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https://github.com/anonymone/ace-nas
https://github.com/anonymone/ace-nas
Last synced: 22 days ago
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- Host: GitHub
- URL: https://github.com/anonymone/ace-nas
- Owner: anonymone
- License: gpl-3.0
- Created: 2019-04-03T02:01:36.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-05-03T20:13:36.000Z (6 months ago)
- Last Synced: 2024-05-03T21:31:06.931Z (6 months ago)
- Language: Python
- Size: 3.25 MB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ACEncoding
Code accompanying the paper
>*Action Command Encoding for Surrogate Assisted Neural Architecture Search*
ACEncoding is an encoding strategy of NAS. The proposed ACEncoding is inspired by the process of gene expression, where each architecture is constructed by a series of action commands and each action command is encoded by three integers.
## Requirements
```
pip install -r requirements.txt
# Surrogate dependence
git clone https://github.com/anonymone/pytorch-seq2seq.git ./Res/seq2seq
cd ./Res/Seq2Seq/
python3 setup.py install
```
## Neural Architecture Search
```
# Evolutionary Algorithm
sh search_EA.sh
# Reinforcement Learning
sh search_RL.sh
# Gradient Descent
sh search_GD.sh
# Random (Baseline)
sh search_RD.sh
```
## Neural Architecture Validation
Your need to specify the paramenter `$ENCODE = "Your ACEncoding String."`
```
# CIFAR-10, CIFAR-100, Fashion-MNIST
# You need to specify the parameter $DATASET = ["CIFAR10", "CIFAR100", "Fashion-MNIST"]
sh valid_CIFAR.sh
# ImageNet
sh valid_ImageNet.sh
```
If your GPU server has more than one GPU. You can choose the GPU by changing `$DEVICE = cuda:$ID`. `$ID` is the ID of GPUs.
## Visualization
In our framework, each neural architecture is saved as a visual DOT file.
### Install Graphviz
```
# CentOS
sudo yum install graphviz
# Ubuntu
sudo apt install graphviz
# MacOS
brew install graphviz
```
### Usage
```
# Command Line
dot -Tpng YOUR_DOT_FILE.dot -o OUTPUT.png
# We highly recommand to use VScode extension Graphviz Preview.
# And then, you can check your DOT file in a convenience way.
```
## Acknowledgement
Code inspired and modified from [NSGA-Net](https://github.com/ianwhale/nsga-net), [NAO](https://github.com/renqianluo/NAO_pytorch), [MetaQNN](https://github.com/bowenbaker/metaqnn), [ENAS](https://github.com/melodyguan/enas) and [IBM/Seq2Seq-torch](https://github.com/IBM/pytorch-seq2seq).