{"id":34009,"date":"2025-04-28T22:09:32","date_gmt":"2025-04-28T14:09:32","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/34009.html"},"modified":"2025-04-28T22:09:32","modified_gmt":"2025-04-28T14:09:32","slug":"%e3%80%90%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e3%80%91-%e7%a5%9e%e7%bb%8f%e6%9e%b6%e6%9e%84%e6%90%9c%e7%b4%a2%ef%bc%88nas%ef%bc%89","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/34009.html","title":{"rendered":"\u3010\u673a\u5668\u5b66\u4e60\u3011---\u795e\u7ecf\u67b6\u6784\u641c\u7d22\uff08NAS\uff09"},"content":{"rendered":"<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250428140930-680f8c1ae2d49.gif\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> <\/p>\n<h4>\u8fd9\u91cc\u5199\u76ee\u5f55\u6807\u9898<\/h4>\n<ul>\n<li>\n<ul>\n<li>\u5f15\u8a00<\/li>\n<li>1. \u4ec0\u4e48\u662f\u795e\u7ecf\u67b6\u6784\u641c\u7d22&#xff08;NAS&#xff09;<\/li>\n<li>\n<ul>\n<li>1.1 \u4e3a\u4ec0\u4e48\u9700\u8981NAS&#xff1f;<\/li>\n<\/ul>\n<\/li>\n<li>2. NAS\u7684\u4e09\u5927\u7ec4\u4ef6<\/li>\n<li>\n<ul>\n<li>2.1 \u641c\u7d22\u7a7a\u95f4<\/li>\n<li>\n<ul>\n<li>\u641c\u7d22\u7a7a\u95f4\u8bbe\u8ba1\u7684\u8003\u8651\u56e0\u7d20&#xff1a;<\/li>\n<\/ul>\n<\/li>\n<li>2.2 \u641c\u7d22\u7b56\u7565<\/li>\n<li>2.3 \u6027\u80fd\u4f30\u8ba1<\/li>\n<\/ul>\n<\/li>\n<li>3. NAS\u7684\u4e3b\u8981\u65b9\u6cd5<\/li>\n<li>\n<ul>\n<li>3.1 \u57fa\u4e8e\u5f3a\u5316\u5b66\u4e60\u7684NAS<\/li>\n<li>3.2 \u57fa\u4e8e\u8fdb\u5316\u7b97\u6cd5\u7684NAS<\/li>\n<li>3.3 \u57fa\u4e8e\u68af\u5ea6\u7684NAS<\/li>\n<\/ul>\n<\/li>\n<li>4. NAS\u7684\u5e94\u7528<\/li>\n<li>5. \u5b9e\u73b0\u4e00\u4e2a\u7b80\u5355\u7684NAS\u6846\u67b6<\/li>\n<li>6. \u603b\u7ed3<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3>\u5f15\u8a00<\/h3>\n<p>\u968f\u7740\u6df1\u5ea6\u5b66\u4e60\u7684\u6210\u529f\u5e94\u7528&#xff0c;\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\u7684\u8bbe\u8ba1\u53d8\u5f97\u8d8a\u6765\u8d8a\u590d\u6742\u3002\u6a21\u578b\u7684\u6027\u80fd\u4e0d\u4ec5\u4f9d\u8d56\u4e8e\u6570\u636e\u548c\u8bad\u7ec3\u65b9\u6cd5&#xff0c;\u8fd8\u4f9d\u8d56\u4e8e\u7f51\u7edc\u67b6\u6784\u672c\u8eab\u3002\u7136\u800c&#xff0c;\u624b\u5de5\u8bbe\u8ba1\u4e00\u4e2a\u9002\u7528\u4e8e\u4e0d\u540c\u4efb\u52a1\u7684\u9ad8\u6548\u67b6\u6784\u9700\u8981\u5927\u91cf\u7684\u9886\u57df\u77e5\u8bc6\u548c\u5b9e\u9a8c\u3002\u8fd9\u65f6&#xff0c;**\u795e\u7ecf\u67b6\u6784\u641c\u7d22&#xff08;Neural Architecture Search&#xff0c;NAS&#xff09;**\u5e94\u8fd0\u800c\u751f&#xff0c;\u4f5c\u4e3a\u81ea\u52a8\u5316\u5bfb\u627e\u795e\u7ecf\u7f51\u7edc\u6700\u4f73\u67b6\u6784\u7684\u5de5\u5177&#xff0c;\u5b83\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u7f13\u89e3\u4e86\u8bbe\u8ba1\u8005\u7684\u5de5\u4f5c\u91cf&#xff0c;\u5e76\u80fd\u627e\u5230\u6bd4\u4eba\u7c7b\u624b\u5de5\u8bbe\u8ba1\u66f4\u9ad8\u6548\u7684\u67b6\u6784\u3002<\/p>\n<p>\u672c\u7bc7\u6587\u7ae0\u5c06\u8be6\u7ec6\u4ecb\u7ecdNAS\u7684\u80cc\u666f\u3001\u65b9\u6cd5\u3001\u5e94\u7528\u4ee5\u53ca\u5982\u4f55\u5b9e\u73b0NAS\u7b97\u6cd5\u3002<\/p>\n<h3>1. \u4ec0\u4e48\u662f\u795e\u7ecf\u67b6\u6784\u641c\u7d22&#xff08;NAS&#xff09;<\/h3>\n<p>\u795e\u7ecf\u67b6\u6784\u641c\u7d22&#xff08;NAS&#xff09; \u662f\u6307\u901a\u8fc7\u641c\u7d22\u7b97\u6cd5\u81ea\u52a8\u8bbe\u8ba1\u795e\u7ecf\u7f51\u7edc\u67b6\u6784&#xff0c;\u4ece\u800c\u4f18\u5316\u7279\u5b9a\u4efb\u52a1\u7684\u6027\u80fd\u3002NAS\u7684\u76ee\u6807\u662f\u5728\u4e00\u4e2a\u5b9a\u4e49\u597d\u7684\u641c\u7d22\u7a7a\u95f4\u4e2d&#xff0c;\u627e\u5230\u6700\u4f73\u7684\u7f51\u7edc\u7ed3\u6784&#xff0c;\u8be5\u7ed3\u6784\u901a\u5e38\u7531\u6027\u80fd\u6307\u6807&#xff08;\u4f8b\u5982\u51c6\u786e\u7387\u3001\u901f\u5ea6\u3001\u53c2\u6570\u91cf\u7b49&#xff09;\u6765\u8861\u91cf\u3002<\/p>\n<p>NAS\u4e3b\u8981\u5305\u62ec\u4e09\u4e2a\u5173\u952e\u8981\u7d20&#xff1a;<\/p>\n<li>\u641c\u7d22\u7a7a\u95f4&#xff08;Search Space&#xff09;&#xff1a;\u5b9a\u4e49\u4e86\u6240\u6709\u53ef\u80fd\u7684\u7f51\u7edc\u67b6\u6784\u3002<\/li>\n<li>\u641c\u7d22\u7b56\u7565&#xff08;Search Strategy&#xff09;&#xff1a;\u6307\u5bfc\u5982\u4f55\u5728\u641c\u7d22\u7a7a\u95f4\u4e2d\u9ad8\u6548\u5730\u63a2\u7d22\u3002<\/li>\n<li>\u6027\u80fd\u4f30\u8ba1&#xff08;Performance Estimation&#xff09;&#xff1a;\u8bc4\u4f30\u5019\u9009\u67b6\u6784\u7684\u6027\u80fd\u3002<\/li>\n<h4>1.1 \u4e3a\u4ec0\u4e48\u9700\u8981NAS&#xff1f;<\/h4>\n<li>\u51cf\u5c11\u4eba\u7c7b\u5e72\u9884&#xff1a;\u4f20\u7edf\u7684\u7f51\u7edc\u67b6\u6784\u8bbe\u8ba1\u4f9d\u8d56\u4e8e\u7814\u7a76\u4eba\u5458\u7684\u76f4\u89c9\u548c\u7ecf\u9a8c\u3002NAS\u51cf\u5c11\u4e86\u8fd9\u79cd\u4f9d\u8d56&#xff0c;\u901a\u8fc7\u7b97\u6cd5\u81ea\u52a8\u751f\u6210\u67b6\u6784\u3002<\/li>\n<li>\u627e\u5230\u66f4\u4f18\u67b6\u6784&#xff1a;NAS\u53ef\u4ee5\u627e\u5230\u6bd4\u4eba\u7c7b\u624b\u5de5\u8bbe\u8ba1\u66f4\u4f18\u7684\u67b6\u6784\u3002\u4f8b\u5982&#xff0c;Google\u4f7f\u7528NAS\u641c\u7d22\u5230\u4e86\u8457\u540d\u7684MobileNetV3\u3002<\/li>\n<li>\u63d0\u9ad8\u641c\u7d22\u6548\u7387&#xff1a;\u5c3d\u7ba1\u641c\u7d22\u7a7a\u95f4\u5de8\u5927&#xff0c;NAS\u901a\u8fc7\u4f18\u5316\u6280\u672f\u53ef\u4ee5\u6709\u6548\u641c\u7d22\u5230\u4f18\u79c0\u7684\u6a21\u578b\u3002<\/li>\n<h3>2. NAS\u7684\u4e09\u5927\u7ec4\u4ef6<\/h3>\n<h4>2.1 \u641c\u7d22\u7a7a\u95f4<\/h4>\n<p>\u641c\u7d22\u7a7a\u95f4\u5b9a\u4e49\u4e86NAS\u53ef\u4ee5\u63a2\u7d22\u7684\u6240\u6709\u53ef\u80fd\u7f51\u7edc\u7ed3\u6784&#xff0c;\u901a\u5e38\u5305\u62ec\u4ee5\u4e0b\u5143\u7d20&#xff1a;<\/p>\n<ul>\n<li>\u5c42\u7684\u7c7b\u578b&#xff08;\u4f8b\u5982\u5377\u79ef\u5c42\u3001\u6c60\u5316\u5c42\u3001\u5168\u8fde\u63a5\u5c42&#xff09;<\/li>\n<li>\u5c42\u7684\u8d85\u53c2\u6570&#xff08;\u5982\u5377\u79ef\u6838\u5927\u5c0f\u3001\u6b65\u957f\u3001\u6fc0\u6d3b\u51fd\u6570\u7b49&#xff09;<\/li>\n<li>\u7f51\u7edc\u62d3\u6251\u7ed3\u6784&#xff08;\u5982\u5c42\u4e4b\u95f4\u7684\u8fde\u63a5\u65b9\u5f0f&#xff09;<\/li>\n<\/ul>\n<h5>\u641c\u7d22\u7a7a\u95f4\u8bbe\u8ba1\u7684\u8003\u8651\u56e0\u7d20&#xff1a;<\/h5>\n<li>\u5927\u5c0f&#xff1a;\u641c\u7d22\u7a7a\u95f4\u8fc7\u5927\u4f1a\u5bfc\u81f4\u641c\u7d22\u96be\u5ea6\u589e\u52a0&#xff0c;\u8fc7\u5c0f\u5219\u53ef\u80fd\u9650\u5236\u6a21\u578b\u7684\u8868\u73b0\u529b\u3002<\/li>\n<li>\u7075\u6d3b\u6027&#xff1a;\u641c\u7d22\u7a7a\u95f4\u5e94\u6db5\u76d6\u591a\u6837\u5316\u7684\u7f51\u7edc\u7ed3\u6784\u4ee5\u4fdd\u8bc1\u641c\u7d22\u7ed3\u679c\u7684\u591a\u6837\u6027\u3002<\/li>\n<h4>2.2 \u641c\u7d22\u7b56\u7565<\/h4>\n<p>\u641c\u7d22\u7b56\u7565\u51b3\u5b9a\u4e86\u5982\u4f55\u5728\u5b9a\u4e49\u597d\u7684\u641c\u7d22\u7a7a\u95f4\u4e2d\u9ad8\u6548\u5730\u5bfb\u627e\u6700\u4f18\u67b6\u6784\u3002\u76ee\u524d&#xff0c;\u5e38\u7528\u7684\u641c\u7d22\u7b56\u7565\u6709\u4ee5\u4e0b\u51e0\u79cd&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u5f3a\u5316\u5b66\u4e60&#xff08;Reinforcement Learning, RL&#xff09;&#xff1a;\u5c06\u7f51\u7edc\u67b6\u6784\u7684\u641c\u7d22\u8fc7\u7a0b\u89c6\u4e3a\u4e00\u4e2a\u51b3\u7b56\u95ee\u9898&#xff0c;\u4ee3\u7406&#xff08;agent&#xff09;\u901a\u8fc7\u4e0e\u73af\u5883\u4ea4\u4e92\u5b66\u4e60\u6784\u5efa\u66f4\u597d\u7684\u67b6\u6784\u3002<\/p>\n<p> <span class=\"token keyword\">import<\/span> tensorflow <span class=\"token keyword\">as<\/span> tf<br \/>\n<span class=\"token keyword\">import<\/span> numpy <span class=\"token keyword\">as<\/span> np<\/p>\n<p><span class=\"token keyword\">class<\/span> <span class=\"token class-name\">NASAgent<\/span><span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>Model<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">def<\/span> <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> search_space<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span>NASAgent<span class=\"token punctuation\">,<\/span> self<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>__init__<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>search_space <span class=\"token operator\">&#061;<\/span> search_space<br \/>\n        self<span class=\"token punctuation\">.<\/span>policy_network <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>Sequential<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span><br \/>\n            tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Dense<span class=\"token punctuation\">(<\/span><span class=\"token number\">128<\/span><span class=\"token punctuation\">,<\/span> activation<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;relu&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n            tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Dense<span class=\"token punctuation\">(<\/span><span class=\"token builtin\">len<\/span><span class=\"token punctuation\">(<\/span>search_space<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> activation<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;softmax&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">def<\/span> <span class=\"token function\">call<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> state<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token keyword\">return<\/span> self<span class=\"token punctuation\">.<\/span>policy_network<span class=\"token punctuation\">(<\/span>state<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u4f7f\u7528\u5f3a\u5316\u5b66\u4e60\u8fdb\u884c\u641c\u7d22\u7684\u4f2a\u4ee3\u7801<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">search_with_rl<\/span><span class=\"token punctuation\">(<\/span>agent<span class=\"token punctuation\">,<\/span> num_epochs<span class=\"token operator\">&#061;<\/span><span class=\"token number\">100<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> epoch <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span>num_epochs<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        state <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>randn<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u5047\u8bbe\u521d\u59cb\u72b6\u6001<\/span><br \/>\n        action_prob <span class=\"token operator\">&#061;<\/span> agent<span class=\"token punctuation\">(<\/span>state<span class=\"token punctuation\">)<\/span><br \/>\n        action <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>argmax<span class=\"token punctuation\">(<\/span>action_prob<span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token comment\"># \u8fd9\u91cc\u57fa\u4e8eaction\u9009\u62e9\u7f51\u7edc\u67b6\u6784&#xff0c;\u5e76\u8bc4\u4f30\u5176\u6027\u80fd<\/span><br \/>\n        performance <span class=\"token operator\">&#061;<\/span> evaluate_model<span class=\"token punctuation\">(<\/span>action<span class=\"token punctuation\">)<\/span><br \/>\n        agent<span class=\"token punctuation\">.<\/span>update_policy<span class=\"token punctuation\">(<\/span>action<span class=\"token punctuation\">,<\/span> performance<span class=\"token punctuation\">)<\/span>\n <\/li>\n<li>\n<p>\u8fdb\u5316\u7b97\u6cd5&#xff08;Evolutionary Algorithms, EA&#xff09;&#xff1a;\u901a\u8fc7\u6a21\u62df\u751f\u7269\u8fdb\u5316\u8fc7\u7a0b&#xff08;\u5982\u53d8\u5f02\u3001\u4ea4\u53c9\u3001\u9009\u62e9\u7b49&#xff09;\u9010\u6e10\u751f\u6210\u66f4\u597d\u7684\u67b6\u6784\u3002<\/p>\n<p> <span class=\"token keyword\">import<\/span> random<\/p>\n<p><span class=\"token comment\"># \u57fa\u4e8e\u8fdb\u5316\u7b97\u6cd5\u8fdb\u884c\u7f51\u7edc\u641c\u7d22\u7684\u4f2a\u4ee3\u7801<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">evolve_population<\/span><span class=\"token punctuation\">(<\/span>population<span class=\"token punctuation\">,<\/span> generations<span class=\"token operator\">&#061;<\/span><span class=\"token number\">50<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> generation <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span>generations<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        selected_parents <span class=\"token operator\">&#061;<\/span> select_best<span class=\"token punctuation\">(<\/span>population<span class=\"token punctuation\">)<\/span><br \/>\n        offspring <span class=\"token operator\">&#061;<\/span> crossover<span class=\"token punctuation\">(<\/span>selected_parents<span class=\"token punctuation\">)<\/span><br \/>\n        mutated_offspring <span class=\"token operator\">&#061;<\/span> mutate<span class=\"token punctuation\">(<\/span>offspring<span class=\"token punctuation\">)<\/span><br \/>\n        population <span class=\"token operator\">&#061;<\/span> selected_parents <span class=\"token operator\">&#043;<\/span> mutated_offspring<br \/>\n        evaluate_population<span class=\"token punctuation\">(<\/span>population<span class=\"token punctuation\">)<\/span>\n <\/li>\n<li>\n<p>\u968f\u673a\u641c\u7d22&#xff08;Random Search&#xff09;&#xff1a;\u968f\u673a\u9009\u62e9\u67b6\u6784\u8fdb\u884c\u8bc4\u4f30\u3002\u8fd9\u662f\u6700\u7b80\u5355\u7684NAS\u65b9\u6cd5&#xff0c;\u4f46\u6548\u7387\u8f83\u4f4e\u3002<\/p>\n<\/li>\n<li>\n<p>\u8d1d\u53f6\u65af\u4f18\u5316&#xff08;Bayesian Optimization&#xff09;&#xff1a;\u901a\u8fc7\u5efa\u7acb\u5019\u9009\u67b6\u6784\u7684\u4ee3\u7406\u6a21\u578b\u6765\u63a8\u6d4b\u672a\u6d4b\u8bd5\u67b6\u6784\u7684\u6027\u80fd&#xff0c;\u4ece\u800c\u51cf\u5c11\u8bc4\u4f30\u6b21\u6570\u3002<\/p>\n<\/li>\n<\/ul>\n<h4>2.3 \u6027\u80fd\u4f30\u8ba1<\/h4>\n<p>\u6027\u80fd\u4f30\u8ba1\u7684\u76ee\u6807\u662f\u8bc4\u4f30\u6bcf\u4e2a\u5019\u9009\u67b6\u6784\u7684\u8868\u73b0\u3002\u76f4\u63a5\u8bad\u7ec3\u6bcf\u4e2a\u67b6\u6784\u5e76\u8bc4\u4f30\u5176\u6027\u80fd\u662f\u975e\u5e38\u8017\u65f6\u7684&#xff0c;\u56e0\u6b64\u4e00\u4e9b\u52a0\u901f\u65b9\u6cd5\u88ab\u63d0\u51fa&#xff1a;<\/p>\n<li>\u53c2\u6570\u5171\u4eab&#xff08;Weight Sharing&#xff09;&#xff1a;\u4e0d\u540c\u67b6\u6784\u5171\u4eab\u90e8\u5206\u6a21\u578b\u6743\u91cd&#xff0c;\u4ee5\u51cf\u5c11\u91cd\u590d\u8bad\u7ec3\u3002<\/li>\n<li>\u65e9\u671f\u505c\u6b62&#xff08;Early Stopping&#xff09;&#xff1a;\u5728\u9a8c\u8bc1\u96c6\u4e2d\u89c2\u5bdf\u5230\u6027\u80fd\u5f00\u59cb\u6536\u655b\u65f6&#xff0c;\u63d0\u524d\u505c\u6b62\u8bad\u7ec3&#xff0c;\u907f\u514d\u6d6a\u8d39\u8ba1\u7b97\u8d44\u6e90\u3002<\/li>\n<li>\u4ee3\u7406\u6a21\u578b&#xff1a;\u901a\u8fc7\u8bad\u7ec3\u4e00\u4e2a\u4ee3\u7406\u6a21\u578b&#xff0c;\u6765\u4f30\u8ba1\u67b6\u6784\u7684\u6027\u80fd\u800c\u4e0d\u5fc5\u8fdb\u884c\u5b8c\u6574\u8bad\u7ec3\u3002<\/li>\n<p><span class=\"token comment\"># \u53c2\u6570\u5171\u4eab\u793a\u4f8b&#xff1a;\u591a\u4e2a\u67b6\u6784\u5171\u4eab\u90e8\u5206\u5377\u79ef\u5c42\u6743\u91cd<\/span><br \/>\nshared_conv_layer <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Conv2D<span class=\"token punctuation\">(<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">,<\/span> kernel_size<span class=\"token operator\">&#061;<\/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 punctuation\">,<\/span> padding<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;same&#039;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">create_model_with_shared_weights<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    model <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>Sequential<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span><br \/>\n        shared_conv_layer<span class=\"token punctuation\">,<\/span><br \/>\n        tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Conv2D<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> kernel_size<span class=\"token operator\">&#061;<\/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 punctuation\">,<\/span> padding<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;same&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n        tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>GlobalAveragePooling2D<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n        tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Dense<span class=\"token punctuation\">(<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">,<\/span> activation<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;softmax&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> model<\/p>\n<h3>3. NAS\u7684\u4e3b\u8981\u65b9\u6cd5<\/h3>\n<h4>3.1 \u57fa\u4e8e\u5f3a\u5316\u5b66\u4e60\u7684NAS<\/h4>\n<p>\u5f3a\u5316\u5b66\u4e60\u65b9\u6cd5\u6700\u65e9\u7531Baker\u7b49\u4eba\u63d0\u51fa&#xff0c;\u5e76\u5728Google\u7684\u8bba\u6587\u300aNeural Architecture Search with Reinforcement Learning\u300b\u4e2d\u5f97\u5230\u5e7f\u6cdb\u5e94\u7528\u3002\u8be5\u65b9\u6cd5\u901a\u8fc7RNN\u63a7\u5236\u5668\u751f\u6210\u7f51\u7edc\u67b6\u6784&#xff0c;\u5e76\u901a\u8fc7\u8bad\u7ec3\u597d\u7684\u67b6\u6784\u6027\u80fd\u53cd\u9988\u6765\u66f4\u65b0\u63a7\u5236\u5668\u7b56\u7565\u3002<\/p>\n<p><span class=\"token comment\"># \u57fa\u4e8eRNN\u63a7\u5236\u5668\u751f\u6210\u7f51\u7edc\u67b6\u6784<\/span><br \/>\n<span class=\"token keyword\">class<\/span> <span class=\"token class-name\">RNNController<\/span><span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>Model<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">def<\/span> <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span>RNNController<span class=\"token punctuation\">,<\/span> self<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>__init__<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>rnn <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>LSTM<span class=\"token punctuation\">(<\/span><span class=\"token number\">128<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>dense <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Dense<span class=\"token punctuation\">(<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">,<\/span> activation<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;softmax&#039;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">def<\/span> <span class=\"token function\">call<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> inputs<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>rnn<span class=\"token punctuation\">(<\/span>inputs<span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token keyword\">return<\/span> self<span class=\"token punctuation\">.<\/span>dense<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><\/p>\n<h4>3.2 \u57fa\u4e8e\u8fdb\u5316\u7b97\u6cd5\u7684NAS<\/h4>\n<p>\u57fa\u4e8e\u8fdb\u5316\u7b97\u6cd5\u7684NAS\u4e3b\u8981\u6a21\u62df\u4e86\u751f\u7269\u8fdb\u5316\u4e2d\u7684\u81ea\u7136\u9009\u62e9\u8fc7\u7a0b\u3002\u5176\u6838\u5fc3\u601d\u60f3\u662f\u901a\u8fc7\u4e0d\u65ad\u53d8\u5f02\u548c\u4ea4\u53c9\u5df2\u6709\u7684\u67b6\u6784\u6765\u751f\u6210\u65b0\u7684\u67b6\u6784&#xff0c;\u5e76\u6839\u636e\u6027\u80fd\u9009\u62e9\u6700\u4f18\u4e2a\u4f53\u3002<\/p>\n<p><span class=\"token comment\"># \u8fdb\u5316\u7b97\u6cd5\u793a\u4f8b<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">mutate_architecture<\/span><span class=\"token punctuation\">(<\/span>architecture<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token comment\"># \u968f\u673a\u4fee\u6539\u67b6\u6784\u4e2d\u7684\u67d0\u4e2a\u5c42<\/span><br \/>\n    mutated_architecture <span class=\"token operator\">&#061;<\/span> architecture<span class=\"token punctuation\">.<\/span>copy<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    layer_to_mutate <span class=\"token operator\">&#061;<\/span> random<span class=\"token punctuation\">.<\/span>choice<span class=\"token punctuation\">(<\/span>mutated_architecture<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">)<\/span><br \/>\n    mutated_architecture<span class=\"token punctuation\">.<\/span>modify_layer<span class=\"token punctuation\">(<\/span>layer_to_mutate<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> mutated_architecture<\/p>\n<h4>3.3 \u57fa\u4e8e\u68af\u5ea6\u7684NAS<\/h4>\n<p>\u4e00\u79cd\u66f4\u9ad8\u6548\u7684NAS\u65b9\u6cd5\u662f\u57fa\u4e8e\u68af\u5ea6\u7684DARTS&#xff08;Differentiable Architecture Search&#xff09;&#xff0c;\u5b83\u5c06\u67b6\u6784\u641c\u7d22\u8fc7\u7a0b\u8f6c\u6362\u4e3a\u53ef\u5fae\u5206\u7684\u4f18\u5316\u95ee\u9898&#xff0c;\u5141\u8bb8\u901a\u8fc7\u68af\u5ea6\u4e0b\u964d\u8fdb\u884c\u4f18\u5316\u3002<\/p>\n<p><span class=\"token comment\"># DARTS\u65b9\u6cd5\u7684\u4f2a\u4ee3\u7801<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">darts_search<\/span><span class=\"token punctuation\">(<\/span>architecture_space<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    alpha <span class=\"token operator\">&#061;<\/span> initialize_architecture_parameters<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u53ef\u5fae\u7684\u67b6\u6784\u53c2\u6570<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> epoch <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span>num_epochs<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        weights <span class=\"token operator\">&#061;<\/span> train_model<span class=\"token punctuation\">(<\/span>alpha<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u4f7f\u7528\u5f53\u524d\u67b6\u6784\u8bad\u7ec3\u6a21\u578b<\/span><br \/>\n        alpha <span class=\"token operator\">&#061;<\/span> update_architecture_parameters<span class=\"token punctuation\">(<\/span>weights<span class=\"token punctuation\">,<\/span> alpha<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u66f4\u65b0\u67b6\u6784\u53c2\u6570<\/span><\/p>\n<h3>4. NAS\u7684\u5e94\u7528<\/h3>\n<p>NAS\u5df2\u7ecf\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u56fe\u50cf\u5206\u7c7b\u3001\u76ee\u6807\u68c0\u6d4b\u3001\u8bed\u97f3\u8bc6\u522b\u7b49\u591a\u4e2a\u9886\u57df\u3002\u4f8b\u5982&#xff1a;<\/p>\n<li>\u56fe\u50cf\u5206\u7c7b&#xff1a;NASNet\u5728ImageNet\u5206\u7c7b\u4efb\u52a1\u4e0a\u8fbe\u5230\u4e86\u6781\u9ad8\u7684\u6027\u80fd\u3002<\/li>\n<li>\u8bed\u97f3\u8bc6\u522b&#xff1a;\u4f7f\u7528NAS\u627e\u5230\u7684\u6a21\u578b\u5728\u8bed\u97f3\u8bc6\u522b\u4efb\u52a1\u4e0a\u4f18\u4e8e\u4f20\u7edf\u624b\u5de5\u8bbe\u8ba1\u7684\u6a21\u578b\u3002<\/li>\n<li>\u81ea\u52a8\u9a7e\u9a76&#xff1a;\u901a\u8fc7NAS\u4f18\u5316\u4e86\u611f\u77e5\u6a21\u5757\u4e2d\u7684\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\u3002<\/li>\n<h3>5. \u5b9e\u73b0\u4e00\u4e2a\u7b80\u5355\u7684NAS\u6846\u67b6<\/h3>\n<p>\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5316\u7684NAS\u6846\u67b6\u4ee3\u7801&#xff0c;\u57fa\u4e8e\u968f\u673a\u641c\u7d22\u8fdb\u884c\u67b6\u6784\u4f18\u5316\u3002<\/p>\n<p><span class=\"token keyword\">import<\/span> random<br \/>\n<span class=\"token keyword\">import<\/span> tensorflow <span class=\"token keyword\">as<\/span> tf<\/p>\n<p><span class=\"token comment\"># \u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">create_search_space<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> <span class=\"token punctuation\">[<\/span><br \/>\n        <span class=\"token punctuation\">{<\/span><span class=\"token string\">&#039;layer_type&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token string\">&#039;conv&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;filters&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token number\">32<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;kernel_size&#039;<\/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 number\">3<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">}<\/span><span class=\"token punctuation\">,<\/span><br \/>\n        <span class=\"token punctuation\">{<\/span><span class=\"token string\">&#039;layer_type&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token string\">&#039;conv&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;filters&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;kernel_size&#039;<\/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 number\">3<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">}<\/span><span class=\"token punctuation\">,<\/span><br \/>\n        <span class=\"token punctuation\">{<\/span><span class=\"token string\">&#039;layer_type&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token string\">&#039;dense&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;units&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token number\">128<\/span><span class=\"token punctuation\">}<\/span><br \/>\n    <span class=\"token punctuation\">]<\/span><\/p>\n<p><span class=\"token comment\"># \u968f\u673a<\/span><\/p>\n<p>\u751f\u6210\u7f51\u7edc\u67b6\u6784<br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">generate_random_architecture<\/span><span class=\"token punctuation\">(<\/span>search_space<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    model <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>Sequential<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> layer_config <span class=\"token keyword\">in<\/span> search_space<span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token keyword\">if<\/span> layer_config<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;layer_type&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;&#061;<\/span> <span class=\"token string\">&#039;conv&#039;<\/span><span class=\"token punctuation\">:<\/span><br \/>\n            model<span class=\"token punctuation\">.<\/span>add<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Conv2D<span class=\"token punctuation\">(<\/span>filters<span class=\"token operator\">&#061;<\/span>layer_config<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;filters&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span><br \/>\n                                             kernel_size<span class=\"token operator\">&#061;<\/span>layer_config<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;kernel_size&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span><br \/>\n                                             activation<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;relu&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token keyword\">elif<\/span> layer_config<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;layer_type&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;&#061;<\/span> <span class=\"token string\">&#039;dense&#039;<\/span><span class=\"token punctuation\">:<\/span><br \/>\n            model<span class=\"token punctuation\">.<\/span>add<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Dense<span class=\"token punctuation\">(<\/span>units<span class=\"token operator\">&#061;<\/span>layer_config<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;units&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> activation<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;relu&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    model<span class=\"token punctuation\">.<\/span>add<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>GlobalAveragePooling2D<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    model<span class=\"token punctuation\">.<\/span>add<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Dense<span class=\"token punctuation\">(<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">,<\/span> activation<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;softmax&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> model<\/p>\n<p><span class=\"token comment\"># \u8bc4\u4f30\u6a21\u578b<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">evaluate_model<\/span><span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    model<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">compile<\/span><span class=\"token punctuation\">(<\/span>optimizer<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;adam&#039;<\/span><span class=\"token punctuation\">,<\/span> loss<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;sparse_categorical_crossentropy&#039;<\/span><span class=\"token punctuation\">,<\/span> metrics<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;accuracy&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># \u5047\u8bbe\u4f7f\u7528\u968f\u673a\u751f\u6210\u7684\u6570\u636e\u8fdb\u884c\u8bc4\u4f30<\/span><br \/>\n    x_train<span class=\"token punctuation\">,<\/span> y_train <span class=\"token operator\">&#061;<\/span> random_data<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    model<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>x_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">,<\/span> epochs<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> model<span class=\"token punctuation\">.<\/span>evaluate<span class=\"token punctuation\">(<\/span>x_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u968f\u673a\u641c\u7d22NAS<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">random_search_nas<\/span><span class=\"token punctuation\">(<\/span>search_space<span class=\"token punctuation\">,<\/span> num_trials<span class=\"token operator\">&#061;<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    best_architecture <span class=\"token operator\">&#061;<\/span> <span class=\"token boolean\">None<\/span><br \/>\n    best_score <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">float<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;-inf&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> _ <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span>num_trials<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        architecture <span class=\"token operator\">&#061;<\/span> generate_random_architecture<span class=\"token punctuation\">(<\/span>search_space<span class=\"token punctuation\">)<\/span><br \/>\n        score <span class=\"token operator\">&#061;<\/span> evaluate_model<span class=\"token punctuation\">(<\/span>architecture<span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token keyword\">if<\/span> score <span class=\"token operator\">&gt;<\/span> best_score<span class=\"token punctuation\">:<\/span><br \/>\n            best_score <span class=\"token operator\">&#061;<\/span> score<br \/>\n            best_architecture <span class=\"token operator\">&#061;<\/span> architecture<br \/>\n    <span class=\"token keyword\">return<\/span> best_architecture<\/p>\n<h3>6. \u603b\u7ed3<\/h3>\n<p>\u795e\u7ecf\u67b6\u6784\u641c\u7d22&#xff08;NAS&#xff09;\u4f5c\u4e3a\u4e00\u79cd\u81ea\u52a8\u5316\u8bbe\u8ba1\u795e\u7ecf\u7f51\u7edc\u7684\u6280\u672f&#xff0c;\u6781\u5927\u5730\u63d0\u9ad8\u4e86\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u5f00\u53d1\u6548\u7387\u3002\u867d\u7136\u5176\u8ba1\u7b97\u5f00\u9500\u8f83\u5927&#xff0c;\u4f46\u8fd1\u5e74\u6765\u901a\u8fc7\u6743\u91cd\u5171\u4eab\u3001\u4ee3\u7406\u6a21\u578b\u7b49\u6280\u672f\u5927\u5927\u964d\u4f4e\u4e86NAS\u7684\u641c\u7d22\u6210\u672c\u3002\u968f\u7740\u6280\u672f\u7684\u53d1\u5c55&#xff0c;NAS\u5df2\u7ecf\u5e94\u7528\u4e8e\u5404\u79cd\u5b9e\u9645\u4efb\u52a1&#xff0c;\u5e76\u6709\u671b\u6210\u4e3a\u672a\u6765\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8bbe\u8ba1\u7684\u91cd\u8981\u5de5\u5177\u3002<\/p>\n<p>NAS\u7684\u672a\u6765\u65b9\u5411\u53ef\u80fd\u5305\u62ec\u66f4\u9ad8\u6548\u7684\u641c\u7d22\u65b9\u6cd5\u3001\u66f4\u5e7f\u6cdb\u7684\u5e94\u7528\u573a\u666f\u4ee5\u53ca\u7ed3\u5408\u66f4\u591a\u5143\u7684\u4f18\u5316\u76ee\u6807\u3002\u901a\u8fc7\u8fd9\u7bc7\u6587\u7ae0&#xff0c;\u5e0c\u671b\u4f60\u5bf9NAS\u6709\u4e86\u6df1\u5165\u7684\u7406\u89e3&#xff0c;\u5e76\u638c\u63e1\u4e86\u57fa\u672c\u7684\u5b9e\u73b0\u65b9\u6cd5\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb1.6w\u6b21\uff0c\u70b9\u8d5e174\u6b21\uff0c\u6536\u85cf177\u6b21\u3002\u795e\u7ecf\u67b6\u6784\u641c\u7d22\uff08NAS\uff09\u662f\u6307\u901a\u8fc7\u641c\u7d22\u7b97\u6cd5\u81ea\u52a8\u8bbe\u8ba1\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\uff0c\u4ece\u800c\u4f18\u5316\u7279\u5b9a\u4efb\u52a1\u7684\u6027\u80fd\u3002NAS\u7684\u76ee\u6807\u662f\u5728\u4e00\u4e2a\u5b9a\u4e49\u597d\u7684\u641c\u7d22\u7a7a\u95f4\u4e2d\uff0c\u627e\u5230\u6700\u4f73\u7684\u7f51\u7edc\u7ed3\u6784\uff0c\u8be5\u7ed3\u6784\u901a\u5e38\u7531\u6027\u80fd\u6307\u6807\uff08\u4f8b\u5982\u51c6\u786e\u7387\u3001\u901f\u5ea6\u3001\u53c2\u6570\u91cf\u7b49\uff09\u6765\u8861\u91cf\u3002\u641c\u7d22\u7a7a\u95f4\uff08Search 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