Trait neuro::losses::Loss

pub trait Loss {
    fn id(&self) -> u64;
    fn eval(&self, y_pred: &Tensor, y_true: &Tensor) -> PrimitiveType;
    fn grad(&self, y_pred: &Tensor, y_true: &Tensor) -> Tensor;
}

Defines the behaviors of a loss function.

Required methods

fn id(&self) -> u64

Returns a unique identifier.

fn eval(&self, y_pred: &Tensor, y_true: &Tensor) -> PrimitiveType

Computes the value of the loss function from the predicted and true labels.

fn grad(&self, y_pred: &Tensor, y_true: &Tensor) -> Tensor

Computes the gradient of the loss function from the predicted and true labels.

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Implementors

impl Loss for BinaryCrossEntropy

fn id(&self) -> u64

fn eval(&self, y_pred: &Tensor, y_true: &Tensor) -> PrimitiveType

fn grad(&self, y_pred: &Tensor, y_true: &Tensor) -> Tensor

impl Loss for CrossEntropy

fn id(&self) -> u64

fn eval(&self, y_pred: &Tensor, y_true: &Tensor) -> PrimitiveType

fn grad(&self, y_pred: &Tensor, y_true: &Tensor) -> Tensor

impl Loss for MeanAbsoluteError

fn id(&self) -> u64

fn eval(&self, y_pred: &Tensor, y_true: &Tensor) -> PrimitiveType

fn grad(&self, y_pred: &Tensor, y_true: &Tensor) -> Tensor

impl Loss for MeanSquaredError

fn id(&self) -> u64

fn eval(&self, y_pred: &Tensor, y_true: &Tensor) -> PrimitiveType

fn grad(&self, y_pred: &Tensor, y_true: &Tensor) -> Tensor

impl Loss for SoftmaxCrossEntropy

fn id(&self) -> u64

fn eval(&self, y_pred: &Tensor, y_true: &Tensor) -> PrimitiveType

fn grad(&self, y_pred: &Tensor, y_true: &Tensor) -> Tensor

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