五层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：

% 加载数据
data = imageDatastore('path_to_folder', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');

% 将数据集分为训练集和测试集
[trainData, testData] = splitEachLabel(data, 0.8, 'randomized');

% 定义网络结构
layers = [
    imageInputLayer([32 32 3])
    
    convolution2dLayer(3, 32, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2, 'Stride', 2)
    
    convolution2dLayer(3, 64, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2, 'Stride', 2)
    
    convolution2dLayer(3, 128, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2, 'Stride', 2)
    
    convolution2dLayer(3, 256, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2, 'Stride', 2)
    
    fullyConnectedLayer(4)
    softmaxLayer
    classificationLayer
];

% 设置训练选项
options = trainingOptions('adam', ...
    'MaxEpochs', 20, ...
    'MiniBatchSize', 128, ...
    'InitialLearnRate', 0.001, ...
    'Shuffle', 'every-epoch', ...
    'ValidationData', testData, ...
    'ValidationFrequency', 10, ...
    'Plots', 'training-progress');

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

% 对测试集进行分类
predictedLabels = classify(net, testData);

% 计算混淆矩阵
confMat = confusionmat(testData.Labels, predictedLabels);

% 可视化混淆矩阵
figure
cm = confusionchart(confMat);
cm.Title = 'Confusion Matrix';
cm.RowSummary = 'row-normalized';
cm.ColumnSummary = 'column-normalized';

其中，`path_to_folder`需要替换为指定的文件夹路径，`4`需要替换为实际的标签数。训练过程中的参数可以根据具体情况进行调整。