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
Inherited Members
com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()
com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)
com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)
com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)
com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)
Static Methods
public static final Descriptors.Descriptor getDescriptor()
Methods
public XraiAttribution.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
Overrides
public XraiAttribution build()
public XraiAttribution buildPartial()
public XraiAttribution.Builder clear()
Overrides
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;
public XraiAttribution.Builder clearField(Descriptors.FieldDescriptor field)
Overrides
public XraiAttribution.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Overrides
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;
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];
public XraiAttribution.Builder clone()
Overrides
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;
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;
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;
public XraiAttribution getDefaultInstanceForType()
public Descriptors.Descriptor getDescriptorForType()
Overrides
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;
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;
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;
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.
|
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.
|
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.
|
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Overrides
public final boolean isInitialized()
Overrides
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;
public XraiAttribution.Builder mergeFrom(XraiAttribution other)
public XraiAttribution.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Overrides
public XraiAttribution.Builder mergeFrom(Message other)
| Parameter |
|---|
| Name | Description |
other | Message
|
Overrides
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;
public final XraiAttribution.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Overrides
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;
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;
public XraiAttribution.Builder setField(Descriptors.FieldDescriptor field, Object value)
Overrides
public XraiAttribution.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
Overrides
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;
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;
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.
|
public final XraiAttribution.Builder setUnknownFields(UnknownFieldSet unknownFields)
Overrides
