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models.py
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models.py
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from keras import backend as K
from keras.models import (
Sequential,
Model
)
from keras.layers import (
Activation,
AveragePooling2D,
BatchNormalization,
Convolution2D,
Dense,
Dropout,
Flatten,
GlobalAveragePooling2D,
Input,
MaxPooling2D,
merge,
)
from keras.regularizers import l2
from keras.applications import resnet50
def channel_axis():
if K.image_dim_ordering() == 'tf':
return 3
else:
return 1
def load_simple_cnn(input_shape, n_classes, weight_decay):
model = Sequential()
model.add(Convolution2D(
64, 3, 3, border_mode='same',
input_shape=input_shape,
init='he_normal',
W_regularizer=l2(weight_decay)))
model.add(BatchNormalization(
mode=0,
axis=channel_axis(),
gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay)
))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(
64, 3, 3, border_mode='same',
init='he_normal',
W_regularizer=l2(weight_decay)))
model.add(BatchNormalization(
mode=0,
axis=channel_axis(),
gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay)
))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(
64, 3, 3, border_mode='same',
init='he_normal',
W_regularizer=l2(weight_decay)))
model.add(BatchNormalization(
mode=0,
axis=channel_axis(),
gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay)
))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, W_regularizer=l2(weight_decay), bias=False))
model.add(BatchNormalization(
mode=0,
axis=-1,
gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay)
))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(n_classes, W_regularizer=l2(weight_decay)))
model.add(Activation('softmax'))
return model
def load_resnet(input_shape, n_classes, depth, weight_decay, widen):
wd = weight_decay
def bnrelu(x_input):
x_output = BatchNormalization(
mode=0,
axis=channel_axis(),
gamma_regularizer=l2(wd),
beta_regularizer=l2(wd))(x_input)
x_output = Activation('relu')(x_output)
return x_output
def basic_block(x, n_input, n_output, stride):
resmap = bnrelu(x)
if n_input != n_output:
x = resmap
resmap = Convolution2D(
n_output, 3, 3,
init='he_normal',
border_mode='same',
subsample=(stride, stride),
bias=False,
W_regularizer=l2(wd))(resmap)
resmap = bnrelu(resmap)
resmap = Convolution2D(
n_output, 3, 3,
init='he_normal',
border_mode='same',
subsample=(1,1),
bias=False,
W_regularizer=l2(wd))(resmap)
if n_input == n_output:
skip = x # Identity skip connection
else:
skip = Convolution2D(
n_output, 1, 1,
init='he_normal',
border_mode='same',
subsample=(stride, stride),
bias=False,
W_regularizer=l2(wd))(x)
return merge([resmap, skip], mode='sum')
def stage(x, n_input, n_output, n_block, stride):
x = basic_block(x, n_input, n_output, stride)
for i in range(n_block - 1):
x = basic_block(x, n_output, n_output, 1)
return x
stages = [16, 16*widen, 32*widen, 64*widen]
if (depth - 4) % 6 != 0:
error(Exception("depth must be 6n+4."))
n_block = int((depth - 4) / 6)
# Create model
x_input = Input(shape=input_shape)
x = Convolution2D(
stages[0], 3, 3,
init='he_normal',
border_mode='same',
subsample=(1,1),
bias=False,
W_regularizer=l2(wd))(x_input) # spatial size 32x32
x = stage(x, stages[0], stages[1], n_block, 1) # spatial size 32x32
x = stage(x, stages[1], stages[2], n_block, 2) # spatial size 16x16
x = stage(x, stages[2], stages[3], n_block, 2) # spatial size 8x8
x = bnrelu(x)
x = GlobalAveragePooling2D()(x)
x = Dense(
n_classes,
init='glorot_uniform',
W_regularizer=l2(wd))(x)
x_output = Activation('softmax')(x)
model = Model(input=x_input, output=x_output)
return model
def load_resnet50_imagenet(n_classes, weight_decay):
base_model = resnet50.ResNet50(weights='imagenet', include_top=False)
for layer in base_model.layers:
layer.trainable = False
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(n_classes, W_regularizer=l2(weight_decay))(x)
x = Activation('softmax')(x)
return Model(input=base_model.input, output=x)
def load_model(net_type, input_shape, n_classes, depth, weight_decay, widen):
if net_type == 'simple':
model = load_simple_cnn(input_shape, n_classes, weight_decay)
elif net_type == 'resnet':
model = load_resnet(input_shape, n_classes, depth, weight_decay, widen)
elif net_type == 'resnet50imagenet':
model = load_resnet50_imagenet(n_classes, weight_decay)
else:
raise("Invalid net_type.")
return model