Created
June 12, 2018 09:03
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TensorFlow.js learning of the cosine function with realtime loss plot and resulting approximation.
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| <!DOCTYPE html> | |
| <html lang="en" xmlns="http://www.w3.org/1999/xhtml"> | |
| <head> | |
| <meta charset="utf-8" /> | |
| <title></title> | |
| <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.2.1/jquery.min.js"></script> | |
| <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.4/lodash.min.js"></script> | |
| <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/Faker/3.1.0/faker.min.js"></script> | |
| <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.0.0-beta/js/bootstrap.min.js"></script> | |
| <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.0.0-beta/css/bootstrap.min.css"> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]"> | |
| </script> | |
| <script src="https://cdn.plot.ly/plotly-latest.min.js"></script> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <h1>Learning a cosine using TF.js </h1> | |
| <div id="done" style="height: 20px; background-color: limegreen; margin: 50px 0;"></div> | |
| <div id="loss"></div> | |
| <div id="result"></div> | |
| </div> | |
| <script> | |
| const N = 100; | |
| const M = 10; | |
| var loss = []; | |
| async function plot_loss(h) { | |
| loss.push(h.history.loss[0]); | |
| Plotly.newPlot('loss', [{ | |
| x: loss.length, | |
| y: loss, | |
| name: "loss", | |
| type: 'scatter' | |
| }], { | |
| title: "Current loss: " + Math.round(h.history.loss[0]*10000)/10000, | |
| xaxis: { | |
| title: "run" | |
| }, | |
| yaxis: { | |
| title: "loss" | |
| } | |
| }); | |
| } | |
| function sleep(ms) { | |
| return new Promise(resolve => setTimeout(resolve, ms)); | |
| } | |
| async function run() { | |
| const b = _.range(N); | |
| const model = tf.sequential(); | |
| model.add(tf.layers.dense({ | |
| name: "input", | |
| units: 1, | |
| inputShape: [1] | |
| })); | |
| model.add(tf.layers.dense({ | |
| name: "learning_stack_1", | |
| activation: "tanh", | |
| kernelInitializer: "randomNormal", | |
| units: 15 | |
| })); | |
| model.add(tf.layers.dense({ | |
| name: "learning_stack_2", | |
| activation: "tanh", | |
| kernelInitializer: "randomNormal", | |
| units: 15 | |
| })); | |
| model.add(tf.layers.dense({ | |
| name: "learning_stack_3", | |
| activation: "tanh", | |
| kernelInitializer: "randomNormal", | |
| units: 15 | |
| })); | |
| model.add(tf.layers.dense({ | |
| name: "outputter", | |
| activation: "linear", | |
| kernelInitializer: "randomNormal", | |
| units: 1 | |
| })); | |
| model.compile({ | |
| loss: 'meanSquaredError', | |
| optimizer: 'adam' | |
| }); | |
| const xs = tf.tensor2d(b, [N, 1]); | |
| const ys = tf.tensor2d(_.map(b, x => Math.cos(2 * Math.PI * x / N)), [N, 1]); | |
| for (let i = 1; i < M; ++i) { | |
| const h = await model.fit(xs, ys, { | |
| batchSize: 10, | |
| epochs: 10 | |
| }); | |
| //console.log("Loss after Epoch " + i + " : " + h.history.loss[0]); | |
| plot_loss(h); | |
| $("#done").css("width", `${100*i/(M-1)}%`).text(`${Math.round(100*i/(M-1))}%`); | |
| await sleep(100); | |
| } | |
| model.predict(xs).data().then(function(p) { | |
| var actual = { | |
| x: b, | |
| y: _.map(b, x => Math.cos(2 * Math.PI * x / N)), | |
| name: "actual data", | |
| type: 'scatter', | |
| mode: 'lines', | |
| }; | |
| var predicted = { | |
| x: b, | |
| y: p, | |
| name: "predicted data", | |
| type: 'scatter' | |
| }; | |
| Plotly.newPlot('result', [actual, predicted]); | |
| });; | |
| } | |
| //$("#go").click(run); | |
| run(); | |
| </script> | |
| </body> | |
| </html> |
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