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Validation Loss = Nan

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aryan ramesh
aryan ramesh am 6 Feb. 2022
Kommentiert: aryan ramesh am 8 Feb. 2022
Hello, I'm attempting to utilize lstm to categorize data but the validation loss Is Nan.
I reduced the learning rates to 1e-12 but I am still receiving Nan results.
Appreciate any guidance.
Best Regards,
options = trainingOptions("sgdm", ...
"MaxEpochs",400, ...
"InitialLearnRate",0.000000000001, ...
"Shuffle", 'never', ...
"Plots","training-progress",...
"ValidationData",{XValidation,YValidation},...
'ValidationFrequency',1);
%%
layers = [ ...
sequenceInputLayer(1)
bilstmLayer(100,"OutputMode","last")
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];
% displaySequence(tones_cell{1}, label1{1})
net = trainNetwork(XTrain,labelTrain, layers, options )
YPred = classify(net,XTest);
  1 Kommentar
KSSV
KSSV am 7 Feb. 2022
Increase the learning rate and see.

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Akzeptierte Antwort

yanqi liu
yanqi liu am 8 Feb. 2022
yes,sir,may be add dropoutLayer、batchNormalizationLayer to the model
  1 Kommentar
aryan ramesh
aryan ramesh am 8 Feb. 2022
I added the dropoutLayer. Tks

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