Class IntegratedGradientsAttribution.Builder (3.84.0)

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public static final class IntegratedGradientsAttribution.Builder extends GeneratedMessage.Builder<IntegratedGradientsAttribution.Builder> implements IntegratedGradientsAttributionOrBuilder

An attribution method that computes the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365

Protobuf type google.cloud.aiplatform.v1.IntegratedGradientsAttribution

Inheritance

java.lang.Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessage.Builder > IntegratedGradientsAttribution.Builder

Implements

IntegratedGradientsAttributionOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessage.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessage.Builder.clear()

GeneratedMessage.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessage.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessage.Builder.clone()

GeneratedMessage.Builder.getAllFields()

GeneratedMessage.Builder.getDescriptorForType()

GeneratedMessage.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessage.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessage.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessage.Builder.getParentForChildren()

GeneratedMessage.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessage.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessage.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessage.Builder.getUnknownFieldSetBuilder()

GeneratedMessage.Builder.getUnknownFields()

GeneratedMessage.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessage.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessage.Builder.internalGetFieldAccessorTable()

GeneratedMessage.Builder.internalGetMapField(int)

com.google.protobuf.GeneratedMessage.Builder.internalGetMapFieldReflection(int)

GeneratedMessage.Builder.internalGetMutableMapField(int)

com.google.protobuf.GeneratedMessage.Builder.internalGetMutableMapFieldReflection(int)

GeneratedMessage.Builder.isClean()

GeneratedMessage.Builder.isInitialized()

GeneratedMessage.Builder.markClean()

GeneratedMessage.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessage.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessage.Builder.mergeUnknownVarintField(int,int)

GeneratedMessage.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessage.Builder.onBuilt()

GeneratedMessage.Builder.onChanged()

com.google.protobuf.GeneratedMessage.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessage.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessage.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessage.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessage.Builder.setUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessage.Builder.setUnknownFieldsProto3(com.google.protobuf.UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns
Type	Description
Descriptor

Methods

build()

public IntegratedGradientsAttribution build()

Returns
Type	Description
IntegratedGradientsAttribution

buildPartial()

public IntegratedGradientsAttribution buildPartial()

Returns
Type	Description
IntegratedGradientsAttribution

clear()

public IntegratedGradientsAttribution.Builder clear()

Returns
Type	Description
IntegratedGradientsAttribution.Builder

Overrides

GeneratedMessage.Builder<BuilderType>.clear()

clearBlurBaselineConfig()

public IntegratedGradientsAttribution.Builder clearBlurBaselineConfig()

Config for IG with blur baseline.

When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

.google.cloud.aiplatform.v1.BlurBaselineConfig blur_baseline_config = 3;

Returns
Type	Description
IntegratedGradientsAttribution.Builder

clearSmoothGradConfig()

public IntegratedGradientsAttribution.Builder clearSmoothGradConfig()

Config for SmoothGrad approximation of gradients.

When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

.google.cloud.aiplatform.v1.SmoothGradConfig smooth_grad_config = 2;

Returns
Type	Description
IntegratedGradientsAttribution.Builder

clearStepCount()

public IntegratedGradientsAttribution.Builder clearStepCount()

Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range.

Valid range of its value is [1, 100], inclusively.

int32 step_count = 1 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
IntegratedGradientsAttribution.Builder	This builder for chaining.

getBlurBaselineConfig()

public BlurBaselineConfig getBlurBaselineConfig()

Config for IG with blur baseline.

When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

.google.cloud.aiplatform.v1.BlurBaselineConfig blur_baseline_config = 3;

Returns
Type	Description
BlurBaselineConfig	The blurBaselineConfig.

getBlurBaselineConfigBuilder()

public BlurBaselineConfig.Builder getBlurBaselineConfigBuilder()

Config for IG with blur baseline.

When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

.google.cloud.aiplatform.v1.BlurBaselineConfig blur_baseline_config = 3;

Returns
Type	Description
BlurBaselineConfig.Builder

getBlurBaselineConfigOrBuilder()

public BlurBaselineConfigOrBuilder getBlurBaselineConfigOrBuilder()

Config for IG with blur baseline.

When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

.google.cloud.aiplatform.v1.BlurBaselineConfig blur_baseline_config = 3;

Returns
Type	Description
BlurBaselineConfigOrBuilder

getDefaultInstanceForType()

public IntegratedGradientsAttribution getDefaultInstanceForType()

Returns
Type	Description
IntegratedGradientsAttribution

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns
Type	Description
Descriptor

Overrides

GeneratedMessage.Builder<BuilderType>.getDescriptorForType()

getSmoothGradConfig()

public SmoothGradConfig getSmoothGradConfig()

Config for SmoothGrad approximation of gradients.

When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

.google.cloud.aiplatform.v1.SmoothGradConfig smooth_grad_config = 2;

Returns
Type	Description
SmoothGradConfig	The smoothGradConfig.

getSmoothGradConfigBuilder()

public SmoothGradConfig.Builder getSmoothGradConfigBuilder()

Config for SmoothGrad approximation of gradients.

When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

.google.cloud.aiplatform.v1.SmoothGradConfig smooth_grad_config = 2;

Returns
Type	Description
SmoothGradConfig.Builder

getSmoothGradConfigOrBuilder()

public SmoothGradConfigOrBuilder getSmoothGradConfigOrBuilder()

Config for SmoothGrad approximation of gradients.

When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

.google.cloud.aiplatform.v1.SmoothGradConfig smooth_grad_config = 2;

Returns
Type	Description
SmoothGradConfigOrBuilder

getStepCount()

public int getStepCount()

Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range.

Valid range of its value is [1, 100], inclusively.

int32 step_count = 1 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
int	The stepCount.

hasBlurBaselineConfig()

public boolean hasBlurBaselineConfig()

Config for IG with blur baseline.

When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

.google.cloud.aiplatform.v1.BlurBaselineConfig blur_baseline_config = 3;

Returns
Type	Description
boolean	Whether the blurBaselineConfig field is set.

hasSmoothGradConfig()

public boolean hasSmoothGradConfig()

Config for SmoothGrad approximation of gradients.

When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

.google.cloud.aiplatform.v1.SmoothGradConfig smooth_grad_config = 2;

Returns
Type	Description
boolean	Whether the smoothGradConfig field is set.

internalGetFieldAccessorTable()

protected GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()

Returns
Type	Description
FieldAccessorTable

Overrides

GeneratedMessage.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns
Type	Description
boolean

Overrides

GeneratedMessage.Builder<BuilderType>.isInitialized()

mergeBlurBaselineConfig(BlurBaselineConfig value)

public IntegratedGradientsAttribution.Builder mergeBlurBaselineConfig(BlurBaselineConfig value)

Config for IG with blur baseline.

When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

.google.cloud.aiplatform.v1.BlurBaselineConfig blur_baseline_config = 3;

Parameter
Name	Description
value	BlurBaselineConfig

Returns
Type	Description
IntegratedGradientsAttribution.Builder

mergeFrom(IntegratedGradientsAttribution other)

public IntegratedGradientsAttribution.Builder mergeFrom(IntegratedGradientsAttribution other)

Parameter
Name	Description
other	IntegratedGradientsAttribution

Returns
Type	Description
IntegratedGradientsAttribution.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public IntegratedGradientsAttribution.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns
Type	Description
IntegratedGradientsAttribution.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions
Type	Description
IOException

mergeFrom(Message other)

public IntegratedGradientsAttribution.Builder mergeFrom(Message other)

Parameter
Name	Description
other	Message

Returns
Type	Description
IntegratedGradientsAttribution.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeSmoothGradConfig(SmoothGradConfig value)

public IntegratedGradientsAttribution.Builder mergeSmoothGradConfig(SmoothGradConfig value)

Config for SmoothGrad approximation of gradients.

When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

.google.cloud.aiplatform.v1.SmoothGradConfig smooth_grad_config = 2;

Parameter
Name	Description
value	SmoothGradConfig

Returns
Type	Description
IntegratedGradientsAttribution.Builder

setBlurBaselineConfig(BlurBaselineConfig value)

public IntegratedGradientsAttribution.Builder setBlurBaselineConfig(BlurBaselineConfig value)

Config for IG with blur baseline.

When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

.google.cloud.aiplatform.v1.BlurBaselineConfig blur_baseline_config = 3;

Parameter
Name	Description
value	BlurBaselineConfig

Returns
Type	Description
IntegratedGradientsAttribution.Builder

setBlurBaselineConfig(BlurBaselineConfig.Builder builderForValue)

public IntegratedGradientsAttribution.Builder setBlurBaselineConfig(BlurBaselineConfig.Builder builderForValue)

Config for IG with blur baseline.

When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

.google.cloud.aiplatform.v1.BlurBaselineConfig blur_baseline_config = 3;

Parameter
Name	Description
builderForValue	BlurBaselineConfig.Builder

Returns
Type	Description
IntegratedGradientsAttribution.Builder

setSmoothGradConfig(SmoothGradConfig value)

public IntegratedGradientsAttribution.Builder setSmoothGradConfig(SmoothGradConfig value)

Config for SmoothGrad approximation of gradients.

When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

.google.cloud.aiplatform.v1.SmoothGradConfig smooth_grad_config = 2;

Parameter
Name	Description
value	SmoothGradConfig

Returns
Type	Description
IntegratedGradientsAttribution.Builder

setSmoothGradConfig(SmoothGradConfig.Builder builderForValue)

public IntegratedGradientsAttribution.Builder setSmoothGradConfig(SmoothGradConfig.Builder builderForValue)

Config for SmoothGrad approximation of gradients.

When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

.google.cloud.aiplatform.v1.SmoothGradConfig smooth_grad_config = 2;

Parameter
Name	Description
builderForValue	SmoothGradConfig.Builder

Returns
Type	Description
IntegratedGradientsAttribution.Builder

setStepCount(int value)

public IntegratedGradientsAttribution.Builder setStepCount(int value)

Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range.

Valid range of its value is [1, 100], inclusively.

int32 step_count = 1 [(.google.api.field_behavior) = REQUIRED];

Parameter
Name	Description
value	int The stepCount to set.

Returns
Type	Description
IntegratedGradientsAttribution.Builder	This builder for chaining.