Usually you would like to validate your model after each training epoch to get a signal about your model’s ability to generalize, i.e. how high the validation accuracy is (or how low the validation loss gets). Using these validation metrics you could apply e.g. early stopping in order to stop the training once your model starts to overfit.
If you train for some epochs and try to validate (or test) your model afterwards, you will just get the final validation metrics. Running the validation (or test) for several epochs sequentially doesn’t make sense, as the metrics won’t change and you’ll end up with the same values.