Reservoir Computing #3

Introduction to EchoTorch : A pyTorch-based Reservoir Computing framework

Introduction to Reservoir Computing #3 : EchoTorch
November 7, 2018 nschaetti

EchoTorch

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 can be integrated into deep architectures. EchoTorch gives two possible ways to train models :

  • Classical ESN training with Moore Penrose pseudo-inverse or LU decomposition;
  • pyTorch gradient descent optimizer;

This framework consists of:

  • echotorch.datasets : Pre-built datasets for common ESN tasks
  • echotorch.models : Generic pretrained ESN models
  • echotorch.transforms : Data transformations specific to echo state networks
  • echotorch.utils : Tools, functions and measures for echo state networks

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.

  • pyTorch
  • TorchVision

Installation

pip install EchoTorch

Or directly from GitHub.

License

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

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)

 

Nils Schaetti is a doctoral researcher in Switzerland specialised in machine learning and artificial intelligence.

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