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public static final class XraiAttribution.Builder extends GeneratedMessageV3.Builder<XraiAttribution.Builder> implements XraiAttributionOrBuilderAn 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.BuilderImplements
XraiAttributionOrBuilderStatic 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 |
|
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 |
|
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 |
|
clearOneof(Descriptors.OneofDescriptor oneof)
public XraiAttribution.Builder clearOneof(Descriptors.OneofDescriptor oneof)| Parameter | |
|---|---|
| Name | Description |
oneof |
OneofDescriptor |
| Returns | |
|---|---|
| Type | Description |
XraiAttribution.Builder |
|
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 |
|
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 |
|
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 |
|
isInitialized()
public final boolean isInitialized()| Returns | |
|---|---|
| Type | Description |
boolean |
|
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 |
|
| Exceptions | |
|---|---|
| Type | Description |
IOException |
|
mergeFrom(Message other)
public XraiAttribution.Builder mergeFrom(Message other)| Parameter | |
|---|---|
| Name | Description |
other |
Message |
| Returns | |
|---|---|
| Type | Description |
XraiAttribution.Builder |
|
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 |
|
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 |
|
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 |
|
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 |
intThe 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 |
|