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

Define - Model Configuration V3
1 %%writefile model_configuration/v3.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,
19 # loss function 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)
24
25 # Creates a Summary Writer to interface with TensorBoard
26 writer = SummaryWriter('runs/simple_linear_regression') 1
27 # Fetches a single mini-batch so we can use add_graph
28 x_dummy, y_dummy = next(iter(train_loader))
29 writer.add_graph(model, x_dummy.to(device))
1 Creating SummaryWriter to interface with TensorBoard
Run - Model Configuration V3
%run -i model_configuration/v3.py
160 | Chapter 2: Rethinking the Training Loop