基于深度迁移学习的小样本图像分类matlab程序_alexnet图像分类matlab,matlab alexnet 图像缺陷检测

% 加载数据 unzip('MerchData.zip'); imds = imageDatastore('MerchData',... 'IncludeSubfolders',true,... 'LabelSource','foldernames'); % 划分数据集 [imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized'); % 显示数据集的一些图像 numTrainImages = numel(imdsTrain.Labels); idx = randperm(numTrainImages,16); figure for i = 1:16 subplot(4,4,i) I = readimage(imdsTrain,idx(i)); imshow(I) end % 加载预训练网络 net = alexnet; analyzeNetwork(net) inputSize = net.Layers(1).InputSize % 替换最终层 layersTransfer = net.Layers(1:end-3); numClasses = numel(categories(imdsTrain.Labels)) layers = [ layersTransfer fullyConnectedLayer(numClasses,'WeightLearnRateFactor',20,'BiasLearnRateFactor',20) softmaxLayer classificationLayer]; % 开始训练网络 pixelRange = [-30 30]; imageAugmenter = imageDataAugmenter( ... 'RandXReflection',true, ... 'RandXTranslation',pixelRange, ... 'RandYTranslation',pixelRange); augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain, ... 'DataAugmentation',imageAugmenter); augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation); options = trainingOptions('sgdm', ... 'MiniBatchSize',10, ... 'MaxEpochs',6, ... 'InitialLearnRate',1e-4, ... 'Shuffle','every-epoch', ... 'ValidationData',augimdsValidation, ... 'ValidationFrequency',3, ... 'Verbose',false, ... 'Plots','training-progress'); netTransfer = trainNetwork(augimdsTrain,layers,options); % 对图像进行分类 idx = randperm(numel(imdsValidation.Files),4); figure for i = 1:4 subplot(2,2,i) I = readimage(imdsValidation,idx(i)); imshow(I) label = YPred(idx(i)); title(string(label)); end % 分类准确度 YValidation = imdsValidation.Labels; accuracy = mean(YPred == YValidation)