Enum LossFunction 

pub enum LossFunction {
    MeanSquaredError,
    CrossEntropy,
    BinaryCrossEntropy,
}

Loss functions for neural network training

Variants

MeanSquaredError

Mean squared error for regression

CrossEntropy

Cross-entropy for classification

BinaryCrossEntropy

Binary cross-entropy for multi-label classification

Implementations

impl LossFunction

pub fn compute_loss( &self, y_pred: &Array2<Float>, y_true: &Array2<Float>, ) -> Float

Compute loss between predictions and targets

Trait Implementations

impl Clone for LossFunction

fn clone(&self) -> LossFunction

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LossFunction

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

Formats the value using the given formatter.

impl PartialEq for LossFunction

fn eq(&self, other: &LossFunction) -> 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 Copy for LossFunction

impl StructuralPartialEq for LossFunction