EED.py 文件源码

def model_EED():
    _input = Input(shape=(None, None, 1), name='input')

    Feature = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(_input)
    Feature_out = Res_block()(Feature)

    # Upsampling
    Upsampling1 = Conv2D(filters=4, kernel_size=(1, 1), strides=(1, 1), padding='same', activation='relu')(Feature_out)
    Upsampling2 = Conv2DTranspose(filters=4, kernel_size=(14, 14), strides=(2, 2),
                                  padding='same', activation='relu')(Upsampling1)
    Upsampling3 = Conv2D(filters=64, kernel_size=(1, 1), strides=(1, 1), padding='same', activation='relu')(Upsampling2)

    # Mulyi-scale Reconstruction
    Reslayer1 = Res_block()(Upsampling3)

    Reslayer2 = Res_block()(Reslayer1)

    # ***************//
    Multi_scale1 = Conv2D(filters=16, kernel_size=(1, 1), strides=(1, 1), padding='same', activation='relu')(Reslayer2)

    Multi_scale2a = Conv2D(filters=16, kernel_size=(1, 1), strides=(1, 1),
                           padding='same', activation='relu')(Multi_scale1)

    Multi_scale2b = Conv2D(filters=16, kernel_size=(1, 3), strides=(1, 1),
                           padding='same', activation='relu')(Multi_scale1)
    Multi_scale2b = Conv2D(filters=16, kernel_size=(3, 1), strides=(1, 1),
                           padding='same', activation='relu')(Multi_scale2b)

    Multi_scale2c = Conv2D(filters=16, kernel_size=(1, 5), strides=(1, 1),
                           padding='same', activation='relu')(Multi_scale1)
    Multi_scale2c = Conv2D(filters=16, kernel_size=(5, 1), strides=(1, 1),
                           padding='same', activation='relu')(Multi_scale2c)

    Multi_scale2d = Conv2D(filters=16, kernel_size=(1, 7), strides=(1, 1),
                           padding='same', activation='relu')(Multi_scale1)
    Multi_scale2d = Conv2D(filters=16, kernel_size=(7, 1), strides=(1, 1),
                           padding='same', activation='relu')(Multi_scale2d)

    Multi_scale2 = concatenate(inputs=[Multi_scale2a, Multi_scale2b, Multi_scale2c, Multi_scale2d])

    out = Conv2D(filters=1, kernel_size=(1, 1), strides=(1, 1), padding='same', activation='relu')(Multi_scale2)
    model = Model(input=_input, output=out)

    return model