-
Notifications
You must be signed in to change notification settings - Fork 682
/
GAN Generative Adversarial 2
171 lines (140 loc) · 5.44 KB
/
GAN Generative Adversarial 2
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
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from keras.layers import Input, Dense, Lambda
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
np.random.seed(1337)
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.utils import np_utils
from keras import backend as K
import os
os.environ["KERAS_BACKEND"] = "theano"
#os.environ["THEANO_FLAGS"] = "device=gpu%d,lib.cnmem=0"%(random.randint(0,3))
(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:])))
x_train=x_train[1:2]
x_test=x_test[1:2]
y_train=y_train[1:2]
y_test=y_test[1:2]
batch_size = 1
nb_classes = 10
nb_epoch = 1
# input image dimensions
img_rows, img_cols = 28, 28
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
pool_size = (2, 2)
# convolution kernel size
kernel_size = (3, 3)
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
if K.image_dim_ordering() == 'th':
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows,img_cols)
else:
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
X_train = X_train.astype('float32')[1:2]
X_test = X_test.astype('float32')[1:2]
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
generator = Sequential()
generator.add(Convolution2D(28, 1,1,
border_mode='valid',
input_shape=input_shape))
generator.add(Activation('relu'))
generator.add(Convolution2D(28, 1, 1,
init='glorot_uniform'))
generator.add(Activation('relu'))
generator.add(Convolution2D(1, 1, 1,init='glorot_uniform'))
generator.add(Activation('relu'))
generator.compile(loss='binary_crossentropy', optimizer='adam')
generator.summary()
############# DISCRIMINATOR
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
if K.image_dim_ordering() == 'th':
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows,img_cols)
else:
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
X_train = X_train.astype('float32')[1:2]
X_test = X_test.astype('float32')[1:2]
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)[1:2]
Y_test = np_utils.to_categorical(y_test, nb_classes)[1:2]
discriminator = Sequential()
discriminator.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
border_mode='valid',
input_shape=input_shape))
discriminator.add(Activation('relu'))
discriminator.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
discriminator.add(Activation('relu'))
discriminator.add(MaxPooling2D(pool_size=pool_size))
discriminator.add(Dropout(0.25))
discriminator.add(Flatten())
discriminator.add(Dense(64))
discriminator.add(Activation('relu'))
discriminator.add(Dropout(0.5))
discriminator.add(Dense(nb_classes))
discriminator.add(Activation('sigmoid'))
discriminator.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
discriminator.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=1,
verbose=1, validation_data=(X_test, Y_test))
score = discriminator.evaluate(X_test, Y_test, verbose=2)
print('Test score:', score[0])
print('Test accuracy:', score[1])
def not_train(net, val):
net.trainable = val
for k in net.layers:
k.trainable = val
not_train(discriminator, False)
gan_input = Input(batch_shape=(1, 28,28,1))
generator.summary()
discriminator.summary()
gan_level2 = discriminator(generator(gan_input))
GAN = Model(gan_input, gan_level2)
GAN.compile(loss='categorical_crossentropy', optimizer='adam')
(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:])))
x_train=x_train[1:2]
x_test=x_test[1:2]
y_train=y_train[1:2]
y_test=y_test[1:2]
GAN.fit(x_train.reshape((1,28,28,1)), Y_train.reshape((1,10)), batch_size=batch_size, nb_epoch=2,
verbose=1)
bb=GAN.predict(x_test.reshape((1,28,28,1)))
c=np.where(bb[0]==np.max(bb))[0]
Y_train[c]
plt.imshow(x_train[c].reshape((28,28)),cmap='Greys_r')