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README.md

Efficient Neural Architecture Search via Parameter Sharing

Authors' implementation of "Efficient Neural Architecture Search via Parameter Sharing" (2018) in TensorFlow.

Includes code for CIFAR-10 image classification and Penn Tree Bank language modeling tasks.

Paper: https://arxiv.org/abs/1802.03268

Authors: Hieu Pham*, Melody Y. Guan*, Barret Zoph, Quoc V. Le, Jeff Dean

This is not an official Google product.

Penn Treebank

IMPORTANT ERRATA: The implementation of Language Model on this repository is wrong. Please do not use it. The correct implementation is at the new repository. We apologize for the inconvenience.

CIFAR-10

To run the experiments on CIFAR-10, please first download the dataset. Again, all hyper-parameters are specified in the scripts that we descibe below.

To run the ENAS experiments on the macro search space as described in our paper, please use the following scripts:

./scripts/cifar10_macro_search.sh
./scripts/cifar10_macro_final.sh

A macro architecture for a neural network with N layers consists of N parts, indexed by 1, 2, 3, ..., N. Part i consists of:

  • A number in [0, 1, 2, 3, 4, 5] that specifies the operation at layer i-th, corresponding to conv_3x3, separable_conv_3x3, conv_5x5, separable_conv_5x5, average_pooling, max_pooling.
  • A sequence of i - 1 numbers, each is either 0 or 1, indicating whether a skip connection should be formed from a the corresponding past layer to the current layer.

A concrete example can be found in our script ./scripts/cifar10_macro_final.sh.

To run the ENAS experiments on the micro search space as described in our paper, please use the following scripts:

./scripts/cifar10_micro_search.sh
./scripts/cifar10_micro_final.sh

A micro cell with B + 2 blocks can be specified using B blocks, corresponding to blocks numbered 2, 3, ..., B+1, each block consists of 4 numbers

index_1, op_1, index_2, op_2

Here, index_1 and index_2 can be any previous index. op_1 and op_2 can be [0, 1, 2, 3, 4], corresponding to separable_conv_3x3, separable_conv_5x5, average_pooling, max_pooling, identity.

A micro architecture can be specified by two sequences of cells concatenated after each other, as shown in our script ./scripts/cifar10_micro_final.sh

Citations

If you happen to use our work, please consider citing our paper.

@inproceedings{enas,
  title     = {Efficient Neural Architecture Search via Parameter Sharing},
  author    = {Pham, Hieu and
               Guan, Melody Y. and
               Zoph, Barret and
               Le, Quoc V. and
               Dean, Jeff
  },
  booktitle = {ICML},
  year      = {2018}
}

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TensorFlow Code for paper "Efficient Neural Architecture Search via Parameter Sharing"

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