深度学习神经网络实战：多层感知机，手写数字识别

目的

利用tensorflow.js训练模型，搭建神经网络模型，完成手写数字识别

设计

简单三层神经网络

输入层
28*28个神经原，代表每一张手写数字图片的灰度
隐藏层
100个神经原
输出层
-10个神经原，分别代表10个数字

代码

// 导入 TensorFlow.js 库
import tf from "@tensorflow/tfjs";
import * as tfjsnode from "@tensorflow/tfjs-node";
import * as tfvis from "@tensorflow/tfjs-vis";
import fs from "fs";
import plot from "nodeplotlib";
// 定义模型
const model = tf.sequential();// 添加输入层
model.add(tf.layers.dense({ units: 64, inputShape: [784], activation: "relu" })
);// 添加隐藏层
model.add(tf.layers.dense({ units: 100, activation: "relu" }));// 添加输出层
model.add(tf.layers.dense({ units: 10, activation: "softmax" }));// 编译模型
model.compile({optimizer: "sgd",loss: "categoricalCrossentropy",metrics: ["accuracy"],
});
const trainDataLen = 3000;
const testDataLen = 2000;// 加载 MNIST 数据集
import pkg from "mnist";
const { set: Dataset } = pkg;
const set = Dataset(trainDataLen, testDataLen);
const trainingSet = set.training;
const testSet = set.test;const trainXs = [];
const testXs = [];const trainLabels = [];
const testLabels = [];for (let i = 0; i < trainingSet.length; i++) {trainXs.push(trainingSet[i].input);trainLabels.push(trainingSet[i].output.indexOf(1));
}for (let i = 0; i < testSet.length; i++) {testXs.push(testSet[i].input);testLabels.push(testSet[i].output.indexOf(1));
}// 准备数据
const trainXsTensor = tf.tensor(trainXs, [trainDataLen, 784]);
const trainYsOneHot = tf.oneHot(trainLabels, 10);//记录每轮模型训练中的损失和精度，为了绘制曲线图
var accPlot = [];
var lossPlot = [];// 模型训练
model.fit(trainXsTensor, trainYsOneHot, {batchSize: 64,epochs: 100,validationSplit: 0.2,callbacks: {onEpochBegin: (epoch) => console.log(`Epoch ${epoch + 1} started...`),onEpochEnd: async (epoch, logs) => {console.log(`Epoch ${epoch + 1} completed. Loss: ${logs.loss.toFixed(3)}, Accuracy: ${logs.acc.toFixed(3)}`);//记录loss和acc，绘制曲线图accPlot.push(logs.acc.toFixed(3));lossPlot.push(logs.loss.toFixed(3));await tf.nextFrame(); // 防止阻塞},onBatchEnd: async (batch, logs) => {console.log(`Batch ${batch} completed. Loss: ${logs.loss.toFixed(3)}, Accuracy: ${logs.acc.toFixed(3)}`);await tf.nextFrame(); // 防止阻塞},},}).then((history) => {console.log("Training completed!", history);//绘制模型训练过程中的损失函数和模型精度曲线变化const epochs = Array.from({ length: lossPlot.length }, (_, i) => i + 1);plot.plot([{ x: epochs, y: lossPlot, name: "Loss" },{ x: epochs, y: accPlot, name: "Accuracy" },],{filename: "loss_acc.png",});//模型评估const testXsTensor = tf.tensor(testXs, [testDataLen, 784]);const testYsOneHot = tf.oneHot(testLabels, 10);const result = model.evaluate(testXsTensor, testYsOneHot);const testLoss = result[0].dataSync()[0];const testAccuracy = result[1].dataSync()[0];console.log(`Test loss: ${testLoss.toFixed(3)}`);console.log(`Test accuracy: ${testAccuracy.toFixed(3)}`);//保存模型model.save("file://./my-model").then(() => {console.log("Model saved!");});});

package.json

{"name": "neural_network","version": "1.0.0","description": "","type": "module","main": "mlpTest.js","scripts": {"test": "echo \"Error: no test specified\" && exit 1",},"author": "","license": "ISC","dependencies": {"@tensorflow/tfjs": "^4.17.0","@tensorflow/tfjs-node": "^4.17.0","@tensorflow/tfjs-vis": "^1.0.0","mnist": "^1.1.0","nodeplotlib": "^0.7.7"},"devDependencies": {"@babel/core": "^7.0.0","@babel/preset-env": "^7.0.0","babel-loader": "^8.0.0","webpack": "^5.0.0","webpack-cli": "^4.0.0"}
}