'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 |
| Reference manual: | kerastuneR.html , kerastuneR.pdf |
| Vignettes: |
Bayesian Optimization (source, R code) HyperModel subclass (source, R code) Introduction to kerastuneR (source, R code) MNIST hypertuning (source, R code) KerasTuner best practices (source, R code) |
| 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 |
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