我是人工智能和TensorFlow.js的初学者，目前正在跟随Stephen Grider的机器学习课程。运行以下代码后，本应得到输出结果，但却遇到了错误。请帮助我：
代码：linear-regression.js：
const tf = require('@tensorflow/tfjs');class LinearRegression { constructor(features, labels, options) { this.features = tf.tensor(features); this.labels = tf.tensor(labels); this.features = tf.ones([this.features.shape[0], 1]).concat(this.features) //生成马力的一列 this.options = Object.assign( { learningRate: 0.1, iterations: 1000 }, options ); //默认学习率为0.1，如果提供了学习率，则使用提供的值...迭代次数为梯度下降运行的次数 this.weights = tf.zeros([2, 1]); //初始权重m和b的张量为零 } gradientDescent() { const currentGuesses = this.features.matMul(this.weights); //matMul是矩阵乘法，即特征乘以权重 const differences = currentGuesses.sub(this.labels); //(特征 * 权重) - 标签 const slopes = this.features .transpose() .matMul(differences) .div(features.shape[0]); //相对于m和b的MSE斜率。特征 * ((特征 * 权重) - 标签) / 特征总数 this.weights = this.weights.sub(slopes.mul(this.options.learningRate)); } train() { for (let i=0; i < this.options.iterations; i++) { this.gradientDescent(); } /*test(testFeatures, testLabels) { testFeatures = tf.tensor(testFeatures); testLabels = tf.tensor(testLabels); } */ }}module.exports = LinearRegression;index.js：
require('@tensorflow/tfjs-node');const tf = require('@tensorflow/tfjs');const loadCSV = require('./load-csv');const LinearRegression = require('./linear-regression');let { features, labels, testFeatures, testLabels } =loadCSV('./cars.csv', { shuffle: true, splitTest: 50, dataColumns: ['horsepower'], labelColumns: ['mpg']});const regression = new LinearRegression(features, labels, { learningRate: 0.002, iterations: 100});regression.train();console.log( '更新后的M值为:', regression.weights.get(1, 0), '更新后的B值为:', regression.weights.get(0, 0) );错误：
D:\Application Development\MLKits-master\MLKits-master\regressions\node_modules\@tensorflow\tfjs-core\dist\ops\operation.js:32 throw ex; ^Error: 在矩阵乘法中出现错误：内层形状（1）和（2）的张量，其形状分别为684,1和2,1，且transposeA=false和transposeB=false时必须匹配。 at Object.assert (D:\Application Development\MLKits-master\MLKits-master\regressions\node_modules\@tensorflow\tfjs-core\dist\util.js:36:15) at matMul_ (D:\Application Development\MLKits-master\MLKits-master\regressions\node_modules\@tensorflow\tfjs-core\dist\ops\matmul.js:25:10) at Object.matMul (D:\Application Development\MLKits-master\MLKits-master\regressions\node_modules\@tensorflow\tfjs-core\dist\ops\operation.js:23:29) at Tensor.matMul (D:\Application Development\MLKits-master\MLKits-master\regressions\node_modules\@tensorflow\tfjs-core\dist\tensor.js:315:26) at LinearRegression.gradientDescent (D:\Application Development\MLKits-master\MLKits-master\regressions\linear-regression.js:19:46) at LinearRegression.train (D:\Application Development\MLKits-master\MLKits-master\regressions\linear-regression.js:34:18) at Object.<anonymous> (D:\Application Development\MLKits-master\MLKits-master\regressions\index.js:18:12) at Module._compile (internal/modules/cjs/loader.js:1063:30) at Object.Module._extensions..js (internal/modules/cjs/loader.js:1092:10) at Module.load (internal/modules/cjs/loader.js:928:32)回答：
错误是由以下代码抛出的：
this.features.matMul(this.weights)
this.features的形状为[684, 1]，而this.weights的形状为[2, 1]。要使矩阵A（形状为[a, b]）能够与矩阵B（形状为[c, d]）相乘，b和c必须匹配，但这里并不符合这一条件。
要解决这个问题，this.weights应该进行转置：
this.features.matMul(this.weights, false, true)