ghmole / enas

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

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

The Penn Treebank dataset is included at data/ptb. Depending on the system, you may want to run the script data/ptb/process.py to create the pkl version. All hyper-parameters are specified in these scripts.

To run the ENAS search process on Penn Treebank, please use the script

./scripts/ptb_search.sh

To run ENAS with a determined architecture, you have to specify the archiecture using a string. The following is an example script for using the architecture we described in our paper.

./scripts/ptb_final.sh

A sequence of architecture for a cell with N nodes can be specified using a sequence a of 2N + 1 tokens

• a[0] is a number in [0, 1, 2, 3], specifying the activation function to use at the first cell: tanh, ReLU, identity, and sigmoid.
• For each i, a[2*i] specifies a previous index and a[2*i+1] specifies the activation function at the i-th cell.

For a concrete example, the following sequence specifies the architecture we visualize in our paper

0 0 0 1 1 2 1 2 0 2 0 5 1 1 0 6 1 8 1 8 1 8 1

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}
}