public final class IntegratedGradientsAttribution extends GeneratedMessageV3 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
Inherited Members
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT)
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT,int)
com.google.protobuf.GeneratedMessageV3.<T>emptyList(java.lang.Class<T>)
com.google.protobuf.GeneratedMessageV3.internalGetMapFieldReflection(int)
Static Fields
BLUR_BASELINE_CONFIG_FIELD_NUMBER
public static final int BLUR_BASELINE_CONFIG_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
SMOOTH_GRAD_CONFIG_FIELD_NUMBER
public static final int SMOOTH_GRAD_CONFIG_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
STEP_COUNT_FIELD_NUMBER
public static final int STEP_COUNT_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
Static Methods
getDefaultInstance()
public static IntegratedGradientsAttribution getDefaultInstance()
getDescriptor()
public static final Descriptors.Descriptor getDescriptor()
newBuilder()
public static IntegratedGradientsAttribution.Builder newBuilder()
newBuilder(IntegratedGradientsAttribution prototype)
public static IntegratedGradientsAttribution.Builder newBuilder(IntegratedGradientsAttribution prototype)
public static IntegratedGradientsAttribution parseDelimitedFrom(InputStream input)
public static IntegratedGradientsAttribution parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
parseFrom(byte[] data)
public static IntegratedGradientsAttribution parseFrom(byte[] data)
| Parameter |
| Name |
Description |
data |
byte[]
|
parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
public static IntegratedGradientsAttribution parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
parseFrom(ByteString data)
public static IntegratedGradientsAttribution parseFrom(ByteString data)
parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static IntegratedGradientsAttribution parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static IntegratedGradientsAttribution parseFrom(CodedInputStream input)
public static IntegratedGradientsAttribution parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public static IntegratedGradientsAttribution parseFrom(InputStream input)
public static IntegratedGradientsAttribution parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
parseFrom(ByteBuffer data)
public static IntegratedGradientsAttribution parseFrom(ByteBuffer data)
parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
public static IntegratedGradientsAttribution parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
parser()
public static Parser<IntegratedGradientsAttribution> parser()
Methods
equals(Object obj)
public boolean equals(Object obj)
| Parameter |
| Name |
Description |
obj |
Object
|
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;
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;
getDefaultInstanceForType()
public IntegratedGradientsAttribution getDefaultInstanceForType()
getParserForType()
public Parser<IntegratedGradientsAttribution> getParserForType()
Overrides
getSerializedSize()
public int getSerializedSize()
| Returns |
| Type |
Description |
int |
|
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;
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;
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.vertexai.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.vertexai.v1.SmoothGradConfig smooth_grad_config = 2;
| Returns |
| Type |
Description |
boolean |
Whether the smoothGradConfig field is set.
|
hashCode()
| Returns |
| Type |
Description |
int |
|
Overrides
internalGetFieldAccessorTable()
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Overrides
isInitialized()
public final boolean isInitialized()
Overrides
newBuilderForType()
public IntegratedGradientsAttribution.Builder newBuilderForType()
newBuilderForType(GeneratedMessageV3.BuilderParent parent)
protected IntegratedGradientsAttribution.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Overrides
newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
| Returns |
| Type |
Description |
Object |
|
Overrides
toBuilder()
public IntegratedGradientsAttribution.Builder toBuilder()
writeTo(CodedOutputStream output)
public void writeTo(CodedOutputStream output)
Overrides
