From d1369b08857136a734951e7909da7750ead5c7b4 Mon Sep 17 00:00:00 2001 From: Chris Langenberg Date: Mon, 22 Nov 2021 18:18:22 +0100 Subject: [PATCH] Refactored the Code * moved everything to a seperate file * add more layers to the NN * removed unused canvas package * save and load model from disk * added some data sources --- .gitignore | 3 +- ImageTransformer.js | 15 + arbitraryImageDataSource.js | 44 +++ index.js | 144 ++------ mnistDataSource.js | 20 ++ model.js | 67 ++++ package-lock.json | 688 +++++++++++++++++++++++++++++++----- package.json | 5 +- randomDataSource.js | 22 ++ 9 files changed, 813 insertions(+), 195 deletions(-) create mode 100644 ImageTransformer.js create mode 100644 arbitraryImageDataSource.js create mode 100644 mnistDataSource.js create mode 100644 model.js create mode 100644 randomDataSource.js diff --git a/.gitignore b/.gitignore index 397c471..67f2bf6 100644 --- a/.gitignore +++ b/.gitignore @@ -3,4 +3,5 @@ data/ *.jpg node_modules/ .env -.DS_Store \ No newline at end of file +.DS_Store +models/ \ No newline at end of file diff --git a/ImageTransformer.js b/ImageTransformer.js new file mode 100644 index 0000000..650ca13 --- /dev/null +++ b/ImageTransformer.js @@ -0,0 +1,15 @@ +import Jimp from 'jimp' + +/** + * Converts pixel arrays to real images which can be saved to disk + */ +export class ImageTransformer { + saveImage(img, width = 28, height = 28, path) { + new Jimp({ width, height, data: Buffer.from(img) }, (_, img) => img.write(path)) + } + + toImages(data, filePrefix = 'processed', width = 28, height = 28) { + const imgs = data.map(img => img.flatMap(val => [val * 255, val * 255, val * 255, 255])) + imgs.forEach((img, i) => this.saveImage(img, width, height, `output/${filePrefix}_${i}.png`)) + } +} \ No newline at end of file diff --git a/arbitraryImageDataSource.js b/arbitraryImageDataSource.js new file mode 100644 index 0000000..019f187 --- /dev/null +++ b/arbitraryImageDataSource.js @@ -0,0 +1,44 @@ +import fs from 'fs' +import sharp from 'sharp' +import tf from '@tensorflow/tfjs-node' + +/** + * Loads images from the images directory and processes them to fit the model + */ +export class ArbitraryImageDataSource { + constructor(countTraining = 1000, countTest = 10) { + const files = fs.readdirSync('images') + .filter(f => f.endsWith('.jpg') || f.endsWith('.jpeg') || f.endsWith('.png')) + .map(f => `images/${f}`) + + this.trainingFiles = files.shuffle().slice(0, countTraining) + this.testFiles = files.shuffle().slice(0, countTest) + } + + async getTrainingData() { + const data = await Promise.all(this.trainingFiles.map(f => this._processImageFile(f))) + return tf.tensor(data).div(255) + } + + async getTestData() { + const data = await Promise.all(this.testFiles.map(f => this._processImageFile(f))) + return tf.tensor(data).div(255) + } + + _processImageFile(filename) { + return sharp(filename) + .resize(28, 28, { + fit: 'cover' + }) + .gamma() + .greyscale() + .raw() + .toBuffer() + } +} + +Array.prototype.shuffle = function () { + return this.map((value) => ({ value, sort: Math.random() })) + .sort((a, b) => a.sort - b.sort) + .map(({ value }) => value) +} \ No newline at end of file diff --git a/index.js b/index.js index d254457..230957c 100644 --- a/index.js +++ b/index.js @@ -1,119 +1,37 @@ -console.log("Hello Autoencoder 🚂"); +import { Model } from './model.js' +import { MnistDataSource } from './mnistDataSource.js' +import { ImageTransformer } from './ImageTransformer.js' +import { RandomDataSource } from './randomDataSource.js' +import { ArbitraryImageDataSource } from './arbitraryImageDataSource.js' -import * as tf from "@tensorflow/tfjs-node"; -// import canvas from "canvas"; -// const { loadImage } = canvas; -import Jimp from "jimp"; -import numeral from "numeral"; - -main(); +main() async function main() { - // Build the model - const autoencoder = buildModel(); - // load all image data - const images = await loadImages(550); - - // train the model - const x_train = tf.tensor2d(images.slice(0, 500)); - await trainModel(autoencoder, x_train, 250); - - // test the model - const x_test = tf.tensor2d(images.slice(500)); - await generateTests(autoencoder, x_test); -} - -async function generateTests(autoencoder, x_test) { - const output = autoencoder.predict(x_test); - // output.print(); - - const newImages = await output.array(); - for (let i = 0; i < newImages.length; i++) { - const img = newImages[i]; - const buffer = []; - for (let n = 0; n < img.length; n++) { - const val = Math.floor(img[n] * 255); - buffer[n * 4 + 0] = val; - buffer[n * 4 + 1] = val; - buffer[n * 4 + 2] = val; - buffer[n * 4 + 3] = 255; + // Instantiate the model + const model = new Model() + + // Instantiate a data source + const dataSource = new MnistDataSource() + // const dataSource = new RandomDataSource() + // const dataSource = new ArbitraryImageDataSource() + + // Instatiate the Image transformer + const transformer = new ImageTransformer() + + // Check if there is a pretrained model. If it exists load it, or train the model + if (model.pretrainedModelExists()) { + await model.load() + } else { + // Create the layers + model.configure() + // and train + await model.train(await dataSource.getTrainingData(), 200) } - const image = new Jimp( - { - data: Buffer.from(buffer), - width: 28, - height: 28, - }, - (err, image) => { - const num = numeral(i).format("000"); - image.write(`output/square${num}.png`); - } - ); - } -} - -function buildModel() { - const autoencoder = tf.sequential(); - // Build the model - autoencoder.add( - tf.layers.dense({ - units: 256, - inputShape: [784], - activation: "relu", - }) - ); - autoencoder.add( - tf.layers.dense({ - units: 128, - activation: "relu", - }) - ); + // Test the model with testing data from the data source + const testData = await dataSource.getTestData() + const autoEncodedImages = model.autoencode(testData) - autoencoder.add( - tf.layers.dense({ - units: 256, - activation: "sigmoid", - }) - ); - - autoencoder.add( - tf.layers.dense({ - units: 784, - activation: "sigmoid", - }) - ); - autoencoder.compile({ - optimizer: "adam", - loss: "binaryCrossentropy", - metrics: ["accuracy"], - }); - return autoencoder; -} - -async function trainModel(autoencoder, x_train, epochs) { - await autoencoder.fit(x_train, x_train, { - epochs: epochs, - batch_size: 32, - shuffle: true, - verbose: true, - }); -} - -async function loadImages(total) { - const allImages = []; - for (let i = 0; i < total; i++) { - const num = numeral(i).format("000"); - const img = await Jimp.read(`data/square${num}.png`); - - let rawData = []; - for (let n = 0; n < 28 * 28; n++) { - let index = n * 4; - let r = img.bitmap.data[index + 0]; - // let g = img.bitmap.data[n + 1]; - // let b = img.bitmap.data[n + 2]; - rawData[n] = r / 255.0; - } - allImages[i] = rawData; - } - return allImages; + // save the images to disk + transformer.toImages(testData.arraySync(), 'org') + transformer.toImages(autoEncodedImages.arraySync()) } diff --git a/mnistDataSource.js b/mnistDataSource.js new file mode 100644 index 0000000..deb45f3 --- /dev/null +++ b/mnistDataSource.js @@ -0,0 +1,20 @@ +import mnist from 'mnist' +import tf from '@tensorflow/tfjs-node' + +/** + * Load data from the mnist data set + */ +export class MnistDataSource { + constructor(countTraining = 1000, countTest = 10) { + const { training, test } = mnist.set(countTraining, countTest) + this.training = training.map(x => x.input) + this.test = test.map(x => x.input) + } + + getTrainingData() { + return tf.tensor(this.training) + } + getTestData() { + return tf.tensor(this.test) + } +} diff --git a/model.js b/model.js new file mode 100644 index 0000000..d946dd3 --- /dev/null +++ b/model.js @@ -0,0 +1,67 @@ +import fs from 'fs' +import tf from '@tensorflow/tfjs-node' + +/** + * Abstraction for tfjs + */ +export class Model { + pretrainedModelExists() { + return fs.existsSync('models/autoencoder/model.json') && + fs.existsSync('models/encoder/model.json') && + fs.existsSync('models/decoder/model.json') + } + + async load() { + this.autoencoder = await tf.loadLayersModel('file://models/autoencoder/model.json') + this.encoder = await tf.loadLayersModel('file://models/encoder/model.json') + this.decoder = await tf.loadLayersModel('file://models/decoder/model.json') + } + + configure() { + const encoded = [ + tf.layers.dense({ units: 128, inputShape: [784], activation: "relu" }), + tf.layers.dense({ units: 64, activation: "relu" }), + tf.layers.dense({ units: 32, activation: "relu" }), + ] + const decoded = [ + tf.layers.dense({ units: 64, activation: "relu" }), + tf.layers.dense({ units: 128, activation: "relu" }), + tf.layers.dense({ units: 784, activation: "sigmoid" }), + ] + + this.autoencoder = tf.sequential({ layers: [...encoded, ...decoded] }) + this.encoder = tf.sequential({ layers: encoded }) + + const encoded_input = tf.layers.inputLayer({ inputShape: [32] }) + this.decoder = tf.sequential({ layers: [encoded_input, ...decoded] }) + + this.autoencoder.compile({ + optimizer: 'adam', + loss: 'binaryCrossentropy', + }) + } + + async train(x_train, epochs = 100) { + await this.autoencoder.fit(x_train, x_train, { + epochs, + batchSize: 32, + shuffle: true, + }) + fs.mkdirSync('models') + await this.autoencoder.save('file://models/autoencoder') + await this.encoder.save('file://models/encoder') + await this.decoder.save('file://models/decoder') + } + + autoencode(data) { + return this.decode(this.encode(data)) + } + + encode (data) { + return this.encoder.predict(data) + } + + decode(encoded) { + return this.decoder.predict(encoded) + } +} \ No newline at end of file diff --git a/package-lock.json b/package-lock.json index 37a711d..1b69650 100644 --- a/package-lock.json +++ b/package-lock.json @@ -10,9 +10,10 @@ "license": "ISC", "dependencies": { "@tensorflow/tfjs-node": "^3.11.0", - "canvas": "^2.8.0", "jimp": "^0.16.1", - "numeral": "^2.0.6" + "mnist": "^1.1.0", + "numeral": "^2.0.6", + "sharp": "^0.29.3" } }, "node_modules/@babel/runtime": { @@ -869,6 +870,29 @@ } ] }, + "node_modules/bl": { + "version": "4.1.0", + "resolved": "https://registry.npmjs.org/bl/-/bl-4.1.0.tgz", + "integrity": "sha512-1W07cM9gS6DcLperZfFSj+bWLtaPGSOHWhPiGzXmvVJbRLdG82sH/Kn8EtW1VqWVA54AKf2h5k5BbnIbwF3h6w==", + "dependencies": { + "buffer": "^5.5.0", + "inherits": "^2.0.4", + "readable-stream": "^3.4.0" + } + }, + "node_modules/bl/node_modules/readable-stream": { + "version": "3.6.0", + "resolved": "https://registry.npmjs.org/readable-stream/-/readable-stream-3.6.0.tgz", + "integrity": "sha512-BViHy7LKeTz4oNnkcLJ+lVSL6vpiFeX6/d3oSH8zCW7UxP2onchk+vTGB143xuFjHS3deTgkKoXXymXqymiIdA==", + "dependencies": { + "inherits": "^2.0.3", + "string_decoder": "^1.1.1", + "util-deprecate": "^1.0.1" + }, + "engines": { + "node": ">= 6" + } + }, "node_modules/bmp-js": { "version": "0.1.0", "resolved": "https://registry.npmjs.org/bmp-js/-/bmp-js-0.1.0.tgz", @@ -914,20 +938,6 @@ "node": ">=0.4.0" } }, - "node_modules/canvas": { - 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"version": "1.7.1", "resolved": "https://registry.npmjs.org/timm/-/timm-1.7.1.tgz", @@ -3522,6 +4044,14 @@ "resolved": "https://registry.npmjs.org/tr46/-/tr46-0.0.3.tgz", "integrity": "sha1-gYT9NH2snNwYWZLzpmIuFLnZq2o=" }, + "tunnel-agent": { + "version": "0.6.0", + "resolved": "https://registry.npmjs.org/tunnel-agent/-/tunnel-agent-0.6.0.tgz", + "integrity": "sha1-J6XeoGs2sEoKmWZ3SykIaPD8QP0=", + "requires": { + "safe-buffer": "^5.0.1" + } + }, "utif": { "version": "2.0.1", "resolved": "https://registry.npmjs.org/utif/-/utif-2.0.1.tgz", diff --git a/package.json b/package.json index f5ec99c..9fcc571 100644 --- a/package.json +++ b/package.json @@ -11,8 +11,9 @@ "license": "ISC", "dependencies": { "@tensorflow/tfjs-node": "^3.11.0", - "canvas": "^2.8.0", "jimp": "^0.16.1", - "numeral": "^2.0.6" + "numeral": "^2.0.6", + "mnist": "^1.1.0", + "sharp": "^0.29.3" } } diff --git a/randomDataSource.js b/randomDataSource.js new file mode 100644 index 0000000..dcc0996 --- /dev/null +++ b/randomDataSource.js @@ -0,0 +1,22 @@ +import tf from '@tensorflow/tfjs-node' + +/** + * Generate random noise as a data set + */ +export class RandomDataSource { + constructor(countTraining = 1000, countTest = 10) { + this.training = Array.from({ length: countTraining }, _ => Array.from({ length: 28*28 }, _ => getRandomInt(255) / 255)) + this.test = Array.from({ length: countTest }, _ => Array.from({ length: 28*28 }, _ => getRandomInt(255) / 255)) + } + + getTrainingData() { + return tf.tensor(this.training) + } + getTestData() { + return tf.tensor(this.test) + } +} + +function getRandomInt(max) { + return Math.floor(Math.random() * max); +} \ No newline at end of file