forked from jonbruner/generative-adversarial-networks
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathgan-script-fast.py
176 lines (141 loc) · 7.73 KB
/
gan-script-fast.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
"""
This is a straightforward Python implementation of a generative adversarial network.
The code is derived from the O'Reilly interactive tutorial on GANs
(https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners).
The tutorial's code trades efficiency for clarity in explaining how GANs function;
this script refactors a few things to improve performance, especially on GPU machines.
In particular, it uses a TensorFlow operation to generate random z values and pass them
to the generator; this way, more computations are contained entirely within the
TensorFlow graph.
A version of this model with explanatory notes is also available on GitHub
at https://github.com/jonbruner/generative-adversarial-networks.
This script requires TensorFlow and its dependencies in order to run. Please see
the readme for guidance on installing TensorFlow.
This script won't print summary statistics in the terminal during training;
track progress and see sample images in TensorBoard.
"""
import tensorflow as tf
import datetime
# Load MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/")
# Define the discriminator network
def discriminator(images, reuse_variables=None):
with tf.variable_scope(tf.get_variable_scope(), reuse=reuse_variables) as scope:
# First convolutional and pool layers
# This finds 32 different 5 x 5 pixel features
d_w1 = tf.get_variable('d_w1', [5, 5, 1, 32], initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b1 = tf.get_variable('d_b1', [32], initializer=tf.constant_initializer(0))
d1 = tf.nn.conv2d(input=images, filter=d_w1, strides=[1, 1, 1, 1], padding='SAME')
d1 = d1 + d_b1
d1 = tf.nn.relu(d1)
d1 = tf.nn.avg_pool(d1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# Second convolutional and pool layers
# This finds 64 different 5 x 5 pixel features
d_w2 = tf.get_variable('d_w2', [5, 5, 32, 64], initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b2 = tf.get_variable('d_b2', [64], initializer=tf.constant_initializer(0))
d2 = tf.nn.conv2d(input=d1, filter=d_w2, strides=[1, 1, 1, 1], padding='SAME')
d2 = d2 + d_b2
d2 = tf.nn.relu(d2)
d2 = tf.nn.avg_pool(d2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# First fully connected layer
d_w3 = tf.get_variable('d_w3', [7 * 7 * 64, 1024], initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b3 = tf.get_variable('d_b3', [1024], initializer=tf.constant_initializer(0))
d3 = tf.reshape(d2, [-1, 7 * 7 * 64])
d3 = tf.matmul(d3, d_w3)
d3 = d3 + d_b3
d3 = tf.nn.relu(d3)
# Second fully connected layer
d_w4 = tf.get_variable('d_w4', [1024, 1], initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b4 = tf.get_variable('d_b4', [1], initializer=tf.constant_initializer(0))
d4 = tf.matmul(d3, d_w4) + d_b4
# d4 contains unscaled values
return d4
# Define the generator network
def generator(batch_size, z_dim):
z = tf.random_normal([batch_size, z_dim], mean=0, stddev=1, name='z')
g_w1 = tf.get_variable('g_w1', [z_dim, 3136], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.02))
g_b1 = tf.get_variable('g_b1', [3136], initializer=tf.truncated_normal_initializer(stddev=0.02))
g1 = tf.matmul(z, g_w1) + g_b1
g1 = tf.reshape(g1, [-1, 56, 56, 1])
g1 = tf.contrib.layers.batch_norm(g1, epsilon=1e-5, scope='bn1')
g1 = tf.nn.relu(g1)
# Generate 50 features
g_w2 = tf.get_variable('g_w2', [3, 3, 1, z_dim/2], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.02))
g_b2 = tf.get_variable('g_b2', [z_dim/2], initializer=tf.truncated_normal_initializer(stddev=0.02))
g2 = tf.nn.conv2d(g1, g_w2, strides=[1, 2, 2, 1], padding='SAME')
g2 = g2 + g_b2
g2 = tf.contrib.layers.batch_norm(g2, epsilon=1e-5, scope='bn2')
g2 = tf.nn.relu(g2)
g2 = tf.image.resize_images(g2, [56, 56])
# Generate 25 features
g_w3 = tf.get_variable('g_w3', [3, 3, z_dim/2, z_dim/4], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.02))
g_b3 = tf.get_variable('g_b3', [z_dim/4], initializer=tf.truncated_normal_initializer(stddev=0.02))
g3 = tf.nn.conv2d(g2, g_w3, strides=[1, 2, 2, 1], padding='SAME')
g3 = g3 + g_b3
g3 = tf.contrib.layers.batch_norm(g3, epsilon=1e-5, scope='bn3')
g3 = tf.nn.relu(g3)
g3 = tf.image.resize_images(g3, [56, 56])
# Final convolution with one output channel
g_w4 = tf.get_variable('g_w4', [1, 1, z_dim/4, 1], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.02))
g_b4 = tf.get_variable('g_b4', [1], initializer=tf.truncated_normal_initializer(stddev=0.02))
g4 = tf.nn.conv2d(g3, g_w4, strides=[1, 2, 2, 1], padding='SAME')
g4 = g4 + g_b4
g4 = tf.sigmoid(g4)
# Dimensions of g4: batch_size x 28 x 28 x 1
return g4
z_dimensions = 100
batch_size = 50
x_placeholder = tf.placeholder(tf.float32, shape = [None,28,28,1], name='x_placeholder')
# x_placeholder is for feeding input images to the discriminator
Gz = generator(batch_size, z_dimensions)
# Gz holds the generated images
Dx = discriminator(x_placeholder)
# Dx will hold discriminator prediction probabilities
# for the real MNIST images
Dg = discriminator(Gz, reuse_variables=True)
# Dg will hold discriminator prediction probabilities for generated images
# Define losses
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = Dx, labels = tf.ones_like(Dx)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = Dg, labels = tf.zeros_like(Dg)))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = Dg, labels = tf.ones_like(Dg)))
# Define variable lists
tvars = tf.trainable_variables()
d_vars = [var for var in tvars if 'd_' in var.name]
g_vars = [var for var in tvars if 'g_' in var.name]
# Define the optimizers
# Train the discriminator
d_trainer_fake = tf.train.AdamOptimizer(0.0003).minimize(d_loss_fake, var_list=d_vars)
d_trainer_real = tf.train.AdamOptimizer(0.0003).minimize(d_loss_real, var_list=d_vars)
# Train the generator
g_trainer = tf.train.AdamOptimizer(0.0001).minimize(g_loss, var_list=g_vars)
# From this point forward, reuse variables
tf.get_variable_scope().reuse_variables()
sess = tf.Session()
# Send summary statistics to TensorBoard
tf.summary.scalar('Generator_loss', g_loss)
tf.summary.scalar('Discriminator_loss_real', d_loss_real)
tf.summary.scalar('Discriminator_loss_fake', d_loss_fake)
images_for_tensorboard = generator(batch_size, z_dimensions)
tf.summary.image('Generated_images', images_for_tensorboard, 5)
merged = tf.summary.merge_all()
logdir = "tensorboard/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + "/"
writer = tf.summary.FileWriter(logdir, sess.graph)
sess.run(tf.global_variables_initializer())
# Pre-train discriminator
for i in range(300):
real_image_batch = mnist.train.next_batch(batch_size)[0].reshape([batch_size, 28, 28, 1])
_, __ = sess.run([d_trainer_real, d_trainer_fake],
{x_placeholder: real_image_batch})
# Train generator and discriminator together
for i in range(100000):
real_image_batch = mnist.train.next_batch(batch_size)[0].reshape([batch_size, 28, 28, 1])
# Train discriminator on both real and fake images
_, __ = sess.run([d_trainer_real, d_trainer_fake],
{x_placeholder: real_image_batch})
# Train generator
_ = sess.run(g_trainer)
if i % 10 == 0:
# Update TensorBoard with summary statistics
summary = sess.run(merged, {x_placeholder: real_image_batch})
writer.add_summary(summary, i)