RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization
This work addresses the adoption lag of reservoir computers for researchers and practitioners by providing a unified software package, though it is incremental as it builds on existing RC methods with automation.
The authors tackled the problem of reservoir computers (RCs) being sensitive to hyper-parameters and lacking automated tuning tools, by introducing RcTorch, a PyTorch-based package with automated hyper-parameter optimization, and demonstrated its utility by predicting the complex dynamics of a driven pendulum.
Reservoir computers (RCs) are among the fastest to train of all neural networks, especially when they are compared to other recurrent neural networks. RC has this advantage while still handling sequential data exceptionally well. However, RC adoption has lagged other neural network models because of the model's sensitivity to its hyper-parameters (HPs). A modern unified software package that automatically tunes these parameters is missing from the literature. Manually tuning these numbers is very difficult, and the cost of traditional grid search methods grows exponentially with the number of HPs considered, discouraging the use of the RC and limiting the complexity of the RC models which can be devised. We address these problems by introducing RcTorch, a PyTorch based RC neural network package with automated HP tuning. Herein, we demonstrate the utility of RcTorch by using it to predict the complex dynamics of a driven pendulum being acted upon by varying forces. This work includes coding examples. Example Python Jupyter notebooks can be found on our GitHub repository https://github.com/blindedjoy/RcTorch and documentation can be found at https://rctorch.readthedocs.io/.