[scikit-learn] Fwd: Scikit-learn MLPRegressor Help
Alekh Karkada Ashok
alekhka at gmail.com
Sat Dec 3 17:00:04 EST 2016
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 <t3kcit at 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:
> MLPClassifier does not support multi-output (multi-class multi-output),
> which is probably what you want.
> scikit-learn mailing list
> scikit-learn at python.org
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