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

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Book cover Artificial Neural Networks and Machine Learning – ICANN 2020 (ICANN 2020)

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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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Notes

  1. 1.

    See for instance the minimal version of Mantas Lukoševičius saved at https://mantas.info/code/simple_esn or reproduced in examples directory of ReservoirPy: https://github.com/neuronalX/reservoirpy/tree/master/examples.

  2. 2.

    Oger is no longer maintained; archived at https://github.com/neuronalX/Oger.

  3. 3.

    See for instance https://github.com/topics/echo-state-networks.

References

  1. Garofolo, J, et al.: Timit acoustic-phonetic continuous speech corpus. Linguistic Data Consortium LDC93S1 (1993)

    Google Scholar 

  2. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(Feb), 281–305 (2012)

    MathSciNet  MATH  Google Scholar 

  3. Bergstra, J., Yamins, D., Cox, D.D.: Hyperopt: a Python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of 12th SciPy Conference (2013)

    Google Scholar 

  4. Gallicchio, C., Micheli, A., Pedrelli, L.: Deep reservoir computing: a critical experimental analysis. Neurocomputing 268, 87–99 (2017)

    Article  Google Scholar 

  5. Gallicchio, C., Micheli, A., Pedrelli, L.: Fast spectral radius initialization for recurrent neural networks. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds.) INNSBDDL 2019. PINNS, vol. 1, pp. 380–390. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-16841-4_39

    Chapter  Google Scholar 

  6. Hinaut, X., Spranger, M.: Learning to parse grounded language using reservoir computing. In: IEEE ICDL-EpiRob, August 2019

    Google Scholar 

  7. Holzmann, G.: Efficient C++ library for analog reservoir computing neural networks (echo state networks). http://aureservoir.sourceforge.net (2007–2008)

  8. Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. Technical report 148, GNRCIT GMD, Bonn, Germany (2001)

    Google Scholar 

  9. Juven, A., Hinaut, X.: Cross-situational learning with reservoir computing for language acquisition modelling. In: IJCNN (2020)

    Google Scholar 

  10. Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127–149 (2009)

    Article  Google Scholar 

  11. Pedrelli, L., Hinaut, X.: Hierarchical-task reservoir for anytime POS tagging from continuous speech. In: IJCNN (2020)

    Google Scholar 

  12. Strock, A., Hinaut, X., Rougier, N.P.: A robust model of gated working memory. Neural Comput. 32(1), 153–181 (2020)

    Article  MathSciNet  Google Scholar 

  13. Sussillo, D., Abbott, L.: Generating coherent patterns of activity from chaotic neural networks. Neuron 63(4), 544–557 (2009)

    Article  Google Scholar 

  14. Verstraeten, D., Schrauwen, B., d’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Netw. 20(3), 391–403 (2007)

    Article  Google Scholar 

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Authors and Affiliations

  1. INRIA Bordeaux Sud-Ouest, Talence, France

    Nathan Trouvain, Luca Pedrelli, Thanh Trung Dinh & Xavier Hinaut

  2. LaBRI, Bordeaux INP, CNRS, UMR 5800, Talence, France

    Nathan Trouvain, Luca Pedrelli, Thanh Trung Dinh & Xavier Hinaut

  3. Institut des Maladies Neurodégénératives, Université de Bordeaux, CNRS, UMR 5293, Bordeaux, France

    Nathan Trouvain, Luca Pedrelli, Thanh Trung Dinh & Xavier Hinaut

Authors
  1. Nathan Trouvain
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  2. Luca Pedrelli
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  3. Thanh Trung Dinh
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  4. Xavier Hinaut
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Corresponding author

Correspondence to Xavier Hinaut .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark

    Paolo Masulli

  3. Department of Informatics, University of Hamburg, Hamburg, Germany

    Stefan Wermter

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Trouvain, N., Pedrelli, L., Dinh, T.T., Hinaut, X. (2020). ReservoirPy: An Efficient and User-Friendly Library to Design Echo State Networks. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12397. Springer, Cham. https://doi.org/10.1007/978-3-030-61616-8_40

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  • DOI: https://doi.org/10.1007/978-3-030-61616-8_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61615-1

  • Online ISBN: 978-3-030-61616-8

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