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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.
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.
If KerasTuner helps your research, we appreciate your citations.
Here is the BibTeX entry:
@misc{omalley2019kerastuner,
title = {KerasTuner},
author = {O'Malley, Tom and Bursztein, Elie and Long, James and Chollet, Fran\c{c}ois and Jin, Haifeng and Invernizzi, Luca and others},
year = 2019,
howpublished = {\url{https://github.com/keras-team/keras-tuner}}
}