Neural Architecture Search and Automl
# Neural Architecture Search and AutoML
## Introduction & Motivation
Neural Architecture Search (NAS) automates model design exploration, eliminating manual engineering. Critical for specialized domains where optimal architectures are unknown and computational budgets enable automated discovery of superior designs.
Motivation: Automate neural network design for specialized tasks.
Applications: Model design automation, hyperparameter optimization, architecture discovery, efficient network design.
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## Core Concepts & Theory
### Architecture Space
Design parameter ranges.
### Search Strategy
Exploration algorithms.
### Performance Prediction
Early stopping and surrogates.
### Hardware Efficiency
Latency and memory constraints.
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## Mathematical Formulation
Architecture Encoding:
$$\mathbf{a} = [op_1, op_2, \ldots, op_k]$$
Multi-Objective:
$$\max_\mathbf{a} ext{Accuracy}(\mathbf{a}) - \lambda \cdot ext{Latency}(\mathbf{a})$$
Search Reward:
$$R = ext{Validation Accuracy} - \lambda \cdot ext{Computational Cost}$$
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## Advanced Theory & Extensions
### Differentiable Architecture Search
Gradient-based search.
### Evolutionary Algorithms
Population-based search.
### Reinforcement Learning
RL-based architecture generation.
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## Computational Considerations
Search: O(C·S) for C architectures, S searches.
Training: O(A·D·T) for A architectures, D data, T time.
Total: Expensive depending on search space.
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## Practical Implementation Strategies
### Search Space Design
Limiting architecture variations.
### Early Stopping
Quick architecture evaluation.
### Hardware-Aware Search
Deployment constraints.
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## Benchmark Datasets & Evaluation
ImageNet: Vision architectures.
Text: NLP architectures.
TabNet: Tabular data.
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## Key Challenges & Limitations
### Search Cost
Computational expense.
### Transferability
Generalization across datasets.
### Reproducibility
Stochastic search variation.
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## Hyperparameter Tuning
Search population: 10-100.
Generations: 50-500.
Mutation rate: 0.1-0.5.
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## Real-World Applications & Case Studies
Vision: CNN architecture discovery.
NLP: Transformer variants.
Engineering: Domain-specific networks.
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## Integration with Other Methods
NAS + transfer learning; + ensemble methods; + knowledge distillation.
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## Summary & Key Takeaways
NAS automates architecture design discovery.
Principles:
1. Space: Define architecture options.
2. Search: Explore configurations.
3. Evaluation: Assess performance.
4. Selection: Choose best architectures.
5. Deployment: Use discovered designs.
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## Appendix: Practical Labs
### Lab 1: Architecture Encoding
import numpy as np
class ArchitectureEncoding:
def __init__(self, n_operations=5, n_layers=3):
self.n_ops = n_operations
self.n_layers = n_layers
def generate_random_arch(self):
"""Random architecture"""
return np.random.randint(0, self.n_ops, self.n_layers)
def mutate_arch(self, arch, mutation_rate=0.2):
"""Mutate architecture"""
mutated = arch.copy()
for i in range(len(mutated)):
if np.random.random() < mutation_rate:
mutated[i] = np.random.randint(0, self.n_ops)
return mutated
encoder = ArchitectureEncoding(n_operations=5, n_layers=3)
arch = encoder.generate_random_arch()
print(f"✓ Architecture: {arch}")### Lab 2: Performance Prediction
import numpy as np
class PerformancePredictor:
def __init__(self):
self.weights = np.random.randn(5) * 0.1
def predict_accuracy(self, arch_features):
"""Predict validation accuracy"""
return 0.7 + 0.2 * (arch_features @ self.weights)
def predict_latency(self, arch_features):
"""Predict inference latency"""
return 100 * np.exp(0.1 * np.sum(arch_features))
predictor = PerformancePredictor()
features = np.random.rand(5)
acc = predictor.predict_accuracy(features)
lat = predictor.predict_latency(features)
print(f"✓ Predicted accuracy: {acc:.3f}")
print(f"✓ Predicted latency: {lat:.1f} ms")### Lab 3: Evolutionary Search
import numpy as np
class EvolutionaryArchitectureSearch:
def __init__(self, pop_size=10, n_layers=3):
self.pop_size = pop_size
self.n_layers = n_layers
self.population = [np.random.randint(0, 5, n_layers) for _ in range(pop_size)]
self.fitness = np.zeros(pop_size)
def evaluate_fitness(self, arch):
"""Fitness score"""
return np.random.rand() * 0.5 + np.sum(arch) * 0.01
def evolve_generation(self):
"""One evolution step"""
for i in range(self.pop_size):
self.fitness[i] = self.evaluate_fitness(self.population[i])
# Selection
best_idx = np.argsort(self.fitness)[-2:]
# Crossover and mutation
for i in range(self.pop_size):
if np.random.random() < 0.8:
parent = self.population[best_idx[0]]
else:
parent = self.population[best_idx[1]]
self.population[i] = parent.copy()
if np.random.random() < 0.2:
self.population[i][np.random.randint(self.n_layers)] = np.random.randint(0, 5)
nas = EvolutionaryArchitectureSearch(pop_size=10, n_layers=3)
for gen in range(5):
nas.evolve_generation()
best_idx = np.argmax(nas.fitness)
print(f"✓ Best architecture fitness: {nas.fitness[best_idx]:.3f}")### Lab 4: Hardware-Aware Search
import numpy as np
class HardwareAwareNAS:
def __init__(self):
self.latency_budget = 50 # ms
self.memory_budget = 1000 # MB
def estimate_latency(self, architecture):
"""Estimate inference latency"""
return 10 + np.sum(architecture) * 5
def estimate_memory(self, architecture):
"""Estimate model memory"""
return 100 + np.sum(architecture) ** 2 * 10
def is_feasible(self, architecture):
"""Check hardware constraints"""
latency = self.estimate_latency(architecture)
memory = self.estimate_memory(architecture)
return (latency < self.latency_budget) and (memory < self.memory_budget)
def search_feasible_archs(self, n_candidates=100):
"""Find feasible architectures"""
feasible = []
for _ in range(n_candidates):
arch = np.random.randint(0, 5, 3)
if self.is_feasible(arch):
feasible.append(arch)
return feasible
hw_nas = HardwareAwareNAS()
feasible_archs = hw_nas.search_feasible_archs(n_candidates=100)
print(f"✓ Found {len(feasible_archs)} feasible architectures")---