On Tue, May 15, 2012 at 10:31 PM, Andreas Mueller <[email protected]> wrote: > On the same topic: I'm not sure if we decided whether we want minibatch, > batch and online learning. > I have the feeling that it might be possible to do particular > optimizations for online learning, and this > is the algorithm that I favor the most. > > Comments? > > David M., what do you think?
Well, I am not sure what optimizations could be done for online learning, yet. At first I thought it would be possible to use SequentialDataset for online learning, but now I don't think it's a good idea to reimplement matrix operations that will be needed, when we have numpy. If we find optimizations that would make online learning faster than other options than I'd vote for it. But so far I think the batch_size argument is ok. > Btw, two comments on your current code: > I think this looks pretty good already. Atm, the tests are failing, though. > Also, I feel like using squared error for classification is a very bad habit > that for some reason survived the last 20 years in some dark corner. Well, the first test should not fail, it's just XOR, the second one is recognizing hand-written numbers and I don't expect it to be 100% successful, I am just using it as a simple benchmark. Thank you for pointing out what I thought about my Neural Networks course in university, they teach 20 year old things :-D > Did you compare timings and results against my implementation? > Once you are pretty sure that the code is correct, you should disable > the boundscheck > in cython, as this can improve speed a lot :) I haven't yet, will look at it. I have seen boundscheck and other options used in sgd_fast, will have to try them. Thanks David ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
