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
)

## Arguments

objective String or kerastuner.Objective. If a string, the direction of the optimization (min or max) will be inferred. 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. (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. 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. Float. The balancing factor of exploration and exploitation. The larger it is, the more explorative it is. Int. Random seed. HyperParameters class instance. Can be used to override (or register in advance) hyperparamters in the search space. Whether the hypermodel is allowed to request hyperparameter entries not listed in hyperparameters. 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.

## Value

BayesianOptimization tuning with Gaussian process

## Details

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

## Examples


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