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 I wrote the code to better understand the underlying algorithms. I don't use it for anything at the moment, having moved on to using primarily Theano with [networks of rectified linear neurons][http://www.csri.utoronto.ca/~hinton/absps/reluICML.pdf] (PDF). Still, there seems to be some interest in RBMs, so hopefully others will find this package 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, clone the source for this package and install glumpy :
pip install glumpy
Then download the MNIST digits data from http://yann.lecun.com/exdb/mnist/ --
you'll need both the train-*-images.ubyte.gz and train-*-labels.ubyte.gz
files. Then run the test :
python test/mnist.py \
--images *-images.ubyte.gz \
--labels *-labels.ubyte.gz
If you're feeling overconfident, go ahead and try out the gaussian visible units :
python test/mnist.py \
--images *-images.ubyte.gz \
--labels *-labels.ubyte.gz \
--batch-size 257 \
--l2 0.0001 \
--learning-rate 0.2 \
--momentum 0.5 \
--sparsity 0.01 \
--gaussian
The learning parameters can be a bit 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 MNIST images you are feeding it.
You can also try running the test script with --conv to try out a
convolutional filterbank, but I'm not confident that the conv net test is
working correctly. Anyway, if you're thinking of using conv nets for a project,
please have a look at Theano, or for a highly-tuned GPU/C++ implementation,
https://code.google.com/p/cuda-convnet/ (by
[Alex Krizhevsky][www.cs.toronto.edu/~kriz/]).
(The MIT License)
Copyright (c) 2011 Leif Johnson leif@leifjohnson.net
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