SchemaOrBuilder

trait SchemaOrBuilder extends MessageOrBuilder

Source: SchemaOrBuilder.java

Linear Supertypes

MessageOrBuilder, MessageLiteOrBuilder, AnyRef, Any

Known Subclasses

Schema

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MessageOrBuilder
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Abstract Value Members

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;

abstract def findInitializationErrors(): List[String]
Definition Classes
MessageOrBuilder
abstract def getAllFields(): Map[FieldDescriptor, AnyRef]
Definition Classes
MessageOrBuilder

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.

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;

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.

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;

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.

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.

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.

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.

abstract def getDefaultInstanceForType(): Message
Definition Classes
MessageOrBuilder → MessageLiteOrBuilder
abstract def getDescriptorForType(): Descriptor
Definition Classes
MessageOrBuilder
abstract def getFeature(index: Int): Feature
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
abstract def getFeatureCount(): Int
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
abstract def getFeatureList(): List[Feature]
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
abstract def getFeatureOrBuilder(index: Int): FeatureOrBuilder
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
abstract def getFeatureOrBuilderList(): List[_ <: FeatureOrBuilder]
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
abstract def getField(field: FieldDescriptor): AnyRef
Definition Classes
MessageOrBuilder
abstract 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;

abstract 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;

abstract 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;
abstract 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;
abstract 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;
abstract def getInitializationErrorString(): String
Definition Classes
MessageOrBuilder

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;

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;

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;

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;
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;
abstract def getOneofFieldDescriptor(oneof: OneofDescriptor): FieldDescriptor
Definition Classes
MessageOrBuilder
abstract def getRepeatedField(field: FieldDescriptor, index: Int): AnyRef
Definition Classes
MessageOrBuilder
abstract def getRepeatedFieldCount(field: FieldDescriptor): Int
Definition Classes
MessageOrBuilder

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.

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;
abstract def getSparseFeatureCount(): Int
```
Sparse features described in this schema.
```
```
Sparse features described in this schema.
```
repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;
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;
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;
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;

abstract 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.