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$\begingroup$ Thank you for the very detailed answer! There are still a couple of things that I am not sure I understand correctly. You say there are 48 weights + 1 bias for each kernel, so that means that each color has effectively its own filter, so shouldn't the sum over i,j (4,4) be actually over i,j,k (4,4,3)? because if I end up with 6x6x1 then the channel dimension is gone by then. $\endgroup$

Tacratis
– Tacratis

2019-02-13 17:41:55 +00:00
Commented Feb 13, 2019 at 17:41
$\begingroup$ The second question is: with a final vector of 6x6x5 it is clear that each kernel generates its own set of outputs for the neurons of the fully connected layer, but when I inspect CNNs generated by Keras using the exact same parameter, I see that the first fully connected layer has only 6x6 neurons - this is where the initial confusion started for me. Thanks again! $\endgroup$

Tacratis
– Tacratis

2019-02-13 17:42:40 +00:00
Commented Feb 13, 2019 at 17:42

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