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

Let’s build a proper (yet simple) model for our regression task. It should look like
this:
Notebook Cell 1.9 - Building our "Manual" model, creating parameter by parameter!
1 class ManualLinearRegression(nn.Module):
2 def __init__(self):
3 super().__init__()
4 # To make "b" and "w" real parameters of the model,
5 # we need to wrap them with nn.Parameter
6 self.b = nn.Parameter(torch.randn(1,
7 requires_grad=True,
8 dtype=torch.float))
9 self.w = nn.Parameter(torch.randn(1,
10 requires_grad=True,
11 dtype=torch.float))
12
13 def forward(self, x):
14 # Computes the outputs / predictions
15 return self.b + self.w * x
Parameters
In the __init__() method, we define our two parameters, b and w, using the
Parameter class, to tell PyTorch that these tensors, which are attributes of the
ManualLinearRegression class, should be considered parameters of the model the
class represents.
Why should we care about that? By doing so, we can use our model’s parameters()
method to retrieve an iterator over the model’s parameters, including parameters
of nested models. Then we can use it to feed our optimizer (instead of building a list
of parameters ourselves!).
torch.manual_seed(42)
# Creates a "dummy" instance of our ManualLinearRegression model
dummy = ManualLinearRegression()
list(dummy.parameters())
Model | 105