Class XraiAttribution.Builder (3.20.0)

Version 3.20.0

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public static final class XraiAttribution.Builder extends GeneratedMessageV3.Builder<XraiAttribution.Builder> implements XraiAttributionOrBuilder

An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825

Supported only by image Models.

Protobuf type google.cloud.aiplatform.v1beta1.XraiAttribution

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > XraiAttribution.Builder

Implements

XraiAttributionOrBuilder

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)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(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

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public XraiAttribution.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
XraiAttribution.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

build()

public XraiAttribution build()

Returns
Type	Description
XraiAttribution

buildPartial()

public XraiAttribution buildPartial()

Returns
Type	Description
XraiAttribution

clear()

public XraiAttribution.Builder clear()

Returns
Type	Description
XraiAttribution.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearBlurBaselineConfig()

public XraiAttribution.Builder clearBlurBaselineConfig()

Config for XRAI 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.v1beta1.BlurBaselineConfig blur_baseline_config = 3;

Returns
Type	Description
XraiAttribution.Builder

clearField(Descriptors.FieldDescriptor field)

public XraiAttribution.Builder clearField(Descriptors.FieldDescriptor field)

Parameter
Name	Description
field	FieldDescriptor

Returns
Type	Description
XraiAttribution.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

clearOneof(Descriptors.OneofDescriptor oneof)

public XraiAttribution.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter
Name	Description
oneof	OneofDescriptor

Returns
Type	Description
XraiAttribution.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

clearSmoothGradConfig()

public XraiAttribution.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.v1beta1.SmoothGradConfig smooth_grad_config = 2;

Returns
Type	Description
XraiAttribution.Builder

clearStepCount()

public XraiAttribution.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 met 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
XraiAttribution.Builder	This builder for chaining.

clone()

public XraiAttribution.Builder clone()

Returns
Type	Description
XraiAttribution.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getBlurBaselineConfig()

public BlurBaselineConfig getBlurBaselineConfig()

Config for XRAI 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.v1beta1.BlurBaselineConfig blur_baseline_config = 3;

Returns
Type	Description
BlurBaselineConfig	The blurBaselineConfig.

getBlurBaselineConfigBuilder()

public BlurBaselineConfig.Builder getBlurBaselineConfigBuilder()

Config for XRAI 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.v1beta1.BlurBaselineConfig blur_baseline_config = 3;

Returns
Type	Description
BlurBaselineConfig.Builder

getBlurBaselineConfigOrBuilder()

public BlurBaselineConfigOrBuilder getBlurBaselineConfigOrBuilder()

Config for XRAI 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.v1beta1.BlurBaselineConfig blur_baseline_config = 3;

Returns
Type	Description
BlurBaselineConfigOrBuilder

getDefaultInstanceForType()

public XraiAttribution getDefaultInstanceForType()

Returns
Type	Description
XraiAttribution

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns
Type	Description
Descriptor

Overrides

GeneratedMessageV3.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.v1beta1.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.v1beta1.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.v1beta1.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 met 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 XRAI 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.v1beta1.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.v1beta1.SmoothGradConfig smooth_grad_config = 2;

Returns
Type	Description
boolean	Whether the smoothGradConfig field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns
Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns
Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeBlurBaselineConfig(BlurBaselineConfig value)

public XraiAttribution.Builder mergeBlurBaselineConfig(BlurBaselineConfig value)

Config for XRAI 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.v1beta1.BlurBaselineConfig blur_baseline_config = 3;

Parameter
Name	Description
value	BlurBaselineConfig

Returns
Type	Description
XraiAttribution.Builder

mergeFrom(XraiAttribution other)

public XraiAttribution.Builder mergeFrom(XraiAttribution other)

Parameter
Name	Description
other	XraiAttribution

Returns
Type	Description
XraiAttribution.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

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

Parameters
Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns
Type	Description
XraiAttribution.Builder

Overrides

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

Exceptions
Type	Description
IOException

mergeFrom(Message other)

public XraiAttribution.Builder mergeFrom(Message other)

Parameter
Name	Description
other	Message

Returns
Type	Description
XraiAttribution.Builder

Overrides

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

mergeSmoothGradConfig(SmoothGradConfig value)

public XraiAttribution.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.v1beta1.SmoothGradConfig smooth_grad_config = 2;

Parameter
Name	Description
value	SmoothGradConfig

Returns
Type	Description
XraiAttribution.Builder

mergeUnknownFields(UnknownFieldSet unknownFields)

public final XraiAttribution.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
XraiAttribution.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

setBlurBaselineConfig(BlurBaselineConfig value)

public XraiAttribution.Builder setBlurBaselineConfig(BlurBaselineConfig value)

Config for XRAI 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.v1beta1.BlurBaselineConfig blur_baseline_config = 3;

Parameter
Name	Description
value	BlurBaselineConfig

Returns
Type	Description
XraiAttribution.Builder

setBlurBaselineConfig(BlurBaselineConfig.Builder builderForValue)

public XraiAttribution.Builder setBlurBaselineConfig(BlurBaselineConfig.Builder builderForValue)

Config for XRAI 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.v1beta1.BlurBaselineConfig blur_baseline_config = 3;

Parameter
Name	Description
builderForValue	BlurBaselineConfig.Builder

Returns
Type	Description
XraiAttribution.Builder

setField(Descriptors.FieldDescriptor field, Object value)

public XraiAttribution.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
XraiAttribution.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public XraiAttribution.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters
Name	Description
field	FieldDescriptor
index	int
value	Object

Returns
Type	Description
XraiAttribution.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setSmoothGradConfig(SmoothGradConfig value)

public XraiAttribution.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.v1beta1.SmoothGradConfig smooth_grad_config = 2;

Parameter
Name	Description
value	SmoothGradConfig

Returns
Type	Description
XraiAttribution.Builder

setSmoothGradConfig(SmoothGradConfig.Builder builderForValue)

public XraiAttribution.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.v1beta1.SmoothGradConfig smooth_grad_config = 2;

Parameter
Name	Description
builderForValue	SmoothGradConfig.Builder

Returns
Type	Description
XraiAttribution.Builder

setStepCount(int value)

public XraiAttribution.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 met 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
XraiAttribution.Builder	This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final XraiAttribution.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
XraiAttribution.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)