>> I recently posted a gist: https://gist.github.com/2061456 >> And there is also a branch by me: >> https://github.com/amueller/scikit-learn/tree/multilayer_perceptron >> > I'll look into it. One thing I noticed is that you assume only one > hidden layer. Is it feature? I think having one hidden layer makes the implementation more straight forward and is quite good in practice. Also it makes model-selection a lot easier. If there are strong opinions (and good reasons) for supporting multiple hidden layers, than we can also do that.
>> These would be good places to start. >> >> This implements SGD. >> My biggest wish would be to do the SGD in Cython, which >> should result in a major speedup. This can probably >> be done using the recently used helper classes from >> the linear SGD, which might need to be extended. >> > I've skipped after http://wiki.cython.org/tutorials/numpy and Cython > tutorial ( http://conference.scipy.org/proceedings/SciPy2009/paper_1/ > ) and it looks that it shouldn't be that hard to implement the > algorithm in Python and then add types and additional info to improve > speed of slow parts (main loop and propagations). > > Well the algorithm is implemented in the code I linked to ;) It is not fully tested but it should return reasonable results on the datasets included in the scikit. >> As far as deep learning goes: I agree, this is to much >> and I think it is at the moment not in the scope of the >> scikit. >> >> Apart from writing the SGD, there should be support for >> differnt loss-functions. In my current implementation, the >> hinge-loss support is not working yet. >> > I found loss functions in sgd_fast.pyx. Shouldn't they be used? > > These are only for two classes. Depending on how the two-class case will be handled by the network, they could be used there. Cheers, Andy ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
