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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