Vertex AI V1 API - Module Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Encoding (v0.29.0)

Reference documentation and code samples for the Vertex AI V1 API module Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Encoding.

Defines how a feature is encoded. Defaults to IDENTITY.

value: 0
Default value. This is the same as IDENTITY.

value: 1
The tensor represents one feature.

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"]

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"]

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"]

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]

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]]