keras

R 패키지 메타데이터와 수집 신호를 모아 봅니다.

Packages / CRAN / keras

keras

v2.16.1
Repository CRANLicense MIT + file LICENSELifecycle activeNeeds compilation no
Package pageReferenceSourceDOINEWS
DOI
10.32614/CRAN.package.keras
Task views
High-Performance and Parallel Computing with R, Model Deployment with R
Reverse imports
1,412
Reverse depends
83

Core Signals

첫 화면에서 판단해야 할 수집 신호를 먼저 배치합니다.

3
Task views
High-Performance and Parallel Computing with R, Model Deployment with R
Reverse imports
1,412
Reverse depends
83

Supported Backends

DESCRIPTION에서 감지한 backend 관련 package입니다.

0
backend package 신호가 없습니다.

Quick Facts

기본 메타데이터를 작은 카드와 토큰으로 압축합니다.

profile
Repository
CRAN
Version
2.16.1
License
MIT + file LICENSE
Lifecycle
active
Needs compilation
no
Reverse depends
83
Reverse imports
1,412
Last observed
2026-05-30
CRAN
cran.r-project.org/package=keras

수집 소스별 패키지 정보

1개 소스
CRAN
2.16.1
2026-05-30
License
MIT + file LICENSE
Depends
R (>= 3.6)
Imports
generics (>= 0.0.1), reticulate (>= 1.31), tensorflow (>= 2.13.0.9000), tfruns (>= 1.0), magrittr, zeallot, glue, methods, R6, rlang
Suggests
ggplot2, testthat (>= 2.1.0), knitr, rmarkdown, callr, tfdatasets, withr, png, jpeg
Needs compilation
no
Reverse depends
83
Reverse imports
1,412
Lifecycle
active
Last observed
2026-05-30 10:45:11

이 패키지가 의존하는 패키지

5개 표시전체 19개
PackageTypeSpec
generics
CRAN · 2.16.1 · 2026-05-30
Importsgenerics (>= 0.0.1)
glue
CRAN · 2.16.1 · 2026-05-30
Importsglue
magrittr
CRAN · 2.16.1 · 2026-05-30
Importsmagrittr
methods
CRAN · 2.16.1 · 2026-05-30
Importsmethods
R6
CRAN · 2.16.1 · 2026-05-30
ImportsR6
1 / 4

이 패키지를 쓰는 패키지

5개 표시전체 67개
PackageTypeSpec
LDNN
1.10
CRAN · 2026-05-30
Dependskeras
MantaID
1.0.4
CRAN · 2026-05-30
Dependskeras
ARMALSTM
0.1.0
CRAN · 2026-05-30
Importskeras
autokeras
1.0.12
CRAN · 2026-05-30
Importskeras
autotab
1.0.1
CRAN · 2026-05-30
Importskeras
1 / 14

Reverse dependency summary

3 types
TypePackages
Depends2
Imports36
Suggests29

패키지 페이지

Reverse depends
12
Reverse imports
74
Reverse suggests
66
All links
151
Repository
CRAN
Version
2.16.1
Collected
2026-05-27 07:02:03
Package page
https://cran.r-project.org/web/packages/keras/index.html
DOI
10.32614/CRAN.package.keras
CRAN checks
https://cran.r-project.org/web/checks/check_results_keras.html
NEWS
https://cran.r-project.org/web/packages/keras/news/news.html
Reference HTML
https://cran.r-project.org/web/packages/keras/refman/keras.html
Reference PDF
https://cran.r-project.org/web/packages/keras/keras.pdf
Source package
https://cran.r-project.org/src/contrib/keras_2.16.1.tar.gz
Archive
https://CRAN.R-project.org/src/contrib/Archive/keras
In views
HighPerformanceComputingModelDeployment
Page fields
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]
BugReports
https://github.com/rstudio/keras3/issues
CRAN Checks
keras results
DOI
10.32614/CRAN.package.keras
In Views
HighPerformanceComputing , ModelDeployment
License
MIT + file LICENSE
Maintainer
Tomasz Kalinowski <tomasz at posit.co>
Materials
NEWS
NeedsCompilation
no
Old Sources
keras archive
Package Source
keras_2.16.1.tar.gz
Published
2026-02-14
Reference Manual
keras.html , keras.pdf
Reverse Depends
DeepPINCS , GenProSeq , LDNN , MantaID , ttgsea , VAExprs
Reverse Imports
ARMALSTM , autokeras , autotab , codacore , codez , cramR , CRISPRseek , criticality , deepspat , EEMDlstm , gnn , imageseg , iSubGen , janus , LilRhino , MBMethPred , ML2Pvae , mnda , neuralGAM , orthos , PLEXI , ProcData , processpredictR , roseRF , snap , soundClass , SPORTSCausal , tfaddons , tfNeuralODE , tfprobability , TraceAssist , transformerForecasting , TSdeeplearning , TSLSTM , TSLSTMplus , tsLSTMx , TSPred
Reverse Suggests
AnnotationHub , bamlss , bundle , cloudml , condvis2 , counterfactuals , CytoMethIC , dimRed , drake , fastml , iml , infinityFlow , innsight , lime , mlflow , mrbin , multiRL , nn2poly , parsnip , pdp , PhysicalActivity , PortfolioTesteR , qeML , RegimeChange , regtools , RNAmodR.ML , seriation , survivalmodels , targets , tfhub , tidylearn , vetiver , vivid
URL
https://tensorflow.rstudio.com/ , https://github.com/rstudio/keras3/tree/r2
Version
2.16.1
Vignettes
Using Pre-Trained Models ( source , R code ) Writing Custom Keras Layers ( source , R code ) Writing Custom Keras Models ( source , R code ) Frequently Asked Questions ( source , R code ) Guide to the Functional API ( source , R code ) Guide to Keras Basics ( source , R code ) Getting Started with Keras ( source , R code ) Saving and serializing models ( source , R code ) Guide to the Sequential Model ( source , R code ) Training Callbacks ( source , R code ) Training Visualization ( source , R code )
Windows Binaries
r-devel: keras_2.16.1.zip , r-release: keras_2.16.1.zip , r-oldrel: keras_2.16.1.zip
MacOS Binaries
r-release (arm64): keras_2.16.1.tgz , r-oldrel (arm64): keras_2.16.1.tgz , r-release (x86_64): keras_2.16.1.tgz , r-oldrel (x86_64): keras_2.16.1.tgz
Version
2.16.1
Published
2026-02-14
DOI
10.32614/CRAN.package.keras
10.32614/CRAN.package.keras
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]
https://orcid.org/0000-0001-5243-233X
Maintainer
Tomasz Kalinowski <tomasz at posit.co>
BugReports
https://github.com/rstudio/keras3/issues
https://github.com/rstudio/keras3/issues
License
MIT + file LICENSE
MITLICENSE
URL
https://tensorflow.rstudio.com/ , https://github.com/rstudio/keras3/tree/r2
https://tensorflow.rstudio.com/https://github.com/rstudio/keras3/tree/r2
NeedsCompilation
no
Materials
NEWS
NEWS
In Views
HighPerformanceComputing , ModelDeployment
HighPerformanceComputingModelDeployment
CRAN Checks
keras results
keras results
Reference Manual
keras.html , keras.pdf
keras.htmlkeras.pdf
Vignettes
Using Pre-Trained Models ( source , R code ) Writing Custom Keras Layers ( source , R code ) Writing Custom Keras Models ( source , R code ) Frequently Asked Questions ( source , R code ) Guide to the Functional API ( source , R code ) Guide to Keras Basics ( source , R code ) Getting Started with Keras ( source , R code ) Saving and serializing models ( source , R code ) Guide to the Sequential Model ( source , R code ) Training Callbacks ( source , R code ) Training Visualization ( source , R code )
Using Pre-Trained ModelssourceR codeWriting Custom Keras LayerssourceR codeWriting Custom Keras ModelssourceR codeFrequently Asked QuestionssourceR codeGuide to the Functional APIsourceR codeGuide to Keras BasicssourceR codeGetting Started with Kerassource
Package Source
keras_2.16.1.tar.gz
keras_2.16.1.tar.gz
Windows Binaries
r-devel: keras_2.16.1.zip , r-release: keras_2.16.1.zip , r-oldrel: keras_2.16.1.zip
keras_2.16.1.zipkeras_2.16.1.zipkeras_2.16.1.zip
MacOS Binaries
r-release (arm64): keras_2.16.1.tgz , r-oldrel (arm64): keras_2.16.1.tgz , r-release (x86_64): keras_2.16.1.tgz , r-oldrel (x86_64): keras_2.16.1.tgz
keras_2.16.1.tgzkeras_2.16.1.tgzkeras_2.16.1.tgzkeras_2.16.1.tgz
Old Sources
keras archive
keras archive
Reverse Depends
DeepPINCS , GenProSeq , LDNN , MantaID , ttgsea , VAExprs
DeepPINCSGenProSeqLDNNMantaIDttgseaVAExprs
Reverse Imports
ARMALSTM , autokeras , autotab , codacore , codez , cramR , CRISPRseek , criticality , deepspat , EEMDlstm , gnn , imageseg , iSubGen , janus , LilRhino , MBMethPred , ML2Pvae , mnda , neuralGAM , orthos , PLEXI , ProcData , processpredictR , roseRF , snap , soundClass , SPORTSCausal , tfaddons , tfNeuralODE , tfprobability , TraceAssist , transformerForecasting , TSdeeplearning , TSLSTM , TSLSTMplus , tsLSTMx , TSPred
ARMALSTMautokerasautotabcodacorecodezcramRCRISPRseekcriticalitydeepspatEEMDlstmgnnimagesegiSubGenjanusLilRhinoMBMethPredML2PvaemndaneuralGAMorthos
Reverse Suggests
AnnotationHub , bamlss , bundle , cloudml , condvis2 , counterfactuals , CytoMethIC , dimRed , drake , fastml , iml , infinityFlow , innsight , lime , mlflow , mrbin , multiRL , nn2poly , parsnip , pdp , PhysicalActivity , PortfolioTesteR , qeML , RegimeChange , regtools , RNAmodR.ML , seriation , survivalmodels , targets , tfhub , tidylearn , vetiver , vivid
AnnotationHubbamlssbundlecloudmlcondvis2counterfactualsCytoMethICdimReddrakefastmlimlinfinityFlowinnsightlimemlflowmrbinmultiRLnn2polyparsnippdp
Page sections 4
Documentation
Heading
Documentation
Links
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Text
Reference manual: keras.html , keras.pdf Vignettes: Using Pre-Trained Models ( source , R code ) Writing Custom Keras Layers ( source , R code ) Writing Custom Keras Models ( source , R code ) Frequently Asked Questions ( source , R code ) Guide to the Functional API ( source , R code ) Guide to Keras Basics ( source , R code ) Getting Started with Keras ( source , R code ) Saving and serializing models ( source , R code ) Guide to the Sequential Model ( source , R code ) Training Callbacks ( source , R code ) Training Visualization ( source , R code )
Downloads
Heading
Downloads
Links
[{"label":"keras_2.16.1.tar.gz","section":"","type":"","url":"https://cran.r-project.org/src/contrib/keras_2.16.1.tar.gz"},{"label":"keras_2.16.1.zip","section":"","type":"","url":"https://cran.r-project.org/bin/windows/contrib/4.7/keras_2.16.1.zip"},{"label":"keras_2.16.1.zip","section":"","type":"","url":"https://cran.r-project.org/bin/windows/contrib/4.6/keras_2.16.1.zip"},{"label":"keras_2.16.1.zip","section":"","type":"","url":"https://cran.r-project.org/bin/windows/contrib/4.5/keras_2.16.1.zip"},{"label":"keras_2.16.1.tgz","section":"","type":"","url":"https://cran.r-project.org/bin/macosx/sonoma-arm64/contrib/4.6/keras_2.16.1.tgz"},{"label":"keras_2.16.1.tgz","section":"","type":"","url":"https://cran.r-project.org/bin/macosx/big-sur-arm64/contrib/4.5/keras_2.16.1.tgz"},{"label":"keras_2.16.1.tgz","section":"","type":"","url":"https://cran.r-project.org/bin/macosx/big-sur-x86_64/contrib/4.6/keras_2.16.1.tgz"},{"label":"keras_2.16.1.tgz","section":"","type":"","url":"https://cran.r-project.org/bin/macosx/big-sur-x86_64/contrib/4.5/keras_2.16.1.tgz"},{"label":"keras archive","section":"","type":"","url":"https://CRAN.R-project.org/src/contrib/Archive/keras"}]
Text
Package source: keras_2.16.1.tar.gz Windows binaries: r-devel: keras_2.16.1.zip , r-release: keras_2.16.1.zip , r-oldrel: keras_2.16.1.zip macOS binaries: r-release (arm64): keras_2.16.1.tgz , r-oldrel (arm64): keras_2.16.1.tgz , r-release (x86_64): keras_2.16.1.tgz , r-oldrel (x86_64): keras_2.16.1.tgz Old sources: keras archive
Reverse dependencies
Heading
Reverse dependencies
Links
[{"label":"DeepPINCS","section":"","type":"","url":"https://www.bioconductor.org/packages/release/bioc/html/DeepPINCS.html"},{"label":"GenProSeq","section":"","type":"","url":"https://www.bioconductor.org/packages/release/bioc/html/GenProSeq.html"},{"label":"LDNN","section":"","type":"","url":"https://cran.r-project.org/web/packages/LDNN/index.html"},{"label":"MantaID","section":"","type":"","url":"https://cran.r-project.org/web/packages/MantaID/index.html"},{"label":"ttgsea","section":"","type":"","url":"https://www.bioconductor.org/packages/release/bioc/html/ttgsea.html"},{"label":"VAExprs","section":"","type":"","url":"https://www.bioconductor.org/packages/release/bioc/html/VAExprs.html"},{"label":"ARMALSTM","section":"","type":"","url":"https://cran.r-project.org/web/packages/ARMALSTM/index.html"},{"label":"autokeras","section":"","type":"","url":"https://cran.r-project.org/web/packages/autokeras/index.html"},{"label":"autotab","section":"","type":"","url":"https://cran.r-project.org/web/packages/autotab/index.html"},{"label":"codacore","section":"","type":"","url":"https://cran.r-project.org/web/packages/codacore/index.html"},{"label":"codez","section":"","type":"","url":"https://cran.r-project.org/web/packages/codez/index.html"},{"label":"cramR","section":"","type":"","url":"https://cran.r-project.org/web/packages/cramR/index.html"},{"label":"CRISPRseek","section":"","type":"","url":"https://www.bioconductor.org/packages/release/bioc/html/CRISPRseek.html"},{"label":"criticality","section":"","type":"","url":"https://cran.r-project.org/web/packages/criticality/index.html"},{"label":"deepspat","section":"","type":"","url":"https://cran.r-project.org/web/packages/deepspat/index.html"},{"label":"EEMDlstm","section":"","type":"","url":"https://cran.r-project.org/web/packages/EEMDlstm/index.html"},{"label":"gnn","section":"","type":"","url":"https://cran.r-project.org/web/packages/gnn/index.html"},{"label":"imageseg","section":"","type":"","url":"https://cran.r-project.org/web/packages/imageseg/index.html"},{"label":"iSubGen","section":"","type":"","url":"https://cran.r-project.org/web/packages/iSubGen/index.html"},{"label":"janus","section":"","type":"","url":"https://cran.r-project.org/web/packages/janus/index.html"}]
Text
Reverse depends: DeepPINCS , GenProSeq , LDNN , MantaID , ttgsea , VAExprs Reverse imports: ARMALSTM , autokeras , autotab , codacore , codez , cramR , CRISPRseek , criticality , deepspat , EEMDlstm , gnn , imageseg , iSubGen , janus , LilRhino , MBMethPred , ML2Pvae , mnda , neuralGAM , orthos , PLEXI , ProcData , processpredictR , roseRF , snap , soundClass , SPORTSCausal , tfaddons , tfNeuralODE , tfprobability , TraceAssist , transformerForecasting , TSdeeplearning , TSLSTM , TSLSTMplus , tsLSTMx , TSPred Reverse suggests: AnnotationHub , bamlss , bundle , cloudml , condvis2 , counterfactuals , CytoMethIC , dimRed , drake , fastml , iml , infinityFlow , innsight , lime , mlflow , mrbin , multiRL , nn2poly , parsnip , pdp , PhysicalActivity , PortfolioTesteR , qeML , RegimeChange , regtools , RNAmodR.ML , seriation , survivalmodels , targets , tfhub , tidylearn , vetiver , vivid
Linking
Heading
Linking
Links
[{"label":"https://CRAN.R-project.org/package=keras","section":"","type":"","url":"https://CRAN.R-project.org/package=keras"}]
Text
Please use the canonical form https://CRAN.R-project.org/package=keras to link to this page.
Materials 1
NEWS
Documentation 35
keras.htmlkeras.pdfUsing Pre-Trained ModelssourceR codeWriting Custom Keras LayerssourceR codeWriting Custom Keras ModelssourceR codeFrequently Asked QuestionssourceR codeGuide to the Functional APIsourceR codeGuide to Keras BasicssourceR codeGetting Started with KerassourceR codeSaving and serializing modelssourceR codeGuide to the Sequential ModelsourceR codeTraining CallbackssourceR codeTraining VisualizationsourceR code
Vignettes 33
Using Pre-Trained ModelssourceR codeWriting Custom Keras LayerssourceR codeWriting Custom Keras ModelssourceR codeFrequently Asked QuestionssourceR codeGuide to the Functional APIsourceR codeGuide to Keras BasicssourceR codeGetting Started with KerassourceR codeSaving and serializing modelssourceR codeGuide to the Sequential ModelsourceR codeTraining CallbackssourceR codeTraining VisualizationsourceR code
Downloads 9
keras_2.16.1.tar.gzkeras_2.16.1.zipkeras_2.16.1.zipkeras_2.16.1.zipkeras_2.16.1.tgzkeras_2.16.1.tgzkeras_2.16.1.tgzkeras_2.16.1.tgzkeras archive
All page links 120
genericsreticulatetensorflowtfrunsmagrittrzeallotgluemethodsR6rlangggplot2testthatknitrrmarkdowncallrtfdatasetswithrpngjpeg10.32614/CRAN.package.kerashttps://orcid.org/0000-0001-5243-233Xhttps://github.com/rstudio/keras3/issuesMITLICENSEhttps://tensorflow.rstudio.com/https://github.com/rstudio/keras3/tree/r2NEWSHighPerformanceComputingModelDeploymentkeras resultskeras.htmlkeras.pdfUsing Pre-Trained ModelssourceR codeWriting Custom Keras LayerssourceR codeWriting Custom Keras ModelssourceR codeFrequently Asked QuestionssourceR codeGuide to the Functional APIsourceR codeGuide to Keras BasicssourceR codeGetting Started with Kerassourcekeras_2.16.1.tar.gzkeras_2.16.1.zipkeras_2.16.1.zipkeras_2.16.1.zipkeras_2.16.1.tgzkeras_2.16.1.tgzkeras_2.16.1.tgzkeras_2.16.1.tgzkeras archiveDeepPINCSGenProSeqLDNNMantaIDttgseaVAExprsARMALSTMautokerasautotabcodacorecodezcramRCRISPRseekcriticalitydeepspatEEMDlstmgnnimagesegiSubGenjanusLilRhinoMBMethPredML2PvaemndaneuralGAMorthosAnnotationHubbamlssbundlecloudmlcondvis2counterfactualsCytoMethICdimReddrakefastmlimlinfinityFlowinnsightlimemlflowmrbinmultiRLnn2polyparsnippdpR codeSaving and serializing modelssourceR codeGuide to the Sequential ModelsourceR codeTraining CallbackssourceR codeTraining VisualizationsourceR code

버전 이력

RepositoryVersionPublishedFirst seenLast seenDocs
CRAN2.16.12026-05-292026-05-30

보안

표시할 OSV 데이터가 없습니다.

문헌 신호

표시할 OpenAlex 데이터가 없습니다.

CRAN Task Views

High-Performance and Parallel Computing with R · 2026-05-30Model Deployment with R · 2026-05-30