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

Data Preparation
There hasn’t been much data preparation up to this point, to be honest. After
generating our data points in Notebook Cell 1.1, the only preparation step
performed so far has been transforming Numpy arrays into PyTorch tensors, as in
Notebook Cell 1.3, which is reproduced below:
Define - Data Preparation V0
1 %%writefile data_preparation/v0.py
2
3 device = 'cuda' if torch.cuda.is_available() else 'cpu'
4
5 # Our data was in Numpy arrays, but we need to transform them
6 # into PyTorch's Tensors and then send them to the
7 # chosen device
8 x_train_tensor = torch.as_tensor(x_train).float().to(device)
9 y_train_tensor = torch.as_tensor(y_train).float().to(device)
Run - Data Preparation V0
%run -i data_preparation/v0.py
This part will get much more interesting in the next chapter when we get to use
Dataset and DataLoader classes :-)
"What’s the purpose of saving cells to these files?"
We know we have to run the full sequence to train a model: data preparation,
model configuration, and model training. In Chapter 2, we’ll gradually improve each
of these parts, versioning them inside each corresponding folder. So, saving them to
files allows us to run a full sequence using different versions without having to
duplicate code.
Let’s say we start improving model configuration (and we will do exactly that in
Chapter 2), but the other two parts are still the same; how do we run the full
sequence?
116 | Chapter 1: A Simple Regression Problem