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Validation Loss = Nan

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aryan ramesh
aryan ramesh el 6 de Feb. de 2022
Comentada: aryan ramesh el 8 de Feb. de 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 comentario
KSSV
KSSV el 7 de Feb. de 2022
Increase the learning rate and see.

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Respuesta aceptada

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

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