How does back propagation works through layers like maxpooling and padding? [duplicate]Back-propagation through max pooling layersTrying to figure out how to set weights for convolutional networkshow to propagate error from convolutional layer to previous layer？Back-propagation through max pooling layersSteps for back propagation of convolutional layer in CNNHow can you decide the window size on a pooling layer?Understand the shape of this Convolutional Neural NetworkHow is error back-propagated in a multi-layer RNNHow can I perform backpropagation directly in matrix form?How to make a region of interest proposal from convolutional feature maps?

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How does back propagation works through layers like maxpooling and padding? [duplicate]


Back-propagation through max pooling layersTrying to figure out how to set weights for convolutional networkshow to propagate error from convolutional layer to previous layer？Back-propagation through max pooling layersSteps for back propagation of convolutional layer in CNNHow can you decide the window size on a pooling layer?Understand the shape of this Convolutional Neural NetworkHow is error back-propagated in a multi-layer RNNHow can I perform backpropagation directly in matrix form?How to make a region of interest proposal from convolutional feature maps?













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  • Back-propagation through max pooling layers

    1 answer



I know back propagation takes derivatives (changing one quantity wrt other). But how this is applied when there is maxpooling layer in between two Conv2D layers? How it gains its original shape when there is padding added?






machine-learning deep-learning keras tensorflow backpropagation







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marked as duplicate by Esmailian, Ethan, Siong Thye Goh, Sean Owen♦ yesterday


This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.






















    0












    $begingroup$



    This question already has an answer here:



    • Back-propagation through max pooling layers

      1 answer



    I know back propagation takes derivatives (changing one quantity wrt other). But how this is applied when there is maxpooling layer in between two Conv2D layers? How it gains its original shape when there is padding added?






    machine-learning deep-learning keras tensorflow backpropagation







    share|improve this question







    New contributor




    Arshad_221b is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$



    marked as duplicate by Esmailian, Ethan, Siong Thye Goh, Sean Owen♦ yesterday


    This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.




















      0












      0








      0





      $begingroup$



      This question already has an answer here:



      • Back-propagation through max pooling layers

        1 answer



      I know back propagation takes derivatives (changing one quantity wrt other). But how this is applied when there is maxpooling layer in between two Conv2D layers? How it gains its original shape when there is padding added?






      machine-learning deep-learning keras tensorflow backpropagation







      share|improve this question







      New contributor




      Arshad_221b is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$





      This question already has an answer here:



      • Back-propagation through max pooling layers

        1 answer



      I know back propagation takes derivatives (changing one quantity wrt other). But how this is applied when there is maxpooling layer in between two Conv2D layers? How it gains its original shape when there is padding added?





      This question already has an answer here:



      • Back-propagation through max pooling layers

        1 answer






      machine-learning deep-learning keras tensorflow backpropagation




      machine-learning deep-learning keras tensorflow backpropagation






      share|improve this question







      New contributor




      Arshad_221b is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question







      New contributor




      Arshad_221b is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




      share|improve this question






      New contributor




      Arshad_221b is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      asked 2 days ago









      Arshad_221bArshad_221b

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      New contributor




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      New contributor





      Arshad_221b is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      Arshad_221b is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.




      marked as duplicate by Esmailian, Ethan, Siong Thye Goh, Sean Owen♦ yesterday


      This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.









      marked as duplicate by Esmailian, Ethan, Siong Thye Goh, Sean Owen♦ yesterday


      This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.






















          1 Answer
          1






          active

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          0












          $begingroup$

          Max pooling will cancel the effect of not pooled values to the gradients.
          Padded values either have no effect.



          Nice thing about convolution is, that it is basically reducable to a matrix multiplication and the backpropagation is simply the transposed of it. So you have already your backward pass stored in the foward pass.






          share|improve this answer









          $endgroup$



















            1 Answer
            1






            active

            oldest

            votes








            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            0












            $begingroup$

            Max pooling will cancel the effect of not pooled values to the gradients.
            Padded values either have no effect.



            Nice thing about convolution is, that it is basically reducable to a matrix multiplication and the backpropagation is simply the transposed of it. So you have already your backward pass stored in the foward pass.






            share|improve this answer









            $endgroup$

















              0












              $begingroup$

              Max pooling will cancel the effect of not pooled values to the gradients.
              Padded values either have no effect.



              Nice thing about convolution is, that it is basically reducable to a matrix multiplication and the backpropagation is simply the transposed of it. So you have already your backward pass stored in the foward pass.






              share|improve this answer









              $endgroup$















                0












                0








                0





                $begingroup$

                Max pooling will cancel the effect of not pooled values to the gradients.
                Padded values either have no effect.



                Nice thing about convolution is, that it is basically reducable to a matrix multiplication and the backpropagation is simply the transposed of it. So you have already your backward pass stored in the foward pass.






                share|improve this answer









                $endgroup$



                Max pooling will cancel the effect of not pooled values to the gradients.
                Padded values either have no effect.



                Nice thing about convolution is, that it is basically reducable to a matrix multiplication and the backpropagation is simply the transposed of it. So you have already your backward pass stored in the foward pass.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 2 days ago









                Andreas LookAndreas Look

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