In plain words: A free, openly shared toolkit for training neural networks that read sequences, built to run efficiently across several graphics cards and to handle both sound and images of handwriting. It was used to build successful entries in several speech and handwriting contests.
Abstract · RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks
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.
Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilya Kulikov, Ralf Schlüter, Hermann Ney
arXiv:1608.00895 · cs.LG, cs.CL, cs.NE · submitted Aug 2, 2016 · updated Jan 10, 2017
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