12/20/2023 0 Comments Image tunerWe have several Python scripts to review today, including: We’ll then configure our development environment and review our project directory structure. In the first part of this tutorial, we’ll discuss the Keras Tuner package, including how it can help automatically tune your model’s hyperparameters with minimal code. Looking for the source code to this post? Jump Right To The Downloads Section Easy Hyperparameter Tuning with Keras Tuner and TensorFlow To learn how to tune hyperparameters with Keras Tuner, just keep reading. Can boost accuracy with minimal effort on your part.Implements novel hyperparameter tuning algorithms.Integrates into your existing deep learning training pipeline with minimal code changes.As we’ll see, utilizing Keras Tuner in your own deep learning scripts is as simple as a single import followed by single class instantiation - from there, it’s as simple as training your neural network just as you normally would!īesides ease of use, you’ll find that Keras Tuner: However, there are more advanced hyperparameter tuning algorithms, including Bayesian hyperparameter optimization and Hyperband, an adaptation and improvement to traditional randomized hyperparameter searches.īoth Bayesian optimization and Hyperband are implemented inside the keras tuner package. Last week we learned how to use scikit-learn to interface with Keras and TensorFlow to perform a randomized cross-validated hyperparameter search. Easy Hyperparameter Tuning with Keras Tuner and TensorFlow (today’s post).Hyperparameter tuning for Deep Learning with scikit-learn, Keras, and TensorFlow (last week’s post).Grid search hyperparameter tuning with scikit-learn ( GridSearchCV ) (tutorial from two weeks ago).Introduction to hyperparameter tuning with scikit-learn and Python (first tutorial in this series).This tutorial is part four in our four-part series on hyperparameter tuning:
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