BayesianOptimization: BayesianOptimization

Description Usage Arguments Details Value be found in the following link Examples

View source: R/bayesian_optimisation.R

Description

Bayesian optimization oracle.

Usage

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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.

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.

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

Value

BayesianOptimization tuning with Gaussian process

be found in the following link

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

Examples

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## Not run: 
# The usage of 'tf$keras'
library(tensorflow)
tf$keras$Input(shape=list(28L, 28L, 1L))

## End(Not run)

kerastuneR documentation built on Jan. 13, 2021, 6:40 p.m.