KerasTuner
KerasTuner is a library for defining, running, and evaluating automated searches for effective hyperparameter configurations of Keras models.
Deep Learning with R, Third Edition
KerasTuner is a library for defining, running, and evaluating hyperparameter searches for Keras models.
- How it is used
- Automates the search for effective Keras model configurations when training each candidate is expensive and hyperparameters are not differentiable.
- How deep it goes
- Defines search spaces, model builders, tuners, objectives, trials, validation, early stopping, and final model selection.
- Examples
- Uses an MNIST dense classifier whose units and optimizer vary across trials, with validation accuracy guiding selection.
- What you can do after
- Apply it via KerasTuner to define searches, run trials, retrieve top configurations, retrain models, and evaluate them on test data.
Read how Deep Learning with R, Third Edition explains KerasTuner →
Deep Learning with Python, Third Edition
A Keras-related library for automating hyperparameter search over model configurations.
- How it is used
- Automates searching Keras model configurations when manually selecting hyperparameters is expensive and repetitive.
- How deep it goes
- Explains search spaces, model builders, tuners, objectives, trials, and result retrieval, but does not derive optimization theory.
- Examples
- Uses an MNIST multilayer perceptron that searches dense-layer units, optimizer choice, validation accuracy, training epochs, and final test performance.
- What you can do after
- Apply it via KerasTuner to define a search space, run trials, and select or retrain promising Keras models.
Read how Deep Learning with Python, Third Edition explains KerasTuner →
Distributed Machine Learning Patterns
Keras Tuner is a library for searching and selecting hyperparameter configurations for Keras models.
- How it is used
- Uses automated hyperparameter search to improve Keras model selection beyond a single manually chosen configuration.
- How deep it goes
- Explains installation, model construction, tuner configuration, search, selection, retraining, and evaluation without detailing tuner internals.
- Examples
- Demonstrates MNIST tuning with dense-layer width, Adam learning rate, Hyperband, validation splitting, and test-set evaluation.
- What you can do after
- Apply KerasTuner to define a search space, run Hyperband, retrieve the best hyperparameters, retrain, and evaluate a model.
Read how Distributed Machine Learning Patterns explains KerasTuner →
Related concepts
- hyperparameter tuninguses · Hyperparameter tuning is the process of selecting or adjusting configurable model or procedure settings and evaluating them against a chosen objective.
- hyperparameteruses · A hyperparameter is a setting chosen outside ordinary parameter learning that controls a model, algorithm, or learning procedure.
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