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

Data Preparation
1 # Builds tensors from numpy arrays BEFORE split
2 # Modifies the scale of pixel values from [0, 255] to [0, 1]
3 x_tensor = torch.as_tensor(images / 255).float()
4 y_tensor = torch.as_tensor(labels).long()
5
6 # Uses index_splitter to generate indices for training and
7 # validation sets
8 train_idx, val_idx = index_splitter(len(x_tensor), [80, 20])
9 # Uses indices to perform the split
10 x_train_tensor = x_tensor[train_idx]
11 y_train_tensor = y_tensor[train_idx]
12 x_val_tensor = x_tensor[val_idx]
13 y_val_tensor = y_tensor[val_idx]
14
15 # We're not doing any data augmentation now
16 train_composer = Compose([Normalize(mean=(.5,), std=(.5,))])
17 val_composer = Compose([Normalize(mean=(.5,), std=(.5,))])
18
19 # Uses custom dataset to apply composed transforms to each set
20 train_dataset = TransformedTensorDataset(
21 x_train_tensor, y_train_tensor,
22 transform=train_composer
23 )
24 val_dataset = TransformedTensorDataset(
25 x_val_tensor, y_val_tensor,
26 transform=val_composer
27 )
28
29 # Builds a weighted random sampler to handle imbalanced classes
30 sampler = make_balanced_sampler(y_train_tensor)
31
32 # Uses sampler in the training set to get a balanced data loader
33 train_loader = DataLoader(
34 dataset=train_dataset, batch_size=16,
35 sampler=sampler
36 )
37 val_loader = DataLoader(dataset=val_dataset, batch_size=16)
Before defining a model to classify our images, we need to discuss something else:
the loss function.
374 | Chapter 5: Convolutions