trait MetaOptimizationTargetOrBuilder 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
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- MessageOrBuilder
- abstract def getField(field: FieldDescriptor): AnyRef
- Definition Classes
- MessageOrBuilder
- abstract def getInitializationErrorString(): String
- Definition Classes
- MessageOrBuilder
- abstract def getObjectiveCombinationCase(): ObjectiveCombinationCase
- abstract def getOneofFieldDescriptor(oneof: OneofDescriptor): FieldDescriptor
- Definition Classes
- MessageOrBuilder
- abstract def getPerformanceMetric(): PerformanceMetric
The performance metric to be evaluated. The prediction or classification is based upon the task. The label is from the type of the task, or from the override_task.
The performance metric to be evaluated. The prediction or classification is based upon the task. The label is from the type of the task, or from the override_task.
.tensorflow.metadata.v0.PerformanceMetric performance_metric = 3;- returns
The performanceMetric.
- abstract def getPerformanceMetricOrBuilder(): PerformanceMetricOrBuilder
The performance metric to be evaluated. The prediction or classification is based upon the task. The label is from the type of the task, or from the override_task.
The performance metric to be evaluated. The prediction or classification is based upon the task. The label is from the type of the task, or from the override_task.
.tensorflow.metadata.v0.PerformanceMetric performance_metric = 3; - abstract def getRepeatedField(field: FieldDescriptor, index: Int): AnyRef
- Definition Classes
- MessageOrBuilder
- abstract def getRepeatedFieldCount(field: FieldDescriptor): Int
- Definition Classes
- MessageOrBuilder
- abstract def getTaskName(): String
The name of a task in this problem statement producing the prediction or classification for the metric.
The name of a task in this problem statement producing the prediction or classification for the metric.
string task_name = 1;- returns
The taskName.
- abstract def getTaskNameBytes(): ByteString
The name of a task in this problem statement producing the prediction or classification for the metric.
The name of a task in this problem statement producing the prediction or classification for the metric.
string task_name = 1;- returns
The bytes for taskName.
- abstract def getThresholdConfig(): ThresholdConfig
Secondary meta optimization targets can be thresholded, meaning that the optimization process prefers solutions above (or below) the threshold, but need not prefer solutions higher (or lower) on the metric if the threshold is met.
Secondary meta optimization targets can be thresholded, meaning that the optimization process prefers solutions above (or below) the threshold, but need not prefer solutions higher (or lower) on the metric if the threshold is met.
.tensorflow.metadata.v0.MetaOptimizationTarget.ThresholdConfig threshold_config = 5;- returns
The thresholdConfig.
- abstract def getThresholdConfigOrBuilder(): ThresholdConfigOrBuilder
Secondary meta optimization targets can be thresholded, meaning that the optimization process prefers solutions above (or below) the threshold, but need not prefer solutions higher (or lower) on the metric if the threshold is met.
Secondary meta optimization targets can be thresholded, meaning that the optimization process prefers solutions above (or below) the threshold, but need not prefer solutions higher (or lower) on the metric if the threshold is met.
.tensorflow.metadata.v0.MetaOptimizationTarget.ThresholdConfig threshold_config = 5; - 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 hasPerformanceMetric(): Boolean
The performance metric to be evaluated. The prediction or classification is based upon the task. The label is from the type of the task, or from the override_task.
The performance metric to be evaluated. The prediction or classification is based upon the task. The label is from the type of the task, or from the override_task.
.tensorflow.metadata.v0.PerformanceMetric performance_metric = 3;- returns
Whether the performanceMetric field is set.
- abstract def hasThresholdConfig(): Boolean
Secondary meta optimization targets can be thresholded, meaning that the optimization process prefers solutions above (or below) the threshold, but need not prefer solutions higher (or lower) on the metric if the threshold is met.
Secondary meta optimization targets can be thresholded, meaning that the optimization process prefers solutions above (or below) the threshold, but need not prefer solutions higher (or lower) on the metric if the threshold is met.
.tensorflow.metadata.v0.MetaOptimizationTarget.ThresholdConfig threshold_config = 5;- returns
Whether the thresholdConfig field is set.
- abstract def isInitialized(): Boolean
- Definition Classes
- MessageLiteOrBuilder
- abstract def getWeight(): Double
If a model spec has multiple meta optimization targets, the weight of each can be specified. The final objective is then a weighted combination of the multiple objectives. If not specified, value is 1.
If a model spec has multiple meta optimization targets, the weight of each can be specified. The final objective is then a weighted combination of the multiple objectives. If not specified, value is 1.
double weight = 4 [deprecated = true];- returns
The weight.
- Annotations
- @Deprecated
- Deprecated
- abstract def hasWeight(): Boolean
If a model spec has multiple meta optimization targets, the weight of each can be specified. The final objective is then a weighted combination of the multiple objectives. If not specified, value is 1.
If a model spec has multiple meta optimization targets, the weight of each can be specified. The final objective is then a weighted combination of the multiple objectives. If not specified, value is 1.
double weight = 4 [deprecated = true];- returns
Whether the weight field is set.
- Annotations
- @Deprecated
- Deprecated
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