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Encoding(value)Defines how a feature is encoded. Defaults to IDENTITY.
Values: ENCODING_UNSPECIFIED (0): Default value. This is the same as IDENTITY. IDENTITY (1): The tensor represents one feature. BAG_OF_FEATURES (2): 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:
::
input = [27, 6.0, 150]
index_feature_mapping = ["age", "height", "weight"]
BAG_OF_FEATURES_SPARSE (3):
The tensor represents a bag of features where each index
maps to a feature. Zero values in the tensor indicates
feature being non-existent.
<xref uid="google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.index_feature_mapping">InputMetadata.index_feature_mapping</xref>
must be provided for this encoding. For example:
::
input = [2, 0, 5, 0, 1]
index_feature_mapping = ["a", "b", "c", "d", "e"]
INDICATOR (4):
The tensor is a list of binaries representing whether a
feature exists or not (1 indicates existence).
<xref uid="google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.index_feature_mapping">InputMetadata.index_feature_mapping</xref>
must be provided for this encoding. For example:
::
input = [1, 0, 1, 0, 1]
index_feature_mapping = ["a", "b", "c", "d", "e"]
COMBINED_EMBEDDING (5):
The tensor is encoded into a 1-dimensional array represented
by an encoded tensor.
<xref uid="google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.encoded_tensor_name">InputMetadata.encoded_tensor_name</xref>
must be provided for this encoding. For example:
::
input = ["This", "is", "a", "test", "."]
encoded = [0.1, 0.2, 0.3, 0.4, 0.5]
CONCAT_EMBEDDING (6):
Select this encoding when the input tensor is encoded into a
2-dimensional array represented by an encoded tensor.
<xref uid="google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.encoded_tensor_name">InputMetadata.encoded_tensor_name</xref>
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:
::
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]]