Struct KLDivergenceLoss 

pub struct KLDivergenceLoss {
    pub epsilon: f64,
}

Kullback-Leibler Divergence loss. Measures how one probability distribution diverges from a reference distribution.

Fields

epsilon: f64

Epsilon for numerical stability.

Trait Implementations

impl Clone for KLDivergenceLoss

fn clone(&self) -> KLDivergenceLoss

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for KLDivergenceLoss

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

Formats the value using the given formatter.

impl Default for KLDivergenceLoss

fn default() -> Self

Returns the “default value” for a type.

impl Loss for KLDivergenceLoss

fn compute( &self, predictions: &ArrayView<'_, f64, Ix2>, targets: &ArrayView<'_, f64, Ix2>, ) -> TrainResult<f64>

Compute loss value.

fn gradient( &self, predictions: &ArrayView<'_, f64, Ix2>, targets: &ArrayView<'_, f64, Ix2>, ) -> TrainResult<Array<f64, Ix2>>

Compute loss gradient with respect to predictions.

fn name(&self) -> &str

Get the name of the loss function.