This is a small Python library that contains code for using and training Restricted Boltzmann Machines (RBMs), the basic building blocks for many types of deep belief networks. Variations available include the "standard" RBM (with optional sparsity-based hidden layer learning); the temporal net introduced by Taylor, Hinton & Roweis; and convolutional nets with probabilistic max-pooling described by Lee, Grosse, Ranganath & Ng.
Mostly the code is being used for research in our lab. Hopefully others will find it instructive, and maybe even useful !
Just install using the included setup script :
python setup.py install
Or you can install the package from the internets using pip :
pip install lmj.rbm
This library is definitely very alpha; so far I just have one main test that encodes image data. To try things out, first install glumpy :
pip install glumpy
Then run the test :
python test/images.py /path/to/my/images*.jpg
If you're feeling overconfident, go ahead and try out the gaussian visible units :
python test/images.py \ --batch-size 257 \ --l2 0.0001 \ --learning-rate 0.2 \ --momentum 0.5 \ --sparsity 0.01 \ --gaussian /path/to/my/images*.jpg
The learning parameters are squirrely, but if things go right you should see a number of images show up on your screen that represent the "basis functions" that the network has learned when trying to auto-encode the images you are feeding it.
Please fork and contribute if you find this code at all useful !
(The MIT License)
Copyright (c) 2011 Leif Johnson firstname.lastname@example.org
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the 'Software'), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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