用深度信念网络对文件夹内的彩色图像进行分类并可视化其混淆矩阵具体可运行的matlab

代码如下：

%% Load data
clear all;
close all;

% Load image data
imds = imageDatastore('folder_path', ...
'IncludeSubfolders',true,'LabelSource','foldernames');

% Split data into training and test sets
[imdsTrain,imdsTest] = splitEachLabel(imds,0.7,'randomized');

% Display some sample images
figure;
perm = randperm(100,20);
for i = 1:20
subplot(4,5,i);
imshow(imdsTrain.Files{perm(i)});
end

%% Define network architecture
layers = [ ...
imageInputLayer([32 32 3])

% Convolutional layer
convolution2dLayer(3,32,'Padding','same')
batchNormalizationLayer
reluLayer

% Max pooling layer
maxPooling2dLayer(2,'Stride',2)

% Convolutional layer
convolution2dLayer(3,64,'Padding','same')
batchNormalizationLayer
reluLayer

% Max pooling layer
maxPooling2dLayer(2,'Stride',2)

% Convolutional layer
convolution2dLayer(3,128,'Padding','same')
batchNormalizationLayer
reluLayer

% Max pooling layer
maxPooling2dLayer(2,'Stride',2)

% Fully connected layer
fullyConnectedLayer(64)
reluLayer

% Dropout layer
dropoutLayer(0.5)

% Output layer
fullyConnectedLayer(10)
softmaxLayer
classificationLayer];

%% Train network
options = trainingOptions('sgdm', ...
'MaxEpochs',20, ...
'InitialLearnRate',0.001, ...
'MiniBatchSize',128, ...
'Shuffle','every-epoch', ...
'ValidationData',imdsTest, ...
'ValidationFrequency',30, ...
'Verbose',false, ...
'Plots','training-progress');

net = trainNetwork(imdsTrain,layers,options);

%% Test network
YPred = classify(net,imdsTest);
YTest = imdsTest.Labels;

% Calculate accuracy
accuracy = sum(YPred == YTest)/numel(YTest)

% Plot confusion matrix
figure;
plotconfusion(YTest,YPred);

%% Visualize network
figure;
plot(net);