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https://github.com/keras-team/keras-tuner
A Hyperparameter Tuning Library for Keras
https://github.com/keras-team/keras-tuner
automl deep-learning hyperparameter-optimization keras machine-learning tensorflow
Last synced: 6 days ago
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A Hyperparameter Tuning Library for Keras
- Host: GitHub
- URL: https://github.com/keras-team/keras-tuner
- Owner: keras-team
- License: apache-2.0
- Created: 2019-06-06T22:38:21.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-12-01T18:14:29.000Z (about 1 month ago)
- Last Synced: 2025-01-03T15:09:28.248Z (9 days ago)
- Topics: automl, deep-learning, hyperparameter-optimization, keras, machine-learning, tensorflow
- Language: Python
- Homepage: https://keras.io/keras_tuner/
- Size: 1.93 MB
- Stars: 2,862
- Watchers: 61
- Forks: 396
- Open Issues: 225
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Security: SECURITY.md
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README
# 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}}
}
```