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public sealed class ActiveLearningConfig : IMessage<ActiveLearningConfig>, IEquatable<ActiveLearningConfig>, IDeepCloneable<ActiveLearningConfig>, IBufferMessage, IMessage
Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
Implements
IMessage<ActiveLearningConfig>, IEquatable<ActiveLearningConfig>, IDeepCloneable<ActiveLearningConfig>, IBufferMessage, IMessageNamespace
Google.Cloud.AIPlatform.V1Assembly
Google.Cloud.AIPlatform.V1.dll
Constructors
ActiveLearningConfig()
public ActiveLearningConfig()
ActiveLearningConfig(ActiveLearningConfig)
public ActiveLearningConfig(ActiveLearningConfig other)
Parameter | |
---|---|
Name | Description |
other | ActiveLearningConfig |
Properties
HumanLabelingBudgetCase
public ActiveLearningConfig.HumanLabelingBudgetOneofCase HumanLabelingBudgetCase { get; }
Property Value | |
---|---|
Type | Description |
ActiveLearningConfig.HumanLabelingBudgetOneofCase |
MaxDataItemCount
public long MaxDataItemCount { get; set; }
Max number of human labeled DataItems.
Property Value | |
---|---|
Type | Description |
Int64 |
MaxDataItemPercentage
public int MaxDataItemPercentage { get; set; }
Max percent of total DataItems for human labeling.
Property Value | |
---|---|
Type | Description |
Int32 |
SampleConfig
public SampleConfig SampleConfig { get; set; }
Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
Property Value | |
---|---|
Type | Description |
SampleConfig |
TrainingConfig
public TrainingConfig TrainingConfig { get; set; }
CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
Property Value | |
---|---|
Type | Description |
TrainingConfig |