- 1.73.0 (latest)
- 1.72.0
- 1.71.1
- 1.70.0
- 1.69.0
- 1.68.0
- 1.67.1
- 1.66.0
- 1.65.0
- 1.63.0
- 1.62.0
- 1.60.0
- 1.59.0
- 1.58.0
- 1.57.0
- 1.56.0
- 1.55.0
- 1.54.1
- 1.53.0
- 1.52.0
- 1.51.0
- 1.50.0
- 1.49.0
- 1.48.0
- 1.47.0
- 1.46.0
- 1.45.0
- 1.44.0
- 1.43.0
- 1.39.0
- 1.38.1
- 1.37.0
- 1.36.4
- 1.35.0
- 1.34.0
- 1.33.1
- 1.32.0
- 1.31.1
- 1.30.1
- 1.29.0
- 1.28.1
- 1.27.1
- 1.26.1
- 1.25.0
- 1.24.1
- 1.23.0
- 1.22.1
- 1.21.0
- 1.20.0
- 1.19.1
- 1.18.3
- 1.17.1
- 1.16.1
- 1.15.1
- 1.14.0
- 1.13.1
- 1.12.1
- 1.11.0
- 1.10.0
- 1.9.0
- 1.8.1
- 1.7.1
- 1.6.2
- 1.5.0
- 1.4.3
- 1.3.0
- 1.2.0
- 1.1.1
- 1.0.1
- 0.9.0
- 0.8.0
- 0.7.1
- 0.6.0
- 0.5.1
- 0.4.0
- 0.3.1
InputMetadata
(
mapping
=
None
,
*
,
ignore_unknown_fields
=
False
,
**
kwargs
)
Metadata of the input of a feature.
Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.
Attributes
| Name | Description |
input_baselines
|
Sequence[google.protobuf.struct_pb2.Value]
Baseline inputs for this feature. If no baseline is specified, Vertex AI chooses the baseline for this feature. If multiple baselines are specified, Vertex AI returns the average attributions across them in Attribution.feature_attributions . For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape of each baseline must match the shape of the input tensor. If a scalar is provided, we broadcast to the same shape as the input tensor. For custom images, the element of the baselines must be in the same format as the feature's input in the instance []. The schema of any single instance may be specified via Endpoint's DeployedModels' [Model's][google.cloud.aiplatform.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] instance_schema_uri . |
input_tensor_name
|
str
Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow. |
encoding
|
google.cloud.aiplatform_v1beta1.types.ExplanationMetadata.InputMetadata.Encoding
Defines how the feature is encoded into the input tensor. Defaults to IDENTITY. |
modality
|
str
Modality of the feature. Valid values are: numeric, image. Defaults to numeric. |
feature_value_domain
|
google.cloud.aiplatform_v1beta1.types.ExplanationMetadata.InputMetadata.FeatureValueDomain
The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized. |
indices_tensor_name
|
str
Specifies the index of the values of the input tensor. Required when the input tensor is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor. |
dense_shape_tensor_name
|
str
Specifies the shape of the values of the input if the input is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor. |
index_feature_mapping
|
Sequence[str]
A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR. |
encoded_tensor_name
|
str
Encoded tensor is a transformation of the input tensor. Must be provided if choosing [Integrated Gradients attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.integrated_gradients_attribution] or [XRAI attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.xrai_attribution] and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table. |
encoded_baselines
|
Sequence[google.protobuf.struct_pb2.Value]
A list of baselines for the encoded tensor. The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor. |
visualization
|
google.cloud.aiplatform_v1beta1.types.ExplanationMetadata.InputMetadata.Visualization
Visualization configurations for image explanation. |
group_name
|
str
Name of the group that the input belongs to. Features with the same group name will be treated as one feature when computing attributions. Features grouped together can have different shapes in value. If provided, there will be one single attribution generated in Attribution.feature_attributions , keyed by the group name. |
Inheritance
builtins.object > proto.message.Message > InputMetadataClasses
Encoding
Encoding
(
value
)
Defines how a feature is encoded. Defaults to IDENTITY.
FeatureValueDomain
FeatureValueDomain
(
mapping
=
None
,
*
,
ignore_unknown_fields
=
False
,
**
kwargs
)
Domain details of the input feature value. Provides numeric information about the feature, such as its range (min, max). If the feature has been pre-processed, for example with z-scoring, then it provides information about how to recover the original feature. For example, if the input feature is an image and it has been pre-processed to obtain 0-mean and stddev = 1 values, then original_mean, and original_stddev refer to the mean and stddev of the original feature (e.g. image tensor) from which input feature (with mean = 0 and stddev = 1) was obtained.
Visualization
Visualization
(
mapping
=
None
,
*
,
ignore_unknown_fields
=
False
,
**
kwargs
)
Visualization configurations for image explanation.
