deebn

A Clojure library implementing a Deep Belief Network using Restricted Boltzmann Machines, based on Geoffery Hinton's work. This library is the result of my thesis research into deep learning methods.

"Installation"

deebn is available for download or usage through your favorite dependency management tool from Clojars:

Capabilities

There are a few types of model that you can build and train, either for classification or as components of other models:

• Restricted Boltzmann Machine
  • can be used as a component of a Deep Belief Network, or as a standalone discriminatory classifer
    Hyper-parameters:
    • learning rate
    • initial momentum
    • momentum (used after 'momentum-delay' epochs)
    • momentum-delay
    • batch-size
    • epochs
    • gap-delay (epochs to wait before testing for early stopping)
    • gap-stop-delay (consecutive positive energy gap epochs that initiate an early stop)
• Deep Belief Network (composed of layers of RBMs)
  • Can be used to pre-train a Deep Neural Network, or as a discriminatory classifier (Note: a classification DBN is not fine-tuned - performance is sastifactory but not optimal)
    Hyper-parameters:
    • whether to use activations rather than samples from hidden layers when propagating to the next layer
• Deep Neural Network
  • Initialized from a pre-trained DBN, with an additional logistic regression layer added
  • Network output is a softmax unit
  • Logistic regression unit is pre-trained with output from the DBN before moving to a full backprop training regimen
    Hyper-parameters:
    • batch-size
    • epochs
    • learning rate
    • lambda - L2 regularization (weight decay) parameter

Usage

The core namespace aims to offer examples of using the library. The mnist namespace offers examples for bringing in datasets (in this case the MNIST dataset).

License

Copyright © 2014 Chris Sims

Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.