Class IntegratedGradientsAttribution.Builder (0.3.0)

public static final class IntegratedGradientsAttribution.Builder extends GeneratedMessageV3.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.vertexai.v1.IntegratedGradientsAttribution

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
TypeDescription
Descriptor

Methods

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public IntegratedGradientsAttribution.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
Parameters
NameDescription
fieldFieldDescriptor
valueObject
Returns
TypeDescription
IntegratedGradientsAttribution.Builder
Overrides

build()

public IntegratedGradientsAttribution build()
Returns
TypeDescription
IntegratedGradientsAttribution

buildPartial()

public IntegratedGradientsAttribution buildPartial()
Returns
TypeDescription
IntegratedGradientsAttribution

clear()

public IntegratedGradientsAttribution.Builder clear()
Returns
TypeDescription
IntegratedGradientsAttribution.Builder
Overrides

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.vertexai.v1.BlurBaselineConfig blur_baseline_config = 3;

Returns
TypeDescription
IntegratedGradientsAttribution.Builder

clearField(Descriptors.FieldDescriptor field)

public IntegratedGradientsAttribution.Builder clearField(Descriptors.FieldDescriptor field)
Parameter
NameDescription
fieldFieldDescriptor
Returns
TypeDescription
IntegratedGradientsAttribution.Builder
Overrides

clearOneof(Descriptors.OneofDescriptor oneof)

public IntegratedGradientsAttribution.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
NameDescription
oneofOneofDescriptor
Returns
TypeDescription
IntegratedGradientsAttribution.Builder
Overrides

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.vertexai.v1.SmoothGradConfig smooth_grad_config = 2;

Returns
TypeDescription
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
TypeDescription
IntegratedGradientsAttribution.Builder

This builder for chaining.

clone()

public IntegratedGradientsAttribution.Builder clone()
Returns
TypeDescription
IntegratedGradientsAttribution.Builder
Overrides

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.vertexai.v1.BlurBaselineConfig blur_baseline_config = 3;

Returns
TypeDescription
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.vertexai.v1.BlurBaselineConfig blur_baseline_config = 3;

Returns
TypeDescription
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.vertexai.v1.BlurBaselineConfig blur_baseline_config = 3;

Returns
TypeDescription
BlurBaselineConfigOrBuilder

getDefaultInstanceForType()

public IntegratedGradientsAttribution getDefaultInstanceForType()
Returns
TypeDescription
IntegratedGradientsAttribution

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()
Returns
TypeDescription
Descriptor
Overrides

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.vertexai.v1.SmoothGradConfig smooth_grad_config = 2;

Returns
TypeDescription
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.vertexai.v1.SmoothGradConfig smooth_grad_config = 2;

Returns
TypeDescription
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.vertexai.v1.SmoothGradConfig smooth_grad_config = 2;

Returns
TypeDescription
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
TypeDescription
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.vertexai.v1.BlurBaselineConfig blur_baseline_config = 3;

Returns
TypeDescription
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.vertexai.v1.SmoothGradConfig smooth_grad_config = 2;

Returns
TypeDescription
boolean

Whether the smoothGradConfig field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
TypeDescription
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
TypeDescription
boolean
Overrides

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.vertexai.v1.BlurBaselineConfig blur_baseline_config = 3;

Parameter
NameDescription
valueBlurBaselineConfig
Returns
TypeDescription
IntegratedGradientsAttribution.Builder

mergeFrom(IntegratedGradientsAttribution other)

public IntegratedGradientsAttribution.Builder mergeFrom(IntegratedGradientsAttribution other)
Parameter
NameDescription
otherIntegratedGradientsAttribution
Returns
TypeDescription
IntegratedGradientsAttribution.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public IntegratedGradientsAttribution.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputCodedInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
IntegratedGradientsAttribution.Builder
Overrides
Exceptions
TypeDescription
IOException

mergeFrom(Message other)

public IntegratedGradientsAttribution.Builder mergeFrom(Message other)
Parameter
NameDescription
otherMessage
Returns
TypeDescription
IntegratedGradientsAttribution.Builder
Overrides

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.vertexai.v1.SmoothGradConfig smooth_grad_config = 2;

Parameter
NameDescription
valueSmoothGradConfig
Returns
TypeDescription
IntegratedGradientsAttribution.Builder

mergeUnknownFields(UnknownFieldSet unknownFields)

public final IntegratedGradientsAttribution.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
NameDescription
unknownFieldsUnknownFieldSet
Returns
TypeDescription
IntegratedGradientsAttribution.Builder
Overrides

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.vertexai.v1.BlurBaselineConfig blur_baseline_config = 3;

Parameter
NameDescription
valueBlurBaselineConfig
Returns
TypeDescription
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.vertexai.v1.BlurBaselineConfig blur_baseline_config = 3;

Parameter
NameDescription
builderForValueBlurBaselineConfig.Builder
Returns
TypeDescription
IntegratedGradientsAttribution.Builder

setField(Descriptors.FieldDescriptor field, Object value)

public IntegratedGradientsAttribution.Builder setField(Descriptors.FieldDescriptor field, Object value)
Parameters
NameDescription
fieldFieldDescriptor
valueObject
Returns
TypeDescription
IntegratedGradientsAttribution.Builder
Overrides

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

public IntegratedGradientsAttribution.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
Parameters
NameDescription
fieldFieldDescriptor
indexint
valueObject
Returns
TypeDescription
IntegratedGradientsAttribution.Builder
Overrides

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.vertexai.v1.SmoothGradConfig smooth_grad_config = 2;

Parameter
NameDescription
valueSmoothGradConfig
Returns
TypeDescription
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.vertexai.v1.SmoothGradConfig smooth_grad_config = 2;

Parameter
NameDescription
builderForValueSmoothGradConfig.Builder
Returns
TypeDescription
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
NameDescription
valueint

The stepCount to set.

Returns
TypeDescription
IntegratedGradientsAttribution.Builder

This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final IntegratedGradientsAttribution.Builder setUnknownFields(UnknownFieldSet unknownFields)
Parameter
NameDescription
unknownFieldsUnknownFieldSet
Returns
TypeDescription
IntegratedGradientsAttribution.Builder
Overrides