-
Notifications
You must be signed in to change notification settings - Fork 0
/
dcgan.py
141 lines (120 loc) · 4.97 KB
/
dcgan.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
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class DCGAN(object):
"""
Tensorflow implementation of DCGAN, with four CNN layers.
We assume the input images are of size 32x32.
"""
def __init__(self):
# self.image_size = 64
self.image_size = 32
self.noise_size = 256
self.lrelu_alpha = 0.2
self.num_channels = 3
self.lr = 1e-3
self.beta_1 = 0.5
def _create_placeholders(self):
self.input_images = tf.placeholder(
shape=[None, self.image_size, self.image_size, self.num_channels],
dtype=tf.float32,
name="input_images")
self.input_noise = tf.placeholder(
shape=[None, self.noise_size],
dtype=tf.float32,
name="input_noise")
def _create_generator(self):
xav_init = tf.contrib.layers.xavier_initializer
bnorm = tf.layers.batch_normalization
with tf.variable_scope("generator"):
"""
fc_1 = tf.layers.dense(
inputs=self.input_noise, units=4 * 4 * 512, name="fc_1")
"""
fc_1 = tf.layers.dense(
inputs=self.input_noise, units=4 * 4 * 256, name="fc_1")
reshaped_fc_1 = tf.reshape(
fc_1,
shape=[tf.shape(fc_1)[0], 4, 4, 256],
name="reshapsed_noise")
def _create_deconv_bnorm_block(inputs,
name,
filters,
activation=tf.nn.relu):
with tf.variable_scope(name):
deconv = tf.layers.conv2d_transpose(
inputs=inputs,
filters=filters,
kernel_size=[5, 5],
strides=2,
padding="same",
kernel_initializer=xav_init(),
name="deconv")
deconv = activation(deconv)
bnorm_op = bnorm(deconv, name="bnorm")
return bnorm_op
"""
bnorm_1 = _create_deconv_bnorm_block(
inputs=reshaped_fc_1, filters=256, name="block_1")
bnorm_2 = _create_deconv_bnorm_block(
inputs=bnorm_1, filters=128, name="block_2")
"""
bnorm_2 = _create_deconv_bnorm_block(
inputs=reshaped_fc_1, filters=128, name="block_2")
bnorm_3 = _create_deconv_bnorm_block(
inputs=bnorm_2, filters=64, name="block_3")
bnorm_4 = _create_deconv_bnorm_block(
inputs=bnorm_3,
filters=3,
activation=tf.nn.tanh,
name="block_4")
return bnorm_4
def _create_discriminator(self, inputs, reuse=False):
xav_init = tf.contrib.layers.xavier_initializer
bnorm = tf.layers.batch_normalization
with tf.variable_scope("discriminator", reuse=reuse):
def _create_conv_bnorm_block(inputs, filters, name):
with tf.variable_scope(name, reuse=reuse):
conv = tf.layers.conv2d(
inputs=inputs,
filters=filters,
kernel_size=[5, 5],
strides=2,
padding="same",
kernel_initializer=xav_init(),
name="conv")
conv = tf.maximum(conv, self.lrelu_alpha * conv)
bnorm_op = bnorm(conv, name="bnorm")
return bnorm_op
conv_1 = tf.layers.conv2d(
inputs=inputs,
filters=64,
kernel_size=[5, 5],
strides=2,
kernel_initializer=xav_init(),
padding="same",
name="conv_1")
conv_1 = tf.maximum(conv_1, self.lrelu_alpha * conv_1)
bnorm_1 = _create_conv_bnorm_block(
inputs=conv_1, filters=128, name="block_1")
bnorm_2 = _create_conv_bnorm_block(
inputs=bnorm_1, filters=256, name="block_2")
"""
bnorm_3 = _create_conv_bnorm_block(
inputs=bnorm_2, filters=512, name="block_3")
reshaped_bnorm_3 = tf.reshape(
bnorm_3,
shape=[tf.shape(bnorm_3)[0], 4 * 4 * 512],
name="reshaped_bnorm_3")
logits = tf.layers.dense(
inputs=reshaped_bnorm_3, units=1, name="fc_1")
"""
reshaped_bnorm_2 = tf.reshape(
bnorm_2,
shape=[tf.shape(bnorm_2)[0], 4 * 4 * 256],
name="reshaped_bnorm_2")
logits = tf.layers.dense(
inputs=reshaped_bnorm_2, units=1, name="fc_1")
fc_1 = tf.sigmoid(logits)
return fc_1, logits