No, I am not saying it is better than CNN, but my images aren't real-life images but computer generated silhouettes. So CNN seemed to be overkill. I'll revisit CNN. I resized the images and converted it to grayscale. Now I am feeding [1,4800] now and I am getting good output with MLP. I looped over all my images and used partial_fit to train each one. I didn't get what you meant by MLPClassifier doesn't support multi-output. Thanks for the help!
On Sun, Dec 4, 2016 at 2:11 AM, Andy <t3k...@gmail.com> wrote: > > > On 12/03/2016 03:10 PM, Alekh Karkada Ashok wrote: > >> >> Hey All, >> >> I chose MLP because they were images and I have heard MLPs perform better. >> > Better than a convolutional neural net? Whoever told you that was wrong. I > usually don't make absolute statements like this, but this is something > that is pretty certain. > > >> Where do you want to me to open the issue? GitHub? I don't think the >> error is only in documentation. Because when Y is [2030400,1] there is no >> MemoryError (treated as 2030400 samples with a single feature) and when I >> try to fit [1,2030400] it throws MemoryError. If the case was memory, both >> should have thrown the error right? >> > MLPClassifier actually supports multi-label classification (which is not > documented correctly and I made an issue here: > https://github.com/scikit-learn/scikit-learn/issues/7972) > MLPClassifier does not support multi-output (multi-class multi-output), > which is probably what you want. > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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