I'm really surprised that the old one is faster in multiple cores then the
new one. Can you provide a script that shot that? It could be just for some
shapes

Le 4 sept. 2016 12:41, <[email protected]> a écrit :

> Thank you nouiz! Great to know. Does that imply that the new conv2d
> doesn't use full parallelism capabilities as the old  one?
> BTW in both the experiments that I've run I've tested the new conv2d
> against the old with openblas and mkl-intel libraries.
> The results were the same the old one utilizes better the available cores.
> The new one fluctuates a lot. But the down side is that
> the old conv2d doesn't support arguments for strides and padding besides
> the pre-defined ones.
>
> Thanks again.
>
>
> On Thursday, September 1, 2016 at 4:55:05 PM UTC+1, nouiz wrote:
>>
>> They use different implementation. Make sure Theano use a parallel blas
>> library. The new conv2d use it for part of the parallelism.
>>
>> Le 25 août 2016 21:53, <[email protected]> a écrit :
>>
>>> Hi everyone,
>>>
>>> I've recently come across some weird behaviour regarding the new
>>> theano.tensor.nnet.conv2d
>>> and the old
>>> theano.tensor.nnet.conv.conv2d
>>> convolution functions.
>>>
>>> I have 2 different models one uses the old the other the new conv2d
>>> method.
>>> The difference between the two is that the the model that uses the new
>>> conv2d methods has more layers than the other one, plus that I've
>>> explicitly defined padding and stride.
>>>
>>> Other than that everything else is the same. Number of data, training
>>> algorithm, batchSize .... etc. pretty much the same.
>>>
>>> Once I execute them, the smaller model with the old conv2d method
>>> utilizes all the cores in my system ;) great.
>>> The bigger model with the new conv2d method doesn't, which is strange
>>> because in this case the bigger the model the more resources would need.
>>>
>>> Are there any differences in the way the two conv2d methods utilize
>>> openmp?
>>>
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