ยป Keras API reference / KerasTuner / Tuners / BayesianOptimization Tuner

BayesianOptimization Tuner

BayesianOptimization class

keras_tuner.BayesianOptimization(
    hypermodel,
    objective,
    max_trials,
    num_initial_points=2,
    alpha=0.0001,
    beta=2.6,
    seed=None,
    hyperparameters=None,
    tune_new_entries=True,
    allow_new_entries=True,
    **kwargs
)

BayesianOptimization tuning with Gaussian process.

Arguments

  • hypermodel: A HyperModel instance (or callable that takes hyperparameters and returns a Model instance).
  • objective: A string or keras_tuner.Objective instance. If a string, the direction of the optimization (min or max) will be inferred.
  • max_trials: Integer, the total number of trials (model configurations) to test at most. Note that the oracle may interrupt the search before max_trial models have been tested if the search space has been exhausted.
  • num_initial_points: Optional number of randomly generated samples as initial training data for Bayesian optimization. If left unspecified, a value of 3 times the dimensionality of the hyperparameter space is used.
  • alpha: Float, the value added to the diagonal of the kernel matrix during fitting. It represents the expected amount of noise in the observed performances in Bayesian optimization. Defaults to 1e-4.
  • beta: Float, the balancing factor of exploration and exploitation. The larger it is, the more explorative it is. Defaults to 2.6.
  • seed: Optional integer, the random seed.
  • hyperparameters: Optional HyperParameters instance. Can be used to override (or register in advance) hyperparameters in the search space.
  • tune_new_entries: Boolean, whether hyperparameter entries that are requested by the hypermodel but that were not specified in hyperparameters should be added to the search space, or not. If not, then the default value for these parameters will be used. Defaults to True.
  • allow_new_entries: Boolean, whether the hypermodel is allowed to request hyperparameter entries not listed in hyperparameters. Defaults to True.
  • **kwargs: Keyword arguments relevant to all Tuner subclasses. Please see the docstring for Tuner.