KerasTuner
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.
Quick links
Installation
Install the latest release:
pip install keras-tuner --upgrade
You can also check out other versions in our GitHub repository.
Quick introduction
Import KerasTuner and TensorFlow:
import keras_tuner
import keras
Write a function that creates and returns a Keras model.
Use the hp argument to define the hyperparameters during model creation.
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.
tuner = keras_tuner.RandomSearch(
build_model,
objective='val_loss',
max_trials=5)
Start the search and get the best model:
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 the getting stated guide.
Citing KerasTuner
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}}
}