MATLAB与深度学习构建神经网络的实用指南

% 检查是否已安装深度学习工具箱
ver deep_learning_toolbox

% 加载数据集
load fisheriris

% 数据特征
X = meas;

% 数据标签
Y = species;

% 将标签转换为分类的数字表示
Y_numeric = grp2idx(Y);

% 创建前馈神经网络
net = fitcnet(X, Y_numeric);

% 查看网络结构
view(net);

% 训练神经网络
net = train(net, X, Y_numeric);

% 使用训练数据进行预测
YPred = predict(net, X);

% 计算准确率
accuracy = sum(YPred == Y_numeric) / length(Y_numeric);
fprintf('准确率: %.2f%%\n', accuracy * 100);

% 加载MNIST手写数字数据集
[XTrain, YTrain] = digitTrain4DArrayData; 

% 创建卷积神经网络结构
layers = [
    imageInputLayer([28 28 1])
    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

% 训练选项
options = trainingOptions('sgdm', ...
    'MaxEpochs',4, ...
    'Shuffle','every-epoch', ...
    'Verbose',false, ...
    'Plots','training-progress');

% 训练网络
net = trainNetwork(XTrain,YTrain,layers,options);

% 使用测试数据评估模型
[XTest, YTest] = digitTest4DArrayData;
YPred = classify(net,XTest);
accuracy = sum(YPred == YTest) / numel(YTest);
fprintf('测试集准确率: %.2f%%\n', accuracy * 100);

% 定义学习率和批大小的范围
learningRates = [0.001, 0.01, 0.1];
batchSizes = [16, 32, 64];

% 网格搜索
bestAccuracy = 0;
for lr = learningRates
    for bs = batchSizes
        options = trainingOptions('sgdm', ...
            'MaxEpochs', 4, ...
            'MiniBatchSize', bs, ...
            'InitialLearnRate', lr, ...
            'Verbose', false);
        
        % 训练网络
        net = trainNetwork(XTrain, YTrain, layers, options);
        
        % 验证集准确率
        YPred = classify(net, XTest);
        accuracy = sum(YPred == YTest) / numel(YTest);
        
        if accuracy > bestAccuracy
            bestAccuracy = accuracy;
            bestLR = lr;
            bestBS = bs;
        end
    end
end

fprintf('最佳学习率: %.4f，最佳批大小: %d，最佳准确率: %.2f%%\n', bestLR, bestBS, bestAccuracy * 100);

% 创建多个网络
net1 = trainNetwork(XTrain, YTrain, layers, options);
net2 = trainNetwork(XTrain, YTrain, layers, options);
net3 = trainNetwork(XTrain, YTrain, layers, options);

% 集成预测
YPred1 = classify(net1, XTest);
YPred2 = classify(net2, XTest);
YPred3 = classify(net3, XTest);

% 投票机制
YPredFinal = mode([YPred1, YPred2, YPred3], 2);

% 计算准确率
accuracy = sum(YPredFinal == YTest) / numel(YTest);
fprintf('集成模型的准确率: %.2f%%\n', accuracy * 100);

% 可视化网络结构
plot(net);
title('神经网络结构');

options = trainingOptions('sgdm', ...
    'MaxEpochs', 4, ...
    'Plots', 'training-progress');

% 训练网络并实时监控
net = trainNetwork(XTrain, YTrain, layers, options);

% 加载预训练的ResNet-50模型
net = resnet50;

% 修改最后一层以适应新的分类任务
numClasses = 10; % 新任务的类数
layers = replaceLayer(net.Layers(end).Name, fullyConnectedLayer(numClasses, 'WeightLearnRateFactor',10, 'BiasLearnRateFactor',10));

% 定义训练选项
options = trainingOptions('sgdm', ...
    'MaxEpochs', 4, ...
    'Shuffle', 'every-epoch', ...
    'Verbose', false, ...
    'Plots', 'training-progress');

% 训练迁移学习模型
netTransfer = trainNetwork(XTrain, YTrain, layers, options);

% 使用He初始化
layers = [
    imageInputLayer([28 28 1])
    convolution2dLayer(3, 8, 'WeightsInitializer', 'he')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

% 添加Dropout层以防止过拟合
layers = [
    imageInputLayer([28 28 1])
    convolution2dLayer(3, 8)
    batchNormalizationLayer
    reluLayer
    dropoutLayer(0.5)  % 添加Dropout层
    maxPooling2dLayer(2, 'Stride', 2)
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];