{"id":117133,"date":"2026-10-22T16:09:25","date_gmt":"2026-10-22T08:09:25","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/117133.html"},"modified":"2026-10-22T16:09:25","modified_gmt":"2026-10-22T08:09:25","slug":"%e5%9f%ba%e4%ba%8e%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e7%9a%84%e6%95%b0%e6%8d%ae%e5%8a%a0%e5%af%86%e8%af%86%e5%88%ab%ef%bc%88%e5%9b%9b%ef%bc%89%ef%bc%9a1d-cnn-%e6%bf%80%e6%b4%bb%e5%87%bd%e6%95%b0","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/117133.html","title":{"rendered":"\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u6570\u636e\u52a0\u5bc6\u8bc6\u522b\uff08\u56db\uff09\uff1a1D-CNN \u6fc0\u6d3b\u51fd\u6570\u4e0e BatchNorm \u6d88\u878d\u5b9e\u9a8c"},"content":{"rendered":"

\u76ee\u5f55<\/p>\n

\n

\u4e00\u3001\u524d\u8a00<\/p>\n

\u4e8c\u3001\u5b9e\u9a8c\u539f\u7406<\/p>\n

2.1 \u660e\u6587\u4e0e\u5bc6\u6587\u4e8c\u5206\u7c7b<\/p>\n

2.2 \u5b57\u8282\u9891\u7387\u76f4\u65b9\u56fe<\/p>\n

2.3 Z-Score \u6807\u51c6\u5316<\/p>\n

\u4e09\u3001\u4e3a\u4ec0\u4e48\u7814\u7a76\u6fc0\u6d3b\u51fd\u6570？<\/p>\n

3.1 ReLU<\/p>\n

3.2 LeakyReLU<\/p>\n

3.3 GELU<\/p>\n

3.4 SiLU<\/p>\n

\u56db\u3001BatchNorm \u7684\u4f5c\u7528<\/p>\n

\u4e94\u3001\u5b9e\u9a8c\u8bbe\u8ba1<\/p>\n

5.1 \u6570\u636e\u96c6\u751f\u6210<\/p>\n

5.2 CNN \u7f51\u7edc\u7ed3\u6784<\/p>\n

5.3 \u6d88\u878d\u5b9e\u9a8c\u5206\u7ec4<\/p>\n

5.4 \u8bad\u7ec3\u53c2\u6570<\/p>\n

\u516d\u3001\u5b9e\u9a8c\u7ed3\u679c<\/p>\n

6.1 \u4e94\u968f\u673a\u79cd\u5b50\u5e73\u5747\u6027\u80fd<\/p>\n

6.2 \u4e0d\u540c\u6fc0\u6d3b\u51fd\u6570\u5bf9\u6bd4<\/p>\n

6.3 BatchNorm \u6d88\u878d\u7ed3\u679c<\/p>\n

\u4e03\u3001\u4e3a\u4ec0\u4e48 BatchNorm \u53ef\u80fd\u6ca1\u6709\u5e26\u6765\u63d0\u5347？<\/p>\n

7.1 \u8f93\u5165\u5df2\u7ecf\u7ecf\u8fc7 Z-Score \u6807\u51c6\u5316<\/p>\n

7.2 BatchNorm \u7684\u7edf\u8ba1\u91cf\u53ef\u80fd\u5f71\u54cd\u6a21\u578b\u8f93\u51fa<\/p>\n

7.3 \u7f51\u7edc\u7ed3\u6784\u4e0e\u8f93\u5165\u7279\u5f81\u53ef\u80fd\u4e0d\u5339\u914d<\/p>\n

\u516b\u3001CNN \u662f\u5426\u4ecd\u7136\u5b58\u5728\u5355\u7c7b\u522b\u9884\u6d4b\u95ee\u9898？<\/p>\n

\u4e5d\u3001\u4e3a\u4ec0\u4e48 MLP \u7684\u6548\u679c\u6bd4 CNN \u66f4\u597d？<\/p>\n

\u5341\u3001\u5b9e\u9a8c\u5c40\u9650\u6027<\/p>\n

10.1 \u6570\u636e\u4e3b\u8981\u6765\u81ea\u6a21\u62df\u56fe\u50cf<\/p>\n

10.2 \u660e\u6587\u4e0e\u5bc6\u6587\u5206\u7c7b\u4e0d\u7b49\u4e8e\u8bc6\u522b\u52a0\u5bc6\u7b97\u6cd5<\/p>\n

10.3 \u4ecd\u9700\u8981\u8de8\u6570\u636e\u96c6\u6cdb\u5316\u9a8c\u8bc1<\/p>\n

10.4 \u9700\u8981\u8fdb\u4e00\u6b65\u5206\u6790\u6027\u80fd\u5dee\u5f02\u7684\u7edf\u8ba1\u7a33\u5b9a\u6027<\/p>\n

\u5341\u4e00\u3001\u4e0b\u4e00\u6b65\u7814\u7a76\u65b9\u5411<\/p>\n

\u5341\u4e8c\u3001\u603b\u7ed3<\/p>\n

\n

\u4e00\u3001\u524d\u8a00<\/h2>\n

\u5728\u524d\u9762\u7684\u5b9e\u9a8c\u4e2d，\u6211\u4eec\u5c1d\u8bd5\u4f7f\u7528\u591a\u5c42\u611f\u77e5\u673a（MLP）\u548c\u4e00\u7ef4\u5377\u79ef\u795e\u7ecf\u7f51\u7edc（1D-CNN），\u5bf9\u660e\u6587\u6570\u636e\u4e0e AES \u52a0\u5bc6\u6570\u636e\u8fdb\u884c\u4e8c\u5206\u7c7b\u3002\u5b9e\u9a8c\u53d1\u73b0，\u76f4\u63a5\u5c06\u539f\u59cb\u5b57\u8282\u5e8f\u5217\u6216\u5b57\u8282\u9891\u7387\u76f4\u65b9\u56fe\u8f93\u5165 CNN，\u5e76\u4e0d\u4e00\u5b9a\u80fd\u591f\u53d6\u5f97\u7406\u60f3\u7684\u5206\u7c7b\u6548\u679c\u3002\u90e8\u5206\u5b9e\u9a8c\u4e2d，CNN \u751a\u81f3\u4f1a\u5c06\u6240\u6709\u6837\u672c\u9884\u6d4b\u4e3a\u540c\u4e00\u7c7b\u522b，\u5bfc\u81f4\u51c6\u786e\u7387\u505c\u7559\u5728 50% \u5de6\u53f3\u3002<\/p>\n

\u4e3a\u4e86\u5206\u6790\u8fd9\u4e00\u95ee\u9898，\u4e0a\u4e00\u7bc7\u5b9e\u9a8c\u8fdb\u4e00\u6b65\u6bd4\u8f83\u4e86\u4e0d\u540c\u8f93\u5165\u5904\u7406\u65b9\u5f0f，\u5305\u62ec\u539f\u59cb\u5b57\u8282\u3001\u5b57\u8282\u9891\u7387\u76f4\u65b9\u56fe\u3001\u76f4\u65b9\u56fe\u6570\u503c\u653e\u5927\u548c Z-Score \u6807\u51c6\u5316\u3002\u4e94\u968f\u673a\u79cd\u5b50\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e：<\/p>\n\n\n\u6a21\u578b\u5e73\u5747 Accuracy\u5e73\u5747 ROC-AUC<\/tr>\n\n\n\n\n\n
MLP_Hist<\/td>\n71.75%<\/td>\n0.8322<\/td>\n<\/tr>\n
CNN_Raw<\/td>\n53.43%<\/td>\n0.5832<\/td>\n<\/tr>\n
CNN_Hist<\/td>\n50.00%<\/td>\n0.3088<\/td>\n<\/tr>\n
CNN_Hist_Scaled<\/td>\n66.93%<\/td>\n0.6959<\/td>\n<\/tr>\n
CNN_Hist_ZScore<\/td>\n72.05%<\/td>\n0.8041<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u53ef\u4ee5\u770b\u5230，\u8f93\u5165\u7279\u5f81\u7684\u5904\u7406\u65b9\u5f0f\u5bf9 CNN \u6027\u80fd\u4ea7\u751f\u4e86\u660e\u663e\u5f71\u54cd\u3002\u90a3\u4e48，\u5728\u4f7f\u7528 Z-Score \u6807\u51c6\u5316\u7684\u57fa\u7840\u4e0a，\u80fd\u5426\u901a\u8fc7\u8c03\u6574 CNN \u5185\u90e8\u7684\u6fc0\u6d3b\u51fd\u6570\u548c\u5f52\u4e00\u5316\u5c42，\u8fdb\u4e00\u6b65\u6539\u5584\u5206\u7c7b\u6548\u679c？\u672c\u7bc7\u6587\u7ae0\u5c06\u56f4\u7ed5\u8fd9\u4e00\u95ee\u9898\u5c55\u5f00\u5b9e\u9a8c\u3002<\/p>\n

\u672c\u6587\u4e3b\u8981\u7814\u7a76\u4e09\u4e2a\u95ee\u9898：<\/p>\n

  • \n

    ReLU\u3001LeakyReLU\u3001GELU\u3001SiLU \u56db\u79cd\u6fc0\u6d3b\u51fd\u6570\u662f\u5426\u4f1a\u5f71\u54cd CNN \u7684\u5206\u7c7b\u6027\u80fd？<\/p>\n<\/li>\n

  • \n

    \u52a0\u5165 Batch Normalization（BatchNorm）\u662f\u5426\u80fd\u591f\u6539\u5584\u6a21\u578b\u8868\u73b0？<\/p>\n<\/li>\n

  • \n

    \u6539\u8fdb\u540e\u7684 CNN \u80fd\u5426\u8d85\u8fc7\u4f7f\u7528\u76f8\u540c\u8f93\u5165\u7279\u5f81\u7684 MLP？<\/p>\n<\/li>\n

    \u9700\u8981\u8bf4\u660e\u7684\u662f，\u672c\u6587\u7814\u7a76\u7684\u662f\u7279\u5b9a\u6570\u636e\u96c6\u4e0a\u7684\u660e\u6587\u4e0e\u5bc6\u6587\u5206\u7c7b，\u4e0d\u6d89\u53ca AES \u5bc6\u94a5\u6062\u590d，\u4e5f\u4e0d\u80fd\u636e\u6b64\u8ba4\u4e3a\u6a21\u578b\u80fd\u591f\u7834\u89e3 AES\u3002<\/p>\n

    \n

    \u4e8c\u3001\u5b9e\u9a8c\u539f\u7406<\/h2>\n

    2.1 \u660e\u6587\u4e0e\u5bc6\u6587\u4e8c\u5206\u7c7b<\/h4>\n

    \u672c\u5b9e\u9a8c\u5c06\u8f93\u5165\u6570\u636e\u5206\u4e3a\u4e24\u7c7b：<\/p>\n

      \n
    • \n

      \u6807\u7b7e 0：\u660e\u6587\u6587\u4ef6\u6570\u636e\u7247\u6bb5（Plaintext）<\/p>\n<\/li>\n

    • \n

      \u6807\u7b7e 1：AES-256-CTR \u52a0\u5bc6\u540e\u7684\u6587\u4ef6\u6570\u636e\u7247\u6bb5（Encrypted）<\/p>\n<\/li>\n<\/ul>\n

      \u5b9e\u9a8c\u9996\u5148\u751f\u6210 RGB \u56fe\u50cf，\u5206\u522b\u7f16\u7801\u4e3a PNG \u548c JPEG \u6587\u4ef6，\u7136\u540e\u4f7f\u7528 AES-256-CTR \u5bf9\u6587\u4ef6\u5b57\u8282\u8fdb\u884c\u52a0\u5bc6\u3002\u968f\u540e，\u5c06\u660e\u6587\u548c\u5bc6\u6587\u6309\u7167\u56fa\u5b9a\u957f\u5ea6\u5207\u5206\u4e3a\u6570\u636e\u7247\u6bb5，\u6bcf\u4e2a\u7247\u6bb5\u5305\u542b 4096 \u5b57\u8282\u3002\u6a21\u578b\u9700\u8981\u6839\u636e\u8f93\u5165\u7247\u6bb5\u7684\u7edf\u8ba1\u7279\u5f81，\u5224\u65ad\u8be5\u7247\u6bb5\u6765\u81ea\u660e\u6587\u6587\u4ef6\u8fd8\u662f\u52a0\u5bc6\u540e\u7684\u6587\u4ef6\u3002<\/p>\n

      2.2 \u5b57\u8282\u9891\u7387\u76f4\u65b9\u56fe<\/h4>\n

      \u5bf9\u4e8e\u4e00\u4e2a\u957f\u5ea6\u4e3a 4096 \u5b57\u8282\u7684\u6570\u636e\u7247\u6bb5，\u7edf\u8ba1 0～255 \u6bcf\u4e00\u79cd\u5b57\u8282\u53d6\u503c\u51fa\u73b0\u7684\u6b21\u6570\u3002<\/p>\n

      \u8bbe\u5b57\u8282\u503c (i) \u51fa\u73b0\u7684\u6b21\u6570\u4e3a (n_i)，\u5219\u5f52\u4e00\u5316\u9891\u7387\u4e3a：[h_i=\\\\frac{n_i}{4096}]<\/p>\n

      \u6700\u7ec8\u5f97\u5230\u4e00\u4e2a 256 \u7ef4\u7279\u5f81\u5411\u91cf：[H=[h_0,h_1,\\\\ldots,h_{255}]]<\/p>\n

      \u4e0e\u76f4\u63a5\u8f93\u5165 4096 \u4e2a\u539f\u59cb\u5b57\u8282\u4e0d\u540c，\u5b57\u8282\u9891\u7387\u76f4\u65b9\u56fe\u4e0d\u4fdd\u7559\u5b57\u8282\u51fa\u73b0\u7684\u5148\u540e\u987a\u5e8f，\u800c\u662f\u63cf\u8ff0\u6574\u4f53\u9891\u7387\u5206\u5e03\u3002<\/p>\n

      \u8fd9\u79cd\u7279\u5f81\u53ef\u80fd\u53cd\u6620\u6587\u4ef6\u7f16\u7801\u548c\u52a0\u5bc6\u5904\u7406\u540e\u7684\u7edf\u8ba1\u5dee\u5f02，\u4f46\u4e5f\u4f1a\u4e22\u5931\u5b57\u8282\u4e4b\u95f4\u7684\u987a\u5e8f\u5173\u7cfb\u3002<\/p>\n

      2.3 Z-Score \u6807\u51c6\u5316<\/h4>\n

      \u4e3a\u4e86\u51cf\u5c0f\u4e0d\u540c\u7279\u5f81\u7ef4\u5ea6\u4e4b\u95f4\u7684\u6570\u503c\u5c3a\u5ea6\u5dee\u5f02，\u672c\u5b9e\u9a8c\u4f7f\u7528 Z-Score \u6807\u51c6\u5316：[x_i'=\\\\frac{x_i-\\\\mu_i}{\\\\sigma_i}]<\/p>\n

      \u5176\u4e2d：<\/p>\n

        \n
      • \n

        (\\\\mu_i)：\u8bad\u7ec3\u96c6\u4e2d\u7b2c (i) \u4e2a\u7279\u5f81\u7ef4\u5ea6\u7684\u5747\u503c；<\/p>\n<\/li>\n

      • \n

        (\\\\sigma_i)：\u8bad\u7ec3\u96c6\u4e2d\u8be5\u7ef4\u5ea6\u7684\u6807\u51c6\u5dee\u3002<\/p>\n<\/li>\n<\/ul>\n

        \u4e3a\u4e86\u907f\u514d\u6807\u51c6\u5dee\u8fc7\u5c0f\u5f15\u8d77\u6570\u503c\u4e0d\u7a33\u5b9a，\u4ee3\u7801\u4e2d\u8bbe\u7f6e\u4e86\u6700\u5c0f\u6807\u51c6\u5dee：<\/p>\n

        std = np.maximum(std, 1e-6) <\/p>\n

        \u7279\u522b\u6ce8\u610f：\u5747\u503c\u548c\u6807\u51c6\u5dee\u53ea\u6839\u636e\u8bad\u7ec3\u96c6\u8ba1\u7b97，\u518d\u5e94\u7528\u5230\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\u3002<\/p>\n

        \u8fd9\u6837\u53ef\u4ee5\u907f\u514d\u6d4b\u8bd5\u96c6\u7edf\u8ba1\u4fe1\u606f\u6cc4\u6f0f\u5230\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u3002<\/p>\n

        \n

        \u4e09\u3001\u4e3a\u4ec0\u4e48\u7814\u7a76\u6fc0\u6d3b\u51fd\u6570？<\/h2>\n

        \u6fc0\u6d3b\u51fd\u6570\u4e3a\u795e\u7ecf\u7f51\u7edc\u5f15\u5165\u975e\u7ebf\u6027\u8868\u8fbe\u80fd\u529b\u3002<\/p>\n

        \u4e0d\u540c\u6fc0\u6d3b\u51fd\u6570\u5177\u6709\u4e0d\u540c\u7684\u68af\u5ea6\u7279\u6027，\u53ef\u80fd\u5f71\u54cd\u6a21\u578b\u7684\u4f18\u5316\u8fc7\u7a0b\u3001\u6536\u655b\u901f\u5ea6\u548c\u6700\u7ec8\u5206\u7c7b\u6027\u80fd\u3002<\/p>\n

        \u672c\u5b9e\u9a8c\u9009\u62e9\u56db\u79cd\u5e38\u89c1\u6fc0\u6d3b\u51fd\u6570\u8fdb\u884c\u5bf9\u6bd4\u3002<\/p>\n

        3.1 ReLU<\/h4>\n

        ReLU \u7684\u5b9a\u4e49\u4e3a：[ReLU(x)=\\\\max(0,x)]<\/p>\n

        ReLU \u8ba1\u7b97\u7b80\u5355，\u662f\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u4e2d\u5e7f\u6cdb\u4f7f\u7528\u7684\u6fc0\u6d3b\u51fd\u6570\u3002\u4f46\u5bf9\u4e8e\u8d1f\u6570\u8f93\u5165，\u5176\u8f93\u51fa\u548c\u68af\u5ea6\u5747\u4e3a 0，\u90e8\u5206\u795e\u7ecf\u5143\u53ef\u80fd\u957f\u671f\u65e0\u6cd5\u83b7\u5f97\u6709\u6548\u66f4\u65b0\u3002<\/p>\n

        3.2 LeakyReLU<\/h4>\n

        LeakyReLU \u5728\u8d1f\u6570\u533a\u57df\u4fdd\u7559\u4e00\u4e2a\u8f83\u5c0f\u7684\u659c\u7387：<\/p>\n

        [LeakyReLU(x)=\\\\begin{cases}x,&x\\\\geq0\\\\\\\\alpha x,&x<0\\\\end{cases}]<\/p>\n

        \u672c\u5b9e\u9a8c\u8bbe\u7f6e：[\\\\alpha=0.01]\u4e0e ReLU \u76f8\u6bd4，\u5b83\u5141\u8bb8\u8d1f\u6570\u533a\u57df\u7ee7\u7eed\u4f20\u64ad\u68af\u5ea6\u3002<\/p>\n

        3.3 GELU<\/h4>\n

        GELU（Gaussian Error Linear Unit）\u662f\u4e00\u79cd\u5e73\u6ed1\u7684\u975e\u7ebf\u6027\u6fc0\u6d3b\u51fd\u6570：[GELU(x)=x\\\\Phi(x)]<\/p>\n

        \u5176\u4e2d (\\\\Phi(x)) \u8868\u793a\u6807\u51c6\u6b63\u6001\u5206\u5e03\u7684\u7d2f\u79ef\u5206\u5e03\u51fd\u6570\u3002GELU \u4e0d\u4f1a\u50cf ReLU \u90a3\u6837\u76f4\u63a5\u622a\u65ad\u6240\u6709\u8d1f\u6570\u8f93\u5165\u3002<\/p>\n

        3.4 SiLU<\/h4>\n

        SiLU（Sigmoid Linear Unit）\u5b9a\u4e49\u4e3a：[SiLU(x)=x\\\\cdot\\\\sigma(x)]<\/p>\n

        \u5176\u4e2d：[\\\\sigma(x)=\\\\frac{1}{1+e^{-x}}]<\/p>\n

        SiLU \u540c\u6837\u662f\u4e00\u79cd\u5e73\u6ed1\u6fc0\u6d3b\u51fd\u6570，\u5176\u8f93\u51fa\u7531\u8f93\u5165\u4e0e Sigmoid \u51fd\u6570\u5171\u540c\u51b3\u5b9a\u3002\u672c\u5b9e\u9a8c\u5e0c\u671b\u901a\u8fc7\u5b9e\u9645\u6570\u636e\u5224\u65ad：\u8fd9\u4e9b\u51fd\u6570\u7684\u5dee\u5f02，\u662f\u5426\u4f1a\u8f6c\u5316\u4e3a\u660e\u6587\u4e0e\u5bc6\u6587\u5206\u7c7b\u6027\u80fd\u7684\u5dee\u5f02\u3002<\/p>\n

        \n

        \u56db\u3001BatchNorm \u7684\u4f5c\u7528<\/h2>\n

        Batch Normalization \u662f\u6df1\u5ea6\u5b66\u4e60\u4e2d\u5e38\u89c1\u7684\u5f52\u4e00\u5316\u65b9\u6cd5\u3002<\/p>\n

        \u5b83\u901a\u8fc7\u6279\u6b21\u7edf\u8ba1\u91cf\u5bf9\u7f51\u7edc\u5185\u90e8\u7279\u5f81\u8fdb\u884c\u6807\u51c6\u5316，\u5e76\u5f15\u5165\u53ef\u5b66\u4e60\u7684\u7f29\u653e\u4e0e\u504f\u79fb\u53c2\u6570\u3002<\/p>\n

        \u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，BatchNorm \u901a\u5e38\u4f7f\u7528\u5f53\u524d\u6279\u6b21\u7684\u7edf\u8ba1\u91cf；\u5728\u63a8\u7406\u8fc7\u7a0b\u4e2d，\u5219\u4f7f\u7528\u8bad\u7ec3\u9636\u6bb5\u79ef\u7d2f\u7684\u8fd0\u884c\u7edf\u8ba1\u91cf\u3002<\/p>\n

        BatchNorm \u6709\u65f6\u80fd\u591f\u6539\u5584\u4f18\u5316\u8fc7\u7a0b，\u4f46\u5b83\u5e76\u4e0d\u4fdd\u8bc1\u5728\u6240\u6709\u4efb\u52a1\u548c\u7f51\u7edc\u7ed3\u6784\u4e2d\u90fd\u6709\u6548\u3002<\/p>\n

        \u56e0\u6b64，\u672c\u5b9e\u9a8c\u5206\u522b\u6784\u5efa：<\/p>\n

          \n
        • \n

          \u4e0d\u4f7f\u7528 BatchNorm \u7684 CNN；<\/p>\n<\/li>\n

        • \n

          \u5728\u5377\u79ef\u5c42\u540e\u52a0\u5165 BatchNorm \u7684 CNN\u3002<\/p>\n<\/li>\n<\/ul>\n

          \u901a\u8fc7\u63a7\u5236\u5176\u4ed6\u6761\u4ef6\u4e00\u81f4，\u89c2\u5bdf BatchNorm \u662f\u5426\u771f\u6b63\u6539\u5584\u6a21\u578b\u6027\u80fd\u3002<\/p>\n

          \n

          \u4e94\u3001\u5b9e\u9a8c\u8bbe\u8ba1<\/h2>\n

          5.1 \u6570\u636e\u96c6\u751f\u6210<\/h4>\n

          \u672c\u5b9e\u9a8c\u4f7f\u7528 Python \u751f\u6210\u6a21\u62df RGB \u56fe\u50cf，\u6bcf\u5f20\u56fe\u50cf\u5206\u522b\u4fdd\u5b58\u4e3a PNG \u548c JPEG \u683c\u5f0f\u3002\u4e4b\u540e\u5bf9\u6587\u4ef6\u5b57\u8282\u8fdb\u884c AES-256-CTR \u52a0\u5bc6，\u518d\u5206\u522b\u63d0\u53d6\u660e\u6587\u4e0e\u5bc6\u6587\u6570\u636e\u7247\u6bb5\u3002<\/p>\n

          \u4e3b\u8981\u914d\u7f6e\u5982\u4e0b：<\/p>\n\n\n\u53c2\u6570\u8bbe\u7f6e<\/tr>\n\n\n\n\n\n\n\n\n\n\n
          \u56fe\u50cf\u6570\u91cf<\/td>\n60<\/td>\n<\/tr>\n
          \u56fe\u50cf\u5c3a\u5bf8<\/td>\n128 \u00d7 128<\/td>\n<\/tr>\n
          \u6587\u4ef6\u683c\u5f0f<\/td>\nPNG\u3001JPEG<\/td>\n<\/tr>\n
          \u52a0\u5bc6\u7b97\u6cd5<\/td>\nAES-256-CTR<\/td>\n<\/tr>\n
          \u6570\u636e\u7247\u6bb5\u957f\u5ea6<\/td>\n4096 \u5b57\u8282<\/td>\n<\/tr>\n
          \u6bcf\u4e2a\u6587\u4ef6\u6700\u591a\u63d0\u53d6\u7247\u6bb5<\/td>\n8<\/td>\n<\/tr>\n
          \u8f93\u5165\u7279\u5f81<\/td>\n256 \u7ef4\u5b57\u8282\u9891\u7387\u76f4\u65b9\u56fe<\/td>\n<\/tr>\n
          \u7279\u5f81\u9884\u5904\u7406<\/td>\nZ-Score<\/td>\n<\/tr>\n
          \u6570\u636e\u5212\u5206<\/td>\n\u8bad\u7ec3\u96c6 60%\u3001\u9a8c\u8bc1\u96c6 20%\u3001\u6d4b\u8bd5\u96c6 20%<\/td>\n<\/tr>\n
          \u968f\u673a\u79cd\u5b50<\/td>\n2026\u30012027\u30012028\u30012029\u30012030<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u4e3a\u4e86\u964d\u4f4e\u6570\u636e\u6cc4\u6f0f\u98ce\u9669，\u5b9e\u9a8c\u6309\u7167\u539f\u59cb\u56fe\u50cf\u8fdb\u884c\u5206\u7ec4\u5212\u5206\u3002<\/p>\n

          \u540c\u4e00\u5f20\u539f\u59cb\u56fe\u50cf\u4ea7\u751f\u7684 PNG\u3001JPEG\u3001\u660e\u6587\u53ca\u5bc6\u6587\u7247\u6bb5\u88ab\u5206\u914d\u5230\u540c\u4e00\u4e2a\u6570\u636e\u5b50\u96c6，\u4e0d\u4f1a\u8de8\u8bad\u7ec3\u96c6\u3001\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\u51fa\u73b0\u3002<\/p>\n

          5.2 CNN \u7f51\u7edc\u7ed3\u6784<\/h4>\n

          \u672c\u5b9e\u9a8c\u4f7f\u7528\u7684 1D-CNN \u7ed3\u6784\u5982\u4e0b：<\/p>\n

          \u8f93\u5165：256 \u7ef4\u76f4\u65b9\u56fe
          \n \u2502
          \n \u25bc
          \nConv1D(1 \u2192 32, kernel=7)
          \n \u2502
          \n [\u53ef\u9009 BatchNorm]
          \n \u2502
          \n \u6fc0\u6d3b\u51fd\u6570
          \n \u2502
          \n MaxPool1D
          \n \u2502
          \n \u25bc
          \nConv1D(32 \u2192 64, kernel=5)
          \n \u2502
          \n [\u53ef\u9009 BatchNorm]
          \n \u2502
          \n \u6fc0\u6d3b\u51fd\u6570
          \n \u2502
          \n MaxPool1D
          \n \u2502
          \n \u25bc
          \nConv1D(64 \u2192 64, kernel=3)
          \n \u2502
          \n [\u53ef\u9009 BatchNorm]
          \n \u2502
          \n \u6fc0\u6d3b\u51fd\u6570
          \n \u2502
          \nAdaptiveAvgPool1D(1)
          \n \u2502
          \n \u25bc
          \nLinear(64 \u2192 32)
          \n \u2502
          \n \u6fc0\u6d3b\u51fd\u6570
          \n \u2502
          \n Dropout(0.2)
          \n \u2502
          \nLinear(32 \u2192 1)
          \n \u2502
          \n \u25bc
          \n\u8f93\u51fa Logit
          \n \u2502
          \n Sigmoid
          \n \u2502
          \n\u660e\u6587 \/ \u5bc6\u6587\u9884\u6d4b <\/p>\n

          \u5176\u4e2d，Sigmoid \u4ec5\u7528\u4e8e\u5c06\u8f93\u51fa Logit \u8f6c\u6362\u4e3a\u9884\u6d4b\u5206\u6570\u3002<\/p>\n

          \u8bad\u7ec3\u9636\u6bb5\u76f4\u63a5\u4f7f\u7528 BCEWithLogitsLoss，\u907f\u514d\u5728\u635f\u5931\u51fd\u6570\u4e4b\u524d\u91cd\u590d\u6267\u884c Sigmoid\u3002<\/p>\n

          5.3 \u6d88\u878d\u5b9e\u9a8c\u5206\u7ec4<\/h4>\n

          \u672c\u5b9e\u9a8c\u6bd4\u8f83\u4ee5\u4e0b\u516b\u79cd CNN \u914d\u7f6e：<\/p>\n\n\n\u6fc0\u6d3b\u51fd\u6570\u4e0d\u4f7f\u7528 BatchNorm\u4f7f\u7528 BatchNorm<\/tr>\n\n\n\n\n
          ReLU<\/td>\nCNN_ReLU_NoBN<\/td>\nCNN_ReLU_BN<\/td>\n<\/tr>\n
          LeakyReLU<\/td>\nCNN_LeakyReLU_NoBN<\/td>\nCNN_LeakyReLU_BN<\/td>\n<\/tr>\n
          GELU<\/td>\nCNN_GELU_NoBN<\/td>\nCNN_GELU_BN<\/td>\n<\/tr>\n
          SiLU<\/td>\nCNN_SiLU_NoBN<\/td>\nCNN_SiLU_BN<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u6bcf\u79cd\u914d\u7f6e\u8fd0\u884c\u4e94\u4e2a\u968f\u673a\u79cd\u5b50，\u5171\u8fdb\u884c：[4\\\\times2\\\\times5=40]\u6b21 CNN \u8bad\u7ec3\u3002<\/p>\n

          \u53e6\u5916，\u6bcf\u4e2a\u968f\u673a\u79cd\u5b50\u8bad\u7ec3\u4e00\u6b21 MLP，\u4f5c\u4e3a\u5bf9\u7167\u7ec4\u3002<\/p>\n

          5.4 \u8bad\u7ec3\u53c2\u6570<\/h4>\n\n\n\u53c2\u6570\u8bbe\u7f6e<\/tr>\n\n\n\n\n\n\n\n
          \u4f18\u5316\u5668<\/td>\nAdam<\/td>\n<\/tr>\n
          \u5b66\u4e60\u7387<\/td>\n0.001<\/td>\n<\/tr>\n
          Batch Size<\/td>\n64<\/td>\n<\/tr>\n
          \u6700\u5927 Epoch<\/td>\n30<\/td>\n<\/tr>\n
          Early Stopping Patience<\/td>\n6<\/td>\n<\/tr>\n
          \u635f\u5931\u51fd\u6570<\/td>\nBCEWithLogitsLoss<\/td>\n<\/tr>\n
          \u56fa\u5b9a\u5206\u7c7b\u9608\u503c<\/td>\n0.5<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u6a21\u578b\u4f7f\u7528\u9a8c\u8bc1\u96c6\u635f\u5931\u8fdb\u884c Early Stopping，\u5e76\u6062\u590d\u9a8c\u8bc1\u635f\u5931\u6700\u4f4e\u65f6\u7684\u6a21\u578b\u53c2\u6570\u3002<\/p>\n

          \u5b9e\u9a8c\u540c\u65f6\u8bb0\u5f55\u56fa\u5b9a\u9608\u503c 0.5 \u548c\u9a8c\u8bc1\u96c6\u4f18\u5316\u9608\u503c\u7684\u7ed3\u679c\u3002<\/p>\n

          \u4e3a\u4fbf\u4e8e\u6bd4\u8f83，\u672c\u6587\u4e3b\u8981\u5c55\u793a\u56fa\u5b9a\u9608\u503c 0.5 \u4e0b\u7684\u5b9e\u9a8c\u6307\u6807\u3002<\/p>\n

          \n

          \u516d\u3001\u5b9e\u9a8c\u7ed3\u679c<\/h2>\n

          6.1 \u4e94\u968f\u673a\u79cd\u5b50\u5e73\u5747\u6027\u80fd<\/h4>\n

          \u6839\u636e\u5b9e\u9645\u8fd0\u884c\u751f\u6210\u7684 five_seed_summary.csv，\u5f97\u5230\u4ee5\u4e0b\u7ed3\u679c：<\/p>\n\n\n\u6a21\u578bAccuracyF1ROC-AUC<\/tr>\n\n\n\n\n\n\n\n\n\n
          MLP_Hist<\/td>\n93.67%<\/td>\n0.9367<\/td>\n0.9849<\/td>\n<\/tr>\n
          CNN_SiLU_NoBN<\/td>\n81.33%<\/td>\n0.8260<\/td>\n0.9124<\/td>\n<\/tr>\n
          CNN_ReLU_NoBN<\/td>\n79.75%<\/td>\n0.8080<\/td>\n0.8943<\/td>\n<\/tr>\n
          CNN_LeakyReLU_NoBN<\/td>\n78.92%<\/td>\n0.8024<\/td>\n0.8878<\/td>\n<\/tr>\n
          CNN_GELU_NoBN<\/td>\n76.67%<\/td>\n0.7720<\/td>\n0.8646<\/td>\n<\/tr>\n
          CNN_SiLU_BN<\/td>\n69.67%<\/td>\n0.7497<\/td>\n0.8168<\/td>\n<\/tr>\n
          CNN_LeakyReLU_BN<\/td>\n70.58%<\/td>\n0.7298<\/td>\n0.8130<\/td>\n<\/tr>\n
          CNN_GELU_BN<\/td>\n69.50%<\/td>\n0.7354<\/td>\n0.7922<\/td>\n<\/tr>\n
          CNN_ReLU_BN<\/td>\n66.92%<\/td>\n0.7020<\/td>\n0.7793<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u4ece\u7ed3\u679c\u53ef\u4ee5\u89c2\u5bdf\u5230：<\/p>\n

          \u7b2c\u4e00，\u5728\u56db\u79cd\u6fc0\u6d3b\u51fd\u6570\u4e2d，SiLU \u7684 CNN \u8868\u73b0\u6700\u597d\u3002<\/p>\n

          \u4e0d\u4f7f\u7528 BatchNorm \u65f6，CNN_SiLU_NoBN \u7684\u5e73\u5747 Accuracy \u4e3a 81.33%，ROC-AUC \u4e3a 0.9124\u3002<\/p>\n

          \u7b2c\u4e8c，\u52a0\u5165 BatchNorm \u540e，\u56db\u79cd CNN \u7684\u5e73\u5747 ROC-AUC \u5747\u4e0b\u964d\u3002<\/p>\n

          \u7b2c\u4e09，MLP \u5728\u672c\u6b21\u6570\u636e\u96c6\u4e0a\u7684\u8868\u73b0\u660e\u663e\u4f18\u4e8e\u5168\u90e8 CNN \u914d\u7f6e\u3002<\/p>\n

          MLP \u7684\u5e73\u5747 ROC-AUC \u8fbe\u5230 0.9849，\u800c\u8868\u73b0\u6700\u597d\u7684 CNN \u4e3a 0.9124\u3002<\/p>\n

          6.2 \u4e0d\u540c\u6fc0\u6d3b\u51fd\u6570\u5bf9\u6bd4<\/h4>\n

          \u5148\u53ea\u6bd4\u8f83\u4e0d\u4f7f\u7528 BatchNorm \u7684\u56db\u79cd CNN：<\/p>\n\n\n\u6fc0\u6d3b\u51fd\u6570AccuracyROC-AUC<\/tr>\n\n\n\n\n
          SiLU<\/td>\n81.33%<\/td>\n0.9124<\/td>\n<\/tr>\n
          ReLU<\/td>\n79.75%<\/td>\n0.8943<\/td>\n<\/tr>\n
          LeakyReLU<\/td>\n78.92%<\/td>\n0.8878<\/td>\n<\/tr>\n
          GELU<\/td>\n76.67%<\/td>\n0.8646<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u7ed3\u679c\u8868\u660e，\u5728\u5f53\u524d\u7f51\u7edc\u7ed3\u6784\u3001\u8f93\u5165\u7279\u5f81\u548c\u8bad\u7ec3\u914d\u7f6e\u4e0b，SiLU \u83b7\u5f97\u4e86\u6700\u597d\u7684\u5e73\u5747\u6027\u80fd\u3002\u4e0d\u8fc7，\u4e0d\u80fd\u636e\u6b64\u5f97\u51fa\u201cSiLU \u5728\u6240\u6709\u52a0\u5bc6\u6570\u636e\u5206\u7c7b\u4efb\u52a1\u4e2d\u90fd\u4f18\u4e8e ReLU\u201d\u7684\u7ed3\u8bba\u3002\u6fc0\u6d3b\u51fd\u6570\u7684\u6548\u679c\u4ecd\u7136\u53ef\u80fd\u53d7\u5230\u7f51\u7edc\u6df1\u5ea6\u3001\u6570\u636e\u5206\u5e03\u3001\u5b66\u4e60\u7387\u53ca\u5176\u4ed6\u8d85\u53c2\u6570\u7684\u5f71\u54cd\u3002<\/p>\n

          6.3 BatchNorm \u6d88\u878d\u7ed3\u679c<\/h4>\n

          \u5c06\u76f8\u540c\u6fc0\u6d3b\u51fd\u6570\u4e0b\u7684\u6709\u65e0 BatchNorm \u7ed3\u679c\u8fdb\u884c\u6bd4\u8f83：<\/p>\n\n\n\u6fc0\u6d3b\u51fd\u6570\u65e0 BN \u7684 ROC-AUC\u6709 BN \u7684 ROC-AUC\u53d8\u5316<\/tr>\n\n\n\n\n
          ReLU<\/td>\n0.8943<\/td>\n0.7793<\/td>\n-0.1150<\/td>\n<\/tr>\n
          LeakyReLU<\/td>\n0.8878<\/td>\n0.8130<\/td>\n-0.0748<\/td>\n<\/tr>\n
          GELU<\/td>\n0.8646<\/td>\n0.7922<\/td>\n-0.0724<\/td>\n<\/tr>\n
          SiLU<\/td>\n0.9124<\/td>\n0.8168<\/td>\n-0.0956<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u53ef\u4ee5\u770b\u5230，\u56db\u79cd\u6fc0\u6d3b\u51fd\u6570\u52a0\u5165 BatchNorm \u540e，\u5e73\u5747 ROC-AUC \u90fd\u51fa\u73b0\u4e0b\u964d\u3002\u5176\u4e2d，ReLU \u7684\u4e0b\u964d\u5e45\u5ea6\u6700\u5927，\u7ea6\u4e3a 0.1150\u3002\u8fd9\u4e00\u7ed3\u679c\u4e0e\u201c\u52a0\u5165\u5f52\u4e00\u5316\u5c42\u5c31\u80fd\u63d0\u5347\u6027\u80fd\u201d\u7684\u76f4\u89c9\u5e76\u4e0d\u4e00\u81f4\u3002\u4f46\u5b83\u5e76\u4e0d\u610f\u5473\u7740 BatchNorm \u672c\u8eab\u5b58\u5728\u95ee\u9898，\u800c\u662f\u8bf4\u660e：<\/p>\n

          BatchNorm \u662f\u5426\u6709\u6548，\u9700\u8981\u7ed3\u5408\u5177\u4f53\u7684\u6570\u636e\u8868\u793a\u3001\u7f51\u7edc\u7ed3\u6784\u548c\u8bad\u7ec3\u6761\u4ef6\u8fdb\u884c\u9a8c\u8bc1\u3002<\/p>\n

          \n

          \u4e03\u3001\u4e3a\u4ec0\u4e48 BatchNorm \u53ef\u80fd\u6ca1\u6709\u5e26\u6765\u63d0\u5347？<\/h2>\n

          \u5bf9\u4e8e\u8fd9\u4e00\u73b0\u8c61，\u53ef\u4ee5\u63d0\u51fa\u51e0\u4e2a\u53ef\u80fd\u7684\u89e3\u91ca\u3002<\/p>\n

          7.1 \u8f93\u5165\u5df2\u7ecf\u7ecf\u8fc7 Z-Score \u6807\u51c6\u5316<\/h4>\n

          \u672c\u5b9e\u9a8c\u8f93\u5165\u7684 256 \u7ef4\u76f4\u65b9\u56fe\u5df2\u7ecf\u8fdb\u884c\u4e86 Z-Score \u5904\u7406\u3002BatchNorm \u53c8\u5728\u5377\u79ef\u5c42\u5185\u90e8\u5f15\u5165\u5f52\u4e00\u5316\u64cd\u4f5c\u3002\u8fd9\u4e24\u79cd\u5f52\u4e00\u5316\u5e76\u4e0d\u7b49\u4ef7，\u4e5f\u4e0d\u80fd\u7b80\u5355\u8ba4\u4e3a\u5b83\u4eec\u53d1\u751f\u4e86\u201c\u91cd\u590d\u6807\u51c6\u5316\u201d，\u4f46\u5b83\u4eec\u53ef\u80fd\u5171\u540c\u6539\u53d8\u7f51\u7edc\u7684\u7279\u5f81\u5206\u5e03\u4e0e\u4f18\u5316\u8fc7\u7a0b\u3002\u662f\u5426\u5b58\u5728\u4e0d\u5229\u7684\u76f8\u4e92\u4f5c\u7528，\u8fd8\u9700\u8981\u8fdb\u4e00\u6b65\u5b9e\u9a8c\u9a8c\u8bc1\u3002<\/p>\n

          7.2 BatchNorm \u7684\u7edf\u8ba1\u91cf\u53ef\u80fd\u5f71\u54cd\u6a21\u578b\u8f93\u51fa<\/h4>\n

          BatchNorm \u5728\u8bad\u7ec3\u548c\u63a8\u7406\u9636\u6bb5\u91c7\u7528\u4e0d\u540c\u7684\u7edf\u8ba1\u91cf\u5904\u7406\u65b9\u5f0f\u3002\u5982\u679c\u8bad\u7ec3\u6279\u6b21\u7684\u7edf\u8ba1\u7279\u5f81\u4e0e\u9a8c\u8bc1\u96c6\u3001\u6d4b\u8bd5\u96c6\u5b58\u5728\u5dee\u5f02，\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\u53ef\u80fd\u53d7\u5230\u5f71\u54cd\u3002\u53ef\u4ee5\u901a\u8fc7\u89c2\u5bdf\u8bad\u7ec3\u635f\u5931\u3001\u9a8c\u8bc1\u635f\u5931\u548c\u9884\u6d4b\u6982\u7387\u5206\u5e03，\u8fdb\u4e00\u6b65\u68c0\u67e5\u8fd9\u79cd\u53ef\u80fd\u6027\u3002<\/p>\n

          7.3 \u7f51\u7edc\u7ed3\u6784\u4e0e\u8f93\u5165\u7279\u5f81\u53ef\u80fd\u4e0d\u5339\u914d<\/h4>\n

          \u8fd9\u662f\u53e6\u4e00\u4e2a\u503c\u5f97\u6ce8\u610f\u7684\u95ee\u9898\u3002\u5b57\u8282\u9891\u7387\u76f4\u65b9\u56fe\u7684 256 \u4e2a\u4f4d\u7f6e\u5206\u522b\u5bf9\u5e94\u5b57\u8282\u503c 0～255\u3002\u5728\u8fd9\u79cd\u8868\u793a\u4e2d，\u76f4\u65b9\u56fe\u4e2d\u76f8\u90bb\u7684\u4f4d\u7f6e\u8868\u793a\u76f8\u90bb\u7684\u5b57\u8282\u53d6\u503c，\u4f46\u4e0d\u4e00\u5b9a\u5177\u6709\u9002\u5408\u5377\u79ef\u63d0\u53d6\u7684\u5c40\u90e8\u8bed\u4e49\u3002\u4f8b\u5982，\u5b57\u8282\u503c 100 \u548c 101 \u7684\u9891\u7387\u76f8\u90bb，\u5e76\u4e0d\u610f\u5473\u7740\u5b83\u4eec\u4e4b\u95f4\u4e00\u5b9a\u5b58\u5728\u7c7b\u4f3c\u56fe\u50cf\u76f8\u90bb\u50cf\u7d20\u7684\u5c40\u90e8\u7ed3\u6784\u3002<\/p>\n

          \u56e0\u6b64，1D-CNN \u7684\u5c40\u90e8\u5377\u79ef\u4f18\u52bf\u53ef\u80fd\u65e0\u6cd5\u5145\u5206\u53d1\u6325\u3002<\/p>\n

          \u8fd9\u4e5f\u53ef\u80fd\u89e3\u91ca\u4e3a\u4ec0\u4e48\u672c\u6b21 MLP \u7684\u8868\u73b0\u4f18\u4e8e CNN\u3002\u9700\u8981\u5f3a\u8c03\u7684\u662f，\u4ee5\u4e0a\u89e3\u91ca\u90fd\u5c5e\u4e8e\u57fa\u4e8e\u5b9e\u9a8c\u73b0\u8c61\u63d0\u51fa\u7684\u7814\u7a76\u5047\u8bbe，\u5c1a\u672a\u901a\u8fc7\u72ec\u7acb\u5b9e\u9a8c\u786e\u5b9a\u5177\u4f53\u539f\u56e0\u3002<\/p>\n

          \n

          \u516b\u3001CNN \u662f\u5426\u4ecd\u7136\u5b58\u5728\u5355\u7c7b\u522b\u9884\u6d4b\u95ee\u9898？<\/h2>\n

          \u5728\u65e9\u671f\u5b9e\u9a8c\u4e2d，CNN \u66fe\u51fa\u73b0\u6240\u6709\u6837\u672c\u90fd\u88ab\u9884\u6d4b\u4e3a\u660e\u6587\u7684\u60c5\u51b5\u3002\u4f8b\u5982，\u5728\u4e00\u4e2a\u660e\u6587\u4e0e\u5bc6\u6587\u6570\u91cf\u76f8\u7b49\u7684\u6d4b\u8bd5\u96c6\u4e2d：<\/p>\n\n\n\u771f\u5b9e\u7c7b\u522b \/ \u9884\u6d4b\u7c7b\u522b\u660e\u6587\u5bc6\u6587<\/tr>\n\n\n
          \u771f\u5b9e\u660e\u6587<\/td>\n270<\/td>\n0<\/td>\n<\/tr>\n
          \u771f\u5b9e\u5bc6\u6587<\/td>\n270<\/td>\n0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u8fd9\u79cd\u60c5\u51b5\u4e0b，\u6a21\u578b\u51c6\u786e\u7387\u4e3a 50%，\u5e76\u6ca1\u6709\u6b63\u786e\u8bc6\u522b\u51fa\u4efb\u4f55\u5bc6\u6587\u3002\u672c\u6b21\u6d88\u878d\u5b9e\u9a8c\u7684\u60c5\u51b5\u6709\u6240\u6539\u5584\u3002<\/p>\n

          \u68c0\u67e5\u4e94\u968f\u673a\u79cd\u5b50\u4e0b\u7684\u56fa\u5b9a\u9608\u503c\u5b9e\u9a8c\u7ed3\u679c，\u6ca1\u6709\u51fa\u73b0\u6240\u6709\u6837\u672c\u90fd\u88ab\u9884\u6d4b\u4e3a\u660e\u6587\u7684\u60c5\u51b5\u3002<\/p>\n

          \u4ee5 CNN_SiLU_NoBN \u4e3a\u4f8b，\u5176\u4e94\u6b21\u5b9e\u9a8c\u7684\u5e73\u5747\u6df7\u6dc6\u77e9\u9635\u4e3a：<\/p>\n\n\n\u771f\u5b9e\u7c7b\u522b \/ \u9884\u6d4b\u7c7b\u522b\u9884\u6d4b\u660e\u6587\u9884\u6d4b\u5bc6\u6587<\/tr>\n\n\n
          \u771f\u5b9e\u660e\u6587<\/td>\n88.4<\/td>\n31.6<\/td>\n<\/tr>\n
          \u771f\u5b9e\u5bc6\u6587<\/td>\n13.2<\/td>\n106.8<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u8fd9\u91cc\u7684\u6570\u503c\u662f\u4e94\u6b21\u5b9e\u9a8c\u7684\u5e73\u5747\u503c，\u56e0\u6b64\u5305\u542b\u5c0f\u6570\u3002<\/p>\n

          \u53ef\u4ee5\u770b\u5230，\u6a21\u578b\u5df2\u7ecf\u80fd\u591f\u6b63\u786e\u8bc6\u522b\u591a\u6570\u5bc6\u6587，\u4f46\u4ecd\u5b58\u5728\u660e\u6587\u88ab\u8bef\u5224\u4e3a\u5bc6\u6587\u7684\u60c5\u51b5\u3002\u8fd9\u8bf4\u660e\u5f53\u524d CNN \u4e0d\u518d\u53ea\u662f\u8f93\u51fa\u5355\u4e00\u7c7b\u522b，\u800c\u662f\u5df2\u7ecf\u8868\u73b0\u51fa\u4e00\u5b9a\u7684\u5206\u7c7b\u80fd\u529b\u3002\u4e0d\u8fc7，\u4e0d\u80fd\u4ec5\u51ed\u8fd9\u4e00\u73b0\u8c61\u5224\u65ad\u6a21\u578b\u5df2\u7ecf\u5145\u5206\u5b66\u4e60\u5230\u5177\u6709\u666e\u904d\u6027\u7684\u52a0\u5bc6\u6570\u636e\u7279\u5f81\u3002<\/p>\n

          \n

          \u4e5d\u3001\u4e3a\u4ec0\u4e48 MLP \u7684\u6548\u679c\u6bd4 CNN \u66f4\u597d？<\/h2>\n

          \u672c\u6b21\u5b9e\u9a8c\u4e2d，MLP_Hist \u7684\u5e73\u5747 ROC-AUC \u8fbe\u5230 0.9849，\u660e\u663e\u9ad8\u4e8e\u5168\u90e8 CNN \u914d\u7f6e\u3002\u8fd9\u4e00\u73b0\u8c61\u503c\u5f97\u5173\u6ce8\u3002MLP \u76f4\u63a5\u5bf9 256 \u7ef4\u76f4\u65b9\u56fe\u8fdb\u884c\u5168\u8fde\u63a5\u7279\u5f81\u5b66\u4e60，\u80fd\u591f\u5efa\u7acb\u4e0d\u540c\u5b57\u8282\u9891\u7387\u7ef4\u5ea6\u4e4b\u95f4\u7684\u7ec4\u5408\u5173\u7cfb\u3002\u800c 1D-CNN \u4e3b\u8981\u4f9d\u8d56\u5377\u79ef\u6838\u63d0\u53d6\u5c40\u90e8\u7279\u5f81\u3002\u5bf9\u4e8e\u5b57\u8282\u9891\u7387\u76f4\u65b9\u56fe\u800c\u8a00，\u5c40\u90e8\u76f8\u90bb\u5173\u7cfb\u672a\u5fc5\u662f\u6700\u6709\u4ef7\u503c\u7684\u7ed3\u6784\u4fe1\u606f\u3002\u56e0\u6b64，MLP \u5728\u8fd9\u79cd\u7279\u5f81\u8868\u793a\u4e0a\u83b7\u5f97\u66f4\u597d\u7684\u7ed3\u679c\u662f\u53ef\u4ee5\u7406\u89e3\u7684\u3002<\/p>\n

          \u4f46\u8fd9\u91cc\u8fd8\u5b58\u5728\u4e00\u4e2a\u91cd\u8981\u95ee\u9898：\u5728\u4e0a\u4e00\u7bc7\u5b9e\u9a8c\u4e2d，MLP_Hist \u7684\u5e73\u5747 ROC-AUC \u7ea6\u4e3a 0.8322，\u800c\u672c\u6b21\u8fbe\u5230\u4e86 0.9849\u3002\u4e24\u6b21\u5b9e\u9a8c\u7684\u6570\u636e\u751f\u6210\u6d41\u7a0b\u3001\u6837\u672c\u6784\u6210\u53ca\u9884\u5904\u7406\u914d\u7f6e\u5e76\u4e0d\u5b8c\u5168\u76f8\u540c，\u56e0\u6b64\u4e0d\u80fd\u76f4\u63a5\u5c06\u8fd9\u4e2a\u5dee\u8ddd\u89e3\u91ca\u4e3a\u6a21\u578b\u7ed3\u6784\u7684\u6027\u80fd\u63d0\u5347\u3002<\/p>\n

          \u672c\u6b21\u5b9e\u9a8c\u5185\u90e8\u7684 MLP \u4e0e CNN \u53ef\u4ee5\u8fdb\u884c\u5bf9\u6bd4，\u4f46\u4e0d\u540c\u7bc7\u6587\u7ae0\u4e2d\u7684\u6570\u503c\u4e0d\u5e94\u5728\u7f3a\u5c11\u7edf\u4e00\u5b9e\u9a8c\u6761\u4ef6\u7684\u60c5\u51b5\u4e0b\u76f4\u63a5\u6bd4\u8f83\u3002<\/p>\n

          \n

          \u5341\u3001\u5b9e\u9a8c\u5c40\u9650\u6027<\/h2>\n

          \u867d\u7136\u672c\u6b21\u5b9e\u9a8c\u83b7\u5f97\u4e86\u8f83\u597d\u7684\u5206\u7c7b\u7ed3\u679c，\u4f46\u4ecd\u5b58\u5728\u4e00\u4e9b\u9650\u5236\u3002<\/p>\n

          10.1 \u6570\u636e\u4e3b\u8981\u6765\u81ea\u6a21\u62df\u56fe\u50cf<\/h4>\n

          \u5b9e\u9a8c\u4f7f\u7528\u968f\u673a\u751f\u6210\u7684 RGB \u56fe\u50cf，\u5e76\u5c06\u5176\u7f16\u7801\u4e3a PNG\u3001JPEG \u6587\u4ef6\u3002\u8fd9\u4e9b\u6570\u636e\u4e0d\u80fd\u5b8c\u5168\u4ee3\u8868\u771f\u5b9e\u73af\u5883\u4e2d\u7684\u6587\u4ef6\u5206\u5e03\u3002\u7279\u522b\u662f\u4e0d\u540c\u6587\u4ef6\u683c\u5f0f\u7684\u7f16\u7801\u65b9\u5f0f\u53ef\u80fd\u4ea7\u751f\u660e\u663e\u7684\u5b57\u8282\u7edf\u8ba1\u5dee\u5f02，\u6a21\u578b\u53ef\u80fd\u5229\u7528\u8fd9\u4e9b\u5dee\u5f02\u8fdb\u884c\u5206\u7c7b\u3002<\/p>\n

          10.2 \u660e\u6587\u4e0e\u5bc6\u6587\u5206\u7c7b\u4e0d\u7b49\u4e8e\u8bc6\u522b\u52a0\u5bc6\u7b97\u6cd5<\/h4>\n

          \u672c\u6587\u7684\u4efb\u52a1\u662f\u5224\u65ad\u6570\u636e\u7247\u6bb5\u6765\u81ea\u660e\u6587\u6587\u4ef6\u8fd8\u662f AES-CTR \u52a0\u5bc6\u540e\u7684\u6587\u4ef6\u3002\u5373\u4f7f\u5206\u7c7b\u51c6\u786e\u7387\u8f83\u9ad8，\u4e5f\u4e0d\u4ee3\u8868\u6a21\u578b\u80fd\u591f\u8bc6\u522b\u672a\u77e5\u52a0\u5bc6\u7b97\u6cd5，\u66f4\u4e0d\u4ee3\u8868\u53ef\u4ee5\u6062\u590d AES \u5bc6\u94a5\u3002<\/p>\n

          10.3 \u4ecd\u9700\u8981\u8de8\u6570\u636e\u96c6\u6cdb\u5316\u9a8c\u8bc1<\/h4>\n

          \u76ee\u524d\u7684\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u867d\u7136\u6309\u7167\u6e90\u56fe\u50cf\u8fdb\u884c\u4e86\u5206\u7ec4，\u4f46\u4ecd\u7136\u6765\u81ea\u76f8\u4f3c\u7684\u6570\u636e\u751f\u6210\u6d41\u7a0b\u3002\u5982\u679c\u66f4\u6362\u56fe\u50cf\u6765\u6e90\u3001\u6587\u4ef6\u683c\u5f0f\u6216\u6587\u4ef6\u7247\u6bb5\u4f4d\u7f6e，\u6a21\u578b\u6027\u80fd\u53ef\u80fd\u53d1\u751f\u660e\u663e\u53d8\u5316\u3002<\/p>\n

          10.4 \u9700\u8981\u8fdb\u4e00\u6b65\u5206\u6790\u6027\u80fd\u5dee\u5f02\u7684\u7edf\u8ba1\u7a33\u5b9a\u6027<\/h4>\n

          \u672c\u6b21\u4f7f\u7528\u4e94\u4e2a\u968f\u673a\u79cd\u5b50，\u80fd\u591f\u521d\u6b65\u89c2\u5bdf\u6a21\u578b\u7684\u7a33\u5b9a\u6027\u3002\u4f46\u82e5\u8981\u5f97\u51fa\u66f4\u5f3a\u7684\u7ed3\u8bba，\u8fd8\u9700\u8981\u7ed3\u5408\u6807\u51c6\u5dee\u3001\u914d\u5bf9\u6bd4\u8f83\u548c\u66f4\u5927\u89c4\u6a21\u7684\u72ec\u7acb\u6d4b\u8bd5\u96c6\u3002<\/p>\n

          \n

          \u5341\u4e00\u3001\u4e0b\u4e00\u6b65\u7814\u7a76\u65b9\u5411<\/h2>\n

          \u6839\u636e\u672c\u6b21\u5b9e\u9a8c，\u540e\u7eed\u53ef\u4ee5\u91cd\u70b9\u7814\u7a76\u4e09\u4e2a\u65b9\u5411\u3002<\/p>\n

          \u65b9\u5411\u4e00：\u7814\u7a76 CNN \u4e0e\u76f4\u65b9\u56fe\u7279\u5f81\u7684\u5339\u914d\u95ee\u9898\u3002<\/p>\n

          \u5bf9\u6bd4\u539f\u59cb\u5b57\u8282\u3001\u5b57\u8282\u9891\u7387\u76f4\u65b9\u56fe\u3001\u76f8\u90bb\u5b57\u8282\u8f6c\u79fb\u7edf\u8ba1\u7b49\u8f93\u5165\u8868\u793a，\u5224\u65ad\u54ea\u4e9b\u7279\u5f81\u66f4\u9002\u5408\u5377\u79ef\u7f51\u7edc\u3002<\/p>\n

          \u65b9\u5411\u4e8c：\u8fdb\u4e00\u6b65\u9a8c\u8bc1 BatchNorm \u7684\u5f71\u54cd\u3002<\/p>\n

          \u7ed3\u5408\u8bad\u7ec3\u635f\u5931\u66f2\u7ebf\u3001\u4e0d\u540c\u6279\u91cf\u5927\u5c0f\u4ee5\u53ca\u5176\u4ed6\u5f52\u4e00\u5316\u65b9\u6cd5，\u5206\u6790 BatchNorm \u6027\u80fd\u4e0b\u964d\u7684\u539f\u56e0\u3002<\/p>\n

          \u65b9\u5411\u4e09：\u5f00\u5c55\u8de8\u6570\u636e\u96c6\u6cdb\u5316\u5b9e\u9a8c\u3002<\/p>\n

          \u4f7f\u7528\u771f\u5b9e\u6587\u4ef6\u548c\u4e0d\u540c\u6570\u636e\u6765\u6e90，\u6d4b\u8bd5\u6a21\u578b\u662f\u5426\u4ecd\u7136\u80fd\u591f\u533a\u5206\u660e\u6587\u4e0e\u5bc6\u6587，\u6392\u9664\u6a21\u578b\u4f9d\u8d56\u7279\u5b9a\u6587\u4ef6\u683c\u5f0f\u6216\u6570\u636e\u751f\u6210\u65b9\u5f0f\u7684\u53ef\u80fd\u6027\u3002\u5176\u4e2d，\u7b2c\u4e09\u4e2a\u65b9\u5411\u5bf9\u4e8e\u540e\u7eed\u8bba\u6587\u7814\u7a76\u5c24\u5176\u91cd\u8981\u3002<\/p>\n

          \n

          \u5341\u4e8c\u3001\u603b\u7ed3<\/h2>\n

          \u672c\u6587\u56f4\u7ed5 1D-CNN \u7684\u6fc0\u6d3b\u51fd\u6570\u548c BatchNorm \u8fdb\u884c\u4e86\u6d88\u878d\u5b9e\u9a8c\u3002<\/p>\n

          \u5728\u76f8\u540c\u7684\u6570\u636e\u5904\u7406\u548c\u8bad\u7ec3\u6761\u4ef6\u4e0b，\u5bf9 ReLU\u3001LeakyReLU\u3001GELU\u3001SiLU \u56db\u79cd\u6fc0\u6d3b\u51fd\u6570，\u4ee5\u53ca\u662f\u5426\u4f7f\u7528 BatchNorm \u8fdb\u884c\u4e86\u7cfb\u7edf\u6bd4\u8f83\u3002<\/p>\n

          \u6839\u636e\u4e94\u968f\u673a\u79cd\u5b50\u7684\u5b9e\u9a8c\u7ed3\u679c，\u53ef\u4ee5\u5f97\u5230\u4ee5\u4e0b\u7ed3\u8bba：<\/p>\n

        • \n

          SiLU \u662f\u672c\u6b21\u56db\u79cd\u6fc0\u6d3b\u51fd\u6570\u4e2d\u8868\u73b0\u6700\u597d\u7684 CNN \u914d\u7f6e\u3002 \u4e0d\u4f7f\u7528 BatchNorm \u65f6，\u5e73\u5747 Accuracy \u4e3a 81.33%，ROC-AUC \u4e3a 0.9124\u3002<\/p>\n<\/li>\n

        • \n

          BatchNorm \u5e76\u6ca1\u6709\u63d0\u5347\u5f53\u524d CNN \u7684\u6027\u80fd\u3002 \u56db\u79cd\u6fc0\u6d3b\u51fd\u6570\u5728\u52a0\u5165 BatchNorm \u540e，\u5e73\u5747 ROC-AUC \u5747\u4e0b\u964d\u3002<\/p>\n<\/li>\n

        • \n

          MLP \u5728\u672c\u6b21\u5b57\u8282\u9891\u7387\u76f4\u65b9\u56fe\u6570\u636e\u96c6\u4e0a\u4f18\u4e8e CNN\u3002 \u5176\u5e73\u5747 ROC-AUC \u8fbe\u5230 0.9849\u3002<\/p>\n<\/li>\n

        • \n

          \u6539\u8fdb\u540e\u7684 CNN \u4e0d\u518d\u51fa\u73b0\u5168\u90e8\u6837\u672c\u88ab\u9884\u6d4b\u4e3a\u660e\u6587\u7684\u60c5\u51b5\u3002 \u4f46\u5206\u7c7b\u6027\u80fd\u4ecd\u53d7\u8f93\u5165\u8868\u793a\u548c\u7f51\u7edc\u7ed3\u6784\u5f71\u54cd\u3002<\/p>\n<\/li>\n

        • \n

          \u9ad8\u51c6\u786e\u7387\u4e0d\u4ee3\u8868\u6a21\u578b\u5177\u5907\u8de8\u6570\u636e\u96c6\u6cdb\u5316\u80fd\u529b\u3002 \u540e\u7eed\u4ecd\u9700\u8981\u901a\u8fc7\u771f\u5b9e\u6587\u4ef6\u548c\u72ec\u7acb\u6570\u636e\u6765\u6e90\u8fdb\u884c\u9a8c\u8bc1\u3002<\/p>\n<\/li>\n

          \u672c\u6b21\u5b9e\u9a8c\u8bf4\u660e，\u5728\u52a0\u5bc6\u6570\u636e\u5206\u7c7b\u4efb\u52a1\u4e2d，\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u5e76\u4e0d\u662f\u8d8a\u590d\u6742\u8d8a\u597d\u3002<\/p>\n

          \u8f93\u5165\u7279\u5f81\u662f\u5426\u9002\u5408\u7f51\u7edc\u7ed3\u6784，\u4ee5\u53ca\u5b9e\u9a8c\u6570\u636e\u662f\u5426\u80fd\u591f\u652f\u6301\u53ef\u9760\u7684\u6cdb\u5316\u7ed3\u8bba，\u53ef\u80fd\u6bd4\u5355\u7eaf\u589e\u52a0\u7f51\u7edc\u5c42\u6570\u66f4\u91cd\u8981\u3002<\/p>\n

          \u4e0b\u4e00\u7bc7\u5c06\u8fdb\u4e00\u6b65\u7814\u7a76\u4e0d\u540c\u8f93\u5165\u7279\u5f81\u4e0e\u6570\u636e\u6765\u6e90\u5bf9\u5206\u7c7b\u6027\u80fd\u7684\u5f71\u54cd，\u63a2\u7d22\u6a21\u578b\u7a76\u7adf\u5b66\u4e60\u5230\u4e86\u4ec0\u4e48\u3002<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"

          \u76ee\u5f55 \u4e00\u3001\u524d\u8a00
          \n\u4e8c\u3001\u5b9e\u9a8c\u539f\u7406
          \n2.1 \u660e\u6587\u4e0e\u5bc6\u6587\u4e8c\u5206\u7c7b
          \n2.2 \u5b57\u8282\u9891\u7387\u76f4\u65b9\u56fe
          \n2.3 Z-Score \u6807\u51c6\u5316
          \n\u4e09\u3001\u4e3a\u4ec0\u4e48\u7814\u7a76\u6fc0\u6d3b\u51fd\u6570？
          \n3.1 ReLU
          \n3.2 LeakyReLU
          \n3.3 GELU
          \n3.4 SiLU
          \n\u56db\u3001BatchNorm \u7684\u4f5c\u7528
          \n\u4e94\u3001\u5b9e\u9a8c\u8bbe\u8ba1
          \n5.1 \u6570\u636e\u96c6\u751f\u6210
          \n5.2 CNN \u7f51\u7edc\u7ed3\u6784
          \n5.3 \u6d88\u878d\u5b9e\u9a8c\u5206\u7ec4
          \n5.4 \u8bad\u7ec3\u53c2\u6570
          \n\u516d\u3001\u5b9e\u9a8c\u7ed3\u679c
          \n6.1 \u4e94\u968f\u673a\u79cd\u5b50\u5e73\u5747\u6027\u80fd
          \n6.2 \u4e0d\u540c\u6fc0\u6d3b\u51fd\u6570\u5bf9\u6bd4
          \n6.3 <\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[2394,50,86],"topic":[],"class_list":["post-117133","post","type-post","status-publish","format-standard","hentry","category-server","tag-cnn","tag-50","tag-86"],"yoast_head":"\n\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u6570\u636e\u52a0\u5bc6\u8bc6\u522b\uff08\u56db\uff09\uff1a1D-CNN \u6fc0\u6d3b\u51fd\u6570\u4e0e BatchNorm \u6d88\u878d\u5b9e\u9a8c - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/117133.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u6570\u636e\u52a0\u5bc6\u8bc6\u522b\uff08\u56db\uff09\uff1a1D-CNN \u6fc0\u6d3b\u51fd\u6570\u4e0e BatchNorm \u6d88\u878d\u5b9e\u9a8c - 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