ยป Keras API reference / KerasTuner / Tuners / BayesianOptimization Tuner

BayesianOptimization Tuner

[source]

BayesianOptimization class

keras_tuner.BayesianOptimization(
    hypermodel=None,
    objective=None,
    max_trials=10,
    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: Instance of HyperModel class (or callable that takes hyperparameters and returns a Model instance). It is optional when Tuner.run_trial() is overriden and does not use self.hypermodel.
  • objective: A string, keras_tuner.Objective instance, or a list of keras_tuner.Objectives and strings. If a string, the direction of the optimization (min or max) will be inferred. If a list of keras_tuner.Objective, we will minimize the sum of all the objectives to minimize subtracting the sum of all the objectives to maximize. The objective argument is optional when Tuner.run_trial() or HyperModel.fit() returns a single float as the objective to minimize.
  • 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. Defaults to 10.
  • 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.