Enum ExplanationMetadata.Types.InputMetadata.Types.Encoding (1.8.0)

Stay organized with collections Save and categorize content based on your preferences.

The tensor represents a bag of features where each index maps to a feature. [InputMetadata.index_feature_mapping][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.index_feature_mapping] must be provided for this encoding. For example:

input = [27, 6.0, 150]
index_feature_mapping = ["age", "height", "weight"]

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.v1.ExplanationMetadata.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"]

The tensor is encoded into a 1-dimensional array represented by an encoded tensor. [InputMetadata.encoded_tensor_name][google.cloud.aiplatform.v1.ExplanationMetadata.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]

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.v1.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:

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]]

The tensor represents one feature.

The tensor is a list of binaries representing whether a feature exists or not (1 indicates existence). [InputMetadata.index_feature_mapping][google.cloud.aiplatform.v1.ExplanationMetadata.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"]

Default value. This is the same as IDENTITY.