Struct rusty_machine::learning::nnet::MSECriterion [] [src]

pub struct MSECriterion { /* fields omitted */ }

The mean squared error criterion.

Uses the Linear activation function and the mean squared error.

Methods

impl MSECriterion
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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
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Returns a copy of the value. Read more

Performs copy-assignment from source. Read more

impl Copy for MSECriterion
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impl Debug for MSECriterion
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Formats the value using the given formatter.

impl Criterion for MSECriterion
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The activation function for the criterion.

The cost function for the criterion.

Returns the regularization for this criterion. Read more

The activation function applied to a matrix.

The gradient of the activation function applied to a matrix.

The cost function. Read more

The gradient of the cost function. Read more

Checks if the current criterion includes regularization. Read more

Returns the regularization cost for the criterion. Read more

Returns the regularization gradient for the criterion. Read more

impl Default for MSECriterion
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Creates an MSE Criterion without any regularization.

Returns the "default value" for a type. Read more