Thanks guys, I've spent a bunch of time playing convnet sudoku and I'm converging towards agreement that there's no simple way to do it with tricks or reshapes. I think you might be able to do some sort of factorization where you do an elementwise multiply that spans the batch dimension but since I'm just exploring I think I'll go with Michael's solution. Thanks again!
-Andy On Tuesday, December 13, 2016 at 10:24:26 PM UTC, Pascal Lamblin wrote: > > Hi, > > Unfortunately, I don't think there is an easy way yet. > > If the batch size is small enough, you can try to define a "diagonal" > set of 3D filters and use 3D convolutions. > > Otherwise, if we have "grouped" convolutions, that might help, but I > think none of the current convolution back-ends implement that. > > On Tue, Dec 13, 2016, Andrew Brock wrote: > > Hi guys, > > > > I'm doing some work where I'd like to have a data-dependent > convolutional > > layer, where a different filterbank is used on each element of a given > > batch. This is easy when doing things with a single batch, but for > > minibatches I'd end up with a 5D Batch x Num_filters x Channels x H x W > > tensor, and I can't think of an easy way to apply this on a 4D Batch x > > Channel x H x W tensor. Is there some trick with backward passes, > > dimshuffles and 3D reshapes, or something else that anyone knows of that > > would give me an efficient way to do this? > > > > Thanks, > > > > Andy > > > > -- > > > > --- > > 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 [email protected] <javascript:>. > > For more options, visit https://groups.google.com/d/optout. > > > -- > Pascal > -- --- 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 [email protected]. For more options, visit https://groups.google.com/d/optout.
