If you look at the error the shapes don't match. the conv_out is 1x32x16x16
while the bias is 1x1x1x32.
I guess your bias you did wrong the dimshuffle.
On Saturday, 8 July 2017 01:53:58 UTC+1, zxzh...@gmail.com wrote:
>
> conv_out is the output of dnn.dnn_conv. I tried to add the bias to the
>
So the scan checkpointing seems very ineteresting from the prespective that
it can be used for things like learning-to-learn.
However, my question is can we tell Theano which part of each N-th
iteration it to store and which not? For instance in the learning-to-learn
framework where we unroll
of
> theano.function). Then, feed pygpu.gpuarray.GpuArray object directly to
> the compiled function. pygpu.gpuarray.asarray can be used to move numpy
> array to GPU.
>
> On Tuesday, May 9, 2017 at 5:01:42 PM UTC+8, Alexander Botev wrote:
>>
>> Actually one thing I've ju
So recently I was wondering if there is any way that after compiling a
theano function, rather than taking numpy arrays / native lists / native
numbers it can accept as an input something like a libgpuarray or anything
else that lives on the GPU. However, I know that in the computation graph
So I was wondering if there is any significant difference between having a
single MRG_RandomStreams or several of them?
Particularly, I'm used to having one single stream, such that I can easily
set it and recover top to bottom the exact behaviour as previous iterations.
However, I was just
I have the following code:
>>> a = T.fmatrix()
>>> b = T.sqr(a)
>>> c = T.nnet.sigmoid(a)
>>> g = T.fmatrix()
>>> d = T.Lop(c, a, g)
>>> f = theano.function([a, g], d)
Using debug print I get:
>>> theano.printing.debugprint(f)
Elemwise{mul} [id A] '' 5
|Elemwise{mul} [id B] '' 3
|
, Adam Becker wrote:
>
> > when it can just reuse that computation
>
> That's what optimization does. Try running it with device=cpu and
> optimizer=fast_run
>
> On Saturday, May 20, 2017 at 11:55:19 PM UTC+8, Alexander Botev wrote:
>>
>> I have the fol
I think pip is the best in anaconda if you want the bleeding edge theano.
Otherwise just do
conda install theano pygpu
On Monday, 29 May 2017 14:44:24 UTC+1, Chuck Anderson wrote:
>
> What is the best way to install theano within an anaconda distribution of
> python 3.6 on linux?
>
> Thank
Hi to all Theano users.
So we had this discussion on github about Theano T-shirts
(https://github.com/Theano/Theano/issues/5984).
As we would welcome any interesting suggestions you can think for Theano!
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