forked from TobiasLee/Chinese-Hip-pop-Generation
-
Notifications
You must be signed in to change notification settings - Fork 0
/
g_beta.py
284 lines (234 loc) · 12 KB
/
g_beta.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
import tensorflow as tf
import numpy as np
from tensorflow.python.ops import tensor_array_ops, control_flow_ops
from rhyme import calc_rhyme
class G_beta:
def __init__(self, lstm, update_rate):
self.lstm = lstm
self.update_rate = update_rate
# copy parameters from lstm model
self.num_emb = self.lstm.num_emb
self.batch_size = self.lstm.batch_size
self.emb_dim = self.lstm.emb_dim
self.hidden_dim = self.lstm.hidden_dim
self.sequence_length = self.lstm.sequence_length
self.start_token = tf.identity(self.lstm.start_token)
self.learning_rate = self.lstm.learning_rate
self.g_embeddings = tf.identity(self.lstm.g_embeddings)
self.g_recurrent_unit = self.create_recurrent_unit() # maps h_tm1 to h_t for generator
self.g_output_unit = self.create_output_unit() # maps h_t to o_t (output token logits)
# placeholder
self.x = tf.placeholder(tf.int32, shape=[self.batch_size, self.sequence_length])
self.given_num = tf.placeholder(tf.int32)
# embedded input
with tf.device("/cpu:0"):
self.processed_x = tf.transpose(tf.nn.embedding_lookup(self.g_embeddings, self.x),
perm=[1, 0, 2]) # seq * batch * emb_size
ta_emb_x = tensor_array_ops.TensorArray(
dtype=tf.float32, size=self.sequence_length
)
ta_emb_x = ta_emb_x.unstack(self.processed_x) # seq * emb_size
ta_x = tensor_array_ops.TensorArray(
dtype=tf.int32, size=self.sequence_length
)
ta_x = ta_x.unstack(tf.transpose(self.x, perm=[1, 0])) # seq * batch
self.h0 = lstm.h0
gen_x = tensor_array_ops.TensorArray(dtype=tf.int32, size=self.sequence_length,
dynamic_size=False, infer_shape=True)
# while i < given num , using the provided tokens as input
def _g_recurrence_1(i, x_t, h_tm1, given_num, gen_x):
h_t = self.g_recurrent_unit(x_t, h_tm1) # hidden_memory_tuple
x_tp1 = ta_emb_x.read(i)
gen_x = gen_x.write(i, ta_x.read(i))
return i + 1, x_tp1, h_t, given_num, gen_x
def _g_recurrence_2(i, x_t, h_tm1, gen_x):
h_t = self.g_recurrent_unit(x_t, h_tm1)
o_t = self.g_output_unit(h_t) # logits : batch x vocab
log_prob = tf.log(tf.nn.softmax(o_t)) # log prob
next_token = tf.cast(tf.reshape(tf.multinomial(log_prob, 1), [self.batch_size]), tf.int32)
x_tp1 = tf.nn.embedding_lookup(self.g_embeddings, next_token)
gen_x = gen_x.write(i, next_token)
return i + 1, x_tp1, h_t, gen_x
i, x_t, h_tm1, given_num, self.gen_x = control_flow_ops.while_loop(
cond=lambda i, _1, _2, given_num, _4: i < given_num,
body=_g_recurrence_1,
loop_vars=(tf.constant(0, dtype=tf.int32),
tf.nn.embedding_lookup(self.g_embeddings, self.start_token), self.h0, self.given_num, gen_x))
_, _, _, self.gen_x = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3: i < self.sequence_length,
body=_g_recurrence_2,
loop_vars=(i, x_t, h_tm1, self.gen_x)
)
self.gen_x = self.gen_x.stack() # seq_length x batch_size
self.gen_x = tf.transpose(self.gen_x, perm=[1, 0]) # batch_size x seq_length
def get_reward(self, sess, target, input_x, roll_out_num, discriminator):
rewards = []
for i in range(roll_out_num): # sample times
for given_num in range(1, self.sequence_length): # 1 -> 19
feed = {self.x: target, self.given_num: given_num, self.lstm.inputs: input_x}
samples = sess.run(self.gen_x, feed)
feed = {discriminator.input_x: samples, discriminator.dropout_keep_prob: 1.0}
ypred_for_auc = sess.run(discriminator.ypred_for_auc, feed)
ypred = np.array([item[0] for item in ypred_for_auc]) # probability of being real data
if i == 0:
rewards.append(ypred)
else:
rewards[given_num - 1] += ypred
# the last token reward
feed = {discriminator.input_x: target, discriminator.dropout_keep_prob: 1.0}
ypred_for_auc = sess.run(discriminator.ypred_for_auc, feed)
# probability of being fake
ypred = np.array([item[0] for item in ypred_for_auc])
if i == 0:
rewards.append(ypred)
else:
rewards[self.sequence_length - 1] += ypred # seq_len * batch_size
rewards = np.transpose(np.array(rewards)) / (1.0 * roll_out_num) # batch_size x seq_length
# baseline = np.mean(rewards, axis=0)
return rewards
def get_reward_rhyme(self, sess, input_x, input_y, roll_out_num, discriminator, reward_weight, idx2word):
"""
calculate sentence reward based on rhyme and discriminator
Args:
sess: session to run
input_x: the x of original x-y pair
input_y: the generated y_hat corresponding to x, we calculate reward of input_y
roll_out_num: MC rollout times
discriminator: discriminator model to evaluate probability of being real
reward_weight: reward weight of rhyme reward
idx2word: dict, map word idx to real word, in order to
"""
rewards = []
for i in range(roll_out_num): # sample times
for given_num in range(1, self.sequence_length): # 1 -> 19
feed = {self.x: input_y, self.given_num: given_num}
# print(input_x.shape)
samples = sess.run(self.gen_x, feed)
feed = {discriminator.input_x: samples, discriminator.dropout_keep_prob: 1.0}
ypred_for_auc = sess.run(discriminator.ypred_for_auc, feed)
ypred = np.array([item[1] for item in ypred_for_auc]) # probability of being real data
# print(ypred.shape)
if i == 0:
rewards.append(ypred)
else:
rewards[given_num - 1] += ypred
# the last token reward
feed = {discriminator.input_x: input_y, discriminator.dropout_keep_prob: 1.0}
ypred_for_auc = sess.run(discriminator.ypred_for_auc, feed)
ypred = np.array([item[1] for item in ypred_for_auc])
if i == 0:
rewards.append(ypred)
else:
rewards[self.sequence_length - 1] += ypred # seq_len * batch_size
rewards = np.transpose(np.array(rewards)) / (1.0 * roll_out_num) # batch_size x seq_length
rhyme_rewards = calc_rhyme(input_x, input_y, idx2word, reverse=True)
baseline = np.mean(rewards, axis=0)
return rewards - baseline, np.mean(rhyme_rewards, axis=0)
def update_params(self):
self.g_embeddings = tf.identity(self.lstm.g_embeddings)
self.g_recurrent_unit = self.update_recurrent_unit
self.g_output_unit = self.update_output_unit
def create_output_unit(self):
self.Wo = tf.identity(self.lstm.Wo)
self.bo = tf.identity(self.lstm.bo)
def unit(hidden_memory_tuple):
hidden_state, c_prev = tf.unstack(hidden_memory_tuple)
# hidden_state : batch x hidden_dim
logits = tf.matmul(hidden_state, self.Wo) + self.bo
# output = tf.nn.softmax(logits)
return logits
return unit
def update_recurrent_unit(self):
# Weights and Bias for input and hidden tensor with weight decay
with tf.variable_scope("lstm_beta"):
self.Wi = self.update_rate * self.Wi + (1 - self.update_rate) * tf.identity(self.lstm.Wi)
self.Ui = self.update_rate * self.Ui + (1 - self.update_rate) * tf.identity(self.lstm.Ui)
self.bi = self.update_rate * self.bi + (1 - self.update_rate) * tf.identity(self.lstm.bi)
self.Wf = self.update_rate * self.Wf + (1 - self.update_rate) * tf.identity(self.lstm.Wf)
self.Uf = self.update_rate * self.Uf + (1 - self.update_rate) * tf.identity(self.lstm.Uf)
self.bf = self.update_rate * self.bf + (1 - self.update_rate) * tf.identity(self.lstm.bf)
self.Wog = self.update_rate * self.Wog + (1 - self.update_rate) * tf.identity(self.lstm.Wog)
self.Uog = self.update_rate * self.Uog + (1 - self.update_rate) * tf.identity(self.lstm.Uog)
self.bog = self.update_rate * self.bog + (1 - self.update_rate) * tf.identity(self.lstm.bog)
self.Wc = self.update_rate * self.Wc + (1 - self.update_rate) * tf.identity(self.lstm.Wc)
self.Uc = self.update_rate * self.Uc + (1 - self.update_rate) * tf.identity(self.lstm.Uc)
self.bc = self.update_rate * self.bc + (1 - self.update_rate) * tf.identity(self.lstm.bc)
def unit(x, hidden_memory_tm1):
previous_hidden_state, c_prev = tf.unstack(hidden_memory_tm1)
# Input Gate
i = tf.sigmoid(
tf.matmul(x, self.Wi) +
tf.matmul(previous_hidden_state, self.Ui) + self.bi
)
# Forget Gate
f = tf.sigmoid(
tf.matmul(x, self.Wf) +
tf.matmul(previous_hidden_state, self.Uf) + self.bf
)
# Output Gate
o = tf.sigmoid(
tf.matmul(x, self.Wog) +
tf.matmul(previous_hidden_state, self.Uog) + self.bog
)
# New Memory Cell
c_ = tf.nn.tanh(
tf.matmul(x, self.Wc) +
tf.matmul(previous_hidden_state, self.Uc) + self.bc
)
# Final Memory cell
c = f * c_prev + i * c_
# Current Hidden state
current_hidden_state = o * tf.nn.tanh(c)
return tf.stack([current_hidden_state, c])
return unit
def create_recurrent_unit(self):
# Weights and Bias for input and hidden tensor
self.Wi = tf.identity(self.lstm.Wi)
self.Ui = tf.identity(self.lstm.Ui)
self.bi = tf.identity(self.lstm.bi)
self.Wf = tf.identity(self.lstm.Wf)
self.Uf = tf.identity(self.lstm.Uf)
self.bf = tf.identity(self.lstm.bf)
self.Wog = tf.identity(self.lstm.Wog)
self.Uog = tf.identity(self.lstm.Uog)
self.bog = tf.identity(self.lstm.bog)
self.Wc = tf.identity(self.lstm.Wc)
self.Uc = tf.identity(self.lstm.Uc)
self.bc = tf.identity(self.lstm.bc)
def unit(x, hidden_memory_tm1):
previous_hidden_state, c_prev = tf.unstack(hidden_memory_tm1)
# Input Gate
i = tf.sigmoid(
tf.matmul(x, self.Wi) +
tf.matmul(previous_hidden_state, self.Ui) + self.bi
)
# Forget Gate
f = tf.sigmoid(
tf.matmul(x, self.Wf) +
tf.matmul(previous_hidden_state, self.Uf) + self.bf
)
# Output Gate
o = tf.sigmoid(
tf.matmul(x, self.Wog) +
tf.matmul(previous_hidden_state, self.Uog) + self.bog
)
# New Memory Cell
c_ = tf.nn.tanh(
tf.matmul(x, self.Wc) +
tf.matmul(previous_hidden_state, self.Uc) + self.bc
)
# Final Memory cell
c = f * c_prev + i * c_
# Current Hidden state
current_hidden_state = o * tf.nn.tanh(c)
return tf.stack([current_hidden_state, c])
return unit
def update_output_unit(self):
self.Wo = self.update_rate * self.Wo + (1 - self.update_rate) * tf.identity(self.lstm.Wo)
self.bo = self.update_rate * self.bo + (1 - self.update_rate) * tf.identity(self.lstm.bo)
def unit(hidden_memory_tuple):
hidden_state, c_prev = tf.unstack(hidden_memory_tuple)
# hidden_state : batch x hidden_dim
logits = tf.matmul(hidden_state, self.Wo) + self.bo
return logits
return unit