Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1IntegratedGradientsAttribution

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1/classes.rb,
lib/google/apis/aiplatform_v1/representations.rb,
lib/google/apis/aiplatform_v1/representations.rb

Overview

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

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1IntegratedGradientsAttribution

Returns a new instance of GoogleCloudAiplatformV1IntegratedGradientsAttribution.



9218
9219
9220
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 9218

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#blur_baseline_config ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1BlurBaselineConfig

Config for 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 Corresponds to the JSON property blurBaselineConfig

Returns:



9200
9201
9202
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 9200

def blur_baseline_config
  @blur_baseline_config
end

#smooth_grad_config ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SmoothGradConfig

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 Corresponds to the JSON property smoothGradConfig

Returns:



9208
9209
9210
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 9208

def smooth_grad_config
  @smooth_grad_config
end

#step_count ⇒ Fixnum

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. Corresponds to the JSON property stepCount

Returns:

  • (Fixnum) 


9216
9217
9218
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 9216

def step_count
  @step_count
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



9223
9224
9225
9226
9227
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 9223

def update!(**args)
  @blur_baseline_config = args[:blur_baseline_config] if args.key?(:blur_baseline_config)
  @smooth_grad_config = args[:smooth_grad_config] if args.key?(:smooth_grad_config)
  @step_count = args[:step_count] if args.key?(:step_count)
end