30:T4e1,training = { setTimeout(()=>{ var trainBt=document.getElementById('trainBt') trainBt.onclick=()=>{ console.log('started at '+Date()) var msg = document.getElementById('msg') msg.innerHTML = ' training started at '+Date() msg.style.color='green' var xdt = document.getElementById('xdt') var ydt = document.getElementById('ydt') //debugger var trace = { x:JSON.parse(xdt.value), y:JSON.parse(ydt.value), mode: 'markers', type: 'scatter' } console.log(trace) // plot data //plt.newPlot("plot",[trace]) // prepare data for training var xs = tf.tensor2d(trace.x, [trace.x.length, 1]); var ys = tf.tensor2d(trace.y, [trace.y.length, 1]); model.fit(xs, ys).then(() => { var py = predict(JSON.parse(document.getElementById('xdt').value)) document.getElementById('pdt').value=JSON.stringify(py) // update text area var traceModel = { x:JSON.parse(document.getElementById('xdt').value), y:py, mode: 'line', type: 'scatter' } plt.newPlot("plot",[trace,traceModel]) }) //train() } },100) return 'click on button to train' }31:[] 2e:{"title":"TensorFlow","cells":[{"id":0,"value":"md`# TensorFlow\n

\nTensor Flow is now a full citizen of the Web computer. Let's give it a try ...\n

Original announcement https://youtu.be/kSa3UObNS6o?t=790.

Project page: js.tensorflow.org.

Tutorial at js.tensorflow.org/tutorials.

20 min video on js-based TF.

TF fun at jonasalmeida.github.io/tf.

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"html`\n

tf = require ...

\ndirectly from https://cdn.jsdelivr.net/npm/@tensorflow/tfjs.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":11,"value":"tf = require('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs')","pinned":false,"hidden":false,"mode":"ojs"},{"id":33,"value":"html`\n

Getting Started

\nFollowing tutorial at js.tensorflow.org/#getting-started.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":36,"value":"model = {\n const model = tf.sequential()\n //debugger\n model.add(tf.layers.dense({units: 1, inputShape: [1]}))\n // Prepare the model for training: Specify the loss and the optimizer.\n model.compile({loss: 'meanSquaredError', optimizer: 'sgd'})\n return model\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":38,"value":"div = {\n var div = DOM.element('div')\n var h = '

Training data

'\n h += ''\n h += ''\n h += ''\n h += ''\n h += ''\n h += ''\n h += ''\n h += '

X	Y
[1,2,3,4]	[1,2,6,7]	prediction ...

'\n h += ' '\n h += '

'\n div.innerHTML=h\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":74,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":86,"value":"train = (t)=>{\n console.log('model',model)\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":54,"value":"predict=(x)=>{\n var fun = y=>parseFloat(model.predict(tf.tensor2d([y], [1, 1])).toString().match(/\\[\\[[^\\]]*/)[0].slice(2))\n if(Array.isArray(x)){\n var y=x.map(xi=>fun(xi))\n }\n else{\n var y=fun(x)\n }\n return y\n}//model.predict(tf.tensor2d([5], [1, 1])).print()","pinned":false,"hidden":false,"mode":"ojs"},{"id":153,"value":"plt = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}