Nas

Neural Architecture Search (NAS)

What is NAS?
Automated process of discovering optimal neural network architectures for given tasks, replacing manual architecture design.

NAS Components

Search Space
Define what architectures are possible:

search_space = {
    "num_layers": [4, 6, 8, 12],
    "hidden_size": [256, 512, 768, 1024],
    "num_heads": [4, 8, 12],
    "activation": ["relu", "gelu", "swish"],
    "dropout": [0.0, 0.1, 0.2]
}

Search Strategy
| Strategy | Description |
|----------|-------------|
| Random Search | Sample randomly from space |
| Grid Search | Exhaustive search (expensive) |
| Bayesian Optimization | Model-based search |
| Evolution | Genetic algorithms |
| Reinforcement Learning | RL controller picks architectures |
| Differentiable (DARTS) | Gradient-based search |

Performance Estimation
| Method | Speed | Accuracy |
|--------|-------|----------|
| Full training | Slow | High |
| Early stopping | Faster | Medium |
| Weight sharing | Fast | Variable |
| Predictors | Very fast | Variable |

DARTS (Differentiable Architecture Search)

# Continuous relaxation of architecture choice
alpha = nn.Parameter(torch.randn(num_ops))  # Architecture weights

def forward(x):
    ops_outputs = [op(x) for op in operations]
    weights = F.softmax(alpha, dim=0)
    return sum(w * o for w, o in zip(weights, ops_outputs))

# After training, select highest-weight operations
final_arch = alpha.argmax(dim=0)

AutoML Platforms
| Platform | Features |
|----------|----------|
| AutoGluon | Tabular, image, text |
| Auto-sklearn | Classical ML |
| H2O AutoML | Enterprise AutoML |
| Ludwig | Declarative deep learning |
| Ray Tune | Hyperparameter tuning |

Use Cases
- Find efficient architectures for deployment
- Discover architectures for new domains
- Optimize for specific hardware constraints
- Automate ML pipeline development

Best Practices
- Define search space based on domain knowledge
- Use early stopping for efficiency
- Validate on held-out data
- Consider transfer from similar tasks