我要对它的分类进行一次测试。当我选择该图像时，它将显示其标签。我如何选择图像而不从火车上拆分数据。尽管我选择了numoftest = 1的随机照片并将其随机化。我想指定要分类的图像。
trainPath = fullfile(pwd, 'trainingDataset');
testPath = fullfile(pwd, 'testDataset');
imds =imageDatastore(trainPath, 'IncludeSubfolders', true, 'LabelSource',
'foldernames');
imds1 =imageDatastore(testPath, 'IncludeSubfolders', true, 'LabelSource',
'foldernames');
labelCount = countEachLabel(imds);
labelCount1 = countEachLabel(imds1);
img = readimage(imds,1);
size(img);
numTrainFiles = 41;
[imdsTrain] = splitEachLabel(imds,numTrainFiles,'randomize');
numTestFiles = 1;
[imdsValidation] = splitEachLabel(imds1,numTestFiles,'randomize');
layers = [
imageInputLayer([325 720 3])
convolution2dLayer(3,8,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,16,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,32,'Padding','same')
batchNormalizationLayer
reluLayer
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm', ...
'InitialLearnRate',0.01, ...
'MaxEpochs',4, ...
'Shuffle','every-epoch', ...
'ValidationData',imdsValidation, ...
'ValidationFrequency',30, ...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(imdsTrain,layers,options);
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
accuracy = sum(YPred == YValidation)/numel(YValidation);