-
Notifications
You must be signed in to change notification settings - Fork 257
/
Copy pathdeep_learning_tutorial.html
1054 lines (847 loc) ยท 70.6 KB
/
deep_learning_tutorial.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>PyTorch๋ฅผ ์ด์ฉํ ๋ฅ๋ฌ๋ — PyTorch Tutorials 1.10.2+cu102 documentation</title>
<link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
<!-- <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" /> -->
<link rel="stylesheet" href="../../_static/copybutton.css" type="text/css" />
<link rel="stylesheet" href="../../_static/gallery.css" type="text/css" />
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css" type="text/css" />
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" type="text/css" />
<link rel="stylesheet" href="../../_static/katex-math.css" type="text/css" />
<link rel="index" title="Index" href="../../genindex.html" />
<link rel="search" title="Search" href="../../search.html" />
<script src="../../_static/js/modernizr.min.js"></script>
<!-- Preload the theme fonts -->
<link rel="preload" href="../../_static/fonts/FreightSans/freight-sans-book.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../_static/fonts/FreightSans/freight-sans-medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../_static/fonts/IBMPlexMono/IBMPlexMono-Medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../_static/fonts/FreightSans/freight-sans-bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../_static/fonts/FreightSans/freight-sans-medium-italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../_static/fonts/IBMPlexMono/IBMPlexMono-SemiBold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<!-- Preload the katex fonts -->
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Math-Italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Main-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Main-Bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size1-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size4-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size2-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size3-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Caligraphic-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.15.2/css/all.css" integrity="sha384-vSIIfh2YWi9wW0r9iZe7RJPrKwp6bG+s9QZMoITbCckVJqGCCRhc+ccxNcdpHuYu" crossorigin="anonymous">
</head>
<div class="container-fluid header-holder tutorials-header" id="header-holder">
<div class="container">
<div class="header-container">
<a class="header-logo" href="https://pytorch.kr/" aria-label="PyTorch"></a>
<div class="main-menu">
<ul>
<li>
<a href="https://pytorch.kr/get-started">์์ํ๊ธฐ</a>
</li>
<li class="active">
<a href="https://tutorials.pytorch.kr">ํํ ๋ฆฌ์ผ</a>
</li>
<li>
<a href="https://pytorch.kr/hub">ํ๋ธ</a>
</li>
<li>
<a href="https://discuss.pytorch.kr">์ปค๋ฎค๋ํฐ</a>
</li>
</ul>
</div>
<a class="main-menu-open-button" href="#" data-behavior="open-mobile-menu"></a>
</div>
</div>
</div>
<body class="pytorch-body">
<div class="table-of-contents-link-wrapper">
<span>Table of Contents</span>
<a href="#" class="toggle-table-of-contents" data-behavior="toggle-table-of-contents"></a>
</div>
<nav data-toggle="wy-nav-shift" class="pytorch-left-menu" id="pytorch-left-menu">
<div class="pytorch-side-scroll">
<div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
<div class="pytorch-left-menu-search">
<div class="version">
1.10.2+cu102
</div>
<div role="search">
<form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
<input type="text" name="q" placeholder="Search Tutorials" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../recipes/recipes_index.html">๋ชจ๋ ๋ ์ํผ ๋ณด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../prototype/prototype_index.html">๋ชจ๋ ํ๋กํ ํ์
๋ ์ํผ ๋ณด๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ์์ํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../basics/data_tutorial.html">Dataset๊ณผ DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../basics/autogradqs_tutorial.html"><code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code>๋ฅผ ์ฌ์ฉํ ์๋ ๋ฏธ๋ถ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../introyt.html">Introduction to PyTorch - YouTube Series</a></li>
<li class="toctree-l1"><a class="reference internal" href="../introyt/introyt1_tutorial.html">Introduction to PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../introyt/tensors_deeper_tutorial.html">Introduction to PyTorch Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="../introyt/autogradyt_tutorial.html">The Fundamentals of Autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="../introyt/modelsyt_tutorial.html">Building Models with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../introyt/tensorboardyt_tutorial.html">PyTorch TensorBoard Support</a></li>
<li class="toctree-l1"><a class="reference internal" href="../introyt/trainingyt.html">Training with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../introyt/captumyt.html">Model Understanding with Captum</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../nn_tutorial.html"><cite>torch.nn</cite> ์ด <em>์ค์ ๋ก</em> ๋ฌด์์ธ๊ฐ์?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/tensorboard_tutorial.html">TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/torchvision_tutorial.html">TorchVision ๊ฐ์ฒด ๊ฒ์ถ ๋ฏธ์ธ์กฐ์ (Finetuning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dcgan_faces_tutorial.html">DCGAN ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ค๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../audio_io_tutorial.html">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="../audio_resampling_tutorial.html">Audio Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../audio_feature_extractions_tutorial.html">Audio Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../audio_feature_augmentation_tutorial.html">Audio Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../audio_datasets_tutorial.html">Audio Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/speech_recognition_pipeline_tutorial.html">Speech Recognition with Wav2Vec2</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/speech_command_classification_with_torchaudio_tutorial.html">Speech Command Classification with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/text_to_speech_with_torchaudio.html">Text-to-speech with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/forced_alignment_with_torchaudio_tutorial.html">Forced Alignment with Wav2Vec2</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ
์คํธ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../transformer_tutorial.html">nn.Transformer ์ TorchText ๋ก ์ํ์ค-ํฌ-์ํ์ค(Sequence-to-Sequence) ๋ชจ๋ธ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/char_rnn_classification_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/char_rnn_generation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ์์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/seq2seq_translation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: Sequence to Sequence ๋คํธ์ํฌ์ Attention์ ์ด์ฉํ ๋ฒ์ญ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../text_sentiment_ngrams_tutorial.html">torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../translation_transformer.html">nn.Transformer์ torchtext๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">๊ฐํํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/reinforcement_q_learning.html">๊ฐํ ํ์ต (DQN) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/mario_rl_tutorial.html">Train a Mario-playing RL Agent</a></li>
</ul>
<p class="caption"><span class="caption-text">PyTorch ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/flask_rest_api_tutorial.html">Flask๋ฅผ ์ฌ์ฉํ์ฌ Python์์ PyTorch๋ฅผ REST API๋ก ๋ฐฐํฌํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../Intro_to_TorchScript_tutorial.html">TorchScript ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_export.html">C++์์ TorchScript ๋ชจ๋ธ ๋ก๋ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/super_resolution_with_onnxruntime.html">(์ ํ) PyTorch ๋ชจ๋ธ์ ONNX์ผ๋ก ๋ณํํ๊ณ ONNX ๋ฐํ์์์ ์คํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">Code Transforms with FX</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/fx_conv_bn_fuser.html">(๋ฒ ํ) FX์์ ํฉ์ฑ๊ณฑ/๋ฐฐ์น ์ ๊ทํ(Convolution/Batch Norm) ๊ฒฐํฉ๊ธฐ(Fuser) ๋ง๋ค๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/fx_profiling_tutorial.html">(beta) Building a Simple CPU Performance Profiler with FX</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ๋ก ํธ์๋ API</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(๋ฒ ํ) PyTorch๋ฅผ ์ฌ์ฉํ Channels Last ๋ฉ๋ชจ๋ฆฌ ํ์</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/forward_ad_usage.html">Forward-mode Automatic Differentiation (Beta)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">PyTorch C++ ํ๋ก ํธ์๋ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/torch-script-parallelism.html">TorchScript์ ๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ(Dynamic Parallelism)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_autograd.html">C++ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ (autograd)</a></li>
</ul>
<p class="caption"><span class="caption-text">PyTorch ํ์ฅํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/custom_function_conv_bn_tutorial.html">Fusing Convolution and Batch Norm using Custom Function</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_classes.html">์ปค์คํ
C++ ํด๋์ค๋ก TorchScript ํ์ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/dispatcher.html">Registering a Dispatched Operator in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/extend_dispatcher.html">Extending dispatcher for a new backend in C++</a></li>
</ul>
<p class="caption"><span class="caption-text">๋ชจ๋ธ ์ต์ ํ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋ง ํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/tensorboard_profiler_tutorial.html">PyTorch Profiler With TensorBoard</a></li>
<li class="toctree-l1"><a class="reference internal" href="../hyperparameter_tuning_tutorial.html">Hyperparameter tuning with Ray Tune</a></li>
<li class="toctree-l1"><a class="reference internal" href="../vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/parametrizations.html">Parametrizations Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">๊ฐ์ง์น๊ธฐ ๊ธฐ๋ฒ(Pruning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(๋ฒ ํ) LSTM ๊ธฐ๋ฐ ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ์ ๋์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(๋ฒ ํ) BERT ๋ชจ๋ธ ๋์ ์์ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(๋ฒ ํ) ์ปดํจํฐ ๋น์ ํํ ๋ฆฌ์ผ์ ์ํ ์์ํ๋ ์ ์ดํ์ต(Quantized Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
</ul>
<p class="caption"><span class="caption-text">๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../dist_overview.html">PyTorch Distributed Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/model_parallel_tutorial.html">๋จ์ผ ๋จธ์ ์ ์ฌ์ฉํ ๋ชจ๋ธ ๋ณ๋ ฌํ ๋ชจ๋ฒ ์ฌ๋ก</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ ์์ํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/dist_tuto.html">PyTorch๋ก ๋ถ์ฐ ์ดํ๋ฆฌ์ผ์ด์
๊ฐ๋ฐํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/rpc_param_server_tutorial.html">Implementing a Parameter Server Using Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/dist_pipeline_parallel_tutorial.html">Distributed Pipeline Parallelism Using RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/rpc_async_execution.html">Implementing Batch RPC Processing Using Asynchronous Executions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/rpc_ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ(DDP)๊ณผ ๋ถ์ฐ RPC ํ๋ ์์ํฌ ๊ฒฐํฉ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../intermediate/pipeline_tutorial.html">ํ์ดํ๋ผ์ธ ๋ณ๋ ฌํ๋ก ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต์ํค๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/ddp_pipeline.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ์ ๋ณ๋ ฌ ์ฒ๋ฆฌ ํ์ดํ๋ผ์ธ์ ์ฌ์ฉํ ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
</ul>
<p class="caption"><span class="caption-text">Mobile</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../deeplabv3_on_ios.html">iOS์์์ ์ด๋ฏธ์ง ๋ถํ DeepLapV3</a></li>
<li class="toctree-l1"><a class="reference internal" href="../deeplabv3_on_android.html">์๋๋ก์ด๋์์์ ์ด๋ฏธ์ง ๋ถํ DeepLapV3</a></li>
</ul>
</div>
</div>
</nav>
<div class="pytorch-container">
<div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
<div class="pytorch-breadcrumbs-wrapper">
<div role="navigation" aria-label="breadcrumbs navigation">
<ul class="pytorch-breadcrumbs">
<li>
<a href="../../index.html">
Tutorials
</a> >
</li>
<li>PyTorch๋ฅผ ์ด์ฉํ ๋ฅ๋ฌ๋</li>
<li class="pytorch-breadcrumbs-aside">
<a href="../../_sources/beginner/nlp/deep_learning_tutorial.rst.txt" rel="nofollow"><img src="../../_static/images/view-page-source-icon.svg"></a>
</li>
</ul>
</div>
</div>
<div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
Shortcuts
</div>
</div>
<section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
<div class="pytorch-content-left">
<div class="pytorch-call-to-action-links">
<div id="tutorial-type">beginner/nlp/deep_learning_tutorial</div>
<div id="google-colab-link">
<img class="call-to-action-img" src="../../_static/images/pytorch-colab.svg"/>
<div class="call-to-action-desktop-view">Run in Google Colab</div>
<div class="call-to-action-mobile-view">Colab</div>
</div>
<div id="download-notebook-link">
<img class="call-to-action-notebook-img" src="../../_static/images/pytorch-download.svg"/>
<div class="call-to-action-desktop-view">Download Notebook</div>
<div class="call-to-action-mobile-view">Notebook</div>
</div>
<div id="github-view-link">
<img class="call-to-action-img" src="../../_static/images/pytorch-github.svg"/>
<div class="call-to-action-desktop-view">View on GitHub</div>
<div class="call-to-action-mobile-view">GitHub</div>
</div>
</div>
<div class="rst-content">
<div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
<article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
<div class="sphx-glr-download-link-note admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Click <a class="reference internal" href="#sphx-glr-download-beginner-nlp-deep-learning-tutorial-py"><span class="std std-ref">here</span></a> to download the full example code</p>
</div>
<div class="sphx-glr-example-title section" id="pytorch">
<span id="sphx-glr-beginner-nlp-deep-learning-tutorial-py"></span><h1>PyTorch๋ฅผ ์ด์ฉํ ๋ฅ๋ฌ๋<a class="headerlink" href="#pytorch" title="Permalink to this headline">ยถ</a></h1>
<p><strong>๋ฒ์ญ</strong>: <a class="reference external" href="https://github.com/adonisues">ํฉ์ฑ์</a></p>
<div class="section" id="affine-maps">
<h2>๋ฅ๋ฌ๋ ๋ธ๋ก ๊ตฌ์ถ : ์ํ ๋งต(affine maps), ๋น์ ํ์ฑ, ๊ฐ์ฒด<a class="headerlink" href="#affine-maps" title="Permalink to this headline">ยถ</a></h2>
<p>๋ฅ๋ฌ๋์ ์๋ฆฌํ ๋ฐฉ๋ฒ์ผ๋ก ๋น์ ํ์ฑ์ ๊ฐ์ง ์ ํ์ฑ์ ๊ตฌ์ฑํ๋ ๊ฒ์ผ๋ก
์ด๋ฃจ์ด์ง๋๋ค. ๋น์ ํ์ฑ์ ๋์
์ ๊ฐ๋ ฅํ ๋ชจ๋ธ์ ๊ฐ๋ฅํ๊ฒ ํฉ๋๋ค.
์ด ์น์
์์ ์ด ํต์ฌ ๊ตฌ์ฑ ์์๋ฅผ ๋ค๋ฃจ๊ณ , ๊ฐ์ฒด ํจ์๋ฅผ ๋ง๋ค๊ณ , ์ด๋ป๊ฒ
๋ชจ๋ธ์ด ํ์ต๋์ง๋ ์ดํด๋ด
์๋ค.</p>
<div class="section" id="id2">
<h3>์ํ ๋งต<a class="headerlink" href="#id2" title="Permalink to this headline">ยถ</a></h3>
<p>๋ฅ๋ฌ๋์ ํต์ฌ ์์
์ ์ค ํ๋๋ ์ํ ๋งต ์
๋๋ค.
์ด ํจ์ <span class="math">\(f(x)\)</span> ๋ ๋ค์๊ณผ ๊ฐ์ต๋๋ค.</p>
<div class="math">
\[f(x) = Ax + b
\]</div>
<p>์ฌ๊ธฐ์ <span class="math">\(A\)</span> ๋ ํ๋ ฌ, <span class="math">\(x, b\)</span> ๋ ๋ฒกํฐ ์
๋๋ค.
์ฌ๊ธฐ์ ํ์ต๋๋ ๋ณ์๋ <span class="math">\(A\)</span> ์ <span class="math">\(b\)</span> ์
๋๋ค.
์ข
์ข
<span class="math">\(b\)</span> ๋ <em>ํธํฅ(Bias)</em> ์ด๋ผ ๋ถ๋ฆฝ๋๋ค.</p>
<p>PyTorch ์ ๋๋ถ๋ถ์ ๋ค๋ฅธ ๋ฅ๋ฌ๋ ํ๋ ์์ํฌ๋ค์ ๊ณ ์ ์ ์ธ ์ ํ ๋์ํ์
์กฐ๊ธ ๋ค๋ฅด๊ฒ ๋์ํฉ๋๋ค. ์
๋ ฅ์ ์ด ๋์ ์ ํ์ผ๋ก ๋งคํํฉ๋๋ค.
์ฆ ์ฃผ์ด์ง <span class="math">\(A\)</span> ์์ ์ถ๋ ฅ์ <span class="math">\(i\)</span> ๋ฒ์งธ ํ์
์
๋ ฅ์ <span class="math">\(i\)</span> ๋ฒ์งธ ํ์ ๋งคํ๋๊ณ ํธํฅ(Bias)์ ๋ํฉ๋๋ค.
์๋ ์์๋ฅผ ์ดํด๋ณด์ญ์์ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Robert Guthrie</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">lin</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> <span class="c1"># maps from R^5 to R^3, parameters A, b</span>
<span class="c1"># data is 2x5. A maps from 5 to 3... can we map "data" under A?</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">lin</span><span class="p">(</span><span class="n">data</span><span class="p">))</span> <span class="c1"># yes</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[ 0.1755, -0.3268, -0.5069],
[-0.6602, 0.2260, 0.1089]], grad_fn=<AddmmBackward>)
</pre></div>
</div>
</div>
<div class="section" id="id3">
<h3>๋น์ ํ์ฑ<a class="headerlink" href="#id3" title="Permalink to this headline">ยถ</a></h3>
<p>๋จผ์ ์ ๋น์ ํ์ฑ์ด ํ์ํ์ง ์ค๋ช
ํ๋ ๋ค์ ์ฌ์ค์ ์ฃผ๋ชฉํ์ญ์์ค.
<span class="math">\(f(x) = Ax + b\)</span> ์ <span class="math">\(g(x) = Cx + d\)</span> ๋๊ฐ์ ์ํ๋งต์ด ์๋ค๊ณ ๊ฐ์ ํฉ๋๋ค.
<span class="math">\(f(g(x))\)</span> ๋ ๋ฌด์์ผ๊น์?</p>
<div class="math">
\[f(g(x)) = A(Cx + d) + b = ACx + (Ad + b)
\]</div>
<p><span class="math">\(AC\)</span> ๋ ํ๋ ฌ์ด๊ณ <span class="math">\(Ad + b\)</span> ๋ ๋ฒกํฐ์ด๋ฏ๋ก ์ํ๋งต ๊ตฌ์ฑ์
์ํ๋งต์ด ์ฃผ์ด์ง๋๋ค.</p>
<p>์ด๊ฒ์ผ๋ก๋ถํฐ, ์ ๊ฒฝ๋ง์ด ์ํ ๊ตฌ์ฑ์ ๊ธด ์ฒด์ธ์ด ๋๊ธธ ์ํ๋ค๋ฉด,
๋จ์ผ ์ํ ๋งต์ ์์ฑํ๋ ๊ฒ๋ณด๋ค ์ด๊ฒ์ด ๋ชจ๋ธ์ ์ถ๊ฐํ๋ ์๋ก์ด ํ์ด
์๋ค๋ ๊ฒ์ ์ ์ ์์ต๋๋ค.</p>
<p>์ํ ๊ณ์ธต ์ฌ์ด์ ๋ง์ฝ ๋น์ ํ์ฑ์ ์ ์ฉํ๋ค๋ฉด
์ด๊ฒ์ ์ ๊ฒฝ์ฐ์ ๋ฌ๋ฆฌ ๋์ฑ ๋ ๊ฐ๋ ฅํ ๋ชจ๋ธ์ ๊ตฌ์ถํ ์ ์์ต๋๋ค.</p>
<p>ํต์ฌ์ ์ธ ๋น์ ํ์ฑ <span class="math">\(\tanh(x), \sigma(x), \text{ReLU}(x)\)</span> ๋ค์ด ๊ฐ์ฅ
์ผ๋ฐ์ ์
๋๋ค. ์๋ง ์๋ฌธ์ด ์๊ธธ๊ฒ๋๋ค : โ์ ์ด๋ฐ ํจ์๋ค์ด์ง? ๋๋ ๋ค๋ฅธ ๋ง์
๋น์ ํ์ฑ์ ์๊ฐํ ์ ์๋๋ฐโ. ๊ทธ ์ด์ ๋ ๊ทธ๋ค์ด ๋ณํ๋(gradient)๋ฅผ ๊ณ์ฐํ๊ธฐ ์ฝ๊ณ ,
๋ณํ๋ ์ฐ์ฐ์ ํ์ต์ ํ์์ ์ด๊ธฐ ๋๋ฌธ์
๋๋ค.
์๋ฅผ ๋ค์ด์</p>
<div class="math">
\[\frac{d\sigma}{dx} = \sigma(x)(1 - \sigma(x))
\]</div>
<p>๋น ๋ฅธ ์ฐธ๊ณ : AI ํด๋์ค์ ๋ํ ์๊ฐ์์ ์ผ๋ถ ์ ๊ฒฝ๋ง์ ๋ฐฐ์ ์ง๋ง <span class="math">\(\sigma(x)\)</span> ๊ฐ ๊ธฐ๋ณธ์ด์์ ๊ฒ์
๋๋ค.
์ผ๋ฐ์ ์ผ๋ก ์ฌ๋๋ค์ ์ค์ ๋ก ๊ทธ๊ฒ์ ์ฌ์ฉํ์ง ์๊ณ ํผํฉ๋๋ค.
์ด๊ฒ์ ๋ณํ๋๊ฐ ์ธ์์ ์ ๋ ๊ฐ์ด ์ปค์ง์ ๋ฐ๋ผ ๋งค์ฐ ๋นจ๋ฆฌ <em>์ฌ๋ผ์ง๊ธฐ</em> ๋๋ฌธ์
๋๋ค.
์์ ๋ณํ๋๋ ํ์ตํ๊ธฐ ์ด๋ ต๋ค๋ ๊ฒ์ ์๋ฏธํฉ๋๋ค.
๋๋ถ๋ถ์ ์ฌ๋๋ค์ tanh ๋๋ ReLU๋ฅผ ๊ธฐ๋ณธ๊ฐ์ผ๋ก ์ฌ์ฉํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Pytorch์์ ๋๋ถ๋ถ์ ๋น์ ํ์ฑ์ torch.functional์ ์์ต๋๋ค ( F ๋ก ๊ฐ์ ธ์ต๋๋ค)</span>
<span class="c1"># ์ผ๋ฐ์ ์ผ๋ก ๋น์ ํ์ฑ์ ์ํ๋งต๊ณผ ๊ฐ์ ํ๋ผ๋ฏธํฐ๋ฅผ ๊ฐ์ง๊ณ ์์ง ์์ต๋๋ค.</span>
<span class="c1"># ์ฆ, ํ์ต ์ค์ ์
๋ฐ์ดํธ๋๋ ๊ฐ์ค์น๊ฐ ์์ต๋๋ค.</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[-0.5404, -2.2102],
[ 2.1130, -0.0040]])
tensor([[0.0000, 0.0000],
[2.1130, 0.0000]])
</pre></div>
</div>
</div>
<div class="section" id="softmax">
<h3>Softmax ๋ฐ ํ๋ฅ <a class="headerlink" href="#softmax" title="Permalink to this headline">ยถ</a></h3>
<p>ํจ์ <span class="math">\(\text{Softmax}(x)\)</span> ๋ํ ๋จ์ง ๋น์ ํ์ฑ ์ด์ง๋ง, ์ผ๋ฐ์ ์ผ๋ก ๋คํธ์ํฌ์์
๋ง์ง๋ง์ผ๋ก ์ํ๋๋ ์์
์ด๋ผ๋ ์ ์์ ํน๋ณํฉ๋๋ค.
์ด๋ ์ค์์ ๋ฒกํฐ๋ฅผ ์ทจํ์ฌ ํ๋ฅ ๋ถํฌ๋ฅผ ๋ฐํํ๊ธฐ ๋๋ฌธ์
๋๋ค.
์ ์๋ ๋ค์๊ณผ ๊ฐ์ต๋๋ค. <span class="math">\(x\)</span> ๋ ์ค์ ๋ฒกํฐ(์์, ์์ , ์ ์ฝ ์์)๋ผ๊ณ ํ๋ฉด,
i๋ฒ์งธ ๊ตฌ์ฑ ์์๋ <span class="math">\(\text{Softmax}(x)\)</span> ๋</p>
<div class="math">
\[\frac{\exp(x_i)}{\sum_j \exp(x_j)}
\]</div>
<p>์ถ๋ ฅ์ ํ๋ฅ ๋ถํฌ๋ผ๋ ๊ฒ์ด ๋ถ๋ช
ํด์ผํฉ๋๋ค:
๊ฐ ์์๋ ์์๊ฐ ์๋๋ฉฐ ๋ชจ๋ ๊ตฌ์ฑ ์์์ ํฉ์ 1์
๋๋ค.</p>
<p>๋ชจ๋ ์์๊ฐ ์๋๊ฒ ํ๊ธฐ ์ํด์ ์
๋ ฅ์ ์์ ๋จ์์ ์ง์ ์ฐ์ฐ์๋ฅผ ์ ์ฉํ ๋ค์
์ ๊ทํ ์์๋ก ๋๋๋ ๊ฒ๋ ์๊ฐํ ์ ์์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Softmax ๋ torch.nn.functional ์ ์์ต๋๋ค.</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span> <span class="c1"># ํ๋ฅ ๋ถํฌ์ด๊ธฐ ๋๋ฌธ์ ํฉ์ด 1 ์
๋๋ค!</span>
<span class="nb">print</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span> <span class="c1"># log_softmax ๋ ์์ต๋๋ค.</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([ 1.3800, -1.3505, 0.3455, 0.5046, 1.8213])
tensor([0.2948, 0.0192, 0.1048, 0.1228, 0.4584])
tensor(1.)
tensor([-1.2214, -3.9519, -2.2560, -2.0969, -0.7801])
</pre></div>
</div>
</div>
<div class="section" id="objective-functions">
<h3>๋ชฉ์ ํจ์(Objective Functions)<a class="headerlink" href="#objective-functions" title="Permalink to this headline">ยถ</a></h3>
<p>๋ชฉ์ ํจ์๋ ๋คํธ์ํฌ๊ฐ ์ต์ํํ๋๋ก ํ์ต๋๋ ํจ์์
๋๋ค
( <em>์์ค ํจ์</em> ๋๋ <em>๋น์ฉ ํจ์</em> ๋ผ๊ณ ํจ).
๋จผ์ ํ์ต ์ธ์คํด์ค๋ฅผ ์ ํํ๊ณ ์ ๊ฒฝ๋ง์ ํตํด ์คํํ ๋ค์ ์ถ๋ ฅ์ ์์ค์ ๊ณ์ฐํฉ๋๋ค.
๊ทธ๋ฐ ๋ค์ ์์ค ํจ์์ ๋ฏธ๋ถ์ ์ทจํจ์ผ๋ก์จ ๋ชจ๋ธ์ ํ๋ผ๋ฏธํฐ๊ฐ ์
๋ฐ์ดํธ๋ฉ๋๋ค.
์ง๊ด์ ์ผ๋ก ๋ชจ๋ธ์ด ์์ ์ ๋๋ต์ ์์ ํ ํ์ ํ๊ณ ๋๋ต์ด ์๋ชป๋๋ฉด ์์ค์ด ๋์์ง๋๋ค.
๋ต๋ณ์ ์์ ์ด ์๊ณ ๋ต๋ณ์ด ๋ง์ผ๋ฉด ์์ค์ด ์ ์ต๋๋ค.</p>
<p>ํ์ต ์์ ์์ ์์ค ํจ์๋ฅผ ์ต์ํํ๋ ค๋ ์์ด๋์ด๋
๋คํธ์ํฌ๊ฐ ์ ์ผ๋ฐํ๋๊ณ ๊ฐ๋ฐ์ ์ธํธ, ํ
์คํธ ์ธํธ ๋๋ ํ๋ก๋์
์์
๋ํ๋์ง ์์๋ ์์ (unseen examples)์ ๋ํด ์์ ์์ค์ ๊ฐ์ง๊ธฐ๋ฅผ ๋ฐ๋๋๋ค.
์์ค ํจ์์ ์๋ก <em>์์ ๋ก๊ทธ ์ฐ๋ ์์ค(negative log likelihood loss)</em> ์์ต๋๋ค.
์ด ๊ฒ์ ๋ค์ค ํด๋์ค ๋ถ๋ฅ์์ ๋งค์ฐ ์์ฃผ ์ฌ์ฉ๋๋ ๋ชฉ์ ํจ์์
๋๋ค.
๊ฐ๋
๋ค์ค ํด๋์ค ๋ถ๋ฅ์ ๊ฒฝ์ฐ์๋ ์ฌ๋ฐ๋ฅธ ์ถ๋ ฅ(์ ๋ต์ ๋ง์ถ ์ถ๋ ฅ)์ ์์ ๋ก๊ทธ ํ๋ฅ ์
์ต์ํํ๋๋ก ๋คํธ์ํฌ๋ฅผ ๊ต์กํ๋ ๊ฒ์ ์๋ฏธํฉ๋๋ค.
(๋๋ ์ด์ ๋๋ฑํ๊ฒ ์ฌ๋ฐ๋ฅธ ์ถ๋ ฅ์ ๋ก๊ทธ ํ๋ฅ ์ ์ต๋ํํ์ญ์์ค)</p>
</div>
</div>
<div class="section" id="id4">
<h2>์ต์ ํ์ ํ์ต<a class="headerlink" href="#id4" title="Permalink to this headline">ยถ</a></h2>
<p>๊ทธ๋ผ ์ธ์คํด์ค์ ๋ํด ์์ค ํจ์๋ฅผ ๊ณ์ฐํ ์ ์๋ค๋ ๊ฒ์ ๋ฌด์์
๋๊น? ๊ทธ๊ฑธ ์ด๋ป๊ฒ ํ ๊น์?
์ฐ๋ฆฌ๋ ์ด์ ์ Tensor๊ฐ ๊ทธ๊ฒ์ ๊ณ์ฐํ๋๋ฐ ์ฌ์ฉ๋ ๊ฒ๋ค์ ํด๋นํ๋ ๋ณํ๋๋ฅผ
๊ณ์ฐํ๋ ๋ฐฉ๋ฒ์ ์๊ณ ์๋ค๋ ๊ฒ์ ๋ณด์์ต๋๋ค.
์์ค์ Tensor์ด๊ธฐ ๋๋ฌธ์ ๊ทธ๊ฒ์ ๊ณ์ฐํ๋๋ฐ ์ฌ์ฉ๋ ๋ชจ๋ ํ๋ผ๋ฏธํฐ์ ๊ด๋ จํ์ฌ
๋ณํ๋๋ฅผ ๊ณ์ฐํ ์ ์์ต๋๋ค! ๊ทธ๋ฐ ๋ค์ ํ์ค ๋ณํ๋ ์
๋ฐ์ดํธ๋ฅผ ์ํ ํ ์ ์์ต๋๋ค.
<span class="math">\(\theta\)</span> ๊ฐ ์ฐ๋ฆฌ์ ํ๋ผ๋ฏธํฐ๋ผ๊ณ ํฉ์๋ค.
<span class="math">\(L(\theta)\)</span> ๋ ์์ค ํจ์, ๊ทธ๋ฆฌ๊ณ <span class="math">\(\eta\)</span> ๋ ์์ ๋ฌ๋ ๋ ์ดํธ ์
๋๋ค. ๊ทธ๋ฌ๋ฉด</p>
<div class="math">
\[\theta^{(t+1)} = \theta^{(t)} - \eta \nabla_\theta L(\theta)
\]</div>
<p>์ด ๊ธฐ๋ณธ์ ์ธ ๊ทธ๋ ๋์ธํธ ์
๋ฐ์ดํธ ์ด์์ ๊ฒ์ ํ๊ธฐ ์ํด์ ๋ง์ ์๊ณ ๋ฆฌ์ฆ๊ณผ
์๋๋๊ณ ์๋ ํ๋ฐํ ์ฐ๊ตฌ๋ค์ด ์์ต๋๋ค.
๋ง์ ์๋๋ค์ ํ์ต ์๊ฐ์ ์ผ์ด๋๋ ๊ฒ์ ๊ธฐ๋ฐํ ๋ฌ๋ ๋ ์ดํธ๋ฅผ ๋ณ๊ฒฝํด๋ด
๋๋ค.
๋น์ ์ด ์ ๋ง ๊ด์ฌ์ด ์๋ค๋ฉด ํน๋ณํ ์ด๋ค ์๊ณ ๋ฆฌ์ฆ์ด ๋ฌด์์ ํ๋์ง ๊ฑฑ์ ํ
ํ์๊ฐ ์์ต๋๋ค. Torch๋ torch.optim ํจํค์ง์์ ๋ง์ ๊ฒ์ ์ ๊ณตํ๋ฉฐ
์์ ํ ๊ณต๊ฐ๋์ด ์์ต๋๋ค. ๊ฐ์ฅ ๋จ์ํ ๋ณํ๋ ์
๋ฐ์ดํธ ์ฌ์ฉ์
๋ ๋ณต์กํ ์๊ณ ๋ฆฌ์ฆ์ ์ฌ์ฉํ๋ ๊ฒ๊ณผ ๋์ผํฉ๋๋ค.
๋ค๋ฅธ ์
๋ฐ์ดํธ ์๊ณ ๋ฆฌ์ฆ๊ณผ ์
๋ฐ์ดํธ ์๊ณ ๋ฆฌ์ฆ์ ์ํ ๋ค๋ฅธ ํ๋ผ๋ฏธํฐ(๋ค๋ฅธ ์ด๊ธฐ ๋ฌ๋ ๋ ์ดํธ)๋ฅผ
์๋ํด ๋ณด๋ ๊ฒ์ ๋คํธ์ํฌ์ ์ฑ๋ฅ์ ์ต์ ํํ๋๋ฐ ์ค์ํฉ๋๋ค.
์ข
์ข
๊ธฐ๋ณธ SGD๋ฅผ Adam ๋๋ RMSprop ์ผ๋ก ๊ต์ฒดํ๋ ๊ฒ์ด ๋์ ๋๊ฒ ์ฑ๋ฅ
ํฅ์ ์ํต๋๋ค.</p>
</div>
<div class="section" id="id5">
<h2>Pytorch ์์ ๋คํธ์ํฌ ๊ตฌ์ฑ์์ ์์ฑํ๊ธฐ<a class="headerlink" href="#id5" title="Permalink to this headline">ยถ</a></h2>
<p>NLP์ ์ด์ ์ ๋ง์ถ๊ธฐ ์ ์, PyTorch์์ ์ํ ๋งต๊ณผ ๋น์ ํ์ฑ๋ง์ ์ฌ์ฉํ์ฌ
๋คํธ์ํฌ๋ฅผ ๊ตฌ์ถํ๋ ์ฃผ์ ์ฒ๋ฆฌ๋ ์์ ๋ฅผ ์ํ ํ ์ ์์ต๋๋ค.
๋ํ ์์ค ํจ์๋ฅผ ๊ณ์ฐํ๋ ๋ฐฉ๋ฒ, PyTorch์ ๋ด์ฅ๋ ์์ ๋ก๊ทธ ์ฐ๋๋ฅผ ์ฌ์ฉํ๋ ๋ฐฉ๋ฒ,
์ญ์ ํ๋ฅผ ํตํด ๋งค๊ฐ ๋ณ์๋ฅผ ์
๋ฐ์ดํธํ๋ ๋ฐฉ๋ฒ์ ๋ณผ ๊ฒ์
๋๋ค.</p>
<p>๋ชจ๋ ๋คํธ์ํฌ ๊ตฌ์ฑ ์์๋ nn.Module์์ ์์ ๋ฐ์ forward() ๋ฉ์๋๋ฅผ ์ฌ์ ์ํด์ผํฉ๋๋ค.
์ด๊ฒ์ ์์ฉ๊ตฌ์ ๊ดํ ๊ฒ์
๋๋ค. nn.Module์์์ ์์์ ๊ตฌ์ฑ ์์์ ๊ธฐ๋ฅ์ ์ ๊ณตํฉ๋๋ค.
์๋ฅผ ๋ค์ด ๊ทธ๊ฒ์ ํ์ต ๊ฐ๋ฅํ ํ๋ผ๋ฏธํฐ๋ฅผ ์ถ์ ํ๋๋ก ๋ง๋ค๊ณ ,
<code class="docutils literal notranslate"><span class="pre">.to(device)</span></code> ๋ก CPU์ GPU ๋ฅผ ๊ตํํ ์ ์์ต๋๋ค.
<code class="docutils literal notranslate"><span class="pre">torch.device("cpu")</span></code> ๋ CPU ์ฅ์น๋ฅผ <code class="docutils literal notranslate"><span class="pre">torch.device("cuda:0")</span></code> ๋ GPU ์ฅ์น๋ฅผ ์ฌ์ฉํฉ๋๋ค.</p>
<p>ํฌ์ํ Bag-of-Words Representation ์ ๋ฐ์์ ๋๊ฐ์ ๋ ์ด๋ธ โ์์ดโ์ โ์คํ์ธ์ดโ์ ํ๋ฅ ๋ถํฌ
์ถ๋ ฅํ๋ ๋คํธ์ํฌ์ ์ฃผ์์ด ๋ฌ๋ฆฐ ์์๋ฅผ ์์ฑํด ๋ด
์๋ค.
์ด ๋ชจ๋ธ์ ๋จ์ํ ๋
ผ๋ฆฌ ํ๊ท ์
๋๋ค.</p>
<div class="section" id="bag-of-words">
<h3>์์ : ๋
ผ๋ฆฌ ํ๊ท Bag-of-Words ๋ถ๋ฅ๊ธฐ<a class="headerlink" href="#bag-of-words" title="Permalink to this headline">ยถ</a></h3>
<p>์ฐ๋ฆฌ ๋ชจ๋ธ์ ํฌ์ํ BoW ํํ์ ๋ ์ด๋ธ์ ๋ํ ๋ก๊ทธ ํ๋ฅ ๋ก ๋งคํํฉ๋๋ค.
์ฌ์ ์ ๊ฐ ๋จ์ด์ ํ๋์ ์์ธ์ ํ ๋นํฉ๋๋ค.
์๋ฅผ ๋ค์ด์ ์ ์ฒด ์ฌ์ ์ด ๊ฐ๊ฐ 0๊ณผ 1์ ์์ธ์ ๊ฐ์ง ๋๊ฐ์ ๋จ์ด โhelloโ ์ โworldโ ๋ผ๊ณ ํฉ์๋ค.
โhello hello hello helloโ ๋ฌธ์ฅ์ BoW ๋ฒกํฐ๋ ๋ค์๊ณผ ๊ฐ์ต๋๋ค.</p>
<div class="math">
\[\left[ 4, 0 \right]
"hello world world hello"๋ ๋ค์๊ณผ ๊ฐ์ต๋๋ค.\]</div>
<div class="math">
\[\left[ 2, 2 \right]
\]</div>
<p>์ผ๋ฐํ ํ๋ฉด ๋ค์๊ณผ ๊ฐ์ต๋๋ค.</p>
<div class="math">
\[\left[ \text{Count}(\text{hello}), \text{Count}(\text{world}) \right]
\]</div>
<p>์ด BOW ๋ฒกํฐ๋ฅผ <span class="math">\(x\)</span> ๋ผํ๋ฉด ๋คํธ์ํฌ์ ์ถ๋ ฅ์ ๋ค์๊ณผ ๊ฐ์ต๋๋ค:</p>
<div class="math">
\[\log \text{Softmax}(Ax + b)
\]</div>
<p>์ฆ, ์ํ๋งต์ ์
๋ ฅ์ ์ฃผ๊ณ ๊ทธ ๋ค์ Log Softmax ๋ฅผ ํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[(</span><span class="s2">"me gusta comer en la cafeteria"</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="s2">"SPANISH"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"Give it to me"</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="s2">"ENGLISH"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"No creo que sea una buena idea"</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="s2">"SPANISH"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"No it is not a good idea to get lost at sea"</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="s2">"ENGLISH"</span><span class="p">)]</span>
<span class="n">test_data</span> <span class="o">=</span> <span class="p">[(</span><span class="s2">"Yo creo que si"</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="s2">"SPANISH"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"it is lost on me"</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="s2">"ENGLISH"</span><span class="p">)]</span>
<span class="c1"># word_to_ix ๋ ์ฌ์ ์ ๊ฐ ๋จ์ด๋ฅผ ๊ณ ์ ํ ์ ์๋ก ๋งคํํ๊ณ </span>
<span class="c1"># ๊ทธ๊ฒ์ BoW ๋ฒกํฐ์์ ์์ ์ ์์ธ์ด ๋ฉ๋๋ค.</span>
<span class="n">word_to_ix</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">sent</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">data</span> <span class="o">+</span> <span class="n">test_data</span><span class="p">:</span>
<span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">sent</span><span class="p">:</span>
<span class="k">if</span> <span class="n">word</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">word_to_ix</span><span class="p">:</span>
<span class="n">word_to_ix</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">word_to_ix</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">word_to_ix</span><span class="p">)</span>
<span class="n">VOCAB_SIZE</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">word_to_ix</span><span class="p">)</span>
<span class="n">NUM_LABELS</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">class</span> <span class="nc">BoWClassifier</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span> <span class="c1"># nn.Module๋ก ๋ถํฐ ์์ ๋ฐ๊ธฐ !</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">):</span>
<span class="c1"># calls the init function of nn.Module์ ์ด๊ธฐํ ํจ์ ํธ์ถ. Dont get confused by syntax,</span>
<span class="c1"># ๋ฌธ๋ฒ์ ํผ๋์ค๋ฌ์ ํ์ง ๋ง์๊ณ ๋จ์ง ํญ์ nn.Module ์์ ์ํํ์ญ์์ค.</span>
<span class="nb">super</span><span class="p">(</span><span class="n">BoWClassifier</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># ํ์ํ ํ๋ผ๋ฏธํฐ๋ฅผ ์ ์ ํ์ญ์์ค. ์ด ๊ฒฝ์ฐ์๋ ์ํ ๋งคํ์ ๋งค๊ฐ ๋ณ์ ์ธ A์ b๊ฐ ํ์ํฉ๋๋ค.</span>
<span class="c1"># Torch๋ ์ํ ๋งต์ ์ ๊ณตํ๋ nn.Linear()๋ฅผ ์ ์ํฉ๋๋ค</span>
<span class="c1"># ์
๋ ฅ ์ฐจ์์ด vocab_size์ด๊ณ ์ถ๋ ฅ์ด num_labels ์ธ ์ด์ ๋ฅผ ์ดํดํ๋์ง ํ์ธํ์ญ์์ค!</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">)</span>
<span class="c1"># ์ฃผ์! ๋น์ ํ์ฑ Log Softmax์๋ ํ๋ผ๋ฏธํฐ๊ฐ ์์ต๋๋ค!</span>
<span class="c1"># ๊ทธ๋์ ์ฌ๊ธฐ์ ๋ํด ๊ฑฑ์ ํ ํ์๊ฐ ์์ต๋๋ค.</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bow_vec</span><span class="p">):</span>
<span class="c1"># ์ ํ ๊ณ์ธต๋ฅผ ํตํด ์
๋ ฅ์ ์ ๋ฌํ ๋ค์ log_softmax๋ก ์ ๋ฌํฉ๋๋ค.</span>
<span class="c1"># ๋ง์ ๋น์ ํ์ฑ ๋ฐ ๊ธฐํ ๊ธฐ๋ฅ์ด torch.nn.functional ์ ์์ต๋๋ค</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">bow_vec</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">make_bow_vector</span><span class="p">(</span><span class="n">sentence</span><span class="p">,</span> <span class="n">word_to_ix</span><span class="p">):</span>
<span class="n">vec</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">word_to_ix</span><span class="p">))</span>
<span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">sentence</span><span class="p">:</span>
<span class="n">vec</span><span class="p">[</span><span class="n">word_to_ix</span><span class="p">[</span><span class="n">word</span><span class="p">]]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">vec</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">make_target</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">label_to_ix</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">([</span><span class="n">label_to_ix</span><span class="p">[</span><span class="n">label</span><span class="p">]])</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">BoWClassifier</span><span class="p">(</span><span class="n">NUM_LABELS</span><span class="p">,</span> <span class="n">VOCAB_SIZE</span><span class="p">)</span>
<span class="c1"># ๋ชจ๋ธ์ ์์ ์ ํ๋ผ๋ฏธํฐ๋ฅผ ์๊ณ ์์ต๋๋ค. ์๋์ ์๋ ์ฒซ๋ฒ์งธ ์ถ๋ ฅ์ A ๋๋ฒ์งธ๋ b ์
๋๋ค.</span>
<span class="c1"># ๋ชจ๋์ __init__ ํจ์์์ ํด๋์ค ๋ณ์์ ๊ตฌ์ฑ ์์๋ฅผ ํ ๋น ํ ๋๋ง๋ค ๋ค์ ํ์ ์ฌ์ฉํ์ฌ ์๋ฃํฉ๋๋ค.</span>
<span class="c1"># self.linear = nn.Linear(...)</span>
<span class="c1"># ๊ทธ๋ฐ ๋ค์ PyTorch ๊ฐ๋ฐ์์ Python ๋ง๋ฒ์ ํตํด, ๋ชจ๋(์ด ๊ฒฝ์ฐ BoWClassifier)์</span>
<span class="c1"># nn.Linear ํ๋ผ๋ฏธํฐ์ ๋ํ ์ง์์ ์ ์ฅํฉ๋๋ค</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="n">param</span><span class="p">)</span>
<span class="c1"># ๋ชจ๋ธ์ ์คํํ๋ ค๋ฉด BoW ๋ฒกํฐ๋ฅผ ์ ๋ฌํฉ๋๋ค.</span>
<span class="c1"># ์ฌ๊ธฐ์ ์ฐ๋ฆฌ๋ ํ์ต ํ ํ์๊ฐ ์์ผ๋ฏ๋ก ์ฝ๋๋ torch.no_grad()๋ก ์ธ์ฌ ์์ต๋๋ค.</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">bow_vector</span> <span class="o">=</span> <span class="n">make_bow_vector</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">word_to_ix</span><span class="p">)</span>
<span class="n">log_probs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">bow_vector</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">log_probs</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{'me': 0, 'gusta': 1, 'comer': 2, 'en': 3, 'la': 4, 'cafeteria': 5, 'Give': 6, 'it': 7, 'to': 8, 'No': 9, 'creo': 10, 'que': 11, 'sea': 12, 'una': 13, 'buena': 14, 'idea': 15, 'is': 16, 'not': 17, 'a': 18, 'good': 19, 'get': 20, 'lost': 21, 'at': 22, 'Yo': 23, 'si': 24, 'on': 25}
Parameter containing:
tensor([[ 0.1194, 0.0609, -0.1268, 0.1274, 0.1191, 0.1739, -0.1099, -0.0323,
-0.0038, 0.0286, -0.1488, -0.1392, 0.1067, -0.0460, 0.0958, 0.0112,
0.0644, 0.0431, 0.0713, 0.0972, -0.1816, 0.0987, -0.1379, -0.1480,
0.0119, -0.0334],
[ 0.1152, -0.1136, -0.1743, 0.1427, -0.0291, 0.1103, 0.0630, -0.1471,
0.0394, 0.0471, -0.1313, -0.0931, 0.0669, 0.0351, -0.0834, -0.0594,
0.1796, -0.0363, 0.1106, 0.0849, -0.1268, -0.1668, 0.1882, 0.0102,
0.1344, 0.0406]], requires_grad=True)
Parameter containing:
tensor([0.0631, 0.1465], requires_grad=True)
tensor([[-0.5378, -0.8771]])
</pre></div>
</div>
<p>์์ ๊ฐ ์ค ์ด๋ ๊ฒ์ด ENGLISH์ SPANISH์ ๋ก๊ทธ ํ๋ฅ ์ ํด๋นํ๋ ๊ฐ์ผ๊น์?
์ฐ๋ฆฌ๋ ์ ์ํ์ง ์์์ง๋ง, ํ์ตํ๊ธฐ๋ฅผ ์ํ๋ค๋ฉด ํ์ํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">label_to_ix</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"SPANISH"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"ENGLISH"</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>
</pre></div>
</div>
<p>๊ทธ๋ผ ํ์ต์ ํด๋ด
์๋ค! ์ด๋ฅผ ์ํด ๋ก๊ทธ ํ๋ฅ ์ ์ป๊ณ , ์์ค ํจ์๋ฅผ ๊ณ์ฐํ๊ณ ,
์์ค ํจ์์ ๋ณํ๋๋ฅผ ๊ณ์ฐํ ๋ค์ ๋ณํ๋ ๋จ๊ณ๋ก ํ๋ผ๋ฏธํฐ๋ฅผ
์
๋ฐ์ดํธํ๊ธฐ ์ํด ์ธ์คํด์ค๋ฅผ ํต๊ณผ์ํต๋๋ค. ์์ค ๊ธฐ๋ฅ์ nn ํจํค์ง์ Torch์์ ์ ๊ณตํฉ๋๋ค.
nn.NLLLoss()๋ ์ํ๋ ์์ ๋ก๊ทธ ์ฐ๋ ์์ค์
๋๋ค. ๋ํ torch.optim์์ ์ต์ ํ ํจ์๋ฅผ ์ ์ํฉ๋๋ค.
์ฌ๊ธฐ์๋ SGD ๋ง ์ฌ์ฉํฉ๋๋ค.</p>
<p>NLLLoss์ ๋ํ <em>์
๋ ฅ</em> ์ ๋ก๊ทธ ํ๋ฅ ์ ๋ฒกํฐ์ด๊ณ ๋ชฉํ ๋ ์ด๋ธ์
๋๋ค.
์ฐ๋ฆฌ๋ฅผ ์ํ ๋ก๊ทธ ํ๋ฅ ์ ๊ณ์ฐํ์ง ์์ต๋๋ค. ์ด๊ฒ์ด ๋คํธ์ํฌ์ ๋ง์ง๋ง ๊ณ์ธต์ด
Log softmax ์ธ ์ด์ ์
๋๋ค. ์์ค ํจ์ nn.CrossEntropyLoss()๋ Log softmax๋ฅผ ์ ์ธํ๊ณ ๋ NLLLoss()์ ๊ฐ์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># ํ๋ จํ๊ธฐ ์ ์ ํ
์คํธ ๋ฐ์ดํฐ๋ฅผ ์คํํ์ฌ ์ ํ๋ฅผ ๋ณผ ์ ์์ต๋๋ค.</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="k">for</span> <span class="n">instance</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">test_data</span><span class="p">:</span>
<span class="n">bow_vec</span> <span class="o">=</span> <span class="n">make_bow_vector</span><span class="p">(</span><span class="n">instance</span><span class="p">,</span> <span class="n">word_to_ix</span><span class="p">)</span>
<span class="n">log_probs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">bow_vec</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">log_probs</span><span class="p">)</span>
<span class="c1"># "creo"์ ํด๋นํ๋ ํ๋ ฌ ์ด์ ์ธ์ํ์ญ์์ค.</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())[:,</span> <span class="n">word_to_ix</span><span class="p">[</span><span class="s2">"creo"</span><span class="p">]])</span>
<span class="n">loss_function</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="c1"># ์ผ๋ฐ์ ์ผ๋ก ๊ต์ก ๋ฐ์ดํฐ๋ฅผ ์ฌ๋ฌ ๋ฒ ์ ๋ฌ ํฉ๋๋ค.</span>
<span class="c1"># 100์ ์ค์ ๋ฐ์ดํฐ ์ธํธ๋ณด๋ค ํจ์ฌ ๋ ํฌ์ง ๋ง ์ค์ ๋ฐ์ดํฐ ์ธํธ๋ ๋ ๊ฐ ์ด์์ ์ธ์คํด์ค๋ฅผ ๊ฐ์ง๋๋ค.</span>
<span class="c1"># ์ผ๋ฐ์ ์ผ๋ก 5 ~ 30 ๊ฐ ์ํฌํฌ๊ฐ ์ ๋นํฉ๋๋ค.</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
<span class="k">for</span> <span class="n">instance</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
<span class="c1"># 1 ๋จ๊ณ. PyTorch๋ ๊ทธ๋ผ๋ฐ์ด์
์ ์ถ์ ํฉ๋๋ค.</span>
<span class="c1"># ๊ฐ ์ธ์คํด์ค ์ ์ ๊ทธ๋ค์ ์ ๊ฑฐํด์ผํฉ๋๋ค.</span>
<span class="n">model</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="c1"># 2 ๋จ๊ณ. BOW ๋ฒกํฐ๋ฅผ ๋ง๋ค๊ณ ์ ์๋ก ํ
์๋ก ๋์์ ์ธ์ผํฉ๋๋ค.</span>
<span class="c1"># ์๋ฅผ ๋ค์ด, ๋์์ด SPANISH์ด๋ฉด ์ ์ 0์ผ๋ก ํฉ๋๋ค.</span>
<span class="c1"># ์์ค ํจ์๋ ๋ก๊ทธ ํ๋ฅ ์ 0๋ฒ์งธ ์์๊ฐ SPANISH์ ํด๋นํ๋ ๋ก๊ทธ ํ๋ฅ ์์ ์ ์ ์์ต๋๋ค</span>
<span class="n">bow_vec</span> <span class="o">=</span> <span class="n">make_bow_vector</span><span class="p">(</span><span class="n">instance</span><span class="p">,</span> <span class="n">word_to_ix</span><span class="p">)</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">make_target</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">label_to_ix</span><span class="p">)</span>
<span class="c1"># 3 ๋จ๊ณ. ์์ ํ๋ฅผ ์คํํฉ๋๋ค.</span>
<span class="n">log_probs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">bow_vec</span><span class="p">)</span>
<span class="c1"># 4 ๋จ๊ณ. optimizer.step()์ ํธ์ถํ์ฌ ์์ค, ๋ณํ๋๋ฅผ ๊ณ์ฐํ๊ณ ํ๋ผ๋ฏธํฐ๋ฅผ ์
๋ฐ์ดํธํฉ๋๋ค.</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">loss_function</span><span class="p">(</span><span class="n">log_probs</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="k">for</span> <span class="n">instance</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">test_data</span><span class="p">:</span>
<span class="n">bow_vec</span> <span class="o">=</span> <span class="n">make_bow_vector</span><span class="p">(</span><span class="n">instance</span><span class="p">,</span> <span class="n">word_to_ix</span><span class="p">)</span>
<span class="n">log_probs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">bow_vec</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">log_probs</span><span class="p">)</span>
<span class="c1"># ์คํ์ธ์ด์ ํด๋นํ๋ ์์ธ์ด ์ฌ๋ผ๊ฐ๋๋ค. ์์ด๊ฐ ๋ด๋ ค๊ฐ๋๋ค!!</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())[:,</span> <span class="n">word_to_ix</span><span class="p">[</span><span class="s2">"creo"</span><span class="p">]])</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[-0.9297, -0.5020]])
tensor([[-0.6388, -0.7506]])
tensor([-0.1488, -0.1313], grad_fn=<SelectBackward>)
tensor([[-0.2093, -1.6669]])
tensor([[-2.5330, -0.0828]])
tensor([ 0.2803, -0.5605], grad_fn=<SelectBackward>)
</pre></div>
</div>
<p>์ ๋ต์ ์ป์์ต๋๋ค! ์ฒซ ๋ฒ์งธ ์์ ์์๋ ์คํ์ธ์ด์ ๋ก๊ทธ ํ๋ฅ ์ด โโํจ์ฌ ๋๊ณ
์์ด์ ๋ก๊ทธ ํ๋ฅ ์ ํ
์คํธ ๋ฐ์ดํฐ์ ๋ ๋ฒ์งธ์์ ํจ์ฌ ๋๋ค๋ ๊ฒ์ ์ ์ ์์ต๋๋ค.</p>
<p>์ด์ PyTorch ๊ตฌ์ฑ ์์๋ฅผ ๋ง๋ค๊ณ ์ด๋ฅผ ํตํด ์ผ๋ถ ๋ฐ์ดํฐ๋ฅผ ์ ๋ฌํ๊ณ
๋ณํ๋ ์
๋ฐ์ดํธ๋ฅผ ์ํํ๋ ๋ฐฉ๋ฒ์ ์ดํด ๋ณด์์ต๋๋ค.
์ฐ๋ฆฌ๋ ์ฌ๋์๋ NLP๊ฐ ์ ๊ณตํด์ผํ๋ ๊ฒ์ ๋ ๊น์ด ํ๊ณ ๋ค ์ค๋น๊ฐ๋์์ต๋๋ค.</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.150 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-nlp-deep-learning-tutorial-py">
<div class="sphx-glr-download docutils container">
<a class="reference download internal" download="" href="../../_downloads/df7502a0f8c6d7dcbf3e27a329b05d03/deep_learning_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deep_learning_tutorial.py</span></code></a></div>
<div class="sphx-glr-download docutils container">
<a class="reference download internal" download="" href="../../_downloads/408b3f799713e4020b578f67d80604f6/deep_learning_tutorial.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">deep_learning_tutorial.ipynb</span></code></a></div>
</div>
<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.readthedocs.io">Gallery generated by Sphinx-Gallery</a></p>
</div>
</div>
</div>
</article>
</div>
<footer>
<hr class="rating-hr hr-top">
<div class="rating-container">
<div class="rating-prompt">Rate this Tutorial</div>
<div class="stars-outer">
<i class="far fa-star" title="1 Star" data-behavior="tutorial-rating" data-count="1"></i>
<i class="far fa-star" title="2 Stars" data-behavior="tutorial-rating" data-count="2"></i>
<i class="far fa-star" title="3 Stars" data-behavior="tutorial-rating" data-count="3"></i>
<i class="far fa-star" title="4 Stars" data-behavior="tutorial-rating" data-count="4"></i>
<i class="far fa-star" title="5 Stars" data-behavior="tutorial-rating" data-count="5"></i>
</div>
</div>
<hr class="rating-hr hr-bottom"/>
<div role="contentinfo">
<p>
© Copyright 2021, PyTorch & PyTorch Korea Community.
</p>
</div>
<div>
Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
</div>
</footer>
</div>
</div>
<div class="pytorch-content-right" id="pytorch-content-right">
<div class="pytorch-right-menu" id="pytorch-right-menu">
<div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
<ul>
<li><a class="reference internal" href="#">PyTorch๋ฅผ ์ด์ฉํ ๋ฅ๋ฌ๋</a><ul>
<li><a class="reference internal" href="#affine-maps">๋ฅ๋ฌ๋ ๋ธ๋ก ๊ตฌ์ถ : ์ํ ๋งต(affine maps), ๋น์ ํ์ฑ, ๊ฐ์ฒด</a><ul>
<li><a class="reference internal" href="#id2">์ํ ๋งต</a></li>
<li><a class="reference internal" href="#id3">๋น์ ํ์ฑ</a></li>
<li><a class="reference internal" href="#softmax">Softmax ๋ฐ ํ๋ฅ </a></li>
<li><a class="reference internal" href="#objective-functions">๋ชฉ์ ํจ์(Objective Functions)</a></li>
</ul>
</li>
<li><a class="reference internal" href="#id4">์ต์ ํ์ ํ์ต</a></li>
<li><a class="reference internal" href="#id5">Pytorch ์์ ๋คํธ์ํฌ ๊ตฌ์ฑ์์ ์์ฑํ๊ธฐ</a><ul>
<li><a class="reference internal" href="#bag-of-words">์์ : ๋
ผ๋ฆฌ ํ๊ท Bag-of-Words ๋ถ๋ฅ๊ธฐ</a></li>
</ul>
</li>
</ul>
</li>
</ul>
</div>
</div>
</div>
</section>
</div>
<script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
<script type="text/javascript" src="../../_static/jquery.js"></script>
<script type="text/javascript" src="../../_static/underscore.js"></script>
<script type="text/javascript" src="../../_static/doctools.js"></script>
<script type="text/javascript" src="../../_static/clipboard.min.js"></script>
<script type="text/javascript" src="../../_static/copybutton.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/contrib/auto-render.min.js"></script>
<script type="text/javascript" src="../../_static/katex_autorenderer.js"></script>
<script type="text/javascript" src="../../_static/js/vendor/popper.min.js"></script>
<script type="text/javascript" src="../../_static/js/vendor/bootstrap.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/list.js/1.5.0/list.min.js"></script>
<script type="text/javascript" src="../../_static/js/theme.js"></script>
<script type="text/javascript">
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
});
</script>
<script>
//add microsoft link
if(window.location.href.indexOf("/beginner/basics/")!= -1)
{
var url="https://docs.microsoft.com/learn/paths/pytorch-fundamentals/?wt.mc_id=aiml-7486-cxa";
switch(window.location.pathname.split("/").pop().replace('.html',''))
{
case"quickstart_tutorial":
url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/9-quickstart?WT.mc_id=aiml-7486-cxa";
break;
case"tensorqs_tutorial":
url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/2-tensors?WT.mc_id=aiml-7486-cxa";
break;
case"data_tutorial":
url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/3-data?WT.mc_id=aiml-7486-cxa";
break;
case"transforms_tutorial":
url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/4-transforms?WT.mc_id=aiml-7486-cxa";
break;
case"buildmodel_tutorial":
url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/5-model?WT.mc_id=aiml-7486-cxa";
break;
case"autogradqs_tutorial":
url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/6-autograd?WT.mc_id=aiml-7486-cxa";
break;
case"optimization_tutorial":
url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/7-optimization?WT.mc_id=aiml-7486-cxa";
break;
case"saveloadrun_tutorial":
url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/8-inference?WT.mc_id=aiml-7486-cxa";
}
$(".pytorch-call-to-action-links").children().first().before("<a href="+url+' data-behavior="call-to-action-event" data-response="Run in Microsoft Learn" target="_blank"><div id="microsoft-learn-link" style="padding-bottom: 0.625rem;border-bottom: 1px solid #f3f4f7;padding-right: 2.5rem;display: -webkit-box; display: -ms-flexbox; isplay: flex; -webkit-box-align: center;-ms-flex-align: center;align-items: center;"><img class="call-to-action-img" src="../../_static/images/microsoft-logo.svg"/><div class="call-to-action-desktop-view">Run in Microsoft Learn</div><div class="call-to-action-mobile-view">Learn</div></div></a>')
}
</script>
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-71919972-3"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-71919972-3');
</script>
<script>
$("[data-behavior='call-to-action-event']").on('click', function(){
ga('send', {
hitType: 'event',
eventCategory: $(this).attr("data-response"),
eventAction: 'click',
eventLabel: window.location.href
});
gtag('event', 'click', {
'event_category': $(this).attr("data-response"),
'event_label': $("h1").first().text(),
'tutorial_link': window.location.href
});
});
$("[data-behavior='tutorial-rating']").on('click', function(){
gtag('event', 'click', {
'event_category': 'Tutorial Rating',
'event_label': $("h1").first().text(),
'value': $(this).attr("data-count")
});
});
if (location.pathname == "/") {
$(".rating-container").hide();
$(".hr-bottom").hide();
}
</script>
<script type="text/javascript">
var collapsedSections = ['ํ์ดํ ์น(PyTorch) ๋ ์ํผ', 'ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ', '์ด๋ฏธ์ง/๋น๋์ค', '์ค๋์ค', 'ํ
์คํธ', '๊ฐํํ์ต', 'PyTorch ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ', 'Code Transforms with FX', 'ํ๋ก ํธ์๋ API', 'PyTorch ํ์ฅํ๊ธฐ', '๋ชจ๋ธ ์ต์ ํ', '๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต', 'Mobile'];
</script>
<!-- Begin Footer -->
<div class="container-fluid docs-tutorials-resources" id="docs-tutorials-resources">
<div class="container">
<div class="row">
<div class="col-md-4 text-center">
<h2>๊ณต์ ๋ฌธ์ (์์ด)</h2>
<p>PyTorch ๊ณต์ ๋ฌธ์์
๋๋ค.</p>
<a id="orgTutorialLink" class="with-right-arrow" href="https://pytorch.org/docs/stable/index.html" target="_blank">๊ณต์ ๋ฌธ์๋ก ์ด๋</a>
</div>
<div class="col-md-4 text-center">
<h2>ํ๊ตญ์ด ํํ ๋ฆฌ์ผ</h2>
<p>ํ๊ตญ์ด๋ก ๋ฒ์ญ ์ค์ธ PyTorch ํํ ๋ฆฌ์ผ์
๋๋ค.</p>
<a class="with-right-arrow" href="https://tutorials.pytorch.kr">ํํ ๋ฆฌ์ผ๋ก ์ด๋</a>
</div>
<div class="col-md-4 text-center">
<h2>์ปค๋ฎค๋ํฐ</h2>
<p>๋ค๋ฅธ ์ฌ์ฉ์๋ค๊ณผ ์๊ฒฌ์ ๋๋ ๋ณด์ธ์!</p>
<a class="with-right-arrow" href="https://discuss.pytorch.kr">์ปค๋ฎค๋ํฐ๋ก ์ด๋</a>
</div>
</div>
</div>
</div>
<footer class="site-footer">
<div class="container footer-container">
<div class="footer-logo-wrapper">
<a href="https://pytorch.kr/" class="footer-logo"></a>
</div>
<div class="footer-links-wrapper">
<div class="footer-links-col">
<ul>
<li class="list-title"><a href="https://pytorch.org">PyTorch ํํ์ด์ง (๊ณต์)</a></li>
<li><a href="https://pytorch.org" target="_blank">๊ณต์ ํํ์ด์ง</a></li>
<li><a href="https://pytorch.org/tutorials" target="_blank">๊ณต์ ํํ ๋ฆฌ์ผ</a></li>
<li><a href="https://pytorch.org/docs" target="_blank">๊ณต์ ๋ฌธ์</a></li>
</ul>
</div>
<div class="footer-links-col">
<ul>
<li class="list-title"><a href="">ํ๊ตญ ์ฌ์ฉ์ ๋ชจ์</a></li>
<li><a href="https://pytorch.kr/about">์ฌ์ดํธ ์๊ฐ</a></li>
<li><a href="https://tutorials.pytorch.kr/">ํ๊ตญ์ด ํํ ๋ฆฌ์ผ</a></li>
<li><a href="https://github.com/9bow/PyTorch-tutorials-kr" target="_blank">ํ๊ตญ์ด ํํ ๋ฆฌ์ผ ์ ์ฅ์</a></li>
</ul>
</div>
</div>
<div class="trademark-disclaimer">
<ul>
<li>์ด ์ฌ์ดํธ๋ PyTorch ํ๊ตญ ์ฌ์ฉ์ ์ปค๋ฎค๋ํฐ๋ก Facebook, Inc์์ ์ด์ํ๋ ์ฌ์ดํธ๊ฐ ์๋๋๋ค. PyTorch, PyTorch ๋ก๊ณ ๋ฐ ๋ชจ๋ ๊ด๋ จ ํ๊ธฐ๋ Facebook, Inc์ ์ํ์
๋๋ค.</li>
<li>This site is a user community and is not operated by Facebook, Inc. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.</li>
</ul>
</div>
</div>
</footer>
<!-- End Footer -->
<!-- Begin Mobile Menu -->
<div class="mobile-main-menu">
<div class="container-fluid">