How can I validate CNN after training?

19 次查看（过去 30 天）
Kwasi
Kwasi 2022-3-14
评论： Kwasi 2022-3-21
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

请先登录，再回答此问题。

采纳的回答

Joss Knight
Joss Knight 2022-3-16
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 个评论
显示 2更早的评论
Kwasi
Kwasi 2022-3-21
Thank you for responding and the answer.

请先登录，再进行评论。

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Recognition, Object Detection, and Semantic Segmentation 的更多信息

标签

产品


版本

R2019b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by