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:	2.9.0
Depends:	R (≥ 3.4)
Imports:	generics (≥ 0.0.1), reticulate (> 1.22), tensorflow (≥ 2.8.0), tfruns (≥ 1.0), magrittr, zeallot, glue, methods, R6, ellipsis, rlang
Suggests:	ggplot2, testthat (≥ 2.1.0), knitr, rmarkdown, callr, tfdatasets, withr, png, jpeg
Published:	2022-05-23
Author:	Tomasz Kalinowski [ctb, cph, cre], Daniel Falbel [ctb, cph], JJ Allaire [aut, cph], François Chollet [aut, cph], RStudio [ctb, cph, fnd], Google [ctb, cph, fnd], Yuan Tang [ctb, cph], Wouter Van Der Bijl [ctb, cph], Martin Studer [ctb, cph], Sigrid Keydana [ctb]
Maintainer:	Tomasz Kalinowski <tomasz.kalinowski at rstudio.com>
BugReports:	https://github.com/rstudio/keras/issues
License:	MIT + file LICENSE
URL:	https://keras.rstudio.com
NeedsCompilation:	no
Materials:	NEWS
In views:	HighPerformanceComputing, ModelDeployment
CRAN checks:	keras results

Documentation:

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

Downloads:

Package source:	keras_2.9.0.tar.gz
Windows binaries:	r-devel: keras_2.9.0.zip, r-release: keras_2.9.0.zip, r-oldrel: keras_2.9.0.zip
macOS binaries:	r-release (arm64): keras_2.9.0.tgz, r-oldrel (arm64): keras_2.9.0.tgz, r-release (x86_64): keras_2.9.0.tgz, r-oldrel (x86_64): keras_2.9.0.tgz
Old sources:	keras archive

Reverse dependencies:

Reverse depends:	DeepPINCS, GenProSeq, LDNN, NeuCA, ttgsea, VAExprs
Reverse imports:	animl, autokeras, codacore, CRISPRseek, criticality, deepredeff, deepregression, digitalDLSorteR, downscaledl, embed, FuncNN, gnn, imageseg, iSubGen, kerastuneR, LilRhino, ML2Pvae, pareg, ProcData, ruta, SCFA, snap, soundClass, SPORTSCausal, SPOTMisc, tfaddons, tfprobability, TraceAssist, TSdeeplearning, TSLSTM, TSPred, utr.annotation
Reverse suggests:	bamlss, cloudml, condvis2, dimRed, drake, iForecast, iml, infinityFlow, innsight, lime, mlflow, modelplotr, parsnip, pdp, PhysicalActivity, Platypus, regtools, RNAmodR.ML, rsleep, survivalmodels, targets, tensorflow, tfdatasets, tfhub, vip

Linking:

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