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

Model Training
We start by instantiating the StepByStep class with the corresponding arguments.
Next, we set its loaders using the appropriately named method set_loaders().
Then, we set up an interface with TensorBoard and name our experiment classy
(what else could it be?!).
Notebook Cell 2.1.1
1 sbs = StepByStep(model, loss_fn, optimizer)
2 sbs.set_loaders(train_loader, val_loader)
3 sbs.set_tensorboard('classy')
One important thing to notice is that the model attribute of the sbs object is the
same object as the model variable created in the model configuration. It is not a
copy! We can easily verify this:
print(sbs.model == model)
print(sbs.model)
Output
True
Sequential(
(0): Linear(in_features=1, out_features=1, bias=True)
)
As expected, the equality holds. If we print the model itself, we get our simple one
input-one output model.
Let’s train the model now, using the same 200 epochs as before:
Notebook Cell 2.1.2
1 sbs.train(n_epochs=200)
Done! It is trained! Really? Really! Let’s check it out:
Classy Pipeline | 197