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

In the next few pages, I will present four chunks of code showing
different attempts at creating parameters.
The first three attempts are shown to build up to a solution. The
first one only works well if you never use a GPU. The second one
doesn’t work at all. The third one works, but it is too verbose.
The recommended way of creating parameters is the last:
Notebook Cell 1.4.
The first chunk of code below creates two tensors for our parameters, including
gradients and all. But they are CPU tensors, by default.
# FIRST
# Initializes parameters "b" and "w" randomly, ALMOST as we
# did in Numpy, since we want to apply gradient descent on
# these parameters we need to set REQUIRES_GRAD = TRUE
torch.manual_seed(42)
b = torch.randn(1, requires_grad=True, dtype=torch.float)
w = torch.randn(1, requires_grad=True, dtype=torch.float)
print(b, w)
Output
tensor([0.3367], requires_grad=True)
tensor([0.1288], requires_grad=True)
Never forget to set the seed to ensure reproducibility, just like
we did before while using Numpy. PyTorch’s equivalent is
torch.manual_seed().
"If I use the same seed in PyTorch as I used in Numpy (or, to put it
differently, if I use 42 everywhere), will I get the same numbers?"
Unfortunately, NO.
You’ll get the same numbers for the same seed in the same package. PyTorch
generates a number sequence that is different from the one generated by Numpy,
even if you use the same seed in both.
82 | Chapter 1: A Simple Regression Problem