Rebuild

Author: 9bow

docs/.nojekyll

@@ -94,7 +94,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ud14c\uc2a4\ud305 \ud568\uc218\n~~~~~~~~~~~~~~~~\n\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \ud575\uc2ec \uacb0\uacfc\ub294 ``\ud14c\uc2a4\ud2b8`` \ud568\uc218\uc5d0\uc11c \uc624\uac8c \ub429\ub2c8\ub2e4.\n\uc774 \ud14c\uc2a4\ud2b8 \uae30\ub2a5\uc744 \ud638\ucd9c \ud560 \ub54c\ub9c8\ub2e4 MNIST \ud14c\uc2a4\ud2b8 \uc14b\uc5d0\uc11c \uc804\uccb4 \ud14c\uc2a4\ud2b8 \ub2e8\uacc4\ub97c \uc218\ud589\ud558\uace0 \ucd5c\uc885 \uc815\ud655\ub3c4\ub97c \ubcf4\uace0\ud569\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098 \uc774 \ud568\uc218\uc5d0\ub294 *\uc5e1\uc2e4\ub860* \uc785\ub825\ub3c4 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub294 ``\ud14c\uc2a4\ud2b8``` \ud568\uc218\uac00 $\\epsilon$ \ud06c\uae30\uc5d0 \ub530\ub77c \uacf5\uaca9\uc790\uc758 \uacf5\uaca9\uc744 \ubc1b\ub294 \ubaa8\ub378\uc758\n\uc815\ud655\ub3c4\uc744 \ubcf4\uace0\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. \ub354 \uad6c\uccb4\uc801\uc73c\ub85c \ubcf4\uba74 \ud14c\uc2a4\ud2b8 \uc14b\uc758 \uac01\uac01\uc758 \uc0d8\ud50c\uc5d0\uc11c \ud14c\uc2a4\ud2b8 \ud568\uc218\ub294 \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc190\uc2e4 \ubcc0\ud654\ub3c4( $data\\_grad$ )\ub97c \uacc4\uc0b0\ud558\uace0,\n``FGSM \uacf5\uaca9`` ($perturbed\\_data$) \uc744 \ubc1b\uc740 \uc791\uc740 \ubcc0\ud654\uac00 \uc801\uc6a9\ub41c \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4\uace0 \ub098\uc11c \uc791\uc740 \ubcc0\ud654\uac00 \uc801\uc6a9\ub41c \uc774\ubbf8\uc9c0\uac00 \uc801\ub300\uc801\uc778\uc9c0 \ud655\uc778\uc744 \ud569\ub2c8\ub2e4.\n\ucd94\uac00\ub85c \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub97c \ud14c\uc2a4\ud2b8\ud558\uae30 \uc704\ud574\uc11c \ud14c\uc2a4\ud2b8 \ud568\uc218\ub294 \ub098\uc911\uc5d0 \uc2dc\uac01\ud654\ud558\uc5ec \ubcfc \uc218 \uc788\ub3c4\ub85d \uc131\uacf5\uc801\uc73c\ub85c \uc5bb\uc740 \uc801\ub300\uc801 \uc774\ubbf8\uc9c0\ub97c \uc800\uc7a5\ud558\uace0 \ubc18\ud658\ud569\ub2c8\ub2e4.\n\n\n"
+        "\ud14c\uc2a4\ud305 \ud568\uc218\n~~~~~~~~~~~~~~~~\n\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \ud575\uc2ec \uacb0\uacfc\ub294 ``\ud14c\uc2a4\ud2b8`` \ud568\uc218\uc5d0\uc11c \uc624\uac8c \ub429\ub2c8\ub2e4.\n\uc774 \ud14c\uc2a4\ud2b8 \uae30\ub2a5\uc744 \ud638\ucd9c \ud560 \ub54c\ub9c8\ub2e4 MNIST \ud14c\uc2a4\ud2b8 \uc14b\uc5d0\uc11c \uc804\uccb4 \ud14c\uc2a4\ud2b8 \ub2e8\uacc4\ub97c \uc218\ud589\ud558\uace0 \ucd5c\uc885 \uc815\ud655\ub3c4\ub97c \ubcf4\uace0\ud569\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098 \uc774 \ud568\uc218\uc5d0\ub294 *\uc5e1\uc2e4\ub860* \uc785\ub825\ub3c4 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub294 ``\ud14c\uc2a4\ud2b8`` \ud568\uc218\uac00 $\\epsilon$ \ud06c\uae30\uc5d0 \ub530\ub77c \uacf5\uaca9\uc790\uc758 \uacf5\uaca9\uc744 \ubc1b\ub294 \ubaa8\ub378\uc758\n\uc815\ud655\ub3c4\uc744 \ubcf4\uace0\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. \ub354 \uad6c\uccb4\uc801\uc73c\ub85c \ubcf4\uba74 \ud14c\uc2a4\ud2b8 \uc14b\uc758 \uac01\uac01\uc758 \uc0d8\ud50c\uc5d0\uc11c \ud14c\uc2a4\ud2b8 \ud568\uc218\ub294 \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc190\uc2e4 \ubcc0\ud654\ub3c4( $data\\_grad$ )\ub97c \uacc4\uc0b0\ud558\uace0,\n``FGSM \uacf5\uaca9`` ($perturbed\\_data$) \uc744 \ubc1b\uc740 \uc791\uc740 \ubcc0\ud654\uac00 \uc801\uc6a9\ub41c \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4\uace0 \ub098\uc11c \uc791\uc740 \ubcc0\ud654\uac00 \uc801\uc6a9\ub41c \uc774\ubbf8\uc9c0\uac00 \uc801\ub300\uc801\uc778\uc9c0 \ud655\uc778\uc744 \ud569\ub2c8\ub2e4.\n\ucd94\uac00\ub85c \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub97c \ud14c\uc2a4\ud2b8\ud558\uae30 \uc704\ud574\uc11c \ud14c\uc2a4\ud2b8 \ud568\uc218\ub294 \ub098\uc911\uc5d0 \uc2dc\uac01\ud654\ud558\uc5ec \ubcfc \uc218 \uc788\ub3c4\ub85d \uc131\uacf5\uc801\uc73c\ub85c \uc5bb\uc740 \uc801\ub300\uc801 \uc774\ubbf8\uc9c0\ub97c \uc800\uc7a5\ud558\uace0 \ubc18\ud658\ud569\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {

docs/CNAME

@@ -197,7 +197,7 @@ def fgsm_attack(image, epsilon, data_grad):
 #
 # 마지막으로 본 튜토리얼의 핵심 결과는 ``테스트`` 함수에서 오게 됩니다.
 # 이 테스트 기능을 호출 할 때마다 MNIST 테스트 셋에서 전체 테스트 단계를 수행하고 최종 정확도를 보고합니다.
-# 그러나 이 함수에는 *엡실론* 입력도 필요합니다. 이는 ``테스트``` 함수가 :math:`\epsilon` 크기에 따라 공격자의 공격을 받는 모델의
+# 그러나 이 함수에는 *엡실론* 입력도 필요합니다. 이는 ``테스트`` 함수가 :math:`\epsilon` 크기에 따라 공격자의 공격을 받는 모델의
 # 정확도을 보고하기 때문입니다. 더 구체적으로 보면 테스트 셋의 각각의 샘플에서 테스트 함수는 입력 데이터에 대한 손실 변화도( :math:`data\_grad` )를 계산하고,
 # ``FGSM 공격`` (:math:`perturbed\_data`) 을 받은 작은 변화가 적용된 이미지를 만들고 나서 작은 변화가 적용된 이미지가 적대적인지 확인을 합니다.
 # 추가로 모델의 정확도를 테스트하기 위해서 테스트 함수는 나중에 시각화하여 볼 수 있도록 성공적으로 얻은 적대적 이미지를 저장하고 반환합니다.

docs/_downloads/2db6a7dd5fbf3ce6acb5f93d7d5373c5/fgsm_tutorial.ipynb

@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nf = wt_compare_dict['conv1.weight']['float'].flatten()\nplt.hist(f, bins = 100)\nplt.title(\"Floating point model weights of conv1\")\nplt.show()\n\nq = wt_compare_dict['conv1.weight']['quantized'].flatten().dequantize()\nplt.hist(q, bins = 100)\nplt.title(\"Quantized model weights of conv1\")\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nf = wt_compare_dict['conv1.weight']['float'].flatten()\n#plt.hist(f, bins = 100)\n#plt.title(\"Floating point model weights of conv1\")\n#plt.show()\n\nq = wt_compare_dict['conv1.weight']['quantized'].flatten().dequantize()\n#plt.hist(q, bins = 100)\n#plt.title(\"Quantized model weights of conv1\")\n#plt.show()"
       ]
     },
     {

docs/_downloads/3829e7d2fa2343bb4bd51456f33d3f84/fgsm_tutorial.py

@@ -91,14 +91,14 @@ def compute_error(x, y):
 import matplotlib.pyplot as plt
 
 f = wt_compare_dict['conv1.weight']['float'].flatten()
-plt.hist(f, bins = 100)
-plt.title("Floating point model weights of conv1")
-plt.show()
+#plt.hist(f, bins = 100)
+#plt.title("Floating point model weights of conv1")
+#plt.show()
 
 q = wt_compare_dict['conv1.weight']['quantized'].flatten().dequantize()
-plt.hist(q, bins = 100)
-plt.title("Quantized model weights of conv1")
-plt.show()
+#plt.hist(q, bins = 100)
+#plt.title("Quantized model weights of conv1")
+#plt.show()