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.

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.

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-.

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

• PyTorch (cuda)
• NasBench
• h5py
• matplotlib

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

Then generate train/eval split:

python tools/split_train_val.py

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

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.

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

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.

• 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%).

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.

• graph-cnn.pytorch
• pygcn