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

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

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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.

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.

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FeatureNoiseSigma(FeatureNoiseSigma)

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.

Implementations§

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impl GradientNoiseSigma

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pub fn encode(&self, buf: &mut impl BufMut)

Encodes the message to a buffer.

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

Decodes an instance of the message from a buffer, and merges it into self.

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pub fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

Trait Implementations§

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impl Clone for GradientNoiseSigma

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fn clone(&self) -> GradientNoiseSigma

Returns a copy of the value. Read more
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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for GradientNoiseSigma

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

Formats the value using the given formatter. Read more
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impl PartialEq for GradientNoiseSigma

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

This method tests for self and other values to be equal, and is used by ==.
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fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.
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impl StructuralPartialEq for GradientNoiseSigma

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impl<T> Any for T
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fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
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fn borrow(&self) -> &T

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impl<T> BorrowMut<T> for T
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fn borrow_mut(&mut self) -> &mut T

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impl<T> From<T> for T

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fn from(t: T) -> T

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impl<T> FromRef<T> for T
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fn instrument(self, span: Span) -> Instrumented<Self>

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fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

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fn into_request(self) -> Request<T>

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where T: Clone,

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type Owned = T

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fn to_owned(&self) -> T

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fn clone_into(&self, target: &mut T)

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impl<T, U> TryFrom<U> for T
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type Error = Infallible

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fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
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impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

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type Error = <U as TryFrom<T>>::Error

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fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.
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