Is it possible to see the optimized graph than, or somehow get things
identified that they are reused on the graph? If I make a picture with
pydotprint there are still 2 separate nodes with the sigmoid, while I want
to have a graph where there is only one.
On Sunday, 21 May 2017 00:59:34 UTC+1, 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 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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