Enum ExplanationMetadata.InputMetadata.Encoding (0.8.0)

publicenumExplanationMetadata.InputMetadata.EncodingextendsEnum<ExplanationMetadata.InputMetadata.Encoding>implementsProtocolMessageEnum

Defines how a feature is encoded. Defaults to IDENTITY.

Protobuf enum google.cloud.vertexai.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()`

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Implements

Inherited Members

Static Fields

Static Methods

Methods

Enum ExplanationMetadata.InputMetadata.Encoding (0.8.0)