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t

org.tensorflow.metadata.v0

SchemaOrBuilder

trait SchemaOrBuilder extends MessageOrBuilder

Annotations
@Generated()
Source
SchemaOrBuilder.java
Linear Supertypes
MessageOrBuilder, MessageLiteOrBuilder, AnyRef, Any
Known Subclasses
Schema
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  1. SchemaOrBuilder
  2. MessageOrBuilder
  3. MessageLiteOrBuilder
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Abstract Value Members

  1. abstract def containsTensorRepresentationGroup(key: String): Boolean

    TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.


    TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

    map<string, .tensorflow.metadata.v0.TensorRepresentationGroup> tensor_representation_group = 13;

  2. abstract def findInitializationErrors(): List[String]
    Definition Classes
    MessageOrBuilder
  3. abstract def getAllFields(): Map[FieldDescriptor, AnyRef]
    Definition Classes
    MessageOrBuilder
  4. abstract def getAnnotation(): Annotation

    Additional information about the schema as a whole. Features may also
be annotated individually.


    Additional information about the schema as a whole. Features may also
be annotated individually.

    optional .tensorflow.metadata.v0.Annotation annotation = 8;

    returns

    The annotation.

  5. abstract def getAnnotationOrBuilder(): AnnotationOrBuilder

    Additional information about the schema as a whole. Features may also
be annotated individually.


    Additional information about the schema as a whole. Features may also
be annotated individually.

    optional .tensorflow.metadata.v0.Annotation annotation = 8;

  6. abstract def getDatasetConstraints(): DatasetConstraints

    Dataset-level constraints. This is currently used for specifying
information about changes in num_examples.


    Dataset-level constraints. This is currently used for specifying
information about changes in num_examples.

    optional .tensorflow.metadata.v0.DatasetConstraints dataset_constraints = 11;

    returns

    The datasetConstraints.

  7. abstract def getDatasetConstraintsOrBuilder(): DatasetConstraintsOrBuilder

    Dataset-level constraints. This is currently used for specifying
information about changes in num_examples.


    Dataset-level constraints. This is currently used for specifying
information about changes in num_examples.

    optional .tensorflow.metadata.v0.DatasetConstraints dataset_constraints = 11;

  8. abstract def getDefaultEnvironment(index: Int): String

    Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
three models are available for the ensemble model, there may be
TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.


    Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
three models are available for the ensemble model, there may be
TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.

    repeated string default_environment = 5;

    index

    The index of the element to return.

    returns

    The defaultEnvironment at the given index.

  9. abstract def getDefaultEnvironmentBytes(index: Int): ByteString

    Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
three models are available for the ensemble model, there may be
TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.


    Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
three models are available for the ensemble model, there may be
TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.

    repeated string default_environment = 5;

    index

    The index of the value to return.

    returns

    The bytes of the defaultEnvironment at the given index.

  10. abstract def getDefaultEnvironmentCount(): Int

    Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
three models are available for the ensemble model, there may be
TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.


    Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
three models are available for the ensemble model, there may be
TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.

    repeated string default_environment = 5;

    returns

    The count of defaultEnvironment.

  11. abstract def getDefaultEnvironmentList(): List[String]

    Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
three models are available for the ensemble model, there may be
TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.


    Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
three models are available for the ensemble model, there may be
TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.

    repeated string default_environment = 5;

    returns

    A list containing the defaultEnvironment.

  12. abstract def getDefaultInstanceForType(): Message
    Definition Classes
    MessageOrBuilder → MessageLiteOrBuilder
  13. abstract def getDescriptorForType(): Descriptor
    Definition Classes
    MessageOrBuilder
  14. abstract def getFeature(index: Int): Feature

    Features described in this schema.


    Features described in this schema.

    repeated .tensorflow.metadata.v0.Feature feature = 1;

  15. abstract def getFeatureCount(): Int

    Features described in this schema.


    Features described in this schema.

    repeated .tensorflow.metadata.v0.Feature feature = 1;

  16. abstract def getFeatureList(): List[Feature]

    Features described in this schema.


    Features described in this schema.

    repeated .tensorflow.metadata.v0.Feature feature = 1;

  17. abstract def getFeatureOrBuilder(index: Int): FeatureOrBuilder

    Features described in this schema.


    Features described in this schema.

    repeated .tensorflow.metadata.v0.Feature feature = 1;

  18. abstract def getFeatureOrBuilderList(): List[_ <: FeatureOrBuilder]

    Features described in this schema.


    Features described in this schema.

    repeated .tensorflow.metadata.v0.Feature feature = 1;

  19. abstract def getField(field: FieldDescriptor): AnyRef
    Definition Classes
    MessageOrBuilder
  20. abstract def getFloatDomain(index: Int): FloatDomain

    TOP LEVEL FLOAT AND INT DOMAINS ARE UNSUPPORTED IN TFDV.
TODO(b/63664182): Support this.
top level float domains that can be reused by features


    TOP LEVEL FLOAT AND INT DOMAINS ARE UNSUPPORTED IN TFDV.
TODO(b/63664182): Support this.
top level float domains that can be reused by features

    repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;

  21. abstract def getFloatDomainCount(): Int

    TOP LEVEL FLOAT AND INT DOMAINS ARE UNSUPPORTED IN TFDV.
TODO(b/63664182): Support this.
top level float domains that can be reused by features


    TOP LEVEL FLOAT AND INT DOMAINS ARE UNSUPPORTED IN TFDV.
TODO(b/63664182): Support this.
top level float domains that can be reused by features

    repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;

  22. abstract def getFloatDomainList(): List[FloatDomain]

    TOP LEVEL FLOAT AND INT DOMAINS ARE UNSUPPORTED IN TFDV.
TODO(b/63664182): Support this.
top level float domains that can be reused by features


    TOP LEVEL FLOAT AND INT DOMAINS ARE UNSUPPORTED IN TFDV.
TODO(b/63664182): Support this.
top level float domains that can be reused by features

    repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;

  23. abstract def getFloatDomainOrBuilder(index: Int): FloatDomainOrBuilder

    TOP LEVEL FLOAT AND INT DOMAINS ARE UNSUPPORTED IN TFDV.
TODO(b/63664182): Support this.
top level float domains that can be reused by features


    TOP LEVEL FLOAT AND INT DOMAINS ARE UNSUPPORTED IN TFDV.
TODO(b/63664182): Support this.
top level float domains that can be reused by features

    repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;

  24. abstract def getFloatDomainOrBuilderList(): List[_ <: FloatDomainOrBuilder]

    TOP LEVEL FLOAT AND INT DOMAINS ARE UNSUPPORTED IN TFDV.
TODO(b/63664182): Support this.
top level float domains that can be reused by features


    TOP LEVEL FLOAT AND INT DOMAINS ARE UNSUPPORTED IN TFDV.
TODO(b/63664182): Support this.
top level float domains that can be reused by features

    repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;

  25. abstract def getInitializationErrorString(): String
    Definition Classes
    MessageOrBuilder
  26. abstract def getIntDomain(index: Int): IntDomain

    top level int domains that can be reused by features


    top level int domains that can be reused by features

    repeated .tensorflow.metadata.v0.IntDomain int_domain = 10;

  27. abstract def getIntDomainCount(): Int

    top level int domains that can be reused by features


    top level int domains that can be reused by features

    repeated .tensorflow.metadata.v0.IntDomain int_domain = 10;

  28. abstract def getIntDomainList(): List[IntDomain]

    top level int domains that can be reused by features


    top level int domains that can be reused by features

    repeated .tensorflow.metadata.v0.IntDomain int_domain = 10;

  29. abstract def getIntDomainOrBuilder(index: Int): IntDomainOrBuilder

    top level int domains that can be reused by features


    top level int domains that can be reused by features

    repeated .tensorflow.metadata.v0.IntDomain int_domain = 10;

  30. abstract def getIntDomainOrBuilderList(): List[_ <: IntDomainOrBuilder]

    top level int domains that can be reused by features


    top level int domains that can be reused by features

    repeated .tensorflow.metadata.v0.IntDomain int_domain = 10;

  31. abstract def getOneofFieldDescriptor(oneof: OneofDescriptor): FieldDescriptor
    Definition Classes
    MessageOrBuilder
  32. abstract def getRepeatedField(field: FieldDescriptor, index: Int): AnyRef
    Definition Classes
    MessageOrBuilder
  33. abstract def getRepeatedFieldCount(field: FieldDescriptor): Int
    Definition Classes
    MessageOrBuilder
  34. abstract def getRepresentVariableLengthAsRagged(): Boolean

    Whether to represent variable length features as RaggedTensors. By default
they are represented as ragged left-alighned SparseTensors. RaggedTensor
representation is more memory efficient. Therefore, turning this on will
likely yield data processing performance improvement.
Experimental and may be subject to change.


    Whether to represent variable length features as RaggedTensors. By default
they are represented as ragged left-alighned SparseTensors. RaggedTensor
representation is more memory efficient. Therefore, turning this on will
likely yield data processing performance improvement.
Experimental and may be subject to change.

    optional bool represent_variable_length_as_ragged = 14;

    returns

    The representVariableLengthAsRagged.

  35. abstract def getSparseFeature(index: Int): SparseFeature

    Sparse features described in this schema.


    Sparse features described in this schema.

    repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;

  36. abstract def getSparseFeatureCount(): Int

    Sparse features described in this schema.


    Sparse features described in this schema.

    repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;

  37. abstract def getSparseFeatureList(): List[SparseFeature]

    Sparse features described in this schema.


    Sparse features described in this schema.

    repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;

  38. abstract def getSparseFeatureOrBuilder(index: Int): SparseFeatureOrBuilder

    Sparse features described in this schema.


    Sparse features described in this schema.

    repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;

  39. abstract def getSparseFeatureOrBuilderList(): List[_ <: SparseFeatureOrBuilder]

    Sparse features described in this schema.


    Sparse features described in this schema.

    repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;

  40. abstract def getStringDomain(index: Int): StringDomain

    String domains referenced in the features.


    String domains referenced in the features.

    repeated .tensorflow.metadata.v0.StringDomain string_domain = 4;

  41. abstract def getStringDomainCount(): Int

    String domains referenced in the features.


    String domains referenced in the features.

    repeated .tensorflow.metadata.v0.StringDomain string_domain = 4;

  42. abstract def getStringDomainList(): List[StringDomain]

    String domains referenced in the features.


    String domains referenced in the features.

    repeated .tensorflow.metadata.v0.StringDomain string_domain = 4;

  43. abstract def getStringDomainOrBuilder(index: Int): StringDomainOrBuilder

    String domains referenced in the features.


    String domains referenced in the features.

    repeated .tensorflow.metadata.v0.StringDomain string_domain = 4;

  44. abstract def getStringDomainOrBuilderList(): List[_ <: StringDomainOrBuilder]

    String domains referenced in the features.


    String domains referenced in the features.

    repeated .tensorflow.metadata.v0.StringDomain string_domain = 4;

  45. abstract def getTensorRepresentationGroupCount(): Int

    TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.


    TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

    map<string, .tensorflow.metadata.v0.TensorRepresentationGroup> tensor_representation_group = 13;

  46. abstract def getTensorRepresentationGroupMap(): Map[String, TensorRepresentationGroup]

    TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.


    TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

    map<string, .tensorflow.metadata.v0.TensorRepresentationGroup> tensor_representation_group = 13;

  47. abstract def getTensorRepresentationGroupOrDefault(key: String, defaultValue: TensorRepresentationGroup): TensorRepresentationGroup

    TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.


    TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

    map<string, .tensorflow.metadata.v0.TensorRepresentationGroup> tensor_representation_group = 13;

  48. abstract def getTensorRepresentationGroupOrThrow(key: String): TensorRepresentationGroup

    TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.


    TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

    map<string, .tensorflow.metadata.v0.TensorRepresentationGroup> tensor_representation_group = 13;

  49. abstract def getUnknownFields(): UnknownFieldSet
    Definition Classes
    MessageOrBuilder
  50. abstract def getWeightedFeature(index: Int): WeightedFeature

    Weighted features described in this schema.


    Weighted features described in this schema.

    repeated .tensorflow.metadata.v0.WeightedFeature weighted_feature = 12;

  51. abstract def getWeightedFeatureCount(): Int

    Weighted features described in this schema.


    Weighted features described in this schema.

    repeated .tensorflow.metadata.v0.WeightedFeature weighted_feature = 12;

  52. abstract def getWeightedFeatureList(): List[WeightedFeature]

    Weighted features described in this schema.


    Weighted features described in this schema.

    repeated .tensorflow.metadata.v0.WeightedFeature weighted_feature = 12;

  53. abstract def getWeightedFeatureOrBuilder(index: Int): WeightedFeatureOrBuilder

    Weighted features described in this schema.


    Weighted features described in this schema.

    repeated .tensorflow.metadata.v0.WeightedFeature weighted_feature = 12;

  54. abstract def getWeightedFeatureOrBuilderList(): List[_ <: WeightedFeatureOrBuilder]

    Weighted features described in this schema.


    Weighted features described in this schema.

    repeated .tensorflow.metadata.v0.WeightedFeature weighted_feature = 12;

  55. abstract def hasAnnotation(): Boolean

    Additional information about the schema as a whole. Features may also
be annotated individually.


    Additional information about the schema as a whole. Features may also
be annotated individually.

    optional .tensorflow.metadata.v0.Annotation annotation = 8;

    returns

    Whether the annotation field is set.

  56. abstract def hasDatasetConstraints(): Boolean

    Dataset-level constraints. This is currently used for specifying
information about changes in num_examples.


    Dataset-level constraints. This is currently used for specifying
information about changes in num_examples.

    optional .tensorflow.metadata.v0.DatasetConstraints dataset_constraints = 11;

    returns

    Whether the datasetConstraints field is set.

  57. abstract def hasField(field: FieldDescriptor): Boolean
    Definition Classes
    MessageOrBuilder
  58. abstract def hasOneof(oneof: OneofDescriptor): Boolean
    Definition Classes
    MessageOrBuilder
  59. abstract def hasRepresentVariableLengthAsRagged(): Boolean

    Whether to represent variable length features as RaggedTensors. By default
they are represented as ragged left-alighned SparseTensors. RaggedTensor
representation is more memory efficient. Therefore, turning this on will
likely yield data processing performance improvement.
Experimental and may be subject to change.


    Whether to represent variable length features as RaggedTensors. By default
they are represented as ragged left-alighned SparseTensors. RaggedTensor
representation is more memory efficient. Therefore, turning this on will
likely yield data processing performance improvement.
Experimental and may be subject to change.

    optional bool represent_variable_length_as_ragged = 14;

    returns

    Whether the representVariableLengthAsRagged field is set.

  60. abstract def isInitialized(): Boolean
    Definition Classes
    MessageLiteOrBuilder
  61. abstract def getTensorRepresentationGroup(): Map[String, TensorRepresentationGroup]

    Use #getTensorRepresentationGroupMap() instead.

    Use #getTensorRepresentationGroupMap() instead.

    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