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In a CNN, if the input size 5 X 5 and the filter size is 7 X 7, then what would be the size of the output in Deep learning?

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This is a pretty intuitive answer. As we saw above, we perform the convolution on ‘x’ one step at a time, to the right, and in the end, we got Z with dimensions 2 X 2, for X with dimensions 3 X 3.

Thus, to make the input size similar to the filter size, we make use of padding – adding 0s to the input matrix such that its new size becomes at least 7 X 7. Thus, the output size would be using the formula:

Dimension of image = (n, n) = 5 X 5

Dimension of filter = (f,f)  = 7 X 7

Padding = 1 (adding 1 pixel with value 0 all around the edges)

Dimension of output will be (n+2p-f+1) X (n+2p-f+1) = 1 X 1

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