-
Notifications
You must be signed in to change notification settings - Fork 0
/
model_generator.py
387 lines (266 loc) · 14.8 KB
/
model_generator.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
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
# Modifications Copyright 2017 Abigail See
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""This file contains code to build and run the tensorflow graph for the sequence-to-sequence model"""
import os
import time
import numpy as np
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
FLAGS = tf.app.flags.FLAGS
def sample_output(embedding, embedding_dec, output_projection=None,
given_number=None):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
def loop_function(prev,_):
prev = tf.nn.xw_plus_b(
prev, output_projection[0], output_projection[1])
prev_symbol = tf.cast(tf.reshape(tf.multinomial(tf.log(prev), 1), [FLAGS.batch_size]), tf.int32)
emb_prev = tf.nn.embedding_lookup(embedding, prev_symbol)
return emb_prev
def loop_function_max(prev,_):
"""function that feed previous model output rather than ground truth."""
if output_projection is not None:
prev = tf.nn.xw_plus_b(
prev, output_projection[0], output_projection[1])
prev_symbol = tf.argmax(prev, 1)
emb_prev = tf.nn.embedding_lookup(embedding, prev_symbol)
#emb_prev = tf.stop_gradient(emb_prev)
return emb_prev
'''def f1(prev,i):
prev = tf.nn.xw_plus_b(
prev, output_projection[0], output_projection[1])
prev_symbol = tf.cast(tf.reshape(tf.multinomial(prev, 1), [FLAGS.batch_size]), tf.int32)
emb_prev = tf.nn.embedding_lookup(embedding, prev_symbol)
return emb_prev
def f2(prev,i):
emb_prev = embedding_dec[i]
return emb_prev'''
def loop_given_function(prev, i):
return tf.cond(tf.less(i,2), lambda :loop_function(prev,i), lambda:loop_function_max(prev,i))
return loop_function,loop_function_max,loop_given_function
class Generator(object):
def __init__(self, hps, vocab):
self._hps = hps
self._vocab = vocab
def _add_placeholders(self):
"""Add placeholders to the graph. These are entry points for any input data."""
hps = self._hps
self._enc_batch = tf.placeholder(tf.int32, [hps.batch_size, hps.max_enc_steps],
name='enc_batch')
self._enc_lens = tf.placeholder(tf.int32, [hps.batch_size], name='enc_sen_lens')
self._weight = tf.placeholder(tf.float32, [hps.batch_size,hps.max_enc_steps], name = "weight")
self.score = tf.placeholder(tf.int32, name = 'score')
#self._given_number = tf.placeholder(tf.int32, name = "given_number")
#self._enc_padding_mask = tf.placeholder(tf.float32, [hps.batch_size, None], name='enc_padding_mask')
self._dec_batch = tf.placeholder(tf.int32, [hps.batch_size, hps.max_dec_steps], name='dec_batch')
self._target_batch = tf.placeholder(tf.int32, [hps.batch_size, hps.max_dec_steps], name='target_batch')
self._dec_padding_mask = tf.placeholder(tf.float32, [hps.batch_size, hps.max_dec_steps], name='dec_padding_mask')
self.reward = tf.placeholder(tf.float32, [hps.batch_size], name='reward')
def _make_feed_dict(self, batch, just_enc=False):
feed_dict = {}
feed_dict[self._enc_batch] = batch.enc_batch
feed_dict[self._enc_lens] = batch.enc_lens
#feed_dict[self._enc_padding_mask] = batch.enc_padding_mask
feed_dict[self._dec_batch] = batch.dec_batch
feed_dict[self.score] = batch.score
feed_dict[self._weight] = batch.weight
feed_dict[self.reward]= batch.reward
feed_dict[self._target_batch] = batch.target_batch
feed_dict[self._dec_padding_mask] = batch.dec_padding_mask
return feed_dict
def _add_encoder(self, encoder_inputs, seq_len):
with tf.variable_scope('encoder'):
cell_fw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)
#cell_bw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)
(encoder_outputs, (fw_st)) = tf.nn.dynamic_rnn(cell_fw, encoder_inputs, dtype=tf.float32, sequence_length=seq_len, swap_memory=True)
return fw_st
def _add_decoder(self, loop_function, loop_function_max, input): # input batch sequence dim
hps = self._hps
#input = tf.unstack(input, axis=1)
cell = tf.contrib.rnn.LSTMCell(
hps.hidden_dim,
initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=113),
state_is_tuple=True)
decoder_outputs_pretrain,_ = tf.contrib.legacy_seq2seq.rnn_decoder(
input, self._dec_in_state,
cell, loop_function=None
)
decoder_outputs_max_generator, _ = tf.contrib.legacy_seq2seq.rnn_decoder(
input, self._dec_in_state,
cell, loop_function=loop_function_max
)
decoder_outputs_pretrain = tf.stack(decoder_outputs_pretrain, axis=1)
decoder_outputs_max_generator = tf.stack(decoder_outputs_max_generator, axis=1)
#decoder_outputs_generator_rollout = tf.transpose(decoder_outputs_generator_rollout, [1, 0, 2])
return decoder_outputs_pretrain,decoder_outputs_max_generator
def add_positive_decoder(self, embedding, emb_dec_inputs, vsize, hps):
with tf.variable_scope('positive_decoder'):
with tf.variable_scope('output_projection'):
w = tf.get_variable(
'w', [hps.hidden_dim, vsize], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=1e-4))
v = tf.get_variable(
'v', [vsize], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=1e-4))
# Add the decoder.
with tf.variable_scope('decoder'):
loop_function, loop_function_max,loop_given_function = sample_output(
embedding, emb_dec_inputs, (w, v))
decoder_outputs_pretrain, decoder_outputs_max_generator= self._add_decoder(loop_function = loop_function, loop_function_max = loop_function_max, input=emb_dec_inputs)
decoder_outputs_pretrain = tf.reshape(decoder_outputs_pretrain,
[hps.batch_size* hps.max_dec_steps, hps.hidden_dim])
decoder_outputs_pretrain = tf.nn.xw_plus_b(decoder_outputs_pretrain, w, v)
self.decoder_outputs_pretrain = tf.reshape(decoder_outputs_pretrain,
[hps.batch_size, hps.max_dec_steps, vsize])
decoder_outputs_max_generator = tf.reshape(decoder_outputs_max_generator,
[hps.batch_size * hps.max_dec_steps, hps.hidden_dim])
decoder_outputs_max_generator = tf.nn.xw_plus_b(decoder_outputs_max_generator, w, v)
self._max_best_output = tf.reshape(tf.argmax(decoder_outputs_max_generator, 1),
[hps.batch_size, hps.max_dec_steps])
return self.decoder_outputs_pretrain, self._max_best_output
def add_negetive_decoder(self, embedding, emb_dec_inputs, vsize, hps):
with tf.variable_scope('negetive_decoder'):
with tf.variable_scope('output_projection'):
w = tf.get_variable(
'w', [hps.hidden_dim, vsize], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=1e-4))
v = tf.get_variable(
'v', [vsize], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=1e-4))
# Add the decoder.
with tf.variable_scope('decoder'):
loop_function, loop_function_max, loop_given_function = sample_output(
embedding, emb_dec_inputs, (w, v))
decoder_outputs_pretrain, decoder_outputs_max_generator = self._add_decoder(loop_function = loop_function,
loop_function_max=loop_function_max, input=emb_dec_inputs )
decoder_outputs_pretrain = tf.reshape(decoder_outputs_pretrain,
[hps.batch_size * hps.max_dec_steps, hps.hidden_dim])
decoder_outputs_pretrain = tf.nn.xw_plus_b(decoder_outputs_pretrain, w, v)
self.decoder_outputs_pretrain = tf.reshape(decoder_outputs_pretrain,
[hps.batch_size, hps.max_dec_steps, vsize])
decoder_outputs_max_generator = tf.reshape(decoder_outputs_max_generator,
[hps.batch_size * hps.max_dec_steps, hps.hidden_dim])
decoder_outputs_max_generator = tf.nn.xw_plus_b(decoder_outputs_max_generator, w, v)
self._max_best_output = tf.reshape(tf.argmax(decoder_outputs_max_generator, 1),
[hps.batch_size, hps.max_dec_steps])
return self.decoder_outputs_pretrain, self._max_best_output
def add_decoder(self,embedding, emb_dec_inputs, vsize, hps):
return tf.cond(tf.less(self.score, 1), lambda: self.add_negetive_decoder(embedding, emb_dec_inputs, vsize, hps), lambda: self.add_positive_decoder(embedding, emb_dec_inputs, vsize, hps),)
def _build_model(self):
"""Add the whole generator model to the graph."""
hps = self._hps
vsize = self._vocab.size() # size of the vocabulary
with tf.variable_scope('seq2seq'):
# Some initializers
self.rand_unif_init = tf.random_uniform_initializer(-hps.rand_unif_init_mag, hps.rand_unif_init_mag, seed=123)
self.trunc_norm_init = tf.truncated_normal_initializer(stddev=hps.trunc_norm_init_std)
# Add embedding matrix (shared by the encoder and decoder inputs)
with tf.variable_scope('embedding'):
embedding = tf.get_variable('embedding', [vsize, hps.emb_dim], dtype=tf.float32, initializer=self.trunc_norm_init)
#embedding_score = tf.get_variable('embedding_score', [5, hps.hidden_dim], dtype=tf.float32, initializer=self.trunc_norm_init)
emb_dec_inputs = tf.nn.embedding_lookup(embedding, self._dec_batch) # list length max_dec_steps containing shape (batch_size, emb_size)
emb_dec_inputs = tf.unstack(emb_dec_inputs, axis=1)
emb_enc_inputs = tf.nn.embedding_lookup(embedding,
self._enc_batch) # tensor with shape (batch_size, max_enc_steps, emb_size)
emb_enc_inputs = emb_enc_inputs * tf.expand_dims(self._weight, axis = -1)
fw_st = self._add_encoder(emb_enc_inputs, self._enc_lens)
self.return_hidden = fw_st.h
self._dec_in_state = tf.contrib.rnn.LSTMStateTuple(fw_st.h, fw_st.h)#self._reduce_states(fw_st, bw_st)
self.decoder_outputs_pretrain, self._max_best_output =self.add_decoder(embedding, emb_dec_inputs, vsize, hps)
loss = tf.contrib.seq2seq.sequence_loss(
self.decoder_outputs_pretrain,
self._target_batch,
self._dec_padding_mask,
average_across_timesteps=True,
average_across_batch=False)
reward_loss = tf.contrib.seq2seq.sequence_loss(
self.decoder_outputs_pretrain,
self._target_batch,
self._dec_padding_mask,
average_across_timesteps=True,
average_across_batch=False) * self.reward
# Update the cost
self._cost = tf.reduce_mean(loss)
self._reward_cost = tf.reduce_mean(reward_loss)
self.optimizer = tf.train.AdagradOptimizer(self._hps.lr, initial_accumulator_value=self._hps.adagrad_init_acc)
def _add_train_op(self):
loss_to_minimize = self._cost
tvars = tf.trainable_variables()
gradients = tf.gradients(loss_to_minimize, tvars, aggregation_method=tf.AggregationMethod.EXPERIMENTAL_TREE)
# Clip the gradients
grads, global_norm = tf.clip_by_global_norm(gradients, self._hps.max_grad_norm)
# Add a summary
tf.summary.scalar('global_norm', global_norm)
# Apply adagrad optimizer
self._train_op = self.optimizer.apply_gradients(zip(grads, tvars), global_step=self.global_step, name='train_step')
def _add_reward_train_op(self):
loss_to_minimize = self._reward_cost
tvars = tf.trainable_variables()
gradients = tf.gradients(loss_to_minimize, tvars, aggregation_method=tf.AggregationMethod.EXPERIMENTAL_TREE)
# Clip the gradients
grads, global_norm = tf.clip_by_global_norm(gradients, self._hps.max_grad_norm)
self._train_reward_op = self.optimizer.apply_gradients(zip(grads, tvars), global_step=self.global_step, name='train_step')
def build_graph(self):
"""Add the placeholders, model, global step, train_op and summaries to the graph"""
with tf.device("/gpu:"+str(FLAGS.gpuid)):
tf.logging.info('Building generator graph...')
t0 = time.time()
self._add_placeholders()
self._build_model()
self.global_step = tf.Variable(0, name='global_step', trainable=False)
self._add_train_op()
self._add_reward_train_op()
t1 = time.time()
tf.logging.info('Time to build graph: %i seconds', t1 - t0)
def run_pre_train_step(self, sess, batch):
"""Runs one training iteration. Returns a dictionary containing train op, summaries, loss, global_step and (optionally) coverage loss."""
feed_dict = self._make_feed_dict(batch)
to_return = {
'train_op': self._train_op,
'loss': self._cost,
'global_step': self.global_step,
}
return sess.run(to_return, feed_dict)
def run_hidden_step(self, sess, batch):
feed_dict = self._make_feed_dict(batch)
to_return = {
'hidden': self.return_hidden,
}
return sess.run(to_return, feed_dict)
def run_train_step(self, sess, batch):
"""Runs one training iteration. Returns a dictionary containing train op, summaries, loss, global_step and (optionally) coverage loss."""
feed_dict = self._make_feed_dict(batch)
to_return = {
'train_op': self._train_reward_op,
'generated': self._max_best_output,
'loss': self._reward_cost,
'global_step': self.global_step,
}
return sess.run(to_return, feed_dict)
def max_generator(self,sess, batch):
feed_dict = self._make_feed_dict(batch)
to_return = {
'generated': self._max_best_output,
}
return sess.run(to_return, feed_dict)