python VGG16 + BatchNormalization

Posted 2021-05-09

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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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