forked from sreedath/one-pixel-attack-keras
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathresnet.py
190 lines (159 loc) · 7.62 KB
/
resnet.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
177
178
179
180
181
182
183
184
185
186
187
188
189
190
import keras
import numpy as np
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Conv2D, Dense, Input, add, Activation, GlobalAveragePooling2D, BatchNormalization
from keras.callbacks import LearningRateScheduler, TensorBoard, ModelCheckpoint
from keras.models import Model, load_model
from keras import optimizers, regularizers
from networks.train_plot import PlotLearning
# Code taken from https://github.com/BIGBALLON/cifar-10-cnn
class ResNet:
def __init__(self, epochs=200, batch_size=128, load_weights=True):
self.name = 'resnet'
self.model_filename = 'networks/models/resnet.h5'
self.stack_n = 5
self.num_classes = 10
self.img_rows, self.img_cols = 32, 32
self.img_channels = 3
self.batch_size = batch_size
self.epochs = epochs
self.iterations = 50000 // self.batch_size
self.weight_decay = 0.0001
self.log_filepath = r'networks/models/resnet/'
if load_weights:
try:
self._model = load_model(self.model_filename)
print('Successfully loaded', self.name)
except (ImportError, ValueError, OSError):
print('Failed to load', self.name)
def count_params(self):
return self._model.count_params()
def color_preprocessing(self, x_train,x_test):
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
mean = [125.307, 122.95, 113.865]
std = [62.9932, 62.0887, 66.7048]
for i in range(3):
x_train[:,:,:,i] = (x_train[:,:,:,i] - mean[i]) / std[i]
x_test[:,:,:,i] = (x_test[:,:,:,i] - mean[i]) / std[i]
return x_train, x_test
def scheduler(self, epoch):
if epoch < 80:
return 0.1
if epoch < 150:
return 0.01
return 0.001
def residual_network(self, img_input,classes_num=10,stack_n=5):
def residual_block(intput,out_channel,increase=False):
if increase:
stride = (2,2)
else:
stride = (1,1)
pre_bn = BatchNormalization()(intput)
pre_relu = Activation('relu')(pre_bn)
conv_1 = Conv2D(out_channel,kernel_size=(3,3),strides=stride,padding='same',
kernel_initializer="he_normal",
kernel_regularizer=regularizers.l2(self.weight_decay))(pre_relu)
bn_1 = BatchNormalization()(conv_1)
relu1 = Activation('relu')(bn_1)
conv_2 = Conv2D(out_channel,kernel_size=(3,3),strides=(1,1),padding='same',
kernel_initializer="he_normal",
kernel_regularizer=regularizers.l2(self.weight_decay))(relu1)
if increase:
projection = Conv2D(out_channel,
kernel_size=(1,1),
strides=(2,2),
padding='same',
kernel_initializer="he_normal",
kernel_regularizer=regularizers.l2(self.weight_decay))(intput)
block = add([conv_2, projection])
else:
block = add([intput,conv_2])
return block
# build model
# total layers = stack_n * 3 * 2 + 2
# stack_n = 5 by default, total layers = 32
# input: 32x32x3 output: 32x32x16
x = Conv2D(filters=16,kernel_size=(3,3),strides=(1,1),padding='same',
kernel_initializer="he_normal",
kernel_regularizer=regularizers.l2(self.weight_decay))(img_input)
# input: 32x32x16 output: 32x32x16
for _ in range(stack_n):
x = residual_block(x,16,False)
# input: 32x32x16 output: 16x16x32
x = residual_block(x,32,True)
for _ in range(1,stack_n):
x = residual_block(x,32,False)
# input: 16x16x32 output: 8x8x64
x = residual_block(x,64,True)
for _ in range(1,stack_n):
x = residual_block(x,64,False)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = GlobalAveragePooling2D()(x)
# input: 64 output: 10
x = Dense(classes_num,activation='softmax',
kernel_initializer="he_normal",
kernel_regularizer=regularizers.l2(self.weight_decay))(x)
return x
def train(self):
# load data
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = keras.utils.to_categorical(y_train, self.num_classes)
y_test = keras.utils.to_categorical(y_test, self.num_classes)
# color preprocessing
x_train, x_test = self.color_preprocessing(x_train, x_test)
# build network
img_input = Input(shape=(self.img_rows,self.img_cols,self.img_channels))
output = self.residual_network(img_input,self.num_classes,self.stack_n)
resnet = Model(img_input, output)
resnet.summary()
# set optimizer
sgd = optimizers.SGD(lr=.1, momentum=0.9, nesterov=True)
resnet.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
# set callback
tb_cb = TensorBoard(log_dir=self.log_filepath, histogram_freq=0)
change_lr = LearningRateScheduler(self.scheduler)
checkpoint = ModelCheckpoint(self.model_filename,
monitor='val_loss', verbose=0, save_best_only= True, mode='auto')
plot_callback = PlotLearning()
cbks = [change_lr,tb_cb,checkpoint,plot_callback]
# set data augmentation
print('Using real-time data augmentation.')
datagen = ImageDataGenerator(horizontal_flip=True,
width_shift_range=0.125,
height_shift_range=0.125,
fill_mode='constant',cval=0.)
datagen.fit(x_train)
# start training
resnet.fit_generator(datagen.flow(x_train, y_train,batch_size=self.batch_size),
steps_per_epoch=self.iterations,
epochs=self.epochs,
callbacks=cbks,
validation_data=(x_test, y_test))
resnet.save(self.model_filename)
self._model = resnet
self.param_count = self._model.count_params()
def color_process(self, imgs):
if imgs.ndim < 4:
imgs = np.array([imgs])
imgs = imgs.astype('float32')
mean = [125.307, 122.95, 113.865]
std = [62.9932, 62.0887, 66.7048]
for img in imgs:
for i in range(3):
img[:,:,i] = (img[:,:,i] - mean[i]) / std[i]
return imgs
def predict(self, img):
processed = self.color_process(img)
return self._model.predict(processed, batch_size=self.batch_size)
def predict_one(self, img):
return self.predict(img)[0]
def accuracy(self):
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = keras.utils.to_categorical(y_train, self.num_classes)
y_test = keras.utils.to_categorical(y_test, self.num_classes)
# color preprocessing
x_train, x_test = self.color_preprocessing(x_train, x_test)
return self._model.evaluate(x_test, y_test, verbose=0)[1]