Keras documentation: KerasTuner (original) (raw)

► KerasTuner

Star

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

• Getting started with KerasTuner
• Developer guides
• API documentation
• KerasTuner on GitHub

Installation

Install the latest release:

pip install keras-tuner --upgrade

You can also check out other versions in ourGitHub 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 thegetting 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}} }