I think this idea would be something like y = [1, 2, 3, 0]
y_current_avgpool = (1 + 2 + 3 + 0) / 4 y_new_avgpool = (1 + 2 + 3) / 3 I'm not sure that there is a simple way to do this currently. You could do sum pooling first, then compute the divisors by looking at the number of non-zero elements using this http://deeplearning.net/software/theano/library/tensor/nnet/neighbours.html#theano.tensor.nnet.neighbours.images2neibs and T.switch On Wednesday, August 9, 2017 at 11:36:29 AM UTC-7, nouiz wrote: > > I don't understand the problem with using normal operation. Can you give > this code? I don't see more problem with that implementation vs a normal > average pooling. > > Le mar. 8 août 2017 07:36, Feras Almasri <fsal...@gmail.com <javascript:>> > a écrit : > >> I want to have an node that take the average of the only activated points >> in the last feature map. what I mean by activated points is any pixel >> higher than zero. instead of taking the global average of the full feature >> map I'd rather take it of the only activated pixels. >> If I just do this in normal operation then gradient descent will be >> discontinued in a way that location for back prorogation are not there. >> >> Any hint or advice would be appreciated, thank you. >> >> -- >> >> --- >> You received this message because you are subscribed to the Google Groups >> "theano-users" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to theano-users...@googlegroups.com <javascript:>. >> For more options, visit https://groups.google.com/d/optout. >> > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to theano-users+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
