Enum ExplanationMetadata.InputMetadata.Encoding (3.52.0)

Version latest

• 3.52.0 (latest)
• 3.50.0
• 3.49.0
• 3.48.0
• 3.47.0
• 3.46.0
• 3.45.0
• 3.44.0
• 3.43.0
• 3.42.0
• 3.41.0
• 3.40.0
• 3.38.0
• 3.37.0
• 3.36.0
• 3.35.0
• 3.34.0
• 3.33.0
• 3.32.0
• 3.31.0
• 3.30.0
• 3.29.0
• 3.28.0
• 3.25.0
• 3.24.0
• 3.23.0
• 3.22.0
• 3.21.0
• 3.20.0
• 3.19.0
• 3.18.0
• 3.17.0
• 3.16.0
• 3.15.0
• 3.14.0
• 3.13.0
• 3.12.0
• 3.11.0
• 3.10.0
• 3.9.0
• 3.8.0
• 3.7.0
• 3.6.0
• 3.5.0
• 3.4.2
• 3.3.0
• 3.2.0
• 3.0.0
• 2.9.8
• 2.8.9
• 2.7.4
• 2.5.3
• 2.4.0

public enum ExplanationMetadata.InputMetadata.Encoding extends Enum<ExplanationMetadata.InputMetadata.Encoding> implements ProtocolMessageEnum

Defines how a feature is encoded. Defaults to IDENTITY.

Protobuf enum google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Encoding

Implements

ProtocolMessageEnum

Inherited Members

Enum.<T>valueOf(Class<T>,String)

Enum.clone()

Enum.compareTo(E)

Enum.equals(Object)

Enum.finalize()

Enum.getDeclaringClass()

Enum.hashCode()

Enum.name()

Enum.ordinal()

Enum.toString()

Object.getClass()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Fields
Name	Description
BAG_OF_FEATURES	The tensor represents a bag of features where each index maps to a feature. InputMetadata.index_feature_mapping must be provided for this encoding. For example: <code><code> input = [27, 6.0, 150] index_feature_mapping = ["age", "height", "weight"] </code></code> BAG_OF_FEATURES = 2;
BAG_OF_FEATURES_SPARSE	The tensor represents a bag of features where each index maps to a feature. Zero values in the tensor indicates feature being non-existent. InputMetadata.index_feature_mapping must be provided for this encoding. For example: <code><code> input = [2, 0, 5, 0, 1] index_feature_mapping = ["a", "b", "c", "d", "e"] </code></code> BAG_OF_FEATURES_SPARSE = 3;
BAG_OF_FEATURES_SPARSE_VALUE	The tensor represents a bag of features where each index maps to a feature. Zero values in the tensor indicates feature being non-existent. InputMetadata.index_feature_mapping must be provided for this encoding. For example: <code><code> input = [2, 0, 5, 0, 1] index_feature_mapping = ["a", "b", "c", "d", "e"] </code></code> BAG_OF_FEATURES_SPARSE = 3;
BAG_OF_FEATURES_VALUE	The tensor represents a bag of features where each index maps to a feature. InputMetadata.index_feature_mapping must be provided for this encoding. For example: <code><code> input = [27, 6.0, 150] index_feature_mapping = ["age", "height", "weight"] </code></code> BAG_OF_FEATURES = 2;
COMBINED_EMBEDDING	The tensor is encoded into a 1-dimensional array represented by an encoded tensor. InputMetadata.encoded_tensor_name must be provided for this encoding. For example: <code><code> input = ["This", "is", "a", "test", "."] encoded = [0.1, 0.2, 0.3, 0.4, 0.5] </code></code> COMBINED_EMBEDDING = 5;
COMBINED_EMBEDDING_VALUE	The tensor is encoded into a 1-dimensional array represented by an encoded tensor. InputMetadata.encoded_tensor_name must be provided for this encoding. For example: <code><code> input = ["This", "is", "a", "test", "."] encoded = [0.1, 0.2, 0.3, 0.4, 0.5] </code></code> COMBINED_EMBEDDING = 5;
CONCAT_EMBEDDING	Select this encoding when the input tensor is encoded into a 2-dimensional array represented by an encoded tensor. InputMetadata.encoded_tensor_name must be provided for this encoding. The first dimension of the encoded tensor's shape is the same as the input tensor's shape. For example: <code><code> input = ["This", "is", "a", "test", "."] encoded = [[0.1, 0.2, 0.3, 0.4, 0.5], [0.2, 0.1, 0.4, 0.3, 0.5], [0.5, 0.1, 0.3, 0.5, 0.4], [0.5, 0.3, 0.1, 0.2, 0.4], [0.4, 0.3, 0.2, 0.5, 0.1]] </code></code> CONCAT_EMBEDDING = 6;
CONCAT_EMBEDDING_VALUE	Select this encoding when the input tensor is encoded into a 2-dimensional array represented by an encoded tensor. InputMetadata.encoded_tensor_name must be provided for this encoding. The first dimension of the encoded tensor's shape is the same as the input tensor's shape. For example: <code><code> input = ["This", "is", "a", "test", "."] encoded = [[0.1, 0.2, 0.3, 0.4, 0.5], [0.2, 0.1, 0.4, 0.3, 0.5], [0.5, 0.1, 0.3, 0.5, 0.4], [0.5, 0.3, 0.1, 0.2, 0.4], [0.4, 0.3, 0.2, 0.5, 0.1]] </code></code> CONCAT_EMBEDDING = 6;
ENCODING_UNSPECIFIED	Default value. This is the same as IDENTITY. ENCODING_UNSPECIFIED = 0;
ENCODING_UNSPECIFIED_VALUE	Default value. This is the same as IDENTITY. ENCODING_UNSPECIFIED = 0;
IDENTITY	The tensor represents one feature. IDENTITY = 1;
IDENTITY_VALUE	The tensor represents one feature. IDENTITY = 1;
INDICATOR	The tensor is a list of binaries representing whether a feature exists or not (1 indicates existence). InputMetadata.index_feature_mapping must be provided for this encoding. For example: <code><code> input = [1, 0, 1, 0, 1] index_feature_mapping = ["a", "b", "c", "d", "e"] </code></code> INDICATOR = 4;
INDICATOR_VALUE	The tensor is a list of binaries representing whether a feature exists or not (1 indicates existence). InputMetadata.index_feature_mapping must be provided for this encoding. For example: <code><code> input = [1, 0, 1, 0, 1] index_feature_mapping = ["a", "b", "c", "d", "e"] </code></code> INDICATOR = 4;
UNRECOGNIZED

Static Methods
Name	Description
forNumber(int value)
getDescriptor()
internalGetValueMap()
valueOf(Descriptors.EnumValueDescriptor desc)
valueOf(int value)	Deprecated. Use #forNumber(int) instead.
valueOf(String name)
values()

Methods
Name	Description
getDescriptorForType()
getNumber()
getValueDescriptor()