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public interface IntegratedGradientsAttributionOrBuilder extends MessageOrBuilderImplements
MessageOrBuilderMethods
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.v1.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.v1.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.v1.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.v1.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.v1.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.v1.SmoothGradConfig smooth_grad_config = 2;
| Returns | |
|---|---|
| Type | Description |
boolean |
Whether the smoothGradConfig field is set. |