Support of strides in the convolutional layers #239
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jvdp1
requested review from
Riccardo231 and
milancurcic
and removed request for
milancurcic
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Yes, it's exactly how I would do it, thanks Jeremie. |
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I think it is a good implementation. Thanks for your job |
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I'd like to offer my support whenever it is needed, feel free to contact me, right now I'm busy to develop improvements on my own but I can cooperate with someone else |
| self % gradient(k,iws:iwe,jws:jwe) = self % gradient(k,iws:iwe,jws:jwe) & | ||
| + gdz(n,iws:iwe,jws:jwe) * self % kernel(n,k,1:iwe-iws+1,1:jwe-jws+1) |
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@Riccardo231 Could you check this, please? I think it has a different behaviour now. However, I am not sure what was the goal before the change, because all entries of self % gradient were not updated (that is only the entries between istart:iend and jstart:jend were updated).
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I think I implemented the conv2d variant.. I'll look at this carefully over the weekend. It's possible that the original code was bad.
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I'll have a look tomorrow.
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Sorry, just to inform you these days I'm really busy with school. Can probably have a look after the 5th. Sorry for the delay
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Sorry, just to inform you these days I'm really busy with school. Can probably have a look after the 5th. Sorry for the delay
No worries. Whenever you have time. Thank you.
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@milancurcic @Riccardo231 Pending a comment/question, this PR is ready for review and/or to be merged. |
3 participants
Proposal to support strides in the convolutional layers
@Riccardo231 @milancurcic does this approach make sense? If yes, I will continue its implementation.