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EchoTorch is a python module based on PyTorch to implement and test various flavours of Echo State Network models. EchoTorch is not intended to be put into production but for research purposes. As it is based on PyTorch, EchoTorch's layers are designed to be integrated into deep architectures for future work and research.

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Development status

PyPI - Python Version Documentation Status

Builds

Master

Build Status

Dev

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Index

This repository consists of:

Examples

Here is some examples of what you can do with EchoTorch.

Tutorials

In addition to examples, here are some Jupyter tutorials to learn how Reservoir Computing works.

Code and papers

Here are some experimences done with ESN and reproduced with EchoTorch :

Getting started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

You need to following package to install EchoTorch.

  • sphinx_bootstrap_theme
  • future
  • numpy
  • scipy
  • scikit-learn
  • matplotlib
  • torch==1.3.0
  • torchvision==0.4.1

Installation

pip install EchoTorch

Authors

License

This project is licensed under the GPLv3 License - see the LICENSE file for details.

Citing

If you find EchoTorch useful for an academic publication, then please use the following BibTeX to cite it:

@misc{echotorch,
  author = {Schaetti, Nils},
  title = {EchoTorch: Reservoir Computing with pyTorch},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/nschaetti/EchoTorch}},
}

A short introduction

Classical ESN training

You can simply create an ESN with the ESN or LiESN objects in the nn module.

esn = etnn.LiESN(
    input_dim,
    n_hidden,
    output_dim,
    spectral_radius,
    learning_algo='inv',
    leaky_rate=leaky_rate
)

Where

  • input_dim is the input dimensionality;
  • h_hidden is the size of the reservoir;
  • output_dim is the output dimensionality;
  • spectral_radius is the spectral radius with a default value of 0.9;
  • learning_algo allows you to choose with training algorithms to use. The possible values are inv, LU and sdg;

You now just have to give the ESN the inputs and the attended outputs.

for data in trainloader:
    # Inputs and outputs
    inputs, targets = data

    # To variable
    inputs, targets = Variable(inputs), Variable(targets)

    # Give the example to EchoTorch
    esn(inputs, targets)
# end for

After giving all examples to EchoTorch, you just have to call the finalize method.

esn.finalize()

The model is now trained and you can call the esn object to get a prediction.

predicted = esn(test_input)

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