KerasTuner

KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search algorithms.
Install the latest release:
You can also check out other versions in our GitHub repository.
Import KerasTuner and TensorFlow:
Write a function that creates and returns a Keras model. Use the hp argument to define the hyperparameters during model creation.
Initialize a tuner (here, RandomSearch). We use objective to specify the objective to select the best models, and we use max_trials to specify the number of different models to try.
Start the search and get the best model:
To learn more about KerasTuner, check out the getting stated guide.
If KerasTuner helps your research, we appreciate your citations. Here is the BibTeX entry: