Struct googapis::google::cloud::aiplatform::v1::explanation_metadata::InputMetadata [−][src]
pub struct InputMetadata {
pub input_baselines: Vec<Value>,
pub input_tensor_name: String,
pub encoding: i32,
pub modality: String,
pub feature_value_domain: Option<FeatureValueDomain>,
pub indices_tensor_name: String,
pub dense_shape_tensor_name: String,
pub index_feature_mapping: Vec<String>,
pub encoded_tensor_name: String,
pub encoded_baselines: Vec<Value>,
pub visualization: Option<Visualization>,
pub group_name: String,
}
Expand description
Metadata of the input of a feature.
Fields other than [InputMetadata.input_baselines][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.input_baselines] are applicable only for Models that are using Vertex AI-provided images for Tensorflow.
Fields
input_baselines: Vec<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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. 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][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
input_tensor_name: String
Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
encoding: i32
Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
modality: String
Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
feature_value_domain: Option<FeatureValueDomain>
The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
indices_tensor_name: 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.
dense_shape_tensor_name: 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.
index_feature_mapping: Vec<String>
A list of feature names for each index in the input tensor. Required when the input [InputMetadata.encoding][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding] is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
encoded_tensor_name: 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_baselines: Vec<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: Option<Visualization>
Visualization configurations for image explanation.
group_name: 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][google.cloud.aiplatform.v1.Attribution.feature_attributions], keyed by the group name.
Implementations
Trait Implementations
fn merge_field<B>(
&mut self,
tag: u32,
wire_type: WireType,
buf: &mut B,
ctx: DecodeContext
) -> Result<(), DecodeError> where
B: Buf,
Returns the encoded length of the message without a length delimiter.
Encodes the message to a buffer. Read more
Encodes the message to a newly allocated buffer.
Encodes the message with a length-delimiter to a buffer. Read more
Encodes the message with a length-delimiter to a newly allocated buffer.
Decodes an instance of the message from a buffer. Read more
fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError> where
Self: Default,
B: Buf,
fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError> where
Self: Default,
B: Buf,
Decodes a length-delimited instance of the message from the buffer.
Decodes an instance of the message from a buffer, and merges it into self
. Read more
Decodes a length-delimited instance of the message from buffer, and
merges it into self
. Read more
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
This method tests for !=
.
Auto Trait Implementations
impl RefUnwindSafe for InputMetadata
impl Send for InputMetadata
impl Sync for InputMetadata
impl Unpin for InputMetadata
impl UnwindSafe for InputMetadata
Blanket Implementations
Mutably borrows from an owned value. Read more
Wrap the input message T
in a tonic::Request
pub fn vzip(self) -> V
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more