Google Cloud Ai Platform V1 Client - Class IntegratedGradientsAttribution (0.13.0)

Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class IntegratedGradientsAttribution.

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

Generated from protobuf message google.cloud.aiplatform.v1.IntegratedGradientsAttribution

Methods

__construct

Constructor.

Parameters
NameDescription
data array

Optional. Data for populating the Message object.

↳ step_count int

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.

↳ smooth_grad_config Google\Cloud\AIPlatform\V1\SmoothGradConfig

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

↳ blur_baseline_config Google\Cloud\AIPlatform\V1\BlurBaselineConfig

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

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.

Returns
TypeDescription
int

setStepCount

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.

Parameter
NameDescription
var int
Returns
TypeDescription
$this

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

Returns
TypeDescription
Google\Cloud\AIPlatform\V1\SmoothGradConfig|null

hasSmoothGradConfig

clearSmoothGradConfig

setSmoothGradConfig

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

Parameter
NameDescription
var Google\Cloud\AIPlatform\V1\SmoothGradConfig
Returns
TypeDescription
$this

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

Returns
TypeDescription
Google\Cloud\AIPlatform\V1\BlurBaselineConfig|null

hasBlurBaselineConfig

clearBlurBaselineConfig

setBlurBaselineConfig

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

Parameter
NameDescription
var Google\Cloud\AIPlatform\V1\BlurBaselineConfig
Returns
TypeDescription
$this