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: 2020-05-19
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: Using Pre-Trained Models
Writing Custom Keras Layers
Writing Custom Keras Models
Frequently Asked Questions
Guide to the Functional API
Guide to Keras Basics
Getting Started with Keras
Saving and serializing models
Guide to the Sequential Model
Training Callbacks
Training Visualization
Package source: keras_2.3.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: keras_2.3.0.0.tgz, r-oldrel: keras_2.3.0.0.tgz
Old sources: keras archive

Reverse dependencies:

Reverse depends: LDNN
Reverse imports: autokeras, deepredeff, downscaledl, embed, FuncNN, gnn, iml, kerastuneR, LilRhino, ML2Pvae, ProcData, ruta, SCFA, tfaddons, tfprobability, TSPred
Reverse suggests: bamlss, cloudml, condvis2, dimRed, drake, iForecast, infinityFlow, JFE, lime, mlflow, modelplotr, parsnip, pdp, PhysicalActivity, RNAmodR.ML, survivalmodels, targets, tensorflow, tfautograph, tfdatasets, tfhub, vip


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