Packages

c

org.tensorflow.metadata.v0

ProblemStatement

final class ProblemStatement extends GeneratedMessageV3 with ProblemStatementOrBuilder

Protobuf type tensorflow.metadata.v0.ProblemStatement

Source
ProblemStatement.java
Linear Supertypes
ProblemStatementOrBuilder, GeneratedMessageV3, Serializable, AbstractMessage, Message, MessageOrBuilder, AbstractMessageLite[MessageType, BuilderType], MessageLite, MessageLiteOrBuilder, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. ProblemStatement
  2. ProblemStatementOrBuilder
  3. GeneratedMessageV3
  4. Serializable
  5. AbstractMessage
  6. Message
  7. MessageOrBuilder
  8. AbstractMessageLite
  9. MessageLite
  10. MessageLiteOrBuilder
  11. AnyRef
  12. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. Protected

Value Members

  1. def equals(obj: AnyRef): Boolean
    Definition Classes
    ProblemStatement → AbstractMessage → Message → AnyRef → Any
    Annotations
    @Override()
  2. def findInitializationErrors(): List[String]
    Definition Classes
    AbstractMessage → MessageOrBuilder
  3. def getAllFields(): Map[FieldDescriptor, AnyRef]
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  4. def getDefaultInstanceForType(): ProblemStatement
    Definition Classes
    ProblemStatement → MessageOrBuilder → MessageLiteOrBuilder
    Annotations
    @Override()
  5. 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.

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  6. 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.

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  7. def getDescriptorForType(): Descriptor
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  8. 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.

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  9. 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.

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  10. def getField(field: FieldDescriptor): AnyRef
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  11. def getInitializationErrorString(): String
    Definition Classes
    AbstractMessage → MessageOrBuilder
  12. 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;

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  13. 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;

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  14. 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;

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  15. 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;

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  16. 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;

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  17. def getOneofFieldDescriptor(oneof: OneofDescriptor): FieldDescriptor
    Definition Classes
    GeneratedMessageV3 → AbstractMessage → MessageOrBuilder
  18. 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.

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
  19. 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.

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
  20. def getOwnerCount(): Int

    repeated string owner = 3;

    repeated string owner = 3;

    returns

    The count of owner.

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
  21. def getOwnerList(): ProtocolStringList

    repeated string owner = 3;

    repeated string owner = 3;

    returns

    A list containing the owner.

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
  22. def getParserForType(): Parser[ProblemStatement]
    Definition Classes
    ProblemStatement → GeneratedMessageV3 → Message → MessageLite
    Annotations
    @Override()
  23. def getRepeatedField(field: FieldDescriptor, index: Int): AnyRef
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  24. def getRepeatedFieldCount(field: FieldDescriptor): Int
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  25. def getSerializedSize(): Int
    Definition Classes
    ProblemStatement → GeneratedMessageV3 → AbstractMessage → MessageLite
    Annotations
    @Override()
  26. 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;

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  27. 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;

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  28. 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;

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  29. 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;

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  30. 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;

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override()
  31. def getUnknownFields(): UnknownFieldSet
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  32. def hasField(field: FieldDescriptor): Boolean
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  33. def hasOneof(oneof: OneofDescriptor): Boolean
    Definition Classes
    GeneratedMessageV3 → AbstractMessage → MessageOrBuilder
  34. def hashCode(): Int
    Definition Classes
    ProblemStatement → AbstractMessage → Message → AnyRef → Any
    Annotations
    @Override()
  35. final def isInitialized(): Boolean
    Definition Classes
    ProblemStatement → GeneratedMessageV3 → AbstractMessage → MessageLiteOrBuilder
    Annotations
    @Override()
  36. def newBuilderForType(): Builder
    Definition Classes
    ProblemStatement → Message → MessageLite
    Annotations
    @Override()
  37. def toBuilder(): Builder
    Definition Classes
    ProblemStatement → Message → MessageLite
    Annotations
    @Override()
  38. def toByteArray(): Array[Byte]
    Definition Classes
    AbstractMessageLite → MessageLite
  39. def toByteString(): ByteString
    Definition Classes
    AbstractMessageLite → MessageLite
  40. final def toString(): String
    Definition Classes
    AbstractMessage → Message → AnyRef → Any
  41. def writeDelimitedTo(output: OutputStream): Unit
    Definition Classes
    AbstractMessageLite → MessageLite
    Annotations
    @throws(classOf[java.io.IOException])
  42. def writeTo(output: CodedOutputStream): Unit
    Definition Classes
    ProblemStatement → GeneratedMessageV3 → AbstractMessage → MessageLite
    Annotations
    @Override()
  43. def writeTo(output: OutputStream): Unit
    Definition Classes
    AbstractMessageLite → MessageLite
    Annotations
    @throws(classOf[java.io.IOException])

Deprecated Value Members

  1. def getMultiObjective(): Boolean

    bool multi_objective = 8 [deprecated = true];

    bool multi_objective = 8 [deprecated = true];

    returns

    The multiObjective.

    Definition Classes
    ProblemStatement → ProblemStatementOrBuilder
    Annotations
    @Override() @Deprecated
    Deprecated