encoding property
Defines how the feature is encoded into the input tensor.
Defaults to IDENTITY. Possible string values are:
- "ENCODING_UNSPECIFIED" : Default value. This is the same as IDENTITY.
- "IDENTITY" : The tensor represents one feature.
- "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:
input = [27, 6.0, 150] index_feature_mapping = ["age", "height", "weight"]
- "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:
input = [2, 0, 5, 0, 1] index_feature_mapping = ["a", "b", "c", "d", "e"]
- "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:
input = [1, 0, 1, 0, 1] index_feature_mapping = ["a", "b", "c", "d", "e"]
- "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:
input = ["This", "is", "a", "test", "."] encoded = [0.1, 0.2, 0.3, 0.4, 0.5]
- "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: ``` 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
]
Implementation
core.String? encoding;