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]