[MRG] Generic multi layer perceptron

Currently I am implementing `layers_coef_` to allow for any number of hidden layers.

This pull request is to implement the generic Multi-layer perceptron as part of the GSoC 2014 proposal. 

The expected time to finish this pull request is June 15

The goal is to extend Multi-layer Perceptron to support more than one hidden layer and to support having a pre-training phase (initializing weights through Restricted Boltzmann Machines) and a fine-tuning phase; and write its documentation.

This directly follows from this pull-request: https://github.com/scikit-learn/scikit-learn/pull/2120
`make_multilabel_classification` is yet to be fixed for this (@hamsal, is
it to be yours?)


On 2 August 2014 22:49, Issam H. Laradji <notifications@github.com> wrote:

>  In sklearn/neural_network/multilayer_perceptron.py:
>
> > +                             "to partial_fit.")
> > +        elif self.classes_ is not None and classes is not None:
> > +            if np.any(self.classes_ != np.unique(classes)):
> > +                raise ValueError("`classes` is not the same as on last call "
> > +                                 "to partial_fit.")
> > +        elif classes is not None:
> > +            self.classes_ = classes
> > +
> > +        if not hasattr(self, '_lbin'):
> > +            self._lbin = LabelBinarizer()
> > +            self._lbin._classes = classes
> > +
> > +        X, y = check_X_y(X, y, accept_sparse='csr')
> > +
> > +        # needs a better way to check multi-label instances
> > +        if isinstance(np.reshape(y, (-1, 1))[0][0], list):
>
> Thanks @jnothman <https://github.com/jnothman> . I read all about the
> issues with having lists of lists for multi-label instances - it makes
> sense why its support is being deprecated.
>
> I was having problems with make_multilabel_classification, but by setting
> return_indicator to True, it returned a friendly format for LabelBinarizer
> that worked nicely. Thanks :)
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/scikit-learn/scikit-learn/pull/3204/files#r15728880>.
>