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

convolutional layers to our model and defining a different loss function to handle
the multiclass classification problem. We also added some more methods to our
class, such that we can visualize the filters learned by our model, attach hooks to
the model’s forward pass, and use the captured results to visualize the
corresponding feature maps.
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 # Builds a weighted random sampler to handle imbalanced classes
29 sampler = make_balanced_sampler(y_train_tensor)
30 # Uses sampler in the training set to get a balanced data loader
31 train_loader = DataLoader(
32 dataset=train_dataset, batch_size=16, sampler=sampler
33 )
34 val_loader = DataLoader(dataset=val_dataset, batch_size=16)
Putting It All Together | 411