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A Hyperparameter Tuning Library for Keras
| Date | Stars |
|---|---|
| 2026-07-24 | 2922 |
| 2026-07-25 | 2922 |
| 2026-07-28 | 2922 |
| 2026-07-30 | 2922 |
| 2026-08-06 | 2922 |
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# KerasTuner
[](https://github.com/keras-team/keras-tuner/actions?query=workflow%3ATests+branch%3Amaster)
[](https://codecov.io/gh/keras-team/keras-tuner)
[](https://badge.fury.io/py/keras-tuner)
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.
Official Website: [https://keras.io/keras_tuner/](https://keras.io/keras_tuner/)
## Quick links
* [Getting started with KerasTuner](https://keras.io/guides/keras_tuner/getting_started)
* [KerasTuner developer guides](https://keras.io/guides/keras_tuner/)
* [KerasTuner API reference](https://keras.io/api/keras_tuner/)
## Installation
KerasTuner requires **Python 3.8+** and **TensorFlow 2.0+**.
Install the latest release:
```
pip install keras-tuner
```
You can also check out other versions in our
[GitHub repository](https://github.com/keras-team/keras-tuner).
## Quick introduction
Import KerasTuner and TensorFlow:
```python
import keras_tuner
from tensorflow import keras
```
Write a function that creates and returns a Keras model.
Use the `hp` argument to define the hyperparameters during model creation.
```python
def build_model(hp):
model = keras.Sequential()
model.add(keras.layers.Dense(
hp.Choice('units', [8, 16, 32]),
activation='relu'))
model.add(keras.layers.Dense(1, activation='relu'))
model.compile(loss='mse')
return model
```
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.
```python
tuner = keras_tuner.RandomSearch(
build_model,
objective='val_loss',
max_trials=5)
```
Start the search and get the best model:
```python
tuner.search(x_train, y_train, epochs=5, validation_data=(x_val, y_val))
best_model = tuner.get_best_models()[0]
```
To learn more about KerasTuner, check out [this starter guide](https://keras.io/guides/keras_tuner/getting_started/).
## Contributing Guide
Please refer to the [CONTRIBUTING.md](https://github.com/keras-team/keras-tuner/blob/master/CONTRIBUTING.md) for the contributing guide.
Thank all the contributors!
[](https://github.com/keras-team/keras-tuner/graphs/contributors)
## Community
Ask your questions on our [GitHub Discussions](https://github.com/keras-team/keras-tuner/discussions).
## Citing KerasTuner
If KerasTuner helps your research, we appreciate your citations.
Here is the BibTeX entry:
```bibtex
@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}}
}
```
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Haifeng Jin
312
179
Elie Bursztein · @google · United States
146
François Chollet
111
101
Luca Invernizzi · Google · United States
36
Gabriel de Marmiesse · Kyutai · France
19
Anselm Hahn · Switzerland
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8
4
Sagi Perel · Google Deepmind
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4
Florian Schäfer · Germany
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Brydon Eastman
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Pedro Kaj Kjellerup Nacht · @googlers · Brazil
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You Quan Chong
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3
Avichal Goel
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matched fp:a2a7aa03dc2a7235, topic:deep-learning, topic:tensorflow