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

computation.
If you chose "Local Installation" in the "Setup Guide" and skipped
or had issues with Step 5 ("Install GraphViz software and
TorchViz package"), you will get an error when trying to visualize
the graphs using make_dot.
So, let’s stick with the bare minimum: two (gradient-computing) tensors for our
parameters, predictions, errors, and loss—these are Steps 0, 1, and 2.
# Step 0 - Initializes parameters "b" and "w" randomly
torch.manual_seed(42)
b = torch.randn(1, requires_grad=True, \
dtype=torch.float, device=device)
w = torch.randn(1, requires_grad=True, \
dtype=torch.float, device=device)
# Step 1 - Computes our model's predicted output - forward pass
yhat = b + w * x_train_tensor
# Step 2 - Computes the loss
error = (yhat - y_train_tensor)
loss = (error ** 2).mean()
# We can try plotting the graph for any variable: yhat, error, loss
make_dot(yhat)
Running the code above will produce the graph below:
Figure 1.5 - Computation graph generated for yhat; Obs.: the corresponding variable names were
inserted manually
Dynamic Computation Graph | 93