Neural Network input images size error
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Hello Matlab Community,
I try to train a convolutional nn with labelled images. Currently i just want to understand the usage, its not a specific task.
The inputs are:
files = imageDatastore({'D:\...\containingDirectory1','D:\...\containingDirectory2'});
Which are 200x200x1 grayscale images. I cropped them to 200x200 with a function and checked the sizes manually.
Labels made manually into a categorical struct, then added to the files:
files.Labels = labels;
Layers are:
layers = [ imageInputLayer([200,200,1])
convolution2dLayer(80,80)
reluLayer
maxPooling2dLayer(40,'Stride',40)
fullyConnectedLayer(2)
softmaxLayer
classificationLayer
];
Training options are:
opts = trainingOptions('sgdm','ExecutionEnvironment','cpu');
('cpu' because my GPU capacity is not enough)
Training:
convnet = trainNetwork(files, layers, opts)
Error:
Error using nnet.internal.cnn.ImageDatastoreDispatcher>iCellTo4DArray (line 246)
Unexpected image size: All images must have the same size.
The heights, widths, color channels and bit depths are the same. All of them are jpg-s. I tried to use just 2 images but it resulted the same error. I tried 2 full black images, one copied from the other, and the "training" ran successfully.
I upload two training images for an example.
4 Comments
Answers (1)
Aasma Aslam
on 4 Jan 2018
inputSize = net.Layers(1).InputSize(1:2) images.ReadFcn = @(loc)imresize(imread(loc),inputSize); use this command for resizing it will work.
4 Comments
Rabia Afzal
on 1 Feb 2018
And what is this InputSize. Where should I use it. Plz help
if true
DatasetPath = fullfile(matlabroot,'MYDATA');
digitData = imageDatastore(DatasetPath,...
'IncludeSubfolders',true,'LabelSource','foldernames');
% inputSize = net.Layers(1).InputSize(1:2) images.ReadFcn = @(loc)imresize(imread(loc),inputSize);
% net=alexnet; % inputSize = net.Layers(1).InputSize(1:2) % im = imresize(im,inputSize); %digitData.ReadFcn = @(loc)imresize(imread(loc),inputSize);
figure; perm = randperm(20000,20); for i = 1:20 subplot(4,5,i); imshow(digitData.Files{perm(i)}); end
labelCount = countEachLabel(digitData)
img = readimage(digitData,20); size(img)
% trainNumFiles = 3000; % [trainDigitData,valDigitData] = splitEachLabel(digitData,trainNumFiles,'randomize'); % % % Define the convolutional neural network architecture. % layers = [ % imageInputLayer([480 640 3]) % % convolution2dLayer(3,16,'Padding',1) % batchNormalizationLayer % reluLayer % % maxPooling2dLayer(2,'Stride',2) % % convolution2dLayer(3,32,'Padding',1) % batchNormalizationLayer % reluLayer % % maxPooling2dLayer(2,'Stride',2) % % convolution2dLayer(3,64,'Padding',1) % batchNormalizationLayer % reluLayer % % fullyConnectedLayer(4) % softmaxLayer % classificationLayer]; % % options = trainingOptions('sgdm',... % 'MaxEpochs',3, ... % 'ValidationData',valDigitData,... % 'ValidationFrequency',30,... % 'Verbose',false,... % 'Plots','training-progress'); % % % net = trainNetwork(trainDigitData,layers,options); % % % predictedLabels = classify(net,valDigitData); % valLabels = valDigitData.Labels; % % accuracy = sum(predictedLabels == valLabels)/numel(valLabels) end
Limyandi Vicotrico
on 3 Oct 2018
can someone elaborate more on how it work again? I cant get it working.
inputSize = net.Layers(1).InputSize(1:2);
imds.ReadFcn = @(loc)imresize(imread(loc),inputSize);
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