Description:

The DeeBNet (Deep Belief Network) is an object oriented MATLAB toolbox to provide tools for conducting research using Deep Belief Networks. The toolbox has two packages with some classes and functions for managing data and sampling methods and also has some classes to define different RBMs and DBN.

DataClasses package has one class to manage training, testing and validation of data. The DataStore class has some useful functions such as normalize and shuffle functions for normalizing and shuffling of data. The second package includes the implementation of some different sampling methods. These sampling methods are Gibbs, CD, PCD and FEPCD, where FEPCD is our new sampling method.

The toolbox has also six types of RBM classes. The first one, RBM class, is an abstract class that defines all necessary functions (such as training method) and features (like sampler object) in all types of RBMs and therefore , we can't create an object from it. Other RBM classes are inherited from this abstract class. These RBMs are GenerativeRBM, DiscriminativeRBM, SparseRBM, SparseGenerativeRBM and SparseDiscriminativeRBM. The SparseRBM can use three different types of sparsity methods where one of them is our new method namely “normal sparse RBM method”.

In addition, some useful codes are implemented. These codes are the functions to read different datasets and different scripts showing how the toolbox can be used in different applications (MNIST for image recognition, ISOLET for speech recognition and 20 Newsgroups for text categorization) and can be applied to different problems (classification, feature extraction, data reconstructing, noise reduction, new data generation, etc).

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Supported Operating Systems: Platform Independent

Data Formats: Matlab

Tags: Classification, Deep Belief Networks, Feature Extraction, Artificial Neural Network, Matlab Toolbox, Restricted Boltzmann Machine

Archive: download here

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