scalar-autograd/dist/Losses.spec.js

Scalar-based reverse-mode automatic differentiation in TypeScript.

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const Value_1 = require("./Value");
const Losses_1 = require("./Losses");
describe("Losses", () => {
    it("mse computes value and gradients correctly", () => {
        const x = new Value_1.Value(2, "x", true);
        const y = new Value_1.Value(3, "y", true);
        const tx = new Value_1.Value(5, "tx");
        const ty = new Value_1.Value(1, "ty");
        const loss = Losses_1.Losses.mse([x, y], [tx, ty]); // (1/2)*((2-5)^2 + (3-1)^2) = (1/2)*(9+4) = 6.5
        expect(loss.data).toBeCloseTo(6.5);
        loss.backward();
        expect(x.grad).toBeCloseTo(-3);
        expect(y.grad).toBeCloseTo(2);
    });
    it("mae computes value and gradients correctly", () => {
        const x = new Value_1.Value(2, "x", true);
        const y = new Value_1.Value(-3, "y", true);
        const tx = new Value_1.Value(5, "tx");
        const ty = new Value_1.Value(2, "ty");
        const loss = Losses_1.Losses.mae([x, y], [tx, ty]); // (1/2)*(abs(2-5)+abs(-3-2)) = (1/2)*(3+5)=4
        expect(loss.data).toBeCloseTo(4);
        loss.backward();
        expect(x.grad).toBeCloseTo(-0.5);
        expect(y.grad).toBeCloseTo(-0.5);
    });
    it("binaryCrossEntropy computes value and gradients correctly for easy case", () => {
        const out = new Value_1.Value(0.9, "out", true);
        const target = new Value_1.Value(1, "target");
        const loss = Losses_1.Losses.binaryCrossEntropy([out], [target]);
        expect(loss.data).toBeCloseTo(-Math.log(0.9));
        loss.backward();
        expect(out.grad).toBeCloseTo(-1 / 0.9, 4);
    });
    it("categoricalCrossEntropy computes value and gradients (softmax+NLL)", () => {
        // logits: [2, 1, 0], true = 0
        const a = new Value_1.Value(2, "a", true);
        const b = new Value_1.Value(1, "b", true);
        const c = new Value_1.Value(0, "c", true);
        const targets = [0];
        const loss = Losses_1.Losses.categoricalCrossEntropy([a, b, c], targets);
        const softmax = [
            Math.exp(2) / (Math.exp(2) + Math.exp(1) + Math.exp(0)),
            Math.exp(1) / (Math.exp(2) + Math.exp(1) + Math.exp(0)),
            Math.exp(0) / (Math.exp(2) + Math.exp(1) + Math.exp(0))
        ];
        expect(loss.data).toBeCloseTo(-Math.log(softmax[0]), 4);
        loss.backward();
        expect(a.grad).toBeCloseTo(softmax[0] - 1, 4);
        expect(b.grad).toBeCloseTo(softmax[1], 4);
        expect(c.grad).toBeCloseTo(softmax[2], 4);
    });
});