How to perform hyperparameter tuning in Keras?

5 months ago

2 minutes

In one using the following methods:

Grid Search: By defining a set of combinations of hyperparameters, training and validating are performed on these combinations, ultimately selecting the best-performing combination. Keras provides the GridSearchCV class, which can be used in conjunction with the Scikit-learn library for grid searching.
Random Search is similar to Grid Search, but instead of systematically trying out all possible hyperparameter combinations, Random Search randomly selects combinations to train and validate within a given hyperparameter space. Keras provides the RandomizedSearchCV class, which can also be used in conjunction with the Scikit-learn library for random search.
Bayesian Optimization: Utilize Bayesian optimization algorithms like BayesianOptimization or hyperopt to select the best combination of hyperparameters. This method can efficiently adjust hyperparameters.
Automatic tuning tools: Keras also offers some automatic tuning tools, such as kerastuner, which can help users quickly find the best hyperparameter combinations.

Regardless of the method used, the purpose of hyperparameter tuning is to find the best model performance, to improve accuracy and generalization. It is recommended to incorporate cross-validation when tuning hyperparameters to evaluate the model’s performance, in order to avoid overfitting and enhance stability.