You can try to implement it with tensor.alloc() and set_subtensor with
broadcasting.

this will work with gradient and on the GPU.

On Thu, May 3, 2018 at 7:02 PM Pascal Lamblin <[email protected]>
wrote:

> Oh, right.
>
> Then, I don't think it will be implemented.
> And I don't think RepeatOp is optimized to use the GPU anyway.
>
>
> Sorry about that
>
> On 2018-05-03 06:01 PM, Kristjan Arumae wrote:
> > No, it only works with two vectors.  2 Matrixes is not supported in
> > numpy either I don't think, since it is likely to mess up the output
> shape.
> >
> > On Thursday, May 3, 2018 at 4:03:15 PM UTC-4, Pascal Lamblin wrote:
> >
> >     Does it work with two matrices?
> >     If so, you can try to use dimshuffle to make v a "row" instead of a
> >     "vector".
> >
> >     On 2018-05-03 03:24 PM, Kristjan Arumae wrote:
> >      > An example of what I am doing:
> >      >
> >      > Here m is an fmatrix, and v is an ivector
> >      >
> >      > out = T.repeat(m, v, axis=0)
> >      >
> >      > The forward pass works fine, but there is no gradient code
> >     implemented.
> >      >
> >      > This works fine when both inputs are vectors but not as above.
> >      >
> >      > I am not familiar with theano enough to fill in the missing code
> in
> >      > grad() for class RepeatOp().  Does anyone have suggestions as to a
> >      > workaround?  I have not found anything even remotely helpful so
> far.
> >      > I've tried using tile with scan, but to no end.
> >      >
> >      > Thanks.
> >      >
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