Struct SmoothGradConfig 

#[non_exhaustive]
pub struct SmoothGradConfig {
    pub noisy_sample_count: i32,
    pub gradient_noise_sigma: Option<GradientNoiseSigma>,
    /* private fields */
}

Available on crate features dataset-service or deployment-resource-pool-service or endpoint-service or job-service or model-service or pipeline-service or prediction-service only.

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

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

noisy_sample_count: i32

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.

gradient_noise_sigma: Option<GradientNoiseSigma>

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

Implementations

impl SmoothGradConfig

pub fn new() -> Self

pub fn set_noisy_sample_count<T: Into<i32>>(self, v: T) -> Self

Sets the value of noisy_sample_count.

Example

let x = SmoothGradConfig::new().set_noisy_sample_count(42);

pub fn set_gradient_noise_sigma<T: Into<Option<GradientNoiseSigma>>>( self, v: T, ) -> Self

Sets the value of gradient_noise_sigma.

Note that all the setters affecting gradient_noise_sigma are mutually exclusive.

Example

use google_cloud_aiplatform_v1::model::smooth_grad_config::GradientNoiseSigma;
let x = SmoothGradConfig::new().set_gradient_noise_sigma(Some(GradientNoiseSigma::NoiseSigma(42.0)));

pub fn noise_sigma(&self) -> Option<&f32>

The value of gradient_noise_sigma if it holds a NoiseSigma, None if the field is not set or holds a different branch.

pub fn set_noise_sigma<T: Into<f32>>(self, v: T) -> Self

Sets the value of gradient_noise_sigma to hold a NoiseSigma.

Note that all the setters affecting gradient_noise_sigma are mutually exclusive.

Example

let x = SmoothGradConfig::new().set_noise_sigma(42.0);
assert!(x.noise_sigma().is_some());
assert!(x.feature_noise_sigma().is_none());

pub fn feature_noise_sigma(&self) -> Option<&Box<FeatureNoiseSigma>>

The value of gradient_noise_sigma if it holds a FeatureNoiseSigma, None if the field is not set or holds a different branch.

pub fn set_feature_noise_sigma<T: Into<Box<FeatureNoiseSigma>>>( self, v: T, ) -> Self

Sets the value of gradient_noise_sigma to hold a FeatureNoiseSigma.

Note that all the setters affecting gradient_noise_sigma are mutually exclusive.

Example

use google_cloud_aiplatform_v1::model::FeatureNoiseSigma;
let x = SmoothGradConfig::new().set_feature_noise_sigma(FeatureNoiseSigma::default()/* use setters */);
assert!(x.feature_noise_sigma().is_some());
assert!(x.noise_sigma().is_none());

Trait Implementations

impl Clone for SmoothGradConfig

fn clone(&self) -> SmoothGradConfig

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for SmoothGradConfig

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

Formats the value using the given formatter.

impl Default for SmoothGradConfig

fn default() -> SmoothGradConfig

Returns the “default value” for a type.

impl Message for SmoothGradConfig

fn typename() -> &'static str

The typename of this message.

impl PartialEq for SmoothGradConfig

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

Tests for self and other values to be equal, and is used by ==.

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

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for SmoothGradConfig