A MATLAB / MEX / CUDA-MEX implementation of Convolutional Restricted Boltzmann Machines.
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README.md

#convolutionalRBM.m

A MATLAB / MEX / CUDA-MEX implementation of Convolutional Restricted Boltzmann Machines.

General Information

Please refer to the project introduction page on my website.

Current status (updated: Feb. 7, 2014)

The current version contains a tested CPU version only. The GPU code in this version is incompatible with the CPU implementation, and may result in memory leakage or other issues. The GPU version is still in development. Any updates will be posted here.

The development of this project is active. The data structures and interfaces are subject to change, and the documentation in the .m files will be updated during development.

CUDA compilation support for multiple platforms is under construction, and a documentation on how to setup nvmex on multiple platforms will be available after the code support is ready.

For general information on functions in this project, try help function_name, as most .m files in this project are self-documented.

Recent FAQ

I'm sincerely sorry if I did not respond in a timely manner on GitHub. In this case, the most efficient way to reach me is via Email (Please begin your email subject with "[CRBM Issue]", which should have automatically added if you follow this link).

Here's some questions from recent comments

  • How do I get the data and parameters for trainCRBM?

You'll have to download a dataset on your own, and transform it to a format that is compatible with trainCRBM. For format of the input, try using the MATLAB help command on the function.

As for the param parameter in that function, try the getparams function for an example.

  • What is the oldModel parameter in trainCRBM?

Firstly, this parameter is optional. That is, you can simply ignore it when you first train your CRBM.

This parameter sort of a fail-safe. As is known to researchers, CDBN's can take a relatively long time to train, and there's usually no guarantee for the system state of your machine running the traning process. Therefore, in my implementation, the model parameters are saved to a .mat file periodically during the training process.

In case your training was interrupted accidentally, you can load the intermediate model from the fail-safe .mat file, and use the model there as the oldModel parameter in this function. The training process will instantly continue based on the saved progress.