我正在尝试构建一个简单的线性回归模型，我的数据集是从1到10的数字。我试图训练模型，以便对于任何给定的输入，例如3，输出应该等于输入的值（y = x）。
预测结果总是错误。请问有人能解释一下我哪里做错了么？
const tf = require("@tensorflow/tfjs");const xArray = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10];const yArray = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10];const createModel = () => { const model = tf.sequential(); model.add(tf.layers.dense({ inputShape: [1], units: 1, useBias: true })); //input layer model.add(tf.layers.dense({ units: 1, useBias: true })); //output layer return model;};const convertToTensor = () => { return tf.tidy(() => { const inputTensor = tf.tensor2d(xArray, [xArray.length, 1]); const outputTensor = tf.tensor2d(yArray, [yArray.length, 1]); return { inputs: inputTensor, outputs: outputTensor, }; });};async function trainModel(model, inputs, trueValues) { model.compile({ optimizer: tf.train.adam(), loss: tf.losses.meanSquaredError, metrics: ["mse"] }); return await model.fit(inputs, trueValues, { batchSize: 2, epochs: 5, learningRate: 0.04 });}function testModel(model, testValue) { return tf.tidy(() => model.predict(tf.tensor2d([testValue], [1, 1]));}const run = async testValue => { const model = createModel(); const tensorData = convertToTensor(); await trainModel(model, tensorData.inputs, tensorData.outputs); const prediction = testModel(model, testValue); console.log(prediction.toString());};run(5);回答：
你的代码有两个问题：
- 你没有正确设置
learningRate的值。你需要将它作为第一个参数传递给tf.train.adam()函数 - 你只学习了
5个周期，这在你的情况下是不够的。
自己尝试
我从你的代码中删除了不必要的部分。你可以更改epochs和learning rate的值，看看它们如何影响5的预测结果。我将epoch的默认值改为50。预测结果最终会非常接近5。
document.querySelector('button').addEventListener('click', async () => { const learningRate = document.querySelector('#learning_rate').value; const epochs = document.querySelector('#epochs').value; const xArray = [0,1,2,3,4,5,6,7,8,9]; const yArray = [0,1,2,3,4,5,6,7,8,9]; const createModel = () => { const model = tf.sequential(); model.add(tf.layers.dense({ inputShape: [1], units: 1, useBias: true })); model.add(tf.layers.dense({ units: 1, useBias: true })); return model; }; const convertToTensor = () => { return tf.tidy(() => { const inputTensor = tf.tensor2d(xArray, [xArray.length, 1]); const outputTensor = tf.tensor2d(yArray, [yArray.length, 1]); return { inputs: inputTensor, outputs: outputTensor, }; }); }; async function trainModel(model, inputs, trueValues) { model.compile({ optimizer: tf.train.adam(learningRate), loss: tf.losses.meanSquaredError, metrics: ["mse"] }); const batchSize = 2; return await model.fit(inputs, trueValues, { batchSize, epochs, }); } function testModel(model, testValue) { return tf.tidy(() => model.predict(tf.tensor2d([testValue], [1, 1]))); } const run = async testValue => { const model = createModel(); const tensorData = convertToTensor(); await trainModel(model, tensorData.inputs, tensorData.outputs); const prediction = testModel(model, testValue); console.log(`Predction for 5: ${prediction.toString()}`); }; run(5);});<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script>epochs: <input type="number" id="epochs" value="50" />learning rate: <input type="number" id="learning_rate" value="0.04" /><button id="train">Train</button>