{"id":4883,"date":"2024-10-07T23:01:01","date_gmt":"2024-10-07T15:01:01","guid":{"rendered":""},"modified":"2024-10-07T23:01:01","modified_gmt":"2024-10-07T15:01:01","slug":"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9","status":"publish","type":"post","link":"https:\/\/mushiming.com\/4883.html","title":{"rendered":"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9"},"content":{"rendered":"<p><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"display: none;\"> \n <path stroke-linecap=\"round\" d=\"M5,0 0,2.5 5,5z\" id=\"raphael-marker-block\" style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0);\"><\/path> \n<\/svg> <\/p>\n<div class=\"toc\">\n<h4>\u6587\u7ae0\u76ee\u5f55<\/h4>\n<ul>\n<li>0 \u524d\u8a00<\/li>\n<li>1 \u9879\u76ee\u80cc\u666f<\/li>\n<li>2 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<strong>\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u82b1\u5349\u8bc6\u522b<\/strong><\/p>\n<p>\ud83e\udd47\u5b66\u957f\u8fd9\u91cc\u7ed9\u4e00\u4e2a\u9898\u76ee\u7efc\u5408\u8bc4\u5206(\u6bcf\u9879\u6ee1\u52065\u5206)<\/p>\n<ul>\n<li>\u96be\u5ea6\u7cfb\u6570\uff1a4\u5206<\/li>\n<li>\u5de5\u4f5c\u91cf\uff1a4\u5206<\/li>\n<li>\u521b\u65b0\u70b9\uff1a3\u5206<\/li>\n<\/ul>\n<p>\ud83e\uddff <strong>\u9009\u9898\u6307\u5bfc, \u9879\u76ee\u5206\u4eab\uff1a<\/strong><\/p>\n<p>https:\/\/gitee.com\/yaa-dc\/BJH\/blob\/master\/gg\/cc\/README.md<\/p>\n<h2>1 \u9879\u76ee\u80cc\u666f<\/h2>\n<p>\u5728\u6211\u56fd\u6709\u7740\u6210\u5343\u4e0a\u4e07\u79cd\u82b1\u5349\uff0c \u4f46\u5982\u4f55\u80fd\u65b9\u4fbf\u5feb\u6377\u7684\u8bc6\u522b\u8fa8\u8bc6\u51fa\u8fd9\u4e9b\u82b1\u5349\u7684\u79cd\u7c7b\u6210\u4e3a\u4e86\u690d\u7269\u5b66\u9886\u57df\u7684\u91cd\u8981\u7814\u7a76\u8bfe\u9898\u3002 \u6211\u56fd\u7684\u82b1\u5349\u7814\u7a76\u5386\u53f2\u60a0\u4e45\uff0c 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\u8fd9\u51e0\u4e2a\u8fc7\u7a0b\u76f8\u4e92\u5173\u8054\u800c\u53c8\u6709\u660e\u663e\u533a\u522b\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/img-blog.csdnimg.cn\/64778fa48b47445ebea470aed72297e3.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9 (https:\/\/mushiming.com\/)  \u7b2c1\u5f20\" title=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9  \u7b2c1\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><\/p>\n<h2>3 \u7b97\u6cd5\u5b9e\u73b0<\/h2>\n<h3>3.1 \u9884\u5904\u7406<\/h3>\n<p>\u9884\u5904\u7406\u662f\u5bf9\u5904\u4e8e\u6700\u4f4e\u62bd\u8c61\u7ea7\u522b\u7684\u56fe\u50cf\u8fdb\u884c\u64cd\u4f5c\u7684\u901a\u7528\u540d\u79f0\uff0c \u8f93\u5165\u548c\u8f93\u51fa\u5747\u4e3a\u5f3a\u5ea6\u56fe\u50cf\u3002 \u4e3a\u4e86\u4f7f\u5b9e\u9a8c\u7ed3\u679c\u66f4\u7cbe\u51c6\uff0c \u9700\u8981\u5bf9\u56fe\u50cf\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c \u6bd4\u5982\uff0c \u6839\u636e\u9700\u8981\u589e\u5f3a\u56fe\u50cf\u8d28\u91cf\u3001 \u5c06\u56fe\u50cf\u88c1\u526a\u6210\u5927\u5c0f\u4e00\u81f4\u7684\u5f62\u72b6\u3001 \u907f\u514d\u4e0d\u5fc5\u8981\u7684\u5931\u771f\u7b49\u7b49\u3002<\/p>\n<h3>3.2 \u7279\u5f81\u63d0\u53d6\u548c\u9009\u62e9<\/h3>\n<p>\u8981\u60f3\u83b7\u53d6\u82b1\u5349\u56fe\u50cf\u4e2d\u7684\u6700\u5177\u4ee3\u8868\u6027\u7684\u9690\u542b\u4fe1\u606f\uff0c \u5c31\u5fc5\u987b\u5bf9\u82b1\u5349\u56fe\u50cf\u6570\u636e\u96c6\u8fdb\u884c\u76f8\u5e94\u7684\u53d8\u6362\u3002<\/p>\n<p>\u7279\u5f81\u63d0\u53d6\u65e8\u5728\u901a\u8fc7\u4ece\u73b0\u6709\u7279\u5f81\u4e2d\u521b\u5efa\u65b0\u7279\u5f81\uff08\u7136\u540e\u4e22\u5f03\u539f\u59cb\u7279\u5f81\uff09 \u6765\u51cf\u5c11\u6570\u636e\u96c6\u4e2d\u7684\u7279\u5f81\u6570\u91cf\u3002 \u7136\u540e\uff0c \u8fd9\u4e9b\u65b0\u7684\u7b80\u5316\u529f\u80fd\u96c6\u5e94\u8be5\u80fd\u591f\u6c47\u603b\u539f\u59cb\u529f\u80fd\u96c6\u4e2d\u5305\u542b\u7684\u5927\u591a\u6570\u4fe1\u606f\u3002 \u8fd9\u6837\uff0c \u53ef\u4ee5\u4ece\u539f\u59cb\u96c6\u5408\u7684\u7ec4\u5408\u4e2d\u521b\u5efa\u539f\u59cb\u7279\u5f81\u7684\u6458\u8981\u7248\u672c\u3002 \u5bf9\u6240\u83b7\u53d6\u7684\u4fe1\u606f\u5b9e\u73b0\u4ece\u6d4b\u91cf\u7a7a\u95f4\u5230\u7279\u5f81\u7a7a\u95f4\u7684\u8f6c\u6362\u3002<\/p>\n<h3>3.3 \u5206\u7c7b\u5668\u8bbe\u8ba1\u548c\u51b3\u7b56<\/h3>\n<p>\u6784\u5efa\u5b8c\u6574\u7cfb\u7edf\u7684\u9002\u5f53\u5206\u7c7b\u5668\u7ec4\u4ef6\u7684\u4efb\u52a1\u662f\u4f7f\u7528\u7279\u5f81\u63d0\u53d6\u5668\u63d0\u4f9b\u7684\u7279\u5f81\u5411\u91cf\u5c06\u5bf9\u8c61\u5206\u914d\u7ed9\u7c7b\u522b\u3002 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\u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9  \u7b2c2\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><br \/> <img decoding=\"async\" src=\"https:\/\/img-blog.csdnimg.cn\/85d6e08d5129462a81d598f8131d4ee2.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9 (https:\/\/mushiming.com\/)  \u7b2c3\u5f20\" title=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9  \u7b2c3\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><\/p>\n<p>\u5728\u8bbe\u8ba1\u9636\u6bb5\uff0c \u51b3\u7b56\u529f\u80fd\u5fc5\u987b\u91cd\u590d\u591a\u6b21\uff0c \u76f4\u5230\u9519\u8bef\u8fbe\u5230\u7279\u5b9a\u6761\u4ef6\u4e3a\u6b62\u3002 \u5206\u7c7b\u51b3\u7b56\u662f\u5728\u5206\u7c7b\u5668\u8bbe\u8ba1\u9636\u6bb5\u57fa\u4e8e\u9884\u5904\u7406\u3001 \u7279\u5f81\u63d0\u53d6\u4e0e\u9009\u62e9\u53ca\u5224\u51b3\u51fd\u6570\u5efa\u7acb\u7684\u6a21\u578b\uff0c \u5bf9\u63a5\u6536\u5230\u7684\u6837\u672c\u6570\u636e\u8fdb\u884c\u5f52\u7c7b\uff0c \u7136\u540e\u8f93\u51fa\u5206\u7c7b\u7ed3\u679c\u3002<\/p>\n<h3>3.4 \u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u57fa\u672c\u539f\u7406<\/h3>\n<p>\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u662f\u53d7\u5230\u751f\u7269\u5b66\u542f\u53d1\u7684\u6df1\u5ea6\u5b66\u4e60\u7ecf\u5178\u7684\u591a\u5c42\u524d\u9988\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u3002 \u662f\u4e00\u79cd\u5728\u56fe\u50cf\u5206\u7c7b\u4e2d\u5e7f\u6cdb\u4f7f\u7528\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u3002<\/p>\n<p>CNN \u7684\u7075\u611f\u6765\u81ea\u6211\u4eec\u4eba\u7c7b\u5b9e\u9645\u770b\u5230\u5e76\u8bc6\u522b\u7269\u4f53\u7684\u65b9\u5f0f\u3002 \u8fd9\u662f\u57fa\u4e8e\u4e00\u79cd\u65b9\u6cd5\uff0c\u5373\u6211\u4eec\u773c\u775b\u4e2d\u7684\u795e\u7ecf\u5143\u7ec6\u80de\u53ea\u63a5\u6536\u5230\u6574\u4e2a\u5bf9\u8c61\u7684\u4e00\u5c0f\u90e8\u5206\uff0c\u800c\u8fd9\u4e9b\u5c0f\u5757\uff08\u79f0\u4e3a\u63a5\u53d7\u573a\uff09 \u88ab\u7ec4\u5408\u5728\u4e00\u8d77\u4ee5\u5f62\u6210\u6574\u4e2a\u5bf9\u8c61\u3002\u4e0e\u5176\u4ed6\u7684\u4eba\u5de5\u89c6\u89c9\u7b97\u6cd5\u4e0d\u4e00\u6837\u7684\u662f CNN \u53ef\u4ee5\u5904\u7406\u7279\u5b9a\u4efb\u52a1\u7684\u591a\u4e2a\u9636\u6bb5\u7684\u4e0d\u53d8\u7279\u5f81\u3002 \u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u4f7f\u7528\u7684\u5e76\u4e0d\u50cf\u7ecf\u5178\u7684\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u90a3\u6837\u7684\u5168\u8fde\u63a5\u5c42\uff0c \u800c\u662f\u901a\u8fc7\u91c7\u53d6\u5c40\u90e8\u8fde\u63a5\u548c\u6743\u503c\u5171\u4eab\u7684\u65b9\u6cd5\uff0c \u6765\u4f7f\u8bad\u7ec3\u7684\u53c2\u6570\u91cf\u51cf\u5c11\uff0c \u964d\u4f4e\u6a21\u578b\u7684\u8bad\u7ec3\u590d\u6742\u5ea6\u3002<\/p>\n<p>CNN \u5728\u56fe\u50cf\u5206\u7c7b\u548c\u5176\u4ed6\u8bc6\u522b\u4efb\u52a1\u65b9\u9762\u5df2\u7ecf\u4f7f\u4f20\u7edf\u6280\u672f\u7684\u8bc6\u522b\u6548\u679c\u5f97\u5230\u663e\u8457\u7684\u6539\u5584\u3002 \u7531\u4e8e\u5728\u8fc7\u53bb\u7684\u51e0\u5e74\u4e2d\u5377\u79ef\u7f51\u7edc\u7684\u5feb\u901f\u53d1\u5c55\uff0c \u5bf9\u8c61\u5206\u7c7b\u548c\u76ee\u6807\u68c0\u6d4b\u80fd\u529b\u53d6\u5f97\u559c\u4eba\u7684\u6210\u7ee9\u3002<\/p>\n<p>\u5178\u578b\u7684 CNN \u542b\u6709\u591a\u4e2a\u5377\u79ef\u5c42\u548c\u6c60\u5316\u5c42\uff0c \u5e76\u5177\u6709\u5168\u8fde\u63a5\u5c42\u4ee5\u4ea7\u751f\u4efb\u52a1\u7684\u6700\u7ec8\u7ed3\u679c\u3002 \u5728\u56fe\u50cf\u5206\u7c7b\u4e2d\uff0c \u6700\u540e\u4e00\u5c42\u7684\u6bcf\u4e2a\u5355\u5143\u8868\u793a\u5206\u7c7b\u6982\u7387\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/img-blog.csdnimg.cn\/1a73ddd7faf94f9e9a9773d12137cb78.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9 (https:\/\/mushiming.com\/)  \u7b2c4\u5f20\" title=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9  \u7b2c4\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><\/p>\n<h2>4 \u7b97\u6cd5\u5b9e\u73b0<\/h2>\n<h3>4.1 \u82b1\u5349\u56fe\u50cf\u6570\u636e<\/h3>\n<p>\u82b1\u5349\u56fe\u50cf\u7684\u83b7\u53d6\u9664\u4e86\u901a\u8fc7\u7528\u62cd\u6444\u8bbe\u5907\u624b\u5de5\u6536\u96c6\u6216\u662f\u901a\u8fc7\u7f51\u7edc\u4e0b\u8f7d\u5df2\u7ecf\u6574\u7406\u597d\u7684\u73b0\u6709\u6570\u636e\u96c6\uff0c \u8fd8\u53ef\u4ee5\u901a\u8fc7\u7f51\u7edc\u722c\u866b\u6280\u672f\u6536\u96c6\u6574\u7406\u81ea\u5df1\u7684\u6570\u636e\u96c6\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/img-blog.csdnimg.cn\/b598b7a7d0884cb4a41b315b07fe8610.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9 (https:\/\/mushiming.com\/)  \u7b2c5\u5f20\" title=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9  \u7b2c5\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><\/p>\n<p>\u4ee5roses\u79cd\u7c7b\u7684\u8bad\u7ec3\u6570\u636e\u4e3a\u4f8b\uff0c\u6587\u4ef6\u5939\u5185\u90e8\u5747\u4e3a\u8be5\u79cd\u7c7b\u82b1\u7684\u56fe\u50cf\u6587\u4ef6<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/img-blog.csdnimg.cn\/d6e428b5c7964dedaedf9906ff888344.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9 (https:\/\/mushiming.com\/)  \u7b2c6\u5f20\" title=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9  \u7b2c6\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><\/p>\n<h3>4.2 \u6a21\u5757\u7ec4\u6210<\/h3>\n<p>\u793a\u4f8b\u4ee3\u7801\u4e3b\u8981\u7531\u56db\u4e2a\u6a21\u5757\u7ec4\u6210\uff1a<\/p>\n<ul>\n<li>input_data.py\u2014\u2014\u56fe\u50cf\u7279\u5f81\u63d0\u53d6\u6a21\u5757\uff0c\u6a21\u5757\u751f\u6210\u56db\u79cd\u82b1\u7684\u54c1\u7c7b\u56fe\u7247\u8def\u5f84\u53ca\u5bf9\u5e94\u6807\u7b7e\u7684List<\/li>\n<li>model.py\u2014\u2014\u6a21\u578b\u6a21\u5757\uff0c\u6784\u5efa\u5b8c\u6574\u7684CNN\u6a21\u578b<\/li>\n<li>train.py\u2014\u2014\u8bad\u7ec3\u6a21\u5757\uff0c\u8bad\u7ec3\u6a21\u578b\uff0c\u5e76\u4fdd\u5b58\u8bad\u7ec3\u6a21\u578b\u7ed3\u679c<\/li>\n<li>test.py\u2014\u2014\u6d4b\u8bd5\u6a21\u5757\uff0c\u6d4b\u8bd5\u6a21\u578b\u5bf9\u56fe\u7247\u8bc6\u522b\u7684\u51c6\u786e\u5ea6<\/li>\n<\/ul>\n<p>\u9879\u76ee\u6a21\u5757\u6267\u884c\u987a\u5e8f<\/p>\n<p>\u8fd0\u884ctrain.py\u5f00\u59cb\u8bad\u7ec3\u3002<br \/> \u8bad\u7ec3\u5b8c\u6210\u540e- \u8fd0\u884ctest.py\uff0c\u67e5\u770b\u5b9e\u9645\u6d4b\u8bd5\u7ed3\u679c<br \/> input_data.py\u2014\u2014\u56fe\u50cf\u7279\u5f81\u63d0\u53d6\u6a21\u5757\uff0c\u6a21\u5757\u751f\u6210\u56db\u79cd\u82b1\u7684\u54c1\u7c7b\u56fe\u7247\u8def\u5f84\u53ca\u5bf9\u5e94\u6807\u7b7e\u7684List<\/p>\n<pre class=\"language-java prettyprint linenums\"><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> os <span class=\"token keyword\">import<\/span> math <span class=\"token keyword\">import<\/span> numpy <span class=\"token keyword\">as<\/span> np <span class=\"token keyword\">import<\/span> tensorflow <span class=\"token keyword\">as<\/span> tf <span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt <span class=\"token comment\"># -----------------\u751f\u6210\u56fe\u7247\u8def\u5f84\u548c\u6807\u7b7e\u7684List------------------------------------<\/span> train_dir <span class=\"token operator\">=<\/span> <span class=\"token string\">'D:\/ML\/flower\/input_data'<\/span> roses <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span> label_roses <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span> tulips <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span> label_tulips <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span> dandelion <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span> label_dandelion <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span> sunflowers <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span> label_sunflowers <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span> <\/code><\/pre>\n<p><strong>\u5b9a\u4e49\u51fd\u6570get_files,\u83b7\u53d6\u56fe\u7247\u5217\u8868\u53ca\u6807\u7b7e\u5217\u8868<\/strong><\/p>\n<pre class=\"language-java prettyprint linenums\"><code class=\"prism language-python\"><span class=\"token comment\"># step1\uff1a\u83b7\u53d6\u6240\u6709\u7684\u56fe\u7247\u8def\u5f84\u540d\uff0c\u5b58\u653e\u5230<\/span> <span class=\"token comment\"># \u5bf9\u5e94\u7684\u5217\u8868\u4e2d\uff0c\u540c\u65f6\u8d34\u4e0a\u6807\u7b7e\uff0c\u5b58\u653e\u5230label\u5217\u8868\u4e2d\u3002<\/span> <span class=\"token keyword\">def<\/span> <span class=\"token function\">get_files<\/span><span class=\"token punctuation\">(<\/span>file_dir<span class=\"token punctuation\">,<\/span> ratio<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token keyword\">for<\/span> <span class=\"token builtin\">file<\/span> <span class=\"token keyword\">in<\/span> os<span class=\"token punctuation\">.<\/span>listdir<span class=\"token punctuation\">(<\/span>file_dir <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/roses'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> roses<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>file_dir <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/roses'<\/span> <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/'<\/span> <span class=\"token operator\">+<\/span> <span class=\"token builtin\">file<\/span><span class=\"token punctuation\">)<\/span> label_roses<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">for<\/span> <span class=\"token builtin\">file<\/span> <span class=\"token keyword\">in<\/span> os<span class=\"token punctuation\">.<\/span>listdir<span class=\"token punctuation\">(<\/span>file_dir <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/tulips'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> tulips<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>file_dir <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/tulips'<\/span> <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/'<\/span> <span class=\"token operator\">+<\/span> <span class=\"token builtin\">file<\/span><span class=\"token punctuation\">)<\/span> label_tulips<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">for<\/span> <span class=\"token builtin\">file<\/span> <span class=\"token keyword\">in<\/span> os<span class=\"token punctuation\">.<\/span>listdir<span class=\"token punctuation\">(<\/span>file_dir <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/dandelion'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> dandelion<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>file_dir <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/dandelion'<\/span> <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/'<\/span> <span class=\"token operator\">+<\/span> <span class=\"token builtin\">file<\/span><span class=\"token punctuation\">)<\/span> label_dandelion<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">for<\/span> <span class=\"token builtin\">file<\/span> <span class=\"token keyword\">in<\/span> os<span class=\"token punctuation\">.<\/span>listdir<span class=\"token punctuation\">(<\/span>file_dir <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/sunflowers'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> sunflowers<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>file_dir <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/sunflowers'<\/span> <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/'<\/span> <span class=\"token operator\">+<\/span> <span class=\"token builtin\">file<\/span><span class=\"token punctuation\">)<\/span> label_sunflowers<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># step2\uff1a\u5bf9\u751f\u6210\u7684\u56fe\u7247\u8def\u5f84\u548c\u6807\u7b7eList\u505a\u6253\u4e71\u5904\u7406<\/span> image_list <span class=\"token operator\">=<\/span> np<span class=\"token punctuation\">.<\/span>hstack<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">(<\/span>roses<span class=\"token punctuation\">,<\/span> tulips<span class=\"token punctuation\">,<\/span> dandelion<span class=\"token punctuation\">,<\/span> sunflowers<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> label_list <span class=\"token operator\">=<\/span> np<span class=\"token punctuation\">.<\/span>hstack<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">(<\/span>label_roses<span class=\"token punctuation\">,<\/span> label_tulips<span class=\"token punctuation\">,<\/span> label_dandelion<span class=\"token punctuation\">,<\/span> label_sunflowers<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u5229\u7528shuffle\u6253\u4e71\u987a\u5e8f<\/span> temp <span class=\"token operator\">=<\/span> np<span class=\"token punctuation\">.<\/span>array<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span>image_list<span class=\"token punctuation\">,<\/span> label_list<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> temp <span class=\"token operator\">=<\/span> temp<span class=\"token punctuation\">.<\/span>transpose<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>shuffle<span class=\"token punctuation\">(<\/span>temp<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u5c06\u6240\u6709\u7684img\u548clab\u8f6c\u6362\u6210list<\/span> all_image_list <span class=\"token operator\">=<\/span> <span class=\"token builtin\">list<\/span><span class=\"token punctuation\">(<\/span>temp<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> all_label_list <span class=\"token operator\">=<\/span> <span class=\"token builtin\">list<\/span><span class=\"token punctuation\">(<\/span>temp<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u5c06\u6240\u5f97List\u5206\u4e3a\u4e24\u90e8\u5206\uff0c\u4e00\u90e8\u5206\u7528\u6765\u8bad\u7ec3tra\uff0c\u4e00\u90e8\u5206\u7528\u6765\u6d4b\u8bd5val<\/span> <span class=\"token comment\"># ratio\u662f\u6d4b\u8bd5\u96c6\u7684\u6bd4\u4f8b<\/span> n_sample <span class=\"token operator\">=<\/span> <span class=\"token builtin\">len<\/span><span class=\"token punctuation\">(<\/span>all_label_list<span class=\"token punctuation\">)<\/span> n_val <span class=\"token operator\">=<\/span> <span class=\"token builtin\">int<\/span><span class=\"token punctuation\">(<\/span>math<span class=\"token punctuation\">.<\/span>ceil<span class=\"token punctuation\">(<\/span>n_sample <span class=\"token operator\">*<\/span> ratio<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u6d4b\u8bd5\u6837\u672c\u6570<\/span> n_train <span class=\"token operator\">=<\/span> n_sample <span class=\"token operator\">-<\/span> n_val <span class=\"token comment\"># \u8bad\u7ec3\u6837\u672c\u6570<\/span> tra_images <span class=\"token operator\">=<\/span> all_image_list<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">:<\/span>n_train<span class=\"token punctuation\">]<\/span> tra_labels <span class=\"token operator\">=<\/span> all_label_list<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">:<\/span>n_train<span class=\"token punctuation\">]<\/span> tra_labels <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token builtin\">int<\/span><span class=\"token punctuation\">(<\/span><span class=\"token builtin\">float<\/span><span class=\"token punctuation\">(<\/span>i<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">for<\/span> i <span class=\"token keyword\">in<\/span> tra_labels<span class=\"token punctuation\">]<\/span> val_images <span class=\"token operator\">=<\/span> all_image_list<span class=\"token punctuation\">[<\/span>n_train<span class=\"token punctuation\">:<\/span><span class=\"token operator\">-<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span> val_labels <span class=\"token operator\">=<\/span> all_label_list<span class=\"token punctuation\">[<\/span>n_train<span class=\"token punctuation\">:<\/span><span class=\"token operator\">-<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span> val_labels <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token builtin\">int<\/span><span class=\"token punctuation\">(<\/span><span class=\"token builtin\">float<\/span><span class=\"token punctuation\">(<\/span>i<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">for<\/span> i <span class=\"token keyword\">in<\/span> val_labels<span class=\"token punctuation\">]<\/span> <span class=\"token keyword\">return<\/span> tra_images<span class=\"token punctuation\">,<\/span> tra_labels<span class=\"token punctuation\">,<\/span> val_images<span class=\"token punctuation\">,<\/span> val_labels <\/code><\/pre>\n<p><strong>\u5b9a\u4e49\u51fd\u6570get_batch,\u751f\u6210\u8bad\u7ec3\u6279\u6b21\u6570\u636e<\/strong><\/p>\n<pre class=\"language-java prettyprint linenums\"><code class=\"prism language-python\"><span class=\"token comment\"># --------------------\u751f\u6210Batch----------------------------------------------<\/span> <span class=\"token comment\"># step1\uff1a\u5c06\u4e0a\u9762\u751f\u6210\u7684List\u4f20\u5165get_batch() \uff0c\u8f6c\u6362\u7c7b\u578b\uff0c\u4ea7\u751f\u4e00\u4e2a\u8f93\u5165\u961f\u5217queue\uff0c\u56e0\u4e3aimg\u548clab<\/span> <span class=\"token comment\"># \u662f\u5206\u5f00\u7684\uff0c\u6240\u4ee5\u4f7f\u7528tf.train.slice_input_producer()\uff0c\u7136\u540e\u7528tf.read_file()\u4ece\u961f\u5217\u4e2d\u8bfb\u53d6\u56fe\u50cf<\/span> <span class=\"token comment\"># image_W, image_H, \uff1a\u8bbe\u7f6e\u597d\u56fa\u5b9a\u7684\u56fe\u50cf\u9ad8\u5ea6\u548c\u5bbd\u5ea6<\/span> <span class=\"token comment\"># \u8bbe\u7f6ebatch_size\uff1a\u6bcf\u4e2abatch\u8981\u653e\u591a\u5c11\u5f20\u56fe\u7247<\/span> <span class=\"token comment\"># capacity\uff1a\u4e00\u4e2a\u961f\u5217\u6700\u5927\u591a\u5c11<\/span> \u5b9a\u4e49\u51fd\u6570get_batch<span class=\"token punctuation\">,<\/span>\u751f\u6210\u8bad\u7ec3\u6279\u6b21\u6570\u636e <span class=\"token keyword\">def<\/span> <span class=\"token function\">get_batch<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> label<span class=\"token punctuation\">,<\/span> image_W<span class=\"token punctuation\">,<\/span> image_H<span class=\"token punctuation\">,<\/span> batch_size<span class=\"token punctuation\">,<\/span> capacity<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token comment\"># \u8f6c\u6362\u7c7b\u578b<\/span> image <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>cast<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> tf<span class=\"token punctuation\">.<\/span>string<span class=\"token punctuation\">)<\/span> label <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>cast<span class=\"token punctuation\">(<\/span>label<span class=\"token punctuation\">,<\/span> tf<span class=\"token punctuation\">.<\/span>int32<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># make an input queue<\/span> input_queue <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>train<span class=\"token punctuation\">.<\/span>slice_input_producer<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span>image<span class=\"token punctuation\">,<\/span> label<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> label <span class=\"token operator\">=<\/span> input_queue<span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span> image_contents <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>read_file<span class=\"token punctuation\">(<\/span>input_queue<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># read img from a queue<\/span> <span class=\"token comment\"># step2\uff1a\u5c06\u56fe\u50cf\u89e3\u7801\uff0c\u4e0d\u540c\u7c7b\u578b\u7684\u56fe\u50cf\u4e0d\u80fd\u6df7\u5728\u4e00\u8d77\uff0c\u8981\u4e48\u53ea\u7528jpeg\uff0c\u8981\u4e48\u53ea\u7528png\u7b49\u3002<\/span> image <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>image<span class=\"token punctuation\">.<\/span>decode_jpeg<span class=\"token punctuation\">(<\/span>image_contents<span class=\"token punctuation\">,<\/span> channels<span class=\"token operator\">=<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># step3\uff1a\u6570\u636e\u9884\u5904\u7406\uff0c\u5bf9\u56fe\u50cf\u8fdb\u884c\u65cb\u8f6c\u3001\u7f29\u653e\u3001\u88c1\u526a\u3001\u5f52\u4e00\u5316\u7b49\u64cd\u4f5c\uff0c\u8ba9\u8ba1\u7b97\u51fa\u7684\u6a21\u578b\u66f4\u5065\u58ee\u3002<\/span> image <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>image<span class=\"token punctuation\">.<\/span>resize_image_with_crop_or_pad<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> image_W<span class=\"token punctuation\">,<\/span> image_H<span class=\"token punctuation\">)<\/span> image <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>image<span class=\"token punctuation\">.<\/span>per_image_standardization<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># step4\uff1a\u751f\u6210batch<\/span> <span class=\"token comment\"># image_batch: 4D tensor [batch_size, width, height, 3],dtype=tf.float32<\/span> <span class=\"token comment\"># label_batch: 1D tensor [batch_size], dtype=tf.int32<\/span> image_batch<span class=\"token punctuation\">,<\/span> label_batch <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>train<span class=\"token punctuation\">.<\/span>batch<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span>image<span class=\"token punctuation\">,<\/span> label<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> batch_size<span class=\"token operator\">=<\/span>batch_size<span class=\"token punctuation\">,<\/span> num_threads<span class=\"token operator\">=<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">,<\/span> capacity<span class=\"token operator\">=<\/span>capacity<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u91cd\u65b0\u6392\u5217label\uff0c\u884c\u6570\u4e3a[batch_size]<\/span> label_batch <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>reshape<span class=\"token punctuation\">(<\/span>label_batch<span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">[<\/span>batch_size<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> image_batch <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>cast<span class=\"token punctuation\">(<\/span>image_batch<span class=\"token punctuation\">,<\/span> tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">return<\/span> image_batch<span class=\"token punctuation\">,<\/span> label_batch <\/code><\/pre>\n<p><strong>model.py\u2014\u2014CN\u6a21\u578b\u6784\u5efa<\/strong><\/p>\n<pre class=\"language-java prettyprint linenums\"><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> tensorflow <span class=\"token keyword\">as<\/span> tf <span class=\"token comment\">#\u5b9a\u4e49\u51fd\u6570infence\uff0c\u5b9a\u4e49CNN\u7f51\u7edc\u7ed3\u6784<\/span> <span class=\"token comment\">#\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff0c\u5377\u79ef\u52a0\u6c60\u5316*2\uff0c\u5168\u8fde\u63a5*2\uff0csoftmax\u5206\u7c7b<\/span> <span class=\"token comment\">#\u5377\u79ef\u5c421<\/span> <span class=\"token keyword\">def<\/span> <span class=\"token function\">inference<\/span><span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">,<\/span> batch_size<span class=\"token punctuation\">,<\/span> n_classes<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token keyword\">with<\/span> tf<span class=\"token punctuation\">.<\/span>variable_scope<span class=\"token punctuation\">(<\/span><span class=\"token string\">'conv1'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">as<\/span> scope<span class=\"token punctuation\">:<\/span> weights <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>Variable<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>truncated_normal<span class=\"token punctuation\">(<\/span>shape<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span>stddev<span class=\"token operator\">=<\/span><span class=\"token number\">1.0<\/span><span class=\"token punctuation\">,<\/span>dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> name <span class=\"token operator\">=<\/span> <span class=\"token string\">'weights'<\/span><span class=\"token punctuation\">,<\/span>dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> biases <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>Variable<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>constant<span class=\"token punctuation\">(<\/span>value<span class=\"token operator\">=<\/span><span class=\"token number\">0.1<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">,<\/span> shape<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'biases'<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> conv <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>conv2d<span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">,<\/span> weights<span class=\"token punctuation\">,<\/span> strides<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> padding<span class=\"token operator\">=<\/span><span class=\"token string\">'SAME'<\/span><span class=\"token punctuation\">)<\/span> pre_activation <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>bias_add<span class=\"token punctuation\">(<\/span>conv<span class=\"token punctuation\">,<\/span> biases<span class=\"token punctuation\">)<\/span> conv1 <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>relu<span class=\"token punctuation\">(<\/span>pre_activation<span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span>scope<span class=\"token punctuation\">.<\/span>name<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u6c60\u5316\u5c421<\/span> <span class=\"token comment\"># 3x3\u6700\u5927\u6c60\u5316\uff0c\u6b65\u957fstrides\u4e3a2\uff0c\u6c60\u5316\u540e\u6267\u884clrn()\u64cd\u4f5c\uff0c\u5c40\u90e8\u54cd\u5e94\u5f52\u4e00\u5316\uff0c\u5bf9\u8bad\u7ec3\u6709\u5229\u3002<\/span> <span class=\"token keyword\">with<\/span> tf<span class=\"token punctuation\">.<\/span>variable_scope<span class=\"token punctuation\">(<\/span><span class=\"token string\">'pooling1_lrn'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">as<\/span> scope<span class=\"token punctuation\">:<\/span> pool1 <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>max_pool<span class=\"token punctuation\">(<\/span>conv1<span class=\"token punctuation\">,<\/span> ksize<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> strides<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> padding<span class=\"token operator\">=<\/span><span class=\"token string\">'SAME'<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'pooling1'<\/span><span class=\"token punctuation\">)<\/span> norm1 <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>lrn<span class=\"token punctuation\">(<\/span>pool1<span class=\"token punctuation\">,<\/span> depth_radius<span class=\"token operator\">=<\/span><span class=\"token number\">4<\/span><span class=\"token punctuation\">,<\/span> bias<span class=\"token operator\">=<\/span><span class=\"token number\">1.0<\/span><span class=\"token punctuation\">,<\/span> alpha<span class=\"token operator\">=<\/span><span class=\"token number\">0.001<\/span> <span class=\"token operator\">\/<\/span> <span class=\"token number\">9.0<\/span><span class=\"token punctuation\">,<\/span> beta<span class=\"token operator\">=<\/span><span class=\"token number\">0.75<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'norm1'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u5377\u79ef\u5c422<\/span> <span class=\"token comment\"># 16\u4e2a3x3\u7684\u5377\u79ef\u6838\uff0816\u901a\u9053\uff09\uff0cpadding=\u2019SAME\u2019\uff0c\u8868\u793apadding\u540e\u5377\u79ef\u7684\u56fe\u4e0e\u539f\u56fe\u5c3a\u5bf8\u4e00\u81f4\uff0c\u6fc0\u6d3b\u51fd\u6570relu()<\/span> <span class=\"token keyword\">with<\/span> tf<span class=\"token punctuation\">.<\/span>variable_scope<span class=\"token punctuation\">(<\/span><span class=\"token string\">'conv2'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">as<\/span> scope<span class=\"token punctuation\">:<\/span> weights <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>Variable<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>truncated_normal<span class=\"token punctuation\">(<\/span>shape<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">16<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> stddev<span class=\"token operator\">=<\/span><span class=\"token number\">0.1<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'weights'<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> biases <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>Variable<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>constant<span class=\"token punctuation\">(<\/span>value<span class=\"token operator\">=<\/span><span class=\"token number\">0.1<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">,<\/span> shape<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">16<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'biases'<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> conv <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>conv2d<span class=\"token punctuation\">(<\/span>norm1<span class=\"token punctuation\">,<\/span> weights<span class=\"token punctuation\">,<\/span> strides<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> padding<span class=\"token operator\">=<\/span><span class=\"token string\">'SAME'<\/span><span class=\"token punctuation\">)<\/span> pre_activation <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>bias_add<span class=\"token punctuation\">(<\/span>conv<span class=\"token punctuation\">,<\/span> biases<span class=\"token punctuation\">)<\/span> conv2 <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>relu<span class=\"token punctuation\">(<\/span>pre_activation<span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'conv2'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u6c60\u5316\u5c422<\/span> <span class=\"token comment\"># 3x3\u6700\u5927\u6c60\u5316\uff0c\u6b65\u957fstrides\u4e3a2\uff0c\u6c60\u5316\u540e\u6267\u884clrn()\u64cd\u4f5c\uff0c<\/span> <span class=\"token comment\"># pool2 and norm2<\/span> <span class=\"token keyword\">with<\/span> tf<span class=\"token punctuation\">.<\/span>variable_scope<span class=\"token punctuation\">(<\/span><span class=\"token string\">'pooling2_lrn'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">as<\/span> scope<span class=\"token punctuation\">:<\/span> norm2 <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>lrn<span class=\"token punctuation\">(<\/span>conv2<span class=\"token punctuation\">,<\/span> depth_radius<span class=\"token operator\">=<\/span><span class=\"token number\">4<\/span><span class=\"token punctuation\">,<\/span> bias<span class=\"token operator\">=<\/span><span class=\"token number\">1.0<\/span><span class=\"token punctuation\">,<\/span> alpha<span class=\"token operator\">=<\/span><span class=\"token number\">0.001<\/span> <span class=\"token operator\">\/<\/span> <span class=\"token number\">9.0<\/span><span class=\"token punctuation\">,<\/span> beta<span class=\"token operator\">=<\/span><span class=\"token number\">0.75<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'norm2'<\/span><span class=\"token punctuation\">)<\/span> pool2 <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>max_pool<span class=\"token punctuation\">(<\/span>norm2<span class=\"token punctuation\">,<\/span> ksize<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> strides<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> padding<span class=\"token operator\">=<\/span><span class=\"token string\">'SAME'<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'pooling2'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u5168\u8fde\u63a5\u5c423<\/span> <span class=\"token comment\"># 128\u4e2a\u795e\u7ecf\u5143\uff0c\u5c06\u4e4b\u524dpool\u5c42\u7684\u8f93\u51fareshape\u6210\u4e00\u884c\uff0c\u6fc0\u6d3b\u51fd\u6570relu()<\/span> <span class=\"token keyword\">with<\/span> tf<span class=\"token punctuation\">.<\/span>variable_scope<span class=\"token punctuation\">(<\/span><span class=\"token string\">'local3'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">as<\/span> scope<span class=\"token punctuation\">:<\/span> reshape <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>reshape<span class=\"token punctuation\">(<\/span>pool2<span class=\"token punctuation\">,<\/span> shape<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span>batch_size<span class=\"token punctuation\">,<\/span> <span class=\"token operator\">-<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> dim <span class=\"token operator\">=<\/span> reshape<span class=\"token punctuation\">.<\/span>get_shape<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>value weights <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>Variable<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>truncated_normal<span class=\"token punctuation\">(<\/span>shape<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span>dim<span class=\"token punctuation\">,<\/span> <span class=\"token number\">128<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> stddev<span class=\"token operator\">=<\/span><span class=\"token number\">0.005<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'weights'<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> biases <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>Variable<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>constant<span class=\"token punctuation\">(<\/span>value<span class=\"token operator\">=<\/span><span class=\"token number\">0.1<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">,<\/span> shape<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">128<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'biases'<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> local3 <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>relu<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>matmul<span class=\"token punctuation\">(<\/span>reshape<span class=\"token punctuation\">,<\/span> weights<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">+<\/span> biases<span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span>scope<span class=\"token punctuation\">.<\/span>name<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u5168\u8fde\u63a5\u5c424<\/span> <span class=\"token comment\"># 128\u4e2a\u795e\u7ecf\u5143\uff0c\u6fc0\u6d3b\u51fd\u6570relu()<\/span> <span class=\"token keyword\">with<\/span> tf<span class=\"token punctuation\">.<\/span>variable_scope<span class=\"token punctuation\">(<\/span><span class=\"token string\">'local4'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">as<\/span> scope<span class=\"token punctuation\">:<\/span> weights <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>Variable<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>truncated_normal<span class=\"token punctuation\">(<\/span>shape<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">128<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">128<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> stddev<span class=\"token operator\">=<\/span><span class=\"token number\">0.005<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'weights'<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> biases <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>Variable<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>constant<span class=\"token punctuation\">(<\/span>value<span class=\"token operator\">=<\/span><span class=\"token number\">0.1<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">,<\/span> shape<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">128<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'biases'<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> local4 <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>relu<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>matmul<span class=\"token punctuation\">(<\/span>local3<span class=\"token punctuation\">,<\/span> weights<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">+<\/span> biases<span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'local4'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># dropout\u5c42<\/span> <span class=\"token comment\"># with tf.variable_scope('dropout') as scope:<\/span> <span class=\"token comment\"># drop_out = tf.nn.dropout(local4, 0.8)<\/span> <span class=\"token comment\"># Softmax\u56de\u5f52\u5c42<\/span> <span class=\"token comment\"># \u5c06\u524d\u9762\u7684FC\u5c42\u8f93\u51fa\uff0c\u505a\u4e00\u4e2a\u7ebf\u6027\u56de\u5f52\uff0c\u8ba1\u7b97\u51fa\u6bcf\u4e00\u7c7b\u7684\u5f97\u5206<\/span> <span class=\"token keyword\">with<\/span> tf<span class=\"token punctuation\">.<\/span>variable_scope<span class=\"token punctuation\">(<\/span><span class=\"token string\">'softmax_linear'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">as<\/span> scope<span class=\"token punctuation\">:<\/span> weights <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>Variable<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>truncated_normal<span class=\"token punctuation\">(<\/span>shape<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">128<\/span><span class=\"token punctuation\">,<\/span> n_classes<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> stddev<span class=\"token operator\">=<\/span><span class=\"token number\">0.005<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'softmax_linear'<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> biases <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>Variable<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>constant<span class=\"token punctuation\">(<\/span>value<span class=\"token operator\">=<\/span><span class=\"token number\">0.1<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">,<\/span> shape<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span>n_classes<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'biases'<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> softmax_linear <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>add<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>matmul<span class=\"token punctuation\">(<\/span>local4<span class=\"token punctuation\">,<\/span> weights<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> biases<span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'softmax_linear'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">return<\/span> softmax_linear <span class=\"token comment\"># -----------------------------------------------------------------------------<\/span> <span class=\"token comment\"># loss\u8ba1\u7b97<\/span> <span class=\"token comment\"># \u4f20\u5165\u53c2\u6570\uff1alogits\uff0c\u7f51\u7edc\u8ba1\u7b97\u8f93\u51fa\u503c\u3002labels\uff0c\u771f\u5b9e\u503c\uff0c\u5728\u8fd9\u91cc\u662f0\u6216\u80051<\/span> <span class=\"token comment\"># \u8fd4\u56de\u53c2\u6570\uff1aloss\uff0c\u635f\u5931\u503c<\/span> <span class=\"token keyword\">def<\/span> <span class=\"token function\">losses<\/span><span class=\"token punctuation\">(<\/span>logits<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token keyword\">with<\/span> tf<span class=\"token punctuation\">.<\/span>variable_scope<span class=\"token punctuation\">(<\/span><span class=\"token string\">'loss'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">as<\/span> scope<span class=\"token punctuation\">:<\/span> cross_entropy <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>sparse_softmax_cross_entropy_with_logits<span class=\"token punctuation\">(<\/span>logits<span class=\"token operator\">=<\/span>logits<span class=\"token punctuation\">,<\/span> labels<span class=\"token operator\">=<\/span>labels<span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'xentropy_per_example'<\/span><span class=\"token punctuation\">)<\/span> loss <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>reduce_mean<span class=\"token punctuation\">(<\/span>cross_entropy<span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'loss'<\/span><span class=\"token punctuation\">)<\/span> tf<span class=\"token punctuation\">.<\/span>summary<span class=\"token punctuation\">.<\/span>scalar<span class=\"token punctuation\">(<\/span>scope<span class=\"token punctuation\">.<\/span>name <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/loss'<\/span><span class=\"token punctuation\">,<\/span> loss<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">return<\/span> loss <span class=\"token comment\"># --------------------------------------------------------------------------<\/span> <span class=\"token comment\"># loss\u635f\u5931\u503c\u4f18\u5316<\/span> <span class=\"token comment\"># \u8f93\u5165\u53c2\u6570\uff1aloss\u3002learning_rate\uff0c\u5b66\u4e60\u901f\u7387\u3002<\/span> <span class=\"token comment\"># \u8fd4\u56de\u53c2\u6570\uff1atrain_op\uff0c\u8bad\u7ec3op\uff0c\u8fd9\u4e2a\u53c2\u6570\u8981\u8f93\u5165sess.run\u4e2d\u8ba9\u6a21\u578b\u53bb\u8bad\u7ec3\u3002<\/span> <span class=\"token keyword\">def<\/span> <span class=\"token function\">trainning<\/span><span class=\"token punctuation\">(<\/span>loss<span class=\"token punctuation\">,<\/span> learning_rate<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token keyword\">with<\/span> tf<span class=\"token punctuation\">.<\/span>name_scope<span class=\"token punctuation\">(<\/span><span class=\"token string\">'optimizer'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> optimizer <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>train<span class=\"token punctuation\">.<\/span>AdamOptimizer<span class=\"token punctuation\">(<\/span>learning_rate<span class=\"token operator\">=<\/span>learning_rate<span class=\"token punctuation\">)<\/span> global_step <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>Variable<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span><span class=\"token string\">'global_step'<\/span><span class=\"token punctuation\">,<\/span> trainable<span class=\"token operator\">=<\/span><span class=\"token boolean\">False<\/span><span class=\"token punctuation\">)<\/span> train_op <span class=\"token operator\">=<\/span> optimizer<span class=\"token punctuation\">.<\/span>minimize<span class=\"token punctuation\">(<\/span>loss<span class=\"token punctuation\">,<\/span> global_step<span class=\"token operator\">=<\/span>global_step<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">return<\/span> train_op <span class=\"token comment\"># -----------------------------------------------------------------------<\/span> <span class=\"token comment\"># \u8bc4\u4ef7\/\u51c6\u786e\u7387\u8ba1\u7b97<\/span> <span class=\"token comment\"># \u8f93\u5165\u53c2\u6570\uff1alogits\uff0c\u7f51\u7edc\u8ba1\u7b97\u503c\u3002labels\uff0c\u6807\u7b7e\uff0c\u4e5f\u5c31\u662f\u771f\u5b9e\u503c\uff0c\u5728\u8fd9\u91cc\u662f0\u6216\u80051\u3002<\/span> <span class=\"token comment\"># \u8fd4\u56de\u53c2\u6570\uff1aaccuracy\uff0c\u5f53\u524dstep\u7684\u5e73\u5747\u51c6\u786e\u7387\uff0c\u4e5f\u5c31\u662f\u5728\u8fd9\u4e9bbatch\u4e2d\u591a\u5c11\u5f20\u56fe\u7247\u88ab\u6b63\u786e\u5206\u7c7b\u4e86\u3002<\/span> <span class=\"token keyword\">def<\/span> <span class=\"token function\">evaluation<\/span><span class=\"token punctuation\">(<\/span>logits<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token keyword\">with<\/span> tf<span class=\"token punctuation\">.<\/span>variable_scope<span class=\"token punctuation\">(<\/span><span class=\"token string\">'accuracy'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">as<\/span> scope<span class=\"token punctuation\">:<\/span> correct <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>in_top_k<span class=\"token punctuation\">(<\/span>logits<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span> correct <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>cast<span class=\"token punctuation\">(<\/span>correct<span class=\"token punctuation\">,<\/span> tf<span class=\"token punctuation\">.<\/span>float16<span class=\"token punctuation\">)<\/span> accuracy <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>reduce_mean<span class=\"token punctuation\">(<\/span>correct<span class=\"token punctuation\">)<\/span> tf<span class=\"token punctuation\">.<\/span>summary<span class=\"token punctuation\">.<\/span>scalar<span class=\"token punctuation\">(<\/span>scope<span class=\"token punctuation\">.<\/span>name <span class=\"token operator\">+<\/span> <span class=\"token string\">'\/accuracy'<\/span><span class=\"token punctuation\">,<\/span> accuracy<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">return<\/span> accuracy <\/code><\/pre>\n<p><strong>train.py\u2014\u2014\u5229\u7528D:\/ML\/flower\/input_data\/\u8def\u5f84\u4e0b\u7684\u8bad\u7ec3\u6570\u636e\uff0c\u5bf9CNN\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3<\/strong><\/p>\n<pre class=\"language-java prettyprint linenums\"><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> input_data <span class=\"token keyword\">import<\/span> model <span class=\"token comment\"># \u53d8\u91cf\u58f0\u660e<\/span> N_CLASSES <span class=\"token operator\">=<\/span> <span class=\"token number\">4<\/span> <span class=\"token comment\"># \u56db\u79cd\u82b1\u7c7b\u578b<\/span> IMG_W <span class=\"token operator\">=<\/span> <span class=\"token number\">64<\/span> <span class=\"token comment\"># resize\u56fe\u50cf\uff0c\u592a\u5927\u7684\u8bdd\u8bad\u7ec3\u65f6\u95f4\u4e45<\/span> IMG_H <span class=\"token operator\">=<\/span> <span class=\"token number\">64<\/span> BATCH_SIZE <span class=\"token operator\">=<\/span> <span class=\"token number\">20<\/span> CAPACITY <span class=\"token operator\">=<\/span> <span class=\"token number\">200<\/span> MAX_STEP <span class=\"token operator\">=<\/span> <span class=\"token number\">2000<\/span> <span class=\"token comment\"># \u4e00\u822c\u5927\u4e8e10K<\/span> learning_rate <span class=\"token operator\">=<\/span> <span class=\"token number\">0.0001<\/span> <span class=\"token comment\"># \u4e00\u822c\u5c0f\u4e8e0.0001<\/span> <span class=\"token comment\"># \u83b7\u53d6\u6279\u6b21batch<\/span> train_dir <span class=\"token operator\">=<\/span> <span class=\"token string\">'F:\/input_data'<\/span> <span class=\"token comment\"># \u8bad\u7ec3\u6837\u672c\u7684\u8bfb\u5165\u8def\u5f84<\/span> logs_train_dir <span class=\"token operator\">=<\/span> <span class=\"token string\">'F:\/save'<\/span> <span class=\"token comment\"># logs\u5b58\u50a8\u8def\u5f84<\/span> <span class=\"token comment\"># train, train_label = input_data.get_files(train_dir)<\/span> train<span class=\"token punctuation\">,<\/span> train_label<span class=\"token punctuation\">,<\/span> val<span class=\"token punctuation\">,<\/span> val_label <span class=\"token operator\">=<\/span> input_data<span class=\"token punctuation\">.<\/span>get_files<span class=\"token punctuation\">(<\/span>train_dir<span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.3<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u8bad\u7ec3\u6570\u636e\u53ca\u6807\u7b7e<\/span> train_batch<span class=\"token punctuation\">,<\/span> train_label_batch <span class=\"token operator\">=<\/span> input_data<span class=\"token punctuation\">.<\/span>get_batch<span class=\"token punctuation\">(<\/span>train<span class=\"token punctuation\">,<\/span> train_label<span class=\"token punctuation\">,<\/span> IMG_W<span class=\"token punctuation\">,<\/span> IMG_H<span class=\"token punctuation\">,<\/span> BATCH_SIZE<span class=\"token punctuation\">,<\/span> CAPACITY<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u6d4b\u8bd5\u6570\u636e\u53ca\u6807\u7b7e<\/span> val_batch<span class=\"token punctuation\">,<\/span> val_label_batch <span class=\"token operator\">=<\/span> input_data<span class=\"token punctuation\">.<\/span>get_batch<span class=\"token punctuation\">(<\/span>val<span class=\"token punctuation\">,<\/span> val_label<span class=\"token punctuation\">,<\/span> IMG_W<span class=\"token punctuation\">,<\/span> IMG_H<span class=\"token punctuation\">,<\/span> BATCH_SIZE<span class=\"token punctuation\">,<\/span> CAPACITY<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u8bad\u7ec3\u64cd\u4f5c\u5b9a\u4e49<\/span> train_logits <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">.<\/span>inference<span class=\"token punctuation\">(<\/span>train_batch<span class=\"token punctuation\">,<\/span> BATCH_SIZE<span class=\"token punctuation\">,<\/span> N_CLASSES<span class=\"token punctuation\">)<\/span> train_loss <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">.<\/span>losses<span class=\"token punctuation\">(<\/span>train_logits<span class=\"token punctuation\">,<\/span> train_label_batch<span class=\"token punctuation\">)<\/span> train_op <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">.<\/span>trainning<span class=\"token punctuation\">(<\/span>train_loss<span class=\"token punctuation\">,<\/span> learning_rate<span class=\"token punctuation\">)<\/span> train_acc <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">.<\/span>evaluation<span class=\"token punctuation\">(<\/span>train_logits<span class=\"token punctuation\">,<\/span> train_label_batch<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u6d4b\u8bd5\u64cd\u4f5c\u5b9a\u4e49<\/span> test_logits <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">.<\/span>inference<span class=\"token punctuation\">(<\/span>val_batch<span class=\"token punctuation\">,<\/span> BATCH_SIZE<span class=\"token punctuation\">,<\/span> N_CLASSES<span class=\"token punctuation\">)<\/span> test_loss <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">.<\/span>losses<span class=\"token punctuation\">(<\/span>test_logits<span class=\"token punctuation\">,<\/span> val_label_batch<span class=\"token punctuation\">)<\/span> test_acc <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">.<\/span>evaluation<span class=\"token punctuation\">(<\/span>test_logits<span class=\"token punctuation\">,<\/span> val_label_batch<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u8fd9\u4e2a\u662flog\u6c47\u603b\u8bb0\u5f55<\/span> summary_op <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>summary<span class=\"token punctuation\">.<\/span>merge_all<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u4ea7\u751f\u4e00\u4e2a\u4f1a\u8bdd<\/span> sess <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>Session<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u4ea7\u751f\u4e00\u4e2awriter\u6765\u5199log\u6587\u4ef6<\/span> train_writer <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>summary<span class=\"token punctuation\">.<\/span>FileWriter<span class=\"token punctuation\">(<\/span>logs_train_dir<span class=\"token punctuation\">,<\/span> sess<span class=\"token punctuation\">.<\/span>graph<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># val_writer = tf.summary.FileWriter(logs_test_dir, sess.graph)<\/span> <span class=\"token comment\"># \u4ea7\u751f\u4e00\u4e2asaver\u6765\u5b58\u50a8\u8bad\u7ec3\u597d\u7684\u6a21\u578b<\/span> saver <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>train<span class=\"token punctuation\">.<\/span>Saver<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u6240\u6709\u8282\u70b9\u521d\u59cb\u5316<\/span> sess<span class=\"token punctuation\">.<\/span>run<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>global_variables_initializer<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u961f\u5217\u76d1\u63a7<\/span> coord <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>train<span class=\"token punctuation\">.<\/span>Coordinator<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> threads <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>train<span class=\"token punctuation\">.<\/span>start_queue_runners<span class=\"token punctuation\">(<\/span>sess<span class=\"token operator\">=<\/span>sess<span class=\"token punctuation\">,<\/span> coord<span class=\"token operator\">=<\/span>coord<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u8fdb\u884cbatch\u7684\u8bad\u7ec3<\/span> <span class=\"token keyword\">try<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token comment\"># \u6267\u884cMAX_STEP\u6b65\u7684\u8bad\u7ec3\uff0c\u4e00\u6b65\u4e00\u4e2abatch<\/span> <span class=\"token keyword\">for<\/span> step <span class=\"token keyword\">in<\/span> np<span class=\"token punctuation\">.<\/span>arange<span class=\"token punctuation\">(<\/span>MAX_STEP<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token keyword\">if<\/span> coord<span class=\"token punctuation\">.<\/span>should_stop<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token keyword\">break<\/span> _<span class=\"token punctuation\">,<\/span> tra_loss<span class=\"token punctuation\">,<\/span> tra_acc <span class=\"token operator\">=<\/span> sess<span class=\"token punctuation\">.<\/span>run<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span>train_op<span class=\"token punctuation\">,<\/span> train_loss<span class=\"token punctuation\">,<\/span> train_acc<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u6bcf\u969450\u6b65\u6253\u5370\u4e00\u6b21\u5f53\u524d\u7684loss\u4ee5\u53caacc\uff0c\u540c\u65f6\u8bb0\u5f55log\uff0c\u5199\u5165writer<\/span> <span class=\"token keyword\">if<\/span> step <span class=\"token operator\">%<\/span> <span class=\"token number\">10<\/span> <span class=\"token operator\">==<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'Step %d, train loss = %.2f, train accuracy = %.2f%%'<\/span> <span class=\"token operator\">%<\/span> <span class=\"token punctuation\">(<\/span>step<span class=\"token punctuation\">,<\/span> tra_loss<span class=\"token punctuation\">,<\/span> tra_acc <span class=\"token operator\">*<\/span> <span class=\"token number\">100.0<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> summary_str <span class=\"token operator\">=<\/span> sess<span class=\"token punctuation\">.<\/span>run<span class=\"token punctuation\">(<\/span>summary_op<span class=\"token punctuation\">)<\/span> train_writer<span class=\"token punctuation\">.<\/span>add_summary<span class=\"token punctuation\">(<\/span>summary_str<span class=\"token punctuation\">,<\/span> step<span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># \u6bcf\u9694100\u6b65\uff0c\u4fdd\u5b58\u4e00\u6b21\u8bad\u7ec3\u597d\u7684\u6a21\u578b<\/span> <span class=\"token keyword\">if<\/span> <span class=\"token punctuation\">(<\/span>step <span class=\"token operator\">+<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">==<\/span> MAX_STEP<span class=\"token punctuation\">:<\/span> checkpoint_path <span class=\"token operator\">=<\/span> os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span>join<span class=\"token punctuation\">(<\/span>logs_train_dir<span class=\"token punctuation\">,<\/span> <span class=\"token string\">'model.ckpt'<\/span><span class=\"token punctuation\">)<\/span> saver<span class=\"token punctuation\">.<\/span>save<span class=\"token punctuation\">(<\/span>sess<span class=\"token punctuation\">,<\/span> checkpoint_path<span class=\"token punctuation\">,<\/span> global_step<span class=\"token operator\">=<\/span>step<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">except<\/span> tf<span class=\"token punctuation\">.<\/span>errors<span class=\"token punctuation\">.<\/span>OutOfRangeError<span class=\"token punctuation\">:<\/span> <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'Done training -- epoch limit reached'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">finally<\/span><span class=\"token punctuation\">:<\/span> coord<span class=\"token punctuation\">.<\/span>request_stop<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <\/code><\/pre>\n<p><strong>test.py\u2014\u2014\u5229\u7528D:\/ML\/flower\/flower_photos\/roses\u8def\u5f84\u4e0b\u7684\u6d4b\u8bd5\u6570\u636e\uff0c\u67e5\u770b\u8bc6\u522b\u6548\u679c<\/strong><\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt <span class=\"token keyword\">import<\/span> model <span class=\"token keyword\">from<\/span> input_data <span class=\"token keyword\">import<\/span> get_files <span class=\"token comment\"># \u83b7\u53d6\u4e00\u5f20\u56fe\u7247<\/span> <span class=\"token keyword\">def<\/span> <span class=\"token function\">get_one_image<\/span><span class=\"token punctuation\">(<\/span>train<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token comment\"># \u8f93\u5165\u53c2\u6570\uff1atrain,\u8bad\u7ec3\u56fe\u7247\u7684\u8def\u5f84<\/span> <span class=\"token comment\"># \u8fd4\u56de\u53c2\u6570\uff1aimage\uff0c\u4ece\u8bad\u7ec3\u56fe\u7247\u4e2d\u968f\u673a\u62bd\u53d6\u4e00\u5f20\u56fe\u7247<\/span> n <span class=\"token operator\">=<\/span> <span class=\"token builtin\">len<\/span><span class=\"token punctuation\">(<\/span>train<span class=\"token punctuation\">)<\/span> ind <span class=\"token operator\">=<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>randint<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">,<\/span> n<span class=\"token punctuation\">)<\/span> img_dir <span class=\"token operator\">=<\/span> train<span class=\"token punctuation\">[<\/span>ind<span class=\"token punctuation\">]<\/span> <span class=\"token comment\"># \u968f\u673a\u9009\u62e9\u6d4b\u8bd5\u7684\u56fe\u7247<\/span> img <span class=\"token operator\">=<\/span> Image<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">open<\/span><span class=\"token punctuation\">(<\/span>img_dir<span class=\"token punctuation\">)<\/span> plt<span class=\"token punctuation\">.<\/span>imshow<span class=\"token punctuation\">(<\/span>img<span class=\"token punctuation\">)<\/span> plt<span class=\"token punctuation\">.<\/span>show<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> image <span class=\"token operator\">=<\/span> np<span class=\"token punctuation\">.<\/span>array<span class=\"token punctuation\">(<\/span>img<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">return<\/span> image <span class=\"token comment\"># \u6d4b\u8bd5\u56fe\u7247<\/span> <span class=\"token keyword\">def<\/span> <span class=\"token function\">evaluate_one_image<\/span><span class=\"token punctuation\">(<\/span>image_array<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token keyword\">with<\/span> tf<span class=\"token punctuation\">.<\/span>Graph<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>as_default<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span> BATCH_SIZE <span class=\"token operator\">=<\/span> <span class=\"token number\">1<\/span> N_CLASSES <span class=\"token operator\">=<\/span> <span class=\"token number\">4<\/span> image <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>cast<span class=\"token punctuation\">(<\/span>image_array<span class=\"token punctuation\">,<\/span> tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> image <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>image<span class=\"token punctuation\">.<\/span>per_image_standardization<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span> image <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>reshape<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> logit <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">.<\/span>inference<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> BATCH_SIZE<span class=\"token punctuation\">,<\/span> N_CLASSES<span class=\"token punctuation\">)<\/span> logit <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>softmax<span class=\"token punctuation\">(<\/span>logit<span class=\"token punctuation\">)<\/span> x <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>placeholder<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">,<\/span> shape<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token comment\"># you need to change the directories to yours.<\/span> logs_train_dir <span class=\"token operator\">=<\/span> <span class=\"token string\">'F:\/save\/'<\/span> saver <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>train<span class=\"token punctuation\">.<\/span>Saver<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">with<\/span> tf<span class=\"token punctuation\">.<\/span>Session<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">as<\/span> sess<span class=\"token punctuation\">:<\/span> <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">\"Reading checkpoints...\"<\/span><span class=\"token punctuation\">)<\/span> ckpt <span class=\"token operator\">=<\/span> tf<span class=\"token punctuation\">.<\/span>train<span class=\"token punctuation\">.<\/span>get_checkpoint_state<span class=\"token punctuation\">(<\/span>logs_train_dir<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">if<\/span> ckpt <span class=\"token keyword\">and<\/span> ckpt<span class=\"token punctuation\">.<\/span>model_checkpoint_path<span class=\"token punctuation\">:<\/span> global_step <span class=\"token operator\">=<\/span> ckpt<span class=\"token punctuation\">.<\/span>model_checkpoint_path<span class=\"token punctuation\">.<\/span>split<span class=\"token punctuation\">(<\/span><span class=\"token string\">'\/'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">[<\/span><span class=\"token operator\">-<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>split<span class=\"token punctuation\">(<\/span><span class=\"token string\">'-'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">[<\/span><span class=\"token operator\">-<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span> saver<span class=\"token punctuation\">.<\/span>restore<span class=\"token punctuation\">(<\/span>sess<span class=\"token punctuation\">,<\/span> ckpt<span class=\"token punctuation\">.<\/span>model_checkpoint_path<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'Loading success, global_step is %s'<\/span> <span class=\"token operator\">%<\/span> global_step<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">else<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'No checkpoint file found'<\/span><span class=\"token punctuation\">)<\/span> prediction <span class=\"token operator\">=<\/span> sess<span class=\"token punctuation\">.<\/span>run<span class=\"token punctuation\">(<\/span>logit<span class=\"token punctuation\">,<\/span> feed_dict<span class=\"token operator\">=<\/span><span class=\"token punctuation\">{ \n   <!-- --><\/span>x<span class=\"token punctuation\">:<\/span> image_array<span class=\"token punctuation\">}<\/span><span class=\"token punctuation\">)<\/span> max_index <span class=\"token operator\">=<\/span> np<span class=\"token punctuation\">.<\/span>argmax<span class=\"token punctuation\">(<\/span>prediction<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">if<\/span> max_index <span class=\"token operator\">==<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">:<\/span> result <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token string\">'\u8fd9\u662f\u73ab\u7470\u82b1\u7684\u53ef\u80fd\u6027\u4e3a\uff1a %.6f'<\/span> <span class=\"token operator\">%<\/span> prediction<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">elif<\/span> max_index <span class=\"token operator\">==<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">:<\/span> result <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token string\">'\u8fd9\u662f\u90c1\u91d1\u9999\u7684\u53ef\u80fd\u6027\u4e3a\uff1a %.6f'<\/span> <span class=\"token operator\">%<\/span> prediction<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">elif<\/span> max_index <span class=\"token operator\">==<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">:<\/span> result <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token string\">'\u8fd9\u662f\u84b2\u516c\u82f1\u7684\u53ef\u80fd\u6027\u4e3a\uff1a %.6f'<\/span> <span class=\"token operator\">%<\/span> prediction<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">else<\/span><span class=\"token punctuation\">:<\/span> result <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token string\">'\u8fd9\u662f\u8fd9\u662f\u5411\u65e5\u8475\u7684\u53ef\u80fd\u6027\u4e3a\uff1a %.6f'<\/span> <span class=\"token operator\">%<\/span> prediction<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">return<\/span> result <span class=\"token comment\"># ------------------------------------------------------------------------<\/span> <span class=\"token keyword\">if<\/span> __name__ <span class=\"token operator\">==<\/span> <span class=\"token string\">'__main__'<\/span><span class=\"token punctuation\">:<\/span> img <span class=\"token operator\">=<\/span> Image<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">open<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'F:\/input_data\/dandelion\/1451samples2.jpg'<\/span><span class=\"token punctuation\">)<\/span> plt<span class=\"token punctuation\">.<\/span>imshow<span class=\"token punctuation\">(<\/span>img<span class=\"token punctuation\">)<\/span> plt<span class=\"token punctuation\">.<\/span>show<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> imag <span class=\"token operator\">=<\/span> img<span class=\"token punctuation\">.<\/span>resize<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">64<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> image <span class=\"token operator\">=<\/span> np<span class=\"token punctuation\">.<\/span>array<span class=\"token punctuation\">(<\/span>imag<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>evaluate_one_image<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> <\/code><\/pre>\n<h2>5 \u9879\u76ee\u6267\u884c\u7ed3\u679c<\/h2>\n<p><strong>\u6267\u884ctrain\u6a21\u5757\uff0c\u7ed3\u679c\u5982\u4e0b\uff1a<\/strong><br \/> <img decoding=\"async\" src=\"https:\/\/img-blog.csdnimg.cn\/73e18f3b9d3f4f30bb57092da9888e1a.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9 (https:\/\/mushiming.com\/)  \u7b2c7\u5f20\" title=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9  \u7b2c7\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><br \/> \u540c\u65f6\uff0c\u8bad\u7ec3\u7ed3\u675f\u540e\uff0c\u5728\u7535\u8111\u6307\u5b9a\u7684\u8bad\u7ec3\u6a21\u578b\u5b58\u50a8\u8def\u5f84\u53ef\u770b\u5230\u4fdd\u5b58\u7684\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6570\u636e\u3002<br \/> <img decoding=\"async\" src=\"https:\/\/img-blog.csdnimg.cn\/895b355b39c2480fa98387ab9f4b6d24.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9 (https:\/\/mushiming.com\/)  \u7b2c8\u5f20\" title=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9  \u7b2c8\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><\/p>\n<p><strong>\u6267\u884ctest\u6a21\u5757\uff0c\u7ed3\u679c\u5982\u4e0b\uff1a<\/strong><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/img-blog.csdnimg.cn\/2c9e9c5cffc5470c93b688fec0d686c1.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9 (https:\/\/mushiming.com\/)  \u7b2c9\u5f20\" title=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9  \u7b2c9\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><br \/> \u5173\u95ed\u663e\u793a\u7684\u6d4b\u8bd5\u56fe\u7247\u540e\uff0cconsole\u67e5\u770b\u6d4b\u8bd5\u7ed3\u679c\u5982\u4e0b\uff1a<br \/> <img decoding=\"async\" src=\"https:\/\/img-blog.csdnimg.cn\/073491de9c394dd39e50e881111317c1.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9 (https:\/\/mushiming.com\/)  \u7b2c10\u5f20\" title=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9  \u7b2c10\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><\/p>\n<p><strong>\u505a\u4e00\u4e2aGUI\u4ea4\u4e92\u754c\u9762<\/strong><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/img-blog.csdnimg.cn\/f90545a05db445cfb71c01c0c1f7827d.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9 (https:\/\/mushiming.com\/)  \u7b2c11\u5f20\" title=\"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9  \u7b2c11\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><\/p>\n<h2>6 \u6700\u540e<\/h2>\n","protected":false},"excerpt":{"rendered":"\u3010\u6bd5\u4e1a\u8bbe\u8ba1\u3011\u6df1\u5ea6\u5b66\u4e60\u82b1\u5349\u8bc6\u522b\u7cfb\u7edf - \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u673a\u5668\u89c6\u89c9\ud83d\udd25Hi\uff0c\u5927\u5bb6\u597d\uff0c\u8fd9\u91cc\u662f\u4e39\u6210\u5b66\u957f\u7684\u6bd5\u8bbe\u7cfb\u5217\u6587\u7ae0\uff01\ud83d\udd25\u5bf9\u6bd5\u8bbe\u6709\u4efb\u4f55\u7591\u95ee...","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"_links":{"self":[{"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/posts\/4883"}],"collection":[{"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/comments?post=4883"}],"version-history":[{"count":0,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/posts\/4883\/revisions"}],"wp:attachment":[{"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/media?parent=4883"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/categories?post=4883"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/tags?post=4883"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}