The tensor represents a bag of features where each index maps to
a feature.
[InputMetadata.index_feature_mapping][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.index_feature_mapping]
must be provided for this encoding. For example:
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][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.index_feature_mapping]
must be provided for this encoding. For example:
The tensor is encoded into a 1-dimensional array represented by an
encoded tensor.
[InputMetadata.encoded_tensor_name][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.encoded_tensor_name]
must be provided for this encoding. For example:
Select this encoding when the input tensor is encoded into a
2-dimensional array represented by an encoded tensor.
[InputMetadata.encoded_tensor_name][google.cloud.aiplatform.v1beta1.ExplanationMetadata.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:
The tensor is a list of binaries representing whether a feature exists
or not (1 indicates existence).
[InputMetadata.index_feature_mapping][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.index_feature_mapping]
must be provided for this encoding. For example:
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-03-31 UTC."],[[["This documentation provides reference information for the `ExplanationMetadata.Types.InputMetadata.Types.Encoding` enum within the Cloud AI Platform v1beta1 API, specifically for version 1.0.0-beta09, with a link to the latest version 1.0.0-beta21."],["The `Encoding` enum defines how a feature is encoded in AI Platform models, with a default setting of `IDENTITY` if not otherwise specified."],["Available encoding types include `BagOfFeatures`, `BagOfFeaturesSparse`, `CombinedEmbedding`, `ConcatEmbedding`, `Identity`, `Indicator`, and `Unspecified`, each with specific requirements for tensor representation and data structure."],["Certain encodings like `BagOfFeatures`, `BagOfFeaturesSparse`, and `Indicator` necessitate the provision of `InputMetadata.index_feature_mapping` to correspond indices to features, while `CombinedEmbedding` and `ConcatEmbedding` require `InputMetadata.encoded_tensor_name` for encoded tensor information."]]],[]]