Initializing the weights of a MLP with the RBM weights

Question

I want to build a Deep Believe Network with scikit-learn. As I know one should train many Restricted Boltzmann Machines (RBM) individually. Then one should create a Multilayer Perceptron (MLP) that has the same number of layers as the number of (RBMs), and the weights of the MLP should be initialized with the weights of the RBMs. However I'm unable to find a way to get the weights of the RBMs from scikit-learn's BernoulliRBM. Also it doesn't seem to be a way also to initialize the weights of a MLP in scikit-learn.

Is there a way to do what I described?

Answer 1

scikit-learn does not currently have an MLP implemented which you can initialize via an RBM, but you can still access the weights which are stored in the components_ attribute and the bias which is stored in the intercept_hidden_ attribute.

If you're interested in using modern MLPs, torch7, pylearn2, and deepnet are all modern libraries and most of them contain pretraining routines like you describe.