Could you explain me the output of keras at each iteration?
As far as I can tell the output of the keras function is a running average loss and the loss is quite a lot larger at the beginning of the epoch, than in the end. The loss is reset after each epoch and a new running average is formed. Therefore, the old running average is quite a bit higher (or at least different), than the beginning loss in the next epoch.
When I train a sequential model with keras using the method fit_generator, I see this output

Epoch 1/N_epochs
n/N [====================>..............] - ETA xxxx - loss: yyyy

I noticed that the loss decreased gradually with the number of steps, as expected. My problem is that I also noticed that when one epoch finishes and another one starts, the value of the loss is quite different from the one that I see at the end of the previous epoch.
Why so? I thought that epoch and number of steps per epochs was arbitrary values and using, for instance, 10 epochs with 1000 steps should be the same of 1000 epochs with 10 steps. But what does exactly happen between one epoch and the next one in Keras 2.0?
Disclaimer: I know the definition of epoch and how the number of steps should be decided using a batch generator, but I have too many data and I cannot apply this rule.