diff --git a/docz/docs/03-demos/01-math/11-tensorflow.md b/docz/docs/03-demos/01-math/11-tensorflow.md index 43d0ab4..90afe7b 100644 --- a/docz/docs/03-demos/01-math/11-tensorflow.md +++ b/docz/docs/03-demos/01-math/11-tensorflow.md @@ -5,6 +5,11 @@ pagination_prev: demos/index pagination_next: demos/frontend/index --- +import current from '/version.js'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import CodeBlock from '@theme/CodeBlock'; + @@ -25,27 +30,151 @@ results back to spreadsheets. - ["JS Array Interchange"](#js-array-interchange) uses SheetJS to process sheets and generate rows of objects that can be post-processed. -:::info pass - -Live code blocks in this page use the TF.js `4.14.0` standalone build. - -For use in web frameworks, the `@tensorflow/tfjs` module should be used. - -For use in NodeJS, the native bindings module is `@tensorflow/tfjs-node`. - -::: - :::note Tested Deployments Each browser demo was tested in the following environments: | Browser | TF.js version | Date | |:------------|:--------------|:-----------| -| Chrome 122 | `4.14.0` | 2024-04-07 | -| Safari 17.4 | `4.14.0` | 2024-03-23 | +| Chrome 127 | `4.20.0` | 2024-08-16 | +| Safari 17.4 | `4.20.0` | 2024-08-16 | + +The NodeJS demo was tested in the following environments: + +| NodeJS | TF.js version | Date | +|:---------|:------------------------------|:-----------| +| `22.3.0` | `4.20.0` (`@tensorflow/tfjs`) | 2024-08-16 | + +The Kaioken demo was tested in the following environments: + +| Kaioken | TF.js version | Date | +|:----------|:--------------|:-----------| +| `0.25.3` | `4.20.0` | 2024-08-16 | ::: +## Installation + +#### Standalone Browser Scripts + +Live code blocks in this page use the TF.js `4.20.0` standalone build. + +Standalone scripts are available on various CDNs including UNPKG. The latest +version can be loaded with the following `SCRIPT` tag. + +The [SheetJS Standalone scripts](/docs/getting-started/installation/standalone) +can be loaded after the TF.js standalone script. + +{`\ + + + +`} + + +#### Frameworks and Bundlers + +[The "Frameworks" section](/docs/getting-started/installation/frameworks) covers +installation with Yarn and other package managers. + +`@tensorflow/tfjs` and SheetJS modules should be installed using a package manager: + + + +{`\ +npm i --save https://cdn.sheetjs.com/xlsx-${current}/xlsx-${current}.tgz @tensorflow/tfjs`} + + + +{`\ +pnpm install --save https://cdn.sheetjs.com/xlsx-${current}/xlsx-${current}.tgz @tensorflow/tfjs`} + + + +{`\ +yarn add https://cdn.sheetjs.com/xlsx-${current}/xlsx-${current}.tgz @tensorflow/tfjs`} + + +:::caution pass + +Newer releases of Yarn may throw an error: + +``` +Usage Error: It seems you are trying to add a package using a https:... url; we now require package names to be explicitly specified. +Try running the command again with the package name prefixed: yarn add my-package@https:... +``` + +The workaround is to prepend the URL with `xlsx@`: + +{`\ +yarn add xlsx@https://cdn.sheetjs.com/xlsx-${current}/xlsx-${current}.tgz @tensorflow/tfjs`} + + +::: + + + + +#### NodeJS + +The [SheetJS NodeJS module](/docs/getting-started/installation/nodejs) can be +imported in NodeJS scripts that use TF.js. + +There are two options for NodeJS: + +- the pure JavaScript bindings module is `@tensorflow/tfjs` +- the native bindings module is `@tensorflow/tfjs-node` + +:::danger pass + +When this demo was last tested, there were issues with the native binding: + +``` +Error: The specified module could not be found. +\\?\C:\Users\SheetJS\node_modules\@tensorflow\tfjs-node\lib\napi-v8\tfjs_binding.node +``` + +For general compatibility, the demos use the pure `@tensorflow/tfjs` binding. + +::: + + + + +{`\ +npm i --save https://cdn.sheetjs.com/xlsx-${current}/xlsx-${current}.tgz @tensorflow/tfjs @tensorflow/tfjs-node`} + + + +{`\ +pnpm install --save https://cdn.sheetjs.com/xlsx-${current}/xlsx-${current}.tgz @tensorflow/tfjs @tensorflow/tfjs-node`} + + + +{`\ +yarn add https://cdn.sheetjs.com/xlsx-${current}/xlsx-${current}.tgz @tensorflow/tfjs @tensorflow/tfjs-node`} + + +:::caution pass + +Newer releases of Yarn may throw an error: + +``` +Usage Error: It seems you are trying to add a package using a https:... url; we now require package names to be explicitly specified. +Try running the command again with the package name prefixed: yarn add my-package@https:... +``` + +The workaround is to prepend the URL with `xlsx@`: + +{`\ +yarn add xlsx@https://cdn.sheetjs.com/xlsx-${current}/xlsx-${current}.tgz @tensorflow/tfjs @tensorflow/tfjs-node`} + + +::: + + + + ## CSV Data Interchange `tf.data.csv`[^1] generates a Dataset from CSV data. The function expects a URL. @@ -126,6 +255,8 @@ The demo builds a model for predicting MPG from Horsepower data. It: - builds a model and trains with `fitDataset`[^8] - predicts MPG from a set of sample inputs and displays results in a table +#### Live Demo +
Live Demo (click to show) @@ -186,8 +317,8 @@ function SheetJSToTFJSCSV() { hasHeader: true, configuredColumnsOnly: true, columnConfigs:{ - "Horsepower": {required: false, default: 0}, - "Miles_per_Gallon":{required: false, default: 0, isLabel:true} + "Horsepower": { required: false, default: 0}, + "Miles_per_Gallon": { required: false, default: 0, isLabel: true } } }); @@ -234,6 +365,111 @@ function SheetJSToTFJSCSV() {
+#### NodeJS Demo + +0) Create a new project: + +```bash +mkdir sheetjs-tfjs-csv +cd sheetjs-tfjs-csv +npm init -y +``` + +1) Download [`SheetJSTF.js`](pathname:///tfjs/SheetJSTF.js): + +```bash +curl -LO https://docs.sheetjs.com/tfjs/SheetJSTF.js +``` + +2) Install SheetJS and TF.js dependencies: + +{`\ +npm i --save https://cdn.sheetjs.com/xlsx-${current}/xlsx-${current}.tgz @tensorflow/tfjs @tensorflow/tfjs-node`} + + +3) Run the script: + +```bash +node SheetJSTF.js +``` + +#### Kaioken Demo + +:::tip pass + +[Kaioken](/docs/demos/frontend/kaioken) is a popular front-end framework that +uses patterns that will be familiar to ReactJS developers. + +The SheetJS team strongly recommends using Kaioken in projects using TF.js. + +::: + +1) Create a new site. + +```bash +npm create vite sheetjs-tfjs-kaioken -- --template vanilla-ts +cd sheetjs-tfjs-kaioken +npm add --save kaioken +npm add --save vite-plugin-kaioken -D +``` + +2) Create a new file `vite.config.ts` with the following content: + +```ts title="vite.config.ts (create new file)" +import { defineConfig } from "vite" +import kaioken from "vite-plugin-kaioken" + +export default defineConfig({ + plugins: [kaioken()], +}) +``` + +3) Edit `tsconfig.json` and add `"jsx": "preserve"` within `compilerOptions`: + +```js title="tsconfig.json (add highlighted line)" +{ + "compilerOptions": { +// highlight-next-line + "jsx": "preserve", +``` + +4) Replace `src/main.ts` with the following codeblock: + +```js title="src/main.ts" +import { mount } from "kaioken"; +import App from "./SheetJSTF"; + +const root = document.getElementById("app"); +mount(App, root!); +``` + +5) Download [`SheetJSTF.tsx`](pathname:///tfjs/SheetJSTF.tsx) to the `src` directory: + +```bash +curl -L -o src/SheetJSTF.tsx https://docs.sheetjs.com/tfjs/SheetJSTF.tsx +``` + +6) Install SheetJS and TF.js dependencies: + +{`\ +npm i --save https://cdn.sheetjs.com/xlsx-${current}/xlsx-${current}.tgz @tensorflow/tfjs`} + + +7) Start the development server: + +```bash +npm run dev +``` + +The process will display a URL: + +``` + ➜ Local: http://localhost:5173/ +``` + +Open the displayed URL (`http://localhost:5173/` in this example) with a web +browser. Click the "Click to Run" button to see the results. + ## JS Array Interchange [The official Linear Regression tutorial](https://www.tensorflow.org/js/tutorials/training/linear_regression) @@ -306,7 +542,7 @@ The SheetJS `sheet_to_json` method[^10] will translate worksheet objects into an array of row objects: ```js -var aoo = [ +const aoo = [ {"sepal length": 5.1, "sepal width": 3.5, ...}, {"sepal length": 4.9, "sepal width": 3, ...}, ... @@ -316,18 +552,18 @@ var aoo = [ TF.js and other libraries tend to operate on individual columns, equivalent to: ```js -var sepal_lengths = [5.1, 4.9, ...]; -var sepal_widths = [3.5, 3, ...]; +const sepal_lengths = [5.1, 4.9, ...]; +const sepal_widths = [3.5, 3, ...]; ``` When a `tensor2d` can be exported, it will look different from the spreadsheet: ```js -var data_set_2d = [ +const data_set_2d = [ [5.1, 4.9, ...], [3.5, 3, ...], - ... -] + // ... +]; ``` This is the transpose of how people use spreadsheets! @@ -340,35 +576,35 @@ transposed. To export multiple data sets, the data should be transposed: ```js /* assuming data is an array of typed arrays */ -var aoa = []; -for(var i = 0; i < data.length; ++i) { - for(var j = 0; j < data[i].length; ++j) { +const aoa = []; +for(let i = 0; i < data.length; ++i) { + for(let j = 0; j < data[i].length; ++j) { if(!aoa[j]) aoa[j] = []; aoa[j][i] = data[i][j]; } } /* aoa can be directly converted to a worksheet object */ -var ws = XLSX.utils.aoa_to_sheet(aoa); +const ws = XLSX.utils.aoa_to_sheet(aoa); ``` ### Importing Data from a Spreadsheet -`sheet_to_json` with the option `header:1`[^12] will generate a row-major array +`sheet_to_json` with the option `header: 1`[^12] will generate a row-major array of arrays that can be transposed. However, it is more efficient to walk the sheet manually: ```js /* find worksheet range */ -var range = XLSX.utils.decode_range(ws['!ref']); -var out = [] +const range = XLSX.utils.decode_range(ws['!ref']); +const out = [] /* walk the columns */ -for(var C = range.s.c; C <= range.e.c; ++C) { +for(let C = range.s.c; C <= range.e.c; ++C) { /* create the typed array */ - var ta = new Float32Array(range.e.r - range.s.r + 1); + const ta = new Float32Array(range.e.r - range.s.r + 1); /* walk the rows */ - for(var R = range.s.r; R <= range.e.r; ++R) { + for(let R = range.s.r; R <= range.e.r; ++R) { /* find the cell, skip it if the cell isn't numeric or boolean */ - var cell = ws["!data"] ? (ws["!data"][R]||[])[C] : ws[XLSX.utils.encode_cell({r:R, c:C})]; + const cell = ws["!data"] ? (ws["!data"][R]||[])[C] : ws[XLSX.utils.encode_cell({r:R, c:C})]; if(!cell || cell.t != 'n' && cell.t != 'b') continue; /* assign to the typed array */ ta[R - range.s.r] = cell.v; @@ -393,38 +629,38 @@ const tensor = tf.tensor1d(lengths); `tf.Tensor` objects can be directly transposed using `transpose`: ```js -var aoo = XLSX.utils.sheet_to_json(worksheet); +const aoo = XLSX.utils.sheet_to_json(worksheet); // "x" and "y" are the fields we want to pull from the data -var data = aoo.map(row => ([row["x"], row["y"]])); +con st data = aoo.map(row => ([row["x"], row["y"]])); // create a tensor representing two column datasets -var tensor = tf.tensor2d(data).transpose(); +const tensor = tf.tensor2d(data).transpose(); // individual columns can be accessed -var col1 = tensor.slice([0,0], [1,tensor.shape[1]]).flatten(); -var col2 = tensor.slice([1,0], [1,tensor.shape[1]]).flatten(); +const col1 = tensor.slice([0,0], [1,tensor.shape[1]]).flatten(); +const col2 = tensor.slice([1,0], [1,tensor.shape[1]]).flatten(); ``` For exporting, `stack` can be used to collapse the columns into a linear array: ```js /* pull data into a Float32Array */ -var result = tf.stack([col1, col2]).transpose(); -var shape = tensor.shape; -var f32 = tensor.dataSync(); +const result = tf.stack([col1, col2]).transpose(); +const shape = tensor.shape; +const f32 = tensor.dataSync(); /* construct an array of arrays of the data in spreadsheet order */ -var aoa = []; -for(var j = 0; j < shape[0]; ++j) { +const aoa = []; +for(let j = 0; j < shape[0]; ++j) { aoa[j] = []; - for(var i = 0; i < shape[1]; ++i) aoa[j][i] = f32[j * shape[1] + i]; + for(let i = 0; i < shape[1]; ++i) aoa[j][i] = f32[j * shape[1] + i]; } /* add headers to the top */ aoa.unshift(["x", "y"]); /* generate worksheet */ -var worksheet = XLSX.utils.aoa_to_sheet(aoa); +const worksheet = XLSX.utils.aoa_to_sheet(aoa); ``` [^1]: See [`tf.data.csv`](https://js.tensorflow.org/api/latest/#data.csv) in the TensorFlow.js documentation diff --git a/docz/docs/03-demos/17-mobile/02-nativescript.md b/docz/docs/03-demos/17-mobile/02-nativescript.md index a801711..e1683e3 100644 --- a/docz/docs/03-demos/17-mobile/02-nativescript.md +++ b/docz/docs/03-demos/17-mobile/02-nativescript.md @@ -828,4 +828,5 @@ file named `SheetJSNS.xls`. [^4]: See [`write` in "Writing Files"](/docs/api/write-options) [^5]: See [`json_to_sheet` in "Utilities"](/docs/api/utilities/array#array-of-objects-input) [^6]: See ["Workbook Helpers" in "Utilities"](/docs/api/utilities/wb) for details on `book_new` and `book_append_sheet`. -[^7]: See [`read` in "Reading Files"](/docs/api/parse-options) \ No newline at end of file +[^7]: See [`read` in "Reading Files"](/docs/api/parse-options) +[^8]: See ["Local setup"](https://docs.nativescript.org/setup/#local-setup) in the NativeScript documentation. For Windows and Linux, follow the "Android" instructions. For macOS, follow both the iOS and Android instructions. diff --git a/docz/static/tfjs/SheetJSTF.js b/docz/static/tfjs/SheetJSTF.js new file mode 100644 index 0000000..5b74795 --- /dev/null +++ b/docz/static/tfjs/SheetJSTF.js @@ -0,0 +1,68 @@ +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; + +export default function SheetJSToTFJSCSV() { + const [output, setOutput] = useState(""); + const [results, setResults] = useState<[number, number][]>([]); + const [disabled, setDisabled] = useState(false); + + function worksheet_to_csv_url(worksheet: WorkSheet) { + /* generate CSV */ + const csv = 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); + } + + const doit = useCallback(async () => { + setResults([]); setOutput(""); setDisabled(true); + 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 = 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 = 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 as unknown as DSet) + .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))})); + let batch = flat.batch(32); + + /* build and train model */ + const model = sequential(); + model.add(layers.dense({inputShape: [1], units: 1})); + model.compile({ optimizer: train.sgd(0.000001), loss: 'meanSquaredError' }); + await model.fitDataset(batch, { epochs: 100, callbacks: { onEpochEnd: async (epoch, logs) => { + setOutput(`${epoch}:${logs?.loss}`); + }}}); + + /* predict values */ + const inp = linspace(0, 1, 9); + const pred = model.predict(inp) as Tensor; + const xs = await inp.dataSync(), ys = await pred.dataSync(); + setResults(Array.from(xs).map((x, i) => [ x * (maxX - minX) + minX, ys[i] * (maxY - minY) + minY ])); + setOutput(""); + } catch(e) { setOutput(`ERROR: ${String(e)}`); } finally { setDisabled(false);} + }, []); + + return ( <> +
+ {output && 
{output}
|| <>} + {results.length && + {results.map((r,i) => )} +
HorsepowerMPG
{r[0]}{r[1].toFixed(2)}
|| <>} + ); +} diff --git a/docz/static/tfjs/tf.min.js b/docz/static/tfjs/tf.min.js index 00adab9..29e4008 100644 --- a/docz/static/tfjs/tf.min.js +++ b/docz/static/tfjs/tf.min.js @@ -1,6 +1,6 @@ /** * @license - * Copyright 2023 Google LLC. All Rights Reserved. + * Copyright 2024 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at @@ -14,5 +14,5 @@ * limitations under the License. * ============================================================================= */ -!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).tf=e.tf||{})}(this,(function(e){"use strict";function t(e,t){return t.forEach((function(t){t&&"string"!=typeof t&&!Array.isArray(t)&&Object.keys(t).forEach((function(n){if("default"!==n&&!(n in e)){var r=Object.getOwnPropertyDescriptor(t,n);Object.defineProperty(e,n,r.get?r:{enumerable:!0,get:function(){return t[n]}})}}))})),e}var n="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof 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