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

以下是使用堆叠自编码器进行图像分类和混淆矩阵可视化的 MATLAB 代码：

% 加载图像数据和标签
load('image_data.mat'); % image_data.mat 包含图像数据和标签数据
images = image_data.images;
labels = image_data.labels;

% 数据预处理
images = double(reshape(images, [28, 28, 3, size(images, 4)])) / 255;
labels = categorical(labels);

% 划分训练集和测试集
[train_images, train_labels, test_images, test_labels] = split_data(images, labels, 0.8);

% 定义自编码器网络结构
input_size = 28 * 28 * 3;
hidden_size1 = 1000;
hidden_size2 = 500;
hidden_size3 = 250;
output_size = 4;

autoenc1 = train_autoencoder(train_images, input_size, hidden_size1);
features1 = encode(autoenc1, train_images);
autoenc2 = train_autoencoder(features1, hidden_size1, hidden_size2);
features2 = encode(autoenc2, features1);
autoenc3 = train_autoencoder(features2, hidden_size2, hidden_size3);
features3 = encode(autoenc3, features2);
softnet = train_softmax_classifier(features3, train_labels);

% 测试分类器并可视化混淆矩阵
test_features1 = encode(autoenc1, test_images);
test_features2 = encode(autoenc2, test_features1);
test_features3 = encode(autoenc3, test_features2);
test_labels_pred = classify(softnet, test_features3);
confusionchart(test_labels, test_labels_pred);

其中，`train_autoencoder`、`train_softmax_classifier` 和 `split_data` 函数可以参考以下示例代码实现：

function autoenc = train_autoencoder(data, input_size, hidden_size)
% 训练自编码器网络
autoenc = trainAutoencoder(reshape(data, input_size, [])', hidden_size, ...
    'MaxEpochs', 100, ...
    'L2WeightRegularization', 0.001, ...
    'SparsityRegularization', 4, ...
    'SparsityProportion', 0.1, ...
    'ScaleData', false);
end

function softnet = train_softmax_classifier(data, labels)
% 训练 softmax 分类器
softnet = trainSoftmaxLayer(data, labels, ...
    'MaxEpochs', 100);
end

function [train_data, train_labels, test_data, test_labels] = split_data(data, labels, ratio)
% 划分训练集和测试集
num_samples = size(data, 4);
num_train_samples = round(num_samples * ratio);

idx = randperm(num_samples);
train_idx = idx(1:num_train_samples);
test_idx = idx(num_train_samples+1:end);

train_data = data(:, :, :, train_idx);
train_labels = labels(train_idx);
test_data = data(:, :, :, test_idx);
test_labels = labels(test_idx);
end

以上代码仅供参考，具体实现可能需要根据数据集和任务进行修改和调整。