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Here you can Sed posuere consectetur est at lobortis. Donec ullamcorper nulla non metus auctor fringilla. Maecenas sed diam eget risus varius blandit sit amet non magna. Donec id elit non mi porta gravida at eget metus. Praesent commodo cursus magna, vel scelerisque nisl consectetur et.


              <!DOCTYPE html>
	<meta charset="UTF-8">
	<meta http-equiv="X-UA-Compatible" content="IE=edge">
	<meta name="viewport" content="width=device-width, initial-scale=1">

	<title>linear regression with tensorflow</title>

	<script src=""> </script>
	<script src=""></script>
	<script src=""></script>
	<script src="sketch.js"></script>





              // Daniel Shiffman

// Linear Regression with TensorFlow.js
// Video:

let x_vals = [];
let y_vals = [];

let m, b;

const learningRate = 0.5;
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);

function draw() {

  tf.tidy(() => {
    if (x_vals.length > 0) {
      const ys = tf.tensor1d(y_vals);
      optimizer.minimize(() => loss(predict(x_vals), ys));


  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);

  const lineX = [0, 1];

  const ys = tf.tidy(() => predict(lineX));
  let lineY = ys.dataSync();

  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);