Thank you Ralf and Kyle for your input! They sound really useful. I will try them out.
Again thanks a lot for introducing two hilarious libraries! -Shawn On Mon, Apr 7, 2014 at 3:51 PM, Ralf Gunter <[email protected]> wrote: > 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 -- Yuxiang "Shawn" Wang Gerling Research Lab University of Virginia [email protected] +1 (434) 284-0836 https://sites.google.com/a/virginia.edu/yw5aj/ ------------------------------------------------------------------------------ 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
