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Query about weight filter size in AlexNet

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deepika s
deepika s 2021년 8월 14일
댓글: deepika s 2021년 8월 18일
I have analysed the pretrained alexnet model. I have attached the screen shot. In that conv2 row, weights are given as 5*5*48*256. what is 48 here?. As per my knowledge, 48 represents the previous layer (number of channels in previous layer). but number of previous layer is 96 after pool1. likewise, same doubt in conv4 and conv5.
Can anyone explain this question?

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Harikrishnan Balachandran Nair
Harikrishnan Balachandran Nair 2021년 8월 17일
The reason that the third dimension of weights in the mentioned layer is not the same as the number of channels in input is that the corresponding layer in AlexNet perform grouped Convolution. You can refer to the following Documentation to learn more about AlexNet : https://www.mathworks.com/help/deeplearning/ref/alexnet.html.
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deepika s
deepika s 2021년 8월 18일
Yes. but how the third dimesion (48) of weight in Conv2 is convolves with number of channels (nothing but dimension 96) in the pool1 output? Both dimensions are different.

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