Class SmoothGradConfig (2.0.0)

public sealed class SmoothGradConfig : IMessage<SmoothGradConfig>, IEquatable<SmoothGradConfig>, IDeepCloneable<SmoothGradConfig>, IBufferMessage, IMessage

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

Inheritance

Object > SmoothGradConfig

Implements

IMessage<SmoothGradConfig>, IEquatable<SmoothGradConfig>, IDeepCloneable<SmoothGradConfig>, IBufferMessage, IMessage

Inherited Members

Object.GetHashCode()

Object.GetType()

Object.MemberwiseClone()

Object.ToString()

Namespace

Google.Cloud.AIPlatform.V1

Assembly

Google.Cloud.AIPlatform.V1.dll

Constructors

SmoothGradConfig()

public SmoothGradConfig()

SmoothGradConfig(SmoothGradConfig)

public SmoothGradConfig(SmoothGradConfig other)

Parameter
Name	Description
`other`	`SmoothGradConfig`

Properties

FeatureNoiseSigma

public FeatureNoiseSigma FeatureNoiseSigma { get; set; }

This is similar to [noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.noise_sigma], but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, [noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.noise_sigma] will be used for all features.

Property Value
Type	Description
`FeatureNoiseSigma`

GradientNoiseSigmaCase

public SmoothGradConfig.GradientNoiseSigmaOneofCase GradientNoiseSigmaCase { get; }

Property Value
Type	Description
`SmoothGradConfig.GradientNoiseSigmaOneofCase`

NoiseSigma

public float NoiseSigma { get; set; }

This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization.

For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1.

If the distribution is different per feature, set [feature_noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.feature_noise_sigma] instead for each feature.

Property Value
Type	Description
`Single`

NoisySampleCount

public int NoisySampleCount { get; set; }

The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.

Property Value
Type	Description
`Int32`