Keras Tuner
An easy-to-use, scalable hyperparameter optimization framework for Keras models with define-by-run syntax and built-in search algorithms.
What is Keras Tuner?
KerasTuner is a hyperparameter tuning library for Keras that automates the search for optimal model configurations. It solves the pain points of manual hyperparameter optimization by providing scalable search algorithms and an intuitive define-by-run syntax. The framework helps machine learning practitioners efficiently improve model performance through systematic experimentation.
Machine learning engineers and researchers using Keras/TensorFlow who need to optimize model hyperparameters systematically. It's particularly valuable for those building deep learning models where manual tuning is time-consuming.
Developers choose KerasTuner for its seamless Keras integration, built-in search algorithms, and easy-to-use API that reduces the complexity of hyperparameter optimization. Its extensible design also appeals to researchers wanting to experiment with custom search methods.
Overview
A Hyperparameter Tuning Library for Keras
Use Cases
Best For
- Automating hyperparameter search for Keras deep learning models
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