-
Notifications
You must be signed in to change notification settings - Fork 37
/
discriminator.py
141 lines (122 loc) · 6.52 KB
/
discriminator.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
import tensorflow as tf
import numpy as np
# An alternative to tf.nn.rnn_cell._linear function, which has been removed in Tensorfow 1.0.1
# The highway layer is borrowed from https://github.com/mkroutikov/tf-lstm-char-cnn
def linear(input_, output_size, scope=None):
'''
Linear map: output[k] = sum_i(Matrix[k, i] * input_[i] ) + Bias[k]
Args:
input_: a tensor or a list of 2D, batch x n, Tensors.
output_size: int, second dimension of W[i].
scope: VariableScope for the created subgraph; defaults to "Linear".
Returns:
A 2D Tensor with shape [batch x output_size] equal to
sum_i(input_[i] * W[i]), where W[i]s are newly created matrices.
Raises:
ValueError: if some of the arguments has unspecified or wrong shape.
'''
shape = input_.get_shape().as_list()
if len(shape) != 2:
raise ValueError("Linear is expecting 2D arguments: %s" % str(shape))
if not shape[1]:
raise ValueError("Linear expects shape[1] of arguments: %s" % str(shape))
input_size = shape[1]
# Now the computation.
with tf.variable_scope(scope or "SimpleLinear"):
matrix = tf.get_variable("Matrix", [output_size, input_size], dtype=input_.dtype)
bias_term = tf.get_variable("Bias", [output_size], dtype=input_.dtype)
return tf.matmul(input_, tf.transpose(matrix)) + bias_term
def highway(input_, size, num_layers=1, bias=-2.0, f=tf.nn.relu, scope='Highway'):
"""Highway Network (cf. http://arxiv.org/abs/1505.00387).
t = sigmoid(Wy + b)
z = t * g(Wy + b) + (1 - t) * y
where g is nonlinearity, t is transform gate, and (1 - t) is carry gate.
"""
with tf.variable_scope(scope):
for idx in range(num_layers):
g = f(linear(input_, size, scope='highway_lin_%d' % idx))
t = tf.sigmoid(linear(input_, size, scope='highway_gate_%d' % idx) + bias)
output = t * g + (1. - t) * input_
input_ = output
return output
class Discriminator(object):
"""
A CNN for text classification.
Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
"""
def __init__(self, config):
# Placeholders for input, output and dropout
self.sequence_length = config.sequence_length
self.num_classes = config.num_classes
self.vocab_size = config.vocab_size
self.filter_sizes = config.dis_filter_sizes
self.num_filters = config.dis_num_filters
self.vocab_size = config.vocab_size
self.dis_learning_rate = config.dis_learning_rate
self.embedding_size = config.dis_embedding_dim
self.l2_reg_lambda = config.dis_l2_reg_lambda
self.input_x = tf.placeholder(tf.int32, [None, self.sequence_length], name="input_x")
self.input_y = tf.placeholder(tf.float32, [None, self.num_classes], name="input_y")
self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
# Keeping track of l2 regularization loss (optional)
self.l2_loss = tf.constant(0.0)
def build_discriminator(self):
with tf.variable_scope('discriminator'):
# Embedding layer
with tf.device('/cpu:0'), tf.name_scope("embedding"):
self.W = tf.Variable(
tf.random_uniform([self.vocab_size, self.embedding_size], -1.0, 1.0),
name="W")
self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)
# Create a convolution + maxpool layer for each filter size
pooled_outputs = []
for filter_size, num_filter in zip(self.filter_sizes, self.num_filters):
with tf.name_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
filter_shape = [filter_size, self.embedding_size, 1, num_filter]
W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[num_filter]), name="b")
conv = tf.nn.conv2d(
self.embedded_chars_expanded,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
# Maxpooling over the outputs
pooled = tf.nn.max_pool(
h,
ksize=[1, self.sequence_length - filter_size + 1, 1, 1],
strides=[1, 1, 1, 1],
padding='VALID',
name="pool")
pooled_outputs.append(pooled)
# Combine all the pooled features
num_filters_total = sum(self.num_filters)
self.h_pool = tf.concat(pooled_outputs, 3)
self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
# Add highway
with tf.name_scope("highway"):
self.h_highway = highway(self.h_pool_flat, self.h_pool_flat.get_shape()[1], 1, 0)
# Add dropout
with tf.name_scope("dropout"):
self.h_drop = tf.nn.dropout(self.h_highway, self.dropout_keep_prob)
# Final (unnormalized) scores and predictions
with tf.name_scope("output"):
W = tf.Variable(tf.truncated_normal([num_filters_total, self.num_classes], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[self.num_classes]), name="b")
self.l2_loss += tf.nn.l2_loss(W)
self.l2_loss += tf.nn.l2_loss(b)
self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores")
self.ypred_for_auc = tf.nn.softmax(self.scores)
self.predictions = tf.argmax(self.scores, 1, name="predictions")
# CalculateMean cross-entropy loss
with tf.name_scope("loss"):
losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)
self.loss = tf.reduce_mean(losses) + self.l2_reg_lambda * self.l2_loss
self.params = [param for param in tf.trainable_variables() if 'discriminator' in param.name]
d_optimizer = tf.train.AdamOptimizer(self.dis_learning_rate)
grads_and_vars = d_optimizer.compute_gradients(self.loss, self.params, aggregation_method=2)
self.train_op = d_optimizer.apply_gradients(grads_and_vars)