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sketch.js
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sketch.js
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let X = [];
let Y = [];
let m, b;
let slider;
let optimizerSelect;
let learningRate = 0.3;
const f = (pred, label) => pred.sub(label).square().mean(); // mean squared error
function setup() {
canvas = createCanvas(windowWidth, 400);
canvas.parent('canvas');
canvas.mouseClicked(() => {
if (mouseButton === LEFT) {
// Normalize x, y to (-1, 1) and add it to data
let x = map(mouseX, 0, width, -1, 1);
let y = map(mouseY, 0, height, 1, -1);
X.push(x);
Y.push(y);
}
});
slider = createSlider(0.001, 1, 0.3, 0.001);
slider.parent('canvas-wrapper');
slider.style('width', '150px');
slider.style('display', 'block');
slider.changed(resetCanvas); // When the slider is changed, reset the canvas
optimizerSelect = createSelect();
optimizerSelect.parent('canvas-wrapper');
optimizerSelect.option('sgd');
optimizerSelect.option('adam');
optimizerSelect.changed(resetCanvas);
let show = createButton('Show Data');
show.parent('canvas-wrapper');
show.mouseClicked(showData);
let stop = createButton('Stop');
stop.parent('canvas-wrapper');
stop.mouseClicked(() => {
noLoop();
});
let clear = createButton('Reset');
clear.parent('canvas-wrapper');
clear.mouseClicked(() => {
resetCanvas();
});
resetCanvas();
}
function draw() {
background(0);
learningRate = slider.value();
fill(255).strokeWeight(0).textSize(15);
text(`learning rate = ${learningRate}`, 20, 20);
text(`y = ${m.dataSync()}x + ${b.dataSync()}`, 20, 35);
tf.tidy(() => {
if (X.length > 0) {
const y = tf.tensor1d(Y);
optimizer.minimize(() => {
loss = f(tf.tensor1d(X).mul(m).add(b), y);
loss.data().then((mse) => {
fill(255).strokeWeight(0).textSize(15);
text(`mean squared error = ${mse}`, 20, 50);
});
return loss;
});
const lineX = [-1, 1];
const ys = tf.tensor1d(lineX).mul(m).add(b);
let lineY = ys.data().then((y) => {
// reverse normalization of x, y
let x1 = map(lineX[0], -1, 1, 0, width);
let x2 = map(lineX[1], -1, 1, 0, width);
let y1 = map(y[0], -1, 1, height, 0);
let y2 = map(y[1], -1, 1, height, 0);
stroke(139, 0, 139);
strokeWeight(2);
line(x1, y1, x2, y2);
});
}
});
strokeWeight(8)
stroke(250);
for (let i = 0; i < X.length; i++) {
let px = map(X[i], -1, 1, 0, width);
let py = map(Y[i], -1, 1, height, 0);
point(px, py);
}
}
function showData() {
let data = document.getElementById('data');
data.innerText = null;
if (X.length > 0) {
// Show x, y pairs
for (let i = 0; i < X.length; i++) {
data.innerText += `[${X[i]}, ${Y[i]}]\r\n`;
}
}
}
/* resetCanvas is causing a memory leak...Each time resetCanvas is called
more and more tensors are created. A fix is coming soon.
It is most likely from recreating the optimizers.
*/
function resetCanvas() {
X = [];
Y = [];
optimizer = tf.tidy(() => {
m = tf.variable(tf.scalar(0));
b = tf.variable(tf.scalar(0));
const optimizers = {
'adam': tf.train.adam(learningRate),
'sgd': tf.train.sgd(learningRate)
}
return optimizers[optimizerSelect.value()];
});
// Clear #data
let data = document.getElementById('data');
data.innerText = null;
loop();
}
function windowResized() {
resizeCanvas(windowWidth, height);
}