keras_functional_api.py 文件源码

def mnist_cnn(args, input_image):
    shape = (args.channels, args.height, args.width)
    x = Convolution2D(32, 5, 5, 
        activation='relu', 
        border_mode='valid', 
        input_shape=shape)(input_image)
    x = MaxPooling2D((2,2))(x)          
    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(x)
    x = Dropout(0.2)(x)
    x = MaxPooling2D((2,2))(x)  
    x = Flatten()(x)
    x = Dense(128, activation='relu')(x)
    x = Dense(64, activation='relu')(x)

    predictions = Dense(args.num_labels, activation='softmax')(x)

    # this creates a model that includes
    # the Input layer and three Dense layers
    model = Model(input=input_image, output=predictions)
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    model.summary()
    return model