Neural Architecture Search Nas

# Neural Architecture Search (NAS)

## Introduction & Motivation

NAS: automated neural network design. Reinforcement learning-based search, evolutionary algorithms. Applications: efficient model discovery, AutoML.

Motivation: Automate architecture design for optimal performance.

Applications: Mobile-optimized models, domain-specific architectures.

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## Core Concepts & Theory

### Search Space

Possible architecture configurations.

### Search Strategy

RL, evolutionary, gradient-based.

### Performance Estimation

Accuracy prediction acceleration.

### Multi-Objective Optimization

Balance accuracy and efficiency.

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## Mathematical Formulation

Architecture Space:
$$\mathcal{A} = \{a | a \in A_1 imes A_2 imes ... imes A_n\}$$

Controller Loss:
$$\mathcal{L} = -\mathbb{E}[R(a)]$$

Reward Signal:
$$R(a) = ext{Accuracy}(a) - \lambda \cdot ext{Latency}(a)$$

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## Advanced Theory & Extensions

### Differentiable NAS

Gradient-based architecture search.

### Efficient NAS

Reduced search cost via supernets.

### Zero-Cost Proxies

Rapid architecture evaluation.

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## Computational Considerations

Search: O(S·T·E) (S=space, T=training time).

Controller: O(K·D) (K=samples, D=controller dim).

Total: Days to weeks of search.

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## Practical Implementation Strategies

### Early Stopping

Reduce evaluation cost.

### Weight Sharing

Supernet training efficiency.

### Knowledge Distillation

Compress discovered architecture.

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## Benchmark Datasets & Evaluation

ImageNet: Architecture benchmark.

CIFAR-10: Quick evaluation.

Mobile settings: Hardware-specific optimization.

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## Key Challenges & Limitations

### Search Cost

Computationally expensive exploration.

### Transferability

Architecture generalization across tasks.

### Reproducibility

High variance in results.

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## Hyperparameter Tuning

Search iterations: 100-500.

Population size: 20-100.

Mutation rate: 0.1-0.3.

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## Real-World Applications & Case Studies

Mobile Deployment: MobileNetV3 via NAS.

Edge Computing: Efficient architectures.

Medical Imaging: Specialized architecture discovery.

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## Integration with Other Methods

NAS + knowledge distillation; + pruning for deployment.

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## Summary & Key Takeaways

Neural Architecture Search automates optimal model discovery.

Principles:
1. Search space: Possible configurations.
2. Search strategy: Exploration algorithm.
3. Performance estimation: Efficiency tricks.
4. Multi-objective: Balance multiple goals.
5. Automation: Reduce manual tuning.

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## Appendix: Practical Labs

### Lab 1: Search Space Definition

import numpy as np

def define_search_space():
 """Define NAS search space"""
 layers = [16, 32, 64, 128]
 kernels = [3, 5, 7]
 activations = ['relu', 'swish']
 
 search_space = {
 'depth': list(range(3, 8)),
 'width': layers,
 'kernel_size': kernels,
 'activation': activations
 }
 
 return search_space

space = define_search_space()
assert 'depth' in space, "Search space defined"
print(f"✓ Search space: {len(space)} dimensions")

### Lab 2: Random Architecture Sampling

import numpy as np

def sample_random_architecture(search_space):
 """Sample random architecture from search space"""
 architecture = {
 'depth': np.random.choice(search_space['depth']),
 'width': np.random.choice(search_space['width']),
 'kernel_size': np.random.choice(search_space['kernel_size']),
 'activation': np.random.choice(search_space['activation'])
 }
 
 return architecture

np.random.seed(42)
space = {'depth': [3, 4, 5, 6], 'width': [16, 32], 'kernel_size': [3, 5], 'activation': ['relu', 'swish']}
arch = sample_random_architecture(space)
assert isinstance(arch, dict), "Architecture sampled"
print(f"✓ Sampled architecture: {arch}")

### Lab 3: Architecture Encoding

import numpy as np

def encode_architecture(architecture, search_space):
 """Encode architecture to vector"""
 encoding = []
 
 for key, value in architecture.items():
 idx = search_space[key].index(value) if isinstance(search_space[key][0], str) else np.where(np.array(search_space[key]) == value)[0][0]
 encoding.append(idx)
 
 return np.array(encoding)

np.random.seed(42)
space = {'depth': [3, 4, 5], 'width': [16, 32], 'kernel': [3, 5], 'act': ['relu', 'swish']}
arch = {'depth': 4, 'width': 32, 'kernel': 3, 'act': 'relu'}
encoding = encode_architecture(arch, space)
assert len(encoding) == 4, "Correct encoding"
print("✓ Architecture encoding working")

### Lab 4: Multi-Objective Scoring

import numpy as np

def multi_objective_score(accuracy, latency, lambda_param=0.1):
 """Compute multi-objective reward"""
 score = accuracy - lambda_param * latency
 return score

accuracy = 0.95
latency = 100 # ms
score = multi_objective_score(accuracy, latency, lambda_param=0.001)
assert isinstance(score, (int, float, np.number)), "Valid score"
print(f"✓ Multi-objective score: {score:.4f}")

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