You could use the ImageNet example or the following manual approach:
for epoch in range(num_epochs):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
running_loss =+ loss.item() * images.size(0)
loss_values.append(running_loss / len(train_dataset))
plt.plot(loss_values)
This code would plot a single loss value for each epoch. Would that work?