# 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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"description": "# KerasTuner\n\n[](https://github.com/keras-team/keras-tuner/actions?query=workflow%3ATests+branch%3Amaster)\n[](https://codecov.io/gh/keras-team/keras-tuner)\n[](https://badge.fury.io/py/keras-tuner)\n\nKerasTuner is an easy-to-use, scalable hyperparameter optimization framework\nthat solves the pain points of hyperparameter search. Easily configure your\nsearch space with a define-by-run syntax, then leverage one of the available\nsearch algorithms to find the best hyperparameter values for your models.\nKerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms\nbuilt-in, and is also designed to be easy for researchers to extend in order to\nexperiment with new search algorithms.\n\nOfficial Website: [https://keras.io/keras_tuner/](https://keras.io/keras_tuner/)\n\n## Quick links\n\n* [Getting started with KerasTuner](https://keras.io/guides/keras_tuner/getting_started)\n* [KerasTuner developer guides](https://keras.io/guides/keras_tuner/)\n* [KerasTuner API reference](https://keras.io/api/keras_tuner/)\n\n\n## Installation\n\nKerasTuner requires **Python 3.8+** and **TensorFlow 2.0+**.\n\nInstall the latest release:\n\n```\npip install keras-tuner\n```\n\nYou can also check out other versions in our\n[GitHub repository](https://github.com/keras-team/keras-tuner).\n\n\n## Quick introduction\n\nImport KerasTuner and TensorFlow:\n\n```python\nimport keras_tuner\nfrom tensorflow import keras\n```\n\nWrite a function that creates and returns a Keras model.\nUse the `hp` argument to define the hyperparameters during model creation.\n\n```python\ndef build_model(hp):\n model = keras.Sequential()\n model.add(keras.layers.Dense(\n hp.Choice('units', [8, 16, 32]),\n activation='relu'))\n model.add(keras.layers.Dense(1, activation='relu'))\n model.compile(loss='mse')\n return model\n```\n\nInitialize a tuner (here, `RandomSearch`).\nWe use `objective` to specify the objective to select the best models,\nand we use `max_trials` to specify the number of different models to try.\n\n```python\ntuner = keras_tuner.RandomSearch(\n build_model,\n objective='val_loss',\n max_trials=5)\n```\n\nStart the search and get the best model:\n\n```python\ntuner.search(x_train, y_train, epochs=5, validation_data=(x_val, y_val))\nbest_model = tuner.get_best_models()[0]\n```\n\nTo learn more about KerasTuner, check out [this starter guide](https://keras.io/guides/keras_tuner/getting_started/).\n\n## Contributing Guide\n\nPlease refer to the [CONTRIBUTING.md](https://github.com/keras-team/keras-tuner/blob/master/CONTRIBUTING.md) for the contributing guide.\n\nThank all the contributors!\n\n[](https://github.com/keras-team/keras-tuner/graphs/contributors)\n\n## Community\n\nAsk your questions on our [GitHub Discussions](https://github.com/keras-team/keras-tuner/discussions).\n\n## Citing KerasTuner\n\nIf KerasTuner helps your research, we appreciate your citations.\nHere is the BibTeX entry:\n\n```bibtex\n@misc{omalley2019kerastuner,\n\ttitle = {KerasTuner},\n\tauthor = {O'Malley, Tom and Bursztein, Elie and Long, James and Chollet, Fran\\c{c}ois and Jin, Haifeng and Invernizzi, Luca and others},\n\tyear = 2019,\n\thowpublished = {\\url{https://github.com/keras-team/keras-tuner}}\n}\n```\n",
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