Enum googapis::google::cloud::aiplatform::v1beta1::smooth_grad_config::GradientNoiseSigma

pub enum GradientNoiseSigma {
    NoiseSigma(f32),
    FeatureNoiseSigma(FeatureNoiseSigma),
}

Represents the standard deviation of the gaussian kernel that will be used to add noise to the interpolated inputs prior to computing gradients.

Variants

NoiseSigma(f32)

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](https://developers.google.com/machine-learning/data-prep/transform/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.v1beta1.SmoothGradConfig.feature_noise_sigma] instead for each feature.

Tuple Fields of NoiseSigma

0: f32

FeatureNoiseSigma(FeatureNoiseSigma)

This is similar to [noise_sigma][google.cloud.aiplatform.v1beta1.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.v1beta1.SmoothGradConfig.noise_sigma] will be used for all features.

Tuple Fields of FeatureNoiseSigma

0: FeatureNoiseSigma

Implementations

impl GradientNoiseSigma

pub fn encode<B>(&self, buf: &mut B) where
    B: BufMut, 

pub fn merge<B>(
    field: &mut Option<GradientNoiseSigma>,
    tag: u32,
    wire_type: WireType,
    buf: &mut B,
    ctx: DecodeContext
) -> Result<(), DecodeError> where
    B: Buf, 

pub fn encoded_len(&self) -> usize

Trait Implementations

impl Clone for GradientNoiseSigma

fn clone(&self) -> GradientNoiseSigma

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for GradientNoiseSigma

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq<GradientNoiseSigma> for GradientNoiseSigma

fn eq(&self, other: &GradientNoiseSigma) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &GradientNoiseSigma) -> bool

This method tests for !=.

impl StructuralPartialEq for GradientNoiseSigma