On Fri, Nov 4, 2011 at 10:49 AM, Andreas Müller <[email protected]> wrote: > On 11/04/2011 03:42 PM, Alexandre Passos wrote: >> On Fri, Nov 4, 2011 at 10:34, Lars Buitinck <[email protected]> wrote: >>> 2011/11/4 Alexandre Passos <[email protected]>: >>>> I have a question: why not just use Theano for this? I doubt that we >>>> can write neural network code that's as fast as their automatically >>>> generated code. >>> Would that mean an extra run-time dependency? >> >> Yes, as theano needs a compiler (gcc or nvcc if you want to use cuda) >> available at run time, but even still it's faster even than most >> hand-coded implementations of neural networks. James Bergstra reads >> this list occasionally, and he's one of the main people behind theano, >> so he can give more info here. >> >> > I think sklearn does not aim at beating cuda implementations. > For using theano: that's a huge and imho unnecessary dependency. > For a simple mlp, I think theano will not beat a hand implemented version. > Afaik, torch7 is faster than theano for cnns and mlps and there > is no compilation of algorithms there. If you want to vary your > implementation > a lot and want to do fancy things, theano is probably faster. > But I thought more about an easy to use classifier.
Yes Theano is a huge dependency. Yes there is no MLP implementation in Theano, but there is the Deep Learning Tutorial[1] that have one. It also have implementation for Logistic Regression, Deep Convolutional Network, Stacked Denoising Auto-Encoders, Restricted Boltzmann Machines and Deep Belief Networks. Those implementation are in a tutorial format. They are not in a librarie with an easy to use interface like what scikit.learn use. But many people already modified them to there need. We want from a long time to have an easier interface, but we don't know how well to do automatically the hyper-parameter selection. I don't have a good understanding of scikit.learn, but I think that all the hyper-parameter selection is a hot research topic for now. How do you plan to include this in the current scikit.learn interface of the fit method? About torch7 being faster then Theano. I have heard that a few times, but never see the papers, numbers, code or whatever subtential for this. I would love to have any number with something about that. Do you have some? But don't forget that in the Theano framework, we can just implement all the trick that other people used to beat Theano. So if torch7 is faster in some case, this will tell us where we can make Theano faster! Can you tell us more about the comparison you refer to? Just a side note. I don't imply the comparison you refer to is biased, but benchmarking is VERY HARD. So I like to have information on how the comparison is done. We tried to make the Theano comparison as fair as we could at that time. We spend days compiling each applications with the same blas and other stuff like that. But since them torch have new version released. Thanks and I hope to have more info on the comparison people used to tell that torch7 is faster then Theano and how you plan to work around the hyper-paramete selection problem. That would be very valuable to every body I think. Frédéric Bastien [1] http://deeplearning.net/tutorial/ p.s. I'm one of the core Theano developer. ------------------------------------------------------------------------------ RSA(R) Conference 2012 Save $700 by Nov 18 Register now http://p.sf.net/sfu/rsa-sfdev2dev1 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
