class Google::Apis::MlV1::GoogleCloudMlV1HyperparameterSpec

Represents a set of hyperparameters to optimize.

Attributes

algorithm[RW]

Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified. Corresponds to the JSON property `algorithm` @return [String]

enable_trial_early_stopping[RW]

Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping. Corresponds to the JSON property `enableTrialEarlyStopping` @return [Boolean]

enable_trial_early_stopping?[RW]

Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping. Corresponds to the JSON property `enableTrialEarlyStopping` @return [Boolean]

goal[RW]

Required. The type of goal to use for tuning. Available types are `MAXIMIZE` and `MINIMIZE`. Defaults to `MAXIMIZE`. Corresponds to the JSON property `goal` @return [String]

hyperparameter_metric_tag[RW]

Optional. The TensorFlow summary tag name to use for optimizing trials. For current versions of TensorFlow, this tag name should exactly match what is shown in TensorBoard, including all scopes. For versions of TensorFlow prior to 0.12, this should be only the tag passed to tf.Summary. By default, “ training/hptuning/metric” will be used. Corresponds to the JSON property `hyperparameterMetricTag` @return [String]

max_failed_trials[RW]

Optional. The number of failed trials that need to be seen before failing the hyperparameter tuning job. You can specify this field to override the default failing criteria for AI Platform hyperparameter tuning jobs. Defaults to zero, which means the service decides when a hyperparameter job should fail. Corresponds to the JSON property `maxFailedTrials` @return [Fixnum]

max_parallel_trials[RW]

Optional. The number of training trials to run concurrently. You can reduce the time it takes to perform hyperparameter tuning by adding trials in parallel. However, each trail only benefits from the information gained in completed trials. That means that a trial does not get access to the results of trials running at the same time, which could reduce the quality of the overall optimization. Each trial will use the same scale tier and machine types. Defaults to one. Corresponds to the JSON property `maxParallelTrials` @return [Fixnum]

max_trials[RW]

Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one. Corresponds to the JSON property `maxTrials` @return [Fixnum]

params[RW]

Required. The set of parameters to tune. Corresponds to the JSON property `params` @return [Array<Google::Apis::MlV1::GoogleCloudMlV1ParameterSpec>]

resume_previous_job_id[RW]

Optional. The prior hyperparameter tuning job id that users hope to continue with. The job id will be used to find the corresponding vizier study guid and resume the study. Corresponds to the JSON property `resumePreviousJobId` @return [String]

Public Class Methods

new(**args) click to toggle source
# File lib/google/apis/ml_v1/classes.rb, line 1310
def initialize(**args)
   update!(**args)
end

Public Instance Methods

update!(**args) click to toggle source

Update properties of this object

# File lib/google/apis/ml_v1/classes.rb, line 1315
def update!(**args)
  @algorithm = args[:algorithm] if args.key?(:algorithm)
  @enable_trial_early_stopping = args[:enable_trial_early_stopping] if args.key?(:enable_trial_early_stopping)
  @goal = args[:goal] if args.key?(:goal)
  @hyperparameter_metric_tag = args[:hyperparameter_metric_tag] if args.key?(:hyperparameter_metric_tag)
  @max_failed_trials = args[:max_failed_trials] if args.key?(:max_failed_trials)
  @max_parallel_trials = args[:max_parallel_trials] if args.key?(:max_parallel_trials)
  @max_trials = args[:max_trials] if args.key?(:max_trials)
  @params = args[:params] if args.key?(:params)
  @resume_previous_job_id = args[:resume_previous_job_id] if args.key?(:resume_previous_job_id)
end