我在尝试使用 Tensorflow.js 中的 knnClassifier 和 mobileNet 图像识别模型进行迁移学习时，遇到了以下错误：

大小(28672) 必须与形状 28,3072 的乘积匹配

我不知道如何解决这个问题，我尝试过创建 tensor3D，使用双线性和最近邻方法调整大小，但都没有成功。我想知道这里是否有人可以检查一下这个问题。

请注意，我的想法是训练来自某些文件夹的图像，并使用 knnClassifier 的添加示例将其分配到它们的类别。我有一个函数可以从路径读取图像，还有一个异步函数可以训练模型并从图像中进行预测。

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const tf = require('@tensorflow/tfjs');//MobileNet : pre-trained model for TensorFlow.jsconst mobilenet = require('@tensorflow-models/mobilenet');//The module provides native TensorFlow execution//in backend JavaScript applications under the Node.js runtime.const tfnode = require('@tensorflow/tfjs-node');const knnClassifier = require('./node_modules/@tensorflow-models/knn-classifier/dist/knn-classifier');var glob = require('glob')//The fs module provides an API for interacting with the file system.const fs = require('fs');const readImage = path => {  //reads the entire contents of a file.  //readFileSync() is synchronous and blocks execution until finished.  const imageBuffer = fs.readFileSync(path);  //Given the encoded bytes of an image,  //it returns a 3D or 4D tensor of the decoded image. Supports BMP, GIF, JPEG and PNG formats.  var tfimage = tfnode.node.decodeImage(imageBuffer);  // const t3d = tf.tensor3d(Array.from(tfimage.dataSync()),[tfimage.shape[0], tfimage.shape[1], 1])  const smalImg = tf.image.resizeNearestNeighbor(tfimage, [32, 32]);  const resized = tf.cast(smalImg, 'float32');  // t3d.reshape([32,32,3])  // var smalImg = tf.image.resizeBilinear(tfimage, [368, 432]);  // const resized = tf.cast(smalImg, 'float32');  return resized;}var mainDirectory = "./img_samples/";const imageClassification = async path => {  const classifier = await knnClassifier.create();  const image = await readImage(path);  // Load the model.  const model = await mobilenet.load();  // Classify the image.  const predictions = await model.classify(image);  // print results on terminal  console.log('Classification Results:', predictions);  var folders = fs.readdirSync(mainDirectory);  var filesPerClass = [];  for(var i=0;i<folders.length;i++){    files = fs.readdirSync(mainDirectory+folders[i]);    var files_complete = [];    for(var j=0;j<files.length;j++){      files_complete.push(mainDirectory+folders[i]+"/"+files[j]);    }    filesPerClass.push(files_complete);  }  for(var i=0;i<filesPerClass.length;i++){    for(var j=0;j<filesPerClass[i].length;j++){      imageSample = readImage(filesPerClass[i][j]);      console.log(imageSample);      activation = await model.infer(imageSample, 'conv_preds');  //main directory      classifier.addExample(activation,i);    }  }  console.log(readImage('./hospitalTest.jpg'))  const predictionsTest = await classifier.predictClass(readImage('./hospitalTest.jpg'));     console.log('classficationTest:',predictionsTest);}if (process.argv.length !== 3) throw new Error('Incorrect arguments: node classify.js <IMAGE_FILE>');imageClassification(process.argv[2]);

回答：

由于 knn 分类器是使用 mobilenet 的节点输出进行训练的，因此预测也需要以相同的方式进行

outputMobilenet = await model.infer(readImage('./hospitalTest.jpg'), 'conv_preds')predicted = await classifier.predictClass(outputMobilenet)