I was trying to build a cnn to with Pytorch, and had difficulty in maxpooling. I have taken the cs231n held by Stanford. As I recalled, maxpooling can be used as a dimensional deduction step, for example, I have this (1, 20, height, width) input ot max\_pool2d (assuming my batch\_size is 1). And if I use (1, 1) kernel, I want to get output like this: (1, 1, height, width), which means the kernel should be slide over the channel dimension. However, after checking the pytorch docs, it says the kernel slides over height and width. And thanks to @ImgPrcSng on Pytorch forum who told me to use max\_pool3d, and it turned out worked well. But there is still a reshape operation between the output of the conv2d layer and the input of the max\_pool3d layer. So it is hard to be aggregated into a nn.Sequential, so I wonder is there another way to do this?