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How can I validate CNN after training?

16 vues (au cours des 30 derniers jours)
Kwasi
Kwasi le 14 Mar 2022
Commenté : Kwasi le 21 Mar 2022
I have 4 samples, each sample contains about 51,000 images. I train the network but each training ends with a suden fall of the validation accuracy. I have tried to increase the minibatch size, it helped a little but now I cannot increase further because I'm running out of memory
After reading some few helps online I think I can train the network without validation data but how do I validate after training?
Thank you in advance.
% reset(gpuDevice(1));
cz1 = fullfile('v1');
imds = imageDatastore(cz1,'LabelSource','none','IncludeSubfolders',true,'FileExtensions','.mat','ReadFcn',@(filename)customreader(filename));
tbl = countEachLabel(imds)
[trainingSet,validationSet, testSet] = splitEachLabel(imds,0.8, 0.1, 0.1 ...
...%399,49,49 ...
...%100,10,10 ...
,'randomized');
layers = [
imageInputLayer([480 640 1],'Normalization', ...
'none','Name','input')
%layer 1
convolution2dLayer([3 3],64,'Stride',[1 1],'Padding',1)
batchNormalizationLayer('Name','BN1')
reluLayer('Name','relu1')
maxPooling2dLayer([3 3],'Stride',2,'Name','MP1')
%layer 4
convolution2dLayer([3 3],64,'Stride',[1 1],'Padding',1,'Name','conv4')
batchNormalizationLayer('Name','BN4')
reluLayer('Name','relu4')
%layer 5
convolution2dLayer([3 3],64,'Stride',[2 2],'Padding',1,'Name','conv5')
additionLayer(2,'Name','add2')
batchNormalizationLayer('Name','BN5')
fullyConnectedLayer(512,'Name','fc1')
batchNormalizationLayer('Name','BN7')
reluLayer('Name','relu7')
%warstwa 7
fullyConnectedLayer(256,'Name','fc2')
reluLayer('Name','relu8')
%warstwa 8
fullyConnectedLayer(4,'Name','fc3')
softmaxLayer('Name','softmax')
classificationLayer('Name','classif')
]
lgraph = layerGraph(layers);
skipConv1 = convolution2dLayer(1,64,'Stride',2,'Name','skipConv1');
lgraph = addLayers(lgraph,skipConv1);
lgraph = connectLayers(lgraph,'MP1','skipConv1');
lgraph = connectLayers(lgraph,'skipConv1','add1/in2')
skipConv2 = convolution2dLayer(1,64,'Stride',2,'Name','skipConv2');
lgraph = addLayers(lgraph,skipConv2);
lgraph = connectLayers(lgraph,'MP3','skipConv2');
lgraph = connectLayers(lgraph,'skipConv2','add2/in2')
options = trainingOptions('sgdm', ...
'InitialLearnRate',0.001, ...
'MaxEpochs',10, ...
'Shuffle','every-epoch', ...
'ValidationData',validationSet, ...
'ValidationFrequency',4000, ...
'ValidationPatience',1, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropPeriod',1,...
'Verbose',false, ...
'Plots','training-progress', ...
'MiniBatchSize', 20, ...
'CheckpointPath', 't7g');
net = trainNetwork(trainingSet,lgraph,options);
[YPred,scores] = classify(net,testSet,'MiniBatchSize',20);
[S,I] = maxk(scores',5);
YValidation = testSet.Labels;
accuracy = sum(YPred == YValidation)/numel(YValidation)
top5 = sum(sum(tbl.Label(I)' == YValidation))/numel(YValidation)
function data = customreader(filename)
load(filename,'frame');
end

Réponse acceptée

Joss Knight
Joss Knight le 16 Mar 2022
The easiest way to validate after training for classification is to do exactly what you do in your example code to check the accuracy of your test set, but with your validation set. To compute the cross-entropy loss rather than accuracy you might need to implement the crossentropy function yourself. You could just pass your validation data in instead of training data and train for a few iterations to get some numbers.
The simplest way to get rid of this unusual fall in accuracy at the final iteration is to use moving average statistics for your batch normalization layers. Set the BatchNormalizationStatistics training option to 'moving'. (See the documentation.)
  4 commentaires
Joss Knight
Joss Knight le 21 Mar 2022
No, it was introduced in R2021a.
Kwasi
Kwasi le 21 Mar 2022
Thank you for responding and the answer.

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