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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

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A Hyperparameter Tuning Library for Keras

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README 

        
# KerasTuner



[![](https://github.com/keras-team/keras-tuner/workflows/Tests/badge.svg?branch=master)](https://github.com/keras-team/keras-tuner/actions?query=workflow%3ATests+branch%3Amaster)

[![codecov](https://codecov.io/gh/keras-team/keras-tuner/branch/master/graph/badge.svg)](https://codecov.io/gh/keras-team/keras-tuner)

[![PyPI version](https://badge.fury.io/py/keras-tuner.svg)](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!



[![The contributors](https://raw.githubusercontent.com/keras-team/keras-tuner/master/docs/contributors.svg)](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}}

}

```