Interface IntegratedGradientsAttributionOrBuilder (3.22.0)

Version 3.22.0

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public interface IntegratedGradientsAttributionOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getBlurBaselineConfig()

public abstract 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.aiplatform.v1beta1.BlurBaselineConfig blur_baseline_config = 3;

Returns
Type	Description
BlurBaselineConfig	The blurBaselineConfig.

getBlurBaselineConfigOrBuilder()

public abstract 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.aiplatform.v1beta1.BlurBaselineConfig blur_baseline_config = 3;

Returns
Type	Description
BlurBaselineConfigOrBuilder

getSmoothGradConfig()

public abstract 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.

getSmoothGradConfigOrBuilder()

public abstract 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 abstract 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 abstract 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.aiplatform.v1beta1.BlurBaselineConfig blur_baseline_config = 3;

Returns
Type	Description
boolean	Whether the blurBaselineConfig field is set.

hasSmoothGradConfig()

public abstract 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.