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