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I have got a good grasp of backpropagation algorithm for fully connected neural networks and I can derive the vectorized implementation for it but after getting into convulutional networks, I think due to my not-so-strong linear algebra background, I have a lot of trouble trying to come up with a fully vectorized implementation for backpropagation.

The network architecture is as follows :-

Input->conv->relu->pool->fully_connected_network

I have used only one convolutional Layer for simplifaication

How can the vectorized implementation of this network be derived ?

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  • $\begingroup$ Where are you stuck? $\endgroup$
    – Aaron
    Aug 15, 2017 at 16:37
  • $\begingroup$ @Aaron Well I know the formula for convolution so I can calculate element-wise derivative but I want to vectorize it. $\endgroup$ Aug 16, 2017 at 1:09
  • $\begingroup$ Start with one-dimensional case with only 1 variable. Derive derivatives. Extend to slightly harder case, again, derive derivatives, combine them into vector form (should be easy once correct derivatives are computed); alternatively you can lookup a the answer in the book. Then move to the most general case which is for images. $\endgroup$
    – Gnattuha
    Aug 20, 2017 at 19:14
  • $\begingroup$ @Gnattuha I have read an article and understood most of the derivation but I'm stuck . here are more details about my problem. Please help. $\endgroup$ Sep 2, 2017 at 8:36

1 Answer 1

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  1. Convert all kernels to columns and get a kernels matrix
  2. Split your input (image) on slices for convolution then convert to columns and get an input matrix. You can append other inputs (images) to form a mini-batch
  3. multiply transposed input matrix on kernels matrix. In the result matrix each column is one feature map.

Look here for more details

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