Actually Olivier and me added some things to the MLP since then, and we
still want to add early stopping and Nesterov's momentum before merging it:
https://github.com/scikit-learn/scikit-learn/pull/3939
On 04/07/2015 07:16 AM, Joel Nothman wrote:
Issam Laradji implemented a multilayer perceptron and extreme learning
machines for last year's GSoC. Both are awaiting final reviews before
being merged. They should be functional and can be found in the Issue
Tracker.
On 7 April 2015 at 21:09, Vlad Ionescu <ionescu.vl...@gmail.com
<mailto:ionescu.vl...@gmail.com>> wrote:
Hello,
I was wondering why there isn't a classic neural network
implementation in scikit-learn (a multilayer perceptron). This
could have varying levels of complexity: it could be hardcoded to
just one hidden layer, allowing one to specify the type of neurons
in it (sigmoid, tanh, rectified linear etc.), the learning rate
and values for weight decay and momentum.
It could also be made to accept multiple hidden layers, with the
ability to specify the number of neurons and their type for each one.
Has this been considered before but no one has gotten around to
it? Would it be of interest for you?
There are of course more sophisticated methods that would be nice
to have as well. I'm only asking about the basic type because that
is what I currently would be willing to help with, but it would be
great if more were under consideration.
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