I am confused about how to evaluate in stratified kfold CV. According to the documentation, performance is evaluated the average, but I do not know what the average means.
So far, I split the fold using stratified kfold CV, and training and validation for each fold. After k repetitions, the test proceeded only once.
My result as follow
1-fold

epoch 15/149

train Loss: 0.0264 Acc: 88.0000

valid Loss: 0.0220 Acc: 89.0000

Training process is stopped early....

2-fold

epoch 16/149

train Loss: 0.0201 Acc: 91.0000

valid Loss: 0.0124 Acc: 91.0000

Training process is stopped early....

. . .

k-fold

epoch 14/149

train Loss: 0.0239 Acc: 89.0000

valid Loss: 0.0254 Acc: 90.0000

Training process is stopped early....

test Acc: 93.0000

Confusion Matrix : 
([[145., 10.],
  [ 11., 139.]])

I wonder if do I have to test each fold?
Do I have to train by applying the calculated value of the epochs average?
When I averaged performance, do I average epochs and train again with that value?
After all the hyper parameters have been tuned, do I train again with kfold or do I train only once training data divided into train set and test set?