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:

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