Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

And then we modify the train() method to include a call to the protected method
defined above. It should come after the validation inner loop.
StepByStep Method
def train(self, n_epochs, seed=42):
# To ensure reproducibility of the training process
self.set_seed(seed)
for epoch in range(n_epochs):
# Keeps track of the number of epochs
# by updating the corresponding attribute
self.total_epochs += 1
# inner loop
# Performs training using mini-batches
loss = self._mini_batch(validation=False)
self.losses.append(loss)
# VALIDATION
# no gradients in validation!
with torch.no_grad():
# Performs evaluation using mini-batches
val_loss = self._mini_batch(validation=True)
self.val_losses.append(val_loss)
self._epoch_schedulers(val_loss)
1
# If a SummaryWriter has been set...
if self.writer:
scalars = {'training': loss}
if val_loss is not None:
scalars.update({'validation': val_loss})
# Records both losses for each epoch under tag "loss"
self.writer.add_scalars(main_tag='loss',
tag_scalar_dict=scalars,
global_step=epoch)
if self.writer:
# Closes the writer
self.writer.close()
setattr(StepByStep, 'train', train)
1 Calls the learning rate scheduler at the end of every epoch
484 | Chapter 6: Rock, Paper, Scissors