docs.sheetjs.com/docz/static/tfjs/SheetJSTF.js

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2024-08-16 11:15:33 +00:00
const XLSX = require('xlsx');
const tf = require("@tensorflow/tfjs");
//const tf = require("@tensorflow/tfjs-node");
function worksheet_to_csv_url(worksheet) {
/* generate CSV */
const csv = XLSX.utils.sheet_to_csv(worksheet);
/* CSV -> Uint8Array -> Blob */
const u8 = new TextEncoder().encode(csv);
const blob = new Blob([u8], { type: "text/csv" });
/* generate a blob URL */
return URL.createObjectURL(blob);
}
(async() => { try {
/* fetch file */
const f = await fetch("https://docs.sheetjs.com/cd.xls");
const ab = await f.arrayBuffer();
/* parse file and get first worksheet */
const wb = XLSX.read(ab);
const ws = wb.Sheets[wb.SheetNames[0]];
/* generate blob URL */
const url = worksheet_to_csv_url(ws);
/* feed to tf.js */
const dataset = tf.data.csv(url, {
hasHeader: true,
configuredColumnsOnly: true,
columnConfigs:{
"Horsepower": {required: false, default: 0},
"Miles_per_Gallon":{required: false, default: 0, isLabel:true}
}
});
/* pre-process data */
let flat = dataset
.map(({xs,ys}) =>({xs: Object.values(xs), ys: Object.values(ys)}))
.filter(({xs,ys}) => [...xs,...ys].every(v => v>0));
/* normalize manually :( */
let minX = Infinity, maxX = -Infinity, minY = Infinity, maxY = -Infinity;
await flat.forEachAsync(({xs, ys}) => {
minX = Math.min(minX, xs[0]); maxX = Math.max(maxX, xs[0]);
minY = Math.min(minY, ys[0]); maxY = Math.max(maxY, ys[0]);
});
flat = flat.map(({xs, ys}) => ({xs:xs.map(v => (v-minX)/(maxX - minX)),ys:ys.map(v => (v-minY)/(maxY-minY))}));
flat = flat.batch(32);
/* build and train model */
const model = tf.sequential();
model.add(tf.layers.dense({inputShape: [1], units: 1}));
model.compile({ optimizer: tf.train.sgd(0.000001), loss: 'meanSquaredError' });
await model.fitDataset(flat, { epochs: 100, callbacks: { onEpochEnd: async (epoch, logs) => {
console.error(`${epoch}:${logs.loss}`);
}}});
/* predict values */
const inp = tf.linspace(0, 1, 9);
const pred = model.predict(inp);
const xs = await inp.dataSync(), ys = await pred.dataSync();
for (let i=0; i<xs.length; ++i) {
console.log([xs[i] * (maxX - minX) + minX, ys[i] * (maxY - minY) + minY].join(" "));
}
} catch(e) { console.error(`ERROR: ${String(e)}`); }})();