skyunyoo:

Thank you for your reply.
But, I still have questions about the model selection section with K-fold cross validation…
“the average of the validation accuracies”
Does this mean Best valid acc or valid acc when stopped due to early stopping?


Say you use 5-fold cross validations. Then, you have 5 iterations with 1 training and 1 validation fold in each iteration. So, in total, once you finished iterating, you have 5 validation accuracies. Here, the “the average of the validation accuracies” then means that these are averaged: sum(valid1 + valid2 … + valid5) / 5.



 skyunyoo:

“The final model might be trained using the whole training data”
Does this mean that I need to train the entire training data only once, like a holdout, rather than k repeating train like KFold? Is there no need to k-split the train data in this part?


In a real-world application, you usually care about using your model in some production system. So, in this case, after you evaluated the performance of the model, it makes sense to use even more data for training, because based on theoretical assumptions, this can only improve the model.
So, yes, for production, you may re-train your model on the whole training set after evaluation. However, note that in DL, this is typically not as necessary as in traditional machine learning, because the training set is already relatively large to begin with.