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

# Creates the val_step function for our model and loss
self.val_step_fn = self._make_val_step_fn()
def to(self, device):
# This method allows the user to specify a different device
# It sets the corresponding attribute (to be used later in
# the mini-batches) and sends the model to the device
try:
self.device = device
self.model.to(self.device)
except RuntimeError:
self.device = ('cuda' if torch.cuda.is_available()
else 'cpu')
print(f"Couldn't send it to {device}, \
sending it to {self.device} instead.")
self.model.to(self.device)
def set_loaders(self, train_loader, val_loader=None):
# This method allows the user to define which train_loader
# (and val_loader, optionally) to use
# Both loaders are then assigned to attributes of the class
# So they can be referred to later
self.train_loader = train_loader
self.val_loader = val_loader
def set_tensorboard(self, name, folder='runs'):
# This method allows the user to create a SummaryWriter to
# interface with TensorBoard
suffix = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
self.writer = SummaryWriter(f'{folder}/{name}_{suffix}')
Sure, we are still missing both _make_train_step_fn() and _make_val_step_fn()
functions. Both are pretty much the same as before, except that they refer to the
class attributes self.model, self.loss_fn, and self.optimizer, instead of taking
them as arguments. They look like this now:
Step Methods
def _make_train_step_fn(self):
# This method does not need ARGS... it can use directly
# the attributes: self.model, self.loss_fn and self.optimizer
182 | Chapter 2.1: Going Classy