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In this work we release our extensible and easily configurable neural networktraining software. It provides a rich set of functional layers with aparticular focus on efficient training of recurrent neural network topologieson multiple GPUs. The source of the software package is public and freelyavailable for academic research purposes and can be used as a framework or as astandalone tool which supports a flexible configuration. The software allows totrain state-of-the-art deep bidirectional long short-term memory (LSTM) modelson both one dimensional data like speech or two dimensional data likehandwritten text and was used to develop successful submission systems inseveral evaluation campaigns.

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks