keras: R Interface to 'Keras'

Interface to 'Keras' <>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.

Depends: R (≥ 3.2)
Imports: generics (≥ 0.0.1), reticulate (≥ 1.10), tensorflow (≥ 2.0.0), tfruns (≥ 1.0), magrittr, zeallot, methods, R6
Suggests: ggplot2, testthat (≥ 2.1.0), knitr, rmarkdown, tfdatasets, jpeg
Published: 2019-10-08
Author: Daniel Falbel [ctb, cph, cre], JJ Allaire [aut, cph], Fran├žois Chollet [aut, cph], RStudio [ctb, cph, fnd], Google [ctb, cph, fnd], Yuan Tang ORCID iD [ctb, cph], Wouter Van Der Bijl [ctb, cph], Martin Studer [ctb, cph], Sigrid Keydana [ctb]
Maintainer: Daniel Falbel <daniel at>
License: MIT + file LICENSE
NeedsCompilation: no
SystemRequirements: Keras >= 2.0 (
Materials: NEWS
In views: HighPerformanceComputing, ModelDeployment
CRAN checks: keras results


Reference manual: keras.pdf
Vignettes: About Keras Layers
About Keras Models
Using Pre-Trained Models
Keras Backend
Writing Custom Keras Layers
Writing Custom Keras Models
Writing Custom Keras Wrappers
Keras with eager execution
Frequently Asked Questions
Guide to the Functional API
Getting Started with Keras
Guide to Keras Basics
Saving and serializing models
Guide to the Sequential Model
Training Callbacks
Training Visualization
Tutorial: Basic Classification
Tutorial: Basic Regression
Tutorial: Text Classification
Tutorial: Overfitting and Underfitting
Tutorial: Save and Restore Models
Why Use Keras?
Package source: keras_2.2.5.0.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: keras_2.2.5.0.tgz, r-oldrel: keras_2.2.5.0.tgz
Old sources: keras archive

Reverse dependencies:

Reverse imports: autokeras, downscaledl, embed, gnn, LilRhino, resautonet, ruta, tfprobability
Reverse suggests: bamlss, cloudml, condvis2, dimRed, drake, iml, kerastuneR, lime, mlflow, modelplotr, OSTSC, parsnip, pdp, reinforcelearn, RNAmodR.ML, tensorflow, tfautograph, tfdatasets, tfhub, vip


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