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