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sketch.js
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sketch.js
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// ************************************************
// Neural network
// Horribly coded by : Kevin Le Teugg, 2022
// File : sketch.js
// Description :
// ************************************************
const CANVAS_WIDTH = 800;
const CANVAS_HEIGHT = 600;
// First and last entries of nnInputs must be respectively linked to the (training) data and expected outputs
let nnInputs = [2, 2, 1];
let nn;
let td;
let guiNetwork;
let isNeuRequested = false;
let isConRequested = false;
let isInpRequested = true;
let isOutRequested = true;
let isWeiRequested = true;
let cbToggleConnections;
let buttonSaveNeuralNetworkJSON;
let cbToggleNeurons;
let img;
let img2;
let trainingData = [];
// Desired output linked to the training data that was fed to the network
let desiredOutputs = [];
function preload() {
img = loadImage('td/4-0.png');
img2 = loadImage('td/4-1.png');
//TestImg = loadImage('test_img.png');
}
function setup() {
// Canvas creation
canvas = createCanvas(CANVAS_WIDTH, CANVAS_HEIGHT);
background(60);
guiNetwork = new GUINetwork();
guiNetwork.init();
td = new TrainingData([[img, loadJSON('td/4-0.json')],[img2, loadJSON('td/4-1.json')]]);
// Maybe put this DOM elements code in a GUI class
buttonSaveNeuralNetworkJSON = createButton('Save JSON');
buttonSaveNeuralNetworkJSON.position(CANVAS_WIDTH / 2, CANVAS_HEIGHT);
buttonSaveNeuralNetworkJSON.mousePressed(saveNeuralNetworkJSON);
cbToggleNeurons = createCheckbox('Toggle neurons');
cbToggleNeurons.position(CANVAS_WIDTH / 2, CANVAS_HEIGHT - 100);
cbToggleNeurons.mousePressed(toggleNeurons);
nn = new NeuralNetwork(nnInputs);
nn.initNetwork();
//nn.feedInputs(td.imgs[0]);
//nn.feedInputs([0.39, 0.54, 0.23, 0.77]);
nn.feedInputs([0.12, 0.007]);
//nn.feedInputs([-0.63, 0.26, 0.543, -0.12]);
desiredOutputs = [0.8]; // has to be the same dimension as the last layer of network
// Forward propagation
for (var i = 0; i < nn.network.length; i++) {
nn.calculateOutputs(i);
// Stop mapping at the penultimate network layer, hence the -1
if (i < nn.network.length - 1) {
nn.inOutMap(i);
}
}
// Backpropagation test
nn.calculateOutputError(desiredOutputs);
// Error calculation test
for (var i = nn.network.length - 2; i > 0; i--) {
nn.calculateError(i);
}
}
function draw() {
// "Animation test"
/* nn.feedInputs([random(-1,1), random(-1,1), random(-1,1), random(-1,1)]);
for (var i = 0; i < nn.network.length; i++) {
nn.calculateOutputs(i);
if (i < nn.network.length - 1) {
nn.inOutMap(i);
}
}
nn.calculateOutputError([random(0,1), random(0,1), random(0,1)]);
for (var i = nn.network.length - 2; i > 0; i--) {
nn.calculateError(i);
} */
background(60);
guiNetwork.setFrameRate();
guiNetwork.showFrameRate();
nn.showNeurons(isNeuRequested);
nn.showConnections(isConRequested);
nn.showInputs(isInpRequested);
nn.showOutputs(isOutRequested);
nn.showWeights(isWeiRequested);
// Show all inputs for each neuron, debug only
for (var i = 1; i < nn.network.length; i++) {
for (var j = 0; j < nn.network[i].length; j++) {
for (var k = 0; k < nn.network[i][j].inputs.length; k++) {
push();
textSize(10);
fill(255, 150, 100);
translate(nn.network[i][j].x, nn.network[i][j].y);
text((Math.round(nn.network[i][j].inputs[k] * 1000)) / 1000, -40, k*10 - 20);
pop();
}
}
}
// Show all inputs for each neuron, debug only
for (var i = 0; i < nn.network.length; i++) {
for (var j = 0; j < nn.network[i].length; j++) {
push();
textSize(10);
fill(200, 200, 50);
translate(nn.network[i][j].x, nn.network[i][j].y);
text((Math.round(nn.network[i][j].err * 1000)) / 1000, 15, 5);
pop();
}
}
// Display desired outputs
push();
textAlign(CENTER, CENTER);
fill(255);
text('Desired output : ' + desiredOutputs, 80, 20);
pop();
}
function saveNeuralNetworkJSON() {
nn.saveToJSON();
}
function toggleNeurons() {
if (cbToggleNeurons.checked()) {
isNeuRequested = false;
} else {
isNeuRequested = true;
clear();
background(60);
}
}