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

Losses
def plot_losses(self):
fig = plt.figure(figsize=(10, 4))
plt.plot(self.losses, label='Training Loss', c='b')
if self.val_loader:
plt.plot(self.val_losses, label='Validation Loss', c='r')
plt.yscale('log')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.tight_layout()
return fig
setattr(StepByStep, 'plot_losses', plot_losses)
Finally, if both training loader and TensorBoard were already configured, we can
use the former to fetch a single mini-batch and the latter to build the model graph
in TensorBoard:
Model Graph
def add_graph(self):
if self.train_loader and self.writer:
# Fetches a single mini-batch so we can use add_graph
x_dummy, y_dummy = next(iter(self.train_loader))
self.writer.add_graph(self.model, x_dummy.to(self.device))
setattr(StepByStep, 'add_graph', add_graph)
The Full Code
If you’d like to check what the full code of the class looks like, you can see it here [67]
or in the Jupyter notebook of this chapter.
We are classy now, so let’s build a classy pipeline too!
Going Classy | 193