Is training accuracy of a model calculated on the mini-batch?
Ideally training accuracy should be calculated including every sample, but practically, it's all right to use a subset of the data as long as its representative of all data.  Whether a random batch of 100 samples is representative of your data or not, depends on the problem at hand.  For instance if you were classifying a sample as one of one thousand possible classes, this is clearly not enough.
I generally keep a running average of the loss over a given epoch during training.  Since the training loss is almost always monotonically decreasing, this will tend to overestimate loss (and underestimate accuracy.  However, the discrepancy should disappear as the model converges.
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?