get_model.py 文件源码

def Alexnet(height, width, weights_path=None):

    model = Sequential()
    model.add(ZeroPadding2D((1, 1), input_shape=(3, height, width)))
    model.add(Convolution2D(64, 11, 11, border_mode="same", activation="relu"))
    model.add(BatchNormalization())
    model.add(ZeroPadding2D((1, 1)))
    model.add(MaxPooling2D(pool_size=(3, 3)))

    model.add(Convolution2D(128, 7, 7, border_mode="same", activation="relu"))
    model.add(BatchNormalization())
    model.add(ZeroPadding2D((1, 1)))
    model.add(MaxPooling2D(pool_size=(3, 3)))

    model.add(Convolution2D(192, 3, 3, border_mode="same", activation="relu"))
    model.add(BatchNormalization())
    model.add(ZeroPadding2D((1, 1)))
    model.add(MaxPooling2D(pool_size=(3, 3)))

    model.add(Convolution2D(256, 3, 3, border_mode="same", activation="relu"))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(3, 3)))

    model.add(Flatten())
    model.add(Dense(4096, init='normal', activation="relu"))
    model.add(BatchNormalization())
    model.add(Dense(512, init='normal', activation="relu"))
    model.add(BatchNormalization())
    model.add(Dense(2, init='normal', activation="softmax"))

    if weights_path:
        print("Loading weights...", end='\t')
        model.load_weights(weights_path)
        print("Finished.")

    return model