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wottpal/vgg16bn.py

Created August 31, 2018 06:14
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vgg16bn.py
def VGG_16_BN(input_shape):
model = models.Sequential()
model.add(Convolution2D(64, (3,3), input_shape=input_shape, activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(Convolution2D(64, (3,3), activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Convolution2D(128, (3,3), activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(Convolution2D(128, (3,3), activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Convolution2D(256, (3,3), activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(Convolution2D(256, (3,3), activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(Convolution2D(256, (3,3), activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Convolution2D(512, (3,3), activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(Convolution2D(512, (3,3), activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(Convolution2D(512, (3,3), activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Convolution2D(512, (3,3), activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(Convolution2D(512, (3,3), activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(Convolution2D(512, (3,3), activation='relu', padding='same', use_bias=False))
model.add(BatchNormalization())
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Flatten())
model.add(Dense(4096, activation='relu', use_bias=False))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu', use_bias=False))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
return model
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