我在做卷积式自动编码器教程,作者是Keras库: https://blog.keras.io/building-autoencoders-in-keras.html
但是,当我启动完全相同的代码,并使用summary()分析网络的架构时,似乎输出大小与输入大小不兼容(对于自动编码器是必要的)。下面是summary()的输出:
**____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 1, 28, 28) 0
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D) (None, 16, 28, 28) 160 input_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 16, 14, 14) 0 convolution2d_1[0][0]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, 8, 14, 14) 1160 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_2 (MaxPooling2D) (None, 8, 7, 7) 0 convolution2d_2[0][0]
____________________________________________________________________________________________________
convolution2d_3 (Convolution2D) (None, 8, 7, 7) 584 maxpooling2d_2[0][0]
____________________________________________________________________________________________________
maxpooling2d_3 (MaxPooling2D) (None, 8, 3, 3) 0 convolution2d_3[0][0]
____________________________________________________________________________________________________
convolution2d_4 (Convolution2D) (None, 8, 3, 3) 584 maxpooling2d_3[0][0]
____________________________________________________________________________________________________
upsampling2d_1 (UpSampling2D) (None, 8, 6, 6) 0 convolution2d_4[0][0]
____________________________________________________________________________________________________
convolution2d_5 (Convolution2D) (None, 8, 6, 6) 584 upsampling2d_1[0][0]
____________________________________________________________________________________________________
upsampling2d_2 (UpSampling2D) (None, 8, 12, 12) 0 convolution2d_5[0][0]
____________________________________________________________________________________________________
convolution2d_6 (Convolution2D) (None, 16, 10, 10) 1168 upsampling2d_2[0][0]
____________________________________________________________________________________________________
upsampling2d_3 (UpSampling2D) (None, 16, 20, 20) 0 convolution2d_6[0][0]
____________________________________________________________________________________________________
convolution2d_7 (Convolution2D) (None, 1, 20, 20) 145 upsampling2d_3[0][0]
====================================================================================================
Total params: 4385
____________________________________________________________________________________________________**
请注意,在pre-last卷积层中缺少一个
border_mode
选项。在这应该行得通
相关问题 更多 >
编程相关推荐