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

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);

Answer 1

[John D'Errico]

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.

[Kimberly Cardillo]

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?