> 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 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
> | |<TensorType(float32, matrix)> [id C]
> | |Elemwise{scalar_sigmoid} [id D] '' 1
> | |<TensorType(float32, matrix)> [id E]
> |Elemwise{sub} [id F] '' 4
> |InplaceDimShuffle{x,x} [id G] '' 2
> | |TensorConstant{1.0} [id H]
> |Elemwise{scalar_sigmoid} [id I] '' 0
> |<TensorType(float32, matrix)> [id E]
>
> My question is why does it compute the Sigmoid 2 times, when it can just
> reuse that computation? Or if it does this how can I notice it on the
> graph. I have not switched any of the optimisations.
>
>
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