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? -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
