|
8 | 8 | "source": [ |
9 | 9 | "# Optimizing AUPRC Loss on imbalanced dataset\n", |
10 | 10 | "\n", |
11 | | - "**Author**: Gang Li \\\\\n", |
12 | | - "**Edited by:** Zhuoning Yuan\n", |
| 11 | + "**Author**: Gang Li \n", |
| 12 | + "**Edited by**: Zhuoning Yuan\n", |
13 | 13 | "\n", |
14 | 14 | "In this tutorial, you will learn how to quickly train a Resnet18 model by optimizing **AUPRC** loss with **SOAP** optimizer [[ref]](https://arxiv.org/abs/2104.08736) on a binary image classification task with CIFAR-10 dataset. After completion of this tutorial, you should be able to use LibAUC to train your own models on your own datasets.\n", |
15 | 15 | "\n", |
|
87 | 87 | }, |
88 | 88 | { |
89 | 89 | "cell_type": "code", |
90 | | - "execution_count": 43, |
| 90 | + "execution_count": null, |
91 | 91 | "metadata": { |
92 | 92 | "id": "8adcc7b0" |
93 | 93 | }, |
|
133 | 133 | }, |
134 | 134 | { |
135 | 135 | "cell_type": "code", |
136 | | - "execution_count": 44, |
| 136 | + "execution_count": null, |
137 | 137 | "metadata": { |
138 | 138 | "id": "27ef8526" |
139 | 139 | }, |
|
172 | 172 | }, |
173 | 173 | { |
174 | 174 | "cell_type": "code", |
175 | | - "execution_count": 45, |
| 175 | + "execution_count": null, |
176 | 176 | "metadata": { |
177 | 177 | "colab": { |
178 | 178 | "base_uri": "https://localhost:8080/", |
|
250 | 250 | }, |
251 | 251 | { |
252 | 252 | "cell_type": "code", |
253 | | - "execution_count": 50, |
| 253 | + "execution_count": null, |
254 | 254 | "metadata": { |
255 | 255 | "id": "b90ddd75" |
256 | 256 | }, |
|
307 | 307 | }, |
308 | 308 | { |
309 | 309 | "cell_type": "code", |
310 | | - "execution_count": 51, |
| 310 | + "execution_count": null, |
311 | 311 | "metadata": { |
312 | 312 | "id": "e5c396c4" |
313 | 313 | }, |
|
410 | 410 | }, |
411 | 411 | { |
412 | 412 | "cell_type": "code", |
413 | | - "execution_count": 55, |
| 413 | + "execution_count": null, |
414 | 414 | "metadata": { |
415 | 415 | "id": "5a73b4e7" |
416 | 416 | }, |
|
438 | 438 | }, |
439 | 439 | { |
440 | 440 | "cell_type": "code", |
441 | | - "execution_count": 56, |
| 441 | + "execution_count": null, |
442 | 442 | "metadata": { |
443 | 443 | "id": "08370a67" |
444 | 444 | }, |
|
465 | 465 | }, |
466 | 466 | { |
467 | 467 | "cell_type": "code", |
468 | | - "execution_count": 57, |
| 468 | + "execution_count": null, |
469 | 469 | "metadata": { |
470 | 470 | "colab": { |
471 | 471 | "base_uri": "https://localhost:8080/" |
|
611 | 611 | }, |
612 | 612 | { |
613 | 613 | "cell_type": "code", |
614 | | - "execution_count": 58, |
| 614 | + "execution_count": null, |
615 | 615 | "metadata": { |
616 | 616 | "id": "b18d7de4" |
617 | 617 | }, |
|
637 | 637 | }, |
638 | 638 | { |
639 | 639 | "cell_type": "code", |
640 | | - "execution_count": 64, |
| 640 | + "execution_count": null, |
641 | 641 | "metadata": { |
642 | 642 | "colab": { |
643 | 643 | "base_uri": "https://localhost:8080/", |
|
0 commit comments