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Refactor #3

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3 changes: 2 additions & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -3,4 +3,5 @@ data/
*.jpg
node_modules/
.env
.DS_Store
.DS_Store
models/
15 changes: 15 additions & 0 deletions ImageTransformer.js
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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`))
}
}
44 changes: 44 additions & 0 deletions arbitraryImageDataSource.js
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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)
}
144 changes: 31 additions & 113 deletions index.js
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@@ -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())
}
20 changes: 20 additions & 0 deletions mnistDataSource.js
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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)
}
}
67 changes: 67 additions & 0 deletions model.js
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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)
}
}
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