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

train_composer = Compose([RandomHorizontalFlip(p=.5),
Normalize(mean=(.5,), std=(.5,))])
val_composer = Compose([Normalize(mean=(.5,), std=(.5,))])
Next, we use them to create two datasets and their corresponding data loaders:
train_dataset = TransformedTensorDataset(
x_train_tensor, y_train_tensor, transform=train_composer
)
val_dataset = TransformedTensorDataset(
x_val_tensor, y_val_tensor, transform=val_composer
)
# Builds a loader of each set
train_loader = DataLoader(
dataset=train_dataset, batch_size=16, shuffle=True
)
val_loader = DataLoader(dataset=val_dataset, batch_size=16)
And, since we’re not using a sampler to perform the split anymore, we can (and
should) set shuffle to True.
If you do not perform data augmentation, you may keep using
samplers and a single dataset.
Disappointed with the apparently short-lived use of samplers? Don’t be! I saved
the best sampler for last.
WeightedRandomSampler
We have already talked about imbalanced datasets when learning about binary
cross-entropy losses in Chapter 3. We adjusted the loss weight for points in the
positive class to compensate for the imbalance. It wasn’t quite the weighted average
one would expect, though. Now, we can tackle the imbalance using a different
approach: A weighted sampler.
The reasoning is pretty much the same but, instead of weighted losses, we use
weights for sampling: The class with fewer data points (minority class) should get
Data Preparation | 289