Package: kerastuneR 0.1.0.7
Turgut Abdullayev
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.
Authors:
kerastuneR_0.1.0.7.tar.gz
kerastuneR_0.1.0.7.zip(r-4.5)kerastuneR_0.1.0.7.zip(r-4.4)kerastuneR_0.1.0.7.zip(r-4.3)
kerastuneR_0.1.0.7.tgz(r-4.4-any)kerastuneR_0.1.0.7.tgz(r-4.3-any)
kerastuneR_0.1.0.7.tar.gz(r-4.5-noble)kerastuneR_0.1.0.7.tar.gz(r-4.4-noble)
kerastuneR_0.1.0.7.tgz(r-4.4-emscripten)kerastuneR_0.1.0.7.tgz(r-4.3-emscripten)
kerastuneR.pdf |kerastuneR.html✨
kerastuneR/json (API)
| # Install 'kerastuneR' in R: |
| install.packages('kerastuneR', repos = c('https://eagerai.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/eagerai/kerastuner/issues
hyperparameter-tuninghypertuningkeraskeras-tunertensorflowtrial
Last updated 7 months agofrom:d9ab72f540. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 09 2024 |
| R-4.5-win | OK | Nov 09 2024 |
| R-4.5-linux | OK | Nov 09 2024 |
| R-4.4-win | OK | Nov 09 2024 |
| R-4.4-mac | OK | Nov 09 2024 |
| R-4.3-win | OK | Nov 09 2024 |
| R-4.3-mac | OK | Nov 09 2024 |
Exports:BaseTunerBayesianOptimizationcallback_tunerfit_tunerget_best_modelsHyperbandHyperModel_classHyperParametersHyperResNetHyperXceptioninstall_kerastunerkeras_tuner_versionload_modelObjectiveOracleplot_keras_modelplot_tunerPyClassRandomSearchresults_summarysave_modelsearch_summaryTensorBoardtfTuner_classuse_python
Dependencies:askpassassertthatbackportsbase64encbroombslibcachemclicolorspacecommonmarkconfigcorrplotcountrycodecpp11crayoncrosstalkcurldata.tabledigestdplyrecharts4revaluatefansifarverfastmapfontawesomefsgenericsggplot2gluegtableherehighrhtmltoolshtmlwidgetshttpuvhttrisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclemagickmagrittrMASSMatrixmemoisemgcvmimemunsellnlmeopensslpillarpkgconfigplotlypngprocessxpromisespspurrrR6rappdirsRColorBrewerRcppRcppTOMLreticulaterjsonRJSONIOrlangrmarkdownrprojrootrstudioapisassscalesshinysourcetoolsstringistringrsystensorflowtfautographtfrunstibbletidyjsontidyrtidyselecttinytexutf8vctrsviridisLitewhiskerwithrxfunxtableyaml
Bayesian Optimization
Rendered fromBayesianOptimisation.Rmdusingknitr::rmarkdownon Nov 09 2024.Last update: 2024-04-13
Started: 2020-02-02
HyperModel subclass
Rendered fromHyperModel_subclass.Rmdusingknitr::rmarkdownon Nov 09 2024.Last update: 2024-04-13
Started: 2020-01-08
Introduction to kerastuneR
Rendered fromIntroduction.Rmdusingknitr::rmarkdownon Nov 09 2024.Last update: 2022-01-10
Started: 2020-01-01
KerasTuner best practices
Rendered frombest_practice.Rmdusingknitr::rmarkdownon Nov 09 2024.Last update: 2024-04-13
Started: 2020-10-04
MNIST hypertuning
Rendered fromMNIST.Rmdusingknitr::rmarkdownon Nov 09 2024.Last update: 2024-04-13
Started: 2020-01-02
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Base Tuner | BaseTuner |
| Bayesian Optimization | BayesianOptimization |
| Tuner Callback | callback_tuner |
| Search | fit_tuner |
| Get best models | get_best_models |
| Hyperband | Hyperband |
| HyperModel | HyperModel_class |
| HyperParameters | HyperParameters |
| HyperResNet | HyperResNet |
| HyperXception | HyperXception |
| Install Keras Tuner | install_kerastuner |
| Version of Keras Tuner | keras_tuner_version |
| Load model | load_model |
| Objective | Objective |
| Oracle | Oracle |
| Plot Keras model | plot_keras_model |
| Plot the tuner results with 'plotly' | plot_tuner |
| RandomSearch | RandomSearch |
| Results summary | results_summary |
| Save model | save_model |
| Search summary | search_summary |
| TensorBoard | TensorBoard |
| Tuner | Tuner_class |
