GoogleCloudMlV1HyperparameterSpecResponse class

Represents a set of hyperparameters to optimize.

Constructors

GoogleCloudMlV1HyperparameterSpecResponse({required Input<String> algorithm, required Input<bool> enableTrialEarlyStopping, required Input<String> goal, required Input<String> hyperparameterMetricTag, required Input<int> maxFailedTrials, required Input<int> maxParallelTrials, required Input<int> maxTrials, required Input<List<GoogleCloudMlV1ParameterSpecResponse>> params, required Input<String> resumePreviousJobId})
Creates a new GoogleCloudMlV1HyperparameterSpecResponse. algorithm Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified. enableTrialEarlyStopping Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping. goal The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE. Defaults to MAXIMIZE. hyperparameterMetricTag 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. maxFailedTrials 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. maxParallelTrials 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. maxTrials Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one. params The set of parameters to tune. resumePreviousJobId 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.
const
GoogleCloudMlV1HyperparameterSpecResponse.fromMap(Map<String, dynamic> map)
factory

Properties

algorithm → Input<String>
Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
final
enableTrialEarlyStopping → Input<bool>
Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
final
goal → Input<String>
The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE. Defaults to MAXIMIZE.
final
hashCode → int
The hash code for this object.
no setterinherited
hyperparameterMetricTag → Input<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.
final
maxFailedTrials → Input<int>
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.
final
maxParallelTrials → Input<int>
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.
final
maxTrials → Input<int>
Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
final
params → Input<List<GoogleCloudMlV1ParameterSpecResponse>>
The set of parameters to tune.
final
resumePreviousJobId → Input<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.
final
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited

Methods

noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
toMap() → Map<String, dynamic>
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited