Google Cloud Ai Platform V1 Client - Class Encoding (0.22.0)

Version 0.22.0

• 1.12.0 (latest)
• 1.11.0
• 1.10.0
• 1.9.0
• 1.8.0
• 1.7.0
• 1.6.0
• 1.5.0
• 1.4.0
• 1.3.0
• 1.2.0
• 1.1.0
• 1.0.0
• 0.39.0
• 0.38.0
• 0.37.1
• 0.32.0
• 0.31.0
• 0.30.0
• 0.29.0
• 0.28.0
• 0.27.0
• 0.26.2
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.0
• 0.19.0
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.13.0
• 0.12.0
• 0.11.1
• 0.10.0

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 \ InputMetadata

Methods

static::name

Parameter
Name	Description
value	mixed

static::value

Parameter
Name	Description
name	mixed

Constants

ENCODING_UNSPECIFIED

Value: 0

Default value. This is the same as IDENTITY.

Generated from protobuf enum ENCODING_UNSPECIFIED = 0;

IDENTITY

Value: 1

The tensor represents one feature.

Generated from protobuf enum IDENTITY = 1;

BAG_OF_FEATURES

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"]

Generated from protobuf enum BAG_OF_FEATURES = 2;

BAG_OF_FEATURES_SPARSE

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"]

Generated from protobuf enum BAG_OF_FEATURES_SPARSE = 3;

INDICATOR

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"]

Generated from protobuf enum INDICATOR = 4;

COMBINED_EMBEDDING

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]

Generated from protobuf enum COMBINED_EMBEDDING = 5;

CONCAT_EMBEDDING

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]]

Generated from protobuf enum CONCAT_EMBEDDING = 6;