Unexpected image size: All images must have the same size.
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Andre Brandao
le 5 Juil 2019
Commenté : Mohamed Nasr
le 28 Avr 2020
Hi, I'm having some problems with a bench of chest xray images. I tryed to use the code from the link below, but it did not work.
Error using trainNetwork (line 165)
Unexpected image size: All images must have the same size.
Error in chestXray1 (line 49)
net = trainNetwork(imdsTrain,layers,options);
inputSize = [224 224 1];
numClasses = 2;
layers = [
imageInputLayer(inputSize)
convolution2dLayer(5,20)
batchNormalizationLayer
reluLayer
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm', ...
'MaxEpochs',3, ...
'ValidationData',imdsValidation, ...
'ValidationFrequency',30, ...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(imdsTrain,layers,options);
8 commentaires
Geoff Hayes
le 9 Juil 2019
try putting a breakpoint at the line
allfiles = fullfile({dinfo.folder}, {dinfo.name});
and then run the code. When the debugger pauses at thisline, step through the subsequent lines. What is thisfile set to? What is thisinfo?
Réponse acceptée
Dheeraj Singh
le 5 Août 2019
Use the following code for your problem:
dataChest = fullfile('/Users/andrebr4/Documents/MATLAB/chestXray/chest_xray');
imds = imageDatastore(dataChest, ...
'IncludeSubfolders',true, ...
'LabelSource','foldernames');
%% Dividir o conjunto de dados em cada categoria
numTrainingFiles = 750;
[imdsTrain,imdsValidation] = splitEachLabel(imds,numTrainingFiles,'randomize');
%%%%%%%code for resizing
inputSize=[224 224 1];
imdsTrain=augmentedImageDatastore(inputSize, imdsTrain,'ColorPreprocessing','rgb2gray');
imdsValidation=augmentedImageDatastore(inputSize, imdsValidation,'ColorPreprocessing','rgb2gray');
%% Configurar a rede neural
inputSize = [224 224 1];
numClasses = 2;
layers = [
imageInputLayer(inputSize)
convolution2dLayer(5,20)
batchNormalizationLayer
reluLayer
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer];
%% Opções de treino
options = trainingOptions('sgdm', ...
'MaxEpochs',5, ...
'ValidationData',imdsValidation, ...
'ValidationFrequency',30, ...
'Verbose',false, ...
'Plots','training-progress');
%% Treinar a rede neural
net = trainNetwork(imdsTrain,layers,options);
%% Executar rede treinada no conjunto de teste
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
%% Calcular a precisão
accuracy = sum(YPred == YValidation)/numel(YValidation)
3 commentaires
Mohamed Nasr
le 28 Avr 2020
and make error in YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
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