[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
Was working on this and found it to be a really cool idea that could avoid packing and unpacking of the parameters. :)
But what if there is zero element in one of the parameters ? then `.data` will not return all the elements since it ignores the zeros.
I am facing this issue right now while storing the parameters as `csr_matrix`. 
Thanks