Questions

I manually calculated all the conv2d values
and also the FC1 and FC2 dimensions. Is there a way that it can be calculated by pytorch?

**Coincidentally, here with the use of padding 1, input equates to output before Pooling, hence
it’s slightly easier over here.


At line 41, is -1 referring to the batch size since we converted the rest to 4W * 4H * 64 final depth ?


I understand that I did a resnet transfer learning before and it can achieve >90% but when I use CNN, I only managed to obtain ~70% based on this network. Conceptually would it be better to just use transfer learning instead OR this is just an ‘entry level’ CNN that will lead to a much better one that may or may not be better than transfer learning?


Thanks.
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