fgdfg

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.idea/workspace.xml

@@ -17,12 +17,24 @@
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+      <file pinned="false" current-in-tab="true">
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@@ -65,9 +77,9 @@
         <option value="$PROJECT_DIR$/SecondSection/Tensor.md" />
         <option value="$PROJECT_DIR$/tensorTest.py" />
         <option value="$PROJECT_DIR$/SecondSection/Autograd.md" />
-        <option value="$PROJECT_DIR$/fourSection/AdversarialExampleGene.md" />
         <option value="$PROJECT_DIR$/Content.md" />
         <option value="$PROJECT_DIR$/fourSection/ONNX.md" />
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@@ -191,7 +203,7 @@
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@@ -322,16 +334,6 @@
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+            <caret line="158" selection-start-line="158" selection-end-line="158" />
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+          <second_editor />
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+    <entry file="file://$PROJECT_DIR$/fourSection/AdversarialExampleGene.md">
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fourSection/AdversarialExampleGene.md

@@ -19,6 +19,7 @@ FGSM 攻击是一种白盒攻击，其目标是错误分类。有了这些背景
 换句话说，攻击是利用损失函数的梯度，然后调整输入数据以最大化损失。
 
 在进入代码之前，先讲一下著名的 FGSM 熊猫示例并提取一些符号。
+
 ![](image/15.png)
 
 从图中可以看出，![](notation/x.gif)是正确分类为“熊猫”的原始输入图像，![](notation/y.gif)是![](notation/x.gif)的基本事实标签，
@@ -243,6 +244,7 @@ plt.xlabel("Epsilon")
 plt.ylabel("Accuracy")
 plt.show()
 ```
+
 ![](image/16.png)
 
 #### 4.2 样本对抗性示例
@@ -268,6 +270,7 @@ for i in range(len(epsilons)):
 plt.tight_layout()
 plt.show()
 ```
+
 ![](image/17.png)
 
 ### 5.展望

fourSection/ONNX.md

@@ -156,7 +156,9 @@ with open('predict_net.pb', "wb") as fopen:
 的`init_net`和`predict_net`，并在正常的 Caffe2 后端和移动设备中运行它们，并验证两次运行中生成的输出高分辨率猫咪图像是否相同。
 
 在本教程中，我们将使用广泛使用的著名猫咪图像，如下所示：
+
 ![](image/19.jpg)
+
 ```buildoutcfg
 # 一些必备的导入包
 from caffe2.proto import caffe2_pb2