What is overfitting and what regularization techniques does PyTorch support to address it?

Overfitting occurs when a model memorises the training data instead of learning generalisable patterns — visible as low training loss but high validation loss. PyTorch provides several built-in tools to combat overfitting.

import torch
import torch.nn as nn

class RegularizedNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1  = nn.Linear(784, 256)
        self.bn1  = nn.BatchNorm1d(256)
        self.drop = nn.Dropout(p=0.5)        # 50% dropout
        self.fc2  = nn.Linear(256, 10)

    def forward(self, x):
        x = torch.relu(self.bn1(self.fc1(x)))
        x = self.drop(x)                      # active in train(), off in eval()
        return self.fc2(x)

model = RegularizedNet()

# Weight decay — L2 penalty added by the optimizer
optimizer = torch.optim.AdamW(
    model.parameters(),
    lr=1e-3,
    weight_decay=1e-2,    # penalise large weights
)

# Label smoothing — softens hard one-hot targets
criterion = nn.CrossEntropyLoss(label_smoothing=0.1)

# Early stopping pattern
best_val_loss = float("inf")
patience, patience_counter = 5, 0

for epoch in range(100):
    train_loss = train_one_epoch(model, train_loader, optimizer, criterion, device)
    val_loss, _ = validate(model, val_loader, criterion, device)

    if val_loss < best_val_loss:
        best_val_loss = val_loss
        patience_counter = 0
        torch.save(model.state_dict(), "best_model.pt")  # save best checkpoint
    else:
        patience_counter += 1

    if patience_counter >= patience:
        print(f"Early stopping at epoch {epoch}")
        break

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