Cloud AI Platform v1 API - Class StudySpec.Types.ConvexAutomatedStoppingSpec (2.8.0) 

public sealed class ConvexAutomatedStoppingSpec : IMessage<StudySpec.Types.ConvexAutomatedStoppingSpec>, IEquatable<StudySpec.Types.ConvexAutomatedStoppingSpec>, IDeepCloneable<StudySpec.Types.ConvexAutomatedStoppingSpec>, IBufferMessage, IMessage

Reference documentation and code samples for the Cloud AI Platform v1 API class StudySpec.Types.ConvexAutomatedStoppingSpec.

Configuration for ConvexAutomatedStoppingSpec. When there are enough completed trials (configured by min_measurement_count), for pending trials with enough measurements and steps, the policy first computes an overestimate of the objective value at max_num_steps according to the slope of the incomplete objective value curve. No prediction can be made if the curve is completely flat. If the overestimation is worse than the best objective value of the completed trials, this pending trial will be early-stopped, but a last measurement will be added to the pending trial with max_num_steps and predicted objective value from the autoregression model.

Inheritance

Object > StudySpec.Types.ConvexAutomatedStoppingSpec

Namespace

Google.Cloud.AIPlatform.V1

Assembly

Google.Cloud.AIPlatform.V1.dll

Constructors

ConvexAutomatedStoppingSpec()

public ConvexAutomatedStoppingSpec()

ConvexAutomatedStoppingSpec(StudySpec.Types.ConvexAutomatedStoppingSpec)

public ConvexAutomatedStoppingSpec(StudySpec.Types.ConvexAutomatedStoppingSpec other)
Parameter
NameDescription
otherStudySpec.Types.ConvexAutomatedStoppingSpec

Properties

HasUpdateAllStoppedTrials

public bool HasUpdateAllStoppedTrials { get; }

Gets whether the "update_all_stopped_trials" field is set

Property Value
TypeDescription
Boolean

LearningRateParameterName

public string LearningRateParameterName { get; set; }

The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.

Property Value
TypeDescription
String

MaxStepCount

public long MaxStepCount { get; set; }

Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.

Property Value
TypeDescription
Int64

MinMeasurementCount

public long MinMeasurementCount { get; set; }

The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.

Property Value
TypeDescription
Int64

MinStepCount

public long MinStepCount { get; set; }

Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.

Property Value
TypeDescription
Int64

UpdateAllStoppedTrials

public bool UpdateAllStoppedTrials { get; set; }

ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.

Property Value
TypeDescription
Boolean

UseElapsedDuration

public bool UseElapsedDuration { get; set; }

This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.

Property Value
TypeDescription
Boolean