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199 lines
4.3 KiB
HTML
199 lines
4.3 KiB
HTML
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<html>
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<head>
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<title>neural network art</title>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=320, initial-scale=1.0, maximum-scale=1.0, user-scalable=0"/>
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<meta name="apple-mobile-web-app-capable" content="yes" />
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<meta http-equiv="X-UA-Compatible" content="IE=edge">
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<meta name="mobile-web-app-capable" content="yes">
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<meta name="description" content="ōtoro.net">
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<meta name="author" content="hardmaru">
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<!-- extra styles -->
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<style>
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head {
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margin: 0;
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padding: 0;
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}
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body {
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margin: 0;
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padding: 0;
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}
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p {
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padding: 10px;
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font-family: Courier, "Helvetica Neue",Helvetica,Arial,sans-serif;
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font-weight: 100;
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font-size: 0.6em;
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}
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textarea {
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padding: 5;
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font-family: Courier, "Helvetica Neue",Helvetica,Arial,sans-serif;
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font-weight: 100;
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font-size: 0.5em;
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}
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</style>
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</head>
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<body>
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<div id="p5Container">
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</div>
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<!-- jQuery -->
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<script src="http://ajax.googleapis.com/ajax/libs/jquery/1.8.3/jquery.min.js"></script>
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<script src="lib/p5.min.js"></script>
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<script src="lib/recurrent.js"></script>
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<script>
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// neural network random art generator
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// settings
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// actual size of generated image
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var sizeh = 300;
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var sizew = 300;
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var sizeImage = sizeh*sizew;
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var nH, nW, nImage;
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var mask;
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// settings of nnet:
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var networkSize = 8;
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var nHidden = 10;
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var nOut = 3; // r, g, b layers
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// support variables:
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var img;
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var img2;
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var G = new R.Graph(false);
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var initModel = function() {
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"use strict";
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var model = [];
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var i;
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var randomSize = 1.0;
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// define the model below:
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model.w_in = R.RandMat(networkSize, 3, 0, randomSize); // x, y, and bias
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// model['w_0'] = R.RandMat(networkSize, networkSize, 0, randomSize);
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for (i = 0; i < nHidden; i++) {
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model['w_'+i] = R.RandMat(networkSize, networkSize, 0, randomSize);
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}
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model.w_out = R.RandMat(nOut, networkSize, 0, randomSize); // output layer
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console.log(model)
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return model;
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};
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var forwardNetwork = function(G, model, x_, y_) {
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// x_, y_ is a normal javascript float, will be converted to a mat object below
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// G is a graph to amend ops to
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var x = new R.Mat(3, 1); // input
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var i;
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x.set(0, 0, x_);
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x.set(1, 0, y_);
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x.set(2, 0, 1.0); // bias.
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var out;
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out = G.tanh(G.mul(model.w_in, x));
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for (i = 0; i < nHidden; i++) {
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out = G.tanh(G.mul(model['w_'+i], out));
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}
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out = G.sigmoid(G.mul(model.w_out, out));
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console.log(out)
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return out;
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};
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function getColorAt(model, x, y) {
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// function that returns a color given coordintes (x, y)
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// (x, y) are scaled to -0.5 -> 0.5 for image recognition later
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// but it can be behond the +/- 0.5 for generation above and beyond
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// recognition limits
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var r, g, b;
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var out = forwardNetwork(G, model, x, y);
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r = out.w[0]*255.0;
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g = out.w[1]*255.0;
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b = out.w[2]*255.0;
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return color(r, g, b);
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}
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function genImage(img, model) {
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var i, j, m, n;
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img.loadPixels();
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for (i = 0, m=img.width; i < m; i++) {
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for (j = 0, n=img.height; j < n; j++) {
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img.set(i, j, getColorAt(model, i/sizeh-0.5,j/sizew-0.5));
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}
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}
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img.updatePixels();
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}
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function setup() {
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"use strict";
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var myCanvas;
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myCanvas = createCanvas(windowWidth,windowHeight);
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myCanvas.parent('p5Container');
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nW = Math.max(Math.floor(windowWidth/sizew), 1);
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nH = Math.max(Math.floor(windowHeight/sizeh), 1);
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nImage = nH*nW;
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mask = R.zeros(nImage);
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//img.resize(320*1.0, 320*1.0);
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//img.save('genart.png','png');
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//noLoop();
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img = createImage(sizeh, sizew);
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frameRate(30);
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}
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function getRandomLocation() {
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var i, result=0, r;
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for (i=0;i<nImage;i++) {
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result += mask[i];
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}
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if (result === nImage) {
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mask = R.zeros(nImage);
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}
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do {
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r = R.randi(0, nImage);
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} while (mask[r] !== 0);
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mask[r] = 1;
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return r;
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}
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function displayImage(n) {
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var row = Math.floor(n/nW);
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var col = n % nW;
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image(img, col*sizew, row*sizeh);
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}
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function draw() {
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model = initModel();
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genImage(img, model);
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displayImage(getRandomLocation());
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}
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</script>
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</body>
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</html>
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