Is a large number of epochs good or bad idea in CNN
Assuming you track the performance with a validation set, as long as validation error is decreasing, more epochs are beneficial, model is improving on seen (training) and unseen (validation) data.

As soon as validation error starts to increase, it signals that model is over-fitting on training data, thus the learning process should be stopped. This method is independent of the details of a model.

In practice, save the model corresponding to the best validation error found so far, and stop the process only when validation error is consistently increasing. This can be done manually or with an ad-hoc threshold based on the distance of current validation error from the minimum error so far. This way, learning will not be stopped prematurely due to fluctuations in validation error.