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Defines how a feature is encoded. Defaults to IDENTITY.
Constants
ENCODING_UNSPECIFIED
value: 0
Default value. This is the same as IDENTITY.
IDENTITY
value: 1
The tensor represents one feature.
BAG_OF_FEATURES
value: 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
value: 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. InputMetadata.index_feature_mapping must be provided
for this encoding. For example:
input = [2, 0, 5, 0, 1]
index_feature_mapping = ["a", "b", "c", "d", "e"]
INDICATOR
value: 4
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:
input = [1, 0, 1, 0, 1]
index_feature_mapping = ["a", "b", "c", "d", "e"]
COMBINED_EMBEDDING
value: 5
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:
input = ["This", "is", "a", "test", "."]
encoded = [0.1, 0.2, 0.3, 0.4, 0.5]
CONCAT_EMBEDDING
value: 6
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:
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]]