scalar-autograd/dist/Losses.d.ts

Scalar-based reverse-mode automatic differentiation in TypeScript.

import { Value } from "./Value";
export declare class Losses {
    /**
     * Computes mean squared error (MSE) loss between outputs and targets.
     * @param outputs Array of Value predictions.
     * @param targets Array of Value targets.
     * @returns Mean squared error as a Value.
     */
    static mse(outputs: Value[], targets: Value[]): Value;
    /**
     * Computes mean absolute error (MAE) loss between outputs and targets.
     * @param outputs Array of Value predictions.
     * @param targets Array of Value targets.
     * @returns Mean absolute error as a Value.
     */
    static mae(outputs: Value[], targets: Value[]): Value;
    static EPS: number;
    /**
     * Computes binary cross-entropy loss between predicted outputs and targets (after sigmoid).
     * @param outputs Array of Value predictions (expected in (0,1)).
     * @param targets Array of Value targets (typically 0 or 1).
     * @returns Binary cross-entropy loss as a Value.
     */
    static binaryCrossEntropy(outputs: Value[], targets: Value[]): Value;
    /**
     * Computes categorical cross-entropy loss between outputs (logits) and integer target classes.
     * @param outputs Array of Value logits for each class.
     * @param targets Array of integer class indices (0-based, one per sample).
     * @returns Categorical cross-entropy loss as a Value.
     */
    static categoricalCrossEntropy(outputs: Value[], targets: number[]): Value;
    /**
     * Computes Huber loss between outputs and targets.
     * Combines quadratic loss for small residuals and linear loss for large residuals.
     * @param outputs Array of Value predictions.
     * @param targets Array of Value targets.
     * @param delta Threshold at which to switch from quadratic to linear (default: 1.0).
     * @returns Huber loss as a Value.
     */
    static huber(outputs: Value[], targets: Value[], delta?: number): Value;
    /**
     * Computes Tukey loss between outputs and targets.
     * This robust loss function saturates for large residuals.
     *
     * @param outputs Array of Value predictions.
     * @param targets Array of Value targets.
     * @param c Threshold constant (typically 4.685).
     * @returns Tukey loss as a Value.
     */
    static tukey(outputs: Value[], targets: Value[], c?: number): Value;
}