random_parameter_sampling: Define random sampling over a hyperparameter search space

In azuremlsdk: Interface to the 'Azure Machine Learning' 'SDK'

Description

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.

Usage

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

## Not run: 
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))))

## End(Not run)

azuremlsdk documentation built on Oct. 23, 2020, 8:22 p.m.