Neural Architecture Search (NAS)
Automate model design with evolutionary algorithms and reinforcement learning. Master optimizing architectures for efficiency and accuracy.
Step-by-Step NAS Guide
Define Search Space
Specify possible operations (convolutions, skip connections) and network structure. We'll use a simplified cell-based search space.
Choose Strategy
Implement evolutionary search or reinforcement learning. We'll demonstrate evolutionary algorithms with Pytorch.
Code Example: NAS Pipeline
import torch
from nasbench_nlp import AutoNAS
# Initialize search
search = AutoNAS(space="nlp", dataset="cnn-10")
search.set_optimizer("evolution")
# Evolve architectures
results = search.evolve(generations=5,
population=100,
metrics='accuracy')
This example demonstrates an evolutionary search for Nlp architectures using CNN-10 dataset.
Customize this code →NAS Challenge
Task: Optimize MobileNet Variants
Modify the search space to prioritize mobile deployment. Balance FLOPS and accuracy for edge devices.
class MobileSearchSpace:
def __init__(self):
self.width_mult = [0.75, 1.0, 1.25]
self.depth_mult = [0.9, 1.1]
def get_config(self):
# Return random configuration
Ready to Deploy?
Your optimized architectures are now ready. Test them on real-world edge devices and compare performance to baseline models.