forked from RubensZimbres/Repo-2017
-
Notifications
You must be signed in to change notification settings - Fork 0
/
GAN Siamese Autoencoders
205 lines (178 loc) · 6.79 KB
/
GAN Siamese Autoencoders
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
import keras
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from keras.layers import Input, Dense, Lambda, Merge
from keras.models import Model
from keras import backend as K
from keras import objectives
from keras.datasets import mnist
from keras.layers.core import Reshape
from __future__ import print_function
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D, UpSampling2D
from keras.utils import np_utils
from keras.layers.normalization import BatchNormalization
from keras.callbacks import ModelCheckpoint,LearningRateScheduler
import os
from keras.optimizers import SGD
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
bb=np.where(y_train==0)[0][0:10]
cc=np.where(y_train==1)[0][0:10]
x_train0=np.array([x_train[i] for i in bb])
x_train1=np.array([x_train[i] for i in cc])
x_train0=x_train0.reshape((10,28,28,1))
x_train1=x_train1.reshape((10,28,28,1))
x_train_parallel_left=np.array([x_train0[7]])
x_train_parallel_right=np.array([x_train1[3]])
x_train_CNN=np.array([x_train0[7]])
n = 10
plt.figure(figsize=(10, 2))
for i in range(0,n):
ax = plt.subplot(1, n, i+1)
plt.imshow(x_train0[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
batch_size = 30
nb_classes = 10
img_rows, img_cols = 28, 28
nb_filters = 32
pool_size = (2, 2)
kernel_size = (3, 3)
input_shape=(28,28,1)
epochs=80
learning_rate = 0.027
decay_rate = 5e-5
momentum = 0.6
sgd = SGD(lr=learning_rate,momentum=momentum, decay=decay_rate, nesterov=False)
denoise_left = Sequential()
denoise_left.add(Convolution2D(20, 3,3,
border_mode='valid',
input_shape=input_shape))
denoise_left.add(BatchNormalization(mode=2))
denoise_left.add(Activation('relu'))
denoise_left.add(MaxPooling2D(pool_size=(2,2)))
denoise_left.add(Convolution2D(20, 3, 3,
init='glorot_uniform'))
denoise_left.add(BatchNormalization(mode=2))
denoise_left.add(Activation('relu'))
denoise_left.add(Convolution2D(8, 3, 3,init='glorot_uniform'))
denoise_left.add(BatchNormalization(mode=2))
denoise_left.add(Activation('relu'))
denoise_left.add(UpSampling2D(size=(2, 2)))
denoise_left.add(Convolution2D(8, 3, 3,init='glorot_uniform'))
denoise_left.add(BatchNormalization(mode=2))
denoise_left.add(Activation('relu'))
denoise_left.add(UpSampling2D(size=(2, 2)))
denoise_left.add(Convolution2D(8, 3, 3,init='glorot_uniform'))
denoise_left.add(BatchNormalization(mode=2))
denoise_left.add(Activation('relu'))
denoise_left.add(Convolution2D(1, 3, 3,init='glorot_uniform'))
denoise_left.add(BatchNormalization(mode=2))
denoise_left.add(Activation('sigmoid'))
denoise_right = Sequential()
denoise_right.add(Convolution2D(20, 3,3,
border_mode='valid',
input_shape=input_shape))
denoise_right.add(BatchNormalization(mode=2))
denoise_right.add(Activation('relu'))
denoise_right.add(MaxPooling2D(pool_size=(2,2)))
denoise_right.add(Convolution2D(20, 3, 3,
init='glorot_uniform'))
denoise_right.add(BatchNormalization(mode=2))
denoise_right.add(Activation('relu'))
denoise_right.add(Convolution2D(8, 3, 3,init='glorot_uniform'))
denoise_right.add(BatchNormalization(mode=2))
denoise_right.add(Activation('relu'))
denoise_right.add(UpSampling2D(size=(2, 2)))
denoise_right.add(Convolution2D(8, 3, 3,init='glorot_uniform'))
denoise_right.add(BatchNormalization(mode=2))
denoise_right.add(Activation('relu'))
denoise_right.add(UpSampling2D(size=(2, 2)))
denoise_right.add(Convolution2D(8, 3, 3,init='glorot_uniform'))
denoise_right.add(BatchNormalization(mode=2))
denoise_right.add(Activation('relu'))
denoise_right.add(Convolution2D(1, 3, 3,init='glorot_uniform'))
denoise_right.add(BatchNormalization(mode=2))
denoise_right.add(Activation('sigmoid'))
denoise0 = Sequential()
denoise0.add(Merge([denoise_left,denoise_right],mode = 'ave'))
denoise0.compile(loss='mean_squared_error', optimizer=sgd,metrics = ['accuracy'])
denoise = Sequential()
denoise.add(Convolution2D(20, 3,3,
border_mode='valid',
input_shape=input_shape))
denoise.add(BatchNormalization(mode=2))
denoise.add(Activation('relu'))
denoise.add(MaxPooling2D(pool_size=(2,2)))
denoise.add(Convolution2D(20, 3, 3,
init='glorot_uniform'))
denoise.add(BatchNormalization(mode=2))
denoise.add(Activation('relu'))
denoise.add(Convolution2D(8, 3, 3,init='glorot_uniform'))
denoise.add(BatchNormalization(mode=2))
denoise.add(Activation('relu'))
denoise.add(UpSampling2D(size=(2, 2)))
denoise.add(Convolution2D(8, 3, 3,init='glorot_uniform'))
denoise.add(BatchNormalization(mode=2))
denoise.add(Activation('relu'))
denoise.add(UpSampling2D(size=(2, 2)))
denoise.add(Convolution2D(8, 3, 3,init='glorot_uniform'))
denoise.add(BatchNormalization(mode=2))
denoise.add(Activation('relu'))
denoise.add(Convolution2D(1, 3, 3,init='glorot_uniform'))
denoise.add(BatchNormalization(mode=2))
denoise.add(Activation('sigmoid'))
denoise.compile(loss='mean_squared_error', optimizer=sgd,metrics = ['accuracy'])
denoise.summary()
denoise.fit(x_train_CNN, x_train_CNN,
nb_epoch=epochs,
batch_size=30,verbose=1)
a1=denoise.predict(x_train_CNN,verbose=1)
plt.figure(figsize=(10, 10))
ax = plt.subplot(1, 2, 1)
plt.imshow(x_train_CNN.reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax = plt.subplot(1, 2, 2)
plt.imshow(a1.reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
################## GAN
def not_train(net, val):
net.trainable = val
for k in net.layers:
k.trainable = val
not_train(denoise0, False)
gan_input = Input(batch_shape=(1, 28,28,1))
gan_level2 = denoise(denoise0([gan_input,gan_input]))
GAN = Model(gan_input, gan_level2)
GAN.compile(loss='mean_squared_error', optimizer='adam',metrics = ['accuracy'])
GAN.fit(x_train_parallel_left, x_train_parallel_right,
batch_size=30, nb_epoch=epochs,verbose=1)
a=GAN.predict(x_train_parallel_right,verbose=1)
plt.figure(figsize=(10, 10))
ax = plt.subplot(1, 2, 1)
plt.imshow(x_train_parallel_right.reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax = plt.subplot(1, 2, 2)
plt.imshow(a.reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()