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.
| 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-.
- 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:
- 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.
- 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.
