[scikit-learn] Fwd: Scikit-learn MLPRegressor Help

[federico vaggi]

As long as the feature ordering has a meaningful spatial component (as is
almost always the case when you are dealing with raw pixels as features)
CNNs will almost always be better.  CNNs actually have a lot fewer
parameters than MLPs (depending on architecture of course) because of
weight sharing among the parameters of the convolutional kernel within a
feature map.

On Sat, 3 Dec 2016 at 23:00 Alekh Karkada Ashok <alekhka at gmail.com> wrote:

> 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:
> 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.
>
>
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