pyrenn

pyrenn is a recurrent neural network toolbox for Python and Matlab.

Features

pyrenn allows to create a wide range of (recurrent) neural network configurations
It is very easy to create, train and use neural networks
It uses the Levenberg–Marquardt algorithm (a second-order Quasi-Newton optimization method) for training, which is much faster than first-order methods like gradient descent. In the matlab version additionally the Broyden–Fletcher–Goldfarb–Shanno algorithm is implemented
The python version is written in pure python and numpy and the matlab version in pure matlab (no toolboxes needed)
Real-Time Recurrent Learning (RTRL) algorithm and Backpropagation Through Time (BPTT) algorithm are implemented and can be used to implement further training algorithms
It comes with various examples which show how to create, train and use the neural network

Installation

Install with pip (python only)

From your command line, run:

pip install pyrenn

Install manually

download or clone (with git) this repository to a directory of your choice.
- Python: Copy the pyrenn.py file in the python folder to a directory which is already in python's search path or add the python folder to python's search path (sys.path) (how to)
- Matlab: Add the matlab folder to Matlab's search path (how to)

Get Started

Run the given examples in the examples folder.
Read the documenation and create your own neural network

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/

pizzooid/pyrenn