---
id: wiki-2026-0508-cross-entropy-loss
title: Cross-Entropy Loss
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [cross-entropy, NLL, log loss, focal loss, label smoothing, KL divergence]
duplicate_of: none
source_trust_level: A
confidence_score: 0.95
verification_status: applied
tags: [loss-function, cross-entropy, classification, deep-learning, focal-loss, label-smoothing, llm-pretraining]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: PyTorch / TensorFlow / JAX
---

# Cross-Entropy Loss

## 매 한 줄
> **"매 prediction 의 truth 와 의 distance"**. 매 entropy + 매 KL divergence 의 base. 매 classification 의 standard. 매 LLM pretraining (next-token prediction) 의 same. 매 modern: focal loss, label smoothing, soft target.

## 매 핵심

### 매 formula
$$H(p, q) = -\sum_x p(x) \log q(x)$$

- 매 p = 매 ground truth (one-hot for classification).
- 매 q = 매 model 의 prediction.

### 매 binary case
$$L = -[y \log \hat{y} + (1-y) \log(1-\hat{y})]$$

### 매 multi-class
$$L = -\sum_c y_c \log \hat{y}_c$$

→ 매 one-hot 의 case 의 매 negative log likelihood (NLL).

### 매 vs MSE
- 매 MSE 의 sigmoid 의 vanishing gradient.
- 매 cross-entropy 의 sigmoid + linear gradient.
- 매 classification 의 standard.

### 매 information theory connection
- 매 H(p) = 매 entropy.
- 매 KL(p || q) = 매 H(p, q) - H(p).
- 매 cross-entropy 의 minimize ≡ 매 KL 의 minimize (with fixed p).

### 매 변형

#### Focal Loss (Lin 2017)
- 매 imbalanced class 의 hard 예제 의 focus.
- 매 (1 - p_t)^γ 의 weight.

#### Label Smoothing
- 매 one-hot → soft (e.g., 0.9 / 0.025 / ...).
- 매 over-confidence 의 mitigate.
- 매 calibration 향상.

#### Class weight
- 매 imbalanced 의 weight.
- 매 minority class 의 보강.

#### Soft target (knowledge distillation)
- 매 teacher 의 distribution 의 student 의 target.

#### Soft cross-entropy
- 매 p 의 distribution (e.g., LLM token prediction 의 entropy regularize).

### 매 numerical stability
- 매 logsoftmax + NLL > 매 softmax + log.
- 매 PyTorch `F.cross_entropy` = 매 logits + integer label (combined).

### 매 LLM pretraining
- 매 standard: 매 next-token cross-entropy.
- 매 perplexity = exp(loss).
- 매 매 token 의 매 vocab distribution.

## 💻 패턴

### Binary CE (PyTorch)
```python
import torch
import torch.nn.functional as F

# 매 logits (raw scores) + label (0 / 1)
logits = torch.randn(32, 1)  # 매 batch 32, binary
labels = torch.randint(0, 2, (32, 1)).float()
loss = F.binary_cross_entropy_with_logits(logits, labels)

# 매 numerical stable
```

### Multi-class CE
```python
# 매 logits (B, C) + label (B,) integer
logits = torch.randn(32, 10)  # 매 10 class
labels = torch.randint(0, 10, (32,))
loss = F.cross_entropy(logits, labels)

# 매 inside: 매 logsoftmax + NLL
```

### With class weight (imbalanced)
```python
class_weights = torch.tensor([1.0, 5.0, 2.0])  # 매 minority class 의 5×
loss = F.cross_entropy(logits, labels, weight=class_weights)
```

### Focal loss
```python
def focal_loss(logits, labels, gamma=2.0, alpha=0.25):
    ce_loss = F.cross_entropy(logits, labels, reduction='none')
    pt = torch.exp(-ce_loss)  # 매 prob of correct class
    focal = alpha * (1 - pt) ** gamma * ce_loss
    return focal.mean()
```

### Label smoothing
```python
# 매 PyTorch 1.10+
loss = F.cross_entropy(logits, labels, label_smoothing=0.1)

# 매 manual
def cross_entropy_smooth(logits, labels, smoothing=0.1, num_classes=10):
    log_probs = F.log_softmax(logits, dim=-1)
    nll_loss = -log_probs.gather(dim=-1, index=labels.unsqueeze(1)).squeeze(1)
    smooth_loss = -log_probs.mean(dim=-1)
    return ((1 - smoothing) * nll_loss + smoothing * smooth_loss).mean()
```

### LLM pretraining (next-token)
```python
def next_token_loss(model, input_ids):
    logits = model(input_ids).logits  # (B, T, V)
    # 매 shift: 매 t → 매 t+1 prediction
    shift_logits = logits[:, :-1, :].contiguous()
    shift_labels = input_ids[:, 1:].contiguous()
    
    loss = F.cross_entropy(
        shift_logits.view(-1, shift_logits.size(-1)),
        shift_labels.view(-1),
    )
    return loss

# 매 perplexity
perplexity = torch.exp(loss).item()
```

### Knowledge distillation (soft target)
```python
def distillation_loss(student_logits, teacher_logits, labels, T=4, alpha=0.7):
    # 매 soft target (KL divergence with temperature)
    soft_loss = F.kl_div(
        F.log_softmax(student_logits / T, dim=-1),
        F.softmax(teacher_logits / T, dim=-1),
        reduction='batchmean',
    ) * T * T
    
    # 매 hard target (regular CE)
    hard_loss = F.cross_entropy(student_logits, labels)
    
    return alpha * soft_loss + (1 - alpha) * hard_loss
```

### Calibration check
```python
def expected_calibration_error(logits, labels, n_bins=10):
    probs = F.softmax(logits, dim=-1)
    confidences, predictions = probs.max(-1)
    accuracies = (predictions == labels).float()
    
    bin_boundaries = torch.linspace(0, 1, n_bins + 1)
    ece = 0
    for i in range(n_bins):
        in_bin = (confidences > bin_boundaries[i]) & (confidences <= bin_boundaries[i+1])
        if in_bin.sum() > 0:
            avg_conf = confidences[in_bin].mean()
            avg_acc = accuracies[in_bin].mean()
            ece += abs(avg_conf - avg_acc) * in_bin.float().mean()
    return ece.item()
```

### Soft cross-entropy (for distribution target)
```python
def soft_cross_entropy(logits, target_probs):
    log_probs = F.log_softmax(logits, dim=-1)
    return -(target_probs * log_probs).sum(dim=-1).mean()
```

### Mixup (regularization with soft label)
```python
def mixup(x, y, alpha=0.2):
    lam = np.random.beta(alpha, alpha)
    idx = torch.randperm(x.size(0))
    mixed_x = lam * x + (1 - lam) * x[idx]
    return mixed_x, y, y[idx], lam

def mixup_loss(logits, y_a, y_b, lam):
    return lam * F.cross_entropy(logits, y_a) + (1 - lam) * F.cross_entropy(logits, y_b)
```

## 매 결정 기준
| 상황 | Loss |
|---|---|
| Standard classification | Cross-entropy |
| Imbalanced | Focal loss / class weight |
| Calibration | + label smoothing |
| Distillation | Soft target + KL |
| Long-tail | Focal + class-balanced |
| Hard examples | Focal (γ=2) |
| LLM pretrain | Next-token CE |

**기본값**: F.cross_entropy + label_smoothing 0.1 (대부분).

## 🔗 Graph
- 부모: [[Loss-Function]] · [[Information_Theory|Information-Theory]] · [[Deep-Learning]]
- 변형: [[Focal-Loss]] · [[Label-Smoothing]] · [[LLM_Optimization_and_Deployment_Strategies|Knowledge-Distillation]] · [[KL-Divergence]]
- 응용: [[Image-Classification-Mastery]]
- Adjacent: [[Bias-vs-Variance]] · [[Bias-Correction-Algorithm]] · [[Cognitive-Biases]]

## 🤖 LLM 활용
**언제**: 매 classification model. 매 LLM training. 매 distillation.
**언제 X**: 매 regression (use MSE / Huber). 매 ranking (use ListNet, etc).

## ❌ 안티패턴
- **MSE for classification**: 매 vanishing gradient.
- **One-hot 의 hard label** + small data: 매 over-confidence.
- **No class weight** (imbalanced): 매 majority class dominate.
- **Softmax + log (separate)**: 매 numerical instability — 매 logsoftmax 의 use.
- **Label smoothing 의 too high**: 매 calibration over-correct.

## 🧪 검증 / 중복
- Verified (Bishop "Pattern Recognition", Lin Focal Loss, Hinton distillation).
- 신뢰도 A.
- Related: [[Information_Theory|Information-Theory]] · [[Bias-vs-Variance]] · [[Bias-Correction-Algorithm]] · [[Best-of-N_Sampling]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — formula + variant + 매 binary / multi / focal / smoothing / distill code |