-
Notifications
You must be signed in to change notification settings - Fork 231
/
dssm_rnn.py
231 lines (197 loc) · 10.7 KB
/
dssm_rnn.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
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
# coding=utf8
"""
python=3.5
TensorFlow=1.2.1
"""
import time
import numpy as np
import tensorflow as tf
import data_input
from config import Config
import random
random.seed(9102)
start = time.time()
# 是否加BN层
norm, epsilon = False, 0.001
# TRIGRAM_D = 21128
TRIGRAM_D = 100
# negative sample
NEG = 4
# query batch size
query_BS = 100
# batch size
BS = query_BS * NEG
# 读取数据
conf = Config()
data_train = data_input.get_data(conf.file_train)
data_vali = data_input.get_data(conf.file_vali)
# print(len(data_train['query']), query_BS, len(data_train['query']) / query_BS)
train_epoch_steps = int(len(data_train['query']) / query_BS) - 1
vali_epoch_steps = int(len(data_vali['query']) / query_BS) - 1
def variable_summaries(var, name):
"""Attach a lot of summaries to a Tensor."""
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean/' + name, mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
tf.summary.scalar('sttdev/' + name, stddev)
tf.summary.scalar('max/' + name, tf.reduce_max(var))
tf.summary.scalar('min/' + name, tf.reduce_min(var))
tf.summary.histogram(name, var)
with tf.name_scope('input'):
# 预测时只用输入query即可，将其embedding为向量。
query_batch = tf.placeholder(tf.int32, shape=[None, None], name='query_batch')
doc_pos_batch = tf.placeholder(tf.int32, shape=[None, None], name='doc_positive_batch')
doc_neg_batch = tf.placeholder(tf.int32, shape=[None, None], name='doc_negative_batch')
query_seq_length = tf.placeholder(tf.int32, shape=[None], name='query_sequence_length')
pos_seq_length = tf.placeholder(tf.int32, shape=[None], name='pos_seq_length')
neg_seq_length = tf.placeholder(tf.int32, shape=[None], name='neg_sequence_length')
on_train = tf.placeholder(tf.bool)
drop_out_prob = tf.placeholder(tf.float32, name='drop_out_prob')
with tf.name_scope('word_embeddings_layer'):
_word_embedding = tf.get_variable(name="word_embedding_arr", dtype=tf.float32,
shape=[conf.nwords, TRIGRAM_D])
query_embed = tf.nn.embedding_lookup(_word_embedding, query_batch, name='query_batch_embed')
doc_pos_embed = tf.nn.embedding_lookup(_word_embedding, doc_pos_batch, name='doc_positive_embed')
doc_neg_embed = tf.nn.embedding_lookup(_word_embedding, doc_neg_batch, name='doc_negative_embed')
with tf.name_scope('RNN'):
# Abandon bag of words, use GRU, you can use stacked gru
# query_l1 = add_layer(query_batch, TRIGRAM_D, L1_N, activation_function=None) # tf.nn.relu()
# doc_positive_l1 = add_layer(doc_positive_batch, TRIGRAM_D, L1_N, activation_function=None)
# doc_negative_l1 = add_layer(doc_negative_batch, TRIGRAM_D, L1_N, activation_function=None)
if conf.use_stack_rnn:
cell_fw = tf.contrib.rnn.GRUCell(conf.hidden_size_rnn, reuse=tf.AUTO_REUSE)
stacked_gru_fw = tf.contrib.rnn.MultiRNNCell([cell_fw], state_is_tuple=True)
cell_bw = tf.contrib.rnn.GRUCell(conf.hidden_size_rnn, reuse=tf.AUTO_REUSE)
stacked_gru_bw = tf.contrib.rnn.MultiRNNCell([cell_fw], state_is_tuple=True)
(output_fw, output_bw), (_, _) = tf.nn.bidirectional_dynamic_rnn(stacked_gru_fw, stacked_gru_bw)
# not ready, to be continue ...
else:
cell_fw = tf.contrib.rnn.GRUCell(conf.hidden_size_rnn, reuse=tf.AUTO_REUSE)
cell_bw = tf.contrib.rnn.GRUCell(conf.hidden_size_rnn, reuse=tf.AUTO_REUSE)
# query
(_, _), (query_output_fw, query_output_bw) = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, query_embed,
sequence_length=query_seq_length,
dtype=tf.float32)
query_rnn_output = tf.concat([query_output_fw, query_output_bw], axis=-1)
query_rnn_output = tf.nn.dropout(query_rnn_output, drop_out_prob)
# doc_pos
(_, _), (doc_pos_output_fw, doc_pos_output_bw) = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw,
doc_pos_embed,
sequence_length=pos_seq_length,
dtype=tf.float32)
doc_pos_rnn_output = tf.concat([doc_pos_output_fw, doc_pos_output_bw], axis=-1)
doc_pos_rnn_output = tf.nn.dropout(doc_pos_rnn_output, drop_out_prob)
# doc_neg
(_, _), (doc_neg_output_fw, doc_neg_output_bw) = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw,
doc_neg_embed,
sequence_length=neg_seq_length,
dtype=tf.float32)
doc_neg_rnn_output = tf.concat([doc_neg_output_fw, doc_neg_output_bw], axis=-1)
doc_neg_rnn_output = tf.nn.dropout(doc_neg_rnn_output, drop_out_prob)
with tf.name_scope('Merge_Negative_Doc'):
# 合并负样本，tile可选择是否扩展负样本。
# doc_y = tf.tile(doc_positive_y, [1, 1])
doc_y = tf.tile(doc_pos_rnn_output, [1, 1])
for i in range(NEG):
for j in range(query_BS):
# slice(input_, begin, size)切片API
# doc_y = tf.concat([doc_y, tf.slice(doc_negative_y, [j * NEG + i, 0], [1, -1])], 0)
doc_y = tf.concat([doc_y, tf.slice(doc_neg_rnn_output, [j * NEG + i, 0], [1, -1])], 0)
with tf.name_scope('Cosine_Similarity'):
# Cosine similarity
# query_norm = sqrt(sum(each x^2))
query_norm = tf.tile(tf.sqrt(tf.reduce_sum(tf.square(query_rnn_output), 1, True)), [NEG + 1, 1])
# doc_norm = sqrt(sum(each x^2))
doc_norm = tf.sqrt(tf.reduce_sum(tf.square(doc_y), 1, True))
prod = tf.reduce_sum(tf.multiply(tf.tile(query_rnn_output, [NEG + 1, 1]), doc_y), 1, True)
norm_prod = tf.multiply(query_norm, doc_norm)
# cos_sim_raw = query * doc / (||query|| * ||doc||)
cos_sim_raw = tf.truediv(prod, norm_prod)
# gamma = 20
cos_sim = tf.transpose(tf.reshape(tf.transpose(cos_sim_raw), [NEG + 1, query_BS])) * 20
with tf.name_scope('Loss'):
# Train Loss
# 转化为softmax概率矩阵。
prob = tf.nn.softmax(cos_sim)
# 只取第一列，即正样本列概率。
hit_prob = tf.slice(prob, [0, 0], [-1, 1])
loss = -tf.reduce_sum(tf.log(hit_prob))
tf.summary.scalar('loss', loss)
with tf.name_scope('Training'):
# Optimizer
train_step = tf.train.AdamOptimizer(conf.learning_rate).minimize(loss)
# with tf.name_scope('Accuracy'):
# correct_prediction = tf.equal(tf.argmax(prob, 1), 0)
# accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# tf.summary.scalar('accuracy', accuracy)
merged = tf.summary.merge_all()
with tf.name_scope('Test'):
average_loss = tf.placeholder(tf.float32)
loss_summary = tf.summary.scalar('average_loss', average_loss)
with tf.name_scope('Train'):
train_average_loss = tf.placeholder(tf.float32)
train_loss_summary = tf.summary.scalar('train_average_loss', train_average_loss)
def pull_batch(data_map, batch_id):
query_in = data_map['query'][batch_id * query_BS:(batch_id + 1) * query_BS]
query_len = data_map['query_len'][batch_id * query_BS:(batch_id + 1) * query_BS]
doc_positive_in = data_map['doc_pos'][batch_id * query_BS:(batch_id + 1) * query_BS]
doc_positive_len = data_map['doc_pos_len'][batch_id * query_BS:(batch_id + 1) * query_BS]
doc_negative_in = data_map['doc_neg'][batch_id * query_BS * NEG:(batch_id + 1) * query_BS * NEG]
doc_negative_len = data_map['doc_neg_len'][batch_id * query_BS * NEG:(batch_id + 1) * query_BS * NEG]
# query_in, doc_positive_in, doc_negative_in = pull_all(query_in, doc_positive_in, doc_negative_in)
return query_in, doc_positive_in, doc_negative_in, query_len, doc_positive_len, doc_negative_len
def feed_dict(on_training, data_set, batch_id, drop_prob):
query_in, doc_positive_in, doc_negative_in, query_seq_len, pos_seq_len, neg_seq_len = pull_batch(data_set,
batch_id)
query_len = len(query_in)
query_seq_len = [conf.max_seq_len] * query_len
pos_seq_len = [conf.max_seq_len] * query_len
neg_seq_len = [conf.max_seq_len] * query_len * NEG
return {query_batch: query_in, doc_pos_batch: doc_positive_in, doc_neg_batch: doc_negative_in,
on_train: on_training, drop_out_prob: drop_prob, query_seq_length: query_seq_len,
neg_seq_length: neg_seq_len, pos_seq_length: pos_seq_len}
# config = tf.ConfigProto() # log_device_placement=True)
# config.gpu_options.allow_growth = True
# if not config.gpu:
# config = tf.ConfigProto(device_count= {'GPU' : 0})
# 创建一个Saver对象，选择性保存变量或者模型。
saver = tf.train.Saver()
# with tf.Session(config=config) as sess:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_writer = tf.summary.FileWriter(conf.summaries_dir + '/train', sess.graph)
start = time.time()
for epoch in range(conf.num_epoch):
batch_ids = [i for i in range(train_epoch_steps)]
random.shuffle(batch_ids)
for batch_id in batch_ids:
# print(batch_id)
sess.run(train_step, feed_dict=feed_dict(True, data_train, batch_id, 0.5))
end = time.time()
# train loss
epoch_loss = 0
for i in range(train_epoch_steps):
loss_v = sess.run(loss, feed_dict=feed_dict(False, data_train, i, 1))
epoch_loss += loss_v
epoch_loss /= (train_epoch_steps)
train_loss = sess.run(train_loss_summary, feed_dict={train_average_loss: epoch_loss})
train_writer.add_summary(train_loss, epoch + 1)
print("\nEpoch #%d | Train Loss: %-4.3f | PureTrainTime: %-3.3fs" %
(epoch, epoch_loss, end - start))
# test loss
start = time.time()
epoch_loss = 0
for i in range(vali_epoch_steps):
loss_v = sess.run(loss, feed_dict=feed_dict(False, data_vali, i, 1))
epoch_loss += loss_v
epoch_loss /= (vali_epoch_steps)
test_loss = sess.run(loss_summary, feed_dict={average_loss: epoch_loss})
train_writer.add_summary(test_loss, epoch + 1)
# test_writer.add_summary(test_loss, step + 1)
print("Epoch #%d | Test Loss: %-4.3f | Calc_LossTime: %-3.3fs" %
(epoch, epoch_loss, start - end))
# 保存模型
save_path = saver.save(sess, "model/model_1.ckpt")
print("Model saved in file: ", save_path)