diff --git a/docs/_downloads/07d05907b3ff859aeed5f76f1acc5df4/Intro_to_TorchScript_tutorial.py b/docs/_downloads/07d05907b3ff859aeed5f76f1acc5df4/Intro_to_TorchScript_tutorial.py index b341cda8d..47da92ee6 100644 --- a/docs/_downloads/07d05907b3ff859aeed5f76f1acc5df4/Intro_to_TorchScript_tutorial.py +++ b/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 diff --git a/docs/_downloads/61a76849444a0a65d843361c26d1de16/Intro_to_TorchScript_tutorial.ipynb b/docs/_downloads/61a76849444a0a65d843361c26d1de16/Intro_to_TorchScript_tutorial.ipynb index 0ca63a4c1..8ac99f76d 100644 --- a/docs/_downloads/61a76849444a0a65d843361c26d1de16/Intro_to_TorchScript_tutorial.ipynb +++ b/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" ] }, { diff --git a/docs/beginner/Intro_to_TorchScript_tutorial.html b/docs/beginner/Intro_to_TorchScript_tutorial.html index 6f5633816..f3c073221 100644 --- a/docs/beginner/Intro_to_TorchScript_tutorial.html +++ b/docs/beginner/Intro_to_TorchScript_tutorial.html @@ -463,11 +463,11 @@
(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]]))
+(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]]))
우리는 다음 작업을 수행했습니다.:
@@ -502,11 +502,11 @@ PyTorch 모델 작성의 기초MyCell(
(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=<TanhBackward0>), 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=<TanhBackward0>))
+(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=<TanhBackward0>), 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=<TanhBackward0>))
모듈 MyCell
을 재정의했지만, 이번에는 self.linear
속성을 추가하고
@@ -551,11 +551,11 @@
Module
(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=<TanhBackward0>), 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=<TanhBackward0>))
+(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=<TanhBackward0>), 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=<TanhBackward0>))
살짝 앞으로 돌아가 MyCell
의 두 번째 버전을 가져왔습니다. 이전에 이것을
@@ -661,17 +661,17 @@
Module
print(traced_cell(x, h))
-(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=<TanhBackward0>), 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=<TanhBackward0>))
-(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=<DifferentiableGraphBackward>), 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]],
+(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=<TanhBackward0>), 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=<TanhBackward0>))
+(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=<DifferentiableGraphBackward>), 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=<DifferentiableGraphBackward>))
@@ -765,11 +765,11 @@ 스크립팅을 사용하여 모듈 변환traced_cell(x, h)
-(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=<TanhBackward0>), 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=<TanhBackward0>))
+(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=<TanhBackward0>), 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=<TanhBackward0>))
@@ -875,7 +875,7 @@ 더 읽을거리https://colab.research.google.com/drive/1HiICg6jRkBnr5hvK2-VnMi88Vi9pUzEJ
-
Total running time of the script: ( 0 minutes 0.671 seconds)
+Total running time of the script: ( 0 minutes 0.368 seconds)
Download Python source code: Intro_to_TorchScript_tutorial.py
diff --git a/docs/beginner/sg_execution_times.html b/docs/beginner/sg_execution_times.html
index cb4a10d59..befbc9829 100644
--- a/docs/beginner/sg_execution_times.html
+++ b/docs/beginner/sg_execution_times.html
@@ -13,7 +13,7 @@
-
+
@@ -398,7 +398,7 @@
Computation times¶
-00:10.709 total execution time for beginner files:
+00:09.389 total execution time for beginner files: