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              <!DOCTYPE html>
<html>
<head>
	<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="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.11.1"> </script>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.6.0/p5.js"></script>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.6.0/addons/p5.dom.js"></script>
	<script src="sketch.js"></script>
</head>
<body>
</body>
</html>

            
          
!
            
              // Daniel Shiffman
// http://codingtra.in
// http://patreon.com/codingtrain

// Linear Regression with TensorFlow.js
// Video: https://www.youtube.com/watch?v=dLp10CFIvxI

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);
  x_vals.push(x);
  y_vals.push(y);
}

function draw() {

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

  background(0);

  stroke(255);
  strokeWeight(8);
  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();
  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);

  strokeWeight(2);
  line(x1, y1, x2, y2);


  console.log(tf.memory().numTensors);
  //noLoop();
}

            
          
!
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