why do we need to flip kernel before using conv2 in CNN?

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We know that function conv2 can prefom convolution (between image and kernel ) and flip kernel before apply convolution to image according to defnition of convolution
y = conv2(image, kernel, 'valid')
.However, in convolution neural network(CNN) ,they flip the kernel before the use conv2
kernel = rot90(kernel, 2);
y = conv2(image, kernel, 'valid');
which means the kernel flip twice and this correlation not convolution why
Mohammedee on 4 Jul 2022
Edited: Mohammedee on 4 Jul 2022
Look to this code..kernel rotated 180 Then pass it to conv2 And we know that conv2 will rotate kernel 180 again... This mean kernel rotated twice 180..

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Answers (1)

Matt J
Matt J on 4 Jul 2022
Edited: Matt J on 4 Jul 2022
The field of neural networks uses the term "convolution" loosely. There are other differences as well. We also know that in traditional DSP theory, convolution operations don't contain a stride parameter, but in the NN world, they do.
Matt J
Matt J on 11 Jul 2022
Edited: Matt J on 11 Jul 2022
If you use conv2(image, W), MATLAB will first "flip" W, reversing its rows and columns
Yes, conv2 will flip W internally and that is the correct thing for it to do, because that is the way convolution is defined. This definition ensures that conv2(1,W) = W. Example:
W=[1 2;3 4]
W = 2×2
1 2 3 4
ans = 2×2
1 2 3 4
If you were to flip W manually, prior to giving it to conv2, it would mess this up:
ans = 2×2
4 3 2 1

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