trait TaskOrBuilder extends MessageOrBuilder
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Abstract Value Members
- abstract def findInitializationErrors(): List[String]
- Definition Classes
- MessageOrBuilder
- abstract def getAllFields(): Map[FieldDescriptor, AnyRef]
- Definition Classes
- MessageOrBuilder
- abstract def getDefaultInstanceForType(): Message
- Definition Classes
- MessageOrBuilder → MessageLiteOrBuilder
- abstract def getDescriptorForType(): Descriptor
- Definition Classes
- MessageOrBuilder
- abstract def getField(field: FieldDescriptor): AnyRef
- Definition Classes
- MessageOrBuilder
- abstract def getInitializationErrorString(): String
- Definition Classes
- MessageOrBuilder
- abstract def getIsAuxiliary(): Boolean
True to indicate the task is an auxiliary task in a multi-task setting. Auxiliary tasks are of minor relevance for the application and they are added only to improve the performance on a primary task (by providing additional regularization or data augmentation), and thus are not considered in the meta optimization process (but may be utilized in the learner optimization).
True to indicate the task is an auxiliary task in a multi-task setting. Auxiliary tasks are of minor relevance for the application and they are added only to improve the performance on a primary task (by providing additional regularization or data augmentation), and thus are not considered in the meta optimization process (but may be utilized in the learner optimization).
bool is_auxiliary = 6;
- returns
The isAuxiliary.
- abstract def getName(): String
The task name. Tasks within the same ProblemStatement should have unique names. This a REQUIRED field in case of multi-task learning problems.
The task name. Tasks within the same ProblemStatement should have unique names. This a REQUIRED field in case of multi-task learning problems.
string name = 5;
- returns
The name.
- abstract def getNameBytes(): ByteString
The task name. Tasks within the same ProblemStatement should have unique names. This a REQUIRED field in case of multi-task learning problems.
The task name. Tasks within the same ProblemStatement should have unique names. This a REQUIRED field in case of multi-task learning problems.
string name = 5;
- returns
The bytes for name.
- abstract def getOneofFieldDescriptor(oneof: OneofDescriptor): FieldDescriptor
- Definition Classes
- MessageOrBuilder
- abstract def getPerformanceMetric(index: Int): PerformanceMetric
This field includes performance metrics of this head that are important to the problem owner and need to be monitored and reported. However, unlike fields such as "meta_optimization_target", these metrics are not not automatically used in meta-optimization.
This field includes performance metrics of this head that are important to the problem owner and need to be monitored and reported. However, unlike fields such as "meta_optimization_target", these metrics are not not automatically used in meta-optimization.
repeated .tensorflow.metadata.v0.PerformanceMetric performance_metric = 4;
- abstract def getPerformanceMetricCount(): Int
This field includes performance metrics of this head that are important to the problem owner and need to be monitored and reported. However, unlike fields such as "meta_optimization_target", these metrics are not not automatically used in meta-optimization.
This field includes performance metrics of this head that are important to the problem owner and need to be monitored and reported. However, unlike fields such as "meta_optimization_target", these metrics are not not automatically used in meta-optimization.
repeated .tensorflow.metadata.v0.PerformanceMetric performance_metric = 4;
- abstract def getPerformanceMetricList(): List[PerformanceMetric]
This field includes performance metrics of this head that are important to the problem owner and need to be monitored and reported. However, unlike fields such as "meta_optimization_target", these metrics are not not automatically used in meta-optimization.
This field includes performance metrics of this head that are important to the problem owner and need to be monitored and reported. However, unlike fields such as "meta_optimization_target", these metrics are not not automatically used in meta-optimization.
repeated .tensorflow.metadata.v0.PerformanceMetric performance_metric = 4;
- abstract def getPerformanceMetricOrBuilder(index: Int): PerformanceMetricOrBuilder
This field includes performance metrics of this head that are important to the problem owner and need to be monitored and reported. However, unlike fields such as "meta_optimization_target", these metrics are not not automatically used in meta-optimization.
This field includes performance metrics of this head that are important to the problem owner and need to be monitored and reported. However, unlike fields such as "meta_optimization_target", these metrics are not not automatically used in meta-optimization.
repeated .tensorflow.metadata.v0.PerformanceMetric performance_metric = 4;
- abstract def getPerformanceMetricOrBuilderList(): List[_ <: PerformanceMetricOrBuilder]
This field includes performance metrics of this head that are important to the problem owner and need to be monitored and reported. However, unlike fields such as "meta_optimization_target", these metrics are not not automatically used in meta-optimization.
This field includes performance metrics of this head that are important to the problem owner and need to be monitored and reported. However, unlike fields such as "meta_optimization_target", these metrics are not not automatically used in meta-optimization.
repeated .tensorflow.metadata.v0.PerformanceMetric performance_metric = 4;
- abstract def getRepeatedField(field: FieldDescriptor, index: Int): AnyRef
- Definition Classes
- MessageOrBuilder
- abstract def getRepeatedFieldCount(field: FieldDescriptor): Int
- Definition Classes
- MessageOrBuilder
- abstract def getType(): Type
Specification of the label and weight columns, and the type of the prediction or classification.
Specification of the label and weight columns, and the type of the prediction or classification.
.tensorflow.metadata.v0.Type type = 1;
- returns
The type.
- abstract def getTypeOrBuilder(): TypeOrBuilder
Specification of the label and weight columns, and the type of the prediction or classification.
Specification of the label and weight columns, and the type of the prediction or classification.
.tensorflow.metadata.v0.Type type = 1;
- abstract def getUnknownFields(): UnknownFieldSet
- Definition Classes
- MessageOrBuilder
- abstract def hasField(field: FieldDescriptor): Boolean
- Definition Classes
- MessageOrBuilder
- abstract def hasOneof(oneof: OneofDescriptor): Boolean
- Definition Classes
- MessageOrBuilder
- abstract def hasType(): Boolean
Specification of the label and weight columns, and the type of the prediction or classification.
Specification of the label and weight columns, and the type of the prediction or classification.
.tensorflow.metadata.v0.Type type = 1;
- returns
Whether the type field is set.
- abstract def isInitialized(): Boolean
- Definition Classes
- MessageLiteOrBuilder
- abstract def getTaskWeight(): Double
If a Problem is composed of mulitple sub-tasks, the weight of each task determines the importance of solving each sub-task. It is used to rank and select the best solution for multi-task problems. Not meaningful for a problem with one task. If the problem has multiple tasks and all task_weight=0 (unset) then all tasks are weighted equally.
If a Problem is composed of mulitple sub-tasks, the weight of each task determines the importance of solving each sub-task. It is used to rank and select the best solution for multi-task problems. Not meaningful for a problem with one task. If the problem has multiple tasks and all task_weight=0 (unset) then all tasks are weighted equally.
double task_weight = 2 [deprecated = true];
- returns
The taskWeight.
- Annotations
- @Deprecated
- Deprecated
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