Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata (v0.61.0)

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata.

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.

Inherits

  • Object

Extended By

  • Google::Protobuf::MessageExts::ClassMethods

Includes

  • Google::Protobuf::MessageExts

Methods

#dense_shape_tensor_name

def dense_shape_tensor_name() -> ::String
Returns
  • (::String) — 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.

#dense_shape_tensor_name=

def dense_shape_tensor_name=(value) -> ::String
Parameter
  • value (::String) — 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.
Returns
  • (::String) — 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.

#encoded_baselines

def encoded_baselines() -> ::Array<::Google::Protobuf::Value>
Returns
  • (::Array<::Google::Protobuf::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.

#encoded_baselines=

def encoded_baselines=(value) -> ::Array<::Google::Protobuf::Value>
Parameter
  • value (::Array<::Google::Protobuf::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.

Returns
  • (::Array<::Google::Protobuf::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.

#encoded_tensor_name

def encoded_tensor_name() -> ::String
Returns
  • (::String) — Encoded tensor is a transformation of the input tensor. Must be provided if choosing [Integrated Gradients attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution] or [XRAI attribution][google.cloud.aiplatform.v1.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_tensor_name=

def encoded_tensor_name=(value) -> ::String
Parameter
  • value (::String) — Encoded tensor is a transformation of the input tensor. Must be provided if choosing [Integrated Gradients attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution] or [XRAI attribution][google.cloud.aiplatform.v1.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.

Returns
  • (::String) — Encoded tensor is a transformation of the input tensor. Must be provided if choosing [Integrated Gradients attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution] or [XRAI attribution][google.cloud.aiplatform.v1.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.

#encoding

def encoding() -> ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Encoding
Returns

#encoding=

def encoding=(value) -> ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Encoding
Parameter
Returns

#feature_value_domain

def feature_value_domain() -> ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::FeatureValueDomain
Returns

#feature_value_domain=

def feature_value_domain=(value) -> ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::FeatureValueDomain
Parameter
Returns

#group_name

def group_name() -> ::String
Returns
  • (::String) — 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.

#group_name=

def group_name=(value) -> ::String
Parameter
  • value (::String) — 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.
Returns
  • (::String) — 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.

#index_feature_mapping

def index_feature_mapping() -> ::Array<::String>
Returns
  • (::Array<::String>) — 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.

#index_feature_mapping=

def index_feature_mapping=(value) -> ::Array<::String>
Parameter
  • value (::Array<::String>) — 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.
Returns
  • (::Array<::String>) — 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.

#indices_tensor_name

def indices_tensor_name() -> ::String
Returns
  • (::String) — 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.

#indices_tensor_name=

def indices_tensor_name=(value) -> ::String
Parameter
  • value (::String) — 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.
Returns
  • (::String) — 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.

#input_baselines

def input_baselines() -> ::Array<::Google::Protobuf::Value>
Returns
  • (::Array<::Google::Protobuf::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.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri.

#input_baselines=

def input_baselines=(value) -> ::Array<::Google::Protobuf::Value>
Parameter
  • value (::Array<::Google::Protobuf::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.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri.

Returns
  • (::Array<::Google::Protobuf::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.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri.

#input_tensor_name

def input_tensor_name() -> ::String
Returns
  • (::String) — Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.

#input_tensor_name=

def input_tensor_name=(value) -> ::String
Parameter
  • value (::String) — Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
Returns
  • (::String) — Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.

#modality

def modality() -> ::String
Returns
  • (::String) — Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

#modality=

def modality=(value) -> ::String
Parameter
  • value (::String) — Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
Returns
  • (::String) — Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

#visualization

def visualization() -> ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization
Returns

#visualization=

def visualization=(value) -> ::Google::Cloud::AIPlatform::V1::ExplanationMetadata::InputMetadata::Visualization
Parameter
Returns