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

Notebook Cell 1.10 - PyTorch’s model in action: no more manual prediction / forward step!
1 # Sets learning rate - this is "eta" ~ the "n"-like
2 # Greek letter
3 lr = 0.1
4
5 # Step 0 - Initializes parameters "b" and "w" randomly
6 torch.manual_seed(42)
7 # Now we can create a model and send it at once to the device
8 model = ManualLinearRegression().to(device) 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')
16
17 # Defines number of epochs
18 n_epochs = 1000
19
20 for epoch in range(n_epochs):
21 model.train() # What is this?!? 2
22
23 # Step 1 - Computes model's predicted output - forward pass
24 # No more manual prediction!
25 yhat = model(x_train_tensor) 3
26
27 # Step 2 - Computes the loss
28 loss = loss_fn(yhat, y_train_tensor)
29
30 # Step 3 - Computes gradients for both "b" and "w" parameters
31 loss.backward()
32
33 # Step 4 - Updates parameters using gradients and
34 # the learning rate
35 optimizer.step()
36 optimizer.zero_grad()
37
38 # We can also inspect its parameters using its state_dict
39 print(model.state_dict())
108 | Chapter 1: A Simple Regression Problem