๐งช Keras Tuner Flashcards
Efficient Hyperparameter Tuning Library for Keras and TensorFlow
๐ What is Keras Tuner?
Keras Tuner is a scalable and easy-to-use hyperparameter optimization framework for Keras and TensorFlow models.
๐ฆ Installation
Install it using pip install keras-tuner
. It supports both standalone and TensorFlow integrations.
๐งฑ HyperModel Class
Create a custom model-building function by subclassing HyperModel
or using a simple callable method.
โ๏ธ Supported Search Algorithms
Includes Random Search, Hyperband, Bayesian Optimization, and Sklearn Tuner for flexible search strategies.
๐ Objective
Set your optimization target (e.g., 'val_accuracy'
) for the tuner to find the best configuration.
๐ Tuner Initialization
Initialize a tuner class with MyTuner(...)
providing search space, objective, max_trials, etc.
๐ Search Space
Define ranges for hyperparameters like learning rate, dropout rate, number of layers, and units per layer.
๐ฏ tuner.search()
Executes the tuning job across the hyperparameter space using training and validation datasets.
๐ get_best_hyperparameters()
Retrieve the best performing hyperparameter configuration after the search completes.
๐ tuner.results_summary()
Prints a summary of trials, including their scores and hyperparameter values used in each.
๐ Reuse Saved Results
Persist tuner results to disk for resuming or analyzing experiments later via directory arguments.
๐ Integration with TensorBoard
Easily visualize tuning progress and loss/accuracy curves using built-in TensorBoard logging support.