-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrain00.py
176 lines (146 loc) · 7.05 KB
/
train00.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
# encoding=utf8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
from skimage.io import imsave
import os
import shutil
img_height = 28
img_width = 28
img_size = img_height * img_width
to_train = False
to_restore = False
output_path = "output"
# 总迭代次数500
max_epoch = 500
h1_size = 150
h2_size = 300
z_size = 100
batch_size = 256
# generate (model 1)
def build_generator(z_prior):
w1 = tf.Variable(tf.truncated_normal([z_size, h1_size], stddev=0.1), name="g_w1", dtype=tf.float32)
b1 = tf.Variable(tf.zeros([h1_size]), name="g_b1", dtype=tf.float32)
h1 = tf.nn.relu(tf.matmul(z_prior, w1) + b1)
w2 = tf.Variable(tf.truncated_normal([h1_size, h2_size], stddev=0.1), name="g_w2", dtype=tf.float32)
b2 = tf.Variable(tf.zeros([h2_size]), name="g_b2", dtype=tf.float32)
h2 = tf.nn.relu(tf.matmul(h1, w2) + b2)
w3 = tf.Variable(tf.truncated_normal([h2_size, img_size], stddev=0.1), name="g_w3", dtype=tf.float32)
b3 = tf.Variable(tf.zeros([img_size]), name="g_b3", dtype=tf.float32)
h3 = tf.matmul(h2, w3) + b3
x_generate = tf.nn.tanh(h3)
g_params = [w1, b1, w2, b2, w3, b3]
return x_generate, g_params
# discriminator (model 2)
def build_discriminator(x_data, x_generated, keep_prob):
# tf.concat
#t1 = [[1, 2, 3], [4, 5, 6]]
#t2 = [[7, 8, 9], [10, 11, 12]]
#tf.concat(0, [t1, t2]) == > [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
x_in = tf.concat([x_data, x_generated], 0)
w1 = tf.Variable(tf.truncated_normal([img_size, h2_size], stddev=0.1), name="d_w1", dtype=tf.float32)
b1 = tf.Variable(tf.zeros([h2_size]), name="d_b1", dtype=tf.float32)
h1 = tf.nn.dropout(tf.nn.relu(tf.matmul(x_in, w1) + b1), keep_prob)
w2 = tf.Variable(tf.truncated_normal([h2_size, h1_size], stddev=0.1), name="d_w2", dtype=tf.float32)
b2 = tf.Variable(tf.zeros([h1_size]), name="d_b2", dtype=tf.float32)
h2 = tf.nn.dropout(tf.nn.relu(tf.matmul(h1, w2) + b2), keep_prob)
w3 = tf.Variable(tf.truncated_normal([h1_size, 1], stddev=0.1), name="d_w3", dtype=tf.float32)
b3 = tf.Variable(tf.zeros([1]), name="d_b3", dtype=tf.float32)
h3 = tf.matmul(h2, w3) + b3
#[0, 0]是起点位置
#[batch_size, -1]切出多大，即行数和列数,-1表示全部列数
y_data = tf.nn.sigmoid(tf.slice(h3, [0, 0], [batch_size, -1], name=None))
print(y_data.eval)
y_generated = tf.nn.sigmoid(tf.slice(h3, [batch_size, 0], [-1, -1], name=None))
d_params = [w1, b1, w2, b2, w3, b3]
return y_data, y_generated, d_params
#
def show_result(batch_res, fname, grid_size=(8, 8), grid_pad=5):
batch_res = 0.5 * batch_res.reshape((batch_res.shape[0], img_height, img_width)) + 0.5
img_h, img_w = batch_res.shape[1], batch_res.shape[2]
grid_h = img_h * grid_size[0] + grid_pad * (grid_size[0] - 1)
grid_w = img_w * grid_size[1] + grid_pad * (grid_size[1] - 1)
img_grid = np.zeros((grid_h, grid_w), dtype=np.uint8)
for i, res in enumerate(batch_res):
if i >= grid_size[0] * grid_size[1]:
break
img = (res) * 255
img = img.astype(np.uint8)
row = (i // grid_size[0]) * (img_h + grid_pad)
col = (i % grid_size[1]) * (img_w + grid_pad)
img_grid[row:row + img_h, col:col + img_w] = img
imsave(fname, img_grid)
def train():
# load data（mnist手写数据集）
mnist = input_data.read_data_sets('./data', one_hot=True)
x_data = tf.placeholder(tf.float32, [batch_size, img_size], name="x_data")
z_prior = tf.placeholder(tf.float32, [batch_size, z_size], name="z_prior")
keep_prob = tf.placeholder(tf.float32, name="keep_prob")
global_step = tf.Variable(0, name="global_step", trainable=False)
# 创建生成模型
x_generated, g_params = build_generator(z_prior)
# 创建判别模型
y_data, y_generated, d_params = build_discriminator(x_data, x_generated, keep_prob)
# 损失函数的设置
d_loss = - (tf.log(y_data) + tf.log(1 -y_generated))
g_loss = - tf.log(y_generated)
optimizer = tf.train.AdamOptimizer(0.0001)
# 两个模型的优化函数
d_trainer = optimizer.minimize(d_loss, var_list=d_params)
g_trainer = optimizer.minimize(g_loss, var_list=g_params)
init = tf.initialize_all_variables()
saver = tf.train.Saver()
# 启动默认图
sess = tf.Session()
# 初始化
sess.run(init)
if to_restore:
chkpt_fname = tf.train.latest_checkpoint(output_path)
saver.restore(sess, chkpt_fname)
else:
if not os.path.exists(output_path):
os.mkdir(output_path)
z_sample_val = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32)
steps = 60000 / batch_size
for i in range(sess.run(global_step), max_epoch):
for j in np.arange(steps):
# for j in range(steps):
print("epoch:%s, iter:%s" % (i, j))
# 每一步迭代，我们都会加载256个训练样本，然后执行一次train_step
x_value, _ = mnist.train.next_batch(batch_size)
x_value = 2 * x_value.astype(np.float32) - 1
z_value = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32)
# 执行生成
#sess.run([y_data,y_generated,d_trainer],
#feed_dict={x_data: x_value, z_prior: z_value, keep_prob: np.sum(0.7).astype(np.float32)})
a,b,c=sess.run([y_data,y_generated,d_trainer],feed_dict={x_data: x_value, z_prior: z_value, keep_prob: np.sum(0.7).astype(np.float32)})
# run(fetches)
# 执行判别
if j % 10 == 0:
sess.run(g_trainer,
feed_dict={x_data: x_value, z_prior: z_value, keep_prob: np.sum(0.7).astype(np.float32)})
x_gen_val = sess.run(x_generated, feed_dict={z_prior: z_sample_val})
show_result(x_gen_val, "output/sample{0}.jpg".format(i))
z_random_sample_val = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32)
x_gen_val = sess.run(x_generated, feed_dict={z_prior: z_random_sample_val})
show_result(x_gen_val, "output/random_sample{0}.jpg".format(i))
sess.run(tf.assign(global_step, i + 1))
if i%100==0:
saver.save(sess, os.path.join(output_path, "model"), global_step=global_step)
def test():
z_prior = tf.placeholder(tf.float32, [batch_size, z_size], name="z_prior")
x_generated, _ = build_generator(z_prior)
chkpt_fname = tf.train.latest_checkpoint(output_path)
init = tf.initialize_all_variables()
sess = tf.Session()
saver = tf.train.Saver()
sess.run(init)
saver.restore(sess, chkpt_fname)
z_test_value = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32)
x_gen_val = sess.run(x_generated, feed_dict={z_prior: z_test_value})
show_result(x_gen_val, "output/test_result.jpg")
if __name__ == '__main__':
if 1:
train()
else:
test()