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

print(model.state_dict()) # remember, model == sbs.model
print(sbs.total_epochs)
Output
OrderedDict([('0.weight', tensor([[1.9414]], device='cuda:0')),
'0.bias', tensor([1.0233], device='cuda:0'))])
200
Our class sent the model to the available device (a GPU, in this case), and now the
model’s parameters are GPU tensors.
The weights of our trained model are quite close to the ones we got in Chapter 2.
They are slightly different, though, because we are now using yet another random
seed before starting the training loop. The total number of epochs was tracked by
the total_epochs attribute, as expected.
Let’s take a look at the losses:
fig = sbs.plot_losses()
Figure 2.1.2 - Losses
Again, no surprises here; what about making predictions for new, never seen
before data points?
198 | Chapter 2.1: Going Classy