Source: a convenient blog While using convolutional neural network, we don’t have to manually calculate the dimension (the spatial size) of the output(s), but it’s a good idea to do so to keep a mental account of how our inputs are being transformed at each step. We can compute the spatial size on each dimension (width/height/depth(channel)).
$$W_2= \frac{W_1-F_w + 2P}{S_w} + 1$$ $$H_2= \frac{H_1-F_h + 2P}{S_h} + 1$$ Goal $W_2== W_1$; provided $S_w=1$
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