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seq2seq.py
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seq2seq.py
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import tensorflow as tf
from tensorflow.contrib import rnn
from tensorflow.python.layers import core as layers_core
def getLayeredCell(layer_size, num_units, input_keep_prob,
output_keep_prob=1.0):
return rnn.MultiRNNCell([rnn.DropoutWrapper(rnn.BasicLSTMCell(num_units),
input_keep_prob, output_keep_prob) for i in range(layer_size)])
def bi_encoder(embed_input, in_seq_len, num_units, layer_size, input_keep_prob):
# encode input into a vector
bi_layer_size = int(layer_size / 2)
encode_cell_fw = getLayeredCell(bi_layer_size, num_units, input_keep_prob)
encode_cell_bw = getLayeredCell(bi_layer_size, num_units, input_keep_prob)
bi_encoder_output, bi_encoder_state = tf.nn.bidirectional_dynamic_rnn(
cell_fw = encode_cell_fw,
cell_bw = encode_cell_bw,
inputs = embed_input,
sequence_length = in_seq_len,
dtype = embed_input.dtype,
time_major = False)
# concat encode output and state
encoder_output = tf.concat(bi_encoder_output, -1)
encoder_state = []
for layer_id in range(bi_layer_size):
encoder_state.append(bi_encoder_state[0][layer_id])
encoder_state.append(bi_encoder_state[1][layer_id])
encoder_state = tuple(encoder_state)
return encoder_output, encoder_state
def attention_decoder_cell(encoder_output, in_seq_len, num_units, layer_size,
input_keep_prob):
attention_mechanim = tf.contrib.seq2seq.BahdanauAttention(num_units,
encoder_output, in_seq_len, normalize = True)
# attention_mechanim = tf.contrib.seq2seq.LuongAttention(num_units,
# encoder_output, in_seq_len, scale = True)
cell = getLayeredCell(layer_size, num_units, input_keep_prob)
cell = tf.contrib.seq2seq.AttentionWrapper(cell, attention_mechanim,
attention_layer_size=num_units)
return cell
def decoder_projection(output, output_size):
return tf.layers.dense(output, output_size, activation=None,
use_bias=False, name='output_mlp')
def train_decoder(encoder_output, in_seq_len, target_seq, target_seq_len,
encoder_state, num_units, layers, embedding, output_size,
input_keep_prob, projection_layer):
decoder_cell = attention_decoder_cell(encoder_output, in_seq_len, num_units,
layers, input_keep_prob)
batch_size = tf.shape(in_seq_len)[0]
init_state = decoder_cell.zero_state(batch_size, tf.float32).clone(
cell_state=encoder_state)
helper = tf.contrib.seq2seq.TrainingHelper(
target_seq, target_seq_len, time_major=False)
decoder = tf.contrib.seq2seq.BasicDecoder(decoder_cell, helper,
init_state, output_layer=projection_layer)
outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder,
maximum_iterations=100)
return outputs.rnn_output
def infer_decoder(encoder_output, in_seq_len, encoder_state, num_units, layers,
embedding, output_size, input_keep_prob, projection_layer):
decoder_cell = attention_decoder_cell(encoder_output, in_seq_len, num_units,
layers, input_keep_prob)
batch_size = tf.shape(in_seq_len)[0]
init_state = decoder_cell.zero_state(batch_size, tf.float32).clone(
cell_state=encoder_state)
# TODO: start tokens and end tokens are hard code
"""
helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
embedding, tf.fill([batch_size], 0), 1)
decoder = tf.contrib.seq2seq.BasicDecoder(decoder_cell, helper,
init_state, output_layer=projection_layer)
"""
decoder = tf.contrib.seq2seq.BeamSearchDecoder(
cell=decoder_cell,
embedding=embedding,
start_tokens=tf.fill([batch_size], 0),
end_token=1,
initial_state=init_state,
beam_width=10,
output_layer=projection_layer,
length_penalty_weight=1.0)
outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder,
maximum_iterations=100)
return outputs.sample_id
def seq2seq(in_seq, in_seq_len, target_seq, target_seq_len, vocab_size,
num_units, layers, dropout):
in_shape = tf.shape(in_seq)
batch_size = in_shape[0]
if target_seq != None:
input_keep_prob = 1 - dropout
else:
input_keep_prob = 1
projection_layer=layers_core.Dense(vocab_size, use_bias=False)
# embedding input and target sequence
with tf.device('/cpu:0'):
embedding = tf.get_variable(
name = 'embedding',
shape = [vocab_size, num_units])
embed_input = tf.nn.embedding_lookup(embedding, in_seq, name='embed_input')
# encode and decode
encoder_output, encoder_state = bi_encoder(embed_input, in_seq_len,
num_units, layers, input_keep_prob)
decoder_cell = attention_decoder_cell(encoder_output, in_seq_len, num_units,
layers, input_keep_prob)
batch_size = tf.shape(in_seq_len)[0]
init_state = decoder_cell.zero_state(batch_size, tf.float32).clone(
cell_state=encoder_state)
if target_seq != None:
embed_target = tf.nn.embedding_lookup(embedding, target_seq,
name='embed_target')
helper = tf.contrib.seq2seq.TrainingHelper(
embed_target, target_seq_len, time_major=False)
else:
# TODO: start tokens and end tokens are hard code
helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
embedding, tf.fill([batch_size], 0), 1)
decoder = tf.contrib.seq2seq.BasicDecoder(decoder_cell, helper,
init_state, output_layer=projection_layer)
outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder,
maximum_iterations=100)
if target_seq != None:
return outputs.rnn_output
else:
return outputs.sample_id
def seq_loss(output, target, seq_len):
target = target[:, 1:]
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=output,
labels=target)
batch_size = tf.shape(target)[0]
loss_mask = tf.sequence_mask(seq_len, tf.shape(output)[1])
cost = cost * tf.to_float(loss_mask)
return tf.reduce_sum(cost) / tf.to_float(batch_size)