Overfitting on the loss graph, but not the accuracy graph I am looking at learning curves (CNN for text classification, which is based on this paper) and trying to play with regularization to prevent overfitting. This model uses L2 regularization and dropout.
What is interesting is that by looking at the accuracy graph I cannot really tell which model is the best. On the other hand the loss graph shows some differences. See pictures below.
Here are my question: 


*

*should we always look at the loss curves to check for overfitting?

*the accuracy graph is not very precise because accuracy is discrete and a lot of information gets lost when we compute it? 




 A: Accuracy is not a great way to report machine learning results. (I've never found a need to report accuracy, except when explaining my results to a non-technical audience.) Accuracy only compares a predicted score $t$ to some cutoff $c$, which is not a proper scoring rule and conceals important information about model fitness.
I assume you're using some sort of proper loss function in the "loss" graph, such as cross-entropy loss. Cross-entropy loss is more useful than accuracy because it is sensitive to "how wrong" its results are: if the label is $1$ but $t=0.9$, the cross-entropy is lower than when the label is $1$ but $t=0.1$. 
The phenomenon you're seeing when comparing these two graphs -- accuracy is flat but loss is increasing -- happens because $t>c$ is satisfied in the accuracy graph, but the predicted scores are poorly aligned to their labels.
This is intimately related to this similar issue with AUC: Why is AUC higher for a classifier that is less accurate than for one that is more accurate?
