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# Nueral Architecture Search
# Nueral Architecture Search (NAS)
This project contains the following neural architecture search algorithms, implemented in [PyTorch](http://pytorch.org). More NAS resources can be found in [Awesome-NAS](https://github.com/D-X-Y/Awesome-NAS).
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<img align="right" src="https://d-x-y.github.com/resources/paper-icon/ICCV-2019-SETN.png" width="450">
Highlight: we equip one-shot NAS with an architecture sampler and train network weights using uniformly sampling.
<strong>Highlight</strong>: we equip one-shot NAS with an architecture sampler and train network weights using uniformly sampling.
### Usage
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<img align="right" src="https://d-x-y.github.com/resources/paper-icon/CVPR-2019-GDAS.png" width="300">
We proposed a gradient-based searching algorithm using differentiable architecture sampling (improving DARTS with Gumbel-softmax sampling).
We proposed a Gradient-based searching algorithm using Differentiable Architecture Sampling (GDAS). GDAS is baseed on DARTS and improves it with Gumbel-softmax sampling.
Experiments on CIFAR-10, CIFAR-100, ImageNet, PTB, and WT2 are reported.
The old version is located at [`others/GDAS`](https://github.com/D-X-Y/NAS-Projects/tree/master/others/GDAS) and a paddlepaddle implementation is locate at [`others/paddlepaddle`](https://github.com/D-X-Y/NAS-Projects/tree/master/others/paddlepaddle).
### Usage
Please use the following scripts to train the searched GDAS-searched CNN on CIFAR-10, CIFAR-100, and ImageNet.