Welcome to RETURNN

RETURNN paper.

RETURNN - RWTH extensible training framework for universal recurrent neural networks, is a Theano/TensorFlow-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 including our own fast CUDA kernel
  • Multidimensional LSTM (GPU only, there is no CPU version)
  • Memory management for large data sets
  • Work distribution across multiple devices

See Basic usage and Technological overview.

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.