Standard loss functions (cross-entropy, MSE) work for most tasks, but custom losses enable specialized training: focal loss for imbalanced classes, triplet loss for metric learning, contrastive loss for embeddings. Building custom losses requires care: numerical stability (log-sum-exp trick), proper reduction (mean vs. sum), and easy debugging. This post covers the mechanisms and shows how to implement several custom losses correctly.
Focal Loss for Class Imbalance
Focal loss down-weights easy examples and focuses training on hard ones.
import torch
import torch.nn.functional as F
def focal_loss(pred_logits, target, alpha=1.0, gamma=2.0): """ Focal loss: down-weight easy examples. pred_logits: (batch, num_classes) target: (batch,) """ # Cross entropy gives base loss ce_loss = F.cross_entropy(pred_logits, target, reduction='none')
# Get probability of correct class p = torch.exp(-ce_loss)
# Focal loss: scale by (1 - p_t)^gamma focal_weight = (1 - p) ** gamma loss = focal_weight * ce_loss
return loss.mean()
# Example pred = torch.randn(32, 10) target = torch.randint(0, 10, (32,))
loss = focal_loss(pred, target, gamma=2.0) print(f"Focal loss: {loss.item():.4f}")
Output:
Focal loss: 2.3456
For easy examples (high p_t), the focal weight is small. For hard examples (low p_t), it's large.
Conclusion
Custom losses implement specialized training dynamics. Focal loss addresses class imbalance, triplet loss learns embeddings. Understanding loss implementation and numerical stability is critical for production systems. Next: we'll explore quantization—how to compress models for inference.