Created
August 31, 2018 06:14
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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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