Class GoogleCloudMlV1HyperparameterSpec

Represents a set of hyperparameters to optimize.

Inheritance

object

GoogleCloudMlV1HyperparameterSpec

Implements

IDirectResponseSchema

Inherited Members

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Namespace: Google.Apis.CloudMachineLearningEngine.v1.Data

Assembly: Google.Apis.CloudMachineLearningEngine.v1.dll

Syntax

public class GoogleCloudMlV1HyperparameterSpec : IDirectResponseSchema

Properties

Algorithm

Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.

Declaration

[JsonProperty("algorithm")]
public virtual string Algorithm { get; set; }

Property Value

Type	Description
string

ETag

The ETag of the item.

Declaration

public virtual string ETag { get; set; }

Property Value

Type	Description
string

EnableTrialEarlyStopping

Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.

Declaration

[JsonProperty("enableTrialEarlyStopping")]
public virtual bool? EnableTrialEarlyStopping { get; set; }

Property Value

Type	Description
bool?

Goal

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

Declaration

[JsonProperty("goal")]
public virtual string Goal { get; set; }

Property Value

Type	Description
string

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.

Declaration

[JsonProperty("hyperparameterMetricTag")]
public virtual string HyperparameterMetricTag { get; set; }

Property Value

Type	Description
string

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.

Declaration

[JsonProperty("maxFailedTrials")]
public virtual int? MaxFailedTrials { get; set; }

Property Value

Type	Description
int?

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.

Declaration

[JsonProperty("maxParallelTrials")]
public virtual int? MaxParallelTrials { get; set; }

Property Value

Type	Description
int?

MaxTrials

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

Declaration

[JsonProperty("maxTrials")]
public virtual int? MaxTrials { get; set; }

Property Value

Type	Description
int?

Params__

Required. The set of parameters to tune.

Declaration

[JsonProperty("params")]
public virtual IList<GoogleCloudMlV1ParameterSpec> Params__ { get; set; }

Property Value

Type	Description
IList<GoogleCloudMlV1ParameterSpec>

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.

Declaration

[JsonProperty("resumePreviousJobId")]
public virtual string ResumePreviousJobId { get; set; }

Property Value

Type	Description
string

Implements

IDirectResponseSchema