On the convolutional neural network, there used one or more pooling layers. As far as I know many tutorials instruct you to set it either 2 or 3 for the window size. For example, in this tutorial:Pooling LayersAfter some ReLU layers, programmers may choose to apply a pooling layer. It is also referred to as a downsampling layer. In this category, there are also several layer options, with maxpooling being the most popular. This basically takes a filter (normally of size 2x2) and a stride of the same length. It then applies it to the input volume and outputs the maximum number in every subregion that the filter convolves around.I make the relevant part bold. But why is it usually set to 2 (or in some cases 3)? How can I know what size is appropriate? Should I just tweak the parameter via a trial and error like a brute-force way, or is there any tips on deciding the window size?
How can you decide the window size on a pooling layer?
deep learning;hyperparameter;convolution