On 11/04/2011 02:49 PM, Olivier Grisel wrote: > 2011/11/4 Andreas Müller <[email protected]>: >> 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. > In the past (before getting involved in scikit-learn) I had started an > unfinished library in pure C + python ctypes bindings for MLP and > stacked autoencoders. This is basically the same datastructure and > algorithms but one is supervised and the other is unsupervised. > > https://bitbucket.org/ogrisel/libsgd/wiki/Home > > I think it should be pretty straightforward to rewrite this in cython > directly. The important trick is to pre-allocate the memory buffer of > the minibatch size for both the hidden and output layers. > Why not wrap your C in cython? Then we could take advantage of your SSE code.
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