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

Apart from returning the loss value, the inner perform_train_step_fn() function
below is exactly the same as the code inside the loop in Model Training V0. The
code should look like this:
Helper Function #1
1 def make_train_step_fn(model, loss_fn, optimizer):
2 # Builds function that performs a step in the train loop
3 def perform_train_step_fn(x, y):
4 # Sets model to TRAIN mode
5 model.train()
6
7 # Step 1 - Computes model's predictions - forward pass
8 yhat = model(x)
9 # Step 2 - Computes the loss
10 loss = loss_fn(yhat, y)
11 # Step 3 - Computes gradients for "b" and "w" parameters
12 loss.backward()
13 # Step 4 - Updates parameters using gradients and
14 # the learning rate
15 optimizer.step()
16 optimizer.zero_grad()
17
18 # Returns the loss
19 return loss.item()
20
21 # Returns the function that will be called inside the
22 # train loop
23 return perform_train_step_fn
Then we need to update our model configuration code (adding line 20 in the next
snippet) to call this higher-order function to build a train_step_fn() function. But
we need to run a data preparation script first.
Run - Data Preparation V0
%run -i data_preparation/v0.py
Rethinking the Training Loop | 131