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The accuracy on my LSTM deep learning neural network will not exceed 62.96%

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Kimberly Cardillo
Kimberly Cardillo on 3 Aug 2020
I am training an LSTM network and the accuracy will not exceed 62.96% and I cannot figure out why. I have tried changing the number of nodes, the max epochs, initial learn rate, etc and i cannot figure out what is wrong. The data is being predicted all into one classification. Sometimes when I change around my training and testing data, the accuracy then will not surpass 60%. Can someone help me figure out what may be wrong?
inLayer = sequenceInputLayer(1);
lstm = bilstmLayer(100,'OutputMode','last');
outLayers = [
fullyConnectedLayer(3)
softmaxLayer()
classificationLayer()
];
layers = [inLayer;lstm;outLayers];
options = trainingOptions('adam', ...
'MaxEpochs',25, ...
'InitialLearnRate',0.01, ...
'Shuffle','every-epoch', ...
'Plots','training-progress');
net = trainNetwork(dataTrain,fTrain,layers,options);

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Answers (1)

John D'Errico
John D'Errico on 3 Aug 2020
You have shown no data, so we cannot truly say if you are doing something wrong. However, it sounds as it what you are doing is:
Suppose I have data where there is some signal, but there is a significant amount of random noise, so a significant unpredictable part.
Would you expect a model to predict perfectly? All you end up doing at some point is predicting noise, thus overfitting the data.
You can't squeeze blood from a rock, though sometimes, if you try too hard, the blood may be coming from your own hands. Try not to squeeze too hard.

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Kimberly Cardillo
Kimberly Cardillo on 3 Aug 2020
My signals do have some noise but I can tell the difference between the classes with just my eyes so shouldn't running it through a deep learning network be able to do the same thing?

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