You’re right @ptrblck , I was checking the highest validation accuracy!
EMNIST-Letters is by default split into a training set (124800 samples) and test set (20800).
Now I decided to split the training data into training set (104000 samples) and validation set (20800).
In other words the data is split up in the following way; training set (70%), validation set (15%) and test set (15%).
However, when training the model and then measuring its accuracy on the test set, I see that test accuracy is significantly higher than training accuracy, which feels a bit weird. Sometimes the test an training accuracy are differing by 10 percentage units.
Are there any reasons for this output? I was thinking that it might have to do with the training set being significantly bigger than the test set…
EDIT: Reading in other forums it seems like the problem can arise since I am augmenting my training set and validation set which makes it harder for the model to make predictions, i.e lower training accuracy. In the test set, the data might be easier which result in higher test accuracy.