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README.md

Neural Predictor for Neural Architecture Search

Wei Wen, Hanxiao Liu, Hai Li, Yiran Chen, Gabriel Bender, Pieter-Jan Kindermans. "Neural Predictor for Neural Architecture Search". arXiv:1912.00848. Paper link.

This is a open source reproduction in PyTorch.

Reproduction Results

All the results are run with the hyper-parameters provided in paper (default value in train.py), unless otherwise specified.

The following results are MSE. The lower, the better.

Train Split Eval Split Paper Reproduction Comments
172 all 1.95 3.62
860 all NA 2.94
172 denoise-80 NA 1.90
91-172 denoise-91 0.66 0.74 Paper used classifier to denoise
91-172 denoise-91 NA 0.56 epochs = 600, lr = 2e-4

NOTE: As the classifier is not ready, we cheated a little by directly filtering out all the architectures below 91%. The splits are called 91-.

TODO Items

  • Classifier (first stage)
  • Cross validation
  • E2E Architecture Selection

Preparation

Dependencies

  • PyTorch (cuda)
  • NasBench
  • h5py
  • matplotlib

Dataset

Download HDF5 version of NasBench from here and put it under data.

Then generate train/eval split:

python tools/split_train_val.py

Advanced: Build HDF5 Dataset from Scratch

Skip this step if you have downloaded the data from last step.

This step is to convert the tfrecord into a hdf5 file, as the official asset Google has provided is too slow to read (and very large in volume).

Download nasbench_full.tfrecord from NasBench, and put it under data. Then run

python tools/nasbench_tfrecord_converter.py

Splits

The following splits are provided for now:

  • 172, 334, 860: Randomly sampled architectures from NasBench.
  • 91-172, 91-334, 91-860: The splits above filtered with a threshold (validation accuracy 91% on seed 0).
  • denoise-91, denoise-80: All architectures filtered with threshold 91% and 80%.
  • all.

Train and Evaluation

Refer to python train.py -h for options. Training and evaluation are very fast (about 90 seconds on P100).

Implementation Details

HDF5 Format

The HDF5 is quite self-explanatory. You can refer to dataset.py for how to read it. The only thing I believe should be highlighted is that metrics is a 423624 x 4 (epochs: 4, 12, 36, 108) x 3 (seed: 0, 1, 2) x 2 (halfway, total) x 4 (training_time, train_accuracy, validation_accuracy, test_accuracy) matrix.

Modeling and Training

  • The paper didn't mention where to put dropout. So dropout is added after every layer. Nevertheless, the model still tends to overfit.
  • Uses xavier uniform for initialization. Bias for linear layer is turned off.
  • Paper didn't mention hyper-parameters other than lr and weight decay in Adam. We follow default settings in tensorflow 1.15.
  • We drop the last samples (less than batch size) in every epoch.
  • We normalize the labels (validation accuracy) with MEAN (90.8) and STD (2.4).
  • Resample with different seed in case the validation accuracy belows (< 15%).

Bad results for evaluation on "all"

A brief case study reveals that the bad results are mainly due to the "noise" in NasBench. In NasBench, there are two types of noises:

  1. Some training (0.63%) "blows". The results are about ~10%. We resample on such case. We can handle 99.85% (422979/423624), with others using mean accuracy directly.
  2. The result diversifies. About 1.2% of the architectures get accuracy lower than 80%, but they contribute a lot to MSE. We found that without sampling them in testing, the results improve and are on par with paper.

References

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Open source reproduction in PyTorch of "Neural Predictor for Neural Architecture Search".

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