{"id":257,"date":"2020-05-03T19:12:37","date_gmt":"2020-05-03T11:12:37","guid":{"rendered":"http:\/\/hk.khscience.cn\/docs\/?p=257"},"modified":"2024-05-01T00:16:46","modified_gmt":"2024-04-30T16:16:46","slug":"network-2","status":"publish","type":"post","link":"https:\/\/www.khsci.com\/docs\/index.php\/2020\/05\/03\/network-2\/","title":{"rendered":"BP\u795e\u7ecf\u7f51\u7edc\u201c\u5206\u7c7b\u201d\u4ee3\u7801"},"content":{"rendered":"\n
\n

\u795e\u7ecf\u7f51\u7edc\u201c\u5206\u7c7b\u201d\u5de5\u5177\u7bb1\u662f\u4e13\u4e3a\u4f7f\u7528\u795e\u7ecf\u7f51\u7edc\u8fdb\u884c\u5206\u7c7b\u7684\u5de5\u5177\u7bb1\u3002<\/p>\n\n\n\n

\u5de5\u5177\u7bb1\u4e2d\u7684\u4ee3\u7801\u5728\u4e0d\u5b9a\u671f\u66f4\u65b0\u4e2d\uff0c\u4e0d\u65ad\u5b8c\u5584\u548c\u6dfb\u52a0\u65b0\u7684\u529f\u80fd\u3002<\/p>\n\n\n\n

\u81f4\u529b\u4e8e\u5b9e\u73b0\u4f18\u8d28\u3001\u6613\u7528\u7684\u7a0b\u5e8f\uff0c\u5e2e\u52a9\u7814\u7a76\u4eba\u5458\u5feb\u901f\u5b9e\u73b0\u76ee\u6807<\/p>\n<\/blockquote>\n\n\n\n

\u4e00\u3001\u4ee3\u7801\u8fd0\u884c\u73af\u5883\uff1a<\/strong><\/span><\/h4>\n\n\n\n

MATLAB2016\u53ca\u66f4\u65b0\u7248\u672c\u3002<\/p>\n\n\n\n

\u4e8c\u3001\u6587\u4ef6\u8bf4\u660e<\/strong><\/span><\/p>\n\n\n

\n
\"\"<\/figure><\/div>\n\n\n

\u6ce8\uff1a\u56fe\u6807\u4ee3\u8868\u8be5m\u6587\u4ef6\u4e3a\u811a\u672c\u6587\u4ef6\uff0c\u53ef\u4ee5\u76f4\u63a5\u8fd0\u884c\uff1b\u56fe\u6807\u4ee3\u8868\u51fd\u6570\u6587\u4ef6\uff0c\u5728\u6ca1\u6709\u8f93\u5165\u53d8\u91cf\u7684\u60c5\u51b5\u4e0b\u65e0\u6cd5\u76f4\u63a5\u8fd0\u884c\u3002\u66f4\u8be6\u7ec6\u7684\u89e3\u91ca\u53ef\u4ee5\u770b\u8fd9\u91cc<\/a>\u3002<\/p>\n\n\n\n

1. <\/strong>demoBPclass.m<\/b><\/span><\/p>\n\n\n\n

\u4f7f\u7528BP\u795e\u7ecf\u7f51\u7edc\u8fdb\u884c\u5206\u7c7b\u7684\u811a\u672c\u4ee3\u7801\uff0c\u53ef\u4ee5\u76f4\u63a5\u8fd0\u884c\uff0c\u4e3b\u8981\u7528\u4e8e\u6f14\u793abpPatternnet\u51fd\u6570\u7684\u7528\u6cd5\u3002\u8be5\u811a\u672c\u4e2d\u4f7f\u7528\u4e86\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\u6765\u8fdb\u884c\u6f14\u793a\u3002\u7a0b\u5e8f\u8fd0\u884c\u5b8c\u6210\u540e\uff0c\u5c06\u4f1a\u5f97\u5230\u4ee5\u4e0b\u51e0\u5f20\u56fe\u7247\u548c\u8fd0\u884c\u7ed3\u679c\u3002<\/p>\n\n\n

\n
\"\"<\/figure><\/div>\n\n
\n
\"\"<\/figure><\/div>\n\n
\n
\"\"<\/figure><\/div>\n\n\n

\u53e6\u5916\uff0c\u5728\u547d\u4ee4\u884c\u7a97\u53e3\uff0c\u4f1a\u6253\u5370\u51fa\u6a21\u578b\u8bc6\u522b\u7684\u6b63\u786e\u7387\u3001\u53ec\u56de\u7387\u3001\u7cbe\u786e\u7387\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

2.bpPatternnet.m<\/mark><\/strong><\/p>\n\n\n\n

\u5feb\u901f\u6a21\u5f0f\u8bc6\u522b(\u5206\u7c7b)\u795e\u7ecf\u7f51\u7edc\u7684\u5c01\u88c5\u51fd\u6570\uff0c\u53ea\u9700\u8981\u5bfc\u5165\u8f93\u5165\u548c\u8f93\u51fa\u6570\u636e\uff0c\u4ee5\u53ca\u4e00\u4e9b\u5fc5\u8981\u7684\u53c2\u6570\uff0c\u5c31\u53ef\u4ee5\u81ea\u52a8\u5b9e\u73b0\u8bad\u7ec3\u548c\u753b\u56fe\uff0c\u5e76\u5f97\u5230\u6d4b\u8bd5\u96c6\u5206\u7c7b\u6b63\u786e\u7387\u3002\u8be5\u51fd\u6570\u4e0d\u80fd\u76f4\u63a5\u8fd0\u884c\uff0c\u800c\u662f\u9700\u8981\u8c03\u7528\u3002\u8c03\u7528\u65b9\u6cd5\u5728demoBPclass.m<\/b><\/span>\u6587\u4ef6\u4e2d\u6f14\u793a\u3002<\/p>\n\n\n\n

function [net,acc,recall, precision] = bpPatternnet(dataX,dataY,trainRatio,hiddenSizes,auto,set,figflag)\n% \u5feb\u901f\u6a21\u5f0f\u8bc6\u522b(\u5206\u7c7b)\u795e\u7ecf\u7f51\u7edc,\u53ef\u4ee5\u81ea\u4e3b\u8bbe\u5b9a\u8bad\u7ec3\u96c6\u6bd4\u4f8b,\u5e76\u5f97\u5230\u6d4b\u8bd5\u96c6\u5206\u7c7b\u6b63\u786e\u7387\n% \u8f93\u5165:\n% dataX:\u8f93\u5165\u7684\u8bad\u7ec3\u96c6,R*Q\u7684\u77e9\u9635,R\u4e3a\u7279\u5f81\u7ef4\u5ea6,Q\u4e3a\u6279\u6b21\u6570,\u8f93\u5165\u8be5\u53d8\u91cf\u65f6\u4e00\u5b9a\u8981\u6ce8\u610f\u884c\u5217\u65b9\u5411\u662f\u5426\u6b63\u786e  \n% dataY:\u8bad\u7ec3\u96c6\u7684\u6807\u7b7e\u503c,U*Q\u7684\u77e9\u9635\u62161*Q\u7684cell\u5411\u91cf,U\u4e3a\u6807\u7b7e\u79cd\u7c7b\u6570,Q\u4e3a\u6279\u6b21\u6570,\u8f93\u5165\u8be5\u53d8\u91cf\u65f6\u4e00\u5b9a\u8981\u6ce8\u610f\u884c\u5217\u65b9\u5411\u662f\u5426\u6b63\u786e\n% trainRatio:\u8bad\u7ec3\u96c6\u5360\u603b\u6570\u636e\u7684\u6bd4\u4f8b,\u5c0f\u4e8e1\u7684\u6570\u503c,\u4f8b\u5982trainRatio=0.8\u65f6,80%\u6570\u636e\u7528\u4e8e\u8bad\u7ec3\n% hiddenSizes:\u795e\u7ecf\u7f51\u7edc\u9690\u85cf\u5c42\u6570\n% auto:\u662f\u5426\u8fdb\u884c\u81ea\u52a8\u7ea0\u9519,'on'\u4e3a\u662f,\u5426\u5219\u4e3a\u5426\u3002\u5f00\u542f\u81ea\u52a8\u7ea0\u9519\u540e\u4f1a\u667a\u80fd\u8c03\u6574\u8bad\u7ec3\u3001\u6d4b\u8bd5\u96c6\u7684\u884c\u5217\u65b9\u5411\u3002\n% set:\u7f51\u7edc\u7684\u989d\u5916\u8bbe\u7f6e,\u5177\u4f53\u8bbe\u7f6e\u5305\u62ec:\n%     - trainFcn:\u8bad\u7ec3\u51fd\u6570,\u9ed8\u8ba4\u4e3a'trainscg',\u53ef\u9009\u7684\u8bad\u7ec3\u51fd\u6570\u6709:\n%         - 'trainscg':\u6bd4\u4f8b\u5171\u8f6d\u68af\u5ea6\u65b9\u5411\u4f20\u64ad\n%         - 'trainbfg':BFGS\u51c6\u725b\u987f\u53cd\u5411\u4f20\u64ad\n%         - 'traincgb':\u5177\u6709Powell-Beale\u91cd\u542f\u7684\u5171\u8f6d\u68af\u5ea6\u65b9\u5411\u4f20\u64ad\n%         - 'traincgf':\u5177\u6709Fletcher-Reeves\u66f4\u65b0\u7684\u5171\u8f6d\u68af\u5ea6\u53cd\u5411\u4f20\u64ad\n%         - 'traincgp':\u5177\u6709Polak-Ribi\u00e9re\u66f4\u65b0\u7684\u5171\u8f6d\u68af\u5ea6\u53cd\u5411\u4f20\u64ad\n%         - 'traingda':\u81ea\u9002\u5e94\u5b66\u4e60\u901f\u7387\u65b9\u5411\u4f20\u64ad\u7684\u68af\u5ea6\u4e0b\u964d\n%         - 'traingdm':\u52a8\u91cf\u65b9\u5411\u4f20\u64ad\u7684\u68af\u5ea6\u4e0b\u964d\n%         - 'traingdx':\u52a8\u91cf\u68af\u5ea6\u4e0b\u964d\u548c\u81ea\u9002\u5e94\u5b66\u4e60\u901f\u7387\u53cd\u5411\u4f20\u64ad\n%         - 'trainlm':Levenberg-Marquardt\u53cd\u5411\u4f20\u64ad\n%         - 'trainoss':\u4e00\u6b65\u5272\u7ebf\u53cd\u5411\u4f20\u64ad\n%         - 'trainrp':\u5f39\u6027\u53cd\u5411\u4f20\u64ad\n%     - transferFcn:\u4f20\u9012\u51fd\u6570,\u9ed8\u8ba4\u4e3a'tansig',\u53ef\u9009\u7684\u4f20\u9012\u51fd\u6570\u6709:\n%         - 'tansig':\u53cc\u66f2\u6b63\u5207sigmoid\u51fd\u6570\n%         - 'logsig':\u5bf9\u6570sigmoid\u51fd\u6570\n%         - 'purelin':\u7ebf\u6027\u4f20\u9012\u51fd\u6570\n%     - epochs:\u6700\u5927\u8fed\u4ee3\u6b21\u6570,\u9ed8\u8ba4\u4e3a1000,\u7528\u4e8e\u63a7\u5236\u795e\u7ecf\u7f51\u7edc\u7684\u8bad\u7ec3\u8fed\u4ee3\u6b21\u6570\u4e0a\u9650\n% figflag:\u662f\u5426\u753b\u56fe,\u8bbe\u7f6e\u4e3a1\u65f6\u753b\u6df7\u6dc6\u77e9\u9635\u56fe\u3001\u771f\u5b9e\u7c7b\u522b\u4e0e\u9884\u6d4b\u7c7b\u522b\u5bf9\u6bd4\u56fe\n% \u8f93\u51fa:\n% net:\u8bad\u7ec3\u5b8c\u6210\u7684\u795e\u7ecf\u7f51\u7edc\n% acc:\u4f7f\u7528\u6d4b\u8bd5\u96c6\u5f97\u5230\u7684\u6a21\u5f0f\u8bc6\u522b\u6b63\u786e\u7387,1\u5bf9\u5e94100%\u6b63\u786e\n% recall\uff1a\u53ec\u56de\u7387\n% precision\uff1a\u7cbe\u786e\u7387<\/code><\/pre>\n\n\n\n
3. <\/strong>class2label.m<\/b><\/span><\/p>\n\n\n\n
\u5c06class\u8f6c\u4e3alabel \u4f8b\u5982\u5c06[3,2,1,1]\u8f6c\u6362\u4e3a[0,0,1;0,1,0;1,0,0;1,0,0] p\u6587\u4ef6\uff0c\u53ef\u4ee5\u8c03\u7528<\/p>\n\n\n\n
4. <\/strong>class2label.m<\/b><\/span><\/p>\n\n\n\n
\u5c06label\u8f6c\u4e3aclass \u4f8b\u5982\u5c06[0,0,1;0,1,0;1,0,0;1,0,0]\u8f6c\u6362\u4e3a[3,2,1,1] p\u6587\u4ef6\uff0c\u53ef\u4ee5\u8c03\u7528<\/p>\n\n\n\n
5.EvaluteClassResultsV2.m<\/strong><\/mark><\/p>\n\n\n\n
\u7528\u4e8e\u8bc4\u4f30\u5206\u7c7b\u7ed3\u679c\u7684\u51fd\u6570\u3002\u8c03\u7528\u8be5\u51fd\u6570\u53ef\u4ee5\u5b9e\u73b0\u7ed8\u5236\u6df7\u6dc6\u77e9\u9635\uff08\u4e24\u79cd\uff09\uff0c\u53ef\u89c6\u5316\u6d4b\u8bd5\u96c6\u771f\u5b9e\u7c7b\u522b\u548c\u9884\u6d4b\u7c7b\u522b\uff0c\u4ee5\u53ca\u8ba1\u7b97\u53ec\u56de\u7387\u548c\u51c6\u786e\u7387\u3002<\/p>\n\n\n\n
function [recall, precision] = EvaluteClassResultsV2(outputs,targets,figflag)\n% \u5bf9\u5206\u7c7b\u7ed3\u679c\u8fdb\u884c\u8bc4\u4ef7\uff0c\u5305\u62ec\uff1a\n% \u4e24\u79cd\u6df7\u6dc6\u77e9\u9635\u56fe\u3001\u53ef\u89c6\u5316\u6d4b\u8bd5\u96c6\u771f\u5b9e\u7c7b\u522b\u548c\u9884\u6d4b\u7c7b\u522b\n% \u8ba1\u7b97 weigthed-Precision, weigthed-Recall\u548cF1-score  \u53c2\u8003 https:\/\/zhuanlan.zhihu.com\/p\/147663370?from_voters_page=true\n% \u8f93\u5165\uff1a\n% outputs\uff1a\u7ecf\u8fc7\u5206\u7c7b\u7b97\u6cd5\u5f97\u5230\u7684\u5206\u7c7b\u7ed3\u679c\uff0c\u4e00\u7ef4\u6b63\u6574\u6570\n% targets\uff1a\u76ee\u6807\u5206\u7c7b\u7ed3\u679c\uff0c\u4e00\u7ef4\u6b63\u6574\u6570\n% figflag\uff1a\u662f\u5426\u753b\u56fe\uff0c'on'\u4e3a\u753b\u56fe\uff0c'off'\u4e3a\u4e0d\u753b\n% \u8f93\u51fa\uff1a\n% recall\uff1a \u53ec\u56de\u7387\n% precision\uff1a \u7cbe\u786e\u7387<\/code><\/pre>\n\n\n\n
6.fisheriris.mat<\/mark><\/strong><\/p>\n\n\n\n
\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u5305\u542bmeas\u53d8\u91cf\uff0c\u662f\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\u7684\u7279\u5f81\uff0c\u7528\u4e8e\u6a21\u578b\u7684\u8f93\u5165\u3002species\u662f\u7c7b\u522b\u6570\u636e\uff0c\u7528\u4e8e\u6a21\u578b\u7684\u8f93\u51fa\u3002<\/p>\n\n\n\n
\u4e09\u3001\u5feb\u901f\u5f00\u59cb<\/h4>\n\n\n\n
1.\u8fd0\u884c\u6d4b\u8bd5\u811a\u672c<\/strong><\/p>\n\n\n\n
\u5148\u5728MATLAB\u91cc\u6253\u5f00\u4e0b\u8f7d\u597d\u7684\u6587\u4ef6\u5939\uff0c\u7136\u540e\u8fd0\u884cdemoBPclass.m<\/b><\/span>\u7a0b\u5e8f\uff0c\u7a0b\u5e8f\u8fd0\u884c\u5b8c\u6bd5\u540e\u5982\u679c\u6ca1\u62a5\u9519\uff0c\u4e14\u6b63\u5e38\u753b\u51fa\u6df7\u6dc6\u77e9\u9635\u7b49\u56fe\u50cf\uff0c\u5219\u8bf4\u660e\u8fd0\u884c\u73af\u5883\u6b63\u5e38\uff0c\u7a0b\u5e8f\u6b63\u786e\u3002<\/p>\n\n\n\n
2.\u4fee\u6539\u4eff\u771f\u6570\u636e\/\u5bfc\u5165\u6570\u636e<\/strong><\/p>\n\n\n\n
\u590d\u5236\u4e00\u4e2ademoBPclass.m<\/b><\/span>\u7684\u6587\u4ef6\u526f\u672c\uff0c\u5728\u8fd9\u4e2a\u526f\u672c\u91cc\u505a\u5982\u4e0b\u4fee\u6539\uff1a<\/p>\n\n\n\n
\u6839\u636e\u4f60\u7684\u6587\u4ef6\u7c7b\u578b\u7684\u4e0d\u540c\uff08excel,txt,csv\u7b49\uff09\uff0c\u5c06\u6570\u636e\u5bfc\u5165MATLAB\u7684\u65b9\u6cd5\u6709\u6240\u4e0d\u540c\u3002\u540c\u5b66\u4eec\u53ef\u4ee5\u770b\u535a\u4e3b\u9488\u5bf9\u5e38\u7528\u6587\u4ef6\u7684\u5bfc\u5165\u65b9\u6cd5\u7684\u8fd9\u4e2a\u6559\u7a0b<\/a>\uff0c\u6559\u7a0b\u4e0a\u672a\u5305\u542b\u7684\u6570\u636e\u7c7b\u578b\uff0c\u5927\u5bb6\u53ef\u4ee5\u518d\u53c2\u8003\u8fd9\u4e2a\u6587\u6863<\/a>\u3002<\/p>\n\n\n\n
\u9700\u8981\u6ce8\u610f\uff0c\u5bfc\u5165\u7684\u6570\u636e\u9700\u8981\u6ee1\u8db3\u6570\u636e\u683c\u5f0f\u8981\u6c42\uff1a<\/p>\n\n\n
% dataX:\u8f93\u5165\u7684\u8bad\u7ec3\u96c6,R*Q\u7684\u77e9\u9635,R\u4e3a\u7279\u5f81\u7ef4\u5ea6,Q\u4e3a\u6279\u6b21\u6570,\u8f93\u5165\u8be5\u53d8\u91cf\u65f6\u4e00\u5b9a\u8981\u6ce8\u610f\u884c\u5217\u65b9\u5411\u662f\u5426\u6b63\u786e  \n% dataY:\u8bad\u7ec3\u96c6\u7684\u6807\u7b7e\u503c,U*Q\u7684\u77e9\u9635\u62161*Q\u7684cell\u5411\u91cf,U\u4e3a\u6807\u7b7e\u79cd\u7c7b\u6570,Q\u4e3a\u6279\u6b21\u6570,\u8f93\u5165\u8be5\u53d8\u91cf\u65f6\u4e00\u5b9a\u8981\u6ce8\u610f\u884c\u5217\u65b9\u5411\u662f\u5426\u6b63\u786e<\/code><\/div><\/pre>\n\n\n
3.\u5b9e\u73b0BP\u5206\u7c7b<\/strong><\/p>\n\n\n\n
\u5206\u7c7b\u7684\u4e00\u822c\u5b9e\u73b0\u65b9\u6cd5\u5728\u6587\u4ef6\u5939\u4e2d\u7684demoBPclass\u6587\u4ef6\u4e2d\u6709\u6240\u5c55\u793a\uff0c\u4ee3\u7801\u5206\u4e3a\u5bfc\u5165\u6570\u636e\u548c\u8c03\u7528\u51fd\u6570\u4e24\u6b65\u3002\u7528\u6237\u53ea\u9700\u8981\u5c06\u5bfc\u5165\u6570\u636e\u90e8\u5206\u6362\u6210\u81ea\u5df1\u7684\u5bfc\u5165\u7a0b\u5e8f\uff0c\u518d\u8bbe\u7f6e\u597d\u8c03\u7528\u51fd\u6570\u7684\u5165\u53e3\u53c2\u6570\u5373\u53ef\u3002<\/p>\n\n\n\n
\u5176\u4e2d\u5927\u90e8\u5206\u53c2\u6570\u8bbe\u7f6e\u65b9\u6cd5\u5728\u6ce8\u91ca\u4e2d\u90fd\u6709\u6240\u4ecb\u7ecd\uff0c\u4e0d\u8fc7\u5176\u4e2d\u7684set\u9700\u8981\u989d\u5916\u8bf4\u660e\u4e00\u4e0b\uff1a<\/p>\n\n\n\n
set\u4e2d\u76ee\u524d\u53ef\u4ee5\u8bbe\u7f6e\u7684\u5c5e\u6027\u5305\u62ectrainFcn\u548ctransferFcn\u3001epochs\u4e09\u79cd\uff0c\u5206\u522b\u4ee3\u8868\u795e\u7ecf\u7f51\u7edc\u7684\u8bad\u7ec3\u51fd\u6570\u548c\u6fc0\u6d3b\u51fd\u6570\u3001\u6700\u5927\u8fed\u4ee3\u6b21\u6570\u3002trainFcn\u548ctransferFcn\u53ef\u9009\u7684\u5c5e\u6027\u4ee5\u53ca\u76f8\u5e94\u7684\u63cf\u8ff0\u5982\u4e0b\uff1a<\/p>\n\n\n\n
trainFcn<\/strong><\/p>\n\n\n\n
trainscg\uff08\u9ed8\u8ba4\uff09:\u6bd4\u4f8b\u5171\u8f6d\u68af\u5ea6\u65b9\u5411\u4f20\u64ad<\/a><\/p>\n\n\n\n
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traincgb\uff1a\u5177\u6709Powell-Beale\u91cd\u542f\u7684\u5171\u8f6d\u68af\u5ea6\u65b9\u5411\u4f20\u64ad<\/a><\/p>\n\n\n\n
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traingda\uff1a\u81ea\u9002\u5e94\u5b66\u4e60\u901f\u7387\u65b9\u5411\u4f20\u64ad\u7684\u68af\u5ea6\u4e0b\u964d<\/a><\/p>\n\n\n\n
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trainlm\uff1aLevenberg-Marquardt\u53cd\u5411\u4f20\u64ad<\/a><\/p>\n\n\n\n
trainoss\uff1a\u4e00\u6b65\u5272\u7ebf\u53cd\u5411\u4f20\u64ad<\/a><\/p>\n\n\n\n
trainrp\uff1a\u5f39\u6027\u53cd\u5411\u4f20\u64ad<\/a><\/p>\n\n\n\n
transferFcn<\/strong><\/p>\n\n\n\n
logsig<\/mark><\/p>\n\n\n\n
Log-sigmoid\u8f6c\u79fb\u51fd\u6570\uff0c\u8868\u8fbe\u5f0f\uff1a<\/p>\n\n\n\n
logsig(n) = 1 \/ (1 + exp(-n))<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
tansig<\/mark><\/p>\n\n\n\n
\u8868\u8fbe\u5f0f\uff1a<\/p>\n\n\n\n
a = tansig(n) = 2\/(1+exp(-2*n))-1<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
poslin\uff08\u5c31\u662fReLU\uff09<\/mark><\/p>\n\n\n\n
\u8868\u8fbe\u5f0f\uff1aposlin(n) = n, if n >= 0 = 0, if n <= 0 <\/p>\n\n\n\n
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\n\n    
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Bp\u7f51\u7edc\u5b9e\u73b0\u5206\u7c7b\u516c\u5f00\u7248\u4ee3\u7801<\/h3>\n        
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\u6587\u4ef6\u5c3a\u5bf8: 161.23 KB<\/div>\n                    
\u66f4\u65b0: 30-04-2024<\/div>\n        <\/div>\n    <\/div>\n    
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