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] <javascript:>> 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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