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利用tensorflow.js训练模型#xff0c;搭建神经网络模型#xff0c;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#xff0c;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#xff0c;分别代表10个数字
代码
// 导入 Ten…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}
}模型结果 损失函数与模型精度变化
