2011/11/4 Andreas Müller <[email protected]>: > Are you using pure Python at the moment? > Where can I find your code? And is the goal of your code to > be included in the scikits?
My goal is to improve on somebody else's result and get a paper published ;), but if the sklearn community can peer review and adopt the code I use to obtain that result, I'd be more than happy. This is more or less what I used: https://github.com/larsmans/scikit-learn/tree/mlperceptron Again, with weight vectors loaded from a Matlab file by hand, so no fit yet. > I think it is necessary to have minibatch learning and so I think > building that into the code from the beginning is good. Alright. >> Logistic activation functions seem fashionable; that's what Bishop and >> other textbooks use. I'm not sure if there's a big difference, but it >> seems to me that gradient computations might be slightly more >> efficient (guesswork, I admit). We can always add a steepness >> parameter later. > In my personal experience, tanh works better. LeCun uses tanh ;) That's always a good argument ;) > RPROP is very easy to implement. I use it in my lab all the time. > I have no personal experience with IRPROP-? How is that different > than IRPROP? What is RPROP+? Can you give me references? http://sci2s.ugr.es/keel/pdf/algorithm/articulo/2003-Neuro-Igel-IRprop+.pdf The difference between RPROP+ and - is that + does backtracking, so it needs more memory. In the Improved RPROP variant, + or - hardly makes any difference. -- Lars Buitinck Scientific programmer, ILPS University of Amsterdam ------------------------------------------------------------------------------ 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
