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kerastuneR: Interface to 'Keras Tuner'

'Keras Tuner' <https://keras-team.github.io/keras-tuner/> is a hypertuning framework made for humans. It aims at making the life of AI practitioners, hypertuner algorithm creators and model designers as simple as possible by providing them with a clean and easy to use API for hypertuning. 'Keras Tuner' makes moving from a base model to a hypertuned one quick and easy by only requiring you to change a few lines of code.

Version: 0.1.0.7
Imports: reticulate, tensorflow, rstudioapi, plotly, data.table, RJSONIO, rjson, tidyjson, dplyr, echarts4r, crayon, magick
Suggests: keras3, knitr, tfdatasets, testthat, purrr, rmarkdown
Published: 2024-04-13
DOI: 10.32614/CRAN.package.kerastuneR
Author: Turgut Abdullayev [aut, cre], Google Inc. [cph]
Maintainer: Turgut Abdullayev <turqut.a.314 at gmail.com>
BugReports: https://github.com/EagerAI/kerastuneR/issues/
License: Apache License 2.0
URL: https://github.com/EagerAI/kerastuneR/
NeedsCompilation: no
SystemRequirements: TensorFlow >= 2.0 (https://www.tensorflow.org/)
Materials: README
CRAN checks: kerastuneR results

Documentation:

Reference manual: kerastuneR.pdf
Vignettes: Bayesian Optimization
HyperModel subclass
Introduction to kerastuneR
MNIST hypertuning
KerasTuner best practices

Downloads:

Package source: kerastuneR_0.1.0.7.tar.gz
Windows binaries: r-devel: kerastuneR_0.1.0.7.zip, r-release: kerastuneR_0.1.0.7.zip, r-oldrel: kerastuneR_0.1.0.7.zip
macOS binaries: r-release (arm64): kerastuneR_0.1.0.7.tgz, r-oldrel (arm64): kerastuneR_0.1.0.7.tgz, r-release (x86_64): kerastuneR_0.1.0.7.tgz, r-oldrel (x86_64): kerastuneR_0.1.0.7.tgz
Old sources: kerastuneR archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=kerastuneR to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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