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D-X-Y 2021-03-01 20:34:34 +08:00
parent 429cfd3b9a
commit af3ce50a65
5 changed files with 97 additions and 2 deletions
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3
.gitmodules vendored
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[submodule "qlib-git"]
path = .latent-data/qlib
url = git@github.com:microsoft/qlib.git
[submodule ".latent-data/qlib"]
path = .latent-data/qlib
url = git@github.com:microsoft/qlib.git

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.latent-data/qlib Submodule

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Subproject commit a96f0c2e5fa427ce33660c8791fa2057c6f423fc

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README.md
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@ -113,7 +113,7 @@ If you find that this project helps your research, please consider citing the re
note = {\mbox{doi}:\url{10.1109/TPAMI.2021.3054824}}
}
@inproceedings{dong2020nasbench201,
title = {NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
title = {{NAS-Bench-201}: Extending the Scope of Reproducible Neural Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {International Conference on Learning Representations (ICLR)},
url = {https://openreview.net/forum?id=HJxyZkBKDr},

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README_CN.md
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@ -107,7 +107,7 @@ Some methods use knowledge distillation (KD), which require pre-trained models.
note = {\mbox{doi}:\url{10.1109/TPAMI.2021.3054824}}
}
@inproceedings{dong2020nasbench201,
title = {NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
title = {{NAS-Bench-201}: Extending the Scope of Reproducible Neural Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {International Conference on Learning Representations (ICLR)},
url = {https://openreview.net/forum?id=HJxyZkBKDr},

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notebooks/NATS-Bench/issue-96.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2021-03-01 12:28:12] Try to use the default NATS-Bench (topology) path from fast_mode=True and path=None.\n"
]
}
],
"source": [
"from nats_bench import create\n",
"import numpy as np\n",
"\n",
"def get_correlation(A, B):\n",
" return float(np.corrcoef(A, B)[0,1])\n",
"\n",
"# Create the API for tologoy search space\n",
"api = create(None, 'tss', fast_mode=True, verbose=False)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 15625 architectures on the topology search space\n"
]
}
],
"source": [
"print('There are {:} architectures on the topology search space'.format(len(api)))\n",
"accuracies_12, accuracies_200 = [], []\n",
"for i, arch in enumerate(api):\n",
" info_a = api.get_more_info(i, dataset='cifar10-valid', hp='12', is_random=False)\n",
" accuracies_12.append(info_a['valid-accuracy'])\n",
"\n",
" info_b = api.get_more_info(i, dataset='cifar10-valid', hp='200', is_random=False)\n",
" accuracies_200.append(info_b['test-accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CIFAR-10] The correlation between 12-epoch validation accuracy and 200-epoch test accuracy is: 91.18%\n"
]
}
],
"source": [
"correlation = get_correlation(accuracies_12, accuracies_200)\n",
"print('[CIFAR-10] The correlation between 12-epoch validation accuracy and 200-epoch test accuracy is: {:.2f}%'.format(correlation * 100))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}