ML - image class code

ML - image class code

problem: await dont work

import * as tf from '@tensorflow/tfjs';import drum from './data/pretrained-model-data/drum.jpg';import drum2 from './data/pretrained-model-data/index.jpg';import drum3 from './data/pretrained-model-data/index2.jpg';import drum4 from './data/pretrained-model-data/index3.jpg';// import labels from './imagenet_labels.json';import blue1 from './data/colors/training/blue/blue-1.png';import blue2 from './data/colors/training/blue/blue-2.png';import blue3 from './data/colors/validation/blue/blue-3.png';import red1 from './data/colors/training/red/red-1.png';import red2 from './data/colors/training/red/red-2.png';import red3 from './data/colors/validation/red/red-3.png';const training = [ blue1, blue2, red1, red2,];// labels should match the positions of their associated imagesconst labels = [ 'blue', 'blue', 'red', 'red',];// function makePrediction(pretrainedModel, image, expectedLabel,model) {// loadImage(image).then(loadedImage => {// return loadAndProcessImage(loadedImage, pretrainedModel);// }).then(loadedImage => {// const prediction2 = pretrainedModel.predict(loadedImage);// console.log('Expected Label', expectedLabel );// console.log('Predicted Label', model.predict(prediction2).as1D().argMax().data());// // loadedImage.dispose();// });// }// function makePrediction(pretrainedModel, image, expectedLabel) {// loadImage(image).then(loadedImage => {// return loadAndProcessImage(loadedImage, pretrainedModel);// }).then(loadedImage => {// console.log('Expected Label', expectedLabel);// console.log('Predicted Label', predict(model, loadedImage));// loadedImage.dispose();// });// }buildPretrainedModel().then(pretrainedModel => { loadImages(training, pretrainedModel).then(xs => { console.log(xs); const prediction = pretrainedModel.predict(xs); console.log(prediction); const ys = addLabels(labels); const model = getModel(2); model.fit(prediction, ys, { epochs: 20, shuffle: true, }).then(history => { // makePrediction(pretrainedModel, blue3, "0"); // makePrediction(pretrainedModel, red3, "1"); // make predictions loadImage(blue3).then(loadedImage => { return loadAndProcessImage(loadedImage, pretrainedModel); }).then(loadedImage => { const prediction2 = pretrainedModel.predict(loadedImage); console.log('Expected Label', "0"); console.log('Predicted Label', model.predict(prediction2).as1D().argMax().data() ); // async function print(){ // const prediction3 = model.predict(prediction2); // const predictedClass =prediction3.as1D().argMax(); // const classID = (await predictedClass.data())[0]; // } // loadedImage.dispose(); }); // loadImage(red3).then(async (loadedImage) => { // return loadAndProcessImage(loadedImage, pretrainedModel); // }).then(loadedImage => { // const prediction2 = pretrainedModel.predict(loadedImage); // console.log('Expected Label', "1"); // console.log('Predicted Label', (await (model.predict(prediction2).as1D().argMax().data()) )[0] ); // // loadedImage.dispose(); // }); // makePrediction(pretrainedModel, blue3, "0",model); // makePrediction(pretrainedModel, red3, "1",model); }); });});// buildPretrainedModel().then(pretrainedModel => {// loadImages(training, pretrainedModel).then(xs => {// const xxs = pretrainedModel.predict(xs)// const ys = addLabels(labels);// const model = getModel(2);// model.fit(xxs, ys, {// epochs: 20,// shuffle: true,// }).then(history => {// // make predictions// loadImage(red3).then(loadedImage => {// return loadAndProcessImage(loadedImage, pretrainedModel);// }).then(loadedImage => { // console.log('Expected Label', '1');// console.log('Predicted Label', model.predict(loadedImage));// // loadedImage.dispose();// });// // makePrediction(pretrainedModel, blue3, "0");// // makePrediction(pretrainedModel, red3, "1");// });// });// });function getModel(numberOfClasses) { const model = tf.sequential({ layers: [ tf.layers.flatten({inputShape: [7, 7, 256]}), tf.layers.dense({ units: 100, activation: 'relu', kernelInitializer: 'varianceScaling', useBias: true }), tf.layers.dense({ units: numberOfClasses, kernelInitializer: 'varianceScaling', useBias: false, activation: 'softmax' }) ], }); model.compile({ optimizer: tf.train.adam(0.0001), loss: 'categoricalCrossentropy', metrics: ['accuracy'], }); return model;}// buildPretrainedModel().then(pretrainedModel => {// loadImages(training, pretrainedModel).then(xs => {// console.log("===========================");// xs.print();// })// pretrainedModel.summary();// loadImage(drum4).then(img => {// const processedImage = loadAndProcessImage(img);// const prediction = pretrainedModel.predict(processedImage);// // Because of the way Tensorflow.js works, you must call print on a Tensor instead of console.log.// prediction.print();// const labelPrediction = prediction.as1D().argMax().dataSync()[0];// // console.log(`// // Numeric prediction is ${labelPrediction}// // The predicted label is ${labels[labelPrediction]}// // The actual label is drum, membranophone, tympan// // `);// });// });function oneHot(labelIndex, classLength) { return tf.tidy(() => tf.oneHot(tf.tensor1d([labelIndex]).toInt(), classLength));};function getLabelsAsObject(labels) { let labelObject = {}; for (let i = 0; i < labels.length; i++) { const label = labels[i]; if (labelObject[label] === undefined) { // only assign it if we haven't seen it before labelObject[label] = Object.keys(labelObject).length; } } return labelObject;}function addLabels(labels) { return tf.tidy(() => { const classes = getLabelsAsObject(labels); const classLength = Object.keys(classes).length; let ys; for (let i = 0; i < labels.length; i++) { const label = labels[i]; const labelIndex = classes[label]; const y = oneHot(labelIndex, classLength); if (i === 0) { ys = y; } else { ys = ys.concat(y, 0); } } return ys; });};function loadImages(images, pretrainedModel) { let promise = Promise.resolve(); for (let i = 0; i < images.length; i++) { const image = images[i]; promise = promise.then(data => { return loadImage(image).then(loadedImage => { // Note the use of `tf.tidy` and `.dispose()`. These are two memory management // functions that Tensorflow.js exposes. // // // Handling memory management is crucial for building a performant machine learning // model in a browser. return tf.tidy(() => { const processedImage = loadAndProcessImage(loadedImage, pretrainedModel); if (data) { const newData = data.concat(processedImage); data.dispose(); return newData; } return tf.keep(processedImage); }); }); }); } return promise;}function buildPretrainedModel() { return loadMobilenet().then(mobilenet => { const layer = mobilenet.getLayer('conv_pw_13_relu'); return tf.model({ inputs: mobilenet.inputs, outputs: layer.output, }); });}function loadMobilenet() { return tf.loadModel('loadImage(src) { return new Promise((resolve, reject) => { const img = new Image(); img.src = src; img.onload = () => resolve(tf.fromPixels(img)); img.onerror = (err) => reject(err); });}function cropImage(img) { const width = img.shape[0]; const height = img.shape[1]; // use the shorter side as the size to which we will crop const shorterSide = Math.min(img.shape[0], img.shape[1]); // calculate beginning and ending crop points const startingHeight = (height - shorterSide) / 2; const startingWidth = (width - shorterSide) / 2; const endingHeight = startingHeight + shorterSide; const endingWidth = startingWidth + shorterSide; // return image data cropped to those points return img.slice([startingWidth, startingHeight, 0], [endingWidth, endingHeight, 3]);}function batchImage(image) { // Expand our tensor to have an additional dimension, whose size is 1 const batchedImage = image.expandDims(0); // Turn pixel data into a float between -1 and 1. return batchedImage.toFloat().div(tf.scalar(127)).sub(tf.scalar(1));}function resizeImage(image) { return tf.image.resizeBilinear(image, [224, 224]);}function loadAndProcessImage(image) { const croppedImage = cropImage(image); const resizedImage = resizeImage(croppedImage); const batchedImage = batchImage(resizedImage); return batchedImage;}

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