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; Flexible and fast architecture which allows all kinds of encoder-attention-decoder models.
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References in zbMATH (referenced in 2 articles , 1 standard article )
Showing results 1 to 2 of 2.
- Albert Zeyer, Tamer Alkhouli, Hermann Ney: RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition (2018) arXiv
- Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilya Kulikov, Ralf Schlüter, Hermann Ney: RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks (2016) arXiv