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

Define - Model Configuration V1
1 %%writefile model_configuration/v1.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) 1
1 Creating a function that performs a training step
Run - Model Configuration V1
%run -i model_configuration/v1.py
Let’s check our train_step_fn() function out!
train_step_fn
Output
<function __main__.make_train_step_fn.<locals>\
.perform_train_step_fn(x, y)>
Looking good! Now we need to update our model training to replace the code
inside the loop with a call to our newly created function.
132 | Chapter 2: Rethinking the Training Loop