Schema

final class Schema extends GeneratedMessageV3 with SchemaOrBuilder

Message to represent schema information.
NextID: 14

Protobuf type tensorflow.metadata.v0.Schema

Linear Supertypes

SchemaOrBuilder, GeneratedMessageV3, Serializable, AbstractMessage, Message, MessageOrBuilder, AbstractMessageLite[MessageType, BuilderType], MessageLite, MessageLiteOrBuilder, AnyRef, Any

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Schema
SchemaOrBuilder
GeneratedMessageV3
Serializable
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Message
MessageOrBuilder
AbstractMessageLite
MessageLite
MessageLiteOrBuilder
AnyRef
Any

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final def !=(arg0: Any): Boolean
Definition Classes
AnyRef → Any
final def ##: Int
Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean
Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0
Definition Classes
Any
def clone(): AnyRef
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()

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. DO NOT USE.

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. DO NOT USE.

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

Definition Classes: Schema → SchemaOrBuilder

final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
def equals(obj: AnyRef): Boolean
Definition Classes
Schema → AbstractMessage → Message → AnyRef → Any
Annotations
@Override()
def findInitializationErrors(): List[String]
Definition Classes
AbstractMessage → MessageOrBuilder
def getAllFields(): Map[FieldDescriptor, AnyRef]
Definition Classes
GeneratedMessageV3 → MessageOrBuilder

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.

Definition Classes: Schema → SchemaOrBuilder

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;

Definition Classes: Schema → SchemaOrBuilder

final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@native() @HotSpotIntrinsicCandidate()

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.

Definition Classes: Schema → SchemaOrBuilder

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;

Definition Classes: Schema → SchemaOrBuilder

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.

Definition Classes: Schema → SchemaOrBuilder

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.

Definition Classes: Schema → SchemaOrBuilder

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.

Definition Classes: Schema → SchemaOrBuilder

def getDefaultEnvironmentList(): ProtocolStringList

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.

Definition Classes: Schema → SchemaOrBuilder

def getDefaultInstanceForType(): Schema
Definition Classes
Schema → MessageOrBuilder → MessageLiteOrBuilder
Annotations
@Override()
def getDescriptorForType(): Descriptor
Definition Classes
GeneratedMessageV3 → MessageOrBuilder
def getFeature(index: Int): Feature
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
Definition Classes
Schema → SchemaOrBuilder
def getFeatureCount(): Int
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
Definition Classes
Schema → SchemaOrBuilder
def getFeatureList(): List[Feature]
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
Definition Classes
Schema → SchemaOrBuilder
def getFeatureOrBuilder(index: Int): FeatureOrBuilder
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
Definition Classes
Schema → SchemaOrBuilder
def getFeatureOrBuilderList(): List[_ <: FeatureOrBuilder]
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
Definition Classes
Schema → SchemaOrBuilder
def getField(arg0: FieldDescriptor): AnyRef
Definition Classes
GeneratedMessageV3 → MessageOrBuilder
def getFloatDomain(index: Int): FloatDomain
```
top level float domains that can be reused by features
```
```
top level float domains that can be reused by features
```
repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;
Definition Classes
Schema → SchemaOrBuilder
def getFloatDomainCount(): Int
```
top level float domains that can be reused by features
```
```
top level float domains that can be reused by features
```
repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;
Definition Classes
Schema → SchemaOrBuilder
def getFloatDomainList(): List[FloatDomain]
```
top level float domains that can be reused by features
```
```
top level float domains that can be reused by features
```
repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;
Definition Classes
Schema → SchemaOrBuilder
def getFloatDomainOrBuilder(index: Int): FloatDomainOrBuilder
```
top level float domains that can be reused by features
```
```
top level float domains that can be reused by features
```
repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;
Definition Classes
Schema → SchemaOrBuilder
def getFloatDomainOrBuilderList(): List[_ <: FloatDomainOrBuilder]
```
top level float domains that can be reused by features
```
```
top level float domains that can be reused by features
```
repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;
Definition Classes
Schema → SchemaOrBuilder
def getInitializationErrorString(): String
Definition Classes
AbstractMessage → MessageOrBuilder
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;
Definition Classes
Schema → SchemaOrBuilder
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;
Definition Classes
Schema → SchemaOrBuilder
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;
Definition Classes
Schema → SchemaOrBuilder
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;
Definition Classes
Schema → SchemaOrBuilder
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;
Definition Classes
Schema → SchemaOrBuilder
def getOneofFieldDescriptor(arg0: OneofDescriptor): FieldDescriptor
Definition Classes
GeneratedMessageV3 → AbstractMessage → MessageOrBuilder
def getParserForType(): Parser[Schema]
Definition Classes
Schema → GeneratedMessageV3 → Message → MessageLite
Annotations
@Override()
def getRepeatedField(arg0: FieldDescriptor, arg1: Int): AnyRef
Definition Classes
GeneratedMessageV3 → MessageOrBuilder
def getRepeatedFieldCount(arg0: FieldDescriptor): Int
Definition Classes
GeneratedMessageV3 → MessageOrBuilder
def getSerializedSize(): Int
Definition Classes
Schema → GeneratedMessageV3 → AbstractMessage → MessageLite
Annotations
@Override()
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;
Definition Classes
Schema → SchemaOrBuilder
def getSparseFeatureCount(): Int
```
Sparse features described in this schema.
```
```
Sparse features described in this schema.
```
repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;
Definition Classes
Schema → SchemaOrBuilder
def getSparseFeatureList(): List[SparseFeature]
```
Sparse features described in this schema.
```
```
Sparse features described in this schema.
```
repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;
Definition Classes
Schema → SchemaOrBuilder
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;
Definition Classes
Schema → SchemaOrBuilder
def getSparseFeatureOrBuilderList(): List[_ <: SparseFeatureOrBuilder]
```
Sparse features described in this schema.
```
```
Sparse features described in this schema.
```
repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;
Definition Classes
Schema → SchemaOrBuilder

def getStringDomain(index: Int): StringDomain

declared as top-level features in <feature>.
String domains referenced in the features.

declared as top-level features in <feature>.
String domains referenced in the features.