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import tensorflow as tf
from tensorflow.python.ops import rnn_cell
from tensorflow.python.ops import seq2seq
import random
import numpy as np
class Model():
def __init__(self, args, infer=False):
self.args = args
if infer:
args.batch_size = 1
args.seq_length = 1
if args.model == 'rnn':
cell_fn = rnn_cell.BasicRNNCell
elif args.model == 'gru':
cell_fn = rnn_cell.GRUCell
elif args.model == 'lstm':
cell_fn = rnn_cell.BasicLSTMCell
else:
raise Exception("model type not supported: {}".format(args.model))
cell = cell_fn(args.rnn_size)
self.cell = cell = rnn_cell.MultiRNNCell([cell] * args.num_layers)
self.input_data = tf.placeholder(tf.int32, [args.batch_size, args.seq_length])
self.targets = tf.placeholder(tf.int32, [args.batch_size, args.seq_length])
self.initial_state = cell.zero_state(args.batch_size, tf.float32)
with tf.variable_scope('rnnlm'):
softmax_w = tf.get_variable("softmax_w", [args.rnn_size, args.vocab_size])
softmax_b = tf.get_variable("softmax_b", [args.vocab_size])
with tf.device("/cpu:0"):
embedding = tf.get_variable("embedding", [args.vocab_size, args.rnn_size])
inputs = tf.split(1, args.seq_length, tf.nn.embedding_lookup(embedding, self.input_data))
inputs = [tf.squeeze(input_, [1]) for input_ in inputs]
def loop(prev, _):
prev = tf.matmul(prev, softmax_w) + softmax_b
prev_symbol = tf.stop_gradient(tf.argmax(prev, 1))
return tf.nn.embedding_lookup(embedding, prev_symbol)
outputs, last_state = seq2seq.rnn_decoder(inputs, self.initial_state, cell, loop_function=loop if infer else None, scope='rnnlm')
output = tf.reshape(tf.concat(1, outputs), [-1, args.rnn_size])
self.logits = tf.matmul(output, softmax_w) + softmax_b
self.probs = tf.nn.softmax(self.logits)
loss = seq2seq.sequence_loss_by_example([self.logits],
[tf.reshape(self.targets, [-1])],
[tf.ones([args.batch_size * args.seq_length])],
args.vocab_size)
self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length
self.final_state = last_state
self.lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),
args.grad_clip)
optimizer = tf.train.AdamOptimizer(self.lr)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))
def sample(self, sess, words, vocab, num=200, prime='first all', sampling_type=1):
state = sess.run(self.cell.zero_state(1, tf.float32))
if not len(prime) or prime == " ":
prime = random.choice(list(vocab.keys()))
print (prime)
for word in prime.split()[:-1]:
print (word)
x = np.zeros((1, 1))
x[0, 0] = vocab.get(word,0)
feed = {self.input_data: x, self.initial_state:state}
[state] = sess.run([self.final_state], feed)
def weighted_pick(weights):
t = np.cumsum(weights)
s = np.sum(weights)
return(int(np.searchsorted(t, np.random.rand(1)*s)))
ret = prime
word = prime.split()[-1]
for n in range(num):
x = np.zeros((1, 1))
x[0, 0] = vocab.get(word,0)
feed = {self.input_data: x, self.initial_state:state}
[probs, state] = sess.run([self.probs, self.final_state], feed)
p = probs[0]
if sampling_type == 0:
sample = np.argmax(p)
elif sampling_type == 2:
if word == '\n':
sample = weighted_pick(p)
else:
sample = np.argmax(p)
else: # sampling_type == 1 default:
sample = weighted_pick(p)
pred = words[sample]
ret += ' ' + pred
word = pred
return ret
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