kindling

Package overview

Title: Higher-Level Interface of ‘torch’ Package to Auto-Train Neural Networks

Whether you’re generating neural network architecture expressions or directly fitting/training models, {kindling} minimizes boilerplate code while preserving {torch}. Since this package uses {torch} as its backend, GPU acceleration is supported.

{kindling} also bridges the gap between {torch} and {tidymodels}. It works seamlessly with {parsnip}, {recipes}, and {workflows} to bring deep learning into your existing {tidymodels} modeling pipeline. This enables a streamlined interface for building, training, and tuning deep learning models within the familiar {tidymodels} ecosystem.

Main Features

• Code generation of {torch} expression

• Multiple architectures available

  • Base models interface: feedforward networks (MLP/DNN/FFNN) and recurrent variants (RNN, LSTM, GRU)
  • Generalized neural network trainer that has the same topology as MLPs

• Native support for R ML workflows and pipelines (currently {tidymodels}; {mlr3} planned)

• Fine-grained control over network depth, layer sizes, and activation functions

• GPU acceleration support via {torch} tensors

Installation

You can install {kindling} on CRAN:

install.packages('kindling')

Or install the development version from GitHub:

# install.packages("pak")
# pak::pak("joshuamarie/kindling")
install.packages("kindling", repos = c('https://joshuamarie.r-universe.dev', 'https://cloud.r-project.org'))
## devtools::install_github("joshuamarie/kindling")

Learn more

• Getting Started with kindling
• Tuning Capabilities
• Custom Activation Function
• Special Cases: Linear and Logistic Regression
• Similar Packages and Comparison

References

Falbel D, Luraschi J (2023). torch: Tensors and Neural Networks with ‘GPU’ Acceleration. R package version 0.13.0, https://torch.mlverse.org, https://github.com/mlverse/torch.

Wickham H (2019). Advanced R, 2nd edition. Chapman and Hall/CRC. ISBN 978-0815384571, https://adv-r.hadley.nz/.

Goodfellow I, Bengio Y, Courville A (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/.

Citation

If you use {kindling} in a publication, please cite it. Run citation("kindling") in R to get the current citation, or see the CITATION file.

License

MIT + file LICENSE

Code of Conduct

Please note that the kindling project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.