用堆叠自编码器进行对指定文件夹内的4类彩色图像进行分类并可视化混淆矩阵matlab代码

以下是用堆叠自编码器进行对指定文件夹内的4类彩色图像进行分类并可视化混淆矩阵的 Matlab 代码：

% 设置参数
numClasses = 4; % 类别数
imageSize = [32 32 3]; % 图像大小
hiddenSize = 100; % 自编码器的隐层大小
sparsityParam = 0.1; % 稀疏性参数
lambda = 3e-3; % 权重衰减参数
beta = 3; % 稀疏性惩罚参数
epochs = 100; % 迭代次数
batchSize = 100; % 批处理大小

% 加载数据
data = imageDatastore('文件夹路径', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
[trainData, testData] = splitEachLabel(data, 0.8, 'randomized');

% 堆叠自编码器网络
inputSize = prod(imageSize);
autoenc1 = trainAutoencoder(trainData, hiddenSize, ...
    'MaxEpochs', epochs, ...
    'L2WeightRegularization', lambda, ...
    'SparsityRegularization', beta, ...
    'SparsityProportion', sparsityParam, ...
    'ScaleData', false);
feat1 = encode(autoenc1, trainData);

autoenc2 = trainAutoencoder(feat1, hiddenSize, ...
    'MaxEpochs', epochs, ...
    'L2WeightRegularization', lambda, ...
    'SparsityRegularization', beta, ...
    'SparsityProportion', sparsityParam, ...
    'ScaleData', false);
feat2 = encode(autoenc2, feat1);

softnet = trainSoftmaxLayer(feat2, trainData.Labels, ...
    'MaxEpochs', epochs, ...
    'L2WeightRegularization', lambda);

deepnet = stack(autoenc1, autoenc2, softnet);

% 训练深度网络
options = trainingOptions('sgdm', ...
    'Plots', 'training-progress', ...
    'MaxEpochs', epochs, ...
    'MiniBatchSize', batchSize);
deepnet = train(deepnet, trainData, options);

% 测试深度网络
predictLabels = classify(deepnet, testData);
testLabels = testData.Labels;

% 计算混淆矩阵
confusionMat = confusionmat(testLabels, predictLabels);

% 可视化混淆矩阵
figure;
heatmap(confusionMat);
xlabel('Predicted Labels');
ylabel('True Labels');

请将 `文件夹路径` 替换为你的图像文件夹路径。