pyrenn: A recurrent neural network toolbox for python and matlab¶
|Maintainer:||Dennis Atabay, <firstname.lastname@example.org>|
|Organization:||Institute for Energy Economy and Application Technology, Technische Universität München|
|Date:||Jun 30, 2018|
|Copyright:||This documentation is licensed under a Creative Commons Attribution 4.0 International license.|
This documentation contains the following pages:
- 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
- download or clone (with git) this repository to a directory of your choice.
- Run the given examples in the examples folder.
- Create your own neural network.