深度信念网络对指定文件夹内的具有四个标签的图像进行分类并可视化混淆矩阵具体matlab

代码如下：

% 设置文件夹路径和标签
folder = 'path/to/folder';
labels = {'label1', 'label2', 'label3', 'label4'};

% 加载图像和标签
imds = imageDatastore(folder,'IncludeSubfolders',true,'LabelSource','foldernames');
imds.Labels = categorical(imds.Labels, labels);

% 划分训练集和测试集
[trainImds, testImds] = splitEachLabel(imds,0.8);

% 创建深度信念网络
layers = [ ...
    imageInputLayer([32 32 3])
    convolution2dLayer(3,16,'Padding',1)
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,32,'Padding',1)
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,64,'Padding',1)
    batchNormalizationLayer
    reluLayer
    fullyConnectedLayer(4)
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'MaxEpochs',20, ...
    'MiniBatchSize',200, ...
    'InitialLearnRate',0.001, ...
    'Shuffle','every-epoch', ...
    'ValidationData',testImds, ...
    'ValidationFrequency',30, ...
    'Plots','training-progress');
net = trainNetwork(trainImds,layers,options);

% 测试网络
predictedLabels = classify(net,testImds);
accuracy = sum(predictedLabels == testImds.Labels)/numel(testImds.Labels);
disp(['准确率为：' num2str(accuracy*100) '%']);

% 可视化混淆矩阵
figure;
plotconfusion(testImds.Labels, predictedLabels);
title('混淆矩阵');