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Why the final Validation accuracy appears on the plot different than the accuracy that is calculated by the law of accuracy ?

Nusaiba Mnayyis さんによって質問されました 2018 年 3 月 19 日
最新アクティビティ Xinlong Liu さんによって コメントされました 2019 年 7 月 30 日 17:34
I create and train a simple convolutional neural network for deep learning classification on Matlab, when training finishes, the final validation accuracy that appears on the right side of the plot is different than the accuracy I have gotten from the following law for the validation set
accuracy = sum(predictedLabels == valLabels)/numel(valLabels);

  1 件のコメント

Hi Nusaiba,
Which MATLAB version are you using?

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1 件の回答

回答者: Manlin Wang 2019 年 4 月 21 日

I have the same problem. The validation accuracy showed in the training process plot is different from the law of accuracy.The codes are posted as follows:
indices=crossvalind('Kfold',length(XTrain),5);
validate_data_in = (indices == 1);
train = ~validate_data_in;
Xvalidate_data=XTrain(validate_data_in,:);%测试集为20%数据
Yvalidate_label=YTrain(validate_data_in,:);%测试指标
Xtrain_data=XTrain(train,:);
YTrain_label=YTrain(train,:);
indices1=crossvalind('Kfold',length(Xtrain_data),10);
acc=zeros(2,1);
for k=1:2
test = (indices1 == k);
train = ~test;
X_train=Xtrain_data(train,:);
Y_Trainlabel=YTrain_label(train,:);
test_data=Xtrain_data(test,:);
test_target=YTrain_label(test,:);
inputSize = 31;
numHiddenUnits = 120;
numClasses = 2;
layers = [ ...
sequenceInputLayer(inputSize)
bilstmLayer(numHiddenUnits,'OutputMode','sequence')
dropoutLayer(0.2)
bilstmLayer(100,'OutputMode','sequence')
dropoutLayer(0.2)
bilstmLayer(50,'OutputMode','last')
dropoutLayer(0.2)
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer];
%
maxEpochs = 100;
miniBatchSize = 100;
% lgraph = layerGraph(layers);
% lgraph = connectLayers(lgraph,'fold/miniBatchSize','unfold/miniBatchSize');
options = trainingOptions('sgdm', ...
'ExecutionEnvironment','cpu', ...
'GradientThreshold',1, ...
'MaxEpochs',maxEpochs, ...
'MiniBatchSize',miniBatchSize, ...
'InitialLearnRate',1e-3, ...
'SequenceLength','longest', ...
'ValidationData',{test_data,test_target}, ...
'Shuffle','every-epoch', ...
'Verbose',false, ...
'Plots','training-progress');
%train LSTM network
net = trainNetwork(X_train,Y_Trainlabel,layers,options);
%Test LSTM Network
YPred = classify(net,test_data);
acc(k) = sum(YPred == test_target)./numel(test_target)
end

  2 件のコメント

Which MATLAB version are you using?
Hi Maria,
I have the same problem. I am using MATLAB R2017b. Are there any solutions?

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