30:[] 2e:{"title":"Tensor Flow","cells":[{"id":0,"value":"md`# Tensor Flow`","pinned":false,"hidden":false,"mode":"ojs"},{"id":6,"value":"md`\n## References\n### Open Source\n- [Tensorflow.js](https://github.com/tensorflow/tfjs)\n- [TensorFlow Playground](https://playground.tensorflow.org/)\n\n### Learning\n- [Tensorflow.js](https://www.tensorflow.org/js/)\n- [TensorFlow Javascript API](https://js.tensorflow.org/api/latest/)\n- [Learn ML](https://www.tensorflow.org/resources/learn-ml)\n- [Basics of TensorFlow for JavaScript development](https://www.tensorflow.org/resources/learn-ml/basics-of-tensorflow-for-js-development/)\n- [Deep Learning with Javascript](https://www.manning.com/books/deep-learning-with-javascript)\n\n### Resources\n- [Gallery](https://github.com/tensorflow/tfjs/blob/master/GALLERY.md)\n- [TensorFlow Blog](https://blog.tensorflow.org/)\n\n### Relatives\n- [ONNX](https://github.com/Microsoft/onnxjs): ONNX.js: run ONNX models using JavaScript\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"md`\n## QuickStart\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":22,"value":"md`\n### Linear Prediction\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":39,"value":"viewof linear = {\n const form = html``;\n 2;\n form.oninput = () =>\n (form.value = {\n a: parseFloat(form.a.value),\n b: parseFloat(form.b.value),\n sample: parseInt(form.sample.value),\n predict: parseFloat(form.predict.value)\n });\n form.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":64,"value":"{\n const columns = linear.sample;\n const xs = [];\n const ys = [];\n\n for (let i = 0; i < columns; i++) {\n xs.push(i);\n ys.push(linear.a * i + linear.b);\n }\n\n const model = await learnLinear(xs, ys, [columns, 1]);\n const predicted = model.predict(tfjs.tensor2d([linear.predict], [1, 1]));\n const predictedValue = await predicted.array();\n\n xs.push(linear.predict);\n ys.push(linear.a * linear.predict + linear.b);\n\n const line = {\n x: xs,\n y: ys,\n type: 'line',\n name: 'real'\n };\n\n const div = DOM.element('div', {\n style: `width:${width}px`\n });\n Plotly.newPlot(\n div,\n [\n line,\n {\n x: [linear.predict],\n y: predictedValue[0],\n mode: 'markers',\n type: 'scatter',\n name: 'predicted'\n }\n ],\n {\n showLegend: true\n },\n {\n displayModeBar: true\n }\n );\n return div;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":26,"value":"learnLinear = async (x, y, shape) => {\n const model = tfjs.sequential();\n model.add(\n tfjs.layers.dense({\n units: 1,\n inputShape: [1]\n })\n );\n\n model.compile({\n loss: 'meanSquaredError',\n optimizer: 'sgd'\n });\n\n const xs = tfjs.tensor2d(x, shape);\n const ys = tfjs.tensor2d(y, shape);\n\n await model.fit(xs, ys, {\n epochs: 250\n });\n\n return model\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"md`\n## Imports\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":61,"value":"Plotly = require('plotly.js-dist')","pinned":false,"hidden":false,"mode":"ojs"},{"id":16,"value":"tfjs = require('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js')","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W30","stdlib":"1","titling":"auto"}