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

Run - Model Configuration V4
1 # %load model_configuration/v4.py
2
3 # Sets learning rate - this is "eta" ~ the "n"-like Greek letter
4 lr = 0.1
5
6 torch.manual_seed(42)
7 # Now we can create a model
8 model = nn.Sequential(nn.Linear(1, 1))
9
10 # Defines an SGD optimizer to update the parameters
11 # (now retrieved directly from the model)
12 optimizer = optim.SGD(model.parameters(), lr=lr)
13
14 # Defines an MSE loss function
15 loss_fn = nn.MSELoss(reduction='mean')
Run - Model Training
1 n_epochs = 200
2
3 sbs = StepByStep(model, loss_fn, optimizer)
4 sbs.set_loaders(train_loader, val_loader)
5 sbs.set_tensorboard('classy')
6 sbs.train(n_epochs=n_epochs)
print(model.state_dict())
Output
OrderedDict([('0.weight', tensor([[1.9414]], device='cuda:0')),
('0.bias', tensor([1.0233], device='cuda:0'))])
Recap
In this chapter, we’ve revisited and reimplemented many methods. This is what
we’ve covered:
204 | Chapter 2.1: Going Classy