refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-exploding-gradient-problem
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title: Exploding Gradient Problem
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [exploding gradient, gradient clipping, RNN explosion, weight init, Xavier He]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.98
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verification_status: applied
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tags: [deep-learning, training, gradient-explosion, gradient-clipping, weight-initialization]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: PyTorch / TensorFlow
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---
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# Exploding Gradient Problem
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## 매 한 줄
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> **"매 deep / recurrent network 의 gradient 의 magnitude 의 explode"**. 매 loss = NaN. 매 cause: 매 deep chain rule + 매 weight > 1 의 multiply. 매 mitigation: gradient clipping, 매 better init (Xavier/He), 매 LayerNorm, 매 residual.
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## 매 핵심
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### 매 cause
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- **Chain rule**: 매 ∏ (∂h/∂h_prev) > 1 → exp.
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- **RNN**: 매 same weight 의 매 timestep multiply.
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- **Bad init**: 매 weight scale.
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- **High learning rate**.
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- **No normalization**.
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### 매 detection
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- **Loss = NaN**.
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- **Grad norm = inf**.
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- **Weight diverge**.
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- **Activation overflow**.
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### 매 mitigation
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1. **Gradient clipping** (norm or value).
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2. **Weight init** (Xavier, He, orthogonal).
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3. **LayerNorm / BatchNorm**.
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4. **Residual connection**.
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5. **LSTM / GRU** (gating).
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6. **Smaller learning rate**.
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7. **Gradient check**.
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### 매 응용
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- **RNN / LSTM training**.
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- **Deep Transformer**.
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- **GAN training**.
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- **RL policy**.
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## 💻 패턴
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### Gradient clipping (norm)
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```python
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import torch
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def train_step(model, loss, optim, max_norm=1.0):
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optim.zero_grad()
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
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optim.step()
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```
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### Gradient clipping (value)
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```python
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torch.nn.utils.clip_grad_value_(model.parameters(), clip_value=0.5)
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```
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### Adaptive clip (recent)
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```python
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def adaptive_clip(grads, percentile=99):
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norms = [g.norm() for g in grads]
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threshold = np.percentile([n.item() for n in norms], percentile)
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for g in grads: g.data.mul_(min(1, threshold / max(g.norm(), 1e-6)))
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```
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### Xavier / Glorot init
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```python
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import torch.nn as nn
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for m in model.modules():
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if isinstance(m, nn.Linear):
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nn.init.xavier_uniform_(m.weight)
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nn.init.zeros_(m.bias)
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```
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### He init (ReLU)
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```python
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for m in model.modules():
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if isinstance(m, nn.Linear):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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```
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### Orthogonal init (RNN)
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```python
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for name, param in model.named_parameters():
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if 'weight_hh' in name:
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nn.init.orthogonal_(param)
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```
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### LayerNorm
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```python
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class TransformerBlock(nn.Module):
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def __init__(self, d):
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super().__init__()
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self.ln1 = nn.LayerNorm(d)
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self.ln2 = nn.LayerNorm(d)
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self.attn = MultiHeadAttention(d)
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self.mlp = nn.Sequential(nn.Linear(d, 4*d), nn.GELU(), nn.Linear(4*d, d))
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def forward(self, x):
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x = x + self.attn(self.ln1(x)) # 매 pre-norm
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x = x + self.mlp(self.ln2(x))
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return x
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```
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### Residual connection
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```python
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class ResBlock(nn.Module):
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def __init__(self, d):
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super().__init__()
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self.layers = nn.Sequential(nn.Linear(d, d), nn.ReLU(), nn.Linear(d, d))
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def forward(self, x):
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return x + self.layers(x) # 매 gradient highway
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```
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### Detect explosion (logging)
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```python
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def grad_norm(model):
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total = 0
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for p in model.parameters():
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if p.grad is not None: total += p.grad.norm().item() ** 2
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return total ** 0.5
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# 매 monitor + alert
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gn = grad_norm(model)
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if gn > 100 or np.isnan(gn):
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print(f'⚠️ Grad norm: {gn}')
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```
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### NaN guard
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```python
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def safe_train_step(model, loss, optim):
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if torch.isnan(loss).any() or torch.isinf(loss).any():
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print('Skipping NaN/Inf loss')
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return
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optim.zero_grad()
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loss.backward()
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grad_ok = all(not torch.isnan(p.grad).any() for p in model.parameters() if p.grad is not None)
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if grad_ok: optim.step()
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```
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### LR warmup (transformer)
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```python
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def warmup_cosine(step, warmup=4000, total=100000, peak_lr=1e-4):
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if step < warmup: return peak_lr * step / warmup
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progress = (step - warmup) / (total - warmup)
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return peak_lr * 0.5 * (1 + np.cos(np.pi * progress))
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```
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### Mixed precision (with GradScaler)
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```python
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from torch.cuda.amp import autocast, GradScaler
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scaler = GradScaler()
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with autocast():
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loss = model(batch)
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scaler.scale(loss).backward()
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scaler.unscale_(optim)
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
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scaler.step(optim)
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scaler.update()
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```
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### LSTM (gating fix for RNN)
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```python
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self.lstm = nn.LSTM(input_size, hidden_size, num_layers=2, batch_first=True)
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# 매 forget gate 의 의 의 long sequence stabilize
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```
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### TBPTT (truncated BPTT)
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```python
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def tbptt_train(model, sequence, chunk=20):
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h = None
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losses = []
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for i in range(0, len(sequence), chunk):
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chunk_data = sequence[i:i+chunk]
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out, h = model(chunk_data, h)
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h = (h[0].detach(), h[1].detach()) # 매 detach 의 backprop 의 limit
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loss = compute_loss(out, target)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
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optim.step(); optim.zero_grad()
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losses.append(loss.item())
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return losses
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```
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## 매 결정 기준
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| 상황 | Mitigation |
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|---|---|
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| Default DL | Clip + LayerNorm + He init |
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| RNN | + orthogonal + TBPTT |
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| Transformer | LayerNorm + warmup |
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| GAN | Spectral norm + low LR |
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| Mixed precision | + GradScaler |
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**기본값**: 매 grad clip 1.0 + 매 He init + 매 LayerNorm + 매 LR warmup + 매 NaN guard.
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## 🔗 Graph
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- 부모: [[Deep Learning]] · [[Optimization]]
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- 변형: [[Gradient-Clipping]]
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- 응용: [[데이터 사이언스 및 ML 엔지니어링|RNN]] · [[Transformer]] · [[Generative-Adversarial-Networks|GAN]]
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- Adjacent: [[LayerNorm]]
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## 🤖 LLM 활용
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**언제**: 매 deep model train. 매 RNN. 매 GAN.
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**언제 X**: 매 shallow / pretrained inference.
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## ❌ 안티패턴
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- **No clip in RNN**: 매 NaN guarantee.
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- **Default init in deep ReLU**: 매 He init 의 use.
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- **No LR warmup**: 매 transformer fail.
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- **Ignore NaN once**: 매 cascade.
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- **Clip 너무 작게**: 매 underfit.
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## 🧪 검증 / 중복
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- Verified (Pascanu 2013, He init 2015, Goodfellow Deep Learning).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-04-26 | GRAD-EXPL auto |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — clip / init / norm / TBPTT / mixed-prec code |
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