[ctb,
cph],
Wouter Van Der Bijl [ctb, cph],
Martin Studer [ctb, cph],
Sigrid Keydana [ctb]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],
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: | 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 |
| 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 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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