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

Putting It All Together
We have updated each of the three fundamental parts of our code at least twice. It
is time to put it all together to get an overall view of what we have achieved so far.
Behold your pipeline: Data Preparation V2, Model Configuration V3, and Model
Training V5!
Run - Data Preparation V2
1 # %load data_preparation/v2.py
2
3 torch.manual_seed(13)
4
5 # Builds tensors from numpy arrays BEFORE split
6 x_tensor = torch.as_tensor(x).float()
7 y_tensor = torch.as_tensor(y).float()
8
9 # Builds dataset containing ALL data points
10 dataset = TensorDataset(x_tensor, y_tensor)
11
12 # Performs the split
13 ratio = .8
14 n_total = len(dataset)
15 n_train = int(n_total * ratio)
16 n_val = n_total - n_train
17 train_data, val_data = random_split(dataset, [n_train, n_val])
18 # Builds a loader of each set
19 train_loader = DataLoader(
20 dataset=train_data,
21 batch_size=16,
22 shuffle=True,
23 )
24 val_loader = DataLoader(dataset=val_data, batch_size=16)
170 | Chapter 2: Rethinking the Training Loop