{"id":57611,"date":"2025-08-15T16:19:57","date_gmt":"2025-08-15T08:19:57","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/57611.html"},"modified":"2025-08-15T16:19:57","modified_gmt":"2025-08-15T08:19:57","slug":"%e5%8d%b7%e7%a7%af%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%ef%bc%88cnn%ef%bc%89%e5%ad%a6%e4%b9%a0%e7%ac%94%e8%ae%b0","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/57611.html","title":{"rendered":"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u5b66\u4e60\u7b14\u8bb0"},"content":{"rendered":"<h3>1. CNN \u7684\u76f4\u89c2\u7406\u89e3&#xff1a;\u4e00\u4e2a\u201c\u4f1a\u81ea\u5df1\u5b66\u7279\u5f81\u7684\u6444\u50cf\u961f\u201d<\/h3>\n<p>\u60f3\u8c61\u4e00\u4e0b&#xff0c;\u5982\u679c\u4f60\u8981\u62cd\u6444\u4e00\u90e8\u5173\u4e8e\u201c\u8bc6\u522b\u7269\u4f53\u201d\u7684\u7eaa\u5f55\u7247&#xff0c;\u4f60\u4f1a\u600e\u4e48\u505a&#xff1f;\u4f20\u7edf\u7684\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u53ef\u80fd\u9700\u8981\u4f60\u624b\u52a8\u544a\u8bc9\u6444\u50cf\u5e08&#xff1a;\u201c\u5148\u62cd\u8fb9\u7f18&#xff0c;\u518d\u62cd\u989c\u8272&#xff0c;\u7136\u540e\u628a\u8fd9\u4e9b\u4fe1\u606f\u7ec4\u5408\u8d77\u6765\u3002\u201d\u800c\u5377\u79ef\u795e\u7ecf\u7f51\u7edc&#xff08;CNN&#xff09;\u5219\u50cf\u4e00\u652f\u62e5\u6709\u201c\u5b66\u4e60\u80fd\u529b\u201d\u7684\u4e13\u4e1a\u6444\u50cf\u961f&#xff0c;\u5b83\u80fd\u81ea\u5df1\u6478\u7d22\u51fa\u6700\u4f73\u7684\u62cd\u6444\u548c\u526a\u8f91\u624b\u6cd5\u3002<\/p>\n<ul>\n<li>\n<p>\u5377\u79ef\u5c42&#xff08;Convolutional Layer&#xff09;&#xff1a;\u8fd9\u5c31\u50cf\u6444\u50cf\u961f\u7684\u201c\u7279\u5199\u955c\u5934\u201d\u3002\u5b83\u4f1a\u81ea\u52a8\u6355\u6349\u753b\u9762\u4e2d\u7684\u8fb9\u7f18\u3001\u7eb9\u7406\u3001\u989c\u8272\u3001\u5f62\u72b6\u7b49\u5c40\u90e8\u7ec6\u8282\u4fe1\u606f\u3002\u800c\u4e14&#xff0c;\u8fd9\u4e9b\u201c\u7279\u5199\u955c\u5934\u201d\u7684\u62cd\u6444\u65b9\u5f0f&#xff08;\u5373\u5377\u79ef\u6838\u7684\u53c2\u6570&#xff09;\u662f\u53ef\u4ee5\u901a\u8fc7\u8bad\u7ec3\u81ea\u52a8\u5b66\u4e60\u548c\u4f18\u5316\u7684&#xff0c;\u65e0\u9700\u4eba\u5de5\u5e72\u9884\u3002<\/p>\n<\/li>\n<li>\n<p>\u591a\u5c42\u53e0\u52a0&#xff1a;\u4e00\u652f\u4f18\u79c0\u7684\u6444\u50cf\u961f\u4e0d\u4f1a\u53ea\u7528\u4e00\u4e2a\u955c\u5934\u62cd\u5230\u5e95\u3002CNN \u4e5f\u662f\u5982\u6b64&#xff0c;\u5b83\u7531\u591a\u5c42\u5377\u79ef\u5c42\u5806\u53e0\u800c\u6210\u3002\u8fd9\u5c31\u50cf\u62cd\u6444\u56e2\u961f\u7684\u9ed8\u5951\u914d\u5408&#xff0c;\u4ece\u6700\u521d\u6355\u6349\u7ec6\u5fae\u7684\u50cf\u7d20\u53d8\u5316&#xff08;\u4f4e\u7ea7\u7279\u5f81&#xff09;&#xff0c;\u9010\u6b65\u7ec4\u5408\u6210\u66f4\u590d\u6742\u7684\u5c40\u90e8\u7ed3\u6784&#xff08;\u4e2d\u7ea7\u7279\u5f81&#xff09;&#xff0c;\u6700\u7ec8\u7406\u89e3\u6574\u4e2a\u573a\u666f\u7684\u5b8f\u89c2\u5e03\u5c40&#xff08;\u9ad8\u7ea7\u8bed\u4e49\u7279\u5f81&#xff09;\u3002<\/p>\n<\/li>\n<li>\n<p>\u6c60\u5316\u5c42&#xff08;Pooling Layer&#xff09;&#xff1a;\u8fd9\u597d\u6bd4\u6444\u50cf\u961f\u5728\u62cd\u6444\u5b8c\u5927\u91cf\u7d20\u6750\u540e&#xff0c;\u8fdb\u884c\u201c\u7f29\u7565\u526a\u8f91\u201d\u3002\u5b83\u4f1a\u628a\u6bcf\u4e2a\u7279\u5199\u955c\u5934\u4e2d\u6700\u91cd\u8981\u3001\u6700\u5177\u4ee3\u8868\u6027\u7684\u4fe1\u606f\u63d0\u53d6\u51fa\u6765\u5e76\u538b\u7f29&#xff0c;\u540c\u65f6\u5ffd\u7565\u6389\u4e00\u4e9b\u7ec6\u679d\u672b\u8282\u7684\u4f4d\u7f6e\u4fe1\u606f\u3002\u8fd9\u6837\u65e2\u80fd\u51cf\u5c11\u6570\u636e\u91cf&#xff0c;\u53c8\u80fd\u8ba9\u6a21\u578b\u5bf9\u7269\u4f53\u4f4d\u7f6e\u7684\u5fae\u5c0f\u53d8\u5316\u4e0d\u90a3\u4e48\u654f\u611f\u3002<\/p>\n<\/li>\n<li>\n<p>\u5168\u8fde\u63a5\u5c42&#xff08;Fully Connected Layer&#xff09;&#xff1a;\u6700\u540e&#xff0c;\u6240\u6709\u7684\u955c\u5934\u7d20\u6750\u90fd\u4ea4\u5230\u4e86\u201c\u5bfc\u6f14\u201d\u624b\u4e2d\u3002\u5168\u8fde\u63a5\u5c42\u5c31\u50cf\u8fd9\u4f4d\u5bfc\u6f14&#xff0c;\u5b83\u4f1a\u7efc\u5408\u6240\u6709\u7279\u5199\u548c\u7f29\u7565\u955c\u5934\u7684\u4fe1\u606f&#xff0c;\u8fdb\u884c\u6700\u7ec8\u7684\u603b\u7ed3\u548c\u5224\u65ad&#xff0c;\u6bd4\u5982\u5224\u65ad\u753b\u9762\u4e2d\u662f\u201c\u732b\u201d\u8fd8\u662f\u201c\u72d7\u201d&#xff0c;\u6216\u8005\u8bc6\u522b\u51fa\u753b\u9762\u4e2d\u7684\u5177\u4f53\u7269\u4f53\u4f4d\u7f6e\u3002\u8fd9\u5c31\u662f CNN \u8fdb\u884c\u5206\u7c7b\u3001\u68c0\u6d4b\u7b49\u4efb\u52a1\u7684\u6700\u7ec8\u51b3\u7b56\u8fc7\u7a0b\u3002<\/p>\n<\/li>\n<\/ul>\n<h3>2. CNN \u7684\u6838\u5fc3\u601d\u60f3&#xff1a;\u81ea\u52a8\u7279\u5f81\u63d0\u53d6\u7684\u201c\u6ee4\u955c\u201d\u9b54\u6cd5<\/h3>\n<p>CNN \u4e4b\u6240\u4ee5\u5f3a\u5927&#xff0c;\u5176\u6838\u5fc3\u5728\u4e8e\u201c\u5377\u79ef&#xff08;Convolution&#xff09;\u201d\u64cd\u4f5c\u3002\u5b83\u4e0d\u518d\u9700\u8981\u6211\u4eec\u624b\u52a8\u8bbe\u8ba1\u590d\u6742\u7684\u7279\u5f81\u63d0\u53d6\u5668&#xff0c;\u800c\u662f\u8ba9\u7f51\u7edc\u81ea\u5df1\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u5982\u4f55\u63d0\u53d6\u6709\u7528\u7684\u7279\u5f81\u3002<\/p>\n<h4>2.1 \u5377\u79ef\u64cd\u4f5c&#xff1a;\u6ed1\u52a8\u201c\u6ee4\u955c\u201d\u6355\u6349\u5c40\u90e8\u4fe1\u606f<\/h4>\n<p>\u5377\u79ef\u64cd\u4f5c\u5c31\u50cf\u7528\u4e00\u4e2a\u5c0f\u7684\u201c\u6ee4\u955c\u201d&#xff08;\u4e5f\u53eb\u5377\u79ef\u6838\u6216\u6838 \/ kernel&#xff09;\u5728\u56fe\u7247\u4e0a\u4ece\u5de6\u5230\u53f3\u3001\u4ece\u4e0a\u5230\u4e0b\u6ed1\u52a8\u3002\u6bcf\u6ed1\u52a8\u5230\u4e00\u4e2a\u4f4d\u7f6e&#xff0c;\u6ee4\u955c\u5c31\u4f1a\u4e0e\u56fe\u7247\u5bf9\u5e94\u533a\u57df\u7684\u50cf\u7d20\u8fdb\u884c\u4e58\u52a0\u8fd0\u7b97&#xff0c;\u7136\u540e\u628a\u7ed3\u679c\u4f5c\u4e3a\u4e00\u4e2a\u65b0\u7684\u50cf\u7d20\u503c\u8f93\u51fa\u5230\u4e00\u5f20\u65b0\u7684\u56fe\u7247\u4e0a&#xff0c;\u8fd9\u5f20\u65b0\u56fe\u7247\u5c31\u662f\u7279\u5f81\u56fe&#xff08;Feature Map&#xff09;\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u76f8\u5f53\u4e8e\u5728\u539f\u56fe\u4e2d\u5bfb\u627e\u5e76\u7a81\u51fa\u67d0\u79cd\u7279\u5b9a\u7684\u5c40\u90e8\u6a21\u5f0f\u3002<\/p>\n<h4>2.2 \u53ef\u5b66\u4e60\u7684\u201c\u6ee4\u955c\u201d\u53c2\u6570<\/h4>\n<p>\u6700\u5173\u952e\u7684\u662f&#xff0c;\u8fd9\u4e9b\u201c\u6ee4\u955c\u201d\u91cc\u7684\u53c2\u6570&#xff08;\u4e5f\u5c31\u662f\u5377\u79ef\u6838\u4e2d\u7684\u6570\u503c&#xff09;\u4e0d\u662f\u6211\u4eec\u9884\u5148\u8bbe\u5b9a\u597d\u7684&#xff0c;\u800c\u662f\u53ef\u5b66\u4e60\u7684\u3002\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d&#xff0c;CNN \u4f1a\u81ea\u52a8\u8c03\u6574\u8fd9\u4e9b\u53c2\u6570&#xff0c;\u4f7f\u5f97\u4e0d\u540c\u7684\u5377\u79ef\u6838\u80fd\u591f\u5b66\u4e60\u5230\u4e0d\u540c\u7684\u7279\u5f81&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u6709\u7684\u5377\u79ef\u6838\u53ef\u80fd\u5b66\u4f1a\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u8fb9\u7f18&#xff08;\u6bd4\u5982\u6c34\u5e73\u8fb9\u7f18\u3001\u5782\u76f4\u8fb9\u7f18&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u6709\u7684\u53ef\u80fd\u4e13\u6ce8\u4e8e\u6355\u6349\u7eb9\u7406&#xff08;\u6bd4\u5982\u7c97\u7cd9\u7684\u8868\u9762\u3001\u5149\u6ed1\u7684\u8868\u9762&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u8fd8\u6709\u7684\u53ef\u80fd\u64c5\u957f\u8bc6\u522b\u7279\u5b9a\u7684\u989c\u8272\u6a21\u5f0f\u6216\u5f62\u72b6\u7ec4\u5408\u3002<\/p>\n<\/li>\n<\/ul>\n<h4>2.3 \u5377\u79ef\u64cd\u4f5c\u7684\u4e24\u5927\u4f18\u52bf<\/h4>\n<p>\u5377\u79ef\u64cd\u4f5c\u4e4b\u6240\u4ee5\u9ad8\u6548\u4e14\u9002\u7528\u4e8e\u56fe\u50cf\u5904\u7406&#xff0c;\u5f97\u76ca\u4e8e\u5176\u4e24\u5927\u7279\u6027&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u6743\u503c\u5171\u4eab&#xff08;Weight Sharing&#xff09;&#xff1a;\u540c\u4e00\u4e2a\u5377\u79ef\u6838\u4f1a\u5728\u6574\u5f20\u56fe\u7247\u4e0a\u6ed1\u52a8\u5e76\u91cd\u590d\u4f7f\u7528\u3002\u8fd9\u610f\u5473\u7740\u6211\u4eec\u4e0d\u9700\u8981\u4e3a\u56fe\u7247\u4e2d\u7684\u6bcf\u4e2a\u4f4d\u7f6e\u90fd\u5b66\u4e60\u4e00\u4e2a\u72ec\u7acb\u7684\u7279\u5f81\u68c0\u6d4b\u5668&#xff0c;\u5927\u5927\u51cf\u5c11\u4e86\u6a21\u578b\u7684\u53c2\u6570\u91cf\u3002\u53c2\u6570\u5c11\u4e86&#xff0c;\u6a21\u578b\u8bad\u7ec3\u8d77\u6765\u5c31\u66f4\u5bb9\u6613&#xff0c;\u4e5f\u66f4\u4e0d\u5bb9\u6613\u8fc7\u62df\u5408\u3002<\/p>\n<\/li>\n<li>\n<p>\u5c40\u90e8\u8fde\u63a5&#xff08;Local Connectivity&#xff09;&#xff1a;\u6bcf\u4e2a\u795e\u7ecf\u5143&#xff08;\u7279\u5f81\u56fe\u4e0a\u7684\u4e00\u4e2a\u70b9&#xff09;\u53ea\u4e0e\u8f93\u5165\u56fe\u7247\u7684\u4e00\u4e2a\u5c40\u90e8\u533a\u57df\u76f8\u8fde\u63a5&#xff0c;\u800c\u4e0d\u662f\u4e0e\u6574\u5f20\u56fe\u7247\u7684\u6240\u6709\u50cf\u7d20\u76f8\u8fde\u63a5\u3002\u8fd9\u6a21\u4eff\u4e86\u4eba\u7c7b\u89c6\u89c9\u7cfb\u7edf\u7684\u5de5\u4f5c\u65b9\u5f0f\u2014\u2014\u6211\u4eec\u901a\u5e38\u4e5f\u662f\u5148\u5173\u6ce8\u5c40\u90e8\u7ec6\u8282&#xff0c;\u518d\u5c06\u8fd9\u4e9b\u5c40\u90e8\u4fe1\u606f\u7ec4\u5408\u8d77\u6765\u7406\u89e3\u6574\u4f53\u3002\u8fd9\u79cd\u5c40\u90e8\u6027\u4f7f\u5f97\u7f51\u7edc\u80fd\u591f\u9ad8\u6548\u5730\u6355\u6349\u5230\u56fe\u50cf\u7684\u5c40\u90e8\u7279\u5f81\u3002<\/p>\n<\/li>\n<\/ul>\n<h3>3. CNN \u7684\u201c\u6d41\u6c34\u7ebf\u201d&#xff1a;\u7f51\u7edc\u7ed3\u6784\u6a21\u5757\u62c6\u89e3<\/h3>\n<p>\u4e00\u4e2a\u5178\u578b\u7684 CNN \u6a21\u578b\u5c31\u50cf\u4e00\u6761\u9ad8\u6548\u7684\u751f\u4ea7\u7ebf&#xff0c;\u6bcf\u4e2a\u73af\u8282\u90fd\u6709\u5176\u72ec\u7279\u7684\u529f\u80fd&#xff1a;<\/p>\n<h4>3.1 \u5377\u79ef\u5c42&#xff08;Convolutional Layer&#xff09;<\/h4>\n<ul>\n<li>\n<p>\u8f93\u5165&#xff1a;\u901a\u5e38\u662f\u539f\u59cb\u56fe\u50cf&#xff08;\u4f8b\u5982&#xff0c;RGB \u5f69\u8272\u56fe\u50cf\u4f1a\u6709\u7ea2\u3001\u7eff\u3001\u84dd\u4e09\u4e2a\u901a\u9053&#xff09;&#xff0c;\u6216\u8005\u662f\u524d\u4e00\u4e2a\u5377\u79ef\u5c42\u6216\u6c60\u5316\u5c42\u8f93\u51fa\u7684\u7279\u5f81\u56fe\u3002<\/p>\n<\/li>\n<li>\n<p>\u64cd\u4f5c&#xff1a;\u5e94\u7528\u4e00\u7ec4\u53ef\u5b66\u4e60\u7684\u5377\u79ef\u6838\u5728\u8f93\u5165\u4e0a\u6ed1\u52a8&#xff0c;\u6267\u884c\u5377\u79ef\u8fd0\u7b97&#xff0c;\u63d0\u53d6\u5c40\u90e8\u7279\u5f81\u3002<\/p>\n<\/li>\n<li>\n<p>\u8f93\u51fa&#xff1a;\u751f\u6210\u4e00\u7cfb\u5217\u7279\u5f81\u56fe&#xff08;Feature Map&#xff09;\u3002\u6bcf\u4e2a\u7279\u5f81\u56fe\u90fd\u5bf9\u5e94\u4e00\u4e2a\u5377\u79ef\u6838\u6240\u63d0\u53d6\u5230\u7684\u7279\u5b9a\u7279\u5f81\u7684\u54cd\u5e94\u5f3a\u5ea6\u3002<\/p>\n<\/li>\n<\/ul>\n<h4>3.2 \u6fc0\u6d3b\u5c42&#xff08;Activation Layer&#xff09;<\/h4>\n<ul>\n<li>\n<p>\u5e38\u7528\u6fc0\u6d3b\u51fd\u6570&#xff1a;\u6700\u5e38\u7528\u7684\u662f ReLU&#xff08;Rectified Linear Unit&#xff09;&#xff0c;\u5b83\u7684\u4f5c\u7528\u5f88\u7b80\u5355&#xff1a;\u628a\u6240\u6709\u8d1f\u6570\u90fd\u53d8\u6210 0&#xff0c;\u6b63\u6570\u4fdd\u6301\u4e0d\u53d8\u3002\u6b64\u5916&#xff0c;\u8fd8\u6709 LeakyReLU\u3001Sigmoid\u3001Tanh \u7b49\u3002<\/p>\n<\/li>\n<li>\n<p>\u4f5c\u7528&#xff1a;\u5f15\u5165\u975e\u7ebf\u6027\u3002\u5982\u679c\u6ca1\u6709\u6fc0\u6d3b\u51fd\u6570&#xff0c;\u65e0\u8bba\u7f51\u7edc\u6709\u591a\u5c11\u5c42&#xff0c;\u5b83\u90fd\u53ea\u80fd\u5b66\u4e60\u5230\u7ebf\u6027\u5173\u7cfb&#xff0c;\u8fd9\u9650\u5236\u4e86\u6a21\u578b\u8868\u8fbe\u590d\u6742\u7279\u5f81\u7684\u80fd\u529b\u3002\u5f15\u5165\u975e\u7ebf\u6027\u540e&#xff0c;\u6a21\u578b\u624d\u80fd\u5b66\u4e60\u548c\u8868\u793a\u56fe\u50cf\u4e2d\u66f4\u590d\u6742\u7684\u3001\u975e\u7ebf\u6027\u7684\u7279\u5f81\u6a21\u5f0f&#xff0c;\u6bd4\u5982\u66f2\u7ebf\u3001\u590d\u6742\u7684\u5f62\u72b6\u7ec4\u5408\u7b49\u3002<\/p>\n<\/li>\n<\/ul>\n<h4>3.3 \u6c60\u5316\u5c42&#xff08;Pooling Layer&#xff09;<\/h4>\n<ul>\n<li>\n<p>\u5e38\u89c1\u7c7b\u578b&#xff1a;<\/p>\n<ul>\n<li>\n<p>Max Pooling&#xff08;\u6700\u5927\u6c60\u5316&#xff09;&#xff1a;\u5728\u6c60\u5316\u7a97\u53e3\u5185&#xff0c;\u53ea\u4fdd\u7559\u6700\u5927\u7684\u90a3\u4e2a\u503c\u3002\u8fd9\u5c31\u50cf\u201c\u53bb\u829c\u5b58\u83c1\u201d&#xff0c;\u53ea\u7559\u4e0b\u6700\u663e\u8457\u7684\u7279\u5f81\u3002<\/p>\n<\/li>\n<li>\n<p>Average Pooling&#xff08;\u5e73\u5747\u6c60\u5316&#xff09;&#xff1a;\u5728\u6c60\u5316\u7a97\u53e3\u5185&#xff0c;\u8ba1\u7b97\u6240\u6709\u503c\u7684\u5e73\u5747\u503c\u3002\u8fd9\u63d0\u4f9b\u4e86\u4e00\u79cd\u66f4\u5e73\u6ed1\u7684\u7279\u5f81\u8868\u793a\u3002<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u4f5c\u7528&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u964d\u7ef4&#xff1a;\u663e\u8457\u51cf\u5c11\u7279\u5f81\u56fe\u7684\u5c3a\u5bf8&#xff0c;\u4ece\u800c\u51cf\u5c11\u540e\u7eed\u5c42\u7684\u8ba1\u7b97\u91cf\u548c\u53c2\u6570\u91cf\u3002<\/p>\n<\/li>\n<li>\n<p>\u9632\u6b62\u8fc7\u62df\u5408&#xff1a;\u901a\u8fc7\u51cf\u5c11\u7279\u5f81\u7684\u6570\u91cf&#xff0c;\u964d\u4f4e\u4e86\u6a21\u578b\u7684\u590d\u6742\u5ea6&#xff0c;\u6709\u52a9\u4e8e\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n<\/li>\n<li>\n<p>\u589e\u5f3a\u5e73\u79fb\u4e0d\u53d8\u6027&#xff08;Translation Invariance&#xff09;&#xff1a;\u5373\u4f7f\u56fe\u50cf\u4e2d\u7684\u7269\u4f53\u53d1\u751f\u5fae\u5c0f\u7684\u5e73\u79fb&#xff0c;\u6c60\u5316\u64cd\u4f5c\u4e5f\u80fd\u4fdd\u8bc1\u63d0\u53d6\u5230\u7684\u7279\u5f81\u57fa\u672c\u4e0d\u53d8\u3002\u56e0\u4e3a\u6211\u4eec\u53ea\u5173\u5fc3\u67d0\u4e2a\u7279\u5f81\u662f\u5426\u5b58\u5728\u4e8e\u67d0\u4e2a\u533a\u57df&#xff0c;\u800c\u4e0d\u90a3\u4e48\u5173\u5fc3\u5b83\u7cbe\u786e\u7684\u4f4d\u7f6e\u3002<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4>3.4 \u5168\u8fde\u63a5\u5c42&#xff08;Fully Connected Layer \/ FC Layer&#xff09;<\/h4>\n<ul>\n<li>\n<p>\u4f5c\u7528&#xff1a;\u5728\u7ecf\u8fc7\u591a\u5c42\u5377\u79ef\u548c\u6c60\u5316\u64cd\u4f5c\u540e&#xff0c;\u56fe\u50cf\u7684\u539f\u59cb\u50cf\u7d20\u4fe1\u606f\u5df2\u7ecf\u88ab\u62bd\u8c61\u6210\u4e86\u9ad8\u5c42\u6b21\u7684\u7279\u5f81\u8868\u793a\u3002\u5168\u8fde\u63a5\u5c42\u7684\u4f5c\u7528\u5c31\u662f\u5c06\u8fd9\u4e9b\u9ad8\u7ef4\u7684\u3001\u62bd\u8c61\u7684\u7279\u5f81\u201c\u5c55\u5e73\u201d&#xff0c;\u7136\u540e\u5c06\u5b83\u4eec\u7ec4\u5408\u8d77\u6765&#xff0c;\u6620\u5c04\u5230\u6700\u7ec8\u7684\u8f93\u51fa\u7ed3\u679c\u4e0a\u3002\u4f8b\u5982&#xff0c;\u5728\u5206\u7c7b\u4efb\u52a1\u4e2d&#xff0c;\u5b83\u4f1a\u6839\u636e\u63d0\u53d6\u5230\u7684\u6240\u6709\u7279\u5f81&#xff0c;\u8ba1\u7b97\u51fa\u8f93\u5165\u56fe\u50cf\u5c5e\u4e8e\u6bcf\u4e2a\u7c7b\u522b\u7684\u6982\u7387\u3002<\/p>\n<\/li>\n<\/ul>\n<h4>3.5 Softmax \u5c42&#xff08;\u9488\u5bf9\u5206\u7c7b\u4efb\u52a1&#xff09;<\/h4>\n<ul>\n<li>\n<p>\u4f5c\u7528&#xff1a;\u901a\u5e38\u4f4d\u4e8e\u5168\u8fde\u63a5\u5c42\u7684\u672b\u7aef&#xff0c;\u7279\u522b\u7528\u4e8e\u591a\u5206\u7c7b\u4efb\u52a1\u3002\u5b83\u5c06\u5168\u8fde\u63a5\u5c42\u7684\u8f93\u51fa&#xff08;\u901a\u5e38\u662f\u672a\u7ecf\u5f52\u4e00\u5316\u7684\u5206\u6570&#xff09;\u8f6c\u6362\u6210\u4e00\u4e2a\u6982\u7387\u5206\u5e03\u3002\u6bcf\u4e2a\u8f93\u51fa\u503c\u90fd\u4ecb\u4e8e 0 \u5230 1 \u4e4b\u95f4&#xff0c;\u5e76\u4e14\u6240\u6709\u8f93\u51fa\u503c\u7684\u548c\u4e3a 1\u3002\u8fd9\u4f7f\u5f97\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\u66f4\u6613\u4e8e\u89e3\u91ca\u548c\u51b3\u7b56&#xff0c;\u4f8b\u5982&#xff0c;\u201c\u8fd9\u5f20\u56fe\u7247\u662f\u732b\u7684\u6982\u7387\u662f 90%&#xff0c;\u662f\u72d7\u7684\u6982\u7387\u662f 10%\u3002\u201d<\/p>\n<\/li>\n<\/ul>\n<h3>4. CNN \u7684\u201c\u6210\u957f\u201d&#xff1a;\u5de5\u4f5c\u539f\u7406\u4e3e\u4f8b&#xff08;\u732b\u72d7\u5206\u7c7b&#xff09;<\/h3>\n<p>\u8ba9\u6211\u4eec\u4ee5\u4e00\u4e2a\u7ecf\u5178\u7684\u732b\u72d7\u5206\u7c7b\u4efb\u52a1\u4e3a\u4f8b&#xff0c;\u770b\u770b CNN \u662f\u5982\u4f55\u4e00\u6b65\u6b65\u201c\u7406\u89e3\u201d\u56fe\u50cf\u7684&#xff1a;<\/p>\n<li>\n<p>\u7b2c\u4e00\u5c42\u5377\u79ef&#xff1a;\u5f53\u539f\u59cb\u56fe\u7247\u8f93\u5165\u5230\u7b2c\u4e00\u5c42\u5377\u79ef\u5c42\u65f6&#xff0c;\u5b83\u4f1a\u5b66\u4e60\u5e76\u63d0\u53d6\u6700\u57fa\u7840\u7684\u7279\u5f81&#xff0c;\u6bd4\u5982\u56fe\u50cf\u4e2d\u7684\u8fb9\u7f18&#xff08;\u6c34\u5e73\u3001\u5782\u76f4\u3001\u5bf9\u89d2\u7ebf&#xff09;\u3001\u989c\u8272\u53d8\u5316\u3001\u4ee5\u53ca\u4e00\u4e9b\u7b80\u5355\u7684\u7eb9\u7406\u3002\u8fd9\u4e9b\u88ab\u79f0\u4e3a\u4f4e\u5c42\u7279\u5f81\u3002<\/p>\n<\/li>\n<li>\n<p>\u7b2c\u4e8c\u3001\u4e09\u5c42\u5377\u79ef&#xff1a;\u968f\u7740\u4fe1\u606f\u6df1\u5165\u5230\u66f4\u6df1\u7684\u5377\u79ef\u5c42&#xff0c;\u7f51\u7edc\u5f00\u59cb\u5c06\u8fd9\u4e9b\u4f4e\u5c42\u7279\u5f81\u7ec4\u5408\u8d77\u6765&#xff0c;\u5f62\u6210\u66f4\u590d\u6742\u3001\u66f4\u6709\u610f\u4e49\u7684\u4e2d\u5c42\u7279\u5f81\u3002\u4f8b\u5982&#xff0c;\u5b83\u53ef\u80fd\u4f1a\u8bc6\u522b\u51fa\u773c\u775b\u7684\u5f62\u72b6\u3001\u8033\u6735\u7684\u8f6e\u5ed3\u3001\u9f3b\u5b50\u7684\u7ed3\u6784\u7b49\u5c40\u90e8\u7ec4\u4ef6\u3002<\/p>\n<\/li>\n<li>\n<p>\u66f4\u6df1\u5c42\u5377\u79ef&#xff1a;\u518d\u5f80\u6df1\u5c42\u8d70&#xff0c;\u7f51\u7edc\u4f1a\u8fdb\u4e00\u6b65\u5c06\u4e2d\u5c42\u7279\u5f81\u7ec4\u5408&#xff0c;\u5b66\u4e60\u5230\u66f4\u62bd\u8c61\u3001\u66f4\u5177\u8bed\u4e49\u7684\u9ad8\u5c42\u7279\u5f81\u3002\u4f8b\u5982&#xff0c;\u5b83\u53ef\u80fd\u4f1a\u8bc6\u522b\u51fa\u201c\u6574\u4e2a\u732b\u8138\u201d\u7684\u7ec4\u5408\u6a21\u5f0f&#xff0c;\u6216\u8005\u201c\u72d7\u7684\u8eab\u4f53\u201d\u7684\u6574\u4f53\u7ed3\u6784\u3002\u6b64\u65f6&#xff0c;\u7f51\u7edc\u5df2\u7ecf\u80fd\u591f\u7406\u89e3\u56fe\u50cf\u4e2d\u7684\u9ad8\u7ea7\u8bed\u4e49\u4fe1\u606f\u3002<\/p>\n<\/li>\n<li>\n<p>\u5168\u8fde\u63a5\u5c42&#xff1a;\u6700\u540e&#xff0c;\u8fd9\u4e9b\u4ece\u4f4e\u5230\u9ad8\u5c42\u7ea7\u63d0\u53d6\u5230\u7684\u6240\u6709\u7279\u5f81&#xff08;\u8fb9\u7f18\u3001\u7eb9\u7406\u3001\u773c\u775b\u3001\u8033\u6735\u3001\u732b\u8138\u3001\u72d7\u8eab\u7b49&#xff09;\u90fd\u4f1a\u88ab\u4f20\u9012\u7ed9\u5168\u8fde\u63a5\u5c42\u3002\u5168\u8fde\u63a5\u5c42\u4f1a\u7efc\u5408\u8fd9\u4e9b\u4fe1\u606f&#xff0c;\u8fdb\u884c\u6700\u7ec8\u7684\u5224\u65ad&#xff0c;\u8f93\u51fa\u201c\u8fd9\u662f\u732b\u201d\u6216\u201c\u8fd9\u662f\u72d7\u201d\u7684\u5206\u7c7b\u7ed3\u679c\u3002<\/p>\n<\/li>\n<p>\u8fd9\u4e2a\u8fc7\u7a0b\u5c31\u50cf\u4e00\u4e2a\u4fa6\u63a2&#xff0c;\u4ece\u6700\u7ec6\u5fae\u7684\u7ebf\u7d22&#xff08;\u8fb9\u7f18&#xff09;\u5f00\u59cb&#xff0c;\u9010\u6b65\u62fc\u51d1\u51fa\u5c40\u90e8\u8bc1\u636e&#xff08;\u773c\u775b&#xff09;&#xff0c;\u6700\u7ec8\u5f62\u6210\u5bf9\u6574\u4e2a\u6848\u4ef6&#xff08;\u732b\u6216\u72d7&#xff09;\u7684\u5b8c\u6574\u5224\u65ad\u3002<\/p>\n<h3>5. \u4e3a\u4ec0\u4e48 CNN \u5982\u6b64\u6709\u6548&#xff1f;<\/h3>\n<p>CNN \u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\u53d6\u5f97\u5de8\u5927\u6210\u529f\u5e76\u975e\u5076\u7136&#xff0c;\u8fd9\u5f97\u76ca\u4e8e\u5176\u8bbe\u8ba1\u4e2d\u8574\u542b\u7684\u51e0\u4e2a\u5173\u952e\u601d\u60f3&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u5c40\u90e8\u6027\u5047\u8bbe&#xff08;Locality Assumption&#xff09;&#xff1a;\u56fe\u50cf\u4e2d\u7684\u50cf\u7d20\u70b9\u4e4b\u95f4&#xff0c;\u901a\u5e38\u662f\u76f8\u90bb\u7684\u50cf\u7d20\u70b9\u5173\u7cfb\u66f4\u4e3a\u7d27\u5bc6\u548c\u76f8\u5173&#xff0c;\u800c\u8ddd\u79bb\u8f83\u8fdc\u7684\u50cf\u7d20\u70b9\u4e4b\u95f4\u7684\u76f4\u63a5\u5173\u8054\u6027\u8f83\u5f31\u3002CNN \u7684\u5c40\u90e8\u8fde\u63a5\u7279\u6027\u5b8c\u7f8e\u5951\u5408\u4e86\u8fd9\u4e00\u5047\u8bbe&#xff0c;\u4f7f\u5f97\u7f51\u7edc\u80fd\u591f\u9ad8\u6548\u5730\u6355\u6349\u5230\u56fe\u50cf\u7684\u5c40\u90e8\u7ed3\u6784\u4fe1\u606f\u3002<\/p>\n<\/li>\n<li>\n<p>\u5e73\u79fb\u4e0d\u53d8\u6027&#xff08;Translation Invariance&#xff09;&#xff1a;\u65e0\u8bba\u56fe\u50cf\u4e2d\u7684\u7269\u4f53\u51fa\u73b0\u5728\u54ea\u4e2a\u4f4d\u7f6e&#xff0c;\u6216\u8005\u53d1\u751f\u5fae\u5c0f\u7684\u5e73\u79fb\u3001\u65cb\u8f6c&#xff0c;\u5176\u57fa\u672c\u7279\u5f81&#xff08;\u5982\u8fb9\u7f18\u3001\u7eb9\u7406&#xff09;\u5e76\u4e0d\u4f1a\u53d1\u751f\u592a\u5927\u53d8\u5316\u3002CNN \u901a\u8fc7\u6743\u503c\u5171\u4eab&#xff08;\u540c\u4e00\u4e2a\u5377\u79ef\u6838\u5728\u4e0d\u540c\u4f4d\u7f6e\u68c0\u6d4b\u76f8\u540c\u7279\u5f81&#xff09;\u548c\u6c60\u5316\u64cd\u4f5c&#xff08;\u5bf9\u4f4d\u7f6e\u4fe1\u606f\u8fdb\u884c\u4e00\u5b9a\u7a0b\u5ea6\u7684\u6a21\u7cca&#xff09;&#xff0c;\u4f7f\u5f97\u6a21\u578b\u5bf9\u7269\u4f53\u4f4d\u7f6e\u7684\u5fae\u5c0f\u53d8\u5316\u5177\u6709\u9c81\u68d2\u6027&#xff0c;\u5373\u201c\u5e73\u79fb\u4e0d\u53d8\u6027\u201d\u3002<\/p>\n<\/li>\n<li>\n<p>\u53c2\u6570\u6548\u7387\u9ad8&#xff08;High Parameter Efficiency&#xff09;&#xff1a;\u7531\u4e8e\u6743\u503c\u5171\u4eab\u7684\u7279\u6027&#xff0c;CNN \u7684\u53c2\u6570\u91cf\u76f8\u6bd4\u5168\u8fde\u63a5\u7f51\u7edc\u5927\u5927\u51cf\u5c11\u3002\u4f8b\u5982&#xff0c;\u4e00\u4e2a\u5377\u79ef\u6838\u65e0\u8bba\u5728\u591a\u5927\u7684\u56fe\u50cf\u4e0a\u6ed1\u52a8&#xff0c;\u5176\u81ea\u8eab\u7684\u53c2\u6570\u6570\u91cf\u662f\u56fa\u5b9a\u7684\u3002\u53c2\u6570\u91cf\u7684\u51cf\u5c11\u4e0d\u4ec5\u964d\u4f4e\u4e86\u6a21\u578b\u7684\u5b58\u50a8\u9700\u6c42&#xff0c;\u4e5f\u663e\u8457\u51cf\u5c11\u4e86\u8bad\u7ec3\u7684\u96be\u5ea6\u548c\u8ba1\u7b97\u91cf&#xff0c;\u540c\u65f6\u964d\u4f4e\u4e86\u8fc7\u62df\u5408\u7684\u98ce\u9669\u3002<\/p>\n<\/li>\n<li>\n<p>\u5c42\u7ea7\u7279\u5f81\u5b66\u4e60&#xff08;Hierarchical Feature Learning&#xff09;&#xff1a;CNN \u80fd\u591f\u81ea\u52a8\u5730\u4ece\u539f\u59cb\u50cf\u7d20\u6570\u636e\u4e2d\u5b66\u4e60\u5230\u4e0d\u540c\u62bd\u8c61\u5c42\u6b21\u7684\u7279\u5f81\u3002\u4ece\u6d45\u5c42\u7684\u8fb9\u7f18\u3001\u7eb9\u7406\u7b49\u4f4e\u7ea7\u7279\u5f81&#xff0c;\u5230\u4e2d\u5c42\u7684\u5c40\u90e8\u5f62\u72b6\u3001\u90e8\u4ef6&#xff0c;\u518d\u5230\u6df1\u5c42\u7684\u9ad8\u7ea7\u8bed\u4e49\u7279\u5f81&#xff08;\u5982\u201c\u732b\u8138\u201d\u3001\u201c\u6c7d\u8f66\u201d&#xff09;&#xff0c;\u8fd9\u79cd\u7531\u7b80\u5230\u7e41\u3001\u7531\u5c40\u90e8\u5230\u5168\u5c40\u7684\u5c42\u7ea7\u5f0f\u7279\u5f81\u63d0\u53d6\u80fd\u529b&#xff0c;\u4f7f\u5f97 CNN \u80fd\u591f\u5bf9\u56fe\u50cf\u5185\u5bb9\u8fdb\u884c\u6df1\u5165\u7684\u7406\u89e3\u3002<\/p>\n<\/li>\n<\/ul>\n<h3>6. CNN \u7684\u53d1\u5c55\u5386\u7a0b&#xff1a;\u7ecf\u5178\u6539\u826f\u4e0e\u4ee3\u8868\u4f5c<\/h3>\n<p>CNN \u7684\u53d1\u5c55\u5e76\u975e\u4e00\u8e74\u800c\u5c31&#xff0c;\u800c\u662f\u7ecf\u8fc7\u4e86\u4f17\u591a\u7814\u7a76\u8005\u7684\u4e0d\u65ad\u63a2\u7d22\u548c\u521b\u65b0&#xff1a;<\/p>\n<ul>\n<li>\n<p>LeNet-5 (1998)&#xff1a;\u7531 Yann LeCun \u7b49\u4eba\u63d0\u51fa&#xff0c;\u662f\u65e9\u671f\u6210\u529f\u7684 CNN \u6a21\u578b&#xff0c;\u4e3b\u8981\u7528\u4e8e\u624b\u5199\u6570\u5b57\u8bc6\u522b\u3002\u5b83\u5960\u5b9a\u4e86\u73b0\u4ee3 CNN \u7684\u57fa\u672c\u67b6\u6784&#xff0c;\u5305\u62ec\u5377\u79ef\u5c42\u3001\u6c60\u5316\u5c42\u548c\u5168\u8fde\u63a5\u5c42\u3002<\/p>\n<\/li>\n<li>\n<p>AlexNet (2012)&#xff1a;\u5728 ImageNet \u5927\u89c4\u6a21\u56fe\u50cf\u8bc6\u522b\u7ade\u8d5b\u4e2d\u593a\u51a0&#xff0c;\u5f00\u542f\u4e86\u6df1\u5ea6 CNN \u7684\u6d6a\u6f6e\u3002\u5b83\u8bc1\u660e\u4e86\u6df1\u5ea6\u5377\u79ef\u7f51\u7edc\u5728\u5927\u578b\u6570\u636e\u96c6\u4e0a\u7684\u5f3a\u5927\u80fd\u529b&#xff0c;\u5e76\u5f15\u5165\u4e86 ReLU \u6fc0\u6d3b\u51fd\u6570\u3001Dropout \u7b49\u6280\u672f\u6765\u63d0\u9ad8\u6027\u80fd\u548c\u9632\u6b62\u8fc7\u62df\u5408\u3002<\/p>\n<\/li>\n<li>\n<p>VGGNet (2014)&#xff1a;\u4ee5\u5176\u7b80\u6d01\u800c\u6df1\u5ea6\u7684\u7ed3\u6784\u8457\u79f0\u3002\u5b83\u901a\u8fc7\u5806\u53e0\u591a\u4e2a\u5c0f\u5c3a\u5bf8&#xff08;3&#215;3&#xff09;\u7684\u5377\u79ef\u6838\u6765\u6784\u5efa\u975e\u5e38\u6df1\u7684\u7f51\u7edc&#xff0c;\u8bc1\u660e\u4e86\u589e\u52a0\u7f51\u7edc\u6df1\u5ea6\u80fd\u591f\u6709\u6548\u63d0\u5347\u6027\u80fd&#xff0c;\u540c\u65f6\u4fdd\u6301\u4e86\u6a21\u578b\u7684\u7b80\u6d01\u6027\u3002<\/p>\n<\/li>\n<li>\n<p>GoogLeNet \/ Inception (2014)&#xff1a;\u5f15\u5165\u4e86 Inception \u6a21\u5757&#xff0c;\u5141\u8bb8\u7f51\u7edc\u5728\u540c\u4e00\u5c42\u7ea7\u4e0a\u5e76\u884c\u5730\u6267\u884c\u4e0d\u540c\u5c3a\u5ea6\u7684\u5377\u79ef\u64cd\u4f5c&#xff08;\u5982 1&#215;1, 3&#215;3, 5&#215;5 \u5377\u79ef\u548c\u6c60\u5316&#xff09;&#xff0c;\u7136\u540e\u5c06\u5b83\u4eec\u7684\u8f93\u51fa\u62fc\u63a5\u8d77\u6765\u3002\u8fd9\u4f7f\u5f97\u7f51\u7edc\u80fd\u591f\u66f4\u597d\u5730\u6355\u6349\u591a\u5c3a\u5ea6\u4fe1\u606f&#xff0c;\u540c\u65f6\u901a\u8fc7 1&#215;1 \u5377\u79ef\u6838\u6709\u6548\u63a7\u5236\u4e86\u53c2\u6570\u91cf\u3002<\/p>\n<\/li>\n<li>\n<p>ResNet (Residual Network, 2015)&#xff1a;\u7531\u5fae\u8f6f\u4e9a\u6d32\u7814\u7a76\u9662\u63d0\u51fa&#xff0c;\u89e3\u51b3\u4e86\u6df1\u5ea6\u7f51\u7edc\u8bad\u7ec3\u4e2d\u7684\u201c\u68af\u5ea6\u6d88\u5931\u201d\u548c\u201c\u9000\u5316\u201d\u95ee\u9898\u3002\u5b83\u5f15\u5165\u4e86\u6b8b\u5dee\u8fde\u63a5&#xff08;Residual Connection&#xff09;&#xff0c;\u5141\u8bb8\u4fe1\u606f\u8df3\u8fc7\u4e00\u5c42\u6216\u591a\u5c42\u76f4\u63a5\u4f20\u9012&#xff0c;\u4f7f\u5f97\u7f51\u7edc\u53ef\u4ee5\u6784\u5efa\u5f97\u975e\u5e38\u6df1&#xff08;\u4f8b\u5982 152 \u5c42&#xff09;&#xff0c;\u800c\u6027\u80fd\u4e0d\u4f1a\u4e0b\u964d&#xff0c;\u751a\u81f3\u80fd\u6301\u7eed\u63d0\u5347\u3002<\/p>\n<\/li>\n<li>\n<p>DenseNet (Dense Convolutional Network, 2017)&#xff1a;\u8fdb\u4e00\u6b65\u53d1\u5c55\u4e86\u8fde\u63a5\u601d\u60f3&#xff0c;\u63d0\u51fa\u4e86\u5bc6\u96c6\u8fde\u63a5&#xff08;Dense Connection&#xff09;\u3002\u5728 DenseNet \u4e2d&#xff0c;\u6bcf\u4e00\u5c42\u7684\u8f93\u5165\u90fd\u8fde\u63a5\u5230\u524d\u9762\u6240\u6709\u5c42\u7684\u8f93\u51fa&#xff0c;\u5e76\u4e14\u5c06\u5b83\u4eec\u62fc\u63a5\u8d77\u6765\u4f5c\u4e3a\u81ea\u5df1\u7684\u8f93\u5165\u3002\u8fd9\u79cd\u5bc6\u96c6\u8fde\u63a5\u6a21\u5f0f\u4fc3\u8fdb\u4e86\u7279\u5f81\u7684\u91cd\u7528&#xff0c;\u51cf\u5c11\u4e86\u53c2\u6570\u91cf&#xff0c;\u5e76\u7f13\u89e3\u4e86\u68af\u5ea6\u6d88\u5931\u95ee\u9898\u3002<\/p>\n<\/li>\n<\/ul>\n<h3>7. \u5e38\u89c1\u75db\u70b9\u4e0e\u4f18\u5316\u65b9\u5411<\/h3>\n<p>\u5c3d\u7ba1 CNN \u5f3a\u5927&#xff0c;\u4f46\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u4e5f\u4f1a\u9047\u5230\u4e00\u4e9b\u6311\u6218&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u8fc7\u62df\u5408&#xff08;Overfitting&#xff09;&#xff1a;\u6a21\u578b\u5728\u8bad\u7ec3\u6570\u636e\u4e0a\u8868\u73b0\u5f88\u597d&#xff0c;\u4f46\u5728\u672a\u89c1\u8fc7\u7684\u65b0\u6570\u636e\u4e0a\u8868\u73b0\u5f88\u5dee\u3002\u8fd9\u901a\u5e38\u662f\u56e0\u4e3a\u6a21\u578b\u8fc7\u4e8e\u590d\u6742&#xff0c;\u8fc7\u5ea6\u5b66\u4e60\u4e86\u8bad\u7ec3\u6570\u636e\u4e2d\u7684\u566a\u58f0\u3002<\/p>\n<ul>\n<li>\n<p>\u4f18\u5316\u65b9\u5411&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u6570\u636e\u589e\u5f3a&#xff08;Data Augmentation&#xff09;&#xff1a;\u901a\u8fc7\u5bf9\u8bad\u7ec3\u56fe\u7247\u8fdb\u884c\u968f\u673a\u7ffb\u8f6c\u3001\u88c1\u526a\u3001\u65cb\u8f6c\u3001\u989c\u8272\u6296\u52a8\u7b49\u64cd\u4f5c&#xff0c;\u6269\u5145\u6570\u636e\u96c6&#xff0c;\u589e\u52a0\u6570\u636e\u7684\u591a\u6837\u6027\u3002<\/p>\n<\/li>\n<li>\n<p>Dropout&#xff1a;\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u968f\u673a\u201c\u5173\u95ed\u201d\u4e00\u90e8\u5206\u795e\u7ecf\u5143&#xff0c;\u5f3a\u5236\u7f51\u7edc\u5b66\u4e60\u66f4\u9c81\u68d2\u7684\u7279\u5f81\u3002<\/p>\n<\/li>\n<li>\n<p>\u6743\u91cd\u8870\u51cf&#xff08;Weight Decay \/ L2 \u6b63\u5219\u5316&#xff09;&#xff1a;\u5728\u635f\u5931\u51fd\u6570\u4e2d\u52a0\u5165\u5bf9\u6a21\u578b\u6743\u91cd\u5927\u5c0f\u7684\u60e9\u7f5a\u9879&#xff0c;\u9650\u5236\u6a21\u578b\u590d\u6742\u5ea6\u3002<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u68af\u5ea6\u6d88\u5931&#xff08;Vanishing Gradients&#xff09;&#xff1a;\u5728\u6df1\u5ea6\u7f51\u7edc\u4e2d&#xff0c;\u68af\u5ea6\u5728\u53cd\u5411\u4f20\u64ad\u8fc7\u7a0b\u4e2d\u53d8\u5f97\u8d8a\u6765\u8d8a\u5c0f&#xff0c;\u5bfc\u81f4\u6d45\u5c42\u7f51\u7edc\u7684\u53c2\u6570\u96be\u4ee5\u66f4\u65b0&#xff0c;\u6a21\u578b\u65e0\u6cd5\u6709\u6548\u5b66\u4e60\u3002<\/p>\n<ul>\n<li>\n<p>\u4f18\u5316\u65b9\u5411&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u4f7f\u7528 ReLU \/ LeakyReLU \u7b49\u975e\u9971\u548c\u6fc0\u6d3b\u51fd\u6570&#xff0c;\u5b83\u4eec\u5728\u6b63\u533a\u95f4\u5185\u68af\u5ea6\u6052\u5b9a&#xff0c;\u907f\u514d\u4e86\u68af\u5ea6\u6d88\u5931\u3002<\/p>\n<\/li>\n<li>\n<p>\u5f15\u5165 ResNet \u7684\u6b8b\u5dee\u7ed3\u6784&#xff0c;\u901a\u8fc7\u8df3\u8dc3\u8fde\u63a5\u76f4\u63a5\u4f20\u9012\u68af\u5ea6&#xff0c;\u6709\u6548\u7f13\u89e3\u4e86\u68af\u5ea6\u6d88\u5931\u95ee\u9898\u3002<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u8ba1\u7b97\u91cf\u5927&#xff08;High Computational Cost&#xff09;&#xff1a;\u5c24\u5176\u662f\u5728\u79fb\u52a8\u8bbe\u5907\u6216\u5d4c\u5165\u5f0f\u8bbe\u5907\u4e0a\u90e8\u7f72\u65f6&#xff0c;\u6a21\u578b\u7684\u8ba1\u7b97\u91cf\u548c\u53c2\u6570\u91cf\u53ef\u80fd\u6210\u4e3a\u74f6\u9888\u3002<\/p>\n<ul>\n<li>\n<p>\u4f18\u5316\u65b9\u5411&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u4f7f\u7528\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef&#xff08;Depthwise Separable Convolution&#xff09;&#xff1a;\u5982 MobileNet \u7cfb\u5217\u7f51\u7edc&#xff0c;\u5c06\u6807\u51c6\u5377\u79ef\u5206\u89e3\u4e3a\u6df1\u5ea6\u5377\u79ef\u548c\u9010\u70b9\u5377\u79ef&#xff0c;\u5927\u5927\u51cf\u5c11\u4e86\u8ba1\u7b97\u91cf\u548c\u53c2\u6570\u91cf&#xff0c;\u540c\u65f6\u4fdd\u6301\u4e86\u8f83\u9ad8\u7684\u6027\u80fd\u3002<\/p>\n<\/li>\n<li>\n<p>\u6a21\u578b\u526a\u679d&#xff08;Pruning&#xff09;&#xff1a;\u79fb\u9664\u7f51\u7edc\u4e2d\u4e0d\u91cd\u8981\u7684\u8fde\u63a5\u6216\u795e\u7ecf\u5143\u3002<\/p>\n<\/li>\n<li>\n<p>\u6a21\u578b\u91cf\u5316&#xff08;Quantization&#xff09;&#xff1a;\u5c06\u6a21\u578b\u53c2\u6570\u4ece\u6d6e\u70b9\u6570\u8f6c\u6362\u4e3a\u4f4e\u7cbe\u5ea6\u6574\u6570&#xff0c;\u51cf\u5c11\u6a21\u578b\u5927\u5c0f\u548c\u8ba1\u7b97\u91cf\u3002<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u6a21\u578b\u6cdb\u5316\u5dee&#xff08;Poor Generalization&#xff09;&#xff1a;\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e4b\u5916\u7684\u6570\u636e\u4e0a\u8868\u73b0\u4e0d\u4f73&#xff0c;\u53ef\u80fd\u4e0e\u6570\u636e\u91cf\u4e0d\u8db3\u6216\u6570\u636e\u5206\u5e03\u4e0d\u5747\u6709\u5173\u3002<\/p>\n<ul>\n<li>\n<p>\u4f18\u5316\u65b9\u5411&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u589e\u52a0\u6570\u636e\u591a\u6837\u6027&#xff1a;\u6536\u96c6\u66f4\u591a\u4e0d\u540c\u573a\u666f\u3001\u4e0d\u540c\u6761\u4ef6\u4e0b\u7684\u6570\u636e\u3002<\/p>\n<\/li>\n<li>\n<p>\u8fc1\u79fb\u5b66\u4e60&#xff08;Transfer Learning&#xff09;&#xff1a;\u5229\u7528\u5728\u5927\u89c4\u6a21\u6570\u636e\u96c6&#xff08;\u5982 ImageNet&#xff09;\u4e0a\u9884\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u4f5c\u4e3a\u8d77\u70b9&#xff0c;\u7136\u540e\u5728\u81ea\u5df1\u7684\u5c0f\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u5fae\u8c03\u3002\u8fd9\u662f\u4e00\u79cd\u975e\u5e38\u6709\u6548\u7684\u7b56\u7565&#xff0c;\u5c24\u5176\u662f\u5728\u6570\u636e\u91cf\u6709\u9650\u7684\u60c5\u51b5\u4e0b\u3002<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3>8. 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