Bayesian optimization oracle.

BayesianOptimization( objective, max_trials, num_initial_points = NULL, alpha = 1e-04, beta = 2.6, seed = NULL, hyperparameters = NULL, allow_new_entries = TRUE, tune_new_entries = TRUE )

objective | String or `kerastuner.Objective`. If a string, the direction of the optimization (min or max) will be inferred. |
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max_trials | Int. 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) Int. The number of randomly generated samples as initial training data for Bayesian optimization. If not specified, a value of 3 times the dimensionality of the hyperparameter space is used. |

alpha | Float. 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. |

beta | Float. The balancing factor of exploration and exploitation. The larger it is, the more explorative it is. |

seed | Int. Random seed. |

hyperparameters | HyperParameters class instance. Can be used to override (or register in advance) hyperparamters in the search space. |

allow_new_entries | Whether the hypermodel is allowed to request hyperparameter entries not listed in `hyperparameters`. |

tune_new_entries | 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. |

BayesianOptimization tuning with Gaussian process

It uses Bayesian optimization with a underlying Gaussian process model. The acquisition function used is upper confidence bound (UCB), which can be found in the following link: https://www.cse.wustl.edu/~garnett/cse515t/spring_2015/files/lecture_notes/12.pdf

https://www.cse.wustl.edu/~garnett/cse515t/spring_2015/files/lecture_notes/12.pdf

if (FALSE) { # The usage of 'tf$keras' library(tensorflow) tf$keras$Input(shape=list(28L, 28L, 1L)) }