966, pruned_loss=0.08894, over 7366.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2708, pruned_loss=0.04939, over 1428197.74 frames.], batch size: 23, lr: 2.60e-04
2022-05-28 06:52:54,472 INFO [train.py:842] (2/4) Epoch 21, batch 8100, loss[loss=0.2224, simple_loss=0.3012, pruned_loss=0.07182, over 7302.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2711, pruned_loss=0.04968, over 1426886.00 frames.], batch size: 24, lr: 2.60e-04
2022-05-28 06:53:32,525 INFO [train.py:842] (2/4) Epoch 21, batch 8150, loss[loss=0.2055, simple_loss=0.2799, pruned_loss=0.06552, over 7326.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2716, pruned_loss=0.04947, over 1426491.00 frames.], batch size: 20, lr: 2.59e-04
2022-05-28 06:54:13,535 INFO [train.py:842] (2/4) Epoch 21, batch 8200, loss[loss=0.1881, simple_loss=0.2727, pruned_loss=0.05176, over 7060.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2721, pruned_loss=0.04958, over 1428785.92 frames.], batch size: 18, lr: 2.59e-04
2022-05-28 06:54:51,647 INFO [train.py:842] (2/4) Epoch 21, batch 8250, loss[loss=0.2446, simple_loss=0.3289, pruned_loss=0.08014, over 5011.00 frames.], tot_loss[loss=0.1842, simple_loss=0.271, pruned_loss=0.04867, over 1428087.97 frames.], batch size: 52, lr: 2.59e-04
2022-05-28 06:55:30,092 INFO [train.py:842] (2/4) Epoch 21, batch 8300, loss[loss=0.1965, simple_loss=0.2859, pruned_loss=0.05352, over 7295.00 frames.], tot_loss[loss=0.183, simple_loss=0.27, pruned_loss=0.04802, over 1427728.14 frames.], batch size: 25, lr: 2.59e-04
2022-05-28 06:56:07,848 INFO [train.py:842] (2/4) Epoch 21, batch 8350, loss[loss=0.2281, simple_loss=0.3193, pruned_loss=0.06841, over 7415.00 frames.], tot_loss[loss=0.1838, simple_loss=0.271, pruned_loss=0.04832, over 1427112.17 frames.], batch size: 21, lr: 2.59e-04
2022-05-28 06:56:46,223 INFO [train.py:842] (2/4) Epoch 21, batch 8400, loss[loss=0.1676, simple_loss=0.2628, pruned_loss=0.03622, over 7128.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2714, pruned_loss=0.04905, over 1430677.80 frames.], batch size: 26, lr: 2.59e-04
2022-05-28 06:57:24,197 INFO [train.py:842] (2/4) Epoch 21, batch 8450, loss[loss=0.2136, simple_loss=0.2941, pruned_loss=0.06656, over 7140.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2701, pruned_loss=0.04826, over 1425592.72 frames.], batch size: 20, lr: 2.59e-04
2022-05-28 06:58:02,574 INFO [train.py:842] (2/4) Epoch 21, batch 8500, loss[loss=0.1444, simple_loss=0.231, pruned_loss=0.02888, over 7427.00 frames.], tot_loss[loss=0.184, simple_loss=0.2708, pruned_loss=0.04857, over 1424065.58 frames.], batch size: 20, lr: 2.59e-04
2022-05-28 06:58:40,506 INFO [train.py:842] (2/4) Epoch 21, batch 8550, loss[loss=0.1717, simple_loss=0.2437, pruned_loss=0.04985, over 7278.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2697, pruned_loss=0.0478, over 1424889.03 frames.], batch size: 17, lr: 2.59e-04
2022-05-28 06:59:18,639 INFO [train.py:842] (2/4) Epoch 21, batch 8600, loss[loss=0.2014, simple_loss=0.2905, pruned_loss=0.05616, over 7247.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2715, pruned_loss=0.04905, over 1422023.48 frames.], batch size: 25, lr: 2.59e-04
2022-05-28 06:59:56,662 INFO [train.py:842] (2/4) Epoch 21, batch 8650, loss[loss=0.22, simple_loss=0.2879, pruned_loss=0.07606, over 7161.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2711, pruned_loss=0.04919, over 1418791.81 frames.], batch size: 18, lr: 2.59e-04
2022-05-28 07:00:35,010 INFO [train.py:842] (2/4) Epoch 21, batch 8700, loss[loss=0.1667, simple_loss=0.2656, pruned_loss=0.03396, over 7114.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2697, pruned_loss=0.04896, over 1415664.11 frames.], batch size: 21, lr: 2.59e-04
2022-05-28 07:01:12,968 INFO [train.py:842] (2/4) Epoch 21, batch 8750, loss[loss=0.2148, simple_loss=0.3056, pruned_loss=0.06199, over 6643.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2696, pruned_loss=0.04841, over 1416632.58 frames.], batch size: 31, lr: 2.59e-04
2022-05-28 07:01:51,333 INFO [train.py:842] (2/4) Epoch 21, batch 8800, loss[loss=0.184, simple_loss=0.2599, pruned_loss=0.05402, over 7286.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2689, pruned_loss=0.04767, over 1420236.74 frames.], batch size: 17, lr: 2.59e-04
2022-05-28 07:02:29,285 INFO [train.py:842] (2/4) Epoch 21, batch 8850, loss[loss=0.1681, simple_loss=0.265, pruned_loss=0.03559, over 6397.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2696, pruned_loss=0.04805, over 1418001.79 frames.], batch size: 38, lr: 2.59e-04
2022-05-28 07:03:07,722 INFO [train.py:842] (2/4) Epoch 21, batch 8900, loss[loss=0.2059, simple_loss=0.293, pruned_loss=0.05934, over 7119.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2695, pruned_loss=0.04815, over 1420014.80 frames.], batch size: 21, lr: 2.59e-04
2022-05-28 07:03:45,611 INFO [train.py:842] (2/4) Epoch 21, batch 8950, loss[loss=0.1668, simple_loss=0.2511, pruned_loss=0.04123, over 7145.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2696, pruned_loss=0.04852, over 1411203.58 frames.], batch size: 20, lr: 2.59e-04
2022-05-28 07:04:23,622 INFO [train.py:842] (2/4) Epoch 21, batch 9000, loss[loss=0.1884, simple_loss=0.2673, pruned_loss=0.05472, over 6532.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2712, pruned_loss=0.04971, over 1396968.03 frames.], batch size: 38, lr: 2.59e-04
2022-05-28 07:04:23,622 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 07:04:32,661 INFO [train.py:871] (2/4) Epoch 21, validation: loss=0.1634, simple_loss=0.2627, pruned_loss=0.03203, over 868885.00 frames.
2022-05-28 07:05:10,717 INFO [train.py:842] (2/4) Epoch 21, batch 9050, loss[loss=0.1567, simple_loss=0.2383, pruned_loss=0.03755, over 6806.00 frames.], tot_loss[loss=0.185, simple_loss=0.2705, pruned_loss=0.04975, over 1384618.85 frames.], batch size: 15, lr: 2.59e-04
2022-05-28 07:05:48,354 INFO [train.py:842] (2/4) Epoch 21, batch 9100, loss[loss=0.2234, simple_loss=0.3072, pruned_loss=0.0698, over 4947.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2715, pruned_loss=0.05014, over 1356754.20 frames.], batch size: 53, lr: 2.59e-04
2022-05-28 07:06:25,190 INFO [train.py:842] (2/4) Epoch 21, batch 9150, loss[loss=0.2719, simple_loss=0.3344, pruned_loss=0.1047, over 7113.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2749, pruned_loss=0.05175, over 1321539.26 frames.], batch size: 21, lr: 2.59e-04
2022-05-28 07:07:15,922 INFO [train.py:842] (2/4) Epoch 22, batch 0, loss[loss=0.1983, simple_loss=0.2945, pruned_loss=0.05106, over 7293.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2945, pruned_loss=0.05106, over 7293.00 frames.], batch size: 25, lr: 2.53e-04
2022-05-28 07:07:53,995 INFO [train.py:842] (2/4) Epoch 22, batch 50, loss[loss=0.1343, simple_loss=0.2278, pruned_loss=0.02037, over 7154.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2718, pruned_loss=0.04899, over 318200.35 frames.], batch size: 18, lr: 2.53e-04
2022-05-28 07:08:32,413 INFO [train.py:842] (2/4) Epoch 22, batch 100, loss[loss=0.1803, simple_loss=0.2769, pruned_loss=0.04185, over 7109.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2699, pruned_loss=0.04726, over 564579.30 frames.], batch size: 21, lr: 2.53e-04
2022-05-28 07:09:10,275 INFO [train.py:842] (2/4) Epoch 22, batch 150, loss[loss=0.1847, simple_loss=0.2739, pruned_loss=0.04772, over 7334.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2706, pruned_loss=0.04782, over 753755.26 frames.], batch size: 21, lr: 2.53e-04
2022-05-28 07:09:48,502 INFO [train.py:842] (2/4) Epoch 22, batch 200, loss[loss=0.171, simple_loss=0.264, pruned_loss=0.03899, over 7330.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2709, pruned_loss=0.0482, over 902152.65 frames.], batch size: 22, lr: 2.53e-04
2022-05-28 07:10:26,523 INFO [train.py:842] (2/4) Epoch 22, batch 250, loss[loss=0.1975, simple_loss=0.2799, pruned_loss=0.05752, over 7249.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2701, pruned_loss=0.04811, over 1015413.23 frames.], batch size: 19, lr: 2.53e-04
2022-05-28 07:11:04,686 INFO [train.py:842] (2/4) Epoch 22, batch 300, loss[loss=0.1778, simple_loss=0.2703, pruned_loss=0.04269, over 7232.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2715, pruned_loss=0.04845, over 1107739.15 frames.], batch size: 20, lr: 2.53e-04
2022-05-28 07:11:42,632 INFO [train.py:842] (2/4) Epoch 22, batch 350, loss[loss=0.1694, simple_loss=0.2518, pruned_loss=0.04351, over 7154.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2707, pruned_loss=0.0482, over 1178311.27 frames.], batch size: 19, lr: 2.53e-04
2022-05-28 07:12:20,792 INFO [train.py:842] (2/4) Epoch 22, batch 400, loss[loss=0.1837, simple_loss=0.2773, pruned_loss=0.04506, over 7214.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2703, pruned_loss=0.04755, over 1230501.03 frames.], batch size: 21, lr: 2.53e-04
2022-05-28 07:12:58,840 INFO [train.py:842] (2/4) Epoch 22, batch 450, loss[loss=0.2151, simple_loss=0.2931, pruned_loss=0.06861, over 5013.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2688, pruned_loss=0.04702, over 1273547.84 frames.], batch size: 52, lr: 2.53e-04
2022-05-28 07:13:36,979 INFO [train.py:842] (2/4) Epoch 22, batch 500, loss[loss=0.1918, simple_loss=0.2897, pruned_loss=0.04691, over 7297.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2691, pruned_loss=0.0466, over 1308840.97 frames.], batch size: 25, lr: 2.53e-04
2022-05-28 07:14:14,811 INFO [train.py:842] (2/4) Epoch 22, batch 550, loss[loss=0.1818, simple_loss=0.2706, pruned_loss=0.04654, over 7426.00 frames.], tot_loss[loss=0.1831, simple_loss=0.271, pruned_loss=0.04759, over 1332529.06 frames.], batch size: 20, lr: 2.53e-04
2022-05-28 07:14:53,169 INFO [train.py:842] (2/4) Epoch 22, batch 600, loss[loss=0.1775, simple_loss=0.2833, pruned_loss=0.03586, over 7329.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2698, pruned_loss=0.04727, over 1353362.02 frames.], batch size: 22, lr: 2.53e-04
2022-05-28 07:15:30,881 INFO [train.py:842] (2/4) Epoch 22, batch 650, loss[loss=0.1826, simple_loss=0.2734, pruned_loss=0.04592, over 7344.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2715, pruned_loss=0.04797, over 1369070.95 frames.], batch size: 22, lr: 2.53e-04
2022-05-28 07:16:09,254 INFO [train.py:842] (2/4) Epoch 22, batch 700, loss[loss=0.1758, simple_loss=0.2706, pruned_loss=0.04055, over 7297.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2708, pruned_loss=0.04821, over 1377695.95 frames.], batch size: 25, lr: 2.53e-04
2022-05-28 07:16:47,343 INFO [train.py:842] (2/4) Epoch 22, batch 750, loss[loss=0.1915, simple_loss=0.2678, pruned_loss=0.05757, over 7161.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2697, pruned_loss=0.04787, over 1386874.41 frames.], batch size: 18, lr: 2.53e-04
2022-05-28 07:17:25,593 INFO [train.py:842] (2/4) Epoch 22, batch 800, loss[loss=0.1656, simple_loss=0.2571, pruned_loss=0.03709, over 7302.00 frames.], tot_loss[loss=0.1828, simple_loss=0.27, pruned_loss=0.04778, over 1399211.53 frames.], batch size: 25, lr: 2.53e-04
2022-05-28 07:18:03,628 INFO [train.py:842] (2/4) Epoch 22, batch 850, loss[loss=0.1655, simple_loss=0.2595, pruned_loss=0.03573, over 7418.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2706, pruned_loss=0.04816, over 1404879.51 frames.], batch size: 18, lr: 2.52e-04
2022-05-28 07:18:41,829 INFO [train.py:842] (2/4) Epoch 22, batch 900, loss[loss=0.1758, simple_loss=0.275, pruned_loss=0.03824, over 6305.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2706, pruned_loss=0.04806, over 1408910.17 frames.], batch size: 37, lr: 2.52e-04
2022-05-28 07:19:19,850 INFO [train.py:842] (2/4) Epoch 22, batch 950, loss[loss=0.1921, simple_loss=0.2838, pruned_loss=0.05022, over 7292.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2704, pruned_loss=0.04799, over 1410778.78 frames.], batch size: 18, lr: 2.52e-04
2022-05-28 07:19:57,841 INFO [train.py:842] (2/4) Epoch 22, batch 1000, loss[loss=0.1683, simple_loss=0.2614, pruned_loss=0.03756, over 7155.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2723, pruned_loss=0.04925, over 1410988.51 frames.], batch size: 19, lr: 2.52e-04
2022-05-28 07:20:36,020 INFO [train.py:842] (2/4) Epoch 22, batch 1050, loss[loss=0.2108, simple_loss=0.2973, pruned_loss=0.06214, over 7337.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2715, pruned_loss=0.04892, over 1414422.00 frames.], batch size: 22, lr: 2.52e-04
2022-05-28 07:21:14,301 INFO [train.py:842] (2/4) Epoch 22, batch 1100, loss[loss=0.1921, simple_loss=0.2786, pruned_loss=0.0528, over 6407.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2714, pruned_loss=0.04894, over 1417848.67 frames.], batch size: 37, lr: 2.52e-04
2022-05-28 07:21:52,336 INFO [train.py:842] (2/4) Epoch 22, batch 1150, loss[loss=0.1907, simple_loss=0.2778, pruned_loss=0.0518, over 7262.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2708, pruned_loss=0.04854, over 1419813.26 frames.], batch size: 19, lr: 2.52e-04
2022-05-28 07:22:30,715 INFO [train.py:842] (2/4) Epoch 22, batch 1200, loss[loss=0.2287, simple_loss=0.3112, pruned_loss=0.07312, over 7306.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2697, pruned_loss=0.04836, over 1421532.18 frames.], batch size: 25, lr: 2.52e-04
2022-05-28 07:23:08,666 INFO [train.py:842] (2/4) Epoch 22, batch 1250, loss[loss=0.1516, simple_loss=0.2212, pruned_loss=0.04102, over 7008.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2699, pruned_loss=0.04842, over 1420818.76 frames.], batch size: 16, lr: 2.52e-04
2022-05-28 07:23:46,945 INFO [train.py:842] (2/4) Epoch 22, batch 1300, loss[loss=0.1653, simple_loss=0.2448, pruned_loss=0.04291, over 7153.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2694, pruned_loss=0.04812, over 1419662.26 frames.], batch size: 19, lr: 2.52e-04
2022-05-28 07:24:25,110 INFO [train.py:842] (2/4) Epoch 22, batch 1350, loss[loss=0.1718, simple_loss=0.2604, pruned_loss=0.04154, over 7405.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2691, pruned_loss=0.04804, over 1423768.66 frames.], batch size: 21, lr: 2.52e-04
2022-05-28 07:25:03,515 INFO [train.py:842] (2/4) Epoch 22, batch 1400, loss[loss=0.2437, simple_loss=0.3341, pruned_loss=0.07667, over 7200.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2693, pruned_loss=0.04805, over 1420300.79 frames.], batch size: 22, lr: 2.52e-04
2022-05-28 07:25:41,554 INFO [train.py:842] (2/4) Epoch 22, batch 1450, loss[loss=0.1486, simple_loss=0.2344, pruned_loss=0.03133, over 7424.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2702, pruned_loss=0.04856, over 1424293.70 frames.], batch size: 20, lr: 2.52e-04
2022-05-28 07:26:19,928 INFO [train.py:842] (2/4) Epoch 22, batch 1500, loss[loss=0.1622, simple_loss=0.2498, pruned_loss=0.03732, over 7232.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2695, pruned_loss=0.0489, over 1426354.82 frames.], batch size: 20, lr: 2.52e-04
2022-05-28 07:26:58,143 INFO [train.py:842] (2/4) Epoch 22, batch 1550, loss[loss=0.1674, simple_loss=0.2581, pruned_loss=0.03831, over 7249.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2684, pruned_loss=0.04796, over 1428449.35 frames.], batch size: 20, lr: 2.52e-04
2022-05-28 07:27:45,582 INFO [train.py:842] (2/4) Epoch 22, batch 1600, loss[loss=0.1721, simple_loss=0.2517, pruned_loss=0.0463, over 7193.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2691, pruned_loss=0.04815, over 1429404.90 frames.], batch size: 16, lr: 2.52e-04
2022-05-28 07:28:23,576 INFO [train.py:842] (2/4) Epoch 22, batch 1650, loss[loss=0.1603, simple_loss=0.2559, pruned_loss=0.03233, over 6752.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2691, pruned_loss=0.04788, over 1431362.49 frames.], batch size: 31, lr: 2.52e-04
2022-05-28 07:29:02,070 INFO [train.py:842] (2/4) Epoch 22, batch 1700, loss[loss=0.1739, simple_loss=0.2678, pruned_loss=0.03999, over 7351.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2673, pruned_loss=0.04706, over 1433246.57 frames.], batch size: 22, lr: 2.52e-04
2022-05-28 07:29:40,021 INFO [train.py:842] (2/4) Epoch 22, batch 1750, loss[loss=0.1722, simple_loss=0.2649, pruned_loss=0.03978, over 7242.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2688, pruned_loss=0.0478, over 1433197.97 frames.], batch size: 20, lr: 2.52e-04
2022-05-28 07:30:27,697 INFO [train.py:842] (2/4) Epoch 22, batch 1800, loss[loss=0.1594, simple_loss=0.2374, pruned_loss=0.04072, over 7275.00 frames.], tot_loss[loss=0.182, simple_loss=0.2683, pruned_loss=0.04785, over 1430068.83 frames.], batch size: 17, lr: 2.52e-04
2022-05-28 07:31:05,562 INFO [train.py:842] (2/4) Epoch 22, batch 1850, loss[loss=0.1755, simple_loss=0.2739, pruned_loss=0.03855, over 6324.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2683, pruned_loss=0.04806, over 1425873.54 frames.], batch size: 37, lr: 2.52e-04
2022-05-28 07:31:53,092 INFO [train.py:842] (2/4) Epoch 22, batch 1900, loss[loss=0.2383, simple_loss=0.3266, pruned_loss=0.07504, over 5010.00 frames.], tot_loss[loss=0.1826, simple_loss=0.269, pruned_loss=0.04809, over 1424059.20 frames.], batch size: 52, lr: 2.52e-04
2022-05-28 07:32:31,069 INFO [train.py:842] (2/4) Epoch 22, batch 1950, loss[loss=0.1469, simple_loss=0.2266, pruned_loss=0.03361, over 7268.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2685, pruned_loss=0.04766, over 1424732.25 frames.], batch size: 17, lr: 2.52e-04
2022-05-28 07:33:09,366 INFO [train.py:842] (2/4) Epoch 22, batch 2000, loss[loss=0.1908, simple_loss=0.2783, pruned_loss=0.05169, over 7333.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2686, pruned_loss=0.04785, over 1427174.53 frames.], batch size: 20, lr: 2.52e-04
2022-05-28 07:33:47,296 INFO [train.py:842] (2/4) Epoch 22, batch 2050, loss[loss=0.1945, simple_loss=0.2664, pruned_loss=0.06129, over 7284.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2693, pruned_loss=0.04806, over 1428276.70 frames.], batch size: 17, lr: 2.52e-04
2022-05-28 07:34:25,510 INFO [train.py:842] (2/4) Epoch 22, batch 2100, loss[loss=0.2371, simple_loss=0.3107, pruned_loss=0.08174, over 7413.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2695, pruned_loss=0.04788, over 1427525.03 frames.], batch size: 18, lr: 2.52e-04
2022-05-28 07:35:03,400 INFO [train.py:842] (2/4) Epoch 22, batch 2150, loss[loss=0.1631, simple_loss=0.2635, pruned_loss=0.0313, over 7178.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2698, pruned_loss=0.04806, over 1423687.40 frames.], batch size: 18, lr: 2.52e-04
2022-05-28 07:35:41,676 INFO [train.py:842] (2/4) Epoch 22, batch 2200, loss[loss=0.1845, simple_loss=0.282, pruned_loss=0.04354, over 7113.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2693, pruned_loss=0.04761, over 1426496.96 frames.], batch size: 21, lr: 2.52e-04
2022-05-28 07:36:19,630 INFO [train.py:842] (2/4) Epoch 22, batch 2250, loss[loss=0.1711, simple_loss=0.2516, pruned_loss=0.04533, over 6819.00 frames.], tot_loss[loss=0.1815, simple_loss=0.269, pruned_loss=0.04701, over 1423863.53 frames.], batch size: 15, lr: 2.52e-04
2022-05-28 07:36:57,801 INFO [train.py:842] (2/4) Epoch 22, batch 2300, loss[loss=0.2198, simple_loss=0.2987, pruned_loss=0.07047, over 4935.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2693, pruned_loss=0.04713, over 1424762.53 frames.], batch size: 52, lr: 2.52e-04
2022-05-28 07:37:35,869 INFO [train.py:842] (2/4) Epoch 22, batch 2350, loss[loss=0.1956, simple_loss=0.2791, pruned_loss=0.05604, over 6344.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2684, pruned_loss=0.04699, over 1426210.94 frames.], batch size: 37, lr: 2.52e-04
2022-05-28 07:38:14,394 INFO [train.py:842] (2/4) Epoch 22, batch 2400, loss[loss=0.1921, simple_loss=0.2725, pruned_loss=0.05587, over 7126.00 frames.], tot_loss[loss=0.18, simple_loss=0.267, pruned_loss=0.04653, over 1425647.78 frames.], batch size: 17, lr: 2.51e-04
2022-05-28 07:38:52,298 INFO [train.py:842] (2/4) Epoch 22, batch 2450, loss[loss=0.1514, simple_loss=0.2296, pruned_loss=0.03661, over 7293.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2666, pruned_loss=0.04618, over 1424322.69 frames.], batch size: 17, lr: 2.51e-04
2022-05-28 07:39:30,626 INFO [train.py:842] (2/4) Epoch 22, batch 2500, loss[loss=0.1716, simple_loss=0.2641, pruned_loss=0.03954, over 7424.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2662, pruned_loss=0.04578, over 1422501.13 frames.], batch size: 21, lr: 2.51e-04
2022-05-28 07:40:08,485 INFO [train.py:842] (2/4) Epoch 22, batch 2550, loss[loss=0.165, simple_loss=0.2614, pruned_loss=0.03425, over 7082.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2661, pruned_loss=0.04577, over 1421008.98 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:40:46,512 INFO [train.py:842] (2/4) Epoch 22, batch 2600, loss[loss=0.1716, simple_loss=0.2579, pruned_loss=0.04266, over 7161.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2684, pruned_loss=0.04689, over 1416908.41 frames.], batch size: 19, lr: 2.51e-04
2022-05-28 07:41:24,749 INFO [train.py:842] (2/4) Epoch 22, batch 2650, loss[loss=0.2081, simple_loss=0.2892, pruned_loss=0.06352, over 7256.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2676, pruned_loss=0.04679, over 1421417.08 frames.], batch size: 19, lr: 2.51e-04
2022-05-28 07:42:02,990 INFO [train.py:842] (2/4) Epoch 22, batch 2700, loss[loss=0.1827, simple_loss=0.2709, pruned_loss=0.04729, over 7170.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2677, pruned_loss=0.04691, over 1421192.77 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:42:40,714 INFO [train.py:842] (2/4) Epoch 22, batch 2750, loss[loss=0.1552, simple_loss=0.2397, pruned_loss=0.03534, over 7055.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2683, pruned_loss=0.04722, over 1420547.90 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:43:18,874 INFO [train.py:842] (2/4) Epoch 22, batch 2800, loss[loss=0.16, simple_loss=0.2458, pruned_loss=0.03706, over 7269.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2678, pruned_loss=0.04663, over 1420997.00 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:43:56,991 INFO [train.py:842] (2/4) Epoch 22, batch 2850, loss[loss=0.169, simple_loss=0.2517, pruned_loss=0.04308, over 7161.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2677, pruned_loss=0.04677, over 1419876.30 frames.], batch size: 19, lr: 2.51e-04
2022-05-28 07:44:35,180 INFO [train.py:842] (2/4) Epoch 22, batch 2900, loss[loss=0.1573, simple_loss=0.2518, pruned_loss=0.0314, over 7154.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2687, pruned_loss=0.04737, over 1421831.59 frames.], batch size: 19, lr: 2.51e-04
2022-05-28 07:45:13,313 INFO [train.py:842] (2/4) Epoch 22, batch 2950, loss[loss=0.1929, simple_loss=0.2815, pruned_loss=0.05215, over 7422.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2685, pruned_loss=0.04724, over 1421661.76 frames.], batch size: 21, lr: 2.51e-04
2022-05-28 07:45:51,549 INFO [train.py:842] (2/4) Epoch 22, batch 3000, loss[loss=0.1418, simple_loss=0.2337, pruned_loss=0.02494, over 7165.00 frames.], tot_loss[loss=0.181, simple_loss=0.2679, pruned_loss=0.04706, over 1425599.47 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:45:51,551 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 07:46:00,591 INFO [train.py:871] (2/4) Epoch 22, validation: loss=0.1649, simple_loss=0.2645, pruned_loss=0.03269, over 868885.00 frames.
2022-05-28 07:46:38,640 INFO [train.py:842] (2/4) Epoch 22, batch 3050, loss[loss=0.1721, simple_loss=0.2635, pruned_loss=0.04033, over 7056.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2672, pruned_loss=0.04665, over 1427549.05 frames.], batch size: 28, lr: 2.51e-04
2022-05-28 07:47:17,290 INFO [train.py:842] (2/4) Epoch 22, batch 3100, loss[loss=0.186, simple_loss=0.2786, pruned_loss=0.04667, over 5074.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2671, pruned_loss=0.04693, over 1428484.48 frames.], batch size: 52, lr: 2.51e-04
2022-05-28 07:47:55,458 INFO [train.py:842] (2/4) Epoch 22, batch 3150, loss[loss=0.1983, simple_loss=0.2841, pruned_loss=0.05622, over 7418.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2665, pruned_loss=0.04646, over 1425992.23 frames.], batch size: 21, lr: 2.51e-04
2022-05-28 07:48:33,859 INFO [train.py:842] (2/4) Epoch 22, batch 3200, loss[loss=0.161, simple_loss=0.2562, pruned_loss=0.03294, over 7070.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2669, pruned_loss=0.0465, over 1427423.63 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:49:11,738 INFO [train.py:842] (2/4) Epoch 22, batch 3250, loss[loss=0.1675, simple_loss=0.2548, pruned_loss=0.04012, over 7002.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2681, pruned_loss=0.04686, over 1428484.62 frames.], batch size: 16, lr: 2.51e-04
2022-05-28 07:49:49,917 INFO [train.py:842] (2/4) Epoch 22, batch 3300, loss[loss=0.1794, simple_loss=0.2683, pruned_loss=0.04528, over 7428.00 frames.], tot_loss[loss=0.181, simple_loss=0.2683, pruned_loss=0.04682, over 1430459.41 frames.], batch size: 20, lr: 2.51e-04
2022-05-28 07:50:27,863 INFO [train.py:842] (2/4) Epoch 22, batch 3350, loss[loss=0.1673, simple_loss=0.2489, pruned_loss=0.04279, over 7360.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2688, pruned_loss=0.04696, over 1429438.26 frames.], batch size: 19, lr: 2.51e-04
2022-05-28 07:51:05,905 INFO [train.py:842] (2/4) Epoch 22, batch 3400, loss[loss=0.1591, simple_loss=0.2455, pruned_loss=0.03641, over 7132.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2682, pruned_loss=0.04679, over 1425952.33 frames.], batch size: 17, lr: 2.51e-04
2022-05-28 07:51:43,851 INFO [train.py:842] (2/4) Epoch 22, batch 3450, loss[loss=0.171, simple_loss=0.2766, pruned_loss=0.03264, over 7340.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2675, pruned_loss=0.04642, over 1427398.23 frames.], batch size: 22, lr: 2.51e-04
2022-05-28 07:52:22,331 INFO [train.py:842] (2/4) Epoch 22, batch 3500, loss[loss=0.1928, simple_loss=0.2707, pruned_loss=0.05747, over 7332.00 frames.], tot_loss[loss=0.181, simple_loss=0.2682, pruned_loss=0.04693, over 1429704.46 frames.], batch size: 22, lr: 2.51e-04
2022-05-28 07:53:00,184 INFO [train.py:842] (2/4) Epoch 22, batch 3550, loss[loss=0.213, simple_loss=0.2989, pruned_loss=0.06354, over 6755.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2684, pruned_loss=0.04703, over 1427551.11 frames.], batch size: 31, lr: 2.51e-04
2022-05-28 07:53:38,512 INFO [train.py:842] (2/4) Epoch 22, batch 3600, loss[loss=0.1473, simple_loss=0.2324, pruned_loss=0.03105, over 7293.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2679, pruned_loss=0.04693, over 1422178.56 frames.], batch size: 17, lr: 2.51e-04
2022-05-28 07:54:16,513 INFO [train.py:842] (2/4) Epoch 22, batch 3650, loss[loss=0.1567, simple_loss=0.2432, pruned_loss=0.03506, over 7263.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2678, pruned_loss=0.04662, over 1424623.87 frames.], batch size: 19, lr: 2.51e-04
2022-05-28 07:54:54,675 INFO [train.py:842] (2/4) Epoch 22, batch 3700, loss[loss=0.1915, simple_loss=0.289, pruned_loss=0.04698, over 7151.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2677, pruned_loss=0.04596, over 1426187.20 frames.], batch size: 20, lr: 2.51e-04
2022-05-28 07:55:32,496 INFO [train.py:842] (2/4) Epoch 22, batch 3750, loss[loss=0.1964, simple_loss=0.2908, pruned_loss=0.05103, over 7274.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2676, pruned_loss=0.04596, over 1428400.98 frames.], batch size: 24, lr: 2.51e-04
2022-05-28 07:56:11,022 INFO [train.py:842] (2/4) Epoch 22, batch 3800, loss[loss=0.2513, simple_loss=0.3306, pruned_loss=0.08603, over 4695.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2676, pruned_loss=0.04663, over 1424463.36 frames.], batch size: 52, lr: 2.51e-04
2022-05-28 07:56:49,074 INFO [train.py:842] (2/4) Epoch 22, batch 3850, loss[loss=0.2311, simple_loss=0.286, pruned_loss=0.0881, over 7284.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2685, pruned_loss=0.04735, over 1425875.61 frames.], batch size: 18, lr: 2.51e-04
2022-05-28 07:57:27,545 INFO [train.py:842] (2/4) Epoch 22, batch 3900, loss[loss=0.2129, simple_loss=0.2954, pruned_loss=0.06525, over 7326.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2689, pruned_loss=0.048, over 1428064.48 frames.], batch size: 20, lr: 2.51e-04
2022-05-28 07:58:05,556 INFO [train.py:842] (2/4) Epoch 22, batch 3950, loss[loss=0.1879, simple_loss=0.2761, pruned_loss=0.0499, over 7418.00 frames.], tot_loss[loss=0.182, simple_loss=0.2683, pruned_loss=0.04786, over 1427427.51 frames.], batch size: 21, lr: 2.50e-04
2022-05-28 07:58:43,886 INFO [train.py:842] (2/4) Epoch 22, batch 4000, loss[loss=0.17, simple_loss=0.2599, pruned_loss=0.04002, over 6738.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2678, pruned_loss=0.04747, over 1427744.84 frames.], batch size: 31, lr: 2.50e-04
2022-05-28 07:59:21,788 INFO [train.py:842] (2/4) Epoch 22, batch 4050, loss[loss=0.1681, simple_loss=0.2624, pruned_loss=0.0369, over 7422.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2672, pruned_loss=0.0473, over 1426107.08 frames.], batch size: 21, lr: 2.50e-04
2022-05-28 08:00:00,257 INFO [train.py:842] (2/4) Epoch 22, batch 4100, loss[loss=0.2056, simple_loss=0.2992, pruned_loss=0.05601, over 7324.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2668, pruned_loss=0.04707, over 1424819.33 frames.], batch size: 22, lr: 2.50e-04
2022-05-28 08:00:38,143 INFO [train.py:842] (2/4) Epoch 22, batch 4150, loss[loss=0.2162, simple_loss=0.2973, pruned_loss=0.06756, over 7325.00 frames.], tot_loss[loss=0.182, simple_loss=0.2682, pruned_loss=0.04794, over 1427831.46 frames.], batch size: 22, lr: 2.50e-04
2022-05-28 08:01:16,330 INFO [train.py:842] (2/4) Epoch 22, batch 4200, loss[loss=0.2658, simple_loss=0.3416, pruned_loss=0.09504, over 4800.00 frames.], tot_loss[loss=0.182, simple_loss=0.268, pruned_loss=0.048, over 1420793.14 frames.], batch size: 52, lr: 2.50e-04
2022-05-28 08:01:54,109 INFO [train.py:842] (2/4) Epoch 22, batch 4250, loss[loss=0.1851, simple_loss=0.2768, pruned_loss=0.04668, over 5113.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2685, pruned_loss=0.04824, over 1416052.36 frames.], batch size: 52, lr: 2.50e-04
2022-05-28 08:02:32,397 INFO [train.py:842] (2/4) Epoch 22, batch 4300, loss[loss=0.1738, simple_loss=0.2595, pruned_loss=0.04403, over 7418.00 frames.], tot_loss[loss=0.1838, simple_loss=0.27, pruned_loss=0.04881, over 1418435.84 frames.], batch size: 18, lr: 2.50e-04
2022-05-28 08:03:10,569 INFO [train.py:842] (2/4) Epoch 22, batch 4350, loss[loss=0.1569, simple_loss=0.2481, pruned_loss=0.03282, over 7286.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2682, pruned_loss=0.04757, over 1419112.05 frames.], batch size: 17, lr: 2.50e-04
2022-05-28 08:03:48,874 INFO [train.py:842] (2/4) Epoch 22, batch 4400, loss[loss=0.1722, simple_loss=0.2684, pruned_loss=0.03796, over 7333.00 frames.], tot_loss[loss=0.182, simple_loss=0.2689, pruned_loss=0.04754, over 1420964.16 frames.], batch size: 21, lr: 2.50e-04
2022-05-28 08:04:26,816 INFO [train.py:842] (2/4) Epoch 22, batch 4450, loss[loss=0.1842, simple_loss=0.2761, pruned_loss=0.04609, over 7288.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2685, pruned_loss=0.04751, over 1417500.96 frames.], batch size: 24, lr: 2.50e-04
2022-05-28 08:05:05,054 INFO [train.py:842] (2/4) Epoch 22, batch 4500, loss[loss=0.2137, simple_loss=0.2961, pruned_loss=0.06571, over 7387.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2694, pruned_loss=0.04782, over 1420368.19 frames.], batch size: 23, lr: 2.50e-04
2022-05-28 08:05:43,122 INFO [train.py:842] (2/4) Epoch 22, batch 4550, loss[loss=0.1456, simple_loss=0.2257, pruned_loss=0.03276, over 7164.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2685, pruned_loss=0.04747, over 1421183.67 frames.], batch size: 18, lr: 2.50e-04
2022-05-28 08:06:21,009 INFO [train.py:842] (2/4) Epoch 22, batch 4600, loss[loss=0.1544, simple_loss=0.2537, pruned_loss=0.02755, over 7231.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2695, pruned_loss=0.0477, over 1420480.62 frames.], batch size: 20, lr: 2.50e-04
2022-05-28 08:06:58,735 INFO [train.py:842] (2/4) Epoch 22, batch 4650, loss[loss=0.1627, simple_loss=0.2528, pruned_loss=0.03632, over 7072.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2695, pruned_loss=0.04768, over 1417690.06 frames.], batch size: 18, lr: 2.50e-04
2022-05-28 08:07:37,051 INFO [train.py:842] (2/4) Epoch 22, batch 4700, loss[loss=0.1634, simple_loss=0.2509, pruned_loss=0.03801, over 7352.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2695, pruned_loss=0.04814, over 1418659.27 frames.], batch size: 19, lr: 2.50e-04
2022-05-28 08:08:15,470 INFO [train.py:842] (2/4) Epoch 22, batch 4750, loss[loss=0.1744, simple_loss=0.2513, pruned_loss=0.0488, over 7283.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2678, pruned_loss=0.04752, over 1423849.07 frames.], batch size: 18, lr: 2.50e-04
2022-05-28 08:08:53,582 INFO [train.py:842] (2/4) Epoch 22, batch 4800, loss[loss=0.1715, simple_loss=0.2646, pruned_loss=0.03919, over 4930.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2698, pruned_loss=0.0483, over 1419117.16 frames.], batch size: 52, lr: 2.50e-04
2022-05-28 08:09:31,583 INFO [train.py:842] (2/4) Epoch 22, batch 4850, loss[loss=0.169, simple_loss=0.2707, pruned_loss=0.03371, over 7115.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2701, pruned_loss=0.04849, over 1421299.85 frames.], batch size: 21, lr: 2.50e-04
2022-05-28 08:10:09,776 INFO [train.py:842] (2/4) Epoch 22, batch 4900, loss[loss=0.174, simple_loss=0.2697, pruned_loss=0.03912, over 7183.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2705, pruned_loss=0.04866, over 1419857.57 frames.], batch size: 23, lr: 2.50e-04
2022-05-28 08:10:47,869 INFO [train.py:842] (2/4) Epoch 22, batch 4950, loss[loss=0.1585, simple_loss=0.2531, pruned_loss=0.03191, over 7253.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2698, pruned_loss=0.04844, over 1415016.33 frames.], batch size: 19, lr: 2.50e-04
2022-05-28 08:11:25,835 INFO [train.py:842] (2/4) Epoch 22, batch 5000, loss[loss=0.208, simple_loss=0.3032, pruned_loss=0.05639, over 6408.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2712, pruned_loss=0.04896, over 1412568.42 frames.], batch size: 38, lr: 2.50e-04
2022-05-28 08:12:03,911 INFO [train.py:842] (2/4) Epoch 22, batch 5050, loss[loss=0.1496, simple_loss=0.2338, pruned_loss=0.03271, over 7405.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2702, pruned_loss=0.0485, over 1416269.15 frames.], batch size: 18, lr: 2.50e-04
2022-05-28 08:12:42,390 INFO [train.py:842] (2/4) Epoch 22, batch 5100, loss[loss=0.2002, simple_loss=0.2935, pruned_loss=0.0535, over 7311.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2695, pruned_loss=0.04811, over 1420335.49 frames.], batch size: 21, lr: 2.50e-04
2022-05-28 08:13:20,353 INFO [train.py:842] (2/4) Epoch 22, batch 5150, loss[loss=0.1804, simple_loss=0.2771, pruned_loss=0.0419, over 7341.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2704, pruned_loss=0.04821, over 1427048.33 frames.], batch size: 22, lr: 2.50e-04
2022-05-28 08:13:58,902 INFO [train.py:842] (2/4) Epoch 22, batch 5200, loss[loss=0.1725, simple_loss=0.2784, pruned_loss=0.0333, over 7330.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2705, pruned_loss=0.04901, over 1425254.94 frames.], batch size: 20, lr: 2.50e-04
2022-05-28 08:14:36,955 INFO [train.py:842] (2/4) Epoch 22, batch 5250, loss[loss=0.1655, simple_loss=0.2585, pruned_loss=0.03623, over 7092.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2693, pruned_loss=0.04843, over 1421851.25 frames.], batch size: 28, lr: 2.50e-04
2022-05-28 08:15:14,907 INFO [train.py:842] (2/4) Epoch 22, batch 5300, loss[loss=0.17, simple_loss=0.2689, pruned_loss=0.03551, over 7324.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2703, pruned_loss=0.04848, over 1422858.69 frames.], batch size: 22, lr: 2.50e-04
2022-05-28 08:15:52,743 INFO [train.py:842] (2/4) Epoch 22, batch 5350, loss[loss=0.1783, simple_loss=0.2654, pruned_loss=0.04562, over 6713.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2706, pruned_loss=0.04893, over 1423556.25 frames.], batch size: 31, lr: 2.50e-04
2022-05-28 08:16:30,988 INFO [train.py:842] (2/4) Epoch 22, batch 5400, loss[loss=0.1742, simple_loss=0.2556, pruned_loss=0.04643, over 7067.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2712, pruned_loss=0.04904, over 1424505.73 frames.], batch size: 18, lr: 2.50e-04
2022-05-28 08:17:09,159 INFO [train.py:842] (2/4) Epoch 22, batch 5450, loss[loss=0.1782, simple_loss=0.2651, pruned_loss=0.0456, over 7434.00 frames.], tot_loss[loss=0.184, simple_loss=0.2705, pruned_loss=0.04872, over 1424758.65 frames.], batch size: 20, lr: 2.50e-04
2022-05-28 08:17:47,415 INFO [train.py:842] (2/4) Epoch 22, batch 5500, loss[loss=0.173, simple_loss=0.2701, pruned_loss=0.03792, over 7415.00 frames.], tot_loss[loss=0.183, simple_loss=0.27, pruned_loss=0.04801, over 1422614.64 frames.], batch size: 21, lr: 2.49e-04
2022-05-28 08:18:25,315 INFO [train.py:842] (2/4) Epoch 22, batch 5550, loss[loss=0.1737, simple_loss=0.2563, pruned_loss=0.04554, over 7159.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2702, pruned_loss=0.04842, over 1420112.73 frames.], batch size: 18, lr: 2.49e-04
2022-05-28 08:19:03,348 INFO [train.py:842] (2/4) Epoch 22, batch 5600, loss[loss=0.206, simple_loss=0.2917, pruned_loss=0.06011, over 7153.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2703, pruned_loss=0.04832, over 1421187.17 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:19:41,113 INFO [train.py:842] (2/4) Epoch 22, batch 5650, loss[loss=0.1917, simple_loss=0.2769, pruned_loss=0.05325, over 7362.00 frames.], tot_loss[loss=0.1834, simple_loss=0.27, pruned_loss=0.0484, over 1419964.51 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:20:19,519 INFO [train.py:842] (2/4) Epoch 22, batch 5700, loss[loss=0.1781, simple_loss=0.2648, pruned_loss=0.04563, over 7342.00 frames.], tot_loss[loss=0.184, simple_loss=0.2702, pruned_loss=0.04893, over 1425875.81 frames.], batch size: 22, lr: 2.49e-04
2022-05-28 08:20:57,462 INFO [train.py:842] (2/4) Epoch 22, batch 5750, loss[loss=0.1992, simple_loss=0.2701, pruned_loss=0.0642, over 7408.00 frames.], tot_loss[loss=0.184, simple_loss=0.2705, pruned_loss=0.04871, over 1427813.74 frames.], batch size: 18, lr: 2.49e-04
2022-05-28 08:21:35,774 INFO [train.py:842] (2/4) Epoch 22, batch 5800, loss[loss=0.155, simple_loss=0.2375, pruned_loss=0.03631, over 7142.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2704, pruned_loss=0.04927, over 1428258.05 frames.], batch size: 17, lr: 2.49e-04
2022-05-28 08:22:13,661 INFO [train.py:842] (2/4) Epoch 22, batch 5850, loss[loss=0.2404, simple_loss=0.3205, pruned_loss=0.08012, over 7293.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2712, pruned_loss=0.04921, over 1429790.85 frames.], batch size: 24, lr: 2.49e-04
2022-05-28 08:22:52,078 INFO [train.py:842] (2/4) Epoch 22, batch 5900, loss[loss=0.179, simple_loss=0.2699, pruned_loss=0.04406, over 7204.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2697, pruned_loss=0.04854, over 1432559.58 frames.], batch size: 23, lr: 2.49e-04
2022-05-28 08:23:29,852 INFO [train.py:842] (2/4) Epoch 22, batch 5950, loss[loss=0.1613, simple_loss=0.2526, pruned_loss=0.03506, over 7323.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2702, pruned_loss=0.04913, over 1426698.23 frames.], batch size: 22, lr: 2.49e-04
2022-05-28 08:24:08,105 INFO [train.py:842] (2/4) Epoch 22, batch 6000, loss[loss=0.1616, simple_loss=0.2342, pruned_loss=0.04445, over 7390.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2688, pruned_loss=0.0478, over 1428313.23 frames.], batch size: 18, lr: 2.49e-04
2022-05-28 08:24:08,106 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 08:24:17,058 INFO [train.py:871] (2/4) Epoch 22, validation: loss=0.1664, simple_loss=0.2658, pruned_loss=0.03347, over 868885.00 frames.
2022-05-28 08:24:55,053 INFO [train.py:842] (2/4) Epoch 22, batch 6050, loss[loss=0.1499, simple_loss=0.2338, pruned_loss=0.03307, over 7283.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2692, pruned_loss=0.0479, over 1425636.81 frames.], batch size: 17, lr: 2.49e-04
2022-05-28 08:25:33,539 INFO [train.py:842] (2/4) Epoch 22, batch 6100, loss[loss=0.1522, simple_loss=0.2403, pruned_loss=0.03208, over 7149.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2685, pruned_loss=0.04755, over 1426512.77 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:26:11,600 INFO [train.py:842] (2/4) Epoch 22, batch 6150, loss[loss=0.1623, simple_loss=0.2529, pruned_loss=0.03586, over 7074.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2684, pruned_loss=0.04744, over 1421146.37 frames.], batch size: 18, lr: 2.49e-04
2022-05-28 08:26:49,986 INFO [train.py:842] (2/4) Epoch 22, batch 6200, loss[loss=0.1976, simple_loss=0.2784, pruned_loss=0.05846, over 7417.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2681, pruned_loss=0.04702, over 1424084.06 frames.], batch size: 21, lr: 2.49e-04
2022-05-28 08:27:27,697 INFO [train.py:842] (2/4) Epoch 22, batch 6250, loss[loss=0.2079, simple_loss=0.2924, pruned_loss=0.06164, over 6804.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2685, pruned_loss=0.04744, over 1420817.79 frames.], batch size: 31, lr: 2.49e-04
2022-05-28 08:28:05,771 INFO [train.py:842] (2/4) Epoch 22, batch 6300, loss[loss=0.169, simple_loss=0.267, pruned_loss=0.03553, over 7330.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2693, pruned_loss=0.04784, over 1420392.60 frames.], batch size: 22, lr: 2.49e-04
2022-05-28 08:28:43,773 INFO [train.py:842] (2/4) Epoch 22, batch 6350, loss[loss=0.2235, simple_loss=0.2849, pruned_loss=0.08106, over 4981.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2695, pruned_loss=0.04787, over 1422302.70 frames.], batch size: 52, lr: 2.49e-04
2022-05-28 08:29:22,115 INFO [train.py:842] (2/4) Epoch 22, batch 6400, loss[loss=0.1869, simple_loss=0.2674, pruned_loss=0.05319, over 7164.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2713, pruned_loss=0.04874, over 1422810.65 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:30:00,138 INFO [train.py:842] (2/4) Epoch 22, batch 6450, loss[loss=0.1676, simple_loss=0.2588, pruned_loss=0.03821, over 7255.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2713, pruned_loss=0.04909, over 1423118.81 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:30:38,600 INFO [train.py:842] (2/4) Epoch 22, batch 6500, loss[loss=0.2055, simple_loss=0.2695, pruned_loss=0.07074, over 7001.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2709, pruned_loss=0.04936, over 1424374.43 frames.], batch size: 16, lr: 2.49e-04
2022-05-28 08:31:16,546 INFO [train.py:842] (2/4) Epoch 22, batch 6550, loss[loss=0.1717, simple_loss=0.2529, pruned_loss=0.04524, over 7073.00 frames.], tot_loss[loss=0.1837, simple_loss=0.27, pruned_loss=0.04867, over 1420385.25 frames.], batch size: 18, lr: 2.49e-04
2022-05-28 08:31:54,815 INFO [train.py:842] (2/4) Epoch 22, batch 6600, loss[loss=0.1809, simple_loss=0.2759, pruned_loss=0.04291, over 7257.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2695, pruned_loss=0.04834, over 1418012.15 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:32:32,830 INFO [train.py:842] (2/4) Epoch 22, batch 6650, loss[loss=0.1892, simple_loss=0.2882, pruned_loss=0.04504, over 7339.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2691, pruned_loss=0.04793, over 1421537.16 frames.], batch size: 22, lr: 2.49e-04
2022-05-28 08:33:11,311 INFO [train.py:842] (2/4) Epoch 22, batch 6700, loss[loss=0.1496, simple_loss=0.2333, pruned_loss=0.03295, over 7267.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2683, pruned_loss=0.04755, over 1425546.42 frames.], batch size: 19, lr: 2.49e-04
2022-05-28 08:33:49,372 INFO [train.py:842] (2/4) Epoch 22, batch 6750, loss[loss=0.1885, simple_loss=0.2688, pruned_loss=0.05407, over 7238.00 frames.], tot_loss[loss=0.183, simple_loss=0.2693, pruned_loss=0.04838, over 1423991.62 frames.], batch size: 20, lr: 2.49e-04
2022-05-28 08:34:27,413 INFO [train.py:842] (2/4) Epoch 22, batch 6800, loss[loss=0.1723, simple_loss=0.2653, pruned_loss=0.03964, over 6523.00 frames.], tot_loss[loss=0.1825, simple_loss=0.269, pruned_loss=0.04794, over 1421699.93 frames.], batch size: 38, lr: 2.49e-04
2022-05-28 08:35:05,254 INFO [train.py:842] (2/4) Epoch 22, batch 6850, loss[loss=0.1644, simple_loss=0.2655, pruned_loss=0.03162, over 7272.00 frames.], tot_loss[loss=0.182, simple_loss=0.2689, pruned_loss=0.0475, over 1419717.55 frames.], batch size: 24, lr: 2.49e-04
2022-05-28 08:35:43,335 INFO [train.py:842] (2/4) Epoch 22, batch 6900, loss[loss=0.2044, simple_loss=0.3005, pruned_loss=0.05412, over 7226.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2705, pruned_loss=0.04841, over 1415640.29 frames.], batch size: 21, lr: 2.49e-04
2022-05-28 08:36:21,220 INFO [train.py:842] (2/4) Epoch 22, batch 6950, loss[loss=0.1751, simple_loss=0.2637, pruned_loss=0.04325, over 7423.00 frames.], tot_loss[loss=0.183, simple_loss=0.2703, pruned_loss=0.04779, over 1412308.07 frames.], batch size: 20, lr: 2.49e-04
2022-05-28 08:37:02,015 INFO [train.py:842] (2/4) Epoch 22, batch 7000, loss[loss=0.1732, simple_loss=0.266, pruned_loss=0.04022, over 7315.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2714, pruned_loss=0.0486, over 1414637.81 frames.], batch size: 21, lr: 2.49e-04
2022-05-28 08:37:39,799 INFO [train.py:842] (2/4) Epoch 22, batch 7050, loss[loss=0.2235, simple_loss=0.3116, pruned_loss=0.06774, over 7301.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2713, pruned_loss=0.04828, over 1416383.04 frames.], batch size: 24, lr: 2.49e-04
2022-05-28 08:38:17,977 INFO [train.py:842] (2/4) Epoch 22, batch 7100, loss[loss=0.2056, simple_loss=0.277, pruned_loss=0.06708, over 7008.00 frames.], tot_loss[loss=0.1849, simple_loss=0.272, pruned_loss=0.04887, over 1417834.95 frames.], batch size: 16, lr: 2.49e-04
2022-05-28 08:38:55,910 INFO [train.py:842] (2/4) Epoch 22, batch 7150, loss[loss=0.1521, simple_loss=0.2347, pruned_loss=0.03476, over 7432.00 frames.], tot_loss[loss=0.1849, simple_loss=0.272, pruned_loss=0.04889, over 1417711.19 frames.], batch size: 18, lr: 2.48e-04
2022-05-28 08:39:34,202 INFO [train.py:842] (2/4) Epoch 22, batch 7200, loss[loss=0.1604, simple_loss=0.2552, pruned_loss=0.03284, over 7224.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2722, pruned_loss=0.04945, over 1411362.23 frames.], batch size: 20, lr: 2.48e-04
2022-05-28 08:40:12,372 INFO [train.py:842] (2/4) Epoch 22, batch 7250, loss[loss=0.2177, simple_loss=0.2963, pruned_loss=0.06958, over 7198.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2717, pruned_loss=0.04927, over 1415036.71 frames.], batch size: 23, lr: 2.48e-04
2022-05-28 08:40:50,458 INFO [train.py:842] (2/4) Epoch 22, batch 7300, loss[loss=0.154, simple_loss=0.2319, pruned_loss=0.03806, over 7282.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2711, pruned_loss=0.04898, over 1413582.25 frames.], batch size: 17, lr: 2.48e-04
2022-05-28 08:41:28,163 INFO [train.py:842] (2/4) Epoch 22, batch 7350, loss[loss=0.2182, simple_loss=0.3113, pruned_loss=0.06255, over 7140.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2725, pruned_loss=0.04934, over 1416632.91 frames.], batch size: 20, lr: 2.48e-04
2022-05-28 08:42:06,480 INFO [train.py:842] (2/4) Epoch 22, batch 7400, loss[loss=0.2205, simple_loss=0.3031, pruned_loss=0.06897, over 7291.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2723, pruned_loss=0.04902, over 1419102.57 frames.], batch size: 25, lr: 2.48e-04
2022-05-28 08:42:44,748 INFO [train.py:842] (2/4) Epoch 22, batch 7450, loss[loss=0.1808, simple_loss=0.2697, pruned_loss=0.04593, over 7168.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2719, pruned_loss=0.04934, over 1417784.51 frames.], batch size: 18, lr: 2.48e-04
2022-05-28 08:43:22,937 INFO [train.py:842] (2/4) Epoch 22, batch 7500, loss[loss=0.1474, simple_loss=0.2296, pruned_loss=0.03256, over 7427.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2718, pruned_loss=0.0489, over 1418622.22 frames.], batch size: 18, lr: 2.48e-04
2022-05-28 08:44:01,046 INFO [train.py:842] (2/4) Epoch 22, batch 7550, loss[loss=0.2918, simple_loss=0.3591, pruned_loss=0.1123, over 7193.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2704, pruned_loss=0.04837, over 1417310.65 frames.], batch size: 23, lr: 2.48e-04
2022-05-28 08:44:39,275 INFO [train.py:842] (2/4) Epoch 22, batch 7600, loss[loss=0.2144, simple_loss=0.2921, pruned_loss=0.06836, over 7196.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2702, pruned_loss=0.04854, over 1417982.09 frames.], batch size: 22, lr: 2.48e-04
2022-05-28 08:45:17,058 INFO [train.py:842] (2/4) Epoch 22, batch 7650, loss[loss=0.191, simple_loss=0.2761, pruned_loss=0.05299, over 7338.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2706, pruned_loss=0.04854, over 1419777.29 frames.], batch size: 20, lr: 2.48e-04
2022-05-28 08:45:55,431 INFO [train.py:842] (2/4) Epoch 22, batch 7700, loss[loss=0.1791, simple_loss=0.2835, pruned_loss=0.03731, over 7226.00 frames.], tot_loss[loss=0.183, simple_loss=0.2697, pruned_loss=0.0481, over 1420855.33 frames.], batch size: 21, lr: 2.48e-04
2022-05-28 08:46:33,279 INFO [train.py:842] (2/4) Epoch 22, batch 7750, loss[loss=0.1957, simple_loss=0.2939, pruned_loss=0.04874, over 7142.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2704, pruned_loss=0.04825, over 1423480.83 frames.], batch size: 20, lr: 2.48e-04
2022-05-28 08:47:11,741 INFO [train.py:842] (2/4) Epoch 22, batch 7800, loss[loss=0.1856, simple_loss=0.2767, pruned_loss=0.04728, over 7297.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2698, pruned_loss=0.04801, over 1419425.92 frames.], batch size: 25, lr: 2.48e-04
2022-05-28 08:47:49,715 INFO [train.py:842] (2/4) Epoch 22, batch 7850, loss[loss=0.1759, simple_loss=0.2663, pruned_loss=0.04278, over 7317.00 frames.], tot_loss[loss=0.1832, simple_loss=0.27, pruned_loss=0.04824, over 1420156.05 frames.], batch size: 25, lr: 2.48e-04
2022-05-28 08:48:27,740 INFO [train.py:842] (2/4) Epoch 22, batch 7900, loss[loss=0.1902, simple_loss=0.2613, pruned_loss=0.05952, over 7073.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2711, pruned_loss=0.04913, over 1414587.31 frames.], batch size: 18, lr: 2.48e-04
2022-05-28 08:49:05,723 INFO [train.py:842] (2/4) Epoch 22, batch 7950, loss[loss=0.1817, simple_loss=0.2781, pruned_loss=0.04262, over 7300.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2708, pruned_loss=0.04843, over 1412042.85 frames.], batch size: 25, lr: 2.48e-04
2022-05-28 08:49:43,860 INFO [train.py:842] (2/4) Epoch 22, batch 8000, loss[loss=0.2411, simple_loss=0.3249, pruned_loss=0.07866, over 7032.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2728, pruned_loss=0.04994, over 1413684.41 frames.], batch size: 28, lr: 2.48e-04
2022-05-28 08:50:21,586 INFO [train.py:842] (2/4) Epoch 22, batch 8050, loss[loss=0.1512, simple_loss=0.2393, pruned_loss=0.03159, over 6995.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2741, pruned_loss=0.05071, over 1410566.22 frames.], batch size: 16, lr: 2.48e-04
2022-05-28 08:50:59,773 INFO [train.py:842] (2/4) Epoch 22, batch 8100, loss[loss=0.3012, simple_loss=0.3712, pruned_loss=0.1156, over 7072.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2739, pruned_loss=0.0513, over 1406766.48 frames.], batch size: 18, lr: 2.48e-04
2022-05-28 08:51:37,735 INFO [train.py:842] (2/4) Epoch 22, batch 8150, loss[loss=0.1539, simple_loss=0.2378, pruned_loss=0.03504, over 7286.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2737, pruned_loss=0.05091, over 1412610.19 frames.], batch size: 17, lr: 2.48e-04
2022-05-28 08:52:15,699 INFO [train.py:842] (2/4) Epoch 22, batch 8200, loss[loss=0.1813, simple_loss=0.2721, pruned_loss=0.0453, over 6338.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2733, pruned_loss=0.05009, over 1416420.95 frames.], batch size: 38, lr: 2.48e-04
2022-05-28 08:52:53,587 INFO [train.py:842] (2/4) Epoch 22, batch 8250, loss[loss=0.1725, simple_loss=0.2675, pruned_loss=0.03873, over 7062.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2723, pruned_loss=0.04935, over 1416989.13 frames.], batch size: 28, lr: 2.48e-04
2022-05-28 08:53:31,678 INFO [train.py:842] (2/4) Epoch 22, batch 8300, loss[loss=0.1858, simple_loss=0.2789, pruned_loss=0.04639, over 7280.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2718, pruned_loss=0.04889, over 1417805.32 frames.], batch size: 24, lr: 2.48e-04
2022-05-28 08:54:09,568 INFO [train.py:842] (2/4) Epoch 22, batch 8350, loss[loss=0.1841, simple_loss=0.2775, pruned_loss=0.04537, over 7220.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2712, pruned_loss=0.04827, over 1419280.36 frames.], batch size: 21, lr: 2.48e-04
2022-05-28 08:54:47,763 INFO [train.py:842] (2/4) Epoch 22, batch 8400, loss[loss=0.1958, simple_loss=0.2903, pruned_loss=0.05062, over 7227.00 frames.], tot_loss[loss=0.185, simple_loss=0.2719, pruned_loss=0.04902, over 1421484.03 frames.], batch size: 21, lr: 2.48e-04
2022-05-28 08:55:25,639 INFO [train.py:842] (2/4) Epoch 22, batch 8450, loss[loss=0.2176, simple_loss=0.2983, pruned_loss=0.06841, over 7329.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2719, pruned_loss=0.04897, over 1418335.51 frames.], batch size: 20, lr: 2.48e-04
2022-05-28 08:56:03,902 INFO [train.py:842] (2/4) Epoch 22, batch 8500, loss[loss=0.1385, simple_loss=0.2276, pruned_loss=0.0247, over 7000.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2713, pruned_loss=0.04826, over 1420754.21 frames.], batch size: 16, lr: 2.48e-04
2022-05-28 08:56:41,566 INFO [train.py:842] (2/4) Epoch 22, batch 8550, loss[loss=0.2167, simple_loss=0.3015, pruned_loss=0.0659, over 7296.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2715, pruned_loss=0.04841, over 1415877.58 frames.], batch size: 25, lr: 2.48e-04
2022-05-28 08:57:19,534 INFO [train.py:842] (2/4) Epoch 22, batch 8600, loss[loss=0.1704, simple_loss=0.2685, pruned_loss=0.03614, over 7292.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2714, pruned_loss=0.04814, over 1418100.36 frames.], batch size: 24, lr: 2.48e-04
2022-05-28 08:57:57,115 INFO [train.py:842] (2/4) Epoch 22, batch 8650, loss[loss=0.1622, simple_loss=0.2503, pruned_loss=0.03705, over 7168.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2719, pruned_loss=0.04935, over 1411553.75 frames.], batch size: 18, lr: 2.48e-04
2022-05-28 08:58:35,186 INFO [train.py:842] (2/4) Epoch 22, batch 8700, loss[loss=0.1848, simple_loss=0.2733, pruned_loss=0.04818, over 7356.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2729, pruned_loss=0.0498, over 1412625.53 frames.], batch size: 19, lr: 2.48e-04
2022-05-28 08:59:12,996 INFO [train.py:842] (2/4) Epoch 22, batch 8750, loss[loss=0.1954, simple_loss=0.2872, pruned_loss=0.05184, over 7330.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2726, pruned_loss=0.04943, over 1414786.44 frames.], batch size: 22, lr: 2.47e-04
2022-05-28 08:59:50,866 INFO [train.py:842] (2/4) Epoch 22, batch 8800, loss[loss=0.1636, simple_loss=0.2498, pruned_loss=0.03873, over 7157.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2715, pruned_loss=0.04836, over 1412301.85 frames.], batch size: 18, lr: 2.47e-04
2022-05-28 09:00:28,599 INFO [train.py:842] (2/4) Epoch 22, batch 8850, loss[loss=0.1724, simple_loss=0.2675, pruned_loss=0.0386, over 7437.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2728, pruned_loss=0.04882, over 1409760.69 frames.], batch size: 20, lr: 2.47e-04
2022-05-28 09:01:06,625 INFO [train.py:842] (2/4) Epoch 22, batch 8900, loss[loss=0.1513, simple_loss=0.2403, pruned_loss=0.03117, over 7235.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2722, pruned_loss=0.04869, over 1411628.41 frames.], batch size: 20, lr: 2.47e-04
2022-05-28 09:01:44,248 INFO [train.py:842] (2/4) Epoch 22, batch 8950, loss[loss=0.1794, simple_loss=0.2712, pruned_loss=0.04378, over 7298.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2721, pruned_loss=0.04857, over 1405023.39 frames.], batch size: 25, lr: 2.47e-04
2022-05-28 09:02:22,231 INFO [train.py:842] (2/4) Epoch 22, batch 9000, loss[loss=0.1839, simple_loss=0.262, pruned_loss=0.05292, over 7002.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2728, pruned_loss=0.049, over 1399315.90 frames.], batch size: 16, lr: 2.47e-04
2022-05-28 09:02:22,232 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 09:02:31,315 INFO [train.py:871] (2/4) Epoch 22, validation: loss=0.1647, simple_loss=0.2634, pruned_loss=0.03302, over 868885.00 frames.
2022-05-28 09:03:08,929 INFO [train.py:842] (2/4) Epoch 22, batch 9050, loss[loss=0.2046, simple_loss=0.2876, pruned_loss=0.06081, over 4962.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2727, pruned_loss=0.049, over 1393160.27 frames.], batch size: 52, lr: 2.47e-04
2022-05-28 09:03:46,322 INFO [train.py:842] (2/4) Epoch 22, batch 9100, loss[loss=0.2402, simple_loss=0.3157, pruned_loss=0.08232, over 5075.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2737, pruned_loss=0.04952, over 1367385.57 frames.], batch size: 52, lr: 2.47e-04
2022-05-28 09:04:23,080 INFO [train.py:842] (2/4) Epoch 22, batch 9150, loss[loss=0.2556, simple_loss=0.3246, pruned_loss=0.09332, over 4856.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2782, pruned_loss=0.05344, over 1285571.74 frames.], batch size: 53, lr: 2.47e-04
2022-05-28 09:05:08,931 INFO [train.py:842] (2/4) Epoch 23, batch 0, loss[loss=0.1649, simple_loss=0.2432, pruned_loss=0.0433, over 6785.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2432, pruned_loss=0.0433, over 6785.00 frames.], batch size: 15, lr: 2.42e-04
2022-05-28 09:05:47,157 INFO [train.py:842] (2/4) Epoch 23, batch 50, loss[loss=0.2311, simple_loss=0.3151, pruned_loss=0.07356, over 7149.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2689, pruned_loss=0.04774, over 319705.89 frames.], batch size: 19, lr: 2.42e-04
2022-05-28 09:06:25,602 INFO [train.py:842] (2/4) Epoch 23, batch 100, loss[loss=0.1711, simple_loss=0.2568, pruned_loss=0.04272, over 7268.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2674, pruned_loss=0.04718, over 566893.90 frames.], batch size: 18, lr: 2.42e-04
2022-05-28 09:07:03,393 INFO [train.py:842] (2/4) Epoch 23, batch 150, loss[loss=0.182, simple_loss=0.2788, pruned_loss=0.04262, over 7302.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2699, pruned_loss=0.04764, over 754637.24 frames.], batch size: 24, lr: 2.42e-04
2022-05-28 09:07:41,556 INFO [train.py:842] (2/4) Epoch 23, batch 200, loss[loss=0.2816, simple_loss=0.3489, pruned_loss=0.1071, over 6528.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2704, pruned_loss=0.04727, over 902788.05 frames.], batch size: 38, lr: 2.42e-04
2022-05-28 09:08:19,369 INFO [train.py:842] (2/4) Epoch 23, batch 250, loss[loss=0.1821, simple_loss=0.2803, pruned_loss=0.04193, over 7189.00 frames.], tot_loss[loss=0.1834, simple_loss=0.271, pruned_loss=0.0479, over 1017239.95 frames.], batch size: 23, lr: 2.42e-04
2022-05-28 09:08:57,665 INFO [train.py:842] (2/4) Epoch 23, batch 300, loss[loss=0.1626, simple_loss=0.2421, pruned_loss=0.04159, over 7151.00 frames.], tot_loss[loss=0.1827, simple_loss=0.27, pruned_loss=0.04767, over 1103325.21 frames.], batch size: 19, lr: 2.42e-04
2022-05-28 09:09:35,669 INFO [train.py:842] (2/4) Epoch 23, batch 350, loss[loss=0.1809, simple_loss=0.2752, pruned_loss=0.04327, over 7332.00 frames.], tot_loss[loss=0.1817, simple_loss=0.269, pruned_loss=0.04718, over 1177584.37 frames.], batch size: 22, lr: 2.42e-04
2022-05-28 09:10:13,899 INFO [train.py:842] (2/4) Epoch 23, batch 400, loss[loss=0.1933, simple_loss=0.2871, pruned_loss=0.04974, over 7197.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2679, pruned_loss=0.04679, over 1230072.49 frames.], batch size: 23, lr: 2.42e-04
2022-05-28 09:10:51,790 INFO [train.py:842] (2/4) Epoch 23, batch 450, loss[loss=0.2025, simple_loss=0.3009, pruned_loss=0.05209, over 7301.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2689, pruned_loss=0.04706, over 1271691.26 frames.], batch size: 24, lr: 2.42e-04
2022-05-28 09:11:30,075 INFO [train.py:842] (2/4) Epoch 23, batch 500, loss[loss=0.1831, simple_loss=0.2523, pruned_loss=0.0569, over 7221.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2683, pruned_loss=0.04708, over 1306968.47 frames.], batch size: 16, lr: 2.42e-04
2022-05-28 09:12:08,360 INFO [train.py:842] (2/4) Epoch 23, batch 550, loss[loss=0.1812, simple_loss=0.2668, pruned_loss=0.04774, over 7279.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2671, pruned_loss=0.04651, over 1336915.69 frames.], batch size: 24, lr: 2.42e-04
2022-05-28 09:12:46,590 INFO [train.py:842] (2/4) Epoch 23, batch 600, loss[loss=0.1634, simple_loss=0.2569, pruned_loss=0.0349, over 7110.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2678, pruned_loss=0.04669, over 1359454.61 frames.], batch size: 21, lr: 2.42e-04
2022-05-28 09:13:24,491 INFO [train.py:842] (2/4) Epoch 23, batch 650, loss[loss=0.1704, simple_loss=0.2668, pruned_loss=0.03703, over 6785.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2692, pruned_loss=0.04701, over 1375088.70 frames.], batch size: 31, lr: 2.42e-04
2022-05-28 09:14:02,707 INFO [train.py:842] (2/4) Epoch 23, batch 700, loss[loss=0.1765, simple_loss=0.259, pruned_loss=0.04701, over 5441.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2689, pruned_loss=0.04662, over 1381540.28 frames.], batch size: 53, lr: 2.42e-04
2022-05-28 09:14:40,546 INFO [train.py:842] (2/4) Epoch 23, batch 750, loss[loss=0.1932, simple_loss=0.2866, pruned_loss=0.0499, over 7195.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2697, pruned_loss=0.04686, over 1392175.54 frames.], batch size: 23, lr: 2.41e-04
2022-05-28 09:15:18,798 INFO [train.py:842] (2/4) Epoch 23, batch 800, loss[loss=0.18, simple_loss=0.2653, pruned_loss=0.04734, over 7364.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2693, pruned_loss=0.04712, over 1396324.73 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:16:05,927 INFO [train.py:842] (2/4) Epoch 23, batch 850, loss[loss=0.2249, simple_loss=0.2987, pruned_loss=0.07562, over 7435.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2697, pruned_loss=0.04742, over 1404881.88 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:16:44,108 INFO [train.py:842] (2/4) Epoch 23, batch 900, loss[loss=0.2284, simple_loss=0.2964, pruned_loss=0.08025, over 7159.00 frames.], tot_loss[loss=0.1824, simple_loss=0.27, pruned_loss=0.04737, over 1409353.69 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:17:21,900 INFO [train.py:842] (2/4) Epoch 23, batch 950, loss[loss=0.2245, simple_loss=0.3126, pruned_loss=0.06821, over 7080.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2705, pruned_loss=0.04747, over 1410437.17 frames.], batch size: 28, lr: 2.41e-04
2022-05-28 09:18:00,183 INFO [train.py:842] (2/4) Epoch 23, batch 1000, loss[loss=0.1756, simple_loss=0.2596, pruned_loss=0.04576, over 7356.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2691, pruned_loss=0.04662, over 1417595.15 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:18:38,351 INFO [train.py:842] (2/4) Epoch 23, batch 1050, loss[loss=0.2031, simple_loss=0.2831, pruned_loss=0.06157, over 5405.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2689, pruned_loss=0.04723, over 1418317.11 frames.], batch size: 52, lr: 2.41e-04
2022-05-28 09:19:16,279 INFO [train.py:842] (2/4) Epoch 23, batch 1100, loss[loss=0.147, simple_loss=0.2244, pruned_loss=0.03479, over 7268.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2694, pruned_loss=0.04756, over 1417910.78 frames.], batch size: 17, lr: 2.41e-04
2022-05-28 09:19:54,200 INFO [train.py:842] (2/4) Epoch 23, batch 1150, loss[loss=0.1516, simple_loss=0.2493, pruned_loss=0.02697, over 7439.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2693, pruned_loss=0.04742, over 1421732.54 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:20:32,490 INFO [train.py:842] (2/4) Epoch 23, batch 1200, loss[loss=0.1938, simple_loss=0.267, pruned_loss=0.0603, over 7280.00 frames.], tot_loss[loss=0.1828, simple_loss=0.27, pruned_loss=0.04781, over 1421713.07 frames.], batch size: 18, lr: 2.41e-04
2022-05-28 09:21:10,632 INFO [train.py:842] (2/4) Epoch 23, batch 1250, loss[loss=0.2325, simple_loss=0.2868, pruned_loss=0.08909, over 6839.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2698, pruned_loss=0.04797, over 1425294.71 frames.], batch size: 15, lr: 2.41e-04
2022-05-28 09:21:48,973 INFO [train.py:842] (2/4) Epoch 23, batch 1300, loss[loss=0.1978, simple_loss=0.285, pruned_loss=0.05531, over 7230.00 frames.], tot_loss[loss=0.1818, simple_loss=0.269, pruned_loss=0.04732, over 1428230.36 frames.], batch size: 23, lr: 2.41e-04
2022-05-28 09:22:27,065 INFO [train.py:842] (2/4) Epoch 23, batch 1350, loss[loss=0.15, simple_loss=0.2396, pruned_loss=0.03019, over 7286.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2682, pruned_loss=0.04715, over 1428219.47 frames.], batch size: 18, lr: 2.41e-04
2022-05-28 09:23:05,259 INFO [train.py:842] (2/4) Epoch 23, batch 1400, loss[loss=0.1604, simple_loss=0.2562, pruned_loss=0.03228, over 7123.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2685, pruned_loss=0.04687, over 1427123.55 frames.], batch size: 21, lr: 2.41e-04
2022-05-28 09:23:43,274 INFO [train.py:842] (2/4) Epoch 23, batch 1450, loss[loss=0.1551, simple_loss=0.2388, pruned_loss=0.03565, over 7406.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2687, pruned_loss=0.04753, over 1420907.50 frames.], batch size: 18, lr: 2.41e-04
2022-05-28 09:24:21,883 INFO [train.py:842] (2/4) Epoch 23, batch 1500, loss[loss=0.2001, simple_loss=0.2935, pruned_loss=0.05339, over 7091.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2661, pruned_loss=0.04637, over 1422819.81 frames.], batch size: 28, lr: 2.41e-04
2022-05-28 09:24:59,673 INFO [train.py:842] (2/4) Epoch 23, batch 1550, loss[loss=0.1679, simple_loss=0.2513, pruned_loss=0.04224, over 7366.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2675, pruned_loss=0.04699, over 1414098.79 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:25:37,903 INFO [train.py:842] (2/4) Epoch 23, batch 1600, loss[loss=0.1898, simple_loss=0.2811, pruned_loss=0.04926, over 7223.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2676, pruned_loss=0.04699, over 1412954.90 frames.], batch size: 21, lr: 2.41e-04
2022-05-28 09:26:16,015 INFO [train.py:842] (2/4) Epoch 23, batch 1650, loss[loss=0.1872, simple_loss=0.2832, pruned_loss=0.04557, over 7390.00 frames.], tot_loss[loss=0.181, simple_loss=0.2676, pruned_loss=0.04718, over 1415844.59 frames.], batch size: 23, lr: 2.41e-04
2022-05-28 09:26:54,165 INFO [train.py:842] (2/4) Epoch 23, batch 1700, loss[loss=0.1506, simple_loss=0.2395, pruned_loss=0.0309, over 7423.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2676, pruned_loss=0.04713, over 1416593.19 frames.], batch size: 18, lr: 2.41e-04
2022-05-28 09:27:31,882 INFO [train.py:842] (2/4) Epoch 23, batch 1750, loss[loss=0.1976, simple_loss=0.2845, pruned_loss=0.05541, over 7114.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2685, pruned_loss=0.04702, over 1414530.41 frames.], batch size: 26, lr: 2.41e-04
2022-05-28 09:28:10,169 INFO [train.py:842] (2/4) Epoch 23, batch 1800, loss[loss=0.2582, simple_loss=0.336, pruned_loss=0.0902, over 5035.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2701, pruned_loss=0.04811, over 1412162.81 frames.], batch size: 53, lr: 2.41e-04
2022-05-28 09:28:48,255 INFO [train.py:842] (2/4) Epoch 23, batch 1850, loss[loss=0.1761, simple_loss=0.2634, pruned_loss=0.04439, over 7434.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2696, pruned_loss=0.04786, over 1417159.59 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:29:26,609 INFO [train.py:842] (2/4) Epoch 23, batch 1900, loss[loss=0.2685, simple_loss=0.3399, pruned_loss=0.09857, over 7143.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2695, pruned_loss=0.04791, over 1420358.57 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:30:04,581 INFO [train.py:842] (2/4) Epoch 23, batch 1950, loss[loss=0.1727, simple_loss=0.2758, pruned_loss=0.0348, over 7138.00 frames.], tot_loss[loss=0.184, simple_loss=0.2706, pruned_loss=0.04871, over 1417333.88 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:30:42,752 INFO [train.py:842] (2/4) Epoch 23, batch 2000, loss[loss=0.1608, simple_loss=0.2548, pruned_loss=0.03338, over 7261.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2715, pruned_loss=0.04844, over 1420499.03 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:31:20,833 INFO [train.py:842] (2/4) Epoch 23, batch 2050, loss[loss=0.1932, simple_loss=0.2819, pruned_loss=0.05228, over 7231.00 frames.], tot_loss[loss=0.1834, simple_loss=0.271, pruned_loss=0.04794, over 1425144.53 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:31:59,065 INFO [train.py:842] (2/4) Epoch 23, batch 2100, loss[loss=0.2182, simple_loss=0.3006, pruned_loss=0.06792, over 7198.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2709, pruned_loss=0.04816, over 1419930.78 frames.], batch size: 23, lr: 2.41e-04
2022-05-28 09:32:37,114 INFO [train.py:842] (2/4) Epoch 23, batch 2150, loss[loss=0.1593, simple_loss=0.2531, pruned_loss=0.03274, over 7149.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2703, pruned_loss=0.04765, over 1421178.27 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:33:15,367 INFO [train.py:842] (2/4) Epoch 23, batch 2200, loss[loss=0.1738, simple_loss=0.2727, pruned_loss=0.03745, over 7138.00 frames.], tot_loss[loss=0.1826, simple_loss=0.27, pruned_loss=0.04754, over 1417764.57 frames.], batch size: 20, lr: 2.41e-04
2022-05-28 09:33:53,111 INFO [train.py:842] (2/4) Epoch 23, batch 2250, loss[loss=0.1465, simple_loss=0.2328, pruned_loss=0.03015, over 7159.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2703, pruned_loss=0.04738, over 1412989.25 frames.], batch size: 19, lr: 2.41e-04
2022-05-28 09:34:31,493 INFO [train.py:842] (2/4) Epoch 23, batch 2300, loss[loss=0.188, simple_loss=0.2822, pruned_loss=0.04691, over 7320.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2688, pruned_loss=0.047, over 1414568.84 frames.], batch size: 21, lr: 2.41e-04
2022-05-28 09:35:09,539 INFO [train.py:842] (2/4) Epoch 23, batch 2350, loss[loss=0.1503, simple_loss=0.2456, pruned_loss=0.02746, over 7332.00 frames.], tot_loss[loss=0.1819, simple_loss=0.269, pruned_loss=0.04741, over 1416517.05 frames.], batch size: 22, lr: 2.41e-04
2022-05-28 09:35:47,646 INFO [train.py:842] (2/4) Epoch 23, batch 2400, loss[loss=0.1898, simple_loss=0.2887, pruned_loss=0.0455, over 7276.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2697, pruned_loss=0.04791, over 1418467.78 frames.], batch size: 24, lr: 2.41e-04
2022-05-28 09:36:25,391 INFO [train.py:842] (2/4) Epoch 23, batch 2450, loss[loss=0.1752, simple_loss=0.2745, pruned_loss=0.0379, over 7215.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2714, pruned_loss=0.04838, over 1422882.68 frames.], batch size: 22, lr: 2.40e-04
2022-05-28 09:37:03,845 INFO [train.py:842] (2/4) Epoch 23, batch 2500, loss[loss=0.1951, simple_loss=0.286, pruned_loss=0.05216, over 6272.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2704, pruned_loss=0.04852, over 1421152.01 frames.], batch size: 37, lr: 2.40e-04
2022-05-28 09:37:41,686 INFO [train.py:842] (2/4) Epoch 23, batch 2550, loss[loss=0.1615, simple_loss=0.2556, pruned_loss=0.03373, over 7385.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2704, pruned_loss=0.04845, over 1421886.32 frames.], batch size: 23, lr: 2.40e-04
2022-05-28 09:38:20,087 INFO [train.py:842] (2/4) Epoch 23, batch 2600, loss[loss=0.1934, simple_loss=0.2828, pruned_loss=0.05197, over 7334.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2702, pruned_loss=0.04806, over 1426414.42 frames.], batch size: 22, lr: 2.40e-04
2022-05-28 09:38:58,228 INFO [train.py:842] (2/4) Epoch 23, batch 2650, loss[loss=0.1787, simple_loss=0.2651, pruned_loss=0.04617, over 7316.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2688, pruned_loss=0.0475, over 1423382.91 frames.], batch size: 25, lr: 2.40e-04
2022-05-28 09:39:36,475 INFO [train.py:842] (2/4) Epoch 23, batch 2700, loss[loss=0.1472, simple_loss=0.2351, pruned_loss=0.02966, over 7150.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2694, pruned_loss=0.04797, over 1423166.68 frames.], batch size: 19, lr: 2.40e-04
2022-05-28 09:40:14,497 INFO [train.py:842] (2/4) Epoch 23, batch 2750, loss[loss=0.162, simple_loss=0.2514, pruned_loss=0.03632, over 7165.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2689, pruned_loss=0.04776, over 1420877.85 frames.], batch size: 18, lr: 2.40e-04
2022-05-28 09:40:52,718 INFO [train.py:842] (2/4) Epoch 23, batch 2800, loss[loss=0.1606, simple_loss=0.2488, pruned_loss=0.03617, over 7161.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2685, pruned_loss=0.04739, over 1419439.28 frames.], batch size: 18, lr: 2.40e-04
2022-05-28 09:41:30,788 INFO [train.py:842] (2/4) Epoch 23, batch 2850, loss[loss=0.1956, simple_loss=0.2768, pruned_loss=0.05721, over 7067.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2686, pruned_loss=0.04739, over 1420920.89 frames.], batch size: 28, lr: 2.40e-04
2022-05-28 09:42:08,983 INFO [train.py:842] (2/4) Epoch 23, batch 2900, loss[loss=0.1896, simple_loss=0.2726, pruned_loss=0.05328, over 7275.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2686, pruned_loss=0.04724, over 1422821.68 frames.], batch size: 25, lr: 2.40e-04
2022-05-28 09:42:46,952 INFO [train.py:842] (2/4) Epoch 23, batch 2950, loss[loss=0.1705, simple_loss=0.2639, pruned_loss=0.03857, over 7208.00 frames.], tot_loss[loss=0.182, simple_loss=0.2689, pruned_loss=0.04752, over 1423817.80 frames.], batch size: 22, lr: 2.40e-04
2022-05-28 09:43:25,056 INFO [train.py:842] (2/4) Epoch 23, batch 3000, loss[loss=0.1477, simple_loss=0.2305, pruned_loss=0.03251, over 7002.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2691, pruned_loss=0.04707, over 1423766.93 frames.], batch size: 16, lr: 2.40e-04
2022-05-28 09:43:25,057 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 09:43:34,050 INFO [train.py:871] (2/4) Epoch 23, validation: loss=0.1673, simple_loss=0.2658, pruned_loss=0.03441, over 868885.00 frames.
2022-05-28 09:44:12,028 INFO [train.py:842] (2/4) Epoch 23, batch 3050, loss[loss=0.1721, simple_loss=0.2528, pruned_loss=0.04565, over 7151.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2698, pruned_loss=0.048, over 1426589.04 frames.], batch size: 19, lr: 2.40e-04
2022-05-28 09:44:50,427 INFO [train.py:842] (2/4) Epoch 23, batch 3100, loss[loss=0.1854, simple_loss=0.2668, pruned_loss=0.05199, over 7232.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2696, pruned_loss=0.04797, over 1425763.48 frames.], batch size: 20, lr: 2.40e-04
2022-05-28 09:45:28,469 INFO [train.py:842] (2/4) Epoch 23, batch 3150, loss[loss=0.1865, simple_loss=0.2769, pruned_loss=0.04808, over 7327.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2689, pruned_loss=0.04784, over 1427544.23 frames.], batch size: 20, lr: 2.40e-04
2022-05-28 09:46:06,786 INFO [train.py:842] (2/4) Epoch 23, batch 3200, loss[loss=0.1619, simple_loss=0.2598, pruned_loss=0.03199, over 7113.00 frames.], tot_loss[loss=0.1811, simple_loss=0.268, pruned_loss=0.04708, over 1428114.98 frames.], batch size: 21, lr: 2.40e-04
2022-05-28 09:46:44,583 INFO [train.py:842] (2/4) Epoch 23, batch 3250, loss[loss=0.2227, simple_loss=0.3062, pruned_loss=0.06964, over 6325.00 frames.], tot_loss[loss=0.1828, simple_loss=0.269, pruned_loss=0.04827, over 1422452.97 frames.], batch size: 38, lr: 2.40e-04
2022-05-28 09:47:22,766 INFO [train.py:842] (2/4) Epoch 23, batch 3300, loss[loss=0.2025, simple_loss=0.2853, pruned_loss=0.05984, over 7287.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2695, pruned_loss=0.04771, over 1423722.41 frames.], batch size: 24, lr: 2.40e-04
2022-05-28 09:48:00,967 INFO [train.py:842] (2/4) Epoch 23, batch 3350, loss[loss=0.1792, simple_loss=0.2629, pruned_loss=0.04775, over 7163.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2685, pruned_loss=0.04764, over 1427814.58 frames.], batch size: 26, lr: 2.40e-04
2022-05-28 09:48:39,179 INFO [train.py:842] (2/4) Epoch 23, batch 3400, loss[loss=0.1496, simple_loss=0.2328, pruned_loss=0.03325, over 7166.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2694, pruned_loss=0.04819, over 1429144.12 frames.], batch size: 19, lr: 2.40e-04
2022-05-28 09:49:17,342 INFO [train.py:842] (2/4) Epoch 23, batch 3450, loss[loss=0.1531, simple_loss=0.238, pruned_loss=0.03409, over 7231.00 frames.], tot_loss[loss=0.1819, simple_loss=0.269, pruned_loss=0.04739, over 1430893.70 frames.], batch size: 16, lr: 2.40e-04
2022-05-28 09:49:55,692 INFO [train.py:842] (2/4) Epoch 23, batch 3500, loss[loss=0.1364, simple_loss=0.2181, pruned_loss=0.02733, over 7216.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2684, pruned_loss=0.04694, over 1431669.44 frames.], batch size: 16, lr: 2.40e-04
2022-05-28 09:50:33,623 INFO [train.py:842] (2/4) Epoch 23, batch 3550, loss[loss=0.1626, simple_loss=0.241, pruned_loss=0.04209, over 7414.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2688, pruned_loss=0.04712, over 1431167.40 frames.], batch size: 18, lr: 2.40e-04
2022-05-28 09:51:11,853 INFO [train.py:842] (2/4) Epoch 23, batch 3600, loss[loss=0.1477, simple_loss=0.2314, pruned_loss=0.03203, over 7281.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2696, pruned_loss=0.04756, over 1431691.06 frames.], batch size: 17, lr: 2.40e-04
2022-05-28 09:51:49,915 INFO [train.py:842] (2/4) Epoch 23, batch 3650, loss[loss=0.1927, simple_loss=0.2821, pruned_loss=0.05164, over 6450.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2703, pruned_loss=0.04763, over 1431400.45 frames.], batch size: 37, lr: 2.40e-04
2022-05-28 09:52:28,067 INFO [train.py:842] (2/4) Epoch 23, batch 3700, loss[loss=0.1816, simple_loss=0.2739, pruned_loss=0.04464, over 7158.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2706, pruned_loss=0.04791, over 1430154.62 frames.], batch size: 19, lr: 2.40e-04
2022-05-28 09:53:05,868 INFO [train.py:842] (2/4) Epoch 23, batch 3750, loss[loss=0.2394, simple_loss=0.2865, pruned_loss=0.09614, over 7268.00 frames.], tot_loss[loss=0.186, simple_loss=0.2721, pruned_loss=0.0499, over 1427775.29 frames.], batch size: 17, lr: 2.40e-04
2022-05-28 09:53:44,123 INFO [train.py:842] (2/4) Epoch 23, batch 3800, loss[loss=0.1728, simple_loss=0.2604, pruned_loss=0.04263, over 7382.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2723, pruned_loss=0.04995, over 1429486.14 frames.], batch size: 23, lr: 2.40e-04
2022-05-28 09:54:22,214 INFO [train.py:842] (2/4) Epoch 23, batch 3850, loss[loss=0.1739, simple_loss=0.2648, pruned_loss=0.04145, over 7060.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2711, pruned_loss=0.04889, over 1431154.85 frames.], batch size: 28, lr: 2.40e-04
2022-05-28 09:55:00,585 INFO [train.py:842] (2/4) Epoch 23, batch 3900, loss[loss=0.1734, simple_loss=0.2741, pruned_loss=0.03637, over 7121.00 frames.], tot_loss[loss=0.1841, simple_loss=0.271, pruned_loss=0.04854, over 1430870.61 frames.], batch size: 21, lr: 2.40e-04
2022-05-28 09:55:38,443 INFO [train.py:842] (2/4) Epoch 23, batch 3950, loss[loss=0.1592, simple_loss=0.2563, pruned_loss=0.03108, over 7154.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2708, pruned_loss=0.04814, over 1430041.43 frames.], batch size: 19, lr: 2.40e-04
2022-05-28 09:56:16,610 INFO [train.py:842] (2/4) Epoch 23, batch 4000, loss[loss=0.1706, simple_loss=0.2508, pruned_loss=0.04519, over 7300.00 frames.], tot_loss[loss=0.184, simple_loss=0.2712, pruned_loss=0.0484, over 1427060.73 frames.], batch size: 17, lr: 2.40e-04
2022-05-28 09:56:54,389 INFO [train.py:842] (2/4) Epoch 23, batch 4050, loss[loss=0.1539, simple_loss=0.2372, pruned_loss=0.03532, over 6788.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2714, pruned_loss=0.04857, over 1421510.43 frames.], batch size: 15, lr: 2.40e-04
2022-05-28 09:57:32,627 INFO [train.py:842] (2/4) Epoch 23, batch 4100, loss[loss=0.2014, simple_loss=0.2857, pruned_loss=0.05859, over 7144.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2709, pruned_loss=0.04859, over 1418870.53 frames.], batch size: 20, lr: 2.40e-04
2022-05-28 09:58:10,723 INFO [train.py:842] (2/4) Epoch 23, batch 4150, loss[loss=0.1656, simple_loss=0.2548, pruned_loss=0.03816, over 7058.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2689, pruned_loss=0.04747, over 1418149.88 frames.], batch size: 18, lr: 2.39e-04
2022-05-28 09:58:48,822 INFO [train.py:842] (2/4) Epoch 23, batch 4200, loss[loss=0.1593, simple_loss=0.2494, pruned_loss=0.0346, over 7426.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2703, pruned_loss=0.04812, over 1421270.60 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 09:59:26,827 INFO [train.py:842] (2/4) Epoch 23, batch 4250, loss[loss=0.122, simple_loss=0.2055, pruned_loss=0.01928, over 7274.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2696, pruned_loss=0.04752, over 1426064.37 frames.], batch size: 17, lr: 2.39e-04
2022-05-28 10:00:04,964 INFO [train.py:842] (2/4) Epoch 23, batch 4300, loss[loss=0.2484, simple_loss=0.3298, pruned_loss=0.08349, over 6783.00 frames.], tot_loss[loss=0.1828, simple_loss=0.27, pruned_loss=0.04782, over 1427763.68 frames.], batch size: 31, lr: 2.39e-04
2022-05-28 10:00:42,793 INFO [train.py:842] (2/4) Epoch 23, batch 4350, loss[loss=0.1921, simple_loss=0.2721, pruned_loss=0.05603, over 7408.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2701, pruned_loss=0.04784, over 1426923.58 frames.], batch size: 21, lr: 2.39e-04
2022-05-28 10:01:30,388 INFO [train.py:842] (2/4) Epoch 23, batch 4400, loss[loss=0.1715, simple_loss=0.2658, pruned_loss=0.03855, over 6805.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2694, pruned_loss=0.0475, over 1427695.89 frames.], batch size: 31, lr: 2.39e-04
2022-05-28 10:02:08,603 INFO [train.py:842] (2/4) Epoch 23, batch 4450, loss[loss=0.131, simple_loss=0.217, pruned_loss=0.02253, over 7129.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2688, pruned_loss=0.0472, over 1428200.05 frames.], batch size: 17, lr: 2.39e-04
2022-05-28 10:02:46,913 INFO [train.py:842] (2/4) Epoch 23, batch 4500, loss[loss=0.1668, simple_loss=0.25, pruned_loss=0.04182, over 7404.00 frames.], tot_loss[loss=0.181, simple_loss=0.2685, pruned_loss=0.04673, over 1426398.71 frames.], batch size: 18, lr: 2.39e-04
2022-05-28 10:03:24,942 INFO [train.py:842] (2/4) Epoch 23, batch 4550, loss[loss=0.1951, simple_loss=0.2817, pruned_loss=0.05427, over 7205.00 frames.], tot_loss[loss=0.18, simple_loss=0.2678, pruned_loss=0.04606, over 1429499.97 frames.], batch size: 22, lr: 2.39e-04
2022-05-28 10:04:12,779 INFO [train.py:842] (2/4) Epoch 23, batch 4600, loss[loss=0.186, simple_loss=0.2662, pruned_loss=0.05284, over 7372.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2685, pruned_loss=0.04637, over 1425050.19 frames.], batch size: 23, lr: 2.39e-04
2022-05-28 10:04:50,907 INFO [train.py:842] (2/4) Epoch 23, batch 4650, loss[loss=0.168, simple_loss=0.2589, pruned_loss=0.03853, over 7435.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2677, pruned_loss=0.0461, over 1429046.93 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 10:05:38,560 INFO [train.py:842] (2/4) Epoch 23, batch 4700, loss[loss=0.1819, simple_loss=0.2801, pruned_loss=0.0418, over 7411.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2675, pruned_loss=0.04574, over 1430612.14 frames.], batch size: 21, lr: 2.39e-04
2022-05-28 10:06:16,320 INFO [train.py:842] (2/4) Epoch 23, batch 4750, loss[loss=0.1793, simple_loss=0.2753, pruned_loss=0.04168, over 7144.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2691, pruned_loss=0.04653, over 1424345.43 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 10:06:54,360 INFO [train.py:842] (2/4) Epoch 23, batch 4800, loss[loss=0.1701, simple_loss=0.26, pruned_loss=0.04007, over 7067.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2701, pruned_loss=0.04703, over 1420398.77 frames.], batch size: 18, lr: 2.39e-04
2022-05-28 10:07:32,376 INFO [train.py:842] (2/4) Epoch 23, batch 4850, loss[loss=0.1533, simple_loss=0.2342, pruned_loss=0.03623, over 7407.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2684, pruned_loss=0.04659, over 1420386.18 frames.], batch size: 18, lr: 2.39e-04
2022-05-28 10:08:10,711 INFO [train.py:842] (2/4) Epoch 23, batch 4900, loss[loss=0.1966, simple_loss=0.2862, pruned_loss=0.0535, over 7195.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2697, pruned_loss=0.04761, over 1423932.27 frames.], batch size: 22, lr: 2.39e-04
2022-05-28 10:08:48,734 INFO [train.py:842] (2/4) Epoch 23, batch 4950, loss[loss=0.1851, simple_loss=0.2777, pruned_loss=0.04623, over 7410.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2685, pruned_loss=0.04725, over 1423699.47 frames.], batch size: 21, lr: 2.39e-04
2022-05-28 10:09:27,010 INFO [train.py:842] (2/4) Epoch 23, batch 5000, loss[loss=0.1722, simple_loss=0.2562, pruned_loss=0.04416, over 7440.00 frames.], tot_loss[loss=0.1822, simple_loss=0.269, pruned_loss=0.0477, over 1422437.53 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 10:10:05,040 INFO [train.py:842] (2/4) Epoch 23, batch 5050, loss[loss=0.2103, simple_loss=0.2876, pruned_loss=0.06649, over 7163.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2674, pruned_loss=0.047, over 1421050.83 frames.], batch size: 19, lr: 2.39e-04
2022-05-28 10:10:43,283 INFO [train.py:842] (2/4) Epoch 23, batch 5100, loss[loss=0.2441, simple_loss=0.326, pruned_loss=0.08115, over 7272.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2681, pruned_loss=0.04735, over 1422509.52 frames.], batch size: 24, lr: 2.39e-04
2022-05-28 10:11:21,303 INFO [train.py:842] (2/4) Epoch 23, batch 5150, loss[loss=0.2125, simple_loss=0.3017, pruned_loss=0.06168, over 7410.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2691, pruned_loss=0.0477, over 1426728.02 frames.], batch size: 21, lr: 2.39e-04
2022-05-28 10:11:59,863 INFO [train.py:842] (2/4) Epoch 23, batch 5200, loss[loss=0.1863, simple_loss=0.2809, pruned_loss=0.04585, over 7391.00 frames.], tot_loss[loss=0.1821, simple_loss=0.269, pruned_loss=0.04766, over 1428748.85 frames.], batch size: 23, lr: 2.39e-04
2022-05-28 10:12:37,834 INFO [train.py:842] (2/4) Epoch 23, batch 5250, loss[loss=0.182, simple_loss=0.2624, pruned_loss=0.05086, over 7325.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2704, pruned_loss=0.04836, over 1430867.69 frames.], batch size: 22, lr: 2.39e-04
2022-05-28 10:13:16,069 INFO [train.py:842] (2/4) Epoch 23, batch 5300, loss[loss=0.2464, simple_loss=0.3225, pruned_loss=0.08512, over 6578.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2697, pruned_loss=0.04792, over 1429377.62 frames.], batch size: 38, lr: 2.39e-04
2022-05-28 10:13:54,135 INFO [train.py:842] (2/4) Epoch 23, batch 5350, loss[loss=0.2206, simple_loss=0.297, pruned_loss=0.07215, over 7103.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2695, pruned_loss=0.0481, over 1426443.97 frames.], batch size: 21, lr: 2.39e-04
2022-05-28 10:14:32,591 INFO [train.py:842] (2/4) Epoch 23, batch 5400, loss[loss=0.1756, simple_loss=0.2783, pruned_loss=0.03647, over 7335.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2694, pruned_loss=0.04801, over 1430384.20 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 10:15:10,492 INFO [train.py:842] (2/4) Epoch 23, batch 5450, loss[loss=0.1845, simple_loss=0.2691, pruned_loss=0.04998, over 6976.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2697, pruned_loss=0.0479, over 1431384.27 frames.], batch size: 28, lr: 2.39e-04
2022-05-28 10:15:48,465 INFO [train.py:842] (2/4) Epoch 23, batch 5500, loss[loss=0.1858, simple_loss=0.2831, pruned_loss=0.04423, over 7170.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2715, pruned_loss=0.04892, over 1425099.57 frames.], batch size: 26, lr: 2.39e-04
2022-05-28 10:16:26,510 INFO [train.py:842] (2/4) Epoch 23, batch 5550, loss[loss=0.1943, simple_loss=0.293, pruned_loss=0.04778, over 7209.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2704, pruned_loss=0.04851, over 1426990.88 frames.], batch size: 22, lr: 2.39e-04
2022-05-28 10:17:04,852 INFO [train.py:842] (2/4) Epoch 23, batch 5600, loss[loss=0.1702, simple_loss=0.2493, pruned_loss=0.04548, over 6806.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2703, pruned_loss=0.04863, over 1427215.21 frames.], batch size: 15, lr: 2.39e-04
2022-05-28 10:17:43,158 INFO [train.py:842] (2/4) Epoch 23, batch 5650, loss[loss=0.1443, simple_loss=0.2419, pruned_loss=0.02334, over 7425.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2683, pruned_loss=0.04763, over 1430192.66 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 10:18:21,418 INFO [train.py:842] (2/4) Epoch 23, batch 5700, loss[loss=0.1899, simple_loss=0.2888, pruned_loss=0.04546, over 7151.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2684, pruned_loss=0.04776, over 1426309.52 frames.], batch size: 20, lr: 2.39e-04
2022-05-28 10:18:59,405 INFO [train.py:842] (2/4) Epoch 23, batch 5750, loss[loss=0.1482, simple_loss=0.2311, pruned_loss=0.03266, over 7152.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2679, pruned_loss=0.04764, over 1424957.93 frames.], batch size: 17, lr: 2.39e-04
2022-05-28 10:19:40,410 INFO [train.py:842] (2/4) Epoch 23, batch 5800, loss[loss=0.159, simple_loss=0.2483, pruned_loss=0.03487, over 7072.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2667, pruned_loss=0.04692, over 1426164.45 frames.], batch size: 18, lr: 2.39e-04
2022-05-28 10:20:18,494 INFO [train.py:842] (2/4) Epoch 23, batch 5850, loss[loss=0.1829, simple_loss=0.2803, pruned_loss=0.0428, over 7323.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2666, pruned_loss=0.04695, over 1428474.03 frames.], batch size: 21, lr: 2.39e-04
2022-05-28 10:20:56,744 INFO [train.py:842] (2/4) Epoch 23, batch 5900, loss[loss=0.1503, simple_loss=0.2382, pruned_loss=0.03117, over 7431.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2666, pruned_loss=0.04686, over 1424021.38 frames.], batch size: 20, lr: 2.38e-04
2022-05-28 10:21:34,796 INFO [train.py:842] (2/4) Epoch 23, batch 5950, loss[loss=0.1268, simple_loss=0.2123, pruned_loss=0.02065, over 7000.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2665, pruned_loss=0.04695, over 1419738.62 frames.], batch size: 16, lr: 2.38e-04
2022-05-28 10:22:13,044 INFO [train.py:842] (2/4) Epoch 23, batch 6000, loss[loss=0.186, simple_loss=0.2825, pruned_loss=0.04474, over 6791.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2677, pruned_loss=0.04706, over 1419184.47 frames.], batch size: 31, lr: 2.38e-04
2022-05-28 10:22:13,045 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 10:22:22,049 INFO [train.py:871] (2/4) 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] (2/4) Epoch 23, batch 6050, loss[loss=0.1639, simple_loss=0.2511, pruned_loss=0.03831, over 7406.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2691, pruned_loss=0.04716, over 1418565.82 frames.], batch size: 18, lr: 2.38e-04
2022-05-28 10:23:37,967 INFO [train.py:842] (2/4) Epoch 23, batch 6100, loss[loss=0.158, simple_loss=0.243, pruned_loss=0.03656, over 6803.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2697, pruned_loss=0.04761, over 1421307.71 frames.], batch size: 31, lr: 2.38e-04
2022-05-28 10:24:16,055 INFO [train.py:842] (2/4) Epoch 23, batch 6150, loss[loss=0.1926, simple_loss=0.2805, pruned_loss=0.05238, over 7289.00 frames.], tot_loss[loss=0.1828, simple_loss=0.27, pruned_loss=0.0478, over 1422273.01 frames.], batch size: 24, lr: 2.38e-04
2022-05-28 10:24:54,366 INFO [train.py:842] (2/4) Epoch 23, batch 6200, loss[loss=0.1593, simple_loss=0.2497, pruned_loss=0.03442, over 7180.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2701, pruned_loss=0.0482, over 1424700.91 frames.], batch size: 26, lr: 2.38e-04
2022-05-28 10:25:32,197 INFO [train.py:842] (2/4) Epoch 23, batch 6250, loss[loss=0.1641, simple_loss=0.2519, pruned_loss=0.0382, over 6686.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2703, pruned_loss=0.04826, over 1421823.44 frames.], batch size: 31, lr: 2.38e-04
2022-05-28 10:26:10,648 INFO [train.py:842] (2/4) Epoch 23, batch 6300, loss[loss=0.1664, simple_loss=0.2637, pruned_loss=0.03454, over 7294.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2695, pruned_loss=0.04781, over 1423116.13 frames.], batch size: 25, lr: 2.38e-04
2022-05-28 10:26:48,651 INFO [train.py:842] (2/4) Epoch 23, batch 6350, loss[loss=0.2225, simple_loss=0.3083, pruned_loss=0.06833, over 7153.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2685, pruned_loss=0.04744, over 1421663.55 frames.], batch size: 26, lr: 2.38e-04
2022-05-28 10:27:27,045 INFO [train.py:842] (2/4) Epoch 23, batch 6400, loss[loss=0.1765, simple_loss=0.265, pruned_loss=0.04403, over 7154.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2673, pruned_loss=0.04708, over 1425090.30 frames.], batch size: 28, lr: 2.38e-04
2022-05-28 10:28:05,059 INFO [train.py:842] (2/4) Epoch 23, batch 6450, loss[loss=0.2391, simple_loss=0.3195, pruned_loss=0.07935, over 7339.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2671, pruned_loss=0.04717, over 1423003.95 frames.], batch size: 20, lr: 2.38e-04
2022-05-28 10:28:43,391 INFO [train.py:842] (2/4) Epoch 23, batch 6500, loss[loss=0.1891, simple_loss=0.2725, pruned_loss=0.05284, over 7145.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2678, pruned_loss=0.04745, over 1423035.73 frames.], batch size: 18, lr: 2.38e-04
2022-05-28 10:29:21,290 INFO [train.py:842] (2/4) Epoch 23, batch 6550, loss[loss=0.1916, simple_loss=0.2766, pruned_loss=0.05333, over 7262.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2685, pruned_loss=0.04746, over 1423372.70 frames.], batch size: 19, lr: 2.38e-04
2022-05-28 10:29:59,627 INFO [train.py:842] (2/4) Epoch 23, batch 6600, loss[loss=0.1819, simple_loss=0.2767, pruned_loss=0.04356, over 6883.00 frames.], tot_loss[loss=0.1819, simple_loss=0.269, pruned_loss=0.04738, over 1427207.12 frames.], batch size: 31, lr: 2.38e-04
2022-05-28 10:30:37,531 INFO [train.py:842] (2/4) Epoch 23, batch 6650, loss[loss=0.2024, simple_loss=0.288, pruned_loss=0.05841, over 7318.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2686, pruned_loss=0.04697, over 1428893.08 frames.], batch size: 21, lr: 2.38e-04
2022-05-28 10:31:15,858 INFO [train.py:842] (2/4) Epoch 23, batch 6700, loss[loss=0.1744, simple_loss=0.2512, pruned_loss=0.04882, over 7358.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2681, pruned_loss=0.04716, over 1428290.76 frames.], batch size: 19, lr: 2.38e-04
2022-05-28 10:31:54,040 INFO [train.py:842] (2/4) Epoch 23, batch 6750, loss[loss=0.1805, simple_loss=0.271, pruned_loss=0.04497, over 7402.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2671, pruned_loss=0.04706, over 1429913.55 frames.], batch size: 21, lr: 2.38e-04
2022-05-28 10:32:32,268 INFO [train.py:842] (2/4) Epoch 23, batch 6800, loss[loss=0.1696, simple_loss=0.2517, pruned_loss=0.04378, over 7361.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2684, pruned_loss=0.04748, over 1432124.62 frames.], batch size: 19, lr: 2.38e-04
2022-05-28 10:33:10,268 INFO [train.py:842] (2/4) Epoch 23, batch 6850, loss[loss=0.1496, simple_loss=0.2349, pruned_loss=0.03214, over 7298.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2686, pruned_loss=0.04785, over 1426560.15 frames.], batch size: 18, lr: 2.38e-04
2022-05-28 10:33:48,535 INFO [train.py:842] (2/4) Epoch 23, batch 6900, loss[loss=0.179, simple_loss=0.272, pruned_loss=0.04303, over 7421.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2692, pruned_loss=0.04803, over 1425329.00 frames.], batch size: 21, lr: 2.38e-04
2022-05-28 10:34:26,510 INFO [train.py:842] (2/4) Epoch 23, batch 6950, loss[loss=0.1412, simple_loss=0.2275, pruned_loss=0.02746, over 6997.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2687, pruned_loss=0.0474, over 1427163.76 frames.], batch size: 16, lr: 2.38e-04
2022-05-28 10:35:04,766 INFO [train.py:842] (2/4) Epoch 23, batch 7000, loss[loss=0.2464, simple_loss=0.3104, pruned_loss=0.09122, over 5139.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2689, pruned_loss=0.04746, over 1426038.75 frames.], batch size: 52, lr: 2.38e-04
2022-05-28 10:35:42,812 INFO [train.py:842] (2/4) Epoch 23, batch 7050, loss[loss=0.1689, simple_loss=0.2568, pruned_loss=0.04047, over 7245.00 frames.], tot_loss[loss=0.1813, simple_loss=0.268, pruned_loss=0.04734, over 1426444.63 frames.], batch size: 20, lr: 2.38e-04
2022-05-28 10:36:21,010 INFO [train.py:842] (2/4) Epoch 23, batch 7100, loss[loss=0.2241, simple_loss=0.3023, pruned_loss=0.07292, over 7303.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2689, pruned_loss=0.04735, over 1421899.12 frames.], batch size: 24, lr: 2.38e-04
2022-05-28 10:36:58,900 INFO [train.py:842] (2/4) Epoch 23, batch 7150, loss[loss=0.1848, simple_loss=0.2735, pruned_loss=0.04802, over 7281.00 frames.], tot_loss[loss=0.1817, simple_loss=0.269, pruned_loss=0.04723, over 1424785.19 frames.], batch size: 25, lr: 2.38e-04
2022-05-28 10:37:37,087 INFO [train.py:842] (2/4) Epoch 23, batch 7200, loss[loss=0.1966, simple_loss=0.2867, pruned_loss=0.05329, over 7330.00 frames.], tot_loss[loss=0.1828, simple_loss=0.27, pruned_loss=0.04777, over 1418636.58 frames.], batch size: 20, lr: 2.38e-04
2022-05-28 10:38:15,058 INFO [train.py:842] (2/4) Epoch 23, batch 7250, loss[loss=0.1697, simple_loss=0.2632, pruned_loss=0.0381, over 7159.00 frames.], tot_loss[loss=0.184, simple_loss=0.2711, pruned_loss=0.04842, over 1416922.61 frames.], batch size: 19, lr: 2.38e-04
2022-05-28 10:38:53,290 INFO [train.py:842] (2/4) Epoch 23, batch 7300, loss[loss=0.2003, simple_loss=0.286, pruned_loss=0.05735, over 7207.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2704, pruned_loss=0.04822, over 1416001.88 frames.], batch size: 26, lr: 2.38e-04
2022-05-28 10:39:31,502 INFO [train.py:842] (2/4) Epoch 23, batch 7350, loss[loss=0.1914, simple_loss=0.2749, pruned_loss=0.05392, over 5217.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2693, pruned_loss=0.04801, over 1418985.69 frames.], batch size: 52, lr: 2.38e-04
2022-05-28 10:40:09,507 INFO [train.py:842] (2/4) Epoch 23, batch 7400, loss[loss=0.1603, simple_loss=0.2595, pruned_loss=0.03052, over 7146.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2691, pruned_loss=0.04766, over 1420832.32 frames.], batch size: 20, lr: 2.38e-04
2022-05-28 10:40:47,420 INFO [train.py:842] (2/4) Epoch 23, batch 7450, loss[loss=0.1914, simple_loss=0.2808, pruned_loss=0.051, over 7154.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2686, pruned_loss=0.04702, over 1423090.08 frames.], batch size: 19, lr: 2.38e-04
2022-05-28 10:41:25,542 INFO [train.py:842] (2/4) Epoch 23, batch 7500, loss[loss=0.2332, simple_loss=0.3179, pruned_loss=0.07427, over 7207.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2693, pruned_loss=0.0472, over 1417140.33 frames.], batch size: 22, lr: 2.38e-04
2022-05-28 10:42:03,560 INFO [train.py:842] (2/4) Epoch 23, batch 7550, loss[loss=0.1891, simple_loss=0.2845, pruned_loss=0.04683, over 7408.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2694, pruned_loss=0.04717, over 1421307.98 frames.], batch size: 21, lr: 2.38e-04
2022-05-28 10:42:41,661 INFO [train.py:842] (2/4) Epoch 23, batch 7600, loss[loss=0.2042, simple_loss=0.2833, pruned_loss=0.06257, over 4843.00 frames.], tot_loss[loss=0.182, simple_loss=0.2696, pruned_loss=0.04724, over 1416932.60 frames.], batch size: 52, lr: 2.38e-04
2022-05-28 10:43:19,664 INFO [train.py:842] (2/4) Epoch 23, batch 7650, loss[loss=0.1727, simple_loss=0.2676, pruned_loss=0.03892, over 6909.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2689, pruned_loss=0.04701, over 1414534.86 frames.], batch size: 31, lr: 2.37e-04
2022-05-28 10:43:57,920 INFO [train.py:842] (2/4) Epoch 23, batch 7700, loss[loss=0.1814, simple_loss=0.2642, pruned_loss=0.0493, over 7154.00 frames.], tot_loss[loss=0.1817, simple_loss=0.269, pruned_loss=0.04717, over 1415255.55 frames.], batch size: 20, lr: 2.37e-04
2022-05-28 10:44:35,745 INFO [train.py:842] (2/4) Epoch 23, batch 7750, loss[loss=0.1926, simple_loss=0.2861, pruned_loss=0.04957, over 7407.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2681, pruned_loss=0.04678, over 1418951.89 frames.], batch size: 21, lr: 2.37e-04
2022-05-28 10:45:14,278 INFO [train.py:842] (2/4) Epoch 23, batch 7800, loss[loss=0.2599, simple_loss=0.3425, pruned_loss=0.08862, over 7131.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2673, pruned_loss=0.0462, over 1421955.79 frames.], batch size: 26, lr: 2.37e-04
2022-05-28 10:45:52,171 INFO [train.py:842] (2/4) Epoch 23, batch 7850, loss[loss=0.1936, simple_loss=0.2749, pruned_loss=0.05621, over 4711.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2675, pruned_loss=0.04698, over 1416353.16 frames.], batch size: 52, lr: 2.37e-04
2022-05-28 10:46:30,462 INFO [train.py:842] (2/4) Epoch 23, batch 7900, loss[loss=0.2037, simple_loss=0.2958, pruned_loss=0.05578, over 4715.00 frames.], tot_loss[loss=0.1812, simple_loss=0.268, pruned_loss=0.04716, over 1417336.31 frames.], batch size: 53, lr: 2.37e-04
2022-05-28 10:47:08,307 INFO [train.py:842] (2/4) Epoch 23, batch 7950, loss[loss=0.1841, simple_loss=0.26, pruned_loss=0.0541, over 7123.00 frames.], tot_loss[loss=0.1812, simple_loss=0.268, pruned_loss=0.04716, over 1418544.19 frames.], batch size: 17, lr: 2.37e-04
2022-05-28 10:47:46,446 INFO [train.py:842] (2/4) Epoch 23, batch 8000, loss[loss=0.1923, simple_loss=0.282, pruned_loss=0.05135, over 7164.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2683, pruned_loss=0.04719, over 1422542.69 frames.], batch size: 26, lr: 2.37e-04
2022-05-28 10:48:24,472 INFO [train.py:842] (2/4) Epoch 23, batch 8050, loss[loss=0.1747, simple_loss=0.2574, pruned_loss=0.04596, over 7158.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2675, pruned_loss=0.0469, over 1422681.94 frames.], batch size: 18, lr: 2.37e-04
2022-05-28 10:49:02,718 INFO [train.py:842] (2/4) Epoch 23, batch 8100, loss[loss=0.147, simple_loss=0.2318, pruned_loss=0.03107, over 7281.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2673, pruned_loss=0.04705, over 1424737.52 frames.], batch size: 17, lr: 2.37e-04
2022-05-28 10:49:40,299 INFO [train.py:842] (2/4) Epoch 23, batch 8150, loss[loss=0.1751, simple_loss=0.2711, pruned_loss=0.03958, over 7227.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2683, pruned_loss=0.04704, over 1420624.88 frames.], batch size: 21, lr: 2.37e-04
2022-05-28 10:50:18,852 INFO [train.py:842] (2/4) Epoch 23, batch 8200, loss[loss=0.1927, simple_loss=0.2875, pruned_loss=0.04894, over 7280.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2666, pruned_loss=0.04646, over 1422339.40 frames.], batch size: 25, lr: 2.37e-04
2022-05-28 10:50:56,564 INFO [train.py:842] (2/4) Epoch 23, batch 8250, loss[loss=0.172, simple_loss=0.278, pruned_loss=0.03301, over 7194.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2667, pruned_loss=0.04595, over 1420855.31 frames.], batch size: 22, lr: 2.37e-04
2022-05-28 10:51:34,715 INFO [train.py:842] (2/4) Epoch 23, batch 8300, loss[loss=0.1766, simple_loss=0.2634, pruned_loss=0.04489, over 7054.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2668, pruned_loss=0.04605, over 1415256.74 frames.], batch size: 18, lr: 2.37e-04
2022-05-28 10:52:12,731 INFO [train.py:842] (2/4) Epoch 23, batch 8350, loss[loss=0.1823, simple_loss=0.2706, pruned_loss=0.04701, over 6434.00 frames.], tot_loss[loss=0.179, simple_loss=0.2661, pruned_loss=0.0459, over 1413508.27 frames.], batch size: 38, lr: 2.37e-04
2022-05-28 10:52:50,724 INFO [train.py:842] (2/4) Epoch 23, batch 8400, loss[loss=0.1636, simple_loss=0.254, pruned_loss=0.03658, over 7063.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2673, pruned_loss=0.04618, over 1408764.85 frames.], batch size: 18, lr: 2.37e-04
2022-05-28 10:53:28,671 INFO [train.py:842] (2/4) Epoch 23, batch 8450, loss[loss=0.1478, simple_loss=0.2212, pruned_loss=0.03716, over 7012.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2672, pruned_loss=0.04647, over 1407442.79 frames.], batch size: 16, lr: 2.37e-04
2022-05-28 10:54:06,868 INFO [train.py:842] (2/4) Epoch 23, batch 8500, loss[loss=0.1447, simple_loss=0.2203, pruned_loss=0.03457, over 6823.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2669, pruned_loss=0.04629, over 1408510.61 frames.], batch size: 15, lr: 2.37e-04
2022-05-28 10:54:44,742 INFO [train.py:842] (2/4) Epoch 23, batch 8550, loss[loss=0.1873, simple_loss=0.2774, pruned_loss=0.04862, over 7213.00 frames.], tot_loss[loss=0.1807, simple_loss=0.268, pruned_loss=0.04668, over 1409755.83 frames.], batch size: 21, lr: 2.37e-04
2022-05-28 10:55:22,735 INFO [train.py:842] (2/4) Epoch 23, batch 8600, loss[loss=0.1993, simple_loss=0.2867, pruned_loss=0.05594, over 7365.00 frames.], tot_loss[loss=0.1816, simple_loss=0.269, pruned_loss=0.04705, over 1409170.13 frames.], batch size: 23, lr: 2.37e-04
2022-05-28 10:56:00,902 INFO [train.py:842] (2/4) Epoch 23, batch 8650, loss[loss=0.1778, simple_loss=0.2578, pruned_loss=0.04892, over 7310.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2686, pruned_loss=0.04713, over 1414633.44 frames.], batch size: 17, lr: 2.37e-04
2022-05-28 10:56:39,171 INFO [train.py:842] (2/4) Epoch 23, batch 8700, loss[loss=0.1595, simple_loss=0.2344, pruned_loss=0.04227, over 6982.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2678, pruned_loss=0.04675, over 1414165.46 frames.], batch size: 16, lr: 2.37e-04
2022-05-28 10:57:17,036 INFO [train.py:842] (2/4) Epoch 23, batch 8750, loss[loss=0.177, simple_loss=0.2548, pruned_loss=0.04961, over 7115.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2676, pruned_loss=0.04689, over 1412024.26 frames.], batch size: 17, lr: 2.37e-04
2022-05-28 10:57:55,223 INFO [train.py:842] (2/4) Epoch 23, batch 8800, loss[loss=0.1829, simple_loss=0.273, pruned_loss=0.04637, over 7303.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2684, pruned_loss=0.0475, over 1407585.33 frames.], batch size: 24, lr: 2.37e-04
2022-05-28 10:58:33,254 INFO [train.py:842] (2/4) Epoch 23, batch 8850, loss[loss=0.1929, simple_loss=0.2809, pruned_loss=0.05246, over 7110.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2678, pruned_loss=0.04678, over 1409499.80 frames.], batch size: 21, lr: 2.37e-04
2022-05-28 10:59:11,041 INFO [train.py:842] (2/4) Epoch 23, batch 8900, loss[loss=0.1946, simple_loss=0.2816, pruned_loss=0.05379, over 7166.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2687, pruned_loss=0.04688, over 1402681.16 frames.], batch size: 26, lr: 2.37e-04
2022-05-28 10:59:48,797 INFO [train.py:842] (2/4) Epoch 23, batch 8950, loss[loss=0.1791, simple_loss=0.2676, pruned_loss=0.04537, over 6388.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2694, pruned_loss=0.04762, over 1395909.68 frames.], batch size: 37, lr: 2.37e-04
2022-05-28 11:00:26,424 INFO [train.py:842] (2/4) Epoch 23, batch 9000, loss[loss=0.2086, simple_loss=0.2828, pruned_loss=0.06718, over 5204.00 frames.], tot_loss[loss=0.184, simple_loss=0.2719, pruned_loss=0.04809, over 1391904.03 frames.], batch size: 52, lr: 2.37e-04
2022-05-28 11:00:26,425 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 11:00:35,435 INFO [train.py:871] (2/4) Epoch 23, validation: loss=0.1641, simple_loss=0.2627, pruned_loss=0.03273, over 868885.00 frames.
2022-05-28 11:01:12,462 INFO [train.py:842] (2/4) Epoch 23, batch 9050, loss[loss=0.176, simple_loss=0.2656, pruned_loss=0.04323, over 6790.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2739, pruned_loss=0.04874, over 1379641.78 frames.], batch size: 31, lr: 2.37e-04
2022-05-28 11:01:49,740 INFO [train.py:842] (2/4) Epoch 23, batch 9100, loss[loss=0.1733, simple_loss=0.2639, pruned_loss=0.04132, over 7163.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2766, pruned_loss=0.05052, over 1344358.14 frames.], batch size: 26, lr: 2.37e-04
2022-05-28 11:02:26,560 INFO [train.py:842] (2/4) Epoch 23, batch 9150, loss[loss=0.2118, simple_loss=0.283, pruned_loss=0.07033, over 5371.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2796, pruned_loss=0.05336, over 1282179.61 frames.], batch size: 52, lr: 2.37e-04
2022-05-28 11:03:11,882 INFO [train.py:842] (2/4) Epoch 24, batch 0, loss[loss=0.1399, simple_loss=0.2143, pruned_loss=0.03274, over 7213.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2143, pruned_loss=0.03274, over 7213.00 frames.], batch size: 16, lr: 2.32e-04
2022-05-28 11:03:49,765 INFO [train.py:842] (2/4) Epoch 24, batch 50, loss[loss=0.1436, simple_loss=0.2301, pruned_loss=0.02859, over 7281.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2605, pruned_loss=0.04194, over 317424.14 frames.], batch size: 17, lr: 2.32e-04
2022-05-28 11:04:28,172 INFO [train.py:842] (2/4) Epoch 24, batch 100, loss[loss=0.2294, simple_loss=0.3007, pruned_loss=0.0791, over 7326.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2649, pruned_loss=0.04406, over 567779.92 frames.], batch size: 20, lr: 2.32e-04
2022-05-28 11:05:05,926 INFO [train.py:842] (2/4) Epoch 24, batch 150, loss[loss=0.2143, simple_loss=0.2845, pruned_loss=0.07198, over 7370.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2678, pruned_loss=0.04664, over 753254.66 frames.], batch size: 23, lr: 2.32e-04
2022-05-28 11:05:44,209 INFO [train.py:842] (2/4) Epoch 24, batch 200, loss[loss=0.2134, simple_loss=0.3043, pruned_loss=0.06127, over 7201.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2671, pruned_loss=0.04618, over 904416.77 frames.], batch size: 22, lr: 2.32e-04
2022-05-28 11:06:22,171 INFO [train.py:842] (2/4) Epoch 24, batch 250, loss[loss=0.1594, simple_loss=0.249, pruned_loss=0.03492, over 7412.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2671, pruned_loss=0.04605, over 1016753.12 frames.], batch size: 21, lr: 2.32e-04
2022-05-28 11:07:00,465 INFO [train.py:842] (2/4) Epoch 24, batch 300, loss[loss=0.1757, simple_loss=0.2696, pruned_loss=0.0409, over 7148.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2674, pruned_loss=0.04618, over 1107601.57 frames.], batch size: 20, lr: 2.32e-04
2022-05-28 11:07:38,461 INFO [train.py:842] (2/4) Epoch 24, batch 350, loss[loss=0.2087, simple_loss=0.296, pruned_loss=0.06066, over 7273.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2672, pruned_loss=0.04608, over 1179583.92 frames.], batch size: 25, lr: 2.32e-04
2022-05-28 11:08:16,626 INFO [train.py:842] (2/4) Epoch 24, batch 400, loss[loss=0.1856, simple_loss=0.2727, pruned_loss=0.04921, over 7279.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2672, pruned_loss=0.0461, over 1229468.83 frames.], batch size: 24, lr: 2.32e-04
2022-05-28 11:08:54,681 INFO [train.py:842] (2/4) Epoch 24, batch 450, loss[loss=0.1993, simple_loss=0.2856, pruned_loss=0.05647, over 7152.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2668, pruned_loss=0.04593, over 1275318.76 frames.], batch size: 20, lr: 2.32e-04
2022-05-28 11:09:32,935 INFO [train.py:842] (2/4) Epoch 24, batch 500, loss[loss=0.2111, simple_loss=0.2819, pruned_loss=0.07016, over 7353.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2678, pruned_loss=0.04642, over 1306899.69 frames.], batch size: 19, lr: 2.31e-04
2022-05-28 11:10:11,037 INFO [train.py:842] (2/4) Epoch 24, batch 550, loss[loss=0.1825, simple_loss=0.2842, pruned_loss=0.04043, over 7205.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2684, pruned_loss=0.04665, over 1335673.17 frames.], batch size: 22, lr: 2.31e-04
2022-05-28 11:10:49,409 INFO [train.py:842] (2/4) Epoch 24, batch 600, loss[loss=0.1928, simple_loss=0.2798, pruned_loss=0.05297, over 7359.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2673, pruned_loss=0.04653, over 1352778.58 frames.], batch size: 19, lr: 2.31e-04
2022-05-28 11:11:27,460 INFO [train.py:842] (2/4) Epoch 24, batch 650, loss[loss=0.1466, simple_loss=0.2399, pruned_loss=0.02668, over 7352.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2671, pruned_loss=0.04668, over 1363709.74 frames.], batch size: 19, lr: 2.31e-04
2022-05-28 11:12:06,160 INFO [train.py:842] (2/4) Epoch 24, batch 700, loss[loss=0.1843, simple_loss=0.2722, pruned_loss=0.04817, over 7165.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2657, pruned_loss=0.04628, over 1380808.28 frames.], batch size: 26, lr: 2.31e-04
2022-05-28 11:12:44,041 INFO [train.py:842] (2/4) Epoch 24, batch 750, loss[loss=0.1714, simple_loss=0.2494, pruned_loss=0.04674, over 6983.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2657, pruned_loss=0.04579, over 1391174.44 frames.], batch size: 16, lr: 2.31e-04
2022-05-28 11:13:22,544 INFO [train.py:842] (2/4) Epoch 24, batch 800, loss[loss=0.1591, simple_loss=0.2529, pruned_loss=0.03268, over 7263.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2654, pruned_loss=0.04569, over 1398264.00 frames.], batch size: 19, lr: 2.31e-04
2022-05-28 11:14:00,595 INFO [train.py:842] (2/4) Epoch 24, batch 850, loss[loss=0.1744, simple_loss=0.254, pruned_loss=0.04746, over 6765.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2647, pruned_loss=0.04519, over 1404526.04 frames.], batch size: 31, lr: 2.31e-04
2022-05-28 11:14:38,791 INFO [train.py:842] (2/4) Epoch 24, batch 900, loss[loss=0.1601, simple_loss=0.2486, pruned_loss=0.03583, over 7435.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2659, pruned_loss=0.0454, over 1410743.10 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:15:16,791 INFO [train.py:842] (2/4) Epoch 24, batch 950, loss[loss=0.1521, simple_loss=0.2415, pruned_loss=0.03142, over 6633.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2668, pruned_loss=0.04639, over 1416003.32 frames.], batch size: 38, lr: 2.31e-04
2022-05-28 11:15:55,338 INFO [train.py:842] (2/4) Epoch 24, batch 1000, loss[loss=0.1852, simple_loss=0.2809, pruned_loss=0.04477, over 7321.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2671, pruned_loss=0.04653, over 1417861.12 frames.], batch size: 21, lr: 2.31e-04
2022-05-28 11:16:33,148 INFO [train.py:842] (2/4) Epoch 24, batch 1050, loss[loss=0.1591, simple_loss=0.2549, pruned_loss=0.03166, over 7238.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2677, pruned_loss=0.04678, over 1412792.14 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:17:11,282 INFO [train.py:842] (2/4) Epoch 24, batch 1100, loss[loss=0.1913, simple_loss=0.281, pruned_loss=0.05083, over 7139.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2677, pruned_loss=0.04679, over 1411828.36 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:17:49,521 INFO [train.py:842] (2/4) Epoch 24, batch 1150, loss[loss=0.1649, simple_loss=0.2597, pruned_loss=0.03504, over 6373.00 frames.], tot_loss[loss=0.18, simple_loss=0.2669, pruned_loss=0.04655, over 1415567.94 frames.], batch size: 37, lr: 2.31e-04
2022-05-28 11:18:27,584 INFO [train.py:842] (2/4) Epoch 24, batch 1200, loss[loss=0.1508, simple_loss=0.242, pruned_loss=0.02983, over 7173.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2672, pruned_loss=0.04648, over 1418669.80 frames.], batch size: 18, lr: 2.31e-04
2022-05-28 11:19:05,764 INFO [train.py:842] (2/4) Epoch 24, batch 1250, loss[loss=0.2003, simple_loss=0.2879, pruned_loss=0.05635, over 7325.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2674, pruned_loss=0.04678, over 1419808.92 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:19:44,005 INFO [train.py:842] (2/4) Epoch 24, batch 1300, loss[loss=0.2047, simple_loss=0.2798, pruned_loss=0.06483, over 6799.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2667, pruned_loss=0.04633, over 1421555.39 frames.], batch size: 31, lr: 2.31e-04
2022-05-28 11:20:21,956 INFO [train.py:842] (2/4) Epoch 24, batch 1350, loss[loss=0.1387, simple_loss=0.2291, pruned_loss=0.02414, over 7398.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2679, pruned_loss=0.04665, over 1426610.64 frames.], batch size: 18, lr: 2.31e-04
2022-05-28 11:21:00,272 INFO [train.py:842] (2/4) Epoch 24, batch 1400, loss[loss=0.211, simple_loss=0.303, pruned_loss=0.0595, over 7191.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2688, pruned_loss=0.04741, over 1424996.85 frames.], batch size: 26, lr: 2.31e-04
2022-05-28 11:21:38,255 INFO [train.py:842] (2/4) Epoch 24, batch 1450, loss[loss=0.174, simple_loss=0.2669, pruned_loss=0.04054, over 7147.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2687, pruned_loss=0.04728, over 1423154.06 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:22:16,538 INFO [train.py:842] (2/4) Epoch 24, batch 1500, loss[loss=0.1766, simple_loss=0.2669, pruned_loss=0.04314, over 7148.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2686, pruned_loss=0.04716, over 1421381.90 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:22:54,642 INFO [train.py:842] (2/4) Epoch 24, batch 1550, loss[loss=0.2251, simple_loss=0.3146, pruned_loss=0.06775, over 6798.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2693, pruned_loss=0.04748, over 1422060.34 frames.], batch size: 31, lr: 2.31e-04
2022-05-28 11:23:32,822 INFO [train.py:842] (2/4) Epoch 24, batch 1600, loss[loss=0.167, simple_loss=0.2461, pruned_loss=0.04391, over 7335.00 frames.], tot_loss[loss=0.183, simple_loss=0.2705, pruned_loss=0.04775, over 1423488.47 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:24:10,609 INFO [train.py:842] (2/4) Epoch 24, batch 1650, loss[loss=0.1372, simple_loss=0.2213, pruned_loss=0.02654, over 6825.00 frames.], tot_loss[loss=0.184, simple_loss=0.2715, pruned_loss=0.04823, over 1415346.07 frames.], batch size: 15, lr: 2.31e-04
2022-05-28 11:24:48,819 INFO [train.py:842] (2/4) Epoch 24, batch 1700, loss[loss=0.1823, simple_loss=0.281, pruned_loss=0.04177, over 7312.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2712, pruned_loss=0.0479, over 1418919.60 frames.], batch size: 21, lr: 2.31e-04
2022-05-28 11:25:26,711 INFO [train.py:842] (2/4) Epoch 24, batch 1750, loss[loss=0.1756, simple_loss=0.2634, pruned_loss=0.04388, over 7066.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2704, pruned_loss=0.04768, over 1421012.77 frames.], batch size: 18, lr: 2.31e-04
2022-05-28 11:26:05,003 INFO [train.py:842] (2/4) Epoch 24, batch 1800, loss[loss=0.2153, simple_loss=0.2989, pruned_loss=0.06586, over 7340.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2697, pruned_loss=0.04745, over 1421797.97 frames.], batch size: 22, lr: 2.31e-04
2022-05-28 11:26:42,917 INFO [train.py:842] (2/4) Epoch 24, batch 1850, loss[loss=0.2235, simple_loss=0.3193, pruned_loss=0.06389, over 7268.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2688, pruned_loss=0.04704, over 1425150.15 frames.], batch size: 24, lr: 2.31e-04
2022-05-28 11:27:21,185 INFO [train.py:842] (2/4) Epoch 24, batch 1900, loss[loss=0.1894, simple_loss=0.2871, pruned_loss=0.04582, over 7076.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2691, pruned_loss=0.04724, over 1423148.00 frames.], batch size: 28, lr: 2.31e-04
2022-05-28 11:27:59,203 INFO [train.py:842] (2/4) Epoch 24, batch 1950, loss[loss=0.1826, simple_loss=0.2796, pruned_loss=0.04281, over 7116.00 frames.], tot_loss[loss=0.1808, simple_loss=0.268, pruned_loss=0.04679, over 1424233.40 frames.], batch size: 21, lr: 2.31e-04
2022-05-28 11:28:37,366 INFO [train.py:842] (2/4) Epoch 24, batch 2000, loss[loss=0.1816, simple_loss=0.2671, pruned_loss=0.04804, over 5368.00 frames.], tot_loss[loss=0.1816, simple_loss=0.269, pruned_loss=0.04704, over 1422732.23 frames.], batch size: 53, lr: 2.31e-04
2022-05-28 11:29:15,375 INFO [train.py:842] (2/4) Epoch 24, batch 2050, loss[loss=0.1893, simple_loss=0.2681, pruned_loss=0.05523, over 7436.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2688, pruned_loss=0.04701, over 1422625.96 frames.], batch size: 20, lr: 2.31e-04
2022-05-28 11:29:53,648 INFO [train.py:842] (2/4) Epoch 24, batch 2100, loss[loss=0.1663, simple_loss=0.2553, pruned_loss=0.03864, over 7002.00 frames.], tot_loss[loss=0.1805, simple_loss=0.268, pruned_loss=0.04646, over 1423538.98 frames.], batch size: 16, lr: 2.31e-04
2022-05-28 11:30:31,675 INFO [train.py:842] (2/4) Epoch 24, batch 2150, loss[loss=0.2258, simple_loss=0.3042, pruned_loss=0.07372, over 5124.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2678, pruned_loss=0.04665, over 1420947.16 frames.], batch size: 52, lr: 2.31e-04
2022-05-28 11:31:10,137 INFO [train.py:842] (2/4) Epoch 24, batch 2200, loss[loss=0.1509, simple_loss=0.2366, pruned_loss=0.03256, over 7139.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2674, pruned_loss=0.04685, over 1419843.97 frames.], batch size: 17, lr: 2.31e-04
2022-05-28 11:31:47,812 INFO [train.py:842] (2/4) Epoch 24, batch 2250, loss[loss=0.1794, simple_loss=0.263, pruned_loss=0.04797, over 7287.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2692, pruned_loss=0.04769, over 1410168.53 frames.], batch size: 25, lr: 2.31e-04
2022-05-28 11:32:26,192 INFO [train.py:842] (2/4) Epoch 24, batch 2300, loss[loss=0.1698, simple_loss=0.253, pruned_loss=0.04336, over 7270.00 frames.], tot_loss[loss=0.181, simple_loss=0.2683, pruned_loss=0.04686, over 1416984.23 frames.], batch size: 17, lr: 2.31e-04
2022-05-28 11:33:04,223 INFO [train.py:842] (2/4) Epoch 24, batch 2350, loss[loss=0.1619, simple_loss=0.2546, pruned_loss=0.03458, over 7342.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2687, pruned_loss=0.04678, over 1419396.73 frames.], batch size: 22, lr: 2.30e-04
2022-05-28 11:33:42,613 INFO [train.py:842] (2/4) Epoch 24, batch 2400, loss[loss=0.1552, simple_loss=0.2361, pruned_loss=0.03716, over 6848.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2694, pruned_loss=0.04699, over 1421864.81 frames.], batch size: 15, lr: 2.30e-04
2022-05-28 11:34:20,476 INFO [train.py:842] (2/4) Epoch 24, batch 2450, loss[loss=0.1844, simple_loss=0.2804, pruned_loss=0.04419, over 7230.00 frames.], tot_loss[loss=0.182, simple_loss=0.2692, pruned_loss=0.04739, over 1418253.01 frames.], batch size: 20, lr: 2.30e-04
2022-05-28 11:34:58,777 INFO [train.py:842] (2/4) Epoch 24, batch 2500, loss[loss=0.156, simple_loss=0.2552, pruned_loss=0.02838, over 7319.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2686, pruned_loss=0.04708, over 1418607.23 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:35:36,690 INFO [train.py:842] (2/4) Epoch 24, batch 2550, loss[loss=0.1753, simple_loss=0.2605, pruned_loss=0.04501, over 4908.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2683, pruned_loss=0.0473, over 1414094.81 frames.], batch size: 52, lr: 2.30e-04
2022-05-28 11:36:14,957 INFO [train.py:842] (2/4) Epoch 24, batch 2600, loss[loss=0.138, simple_loss=0.2173, pruned_loss=0.02935, over 7281.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2693, pruned_loss=0.04743, over 1417386.56 frames.], batch size: 18, lr: 2.30e-04
2022-05-28 11:36:52,929 INFO [train.py:842] (2/4) Epoch 24, batch 2650, loss[loss=0.1738, simple_loss=0.2682, pruned_loss=0.03975, over 7310.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2695, pruned_loss=0.04775, over 1417346.28 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:37:31,146 INFO [train.py:842] (2/4) Epoch 24, batch 2700, loss[loss=0.2099, simple_loss=0.3107, pruned_loss=0.05458, over 7345.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2695, pruned_loss=0.04775, over 1422094.84 frames.], batch size: 22, lr: 2.30e-04
2022-05-28 11:38:09,217 INFO [train.py:842] (2/4) Epoch 24, batch 2750, loss[loss=0.1635, simple_loss=0.2619, pruned_loss=0.03259, over 7413.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2689, pruned_loss=0.0471, over 1424940.86 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:38:47,216 INFO [train.py:842] (2/4) Epoch 24, batch 2800, loss[loss=0.1786, simple_loss=0.2598, pruned_loss=0.04869, over 7237.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2703, pruned_loss=0.04753, over 1421574.38 frames.], batch size: 20, lr: 2.30e-04
2022-05-28 11:39:25,096 INFO [train.py:842] (2/4) Epoch 24, batch 2850, loss[loss=0.2083, simple_loss=0.2917, pruned_loss=0.06244, over 7352.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2703, pruned_loss=0.04764, over 1422673.02 frames.], batch size: 19, lr: 2.30e-04
2022-05-28 11:40:03,390 INFO [train.py:842] (2/4) Epoch 24, batch 2900, loss[loss=0.223, simple_loss=0.3039, pruned_loss=0.07104, over 7271.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2701, pruned_loss=0.04762, over 1422270.70 frames.], batch size: 25, lr: 2.30e-04
2022-05-28 11:40:41,532 INFO [train.py:842] (2/4) Epoch 24, batch 2950, loss[loss=0.1741, simple_loss=0.2586, pruned_loss=0.04476, over 7278.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2698, pruned_loss=0.0473, over 1426396.23 frames.], batch size: 17, lr: 2.30e-04
2022-05-28 11:41:19,736 INFO [train.py:842] (2/4) Epoch 24, batch 3000, loss[loss=0.1732, simple_loss=0.2714, pruned_loss=0.03754, over 7127.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2699, pruned_loss=0.04763, over 1422454.30 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:41:19,737 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 11:41:28,713 INFO [train.py:871] (2/4) Epoch 24, validation: loss=0.1662, simple_loss=0.2647, pruned_loss=0.03391, over 868885.00 frames.
2022-05-28 11:42:06,676 INFO [train.py:842] (2/4) Epoch 24, batch 3050, loss[loss=0.1371, simple_loss=0.2175, pruned_loss=0.02839, over 7284.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2688, pruned_loss=0.04704, over 1417201.15 frames.], batch size: 18, lr: 2.30e-04
2022-05-28 11:42:45,176 INFO [train.py:842] (2/4) Epoch 24, batch 3100, loss[loss=0.1919, simple_loss=0.2823, pruned_loss=0.05076, over 6733.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2679, pruned_loss=0.04645, over 1420322.04 frames.], batch size: 31, lr: 2.30e-04
2022-05-28 11:43:23,173 INFO [train.py:842] (2/4) Epoch 24, batch 3150, loss[loss=0.1552, simple_loss=0.247, pruned_loss=0.03172, over 7001.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2679, pruned_loss=0.04653, over 1422249.48 frames.], batch size: 16, lr: 2.30e-04
2022-05-28 11:44:01,730 INFO [train.py:842] (2/4) Epoch 24, batch 3200, loss[loss=0.1878, simple_loss=0.2768, pruned_loss=0.04937, over 7316.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2674, pruned_loss=0.04611, over 1426268.15 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:44:39,930 INFO [train.py:842] (2/4) Epoch 24, batch 3250, loss[loss=0.1497, simple_loss=0.2375, pruned_loss=0.03089, over 7168.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2674, pruned_loss=0.04619, over 1427591.72 frames.], batch size: 18, lr: 2.30e-04
2022-05-28 11:45:18,234 INFO [train.py:842] (2/4) Epoch 24, batch 3300, loss[loss=0.1977, simple_loss=0.2927, pruned_loss=0.05135, over 7285.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2674, pruned_loss=0.04586, over 1427624.34 frames.], batch size: 24, lr: 2.30e-04
2022-05-28 11:45:56,717 INFO [train.py:842] (2/4) Epoch 24, batch 3350, loss[loss=0.2595, simple_loss=0.3466, pruned_loss=0.08627, over 7294.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2687, pruned_loss=0.04682, over 1423710.24 frames.], batch size: 24, lr: 2.30e-04
2022-05-28 11:46:35,160 INFO [train.py:842] (2/4) Epoch 24, batch 3400, loss[loss=0.1787, simple_loss=0.264, pruned_loss=0.04674, over 7364.00 frames.], tot_loss[loss=0.1806, simple_loss=0.268, pruned_loss=0.04661, over 1428104.48 frames.], batch size: 19, lr: 2.30e-04
2022-05-28 11:47:13,091 INFO [train.py:842] (2/4) Epoch 24, batch 3450, loss[loss=0.1809, simple_loss=0.2714, pruned_loss=0.04519, over 7321.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2694, pruned_loss=0.04691, over 1422824.33 frames.], batch size: 22, lr: 2.30e-04
2022-05-28 11:47:51,628 INFO [train.py:842] (2/4) Epoch 24, batch 3500, loss[loss=0.1715, simple_loss=0.2476, pruned_loss=0.04773, over 7201.00 frames.], tot_loss[loss=0.1796, simple_loss=0.267, pruned_loss=0.04611, over 1421124.48 frames.], batch size: 16, lr: 2.30e-04
2022-05-28 11:48:29,729 INFO [train.py:842] (2/4) Epoch 24, batch 3550, loss[loss=0.2337, simple_loss=0.3103, pruned_loss=0.07856, over 7119.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2658, pruned_loss=0.0455, over 1423412.59 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:49:17,336 INFO [train.py:842] (2/4) Epoch 24, batch 3600, loss[loss=0.1698, simple_loss=0.2602, pruned_loss=0.03969, over 7074.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2659, pruned_loss=0.04529, over 1422767.35 frames.], batch size: 18, lr: 2.30e-04
2022-05-28 11:49:55,160 INFO [train.py:842] (2/4) Epoch 24, batch 3650, loss[loss=0.1466, simple_loss=0.237, pruned_loss=0.02809, over 7362.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2663, pruned_loss=0.04528, over 1423209.78 frames.], batch size: 19, lr: 2.30e-04
2022-05-28 11:50:33,269 INFO [train.py:842] (2/4) Epoch 24, batch 3700, loss[loss=0.1836, simple_loss=0.2759, pruned_loss=0.04568, over 6402.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2664, pruned_loss=0.04523, over 1419855.89 frames.], batch size: 38, lr: 2.30e-04
2022-05-28 11:51:11,247 INFO [train.py:842] (2/4) Epoch 24, batch 3750, loss[loss=0.1559, simple_loss=0.2433, pruned_loss=0.03423, over 7266.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2679, pruned_loss=0.04637, over 1421883.38 frames.], batch size: 18, lr: 2.30e-04
2022-05-28 11:51:49,639 INFO [train.py:842] (2/4) Epoch 24, batch 3800, loss[loss=0.1659, simple_loss=0.254, pruned_loss=0.03889, over 7426.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2679, pruned_loss=0.04631, over 1424208.57 frames.], batch size: 20, lr: 2.30e-04
2022-05-28 11:52:27,526 INFO [train.py:842] (2/4) Epoch 24, batch 3850, loss[loss=0.2599, simple_loss=0.3351, pruned_loss=0.09236, over 5055.00 frames.], tot_loss[loss=0.181, simple_loss=0.2683, pruned_loss=0.04687, over 1419627.06 frames.], batch size: 52, lr: 2.30e-04
2022-05-28 11:53:05,680 INFO [train.py:842] (2/4) Epoch 24, batch 3900, loss[loss=0.1846, simple_loss=0.2819, pruned_loss=0.04366, over 6846.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2687, pruned_loss=0.04719, over 1416242.68 frames.], batch size: 31, lr: 2.30e-04
2022-05-28 11:53:43,320 INFO [train.py:842] (2/4) Epoch 24, batch 3950, loss[loss=0.1989, simple_loss=0.2867, pruned_loss=0.05553, over 7318.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2699, pruned_loss=0.04763, over 1417220.36 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:54:21,407 INFO [train.py:842] (2/4) Epoch 24, batch 4000, loss[loss=0.1705, simple_loss=0.2662, pruned_loss=0.03741, over 7156.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2699, pruned_loss=0.04762, over 1417852.21 frames.], batch size: 19, lr: 2.30e-04
2022-05-28 11:54:59,369 INFO [train.py:842] (2/4) Epoch 24, batch 4050, loss[loss=0.1615, simple_loss=0.249, pruned_loss=0.03705, over 7411.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2692, pruned_loss=0.04747, over 1421429.72 frames.], batch size: 21, lr: 2.30e-04
2022-05-28 11:55:37,619 INFO [train.py:842] (2/4) Epoch 24, batch 4100, loss[loss=0.1921, simple_loss=0.2808, pruned_loss=0.05171, over 7287.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2689, pruned_loss=0.04747, over 1420508.63 frames.], batch size: 24, lr: 2.30e-04
2022-05-28 11:56:15,699 INFO [train.py:842] (2/4) Epoch 24, batch 4150, loss[loss=0.1802, simple_loss=0.2736, pruned_loss=0.04339, over 7257.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2705, pruned_loss=0.04835, over 1425614.76 frames.], batch size: 19, lr: 2.30e-04
2022-05-28 11:56:53,999 INFO [train.py:842] (2/4) Epoch 24, batch 4200, loss[loss=0.1904, simple_loss=0.2737, pruned_loss=0.05352, over 5148.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2695, pruned_loss=0.04743, over 1423850.27 frames.], batch size: 53, lr: 2.29e-04
2022-05-28 11:57:31,909 INFO [train.py:842] (2/4) Epoch 24, batch 4250, loss[loss=0.1685, simple_loss=0.2488, pruned_loss=0.04412, over 7138.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2708, pruned_loss=0.0485, over 1427647.73 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 11:58:10,158 INFO [train.py:842] (2/4) Epoch 24, batch 4300, loss[loss=0.1781, simple_loss=0.2799, pruned_loss=0.03813, over 7217.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2703, pruned_loss=0.04792, over 1423851.98 frames.], batch size: 21, lr: 2.29e-04
2022-05-28 11:58:48,027 INFO [train.py:842] (2/4) Epoch 24, batch 4350, loss[loss=0.1756, simple_loss=0.2543, pruned_loss=0.04846, over 7327.00 frames.], tot_loss[loss=0.183, simple_loss=0.2701, pruned_loss=0.04796, over 1422461.55 frames.], batch size: 20, lr: 2.29e-04
2022-05-28 11:59:26,287 INFO [train.py:842] (2/4) Epoch 24, batch 4400, loss[loss=0.2022, simple_loss=0.296, pruned_loss=0.05418, over 7420.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2692, pruned_loss=0.04709, over 1423631.55 frames.], batch size: 21, lr: 2.29e-04
2022-05-28 12:00:04,050 INFO [train.py:842] (2/4) Epoch 24, batch 4450, loss[loss=0.153, simple_loss=0.2308, pruned_loss=0.03755, over 7133.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2691, pruned_loss=0.04687, over 1422171.39 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 12:00:42,349 INFO [train.py:842] (2/4) Epoch 24, batch 4500, loss[loss=0.15, simple_loss=0.2313, pruned_loss=0.03438, over 7130.00 frames.], tot_loss[loss=0.181, simple_loss=0.2684, pruned_loss=0.04681, over 1424012.44 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 12:01:20,176 INFO [train.py:842] (2/4) Epoch 24, batch 4550, loss[loss=0.1609, simple_loss=0.2458, pruned_loss=0.03801, over 7363.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2682, pruned_loss=0.04634, over 1422748.51 frames.], batch size: 19, lr: 2.29e-04
2022-05-28 12:02:01,222 INFO [train.py:842] (2/4) Epoch 24, batch 4600, loss[loss=0.1444, simple_loss=0.2302, pruned_loss=0.02934, over 7265.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2674, pruned_loss=0.04569, over 1427771.26 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 12:02:38,967 INFO [train.py:842] (2/4) Epoch 24, batch 4650, loss[loss=0.2315, simple_loss=0.3111, pruned_loss=0.07597, over 6851.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2686, pruned_loss=0.04621, over 1426191.73 frames.], batch size: 31, lr: 2.29e-04
2022-05-28 12:03:17,222 INFO [train.py:842] (2/4) Epoch 24, batch 4700, loss[loss=0.1623, simple_loss=0.2442, pruned_loss=0.04022, over 7151.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2692, pruned_loss=0.04675, over 1424569.13 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 12:03:55,140 INFO [train.py:842] (2/4) Epoch 24, batch 4750, loss[loss=0.1686, simple_loss=0.2468, pruned_loss=0.0452, over 7170.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2687, pruned_loss=0.04651, over 1424679.86 frames.], batch size: 18, lr: 2.29e-04
2022-05-28 12:04:33,317 INFO [train.py:842] (2/4) Epoch 24, batch 4800, loss[loss=0.1615, simple_loss=0.2549, pruned_loss=0.03401, over 7049.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2687, pruned_loss=0.04637, over 1425292.44 frames.], batch size: 28, lr: 2.29e-04
2022-05-28 12:05:10,964 INFO [train.py:842] (2/4) Epoch 24, batch 4850, loss[loss=0.1729, simple_loss=0.262, pruned_loss=0.04185, over 6578.00 frames.], tot_loss[loss=0.1808, simple_loss=0.269, pruned_loss=0.04632, over 1419652.35 frames.], batch size: 38, lr: 2.29e-04
2022-05-28 12:05:49,019 INFO [train.py:842] (2/4) Epoch 24, batch 4900, loss[loss=0.1821, simple_loss=0.263, pruned_loss=0.05053, over 7418.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2686, pruned_loss=0.04643, over 1418596.95 frames.], batch size: 18, lr: 2.29e-04
2022-05-28 12:06:27,195 INFO [train.py:842] (2/4) Epoch 24, batch 4950, loss[loss=0.1533, simple_loss=0.2408, pruned_loss=0.03292, over 7268.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2688, pruned_loss=0.04698, over 1417056.98 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 12:07:05,411 INFO [train.py:842] (2/4) Epoch 24, batch 5000, loss[loss=0.2032, simple_loss=0.2977, pruned_loss=0.05434, over 7205.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2699, pruned_loss=0.04779, over 1417220.39 frames.], batch size: 22, lr: 2.29e-04
2022-05-28 12:07:43,335 INFO [train.py:842] (2/4) Epoch 24, batch 5050, loss[loss=0.1644, simple_loss=0.2313, pruned_loss=0.04876, over 6830.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2699, pruned_loss=0.04749, over 1418283.28 frames.], batch size: 15, lr: 2.29e-04
2022-05-28 12:08:21,582 INFO [train.py:842] (2/4) Epoch 24, batch 5100, loss[loss=0.2353, simple_loss=0.3035, pruned_loss=0.08353, over 4858.00 frames.], tot_loss[loss=0.1813, simple_loss=0.269, pruned_loss=0.04682, over 1418058.08 frames.], batch size: 53, lr: 2.29e-04
2022-05-28 12:08:59,669 INFO [train.py:842] (2/4) Epoch 24, batch 5150, loss[loss=0.257, simple_loss=0.3182, pruned_loss=0.09792, over 7350.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2696, pruned_loss=0.04728, over 1422528.56 frames.], batch size: 19, lr: 2.29e-04
2022-05-28 12:09:37,927 INFO [train.py:842] (2/4) Epoch 24, batch 5200, loss[loss=0.1529, simple_loss=0.2416, pruned_loss=0.0321, over 7358.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2685, pruned_loss=0.04662, over 1426210.55 frames.], batch size: 19, lr: 2.29e-04
2022-05-28 12:10:15,997 INFO [train.py:842] (2/4) Epoch 24, batch 5250, loss[loss=0.1723, simple_loss=0.2611, pruned_loss=0.04179, over 7391.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2674, pruned_loss=0.04646, over 1428155.40 frames.], batch size: 23, lr: 2.29e-04
2022-05-28 12:10:54,303 INFO [train.py:842] (2/4) Epoch 24, batch 5300, loss[loss=0.1947, simple_loss=0.287, pruned_loss=0.05119, over 7117.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2679, pruned_loss=0.04627, over 1430145.43 frames.], batch size: 26, lr: 2.29e-04
2022-05-28 12:11:32,300 INFO [train.py:842] (2/4) Epoch 24, batch 5350, loss[loss=0.184, simple_loss=0.2681, pruned_loss=0.04999, over 7419.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2673, pruned_loss=0.04611, over 1427241.49 frames.], batch size: 21, lr: 2.29e-04
2022-05-28 12:12:10,620 INFO [train.py:842] (2/4) Epoch 24, batch 5400, loss[loss=0.2051, simple_loss=0.2852, pruned_loss=0.06255, over 5156.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2688, pruned_loss=0.0472, over 1427833.71 frames.], batch size: 52, lr: 2.29e-04
2022-05-28 12:12:48,583 INFO [train.py:842] (2/4) Epoch 24, batch 5450, loss[loss=0.2062, simple_loss=0.3015, pruned_loss=0.05539, over 7178.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2684, pruned_loss=0.04674, over 1431229.63 frames.], batch size: 23, lr: 2.29e-04
2022-05-28 12:13:26,954 INFO [train.py:842] (2/4) Epoch 24, batch 5500, loss[loss=0.2157, simple_loss=0.3071, pruned_loss=0.06218, over 7098.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2681, pruned_loss=0.04672, over 1425505.29 frames.], batch size: 26, lr: 2.29e-04
2022-05-28 12:14:04,954 INFO [train.py:842] (2/4) Epoch 24, batch 5550, loss[loss=0.1605, simple_loss=0.2631, pruned_loss=0.02892, over 7281.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2689, pruned_loss=0.04698, over 1425663.00 frames.], batch size: 25, lr: 2.29e-04
2022-05-28 12:14:43,505 INFO [train.py:842] (2/4) Epoch 24, batch 5600, loss[loss=0.1681, simple_loss=0.248, pruned_loss=0.04415, over 7002.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2673, pruned_loss=0.04641, over 1423460.20 frames.], batch size: 16, lr: 2.29e-04
2022-05-28 12:15:21,845 INFO [train.py:842] (2/4) Epoch 24, batch 5650, loss[loss=0.1736, simple_loss=0.2689, pruned_loss=0.0391, over 7139.00 frames.], tot_loss[loss=0.1797, simple_loss=0.267, pruned_loss=0.04622, over 1422662.32 frames.], batch size: 20, lr: 2.29e-04
2022-05-28 12:16:00,295 INFO [train.py:842] (2/4) Epoch 24, batch 5700, loss[loss=0.1656, simple_loss=0.2654, pruned_loss=0.03292, over 7288.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2685, pruned_loss=0.04696, over 1423791.69 frames.], batch size: 25, lr: 2.29e-04
2022-05-28 12:16:38,562 INFO [train.py:842] (2/4) Epoch 24, batch 5750, loss[loss=0.1471, simple_loss=0.2308, pruned_loss=0.03166, over 7270.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2692, pruned_loss=0.04759, over 1421644.56 frames.], batch size: 17, lr: 2.29e-04
2022-05-28 12:17:17,296 INFO [train.py:842] (2/4) Epoch 24, batch 5800, loss[loss=0.1634, simple_loss=0.2594, pruned_loss=0.03371, over 7199.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2709, pruned_loss=0.049, over 1421722.46 frames.], batch size: 22, lr: 2.29e-04
2022-05-28 12:17:55,618 INFO [train.py:842] (2/4) Epoch 24, batch 5850, loss[loss=0.1629, simple_loss=0.2586, pruned_loss=0.0336, over 7203.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2697, pruned_loss=0.04853, over 1419902.87 frames.], batch size: 23, lr: 2.29e-04
2022-05-28 12:18:34,454 INFO [train.py:842] (2/4) Epoch 24, batch 5900, loss[loss=0.1677, simple_loss=0.2484, pruned_loss=0.04349, over 6857.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2678, pruned_loss=0.04784, over 1421097.56 frames.], batch size: 15, lr: 2.29e-04
2022-05-28 12:19:12,839 INFO [train.py:842] (2/4) Epoch 24, batch 5950, loss[loss=0.1738, simple_loss=0.2651, pruned_loss=0.0412, over 7170.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2677, pruned_loss=0.04724, over 1420680.92 frames.], batch size: 26, lr: 2.29e-04
2022-05-28 12:19:51,436 INFO [train.py:842] (2/4) Epoch 24, batch 6000, loss[loss=0.2229, simple_loss=0.2998, pruned_loss=0.07301, over 4983.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2677, pruned_loss=0.04702, over 1418971.27 frames.], batch size: 53, lr: 2.29e-04
2022-05-28 12:19:51,438 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 12:20:00,718 INFO [train.py:871] (2/4) Epoch 24, validation: loss=0.166, simple_loss=0.2648, pruned_loss=0.03361, over 868885.00 frames.
2022-05-28 12:20:38,957 INFO [train.py:842] (2/4) Epoch 24, batch 6050, loss[loss=0.2067, simple_loss=0.2927, pruned_loss=0.06039, over 6829.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2683, pruned_loss=0.04702, over 1421423.85 frames.], batch size: 31, lr: 2.29e-04
2022-05-28 12:21:17,782 INFO [train.py:842] (2/4) Epoch 24, batch 6100, loss[loss=0.1806, simple_loss=0.2748, pruned_loss=0.04318, over 7369.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2664, pruned_loss=0.04599, over 1423325.51 frames.], batch size: 23, lr: 2.28e-04
2022-05-28 12:21:56,408 INFO [train.py:842] (2/4) Epoch 24, batch 6150, loss[loss=0.1492, simple_loss=0.2429, pruned_loss=0.02771, over 7421.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2657, pruned_loss=0.0458, over 1423785.18 frames.], batch size: 20, lr: 2.28e-04
2022-05-28 12:22:35,162 INFO [train.py:842] (2/4) Epoch 24, batch 6200, loss[loss=0.1986, simple_loss=0.2854, pruned_loss=0.05596, over 7321.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2668, pruned_loss=0.04623, over 1423418.75 frames.], batch size: 25, lr: 2.28e-04
2022-05-28 12:23:13,724 INFO [train.py:842] (2/4) Epoch 24, batch 6250, loss[loss=0.1851, simple_loss=0.281, pruned_loss=0.04457, over 7162.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2656, pruned_loss=0.04575, over 1425133.46 frames.], batch size: 28, lr: 2.28e-04
2022-05-28 12:23:52,505 INFO [train.py:842] (2/4) Epoch 24, batch 6300, loss[loss=0.1958, simple_loss=0.2778, pruned_loss=0.05685, over 7400.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2672, pruned_loss=0.04697, over 1423529.53 frames.], batch size: 21, lr: 2.28e-04
2022-05-28 12:24:30,656 INFO [train.py:842] (2/4) Epoch 24, batch 6350, loss[loss=0.1777, simple_loss=0.2768, pruned_loss=0.03929, over 6676.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2683, pruned_loss=0.0472, over 1420032.51 frames.], batch size: 31, lr: 2.28e-04
2022-05-28 12:25:09,317 INFO [train.py:842] (2/4) Epoch 24, batch 6400, loss[loss=0.1747, simple_loss=0.2542, pruned_loss=0.04762, over 6995.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2678, pruned_loss=0.04694, over 1421130.22 frames.], batch size: 16, lr: 2.28e-04
2022-05-28 12:25:47,747 INFO [train.py:842] (2/4) Epoch 24, batch 6450, loss[loss=0.2644, simple_loss=0.3335, pruned_loss=0.09761, over 7030.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2682, pruned_loss=0.04705, over 1421548.03 frames.], batch size: 32, lr: 2.28e-04
2022-05-28 12:26:26,358 INFO [train.py:842] (2/4) Epoch 24, batch 6500, loss[loss=0.149, simple_loss=0.2244, pruned_loss=0.03677, over 6996.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2679, pruned_loss=0.04682, over 1421312.26 frames.], batch size: 16, lr: 2.28e-04
2022-05-28 12:27:04,744 INFO [train.py:842] (2/4) Epoch 24, batch 6550, loss[loss=0.1693, simple_loss=0.2651, pruned_loss=0.03675, over 7294.00 frames.], tot_loss[loss=0.179, simple_loss=0.2667, pruned_loss=0.04571, over 1425112.62 frames.], batch size: 24, lr: 2.28e-04
2022-05-28 12:27:43,574 INFO [train.py:842] (2/4) Epoch 24, batch 6600, loss[loss=0.1942, simple_loss=0.2866, pruned_loss=0.05091, over 7104.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2671, pruned_loss=0.04619, over 1426490.07 frames.], batch size: 28, lr: 2.28e-04
2022-05-28 12:28:22,107 INFO [train.py:842] (2/4) Epoch 24, batch 6650, loss[loss=0.2081, simple_loss=0.2932, pruned_loss=0.06155, over 7299.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2674, pruned_loss=0.04596, over 1428424.45 frames.], batch size: 24, lr: 2.28e-04
2022-05-28 12:29:00,906 INFO [train.py:842] (2/4) Epoch 24, batch 6700, loss[loss=0.1466, simple_loss=0.232, pruned_loss=0.03063, over 7287.00 frames.], tot_loss[loss=0.179, simple_loss=0.2664, pruned_loss=0.04583, over 1430697.32 frames.], batch size: 17, lr: 2.28e-04
2022-05-28 12:29:39,678 INFO [train.py:842] (2/4) Epoch 24, batch 6750, loss[loss=0.1798, simple_loss=0.2782, pruned_loss=0.04067, over 7156.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2673, pruned_loss=0.04686, over 1430014.49 frames.], batch size: 19, lr: 2.28e-04
2022-05-28 12:30:18,212 INFO [train.py:842] (2/4) Epoch 24, batch 6800, loss[loss=0.1835, simple_loss=0.2752, pruned_loss=0.04592, over 7147.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2682, pruned_loss=0.04717, over 1427654.38 frames.], batch size: 20, lr: 2.28e-04
2022-05-28 12:30:56,684 INFO [train.py:842] (2/4) Epoch 24, batch 6850, loss[loss=0.2037, simple_loss=0.2914, pruned_loss=0.05796, over 6587.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2684, pruned_loss=0.04736, over 1427595.90 frames.], batch size: 38, lr: 2.28e-04
2022-05-28 12:31:35,007 INFO [train.py:842] (2/4) Epoch 24, batch 6900, loss[loss=0.1906, simple_loss=0.2676, pruned_loss=0.05678, over 7186.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2683, pruned_loss=0.04729, over 1427485.95 frames.], batch size: 23, lr: 2.28e-04
2022-05-28 12:32:13,549 INFO [train.py:842] (2/4) Epoch 24, batch 6950, loss[loss=0.1604, simple_loss=0.247, pruned_loss=0.03693, over 6809.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2683, pruned_loss=0.0472, over 1428226.31 frames.], batch size: 15, lr: 2.28e-04
2022-05-28 12:32:52,257 INFO [train.py:842] (2/4) Epoch 24, batch 7000, loss[loss=0.1372, simple_loss=0.2284, pruned_loss=0.02301, over 6825.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2678, pruned_loss=0.04676, over 1426665.53 frames.], batch size: 15, lr: 2.28e-04
2022-05-28 12:33:30,584 INFO [train.py:842] (2/4) Epoch 24, batch 7050, loss[loss=0.1602, simple_loss=0.2502, pruned_loss=0.03507, over 7144.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2682, pruned_loss=0.04709, over 1427157.54 frames.], batch size: 26, lr: 2.28e-04
2022-05-28 12:34:09,117 INFO [train.py:842] (2/4) Epoch 24, batch 7100, loss[loss=0.1854, simple_loss=0.2715, pruned_loss=0.04965, over 7136.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2687, pruned_loss=0.04701, over 1426970.12 frames.], batch size: 28, lr: 2.28e-04
2022-05-28 12:34:47,728 INFO [train.py:842] (2/4) Epoch 24, batch 7150, loss[loss=0.1797, simple_loss=0.2621, pruned_loss=0.04866, over 7150.00 frames.], tot_loss[loss=0.18, simple_loss=0.2673, pruned_loss=0.04642, over 1425904.97 frames.], batch size: 19, lr: 2.28e-04
2022-05-28 12:35:36,394 INFO [train.py:842] (2/4) Epoch 24, batch 7200, loss[loss=0.1951, simple_loss=0.2893, pruned_loss=0.05047, over 7126.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2687, pruned_loss=0.04713, over 1426207.53 frames.], batch size: 21, lr: 2.28e-04
2022-05-28 12:36:14,993 INFO [train.py:842] (2/4) Epoch 24, batch 7250, loss[loss=0.2246, simple_loss=0.3172, pruned_loss=0.06601, over 7331.00 frames.], tot_loss[loss=0.1821, simple_loss=0.269, pruned_loss=0.04756, over 1431276.81 frames.], batch size: 22, lr: 2.28e-04
2022-05-28 12:36:53,727 INFO [train.py:842] (2/4) Epoch 24, batch 7300, loss[loss=0.157, simple_loss=0.2485, pruned_loss=0.03278, over 7449.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2682, pruned_loss=0.04716, over 1431101.05 frames.], batch size: 19, lr: 2.28e-04
2022-05-28 12:37:32,028 INFO [train.py:842] (2/4) Epoch 24, batch 7350, loss[loss=0.1447, simple_loss=0.2171, pruned_loss=0.03613, over 6996.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2684, pruned_loss=0.04701, over 1432225.81 frames.], batch size: 16, lr: 2.28e-04
2022-05-28 12:38:10,691 INFO [train.py:842] (2/4) Epoch 24, batch 7400, loss[loss=0.2187, simple_loss=0.3011, pruned_loss=0.0681, over 7244.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2693, pruned_loss=0.04724, over 1431615.31 frames.], batch size: 20, lr: 2.28e-04
2022-05-28 12:38:59,109 INFO [train.py:842] (2/4) Epoch 24, batch 7450, loss[loss=0.1707, simple_loss=0.2654, pruned_loss=0.03798, over 7431.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2688, pruned_loss=0.04676, over 1430194.36 frames.], batch size: 20, lr: 2.28e-04
2022-05-28 12:39:37,696 INFO [train.py:842] (2/4) Epoch 24, batch 7500, loss[loss=0.1785, simple_loss=0.2726, pruned_loss=0.04217, over 7261.00 frames.], tot_loss[loss=0.1812, simple_loss=0.269, pruned_loss=0.04668, over 1428526.93 frames.], batch size: 19, lr: 2.28e-04
2022-05-28 12:40:26,158 INFO [train.py:842] (2/4) Epoch 24, batch 7550, loss[loss=0.145, simple_loss=0.2306, pruned_loss=0.02972, over 7354.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2685, pruned_loss=0.04643, over 1427783.48 frames.], batch size: 19, lr: 2.28e-04
2022-05-28 12:41:04,824 INFO [train.py:842] (2/4) Epoch 24, batch 7600, loss[loss=0.191, simple_loss=0.2805, pruned_loss=0.05069, over 7101.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2699, pruned_loss=0.04701, over 1428933.69 frames.], batch size: 26, lr: 2.28e-04
2022-05-28 12:41:43,339 INFO [train.py:842] (2/4) Epoch 24, batch 7650, loss[loss=0.2284, simple_loss=0.3166, pruned_loss=0.0701, over 7280.00 frames.], tot_loss[loss=0.1813, simple_loss=0.269, pruned_loss=0.04682, over 1431153.09 frames.], batch size: 25, lr: 2.28e-04
2022-05-28 12:42:22,065 INFO [train.py:842] (2/4) Epoch 24, batch 7700, loss[loss=0.1752, simple_loss=0.2681, pruned_loss=0.04117, over 7017.00 frames.], tot_loss[loss=0.1805, simple_loss=0.268, pruned_loss=0.04652, over 1429199.58 frames.], batch size: 28, lr: 2.28e-04
2022-05-28 12:43:00,403 INFO [train.py:842] (2/4) Epoch 24, batch 7750, loss[loss=0.1683, simple_loss=0.258, pruned_loss=0.03936, over 7362.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2692, pruned_loss=0.04661, over 1430794.83 frames.], batch size: 19, lr: 2.28e-04
2022-05-28 12:43:39,187 INFO [train.py:842] (2/4) Epoch 24, batch 7800, loss[loss=0.2079, simple_loss=0.2978, pruned_loss=0.05902, over 7377.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2711, pruned_loss=0.04803, over 1431141.35 frames.], batch size: 23, lr: 2.28e-04
2022-05-28 12:44:17,731 INFO [train.py:842] (2/4) Epoch 24, batch 7850, loss[loss=0.2076, simple_loss=0.2903, pruned_loss=0.06242, over 5426.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2699, pruned_loss=0.04764, over 1427447.79 frames.], batch size: 53, lr: 2.28e-04
2022-05-28 12:44:56,228 INFO [train.py:842] (2/4) Epoch 24, batch 7900, loss[loss=0.2101, simple_loss=0.2956, pruned_loss=0.06234, over 7399.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2716, pruned_loss=0.04883, over 1422649.75 frames.], batch size: 18, lr: 2.28e-04
2022-05-28 12:45:34,561 INFO [train.py:842] (2/4) Epoch 24, batch 7950, loss[loss=0.2188, simple_loss=0.2957, pruned_loss=0.07091, over 6415.00 frames.], tot_loss[loss=0.185, simple_loss=0.2718, pruned_loss=0.04907, over 1424775.86 frames.], batch size: 38, lr: 2.28e-04
2022-05-28 12:46:13,251 INFO [train.py:842] (2/4) Epoch 24, batch 8000, loss[loss=0.2046, simple_loss=0.2999, pruned_loss=0.05462, over 7270.00 frames.], tot_loss[loss=0.1844, simple_loss=0.271, pruned_loss=0.04889, over 1416812.80 frames.], batch size: 24, lr: 2.27e-04
2022-05-28 12:46:51,901 INFO [train.py:842] (2/4) Epoch 24, batch 8050, loss[loss=0.1978, simple_loss=0.297, pruned_loss=0.04926, over 6697.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2703, pruned_loss=0.0486, over 1415786.54 frames.], batch size: 31, lr: 2.27e-04
2022-05-28 12:47:30,405 INFO [train.py:842] (2/4) Epoch 24, batch 8100, loss[loss=0.2267, simple_loss=0.3095, pruned_loss=0.07195, over 7309.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2704, pruned_loss=0.04842, over 1414657.10 frames.], batch size: 24, lr: 2.27e-04
2022-05-28 12:48:08,866 INFO [train.py:842] (2/4) Epoch 24, batch 8150, loss[loss=0.1529, simple_loss=0.2466, pruned_loss=0.02956, over 7072.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2695, pruned_loss=0.04783, over 1412647.62 frames.], batch size: 18, lr: 2.27e-04
2022-05-28 12:48:47,288 INFO [train.py:842] (2/4) Epoch 24, batch 8200, loss[loss=0.1837, simple_loss=0.2773, pruned_loss=0.04507, over 7167.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2695, pruned_loss=0.04762, over 1417027.30 frames.], batch size: 19, lr: 2.27e-04
2022-05-28 12:49:25,439 INFO [train.py:842] (2/4) Epoch 24, batch 8250, loss[loss=0.2234, simple_loss=0.3067, pruned_loss=0.07004, over 6459.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2695, pruned_loss=0.04807, over 1418584.43 frames.], batch size: 38, lr: 2.27e-04
2022-05-28 12:50:04,139 INFO [train.py:842] (2/4) Epoch 24, batch 8300, loss[loss=0.1645, simple_loss=0.264, pruned_loss=0.0325, over 7324.00 frames.], tot_loss[loss=0.182, simple_loss=0.2691, pruned_loss=0.04746, over 1422407.83 frames.], batch size: 21, lr: 2.27e-04
2022-05-28 12:50:42,560 INFO [train.py:842] (2/4) Epoch 24, batch 8350, loss[loss=0.1817, simple_loss=0.2689, pruned_loss=0.04721, over 7045.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2681, pruned_loss=0.04683, over 1421058.94 frames.], batch size: 28, lr: 2.27e-04
2022-05-28 12:51:21,494 INFO [train.py:842] (2/4) Epoch 24, batch 8400, loss[loss=0.1819, simple_loss=0.2683, pruned_loss=0.04774, over 7419.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2669, pruned_loss=0.0463, over 1425912.51 frames.], batch size: 18, lr: 2.27e-04
2022-05-28 12:51:59,857 INFO [train.py:842] (2/4) Epoch 24, batch 8450, loss[loss=0.1744, simple_loss=0.2639, pruned_loss=0.04241, over 6429.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2674, pruned_loss=0.04636, over 1424789.27 frames.], batch size: 37, lr: 2.27e-04
2022-05-28 12:52:39,114 INFO [train.py:842] (2/4) Epoch 24, batch 8500, loss[loss=0.1539, simple_loss=0.2448, pruned_loss=0.03144, over 7202.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2678, pruned_loss=0.04646, over 1427278.98 frames.], batch size: 22, lr: 2.27e-04
2022-05-28 12:53:18,140 INFO [train.py:842] (2/4) Epoch 24, batch 8550, loss[loss=0.211, simple_loss=0.3, pruned_loss=0.061, over 7202.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2659, pruned_loss=0.04549, over 1428460.27 frames.], batch size: 26, lr: 2.27e-04
2022-05-28 12:53:57,152 INFO [train.py:842] (2/4) Epoch 24, batch 8600, loss[loss=0.1581, simple_loss=0.2452, pruned_loss=0.03553, over 7172.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2662, pruned_loss=0.04539, over 1431003.90 frames.], batch size: 18, lr: 2.27e-04
2022-05-28 12:54:35,695 INFO [train.py:842] (2/4) Epoch 24, batch 8650, loss[loss=0.1579, simple_loss=0.2386, pruned_loss=0.03863, over 7233.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2677, pruned_loss=0.04605, over 1425129.48 frames.], batch size: 20, lr: 2.27e-04
2022-05-28 12:55:14,296 INFO [train.py:842] (2/4) Epoch 24, batch 8700, loss[loss=0.2045, simple_loss=0.2903, pruned_loss=0.05936, over 7148.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2693, pruned_loss=0.04644, over 1419449.73 frames.], batch size: 18, lr: 2.27e-04
2022-05-28 12:55:52,911 INFO [train.py:842] (2/4) Epoch 24, batch 8750, loss[loss=0.2534, simple_loss=0.3344, pruned_loss=0.0862, over 7210.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2681, pruned_loss=0.04617, over 1421015.49 frames.], batch size: 23, lr: 2.27e-04
2022-05-28 12:56:31,829 INFO [train.py:842] (2/4) Epoch 24, batch 8800, loss[loss=0.1538, simple_loss=0.2496, pruned_loss=0.02901, over 7227.00 frames.], tot_loss[loss=0.181, simple_loss=0.2688, pruned_loss=0.04656, over 1421985.68 frames.], batch size: 21, lr: 2.27e-04
2022-05-28 12:57:10,117 INFO [train.py:842] (2/4) Epoch 24, batch 8850, loss[loss=0.1743, simple_loss=0.2667, pruned_loss=0.0409, over 6505.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2691, pruned_loss=0.04728, over 1411739.27 frames.], batch size: 38, lr: 2.27e-04
2022-05-28 12:57:48,730 INFO [train.py:842] (2/4) Epoch 24, batch 8900, loss[loss=0.1905, simple_loss=0.2798, pruned_loss=0.05063, over 7210.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2695, pruned_loss=0.04761, over 1402215.19 frames.], batch size: 22, lr: 2.27e-04
2022-05-28 12:58:27,159 INFO [train.py:842] (2/4) Epoch 24, batch 8950, loss[loss=0.2521, simple_loss=0.3173, pruned_loss=0.09342, over 4854.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2691, pruned_loss=0.04765, over 1398403.45 frames.], batch size: 52, lr: 2.27e-04
2022-05-28 12:59:05,943 INFO [train.py:842] (2/4) Epoch 24, batch 9000, loss[loss=0.1669, simple_loss=0.2432, pruned_loss=0.04535, over 7195.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2693, pruned_loss=0.04847, over 1392096.75 frames.], batch size: 16, lr: 2.27e-04
2022-05-28 12:59:05,944 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 12:59:15,343 INFO [train.py:871] (2/4) Epoch 24, validation: loss=0.1628, simple_loss=0.2615, pruned_loss=0.03209, over 868885.00 frames.
2022-05-28 12:59:53,634 INFO [train.py:842] (2/4) Epoch 24, batch 9050, loss[loss=0.2067, simple_loss=0.29, pruned_loss=0.06166, over 7368.00 frames.], tot_loss[loss=0.1858, simple_loss=0.272, pruned_loss=0.0498, over 1380155.44 frames.], batch size: 23, lr: 2.27e-04
2022-05-28 13:00:31,859 INFO [train.py:842] (2/4) Epoch 24, batch 9100, loss[loss=0.1846, simple_loss=0.2672, pruned_loss=0.05099, over 4850.00 frames.], tot_loss[loss=0.1885, simple_loss=0.274, pruned_loss=0.05143, over 1333129.22 frames.], batch size: 53, lr: 2.27e-04
2022-05-28 13:01:09,340 INFO [train.py:842] (2/4) Epoch 24, batch 9150, loss[loss=0.2842, simple_loss=0.3638, pruned_loss=0.1023, over 5188.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2783, pruned_loss=0.0545, over 1262507.49 frames.], batch size: 52, lr: 2.27e-04
2022-05-28 13:02:00,921 INFO [train.py:842] (2/4) Epoch 25, batch 0, loss[loss=0.189, simple_loss=0.2793, pruned_loss=0.0493, over 7075.00 frames.], tot_loss[loss=0.189, simple_loss=0.2793, pruned_loss=0.0493, over 7075.00 frames.], batch size: 18, lr: 2.22e-04
2022-05-28 13:02:39,758 INFO [train.py:842] (2/4) Epoch 25, batch 50, loss[loss=0.1592, simple_loss=0.2539, pruned_loss=0.03224, over 7253.00 frames.], tot_loss[loss=0.183, simple_loss=0.2696, pruned_loss=0.04821, over 322488.14 frames.], batch size: 19, lr: 2.22e-04
2022-05-28 13:03:18,865 INFO [train.py:842] (2/4) Epoch 25, batch 100, loss[loss=0.1719, simple_loss=0.2655, pruned_loss=0.03912, over 7329.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2685, pruned_loss=0.04767, over 570397.71 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:03:57,400 INFO [train.py:842] (2/4) Epoch 25, batch 150, loss[loss=0.1592, simple_loss=0.2577, pruned_loss=0.03041, over 7331.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2673, pruned_loss=0.04642, over 761647.04 frames.], batch size: 21, lr: 2.22e-04
2022-05-28 13:04:36,560 INFO [train.py:842] (2/4) Epoch 25, batch 200, loss[loss=0.1412, simple_loss=0.2289, pruned_loss=0.02674, over 6763.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2662, pruned_loss=0.04563, over 906909.18 frames.], batch size: 15, lr: 2.22e-04
2022-05-28 13:05:14,871 INFO [train.py:842] (2/4) Epoch 25, batch 250, loss[loss=0.1803, simple_loss=0.2731, pruned_loss=0.04374, over 7229.00 frames.], tot_loss[loss=0.179, simple_loss=0.2663, pruned_loss=0.04586, over 1019872.90 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:05:53,572 INFO [train.py:842] (2/4) Epoch 25, batch 300, loss[loss=0.1549, simple_loss=0.2469, pruned_loss=0.0314, over 7153.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2678, pruned_loss=0.04641, over 1113623.75 frames.], batch size: 19, lr: 2.22e-04
2022-05-28 13:06:31,936 INFO [train.py:842] (2/4) Epoch 25, batch 350, loss[loss=0.2042, simple_loss=0.2926, pruned_loss=0.0579, over 7206.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2676, pruned_loss=0.04664, over 1182405.88 frames.], batch size: 23, lr: 2.22e-04
2022-05-28 13:07:10,738 INFO [train.py:842] (2/4) Epoch 25, batch 400, loss[loss=0.1668, simple_loss=0.2592, pruned_loss=0.03719, over 7240.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2672, pruned_loss=0.04652, over 1236228.76 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:07:49,165 INFO [train.py:842] (2/4) Epoch 25, batch 450, loss[loss=0.1635, simple_loss=0.2539, pruned_loss=0.03658, over 7123.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2673, pruned_loss=0.04662, over 1276283.76 frames.], batch size: 28, lr: 2.22e-04
2022-05-28 13:08:27,846 INFO [train.py:842] (2/4) Epoch 25, batch 500, loss[loss=0.1542, simple_loss=0.2375, pruned_loss=0.03543, over 7169.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2671, pruned_loss=0.04653, over 1311259.10 frames.], batch size: 18, lr: 2.22e-04
2022-05-28 13:09:06,208 INFO [train.py:842] (2/4) Epoch 25, batch 550, loss[loss=0.1932, simple_loss=0.2667, pruned_loss=0.05986, over 7153.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2681, pruned_loss=0.04719, over 1338646.27 frames.], batch size: 18, lr: 2.22e-04
2022-05-28 13:09:45,033 INFO [train.py:842] (2/4) Epoch 25, batch 600, loss[loss=0.1917, simple_loss=0.2823, pruned_loss=0.05053, over 7224.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2683, pruned_loss=0.04717, over 1358382.18 frames.], batch size: 23, lr: 2.22e-04
2022-05-28 13:10:23,508 INFO [train.py:842] (2/4) Epoch 25, batch 650, loss[loss=0.1654, simple_loss=0.2374, pruned_loss=0.04671, over 7274.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2664, pruned_loss=0.04642, over 1370845.81 frames.], batch size: 17, lr: 2.22e-04
2022-05-28 13:11:02,299 INFO [train.py:842] (2/4) Epoch 25, batch 700, loss[loss=0.1706, simple_loss=0.2474, pruned_loss=0.04686, over 7261.00 frames.], tot_loss[loss=0.179, simple_loss=0.2662, pruned_loss=0.04594, over 1387184.47 frames.], batch size: 16, lr: 2.22e-04
2022-05-28 13:11:40,747 INFO [train.py:842] (2/4) Epoch 25, batch 750, loss[loss=0.1535, simple_loss=0.2345, pruned_loss=0.03623, over 7225.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2664, pruned_loss=0.04559, over 1398477.17 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:12:19,433 INFO [train.py:842] (2/4) Epoch 25, batch 800, loss[loss=0.1738, simple_loss=0.2688, pruned_loss=0.03939, over 7403.00 frames.], tot_loss[loss=0.18, simple_loss=0.2676, pruned_loss=0.04621, over 1405606.31 frames.], batch size: 21, lr: 2.22e-04
2022-05-28 13:12:57,613 INFO [train.py:842] (2/4) Epoch 25, batch 850, loss[loss=0.1992, simple_loss=0.2803, pruned_loss=0.05903, over 7320.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2678, pruned_loss=0.04675, over 1407791.37 frames.], batch size: 21, lr: 2.22e-04
2022-05-28 13:13:36,190 INFO [train.py:842] (2/4) Epoch 25, batch 900, loss[loss=0.169, simple_loss=0.2605, pruned_loss=0.03878, over 7327.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2686, pruned_loss=0.04679, over 1409940.55 frames.], batch size: 25, lr: 2.22e-04
2022-05-28 13:14:14,459 INFO [train.py:842] (2/4) Epoch 25, batch 950, loss[loss=0.2434, simple_loss=0.3184, pruned_loss=0.08414, over 5013.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2699, pruned_loss=0.04796, over 1404628.29 frames.], batch size: 52, lr: 2.22e-04
2022-05-28 13:14:53,224 INFO [train.py:842] (2/4) Epoch 25, batch 1000, loss[loss=0.1889, simple_loss=0.274, pruned_loss=0.05187, over 7414.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2694, pruned_loss=0.04762, over 1411479.14 frames.], batch size: 21, lr: 2.22e-04
2022-05-28 13:15:31,610 INFO [train.py:842] (2/4) Epoch 25, batch 1050, loss[loss=0.1568, simple_loss=0.2485, pruned_loss=0.0326, over 7323.00 frames.], tot_loss[loss=0.183, simple_loss=0.2701, pruned_loss=0.04794, over 1418364.84 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:16:10,345 INFO [train.py:842] (2/4) Epoch 25, batch 1100, loss[loss=0.1837, simple_loss=0.2765, pruned_loss=0.04543, over 7338.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2695, pruned_loss=0.04758, over 1421146.94 frames.], batch size: 22, lr: 2.22e-04
2022-05-28 13:16:48,919 INFO [train.py:842] (2/4) Epoch 25, batch 1150, loss[loss=0.1706, simple_loss=0.271, pruned_loss=0.0351, over 7211.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2688, pruned_loss=0.04748, over 1424314.36 frames.], batch size: 23, lr: 2.22e-04
2022-05-28 13:17:27,818 INFO [train.py:842] (2/4) Epoch 25, batch 1200, loss[loss=0.1954, simple_loss=0.2836, pruned_loss=0.05359, over 7387.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2685, pruned_loss=0.04758, over 1424020.55 frames.], batch size: 23, lr: 2.22e-04
2022-05-28 13:18:06,179 INFO [train.py:842] (2/4) Epoch 25, batch 1250, loss[loss=0.1755, simple_loss=0.2685, pruned_loss=0.04127, over 7156.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2678, pruned_loss=0.04722, over 1422309.26 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:18:44,991 INFO [train.py:842] (2/4) Epoch 25, batch 1300, loss[loss=0.2469, simple_loss=0.299, pruned_loss=0.09741, over 6822.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2683, pruned_loss=0.04796, over 1421458.89 frames.], batch size: 15, lr: 2.22e-04
2022-05-28 13:19:23,289 INFO [train.py:842] (2/4) Epoch 25, batch 1350, loss[loss=0.1984, simple_loss=0.2897, pruned_loss=0.05354, over 6544.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2688, pruned_loss=0.04821, over 1421786.58 frames.], batch size: 38, lr: 2.22e-04
2022-05-28 13:20:01,954 INFO [train.py:842] (2/4) Epoch 25, batch 1400, loss[loss=0.1735, simple_loss=0.262, pruned_loss=0.04251, over 7287.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2692, pruned_loss=0.04784, over 1426668.92 frames.], batch size: 17, lr: 2.22e-04
2022-05-28 13:20:40,264 INFO [train.py:842] (2/4) Epoch 25, batch 1450, loss[loss=0.1696, simple_loss=0.2718, pruned_loss=0.03372, over 7147.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2691, pruned_loss=0.0478, over 1423104.41 frames.], batch size: 20, lr: 2.22e-04
2022-05-28 13:21:18,922 INFO [train.py:842] (2/4) Epoch 25, batch 1500, loss[loss=0.1904, simple_loss=0.2865, pruned_loss=0.04716, over 6711.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2696, pruned_loss=0.04805, over 1421669.65 frames.], batch size: 31, lr: 2.22e-04
2022-05-28 13:21:57,241 INFO [train.py:842] (2/4) Epoch 25, batch 1550, loss[loss=0.1568, simple_loss=0.2363, pruned_loss=0.03867, over 7277.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2696, pruned_loss=0.04711, over 1422905.36 frames.], batch size: 18, lr: 2.22e-04
2022-05-28 13:22:36,324 INFO [train.py:842] (2/4) Epoch 25, batch 1600, loss[loss=0.1457, simple_loss=0.2342, pruned_loss=0.02854, over 6789.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2696, pruned_loss=0.0475, over 1420985.30 frames.], batch size: 15, lr: 2.22e-04
2022-05-28 13:23:14,830 INFO [train.py:842] (2/4) Epoch 25, batch 1650, loss[loss=0.1932, simple_loss=0.2863, pruned_loss=0.05001, over 7219.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2692, pruned_loss=0.04748, over 1421140.10 frames.], batch size: 21, lr: 2.22e-04
2022-05-28 13:23:53,567 INFO [train.py:842] (2/4) Epoch 25, batch 1700, loss[loss=0.191, simple_loss=0.2855, pruned_loss=0.0482, over 7357.00 frames.], tot_loss[loss=0.1829, simple_loss=0.27, pruned_loss=0.04792, over 1419894.26 frames.], batch size: 23, lr: 2.22e-04
2022-05-28 13:24:31,714 INFO [train.py:842] (2/4) Epoch 25, batch 1750, loss[loss=0.1429, simple_loss=0.2273, pruned_loss=0.02919, over 7144.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2695, pruned_loss=0.04714, over 1422342.46 frames.], batch size: 17, lr: 2.22e-04
2022-05-28 13:25:10,181 INFO [train.py:842] (2/4) Epoch 25, batch 1800, loss[loss=0.1696, simple_loss=0.2459, pruned_loss=0.0466, over 6989.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2711, pruned_loss=0.0477, over 1422389.49 frames.], batch size: 16, lr: 2.21e-04
2022-05-28 13:25:48,946 INFO [train.py:842] (2/4) Epoch 25, batch 1850, loss[loss=0.203, simple_loss=0.2747, pruned_loss=0.06563, over 7154.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2689, pruned_loss=0.04695, over 1420453.59 frames.], batch size: 16, lr: 2.21e-04
2022-05-28 13:26:27,663 INFO [train.py:842] (2/4) Epoch 25, batch 1900, loss[loss=0.1877, simple_loss=0.2784, pruned_loss=0.04854, over 7280.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2683, pruned_loss=0.04696, over 1422136.98 frames.], batch size: 25, lr: 2.21e-04
2022-05-28 13:27:06,011 INFO [train.py:842] (2/4) Epoch 25, batch 1950, loss[loss=0.1685, simple_loss=0.2631, pruned_loss=0.03695, over 7251.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2683, pruned_loss=0.04721, over 1424201.53 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:27:44,807 INFO [train.py:842] (2/4) Epoch 25, batch 2000, loss[loss=0.1505, simple_loss=0.2381, pruned_loss=0.0314, over 7176.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2687, pruned_loss=0.04703, over 1424924.10 frames.], batch size: 18, lr: 2.21e-04
2022-05-28 13:28:23,557 INFO [train.py:842] (2/4) Epoch 25, batch 2050, loss[loss=0.1854, simple_loss=0.2733, pruned_loss=0.04873, over 7320.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2672, pruned_loss=0.04679, over 1427550.80 frames.], batch size: 21, lr: 2.21e-04
2022-05-28 13:29:02,057 INFO [train.py:842] (2/4) Epoch 25, batch 2100, loss[loss=0.1759, simple_loss=0.2538, pruned_loss=0.04898, over 7251.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2684, pruned_loss=0.04748, over 1423947.39 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:29:40,344 INFO [train.py:842] (2/4) Epoch 25, batch 2150, loss[loss=0.1332, simple_loss=0.2317, pruned_loss=0.01728, over 7429.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2686, pruned_loss=0.0473, over 1422697.64 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:30:19,183 INFO [train.py:842] (2/4) Epoch 25, batch 2200, loss[loss=0.1597, simple_loss=0.2416, pruned_loss=0.03891, over 6779.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2686, pruned_loss=0.04765, over 1421207.47 frames.], batch size: 15, lr: 2.21e-04
2022-05-28 13:30:57,858 INFO [train.py:842] (2/4) Epoch 25, batch 2250, loss[loss=0.1691, simple_loss=0.2612, pruned_loss=0.03846, over 7062.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2686, pruned_loss=0.04776, over 1417794.58 frames.], batch size: 18, lr: 2.21e-04
2022-05-28 13:31:36,586 INFO [train.py:842] (2/4) Epoch 25, batch 2300, loss[loss=0.1708, simple_loss=0.2552, pruned_loss=0.04326, over 6792.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2677, pruned_loss=0.04667, over 1419238.90 frames.], batch size: 15, lr: 2.21e-04
2022-05-28 13:32:14,938 INFO [train.py:842] (2/4) Epoch 25, batch 2350, loss[loss=0.1993, simple_loss=0.2915, pruned_loss=0.0535, over 7309.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2669, pruned_loss=0.04645, over 1419113.25 frames.], batch size: 21, lr: 2.21e-04
2022-05-28 13:32:53,624 INFO [train.py:842] (2/4) Epoch 25, batch 2400, loss[loss=0.1858, simple_loss=0.2693, pruned_loss=0.0512, over 7357.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2679, pruned_loss=0.04655, over 1423649.91 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:33:32,095 INFO [train.py:842] (2/4) Epoch 25, batch 2450, loss[loss=0.1271, simple_loss=0.2154, pruned_loss=0.0194, over 7128.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2679, pruned_loss=0.0466, over 1423133.17 frames.], batch size: 17, lr: 2.21e-04
2022-05-28 13:34:10,901 INFO [train.py:842] (2/4) Epoch 25, batch 2500, loss[loss=0.2353, simple_loss=0.3168, pruned_loss=0.07689, over 7420.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2681, pruned_loss=0.04658, over 1423443.68 frames.], batch size: 21, lr: 2.21e-04
2022-05-28 13:34:49,183 INFO [train.py:842] (2/4) Epoch 25, batch 2550, loss[loss=0.1648, simple_loss=0.2563, pruned_loss=0.03663, over 7434.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2685, pruned_loss=0.04649, over 1424343.50 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:35:27,916 INFO [train.py:842] (2/4) Epoch 25, batch 2600, loss[loss=0.1388, simple_loss=0.2211, pruned_loss=0.02827, over 7145.00 frames.], tot_loss[loss=0.181, simple_loss=0.2685, pruned_loss=0.04671, over 1420904.19 frames.], batch size: 17, lr: 2.21e-04
2022-05-28 13:36:06,372 INFO [train.py:842] (2/4) Epoch 25, batch 2650, loss[loss=0.2074, simple_loss=0.2909, pruned_loss=0.06192, over 7218.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2684, pruned_loss=0.04614, over 1423118.19 frames.], batch size: 22, lr: 2.21e-04
2022-05-28 13:36:45,200 INFO [train.py:842] (2/4) Epoch 25, batch 2700, loss[loss=0.1725, simple_loss=0.25, pruned_loss=0.04751, over 7062.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2672, pruned_loss=0.04582, over 1424975.65 frames.], batch size: 18, lr: 2.21e-04
2022-05-28 13:37:23,654 INFO [train.py:842] (2/4) Epoch 25, batch 2750, loss[loss=0.1542, simple_loss=0.2459, pruned_loss=0.03131, over 7154.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2662, pruned_loss=0.04569, over 1420797.69 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:38:02,433 INFO [train.py:842] (2/4) Epoch 25, batch 2800, loss[loss=0.1528, simple_loss=0.2463, pruned_loss=0.0296, over 7251.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2668, pruned_loss=0.04572, over 1421440.06 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:38:40,761 INFO [train.py:842] (2/4) Epoch 25, batch 2850, loss[loss=0.1648, simple_loss=0.259, pruned_loss=0.03528, over 7425.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2664, pruned_loss=0.04539, over 1419386.79 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:39:19,308 INFO [train.py:842] (2/4) Epoch 25, batch 2900, loss[loss=0.2074, simple_loss=0.2932, pruned_loss=0.0608, over 7195.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2673, pruned_loss=0.04574, over 1419927.19 frames.], batch size: 23, lr: 2.21e-04
2022-05-28 13:39:57,703 INFO [train.py:842] (2/4) Epoch 25, batch 2950, loss[loss=0.1734, simple_loss=0.2693, pruned_loss=0.03876, over 7126.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2667, pruned_loss=0.04538, over 1425693.64 frames.], batch size: 21, lr: 2.21e-04
2022-05-28 13:40:36,695 INFO [train.py:842] (2/4) Epoch 25, batch 3000, loss[loss=0.1686, simple_loss=0.2673, pruned_loss=0.03489, over 6710.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2654, pruned_loss=0.04484, over 1428512.80 frames.], batch size: 31, lr: 2.21e-04
2022-05-28 13:40:36,696 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 13:40:46,045 INFO [train.py:871] (2/4) Epoch 25, validation: loss=0.1651, simple_loss=0.2634, pruned_loss=0.03341, over 868885.00 frames.
2022-05-28 13:41:24,598 INFO [train.py:842] (2/4) Epoch 25, batch 3050, loss[loss=0.1702, simple_loss=0.2766, pruned_loss=0.03189, over 7122.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2653, pruned_loss=0.04474, over 1429115.56 frames.], batch size: 21, lr: 2.21e-04
2022-05-28 13:42:03,495 INFO [train.py:842] (2/4) Epoch 25, batch 3100, loss[loss=0.2131, simple_loss=0.2757, pruned_loss=0.07525, over 7177.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2651, pruned_loss=0.04536, over 1429263.34 frames.], batch size: 16, lr: 2.21e-04
2022-05-28 13:42:41,825 INFO [train.py:842] (2/4) Epoch 25, batch 3150, loss[loss=0.1666, simple_loss=0.2596, pruned_loss=0.03686, over 7261.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2659, pruned_loss=0.04534, over 1430929.46 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:43:20,563 INFO [train.py:842] (2/4) Epoch 25, batch 3200, loss[loss=0.2552, simple_loss=0.3331, pruned_loss=0.08862, over 4989.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2661, pruned_loss=0.04569, over 1429513.02 frames.], batch size: 52, lr: 2.21e-04
2022-05-28 13:43:59,045 INFO [train.py:842] (2/4) Epoch 25, batch 3250, loss[loss=0.2232, simple_loss=0.3176, pruned_loss=0.06439, over 7233.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2669, pruned_loss=0.04604, over 1426838.71 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:44:37,658 INFO [train.py:842] (2/4) Epoch 25, batch 3300, loss[loss=0.1764, simple_loss=0.2584, pruned_loss=0.04718, over 7156.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2669, pruned_loss=0.04601, over 1425765.36 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:45:16,119 INFO [train.py:842] (2/4) Epoch 25, batch 3350, loss[loss=0.1504, simple_loss=0.2463, pruned_loss=0.02722, over 7257.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2663, pruned_loss=0.04561, over 1422415.51 frames.], batch size: 19, lr: 2.21e-04
2022-05-28 13:45:57,674 INFO [train.py:842] (2/4) Epoch 25, batch 3400, loss[loss=0.1598, simple_loss=0.233, pruned_loss=0.04325, over 7264.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2663, pruned_loss=0.04597, over 1424090.75 frames.], batch size: 17, lr: 2.21e-04
2022-05-28 13:46:36,037 INFO [train.py:842] (2/4) Epoch 25, batch 3450, loss[loss=0.1563, simple_loss=0.2584, pruned_loss=0.02707, over 7222.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2666, pruned_loss=0.04625, over 1420896.85 frames.], batch size: 21, lr: 2.21e-04
2022-05-28 13:47:14,709 INFO [train.py:842] (2/4) Epoch 25, batch 3500, loss[loss=0.1429, simple_loss=0.2233, pruned_loss=0.03123, over 7126.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2668, pruned_loss=0.04622, over 1422632.43 frames.], batch size: 17, lr: 2.21e-04
2022-05-28 13:47:52,980 INFO [train.py:842] (2/4) Epoch 25, batch 3550, loss[loss=0.1637, simple_loss=0.2598, pruned_loss=0.03379, over 7327.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2681, pruned_loss=0.04661, over 1424327.85 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:48:31,600 INFO [train.py:842] (2/4) Epoch 25, batch 3600, loss[loss=0.2231, simple_loss=0.3016, pruned_loss=0.07224, over 7211.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2684, pruned_loss=0.04691, over 1422498.60 frames.], batch size: 23, lr: 2.21e-04
2022-05-28 13:49:09,892 INFO [train.py:842] (2/4) Epoch 25, batch 3650, loss[loss=0.1846, simple_loss=0.2672, pruned_loss=0.05107, over 6494.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2683, pruned_loss=0.04654, over 1419530.06 frames.], batch size: 38, lr: 2.21e-04
2022-05-28 13:49:48,740 INFO [train.py:842] (2/4) Epoch 25, batch 3700, loss[loss=0.147, simple_loss=0.2426, pruned_loss=0.02573, over 7422.00 frames.], tot_loss[loss=0.181, simple_loss=0.2684, pruned_loss=0.04679, over 1422702.57 frames.], batch size: 20, lr: 2.21e-04
2022-05-28 13:50:27,334 INFO [train.py:842] (2/4) Epoch 25, batch 3750, loss[loss=0.1811, simple_loss=0.2686, pruned_loss=0.04682, over 7391.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2678, pruned_loss=0.04627, over 1425087.05 frames.], batch size: 23, lr: 2.21e-04
2022-05-28 13:51:06,204 INFO [train.py:842] (2/4) Epoch 25, batch 3800, loss[loss=0.2458, simple_loss=0.3188, pruned_loss=0.08643, over 4700.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2678, pruned_loss=0.04667, over 1422809.85 frames.], batch size: 52, lr: 2.21e-04
2022-05-28 13:51:44,466 INFO [train.py:842] (2/4) Epoch 25, batch 3850, loss[loss=0.1543, simple_loss=0.2369, pruned_loss=0.03588, over 7271.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2681, pruned_loss=0.0467, over 1422033.47 frames.], batch size: 18, lr: 2.20e-04
2022-05-28 13:52:23,073 INFO [train.py:842] (2/4) Epoch 25, batch 3900, loss[loss=0.1659, simple_loss=0.2437, pruned_loss=0.044, over 7273.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2675, pruned_loss=0.04615, over 1422327.11 frames.], batch size: 19, lr: 2.20e-04
2022-05-28 13:53:01,448 INFO [train.py:842] (2/4) Epoch 25, batch 3950, loss[loss=0.1366, simple_loss=0.2221, pruned_loss=0.02558, over 7429.00 frames.], tot_loss[loss=0.1792, simple_loss=0.267, pruned_loss=0.04566, over 1424443.05 frames.], batch size: 18, lr: 2.20e-04
2022-05-28 13:53:40,190 INFO [train.py:842] (2/4) Epoch 25, batch 4000, loss[loss=0.1814, simple_loss=0.2713, pruned_loss=0.04576, over 7404.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2667, pruned_loss=0.04558, over 1425984.85 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 13:54:18,685 INFO [train.py:842] (2/4) Epoch 25, batch 4050, loss[loss=0.1445, simple_loss=0.2306, pruned_loss=0.02917, over 7130.00 frames.], tot_loss[loss=0.178, simple_loss=0.2655, pruned_loss=0.0453, over 1424655.11 frames.], batch size: 17, lr: 2.20e-04
2022-05-28 13:54:57,384 INFO [train.py:842] (2/4) Epoch 25, batch 4100, loss[loss=0.1751, simple_loss=0.2775, pruned_loss=0.03634, over 7313.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2674, pruned_loss=0.04622, over 1427985.00 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 13:55:35,864 INFO [train.py:842] (2/4) Epoch 25, batch 4150, loss[loss=0.1786, simple_loss=0.2695, pruned_loss=0.04378, over 7413.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2671, pruned_loss=0.0466, over 1424791.89 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 13:56:14,602 INFO [train.py:842] (2/4) Epoch 25, batch 4200, loss[loss=0.1687, simple_loss=0.2658, pruned_loss=0.03578, over 7236.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2669, pruned_loss=0.04641, over 1421637.23 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 13:56:52,840 INFO [train.py:842] (2/4) Epoch 25, batch 4250, loss[loss=0.1987, simple_loss=0.2846, pruned_loss=0.05643, over 7360.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2677, pruned_loss=0.04623, over 1423632.01 frames.], batch size: 23, lr: 2.20e-04
2022-05-28 13:57:31,628 INFO [train.py:842] (2/4) Epoch 25, batch 4300, loss[loss=0.1587, simple_loss=0.2541, pruned_loss=0.0317, over 7058.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2673, pruned_loss=0.04628, over 1425135.24 frames.], batch size: 28, lr: 2.20e-04
2022-05-28 13:58:10,169 INFO [train.py:842] (2/4) Epoch 25, batch 4350, loss[loss=0.1424, simple_loss=0.2285, pruned_loss=0.02814, over 7417.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2659, pruned_loss=0.04552, over 1425086.68 frames.], batch size: 18, lr: 2.20e-04
2022-05-28 13:58:48,803 INFO [train.py:842] (2/4) Epoch 25, batch 4400, loss[loss=0.1861, simple_loss=0.2816, pruned_loss=0.0453, over 7235.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2661, pruned_loss=0.04551, over 1423060.67 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 13:59:27,181 INFO [train.py:842] (2/4) Epoch 25, batch 4450, loss[loss=0.2126, simple_loss=0.3087, pruned_loss=0.05823, over 7296.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2667, pruned_loss=0.04556, over 1422591.25 frames.], batch size: 24, lr: 2.20e-04
2022-05-28 14:00:05,956 INFO [train.py:842] (2/4) Epoch 25, batch 4500, loss[loss=0.1691, simple_loss=0.2665, pruned_loss=0.0358, over 7149.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2663, pruned_loss=0.04519, over 1423360.85 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 14:00:44,413 INFO [train.py:842] (2/4) Epoch 25, batch 4550, loss[loss=0.1916, simple_loss=0.2885, pruned_loss=0.04741, over 6386.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2665, pruned_loss=0.04513, over 1423477.73 frames.], batch size: 37, lr: 2.20e-04
2022-05-28 14:01:23,178 INFO [train.py:842] (2/4) Epoch 25, batch 4600, loss[loss=0.1946, simple_loss=0.2815, pruned_loss=0.05386, over 6657.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2655, pruned_loss=0.04443, over 1422086.35 frames.], batch size: 31, lr: 2.20e-04
2022-05-28 14:02:01,597 INFO [train.py:842] (2/4) Epoch 25, batch 4650, loss[loss=0.2397, simple_loss=0.33, pruned_loss=0.07473, over 7147.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2656, pruned_loss=0.0447, over 1421317.82 frames.], batch size: 26, lr: 2.20e-04
2022-05-28 14:02:40,409 INFO [train.py:842] (2/4) Epoch 25, batch 4700, loss[loss=0.205, simple_loss=0.2698, pruned_loss=0.07014, over 7018.00 frames.], tot_loss[loss=0.178, simple_loss=0.2658, pruned_loss=0.04512, over 1417349.63 frames.], batch size: 16, lr: 2.20e-04
2022-05-28 14:03:18,771 INFO [train.py:842] (2/4) Epoch 25, batch 4750, loss[loss=0.2132, simple_loss=0.305, pruned_loss=0.06071, over 7287.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2661, pruned_loss=0.04511, over 1418766.84 frames.], batch size: 24, lr: 2.20e-04
2022-05-28 14:03:57,591 INFO [train.py:842] (2/4) Epoch 25, batch 4800, loss[loss=0.1721, simple_loss=0.2529, pruned_loss=0.04565, over 7212.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2654, pruned_loss=0.04482, over 1422165.15 frames.], batch size: 22, lr: 2.20e-04
2022-05-28 14:04:36,159 INFO [train.py:842] (2/4) Epoch 25, batch 4850, loss[loss=0.2245, simple_loss=0.3029, pruned_loss=0.07301, over 7319.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2654, pruned_loss=0.04476, over 1427394.77 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 14:05:14,790 INFO [train.py:842] (2/4) Epoch 25, batch 4900, loss[loss=0.1627, simple_loss=0.2501, pruned_loss=0.03761, over 7387.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2661, pruned_loss=0.04487, over 1420861.90 frames.], batch size: 23, lr: 2.20e-04
2022-05-28 14:05:53,358 INFO [train.py:842] (2/4) Epoch 25, batch 4950, loss[loss=0.2556, simple_loss=0.333, pruned_loss=0.0891, over 5107.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2675, pruned_loss=0.04605, over 1419083.03 frames.], batch size: 52, lr: 2.20e-04
2022-05-28 14:06:31,859 INFO [train.py:842] (2/4) Epoch 25, batch 5000, loss[loss=0.1483, simple_loss=0.2315, pruned_loss=0.03255, over 7441.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2675, pruned_loss=0.04606, over 1415762.80 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 14:07:10,243 INFO [train.py:842] (2/4) Epoch 25, batch 5050, loss[loss=0.1822, simple_loss=0.2797, pruned_loss=0.04232, over 7323.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2682, pruned_loss=0.04637, over 1422882.29 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 14:07:49,139 INFO [train.py:842] (2/4) Epoch 25, batch 5100, loss[loss=0.1819, simple_loss=0.2781, pruned_loss=0.04278, over 7155.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2678, pruned_loss=0.0465, over 1424687.59 frames.], batch size: 19, lr: 2.20e-04
2022-05-28 14:08:27,760 INFO [train.py:842] (2/4) Epoch 25, batch 5150, loss[loss=0.1735, simple_loss=0.2672, pruned_loss=0.03988, over 7099.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2679, pruned_loss=0.04686, over 1424811.39 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 14:09:06,403 INFO [train.py:842] (2/4) Epoch 25, batch 5200, loss[loss=0.1756, simple_loss=0.2663, pruned_loss=0.04248, over 7146.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2689, pruned_loss=0.04717, over 1425949.97 frames.], batch size: 26, lr: 2.20e-04
2022-05-28 14:09:44,927 INFO [train.py:842] (2/4) Epoch 25, batch 5250, loss[loss=0.1828, simple_loss=0.263, pruned_loss=0.05131, over 7360.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2677, pruned_loss=0.0468, over 1423921.33 frames.], batch size: 19, lr: 2.20e-04
2022-05-28 14:10:23,854 INFO [train.py:842] (2/4) Epoch 25, batch 5300, loss[loss=0.2073, simple_loss=0.282, pruned_loss=0.06626, over 7323.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2676, pruned_loss=0.04686, over 1421843.69 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 14:11:02,326 INFO [train.py:842] (2/4) Epoch 25, batch 5350, loss[loss=0.1953, simple_loss=0.2813, pruned_loss=0.05458, over 7340.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2674, pruned_loss=0.04655, over 1416970.02 frames.], batch size: 19, lr: 2.20e-04
2022-05-28 14:11:41,150 INFO [train.py:842] (2/4) Epoch 25, batch 5400, loss[loss=0.1748, simple_loss=0.2653, pruned_loss=0.04217, over 7074.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2658, pruned_loss=0.04565, over 1422318.24 frames.], batch size: 18, lr: 2.20e-04
2022-05-28 14:12:19,664 INFO [train.py:842] (2/4) Epoch 25, batch 5450, loss[loss=0.1672, simple_loss=0.2568, pruned_loss=0.03875, over 7439.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2665, pruned_loss=0.04609, over 1419111.15 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 14:12:58,311 INFO [train.py:842] (2/4) Epoch 25, batch 5500, loss[loss=0.1949, simple_loss=0.2851, pruned_loss=0.05236, over 6496.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2664, pruned_loss=0.04609, over 1418862.91 frames.], batch size: 38, lr: 2.20e-04
2022-05-28 14:13:36,707 INFO [train.py:842] (2/4) Epoch 25, batch 5550, loss[loss=0.175, simple_loss=0.2738, pruned_loss=0.03811, over 7411.00 frames.], tot_loss[loss=0.1788, simple_loss=0.266, pruned_loss=0.04583, over 1423476.86 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 14:14:15,456 INFO [train.py:842] (2/4) Epoch 25, batch 5600, loss[loss=0.2009, simple_loss=0.2938, pruned_loss=0.05401, over 7211.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2643, pruned_loss=0.04495, over 1427092.90 frames.], batch size: 21, lr: 2.20e-04
2022-05-28 14:14:53,913 INFO [train.py:842] (2/4) Epoch 25, batch 5650, loss[loss=0.181, simple_loss=0.2752, pruned_loss=0.04336, over 6992.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2661, pruned_loss=0.04544, over 1430837.60 frames.], batch size: 28, lr: 2.20e-04
2022-05-28 14:15:32,483 INFO [train.py:842] (2/4) Epoch 25, batch 5700, loss[loss=0.1879, simple_loss=0.2769, pruned_loss=0.04944, over 7328.00 frames.], tot_loss[loss=0.178, simple_loss=0.2655, pruned_loss=0.04524, over 1427236.15 frames.], batch size: 22, lr: 2.20e-04
2022-05-28 14:16:11,067 INFO [train.py:842] (2/4) Epoch 25, batch 5750, loss[loss=0.1763, simple_loss=0.2565, pruned_loss=0.04806, over 7146.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2649, pruned_loss=0.045, over 1429145.31 frames.], batch size: 17, lr: 2.20e-04
2022-05-28 14:16:49,543 INFO [train.py:842] (2/4) Epoch 25, batch 5800, loss[loss=0.1662, simple_loss=0.2708, pruned_loss=0.03076, over 7146.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2649, pruned_loss=0.04469, over 1431195.98 frames.], batch size: 20, lr: 2.20e-04
2022-05-28 14:17:27,564 INFO [train.py:842] (2/4) Epoch 25, batch 5850, loss[loss=0.1715, simple_loss=0.2679, pruned_loss=0.03754, over 6494.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2654, pruned_loss=0.04476, over 1424646.80 frames.], batch size: 38, lr: 2.20e-04
2022-05-28 14:18:06,220 INFO [train.py:842] (2/4) Epoch 25, batch 5900, loss[loss=0.1698, simple_loss=0.2685, pruned_loss=0.03556, over 7339.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2662, pruned_loss=0.04536, over 1423674.32 frames.], batch size: 22, lr: 2.19e-04
2022-05-28 14:18:44,329 INFO [train.py:842] (2/4) Epoch 25, batch 5950, loss[loss=0.1936, simple_loss=0.2814, pruned_loss=0.05295, over 7427.00 frames.], tot_loss[loss=0.1804, simple_loss=0.268, pruned_loss=0.04645, over 1421951.97 frames.], batch size: 20, lr: 2.19e-04
2022-05-28 14:19:23,244 INFO [train.py:842] (2/4) Epoch 25, batch 6000, loss[loss=0.1721, simple_loss=0.2607, pruned_loss=0.04169, over 7329.00 frames.], tot_loss[loss=0.1797, simple_loss=0.267, pruned_loss=0.04619, over 1423306.03 frames.], batch size: 22, lr: 2.19e-04
2022-05-28 14:19:23,244 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 14:19:32,870 INFO [train.py:871] (2/4) Epoch 25, validation: loss=0.1658, simple_loss=0.2641, pruned_loss=0.03372, over 868885.00 frames.
2022-05-28 14:20:11,399 INFO [train.py:842] (2/4) Epoch 25, batch 6050, loss[loss=0.1764, simple_loss=0.2657, pruned_loss=0.04353, over 7197.00 frames.], tot_loss[loss=0.18, simple_loss=0.267, pruned_loss=0.04652, over 1424839.17 frames.], batch size: 23, lr: 2.19e-04
2022-05-28 14:20:50,170 INFO [train.py:842] (2/4) Epoch 25, batch 6100, loss[loss=0.1737, simple_loss=0.2506, pruned_loss=0.04839, over 7015.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2665, pruned_loss=0.04628, over 1426927.16 frames.], batch size: 16, lr: 2.19e-04
2022-05-28 14:21:28,824 INFO [train.py:842] (2/4) Epoch 25, batch 6150, loss[loss=0.1692, simple_loss=0.2648, pruned_loss=0.03678, over 7115.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2658, pruned_loss=0.04614, over 1425919.58 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:22:07,507 INFO [train.py:842] (2/4) Epoch 25, batch 6200, loss[loss=0.1595, simple_loss=0.2541, pruned_loss=0.03251, over 7338.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2663, pruned_loss=0.04625, over 1422457.16 frames.], batch size: 20, lr: 2.19e-04
2022-05-28 14:22:45,941 INFO [train.py:842] (2/4) Epoch 25, batch 6250, loss[loss=0.193, simple_loss=0.2927, pruned_loss=0.04666, over 7214.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2675, pruned_loss=0.04707, over 1420068.07 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:23:34,548 INFO [train.py:842] (2/4) Epoch 25, batch 6300, loss[loss=0.1856, simple_loss=0.2753, pruned_loss=0.0479, over 7337.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2674, pruned_loss=0.04651, over 1420847.93 frames.], batch size: 22, lr: 2.19e-04
2022-05-28 14:24:12,964 INFO [train.py:842] (2/4) Epoch 25, batch 6350, loss[loss=0.1711, simple_loss=0.2667, pruned_loss=0.03776, over 7420.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2676, pruned_loss=0.04634, over 1420149.79 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:24:51,897 INFO [train.py:842] (2/4) Epoch 25, batch 6400, loss[loss=0.17, simple_loss=0.2615, pruned_loss=0.03925, over 7252.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2681, pruned_loss=0.04625, over 1421467.59 frames.], batch size: 19, lr: 2.19e-04
2022-05-28 14:25:30,512 INFO [train.py:842] (2/4) Epoch 25, batch 6450, loss[loss=0.1498, simple_loss=0.2283, pruned_loss=0.03561, over 6986.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2673, pruned_loss=0.04617, over 1425369.70 frames.], batch size: 16, lr: 2.19e-04
2022-05-28 14:26:09,238 INFO [train.py:842] (2/4) Epoch 25, batch 6500, loss[loss=0.1732, simple_loss=0.2711, pruned_loss=0.03764, over 6698.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2673, pruned_loss=0.04619, over 1424502.93 frames.], batch size: 38, lr: 2.19e-04
2022-05-28 14:26:47,616 INFO [train.py:842] (2/4) Epoch 25, batch 6550, loss[loss=0.1497, simple_loss=0.2298, pruned_loss=0.03483, over 7168.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2665, pruned_loss=0.04597, over 1422027.88 frames.], batch size: 19, lr: 2.19e-04
2022-05-28 14:27:26,212 INFO [train.py:842] (2/4) Epoch 25, batch 6600, loss[loss=0.1838, simple_loss=0.2669, pruned_loss=0.05036, over 7229.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2664, pruned_loss=0.04577, over 1424395.21 frames.], batch size: 20, lr: 2.19e-04
2022-05-28 14:28:04,726 INFO [train.py:842] (2/4) Epoch 25, batch 6650, loss[loss=0.1931, simple_loss=0.2897, pruned_loss=0.0483, over 6754.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2663, pruned_loss=0.04565, over 1425994.52 frames.], batch size: 31, lr: 2.19e-04
2022-05-28 14:28:43,448 INFO [train.py:842] (2/4) Epoch 25, batch 6700, loss[loss=0.1471, simple_loss=0.242, pruned_loss=0.02604, over 7167.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2675, pruned_loss=0.04645, over 1426994.59 frames.], batch size: 19, lr: 2.19e-04
2022-05-28 14:29:22,075 INFO [train.py:842] (2/4) Epoch 25, batch 6750, loss[loss=0.1676, simple_loss=0.2659, pruned_loss=0.03466, over 7138.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2677, pruned_loss=0.04679, over 1428412.70 frames.], batch size: 20, lr: 2.19e-04
2022-05-28 14:30:00,924 INFO [train.py:842] (2/4) Epoch 25, batch 6800, loss[loss=0.1892, simple_loss=0.2681, pruned_loss=0.05515, over 4905.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2667, pruned_loss=0.04635, over 1425214.20 frames.], batch size: 52, lr: 2.19e-04
2022-05-28 14:30:39,282 INFO [train.py:842] (2/4) Epoch 25, batch 6850, loss[loss=0.1849, simple_loss=0.28, pruned_loss=0.04491, over 7425.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2683, pruned_loss=0.0468, over 1428160.52 frames.], batch size: 20, lr: 2.19e-04
2022-05-28 14:31:18,197 INFO [train.py:842] (2/4) Epoch 25, batch 6900, loss[loss=0.1701, simple_loss=0.2502, pruned_loss=0.04501, over 7155.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2687, pruned_loss=0.04686, over 1429793.90 frames.], batch size: 17, lr: 2.19e-04
2022-05-28 14:31:56,680 INFO [train.py:842] (2/4) Epoch 25, batch 6950, loss[loss=0.155, simple_loss=0.2495, pruned_loss=0.03022, over 7332.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2687, pruned_loss=0.04715, over 1430080.97 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:32:35,579 INFO [train.py:842] (2/4) Epoch 25, batch 7000, loss[loss=0.1378, simple_loss=0.2282, pruned_loss=0.0237, over 7269.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2673, pruned_loss=0.04646, over 1432921.88 frames.], batch size: 18, lr: 2.19e-04
2022-05-28 14:33:13,742 INFO [train.py:842] (2/4) Epoch 25, batch 7050, loss[loss=0.1763, simple_loss=0.2635, pruned_loss=0.04459, over 7250.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2674, pruned_loss=0.04651, over 1428268.94 frames.], batch size: 19, lr: 2.19e-04
2022-05-28 14:33:52,528 INFO [train.py:842] (2/4) Epoch 25, batch 7100, loss[loss=0.1961, simple_loss=0.2942, pruned_loss=0.04905, over 7317.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2685, pruned_loss=0.04724, over 1428328.59 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:34:31,048 INFO [train.py:842] (2/4) Epoch 25, batch 7150, loss[loss=0.164, simple_loss=0.2488, pruned_loss=0.03967, over 7292.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2679, pruned_loss=0.04708, over 1426864.84 frames.], batch size: 17, lr: 2.19e-04
2022-05-28 14:35:09,889 INFO [train.py:842] (2/4) Epoch 25, batch 7200, loss[loss=0.1624, simple_loss=0.2638, pruned_loss=0.03055, over 7322.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2675, pruned_loss=0.04667, over 1427837.83 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:35:48,497 INFO [train.py:842] (2/4) Epoch 25, batch 7250, loss[loss=0.1755, simple_loss=0.2731, pruned_loss=0.0389, over 7144.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2651, pruned_loss=0.04568, over 1428129.63 frames.], batch size: 26, lr: 2.19e-04
2022-05-28 14:36:26,923 INFO [train.py:842] (2/4) Epoch 25, batch 7300, loss[loss=0.1887, simple_loss=0.2803, pruned_loss=0.04853, over 7325.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2667, pruned_loss=0.04581, over 1424898.79 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:37:05,547 INFO [train.py:842] (2/4) Epoch 25, batch 7350, loss[loss=0.1983, simple_loss=0.2839, pruned_loss=0.05639, over 7217.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2658, pruned_loss=0.04517, over 1427204.88 frames.], batch size: 22, lr: 2.19e-04
2022-05-28 14:37:44,466 INFO [train.py:842] (2/4) Epoch 25, batch 7400, loss[loss=0.1878, simple_loss=0.2731, pruned_loss=0.05125, over 7064.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2657, pruned_loss=0.04564, over 1426108.03 frames.], batch size: 18, lr: 2.19e-04
2022-05-28 14:38:22,833 INFO [train.py:842] (2/4) Epoch 25, batch 7450, loss[loss=0.1765, simple_loss=0.2625, pruned_loss=0.04526, over 7277.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2655, pruned_loss=0.04502, over 1426763.53 frames.], batch size: 24, lr: 2.19e-04
2022-05-28 14:39:01,268 INFO [train.py:842] (2/4) Epoch 25, batch 7500, loss[loss=0.1634, simple_loss=0.2531, pruned_loss=0.03685, over 7073.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2675, pruned_loss=0.0465, over 1425395.41 frames.], batch size: 18, lr: 2.19e-04
2022-05-28 14:39:39,756 INFO [train.py:842] (2/4) Epoch 25, batch 7550, loss[loss=0.1713, simple_loss=0.2609, pruned_loss=0.04079, over 7313.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2674, pruned_loss=0.04649, over 1428040.86 frames.], batch size: 25, lr: 2.19e-04
2022-05-28 14:40:18,789 INFO [train.py:842] (2/4) Epoch 25, batch 7600, loss[loss=0.1889, simple_loss=0.2807, pruned_loss=0.04859, over 7385.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2659, pruned_loss=0.04574, over 1433020.57 frames.], batch size: 23, lr: 2.19e-04
2022-05-28 14:40:57,020 INFO [train.py:842] (2/4) Epoch 25, batch 7650, loss[loss=0.1817, simple_loss=0.2748, pruned_loss=0.04432, over 7112.00 frames.], tot_loss[loss=0.1795, simple_loss=0.267, pruned_loss=0.04603, over 1433057.60 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:41:35,617 INFO [train.py:842] (2/4) Epoch 25, batch 7700, loss[loss=0.1548, simple_loss=0.2505, pruned_loss=0.02958, over 7054.00 frames.], tot_loss[loss=0.1797, simple_loss=0.267, pruned_loss=0.04618, over 1431831.43 frames.], batch size: 18, lr: 2.19e-04
2022-05-28 14:42:13,975 INFO [train.py:842] (2/4) Epoch 25, batch 7750, loss[loss=0.2053, simple_loss=0.2944, pruned_loss=0.05806, over 5063.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2661, pruned_loss=0.04571, over 1431180.29 frames.], batch size: 52, lr: 2.19e-04
2022-05-28 14:42:52,790 INFO [train.py:842] (2/4) Epoch 25, batch 7800, loss[loss=0.2387, simple_loss=0.3231, pruned_loss=0.07712, over 6737.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2662, pruned_loss=0.04577, over 1427763.43 frames.], batch size: 31, lr: 2.19e-04
2022-05-28 14:43:31,078 INFO [train.py:842] (2/4) Epoch 25, batch 7850, loss[loss=0.187, simple_loss=0.2804, pruned_loss=0.04679, over 7316.00 frames.], tot_loss[loss=0.179, simple_loss=0.2663, pruned_loss=0.04588, over 1427206.39 frames.], batch size: 21, lr: 2.19e-04
2022-05-28 14:44:10,160 INFO [train.py:842] (2/4) Epoch 25, batch 7900, loss[loss=0.1621, simple_loss=0.2428, pruned_loss=0.04069, over 7275.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2668, pruned_loss=0.04581, over 1428375.38 frames.], batch size: 18, lr: 2.19e-04
2022-05-28 14:44:48,430 INFO [train.py:842] (2/4) Epoch 25, batch 7950, loss[loss=0.1436, simple_loss=0.23, pruned_loss=0.0286, over 6985.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2669, pruned_loss=0.04569, over 1426984.52 frames.], batch size: 16, lr: 2.18e-04
2022-05-28 14:45:27,119 INFO [train.py:842] (2/4) Epoch 25, batch 8000, loss[loss=0.2164, simple_loss=0.3012, pruned_loss=0.06578, over 7335.00 frames.], tot_loss[loss=0.18, simple_loss=0.2679, pruned_loss=0.04604, over 1423553.02 frames.], batch size: 20, lr: 2.18e-04
2022-05-28 14:46:05,217 INFO [train.py:842] (2/4) Epoch 25, batch 8050, loss[loss=0.1565, simple_loss=0.2311, pruned_loss=0.04093, over 7284.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2692, pruned_loss=0.04688, over 1420366.68 frames.], batch size: 18, lr: 2.18e-04
2022-05-28 14:46:43,909 INFO [train.py:842] (2/4) Epoch 25, batch 8100, loss[loss=0.1742, simple_loss=0.2674, pruned_loss=0.04046, over 7206.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2693, pruned_loss=0.0467, over 1423564.12 frames.], batch size: 22, lr: 2.18e-04
2022-05-28 14:47:22,084 INFO [train.py:842] (2/4) Epoch 25, batch 8150, loss[loss=0.1813, simple_loss=0.2662, pruned_loss=0.04818, over 7235.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2693, pruned_loss=0.04694, over 1417696.77 frames.], batch size: 20, lr: 2.18e-04
2022-05-28 14:48:00,735 INFO [train.py:842] (2/4) Epoch 25, batch 8200, loss[loss=0.1349, simple_loss=0.2224, pruned_loss=0.02369, over 7284.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2683, pruned_loss=0.04651, over 1414277.34 frames.], batch size: 17, lr: 2.18e-04
2022-05-28 14:48:39,273 INFO [train.py:842] (2/4) Epoch 25, batch 8250, loss[loss=0.1518, simple_loss=0.2475, pruned_loss=0.02807, over 7159.00 frames.], tot_loss[loss=0.1794, simple_loss=0.267, pruned_loss=0.04586, over 1419335.79 frames.], batch size: 19, lr: 2.18e-04
2022-05-28 14:49:18,041 INFO [train.py:842] (2/4) Epoch 25, batch 8300, loss[loss=0.1609, simple_loss=0.2467, pruned_loss=0.03759, over 7072.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2677, pruned_loss=0.04592, over 1421429.80 frames.], batch size: 18, lr: 2.18e-04
2022-05-28 14:49:56,272 INFO [train.py:842] (2/4) Epoch 25, batch 8350, loss[loss=0.1508, simple_loss=0.2284, pruned_loss=0.03665, over 7283.00 frames.], tot_loss[loss=0.1804, simple_loss=0.268, pruned_loss=0.04641, over 1416842.58 frames.], batch size: 17, lr: 2.18e-04
2022-05-28 14:50:35,120 INFO [train.py:842] (2/4) Epoch 25, batch 8400, loss[loss=0.1565, simple_loss=0.2569, pruned_loss=0.02809, over 7220.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2672, pruned_loss=0.04621, over 1415745.61 frames.], batch size: 21, lr: 2.18e-04
2022-05-28 14:51:13,371 INFO [train.py:842] (2/4) Epoch 25, batch 8450, loss[loss=0.1983, simple_loss=0.2976, pruned_loss=0.04945, over 7143.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2693, pruned_loss=0.04688, over 1416511.51 frames.], batch size: 26, lr: 2.18e-04
2022-05-28 14:51:51,812 INFO [train.py:842] (2/4) Epoch 25, batch 8500, loss[loss=0.2001, simple_loss=0.2729, pruned_loss=0.06366, over 7069.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2705, pruned_loss=0.04788, over 1415759.44 frames.], batch size: 18, lr: 2.18e-04
2022-05-28 14:52:29,806 INFO [train.py:842] (2/4) Epoch 25, batch 8550, loss[loss=0.1721, simple_loss=0.2531, pruned_loss=0.04561, over 7393.00 frames.], tot_loss[loss=0.183, simple_loss=0.2708, pruned_loss=0.04764, over 1412582.44 frames.], batch size: 18, lr: 2.18e-04
2022-05-28 14:53:08,332 INFO [train.py:842] (2/4) Epoch 25, batch 8600, loss[loss=0.2252, simple_loss=0.3054, pruned_loss=0.07248, over 7120.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2706, pruned_loss=0.04731, over 1413920.63 frames.], batch size: 21, lr: 2.18e-04
2022-05-28 14:53:46,538 INFO [train.py:842] (2/4) Epoch 25, batch 8650, loss[loss=0.1692, simple_loss=0.2609, pruned_loss=0.0388, over 7295.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2697, pruned_loss=0.04647, over 1418129.98 frames.], batch size: 24, lr: 2.18e-04
2022-05-28 14:54:25,087 INFO [train.py:842] (2/4) Epoch 25, batch 8700, loss[loss=0.1889, simple_loss=0.2695, pruned_loss=0.0542, over 7273.00 frames.], tot_loss[loss=0.18, simple_loss=0.2685, pruned_loss=0.04575, over 1420216.24 frames.], batch size: 18, lr: 2.18e-04
2022-05-28 14:55:03,386 INFO [train.py:842] (2/4) Epoch 25, batch 8750, loss[loss=0.2086, simple_loss=0.2934, pruned_loss=0.06193, over 7204.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2687, pruned_loss=0.04583, over 1423004.09 frames.], batch size: 23, lr: 2.18e-04
2022-05-28 14:55:42,056 INFO [train.py:842] (2/4) Epoch 25, batch 8800, loss[loss=0.1635, simple_loss=0.2555, pruned_loss=0.03577, over 7074.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2685, pruned_loss=0.0462, over 1421382.87 frames.], batch size: 18, lr: 2.18e-04
2022-05-28 14:56:20,240 INFO [train.py:842] (2/4) Epoch 25, batch 8850, loss[loss=0.1569, simple_loss=0.2511, pruned_loss=0.03139, over 7236.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2687, pruned_loss=0.04619, over 1421522.61 frames.], batch size: 21, lr: 2.18e-04
2022-05-28 14:56:58,669 INFO [train.py:842] (2/4) Epoch 25, batch 8900, loss[loss=0.1824, simple_loss=0.2677, pruned_loss=0.04851, over 7089.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2689, pruned_loss=0.04647, over 1407589.51 frames.], batch size: 28, lr: 2.18e-04
2022-05-28 14:57:36,568 INFO [train.py:842] (2/4) Epoch 25, batch 8950, loss[loss=0.2767, simple_loss=0.3553, pruned_loss=0.09906, over 4896.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2707, pruned_loss=0.04741, over 1399868.92 frames.], batch size: 52, lr: 2.18e-04
2022-05-28 14:58:14,362 INFO [train.py:842] (2/4) Epoch 25, batch 9000, loss[loss=0.1993, simple_loss=0.2918, pruned_loss=0.0534, over 6333.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2714, pruned_loss=0.04772, over 1384375.13 frames.], batch size: 37, lr: 2.18e-04
2022-05-28 14:58:14,363 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 14:58:23,547 INFO [train.py:871] (2/4) Epoch 25, validation: loss=0.1658, simple_loss=0.265, pruned_loss=0.03327, over 868885.00 frames.
2022-05-28 14:59:00,532 INFO [train.py:842] (2/4) Epoch 25, batch 9050, loss[loss=0.1641, simple_loss=0.2618, pruned_loss=0.03316, over 6572.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2729, pruned_loss=0.04877, over 1349417.33 frames.], batch size: 38, lr: 2.18e-04
2022-05-28 14:59:37,684 INFO [train.py:842] (2/4) Epoch 25, batch 9100, loss[loss=0.2064, simple_loss=0.2817, pruned_loss=0.06554, over 4953.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2761, pruned_loss=0.0509, over 1299726.52 frames.], batch size: 52, lr: 2.18e-04
2022-05-28 15:00:14,852 INFO [train.py:842] (2/4) Epoch 25, batch 9150, loss[loss=0.168, simple_loss=0.2525, pruned_loss=0.04169, over 4789.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2786, pruned_loss=0.05294, over 1242198.34 frames.], batch size: 52, lr: 2.18e-04
2022-05-28 15:01:00,944 INFO [train.py:842] (2/4) Epoch 26, batch 0, loss[loss=0.1867, simple_loss=0.2867, pruned_loss=0.04332, over 7208.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2867, pruned_loss=0.04332, over 7208.00 frames.], batch size: 21, lr: 2.14e-04
2022-05-28 15:01:39,898 INFO [train.py:842] (2/4) Epoch 26, batch 50, loss[loss=0.1654, simple_loss=0.2702, pruned_loss=0.03032, over 7319.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2595, pruned_loss=0.04, over 322726.80 frames.], batch size: 21, lr: 2.14e-04
2022-05-28 15:02:18,129 INFO [train.py:842] (2/4) Epoch 26, batch 100, loss[loss=0.2336, simple_loss=0.314, pruned_loss=0.07657, over 5422.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2663, pruned_loss=0.04421, over 567025.84 frames.], batch size: 52, lr: 2.14e-04
2022-05-28 15:02:56,774 INFO [train.py:842] (2/4) Epoch 26, batch 150, loss[loss=0.1421, simple_loss=0.2263, pruned_loss=0.02898, over 7273.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2664, pruned_loss=0.04426, over 761072.79 frames.], batch size: 17, lr: 2.14e-04
2022-05-28 15:03:35,205 INFO [train.py:842] (2/4) Epoch 26, batch 200, loss[loss=0.1927, simple_loss=0.2821, pruned_loss=0.05167, over 7383.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2665, pruned_loss=0.04442, over 907631.76 frames.], batch size: 23, lr: 2.14e-04
2022-05-28 15:04:13,820 INFO [train.py:842] (2/4) Epoch 26, batch 250, loss[loss=0.1831, simple_loss=0.2719, pruned_loss=0.0472, over 7221.00 frames.], tot_loss[loss=0.179, simple_loss=0.2677, pruned_loss=0.04518, over 1021346.67 frames.], batch size: 22, lr: 2.14e-04
2022-05-28 15:04:51,890 INFO [train.py:842] (2/4) Epoch 26, batch 300, loss[loss=0.1695, simple_loss=0.2647, pruned_loss=0.03715, over 7332.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2688, pruned_loss=0.04588, over 1107003.87 frames.], batch size: 20, lr: 2.14e-04
2022-05-28 15:05:30,449 INFO [train.py:842] (2/4) Epoch 26, batch 350, loss[loss=0.1495, simple_loss=0.2314, pruned_loss=0.03373, over 7169.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2678, pruned_loss=0.04618, over 1176721.01 frames.], batch size: 18, lr: 2.14e-04
2022-05-28 15:06:08,846 INFO [train.py:842] (2/4) Epoch 26, batch 400, loss[loss=0.1572, simple_loss=0.2428, pruned_loss=0.03579, over 7384.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2666, pruned_loss=0.0454, over 1234765.82 frames.], batch size: 18, lr: 2.14e-04
2022-05-28 15:06:47,614 INFO [train.py:842] (2/4) Epoch 26, batch 450, loss[loss=0.179, simple_loss=0.2773, pruned_loss=0.04041, over 7409.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2655, pruned_loss=0.0446, over 1275980.81 frames.], batch size: 21, lr: 2.14e-04
2022-05-28 15:07:25,845 INFO [train.py:842] (2/4) Epoch 26, batch 500, loss[loss=0.1999, simple_loss=0.2881, pruned_loss=0.05588, over 7368.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2662, pruned_loss=0.04499, over 1303977.81 frames.], batch size: 23, lr: 2.14e-04
2022-05-28 15:08:04,617 INFO [train.py:842] (2/4) Epoch 26, batch 550, loss[loss=0.1689, simple_loss=0.261, pruned_loss=0.0384, over 7236.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2663, pruned_loss=0.04511, over 1330165.61 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:08:43,125 INFO [train.py:842] (2/4) Epoch 26, batch 600, loss[loss=0.2224, simple_loss=0.3172, pruned_loss=0.06377, over 7022.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2663, pruned_loss=0.04509, over 1348527.99 frames.], batch size: 28, lr: 2.13e-04
2022-05-28 15:09:22,090 INFO [train.py:842] (2/4) Epoch 26, batch 650, loss[loss=0.1623, simple_loss=0.2551, pruned_loss=0.03478, over 7322.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2652, pruned_loss=0.04499, over 1362340.98 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:10:10,874 INFO [train.py:842] (2/4) Epoch 26, batch 700, loss[loss=0.2261, simple_loss=0.3024, pruned_loss=0.07484, over 7146.00 frames.], tot_loss[loss=0.1771, simple_loss=0.265, pruned_loss=0.0446, over 1375126.10 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:10:49,685 INFO [train.py:842] (2/4) Epoch 26, batch 750, loss[loss=0.1578, simple_loss=0.2409, pruned_loss=0.03736, over 7422.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2632, pruned_loss=0.04367, over 1390343.84 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:11:27,859 INFO [train.py:842] (2/4) Epoch 26, batch 800, loss[loss=0.1886, simple_loss=0.2821, pruned_loss=0.04754, over 6706.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2631, pruned_loss=0.04336, over 1395253.88 frames.], batch size: 31, lr: 2.13e-04
2022-05-28 15:12:06,491 INFO [train.py:842] (2/4) Epoch 26, batch 850, loss[loss=0.2352, simple_loss=0.306, pruned_loss=0.08224, over 7118.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2645, pruned_loss=0.04417, over 1405690.29 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:12:54,992 INFO [train.py:842] (2/4) Epoch 26, batch 900, loss[loss=0.161, simple_loss=0.2358, pruned_loss=0.04311, over 6782.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2654, pruned_loss=0.04498, over 1405785.23 frames.], batch size: 15, lr: 2.13e-04
2022-05-28 15:13:43,627 INFO [train.py:842] (2/4) Epoch 26, batch 950, loss[loss=0.1514, simple_loss=0.2447, pruned_loss=0.0291, over 7275.00 frames.], tot_loss[loss=0.1774, simple_loss=0.265, pruned_loss=0.04489, over 1412465.97 frames.], batch size: 17, lr: 2.13e-04
2022-05-28 15:14:21,982 INFO [train.py:842] (2/4) Epoch 26, batch 1000, loss[loss=0.2165, simple_loss=0.301, pruned_loss=0.06606, over 7107.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2657, pruned_loss=0.0454, over 1411873.49 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:15:00,535 INFO [train.py:842] (2/4) Epoch 26, batch 1050, loss[loss=0.2055, simple_loss=0.2884, pruned_loss=0.06129, over 5002.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2659, pruned_loss=0.04522, over 1412400.70 frames.], batch size: 52, lr: 2.13e-04
2022-05-28 15:15:38,917 INFO [train.py:842] (2/4) Epoch 26, batch 1100, loss[loss=0.1981, simple_loss=0.2936, pruned_loss=0.05132, over 7110.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2654, pruned_loss=0.04523, over 1413900.18 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:16:17,613 INFO [train.py:842] (2/4) Epoch 26, batch 1150, loss[loss=0.2018, simple_loss=0.2876, pruned_loss=0.05802, over 7373.00 frames.], tot_loss[loss=0.1781, simple_loss=0.266, pruned_loss=0.04509, over 1417610.15 frames.], batch size: 23, lr: 2.13e-04
2022-05-28 15:16:56,067 INFO [train.py:842] (2/4) Epoch 26, batch 1200, loss[loss=0.1736, simple_loss=0.2551, pruned_loss=0.04608, over 7113.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2658, pruned_loss=0.04546, over 1421117.87 frames.], batch size: 17, lr: 2.13e-04
2022-05-28 15:17:34,815 INFO [train.py:842] (2/4) Epoch 26, batch 1250, loss[loss=0.2144, simple_loss=0.2962, pruned_loss=0.06626, over 7314.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2653, pruned_loss=0.04506, over 1424392.70 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:18:13,310 INFO [train.py:842] (2/4) Epoch 26, batch 1300, loss[loss=0.1682, simple_loss=0.2583, pruned_loss=0.0391, over 7424.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2655, pruned_loss=0.04495, over 1427783.59 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:18:51,816 INFO [train.py:842] (2/4) Epoch 26, batch 1350, loss[loss=0.183, simple_loss=0.2811, pruned_loss=0.04249, over 7333.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2654, pruned_loss=0.04476, over 1427329.32 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:19:30,186 INFO [train.py:842] (2/4) Epoch 26, batch 1400, loss[loss=0.1939, simple_loss=0.2933, pruned_loss=0.04729, over 7333.00 frames.], tot_loss[loss=0.1778, simple_loss=0.266, pruned_loss=0.04486, over 1426911.66 frames.], batch size: 22, lr: 2.13e-04
2022-05-28 15:20:08,705 INFO [train.py:842] (2/4) Epoch 26, batch 1450, loss[loss=0.1573, simple_loss=0.2343, pruned_loss=0.04013, over 6996.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2658, pruned_loss=0.04494, over 1428854.24 frames.], batch size: 16, lr: 2.13e-04
2022-05-28 15:20:47,186 INFO [train.py:842] (2/4) Epoch 26, batch 1500, loss[loss=0.1757, simple_loss=0.2711, pruned_loss=0.04016, over 7211.00 frames.], tot_loss[loss=0.177, simple_loss=0.2649, pruned_loss=0.0445, over 1427418.40 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:21:25,991 INFO [train.py:842] (2/4) Epoch 26, batch 1550, loss[loss=0.1468, simple_loss=0.2399, pruned_loss=0.02692, over 7146.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2654, pruned_loss=0.04493, over 1426728.55 frames.], batch size: 17, lr: 2.13e-04
2022-05-28 15:22:03,995 INFO [train.py:842] (2/4) Epoch 26, batch 1600, loss[loss=0.183, simple_loss=0.2783, pruned_loss=0.04384, over 7153.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2671, pruned_loss=0.04535, over 1424118.55 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:22:43,191 INFO [train.py:842] (2/4) Epoch 26, batch 1650, loss[loss=0.161, simple_loss=0.2541, pruned_loss=0.034, over 7114.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2657, pruned_loss=0.04472, over 1425810.20 frames.], batch size: 28, lr: 2.13e-04
2022-05-28 15:23:21,396 INFO [train.py:842] (2/4) Epoch 26, batch 1700, loss[loss=0.1583, simple_loss=0.2501, pruned_loss=0.03324, over 7310.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2674, pruned_loss=0.04569, over 1425725.73 frames.], batch size: 21, lr: 2.13e-04
2022-05-28 15:24:00,053 INFO [train.py:842] (2/4) Epoch 26, batch 1750, loss[loss=0.151, simple_loss=0.2339, pruned_loss=0.03405, over 7138.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2668, pruned_loss=0.04531, over 1424777.67 frames.], batch size: 17, lr: 2.13e-04
2022-05-28 15:24:38,390 INFO [train.py:842] (2/4) Epoch 26, batch 1800, loss[loss=0.2265, simple_loss=0.3085, pruned_loss=0.07224, over 7141.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2665, pruned_loss=0.04518, over 1420890.52 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:25:17,107 INFO [train.py:842] (2/4) Epoch 26, batch 1850, loss[loss=0.1487, simple_loss=0.2409, pruned_loss=0.02822, over 7426.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2665, pruned_loss=0.04517, over 1421995.61 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:25:55,281 INFO [train.py:842] (2/4) Epoch 26, batch 1900, loss[loss=0.1436, simple_loss=0.2255, pruned_loss=0.03084, over 7137.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2669, pruned_loss=0.04532, over 1422557.25 frames.], batch size: 17, lr: 2.13e-04
2022-05-28 15:26:33,986 INFO [train.py:842] (2/4) Epoch 26, batch 1950, loss[loss=0.2191, simple_loss=0.3032, pruned_loss=0.06747, over 5116.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2674, pruned_loss=0.04595, over 1420849.86 frames.], batch size: 52, lr: 2.13e-04
2022-05-28 15:27:12,148 INFO [train.py:842] (2/4) Epoch 26, batch 2000, loss[loss=0.1818, simple_loss=0.2601, pruned_loss=0.05177, over 7157.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2672, pruned_loss=0.04615, over 1417109.12 frames.], batch size: 19, lr: 2.13e-04
2022-05-28 15:27:51,022 INFO [train.py:842] (2/4) Epoch 26, batch 2050, loss[loss=0.1817, simple_loss=0.2794, pruned_loss=0.042, over 7318.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2664, pruned_loss=0.0459, over 1418636.28 frames.], batch size: 20, lr: 2.13e-04
2022-05-28 15:28:29,355 INFO [train.py:842] (2/4) Epoch 26, batch 2100, loss[loss=0.1793, simple_loss=0.276, pruned_loss=0.04126, over 7207.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2672, pruned_loss=0.04629, over 1418234.68 frames.], batch size: 22, lr: 2.13e-04
2022-05-28 15:29:07,767 INFO [train.py:842] (2/4) Epoch 26, batch 2150, loss[loss=0.1684, simple_loss=0.2553, pruned_loss=0.04075, over 7169.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2672, pruned_loss=0.04575, over 1419632.41 frames.], batch size: 18, lr: 2.13e-04
2022-05-28 15:29:46,150 INFO [train.py:842] (2/4) Epoch 26, batch 2200, loss[loss=0.1975, simple_loss=0.275, pruned_loss=0.05995, over 7080.00 frames.], tot_loss[loss=0.178, simple_loss=0.2658, pruned_loss=0.04512, over 1422484.17 frames.], batch size: 28, lr: 2.13e-04
2022-05-28 15:30:27,789 INFO [train.py:842] (2/4) Epoch 26, batch 2250, loss[loss=0.1577, simple_loss=0.2573, pruned_loss=0.02911, over 7366.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2651, pruned_loss=0.04453, over 1424149.14 frames.], batch size: 23, lr: 2.13e-04
2022-05-28 15:31:06,150 INFO [train.py:842] (2/4) Epoch 26, batch 2300, loss[loss=0.2104, simple_loss=0.2899, pruned_loss=0.06548, over 7059.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2655, pruned_loss=0.04449, over 1424469.35 frames.], batch size: 18, lr: 2.13e-04
2022-05-28 15:31:45,074 INFO [train.py:842] (2/4) Epoch 26, batch 2350, loss[loss=0.1889, simple_loss=0.281, pruned_loss=0.04846, over 7251.00 frames.], tot_loss[loss=0.1771, simple_loss=0.265, pruned_loss=0.04457, over 1425165.49 frames.], batch size: 19, lr: 2.13e-04
2022-05-28 15:32:23,718 INFO [train.py:842] (2/4) Epoch 26, batch 2400, loss[loss=0.2022, simple_loss=0.2851, pruned_loss=0.05963, over 7375.00 frames.], tot_loss[loss=0.178, simple_loss=0.2656, pruned_loss=0.04522, over 1423067.48 frames.], batch size: 23, lr: 2.13e-04
2022-05-28 15:33:02,365 INFO [train.py:842] (2/4) Epoch 26, batch 2450, loss[loss=0.1491, simple_loss=0.237, pruned_loss=0.03065, over 6721.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2658, pruned_loss=0.04555, over 1421547.23 frames.], batch size: 31, lr: 2.13e-04
2022-05-28 15:33:40,962 INFO [train.py:842] (2/4) Epoch 26, batch 2500, loss[loss=0.1684, simple_loss=0.2556, pruned_loss=0.04059, over 7351.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2651, pruned_loss=0.04508, over 1422826.42 frames.], batch size: 19, lr: 2.13e-04
2022-05-28 15:34:19,816 INFO [train.py:842] (2/4) Epoch 26, batch 2550, loss[loss=0.1569, simple_loss=0.2489, pruned_loss=0.03248, over 7415.00 frames.], tot_loss[loss=0.1786, simple_loss=0.266, pruned_loss=0.04557, over 1425206.96 frames.], batch size: 18, lr: 2.13e-04
2022-05-28 15:34:58,257 INFO [train.py:842] (2/4) Epoch 26, batch 2600, loss[loss=0.1621, simple_loss=0.242, pruned_loss=0.04115, over 7159.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2672, pruned_loss=0.0461, over 1423724.56 frames.], batch size: 19, lr: 2.13e-04
2022-05-28 15:35:36,566 INFO [train.py:842] (2/4) Epoch 26, batch 2650, loss[loss=0.2389, simple_loss=0.3201, pruned_loss=0.07886, over 7065.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2675, pruned_loss=0.04605, over 1419601.40 frames.], batch size: 28, lr: 2.13e-04
2022-05-28 15:36:14,915 INFO [train.py:842] (2/4) Epoch 26, batch 2700, loss[loss=0.2117, simple_loss=0.2917, pruned_loss=0.06584, over 7267.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2675, pruned_loss=0.04574, over 1420224.69 frames.], batch size: 19, lr: 2.13e-04
2022-05-28 15:36:53,413 INFO [train.py:842] (2/4) Epoch 26, batch 2750, loss[loss=0.1953, simple_loss=0.2806, pruned_loss=0.05502, over 7295.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2677, pruned_loss=0.04595, over 1414673.62 frames.], batch size: 25, lr: 2.12e-04
2022-05-28 15:37:32,297 INFO [train.py:842] (2/4) Epoch 26, batch 2800, loss[loss=0.1676, simple_loss=0.2675, pruned_loss=0.03383, over 7281.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2674, pruned_loss=0.04609, over 1417823.31 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 15:38:11,412 INFO [train.py:842] (2/4) Epoch 26, batch 2850, loss[loss=0.1988, simple_loss=0.2959, pruned_loss=0.05089, over 7426.00 frames.], tot_loss[loss=0.179, simple_loss=0.2666, pruned_loss=0.04574, over 1412862.09 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 15:38:50,426 INFO [train.py:842] (2/4) Epoch 26, batch 2900, loss[loss=0.1752, simple_loss=0.2633, pruned_loss=0.04357, over 7140.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2666, pruned_loss=0.04579, over 1418342.43 frames.], batch size: 20, lr: 2.12e-04
2022-05-28 15:39:29,980 INFO [train.py:842] (2/4) Epoch 26, batch 2950, loss[loss=0.1496, simple_loss=0.2349, pruned_loss=0.03215, over 7335.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2669, pruned_loss=0.04605, over 1419236.31 frames.], batch size: 20, lr: 2.12e-04
2022-05-28 15:40:09,198 INFO [train.py:842] (2/4) Epoch 26, batch 3000, loss[loss=0.2006, simple_loss=0.2915, pruned_loss=0.05489, over 6386.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2665, pruned_loss=0.04567, over 1423130.33 frames.], batch size: 38, lr: 2.12e-04
2022-05-28 15:40:09,199 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 15:40:19,619 INFO [train.py:871] (2/4) Epoch 26, validation: loss=0.166, simple_loss=0.2639, pruned_loss=0.0341, over 868885.00 frames.
2022-05-28 15:40:59,104 INFO [train.py:842] (2/4) Epoch 26, batch 3050, loss[loss=0.1635, simple_loss=0.2666, pruned_loss=0.0302, over 7336.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2681, pruned_loss=0.04627, over 1421946.44 frames.], batch size: 22, lr: 2.12e-04
2022-05-28 15:41:38,592 INFO [train.py:842] (2/4) Epoch 26, batch 3100, loss[loss=0.1584, simple_loss=0.2443, pruned_loss=0.03629, over 7263.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2684, pruned_loss=0.04627, over 1420400.82 frames.], batch size: 19, lr: 2.12e-04
2022-05-28 15:42:18,083 INFO [train.py:842] (2/4) Epoch 26, batch 3150, loss[loss=0.1326, simple_loss=0.2143, pruned_loss=0.02546, over 7122.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2684, pruned_loss=0.04654, over 1418846.52 frames.], batch size: 17, lr: 2.12e-04
2022-05-28 15:42:57,579 INFO [train.py:842] (2/4) Epoch 26, batch 3200, loss[loss=0.2119, simple_loss=0.2957, pruned_loss=0.0641, over 7149.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2682, pruned_loss=0.0461, over 1421670.75 frames.], batch size: 19, lr: 2.12e-04
2022-05-28 15:43:37,434 INFO [train.py:842] (2/4) Epoch 26, batch 3250, loss[loss=0.1558, simple_loss=0.2412, pruned_loss=0.03522, over 7288.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2668, pruned_loss=0.04555, over 1424567.66 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 15:44:16,456 INFO [train.py:842] (2/4) Epoch 26, batch 3300, loss[loss=0.2131, simple_loss=0.3031, pruned_loss=0.06151, over 7188.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2673, pruned_loss=0.04573, over 1417575.37 frames.], batch size: 26, lr: 2.12e-04
2022-05-28 15:44:55,941 INFO [train.py:842] (2/4) Epoch 26, batch 3350, loss[loss=0.1704, simple_loss=0.2676, pruned_loss=0.03663, over 7328.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2672, pruned_loss=0.04572, over 1414034.47 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 15:45:35,703 INFO [train.py:842] (2/4) Epoch 26, batch 3400, loss[loss=0.1687, simple_loss=0.2523, pruned_loss=0.0426, over 6507.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2664, pruned_loss=0.04567, over 1419795.73 frames.], batch size: 38, lr: 2.12e-04
2022-05-28 15:46:15,384 INFO [train.py:842] (2/4) Epoch 26, batch 3450, loss[loss=0.1516, simple_loss=0.236, pruned_loss=0.03358, over 7160.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2669, pruned_loss=0.04633, over 1420424.14 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 15:46:54,503 INFO [train.py:842] (2/4) Epoch 26, batch 3500, loss[loss=0.1749, simple_loss=0.2628, pruned_loss=0.04345, over 7376.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2679, pruned_loss=0.04644, over 1419262.20 frames.], batch size: 23, lr: 2.12e-04
2022-05-28 15:47:33,968 INFO [train.py:842] (2/4) Epoch 26, batch 3550, loss[loss=0.2141, simple_loss=0.3051, pruned_loss=0.06152, over 7407.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2668, pruned_loss=0.04539, over 1421712.88 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 15:48:13,486 INFO [train.py:842] (2/4) Epoch 26, batch 3600, loss[loss=0.1802, simple_loss=0.2788, pruned_loss=0.0408, over 7192.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2655, pruned_loss=0.04469, over 1425880.31 frames.], batch size: 23, lr: 2.12e-04
2022-05-28 15:48:53,144 INFO [train.py:842] (2/4) Epoch 26, batch 3650, loss[loss=0.1471, simple_loss=0.2388, pruned_loss=0.02766, over 7267.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2655, pruned_loss=0.04492, over 1427484.66 frames.], batch size: 19, lr: 2.12e-04
2022-05-28 15:49:32,409 INFO [train.py:842] (2/4) Epoch 26, batch 3700, loss[loss=0.1454, simple_loss=0.236, pruned_loss=0.02742, over 7068.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2652, pruned_loss=0.0448, over 1424422.10 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 15:50:12,037 INFO [train.py:842] (2/4) Epoch 26, batch 3750, loss[loss=0.1719, simple_loss=0.2629, pruned_loss=0.04046, over 7148.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2662, pruned_loss=0.04497, over 1423480.04 frames.], batch size: 19, lr: 2.12e-04
2022-05-28 15:50:51,108 INFO [train.py:842] (2/4) Epoch 26, batch 3800, loss[loss=0.1659, simple_loss=0.2541, pruned_loss=0.03886, over 6350.00 frames.], tot_loss[loss=0.179, simple_loss=0.2672, pruned_loss=0.04543, over 1421895.02 frames.], batch size: 37, lr: 2.12e-04
2022-05-28 15:51:30,398 INFO [train.py:842] (2/4) Epoch 26, batch 3850, loss[loss=0.189, simple_loss=0.2718, pruned_loss=0.05306, over 7142.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2668, pruned_loss=0.04578, over 1419909.00 frames.], batch size: 20, lr: 2.12e-04
2022-05-28 15:52:09,597 INFO [train.py:842] (2/4) Epoch 26, batch 3900, loss[loss=0.1786, simple_loss=0.2745, pruned_loss=0.04136, over 7236.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2684, pruned_loss=0.04664, over 1421781.05 frames.], batch size: 20, lr: 2.12e-04
2022-05-28 15:52:49,317 INFO [train.py:842] (2/4) Epoch 26, batch 3950, loss[loss=0.2166, simple_loss=0.2998, pruned_loss=0.06666, over 6667.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2675, pruned_loss=0.04607, over 1426609.88 frames.], batch size: 31, lr: 2.12e-04
2022-05-28 15:53:28,759 INFO [train.py:842] (2/4) Epoch 26, batch 4000, loss[loss=0.1769, simple_loss=0.2815, pruned_loss=0.03614, over 7113.00 frames.], tot_loss[loss=0.1793, simple_loss=0.267, pruned_loss=0.04577, over 1418858.11 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 15:54:08,327 INFO [train.py:842] (2/4) Epoch 26, batch 4050, loss[loss=0.1574, simple_loss=0.2486, pruned_loss=0.0331, over 6451.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2663, pruned_loss=0.04507, over 1421188.03 frames.], batch size: 38, lr: 2.12e-04
2022-05-28 15:54:48,072 INFO [train.py:842] (2/4) Epoch 26, batch 4100, loss[loss=0.141, simple_loss=0.2257, pruned_loss=0.02813, over 7259.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2658, pruned_loss=0.04532, over 1418661.14 frames.], batch size: 17, lr: 2.12e-04
2022-05-28 15:55:28,400 INFO [train.py:842] (2/4) Epoch 26, batch 4150, loss[loss=0.2055, simple_loss=0.2877, pruned_loss=0.06163, over 7076.00 frames.], tot_loss[loss=0.1775, simple_loss=0.265, pruned_loss=0.04502, over 1422223.93 frames.], batch size: 28, lr: 2.12e-04
2022-05-28 15:56:07,748 INFO [train.py:842] (2/4) Epoch 26, batch 4200, loss[loss=0.1474, simple_loss=0.2311, pruned_loss=0.03185, over 7277.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2645, pruned_loss=0.04436, over 1421832.92 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 15:56:47,350 INFO [train.py:842] (2/4) Epoch 26, batch 4250, loss[loss=0.212, simple_loss=0.2992, pruned_loss=0.06244, over 7228.00 frames.], tot_loss[loss=0.177, simple_loss=0.2645, pruned_loss=0.04474, over 1423562.99 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 15:57:26,570 INFO [train.py:842] (2/4) Epoch 26, batch 4300, loss[loss=0.2142, simple_loss=0.2991, pruned_loss=0.06468, over 7430.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2657, pruned_loss=0.04522, over 1422782.08 frames.], batch size: 20, lr: 2.12e-04
2022-05-28 15:58:06,184 INFO [train.py:842] (2/4) Epoch 26, batch 4350, loss[loss=0.2146, simple_loss=0.2989, pruned_loss=0.06511, over 7386.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2655, pruned_loss=0.04542, over 1423877.21 frames.], batch size: 23, lr: 2.12e-04
2022-05-28 15:58:45,545 INFO [train.py:842] (2/4) Epoch 26, batch 4400, loss[loss=0.174, simple_loss=0.2623, pruned_loss=0.04287, over 7216.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2649, pruned_loss=0.04512, over 1423280.96 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 15:59:24,703 INFO [train.py:842] (2/4) Epoch 26, batch 4450, loss[loss=0.1757, simple_loss=0.2559, pruned_loss=0.04778, over 7276.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2655, pruned_loss=0.04532, over 1416625.33 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 16:00:03,656 INFO [train.py:842] (2/4) Epoch 26, batch 4500, loss[loss=0.2258, simple_loss=0.3175, pruned_loss=0.06704, over 6656.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2673, pruned_loss=0.04589, over 1417048.69 frames.], batch size: 38, lr: 2.12e-04
2022-05-28 16:00:44,586 INFO [train.py:842] (2/4) Epoch 26, batch 4550, loss[loss=0.1577, simple_loss=0.2573, pruned_loss=0.02902, over 7114.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2671, pruned_loss=0.04576, over 1415480.04 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 16:01:23,779 INFO [train.py:842] (2/4) Epoch 26, batch 4600, loss[loss=0.1509, simple_loss=0.2321, pruned_loss=0.03486, over 7066.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2669, pruned_loss=0.04608, over 1418888.94 frames.], batch size: 18, lr: 2.12e-04
2022-05-28 16:02:03,268 INFO [train.py:842] (2/4) Epoch 26, batch 4650, loss[loss=0.1765, simple_loss=0.2721, pruned_loss=0.04043, over 6403.00 frames.], tot_loss[loss=0.181, simple_loss=0.2683, pruned_loss=0.04682, over 1414223.04 frames.], batch size: 37, lr: 2.12e-04
2022-05-28 16:02:42,689 INFO [train.py:842] (2/4) Epoch 26, batch 4700, loss[loss=0.1742, simple_loss=0.265, pruned_loss=0.04163, over 7194.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2677, pruned_loss=0.04677, over 1416934.39 frames.], batch size: 22, lr: 2.12e-04
2022-05-28 16:03:22,494 INFO [train.py:842] (2/4) Epoch 26, batch 4750, loss[loss=0.1852, simple_loss=0.2713, pruned_loss=0.04955, over 7366.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2667, pruned_loss=0.0463, over 1416800.20 frames.], batch size: 23, lr: 2.12e-04
2022-05-28 16:04:03,191 INFO [train.py:842] (2/4) Epoch 26, batch 4800, loss[loss=0.2238, simple_loss=0.3191, pruned_loss=0.06421, over 7324.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2662, pruned_loss=0.04561, over 1420910.77 frames.], batch size: 21, lr: 2.12e-04
2022-05-28 16:04:43,140 INFO [train.py:842] (2/4) Epoch 26, batch 4850, loss[loss=0.1678, simple_loss=0.2647, pruned_loss=0.03542, over 7345.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2651, pruned_loss=0.04492, over 1424146.38 frames.], batch size: 22, lr: 2.12e-04
2022-05-28 16:05:22,327 INFO [train.py:842] (2/4) Epoch 26, batch 4900, loss[loss=0.1692, simple_loss=0.2539, pruned_loss=0.04224, over 7154.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2663, pruned_loss=0.04552, over 1421406.29 frames.], batch size: 19, lr: 2.12e-04
2022-05-28 16:06:02,535 INFO [train.py:842] (2/4) Epoch 26, batch 4950, loss[loss=0.1728, simple_loss=0.2568, pruned_loss=0.04442, over 7074.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2659, pruned_loss=0.04511, over 1418546.81 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:06:42,093 INFO [train.py:842] (2/4) Epoch 26, batch 5000, loss[loss=0.1611, simple_loss=0.2569, pruned_loss=0.0327, over 7127.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2664, pruned_loss=0.04509, over 1420854.89 frames.], batch size: 21, lr: 2.11e-04
2022-05-28 16:07:21,670 INFO [train.py:842] (2/4) Epoch 26, batch 5050, loss[loss=0.1428, simple_loss=0.226, pruned_loss=0.02983, over 6832.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2669, pruned_loss=0.04537, over 1420056.48 frames.], batch size: 15, lr: 2.11e-04
2022-05-28 16:08:00,698 INFO [train.py:842] (2/4) Epoch 26, batch 5100, loss[loss=0.2372, simple_loss=0.3103, pruned_loss=0.08203, over 5144.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2651, pruned_loss=0.04477, over 1418694.16 frames.], batch size: 52, lr: 2.11e-04
2022-05-28 16:08:40,304 INFO [train.py:842] (2/4) Epoch 26, batch 5150, loss[loss=0.1585, simple_loss=0.2517, pruned_loss=0.03267, over 7337.00 frames.], tot_loss[loss=0.1771, simple_loss=0.265, pruned_loss=0.04457, over 1420997.42 frames.], batch size: 22, lr: 2.11e-04
2022-05-28 16:09:19,782 INFO [train.py:842] (2/4) Epoch 26, batch 5200, loss[loss=0.2577, simple_loss=0.3375, pruned_loss=0.08898, over 6438.00 frames.], tot_loss[loss=0.1774, simple_loss=0.265, pruned_loss=0.04493, over 1420830.86 frames.], batch size: 38, lr: 2.11e-04
2022-05-28 16:09:59,235 INFO [train.py:842] (2/4) Epoch 26, batch 5250, loss[loss=0.1915, simple_loss=0.2773, pruned_loss=0.05285, over 7238.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2652, pruned_loss=0.04513, over 1421740.32 frames.], batch size: 20, lr: 2.11e-04
2022-05-28 16:10:38,547 INFO [train.py:842] (2/4) Epoch 26, batch 5300, loss[loss=0.1536, simple_loss=0.2393, pruned_loss=0.03391, over 7282.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2661, pruned_loss=0.04543, over 1421694.60 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:11:17,716 INFO [train.py:842] (2/4) Epoch 26, batch 5350, loss[loss=0.1445, simple_loss=0.2338, pruned_loss=0.02761, over 7166.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2658, pruned_loss=0.04517, over 1419022.16 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:11:56,899 INFO [train.py:842] (2/4) Epoch 26, batch 5400, loss[loss=0.1831, simple_loss=0.2733, pruned_loss=0.04641, over 7156.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2664, pruned_loss=0.04567, over 1416777.42 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:12:36,482 INFO [train.py:842] (2/4) Epoch 26, batch 5450, loss[loss=0.1438, simple_loss=0.2355, pruned_loss=0.02606, over 7160.00 frames.], tot_loss[loss=0.18, simple_loss=0.2675, pruned_loss=0.04624, over 1417249.41 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:13:15,609 INFO [train.py:842] (2/4) Epoch 26, batch 5500, loss[loss=0.1621, simple_loss=0.2381, pruned_loss=0.04304, over 6985.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2678, pruned_loss=0.04638, over 1412544.51 frames.], batch size: 16, lr: 2.11e-04
2022-05-28 16:13:54,900 INFO [train.py:842] (2/4) Epoch 26, batch 5550, loss[loss=0.1865, simple_loss=0.2744, pruned_loss=0.04925, over 7225.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2672, pruned_loss=0.04615, over 1414990.16 frames.], batch size: 21, lr: 2.11e-04
2022-05-28 16:14:34,126 INFO [train.py:842] (2/4) Epoch 26, batch 5600, loss[loss=0.1867, simple_loss=0.269, pruned_loss=0.05218, over 7205.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2677, pruned_loss=0.04654, over 1417030.47 frames.], batch size: 22, lr: 2.11e-04
2022-05-28 16:15:15,707 INFO [train.py:842] (2/4) Epoch 26, batch 5650, loss[loss=0.177, simple_loss=0.2762, pruned_loss=0.03893, over 7215.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2682, pruned_loss=0.04619, over 1417711.12 frames.], batch size: 23, lr: 2.11e-04
2022-05-28 16:15:55,736 INFO [train.py:842] (2/4) Epoch 26, batch 5700, loss[loss=0.185, simple_loss=0.2794, pruned_loss=0.0453, over 7160.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2678, pruned_loss=0.04578, over 1419712.12 frames.], batch size: 26, lr: 2.11e-04
2022-05-28 16:16:35,343 INFO [train.py:842] (2/4) Epoch 26, batch 5750, loss[loss=0.1503, simple_loss=0.242, pruned_loss=0.02926, over 7155.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2669, pruned_loss=0.04564, over 1418940.55 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:17:14,696 INFO [train.py:842] (2/4) Epoch 26, batch 5800, loss[loss=0.2095, simple_loss=0.2964, pruned_loss=0.06132, over 7235.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2669, pruned_loss=0.04535, over 1420001.33 frames.], batch size: 20, lr: 2.11e-04
2022-05-28 16:17:54,292 INFO [train.py:842] (2/4) Epoch 26, batch 5850, loss[loss=0.1675, simple_loss=0.2447, pruned_loss=0.04515, over 7285.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2671, pruned_loss=0.04576, over 1419884.15 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:18:34,351 INFO [train.py:842] (2/4) Epoch 26, batch 5900, loss[loss=0.1785, simple_loss=0.2586, pruned_loss=0.04919, over 7287.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2666, pruned_loss=0.04561, over 1421459.26 frames.], batch size: 17, lr: 2.11e-04
2022-05-28 16:19:16,476 INFO [train.py:842] (2/4) Epoch 26, batch 5950, loss[loss=0.1671, simple_loss=0.2612, pruned_loss=0.03649, over 7221.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2653, pruned_loss=0.04503, over 1422354.64 frames.], batch size: 21, lr: 2.11e-04
2022-05-28 16:19:56,734 INFO [train.py:842] (2/4) Epoch 26, batch 6000, loss[loss=0.1869, simple_loss=0.283, pruned_loss=0.04539, over 6659.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2672, pruned_loss=0.04597, over 1420411.65 frames.], batch size: 31, lr: 2.11e-04
2022-05-28 16:19:56,735 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 16:20:06,474 INFO [train.py:871] (2/4) Epoch 26, validation: loss=0.164, simple_loss=0.2621, pruned_loss=0.03292, over 868885.00 frames.
2022-05-28 16:20:46,094 INFO [train.py:842] (2/4) Epoch 26, batch 6050, loss[loss=0.1922, simple_loss=0.2807, pruned_loss=0.0519, over 7145.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2679, pruned_loss=0.04627, over 1421271.23 frames.], batch size: 20, lr: 2.11e-04
2022-05-28 16:21:25,129 INFO [train.py:842] (2/4) Epoch 26, batch 6100, loss[loss=0.1537, simple_loss=0.2504, pruned_loss=0.02855, over 7162.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2676, pruned_loss=0.04584, over 1422106.35 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:22:05,075 INFO [train.py:842] (2/4) Epoch 26, batch 6150, loss[loss=0.1852, simple_loss=0.2753, pruned_loss=0.04759, over 7277.00 frames.], tot_loss[loss=0.1784, simple_loss=0.266, pruned_loss=0.04542, over 1425516.85 frames.], batch size: 24, lr: 2.11e-04
2022-05-28 16:22:44,670 INFO [train.py:842] (2/4) Epoch 26, batch 6200, loss[loss=0.1673, simple_loss=0.2584, pruned_loss=0.03811, over 7152.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2652, pruned_loss=0.04518, over 1426485.64 frames.], batch size: 26, lr: 2.11e-04
2022-05-28 16:23:24,214 INFO [train.py:842] (2/4) Epoch 26, batch 6250, loss[loss=0.1916, simple_loss=0.2804, pruned_loss=0.05138, over 7371.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2664, pruned_loss=0.0457, over 1429337.26 frames.], batch size: 23, lr: 2.11e-04
2022-05-28 16:24:03,358 INFO [train.py:842] (2/4) Epoch 26, batch 6300, loss[loss=0.2021, simple_loss=0.2926, pruned_loss=0.05585, over 7241.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2659, pruned_loss=0.0455, over 1425146.64 frames.], batch size: 25, lr: 2.11e-04
2022-05-28 16:24:42,803 INFO [train.py:842] (2/4) Epoch 26, batch 6350, loss[loss=0.1498, simple_loss=0.2324, pruned_loss=0.03361, over 7143.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2662, pruned_loss=0.04556, over 1422140.83 frames.], batch size: 17, lr: 2.11e-04
2022-05-28 16:25:22,044 INFO [train.py:842] (2/4) Epoch 26, batch 6400, loss[loss=0.1673, simple_loss=0.2522, pruned_loss=0.04121, over 7431.00 frames.], tot_loss[loss=0.178, simple_loss=0.2658, pruned_loss=0.04513, over 1425390.96 frames.], batch size: 20, lr: 2.11e-04
2022-05-28 16:26:01,626 INFO [train.py:842] (2/4) Epoch 26, batch 6450, loss[loss=0.1731, simple_loss=0.2581, pruned_loss=0.04406, over 7255.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2651, pruned_loss=0.04474, over 1420983.77 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:26:41,170 INFO [train.py:842] (2/4) Epoch 26, batch 6500, loss[loss=0.1559, simple_loss=0.2446, pruned_loss=0.03357, over 7068.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2653, pruned_loss=0.04441, over 1424425.30 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:27:20,705 INFO [train.py:842] (2/4) Epoch 26, batch 6550, loss[loss=0.1571, simple_loss=0.2519, pruned_loss=0.03112, over 7437.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2667, pruned_loss=0.04531, over 1420453.71 frames.], batch size: 20, lr: 2.11e-04
2022-05-28 16:28:00,088 INFO [train.py:842] (2/4) Epoch 26, batch 6600, loss[loss=0.1988, simple_loss=0.2852, pruned_loss=0.05619, over 7202.00 frames.], tot_loss[loss=0.177, simple_loss=0.2652, pruned_loss=0.04434, over 1423002.04 frames.], batch size: 22, lr: 2.11e-04
2022-05-28 16:28:39,896 INFO [train.py:842] (2/4) Epoch 26, batch 6650, loss[loss=0.2374, simple_loss=0.3242, pruned_loss=0.07533, over 7382.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2658, pruned_loss=0.04488, over 1425737.35 frames.], batch size: 23, lr: 2.11e-04
2022-05-28 16:29:19,203 INFO [train.py:842] (2/4) Epoch 26, batch 6700, loss[loss=0.1703, simple_loss=0.2571, pruned_loss=0.04178, over 7273.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2661, pruned_loss=0.0454, over 1427474.62 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:29:59,198 INFO [train.py:842] (2/4) Epoch 26, batch 6750, loss[loss=0.2089, simple_loss=0.3016, pruned_loss=0.05805, over 7064.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2659, pruned_loss=0.04529, over 1429568.24 frames.], batch size: 18, lr: 2.11e-04
2022-05-28 16:30:38,526 INFO [train.py:842] (2/4) Epoch 26, batch 6800, loss[loss=0.1701, simple_loss=0.2536, pruned_loss=0.04332, over 7341.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2671, pruned_loss=0.04591, over 1431003.54 frames.], batch size: 20, lr: 2.11e-04
2022-05-28 16:31:18,203 INFO [train.py:842] (2/4) Epoch 26, batch 6850, loss[loss=0.1866, simple_loss=0.2822, pruned_loss=0.04547, over 7359.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2661, pruned_loss=0.04523, over 1430895.11 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:31:57,843 INFO [train.py:842] (2/4) Epoch 26, batch 6900, loss[loss=0.1578, simple_loss=0.2453, pruned_loss=0.03517, over 7256.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2665, pruned_loss=0.04583, over 1430604.59 frames.], batch size: 19, lr: 2.11e-04
2022-05-28 16:32:37,592 INFO [train.py:842] (2/4) Epoch 26, batch 6950, loss[loss=0.175, simple_loss=0.2609, pruned_loss=0.04459, over 7134.00 frames.], tot_loss[loss=0.1774, simple_loss=0.265, pruned_loss=0.04491, over 1427312.39 frames.], batch size: 17, lr: 2.11e-04
2022-05-28 16:33:16,714 INFO [train.py:842] (2/4) Epoch 26, batch 7000, loss[loss=0.1507, simple_loss=0.2297, pruned_loss=0.03582, over 7276.00 frames.], tot_loss[loss=0.178, simple_loss=0.2655, pruned_loss=0.04525, over 1428397.53 frames.], batch size: 17, lr: 2.11e-04
2022-05-28 16:33:56,342 INFO [train.py:842] (2/4) Epoch 26, batch 7050, loss[loss=0.2765, simple_loss=0.342, pruned_loss=0.1055, over 5072.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2655, pruned_loss=0.04552, over 1425828.42 frames.], batch size: 52, lr: 2.11e-04
2022-05-28 16:34:35,516 INFO [train.py:842] (2/4) Epoch 26, batch 7100, loss[loss=0.2043, simple_loss=0.2896, pruned_loss=0.05947, over 7085.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2653, pruned_loss=0.04505, over 1421116.09 frames.], batch size: 28, lr: 2.11e-04
2022-05-28 16:35:15,055 INFO [train.py:842] (2/4) Epoch 26, batch 7150, loss[loss=0.2227, simple_loss=0.3037, pruned_loss=0.0708, over 7279.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2666, pruned_loss=0.04584, over 1423451.15 frames.], batch size: 25, lr: 2.11e-04
2022-05-28 16:35:54,027 INFO [train.py:842] (2/4) Epoch 26, batch 7200, loss[loss=0.1893, simple_loss=0.2794, pruned_loss=0.04957, over 7409.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2678, pruned_loss=0.0466, over 1423684.76 frames.], batch size: 21, lr: 2.10e-04
2022-05-28 16:36:33,701 INFO [train.py:842] (2/4) Epoch 26, batch 7250, loss[loss=0.1953, simple_loss=0.2848, pruned_loss=0.05293, over 7281.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2672, pruned_loss=0.04626, over 1425565.53 frames.], batch size: 24, lr: 2.10e-04
2022-05-28 16:37:12,951 INFO [train.py:842] (2/4) Epoch 26, batch 7300, loss[loss=0.1522, simple_loss=0.2284, pruned_loss=0.03807, over 7303.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2679, pruned_loss=0.04633, over 1425034.25 frames.], batch size: 17, lr: 2.10e-04
2022-05-28 16:37:52,152 INFO [train.py:842] (2/4) Epoch 26, batch 7350, loss[loss=0.1814, simple_loss=0.2618, pruned_loss=0.05052, over 7072.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2678, pruned_loss=0.04628, over 1424120.19 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:38:31,852 INFO [train.py:842] (2/4) Epoch 26, batch 7400, loss[loss=0.1885, simple_loss=0.2703, pruned_loss=0.05339, over 7275.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2678, pruned_loss=0.04619, over 1428149.58 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:39:11,456 INFO [train.py:842] (2/4) Epoch 26, batch 7450, loss[loss=0.1506, simple_loss=0.2333, pruned_loss=0.03393, over 7349.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2679, pruned_loss=0.04624, over 1426608.88 frames.], batch size: 19, lr: 2.10e-04
2022-05-28 16:39:50,864 INFO [train.py:842] (2/4) Epoch 26, batch 7500, loss[loss=0.1786, simple_loss=0.2644, pruned_loss=0.04639, over 7179.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2672, pruned_loss=0.04594, over 1431020.58 frames.], batch size: 26, lr: 2.10e-04
2022-05-28 16:40:30,567 INFO [train.py:842] (2/4) Epoch 26, batch 7550, loss[loss=0.1396, simple_loss=0.2324, pruned_loss=0.02338, over 7157.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2659, pruned_loss=0.04544, over 1424309.28 frames.], batch size: 19, lr: 2.10e-04
2022-05-28 16:41:09,960 INFO [train.py:842] (2/4) Epoch 26, batch 7600, loss[loss=0.2149, simple_loss=0.3129, pruned_loss=0.05846, over 7140.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2665, pruned_loss=0.04584, over 1426602.45 frames.], batch size: 20, lr: 2.10e-04
2022-05-28 16:41:49,251 INFO [train.py:842] (2/4) Epoch 26, batch 7650, loss[loss=0.2101, simple_loss=0.2967, pruned_loss=0.06173, over 7206.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2675, pruned_loss=0.04593, over 1426909.91 frames.], batch size: 23, lr: 2.10e-04
2022-05-28 16:42:28,506 INFO [train.py:842] (2/4) Epoch 26, batch 7700, loss[loss=0.1413, simple_loss=0.2311, pruned_loss=0.02571, over 7433.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2668, pruned_loss=0.0458, over 1427673.41 frames.], batch size: 20, lr: 2.10e-04
2022-05-28 16:43:08,332 INFO [train.py:842] (2/4) Epoch 26, batch 7750, loss[loss=0.1587, simple_loss=0.2456, pruned_loss=0.03592, over 7165.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2671, pruned_loss=0.04598, over 1429379.02 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:43:47,829 INFO [train.py:842] (2/4) Epoch 26, batch 7800, loss[loss=0.1719, simple_loss=0.2557, pruned_loss=0.04404, over 6994.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2664, pruned_loss=0.04569, over 1427458.90 frames.], batch size: 16, lr: 2.10e-04
2022-05-28 16:44:27,486 INFO [train.py:842] (2/4) Epoch 26, batch 7850, loss[loss=0.1644, simple_loss=0.2544, pruned_loss=0.03719, over 6262.00 frames.], tot_loss[loss=0.179, simple_loss=0.2666, pruned_loss=0.04572, over 1423233.29 frames.], batch size: 37, lr: 2.10e-04
2022-05-28 16:45:06,412 INFO [train.py:842] (2/4) Epoch 26, batch 7900, loss[loss=0.1632, simple_loss=0.2429, pruned_loss=0.04181, over 7228.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2665, pruned_loss=0.04579, over 1420129.18 frames.], batch size: 16, lr: 2.10e-04
2022-05-28 16:45:46,031 INFO [train.py:842] (2/4) Epoch 26, batch 7950, loss[loss=0.1514, simple_loss=0.2422, pruned_loss=0.03024, over 7146.00 frames.], tot_loss[loss=0.1778, simple_loss=0.265, pruned_loss=0.0453, over 1417799.25 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:46:25,435 INFO [train.py:842] (2/4) Epoch 26, batch 8000, loss[loss=0.1819, simple_loss=0.2653, pruned_loss=0.04921, over 7162.00 frames.], tot_loss[loss=0.1764, simple_loss=0.264, pruned_loss=0.04442, over 1423590.55 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:47:04,794 INFO [train.py:842] (2/4) Epoch 26, batch 8050, loss[loss=0.1676, simple_loss=0.2458, pruned_loss=0.04473, over 6847.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2651, pruned_loss=0.04469, over 1426493.95 frames.], batch size: 15, lr: 2.10e-04
2022-05-28 16:47:43,846 INFO [train.py:842] (2/4) Epoch 26, batch 8100, loss[loss=0.1655, simple_loss=0.2537, pruned_loss=0.03864, over 7430.00 frames.], tot_loss[loss=0.1773, simple_loss=0.265, pruned_loss=0.04477, over 1429151.87 frames.], batch size: 20, lr: 2.10e-04
2022-05-28 16:48:23,596 INFO [train.py:842] (2/4) Epoch 26, batch 8150, loss[loss=0.1577, simple_loss=0.2514, pruned_loss=0.03196, over 7318.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2658, pruned_loss=0.0453, over 1431530.02 frames.], batch size: 21, lr: 2.10e-04
2022-05-28 16:49:02,871 INFO [train.py:842] (2/4) Epoch 26, batch 8200, loss[loss=0.1531, simple_loss=0.2382, pruned_loss=0.03403, over 7247.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2667, pruned_loss=0.04557, over 1430923.98 frames.], batch size: 19, lr: 2.10e-04
2022-05-28 16:49:42,442 INFO [train.py:842] (2/4) Epoch 26, batch 8250, loss[loss=0.17, simple_loss=0.2513, pruned_loss=0.04435, over 7416.00 frames.], tot_loss[loss=0.1777, simple_loss=0.265, pruned_loss=0.04524, over 1430657.67 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:50:21,818 INFO [train.py:842] (2/4) Epoch 26, batch 8300, loss[loss=0.1715, simple_loss=0.2576, pruned_loss=0.04267, over 7294.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2651, pruned_loss=0.04533, over 1432021.86 frames.], batch size: 25, lr: 2.10e-04
2022-05-28 16:51:01,200 INFO [train.py:842] (2/4) Epoch 26, batch 8350, loss[loss=0.1704, simple_loss=0.2614, pruned_loss=0.03971, over 7348.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2666, pruned_loss=0.04594, over 1422773.52 frames.], batch size: 19, lr: 2.10e-04
2022-05-28 16:51:40,155 INFO [train.py:842] (2/4) Epoch 26, batch 8400, loss[loss=0.1608, simple_loss=0.2503, pruned_loss=0.03565, over 7166.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2665, pruned_loss=0.04543, over 1419363.99 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:52:19,790 INFO [train.py:842] (2/4) Epoch 26, batch 8450, loss[loss=0.2221, simple_loss=0.3012, pruned_loss=0.07152, over 4814.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2662, pruned_loss=0.0455, over 1420308.71 frames.], batch size: 52, lr: 2.10e-04
2022-05-28 16:52:58,968 INFO [train.py:842] (2/4) Epoch 26, batch 8500, loss[loss=0.1419, simple_loss=0.2272, pruned_loss=0.02833, over 7272.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2665, pruned_loss=0.04604, over 1418876.50 frames.], batch size: 19, lr: 2.10e-04
2022-05-28 16:53:38,732 INFO [train.py:842] (2/4) Epoch 26, batch 8550, loss[loss=0.1625, simple_loss=0.255, pruned_loss=0.03496, over 7083.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2664, pruned_loss=0.04536, over 1421330.43 frames.], batch size: 28, lr: 2.10e-04
2022-05-28 16:54:18,373 INFO [train.py:842] (2/4) Epoch 26, batch 8600, loss[loss=0.1553, simple_loss=0.2405, pruned_loss=0.03509, over 7152.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2652, pruned_loss=0.04472, over 1425232.93 frames.], batch size: 17, lr: 2.10e-04
2022-05-28 16:54:58,088 INFO [train.py:842] (2/4) Epoch 26, batch 8650, loss[loss=0.1454, simple_loss=0.2322, pruned_loss=0.02928, over 7147.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2658, pruned_loss=0.04535, over 1419739.96 frames.], batch size: 17, lr: 2.10e-04
2022-05-28 16:55:37,257 INFO [train.py:842] (2/4) Epoch 26, batch 8700, loss[loss=0.1612, simple_loss=0.2583, pruned_loss=0.03209, over 7317.00 frames.], tot_loss[loss=0.178, simple_loss=0.2654, pruned_loss=0.0453, over 1415796.66 frames.], batch size: 20, lr: 2.10e-04
2022-05-28 16:56:16,923 INFO [train.py:842] (2/4) Epoch 26, batch 8750, loss[loss=0.1857, simple_loss=0.274, pruned_loss=0.0487, over 7211.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2657, pruned_loss=0.04496, over 1420979.60 frames.], batch size: 26, lr: 2.10e-04
2022-05-28 16:56:56,120 INFO [train.py:842] (2/4) Epoch 26, batch 8800, loss[loss=0.2452, simple_loss=0.3215, pruned_loss=0.08443, over 7294.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2683, pruned_loss=0.04641, over 1421997.20 frames.], batch size: 24, lr: 2.10e-04
2022-05-28 16:57:35,760 INFO [train.py:842] (2/4) Epoch 26, batch 8850, loss[loss=0.1495, simple_loss=0.24, pruned_loss=0.02947, over 7067.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2686, pruned_loss=0.04602, over 1419945.27 frames.], batch size: 18, lr: 2.10e-04
2022-05-28 16:58:14,971 INFO [train.py:842] (2/4) Epoch 26, batch 8900, loss[loss=0.192, simple_loss=0.2773, pruned_loss=0.05334, over 7145.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2682, pruned_loss=0.04609, over 1420042.38 frames.], batch size: 20, lr: 2.10e-04
2022-05-28 16:59:04,917 INFO [train.py:842] (2/4) Epoch 26, batch 8950, loss[loss=0.1786, simple_loss=0.2698, pruned_loss=0.04369, over 7130.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2687, pruned_loss=0.04604, over 1417575.52 frames.], batch size: 26, lr: 2.10e-04
2022-05-28 16:59:43,828 INFO [train.py:842] (2/4) Epoch 26, batch 9000, loss[loss=0.2095, simple_loss=0.2924, pruned_loss=0.06326, over 5074.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2694, pruned_loss=0.04593, over 1413242.06 frames.], batch size: 52, lr: 2.10e-04
2022-05-28 16:59:43,828 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 16:59:53,411 INFO [train.py:871] (2/4) 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] (2/4) Epoch 26, batch 9050, loss[loss=0.1778, simple_loss=0.2609, pruned_loss=0.04731, over 4921.00 frames.], tot_loss[loss=0.1815, simple_loss=0.27, pruned_loss=0.04649, over 1390305.41 frames.], batch size: 52, lr: 2.10e-04
2022-05-28 17:01:10,198 INFO [train.py:842] (2/4) Epoch 26, batch 9100, loss[loss=0.217, simple_loss=0.3033, pruned_loss=0.06533, over 5005.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2724, pruned_loss=0.04806, over 1344943.63 frames.], batch size: 52, lr: 2.10e-04
2022-05-28 17:01:48,404 INFO [train.py:842] (2/4) Epoch 26, batch 9150, loss[loss=0.1663, simple_loss=0.2514, pruned_loss=0.04058, over 5534.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2755, pruned_loss=0.05074, over 1277739.48 frames.], batch size: 52, lr: 2.10e-04
2022-05-28 17:02:39,069 INFO [train.py:842] (2/4) Epoch 27, batch 0, loss[loss=0.1597, simple_loss=0.2463, pruned_loss=0.03652, over 7152.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2463, pruned_loss=0.03652, over 7152.00 frames.], batch size: 18, lr: 2.06e-04
2022-05-28 17:03:18,930 INFO [train.py:842] (2/4) Epoch 27, batch 50, loss[loss=0.1254, simple_loss=0.2169, pruned_loss=0.01697, over 7298.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2594, pruned_loss=0.04172, over 318734.59 frames.], batch size: 17, lr: 2.06e-04
2022-05-28 17:03:58,079 INFO [train.py:842] (2/4) Epoch 27, batch 100, loss[loss=0.175, simple_loss=0.247, pruned_loss=0.0515, over 7279.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2616, pruned_loss=0.04212, over 562358.65 frames.], batch size: 17, lr: 2.06e-04
2022-05-28 17:04:37,588 INFO [train.py:842] (2/4) Epoch 27, batch 150, loss[loss=0.1607, simple_loss=0.248, pruned_loss=0.0367, over 6443.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2647, pruned_loss=0.04344, over 751017.14 frames.], batch size: 39, lr: 2.06e-04
2022-05-28 17:05:16,864 INFO [train.py:842] (2/4) Epoch 27, batch 200, loss[loss=0.1814, simple_loss=0.2777, pruned_loss=0.04255, over 7164.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2673, pruned_loss=0.04525, over 894562.12 frames.], batch size: 26, lr: 2.06e-04
2022-05-28 17:05:56,174 INFO [train.py:842] (2/4) Epoch 27, batch 250, loss[loss=0.1605, simple_loss=0.2631, pruned_loss=0.02902, over 6392.00 frames.], tot_loss[loss=0.178, simple_loss=0.2669, pruned_loss=0.0446, over 1006973.59 frames.], batch size: 38, lr: 2.06e-04
2022-05-28 17:06:35,390 INFO [train.py:842] (2/4) Epoch 27, batch 300, loss[loss=0.1815, simple_loss=0.2682, pruned_loss=0.04739, over 6393.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2655, pruned_loss=0.044, over 1101099.13 frames.], batch size: 37, lr: 2.06e-04
2022-05-28 17:07:15,022 INFO [train.py:842] (2/4) Epoch 27, batch 350, loss[loss=0.1639, simple_loss=0.2571, pruned_loss=0.03532, over 6694.00 frames.], tot_loss[loss=0.1757, simple_loss=0.264, pruned_loss=0.04374, over 1169574.86 frames.], batch size: 31, lr: 2.06e-04
2022-05-28 17:07:54,287 INFO [train.py:842] (2/4) Epoch 27, batch 400, loss[loss=0.1774, simple_loss=0.2663, pruned_loss=0.0442, over 7143.00 frames.], tot_loss[loss=0.175, simple_loss=0.2636, pruned_loss=0.04317, over 1229550.22 frames.], batch size: 20, lr: 2.06e-04
2022-05-28 17:08:33,801 INFO [train.py:842] (2/4) Epoch 27, batch 450, loss[loss=0.1867, simple_loss=0.2803, pruned_loss=0.04651, over 7231.00 frames.], tot_loss[loss=0.1755, simple_loss=0.264, pruned_loss=0.04349, over 1276908.32 frames.], batch size: 20, lr: 2.06e-04
2022-05-28 17:09:13,072 INFO [train.py:842] (2/4) Epoch 27, batch 500, loss[loss=0.188, simple_loss=0.2623, pruned_loss=0.05683, over 4749.00 frames.], tot_loss[loss=0.175, simple_loss=0.2633, pruned_loss=0.04337, over 1308119.13 frames.], batch size: 52, lr: 2.06e-04
2022-05-28 17:09:52,617 INFO [train.py:842] (2/4) Epoch 27, batch 550, loss[loss=0.2155, simple_loss=0.2962, pruned_loss=0.06742, over 7198.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2648, pruned_loss=0.04403, over 1332702.46 frames.], batch size: 22, lr: 2.06e-04
2022-05-28 17:10:31,927 INFO [train.py:842] (2/4) Epoch 27, batch 600, loss[loss=0.1618, simple_loss=0.241, pruned_loss=0.04133, over 7262.00 frames.], tot_loss[loss=0.176, simple_loss=0.2645, pruned_loss=0.04381, over 1355318.29 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:11:11,835 INFO [train.py:842] (2/4) Epoch 27, batch 650, loss[loss=0.1365, simple_loss=0.2205, pruned_loss=0.02624, over 7278.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2643, pruned_loss=0.04388, over 1371431.43 frames.], batch size: 18, lr: 2.05e-04
2022-05-28 17:11:50,949 INFO [train.py:842] (2/4) Epoch 27, batch 700, loss[loss=0.1816, simple_loss=0.2786, pruned_loss=0.04229, over 7108.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2656, pruned_loss=0.04411, over 1380724.33 frames.], batch size: 21, lr: 2.05e-04
2022-05-28 17:12:30,601 INFO [train.py:842] (2/4) Epoch 27, batch 750, loss[loss=0.173, simple_loss=0.2638, pruned_loss=0.0411, over 7146.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2664, pruned_loss=0.04446, over 1388897.20 frames.], batch size: 20, lr: 2.05e-04
2022-05-28 17:13:09,846 INFO [train.py:842] (2/4) Epoch 27, batch 800, loss[loss=0.2015, simple_loss=0.2906, pruned_loss=0.05625, over 7235.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2663, pruned_loss=0.04458, over 1395147.15 frames.], batch size: 20, lr: 2.05e-04
2022-05-28 17:13:49,271 INFO [train.py:842] (2/4) Epoch 27, batch 850, loss[loss=0.2139, simple_loss=0.2848, pruned_loss=0.0715, over 5191.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2667, pruned_loss=0.04476, over 1397499.23 frames.], batch size: 52, lr: 2.05e-04
2022-05-28 17:14:28,657 INFO [train.py:842] (2/4) Epoch 27, batch 900, loss[loss=0.147, simple_loss=0.2241, pruned_loss=0.03497, over 7414.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2659, pruned_loss=0.0446, over 1406846.39 frames.], batch size: 18, lr: 2.05e-04
2022-05-28 17:15:08,236 INFO [train.py:842] (2/4) Epoch 27, batch 950, loss[loss=0.1796, simple_loss=0.255, pruned_loss=0.05208, over 6783.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2673, pruned_loss=0.04528, over 1407548.51 frames.], batch size: 15, lr: 2.05e-04
2022-05-28 17:15:47,431 INFO [train.py:842] (2/4) Epoch 27, batch 1000, loss[loss=0.1815, simple_loss=0.2704, pruned_loss=0.04633, over 7304.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2666, pruned_loss=0.04495, over 1411881.94 frames.], batch size: 24, lr: 2.05e-04
2022-05-28 17:16:29,702 INFO [train.py:842] (2/4) Epoch 27, batch 1050, loss[loss=0.1697, simple_loss=0.2721, pruned_loss=0.03366, over 7186.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2665, pruned_loss=0.04457, over 1418313.77 frames.], batch size: 23, lr: 2.05e-04
2022-05-28 17:17:09,236 INFO [train.py:842] (2/4) Epoch 27, batch 1100, loss[loss=0.1734, simple_loss=0.2689, pruned_loss=0.03899, over 7200.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2645, pruned_loss=0.04412, over 1422474.65 frames.], batch size: 22, lr: 2.05e-04
2022-05-28 17:17:48,444 INFO [train.py:842] (2/4) Epoch 27, batch 1150, loss[loss=0.2108, simple_loss=0.2963, pruned_loss=0.06269, over 7173.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2641, pruned_loss=0.0437, over 1423593.44 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:18:27,744 INFO [train.py:842] (2/4) Epoch 27, batch 1200, loss[loss=0.1798, simple_loss=0.2796, pruned_loss=0.03995, over 7305.00 frames.], tot_loss[loss=0.177, simple_loss=0.265, pruned_loss=0.04448, over 1426831.71 frames.], batch size: 24, lr: 2.05e-04
2022-05-28 17:19:07,422 INFO [train.py:842] (2/4) Epoch 27, batch 1250, loss[loss=0.1825, simple_loss=0.2776, pruned_loss=0.04367, over 6287.00 frames.], tot_loss[loss=0.177, simple_loss=0.2652, pruned_loss=0.0444, over 1426228.24 frames.], batch size: 37, lr: 2.05e-04
2022-05-28 17:19:46,477 INFO [train.py:842] (2/4) Epoch 27, batch 1300, loss[loss=0.1864, simple_loss=0.256, pruned_loss=0.05836, over 7271.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2647, pruned_loss=0.04424, over 1422180.40 frames.], batch size: 18, lr: 2.05e-04
2022-05-28 17:20:26,354 INFO [train.py:842] (2/4) Epoch 27, batch 1350, loss[loss=0.1605, simple_loss=0.2485, pruned_loss=0.03629, over 7413.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2637, pruned_loss=0.04444, over 1426405.98 frames.], batch size: 18, lr: 2.05e-04
2022-05-28 17:21:05,297 INFO [train.py:842] (2/4) Epoch 27, batch 1400, loss[loss=0.1744, simple_loss=0.265, pruned_loss=0.04188, over 7182.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2644, pruned_loss=0.04461, over 1418621.31 frames.], batch size: 23, lr: 2.05e-04
2022-05-28 17:21:44,752 INFO [train.py:842] (2/4) Epoch 27, batch 1450, loss[loss=0.1519, simple_loss=0.2402, pruned_loss=0.03185, over 7293.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2648, pruned_loss=0.04467, over 1420818.44 frames.], batch size: 18, lr: 2.05e-04
2022-05-28 17:22:23,943 INFO [train.py:842] (2/4) Epoch 27, batch 1500, loss[loss=0.2111, simple_loss=0.2901, pruned_loss=0.06601, over 4931.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2648, pruned_loss=0.04471, over 1416559.92 frames.], batch size: 52, lr: 2.05e-04
2022-05-28 17:23:03,656 INFO [train.py:842] (2/4) Epoch 27, batch 1550, loss[loss=0.1885, simple_loss=0.2731, pruned_loss=0.05193, over 7107.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2641, pruned_loss=0.04416, over 1420126.08 frames.], batch size: 21, lr: 2.05e-04
2022-05-28 17:23:43,237 INFO [train.py:842] (2/4) Epoch 27, batch 1600, loss[loss=0.1741, simple_loss=0.2734, pruned_loss=0.03736, over 7247.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2636, pruned_loss=0.04414, over 1423428.52 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:24:22,824 INFO [train.py:842] (2/4) Epoch 27, batch 1650, loss[loss=0.1876, simple_loss=0.2788, pruned_loss=0.0482, over 7175.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2647, pruned_loss=0.04473, over 1427720.61 frames.], batch size: 26, lr: 2.05e-04
2022-05-28 17:25:02,017 INFO [train.py:842] (2/4) Epoch 27, batch 1700, loss[loss=0.1856, simple_loss=0.2863, pruned_loss=0.04247, over 7342.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2642, pruned_loss=0.04378, over 1429491.77 frames.], batch size: 22, lr: 2.05e-04
2022-05-28 17:25:41,539 INFO [train.py:842] (2/4) Epoch 27, batch 1750, loss[loss=0.17, simple_loss=0.2607, pruned_loss=0.03964, over 7132.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2644, pruned_loss=0.04371, over 1430213.47 frames.], batch size: 26, lr: 2.05e-04
2022-05-28 17:26:20,758 INFO [train.py:842] (2/4) Epoch 27, batch 1800, loss[loss=0.1496, simple_loss=0.2502, pruned_loss=0.02449, over 7112.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2645, pruned_loss=0.04413, over 1427977.90 frames.], batch size: 21, lr: 2.05e-04
2022-05-28 17:27:00,462 INFO [train.py:842] (2/4) Epoch 27, batch 1850, loss[loss=0.1932, simple_loss=0.2768, pruned_loss=0.05481, over 5021.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2638, pruned_loss=0.04388, over 1428420.40 frames.], batch size: 52, lr: 2.05e-04
2022-05-28 17:27:40,044 INFO [train.py:842] (2/4) Epoch 27, batch 1900, loss[loss=0.153, simple_loss=0.2363, pruned_loss=0.03478, over 7351.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2639, pruned_loss=0.04441, over 1428112.43 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:28:19,448 INFO [train.py:842] (2/4) Epoch 27, batch 1950, loss[loss=0.2589, simple_loss=0.3286, pruned_loss=0.09461, over 6552.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2651, pruned_loss=0.04503, over 1424742.89 frames.], batch size: 39, lr: 2.05e-04
2022-05-28 17:28:58,925 INFO [train.py:842] (2/4) Epoch 27, batch 2000, loss[loss=0.1823, simple_loss=0.2717, pruned_loss=0.04648, over 6774.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2652, pruned_loss=0.04479, over 1423176.39 frames.], batch size: 31, lr: 2.05e-04
2022-05-28 17:29:38,272 INFO [train.py:842] (2/4) Epoch 27, batch 2050, loss[loss=0.1876, simple_loss=0.2803, pruned_loss=0.04747, over 7195.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2658, pruned_loss=0.04454, over 1426032.16 frames.], batch size: 26, lr: 2.05e-04
2022-05-28 17:30:17,490 INFO [train.py:842] (2/4) Epoch 27, batch 2100, loss[loss=0.2351, simple_loss=0.3254, pruned_loss=0.0724, over 7206.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2646, pruned_loss=0.04403, over 1424598.96 frames.], batch size: 22, lr: 2.05e-04
2022-05-28 17:30:56,961 INFO [train.py:842] (2/4) Epoch 27, batch 2150, loss[loss=0.2117, simple_loss=0.3014, pruned_loss=0.06094, over 7287.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2661, pruned_loss=0.04471, over 1428166.35 frames.], batch size: 25, lr: 2.05e-04
2022-05-28 17:31:36,225 INFO [train.py:842] (2/4) Epoch 27, batch 2200, loss[loss=0.163, simple_loss=0.2503, pruned_loss=0.03783, over 7236.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2667, pruned_loss=0.04497, over 1426074.80 frames.], batch size: 20, lr: 2.05e-04
2022-05-28 17:32:15,917 INFO [train.py:842] (2/4) Epoch 27, batch 2250, loss[loss=0.2168, simple_loss=0.2824, pruned_loss=0.07561, over 6999.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2669, pruned_loss=0.04517, over 1431273.26 frames.], batch size: 16, lr: 2.05e-04
2022-05-28 17:32:55,014 INFO [train.py:842] (2/4) Epoch 27, batch 2300, loss[loss=0.1568, simple_loss=0.2326, pruned_loss=0.04047, over 7150.00 frames.], tot_loss[loss=0.1776, simple_loss=0.266, pruned_loss=0.04457, over 1432997.29 frames.], batch size: 17, lr: 2.05e-04
2022-05-28 17:33:34,531 INFO [train.py:842] (2/4) Epoch 27, batch 2350, loss[loss=0.1726, simple_loss=0.2683, pruned_loss=0.03845, over 7150.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2676, pruned_loss=0.04542, over 1431199.17 frames.], batch size: 20, lr: 2.05e-04
2022-05-28 17:34:13,856 INFO [train.py:842] (2/4) Epoch 27, batch 2400, loss[loss=0.1675, simple_loss=0.2533, pruned_loss=0.04082, over 7297.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2682, pruned_loss=0.04565, over 1432584.03 frames.], batch size: 24, lr: 2.05e-04
2022-05-28 17:34:53,582 INFO [train.py:842] (2/4) Epoch 27, batch 2450, loss[loss=0.2036, simple_loss=0.2949, pruned_loss=0.05613, over 7230.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2678, pruned_loss=0.04527, over 1435172.38 frames.], batch size: 20, lr: 2.05e-04
2022-05-28 17:35:32,762 INFO [train.py:842] (2/4) Epoch 27, batch 2500, loss[loss=0.15, simple_loss=0.246, pruned_loss=0.02702, over 7225.00 frames.], tot_loss[loss=0.1795, simple_loss=0.268, pruned_loss=0.04549, over 1436903.72 frames.], batch size: 21, lr: 2.05e-04
2022-05-28 17:36:12,378 INFO [train.py:842] (2/4) Epoch 27, batch 2550, loss[loss=0.1709, simple_loss=0.2702, pruned_loss=0.03582, over 6712.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2667, pruned_loss=0.04482, over 1433836.22 frames.], batch size: 31, lr: 2.05e-04
2022-05-28 17:36:51,705 INFO [train.py:842] (2/4) Epoch 27, batch 2600, loss[loss=0.163, simple_loss=0.2452, pruned_loss=0.04042, over 7227.00 frames.], tot_loss[loss=0.178, simple_loss=0.2664, pruned_loss=0.04482, over 1434829.05 frames.], batch size: 16, lr: 2.05e-04
2022-05-28 17:37:31,348 INFO [train.py:842] (2/4) Epoch 27, batch 2650, loss[loss=0.1909, simple_loss=0.2771, pruned_loss=0.05237, over 7283.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2665, pruned_loss=0.04506, over 1431135.81 frames.], batch size: 24, lr: 2.05e-04
2022-05-28 17:38:10,517 INFO [train.py:842] (2/4) Epoch 27, batch 2700, loss[loss=0.1809, simple_loss=0.2735, pruned_loss=0.04408, over 7326.00 frames.], tot_loss[loss=0.178, simple_loss=0.2661, pruned_loss=0.04497, over 1429198.61 frames.], batch size: 22, lr: 2.05e-04
2022-05-28 17:38:50,239 INFO [train.py:842] (2/4) Epoch 27, batch 2750, loss[loss=0.1551, simple_loss=0.2376, pruned_loss=0.03631, over 7163.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2656, pruned_loss=0.04464, over 1428570.97 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:39:29,433 INFO [train.py:842] (2/4) Epoch 27, batch 2800, loss[loss=0.1986, simple_loss=0.2837, pruned_loss=0.05676, over 7327.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2659, pruned_loss=0.04484, over 1427918.35 frames.], batch size: 25, lr: 2.05e-04
2022-05-28 17:40:08,860 INFO [train.py:842] (2/4) Epoch 27, batch 2850, loss[loss=0.1653, simple_loss=0.252, pruned_loss=0.03931, over 7260.00 frames.], tot_loss[loss=0.1791, simple_loss=0.267, pruned_loss=0.04556, over 1427007.76 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:40:48,044 INFO [train.py:842] (2/4) Epoch 27, batch 2900, loss[loss=0.217, simple_loss=0.2867, pruned_loss=0.07363, over 7162.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2672, pruned_loss=0.04564, over 1425871.91 frames.], batch size: 19, lr: 2.05e-04
2022-05-28 17:41:27,457 INFO [train.py:842] (2/4) Epoch 27, batch 2950, loss[loss=0.1636, simple_loss=0.263, pruned_loss=0.03214, over 7100.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2671, pruned_loss=0.04534, over 1419254.53 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 17:42:06,536 INFO [train.py:842] (2/4) Epoch 27, batch 3000, loss[loss=0.1645, simple_loss=0.2658, pruned_loss=0.03158, over 7417.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2662, pruned_loss=0.04507, over 1418490.87 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 17:42:06,537 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 17:42:16,299 INFO [train.py:871] (2/4) Epoch 27, validation: loss=0.1636, simple_loss=0.2622, pruned_loss=0.03251, over 868885.00 frames.
2022-05-28 17:42:56,071 INFO [train.py:842] (2/4) Epoch 27, batch 3050, loss[loss=0.1727, simple_loss=0.2736, pruned_loss=0.03586, over 7124.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2658, pruned_loss=0.04498, over 1411057.88 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 17:43:35,204 INFO [train.py:842] (2/4) Epoch 27, batch 3100, loss[loss=0.176, simple_loss=0.2734, pruned_loss=0.03927, over 7317.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2669, pruned_loss=0.04505, over 1416637.15 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 17:44:14,995 INFO [train.py:842] (2/4) Epoch 27, batch 3150, loss[loss=0.1779, simple_loss=0.2761, pruned_loss=0.03981, over 7214.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2668, pruned_loss=0.04513, over 1416719.32 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:44:54,119 INFO [train.py:842] (2/4) Epoch 27, batch 3200, loss[loss=0.2067, simple_loss=0.2965, pruned_loss=0.05847, over 7181.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2675, pruned_loss=0.04577, over 1418782.57 frames.], batch size: 23, lr: 2.04e-04
2022-05-28 17:45:33,918 INFO [train.py:842] (2/4) Epoch 27, batch 3250, loss[loss=0.1824, simple_loss=0.2691, pruned_loss=0.04782, over 6371.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2672, pruned_loss=0.04609, over 1419382.94 frames.], batch size: 38, lr: 2.04e-04
2022-05-28 17:46:13,052 INFO [train.py:842] (2/4) Epoch 27, batch 3300, loss[loss=0.1902, simple_loss=0.2832, pruned_loss=0.04862, over 6660.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2671, pruned_loss=0.04609, over 1418802.18 frames.], batch size: 31, lr: 2.04e-04
2022-05-28 17:46:52,294 INFO [train.py:842] (2/4) Epoch 27, batch 3350, loss[loss=0.1997, simple_loss=0.2884, pruned_loss=0.0555, over 7339.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2672, pruned_loss=0.0455, over 1419795.38 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:47:31,367 INFO [train.py:842] (2/4) Epoch 27, batch 3400, loss[loss=0.1732, simple_loss=0.2612, pruned_loss=0.04256, over 7154.00 frames.], tot_loss[loss=0.1792, simple_loss=0.267, pruned_loss=0.04566, over 1417395.20 frames.], batch size: 20, lr: 2.04e-04
2022-05-28 17:48:10,898 INFO [train.py:842] (2/4) Epoch 27, batch 3450, loss[loss=0.1493, simple_loss=0.2501, pruned_loss=0.0243, over 7332.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2666, pruned_loss=0.04508, over 1421145.41 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:49:02,123 INFO [train.py:842] (2/4) Epoch 27, batch 3500, loss[loss=0.1408, simple_loss=0.2219, pruned_loss=0.02983, over 6816.00 frames.], tot_loss[loss=0.1768, simple_loss=0.265, pruned_loss=0.04431, over 1423558.44 frames.], batch size: 15, lr: 2.04e-04
2022-05-28 17:49:41,575 INFO [train.py:842] (2/4) Epoch 27, batch 3550, loss[loss=0.2678, simple_loss=0.3471, pruned_loss=0.09425, over 5109.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2649, pruned_loss=0.04433, over 1418265.97 frames.], batch size: 52, lr: 2.04e-04
2022-05-28 17:50:20,524 INFO [train.py:842] (2/4) Epoch 27, batch 3600, loss[loss=0.1998, simple_loss=0.2842, pruned_loss=0.05766, over 7155.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2652, pruned_loss=0.04454, over 1415994.41 frames.], batch size: 19, lr: 2.04e-04
2022-05-28 17:51:00,298 INFO [train.py:842] (2/4) Epoch 27, batch 3650, loss[loss=0.1776, simple_loss=0.2587, pruned_loss=0.04823, over 7066.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2654, pruned_loss=0.04456, over 1414821.16 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 17:51:50,565 INFO [train.py:842] (2/4) Epoch 27, batch 3700, loss[loss=0.1907, simple_loss=0.2851, pruned_loss=0.04814, over 7196.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2651, pruned_loss=0.04468, over 1412760.67 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:52:41,169 INFO [train.py:842] (2/4) Epoch 27, batch 3750, loss[loss=0.153, simple_loss=0.2349, pruned_loss=0.03561, over 7152.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2649, pruned_loss=0.04484, over 1415900.46 frames.], batch size: 19, lr: 2.04e-04
2022-05-28 17:53:20,683 INFO [train.py:842] (2/4) Epoch 27, batch 3800, loss[loss=0.1551, simple_loss=0.235, pruned_loss=0.03766, over 7418.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2633, pruned_loss=0.04389, over 1419219.92 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 17:54:00,217 INFO [train.py:842] (2/4) Epoch 27, batch 3850, loss[loss=0.1711, simple_loss=0.2613, pruned_loss=0.04049, over 7189.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2641, pruned_loss=0.04429, over 1413155.54 frames.], batch size: 23, lr: 2.04e-04
2022-05-28 17:54:39,427 INFO [train.py:842] (2/4) Epoch 27, batch 3900, loss[loss=0.1838, simple_loss=0.2785, pruned_loss=0.04461, over 7224.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2644, pruned_loss=0.04431, over 1411431.37 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:55:18,908 INFO [train.py:842] (2/4) Epoch 27, batch 3950, loss[loss=0.1768, simple_loss=0.2674, pruned_loss=0.04312, over 7328.00 frames.], tot_loss[loss=0.177, simple_loss=0.2647, pruned_loss=0.04459, over 1416590.06 frames.], batch size: 20, lr: 2.04e-04
2022-05-28 17:55:58,111 INFO [train.py:842] (2/4) Epoch 27, batch 4000, loss[loss=0.1445, simple_loss=0.2262, pruned_loss=0.03137, over 7415.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2643, pruned_loss=0.04435, over 1423490.71 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 17:56:37,743 INFO [train.py:842] (2/4) Epoch 27, batch 4050, loss[loss=0.2096, simple_loss=0.3022, pruned_loss=0.05846, over 7202.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2642, pruned_loss=0.04447, over 1426908.95 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:57:17,354 INFO [train.py:842] (2/4) Epoch 27, batch 4100, loss[loss=0.1461, simple_loss=0.2398, pruned_loss=0.02623, over 7257.00 frames.], tot_loss[loss=0.1766, simple_loss=0.264, pruned_loss=0.04454, over 1428664.53 frames.], batch size: 19, lr: 2.04e-04
2022-05-28 17:57:56,832 INFO [train.py:842] (2/4) Epoch 27, batch 4150, loss[loss=0.1698, simple_loss=0.2701, pruned_loss=0.03474, over 7328.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2642, pruned_loss=0.04456, over 1423622.27 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 17:58:36,178 INFO [train.py:842] (2/4) Epoch 27, batch 4200, loss[loss=0.1676, simple_loss=0.2631, pruned_loss=0.03606, over 7070.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2648, pruned_loss=0.0451, over 1423778.21 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 17:59:15,948 INFO [train.py:842] (2/4) Epoch 27, batch 4250, loss[loss=0.1657, simple_loss=0.2611, pruned_loss=0.03514, over 7322.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2649, pruned_loss=0.04534, over 1424856.68 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 17:59:55,126 INFO [train.py:842] (2/4) Epoch 27, batch 4300, loss[loss=0.222, simple_loss=0.2809, pruned_loss=0.08154, over 7132.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2667, pruned_loss=0.04628, over 1422203.44 frames.], batch size: 17, lr: 2.04e-04
2022-05-28 18:00:34,753 INFO [train.py:842] (2/4) Epoch 27, batch 4350, loss[loss=0.228, simple_loss=0.3176, pruned_loss=0.06916, over 7295.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2671, pruned_loss=0.04625, over 1419610.53 frames.], batch size: 24, lr: 2.04e-04
2022-05-28 18:01:13,848 INFO [train.py:842] (2/4) Epoch 27, batch 4400, loss[loss=0.167, simple_loss=0.2643, pruned_loss=0.03486, over 7198.00 frames.], tot_loss[loss=0.181, simple_loss=0.2685, pruned_loss=0.04677, over 1419924.09 frames.], batch size: 23, lr: 2.04e-04
2022-05-28 18:01:53,413 INFO [train.py:842] (2/4) Epoch 27, batch 4450, loss[loss=0.1957, simple_loss=0.2928, pruned_loss=0.04934, over 7094.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2678, pruned_loss=0.04661, over 1418876.80 frames.], batch size: 28, lr: 2.04e-04
2022-05-28 18:02:32,859 INFO [train.py:842] (2/4) Epoch 27, batch 4500, loss[loss=0.2456, simple_loss=0.3298, pruned_loss=0.08067, over 7410.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2675, pruned_loss=0.04613, over 1424665.51 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 18:03:12,533 INFO [train.py:842] (2/4) Epoch 27, batch 4550, loss[loss=0.2197, simple_loss=0.3079, pruned_loss=0.06574, over 7420.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2662, pruned_loss=0.04533, over 1418031.28 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 18:03:51,738 INFO [train.py:842] (2/4) Epoch 27, batch 4600, loss[loss=0.1728, simple_loss=0.2499, pruned_loss=0.0479, over 7291.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2662, pruned_loss=0.04538, over 1419302.08 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 18:04:31,171 INFO [train.py:842] (2/4) Epoch 27, batch 4650, loss[loss=0.1253, simple_loss=0.2138, pruned_loss=0.0184, over 7000.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2649, pruned_loss=0.04463, over 1422142.65 frames.], batch size: 16, lr: 2.04e-04
2022-05-28 18:05:10,260 INFO [train.py:842] (2/4) Epoch 27, batch 4700, loss[loss=0.145, simple_loss=0.2383, pruned_loss=0.02585, over 7249.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2654, pruned_loss=0.04482, over 1421533.61 frames.], batch size: 19, lr: 2.04e-04
2022-05-28 18:05:49,557 INFO [train.py:842] (2/4) Epoch 27, batch 4750, loss[loss=0.1971, simple_loss=0.2844, pruned_loss=0.05487, over 7220.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2667, pruned_loss=0.04482, over 1424500.17 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 18:06:28,920 INFO [train.py:842] (2/4) Epoch 27, batch 4800, loss[loss=0.1497, simple_loss=0.2397, pruned_loss=0.02988, over 7277.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2658, pruned_loss=0.0442, over 1425192.98 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 18:07:08,570 INFO [train.py:842] (2/4) Epoch 27, batch 4850, loss[loss=0.1722, simple_loss=0.273, pruned_loss=0.03576, over 7324.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2651, pruned_loss=0.04396, over 1426262.16 frames.], batch size: 21, lr: 2.04e-04
2022-05-28 18:07:47,939 INFO [train.py:842] (2/4) Epoch 27, batch 4900, loss[loss=0.1944, simple_loss=0.2792, pruned_loss=0.05475, over 7204.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2656, pruned_loss=0.04411, over 1427654.45 frames.], batch size: 22, lr: 2.04e-04
2022-05-28 18:08:27,457 INFO [train.py:842] (2/4) Epoch 27, batch 4950, loss[loss=0.1478, simple_loss=0.2419, pruned_loss=0.02685, over 7278.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2653, pruned_loss=0.04416, over 1422288.45 frames.], batch size: 17, lr: 2.04e-04
2022-05-28 18:09:06,627 INFO [train.py:842] (2/4) Epoch 27, batch 5000, loss[loss=0.1782, simple_loss=0.2717, pruned_loss=0.04236, over 7278.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2659, pruned_loss=0.04467, over 1421139.56 frames.], batch size: 25, lr: 2.04e-04
2022-05-28 18:09:46,193 INFO [train.py:842] (2/4) Epoch 27, batch 5050, loss[loss=0.1598, simple_loss=0.2573, pruned_loss=0.03112, over 7237.00 frames.], tot_loss[loss=0.177, simple_loss=0.2657, pruned_loss=0.04418, over 1424477.24 frames.], batch size: 20, lr: 2.04e-04
2022-05-28 18:10:25,423 INFO [train.py:842] (2/4) Epoch 27, batch 5100, loss[loss=0.1568, simple_loss=0.2379, pruned_loss=0.03788, over 7285.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2642, pruned_loss=0.04341, over 1424031.27 frames.], batch size: 17, lr: 2.04e-04
2022-05-28 18:11:05,099 INFO [train.py:842] (2/4) Epoch 27, batch 5150, loss[loss=0.1895, simple_loss=0.285, pruned_loss=0.04701, over 7210.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2645, pruned_loss=0.0439, over 1425747.68 frames.], batch size: 23, lr: 2.04e-04
2022-05-28 18:11:44,276 INFO [train.py:842] (2/4) Epoch 27, batch 5200, loss[loss=0.2421, simple_loss=0.3194, pruned_loss=0.08233, over 6449.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2657, pruned_loss=0.04471, over 1425833.61 frames.], batch size: 38, lr: 2.04e-04
2022-05-28 18:12:24,101 INFO [train.py:842] (2/4) Epoch 27, batch 5250, loss[loss=0.1412, simple_loss=0.2329, pruned_loss=0.02477, over 7148.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2652, pruned_loss=0.04473, over 1429281.33 frames.], batch size: 19, lr: 2.04e-04
2022-05-28 18:13:03,665 INFO [train.py:842] (2/4) Epoch 27, batch 5300, loss[loss=0.1715, simple_loss=0.2584, pruned_loss=0.04228, over 7062.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2654, pruned_loss=0.04501, over 1431784.40 frames.], batch size: 18, lr: 2.04e-04
2022-05-28 18:13:43,393 INFO [train.py:842] (2/4) Epoch 27, batch 5350, loss[loss=0.1736, simple_loss=0.2625, pruned_loss=0.04233, over 7117.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2655, pruned_loss=0.04536, over 1429644.97 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:14:22,784 INFO [train.py:842] (2/4) Epoch 27, batch 5400, loss[loss=0.1688, simple_loss=0.2585, pruned_loss=0.03956, over 7430.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2656, pruned_loss=0.04501, over 1430688.06 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:15:02,175 INFO [train.py:842] (2/4) Epoch 27, batch 5450, loss[loss=0.1622, simple_loss=0.2594, pruned_loss=0.03249, over 7065.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2663, pruned_loss=0.04577, over 1428513.36 frames.], batch size: 18, lr: 2.03e-04
2022-05-28 18:15:41,456 INFO [train.py:842] (2/4) Epoch 27, batch 5500, loss[loss=0.2082, simple_loss=0.2862, pruned_loss=0.06504, over 7138.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2657, pruned_loss=0.04545, over 1428075.73 frames.], batch size: 26, lr: 2.03e-04
2022-05-28 18:16:21,209 INFO [train.py:842] (2/4) Epoch 27, batch 5550, loss[loss=0.1767, simple_loss=0.2742, pruned_loss=0.03958, over 7419.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2659, pruned_loss=0.04585, over 1427016.23 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:17:00,330 INFO [train.py:842] (2/4) Epoch 27, batch 5600, loss[loss=0.1456, simple_loss=0.2232, pruned_loss=0.03404, over 7138.00 frames.], tot_loss[loss=0.1776, simple_loss=0.265, pruned_loss=0.04508, over 1427630.91 frames.], batch size: 17, lr: 2.03e-04
2022-05-28 18:17:39,876 INFO [train.py:842] (2/4) Epoch 27, batch 5650, loss[loss=0.1778, simple_loss=0.2735, pruned_loss=0.04108, over 7222.00 frames.], tot_loss[loss=0.178, simple_loss=0.2656, pruned_loss=0.04517, over 1428197.32 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:18:19,108 INFO [train.py:842] (2/4) Epoch 27, batch 5700, loss[loss=0.1731, simple_loss=0.2672, pruned_loss=0.03947, over 7407.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2659, pruned_loss=0.04494, over 1421573.15 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:18:58,794 INFO [train.py:842] (2/4) Epoch 27, batch 5750, loss[loss=0.1856, simple_loss=0.2646, pruned_loss=0.05329, over 7429.00 frames.], tot_loss[loss=0.178, simple_loss=0.2663, pruned_loss=0.04488, over 1424308.59 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:19:38,100 INFO [train.py:842] (2/4) Epoch 27, batch 5800, loss[loss=0.1742, simple_loss=0.2531, pruned_loss=0.0477, over 7420.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2668, pruned_loss=0.04535, over 1421114.83 frames.], batch size: 17, lr: 2.03e-04
2022-05-28 18:20:17,632 INFO [train.py:842] (2/4) Epoch 27, batch 5850, loss[loss=0.22, simple_loss=0.2922, pruned_loss=0.07387, over 7201.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2667, pruned_loss=0.04525, over 1420916.48 frames.], batch size: 26, lr: 2.03e-04
2022-05-28 18:20:56,984 INFO [train.py:842] (2/4) Epoch 27, batch 5900, loss[loss=0.1975, simple_loss=0.2833, pruned_loss=0.05585, over 7423.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2663, pruned_loss=0.04539, over 1424681.18 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:21:36,824 INFO [train.py:842] (2/4) Epoch 27, batch 5950, loss[loss=0.1803, simple_loss=0.281, pruned_loss=0.03983, over 7231.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2645, pruned_loss=0.0448, over 1422905.42 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:22:15,967 INFO [train.py:842] (2/4) Epoch 27, batch 6000, loss[loss=0.1942, simple_loss=0.2818, pruned_loss=0.05334, over 7338.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2654, pruned_loss=0.04493, over 1421173.65 frames.], batch size: 22, lr: 2.03e-04
2022-05-28 18:22:15,968 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 18:22:25,576 INFO [train.py:871] (2/4) Epoch 27, validation: loss=0.1638, simple_loss=0.2621, pruned_loss=0.03281, over 868885.00 frames.
2022-05-28 18:23:04,725 INFO [train.py:842] (2/4) Epoch 27, batch 6050, loss[loss=0.1396, simple_loss=0.2333, pruned_loss=0.0229, over 7347.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2666, pruned_loss=0.04602, over 1416670.48 frames.], batch size: 19, lr: 2.03e-04
2022-05-28 18:23:44,263 INFO [train.py:842] (2/4) Epoch 27, batch 6100, loss[loss=0.1593, simple_loss=0.2537, pruned_loss=0.0325, over 7105.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2655, pruned_loss=0.04572, over 1417274.80 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:24:23,888 INFO [train.py:842] (2/4) Epoch 27, batch 6150, loss[loss=0.1729, simple_loss=0.2587, pruned_loss=0.04351, over 7148.00 frames.], tot_loss[loss=0.1794, simple_loss=0.267, pruned_loss=0.04596, over 1422720.33 frames.], batch size: 17, lr: 2.03e-04
2022-05-28 18:25:03,236 INFO [train.py:842] (2/4) Epoch 27, batch 6200, loss[loss=0.1395, simple_loss=0.2272, pruned_loss=0.02585, over 7273.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2669, pruned_loss=0.04545, over 1425696.58 frames.], batch size: 18, lr: 2.03e-04
2022-05-28 18:25:42,826 INFO [train.py:842] (2/4) Epoch 27, batch 6250, loss[loss=0.175, simple_loss=0.2694, pruned_loss=0.04029, over 7321.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2662, pruned_loss=0.0448, over 1423748.23 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:26:22,133 INFO [train.py:842] (2/4) Epoch 27, batch 6300, loss[loss=0.2023, simple_loss=0.2962, pruned_loss=0.05416, over 7332.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2664, pruned_loss=0.04498, over 1420100.67 frames.], batch size: 22, lr: 2.03e-04
2022-05-28 18:27:01,744 INFO [train.py:842] (2/4) Epoch 27, batch 6350, loss[loss=0.1511, simple_loss=0.2398, pruned_loss=0.03115, over 7325.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2656, pruned_loss=0.04477, over 1420269.19 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:27:40,931 INFO [train.py:842] (2/4) Epoch 27, batch 6400, loss[loss=0.1593, simple_loss=0.2601, pruned_loss=0.02927, over 7330.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2653, pruned_loss=0.04464, over 1420257.98 frames.], batch size: 22, lr: 2.03e-04
2022-05-28 18:28:20,559 INFO [train.py:842] (2/4) Epoch 27, batch 6450, loss[loss=0.1857, simple_loss=0.284, pruned_loss=0.04366, over 7125.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2666, pruned_loss=0.0451, over 1416183.22 frames.], batch size: 28, lr: 2.03e-04
2022-05-28 18:28:59,827 INFO [train.py:842] (2/4) Epoch 27, batch 6500, loss[loss=0.1904, simple_loss=0.2715, pruned_loss=0.05466, over 7213.00 frames.], tot_loss[loss=0.178, simple_loss=0.2662, pruned_loss=0.04486, over 1420444.34 frames.], batch size: 22, lr: 2.03e-04
2022-05-28 18:29:39,275 INFO [train.py:842] (2/4) Epoch 27, batch 6550, loss[loss=0.1663, simple_loss=0.2578, pruned_loss=0.03739, over 7218.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2657, pruned_loss=0.04497, over 1422840.32 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:30:18,666 INFO [train.py:842] (2/4) Epoch 27, batch 6600, loss[loss=0.1867, simple_loss=0.2658, pruned_loss=0.05379, over 7432.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2664, pruned_loss=0.04534, over 1423662.62 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:30:58,057 INFO [train.py:842] (2/4) Epoch 27, batch 6650, loss[loss=0.212, simple_loss=0.3023, pruned_loss=0.06085, over 7219.00 frames.], tot_loss[loss=0.1801, simple_loss=0.268, pruned_loss=0.0461, over 1422185.07 frames.], batch size: 26, lr: 2.03e-04
2022-05-28 18:31:37,214 INFO [train.py:842] (2/4) Epoch 27, batch 6700, loss[loss=0.1737, simple_loss=0.2594, pruned_loss=0.04399, over 7189.00 frames.], tot_loss[loss=0.1799, simple_loss=0.268, pruned_loss=0.04593, over 1423969.03 frames.], batch size: 26, lr: 2.03e-04
2022-05-28 18:32:16,872 INFO [train.py:842] (2/4) Epoch 27, batch 6750, loss[loss=0.1543, simple_loss=0.2347, pruned_loss=0.03693, over 6864.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2672, pruned_loss=0.04598, over 1422223.02 frames.], batch size: 15, lr: 2.03e-04
2022-05-28 18:32:56,065 INFO [train.py:842] (2/4) Epoch 27, batch 6800, loss[loss=0.1542, simple_loss=0.2402, pruned_loss=0.03411, over 7294.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2671, pruned_loss=0.04572, over 1423342.50 frames.], batch size: 17, lr: 2.03e-04
2022-05-28 18:33:35,640 INFO [train.py:842] (2/4) Epoch 27, batch 6850, loss[loss=0.1784, simple_loss=0.2559, pruned_loss=0.0505, over 7165.00 frames.], tot_loss[loss=0.18, simple_loss=0.2677, pruned_loss=0.04613, over 1422251.91 frames.], batch size: 18, lr: 2.03e-04
2022-05-28 18:34:14,902 INFO [train.py:842] (2/4) Epoch 27, batch 6900, loss[loss=0.1871, simple_loss=0.2764, pruned_loss=0.04887, over 7158.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2675, pruned_loss=0.04593, over 1425355.37 frames.], batch size: 19, lr: 2.03e-04
2022-05-28 18:34:54,547 INFO [train.py:842] (2/4) Epoch 27, batch 6950, loss[loss=0.1605, simple_loss=0.2588, pruned_loss=0.03106, over 6699.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2678, pruned_loss=0.04626, over 1425586.47 frames.], batch size: 31, lr: 2.03e-04
2022-05-28 18:35:33,895 INFO [train.py:842] (2/4) Epoch 27, batch 7000, loss[loss=0.138, simple_loss=0.222, pruned_loss=0.02701, over 7426.00 frames.], tot_loss[loss=0.18, simple_loss=0.2676, pruned_loss=0.04622, over 1426851.58 frames.], batch size: 18, lr: 2.03e-04
2022-05-28 18:36:13,575 INFO [train.py:842] (2/4) Epoch 27, batch 7050, loss[loss=0.1878, simple_loss=0.2782, pruned_loss=0.04871, over 7324.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2671, pruned_loss=0.04572, over 1426996.06 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:36:52,885 INFO [train.py:842] (2/4) Epoch 27, batch 7100, loss[loss=0.1936, simple_loss=0.2806, pruned_loss=0.05332, over 7248.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2659, pruned_loss=0.04523, over 1422223.48 frames.], batch size: 19, lr: 2.03e-04
2022-05-28 18:37:32,335 INFO [train.py:842] (2/4) Epoch 27, batch 7150, loss[loss=0.1874, simple_loss=0.2826, pruned_loss=0.04611, over 7279.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2664, pruned_loss=0.04532, over 1417632.01 frames.], batch size: 24, lr: 2.03e-04
2022-05-28 18:38:11,638 INFO [train.py:842] (2/4) Epoch 27, batch 7200, loss[loss=0.1421, simple_loss=0.2277, pruned_loss=0.02822, over 7281.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2658, pruned_loss=0.04478, over 1420744.77 frames.], batch size: 17, lr: 2.03e-04
2022-05-28 18:38:51,100 INFO [train.py:842] (2/4) Epoch 27, batch 7250, loss[loss=0.1809, simple_loss=0.2654, pruned_loss=0.04821, over 7411.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2662, pruned_loss=0.04525, over 1422848.50 frames.], batch size: 18, lr: 2.03e-04
2022-05-28 18:39:30,083 INFO [train.py:842] (2/4) Epoch 27, batch 7300, loss[loss=0.1767, simple_loss=0.2714, pruned_loss=0.04097, over 7415.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2657, pruned_loss=0.04497, over 1424256.29 frames.], batch size: 21, lr: 2.03e-04
2022-05-28 18:40:09,842 INFO [train.py:842] (2/4) Epoch 27, batch 7350, loss[loss=0.158, simple_loss=0.2567, pruned_loss=0.02961, over 7151.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2651, pruned_loss=0.04481, over 1424421.02 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:40:49,107 INFO [train.py:842] (2/4) Epoch 27, batch 7400, loss[loss=0.1752, simple_loss=0.2688, pruned_loss=0.0408, over 7191.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2646, pruned_loss=0.04465, over 1422636.65 frames.], batch size: 23, lr: 2.03e-04
2022-05-28 18:41:28,895 INFO [train.py:842] (2/4) Epoch 27, batch 7450, loss[loss=0.1446, simple_loss=0.2317, pruned_loss=0.02881, over 7275.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2648, pruned_loss=0.04487, over 1427007.53 frames.], batch size: 17, lr: 2.03e-04
2022-05-28 18:42:08,151 INFO [train.py:842] (2/4) Epoch 27, batch 7500, loss[loss=0.2112, simple_loss=0.2916, pruned_loss=0.06541, over 5368.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2662, pruned_loss=0.04557, over 1424669.39 frames.], batch size: 53, lr: 2.03e-04
2022-05-28 18:42:47,787 INFO [train.py:842] (2/4) Epoch 27, batch 7550, loss[loss=0.1754, simple_loss=0.2749, pruned_loss=0.038, over 7338.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2649, pruned_loss=0.0444, over 1426385.29 frames.], batch size: 20, lr: 2.03e-04
2022-05-28 18:43:27,178 INFO [train.py:842] (2/4) Epoch 27, batch 7600, loss[loss=0.1662, simple_loss=0.2644, pruned_loss=0.03395, over 7319.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2649, pruned_loss=0.04441, over 1427983.13 frames.], batch size: 22, lr: 2.03e-04
2022-05-28 18:44:07,134 INFO [train.py:842] (2/4) Epoch 27, batch 7650, loss[loss=0.2034, simple_loss=0.2931, pruned_loss=0.05689, over 7294.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2636, pruned_loss=0.04393, over 1432249.80 frames.], batch size: 24, lr: 2.03e-04
2022-05-28 18:44:46,342 INFO [train.py:842] (2/4) Epoch 27, batch 7700, loss[loss=0.1878, simple_loss=0.2822, pruned_loss=0.04666, over 7210.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2628, pruned_loss=0.04325, over 1426629.80 frames.], batch size: 22, lr: 2.03e-04
2022-05-28 18:45:26,187 INFO [train.py:842] (2/4) Epoch 27, batch 7750, loss[loss=0.193, simple_loss=0.2903, pruned_loss=0.04783, over 7194.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2627, pruned_loss=0.04328, over 1425923.75 frames.], batch size: 22, lr: 2.02e-04
2022-05-28 18:46:05,417 INFO [train.py:842] (2/4) Epoch 27, batch 7800, loss[loss=0.1746, simple_loss=0.2646, pruned_loss=0.04233, over 7274.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2634, pruned_loss=0.04386, over 1427815.21 frames.], batch size: 25, lr: 2.02e-04
2022-05-28 18:46:45,110 INFO [train.py:842] (2/4) Epoch 27, batch 7850, loss[loss=0.1441, simple_loss=0.2204, pruned_loss=0.03388, over 7245.00 frames.], tot_loss[loss=0.1758, simple_loss=0.263, pruned_loss=0.04426, over 1426962.43 frames.], batch size: 16, lr: 2.02e-04
2022-05-28 18:47:24,271 INFO [train.py:842] (2/4) Epoch 27, batch 7900, loss[loss=0.209, simple_loss=0.299, pruned_loss=0.05954, over 7308.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2622, pruned_loss=0.04358, over 1425326.36 frames.], batch size: 21, lr: 2.02e-04
2022-05-28 18:48:03,876 INFO [train.py:842] (2/4) Epoch 27, batch 7950, loss[loss=0.203, simple_loss=0.2771, pruned_loss=0.06443, over 7284.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2621, pruned_loss=0.04378, over 1422623.17 frames.], batch size: 17, lr: 2.02e-04
2022-05-28 18:48:42,909 INFO [train.py:842] (2/4) Epoch 27, batch 8000, loss[loss=0.1671, simple_loss=0.2441, pruned_loss=0.04507, over 7156.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2637, pruned_loss=0.04488, over 1416609.87 frames.], batch size: 17, lr: 2.02e-04
2022-05-28 18:49:22,391 INFO [train.py:842] (2/4) Epoch 27, batch 8050, loss[loss=0.1769, simple_loss=0.2675, pruned_loss=0.0432, over 7176.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2637, pruned_loss=0.04451, over 1416128.28 frames.], batch size: 26, lr: 2.02e-04
2022-05-28 18:50:01,718 INFO [train.py:842] (2/4) Epoch 27, batch 8100, loss[loss=0.1705, simple_loss=0.2663, pruned_loss=0.03734, over 7232.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2646, pruned_loss=0.04453, over 1419465.21 frames.], batch size: 20, lr: 2.02e-04
2022-05-28 18:50:41,125 INFO [train.py:842] (2/4) Epoch 27, batch 8150, loss[loss=0.1803, simple_loss=0.2646, pruned_loss=0.04795, over 7356.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2657, pruned_loss=0.045, over 1417754.13 frames.], batch size: 19, lr: 2.02e-04
2022-05-28 18:51:20,497 INFO [train.py:842] (2/4) Epoch 27, batch 8200, loss[loss=0.1625, simple_loss=0.2474, pruned_loss=0.03881, over 6878.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2659, pruned_loss=0.04491, over 1421162.36 frames.], batch size: 15, lr: 2.02e-04
2022-05-28 18:51:59,945 INFO [train.py:842] (2/4) Epoch 27, batch 8250, loss[loss=0.166, simple_loss=0.2502, pruned_loss=0.04086, over 6760.00 frames.], tot_loss[loss=0.1773, simple_loss=0.265, pruned_loss=0.04483, over 1417547.40 frames.], batch size: 31, lr: 2.02e-04
2022-05-28 18:52:39,223 INFO [train.py:842] (2/4) Epoch 27, batch 8300, loss[loss=0.1917, simple_loss=0.2803, pruned_loss=0.05158, over 7114.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2663, pruned_loss=0.04544, over 1421003.66 frames.], batch size: 21, lr: 2.02e-04
2022-05-28 18:53:18,878 INFO [train.py:842] (2/4) Epoch 27, batch 8350, loss[loss=0.154, simple_loss=0.2388, pruned_loss=0.03458, over 7119.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2654, pruned_loss=0.04471, over 1420097.73 frames.], batch size: 17, lr: 2.02e-04
2022-05-28 18:53:58,057 INFO [train.py:842] (2/4) Epoch 27, batch 8400, loss[loss=0.1608, simple_loss=0.2521, pruned_loss=0.03477, over 7406.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2658, pruned_loss=0.04422, over 1422670.17 frames.], batch size: 21, lr: 2.02e-04
2022-05-28 18:54:37,714 INFO [train.py:842] (2/4) Epoch 27, batch 8450, loss[loss=0.1912, simple_loss=0.2829, pruned_loss=0.04977, over 7164.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2672, pruned_loss=0.04518, over 1422922.36 frames.], batch size: 18, lr: 2.02e-04
2022-05-28 18:55:16,707 INFO [train.py:842] (2/4) Epoch 27, batch 8500, loss[loss=0.2023, simple_loss=0.2871, pruned_loss=0.0587, over 7225.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2672, pruned_loss=0.04514, over 1425419.76 frames.], batch size: 22, lr: 2.02e-04
2022-05-28 18:55:56,066 INFO [train.py:842] (2/4) Epoch 27, batch 8550, loss[loss=0.2091, simple_loss=0.3004, pruned_loss=0.05891, over 6730.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2677, pruned_loss=0.04567, over 1424359.10 frames.], batch size: 31, lr: 2.02e-04
2022-05-28 18:56:35,099 INFO [train.py:842] (2/4) Epoch 27, batch 8600, loss[loss=0.1723, simple_loss=0.2678, pruned_loss=0.03837, over 7117.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2679, pruned_loss=0.04559, over 1421605.81 frames.], batch size: 21, lr: 2.02e-04
2022-05-28 18:57:14,398 INFO [train.py:842] (2/4) Epoch 27, batch 8650, loss[loss=0.2081, simple_loss=0.288, pruned_loss=0.06416, over 7219.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2669, pruned_loss=0.04524, over 1415188.36 frames.], batch size: 21, lr: 2.02e-04
2022-05-28 18:57:53,640 INFO [train.py:842] (2/4) Epoch 27, batch 8700, loss[loss=0.191, simple_loss=0.2823, pruned_loss=0.04982, over 7201.00 frames.], tot_loss[loss=0.179, simple_loss=0.2672, pruned_loss=0.04544, over 1417269.51 frames.], batch size: 22, lr: 2.02e-04
2022-05-28 18:58:33,140 INFO [train.py:842] (2/4) Epoch 27, batch 8750, loss[loss=0.192, simple_loss=0.2767, pruned_loss=0.05361, over 7216.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2668, pruned_loss=0.04543, over 1413657.22 frames.], batch size: 22, lr: 2.02e-04
2022-05-28 18:59:12,369 INFO [train.py:842] (2/4) Epoch 27, batch 8800, loss[loss=0.1445, simple_loss=0.2294, pruned_loss=0.02984, over 7279.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2664, pruned_loss=0.04491, over 1409717.39 frames.], batch size: 16, lr: 2.02e-04
2022-05-28 18:59:51,553 INFO [train.py:842] (2/4) Epoch 27, batch 8850, loss[loss=0.1497, simple_loss=0.2382, pruned_loss=0.03062, over 7074.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2657, pruned_loss=0.04472, over 1412257.64 frames.], batch size: 18, lr: 2.02e-04
2022-05-28 19:00:30,272 INFO [train.py:842] (2/4) Epoch 27, batch 8900, loss[loss=0.1637, simple_loss=0.2579, pruned_loss=0.03478, over 7202.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2668, pruned_loss=0.04546, over 1406227.43 frames.], batch size: 22, lr: 2.02e-04
2022-05-28 19:01:09,167 INFO [train.py:842] (2/4) Epoch 27, batch 8950, loss[loss=0.15, simple_loss=0.2254, pruned_loss=0.03725, over 7433.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2669, pruned_loss=0.0452, over 1403012.68 frames.], batch size: 17, lr: 2.02e-04
2022-05-28 19:01:47,928 INFO [train.py:842] (2/4) Epoch 27, batch 9000, loss[loss=0.1689, simple_loss=0.2657, pruned_loss=0.036, over 7051.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2665, pruned_loss=0.04538, over 1396129.06 frames.], batch size: 28, lr: 2.02e-04
2022-05-28 19:01:47,929 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 19:01:58,049 INFO [train.py:871] (2/4) Epoch 27, validation: loss=0.1653, simple_loss=0.2632, pruned_loss=0.03366, over 868885.00 frames.
2022-05-28 19:02:39,558 INFO [train.py:842] (2/4) Epoch 27, batch 9050, loss[loss=0.19, simple_loss=0.2744, pruned_loss=0.05275, over 6316.00 frames.], tot_loss[loss=0.1802, simple_loss=0.268, pruned_loss=0.04625, over 1372654.99 frames.], batch size: 38, lr: 2.02e-04
2022-05-28 19:03:17,098 INFO [train.py:842] (2/4) Epoch 27, batch 9100, loss[loss=0.155, simple_loss=0.2576, pruned_loss=0.02618, over 6814.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2717, pruned_loss=0.04807, over 1336230.07 frames.], batch size: 31, lr: 2.02e-04
2022-05-28 19:03:55,291 INFO [train.py:842] (2/4) Epoch 27, batch 9150, loss[loss=0.2327, simple_loss=0.3169, pruned_loss=0.07427, over 5006.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2747, pruned_loss=0.05029, over 1270293.04 frames.], batch size: 52, lr: 2.02e-04
2022-05-28 19:04:48,243 INFO [train.py:842] (2/4) Epoch 28, batch 0, loss[loss=0.1659, simple_loss=0.2586, pruned_loss=0.03657, over 7256.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2586, pruned_loss=0.03657, over 7256.00 frames.], batch size: 19, lr: 1.98e-04
2022-05-28 19:05:27,959 INFO [train.py:842] (2/4) Epoch 28, batch 50, loss[loss=0.1903, simple_loss=0.2733, pruned_loss=0.05371, over 7264.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2632, pruned_loss=0.04325, over 321064.53 frames.], batch size: 19, lr: 1.98e-04
2022-05-28 19:06:07,167 INFO [train.py:842] (2/4) Epoch 28, batch 100, loss[loss=0.2648, simple_loss=0.3337, pruned_loss=0.09799, over 7138.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2637, pruned_loss=0.04328, over 565496.71 frames.], batch size: 20, lr: 1.98e-04
2022-05-28 19:06:46,681 INFO [train.py:842] (2/4) Epoch 28, batch 150, loss[loss=0.158, simple_loss=0.2534, pruned_loss=0.03131, over 6637.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2639, pruned_loss=0.04295, over 753821.71 frames.], batch size: 38, lr: 1.98e-04
2022-05-28 19:07:25,757 INFO [train.py:842] (2/4) Epoch 28, batch 200, loss[loss=0.188, simple_loss=0.2854, pruned_loss=0.04534, over 7191.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2631, pruned_loss=0.0426, over 899623.61 frames.], batch size: 23, lr: 1.98e-04
2022-05-28 19:08:05,253 INFO [train.py:842] (2/4) Epoch 28, batch 250, loss[loss=0.1959, simple_loss=0.2882, pruned_loss=0.05183, over 7323.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2632, pruned_loss=0.04254, over 1015834.95 frames.], batch size: 24, lr: 1.98e-04
2022-05-28 19:08:44,440 INFO [train.py:842] (2/4) Epoch 28, batch 300, loss[loss=0.1606, simple_loss=0.2575, pruned_loss=0.03188, over 6827.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2657, pruned_loss=0.04367, over 1105838.85 frames.], batch size: 31, lr: 1.98e-04
2022-05-28 19:09:23,883 INFO [train.py:842] (2/4) Epoch 28, batch 350, loss[loss=0.2019, simple_loss=0.2911, pruned_loss=0.05628, over 7162.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2644, pruned_loss=0.04302, over 1177603.87 frames.], batch size: 19, lr: 1.98e-04
2022-05-28 19:10:03,148 INFO [train.py:842] (2/4) Epoch 28, batch 400, loss[loss=0.1723, simple_loss=0.2474, pruned_loss=0.04861, over 7145.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2647, pruned_loss=0.04357, over 1233998.55 frames.], batch size: 17, lr: 1.98e-04
2022-05-28 19:10:42,622 INFO [train.py:842] (2/4) Epoch 28, batch 450, loss[loss=0.1709, simple_loss=0.2655, pruned_loss=0.03821, over 7296.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2655, pruned_loss=0.04429, over 1270801.49 frames.], batch size: 25, lr: 1.98e-04
2022-05-28 19:11:21,993 INFO [train.py:842] (2/4) Epoch 28, batch 500, loss[loss=0.1663, simple_loss=0.269, pruned_loss=0.03178, over 7322.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2653, pruned_loss=0.04387, over 1308372.94 frames.], batch size: 21, lr: 1.98e-04
2022-05-28 19:12:01,648 INFO [train.py:842] (2/4) Epoch 28, batch 550, loss[loss=0.1657, simple_loss=0.2577, pruned_loss=0.03684, over 7079.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2648, pruned_loss=0.04412, over 1330941.73 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:12:41,048 INFO [train.py:842] (2/4) Epoch 28, batch 600, loss[loss=0.1657, simple_loss=0.261, pruned_loss=0.03518, over 7321.00 frames.], tot_loss[loss=0.1756, simple_loss=0.264, pruned_loss=0.04363, over 1349052.03 frames.], batch size: 20, lr: 1.98e-04
2022-05-28 19:13:20,388 INFO [train.py:842] (2/4) Epoch 28, batch 650, loss[loss=0.1641, simple_loss=0.2534, pruned_loss=0.03733, over 7033.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2645, pruned_loss=0.04364, over 1366432.45 frames.], batch size: 28, lr: 1.98e-04
2022-05-28 19:13:59,783 INFO [train.py:842] (2/4) Epoch 28, batch 700, loss[loss=0.1523, simple_loss=0.2451, pruned_loss=0.02973, over 7058.00 frames.], tot_loss[loss=0.1758, simple_loss=0.264, pruned_loss=0.04385, over 1379640.85 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:14:39,578 INFO [train.py:842] (2/4) Epoch 28, batch 750, loss[loss=0.1595, simple_loss=0.2555, pruned_loss=0.03179, over 7208.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2632, pruned_loss=0.04382, over 1390974.53 frames.], batch size: 21, lr: 1.98e-04
2022-05-28 19:15:18,778 INFO [train.py:842] (2/4) Epoch 28, batch 800, loss[loss=0.1842, simple_loss=0.2757, pruned_loss=0.04634, over 7105.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2635, pruned_loss=0.04378, over 1397401.40 frames.], batch size: 28, lr: 1.98e-04
2022-05-28 19:15:58,591 INFO [train.py:842] (2/4) Epoch 28, batch 850, loss[loss=0.183, simple_loss=0.2798, pruned_loss=0.0431, over 7304.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2635, pruned_loss=0.04348, over 1404861.98 frames.], batch size: 25, lr: 1.98e-04
2022-05-28 19:16:37,596 INFO [train.py:842] (2/4) Epoch 28, batch 900, loss[loss=0.1931, simple_loss=0.2677, pruned_loss=0.0592, over 6988.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2642, pruned_loss=0.04372, over 1407250.59 frames.], batch size: 16, lr: 1.98e-04
2022-05-28 19:17:16,981 INFO [train.py:842] (2/4) Epoch 28, batch 950, loss[loss=0.1737, simple_loss=0.2569, pruned_loss=0.0453, over 7175.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2652, pruned_loss=0.04417, over 1409417.01 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:17:56,516 INFO [train.py:842] (2/4) Epoch 28, batch 1000, loss[loss=0.1729, simple_loss=0.2619, pruned_loss=0.04192, over 7438.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2647, pruned_loss=0.04399, over 1415766.51 frames.], batch size: 20, lr: 1.98e-04
2022-05-28 19:18:36,068 INFO [train.py:842] (2/4) Epoch 28, batch 1050, loss[loss=0.2188, simple_loss=0.3149, pruned_loss=0.06133, over 7410.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2648, pruned_loss=0.04399, over 1415553.63 frames.], batch size: 21, lr: 1.98e-04
2022-05-28 19:19:15,247 INFO [train.py:842] (2/4) Epoch 28, batch 1100, loss[loss=0.1718, simple_loss=0.261, pruned_loss=0.04136, over 7068.00 frames.], tot_loss[loss=0.1768, simple_loss=0.265, pruned_loss=0.0443, over 1414871.39 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:19:55,057 INFO [train.py:842] (2/4) Epoch 28, batch 1150, loss[loss=0.2314, simple_loss=0.3157, pruned_loss=0.07352, over 7208.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2653, pruned_loss=0.04507, over 1420519.18 frames.], batch size: 23, lr: 1.98e-04
2022-05-28 19:20:34,406 INFO [train.py:842] (2/4) Epoch 28, batch 1200, loss[loss=0.1273, simple_loss=0.2146, pruned_loss=0.01999, over 7135.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2644, pruned_loss=0.04452, over 1424924.08 frames.], batch size: 17, lr: 1.98e-04
2022-05-28 19:21:13,767 INFO [train.py:842] (2/4) Epoch 28, batch 1250, loss[loss=0.1447, simple_loss=0.2276, pruned_loss=0.03096, over 7152.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2655, pruned_loss=0.04502, over 1422450.96 frames.], batch size: 17, lr: 1.98e-04
2022-05-28 19:21:52,976 INFO [train.py:842] (2/4) Epoch 28, batch 1300, loss[loss=0.1475, simple_loss=0.2267, pruned_loss=0.03422, over 7285.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2662, pruned_loss=0.04574, over 1419165.30 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:22:32,600 INFO [train.py:842] (2/4) Epoch 28, batch 1350, loss[loss=0.1876, simple_loss=0.2734, pruned_loss=0.05094, over 7357.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2653, pruned_loss=0.04518, over 1419720.81 frames.], batch size: 19, lr: 1.98e-04
2022-05-28 19:23:11,742 INFO [train.py:842] (2/4) Epoch 28, batch 1400, loss[loss=0.1985, simple_loss=0.2793, pruned_loss=0.05885, over 7068.00 frames.], tot_loss[loss=0.1781, simple_loss=0.266, pruned_loss=0.04512, over 1420273.15 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:23:51,620 INFO [train.py:842] (2/4) Epoch 28, batch 1450, loss[loss=0.2118, simple_loss=0.2954, pruned_loss=0.06412, over 7322.00 frames.], tot_loss[loss=0.1773, simple_loss=0.265, pruned_loss=0.04476, over 1422530.75 frames.], batch size: 20, lr: 1.98e-04
2022-05-28 19:24:30,667 INFO [train.py:842] (2/4) Epoch 28, batch 1500, loss[loss=0.1681, simple_loss=0.2606, pruned_loss=0.03782, over 7104.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2661, pruned_loss=0.04504, over 1423749.89 frames.], batch size: 21, lr: 1.98e-04
2022-05-28 19:25:10,188 INFO [train.py:842] (2/4) Epoch 28, batch 1550, loss[loss=0.1349, simple_loss=0.2235, pruned_loss=0.02312, over 7196.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2654, pruned_loss=0.04506, over 1420656.84 frames.], batch size: 16, lr: 1.98e-04
2022-05-28 19:25:49,456 INFO [train.py:842] (2/4) Epoch 28, batch 1600, loss[loss=0.1904, simple_loss=0.2814, pruned_loss=0.04965, over 7409.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2653, pruned_loss=0.04479, over 1425172.41 frames.], batch size: 21, lr: 1.98e-04
2022-05-28 19:26:29,080 INFO [train.py:842] (2/4) Epoch 28, batch 1650, loss[loss=0.1798, simple_loss=0.2587, pruned_loss=0.05052, over 7052.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2641, pruned_loss=0.04442, over 1425688.21 frames.], batch size: 18, lr: 1.98e-04
2022-05-28 19:27:08,104 INFO [train.py:842] (2/4) Epoch 28, batch 1700, loss[loss=0.1601, simple_loss=0.2528, pruned_loss=0.03367, over 7355.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2642, pruned_loss=0.04398, over 1426709.55 frames.], batch size: 19, lr: 1.98e-04
2022-05-28 19:27:48,082 INFO [train.py:842] (2/4) Epoch 28, batch 1750, loss[loss=0.1827, simple_loss=0.282, pruned_loss=0.04174, over 6607.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2633, pruned_loss=0.04366, over 1428554.88 frames.], batch size: 31, lr: 1.98e-04
2022-05-28 19:28:27,184 INFO [train.py:842] (2/4) Epoch 28, batch 1800, loss[loss=0.1725, simple_loss=0.2638, pruned_loss=0.04063, over 7232.00 frames.], tot_loss[loss=0.1759, simple_loss=0.264, pruned_loss=0.04387, over 1427887.66 frames.], batch size: 20, lr: 1.98e-04
2022-05-28 19:29:06,933 INFO [train.py:842] (2/4) Epoch 28, batch 1850, loss[loss=0.1431, simple_loss=0.2387, pruned_loss=0.02376, over 7164.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2641, pruned_loss=0.04382, over 1430602.51 frames.], batch size: 19, lr: 1.98e-04
2022-05-28 19:29:46,148 INFO [train.py:842] (2/4) Epoch 28, batch 1900, loss[loss=0.1295, simple_loss=0.2189, pruned_loss=0.0201, over 7290.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2642, pruned_loss=0.04328, over 1430687.32 frames.], batch size: 17, lr: 1.98e-04
2022-05-28 19:30:25,829 INFO [train.py:842] (2/4) Epoch 28, batch 1950, loss[loss=0.2119, simple_loss=0.3042, pruned_loss=0.05983, over 6568.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2639, pruned_loss=0.04363, over 1426045.35 frames.], batch size: 38, lr: 1.98e-04
2022-05-28 19:31:05,153 INFO [train.py:842] (2/4) Epoch 28, batch 2000, loss[loss=0.2084, simple_loss=0.3185, pruned_loss=0.04915, over 7206.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2649, pruned_loss=0.04444, over 1425327.72 frames.], batch size: 21, lr: 1.98e-04
2022-05-28 19:31:44,619 INFO [train.py:842] (2/4) Epoch 28, batch 2050, loss[loss=0.1954, simple_loss=0.2895, pruned_loss=0.0506, over 7205.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2656, pruned_loss=0.04484, over 1423464.58 frames.], batch size: 23, lr: 1.97e-04
2022-05-28 19:32:34,444 INFO [train.py:842] (2/4) Epoch 28, batch 2100, loss[loss=0.1787, simple_loss=0.2632, pruned_loss=0.04711, over 7309.00 frames.], tot_loss[loss=0.177, simple_loss=0.2649, pruned_loss=0.0446, over 1423473.79 frames.], batch size: 25, lr: 1.97e-04
2022-05-28 19:33:13,877 INFO [train.py:842] (2/4) Epoch 28, batch 2150, loss[loss=0.1885, simple_loss=0.2651, pruned_loss=0.05593, over 7145.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2658, pruned_loss=0.04496, over 1422190.05 frames.], batch size: 17, lr: 1.97e-04
2022-05-28 19:33:53,204 INFO [train.py:842] (2/4) Epoch 28, batch 2200, loss[loss=0.2033, simple_loss=0.2858, pruned_loss=0.06037, over 7322.00 frames.], tot_loss[loss=0.178, simple_loss=0.2659, pruned_loss=0.04511, over 1420500.38 frames.], batch size: 24, lr: 1.97e-04
2022-05-28 19:34:32,691 INFO [train.py:842] (2/4) Epoch 28, batch 2250, loss[loss=0.1843, simple_loss=0.2855, pruned_loss=0.04154, over 7346.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2656, pruned_loss=0.04499, over 1423290.65 frames.], batch size: 22, lr: 1.97e-04
2022-05-28 19:35:11,840 INFO [train.py:842] (2/4) Epoch 28, batch 2300, loss[loss=0.186, simple_loss=0.2744, pruned_loss=0.04884, over 7139.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2661, pruned_loss=0.04525, over 1421010.18 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:35:51,234 INFO [train.py:842] (2/4) Epoch 28, batch 2350, loss[loss=0.1673, simple_loss=0.2628, pruned_loss=0.03592, over 7157.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2665, pruned_loss=0.04536, over 1419814.06 frames.], batch size: 19, lr: 1.97e-04
2022-05-28 19:36:30,494 INFO [train.py:842] (2/4) Epoch 28, batch 2400, loss[loss=0.1666, simple_loss=0.2597, pruned_loss=0.03678, over 7181.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2667, pruned_loss=0.04552, over 1423088.89 frames.], batch size: 23, lr: 1.97e-04
2022-05-28 19:37:10,235 INFO [train.py:842] (2/4) Epoch 28, batch 2450, loss[loss=0.1741, simple_loss=0.2568, pruned_loss=0.04566, over 6272.00 frames.], tot_loss[loss=0.178, simple_loss=0.2661, pruned_loss=0.04491, over 1423676.33 frames.], batch size: 37, lr: 1.97e-04
2022-05-28 19:37:49,529 INFO [train.py:842] (2/4) Epoch 28, batch 2500, loss[loss=0.1376, simple_loss=0.2184, pruned_loss=0.0284, over 6809.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2648, pruned_loss=0.0443, over 1420340.51 frames.], batch size: 15, lr: 1.97e-04
2022-05-28 19:38:29,266 INFO [train.py:842] (2/4) Epoch 28, batch 2550, loss[loss=0.1928, simple_loss=0.2706, pruned_loss=0.05751, over 7261.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2655, pruned_loss=0.04457, over 1421505.74 frames.], batch size: 19, lr: 1.97e-04
2022-05-28 19:39:08,391 INFO [train.py:842] (2/4) Epoch 28, batch 2600, loss[loss=0.1645, simple_loss=0.2472, pruned_loss=0.04091, over 7229.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2645, pruned_loss=0.04387, over 1421301.46 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:39:48,062 INFO [train.py:842] (2/4) Epoch 28, batch 2650, loss[loss=0.1287, simple_loss=0.206, pruned_loss=0.02567, over 6985.00 frames.], tot_loss[loss=0.1768, simple_loss=0.265, pruned_loss=0.04426, over 1419545.92 frames.], batch size: 16, lr: 1.97e-04
2022-05-28 19:40:27,358 INFO [train.py:842] (2/4) Epoch 28, batch 2700, loss[loss=0.1738, simple_loss=0.2754, pruned_loss=0.03615, over 7331.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2653, pruned_loss=0.04408, over 1421809.78 frames.], batch size: 21, lr: 1.97e-04
2022-05-28 19:41:06,905 INFO [train.py:842] (2/4) Epoch 28, batch 2750, loss[loss=0.1549, simple_loss=0.2518, pruned_loss=0.029, over 7249.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2658, pruned_loss=0.0445, over 1419753.66 frames.], batch size: 19, lr: 1.97e-04
2022-05-28 19:41:45,954 INFO [train.py:842] (2/4) Epoch 28, batch 2800, loss[loss=0.2021, simple_loss=0.2961, pruned_loss=0.05404, over 7229.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2656, pruned_loss=0.04445, over 1416498.76 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:42:25,443 INFO [train.py:842] (2/4) Epoch 28, batch 2850, loss[loss=0.1404, simple_loss=0.2231, pruned_loss=0.02885, over 7147.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2663, pruned_loss=0.04504, over 1420172.61 frames.], batch size: 17, lr: 1.97e-04
2022-05-28 19:43:04,598 INFO [train.py:842] (2/4) Epoch 28, batch 2900, loss[loss=0.205, simple_loss=0.2873, pruned_loss=0.06132, over 7283.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2669, pruned_loss=0.04564, over 1418706.17 frames.], batch size: 25, lr: 1.97e-04
2022-05-28 19:43:43,925 INFO [train.py:842] (2/4) Epoch 28, batch 2950, loss[loss=0.1882, simple_loss=0.2871, pruned_loss=0.04464, over 7197.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2673, pruned_loss=0.04611, over 1421803.19 frames.], batch size: 23, lr: 1.97e-04
2022-05-28 19:44:22,910 INFO [train.py:842] (2/4) Epoch 28, batch 3000, loss[loss=0.1915, simple_loss=0.2909, pruned_loss=0.0461, over 7049.00 frames.], tot_loss[loss=0.1788, simple_loss=0.267, pruned_loss=0.04533, over 1423854.30 frames.], batch size: 28, lr: 1.97e-04
2022-05-28 19:44:22,911 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 19:44:32,547 INFO [train.py:871] (2/4) Epoch 28, validation: loss=0.165, simple_loss=0.263, pruned_loss=0.03349, over 868885.00 frames.
2022-05-28 19:45:12,201 INFO [train.py:842] (2/4) Epoch 28, batch 3050, loss[loss=0.1506, simple_loss=0.2281, pruned_loss=0.03652, over 7132.00 frames.], tot_loss[loss=0.178, simple_loss=0.2665, pruned_loss=0.0448, over 1425585.38 frames.], batch size: 17, lr: 1.97e-04
2022-05-28 19:45:51,484 INFO [train.py:842] (2/4) Epoch 28, batch 3100, loss[loss=0.2149, simple_loss=0.3134, pruned_loss=0.05819, over 7379.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2664, pruned_loss=0.04494, over 1424443.21 frames.], batch size: 23, lr: 1.97e-04
2022-05-28 19:46:31,127 INFO [train.py:842] (2/4) Epoch 28, batch 3150, loss[loss=0.1616, simple_loss=0.2361, pruned_loss=0.04358, over 7425.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2664, pruned_loss=0.04526, over 1423029.06 frames.], batch size: 18, lr: 1.97e-04
2022-05-28 19:47:10,206 INFO [train.py:842] (2/4) Epoch 28, batch 3200, loss[loss=0.1818, simple_loss=0.2763, pruned_loss=0.04362, over 7313.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2661, pruned_loss=0.04502, over 1423328.17 frames.], batch size: 21, lr: 1.97e-04
2022-05-28 19:47:50,112 INFO [train.py:842] (2/4) Epoch 28, batch 3250, loss[loss=0.1692, simple_loss=0.2593, pruned_loss=0.03957, over 7146.00 frames.], tot_loss[loss=0.1769, simple_loss=0.265, pruned_loss=0.04438, over 1422528.11 frames.], batch size: 18, lr: 1.97e-04
2022-05-28 19:48:29,289 INFO [train.py:842] (2/4) Epoch 28, batch 3300, loss[loss=0.1355, simple_loss=0.2164, pruned_loss=0.02725, over 6996.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2656, pruned_loss=0.04488, over 1421298.07 frames.], batch size: 16, lr: 1.97e-04
2022-05-28 19:49:08,803 INFO [train.py:842] (2/4) Epoch 28, batch 3350, loss[loss=0.1711, simple_loss=0.2665, pruned_loss=0.03783, over 7366.00 frames.], tot_loss[loss=0.177, simple_loss=0.265, pruned_loss=0.04453, over 1419881.64 frames.], batch size: 23, lr: 1.97e-04
2022-05-28 19:49:48,107 INFO [train.py:842] (2/4) Epoch 28, batch 3400, loss[loss=0.1711, simple_loss=0.2639, pruned_loss=0.03912, over 7321.00 frames.], tot_loss[loss=0.177, simple_loss=0.2649, pruned_loss=0.04452, over 1421983.19 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:50:27,828 INFO [train.py:842] (2/4) Epoch 28, batch 3450, loss[loss=0.2279, simple_loss=0.3096, pruned_loss=0.07309, over 7214.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2653, pruned_loss=0.04498, over 1422838.94 frames.], batch size: 22, lr: 1.97e-04
2022-05-28 19:51:07,202 INFO [train.py:842] (2/4) Epoch 28, batch 3500, loss[loss=0.1558, simple_loss=0.2356, pruned_loss=0.03799, over 7075.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2648, pruned_loss=0.04472, over 1422560.06 frames.], batch size: 18, lr: 1.97e-04
2022-05-28 19:51:46,794 INFO [train.py:842] (2/4) Epoch 28, batch 3550, loss[loss=0.1941, simple_loss=0.2845, pruned_loss=0.05187, over 7316.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2646, pruned_loss=0.04413, over 1423007.01 frames.], batch size: 22, lr: 1.97e-04
2022-05-28 19:52:25,754 INFO [train.py:842] (2/4) Epoch 28, batch 3600, loss[loss=0.1679, simple_loss=0.2555, pruned_loss=0.04012, over 7064.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2649, pruned_loss=0.04407, over 1421529.30 frames.], batch size: 18, lr: 1.97e-04
2022-05-28 19:53:05,423 INFO [train.py:842] (2/4) Epoch 28, batch 3650, loss[loss=0.1783, simple_loss=0.2547, pruned_loss=0.05091, over 7125.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2644, pruned_loss=0.04445, over 1422481.67 frames.], batch size: 17, lr: 1.97e-04
2022-05-28 19:53:44,327 INFO [train.py:842] (2/4) Epoch 28, batch 3700, loss[loss=0.1645, simple_loss=0.2476, pruned_loss=0.04069, over 7425.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2641, pruned_loss=0.04433, over 1422689.04 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:54:23,826 INFO [train.py:842] (2/4) Epoch 28, batch 3750, loss[loss=0.1634, simple_loss=0.2525, pruned_loss=0.03717, over 7317.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2641, pruned_loss=0.04421, over 1423571.79 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:55:02,978 INFO [train.py:842] (2/4) Epoch 28, batch 3800, loss[loss=0.1898, simple_loss=0.2799, pruned_loss=0.04991, over 7204.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2646, pruned_loss=0.04428, over 1422197.48 frames.], batch size: 22, lr: 1.97e-04
2022-05-28 19:55:42,642 INFO [train.py:842] (2/4) Epoch 28, batch 3850, loss[loss=0.2251, simple_loss=0.3351, pruned_loss=0.05753, over 7143.00 frames.], tot_loss[loss=0.1764, simple_loss=0.265, pruned_loss=0.04396, over 1427126.17 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 19:56:21,900 INFO [train.py:842] (2/4) Epoch 28, batch 3900, loss[loss=0.1372, simple_loss=0.2178, pruned_loss=0.02831, over 6995.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2636, pruned_loss=0.0433, over 1426928.72 frames.], batch size: 16, lr: 1.97e-04
2022-05-28 19:57:01,250 INFO [train.py:842] (2/4) Epoch 28, batch 3950, loss[loss=0.166, simple_loss=0.2446, pruned_loss=0.04374, over 7006.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2642, pruned_loss=0.04355, over 1427222.89 frames.], batch size: 16, lr: 1.97e-04
2022-05-28 19:57:40,565 INFO [train.py:842] (2/4) Epoch 28, batch 4000, loss[loss=0.1634, simple_loss=0.259, pruned_loss=0.03389, over 7319.00 frames.], tot_loss[loss=0.1752, simple_loss=0.264, pruned_loss=0.04325, over 1427708.56 frames.], batch size: 21, lr: 1.97e-04
2022-05-28 19:58:20,474 INFO [train.py:842] (2/4) Epoch 28, batch 4050, loss[loss=0.209, simple_loss=0.3024, pruned_loss=0.05784, over 7285.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2629, pruned_loss=0.04309, over 1429204.32 frames.], batch size: 24, lr: 1.97e-04
2022-05-28 19:58:59,888 INFO [train.py:842] (2/4) Epoch 28, batch 4100, loss[loss=0.1703, simple_loss=0.2464, pruned_loss=0.04712, over 6780.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2636, pruned_loss=0.04385, over 1424554.82 frames.], batch size: 15, lr: 1.97e-04
2022-05-28 19:59:39,533 INFO [train.py:842] (2/4) Epoch 28, batch 4150, loss[loss=0.1483, simple_loss=0.2499, pruned_loss=0.02331, over 7414.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2642, pruned_loss=0.04397, over 1424148.16 frames.], batch size: 21, lr: 1.97e-04
2022-05-28 20:00:18,979 INFO [train.py:842] (2/4) Epoch 28, batch 4200, loss[loss=0.1533, simple_loss=0.2375, pruned_loss=0.03455, over 7150.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2637, pruned_loss=0.04372, over 1426315.29 frames.], batch size: 17, lr: 1.97e-04
2022-05-28 20:00:58,721 INFO [train.py:842] (2/4) Epoch 28, batch 4250, loss[loss=0.1804, simple_loss=0.2574, pruned_loss=0.05173, over 7240.00 frames.], tot_loss[loss=0.176, simple_loss=0.2644, pruned_loss=0.04383, over 1428032.80 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 20:01:38,106 INFO [train.py:842] (2/4) Epoch 28, batch 4300, loss[loss=0.1652, simple_loss=0.2582, pruned_loss=0.03612, over 7158.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2647, pruned_loss=0.04397, over 1426495.92 frames.], batch size: 19, lr: 1.97e-04
2022-05-28 20:02:17,348 INFO [train.py:842] (2/4) Epoch 28, batch 4350, loss[loss=0.1799, simple_loss=0.2602, pruned_loss=0.04981, over 7248.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2662, pruned_loss=0.04448, over 1420545.89 frames.], batch size: 20, lr: 1.97e-04
2022-05-28 20:02:56,679 INFO [train.py:842] (2/4) Epoch 28, batch 4400, loss[loss=0.1287, simple_loss=0.2086, pruned_loss=0.02439, over 7001.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2662, pruned_loss=0.0443, over 1421755.11 frames.], batch size: 16, lr: 1.97e-04
2022-05-28 20:03:36,364 INFO [train.py:842] (2/4) Epoch 28, batch 4450, loss[loss=0.1538, simple_loss=0.239, pruned_loss=0.03437, over 7275.00 frames.], tot_loss[loss=0.1766, simple_loss=0.265, pruned_loss=0.0441, over 1426222.79 frames.], batch size: 18, lr: 1.97e-04
2022-05-28 20:04:15,409 INFO [train.py:842] (2/4) Epoch 28, batch 4500, loss[loss=0.1677, simple_loss=0.2673, pruned_loss=0.03405, over 7341.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2658, pruned_loss=0.0445, over 1426263.15 frames.], batch size: 22, lr: 1.97e-04
2022-05-28 20:04:54,890 INFO [train.py:842] (2/4) Epoch 28, batch 4550, loss[loss=0.1587, simple_loss=0.2414, pruned_loss=0.03801, over 7167.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2656, pruned_loss=0.04417, over 1421452.48 frames.], batch size: 18, lr: 1.97e-04
2022-05-28 20:05:33,872 INFO [train.py:842] (2/4) Epoch 28, batch 4600, loss[loss=0.1646, simple_loss=0.2446, pruned_loss=0.04234, over 7168.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2658, pruned_loss=0.04417, over 1424256.05 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:06:13,390 INFO [train.py:842] (2/4) Epoch 28, batch 4650, loss[loss=0.1881, simple_loss=0.2776, pruned_loss=0.04933, over 6737.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2662, pruned_loss=0.04459, over 1424599.48 frames.], batch size: 31, lr: 1.96e-04
2022-05-28 20:06:52,735 INFO [train.py:842] (2/4) Epoch 28, batch 4700, loss[loss=0.1595, simple_loss=0.242, pruned_loss=0.03847, over 7272.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2666, pruned_loss=0.0452, over 1423170.85 frames.], batch size: 18, lr: 1.96e-04
2022-05-28 20:07:32,425 INFO [train.py:842] (2/4) Epoch 28, batch 4750, loss[loss=0.1933, simple_loss=0.2749, pruned_loss=0.05587, over 7207.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2658, pruned_loss=0.04435, over 1422597.73 frames.], batch size: 22, lr: 1.96e-04
2022-05-28 20:08:11,801 INFO [train.py:842] (2/4) Epoch 28, batch 4800, loss[loss=0.161, simple_loss=0.2506, pruned_loss=0.03574, over 6715.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2635, pruned_loss=0.04352, over 1417246.29 frames.], batch size: 31, lr: 1.96e-04
2022-05-28 20:08:51,418 INFO [train.py:842] (2/4) Epoch 28, batch 4850, loss[loss=0.1954, simple_loss=0.2878, pruned_loss=0.05152, over 7328.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2633, pruned_loss=0.04309, over 1420139.45 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:09:30,546 INFO [train.py:842] (2/4) Epoch 28, batch 4900, loss[loss=0.1633, simple_loss=0.2461, pruned_loss=0.04029, over 7331.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2644, pruned_loss=0.04351, over 1421374.46 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:10:10,252 INFO [train.py:842] (2/4) Epoch 28, batch 4950, loss[loss=0.1595, simple_loss=0.2521, pruned_loss=0.03344, over 7336.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2648, pruned_loss=0.04368, over 1422127.73 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:10:49,481 INFO [train.py:842] (2/4) Epoch 28, batch 5000, loss[loss=0.2263, simple_loss=0.3137, pruned_loss=0.06943, over 7314.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2646, pruned_loss=0.04356, over 1426388.39 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:11:28,961 INFO [train.py:842] (2/4) Epoch 28, batch 5050, loss[loss=0.1468, simple_loss=0.2271, pruned_loss=0.03319, over 7279.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2638, pruned_loss=0.0437, over 1419839.92 frames.], batch size: 16, lr: 1.96e-04
2022-05-28 20:12:08,109 INFO [train.py:842] (2/4) Epoch 28, batch 5100, loss[loss=0.1585, simple_loss=0.2552, pruned_loss=0.03086, over 7233.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2639, pruned_loss=0.04374, over 1417337.57 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:12:47,611 INFO [train.py:842] (2/4) Epoch 28, batch 5150, loss[loss=0.1743, simple_loss=0.2575, pruned_loss=0.04551, over 7271.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2628, pruned_loss=0.04346, over 1416138.90 frames.], batch size: 18, lr: 1.96e-04
2022-05-28 20:13:26,657 INFO [train.py:842] (2/4) Epoch 28, batch 5200, loss[loss=0.1689, simple_loss=0.2651, pruned_loss=0.03633, over 7312.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2638, pruned_loss=0.04442, over 1418084.43 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:14:06,371 INFO [train.py:842] (2/4) Epoch 28, batch 5250, loss[loss=0.156, simple_loss=0.24, pruned_loss=0.03595, over 7361.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2631, pruned_loss=0.04385, over 1420117.24 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:14:45,248 INFO [train.py:842] (2/4) Epoch 28, batch 5300, loss[loss=0.2063, simple_loss=0.2948, pruned_loss=0.05896, over 7378.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2651, pruned_loss=0.04499, over 1415217.43 frames.], batch size: 23, lr: 1.96e-04
2022-05-28 20:15:25,173 INFO [train.py:842] (2/4) Epoch 28, batch 5350, loss[loss=0.1745, simple_loss=0.2532, pruned_loss=0.04789, over 7385.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2645, pruned_loss=0.04451, over 1417103.31 frames.], batch size: 23, lr: 1.96e-04
2022-05-28 20:16:04,654 INFO [train.py:842] (2/4) Epoch 28, batch 5400, loss[loss=0.1509, simple_loss=0.2455, pruned_loss=0.02816, over 6709.00 frames.], tot_loss[loss=0.1767, simple_loss=0.264, pruned_loss=0.04474, over 1420528.29 frames.], batch size: 31, lr: 1.96e-04
2022-05-28 20:16:44,120 INFO [train.py:842] (2/4) Epoch 28, batch 5450, loss[loss=0.1735, simple_loss=0.2668, pruned_loss=0.04009, over 7272.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2646, pruned_loss=0.04451, over 1416428.29 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:17:23,298 INFO [train.py:842] (2/4) Epoch 28, batch 5500, loss[loss=0.2409, simple_loss=0.3129, pruned_loss=0.08447, over 7303.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2653, pruned_loss=0.04518, over 1416093.15 frames.], batch size: 24, lr: 1.96e-04
2022-05-28 20:18:02,975 INFO [train.py:842] (2/4) Epoch 28, batch 5550, loss[loss=0.1463, simple_loss=0.2303, pruned_loss=0.03118, over 7272.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2653, pruned_loss=0.04523, over 1421456.91 frames.], batch size: 18, lr: 1.96e-04
2022-05-28 20:18:42,320 INFO [train.py:842] (2/4) Epoch 28, batch 5600, loss[loss=0.2486, simple_loss=0.324, pruned_loss=0.08657, over 7326.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2647, pruned_loss=0.04506, over 1420957.18 frames.], batch size: 22, lr: 1.96e-04
2022-05-28 20:19:22,320 INFO [train.py:842] (2/4) Epoch 28, batch 5650, loss[loss=0.2116, simple_loss=0.2974, pruned_loss=0.06293, over 7342.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2644, pruned_loss=0.04469, over 1426683.68 frames.], batch size: 22, lr: 1.96e-04
2022-05-28 20:20:01,609 INFO [train.py:842] (2/4) Epoch 28, batch 5700, loss[loss=0.1968, simple_loss=0.2869, pruned_loss=0.0533, over 7149.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2645, pruned_loss=0.04523, over 1429492.33 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:20:41,277 INFO [train.py:842] (2/4) Epoch 28, batch 5750, loss[loss=0.1756, simple_loss=0.2631, pruned_loss=0.04409, over 7326.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2661, pruned_loss=0.04609, over 1426917.67 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:21:20,849 INFO [train.py:842] (2/4) Epoch 28, batch 5800, loss[loss=0.2074, simple_loss=0.2931, pruned_loss=0.06082, over 7152.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2656, pruned_loss=0.04566, over 1431154.86 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:22:00,584 INFO [train.py:842] (2/4) Epoch 28, batch 5850, loss[loss=0.1784, simple_loss=0.2643, pruned_loss=0.04623, over 7163.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2644, pruned_loss=0.04507, over 1432813.80 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:22:39,845 INFO [train.py:842] (2/4) Epoch 28, batch 5900, loss[loss=0.153, simple_loss=0.2509, pruned_loss=0.02756, over 7432.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2636, pruned_loss=0.04424, over 1435789.80 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:23:19,385 INFO [train.py:842] (2/4) Epoch 28, batch 5950, loss[loss=0.242, simple_loss=0.3209, pruned_loss=0.08151, over 7328.00 frames.], tot_loss[loss=0.177, simple_loss=0.2649, pruned_loss=0.04459, over 1436342.79 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:23:58,872 INFO [train.py:842] (2/4) Epoch 28, batch 6000, loss[loss=0.1985, simple_loss=0.2899, pruned_loss=0.05356, over 7224.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2635, pruned_loss=0.04409, over 1436084.02 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:23:58,873 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 20:24:08,607 INFO [train.py:871] (2/4) Epoch 28, validation: loss=0.1653, simple_loss=0.2626, pruned_loss=0.03405, over 868885.00 frames.
2022-05-28 20:24:48,317 INFO [train.py:842] (2/4) Epoch 28, batch 6050, loss[loss=0.2213, simple_loss=0.2949, pruned_loss=0.07386, over 7028.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2641, pruned_loss=0.04439, over 1431853.15 frames.], batch size: 28, lr: 1.96e-04
2022-05-28 20:25:27,638 INFO [train.py:842] (2/4) Epoch 28, batch 6100, loss[loss=0.151, simple_loss=0.2386, pruned_loss=0.03172, over 7063.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2641, pruned_loss=0.04466, over 1429720.33 frames.], batch size: 18, lr: 1.96e-04
2022-05-28 20:26:07,236 INFO [train.py:842] (2/4) Epoch 28, batch 6150, loss[loss=0.223, simple_loss=0.3012, pruned_loss=0.07238, over 7292.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2641, pruned_loss=0.04442, over 1429642.67 frames.], batch size: 25, lr: 1.96e-04
2022-05-28 20:26:46,796 INFO [train.py:842] (2/4) Epoch 28, batch 6200, loss[loss=0.2195, simple_loss=0.3016, pruned_loss=0.06868, over 7203.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2646, pruned_loss=0.04492, over 1427113.55 frames.], batch size: 22, lr: 1.96e-04
2022-05-28 20:27:26,685 INFO [train.py:842] (2/4) Epoch 28, batch 6250, loss[loss=0.174, simple_loss=0.2599, pruned_loss=0.044, over 7254.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2646, pruned_loss=0.04493, over 1426295.26 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:28:17,031 INFO [train.py:842] (2/4) Epoch 28, batch 6300, loss[loss=0.1796, simple_loss=0.2724, pruned_loss=0.04342, over 7217.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2645, pruned_loss=0.04496, over 1423449.06 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:28:56,525 INFO [train.py:842] (2/4) Epoch 28, batch 6350, loss[loss=0.1853, simple_loss=0.2714, pruned_loss=0.04966, over 7146.00 frames.], tot_loss[loss=0.1777, simple_loss=0.265, pruned_loss=0.04517, over 1420573.65 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:29:35,525 INFO [train.py:842] (2/4) Epoch 28, batch 6400, loss[loss=0.1926, simple_loss=0.2876, pruned_loss=0.04874, over 7156.00 frames.], tot_loss[loss=0.177, simple_loss=0.2646, pruned_loss=0.04465, over 1417961.77 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:30:15,235 INFO [train.py:842] (2/4) Epoch 28, batch 6450, loss[loss=0.1578, simple_loss=0.2455, pruned_loss=0.03507, over 7367.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2643, pruned_loss=0.04468, over 1413907.52 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:30:54,477 INFO [train.py:842] (2/4) Epoch 28, batch 6500, loss[loss=0.1692, simple_loss=0.2674, pruned_loss=0.03551, over 7142.00 frames.], tot_loss[loss=0.1787, simple_loss=0.266, pruned_loss=0.04569, over 1414845.18 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:31:33,940 INFO [train.py:842] (2/4) Epoch 28, batch 6550, loss[loss=0.2138, simple_loss=0.291, pruned_loss=0.06829, over 4516.00 frames.], tot_loss[loss=0.179, simple_loss=0.2665, pruned_loss=0.04578, over 1413128.92 frames.], batch size: 52, lr: 1.96e-04
2022-05-28 20:32:34,854 INFO [train.py:842] (2/4) Epoch 28, batch 6600, loss[loss=0.1587, simple_loss=0.237, pruned_loss=0.04023, over 7137.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2672, pruned_loss=0.04665, over 1410268.71 frames.], batch size: 17, lr: 1.96e-04
2022-05-28 20:33:14,396 INFO [train.py:842] (2/4) Epoch 28, batch 6650, loss[loss=0.2007, simple_loss=0.2935, pruned_loss=0.05401, over 7193.00 frames.], tot_loss[loss=0.1786, simple_loss=0.266, pruned_loss=0.04559, over 1414642.49 frames.], batch size: 23, lr: 1.96e-04
2022-05-28 20:33:53,584 INFO [train.py:842] (2/4) Epoch 28, batch 6700, loss[loss=0.2222, simple_loss=0.3115, pruned_loss=0.06645, over 7243.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2665, pruned_loss=0.04531, over 1420429.17 frames.], batch size: 26, lr: 1.96e-04
2022-05-28 20:34:33,412 INFO [train.py:842] (2/4) Epoch 28, batch 6750, loss[loss=0.1722, simple_loss=0.264, pruned_loss=0.04021, over 7390.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2655, pruned_loss=0.0448, over 1423011.43 frames.], batch size: 23, lr: 1.96e-04
2022-05-28 20:35:12,641 INFO [train.py:842] (2/4) Epoch 28, batch 6800, loss[loss=0.1612, simple_loss=0.2502, pruned_loss=0.03609, over 7301.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2657, pruned_loss=0.04478, over 1424420.06 frames.], batch size: 18, lr: 1.96e-04
2022-05-28 20:35:52,294 INFO [train.py:842] (2/4) Epoch 28, batch 6850, loss[loss=0.1748, simple_loss=0.2594, pruned_loss=0.0451, over 7055.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2663, pruned_loss=0.04492, over 1419219.65 frames.], batch size: 28, lr: 1.96e-04
2022-05-28 20:36:31,675 INFO [train.py:842] (2/4) Epoch 28, batch 6900, loss[loss=0.1677, simple_loss=0.2647, pruned_loss=0.03535, over 7119.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2661, pruned_loss=0.04509, over 1420097.73 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:37:11,519 INFO [train.py:842] (2/4) Epoch 28, batch 6950, loss[loss=0.1726, simple_loss=0.2649, pruned_loss=0.04014, over 7293.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2651, pruned_loss=0.04464, over 1422373.70 frames.], batch size: 25, lr: 1.96e-04
2022-05-28 20:37:50,742 INFO [train.py:842] (2/4) Epoch 28, batch 7000, loss[loss=0.2041, simple_loss=0.2913, pruned_loss=0.0584, over 7145.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2645, pruned_loss=0.04443, over 1423171.85 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:38:30,281 INFO [train.py:842] (2/4) Epoch 28, batch 7050, loss[loss=0.177, simple_loss=0.2664, pruned_loss=0.04382, over 7359.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2635, pruned_loss=0.04372, over 1422825.06 frames.], batch size: 19, lr: 1.96e-04
2022-05-28 20:39:09,597 INFO [train.py:842] (2/4) Epoch 28, batch 7100, loss[loss=0.1817, simple_loss=0.2662, pruned_loss=0.04857, over 7429.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2633, pruned_loss=0.04345, over 1425239.94 frames.], batch size: 20, lr: 1.96e-04
2022-05-28 20:39:49,129 INFO [train.py:842] (2/4) Epoch 28, batch 7150, loss[loss=0.1498, simple_loss=0.2443, pruned_loss=0.02767, over 7113.00 frames.], tot_loss[loss=0.1771, simple_loss=0.265, pruned_loss=0.04453, over 1424898.60 frames.], batch size: 21, lr: 1.96e-04
2022-05-28 20:40:28,340 INFO [train.py:842] (2/4) Epoch 28, batch 7200, loss[loss=0.1477, simple_loss=0.23, pruned_loss=0.03275, over 7020.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2663, pruned_loss=0.0453, over 1422058.48 frames.], batch size: 16, lr: 1.95e-04
2022-05-28 20:41:07,987 INFO [train.py:842] (2/4) Epoch 28, batch 7250, loss[loss=0.1583, simple_loss=0.2455, pruned_loss=0.03554, over 6803.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2655, pruned_loss=0.04489, over 1419415.97 frames.], batch size: 15, lr: 1.95e-04
2022-05-28 20:41:46,963 INFO [train.py:842] (2/4) Epoch 28, batch 7300, loss[loss=0.1723, simple_loss=0.2615, pruned_loss=0.04151, over 7227.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2651, pruned_loss=0.04462, over 1418895.91 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 20:42:26,428 INFO [train.py:842] (2/4) Epoch 28, batch 7350, loss[loss=0.1839, simple_loss=0.2667, pruned_loss=0.05054, over 7171.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2658, pruned_loss=0.04482, over 1421302.78 frames.], batch size: 18, lr: 1.95e-04
2022-05-28 20:43:05,477 INFO [train.py:842] (2/4) Epoch 28, batch 7400, loss[loss=0.1597, simple_loss=0.252, pruned_loss=0.03368, over 7322.00 frames.], tot_loss[loss=0.1793, simple_loss=0.267, pruned_loss=0.04587, over 1417552.62 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 20:43:45,186 INFO [train.py:842] (2/4) Epoch 28, batch 7450, loss[loss=0.1998, simple_loss=0.2999, pruned_loss=0.04983, over 7427.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2671, pruned_loss=0.0456, over 1423142.91 frames.], batch size: 20, lr: 1.95e-04
2022-05-28 20:44:24,583 INFO [train.py:842] (2/4) Epoch 28, batch 7500, loss[loss=0.1902, simple_loss=0.2837, pruned_loss=0.04837, over 7179.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2679, pruned_loss=0.04581, over 1425447.77 frames.], batch size: 26, lr: 1.95e-04
2022-05-28 20:45:04,407 INFO [train.py:842] (2/4) Epoch 28, batch 7550, loss[loss=0.1665, simple_loss=0.261, pruned_loss=0.03596, over 7322.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2675, pruned_loss=0.04588, over 1427089.75 frames.], batch size: 22, lr: 1.95e-04
2022-05-28 20:45:43,643 INFO [train.py:842] (2/4) Epoch 28, batch 7600, loss[loss=0.1567, simple_loss=0.2457, pruned_loss=0.03389, over 6787.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2663, pruned_loss=0.04548, over 1426786.84 frames.], batch size: 15, lr: 1.95e-04
2022-05-28 20:46:23,174 INFO [train.py:842] (2/4) Epoch 28, batch 7650, loss[loss=0.1715, simple_loss=0.2517, pruned_loss=0.04566, over 6850.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2663, pruned_loss=0.04499, over 1426822.26 frames.], batch size: 15, lr: 1.95e-04
2022-05-28 20:47:02,345 INFO [train.py:842] (2/4) Epoch 28, batch 7700, loss[loss=0.1858, simple_loss=0.2766, pruned_loss=0.0475, over 7204.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2669, pruned_loss=0.04533, over 1425793.61 frames.], batch size: 22, lr: 1.95e-04
2022-05-28 20:47:42,012 INFO [train.py:842] (2/4) Epoch 28, batch 7750, loss[loss=0.1464, simple_loss=0.2336, pruned_loss=0.02956, over 7156.00 frames.], tot_loss[loss=0.1769, simple_loss=0.265, pruned_loss=0.04437, over 1421174.41 frames.], batch size: 18, lr: 1.95e-04
2022-05-28 20:48:21,318 INFO [train.py:842] (2/4) Epoch 28, batch 7800, loss[loss=0.1702, simple_loss=0.2708, pruned_loss=0.03484, over 7313.00 frames.], tot_loss[loss=0.178, simple_loss=0.2662, pruned_loss=0.0449, over 1424580.59 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 20:49:04,096 INFO [train.py:842] (2/4) Epoch 28, batch 7850, loss[loss=0.1698, simple_loss=0.262, pruned_loss=0.03874, over 6683.00 frames.], tot_loss[loss=0.179, simple_loss=0.2669, pruned_loss=0.04557, over 1424286.52 frames.], batch size: 31, lr: 1.95e-04
2022-05-28 20:49:43,306 INFO [train.py:842] (2/4) Epoch 28, batch 7900, loss[loss=0.1655, simple_loss=0.2688, pruned_loss=0.0311, over 7432.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2663, pruned_loss=0.04481, over 1425882.22 frames.], batch size: 20, lr: 1.95e-04
2022-05-28 20:50:22,488 INFO [train.py:842] (2/4) Epoch 28, batch 7950, loss[loss=0.1524, simple_loss=0.2431, pruned_loss=0.03084, over 7323.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2658, pruned_loss=0.04473, over 1422178.94 frames.], batch size: 20, lr: 1.95e-04
2022-05-28 20:51:01,512 INFO [train.py:842] (2/4) Epoch 28, batch 8000, loss[loss=0.2154, simple_loss=0.3079, pruned_loss=0.06148, over 7118.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2652, pruned_loss=0.0442, over 1419991.40 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 20:51:40,843 INFO [train.py:842] (2/4) Epoch 28, batch 8050, loss[loss=0.144, simple_loss=0.2406, pruned_loss=0.02373, over 7229.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2647, pruned_loss=0.04435, over 1419466.15 frames.], batch size: 20, lr: 1.95e-04
2022-05-28 20:52:19,872 INFO [train.py:842] (2/4) Epoch 28, batch 8100, loss[loss=0.1531, simple_loss=0.2557, pruned_loss=0.0252, over 7295.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2655, pruned_loss=0.04452, over 1420155.04 frames.], batch size: 24, lr: 1.95e-04
2022-05-28 20:52:59,280 INFO [train.py:842] (2/4) Epoch 28, batch 8150, loss[loss=0.1697, simple_loss=0.2512, pruned_loss=0.04411, over 7391.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2662, pruned_loss=0.04463, over 1415052.36 frames.], batch size: 18, lr: 1.95e-04
2022-05-28 20:53:38,459 INFO [train.py:842] (2/4) Epoch 28, batch 8200, loss[loss=0.1709, simple_loss=0.2629, pruned_loss=0.03941, over 7274.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2665, pruned_loss=0.04487, over 1418020.07 frames.], batch size: 24, lr: 1.95e-04
2022-05-28 20:54:18,193 INFO [train.py:842] (2/4) Epoch 28, batch 8250, loss[loss=0.1812, simple_loss=0.2736, pruned_loss=0.04445, over 7353.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2658, pruned_loss=0.04418, over 1420082.22 frames.], batch size: 22, lr: 1.95e-04
2022-05-28 20:54:57,234 INFO [train.py:842] (2/4) Epoch 28, batch 8300, loss[loss=0.1633, simple_loss=0.2533, pruned_loss=0.03662, over 7217.00 frames.], tot_loss[loss=0.177, simple_loss=0.2655, pruned_loss=0.04421, over 1422242.53 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 20:55:36,901 INFO [train.py:842] (2/4) Epoch 28, batch 8350, loss[loss=0.1634, simple_loss=0.2545, pruned_loss=0.03615, over 6877.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2665, pruned_loss=0.04453, over 1424077.26 frames.], batch size: 15, lr: 1.95e-04
2022-05-28 20:56:16,235 INFO [train.py:842] (2/4) Epoch 28, batch 8400, loss[loss=0.1497, simple_loss=0.2275, pruned_loss=0.03596, over 7146.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2662, pruned_loss=0.04452, over 1423860.08 frames.], batch size: 17, lr: 1.95e-04
2022-05-28 20:56:55,847 INFO [train.py:842] (2/4) Epoch 28, batch 8450, loss[loss=0.1374, simple_loss=0.2284, pruned_loss=0.02318, over 7144.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2669, pruned_loss=0.04497, over 1418629.23 frames.], batch size: 17, lr: 1.95e-04
2022-05-28 20:57:34,963 INFO [train.py:842] (2/4) Epoch 28, batch 8500, loss[loss=0.2006, simple_loss=0.2966, pruned_loss=0.05231, over 7373.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2677, pruned_loss=0.04531, over 1416462.32 frames.], batch size: 23, lr: 1.95e-04
2022-05-28 20:58:14,419 INFO [train.py:842] (2/4) Epoch 28, batch 8550, loss[loss=0.2186, simple_loss=0.3063, pruned_loss=0.0654, over 7390.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2678, pruned_loss=0.04575, over 1413978.48 frames.], batch size: 23, lr: 1.95e-04
2022-05-28 20:58:53,559 INFO [train.py:842] (2/4) Epoch 28, batch 8600, loss[loss=0.1872, simple_loss=0.2697, pruned_loss=0.05231, over 7375.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2679, pruned_loss=0.04585, over 1408924.71 frames.], batch size: 23, lr: 1.95e-04
2022-05-28 20:59:32,621 INFO [train.py:842] (2/4) Epoch 28, batch 8650, loss[loss=0.1812, simple_loss=0.2705, pruned_loss=0.04594, over 7427.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2693, pruned_loss=0.04608, over 1409023.57 frames.], batch size: 20, lr: 1.95e-04
2022-05-28 21:00:11,770 INFO [train.py:842] (2/4) Epoch 28, batch 8700, loss[loss=0.186, simple_loss=0.2862, pruned_loss=0.04289, over 6272.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2672, pruned_loss=0.04489, over 1410843.49 frames.], batch size: 37, lr: 1.95e-04
2022-05-28 21:00:51,063 INFO [train.py:842] (2/4) Epoch 28, batch 8750, loss[loss=0.169, simple_loss=0.2647, pruned_loss=0.03665, over 7116.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2674, pruned_loss=0.04477, over 1409177.87 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 21:01:30,136 INFO [train.py:842] (2/4) Epoch 28, batch 8800, loss[loss=0.1794, simple_loss=0.2807, pruned_loss=0.03907, over 7365.00 frames.], tot_loss[loss=0.179, simple_loss=0.2681, pruned_loss=0.04492, over 1406063.08 frames.], batch size: 23, lr: 1.95e-04
2022-05-28 21:02:09,481 INFO [train.py:842] (2/4) Epoch 28, batch 8850, loss[loss=0.1494, simple_loss=0.2385, pruned_loss=0.03015, over 7161.00 frames.], tot_loss[loss=0.178, simple_loss=0.2674, pruned_loss=0.04436, over 1404994.31 frames.], batch size: 26, lr: 1.95e-04
2022-05-28 21:02:48,607 INFO [train.py:842] (2/4) Epoch 28, batch 8900, loss[loss=0.2178, simple_loss=0.2912, pruned_loss=0.0722, over 4888.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2682, pruned_loss=0.04526, over 1393564.27 frames.], batch size: 52, lr: 1.95e-04
2022-05-28 21:03:28,090 INFO [train.py:842] (2/4) Epoch 28, batch 8950, loss[loss=0.1654, simple_loss=0.26, pruned_loss=0.03541, over 7120.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2668, pruned_loss=0.04493, over 1390335.43 frames.], batch size: 21, lr: 1.95e-04
2022-05-28 21:04:07,013 INFO [train.py:842] (2/4) Epoch 28, batch 9000, loss[loss=0.1823, simple_loss=0.2724, pruned_loss=0.04613, over 7161.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2669, pruned_loss=0.04512, over 1382174.80 frames.], batch size: 19, lr: 1.95e-04
2022-05-28 21:04:07,014 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 21:04:16,595 INFO [train.py:871] (2/4) Epoch 28, validation: loss=0.1632, simple_loss=0.2611, pruned_loss=0.03268, over 868885.00 frames.
2022-05-28 21:04:55,658 INFO [train.py:842] (2/4) Epoch 28, batch 9050, loss[loss=0.1824, simple_loss=0.2704, pruned_loss=0.04718, over 6439.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2664, pruned_loss=0.04585, over 1360307.16 frames.], batch size: 37, lr: 1.95e-04
2022-05-28 21:05:34,580 INFO [train.py:842] (2/4) Epoch 28, batch 9100, loss[loss=0.1853, simple_loss=0.2706, pruned_loss=0.04998, over 5160.00 frames.], tot_loss[loss=0.179, simple_loss=0.2661, pruned_loss=0.0459, over 1332919.71 frames.], batch size: 53, lr: 1.95e-04
2022-05-28 21:06:12,605 INFO [train.py:842] (2/4) Epoch 28, batch 9150, loss[loss=0.1888, simple_loss=0.2823, pruned_loss=0.04771, over 6397.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2688, pruned_loss=0.04716, over 1300615.54 frames.], batch size: 37, lr: 1.95e-04
2022-05-28 21:07:05,011 INFO [train.py:842] (2/4) Epoch 29, batch 0, loss[loss=0.2167, simple_loss=0.3171, pruned_loss=0.0581, over 7109.00 frames.], tot_loss[loss=0.2167, simple_loss=0.3171, pruned_loss=0.0581, over 7109.00 frames.], batch size: 28, lr: 1.91e-04
2022-05-28 21:07:44,648 INFO [train.py:842] (2/4) Epoch 29, batch 50, loss[loss=0.1753, simple_loss=0.2722, pruned_loss=0.03924, over 7307.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2685, pruned_loss=0.04682, over 323614.62 frames.], batch size: 24, lr: 1.91e-04
2022-05-28 21:08:24,030 INFO [train.py:842] (2/4) Epoch 29, batch 100, loss[loss=0.1932, simple_loss=0.2833, pruned_loss=0.05156, over 7311.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2675, pruned_loss=0.04636, over 569859.82 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:09:03,647 INFO [train.py:842] (2/4) Epoch 29, batch 150, loss[loss=0.2106, simple_loss=0.2893, pruned_loss=0.06594, over 7236.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2677, pruned_loss=0.0458, over 759875.91 frames.], batch size: 20, lr: 1.91e-04
2022-05-28 21:09:43,194 INFO [train.py:842] (2/4) Epoch 29, batch 200, loss[loss=0.1636, simple_loss=0.2472, pruned_loss=0.04004, over 7069.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2656, pruned_loss=0.04478, over 909047.07 frames.], batch size: 18, lr: 1.91e-04
2022-05-28 21:10:22,666 INFO [train.py:842] (2/4) Epoch 29, batch 250, loss[loss=0.1998, simple_loss=0.2942, pruned_loss=0.05277, over 5121.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2667, pruned_loss=0.04548, over 1019598.17 frames.], batch size: 53, lr: 1.91e-04
2022-05-28 21:11:01,719 INFO [train.py:842] (2/4) Epoch 29, batch 300, loss[loss=0.1962, simple_loss=0.2756, pruned_loss=0.05839, over 7176.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2662, pruned_loss=0.04554, over 1108926.38 frames.], batch size: 18, lr: 1.91e-04
2022-05-28 21:11:41,208 INFO [train.py:842] (2/4) Epoch 29, batch 350, loss[loss=0.1481, simple_loss=0.2374, pruned_loss=0.0294, over 7062.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2658, pruned_loss=0.04448, over 1180576.11 frames.], batch size: 18, lr: 1.91e-04
2022-05-28 21:12:20,484 INFO [train.py:842] (2/4) Epoch 29, batch 400, loss[loss=0.2459, simple_loss=0.3186, pruned_loss=0.08657, over 7142.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2656, pruned_loss=0.04441, over 1235781.75 frames.], batch size: 20, lr: 1.91e-04
2022-05-28 21:13:00,122 INFO [train.py:842] (2/4) Epoch 29, batch 450, loss[loss=0.1486, simple_loss=0.2454, pruned_loss=0.0259, over 7113.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2648, pruned_loss=0.04379, over 1281903.46 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:13:39,176 INFO [train.py:842] (2/4) Epoch 29, batch 500, loss[loss=0.1648, simple_loss=0.2543, pruned_loss=0.03767, over 4957.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2643, pruned_loss=0.0437, over 1309564.09 frames.], batch size: 52, lr: 1.91e-04
2022-05-28 21:14:18,723 INFO [train.py:842] (2/4) Epoch 29, batch 550, loss[loss=0.1703, simple_loss=0.2614, pruned_loss=0.03955, over 7215.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2647, pruned_loss=0.04408, over 1332362.50 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:14:58,120 INFO [train.py:842] (2/4) Epoch 29, batch 600, loss[loss=0.18, simple_loss=0.2603, pruned_loss=0.04986, over 7259.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2648, pruned_loss=0.04473, over 1349421.02 frames.], batch size: 19, lr: 1.91e-04
2022-05-28 21:15:37,661 INFO [train.py:842] (2/4) Epoch 29, batch 650, loss[loss=0.1853, simple_loss=0.2761, pruned_loss=0.04722, over 7066.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2649, pruned_loss=0.04487, over 1368005.37 frames.], batch size: 18, lr: 1.91e-04
2022-05-28 21:16:17,140 INFO [train.py:842] (2/4) Epoch 29, batch 700, loss[loss=0.1945, simple_loss=0.2766, pruned_loss=0.05617, over 5147.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2655, pruned_loss=0.04496, over 1375901.99 frames.], batch size: 52, lr: 1.91e-04
2022-05-28 21:16:56,515 INFO [train.py:842] (2/4) Epoch 29, batch 750, loss[loss=0.1489, simple_loss=0.235, pruned_loss=0.03144, over 7427.00 frames.], tot_loss[loss=0.1768, simple_loss=0.265, pruned_loss=0.04425, over 1383318.31 frames.], batch size: 20, lr: 1.91e-04
2022-05-28 21:17:35,593 INFO [train.py:842] (2/4) Epoch 29, batch 800, loss[loss=0.1534, simple_loss=0.2452, pruned_loss=0.0308, over 7112.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2654, pruned_loss=0.04463, over 1389449.52 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:18:15,125 INFO [train.py:842] (2/4) Epoch 29, batch 850, loss[loss=0.178, simple_loss=0.2647, pruned_loss=0.0457, over 6491.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2664, pruned_loss=0.04523, over 1393632.83 frames.], batch size: 38, lr: 1.91e-04
2022-05-28 21:18:54,078 INFO [train.py:842] (2/4) Epoch 29, batch 900, loss[loss=0.159, simple_loss=0.2545, pruned_loss=0.03176, over 6879.00 frames.], tot_loss[loss=0.1788, simple_loss=0.267, pruned_loss=0.04533, over 1400430.84 frames.], batch size: 31, lr: 1.91e-04
2022-05-28 21:19:33,690 INFO [train.py:842] (2/4) Epoch 29, batch 950, loss[loss=0.1672, simple_loss=0.2533, pruned_loss=0.04051, over 7211.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2661, pruned_loss=0.04517, over 1409232.45 frames.], batch size: 22, lr: 1.91e-04
2022-05-28 21:20:13,153 INFO [train.py:842] (2/4) Epoch 29, batch 1000, loss[loss=0.1747, simple_loss=0.2521, pruned_loss=0.04861, over 6842.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2646, pruned_loss=0.04474, over 1415533.86 frames.], batch size: 15, lr: 1.91e-04
2022-05-28 21:20:52,890 INFO [train.py:842] (2/4) Epoch 29, batch 1050, loss[loss=0.181, simple_loss=0.2716, pruned_loss=0.04525, over 7417.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2651, pruned_loss=0.04489, over 1420682.42 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:21:32,449 INFO [train.py:842] (2/4) Epoch 29, batch 1100, loss[loss=0.14, simple_loss=0.2166, pruned_loss=0.03167, over 7288.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2656, pruned_loss=0.0453, over 1423470.73 frames.], batch size: 17, lr: 1.91e-04
2022-05-28 21:22:11,772 INFO [train.py:842] (2/4) Epoch 29, batch 1150, loss[loss=0.173, simple_loss=0.2603, pruned_loss=0.04288, over 7065.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2663, pruned_loss=0.04533, over 1422235.87 frames.], batch size: 28, lr: 1.91e-04
2022-05-28 21:22:50,766 INFO [train.py:842] (2/4) Epoch 29, batch 1200, loss[loss=0.176, simple_loss=0.2714, pruned_loss=0.04032, over 7050.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2666, pruned_loss=0.04501, over 1423895.81 frames.], batch size: 28, lr: 1.91e-04
2022-05-28 21:23:30,549 INFO [train.py:842] (2/4) Epoch 29, batch 1250, loss[loss=0.1787, simple_loss=0.2827, pruned_loss=0.03733, over 7199.00 frames.], tot_loss[loss=0.177, simple_loss=0.2653, pruned_loss=0.04436, over 1417867.37 frames.], batch size: 22, lr: 1.91e-04
2022-05-28 21:24:09,895 INFO [train.py:842] (2/4) Epoch 29, batch 1300, loss[loss=0.1812, simple_loss=0.285, pruned_loss=0.03868, over 7146.00 frames.], tot_loss[loss=0.176, simple_loss=0.2643, pruned_loss=0.04386, over 1421055.74 frames.], batch size: 20, lr: 1.91e-04
2022-05-28 21:24:49,641 INFO [train.py:842] (2/4) Epoch 29, batch 1350, loss[loss=0.1942, simple_loss=0.2923, pruned_loss=0.04803, over 7113.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2641, pruned_loss=0.04361, over 1426470.59 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:25:29,113 INFO [train.py:842] (2/4) Epoch 29, batch 1400, loss[loss=0.1495, simple_loss=0.2288, pruned_loss=0.03513, over 7284.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2647, pruned_loss=0.04404, over 1427477.70 frames.], batch size: 17, lr: 1.91e-04
2022-05-28 21:26:08,823 INFO [train.py:842] (2/4) Epoch 29, batch 1450, loss[loss=0.1545, simple_loss=0.2542, pruned_loss=0.02738, over 7286.00 frames.], tot_loss[loss=0.1752, simple_loss=0.264, pruned_loss=0.0432, over 1431179.75 frames.], batch size: 24, lr: 1.91e-04
2022-05-28 21:26:47,903 INFO [train.py:842] (2/4) Epoch 29, batch 1500, loss[loss=0.1769, simple_loss=0.2762, pruned_loss=0.03879, over 7322.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2644, pruned_loss=0.04355, over 1428569.48 frames.], batch size: 20, lr: 1.91e-04
2022-05-28 21:27:27,456 INFO [train.py:842] (2/4) Epoch 29, batch 1550, loss[loss=0.1818, simple_loss=0.2701, pruned_loss=0.04671, over 7218.00 frames.], tot_loss[loss=0.1761, simple_loss=0.265, pruned_loss=0.04362, over 1430192.37 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:28:06,653 INFO [train.py:842] (2/4) Epoch 29, batch 1600, loss[loss=0.1534, simple_loss=0.2392, pruned_loss=0.0338, over 7253.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2659, pruned_loss=0.04396, over 1428016.44 frames.], batch size: 16, lr: 1.91e-04
2022-05-28 21:28:46,409 INFO [train.py:842] (2/4) Epoch 29, batch 1650, loss[loss=0.1751, simple_loss=0.2603, pruned_loss=0.04495, over 6736.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2645, pruned_loss=0.0434, over 1429407.70 frames.], batch size: 15, lr: 1.91e-04
2022-05-28 21:29:25,920 INFO [train.py:842] (2/4) Epoch 29, batch 1700, loss[loss=0.1707, simple_loss=0.2619, pruned_loss=0.03973, over 7264.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2645, pruned_loss=0.04393, over 1431701.42 frames.], batch size: 19, lr: 1.91e-04
2022-05-28 21:30:05,644 INFO [train.py:842] (2/4) Epoch 29, batch 1750, loss[loss=0.1651, simple_loss=0.2541, pruned_loss=0.03809, over 7115.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2635, pruned_loss=0.04334, over 1433477.41 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:30:44,892 INFO [train.py:842] (2/4) Epoch 29, batch 1800, loss[loss=0.15, simple_loss=0.2288, pruned_loss=0.03564, over 6986.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2637, pruned_loss=0.04389, over 1423700.84 frames.], batch size: 16, lr: 1.91e-04
2022-05-28 21:31:24,544 INFO [train.py:842] (2/4) Epoch 29, batch 1850, loss[loss=0.1575, simple_loss=0.2384, pruned_loss=0.03826, over 7402.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2651, pruned_loss=0.04469, over 1425398.24 frames.], batch size: 18, lr: 1.91e-04
2022-05-28 21:32:03,711 INFO [train.py:842] (2/4) Epoch 29, batch 1900, loss[loss=0.167, simple_loss=0.2551, pruned_loss=0.03945, over 7178.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2642, pruned_loss=0.04382, over 1426370.38 frames.], batch size: 26, lr: 1.91e-04
2022-05-28 21:32:43,288 INFO [train.py:842] (2/4) Epoch 29, batch 1950, loss[loss=0.1968, simple_loss=0.2857, pruned_loss=0.05393, over 7340.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2647, pruned_loss=0.04416, over 1428637.91 frames.], batch size: 25, lr: 1.91e-04
2022-05-28 21:33:22,687 INFO [train.py:842] (2/4) Epoch 29, batch 2000, loss[loss=0.1862, simple_loss=0.274, pruned_loss=0.04925, over 7176.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2652, pruned_loss=0.04492, over 1431455.75 frames.], batch size: 23, lr: 1.91e-04
2022-05-28 21:34:02,047 INFO [train.py:842] (2/4) Epoch 29, batch 2050, loss[loss=0.1746, simple_loss=0.2701, pruned_loss=0.03953, over 7310.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2659, pruned_loss=0.04525, over 1424623.77 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:34:41,395 INFO [train.py:842] (2/4) Epoch 29, batch 2100, loss[loss=0.2266, simple_loss=0.3099, pruned_loss=0.07168, over 7312.00 frames.], tot_loss[loss=0.1772, simple_loss=0.265, pruned_loss=0.04466, over 1426452.91 frames.], batch size: 25, lr: 1.91e-04
2022-05-28 21:35:21,026 INFO [train.py:842] (2/4) Epoch 29, batch 2150, loss[loss=0.1962, simple_loss=0.2989, pruned_loss=0.04672, over 7232.00 frames.], tot_loss[loss=0.177, simple_loss=0.2653, pruned_loss=0.04432, over 1427218.20 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:35:59,972 INFO [train.py:842] (2/4) Epoch 29, batch 2200, loss[loss=0.1992, simple_loss=0.2972, pruned_loss=0.05054, over 7293.00 frames.], tot_loss[loss=0.177, simple_loss=0.2653, pruned_loss=0.04436, over 1420821.91 frames.], batch size: 25, lr: 1.91e-04
2022-05-28 21:36:39,489 INFO [train.py:842] (2/4) Epoch 29, batch 2250, loss[loss=0.1523, simple_loss=0.2551, pruned_loss=0.02481, over 7118.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2641, pruned_loss=0.04357, over 1424854.75 frames.], batch size: 21, lr: 1.91e-04
2022-05-28 21:37:18,731 INFO [train.py:842] (2/4) Epoch 29, batch 2300, loss[loss=0.1672, simple_loss=0.2531, pruned_loss=0.04063, over 7294.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2638, pruned_loss=0.04368, over 1426258.00 frames.], batch size: 24, lr: 1.91e-04
2022-05-28 21:37:58,191 INFO [train.py:842] (2/4) Epoch 29, batch 2350, loss[loss=0.2219, simple_loss=0.3022, pruned_loss=0.07075, over 7069.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2636, pruned_loss=0.04352, over 1423855.90 frames.], batch size: 18, lr: 1.91e-04
2022-05-28 21:38:37,550 INFO [train.py:842] (2/4) Epoch 29, batch 2400, loss[loss=0.1554, simple_loss=0.2582, pruned_loss=0.02633, over 7363.00 frames.], tot_loss[loss=0.174, simple_loss=0.2623, pruned_loss=0.04285, over 1426038.67 frames.], batch size: 19, lr: 1.90e-04
2022-05-28 21:39:16,874 INFO [train.py:842] (2/4) Epoch 29, batch 2450, loss[loss=0.1743, simple_loss=0.2697, pruned_loss=0.03943, over 7444.00 frames.], tot_loss[loss=0.1758, simple_loss=0.264, pruned_loss=0.04384, over 1416600.39 frames.], batch size: 22, lr: 1.90e-04
2022-05-28 21:39:56,303 INFO [train.py:842] (2/4) Epoch 29, batch 2500, loss[loss=0.1666, simple_loss=0.2436, pruned_loss=0.04478, over 7410.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2634, pruned_loss=0.04351, over 1420116.24 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:40:35,881 INFO [train.py:842] (2/4) Epoch 29, batch 2550, loss[loss=0.1523, simple_loss=0.2439, pruned_loss=0.03029, over 7169.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2635, pruned_loss=0.04376, over 1418007.95 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:41:14,987 INFO [train.py:842] (2/4) Epoch 29, batch 2600, loss[loss=0.2119, simple_loss=0.2937, pruned_loss=0.06501, over 7211.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2639, pruned_loss=0.04368, over 1416188.86 frames.], batch size: 23, lr: 1.90e-04
2022-05-28 21:41:54,608 INFO [train.py:842] (2/4) Epoch 29, batch 2650, loss[loss=0.1635, simple_loss=0.2444, pruned_loss=0.04131, over 7410.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2637, pruned_loss=0.04347, over 1419287.93 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:42:33,897 INFO [train.py:842] (2/4) Epoch 29, batch 2700, loss[loss=0.2336, simple_loss=0.3237, pruned_loss=0.07177, over 5213.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2628, pruned_loss=0.04322, over 1419722.03 frames.], batch size: 54, lr: 1.90e-04
2022-05-28 21:43:13,536 INFO [train.py:842] (2/4) Epoch 29, batch 2750, loss[loss=0.2252, simple_loss=0.3122, pruned_loss=0.06909, over 7318.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2648, pruned_loss=0.04444, over 1415055.24 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 21:43:52,879 INFO [train.py:842] (2/4) Epoch 29, batch 2800, loss[loss=0.1568, simple_loss=0.2601, pruned_loss=0.02682, over 7333.00 frames.], tot_loss[loss=0.1756, simple_loss=0.264, pruned_loss=0.0436, over 1418199.60 frames.], batch size: 22, lr: 1.90e-04
2022-05-28 21:44:32,561 INFO [train.py:842] (2/4) Epoch 29, batch 2850, loss[loss=0.1558, simple_loss=0.2465, pruned_loss=0.03256, over 7263.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2627, pruned_loss=0.04325, over 1418635.18 frames.], batch size: 19, lr: 1.90e-04
2022-05-28 21:45:11,769 INFO [train.py:842] (2/4) Epoch 29, batch 2900, loss[loss=0.1381, simple_loss=0.2211, pruned_loss=0.02752, over 7269.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2618, pruned_loss=0.04242, over 1417068.95 frames.], batch size: 17, lr: 1.90e-04
2022-05-28 21:45:51,700 INFO [train.py:842] (2/4) Epoch 29, batch 2950, loss[loss=0.1594, simple_loss=0.2347, pruned_loss=0.04208, over 7145.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2608, pruned_loss=0.0424, over 1417697.74 frames.], batch size: 17, lr: 1.90e-04
2022-05-28 21:46:30,712 INFO [train.py:842] (2/4) Epoch 29, batch 3000, loss[loss=0.1746, simple_loss=0.2632, pruned_loss=0.04306, over 7230.00 frames.], tot_loss[loss=0.174, simple_loss=0.2623, pruned_loss=0.04287, over 1419166.62 frames.], batch size: 20, lr: 1.90e-04
2022-05-28 21:46:30,713 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 21:46:40,406 INFO [train.py:871] (2/4) Epoch 29, validation: loss=0.1638, simple_loss=0.2614, pruned_loss=0.03305, over 868885.00 frames.
2022-05-28 21:47:20,123 INFO [train.py:842] (2/4) Epoch 29, batch 3050, loss[loss=0.1632, simple_loss=0.2493, pruned_loss=0.03854, over 7163.00 frames.], tot_loss[loss=0.174, simple_loss=0.2621, pruned_loss=0.04296, over 1421822.39 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:47:59,312 INFO [train.py:842] (2/4) Epoch 29, batch 3100, loss[loss=0.1869, simple_loss=0.2552, pruned_loss=0.05931, over 7281.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2622, pruned_loss=0.04313, over 1419048.11 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:48:38,766 INFO [train.py:842] (2/4) Epoch 29, batch 3150, loss[loss=0.1907, simple_loss=0.2869, pruned_loss=0.04725, over 7220.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2635, pruned_loss=0.04357, over 1422438.74 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 21:49:18,080 INFO [train.py:842] (2/4) Epoch 29, batch 3200, loss[loss=0.1799, simple_loss=0.2705, pruned_loss=0.04469, over 7123.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2646, pruned_loss=0.04434, over 1422230.57 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 21:49:57,932 INFO [train.py:842] (2/4) Epoch 29, batch 3250, loss[loss=0.1338, simple_loss=0.221, pruned_loss=0.02328, over 7232.00 frames.], tot_loss[loss=0.1759, simple_loss=0.264, pruned_loss=0.04389, over 1422721.59 frames.], batch size: 16, lr: 1.90e-04
2022-05-28 21:50:37,004 INFO [train.py:842] (2/4) Epoch 29, batch 3300, loss[loss=0.1554, simple_loss=0.2464, pruned_loss=0.03226, over 7224.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2649, pruned_loss=0.04438, over 1421887.63 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 21:51:16,442 INFO [train.py:842] (2/4) Epoch 29, batch 3350, loss[loss=0.1734, simple_loss=0.2637, pruned_loss=0.04153, over 7120.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2647, pruned_loss=0.04459, over 1419312.12 frames.], batch size: 28, lr: 1.90e-04
2022-05-28 21:51:55,684 INFO [train.py:842] (2/4) Epoch 29, batch 3400, loss[loss=0.1829, simple_loss=0.2665, pruned_loss=0.0496, over 7080.00 frames.], tot_loss[loss=0.178, simple_loss=0.2658, pruned_loss=0.04516, over 1418362.95 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:52:35,502 INFO [train.py:842] (2/4) Epoch 29, batch 3450, loss[loss=0.1559, simple_loss=0.2418, pruned_loss=0.03496, over 7266.00 frames.], tot_loss[loss=0.176, simple_loss=0.2641, pruned_loss=0.04393, over 1420934.05 frames.], batch size: 17, lr: 1.90e-04
2022-05-28 21:53:14,678 INFO [train.py:842] (2/4) Epoch 29, batch 3500, loss[loss=0.175, simple_loss=0.2638, pruned_loss=0.04304, over 6755.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2637, pruned_loss=0.04352, over 1420921.05 frames.], batch size: 31, lr: 1.90e-04
2022-05-28 21:53:54,298 INFO [train.py:842] (2/4) Epoch 29, batch 3550, loss[loss=0.1743, simple_loss=0.2555, pruned_loss=0.04653, over 7272.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2622, pruned_loss=0.04252, over 1423775.08 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:54:33,683 INFO [train.py:842] (2/4) Epoch 29, batch 3600, loss[loss=0.1895, simple_loss=0.2586, pruned_loss=0.06016, over 6810.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2637, pruned_loss=0.04358, over 1424312.26 frames.], batch size: 15, lr: 1.90e-04
2022-05-28 21:55:13,258 INFO [train.py:842] (2/4) Epoch 29, batch 3650, loss[loss=0.2161, simple_loss=0.2938, pruned_loss=0.06919, over 7342.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2637, pruned_loss=0.04351, over 1427334.22 frames.], batch size: 22, lr: 1.90e-04
2022-05-28 21:55:52,636 INFO [train.py:842] (2/4) Epoch 29, batch 3700, loss[loss=0.2365, simple_loss=0.333, pruned_loss=0.07003, over 7208.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2642, pruned_loss=0.0436, over 1426802.15 frames.], batch size: 23, lr: 1.90e-04
2022-05-28 21:56:32,178 INFO [train.py:842] (2/4) Epoch 29, batch 3750, loss[loss=0.1933, simple_loss=0.279, pruned_loss=0.05377, over 5211.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2652, pruned_loss=0.04387, over 1426151.61 frames.], batch size: 52, lr: 1.90e-04
2022-05-28 21:57:11,455 INFO [train.py:842] (2/4) Epoch 29, batch 3800, loss[loss=0.158, simple_loss=0.2452, pruned_loss=0.03537, over 7062.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2659, pruned_loss=0.04451, over 1429254.99 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:57:51,284 INFO [train.py:842] (2/4) Epoch 29, batch 3850, loss[loss=0.1283, simple_loss=0.2179, pruned_loss=0.01934, over 6804.00 frames.], tot_loss[loss=0.1766, simple_loss=0.265, pruned_loss=0.04413, over 1426878.26 frames.], batch size: 15, lr: 1.90e-04
2022-05-28 21:58:30,601 INFO [train.py:842] (2/4) Epoch 29, batch 3900, loss[loss=0.1799, simple_loss=0.2615, pruned_loss=0.04916, over 7426.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2657, pruned_loss=0.04429, over 1428934.40 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 21:59:10,096 INFO [train.py:842] (2/4) Epoch 29, batch 3950, loss[loss=0.1743, simple_loss=0.2585, pruned_loss=0.04508, over 7105.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2654, pruned_loss=0.04419, over 1429346.53 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 21:59:49,034 INFO [train.py:842] (2/4) Epoch 29, batch 4000, loss[loss=0.1829, simple_loss=0.2811, pruned_loss=0.04236, over 7117.00 frames.], tot_loss[loss=0.177, simple_loss=0.2655, pruned_loss=0.04425, over 1429136.95 frames.], batch size: 21, lr: 1.90e-04
2022-05-28 22:00:28,501 INFO [train.py:842] (2/4) Epoch 29, batch 4050, loss[loss=0.1759, simple_loss=0.2601, pruned_loss=0.04581, over 7001.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2648, pruned_loss=0.04379, over 1428772.93 frames.], batch size: 28, lr: 1.90e-04
2022-05-28 22:01:07,542 INFO [train.py:842] (2/4) Epoch 29, batch 4100, loss[loss=0.2275, simple_loss=0.3136, pruned_loss=0.07073, over 7099.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2653, pruned_loss=0.04438, over 1429705.08 frames.], batch size: 28, lr: 1.90e-04
2022-05-28 22:01:47,161 INFO [train.py:842] (2/4) Epoch 29, batch 4150, loss[loss=0.2067, simple_loss=0.3107, pruned_loss=0.05136, over 7234.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2654, pruned_loss=0.04443, over 1432021.72 frames.], batch size: 20, lr: 1.90e-04
2022-05-28 22:02:26,325 INFO [train.py:842] (2/4) Epoch 29, batch 4200, loss[loss=0.1997, simple_loss=0.3088, pruned_loss=0.04534, over 7340.00 frames.], tot_loss[loss=0.1777, simple_loss=0.266, pruned_loss=0.04467, over 1427308.73 frames.], batch size: 22, lr: 1.90e-04
2022-05-28 22:03:05,988 INFO [train.py:842] (2/4) Epoch 29, batch 4250, loss[loss=0.1765, simple_loss=0.2609, pruned_loss=0.0461, over 7254.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2664, pruned_loss=0.04457, over 1425715.24 frames.], batch size: 19, lr: 1.90e-04
2022-05-28 22:03:45,578 INFO [train.py:842] (2/4) Epoch 29, batch 4300, loss[loss=0.1611, simple_loss=0.2394, pruned_loss=0.04141, over 6777.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2657, pruned_loss=0.04456, over 1427863.95 frames.], batch size: 15, lr: 1.90e-04
2022-05-28 22:04:25,098 INFO [train.py:842] (2/4) Epoch 29, batch 4350, loss[loss=0.2172, simple_loss=0.2989, pruned_loss=0.06776, over 7165.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2661, pruned_loss=0.0445, over 1429986.29 frames.], batch size: 26, lr: 1.90e-04
2022-05-28 22:05:04,059 INFO [train.py:842] (2/4) Epoch 29, batch 4400, loss[loss=0.2239, simple_loss=0.2992, pruned_loss=0.07435, over 5357.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2665, pruned_loss=0.04503, over 1425948.41 frames.], batch size: 53, lr: 1.90e-04
2022-05-28 22:05:43,312 INFO [train.py:842] (2/4) Epoch 29, batch 4450, loss[loss=0.1571, simple_loss=0.2584, pruned_loss=0.0279, over 6840.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2662, pruned_loss=0.0445, over 1426090.17 frames.], batch size: 31, lr: 1.90e-04
2022-05-28 22:06:22,607 INFO [train.py:842] (2/4) Epoch 29, batch 4500, loss[loss=0.1692, simple_loss=0.2736, pruned_loss=0.03245, over 7325.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2651, pruned_loss=0.04379, over 1428578.81 frames.], batch size: 22, lr: 1.90e-04
2022-05-28 22:07:02,126 INFO [train.py:842] (2/4) Epoch 29, batch 4550, loss[loss=0.1474, simple_loss=0.2266, pruned_loss=0.03403, over 7222.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2642, pruned_loss=0.04349, over 1428364.66 frames.], batch size: 16, lr: 1.90e-04
2022-05-28 22:07:41,329 INFO [train.py:842] (2/4) Epoch 29, batch 4600, loss[loss=0.1637, simple_loss=0.2626, pruned_loss=0.03236, over 7430.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2649, pruned_loss=0.0435, over 1429174.50 frames.], batch size: 20, lr: 1.90e-04
2022-05-28 22:08:21,007 INFO [train.py:842] (2/4) Epoch 29, batch 4650, loss[loss=0.1966, simple_loss=0.2827, pruned_loss=0.0553, over 7196.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2665, pruned_loss=0.04566, over 1428656.34 frames.], batch size: 22, lr: 1.90e-04
2022-05-28 22:09:00,236 INFO [train.py:842] (2/4) Epoch 29, batch 4700, loss[loss=0.18, simple_loss=0.2697, pruned_loss=0.0451, over 7163.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2666, pruned_loss=0.04591, over 1424072.15 frames.], batch size: 19, lr: 1.90e-04
2022-05-28 22:09:50,600 INFO [train.py:842] (2/4) Epoch 29, batch 4750, loss[loss=0.1846, simple_loss=0.2684, pruned_loss=0.05039, over 7310.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2676, pruned_loss=0.04608, over 1423340.35 frames.], batch size: 24, lr: 1.90e-04
2022-05-28 22:10:29,982 INFO [train.py:842] (2/4) Epoch 29, batch 4800, loss[loss=0.1711, simple_loss=0.2558, pruned_loss=0.04323, over 7253.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2671, pruned_loss=0.04637, over 1427214.07 frames.], batch size: 19, lr: 1.90e-04
2022-05-28 22:11:09,584 INFO [train.py:842] (2/4) Epoch 29, batch 4850, loss[loss=0.1749, simple_loss=0.2663, pruned_loss=0.04173, over 7232.00 frames.], tot_loss[loss=0.179, simple_loss=0.2665, pruned_loss=0.04575, over 1427631.67 frames.], batch size: 20, lr: 1.90e-04
2022-05-28 22:11:48,942 INFO [train.py:842] (2/4) Epoch 29, batch 4900, loss[loss=0.1577, simple_loss=0.2427, pruned_loss=0.03639, over 7069.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2647, pruned_loss=0.0443, over 1428098.59 frames.], batch size: 18, lr: 1.90e-04
2022-05-28 22:12:28,557 INFO [train.py:842] (2/4) Epoch 29, batch 4950, loss[loss=0.214, simple_loss=0.3059, pruned_loss=0.06104, over 6431.00 frames.], tot_loss[loss=0.1768, simple_loss=0.265, pruned_loss=0.04437, over 1428004.75 frames.], batch size: 37, lr: 1.90e-04
2022-05-28 22:13:07,898 INFO [train.py:842] (2/4) Epoch 29, batch 5000, loss[loss=0.1854, simple_loss=0.2797, pruned_loss=0.04553, over 7383.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2649, pruned_loss=0.04505, over 1423985.33 frames.], batch size: 23, lr: 1.90e-04
2022-05-28 22:13:47,734 INFO [train.py:842] (2/4) Epoch 29, batch 5050, loss[loss=0.1452, simple_loss=0.2374, pruned_loss=0.02648, over 7282.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2643, pruned_loss=0.04505, over 1430143.28 frames.], batch size: 17, lr: 1.90e-04
2022-05-28 22:14:27,103 INFO [train.py:842] (2/4) Epoch 29, batch 5100, loss[loss=0.1741, simple_loss=0.273, pruned_loss=0.03765, over 7143.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2638, pruned_loss=0.04431, over 1430674.53 frames.], batch size: 20, lr: 1.90e-04
2022-05-28 22:15:06,694 INFO [train.py:842] (2/4) Epoch 29, batch 5150, loss[loss=0.1878, simple_loss=0.2831, pruned_loss=0.04622, over 6313.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2643, pruned_loss=0.04418, over 1431843.27 frames.], batch size: 37, lr: 1.89e-04
2022-05-28 22:15:45,851 INFO [train.py:842] (2/4) Epoch 29, batch 5200, loss[loss=0.1536, simple_loss=0.2483, pruned_loss=0.02948, over 7069.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2634, pruned_loss=0.04404, over 1429827.84 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:16:25,562 INFO [train.py:842] (2/4) Epoch 29, batch 5250, loss[loss=0.1363, simple_loss=0.2353, pruned_loss=0.01865, over 7155.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2636, pruned_loss=0.04371, over 1431362.28 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:17:04,959 INFO [train.py:842] (2/4) Epoch 29, batch 5300, loss[loss=0.1853, simple_loss=0.2784, pruned_loss=0.04612, over 7119.00 frames.], tot_loss[loss=0.1754, simple_loss=0.264, pruned_loss=0.04341, over 1431699.43 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:17:44,492 INFO [train.py:842] (2/4) Epoch 29, batch 5350, loss[loss=0.1464, simple_loss=0.2277, pruned_loss=0.03258, over 7284.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2627, pruned_loss=0.04273, over 1428939.90 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:18:23,822 INFO [train.py:842] (2/4) Epoch 29, batch 5400, loss[loss=0.1646, simple_loss=0.2561, pruned_loss=0.03656, over 7371.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.04227, over 1428174.04 frames.], batch size: 23, lr: 1.89e-04
2022-05-28 22:19:03,666 INFO [train.py:842] (2/4) Epoch 29, batch 5450, loss[loss=0.1878, simple_loss=0.2748, pruned_loss=0.05044, over 7331.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2645, pruned_loss=0.0436, over 1430136.54 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:19:42,619 INFO [train.py:842] (2/4) Epoch 29, batch 5500, loss[loss=0.1512, simple_loss=0.2572, pruned_loss=0.02255, over 7223.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2664, pruned_loss=0.04434, over 1430314.82 frames.], batch size: 22, lr: 1.89e-04
2022-05-28 22:20:22,050 INFO [train.py:842] (2/4) Epoch 29, batch 5550, loss[loss=0.2278, simple_loss=0.3014, pruned_loss=0.07715, over 4664.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2659, pruned_loss=0.04442, over 1426247.17 frames.], batch size: 52, lr: 1.89e-04
2022-05-28 22:21:01,142 INFO [train.py:842] (2/4) Epoch 29, batch 5600, loss[loss=0.1429, simple_loss=0.2204, pruned_loss=0.03263, over 7268.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2674, pruned_loss=0.04497, over 1427800.05 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:21:40,625 INFO [train.py:842] (2/4) Epoch 29, batch 5650, loss[loss=0.1451, simple_loss=0.2373, pruned_loss=0.02641, over 7286.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2655, pruned_loss=0.04402, over 1428950.22 frames.], batch size: 17, lr: 1.89e-04
2022-05-28 22:22:19,829 INFO [train.py:842] (2/4) Epoch 29, batch 5700, loss[loss=0.2637, simple_loss=0.3345, pruned_loss=0.09639, over 6791.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2656, pruned_loss=0.0443, over 1430060.42 frames.], batch size: 31, lr: 1.89e-04
2022-05-28 22:22:59,387 INFO [train.py:842] (2/4) Epoch 29, batch 5750, loss[loss=0.1516, simple_loss=0.235, pruned_loss=0.0341, over 7289.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2663, pruned_loss=0.04478, over 1428775.13 frames.], batch size: 17, lr: 1.89e-04
2022-05-28 22:23:38,531 INFO [train.py:842] (2/4) Epoch 29, batch 5800, loss[loss=0.2137, simple_loss=0.3021, pruned_loss=0.06266, over 7144.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2666, pruned_loss=0.04454, over 1424621.65 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:24:18,036 INFO [train.py:842] (2/4) Epoch 29, batch 5850, loss[loss=0.1795, simple_loss=0.2699, pruned_loss=0.04457, over 7410.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2667, pruned_loss=0.0445, over 1420301.10 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:24:57,136 INFO [train.py:842] (2/4) Epoch 29, batch 5900, loss[loss=0.1435, simple_loss=0.2391, pruned_loss=0.02401, over 7144.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2658, pruned_loss=0.04417, over 1423338.22 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:25:36,552 INFO [train.py:842] (2/4) Epoch 29, batch 5950, loss[loss=0.1909, simple_loss=0.2764, pruned_loss=0.05271, over 7225.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2651, pruned_loss=0.04428, over 1418564.61 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:26:15,712 INFO [train.py:842] (2/4) Epoch 29, batch 6000, loss[loss=0.1526, simple_loss=0.2376, pruned_loss=0.03375, over 7129.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2649, pruned_loss=0.04446, over 1418094.99 frames.], batch size: 17, lr: 1.89e-04
2022-05-28 22:26:15,712 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 22:26:26,013 INFO [train.py:871] (2/4) Epoch 29, validation: loss=0.1668, simple_loss=0.2645, pruned_loss=0.03459, over 868885.00 frames.
2022-05-28 22:27:05,799 INFO [train.py:842] (2/4) Epoch 29, batch 6050, loss[loss=0.1764, simple_loss=0.2666, pruned_loss=0.0431, over 7229.00 frames.], tot_loss[loss=0.176, simple_loss=0.2642, pruned_loss=0.04392, over 1420046.27 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:27:44,941 INFO [train.py:842] (2/4) Epoch 29, batch 6100, loss[loss=0.2339, simple_loss=0.3165, pruned_loss=0.0757, over 7229.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2635, pruned_loss=0.04335, over 1420444.98 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:28:24,678 INFO [train.py:842] (2/4) Epoch 29, batch 6150, loss[loss=0.1326, simple_loss=0.2093, pruned_loss=0.02797, over 7282.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2629, pruned_loss=0.04371, over 1422410.17 frames.], batch size: 17, lr: 1.89e-04
2022-05-28 22:29:03,805 INFO [train.py:842] (2/4) Epoch 29, batch 6200, loss[loss=0.1595, simple_loss=0.2434, pruned_loss=0.03777, over 7431.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2633, pruned_loss=0.04398, over 1419785.01 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:29:43,483 INFO [train.py:842] (2/4) Epoch 29, batch 6250, loss[loss=0.1698, simple_loss=0.2656, pruned_loss=0.037, over 7211.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2637, pruned_loss=0.04431, over 1424060.05 frames.], batch size: 22, lr: 1.89e-04
2022-05-28 22:30:22,882 INFO [train.py:842] (2/4) Epoch 29, batch 6300, loss[loss=0.1563, simple_loss=0.2502, pruned_loss=0.03119, over 7314.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2634, pruned_loss=0.04399, over 1427136.45 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:31:02,413 INFO [train.py:842] (2/4) Epoch 29, batch 6350, loss[loss=0.1929, simple_loss=0.2891, pruned_loss=0.04828, over 7038.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2642, pruned_loss=0.04448, over 1426531.37 frames.], batch size: 28, lr: 1.89e-04
2022-05-28 22:31:41,491 INFO [train.py:842] (2/4) Epoch 29, batch 6400, loss[loss=0.1483, simple_loss=0.2416, pruned_loss=0.02748, over 7430.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2654, pruned_loss=0.04501, over 1423433.78 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:32:20,971 INFO [train.py:842] (2/4) Epoch 29, batch 6450, loss[loss=0.1613, simple_loss=0.2428, pruned_loss=0.03988, over 7175.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2658, pruned_loss=0.04445, over 1424078.37 frames.], batch size: 19, lr: 1.89e-04
2022-05-28 22:33:00,143 INFO [train.py:842] (2/4) Epoch 29, batch 6500, loss[loss=0.168, simple_loss=0.2587, pruned_loss=0.03869, over 7222.00 frames.], tot_loss[loss=0.1773, simple_loss=0.266, pruned_loss=0.04432, over 1422697.01 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:33:39,845 INFO [train.py:842] (2/4) Epoch 29, batch 6550, loss[loss=0.1751, simple_loss=0.2721, pruned_loss=0.03902, over 7316.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2645, pruned_loss=0.04406, over 1421413.29 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:34:18,914 INFO [train.py:842] (2/4) Epoch 29, batch 6600, loss[loss=0.1957, simple_loss=0.2842, pruned_loss=0.05358, over 7143.00 frames.], tot_loss[loss=0.178, simple_loss=0.2658, pruned_loss=0.04507, over 1419903.50 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:35:01,101 INFO [train.py:842] (2/4) Epoch 29, batch 6650, loss[loss=0.1854, simple_loss=0.2821, pruned_loss=0.04436, over 7365.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2656, pruned_loss=0.04458, over 1421759.55 frames.], batch size: 23, lr: 1.89e-04
2022-05-28 22:35:40,344 INFO [train.py:842] (2/4) Epoch 29, batch 6700, loss[loss=0.133, simple_loss=0.2201, pruned_loss=0.02289, over 7285.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2645, pruned_loss=0.04405, over 1415784.18 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:36:19,856 INFO [train.py:842] (2/4) Epoch 29, batch 6750, loss[loss=0.1761, simple_loss=0.2691, pruned_loss=0.04152, over 7198.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2644, pruned_loss=0.04387, over 1414710.77 frames.], batch size: 23, lr: 1.89e-04
2022-05-28 22:36:58,908 INFO [train.py:842] (2/4) Epoch 29, batch 6800, loss[loss=0.2001, simple_loss=0.2844, pruned_loss=0.05791, over 6749.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2645, pruned_loss=0.04413, over 1417551.42 frames.], batch size: 31, lr: 1.89e-04
2022-05-28 22:37:38,493 INFO [train.py:842] (2/4) Epoch 29, batch 6850, loss[loss=0.1946, simple_loss=0.2783, pruned_loss=0.05547, over 5128.00 frames.], tot_loss[loss=0.177, simple_loss=0.2653, pruned_loss=0.04433, over 1415725.07 frames.], batch size: 55, lr: 1.89e-04
2022-05-28 22:38:17,569 INFO [train.py:842] (2/4) Epoch 29, batch 6900, loss[loss=0.1937, simple_loss=0.2783, pruned_loss=0.05453, over 6828.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2654, pruned_loss=0.04409, over 1418001.13 frames.], batch size: 31, lr: 1.89e-04
2022-05-28 22:38:57,126 INFO [train.py:842] (2/4) Epoch 29, batch 6950, loss[loss=0.1871, simple_loss=0.2565, pruned_loss=0.0589, over 7143.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2649, pruned_loss=0.04408, over 1416997.50 frames.], batch size: 17, lr: 1.89e-04
2022-05-28 22:39:36,332 INFO [train.py:842] (2/4) Epoch 29, batch 7000, loss[loss=0.1712, simple_loss=0.2591, pruned_loss=0.04166, over 7165.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2642, pruned_loss=0.04382, over 1417445.54 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:40:15,830 INFO [train.py:842] (2/4) Epoch 29, batch 7050, loss[loss=0.146, simple_loss=0.2364, pruned_loss=0.02782, over 7077.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2642, pruned_loss=0.04357, over 1419795.58 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:40:54,876 INFO [train.py:842] (2/4) Epoch 29, batch 7100, loss[loss=0.2384, simple_loss=0.3171, pruned_loss=0.07982, over 7221.00 frames.], tot_loss[loss=0.1763, simple_loss=0.265, pruned_loss=0.04381, over 1416178.85 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:41:34,446 INFO [train.py:842] (2/4) Epoch 29, batch 7150, loss[loss=0.1461, simple_loss=0.2404, pruned_loss=0.02595, over 7157.00 frames.], tot_loss[loss=0.1762, simple_loss=0.265, pruned_loss=0.04368, over 1417132.50 frames.], batch size: 19, lr: 1.89e-04
2022-05-28 22:42:14,004 INFO [train.py:842] (2/4) Epoch 29, batch 7200, loss[loss=0.1751, simple_loss=0.2632, pruned_loss=0.04354, over 7277.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2641, pruned_loss=0.04327, over 1420264.17 frames.], batch size: 24, lr: 1.89e-04
2022-05-28 22:42:53,895 INFO [train.py:842] (2/4) Epoch 29, batch 7250, loss[loss=0.1553, simple_loss=0.2553, pruned_loss=0.02766, over 7225.00 frames.], tot_loss[loss=0.1747, simple_loss=0.263, pruned_loss=0.04319, over 1426782.29 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:43:33,139 INFO [train.py:842] (2/4) Epoch 29, batch 7300, loss[loss=0.232, simple_loss=0.3187, pruned_loss=0.07264, over 6233.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2637, pruned_loss=0.04345, over 1427892.25 frames.], batch size: 37, lr: 1.89e-04
2022-05-28 22:44:12,675 INFO [train.py:842] (2/4) Epoch 29, batch 7350, loss[loss=0.192, simple_loss=0.2843, pruned_loss=0.04984, over 7419.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2631, pruned_loss=0.04299, over 1428364.43 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:44:51,881 INFO [train.py:842] (2/4) Epoch 29, batch 7400, loss[loss=0.1839, simple_loss=0.2763, pruned_loss=0.04578, over 6703.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2627, pruned_loss=0.04301, over 1426141.15 frames.], batch size: 31, lr: 1.89e-04
2022-05-28 22:45:31,614 INFO [train.py:842] (2/4) Epoch 29, batch 7450, loss[loss=0.1628, simple_loss=0.2558, pruned_loss=0.03495, over 7156.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2631, pruned_loss=0.04351, over 1424023.97 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:46:10,870 INFO [train.py:842] (2/4) Epoch 29, batch 7500, loss[loss=0.1597, simple_loss=0.2488, pruned_loss=0.03526, over 7139.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2639, pruned_loss=0.04398, over 1423290.75 frames.], batch size: 17, lr: 1.89e-04
2022-05-28 22:46:50,592 INFO [train.py:842] (2/4) Epoch 29, batch 7550, loss[loss=0.1485, simple_loss=0.2392, pruned_loss=0.02888, over 7067.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2629, pruned_loss=0.04339, over 1425887.54 frames.], batch size: 18, lr: 1.89e-04
2022-05-28 22:47:29,666 INFO [train.py:842] (2/4) Epoch 29, batch 7600, loss[loss=0.1719, simple_loss=0.2659, pruned_loss=0.0389, over 7318.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2627, pruned_loss=0.04301, over 1423490.24 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:48:09,240 INFO [train.py:842] (2/4) Epoch 29, batch 7650, loss[loss=0.1788, simple_loss=0.272, pruned_loss=0.04285, over 7318.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2625, pruned_loss=0.04307, over 1422220.06 frames.], batch size: 21, lr: 1.89e-04
2022-05-28 22:48:48,361 INFO [train.py:842] (2/4) Epoch 29, batch 7700, loss[loss=0.1928, simple_loss=0.2766, pruned_loss=0.05447, over 7143.00 frames.], tot_loss[loss=0.176, simple_loss=0.2647, pruned_loss=0.04366, over 1423999.20 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:49:27,967 INFO [train.py:842] (2/4) Epoch 29, batch 7750, loss[loss=0.157, simple_loss=0.2561, pruned_loss=0.02895, over 7230.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2653, pruned_loss=0.0436, over 1421682.95 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:50:07,212 INFO [train.py:842] (2/4) Epoch 29, batch 7800, loss[loss=0.1685, simple_loss=0.2664, pruned_loss=0.03524, over 7134.00 frames.], tot_loss[loss=0.175, simple_loss=0.2639, pruned_loss=0.04308, over 1420037.12 frames.], batch size: 20, lr: 1.89e-04
2022-05-28 22:50:46,772 INFO [train.py:842] (2/4) Epoch 29, batch 7850, loss[loss=0.1974, simple_loss=0.3011, pruned_loss=0.04686, over 6428.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2647, pruned_loss=0.04351, over 1419513.71 frames.], batch size: 37, lr: 1.89e-04
2022-05-28 22:51:26,025 INFO [train.py:842] (2/4) Epoch 29, batch 7900, loss[loss=0.1993, simple_loss=0.2907, pruned_loss=0.05395, over 7335.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2645, pruned_loss=0.04383, over 1420588.81 frames.], batch size: 22, lr: 1.89e-04
2022-05-28 22:52:05,539 INFO [train.py:842] (2/4) Epoch 29, batch 7950, loss[loss=0.164, simple_loss=0.2374, pruned_loss=0.0453, over 7270.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2656, pruned_loss=0.0448, over 1420430.31 frames.], batch size: 17, lr: 1.88e-04
2022-05-28 22:52:44,470 INFO [train.py:842] (2/4) Epoch 29, batch 8000, loss[loss=0.1437, simple_loss=0.2426, pruned_loss=0.02241, over 7339.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2666, pruned_loss=0.04539, over 1419492.96 frames.], batch size: 22, lr: 1.88e-04
2022-05-28 22:53:24,164 INFO [train.py:842] (2/4) Epoch 29, batch 8050, loss[loss=0.1522, simple_loss=0.2324, pruned_loss=0.03595, over 7427.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2651, pruned_loss=0.04463, over 1425374.80 frames.], batch size: 20, lr: 1.88e-04
2022-05-28 22:54:03,218 INFO [train.py:842] (2/4) Epoch 29, batch 8100, loss[loss=0.1809, simple_loss=0.2745, pruned_loss=0.04363, over 7305.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2656, pruned_loss=0.04459, over 1425284.36 frames.], batch size: 25, lr: 1.88e-04
2022-05-28 22:54:43,031 INFO [train.py:842] (2/4) Epoch 29, batch 8150, loss[loss=0.1479, simple_loss=0.2305, pruned_loss=0.03263, over 7286.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2644, pruned_loss=0.04438, over 1424578.75 frames.], batch size: 17, lr: 1.88e-04
2022-05-28 22:55:22,210 INFO [train.py:842] (2/4) Epoch 29, batch 8200, loss[loss=0.1629, simple_loss=0.261, pruned_loss=0.03241, over 7226.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2642, pruned_loss=0.04439, over 1422191.40 frames.], batch size: 20, lr: 1.88e-04
2022-05-28 22:56:01,676 INFO [train.py:842] (2/4) Epoch 29, batch 8250, loss[loss=0.1952, simple_loss=0.2836, pruned_loss=0.05338, over 7154.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2636, pruned_loss=0.04389, over 1424818.72 frames.], batch size: 19, lr: 1.88e-04
2022-05-28 22:56:40,919 INFO [train.py:842] (2/4) Epoch 29, batch 8300, loss[loss=0.1655, simple_loss=0.2601, pruned_loss=0.03544, over 7334.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2635, pruned_loss=0.04394, over 1424775.46 frames.], batch size: 20, lr: 1.88e-04
2022-05-28 22:57:20,383 INFO [train.py:842] (2/4) Epoch 29, batch 8350, loss[loss=0.139, simple_loss=0.2307, pruned_loss=0.02362, over 7412.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2625, pruned_loss=0.04302, over 1422671.99 frames.], batch size: 17, lr: 1.88e-04
2022-05-28 22:57:59,596 INFO [train.py:842] (2/4) Epoch 29, batch 8400, loss[loss=0.2006, simple_loss=0.2889, pruned_loss=0.05614, over 7430.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2638, pruned_loss=0.04346, over 1421140.72 frames.], batch size: 20, lr: 1.88e-04
2022-05-28 22:58:38,753 INFO [train.py:842] (2/4) Epoch 29, batch 8450, loss[loss=0.1946, simple_loss=0.2778, pruned_loss=0.05565, over 7222.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2642, pruned_loss=0.04359, over 1414749.81 frames.], batch size: 23, lr: 1.88e-04
2022-05-28 22:59:17,813 INFO [train.py:842] (2/4) Epoch 29, batch 8500, loss[loss=0.1587, simple_loss=0.2505, pruned_loss=0.03346, over 7330.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2657, pruned_loss=0.04436, over 1418265.66 frames.], batch size: 20, lr: 1.88e-04
2022-05-28 22:59:57,398 INFO [train.py:842] (2/4) Epoch 29, batch 8550, loss[loss=0.2009, simple_loss=0.2751, pruned_loss=0.06331, over 7255.00 frames.], tot_loss[loss=0.177, simple_loss=0.2652, pruned_loss=0.04445, over 1419305.53 frames.], batch size: 19, lr: 1.88e-04
2022-05-28 23:00:36,812 INFO [train.py:842] (2/4) Epoch 29, batch 8600, loss[loss=0.2323, simple_loss=0.3136, pruned_loss=0.07551, over 7330.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2643, pruned_loss=0.04403, over 1422324.26 frames.], batch size: 21, lr: 1.88e-04
2022-05-28 23:01:16,280 INFO [train.py:842] (2/4) Epoch 29, batch 8650, loss[loss=0.2128, simple_loss=0.2774, pruned_loss=0.07407, over 7245.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2655, pruned_loss=0.04464, over 1423292.20 frames.], batch size: 16, lr: 1.88e-04
2022-05-28 23:01:55,590 INFO [train.py:842] (2/4) Epoch 29, batch 8700, loss[loss=0.1455, simple_loss=0.2326, pruned_loss=0.0292, over 7356.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2649, pruned_loss=0.04406, over 1420722.42 frames.], batch size: 19, lr: 1.88e-04
2022-05-28 23:02:35,317 INFO [train.py:842] (2/4) Epoch 29, batch 8750, loss[loss=0.1949, simple_loss=0.2995, pruned_loss=0.04511, over 7313.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2655, pruned_loss=0.04449, over 1424248.85 frames.], batch size: 25, lr: 1.88e-04
2022-05-28 23:03:14,609 INFO [train.py:842] (2/4) Epoch 29, batch 8800, loss[loss=0.1517, simple_loss=0.233, pruned_loss=0.03516, over 7012.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2647, pruned_loss=0.04416, over 1425575.23 frames.], batch size: 16, lr: 1.88e-04
2022-05-28 23:03:54,206 INFO [train.py:842] (2/4) Epoch 29, batch 8850, loss[loss=0.2284, simple_loss=0.3046, pruned_loss=0.07608, over 7158.00 frames.], tot_loss[loss=0.177, simple_loss=0.265, pruned_loss=0.0445, over 1416399.04 frames.], batch size: 19, lr: 1.88e-04
2022-05-28 23:04:33,291 INFO [train.py:842] (2/4) Epoch 29, batch 8900, loss[loss=0.2062, simple_loss=0.2914, pruned_loss=0.06047, over 6814.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2638, pruned_loss=0.04374, over 1413563.67 frames.], batch size: 31, lr: 1.88e-04
2022-05-28 23:05:12,288 INFO [train.py:842] (2/4) Epoch 29, batch 8950, loss[loss=0.1981, simple_loss=0.2927, pruned_loss=0.0517, over 7224.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2648, pruned_loss=0.04428, over 1400342.56 frames.], batch size: 22, lr: 1.88e-04
2022-05-28 23:05:50,689 INFO [train.py:842] (2/4) Epoch 29, batch 9000, loss[loss=0.1556, simple_loss=0.2444, pruned_loss=0.03345, over 6501.00 frames.], tot_loss[loss=0.1779, simple_loss=0.266, pruned_loss=0.04489, over 1381323.44 frames.], batch size: 38, lr: 1.88e-04
2022-05-28 23:05:50,690 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 23:06:00,408 INFO [train.py:871] (2/4) Epoch 29, validation: loss=0.164, simple_loss=0.2612, pruned_loss=0.03344, over 868885.00 frames.
2022-05-28 23:06:39,638 INFO [train.py:842] (2/4) Epoch 29, batch 9050, loss[loss=0.1934, simple_loss=0.2845, pruned_loss=0.05116, over 7063.00 frames.], tot_loss[loss=0.1789, simple_loss=0.267, pruned_loss=0.04544, over 1365343.13 frames.], batch size: 28, lr: 1.88e-04
2022-05-28 23:07:27,901 INFO [train.py:842] (2/4) Epoch 29, batch 9100, loss[loss=0.215, simple_loss=0.2965, pruned_loss=0.06679, over 4921.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2696, pruned_loss=0.04702, over 1310918.12 frames.], batch size: 54, lr: 1.88e-04
2022-05-28 23:08:06,165 INFO [train.py:842] (2/4) Epoch 29, batch 9150, loss[loss=0.2197, simple_loss=0.3006, pruned_loss=0.06944, over 4824.00 frames.], tot_loss[loss=0.187, simple_loss=0.2734, pruned_loss=0.05032, over 1242772.52 frames.], batch size: 52, lr: 1.88e-04
2022-05-28 23:08:53,892 INFO [train.py:842] (2/4) Epoch 30, batch 0, loss[loss=0.159, simple_loss=0.247, pruned_loss=0.03551, over 7331.00 frames.], tot_loss[loss=0.159, simple_loss=0.247, pruned_loss=0.03551, over 7331.00 frames.], batch size: 20, lr: 1.85e-04
2022-05-28 23:09:44,414 INFO [train.py:842] (2/4) Epoch 30, batch 50, loss[loss=0.1415, simple_loss=0.2341, pruned_loss=0.02439, over 7282.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2585, pruned_loss=0.04129, over 324178.35 frames.], batch size: 18, lr: 1.85e-04
2022-05-28 23:10:34,870 INFO [train.py:842] (2/4) Epoch 30, batch 100, loss[loss=0.1611, simple_loss=0.2423, pruned_loss=0.03998, over 7267.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2605, pruned_loss=0.04265, over 572511.62 frames.], batch size: 17, lr: 1.85e-04
2022-05-28 23:11:14,512 INFO [train.py:842] (2/4) Epoch 30, batch 150, loss[loss=0.1729, simple_loss=0.2664, pruned_loss=0.03975, over 7285.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2623, pruned_loss=0.04424, over 749470.23 frames.], batch size: 24, lr: 1.85e-04
2022-05-28 23:11:53,928 INFO [train.py:842] (2/4) Epoch 30, batch 200, loss[loss=0.1469, simple_loss=0.2378, pruned_loss=0.02802, over 7371.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2626, pruned_loss=0.0436, over 899972.19 frames.], batch size: 19, lr: 1.85e-04
2022-05-28 23:12:33,269 INFO [train.py:842] (2/4) Epoch 30, batch 250, loss[loss=0.1562, simple_loss=0.2407, pruned_loss=0.03584, over 6782.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2638, pruned_loss=0.04377, over 1015434.55 frames.], batch size: 15, lr: 1.85e-04
2022-05-28 23:13:12,485 INFO [train.py:842] (2/4) Epoch 30, batch 300, loss[loss=0.1535, simple_loss=0.2373, pruned_loss=0.03481, over 7288.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2643, pruned_loss=0.04414, over 1108188.55 frames.], batch size: 18, lr: 1.85e-04
2022-05-28 23:13:52,317 INFO [train.py:842] (2/4) Epoch 30, batch 350, loss[loss=0.1603, simple_loss=0.2475, pruned_loss=0.03653, over 7335.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2644, pruned_loss=0.04512, over 1180945.82 frames.], batch size: 20, lr: 1.85e-04
2022-05-28 23:14:31,572 INFO [train.py:842] (2/4) Epoch 30, batch 400, loss[loss=0.2126, simple_loss=0.2998, pruned_loss=0.06271, over 7277.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2653, pruned_loss=0.04501, over 1236780.17 frames.], batch size: 24, lr: 1.85e-04
2022-05-28 23:15:11,142 INFO [train.py:842] (2/4) Epoch 30, batch 450, loss[loss=0.1704, simple_loss=0.2589, pruned_loss=0.0409, over 7414.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2644, pruned_loss=0.04417, over 1278952.79 frames.], batch size: 21, lr: 1.85e-04
2022-05-28 23:15:50,239 INFO [train.py:842] (2/4) Epoch 30, batch 500, loss[loss=0.1956, simple_loss=0.2826, pruned_loss=0.05431, over 7317.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2656, pruned_loss=0.04508, over 1307173.54 frames.], batch size: 20, lr: 1.85e-04
2022-05-28 23:16:29,830 INFO [train.py:842] (2/4) Epoch 30, batch 550, loss[loss=0.1666, simple_loss=0.2601, pruned_loss=0.03655, over 7291.00 frames.], tot_loss[loss=0.1773, simple_loss=0.265, pruned_loss=0.0448, over 1334870.84 frames.], batch size: 24, lr: 1.85e-04
2022-05-28 23:17:08,957 INFO [train.py:842] (2/4) Epoch 30, batch 600, loss[loss=0.1913, simple_loss=0.2824, pruned_loss=0.05014, over 7216.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2648, pruned_loss=0.04477, over 1351234.80 frames.], batch size: 22, lr: 1.85e-04
2022-05-28 23:17:48,544 INFO [train.py:842] (2/4) Epoch 30, batch 650, loss[loss=0.1954, simple_loss=0.2772, pruned_loss=0.05673, over 7065.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2647, pruned_loss=0.04439, over 1366600.50 frames.], batch size: 18, lr: 1.85e-04
2022-05-28 23:18:27,661 INFO [train.py:842] (2/4) Epoch 30, batch 700, loss[loss=0.1651, simple_loss=0.2675, pruned_loss=0.03136, over 7332.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2639, pruned_loss=0.04337, over 1375251.59 frames.], batch size: 20, lr: 1.85e-04
2022-05-28 23:19:07,268 INFO [train.py:842] (2/4) Epoch 30, batch 750, loss[loss=0.1804, simple_loss=0.2733, pruned_loss=0.04374, over 7224.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2648, pruned_loss=0.04375, over 1382682.33 frames.], batch size: 20, lr: 1.85e-04
2022-05-28 23:19:46,345 INFO [train.py:842] (2/4) Epoch 30, batch 800, loss[loss=0.1741, simple_loss=0.2706, pruned_loss=0.03877, over 7343.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2645, pruned_loss=0.04366, over 1388675.73 frames.], batch size: 22, lr: 1.85e-04
2022-05-28 23:20:25,932 INFO [train.py:842] (2/4) Epoch 30, batch 850, loss[loss=0.2001, simple_loss=0.2808, pruned_loss=0.05969, over 7070.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2627, pruned_loss=0.04296, over 1397130.76 frames.], batch size: 18, lr: 1.85e-04
2022-05-28 23:21:05,198 INFO [train.py:842] (2/4) Epoch 30, batch 900, loss[loss=0.1732, simple_loss=0.2719, pruned_loss=0.03724, over 7217.00 frames.], tot_loss[loss=0.1746, simple_loss=0.263, pruned_loss=0.04311, over 1401503.35 frames.], batch size: 21, lr: 1.85e-04
2022-05-28 23:21:44,665 INFO [train.py:842] (2/4) Epoch 30, batch 950, loss[loss=0.2159, simple_loss=0.3055, pruned_loss=0.06313, over 7125.00 frames.], tot_loss[loss=0.177, simple_loss=0.2649, pruned_loss=0.0445, over 1406984.61 frames.], batch size: 21, lr: 1.85e-04
2022-05-28 23:22:23,792 INFO [train.py:842] (2/4) Epoch 30, batch 1000, loss[loss=0.1645, simple_loss=0.2618, pruned_loss=0.03356, over 7144.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2655, pruned_loss=0.04411, over 1410631.35 frames.], batch size: 20, lr: 1.85e-04
2022-05-28 23:23:03,144 INFO [train.py:842] (2/4) Epoch 30, batch 1050, loss[loss=0.1352, simple_loss=0.2281, pruned_loss=0.02116, over 7279.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2659, pruned_loss=0.04431, over 1406722.33 frames.], batch size: 18, lr: 1.85e-04
2022-05-28 23:23:42,405 INFO [train.py:842] (2/4) Epoch 30, batch 1100, loss[loss=0.184, simple_loss=0.2733, pruned_loss=0.04733, over 7317.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2663, pruned_loss=0.04406, over 1416240.81 frames.], batch size: 21, lr: 1.85e-04
2022-05-28 23:24:21,911 INFO [train.py:842] (2/4) Epoch 30, batch 1150, loss[loss=0.1715, simple_loss=0.2498, pruned_loss=0.0466, over 6995.00 frames.], tot_loss[loss=0.1767, simple_loss=0.266, pruned_loss=0.04364, over 1417214.34 frames.], batch size: 16, lr: 1.85e-04
2022-05-28 23:25:01,251 INFO [train.py:842] (2/4) Epoch 30, batch 1200, loss[loss=0.1482, simple_loss=0.2367, pruned_loss=0.0299, over 7153.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2653, pruned_loss=0.0434, over 1422492.37 frames.], batch size: 19, lr: 1.85e-04
2022-05-28 23:25:40,932 INFO [train.py:842] (2/4) Epoch 30, batch 1250, loss[loss=0.2314, simple_loss=0.3158, pruned_loss=0.07353, over 4862.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2644, pruned_loss=0.0435, over 1417069.53 frames.], batch size: 52, lr: 1.84e-04
2022-05-28 23:26:20,213 INFO [train.py:842] (2/4) Epoch 30, batch 1300, loss[loss=0.1539, simple_loss=0.2537, pruned_loss=0.027, over 7341.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2645, pruned_loss=0.04344, over 1418075.85 frames.], batch size: 22, lr: 1.84e-04
2022-05-28 23:26:59,737 INFO [train.py:842] (2/4) Epoch 30, batch 1350, loss[loss=0.1919, simple_loss=0.2831, pruned_loss=0.05035, over 6325.00 frames.], tot_loss[loss=0.177, simple_loss=0.266, pruned_loss=0.04403, over 1418814.85 frames.], batch size: 37, lr: 1.84e-04
2022-05-28 23:27:39,241 INFO [train.py:842] (2/4) Epoch 30, batch 1400, loss[loss=0.1472, simple_loss=0.2365, pruned_loss=0.02894, over 7280.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2644, pruned_loss=0.04326, over 1419647.69 frames.], batch size: 16, lr: 1.84e-04
2022-05-28 23:28:18,747 INFO [train.py:842] (2/4) Epoch 30, batch 1450, loss[loss=0.1771, simple_loss=0.2768, pruned_loss=0.03871, over 7113.00 frames.], tot_loss[loss=0.177, simple_loss=0.2657, pruned_loss=0.0442, over 1418393.29 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:28:58,163 INFO [train.py:842] (2/4) Epoch 30, batch 1500, loss[loss=0.1577, simple_loss=0.2508, pruned_loss=0.03229, over 7254.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2658, pruned_loss=0.04457, over 1417642.44 frames.], batch size: 19, lr: 1.84e-04
2022-05-28 23:29:37,424 INFO [train.py:842] (2/4) Epoch 30, batch 1550, loss[loss=0.1644, simple_loss=0.2602, pruned_loss=0.03425, over 7186.00 frames.], tot_loss[loss=0.1765, simple_loss=0.265, pruned_loss=0.04397, over 1418010.38 frames.], batch size: 23, lr: 1.84e-04
2022-05-28 23:30:16,502 INFO [train.py:842] (2/4) Epoch 30, batch 1600, loss[loss=0.1672, simple_loss=0.2579, pruned_loss=0.03824, over 7318.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2649, pruned_loss=0.04374, over 1419643.01 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:30:56,192 INFO [train.py:842] (2/4) Epoch 30, batch 1650, loss[loss=0.1861, simple_loss=0.274, pruned_loss=0.04908, over 7134.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2651, pruned_loss=0.04379, over 1424198.93 frames.], batch size: 26, lr: 1.84e-04
2022-05-28 23:31:35,529 INFO [train.py:842] (2/4) Epoch 30, batch 1700, loss[loss=0.1686, simple_loss=0.2501, pruned_loss=0.04359, over 7125.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2651, pruned_loss=0.04399, over 1426715.06 frames.], batch size: 17, lr: 1.84e-04
2022-05-28 23:32:15,268 INFO [train.py:842] (2/4) Epoch 30, batch 1750, loss[loss=0.1758, simple_loss=0.2685, pruned_loss=0.04158, over 7156.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2641, pruned_loss=0.04337, over 1424512.58 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:32:54,398 INFO [train.py:842] (2/4) Epoch 30, batch 1800, loss[loss=0.2081, simple_loss=0.2806, pruned_loss=0.0678, over 5142.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2639, pruned_loss=0.04358, over 1421947.83 frames.], batch size: 52, lr: 1.84e-04
2022-05-28 23:33:33,954 INFO [train.py:842] (2/4) Epoch 30, batch 1850, loss[loss=0.192, simple_loss=0.28, pruned_loss=0.05199, over 7113.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2634, pruned_loss=0.04352, over 1425742.89 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:34:13,204 INFO [train.py:842] (2/4) Epoch 30, batch 1900, loss[loss=0.1509, simple_loss=0.2396, pruned_loss=0.03114, over 7218.00 frames.], tot_loss[loss=0.1758, simple_loss=0.264, pruned_loss=0.04381, over 1428052.29 frames.], batch size: 16, lr: 1.84e-04
2022-05-28 23:34:52,934 INFO [train.py:842] (2/4) Epoch 30, batch 1950, loss[loss=0.1326, simple_loss=0.2186, pruned_loss=0.02333, over 7275.00 frames.], tot_loss[loss=0.1762, simple_loss=0.264, pruned_loss=0.04422, over 1428988.92 frames.], batch size: 17, lr: 1.84e-04
2022-05-28 23:35:32,323 INFO [train.py:842] (2/4) Epoch 30, batch 2000, loss[loss=0.1805, simple_loss=0.2803, pruned_loss=0.04035, over 7339.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2635, pruned_loss=0.04342, over 1431353.76 frames.], batch size: 22, lr: 1.84e-04
2022-05-28 23:36:11,896 INFO [train.py:842] (2/4) Epoch 30, batch 2050, loss[loss=0.1993, simple_loss=0.2821, pruned_loss=0.0582, over 7182.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2632, pruned_loss=0.04321, over 1431575.57 frames.], batch size: 23, lr: 1.84e-04
2022-05-28 23:36:50,955 INFO [train.py:842] (2/4) Epoch 30, batch 2100, loss[loss=0.1818, simple_loss=0.2745, pruned_loss=0.04456, over 7148.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2634, pruned_loss=0.04293, over 1430837.02 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:37:30,444 INFO [train.py:842] (2/4) Epoch 30, batch 2150, loss[loss=0.1495, simple_loss=0.2299, pruned_loss=0.03456, over 7119.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2624, pruned_loss=0.04236, over 1429207.51 frames.], batch size: 17, lr: 1.84e-04
2022-05-28 23:38:09,548 INFO [train.py:842] (2/4) Epoch 30, batch 2200, loss[loss=0.2359, simple_loss=0.3119, pruned_loss=0.07992, over 7283.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2628, pruned_loss=0.04296, over 1423855.39 frames.], batch size: 24, lr: 1.84e-04
2022-05-28 23:38:49,007 INFO [train.py:842] (2/4) Epoch 30, batch 2250, loss[loss=0.1897, simple_loss=0.2851, pruned_loss=0.04716, over 7163.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2636, pruned_loss=0.04354, over 1422368.79 frames.], batch size: 26, lr: 1.84e-04
2022-05-28 23:39:28,070 INFO [train.py:842] (2/4) Epoch 30, batch 2300, loss[loss=0.173, simple_loss=0.2531, pruned_loss=0.04641, over 7335.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2639, pruned_loss=0.04361, over 1419558.18 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:40:07,924 INFO [train.py:842] (2/4) Epoch 30, batch 2350, loss[loss=0.1511, simple_loss=0.2502, pruned_loss=0.026, over 7340.00 frames.], tot_loss[loss=0.1766, simple_loss=0.265, pruned_loss=0.04413, over 1421116.30 frames.], batch size: 22, lr: 1.84e-04
2022-05-28 23:40:47,291 INFO [train.py:842] (2/4) Epoch 30, batch 2400, loss[loss=0.2056, simple_loss=0.2936, pruned_loss=0.05885, over 7301.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2647, pruned_loss=0.04373, over 1423240.02 frames.], batch size: 25, lr: 1.84e-04
2022-05-28 23:41:27,074 INFO [train.py:842] (2/4) Epoch 30, batch 2450, loss[loss=0.2389, simple_loss=0.317, pruned_loss=0.08043, over 7135.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2638, pruned_loss=0.0436, over 1427770.85 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:42:06,580 INFO [train.py:842] (2/4) Epoch 30, batch 2500, loss[loss=0.144, simple_loss=0.2305, pruned_loss=0.02876, over 6755.00 frames.], tot_loss[loss=0.1746, simple_loss=0.263, pruned_loss=0.04311, over 1430713.20 frames.], batch size: 15, lr: 1.84e-04
2022-05-28 23:42:46,135 INFO [train.py:842] (2/4) Epoch 30, batch 2550, loss[loss=0.1463, simple_loss=0.2303, pruned_loss=0.03117, over 7403.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2626, pruned_loss=0.04301, over 1427946.86 frames.], batch size: 18, lr: 1.84e-04
2022-05-28 23:43:25,265 INFO [train.py:842] (2/4) Epoch 30, batch 2600, loss[loss=0.1693, simple_loss=0.2642, pruned_loss=0.03721, over 7114.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2632, pruned_loss=0.04318, over 1427209.39 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:44:04,872 INFO [train.py:842] (2/4) Epoch 30, batch 2650, loss[loss=0.1615, simple_loss=0.2431, pruned_loss=0.03992, over 7121.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2639, pruned_loss=0.0436, over 1428981.70 frames.], batch size: 17, lr: 1.84e-04
2022-05-28 23:44:43,983 INFO [train.py:842] (2/4) Epoch 30, batch 2700, loss[loss=0.1877, simple_loss=0.2731, pruned_loss=0.05115, over 7107.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2651, pruned_loss=0.04385, over 1428932.99 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:45:23,602 INFO [train.py:842] (2/4) Epoch 30, batch 2750, loss[loss=0.187, simple_loss=0.2707, pruned_loss=0.05169, over 7239.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2658, pruned_loss=0.04417, over 1426039.59 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:46:02,920 INFO [train.py:842] (2/4) Epoch 30, batch 2800, loss[loss=0.1689, simple_loss=0.2622, pruned_loss=0.03776, over 7322.00 frames.], tot_loss[loss=0.1761, simple_loss=0.265, pruned_loss=0.04356, over 1425403.78 frames.], batch size: 22, lr: 1.84e-04
2022-05-28 23:46:42,608 INFO [train.py:842] (2/4) Epoch 30, batch 2850, loss[loss=0.1601, simple_loss=0.2584, pruned_loss=0.03094, over 7232.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2646, pruned_loss=0.04383, over 1418587.37 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:47:21,837 INFO [train.py:842] (2/4) Epoch 30, batch 2900, loss[loss=0.1276, simple_loss=0.2141, pruned_loss=0.02048, over 6992.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2638, pruned_loss=0.04352, over 1421329.74 frames.], batch size: 16, lr: 1.84e-04
2022-05-28 23:48:01,437 INFO [train.py:842] (2/4) Epoch 30, batch 2950, loss[loss=0.1489, simple_loss=0.2393, pruned_loss=0.02922, over 6488.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2636, pruned_loss=0.04382, over 1422638.96 frames.], batch size: 38, lr: 1.84e-04
2022-05-28 23:48:40,737 INFO [train.py:842] (2/4) Epoch 30, batch 3000, loss[loss=0.18, simple_loss=0.2808, pruned_loss=0.03961, over 7116.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2647, pruned_loss=0.04443, over 1424993.29 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:48:40,738 INFO [train.py:862] (2/4) Computing validation loss
2022-05-28 23:48:50,532 INFO [train.py:871] (2/4) Epoch 30, validation: loss=0.1622, simple_loss=0.26, pruned_loss=0.03222, over 868885.00 frames.
2022-05-28 23:49:30,135 INFO [train.py:842] (2/4) Epoch 30, batch 3050, loss[loss=0.1762, simple_loss=0.2762, pruned_loss=0.0381, over 7120.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2652, pruned_loss=0.04401, over 1427097.14 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:50:09,312 INFO [train.py:842] (2/4) Epoch 30, batch 3100, loss[loss=0.1795, simple_loss=0.2812, pruned_loss=0.03885, over 7418.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2652, pruned_loss=0.04359, over 1427683.06 frames.], batch size: 21, lr: 1.84e-04
2022-05-28 23:50:48,912 INFO [train.py:842] (2/4) Epoch 30, batch 3150, loss[loss=0.15, simple_loss=0.2333, pruned_loss=0.03334, over 7167.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2637, pruned_loss=0.0428, over 1423748.98 frames.], batch size: 18, lr: 1.84e-04
2022-05-28 23:51:28,427 INFO [train.py:842] (2/4) Epoch 30, batch 3200, loss[loss=0.1431, simple_loss=0.2254, pruned_loss=0.0304, over 7256.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2628, pruned_loss=0.04303, over 1425770.31 frames.], batch size: 19, lr: 1.84e-04
2022-05-28 23:52:08,003 INFO [train.py:842] (2/4) Epoch 30, batch 3250, loss[loss=0.1867, simple_loss=0.2802, pruned_loss=0.04662, over 7035.00 frames.], tot_loss[loss=0.174, simple_loss=0.2622, pruned_loss=0.04288, over 1420198.92 frames.], batch size: 28, lr: 1.84e-04
2022-05-28 23:52:47,271 INFO [train.py:842] (2/4) Epoch 30, batch 3300, loss[loss=0.1921, simple_loss=0.2811, pruned_loss=0.05151, over 7324.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2631, pruned_loss=0.04309, over 1423636.52 frames.], batch size: 20, lr: 1.84e-04
2022-05-28 23:53:26,874 INFO [train.py:842] (2/4) Epoch 30, batch 3350, loss[loss=0.1568, simple_loss=0.2291, pruned_loss=0.04218, over 7285.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2625, pruned_loss=0.04281, over 1427679.99 frames.], batch size: 17, lr: 1.84e-04
2022-05-28 23:54:06,103 INFO [train.py:842] (2/4) Epoch 30, batch 3400, loss[loss=0.2113, simple_loss=0.2983, pruned_loss=0.06218, over 5142.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2628, pruned_loss=0.04307, over 1424623.93 frames.], batch size: 52, lr: 1.84e-04
2022-05-28 23:54:45,911 INFO [train.py:842] (2/4) Epoch 30, batch 3450, loss[loss=0.201, simple_loss=0.2919, pruned_loss=0.05502, over 7308.00 frames.], tot_loss[loss=0.1738, simple_loss=0.262, pruned_loss=0.04274, over 1422200.75 frames.], batch size: 24, lr: 1.84e-04
2022-05-28 23:55:25,173 INFO [train.py:842] (2/4) Epoch 30, batch 3500, loss[loss=0.1788, simple_loss=0.2707, pruned_loss=0.04351, over 7154.00 frames.], tot_loss[loss=0.174, simple_loss=0.2625, pruned_loss=0.04276, over 1424312.59 frames.], batch size: 26, lr: 1.84e-04
2022-05-28 23:56:04,830 INFO [train.py:842] (2/4) Epoch 30, batch 3550, loss[loss=0.1621, simple_loss=0.2442, pruned_loss=0.03998, over 7154.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2622, pruned_loss=0.0423, over 1423377.82 frames.], batch size: 18, lr: 1.84e-04
2022-05-28 23:56:44,276 INFO [train.py:842] (2/4) Epoch 30, batch 3600, loss[loss=0.1484, simple_loss=0.2396, pruned_loss=0.02863, over 7257.00 frames.], tot_loss[loss=0.173, simple_loss=0.2617, pruned_loss=0.04214, over 1427798.73 frames.], batch size: 19, lr: 1.84e-04
2022-05-28 23:57:23,851 INFO [train.py:842] (2/4) Epoch 30, batch 3650, loss[loss=0.1638, simple_loss=0.2628, pruned_loss=0.03239, over 6790.00 frames.], tot_loss[loss=0.1723, simple_loss=0.261, pruned_loss=0.04177, over 1429199.74 frames.], batch size: 31, lr: 1.84e-04
2022-05-28 23:58:03,111 INFO [train.py:842] (2/4) Epoch 30, batch 3700, loss[loss=0.17, simple_loss=0.2461, pruned_loss=0.04697, over 7279.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2612, pruned_loss=0.04232, over 1429662.66 frames.], batch size: 17, lr: 1.84e-04
2022-05-28 23:58:42,922 INFO [train.py:842] (2/4) Epoch 30, batch 3750, loss[loss=0.1691, simple_loss=0.2641, pruned_loss=0.03703, over 7078.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2613, pruned_loss=0.04226, over 1432822.08 frames.], batch size: 28, lr: 1.84e-04
2022-05-28 23:59:21,902 INFO [train.py:842] (2/4) Epoch 30, batch 3800, loss[loss=0.1857, simple_loss=0.2737, pruned_loss=0.04891, over 7203.00 frames.], tot_loss[loss=0.1757, simple_loss=0.264, pruned_loss=0.04366, over 1425485.67 frames.], batch size: 22, lr: 1.84e-04
2022-05-29 00:00:01,408 INFO [train.py:842] (2/4) Epoch 30, batch 3850, loss[loss=0.1911, simple_loss=0.2945, pruned_loss=0.04384, over 7212.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2636, pruned_loss=0.04331, over 1426539.19 frames.], batch size: 22, lr: 1.84e-04
2022-05-29 00:00:40,343 INFO [train.py:842] (2/4) Epoch 30, batch 3900, loss[loss=0.1747, simple_loss=0.2641, pruned_loss=0.04267, over 7217.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2641, pruned_loss=0.04342, over 1426217.01 frames.], batch size: 21, lr: 1.84e-04
2022-05-29 00:01:19,898 INFO [train.py:842] (2/4) Epoch 30, batch 3950, loss[loss=0.1633, simple_loss=0.2426, pruned_loss=0.04197, over 7362.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2644, pruned_loss=0.04359, over 1423894.97 frames.], batch size: 19, lr: 1.84e-04
2022-05-29 00:01:59,044 INFO [train.py:842] (2/4) Epoch 30, batch 4000, loss[loss=0.1773, simple_loss=0.2534, pruned_loss=0.05063, over 7158.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2644, pruned_loss=0.04362, over 1421975.01 frames.], batch size: 18, lr: 1.84e-04
2022-05-29 00:02:38,739 INFO [train.py:842] (2/4) Epoch 30, batch 4050, loss[loss=0.1696, simple_loss=0.261, pruned_loss=0.03913, over 7314.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2642, pruned_loss=0.0438, over 1422071.16 frames.], batch size: 24, lr: 1.84e-04
2022-05-29 00:03:17,889 INFO [train.py:842] (2/4) Epoch 30, batch 4100, loss[loss=0.166, simple_loss=0.267, pruned_loss=0.0325, over 7206.00 frames.], tot_loss[loss=0.177, simple_loss=0.2649, pruned_loss=0.0445, over 1421597.85 frames.], batch size: 21, lr: 1.84e-04
2022-05-29 00:03:57,520 INFO [train.py:842] (2/4) Epoch 30, batch 4150, loss[loss=0.1652, simple_loss=0.253, pruned_loss=0.03872, over 7271.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2657, pruned_loss=0.04501, over 1425800.75 frames.], batch size: 18, lr: 1.84e-04
2022-05-29 00:04:36,800 INFO [train.py:842] (2/4) Epoch 30, batch 4200, loss[loss=0.1668, simple_loss=0.2589, pruned_loss=0.0373, over 7373.00 frames.], tot_loss[loss=0.1759, simple_loss=0.264, pruned_loss=0.04386, over 1427646.80 frames.], batch size: 23, lr: 1.83e-04
2022-05-29 00:05:16,539 INFO [train.py:842] (2/4) Epoch 30, batch 4250, loss[loss=0.1566, simple_loss=0.2417, pruned_loss=0.03574, over 7135.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2626, pruned_loss=0.04294, over 1426967.11 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:05:55,529 INFO [train.py:842] (2/4) Epoch 30, batch 4300, loss[loss=0.1918, simple_loss=0.2866, pruned_loss=0.0485, over 7305.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2647, pruned_loss=0.04382, over 1426412.21 frames.], batch size: 25, lr: 1.83e-04
2022-05-29 00:06:35,223 INFO [train.py:842] (2/4) Epoch 30, batch 4350, loss[loss=0.1641, simple_loss=0.2396, pruned_loss=0.04424, over 6980.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2654, pruned_loss=0.04447, over 1424910.94 frames.], batch size: 16, lr: 1.83e-04
2022-05-29 00:07:14,674 INFO [train.py:842] (2/4) Epoch 30, batch 4400, loss[loss=0.1312, simple_loss=0.2285, pruned_loss=0.01692, over 7431.00 frames.], tot_loss[loss=0.176, simple_loss=0.2647, pruned_loss=0.0437, over 1427748.27 frames.], batch size: 20, lr: 1.83e-04
2022-05-29 00:07:54,166 INFO [train.py:842] (2/4) Epoch 30, batch 4450, loss[loss=0.179, simple_loss=0.2619, pruned_loss=0.04805, over 7155.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2639, pruned_loss=0.04325, over 1426833.13 frames.], batch size: 26, lr: 1.83e-04
2022-05-29 00:08:33,354 INFO [train.py:842] (2/4) Epoch 30, batch 4500, loss[loss=0.193, simple_loss=0.2791, pruned_loss=0.05343, over 7316.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2636, pruned_loss=0.04304, over 1424986.40 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:09:13,164 INFO [train.py:842] (2/4) Epoch 30, batch 4550, loss[loss=0.1699, simple_loss=0.2609, pruned_loss=0.0394, over 7412.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2624, pruned_loss=0.04238, over 1428356.47 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:09:52,431 INFO [train.py:842] (2/4) Epoch 30, batch 4600, loss[loss=0.175, simple_loss=0.2708, pruned_loss=0.03956, over 7198.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2619, pruned_loss=0.04245, over 1425456.83 frames.], batch size: 22, lr: 1.83e-04
2022-05-29 00:10:32,204 INFO [train.py:842] (2/4) Epoch 30, batch 4650, loss[loss=0.1604, simple_loss=0.2555, pruned_loss=0.0327, over 6700.00 frames.], tot_loss[loss=0.1736, simple_loss=0.262, pruned_loss=0.04258, over 1426426.81 frames.], batch size: 31, lr: 1.83e-04
2022-05-29 00:11:11,575 INFO [train.py:842] (2/4) Epoch 30, batch 4700, loss[loss=0.1742, simple_loss=0.2727, pruned_loss=0.03782, over 7284.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2617, pruned_loss=0.0419, over 1428510.25 frames.], batch size: 24, lr: 1.83e-04
2022-05-29 00:11:51,346 INFO [train.py:842] (2/4) Epoch 30, batch 4750, loss[loss=0.1709, simple_loss=0.2582, pruned_loss=0.04175, over 7159.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2631, pruned_loss=0.04259, over 1428787.49 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:12:30,796 INFO [train.py:842] (2/4) Epoch 30, batch 4800, loss[loss=0.2043, simple_loss=0.2977, pruned_loss=0.0554, over 7370.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2645, pruned_loss=0.04285, over 1429632.06 frames.], batch size: 23, lr: 1.83e-04
2022-05-29 00:13:10,317 INFO [train.py:842] (2/4) Epoch 30, batch 4850, loss[loss=0.1692, simple_loss=0.2581, pruned_loss=0.04016, over 7221.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2657, pruned_loss=0.04378, over 1428176.52 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:13:49,477 INFO [train.py:842] (2/4) Epoch 30, batch 4900, loss[loss=0.1772, simple_loss=0.2615, pruned_loss=0.04648, over 7412.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2661, pruned_loss=0.04406, over 1426948.42 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:14:29,090 INFO [train.py:842] (2/4) Epoch 30, batch 4950, loss[loss=0.2202, simple_loss=0.314, pruned_loss=0.06322, over 7301.00 frames.], tot_loss[loss=0.177, simple_loss=0.2658, pruned_loss=0.04408, over 1422654.12 frames.], batch size: 24, lr: 1.83e-04
2022-05-29 00:15:08,306 INFO [train.py:842] (2/4) Epoch 30, batch 5000, loss[loss=0.1696, simple_loss=0.2487, pruned_loss=0.0452, over 7254.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2651, pruned_loss=0.04335, over 1423543.76 frames.], batch size: 16, lr: 1.83e-04
2022-05-29 00:15:47,616 INFO [train.py:842] (2/4) Epoch 30, batch 5050, loss[loss=0.2023, simple_loss=0.2958, pruned_loss=0.05441, over 7080.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2655, pruned_loss=0.04361, over 1418927.57 frames.], batch size: 28, lr: 1.83e-04
2022-05-29 00:16:27,068 INFO [train.py:842] (2/4) Epoch 30, batch 5100, loss[loss=0.1801, simple_loss=0.2634, pruned_loss=0.04838, over 6816.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2656, pruned_loss=0.04443, over 1415057.52 frames.], batch size: 15, lr: 1.83e-04
2022-05-29 00:17:06,587 INFO [train.py:842] (2/4) Epoch 30, batch 5150, loss[loss=0.1521, simple_loss=0.2388, pruned_loss=0.03271, over 7271.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2664, pruned_loss=0.04519, over 1411388.77 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:17:45,813 INFO [train.py:842] (2/4) Epoch 30, batch 5200, loss[loss=0.1631, simple_loss=0.2456, pruned_loss=0.04037, over 7387.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2666, pruned_loss=0.04508, over 1415801.32 frames.], batch size: 23, lr: 1.83e-04
2022-05-29 00:18:25,601 INFO [train.py:842] (2/4) Epoch 30, batch 5250, loss[loss=0.1905, simple_loss=0.2879, pruned_loss=0.04653, over 7314.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2658, pruned_loss=0.04447, over 1419260.70 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:19:04,590 INFO [train.py:842] (2/4) Epoch 30, batch 5300, loss[loss=0.1867, simple_loss=0.2584, pruned_loss=0.05749, over 7142.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2667, pruned_loss=0.04475, over 1421327.36 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:19:44,223 INFO [train.py:842] (2/4) Epoch 30, batch 5350, loss[loss=0.144, simple_loss=0.2335, pruned_loss=0.0273, over 7159.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2658, pruned_loss=0.04436, over 1423767.77 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:20:23,316 INFO [train.py:842] (2/4) Epoch 30, batch 5400, loss[loss=0.1917, simple_loss=0.2728, pruned_loss=0.05532, over 7126.00 frames.], tot_loss[loss=0.176, simple_loss=0.2644, pruned_loss=0.04376, over 1423596.30 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:21:05,471 INFO [train.py:842] (2/4) Epoch 30, batch 5450, loss[loss=0.1528, simple_loss=0.2385, pruned_loss=0.0335, over 7257.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2648, pruned_loss=0.04351, over 1423645.32 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:21:44,384 INFO [train.py:842] (2/4) Epoch 30, batch 5500, loss[loss=0.2163, simple_loss=0.3083, pruned_loss=0.06212, over 7415.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2647, pruned_loss=0.04349, over 1421900.75 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:22:23,772 INFO [train.py:842] (2/4) Epoch 30, batch 5550, loss[loss=0.18, simple_loss=0.262, pruned_loss=0.04904, over 7330.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2655, pruned_loss=0.04379, over 1420678.13 frames.], batch size: 20, lr: 1.83e-04
2022-05-29 00:23:02,892 INFO [train.py:842] (2/4) Epoch 30, batch 5600, loss[loss=0.1802, simple_loss=0.262, pruned_loss=0.04921, over 7365.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2665, pruned_loss=0.04452, over 1409526.11 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:23:42,325 INFO [train.py:842] (2/4) Epoch 30, batch 5650, loss[loss=0.1515, simple_loss=0.2351, pruned_loss=0.03396, over 7363.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2655, pruned_loss=0.04407, over 1409552.68 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:24:21,731 INFO [train.py:842] (2/4) Epoch 30, batch 5700, loss[loss=0.1735, simple_loss=0.2571, pruned_loss=0.04494, over 7009.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2644, pruned_loss=0.04371, over 1416957.23 frames.], batch size: 16, lr: 1.83e-04
2022-05-29 00:25:01,305 INFO [train.py:842] (2/4) Epoch 30, batch 5750, loss[loss=0.2303, simple_loss=0.3154, pruned_loss=0.07257, over 7313.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2643, pruned_loss=0.04332, over 1420833.08 frames.], batch size: 24, lr: 1.83e-04
2022-05-29 00:25:40,654 INFO [train.py:842] (2/4) Epoch 30, batch 5800, loss[loss=0.1322, simple_loss=0.219, pruned_loss=0.02277, over 7424.00 frames.], tot_loss[loss=0.175, simple_loss=0.2636, pruned_loss=0.04319, over 1421663.36 frames.], batch size: 20, lr: 1.83e-04
2022-05-29 00:26:19,839 INFO [train.py:842] (2/4) Epoch 30, batch 5850, loss[loss=0.1723, simple_loss=0.2683, pruned_loss=0.03821, over 7071.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2649, pruned_loss=0.04407, over 1420962.76 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:26:59,095 INFO [train.py:842] (2/4) Epoch 30, batch 5900, loss[loss=0.1863, simple_loss=0.2769, pruned_loss=0.04782, over 7139.00 frames.], tot_loss[loss=0.1758, simple_loss=0.264, pruned_loss=0.04379, over 1421482.97 frames.], batch size: 20, lr: 1.83e-04
2022-05-29 00:27:38,791 INFO [train.py:842] (2/4) Epoch 30, batch 5950, loss[loss=0.1883, simple_loss=0.281, pruned_loss=0.04779, over 7117.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2641, pruned_loss=0.04357, over 1425187.44 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:28:18,038 INFO [train.py:842] (2/4) Epoch 30, batch 6000, loss[loss=0.1756, simple_loss=0.2716, pruned_loss=0.03975, over 7418.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2641, pruned_loss=0.04334, over 1424632.33 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:28:18,039 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 00:28:27,764 INFO [train.py:871] (2/4) Epoch 30, validation: loss=0.1626, simple_loss=0.2605, pruned_loss=0.03232, over 868885.00 frames.
2022-05-29 00:29:07,505 INFO [train.py:842] (2/4) Epoch 30, batch 6050, loss[loss=0.1639, simple_loss=0.2552, pruned_loss=0.03629, over 7154.00 frames.], tot_loss[loss=0.175, simple_loss=0.2643, pruned_loss=0.04289, over 1425239.41 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:29:46,797 INFO [train.py:842] (2/4) Epoch 30, batch 6100, loss[loss=0.1399, simple_loss=0.23, pruned_loss=0.02492, over 7052.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2638, pruned_loss=0.0426, over 1423799.89 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:30:26,352 INFO [train.py:842] (2/4) Epoch 30, batch 6150, loss[loss=0.1512, simple_loss=0.2386, pruned_loss=0.0319, over 7344.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2637, pruned_loss=0.04286, over 1421425.95 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:31:05,558 INFO [train.py:842] (2/4) Epoch 30, batch 6200, loss[loss=0.2255, simple_loss=0.2996, pruned_loss=0.07571, over 7271.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2638, pruned_loss=0.04315, over 1422015.55 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:31:45,361 INFO [train.py:842] (2/4) Epoch 30, batch 6250, loss[loss=0.1684, simple_loss=0.2633, pruned_loss=0.03677, over 7148.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2633, pruned_loss=0.04264, over 1423609.12 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:32:24,501 INFO [train.py:842] (2/4) Epoch 30, batch 6300, loss[loss=0.2096, simple_loss=0.3009, pruned_loss=0.05918, over 6717.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2643, pruned_loss=0.04308, over 1427889.28 frames.], batch size: 31, lr: 1.83e-04
2022-05-29 00:33:03,721 INFO [train.py:842] (2/4) Epoch 30, batch 6350, loss[loss=0.1457, simple_loss=0.2313, pruned_loss=0.03002, over 7295.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2654, pruned_loss=0.04367, over 1426996.08 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:33:42,838 INFO [train.py:842] (2/4) Epoch 30, batch 6400, loss[loss=0.1577, simple_loss=0.235, pruned_loss=0.04014, over 7129.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2649, pruned_loss=0.04345, over 1423764.96 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:34:22,383 INFO [train.py:842] (2/4) Epoch 30, batch 6450, loss[loss=0.1918, simple_loss=0.2813, pruned_loss=0.05116, over 7298.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2654, pruned_loss=0.04417, over 1426117.89 frames.], batch size: 24, lr: 1.83e-04
2022-05-29 00:35:01,391 INFO [train.py:842] (2/4) Epoch 30, batch 6500, loss[loss=0.2007, simple_loss=0.2875, pruned_loss=0.05694, over 7325.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2657, pruned_loss=0.04439, over 1427615.39 frames.], batch size: 24, lr: 1.83e-04
2022-05-29 00:35:41,137 INFO [train.py:842] (2/4) Epoch 30, batch 6550, loss[loss=0.1762, simple_loss=0.2753, pruned_loss=0.03858, over 7407.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2648, pruned_loss=0.04399, over 1426830.77 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:36:20,417 INFO [train.py:842] (2/4) Epoch 30, batch 6600, loss[loss=0.1669, simple_loss=0.2429, pruned_loss=0.04548, over 7396.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2653, pruned_loss=0.04504, over 1428236.76 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:37:00,147 INFO [train.py:842] (2/4) Epoch 30, batch 6650, loss[loss=0.1629, simple_loss=0.2555, pruned_loss=0.03512, over 7154.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2647, pruned_loss=0.0443, over 1427241.78 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:37:39,116 INFO [train.py:842] (2/4) Epoch 30, batch 6700, loss[loss=0.1452, simple_loss=0.2284, pruned_loss=0.03098, over 7270.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2654, pruned_loss=0.04505, over 1423991.22 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:38:18,808 INFO [train.py:842] (2/4) Epoch 30, batch 6750, loss[loss=0.1583, simple_loss=0.232, pruned_loss=0.04226, over 7394.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2636, pruned_loss=0.0438, over 1426428.04 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:38:57,956 INFO [train.py:842] (2/4) Epoch 30, batch 6800, loss[loss=0.1807, simple_loss=0.2775, pruned_loss=0.04194, over 7291.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2634, pruned_loss=0.04339, over 1426239.75 frames.], batch size: 25, lr: 1.83e-04
2022-05-29 00:39:37,523 INFO [train.py:842] (2/4) Epoch 30, batch 6850, loss[loss=0.2468, simple_loss=0.3125, pruned_loss=0.09048, over 7210.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2642, pruned_loss=0.04395, over 1426233.10 frames.], batch size: 22, lr: 1.83e-04
2022-05-29 00:40:16,989 INFO [train.py:842] (2/4) Epoch 30, batch 6900, loss[loss=0.16, simple_loss=0.2427, pruned_loss=0.03866, over 7283.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2632, pruned_loss=0.04324, over 1425384.03 frames.], batch size: 17, lr: 1.83e-04
2022-05-29 00:40:56,489 INFO [train.py:842] (2/4) Epoch 30, batch 6950, loss[loss=0.1604, simple_loss=0.2458, pruned_loss=0.03752, over 7075.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2639, pruned_loss=0.0437, over 1420593.25 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:41:35,808 INFO [train.py:842] (2/4) Epoch 30, batch 7000, loss[loss=0.1469, simple_loss=0.2438, pruned_loss=0.02502, over 7260.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2643, pruned_loss=0.04414, over 1420540.10 frames.], batch size: 19, lr: 1.83e-04
2022-05-29 00:42:15,299 INFO [train.py:842] (2/4) Epoch 30, batch 7050, loss[loss=0.1844, simple_loss=0.2871, pruned_loss=0.04089, over 7318.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2643, pruned_loss=0.04358, over 1420258.42 frames.], batch size: 21, lr: 1.83e-04
2022-05-29 00:42:54,718 INFO [train.py:842] (2/4) Epoch 30, batch 7100, loss[loss=0.1638, simple_loss=0.2421, pruned_loss=0.04273, over 7407.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2646, pruned_loss=0.04435, over 1416230.11 frames.], batch size: 18, lr: 1.83e-04
2022-05-29 00:43:34,107 INFO [train.py:842] (2/4) Epoch 30, batch 7150, loss[loss=0.1823, simple_loss=0.2731, pruned_loss=0.04569, over 7210.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2642, pruned_loss=0.04436, over 1417282.23 frames.], batch size: 22, lr: 1.82e-04
2022-05-29 00:44:13,142 INFO [train.py:842] (2/4) Epoch 30, batch 7200, loss[loss=0.1799, simple_loss=0.2734, pruned_loss=0.04319, over 7115.00 frames.], tot_loss[loss=0.1774, simple_loss=0.265, pruned_loss=0.0449, over 1416553.30 frames.], batch size: 21, lr: 1.82e-04
2022-05-29 00:44:52,875 INFO [train.py:842] (2/4) Epoch 30, batch 7250, loss[loss=0.2094, simple_loss=0.3012, pruned_loss=0.0588, over 7342.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2647, pruned_loss=0.04444, over 1416503.89 frames.], batch size: 22, lr: 1.82e-04
2022-05-29 00:45:32,249 INFO [train.py:842] (2/4) Epoch 30, batch 7300, loss[loss=0.1414, simple_loss=0.2291, pruned_loss=0.02686, over 7080.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2631, pruned_loss=0.04375, over 1419568.07 frames.], batch size: 18, lr: 1.82e-04
2022-05-29 00:46:12,051 INFO [train.py:842] (2/4) Epoch 30, batch 7350, loss[loss=0.1681, simple_loss=0.2587, pruned_loss=0.03878, over 7063.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2623, pruned_loss=0.04353, over 1422385.72 frames.], batch size: 28, lr: 1.82e-04
2022-05-29 00:47:02,156 INFO [train.py:842] (2/4) Epoch 30, batch 7400, loss[loss=0.1729, simple_loss=0.2565, pruned_loss=0.04462, over 6675.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2627, pruned_loss=0.04396, over 1420818.93 frames.], batch size: 31, lr: 1.82e-04
2022-05-29 00:47:41,628 INFO [train.py:842] (2/4) Epoch 30, batch 7450, loss[loss=0.2583, simple_loss=0.3275, pruned_loss=0.09454, over 7317.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2634, pruned_loss=0.04403, over 1426212.84 frames.], batch size: 21, lr: 1.82e-04
2022-05-29 00:48:20,886 INFO [train.py:842] (2/4) Epoch 30, batch 7500, loss[loss=0.1751, simple_loss=0.2521, pruned_loss=0.04907, over 7060.00 frames.], tot_loss[loss=0.1762, simple_loss=0.264, pruned_loss=0.04424, over 1425753.59 frames.], batch size: 18, lr: 1.82e-04
2022-05-29 00:49:00,492 INFO [train.py:842] (2/4) Epoch 30, batch 7550, loss[loss=0.1928, simple_loss=0.2678, pruned_loss=0.05885, over 7160.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2645, pruned_loss=0.04432, over 1423694.07 frames.], batch size: 19, lr: 1.82e-04
2022-05-29 00:49:39,636 INFO [train.py:842] (2/4) Epoch 30, batch 7600, loss[loss=0.1719, simple_loss=0.2623, pruned_loss=0.04072, over 7323.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2649, pruned_loss=0.04437, over 1423392.78 frames.], batch size: 20, lr: 1.82e-04
2022-05-29 00:50:19,174 INFO [train.py:842] (2/4) Epoch 30, batch 7650, loss[loss=0.2219, simple_loss=0.3117, pruned_loss=0.06607, over 7226.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2654, pruned_loss=0.04491, over 1421870.10 frames.], batch size: 20, lr: 1.82e-04
2022-05-29 00:50:58,425 INFO [train.py:842] (2/4) Epoch 30, batch 7700, loss[loss=0.239, simple_loss=0.3321, pruned_loss=0.07297, over 7306.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2659, pruned_loss=0.04516, over 1418456.68 frames.], batch size: 25, lr: 1.82e-04
2022-05-29 00:51:38,143 INFO [train.py:842] (2/4) Epoch 30, batch 7750, loss[loss=0.1673, simple_loss=0.2565, pruned_loss=0.03906, over 7352.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2662, pruned_loss=0.04476, over 1419278.63 frames.], batch size: 19, lr: 1.82e-04
2022-05-29 00:52:17,471 INFO [train.py:842] (2/4) Epoch 30, batch 7800, loss[loss=0.1427, simple_loss=0.2263, pruned_loss=0.02959, over 7058.00 frames.], tot_loss[loss=0.1766, simple_loss=0.265, pruned_loss=0.04407, over 1420950.99 frames.], batch size: 18, lr: 1.82e-04
2022-05-29 00:52:57,232 INFO [train.py:842] (2/4) Epoch 30, batch 7850, loss[loss=0.1431, simple_loss=0.2262, pruned_loss=0.02998, over 7232.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2651, pruned_loss=0.04409, over 1426601.68 frames.], batch size: 16, lr: 1.82e-04
2022-05-29 00:53:36,707 INFO [train.py:842] (2/4) Epoch 30, batch 7900, loss[loss=0.1965, simple_loss=0.2936, pruned_loss=0.04969, over 7328.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2645, pruned_loss=0.04369, over 1424062.90 frames.], batch size: 20, lr: 1.82e-04
2022-05-29 00:54:16,417 INFO [train.py:842] (2/4) Epoch 30, batch 7950, loss[loss=0.1488, simple_loss=0.2479, pruned_loss=0.0249, over 7155.00 frames.], tot_loss[loss=0.1755, simple_loss=0.264, pruned_loss=0.0435, over 1422998.46 frames.], batch size: 19, lr: 1.82e-04
2022-05-29 00:54:55,569 INFO [train.py:842] (2/4) Epoch 30, batch 8000, loss[loss=0.1821, simple_loss=0.2856, pruned_loss=0.03928, over 7214.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2653, pruned_loss=0.04418, over 1425194.12 frames.], batch size: 22, lr: 1.82e-04
2022-05-29 00:55:35,067 INFO [train.py:842] (2/4) Epoch 30, batch 8050, loss[loss=0.1503, simple_loss=0.2429, pruned_loss=0.0289, over 7236.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2629, pruned_loss=0.0426, over 1428230.63 frames.], batch size: 20, lr: 1.82e-04
2022-05-29 00:56:14,418 INFO [train.py:842] (2/4) Epoch 30, batch 8100, loss[loss=0.1383, simple_loss=0.239, pruned_loss=0.01886, over 7145.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2637, pruned_loss=0.04294, over 1431193.46 frames.], batch size: 20, lr: 1.82e-04
2022-05-29 00:56:53,825 INFO [train.py:842] (2/4) Epoch 30, batch 8150, loss[loss=0.1713, simple_loss=0.2683, pruned_loss=0.03713, over 7344.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2644, pruned_loss=0.04323, over 1422990.37 frames.], batch size: 22, lr: 1.82e-04
2022-05-29 00:57:33,237 INFO [train.py:842] (2/4) Epoch 30, batch 8200, loss[loss=0.1961, simple_loss=0.2868, pruned_loss=0.05269, over 7172.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2642, pruned_loss=0.04364, over 1425653.94 frames.], batch size: 26, lr: 1.82e-04
2022-05-29 00:58:12,722 INFO [train.py:842] (2/4) Epoch 30, batch 8250, loss[loss=0.1541, simple_loss=0.2448, pruned_loss=0.03171, over 7428.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2636, pruned_loss=0.0433, over 1423841.40 frames.], batch size: 18, lr: 1.82e-04
2022-05-29 00:58:51,853 INFO [train.py:842] (2/4) Epoch 30, batch 8300, loss[loss=0.1535, simple_loss=0.2513, pruned_loss=0.02784, over 7228.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2622, pruned_loss=0.0426, over 1424915.46 frames.], batch size: 21, lr: 1.82e-04
2022-05-29 00:59:31,528 INFO [train.py:842] (2/4) Epoch 30, batch 8350, loss[loss=0.2054, simple_loss=0.2985, pruned_loss=0.05613, over 7109.00 frames.], tot_loss[loss=0.174, simple_loss=0.2623, pruned_loss=0.04285, over 1429230.62 frames.], batch size: 28, lr: 1.82e-04
2022-05-29 01:00:10,549 INFO [train.py:842] (2/4) Epoch 30, batch 8400, loss[loss=0.1673, simple_loss=0.2456, pruned_loss=0.04446, over 7030.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2621, pruned_loss=0.04266, over 1426907.42 frames.], batch size: 16, lr: 1.82e-04
2022-05-29 01:00:49,939 INFO [train.py:842] (2/4) Epoch 30, batch 8450, loss[loss=0.1794, simple_loss=0.273, pruned_loss=0.0429, over 7200.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2646, pruned_loss=0.04409, over 1424801.96 frames.], batch size: 22, lr: 1.82e-04
2022-05-29 01:01:28,968 INFO [train.py:842] (2/4) Epoch 30, batch 8500, loss[loss=0.1381, simple_loss=0.2337, pruned_loss=0.02124, over 7231.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2642, pruned_loss=0.04394, over 1423813.59 frames.], batch size: 20, lr: 1.82e-04
2022-05-29 01:02:08,167 INFO [train.py:842] (2/4) Epoch 30, batch 8550, loss[loss=0.1413, simple_loss=0.2141, pruned_loss=0.03423, over 6994.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2648, pruned_loss=0.04402, over 1421451.03 frames.], batch size: 16, lr: 1.82e-04
2022-05-29 01:02:47,540 INFO [train.py:842] (2/4) Epoch 30, batch 8600, loss[loss=0.154, simple_loss=0.2482, pruned_loss=0.02994, over 7233.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2641, pruned_loss=0.04375, over 1417975.35 frames.], batch size: 21, lr: 1.82e-04
2022-05-29 01:03:27,025 INFO [train.py:842] (2/4) Epoch 30, batch 8650, loss[loss=0.1752, simple_loss=0.265, pruned_loss=0.04271, over 7201.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2636, pruned_loss=0.04367, over 1418465.52 frames.], batch size: 23, lr: 1.82e-04
2022-05-29 01:04:06,186 INFO [train.py:842] (2/4) Epoch 30, batch 8700, loss[loss=0.1687, simple_loss=0.2485, pruned_loss=0.04442, over 7184.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2639, pruned_loss=0.04373, over 1416217.72 frames.], batch size: 16, lr: 1.82e-04
2022-05-29 01:04:45,517 INFO [train.py:842] (2/4) Epoch 30, batch 8750, loss[loss=0.2165, simple_loss=0.3055, pruned_loss=0.06373, over 5002.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2641, pruned_loss=0.04347, over 1414560.96 frames.], batch size: 52, lr: 1.82e-04
2022-05-29 01:05:24,837 INFO [train.py:842] (2/4) Epoch 30, batch 8800, loss[loss=0.1947, simple_loss=0.2799, pruned_loss=0.05473, over 7116.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2635, pruned_loss=0.0438, over 1417287.57 frames.], batch size: 21, lr: 1.82e-04
2022-05-29 01:06:04,531 INFO [train.py:842] (2/4) Epoch 30, batch 8850, loss[loss=0.1991, simple_loss=0.2826, pruned_loss=0.05778, over 7203.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2614, pruned_loss=0.04263, over 1420117.48 frames.], batch size: 22, lr: 1.82e-04
2022-05-29 01:06:43,626 INFO [train.py:842] (2/4) Epoch 30, batch 8900, loss[loss=0.1789, simple_loss=0.2728, pruned_loss=0.0425, over 7158.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2616, pruned_loss=0.04277, over 1415051.82 frames.], batch size: 18, lr: 1.82e-04
2022-05-29 01:07:22,760 INFO [train.py:842] (2/4) Epoch 30, batch 8950, loss[loss=0.1713, simple_loss=0.2473, pruned_loss=0.04762, over 7409.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2614, pruned_loss=0.04263, over 1404916.87 frames.], batch size: 18, lr: 1.82e-04
2022-05-29 01:08:01,602 INFO [train.py:842] (2/4) Epoch 30, batch 9000, loss[loss=0.2466, simple_loss=0.3267, pruned_loss=0.0833, over 6836.00 frames.], tot_loss[loss=0.175, simple_loss=0.2631, pruned_loss=0.04349, over 1393458.03 frames.], batch size: 31, lr: 1.82e-04
2022-05-29 01:08:01,603 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 01:08:11,201 INFO [train.py:871] (2/4) Epoch 30, validation: loss=0.1653, simple_loss=0.2631, pruned_loss=0.03376, over 868885.00 frames.
2022-05-29 01:08:49,663 INFO [train.py:842] (2/4) Epoch 30, batch 9050, loss[loss=0.1926, simple_loss=0.2703, pruned_loss=0.0575, over 5177.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2652, pruned_loss=0.04381, over 1375095.48 frames.], batch size: 53, lr: 1.82e-04
2022-05-29 01:09:27,661 INFO [train.py:842] (2/4) Epoch 30, batch 9100, loss[loss=0.1588, simple_loss=0.2608, pruned_loss=0.02838, over 6274.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2676, pruned_loss=0.04537, over 1330135.65 frames.], batch size: 37, lr: 1.82e-04
2022-05-29 01:10:06,050 INFO [train.py:842] (2/4) Epoch 30, batch 9150, loss[loss=0.195, simple_loss=0.2803, pruned_loss=0.05489, over 5077.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2713, pruned_loss=0.04843, over 1263002.77 frames.], batch size: 52, lr: 1.82e-04
2022-05-29 01:10:56,764 INFO [train.py:842] (2/4) Epoch 31, batch 0, loss[loss=0.1537, simple_loss=0.2445, pruned_loss=0.03143, over 7319.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2445, pruned_loss=0.03143, over 7319.00 frames.], batch size: 20, lr: 1.79e-04
2022-05-29 01:11:36,470 INFO [train.py:842] (2/4) Epoch 31, batch 50, loss[loss=0.144, simple_loss=0.2285, pruned_loss=0.02978, over 7254.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2621, pruned_loss=0.04459, over 316418.68 frames.], batch size: 19, lr: 1.79e-04
2022-05-29 01:12:15,781 INFO [train.py:842] (2/4) Epoch 31, batch 100, loss[loss=0.191, simple_loss=0.2952, pruned_loss=0.04339, over 7403.00 frames.], tot_loss[loss=0.1791, simple_loss=0.267, pruned_loss=0.04557, over 561334.06 frames.], batch size: 23, lr: 1.79e-04
2022-05-29 01:12:55,629 INFO [train.py:842] (2/4) Epoch 31, batch 150, loss[loss=0.2289, simple_loss=0.3072, pruned_loss=0.07531, over 7200.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2653, pruned_loss=0.04491, over 756598.56 frames.], batch size: 22, lr: 1.79e-04
2022-05-29 01:13:34,915 INFO [train.py:842] (2/4) Epoch 31, batch 200, loss[loss=0.214, simple_loss=0.2949, pruned_loss=0.06655, over 4903.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2649, pruned_loss=0.04412, over 901509.84 frames.], batch size: 53, lr: 1.79e-04
2022-05-29 01:14:14,352 INFO [train.py:842] (2/4) Epoch 31, batch 250, loss[loss=0.1679, simple_loss=0.2646, pruned_loss=0.03555, over 7321.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2658, pruned_loss=0.04402, over 1015759.58 frames.], batch size: 25, lr: 1.79e-04
2022-05-29 01:14:53,593 INFO [train.py:842] (2/4) Epoch 31, batch 300, loss[loss=0.155, simple_loss=0.2587, pruned_loss=0.02571, over 7310.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2638, pruned_loss=0.04304, over 1107017.62 frames.], batch size: 21, lr: 1.79e-04
2022-05-29 01:15:33,051 INFO [train.py:842] (2/4) Epoch 31, batch 350, loss[loss=0.1491, simple_loss=0.242, pruned_loss=0.02806, over 7168.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2631, pruned_loss=0.04254, over 1173906.66 frames.], batch size: 18, lr: 1.79e-04
2022-05-29 01:16:12,213 INFO [train.py:842] (2/4) Epoch 31, batch 400, loss[loss=0.2111, simple_loss=0.2962, pruned_loss=0.06301, over 7214.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2644, pruned_loss=0.04347, over 1224575.36 frames.], batch size: 21, lr: 1.79e-04
2022-05-29 01:16:51,525 INFO [train.py:842] (2/4) Epoch 31, batch 450, loss[loss=0.193, simple_loss=0.2812, pruned_loss=0.0524, over 7255.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2642, pruned_loss=0.0434, over 1265354.19 frames.], batch size: 26, lr: 1.79e-04
2022-05-29 01:17:30,772 INFO [train.py:842] (2/4) Epoch 31, batch 500, loss[loss=0.1411, simple_loss=0.2262, pruned_loss=0.02796, over 7259.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2641, pruned_loss=0.04367, over 1300395.94 frames.], batch size: 17, lr: 1.79e-04
2022-05-29 01:18:10,323 INFO [train.py:842] (2/4) Epoch 31, batch 550, loss[loss=0.17, simple_loss=0.2636, pruned_loss=0.03821, over 7403.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2649, pruned_loss=0.04413, over 1326990.69 frames.], batch size: 21, lr: 1.79e-04
2022-05-29 01:18:49,306 INFO [train.py:842] (2/4) Epoch 31, batch 600, loss[loss=0.1686, simple_loss=0.2535, pruned_loss=0.04185, over 7061.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2666, pruned_loss=0.04503, over 1346750.15 frames.], batch size: 18, lr: 1.79e-04
2022-05-29 01:19:29,071 INFO [train.py:842] (2/4) Epoch 31, batch 650, loss[loss=0.2077, simple_loss=0.2883, pruned_loss=0.0635, over 7143.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2657, pruned_loss=0.04461, over 1368611.57 frames.], batch size: 20, lr: 1.79e-04
2022-05-29 01:20:08,327 INFO [train.py:842] (2/4) Epoch 31, batch 700, loss[loss=0.1247, simple_loss=0.2021, pruned_loss=0.02363, over 6843.00 frames.], tot_loss[loss=0.1759, simple_loss=0.264, pruned_loss=0.04386, over 1378151.35 frames.], batch size: 15, lr: 1.79e-04
2022-05-29 01:20:47,899 INFO [train.py:842] (2/4) Epoch 31, batch 750, loss[loss=0.1536, simple_loss=0.261, pruned_loss=0.0231, over 7239.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2627, pruned_loss=0.04282, over 1386002.38 frames.], batch size: 20, lr: 1.79e-04
2022-05-29 01:21:27,101 INFO [train.py:842] (2/4) Epoch 31, batch 800, loss[loss=0.195, simple_loss=0.28, pruned_loss=0.05502, over 7318.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2623, pruned_loss=0.04252, over 1395107.03 frames.], batch size: 20, lr: 1.79e-04
2022-05-29 01:22:06,648 INFO [train.py:842] (2/4) Epoch 31, batch 850, loss[loss=0.158, simple_loss=0.2542, pruned_loss=0.03086, over 7425.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2619, pruned_loss=0.04233, over 1399399.93 frames.], batch size: 20, lr: 1.79e-04
2022-05-29 01:22:46,054 INFO [train.py:842] (2/4) Epoch 31, batch 900, loss[loss=0.2084, simple_loss=0.284, pruned_loss=0.06641, over 6848.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2613, pruned_loss=0.04225, over 1403530.30 frames.], batch size: 15, lr: 1.79e-04
2022-05-29 01:23:25,747 INFO [train.py:842] (2/4) Epoch 31, batch 950, loss[loss=0.1947, simple_loss=0.2751, pruned_loss=0.05717, over 7078.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2612, pruned_loss=0.04226, over 1406315.59 frames.], batch size: 28, lr: 1.79e-04
2022-05-29 01:24:04,910 INFO [train.py:842] (2/4) Epoch 31, batch 1000, loss[loss=0.1748, simple_loss=0.2667, pruned_loss=0.04144, over 7346.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2634, pruned_loss=0.04338, over 1409460.54 frames.], batch size: 22, lr: 1.79e-04
2022-05-29 01:24:44,396 INFO [train.py:842] (2/4) Epoch 31, batch 1050, loss[loss=0.1915, simple_loss=0.2821, pruned_loss=0.05046, over 7058.00 frames.], tot_loss[loss=0.1745, simple_loss=0.263, pruned_loss=0.04297, over 1411778.65 frames.], batch size: 28, lr: 1.79e-04
2022-05-29 01:25:23,509 INFO [train.py:842] (2/4) Epoch 31, batch 1100, loss[loss=0.1764, simple_loss=0.2529, pruned_loss=0.04997, over 7079.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2628, pruned_loss=0.0428, over 1416266.09 frames.], batch size: 18, lr: 1.79e-04
2022-05-29 01:26:03,300 INFO [train.py:842] (2/4) Epoch 31, batch 1150, loss[loss=0.1619, simple_loss=0.2463, pruned_loss=0.03877, over 7062.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2616, pruned_loss=0.04227, over 1417626.81 frames.], batch size: 18, lr: 1.79e-04
2022-05-29 01:26:42,690 INFO [train.py:842] (2/4) Epoch 31, batch 1200, loss[loss=0.208, simple_loss=0.2973, pruned_loss=0.0593, over 7213.00 frames.], tot_loss[loss=0.173, simple_loss=0.262, pruned_loss=0.04196, over 1419717.28 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 01:27:22,220 INFO [train.py:842] (2/4) Epoch 31, batch 1250, loss[loss=0.1527, simple_loss=0.2373, pruned_loss=0.03404, over 7414.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2629, pruned_loss=0.04224, over 1419009.95 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:28:01,505 INFO [train.py:842] (2/4) Epoch 31, batch 1300, loss[loss=0.1951, simple_loss=0.2752, pruned_loss=0.05753, over 7130.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2628, pruned_loss=0.04195, over 1418142.54 frames.], batch size: 26, lr: 1.78e-04
2022-05-29 01:28:40,941 INFO [train.py:842] (2/4) Epoch 31, batch 1350, loss[loss=0.1807, simple_loss=0.2632, pruned_loss=0.04915, over 7136.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2642, pruned_loss=0.04312, over 1414393.73 frames.], batch size: 17, lr: 1.78e-04
2022-05-29 01:29:20,139 INFO [train.py:842] (2/4) Epoch 31, batch 1400, loss[loss=0.2088, simple_loss=0.2927, pruned_loss=0.06252, over 7336.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2645, pruned_loss=0.04312, over 1418680.06 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 01:29:59,781 INFO [train.py:842] (2/4) Epoch 31, batch 1450, loss[loss=0.1715, simple_loss=0.2616, pruned_loss=0.04071, over 7148.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2637, pruned_loss=0.04282, over 1419962.06 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:30:38,815 INFO [train.py:842] (2/4) Epoch 31, batch 1500, loss[loss=0.1643, simple_loss=0.2536, pruned_loss=0.03754, over 7275.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2647, pruned_loss=0.0431, over 1425662.53 frames.], batch size: 25, lr: 1.78e-04
2022-05-29 01:31:18,572 INFO [train.py:842] (2/4) Epoch 31, batch 1550, loss[loss=0.2131, simple_loss=0.3023, pruned_loss=0.06196, over 7295.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2643, pruned_loss=0.04338, over 1427181.95 frames.], batch size: 25, lr: 1.78e-04
2022-05-29 01:31:57,812 INFO [train.py:842] (2/4) Epoch 31, batch 1600, loss[loss=0.169, simple_loss=0.2511, pruned_loss=0.04342, over 7264.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2636, pruned_loss=0.04356, over 1427995.61 frames.], batch size: 19, lr: 1.78e-04
2022-05-29 01:32:37,052 INFO [train.py:842] (2/4) Epoch 31, batch 1650, loss[loss=0.1566, simple_loss=0.2526, pruned_loss=0.03033, over 7107.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2654, pruned_loss=0.04408, over 1428072.48 frames.], batch size: 21, lr: 1.78e-04
2022-05-29 01:33:16,435 INFO [train.py:842] (2/4) Epoch 31, batch 1700, loss[loss=0.1693, simple_loss=0.2641, pruned_loss=0.03726, over 7266.00 frames.], tot_loss[loss=0.1756, simple_loss=0.264, pruned_loss=0.04363, over 1424957.61 frames.], batch size: 24, lr: 1.78e-04
2022-05-29 01:33:55,899 INFO [train.py:842] (2/4) Epoch 31, batch 1750, loss[loss=0.2024, simple_loss=0.2805, pruned_loss=0.06218, over 7370.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2641, pruned_loss=0.04352, over 1427557.84 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 01:34:34,937 INFO [train.py:842] (2/4) Epoch 31, batch 1800, loss[loss=0.1571, simple_loss=0.2458, pruned_loss=0.03424, over 7434.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2635, pruned_loss=0.04318, over 1423957.23 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:35:14,393 INFO [train.py:842] (2/4) Epoch 31, batch 1850, loss[loss=0.1404, simple_loss=0.224, pruned_loss=0.02841, over 7122.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2631, pruned_loss=0.04285, over 1422399.28 frames.], batch size: 17, lr: 1.78e-04
2022-05-29 01:35:53,690 INFO [train.py:842] (2/4) Epoch 31, batch 1900, loss[loss=0.1843, simple_loss=0.276, pruned_loss=0.04631, over 7333.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2629, pruned_loss=0.04295, over 1425451.76 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:36:33,262 INFO [train.py:842] (2/4) Epoch 31, batch 1950, loss[loss=0.211, simple_loss=0.3032, pruned_loss=0.05943, over 7370.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2631, pruned_loss=0.04296, over 1425655.75 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 01:37:12,631 INFO [train.py:842] (2/4) Epoch 31, batch 2000, loss[loss=0.1428, simple_loss=0.2356, pruned_loss=0.02499, over 7170.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2618, pruned_loss=0.04259, over 1427401.38 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:37:52,319 INFO [train.py:842] (2/4) Epoch 31, batch 2050, loss[loss=0.2015, simple_loss=0.298, pruned_loss=0.05251, over 7211.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2609, pruned_loss=0.0421, over 1424224.18 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 01:38:31,296 INFO [train.py:842] (2/4) Epoch 31, batch 2100, loss[loss=0.183, simple_loss=0.2832, pruned_loss=0.04144, over 7168.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2622, pruned_loss=0.04268, over 1423077.17 frames.], batch size: 19, lr: 1.78e-04
2022-05-29 01:39:10,882 INFO [train.py:842] (2/4) Epoch 31, batch 2150, loss[loss=0.1786, simple_loss=0.2664, pruned_loss=0.0454, over 7157.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2615, pruned_loss=0.04201, over 1427032.19 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:39:50,100 INFO [train.py:842] (2/4) Epoch 31, batch 2200, loss[loss=0.1941, simple_loss=0.2777, pruned_loss=0.05531, over 7067.00 frames.], tot_loss[loss=0.173, simple_loss=0.2621, pruned_loss=0.04198, over 1428960.53 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:40:29,750 INFO [train.py:842] (2/4) Epoch 31, batch 2250, loss[loss=0.1863, simple_loss=0.286, pruned_loss=0.04328, over 7194.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2625, pruned_loss=0.04154, over 1428209.74 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 01:41:09,305 INFO [train.py:842] (2/4) Epoch 31, batch 2300, loss[loss=0.1636, simple_loss=0.2466, pruned_loss=0.04026, over 7259.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2616, pruned_loss=0.04175, over 1430955.77 frames.], batch size: 19, lr: 1.78e-04
2022-05-29 01:41:48,917 INFO [train.py:842] (2/4) Epoch 31, batch 2350, loss[loss=0.1791, simple_loss=0.2562, pruned_loss=0.05106, over 7064.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2617, pruned_loss=0.04191, over 1429863.43 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:42:27,935 INFO [train.py:842] (2/4) Epoch 31, batch 2400, loss[loss=0.1972, simple_loss=0.2769, pruned_loss=0.0588, over 7222.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2622, pruned_loss=0.04227, over 1428162.97 frames.], batch size: 21, lr: 1.78e-04
2022-05-29 01:43:07,258 INFO [train.py:842] (2/4) Epoch 31, batch 2450, loss[loss=0.2099, simple_loss=0.2862, pruned_loss=0.0668, over 7212.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2638, pruned_loss=0.04287, over 1425098.32 frames.], batch size: 21, lr: 1.78e-04
2022-05-29 01:43:46,448 INFO [train.py:842] (2/4) Epoch 31, batch 2500, loss[loss=0.1596, simple_loss=0.2576, pruned_loss=0.03079, over 7335.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2619, pruned_loss=0.04212, over 1427784.51 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 01:44:37,199 INFO [train.py:842] (2/4) Epoch 31, batch 2550, loss[loss=0.1593, simple_loss=0.2542, pruned_loss=0.03218, over 7201.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2615, pruned_loss=0.04184, over 1429341.03 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 01:45:16,366 INFO [train.py:842] (2/4) Epoch 31, batch 2600, loss[loss=0.1297, simple_loss=0.2163, pruned_loss=0.0215, over 7408.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2607, pruned_loss=0.04146, over 1428190.58 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:45:55,626 INFO [train.py:842] (2/4) Epoch 31, batch 2650, loss[loss=0.1565, simple_loss=0.2536, pruned_loss=0.0297, over 7415.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2613, pruned_loss=0.04169, over 1424910.95 frames.], batch size: 21, lr: 1.78e-04
2022-05-29 01:46:34,541 INFO [train.py:842] (2/4) Epoch 31, batch 2700, loss[loss=0.1688, simple_loss=0.2716, pruned_loss=0.03304, over 7302.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2624, pruned_loss=0.04207, over 1419049.14 frames.], batch size: 25, lr: 1.78e-04
2022-05-29 01:47:14,128 INFO [train.py:842] (2/4) Epoch 31, batch 2750, loss[loss=0.1862, simple_loss=0.2796, pruned_loss=0.04638, over 7149.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2626, pruned_loss=0.0426, over 1419892.21 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:47:53,467 INFO [train.py:842] (2/4) Epoch 31, batch 2800, loss[loss=0.1958, simple_loss=0.2768, pruned_loss=0.05737, over 7165.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2625, pruned_loss=0.04281, over 1421686.33 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 01:48:43,776 INFO [train.py:842] (2/4) Epoch 31, batch 2850, loss[loss=0.1998, simple_loss=0.2831, pruned_loss=0.05825, over 7208.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2629, pruned_loss=0.04308, over 1419336.32 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 01:49:23,031 INFO [train.py:842] (2/4) Epoch 31, batch 2900, loss[loss=0.1566, simple_loss=0.2541, pruned_loss=0.02952, over 7127.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2626, pruned_loss=0.04277, over 1423210.24 frames.], batch size: 21, lr: 1.78e-04
2022-05-29 01:50:02,448 INFO [train.py:842] (2/4) Epoch 31, batch 2950, loss[loss=0.1973, simple_loss=0.2825, pruned_loss=0.05609, over 7247.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2623, pruned_loss=0.04226, over 1422313.49 frames.], batch size: 19, lr: 1.78e-04
2022-05-29 01:50:52,119 INFO [train.py:842] (2/4) Epoch 31, batch 3000, loss[loss=0.1753, simple_loss=0.2739, pruned_loss=0.0383, over 7327.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2617, pruned_loss=0.04208, over 1422474.90 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:50:52,119 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 01:51:02,034 INFO [train.py:871] (2/4) Epoch 31, validation: loss=0.165, simple_loss=0.262, pruned_loss=0.03402, over 868885.00 frames.
2022-05-29 01:51:41,766 INFO [train.py:842] (2/4) Epoch 31, batch 3050, loss[loss=0.1652, simple_loss=0.2512, pruned_loss=0.03964, over 6994.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2619, pruned_loss=0.04218, over 1422035.41 frames.], batch size: 16, lr: 1.78e-04
2022-05-29 01:52:21,214 INFO [train.py:842] (2/4) Epoch 31, batch 3100, loss[loss=0.1641, simple_loss=0.2654, pruned_loss=0.03144, over 7320.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2617, pruned_loss=0.04229, over 1425365.70 frames.], batch size: 25, lr: 1.78e-04
2022-05-29 01:53:00,816 INFO [train.py:842] (2/4) Epoch 31, batch 3150, loss[loss=0.1755, simple_loss=0.2423, pruned_loss=0.05436, over 6985.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2619, pruned_loss=0.04215, over 1424259.52 frames.], batch size: 16, lr: 1.78e-04
2022-05-29 01:53:39,926 INFO [train.py:842] (2/4) Epoch 31, batch 3200, loss[loss=0.2196, simple_loss=0.3, pruned_loss=0.06961, over 7213.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2632, pruned_loss=0.04297, over 1415847.93 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 01:54:19,565 INFO [train.py:842] (2/4) Epoch 31, batch 3250, loss[loss=0.1539, simple_loss=0.2534, pruned_loss=0.02725, over 7142.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2647, pruned_loss=0.04413, over 1416014.90 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:54:58,976 INFO [train.py:842] (2/4) Epoch 31, batch 3300, loss[loss=0.1519, simple_loss=0.2348, pruned_loss=0.03452, over 7275.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2641, pruned_loss=0.04381, over 1422552.03 frames.], batch size: 17, lr: 1.78e-04
2022-05-29 01:55:38,528 INFO [train.py:842] (2/4) Epoch 31, batch 3350, loss[loss=0.1528, simple_loss=0.2479, pruned_loss=0.02882, over 7217.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2637, pruned_loss=0.04383, over 1421291.26 frames.], batch size: 21, lr: 1.78e-04
2022-05-29 01:56:17,506 INFO [train.py:842] (2/4) Epoch 31, batch 3400, loss[loss=0.1954, simple_loss=0.2802, pruned_loss=0.05529, over 7300.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2635, pruned_loss=0.04348, over 1421506.50 frames.], batch size: 25, lr: 1.78e-04
2022-05-29 01:56:57,100 INFO [train.py:842] (2/4) Epoch 31, batch 3450, loss[loss=0.2079, simple_loss=0.2992, pruned_loss=0.05829, over 6508.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2637, pruned_loss=0.04352, over 1426170.42 frames.], batch size: 38, lr: 1.78e-04
2022-05-29 01:57:36,437 INFO [train.py:842] (2/4) Epoch 31, batch 3500, loss[loss=0.2021, simple_loss=0.286, pruned_loss=0.05914, over 7359.00 frames.], tot_loss[loss=0.176, simple_loss=0.2643, pruned_loss=0.04378, over 1427055.66 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 01:58:15,845 INFO [train.py:842] (2/4) Epoch 31, batch 3550, loss[loss=0.1645, simple_loss=0.2545, pruned_loss=0.03726, over 7438.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2649, pruned_loss=0.04387, over 1428530.03 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 01:58:54,903 INFO [train.py:842] (2/4) Epoch 31, batch 3600, loss[loss=0.1606, simple_loss=0.2548, pruned_loss=0.03321, over 7290.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2644, pruned_loss=0.04354, over 1423144.45 frames.], batch size: 24, lr: 1.78e-04
2022-05-29 01:59:34,572 INFO [train.py:842] (2/4) Epoch 31, batch 3650, loss[loss=0.1449, simple_loss=0.2274, pruned_loss=0.03123, over 7134.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2642, pruned_loss=0.04358, over 1422169.61 frames.], batch size: 17, lr: 1.78e-04
2022-05-29 02:00:14,110 INFO [train.py:842] (2/4) Epoch 31, batch 3700, loss[loss=0.174, simple_loss=0.2498, pruned_loss=0.04912, over 7280.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2625, pruned_loss=0.04264, over 1425675.16 frames.], batch size: 17, lr: 1.78e-04
2022-05-29 02:00:53,408 INFO [train.py:842] (2/4) Epoch 31, batch 3750, loss[loss=0.169, simple_loss=0.2486, pruned_loss=0.04469, over 7256.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2618, pruned_loss=0.04256, over 1423297.15 frames.], batch size: 19, lr: 1.78e-04
2022-05-29 02:01:33,018 INFO [train.py:842] (2/4) Epoch 31, batch 3800, loss[loss=0.1852, simple_loss=0.2836, pruned_loss=0.04339, over 7384.00 frames.], tot_loss[loss=0.174, simple_loss=0.2624, pruned_loss=0.04282, over 1427406.67 frames.], batch size: 23, lr: 1.78e-04
2022-05-29 02:02:12,542 INFO [train.py:842] (2/4) Epoch 31, batch 3850, loss[loss=0.1565, simple_loss=0.2355, pruned_loss=0.03875, over 7004.00 frames.], tot_loss[loss=0.175, simple_loss=0.2634, pruned_loss=0.04328, over 1426448.20 frames.], batch size: 16, lr: 1.78e-04
2022-05-29 02:02:51,829 INFO [train.py:842] (2/4) Epoch 31, batch 3900, loss[loss=0.1666, simple_loss=0.2608, pruned_loss=0.03621, over 7333.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2631, pruned_loss=0.04301, over 1430249.93 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 02:03:31,403 INFO [train.py:842] (2/4) Epoch 31, batch 3950, loss[loss=0.1673, simple_loss=0.256, pruned_loss=0.03933, over 7273.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2635, pruned_loss=0.04296, over 1430300.58 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 02:04:10,616 INFO [train.py:842] (2/4) Epoch 31, batch 4000, loss[loss=0.1781, simple_loss=0.2534, pruned_loss=0.0514, over 7435.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2629, pruned_loss=0.04238, over 1432030.17 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 02:04:50,019 INFO [train.py:842] (2/4) Epoch 31, batch 4050, loss[loss=0.136, simple_loss=0.2241, pruned_loss=0.02397, over 7008.00 frames.], tot_loss[loss=0.1748, simple_loss=0.264, pruned_loss=0.04279, over 1428393.83 frames.], batch size: 16, lr: 1.78e-04
2022-05-29 02:05:29,186 INFO [train.py:842] (2/4) Epoch 31, batch 4100, loss[loss=0.1407, simple_loss=0.2298, pruned_loss=0.02582, over 7404.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2639, pruned_loss=0.04267, over 1428875.75 frames.], batch size: 18, lr: 1.78e-04
2022-05-29 02:06:08,834 INFO [train.py:842] (2/4) Epoch 31, batch 4150, loss[loss=0.1531, simple_loss=0.245, pruned_loss=0.03057, over 7146.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2629, pruned_loss=0.04247, over 1430661.13 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 02:06:47,959 INFO [train.py:842] (2/4) Epoch 31, batch 4200, loss[loss=0.1682, simple_loss=0.2565, pruned_loss=0.04001, over 7417.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2638, pruned_loss=0.04286, over 1430604.48 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 02:07:30,414 INFO [train.py:842] (2/4) Epoch 31, batch 4250, loss[loss=0.165, simple_loss=0.2689, pruned_loss=0.03053, over 7330.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2627, pruned_loss=0.04253, over 1431448.50 frames.], batch size: 22, lr: 1.78e-04
2022-05-29 02:08:09,311 INFO [train.py:842] (2/4) Epoch 31, batch 4300, loss[loss=0.1986, simple_loss=0.2822, pruned_loss=0.05748, over 7317.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2626, pruned_loss=0.04263, over 1430973.92 frames.], batch size: 20, lr: 1.78e-04
2022-05-29 02:08:48,978 INFO [train.py:842] (2/4) Epoch 31, batch 4350, loss[loss=0.1753, simple_loss=0.2536, pruned_loss=0.04847, over 7079.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2617, pruned_loss=0.042, over 1431789.06 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:09:28,402 INFO [train.py:842] (2/4) Epoch 31, batch 4400, loss[loss=0.1857, simple_loss=0.2874, pruned_loss=0.04202, over 7064.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2614, pruned_loss=0.04212, over 1433817.16 frames.], batch size: 28, lr: 1.77e-04
2022-05-29 02:10:08,123 INFO [train.py:842] (2/4) Epoch 31, batch 4450, loss[loss=0.1773, simple_loss=0.2676, pruned_loss=0.04346, over 7155.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2611, pruned_loss=0.04196, over 1433532.87 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:10:47,342 INFO [train.py:842] (2/4) Epoch 31, batch 4500, loss[loss=0.1767, simple_loss=0.2651, pruned_loss=0.0441, over 7213.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2616, pruned_loss=0.04188, over 1430085.51 frames.], batch size: 22, lr: 1.77e-04
2022-05-29 02:11:26,823 INFO [train.py:842] (2/4) Epoch 31, batch 4550, loss[loss=0.2443, simple_loss=0.3212, pruned_loss=0.08365, over 5022.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2628, pruned_loss=0.04317, over 1421086.86 frames.], batch size: 53, lr: 1.77e-04
2022-05-29 02:12:06,252 INFO [train.py:842] (2/4) Epoch 31, batch 4600, loss[loss=0.1629, simple_loss=0.2675, pruned_loss=0.0291, over 7138.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2633, pruned_loss=0.04352, over 1424382.22 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:12:45,700 INFO [train.py:842] (2/4) Epoch 31, batch 4650, loss[loss=0.2069, simple_loss=0.298, pruned_loss=0.05789, over 7430.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2641, pruned_loss=0.04351, over 1424691.17 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:13:24,763 INFO [train.py:842] (2/4) Epoch 31, batch 4700, loss[loss=0.1603, simple_loss=0.2415, pruned_loss=0.03962, over 7255.00 frames.], tot_loss[loss=0.176, simple_loss=0.2644, pruned_loss=0.04383, over 1422623.62 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:14:04,489 INFO [train.py:842] (2/4) Epoch 31, batch 4750, loss[loss=0.1923, simple_loss=0.2701, pruned_loss=0.05718, over 7134.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2641, pruned_loss=0.04349, over 1425284.46 frames.], batch size: 17, lr: 1.77e-04
2022-05-29 02:14:43,761 INFO [train.py:842] (2/4) Epoch 31, batch 4800, loss[loss=0.1518, simple_loss=0.2389, pruned_loss=0.03237, over 7153.00 frames.], tot_loss[loss=0.176, simple_loss=0.2646, pruned_loss=0.04367, over 1425164.64 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:15:23,374 INFO [train.py:842] (2/4) Epoch 31, batch 4850, loss[loss=0.1717, simple_loss=0.2594, pruned_loss=0.04203, over 6517.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2639, pruned_loss=0.04344, over 1419596.90 frames.], batch size: 38, lr: 1.77e-04
2022-05-29 02:16:02,460 INFO [train.py:842] (2/4) Epoch 31, batch 4900, loss[loss=0.1504, simple_loss=0.2359, pruned_loss=0.03242, over 7426.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2637, pruned_loss=0.04364, over 1420647.17 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:16:42,088 INFO [train.py:842] (2/4) Epoch 31, batch 4950, loss[loss=0.1676, simple_loss=0.2583, pruned_loss=0.03847, over 7066.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2626, pruned_loss=0.04334, over 1417556.49 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:17:21,104 INFO [train.py:842] (2/4) Epoch 31, batch 5000, loss[loss=0.1418, simple_loss=0.2351, pruned_loss=0.02426, over 7065.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2637, pruned_loss=0.04396, over 1416692.64 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:18:00,727 INFO [train.py:842] (2/4) Epoch 31, batch 5050, loss[loss=0.1713, simple_loss=0.259, pruned_loss=0.04181, over 7160.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2645, pruned_loss=0.04416, over 1416436.25 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:18:39,796 INFO [train.py:842] (2/4) Epoch 31, batch 5100, loss[loss=0.175, simple_loss=0.2602, pruned_loss=0.04485, over 7402.00 frames.], tot_loss[loss=0.1748, simple_loss=0.263, pruned_loss=0.04326, over 1422050.20 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:19:19,591 INFO [train.py:842] (2/4) Epoch 31, batch 5150, loss[loss=0.1908, simple_loss=0.2646, pruned_loss=0.05849, over 7136.00 frames.], tot_loss[loss=0.174, simple_loss=0.2625, pruned_loss=0.04273, over 1424542.46 frames.], batch size: 17, lr: 1.77e-04
2022-05-29 02:19:59,021 INFO [train.py:842] (2/4) Epoch 31, batch 5200, loss[loss=0.1722, simple_loss=0.2562, pruned_loss=0.04411, over 7257.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2623, pruned_loss=0.04264, over 1423936.03 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:20:38,692 INFO [train.py:842] (2/4) Epoch 31, batch 5250, loss[loss=0.1733, simple_loss=0.2711, pruned_loss=0.0377, over 7056.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2621, pruned_loss=0.04266, over 1422758.94 frames.], batch size: 28, lr: 1.77e-04
2022-05-29 02:21:18,156 INFO [train.py:842] (2/4) Epoch 31, batch 5300, loss[loss=0.1814, simple_loss=0.2649, pruned_loss=0.04891, over 7370.00 frames.], tot_loss[loss=0.1734, simple_loss=0.262, pruned_loss=0.04244, over 1424056.09 frames.], batch size: 23, lr: 1.77e-04
2022-05-29 02:21:57,657 INFO [train.py:842] (2/4) Epoch 31, batch 5350, loss[loss=0.2084, simple_loss=0.276, pruned_loss=0.07041, over 5104.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2621, pruned_loss=0.04242, over 1420778.62 frames.], batch size: 52, lr: 1.77e-04
2022-05-29 02:22:36,641 INFO [train.py:842] (2/4) Epoch 31, batch 5400, loss[loss=0.1715, simple_loss=0.2611, pruned_loss=0.04095, over 7308.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2631, pruned_loss=0.04276, over 1422413.16 frames.], batch size: 21, lr: 1.77e-04
2022-05-29 02:23:16,001 INFO [train.py:842] (2/4) Epoch 31, batch 5450, loss[loss=0.1441, simple_loss=0.2271, pruned_loss=0.03054, over 6996.00 frames.], tot_loss[loss=0.174, simple_loss=0.2628, pruned_loss=0.04266, over 1416851.24 frames.], batch size: 16, lr: 1.77e-04
2022-05-29 02:23:55,246 INFO [train.py:842] (2/4) Epoch 31, batch 5500, loss[loss=0.1682, simple_loss=0.2656, pruned_loss=0.03542, over 7313.00 frames.], tot_loss[loss=0.175, simple_loss=0.2639, pruned_loss=0.04301, over 1418989.99 frames.], batch size: 22, lr: 1.77e-04
2022-05-29 02:24:34,772 INFO [train.py:842] (2/4) Epoch 31, batch 5550, loss[loss=0.1562, simple_loss=0.2539, pruned_loss=0.02926, over 7412.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2637, pruned_loss=0.04284, over 1420304.44 frames.], batch size: 21, lr: 1.77e-04
2022-05-29 02:25:13,935 INFO [train.py:842] (2/4) Epoch 31, batch 5600, loss[loss=0.1718, simple_loss=0.2507, pruned_loss=0.04644, over 7021.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2641, pruned_loss=0.04329, over 1419688.81 frames.], batch size: 16, lr: 1.77e-04
2022-05-29 02:25:53,461 INFO [train.py:842] (2/4) Epoch 31, batch 5650, loss[loss=0.126, simple_loss=0.2105, pruned_loss=0.0208, over 7398.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2622, pruned_loss=0.04233, over 1420491.05 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:26:32,542 INFO [train.py:842] (2/4) Epoch 31, batch 5700, loss[loss=0.1606, simple_loss=0.2607, pruned_loss=0.03022, over 7335.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2646, pruned_loss=0.04355, over 1413884.39 frames.], batch size: 22, lr: 1.77e-04
2022-05-29 02:27:12,152 INFO [train.py:842] (2/4) Epoch 31, batch 5750, loss[loss=0.2192, simple_loss=0.3169, pruned_loss=0.06072, over 7124.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2646, pruned_loss=0.04381, over 1419314.29 frames.], batch size: 21, lr: 1.77e-04
2022-05-29 02:27:51,382 INFO [train.py:842] (2/4) Epoch 31, batch 5800, loss[loss=0.1796, simple_loss=0.2591, pruned_loss=0.05008, over 7249.00 frames.], tot_loss[loss=0.1765, simple_loss=0.265, pruned_loss=0.04403, over 1418413.86 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:28:31,160 INFO [train.py:842] (2/4) Epoch 31, batch 5850, loss[loss=0.1465, simple_loss=0.2352, pruned_loss=0.02893, over 7404.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2638, pruned_loss=0.0432, over 1422145.57 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:29:10,375 INFO [train.py:842] (2/4) Epoch 31, batch 5900, loss[loss=0.1628, simple_loss=0.2624, pruned_loss=0.0316, over 7331.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2632, pruned_loss=0.0432, over 1423566.00 frames.], batch size: 22, lr: 1.77e-04
2022-05-29 02:29:50,368 INFO [train.py:842] (2/4) Epoch 31, batch 5950, loss[loss=0.1471, simple_loss=0.2375, pruned_loss=0.02835, over 7160.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2621, pruned_loss=0.04318, over 1428707.51 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:30:29,451 INFO [train.py:842] (2/4) Epoch 31, batch 6000, loss[loss=0.1668, simple_loss=0.2641, pruned_loss=0.0348, over 7375.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2624, pruned_loss=0.0433, over 1426190.01 frames.], batch size: 23, lr: 1.77e-04
2022-05-29 02:30:29,452 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 02:30:38,842 INFO [train.py:871] (2/4) Epoch 31, validation: loss=0.1644, simple_loss=0.2618, pruned_loss=0.0335, over 868885.00 frames.
2022-05-29 02:31:18,334 INFO [train.py:842] (2/4) Epoch 31, batch 6050, loss[loss=0.1496, simple_loss=0.236, pruned_loss=0.03163, over 7409.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2626, pruned_loss=0.04343, over 1424882.13 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:31:57,783 INFO [train.py:842] (2/4) Epoch 31, batch 6100, loss[loss=0.1554, simple_loss=0.2421, pruned_loss=0.03432, over 7357.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2625, pruned_loss=0.04329, over 1429472.64 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:32:37,355 INFO [train.py:842] (2/4) Epoch 31, batch 6150, loss[loss=0.1481, simple_loss=0.238, pruned_loss=0.02911, over 7163.00 frames.], tot_loss[loss=0.1751, simple_loss=0.263, pruned_loss=0.04363, over 1428480.79 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:33:16,221 INFO [train.py:842] (2/4) Epoch 31, batch 6200, loss[loss=0.1466, simple_loss=0.2385, pruned_loss=0.02733, over 7144.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2643, pruned_loss=0.04392, over 1423083.75 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:33:55,498 INFO [train.py:842] (2/4) Epoch 31, batch 6250, loss[loss=0.1767, simple_loss=0.2645, pruned_loss=0.04447, over 6766.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2645, pruned_loss=0.04406, over 1423991.59 frames.], batch size: 31, lr: 1.77e-04
2022-05-29 02:34:34,702 INFO [train.py:842] (2/4) Epoch 31, batch 6300, loss[loss=0.1888, simple_loss=0.2871, pruned_loss=0.04529, over 7343.00 frames.], tot_loss[loss=0.1765, simple_loss=0.265, pruned_loss=0.04398, over 1422673.83 frames.], batch size: 22, lr: 1.77e-04
2022-05-29 02:35:14,300 INFO [train.py:842] (2/4) Epoch 31, batch 6350, loss[loss=0.16, simple_loss=0.2552, pruned_loss=0.03239, over 7146.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2656, pruned_loss=0.04413, over 1427266.06 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:35:53,423 INFO [train.py:842] (2/4) Epoch 31, batch 6400, loss[loss=0.1884, simple_loss=0.279, pruned_loss=0.04888, over 6458.00 frames.], tot_loss[loss=0.1773, simple_loss=0.266, pruned_loss=0.04428, over 1425043.72 frames.], batch size: 38, lr: 1.77e-04
2022-05-29 02:36:33,074 INFO [train.py:842] (2/4) Epoch 31, batch 6450, loss[loss=0.2373, simple_loss=0.313, pruned_loss=0.08077, over 7428.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2657, pruned_loss=0.0439, over 1421510.76 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:37:12,496 INFO [train.py:842] (2/4) Epoch 31, batch 6500, loss[loss=0.194, simple_loss=0.2772, pruned_loss=0.05539, over 7262.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2648, pruned_loss=0.04323, over 1427737.09 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:37:52,268 INFO [train.py:842] (2/4) Epoch 31, batch 6550, loss[loss=0.1414, simple_loss=0.2253, pruned_loss=0.02868, over 6995.00 frames.], tot_loss[loss=0.175, simple_loss=0.2642, pruned_loss=0.0429, over 1424496.27 frames.], batch size: 16, lr: 1.77e-04
2022-05-29 02:38:31,477 INFO [train.py:842] (2/4) Epoch 31, batch 6600, loss[loss=0.2221, simple_loss=0.3306, pruned_loss=0.05685, over 7204.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2649, pruned_loss=0.04319, over 1423434.93 frames.], batch size: 23, lr: 1.77e-04
2022-05-29 02:39:11,139 INFO [train.py:842] (2/4) Epoch 31, batch 6650, loss[loss=0.1458, simple_loss=0.2425, pruned_loss=0.02449, over 7425.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2645, pruned_loss=0.04327, over 1427724.13 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:39:50,717 INFO [train.py:842] (2/4) Epoch 31, batch 6700, loss[loss=0.1863, simple_loss=0.2804, pruned_loss=0.04612, over 7218.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2632, pruned_loss=0.04293, over 1432320.95 frames.], batch size: 21, lr: 1.77e-04
2022-05-29 02:40:30,531 INFO [train.py:842] (2/4) Epoch 31, batch 6750, loss[loss=0.2012, simple_loss=0.2764, pruned_loss=0.06296, over 7250.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2626, pruned_loss=0.04262, over 1430092.92 frames.], batch size: 24, lr: 1.77e-04
2022-05-29 02:41:09,493 INFO [train.py:842] (2/4) Epoch 31, batch 6800, loss[loss=0.1673, simple_loss=0.2557, pruned_loss=0.03946, over 6268.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2627, pruned_loss=0.04232, over 1429578.40 frames.], batch size: 38, lr: 1.77e-04
2022-05-29 02:41:49,158 INFO [train.py:842] (2/4) Epoch 31, batch 6850, loss[loss=0.1349, simple_loss=0.219, pruned_loss=0.02545, over 7271.00 frames.], tot_loss[loss=0.1728, simple_loss=0.262, pruned_loss=0.04181, over 1425818.65 frames.], batch size: 18, lr: 1.77e-04
2022-05-29 02:42:28,733 INFO [train.py:842] (2/4) Epoch 31, batch 6900, loss[loss=0.1847, simple_loss=0.281, pruned_loss=0.04418, over 7110.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.04222, over 1425306.90 frames.], batch size: 21, lr: 1.77e-04
2022-05-29 02:43:08,183 INFO [train.py:842] (2/4) Epoch 31, batch 6950, loss[loss=0.1563, simple_loss=0.2521, pruned_loss=0.03022, over 7318.00 frames.], tot_loss[loss=0.174, simple_loss=0.2634, pruned_loss=0.04228, over 1422627.89 frames.], batch size: 21, lr: 1.77e-04
2022-05-29 02:43:47,649 INFO [train.py:842] (2/4) Epoch 31, batch 7000, loss[loss=0.1918, simple_loss=0.2762, pruned_loss=0.05365, over 7317.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2638, pruned_loss=0.04252, over 1417298.77 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:44:27,059 INFO [train.py:842] (2/4) Epoch 31, batch 7050, loss[loss=0.1779, simple_loss=0.2665, pruned_loss=0.04469, over 7259.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2645, pruned_loss=0.04314, over 1405214.04 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:45:06,184 INFO [train.py:842] (2/4) Epoch 31, batch 7100, loss[loss=0.1722, simple_loss=0.2644, pruned_loss=0.04003, over 7429.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2646, pruned_loss=0.04336, over 1397309.41 frames.], batch size: 20, lr: 1.77e-04
2022-05-29 02:45:46,086 INFO [train.py:842] (2/4) Epoch 31, batch 7150, loss[loss=0.2054, simple_loss=0.2772, pruned_loss=0.0668, over 7251.00 frames.], tot_loss[loss=0.175, simple_loss=0.2632, pruned_loss=0.04337, over 1400131.62 frames.], batch size: 19, lr: 1.77e-04
2022-05-29 02:46:25,440 INFO [train.py:842] (2/4) Epoch 31, batch 7200, loss[loss=0.1481, simple_loss=0.2375, pruned_loss=0.02936, over 6761.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2636, pruned_loss=0.04327, over 1400558.62 frames.], batch size: 15, lr: 1.77e-04
2022-05-29 02:47:05,286 INFO [train.py:842] (2/4) Epoch 31, batch 7250, loss[loss=0.1473, simple_loss=0.2264, pruned_loss=0.03413, over 7217.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2629, pruned_loss=0.04312, over 1407832.90 frames.], batch size: 16, lr: 1.77e-04
2022-05-29 02:47:44,445 INFO [train.py:842] (2/4) Epoch 31, batch 7300, loss[loss=0.1817, simple_loss=0.2532, pruned_loss=0.05512, over 7273.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2619, pruned_loss=0.04264, over 1409652.05 frames.], batch size: 17, lr: 1.77e-04
2022-05-29 02:48:24,354 INFO [train.py:842] (2/4) Epoch 31, batch 7350, loss[loss=0.1601, simple_loss=0.2383, pruned_loss=0.04096, over 7278.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2616, pruned_loss=0.04259, over 1412803.85 frames.], batch size: 17, lr: 1.77e-04
2022-05-29 02:49:03,698 INFO [train.py:842] (2/4) Epoch 31, batch 7400, loss[loss=0.2041, simple_loss=0.289, pruned_loss=0.0596, over 6399.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2622, pruned_loss=0.04282, over 1416904.57 frames.], batch size: 37, lr: 1.77e-04
2022-05-29 02:49:43,393 INFO [train.py:842] (2/4) Epoch 31, batch 7450, loss[loss=0.1692, simple_loss=0.2534, pruned_loss=0.04253, over 6841.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2626, pruned_loss=0.04291, over 1417869.35 frames.], batch size: 15, lr: 1.77e-04
2022-05-29 02:50:22,748 INFO [train.py:842] (2/4) Epoch 31, batch 7500, loss[loss=0.171, simple_loss=0.2635, pruned_loss=0.03926, over 7257.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2626, pruned_loss=0.04305, over 1415449.59 frames.], batch size: 19, lr: 1.76e-04
2022-05-29 02:51:02,353 INFO [train.py:842] (2/4) Epoch 31, batch 7550, loss[loss=0.1718, simple_loss=0.271, pruned_loss=0.03634, over 7154.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2634, pruned_loss=0.04298, over 1415402.24 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 02:51:41,392 INFO [train.py:842] (2/4) Epoch 31, batch 7600, loss[loss=0.1465, simple_loss=0.2472, pruned_loss=0.02289, over 7434.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2629, pruned_loss=0.04278, over 1414072.72 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 02:52:20,813 INFO [train.py:842] (2/4) Epoch 31, batch 7650, loss[loss=0.1592, simple_loss=0.242, pruned_loss=0.03822, over 7266.00 frames.], tot_loss[loss=0.174, simple_loss=0.2632, pruned_loss=0.04241, over 1413819.08 frames.], batch size: 19, lr: 1.76e-04
2022-05-29 02:53:00,130 INFO [train.py:842] (2/4) Epoch 31, batch 7700, loss[loss=0.1823, simple_loss=0.2648, pruned_loss=0.04987, over 5062.00 frames.], tot_loss[loss=0.1738, simple_loss=0.263, pruned_loss=0.04227, over 1416062.99 frames.], batch size: 52, lr: 1.76e-04
2022-05-29 02:53:39,823 INFO [train.py:842] (2/4) Epoch 31, batch 7750, loss[loss=0.1712, simple_loss=0.2659, pruned_loss=0.0382, over 7331.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2635, pruned_loss=0.04276, over 1417558.85 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 02:54:19,334 INFO [train.py:842] (2/4) Epoch 31, batch 7800, loss[loss=0.1619, simple_loss=0.2433, pruned_loss=0.04021, over 7332.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2626, pruned_loss=0.04233, over 1419577.32 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 02:54:58,980 INFO [train.py:842] (2/4) Epoch 31, batch 7850, loss[loss=0.1779, simple_loss=0.2777, pruned_loss=0.03906, over 7308.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2639, pruned_loss=0.04344, over 1418947.40 frames.], batch size: 25, lr: 1.76e-04
2022-05-29 02:55:38,179 INFO [train.py:842] (2/4) Epoch 31, batch 7900, loss[loss=0.152, simple_loss=0.2392, pruned_loss=0.03239, over 7396.00 frames.], tot_loss[loss=0.1765, simple_loss=0.265, pruned_loss=0.04401, over 1418988.11 frames.], batch size: 18, lr: 1.76e-04
2022-05-29 02:56:17,829 INFO [train.py:842] (2/4) Epoch 31, batch 7950, loss[loss=0.1486, simple_loss=0.2317, pruned_loss=0.03279, over 7399.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2645, pruned_loss=0.04406, over 1419066.82 frames.], batch size: 18, lr: 1.76e-04
2022-05-29 02:56:57,327 INFO [train.py:842] (2/4) Epoch 31, batch 8000, loss[loss=0.1875, simple_loss=0.2704, pruned_loss=0.05229, over 7434.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2646, pruned_loss=0.04398, over 1418669.14 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 02:57:36,906 INFO [train.py:842] (2/4) Epoch 31, batch 8050, loss[loss=0.1808, simple_loss=0.2652, pruned_loss=0.04821, over 7215.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2644, pruned_loss=0.04426, over 1414839.81 frames.], batch size: 21, lr: 1.76e-04
2022-05-29 02:58:16,030 INFO [train.py:842] (2/4) Epoch 31, batch 8100, loss[loss=0.1488, simple_loss=0.2504, pruned_loss=0.02358, over 7325.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2643, pruned_loss=0.0442, over 1414199.19 frames.], batch size: 22, lr: 1.76e-04
2022-05-29 02:58:55,594 INFO [train.py:842] (2/4) Epoch 31, batch 8150, loss[loss=0.1527, simple_loss=0.2375, pruned_loss=0.03389, over 7269.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2637, pruned_loss=0.04352, over 1417181.74 frames.], batch size: 17, lr: 1.76e-04
2022-05-29 02:59:34,719 INFO [train.py:842] (2/4) Epoch 31, batch 8200, loss[loss=0.1615, simple_loss=0.234, pruned_loss=0.04451, over 7126.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2632, pruned_loss=0.04337, over 1417098.65 frames.], batch size: 17, lr: 1.76e-04
2022-05-29 03:00:14,415 INFO [train.py:842] (2/4) Epoch 31, batch 8250, loss[loss=0.1745, simple_loss=0.2535, pruned_loss=0.04778, over 7300.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2627, pruned_loss=0.04306, over 1413273.95 frames.], batch size: 17, lr: 1.76e-04
2022-05-29 03:00:53,609 INFO [train.py:842] (2/4) Epoch 31, batch 8300, loss[loss=0.155, simple_loss=0.2375, pruned_loss=0.03625, over 7264.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2628, pruned_loss=0.04316, over 1412305.59 frames.], batch size: 18, lr: 1.76e-04
2022-05-29 03:01:33,300 INFO [train.py:842] (2/4) Epoch 31, batch 8350, loss[loss=0.1697, simple_loss=0.2633, pruned_loss=0.03812, over 7120.00 frames.], tot_loss[loss=0.174, simple_loss=0.2625, pruned_loss=0.04282, over 1413355.74 frames.], batch size: 21, lr: 1.76e-04
2022-05-29 03:02:12,649 INFO [train.py:842] (2/4) Epoch 31, batch 8400, loss[loss=0.1669, simple_loss=0.2529, pruned_loss=0.04051, over 7218.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2618, pruned_loss=0.04269, over 1413107.69 frames.], batch size: 21, lr: 1.76e-04
2022-05-29 03:02:52,219 INFO [train.py:842] (2/4) Epoch 31, batch 8450, loss[loss=0.1659, simple_loss=0.2563, pruned_loss=0.03778, over 7317.00 frames.], tot_loss[loss=0.1748, simple_loss=0.263, pruned_loss=0.04333, over 1414926.87 frames.], batch size: 21, lr: 1.76e-04
2022-05-29 03:03:31,506 INFO [train.py:842] (2/4) Epoch 31, batch 8500, loss[loss=0.2108, simple_loss=0.2978, pruned_loss=0.06187, over 7140.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2632, pruned_loss=0.04387, over 1413979.18 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 03:04:11,209 INFO [train.py:842] (2/4) Epoch 31, batch 8550, loss[loss=0.2334, simple_loss=0.326, pruned_loss=0.07038, over 7413.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2639, pruned_loss=0.04398, over 1412978.71 frames.], batch size: 21, lr: 1.76e-04
2022-05-29 03:04:50,566 INFO [train.py:842] (2/4) Epoch 31, batch 8600, loss[loss=0.183, simple_loss=0.268, pruned_loss=0.04895, over 7315.00 frames.], tot_loss[loss=0.1752, simple_loss=0.263, pruned_loss=0.0437, over 1416609.31 frames.], batch size: 21, lr: 1.76e-04
2022-05-29 03:05:30,272 INFO [train.py:842] (2/4) Epoch 31, batch 8650, loss[loss=0.2285, simple_loss=0.3196, pruned_loss=0.06864, over 6300.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2627, pruned_loss=0.04352, over 1418401.28 frames.], batch size: 37, lr: 1.76e-04
2022-05-29 03:06:09,462 INFO [train.py:842] (2/4) Epoch 31, batch 8700, loss[loss=0.1447, simple_loss=0.2243, pruned_loss=0.03249, over 6991.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2628, pruned_loss=0.04333, over 1419030.13 frames.], batch size: 16, lr: 1.76e-04
2022-05-29 03:06:48,540 INFO [train.py:842] (2/4) Epoch 31, batch 8750, loss[loss=0.1971, simple_loss=0.296, pruned_loss=0.04914, over 6751.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2637, pruned_loss=0.0437, over 1409534.36 frames.], batch size: 31, lr: 1.76e-04
2022-05-29 03:07:28,076 INFO [train.py:842] (2/4) Epoch 31, batch 8800, loss[loss=0.1346, simple_loss=0.2234, pruned_loss=0.0229, over 7180.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2632, pruned_loss=0.04399, over 1408359.79 frames.], batch size: 16, lr: 1.76e-04
2022-05-29 03:08:07,338 INFO [train.py:842] (2/4) Epoch 31, batch 8850, loss[loss=0.158, simple_loss=0.2475, pruned_loss=0.03423, over 7407.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2645, pruned_loss=0.04443, over 1404189.21 frames.], batch size: 18, lr: 1.76e-04
2022-05-29 03:08:46,461 INFO [train.py:842] (2/4) Epoch 31, batch 8900, loss[loss=0.1655, simple_loss=0.2494, pruned_loss=0.04084, over 4931.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2664, pruned_loss=0.04598, over 1395807.71 frames.], batch size: 52, lr: 1.76e-04
2022-05-29 03:09:25,594 INFO [train.py:842] (2/4) Epoch 31, batch 8950, loss[loss=0.1558, simple_loss=0.2479, pruned_loss=0.03188, over 7334.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2663, pruned_loss=0.04539, over 1392940.39 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 03:10:04,422 INFO [train.py:842] (2/4) Epoch 31, batch 9000, loss[loss=0.1519, simple_loss=0.2452, pruned_loss=0.02925, over 7230.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2656, pruned_loss=0.04489, over 1391126.60 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 03:10:04,423 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 03:10:13,976 INFO [train.py:871] (2/4) Epoch 31, validation: loss=0.1628, simple_loss=0.2602, pruned_loss=0.03268, over 868885.00 frames.
2022-05-29 03:10:53,015 INFO [train.py:842] (2/4) Epoch 31, batch 9050, loss[loss=0.1637, simple_loss=0.2494, pruned_loss=0.03903, over 6448.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2667, pruned_loss=0.04534, over 1373118.74 frames.], batch size: 38, lr: 1.76e-04
2022-05-29 03:11:31,594 INFO [train.py:842] (2/4) Epoch 31, batch 9100, loss[loss=0.1588, simple_loss=0.2529, pruned_loss=0.03237, over 7322.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2664, pruned_loss=0.04493, over 1360311.27 frames.], batch size: 20, lr: 1.76e-04
2022-05-29 03:12:10,264 INFO [train.py:842] (2/4) Epoch 31, batch 9150, loss[loss=0.1651, simple_loss=0.2564, pruned_loss=0.03689, over 6452.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2683, pruned_loss=0.04608, over 1328837.79 frames.], batch size: 38, lr: 1.76e-04
2022-05-29 03:13:02,476 INFO [train.py:842] (2/4) Epoch 32, batch 0, loss[loss=0.1734, simple_loss=0.2664, pruned_loss=0.04025, over 4774.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2664, pruned_loss=0.04025, over 4774.00 frames.], batch size: 52, lr: 1.73e-04
2022-05-29 03:13:41,545 INFO [train.py:842] (2/4) Epoch 32, batch 50, loss[loss=0.2178, simple_loss=0.3057, pruned_loss=0.06492, over 6279.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2713, pruned_loss=0.04456, over 319630.72 frames.], batch size: 37, lr: 1.73e-04
2022-05-29 03:14:21,189 INFO [train.py:842] (2/4) Epoch 32, batch 100, loss[loss=0.207, simple_loss=0.2817, pruned_loss=0.06617, over 7292.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2653, pruned_loss=0.04316, over 566599.08 frames.], batch size: 25, lr: 1.73e-04
2022-05-29 03:15:00,467 INFO [train.py:842] (2/4) Epoch 32, batch 150, loss[loss=0.2012, simple_loss=0.2873, pruned_loss=0.0575, over 7159.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2655, pruned_loss=0.04356, over 758233.95 frames.], batch size: 26, lr: 1.73e-04
2022-05-29 03:15:39,787 INFO [train.py:842] (2/4) Epoch 32, batch 200, loss[loss=0.1566, simple_loss=0.2304, pruned_loss=0.04141, over 6999.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2638, pruned_loss=0.04265, over 903104.47 frames.], batch size: 16, lr: 1.73e-04
2022-05-29 03:16:19,188 INFO [train.py:842] (2/4) Epoch 32, batch 250, loss[loss=0.1837, simple_loss=0.2805, pruned_loss=0.04349, over 7296.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2613, pruned_loss=0.04122, over 1022762.10 frames.], batch size: 24, lr: 1.73e-04
2022-05-29 03:16:58,584 INFO [train.py:842] (2/4) Epoch 32, batch 300, loss[loss=0.1752, simple_loss=0.262, pruned_loss=0.04417, over 7297.00 frames.], tot_loss[loss=0.1725, simple_loss=0.262, pruned_loss=0.04154, over 1113080.89 frames.], batch size: 24, lr: 1.73e-04
2022-05-29 03:17:37,860 INFO [train.py:842] (2/4) Epoch 32, batch 350, loss[loss=0.2172, simple_loss=0.3158, pruned_loss=0.05932, over 7061.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2631, pruned_loss=0.04274, over 1181229.02 frames.], batch size: 28, lr: 1.73e-04
2022-05-29 03:18:17,603 INFO [train.py:842] (2/4) Epoch 32, batch 400, loss[loss=0.1729, simple_loss=0.2648, pruned_loss=0.0405, over 7166.00 frames.], tot_loss[loss=0.1755, simple_loss=0.264, pruned_loss=0.04348, over 1236570.07 frames.], batch size: 26, lr: 1.73e-04
2022-05-29 03:18:56,907 INFO [train.py:842] (2/4) Epoch 32, batch 450, loss[loss=0.1551, simple_loss=0.2575, pruned_loss=0.02632, over 7324.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2642, pruned_loss=0.04379, over 1277852.15 frames.], batch size: 21, lr: 1.73e-04
2022-05-29 03:19:36,528 INFO [train.py:842] (2/4) Epoch 32, batch 500, loss[loss=0.172, simple_loss=0.2701, pruned_loss=0.03689, over 7328.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2624, pruned_loss=0.04295, over 1313697.44 frames.], batch size: 22, lr: 1.73e-04
2022-05-29 03:20:15,824 INFO [train.py:842] (2/4) Epoch 32, batch 550, loss[loss=0.1855, simple_loss=0.2741, pruned_loss=0.0485, over 7322.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2629, pruned_loss=0.04323, over 1341398.63 frames.], batch size: 22, lr: 1.73e-04
2022-05-29 03:20:55,406 INFO [train.py:842] (2/4) Epoch 32, batch 600, loss[loss=0.1612, simple_loss=0.2437, pruned_loss=0.03935, over 7141.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2623, pruned_loss=0.04275, over 1363984.48 frames.], batch size: 17, lr: 1.73e-04
2022-05-29 03:21:45,364 INFO [train.py:842] (2/4) Epoch 32, batch 650, loss[loss=0.1505, simple_loss=0.2342, pruned_loss=0.03339, over 7400.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2619, pruned_loss=0.04248, over 1379948.56 frames.], batch size: 17, lr: 1.73e-04
2022-05-29 03:22:24,820 INFO [train.py:842] (2/4) Epoch 32, batch 700, loss[loss=0.1992, simple_loss=0.2771, pruned_loss=0.0607, over 7204.00 frames.], tot_loss[loss=0.175, simple_loss=0.2636, pruned_loss=0.04326, over 1388122.13 frames.], batch size: 23, lr: 1.73e-04
2022-05-29 03:23:04,316 INFO [train.py:842] (2/4) Epoch 32, batch 750, loss[loss=0.1633, simple_loss=0.2516, pruned_loss=0.03744, over 7115.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2643, pruned_loss=0.04354, over 1396449.43 frames.], batch size: 21, lr: 1.73e-04
2022-05-29 03:23:43,753 INFO [train.py:842] (2/4) Epoch 32, batch 800, loss[loss=0.1575, simple_loss=0.2378, pruned_loss=0.03862, over 7279.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2637, pruned_loss=0.04327, over 1401152.25 frames.], batch size: 18, lr: 1.73e-04
2022-05-29 03:24:22,953 INFO [train.py:842] (2/4) Epoch 32, batch 850, loss[loss=0.2005, simple_loss=0.2829, pruned_loss=0.05903, over 7248.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2644, pruned_loss=0.04311, over 1408073.27 frames.], batch size: 25, lr: 1.73e-04
2022-05-29 03:25:02,024 INFO [train.py:842] (2/4) Epoch 32, batch 900, loss[loss=0.1622, simple_loss=0.2568, pruned_loss=0.03381, over 7329.00 frames.], tot_loss[loss=0.176, simple_loss=0.2653, pruned_loss=0.04329, over 1411154.15 frames.], batch size: 22, lr: 1.73e-04
2022-05-29 03:25:41,219 INFO [train.py:842] (2/4) Epoch 32, batch 950, loss[loss=0.1693, simple_loss=0.2432, pruned_loss=0.04772, over 6774.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2643, pruned_loss=0.04299, over 1412440.90 frames.], batch size: 15, lr: 1.73e-04
2022-05-29 03:26:20,878 INFO [train.py:842] (2/4) Epoch 32, batch 1000, loss[loss=0.1728, simple_loss=0.2657, pruned_loss=0.04, over 7426.00 frames.], tot_loss[loss=0.175, simple_loss=0.2641, pruned_loss=0.04298, over 1416252.01 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:27:00,309 INFO [train.py:842] (2/4) Epoch 32, batch 1050, loss[loss=0.1585, simple_loss=0.2514, pruned_loss=0.03283, over 7235.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2629, pruned_loss=0.0428, over 1420338.38 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:27:39,930 INFO [train.py:842] (2/4) Epoch 32, batch 1100, loss[loss=0.2223, simple_loss=0.319, pruned_loss=0.06278, over 7215.00 frames.], tot_loss[loss=0.173, simple_loss=0.2616, pruned_loss=0.04221, over 1419391.06 frames.], batch size: 22, lr: 1.73e-04
2022-05-29 03:28:19,223 INFO [train.py:842] (2/4) Epoch 32, batch 1150, loss[loss=0.1585, simple_loss=0.2468, pruned_loss=0.03508, over 7144.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2617, pruned_loss=0.0418, over 1422048.09 frames.], batch size: 17, lr: 1.73e-04
2022-05-29 03:28:59,077 INFO [train.py:842] (2/4) Epoch 32, batch 1200, loss[loss=0.1574, simple_loss=0.2533, pruned_loss=0.03076, over 7425.00 frames.], tot_loss[loss=0.173, simple_loss=0.2617, pruned_loss=0.04213, over 1424641.62 frames.], batch size: 21, lr: 1.73e-04
2022-05-29 03:29:38,226 INFO [train.py:842] (2/4) Epoch 32, batch 1250, loss[loss=0.1709, simple_loss=0.2801, pruned_loss=0.03086, over 7196.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2617, pruned_loss=0.04203, over 1418598.85 frames.], batch size: 23, lr: 1.73e-04
2022-05-29 03:30:17,916 INFO [train.py:842] (2/4) Epoch 32, batch 1300, loss[loss=0.2039, simple_loss=0.2908, pruned_loss=0.05855, over 7145.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2623, pruned_loss=0.04229, over 1423639.71 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:30:57,301 INFO [train.py:842] (2/4) Epoch 32, batch 1350, loss[loss=0.1914, simple_loss=0.2777, pruned_loss=0.05258, over 7336.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2619, pruned_loss=0.04235, over 1420945.75 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:31:37,059 INFO [train.py:842] (2/4) Epoch 32, batch 1400, loss[loss=0.1878, simple_loss=0.2777, pruned_loss=0.04891, over 7229.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2617, pruned_loss=0.04242, over 1420967.20 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:32:16,171 INFO [train.py:842] (2/4) Epoch 32, batch 1450, loss[loss=0.1401, simple_loss=0.2275, pruned_loss=0.02628, over 7334.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2631, pruned_loss=0.04311, over 1422889.24 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:32:55,635 INFO [train.py:842] (2/4) Epoch 32, batch 1500, loss[loss=0.2379, simple_loss=0.317, pruned_loss=0.0794, over 5424.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2633, pruned_loss=0.04329, over 1422627.86 frames.], batch size: 53, lr: 1.73e-04
2022-05-29 03:33:35,094 INFO [train.py:842] (2/4) Epoch 32, batch 1550, loss[loss=0.2018, simple_loss=0.2767, pruned_loss=0.06346, over 7411.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2638, pruned_loss=0.0436, over 1422155.21 frames.], batch size: 18, lr: 1.73e-04
2022-05-29 03:34:14,483 INFO [train.py:842] (2/4) Epoch 32, batch 1600, loss[loss=0.1502, simple_loss=0.2459, pruned_loss=0.02722, over 7190.00 frames.], tot_loss[loss=0.1744, simple_loss=0.263, pruned_loss=0.04291, over 1419019.78 frames.], batch size: 23, lr: 1.73e-04
2022-05-29 03:34:53,752 INFO [train.py:842] (2/4) Epoch 32, batch 1650, loss[loss=0.1649, simple_loss=0.2553, pruned_loss=0.03724, over 7415.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2632, pruned_loss=0.04261, over 1418208.35 frames.], batch size: 21, lr: 1.73e-04
2022-05-29 03:35:33,284 INFO [train.py:842] (2/4) Epoch 32, batch 1700, loss[loss=0.1539, simple_loss=0.2514, pruned_loss=0.02823, over 7118.00 frames.], tot_loss[loss=0.175, simple_loss=0.2637, pruned_loss=0.04313, over 1412822.50 frames.], batch size: 21, lr: 1.73e-04
2022-05-29 03:36:12,374 INFO [train.py:842] (2/4) Epoch 32, batch 1750, loss[loss=0.2456, simple_loss=0.3243, pruned_loss=0.08344, over 4834.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2639, pruned_loss=0.04322, over 1411277.56 frames.], batch size: 52, lr: 1.73e-04
2022-05-29 03:36:51,778 INFO [train.py:842] (2/4) Epoch 32, batch 1800, loss[loss=0.1627, simple_loss=0.2567, pruned_loss=0.03438, over 7239.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2651, pruned_loss=0.04381, over 1412128.52 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:37:30,776 INFO [train.py:842] (2/4) Epoch 32, batch 1850, loss[loss=0.1676, simple_loss=0.2573, pruned_loss=0.03898, over 6990.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2646, pruned_loss=0.04359, over 1406694.65 frames.], batch size: 16, lr: 1.73e-04
2022-05-29 03:38:10,529 INFO [train.py:842] (2/4) Epoch 32, batch 1900, loss[loss=0.1489, simple_loss=0.2288, pruned_loss=0.03451, over 7368.00 frames.], tot_loss[loss=0.174, simple_loss=0.2625, pruned_loss=0.04275, over 1412974.82 frames.], batch size: 19, lr: 1.73e-04
2022-05-29 03:38:49,852 INFO [train.py:842] (2/4) Epoch 32, batch 1950, loss[loss=0.2201, simple_loss=0.2951, pruned_loss=0.07255, over 7358.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2616, pruned_loss=0.04227, over 1419139.58 frames.], batch size: 19, lr: 1.73e-04
2022-05-29 03:39:29,637 INFO [train.py:842] (2/4) Epoch 32, batch 2000, loss[loss=0.1529, simple_loss=0.2352, pruned_loss=0.03527, over 7278.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2619, pruned_loss=0.04228, over 1420957.12 frames.], batch size: 18, lr: 1.73e-04
2022-05-29 03:40:08,867 INFO [train.py:842] (2/4) Epoch 32, batch 2050, loss[loss=0.1722, simple_loss=0.2675, pruned_loss=0.03846, over 7152.00 frames.], tot_loss[loss=0.173, simple_loss=0.2615, pruned_loss=0.04222, over 1417552.37 frames.], batch size: 20, lr: 1.73e-04
2022-05-29 03:40:48,260 INFO [train.py:842] (2/4) Epoch 32, batch 2100, loss[loss=0.1553, simple_loss=0.239, pruned_loss=0.03584, over 7198.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2629, pruned_loss=0.04277, over 1417111.62 frames.], batch size: 16, lr: 1.73e-04
2022-05-29 03:41:27,426 INFO [train.py:842] (2/4) Epoch 32, batch 2150, loss[loss=0.158, simple_loss=0.2556, pruned_loss=0.03024, over 7236.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2636, pruned_loss=0.04295, over 1420482.50 frames.], batch size: 21, lr: 1.73e-04
2022-05-29 03:42:07,268 INFO [train.py:842] (2/4) Epoch 32, batch 2200, loss[loss=0.1794, simple_loss=0.2676, pruned_loss=0.04561, over 7123.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2629, pruned_loss=0.04282, over 1423399.29 frames.], batch size: 26, lr: 1.73e-04
2022-05-29 03:42:46,575 INFO [train.py:842] (2/4) Epoch 32, batch 2250, loss[loss=0.2332, simple_loss=0.3127, pruned_loss=0.07691, over 7072.00 frames.], tot_loss[loss=0.173, simple_loss=0.262, pruned_loss=0.04201, over 1424607.92 frames.], batch size: 18, lr: 1.73e-04
2022-05-29 03:43:25,992 INFO [train.py:842] (2/4) Epoch 32, batch 2300, loss[loss=0.1839, simple_loss=0.2887, pruned_loss=0.03955, over 7342.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2624, pruned_loss=0.04275, over 1420821.00 frames.], batch size: 22, lr: 1.73e-04
2022-05-29 03:44:05,256 INFO [train.py:842] (2/4) Epoch 32, batch 2350, loss[loss=0.1536, simple_loss=0.2353, pruned_loss=0.03591, over 7294.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2627, pruned_loss=0.04249, over 1424949.64 frames.], batch size: 17, lr: 1.73e-04
2022-05-29 03:44:44,567 INFO [train.py:842] (2/4) Epoch 32, batch 2400, loss[loss=0.153, simple_loss=0.237, pruned_loss=0.03454, over 7332.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2622, pruned_loss=0.04237, over 1420319.12 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:45:24,016 INFO [train.py:842] (2/4) Epoch 32, batch 2450, loss[loss=0.1674, simple_loss=0.2689, pruned_loss=0.03295, over 7157.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2615, pruned_loss=0.04194, over 1422007.44 frames.], batch size: 26, lr: 1.72e-04
2022-05-29 03:46:03,769 INFO [train.py:842] (2/4) Epoch 32, batch 2500, loss[loss=0.135, simple_loss=0.2242, pruned_loss=0.02291, over 7290.00 frames.], tot_loss[loss=0.172, simple_loss=0.2611, pruned_loss=0.04146, over 1424503.74 frames.], batch size: 17, lr: 1.72e-04
2022-05-29 03:46:43,179 INFO [train.py:842] (2/4) Epoch 32, batch 2550, loss[loss=0.1523, simple_loss=0.2522, pruned_loss=0.02619, over 7329.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2624, pruned_loss=0.04204, over 1422305.31 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:47:22,948 INFO [train.py:842] (2/4) Epoch 32, batch 2600, loss[loss=0.1406, simple_loss=0.2214, pruned_loss=0.02987, over 7125.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2621, pruned_loss=0.04217, over 1421227.53 frames.], batch size: 17, lr: 1.72e-04
2022-05-29 03:48:02,150 INFO [train.py:842] (2/4) Epoch 32, batch 2650, loss[loss=0.1975, simple_loss=0.2847, pruned_loss=0.0552, over 7118.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2615, pruned_loss=0.04156, over 1423727.54 frames.], batch size: 26, lr: 1.72e-04
2022-05-29 03:48:41,716 INFO [train.py:842] (2/4) Epoch 32, batch 2700, loss[loss=0.177, simple_loss=0.2635, pruned_loss=0.04523, over 7325.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2624, pruned_loss=0.04209, over 1422347.28 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:49:21,018 INFO [train.py:842] (2/4) Epoch 32, batch 2750, loss[loss=0.1697, simple_loss=0.2682, pruned_loss=0.03566, over 7005.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2622, pruned_loss=0.04166, over 1424628.80 frames.], batch size: 28, lr: 1.72e-04
2022-05-29 03:50:00,715 INFO [train.py:842] (2/4) Epoch 32, batch 2800, loss[loss=0.1375, simple_loss=0.2271, pruned_loss=0.02394, over 7409.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2616, pruned_loss=0.04193, over 1424002.89 frames.], batch size: 18, lr: 1.72e-04
2022-05-29 03:50:40,041 INFO [train.py:842] (2/4) Epoch 32, batch 2850, loss[loss=0.188, simple_loss=0.2812, pruned_loss=0.04734, over 6421.00 frames.], tot_loss[loss=0.1732, simple_loss=0.262, pruned_loss=0.0422, over 1421031.45 frames.], batch size: 37, lr: 1.72e-04
2022-05-29 03:51:19,907 INFO [train.py:842] (2/4) Epoch 32, batch 2900, loss[loss=0.1656, simple_loss=0.2491, pruned_loss=0.04101, over 7232.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2624, pruned_loss=0.04208, over 1425277.12 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:51:58,805 INFO [train.py:842] (2/4) Epoch 32, batch 2950, loss[loss=0.207, simple_loss=0.3033, pruned_loss=0.05539, over 7199.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2626, pruned_loss=0.04219, over 1419178.74 frames.], batch size: 23, lr: 1.72e-04
2022-05-29 03:52:38,223 INFO [train.py:842] (2/4) Epoch 32, batch 3000, loss[loss=0.2423, simple_loss=0.328, pruned_loss=0.0783, over 7430.00 frames.], tot_loss[loss=0.1759, simple_loss=0.265, pruned_loss=0.04337, over 1419541.94 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:52:38,223 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 03:52:48,107 INFO [train.py:871] (2/4) Epoch 32, validation: loss=0.1619, simple_loss=0.2592, pruned_loss=0.03236, over 868885.00 frames.
2022-05-29 03:53:27,752 INFO [train.py:842] (2/4) Epoch 32, batch 3050, loss[loss=0.2124, simple_loss=0.3075, pruned_loss=0.05868, over 7309.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2645, pruned_loss=0.04296, over 1422954.40 frames.], batch size: 25, lr: 1.72e-04
2022-05-29 03:54:10,053 INFO [train.py:842] (2/4) Epoch 32, batch 3100, loss[loss=0.1853, simple_loss=0.2722, pruned_loss=0.04922, over 6987.00 frames.], tot_loss[loss=0.1749, simple_loss=0.264, pruned_loss=0.04292, over 1426931.37 frames.], batch size: 28, lr: 1.72e-04
2022-05-29 03:54:49,461 INFO [train.py:842] (2/4) Epoch 32, batch 3150, loss[loss=0.162, simple_loss=0.2409, pruned_loss=0.0415, over 7268.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2627, pruned_loss=0.04233, over 1424970.93 frames.], batch size: 17, lr: 1.72e-04
2022-05-29 03:55:29,001 INFO [train.py:842] (2/4) Epoch 32, batch 3200, loss[loss=0.1636, simple_loss=0.2423, pruned_loss=0.04241, over 7102.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2638, pruned_loss=0.04245, over 1426879.02 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 03:56:08,331 INFO [train.py:842] (2/4) Epoch 32, batch 3250, loss[loss=0.182, simple_loss=0.2786, pruned_loss=0.04269, over 7333.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2632, pruned_loss=0.04209, over 1428101.80 frames.], batch size: 22, lr: 1.72e-04
2022-05-29 03:56:47,743 INFO [train.py:842] (2/4) Epoch 32, batch 3300, loss[loss=0.1808, simple_loss=0.266, pruned_loss=0.0478, over 7422.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2628, pruned_loss=0.04219, over 1423824.94 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:57:27,327 INFO [train.py:842] (2/4) Epoch 32, batch 3350, loss[loss=0.1729, simple_loss=0.2597, pruned_loss=0.043, over 7317.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2619, pruned_loss=0.04191, over 1425840.91 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 03:58:06,774 INFO [train.py:842] (2/4) Epoch 32, batch 3400, loss[loss=0.156, simple_loss=0.2512, pruned_loss=0.03037, over 7315.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2636, pruned_loss=0.04262, over 1422865.08 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 03:58:45,718 INFO [train.py:842] (2/4) Epoch 32, batch 3450, loss[loss=0.1964, simple_loss=0.3028, pruned_loss=0.04498, over 7196.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2645, pruned_loss=0.0429, over 1425388.42 frames.], batch size: 22, lr: 1.72e-04
2022-05-29 03:59:25,420 INFO [train.py:842] (2/4) Epoch 32, batch 3500, loss[loss=0.1788, simple_loss=0.2739, pruned_loss=0.04183, over 7309.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2648, pruned_loss=0.04329, over 1428901.99 frames.], batch size: 24, lr: 1.72e-04
2022-05-29 04:00:04,735 INFO [train.py:842] (2/4) Epoch 32, batch 3550, loss[loss=0.1919, simple_loss=0.2771, pruned_loss=0.05334, over 7369.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2645, pruned_loss=0.04358, over 1431874.02 frames.], batch size: 23, lr: 1.72e-04
2022-05-29 04:00:44,336 INFO [train.py:842] (2/4) Epoch 32, batch 3600, loss[loss=0.1632, simple_loss=0.2599, pruned_loss=0.03323, over 6429.00 frames.], tot_loss[loss=0.174, simple_loss=0.263, pruned_loss=0.04255, over 1428932.33 frames.], batch size: 38, lr: 1.72e-04
2022-05-29 04:01:23,433 INFO [train.py:842] (2/4) Epoch 32, batch 3650, loss[loss=0.165, simple_loss=0.2521, pruned_loss=0.03896, over 7248.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2645, pruned_loss=0.04321, over 1428522.86 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:02:03,201 INFO [train.py:842] (2/4) Epoch 32, batch 3700, loss[loss=0.1433, simple_loss=0.2217, pruned_loss=0.03246, over 7144.00 frames.], tot_loss[loss=0.1755, simple_loss=0.264, pruned_loss=0.04349, over 1430034.22 frames.], batch size: 17, lr: 1.72e-04
2022-05-29 04:02:41,989 INFO [train.py:842] (2/4) Epoch 32, batch 3750, loss[loss=0.2327, simple_loss=0.3132, pruned_loss=0.07606, over 7195.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2649, pruned_loss=0.04399, over 1424128.93 frames.], batch size: 23, lr: 1.72e-04
2022-05-29 04:03:21,654 INFO [train.py:842] (2/4) Epoch 32, batch 3800, loss[loss=0.1903, simple_loss=0.2782, pruned_loss=0.05121, over 7389.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2642, pruned_loss=0.04335, over 1425634.70 frames.], batch size: 23, lr: 1.72e-04
2022-05-29 04:04:01,022 INFO [train.py:842] (2/4) Epoch 32, batch 3850, loss[loss=0.1721, simple_loss=0.2509, pruned_loss=0.04664, over 7427.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2638, pruned_loss=0.04316, over 1427752.44 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:04:40,440 INFO [train.py:842] (2/4) Epoch 32, batch 3900, loss[loss=0.1468, simple_loss=0.2309, pruned_loss=0.0314, over 7170.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2633, pruned_loss=0.0427, over 1429035.63 frames.], batch size: 18, lr: 1.72e-04
2022-05-29 04:05:19,794 INFO [train.py:842] (2/4) Epoch 32, batch 3950, loss[loss=0.173, simple_loss=0.2726, pruned_loss=0.03667, over 7215.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2633, pruned_loss=0.04296, over 1424158.30 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 04:05:59,355 INFO [train.py:842] (2/4) Epoch 32, batch 4000, loss[loss=0.1395, simple_loss=0.2177, pruned_loss=0.03065, over 7430.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2616, pruned_loss=0.04206, over 1421731.08 frames.], batch size: 18, lr: 1.72e-04
2022-05-29 04:06:38,590 INFO [train.py:842] (2/4) Epoch 32, batch 4050, loss[loss=0.1629, simple_loss=0.2576, pruned_loss=0.03409, over 7379.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2618, pruned_loss=0.04235, over 1419390.91 frames.], batch size: 23, lr: 1.72e-04
2022-05-29 04:07:17,977 INFO [train.py:842] (2/4) Epoch 32, batch 4100, loss[loss=0.1967, simple_loss=0.2968, pruned_loss=0.04834, over 7141.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2639, pruned_loss=0.04288, over 1419447.65 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:07:57,043 INFO [train.py:842] (2/4) Epoch 32, batch 4150, loss[loss=0.2079, simple_loss=0.3003, pruned_loss=0.05771, over 6742.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2647, pruned_loss=0.04309, over 1422469.51 frames.], batch size: 31, lr: 1.72e-04
2022-05-29 04:08:36,761 INFO [train.py:842] (2/4) Epoch 32, batch 4200, loss[loss=0.1782, simple_loss=0.2641, pruned_loss=0.04618, over 7283.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2634, pruned_loss=0.04236, over 1425557.41 frames.], batch size: 24, lr: 1.72e-04
2022-05-29 04:09:15,945 INFO [train.py:842] (2/4) Epoch 32, batch 4250, loss[loss=0.1604, simple_loss=0.2478, pruned_loss=0.03648, over 7224.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2645, pruned_loss=0.04341, over 1420453.07 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:09:55,656 INFO [train.py:842] (2/4) Epoch 32, batch 4300, loss[loss=0.1681, simple_loss=0.2594, pruned_loss=0.03846, over 7137.00 frames.], tot_loss[loss=0.175, simple_loss=0.2636, pruned_loss=0.04324, over 1423577.51 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:10:34,769 INFO [train.py:842] (2/4) Epoch 32, batch 4350, loss[loss=0.1653, simple_loss=0.2698, pruned_loss=0.03038, over 6578.00 frames.], tot_loss[loss=0.1756, simple_loss=0.264, pruned_loss=0.04365, over 1425219.02 frames.], batch size: 38, lr: 1.72e-04
2022-05-29 04:11:14,405 INFO [train.py:842] (2/4) Epoch 32, batch 4400, loss[loss=0.1611, simple_loss=0.254, pruned_loss=0.03409, over 7337.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2629, pruned_loss=0.04307, over 1426062.94 frames.], batch size: 22, lr: 1.72e-04
2022-05-29 04:11:53,983 INFO [train.py:842] (2/4) Epoch 32, batch 4450, loss[loss=0.1798, simple_loss=0.2788, pruned_loss=0.04036, over 7260.00 frames.], tot_loss[loss=0.1727, simple_loss=0.261, pruned_loss=0.04218, over 1430334.91 frames.], batch size: 19, lr: 1.72e-04
2022-05-29 04:12:33,505 INFO [train.py:842] (2/4) Epoch 32, batch 4500, loss[loss=0.1788, simple_loss=0.2796, pruned_loss=0.03903, over 7120.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2621, pruned_loss=0.04261, over 1424694.28 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 04:13:12,630 INFO [train.py:842] (2/4) Epoch 32, batch 4550, loss[loss=0.1677, simple_loss=0.2685, pruned_loss=0.03346, over 7331.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2619, pruned_loss=0.04248, over 1416985.70 frames.], batch size: 22, lr: 1.72e-04
2022-05-29 04:13:52,367 INFO [train.py:842] (2/4) Epoch 32, batch 4600, loss[loss=0.1607, simple_loss=0.2368, pruned_loss=0.04233, over 6990.00 frames.], tot_loss[loss=0.174, simple_loss=0.2625, pruned_loss=0.04271, over 1421157.36 frames.], batch size: 16, lr: 1.72e-04
2022-05-29 04:14:31,870 INFO [train.py:842] (2/4) Epoch 32, batch 4650, loss[loss=0.148, simple_loss=0.2439, pruned_loss=0.02603, over 7211.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2639, pruned_loss=0.04332, over 1425619.85 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 04:15:11,516 INFO [train.py:842] (2/4) Epoch 32, batch 4700, loss[loss=0.1806, simple_loss=0.2701, pruned_loss=0.04558, over 7231.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2638, pruned_loss=0.04323, over 1426471.39 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:15:51,108 INFO [train.py:842] (2/4) Epoch 32, batch 4750, loss[loss=0.1998, simple_loss=0.2815, pruned_loss=0.05908, over 7211.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2631, pruned_loss=0.04299, over 1423504.17 frames.], batch size: 22, lr: 1.72e-04
2022-05-29 04:16:30,597 INFO [train.py:842] (2/4) Epoch 32, batch 4800, loss[loss=0.189, simple_loss=0.2737, pruned_loss=0.05209, over 7318.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2639, pruned_loss=0.04313, over 1420558.61 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 04:17:09,851 INFO [train.py:842] (2/4) Epoch 32, batch 4850, loss[loss=0.1569, simple_loss=0.2608, pruned_loss=0.02656, over 7235.00 frames.], tot_loss[loss=0.1741, simple_loss=0.263, pruned_loss=0.04263, over 1421565.32 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:17:49,580 INFO [train.py:842] (2/4) Epoch 32, batch 4900, loss[loss=0.1866, simple_loss=0.2817, pruned_loss=0.04575, over 7316.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2638, pruned_loss=0.04335, over 1423312.93 frames.], batch size: 25, lr: 1.72e-04
2022-05-29 04:18:28,876 INFO [train.py:842] (2/4) Epoch 32, batch 4950, loss[loss=0.1534, simple_loss=0.247, pruned_loss=0.02986, over 7424.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2632, pruned_loss=0.0428, over 1427001.80 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:19:08,271 INFO [train.py:842] (2/4) Epoch 32, batch 5000, loss[loss=0.2112, simple_loss=0.2993, pruned_loss=0.06155, over 6705.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2637, pruned_loss=0.04248, over 1425226.20 frames.], batch size: 31, lr: 1.72e-04
2022-05-29 04:19:47,594 INFO [train.py:842] (2/4) Epoch 32, batch 5050, loss[loss=0.1377, simple_loss=0.2259, pruned_loss=0.0247, over 7281.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2626, pruned_loss=0.04219, over 1424728.06 frames.], batch size: 18, lr: 1.72e-04
2022-05-29 04:20:27,283 INFO [train.py:842] (2/4) Epoch 32, batch 5100, loss[loss=0.1665, simple_loss=0.2625, pruned_loss=0.03527, over 7333.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2625, pruned_loss=0.04233, over 1422652.50 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 04:21:06,324 INFO [train.py:842] (2/4) Epoch 32, batch 5150, loss[loss=0.163, simple_loss=0.2487, pruned_loss=0.03864, over 7064.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2623, pruned_loss=0.04203, over 1417957.17 frames.], batch size: 18, lr: 1.72e-04
2022-05-29 04:21:45,940 INFO [train.py:842] (2/4) Epoch 32, batch 5200, loss[loss=0.1313, simple_loss=0.2182, pruned_loss=0.02216, over 7276.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2615, pruned_loss=0.0418, over 1419408.56 frames.], batch size: 17, lr: 1.72e-04
2022-05-29 04:22:25,243 INFO [train.py:842] (2/4) Epoch 32, batch 5250, loss[loss=0.2199, simple_loss=0.2916, pruned_loss=0.07411, over 6990.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2628, pruned_loss=0.04241, over 1420530.51 frames.], batch size: 16, lr: 1.72e-04
2022-05-29 04:23:04,866 INFO [train.py:842] (2/4) Epoch 32, batch 5300, loss[loss=0.155, simple_loss=0.2435, pruned_loss=0.03327, over 7235.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2614, pruned_loss=0.04187, over 1421795.63 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:23:43,917 INFO [train.py:842] (2/4) Epoch 32, batch 5350, loss[loss=0.1932, simple_loss=0.2773, pruned_loss=0.05448, over 7380.00 frames.], tot_loss[loss=0.1723, simple_loss=0.261, pruned_loss=0.04182, over 1424201.82 frames.], batch size: 23, lr: 1.72e-04
2022-05-29 04:24:23,404 INFO [train.py:842] (2/4) Epoch 32, batch 5400, loss[loss=0.1874, simple_loss=0.2762, pruned_loss=0.04934, over 7154.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2613, pruned_loss=0.04195, over 1426570.50 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:25:13,741 INFO [train.py:842] (2/4) Epoch 32, batch 5450, loss[loss=0.1449, simple_loss=0.2256, pruned_loss=0.03212, over 6790.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2607, pruned_loss=0.04177, over 1428926.53 frames.], batch size: 15, lr: 1.72e-04
2022-05-29 04:25:53,343 INFO [train.py:842] (2/4) Epoch 32, batch 5500, loss[loss=0.2067, simple_loss=0.2907, pruned_loss=0.0613, over 7193.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2628, pruned_loss=0.04252, over 1426018.00 frames.], batch size: 22, lr: 1.72e-04
2022-05-29 04:26:32,587 INFO [train.py:842] (2/4) Epoch 32, batch 5550, loss[loss=0.1773, simple_loss=0.2728, pruned_loss=0.04091, over 7422.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2623, pruned_loss=0.04242, over 1426394.73 frames.], batch size: 21, lr: 1.72e-04
2022-05-29 04:27:12,133 INFO [train.py:842] (2/4) Epoch 32, batch 5600, loss[loss=0.1924, simple_loss=0.2855, pruned_loss=0.04962, over 4953.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2612, pruned_loss=0.04185, over 1426747.38 frames.], batch size: 52, lr: 1.72e-04
2022-05-29 04:27:51,451 INFO [train.py:842] (2/4) Epoch 32, batch 5650, loss[loss=0.2184, simple_loss=0.2907, pruned_loss=0.07308, over 7329.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2625, pruned_loss=0.04296, over 1427055.07 frames.], batch size: 20, lr: 1.72e-04
2022-05-29 04:28:41,644 INFO [train.py:842] (2/4) Epoch 32, batch 5700, loss[loss=0.1731, simple_loss=0.2575, pruned_loss=0.04434, over 7000.00 frames.], tot_loss[loss=0.1746, simple_loss=0.263, pruned_loss=0.04307, over 1421072.60 frames.], batch size: 16, lr: 1.72e-04
2022-05-29 04:29:21,094 INFO [train.py:842] (2/4) Epoch 32, batch 5750, loss[loss=0.1827, simple_loss=0.2531, pruned_loss=0.05612, over 7059.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2623, pruned_loss=0.04305, over 1422727.10 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 04:30:11,323 INFO [train.py:842] (2/4) Epoch 32, batch 5800, loss[loss=0.1599, simple_loss=0.2542, pruned_loss=0.03282, over 7329.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2626, pruned_loss=0.04282, over 1419710.40 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:30:50,465 INFO [train.py:842] (2/4) Epoch 32, batch 5850, loss[loss=0.158, simple_loss=0.2387, pruned_loss=0.03865, over 7356.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2637, pruned_loss=0.04337, over 1418571.49 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:31:30,311 INFO [train.py:842] (2/4) Epoch 32, batch 5900, loss[loss=0.2103, simple_loss=0.2845, pruned_loss=0.06798, over 7267.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2634, pruned_loss=0.04324, over 1423944.82 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 04:32:09,712 INFO [train.py:842] (2/4) Epoch 32, batch 5950, loss[loss=0.1671, simple_loss=0.2521, pruned_loss=0.04105, over 7223.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2621, pruned_loss=0.0428, over 1421539.10 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:32:49,017 INFO [train.py:842] (2/4) Epoch 32, batch 6000, loss[loss=0.146, simple_loss=0.2213, pruned_loss=0.03539, over 7002.00 frames.], tot_loss[loss=0.174, simple_loss=0.2623, pruned_loss=0.04287, over 1417210.82 frames.], batch size: 16, lr: 1.71e-04
2022-05-29 04:32:49,018 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 04:32:58,750 INFO [train.py:871] (2/4) Epoch 32, validation: loss=0.1638, simple_loss=0.2607, pruned_loss=0.0334, over 868885.00 frames.
2022-05-29 04:33:38,108 INFO [train.py:842] (2/4) Epoch 32, batch 6050, loss[loss=0.1768, simple_loss=0.2423, pruned_loss=0.05563, over 7286.00 frames.], tot_loss[loss=0.174, simple_loss=0.2626, pruned_loss=0.04271, over 1420168.40 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 04:34:17,897 INFO [train.py:842] (2/4) Epoch 32, batch 6100, loss[loss=0.1866, simple_loss=0.2666, pruned_loss=0.05332, over 7438.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2634, pruned_loss=0.04307, over 1422058.39 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:34:57,127 INFO [train.py:842] (2/4) Epoch 32, batch 6150, loss[loss=0.2297, simple_loss=0.3224, pruned_loss=0.06847, over 7308.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2638, pruned_loss=0.04334, over 1420053.63 frames.], batch size: 25, lr: 1.71e-04
2022-05-29 04:35:36,533 INFO [train.py:842] (2/4) Epoch 32, batch 6200, loss[loss=0.1723, simple_loss=0.2795, pruned_loss=0.03254, over 7402.00 frames.], tot_loss[loss=0.1751, simple_loss=0.264, pruned_loss=0.04306, over 1422557.58 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 04:36:16,133 INFO [train.py:842] (2/4) Epoch 32, batch 6250, loss[loss=0.1715, simple_loss=0.2705, pruned_loss=0.03626, over 7413.00 frames.], tot_loss[loss=0.1738, simple_loss=0.263, pruned_loss=0.04228, over 1426668.29 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 04:36:55,646 INFO [train.py:842] (2/4) Epoch 32, batch 6300, loss[loss=0.1666, simple_loss=0.2547, pruned_loss=0.03925, over 7066.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2631, pruned_loss=0.0425, over 1425327.95 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 04:37:34,947 INFO [train.py:842] (2/4) Epoch 32, batch 6350, loss[loss=0.1658, simple_loss=0.2551, pruned_loss=0.03828, over 7353.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2631, pruned_loss=0.0428, over 1424744.07 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:38:14,615 INFO [train.py:842] (2/4) Epoch 32, batch 6400, loss[loss=0.2006, simple_loss=0.2953, pruned_loss=0.05293, over 7286.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2627, pruned_loss=0.04219, over 1423786.85 frames.], batch size: 25, lr: 1.71e-04
2022-05-29 04:38:53,831 INFO [train.py:842] (2/4) Epoch 32, batch 6450, loss[loss=0.1646, simple_loss=0.2571, pruned_loss=0.03603, over 7435.00 frames.], tot_loss[loss=0.1742, simple_loss=0.263, pruned_loss=0.04274, over 1425100.05 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:39:33,293 INFO [train.py:842] (2/4) Epoch 32, batch 6500, loss[loss=0.1543, simple_loss=0.2446, pruned_loss=0.03198, over 7294.00 frames.], tot_loss[loss=0.1753, simple_loss=0.264, pruned_loss=0.04331, over 1423337.58 frames.], batch size: 24, lr: 1.71e-04
2022-05-29 04:40:12,610 INFO [train.py:842] (2/4) Epoch 32, batch 6550, loss[loss=0.1573, simple_loss=0.2353, pruned_loss=0.03962, over 7405.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2632, pruned_loss=0.04274, over 1420782.07 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 04:40:52,220 INFO [train.py:842] (2/4) Epoch 32, batch 6600, loss[loss=0.1674, simple_loss=0.2684, pruned_loss=0.03323, over 6784.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2625, pruned_loss=0.04246, over 1422441.67 frames.], batch size: 31, lr: 1.71e-04
2022-05-29 04:41:31,509 INFO [train.py:842] (2/4) Epoch 32, batch 6650, loss[loss=0.2093, simple_loss=0.2909, pruned_loss=0.06382, over 7312.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2619, pruned_loss=0.04228, over 1420047.07 frames.], batch size: 25, lr: 1.71e-04
2022-05-29 04:42:11,091 INFO [train.py:842] (2/4) Epoch 32, batch 6700, loss[loss=0.1484, simple_loss=0.2389, pruned_loss=0.02896, over 7153.00 frames.], tot_loss[loss=0.1734, simple_loss=0.262, pruned_loss=0.04246, over 1420226.56 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:42:50,472 INFO [train.py:842] (2/4) Epoch 32, batch 6750, loss[loss=0.1876, simple_loss=0.2757, pruned_loss=0.04972, over 7145.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2617, pruned_loss=0.04234, over 1419762.28 frames.], batch size: 26, lr: 1.71e-04
2022-05-29 04:43:30,246 INFO [train.py:842] (2/4) Epoch 32, batch 6800, loss[loss=0.2027, simple_loss=0.2974, pruned_loss=0.05396, over 7235.00 frames.], tot_loss[loss=0.173, simple_loss=0.2618, pruned_loss=0.04214, over 1424784.08 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:44:09,482 INFO [train.py:842] (2/4) Epoch 32, batch 6850, loss[loss=0.1605, simple_loss=0.2549, pruned_loss=0.03304, over 7249.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2617, pruned_loss=0.04185, over 1425348.82 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:44:48,823 INFO [train.py:842] (2/4) Epoch 32, batch 6900, loss[loss=0.1751, simple_loss=0.2598, pruned_loss=0.04517, over 7239.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2611, pruned_loss=0.04156, over 1425195.89 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:45:27,973 INFO [train.py:842] (2/4) Epoch 32, batch 6950, loss[loss=0.1603, simple_loss=0.2538, pruned_loss=0.03341, over 7143.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2618, pruned_loss=0.04201, over 1427968.19 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:46:07,544 INFO [train.py:842] (2/4) Epoch 32, batch 7000, loss[loss=0.1575, simple_loss=0.2521, pruned_loss=0.0315, over 7147.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2624, pruned_loss=0.04262, over 1427001.62 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:46:46,467 INFO [train.py:842] (2/4) Epoch 32, batch 7050, loss[loss=0.1627, simple_loss=0.2567, pruned_loss=0.03438, over 7206.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2629, pruned_loss=0.04204, over 1427519.20 frames.], batch size: 23, lr: 1.71e-04
2022-05-29 04:47:25,840 INFO [train.py:842] (2/4) Epoch 32, batch 7100, loss[loss=0.176, simple_loss=0.2681, pruned_loss=0.04193, over 7205.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2628, pruned_loss=0.04225, over 1426907.45 frames.], batch size: 22, lr: 1.71e-04
2022-05-29 04:48:04,899 INFO [train.py:842] (2/4) Epoch 32, batch 7150, loss[loss=0.1905, simple_loss=0.2759, pruned_loss=0.05256, over 6713.00 frames.], tot_loss[loss=0.173, simple_loss=0.2622, pruned_loss=0.04191, over 1422576.69 frames.], batch size: 31, lr: 1.71e-04
2022-05-29 04:48:44,503 INFO [train.py:842] (2/4) Epoch 32, batch 7200, loss[loss=0.1682, simple_loss=0.2631, pruned_loss=0.03667, over 7219.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2619, pruned_loss=0.04177, over 1425221.15 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 04:49:23,964 INFO [train.py:842] (2/4) Epoch 32, batch 7250, loss[loss=0.1575, simple_loss=0.2549, pruned_loss=0.03003, over 7412.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2618, pruned_loss=0.04129, over 1429670.25 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 04:50:03,695 INFO [train.py:842] (2/4) Epoch 32, batch 7300, loss[loss=0.1688, simple_loss=0.2601, pruned_loss=0.03873, over 7413.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2614, pruned_loss=0.04118, over 1431879.68 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 04:50:43,023 INFO [train.py:842] (2/4) Epoch 32, batch 7350, loss[loss=0.1576, simple_loss=0.2448, pruned_loss=0.03524, over 7361.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2613, pruned_loss=0.04152, over 1431286.94 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:51:22,704 INFO [train.py:842] (2/4) Epoch 32, batch 7400, loss[loss=0.2028, simple_loss=0.2848, pruned_loss=0.06043, over 5123.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2618, pruned_loss=0.04178, over 1427132.01 frames.], batch size: 52, lr: 1.71e-04
2022-05-29 04:52:02,059 INFO [train.py:842] (2/4) Epoch 32, batch 7450, loss[loss=0.1644, simple_loss=0.2643, pruned_loss=0.03228, over 7283.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2625, pruned_loss=0.04243, over 1428996.77 frames.], batch size: 24, lr: 1.71e-04
2022-05-29 04:52:41,502 INFO [train.py:842] (2/4) Epoch 32, batch 7500, loss[loss=0.2326, simple_loss=0.3094, pruned_loss=0.07785, over 7324.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2626, pruned_loss=0.04262, over 1428511.45 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:53:20,824 INFO [train.py:842] (2/4) Epoch 32, batch 7550, loss[loss=0.1754, simple_loss=0.2629, pruned_loss=0.044, over 7287.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2629, pruned_loss=0.04247, over 1428516.46 frames.], batch size: 24, lr: 1.71e-04
2022-05-29 04:54:00,340 INFO [train.py:842] (2/4) Epoch 32, batch 7600, loss[loss=0.1685, simple_loss=0.2498, pruned_loss=0.04355, over 7359.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2628, pruned_loss=0.04249, over 1426250.50 frames.], batch size: 19, lr: 1.71e-04
2022-05-29 04:54:39,803 INFO [train.py:842] (2/4) Epoch 32, batch 7650, loss[loss=0.1588, simple_loss=0.2485, pruned_loss=0.03453, over 7231.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2622, pruned_loss=0.04228, over 1427476.70 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:55:19,477 INFO [train.py:842] (2/4) Epoch 32, batch 7700, loss[loss=0.2147, simple_loss=0.2972, pruned_loss=0.06614, over 7280.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2628, pruned_loss=0.04272, over 1429190.89 frames.], batch size: 24, lr: 1.71e-04
2022-05-29 04:55:58,587 INFO [train.py:842] (2/4) Epoch 32, batch 7750, loss[loss=0.1911, simple_loss=0.2873, pruned_loss=0.04742, over 7140.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2636, pruned_loss=0.04348, over 1423433.08 frames.], batch size: 28, lr: 1.71e-04
2022-05-29 04:56:38,199 INFO [train.py:842] (2/4) Epoch 32, batch 7800, loss[loss=0.1697, simple_loss=0.2533, pruned_loss=0.04309, over 6412.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2625, pruned_loss=0.04304, over 1421908.29 frames.], batch size: 38, lr: 1.71e-04
2022-05-29 04:57:17,414 INFO [train.py:842] (2/4) Epoch 32, batch 7850, loss[loss=0.1274, simple_loss=0.2162, pruned_loss=0.01931, over 7146.00 frames.], tot_loss[loss=0.1739, simple_loss=0.262, pruned_loss=0.04293, over 1421558.82 frames.], batch size: 17, lr: 1.71e-04
2022-05-29 04:57:56,954 INFO [train.py:842] (2/4) Epoch 32, batch 7900, loss[loss=0.1757, simple_loss=0.274, pruned_loss=0.03874, over 7230.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2629, pruned_loss=0.04316, over 1421420.18 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:58:36,023 INFO [train.py:842] (2/4) Epoch 32, batch 7950, loss[loss=0.2051, simple_loss=0.3034, pruned_loss=0.05335, over 6717.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2635, pruned_loss=0.04283, over 1423627.16 frames.], batch size: 31, lr: 1.71e-04
2022-05-29 04:59:15,428 INFO [train.py:842] (2/4) Epoch 32, batch 8000, loss[loss=0.1744, simple_loss=0.2652, pruned_loss=0.04184, over 7337.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2634, pruned_loss=0.04272, over 1423737.77 frames.], batch size: 20, lr: 1.71e-04
2022-05-29 04:59:54,752 INFO [train.py:842] (2/4) Epoch 32, batch 8050, loss[loss=0.1562, simple_loss=0.2505, pruned_loss=0.03097, over 7397.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2643, pruned_loss=0.04357, over 1422113.20 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 05:00:34,456 INFO [train.py:842] (2/4) Epoch 32, batch 8100, loss[loss=0.178, simple_loss=0.2526, pruned_loss=0.0517, over 6751.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2638, pruned_loss=0.04349, over 1421344.92 frames.], batch size: 15, lr: 1.71e-04
2022-05-29 05:01:13,692 INFO [train.py:842] (2/4) Epoch 32, batch 8150, loss[loss=0.1877, simple_loss=0.2772, pruned_loss=0.04908, over 7168.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2639, pruned_loss=0.04352, over 1422739.21 frames.], batch size: 26, lr: 1.71e-04
2022-05-29 05:01:53,211 INFO [train.py:842] (2/4) Epoch 32, batch 8200, loss[loss=0.1783, simple_loss=0.2712, pruned_loss=0.0427, over 7218.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2646, pruned_loss=0.04401, over 1421798.18 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 05:02:32,276 INFO [train.py:842] (2/4) Epoch 32, batch 8250, loss[loss=0.154, simple_loss=0.2451, pruned_loss=0.03143, over 6383.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2639, pruned_loss=0.0439, over 1419292.63 frames.], batch size: 37, lr: 1.71e-04
2022-05-29 05:03:11,977 INFO [train.py:842] (2/4) Epoch 32, batch 8300, loss[loss=0.1704, simple_loss=0.2596, pruned_loss=0.04059, over 7068.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2636, pruned_loss=0.04367, over 1423374.47 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 05:03:50,993 INFO [train.py:842] (2/4) Epoch 32, batch 8350, loss[loss=0.2455, simple_loss=0.3173, pruned_loss=0.08683, over 7069.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2646, pruned_loss=0.04409, over 1424659.65 frames.], batch size: 28, lr: 1.71e-04
2022-05-29 05:04:30,238 INFO [train.py:842] (2/4) Epoch 32, batch 8400, loss[loss=0.1497, simple_loss=0.2375, pruned_loss=0.03098, over 7171.00 frames.], tot_loss[loss=0.176, simple_loss=0.2644, pruned_loss=0.04383, over 1421585.50 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 05:05:09,516 INFO [train.py:842] (2/4) Epoch 32, batch 8450, loss[loss=0.1678, simple_loss=0.2478, pruned_loss=0.04395, over 7285.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2642, pruned_loss=0.04373, over 1418190.67 frames.], batch size: 17, lr: 1.71e-04
2022-05-29 05:05:49,195 INFO [train.py:842] (2/4) Epoch 32, batch 8500, loss[loss=0.1574, simple_loss=0.2473, pruned_loss=0.03377, over 7067.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2637, pruned_loss=0.04378, over 1417689.95 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 05:06:28,101 INFO [train.py:842] (2/4) Epoch 32, batch 8550, loss[loss=0.1871, simple_loss=0.2788, pruned_loss=0.04774, over 7103.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2635, pruned_loss=0.04369, over 1417348.39 frames.], batch size: 28, lr: 1.71e-04
2022-05-29 05:07:07,473 INFO [train.py:842] (2/4) Epoch 32, batch 8600, loss[loss=0.1866, simple_loss=0.272, pruned_loss=0.05059, over 7211.00 frames.], tot_loss[loss=0.1748, simple_loss=0.263, pruned_loss=0.04334, over 1414266.64 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 05:07:46,504 INFO [train.py:842] (2/4) Epoch 32, batch 8650, loss[loss=0.137, simple_loss=0.2235, pruned_loss=0.02528, over 7253.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2617, pruned_loss=0.04258, over 1419015.38 frames.], batch size: 16, lr: 1.71e-04
2022-05-29 05:08:26,013 INFO [train.py:842] (2/4) Epoch 32, batch 8700, loss[loss=0.1361, simple_loss=0.2211, pruned_loss=0.02558, over 7421.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2625, pruned_loss=0.04254, over 1423441.55 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 05:09:05,079 INFO [train.py:842] (2/4) Epoch 32, batch 8750, loss[loss=0.2371, simple_loss=0.329, pruned_loss=0.07255, over 5141.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2636, pruned_loss=0.04334, over 1418455.99 frames.], batch size: 54, lr: 1.71e-04
2022-05-29 05:09:44,410 INFO [train.py:842] (2/4) Epoch 32, batch 8800, loss[loss=0.1908, simple_loss=0.2907, pruned_loss=0.04546, over 7286.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2625, pruned_loss=0.04258, over 1411482.18 frames.], batch size: 24, lr: 1.71e-04
2022-05-29 05:10:23,488 INFO [train.py:842] (2/4) Epoch 32, batch 8850, loss[loss=0.1674, simple_loss=0.2556, pruned_loss=0.03955, over 7059.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2639, pruned_loss=0.04318, over 1411056.90 frames.], batch size: 18, lr: 1.71e-04
2022-05-29 05:11:02,866 INFO [train.py:842] (2/4) Epoch 32, batch 8900, loss[loss=0.2002, simple_loss=0.2812, pruned_loss=0.05956, over 7321.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2647, pruned_loss=0.04342, over 1403425.87 frames.], batch size: 21, lr: 1.71e-04
2022-05-29 05:11:41,574 INFO [train.py:842] (2/4) Epoch 32, batch 8950, loss[loss=0.2457, simple_loss=0.3272, pruned_loss=0.08208, over 5141.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2661, pruned_loss=0.04427, over 1398318.57 frames.], batch size: 52, lr: 1.71e-04
2022-05-29 05:12:20,877 INFO [train.py:842] (2/4) Epoch 32, batch 9000, loss[loss=0.1614, simple_loss=0.2477, pruned_loss=0.0376, over 6773.00 frames.], tot_loss[loss=0.1766, simple_loss=0.265, pruned_loss=0.04411, over 1392565.62 frames.], batch size: 15, lr: 1.71e-04
2022-05-29 05:12:20,878 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 05:12:30,815 INFO [train.py:871] (2/4) Epoch 32, validation: loss=0.1637, simple_loss=0.261, pruned_loss=0.03323, over 868885.00 frames.
2022-05-29 05:13:09,668 INFO [train.py:842] (2/4) Epoch 32, batch 9050, loss[loss=0.1911, simple_loss=0.2808, pruned_loss=0.05065, over 7111.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2655, pruned_loss=0.04457, over 1381800.48 frames.], batch size: 28, lr: 1.71e-04
2022-05-29 05:13:48,016 INFO [train.py:842] (2/4) Epoch 32, batch 9100, loss[loss=0.2207, simple_loss=0.294, pruned_loss=0.07366, over 5294.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2694, pruned_loss=0.04706, over 1332541.52 frames.], batch size: 53, lr: 1.71e-04
2022-05-29 05:14:25,961 INFO [train.py:842] (2/4) Epoch 32, batch 9150, loss[loss=0.2514, simple_loss=0.3232, pruned_loss=0.08978, over 5026.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2729, pruned_loss=0.04943, over 1258960.85 frames.], batch size: 52, lr: 1.71e-04
2022-05-29 05:15:18,072 INFO [train.py:842] (2/4) Epoch 33, batch 0, loss[loss=0.1764, simple_loss=0.2719, pruned_loss=0.04043, over 6871.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2719, pruned_loss=0.04043, over 6871.00 frames.], batch size: 31, lr: 1.68e-04
2022-05-29 05:15:57,446 INFO [train.py:842] (2/4) Epoch 33, batch 50, loss[loss=0.199, simple_loss=0.2873, pruned_loss=0.05539, over 5004.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2659, pruned_loss=0.04346, over 314231.54 frames.], batch size: 52, lr: 1.68e-04
2022-05-29 05:16:36,936 INFO [train.py:842] (2/4) Epoch 33, batch 100, loss[loss=0.1675, simple_loss=0.2579, pruned_loss=0.03851, over 6362.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2666, pruned_loss=0.0449, over 558540.82 frames.], batch size: 37, lr: 1.68e-04
2022-05-29 05:17:16,112 INFO [train.py:842] (2/4) Epoch 33, batch 150, loss[loss=0.1781, simple_loss=0.2708, pruned_loss=0.04267, over 7215.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2664, pruned_loss=0.04438, over 751486.44 frames.], batch size: 23, lr: 1.68e-04
2022-05-29 05:17:55,469 INFO [train.py:842] (2/4) Epoch 33, batch 200, loss[loss=0.1398, simple_loss=0.2163, pruned_loss=0.03167, over 6997.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2645, pruned_loss=0.04322, over 895061.92 frames.], batch size: 16, lr: 1.68e-04
2022-05-29 05:18:34,579 INFO [train.py:842] (2/4) Epoch 33, batch 250, loss[loss=0.1724, simple_loss=0.2563, pruned_loss=0.04429, over 7228.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2656, pruned_loss=0.04428, over 1009591.44 frames.], batch size: 20, lr: 1.68e-04
2022-05-29 05:19:13,836 INFO [train.py:842] (2/4) Epoch 33, batch 300, loss[loss=0.1691, simple_loss=0.2535, pruned_loss=0.0423, over 6844.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2649, pruned_loss=0.04348, over 1092372.33 frames.], batch size: 31, lr: 1.68e-04
2022-05-29 05:19:52,965 INFO [train.py:842] (2/4) Epoch 33, batch 350, loss[loss=0.1751, simple_loss=0.2455, pruned_loss=0.05232, over 7405.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2642, pruned_loss=0.0432, over 1164438.76 frames.], batch size: 18, lr: 1.68e-04
2022-05-29 05:20:32,712 INFO [train.py:842] (2/4) Epoch 33, batch 400, loss[loss=0.1851, simple_loss=0.2726, pruned_loss=0.04875, over 7439.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2629, pruned_loss=0.04277, over 1221624.46 frames.], batch size: 20, lr: 1.68e-04
2022-05-29 05:21:12,009 INFO [train.py:842] (2/4) Epoch 33, batch 450, loss[loss=0.1929, simple_loss=0.2805, pruned_loss=0.05266, over 6900.00 frames.], tot_loss[loss=0.175, simple_loss=0.2634, pruned_loss=0.04333, over 1263323.36 frames.], batch size: 31, lr: 1.68e-04
2022-05-29 05:21:51,592 INFO [train.py:842] (2/4) Epoch 33, batch 500, loss[loss=0.1948, simple_loss=0.275, pruned_loss=0.05727, over 7213.00 frames.], tot_loss[loss=0.175, simple_loss=0.2636, pruned_loss=0.04317, over 1300995.45 frames.], batch size: 23, lr: 1.68e-04
2022-05-29 05:22:30,645 INFO [train.py:842] (2/4) Epoch 33, batch 550, loss[loss=0.202, simple_loss=0.295, pruned_loss=0.05448, over 7312.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2643, pruned_loss=0.04345, over 1329328.37 frames.], batch size: 21, lr: 1.68e-04
2022-05-29 05:23:09,921 INFO [train.py:842] (2/4) Epoch 33, batch 600, loss[loss=0.1924, simple_loss=0.2782, pruned_loss=0.05331, over 7299.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2647, pruned_loss=0.04381, over 1347857.13 frames.], batch size: 24, lr: 1.68e-04
2022-05-29 05:23:49,139 INFO [train.py:842] (2/4) Epoch 33, batch 650, loss[loss=0.1806, simple_loss=0.2718, pruned_loss=0.04471, over 7178.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2647, pruned_loss=0.0438, over 1364819.32 frames.], batch size: 26, lr: 1.68e-04
2022-05-29 05:24:28,729 INFO [train.py:842] (2/4) Epoch 33, batch 700, loss[loss=0.1302, simple_loss=0.2079, pruned_loss=0.02627, over 7136.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2642, pruned_loss=0.04356, over 1375828.45 frames.], batch size: 17, lr: 1.68e-04
2022-05-29 05:25:07,927 INFO [train.py:842] (2/4) Epoch 33, batch 750, loss[loss=0.1663, simple_loss=0.2576, pruned_loss=0.03751, over 7219.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2646, pruned_loss=0.04356, over 1381024.46 frames.], batch size: 21, lr: 1.68e-04
2022-05-29 05:25:47,794 INFO [train.py:842] (2/4) Epoch 33, batch 800, loss[loss=0.2335, simple_loss=0.3042, pruned_loss=0.08135, over 7429.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2631, pruned_loss=0.04317, over 1392639.30 frames.], batch size: 20, lr: 1.68e-04
2022-05-29 05:26:26,897 INFO [train.py:842] (2/4) Epoch 33, batch 850, loss[loss=0.1958, simple_loss=0.294, pruned_loss=0.04874, over 7368.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2635, pruned_loss=0.04283, over 1400059.51 frames.], batch size: 23, lr: 1.68e-04
2022-05-29 05:27:06,452 INFO [train.py:842] (2/4) Epoch 33, batch 900, loss[loss=0.1801, simple_loss=0.2717, pruned_loss=0.04429, over 7189.00 frames.], tot_loss[loss=0.1732, simple_loss=0.262, pruned_loss=0.04221, over 1409321.58 frames.], batch size: 23, lr: 1.68e-04
2022-05-29 05:27:45,832 INFO [train.py:842] (2/4) Epoch 33, batch 950, loss[loss=0.183, simple_loss=0.284, pruned_loss=0.04099, over 7420.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.0422, over 1414130.70 frames.], batch size: 20, lr: 1.68e-04
2022-05-29 05:28:25,576 INFO [train.py:842] (2/4) Epoch 33, batch 1000, loss[loss=0.1678, simple_loss=0.2579, pruned_loss=0.03886, over 7195.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2618, pruned_loss=0.04239, over 1413950.20 frames.], batch size: 23, lr: 1.68e-04
2022-05-29 05:29:04,495 INFO [train.py:842] (2/4) Epoch 33, batch 1050, loss[loss=0.1868, simple_loss=0.281, pruned_loss=0.04634, over 7132.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2621, pruned_loss=0.04217, over 1413225.02 frames.], batch size: 28, lr: 1.68e-04
2022-05-29 05:29:43,865 INFO [train.py:842] (2/4) Epoch 33, batch 1100, loss[loss=0.1676, simple_loss=0.2528, pruned_loss=0.04117, over 7286.00 frames.], tot_loss[loss=0.174, simple_loss=0.2627, pruned_loss=0.04264, over 1418438.46 frames.], batch size: 24, lr: 1.68e-04
2022-05-29 05:30:22,995 INFO [train.py:842] (2/4) Epoch 33, batch 1150, loss[loss=0.1527, simple_loss=0.2483, pruned_loss=0.02851, over 7196.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2635, pruned_loss=0.0426, over 1419430.52 frames.], batch size: 23, lr: 1.68e-04
2022-05-29 05:31:02,264 INFO [train.py:842] (2/4) Epoch 33, batch 1200, loss[loss=0.1824, simple_loss=0.2804, pruned_loss=0.04223, over 7236.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2646, pruned_loss=0.04264, over 1422371.06 frames.], batch size: 26, lr: 1.68e-04
2022-05-29 05:31:41,532 INFO [train.py:842] (2/4) Epoch 33, batch 1250, loss[loss=0.1648, simple_loss=0.2548, pruned_loss=0.03734, over 6376.00 frames.], tot_loss[loss=0.1753, simple_loss=0.265, pruned_loss=0.04277, over 1421005.97 frames.], batch size: 37, lr: 1.68e-04
2022-05-29 05:32:21,183 INFO [train.py:842] (2/4) Epoch 33, batch 1300, loss[loss=0.1754, simple_loss=0.2657, pruned_loss=0.04252, over 7218.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2644, pruned_loss=0.04263, over 1420825.21 frames.], batch size: 21, lr: 1.68e-04
2022-05-29 05:33:00,622 INFO [train.py:842] (2/4) Epoch 33, batch 1350, loss[loss=0.17, simple_loss=0.2459, pruned_loss=0.04706, over 7270.00 frames.], tot_loss[loss=0.174, simple_loss=0.2632, pruned_loss=0.04241, over 1420174.66 frames.], batch size: 17, lr: 1.68e-04
2022-05-29 05:33:40,386 INFO [train.py:842] (2/4) Epoch 33, batch 1400, loss[loss=0.2131, simple_loss=0.3053, pruned_loss=0.06049, over 7142.00 frames.], tot_loss[loss=0.1738, simple_loss=0.263, pruned_loss=0.04235, over 1421544.81 frames.], batch size: 20, lr: 1.68e-04
2022-05-29 05:34:19,591 INFO [train.py:842] (2/4) Epoch 33, batch 1450, loss[loss=0.1816, simple_loss=0.2788, pruned_loss=0.04222, over 6774.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2625, pruned_loss=0.04205, over 1425051.87 frames.], batch size: 31, lr: 1.67e-04
2022-05-29 05:34:59,104 INFO [train.py:842] (2/4) Epoch 33, batch 1500, loss[loss=0.2786, simple_loss=0.3489, pruned_loss=0.1041, over 5254.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2622, pruned_loss=0.04203, over 1422981.89 frames.], batch size: 52, lr: 1.67e-04
2022-05-29 05:35:38,244 INFO [train.py:842] (2/4) Epoch 33, batch 1550, loss[loss=0.1902, simple_loss=0.2766, pruned_loss=0.05187, over 7213.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2628, pruned_loss=0.04248, over 1420179.13 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 05:36:17,798 INFO [train.py:842] (2/4) Epoch 33, batch 1600, loss[loss=0.2056, simple_loss=0.2918, pruned_loss=0.0597, over 7403.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2623, pruned_loss=0.0424, over 1420646.46 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 05:36:57,052 INFO [train.py:842] (2/4) Epoch 33, batch 1650, loss[loss=0.2094, simple_loss=0.2945, pruned_loss=0.06214, over 7213.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2628, pruned_loss=0.0428, over 1422083.89 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 05:37:36,354 INFO [train.py:842] (2/4) Epoch 33, batch 1700, loss[loss=0.2041, simple_loss=0.2976, pruned_loss=0.0553, over 7280.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2638, pruned_loss=0.04326, over 1424039.85 frames.], batch size: 24, lr: 1.67e-04
2022-05-29 05:38:15,200 INFO [train.py:842] (2/4) Epoch 33, batch 1750, loss[loss=0.2105, simple_loss=0.3024, pruned_loss=0.05929, over 6981.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2645, pruned_loss=0.04347, over 1417240.29 frames.], batch size: 28, lr: 1.67e-04
2022-05-29 05:38:54,982 INFO [train.py:842] (2/4) Epoch 33, batch 1800, loss[loss=0.1616, simple_loss=0.252, pruned_loss=0.03555, over 7264.00 frames.], tot_loss[loss=0.174, simple_loss=0.2631, pruned_loss=0.04251, over 1420674.30 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 05:39:34,276 INFO [train.py:842] (2/4) Epoch 33, batch 1850, loss[loss=0.1872, simple_loss=0.289, pruned_loss=0.04267, over 7322.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2638, pruned_loss=0.04287, over 1424023.88 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 05:40:16,630 INFO [train.py:842] (2/4) Epoch 33, batch 1900, loss[loss=0.1776, simple_loss=0.269, pruned_loss=0.04311, over 7370.00 frames.], tot_loss[loss=0.1738, simple_loss=0.263, pruned_loss=0.04226, over 1426582.27 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 05:40:55,640 INFO [train.py:842] (2/4) Epoch 33, batch 1950, loss[loss=0.1706, simple_loss=0.2608, pruned_loss=0.04021, over 7275.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2636, pruned_loss=0.04248, over 1424442.92 frames.], batch size: 24, lr: 1.67e-04
2022-05-29 05:41:35,581 INFO [train.py:842] (2/4) Epoch 33, batch 2000, loss[loss=0.1859, simple_loss=0.2863, pruned_loss=0.04275, over 6492.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2628, pruned_loss=0.0423, over 1425518.83 frames.], batch size: 38, lr: 1.67e-04
2022-05-29 05:42:14,887 INFO [train.py:842] (2/4) Epoch 33, batch 2050, loss[loss=0.1506, simple_loss=0.2443, pruned_loss=0.02842, over 7178.00 frames.], tot_loss[loss=0.1741, simple_loss=0.263, pruned_loss=0.04257, over 1425986.18 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 05:42:54,720 INFO [train.py:842] (2/4) Epoch 33, batch 2100, loss[loss=0.1446, simple_loss=0.2333, pruned_loss=0.02799, over 7170.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2627, pruned_loss=0.04272, over 1427605.28 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 05:43:33,991 INFO [train.py:842] (2/4) Epoch 33, batch 2150, loss[loss=0.1316, simple_loss=0.2241, pruned_loss=0.01962, over 7422.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2631, pruned_loss=0.04282, over 1427870.07 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 05:44:13,546 INFO [train.py:842] (2/4) Epoch 33, batch 2200, loss[loss=0.2058, simple_loss=0.2802, pruned_loss=0.06571, over 4990.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2633, pruned_loss=0.04288, over 1422240.52 frames.], batch size: 52, lr: 1.67e-04
2022-05-29 05:44:52,673 INFO [train.py:842] (2/4) Epoch 33, batch 2250, loss[loss=0.1633, simple_loss=0.251, pruned_loss=0.03778, over 7148.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2627, pruned_loss=0.04284, over 1419961.02 frames.], batch size: 26, lr: 1.67e-04
2022-05-29 05:45:32,545 INFO [train.py:842] (2/4) Epoch 33, batch 2300, loss[loss=0.1684, simple_loss=0.2624, pruned_loss=0.03726, over 7206.00 frames.], tot_loss[loss=0.1737, simple_loss=0.262, pruned_loss=0.04271, over 1418791.40 frames.], batch size: 22, lr: 1.67e-04
2022-05-29 05:46:11,735 INFO [train.py:842] (2/4) Epoch 33, batch 2350, loss[loss=0.1389, simple_loss=0.2218, pruned_loss=0.028, over 7245.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2619, pruned_loss=0.04257, over 1421736.11 frames.], batch size: 16, lr: 1.67e-04
2022-05-29 05:46:51,690 INFO [train.py:842] (2/4) Epoch 33, batch 2400, loss[loss=0.1622, simple_loss=0.2538, pruned_loss=0.03527, over 7433.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2614, pruned_loss=0.04299, over 1424385.02 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 05:47:31,140 INFO [train.py:842] (2/4) Epoch 33, batch 2450, loss[loss=0.1509, simple_loss=0.2323, pruned_loss=0.03475, over 7265.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2614, pruned_loss=0.04253, over 1425945.23 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 05:48:10,855 INFO [train.py:842] (2/4) Epoch 33, batch 2500, loss[loss=0.1694, simple_loss=0.2671, pruned_loss=0.03588, over 7312.00 frames.], tot_loss[loss=0.173, simple_loss=0.2615, pruned_loss=0.04224, over 1427105.12 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 05:48:50,228 INFO [train.py:842] (2/4) Epoch 33, batch 2550, loss[loss=0.1673, simple_loss=0.2659, pruned_loss=0.0344, over 7385.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2619, pruned_loss=0.04245, over 1426186.71 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 05:49:30,007 INFO [train.py:842] (2/4) Epoch 33, batch 2600, loss[loss=0.1876, simple_loss=0.2756, pruned_loss=0.04978, over 7188.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2616, pruned_loss=0.04257, over 1426471.27 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 05:50:08,968 INFO [train.py:842] (2/4) Epoch 33, batch 2650, loss[loss=0.171, simple_loss=0.2525, pruned_loss=0.0447, over 6808.00 frames.], tot_loss[loss=0.173, simple_loss=0.2618, pruned_loss=0.0421, over 1421711.33 frames.], batch size: 15, lr: 1.67e-04
2022-05-29 05:50:48,461 INFO [train.py:842] (2/4) Epoch 33, batch 2700, loss[loss=0.1449, simple_loss=0.236, pruned_loss=0.02696, over 7427.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2624, pruned_loss=0.04247, over 1423468.52 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 05:51:27,568 INFO [train.py:842] (2/4) Epoch 33, batch 2750, loss[loss=0.1641, simple_loss=0.2518, pruned_loss=0.03819, over 7276.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2623, pruned_loss=0.04201, over 1424345.41 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 05:52:07,132 INFO [train.py:842] (2/4) Epoch 33, batch 2800, loss[loss=0.1981, simple_loss=0.2978, pruned_loss=0.04922, over 7213.00 frames.], tot_loss[loss=0.1723, simple_loss=0.262, pruned_loss=0.04135, over 1424049.77 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 05:52:46,420 INFO [train.py:842] (2/4) Epoch 33, batch 2850, loss[loss=0.174, simple_loss=0.2771, pruned_loss=0.0355, over 7317.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2616, pruned_loss=0.04133, over 1425348.08 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 05:53:25,952 INFO [train.py:842] (2/4) Epoch 33, batch 2900, loss[loss=0.172, simple_loss=0.2673, pruned_loss=0.03832, over 7273.00 frames.], tot_loss[loss=0.1726, simple_loss=0.262, pruned_loss=0.04161, over 1424662.95 frames.], batch size: 25, lr: 1.67e-04
2022-05-29 05:54:05,074 INFO [train.py:842] (2/4) Epoch 33, batch 2950, loss[loss=0.1738, simple_loss=0.2696, pruned_loss=0.03905, over 7430.00 frames.], tot_loss[loss=0.173, simple_loss=0.2626, pruned_loss=0.04166, over 1426964.45 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 05:54:44,625 INFO [train.py:842] (2/4) Epoch 33, batch 3000, loss[loss=0.1337, simple_loss=0.2206, pruned_loss=0.0234, over 7081.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2614, pruned_loss=0.04107, over 1426246.09 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 05:54:44,626 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 05:54:54,324 INFO [train.py:871] (2/4) Epoch 33, validation: loss=0.1647, simple_loss=0.2614, pruned_loss=0.03398, over 868885.00 frames.
2022-05-29 05:55:33,745 INFO [train.py:842] (2/4) Epoch 33, batch 3050, loss[loss=0.1877, simple_loss=0.2744, pruned_loss=0.05045, over 6461.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2608, pruned_loss=0.04093, over 1422849.62 frames.], batch size: 38, lr: 1.67e-04
2022-05-29 05:56:13,272 INFO [train.py:842] (2/4) Epoch 33, batch 3100, loss[loss=0.2046, simple_loss=0.2976, pruned_loss=0.05575, over 7364.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2619, pruned_loss=0.04174, over 1423827.65 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 05:56:52,613 INFO [train.py:842] (2/4) Epoch 33, batch 3150, loss[loss=0.1606, simple_loss=0.2475, pruned_loss=0.03683, over 7056.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2607, pruned_loss=0.04157, over 1420940.76 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 05:57:42,968 INFO [train.py:842] (2/4) Epoch 33, batch 3200, loss[loss=0.157, simple_loss=0.2383, pruned_loss=0.03787, over 7217.00 frames.], tot_loss[loss=0.172, simple_loss=0.2606, pruned_loss=0.04167, over 1421196.92 frames.], batch size: 16, lr: 1.67e-04
2022-05-29 05:58:22,123 INFO [train.py:842] (2/4) Epoch 33, batch 3250, loss[loss=0.1504, simple_loss=0.2395, pruned_loss=0.03061, over 7281.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2619, pruned_loss=0.04223, over 1418495.61 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 05:59:02,038 INFO [train.py:842] (2/4) Epoch 33, batch 3300, loss[loss=0.2199, simple_loss=0.3191, pruned_loss=0.06039, over 7227.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2619, pruned_loss=0.04249, over 1424358.48 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 05:59:41,361 INFO [train.py:842] (2/4) Epoch 33, batch 3350, loss[loss=0.2041, simple_loss=0.2897, pruned_loss=0.05927, over 7322.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2636, pruned_loss=0.04333, over 1427650.67 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 06:00:20,943 INFO [train.py:842] (2/4) Epoch 33, batch 3400, loss[loss=0.1414, simple_loss=0.2161, pruned_loss=0.03331, over 7256.00 frames.], tot_loss[loss=0.175, simple_loss=0.2632, pruned_loss=0.04336, over 1427727.66 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 06:01:00,242 INFO [train.py:842] (2/4) Epoch 33, batch 3450, loss[loss=0.1742, simple_loss=0.2595, pruned_loss=0.04444, over 7335.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2639, pruned_loss=0.04337, over 1431510.02 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 06:01:39,822 INFO [train.py:842] (2/4) Epoch 33, batch 3500, loss[loss=0.1586, simple_loss=0.2467, pruned_loss=0.03531, over 7365.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2641, pruned_loss=0.04316, over 1428154.10 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 06:02:19,037 INFO [train.py:842] (2/4) Epoch 33, batch 3550, loss[loss=0.2281, simple_loss=0.2996, pruned_loss=0.07827, over 7416.00 frames.], tot_loss[loss=0.175, simple_loss=0.2641, pruned_loss=0.04299, over 1425987.80 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 06:02:58,528 INFO [train.py:842] (2/4) Epoch 33, batch 3600, loss[loss=0.179, simple_loss=0.2802, pruned_loss=0.03892, over 7336.00 frames.], tot_loss[loss=0.1749, simple_loss=0.264, pruned_loss=0.04286, over 1422399.02 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 06:03:38,019 INFO [train.py:842] (2/4) Epoch 33, batch 3650, loss[loss=0.1777, simple_loss=0.2707, pruned_loss=0.04234, over 7330.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2644, pruned_loss=0.04307, over 1422018.42 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 06:04:17,614 INFO [train.py:842] (2/4) Epoch 33, batch 3700, loss[loss=0.1546, simple_loss=0.2267, pruned_loss=0.04127, over 7280.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2641, pruned_loss=0.04266, over 1425653.12 frames.], batch size: 17, lr: 1.67e-04
2022-05-29 06:04:56,705 INFO [train.py:842] (2/4) Epoch 33, batch 3750, loss[loss=0.1961, simple_loss=0.2961, pruned_loss=0.04802, over 7212.00 frames.], tot_loss[loss=0.1748, simple_loss=0.264, pruned_loss=0.04285, over 1425959.28 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 06:05:36,542 INFO [train.py:842] (2/4) Epoch 33, batch 3800, loss[loss=0.1992, simple_loss=0.2864, pruned_loss=0.05601, over 7223.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2636, pruned_loss=0.0431, over 1427437.32 frames.], batch size: 23, lr: 1.67e-04
2022-05-29 06:06:15,673 INFO [train.py:842] (2/4) Epoch 33, batch 3850, loss[loss=0.1731, simple_loss=0.265, pruned_loss=0.04057, over 7314.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2631, pruned_loss=0.04258, over 1427958.52 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 06:06:55,037 INFO [train.py:842] (2/4) Epoch 33, batch 3900, loss[loss=0.1727, simple_loss=0.2449, pruned_loss=0.05024, over 6801.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2638, pruned_loss=0.04268, over 1428310.93 frames.], batch size: 15, lr: 1.67e-04
2022-05-29 06:07:34,355 INFO [train.py:842] (2/4) Epoch 33, batch 3950, loss[loss=0.1374, simple_loss=0.2149, pruned_loss=0.0299, over 6770.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2639, pruned_loss=0.04271, over 1428813.05 frames.], batch size: 15, lr: 1.67e-04
2022-05-29 06:08:14,160 INFO [train.py:842] (2/4) Epoch 33, batch 4000, loss[loss=0.1997, simple_loss=0.2828, pruned_loss=0.05829, over 5134.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2628, pruned_loss=0.04217, over 1430182.12 frames.], batch size: 52, lr: 1.67e-04
2022-05-29 06:08:53,361 INFO [train.py:842] (2/4) Epoch 33, batch 4050, loss[loss=0.1565, simple_loss=0.2513, pruned_loss=0.03083, over 7259.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2616, pruned_loss=0.04145, over 1425988.73 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 06:09:33,024 INFO [train.py:842] (2/4) Epoch 33, batch 4100, loss[loss=0.2289, simple_loss=0.3083, pruned_loss=0.07473, over 7296.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2616, pruned_loss=0.04193, over 1424748.06 frames.], batch size: 25, lr: 1.67e-04
2022-05-29 06:10:12,266 INFO [train.py:842] (2/4) Epoch 33, batch 4150, loss[loss=0.1617, simple_loss=0.2551, pruned_loss=0.03415, over 7156.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2625, pruned_loss=0.0423, over 1419385.35 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 06:10:52,067 INFO [train.py:842] (2/4) Epoch 33, batch 4200, loss[loss=0.1686, simple_loss=0.267, pruned_loss=0.03506, over 7410.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.04221, over 1425413.38 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 06:11:31,147 INFO [train.py:842] (2/4) Epoch 33, batch 4250, loss[loss=0.1719, simple_loss=0.2639, pruned_loss=0.03993, over 7342.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2636, pruned_loss=0.04244, over 1424402.60 frames.], batch size: 22, lr: 1.67e-04
2022-05-29 06:12:10,756 INFO [train.py:842] (2/4) Epoch 33, batch 4300, loss[loss=0.1691, simple_loss=0.2592, pruned_loss=0.03949, over 7161.00 frames.], tot_loss[loss=0.1736, simple_loss=0.263, pruned_loss=0.04208, over 1424846.13 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 06:12:50,146 INFO [train.py:842] (2/4) Epoch 33, batch 4350, loss[loss=0.1494, simple_loss=0.246, pruned_loss=0.02644, over 7240.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2621, pruned_loss=0.04151, over 1426704.30 frames.], batch size: 20, lr: 1.67e-04
2022-05-29 06:13:29,858 INFO [train.py:842] (2/4) Epoch 33, batch 4400, loss[loss=0.1629, simple_loss=0.2589, pruned_loss=0.03345, over 6292.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2625, pruned_loss=0.0419, over 1425789.26 frames.], batch size: 37, lr: 1.67e-04
2022-05-29 06:14:09,299 INFO [train.py:842] (2/4) Epoch 33, batch 4450, loss[loss=0.1786, simple_loss=0.2681, pruned_loss=0.04451, over 7001.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2636, pruned_loss=0.04243, over 1425075.30 frames.], batch size: 16, lr: 1.67e-04
2022-05-29 06:14:48,769 INFO [train.py:842] (2/4) Epoch 33, batch 4500, loss[loss=0.1825, simple_loss=0.2708, pruned_loss=0.04714, over 7331.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2646, pruned_loss=0.0431, over 1425791.82 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 06:15:28,036 INFO [train.py:842] (2/4) Epoch 33, batch 4550, loss[loss=0.1653, simple_loss=0.2516, pruned_loss=0.03954, over 7280.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2636, pruned_loss=0.04238, over 1423929.23 frames.], batch size: 25, lr: 1.67e-04
2022-05-29 06:16:07,559 INFO [train.py:842] (2/4) Epoch 33, batch 4600, loss[loss=0.1739, simple_loss=0.2735, pruned_loss=0.03714, over 6753.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2639, pruned_loss=0.0429, over 1423209.04 frames.], batch size: 31, lr: 1.67e-04
2022-05-29 06:16:46,694 INFO [train.py:842] (2/4) Epoch 33, batch 4650, loss[loss=0.1587, simple_loss=0.2407, pruned_loss=0.03833, over 7422.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2633, pruned_loss=0.0427, over 1421271.92 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 06:17:26,302 INFO [train.py:842] (2/4) Epoch 33, batch 4700, loss[loss=0.1577, simple_loss=0.2542, pruned_loss=0.03059, over 6409.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2641, pruned_loss=0.04322, over 1423226.74 frames.], batch size: 37, lr: 1.67e-04
2022-05-29 06:18:05,568 INFO [train.py:842] (2/4) Epoch 33, batch 4750, loss[loss=0.1706, simple_loss=0.2395, pruned_loss=0.05085, over 7285.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2635, pruned_loss=0.04312, over 1423794.17 frames.], batch size: 17, lr: 1.67e-04
2022-05-29 06:18:45,235 INFO [train.py:842] (2/4) Epoch 33, batch 4800, loss[loss=0.1739, simple_loss=0.2649, pruned_loss=0.04152, over 7124.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2625, pruned_loss=0.04263, over 1424049.48 frames.], batch size: 21, lr: 1.67e-04
2022-05-29 06:19:24,408 INFO [train.py:842] (2/4) Epoch 33, batch 4850, loss[loss=0.1735, simple_loss=0.266, pruned_loss=0.0405, over 6236.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2623, pruned_loss=0.04279, over 1419399.24 frames.], batch size: 37, lr: 1.67e-04
2022-05-29 06:20:03,997 INFO [train.py:842] (2/4) Epoch 33, batch 4900, loss[loss=0.168, simple_loss=0.2458, pruned_loss=0.04509, over 7252.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2626, pruned_loss=0.04331, over 1419292.05 frames.], batch size: 19, lr: 1.67e-04
2022-05-29 06:20:43,301 INFO [train.py:842] (2/4) Epoch 33, batch 4950, loss[loss=0.1565, simple_loss=0.2419, pruned_loss=0.03557, over 7055.00 frames.], tot_loss[loss=0.1748, simple_loss=0.263, pruned_loss=0.0433, over 1417573.64 frames.], batch size: 18, lr: 1.67e-04
2022-05-29 06:21:22,684 INFO [train.py:842] (2/4) Epoch 33, batch 5000, loss[loss=0.1762, simple_loss=0.2636, pruned_loss=0.04439, over 6775.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2629, pruned_loss=0.04282, over 1414609.58 frames.], batch size: 31, lr: 1.66e-04
2022-05-29 06:22:02,162 INFO [train.py:842] (2/4) Epoch 33, batch 5050, loss[loss=0.1789, simple_loss=0.2707, pruned_loss=0.04349, over 7188.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2627, pruned_loss=0.04244, over 1414922.41 frames.], batch size: 26, lr: 1.66e-04
2022-05-29 06:22:41,613 INFO [train.py:842] (2/4) Epoch 33, batch 5100, loss[loss=0.1377, simple_loss=0.2309, pruned_loss=0.02231, over 7278.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2633, pruned_loss=0.04253, over 1414148.43 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:23:20,489 INFO [train.py:842] (2/4) Epoch 33, batch 5150, loss[loss=0.1746, simple_loss=0.2703, pruned_loss=0.03949, over 7226.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2637, pruned_loss=0.04295, over 1407091.02 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 06:24:00,123 INFO [train.py:842] (2/4) Epoch 33, batch 5200, loss[loss=0.1476, simple_loss=0.2284, pruned_loss=0.03342, over 7000.00 frames.], tot_loss[loss=0.1738, simple_loss=0.263, pruned_loss=0.04235, over 1413393.68 frames.], batch size: 16, lr: 1.66e-04
2022-05-29 06:24:38,956 INFO [train.py:842] (2/4) Epoch 33, batch 5250, loss[loss=0.1623, simple_loss=0.2606, pruned_loss=0.03197, over 7142.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2636, pruned_loss=0.04264, over 1415199.96 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:25:18,428 INFO [train.py:842] (2/4) Epoch 33, batch 5300, loss[loss=0.1961, simple_loss=0.2788, pruned_loss=0.05667, over 7065.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2632, pruned_loss=0.04251, over 1416756.65 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:25:57,780 INFO [train.py:842] (2/4) Epoch 33, batch 5350, loss[loss=0.1562, simple_loss=0.257, pruned_loss=0.02772, over 7217.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2622, pruned_loss=0.04233, over 1419349.04 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 06:26:37,436 INFO [train.py:842] (2/4) Epoch 33, batch 5400, loss[loss=0.1705, simple_loss=0.2653, pruned_loss=0.03786, over 6841.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2622, pruned_loss=0.04257, over 1420509.05 frames.], batch size: 31, lr: 1.66e-04
2022-05-29 06:27:16,746 INFO [train.py:842] (2/4) Epoch 33, batch 5450, loss[loss=0.1534, simple_loss=0.2585, pruned_loss=0.02419, over 7327.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2618, pruned_loss=0.04241, over 1422030.26 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 06:27:56,399 INFO [train.py:842] (2/4) Epoch 33, batch 5500, loss[loss=0.1474, simple_loss=0.2353, pruned_loss=0.02969, over 7290.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2614, pruned_loss=0.04222, over 1425314.93 frames.], batch size: 17, lr: 1.66e-04
2022-05-29 06:28:35,892 INFO [train.py:842] (2/4) Epoch 33, batch 5550, loss[loss=0.1707, simple_loss=0.2533, pruned_loss=0.04407, over 7430.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2602, pruned_loss=0.04172, over 1427065.43 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:29:15,509 INFO [train.py:842] (2/4) Epoch 33, batch 5600, loss[loss=0.1914, simple_loss=0.2712, pruned_loss=0.05583, over 4707.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2606, pruned_loss=0.04198, over 1421366.42 frames.], batch size: 52, lr: 1.66e-04
2022-05-29 06:29:54,944 INFO [train.py:842] (2/4) Epoch 33, batch 5650, loss[loss=0.1876, simple_loss=0.2769, pruned_loss=0.04913, over 7375.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2616, pruned_loss=0.04273, over 1424260.24 frames.], batch size: 23, lr: 1.66e-04
2022-05-29 06:30:34,464 INFO [train.py:842] (2/4) Epoch 33, batch 5700, loss[loss=0.2333, simple_loss=0.3124, pruned_loss=0.0771, over 6290.00 frames.], tot_loss[loss=0.1751, simple_loss=0.263, pruned_loss=0.04358, over 1416797.59 frames.], batch size: 38, lr: 1.66e-04
2022-05-29 06:31:13,649 INFO [train.py:842] (2/4) Epoch 33, batch 5750, loss[loss=0.1683, simple_loss=0.25, pruned_loss=0.04327, over 7075.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2648, pruned_loss=0.04434, over 1420485.06 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:31:53,497 INFO [train.py:842] (2/4) Epoch 33, batch 5800, loss[loss=0.1695, simple_loss=0.2652, pruned_loss=0.03696, over 7328.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2641, pruned_loss=0.04457, over 1420866.77 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 06:32:32,845 INFO [train.py:842] (2/4) Epoch 33, batch 5850, loss[loss=0.1496, simple_loss=0.2292, pruned_loss=0.03496, over 7419.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2636, pruned_loss=0.04442, over 1420100.78 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:33:12,594 INFO [train.py:842] (2/4) Epoch 33, batch 5900, loss[loss=0.1465, simple_loss=0.2391, pruned_loss=0.02698, over 7445.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2624, pruned_loss=0.04356, over 1426068.42 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:33:51,703 INFO [train.py:842] (2/4) Epoch 33, batch 5950, loss[loss=0.1676, simple_loss=0.2644, pruned_loss=0.0354, over 6449.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2632, pruned_loss=0.04347, over 1428765.52 frames.], batch size: 37, lr: 1.66e-04
2022-05-29 06:34:31,358 INFO [train.py:842] (2/4) Epoch 33, batch 6000, loss[loss=0.1655, simple_loss=0.2674, pruned_loss=0.03175, over 7151.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2634, pruned_loss=0.04333, over 1428754.22 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:34:31,359 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 06:34:41,082 INFO [train.py:871] (2/4) Epoch 33, validation: loss=0.1642, simple_loss=0.2614, pruned_loss=0.03347, over 868885.00 frames.
2022-05-29 06:35:20,433 INFO [train.py:842] (2/4) Epoch 33, batch 6050, loss[loss=0.1807, simple_loss=0.2771, pruned_loss=0.04218, over 7208.00 frames.], tot_loss[loss=0.1749, simple_loss=0.263, pruned_loss=0.04342, over 1429770.63 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 06:35:59,986 INFO [train.py:842] (2/4) Epoch 33, batch 6100, loss[loss=0.1945, simple_loss=0.2947, pruned_loss=0.04717, over 7328.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2627, pruned_loss=0.04307, over 1427139.85 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 06:36:39,129 INFO [train.py:842] (2/4) Epoch 33, batch 6150, loss[loss=0.1579, simple_loss=0.248, pruned_loss=0.03391, over 7242.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2619, pruned_loss=0.04226, over 1425946.51 frames.], batch size: 16, lr: 1.66e-04
2022-05-29 06:37:18,634 INFO [train.py:842] (2/4) Epoch 33, batch 6200, loss[loss=0.1678, simple_loss=0.2623, pruned_loss=0.03667, over 7426.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2612, pruned_loss=0.04185, over 1425138.36 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 06:37:57,939 INFO [train.py:842] (2/4) Epoch 33, batch 6250, loss[loss=0.134, simple_loss=0.2126, pruned_loss=0.02769, over 7123.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2606, pruned_loss=0.04152, over 1426429.74 frames.], batch size: 17, lr: 1.66e-04
2022-05-29 06:38:37,891 INFO [train.py:842] (2/4) Epoch 33, batch 6300, loss[loss=0.1868, simple_loss=0.2724, pruned_loss=0.05059, over 7281.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2613, pruned_loss=0.04218, over 1425905.25 frames.], batch size: 24, lr: 1.66e-04
2022-05-29 06:39:17,422 INFO [train.py:842] (2/4) Epoch 33, batch 6350, loss[loss=0.1397, simple_loss=0.229, pruned_loss=0.0252, over 7154.00 frames.], tot_loss[loss=0.1732, simple_loss=0.261, pruned_loss=0.04268, over 1427357.23 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:39:57,020 INFO [train.py:842] (2/4) Epoch 33, batch 6400, loss[loss=0.1743, simple_loss=0.2701, pruned_loss=0.03921, over 7230.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2608, pruned_loss=0.0425, over 1426217.77 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:40:36,393 INFO [train.py:842] (2/4) Epoch 33, batch 6450, loss[loss=0.1465, simple_loss=0.2491, pruned_loss=0.02195, over 7383.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2607, pruned_loss=0.04205, over 1427007.85 frames.], batch size: 23, lr: 1.66e-04
2022-05-29 06:41:16,132 INFO [train.py:842] (2/4) Epoch 33, batch 6500, loss[loss=0.2195, simple_loss=0.3094, pruned_loss=0.06478, over 7404.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2618, pruned_loss=0.04246, over 1429071.51 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 06:41:55,408 INFO [train.py:842] (2/4) Epoch 33, batch 6550, loss[loss=0.1676, simple_loss=0.2433, pruned_loss=0.04597, over 6801.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2623, pruned_loss=0.04252, over 1427970.03 frames.], batch size: 15, lr: 1.66e-04
2022-05-29 06:42:34,833 INFO [train.py:842] (2/4) Epoch 33, batch 6600, loss[loss=0.1493, simple_loss=0.2324, pruned_loss=0.0331, over 7009.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2621, pruned_loss=0.04233, over 1426590.99 frames.], batch size: 16, lr: 1.66e-04
2022-05-29 06:43:14,053 INFO [train.py:842] (2/4) Epoch 33, batch 6650, loss[loss=0.1724, simple_loss=0.2589, pruned_loss=0.04294, over 6741.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2615, pruned_loss=0.04195, over 1424562.16 frames.], batch size: 31, lr: 1.66e-04
2022-05-29 06:43:53,711 INFO [train.py:842] (2/4) Epoch 33, batch 6700, loss[loss=0.1572, simple_loss=0.246, pruned_loss=0.03417, over 7074.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2613, pruned_loss=0.04214, over 1420693.67 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:44:32,942 INFO [train.py:842] (2/4) Epoch 33, batch 6750, loss[loss=0.1444, simple_loss=0.2441, pruned_loss=0.02237, over 7164.00 frames.], tot_loss[loss=0.1736, simple_loss=0.262, pruned_loss=0.04262, over 1421758.55 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:45:12,708 INFO [train.py:842] (2/4) Epoch 33, batch 6800, loss[loss=0.171, simple_loss=0.2682, pruned_loss=0.03688, over 7202.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2607, pruned_loss=0.04211, over 1418990.28 frames.], batch size: 23, lr: 1.66e-04
2022-05-29 06:45:51,925 INFO [train.py:842] (2/4) Epoch 33, batch 6850, loss[loss=0.1938, simple_loss=0.2876, pruned_loss=0.05004, over 7171.00 frames.], tot_loss[loss=0.173, simple_loss=0.2614, pruned_loss=0.04232, over 1423361.32 frames.], batch size: 26, lr: 1.66e-04
2022-05-29 06:46:31,096 INFO [train.py:842] (2/4) Epoch 33, batch 6900, loss[loss=0.1661, simple_loss=0.2594, pruned_loss=0.03638, over 6700.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2619, pruned_loss=0.04263, over 1418901.43 frames.], batch size: 31, lr: 1.66e-04
2022-05-29 06:47:10,486 INFO [train.py:842] (2/4) Epoch 33, batch 6950, loss[loss=0.1845, simple_loss=0.2712, pruned_loss=0.04892, over 7445.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2609, pruned_loss=0.04221, over 1425464.84 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:47:50,203 INFO [train.py:842] (2/4) Epoch 33, batch 7000, loss[loss=0.175, simple_loss=0.2622, pruned_loss=0.0439, over 7070.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2606, pruned_loss=0.04221, over 1426984.06 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:48:29,616 INFO [train.py:842] (2/4) Epoch 33, batch 7050, loss[loss=0.137, simple_loss=0.2321, pruned_loss=0.0209, over 7162.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2606, pruned_loss=0.04217, over 1423852.51 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:49:09,086 INFO [train.py:842] (2/4) Epoch 33, batch 7100, loss[loss=0.1949, simple_loss=0.294, pruned_loss=0.04792, over 7202.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2602, pruned_loss=0.0417, over 1427059.14 frames.], batch size: 26, lr: 1.66e-04
2022-05-29 06:49:48,463 INFO [train.py:842] (2/4) Epoch 33, batch 7150, loss[loss=0.1677, simple_loss=0.262, pruned_loss=0.03669, over 7202.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2614, pruned_loss=0.04198, over 1430535.00 frames.], batch size: 26, lr: 1.66e-04
2022-05-29 06:50:28,351 INFO [train.py:842] (2/4) Epoch 33, batch 7200, loss[loss=0.172, simple_loss=0.2705, pruned_loss=0.03681, over 7295.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2609, pruned_loss=0.0416, over 1430490.55 frames.], batch size: 24, lr: 1.66e-04
2022-05-29 06:51:07,720 INFO [train.py:842] (2/4) Epoch 33, batch 7250, loss[loss=0.2161, simple_loss=0.2978, pruned_loss=0.06714, over 7262.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2614, pruned_loss=0.04182, over 1427120.52 frames.], batch size: 19, lr: 1.66e-04
2022-05-29 06:51:46,994 INFO [train.py:842] (2/4) Epoch 33, batch 7300, loss[loss=0.202, simple_loss=0.2976, pruned_loss=0.05315, over 7180.00 frames.], tot_loss[loss=0.1731, simple_loss=0.262, pruned_loss=0.0421, over 1428983.75 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:52:26,315 INFO [train.py:842] (2/4) Epoch 33, batch 7350, loss[loss=0.1894, simple_loss=0.2914, pruned_loss=0.04374, over 7234.00 frames.], tot_loss[loss=0.1743, simple_loss=0.263, pruned_loss=0.04279, over 1427910.05 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 06:53:05,840 INFO [train.py:842] (2/4) Epoch 33, batch 7400, loss[loss=0.201, simple_loss=0.2808, pruned_loss=0.06054, over 4792.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2633, pruned_loss=0.04277, over 1423962.98 frames.], batch size: 54, lr: 1.66e-04
2022-05-29 06:53:45,063 INFO [train.py:842] (2/4) Epoch 33, batch 7450, loss[loss=0.1449, simple_loss=0.2218, pruned_loss=0.03395, over 7277.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2627, pruned_loss=0.0427, over 1416061.07 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:54:24,431 INFO [train.py:842] (2/4) Epoch 33, batch 7500, loss[loss=0.1527, simple_loss=0.2538, pruned_loss=0.02583, over 6425.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2634, pruned_loss=0.04298, over 1419671.52 frames.], batch size: 38, lr: 1.66e-04
2022-05-29 06:55:03,711 INFO [train.py:842] (2/4) Epoch 33, batch 7550, loss[loss=0.144, simple_loss=0.2318, pruned_loss=0.02806, over 7400.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2611, pruned_loss=0.04219, over 1422157.37 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:55:43,324 INFO [train.py:842] (2/4) Epoch 33, batch 7600, loss[loss=0.2049, simple_loss=0.2921, pruned_loss=0.05884, over 7110.00 frames.], tot_loss[loss=0.173, simple_loss=0.2617, pruned_loss=0.04214, over 1427239.81 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 06:56:22,540 INFO [train.py:842] (2/4) Epoch 33, batch 7650, loss[loss=0.148, simple_loss=0.2327, pruned_loss=0.03164, over 7285.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2616, pruned_loss=0.04202, over 1426749.42 frames.], batch size: 18, lr: 1.66e-04
2022-05-29 06:57:02,120 INFO [train.py:842] (2/4) Epoch 33, batch 7700, loss[loss=0.1796, simple_loss=0.2556, pruned_loss=0.05181, over 6817.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2613, pruned_loss=0.04186, over 1424903.67 frames.], batch size: 15, lr: 1.66e-04
2022-05-29 06:57:41,097 INFO [train.py:842] (2/4) Epoch 33, batch 7750, loss[loss=0.1959, simple_loss=0.2763, pruned_loss=0.0578, over 7437.00 frames.], tot_loss[loss=0.1742, simple_loss=0.263, pruned_loss=0.04268, over 1423964.19 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:58:20,691 INFO [train.py:842] (2/4) Epoch 33, batch 7800, loss[loss=0.1889, simple_loss=0.2701, pruned_loss=0.05386, over 6802.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2634, pruned_loss=0.04298, over 1424191.81 frames.], batch size: 31, lr: 1.66e-04
2022-05-29 06:58:59,892 INFO [train.py:842] (2/4) Epoch 33, batch 7850, loss[loss=0.1743, simple_loss=0.2678, pruned_loss=0.04036, over 7334.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2625, pruned_loss=0.04217, over 1425941.31 frames.], batch size: 20, lr: 1.66e-04
2022-05-29 06:59:39,494 INFO [train.py:842] (2/4) Epoch 33, batch 7900, loss[loss=0.163, simple_loss=0.2623, pruned_loss=0.03187, over 7335.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2631, pruned_loss=0.04292, over 1428636.32 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 07:00:18,978 INFO [train.py:842] (2/4) Epoch 33, batch 7950, loss[loss=0.1812, simple_loss=0.272, pruned_loss=0.0452, over 6390.00 frames.], tot_loss[loss=0.1744, simple_loss=0.263, pruned_loss=0.0429, over 1425653.42 frames.], batch size: 38, lr: 1.66e-04
2022-05-29 07:00:58,316 INFO [train.py:842] (2/4) Epoch 33, batch 8000, loss[loss=0.1641, simple_loss=0.2535, pruned_loss=0.03738, over 7067.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2634, pruned_loss=0.04296, over 1424805.32 frames.], batch size: 28, lr: 1.66e-04
2022-05-29 07:01:37,389 INFO [train.py:842] (2/4) Epoch 33, batch 8050, loss[loss=0.1912, simple_loss=0.2811, pruned_loss=0.05061, over 7123.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2646, pruned_loss=0.04341, over 1424765.58 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 07:02:17,301 INFO [train.py:842] (2/4) Epoch 33, batch 8100, loss[loss=0.175, simple_loss=0.2682, pruned_loss=0.0409, over 7210.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2636, pruned_loss=0.043, over 1424359.93 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 07:02:56,301 INFO [train.py:842] (2/4) Epoch 33, batch 8150, loss[loss=0.1888, simple_loss=0.2734, pruned_loss=0.05207, over 7347.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2638, pruned_loss=0.04349, over 1421482.40 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 07:03:35,681 INFO [train.py:842] (2/4) Epoch 33, batch 8200, loss[loss=0.1851, simple_loss=0.2708, pruned_loss=0.04976, over 5129.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2627, pruned_loss=0.04291, over 1419148.76 frames.], batch size: 52, lr: 1.66e-04
2022-05-29 07:04:25,744 INFO [train.py:842] (2/4) Epoch 33, batch 8250, loss[loss=0.1518, simple_loss=0.2376, pruned_loss=0.03306, over 6976.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2618, pruned_loss=0.04235, over 1425567.13 frames.], batch size: 16, lr: 1.66e-04
2022-05-29 07:05:05,267 INFO [train.py:842] (2/4) Epoch 33, batch 8300, loss[loss=0.1407, simple_loss=0.2203, pruned_loss=0.0305, over 7008.00 frames.], tot_loss[loss=0.1736, simple_loss=0.262, pruned_loss=0.0426, over 1423302.70 frames.], batch size: 16, lr: 1.66e-04
2022-05-29 07:05:44,146 INFO [train.py:842] (2/4) Epoch 33, batch 8350, loss[loss=0.1513, simple_loss=0.2489, pruned_loss=0.02686, over 7220.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2623, pruned_loss=0.04217, over 1422251.70 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 07:06:23,433 INFO [train.py:842] (2/4) Epoch 33, batch 8400, loss[loss=0.1727, simple_loss=0.2706, pruned_loss=0.03738, over 7334.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2619, pruned_loss=0.04183, over 1417635.34 frames.], batch size: 22, lr: 1.66e-04
2022-05-29 07:07:02,628 INFO [train.py:842] (2/4) Epoch 33, batch 8450, loss[loss=0.1835, simple_loss=0.2755, pruned_loss=0.04572, over 7030.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2625, pruned_loss=0.04163, over 1420750.69 frames.], batch size: 28, lr: 1.66e-04
2022-05-29 07:07:54,006 INFO [train.py:842] (2/4) Epoch 33, batch 8500, loss[loss=0.21, simple_loss=0.3022, pruned_loss=0.05893, over 7316.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2609, pruned_loss=0.04098, over 1423610.84 frames.], batch size: 21, lr: 1.66e-04
2022-05-29 07:08:33,097 INFO [train.py:842] (2/4) Epoch 33, batch 8550, loss[loss=0.1869, simple_loss=0.2798, pruned_loss=0.04704, over 6732.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2608, pruned_loss=0.04106, over 1423020.15 frames.], batch size: 31, lr: 1.66e-04
2022-05-29 07:09:23,004 INFO [train.py:842] (2/4) Epoch 33, batch 8600, loss[loss=0.2109, simple_loss=0.2969, pruned_loss=0.06247, over 4788.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2616, pruned_loss=0.0417, over 1416250.09 frames.], batch size: 52, lr: 1.65e-04
2022-05-29 07:10:01,969 INFO [train.py:842] (2/4) Epoch 33, batch 8650, loss[loss=0.1921, simple_loss=0.284, pruned_loss=0.05014, over 7198.00 frames.], tot_loss[loss=0.174, simple_loss=0.2629, pruned_loss=0.04257, over 1407910.64 frames.], batch size: 26, lr: 1.65e-04
2022-05-29 07:10:41,440 INFO [train.py:842] (2/4) Epoch 33, batch 8700, loss[loss=0.1947, simple_loss=0.2732, pruned_loss=0.05813, over 4977.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2643, pruned_loss=0.04316, over 1405208.97 frames.], batch size: 52, lr: 1.65e-04
2022-05-29 07:11:20,631 INFO [train.py:842] (2/4) Epoch 33, batch 8750, loss[loss=0.2178, simple_loss=0.3052, pruned_loss=0.0652, over 6380.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2663, pruned_loss=0.04478, over 1408397.15 frames.], batch size: 37, lr: 1.65e-04
2022-05-29 07:11:59,674 INFO [train.py:842] (2/4) Epoch 33, batch 8800, loss[loss=0.1567, simple_loss=0.2552, pruned_loss=0.02911, over 7142.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2662, pruned_loss=0.04432, over 1403775.65 frames.], batch size: 20, lr: 1.65e-04
2022-05-29 07:12:38,563 INFO [train.py:842] (2/4) Epoch 33, batch 8850, loss[loss=0.161, simple_loss=0.2616, pruned_loss=0.03019, over 7228.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2667, pruned_loss=0.04438, over 1390190.28 frames.], batch size: 21, lr: 1.65e-04
2022-05-29 07:13:17,801 INFO [train.py:842] (2/4) Epoch 33, batch 8900, loss[loss=0.1929, simple_loss=0.2772, pruned_loss=0.05426, over 7132.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2672, pruned_loss=0.04465, over 1390161.37 frames.], batch size: 26, lr: 1.65e-04
2022-05-29 07:13:56,504 INFO [train.py:842] (2/4) Epoch 33, batch 8950, loss[loss=0.2129, simple_loss=0.2948, pruned_loss=0.06548, over 7257.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2678, pruned_loss=0.04521, over 1382056.25 frames.], batch size: 19, lr: 1.65e-04
2022-05-29 07:14:35,678 INFO [train.py:842] (2/4) Epoch 33, batch 9000, loss[loss=0.1464, simple_loss=0.2421, pruned_loss=0.02532, over 7202.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2666, pruned_loss=0.04459, over 1376533.44 frames.], batch size: 23, lr: 1.65e-04
2022-05-29 07:14:35,679 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 07:14:45,195 INFO [train.py:871] (2/4) Epoch 33, validation: loss=0.1641, simple_loss=0.2616, pruned_loss=0.03332, over 868885.00 frames.
2022-05-29 07:15:24,272 INFO [train.py:842] (2/4) Epoch 33, batch 9050, loss[loss=0.1875, simple_loss=0.2789, pruned_loss=0.04809, over 7034.00 frames.], tot_loss[loss=0.177, simple_loss=0.2656, pruned_loss=0.04416, over 1372578.99 frames.], batch size: 28, lr: 1.65e-04
2022-05-29 07:16:03,502 INFO [train.py:842] (2/4) Epoch 33, batch 9100, loss[loss=0.1594, simple_loss=0.244, pruned_loss=0.03743, over 5156.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2662, pruned_loss=0.04427, over 1354786.03 frames.], batch size: 52, lr: 1.65e-04
2022-05-29 07:16:41,718 INFO [train.py:842] (2/4) Epoch 33, batch 9150, loss[loss=0.2243, simple_loss=0.3043, pruned_loss=0.07215, over 5465.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2689, pruned_loss=0.04576, over 1309050.04 frames.], batch size: 52, lr: 1.65e-04
2022-05-29 07:17:29,976 INFO [train.py:842] (2/4) Epoch 34, batch 0, loss[loss=0.1774, simple_loss=0.2638, pruned_loss=0.04548, over 7434.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2638, pruned_loss=0.04548, over 7434.00 frames.], batch size: 20, lr: 1.63e-04
2022-05-29 07:18:09,752 INFO [train.py:842] (2/4) Epoch 34, batch 50, loss[loss=0.1539, simple_loss=0.2585, pruned_loss=0.02471, over 7089.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2558, pruned_loss=0.03966, over 324976.66 frames.], batch size: 28, lr: 1.63e-04
2022-05-29 07:18:49,368 INFO [train.py:842] (2/4) Epoch 34, batch 100, loss[loss=0.1781, simple_loss=0.277, pruned_loss=0.03962, over 7120.00 frames.], tot_loss[loss=0.17, simple_loss=0.2598, pruned_loss=0.0401, over 565741.76 frames.], batch size: 21, lr: 1.63e-04
2022-05-29 07:19:28,896 INFO [train.py:842] (2/4) Epoch 34, batch 150, loss[loss=0.1549, simple_loss=0.2306, pruned_loss=0.03959, over 7071.00 frames.], tot_loss[loss=0.171, simple_loss=0.2596, pruned_loss=0.04123, over 755268.10 frames.], batch size: 18, lr: 1.63e-04
2022-05-29 07:20:08,751 INFO [train.py:842] (2/4) Epoch 34, batch 200, loss[loss=0.1622, simple_loss=0.2459, pruned_loss=0.03924, over 7286.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2585, pruned_loss=0.04042, over 905145.64 frames.], batch size: 17, lr: 1.63e-04
2022-05-29 07:20:48,038 INFO [train.py:842] (2/4) Epoch 34, batch 250, loss[loss=0.2107, simple_loss=0.2956, pruned_loss=0.06291, over 4946.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2587, pruned_loss=0.04071, over 1012326.74 frames.], batch size: 52, lr: 1.63e-04
2022-05-29 07:21:27,647 INFO [train.py:842] (2/4) Epoch 34, batch 300, loss[loss=0.1599, simple_loss=0.2579, pruned_loss=0.03095, over 7376.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2588, pruned_loss=0.04028, over 1102234.42 frames.], batch size: 23, lr: 1.63e-04
2022-05-29 07:22:06,478 INFO [train.py:842] (2/4) Epoch 34, batch 350, loss[loss=0.177, simple_loss=0.2546, pruned_loss=0.04969, over 7136.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2609, pruned_loss=0.04113, over 1166230.97 frames.], batch size: 17, lr: 1.63e-04
2022-05-29 07:22:46,335 INFO [train.py:842] (2/4) Epoch 34, batch 400, loss[loss=0.1591, simple_loss=0.2434, pruned_loss=0.03736, over 7414.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2595, pruned_loss=0.04036, over 1227400.21 frames.], batch size: 21, lr: 1.63e-04
2022-05-29 07:23:25,558 INFO [train.py:842] (2/4) Epoch 34, batch 450, loss[loss=0.1765, simple_loss=0.2618, pruned_loss=0.04562, over 7412.00 frames.], tot_loss[loss=0.172, simple_loss=0.2615, pruned_loss=0.04128, over 1272445.73 frames.], batch size: 18, lr: 1.63e-04
2022-05-29 07:24:05,215 INFO [train.py:842] (2/4) Epoch 34, batch 500, loss[loss=0.1784, simple_loss=0.2629, pruned_loss=0.047, over 7307.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2613, pruned_loss=0.04111, over 1304517.50 frames.], batch size: 24, lr: 1.63e-04
2022-05-29 07:24:44,472 INFO [train.py:842] (2/4) Epoch 34, batch 550, loss[loss=0.159, simple_loss=0.26, pruned_loss=0.029, over 6448.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2624, pruned_loss=0.042, over 1328617.15 frames.], batch size: 38, lr: 1.63e-04
2022-05-29 07:25:24,067 INFO [train.py:842] (2/4) Epoch 34, batch 600, loss[loss=0.1964, simple_loss=0.2918, pruned_loss=0.0505, over 7321.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2629, pruned_loss=0.04212, over 1351378.20 frames.], batch size: 25, lr: 1.63e-04
2022-05-29 07:26:03,435 INFO [train.py:842] (2/4) Epoch 34, batch 650, loss[loss=0.1785, simple_loss=0.2621, pruned_loss=0.04743, over 7167.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2635, pruned_loss=0.04237, over 1370078.28 frames.], batch size: 18, lr: 1.63e-04
2022-05-29 07:26:46,156 INFO [train.py:842] (2/4) Epoch 34, batch 700, loss[loss=0.1797, simple_loss=0.2581, pruned_loss=0.05063, over 7146.00 frames.], tot_loss[loss=0.174, simple_loss=0.2629, pruned_loss=0.04261, over 1377322.31 frames.], batch size: 17, lr: 1.63e-04
2022-05-29 07:27:25,321 INFO [train.py:842] (2/4) Epoch 34, batch 750, loss[loss=0.2381, simple_loss=0.318, pruned_loss=0.07909, over 7187.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2625, pruned_loss=0.04215, over 1388960.38 frames.], batch size: 23, lr: 1.63e-04
2022-05-29 07:28:04,921 INFO [train.py:842] (2/4) Epoch 34, batch 800, loss[loss=0.172, simple_loss=0.2518, pruned_loss=0.04611, over 7290.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2625, pruned_loss=0.04192, over 1394524.50 frames.], batch size: 18, lr: 1.63e-04
2022-05-29 07:28:44,224 INFO [train.py:842] (2/4) Epoch 34, batch 850, loss[loss=0.1788, simple_loss=0.2784, pruned_loss=0.03953, over 6360.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2619, pruned_loss=0.04162, over 1404363.41 frames.], batch size: 38, lr: 1.63e-04
2022-05-29 07:29:23,966 INFO [train.py:842] (2/4) Epoch 34, batch 900, loss[loss=0.2606, simple_loss=0.3331, pruned_loss=0.09404, over 5291.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2601, pruned_loss=0.04119, over 1409346.87 frames.], batch size: 52, lr: 1.63e-04
2022-05-29 07:30:03,396 INFO [train.py:842] (2/4) Epoch 34, batch 950, loss[loss=0.1527, simple_loss=0.2392, pruned_loss=0.03307, over 7292.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2595, pruned_loss=0.04091, over 1407139.28 frames.], batch size: 18, lr: 1.63e-04
2022-05-29 07:30:43,031 INFO [train.py:842] (2/4) Epoch 34, batch 1000, loss[loss=0.1613, simple_loss=0.2512, pruned_loss=0.0357, over 7429.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2597, pruned_loss=0.0408, over 1408493.74 frames.], batch size: 20, lr: 1.63e-04
2022-05-29 07:31:22,407 INFO [train.py:842] (2/4) Epoch 34, batch 1050, loss[loss=0.1667, simple_loss=0.2479, pruned_loss=0.0427, over 7171.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2604, pruned_loss=0.04102, over 1414558.75 frames.], batch size: 19, lr: 1.63e-04
2022-05-29 07:32:01,641 INFO [train.py:842] (2/4) Epoch 34, batch 1100, loss[loss=0.1922, simple_loss=0.2851, pruned_loss=0.04963, over 6259.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2609, pruned_loss=0.04097, over 1411926.68 frames.], batch size: 37, lr: 1.63e-04
2022-05-29 07:32:40,984 INFO [train.py:842] (2/4) Epoch 34, batch 1150, loss[loss=0.1712, simple_loss=0.2504, pruned_loss=0.04595, over 7428.00 frames.], tot_loss[loss=0.1728, simple_loss=0.262, pruned_loss=0.0418, over 1414261.79 frames.], batch size: 20, lr: 1.63e-04
2022-05-29 07:33:20,610 INFO [train.py:842] (2/4) Epoch 34, batch 1200, loss[loss=0.1912, simple_loss=0.2941, pruned_loss=0.04419, over 7206.00 frames.], tot_loss[loss=0.173, simple_loss=0.2622, pruned_loss=0.04193, over 1418921.42 frames.], batch size: 23, lr: 1.63e-04
2022-05-29 07:33:59,718 INFO [train.py:842] (2/4) Epoch 34, batch 1250, loss[loss=0.1534, simple_loss=0.2541, pruned_loss=0.02629, over 7337.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2624, pruned_loss=0.04223, over 1416385.07 frames.], batch size: 22, lr: 1.63e-04
2022-05-29 07:34:39,383 INFO [train.py:842] (2/4) Epoch 34, batch 1300, loss[loss=0.2112, simple_loss=0.304, pruned_loss=0.05919, over 7204.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2626, pruned_loss=0.04278, over 1416618.18 frames.], batch size: 26, lr: 1.63e-04
2022-05-29 07:35:18,855 INFO [train.py:842] (2/4) Epoch 34, batch 1350, loss[loss=0.2028, simple_loss=0.2966, pruned_loss=0.05449, over 7218.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2627, pruned_loss=0.04288, over 1418298.15 frames.], batch size: 21, lr: 1.63e-04
2022-05-29 07:35:58,695 INFO [train.py:842] (2/4) Epoch 34, batch 1400, loss[loss=0.183, simple_loss=0.2631, pruned_loss=0.05149, over 7253.00 frames.], tot_loss[loss=0.1728, simple_loss=0.261, pruned_loss=0.04228, over 1421356.05 frames.], batch size: 19, lr: 1.63e-04
2022-05-29 07:36:38,099 INFO [train.py:842] (2/4) Epoch 34, batch 1450, loss[loss=0.1781, simple_loss=0.271, pruned_loss=0.04264, over 7411.00 frames.], tot_loss[loss=0.173, simple_loss=0.2614, pruned_loss=0.04237, over 1425233.26 frames.], batch size: 21, lr: 1.63e-04
2022-05-29 07:37:17,571 INFO [train.py:842] (2/4) Epoch 34, batch 1500, loss[loss=0.1859, simple_loss=0.2623, pruned_loss=0.05474, over 7379.00 frames.], tot_loss[loss=0.1745, simple_loss=0.263, pruned_loss=0.04294, over 1424468.33 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 07:37:56,877 INFO [train.py:842] (2/4) Epoch 34, batch 1550, loss[loss=0.2005, simple_loss=0.2965, pruned_loss=0.05224, over 7278.00 frames.], tot_loss[loss=0.1742, simple_loss=0.263, pruned_loss=0.0427, over 1421576.81 frames.], batch size: 24, lr: 1.62e-04
2022-05-29 07:38:36,496 INFO [train.py:842] (2/4) Epoch 34, batch 1600, loss[loss=0.1424, simple_loss=0.2367, pruned_loss=0.02402, over 7325.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2628, pruned_loss=0.04237, over 1423126.67 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 07:39:15,592 INFO [train.py:842] (2/4) Epoch 34, batch 1650, loss[loss=0.1778, simple_loss=0.2767, pruned_loss=0.03952, over 7198.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2636, pruned_loss=0.04275, over 1422795.50 frames.], batch size: 22, lr: 1.62e-04
2022-05-29 07:39:54,966 INFO [train.py:842] (2/4) Epoch 34, batch 1700, loss[loss=0.1831, simple_loss=0.277, pruned_loss=0.04455, over 7385.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2631, pruned_loss=0.04224, over 1426679.88 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 07:40:34,176 INFO [train.py:842] (2/4) Epoch 34, batch 1750, loss[loss=0.1713, simple_loss=0.2688, pruned_loss=0.03687, over 7081.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2637, pruned_loss=0.0427, over 1421958.16 frames.], batch size: 28, lr: 1.62e-04
2022-05-29 07:41:13,640 INFO [train.py:842] (2/4) Epoch 34, batch 1800, loss[loss=0.1091, simple_loss=0.1947, pruned_loss=0.0118, over 7282.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2627, pruned_loss=0.04229, over 1423024.88 frames.], batch size: 17, lr: 1.62e-04
2022-05-29 07:41:52,935 INFO [train.py:842] (2/4) Epoch 34, batch 1850, loss[loss=0.151, simple_loss=0.2416, pruned_loss=0.03026, over 7315.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2632, pruned_loss=0.04277, over 1415609.31 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 07:42:32,350 INFO [train.py:842] (2/4) Epoch 34, batch 1900, loss[loss=0.1469, simple_loss=0.2464, pruned_loss=0.02368, over 6736.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2625, pruned_loss=0.04205, over 1410811.30 frames.], batch size: 31, lr: 1.62e-04
2022-05-29 07:43:11,709 INFO [train.py:842] (2/4) Epoch 34, batch 1950, loss[loss=0.148, simple_loss=0.227, pruned_loss=0.03452, over 6993.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2622, pruned_loss=0.04223, over 1416708.34 frames.], batch size: 16, lr: 1.62e-04
2022-05-29 07:43:51,613 INFO [train.py:842] (2/4) Epoch 34, batch 2000, loss[loss=0.1979, simple_loss=0.258, pruned_loss=0.06889, over 7404.00 frames.], tot_loss[loss=0.1729, simple_loss=0.262, pruned_loss=0.04187, over 1421373.21 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 07:44:31,029 INFO [train.py:842] (2/4) Epoch 34, batch 2050, loss[loss=0.1789, simple_loss=0.2806, pruned_loss=0.03856, over 7182.00 frames.], tot_loss[loss=0.172, simple_loss=0.2614, pruned_loss=0.04134, over 1421141.32 frames.], batch size: 26, lr: 1.62e-04
2022-05-29 07:45:10,518 INFO [train.py:842] (2/4) Epoch 34, batch 2100, loss[loss=0.2183, simple_loss=0.3165, pruned_loss=0.06008, over 7191.00 frames.], tot_loss[loss=0.1736, simple_loss=0.263, pruned_loss=0.04208, over 1424291.25 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 07:45:49,750 INFO [train.py:842] (2/4) Epoch 34, batch 2150, loss[loss=0.1647, simple_loss=0.2645, pruned_loss=0.03238, over 7301.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2633, pruned_loss=0.04256, over 1423747.77 frames.], batch size: 24, lr: 1.62e-04
2022-05-29 07:46:29,304 INFO [train.py:842] (2/4) Epoch 34, batch 2200, loss[loss=0.1557, simple_loss=0.251, pruned_loss=0.03023, over 7320.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2629, pruned_loss=0.0421, over 1426676.63 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 07:47:08,693 INFO [train.py:842] (2/4) Epoch 34, batch 2250, loss[loss=0.1373, simple_loss=0.2265, pruned_loss=0.02406, over 7281.00 frames.], tot_loss[loss=0.1729, simple_loss=0.262, pruned_loss=0.04186, over 1423782.08 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 07:47:47,847 INFO [train.py:842] (2/4) Epoch 34, batch 2300, loss[loss=0.1646, simple_loss=0.2488, pruned_loss=0.0402, over 7169.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2629, pruned_loss=0.04217, over 1424561.37 frames.], batch size: 19, lr: 1.62e-04
2022-05-29 07:48:27,162 INFO [train.py:842] (2/4) Epoch 34, batch 2350, loss[loss=0.1839, simple_loss=0.2766, pruned_loss=0.04561, over 7159.00 frames.], tot_loss[loss=0.1726, simple_loss=0.262, pruned_loss=0.04161, over 1425558.61 frames.], batch size: 19, lr: 1.62e-04
2022-05-29 07:49:06,852 INFO [train.py:842] (2/4) Epoch 34, batch 2400, loss[loss=0.1864, simple_loss=0.2761, pruned_loss=0.04838, over 7386.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2611, pruned_loss=0.04118, over 1425805.30 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 07:49:45,768 INFO [train.py:842] (2/4) Epoch 34, batch 2450, loss[loss=0.1577, simple_loss=0.2618, pruned_loss=0.02679, over 7228.00 frames.], tot_loss[loss=0.1737, simple_loss=0.263, pruned_loss=0.04218, over 1420071.89 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 07:50:25,156 INFO [train.py:842] (2/4) Epoch 34, batch 2500, loss[loss=0.1441, simple_loss=0.2262, pruned_loss=0.03094, over 7000.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2624, pruned_loss=0.04213, over 1418600.11 frames.], batch size: 16, lr: 1.62e-04
2022-05-29 07:51:04,355 INFO [train.py:842] (2/4) Epoch 34, batch 2550, loss[loss=0.1774, simple_loss=0.2788, pruned_loss=0.03803, over 7339.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2622, pruned_loss=0.04185, over 1420025.58 frames.], batch size: 22, lr: 1.62e-04
2022-05-29 07:51:44,007 INFO [train.py:842] (2/4) Epoch 34, batch 2600, loss[loss=0.1618, simple_loss=0.2503, pruned_loss=0.0366, over 7465.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2619, pruned_loss=0.04173, over 1419895.19 frames.], batch size: 19, lr: 1.62e-04
2022-05-29 07:52:23,457 INFO [train.py:842] (2/4) Epoch 34, batch 2650, loss[loss=0.1867, simple_loss=0.2654, pruned_loss=0.05397, over 7334.00 frames.], tot_loss[loss=0.1734, simple_loss=0.262, pruned_loss=0.04238, over 1420810.59 frames.], batch size: 22, lr: 1.62e-04
2022-05-29 07:53:03,159 INFO [train.py:842] (2/4) Epoch 34, batch 2700, loss[loss=0.128, simple_loss=0.2171, pruned_loss=0.01951, over 7303.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2623, pruned_loss=0.04229, over 1425528.95 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 07:53:42,403 INFO [train.py:842] (2/4) Epoch 34, batch 2750, loss[loss=0.1864, simple_loss=0.2755, pruned_loss=0.04867, over 7321.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2619, pruned_loss=0.04245, over 1424613.94 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 07:54:21,988 INFO [train.py:842] (2/4) Epoch 34, batch 2800, loss[loss=0.1525, simple_loss=0.2395, pruned_loss=0.03272, over 7429.00 frames.], tot_loss[loss=0.1731, simple_loss=0.262, pruned_loss=0.04211, over 1429736.24 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 07:55:01,266 INFO [train.py:842] (2/4) Epoch 34, batch 2850, loss[loss=0.1897, simple_loss=0.2734, pruned_loss=0.05295, over 7208.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2621, pruned_loss=0.04201, over 1431008.40 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 07:55:40,876 INFO [train.py:842] (2/4) Epoch 34, batch 2900, loss[loss=0.1657, simple_loss=0.2687, pruned_loss=0.03132, over 7147.00 frames.], tot_loss[loss=0.173, simple_loss=0.2623, pruned_loss=0.04186, over 1428153.82 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 07:56:20,265 INFO [train.py:842] (2/4) Epoch 34, batch 2950, loss[loss=0.1591, simple_loss=0.2527, pruned_loss=0.03278, over 7142.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2608, pruned_loss=0.04105, over 1427922.77 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 07:56:59,651 INFO [train.py:842] (2/4) Epoch 34, batch 3000, loss[loss=0.149, simple_loss=0.237, pruned_loss=0.03054, over 7359.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2612, pruned_loss=0.04144, over 1428033.29 frames.], batch size: 19, lr: 1.62e-04
2022-05-29 07:56:59,652 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 07:57:09,215 INFO [train.py:871] (2/4) Epoch 34, validation: loss=0.165, simple_loss=0.2618, pruned_loss=0.03414, over 868885.00 frames.
2022-05-29 07:57:48,390 INFO [train.py:842] (2/4) Epoch 34, batch 3050, loss[loss=0.1435, simple_loss=0.2323, pruned_loss=0.02731, over 7354.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2609, pruned_loss=0.04102, over 1428486.48 frames.], batch size: 19, lr: 1.62e-04
2022-05-29 07:58:28,172 INFO [train.py:842] (2/4) Epoch 34, batch 3100, loss[loss=0.1643, simple_loss=0.2474, pruned_loss=0.0406, over 6762.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2608, pruned_loss=0.04092, over 1429522.39 frames.], batch size: 15, lr: 1.62e-04
2022-05-29 07:59:07,350 INFO [train.py:842] (2/4) Epoch 34, batch 3150, loss[loss=0.1367, simple_loss=0.2204, pruned_loss=0.02655, over 7286.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2591, pruned_loss=0.04022, over 1429102.05 frames.], batch size: 17, lr: 1.62e-04
2022-05-29 07:59:46,872 INFO [train.py:842] (2/4) Epoch 34, batch 3200, loss[loss=0.2324, simple_loss=0.3109, pruned_loss=0.07694, over 5319.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2587, pruned_loss=0.04025, over 1424649.98 frames.], batch size: 52, lr: 1.62e-04
2022-05-29 08:00:26,136 INFO [train.py:842] (2/4) Epoch 34, batch 3250, loss[loss=0.16, simple_loss=0.2429, pruned_loss=0.03851, over 7134.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2592, pruned_loss=0.04064, over 1423793.15 frames.], batch size: 17, lr: 1.62e-04
2022-05-29 08:01:05,720 INFO [train.py:842] (2/4) Epoch 34, batch 3300, loss[loss=0.1665, simple_loss=0.2672, pruned_loss=0.03292, over 7005.00 frames.], tot_loss[loss=0.1714, simple_loss=0.26, pruned_loss=0.04137, over 1419801.97 frames.], batch size: 28, lr: 1.62e-04
2022-05-29 08:01:45,242 INFO [train.py:842] (2/4) Epoch 34, batch 3350, loss[loss=0.1747, simple_loss=0.2625, pruned_loss=0.04346, over 7139.00 frames.], tot_loss[loss=0.1716, simple_loss=0.26, pruned_loss=0.04163, over 1421905.48 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:02:24,788 INFO [train.py:842] (2/4) Epoch 34, batch 3400, loss[loss=0.1853, simple_loss=0.2691, pruned_loss=0.05073, over 7221.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2604, pruned_loss=0.0413, over 1422236.45 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 08:03:04,107 INFO [train.py:842] (2/4) Epoch 34, batch 3450, loss[loss=0.1664, simple_loss=0.2523, pruned_loss=0.04029, over 7004.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2613, pruned_loss=0.04168, over 1428033.34 frames.], batch size: 16, lr: 1.62e-04
2022-05-29 08:03:43,416 INFO [train.py:842] (2/4) Epoch 34, batch 3500, loss[loss=0.1632, simple_loss=0.2638, pruned_loss=0.03137, over 7223.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2625, pruned_loss=0.04213, over 1430196.54 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 08:04:22,878 INFO [train.py:842] (2/4) Epoch 34, batch 3550, loss[loss=0.1594, simple_loss=0.2405, pruned_loss=0.03917, over 7280.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2615, pruned_loss=0.04164, over 1431562.01 frames.], batch size: 17, lr: 1.62e-04
2022-05-29 08:05:02,704 INFO [train.py:842] (2/4) Epoch 34, batch 3600, loss[loss=0.152, simple_loss=0.2491, pruned_loss=0.02747, over 7312.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2622, pruned_loss=0.04212, over 1433283.52 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 08:05:41,991 INFO [train.py:842] (2/4) Epoch 34, batch 3650, loss[loss=0.1842, simple_loss=0.2698, pruned_loss=0.04933, over 7421.00 frames.], tot_loss[loss=0.1741, simple_loss=0.263, pruned_loss=0.04266, over 1430122.60 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:06:21,209 INFO [train.py:842] (2/4) Epoch 34, batch 3700, loss[loss=0.1662, simple_loss=0.2586, pruned_loss=0.03691, over 4981.00 frames.], tot_loss[loss=0.173, simple_loss=0.2614, pruned_loss=0.04224, over 1423685.95 frames.], batch size: 52, lr: 1.62e-04
2022-05-29 08:07:00,390 INFO [train.py:842] (2/4) Epoch 34, batch 3750, loss[loss=0.1978, simple_loss=0.2698, pruned_loss=0.06294, over 7143.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2606, pruned_loss=0.04185, over 1421398.58 frames.], batch size: 17, lr: 1.62e-04
2022-05-29 08:07:40,322 INFO [train.py:842] (2/4) Epoch 34, batch 3800, loss[loss=0.2127, simple_loss=0.3033, pruned_loss=0.06104, over 7235.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2594, pruned_loss=0.04102, over 1423401.21 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:08:19,520 INFO [train.py:842] (2/4) Epoch 34, batch 3850, loss[loss=0.161, simple_loss=0.2568, pruned_loss=0.03257, over 7084.00 frames.], tot_loss[loss=0.1722, simple_loss=0.261, pruned_loss=0.04164, over 1424846.62 frames.], batch size: 28, lr: 1.62e-04
2022-05-29 08:08:59,242 INFO [train.py:842] (2/4) Epoch 34, batch 3900, loss[loss=0.1551, simple_loss=0.2439, pruned_loss=0.03311, over 7364.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2606, pruned_loss=0.04141, over 1427615.42 frames.], batch size: 19, lr: 1.62e-04
2022-05-29 08:09:38,322 INFO [train.py:842] (2/4) Epoch 34, batch 3950, loss[loss=0.1572, simple_loss=0.2587, pruned_loss=0.02781, over 7333.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2601, pruned_loss=0.04111, over 1421446.00 frames.], batch size: 22, lr: 1.62e-04
2022-05-29 08:10:18,065 INFO [train.py:842] (2/4) Epoch 34, batch 4000, loss[loss=0.2448, simple_loss=0.326, pruned_loss=0.08173, over 7213.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2604, pruned_loss=0.04117, over 1425539.06 frames.], batch size: 26, lr: 1.62e-04
2022-05-29 08:10:57,374 INFO [train.py:842] (2/4) Epoch 34, batch 4050, loss[loss=0.1463, simple_loss=0.2362, pruned_loss=0.02825, over 7422.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2598, pruned_loss=0.04126, over 1424585.93 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:11:36,844 INFO [train.py:842] (2/4) Epoch 34, batch 4100, loss[loss=0.1727, simple_loss=0.2665, pruned_loss=0.03943, over 7330.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2618, pruned_loss=0.04227, over 1422524.91 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:12:16,156 INFO [train.py:842] (2/4) Epoch 34, batch 4150, loss[loss=0.1515, simple_loss=0.2371, pruned_loss=0.0329, over 7434.00 frames.], tot_loss[loss=0.173, simple_loss=0.262, pruned_loss=0.04205, over 1423847.87 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:12:55,711 INFO [train.py:842] (2/4) Epoch 34, batch 4200, loss[loss=0.1817, simple_loss=0.2752, pruned_loss=0.04409, over 6804.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2615, pruned_loss=0.0418, over 1420689.59 frames.], batch size: 31, lr: 1.62e-04
2022-05-29 08:13:35,035 INFO [train.py:842] (2/4) Epoch 34, batch 4250, loss[loss=0.1455, simple_loss=0.2408, pruned_loss=0.02505, over 7320.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2621, pruned_loss=0.04203, over 1423514.17 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:14:14,655 INFO [train.py:842] (2/4) Epoch 34, batch 4300, loss[loss=0.1499, simple_loss=0.2537, pruned_loss=0.0231, over 6792.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2613, pruned_loss=0.04149, over 1425692.00 frames.], batch size: 31, lr: 1.62e-04
2022-05-29 08:14:53,567 INFO [train.py:842] (2/4) Epoch 34, batch 4350, loss[loss=0.1791, simple_loss=0.2712, pruned_loss=0.04353, over 7325.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2613, pruned_loss=0.04143, over 1425019.35 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 08:15:33,259 INFO [train.py:842] (2/4) Epoch 34, batch 4400, loss[loss=0.166, simple_loss=0.2597, pruned_loss=0.03618, over 7229.00 frames.], tot_loss[loss=0.1715, simple_loss=0.261, pruned_loss=0.04101, over 1428463.76 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:16:12,433 INFO [train.py:842] (2/4) Epoch 34, batch 4450, loss[loss=0.1997, simple_loss=0.2846, pruned_loss=0.05741, over 7193.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2616, pruned_loss=0.04132, over 1427342.15 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 08:16:52,065 INFO [train.py:842] (2/4) Epoch 34, batch 4500, loss[loss=0.1532, simple_loss=0.2315, pruned_loss=0.03748, over 7198.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2619, pruned_loss=0.04177, over 1427503.19 frames.], batch size: 16, lr: 1.62e-04
2022-05-29 08:17:31,611 INFO [train.py:842] (2/4) Epoch 34, batch 4550, loss[loss=0.1697, simple_loss=0.2628, pruned_loss=0.03827, over 7269.00 frames.], tot_loss[loss=0.173, simple_loss=0.2621, pruned_loss=0.04193, over 1428912.82 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 08:18:11,494 INFO [train.py:842] (2/4) Epoch 34, batch 4600, loss[loss=0.1682, simple_loss=0.2578, pruned_loss=0.03931, over 6299.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2608, pruned_loss=0.04155, over 1422931.43 frames.], batch size: 37, lr: 1.62e-04
2022-05-29 08:18:50,816 INFO [train.py:842] (2/4) Epoch 34, batch 4650, loss[loss=0.1737, simple_loss=0.2693, pruned_loss=0.039, over 7425.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2616, pruned_loss=0.04179, over 1423860.22 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 08:19:30,456 INFO [train.py:842] (2/4) Epoch 34, batch 4700, loss[loss=0.1323, simple_loss=0.2166, pruned_loss=0.02403, over 7283.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2607, pruned_loss=0.04136, over 1423367.27 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 08:20:09,790 INFO [train.py:842] (2/4) Epoch 34, batch 4750, loss[loss=0.1571, simple_loss=0.2532, pruned_loss=0.03056, over 7138.00 frames.], tot_loss[loss=0.173, simple_loss=0.2617, pruned_loss=0.04211, over 1423709.84 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:20:49,488 INFO [train.py:842] (2/4) Epoch 34, batch 4800, loss[loss=0.2806, simple_loss=0.3457, pruned_loss=0.1078, over 7418.00 frames.], tot_loss[loss=0.1731, simple_loss=0.262, pruned_loss=0.04207, over 1425928.83 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 08:21:28,568 INFO [train.py:842] (2/4) Epoch 34, batch 4850, loss[loss=0.221, simple_loss=0.2998, pruned_loss=0.07109, over 7408.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2622, pruned_loss=0.04151, over 1425529.21 frames.], batch size: 21, lr: 1.62e-04
2022-05-29 08:22:08,247 INFO [train.py:842] (2/4) Epoch 34, batch 4900, loss[loss=0.242, simple_loss=0.3203, pruned_loss=0.08191, over 7344.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2628, pruned_loss=0.04233, over 1424162.96 frames.], batch size: 22, lr: 1.62e-04
2022-05-29 08:22:47,428 INFO [train.py:842] (2/4) Epoch 34, batch 4950, loss[loss=0.1423, simple_loss=0.2279, pruned_loss=0.02834, over 7006.00 frames.], tot_loss[loss=0.1729, simple_loss=0.262, pruned_loss=0.04186, over 1421985.05 frames.], batch size: 16, lr: 1.62e-04
2022-05-29 08:23:27,047 INFO [train.py:842] (2/4) Epoch 34, batch 5000, loss[loss=0.1522, simple_loss=0.2465, pruned_loss=0.02894, over 7134.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2616, pruned_loss=0.04151, over 1419305.94 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:24:06,294 INFO [train.py:842] (2/4) Epoch 34, batch 5050, loss[loss=0.1747, simple_loss=0.2544, pruned_loss=0.04745, over 7300.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2616, pruned_loss=0.04132, over 1422776.35 frames.], batch size: 18, lr: 1.62e-04
2022-05-29 08:24:46,025 INFO [train.py:842] (2/4) Epoch 34, batch 5100, loss[loss=0.1646, simple_loss=0.2588, pruned_loss=0.03517, over 7289.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2621, pruned_loss=0.04156, over 1426929.49 frames.], batch size: 25, lr: 1.62e-04
2022-05-29 08:25:25,117 INFO [train.py:842] (2/4) Epoch 34, batch 5150, loss[loss=0.19, simple_loss=0.2753, pruned_loss=0.05238, over 6761.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2614, pruned_loss=0.04165, over 1423580.63 frames.], batch size: 31, lr: 1.62e-04
2022-05-29 08:26:04,886 INFO [train.py:842] (2/4) Epoch 34, batch 5200, loss[loss=0.1346, simple_loss=0.2274, pruned_loss=0.02089, over 7429.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2605, pruned_loss=0.04127, over 1425235.46 frames.], batch size: 20, lr: 1.62e-04
2022-05-29 08:26:44,199 INFO [train.py:842] (2/4) Epoch 34, batch 5250, loss[loss=0.2074, simple_loss=0.2914, pruned_loss=0.06165, over 7375.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2605, pruned_loss=0.04126, over 1426525.64 frames.], batch size: 23, lr: 1.62e-04
2022-05-29 08:27:23,813 INFO [train.py:842] (2/4) Epoch 34, batch 5300, loss[loss=0.1378, simple_loss=0.2203, pruned_loss=0.0276, over 7295.00 frames.], tot_loss[loss=0.1721, simple_loss=0.261, pruned_loss=0.04161, over 1426528.30 frames.], batch size: 17, lr: 1.61e-04
2022-05-29 08:28:03,186 INFO [train.py:842] (2/4) Epoch 34, batch 5350, loss[loss=0.1709, simple_loss=0.251, pruned_loss=0.04543, over 7150.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2616, pruned_loss=0.0419, over 1418969.81 frames.], batch size: 17, lr: 1.61e-04
2022-05-29 08:28:42,824 INFO [train.py:842] (2/4) Epoch 34, batch 5400, loss[loss=0.1898, simple_loss=0.2899, pruned_loss=0.04486, over 7321.00 frames.], tot_loss[loss=0.174, simple_loss=0.2629, pruned_loss=0.0425, over 1420355.11 frames.], batch size: 25, lr: 1.61e-04
2022-05-29 08:29:21,730 INFO [train.py:842] (2/4) Epoch 34, batch 5450, loss[loss=0.2015, simple_loss=0.2918, pruned_loss=0.05558, over 6576.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2633, pruned_loss=0.04264, over 1418369.19 frames.], batch size: 38, lr: 1.61e-04
2022-05-29 08:30:01,365 INFO [train.py:842] (2/4) Epoch 34, batch 5500, loss[loss=0.1763, simple_loss=0.2716, pruned_loss=0.04051, over 7208.00 frames.], tot_loss[loss=0.1745, simple_loss=0.264, pruned_loss=0.04257, over 1421134.79 frames.], batch size: 22, lr: 1.61e-04
2022-05-29 08:30:40,441 INFO [train.py:842] (2/4) Epoch 34, batch 5550, loss[loss=0.1705, simple_loss=0.2717, pruned_loss=0.0347, over 7228.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2653, pruned_loss=0.0433, over 1420317.72 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:31:19,882 INFO [train.py:842] (2/4) Epoch 34, batch 5600, loss[loss=0.1671, simple_loss=0.2626, pruned_loss=0.03583, over 7333.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2654, pruned_loss=0.04352, over 1420313.61 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:31:59,160 INFO [train.py:842] (2/4) Epoch 34, batch 5650, loss[loss=0.1774, simple_loss=0.2772, pruned_loss=0.03886, over 7196.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2643, pruned_loss=0.04277, over 1418921.24 frames.], batch size: 23, lr: 1.61e-04
2022-05-29 08:32:38,748 INFO [train.py:842] (2/4) Epoch 34, batch 5700, loss[loss=0.1912, simple_loss=0.2778, pruned_loss=0.05225, over 7326.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2642, pruned_loss=0.0428, over 1421151.16 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:33:17,956 INFO [train.py:842] (2/4) Epoch 34, batch 5750, loss[loss=0.1631, simple_loss=0.2526, pruned_loss=0.03683, over 7357.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2633, pruned_loss=0.04249, over 1423099.24 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 08:33:57,816 INFO [train.py:842] (2/4) Epoch 34, batch 5800, loss[loss=0.1559, simple_loss=0.2573, pruned_loss=0.02727, over 7315.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2627, pruned_loss=0.04212, over 1423717.60 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:34:37,248 INFO [train.py:842] (2/4) Epoch 34, batch 5850, loss[loss=0.1769, simple_loss=0.2591, pruned_loss=0.04735, over 6323.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2624, pruned_loss=0.04194, over 1425838.97 frames.], batch size: 37, lr: 1.61e-04
2022-05-29 08:35:27,457 INFO [train.py:842] (2/4) Epoch 34, batch 5900, loss[loss=0.1478, simple_loss=0.2428, pruned_loss=0.02638, over 7220.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2612, pruned_loss=0.04088, over 1421295.44 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:36:06,830 INFO [train.py:842] (2/4) Epoch 34, batch 5950, loss[loss=0.1977, simple_loss=0.289, pruned_loss=0.05321, over 6318.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2629, pruned_loss=0.04229, over 1422787.22 frames.], batch size: 37, lr: 1.61e-04
2022-05-29 08:36:46,429 INFO [train.py:842] (2/4) Epoch 34, batch 6000, loss[loss=0.1395, simple_loss=0.2217, pruned_loss=0.0286, over 7006.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2622, pruned_loss=0.04216, over 1422664.30 frames.], batch size: 16, lr: 1.61e-04
2022-05-29 08:36:46,430 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 08:36:56,770 INFO [train.py:871] (2/4) 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] (2/4) Epoch 34, batch 6050, loss[loss=0.1954, simple_loss=0.276, pruned_loss=0.05738, over 4884.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2628, pruned_loss=0.04241, over 1425300.24 frames.], batch size: 52, lr: 1.61e-04
2022-05-29 08:38:15,784 INFO [train.py:842] (2/4) Epoch 34, batch 6100, loss[loss=0.1653, simple_loss=0.26, pruned_loss=0.03529, over 7223.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2634, pruned_loss=0.04252, over 1424960.84 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:38:55,084 INFO [train.py:842] (2/4) Epoch 34, batch 6150, loss[loss=0.1829, simple_loss=0.274, pruned_loss=0.04586, over 7213.00 frames.], tot_loss[loss=0.1736, simple_loss=0.263, pruned_loss=0.04209, over 1427508.63 frames.], batch size: 23, lr: 1.61e-04
2022-05-29 08:39:34,521 INFO [train.py:842] (2/4) Epoch 34, batch 6200, loss[loss=0.1619, simple_loss=0.2407, pruned_loss=0.04156, over 7279.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2622, pruned_loss=0.04182, over 1425734.08 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 08:40:13,620 INFO [train.py:842] (2/4) Epoch 34, batch 6250, loss[loss=0.1682, simple_loss=0.2625, pruned_loss=0.03691, over 7208.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2616, pruned_loss=0.04143, over 1428626.10 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:40:53,104 INFO [train.py:842] (2/4) Epoch 34, batch 6300, loss[loss=0.1512, simple_loss=0.2363, pruned_loss=0.03307, over 7172.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2621, pruned_loss=0.04133, over 1432208.54 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 08:41:32,251 INFO [train.py:842] (2/4) Epoch 34, batch 6350, loss[loss=0.1785, simple_loss=0.2623, pruned_loss=0.04741, over 7125.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2623, pruned_loss=0.04199, over 1428819.27 frames.], batch size: 26, lr: 1.61e-04
2022-05-29 08:42:12,063 INFO [train.py:842] (2/4) Epoch 34, batch 6400, loss[loss=0.1567, simple_loss=0.2417, pruned_loss=0.03587, over 7324.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2623, pruned_loss=0.04216, over 1432682.00 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:42:51,291 INFO [train.py:842] (2/4) Epoch 34, batch 6450, loss[loss=0.1447, simple_loss=0.2361, pruned_loss=0.02666, over 7233.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2617, pruned_loss=0.04177, over 1428667.56 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:43:31,013 INFO [train.py:842] (2/4) Epoch 34, batch 6500, loss[loss=0.1658, simple_loss=0.2452, pruned_loss=0.04326, over 7401.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2618, pruned_loss=0.04134, over 1430009.15 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 08:44:10,195 INFO [train.py:842] (2/4) Epoch 34, batch 6550, loss[loss=0.1536, simple_loss=0.2533, pruned_loss=0.02697, over 7110.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2622, pruned_loss=0.04143, over 1430981.61 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:44:50,125 INFO [train.py:842] (2/4) Epoch 34, batch 6600, loss[loss=0.1447, simple_loss=0.2306, pruned_loss=0.0294, over 6780.00 frames.], tot_loss[loss=0.172, simple_loss=0.2617, pruned_loss=0.0412, over 1433041.58 frames.], batch size: 15, lr: 1.61e-04
2022-05-29 08:45:29,290 INFO [train.py:842] (2/4) Epoch 34, batch 6650, loss[loss=0.1569, simple_loss=0.241, pruned_loss=0.03638, over 7158.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2617, pruned_loss=0.04153, over 1427475.68 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 08:46:08,632 INFO [train.py:842] (2/4) Epoch 34, batch 6700, loss[loss=0.1889, simple_loss=0.2827, pruned_loss=0.04754, over 7228.00 frames.], tot_loss[loss=0.1738, simple_loss=0.263, pruned_loss=0.04227, over 1426878.22 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:46:47,756 INFO [train.py:842] (2/4) Epoch 34, batch 6750, loss[loss=0.1751, simple_loss=0.2686, pruned_loss=0.04081, over 7217.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2644, pruned_loss=0.04297, over 1424307.65 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:47:27,225 INFO [train.py:842] (2/4) Epoch 34, batch 6800, loss[loss=0.1991, simple_loss=0.295, pruned_loss=0.05163, over 7144.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2636, pruned_loss=0.04251, over 1415079.02 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:48:06,408 INFO [train.py:842] (2/4) Epoch 34, batch 6850, loss[loss=0.2407, simple_loss=0.3189, pruned_loss=0.08127, over 6721.00 frames.], tot_loss[loss=0.174, simple_loss=0.2633, pruned_loss=0.04241, over 1415627.83 frames.], batch size: 31, lr: 1.61e-04
2022-05-29 08:48:45,881 INFO [train.py:842] (2/4) Epoch 34, batch 6900, loss[loss=0.178, simple_loss=0.2721, pruned_loss=0.04197, over 6837.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2637, pruned_loss=0.04262, over 1415573.46 frames.], batch size: 31, lr: 1.61e-04
2022-05-29 08:49:25,561 INFO [train.py:842] (2/4) Epoch 34, batch 6950, loss[loss=0.18, simple_loss=0.2719, pruned_loss=0.04406, over 7132.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2625, pruned_loss=0.04183, over 1420407.71 frames.], batch size: 26, lr: 1.61e-04
2022-05-29 08:50:05,177 INFO [train.py:842] (2/4) Epoch 34, batch 7000, loss[loss=0.2221, simple_loss=0.3087, pruned_loss=0.06778, over 7162.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2609, pruned_loss=0.04122, over 1420318.33 frames.], batch size: 26, lr: 1.61e-04
2022-05-29 08:50:44,483 INFO [train.py:842] (2/4) Epoch 34, batch 7050, loss[loss=0.2131, simple_loss=0.2883, pruned_loss=0.06896, over 7067.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2617, pruned_loss=0.042, over 1419926.90 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 08:51:24,116 INFO [train.py:842] (2/4) Epoch 34, batch 7100, loss[loss=0.1813, simple_loss=0.2819, pruned_loss=0.04035, over 7421.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2627, pruned_loss=0.04249, over 1424425.14 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:52:03,400 INFO [train.py:842] (2/4) Epoch 34, batch 7150, loss[loss=0.1516, simple_loss=0.2501, pruned_loss=0.02653, over 7431.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2629, pruned_loss=0.04228, over 1424741.78 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 08:52:43,323 INFO [train.py:842] (2/4) Epoch 34, batch 7200, loss[loss=0.1443, simple_loss=0.24, pruned_loss=0.02434, over 7161.00 frames.], tot_loss[loss=0.1727, simple_loss=0.262, pruned_loss=0.04172, over 1423226.41 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 08:53:22,451 INFO [train.py:842] (2/4) Epoch 34, batch 7250, loss[loss=0.1512, simple_loss=0.2243, pruned_loss=0.03908, over 6760.00 frames.], tot_loss[loss=0.174, simple_loss=0.263, pruned_loss=0.04248, over 1421798.64 frames.], batch size: 15, lr: 1.61e-04
2022-05-29 08:54:02,107 INFO [train.py:842] (2/4) Epoch 34, batch 7300, loss[loss=0.1398, simple_loss=0.2361, pruned_loss=0.02173, over 7150.00 frames.], tot_loss[loss=0.173, simple_loss=0.262, pruned_loss=0.04197, over 1424884.39 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 08:54:41,349 INFO [train.py:842] (2/4) Epoch 34, batch 7350, loss[loss=0.1731, simple_loss=0.2623, pruned_loss=0.04197, over 7100.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2609, pruned_loss=0.04171, over 1420871.26 frames.], batch size: 28, lr: 1.61e-04
2022-05-29 08:55:20,682 INFO [train.py:842] (2/4) Epoch 34, batch 7400, loss[loss=0.1946, simple_loss=0.2734, pruned_loss=0.05786, over 7142.00 frames.], tot_loss[loss=0.1731, simple_loss=0.262, pruned_loss=0.04206, over 1419098.44 frames.], batch size: 28, lr: 1.61e-04
2022-05-29 08:55:59,978 INFO [train.py:842] (2/4) Epoch 34, batch 7450, loss[loss=0.1675, simple_loss=0.2662, pruned_loss=0.03444, over 7122.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2618, pruned_loss=0.04157, over 1421622.05 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:56:39,507 INFO [train.py:842] (2/4) Epoch 34, batch 7500, loss[loss=0.1524, simple_loss=0.2448, pruned_loss=0.03004, over 7323.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2611, pruned_loss=0.04131, over 1423242.20 frames.], batch size: 25, lr: 1.61e-04
2022-05-29 08:57:18,933 INFO [train.py:842] (2/4) Epoch 34, batch 7550, loss[loss=0.1576, simple_loss=0.2352, pruned_loss=0.03998, over 7215.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2595, pruned_loss=0.04041, over 1423748.21 frames.], batch size: 16, lr: 1.61e-04
2022-05-29 08:57:58,693 INFO [train.py:842] (2/4) Epoch 34, batch 7600, loss[loss=0.1725, simple_loss=0.2549, pruned_loss=0.04503, over 7197.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2609, pruned_loss=0.04087, over 1428715.76 frames.], batch size: 16, lr: 1.61e-04
2022-05-29 08:58:37,772 INFO [train.py:842] (2/4) Epoch 34, batch 7650, loss[loss=0.1915, simple_loss=0.2838, pruned_loss=0.04959, over 7110.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2606, pruned_loss=0.04055, over 1429073.34 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 08:59:17,475 INFO [train.py:842] (2/4) Epoch 34, batch 7700, loss[loss=0.1842, simple_loss=0.2718, pruned_loss=0.04835, over 7190.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2602, pruned_loss=0.04051, over 1427454.78 frames.], batch size: 26, lr: 1.61e-04
2022-05-29 08:59:56,660 INFO [train.py:842] (2/4) Epoch 34, batch 7750, loss[loss=0.1855, simple_loss=0.2715, pruned_loss=0.04977, over 7348.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2594, pruned_loss=0.04023, over 1427842.16 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 09:00:36,149 INFO [train.py:842] (2/4) Epoch 34, batch 7800, loss[loss=0.1643, simple_loss=0.2448, pruned_loss=0.04185, over 7277.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2599, pruned_loss=0.04062, over 1425152.80 frames.], batch size: 17, lr: 1.61e-04
2022-05-29 09:01:15,301 INFO [train.py:842] (2/4) Epoch 34, batch 7850, loss[loss=0.2156, simple_loss=0.2884, pruned_loss=0.07144, over 4576.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2602, pruned_loss=0.04085, over 1423847.69 frames.], batch size: 52, lr: 1.61e-04
2022-05-29 09:01:54,545 INFO [train.py:842] (2/4) Epoch 34, batch 7900, loss[loss=0.2138, simple_loss=0.2954, pruned_loss=0.0661, over 4934.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2618, pruned_loss=0.04141, over 1417692.05 frames.], batch size: 53, lr: 1.61e-04
2022-05-29 09:02:33,823 INFO [train.py:842] (2/4) Epoch 34, batch 7950, loss[loss=0.1821, simple_loss=0.2788, pruned_loss=0.04265, over 7282.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2615, pruned_loss=0.04164, over 1420852.73 frames.], batch size: 24, lr: 1.61e-04
2022-05-29 09:03:13,351 INFO [train.py:842] (2/4) Epoch 34, batch 8000, loss[loss=0.212, simple_loss=0.3004, pruned_loss=0.06186, over 7198.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2614, pruned_loss=0.04178, over 1419241.17 frames.], batch size: 23, lr: 1.61e-04
2022-05-29 09:03:52,514 INFO [train.py:842] (2/4) Epoch 34, batch 8050, loss[loss=0.1547, simple_loss=0.2461, pruned_loss=0.03164, over 7172.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2617, pruned_loss=0.04144, over 1415834.26 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 09:04:32,344 INFO [train.py:842] (2/4) Epoch 34, batch 8100, loss[loss=0.1585, simple_loss=0.2352, pruned_loss=0.04086, over 7249.00 frames.], tot_loss[loss=0.171, simple_loss=0.2606, pruned_loss=0.04073, over 1421756.03 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 09:05:11,680 INFO [train.py:842] (2/4) Epoch 34, batch 8150, loss[loss=0.1817, simple_loss=0.282, pruned_loss=0.04069, over 7209.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2602, pruned_loss=0.04099, over 1423254.04 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 09:05:51,179 INFO [train.py:842] (2/4) Epoch 34, batch 8200, loss[loss=0.1814, simple_loss=0.2767, pruned_loss=0.04308, over 7066.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2608, pruned_loss=0.04124, over 1423812.26 frames.], batch size: 28, lr: 1.61e-04
2022-05-29 09:06:30,361 INFO [train.py:842] (2/4) Epoch 34, batch 8250, loss[loss=0.1766, simple_loss=0.2689, pruned_loss=0.04213, over 7284.00 frames.], tot_loss[loss=0.1707, simple_loss=0.26, pruned_loss=0.04067, over 1421508.25 frames.], batch size: 25, lr: 1.61e-04
2022-05-29 09:07:09,889 INFO [train.py:842] (2/4) Epoch 34, batch 8300, loss[loss=0.1873, simple_loss=0.2848, pruned_loss=0.04484, over 5406.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2604, pruned_loss=0.04073, over 1422564.80 frames.], batch size: 52, lr: 1.61e-04
2022-05-29 09:07:49,143 INFO [train.py:842] (2/4) Epoch 34, batch 8350, loss[loss=0.1873, simple_loss=0.2794, pruned_loss=0.04759, over 7161.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2608, pruned_loss=0.04112, over 1420345.66 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 09:08:28,586 INFO [train.py:842] (2/4) Epoch 34, batch 8400, loss[loss=0.1426, simple_loss=0.2342, pruned_loss=0.02547, over 7256.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2607, pruned_loss=0.04182, over 1419859.35 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 09:09:07,978 INFO [train.py:842] (2/4) Epoch 34, batch 8450, loss[loss=0.145, simple_loss=0.2275, pruned_loss=0.03123, over 7138.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2603, pruned_loss=0.04143, over 1420584.03 frames.], batch size: 17, lr: 1.61e-04
2022-05-29 09:09:47,729 INFO [train.py:842] (2/4) Epoch 34, batch 8500, loss[loss=0.1603, simple_loss=0.2559, pruned_loss=0.03234, over 7146.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2607, pruned_loss=0.04155, over 1418806.36 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 09:10:26,685 INFO [train.py:842] (2/4) Epoch 34, batch 8550, loss[loss=0.1932, simple_loss=0.277, pruned_loss=0.05468, over 7205.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2602, pruned_loss=0.04144, over 1417262.79 frames.], batch size: 23, lr: 1.61e-04
2022-05-29 09:11:06,048 INFO [train.py:842] (2/4) Epoch 34, batch 8600, loss[loss=0.1568, simple_loss=0.2361, pruned_loss=0.03877, over 6820.00 frames.], tot_loss[loss=0.173, simple_loss=0.2618, pruned_loss=0.04212, over 1419412.39 frames.], batch size: 15, lr: 1.61e-04
2022-05-29 09:11:45,262 INFO [train.py:842] (2/4) Epoch 34, batch 8650, loss[loss=0.1559, simple_loss=0.2488, pruned_loss=0.03145, over 7276.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2613, pruned_loss=0.04189, over 1417430.95 frames.], batch size: 18, lr: 1.61e-04
2022-05-29 09:12:27,530 INFO [train.py:842] (2/4) Epoch 34, batch 8700, loss[loss=0.1865, simple_loss=0.2846, pruned_loss=0.04424, over 7152.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2624, pruned_loss=0.04209, over 1414432.12 frames.], batch size: 26, lr: 1.61e-04
2022-05-29 09:13:06,644 INFO [train.py:842] (2/4) Epoch 34, batch 8750, loss[loss=0.1649, simple_loss=0.2521, pruned_loss=0.0388, over 7321.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2631, pruned_loss=0.04238, over 1414116.21 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 09:13:45,960 INFO [train.py:842] (2/4) Epoch 34, batch 8800, loss[loss=0.1842, simple_loss=0.2807, pruned_loss=0.04381, over 7332.00 frames.], tot_loss[loss=0.174, simple_loss=0.2631, pruned_loss=0.04251, over 1407114.86 frames.], batch size: 20, lr: 1.61e-04
2022-05-29 09:14:24,641 INFO [train.py:842] (2/4) Epoch 34, batch 8850, loss[loss=0.1792, simple_loss=0.2688, pruned_loss=0.04479, over 7414.00 frames.], tot_loss[loss=0.176, simple_loss=0.2649, pruned_loss=0.04355, over 1406793.13 frames.], batch size: 21, lr: 1.61e-04
2022-05-29 09:15:04,297 INFO [train.py:842] (2/4) Epoch 34, batch 8900, loss[loss=0.2316, simple_loss=0.3112, pruned_loss=0.07603, over 6841.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2648, pruned_loss=0.04343, over 1406709.13 frames.], batch size: 31, lr: 1.61e-04
2022-05-29 09:15:43,383 INFO [train.py:842] (2/4) Epoch 34, batch 8950, loss[loss=0.1615, simple_loss=0.2451, pruned_loss=0.03899, over 7154.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2647, pruned_loss=0.04347, over 1407819.57 frames.], batch size: 19, lr: 1.61e-04
2022-05-29 09:16:22,295 INFO [train.py:842] (2/4) Epoch 34, batch 9000, loss[loss=0.1809, simple_loss=0.2607, pruned_loss=0.05054, over 7212.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2662, pruned_loss=0.04444, over 1395209.46 frames.], batch size: 22, lr: 1.61e-04
2022-05-29 09:16:22,296 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 09:16:31,957 INFO [train.py:871] (2/4) Epoch 34, validation: loss=0.1642, simple_loss=0.2613, pruned_loss=0.03353, over 868885.00 frames.
2022-05-29 09:17:10,337 INFO [train.py:842] (2/4) Epoch 34, batch 9050, loss[loss=0.1637, simple_loss=0.2667, pruned_loss=0.03039, over 6374.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2679, pruned_loss=0.04558, over 1375668.49 frames.], batch size: 37, lr: 1.61e-04
2022-05-29 09:17:48,548 INFO [train.py:842] (2/4) Epoch 34, batch 9100, loss[loss=0.1645, simple_loss=0.2619, pruned_loss=0.03352, over 6480.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2693, pruned_loss=0.0457, over 1337380.81 frames.], batch size: 39, lr: 1.61e-04
2022-05-29 09:18:26,612 INFO [train.py:842] (2/4) Epoch 34, batch 9150, loss[loss=0.2199, simple_loss=0.291, pruned_loss=0.07439, over 5159.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2722, pruned_loss=0.04811, over 1273644.30 frames.], batch size: 52, lr: 1.60e-04
2022-05-29 09:19:15,548 INFO [train.py:842] (2/4) Epoch 35, batch 0, loss[loss=0.1919, simple_loss=0.2789, pruned_loss=0.05241, over 7229.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2789, pruned_loss=0.05241, over 7229.00 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:19:54,663 INFO [train.py:842] (2/4) Epoch 35, batch 50, loss[loss=0.1808, simple_loss=0.2723, pruned_loss=0.04465, over 7302.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2668, pruned_loss=0.04237, over 317925.36 frames.], batch size: 24, lr: 1.58e-04
2022-05-29 09:20:34,661 INFO [train.py:842] (2/4) Epoch 35, batch 100, loss[loss=0.1763, simple_loss=0.2669, pruned_loss=0.04286, over 7210.00 frames.], tot_loss[loss=0.175, simple_loss=0.265, pruned_loss=0.04249, over 567999.36 frames.], batch size: 26, lr: 1.58e-04
2022-05-29 09:21:13,989 INFO [train.py:842] (2/4) Epoch 35, batch 150, loss[loss=0.1765, simple_loss=0.2752, pruned_loss=0.03891, over 7384.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2649, pruned_loss=0.04224, over 761024.18 frames.], batch size: 23, lr: 1.58e-04
2022-05-29 09:21:53,701 INFO [train.py:842] (2/4) Epoch 35, batch 200, loss[loss=0.1438, simple_loss=0.227, pruned_loss=0.03025, over 7069.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2635, pruned_loss=0.04209, over 910067.37 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:22:33,035 INFO [train.py:842] (2/4) Epoch 35, batch 250, loss[loss=0.1584, simple_loss=0.2481, pruned_loss=0.03438, over 7222.00 frames.], tot_loss[loss=0.172, simple_loss=0.2615, pruned_loss=0.04122, over 1027891.77 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:23:12,663 INFO [train.py:842] (2/4) Epoch 35, batch 300, loss[loss=0.1676, simple_loss=0.264, pruned_loss=0.03561, over 7149.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2611, pruned_loss=0.04099, over 1113504.22 frames.], batch size: 19, lr: 1.58e-04
2022-05-29 09:23:51,964 INFO [train.py:842] (2/4) Epoch 35, batch 350, loss[loss=0.2251, simple_loss=0.3178, pruned_loss=0.06619, over 7181.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2609, pruned_loss=0.04094, over 1185347.68 frames.], batch size: 23, lr: 1.58e-04
2022-05-29 09:24:31,408 INFO [train.py:842] (2/4) Epoch 35, batch 400, loss[loss=0.1463, simple_loss=0.249, pruned_loss=0.0218, over 7332.00 frames.], tot_loss[loss=0.1713, simple_loss=0.261, pruned_loss=0.04077, over 1239199.86 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:25:10,959 INFO [train.py:842] (2/4) Epoch 35, batch 450, loss[loss=0.1541, simple_loss=0.2519, pruned_loss=0.02811, over 6792.00 frames.], tot_loss[loss=0.1705, simple_loss=0.26, pruned_loss=0.04047, over 1283675.90 frames.], batch size: 31, lr: 1.58e-04
2022-05-29 09:25:50,501 INFO [train.py:842] (2/4) Epoch 35, batch 500, loss[loss=0.2421, simple_loss=0.3081, pruned_loss=0.08809, over 7332.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2609, pruned_loss=0.04147, over 1313476.31 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:26:29,909 INFO [train.py:842] (2/4) Epoch 35, batch 550, loss[loss=0.1589, simple_loss=0.2449, pruned_loss=0.03642, over 7068.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2609, pruned_loss=0.04191, over 1334494.70 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:27:09,508 INFO [train.py:842] (2/4) Epoch 35, batch 600, loss[loss=0.1543, simple_loss=0.2486, pruned_loss=0.03004, over 7331.00 frames.], tot_loss[loss=0.1723, simple_loss=0.261, pruned_loss=0.04178, over 1353428.05 frames.], batch size: 22, lr: 1.58e-04
2022-05-29 09:27:48,644 INFO [train.py:842] (2/4) Epoch 35, batch 650, loss[loss=0.1426, simple_loss=0.229, pruned_loss=0.02804, over 7164.00 frames.], tot_loss[loss=0.1721, simple_loss=0.261, pruned_loss=0.04162, over 1372101.58 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:28:28,658 INFO [train.py:842] (2/4) Epoch 35, batch 700, loss[loss=0.1446, simple_loss=0.229, pruned_loss=0.0301, over 7286.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2599, pruned_loss=0.04116, over 1385965.41 frames.], batch size: 17, lr: 1.58e-04
2022-05-29 09:29:08,037 INFO [train.py:842] (2/4) Epoch 35, batch 750, loss[loss=0.1389, simple_loss=0.2355, pruned_loss=0.0211, over 7248.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2601, pruned_loss=0.04084, over 1393165.49 frames.], batch size: 19, lr: 1.58e-04
2022-05-29 09:29:47,593 INFO [train.py:842] (2/4) Epoch 35, batch 800, loss[loss=0.1609, simple_loss=0.2604, pruned_loss=0.03069, over 7205.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2609, pruned_loss=0.04099, over 1402009.38 frames.], batch size: 21, lr: 1.58e-04
2022-05-29 09:30:26,779 INFO [train.py:842] (2/4) Epoch 35, batch 850, loss[loss=0.1978, simple_loss=0.2784, pruned_loss=0.05864, over 7332.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2611, pruned_loss=0.04073, over 1402674.16 frames.], batch size: 24, lr: 1.58e-04
2022-05-29 09:31:06,399 INFO [train.py:842] (2/4) Epoch 35, batch 900, loss[loss=0.1835, simple_loss=0.2714, pruned_loss=0.04776, over 5385.00 frames.], tot_loss[loss=0.171, simple_loss=0.261, pruned_loss=0.04051, over 1406230.94 frames.], batch size: 52, lr: 1.58e-04
2022-05-29 09:31:45,707 INFO [train.py:842] (2/4) Epoch 35, batch 950, loss[loss=0.1624, simple_loss=0.2683, pruned_loss=0.02822, over 7257.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2608, pruned_loss=0.04116, over 1410304.82 frames.], batch size: 19, lr: 1.58e-04
2022-05-29 09:32:25,396 INFO [train.py:842] (2/4) Epoch 35, batch 1000, loss[loss=0.1998, simple_loss=0.2835, pruned_loss=0.05803, over 6709.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2604, pruned_loss=0.04115, over 1410465.51 frames.], batch size: 31, lr: 1.58e-04
2022-05-29 09:33:04,776 INFO [train.py:842] (2/4) Epoch 35, batch 1050, loss[loss=0.1777, simple_loss=0.2682, pruned_loss=0.04361, over 7405.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2593, pruned_loss=0.04023, over 1415796.46 frames.], batch size: 21, lr: 1.58e-04
2022-05-29 09:33:44,562 INFO [train.py:842] (2/4) Epoch 35, batch 1100, loss[loss=0.1763, simple_loss=0.2707, pruned_loss=0.04092, over 7357.00 frames.], tot_loss[loss=0.1705, simple_loss=0.26, pruned_loss=0.04049, over 1419738.58 frames.], batch size: 19, lr: 1.58e-04
2022-05-29 09:34:23,658 INFO [train.py:842] (2/4) Epoch 35, batch 1150, loss[loss=0.1889, simple_loss=0.2895, pruned_loss=0.04418, over 7188.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2616, pruned_loss=0.04117, over 1421471.98 frames.], batch size: 23, lr: 1.58e-04
2022-05-29 09:35:03,471 INFO [train.py:842] (2/4) Epoch 35, batch 1200, loss[loss=0.1662, simple_loss=0.2475, pruned_loss=0.04244, over 7289.00 frames.], tot_loss[loss=0.172, simple_loss=0.2619, pruned_loss=0.04112, over 1425323.46 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:35:42,644 INFO [train.py:842] (2/4) Epoch 35, batch 1250, loss[loss=0.1523, simple_loss=0.2566, pruned_loss=0.02404, over 7334.00 frames.], tot_loss[loss=0.1725, simple_loss=0.262, pruned_loss=0.04152, over 1423909.45 frames.], batch size: 22, lr: 1.58e-04
2022-05-29 09:36:21,899 INFO [train.py:842] (2/4) Epoch 35, batch 1300, loss[loss=0.2269, simple_loss=0.316, pruned_loss=0.06887, over 7094.00 frames.], tot_loss[loss=0.1737, simple_loss=0.263, pruned_loss=0.0422, over 1419272.44 frames.], batch size: 28, lr: 1.58e-04
2022-05-29 09:37:00,973 INFO [train.py:842] (2/4) Epoch 35, batch 1350, loss[loss=0.1401, simple_loss=0.2324, pruned_loss=0.02388, over 7041.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2634, pruned_loss=0.04236, over 1422262.86 frames.], batch size: 28, lr: 1.58e-04
2022-05-29 09:37:40,254 INFO [train.py:842] (2/4) Epoch 35, batch 1400, loss[loss=0.1599, simple_loss=0.2539, pruned_loss=0.03296, over 7323.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2628, pruned_loss=0.04185, over 1419845.31 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:38:19,792 INFO [train.py:842] (2/4) Epoch 35, batch 1450, loss[loss=0.1449, simple_loss=0.2333, pruned_loss=0.02824, over 7255.00 frames.], tot_loss[loss=0.1716, simple_loss=0.261, pruned_loss=0.04112, over 1417389.05 frames.], batch size: 19, lr: 1.58e-04
2022-05-29 09:38:59,585 INFO [train.py:842] (2/4) Epoch 35, batch 1500, loss[loss=0.1484, simple_loss=0.2282, pruned_loss=0.03426, over 7122.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2623, pruned_loss=0.04178, over 1419627.28 frames.], batch size: 17, lr: 1.58e-04
2022-05-29 09:39:38,669 INFO [train.py:842] (2/4) Epoch 35, batch 1550, loss[loss=0.1722, simple_loss=0.2585, pruned_loss=0.04292, over 7225.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2628, pruned_loss=0.04183, over 1419248.89 frames.], batch size: 21, lr: 1.58e-04
2022-05-29 09:40:18,450 INFO [train.py:842] (2/4) Epoch 35, batch 1600, loss[loss=0.1655, simple_loss=0.2614, pruned_loss=0.03478, over 7098.00 frames.], tot_loss[loss=0.1725, simple_loss=0.262, pruned_loss=0.04145, over 1421523.89 frames.], batch size: 28, lr: 1.58e-04
2022-05-29 09:40:57,792 INFO [train.py:842] (2/4) Epoch 35, batch 1650, loss[loss=0.1579, simple_loss=0.2451, pruned_loss=0.03528, over 7414.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2617, pruned_loss=0.04172, over 1426027.28 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:41:48,185 INFO [train.py:842] (2/4) Epoch 35, batch 1700, loss[loss=0.2777, simple_loss=0.3511, pruned_loss=0.1022, over 5011.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2626, pruned_loss=0.04231, over 1425330.25 frames.], batch size: 52, lr: 1.58e-04
2022-05-29 09:42:27,676 INFO [train.py:842] (2/4) Epoch 35, batch 1750, loss[loss=0.154, simple_loss=0.2397, pruned_loss=0.03413, over 7158.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2624, pruned_loss=0.04206, over 1425406.60 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:43:07,476 INFO [train.py:842] (2/4) Epoch 35, batch 1800, loss[loss=0.1749, simple_loss=0.2693, pruned_loss=0.04026, over 7271.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2616, pruned_loss=0.04129, over 1429120.62 frames.], batch size: 25, lr: 1.58e-04
2022-05-29 09:43:46,594 INFO [train.py:842] (2/4) Epoch 35, batch 1850, loss[loss=0.1807, simple_loss=0.2594, pruned_loss=0.05104, over 7059.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2616, pruned_loss=0.04143, over 1425288.54 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:44:26,178 INFO [train.py:842] (2/4) Epoch 35, batch 1900, loss[loss=0.1912, simple_loss=0.2801, pruned_loss=0.0512, over 7350.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2618, pruned_loss=0.04148, over 1424533.47 frames.], batch size: 23, lr: 1.58e-04
2022-05-29 09:45:05,545 INFO [train.py:842] (2/4) Epoch 35, batch 1950, loss[loss=0.1396, simple_loss=0.2319, pruned_loss=0.02364, over 7180.00 frames.], tot_loss[loss=0.1727, simple_loss=0.262, pruned_loss=0.04171, over 1423994.16 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:45:55,989 INFO [train.py:842] (2/4) Epoch 35, batch 2000, loss[loss=0.1682, simple_loss=0.2668, pruned_loss=0.03477, over 6557.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2617, pruned_loss=0.04159, over 1419865.93 frames.], batch size: 38, lr: 1.58e-04
2022-05-29 09:46:35,087 INFO [train.py:842] (2/4) Epoch 35, batch 2050, loss[loss=0.1616, simple_loss=0.2618, pruned_loss=0.03069, over 7117.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2617, pruned_loss=0.04153, over 1421254.81 frames.], batch size: 21, lr: 1.58e-04
2022-05-29 09:47:25,664 INFO [train.py:842] (2/4) Epoch 35, batch 2100, loss[loss=0.1756, simple_loss=0.2738, pruned_loss=0.03872, over 7407.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2619, pruned_loss=0.04124, over 1424351.02 frames.], batch size: 21, lr: 1.58e-04
2022-05-29 09:48:05,088 INFO [train.py:842] (2/4) Epoch 35, batch 2150, loss[loss=0.1943, simple_loss=0.2754, pruned_loss=0.05665, over 6379.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2626, pruned_loss=0.04179, over 1428039.75 frames.], batch size: 38, lr: 1.58e-04
2022-05-29 09:48:44,639 INFO [train.py:842] (2/4) Epoch 35, batch 2200, loss[loss=0.1675, simple_loss=0.2548, pruned_loss=0.04007, over 7431.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2612, pruned_loss=0.04123, over 1424299.43 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:49:23,743 INFO [train.py:842] (2/4) Epoch 35, batch 2250, loss[loss=0.1754, simple_loss=0.2571, pruned_loss=0.04691, over 7285.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2606, pruned_loss=0.04094, over 1421796.30 frames.], batch size: 18, lr: 1.58e-04
2022-05-29 09:50:03,197 INFO [train.py:842] (2/4) Epoch 35, batch 2300, loss[loss=0.1979, simple_loss=0.2788, pruned_loss=0.05851, over 7190.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2601, pruned_loss=0.04081, over 1419038.81 frames.], batch size: 26, lr: 1.58e-04
2022-05-29 09:50:42,235 INFO [train.py:842] (2/4) Epoch 35, batch 2350, loss[loss=0.1698, simple_loss=0.2633, pruned_loss=0.03813, over 7067.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2599, pruned_loss=0.04055, over 1416722.29 frames.], batch size: 28, lr: 1.58e-04
2022-05-29 09:51:21,891 INFO [train.py:842] (2/4) Epoch 35, batch 2400, loss[loss=0.1554, simple_loss=0.2395, pruned_loss=0.03561, over 7422.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2605, pruned_loss=0.04116, over 1422158.37 frames.], batch size: 17, lr: 1.58e-04
2022-05-29 09:52:01,207 INFO [train.py:842] (2/4) Epoch 35, batch 2450, loss[loss=0.1695, simple_loss=0.2623, pruned_loss=0.03831, over 7425.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2596, pruned_loss=0.04051, over 1422297.28 frames.], batch size: 20, lr: 1.58e-04
2022-05-29 09:52:41,069 INFO [train.py:842] (2/4) Epoch 35, batch 2500, loss[loss=0.2254, simple_loss=0.3136, pruned_loss=0.06865, over 6417.00 frames.], tot_loss[loss=0.17, simple_loss=0.259, pruned_loss=0.04051, over 1423871.84 frames.], batch size: 38, lr: 1.58e-04
2022-05-29 09:53:20,274 INFO [train.py:842] (2/4) Epoch 35, batch 2550, loss[loss=0.1452, simple_loss=0.2438, pruned_loss=0.02332, over 7121.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2591, pruned_loss=0.04082, over 1423799.82 frames.], batch size: 21, lr: 1.58e-04
2022-05-29 09:53:59,845 INFO [train.py:842] (2/4) Epoch 35, batch 2600, loss[loss=0.16, simple_loss=0.2551, pruned_loss=0.03243, over 7202.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2588, pruned_loss=0.04095, over 1422504.24 frames.], batch size: 22, lr: 1.58e-04
2022-05-29 09:54:38,855 INFO [train.py:842] (2/4) Epoch 35, batch 2650, loss[loss=0.1688, simple_loss=0.2604, pruned_loss=0.03861, over 7216.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2605, pruned_loss=0.04167, over 1421621.51 frames.], batch size: 23, lr: 1.58e-04
2022-05-29 09:55:18,707 INFO [train.py:842] (2/4) Epoch 35, batch 2700, loss[loss=0.1722, simple_loss=0.2672, pruned_loss=0.03865, over 7112.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2612, pruned_loss=0.04164, over 1424039.71 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 09:55:57,866 INFO [train.py:842] (2/4) Epoch 35, batch 2750, loss[loss=0.2012, simple_loss=0.2858, pruned_loss=0.05831, over 7321.00 frames.], tot_loss[loss=0.172, simple_loss=0.2608, pruned_loss=0.04154, over 1423686.83 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 09:56:37,367 INFO [train.py:842] (2/4) Epoch 35, batch 2800, loss[loss=0.1486, simple_loss=0.2486, pruned_loss=0.02428, over 7329.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2606, pruned_loss=0.04103, over 1424894.43 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 09:57:16,515 INFO [train.py:842] (2/4) Epoch 35, batch 2850, loss[loss=0.1911, simple_loss=0.2645, pruned_loss=0.05883, over 7159.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2608, pruned_loss=0.04114, over 1423040.02 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 09:57:56,052 INFO [train.py:842] (2/4) Epoch 35, batch 2900, loss[loss=0.1906, simple_loss=0.2837, pruned_loss=0.04874, over 6664.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2611, pruned_loss=0.0416, over 1422560.75 frames.], batch size: 38, lr: 1.57e-04
2022-05-29 09:58:34,901 INFO [train.py:842] (2/4) Epoch 35, batch 2950, loss[loss=0.173, simple_loss=0.2484, pruned_loss=0.04876, over 6834.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2625, pruned_loss=0.04193, over 1415397.93 frames.], batch size: 15, lr: 1.57e-04
2022-05-29 09:59:14,474 INFO [train.py:842] (2/4) Epoch 35, batch 3000, loss[loss=0.1482, simple_loss=0.2446, pruned_loss=0.02592, over 7378.00 frames.], tot_loss[loss=0.172, simple_loss=0.2613, pruned_loss=0.04131, over 1419970.43 frames.], batch size: 23, lr: 1.57e-04
2022-05-29 09:59:14,475 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 09:59:24,203 INFO [train.py:871] (2/4) Epoch 35, validation: loss=0.1643, simple_loss=0.2608, pruned_loss=0.03387, over 868885.00 frames.
2022-05-29 10:00:03,533 INFO [train.py:842] (2/4) Epoch 35, batch 3050, loss[loss=0.1655, simple_loss=0.2592, pruned_loss=0.03586, over 7238.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2623, pruned_loss=0.04146, over 1422872.87 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:00:42,995 INFO [train.py:842] (2/4) Epoch 35, batch 3100, loss[loss=0.2041, simple_loss=0.2891, pruned_loss=0.0595, over 7393.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2636, pruned_loss=0.04194, over 1419912.87 frames.], batch size: 23, lr: 1.57e-04
2022-05-29 10:01:22,490 INFO [train.py:842] (2/4) Epoch 35, batch 3150, loss[loss=0.1828, simple_loss=0.2768, pruned_loss=0.04442, over 7192.00 frames.], tot_loss[loss=0.1714, simple_loss=0.261, pruned_loss=0.04095, over 1422462.70 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:02:01,956 INFO [train.py:842] (2/4) Epoch 35, batch 3200, loss[loss=0.2388, simple_loss=0.3079, pruned_loss=0.08489, over 7199.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2622, pruned_loss=0.04178, over 1426810.26 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:02:41,281 INFO [train.py:842] (2/4) Epoch 35, batch 3250, loss[loss=0.1597, simple_loss=0.2545, pruned_loss=0.03241, over 7428.00 frames.], tot_loss[loss=0.1718, simple_loss=0.261, pruned_loss=0.04129, over 1425611.76 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:03:20,907 INFO [train.py:842] (2/4) Epoch 35, batch 3300, loss[loss=0.1521, simple_loss=0.2463, pruned_loss=0.02891, over 7439.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2616, pruned_loss=0.04129, over 1427431.80 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:04:00,226 INFO [train.py:842] (2/4) Epoch 35, batch 3350, loss[loss=0.1843, simple_loss=0.2798, pruned_loss=0.04441, over 7430.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2612, pruned_loss=0.0408, over 1430000.12 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:04:39,800 INFO [train.py:842] (2/4) Epoch 35, batch 3400, loss[loss=0.1936, simple_loss=0.2846, pruned_loss=0.05126, over 7276.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2613, pruned_loss=0.04069, over 1426493.09 frames.], batch size: 18, lr: 1.57e-04
2022-05-29 10:05:18,879 INFO [train.py:842] (2/4) Epoch 35, batch 3450, loss[loss=0.1387, simple_loss=0.2254, pruned_loss=0.02598, over 7006.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2614, pruned_loss=0.0411, over 1429398.68 frames.], batch size: 16, lr: 1.57e-04
2022-05-29 10:05:58,327 INFO [train.py:842] (2/4) Epoch 35, batch 3500, loss[loss=0.1887, simple_loss=0.2845, pruned_loss=0.04646, over 7346.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2606, pruned_loss=0.04054, over 1429004.43 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:06:37,464 INFO [train.py:842] (2/4) Epoch 35, batch 3550, loss[loss=0.1758, simple_loss=0.2689, pruned_loss=0.0414, over 6814.00 frames.], tot_loss[loss=0.171, simple_loss=0.2608, pruned_loss=0.0406, over 1422542.45 frames.], batch size: 31, lr: 1.57e-04
2022-05-29 10:07:17,141 INFO [train.py:842] (2/4) Epoch 35, batch 3600, loss[loss=0.1676, simple_loss=0.2562, pruned_loss=0.03947, over 7198.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2602, pruned_loss=0.04038, over 1421478.30 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:07:56,197 INFO [train.py:842] (2/4) Epoch 35, batch 3650, loss[loss=0.168, simple_loss=0.2669, pruned_loss=0.0346, over 7323.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2608, pruned_loss=0.04045, over 1422431.89 frames.], batch size: 25, lr: 1.57e-04
2022-05-29 10:08:35,611 INFO [train.py:842] (2/4) Epoch 35, batch 3700, loss[loss=0.1568, simple_loss=0.2574, pruned_loss=0.02815, over 6405.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2608, pruned_loss=0.04077, over 1421807.91 frames.], batch size: 38, lr: 1.57e-04
2022-05-29 10:09:14,934 INFO [train.py:842] (2/4) Epoch 35, batch 3750, loss[loss=0.1918, simple_loss=0.279, pruned_loss=0.05233, over 5429.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2617, pruned_loss=0.04142, over 1419194.51 frames.], batch size: 52, lr: 1.57e-04
2022-05-29 10:09:54,405 INFO [train.py:842] (2/4) Epoch 35, batch 3800, loss[loss=0.1882, simple_loss=0.2766, pruned_loss=0.04985, over 4942.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2628, pruned_loss=0.04213, over 1419376.33 frames.], batch size: 52, lr: 1.57e-04
2022-05-29 10:10:33,430 INFO [train.py:842] (2/4) Epoch 35, batch 3850, loss[loss=0.1769, simple_loss=0.2485, pruned_loss=0.05266, over 7010.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2634, pruned_loss=0.04258, over 1420723.59 frames.], batch size: 16, lr: 1.57e-04
2022-05-29 10:11:12,937 INFO [train.py:842] (2/4) Epoch 35, batch 3900, loss[loss=0.1447, simple_loss=0.2331, pruned_loss=0.0282, over 7278.00 frames.], tot_loss[loss=0.1746, simple_loss=0.264, pruned_loss=0.0426, over 1417538.44 frames.], batch size: 18, lr: 1.57e-04
2022-05-29 10:11:52,223 INFO [train.py:842] (2/4) Epoch 35, batch 3950, loss[loss=0.1968, simple_loss=0.2714, pruned_loss=0.06113, over 7155.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2632, pruned_loss=0.0422, over 1417714.31 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 10:12:31,911 INFO [train.py:842] (2/4) Epoch 35, batch 4000, loss[loss=0.2254, simple_loss=0.3125, pruned_loss=0.06921, over 7243.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2628, pruned_loss=0.04228, over 1418659.93 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:13:11,416 INFO [train.py:842] (2/4) Epoch 35, batch 4050, loss[loss=0.1882, simple_loss=0.2698, pruned_loss=0.05335, over 7205.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.04221, over 1422454.12 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:13:50,828 INFO [train.py:842] (2/4) Epoch 35, batch 4100, loss[loss=0.1666, simple_loss=0.2581, pruned_loss=0.03756, over 6889.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2635, pruned_loss=0.04247, over 1425229.05 frames.], batch size: 31, lr: 1.57e-04
2022-05-29 10:14:30,195 INFO [train.py:842] (2/4) Epoch 35, batch 4150, loss[loss=0.1374, simple_loss=0.2189, pruned_loss=0.028, over 7289.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2629, pruned_loss=0.04245, over 1428746.60 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:15:09,590 INFO [train.py:842] (2/4) Epoch 35, batch 4200, loss[loss=0.1732, simple_loss=0.261, pruned_loss=0.04269, over 7432.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2627, pruned_loss=0.04257, over 1425701.80 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:15:48,833 INFO [train.py:842] (2/4) Epoch 35, batch 4250, loss[loss=0.1696, simple_loss=0.2478, pruned_loss=0.0457, over 7170.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2631, pruned_loss=0.04293, over 1426786.70 frames.], batch size: 18, lr: 1.57e-04
2022-05-29 10:16:28,502 INFO [train.py:842] (2/4) Epoch 35, batch 4300, loss[loss=0.1645, simple_loss=0.2503, pruned_loss=0.03938, over 7149.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2625, pruned_loss=0.04302, over 1428495.26 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:17:07,786 INFO [train.py:842] (2/4) Epoch 35, batch 4350, loss[loss=0.1628, simple_loss=0.2594, pruned_loss=0.03308, over 7321.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2619, pruned_loss=0.04236, over 1431772.41 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 10:17:47,296 INFO [train.py:842] (2/4) Epoch 35, batch 4400, loss[loss=0.1771, simple_loss=0.2765, pruned_loss=0.03886, over 7214.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2628, pruned_loss=0.04272, over 1424311.70 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:18:26,581 INFO [train.py:842] (2/4) Epoch 35, batch 4450, loss[loss=0.2074, simple_loss=0.2963, pruned_loss=0.05932, over 7296.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2624, pruned_loss=0.0425, over 1421642.26 frames.], batch size: 24, lr: 1.57e-04
2022-05-29 10:19:06,137 INFO [train.py:842] (2/4) Epoch 35, batch 4500, loss[loss=0.1688, simple_loss=0.2514, pruned_loss=0.0431, over 6822.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2614, pruned_loss=0.04168, over 1422069.56 frames.], batch size: 15, lr: 1.57e-04
2022-05-29 10:19:45,396 INFO [train.py:842] (2/4) Epoch 35, batch 4550, loss[loss=0.1725, simple_loss=0.2643, pruned_loss=0.04038, over 7200.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2627, pruned_loss=0.04228, over 1421125.62 frames.], batch size: 23, lr: 1.57e-04
2022-05-29 10:20:25,315 INFO [train.py:842] (2/4) Epoch 35, batch 4600, loss[loss=0.1579, simple_loss=0.2592, pruned_loss=0.02827, over 7142.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2603, pruned_loss=0.04122, over 1421761.56 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:21:04,517 INFO [train.py:842] (2/4) Epoch 35, batch 4650, loss[loss=0.1697, simple_loss=0.261, pruned_loss=0.03927, over 7229.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2603, pruned_loss=0.04145, over 1418423.34 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:21:44,097 INFO [train.py:842] (2/4) Epoch 35, batch 4700, loss[loss=0.1771, simple_loss=0.2716, pruned_loss=0.04136, over 7223.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2598, pruned_loss=0.041, over 1418941.67 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 10:22:23,125 INFO [train.py:842] (2/4) Epoch 35, batch 4750, loss[loss=0.2158, simple_loss=0.3114, pruned_loss=0.06011, over 7207.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2611, pruned_loss=0.04164, over 1419775.84 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:23:02,785 INFO [train.py:842] (2/4) Epoch 35, batch 4800, loss[loss=0.1564, simple_loss=0.249, pruned_loss=0.03186, over 7251.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2609, pruned_loss=0.04143, over 1425851.35 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 10:23:42,031 INFO [train.py:842] (2/4) Epoch 35, batch 4850, loss[loss=0.1817, simple_loss=0.2669, pruned_loss=0.04826, over 5140.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2606, pruned_loss=0.04131, over 1423984.62 frames.], batch size: 52, lr: 1.57e-04
2022-05-29 10:24:21,729 INFO [train.py:842] (2/4) Epoch 35, batch 4900, loss[loss=0.1664, simple_loss=0.253, pruned_loss=0.03987, over 7273.00 frames.], tot_loss[loss=0.171, simple_loss=0.2601, pruned_loss=0.04097, over 1425230.88 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:25:01,003 INFO [train.py:842] (2/4) Epoch 35, batch 4950, loss[loss=0.147, simple_loss=0.246, pruned_loss=0.02404, over 7434.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2594, pruned_loss=0.04073, over 1428910.70 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:25:40,536 INFO [train.py:842] (2/4) Epoch 35, batch 5000, loss[loss=0.2025, simple_loss=0.2882, pruned_loss=0.05839, over 7214.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2601, pruned_loss=0.0411, over 1427805.57 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 10:26:19,957 INFO [train.py:842] (2/4) Epoch 35, batch 5050, loss[loss=0.1613, simple_loss=0.2602, pruned_loss=0.03121, over 7151.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2611, pruned_loss=0.04175, over 1430762.04 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 10:26:59,249 INFO [train.py:842] (2/4) Epoch 35, batch 5100, loss[loss=0.1374, simple_loss=0.2209, pruned_loss=0.02698, over 7269.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2616, pruned_loss=0.04141, over 1427277.82 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:27:38,561 INFO [train.py:842] (2/4) Epoch 35, batch 5150, loss[loss=0.1511, simple_loss=0.2436, pruned_loss=0.0293, over 7325.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2622, pruned_loss=0.04212, over 1427257.78 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 10:28:18,440 INFO [train.py:842] (2/4) Epoch 35, batch 5200, loss[loss=0.171, simple_loss=0.2491, pruned_loss=0.04647, over 7273.00 frames.], tot_loss[loss=0.1731, simple_loss=0.262, pruned_loss=0.04213, over 1428054.75 frames.], batch size: 16, lr: 1.57e-04
2022-05-29 10:28:57,713 INFO [train.py:842] (2/4) Epoch 35, batch 5250, loss[loss=0.1605, simple_loss=0.2491, pruned_loss=0.03598, over 7364.00 frames.], tot_loss[loss=0.172, simple_loss=0.2609, pruned_loss=0.04159, over 1428305.45 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 10:29:37,073 INFO [train.py:842] (2/4) Epoch 35, batch 5300, loss[loss=0.1587, simple_loss=0.2542, pruned_loss=0.03158, over 7309.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2606, pruned_loss=0.04101, over 1427754.37 frames.], batch size: 24, lr: 1.57e-04
2022-05-29 10:30:16,506 INFO [train.py:842] (2/4) Epoch 35, batch 5350, loss[loss=0.1696, simple_loss=0.256, pruned_loss=0.04163, over 7316.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2604, pruned_loss=0.04111, over 1426471.73 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:30:56,215 INFO [train.py:842] (2/4) Epoch 35, batch 5400, loss[loss=0.1629, simple_loss=0.2553, pruned_loss=0.03521, over 7325.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2609, pruned_loss=0.04111, over 1430497.79 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:31:35,720 INFO [train.py:842] (2/4) Epoch 35, batch 5450, loss[loss=0.1675, simple_loss=0.2583, pruned_loss=0.03833, over 7161.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2605, pruned_loss=0.04119, over 1432676.62 frames.], batch size: 18, lr: 1.57e-04
2022-05-29 10:32:14,961 INFO [train.py:842] (2/4) Epoch 35, batch 5500, loss[loss=0.1579, simple_loss=0.2558, pruned_loss=0.03, over 7143.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2605, pruned_loss=0.04111, over 1427943.07 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:32:54,338 INFO [train.py:842] (2/4) Epoch 35, batch 5550, loss[loss=0.1362, simple_loss=0.2248, pruned_loss=0.02375, over 7295.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2606, pruned_loss=0.04131, over 1425837.27 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:33:33,908 INFO [train.py:842] (2/4) Epoch 35, batch 5600, loss[loss=0.2017, simple_loss=0.2844, pruned_loss=0.05955, over 7223.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2604, pruned_loss=0.04103, over 1425644.20 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:34:13,271 INFO [train.py:842] (2/4) Epoch 35, batch 5650, loss[loss=0.1529, simple_loss=0.241, pruned_loss=0.03241, over 7148.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2602, pruned_loss=0.04121, over 1427404.29 frames.], batch size: 18, lr: 1.57e-04
2022-05-29 10:34:52,885 INFO [train.py:842] (2/4) Epoch 35, batch 5700, loss[loss=0.156, simple_loss=0.2512, pruned_loss=0.03037, over 7274.00 frames.], tot_loss[loss=0.171, simple_loss=0.2604, pruned_loss=0.04076, over 1429655.38 frames.], batch size: 24, lr: 1.57e-04
2022-05-29 10:35:32,101 INFO [train.py:842] (2/4) Epoch 35, batch 5750, loss[loss=0.1765, simple_loss=0.2758, pruned_loss=0.03854, over 6703.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2612, pruned_loss=0.041, over 1432881.36 frames.], batch size: 31, lr: 1.57e-04
2022-05-29 10:36:11,749 INFO [train.py:842] (2/4) Epoch 35, batch 5800, loss[loss=0.1849, simple_loss=0.2786, pruned_loss=0.04563, over 7231.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2598, pruned_loss=0.04035, over 1430688.38 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:36:51,123 INFO [train.py:842] (2/4) Epoch 35, batch 5850, loss[loss=0.1962, simple_loss=0.2893, pruned_loss=0.05152, over 7062.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2591, pruned_loss=0.04028, over 1429621.14 frames.], batch size: 28, lr: 1.57e-04
2022-05-29 10:37:30,750 INFO [train.py:842] (2/4) Epoch 35, batch 5900, loss[loss=0.2154, simple_loss=0.312, pruned_loss=0.0594, over 7196.00 frames.], tot_loss[loss=0.17, simple_loss=0.2594, pruned_loss=0.04034, over 1429480.27 frames.], batch size: 23, lr: 1.57e-04
2022-05-29 10:38:10,005 INFO [train.py:842] (2/4) Epoch 35, batch 5950, loss[loss=0.1594, simple_loss=0.2447, pruned_loss=0.03709, over 7269.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2594, pruned_loss=0.04048, over 1430321.87 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 10:38:49,678 INFO [train.py:842] (2/4) Epoch 35, batch 6000, loss[loss=0.1745, simple_loss=0.2743, pruned_loss=0.03736, over 7331.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2596, pruned_loss=0.04074, over 1431864.92 frames.], batch size: 22, lr: 1.57e-04
2022-05-29 10:38:49,678 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 10:38:59,233 INFO [train.py:871] (2/4) Epoch 35, validation: loss=0.1626, simple_loss=0.2599, pruned_loss=0.03269, over 868885.00 frames.
2022-05-29 10:39:38,668 INFO [train.py:842] (2/4) Epoch 35, batch 6050, loss[loss=0.171, simple_loss=0.2572, pruned_loss=0.04238, over 6986.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2595, pruned_loss=0.0408, over 1431042.11 frames.], batch size: 16, lr: 1.57e-04
2022-05-29 10:40:18,094 INFO [train.py:842] (2/4) Epoch 35, batch 6100, loss[loss=0.1743, simple_loss=0.25, pruned_loss=0.04931, over 6995.00 frames.], tot_loss[loss=0.1707, simple_loss=0.26, pruned_loss=0.04072, over 1424099.55 frames.], batch size: 16, lr: 1.57e-04
2022-05-29 10:40:57,375 INFO [train.py:842] (2/4) Epoch 35, batch 6150, loss[loss=0.1671, simple_loss=0.2589, pruned_loss=0.03768, over 7068.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2601, pruned_loss=0.04043, over 1422558.28 frames.], batch size: 28, lr: 1.57e-04
2022-05-29 10:41:36,708 INFO [train.py:842] (2/4) Epoch 35, batch 6200, loss[loss=0.1622, simple_loss=0.2547, pruned_loss=0.03488, over 7241.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2614, pruned_loss=0.04091, over 1425720.48 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:42:16,068 INFO [train.py:842] (2/4) Epoch 35, batch 6250, loss[loss=0.1453, simple_loss=0.2488, pruned_loss=0.02092, over 7426.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2614, pruned_loss=0.04103, over 1429286.95 frames.], batch size: 21, lr: 1.57e-04
2022-05-29 10:42:55,583 INFO [train.py:842] (2/4) Epoch 35, batch 6300, loss[loss=0.1813, simple_loss=0.2641, pruned_loss=0.0493, over 7282.00 frames.], tot_loss[loss=0.172, simple_loss=0.2614, pruned_loss=0.04126, over 1427060.34 frames.], batch size: 18, lr: 1.57e-04
2022-05-29 10:43:34,636 INFO [train.py:842] (2/4) Epoch 35, batch 6350, loss[loss=0.1945, simple_loss=0.2823, pruned_loss=0.05336, over 4974.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2605, pruned_loss=0.04101, over 1427000.62 frames.], batch size: 52, lr: 1.57e-04
2022-05-29 10:44:14,150 INFO [train.py:842] (2/4) Epoch 35, batch 6400, loss[loss=0.1577, simple_loss=0.2474, pruned_loss=0.03396, over 7308.00 frames.], tot_loss[loss=0.1714, simple_loss=0.261, pruned_loss=0.04086, over 1427060.43 frames.], batch size: 24, lr: 1.57e-04
2022-05-29 10:44:53,488 INFO [train.py:842] (2/4) Epoch 35, batch 6450, loss[loss=0.1648, simple_loss=0.2638, pruned_loss=0.03284, over 7144.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2618, pruned_loss=0.04125, over 1427462.45 frames.], batch size: 20, lr: 1.57e-04
2022-05-29 10:45:32,970 INFO [train.py:842] (2/4) Epoch 35, batch 6500, loss[loss=0.1565, simple_loss=0.2521, pruned_loss=0.03047, over 6692.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2621, pruned_loss=0.04105, over 1429079.14 frames.], batch size: 31, lr: 1.57e-04
2022-05-29 10:46:12,218 INFO [train.py:842] (2/4) Epoch 35, batch 6550, loss[loss=0.1775, simple_loss=0.2689, pruned_loss=0.04303, over 7366.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2618, pruned_loss=0.04141, over 1427051.53 frames.], batch size: 19, lr: 1.57e-04
2022-05-29 10:46:51,877 INFO [train.py:842] (2/4) Epoch 35, batch 6600, loss[loss=0.2035, simple_loss=0.2911, pruned_loss=0.05793, over 7116.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2617, pruned_loss=0.04155, over 1419363.34 frames.], batch size: 28, lr: 1.57e-04
2022-05-29 10:47:31,086 INFO [train.py:842] (2/4) Epoch 35, batch 6650, loss[loss=0.163, simple_loss=0.236, pruned_loss=0.04503, over 7132.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2625, pruned_loss=0.04201, over 1418953.21 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:48:10,529 INFO [train.py:842] (2/4) Epoch 35, batch 6700, loss[loss=0.1975, simple_loss=0.2749, pruned_loss=0.06007, over 7283.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2619, pruned_loss=0.04186, over 1416928.87 frames.], batch size: 17, lr: 1.57e-04
2022-05-29 10:48:49,688 INFO [train.py:842] (2/4) Epoch 35, batch 6750, loss[loss=0.165, simple_loss=0.2433, pruned_loss=0.04335, over 7144.00 frames.], tot_loss[loss=0.1734, simple_loss=0.262, pruned_loss=0.04234, over 1414378.99 frames.], batch size: 17, lr: 1.56e-04
2022-05-29 10:49:29,343 INFO [train.py:842] (2/4) Epoch 35, batch 6800, loss[loss=0.2194, simple_loss=0.3066, pruned_loss=0.06616, over 6770.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2617, pruned_loss=0.04198, over 1417755.51 frames.], batch size: 31, lr: 1.56e-04
2022-05-29 10:50:08,368 INFO [train.py:842] (2/4) Epoch 35, batch 6850, loss[loss=0.1706, simple_loss=0.2403, pruned_loss=0.05045, over 6811.00 frames.], tot_loss[loss=0.173, simple_loss=0.2622, pruned_loss=0.04187, over 1419223.03 frames.], batch size: 15, lr: 1.56e-04
2022-05-29 10:50:47,893 INFO [train.py:842] (2/4) Epoch 35, batch 6900, loss[loss=0.1456, simple_loss=0.2368, pruned_loss=0.02723, over 7232.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2603, pruned_loss=0.041, over 1420310.15 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 10:51:27,293 INFO [train.py:842] (2/4) Epoch 35, batch 6950, loss[loss=0.1889, simple_loss=0.2741, pruned_loss=0.05183, over 7383.00 frames.], tot_loss[loss=0.1718, simple_loss=0.261, pruned_loss=0.04128, over 1421653.64 frames.], batch size: 23, lr: 1.56e-04
2022-05-29 10:52:06,978 INFO [train.py:842] (2/4) Epoch 35, batch 7000, loss[loss=0.2052, simple_loss=0.2963, pruned_loss=0.057, over 7215.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2609, pruned_loss=0.04082, over 1426676.92 frames.], batch size: 22, lr: 1.56e-04
2022-05-29 10:52:46,312 INFO [train.py:842] (2/4) Epoch 35, batch 7050, loss[loss=0.1668, simple_loss=0.2624, pruned_loss=0.03561, over 7319.00 frames.], tot_loss[loss=0.172, simple_loss=0.2613, pruned_loss=0.04137, over 1426685.72 frames.], batch size: 21, lr: 1.56e-04
2022-05-29 10:53:25,686 INFO [train.py:842] (2/4) Epoch 35, batch 7100, loss[loss=0.1513, simple_loss=0.2432, pruned_loss=0.02969, over 7162.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2609, pruned_loss=0.04125, over 1427494.69 frames.], batch size: 19, lr: 1.56e-04
2022-05-29 10:54:05,030 INFO [train.py:842] (2/4) Epoch 35, batch 7150, loss[loss=0.1666, simple_loss=0.2494, pruned_loss=0.04195, over 7353.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2606, pruned_loss=0.0412, over 1425560.07 frames.], batch size: 19, lr: 1.56e-04
2022-05-29 10:54:44,602 INFO [train.py:842] (2/4) Epoch 35, batch 7200, loss[loss=0.147, simple_loss=0.244, pruned_loss=0.02504, over 7328.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2615, pruned_loss=0.04158, over 1428164.41 frames.], batch size: 21, lr: 1.56e-04
2022-05-29 10:55:23,844 INFO [train.py:842] (2/4) Epoch 35, batch 7250, loss[loss=0.2163, simple_loss=0.3029, pruned_loss=0.06488, over 7270.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2608, pruned_loss=0.04114, over 1428062.36 frames.], batch size: 25, lr: 1.56e-04
2022-05-29 10:56:03,386 INFO [train.py:842] (2/4) Epoch 35, batch 7300, loss[loss=0.1459, simple_loss=0.2333, pruned_loss=0.02924, over 7324.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2613, pruned_loss=0.04143, over 1427746.79 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 10:56:42,510 INFO [train.py:842] (2/4) Epoch 35, batch 7350, loss[loss=0.137, simple_loss=0.2138, pruned_loss=0.03012, over 7206.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2609, pruned_loss=0.04094, over 1428950.07 frames.], batch size: 16, lr: 1.56e-04
2022-05-29 10:57:22,058 INFO [train.py:842] (2/4) Epoch 35, batch 7400, loss[loss=0.2114, simple_loss=0.3003, pruned_loss=0.06129, over 7285.00 frames.], tot_loss[loss=0.1715, simple_loss=0.261, pruned_loss=0.04097, over 1431353.19 frames.], batch size: 25, lr: 1.56e-04
2022-05-29 10:58:01,208 INFO [train.py:842] (2/4) Epoch 35, batch 7450, loss[loss=0.2324, simple_loss=0.323, pruned_loss=0.07089, over 6802.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2613, pruned_loss=0.04128, over 1426773.14 frames.], batch size: 31, lr: 1.56e-04
2022-05-29 10:58:43,663 INFO [train.py:842] (2/4) Epoch 35, batch 7500, loss[loss=0.1369, simple_loss=0.2404, pruned_loss=0.01668, over 7438.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2608, pruned_loss=0.04078, over 1428330.26 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 10:59:22,995 INFO [train.py:842] (2/4) Epoch 35, batch 7550, loss[loss=0.152, simple_loss=0.2394, pruned_loss=0.03234, over 7362.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2605, pruned_loss=0.04088, over 1430093.96 frames.], batch size: 19, lr: 1.56e-04
2022-05-29 11:00:02,447 INFO [train.py:842] (2/4) Epoch 35, batch 7600, loss[loss=0.1527, simple_loss=0.2484, pruned_loss=0.02846, over 7235.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2613, pruned_loss=0.04154, over 1421850.37 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:00:41,967 INFO [train.py:842] (2/4) Epoch 35, batch 7650, loss[loss=0.151, simple_loss=0.2264, pruned_loss=0.03775, over 6793.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2604, pruned_loss=0.04089, over 1425099.56 frames.], batch size: 15, lr: 1.56e-04
2022-05-29 11:01:21,768 INFO [train.py:842] (2/4) Epoch 35, batch 7700, loss[loss=0.1894, simple_loss=0.2749, pruned_loss=0.05198, over 7055.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2606, pruned_loss=0.04131, over 1425367.78 frames.], batch size: 28, lr: 1.56e-04
2022-05-29 11:02:00,872 INFO [train.py:842] (2/4) Epoch 35, batch 7750, loss[loss=0.1912, simple_loss=0.2792, pruned_loss=0.05158, over 7329.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.04226, over 1430108.45 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:02:40,425 INFO [train.py:842] (2/4) Epoch 35, batch 7800, loss[loss=0.2274, simple_loss=0.3163, pruned_loss=0.06924, over 7200.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2628, pruned_loss=0.04248, over 1429796.60 frames.], batch size: 22, lr: 1.56e-04
2022-05-29 11:03:19,919 INFO [train.py:842] (2/4) Epoch 35, batch 7850, loss[loss=0.1585, simple_loss=0.2548, pruned_loss=0.03111, over 7345.00 frames.], tot_loss[loss=0.1729, simple_loss=0.262, pruned_loss=0.04188, over 1432670.37 frames.], batch size: 22, lr: 1.56e-04
2022-05-29 11:03:59,719 INFO [train.py:842] (2/4) Epoch 35, batch 7900, loss[loss=0.1881, simple_loss=0.2764, pruned_loss=0.04994, over 7219.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2615, pruned_loss=0.04205, over 1431942.99 frames.], batch size: 22, lr: 1.56e-04
2022-05-29 11:04:38,857 INFO [train.py:842] (2/4) Epoch 35, batch 7950, loss[loss=0.1742, simple_loss=0.2637, pruned_loss=0.04234, over 7282.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2609, pruned_loss=0.04193, over 1430475.96 frames.], batch size: 25, lr: 1.56e-04
2022-05-29 11:05:18,275 INFO [train.py:842] (2/4) Epoch 35, batch 8000, loss[loss=0.164, simple_loss=0.2599, pruned_loss=0.03405, over 7292.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2625, pruned_loss=0.04251, over 1430281.31 frames.], batch size: 24, lr: 1.56e-04
2022-05-29 11:05:57,346 INFO [train.py:842] (2/4) Epoch 35, batch 8050, loss[loss=0.242, simple_loss=0.325, pruned_loss=0.07953, over 7029.00 frames.], tot_loss[loss=0.174, simple_loss=0.263, pruned_loss=0.04247, over 1432410.37 frames.], batch size: 28, lr: 1.56e-04
2022-05-29 11:06:36,978 INFO [train.py:842] (2/4) Epoch 35, batch 8100, loss[loss=0.2109, simple_loss=0.302, pruned_loss=0.05987, over 7285.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2624, pruned_loss=0.04197, over 1433281.02 frames.], batch size: 24, lr: 1.56e-04
2022-05-29 11:07:15,990 INFO [train.py:842] (2/4) Epoch 35, batch 8150, loss[loss=0.1664, simple_loss=0.2534, pruned_loss=0.03974, over 7353.00 frames.], tot_loss[loss=0.174, simple_loss=0.2632, pruned_loss=0.0424, over 1428368.68 frames.], batch size: 19, lr: 1.56e-04
2022-05-29 11:07:55,442 INFO [train.py:842] (2/4) Epoch 35, batch 8200, loss[loss=0.1602, simple_loss=0.2575, pruned_loss=0.03149, over 6783.00 frames.], tot_loss[loss=0.1728, simple_loss=0.262, pruned_loss=0.04176, over 1428780.82 frames.], batch size: 31, lr: 1.56e-04
2022-05-29 11:08:34,625 INFO [train.py:842] (2/4) Epoch 35, batch 8250, loss[loss=0.194, simple_loss=0.2862, pruned_loss=0.05088, over 7303.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2618, pruned_loss=0.04182, over 1422969.35 frames.], batch size: 25, lr: 1.56e-04
2022-05-29 11:09:14,313 INFO [train.py:842] (2/4) Epoch 35, batch 8300, loss[loss=0.1524, simple_loss=0.2378, pruned_loss=0.03348, over 7249.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2614, pruned_loss=0.042, over 1421760.02 frames.], batch size: 19, lr: 1.56e-04
2022-05-29 11:09:53,345 INFO [train.py:842] (2/4) Epoch 35, batch 8350, loss[loss=0.1467, simple_loss=0.2344, pruned_loss=0.02954, over 7428.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2616, pruned_loss=0.04155, over 1425520.61 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:10:32,652 INFO [train.py:842] (2/4) Epoch 35, batch 8400, loss[loss=0.1698, simple_loss=0.2684, pruned_loss=0.03559, over 7173.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2613, pruned_loss=0.04163, over 1418214.85 frames.], batch size: 26, lr: 1.56e-04
2022-05-29 11:11:11,766 INFO [train.py:842] (2/4) Epoch 35, batch 8450, loss[loss=0.1532, simple_loss=0.2491, pruned_loss=0.02869, over 7147.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2617, pruned_loss=0.04185, over 1416749.06 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:11:51,481 INFO [train.py:842] (2/4) Epoch 35, batch 8500, loss[loss=0.1506, simple_loss=0.2368, pruned_loss=0.03213, over 7058.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2609, pruned_loss=0.04181, over 1418932.09 frames.], batch size: 18, lr: 1.56e-04
2022-05-29 11:12:41,663 INFO [train.py:842] (2/4) Epoch 35, batch 8550, loss[loss=0.1735, simple_loss=0.2588, pruned_loss=0.04413, over 6749.00 frames.], tot_loss[loss=0.1727, simple_loss=0.261, pruned_loss=0.04226, over 1419243.91 frames.], batch size: 31, lr: 1.56e-04
2022-05-29 11:13:21,153 INFO [train.py:842] (2/4) Epoch 35, batch 8600, loss[loss=0.1753, simple_loss=0.2641, pruned_loss=0.04322, over 5351.00 frames.], tot_loss[loss=0.173, simple_loss=0.2616, pruned_loss=0.04226, over 1412422.91 frames.], batch size: 52, lr: 1.56e-04
2022-05-29 11:14:00,815 INFO [train.py:842] (2/4) Epoch 35, batch 8650, loss[loss=0.1687, simple_loss=0.2655, pruned_loss=0.03592, over 7215.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2613, pruned_loss=0.04222, over 1419198.05 frames.], batch size: 21, lr: 1.56e-04
2022-05-29 11:14:40,494 INFO [train.py:842] (2/4) Epoch 35, batch 8700, loss[loss=0.1639, simple_loss=0.2496, pruned_loss=0.0391, over 7356.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2608, pruned_loss=0.04229, over 1414920.84 frames.], batch size: 19, lr: 1.56e-04
2022-05-29 11:15:19,847 INFO [train.py:842] (2/4) Epoch 35, batch 8750, loss[loss=0.15, simple_loss=0.2321, pruned_loss=0.03392, over 7436.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2604, pruned_loss=0.04204, over 1415175.85 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:15:59,158 INFO [train.py:842] (2/4) Epoch 35, batch 8800, loss[loss=0.168, simple_loss=0.2681, pruned_loss=0.034, over 7328.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2605, pruned_loss=0.04187, over 1411608.79 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:16:38,438 INFO [train.py:842] (2/4) Epoch 35, batch 8850, loss[loss=0.1552, simple_loss=0.2498, pruned_loss=0.03033, over 7220.00 frames.], tot_loss[loss=0.172, simple_loss=0.2602, pruned_loss=0.04188, over 1408253.68 frames.], batch size: 21, lr: 1.56e-04
2022-05-29 11:17:17,821 INFO [train.py:842] (2/4) Epoch 35, batch 8900, loss[loss=0.1671, simple_loss=0.2528, pruned_loss=0.04068, over 7322.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2596, pruned_loss=0.04141, over 1410496.01 frames.], batch size: 20, lr: 1.56e-04
2022-05-29 11:17:56,602 INFO [train.py:842] (2/4) Epoch 35, batch 8950, loss[loss=0.2007, simple_loss=0.2852, pruned_loss=0.05808, over 5097.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2609, pruned_loss=0.04179, over 1401225.00 frames.], batch size: 52, lr: 1.56e-04
2022-05-29 11:18:35,186 INFO [train.py:842] (2/4) Epoch 35, batch 9000, loss[loss=0.1908, simple_loss=0.2798, pruned_loss=0.05091, over 6186.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2641, pruned_loss=0.04337, over 1379108.96 frames.], batch size: 37, lr: 1.56e-04
2022-05-29 11:18:35,187 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 11:18:44,740 INFO [train.py:871] (2/4) Epoch 35, validation: loss=0.1633, simple_loss=0.2602, pruned_loss=0.03324, over 868885.00 frames.
2022-05-29 11:19:22,582 INFO [train.py:842] (2/4) Epoch 35, batch 9050, loss[loss=0.1629, simple_loss=0.2618, pruned_loss=0.03201, over 6467.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2664, pruned_loss=0.04447, over 1348381.74 frames.], batch size: 38, lr: 1.56e-04
2022-05-29 11:20:00,768 INFO [train.py:842] (2/4) Epoch 35, batch 9100, loss[loss=0.2035, simple_loss=0.289, pruned_loss=0.05901, over 5182.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2695, pruned_loss=0.04662, over 1289968.06 frames.], batch size: 52, lr: 1.56e-04
2022-05-29 11:20:38,912 INFO [train.py:842] (2/4) Epoch 35, batch 9150, loss[loss=0.2182, simple_loss=0.3066, pruned_loss=0.06492, over 5296.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2735, pruned_loss=0.04969, over 1227796.22 frames.], batch size: 53, lr: 1.56e-04
2022-05-29 11:21:27,219 INFO [train.py:842] (2/4) Epoch 36, batch 0, loss[loss=0.1936, simple_loss=0.2837, pruned_loss=0.05174, over 7316.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2837, pruned_loss=0.05174, over 7316.00 frames.], batch size: 20, lr: 1.54e-04
2022-05-29 11:22:06,514 INFO [train.py:842] (2/4) Epoch 36, batch 50, loss[loss=0.1573, simple_loss=0.2452, pruned_loss=0.03476, over 7444.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2615, pruned_loss=0.04161, over 316665.88 frames.], batch size: 20, lr: 1.54e-04
2022-05-29 11:22:46,036 INFO [train.py:842] (2/4) Epoch 36, batch 100, loss[loss=0.1777, simple_loss=0.2633, pruned_loss=0.04604, over 5071.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2611, pruned_loss=0.04095, over 562085.11 frames.], batch size: 52, lr: 1.54e-04
2022-05-29 11:23:25,168 INFO [train.py:842] (2/4) Epoch 36, batch 150, loss[loss=0.1483, simple_loss=0.2492, pruned_loss=0.02369, over 7236.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2603, pruned_loss=0.04109, over 751302.84 frames.], batch size: 20, lr: 1.54e-04
2022-05-29 11:24:04,870 INFO [train.py:842] (2/4) Epoch 36, batch 200, loss[loss=0.1794, simple_loss=0.2793, pruned_loss=0.03973, over 7321.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2597, pruned_loss=0.04064, over 901164.55 frames.], batch size: 21, lr: 1.54e-04
2022-05-29 11:24:44,249 INFO [train.py:842] (2/4) Epoch 36, batch 250, loss[loss=0.146, simple_loss=0.2325, pruned_loss=0.02972, over 7171.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2583, pruned_loss=0.03953, over 1021023.34 frames.], batch size: 19, lr: 1.54e-04
2022-05-29 11:25:23,532 INFO [train.py:842] (2/4) Epoch 36, batch 300, loss[loss=0.1917, simple_loss=0.2804, pruned_loss=0.05149, over 7213.00 frames.], tot_loss[loss=0.1704, simple_loss=0.26, pruned_loss=0.04037, over 1106246.05 frames.], batch size: 26, lr: 1.54e-04
2022-05-29 11:26:02,734 INFO [train.py:842] (2/4) Epoch 36, batch 350, loss[loss=0.1748, simple_loss=0.2715, pruned_loss=0.03904, over 6779.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2605, pruned_loss=0.04004, over 1175392.77 frames.], batch size: 31, lr: 1.54e-04
2022-05-29 11:26:41,934 INFO [train.py:842] (2/4) Epoch 36, batch 400, loss[loss=0.1534, simple_loss=0.2377, pruned_loss=0.03453, over 7196.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2622, pruned_loss=0.04125, over 1231389.69 frames.], batch size: 22, lr: 1.54e-04
2022-05-29 11:27:21,325 INFO [train.py:842] (2/4) Epoch 36, batch 450, loss[loss=0.1579, simple_loss=0.2531, pruned_loss=0.03131, over 7180.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2622, pruned_loss=0.04145, over 1278693.96 frames.], batch size: 26, lr: 1.54e-04
2022-05-29 11:28:00,714 INFO [train.py:842] (2/4) Epoch 36, batch 500, loss[loss=0.1643, simple_loss=0.2619, pruned_loss=0.03342, over 7197.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2627, pruned_loss=0.04142, over 1310144.84 frames.], batch size: 23, lr: 1.54e-04
2022-05-29 11:28:39,899 INFO [train.py:842] (2/4) Epoch 36, batch 550, loss[loss=0.146, simple_loss=0.2179, pruned_loss=0.03709, over 7427.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2626, pruned_loss=0.04101, over 1336098.41 frames.], batch size: 20, lr: 1.54e-04
2022-05-29 11:29:19,661 INFO [train.py:842] (2/4) Epoch 36, batch 600, loss[loss=0.1892, simple_loss=0.2681, pruned_loss=0.05512, over 7209.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2616, pruned_loss=0.0406, over 1358382.22 frames.], batch size: 23, lr: 1.54e-04
2022-05-29 11:29:59,115 INFO [train.py:842] (2/4) Epoch 36, batch 650, loss[loss=0.1598, simple_loss=0.253, pruned_loss=0.0333, over 7162.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2609, pruned_loss=0.04062, over 1373358.41 frames.], batch size: 19, lr: 1.54e-04
2022-05-29 11:30:38,713 INFO [train.py:842] (2/4) Epoch 36, batch 700, loss[loss=0.1489, simple_loss=0.2373, pruned_loss=0.03026, over 7249.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2602, pruned_loss=0.0405, over 1384261.99 frames.], batch size: 19, lr: 1.54e-04
2022-05-29 11:31:17,856 INFO [train.py:842] (2/4) Epoch 36, batch 750, loss[loss=0.2041, simple_loss=0.2874, pruned_loss=0.06034, over 7314.00 frames.], tot_loss[loss=0.1708, simple_loss=0.26, pruned_loss=0.04085, over 1384082.56 frames.], batch size: 20, lr: 1.54e-04
2022-05-29 11:31:57,479 INFO [train.py:842] (2/4) Epoch 36, batch 800, loss[loss=0.1655, simple_loss=0.2528, pruned_loss=0.03913, over 7409.00 frames.], tot_loss[loss=0.17, simple_loss=0.2596, pruned_loss=0.04022, over 1392667.73 frames.], batch size: 21, lr: 1.54e-04
2022-05-29 11:32:36,718 INFO [train.py:842] (2/4) Epoch 36, batch 850, loss[loss=0.154, simple_loss=0.2471, pruned_loss=0.03045, over 7214.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2591, pruned_loss=0.03992, over 1393843.99 frames.], batch size: 21, lr: 1.54e-04
2022-05-29 11:33:16,394 INFO [train.py:842] (2/4) Epoch 36, batch 900, loss[loss=0.1637, simple_loss=0.258, pruned_loss=0.0347, over 6788.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2583, pruned_loss=0.03942, over 1400783.30 frames.], batch size: 31, lr: 1.54e-04
2022-05-29 11:33:55,548 INFO [train.py:842] (2/4) Epoch 36, batch 950, loss[loss=0.1433, simple_loss=0.2216, pruned_loss=0.03247, over 7003.00 frames.], tot_loss[loss=0.17, simple_loss=0.2597, pruned_loss=0.04016, over 1404729.30 frames.], batch size: 16, lr: 1.53e-04
2022-05-29 11:34:35,018 INFO [train.py:842] (2/4) Epoch 36, batch 1000, loss[loss=0.1341, simple_loss=0.2216, pruned_loss=0.02329, over 7270.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2602, pruned_loss=0.04033, over 1406550.88 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 11:35:14,282 INFO [train.py:842] (2/4) Epoch 36, batch 1050, loss[loss=0.1545, simple_loss=0.2335, pruned_loss=0.03777, over 7353.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2604, pruned_loss=0.04103, over 1406778.92 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 11:35:53,813 INFO [train.py:842] (2/4) Epoch 36, batch 1100, loss[loss=0.1712, simple_loss=0.2566, pruned_loss=0.04285, over 7197.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2608, pruned_loss=0.0415, over 1408274.14 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 11:36:33,120 INFO [train.py:842] (2/4) Epoch 36, batch 1150, loss[loss=0.1869, simple_loss=0.2819, pruned_loss=0.04594, over 7294.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2603, pruned_loss=0.04142, over 1413217.63 frames.], batch size: 24, lr: 1.53e-04
2022-05-29 11:37:12,456 INFO [train.py:842] (2/4) Epoch 36, batch 1200, loss[loss=0.1457, simple_loss=0.2216, pruned_loss=0.03483, over 7279.00 frames.], tot_loss[loss=0.173, simple_loss=0.2618, pruned_loss=0.04211, over 1409133.29 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 11:37:51,898 INFO [train.py:842] (2/4) Epoch 36, batch 1250, loss[loss=0.1433, simple_loss=0.2235, pruned_loss=0.03151, over 6984.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2611, pruned_loss=0.04173, over 1409752.80 frames.], batch size: 16, lr: 1.53e-04
2022-05-29 11:38:31,196 INFO [train.py:842] (2/4) Epoch 36, batch 1300, loss[loss=0.1659, simple_loss=0.242, pruned_loss=0.04486, over 7132.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2615, pruned_loss=0.04178, over 1414091.73 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 11:39:10,507 INFO [train.py:842] (2/4) Epoch 36, batch 1350, loss[loss=0.1578, simple_loss=0.2524, pruned_loss=0.03163, over 7232.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2606, pruned_loss=0.04089, over 1419082.12 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 11:39:49,838 INFO [train.py:842] (2/4) Epoch 36, batch 1400, loss[loss=0.1505, simple_loss=0.2314, pruned_loss=0.03484, over 7420.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2615, pruned_loss=0.04148, over 1417756.11 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 11:40:29,036 INFO [train.py:842] (2/4) Epoch 36, batch 1450, loss[loss=0.1486, simple_loss=0.2278, pruned_loss=0.03466, over 7184.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2601, pruned_loss=0.04087, over 1415347.48 frames.], batch size: 16, lr: 1.53e-04
2022-05-29 11:41:08,693 INFO [train.py:842] (2/4) Epoch 36, batch 1500, loss[loss=0.1698, simple_loss=0.2622, pruned_loss=0.0387, over 7326.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2606, pruned_loss=0.04139, over 1419143.35 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 11:41:47,969 INFO [train.py:842] (2/4) Epoch 36, batch 1550, loss[loss=0.1773, simple_loss=0.2714, pruned_loss=0.04156, over 7230.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2622, pruned_loss=0.04206, over 1420389.68 frames.], batch size: 20, lr: 1.53e-04
2022-05-29 11:42:27,493 INFO [train.py:842] (2/4) Epoch 36, batch 1600, loss[loss=0.2316, simple_loss=0.3115, pruned_loss=0.07583, over 7369.00 frames.], tot_loss[loss=0.1731, simple_loss=0.262, pruned_loss=0.04206, over 1420835.02 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 11:43:06,825 INFO [train.py:842] (2/4) Epoch 36, batch 1650, loss[loss=0.1878, simple_loss=0.2764, pruned_loss=0.04959, over 7164.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2622, pruned_loss=0.04235, over 1422218.91 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 11:43:46,255 INFO [train.py:842] (2/4) Epoch 36, batch 1700, loss[loss=0.2725, simple_loss=0.3406, pruned_loss=0.1021, over 7281.00 frames.], tot_loss[loss=0.172, simple_loss=0.2611, pruned_loss=0.04151, over 1424392.21 frames.], batch size: 25, lr: 1.53e-04
2022-05-29 11:44:25,384 INFO [train.py:842] (2/4) Epoch 36, batch 1750, loss[loss=0.1862, simple_loss=0.2724, pruned_loss=0.04998, over 7279.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2617, pruned_loss=0.04197, over 1420269.66 frames.], batch size: 18, lr: 1.53e-04
2022-05-29 11:45:04,918 INFO [train.py:842] (2/4) Epoch 36, batch 1800, loss[loss=0.1711, simple_loss=0.2563, pruned_loss=0.0429, over 7207.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2624, pruned_loss=0.04192, over 1422654.46 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 11:45:44,139 INFO [train.py:842] (2/4) Epoch 36, batch 1850, loss[loss=0.1497, simple_loss=0.2359, pruned_loss=0.03174, over 7109.00 frames.], tot_loss[loss=0.1728, simple_loss=0.262, pruned_loss=0.04174, over 1425298.60 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 11:46:23,733 INFO [train.py:842] (2/4) Epoch 36, batch 1900, loss[loss=0.1943, simple_loss=0.2788, pruned_loss=0.0549, over 6797.00 frames.], tot_loss[loss=0.172, simple_loss=0.2613, pruned_loss=0.04133, over 1424812.82 frames.], batch size: 31, lr: 1.53e-04
2022-05-29 11:47:02,853 INFO [train.py:842] (2/4) Epoch 36, batch 1950, loss[loss=0.171, simple_loss=0.2662, pruned_loss=0.03795, over 7237.00 frames.], tot_loss[loss=0.1728, simple_loss=0.262, pruned_loss=0.04178, over 1422297.61 frames.], batch size: 20, lr: 1.53e-04
2022-05-29 11:47:42,039 INFO [train.py:842] (2/4) Epoch 36, batch 2000, loss[loss=0.1541, simple_loss=0.2329, pruned_loss=0.03764, over 7006.00 frames.], tot_loss[loss=0.171, simple_loss=0.2604, pruned_loss=0.04082, over 1419263.54 frames.], batch size: 16, lr: 1.53e-04
2022-05-29 11:48:21,462 INFO [train.py:842] (2/4) Epoch 36, batch 2050, loss[loss=0.1824, simple_loss=0.2784, pruned_loss=0.04319, over 7315.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2618, pruned_loss=0.04165, over 1423796.27 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 11:49:01,242 INFO [train.py:842] (2/4) Epoch 36, batch 2100, loss[loss=0.1571, simple_loss=0.2564, pruned_loss=0.02896, over 7411.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2615, pruned_loss=0.04171, over 1422476.65 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 11:49:40,716 INFO [train.py:842] (2/4) Epoch 36, batch 2150, loss[loss=0.1483, simple_loss=0.2363, pruned_loss=0.0301, over 7250.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2604, pruned_loss=0.04115, over 1425078.48 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 11:50:20,113 INFO [train.py:842] (2/4) Epoch 36, batch 2200, loss[loss=0.1567, simple_loss=0.2379, pruned_loss=0.03773, over 7410.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2615, pruned_loss=0.04174, over 1425462.10 frames.], batch size: 18, lr: 1.53e-04
2022-05-29 11:50:58,854 INFO [train.py:842] (2/4) Epoch 36, batch 2250, loss[loss=0.1586, simple_loss=0.2471, pruned_loss=0.03505, over 7337.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2622, pruned_loss=0.04168, over 1421821.73 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 11:51:38,535 INFO [train.py:842] (2/4) Epoch 36, batch 2300, loss[loss=0.1463, simple_loss=0.2293, pruned_loss=0.03166, over 7146.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2609, pruned_loss=0.04112, over 1424399.32 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 11:52:17,600 INFO [train.py:842] (2/4) Epoch 36, batch 2350, loss[loss=0.2426, simple_loss=0.3279, pruned_loss=0.07863, over 4879.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2603, pruned_loss=0.04051, over 1422587.40 frames.], batch size: 52, lr: 1.53e-04
2022-05-29 11:52:57,227 INFO [train.py:842] (2/4) Epoch 36, batch 2400, loss[loss=0.1596, simple_loss=0.2444, pruned_loss=0.03746, over 7419.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2605, pruned_loss=0.04049, over 1425877.54 frames.], batch size: 18, lr: 1.53e-04
2022-05-29 11:53:36,466 INFO [train.py:842] (2/4) Epoch 36, batch 2450, loss[loss=0.1551, simple_loss=0.2487, pruned_loss=0.03071, over 7153.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2607, pruned_loss=0.04091, over 1422125.98 frames.], batch size: 18, lr: 1.53e-04
2022-05-29 11:54:16,226 INFO [train.py:842] (2/4) Epoch 36, batch 2500, loss[loss=0.1738, simple_loss=0.2693, pruned_loss=0.03915, over 7152.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2603, pruned_loss=0.04104, over 1426417.84 frames.], batch size: 20, lr: 1.53e-04
2022-05-29 11:54:55,458 INFO [train.py:842] (2/4) Epoch 36, batch 2550, loss[loss=0.1622, simple_loss=0.2486, pruned_loss=0.03791, over 7356.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2605, pruned_loss=0.04132, over 1423226.97 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 11:55:34,942 INFO [train.py:842] (2/4) Epoch 36, batch 2600, loss[loss=0.1795, simple_loss=0.2646, pruned_loss=0.04718, over 7151.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2612, pruned_loss=0.04165, over 1424350.36 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 11:56:14,139 INFO [train.py:842] (2/4) Epoch 36, batch 2650, loss[loss=0.2045, simple_loss=0.2756, pruned_loss=0.06673, over 4957.00 frames.], tot_loss[loss=0.171, simple_loss=0.2603, pruned_loss=0.04089, over 1422400.24 frames.], batch size: 53, lr: 1.53e-04
2022-05-29 11:56:54,045 INFO [train.py:842] (2/4) Epoch 36, batch 2700, loss[loss=0.1597, simple_loss=0.2485, pruned_loss=0.03548, over 7328.00 frames.], tot_loss[loss=0.1723, simple_loss=0.261, pruned_loss=0.04184, over 1423146.77 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 11:57:33,294 INFO [train.py:842] (2/4) Epoch 36, batch 2750, loss[loss=0.1721, simple_loss=0.2687, pruned_loss=0.03775, over 7122.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2605, pruned_loss=0.04124, over 1425368.07 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 11:58:12,909 INFO [train.py:842] (2/4) Epoch 36, batch 2800, loss[loss=0.2554, simple_loss=0.3501, pruned_loss=0.08036, over 7211.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2599, pruned_loss=0.04121, over 1426536.41 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 11:58:52,379 INFO [train.py:842] (2/4) Epoch 36, batch 2850, loss[loss=0.1653, simple_loss=0.2496, pruned_loss=0.04048, over 7269.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2594, pruned_loss=0.04105, over 1428446.62 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 11:59:32,214 INFO [train.py:842] (2/4) Epoch 36, batch 2900, loss[loss=0.1534, simple_loss=0.2467, pruned_loss=0.03008, over 7265.00 frames.], tot_loss[loss=0.1707, simple_loss=0.259, pruned_loss=0.04119, over 1426889.96 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 12:00:11,126 INFO [train.py:842] (2/4) Epoch 36, batch 2950, loss[loss=0.1439, simple_loss=0.2311, pruned_loss=0.02833, over 7154.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2602, pruned_loss=0.04152, over 1424795.24 frames.], batch size: 18, lr: 1.53e-04
2022-05-29 12:00:50,571 INFO [train.py:842] (2/4) Epoch 36, batch 3000, loss[loss=0.1657, simple_loss=0.2609, pruned_loss=0.03528, over 7154.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2616, pruned_loss=0.04183, over 1422099.96 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 12:00:50,571 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 12:01:00,560 INFO [train.py:871] (2/4) Epoch 36, validation: loss=0.1657, simple_loss=0.263, pruned_loss=0.03426, over 868885.00 frames.
2022-05-29 12:01:39,783 INFO [train.py:842] (2/4) Epoch 36, batch 3050, loss[loss=0.1622, simple_loss=0.2482, pruned_loss=0.03808, over 7295.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2614, pruned_loss=0.04181, over 1424682.85 frames.], batch size: 24, lr: 1.53e-04
2022-05-29 12:02:19,630 INFO [train.py:842] (2/4) Epoch 36, batch 3100, loss[loss=0.1829, simple_loss=0.2821, pruned_loss=0.04184, over 7311.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2622, pruned_loss=0.04201, over 1428933.17 frames.], batch size: 25, lr: 1.53e-04
2022-05-29 12:02:58,615 INFO [train.py:842] (2/4) Epoch 36, batch 3150, loss[loss=0.1848, simple_loss=0.2786, pruned_loss=0.04547, over 7375.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2626, pruned_loss=0.04215, over 1426088.00 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:03:38,032 INFO [train.py:842] (2/4) Epoch 36, batch 3200, loss[loss=0.1275, simple_loss=0.2042, pruned_loss=0.02538, over 7146.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2629, pruned_loss=0.04249, over 1420189.09 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 12:04:17,312 INFO [train.py:842] (2/4) Epoch 36, batch 3250, loss[loss=0.1743, simple_loss=0.2659, pruned_loss=0.04138, over 5157.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2635, pruned_loss=0.0426, over 1417655.81 frames.], batch size: 52, lr: 1.53e-04
2022-05-29 12:04:56,911 INFO [train.py:842] (2/4) Epoch 36, batch 3300, loss[loss=0.1885, simple_loss=0.2786, pruned_loss=0.04923, over 7218.00 frames.], tot_loss[loss=0.1728, simple_loss=0.262, pruned_loss=0.04179, over 1420962.35 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:05:36,376 INFO [train.py:842] (2/4) Epoch 36, batch 3350, loss[loss=0.1654, simple_loss=0.267, pruned_loss=0.03191, over 7186.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2627, pruned_loss=0.04237, over 1425865.31 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:06:16,104 INFO [train.py:842] (2/4) Epoch 36, batch 3400, loss[loss=0.1871, simple_loss=0.2728, pruned_loss=0.05068, over 7257.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2627, pruned_loss=0.04276, over 1424689.94 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 12:06:55,241 INFO [train.py:842] (2/4) Epoch 36, batch 3450, loss[loss=0.2042, simple_loss=0.2705, pruned_loss=0.06889, over 7283.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2629, pruned_loss=0.04267, over 1422156.87 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 12:07:34,899 INFO [train.py:842] (2/4) Epoch 36, batch 3500, loss[loss=0.1761, simple_loss=0.2771, pruned_loss=0.03758, over 7414.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2619, pruned_loss=0.04213, over 1419473.25 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 12:08:13,989 INFO [train.py:842] (2/4) Epoch 36, batch 3550, loss[loss=0.1703, simple_loss=0.268, pruned_loss=0.03626, over 7066.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2614, pruned_loss=0.04156, over 1423217.61 frames.], batch size: 28, lr: 1.53e-04
2022-05-29 12:08:53,391 INFO [train.py:842] (2/4) Epoch 36, batch 3600, loss[loss=0.1932, simple_loss=0.2945, pruned_loss=0.04594, over 7246.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2611, pruned_loss=0.04099, over 1421789.17 frames.], batch size: 25, lr: 1.53e-04
2022-05-29 12:09:32,771 INFO [train.py:842] (2/4) Epoch 36, batch 3650, loss[loss=0.1945, simple_loss=0.2903, pruned_loss=0.04936, over 7285.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2606, pruned_loss=0.04064, over 1423945.71 frames.], batch size: 24, lr: 1.53e-04
2022-05-29 12:10:12,431 INFO [train.py:842] (2/4) Epoch 36, batch 3700, loss[loss=0.191, simple_loss=0.2786, pruned_loss=0.05172, over 7100.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2602, pruned_loss=0.04059, over 1426688.12 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 12:10:51,872 INFO [train.py:842] (2/4) Epoch 36, batch 3750, loss[loss=0.1612, simple_loss=0.2605, pruned_loss=0.03089, over 7347.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2601, pruned_loss=0.04078, over 1426341.54 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 12:11:31,340 INFO [train.py:842] (2/4) Epoch 36, batch 3800, loss[loss=0.1425, simple_loss=0.2353, pruned_loss=0.02483, over 7363.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2615, pruned_loss=0.04135, over 1428625.86 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 12:12:10,347 INFO [train.py:842] (2/4) Epoch 36, batch 3850, loss[loss=0.1263, simple_loss=0.216, pruned_loss=0.0183, over 6989.00 frames.], tot_loss[loss=0.173, simple_loss=0.2624, pruned_loss=0.04178, over 1424420.71 frames.], batch size: 16, lr: 1.53e-04
2022-05-29 12:12:50,334 INFO [train.py:842] (2/4) Epoch 36, batch 3900, loss[loss=0.1464, simple_loss=0.2333, pruned_loss=0.02981, over 7192.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2615, pruned_loss=0.04147, over 1426121.38 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:13:29,168 INFO [train.py:842] (2/4) Epoch 36, batch 3950, loss[loss=0.1777, simple_loss=0.2701, pruned_loss=0.04267, over 6782.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2622, pruned_loss=0.04153, over 1423900.52 frames.], batch size: 31, lr: 1.53e-04
2022-05-29 12:14:08,406 INFO [train.py:842] (2/4) Epoch 36, batch 4000, loss[loss=0.162, simple_loss=0.2544, pruned_loss=0.03477, over 7061.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2629, pruned_loss=0.04163, over 1423281.01 frames.], batch size: 28, lr: 1.53e-04
2022-05-29 12:14:47,640 INFO [train.py:842] (2/4) Epoch 36, batch 4050, loss[loss=0.17, simple_loss=0.2604, pruned_loss=0.03978, over 6555.00 frames.], tot_loss[loss=0.173, simple_loss=0.2632, pruned_loss=0.04139, over 1425347.40 frames.], batch size: 38, lr: 1.53e-04
2022-05-29 12:15:27,236 INFO [train.py:842] (2/4) Epoch 36, batch 4100, loss[loss=0.182, simple_loss=0.275, pruned_loss=0.04453, over 7231.00 frames.], tot_loss[loss=0.173, simple_loss=0.263, pruned_loss=0.04147, over 1426240.09 frames.], batch size: 20, lr: 1.53e-04
2022-05-29 12:16:06,489 INFO [train.py:842] (2/4) Epoch 36, batch 4150, loss[loss=0.1804, simple_loss=0.2707, pruned_loss=0.04505, over 7330.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2632, pruned_loss=0.04201, over 1423228.65 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 12:16:45,802 INFO [train.py:842] (2/4) Epoch 36, batch 4200, loss[loss=0.1778, simple_loss=0.2663, pruned_loss=0.0447, over 7327.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2633, pruned_loss=0.0421, over 1418249.37 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 12:17:25,127 INFO [train.py:842] (2/4) Epoch 36, batch 4250, loss[loss=0.213, simple_loss=0.3037, pruned_loss=0.06116, over 7202.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2627, pruned_loss=0.04208, over 1418138.27 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 12:18:04,939 INFO [train.py:842] (2/4) Epoch 36, batch 4300, loss[loss=0.1659, simple_loss=0.2643, pruned_loss=0.0338, over 7206.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2633, pruned_loss=0.04249, over 1418540.47 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:18:44,051 INFO [train.py:842] (2/4) Epoch 36, batch 4350, loss[loss=0.1583, simple_loss=0.2524, pruned_loss=0.03209, over 7353.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2637, pruned_loss=0.04252, over 1413495.41 frames.], batch size: 19, lr: 1.53e-04
2022-05-29 12:19:23,720 INFO [train.py:842] (2/4) Epoch 36, batch 4400, loss[loss=0.186, simple_loss=0.2867, pruned_loss=0.04264, over 6815.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2632, pruned_loss=0.04246, over 1417048.55 frames.], batch size: 31, lr: 1.53e-04
2022-05-29 12:20:13,620 INFO [train.py:842] (2/4) Epoch 36, batch 4450, loss[loss=0.1625, simple_loss=0.2503, pruned_loss=0.03735, over 7422.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2627, pruned_loss=0.04146, over 1417737.95 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 12:20:53,261 INFO [train.py:842] (2/4) Epoch 36, batch 4500, loss[loss=0.1417, simple_loss=0.2242, pruned_loss=0.0296, over 7164.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2628, pruned_loss=0.04147, over 1421683.56 frames.], batch size: 18, lr: 1.53e-04
2022-05-29 12:21:32,484 INFO [train.py:842] (2/4) Epoch 36, batch 4550, loss[loss=0.1744, simple_loss=0.2652, pruned_loss=0.04178, over 7375.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2629, pruned_loss=0.04142, over 1422670.61 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:22:11,953 INFO [train.py:842] (2/4) Epoch 36, batch 4600, loss[loss=0.2154, simple_loss=0.3009, pruned_loss=0.06489, over 4942.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2632, pruned_loss=0.04189, over 1420063.53 frames.], batch size: 52, lr: 1.53e-04
2022-05-29 12:22:50,842 INFO [train.py:842] (2/4) Epoch 36, batch 4650, loss[loss=0.1533, simple_loss=0.2356, pruned_loss=0.03549, over 7274.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2629, pruned_loss=0.04139, over 1416374.74 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 12:23:30,497 INFO [train.py:842] (2/4) Epoch 36, batch 4700, loss[loss=0.1917, simple_loss=0.2946, pruned_loss=0.04438, over 6410.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2623, pruned_loss=0.0412, over 1419951.98 frames.], batch size: 38, lr: 1.53e-04
2022-05-29 12:24:09,548 INFO [train.py:842] (2/4) Epoch 36, batch 4750, loss[loss=0.1748, simple_loss=0.2691, pruned_loss=0.04023, over 7123.00 frames.], tot_loss[loss=0.173, simple_loss=0.2627, pruned_loss=0.04163, over 1414061.31 frames.], batch size: 28, lr: 1.53e-04
2022-05-29 12:24:49,162 INFO [train.py:842] (2/4) Epoch 36, batch 4800, loss[loss=0.1568, simple_loss=0.2508, pruned_loss=0.03137, over 7202.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2618, pruned_loss=0.04175, over 1414404.92 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:25:49,844 INFO [train.py:842] (2/4) Epoch 36, batch 4850, loss[loss=0.1973, simple_loss=0.29, pruned_loss=0.05231, over 7112.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2612, pruned_loss=0.04107, over 1415789.00 frames.], batch size: 21, lr: 1.53e-04
2022-05-29 12:26:29,636 INFO [train.py:842] (2/4) Epoch 36, batch 4900, loss[loss=0.1658, simple_loss=0.2451, pruned_loss=0.04327, over 7286.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2598, pruned_loss=0.04094, over 1420397.53 frames.], batch size: 17, lr: 1.53e-04
2022-05-29 12:27:08,922 INFO [train.py:842] (2/4) Epoch 36, batch 4950, loss[loss=0.1634, simple_loss=0.2581, pruned_loss=0.03438, over 7289.00 frames.], tot_loss[loss=0.171, simple_loss=0.2599, pruned_loss=0.04105, over 1420536.82 frames.], batch size: 25, lr: 1.53e-04
2022-05-29 12:27:48,576 INFO [train.py:842] (2/4) Epoch 36, batch 5000, loss[loss=0.1689, simple_loss=0.2671, pruned_loss=0.03532, over 7370.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2598, pruned_loss=0.04083, over 1424926.57 frames.], batch size: 23, lr: 1.53e-04
2022-05-29 12:28:27,750 INFO [train.py:842] (2/4) Epoch 36, batch 5050, loss[loss=0.2046, simple_loss=0.2836, pruned_loss=0.06283, over 5022.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2605, pruned_loss=0.04117, over 1421323.19 frames.], batch size: 52, lr: 1.53e-04
2022-05-29 12:29:07,320 INFO [train.py:842] (2/4) Epoch 36, batch 5100, loss[loss=0.1826, simple_loss=0.2806, pruned_loss=0.04228, over 7070.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2604, pruned_loss=0.04061, over 1422785.91 frames.], batch size: 28, lr: 1.53e-04
2022-05-29 12:29:46,354 INFO [train.py:842] (2/4) Epoch 36, batch 5150, loss[loss=0.1667, simple_loss=0.2692, pruned_loss=0.03213, over 7339.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2619, pruned_loss=0.04157, over 1422806.15 frames.], batch size: 22, lr: 1.53e-04
2022-05-29 12:30:26,068 INFO [train.py:842] (2/4) Epoch 36, batch 5200, loss[loss=0.1424, simple_loss=0.2284, pruned_loss=0.02821, over 7369.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2616, pruned_loss=0.04163, over 1420357.51 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 12:31:05,228 INFO [train.py:842] (2/4) Epoch 36, batch 5250, loss[loss=0.2505, simple_loss=0.3355, pruned_loss=0.08275, over 7122.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2624, pruned_loss=0.0423, over 1424023.78 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 12:31:44,676 INFO [train.py:842] (2/4) Epoch 36, batch 5300, loss[loss=0.2115, simple_loss=0.2968, pruned_loss=0.0631, over 7209.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2623, pruned_loss=0.0417, over 1427482.84 frames.], batch size: 23, lr: 1.52e-04
2022-05-29 12:32:23,853 INFO [train.py:842] (2/4) Epoch 36, batch 5350, loss[loss=0.1936, simple_loss=0.2796, pruned_loss=0.05382, over 7297.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2623, pruned_loss=0.04192, over 1423539.92 frames.], batch size: 24, lr: 1.52e-04
2022-05-29 12:33:03,181 INFO [train.py:842] (2/4) Epoch 36, batch 5400, loss[loss=0.1704, simple_loss=0.2528, pruned_loss=0.04396, over 7074.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2628, pruned_loss=0.04227, over 1418080.12 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:33:42,355 INFO [train.py:842] (2/4) Epoch 36, batch 5450, loss[loss=0.1469, simple_loss=0.2318, pruned_loss=0.03099, over 7155.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2622, pruned_loss=0.04222, over 1417831.09 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:34:21,822 INFO [train.py:842] (2/4) Epoch 36, batch 5500, loss[loss=0.2119, simple_loss=0.2984, pruned_loss=0.06268, over 7217.00 frames.], tot_loss[loss=0.1729, simple_loss=0.262, pruned_loss=0.04191, over 1418894.64 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 12:35:00,638 INFO [train.py:842] (2/4) Epoch 36, batch 5550, loss[loss=0.1693, simple_loss=0.267, pruned_loss=0.03581, over 7332.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2622, pruned_loss=0.04165, over 1415874.52 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:35:40,084 INFO [train.py:842] (2/4) Epoch 36, batch 5600, loss[loss=0.1999, simple_loss=0.3021, pruned_loss=0.04881, over 7334.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2621, pruned_loss=0.04162, over 1417301.52 frames.], batch size: 22, lr: 1.52e-04
2022-05-29 12:36:19,261 INFO [train.py:842] (2/4) Epoch 36, batch 5650, loss[loss=0.224, simple_loss=0.313, pruned_loss=0.06754, over 7364.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2626, pruned_loss=0.04238, over 1418070.74 frames.], batch size: 23, lr: 1.52e-04
2022-05-29 12:36:58,859 INFO [train.py:842] (2/4) Epoch 36, batch 5700, loss[loss=0.1413, simple_loss=0.2281, pruned_loss=0.02726, over 7415.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2612, pruned_loss=0.04194, over 1414667.81 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:37:37,877 INFO [train.py:842] (2/4) Epoch 36, batch 5750, loss[loss=0.1814, simple_loss=0.2719, pruned_loss=0.04543, over 7298.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2615, pruned_loss=0.04195, over 1411625.63 frames.], batch size: 25, lr: 1.52e-04
2022-05-29 12:38:17,303 INFO [train.py:842] (2/4) Epoch 36, batch 5800, loss[loss=0.151, simple_loss=0.2406, pruned_loss=0.03066, over 7021.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2608, pruned_loss=0.04136, over 1415661.59 frames.], batch size: 28, lr: 1.52e-04
2022-05-29 12:38:56,591 INFO [train.py:842] (2/4) Epoch 36, batch 5850, loss[loss=0.1389, simple_loss=0.228, pruned_loss=0.02486, over 7165.00 frames.], tot_loss[loss=0.172, simple_loss=0.2613, pruned_loss=0.04131, over 1419839.69 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:39:36,363 INFO [train.py:842] (2/4) Epoch 36, batch 5900, loss[loss=0.142, simple_loss=0.2226, pruned_loss=0.03076, over 7408.00 frames.], tot_loss[loss=0.172, simple_loss=0.261, pruned_loss=0.0415, over 1421496.25 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:40:15,792 INFO [train.py:842] (2/4) Epoch 36, batch 5950, loss[loss=0.1717, simple_loss=0.2537, pruned_loss=0.04488, over 7151.00 frames.], tot_loss[loss=0.1703, simple_loss=0.259, pruned_loss=0.0408, over 1419904.10 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 12:40:55,445 INFO [train.py:842] (2/4) Epoch 36, batch 6000, loss[loss=0.1689, simple_loss=0.258, pruned_loss=0.03995, over 7207.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2594, pruned_loss=0.04052, over 1420531.60 frames.], batch size: 22, lr: 1.52e-04
2022-05-29 12:40:55,446 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 12:41:05,037 INFO [train.py:871] (2/4) Epoch 36, validation: loss=0.1636, simple_loss=0.2603, pruned_loss=0.03352, over 868885.00 frames.
2022-05-29 12:41:44,531 INFO [train.py:842] (2/4) Epoch 36, batch 6050, loss[loss=0.1423, simple_loss=0.2268, pruned_loss=0.02889, over 7138.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2589, pruned_loss=0.04013, over 1420522.91 frames.], batch size: 17, lr: 1.52e-04
2022-05-29 12:42:24,242 INFO [train.py:842] (2/4) Epoch 36, batch 6100, loss[loss=0.1339, simple_loss=0.2233, pruned_loss=0.02225, over 7152.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2594, pruned_loss=0.04079, over 1423096.84 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 12:43:03,514 INFO [train.py:842] (2/4) Epoch 36, batch 6150, loss[loss=0.1582, simple_loss=0.2542, pruned_loss=0.03109, over 7352.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2595, pruned_loss=0.041, over 1424167.09 frames.], batch size: 22, lr: 1.52e-04
2022-05-29 12:43:43,140 INFO [train.py:842] (2/4) Epoch 36, batch 6200, loss[loss=0.1594, simple_loss=0.2607, pruned_loss=0.02909, over 7150.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2595, pruned_loss=0.04104, over 1422029.09 frames.], batch size: 28, lr: 1.52e-04
2022-05-29 12:44:22,464 INFO [train.py:842] (2/4) Epoch 36, batch 6250, loss[loss=0.2214, simple_loss=0.3024, pruned_loss=0.07018, over 7304.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2594, pruned_loss=0.04102, over 1422377.33 frames.], batch size: 25, lr: 1.52e-04
2022-05-29 12:45:04,920 INFO [train.py:842] (2/4) Epoch 36, batch 6300, loss[loss=0.1463, simple_loss=0.2293, pruned_loss=0.03161, over 7134.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2608, pruned_loss=0.04237, over 1420782.50 frames.], batch size: 17, lr: 1.52e-04
2022-05-29 12:45:44,286 INFO [train.py:842] (2/4) Epoch 36, batch 6350, loss[loss=0.222, simple_loss=0.3085, pruned_loss=0.06772, over 7311.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2603, pruned_loss=0.04167, over 1420035.63 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 12:46:23,704 INFO [train.py:842] (2/4) Epoch 36, batch 6400, loss[loss=0.1793, simple_loss=0.2837, pruned_loss=0.03745, over 7332.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2601, pruned_loss=0.04107, over 1423006.37 frames.], batch size: 22, lr: 1.52e-04
2022-05-29 12:47:02,921 INFO [train.py:842] (2/4) Epoch 36, batch 6450, loss[loss=0.1425, simple_loss=0.2332, pruned_loss=0.02587, over 7274.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2597, pruned_loss=0.04066, over 1424029.05 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 12:47:42,459 INFO [train.py:842] (2/4) Epoch 36, batch 6500, loss[loss=0.1377, simple_loss=0.2327, pruned_loss=0.02132, over 7159.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2597, pruned_loss=0.04033, over 1423318.64 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:48:21,589 INFO [train.py:842] (2/4) Epoch 36, batch 6550, loss[loss=0.154, simple_loss=0.2518, pruned_loss=0.02808, over 7152.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2603, pruned_loss=0.04065, over 1422179.84 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:49:01,322 INFO [train.py:842] (2/4) Epoch 36, batch 6600, loss[loss=0.1565, simple_loss=0.2371, pruned_loss=0.03793, over 7152.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2602, pruned_loss=0.04083, over 1422648.84 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:49:40,671 INFO [train.py:842] (2/4) Epoch 36, batch 6650, loss[loss=0.1686, simple_loss=0.2603, pruned_loss=0.03841, over 6694.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2617, pruned_loss=0.04187, over 1423913.84 frames.], batch size: 31, lr: 1.52e-04
2022-05-29 12:50:20,331 INFO [train.py:842] (2/4) Epoch 36, batch 6700, loss[loss=0.1567, simple_loss=0.2549, pruned_loss=0.02921, over 7220.00 frames.], tot_loss[loss=0.1718, simple_loss=0.261, pruned_loss=0.04132, over 1426098.88 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:50:59,324 INFO [train.py:842] (2/4) Epoch 36, batch 6750, loss[loss=0.1636, simple_loss=0.2688, pruned_loss=0.02918, over 7330.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2621, pruned_loss=0.0418, over 1420858.86 frames.], batch size: 22, lr: 1.52e-04
2022-05-29 12:51:38,830 INFO [train.py:842] (2/4) Epoch 36, batch 6800, loss[loss=0.1711, simple_loss=0.2637, pruned_loss=0.03925, over 7374.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2619, pruned_loss=0.04112, over 1424735.64 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 12:52:18,257 INFO [train.py:842] (2/4) Epoch 36, batch 6850, loss[loss=0.1804, simple_loss=0.2732, pruned_loss=0.04379, over 7132.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2615, pruned_loss=0.04094, over 1425242.03 frames.], batch size: 28, lr: 1.52e-04
2022-05-29 12:52:58,002 INFO [train.py:842] (2/4) Epoch 36, batch 6900, loss[loss=0.1723, simple_loss=0.2597, pruned_loss=0.04243, over 7429.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2603, pruned_loss=0.04054, over 1426476.10 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:53:37,190 INFO [train.py:842] (2/4) Epoch 36, batch 6950, loss[loss=0.1688, simple_loss=0.258, pruned_loss=0.03974, over 7240.00 frames.], tot_loss[loss=0.1715, simple_loss=0.261, pruned_loss=0.04102, over 1428063.53 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:54:16,945 INFO [train.py:842] (2/4) Epoch 36, batch 7000, loss[loss=0.1609, simple_loss=0.2508, pruned_loss=0.0355, over 7436.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2604, pruned_loss=0.0406, over 1429114.07 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:54:56,281 INFO [train.py:842] (2/4) Epoch 36, batch 7050, loss[loss=0.1903, simple_loss=0.2939, pruned_loss=0.04332, over 7436.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2611, pruned_loss=0.04089, over 1424850.14 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 12:55:36,074 INFO [train.py:842] (2/4) Epoch 36, batch 7100, loss[loss=0.1696, simple_loss=0.2623, pruned_loss=0.03843, over 7261.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2601, pruned_loss=0.04084, over 1425610.06 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 12:56:15,254 INFO [train.py:842] (2/4) Epoch 36, batch 7150, loss[loss=0.1779, simple_loss=0.2707, pruned_loss=0.04253, over 7281.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2613, pruned_loss=0.0408, over 1429693.62 frames.], batch size: 24, lr: 1.52e-04
2022-05-29 12:56:54,942 INFO [train.py:842] (2/4) Epoch 36, batch 7200, loss[loss=0.1437, simple_loss=0.2186, pruned_loss=0.03443, over 7286.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2602, pruned_loss=0.04082, over 1428458.73 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 12:57:34,384 INFO [train.py:842] (2/4) Epoch 36, batch 7250, loss[loss=0.1861, simple_loss=0.2697, pruned_loss=0.05127, over 7145.00 frames.], tot_loss[loss=0.1715, simple_loss=0.261, pruned_loss=0.04098, over 1428096.47 frames.], batch size: 26, lr: 1.52e-04
2022-05-29 12:58:13,736 INFO [train.py:842] (2/4) Epoch 36, batch 7300, loss[loss=0.1722, simple_loss=0.2556, pruned_loss=0.04435, over 7054.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2606, pruned_loss=0.0406, over 1426204.27 frames.], batch size: 28, lr: 1.52e-04
2022-05-29 12:58:52,913 INFO [train.py:842] (2/4) Epoch 36, batch 7350, loss[loss=0.1899, simple_loss=0.283, pruned_loss=0.04841, over 6816.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2613, pruned_loss=0.04113, over 1426799.66 frames.], batch size: 15, lr: 1.52e-04
2022-05-29 12:59:32,489 INFO [train.py:842] (2/4) Epoch 36, batch 7400, loss[loss=0.1694, simple_loss=0.2535, pruned_loss=0.04269, over 7425.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2604, pruned_loss=0.04108, over 1424775.29 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 13:00:11,791 INFO [train.py:842] (2/4) Epoch 36, batch 7450, loss[loss=0.1439, simple_loss=0.2286, pruned_loss=0.02964, over 7398.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2599, pruned_loss=0.04052, over 1422206.75 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 13:00:51,496 INFO [train.py:842] (2/4) Epoch 36, batch 7500, loss[loss=0.1597, simple_loss=0.2521, pruned_loss=0.0337, over 7161.00 frames.], tot_loss[loss=0.171, simple_loss=0.2601, pruned_loss=0.04091, over 1425432.53 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 13:01:30,827 INFO [train.py:842] (2/4) Epoch 36, batch 7550, loss[loss=0.1869, simple_loss=0.2816, pruned_loss=0.04614, over 7235.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2604, pruned_loss=0.04088, over 1425367.72 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:02:10,476 INFO [train.py:842] (2/4) Epoch 36, batch 7600, loss[loss=0.1365, simple_loss=0.2187, pruned_loss=0.02716, over 7272.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2615, pruned_loss=0.04139, over 1421797.02 frames.], batch size: 17, lr: 1.52e-04
2022-05-29 13:02:49,601 INFO [train.py:842] (2/4) Epoch 36, batch 7650, loss[loss=0.191, simple_loss=0.2832, pruned_loss=0.04942, over 7380.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2623, pruned_loss=0.04207, over 1422238.04 frames.], batch size: 23, lr: 1.52e-04
2022-05-29 13:03:29,301 INFO [train.py:842] (2/4) Epoch 36, batch 7700, loss[loss=0.1773, simple_loss=0.273, pruned_loss=0.04077, over 7228.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2609, pruned_loss=0.04137, over 1426221.94 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:04:08,584 INFO [train.py:842] (2/4) Epoch 36, batch 7750, loss[loss=0.1463, simple_loss=0.2398, pruned_loss=0.02645, over 7165.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2604, pruned_loss=0.04139, over 1425889.25 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 13:04:48,303 INFO [train.py:842] (2/4) Epoch 36, batch 7800, loss[loss=0.1662, simple_loss=0.2509, pruned_loss=0.04079, over 7435.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2609, pruned_loss=0.04186, over 1425308.40 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 13:05:27,408 INFO [train.py:842] (2/4) Epoch 36, batch 7850, loss[loss=0.1494, simple_loss=0.243, pruned_loss=0.02784, over 7408.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2611, pruned_loss=0.04177, over 1428162.43 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:06:06,996 INFO [train.py:842] (2/4) Epoch 36, batch 7900, loss[loss=0.1521, simple_loss=0.2533, pruned_loss=0.02544, over 7073.00 frames.], tot_loss[loss=0.1718, simple_loss=0.261, pruned_loss=0.04129, over 1427157.68 frames.], batch size: 18, lr: 1.52e-04
2022-05-29 13:06:46,366 INFO [train.py:842] (2/4) Epoch 36, batch 7950, loss[loss=0.2279, simple_loss=0.3146, pruned_loss=0.07058, over 7106.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2609, pruned_loss=0.04143, over 1426184.30 frames.], batch size: 28, lr: 1.52e-04
2022-05-29 13:07:25,914 INFO [train.py:842] (2/4) Epoch 36, batch 8000, loss[loss=0.1968, simple_loss=0.2758, pruned_loss=0.05894, over 7296.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2603, pruned_loss=0.04137, over 1426059.36 frames.], batch size: 24, lr: 1.52e-04
2022-05-29 13:08:05,179 INFO [train.py:842] (2/4) Epoch 36, batch 8050, loss[loss=0.1797, simple_loss=0.2711, pruned_loss=0.04413, over 6665.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2599, pruned_loss=0.04131, over 1423856.53 frames.], batch size: 31, lr: 1.52e-04
2022-05-29 13:08:44,879 INFO [train.py:842] (2/4) Epoch 36, batch 8100, loss[loss=0.1544, simple_loss=0.2416, pruned_loss=0.03359, over 7357.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2597, pruned_loss=0.04058, over 1423981.76 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 13:09:24,146 INFO [train.py:842] (2/4) Epoch 36, batch 8150, loss[loss=0.2174, simple_loss=0.2957, pruned_loss=0.06956, over 7293.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2609, pruned_loss=0.0408, over 1424014.86 frames.], batch size: 25, lr: 1.52e-04
2022-05-29 13:10:03,964 INFO [train.py:842] (2/4) Epoch 36, batch 8200, loss[loss=0.2177, simple_loss=0.3054, pruned_loss=0.06495, over 7164.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2602, pruned_loss=0.04126, over 1427464.96 frames.], batch size: 26, lr: 1.52e-04
2022-05-29 13:10:43,106 INFO [train.py:842] (2/4) Epoch 36, batch 8250, loss[loss=0.1711, simple_loss=0.2603, pruned_loss=0.04096, over 7223.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2607, pruned_loss=0.04153, over 1424442.34 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 13:11:22,786 INFO [train.py:842] (2/4) Epoch 36, batch 8300, loss[loss=0.2094, simple_loss=0.2925, pruned_loss=0.06312, over 7148.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2611, pruned_loss=0.04175, over 1417192.16 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 13:12:02,029 INFO [train.py:842] (2/4) Epoch 36, batch 8350, loss[loss=0.1502, simple_loss=0.2518, pruned_loss=0.02432, over 7312.00 frames.], tot_loss[loss=0.173, simple_loss=0.2622, pruned_loss=0.04192, over 1418642.56 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:12:41,430 INFO [train.py:842] (2/4) Epoch 36, batch 8400, loss[loss=0.1425, simple_loss=0.2295, pruned_loss=0.02773, over 7001.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2604, pruned_loss=0.04091, over 1419033.17 frames.], batch size: 16, lr: 1.52e-04
2022-05-29 13:13:20,667 INFO [train.py:842] (2/4) Epoch 36, batch 8450, loss[loss=0.1874, simple_loss=0.2684, pruned_loss=0.05318, over 5149.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2593, pruned_loss=0.04068, over 1418739.51 frames.], batch size: 52, lr: 1.52e-04
2022-05-29 13:14:00,548 INFO [train.py:842] (2/4) Epoch 36, batch 8500, loss[loss=0.1555, simple_loss=0.2407, pruned_loss=0.03513, over 7119.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2593, pruned_loss=0.04115, over 1418037.28 frames.], batch size: 17, lr: 1.52e-04
2022-05-29 13:14:39,940 INFO [train.py:842] (2/4) Epoch 36, batch 8550, loss[loss=0.1569, simple_loss=0.2512, pruned_loss=0.03129, over 7234.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2597, pruned_loss=0.04146, over 1416533.88 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 13:15:19,727 INFO [train.py:842] (2/4) Epoch 36, batch 8600, loss[loss=0.1869, simple_loss=0.2885, pruned_loss=0.04267, over 7224.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2604, pruned_loss=0.04162, over 1413723.54 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:15:59,157 INFO [train.py:842] (2/4) Epoch 36, batch 8650, loss[loss=0.1935, simple_loss=0.2849, pruned_loss=0.05106, over 5126.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2606, pruned_loss=0.04148, over 1413692.66 frames.], batch size: 53, lr: 1.52e-04
2022-05-29 13:16:38,696 INFO [train.py:842] (2/4) Epoch 36, batch 8700, loss[loss=0.1736, simple_loss=0.2605, pruned_loss=0.04335, over 6583.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2594, pruned_loss=0.04088, over 1411865.04 frames.], batch size: 38, lr: 1.52e-04
2022-05-29 13:17:17,958 INFO [train.py:842] (2/4) Epoch 36, batch 8750, loss[loss=0.1555, simple_loss=0.2551, pruned_loss=0.02797, over 7236.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2605, pruned_loss=0.04139, over 1412117.45 frames.], batch size: 20, lr: 1.52e-04
2022-05-29 13:17:57,587 INFO [train.py:842] (2/4) Epoch 36, batch 8800, loss[loss=0.1816, simple_loss=0.2773, pruned_loss=0.04294, over 7216.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2596, pruned_loss=0.04044, over 1412622.60 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:18:36,653 INFO [train.py:842] (2/4) Epoch 36, batch 8850, loss[loss=0.2158, simple_loss=0.3067, pruned_loss=0.06246, over 4930.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2591, pruned_loss=0.04056, over 1399464.62 frames.], batch size: 53, lr: 1.52e-04
2022-05-29 13:19:16,393 INFO [train.py:842] (2/4) Epoch 36, batch 8900, loss[loss=0.1674, simple_loss=0.248, pruned_loss=0.04333, over 5021.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2599, pruned_loss=0.04116, over 1400091.38 frames.], batch size: 52, lr: 1.52e-04
2022-05-29 13:19:55,419 INFO [train.py:842] (2/4) Epoch 36, batch 8950, loss[loss=0.1457, simple_loss=0.2448, pruned_loss=0.02323, over 7322.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2589, pruned_loss=0.04085, over 1393428.06 frames.], batch size: 21, lr: 1.52e-04
2022-05-29 13:20:34,573 INFO [train.py:842] (2/4) Epoch 36, batch 9000, loss[loss=0.1866, simple_loss=0.2714, pruned_loss=0.05094, over 7244.00 frames.], tot_loss[loss=0.1714, simple_loss=0.26, pruned_loss=0.04136, over 1385757.04 frames.], batch size: 19, lr: 1.52e-04
2022-05-29 13:20:34,574 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 13:20:44,416 INFO [train.py:871] (2/4) Epoch 36, validation: loss=0.1634, simple_loss=0.2603, pruned_loss=0.03321, over 868885.00 frames.
2022-05-29 13:21:23,730 INFO [train.py:842] (2/4) Epoch 36, batch 9050, loss[loss=0.144, simple_loss=0.2352, pruned_loss=0.02634, over 7437.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2595, pruned_loss=0.04104, over 1385226.38 frames.], batch size: 17, lr: 1.52e-04
2022-05-29 13:22:03,038 INFO [train.py:842] (2/4) Epoch 36, batch 9100, loss[loss=0.1627, simple_loss=0.2478, pruned_loss=0.03878, over 4906.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2593, pruned_loss=0.04088, over 1370611.35 frames.], batch size: 52, lr: 1.52e-04
2022-05-29 13:22:41,486 INFO [train.py:842] (2/4) Epoch 36, batch 9150, loss[loss=0.2581, simple_loss=0.3253, pruned_loss=0.09538, over 5104.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2618, pruned_loss=0.04267, over 1322050.33 frames.], batch size: 52, lr: 1.52e-04
2022-05-29 13:23:33,570 INFO [train.py:842] (2/4) Epoch 37, batch 0, loss[loss=0.2329, simple_loss=0.3107, pruned_loss=0.07761, over 7331.00 frames.], tot_loss[loss=0.2329, simple_loss=0.3107, pruned_loss=0.07761, over 7331.00 frames.], batch size: 22, lr: 1.50e-04
2022-05-29 13:24:13,149 INFO [train.py:842] (2/4) Epoch 37, batch 50, loss[loss=0.1526, simple_loss=0.2386, pruned_loss=0.0333, over 7447.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2627, pruned_loss=0.04283, over 321323.23 frames.], batch size: 19, lr: 1.50e-04
2022-05-29 13:24:52,834 INFO [train.py:842] (2/4) Epoch 37, batch 100, loss[loss=0.146, simple_loss=0.2351, pruned_loss=0.0284, over 7333.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2607, pruned_loss=0.04173, over 567228.40 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:25:32,060 INFO [train.py:842] (2/4) Epoch 37, batch 150, loss[loss=0.1816, simple_loss=0.2722, pruned_loss=0.0455, over 7060.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2617, pruned_loss=0.04155, over 754672.10 frames.], batch size: 28, lr: 1.49e-04
2022-05-29 13:26:11,478 INFO [train.py:842] (2/4) Epoch 37, batch 200, loss[loss=0.1659, simple_loss=0.2661, pruned_loss=0.03283, over 7316.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2649, pruned_loss=0.04231, over 905891.27 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 13:26:50,739 INFO [train.py:842] (2/4) Epoch 37, batch 250, loss[loss=0.1531, simple_loss=0.2458, pruned_loss=0.03023, over 7248.00 frames.], tot_loss[loss=0.174, simple_loss=0.2641, pruned_loss=0.04197, over 1017437.76 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:27:30,270 INFO [train.py:842] (2/4) Epoch 37, batch 300, loss[loss=0.1784, simple_loss=0.2718, pruned_loss=0.04251, over 7325.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2629, pruned_loss=0.04186, over 1104206.95 frames.], batch size: 22, lr: 1.49e-04
2022-05-29 13:28:09,435 INFO [train.py:842] (2/4) Epoch 37, batch 350, loss[loss=0.1478, simple_loss=0.2395, pruned_loss=0.028, over 7162.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2619, pruned_loss=0.04136, over 1172826.37 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 13:28:49,094 INFO [train.py:842] (2/4) Epoch 37, batch 400, loss[loss=0.166, simple_loss=0.2612, pruned_loss=0.03535, over 7229.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2614, pruned_loss=0.04105, over 1231824.62 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:29:28,328 INFO [train.py:842] (2/4) Epoch 37, batch 450, loss[loss=0.1863, simple_loss=0.28, pruned_loss=0.04632, over 7138.00 frames.], tot_loss[loss=0.172, simple_loss=0.2617, pruned_loss=0.04113, over 1275910.64 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:30:07,657 INFO [train.py:842] (2/4) Epoch 37, batch 500, loss[loss=0.1486, simple_loss=0.2523, pruned_loss=0.02249, over 7231.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2621, pruned_loss=0.0414, over 1305901.65 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:30:46,585 INFO [train.py:842] (2/4) Epoch 37, batch 550, loss[loss=0.1623, simple_loss=0.2506, pruned_loss=0.03696, over 7070.00 frames.], tot_loss[loss=0.173, simple_loss=0.2626, pruned_loss=0.04168, over 1321994.37 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 13:31:26,468 INFO [train.py:842] (2/4) Epoch 37, batch 600, loss[loss=0.1802, simple_loss=0.2688, pruned_loss=0.04579, over 7430.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2613, pruned_loss=0.04125, over 1346846.02 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:32:06,313 INFO [train.py:842] (2/4) Epoch 37, batch 650, loss[loss=0.1349, simple_loss=0.2209, pruned_loss=0.0244, over 7147.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2586, pruned_loss=0.03983, over 1365970.92 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 13:32:45,847 INFO [train.py:842] (2/4) Epoch 37, batch 700, loss[loss=0.215, simple_loss=0.3007, pruned_loss=0.06463, over 7226.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2598, pruned_loss=0.04032, over 1379820.73 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:33:25,164 INFO [train.py:842] (2/4) Epoch 37, batch 750, loss[loss=0.1376, simple_loss=0.2187, pruned_loss=0.0283, over 7158.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2585, pruned_loss=0.03963, over 1388136.41 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:34:04,854 INFO [train.py:842] (2/4) Epoch 37, batch 800, loss[loss=0.1371, simple_loss=0.218, pruned_loss=0.0281, over 7395.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2587, pruned_loss=0.04007, over 1398577.15 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 13:34:43,852 INFO [train.py:842] (2/4) Epoch 37, batch 850, loss[loss=0.1609, simple_loss=0.2473, pruned_loss=0.03726, over 7254.00 frames.], tot_loss[loss=0.1697, simple_loss=0.259, pruned_loss=0.04021, over 1398078.73 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:35:23,658 INFO [train.py:842] (2/4) Epoch 37, batch 900, loss[loss=0.1394, simple_loss=0.2236, pruned_loss=0.02758, over 7063.00 frames.], tot_loss[loss=0.17, simple_loss=0.259, pruned_loss=0.04046, over 1406580.48 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 13:36:02,982 INFO [train.py:842] (2/4) Epoch 37, batch 950, loss[loss=0.1603, simple_loss=0.2503, pruned_loss=0.03517, over 7287.00 frames.], tot_loss[loss=0.17, simple_loss=0.2591, pruned_loss=0.04049, over 1410824.16 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 13:36:42,544 INFO [train.py:842] (2/4) Epoch 37, batch 1000, loss[loss=0.1678, simple_loss=0.2634, pruned_loss=0.03612, over 6793.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2589, pruned_loss=0.04001, over 1413772.82 frames.], batch size: 31, lr: 1.49e-04
2022-05-29 13:37:22,124 INFO [train.py:842] (2/4) Epoch 37, batch 1050, loss[loss=0.1512, simple_loss=0.239, pruned_loss=0.03171, over 7381.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2589, pruned_loss=0.03996, over 1419135.53 frames.], batch size: 23, lr: 1.49e-04
2022-05-29 13:38:01,726 INFO [train.py:842] (2/4) Epoch 37, batch 1100, loss[loss=0.1674, simple_loss=0.2655, pruned_loss=0.03466, over 7225.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2598, pruned_loss=0.04043, over 1419954.32 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 13:38:41,046 INFO [train.py:842] (2/4) Epoch 37, batch 1150, loss[loss=0.2033, simple_loss=0.2886, pruned_loss=0.05901, over 5166.00 frames.], tot_loss[loss=0.171, simple_loss=0.2601, pruned_loss=0.041, over 1419311.76 frames.], batch size: 53, lr: 1.49e-04
2022-05-29 13:39:20,628 INFO [train.py:842] (2/4) Epoch 37, batch 1200, loss[loss=0.2049, simple_loss=0.2885, pruned_loss=0.06064, over 7143.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2604, pruned_loss=0.04112, over 1420848.16 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:39:59,649 INFO [train.py:842] (2/4) Epoch 37, batch 1250, loss[loss=0.1657, simple_loss=0.2758, pruned_loss=0.02778, over 7224.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2614, pruned_loss=0.04143, over 1420380.36 frames.], batch size: 22, lr: 1.49e-04
2022-05-29 13:40:39,205 INFO [train.py:842] (2/4) Epoch 37, batch 1300, loss[loss=0.1744, simple_loss=0.2556, pruned_loss=0.04654, over 7139.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2621, pruned_loss=0.04166, over 1422820.08 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 13:41:18,368 INFO [train.py:842] (2/4) Epoch 37, batch 1350, loss[loss=0.1544, simple_loss=0.2412, pruned_loss=0.03379, over 7065.00 frames.], tot_loss[loss=0.1718, simple_loss=0.261, pruned_loss=0.04127, over 1419014.08 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 13:41:58,038 INFO [train.py:842] (2/4) Epoch 37, batch 1400, loss[loss=0.1737, simple_loss=0.2455, pruned_loss=0.05094, over 6984.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2613, pruned_loss=0.04146, over 1419352.33 frames.], batch size: 16, lr: 1.49e-04
2022-05-29 13:42:37,197 INFO [train.py:842] (2/4) Epoch 37, batch 1450, loss[loss=0.1598, simple_loss=0.252, pruned_loss=0.03379, over 7300.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2613, pruned_loss=0.04143, over 1420994.74 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 13:43:16,552 INFO [train.py:842] (2/4) Epoch 37, batch 1500, loss[loss=0.1662, simple_loss=0.2615, pruned_loss=0.03545, over 7315.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2619, pruned_loss=0.04167, over 1417884.46 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 13:43:55,720 INFO [train.py:842] (2/4) Epoch 37, batch 1550, loss[loss=0.1829, simple_loss=0.2677, pruned_loss=0.04901, over 6755.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2617, pruned_loss=0.04161, over 1413216.57 frames.], batch size: 31, lr: 1.49e-04
2022-05-29 13:44:35,495 INFO [train.py:842] (2/4) Epoch 37, batch 1600, loss[loss=0.1731, simple_loss=0.269, pruned_loss=0.03864, over 7372.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2611, pruned_loss=0.04137, over 1413212.38 frames.], batch size: 23, lr: 1.49e-04
2022-05-29 13:45:14,784 INFO [train.py:842] (2/4) Epoch 37, batch 1650, loss[loss=0.2028, simple_loss=0.2906, pruned_loss=0.05752, over 7227.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2608, pruned_loss=0.04101, over 1415871.28 frames.], batch size: 22, lr: 1.49e-04
2022-05-29 13:45:53,948 INFO [train.py:842] (2/4) Epoch 37, batch 1700, loss[loss=0.181, simple_loss=0.2639, pruned_loss=0.04903, over 7148.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2613, pruned_loss=0.04116, over 1414270.72 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:46:43,571 INFO [train.py:842] (2/4) Epoch 37, batch 1750, loss[loss=0.1641, simple_loss=0.2543, pruned_loss=0.03701, over 7348.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2618, pruned_loss=0.04161, over 1409325.41 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:47:22,925 INFO [train.py:842] (2/4) Epoch 37, batch 1800, loss[loss=0.1463, simple_loss=0.2431, pruned_loss=0.02476, over 7293.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2617, pruned_loss=0.04152, over 1411519.49 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 13:48:02,170 INFO [train.py:842] (2/4) Epoch 37, batch 1850, loss[loss=0.1592, simple_loss=0.2535, pruned_loss=0.03242, over 7256.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2609, pruned_loss=0.04102, over 1412136.70 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:48:41,581 INFO [train.py:842] (2/4) Epoch 37, batch 1900, loss[loss=0.2176, simple_loss=0.3058, pruned_loss=0.06471, over 6728.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2613, pruned_loss=0.04123, over 1417495.79 frames.], batch size: 31, lr: 1.49e-04
2022-05-29 13:49:20,956 INFO [train.py:842] (2/4) Epoch 37, batch 1950, loss[loss=0.1923, simple_loss=0.2895, pruned_loss=0.04756, over 7216.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2599, pruned_loss=0.04048, over 1420889.12 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 13:50:00,398 INFO [train.py:842] (2/4) Epoch 37, batch 2000, loss[loss=0.1609, simple_loss=0.2587, pruned_loss=0.03158, over 7420.00 frames.], tot_loss[loss=0.1705, simple_loss=0.26, pruned_loss=0.04045, over 1417866.15 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 13:50:39,829 INFO [train.py:842] (2/4) Epoch 37, batch 2050, loss[loss=0.1537, simple_loss=0.2517, pruned_loss=0.02778, over 7218.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2593, pruned_loss=0.04026, over 1420486.86 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:51:19,298 INFO [train.py:842] (2/4) Epoch 37, batch 2100, loss[loss=0.2082, simple_loss=0.2888, pruned_loss=0.0638, over 7145.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2611, pruned_loss=0.04124, over 1420152.75 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:51:58,331 INFO [train.py:842] (2/4) Epoch 37, batch 2150, loss[loss=0.1635, simple_loss=0.2598, pruned_loss=0.0336, over 7414.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2605, pruned_loss=0.04065, over 1417648.78 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 13:52:37,791 INFO [train.py:842] (2/4) Epoch 37, batch 2200, loss[loss=0.1519, simple_loss=0.2343, pruned_loss=0.03478, over 7256.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2596, pruned_loss=0.04043, over 1419427.66 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:53:16,122 INFO [train.py:842] (2/4) Epoch 37, batch 2250, loss[loss=0.1924, simple_loss=0.2909, pruned_loss=0.04696, over 7138.00 frames.], tot_loss[loss=0.171, simple_loss=0.2603, pruned_loss=0.04082, over 1419466.35 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 13:53:54,634 INFO [train.py:842] (2/4) Epoch 37, batch 2300, loss[loss=0.1688, simple_loss=0.2572, pruned_loss=0.04018, over 7181.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2598, pruned_loss=0.04033, over 1418527.98 frames.], batch size: 23, lr: 1.49e-04
2022-05-29 13:54:32,870 INFO [train.py:842] (2/4) Epoch 37, batch 2350, loss[loss=0.1453, simple_loss=0.232, pruned_loss=0.02926, over 7270.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2612, pruned_loss=0.04102, over 1411857.67 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 13:55:11,698 INFO [train.py:842] (2/4) Epoch 37, batch 2400, loss[loss=0.1739, simple_loss=0.2678, pruned_loss=0.04, over 7296.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2601, pruned_loss=0.04073, over 1418129.77 frames.], batch size: 25, lr: 1.49e-04
2022-05-29 13:55:50,271 INFO [train.py:842] (2/4) Epoch 37, batch 2450, loss[loss=0.1642, simple_loss=0.261, pruned_loss=0.03372, over 7150.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2594, pruned_loss=0.04007, over 1423778.58 frames.], batch size: 26, lr: 1.49e-04
2022-05-29 13:56:28,824 INFO [train.py:842] (2/4) Epoch 37, batch 2500, loss[loss=0.1948, simple_loss=0.2801, pruned_loss=0.05479, over 7162.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2587, pruned_loss=0.03959, over 1426818.05 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 13:57:07,132 INFO [train.py:842] (2/4) Epoch 37, batch 2550, loss[loss=0.1702, simple_loss=0.2607, pruned_loss=0.03988, over 7284.00 frames.], tot_loss[loss=0.169, simple_loss=0.2585, pruned_loss=0.03975, over 1427792.06 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 13:57:45,689 INFO [train.py:842] (2/4) Epoch 37, batch 2600, loss[loss=0.1496, simple_loss=0.2324, pruned_loss=0.03341, over 7232.00 frames.], tot_loss[loss=0.17, simple_loss=0.2594, pruned_loss=0.04029, over 1424208.53 frames.], batch size: 16, lr: 1.49e-04
2022-05-29 13:58:24,142 INFO [train.py:842] (2/4) Epoch 37, batch 2650, loss[loss=0.2067, simple_loss=0.2981, pruned_loss=0.05763, over 7213.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2604, pruned_loss=0.04062, over 1427153.99 frames.], batch size: 22, lr: 1.49e-04
2022-05-29 13:59:02,652 INFO [train.py:842] (2/4) Epoch 37, batch 2700, loss[loss=0.1642, simple_loss=0.2683, pruned_loss=0.03009, over 6469.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2604, pruned_loss=0.04062, over 1423269.74 frames.], batch size: 38, lr: 1.49e-04
2022-05-29 13:59:40,785 INFO [train.py:842] (2/4) Epoch 37, batch 2750, loss[loss=0.2002, simple_loss=0.2835, pruned_loss=0.05848, over 4985.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2615, pruned_loss=0.04105, over 1424144.98 frames.], batch size: 52, lr: 1.49e-04
2022-05-29 14:00:19,743 INFO [train.py:842] (2/4) Epoch 37, batch 2800, loss[loss=0.1523, simple_loss=0.237, pruned_loss=0.03382, over 7289.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2612, pruned_loss=0.04088, over 1429490.85 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 14:00:58,253 INFO [train.py:842] (2/4) Epoch 37, batch 2850, loss[loss=0.1541, simple_loss=0.2493, pruned_loss=0.02943, over 6459.00 frames.], tot_loss[loss=0.1722, simple_loss=0.262, pruned_loss=0.04122, over 1427832.80 frames.], batch size: 38, lr: 1.49e-04
2022-05-29 14:01:37,106 INFO [train.py:842] (2/4) Epoch 37, batch 2900, loss[loss=0.136, simple_loss=0.212, pruned_loss=0.02995, over 7009.00 frames.], tot_loss[loss=0.171, simple_loss=0.2607, pruned_loss=0.0407, over 1428764.17 frames.], batch size: 16, lr: 1.49e-04
2022-05-29 14:02:15,628 INFO [train.py:842] (2/4) Epoch 37, batch 2950, loss[loss=0.1585, simple_loss=0.256, pruned_loss=0.03051, over 7424.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2606, pruned_loss=0.0408, over 1424580.09 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 14:02:54,132 INFO [train.py:842] (2/4) Epoch 37, batch 3000, loss[loss=0.1641, simple_loss=0.2597, pruned_loss=0.03425, over 7222.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2607, pruned_loss=0.04095, over 1421131.71 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 14:02:54,133 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 14:03:03,497 INFO [train.py:871] (2/4) Epoch 37, validation: loss=0.1648, simple_loss=0.2616, pruned_loss=0.03398, over 868885.00 frames.
2022-05-29 14:03:41,915 INFO [train.py:842] (2/4) Epoch 37, batch 3050, loss[loss=0.1739, simple_loss=0.2511, pruned_loss=0.0484, over 7168.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2616, pruned_loss=0.04164, over 1420843.28 frames.], batch size: 16, lr: 1.49e-04
2022-05-29 14:04:20,732 INFO [train.py:842] (2/4) Epoch 37, batch 3100, loss[loss=0.1692, simple_loss=0.2461, pruned_loss=0.04618, over 7064.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2615, pruned_loss=0.04156, over 1418692.94 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 14:04:59,168 INFO [train.py:842] (2/4) Epoch 37, batch 3150, loss[loss=0.1855, simple_loss=0.2677, pruned_loss=0.05163, over 7007.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2616, pruned_loss=0.0419, over 1419426.94 frames.], batch size: 16, lr: 1.49e-04
2022-05-29 14:05:37,843 INFO [train.py:842] (2/4) Epoch 37, batch 3200, loss[loss=0.3312, simple_loss=0.3779, pruned_loss=0.1422, over 5155.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2616, pruned_loss=0.04201, over 1420188.94 frames.], batch size: 52, lr: 1.49e-04
2022-05-29 14:06:16,112 INFO [train.py:842] (2/4) Epoch 37, batch 3250, loss[loss=0.1807, simple_loss=0.2716, pruned_loss=0.04493, over 7227.00 frames.], tot_loss[loss=0.173, simple_loss=0.2617, pruned_loss=0.04213, over 1419634.78 frames.], batch size: 22, lr: 1.49e-04
2022-05-29 14:06:54,705 INFO [train.py:842] (2/4) Epoch 37, batch 3300, loss[loss=0.1698, simple_loss=0.2598, pruned_loss=0.03993, over 7416.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2617, pruned_loss=0.04174, over 1416882.24 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 14:07:32,812 INFO [train.py:842] (2/4) Epoch 37, batch 3350, loss[loss=0.1889, simple_loss=0.2705, pruned_loss=0.05367, over 7368.00 frames.], tot_loss[loss=0.174, simple_loss=0.2636, pruned_loss=0.0422, over 1412729.02 frames.], batch size: 23, lr: 1.49e-04
2022-05-29 14:08:11,570 INFO [train.py:842] (2/4) Epoch 37, batch 3400, loss[loss=0.1828, simple_loss=0.2664, pruned_loss=0.04961, over 7131.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2636, pruned_loss=0.04274, over 1417051.13 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 14:08:50,146 INFO [train.py:842] (2/4) Epoch 37, batch 3450, loss[loss=0.157, simple_loss=0.2368, pruned_loss=0.03864, over 7275.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2616, pruned_loss=0.04174, over 1419628.54 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 14:09:28,737 INFO [train.py:842] (2/4) Epoch 37, batch 3500, loss[loss=0.1428, simple_loss=0.2359, pruned_loss=0.0248, over 7372.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2615, pruned_loss=0.04132, over 1418290.19 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 14:10:06,965 INFO [train.py:842] (2/4) Epoch 37, batch 3550, loss[loss=0.1518, simple_loss=0.2229, pruned_loss=0.04033, over 7202.00 frames.], tot_loss[loss=0.171, simple_loss=0.2607, pruned_loss=0.04066, over 1415631.75 frames.], batch size: 16, lr: 1.49e-04
2022-05-29 14:10:45,910 INFO [train.py:842] (2/4) Epoch 37, batch 3600, loss[loss=0.1281, simple_loss=0.211, pruned_loss=0.02253, over 6985.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2589, pruned_loss=0.03972, over 1421821.46 frames.], batch size: 16, lr: 1.49e-04
2022-05-29 14:11:24,122 INFO [train.py:842] (2/4) Epoch 37, batch 3650, loss[loss=0.1652, simple_loss=0.2476, pruned_loss=0.0414, over 7162.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2588, pruned_loss=0.03986, over 1424370.38 frames.], batch size: 19, lr: 1.49e-04
2022-05-29 14:12:02,919 INFO [train.py:842] (2/4) Epoch 37, batch 3700, loss[loss=0.1626, simple_loss=0.265, pruned_loss=0.0301, over 7229.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2588, pruned_loss=0.04001, over 1426781.12 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 14:12:41,216 INFO [train.py:842] (2/4) Epoch 37, batch 3750, loss[loss=0.1991, simple_loss=0.3007, pruned_loss=0.04873, over 7279.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2592, pruned_loss=0.03998, over 1423494.89 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 14:13:19,981 INFO [train.py:842] (2/4) Epoch 37, batch 3800, loss[loss=0.1622, simple_loss=0.2488, pruned_loss=0.03779, over 7283.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2593, pruned_loss=0.04012, over 1424789.38 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 14:13:58,263 INFO [train.py:842] (2/4) Epoch 37, batch 3850, loss[loss=0.2304, simple_loss=0.2983, pruned_loss=0.08123, over 4985.00 frames.], tot_loss[loss=0.1708, simple_loss=0.26, pruned_loss=0.04078, over 1423631.38 frames.], batch size: 53, lr: 1.49e-04
2022-05-29 14:14:37,038 INFO [train.py:842] (2/4) Epoch 37, batch 3900, loss[loss=0.182, simple_loss=0.2748, pruned_loss=0.0446, over 7338.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2596, pruned_loss=0.04069, over 1425575.77 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 14:15:15,723 INFO [train.py:842] (2/4) Epoch 37, batch 3950, loss[loss=0.1478, simple_loss=0.2313, pruned_loss=0.0321, over 7287.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2598, pruned_loss=0.04069, over 1426556.91 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 14:15:54,288 INFO [train.py:842] (2/4) Epoch 37, batch 4000, loss[loss=0.1854, simple_loss=0.2626, pruned_loss=0.05409, over 7063.00 frames.], tot_loss[loss=0.1719, simple_loss=0.261, pruned_loss=0.04137, over 1426858.80 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 14:16:32,610 INFO [train.py:842] (2/4) Epoch 37, batch 4050, loss[loss=0.1833, simple_loss=0.2647, pruned_loss=0.05096, over 7281.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2603, pruned_loss=0.04102, over 1428143.58 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 14:17:11,126 INFO [train.py:842] (2/4) Epoch 37, batch 4100, loss[loss=0.1415, simple_loss=0.242, pruned_loss=0.02045, over 7114.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2604, pruned_loss=0.04057, over 1424943.25 frames.], batch size: 21, lr: 1.49e-04
2022-05-29 14:17:49,517 INFO [train.py:842] (2/4) Epoch 37, batch 4150, loss[loss=0.1728, simple_loss=0.2716, pruned_loss=0.03699, over 7326.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2595, pruned_loss=0.04046, over 1425347.31 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 14:18:28,582 INFO [train.py:842] (2/4) Epoch 37, batch 4200, loss[loss=0.1587, simple_loss=0.2385, pruned_loss=0.03941, over 7278.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2586, pruned_loss=0.04015, over 1428075.55 frames.], batch size: 17, lr: 1.49e-04
2022-05-29 14:19:06,939 INFO [train.py:842] (2/4) Epoch 37, batch 4250, loss[loss=0.1533, simple_loss=0.2462, pruned_loss=0.03022, over 7226.00 frames.], tot_loss[loss=0.1687, simple_loss=0.258, pruned_loss=0.03973, over 1427834.69 frames.], batch size: 20, lr: 1.49e-04
2022-05-29 14:19:45,857 INFO [train.py:842] (2/4) Epoch 37, batch 4300, loss[loss=0.1859, simple_loss=0.2651, pruned_loss=0.05336, over 7396.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2589, pruned_loss=0.04039, over 1431131.40 frames.], batch size: 18, lr: 1.49e-04
2022-05-29 14:20:24,423 INFO [train.py:842] (2/4) Epoch 37, batch 4350, loss[loss=0.1781, simple_loss=0.2719, pruned_loss=0.04221, over 7113.00 frames.], tot_loss[loss=0.17, simple_loss=0.2591, pruned_loss=0.04044, over 1427872.14 frames.], batch size: 28, lr: 1.49e-04
2022-05-29 14:21:03,030 INFO [train.py:842] (2/4) Epoch 37, batch 4400, loss[loss=0.1864, simple_loss=0.276, pruned_loss=0.04846, over 7346.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2602, pruned_loss=0.0411, over 1426288.25 frames.], batch size: 22, lr: 1.49e-04
2022-05-29 14:21:41,580 INFO [train.py:842] (2/4) Epoch 37, batch 4450, loss[loss=0.2048, simple_loss=0.2952, pruned_loss=0.05722, over 7043.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2611, pruned_loss=0.04158, over 1429131.83 frames.], batch size: 28, lr: 1.49e-04
2022-05-29 14:22:20,381 INFO [train.py:842] (2/4) Epoch 37, batch 4500, loss[loss=0.216, simple_loss=0.2955, pruned_loss=0.06826, over 7243.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2608, pruned_loss=0.04154, over 1426301.30 frames.], batch size: 24, lr: 1.49e-04
2022-05-29 14:22:58,722 INFO [train.py:842] (2/4) Epoch 37, batch 4550, loss[loss=0.2027, simple_loss=0.295, pruned_loss=0.05514, over 7259.00 frames.], tot_loss[loss=0.172, simple_loss=0.2609, pruned_loss=0.04154, over 1424752.63 frames.], batch size: 25, lr: 1.48e-04
2022-05-29 14:23:37,637 INFO [train.py:842] (2/4) Epoch 37, batch 4600, loss[loss=0.1587, simple_loss=0.2474, pruned_loss=0.03505, over 7158.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2608, pruned_loss=0.04181, over 1424328.50 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:24:16,116 INFO [train.py:842] (2/4) Epoch 37, batch 4650, loss[loss=0.1754, simple_loss=0.2824, pruned_loss=0.03423, over 7141.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2598, pruned_loss=0.0406, over 1425144.63 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:24:54,742 INFO [train.py:842] (2/4) Epoch 37, batch 4700, loss[loss=0.1485, simple_loss=0.249, pruned_loss=0.02398, over 6774.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2604, pruned_loss=0.04062, over 1424355.49 frames.], batch size: 31, lr: 1.48e-04
2022-05-29 14:25:33,359 INFO [train.py:842] (2/4) Epoch 37, batch 4750, loss[loss=0.1721, simple_loss=0.2679, pruned_loss=0.03812, over 7228.00 frames.], tot_loss[loss=0.1708, simple_loss=0.26, pruned_loss=0.04076, over 1427107.48 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:26:11,904 INFO [train.py:842] (2/4) Epoch 37, batch 4800, loss[loss=0.1516, simple_loss=0.2423, pruned_loss=0.03044, over 7160.00 frames.], tot_loss[loss=0.171, simple_loss=0.2604, pruned_loss=0.04083, over 1426098.30 frames.], batch size: 26, lr: 1.48e-04
2022-05-29 14:26:50,416 INFO [train.py:842] (2/4) Epoch 37, batch 4850, loss[loss=0.1787, simple_loss=0.2713, pruned_loss=0.04305, over 7115.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2608, pruned_loss=0.04113, over 1430578.34 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:27:29,124 INFO [train.py:842] (2/4) Epoch 37, batch 4900, loss[loss=0.1669, simple_loss=0.2423, pruned_loss=0.04576, over 6771.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2606, pruned_loss=0.04066, over 1433085.23 frames.], batch size: 15, lr: 1.48e-04
2022-05-29 14:28:07,578 INFO [train.py:842] (2/4) Epoch 37, batch 4950, loss[loss=0.169, simple_loss=0.263, pruned_loss=0.03744, over 7420.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2603, pruned_loss=0.04056, over 1434384.57 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:28:46,390 INFO [train.py:842] (2/4) Epoch 37, batch 5000, loss[loss=0.1381, simple_loss=0.219, pruned_loss=0.02857, over 7286.00 frames.], tot_loss[loss=0.17, simple_loss=0.2593, pruned_loss=0.0404, over 1430751.65 frames.], batch size: 17, lr: 1.48e-04
2022-05-29 14:29:24,650 INFO [train.py:842] (2/4) Epoch 37, batch 5050, loss[loss=0.1511, simple_loss=0.2466, pruned_loss=0.02779, over 7044.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2598, pruned_loss=0.04038, over 1427882.20 frames.], batch size: 28, lr: 1.48e-04
2022-05-29 14:30:06,271 INFO [train.py:842] (2/4) Epoch 37, batch 5100, loss[loss=0.193, simple_loss=0.2843, pruned_loss=0.0509, over 7230.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2597, pruned_loss=0.04049, over 1420443.19 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:30:44,609 INFO [train.py:842] (2/4) Epoch 37, batch 5150, loss[loss=0.1756, simple_loss=0.2563, pruned_loss=0.04747, over 7116.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2601, pruned_loss=0.04064, over 1420291.54 frames.], batch size: 28, lr: 1.48e-04
2022-05-29 14:31:23,406 INFO [train.py:842] (2/4) Epoch 37, batch 5200, loss[loss=0.1834, simple_loss=0.2606, pruned_loss=0.05308, over 7275.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2601, pruned_loss=0.0408, over 1421734.84 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:32:01,778 INFO [train.py:842] (2/4) Epoch 37, batch 5250, loss[loss=0.1499, simple_loss=0.2301, pruned_loss=0.03488, over 7181.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2605, pruned_loss=0.04065, over 1422948.47 frames.], batch size: 16, lr: 1.48e-04
2022-05-29 14:32:40,356 INFO [train.py:842] (2/4) Epoch 37, batch 5300, loss[loss=0.1929, simple_loss=0.2831, pruned_loss=0.05137, over 7318.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2603, pruned_loss=0.04031, over 1426268.74 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:33:18,729 INFO [train.py:842] (2/4) Epoch 37, batch 5350, loss[loss=0.1731, simple_loss=0.272, pruned_loss=0.03714, over 7341.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2599, pruned_loss=0.03999, over 1426744.20 frames.], batch size: 22, lr: 1.48e-04
2022-05-29 14:33:57,366 INFO [train.py:842] (2/4) Epoch 37, batch 5400, loss[loss=0.1765, simple_loss=0.2626, pruned_loss=0.0452, over 7311.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2606, pruned_loss=0.0405, over 1423135.42 frames.], batch size: 25, lr: 1.48e-04
2022-05-29 14:34:35,833 INFO [train.py:842] (2/4) Epoch 37, batch 5450, loss[loss=0.1713, simple_loss=0.2721, pruned_loss=0.03522, over 7377.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2599, pruned_loss=0.04046, over 1424393.45 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 14:35:14,221 INFO [train.py:842] (2/4) Epoch 37, batch 5500, loss[loss=0.183, simple_loss=0.2731, pruned_loss=0.04648, over 7259.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2598, pruned_loss=0.04024, over 1420100.39 frames.], batch size: 19, lr: 1.48e-04
2022-05-29 14:35:52,476 INFO [train.py:842] (2/4) Epoch 37, batch 5550, loss[loss=0.1696, simple_loss=0.259, pruned_loss=0.04016, over 7212.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2604, pruned_loss=0.04022, over 1417149.84 frames.], batch size: 22, lr: 1.48e-04
2022-05-29 14:36:31,228 INFO [train.py:842] (2/4) Epoch 37, batch 5600, loss[loss=0.2017, simple_loss=0.29, pruned_loss=0.05666, over 7063.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2606, pruned_loss=0.04045, over 1418252.58 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:37:09,317 INFO [train.py:842] (2/4) Epoch 37, batch 5650, loss[loss=0.1698, simple_loss=0.2627, pruned_loss=0.03844, over 7323.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2604, pruned_loss=0.04025, over 1415873.76 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:37:47,958 INFO [train.py:842] (2/4) Epoch 37, batch 5700, loss[loss=0.1473, simple_loss=0.24, pruned_loss=0.02734, over 7162.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2605, pruned_loss=0.04048, over 1417397.54 frames.], batch size: 19, lr: 1.48e-04
2022-05-29 14:38:26,353 INFO [train.py:842] (2/4) Epoch 37, batch 5750, loss[loss=0.1538, simple_loss=0.2505, pruned_loss=0.02857, over 7258.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2601, pruned_loss=0.04051, over 1418869.16 frames.], batch size: 19, lr: 1.48e-04
2022-05-29 14:39:04,945 INFO [train.py:842] (2/4) Epoch 37, batch 5800, loss[loss=0.1722, simple_loss=0.2767, pruned_loss=0.03383, over 7412.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2619, pruned_loss=0.04126, over 1419918.69 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:39:43,219 INFO [train.py:842] (2/4) Epoch 37, batch 5850, loss[loss=0.153, simple_loss=0.2451, pruned_loss=0.03044, over 7073.00 frames.], tot_loss[loss=0.1717, simple_loss=0.262, pruned_loss=0.0407, over 1420321.15 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:40:21,996 INFO [train.py:842] (2/4) Epoch 37, batch 5900, loss[loss=0.1845, simple_loss=0.2842, pruned_loss=0.04239, over 7169.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2616, pruned_loss=0.04063, over 1422053.63 frames.], batch size: 26, lr: 1.48e-04
2022-05-29 14:41:00,163 INFO [train.py:842] (2/4) Epoch 37, batch 5950, loss[loss=0.1595, simple_loss=0.2472, pruned_loss=0.03593, over 7054.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2616, pruned_loss=0.04061, over 1420549.90 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:41:39,107 INFO [train.py:842] (2/4) Epoch 37, batch 6000, loss[loss=0.1636, simple_loss=0.2412, pruned_loss=0.04301, over 7421.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2605, pruned_loss=0.04024, over 1423777.68 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:41:39,107 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 14:41:48,826 INFO [train.py:871] (2/4) Epoch 37, validation: loss=0.1649, simple_loss=0.2613, pruned_loss=0.03429, over 868885.00 frames.
2022-05-29 14:42:27,122 INFO [train.py:842] (2/4) Epoch 37, batch 6050, loss[loss=0.1828, simple_loss=0.2699, pruned_loss=0.04789, over 7333.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2604, pruned_loss=0.04037, over 1420813.74 frames.], batch size: 25, lr: 1.48e-04
2022-05-29 14:43:05,751 INFO [train.py:842] (2/4) Epoch 37, batch 6100, loss[loss=0.1558, simple_loss=0.2497, pruned_loss=0.03097, over 7332.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2601, pruned_loss=0.0397, over 1421906.92 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:43:43,960 INFO [train.py:842] (2/4) Epoch 37, batch 6150, loss[loss=0.1453, simple_loss=0.2246, pruned_loss=0.03301, over 6989.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2602, pruned_loss=0.03982, over 1417534.47 frames.], batch size: 16, lr: 1.48e-04
2022-05-29 14:44:22,777 INFO [train.py:842] (2/4) Epoch 37, batch 6200, loss[loss=0.1883, simple_loss=0.2772, pruned_loss=0.04971, over 7066.00 frames.], tot_loss[loss=0.1708, simple_loss=0.261, pruned_loss=0.04035, over 1416474.39 frames.], batch size: 28, lr: 1.48e-04
2022-05-29 14:45:01,316 INFO [train.py:842] (2/4) Epoch 37, batch 6250, loss[loss=0.1814, simple_loss=0.2565, pruned_loss=0.05314, over 7122.00 frames.], tot_loss[loss=0.1701, simple_loss=0.26, pruned_loss=0.04009, over 1420626.56 frames.], batch size: 17, lr: 1.48e-04
2022-05-29 14:45:40,233 INFO [train.py:842] (2/4) Epoch 37, batch 6300, loss[loss=0.1489, simple_loss=0.2331, pruned_loss=0.03237, over 7015.00 frames.], tot_loss[loss=0.17, simple_loss=0.259, pruned_loss=0.04053, over 1420391.91 frames.], batch size: 16, lr: 1.48e-04
2022-05-29 14:46:18,700 INFO [train.py:842] (2/4) Epoch 37, batch 6350, loss[loss=0.1841, simple_loss=0.2693, pruned_loss=0.04941, over 7191.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2582, pruned_loss=0.04003, over 1421931.11 frames.], batch size: 26, lr: 1.48e-04
2022-05-29 14:46:57,203 INFO [train.py:842] (2/4) Epoch 37, batch 6400, loss[loss=0.1622, simple_loss=0.2533, pruned_loss=0.03554, over 7318.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2577, pruned_loss=0.03964, over 1419746.90 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:47:35,914 INFO [train.py:842] (2/4) Epoch 37, batch 6450, loss[loss=0.1644, simple_loss=0.2545, pruned_loss=0.03711, over 6534.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2584, pruned_loss=0.04031, over 1422210.54 frames.], batch size: 38, lr: 1.48e-04
2022-05-29 14:48:14,928 INFO [train.py:842] (2/4) Epoch 37, batch 6500, loss[loss=0.1491, simple_loss=0.2482, pruned_loss=0.02502, over 7152.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2579, pruned_loss=0.03997, over 1426497.80 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:48:53,195 INFO [train.py:842] (2/4) Epoch 37, batch 6550, loss[loss=0.1854, simple_loss=0.2724, pruned_loss=0.04919, over 7311.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2591, pruned_loss=0.04009, over 1422634.30 frames.], batch size: 24, lr: 1.48e-04
2022-05-29 14:49:31,826 INFO [train.py:842] (2/4) Epoch 37, batch 6600, loss[loss=0.1695, simple_loss=0.2539, pruned_loss=0.04254, over 7376.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2587, pruned_loss=0.04028, over 1418120.91 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 14:50:10,221 INFO [train.py:842] (2/4) Epoch 37, batch 6650, loss[loss=0.1454, simple_loss=0.2473, pruned_loss=0.02173, over 7116.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2582, pruned_loss=0.04027, over 1415662.76 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:50:48,908 INFO [train.py:842] (2/4) Epoch 37, batch 6700, loss[loss=0.1708, simple_loss=0.2603, pruned_loss=0.04063, over 7140.00 frames.], tot_loss[loss=0.1705, simple_loss=0.259, pruned_loss=0.04104, over 1414092.35 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:51:27,552 INFO [train.py:842] (2/4) Epoch 37, batch 6750, loss[loss=0.2265, simple_loss=0.3075, pruned_loss=0.07271, over 7418.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2589, pruned_loss=0.04083, over 1417812.10 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:52:06,466 INFO [train.py:842] (2/4) Epoch 37, batch 6800, loss[loss=0.1869, simple_loss=0.2944, pruned_loss=0.03971, over 7232.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2592, pruned_loss=0.04033, over 1422037.09 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 14:52:44,811 INFO [train.py:842] (2/4) Epoch 37, batch 6850, loss[loss=0.1776, simple_loss=0.2729, pruned_loss=0.04114, over 7205.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2581, pruned_loss=0.03966, over 1416197.33 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 14:53:23,530 INFO [train.py:842] (2/4) Epoch 37, batch 6900, loss[loss=0.1523, simple_loss=0.242, pruned_loss=0.03128, over 7236.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2585, pruned_loss=0.04024, over 1419436.03 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:54:01,943 INFO [train.py:842] (2/4) Epoch 37, batch 6950, loss[loss=0.165, simple_loss=0.2518, pruned_loss=0.03909, over 7364.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2593, pruned_loss=0.04051, over 1420034.46 frames.], batch size: 19, lr: 1.48e-04
2022-05-29 14:54:40,633 INFO [train.py:842] (2/4) Epoch 37, batch 7000, loss[loss=0.1899, simple_loss=0.2876, pruned_loss=0.04606, over 7381.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2604, pruned_loss=0.04157, over 1418056.89 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 14:55:19,147 INFO [train.py:842] (2/4) Epoch 37, batch 7050, loss[loss=0.1671, simple_loss=0.2686, pruned_loss=0.03276, over 7218.00 frames.], tot_loss[loss=0.1722, simple_loss=0.261, pruned_loss=0.04166, over 1418425.74 frames.], batch size: 22, lr: 1.48e-04
2022-05-29 14:55:57,882 INFO [train.py:842] (2/4) Epoch 37, batch 7100, loss[loss=0.1959, simple_loss=0.2804, pruned_loss=0.05575, over 7387.00 frames.], tot_loss[loss=0.171, simple_loss=0.2599, pruned_loss=0.04107, over 1415014.94 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 14:56:36,268 INFO [train.py:842] (2/4) Epoch 37, batch 7150, loss[loss=0.2089, simple_loss=0.3057, pruned_loss=0.05605, over 6392.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2607, pruned_loss=0.04113, over 1417605.03 frames.], batch size: 37, lr: 1.48e-04
2022-05-29 14:57:14,667 INFO [train.py:842] (2/4) Epoch 37, batch 7200, loss[loss=0.1632, simple_loss=0.2433, pruned_loss=0.04155, over 7285.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2611, pruned_loss=0.04106, over 1415103.60 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 14:58:03,030 INFO [train.py:842] (2/4) Epoch 37, batch 7250, loss[loss=0.2771, simple_loss=0.3426, pruned_loss=0.1058, over 6174.00 frames.], tot_loss[loss=0.171, simple_loss=0.2602, pruned_loss=0.04089, over 1411569.31 frames.], batch size: 37, lr: 1.48e-04
2022-05-29 14:58:41,377 INFO [train.py:842] (2/4) Epoch 37, batch 7300, loss[loss=0.1711, simple_loss=0.2759, pruned_loss=0.03316, over 7146.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2608, pruned_loss=0.04118, over 1407153.28 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:59:20,036 INFO [train.py:842] (2/4) Epoch 37, batch 7350, loss[loss=0.1767, simple_loss=0.266, pruned_loss=0.04367, over 7420.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2604, pruned_loss=0.04151, over 1415478.80 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 14:59:58,588 INFO [train.py:842] (2/4) Epoch 37, batch 7400, loss[loss=0.1665, simple_loss=0.2538, pruned_loss=0.03957, over 7196.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2602, pruned_loss=0.04148, over 1411324.96 frames.], batch size: 22, lr: 1.48e-04
2022-05-29 15:00:37,089 INFO [train.py:842] (2/4) Epoch 37, batch 7450, loss[loss=0.2159, simple_loss=0.3014, pruned_loss=0.06517, over 7186.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2592, pruned_loss=0.04089, over 1415629.67 frames.], batch size: 26, lr: 1.48e-04
2022-05-29 15:01:15,783 INFO [train.py:842] (2/4) Epoch 37, batch 7500, loss[loss=0.1593, simple_loss=0.2525, pruned_loss=0.03305, over 7411.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2605, pruned_loss=0.04121, over 1417343.78 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:01:54,113 INFO [train.py:842] (2/4) Epoch 37, batch 7550, loss[loss=0.1623, simple_loss=0.2646, pruned_loss=0.03004, over 6831.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2604, pruned_loss=0.04096, over 1420306.91 frames.], batch size: 31, lr: 1.48e-04
2022-05-29 15:02:32,811 INFO [train.py:842] (2/4) Epoch 37, batch 7600, loss[loss=0.3155, simple_loss=0.3688, pruned_loss=0.1311, over 5323.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2613, pruned_loss=0.04182, over 1417133.25 frames.], batch size: 53, lr: 1.48e-04
2022-05-29 15:03:20,989 INFO [train.py:842] (2/4) Epoch 37, batch 7650, loss[loss=0.1674, simple_loss=0.264, pruned_loss=0.03543, over 7412.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2619, pruned_loss=0.04181, over 1423016.26 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:04:09,697 INFO [train.py:842] (2/4) Epoch 37, batch 7700, loss[loss=0.1758, simple_loss=0.2733, pruned_loss=0.0392, over 7202.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2614, pruned_loss=0.04147, over 1423579.45 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 15:04:48,017 INFO [train.py:842] (2/4) Epoch 37, batch 7750, loss[loss=0.1239, simple_loss=0.2088, pruned_loss=0.01953, over 6800.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2614, pruned_loss=0.04112, over 1423880.82 frames.], batch size: 15, lr: 1.48e-04
2022-05-29 15:05:26,768 INFO [train.py:842] (2/4) Epoch 37, batch 7800, loss[loss=0.1645, simple_loss=0.2567, pruned_loss=0.03611, over 7325.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2618, pruned_loss=0.0414, over 1424810.74 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 15:06:05,352 INFO [train.py:842] (2/4) Epoch 37, batch 7850, loss[loss=0.1778, simple_loss=0.2763, pruned_loss=0.03961, over 6152.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2603, pruned_loss=0.04093, over 1428920.88 frames.], batch size: 37, lr: 1.48e-04
2022-05-29 15:06:44,242 INFO [train.py:842] (2/4) Epoch 37, batch 7900, loss[loss=0.1722, simple_loss=0.2607, pruned_loss=0.04183, over 7358.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2599, pruned_loss=0.04029, over 1431572.86 frames.], batch size: 19, lr: 1.48e-04
2022-05-29 15:07:22,862 INFO [train.py:842] (2/4) Epoch 37, batch 7950, loss[loss=0.2208, simple_loss=0.2957, pruned_loss=0.07298, over 7310.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2597, pruned_loss=0.03997, over 1433689.17 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:08:01,509 INFO [train.py:842] (2/4) Epoch 37, batch 8000, loss[loss=0.1519, simple_loss=0.235, pruned_loss=0.03442, over 7011.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2604, pruned_loss=0.04047, over 1425781.04 frames.], batch size: 16, lr: 1.48e-04
2022-05-29 15:08:39,685 INFO [train.py:842] (2/4) Epoch 37, batch 8050, loss[loss=0.1731, simple_loss=0.2665, pruned_loss=0.03989, over 7150.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2602, pruned_loss=0.04043, over 1423955.41 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 15:09:18,459 INFO [train.py:842] (2/4) Epoch 37, batch 8100, loss[loss=0.1662, simple_loss=0.2616, pruned_loss=0.03536, over 7325.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2603, pruned_loss=0.04049, over 1425767.96 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:09:56,848 INFO [train.py:842] (2/4) Epoch 37, batch 8150, loss[loss=0.156, simple_loss=0.2538, pruned_loss=0.02909, over 7319.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2621, pruned_loss=0.04165, over 1419343.60 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:10:35,179 INFO [train.py:842] (2/4) Epoch 37, batch 8200, loss[loss=0.1737, simple_loss=0.2684, pruned_loss=0.03955, over 7142.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2619, pruned_loss=0.04137, over 1420018.48 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 15:11:13,491 INFO [train.py:842] (2/4) Epoch 37, batch 8250, loss[loss=0.215, simple_loss=0.296, pruned_loss=0.06698, over 7277.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2616, pruned_loss=0.04134, over 1420724.08 frames.], batch size: 24, lr: 1.48e-04
2022-05-29 15:11:52,000 INFO [train.py:842] (2/4) Epoch 37, batch 8300, loss[loss=0.1537, simple_loss=0.2443, pruned_loss=0.03155, over 7163.00 frames.], tot_loss[loss=0.1736, simple_loss=0.263, pruned_loss=0.04215, over 1417964.29 frames.], batch size: 23, lr: 1.48e-04
2022-05-29 15:12:30,177 INFO [train.py:842] (2/4) Epoch 37, batch 8350, loss[loss=0.2129, simple_loss=0.3147, pruned_loss=0.05553, over 7333.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2643, pruned_loss=0.04275, over 1420499.51 frames.], batch size: 22, lr: 1.48e-04
2022-05-29 15:13:09,198 INFO [train.py:842] (2/4) Epoch 37, batch 8400, loss[loss=0.1596, simple_loss=0.2454, pruned_loss=0.03692, over 6797.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2622, pruned_loss=0.04201, over 1422066.81 frames.], batch size: 15, lr: 1.48e-04
2022-05-29 15:13:47,728 INFO [train.py:842] (2/4) Epoch 37, batch 8450, loss[loss=0.1702, simple_loss=0.2486, pruned_loss=0.04586, over 7077.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2618, pruned_loss=0.04257, over 1422057.64 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 15:14:26,394 INFO [train.py:842] (2/4) Epoch 37, batch 8500, loss[loss=0.1765, simple_loss=0.2554, pruned_loss=0.04877, over 7297.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2613, pruned_loss=0.04223, over 1422901.83 frames.], batch size: 17, lr: 1.48e-04
2022-05-29 15:15:04,526 INFO [train.py:842] (2/4) Epoch 37, batch 8550, loss[loss=0.1604, simple_loss=0.2591, pruned_loss=0.03082, over 7103.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2622, pruned_loss=0.0421, over 1422874.18 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:15:43,228 INFO [train.py:842] (2/4) Epoch 37, batch 8600, loss[loss=0.2375, simple_loss=0.3078, pruned_loss=0.08362, over 7015.00 frames.], tot_loss[loss=0.173, simple_loss=0.2618, pruned_loss=0.0421, over 1418149.99 frames.], batch size: 28, lr: 1.48e-04
2022-05-29 15:16:21,378 INFO [train.py:842] (2/4) Epoch 37, batch 8650, loss[loss=0.1481, simple_loss=0.231, pruned_loss=0.03264, over 7441.00 frames.], tot_loss[loss=0.172, simple_loss=0.2609, pruned_loss=0.04155, over 1417979.22 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 15:16:59,580 INFO [train.py:842] (2/4) Epoch 37, batch 8700, loss[loss=0.1667, simple_loss=0.2539, pruned_loss=0.03969, over 7437.00 frames.], tot_loss[loss=0.1721, simple_loss=0.261, pruned_loss=0.04162, over 1412769.89 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 15:17:37,719 INFO [train.py:842] (2/4) Epoch 37, batch 8750, loss[loss=0.1563, simple_loss=0.2499, pruned_loss=0.03133, over 7164.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2622, pruned_loss=0.04223, over 1411698.90 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 15:18:16,303 INFO [train.py:842] (2/4) Epoch 37, batch 8800, loss[loss=0.1723, simple_loss=0.2715, pruned_loss=0.03655, over 7141.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2617, pruned_loss=0.04167, over 1412714.41 frames.], batch size: 20, lr: 1.48e-04
2022-05-29 15:18:54,788 INFO [train.py:842] (2/4) Epoch 37, batch 8850, loss[loss=0.1703, simple_loss=0.2525, pruned_loss=0.04403, over 7271.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2618, pruned_loss=0.04188, over 1413030.94 frames.], batch size: 18, lr: 1.48e-04
2022-05-29 15:19:33,458 INFO [train.py:842] (2/4) Epoch 37, batch 8900, loss[loss=0.1445, simple_loss=0.242, pruned_loss=0.02351, over 6367.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2629, pruned_loss=0.04233, over 1415624.70 frames.], batch size: 37, lr: 1.48e-04
2022-05-29 15:20:11,861 INFO [train.py:842] (2/4) Epoch 37, batch 8950, loss[loss=0.1542, simple_loss=0.2343, pruned_loss=0.0371, over 7140.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2622, pruned_loss=0.04174, over 1413442.32 frames.], batch size: 17, lr: 1.48e-04
2022-05-29 15:20:50,451 INFO [train.py:842] (2/4) Epoch 37, batch 9000, loss[loss=0.1816, simple_loss=0.2668, pruned_loss=0.04822, over 7104.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2626, pruned_loss=0.04215, over 1408694.18 frames.], batch size: 21, lr: 1.48e-04
2022-05-29 15:20:50,452 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 15:20:59,818 INFO [train.py:871] (2/4) Epoch 37, validation: loss=0.1648, simple_loss=0.2612, pruned_loss=0.03416, over 868885.00 frames.
2022-05-29 15:21:38,649 INFO [train.py:842] (2/4) Epoch 37, batch 9050, loss[loss=0.1541, simple_loss=0.2332, pruned_loss=0.03752, over 7118.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2598, pruned_loss=0.04147, over 1402170.58 frames.], batch size: 17, lr: 1.48e-04
2022-05-29 15:22:16,583 INFO [train.py:842] (2/4) Epoch 37, batch 9100, loss[loss=0.1723, simple_loss=0.2583, pruned_loss=0.0432, over 6274.00 frames.], tot_loss[loss=0.174, simple_loss=0.2619, pruned_loss=0.04303, over 1369885.98 frames.], batch size: 37, lr: 1.47e-04
2022-05-29 15:22:53,403 INFO [train.py:842] (2/4) Epoch 37, batch 9150, loss[loss=0.1512, simple_loss=0.2469, pruned_loss=0.02771, over 5147.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2647, pruned_loss=0.04429, over 1319169.27 frames.], batch size: 53, lr: 1.47e-04
2022-05-29 15:23:42,223 INFO [train.py:842] (2/4) Epoch 38, batch 0, loss[loss=0.1593, simple_loss=0.243, pruned_loss=0.03778, over 7367.00 frames.], tot_loss[loss=0.1593, simple_loss=0.243, pruned_loss=0.03778, over 7367.00 frames.], batch size: 19, lr: 1.46e-04
2022-05-29 15:24:21,097 INFO [train.py:842] (2/4) Epoch 38, batch 50, loss[loss=0.1924, simple_loss=0.2804, pruned_loss=0.05218, over 6243.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2542, pruned_loss=0.03714, over 323247.11 frames.], batch size: 38, lr: 1.46e-04
2022-05-29 15:24:59,384 INFO [train.py:842] (2/4) Epoch 38, batch 100, loss[loss=0.1577, simple_loss=0.252, pruned_loss=0.03171, over 7263.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2609, pruned_loss=0.04015, over 561283.20 frames.], batch size: 19, lr: 1.46e-04
2022-05-29 15:25:37,981 INFO [train.py:842] (2/4) Epoch 38, batch 150, loss[loss=0.1544, simple_loss=0.2534, pruned_loss=0.02777, over 7373.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2616, pruned_loss=0.0406, over 749002.59 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 15:26:16,305 INFO [train.py:842] (2/4) Epoch 38, batch 200, loss[loss=0.1611, simple_loss=0.2648, pruned_loss=0.02871, over 7413.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2612, pruned_loss=0.0407, over 897560.79 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 15:26:54,835 INFO [train.py:842] (2/4) Epoch 38, batch 250, loss[loss=0.1372, simple_loss=0.228, pruned_loss=0.02317, over 7354.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2593, pruned_loss=0.03987, over 1016211.93 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:27:33,114 INFO [train.py:842] (2/4) Epoch 38, batch 300, loss[loss=0.1517, simple_loss=0.2418, pruned_loss=0.03076, over 7227.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2601, pruned_loss=0.04054, over 1106535.02 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:28:11,830 INFO [train.py:842] (2/4) Epoch 38, batch 350, loss[loss=0.1804, simple_loss=0.2738, pruned_loss=0.0435, over 7259.00 frames.], tot_loss[loss=0.1707, simple_loss=0.26, pruned_loss=0.04066, over 1173519.16 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:28:50,323 INFO [train.py:842] (2/4) Epoch 38, batch 400, loss[loss=0.1531, simple_loss=0.2371, pruned_loss=0.03454, over 7280.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2605, pruned_loss=0.04071, over 1233421.94 frames.], batch size: 17, lr: 1.45e-04
2022-05-29 15:29:28,789 INFO [train.py:842] (2/4) Epoch 38, batch 450, loss[loss=0.1582, simple_loss=0.2534, pruned_loss=0.0315, over 7124.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2606, pruned_loss=0.04119, over 1276497.93 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 15:30:07,434 INFO [train.py:842] (2/4) Epoch 38, batch 500, loss[loss=0.2262, simple_loss=0.2955, pruned_loss=0.07844, over 7282.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2592, pruned_loss=0.04057, over 1312402.13 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:30:46,048 INFO [train.py:842] (2/4) Epoch 38, batch 550, loss[loss=0.1875, simple_loss=0.287, pruned_loss=0.044, over 7323.00 frames.], tot_loss[loss=0.171, simple_loss=0.2605, pruned_loss=0.04074, over 1336999.10 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:31:24,319 INFO [train.py:842] (2/4) Epoch 38, batch 600, loss[loss=0.1722, simple_loss=0.2678, pruned_loss=0.03827, over 7369.00 frames.], tot_loss[loss=0.171, simple_loss=0.2606, pruned_loss=0.04073, over 1358501.42 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 15:32:03,010 INFO [train.py:842] (2/4) Epoch 38, batch 650, loss[loss=0.173, simple_loss=0.27, pruned_loss=0.03799, over 7347.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2609, pruned_loss=0.04081, over 1374493.01 frames.], batch size: 22, lr: 1.45e-04
2022-05-29 15:32:41,270 INFO [train.py:842] (2/4) Epoch 38, batch 700, loss[loss=0.1631, simple_loss=0.2473, pruned_loss=0.03945, over 7171.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2611, pruned_loss=0.0408, over 1387054.42 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:33:19,927 INFO [train.py:842] (2/4) Epoch 38, batch 750, loss[loss=0.1571, simple_loss=0.2556, pruned_loss=0.02929, over 7371.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2598, pruned_loss=0.03985, over 1401378.81 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 15:33:58,069 INFO [train.py:842] (2/4) Epoch 38, batch 800, loss[loss=0.1302, simple_loss=0.2121, pruned_loss=0.02415, over 7426.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2604, pruned_loss=0.04022, over 1409272.89 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:34:36,759 INFO [train.py:842] (2/4) Epoch 38, batch 850, loss[loss=0.1521, simple_loss=0.2416, pruned_loss=0.03129, over 7363.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2598, pruned_loss=0.03999, over 1411807.40 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:35:15,000 INFO [train.py:842] (2/4) Epoch 38, batch 900, loss[loss=0.1737, simple_loss=0.278, pruned_loss=0.03469, over 7293.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2587, pruned_loss=0.03933, over 1413845.62 frames.], batch size: 24, lr: 1.45e-04
2022-05-29 15:35:53,549 INFO [train.py:842] (2/4) Epoch 38, batch 950, loss[loss=0.1383, simple_loss=0.22, pruned_loss=0.0283, over 7255.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2586, pruned_loss=0.03926, over 1419500.61 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:36:31,941 INFO [train.py:842] (2/4) Epoch 38, batch 1000, loss[loss=0.1819, simple_loss=0.2648, pruned_loss=0.04944, over 7206.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2595, pruned_loss=0.03987, over 1421625.31 frames.], batch size: 22, lr: 1.45e-04
2022-05-29 15:37:10,452 INFO [train.py:842] (2/4) Epoch 38, batch 1050, loss[loss=0.158, simple_loss=0.2453, pruned_loss=0.0353, over 7332.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2605, pruned_loss=0.04026, over 1421735.87 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:37:48,971 INFO [train.py:842] (2/4) Epoch 38, batch 1100, loss[loss=0.145, simple_loss=0.2384, pruned_loss=0.02577, over 6797.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2612, pruned_loss=0.04055, over 1424890.40 frames.], batch size: 15, lr: 1.45e-04
2022-05-29 15:38:27,642 INFO [train.py:842] (2/4) Epoch 38, batch 1150, loss[loss=0.1439, simple_loss=0.2247, pruned_loss=0.03151, over 7269.00 frames.], tot_loss[loss=0.172, simple_loss=0.2622, pruned_loss=0.04094, over 1421704.60 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:39:05,886 INFO [train.py:842] (2/4) Epoch 38, batch 1200, loss[loss=0.1504, simple_loss=0.2411, pruned_loss=0.02986, over 7180.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2622, pruned_loss=0.04116, over 1423468.80 frames.], batch size: 26, lr: 1.45e-04
2022-05-29 15:39:44,559 INFO [train.py:842] (2/4) Epoch 38, batch 1250, loss[loss=0.1801, simple_loss=0.281, pruned_loss=0.03962, over 6468.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2611, pruned_loss=0.04061, over 1427267.38 frames.], batch size: 38, lr: 1.45e-04
2022-05-29 15:40:22,864 INFO [train.py:842] (2/4) Epoch 38, batch 1300, loss[loss=0.1369, simple_loss=0.2283, pruned_loss=0.02272, over 7300.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2609, pruned_loss=0.04041, over 1426853.18 frames.], batch size: 17, lr: 1.45e-04
2022-05-29 15:41:01,502 INFO [train.py:842] (2/4) Epoch 38, batch 1350, loss[loss=0.1666, simple_loss=0.2564, pruned_loss=0.03838, over 7113.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2602, pruned_loss=0.04028, over 1420271.12 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 15:41:40,068 INFO [train.py:842] (2/4) Epoch 38, batch 1400, loss[loss=0.1734, simple_loss=0.2669, pruned_loss=0.03991, over 7305.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2594, pruned_loss=0.04036, over 1420827.70 frames.], batch size: 24, lr: 1.45e-04
2022-05-29 15:42:18,694 INFO [train.py:842] (2/4) Epoch 38, batch 1450, loss[loss=0.1649, simple_loss=0.2601, pruned_loss=0.03491, over 7193.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2598, pruned_loss=0.04035, over 1425014.23 frames.], batch size: 22, lr: 1.45e-04
2022-05-29 15:42:57,037 INFO [train.py:842] (2/4) Epoch 38, batch 1500, loss[loss=0.1799, simple_loss=0.2692, pruned_loss=0.04528, over 7283.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2587, pruned_loss=0.03957, over 1424971.23 frames.], batch size: 25, lr: 1.45e-04
2022-05-29 15:43:35,783 INFO [train.py:842] (2/4) Epoch 38, batch 1550, loss[loss=0.1672, simple_loss=0.2569, pruned_loss=0.03877, over 7244.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2598, pruned_loss=0.04034, over 1422019.25 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:44:14,285 INFO [train.py:842] (2/4) Epoch 38, batch 1600, loss[loss=0.1585, simple_loss=0.2472, pruned_loss=0.0349, over 7251.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2601, pruned_loss=0.0405, over 1424533.75 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:44:53,045 INFO [train.py:842] (2/4) Epoch 38, batch 1650, loss[loss=0.1523, simple_loss=0.2596, pruned_loss=0.0225, over 7092.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2599, pruned_loss=0.04047, over 1424890.79 frames.], batch size: 28, lr: 1.45e-04
2022-05-29 15:45:31,660 INFO [train.py:842] (2/4) Epoch 38, batch 1700, loss[loss=0.1484, simple_loss=0.2408, pruned_loss=0.02797, over 7174.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2591, pruned_loss=0.0404, over 1424356.48 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:46:10,389 INFO [train.py:842] (2/4) Epoch 38, batch 1750, loss[loss=0.2355, simple_loss=0.3172, pruned_loss=0.0769, over 5381.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2591, pruned_loss=0.04012, over 1422519.24 frames.], batch size: 52, lr: 1.45e-04
2022-05-29 15:46:48,915 INFO [train.py:842] (2/4) Epoch 38, batch 1800, loss[loss=0.1818, simple_loss=0.2737, pruned_loss=0.04495, over 7319.00 frames.], tot_loss[loss=0.1701, simple_loss=0.259, pruned_loss=0.04056, over 1421052.72 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:47:27,691 INFO [train.py:842] (2/4) Epoch 38, batch 1850, loss[loss=0.1391, simple_loss=0.2234, pruned_loss=0.02733, over 7266.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2591, pruned_loss=0.04039, over 1423405.18 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:48:06,000 INFO [train.py:842] (2/4) Epoch 38, batch 1900, loss[loss=0.154, simple_loss=0.2291, pruned_loss=0.03949, over 7206.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2595, pruned_loss=0.04075, over 1426297.58 frames.], batch size: 16, lr: 1.45e-04
2022-05-29 15:48:44,640 INFO [train.py:842] (2/4) Epoch 38, batch 1950, loss[loss=0.1614, simple_loss=0.2527, pruned_loss=0.03505, over 7255.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2606, pruned_loss=0.04092, over 1428477.09 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:49:23,245 INFO [train.py:842] (2/4) Epoch 38, batch 2000, loss[loss=0.1468, simple_loss=0.2337, pruned_loss=0.02995, over 7421.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2597, pruned_loss=0.04049, over 1427601.79 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:50:01,697 INFO [train.py:842] (2/4) Epoch 38, batch 2050, loss[loss=0.1698, simple_loss=0.2588, pruned_loss=0.04041, over 7247.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2602, pruned_loss=0.04056, over 1424435.52 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:50:39,899 INFO [train.py:842] (2/4) Epoch 38, batch 2100, loss[loss=0.1825, simple_loss=0.274, pruned_loss=0.04548, over 7161.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2614, pruned_loss=0.04105, over 1418890.94 frames.], batch size: 26, lr: 1.45e-04
2022-05-29 15:51:18,605 INFO [train.py:842] (2/4) Epoch 38, batch 2150, loss[loss=0.1398, simple_loss=0.2266, pruned_loss=0.02646, over 7063.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2613, pruned_loss=0.04105, over 1418396.93 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:51:56,342 INFO [train.py:842] (2/4) Epoch 38, batch 2200, loss[loss=0.1496, simple_loss=0.2416, pruned_loss=0.02878, over 7448.00 frames.], tot_loss[loss=0.1728, simple_loss=0.263, pruned_loss=0.04135, over 1419411.79 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:52:34,385 INFO [train.py:842] (2/4) Epoch 38, batch 2250, loss[loss=0.2038, simple_loss=0.2899, pruned_loss=0.05886, over 6287.00 frames.], tot_loss[loss=0.1729, simple_loss=0.263, pruned_loss=0.04138, over 1418281.68 frames.], batch size: 37, lr: 1.45e-04
2022-05-29 15:53:12,419 INFO [train.py:842] (2/4) Epoch 38, batch 2300, loss[loss=0.1401, simple_loss=0.2341, pruned_loss=0.02304, over 7455.00 frames.], tot_loss[loss=0.1719, simple_loss=0.262, pruned_loss=0.04088, over 1422380.34 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:53:50,466 INFO [train.py:842] (2/4) Epoch 38, batch 2350, loss[loss=0.1511, simple_loss=0.2454, pruned_loss=0.02838, over 7312.00 frames.], tot_loss[loss=0.1719, simple_loss=0.262, pruned_loss=0.0409, over 1420529.40 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:54:28,591 INFO [train.py:842] (2/4) Epoch 38, batch 2400, loss[loss=0.1799, simple_loss=0.261, pruned_loss=0.04939, over 7382.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2613, pruned_loss=0.04098, over 1426187.30 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:55:06,813 INFO [train.py:842] (2/4) Epoch 38, batch 2450, loss[loss=0.1869, simple_loss=0.276, pruned_loss=0.04886, over 7336.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2612, pruned_loss=0.04106, over 1427790.23 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:55:44,797 INFO [train.py:842] (2/4) Epoch 38, batch 2500, loss[loss=0.1405, simple_loss=0.2227, pruned_loss=0.02916, over 7167.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2602, pruned_loss=0.04078, over 1428216.74 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:56:22,957 INFO [train.py:842] (2/4) Epoch 38, batch 2550, loss[loss=0.1438, simple_loss=0.2339, pruned_loss=0.0268, over 7170.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2609, pruned_loss=0.04096, over 1425413.20 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 15:57:00,957 INFO [train.py:842] (2/4) Epoch 38, batch 2600, loss[loss=0.1569, simple_loss=0.2493, pruned_loss=0.03224, over 7421.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2614, pruned_loss=0.041, over 1424084.55 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:57:39,134 INFO [train.py:842] (2/4) Epoch 38, batch 2650, loss[loss=0.1731, simple_loss=0.2645, pruned_loss=0.04085, over 7213.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2629, pruned_loss=0.04183, over 1425505.32 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 15:58:17,262 INFO [train.py:842] (2/4) Epoch 38, batch 2700, loss[loss=0.1899, simple_loss=0.2668, pruned_loss=0.05648, over 7235.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2614, pruned_loss=0.04138, over 1424654.48 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 15:58:55,519 INFO [train.py:842] (2/4) Epoch 38, batch 2750, loss[loss=0.1897, simple_loss=0.2791, pruned_loss=0.05009, over 7364.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2627, pruned_loss=0.04214, over 1425504.55 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 15:59:33,528 INFO [train.py:842] (2/4) Epoch 38, batch 2800, loss[loss=0.1919, simple_loss=0.2834, pruned_loss=0.05026, over 7303.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2633, pruned_loss=0.04286, over 1424246.62 frames.], batch size: 24, lr: 1.45e-04
2022-05-29 16:00:11,769 INFO [train.py:842] (2/4) Epoch 38, batch 2850, loss[loss=0.1789, simple_loss=0.2667, pruned_loss=0.0455, over 7409.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2623, pruned_loss=0.04219, over 1424872.33 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 16:00:49,751 INFO [train.py:842] (2/4) Epoch 38, batch 2900, loss[loss=0.1698, simple_loss=0.2594, pruned_loss=0.04015, over 7143.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2611, pruned_loss=0.04189, over 1425492.90 frames.], batch size: 17, lr: 1.45e-04
2022-05-29 16:01:28,077 INFO [train.py:842] (2/4) Epoch 38, batch 2950, loss[loss=0.1496, simple_loss=0.2284, pruned_loss=0.03541, over 7420.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2613, pruned_loss=0.04186, over 1430006.85 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 16:02:05,954 INFO [train.py:842] (2/4) Epoch 38, batch 3000, loss[loss=0.2071, simple_loss=0.2991, pruned_loss=0.05755, over 7204.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2617, pruned_loss=0.04203, over 1429062.69 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 16:02:05,954 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 16:02:15,341 INFO [train.py:871] (2/4) Epoch 38, validation: loss=0.1634, simple_loss=0.2602, pruned_loss=0.03326, over 868885.00 frames.
2022-05-29 16:02:53,595 INFO [train.py:842] (2/4) Epoch 38, batch 3050, loss[loss=0.195, simple_loss=0.2715, pruned_loss=0.05931, over 7156.00 frames.], tot_loss[loss=0.172, simple_loss=0.2612, pruned_loss=0.04144, over 1429300.44 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 16:03:31,421 INFO [train.py:842] (2/4) Epoch 38, batch 3100, loss[loss=0.1966, simple_loss=0.2824, pruned_loss=0.05544, over 7190.00 frames.], tot_loss[loss=0.172, simple_loss=0.261, pruned_loss=0.0415, over 1422910.09 frames.], batch size: 22, lr: 1.45e-04
2022-05-29 16:04:09,671 INFO [train.py:842] (2/4) Epoch 38, batch 3150, loss[loss=0.1744, simple_loss=0.2756, pruned_loss=0.03664, over 7385.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2606, pruned_loss=0.04131, over 1421473.56 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 16:04:47,694 INFO [train.py:842] (2/4) Epoch 38, batch 3200, loss[loss=0.1872, simple_loss=0.2696, pruned_loss=0.0524, over 7106.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2604, pruned_loss=0.04124, over 1426192.86 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 16:05:26,135 INFO [train.py:842] (2/4) Epoch 38, batch 3250, loss[loss=0.1507, simple_loss=0.2431, pruned_loss=0.02914, over 7288.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2598, pruned_loss=0.04069, over 1427233.78 frames.], batch size: 18, lr: 1.45e-04
2022-05-29 16:06:04,013 INFO [train.py:842] (2/4) Epoch 38, batch 3300, loss[loss=0.2042, simple_loss=0.286, pruned_loss=0.06117, over 7236.00 frames.], tot_loss[loss=0.1696, simple_loss=0.259, pruned_loss=0.04007, over 1426224.35 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:06:41,993 INFO [train.py:842] (2/4) Epoch 38, batch 3350, loss[loss=0.2132, simple_loss=0.2948, pruned_loss=0.0658, over 7201.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2605, pruned_loss=0.04062, over 1426515.27 frames.], batch size: 22, lr: 1.45e-04
2022-05-29 16:07:19,985 INFO [train.py:842] (2/4) Epoch 38, batch 3400, loss[loss=0.1723, simple_loss=0.2635, pruned_loss=0.04053, over 6777.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2599, pruned_loss=0.04026, over 1430351.79 frames.], batch size: 31, lr: 1.45e-04
2022-05-29 16:07:58,232 INFO [train.py:842] (2/4) Epoch 38, batch 3450, loss[loss=0.1574, simple_loss=0.252, pruned_loss=0.03139, over 7432.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2594, pruned_loss=0.04008, over 1431765.23 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:08:36,207 INFO [train.py:842] (2/4) Epoch 38, batch 3500, loss[loss=0.1634, simple_loss=0.2608, pruned_loss=0.03297, over 7226.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2587, pruned_loss=0.03952, over 1430566.62 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:09:14,386 INFO [train.py:842] (2/4) Epoch 38, batch 3550, loss[loss=0.2188, simple_loss=0.3011, pruned_loss=0.06827, over 7148.00 frames.], tot_loss[loss=0.17, simple_loss=0.2601, pruned_loss=0.03993, over 1430634.43 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:09:52,125 INFO [train.py:842] (2/4) Epoch 38, batch 3600, loss[loss=0.193, simple_loss=0.2867, pruned_loss=0.04964, over 6713.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2616, pruned_loss=0.04047, over 1428407.07 frames.], batch size: 31, lr: 1.45e-04
2022-05-29 16:10:30,404 INFO [train.py:842] (2/4) Epoch 38, batch 3650, loss[loss=0.1664, simple_loss=0.2662, pruned_loss=0.03332, over 7131.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2603, pruned_loss=0.03974, over 1431443.57 frames.], batch size: 28, lr: 1.45e-04
2022-05-29 16:11:08,252 INFO [train.py:842] (2/4) Epoch 38, batch 3700, loss[loss=0.1702, simple_loss=0.2702, pruned_loss=0.03506, over 7286.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2604, pruned_loss=0.04026, over 1422404.35 frames.], batch size: 24, lr: 1.45e-04
2022-05-29 16:11:46,334 INFO [train.py:842] (2/4) Epoch 38, batch 3750, loss[loss=0.1907, simple_loss=0.265, pruned_loss=0.05818, over 7158.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2603, pruned_loss=0.04011, over 1417576.30 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 16:12:24,508 INFO [train.py:842] (2/4) Epoch 38, batch 3800, loss[loss=0.1742, simple_loss=0.2621, pruned_loss=0.04312, over 7384.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2601, pruned_loss=0.0402, over 1417716.93 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 16:13:02,701 INFO [train.py:842] (2/4) Epoch 38, batch 3850, loss[loss=0.1782, simple_loss=0.2737, pruned_loss=0.04132, over 7118.00 frames.], tot_loss[loss=0.17, simple_loss=0.2597, pruned_loss=0.04017, over 1419867.04 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 16:13:40,942 INFO [train.py:842] (2/4) Epoch 38, batch 3900, loss[loss=0.1934, simple_loss=0.2776, pruned_loss=0.05458, over 7325.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2598, pruned_loss=0.04059, over 1421505.26 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:14:21,828 INFO [train.py:842] (2/4) Epoch 38, batch 3950, loss[loss=0.1716, simple_loss=0.2706, pruned_loss=0.03633, over 6803.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2595, pruned_loss=0.04069, over 1418468.28 frames.], batch size: 31, lr: 1.45e-04
2022-05-29 16:14:59,657 INFO [train.py:842] (2/4) Epoch 38, batch 4000, loss[loss=0.158, simple_loss=0.2413, pruned_loss=0.0373, over 7149.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2596, pruned_loss=0.04082, over 1418611.33 frames.], batch size: 17, lr: 1.45e-04
2022-05-29 16:15:37,907 INFO [train.py:842] (2/4) Epoch 38, batch 4050, loss[loss=0.1518, simple_loss=0.2339, pruned_loss=0.03486, over 6987.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2588, pruned_loss=0.04037, over 1416913.99 frames.], batch size: 16, lr: 1.45e-04
2022-05-29 16:16:15,905 INFO [train.py:842] (2/4) Epoch 38, batch 4100, loss[loss=0.1863, simple_loss=0.2798, pruned_loss=0.04637, over 7151.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2591, pruned_loss=0.04038, over 1416981.60 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:16:54,257 INFO [train.py:842] (2/4) Epoch 38, batch 4150, loss[loss=0.1413, simple_loss=0.2232, pruned_loss=0.02965, over 6790.00 frames.], tot_loss[loss=0.17, simple_loss=0.2589, pruned_loss=0.04049, over 1419439.21 frames.], batch size: 15, lr: 1.45e-04
2022-05-29 16:17:32,185 INFO [train.py:842] (2/4) Epoch 38, batch 4200, loss[loss=0.2011, simple_loss=0.2858, pruned_loss=0.05822, over 7359.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2587, pruned_loss=0.0405, over 1419282.59 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 16:18:10,305 INFO [train.py:842] (2/4) Epoch 38, batch 4250, loss[loss=0.1967, simple_loss=0.2801, pruned_loss=0.0567, over 7288.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2591, pruned_loss=0.0404, over 1419778.24 frames.], batch size: 24, lr: 1.45e-04
2022-05-29 16:18:57,693 INFO [train.py:842] (2/4) Epoch 38, batch 4300, loss[loss=0.1588, simple_loss=0.2622, pruned_loss=0.02766, over 7330.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2591, pruned_loss=0.04087, over 1423599.76 frames.], batch size: 22, lr: 1.45e-04
2022-05-29 16:19:35,760 INFO [train.py:842] (2/4) Epoch 38, batch 4350, loss[loss=0.1525, simple_loss=0.2222, pruned_loss=0.04142, over 7282.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2591, pruned_loss=0.04061, over 1423232.94 frames.], batch size: 17, lr: 1.45e-04
2022-05-29 16:20:13,803 INFO [train.py:842] (2/4) Epoch 38, batch 4400, loss[loss=0.1964, simple_loss=0.29, pruned_loss=0.05139, over 7135.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2586, pruned_loss=0.04033, over 1423223.75 frames.], batch size: 28, lr: 1.45e-04
2022-05-29 16:20:52,133 INFO [train.py:842] (2/4) Epoch 38, batch 4450, loss[loss=0.2215, simple_loss=0.3076, pruned_loss=0.06766, over 7340.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2592, pruned_loss=0.04055, over 1425283.48 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:21:30,249 INFO [train.py:842] (2/4) Epoch 38, batch 4500, loss[loss=0.17, simple_loss=0.2707, pruned_loss=0.0347, over 7125.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2589, pruned_loss=0.04049, over 1429072.35 frames.], batch size: 21, lr: 1.45e-04
2022-05-29 16:22:08,242 INFO [train.py:842] (2/4) Epoch 38, batch 4550, loss[loss=0.1878, simple_loss=0.2781, pruned_loss=0.0487, over 7243.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2596, pruned_loss=0.04008, over 1430642.26 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 16:22:46,092 INFO [train.py:842] (2/4) Epoch 38, batch 4600, loss[loss=0.1598, simple_loss=0.2454, pruned_loss=0.03713, over 7375.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2599, pruned_loss=0.04027, over 1428664.99 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 16:23:24,372 INFO [train.py:842] (2/4) Epoch 38, batch 4650, loss[loss=0.1871, simple_loss=0.2907, pruned_loss=0.04176, over 7371.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2594, pruned_loss=0.0399, over 1428267.76 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 16:24:02,468 INFO [train.py:842] (2/4) Epoch 38, batch 4700, loss[loss=0.1513, simple_loss=0.2491, pruned_loss=0.0268, over 7192.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2584, pruned_loss=0.03945, over 1424639.91 frames.], batch size: 23, lr: 1.45e-04
2022-05-29 16:24:40,729 INFO [train.py:842] (2/4) Epoch 38, batch 4750, loss[loss=0.1848, simple_loss=0.2651, pruned_loss=0.0522, over 7163.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2578, pruned_loss=0.03941, over 1422345.93 frames.], batch size: 19, lr: 1.45e-04
2022-05-29 16:25:18,456 INFO [train.py:842] (2/4) Epoch 38, batch 4800, loss[loss=0.157, simple_loss=0.2518, pruned_loss=0.03105, over 7152.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2582, pruned_loss=0.03944, over 1422948.42 frames.], batch size: 20, lr: 1.45e-04
2022-05-29 16:25:56,652 INFO [train.py:842] (2/4) Epoch 38, batch 4850, loss[loss=0.1993, simple_loss=0.2929, pruned_loss=0.05286, over 7098.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2579, pruned_loss=0.03942, over 1419946.87 frames.], batch size: 28, lr: 1.44e-04
2022-05-29 16:26:34,235 INFO [train.py:842] (2/4) Epoch 38, batch 4900, loss[loss=0.166, simple_loss=0.2632, pruned_loss=0.03437, over 7217.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2586, pruned_loss=0.0395, over 1413563.11 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 16:27:12,488 INFO [train.py:842] (2/4) Epoch 38, batch 4950, loss[loss=0.1714, simple_loss=0.2554, pruned_loss=0.04375, over 7062.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2586, pruned_loss=0.03933, over 1417380.12 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:27:50,530 INFO [train.py:842] (2/4) Epoch 38, batch 5000, loss[loss=0.1511, simple_loss=0.2492, pruned_loss=0.02654, over 7189.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2586, pruned_loss=0.03951, over 1421065.08 frames.], batch size: 26, lr: 1.44e-04
2022-05-29 16:28:28,902 INFO [train.py:842] (2/4) Epoch 38, batch 5050, loss[loss=0.2109, simple_loss=0.3015, pruned_loss=0.06012, over 6404.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2582, pruned_loss=0.03939, over 1425492.31 frames.], batch size: 37, lr: 1.44e-04
2022-05-29 16:29:07,179 INFO [train.py:842] (2/4) Epoch 38, batch 5100, loss[loss=0.1795, simple_loss=0.2763, pruned_loss=0.04129, over 7306.00 frames.], tot_loss[loss=0.169, simple_loss=0.2583, pruned_loss=0.03983, over 1426380.56 frames.], batch size: 24, lr: 1.44e-04
2022-05-29 16:29:45,462 INFO [train.py:842] (2/4) Epoch 38, batch 5150, loss[loss=0.1707, simple_loss=0.2554, pruned_loss=0.04298, over 7445.00 frames.], tot_loss[loss=0.1685, simple_loss=0.258, pruned_loss=0.03947, over 1428408.32 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:30:23,530 INFO [train.py:842] (2/4) Epoch 38, batch 5200, loss[loss=0.152, simple_loss=0.2499, pruned_loss=0.02708, over 7225.00 frames.], tot_loss[loss=0.1678, simple_loss=0.257, pruned_loss=0.03928, over 1426068.59 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 16:31:01,711 INFO [train.py:842] (2/4) Epoch 38, batch 5250, loss[loss=0.1823, simple_loss=0.2725, pruned_loss=0.046, over 7332.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2587, pruned_loss=0.04052, over 1423199.27 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:31:39,570 INFO [train.py:842] (2/4) Epoch 38, batch 5300, loss[loss=0.1775, simple_loss=0.267, pruned_loss=0.04398, over 5365.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2577, pruned_loss=0.04007, over 1418482.77 frames.], batch size: 52, lr: 1.44e-04
2022-05-29 16:32:17,621 INFO [train.py:842] (2/4) Epoch 38, batch 5350, loss[loss=0.1628, simple_loss=0.2557, pruned_loss=0.03496, over 7281.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2584, pruned_loss=0.0403, over 1411535.74 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:32:55,660 INFO [train.py:842] (2/4) Epoch 38, batch 5400, loss[loss=0.1918, simple_loss=0.2931, pruned_loss=0.04521, over 7336.00 frames.], tot_loss[loss=0.171, simple_loss=0.2601, pruned_loss=0.04093, over 1415843.49 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 16:33:34,071 INFO [train.py:842] (2/4) Epoch 38, batch 5450, loss[loss=0.1599, simple_loss=0.2533, pruned_loss=0.0333, over 7209.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2594, pruned_loss=0.04043, over 1417760.02 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 16:34:11,843 INFO [train.py:842] (2/4) Epoch 38, batch 5500, loss[loss=0.1739, simple_loss=0.2427, pruned_loss=0.0525, over 7412.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2608, pruned_loss=0.04087, over 1419921.25 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:34:50,167 INFO [train.py:842] (2/4) Epoch 38, batch 5550, loss[loss=0.1682, simple_loss=0.2544, pruned_loss=0.04103, over 7332.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2596, pruned_loss=0.04016, over 1420199.28 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:35:27,884 INFO [train.py:842] (2/4) Epoch 38, batch 5600, loss[loss=0.1936, simple_loss=0.2929, pruned_loss=0.0472, over 7340.00 frames.], tot_loss[loss=0.1703, simple_loss=0.26, pruned_loss=0.04035, over 1418435.53 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 16:36:06,392 INFO [train.py:842] (2/4) Epoch 38, batch 5650, loss[loss=0.1494, simple_loss=0.2323, pruned_loss=0.0333, over 7425.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2589, pruned_loss=0.03984, over 1423348.81 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:36:44,327 INFO [train.py:842] (2/4) Epoch 38, batch 5700, loss[loss=0.1561, simple_loss=0.2562, pruned_loss=0.02799, over 7307.00 frames.], tot_loss[loss=0.17, simple_loss=0.2596, pruned_loss=0.04018, over 1423134.60 frames.], batch size: 24, lr: 1.44e-04
2022-05-29 16:37:22,670 INFO [train.py:842] (2/4) Epoch 38, batch 5750, loss[loss=0.1691, simple_loss=0.2537, pruned_loss=0.04223, over 7070.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2599, pruned_loss=0.04017, over 1426392.25 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:38:00,880 INFO [train.py:842] (2/4) Epoch 38, batch 5800, loss[loss=0.1633, simple_loss=0.2476, pruned_loss=0.03951, over 7278.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2584, pruned_loss=0.03949, over 1430151.33 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:38:38,996 INFO [train.py:842] (2/4) Epoch 38, batch 5850, loss[loss=0.1879, simple_loss=0.2731, pruned_loss=0.05138, over 6738.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2594, pruned_loss=0.04023, over 1423694.61 frames.], batch size: 31, lr: 1.44e-04
2022-05-29 16:39:16,841 INFO [train.py:842] (2/4) Epoch 38, batch 5900, loss[loss=0.167, simple_loss=0.2436, pruned_loss=0.04519, over 6781.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2604, pruned_loss=0.04066, over 1421600.25 frames.], batch size: 15, lr: 1.44e-04
2022-05-29 16:39:55,208 INFO [train.py:842] (2/4) Epoch 38, batch 5950, loss[loss=0.1724, simple_loss=0.2702, pruned_loss=0.03726, over 7289.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2601, pruned_loss=0.04025, over 1421644.18 frames.], batch size: 25, lr: 1.44e-04
2022-05-29 16:40:33,178 INFO [train.py:842] (2/4) Epoch 38, batch 6000, loss[loss=0.163, simple_loss=0.252, pruned_loss=0.03702, over 7158.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2606, pruned_loss=0.0408, over 1419399.70 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:40:33,179 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 16:40:42,180 INFO [train.py:871] (2/4) Epoch 38, validation: loss=0.1638, simple_loss=0.2604, pruned_loss=0.03358, over 868885.00 frames.
2022-05-29 16:41:20,628 INFO [train.py:842] (2/4) Epoch 38, batch 6050, loss[loss=0.1496, simple_loss=0.2471, pruned_loss=0.026, over 7255.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2604, pruned_loss=0.04072, over 1416600.52 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:41:58,587 INFO [train.py:842] (2/4) Epoch 38, batch 6100, loss[loss=0.1676, simple_loss=0.2696, pruned_loss=0.03287, over 7329.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2604, pruned_loss=0.04067, over 1416640.15 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 16:42:36,998 INFO [train.py:842] (2/4) Epoch 38, batch 6150, loss[loss=0.2057, simple_loss=0.2934, pruned_loss=0.05902, over 6734.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2602, pruned_loss=0.04076, over 1418364.33 frames.], batch size: 31, lr: 1.44e-04
2022-05-29 16:43:15,002 INFO [train.py:842] (2/4) Epoch 38, batch 6200, loss[loss=0.2306, simple_loss=0.3054, pruned_loss=0.07786, over 7354.00 frames.], tot_loss[loss=0.1717, simple_loss=0.261, pruned_loss=0.04121, over 1421317.36 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 16:43:53,224 INFO [train.py:842] (2/4) Epoch 38, batch 6250, loss[loss=0.1531, simple_loss=0.2437, pruned_loss=0.03121, over 7159.00 frames.], tot_loss[loss=0.1719, simple_loss=0.261, pruned_loss=0.04134, over 1423498.24 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:44:31,152 INFO [train.py:842] (2/4) Epoch 38, batch 6300, loss[loss=0.1545, simple_loss=0.2382, pruned_loss=0.03538, over 7352.00 frames.], tot_loss[loss=0.172, simple_loss=0.2613, pruned_loss=0.04137, over 1424840.10 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:45:09,389 INFO [train.py:842] (2/4) Epoch 38, batch 6350, loss[loss=0.1929, simple_loss=0.2936, pruned_loss=0.04611, over 7375.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2602, pruned_loss=0.04052, over 1426233.19 frames.], batch size: 23, lr: 1.44e-04
2022-05-29 16:45:47,313 INFO [train.py:842] (2/4) Epoch 38, batch 6400, loss[loss=0.157, simple_loss=0.2494, pruned_loss=0.03235, over 7254.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2601, pruned_loss=0.04038, over 1425498.97 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:46:25,806 INFO [train.py:842] (2/4) Epoch 38, batch 6450, loss[loss=0.1801, simple_loss=0.2835, pruned_loss=0.03832, over 7226.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2585, pruned_loss=0.03966, over 1425864.18 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:47:03,927 INFO [train.py:842] (2/4) Epoch 38, batch 6500, loss[loss=0.1713, simple_loss=0.2685, pruned_loss=0.03702, over 7146.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2585, pruned_loss=0.03992, over 1428142.35 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:47:42,056 INFO [train.py:842] (2/4) Epoch 38, batch 6550, loss[loss=0.1541, simple_loss=0.2477, pruned_loss=0.03022, over 7145.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2589, pruned_loss=0.03977, over 1428511.10 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:48:20,069 INFO [train.py:842] (2/4) Epoch 38, batch 6600, loss[loss=0.1638, simple_loss=0.2565, pruned_loss=0.03556, over 7177.00 frames.], tot_loss[loss=0.169, simple_loss=0.2585, pruned_loss=0.03981, over 1424880.57 frames.], batch size: 26, lr: 1.44e-04
2022-05-29 16:48:58,333 INFO [train.py:842] (2/4) Epoch 38, batch 6650, loss[loss=0.1686, simple_loss=0.2591, pruned_loss=0.03901, over 7355.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2588, pruned_loss=0.04022, over 1423110.97 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:49:36,297 INFO [train.py:842] (2/4) Epoch 38, batch 6700, loss[loss=0.1488, simple_loss=0.2377, pruned_loss=0.02991, over 7200.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2585, pruned_loss=0.04001, over 1424798.91 frames.], batch size: 16, lr: 1.44e-04
2022-05-29 16:50:14,380 INFO [train.py:842] (2/4) Epoch 38, batch 6750, loss[loss=0.1827, simple_loss=0.2697, pruned_loss=0.04783, over 7219.00 frames.], tot_loss[loss=0.17, simple_loss=0.2594, pruned_loss=0.04034, over 1419291.30 frames.], batch size: 23, lr: 1.44e-04
2022-05-29 16:50:52,129 INFO [train.py:842] (2/4) Epoch 38, batch 6800, loss[loss=0.1917, simple_loss=0.2835, pruned_loss=0.04993, over 7327.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2604, pruned_loss=0.04073, over 1418014.57 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:51:30,583 INFO [train.py:842] (2/4) Epoch 38, batch 6850, loss[loss=0.1308, simple_loss=0.2158, pruned_loss=0.0229, over 7274.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2599, pruned_loss=0.04028, over 1421935.01 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:52:08,717 INFO [train.py:842] (2/4) Epoch 38, batch 6900, loss[loss=0.1631, simple_loss=0.257, pruned_loss=0.03457, over 7283.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2592, pruned_loss=0.03971, over 1426483.94 frames.], batch size: 24, lr: 1.44e-04
2022-05-29 16:52:46,977 INFO [train.py:842] (2/4) Epoch 38, batch 6950, loss[loss=0.1454, simple_loss=0.2323, pruned_loss=0.0292, over 7404.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2594, pruned_loss=0.04012, over 1426900.12 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:53:24,870 INFO [train.py:842] (2/4) Epoch 38, batch 7000, loss[loss=0.1652, simple_loss=0.2557, pruned_loss=0.03738, over 7064.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2604, pruned_loss=0.04061, over 1427837.13 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:54:03,212 INFO [train.py:842] (2/4) Epoch 38, batch 7050, loss[loss=0.1476, simple_loss=0.2282, pruned_loss=0.0335, over 7360.00 frames.], tot_loss[loss=0.1705, simple_loss=0.26, pruned_loss=0.0405, over 1428014.25 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 16:54:41,059 INFO [train.py:842] (2/4) Epoch 38, batch 7100, loss[loss=0.1645, simple_loss=0.2707, pruned_loss=0.0291, over 7119.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2608, pruned_loss=0.04091, over 1424595.31 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 16:55:19,343 INFO [train.py:842] (2/4) Epoch 38, batch 7150, loss[loss=0.1667, simple_loss=0.266, pruned_loss=0.03364, over 6188.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2604, pruned_loss=0.04065, over 1423459.73 frames.], batch size: 37, lr: 1.44e-04
2022-05-29 16:55:57,115 INFO [train.py:842] (2/4) Epoch 38, batch 7200, loss[loss=0.1706, simple_loss=0.2653, pruned_loss=0.03799, over 7430.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2594, pruned_loss=0.04011, over 1423023.95 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 16:56:35,183 INFO [train.py:842] (2/4) Epoch 38, batch 7250, loss[loss=0.1747, simple_loss=0.2686, pruned_loss=0.04041, over 7386.00 frames.], tot_loss[loss=0.172, simple_loss=0.2615, pruned_loss=0.04125, over 1423882.95 frames.], batch size: 23, lr: 1.44e-04
2022-05-29 16:57:13,239 INFO [train.py:842] (2/4) Epoch 38, batch 7300, loss[loss=0.1305, simple_loss=0.219, pruned_loss=0.02102, over 7429.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2608, pruned_loss=0.04116, over 1428198.16 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 16:57:51,508 INFO [train.py:842] (2/4) Epoch 38, batch 7350, loss[loss=0.1611, simple_loss=0.2401, pruned_loss=0.04105, over 7000.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2599, pruned_loss=0.04089, over 1429949.47 frames.], batch size: 16, lr: 1.44e-04
2022-05-29 16:58:29,501 INFO [train.py:842] (2/4) Epoch 38, batch 7400, loss[loss=0.1946, simple_loss=0.2923, pruned_loss=0.04843, over 7414.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2602, pruned_loss=0.04076, over 1431599.42 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 16:59:07,987 INFO [train.py:842] (2/4) Epoch 38, batch 7450, loss[loss=0.1809, simple_loss=0.2785, pruned_loss=0.04169, over 7042.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2592, pruned_loss=0.04023, over 1434867.84 frames.], batch size: 28, lr: 1.44e-04
2022-05-29 16:59:45,794 INFO [train.py:842] (2/4) Epoch 38, batch 7500, loss[loss=0.2069, simple_loss=0.3036, pruned_loss=0.05511, over 7159.00 frames.], tot_loss[loss=0.1706, simple_loss=0.26, pruned_loss=0.04058, over 1432367.96 frames.], batch size: 26, lr: 1.44e-04
2022-05-29 17:00:23,994 INFO [train.py:842] (2/4) Epoch 38, batch 7550, loss[loss=0.2048, simple_loss=0.2883, pruned_loss=0.06063, over 6764.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2603, pruned_loss=0.04058, over 1432991.47 frames.], batch size: 31, lr: 1.44e-04
2022-05-29 17:01:01,992 INFO [train.py:842] (2/4) Epoch 38, batch 7600, loss[loss=0.1815, simple_loss=0.2791, pruned_loss=0.04192, over 7107.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2592, pruned_loss=0.03978, over 1432840.13 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 17:01:40,327 INFO [train.py:842] (2/4) Epoch 38, batch 7650, loss[loss=0.172, simple_loss=0.2649, pruned_loss=0.03958, over 7229.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2594, pruned_loss=0.04025, over 1432991.50 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 17:02:18,326 INFO [train.py:842] (2/4) Epoch 38, batch 7700, loss[loss=0.2015, simple_loss=0.2877, pruned_loss=0.05766, over 7289.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2586, pruned_loss=0.03985, over 1431891.81 frames.], batch size: 24, lr: 1.44e-04
2022-05-29 17:02:56,561 INFO [train.py:842] (2/4) Epoch 38, batch 7750, loss[loss=0.1688, simple_loss=0.2598, pruned_loss=0.03888, over 7214.00 frames.], tot_loss[loss=0.1696, simple_loss=0.259, pruned_loss=0.04008, over 1431098.94 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 17:03:34,677 INFO [train.py:842] (2/4) Epoch 38, batch 7800, loss[loss=0.1957, simple_loss=0.2931, pruned_loss=0.04911, over 7188.00 frames.], tot_loss[loss=0.1686, simple_loss=0.258, pruned_loss=0.03956, over 1428959.96 frames.], batch size: 23, lr: 1.44e-04
2022-05-29 17:04:13,044 INFO [train.py:842] (2/4) Epoch 38, batch 7850, loss[loss=0.1756, simple_loss=0.271, pruned_loss=0.04011, over 6811.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2584, pruned_loss=0.03955, over 1429620.76 frames.], batch size: 31, lr: 1.44e-04
2022-05-29 17:04:51,067 INFO [train.py:842] (2/4) Epoch 38, batch 7900, loss[loss=0.331, simple_loss=0.3988, pruned_loss=0.1316, over 7191.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2607, pruned_loss=0.04049, over 1427643.67 frames.], batch size: 23, lr: 1.44e-04
2022-05-29 17:05:29,268 INFO [train.py:842] (2/4) Epoch 38, batch 7950, loss[loss=0.1683, simple_loss=0.2653, pruned_loss=0.03563, over 6215.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2597, pruned_loss=0.04022, over 1425160.17 frames.], batch size: 38, lr: 1.44e-04
2022-05-29 17:06:07,451 INFO [train.py:842] (2/4) Epoch 38, batch 8000, loss[loss=0.1667, simple_loss=0.2609, pruned_loss=0.03626, over 7354.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2592, pruned_loss=0.04023, over 1428718.23 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 17:06:45,753 INFO [train.py:842] (2/4) Epoch 38, batch 8050, loss[loss=0.1909, simple_loss=0.2852, pruned_loss=0.04834, over 7313.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2594, pruned_loss=0.04009, over 1430695.63 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 17:07:23,704 INFO [train.py:842] (2/4) Epoch 38, batch 8100, loss[loss=0.1811, simple_loss=0.2718, pruned_loss=0.04516, over 7205.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2596, pruned_loss=0.0404, over 1428537.72 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 17:08:01,981 INFO [train.py:842] (2/4) Epoch 38, batch 8150, loss[loss=0.2018, simple_loss=0.2923, pruned_loss=0.05567, over 7181.00 frames.], tot_loss[loss=0.171, simple_loss=0.2607, pruned_loss=0.0406, over 1430606.59 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 17:08:39,928 INFO [train.py:842] (2/4) Epoch 38, batch 8200, loss[loss=0.1605, simple_loss=0.2448, pruned_loss=0.03806, over 7413.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2615, pruned_loss=0.041, over 1426564.75 frames.], batch size: 17, lr: 1.44e-04
2022-05-29 17:09:18,216 INFO [train.py:842] (2/4) Epoch 38, batch 8250, loss[loss=0.1244, simple_loss=0.2097, pruned_loss=0.01956, over 7009.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2619, pruned_loss=0.04088, over 1425800.23 frames.], batch size: 16, lr: 1.44e-04
2022-05-29 17:09:56,313 INFO [train.py:842] (2/4) Epoch 38, batch 8300, loss[loss=0.2208, simple_loss=0.3054, pruned_loss=0.06811, over 7296.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2616, pruned_loss=0.04097, over 1427409.24 frames.], batch size: 25, lr: 1.44e-04
2022-05-29 17:10:34,655 INFO [train.py:842] (2/4) Epoch 38, batch 8350, loss[loss=0.2164, simple_loss=0.3021, pruned_loss=0.06533, over 7208.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2618, pruned_loss=0.04136, over 1427890.97 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 17:11:12,608 INFO [train.py:842] (2/4) Epoch 38, batch 8400, loss[loss=0.2426, simple_loss=0.3237, pruned_loss=0.08076, over 6736.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2609, pruned_loss=0.04105, over 1425469.51 frames.], batch size: 31, lr: 1.44e-04
2022-05-29 17:11:50,864 INFO [train.py:842] (2/4) Epoch 38, batch 8450, loss[loss=0.1976, simple_loss=0.2954, pruned_loss=0.04989, over 7012.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2615, pruned_loss=0.04145, over 1424944.86 frames.], batch size: 32, lr: 1.44e-04
2022-05-29 17:12:28,626 INFO [train.py:842] (2/4) Epoch 38, batch 8500, loss[loss=0.1616, simple_loss=0.2628, pruned_loss=0.03023, over 7335.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2623, pruned_loss=0.04176, over 1418290.76 frames.], batch size: 22, lr: 1.44e-04
2022-05-29 17:13:06,632 INFO [train.py:842] (2/4) Epoch 38, batch 8550, loss[loss=0.1603, simple_loss=0.25, pruned_loss=0.03526, over 7138.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2623, pruned_loss=0.04159, over 1418115.30 frames.], batch size: 17, lr: 1.44e-04
2022-05-29 17:13:44,591 INFO [train.py:842] (2/4) Epoch 38, batch 8600, loss[loss=0.16, simple_loss=0.2451, pruned_loss=0.03745, over 7163.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2619, pruned_loss=0.04152, over 1417301.68 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 17:14:22,658 INFO [train.py:842] (2/4) Epoch 38, batch 8650, loss[loss=0.1342, simple_loss=0.22, pruned_loss=0.02425, over 7143.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2612, pruned_loss=0.04098, over 1416101.97 frames.], batch size: 17, lr: 1.44e-04
2022-05-29 17:15:00,645 INFO [train.py:842] (2/4) Epoch 38, batch 8700, loss[loss=0.1572, simple_loss=0.252, pruned_loss=0.03127, over 7317.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2604, pruned_loss=0.04093, over 1416918.28 frames.], batch size: 20, lr: 1.44e-04
2022-05-29 17:15:38,995 INFO [train.py:842] (2/4) Epoch 38, batch 8750, loss[loss=0.1706, simple_loss=0.2487, pruned_loss=0.04624, over 6799.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2595, pruned_loss=0.04046, over 1413036.13 frames.], batch size: 15, lr: 1.44e-04
2022-05-29 17:16:17,262 INFO [train.py:842] (2/4) Epoch 38, batch 8800, loss[loss=0.1656, simple_loss=0.2533, pruned_loss=0.03897, over 7356.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2584, pruned_loss=0.04013, over 1411532.78 frames.], batch size: 19, lr: 1.44e-04
2022-05-29 17:16:55,765 INFO [train.py:842] (2/4) Epoch 38, batch 8850, loss[loss=0.1777, simple_loss=0.2695, pruned_loss=0.04292, over 6989.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2581, pruned_loss=0.04009, over 1411465.68 frames.], batch size: 16, lr: 1.44e-04
2022-05-29 17:17:33,404 INFO [train.py:842] (2/4) Epoch 38, batch 8900, loss[loss=0.1527, simple_loss=0.2473, pruned_loss=0.02899, over 7422.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2587, pruned_loss=0.03987, over 1402930.38 frames.], batch size: 21, lr: 1.44e-04
2022-05-29 17:18:11,735 INFO [train.py:842] (2/4) Epoch 38, batch 8950, loss[loss=0.1939, simple_loss=0.2692, pruned_loss=0.05932, over 7291.00 frames.], tot_loss[loss=0.1697, simple_loss=0.259, pruned_loss=0.04019, over 1402315.08 frames.], batch size: 18, lr: 1.44e-04
2022-05-29 17:18:49,633 INFO [train.py:842] (2/4) Epoch 38, batch 9000, loss[loss=0.1534, simple_loss=0.2502, pruned_loss=0.02834, over 6432.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2575, pruned_loss=0.04005, over 1390481.28 frames.], batch size: 38, lr: 1.44e-04
2022-05-29 17:18:49,634 INFO [train.py:862] (2/4) Computing validation loss
2022-05-29 17:18:58,750 INFO [train.py:871] (2/4) Epoch 38, validation: loss=0.165, simple_loss=0.2618, pruned_loss=0.03413, over 868885.00 frames.
2022-05-29 17:19:36,242 INFO [train.py:842] (2/4) Epoch 38, batch 9050, loss[loss=0.1939, simple_loss=0.2769, pruned_loss=0.05543, over 5106.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2593, pruned_loss=0.04155, over 1355568.70 frames.], batch size: 52, lr: 1.44e-04
2022-05-29 17:20:12,621 INFO [train.py:842] (2/4) Epoch 38, batch 9100, loss[loss=0.1683, simple_loss=0.2556, pruned_loss=0.04049, over 5310.00 frames.], tot_loss[loss=0.174, simple_loss=0.2623, pruned_loss=0.04284, over 1304688.88 frames.], batch size: 52, lr: 1.44e-04
2022-05-29 17:20:49,730 INFO [train.py:842] (2/4) Epoch 38, batch 9150, loss[loss=0.1839, simple_loss=0.2763, pruned_loss=0.04571, over 5321.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2656, pruned_loss=0.04512, over 1241319.74 frames.], batch size: 53, lr: 1.44e-04
2022-05-29 17:21:35,360 INFO [train.py:842] (2/4) Epoch 39, batch 0, loss[loss=0.1527, simple_loss=0.2437, pruned_loss=0.03089, over 7262.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2437, pruned_loss=0.03089, over 7262.00 frames.], batch size: 19, lr: 1.42e-04
2022-05-29 17:22:13,619 INFO [train.py:842] (2/4) Epoch 39, batch 50, loss[loss=0.1719, simple_loss=0.2766, pruned_loss=0.03358, over 7143.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2632, pruned_loss=0.04129, over 320005.97 frames.], batch size: 20, lr: 1.42e-04
2022-05-29 17:22:51,511 INFO [train.py:842] (2/4) Epoch 39, batch 100, loss[loss=0.1732, simple_loss=0.2585, pruned_loss=0.04401, over 6821.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2628, pruned_loss=0.04099, over 565737.18 frames.], batch size: 31, lr: 1.42e-04
2022-05-29 17:23:29,995 INFO [train.py:842] (2/4) Epoch 39, batch 150, loss[loss=0.1619, simple_loss=0.2453, pruned_loss=0.03926, over 7161.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2592, pruned_loss=0.03982, over 754651.57 frames.], batch size: 18, lr: 1.42e-04
2022-05-29 17:24:07,930 INFO [train.py:842] (2/4) Epoch 39, batch 200, loss[loss=0.1922, simple_loss=0.2876, pruned_loss=0.04839, over 7425.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2618, pruned_loss=0.04081, over 900884.24 frames.], batch size: 20, lr: 1.42e-04
2022-05-29 17:24:45,987 INFO [train.py:842] (2/4) Epoch 39, batch 250, loss[loss=0.1867, simple_loss=0.2795, pruned_loss=0.04691, over 6288.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2623, pruned_loss=0.04109, over 1017087.82 frames.], batch size: 38, lr: 1.42e-04
2022-05-29 17:25:24,156 INFO [train.py:842] (2/4) Epoch 39, batch 300, loss[loss=0.1416, simple_loss=0.2321, pruned_loss=0.02557, over 7443.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2611, pruned_loss=0.04068, over 1112054.12 frames.], batch size: 20, lr: 1.42e-04
2022-05-29 17:26:02,384 INFO [train.py:842] (2/4) Epoch 39, batch 350, loss[loss=0.1875, simple_loss=0.2731, pruned_loss=0.05091, over 7281.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2608, pruned_loss=0.04105, over 1178342.06 frames.], batch size: 24, lr: 1.42e-04
2022-05-29 17:26:40,282 INFO [train.py:842] (2/4) Epoch 39, batch 400, loss[loss=0.2075, simple_loss=0.3058, pruned_loss=0.05462, over 7218.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2601, pruned_loss=0.04071, over 1227894.36 frames.], batch size: 21, lr: 1.42e-04
2022-05-29 17:27:18,765 INFO [train.py:842] (2/4) Epoch 39, batch 450, loss[loss=0.1792, simple_loss=0.2735, pruned_loss=0.0424, over 7199.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2605, pruned_loss=0.04092, over 1274072.94 frames.], batch size: 23, lr: 1.42e-04
2022-05-29 17:27:56,685 INFO [train.py:842] (2/4) Epoch 39, batch 500, loss[loss=0.1432, simple_loss=0.2435, pruned_loss=0.02149, over 7145.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2586, pruned_loss=0.04012, over 1301097.20 frames.], batch size: 20, lr: 1.42e-04
2022-05-29 17:28:35,068 INFO [train.py:842] (2/4) Epoch 39, batch 550, loss[loss=0.2157, simple_loss=0.2948, pruned_loss=0.06827, over 7423.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2587, pruned_loss=0.0403, over 1326881.11 frames.], batch size: 20, lr: 1.42e-04
2022-05-29 17:29:12,928 INFO [train.py:842] (2/4) Epoch 39, batch 600, loss[loss=0.2205, simple_loss=0.3035, pruned_loss=0.06876, over 7161.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2585, pruned_loss=0.03985, over 1345637.46 frames.], batch size: 18, lr: 1.42e-04
2022-05-29 17:29:51,401 INFO [train.py:842] (2/4) Epoch 39, batch 650, loss[loss=0.1453, simple_loss=0.2292, pruned_loss=0.03067, over 7283.00 frames.], tot_loss[loss=0.168, simple_loss=0.2579, pruned_loss=0.03909, over 1364995.62 frames.], batch size: 17, lr: 1.42e-04
2022-05-29 17:30:39,080 INFO [train.py:842] (2/4) Epoch 39, batch 700, loss[loss=0.1543, simple_loss=0.2434, pruned_loss=0.03261, over 6813.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2571, pruned_loss=0.03909, over 1378101.12 frames.], batch size: 15, lr: 1.42e-04
2022-05-29 17:31:17,519 INFO [train.py:842] (2/4) Epoch 39, batch 750, loss[loss=0.1721, simple_loss=0.2726, pruned_loss=0.03579, over 6365.00 frames.], tot_loss[loss=0.168, simple_loss=0.2573, pruned_loss=0.03935, over 1386810.92 frames.], batch size: 38, lr: 1.42e-04
2022-05-29 17:31:55,665 INFO [train.py:842] (2/4) Epoch 39, batch 800, loss[loss=0.1844, simple_loss=0.2795, pruned_loss=0.04467, over 7237.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2575, pruned_loss=0.03933, over 1399566.74 frames.], batch size: 20, lr: 1.42e-04
2022-05-29 17:32:33,966 INFO [train.py:842] (2/4) Epoch 39, batch 850, loss[loss=0.19, simple_loss=0.2883, pruned_loss=0.04589, over 7011.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2579, pruned_loss=0.03944, over 1405304.84 frames.], batch size: 28, lr: 1.42e-04
2022-05-29 17:33:11,857 INFO [train.py:842] (2/4) Epoch 39, batch 900, loss[loss=0.2, simple_loss=0.2855, pruned_loss=0.05722, over 7397.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2585, pruned_loss=0.03987, over 1404195.55 frames.], batch size: 21, lr: 1.42e-04
2022-05-29 17:33:49,927 INFO [train.py:842] (2/4) Epoch 39, batch 950, loss[loss=0.1616, simple_loss=0.2382, pruned_loss=0.04252, over 7142.00 frames.], tot_loss[loss=0.1696, simple_loss=0.259, pruned_loss=0.04008, over 1405967.05 frames.], batch size: 17, lr: 1.42e-04
2022-05-29 17:34:28,092 INFO [train.py:842] (2/4) Epoch 39, batch 1000, loss[loss=0.1761, simple_loss=0.2627, pruned_loss=0.04473, over 7350.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2592, pruned_loss=0.04002, over 1408356.14 frames.], batch size: 19, lr: 1.42e-04
2022-05-29 17:35:06,298 INFO [train.py:842] (2/4) Epoch 39, batch 1050, loss[loss=0.1606, simple_loss=0.262, pruned_loss=0.02956, over 6869.00 frames.], tot_loss[loss=0.1694, simple_loss=0.259, pruned_loss=0.03984, over 1410801.64 frames.], batch size: 31, lr: 1.42e-04
2022-05-29 17:35:53,920 INFO [train.py:842] (2/4) Epoch 39, batch 1100, loss[loss=0.1539, simple_loss=0.2487, pruned_loss=0.02959, over 7385.00 frames.], tot_loss[loss=0.168, simple_loss=0.2576, pruned_loss=0.03916, over 1415297.40 frames.], batch size: 23, lr: 1.42e-04
2022-05-29 17:36:32,294 INFO [train.py:842] (2/4) Epoch 39, batch 1150, loss[loss=0.1535, simple_loss=0.2308, pruned_loss=0.03809, over 7276.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2574, pruned_loss=0.03966, over 1418952.19 frames.], batch size: 18, lr: 1.42e-04
2022-05-29 17:37:19,671 INFO [train.py:842] (2/4) Epoch 39, batch 1200, loss[loss=0.2067, simple_loss=0.2896, pruned_loss=0.06196, over 6756.00 frames.], tot_loss[loss=0.1694, simple_loss=0.258, pruned_loss=0.04042, over 1421087.75 frames.], batch size: 31, lr: 1.42e-04
2022-05-29 17:37:57,973 INFO [train.py:842] (2/4) Epoch 39, batch 1250, loss[loss=0.1508, simple_loss=0.2369, pruned_loss=0.03238, over 7428.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2593, pruned_loss=0.04063, over 1422524.43 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:38:36,164 INFO [train.py:842] (2/4) Epoch 39, batch 1300, loss[loss=0.1312, simple_loss=0.2171, pruned_loss=0.02262, over 7271.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2587, pruned_loss=0.04042, over 1425593.11 frames.], batch size: 17, lr: 1.41e-04
2022-05-29 17:39:14,319 INFO [train.py:842] (2/4) Epoch 39, batch 1350, loss[loss=0.1513, simple_loss=0.2363, pruned_loss=0.03311, over 7328.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2588, pruned_loss=0.04014, over 1425623.01 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:39:52,321 INFO [train.py:842] (2/4) Epoch 39, batch 1400, loss[loss=0.2149, simple_loss=0.2905, pruned_loss=0.0696, over 7159.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2589, pruned_loss=0.04042, over 1424974.91 frames.], batch size: 19, lr: 1.41e-04
2022-05-29 17:40:30,522 INFO [train.py:842] (2/4) Epoch 39, batch 1450, loss[loss=0.1868, simple_loss=0.2719, pruned_loss=0.05087, over 7331.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2611, pruned_loss=0.04155, over 1425172.73 frames.], batch size: 25, lr: 1.41e-04
2022-05-29 17:41:08,503 INFO [train.py:842] (2/4) Epoch 39, batch 1500, loss[loss=0.1757, simple_loss=0.273, pruned_loss=0.03916, over 7117.00 frames.], tot_loss[loss=0.172, simple_loss=0.2613, pruned_loss=0.04136, over 1424620.78 frames.], batch size: 21, lr: 1.41e-04
2022-05-29 17:41:46,918 INFO [train.py:842] (2/4) Epoch 39, batch 1550, loss[loss=0.1728, simple_loss=0.2641, pruned_loss=0.04071, over 7219.00 frames.], tot_loss[loss=0.172, simple_loss=0.261, pruned_loss=0.04151, over 1423960.07 frames.], batch size: 22, lr: 1.41e-04
2022-05-29 17:42:25,012 INFO [train.py:842] (2/4) Epoch 39, batch 1600, loss[loss=0.1702, simple_loss=0.2612, pruned_loss=0.03965, over 6689.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2587, pruned_loss=0.04033, over 1425342.63 frames.], batch size: 31, lr: 1.41e-04
2022-05-29 17:43:03,329 INFO [train.py:842] (2/4) Epoch 39, batch 1650, loss[loss=0.192, simple_loss=0.2811, pruned_loss=0.05142, over 7225.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2589, pruned_loss=0.03996, over 1424339.58 frames.], batch size: 21, lr: 1.41e-04
2022-05-29 17:43:41,229 INFO [train.py:842] (2/4) Epoch 39, batch 1700, loss[loss=0.1741, simple_loss=0.2628, pruned_loss=0.04269, over 7059.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2595, pruned_loss=0.03994, over 1425811.85 frames.], batch size: 28, lr: 1.41e-04
2022-05-29 17:44:19,439 INFO [train.py:842] (2/4) Epoch 39, batch 1750, loss[loss=0.1628, simple_loss=0.2612, pruned_loss=0.03221, over 7434.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2611, pruned_loss=0.04052, over 1425340.97 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:44:57,267 INFO [train.py:842] (2/4) Epoch 39, batch 1800, loss[loss=0.1771, simple_loss=0.2744, pruned_loss=0.03988, over 7206.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2605, pruned_loss=0.0403, over 1422491.44 frames.], batch size: 23, lr: 1.41e-04
2022-05-29 17:45:35,463 INFO [train.py:842] (2/4) Epoch 39, batch 1850, loss[loss=0.1553, simple_loss=0.2463, pruned_loss=0.03209, over 7159.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2595, pruned_loss=0.03993, over 1420809.97 frames.], batch size: 19, lr: 1.41e-04
2022-05-29 17:46:13,560 INFO [train.py:842] (2/4) Epoch 39, batch 1900, loss[loss=0.1617, simple_loss=0.251, pruned_loss=0.03615, over 7275.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2591, pruned_loss=0.04018, over 1423483.18 frames.], batch size: 18, lr: 1.41e-04
2022-05-29 17:46:51,816 INFO [train.py:842] (2/4) Epoch 39, batch 1950, loss[loss=0.1526, simple_loss=0.2513, pruned_loss=0.02696, over 7319.00 frames.], tot_loss[loss=0.1694, simple_loss=0.259, pruned_loss=0.03989, over 1423384.14 frames.], batch size: 21, lr: 1.41e-04
2022-05-29 17:47:29,742 INFO [train.py:842] (2/4) Epoch 39, batch 2000, loss[loss=0.1433, simple_loss=0.2353, pruned_loss=0.02569, over 7260.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2588, pruned_loss=0.03983, over 1422772.10 frames.], batch size: 19, lr: 1.41e-04
2022-05-29 17:48:07,761 INFO [train.py:842] (2/4) Epoch 39, batch 2050, loss[loss=0.1773, simple_loss=0.2797, pruned_loss=0.03742, over 7329.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2593, pruned_loss=0.03963, over 1420719.40 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:48:45,823 INFO [train.py:842] (2/4) Epoch 39, batch 2100, loss[loss=0.164, simple_loss=0.2486, pruned_loss=0.03968, over 6761.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2595, pruned_loss=0.04007, over 1421827.14 frames.], batch size: 15, lr: 1.41e-04
2022-05-29 17:49:24,059 INFO [train.py:842] (2/4) Epoch 39, batch 2150, loss[loss=0.1479, simple_loss=0.2343, pruned_loss=0.03075, over 7269.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2594, pruned_loss=0.04017, over 1420192.46 frames.], batch size: 19, lr: 1.41e-04
2022-05-29 17:50:01,945 INFO [train.py:842] (2/4) Epoch 39, batch 2200, loss[loss=0.1742, simple_loss=0.2649, pruned_loss=0.04171, over 7193.00 frames.], tot_loss[loss=0.1702, simple_loss=0.26, pruned_loss=0.04021, over 1420694.49 frames.], batch size: 22, lr: 1.41e-04
2022-05-29 17:50:40,597 INFO [train.py:842] (2/4) Epoch 39, batch 2250, loss[loss=0.1648, simple_loss=0.2478, pruned_loss=0.04092, over 7137.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2581, pruned_loss=0.03956, over 1423824.61 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:51:18,398 INFO [train.py:842] (2/4) Epoch 39, batch 2300, loss[loss=0.2275, simple_loss=0.3032, pruned_loss=0.07595, over 7155.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2591, pruned_loss=0.04022, over 1423597.95 frames.], batch size: 19, lr: 1.41e-04
2022-05-29 17:51:56,795 INFO [train.py:842] (2/4) Epoch 39, batch 2350, loss[loss=0.1431, simple_loss=0.2404, pruned_loss=0.02295, over 7232.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2581, pruned_loss=0.0397, over 1424951.09 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:52:34,837 INFO [train.py:842] (2/4) Epoch 39, batch 2400, loss[loss=0.1702, simple_loss=0.2665, pruned_loss=0.0369, over 7148.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2585, pruned_loss=0.03989, over 1428067.56 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:53:13,350 INFO [train.py:842] (2/4) Epoch 39, batch 2450, loss[loss=0.1445, simple_loss=0.2316, pruned_loss=0.02871, over 7401.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2577, pruned_loss=0.0395, over 1428951.64 frames.], batch size: 18, lr: 1.41e-04
2022-05-29 17:53:51,360 INFO [train.py:842] (2/4) Epoch 39, batch 2500, loss[loss=0.1181, simple_loss=0.2037, pruned_loss=0.01627, over 7402.00 frames.], tot_loss[loss=0.1685, simple_loss=0.258, pruned_loss=0.0395, over 1427595.71 frames.], batch size: 18, lr: 1.41e-04
2022-05-29 17:54:29,852 INFO [train.py:842] (2/4) Epoch 39, batch 2550, loss[loss=0.1613, simple_loss=0.2569, pruned_loss=0.03288, over 7435.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2584, pruned_loss=0.03957, over 1431967.16 frames.], batch size: 20, lr: 1.41e-04
2022-05-29 17:55:07,763 INFO [train.py:842] (2/4) Epoch 39, batch 2600, loss[loss=0.1895, simple_loss=0.284, pruned_loss=0.04751, over 7126.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2591, pruned_loss=0.03986, over 1429419.25 frames.], batch size: 26, lr: 1.41e-04
2022-05-29 17:55:46,320 INFO [train.py:842] (2/4) Epoch 39, batch 2650, loss[loss=0.1792, simple_loss=0.2674, pruned_loss=0.04546, over 7150.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2595, pruned_loss=0.04018, over 1430541.59 frames.], batch size: 28, lr: 1.41e-04
2022-05-29 17:56:24,497 INFO [train.py:842] (2/4) Epoch 39, batch 2700, loss[loss=0.1729, simple_loss=0.2701, pruned_loss=0.03782, over 7298.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2595, pruned_loss=0.03986, over 1429070.78 frames.], batch size: 25, lr: 1.41e-04
2022-05-29 17:57:06,505 INFO [train.py:842] (2/4) Epoch 39, batch 2750, loss[loss=0.1551, simple_loss=0.2482, pruned_loss=0.03096, over 7161.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2585, pruned_loss=0.03939, over 1428625.56 frames.], batch size: 19, lr: 1.41e-04
2022-05-29 17:57:44,930 INFO [train.py:842] (2/4) Epoch 39, batch 2800, loss[loss=0.147, simple_loss=0.2392, pruned_loss=0.02745, over 7353.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2592, pruned_loss=0.03968, over 1425530.98 frames.], batch size: 22, lr: 1.41e-04
2022-05-29 17:58:23,855 INFO [train.py:842] (2/4) Epoch 39, batch 2850, loss[loss=0.1699, simple_loss=0.2645, pruned_loss=0.03767, over 6317.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2599, pruned_loss=0.03993, over 1425970.08 frames.], batch size: 37, lr: 1.41e-04