Created
July 4, 2019 03:03
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ここまでのコード
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imds = imageDatastore('/Users/mizuno/Documents/MATLAB/deeplearning/SimpleDeepLearning/trainingSet/','IncludeSubfolders',true,'LabelSource','foldernames'); | |
labelCount = countEachLabel(imds) | |
perm = randperm(40000,20); | |
montage(imds, 'Indices', perm); | |
img = readimage(imds,1); | |
size(img) | |
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.8); | |
layers = [imageInputLayer([28 28 1]) | |
convolution2dLayer(5,20) | |
reluLayer | |
maxPooling2dLayer(2,'Stride',2) | |
fullyConnectedLayer(10) | |
softmaxLayer | |
classificationLayer]; | |
options = trainingOptions('sgdm', ... | |
'InitialLearnRate',0.001, ... | |
'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) | |
[numberconf, numbernames] = confusionmat(YValidation, YPred); | |
heatmap(numbernames, numbernames, numberconf); |
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