Class IntegratedGradientsAttribution.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<IntegratedGradientsAttribution.Builder>
com.google.cloud.aiplatform.v1.IntegratedGradientsAttribution.Builder
All Implemented Interfaces:
IntegratedGradientsAttributionOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
IntegratedGradientsAttribution

public static final class IntegratedGradientsAttribution.Builder extends com.google.protobuf.GeneratedMessage.Builder<IntegratedGradientsAttribution.Builder> 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.aiplatform.v1.IntegratedGradientsAttribution
  • Method Details

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()
    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<IntegratedGradientsAttribution.Builder>
    • clear

      Specified by:
      clear in interface com.google.protobuf.Message.Builder
      Specified by:
      clear in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clear in class com.google.protobuf.GeneratedMessage.Builder<IntegratedGradientsAttribution.Builder>
    • getDescriptorForType

      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface com.google.protobuf.Message.Builder
      Specified by:
      getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
      Overrides:
      getDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<IntegratedGradientsAttribution.Builder>
    • getDefaultInstanceForType

      public IntegratedGradientsAttribution getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder
    • build

      Specified by:
      build in interface com.google.protobuf.Message.Builder
      Specified by:
      build in interface com.google.protobuf.MessageLite.Builder
    • buildPartial

      public IntegratedGradientsAttribution buildPartial()
      Specified by:
      buildPartial in interface com.google.protobuf.Message.Builder
      Specified by:
      buildPartial in interface com.google.protobuf.MessageLite.Builder
    • mergeFrom

      public IntegratedGradientsAttribution.Builder mergeFrom(com.google.protobuf.Message other)
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<IntegratedGradientsAttribution.Builder>
    • mergeFrom

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessage.Builder<IntegratedGradientsAttribution.Builder>
    • mergeFrom

      public IntegratedGradientsAttribution.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Specified by:
      mergeFrom in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<IntegratedGradientsAttribution.Builder>
      Throws:
      IOException
    • 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];
      Specified by:
      getStepCount in interface IntegratedGradientsAttributionOrBuilder
      Returns:
      The stepCount.
    • setStepCount

      public IntegratedGradientsAttribution.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 within the desired error range.
      
       Valid range of its value is [1, 100], inclusively.
       
      int32 step_count = 1 [(.google.api.field_behavior) = REQUIRED];
      Parameters:
      value - The stepCount to set.
      Returns:
      This builder for chaining.
    • clearStepCount

      public IntegratedGradientsAttribution.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 within the desired error range.
      
       Valid range of its value is [1, 100], inclusively.
       
      int32 step_count = 1 [(.google.api.field_behavior) = REQUIRED];
      Returns:
      This builder for chaining.
    • 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.aiplatform.v1.SmoothGradConfig smooth_grad_config = 2;
      Specified by:
      hasSmoothGradConfig in interface IntegratedGradientsAttributionOrBuilder
      Returns:
      Whether the smoothGradConfig field is set.
    • 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.aiplatform.v1.SmoothGradConfig smooth_grad_config = 2;
      Specified by:
      getSmoothGradConfig in interface IntegratedGradientsAttributionOrBuilder
      Returns:
      The smoothGradConfig.
    • setSmoothGradConfig

      public IntegratedGradientsAttribution.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.v1.SmoothGradConfig smooth_grad_config = 2;
    • setSmoothGradConfig

      public IntegratedGradientsAttribution.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.v1.SmoothGradConfig smooth_grad_config = 2;
    • mergeSmoothGradConfig

      public IntegratedGradientsAttribution.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.v1.SmoothGradConfig smooth_grad_config = 2;
    • clearSmoothGradConfig

      public IntegratedGradientsAttribution.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.v1.SmoothGradConfig smooth_grad_config = 2;
    • getSmoothGradConfigBuilder

      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.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.aiplatform.v1.SmoothGradConfig smooth_grad_config = 2;
      Specified by:
      getSmoothGradConfigOrBuilder in interface IntegratedGradientsAttributionOrBuilder
    • 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.aiplatform.v1.BlurBaselineConfig blur_baseline_config = 3;
      Specified by:
      hasBlurBaselineConfig in interface IntegratedGradientsAttributionOrBuilder
      Returns:
      Whether the blurBaselineConfig field is set.
    • 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.aiplatform.v1.BlurBaselineConfig blur_baseline_config = 3;
      Specified by:
      getBlurBaselineConfig in interface IntegratedGradientsAttributionOrBuilder
      Returns:
      The blurBaselineConfig.
    • setBlurBaselineConfig

      public IntegratedGradientsAttribution.Builder setBlurBaselineConfig(BlurBaselineConfig value)
       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;
    • setBlurBaselineConfig

      public IntegratedGradientsAttribution.Builder setBlurBaselineConfig(BlurBaselineConfig.Builder builderForValue)
       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;
    • mergeBlurBaselineConfig

      public IntegratedGradientsAttribution.Builder mergeBlurBaselineConfig(BlurBaselineConfig value)
       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;
    • clearBlurBaselineConfig

      public IntegratedGradientsAttribution.Builder clearBlurBaselineConfig()
       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;
    • getBlurBaselineConfigBuilder

      public BlurBaselineConfig.Builder getBlurBaselineConfigBuilder()
       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;
    • 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.aiplatform.v1.BlurBaselineConfig blur_baseline_config = 3;
      Specified by:
      getBlurBaselineConfigOrBuilder in interface IntegratedGradientsAttributionOrBuilder