Package com.google.cloud.aiplatform.v1

Interface XraiAttributionOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
XraiAttribution, XraiAttribution.Builder
@Generated public interface XraiAttributionOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Details

    • getStepCount

      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:
      The stepCount.

    • hasSmoothGradConfig

      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:
      Whether the smoothGradConfig field is set.

    • getSmoothGradConfig

      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:
      The smoothGradConfig.

    • getSmoothGradConfigOrBuilder

      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;

    • hasBlurBaselineConfig

      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.v1.BlurBaselineConfig blur_baseline_config = 3;
      Returns:
      Whether the blurBaselineConfig field is set.

    • getBlurBaselineConfig

      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.v1.BlurBaselineConfig blur_baseline_config = 3;
      Returns:
      The blurBaselineConfig.

    • getBlurBaselineConfigOrBuilder

      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.v1.BlurBaselineConfig blur_baseline_config = 3;