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name: tuning description: "Routes generic KerasTuner search workflows for HyperParameters, HyperModel, Oracle, Tuner, RandomSearch, GridSearch, Hyperband, BayesianOptimization, reload/resume, and custom black-box tuning loops." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Tuning

Use this sub-skill when the task is about defining a hyperparameter search space, choosing a search algorithm, subclassing Tuner or HyperModel, inspecting trials, or resuming a saved search project.

Include here

• HyperParameters search-space design.
• HyperModel.build() and HyperModel.fit().
• BaseTuner / Tuner subclassing for custom objectives or nonstandard loops.
• RandomSearch, GridSearch, Hyperband, and BayesianOptimization.
• Trial inspection, search_space_summary(), results_summary(), get_best_models(), and get_best_hyperparameters().
• Save/reload behavior for search projects.

Exclude or route elsewhere

• SklearnTuner and estimator pipelines → sub-skills/sklearn-tuning.
• HyperResNet, HyperXception, HyperEfficientNet, HyperImageAugment → sub-skills/image-hypermodels.
• KERASTUNER_ORACLE_* env vars and chief/worker orchestration → sub-skills/distributed-tuning.

Quick workflow

1. Define the search space with hp.Choice, hp.Int, hp.Float, hp.Boolean, or hp.Fixed.
2. Build a Keras model in build_model(hp) or a HyperModel subclass.
3. Choose a tuner:
   • RandomSearch for a baseline.
   • GridSearch for finite, exhaustive coverage.
   • Hyperband for budgeted training with early stopping.
   • BayesianOptimization for a smaller search space with expensive evaluations.
4. Call search(...) with training and validation data.
5. Read back the top trials and optionally save() / reload() the project.

When this route is the right one

• The user says "tune this model", "search hyperparameters", "find the best architecture", or "resume a KerasTuner search".
• The user wants to subclass Tuner to optimize a black-box score or custom training loop.
• The user wants to understand GridSearch coverage, Hyperband rounds, or Bayesian trial selection.

Read next

• references/workflows.md for small end-to-end search examples and restart patterns.
• references/api-reference.md for verified signatures and algorithm differences.
• references/troubleshooting.md for build, retry, and dependency errors.
• sub-skills/tuning/scripts/smoke_search.py when you want a tiny local search smoke test.

Practical notes

• Tuner.run_trial() may return a History, a float, a dictionary, or a list of those values.
• GridSearch only fully exhausts finite search spaces.
• Hyperband injects tuner/trial_id, tuner/initial_epoch, and tuner/epochs during later rounds.
• BayesianOptimization requires SciPy and scikit-learn at construction time.
• The TensorFlow-backed 1.4.8 trial path imports TensorBoard's HParams API; install tensorboard before running Keras model searches.
• save() and reload() persist the project under directory/project_name.

Troubleshooting checklist

• If build(hp) does not return a Keras model, Tuner raises a fatal type error.
• If the search is retrying unexpectedly, check whether your code raises FailedTrialError or a generic exception.
• If GridSearch ends early, make sure the relevant hyperparameters have finite values.
• If a search is very slow, reduce the synthetic data size or the number of trials before debugging the full workflow.