base_enhancement.py 文件源码

def make_init_model():
    input_data = Input(shape=(32, 32, 3))

    init_model_index = random.randint(1, 4)
    init_model_index = 2

    if init_model_index == 1:  # one conv layer with kernel num = 64
        stem_conv_1 = Conv2D(64, (1, 1), padding='same')(input_data)

    elif init_model_index == 2:  # two conv layers with kernel num = 64
        stem_conv_1 = Conv2D(64, (1, 1), padding='same')(input_data)
        stem_conv_2 = Conv2D(64, (1, 1), padding='same')(stem_conv_1)

    elif init_model_index == 3:  # one conv layer with a wider kernel num = 128
        stem_conv_1 = Conv2D(128, (1, 1), padding='same')(input_data)

    elif init_model_index == 4:  # two conv layers with a wider kernel_num = 128
        stem_conv_1 = Conv2D(128, (1, 1), padding='same')(input_data)
        stem_conv_2 = Conv2D(128, (1, 1), padding='same')(stem_conv_1)

    stem_global_pooling_1 = GlobalMaxPooling2D()(stem_conv_1)
    stem_softmax_1 = Activation('softmax')(stem_global_pooling_1)

    model = Model(inputs=input_data, outputs=stem_softmax_1)
    return model