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shiffman's tf.js linear regression
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| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <meta http-equiv="X-UA-Compatible" content="ie=edge"> | |
| <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.6.1/p5.min.js"></script> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]"> | |
| </script> | |
| <script src="./sketch.js"></script> | |
| <title>linear regression</title> | |
| </head> | |
| </html> |
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| let x_vals = []; | |
| let y_vals = []; | |
| let m, b; | |
| const learningRate = .5; | |
| const optimizer = tf.train.sgd(learningRate); | |
| function setup() { | |
| createCanvas(400, 400); | |
| m = tf.variable(tf.scalar(random(1))); | |
| b = tf.variable(tf.scalar(random(1))); | |
| } | |
| function loss(pred, labels) { | |
| return pred.sub(labels).square().mean(); | |
| } | |
| function predict(x) { | |
| const xs = tf.tensor1d(x); | |
| // y = mx + b | |
| const ys = xs.mul(m).add(b); | |
| return ys; | |
| } | |
| function mousePressed() { | |
| let x = map(mouseX, 0, width, 0, 1); | |
| let y = map(mouseY, 0, height, 1, 0); | |
| x_vals.push(x); | |
| y_vals.push(y); | |
| } | |
| function draw() { | |
| background(51); | |
| stroke(255); | |
| strokeWeight(4); | |
| for (let i = 0; i < x_vals.length; i++) { | |
| let px = map(x_vals[i], 0, 1, 0, width); | |
| let py = map(y_vals[i], 0, 1, height, 0); | |
| point(px, py) | |
| } | |
| tf.tidy(() => { | |
| if (x_vals.length > 0) { | |
| const ys = tf.tensor1d(y_vals); | |
| optimizer.minimize(() => loss(predict(x_vals), ys)); | |
| } | |
| }); | |
| const lineX = [0, 1]; | |
| const ys = tf.tidy(() => predict(lineX)); | |
| let lineY = ys.dataSync(); | |
| ys.dispose(); | |
| let x1 = map(lineX[0], 0, 1, 0, width); | |
| let x2 = map(lineX[1], 0, 1, 0, width); | |
| let y1 = map(lineY[0], 0, 1, height, 0); | |
| let y2 = map(lineY[1], 0, 1, height, 0); | |
| line(x1, y1, x2, y2); | |
| } |
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