Define random sampling over a hyperparameter search space

In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.

random_parameter_sampling(parameter_space, properties = NULL)

Arguments

parameter_space	A named list containing each parameter and its distribution, e.g. list("parameter" = distribution).
properties	A named list of additional properties for the algorithm.

Value

The RandomParameterSampling object.

Details

In this sampling algorithm, parameter values are chosen from a set of discrete values or a distribution over a continuous range. Functions you can use include: choice(), randint(), uniform(), quniform(), loguniform(), qloguniform(), normal(), qnormal(), lognormal(), and qlognormal().

See also

choice(), randint(), uniform(), quniform(), loguniform(), qloguniform(), normal(), qnormal(), lognormal(), qlognormal()

Examples

if (FALSE) {
param_sampling <- random_parameter_sampling(list("learning_rate" = normal(10, 3),
                                                 "keep_probability" = uniform(0.05, 0.1),
                                                 "batch_size" = choice(c(16, 32, 64, 128))))
}