How do I get a loss per epoch and not per batch?

Question

In my understanding an epoch is an arbitrarily often repeated run over the whole dataset, which in turn is processed in parts, so called batches. After each train_on_batch a loss is calculated, the weights are updated and the next batch will get better results. These losses are indicators of the quality and learning state of my to NNs.

In several sources the loss is calculated (and printed) per epoch. Therefore I am not sure if I am doing this right.

At the moment my GAN looks like this:

for epoch:
  for batch:

    fakes = generator.predict_on_batch(batch)

    dlc = discriminator.train_on_batch(batch, ..)
    dlf = discriminator.train_on_batch(fakes, ..)
    dis_loss_total = 0.5 *  np.add(dlc, dlf)

    g_loss = gan.train_on_batch(batch,..)

    # save losses to array to work with later

These losses are for each batch. How do I get them for an epoch? As an aside: Do I need losses for an epoch, what for?

Answer 1

There is no direct way to compute the loss for an epoch. Actually, the loss of an epoch is usually defined as the average of the loss of batches in that epoch. So you can accumulate the loss values during an epoch and at the end divide it by the number of batches in the epoch:

epoch_loss = []
for epoch in range(n_epochs):
    acc_loss = 0.
    for batch in range(n_batches):
        # do the training 
        loss = model.train_on_batch(...)
        acc_loss += loss
    epoch_loss.append(acc_loss / n_batches)

As for the other question, one usage of epoch loss might be to use it as an indicator to stop the training (however, the validation loss is usually used for that, not the training loss).