Skip to content
forked from rwth-i6/returnn

The RWTH extensible training framework for universal recurrent neural networks

License

Notifications You must be signed in to change notification settings

tazdriver/returnn

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

RETURNN development tree

RETURNN paper.

RETURNN - RWTH extensible training framework for universal recurrent neural networks, is a Theano-based implementation of modern recurrent neural network architectures. It is optimized for fast and reliable training of recurrent neural networks in a multi-GPU environment.

Features include:

  • Mini-batch training of feed-forward neural networks
  • Sequence-chunking based batch training for recurrent neural networks
  • Long short-term memory recurrent neural networks
  • Multidimensional LSTM (GPU only, there is no CPU version)
  • Memory management for large data sets
  • Work distribution across multiple devices

Please read the documentation for more information.

There are some example demos in /demos which work on artifically generated data, i.e. they should work as-is.

There are some real-world examples here.

Some benchmark setups against other frameworks can be found here. The results are in the RETURNN paper.

About

The RWTH extensible training framework for universal recurrent neural networks

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 93.7%
  • C++ 6.3%