---
id: wiki-2026-0508-leaky-relu-and-activations
title: Leaky ReLU and Activations
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Activation Functions, ReLU Family, GELU, SiLU, Swish]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [activation, relu, gelu, silu, swiglu, deep-learning]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack: { language: Python, framework: PyTorch }
---

# Leaky ReLU and Activations

## 매 한 줄
> **"매 activation = 비선형성"**. ReLU 계열이 base, Transformer는 GELU/SiLU/SwiGLU.

## 매 핵심
### 매 ReLU 계열
- **ReLU**: max(0, x). 빠름, dying ReLU 문제.
- **Leaky ReLU**: max(αx, x), α=0.01. 음수 작게 통과.
- **PReLU**: α 학습 가능 파라미터.
- **ELU**: x>0이면 x, 아니면 α(eˣ-1). 평균 0에 가까움.
- **SELU**: scaled ELU. self-normalizing (FC + lecun_normal init).

### 매 Smooth 계열
- **GELU**: x·Φ(x). BERT/GPT 표준. xerf 또는 tanh 근사.
- **SiLU/Swish**: x·σ(x). PaLM, EfficientNet.
- **Mish**: x·tanh(softplus(x)). YOLOv4.

### 매 Gated 계열 (FFN)
- **GLU**: (xW)⊗σ(xV). 정보 게이팅.
- **SwiGLU**: (xW)⊗SiLU(xV). LLaMA, PaLM FFN. 보통 hidden ×2/3 보정.
- **GeGLU**: GELU 변형.

### 매 Output 전용
- **Sigmoid**: 이진. saturation→gradient vanish.
- **Softmax**: multi-class probability.
- **Tanh**: [-1,1]. RNN, GAN generator.

### 매 직관
- ReLU: 빠르고 단순, but dead neurons
- GELU/SiLU: smooth, 0근처 비선형성↑, deep transformer에 유리
- SwiGLU: gating으로 expressiveness↑, 동일 param 대비 성능↑

## 💻 패턴
### PyTorch built-ins
```python
import torch.nn as nn, torch.nn.functional as F
# 클래스
nn.ReLU(); nn.LeakyReLU(0.01); nn.PReLU()
nn.ELU(alpha=1.0); nn.SELU()
nn.GELU(approximate="tanh"); nn.SiLU(); nn.Mish()
# 함수
F.relu(x); F.leaky_relu(x, 0.01); F.gelu(x); F.silu(x)
```

### SwiGLU FFN (LLaMA-style)
```python
class SwiGLU(nn.Module):
    def __init__(self, d, d_hidden):
        super().__init__()
        self.w_gate = nn.Linear(d, d_hidden, bias=False)
        self.w_up   = nn.Linear(d, d_hidden, bias=False)
        self.w_down = nn.Linear(d_hidden, d, bias=False)
    def forward(self, x):
        return self.w_down(F.silu(self.w_gate(x)) * self.w_up(x))
# 보통 d_hidden = int(8/3 * d), 64 multiple로 round
```

### GELU 직접
```python
import math, torch
def gelu_tanh(x):
    return 0.5 * x * (1 + torch.tanh(math.sqrt(2/math.pi) * (x + 0.044715 * x**3)))
```

### Init과 페어링
```python
# ReLU/Leaky → He (Kaiming)
nn.init.kaiming_normal_(layer.weight, nonlinearity="relu")
# SELU → LeCun normal
nn.init.normal_(layer.weight, std=(1.0 / fan_in)**0.5)
# Tanh → Xavier
nn.init.xavier_normal_(layer.weight)
```

### Dying ReLU 진단
```python
# forward hook으로 zero ratio 측정
zero_ratio = (act == 0).float().mean().item()
# > 0.5 지속 → Leaky/ELU/GELU로 교체
```

## 매 결정 기준
| 모델 | Activation |
|---|---|
| CNN classic | ReLU |
| ResNet/EfficientNet | ReLU / SiLU |
| Transformer (BERT/GPT) | GELU |
| LLaMA / PaLM FFN | SwiGLU |
| GAN generator | Tanh (out), ReLU (hidden) |
| Self-normalizing FC | SELU + lecun_normal |
| YOLO 변형 | Mish |
| Output binary | Sigmoid |
| Output multiclass | Softmax (or none + CE) |

**기본값**: 일반 DL → ReLU. Transformer → GELU. LLM FFN → SwiGLU.

## 🔗 Graph
- 부모: [[Neural-Networks]], [[Deep-Learning]]
- 변형: [[GELU]]
- 응용: [[Transformer]], [[CNN]]
- Adjacent: [[Loss-Functions-Foundations]]

## 🤖 LLM 활용
**언제**: 모델별 표준 activation 추천, 코드 생성.
**언제 X**: 새로운 SoTA activation 검증은 실험 필요.

## ❌ 안티패턴
- ReLU + softmax 출력 hidden에 Sigmoid 끼우기
- SELU에 BatchNorm 같이 쓰기 (self-norm 깨짐)
- Sigmoid를 deep network hidden에 (vanishing)
- SwiGLU 쓰면서 hidden dim 보정 안 함 (param 늘어남)
- Output에 ReLU (negative target 못 표현)
- He init을 GELU/SiLU에도 (괜찮지만 정확히는 다름)

## 🧪 검증 / 중복
- Verified (He 2015, Hendrycks GELU, Ramachandran Swish, Shazeer SwiGLU). 신뢰도 A.
- 중복: 없음.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — SwiGLU/GELU 코드, init pairing |