Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search

This is the code repository for Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search .

Requirements

To install the following dependencies:

• Python >= 3.6.0
• scikit-learn
• nas-bench-201==1.3

Download the NAS-Bench-201 dataset from here and put it in the current folder.

Prestore data for 5000 valid architectures

python prestore_arch_data.py

Compare rank correlation performance of various performance estimators

python rank_correlation_comparison.py