Packages

t

org.tensorflow.metadata.v0

ProblemStatementOrBuilder

trait ProblemStatementOrBuilder extends MessageOrBuilder

Annotations
@Generated()
Source
ProblemStatementOrBuilder.java
Linear Supertypes
MessageOrBuilder, MessageLiteOrBuilder, AnyRef, Any
Known Subclasses
ProblemStatement
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Inherited
  1. ProblemStatementOrBuilder
  2. MessageOrBuilder
  3. MessageLiteOrBuilder
  4. AnyRef
  5. Any
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Visibility
  1. Public
  2. Protected

Abstract Value Members

  1. abstract def findInitializationErrors(): List[String]
    Definition Classes
    MessageOrBuilder
  2. abstract def getAllFields(): Map[FieldDescriptor, AnyRef]
    Definition Classes
    MessageOrBuilder
  3. abstract def getDefaultInstanceForType(): Message
    Definition Classes
    MessageOrBuilder → MessageLiteOrBuilder
  4. abstract def getDescription(): String

    Description of the problem statement. For example, should describe how
the problem statement was arrived at: what experiments were run, what
side-by-sides were considered.


    Description of the problem statement. For example, should describe how
the problem statement was arrived at: what experiments were run, what
side-by-sides were considered.

    string description = 2;

    returns

    The description.

  5. abstract def getDescriptionBytes(): ByteString

    Description of the problem statement. For example, should describe how
the problem statement was arrived at: what experiments were run, what
side-by-sides were considered.


    Description of the problem statement. For example, should describe how
the problem statement was arrived at: what experiments were run, what
side-by-sides were considered.

    string description = 2;

    returns

    The bytes for description.

  6. abstract def getDescriptorForType(): Descriptor
    Definition Classes
    MessageOrBuilder
  7. abstract def getEnvironment(): String

    The environment of the ProblemStatement (optional). Specifies an
environment string in the SchemaProto.


    The environment of the ProblemStatement (optional). Specifies an
environment string in the SchemaProto.

    string environment = 4;

    returns

    The environment.

  8. abstract def getEnvironmentBytes(): ByteString

    The environment of the ProblemStatement (optional). Specifies an
environment string in the SchemaProto.


    The environment of the ProblemStatement (optional). Specifies an
environment string in the SchemaProto.

    string environment = 4;

    returns

    The bytes for environment.

  9. abstract def getField(field: FieldDescriptor): AnyRef
    Definition Classes
    MessageOrBuilder
  10. abstract def getInitializationErrorString(): String
    Definition Classes
    MessageOrBuilder
  11. abstract def getMetaOptimizationTarget(index: Int): MetaOptimizationTarget

    The target used for meta-optimization. This is used to compare multiple
solutions for this problem. For example, if two solutions have different
candidates, a tuning tool can use meta_optimization_target to decide which
candidate performs the best.
A repeated meta-optimization target implies the weighted sum of the
meta_optimization targets of any non-thresholded metrics.


    The target used for meta-optimization. This is used to compare multiple
solutions for this problem. For example, if two solutions have different
candidates, a tuning tool can use meta_optimization_target to decide which
candidate performs the best.
A repeated meta-optimization target implies the weighted sum of the
meta_optimization targets of any non-thresholded metrics.

    repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;

  12. abstract def getMetaOptimizationTargetCount(): Int

    The target used for meta-optimization. This is used to compare multiple
solutions for this problem. For example, if two solutions have different
candidates, a tuning tool can use meta_optimization_target to decide which
candidate performs the best.
A repeated meta-optimization target implies the weighted sum of the
meta_optimization targets of any non-thresholded metrics.


    The target used for meta-optimization. This is used to compare multiple
solutions for this problem. For example, if two solutions have different
candidates, a tuning tool can use meta_optimization_target to decide which
candidate performs the best.
A repeated meta-optimization target implies the weighted sum of the
meta_optimization targets of any non-thresholded metrics.

    repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;

  13. abstract def getMetaOptimizationTargetList(): List[MetaOptimizationTarget]

    The target used for meta-optimization. This is used to compare multiple
solutions for this problem. For example, if two solutions have different
candidates, a tuning tool can use meta_optimization_target to decide which
candidate performs the best.
A repeated meta-optimization target implies the weighted sum of the
meta_optimization targets of any non-thresholded metrics.


    The target used for meta-optimization. This is used to compare multiple
solutions for this problem. For example, if two solutions have different
candidates, a tuning tool can use meta_optimization_target to decide which
candidate performs the best.
A repeated meta-optimization target implies the weighted sum of the
meta_optimization targets of any non-thresholded metrics.

    repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;

  14. abstract def getMetaOptimizationTargetOrBuilder(index: Int): MetaOptimizationTargetOrBuilder

    The target used for meta-optimization. This is used to compare multiple
solutions for this problem. For example, if two solutions have different
candidates, a tuning tool can use meta_optimization_target to decide which
candidate performs the best.
A repeated meta-optimization target implies the weighted sum of the
meta_optimization targets of any non-thresholded metrics.


    The target used for meta-optimization. This is used to compare multiple
solutions for this problem. For example, if two solutions have different
candidates, a tuning tool can use meta_optimization_target to decide which
candidate performs the best.
A repeated meta-optimization target implies the weighted sum of the
meta_optimization targets of any non-thresholded metrics.

    repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;

  15. abstract def getMetaOptimizationTargetOrBuilderList(): List[_ <: MetaOptimizationTargetOrBuilder]

    The target used for meta-optimization. This is used to compare multiple
solutions for this problem. For example, if two solutions have different
candidates, a tuning tool can use meta_optimization_target to decide which
candidate performs the best.
A repeated meta-optimization target implies the weighted sum of the
meta_optimization targets of any non-thresholded metrics.


    The target used for meta-optimization. This is used to compare multiple
solutions for this problem. For example, if two solutions have different
candidates, a tuning tool can use meta_optimization_target to decide which
candidate performs the best.
A repeated meta-optimization target implies the weighted sum of the
meta_optimization targets of any non-thresholded metrics.

    repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;

  16. abstract def getOneofFieldDescriptor(oneof: OneofDescriptor): FieldDescriptor
    Definition Classes
    MessageOrBuilder
  17. abstract def getOwner(index: Int): String

    repeated string owner = 3;

    repeated string owner = 3;

    index

    The index of the element to return.

    returns

    The owner at the given index.

  18. abstract def getOwnerBytes(index: Int): ByteString

    repeated string owner = 3;

    repeated string owner = 3;

    index

    The index of the value to return.

    returns

    The bytes of the owner at the given index.

  19. abstract def getOwnerCount(): Int

    repeated string owner = 3;

    repeated string owner = 3;

    returns

    The count of owner.

  20. abstract def getOwnerList(): List[String]

    repeated string owner = 3;

    repeated string owner = 3;

    returns

    A list containing the owner.

  21. abstract def getRepeatedField(field: FieldDescriptor, index: Int): AnyRef
    Definition Classes
    MessageOrBuilder
  22. abstract def getRepeatedFieldCount(field: FieldDescriptor): Int
    Definition Classes
    MessageOrBuilder
  23. abstract def getTasks(index: Int): Task

    Tasks for heads of the generated model. This field is repeated because some
models are multi-task models. Each task should have a unique name.
If you wish to directly optimize this problem statement, you need
to specify the objective in the task.


    Tasks for heads of the generated model. This field is repeated because some
models are multi-task models. Each task should have a unique name.
If you wish to directly optimize this problem statement, you need
to specify the objective in the task.

    repeated .tensorflow.metadata.v0.Task tasks = 9;

  24. abstract def getTasksCount(): Int

    Tasks for heads of the generated model. This field is repeated because some
models are multi-task models. Each task should have a unique name.
If you wish to directly optimize this problem statement, you need
to specify the objective in the task.


    Tasks for heads of the generated model. This field is repeated because some
models are multi-task models. Each task should have a unique name.
If you wish to directly optimize this problem statement, you need
to specify the objective in the task.

    repeated .tensorflow.metadata.v0.Task tasks = 9;

  25. abstract def getTasksList(): List[Task]

    Tasks for heads of the generated model. This field is repeated because some
models are multi-task models. Each task should have a unique name.
If you wish to directly optimize this problem statement, you need
to specify the objective in the task.


    Tasks for heads of the generated model. This field is repeated because some
models are multi-task models. Each task should have a unique name.
If you wish to directly optimize this problem statement, you need
to specify the objective in the task.

    repeated .tensorflow.metadata.v0.Task tasks = 9;

  26. abstract def getTasksOrBuilder(index: Int): TaskOrBuilder

    Tasks for heads of the generated model. This field is repeated because some
models are multi-task models. Each task should have a unique name.
If you wish to directly optimize this problem statement, you need
to specify the objective in the task.


    Tasks for heads of the generated model. This field is repeated because some
models are multi-task models. Each task should have a unique name.
If you wish to directly optimize this problem statement, you need
to specify the objective in the task.

    repeated .tensorflow.metadata.v0.Task tasks = 9;

  27. abstract def getTasksOrBuilderList(): List[_ <: TaskOrBuilder]

    Tasks for heads of the generated model. This field is repeated because some
models are multi-task models. Each task should have a unique name.
If you wish to directly optimize this problem statement, you need
to specify the objective in the task.


    Tasks for heads of the generated model. This field is repeated because some
models are multi-task models. Each task should have a unique name.
If you wish to directly optimize this problem statement, you need
to specify the objective in the task.

    repeated .tensorflow.metadata.v0.Task tasks = 9;

  28. abstract def getUnknownFields(): UnknownFieldSet
    Definition Classes
    MessageOrBuilder
  29. abstract def hasField(field: FieldDescriptor): Boolean
    Definition Classes
    MessageOrBuilder
  30. abstract def hasOneof(oneof: OneofDescriptor): Boolean
    Definition Classes
    MessageOrBuilder
  31. abstract def isInitialized(): Boolean
    Definition Classes
    MessageLiteOrBuilder
  32. abstract def getMultiObjective(): Boolean

    bool multi_objective = 8 [deprecated = true];

    bool multi_objective = 8 [deprecated = true];

    returns

    The multiObjective.

    Annotations
    @Deprecated
    Deprecated

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  9. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  11. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  12. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  14. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  15. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  16. def toString(): String
    Definition Classes
    AnyRef → Any
  17. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  18. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  19. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()

Inherited from MessageOrBuilder

Inherited from MessageLiteOrBuilder

Inherited from AnyRef

Inherited from Any

Ungrouped