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public static final class ExplanationMetadata.InputMetadata.Builder extends GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder> implements ExplanationMetadata.InputMetadataOrBuilderMetadata 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.
Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata
Inheritance
Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > ExplanationMetadata.InputMetadata.BuilderImplements
ExplanationMetadata.InputMetadataOrBuilderStatic Methods
getDescriptor()
public static final Descriptors.Descriptor getDescriptor()| Type | Description |
| Descriptor |
Methods
addAllEncodedBaselines(Iterable<? extends Value> values)
public ExplanationMetadata.InputMetadata.Builder addAllEncodedBaselines(Iterable<? extends Value> values)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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Name | Description |
| values | Iterable<? extends com.google.protobuf.Value> |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
addAllIndexFeatureMapping(Iterable<String> values)
public ExplanationMetadata.InputMetadata.Builder addAllIndexFeatureMapping(Iterable<String> values)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.
repeated string index_feature_mapping = 8;
| Name | Description |
| values | Iterable<String>The indexFeatureMapping to add. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
addAllInputBaselines(Iterable<? extends Value> values)
public ExplanationMetadata.InputMetadata.Builder addAllInputBaselines(Iterable<? extends Value> values)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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Name | Description |
| values | Iterable<? extends com.google.protobuf.Value> |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
addEncodedBaselines(Value value)
public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(Value 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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Name | Description |
| value | Value |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
addEncodedBaselines(Value.Builder builderForValue)
public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(Value.Builder builderForValue)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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Name | Description |
| builderForValue | Builder |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
addEncodedBaselines(int index, Value value)
public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(int index, Value 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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Name | Description |
| index | int |
| value | Value |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
addEncodedBaselines(int index, Value.Builder builderForValue)
public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(int index, Value.Builder builderForValue)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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Name | Description |
| index | int |
| builderForValue | Builder |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
addEncodedBaselinesBuilder()
public Value.Builder addEncodedBaselinesBuilder()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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Type | Description |
| Builder |
addEncodedBaselinesBuilder(int index)
public Value.Builder addEncodedBaselinesBuilder(int index)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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Name | Description |
| index | int |
| Type | Description |
| Builder |
addIndexFeatureMapping(String value)
public ExplanationMetadata.InputMetadata.Builder addIndexFeatureMapping(String value)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.
repeated string index_feature_mapping = 8;
| Name | Description |
| value | StringThe indexFeatureMapping to add. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
addIndexFeatureMappingBytes(ByteString value)
public ExplanationMetadata.InputMetadata.Builder addIndexFeatureMappingBytes(ByteString value)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.
repeated string index_feature_mapping = 8;
| Name | Description |
| value | ByteStringThe bytes of the indexFeatureMapping to add. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
addInputBaselines(Value value)
public ExplanationMetadata.InputMetadata.Builder addInputBaselines(Value 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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Name | Description |
| value | Value |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
addInputBaselines(Value.Builder builderForValue)
public ExplanationMetadata.InputMetadata.Builder addInputBaselines(Value.Builder builderForValue)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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Name | Description |
| builderForValue | Builder |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
addInputBaselines(int index, Value value)
public ExplanationMetadata.InputMetadata.Builder addInputBaselines(int index, Value 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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Name | Description |
| index | int |
| value | Value |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
addInputBaselines(int index, Value.Builder builderForValue)
public ExplanationMetadata.InputMetadata.Builder addInputBaselines(int index, Value.Builder builderForValue)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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Name | Description |
| index | int |
| builderForValue | Builder |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
addInputBaselinesBuilder()
public Value.Builder addInputBaselinesBuilder()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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Type | Description |
| Builder |
addInputBaselinesBuilder(int index)
public Value.Builder addInputBaselinesBuilder(int index)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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Name | Description |
| index | int |
| Type | Description |
| Builder |
addRepeatedField(Descriptors.FieldDescriptor field, Object value)
public ExplanationMetadata.InputMetadata.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)| Name | Description |
| field | FieldDescriptor |
| value | Object |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
build()
public ExplanationMetadata.InputMetadata build()| Type | Description |
| ExplanationMetadata.InputMetadata |
buildPartial()
public ExplanationMetadata.InputMetadata buildPartial()| Type | Description |
| ExplanationMetadata.InputMetadata |
clear()
public ExplanationMetadata.InputMetadata.Builder clear()| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
clearDenseShapeTensorName()
public ExplanationMetadata.InputMetadata.Builder clearDenseShapeTensorName()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.
string dense_shape_tensor_name = 7;
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
clearEncodedBaselines()
public ExplanationMetadata.InputMetadata.Builder clearEncodedBaselines()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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
clearEncodedTensorName()
public ExplanationMetadata.InputMetadata.Builder clearEncodedTensorName()Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
string encoded_tensor_name = 9;
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
clearEncoding()
public ExplanationMetadata.InputMetadata.Builder clearEncoding()Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
clearFeatureValueDomain()
public ExplanationMetadata.InputMetadata.Builder clearFeatureValueDomain()The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
clearField(Descriptors.FieldDescriptor field)
public ExplanationMetadata.InputMetadata.Builder clearField(Descriptors.FieldDescriptor field)| Name | Description |
| field | FieldDescriptor |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
clearGroupName()
public ExplanationMetadata.InputMetadata.Builder clearGroupName()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.
string group_name = 12;
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
clearIndexFeatureMapping()
public ExplanationMetadata.InputMetadata.Builder clearIndexFeatureMapping()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.
repeated string index_feature_mapping = 8;
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
clearIndicesTensorName()
public ExplanationMetadata.InputMetadata.Builder clearIndicesTensorName()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.
string indices_tensor_name = 6;
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
clearInputBaselines()
public ExplanationMetadata.InputMetadata.Builder clearInputBaselines()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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
clearInputTensorName()
public ExplanationMetadata.InputMetadata.Builder clearInputTensorName()Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
string input_tensor_name = 2;
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
clearModality()
public ExplanationMetadata.InputMetadata.Builder clearModality()Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
string modality = 4;
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
clearOneof(Descriptors.OneofDescriptor oneof)
public ExplanationMetadata.InputMetadata.Builder clearOneof(Descriptors.OneofDescriptor oneof)| Name | Description |
| oneof | OneofDescriptor |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
clearVisualization()
public ExplanationMetadata.InputMetadata.Builder clearVisualization()Visualization configurations for image explanation.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
clone()
public ExplanationMetadata.InputMetadata.Builder clone()| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
getDefaultInstanceForType()
public ExplanationMetadata.InputMetadata getDefaultInstanceForType()| Type | Description |
| ExplanationMetadata.InputMetadata |
getDenseShapeTensorName()
public String getDenseShapeTensorName()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.
string dense_shape_tensor_name = 7;
| Type | Description |
| String | The denseShapeTensorName. |
getDenseShapeTensorNameBytes()
public ByteString getDenseShapeTensorNameBytes()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.
string dense_shape_tensor_name = 7;
| Type | Description |
| ByteString | The bytes for denseShapeTensorName. |
getDescriptorForType()
public Descriptors.Descriptor getDescriptorForType()| Type | Description |
| Descriptor |
getEncodedBaselines(int index)
public Value getEncodedBaselines(int index)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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Name | Description |
| index | int |
| Type | Description |
| Value |
getEncodedBaselinesBuilder(int index)
public Value.Builder getEncodedBaselinesBuilder(int index)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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Name | Description |
| index | int |
| Type | Description |
| Builder |
getEncodedBaselinesBuilderList()
public List<Value.Builder> getEncodedBaselinesBuilderList()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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Type | Description |
| List<Builder> |
getEncodedBaselinesCount()
public int getEncodedBaselinesCount()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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Type | Description |
| int |
getEncodedBaselinesList()
public List<Value> getEncodedBaselinesList()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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Type | Description |
| List<Value> |
getEncodedBaselinesOrBuilder(int index)
public ValueOrBuilder getEncodedBaselinesOrBuilder(int index)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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Name | Description |
| index | int |
| Type | Description |
| ValueOrBuilder |
getEncodedBaselinesOrBuilderList()
public List<? extends ValueOrBuilder> getEncodedBaselinesOrBuilderList()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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Type | Description |
| List<? extends com.google.protobuf.ValueOrBuilder> |
getEncodedTensorName()
public String getEncodedTensorName()Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
string encoded_tensor_name = 9;
| Type | Description |
| String | The encodedTensorName. |
getEncodedTensorNameBytes()
public ByteString getEncodedTensorNameBytes()Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
string encoded_tensor_name = 9;
| Type | Description |
| ByteString | The bytes for encodedTensorName. |
getEncoding()
public ExplanationMetadata.InputMetadata.Encoding getEncoding()Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;
| Type | Description |
| ExplanationMetadata.InputMetadata.Encoding | The encoding. |
getEncodingValue()
public int getEncodingValue()Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;
| Type | Description |
| int | The enum numeric value on the wire for encoding. |
getFeatureValueDomain()
public ExplanationMetadata.InputMetadata.FeatureValueDomain getFeatureValueDomain()The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
| Type | Description |
| ExplanationMetadata.InputMetadata.FeatureValueDomain | The featureValueDomain. |
getFeatureValueDomainBuilder()
public ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder getFeatureValueDomainBuilder()The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
| Type | Description |
| ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder |
getFeatureValueDomainOrBuilder()
public ExplanationMetadata.InputMetadata.FeatureValueDomainOrBuilder getFeatureValueDomainOrBuilder()The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
| Type | Description |
| ExplanationMetadata.InputMetadata.FeatureValueDomainOrBuilder |
getGroupName()
public String getGroupName()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.
string group_name = 12;
| Type | Description |
| String | The groupName. |
getGroupNameBytes()
public ByteString getGroupNameBytes()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.
string group_name = 12;
| Type | Description |
| ByteString | The bytes for groupName. |
getIndexFeatureMapping(int index)
public String getIndexFeatureMapping(int index)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.
repeated string index_feature_mapping = 8;
| Name | Description |
| index | intThe index of the element to return. |
| Type | Description |
| String | The indexFeatureMapping at the given index. |
getIndexFeatureMappingBytes(int index)
public ByteString getIndexFeatureMappingBytes(int index)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.
repeated string index_feature_mapping = 8;
| Name | Description |
| index | intThe index of the value to return. |
| Type | Description |
| ByteString | The bytes of the indexFeatureMapping at the given index. |
getIndexFeatureMappingCount()
public int getIndexFeatureMappingCount()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.
repeated string index_feature_mapping = 8;
| Type | Description |
| int | The count of indexFeatureMapping. |
getIndexFeatureMappingList()
public ProtocolStringList getIndexFeatureMappingList()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.
repeated string index_feature_mapping = 8;
| Type | Description |
| ProtocolStringList | A list containing the indexFeatureMapping. |
getIndicesTensorName()
public String getIndicesTensorName()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.
string indices_tensor_name = 6;
| Type | Description |
| String | The indicesTensorName. |
getIndicesTensorNameBytes()
public ByteString getIndicesTensorNameBytes()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.
string indices_tensor_name = 6;
| Type | Description |
| ByteString | The bytes for indicesTensorName. |
getInputBaselines(int index)
public Value getInputBaselines(int index)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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Name | Description |
| index | int |
| Type | Description |
| Value |
getInputBaselinesBuilder(int index)
public Value.Builder getInputBaselinesBuilder(int index)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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Name | Description |
| index | int |
| Type | Description |
| Builder |
getInputBaselinesBuilderList()
public List<Value.Builder> getInputBaselinesBuilderList()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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Type | Description |
| List<Builder> |
getInputBaselinesCount()
public int getInputBaselinesCount()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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Type | Description |
| int |
getInputBaselinesList()
public List<Value> getInputBaselinesList()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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Type | Description |
| List<Value> |
getInputBaselinesOrBuilder(int index)
public ValueOrBuilder getInputBaselinesOrBuilder(int index)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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Name | Description |
| index | int |
| Type | Description |
| ValueOrBuilder |
getInputBaselinesOrBuilderList()
public List<? extends ValueOrBuilder> getInputBaselinesOrBuilderList()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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Type | Description |
| List<? extends com.google.protobuf.ValueOrBuilder> |
getInputTensorName()
public String getInputTensorName()Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
string input_tensor_name = 2;
| Type | Description |
| String | The inputTensorName. |
getInputTensorNameBytes()
public ByteString getInputTensorNameBytes()Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
string input_tensor_name = 2;
| Type | Description |
| ByteString | The bytes for inputTensorName. |
getModality()
public String getModality()Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
string modality = 4;
| Type | Description |
| String | The modality. |
getModalityBytes()
public ByteString getModalityBytes()Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
string modality = 4;
| Type | Description |
| ByteString | The bytes for modality. |
getVisualization()
public ExplanationMetadata.InputMetadata.Visualization getVisualization()Visualization configurations for image explanation.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
| Type | Description |
| ExplanationMetadata.InputMetadata.Visualization | The visualization. |
getVisualizationBuilder()
public ExplanationMetadata.InputMetadata.Visualization.Builder getVisualizationBuilder()Visualization configurations for image explanation.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
| Type | Description |
| ExplanationMetadata.InputMetadata.Visualization.Builder |
getVisualizationOrBuilder()
public ExplanationMetadata.InputMetadata.VisualizationOrBuilder getVisualizationOrBuilder()Visualization configurations for image explanation.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
| Type | Description |
| ExplanationMetadata.InputMetadata.VisualizationOrBuilder |
hasFeatureValueDomain()
public boolean hasFeatureValueDomain()The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
| Type | Description |
| boolean | Whether the featureValueDomain field is set. |
hasVisualization()
public boolean hasVisualization()Visualization configurations for image explanation.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
| Type | Description |
| boolean | Whether the visualization field is set. |
internalGetFieldAccessorTable()
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()| Type | Description |
| FieldAccessorTable |
isInitialized()
public final boolean isInitialized()| Type | Description |
| boolean |
mergeFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)
public ExplanationMetadata.InputMetadata.Builder mergeFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
| Name | Description |
| value | ExplanationMetadata.InputMetadata.FeatureValueDomain |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
mergeFrom(ExplanationMetadata.InputMetadata other)
public ExplanationMetadata.InputMetadata.Builder mergeFrom(ExplanationMetadata.InputMetadata other)| Name | Description |
| other | ExplanationMetadata.InputMetadata |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public ExplanationMetadata.InputMetadata.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)| Name | Description |
| input | CodedInputStream |
| extensionRegistry | ExtensionRegistryLite |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
| Type | Description |
| IOException |
mergeFrom(Message other)
public ExplanationMetadata.InputMetadata.Builder mergeFrom(Message other)| Name | Description |
| other | Message |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
mergeUnknownFields(UnknownFieldSet unknownFields)
public final ExplanationMetadata.InputMetadata.Builder mergeUnknownFields(UnknownFieldSet unknownFields)| Name | Description |
| unknownFields | UnknownFieldSet |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
mergeVisualization(ExplanationMetadata.InputMetadata.Visualization value)
public ExplanationMetadata.InputMetadata.Builder mergeVisualization(ExplanationMetadata.InputMetadata.Visualization value)Visualization configurations for image explanation.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
| Name | Description |
| value | ExplanationMetadata.InputMetadata.Visualization |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
removeEncodedBaselines(int index)
public ExplanationMetadata.InputMetadata.Builder removeEncodedBaselines(int index)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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Name | Description |
| index | int |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
removeInputBaselines(int index)
public ExplanationMetadata.InputMetadata.Builder removeInputBaselines(int index)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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Name | Description |
| index | int |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
setDenseShapeTensorName(String value)
public ExplanationMetadata.InputMetadata.Builder setDenseShapeTensorName(String value)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.
string dense_shape_tensor_name = 7;
| Name | Description |
| value | StringThe denseShapeTensorName to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setDenseShapeTensorNameBytes(ByteString value)
public ExplanationMetadata.InputMetadata.Builder setDenseShapeTensorNameBytes(ByteString value)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.
string dense_shape_tensor_name = 7;
| Name | Description |
| value | ByteStringThe bytes for denseShapeTensorName to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setEncodedBaselines(int index, Value value)
public ExplanationMetadata.InputMetadata.Builder setEncodedBaselines(int index, Value 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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Name | Description |
| index | int |
| value | Value |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
setEncodedBaselines(int index, Value.Builder builderForValue)
public ExplanationMetadata.InputMetadata.Builder setEncodedBaselines(int index, Value.Builder builderForValue)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.
repeated .google.protobuf.Value encoded_baselines = 10;
| Name | Description |
| index | int |
| builderForValue | Builder |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
setEncodedTensorName(String value)
public ExplanationMetadata.InputMetadata.Builder setEncodedTensorName(String value)Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
string encoded_tensor_name = 9;
| Name | Description |
| value | StringThe encodedTensorName to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setEncodedTensorNameBytes(ByteString value)
public ExplanationMetadata.InputMetadata.Builder setEncodedTensorNameBytes(ByteString value)Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
string encoded_tensor_name = 9;
| Name | Description |
| value | ByteStringThe bytes for encodedTensorName to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setEncoding(ExplanationMetadata.InputMetadata.Encoding value)
public ExplanationMetadata.InputMetadata.Builder setEncoding(ExplanationMetadata.InputMetadata.Encoding value)Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;
| Name | Description |
| value | ExplanationMetadata.InputMetadata.EncodingThe encoding to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setEncodingValue(int value)
public ExplanationMetadata.InputMetadata.Builder setEncodingValue(int value)Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;
| Name | Description |
| value | intThe enum numeric value on the wire for encoding to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)
public ExplanationMetadata.InputMetadata.Builder setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
| Name | Description |
| value | ExplanationMetadata.InputMetadata.FeatureValueDomain |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder builderForValue)
public ExplanationMetadata.InputMetadata.Builder setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder builderForValue)The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
| Name | Description |
| builderForValue | ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
setField(Descriptors.FieldDescriptor field, Object value)
public ExplanationMetadata.InputMetadata.Builder setField(Descriptors.FieldDescriptor field, Object value)| Name | Description |
| field | FieldDescriptor |
| value | Object |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
setGroupName(String value)
public ExplanationMetadata.InputMetadata.Builder setGroupName(String value)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.
string group_name = 12;
| Name | Description |
| value | StringThe groupName to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setGroupNameBytes(ByteString value)
public ExplanationMetadata.InputMetadata.Builder setGroupNameBytes(ByteString value)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.
string group_name = 12;
| Name | Description |
| value | ByteStringThe bytes for groupName to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setIndexFeatureMapping(int index, String value)
public ExplanationMetadata.InputMetadata.Builder setIndexFeatureMapping(int index, String value)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.
repeated string index_feature_mapping = 8;
| Name | Description |
| index | intThe index to set the value at. |
| value | StringThe indexFeatureMapping to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setIndicesTensorName(String value)
public ExplanationMetadata.InputMetadata.Builder setIndicesTensorName(String value)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.
string indices_tensor_name = 6;
| Name | Description |
| value | StringThe indicesTensorName to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setIndicesTensorNameBytes(ByteString value)
public ExplanationMetadata.InputMetadata.Builder setIndicesTensorNameBytes(ByteString value)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.
string indices_tensor_name = 6;
| Name | Description |
| value | ByteStringThe bytes for indicesTensorName to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setInputBaselines(int index, Value value)
public ExplanationMetadata.InputMetadata.Builder setInputBaselines(int index, Value 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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Name | Description |
| index | int |
| value | Value |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
setInputBaselines(int index, Value.Builder builderForValue)
public ExplanationMetadata.InputMetadata.Builder setInputBaselines(int index, Value.Builder builderForValue)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 PredictSchemata's instance_schema_uri.
repeated .google.protobuf.Value input_baselines = 1;
| Name | Description |
| index | int |
| builderForValue | Builder |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
setInputTensorName(String value)
public ExplanationMetadata.InputMetadata.Builder setInputTensorName(String value)Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
string input_tensor_name = 2;
| Name | Description |
| value | StringThe inputTensorName to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setInputTensorNameBytes(ByteString value)
public ExplanationMetadata.InputMetadata.Builder setInputTensorNameBytes(ByteString value)Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
string input_tensor_name = 2;
| Name | Description |
| value | ByteStringThe bytes for inputTensorName to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setModality(String value)
public ExplanationMetadata.InputMetadata.Builder setModality(String value)Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
string modality = 4;
| Name | Description |
| value | StringThe modality to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setModalityBytes(ByteString value)
public ExplanationMetadata.InputMetadata.Builder setModalityBytes(ByteString value)Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
string modality = 4;
| Name | Description |
| value | ByteStringThe bytes for modality to set. |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder | This builder for chaining. |
setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
public ExplanationMetadata.InputMetadata.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)| Name | Description |
| field | FieldDescriptor |
| index | int |
| value | Object |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
setUnknownFields(UnknownFieldSet unknownFields)
public final ExplanationMetadata.InputMetadata.Builder setUnknownFields(UnknownFieldSet unknownFields)| Name | Description |
| unknownFields | UnknownFieldSet |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
setVisualization(ExplanationMetadata.InputMetadata.Visualization value)
public ExplanationMetadata.InputMetadata.Builder setVisualization(ExplanationMetadata.InputMetadata.Visualization value)Visualization configurations for image explanation.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
| Name | Description |
| value | ExplanationMetadata.InputMetadata.Visualization |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |
setVisualization(ExplanationMetadata.InputMetadata.Visualization.Builder builderForValue)
public ExplanationMetadata.InputMetadata.Builder setVisualization(ExplanationMetadata.InputMetadata.Visualization.Builder builderForValue)Visualization configurations for image explanation.
.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
| Name | Description |
| builderForValue | ExplanationMetadata.InputMetadata.Visualization.Builder |
| Type | Description |
| ExplanationMetadata.InputMetadata.Builder |