视觉识别深度学习的Matlab代码

以下是一个简单的基于深度学习的视觉识别 MATLAB 代码示例：

1. 加载数据

imds = imageDatastore('path/to/images','IncludeSubfolders',true,'LabelSource','foldernames');

2. 定义深度学习网络

layers = [
    imageInputLayer([224 224 3])
    
    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
    
    convolution2dLayer(3,256,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,512,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    convolution2dLayer(3,512,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,512,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    convolution2dLayer(3,512,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)
    
    fullyConnectedLayer(4096)
    reluLayer
    
    dropoutLayer(0.5)
    
    fullyConnectedLayer(4096)
    reluLayer
    
    dropoutLayer(0.5)
    
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];

3. 设置训练选项

options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.001, ...
    'MiniBatchSize',32, ...
    'MaxEpochs',10, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');

4. 训练网络

net = trainNetwork(imds,layers,options);

5. 测试网络

[testData,labels] = imdsTest.Labels;
predictedLabels = classify(net,testData);
accuracy = sum(predictedLabels == labels)/numel(labels);

这只是一个简单的示例代码，实际上，视觉识别的深度学习网络可以非常复杂，需要根据具体任务进行调整。