-
Notifications
You must be signed in to change notification settings - Fork 0
/
model_2.py
192 lines (176 loc) · 13.8 KB
/
model_2.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
import numpy as np
import tensorflow as tf
from tensorflow.python.util import nest
class Seq2SeqModel():
def __init__(self, rnn_size, num_layers, embedding_size, learning_rate, word_to_idx, mode, use_attention,
beam_search, beam_size, max_gradient_norm=5.0):
self.learing_rate = learning_rate
self.embedding_size = embedding_size
self.rnn_size = rnn_size
self.num_layers = num_layers
self.word_to_idx = word_to_idx
self.vocab_size = len(self.word_to_idx)
self.mode = mode
self.use_attention = use_attention
self.beam_search = beam_search
self.beam_size = beam_size
self.max_gradient_norm = max_gradient_norm
#执行模型构建部分的代码
self.build_model()
def _create_rnn_cell(self):
def single_rnn_cell():
# 创建单个cell，这里需要注意的是一定要使用一个single_rnn_cell的函数，不然直接把cell放在MultiRNNCell
# 的列表中最终模型会发生错误
single_cell = tf.contrib.rnn.LSTMCell(self.rnn_size)
#添加dropout
cell = tf.contrib.rnn.DropoutWrapper(single_cell, output_keep_prob=self.keep_prob_placeholder)
return cell
#列表中每个元素都是调用single_rnn_cell函数
cell = tf.contrib.rnn.MultiRNNCell([single_rnn_cell() for _ in range(self.num_layers)])
return cell
def build_model(self):
print('building model... ...')
#=================================1, 定义模型的placeholder
self.encoder_inputs = tf.placeholder(tf.int32, [None, None], name='encoder_inputs')
self.encoder_inputs_length = tf.placeholder(tf.int32, [None], name='encoder_inputs_length')
self.batch_size = tf.placeholder(tf.int32, [], name='batch_size')
self.keep_prob_placeholder = tf.placeholder(tf.float32, name='keep_prob_placeholder')
self.decoder_targets = tf.placeholder(tf.int32, [None, None], name='decoder_targets')
self.decoder_targets_length = tf.placeholder(tf.int32, [None], name='decoder_targets_length')
# 根据目标序列长度，选出其中最大值，然后使用该值构建序列长度的mask标志。用一个sequence_mask的例子来说明起作用
# tf.sequence_mask([1, 3, 2], 5)
# [[True, False, False, False, False],
# [True, True, True, False, False],
# [True, True, False, False, False]]
self.max_target_sequence_length = tf.reduce_max(self.decoder_targets_length, name='max_target_len')
self.mask = tf.sequence_mask(self.decoder_targets_length, self.max_target_sequence_length, dtype=tf.float32, name='masks')
#=================================2, 定义模型的encoder部分
with tf.variable_scope('encoder'):
#创建LSTMCell，两层+dropout
encoder_cell = self._create_rnn_cell()
#构建embedding矩阵,encoder和decoder公用该词向量矩阵
embedding = np.load('embedding_matrix.npy')
embedding=tf.get_variable(name="embedding",shape=embedding.shape,initializer=tf.constant_initializer(embedding), trainable=False)
encoder_inputs_embedded = tf.nn.embedding_lookup(embedding, self.encoder_inputs)
# 使用dynamic_rnn构建LSTM模型，将输入编码成隐层向量。
# encoder_outputs用于attention，batch_size*encoder_inputs_length*rnn_size,
# encoder_state用于decoder的初始化状态，batch_size*rnn_szie
encoder_outputs, encoder_state = tf.nn.dynamic_rnn(encoder_cell, encoder_inputs_embedded,
sequence_length=self.encoder_inputs_length,
dtype=tf.float32)
# =================================3, 定义模型的decoder部分
with tf.variable_scope('decoder'):
encoder_inputs_length = self.encoder_inputs_length
if self.beam_search:
# 如果使用beam_search，则需要将encoder的输出进行tile_batch，其实就是复制beam_size份。
print("use beamsearch decoding..")
encoder_outputs = tf.contrib.seq2seq.tile_batch(encoder_outputs, multiplier=self.beam_size)
encoder_state = nest.map_structure(lambda s: tf.contrib.seq2seq.tile_batch(s, self.beam_size), encoder_state)
encoder_inputs_length = tf.contrib.seq2seq.tile_batch(self.encoder_inputs_length, multiplier=self.beam_size)
#定义要使用的attention机制。
attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(num_units=self.rnn_size, memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length)
#attention_mechanism = tf.contrib.seq2seq.LuongAttention(num_units=self.rnn_size, memory=encoder_outputs, memory_sequence_length=encoder_inputs_length)
# 定义decoder阶段要是用的LSTMCell，然后为其封装attention wrapper
decoder_cell = self._create_rnn_cell()
decoder_cell = tf.contrib.seq2seq.AttentionWrapper(cell=decoder_cell, attention_mechanism=attention_mechanism,
attention_layer_size=self.rnn_size, name='Attention_Wrapper')
#如果使用beam_seach则batch_size = self.batch_size * self.beam_size。因为之前已经复制过一次
batch_size = self.batch_size if not self.beam_search else self.batch_size * self.beam_size
#定义decoder阶段的初始化状态，直接使用encoder阶段的最后一个隐层状态进行赋值
decoder_initial_state = decoder_cell.zero_state(batch_size=batch_size, dtype=tf.float32).clone(cell_state=encoder_state)
output_layer = tf.layers.Dense(self.vocab_size, kernel_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1))
if self.mode == 'train':
# 定义decoder阶段的输入，其实就是在decoder的target开始处添加一个<go>,并删除结尾处的<end>,并进行embedding。
# decoder_inputs_embedded的shape为[batch_size, decoder_targets_length, embedding_size]
ending = tf.strided_slice(self.decoder_targets, [0, 0], [self.batch_size, -1], [1, 1])
decoder_input = tf.concat([tf.fill([self.batch_size, 1], self.word_to_idx['^']), ending], 1)
decoder_inputs_embedded = tf.nn.embedding_lookup(embedding, decoder_input)
#训练阶段，使用TrainingHelper+BasicDecoder的组合，这一般是固定的，当然也可以自己定义Helper类，实现自己的功能
training_helper = tf.contrib.seq2seq.TrainingHelper(inputs=decoder_inputs_embedded,
sequence_length=self.decoder_targets_length,
time_major=False, name='training_helper')
training_decoder = tf.contrib.seq2seq.BasicDecoder(cell=decoder_cell, helper=training_helper,
initial_state=decoder_initial_state, output_layer=output_layer)
#调用dynamic_decode进行解码，decoder_outputs是一个namedtuple，里面包含两项(rnn_outputs, sample_id)
# rnn_output: [batch_size, decoder_targets_length, vocab_size]，保存decode每个时刻每个单词的概率，可以用来计算loss
# sample_id: [batch_size], tf.int32，保存最终的编码结果。可以表示最后的答案
decoder_outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder=training_decoder,
impute_finished=True,
maximum_iterations=self.max_target_sequence_length)
# 根据输出计算loss和梯度，并定义进行更新的AdamOptimizer和train_op
self.decoder_logits_train = tf.identity(decoder_outputs.rnn_output)
self.decoder_predict_train = tf.argmax(self.decoder_logits_train, axis=-1, name='decoder_pred_train')
# 使用sequence_loss计算loss，这里需要传入之前定义的mask标志
self.loss = tf.contrib.seq2seq.sequence_loss(logits=self.decoder_logits_train,
targets=self.decoder_targets, weights=self.mask)
# Training summary for the current batch_loss
tf.summary.scalar('loss', self.loss)
self.summary_op = tf.summary.merge_all()
optimizer = tf.train.AdamOptimizer(self.learing_rate)
trainable_params = tf.trainable_variables()
gradients = tf.gradients(self.loss, trainable_params)
clip_gradients, _ = tf.clip_by_global_norm(gradients, self.max_gradient_norm)
self.train_op = optimizer.apply_gradients(zip(clip_gradients, trainable_params))
elif self.mode == 'decode':
start_tokens = tf.ones([self.batch_size, ], tf.int32) * self.word_to_idx['^']
end_token = self.word_to_idx['$']
# decoder阶段根据是否使用beam_search决定不同的组合，
# 如果使用则直接调用BeamSearchDecoder（里面已经实现了helper类）
# 如果不使用则调用GreedyEmbeddingHelper+BasicDecoder的组合进行贪婪式解码
if self.beam_search:
inference_decoder = tf.contrib.seq2seq.BeamSearchDecoder(cell=decoder_cell, embedding=embedding,
start_tokens=start_tokens, end_token=end_token,
initial_state=decoder_initial_state,
beam_width=self.beam_size,
output_layer=output_layer)
else:
decoding_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(embedding=embedding,
start_tokens=start_tokens, end_token=end_token)
inference_decoder = tf.contrib.seq2seq.BasicDecoder(cell=decoder_cell, helper=decoding_helper,
initial_state=decoder_initial_state,
output_layer=output_layer)
decoder_outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder=inference_decoder,
maximum_iterations=10)
# 调用dynamic_decode进行解码，decoder_outputs是一个namedtuple，
# 对于不使用beam_search的时候，它里面包含两项(rnn_outputs, sample_id)
# rnn_output: [batch_size, decoder_targets_length, vocab_size]
# sample_id: [batch_size, decoder_targets_length], tf.int32
# 对于使用beam_search的时候，它里面包含两项(predicted_ids, beam_search_decoder_output)
# predicted_ids: [batch_size, decoder_targets_length, beam_size],保存输出结果
# beam_search_decoder_output: BeamSearchDecoderOutput instance namedtuple(scores, predicted_ids, parent_ids)
# 所以对应只需要返回predicted_ids或者sample_id即可翻译成最终的结果
if self.beam_search:
self.decoder_predict_decode = decoder_outputs.predicted_ids
else:
self.decoder_predict_decode = tf.expand_dims(decoder_outputs.sample_id, -1)
# =================================4, 保存模型
self.saver = tf.train.Saver(tf.global_variables())
def train(self, sess, batch):
#对于训练阶段，需要执行self.train_op, self.loss, self.summary_op三个op，并传入相应的数据
feed_dict = {self.encoder_inputs: batch.encoder_inputs,
self.encoder_inputs_length: batch.encoder_inputs_length,
self.decoder_targets: batch.decoder_targets,
self.decoder_targets_length: batch.decoder_targets_length,
self.keep_prob_placeholder: 0.5,
self.batch_size: len(batch.encoder_inputs)}
_, loss, summary = sess.run([self.train_op, self.loss, self.summary_op], feed_dict=feed_dict)
return loss, summary
def eval(self, sess, batch):
# 对于eval阶段，不需要反向传播，所以只执行self.loss, self.summary_op两个op，并传入相应的数据
feed_dict = {self.encoder_inputs: batch.encoder_inputs,
self.encoder_inputs_length: batch.encoder_inputs_length,
self.decoder_targets: batch.decoder_targets,
self.decoder_targets_length: batch.decoder_targets_length,
self.keep_prob_placeholder: 1.0,
self.batch_size: len(batch.encoder_inputs)}
loss, summary = sess.run([self.loss, self.summary_op], feed_dict=feed_dict)
return loss, summary
def infer(self, sess, batch):
#infer阶段只需要运行最后的结果，不需要计算loss，所以feed_dict只需要传入encoder_input相应的数据即可
feed_dict = {self.encoder_inputs: batch.encoder_inputs,
self.encoder_inputs_length: batch.encoder_inputs_length,
self.keep_prob_placeholder: 1.0,
self.batch_size: len(batch.encoder_inputs)}
predict = sess.run([self.decoder_predict_decode], feed_dict=feed_dict)
return predict