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The first formula you quote is for an image with one input channel and one output channel, it just focuses on height and width. In this case, if we consider a 5x5 convolution, the Kernel will just have size 5x5, $m$ and $n$ and going from -2 to +2. Now if our input has 3 channels (RGB, but could be feature maps). we need to use each channel as an input, and ...


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