Rebuild

Author: 9bow

docs/_downloads/07d05907b3ff859aeed5f76f1acc5df4/Intro_to_TorchScript_tutorial.py

@@ -154,7 +154,7 @@ def forward(self, x, h):
 # 계산할 때 거꾸로 재생합니다. 이런 방식으로, 프레임워크는 언어의 모든 구문에
 # 대한 미분값을 명시적으로 정의할 필요가 없습니다.
 #
-# .. figure:: https://github.com/pytorch/pytorch/raw/master/docs/source/_static/img/dynamic_graph.gif
+# .. figure:: https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/dynamic_graph.gif
 #    :alt: 오토그라드가 작동하는 방식
 #
 #    오토그라드가 작동하는 방식

docs/_downloads/61a76849444a0a65d843361c26d1de16/Intro_to_TorchScript_tutorial.ipynb

@@ -87,7 +87,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "MyCell \ud074\ub798\uc2a4\ub97c \ub2e4\uc2dc \uc815\uc758\ud588\uc9c0\ub9cc, \uc5ec\uae30\uc120 ``MyDecisionGate`` \ub97c \uc815\uc758\ud588\uc2b5\ub2c8\ub2e4.\n\uc774 \ubaa8\ub4c8\uc740 **\uc81c\uc5b4 \ud750\ub984** \uc744 \ud65c\uc6a9\ud569\ub2c8\ub2e4. \uc81c\uc5b4 \ud750\ub984\uc740 \ub8e8\ud504\uc640 ``if`` \uba85\ub839\ubb38\uacfc\n\uac19\uc740 \uac83\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4.\n\n\ub9ce\uc740 \ud504\ub808\uc784\uc6cc\ud06c\ub4e4\uc740 \uc8fc\uc5b4\uc9c4 \ud504\ub85c\uadf8\ub7a8 \ucf54\ub4dc\ub85c\ubd80\ud130 \uae30\ud638\uc2dd \ubbf8\ubd84(symbolic\nderivatives)\uc744 \uacc4\uc0b0\ud558\ub294 \uc811\uadfc\ubc95\uc744 \ucde8\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc, PyTorch\uc5d0\uc11c\ub294 \ubcc0\ud654\ub3c4\n\ud14c\uc774\ud504(gradient tape)\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc5f0\uc0b0\uc774 \ubc1c\uc0dd\ud560 \ub54c \uc774\ub97c \uae30\ub85d\ud558\uace0, \ubbf8\ubd84\uac12\uc744\n\uacc4\uc0b0\ud560 \ub54c \uac70\uafb8\ub85c \uc7ac\uc0dd\ud569\ub2c8\ub2e4. \uc774\ub7f0 \ubc29\uc2dd\uc73c\ub85c, \ud504\ub808\uc784\uc6cc\ud06c\ub294 \uc5b8\uc5b4\uc758 \ubaa8\ub4e0 \uad6c\ubb38\uc5d0\n\ub300\ud55c \ubbf8\ubd84\uac12\uc744 \uba85\uc2dc\uc801\uc73c\ub85c \uc815\uc758\ud560 \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4.\n\n.. figure:: https://github.com/pytorch/pytorch/raw/master/docs/source/_static/img/dynamic_graph.gif\n   :alt: \uc624\ud1a0\uadf8\ub77c\ub4dc\uac00 \uc791\ub3d9\ud558\ub294 \ubc29\uc2dd\n\n   \uc624\ud1a0\uadf8\ub77c\ub4dc\uac00 \uc791\ub3d9\ud558\ub294 \ubc29\uc2dd\n\n\n"
+        "MyCell \ud074\ub798\uc2a4\ub97c \ub2e4\uc2dc \uc815\uc758\ud588\uc9c0\ub9cc, \uc5ec\uae30\uc120 ``MyDecisionGate`` \ub97c \uc815\uc758\ud588\uc2b5\ub2c8\ub2e4.\n\uc774 \ubaa8\ub4c8\uc740 **\uc81c\uc5b4 \ud750\ub984** \uc744 \ud65c\uc6a9\ud569\ub2c8\ub2e4. \uc81c\uc5b4 \ud750\ub984\uc740 \ub8e8\ud504\uc640 ``if`` \uba85\ub839\ubb38\uacfc\n\uac19\uc740 \uac83\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4.\n\n\ub9ce\uc740 \ud504\ub808\uc784\uc6cc\ud06c\ub4e4\uc740 \uc8fc\uc5b4\uc9c4 \ud504\ub85c\uadf8\ub7a8 \ucf54\ub4dc\ub85c\ubd80\ud130 \uae30\ud638\uc2dd \ubbf8\ubd84(symbolic\nderivatives)\uc744 \uacc4\uc0b0\ud558\ub294 \uc811\uadfc\ubc95\uc744 \ucde8\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc, PyTorch\uc5d0\uc11c\ub294 \ubcc0\ud654\ub3c4\n\ud14c\uc774\ud504(gradient tape)\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc5f0\uc0b0\uc774 \ubc1c\uc0dd\ud560 \ub54c \uc774\ub97c \uae30\ub85d\ud558\uace0, \ubbf8\ubd84\uac12\uc744\n\uacc4\uc0b0\ud560 \ub54c \uac70\uafb8\ub85c \uc7ac\uc0dd\ud569\ub2c8\ub2e4. \uc774\ub7f0 \ubc29\uc2dd\uc73c\ub85c, \ud504\ub808\uc784\uc6cc\ud06c\ub294 \uc5b8\uc5b4\uc758 \ubaa8\ub4e0 \uad6c\ubb38\uc5d0\n\ub300\ud55c \ubbf8\ubd84\uac12\uc744 \uba85\uc2dc\uc801\uc73c\ub85c \uc815\uc758\ud560 \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4.\n\n.. figure:: https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/dynamic_graph.gif\n   :alt: \uc624\ud1a0\uadf8\ub77c\ub4dc\uac00 \uc791\ub3d9\ud558\ub294 \ubc29\uc2dd\n\n   \uc624\ud1a0\uadf8\ub77c\ub4dc\uac00 \uc791\ub3d9\ud558\ub294 \ubc29\uc2dd\n\n\n"
       ]
     },
     {

docs/beginner/Intro_to_TorchScript_tutorial.html

@@ -463,11 +463,11 @@ <h2>PyTorch 모델 작성의 기초<a class="headerlink" href="#pytorch" title="
 <span class="nb">print</span><span class="p">(</span><span class="n">my_cell</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(tensor([[0.3753, 0.8249, 0.8435, 0.5375],
-        [0.5665, 0.7241, 0.8127, 0.5566],
-        [0.8290, 0.6250, 0.5181, 0.7576]]), tensor([[0.3753, 0.8249, 0.8435, 0.5375],
-        [0.5665, 0.7241, 0.8127, 0.5566],
-        [0.8290, 0.6250, 0.5181, 0.7576]]))
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(tensor([[0.6129, 0.9023, 0.2475, 0.6643],
+        [0.5896, 0.7913, 0.2361, 0.6807],
+        [0.8149, 0.8679, 0.5661, 0.7660]]), tensor([[0.6129, 0.9023, 0.2475, 0.6643],
+        [0.5896, 0.7913, 0.2361, 0.6807],
+        [0.8149, 0.8679, 0.5661, 0.7660]]))
 </pre></div>
 </div>
 <p>우리는 다음 작업을 수행했습니다.:</p>
@@ -502,11 +502,11 @@ <h2>PyTorch 모델 작성의 기초<a class="headerlink" href="#pytorch" title="
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>MyCell(
   (linear): Linear(in_features=4, out_features=4, bias=True)
 )
-(tensor([[-0.3062,  0.7067,  0.3896, -0.4582],
-        [ 0.1069,  0.5894,  0.6158, -0.3121],
-        [ 0.6238,  0.6729,  0.1078,  0.1524]], grad_fn=&lt;TanhBackward0&gt;), tensor([[-0.3062,  0.7067,  0.3896, -0.4582],
-        [ 0.1069,  0.5894,  0.6158, -0.3121],
-        [ 0.6238,  0.6729,  0.1078,  0.1524]], grad_fn=&lt;TanhBackward0&gt;))
+(tensor([[0.6727, 0.7429, 0.6171, 0.8134],
+        [0.7613, 0.3871, 0.5461, 0.8945],
+        [0.8020, 0.6856, 0.8246, 0.8536]], grad_fn=&lt;TanhBackward0&gt;), tensor([[0.6727, 0.7429, 0.6171, 0.8134],
+        [0.7613, 0.3871, 0.5461, 0.8945],
+        [0.8020, 0.6856, 0.8246, 0.8536]], grad_fn=&lt;TanhBackward0&gt;))
 </pre></div>
 </div>
 <p>모듈 <code class="docutils literal notranslate"><span class="pre">MyCell</span></code> 을 재정의했지만, 이번에는 <code class="docutils literal notranslate"><span class="pre">self.linear</span></code> 속성을 추가하고
@@ -551,11 +551,11 @@ <h2>PyTorch 모델 작성의 기초<a class="headerlink" href="#pytorch" title="
   (dg): MyDecisionGate()
   (linear): Linear(in_features=4, out_features=4, bias=True)
 )
-(tensor([[ 0.2797,  0.6145,  0.6797, -0.0291],
-        [ 0.2716,  0.4800,  0.8101, -0.2632],
-        [ 0.5457,  0.8588,  0.6202, -0.2416]], grad_fn=&lt;TanhBackward0&gt;), tensor([[ 0.2797,  0.6145,  0.6797, -0.0291],
-        [ 0.2716,  0.4800,  0.8101, -0.2632],
-        [ 0.5457,  0.8588,  0.6202, -0.2416]], grad_fn=&lt;TanhBackward0&gt;))
+(tensor([[ 0.3327,  0.4531,  0.6877,  0.3679],
+        [ 0.3763, -0.0695,  0.6067,  0.5236],
+        [ 0.5550,  0.3187,  0.8690,  0.5507]], grad_fn=&lt;TanhBackward0&gt;), tensor([[ 0.3327,  0.4531,  0.6877,  0.3679],
+        [ 0.3763, -0.0695,  0.6067,  0.5236],
+        [ 0.5550,  0.3187,  0.8690,  0.5507]], grad_fn=&lt;TanhBackward0&gt;))
 </pre></div>
 </div>
 <p>MyCell 클래스를 다시 정의했지만, 여기선 <code class="docutils literal notranslate"><span class="pre">MyDecisionGate</span></code> 를 정의했습니다.
@@ -567,7 +567,7 @@ <h2>PyTorch 모델 작성의 기초<a class="headerlink" href="#pytorch" title="
 계산할 때 거꾸로 재생합니다. 이런 방식으로, 프레임워크는 언어의 모든 구문에
 대한 미분값을 명시적으로 정의할 필요가 없습니다.</p>
 <div class="figure align-default" id="id8">
-<img alt="오토그라드가 작동하는 방식" src="https://github.com/pytorch/pytorch/raw/master/docs/source/_static/img/dynamic_graph.gif" />
+<img alt="오토그라드가 작동하는 방식" src="https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/dynamic_graph.gif" />
 <p class="caption"><span class="caption-text">오토그라드가 작동하는 방식</span><a class="headerlink" href="#id8" title="이 이미지에 대한 퍼머링크">¶</a></p>
 </div>
 </div>
@@ -600,11 +600,11 @@ <h3><code class="docutils literal notranslate"><span class="pre">Module</span></
   (linear): Linear(original_name=Linear)
 )
 
-(tensor([[ 0.7324, -0.3243,  0.1321,  0.1625],
-        [ 0.0783,  0.4072,  0.1977, -0.2026],
-        [-0.0253,  0.4354,  0.1660,  0.2262]], grad_fn=&lt;TanhBackward0&gt;), tensor([[ 0.7324, -0.3243,  0.1321,  0.1625],
-        [ 0.0783,  0.4072,  0.1977, -0.2026],
-        [-0.0253,  0.4354,  0.1660,  0.2262]], grad_fn=&lt;TanhBackward0&gt;))
+(tensor([[0.9564, 0.6855, 0.8985, 0.6681],
+        [0.9028, 0.4467, 0.9141, 0.7140],
+        [0.9648, 0.7171, 0.8723, 0.8622]], grad_fn=&lt;TanhBackward0&gt;), tensor([[0.9564, 0.6855, 0.8985, 0.6681],
+        [0.9028, 0.4467, 0.9141, 0.7140],
+        [0.9648, 0.7171, 0.8723, 0.8622]], grad_fn=&lt;TanhBackward0&gt;))
 </pre></div>
 </div>
 <p>살짝 앞으로 돌아가 <code class="docutils literal notranslate"><span class="pre">MyCell</span></code> 의 두 번째 버전을 가져왔습니다. 이전에 이것을
@@ -661,17 +661,17 @@ <h3><code class="docutils literal notranslate"><span class="pre">Module</span></
 <span class="nb">print</span><span class="p">(</span><span class="n">traced_cell</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(tensor([[ 0.7324, -0.3243,  0.1321,  0.1625],
-        [ 0.0783,  0.4072,  0.1977, -0.2026],
-        [-0.0253,  0.4354,  0.1660,  0.2262]], grad_fn=&lt;TanhBackward0&gt;), tensor([[ 0.7324, -0.3243,  0.1321,  0.1625],
-        [ 0.0783,  0.4072,  0.1977, -0.2026],
-        [-0.0253,  0.4354,  0.1660,  0.2262]], grad_fn=&lt;TanhBackward0&gt;))
-(tensor([[ 0.7324, -0.3243,  0.1321,  0.1625],
-        [ 0.0783,  0.4072,  0.1977, -0.2026],
-        [-0.0253,  0.4354,  0.1660,  0.2262]],
-       grad_fn=&lt;DifferentiableGraphBackward&gt;), tensor([[ 0.7324, -0.3243,  0.1321,  0.1625],
-        [ 0.0783,  0.4072,  0.1977, -0.2026],
-        [-0.0253,  0.4354,  0.1660,  0.2262]],
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(tensor([[0.9564, 0.6855, 0.8985, 0.6681],
+        [0.9028, 0.4467, 0.9141, 0.7140],
+        [0.9648, 0.7171, 0.8723, 0.8622]], grad_fn=&lt;TanhBackward0&gt;), tensor([[0.9564, 0.6855, 0.8985, 0.6681],
+        [0.9028, 0.4467, 0.9141, 0.7140],
+        [0.9648, 0.7171, 0.8723, 0.8622]], grad_fn=&lt;TanhBackward0&gt;))
+(tensor([[0.9564, 0.6855, 0.8985, 0.6681],
+        [0.9028, 0.4467, 0.9141, 0.7140],
+        [0.9648, 0.7171, 0.8723, 0.8622]],
+       grad_fn=&lt;DifferentiableGraphBackward&gt;), tensor([[0.9564, 0.6855, 0.8985, 0.6681],
+        [0.9028, 0.4467, 0.9141, 0.7140],
+        [0.9648, 0.7171, 0.8723, 0.8622]],
        grad_fn=&lt;DifferentiableGraphBackward&gt;))
 </pre></div>
 </div>
@@ -765,11 +765,11 @@ <h2>스크립팅을 사용하여 모듈 변환<a class="headerlink" href="#id4"
 <span class="n">traced_cell</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">h</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(tensor([[0.6585, 0.0558, 0.3720, 0.5105],
-        [0.8285, 0.5487, 0.5880, 0.2457],
-        [0.4929, 0.2075, 0.2141, 0.6373]], grad_fn=&lt;TanhBackward0&gt;), tensor([[0.6585, 0.0558, 0.3720, 0.5105],
-        [0.8285, 0.5487, 0.5880, 0.2457],
-        [0.4929, 0.2075, 0.2141, 0.6373]], grad_fn=&lt;TanhBackward0&gt;))
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(tensor([[ 0.0281,  0.9534,  0.0623, -0.0350],
+        [ 0.4146,  0.9282,  0.3834,  0.3907],
+        [ 0.7277,  0.7082,  0.2041, -0.0696]], grad_fn=&lt;TanhBackward0&gt;), tensor([[ 0.0281,  0.9534,  0.0623, -0.0350],
+        [ 0.4146,  0.9282,  0.3834,  0.3907],
+        [ 0.7277,  0.7082,  0.2041, -0.0696]], grad_fn=&lt;TanhBackward0&gt;))
 </pre></div>
 </div>
 <div class="section" id="id5">
@@ -875,7 +875,7 @@ <h3>더 읽을거리<a class="headerlink" href="#id7" title="이 제목에 대
 <p>튜토리얼을 완료했습니다! 관련 데모를 보려면 TorchScript를 사용하여 기계 번역
 모델을 변환하기 위한 NeurIPS 데모를 확인하십시오:
 <a class="reference external" href="https://colab.research.google.com/drive/1HiICg6jRkBnr5hvK2-VnMi88Vi9pUzEJ">https://colab.research.google.com/drive/1HiICg6jRkBnr5hvK2-VnMi88Vi9pUzEJ</a></p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.671 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.368 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-intro-to-torchscript-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/07d05907b3ff859aeed5f76f1acc5df4/Intro_to_TorchScript_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">Intro_to_TorchScript_tutorial.py</span></code></a></p>

docs/beginner/sg_execution_times.html

@@ -13,7 +13,7 @@
 
 <meta property="og:url" content="https://tutorials.pytorch.kr/beginner/sg_execution_times.html" />
 
-<meta property="og:description" content="00:10.709 total execution time for beginner files:,,, TorchText로 언어 번역하기( torchtext_translation.py), 00:10.709, 0.0 MB,, TorchScript 소개( Intro_to_TorchScript_tutorial.py), 00:00.000, 0.0 MB,, 오디오 데이터 증강( audio_data_augmentation_tutorial.py), 00:00.000, 0.0 MB,, Audio Datasets( audio_datasets_tuto..." />
+<meta property="og:description" content="00:09.389 total execution time for beginner files:,,, TorchText로 언어 번역하기( torchtext_translation.py), 00:09.389, 0.0 MB,, TorchScript 소개( Intro_to_TorchScript_tutorial.py), 00:00.000, 0.0 MB,, 오디오 데이터 증강( audio_data_augmentation_tutorial.py), 00:00.000, 0.0 MB,, Audio Datasets( audio_datasets_tuto..." />
 
 <meta property="og:image" content="https://tutorials.pytorch.kr/_static/logos/logo-kr-sm-dark.png" />
 
@@ -398,7 +398,7 @@
 
   <div class="section" id="computation-times">
 <span id="sphx-glr-beginner-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="이 제목에 대한 퍼머링크">¶</a></h1>
-<p><strong>00:10.709</strong> total execution time for <strong>beginner</strong> files:</p>
+<p><strong>00:09.389</strong> total execution time for <strong>beginner</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 86%" />
@@ -407,7 +407,7 @@
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="torchtext_translation.html#sphx-glr-beginner-torchtext-translation-py"><span class="std std-ref">TorchText로 언어 번역하기</span></a> (<code class="docutils literal notranslate"><span class="pre">torchtext_translation.py</span></code>)</p></td>
-<td><p>00:10.709</p></td>
+<td><p>00:09.389</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="Intro_to_TorchScript_tutorial.html#sphx-glr-beginner-intro-to-torchscript-tutorial-py"><span class="std std-ref">TorchScript 소개</span></a> (<code class="docutils literal notranslate"><span class="pre">Intro_to_TorchScript_tutorial.py</span></code>)</p></td>

docs/beginner/torchtext_translation.html

@@ -884,7 +884,7 @@ <h2>다음 단계<a class="headerlink" href="#id2" title="이 제목에 대한 
 <li><p><code class="docutils literal notranslate"><span class="pre">torchtext</span></code> 를 사용한 Ben Trevett의 튜토리얼을 <a class="reference external" href="https://github.com/bentrevett/">이곳</a> 에서 확인할 수 있습니다.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">nn.Transformer</span></code> 와 <code class="docutils literal notranslate"><span class="pre">torchtext</span></code> 의 다른 기능들을 이용한 다음 단어 예측을 통한 언어 모델링 튜토리얼을 살펴보세요.</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  10.709 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  9.389 seconds)</p>
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 <p><a class="reference download internal" download="" href="../_downloads/462f53ac0f7c6840743ad8655c43102c/torchtext_translation.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">torchtext_translation.py</span></code></a></p>