Struct rusty_machine::learning::nnet::MSECriterion

pub struct MSECriterion { /* private fields */ }

The mean squared error criterion.

Uses the Linear activation function and the mean squared error.

Implementations

impl MSECriterion

pub fn new(regularization: Regularization<f64>) -> Self

Constructs a new BCECriterion with the given regularization.

Examples

use rusty_machine::learning::nnet::MSECriterion;
use rusty_machine::learning::toolkit::regularization::Regularization;

// Create a new MSE criterion with L2 regularization of 0.3.
let criterion = MSECriterion::new(Regularization::L2(0.3f64));

Trait Implementations

impl Clone for MSECriterion

fn clone(&self) -> MSECriterion

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Criterion for MSECriterion

type Cost = MeanSqError

The cost function for the criterion.

fn regularization(&self) -> Regularization<f64>

Returns the regularization for this criterion.

fn cost(&self, outputs: &Matrix<f64>, targets: &Matrix<f64>) -> f64

The cost function.

fn cost_grad(&self, outputs: &Matrix<f64>, targets: &Matrix<f64>) -> Matrix<f64>

The gradient of the cost function.

fn is_regularized(&self) -> bool

Checks if the current criterion includes regularization.

fn reg_cost(&self, reg_weights: MatrixSlice<'_, f64>) -> f64

Returns the regularization cost for the criterion.

fn reg_cost_grad(&self, reg_weights: MatrixSlice<'_, f64>) -> Matrix<f64>

Returns the regularization gradient for the criterion.

impl Debug for MSECriterion

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

Formats the value using the given formatter.

impl Default for MSECriterion

Creates an MSE Criterion without any regularization.

fn default() -> Self

Returns the “default value” for a type.

impl Copy for MSECriterion