python - Initializing the weights of a MLP with the RBM weights -

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I want to create a deep trust network with scikit-learn . As I know, many restricted Botanical Machines (RBMs) will be trained individually. After this one should make a multi-level polluton (MLP) in which the number of layers should be in the form of the same number (RBM), and the weight of the MLP should be started with the weight of RBM. However, I am unable to find a way to get RMB weight from BernoulliRBM to learn about education. Apart from this, it is not a way to start the weight of MLP in learning a scientific method.

Am I a way to describe it?

Scikit-learn does not currently implement a MLP which you can initialize through RBM , But you can still reach the weight that is stored in the components I attribute and bias which is stored in the intercept_hidden_ ​​attribute.

If you are interested in using modern MLP, torch7, pylearn2, and in depth, then there are all modern libraries and most of these are routine like you describe

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