Doubt on cross-validation mean error value for a neural network At the end of the cross validation of a multi layer peceptron, do we make the mean of the validation results of the last epoch of the model on each fold or do we make the mean over all the errors?
For instance, suppose i do a 3-fold cross validation ona neural network and i get
1st fold: 300 epochs and 0.5 of error in the 300th epoch (last epoch)
2nd fold: 100 epochs and 0.2 of error in the 100th epoch (last epoch)
3rd fold: 700 epochs and 0.4 of error in the 700th epoch (last epoch)
then to compute the average error for this validation process, what i do is:
1/3 * 0.5+0.2+0.4
or i should do 1/3 * (sum of the error for each epoch in the 1st fold + sum of the error for each epoch in the 2nd fold + sum of the error for each epoch in the 3rd fold)
In the first case i just sum 3 values and divide by three, in the last case i sum 300+100+700 values and divide by 3.
What is the correct approach?

The first option. Previous epoch errors are irrelevant in validation/test performance. They can be used to profile the training characteristics, but not for evaluating the final model.
