keras: R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, 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.

Version: 2.3.0.0
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 rstudio.com>
BugReports: https://github.com/rstudio/keras/issues
License: MIT + file LICENSE
URL: https://keras.rstudio.com
NeedsCompilation: no
SystemRequirements: Keras >= 2.0 (https://keras.io)
Materials: NEWS
In views: HighPerformanceComputing, ModelDeployment
CRAN checks: keras results

Downloads:

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: keras_2.3.0.0.zip, r-release: keras_2.3.0.0.zip, r-oldrel: keras_2.3.0.0.zip
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 imports: autokeras, downscaledl, embed, gnn, kerastuneR, LilRhino, ProcData, ruta, tfaddons, tfprobability
Reverse suggests: bamlss, cloudml, condvis2, dimRed, drake, iml, lime, mlflow, modelplotr, OSTSC, parsnip, pdp, reinforcelearn, RNAmodR.ML, tensorflow, tfautograph, tfdatasets, tfhub, vip

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