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

And then, we update our model configuration code to include the creation of the
corresponding function for the validation step.
Define - Model Configuration V2
1 %%writefile model_configuration/v2.py
2
3 device = 'cuda' if torch.cuda.is_available() else 'cpu'
4
5 # Sets learning rate - this is "eta" ~ the "n"-like Greek letter
6 lr = 0.1
7
8 torch.manual_seed(42)
9 # Now we can create a model and send it at once to the device
10 model = nn.Sequential(nn.Linear(1, 1)).to(device)
11
12 # Defines an SGD optimizer to update the parameters
13 optimizer = optim.SGD(model.parameters(), lr=lr)
14
15 # Defines an MSE loss function
16 loss_fn = nn.MSELoss(reduction='mean')
17
18 # Creates the train_step function for our model, loss function
19 # and optimizer
20 train_step_fn = make_train_step_fn(model, loss_fn, optimizer)
21
22 # Creates the val_step function for our model and loss function
23 val_step_fn = make_val_step_fn(model, loss_fn) 1
1 Creating a function that performs a validation step
Run - Model Configuration V2
%run -i model_configuration/v2.py
Finally, we need to change the training loop to include the evaluation of our model.
The first step is to include another inner loop to handle the mini-batches that come
from the validation loader, sending them to the same device as our model. Then,
inside that inner loop, we use the validation step function to compute the loss.
"Wait, this looks oddly familiar too…" Evaluation | 149