sketch.js

let nn;
let training_data = [{
  inputs: [0, 0],
  targets: [0]
}, {
  inputs: [1, 0],
  targets: [1]
}, {
  inputs: [0, 1],
  targets: [1]
}, {
  inputs: [1, 1],
  targets: [0]
}];

let lr_slider;

function setup() {
  createCanvas(400, 400);
  nn = new NeuralNetwork(2, 2, 1);

  for (let i = 0; i < 50000; i++) {
  	let data = random(training_data);
  	nn.train(data.inputs, data.targets)
  }

  console.log(nn.feedforward([1,1]));
  console.log(nn.feedforward([1,0]));
  console.log(nn.feedforward([0,1]));
  console.log(nn.feedforward([0,0]));
  // lr_slider = createSlider(0.01, 0.1, 0.05, 0.01);
}

// function draw() {
//   background(0);

//   nn.learning_rate = lr_slider.value();

//   for (let i = 0; i < 100; i++) {
//     let data = random(training_data);
//     nn.train(data.inputs, data.targets);
//   }

//   let resolution = 20;
//   let cols = floor(width / resolution);
//   let rows = floor(height / resolution);

//   for (let i = 0; i < cols; i++) {
//     for (let j = 0; j < rows; j++) {
//       let x = i * resolution;
//       let y = j * resolution;
//       let input_1 = i / (cols - 1);
//       let input_2 = j / (rows - 1);
//       let output = nn.feedforward([input_1, input_2]);
//       let col = output[0] * 255;
//       fill(col);
//       noStroke();
//       rect(x, y, resolution, resolution);
//     }
//   }



// }