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

Then, for each subset of data, we’ll build a corresponding DataLoader, so our code
will look like this:
Define - Data Preparation V2
1 %%writefile 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() 1
7 y_tensor = torch.as_tensor(y).float() 1
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]) 2
18
19 # Builds a loader of each set
20 train_loader = DataLoader(
21 dataset=train_data,
22 batch_size=16,
23 shuffle=True,
24 )
25 val_loader = DataLoader(dataset=val_data, batch_size=16) 3
1 Making tensors out of the full dataset (before split)
2 Performing train-validation split in PyTorch
3 Creating a data loader for the validation set
Run - Data Preparation V2
%run -i data_preparation/v2.py
Now that we have a data loader for our validation set, it makes sense to use it for
the…
146 | Chapter 2: Rethinking the Training Loop