Hey All, I chose MLP because they were images and I have heard MLPs perform better. My application is detecting body parts from these images and therefore, the mapping would be pretty non-linear and this was my idea behind selecting MLP. Otherwise, I would have to engineer high dimension features by hand. I have 2030400 pixels and making higher dimensional features would require a lot more memory.
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? I am still a novice but I am fairly good with Python. I am taken aback by scikit's sheer beauty and simplicity. I would love to contribute code to it. Can you please tell me how I can get started? Thanks a lot! On Sat, Dec 3, 2016 at 11:40 PM, Andy <t3k...@gmail.com> wrote: > > > On 12/03/2016 05:29 AM, Gael Varoquaux wrote: > >> On Sat, Dec 03, 2016 at 03:08:00PM +0530, Alekh Karkada Ashok wrote: >> >>> I want use the Scikit-learn's MLPRegressor to map image to image. That >>> is I >>> have a numpy array of size [1000,2030400] (1000 samples, 76800x3 (RGB) >>> pixels). >>> Corresponding labelled images I have. Therefore Y is also [1000,230400]. >>> But >>> according to documentation: >>> >> 1 thousands samples and 2030 thousands features: you are using the wrong >> tool, I multi-layer perceptron model will be too complex and overfit in >> these settings. I would suggest a ridge. >> >> >> These are images! Don't use ridge, use a convolutional neural network. > Our MLP is not convolutional, it will not be useful. > There is a lot of material out there on how to use covolutional neural > networks > for image labeling (it looks like you have one label per pixel, not per > image) > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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