RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks
Machine Learning
2017-01-11 v2 Computation and Language
Neural and Evolutionary Computing
Abstract
In this work we release our extensible and easily configurable neural network training software. It provides a rich set of functional layers with a particular focus on efficient training of recurrent neural network topologies on multiple GPUs. The source of the software package is public and freely available for academic research purposes and can be used as a framework or as a standalone tool which supports a flexible configuration. The software allows to train state-of-the-art deep bidirectional long short-term memory (LSTM) models on both one dimensional data like speech or two dimensional data like handwritten text and was used to develop successful submission systems in several evaluation campaigns.
Cite
@article{arxiv.1608.00895,
title = {RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks},
author = {Patrick Doetsch and Albert Zeyer and Paul Voigtlaender and Ilya Kulikov and Ralf Schlüter and Hermann Ney},
journal= {arXiv preprint arXiv:1608.00895},
year = {2017}
}