我想像这个图像一样可视化keras模型的中间层 .
我在 github.com/EN10/KerasMNIST 中使用了CNN model，它已经有一个预先训练过的模型 . 而模型看起来像这样
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
conv2d_2 (Conv2D) (None, 24, 24, 64) 18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 12, 12, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 9216) 0
_________________________________________________________________
dense_1 (Dense) (None, 128) 1179776
_________________________________________________________________
dropout_2 (Dropout) (None, 128) 0
_________________________________________________________________
dense_2 (Dense) (None, 10) 1290
=================================================================
Total params: 1,199,882
Trainable params: 1,199,882
Non-trainable params: 0
我想要想象这个模型的第一个，第二个卷积和辍学层，并且根据this answer我只需要向我想要可视化的图层进行前向传递 . 问题是图像的维度为 28x28x1 ，而第一和第二卷积层维度分别为 26x26x32 和 24x24x64 ，而丢失层为 12x12x64 . 如何将这些尺寸转换为图像尺寸，以便我们可以获得与上面相似的图像？