110 lines
1.9 KiB
JavaScript
Executable File
110 lines
1.9 KiB
JavaScript
Executable File
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var history;
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let resolution = 50;
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var xs, ys, nn, result;
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let x = [];
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let y = [];
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let predictionArray = [];
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let res = 1 / resolution;
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var inX = [
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[0, 0],
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[0, 1],
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[1, 0],
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[1, 1]
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];
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var inY = [
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[0],
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[1],
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[1],
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[0]
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];
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function setup() {
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createCanvas(900, 900);
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background(50);
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frameRate(10);
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//learningRateSlider = createSlider(1, 100, 35);
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//a = tf.variable(tf.scalar(random(-1,1)));
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tf.setBackend('cpu');
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//trainNN();
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//noLoop();
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nn = tf.sequential();
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xs = tf.tensor2d(inX);
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ys = tf.tensor2d(inY);
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const optimizer = tf.train.sgd(1);
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var hidden = tf.layers.dense({
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units: 4,
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inputShape: [2],
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activation: 'sigmoid'
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});
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var output = tf.layers.dense({
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units: 1,
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activation: 'sigmoid'
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});
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nn.add(hidden);
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nn.add(output);
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nn.compile({
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optimizer: optimizer,
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loss: 'meanSquaredError'
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});
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for (let i = 0; i < 1; i += res) {
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x.push(i);
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y.push(i);
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}
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for (let i = 0; i < resolution; i++) {
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for (let j = 0; j < resolution; j++) {
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predictionArray.push([i / resolution, j / resolution]);
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}
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}
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}
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async function trainNN() {
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let o = await nn.fit(xs, ys, {
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epochs: 100,
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shuffle: true
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});
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print(o.history.loss[0]);
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//o.dispose();
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}
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function draw() {
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clear();
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background(50);
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trainNN().then(drawgrid());
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// tf.tidy(() => {
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// trainNN();
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// });
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//print(tf.memory().numTensors);
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}
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function drawgrid() {
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tf.tidy(() => {
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let td = tf.tensor2d(predictionArray);
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result = nn.predictOnBatch(td).bufferSync().values;
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//let toFill;
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noStroke();
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for (let i = 0; i < resolution; i ++){
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for (let j = 0; j < resolution; j ++){
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fill(result[(i*resolution)+j]*255);
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square(mW(j/resolution),mH(i/resolution)-height/resolution,mW(res));
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}
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}
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//td.dispose();
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//result.dispose();
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});
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}
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function mH(val) {
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return map(val, 0, 1, height, 0);
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}
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function mW(val) {
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return map(val, 0, 1, 0, width);
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} |