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.
Torch @cite_0 uses the Lua programming language and consists of many flexible and modular components that were developed by the community. In contrast to Theano, Torch does not use symbolic expressions and all calculations are done explicitly.
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Keywords: learning Reference EPFL-REPORT-82802 URL: http: publications.idiap.ch downloads reports 2002 rr02-46.pdf Record created on 2006-03-10, modified on 2017-05-10