Google Cloud Ai Platform V1 Client - Class Encoding (1.42.0)
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Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class Encoding.
Defines how a feature is encoded. Defaults to IDENTITY.
Protobuf type google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Encoding
Namespace
Google \ Cloud \ AIPlatform \ V1 \ ExplanationMetadata \ InputMetadataMethods
static::name
| Parameter | |
|---|---|
| Name | Description |
value |
mixed
|
static::value
| Parameter | |
|---|---|
| Name | Description |
name |
mixed
|
Constants
ENCODING_UNSPECIFIED
Value: 0Default value. This is the same as IDENTITY.
Generated from protobuf enum ENCODING_UNSPECIFIED = 0;
IDENTITY
Value: 1The tensor represents one feature.
Generated from protobuf enum IDENTITY = 1;
BAG_OF_FEATURES
Value: 2The 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"]
Generated from protobuf enum BAG_OF_FEATURES = 2;
BAG_OF_FEATURES_SPARSE
Value: 3The 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"]
Generated from protobuf enum BAG_OF_FEATURES_SPARSE = 3;
INDICATOR
Value: 4The 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"]
Generated from protobuf enum INDICATOR = 4;
COMBINED_EMBEDDING
Value: 5The 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]
Generated from protobuf enum COMBINED_EMBEDDING = 5;
CONCAT_EMBEDDING
Value: 6Select 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]]
Generated from protobuf enum CONCAT_EMBEDDING = 6;