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ReservoirPy: an Efficient and User-Friendly Library to Design Echo State Networks

Nathan Trouvain 1 Luca Pedrelli 1 Thanh Trung Dinh 1 Xavier Hinaut 1
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : We present a simple user-friendly library called ReservoirPy based on Python scientific modules. It provides a flexible interface to implement efficient Reservoir Computing (RC) architectures with a particular focus on Echo State Networks (ESN). Advanced features of ReservoirPy allow to improve up to 87.9% of computation time efficiency on a simple laptop compared to basic Python implementation. Overall, we provide tutorials for hyperparameters tuning, offline and online training, fast spectral initialization, parallel and sparse matrix computation on various tasks (MackeyGlass and audio recognition tasks). In particular, we provide graphical tools to easily explore hyperparameters using random search with the help of the hyperopt library.
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https://hal.inria.fr/hal-02595026
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Submitted on : Tuesday, August 25, 2020 - 1:32:00 AM
Last modification on : Friday, January 21, 2022 - 3:11:45 AM
Long-term archiving on: : Tuesday, December 1, 2020 - 5:56:53 PM

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  • HAL Id : hal-02595026, version 2

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Nathan Trouvain, Luca Pedrelli, Thanh Trung Dinh, Xavier Hinaut. ReservoirPy: an Efficient and User-Friendly Library to Design Echo State Networks. ICANN 2020 - 29th International Conference on Artificial Neural Networks, Sep 2020, Bratislava, Slovakia. ⟨hal-02595026v2⟩

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