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

5 Step 3: Computing gradients
6 Step 4: Updating parameters
Output
# b and w after initialization
[0.49671415] [-0.1382643]
# b and w after our gradient descent
[1.02354094] [1.96896411]
"Do we need to run it for 1,000 epochs? Shouldn’t it stop
automatically after getting close enough to the minimum loss?"
Good question: We don’t need to run it for 1,000 epochs. There are ways of
stopping it earlier, once the progress is considered negligible (for instance, if the
loss was barely reduced). These are called, most appropriately, early stopping
methods. For now, since our model is a very simple one, we can afford to train it for
1,000 epochs.
Figure 1.3 - Fully trained model’s predictions
Just to make sure we haven’t made any mistakes in our code, we can use Scikit-
Learn’s linear regression to fit the model and compare the coefficients.
70 | Chapter 1: A Simple Regression Problem