forked from stanfordmlgroup/nlc
-
Notifications
You must be signed in to change notification settings - Fork 0
/
nlc_model.py
205 lines (171 loc) · 9.12 KB
/
nlc_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
# Copyright 2016 Stanford University
#
# 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import random
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import rnn
from tensorflow.python.ops import rnn_cell
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops.math_ops import sigmoid
from tensorflow.python.ops.math_ops import tanh
class GRUCellAttn(rnn_cell.GRUCell):
def __init__(self, num_units, encoder_output, scope=None):
self.hs = encoder_output
with vs.variable_scope(scope or type(self).__name__):
with vs.variable_scope("Attn1"):
hs2d = tf.reshape(self.hs, [-1, num_units])
phi_hs2d = tanh(rnn_cell.linear(hs2d, num_units, True, 1.0))
self.phi_hs = tf.reshape(phi_hs2d, tf.shape(self.hs))
super(GRUCellAttn, self).__init__(num_units)
def __call__(self, inputs, state, scope=None):
gru_out, gru_state = super(GRUCellAttn, self).__call__(inputs, state, scope)
with vs.variable_scope(scope or type(self).__name__):
with vs.variable_scope("Attn2"):
gamma_h = tanh(rnn_cell.linear(gru_out, self._num_units, True, 1.0))
weights = tf.reduce_sum(self.phi_hs * gamma_h, reduction_indices=2, keep_dims=True)
weights = tf.exp(weights - tf.reduce_max(weights, reduction_indices=0, keep_dims=True))
weights = weights / (1e-6 + tf.reduce_sum(weights, reduction_indices=0, keep_dims=True))
context = tf.reduce_sum(self.hs * weights, reduction_indices=0)
with vs.variable_scope("AttnConcat"):
out = tf.nn.relu(rnn_cell.linear([context, gru_out], self._num_units, True, 1.0))
attn_map = tf.reduce_sum(tf.slice(weights, [0, 0, 0], [-1, -1, 1]), reduction_indices=2)
return (out, out)
class NLCModel(object):
def __init__(self, vocab_size, size, num_layers, max_gradient_norm, batch_size, learning_rate,
learning_rate_decay_factor, dropout, forward_only=False):
self.size = size
self.vocab_size = vocab_size
self.batch_size = batch_size
self.num_layers = num_layers
self.keep_prob = 1.0 - dropout
self.learning_rate = tf.Variable(float(learning_rate), trainable=False)
self.learning_rate_decay_op = self.learning_rate.assign(self.learning_rate * learning_rate_decay_factor)
self.global_step = tf.Variable(0, trainable=False)
self.source_tokens = tf.placeholder(tf.int32, shape=[None, self.batch_size], name="source_tokens")
self.target_tokens = tf.placeholder(tf.int32, shape=[None, self.batch_size], name="target_tokens")
self.source_mask = tf.placeholder(tf.int32, shape=[None, self.batch_size], name="source_mask")
self.target_mask = tf.placeholder(tf.int32, shape=[None, self.batch_size], name="target_mask")
self.source_length = tf.reduce_sum(self.source_mask, reduction_indices=0)
self.target_length = tf.reduce_sum(self.target_mask, reduction_indices=0)
self.setup_embeddings()
self.setup_encoder()
self.setup_decoder()
self.setup_loss()
params = tf.trainable_variables()
if not forward_only:
opt = tf.train.AdamOptimizer(self.learning_rate)
gradients = tf.gradients(self.losses, params)
clipped_gradients, norm = tf.clip_by_global_norm(gradients, max_gradient_norm)
self.gradient_norms = norm
self.updates = opt.apply_gradients(
zip(clipped_gradients, params), global_step=self.global_step)
self.saver = tf.train.Saver(tf.all_variables())
def setup_embeddings(self):
with vs.variable_scope("embeddings"):
self.L_enc = tf.get_variable("L_enc", [self.vocab_size, self.size])
self.L_dec = self.L_enc #tf.get_variable("L_dec", [self.vocab_size, self.size])
self.encoder_inputs = embedding_ops.embedding_lookup(self.L_enc, self.source_tokens)
self.decoder_inputs = embedding_ops.embedding_lookup(self.L_dec, self.target_tokens)
def setup_encoder(self):
self.encoder_cell = rnn_cell.GRUCell(self.size)
with vs.variable_scope("PryamidEncoder"):
inp = self.encoder_inputs
out = None
for i in xrange(self.num_layers):
with vs.variable_scope("EncoderCell%d" % i) as scope:
out, _ = self.bidirectional_rnn(self.encoder_cell, self.dropout(inp), self.source_length, scope=scope)
inp = self.downscale(out)
self.encoder_output = out
def setup_decoder(self):
if self.num_layers > 1:
self.decoder_cell = rnn_cell.GRUCell(self.size)
self.attn_cell = GRUCellAttn(self.size, self.encoder_output, scope="DecoderAttnCell")
out = self.decoder_inputs
with vs.variable_scope("Decoder"):
inp = self.decoder_inputs
for i in xrange(self.num_layers - 1):
with vs.variable_scope("DecoderCell%d" % i) as scope:
out, _ = rnn.dynamic_rnn(self.decoder_cell, self.dropout(inp), time_major=True,
dtype=dtypes.float32, sequence_length=self.target_length,
scope=scope)
inp = out
with vs.variable_scope("DecoderAttnCell") as scope:
out, _ = rnn.dynamic_rnn(self.attn_cell, self.dropout(inp), time_major=True,
dtype=dtypes.float32, sequence_length=self.target_length,
scope=scope)
self.decoder_output = out
def setup_loss(self):
with vs.variable_scope("Logistic"):
do2d = tf.reshape(self.decoder_output, [-1, self.size])
logits2d = rnn_cell.linear(do2d, self.vocab_size, True, 1.0)
outputs2d = tf.nn.softmax(logits2d)
self.outputs = tf.reshape(outputs2d, [-1, self.batch_size, self.vocab_size])
targets_no_GO = tf.slice(self.target_tokens, [1, 0], [-1, -1])
masks_no_GO = tf.slice(self.target_mask, [1, 0], [-1, -1])
# easier to pad target/mask than to split decoder input since tensorflow does not support negative indexing
labels1d = tf.reshape(tf.pad(targets_no_GO, [[0, 1], [0, 0]]), [-1])
mask1d = tf.reshape(tf.pad(masks_no_GO, [[0, 1], [0, 0]]), [-1])
losses1d = tf.nn.sparse_softmax_cross_entropy_with_logits(logits2d, labels1d) * tf.to_float(mask1d)
losses2d = tf.reshape(losses1d, [-1, self.batch_size])
self.losses = tf.reduce_sum(losses2d) / self.batch_size
def dropout(self, inp):
return tf.nn.dropout(inp, self.keep_prob)
def downscale(self, inp):
with vs.variable_scope("Downscale"):
inp2d = tf.reshape(tf.transpose(inp, perm=[1, 0, 2]), [-1, 2 * self.size])
out2d = rnn_cell.linear(inp2d, self.size, True, 1.0)
out3d = tf.reshape(out2d, [self.batch_size, -1, self.size])
out3d = tf.transpose(out3d, perm=[1, 0, 2])
out = tanh(out3d)
return out
def bidirectional_rnn(self, cell, inputs, lengths, scope=None):
name = scope.name or "BiRNN"
# Forward direction
with vs.variable_scope(name + "_FW") as fw_scope:
output_fw, output_state_fw = rnn.dynamic_rnn(cell, inputs, time_major=True, dtype=dtypes.float32,
sequence_length=lengths, scope=fw_scope)
# Backward direction
with vs.variable_scope(name + "_BW") as bw_scope:
output_bw, output_state_bw = rnn.dynamic_rnn(cell, inputs, time_major=True, dtype=dtypes.float32,
sequence_length=lengths, scope=bw_scope)
output_bw = tf.reverse_sequence(output_bw, tf.to_int64(lengths), seq_dim=0, batch_dim=1)
outputs = output_fw + output_bw
output_state = output_state_fw + output_state_bw
return (outputs, output_state)
def train(self, session, source_tokens, source_mask, target_tokens, target_mask):
input_feed = {}
input_feed[self.source_tokens] = source_tokens
input_feed[self.target_tokens] = target_tokens
input_feed[self.source_mask] = source_mask
input_feed[self.target_mask] = target_mask
output_feed = [self.updates, self.gradient_norms, self.losses]
outputs = session.run(output_feed, input_feed)
return outputs[1], outputs[2]
def test(self, session, source_tokens, source_mask, target_tokens, target_mask):
input_feed = {}
input_feed[self.source_tokens] = source_tokens
input_feed[self.target_tokens] = target_tokens
input_feed[self.source_mask] = source_mask
input_feed[self.target_mask] = target_mask
output_feed = [self.losses, self.outputs]
outputs = session.run(output_feed, input_feed)
return outputs[0], outputs[1]