Hi everybody. I was thinking about putting some work into making a multi layer perceptron implementation for sklearn. I think it would be a good addition to the other, mostly linear, classifiers in sklearn. Together with the decision trees / boosting that many people are working on at the moment, I think sklearn would cover most of the classifiers used today.
My question is: has anyone started with a mlp implementation yet? Or is there any code lying around that people think is already pretty good? I would try to keep it simple with support only for one hidden layer and do a pure python implementation to start with. I'm also open for any suggestions. My feature list would be: - online, minibatch and batch learning - vanilla gradient descent and rprop - l2 weight decay optional - tanh nonlinearities - a class for regression and one for classification - MSE and cross entropy (for classification only) loss functions I think that would be a reasonable amount of features and should be pretty easy to maintain. Cheers, Andy ------------------------------------------------------------------------------ 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
