Class: Google::Apis::MlV1::GoogleCloudMlV1HyperparameterSpec

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
generated/google/apis/ml_v1/classes.rb,
generated/google/apis/ml_v1/representations.rb,
generated/google/apis/ml_v1/representations.rb

Overview

Represents a set of hyperparameters to optimize.

Instance Attribute Summary collapse

Instance Method Summary collapse

Methods included from Core::JsonObjectSupport

#to_json

Methods included from Core::Hashable

process_value, #to_h

Constructor Details

#initialize(**args) ⇒ GoogleCloudMlV1HyperparameterSpec

Returns a new instance of GoogleCloudMlV1HyperparameterSpec



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# File 'generated/google/apis/ml_v1/classes.rb', line 338

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#enable_trial_early_stopping ⇒ Boolean Also known as: enable_trial_early_stopping?

Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping. Corresponds to the JSON property enableTrialEarlyStopping

Returns:

  • (Boolean) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 288

def enable_trial_early_stopping
  @enable_trial_early_stopping
end

#goal ⇒ String

Required. The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE. Defaults to MAXIMIZE. Corresponds to the JSON property goal

Returns:

  • (String) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 296

def goal
  @goal
end

#hyperparameter_metric_tag ⇒ String

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

Returns:

  • (String) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 305

def hyperparameter_metric_tag
  @hyperparameter_metric_tag
end

#max_parallel_trials ⇒ Fixnum

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

Returns:

  • (Fixnum) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 317

def max_parallel_trials
  @max_parallel_trials
end

#max_trials ⇒ Fixnum

Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one. Corresponds to the JSON property maxTrials

Returns:

  • (Fixnum) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 324

def max_trials
  @max_trials
end

#params ⇒ Array<Google::Apis::MlV1::GoogleCloudMlV1ParameterSpec>

Required. The set of parameters to tune. Corresponds to the JSON property params

Returns:



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# File 'generated/google/apis/ml_v1/classes.rb', line 329

def params
  @params
end

#resume_previous_job_id ⇒ String

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

Returns:

  • (String) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 336

def resume_previous_job_id
  @resume_previous_job_id
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'generated/google/apis/ml_v1/classes.rb', line 343

def update!(**args)
  @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_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