Google Cloud Ai Platform V1 Client - Class XraiAttribution (0.19.0)

Version 0.19.0

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

An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Supported only by image Models.

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

Namespace

Google \ Cloud \ AIPlatform \ V1

Methods

__construct

Constructor.

Parameters
Name	Description
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 met 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 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

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.

Returns
Type	Description
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 met within the desired error range. Valid range of its value is [1, 100], inclusively.

Parameter
Name	Description
var	int

Returns
Type	Description
$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
Type	Description
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
Name	Description
var	Google\Cloud\AIPlatform\V1\SmoothGradConfig

Returns
Type	Description
$this

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

Returns
Type	Description
Google\Cloud\AIPlatform\V1\BlurBaselineConfig|null

hasBlurBaselineConfig

clearBlurBaselineConfig

setBlurBaselineConfig

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

Parameter
Name	Description
var	Google\Cloud\AIPlatform\V1\BlurBaselineConfig

Returns
Type	Description
$this