Metadata-Version: 1.1
Name: lmj.rbm
Version: 0.1.1
Summary: A library of Restricted Boltzmann Machines
Home-page: http://github.com/lmjohns3/py-rbm/
Author: Leif Johnson
Author-email: leif@leifjohnson.net
License: MIT
Description: # py-rbm
        
        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 !
        
        [Taylor, Hinton & Roweis]: http://www.cs.nyu.edu/~gwtaylor/publications/nips2006mhmublv/
        [Lee, Grosse, Ranganath & Ng]: http://cacm.acm.org/magazines/2011/10/131415-unsupervised-learning-of-hierarchical-representations-with-convolutional-deep-belief-networks/fulltext
        
        ## Installation
        
        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
        
        ## Testing
        
        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 !
        
        [glumpy]: http://code.google.com/p/glumpy/
        
        ## License
        
        (The MIT License)
        
        Copyright (c) 2011 Leif Johnson <leif@leifjohnson.net>
        
        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.
        
        THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
        FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
        COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
        IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
        CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
        
Keywords: deep-belief-network restricted-boltzmann-machine machine-learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
