Tuner_class: Tuner
In kerastuneR: Interface to 'Keras Tuner'

Tuner_class

R Documentation

Tuner

Description

Tuner class for Keras models.

Usage

Tuner_class(
  oracle,
  hypermodel,
  max_model_size = NULL,
  optimizer = NULL,
  loss = NULL,
  metrics = NULL,
  distribution_strategy = NULL,
  directory = NULL,
  project_name = NULL,
  logger = NULL,
  tuner_id = NULL,
  overwrite = FALSE,
  executions_per_trial = 1
)

Arguments

`oracle`	Instance of Oracle class.
`hypermodel`	Instance of HyperModel class (or callable that takes hyperparameters and returns a Model instance).
`max_model_size`	Int. Maximum size of weights (in floating point coefficients) for a valid models. Models larger than this are rejected.
`optimizer`	Optional. Optimizer instance. May be used to override the 'optimizer' argument in the 'compile' step for the models. If the hypermodel does not compile the models it generates, then this argument must be specified.
`loss`	Optional. May be used to override the 'loss' argument in the 'compile' step for the models. If the hypermodel does not compile the models it generates, then this argument must be specified.
`metrics`	Optional. May be used to override the 'metrics' argument in the 'compile' step for the models. If the hypermodel does not compile the models it generates, then this argument must be specified.
`distribution_strategy`	Optional. A TensorFlow 'tf$distribute' DistributionStrategy instance. If specified, each trial will run under this scope. For example, ‘tf$distribute.MirroredStrategy([’/gpu:0, /'gpu:1])' will run each trial on two GPUs. Currently only single-worker strategies are supported.
`directory`	String. Path to the working directory (relative).
`project_name`	Name to use as prefix for files saved by this Tuner.
`logger`	Optional. Instance of Logger class, used for streaming data to Cloud Service for monitoring.
`tuner_id`	tuner_id
`overwrite`	Bool, default 'FALSE'. If 'FALSE', reloads an existing project of the same name if one is found. Otherwise, overwrites the project.
`executions_per_trial`	Integer, the number of executions (training a model from scratch, starting from a new initialization) to run per trial (model configuration). Model metrics may vary greatly depending on random initialization, hence it is often a good idea to run several executions per trial in order to evaluate the performance of a given set of hyperparameter values. **kwargs: Arguments for 'BaseTuner'.