Let's say we have a model and have just started to fit it, the first epoch out of many. The first epoch shows awful results. Does it make sense to continue training hoping the results will be better in the next epochs or it's better to stop (and save time/resources) and try to change something?
In my case, I have a classification model, and the first epoch gives loss=5.5, accuracy=1/n where n is the number of outputs. Consequent epochs changed almost nothing, so, the next 10 epochs were just a waste of time and resources.
Is there a rule of thumb to check that you should stop and try something else?