keras_tuner.oracles.BayesianOptimizationOracle( objective=None, max_trials=10, num_initial_points=None, alpha=0.0001, beta=2.6, seed=None, hyperparameters=None, allow_new_entries=True, tune_new_entries=True, max_retries_per_trial=0, max_consecutive_failed_trials=3, )
Bayesian optimization oracle.
It uses Bayesian optimization with a underlying Gaussian process model. The acquisition function used is upper confidence bound (UCB), which can be found here.
keras_tuner.Objectiveinstance, 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
objectiveargument is optional when
HyperModel.fit()returns a single float as the objective to minimize.
max_trialmodels have been tested if the search space has been exhausted. Defaults to 10.
HyperParametersinstance. Can be used to override (or register in advance) hyperparameters in the search space.
hyperparametersshould be added to the search space, or not. If not, then the default value for these parameters will be used. Defaults to True.
hyperparameters. Defaults to True.
Trialif the trial crashed or the results are invalid.
Trials. When this number is reached, the search will be stopped. A
Trialis marked as failed when none of the retries succeeded.