Automatic vs Manual optimisation in Keras_. day 18

First check automatic – keras tuner – which is explained in our previous post Automated Hyperparameter Tuning in Keras Part 1: Automated Approaches for Hyperparameter Tuning in Keras Hyperparameter tuning is a crucial step in machine learning that involves finding the best set of parameters for your model to optimize its performance. Keras provides a robust toolset for this purpose through its KerasTuner library, which offers several powerful, automated methods to explore the hyperparameter space. In this section, we’ll dive into the different models and approaches available in Keras for automated hyperparameter tuning, updated with the latest in 2024. 1. Random Search Random search is one of the simplest and most straightforward hyperparameter tuning methods. It works by randomly sampling hyperparameter combinations from the predefined search space. Despite its simplicity, random search can be surprisingly effective, especially when combined with a well-chosen search space. It’s often used as a baseline method due to its ease of implementation and ability to explore diverse regions of the hyperparameter space. tuner = kt.RandomSearch( build_model, objective='val_accuracy', max_trials=10, executions_per_trial=2, directory='random_search_dir', project_name='random_search' ) tuner.search(x_train, y_train, epochs=5, validation_data=(x_val, y_val)) Here, max_trials defines the number of different hyperparameter combinations to try, while executions_per_trial allows for multiple runs to…