用三层长短时记忆神经网络对指定文件夹内的具有四个标签的图片进行分类并可视化混淆矩阵具体matlab

代码实现如下：

首先，导入数据集并进行预处理：

% 导入数据
imds = imageDatastore('指定文件夹路径', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');

% 计算图像平均尺寸
imgSize = mean([imds.Height, imds.Width]);

% 数据集分割
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');

% 数据增强
imageAugmenter = imageDataAugmenter('RandXReflection',true,'RandRotation',[-30,30],'RandScale',[0.5,1.5],'FillValues',0);
augimdsTrain = augmentedImageDatastore([imgSize imgSize 3],imdsTrain,'DataAugmentation',imageAugmenter);
augimdsValidation = augmentedImageDatastore([imgSize imgSize 3],imdsValidation);

然后，定义模型并进行训练：

% 定义模型
numClasses = 4;
inputSize = [imgSize imgSize 3];
numHiddenUnits = 100;
layers = [ ...
    sequenceInputLayer(inputSize)
    lstmLayer(numHiddenUnits,'OutputMode','last')
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];

% 设置训练参数
options = trainingOptions('adam', ...
    'MiniBatchSize', 16, ...
    'MaxEpochs', 20, ...
    'InitialLearnRate', 0.001, ...
    'Shuffle', 'every-epoch', ...
    'ValidationData', augimdsValidation, ...
    'ValidationFrequency', 10, ...
    'Verbose', false, ...
    'Plots', 'training-progress');

% 训练模型
net = trainNetwork(augimdsTrain,layers,options);

最后，进行预测并可视化混淆矩阵：

% 进行预测
YPred = classify(net,augimdsValidation);
YValidation = imdsValidation.Labels;

% 计算混淆矩阵
confusion = confusionmat(YValidation,YPred);
confusion = bsxfun(@rdivide, confusion, sum(confusion,2));

% 可视化混淆矩阵
figure
heatmap(confusion, imdsValidation.Labels, imdsValidation.Labels, 1, 'Colormap', 'red', 'ColorbarVisible', 'off');
title('Confusion Matrix');
xlabel('Predicted Label');
ylabel('True Label');