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Seq2SeqModel.py
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Seq2SeqModel.py
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import os
import json
import math
import numpy as np
import tensorflow as tf
import tensorflow.contrib.seq2seq as seq2seq
# borrowed from: https://github.com/JayParks/tf-seq2seq/blob/master/seq2seq_model.py
class Seq2SeqModel(object):
def __init__(self, hParams, mode):
self.HP = hParams
self.mode = mode
self.max_gradient_norm = hParams.MAX_GRADIANT_NORM
self.global_step = tf.Variable(0, trainable=False, name='global_step')
self.global_epoch_step = tf.Variable(0, trainable=False, name='global_epoch_step')
self.global_epoch_step_op = tf.assign(self.global_epoch_step, self.global_epoch_step+1)
self.build_model()
def build_model(self):
print("building the model...")
self.init_placeholders()
self.init_embedding()
self.build_encoder()
self.build_decoder()
self.summary_op = tf.summary.merge_all()
def init_placeholders(self):
# encoder inputs are include </s> tokens. e.g: "hello world </s>". So we can use them as decoder_output too.
# shape: [Batch_size, max_timestep]
self.encoder_inputs = tf.placeholder(dtype=tf.int32, shape=(None, None), name="encoder_inputs")
# shape: [Batch_size]
self.encoder_lengths = tf.placeholder(dtype=tf.int32, shape=(None,), name="encoder_inputs_length")
# shape: [Batch_size, max_timestep]
self.decoder_inputs = tf.placeholder(dtype=tf.int32, shape=(None, None), name="decoder_inputs")
# shape: [Batch_size]
self.decoder_inputs_lengths = tf.placeholder(dtype=tf.int32, shape=(None,), name="decoder_inputs_length")
# get dynamic batch size
self.batch_size = tf.shape(self.encoder_inputs)[0]
def init_embedding(self):
self.emb_weights = tf.Variable(tf.constant(0.0,
shape=[self.HP.VOCAB_LEN, self.HP.EMB_DIM],dtype=tf.float32),
trainable=False,
name="embeddingWeights")
self.embedding_placeholder = tf.placeholder(tf.float32,
[self.HP.VOCAB_LEN, self.HP.EMB_DIM],
name="weights_placeholder")
self.embeddings = self.emb_weights.assign(self.embedding_placeholder)
# assign embeding
def assign_embedding(self, sess, weights):
sess.run(self.embeddings, feed_dict={self.embedding_placeholder:weights})
def build_encoder(self):
print("build encoder layer...")
with tf.variable_scope('encoder'):
self.encoder_cell = tf.contrib.rnn.BasicLSTMCell(self.HP.AE_LSTM_UNITS)
# Embedded_inputs: [batch_size, time_step, embedding_size]
with tf.device('/cpu:0'):
self.encoder_inputs_embedded = tf.nn.embedding_lookup(params=self.emb_weights,
ids=self.encoder_inputs)
# Encode input sequences into context vectors:
# encoder_outputs: [batch_size, max_time_step, cell_output_size]
# encoder_state: [batch_size, cell_output_size]
self.encoder_outputs, self.encoder_last_state = tf.nn.dynamic_rnn(cell=self.encoder_cell,
inputs=self.encoder_inputs_embedded,
sequence_length=self.encoder_lengths,
dtype=tf.float32,
time_major=False)
def build_decoder(self):
print("building decoder layer...")
with tf.variable_scope('decoder'):
self.decoder_cell = tf.contrib.rnn.BasicLSTMCell(self.HP.AE_LSTM_UNITS)
# Output projection layer to convert cell_outputs to logits
output_layer = tf.layers.Dense(self.HP.VOCAB_LEN,
kernel_initializer = tf.truncated_normal_initializer(mean = 0.0, stddev=1),
dtype=tf.float32,
name='output_projection')
if self.mode == 'train':
with tf.device('/cpu:0'):
# decoder_inputs_embedded: [batch_size, max_time_step + <sos>, embedding_size]
self.decoder_inputs_embedded = tf.nn.embedding_lookup(params=self.emb_weights,
ids=self.decoder_inputs)
# Helper to feed inputs for training: read inputs from dense ground truth vectors
training_helper = seq2seq.TrainingHelper(inputs=self.decoder_inputs_embedded,
sequence_length=self.decoder_inputs_lengths,
time_major=False,
name='training_helper')
training_decoder = seq2seq.BasicDecoder(cell=self.decoder_cell,
helper=training_helper,
initial_state=self.encoder_last_state,
output_layer=output_layer)
# Maximum encoder time_steps in current batch
max_encoder_length = tf.reduce_max(self.encoder_lengths)
# Maximum encoder time_steps in current batch
max_decoder_length = tf.reduce_max(self.decoder_inputs_lengths)
# decoder_outputs_train: BasicDecoderOutput
# namedtuple(rnn_outputs, sample_id)
# decoder_outputs_train.rnn_output: [batch_size, max_time_step + 1, num_decoder_symbols] if output_time_major=False
# [max_time_step + 1, batch_size, num_decoder_symbols] if output_time_major=True
# decoder_outputs_train.sample_id: [batch_size], tf.int32
(self.decoder_outputs_train,
self.decoder_last_state_train,
self.decoder_outputs_length_train) = (seq2seq.dynamic_decode(
decoder=training_decoder,
output_time_major=False,
impute_finished=True,
maximum_iterations=max_decoder_length))
# logits_train: [batch_size, max_time_step + 1, decoder_vocab_size]
# self.decoder_logits_train = output_layer(self.decoder_outputs_train.rnn_output)
self.decoder_logits_train = tf.identity(self.decoder_outputs_train.rnn_output)
# Use argmax to extract decoder symbols to emit
self.decoder_pred_train = tf.argmax(self.decoder_logits_train,
axis=-1,
name='decoder_pred_train')
# masks: masking for valid and padded time steps, [batch_size, max_time_step + 1]
masks = tf.sequence_mask(lengths=self.encoder_lengths,
maxlen=max_encoder_length,
dtype=tf.float32,
name='masks')
# Computes per word average cross-entropy over a batch
# Internally calls 'nn_ops.sparse_softmax_cross_entropy_with_logits' by default
self.loss = seq2seq.sequence_loss(logits=self.decoder_logits_train,
targets=self.encoder_inputs,
weights=masks,
average_across_timesteps=True,
average_across_batch=True,)
# Training summary for the current batch_loss
tf.summary.scalar('loss', self.loss)
# Construct graphs for minimizing loss
self.init_optimizer()
def init_optimizer(self):
print("setting optimizer..")
trainable_params = tf.trainable_variables()
learning_rate = tf.train.exponential_decay(self.HP.LEARNING_RATE, self.global_step,
self.HP.DECAY_STEP, self.HP.DECAY_RATE, staircase=True)
self.opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
# Compute gradients of loss w.r.t. all trainable variables
gradients = tf.gradients(self.loss, trainable_params)
# Clip gradients by a given maximum_gradient_norm
clip_gradients, _ = tf.clip_by_global_norm(gradients, self.HP.MAX_GRADIANT_NORM)
# Update the model
self.updates = self.opt.apply_gradients(zip(clip_gradients, trainable_params),
global_step=self.global_step)
def restore(self, sess, path, var_list=None):
# var_list = None returns the list of all saveable variables
saver = tf.train.Saver(var_list)
saver.restore(sess, save_path=path)
print('model restored from %s' % path)
def get_encoder(self):
return self.encoder_cell
def train(self, sess, encoder_inputs, encoder_inputs_length, decoder_inputs, decoder_inputs_length):
"""Run a train step of the model feeding the given inputs.
Args:
session: tensorflow session to use.
encoder_inputs: a numpy int matrix of [batch_size, max_source_time_steps]
to feed as encoder inputs
encoder_inputs_length: a numpy int vector of [batch_size]
to feed as sequence lengths for each element in the given batch
Returns:
A triple consisting of gradient norm (or None if we did not do backward),
average perplexity, and the outputs.
"""
# Check if the model is 'training' mode
if self.mode.lower() != 'train':
raise ValueError("train step can only be operated in train mode")
input_feed = {}
input_feed[self.encoder_inputs.name] = encoder_inputs
input_feed[self.encoder_lengths.name] = encoder_inputs_length
input_feed[self.decoder_inputs.name] = decoder_inputs
input_feed[self.decoder_inputs_lengths.name] = decoder_inputs_length
output_feed = [self.updates, # Update Op that does optimization
self.loss, # Loss for current batch
self.summary_op,
self.decoder_logits_train] # Training summary]
outputs = sess.run(output_feed, input_feed)
return outputs[1], outputs[2], outputs[3]
def eval(self, sess, encoder_inputs, encoder_inputs_length, decoder_inputs, decoder_inputs_length):
"""Run a evaluation step of the model feeding the given inputs.
Args:
session: tensorflow session to use.
encoder_inputs: a numpy int matrix of [batch_size, max_source_time_steps]
to feed as encoder inputs
encoder_inputs_length: a numpy int vector of [batch_size]
to feed as sequence lengths for each element in the given batch
Returns:
A triple consisting of gradient norm (or None if we did not do backward),
average perplexity, and the outputs.
"""
input_feed = {}
input_feed[self.encoder_inputs.name] = encoder_inputs
input_feed[self.encoder_lengths.name] = encoder_inputs_length
input_feed[self.decoder_inputs.name] = decoder_inputs
input_feed[self.decoder_inputs_lengths.name] = decoder_inputs_length
output_feed = [self.loss, # Loss for current batch
self.summary_op,
self.decoder_pred_train] # Training summary
outputs = sess.run(output_feed, input_feed)
return outputs[0], outputs[1], outputs[2] # loss
def predict(self, sess, encoder_inputs, encoder_inputs_length):
# Input feeds for dropout
# input_feed[self.keep_prob_placeholder.name] = 1.0
output_feed = [self.decoder_pred_decode]
outputs = sess.run(output_feed, input_feed)
# GreedyDecoder: [batch_size, max_time_step]
return outputs[0]