Is training accuracy of a model calculated on the mini-batch?
I agree with @Cory that ideally training accuracy should be calculated on every training sample. Instead of using an running average (exponential, perhaps), I keep the sum of last, let's say, 50 mini-batches. Then I report the average loss across these mini-batches. You can easily track the model's recent performance (you can report this number much frequently than every epoch).
I'm trying to record the training accuracy of the model at the end of each epoch, but I wasn't sure if the training accuracy is calculated on the entire training data or the last mini-batch used for training. For example, if I have 10000 training data and I'm training with mini-batches of 100, would I calculate the training accuracy using the last mini-batch of 100 (the 100th and last mini-batch in that epoch) or use the entire 10000 training data?