git clone quark0-darts_-_2018-06-26_19-10-09.bundle -b master
Code accompanying the paper
DARTS: Differentiable Architecture Search\ Hanxiao Liu, Karen Simonyan, Yiming Yang.\ arXiv:1806.09055.
The algorithm is based on continuous relaxation and gradient descent in the architecture space. It is able to efficiently design high-performance convolutional architectures for image classification (on CIFAR-10 and ImageNet) and recurrent architectures for language modeling (on Penn Treebank and WikiText-2). Only a single GPU is required.
Python >= 3.5.5, PyTorch == 0.3.1, torchvision >= 0.2.1PyTorch 0.4 will be supported soon.
Instructions for acquiring PTB and WT2 can be found here. While CIFAR-10 can be automatically downloaded by torchvision, ImageNet needs to be manually downloaded (preferably to a SSD) following the instructions here.
To carry out architecture search, run
cd cnn && python train_search.py --unrolled # for conv cells on CIFAR-10cd rnn && python train_search.py --unrolled # for recurrent cells on PTBSnapshots of the most likely convolutional & recurrent cells over time:
To reproduce our results using the best cells, run
cd cnn && python train.py --auxiliary --cutout # CIFAR-10cd rnn && python train.py # PTBcd rnn && python train.py --data ../data/wikitext-2 \ # WT2 --dropouth 0.15 --emsize 700 --nhidlast 700 --nhid 700 --wdecay 5e-7cd cnn && python train_imagenet.py --auxiliary # ImageNetCustomized architectures are supported through the
--arch flag once specified in