First commit.

source/training.js

@@ -0,0 +1,111 @@
+import Shader from "../../framework/WebGpu.js";
+
+const N = 32; // training samples
+const inputSize = 3;
+const outputSize = 1;
+const lr = 0.01;
+const epochs = 50;
+
+function generateTrainingData() {
+    const X = new Float32Array(N * inputSize);
+    const y = new Float32Array(N);
+
+    for (let i = 0; i < N; i++) {
+        const x1 = Math.random() * 2 - 1;
+        const x2 = Math.random() * 2 - 1;
+        const x3 = Math.random() * 2 - 1;
+
+        const label = 3 * x1 + 2 * x2 - 1 * x3 + 0.5;
+
+        X[i * 3 + 0] = x1;
+        X[i * 3 + 1] = x2;
+        X[i * 3 + 2] = x3;
+
+        y[i] = label;
+    }
+    return { X, y };
+}
+
+async function main() {
+    if (!navigator.gpu) {
+        alert("WebGPU not supported!");
+        return;
+    }
+
+    const adapter = await navigator.gpu.requestAdapter();
+    const device = await adapter.requestDevice();
+
+    const { X, y } = generateTrainingData();
+
+    // --- 1: Create shaders ---
+    const forward = new Shader(device);
+    await forward.setup("../../shaders/neural/forward.wgsl");
+
+    const lossBackward = new Shader(device);
+    await lossBackward.setup("../../shaders/neural/loss_backward.wgsl");
+
+    const weightGrad = new Shader(device);
+    await weightGrad.setup("../../shaders/neural/weight_grad.wgsl");
+
+    const sgdUpdate = new Shader(device);
+    await sgdUpdate.setup("../../shaders/neural/sgd_update.wgsl");
+
+    // --- 2: Initialize model parameters ---
+    let W = new Float32Array(inputSize).map(() => Math.random() * 0.1);
+    let b = new Float32Array([0]);
+
+    forward.setVariable("input", X);
+    forward.setVariable("W", W);
+    forward.setVariable("b", b);
+    forward.setVariable("N", N);
+    forward.setVariable("y", y);
+
+
+
+    // link GPU buffers between shaders
+    lossBackward.setBuffer("y", forward.getBuffer("y"));
+    lossBackward.setBuffer("pred", forward.getBuffer("pred"));
+    lossBackward.setVariable("N", N);
+
+    weightGrad.setBuffer("input", forward.getBuffer("input"));
+    weightGrad.setBuffer("dPred", lossBackward.getBuffer("dPred"));
+    weightGrad.setVariable("N", N);
+
+    sgdUpdate.setBuffer("W", forward.getBuffer("W"));
+    sgdUpdate.setBuffer("b", forward.getBuffer("b"));
+    sgdUpdate.setBuffer("dW", weightGrad.getBuffer("dW"));
+    sgdUpdate.setBuffer("db", weightGrad.getBuffer("db"));
+    sgdUpdate.setVariable("lr", lr);
+
+    const log = document.getElementById("log");
+
+    document.getElementById("trainBtn").onclick = async () => {
+        for (let epoch = 0; epoch < epochs; epoch++) {
+            await forward.execute(N);
+            await lossBackward.execute(N);
+            await weightGrad.execute(N);
+            await sgdUpdate.execute(inputSize);
+
+            const lossArr = await lossBackward.debugBuffer("loss");
+            const loss = lossArr.reduce((a, b) => a + b, 0) / N;
+
+            log.textContent += `Epoch ${epoch}: loss = ${loss.toFixed(4)}\n`;
+       
+			// Debug learned weights every 5 epochs
+			if (epoch % 5 === 0) {
+				const w = await forward.debugBuffer("W");
+				console.log(
+				`Epoch ${epoch}: W = ${w.map(v => v.toFixed(2)).join(", ")}`
+				);
+			}
+        }
+
+        const learnedW = await forward.debugBuffer("W");
+        const learnedB = await forward.debugBuffer("b");
+
+        log.textContent += `\nLearned W: ${learnedW.map(v=>v.toFixed(3)).join(", ")}\n`;
+        log.textContent += `Learned b: ${learnedB[0].toFixed(3)}\n`;
+    };
+}
+
+main();