convolutionalRBM.m-master.zip_CRBM_受限波尔兹曼机 matlab_受限玻尔兹曼_玻尔兹曼机

#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](http://qipeng.me/software/convolutional-rbm.html). ## 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](mailto:qipeng.thu@gmail.com?subject=[CRBM%20Issue]) (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.