How to save loss, rmse, mae, and mape in every training epoch?

Is there any suggestion on how to save the loss, rmse, mae, and mape in every training epoch? I want to compare them in condition of different parameters later.

Cheers

FYI I calculate the rmse, mae, and mape in the end like this:

net = trainNetwork(XTrain,YTrain,layers,options);

net = predictAndUpdateState(net,XTrain);
[net,YPred] = predictAndUpdateState(net,XTest);
YPred = sig(1)*YPred + mu(1);
YTest = dataTest(1,:);
rmse = sqrt(mean((YPred-YTest).^2))
mae = mean(abs(YPred-YTest))
mape = mean(abs((YPred-YTest)./YTest))*100

Answer 1

[Pratik]

Hi,

To monitor the metrics such as loss, rmse and etc, training options can be used. Also built in metric object can be used to store the values to use later.

Please refer to the following documentation for more information:

www.mathworks.com/help/deeplearning/ref/trainingoptions.html#bu59f0q_sep_mw_7396cbb9-29fe-473a-87d7-6db858aefa0d