Genetic Neural Architecture Search (GeneticNAS)

The genetic neural architecture search (GeneticNAS) is a neural architecture search method that is based on genetic algorithm which utilized weight sharing accross all candidate network. The project paper:https://arxiv.org/abs/1907.02871

Includes code for CIFAR-10 and CIFAR-100 image classification

Installation

The first is install all the flowing prerequisites using conda:

• pytorch
• graphviz
• pygraphviz
• numpy

    conda install graphviz
    conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
    conda install pygraphviz
    conda install numpy

Examples Run Search

In this section provide exmaple of how to run architecture search on there dataset CIFAR10 and CIFAR100, at the end of search a log folder is create under the current folder

CIFAR 10

    python main.py --dataset_name CIFAR10 --config_file ./configs/config_cnn_search_cifar10.json

CIFAR 100

    python main.py --dataset_name CIFAR100 --config_file ./configs/config_cnn_search_cifar100.json

Examples Run Final Training

In this section provide exmaple of how to run final training search on there dataset CIFAR10 and CIFAR100, where $LOG_DIR is the log folder of the search result.

CIFAR 10

    python main.py --dataset_name CIFAR10 --final 1 --serach_dir $LOG_DIR --config_file ./configs/config_cnn_final_cifar10.json

CIFAR 100

    python main.py --dataset_name CIFAR100 --final 1 --serach_dir $LOG_DIR --config_file ./configs/config_cnn_final_cifar10.json

Result

CIFAR10 Counvulation Cell

CIFAR100 Counvulation Cell

Counvulation cell final result

Dataset	Accuracy[%]
CIFAR10	96%
CIFAR100	80.1%