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

Figure 3.6 - Training and validation losses
"Wait, there is something weird with this plot." you say.
You’re right; the validation loss is lower than the training loss. Shouldn’t it be the
other way around?! Well, generally speaking, YES, it should, but you can learn more
about situations where this swap happens at this great post [71] . In our case, it is
simply that the validation set is easier to classify: If you check Figure 3.1 at the
beginning of the chapter, you’ll notice that the red and blue points in the right plot
(validation) are not as mixed up as the ones in the left plot (training).
Having settled that, it is time to inspect the model’s trained parameters:
print(model.state_dict())
Output
OrderedDict([('linear.weight', tensor([[ 1.1822, -1.8684]], device
='cuda:0')),
('linear.bias', tensor([-0.0587], device='cuda:0'))])
Our model produced logits, right? So we can plug the weights above into the
corresponding logit equation (Equation 3.3), and end up with:
Equation 3.18 - Model’s output
Model Training | 233