Hi,
To be honest, Its up to you. You can print and visualize whatever you want during training/validation. You just need to add accuracy calculation after each epoch and visualize it with something like visdom 1 or simply in a terminal.
E.g.,
for x, y_true in validation_loader:
    ...
    y_pred = output.data.max(1)[1] # output is a result of your model
    total_loss += loss.item()
    num_correct += y_pred.eq(y_true).long().sum().item()
average_loss = total_loss / len(validation_loader)
accuracy = num_correct / len(validation_loader.dataset) * 100

But you never know when you should stop training. There are different recommendations or regularization techniques (e.g., Early stopping 1), but there is no global solution. So, experiment your way