-
Notifications
You must be signed in to change notification settings - Fork 0
/
model.py
281 lines (243 loc) · 14.3 KB
/
model.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
import tensorflow as tf
from layers import initializer, regularizer, residual_block, highway, conv, mask_logits, trilinear, total_params, optimized_trilinear_for_attention, bidirectional_dynamic_rnn, bidirlstm, drnn
from tensorflow.python.util import nest
from tensorflow.python.ops import array_ops
class Model(object):
def __init__(self, config, batch, word_mat=None, char_mat=None, trainable=True, opt=True, demo = False, graph = None):
self.config = config
self.demo = demo
self.graph = graph if graph is not None else tf.Graph()
with self.graph.as_default():
self.global_step = tf.get_variable('global_step', shape=[], dtype=tf.int32,
initializer=tf.constant_initializer(0), trainable=False)
self.dropout = tf.placeholder_with_default(0.0, (), name="dropout")
if self.demo:
self.c = tf.placeholder(tf.int32, [None, config.test_para_limit],"context")
self.q = tf.placeholder(tf.int32, [None, config.test_ques_limit],"question")
self.ch = tf.placeholder(tf.int32, [None, config.test_para_limit, config.char_limit],"context_char")
self.qh = tf.placeholder(tf.int32, [None, config.test_ques_limit, config.char_limit],"question_char")
self.y1 = tf.placeholder(tf.int32, [None, config.test_para_limit],"answer_index1")
self.y2 = tf.placeholder(tf.int32, [None, config.test_para_limit],"answer_index2")
else:
self.c, self.q, self.ch, self.qh, self.y1, self.y2, self.qa_id = batch.get_next()
# self.word_unk = tf.get_variable("word_unk", shape = [config.glove_dim], initializer=initializer())
self.word_mat = tf.get_variable("word_mat", initializer=tf.constant(
word_mat, dtype=tf.float32), trainable=False)
self.char_mat = tf.get_variable(
"char_mat", initializer=tf.constant(char_mat, dtype=tf.float32))
self.c_mask = tf.cast(self.c, tf.bool)
self.q_mask = tf.cast(self.q, tf.bool)
self.c_len = tf.reduce_sum(tf.cast(self.c_mask, tf.int32), axis=1)
self.q_len = tf.reduce_sum(tf.cast(self.q_mask, tf.int32), axis=1)
if opt:
N, CL = config.batch_size if not self.demo else 1, config.char_limit
self.c_maxlen = tf.reduce_max(self.c_len)
self.q_maxlen = tf.reduce_max(self.q_len)
self.c = tf.slice(self.c, [0, 0], [N, self.c_maxlen])
self.q = tf.slice(self.q, [0, 0], [N, self.q_maxlen])
self.c_mask = tf.slice(self.c_mask, [0, 0], [N, self.c_maxlen])
self.q_mask = tf.slice(self.q_mask, [0, 0], [N, self.q_maxlen])
self.ch = tf.slice(self.ch, [0, 0, 0], [N, self.c_maxlen, CL])
self.qh = tf.slice(self.qh, [0, 0, 0], [N, self.q_maxlen, CL])
self.y1 = tf.slice(self.y1, [0, 0], [N, self.c_maxlen])
self.y2 = tf.slice(self.y2, [0, 0], [N, self.c_maxlen])
else:
self.c_maxlen, self.q_maxlen = config.para_limit, config.ques_limit
self.ch_len = tf.reshape(tf.reduce_sum(
tf.cast(tf.cast(self.ch, tf.bool), tf.int32), axis=2), [-1])
self.qh_len = tf.reshape(tf.reduce_sum(
tf.cast(tf.cast(self.qh, tf.bool), tf.int32), axis=2), [-1])
self.forward()
total_params()
if trainable:
self.lr = tf.minimum(config.learning_rate, 0.001 / tf.log(999.) * tf.log(tf.cast(self.global_step, tf.float32) + 1))
self.opt = tf.train.AdamOptimizer(learning_rate = self.lr, beta1 = 0.8, beta2 = 0.999, epsilon = 1e-7)
grads = self.opt.compute_gradients(self.loss)
gradients, variables = zip(*grads)
capped_grads, _ = tf.clip_by_global_norm(
gradients, config.grad_clip)
self.train_op = self.opt.apply_gradients(
zip(capped_grads, variables), global_step=self.global_step)
def forward(self):
config = self.config
N, PL, QL, CL, d, dc, nh = config.batch_size if not self.demo else 1, self.c_maxlen, self.q_maxlen, config.char_limit, config.hidden, config.char_dim, config.num_heads
d_cell = tf.contrib.rnn.BasicLSTMCell(d, forget_bias=1.0, state_is_tuple=True)
with tf.variable_scope("Input_Embedding_Layer"):
ch_emb = tf.reshape(tf.nn.embedding_lookup(
self.char_mat, self.ch), [N * PL, CL, dc])
qh_emb = tf.reshape(tf.nn.embedding_lookup(
self.char_mat, self.qh), [N * QL, CL, dc])
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
# Bidaf style conv-highway encoder
ch_emb = conv(ch_emb, d,
bias = True, activation = tf.nn.relu, kernel_size = 5, name = "char_conv", reuse = None)
qh_emb = conv(qh_emb, d,
bias = True, activation = tf.nn.relu, kernel_size = 5, name = "char_conv", reuse = True)
ch_emb = tf.reduce_max(ch_emb, axis = 1)
qh_emb = tf.reduce_max(qh_emb, axis = 1)
print "ch_emb before", ch_emb.shape[-1]
print "qh_emb before", qh_emb.shape[-1]
ch_emb = tf.reshape(ch_emb, [N, PL, ch_emb.shape[-1]])
qh_emb = tf.reshape(qh_emb, [N, QL, ch_emb.shape[-1]])
print "N", N, "PL", PL, "QL", QL
print "ch_emb", ch_emb.shape
print "qh_emb", qh_emb.shape
c_emb = tf.nn.dropout(tf.nn.embedding_lookup(self.word_mat, self.c), 1.0 - self.dropout)
q_emb = tf.nn.dropout(tf.nn.embedding_lookup(self.word_mat, self.q), 1.0 - self.dropout)
c_emb = tf.concat([c_emb, ch_emb], axis=2)
q_emb = tf.concat([q_emb, qh_emb], axis=2)
c_emb = highway(c_emb, size = d, scope = "highway", dropout = self.dropout, reuse = None)
q_emb = highway(q_emb, size = d, scope = "highway", dropout = self.dropout, reuse = True)
print "c_emb high", c_emb.shape
print "q_emb high", q_emb.shape
with tf.variable_scope("Embedding_Encoder_Layer"):
c_tmp = residual_block(c_emb,
num_blocks = 1,
num_conv_layers = 4,
kernel_size = 7,
mask = self.c_mask,
num_filters = d,
num_heads = nh,
seq_len = self.c_len,
scope = "Encoder_Residual_Block",
bias = False,
dropout = self.dropout)
# c_cell = tf.contrib.rnn.BasicLSTMCell(d, forget_bias=1.0, state_is_tuple=True)
c = drnn(d_cell, c_tmp, d)
q_tmp = residual_block(q_emb,
num_blocks = 1,
num_conv_layers = 4,
kernel_size = 7,
mask = self.q_mask,
num_filters = d,
num_heads = nh,
seq_len = self.q_len,
scope = "Encoder_Residual_Block",
reuse = True, # Share the weights between passage and question
bias = False,
dropout = self.dropout)
# q_cell = tf.contrib.rnn.BasicLSTMCell(d, forget_bias=1.0, state_is_tuple=True)
q = drnn(d_cell, q_tmp, d)
print "embd enc output c", c.shape
print "embd enc output q", q.shape
# exit()
with tf.variable_scope("Context_to_Query_Attention_Layer"):
# C = tf.tile(tf.expand_dims(c,2),[1,1,self.q_maxlen,1])
# Q = tf.tile(tf.expand_dims(q,1),[1,self.c_maxlen,1,1])
# S = trilinear([C, Q, C*Q], input_keep_prob = 1.0 - self.dropout)
S = optimized_trilinear_for_attention([c, q], self.c_maxlen, self.q_maxlen, input_keep_prob = 1.0 - self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask = mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(tf.nn.softmax(mask_logits(S, mask = mask_c), dim = 1),(0,2,1))
self.c2q = tf.matmul(S_, q)
self.q2c = tf.matmul(tf.matmul(S_, S_T), c)
attention_outputs = [c, self.c2q, c * self.c2q, c * self.q2c]
with tf.variable_scope("Model_Encoder_Layer"):
inputs = tf.concat(attention_outputs, axis = -1)
self.enc = [conv(inputs, d, name = "input_projection")]
print "enc len", len(self.enc)
# print self.ch_len.shape
# print self.qh_len.shape
# print self.c_len.shape
# print self.q_len.shape
# print ip_len.shape
print "qh shape", self.qh.shape
print "qh type", self.qh.dtype
print "ip shape", inputs.shape
print "ip type", inputs.dtype
ip_len = tf.reshape(tf.reduce_sum(tf.cast(tf.cast(inputs, tf.bool), tf.float32), axis=2), [-1])
print "ip_len", ip_len.shape
# fw0 = drnn(d_cell, self.enc[0], d)
# f_cell = tf.contrib.rnn.BasicLSTMCell(fw0[2], forget_bias=1.0, state_is_tuple=True)
# fw1 = drnn(d_cell, fw0, d)
# fw2 = drnn(d_cell, fw1, d)
# self.enc.append(fw0)
# self.enc.append(fw1)
# self.enc.append(fw2)
# print "fw1 shape", fw1
#
# (fw0, bw0), _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None,
# initial_state_fw=None, initial_state_bw=None,
# dtype=None, parallel_iterations=None,
# swap_memory=False, time_major=False, scope=None):
# bw_cell = tf.contrib.rnn.BasicLSTMCell(d, forget_bias=1.0, state_is_tuple=True)
# g0 = bidirlstm(fw_cell, bw_cell, inputs, d)
# g1 = bidirlstm(fw_cell, bw_cell, g0, d)
# g2 = bidirlstm(fw_cell, bw_cell, g1, d)
# fw0 = bidirlstm(d_cell, d_cell, inputs, d)
# d_cell1 = tf.contrib.rnn.BasicLSTMCell(fw0[1], forget_bias=1.0, state_is_tuple=True)
# fw1 = bidirlstm(d_cell1, d_cell1, fw0, d)
# (fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(d_cell, d_cell, self.enc[0], dtype='float', scope='g0') # [N, M, JX, 2d]
# g0 = tf.concat([fw_g0, bw_g0], 4)
# (fw_g1, bw_g1) = bidirectional_dynamic_rnn(d_cell, d_cell, fw_g0, dtype='float', scope='g1') # [N, M, JX, 2d]
# print "fw_g0", fw_g0.shape
# print "bw_g0", bw_g0.shape
# print g0.shape
# (fw_g1, bw_g1), _ = bidirlstm(d_cell, d_cell, g0, dtype='float', scope='g1') # [N, M, JX, 2d]
# g1 = tf.concat([fw_g1, bw_g1], 3)
# flat_output_fw = nest.flatten(fw_g0)
# flat_output_bw = nest.flatten(bw_g0)
# flat_outputs = tuple(array_ops.concat(1, [fw, bw])
# for fw, bw in zip(flat_output_fw, flat_output_bw))
# outputs = nest.pack_sequence_as(structure=output_fw,
# flat_sequence=flat_outputs)
# print "output", outputs.shape
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
self.enc[i] = tf.nn.dropout(self.enc[i], 1.0 - self.dropout)
self.enc.append(
drnn(
d_cell,
residual_block(self.enc[i],
num_blocks = 7,
num_conv_layers = 2,
kernel_size = 5,
mask = self.c_mask,
num_filters = d,
num_heads = nh,
seq_len = self.c_len,
scope = "Model_Encoder",
bias = False,
reuse = True if i > 0 else None,
dropout = self.dropout),
d)
)
# print "enc[0] shape", self.enc[0].shape
print "chalala"
# exit()
with tf.variable_scope("Output_Layer"):
start_logits = tf.squeeze(conv(tf.concat([self.enc[1], self.enc[2]],axis = -1),1, bias = False, name = "start_pointer"),-1)
end_logits = tf.squeeze(conv(tf.concat([self.enc[1], self.enc[3]],axis = -1),1, bias = False, name = "end_pointer"), -1)
self.logits = [mask_logits(start_logits, mask = self.c_mask),
mask_logits(end_logits, mask = self.c_mask)]
logits1, logits2 = [l for l in self.logits]
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(logits1), axis=2),
tf.expand_dims(tf.nn.softmax(logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, config.ans_limit)
self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
losses = tf.nn.softmax_cross_entropy_with_logits(
logits=logits1, labels=self.y1)
losses2 = tf.nn.softmax_cross_entropy_with_logits(
logits=logits2, labels=self.y2)
self.loss = tf.reduce_mean(losses + losses2)
if config.l2_norm is not None:
variables = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
l2_loss = tf.contrib.layers.apply_regularization(regularizer, variables)
self.loss += l2_loss
if config.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(config.decay)
ema_op = self.var_ema.apply(tf.trainable_variables())
with tf.control_dependencies([ema_op]):
self.loss = tf.identity(self.loss)
self.assign_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
if v:
self.assign_vars.append(tf.assign(var,v))
def get_loss(self):
return self.loss
def get_global_step(self):
return self.global_step