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
- Memory management for large data sets
- Work distribution across multiple devices
There is some Sphinx documentation in /docs,
but mostly in the code itself.
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.