Efficient Neural Architecture Search via Parameter Sharing - implementation -

Melody mentions on her twitter feed that an implementation of her work is now available.

Efficient Neural Architecture Search via Parameter Sharing by Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean

We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Thanks to parameter sharing between child models, ENAS is fast: it delivers strong empirical performances using much fewer GPU-hours than all existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On the Penn Treebank dataset, ENAS discovers a novel architecture that achieves a test perplexity of 55.8, establishing a new state-of-the-art among all methods without post-training processing. On the CIFAR-10 dataset, ENAS designs novel architectures that achieve a test error of 2.89%, which is on par with NASNet (Zoph et al., 2018), whose test error is 2.65%.

The implementation in TensorFlow is here: https://github.com/melodyguan/enas and in PyTorch: https://github.com/carpedm20/ENAS-pytorch

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