Ah, my apologies, I vaguely remembered reading about opencl somewhere in the theano docs...
I stand corrected. 2014-04-07 14:42 GMT-05:00 Kyle Kastner <[email protected]>: > Also, I think pylearn2 and theano-nets only support CUDA cards (Nvidia), > since Teahno only supports CUDA or CPU to my knowledge. There is some > discussion of supporting an OpenCL backend > (https://groups.google.com/forum/#!msg/theano-dev/NhRUhWA6xzo/lYrIIOlw4D8J), > but I don't know whether that work was ever completed. Maybe someone else > has a better knowledge of this. > > > On Mon, Apr 7, 2014 at 2:39 PM, Kyle Kastner <[email protected]> wrote: >> >> You can also use the python interface to pylearn2, rather than the yaml. >> If you are interested in examples of the python interface for pylearn2, I >> have some examples (I greatly prefer the python interface, but to each their >> own): >> >> >> https://github.com/kastnerkyle/pylearn2-practice/blob/master/cifar10_train.py >> shows how to build a network and test using a pylearn2 builtin dataset >> >> >> https://github.com/kastnerkyle/kaggle-dogs-vs-cats/blob/master/kaggle_train.py >> This shows how to use scikit-learn's train-test split to create training >> and testing classes for new datasets in pylearn2 format. x and y are both >> 2D. Rows are samples, columns are features for x. y generally needs to be a >> "one hot" label matrix for classification, but will be a regression target >> for RMSE/regression tasks. >> >> In general, it is pretty easy to wrap your data into a pylearn2 compatible >> format, though doing "raw" input for convolutional nets can be tricky. I >> have an example of reading pngs from Kaggle's CIFAR10 competition into >> pylearn2 and using a convnet here: >> https://github.com/kastnerkyle/kaggle-cifar10 >> >> All that being said, theano-nets *can* be easier to start with for those >> who are new to neural networks, as it is a little less roll-your-own. >> >> I have had success using both. >> >> Kyle >> >> >> On Mon, Apr 7, 2014 at 11:48 AM, Ralf Gunter <[email protected]> wrote: >>> >>> Two libraries[1,2] come to mind that can additionally support >>> accelerators through opencl. Just take note that it can take a bit to >>> familiarize yourself with pylearn2, especially because they seem >>> adamant in doing everything through yaml scripts. >>> >>> [1] -- https://github.com/lmjohns3/theano-nets (see e.g. >>> examples/xor-classfier.py) >>> [2] -- http://deeplearning.net/software/pylearn2 >>> >>> 2014-04-07 11:18 GMT-05:00 Yuxiang Wang <[email protected]>: >>> > Dear all, >>> > >>> > I am not entirely sure whether this is the best place to post this, >>> > and please do excuse me if this is not the perfect list for this >>> > question. >>> > >>> > Is there any python packages for neural networks for regression >>> > (instead of classification)? >>> > >>> > Any help would be appreciated. Thanks! >>> > >>> > -Shawn >>> > >>> > >>> > ------------------------------------------------------------------------------ >>> > Put Bad Developers to Shame >>> > Dominate Development with Jenkins Continuous Integration >>> > Continuously Automate Build, Test & Deployment >>> > Start a new project now. Try Jenkins in the cloud. >>> > http://p.sf.net/sfu/13600_Cloudbees_APR >>> > _______________________________________________ >>> > Scikit-learn-general mailing list >>> > [email protected] >>> > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >>> >>> ------------------------------------------------------------------------------ >>> Put Bad Developers to Shame >>> Dominate Development with Jenkins Continuous Integration >>> Continuously Automate Build, Test & Deployment >>> Start a new project now. Try Jenkins in the cloud. >>> http://p.sf.net/sfu/13600_Cloudbees_APR >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> [email protected] >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> > > > ------------------------------------------------------------------------------ > Put Bad Developers to Shame > Dominate Development with Jenkins Continuous Integration > Continuously Automate Build, Test & Deployment > Start a new project now. Try Jenkins in the cloud. > http://p.sf.net/sfu/13600_Cloudbees > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ Put Bad Developers to Shame Dominate Development with Jenkins Continuous Integration Continuously Automate Build, Test & Deployment Start a new project now. Try Jenkins in the cloud. http://p.sf.net/sfu/13600_Cloudbees _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
