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]. > 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.
