MatNN: An NN framework for MATLAB

MatNN is a MATLAB framework for neural network training and testing. It aims to provide the similarity of Caffe, and elastic workflow of MatConvNet.

This toolbox requires MatConvNet to be installed in your system and added to your MATLAB path.

Features

MatNN provides some features you may familiar with Caffe or other CNN tools:

• Weight Sharing
• Multiple Losses
• Custom data sampling algorithm
• Custom layer with custom parameters/weights/loss/...
• Multiple GPUs support

Note that we don't provide data layers, you should design your data sampling and fetching routines.

Functionality

MatNN uses the CUDA/C++ code from MatConvNet to make your training progress more efficient, and the entire framework is built on top of MatConvNet.

Goal

• Minimal reuqirement of external libraries. We will provide a pure matlab code version and a matlab+cuda kernel version, so your code is portable (no need to compile external libraries). And also include the basic version based on MatConvNet. Because you can easily customize a layer, so any exists libraries, like CUDA/cuBlas/cuDNN can be added to your workflow if you want. A cuBlas/cuDNN version will be considered after this project is out of beta. Note that even the pure matlab version will have GPU support from parallel computing toolbox of Matlab.
• Provide the elastic workflow and maintain the computation efficiency. We will separate each core functions into modules so that you can do less work to make them fit your need.
• Test your ideas and validate them quickly. Although learning other tools/programming languages may not be a problem, use you exists matlab code with MatNN is time reserved compared to learn a new language. We provide familiar definition of Caffe, if you have learned it, then you can get started quickly.

Installation

Prerequisite: Parallel computing toolbox for Matlab

1. Download source code
2. Install MatConvNet, and set matlab path
3. (optional) use NVCC to compile .cu code into .ptx
4. Done!

License

MatNN is under the Simplified BSD License. If you use MatNN in your work, please refer to the homepage of this project.