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

• Step 1: compute model’s predictions
• Step 2: compute the loss
• Step 3: compute the gradients
• Step 4: update the parameters
This sequence is repeated over and over until the number of epochs is reached.
The corresponding code for this part also comes from Notebook Cell 1.10, lines 17-
36.
"What happened to the random initialization step?"
Since we are not manually creating parameters anymore, the initialization is
handled inside each layer during model creation.
Define - Model Training V0
1 %%writefile model_training/v0.py
2
3 # Defines number of epochs
4 n_epochs = 1000
5
6 for epoch in range(n_epochs):
7 # Sets model to TRAIN mode
8 model.train()
9
10 # Step 1 - Computes model's predicted output - forward pass
11 yhat = model(x_train_tensor)
12
13 # Step 2 - Computes the loss
14 loss = loss_fn(yhat, y_train_tensor)
15
16 # Step 3 - Computes gradients for both "b" and "w" parameters
17 loss.backward()
18
19 # Step 4 - Updates parameters using gradients and
20 # the learning rate
21 optimizer.step()
22 optimizer.zero_grad()
Putting It All Together | 119