Tuner — Tuner_class • kerastuneR

Tuner class for Keras models.

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`.

Value

a tuner object

Details

May be subclassed to create new tuners.