{"id":8765,"date":"2025-04-18T16:57:54","date_gmt":"2025-04-18T08:57:54","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/8765.html"},"modified":"2025-04-18T16:57:54","modified_gmt":"2025-04-18T08:57:54","slug":"%e5%85%84%e5%bc%9f%e4%bb%ac%ef%bc%8c%e4%b8%8d%e4%bc%9a%e6%9c%8d%e5%8a%a1%e5%99%a8%e7%b9%81%e5%bf%99%e7%9a%84deepseek-r1-v3%e7%9c%9f%e6%bb%a1%e8%a1%80%e7%89%88%e6%9d%a5%e4%ba%86%ef%bc%8c%e6%94%af","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/8765.html","title":{"rendered":"\u5144\u5f1f\u4eec\uff0c\u4e0d\u4f1a\u670d\u52a1\u5668\u7e41\u5fd9\u7684DeepSeek R1\/V3\u771f\u6ee1\u8840\u7248\u6765\u4e86\uff0c\u652f\u6301\u7f51\u9875\u7248\u548cAPI\u63a5\u5165\uff0c\u514d\u8d39500\u4e07tokens\u5403\u5230\u9971\uff0c\u7edd\u7edd\u5b50\uff01\uff01\uff01"},"content":{"rendered":"<p>\u672c\u6587\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528\u84dd\u8018\u5143\u751f\u4ee3\u667a\u7b97\u4e91\u5e73\u53f0\u7684DeepSeek\u6ee1\u8840\u7248\u670d\u52a1&#xff0c;\u5305\u62ec\u7f51\u9875\u7248\u8bbf\u95ee\u548cAPI\u63a5\u5165\u65b9\u5f0f\u3002DeepSeek\u662f\u4e00\u6b3e\u5f3a\u5927\u7684\u8bed\u8a00\u6a21\u578b&#xff0c;\u652f\u6301\u6587\u672c\u751f\u6210\u3001\u95ee\u7b54\u7b49\u591a\u79cd\u4efb\u52a1\u3002\u7528\u6237\u53ef\u901a\u8fc7\u84dd\u8018\u5e73\u53f0\u7684\u7f51\u9875\u7248\u76f4\u63a5\u4f7f\u7528&#xff0c;\u907f\u514d\u670d\u52a1\u5668\u7e41\u5fd9\u95ee\u9898\u3002\u6b64\u5916&#xff0c;\u6587\u7ae0\u8fd8\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u5c06DeepSeek\u63a5\u5165Chatbox&#xff0c;\u5b9e\u73b0\u667a\u80fd\u5316\u804a\u5929\u673a\u5668\u4eba\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u521b\u5efaAPI KEY\u3001\u5b89\u88c5Chatbox\u3001\u914d\u7f6e\u81ea\u5b9a\u4e49\u63d0\u4f9b\u65b9&#xff0c;\u5e76\u9a8c\u8bc1\u529f\u80fd\u3002\u84dd\u8018\u5e73\u53f0\u8fd8\u63d0\u4f9b\u7d2f\u8ba11000\u4e07\u514d\u8d39tokens&#xff08;R1\u548cV3\u5404500W&#xff09;\u4f9b\u7528\u6237\u4f7f\u7528\u3002<\/p>\n<hr \/>\n<p>&#x1f9d1; \u535a\u4e3b\u7b80\u4ecb&#xff1a;\u73b0\u4efb\u963f\u91cc\u5df4\u5df4\u5d4c\u5165\u5f0f\u6280\u672f\u4e13\u5bb6&#xff0c;15\u5e74\u5de5\u4f5c\u7ecf\u9a8c&#xff0c;\u6df1\u8015\u5d4c\u5165\u5f0f&#043;\u4eba\u5de5\u667a\u80fd\u9886\u57df&#xff0c;\u7cbe\u901a\u5d4c\u5165\u5f0f\u9886\u57df\u5f00\u53d1\u3001\u6280\u672f\u7ba1\u7406\u3001\u7b80\u5386\u62db\u8058\u9762\u8bd5\u3002CSDN\u4f18\u8d28\u521b\u4f5c\u8005&#xff0c;\u63d0\u4f9b\u4ea7\u54c1\u6d4b\u8bc4\u3001\u5b66\u4e60\u8f85\u5bfc\u3001\u7b80\u5386\u9762\u8bd5\u8f85\u5bfc\u3001\u6bd5\u8bbe\u8f85\u5bfc\u3001\u9879\u76ee\u5f00\u53d1\u3001C\/C&#043;&#043;\/Java\/Python\/Linux\/AI\u7b49\u65b9\u9762\u7684\u670d\u52a1&#xff0c;\u5982\u6709\u9700\u8981\u8bf7\u7ad9\u5185\u79c1\u4fe1\u6216\u8005\u8054\u7cfb\u4efb\u610f\u6587\u7ae0\u5e95\u90e8\u7684\u7684VX\u540d\u7247&#xff08;ID&#xff1a;gylzbk&#xff09;<\/p>\n<p>&#x1f4ac; \u535a\u4e3b\u7c89\u4e1d\u7fa4\u4ecb\u7ecd&#xff1a;\u2460 \u7fa4\u5185\u521d\u4e2d\u751f\u3001\u9ad8\u4e2d\u751f\u3001\u672c\u79d1\u751f\u3001\u7814\u7a76\u751f\u3001\u535a\u58eb\u751f\u904d\u5e03&#xff0c;\u53ef\u4e92\u76f8\u5b66\u4e60&#xff0c;\u4ea4\u6d41\u56f0\u60d1\u3002\u2461 \u70ed\u699ctop10\u7684\u5e38\u5ba2\u4e5f\u5728\u7fa4\u91cc&#xff0c;\u4e5f\u6709\u6570\u4e0d\u6e05\u7684\u4e07\u7c89\u5927\u4f6c&#xff0c;\u53ef\u4ee5\u4ea4\u6d41\u5199\u4f5c\u6280\u5de7&#xff0c;\u4e0a\u699c\u7ecf\u9a8c&#xff0c;\u6da8\u7c89\u79d8\u7c4d\u3002\u2462 \u7fa4\u5185\u4e5f\u6709\u804c\u573a\u7cbe\u82f1&#xff0c;\u5927\u5382\u5927\u4f6c&#xff0c;\u53ef\u4ea4\u6d41\u6280\u672f\u3001\u9762\u8bd5\u3001\u627e\u5de5\u4f5c\u7684\u7ecf\u9a8c\u3002\u2463 \u8fdb\u7fa4\u514d\u8d39\u8d60\u9001\u5199\u4f5c\u79d8\u7c4d\u4e00\u4efd&#xff0c;\u52a9\u4f60\u7531\u5199\u4f5c\u5c0f\u767d\u664b\u5347\u4e3a\u521b\u4f5c\u5927\u4f6c\u3002\u2464 \u8fdb\u7fa4\u8d60\u9001CSDN\u8bc4\u8bba\u9632\u5c01\u811a\u672c&#xff0c;\u9001\u771f\u6d3b\u8dc3\u7c89\u4e1d&#xff0c;\u52a9\u4f60\u63d0\u5347\u6587\u7ae0\u70ed\u5ea6\u3002\u6709\u5174\u8da3\u7684\u52a0\u6587\u672b\u8054\u7cfb\u65b9\u5f0f&#xff0c;\u5907\u6ce8\u81ea\u5df1\u7684CSDN\u6635\u79f0&#xff0c;\u62c9\u4f60\u8fdb\u7fa4&#xff0c;\u4e92\u76f8\u5b66\u4e60\u5171\u540c\u8fdb\u6b65\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085749-6802140db40e6.gif\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<\/p>\n<h4>\u5144\u5f1f\u4eec&#xff0c;\u4e0d\u4f1a\u670d\u52a1\u5668\u7e41\u5fd9\u7684DeepSeek R1\/V3\u771f\u6ee1\u8840\u7248\u6765\u4e86&#xff0c;\u652f\u6301\u7f51\u9875\u7248\u548cAPI\u63a5\u5165&#xff0c;\u514d\u8d39500\u4e07tokens\u5403\u5230\u9971&#xff0c;\u7edd\u7edd\u5b50&#xff01;&#xff01;&#xff01;<\/h4>\n<ul>\n<li>1. \u4ec0\u4e48\u662fDeepSeek&#xff1f;<\/li>\n<li>2. \u7f51\u9875\u7248\u8bbf\u95ee\u4e0d\u4f1a\u670d\u52a1\u5668\u7e41\u5fd9\u7684DeepSeek<\/li>\n<li>\n<ul>\n<li>2.1 \u4f7f\u7528\u8bf4\u660e<\/li>\n<li>2.2 \u529f\u80fd\u5b9e\u6d4b<\/li>\n<\/ul>\n<\/li>\n<li>3. Chatbox\u63a5\u5165API<\/li>\n<li>\n<ul>\n<li>3.1 \u521b\u5efaAPI KEY<\/li>\n<li>3.2 \u4e0b\u8f7d\u5b89\u88c5Chatbox<\/li>\n<li>3.3 \u914d\u7f6eDeepSeek<\/li>\n<li>3.4 \u529f\u80fd\u5b9e\u6d4b<\/li>\n<li>3.5 \u6ce8\u610f\u4e8b\u9879<\/li>\n<\/ul>\n<\/li>\n<li>4. \u603b\u7ed3<\/li>\n<\/ul>\n<h2>1. \u4ec0\u4e48\u662fDeepSeek&#xff1f;<\/h2>\n<p>DeepSeek\u662f\u4e00\u6b3e\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u8bed\u8a00\u6a21\u578b&#xff0c;\u5177\u5907\u5f3a\u5927\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u80fd\u529b\u3002\u5b83\u53ef\u4ee5\u5b8c\u6210\u6587\u672c\u751f\u6210\u3001\u95ee\u7b54\u3001\u4ee3\u7801\u7f16\u5199\u7b49\u591a\u79cd\u4efb\u52a1&#xff0c;\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5ba2\u670d\u7cfb\u7edf\u3001\u5185\u5bb9\u521b\u4f5c\u3001\u6570\u636e\u5206\u6790\u7b49\u9886\u57df\u3002<\/p>\n<p>\u5728\u4e0a\u4e00\u7bc7\u6587\u7ae0\u4e2d&#xff0c;\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e86\u300a\u5982\u4f55\u5728\u84dd\u8018\u7b97\u529b\u5e73\u53f0\u4e0a\u5feb\u901f\u90e8\u7f72DeepSeek\u300b&#xff0c;\u4f46\u662f\u53d7\u9650\u4e8e\u670d\u52a1\u5668\u6210\u672c&#xff0c;\u81ea\u5df1\u90e8\u7f72\u6700\u591a\u53ea\u80fd\u652f\u6301\u523032b&#xff0c;\u867d\u7136\u4e5f\u80fd\u8986\u76d6\u5927\u591a\u6570\u573a\u666f&#xff0c;\u4f46\u79bb\u6ee1\u8840\u7248\u6765\u8bf4&#xff0c;\u8fd8\u662f\u4f1a\u6709\u4e00\u5b9a\u7684\u5dee\u8ddd\u3002\u6240\u4ee5&#xff0c;\u4eca\u5929\u8fd9\u7bc7\u6587\u7ae0\u7ed9\u5927\u5bb6\u91cd\u70b9\u63a8\u8350\u4e00\u4e2a\u5df2\u7ecf\u90e8\u7f72\u597d\u7684\u771f\u6ee1\u8840\u7248DeepSeek&#xff0c;\u5e76\u4e14\u652f\u6301API\u8c03\u7528&#xff0c;\u5b8c\u5168\u53ef\u4ee5\u5e73\u66ff\u6389\u5b98\u65b9DeepSeek&#xff0c;\u7ed9\u5927\u5bb6\u4e00\u4e2a\u66f4\u597d\u7684\u9009\u62e9\u3002<\/p>\n<h2>2. \u7f51\u9875\u7248\u8bbf\u95ee\u4e0d\u4f1a\u670d\u52a1\u5668\u7e41\u5fd9\u7684DeepSeek<\/h2>\n<h3>2.1 \u4f7f\u7528\u8bf4\u660e<\/h3>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085749-6802140dbed4f.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<p>\u5982\u679c\u9700\u8981\u76f4\u63a5\u7f51\u9875\u7aef\u8bbf\u95ee&#xff0c;\u76f4\u63a5\u8bbf\u95ee\u84dd\u8018\u5143\u751f\u4ee3\u667a\u7b97\u4e91\u5e73\u53f0&#xff0c;\u6ce8\u518c\u5e76\u767b\u5f55\u540e\u5c31\u53ef\u4ee5\u53ef\u4ee5\u4e86\u3002\u5ef6\u8fdf\u4f4e&#xff0c;\u901f\u5ea6\u5feb\u3002<\/p>\n<h3>2.2 \u529f\u80fd\u5b9e\u6d4b<\/h3>\n<p>\u6211\u8ba9\u5b83\u7ed9\u6211\u751f\u6210\u4e00\u7bc7\u9898\u4e3a\u6a21\u578b\u538b\u7f29\u4e0e\u91cf\u5316&#xff1a;\u8ba9\u5927\u6a21\u578b\u8d70\u5411\u8f7b\u91cf\u5316\u843d\u5730\u7684\u6280\u672f\u535a\u5ba2\u3002 <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085749-6802140de5bf5.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<p>\u5b83\u7ed9\u6211\u751f\u6210\u7684\u5185\u5bb9\u5982\u4e0b&#xff1a;<\/p>\n<p># \u4e00\u3001\u5f15\u8a00<\/p>\n<p>\u968f\u7740\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u7684\u5feb\u901f\u53d1\u5c55&#xff0c;\u5927\u578b\u795e\u7ecf\u7f51\u7edc\u6a21\u578b&#xff08;\u5982BERT\u3001GPT-3\u7b49&#xff09;\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u8ba1\u7b97\u673a\u89c6\u89c9\u7b49\u9886\u57df\u53d6\u5f97\u4e86\u4ee4\u4eba\u77a9\u76ee\u7684\u6210\u5c31\u3002\u7136\u800c&#xff0c;\u8fd9\u4e9b\u5927\u6a21\u578b\u901a\u5e38\u9700\u8981\u5927\u91cf\u7684\u8ba1\u7b97\u8d44\u6e90\u548c\u5b58\u50a8\u7a7a\u95f4&#xff0c;\u9650\u5236\u4e86\u5b83\u4eec\u5728\u5b9e\u9645\u573a\u666f\u4e2d\u7684\u5e7f\u6cdb\u5e94\u7528\u3002\u5c24\u5176\u662f\u5728\u79fb\u52a8\u8bbe\u5907\u3001\u5d4c\u5165\u5f0f\u7cfb\u7edf\u7b49\u8d44\u6e90\u53d7\u9650\u7684\u73af\u5883\u4e2d&#xff0c;\u76f4\u63a5\u90e8\u7f72\u5927\u578b\u6a21\u578b\u53d8\u5f97\u5f02\u5e38\u56f0\u96be\u3002<\/p>\n<p>\u4e3a\u4e86\u514b\u670d\u8fd9\u4e00\u95ee\u9898&#xff0c;\u6a21\u578b\u538b\u7f29\u4e0e\u91cf\u5316\u6280\u672f\u5e94\u8fd0\u800c\u751f\u3002\u901a\u8fc7\u6a21\u578b\u538b\u7f29\u4e0e\u91cf\u5316&#xff0c;\u6211\u4eec\u53ef\u4ee5\u5728\u4fdd\u6301\u6a21\u578b\u6027\u80fd\u7684\u540c\u65f6&#xff0c;\u663e\u8457\u964d\u4f4e\u6a21\u578b\u7684\u8ba1\u7b97\u590d\u6742\u5ea6\u548c\u5b58\u50a8\u9700\u6c42&#xff0c;\u4ece\u800c\u5b9e\u73b0\u5927\u6a21\u578b\u5728\u8fb9\u7f18\u8bbe\u5907\u4e0a\u7684\u9ad8\u6548\u90e8\u7f72\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u6a21\u578b\u538b\u7f29\u4e0e\u91cf\u5316\u7684\u57fa\u7840\u6982\u5ff5\u3001\u5173\u952e\u6280\u672f\u8def\u5f84\u4ee5\u53ca\u5b9e\u9645\u5e94\u7528\u573a\u666f&#xff0c;\u5e76\u63a2\u8ba8\u8fd9\u4e00\u9886\u57df\u7684\u672a\u6765\u53d1\u5c55\u65b9\u5411\u3002<\/p>\n<hr \/>\n<p># \u4e8c\u3001\u6a21\u578b\u538b\u7f29\u4e0e\u91cf\u5316\u7684\u57fa\u7840\u77e5\u8bc6<\/p>\n<p>## 1. \u4ec0\u4e48\u662f\u6a21\u578b\u538b\u7f29&#xff1f;<\/p>\n<p>\u6a21\u578b\u538b\u7f29\u7684\u76ee\u6807\u662f\u901a\u8fc7\u51cf\u5c11\u6a21\u578b\u7684\u53c2\u6570\u6570\u91cf\u6216\u4f18\u5316\u6a21\u578b\u7ed3\u6784&#xff0c;\u964d\u4f4e\u6a21\u578b\u7684\u590d\u6742\u5ea6\u548c\u8ba1\u7b97\u9700\u6c42\u3002\u5e38\u89c1\u7684\u538b\u7f29\u6280\u672f\u5305\u62ec&#xff1a;<\/p>\n<ul>\n<li>\u526a\u679d&#xff08;Pruning&#xff09;&#xff1a;\u79fb\u9664\u5bf9\u6a21\u578b\u8d21\u732e\u8f83\u5c0f\u7684\u795e\u7ecf\u5143\u6216\u6743\u91cd\u3002<\/li>\n<li>\u84b8\u998f&#xff08;Distillation&#xff09;&#xff1a;\u5c06\u5927\u6a21\u578b\u7684\u77e5\u8bc6\u8fc1\u79fb\u5230\u4e00\u4e2a\u66f4\u5c0f\u3001\u66f4\u8f7b\u91cf\u5316\u7684\u6a21\u578b\u4e2d\u3002<\/li>\n<\/ul>\n<p>## 2. \u91cf\u5316\u7684\u57fa\u672c\u539f\u7406<\/p>\n<p>\u91cf\u5316\u662f\u901a\u8fc7\u964d\u4f4e\u6570\u503c\u7cbe\u5ea6\u6765\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u548c\u8ba1\u7b97\u5f00\u9500\u3002\u4f8b\u5982&#xff0c;\u4f20\u7edf\u7684\u6d6e\u70b9\u6570\u8fd0\u7b97\u4f7f\u752832\u4f4d\u6d6e\u70b9\u6570&#xff08;FP32&#xff09;&#xff0c;\u800c\u91cf\u5316\u6280\u672f\u53ef\u4ee5\u5c06\u8fd9\u4e9b\u53c2\u6570\u538b\u7f29\u52308\u4f4d\u6574\u6570&#xff08;INT8&#xff09;\u6216\u66f4\u4f4e\u7cbe\u5ea6\u3002<\/p>\n<ul>\n<li>\u5b9a\u70b9\u91cf\u5316&#xff1a;\u5c06\u6743\u91cd\u548c\u6fc0\u6d3b\u503c\u8f6c\u6362\u4e3a\u4f4e\u7cbe\u5ea6\u8868\u793a\u3002<\/li>\n<li>\u52a8\u6001\u91cf\u5316 vs \u9759\u6001\u91cf\u5316&#xff1a;\u52a8\u6001\u91cf\u5316\u5728\u63a8\u7406\u8fc7\u7a0b\u4e2d\u5b9e\u65f6\u8c03\u6574\u7f29\u653e\u56e0\u5b50&#xff0c;\u9759\u6001\u91cf\u5316\u5219\u5728\u8bad\u7ec3\u540e\u56fa\u5b9a\u7f29\u653e\u56e0\u5b50\u3002<\/li>\n<li>\u91cf\u5316\u611f\u77e5\u8bad\u7ec3&#xff08;QAT&#xff09;&#xff1a;\u5728\u8bad\u7ec3\u9636\u6bb5\u5f15\u5165\u91cf\u5316\u64cd\u4f5c&#xff0c;\u63d0\u5347\u91cf\u5316\u6a21\u578b\u7684\u6027\u80fd\u3002<\/li>\n<\/ul>\n<p>## 3. \u91cf\u5316 vs \u538b\u7f29&#xff1a;\u533a\u522b\u4e0e\u8054\u7cfb<\/p>\n<p>\u538b\u7f29\u6280\u672f\u4e3b\u8981\u5173\u6ce8\u51cf\u5c11\u53c2\u6570\u6570\u91cf&#xff0c;\u800c\u91cf\u5316\u6280\u672f\u5219\u662f\u901a\u8fc7\u964d\u4f4e\u7cbe\u5ea6\u6765\u4f18\u5316\u8ba1\u7b97\u6548\u7387\u3002\u4e24\u8005\u53ef\u4ee5\u7ed3\u5408\u4f7f\u7528&#xff0c;\u4ee5\u5b9e\u73b0\u66f4\u9ad8\u6548\u7684\u6a21\u578b\u90e8\u7f72\u3002<\/p>\n<hr \/>\n<p># \u4e09\u3001\u6a21\u578b\u538b\u7f29\u4e0e\u91cf\u5316\u7684\u5173\u952e\u6280\u672f\u8def\u5f84<\/p>\n<p>## 1. \u6a21\u578b\u526a\u679d&#xff08;Network Pruning&#xff09;<\/p>\n<p>\u526a\u679d\u662f\u4e00\u79cd\u76f4\u63a5\u51cf\u5c11\u6a21\u578b\u53c2\u6570\u6570\u91cf\u7684\u65b9\u6cd5\u3002\u6839\u636e\u526a\u679d\u7b56\u7565\u7684\u4e0d\u540c&#xff0c;\u53ef\u5206\u4e3a\u4ee5\u4e0b\u4e24\u7c7b&#xff1a;<\/p>\n<ul>\n<li>\u7ed3\u6784\u5316\u526a\u679d&#xff1a;\u79fb\u9664\u6574\u4e2a\u795e\u7ecf\u5143\u6216\u901a\u9053&#xff08;\u5982Channel Pruning&#xff09;&#xff0c;\u4fdd\u8bc1\u526a\u679d\u540e\u7684\u6a21\u578b\u4ecd\u5177\u6709\u89c4\u5219\u7684\u7f51\u7edc\u7ed3\u6784\u3002<\/li>\n<li>\u975e\u7ed3\u6784\u5316\u526a\u80a2&#xff1a;\u968f\u673a\u79fb\u9664\u90e8\u5206\u6743\u91cd&#xff0c;\u53ef\u80fd\u4f1a\u5bfc\u81f4\u4e0d\u89c4\u5219\u7684\u7a00\u758f\u77e9\u9635\u3002<\/li>\n<\/ul>\n<p>\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u901a\u9053\u526a\u679d\u5b9e\u73b0\u793a\u4f8b&#xff08;\u4f7f\u7528Keras&#xff09;&#xff1a;<\/p>\n<p> <span class=\"token keyword\">import<\/span> tensorflow <span class=\"token keyword\">as<\/span> tf<br \/>\n<span class=\"token keyword\">from<\/span> tensorflow<span class=\"token punctuation\">.<\/span>keras <span class=\"token keyword\">import<\/span> layers<span class=\"token punctuation\">,<\/span> models<\/p>\n<p><span class=\"token comment\"># \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b<\/span><br \/>\nmodel <span class=\"token operator\">&#061;<\/span> models<span class=\"token punctuation\">.<\/span>VGG16<span class=\"token punctuation\">(<\/span>weights<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;imagenet&#039;<\/span><span class=\"token punctuation\">,<\/span> include_top<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">False<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u6dfb\u52a0\u5168\u8fde\u63a5\u5c42<\/span><br \/>\nflatten_layer <span class=\"token operator\">&#061;<\/span> layers<span class=\"token punctuation\">.<\/span>Flatten<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\ndense_layer1 <span class=\"token operator\">&#061;<\/span> layers<span class=\"token punctuation\">.<\/span>Dense<span class=\"token punctuation\">(<\/span><span class=\"token number\">4096<\/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><br \/>\ndense_layer2 <span class=\"token operator\">&#061;<\/span> layers<span class=\"token punctuation\">.<\/span>Dense<span class=\"token punctuation\">(<\/span><span class=\"token number\">4096<\/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><br \/>\npredictions <span class=\"token operator\">&#061;<\/span> layers<span class=\"token punctuation\">.<\/span>Dense<span class=\"token punctuation\">(<\/span><span class=\"token number\">1000<\/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>model <span class=\"token operator\">&#061;<\/span> models<span class=\"token punctuation\">.<\/span>Sequential<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span><br \/>\n    model<span class=\"token punctuation\">,<\/span><br \/>\n    flatten_layer<span class=\"token punctuation\">,<\/span><br \/>\n    dense_layer1<span class=\"token punctuation\">,<\/span><br \/>\n    dense_layer2<span class=\"token punctuation\">,<\/span><br \/>\n    predictions<br \/>\n<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u526a\u679d\u51fd\u6570&#xff08;\u901a\u9053\u526a\u679d&#xff09;<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">prune_channels<\/span><span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token comment\"># \u904d\u5386\u5c42&#xff0c;\u627e\u5230\u5377\u79ef\u5c42\u5e76\u526a\u679d<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> layer <span class=\"token keyword\">in<\/span> model<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token keyword\">if<\/span> <span class=\"token builtin\">isinstance<\/span><span class=\"token punctuation\">(<\/span>layer<span class=\"token punctuation\">,<\/span> layers<span class=\"token punctuation\">.<\/span>Conv2D<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n            <span class=\"token comment\"># \u83b7\u53d6\u6743\u91cd\u548c\u63a9\u7801<\/span><br \/>\n            weights <span class=\"token operator\">&#061;<\/span> layer<span class=\"token punctuation\">.<\/span>get_weights<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><br \/>\n            mask <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">abs<\/span><span class=\"token punctuation\">(<\/span>weights<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&lt;<\/span> <span class=\"token number\">1e-3<\/span>  <span class=\"token comment\"># \u526a\u679d\u9608\u503c<\/span><\/p>\n<p>            <span class=\"token comment\"># \u66f4\u65b0\u6743\u91cd<\/span><br \/>\n            pruned_weights <span class=\"token operator\">&#061;<\/span> weights <span class=\"token operator\">*<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span> <span class=\"token operator\">&#8211;<\/span> mask<span class=\"token punctuation\">)<\/span><br \/>\n            layer<span class=\"token punctuation\">.<\/span>set_weights<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span>pruned_weights<span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#043;<\/span> layer<span class=\"token punctuation\">.<\/span>get_weights<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 punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">return<\/span> model<\/p>\n<p><span class=\"token comment\"># \u5e94\u7528\u526a\u679d<\/span><br \/>\npruned_model <span class=\"token operator\">&#061;<\/span> prune_channels<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u91cd\u65b0\u7f16\u8bd1\u6a21\u578b\u5e76\u8bad\u7ec3<\/span><br \/>\npruned_model<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">compile<\/span><span class=\"token punctuation\">(<\/span><br \/>\n    optimizer<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;adam&#039;<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    loss<span class=\"token operator\">&#061;<\/span>tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>losses<span class=\"token punctuation\">.<\/span>CategoricalCrossentropy<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    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><br \/>\n<span class=\"token punctuation\">)<\/span><\/p>\n<p>pruned_model<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>train_dataset<span class=\"token punctuation\">,<\/span> epochs<span class=\"token operator\">&#061;<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>## 2. \u77e5\u8bc6\u84b8\u998f&#xff08;Knowledge Distillation&#xff09;<\/p>\n<p>\u77e5\u8bc6\u84b8\u998f\u7684\u6838\u5fc3\u601d\u60f3\u662f\u5c06\u5927\u6a21\u578b\u7684\u77e5\u8bc6\u8fc1\u79fb\u5230\u4e00\u4e2a\u66f4\u5c0f\u7684\u5b66\u751f\u6a21\u578b\u4e2d\u3002\u5177\u4f53\u6b65\u9aa4\u5982\u4e0b&#xff1a;<\/p>\n<li>\u4f7f\u7528\u5927\u6a21\u578b&#xff08;\u6559\u5e08\u6a21\u578b&#xff09;\u5bf9\u6570\u636e\u8fdb\u884c\u8bad\u7ec3\u3002<\/li>\n<li>\u5c06\u5b66\u751f\u6a21\u578b\u5728\u6559\u5e08\u6a21\u578b\u7684\u6307\u5bfc\u4e0b\u8fdb\u884c\u5fae\u8c03&#xff0c;\u4f7f\u5176\u6a21\u4eff\u6559\u5e08\u6a21\u578b\u7684\u8f93\u51fa\u3002<\/li>\n<p>\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u77e5\u8bc6\u84b8\u998f\u5b9e\u73b0\u793a\u4f8b&#xff08;\u4f7f\u7528PyTorch&#xff09;&#xff1a;<\/p>\n<p> <span class=\"token keyword\">import<\/span> torch<br \/>\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>nn <span class=\"token keyword\">as<\/span> nn<br \/>\n<span class=\"token keyword\">from<\/span> torch<span class=\"token punctuation\">.<\/span>utils<span class=\"token punctuation\">.<\/span>data <span class=\"token keyword\">import<\/span> DataLoader<\/p>\n<p><span class=\"token comment\"># \u6559\u5e08\u6a21\u578b&#xff08;\u590d\u6742\u6a21\u578b&#xff09;<\/span><br \/>\n<span class=\"token keyword\">class<\/span> <span class=\"token class-name\">TeacherModel<\/span><span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Module<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>TeacherModel<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>layers <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Sequential<span class=\"token punctuation\">(<\/span><br \/>\n            nn<span class=\"token punctuation\">.<\/span>Conv2d<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> kernel_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n            nn<span class=\"token punctuation\">.<\/span>ReLU<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n            nn<span class=\"token punctuation\">.<\/span>Conv2d<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">128<\/span><span class=\"token punctuation\">,<\/span> kernel_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n            nn<span class=\"token punctuation\">.<\/span>ReLU<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n            nn<span class=\"token punctuation\">.<\/span>Flatten<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n            nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span><span class=\"token number\">128<\/span> <span class=\"token operator\">*<\/span> <span class=\"token number\">25<\/span> <span class=\"token operator\">*<\/span> <span class=\"token number\">25<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">def<\/span> <span class=\"token function\">forward<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token keyword\">return<\/span> self<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u5b66\u751f\u6a21\u578b&#xff08;\u8f7b\u91cf\u5316\u6a21\u578b&#xff09;<\/span><br \/>\n<span class=\"token keyword\">class<\/span> <span class=\"token class-name\">StudentModel<\/span><span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Module<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>StudentModel<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>layers <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Sequential<span class=\"token punctuation\">(<\/span><br \/>\n            nn<span class=\"token punctuation\">.<\/span>Conv2d<span class=\"token punctuation\">(<\/span><span class=\"token number\">3<\/span><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 number\">3<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n            nn<span class=\"token punctuation\">.<\/span>ReLU<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n            nn<span class=\"token punctuation\">.<\/span>Conv2d<span class=\"token punctuation\">(<\/span><span class=\"token number\">32<\/span><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 number\">3<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n            nn<span class=\"token punctuation\">.<\/span>ReLU<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n            nn<span class=\"token punctuation\">.<\/span>Flatten<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n            nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span> <span class=\"token operator\">*<\/span> <span class=\"token number\">25<\/span> <span class=\"token operator\">*<\/span> <span class=\"token number\">25<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">def<\/span> <span class=\"token function\">forward<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token keyword\">return<\/span> self<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u635f\u5931\u51fd\u6570&#xff08;\u7ed3\u5408\u5206\u7c7b\u635f\u5931\u548c\u84b8\u998f\u635f\u5931&#xff09;<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">distillation_loss<\/span><span class=\"token punctuation\">(<\/span>student_logits<span class=\"token punctuation\">,<\/span> teacher_logits<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">,<\/span> temperature<span class=\"token operator\">&#061;<\/span><span class=\"token number\">2.0<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token comment\"># \u5206\u7c7b\u635f\u5931<\/span><br \/>\n    ce_loss <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>CrossEntropyLoss<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">(<\/span>student_logits<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token comment\"># \u84b8\u998f\u635f\u5931&#xff08;\u8f6f\u76ee\u6807&#xff09;<\/span><br \/>\n    student_softmax <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>functional<span class=\"token punctuation\">.<\/span>softmax<span class=\"token punctuation\">(<\/span>student_logits <span class=\"token operator\">\/<\/span> temperature<span class=\"token punctuation\">,<\/span> dim<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    teacher_softmax <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>functional<span class=\"token punctuation\">.<\/span>softmax<span class=\"token punctuation\">(<\/span>teacher_logits <span class=\"token operator\">\/<\/span> temperature<span class=\"token punctuation\">,<\/span> dim<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    kl_divergence <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>KLDivLoss<span class=\"token punctuation\">(<\/span>reduction<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;batchmean&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">(<\/span>student_softmax<span class=\"token punctuation\">.<\/span>log<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> teacher_softmax<span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">return<\/span> ce_loss <span class=\"token operator\">&#043;<\/span> <span class=\"token punctuation\">(<\/span>kl_divergence <span class=\"token operator\">*<\/span> temperature <span class=\"token operator\">**<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u6570\u636e\u52a0\u8f7d\u5668&#xff08;\u5047\u8bbe\u5df2\u6709\u6570\u636e\u96c6&#xff09;<\/span><br \/>\ntrain_loader <span class=\"token operator\">&#061;<\/span> DataLoader<span class=\"token punctuation\">(<\/span>dataset<span class=\"token punctuation\">,<\/span> batch_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">,<\/span> shuffle<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u521d\u59cb\u5316\u6a21\u578b\u548c\u4f18\u5316\u5668<\/span><br \/>\nteacher_model <span class=\"token operator\">&#061;<\/span> TeacherModel<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nstudent_model <span class=\"token operator\">&#061;<\/span> StudentModel<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\noptimizer <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>optim<span class=\"token punctuation\">.<\/span>Adam<span class=\"token punctuation\">(<\/span>student_model<span class=\"token punctuation\">.<\/span>parameters<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> lr<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.001<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u8bad\u7ec3\u8fc7\u7a0b<\/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    <span class=\"token keyword\">for<\/span> images<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> train_loader<span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token comment\"># \u524d\u5411\u4f20\u64ad<\/span><br \/>\n        teacher_outputs <span class=\"token operator\">&#061;<\/span> teacher_model<span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">)<\/span><br \/>\n        student_outputs <span class=\"token operator\">&#061;<\/span> student_model<span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">)<\/span><\/p>\n<p>        <span class=\"token comment\"># \u8ba1\u7b97\u635f\u5931<\/span><br \/>\n        loss <span class=\"token operator\">&#061;<\/span> distillation_loss<span class=\"token punctuation\">(<\/span>student_outputs<span class=\"token punctuation\">,<\/span> teacher_outputs<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">)<\/span><\/p>\n<p>        <span class=\"token comment\"># \u53cd\u5411\u4f20\u64ad\u548c\u4f18\u5316<\/span><br \/>\n        optimizer<span class=\"token punctuation\">.<\/span>zero_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        optimizer<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;Epoch [<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>epoch<span class=\"token operator\">&#043;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">\/<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>num_epochs<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">], Loss: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>loss<span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u6d4b\u8bd5\u5b66\u751f\u6a21\u578b<\/span><br \/>\nstudent_model<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">eval<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntest_loader <span class=\"token operator\">&#061;<\/span> DataLoader<span class=\"token punctuation\">(<\/span>test_dataset<span class=\"token punctuation\">,<\/span> batch_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">)<\/span><br \/>\ncorrect <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0<\/span><br \/>\ntotal <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0<\/span><\/p>\n<p><span class=\"token keyword\">with<\/span> torch<span class=\"token punctuation\">.<\/span>no_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> images<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> test_loader<span class=\"token punctuation\">:<\/span><br \/>\n        outputs <span class=\"token operator\">&#061;<\/span> student_model<span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">)<\/span><br \/>\n        _<span class=\"token punctuation\">,<\/span> predicted <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">max<\/span><span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        total <span class=\"token operator\">&#043;&#061;<\/span> labels<span class=\"token punctuation\">.<\/span>size<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        correct <span class=\"token operator\">&#043;&#061;<\/span> <span class=\"token punctuation\">(<\/span>predicted <span class=\"token operator\">&#061;&#061;<\/span> labels<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">sum<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;Accuracy of student model: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>correct <span class=\"token operator\">\/<\/span> total <span class=\"token operator\">*<\/span> <span class=\"token number\">100<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.2f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">%&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>## 3. \u91cf\u5316\u6280\u672f&#xff08;Quantization&#xff09;<\/p>\n<p>### \u5b9a\u70b9\u91cf\u5316 \u76f4\u63a5\u5c06\u6743\u91cd\u548c\u6fc0\u6d3b\u503c\u8f6c\u6362\u4e3a\u4f4e\u7cbe\u5ea6\u8868\u793a\u3002\u4f8b\u5982&#xff0c;Post-Training Quantization&#xff08;PTQ&#xff09;\u662f\u540e\u8bad\u7ec3\u91cf\u5316&#xff0c;\u9002\u7528\u4e8e\u5df2\u7ecf\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u3002<\/p>\n<p>\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u5b9a\u70b9\u91cf\u5316\u5b9e\u73b0\u793a\u4f8b&#xff08;\u4f7f\u7528PyTorch&#xff09;&#xff1a;<\/p>\n<p> <span class=\"token keyword\">import<\/span> torch<br \/>\n<span class=\"token keyword\">from<\/span> torch<span class=\"token punctuation\">.<\/span>quantization <span class=\"token keyword\">import<\/span> QuantWrapper<span class=\"token punctuation\">,<\/span> default_qconfig<\/p>\n<p><span class=\"token comment\"># \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b<\/span><br \/>\nmodel <span class=\"token operator\">&#061;<\/span> MobileNetV2<span class=\"token punctuation\">(<\/span>pretrained<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u5b9a\u4e49\u91cf\u5316\u914d\u7f6e<\/span><br \/>\nqconfig <span class=\"token operator\">&#061;<\/span> default_qconfig<br \/>\nquantized_model <span class=\"token operator\">&#061;<\/span> QuantWrapper<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">)<\/span><br \/>\nquantized_model<span class=\"token punctuation\">.<\/span>qconfig <span class=\"token operator\">&#061;<\/span> qconfig<\/p>\n<p><span class=\"token comment\"># \u91cf\u5316\u51c6\u5907\u548c\u8f6c\u6362<\/span><br \/>\ntorch<span class=\"token punctuation\">.<\/span>utils<span class=\"token punctuation\">.<\/span>quantization<span class=\"token punctuation\">.<\/span>prepare<span class=\"token punctuation\">(<\/span>quantized_model<span class=\"token punctuation\">,<\/span> inplace<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntorch<span class=\"token punctuation\">.<\/span>utils<span class=\"token punctuation\">.<\/span>quantization<span class=\"token punctuation\">.<\/span>convert<span class=\"token punctuation\">(<\/span>quantized_model<span class=\"token punctuation\">,<\/span> inplace<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u6d4b\u8bd5\u91cf\u5316\u540e\u7684\u6a21\u578b<\/span><br \/>\ntest_loader <span class=\"token operator\">&#061;<\/span> DataLoader<span class=\"token punctuation\">(<\/span>test_dataset<span class=\"token punctuation\">,<\/span> batch_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">)<\/span><br \/>\ncorrect <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0<\/span><br \/>\ntotal <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0<\/span><\/p>\n<p><span class=\"token keyword\">with<\/span> torch<span class=\"token punctuation\">.<\/span>no_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> images<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> test_loader<span class=\"token punctuation\">:<\/span><br \/>\n        outputs <span class=\"token operator\">&#061;<\/span> quantized_model<span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">)<\/span><br \/>\n        _<span class=\"token punctuation\">,<\/span> predicted <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">max<\/span><span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        total <span class=\"token operator\">&#043;&#061;<\/span> labels<span class=\"token punctuation\">.<\/span>size<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        correct <span class=\"token operator\">&#043;&#061;<\/span> <span class=\"token punctuation\">(<\/span>predicted <span class=\"token operator\">&#061;&#061;<\/span> labels<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">sum<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;Accuracy of quantized model: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>correct <span class=\"token operator\">\/<\/span> total <span class=\"token operator\">*<\/span> <span class=\"token number\">100<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.2f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">%&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>### \u91cf\u5316\u611f\u77e5\u8bad\u7ec3&#xff08;QAT&#xff09; \u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5f15\u5165\u91cf\u5316\u64cd\u4f5c&#xff0c;\u901a\u8fc7\u53cd\u5411\u4f20\u64ad\u4f18\u5316\u91cf\u5316\u540e\u7684\u53c2\u6570\u3002 \u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684QAT\u5b9e\u73b0\u793a\u4f8b&#xff08;\u4f7f\u7528PyTorch&#xff09;&#xff1a;<\/p>\n<p> <span class=\"token keyword\">import<\/span> torch<br \/>\n<span class=\"token keyword\">from<\/span> torch<span class=\"token punctuation\">.<\/span>quantization <span class=\"token keyword\">import<\/span> QuantWrapper<span class=\"token punctuation\">,<\/span> default_qat_qconfig<\/p>\n<p><span class=\"token comment\"># \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b<\/span><br \/>\nmodel <span class=\"token operator\">&#061;<\/span> MobileNetV2<span class=\"token punctuation\">(<\/span>pretrained<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u5b9a\u4e49\u91cf\u5316\u914d\u7f6e<\/span><br \/>\nqconfig <span class=\"token operator\">&#061;<\/span> default_qat_qconfig<br \/>\nquantized_model <span class=\"token operator\">&#061;<\/span> QuantWrapper<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">)<\/span><br \/>\nquantized_model<span class=\"token punctuation\">.<\/span>qconfig <span class=\"token operator\">&#061;<\/span> qconfig<\/p>\n<p><span class=\"token comment\"># \u51c6\u5907QAT<\/span><br \/>\ntorch<span class=\"token punctuation\">.<\/span>utils<span class=\"token punctuation\">.<\/span>quantization<span class=\"token punctuation\">.<\/span>prepare_qat<span class=\"token punctuation\">(<\/span>quantized_model<span class=\"token punctuation\">,<\/span> inplace<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u5b9a\u4e49\u4f18\u5316\u5668\u548c\u635f\u5931\u51fd\u6570<\/span><br \/>\noptimizer <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>optim<span class=\"token punctuation\">.<\/span>Adam<span class=\"token punctuation\">(<\/span>quantized_model<span class=\"token punctuation\">.<\/span>parameters<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> lr<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.001<\/span><span class=\"token punctuation\">)<\/span><br \/>\nloss_fn <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>CrossEntropyLoss<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># QAT\u8bad\u7ec3\u8fc7\u7a0b<\/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    <span class=\"token keyword\">for<\/span> images<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> train_loader<span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token comment\"># \u524d\u5411\u4f20\u64ad<\/span><br \/>\n        outputs <span class=\"token operator\">&#061;<\/span> quantized_model<span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">)<\/span><br \/>\n        loss <span class=\"token operator\">&#061;<\/span> loss_fn<span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">)<\/span><\/p>\n<p>        <span class=\"token comment\"># \u53cd\u5411\u4f20\u64ad\u548c\u4f18\u5316<\/span><br \/>\n        optimizer<span class=\"token punctuation\">.<\/span>zero_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        optimizer<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;Epoch [<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>epoch<span class=\"token operator\">&#043;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">\/<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>num_epochs<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">], Loss: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>loss<span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u8f6c\u6362\u4e3a\u91cf\u5316\u6a21\u578b<\/span><br \/>\ntorch<span class=\"token punctuation\">.<\/span>utils<span class=\"token punctuation\">.<\/span>quantization<span class=\"token punctuation\">.<\/span>convert<span class=\"token punctuation\">(<\/span>quantized_model<span class=\"token punctuation\">,<\/span> inplace<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u6d4b\u8bd5\u6700\u7ec8\u6a21\u578b<\/span><br \/>\ntest_loader <span class=\"token operator\">&#061;<\/span> DataLoader<span class=\"token punctuation\">(<\/span>test_dataset<span class=\"token punctuation\">,<\/span> batch_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">)<\/span><br \/>\ncorrect <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0<\/span><br \/>\ntotal <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0<\/span><\/p>\n<p><span class=\"token keyword\">with<\/span> torch<span class=\"token punctuation\">.<\/span>no_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> images<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> test_loader<span class=\"token punctuation\">:<\/span><br \/>\n        outputs <span class=\"token operator\">&#061;<\/span> quantized_model<span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">)<\/span><br \/>\n        _<span class=\"token punctuation\">,<\/span> predicted <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">max<\/span><span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        total <span class=\"token operator\">&#043;&#061;<\/span> labels<span class=\"token punctuation\">.<\/span>size<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        correct <span class=\"token operator\">&#043;&#061;<\/span> <span class=\"token punctuation\">(<\/span>predicted <span class=\"token operator\">&#061;&#061;<\/span> labels<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">sum<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;Accuracy of QAT model: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>correct <span class=\"token operator\">\/<\/span> total <span class=\"token operator\">*<\/span> <span class=\"token number\">100<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.2f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">%&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>## 4. \u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3<\/p>\n<p>\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3\u7ed3\u5408\u4e86FP16\u548cINT8\u7b49\u4e0d\u540c\u7cbe\u5ea6\u7684\u8ba1\u7b97&#xff0c;\u53ef\u4ee5\u5728\u4fdd\u6301\u6a21\u578b\u6027\u80fd\u7684\u540c\u65f6\u663e\u8457\u63d0\u5347\u8ba1\u7b97\u6548\u7387\u3002\u8fd9\u79cd\u65b9\u6cd5\u9700\u8981\u786c\u4ef6\u652f\u6301&#xff08;\u5982NVIDIA\u7684Tensor Core&#xff09;\u3002<\/p>\n<hr \/>\n<p># \u56db\u3001\u6a21\u578b\u538b\u7f29\u4e0e\u91cf\u5316\u7684\u5b9e\u9645\u5e94\u7528\u573a\u666f<\/p>\n<p>## 1. \u79fb\u52a8\u8bbe\u5907\u4e0a\u7684 AI \u6a21\u578b\u90e8\u7f72<\/p>\n<p>\u5728\u79fb\u52a8\u8bbe\u5907\u4e0a\u8fd0\u884c\u5927\u6a21\u578b\u901a\u5e38\u4f1a\u9762\u4e34\u8ba1\u7b97\u8d44\u6e90\u548c\u5b58\u50a8\u7a7a\u95f4\u7684\u9650\u5236\u3002\u901a\u8fc7\u6a21\u578b\u538b\u7f29\u4e0e\u91cf\u5316&#xff0c;\u53ef\u4ee5\u5728\u624b\u673a\u7aef\u5b9e\u73b0\u9ad8\u6027\u80fd\u63a8\u7406\u3002\u4f8b\u5982&#xff0c;\u82f9\u679c\u7684Core ML\u6846\u67b6\u5c31\u96c6\u6210\u4e86\u591a\u79cd\u538b\u7f29\u4e0e\u91cf\u5316\u6280\u672f\u3002<\/p>\n<p>## 2. \u5d4c\u5165\u5f0f\u8bbe\u5907\u7684\u8f7b\u91cf\u5316\u9700\u6c42<\/p>\n<p>\u5d4c\u5165\u5f0f\u8bbe\u5907&#xff08;\u5982\u667a\u80fd\u5bb6\u5c45\u3001\u81ea\u52a8\u9a7e\u9a76\u7cfb\u7edf&#xff09;\u901a\u5e38\u5177\u6709\u4e25\u683c\u7684\u529f\u8017\u548c\u6210\u672c\u9650\u5236\u3002\u901a\u8fc7\u526a\u679d\u548c\u91cf\u5316&#xff0c;\u53ef\u4ee5\u5c06\u6a21\u578b\u4f18\u5316\u5230\u6ee1\u8db3\u8fd9\u4e9b\u8bbe\u5907\u7684\u8981\u6c42\u3002<\/p>\n<p>## 3. \u7269\u8054\u7f51\u4e2d\u7684\u5b9e\u65f6\u63a8\u7406<\/p>\n<p>\u5728\u7269\u8054\u7f51\u573a\u666f\u4e2d&#xff0c;\u8bbe\u5907\u901a\u5e38\u9700\u8981\u8fdb\u884c\u5b9e\u65f6\u63a8\u7406&#xff0c;\u4f46\u8ba1\u7b97\u8d44\u6e90\u6709\u9650\u3002\u538b\u7f29\u4e0e\u91cf\u5316\u6280\u672f\u53ef\u4ee5\u5e2e\u52a9\u6a21\u578b\u5728\u4f4e\u529f\u8017\u8bbe\u5907\u4e0a\u5feb\u901f\u8fd0\u884c\u3002<\/p>\n<p>## 4. \u81ea\u52a8\u9a7e\u9a76\u7cfb\u7edf<\/p>\n<p>\u81ea\u52a8\u9a7e\u9a76\u7cfb\u7edf\u5bf9\u6a21\u578b\u7684\u5b9e\u65f6\u6027\u548c\u51c6\u786e\u6027\u8981\u6c42\u6781\u9ad8\u3002\u901a\u8fc7\u538b\u7f29\u4e0e\u91cf\u5316\u6280\u672f&#xff0c;\u53ef\u4ee5\u5728\u4fdd\u8bc1\u6027\u80fd\u7684\u540c\u65f6\u964d\u4f4e\u786c\u4ef6\u6210\u672c\u3002<\/p>\n<hr \/>\n<p># \u4e94\u3001\u672a\u6765\u53d1\u5c55\u65b9\u5411<\/p>\n<p>\u5c3d\u7ba1\u6a21\u578b\u538b\u7f29\u4e0e\u91cf\u5316\u5df2\u7ecf\u53d6\u5f97\u4e86\u663e\u8457\u8fdb\u5c55&#xff0c;\u4f46\u4ecd\u6709\u8bb8\u591a\u503c\u5f97\u63a2\u7d22\u7684\u65b9\u5411&#xff1a;<\/p>\n<li>\u81ea\u52a8\u5316\u538b\u7f29\u5de5\u5177&#xff1a;\u5f00\u53d1\u66f4\u52a0\u667a\u80fd\u5316\u7684\u538b\u7f29\u5de5\u5177&#xff0c;\u80fd\u591f\u6839\u636e\u5177\u4f53\u573a\u666f\u81ea\u52a8\u9009\u62e9\u6700\u4f18\u7684\u538b\u7f29\u7b56\u7565\u3002<\/li>\n<li>\u7aef\u5230\u7aef\u91cf\u5316\u8bad\u7ec3&#xff1a;\u5c06\u91cf\u5316\u8fc7\u7a0b\u96c6\u6210\u5230\u6a21\u578b\u8bad\u7ec3\u4e2d&#xff0c;\u8fdb\u4e00\u6b65\u63d0\u5347\u91cf\u5316\u6a21\u578b\u7684\u6027\u80fd\u3002<\/li>\n<li>\u7a00\u758f\u8ba1\u7b97\u786c\u4ef6\u652f\u6301&#xff1a;\u968f\u7740\u7a00\u758f\u8ba1\u7b97\u6280\u672f\u7684\u53d1\u5c55&#xff0c;\u786c\u4ef6\u5382\u5546\u53ef\u4ee5\u4e3a\u526a\u679d\u540e\u7684\u6a21\u578b\u63d0\u4f9b\u66f4\u597d\u7684\u652f\u6301\u3002<\/li>\n<hr \/>\n<p># \u516d\u3001\u603b\u7ed3<\/p>\n<p>\u6a21\u578b\u538b\u7f29\u4e0e\u91cf\u5316\u662f\u63a8\u52a8\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u843d\u5730\u7684\u91cd\u8981\u624b\u6bb5\u3002\u901a\u8fc7\u51cf\u5c11\u6a21\u578b\u7684\u53c2\u6570\u6570\u91cf\u548c\u8ba1\u7b97\u590d\u6742\u5ea6&#xff0c;\u8fd9\u4e00\u6280\u672f\u4f7f\u5f97\u5927\u6a21\u578b\u80fd\u591f\u5728\u8d44\u6e90\u53d7\u9650\u7684\u73af\u5883\u4e2d\u9ad8\u6548\u8fd0\u884c\u3002\u672a\u6765&#xff0c;\u968f\u7740\u7b97\u6cd5\u548c\u786c\u4ef6\u7684\u4e0d\u65ad\u8fdb\u6b65&#xff0c;\u6a21\u578b\u538b\u7f29\u4e0e\u91cf\u5316\u5c06\u5728\u66f4\u591a\u9886\u57df\u53d1\u6325\u91cd\u8981\u4f5c\u7528\u3002<\/p>\n<p>DeepSeek\u751f\u6210\u7684\u8fd9\u7bc7\u6587\u7ae0&#xff0c;\u6211\u4e5f\u5df2\u7ecf\u53d1\u8868\u51fa\u6765\u4e86&#xff0c;\u9080\u8bf7\u5927\u5bb6\u89c2\u6469&#xff1a;\u6a21\u578b\u538b\u7f29\u4e0e\u91cf\u5316&#xff1a;\u8ba9\u5927\u6a21\u578b\u8d70\u5411\u8f7b\u91cf\u5316\u843d\u5730\u3002\u5927\u5bb6\u53ef\u4ee5\u770b\u4e0b\u5b83\u7684\u8d28\u91cf\u600e\u4e48\u6837&#xff1f;\u662f\u4e0d\u662f\u975e\u5e38\u68d2&#xff01;&#xff1f;<\/p>\n<h2>3. Chatbox\u63a5\u5165API<\/h2>\n<p>\u9664\u4e86\u76f4\u63a5\u4f7f\u7528\u7f51\u9875\u7aef\u7684\u5728\u7ebf\u670d\u52a1&#xff0c;\u6211\u4eec\u8fd8\u53ef\u4ee5\u5c06\u84dd\u8018\u63d0\u4f9b\u7684DeepSeek\u6ee1\u8840\u7248\u670d\u52a1\u4ee5api\u7684\u65b9\u5f0f\u96c6\u6210\u5230\u5176\u5b83\u5e94\u7528\u4e2d\u3002<\/p>\n<p>\u84dd\u8018\u5143\u751f\u4ee3\u667a\u7b97\u4e91\u5e73\u53f0\u63d0\u4f9b\u4e86\u4e0eOpenAI\u517c\u5bb9\u7684\u63a5\u53e3&#xff0c;\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528 OpenAI \u5b98\u65b9\u63d0\u4f9b\u7684 SDK \u6765\u8c03\u7528\u5927\u6a21\u578b\u5bf9\u8bdd\u63a5\u53e3\u3002\u53ea\u9700\u8981\u5c06 base_url \u548c api_key \u66ff\u6362\u6210\u76f8\u5173\u914d\u7f6e&#xff0c;\u4e0d\u9700\u8981\u5bf9\u5e94\u7528\u505a\u989d\u5916\u4fee\u6539&#xff0c;\u5373\u53ef\u65e0\u7f1d\u5c06\u60a8\u7684\u5e94\u7528\u5207\u6362\u5230\u76f8\u5e94\u7684\u5927\u6a21\u578b\u3002<\/p>\n<p><span class=\"token literal-property property\">base_url&#xff1a;https<\/span><span class=\"token operator\">:<\/span><span class=\"token operator\">\/<\/span><span class=\"token operator\">\/<\/span>maas<span class=\"token operator\">&#8211;<\/span>api<span class=\"token punctuation\">.<\/span>lanyun<span class=\"token punctuation\">.<\/span>net<span class=\"token operator\">\/<\/span>v1<br \/>\napi_key&#xff1a;\u5982\u9700\u83b7\u53d6\u8bf7\u53c2\u8003\u83b7\u53d6<span class=\"token constant\">API<\/span> <span class=\"token constant\">KEY<\/span><br \/>\n<span class=\"token literal-property property\">\u63a5\u53e3\u5b8c\u6574\u8def\u5f84&#xff1a;https<\/span><span class=\"token operator\">:<\/span><span class=\"token operator\">\/<\/span><span class=\"token operator\">\/<\/span>maas<span class=\"token operator\">&#8211;<\/span>api<span class=\"token punctuation\">.<\/span>lanyun<span class=\"token punctuation\">.<\/span>net<span class=\"token operator\">\/<\/span>v1<span class=\"token operator\">\/<\/span>chat<span class=\"token operator\">\/<\/span>completions<\/p>\n<p>\u5927\u5bb6\u611f\u5174\u8da3\u7684&#xff0c;\u53ef\u4ee5\u81ea\u884c\u53bb\u5c1d\u8bd5\u4e0b\u3002\u672c\u6587\u4e3b\u8981\u4e3a\u5927\u5bb6\u4ecb\u7ecd\u53e6\u5916\u4e00\u4e2a\u65b9\u5f0f&#xff0c;\u5c06DeepSeek\u63a5\u5165\u5230Chatbox\u4e2d&#xff0c;\u6784\u5efa\u4e00\u4e2a\u667a\u80fd\u5316\u7684\u804a\u5929\u673a\u5668\u4eba\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u6b65\u9aa4&#xff1a;<\/p>\n<h3>3.1 \u521b\u5efaAPI KEY<\/h3>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085750-6802140e3758f.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<p>\u8bbf\u95eeDeepSeek\u6ee1\u8840\u7248\u9875\u9762&#xff0c;\u70b9\u51fb\u5de6\u4e0a\u89d2\u7684API\u5f00\u653e\u5e73\u53f0\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085750-6802140e4b3d8.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085750-6802140e7c71e.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<p>\u7136\u540e\u70b9\u51fb\u521b\u5efaAPI KEY\u6309\u94ae\u751f\u6210\u4e00\u7ec4API KEY&#xff0c;\u590d\u5236\u8fd9\u7ec4KEY&#xff0c;\u4e0b\u9762\u4f1a\u7528\u5230\u3002<\/p>\n<h3>3.2 \u4e0b\u8f7d\u5b89\u88c5Chatbox<\/h3>\n<p>Chatbox AI \u662f\u4e00\u6b3eAI\u5ba2\u6237\u7aef\u5e94\u7528\u548c\u667a\u80fd\u52a9\u624b&#xff0c;\u652f\u6301\u4f17\u591a\u5148\u8fdb\u7684 AI \u6a21\u578b\u548c API\u3002\u4f5c\u4e3a\u4e00\u4e2a\u6a21\u578b API \u548c\u672c\u5730\u6a21\u578b\u7684\u8fde\u63a5\u5de5\u5177&#xff0c;\u5176\u4e3b\u8981\u529f\u80fd\u4e00\u76f4\u90fd\u662f\u5b8c\u5168\u514d\u8d39\u7684&#xff0c;\u975e\u5e38\u63a8\u8350\u5927\u5bb6\u4f7f\u7528\u3002 <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085750-6802140edb7de.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u8bbf\u95ee\u5b98\u7f51 https:\/\/chatboxai.app\/zh \u83b7\u53d6\u5404\u5e73\u53f0\u7248\u672c&#xff08;\u652f\u6301Windows\/Mac\/iOS\/Android\/Web&#xff09;&#xff0c;\u4e0b\u8f7d\u5b89\u88c5\u5373\u53ef\u3002\u6587\u672c\u4ee5Windows\u7248\u5ba2\u6237\u7aef\u4e3a\u4f8b\u8fdb\u884c\u6f14\u793a\u3002<\/p>\n<h3>3.3 \u914d\u7f6eDeepSeek<\/h3>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085751-6802140f26360.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u5141\u8bb8\u4e0a\u4e00\u6b65\u5b89\u88c5\u597d\u7684chatbox&#xff0c;\u70b9\u51fb\u5de6\u4e0b\u89d2\u7684\u8bbe\u7f6e\u6309\u94ae\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085751-6802140f8c554.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u6a21\u578b\u63d0\u4f9b\u65b9\u62c9\u5230\u6700\u4e0b\u9762&#xff0c;\u9009\u62e9\u6dfb\u52a0\u81ea\u5b9a\u4e49\u63d0\u4f9b\u65b9&#xff0c;\u7136\u540e\u4eceAPI\u6a21\u5f0f\u5c31\u53ef\u4ee5\u770b\u5230OpenAI API\u517c\u5bb9\u4e86\u3002 <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085752-6802141006c6c.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u5982\u4e0a\u56fe\u6240\u793a&#xff0c;<\/p>\n<li>\u540d\u79f0&#xff1a;\u968f\u4fbf\u586b&#xff0c;\u6bd4\u5982DeepSeep\u6ee1\u8840\u7248\u3002<\/li>\n<li>API\u57df\u540d&#xff1a;\u5fc5\u987b\u586bhttps:\/\/maas-api.lanyun.net\u3002<\/li>\n<li>API\u8def\u5f84&#xff1a;\u5fc5\u987b\u586b\/v1\/chat\/completions\u3002<\/li>\n<li>API\u79d8\u94a5&#xff1a;\u5fc5\u987b\u586b\u7b2c\u4e00\u6b65\u521b\u5efa\u5e76\u590d\u5236\u597d\u7684API KEY\u3002<\/li>\n<li>\u6a21\u578b&#xff1a;\u968f\u4fbf\u586b&#xff0c;\u6bd4\u5982\/maas\/deepseek-ai\/DeepSeek-R1\u3002<\/li>\n<p>\u7136\u540e\u70b9\u51fb\u53f3\u4e0b\u89d2\u7684\u4fdd\u5b58\u6309\u94ae&#xff0c;\u5c31\u5b8c\u6210\u6dfb\u52a0\u4e86&#xff0c;\u662f\u4e0d\u662f\u975e\u5e38\u7b80\u5355&#xff1f;<\/p>\n<h3>3.4 \u529f\u80fd\u5b9e\u6d4b<\/h3>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085752-6802141076c96.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u2460\u9009\u62e9\u4e0a\u4e00\u6b65\u6dfb\u52a0\u7684\u6a21\u578b&#xff0c;\u7136\u540e\u2461\u8f93\u5165\u95ee\u9898&#xff0c;\u70b9\u51fb\u2462\u53d1\u9001\u6309\u94ae\u3002\u5982\u679c\u80fd\u6b63\u5e38\u56de\u590d&#xff0c;\u5c31\u8bf4\u660e\u6211\u4eec\u524d\u9762\u7684\u914d\u7f6e\u5df2\u7ecf\u6210\u529f\u4e86\u3002 <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085752-68021410dc5fb.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u5982\u4e0a\u56fe\u6240\u793a&#xff0c;\u5c31\u8868\u793a\u6dfb\u52a0\u6210\u529f&#xff0c;\u53ef\u4ee5\u6b63\u5e38\u4f7f\u7528\u4e86\u3002\u63a5\u4e0b\u6765\u6211\u4eec\u8ba9chatBox\u91cc\u7684DeepSeek\u518d\u7ed9\u6211\u4eec\u5199\u4e00\u7bc7\u6280\u672f\u535a\u5ba2&#xff0c;\u770b\u770b\u5b83\u7684\u54cd\u5e94\u901f\u5ea6\u5982\u4f55\u3002 <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250418085753-6802141151cf3.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u6d4b\u8bd5\u4e0b\u6765&#xff0c;\u54cd\u5e94\u8fd8\u662f\u6bd4\u8f83\u8fc5\u901f\u7684&#xff0c;\u4e5f\u6709\u601d\u8003\u8fc7\u7a0b\u3002\u4e0b\u9762\u662fDeepSeek\u7684\u751f\u6210\u7684\u5b8c\u6574\u6587\u7ae0&#xff1a;<\/p>\n<p>\u5f15\u8a00 \u5728\u5f53\u4eca\u79d1\u6280\u98de\u901f\u53d1\u5c55\u7684\u65f6\u4ee3&#xff0c;\u4eba\u5de5\u667a\u80fd\u5df2\u7136\u6210\u4e3a\u63a8\u52a8\u5404\u9886\u57df\u53d8\u9769\u7684\u6838\u5fc3\u529b\u91cf&#xff0c;\u5176\u4e2d\u81ea\u7136\u8bed\u8a00\u5904\u7406&#xff08;NLP&#xff09;\u66f4\u662f\u53d6\u5f97\u4e86\u4ee4\u4eba\u60ca\u53f9\u7684\u6210\u5c31\u3002\u5728\u8fd9\u7247\u5145\u6ee1\u521b\u65b0\u4e0e\u7a81\u7834\u7684\u9886\u57df\u4e2d&#xff0c;DeepSeek\u4e0eChatGPT\u72b9\u5982\u4e24\u9897\u7480\u74a8\u7684\u660e\u661f&#xff0c;\u5438\u5f15\u4e86\u5168\u7403\u5f00\u53d1\u8005\u3001\u7814\u7a76\u4eba\u5458\u4ee5\u53ca\u5e7f\u5927\u666e\u901a\u7528\u6237\u7684\u76ee\u5149\u3002\u5b83\u4eec\u4ee3\u8868\u7740\u5f53\u524dAI\u8bed\u8a00\u6a21\u578b\u7684\u9876\u5c16\u6c34\u51c6&#xff0c;\u5f15\u9886\u7740\u81ea\u7136\u8bed\u8a00\u5904\u7406\u6280\u672f\u7684\u524d\u6cbf\u53d1\u5c55\u3002<\/p>\n<p>\u7136\u800c&#xff0c;\u968f\u7740\u4e24\u8005\u5728\u4f17\u591a\u5e94\u7528\u573a\u666f\u4e2d\u7684\u5e7f\u6cdb\u4f7f\u7528&#xff0c;\u4e00\u4e2a\u5907\u53d7\u5173\u6ce8\u7684\u95ee\u9898\u4e5f\u968f\u4e4b\u800c\u6765&#xff1a;\u7a76\u7adf\u8c01\u80fd\u5728\u8fd9\u573a\u6fc0\u70c8\u7684\u7ade\u4e89\u4e2d\u8131\u9896\u800c\u51fa&#xff0c;\u8363\u81ba\u201cAI\u8bed\u8a00\u4e4b\u738b\u201d\u7684\u79f0\u53f7&#xff1f;\u8fd9\u4e0d\u4ec5\u5173\u4e4e\u6280\u672f\u7684\u8f83\u91cf&#xff0c;\u66f4\u5bf9\u672a\u6765\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7684\u53d1\u5c55\u65b9\u5411\u6709\u7740\u6df1\u8fdc\u5f71\u54cd\u3002\u672c\u6587\u5c06\u4ece\u591a\u4e2a\u7ef4\u5ea6\u5bf9DeepSeek\u548cChatGPT\u8fdb\u884c\u6df1\u5165\u5256\u6790&#xff0c;\u5305\u62ec\u6a21\u578b\u67b6\u6784\u3001\u6027\u80fd\u8868\u73b0\u3001\u5e94\u7528\u573a\u666f\u3001\u4ee3\u7801\u5b9e\u73b0\u7b49\u65b9\u9762&#xff0c;\u529b\u6c42\u4e3a\u8bfb\u8005\u5448\u73b0\u4e00\u573a\u5168\u9762\u4e14\u6df1\u5165\u7684\u7ec8\u6781\u5bf9\u51b3\u3002<\/p>\n<p>\u4e00\u3001\u6a21\u578b\u67b6\u6784\u63a2\u79d8 1.1 ChatGPT\u7684\u67b6\u6784 ChatGPT\u57fa\u4e8eGPT&#xff08;Generative Pretrained Transformer&#xff09;\u67b6\u6784\u3002GPT\u7cfb\u5217\u6a21\u578b\u91c7\u7528\u4e86Transformer\u67b6\u6784&#xff0c;\u6452\u5f03\u4e86\u4f20\u7edf\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc&#xff08;RNN&#xff09;\u548c\u5377\u79ef\u795e\u7ecf\u7f51\u7edc&#xff08;CNN&#xff09;&#xff0c;\u4ee5\u81ea\u6ce8\u610f\u529b\u673a\u5236&#xff08;Self &#8211; Attention&#xff09;\u4e3a\u6838\u5fc3\u3002\u81ea\u6ce8\u610f\u529b\u673a\u5236\u80fd\u591f\u8ba9\u6a21\u578b\u5728\u5904\u7406\u5e8f\u5217\u6570\u636e\u65f6&#xff0c;\u540c\u65f6\u5173\u6ce8\u5e8f\u5217\u4e2d\u7684\u4e0d\u540c\u4f4d\u7f6e&#xff0c;\u4ece\u800c\u66f4\u6709\u6548\u5730\u6355\u6349\u957f\u8ddd\u79bb\u4f9d\u8d56\u5173\u7cfb\u3002<\/p>\n<p>\u5728GPT\u6a21\u578b\u4e2d&#xff0c;Transformer\u7531\u591a\u5c42\u7684\u7f16\u7801\u5668\u548c\u89e3\u7801\u5668\u7ec4\u6210\u3002\u5728\u9884\u8bad\u7ec3\u9636\u6bb5&#xff0c;\u6a21\u578b\u901a\u8fc7\u5927\u91cf\u7684\u65e0\u76d1\u7763\u6587\u672c\u6570\u636e\u8fdb\u884c\u8bad\u7ec3&#xff0c;\u5b66\u4e60\u5230\u901a\u7528\u7684\u8bed\u8a00\u8868\u793a\u3002\u7136\u540e\u5728\u5fae\u8c03\u9636\u6bb5&#xff0c;\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u7684\u4efb\u52a1\u9700\u6c42&#xff0c;\u5982\u6587\u672c\u751f\u6210\u3001\u95ee\u7b54\u7cfb\u7edf\u7b49&#xff0c;\u5bf9\u6a21\u578b\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u8bad\u7ec3\u3002<\/p>\n<p>1.2 DeepSeek\u7684\u67b6\u6784 DeepSeek\u540c\u6837\u57fa\u4e8eTransformer\u67b6\u6784&#xff0c;\u4f46\u5728\u4e00\u4e9b\u7ec6\u8282\u4e0a\u8fdb\u884c\u4e86\u4f18\u5316\u548c\u521b\u65b0\u3002\u5b83\u53ef\u80fd\u5728\u5c42\u6570\u3001\u5934\u6570\u3001\u5d4c\u5165\u7ef4\u5ea6\u7b49\u8d85\u53c2\u6570\u4e0a\u8fdb\u884c\u4e86\u8c03\u6574&#xff0c;\u4ee5\u9002\u5e94\u4e0d\u540c\u7684\u6570\u636e\u96c6\u548c\u4efb\u52a1\u9700\u6c42\u3002\u4f8b\u5982&#xff0c;DeepSeek\u53ef\u80fd\u589e\u52a0\u4e86\u66f4\u591a\u7684\u9690\u85cf\u5c42&#xff0c;\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u8868\u793a\u80fd\u529b&#xff1b;\u6216\u8005\u4f18\u5316\u4e86\u6ce8\u610f\u529b\u673a\u5236\u7684\u8ba1\u7b97\u65b9\u5f0f&#xff0c;\u4f7f\u5176\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u66f4\u52a0\u9ad8\u6548\u3002<\/p>\n<p>\u6b64\u5916&#xff0c;DeepSeek\u53ef\u80fd\u5728\u9884\u8bad\u7ec3\u9636\u6bb5\u91c7\u7528\u4e86\u72ec\u7279\u7684\u6570\u636e\u5904\u7406\u65b9\u5f0f&#xff0c;\u6bd4\u5982\u4f7f\u7528\u4e86\u66f4\u5e7f\u6cdb\u7684\u6570\u636e\u6e90&#xff0c;\u5305\u62ec\u4e0d\u540c\u9886\u57df\u3001\u4e0d\u540c\u8bed\u8a00\u7684\u6587\u672c&#xff0c;\u4ee5\u589e\u5f3a\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002\u540c\u65f6&#xff0c;\u5728\u5fae\u8c03\u9636\u6bb5&#xff0c;\u53ef\u80fd\u8bbe\u8ba1\u4e86\u66f4\u7075\u6d3b\u7684\u5fae\u8c03\u7b56\u7565&#xff0c;\u80fd\u591f\u66f4\u597d\u5730\u9002\u5e94\u591a\u6837\u5316\u7684\u4e0b\u6e38\u4efb\u52a1\u3002<\/p>\n<p>\u4e8c\u3001\u6027\u80fd\u8868\u73b0\u5927\u6bd4\u62fc 2.1 \u8bed\u8a00\u7406\u89e3\u80fd\u529b \u4e3a\u4e86\u8bc4\u4f30\u4e24\u8005\u7684\u8bed\u8a00\u7406\u89e3\u80fd\u529b&#xff0c;\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528GLUE&#xff08;General Language Understanding Evaluation&#xff09;\u57fa\u51c6\u6d4b\u8bd5\u3002GLUE\u5305\u542b\u4e86\u591a\u79cd\u81ea\u7136\u8bed\u8a00\u7406\u89e3\u4efb\u52a1&#xff0c;\u5982\u6587\u672c\u8574\u542b\u3001\u60c5\u611f\u5206\u6790\u3001\u8bed\u4e49\u76f8\u4f3c\u5ea6\u7b49\u3002<\/p>\n<p>\u5728\u6587\u672c\u8574\u542b\u4efb\u52a1\u4e2d&#xff0c;\u7ed9\u5b9a\u4e00\u4e2a\u524d\u63d0\u6587\u672c\u548c\u4e00\u4e2a\u5047\u8bbe\u6587\u672c&#xff0c;\u6a21\u578b\u9700\u8981\u5224\u65ad\u5047\u8bbe\u6587\u672c\u662f\u5426\u80fd\u4ece\u524d\u63d0\u6587\u672c\u4e2d\u5408\u7406\u63a8\u65ad\u51fa\u6765\u3002\u4f8b\u5982&#xff1a; \u524d\u63d0&#xff1a;\u201c\u4e00\u7fa4\u4eba\u5728\u516c\u56ed\u91cc\u5f00\u5fc3\u5730\u73a9\u800d\u3002\u201d \u5047\u8bbe&#xff1a;\u201c\u6709\u4e9b\u4eba\u5728\u6237\u5916\u4eab\u53d7\u65f6\u5149\u3002\u201d<\/p>\n<p>\u5728\u60c5\u611f\u5206\u6790\u4efb\u52a1\u4e2d&#xff0c;\u6a21\u578b\u9700\u8981\u5224\u65ad\u4e00\u6bb5\u6587\u672c\u8868\u8fbe\u7684\u60c5\u611f\u662f\u79ef\u6781\u3001\u6d88\u6781\u8fd8\u662f\u4e2d\u6027\u3002\u4ee5\u5f71\u8bc4\u4e3a\u4f8b&#xff1a; \u201c\u8fd9\u90e8\u7535\u5f71\u7684\u5267\u60c5\u7d27\u51d1&#xff0c;\u7279\u6548\u9707\u64bc&#xff0c;\u771f\u7684\u592a\u68d2\u4e86&#xff01;\u201d &#8211; \u79ef\u6781\u60c5\u611f<\/p>\n<p>\u4ece\u516c\u5f00\u7684\u8bc4\u6d4b\u7ed3\u679c\u6765\u770b&#xff0c;ChatGPT\u5728GLUE\u57fa\u51c6\u6d4b\u8bd5\u4e2d\u53d6\u5f97\u4e86\u76f8\u5f53\u4e0d\u9519\u7684\u6210\u7ee9&#xff0c;\u5c55\u73b0\u51fa\u4e86\u5f3a\u5927\u7684\u8bed\u8a00\u7406\u89e3\u80fd\u529b\u3002\u800cDeepSeek\u4e5f\u4e0d\u7518\u793a\u5f31&#xff0c;\u5728\u90e8\u5206\u4efb\u52a1\u4e0a\u751a\u81f3\u8d85\u8d8a\u4e86ChatGPT\u3002\u8fd9\u53ef\u80fd\u5f97\u76ca\u4e8eDeepSeek\u5728\u9884\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5bf9\u591a\u6837\u5316\u6570\u636e\u7684\u5b66\u4e60&#xff0c;\u4f7f\u5176\u80fd\u591f\u66f4\u597d\u5730\u7406\u89e3\u590d\u6742\u7684\u8bed\u8a00\u8bed\u4e49\u3002<\/p>\n<p>2.2 \u6587\u672c\u751f\u6210\u8d28\u91cf \u5bf9\u4e8e\u6587\u672c\u751f\u6210\u4efb\u52a1&#xff0c;\u6211\u4eec\u53ef\u4ee5\u4ece\u751f\u6210\u6587\u672c\u7684\u8fde\u8d2f\u6027\u3001\u903b\u8f91\u6027\u548c\u521b\u65b0\u6027\u7b49\u65b9\u9762\u8fdb\u884c\u8bc4\u4f30\u3002<\/p>\n<p>\u4ee5\u6545\u4e8b\u751f\u6210\u4efb\u52a1\u4e3a\u4f8b&#xff0c;\u7ed9\u5b9a\u4e00\u4e2a\u5f00\u5934&#xff1a;\u201c\u5728\u4e00\u4e2a\u795e\u79d8\u7684\u68ee\u6797\u91cc&#xff0c;\u4f4f\u7740\u4e00\u4e2a\u52c7\u6562\u7684\u5c0f\u63a2\u9669\u5bb6\u3002\u201d \u4f18\u79c0\u7684\u6a21\u578b\u751f\u6210\u7684\u6545\u4e8b\u5e94\u8be5\u5177\u6709\u8fde\u8d2f\u7684\u60c5\u8282\u53d1\u5c55&#xff0c;\u903b\u8f91\u5408\u7406&#xff0c;\u5e76\u4e14\u5177\u6709\u4e00\u5b9a\u7684\u521b\u65b0\u6027\u3002<\/p>\n<p>ChatGPT\u751f\u6210\u7684\u6587\u672c\u901a\u5e38\u5177\u6709\u8f83\u9ad8\u7684\u8fde\u8d2f\u6027&#xff0c;\u80fd\u591f\u6839\u636e\u7ed9\u5b9a\u7684\u5f00\u5934\u5c55\u5f00\u4e30\u5bcc\u7684\u60f3\u8c61&#xff0c;\u6784\u5efa\u51fa\u4e00\u4e2a\u76f8\u5bf9\u5b8c\u6574\u7684\u6545\u4e8b\u3002\u7136\u800c&#xff0c;\u6709\u65f6\u53ef\u80fd\u4f1a\u51fa\u73b0\u4e00\u4e9b\u91cd\u590d\u6216\u6a21\u5f0f\u5316\u7684\u8868\u8ff0\u3002<\/p>\n<p>DeepSeek\u5728\u751f\u6210\u6587\u672c\u65f6&#xff0c;\u6ce8\u91cd\u903b\u8f91\u6027\u548c\u521b\u65b0\u6027\u7684\u5e73\u8861\u3002\u5b83\u80fd\u591f\u751f\u6210\u65e2\u7b26\u5408\u903b\u8f91\u53c8\u5bcc\u6709\u65b0\u610f\u7684\u6545\u4e8b&#xff0c;\u5728\u60c5\u8282\u8f6c\u6298\u548c\u7ec6\u8282\u63cf\u5199\u4e0a\u53ef\u80fd\u4f1a\u7ed9\u4eba\u5e26\u6765\u66f4\u591a\u7684\u60ca\u559c\u3002\u4f8b\u5982&#xff0c;\u5728\u4e00\u4e9b\u79d1\u5e7b\u6545\u4e8b\u751f\u6210\u4e2d&#xff0c;DeepSeek\u53ef\u80fd\u4f1a\u5f15\u5165\u72ec\u7279\u7684\u79d1\u6280\u8bbe\u5b9a\u548c\u4e16\u754c\u89c2&#xff0c;\u4f7f\u6545\u4e8b\u66f4\u5177\u5438\u5f15\u529b\u3002<\/p>\n<p>2.3 \u591a\u8bed\u8a00\u652f\u6301 \u5728\u5168\u7403\u5316\u7684\u4eca\u5929&#xff0c;\u591a\u8bed\u8a00\u652f\u6301\u80fd\u529b\u81f3\u5173\u91cd\u8981\u3002ChatGPT\u5728\u591a\u8bed\u8a00\u65b9\u9762\u6709\u4e00\u5b9a\u7684\u8868\u73b0&#xff0c;\u80fd\u591f\u5904\u7406\u591a\u79cd\u5e38\u89c1\u8bed\u8a00\u7684\u6587\u672c\u3002\u4f46\u5bf9\u4e8e\u4e00\u4e9b\u5c0f\u4f17\u8bed\u8a00\u6216\u7279\u5b9a\u9886\u57df\u7684\u4e13\u4e1a\u672f\u8bed&#xff0c;\u53ef\u80fd\u5b58\u5728\u7406\u89e3\u548c\u751f\u6210\u4e0d\u51c6\u786e\u7684\u95ee\u9898\u3002<\/p>\n<p>DeepSeek\u5219\u5728\u591a\u8bed\u8a00\u652f\u6301\u4e0a\u6295\u5165\u4e86\u66f4\u591a\u7684\u7cbe\u529b\u3002\u5b83\u901a\u8fc7\u5927\u89c4\u6a21\u7684\u591a\u8bed\u8a00\u6570\u636e\u9884\u8bad\u7ec3&#xff0c;\u5bf9\u4e0d\u540c\u8bed\u8a00\u7684\u8bed\u6cd5\u3001\u8bed\u4e49\u548c\u6587\u5316\u80cc\u666f\u6709\u66f4\u6df1\u5165\u7684\u7406\u89e3\u3002\u5728\u7ffb\u8bd1\u4efb\u52a1\u4e2d&#xff0c;DeepSeek\u80fd\u591f\u66f4\u51c6\u786e\u5730\u5c06\u6e90\u8bed\u8a00\u7ffb\u8bd1\u6210\u76ee\u6807\u8bed\u8a00&#xff0c;\u5e76\u4e14\u5728\u751f\u6210\u591a\u8bed\u8a00\u6587\u672c\u65f6&#xff0c;\u80fd\u591f\u9075\u5faa\u76f8\u5e94\u8bed\u8a00\u7684\u8868\u8fbe\u4e60\u60ef\u3002\u4f8b\u5982&#xff0c;\u5728\u5c06\u4e2d\u6587\u53e4\u8bd7\u7ffb\u8bd1\u6210\u82f1\u6587\u65f6&#xff0c;DeepSeek\u80fd\u591f\u66f4\u597d\u5730\u4fdd\u7559\u539f\u8bd7\u7684\u610f\u5883\u548c\u97f5\u5f8b\u3002<\/p>\n<p>\u4e09\u3001\u5e94\u7528\u573a\u666f\u5256\u6790 3.1 \u804a\u5929\u673a\u5668\u4eba ChatGPT\u56e0\u5176\u51fa\u8272\u7684\u8bed\u8a00\u4ea4\u4e92\u80fd\u529b&#xff0c;\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u804a\u5929\u673a\u5668\u4eba\u9886\u57df\u3002\u5b83\u53ef\u4ee5\u4e0e\u7528\u6237\u8fdb\u884c\u81ea\u7136\u6d41\u7545\u7684\u5bf9\u8bdd&#xff0c;\u56de\u7b54\u5404\u79cd\u95ee\u9898&#xff0c;\u4ece\u65e5\u5e38\u95f2\u804a\u5230\u4e13\u4e1a\u77e5\u8bc6\u54a8\u8be2\u3002\u4f8b\u5982&#xff0c;\u5728\u667a\u80fd\u5ba2\u670d\u573a\u666f\u4e2d&#xff0c;ChatGPT\u80fd\u591f\u5feb\u901f\u7406\u89e3\u7528\u6237\u7684\u95ee\u9898&#xff0c;\u5e76\u63d0\u4f9b\u51c6\u786e\u7684\u89e3\u51b3\u65b9\u6848&#xff0c;\u5927\u5927\u63d0\u9ad8\u4e86\u5ba2\u6237\u670d\u52a1\u7684\u6548\u7387\u3002<\/p>\n<p>DeepSeek\u540c\u6837\u9002\u7528\u4e8e\u804a\u5929\u673a\u5668\u4eba\u573a\u666f&#xff0c;\u5e76\u4e14\u5728\u4e00\u4e9b\u7279\u5b9a\u9886\u57df\u7684\u804a\u5929\u673a\u5668\u4eba\u4e2d\u6709\u72ec\u7279\u7684\u4f18\u52bf\u3002\u6bd4\u5982\u5728\u533b\u7597\u9886\u57df&#xff0c;DeepSeek\u53ef\u4ee5\u5229\u7528\u5176\u5bf9\u533b\u5b66\u672f\u8bed\u7684\u51c6\u786e\u7406\u89e3\u548c\u5bf9\u533b\u5b66\u77e5\u8bc6\u7684\u638c\u63e1&#xff0c;\u4e3a\u60a3\u8005\u63d0\u4f9b\u66f4\u4e13\u4e1a\u3001\u51c6\u786e\u7684\u5065\u5eb7\u54a8\u8be2\u670d\u52a1\u3002\u5b83\u80fd\u591f\u66f4\u597d\u5730\u7406\u89e3\u60a3\u8005\u63cf\u8ff0\u7684\u75c7\u72b6&#xff0c;\u5e76\u7ed9\u51fa\u5408\u7406\u7684\u8bca\u65ad\u5efa\u8bae\u548c\u6cbb\u7597\u65b9\u6848\u3002<\/p>\n<p>3.2 \u5185\u5bb9\u521b\u4f5c \u5728\u5185\u5bb9\u521b\u4f5c\u9886\u57df&#xff0c;ChatGPT\u53ef\u4ee5\u5e2e\u52a9\u4f5c\u5bb6\u751f\u6210\u6545\u4e8b\u5927\u7eb2\u3001\u521b\u4f5c\u8bd7\u6b4c\u3001\u64b0\u5199\u65b0\u95fb\u62a5\u9053\u7b49\u3002\u5b83\u80fd\u591f\u5feb\u901f\u751f\u6210\u5927\u91cf\u7684\u6587\u672c\u7d20\u6750&#xff0c;\u4e3a\u521b\u4f5c\u8005\u63d0\u4f9b\u7075\u611f\u3002\u7136\u800c&#xff0c;\u7531\u4e8e\u5176\u751f\u6210\u7684\u5185\u5bb9\u53ef\u80fd\u5b58\u5728\u4e00\u5b9a\u7684\u6a21\u5f0f\u5316&#xff0c;\u6709\u65f6\u9700\u8981\u521b\u4f5c\u8005\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u6da6\u8272\u548c\u4fee\u6539\u3002<\/p>\n<p>DeepSeek\u5728\u5185\u5bb9\u521b\u4f5c\u65b9\u9762\u5219\u66f4\u5177\u4e2a\u6027\u5316\u3002\u5b83\u53ef\u4ee5\u6839\u636e\u521b\u4f5c\u8005\u7684\u98ce\u683c\u504f\u597d\u548c\u9700\u6c42&#xff0c;\u751f\u6210\u66f4\u7b26\u5408\u5176\u671f\u671b\u7684\u5185\u5bb9\u3002\u4f8b\u5982&#xff0c;\u5bf9\u4e8e\u4e00\u4f4d\u64c5\u957f\u5947\u5e7b\u98ce\u683c\u7684\u4f5c\u5bb6&#xff0c;DeepSeek\u53ef\u4ee5\u751f\u6210\u5177\u6709\u72ec\u7279\u5947\u5e7b\u8bbe\u5b9a\u548c\u7cbe\u5f69\u60c5\u8282\u7684\u6545\u4e8b&#xff0c;\u5e2e\u52a9\u4f5c\u5bb6\u8282\u7701\u521b\u4f5c\u65f6\u95f4&#xff0c;\u540c\u65f6\u4fdd\u6301\u4f5c\u54c1\u7684\u72ec\u7279\u98ce\u683c\u3002<\/p>\n<p>3.3 \u667a\u80fd\u8f85\u52a9\u5199\u4f5c \u5728\u667a\u80fd\u8f85\u52a9\u5199\u4f5c\u65b9\u9762&#xff0c;\u4e24\u8005\u90fd\u80fd\u53d1\u6325\u91cd\u8981\u4f5c\u7528\u3002\u5f53\u7528\u6237\u5728\u64b0\u5199\u6587\u6863\u65f6&#xff0c;\u5b83\u4eec\u53ef\u4ee5\u63d0\u4f9b\u8bed\u6cd5\u68c0\u67e5\u3001\u8bcd\u6c47\u63a8\u8350\u3001\u8bed\u53e5\u6da6\u8272\u7b49\u529f\u80fd\u3002ChatGPT\u7684\u8bed\u6cd5\u68c0\u67e5\u529f\u80fd\u8f83\u4e3a\u5168\u9762&#xff0c;\u80fd\u591f\u8bc6\u522b\u5e38\u89c1\u7684\u8bed\u6cd5\u9519\u8bef\u5e76\u7ed9\u51fa\u4fee\u6539\u5efa\u8bae\u3002\u8bcd\u6c47\u63a8\u8350\u4e5f\u6bd4\u8f83\u4e30\u5bcc&#xff0c;\u80fd\u591f\u6839\u636e\u4e0a\u4e0b\u6587\u63d0\u4f9b\u5408\u9002\u7684\u8bcd\u6c47\u9009\u62e9\u3002<\/p>\n<p>DeepSeek\u5728\u667a\u80fd\u8f85\u52a9\u5199\u4f5c\u4e2d\u66f4\u6ce8\u91cd\u8bed\u4e49\u7684\u4f18\u5316\u3002\u5b83\u4e0d\u4ec5\u80fd\u591f\u68c0\u67e5\u8bed\u6cd5\u9519\u8bef&#xff0c;\u8fd8\u80fd\u6df1\u5165\u7406\u89e3\u53e5\u5b50\u7684\u8bed\u4e49&#xff0c;\u63d0\u51fa\u66f4\u5408\u7406\u7684\u8bed\u4e49\u8c03\u6574\u5efa\u8bae\u3002\u4f8b\u5982&#xff0c;\u5f53\u7528\u6237\u8868\u8fbe\u4e00\u4e2a\u89c2\u70b9\u65f6&#xff0c;DeepSeek\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u66f4\u6e05\u6670\u3001\u51c6\u786e\u5730\u9610\u8ff0\u89c2\u70b9&#xff0c;\u4f7f\u6587\u7ae0\u7684\u903b\u8f91\u6027\u66f4\u5f3a\u3002<\/p>\n<p>\u56db\u3001\u4ee3\u7801\u5b9e\u73b0\u4e0e\u5e94\u7528\u793a\u4f8b&#xff08;Java\u8bed\u8a00&#xff09; 4.1 \u4f7f\u7528ChatGPT API\u8fdb\u884c\u6587\u672c\u751f\u6210 \u9996\u5148&#xff0c;\u9700\u8981\u83b7\u53d6ChatGPT\u7684API\u5bc6\u94a5\u3002\u5047\u8bbe\u5df2\u7ecf\u83b7\u53d6\u4e86API\u5bc6\u94a5&#xff0c;\u4ee5\u4e0b\u662f\u4f7f\u7528Java\u8c03\u7528ChatGPT API\u8fdb\u884c\u6587\u672c\u751f\u6210\u7684\u793a\u4f8b\u4ee3\u7801&#xff1a;<\/p>\n<p> <span class=\"token keyword\">import<\/span> <span class=\"token import\"><span class=\"token namespace\">java<span class=\"token punctuation\">.<\/span>io<span class=\"token punctuation\">.<\/span><\/span><span class=\"token class-name\">BufferedReader<\/span><\/span><span class=\"token punctuation\">;<\/span><br \/>\n<span class=\"token keyword\">import<\/span> <span class=\"token import\"><span class=\"token namespace\">java<span class=\"token punctuation\">.<\/span>io<span class=\"token punctuation\">.<\/span><\/span><span class=\"token class-name\">IOException<\/span><\/span><span class=\"token punctuation\">;<\/span><br \/>\n<span class=\"token keyword\">import<\/span> <span class=\"token import\"><span class=\"token namespace\">java<span class=\"token punctuation\">.<\/span>io<span class=\"token punctuation\">.<\/span><\/span><span class=\"token class-name\">InputStreamReader<\/span><\/span><span class=\"token punctuation\">;<\/span><br \/>\n<span class=\"token keyword\">import<\/span> <span class=\"token import\"><span class=\"token namespace\">java<span class=\"token punctuation\">.<\/span>io<span class=\"token punctuation\">.<\/span><\/span><span class=\"token class-name\">OutputStream<\/span><\/span><span class=\"token punctuation\">;<\/span><br \/>\n<span class=\"token keyword\">import<\/span> <span class=\"token import\"><span class=\"token namespace\">java<span class=\"token punctuation\">.<\/span>net<span class=\"token punctuation\">.<\/span><\/span><span class=\"token class-name\">HttpURLConnection<\/span><\/span><span class=\"token punctuation\">;<\/span><br \/>\n<span class=\"token keyword\">import<\/span> <span class=\"token import\"><span class=\"token namespace\">java<span class=\"token punctuation\">.<\/span>net<span class=\"token punctuation\">.<\/span><\/span><span class=\"token class-name\">URL<\/span><\/span><span class=\"token punctuation\">;<\/span><br \/>\n<span class=\"token keyword\">import<\/span> <span class=\"token import\"><span class=\"token namespace\">java<span class=\"token punctuation\">.<\/span>nio<span class=\"token punctuation\">.<\/span>charset<span class=\"token punctuation\">.<\/span><\/span><span class=\"token class-name\">StandardCharsets<\/span><\/span><span class=\"token punctuation\">;<\/span><br \/>\n<span class=\"token keyword\">import<\/span> <span class=\"token import\"><span class=\"token namespace\">com<span class=\"token punctuation\">.<\/span>google<span class=\"token punctuation\">.<\/span>gson<span class=\"token punctuation\">.<\/span><\/span><span class=\"token class-name\">JsonObject<\/span><\/span><span class=\"token punctuation\">;<\/span><br \/>\n<span class=\"token keyword\">import<\/span> <span class=\"token import\"><span class=\"token namespace\">com<span class=\"token punctuation\">.<\/span>google<span class=\"token punctuation\">.<\/span>gson<span class=\"token punctuation\">.<\/span><\/span><span class=\"token class-name\">JsonParser<\/span><\/span><span class=\"token punctuation\">;<\/span><\/p>\n<p><span class=\"token keyword\">public<\/span> <span class=\"token keyword\">class<\/span> <span class=\"token class-name\">ChatGPTTextGenerator<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n    <span class=\"token keyword\">private<\/span> <span class=\"token keyword\">static<\/span> <span class=\"token keyword\">final<\/span> <span class=\"token class-name\">String<\/span> <span class=\"token constant\">API_URL<\/span> <span class=\"token operator\">&#061;<\/span> <span class=\"token string\">&#034;https:\/\/api.openai.com\/v1\/engines\/davinci\/completions&#034;<\/span><span class=\"token punctuation\">;<\/span><br \/>\n    <span class=\"token keyword\">private<\/span> <span class=\"token keyword\">static<\/span> <span class=\"token keyword\">final<\/span> <span class=\"token class-name\">String<\/span> <span class=\"token constant\">API_KEY<\/span> <span class=\"token operator\">&#061;<\/span> <span class=\"token string\">&#034;YOUR_API_KEY&#034;<\/span><span class=\"token punctuation\">;<\/span><\/p>\n<p>    <span class=\"token keyword\">public<\/span> <span class=\"token keyword\">static<\/span> <span class=\"token class-name\">String<\/span> <span class=\"token function\">generateText<\/span><span class=\"token punctuation\">(<\/span><span class=\"token class-name\">String<\/span> prompt<span class=\"token punctuation\">)<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n        <span class=\"token keyword\">try<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n            <span class=\"token class-name\">URL<\/span> url <span class=\"token operator\">&#061;<\/span> <span class=\"token keyword\">new<\/span> <span class=\"token class-name\">URL<\/span><span class=\"token punctuation\">(<\/span><span class=\"token constant\">API_URL<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n            <span class=\"token class-name\">HttpURLConnection<\/span> connection <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token class-name\">HttpURLConnection<\/span><span class=\"token punctuation\">)<\/span> url<span class=\"token punctuation\">.<\/span><span class=\"token function\">openConnection<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n            connection<span class=\"token punctuation\">.<\/span><span class=\"token function\">setRequestMethod<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;POST&#034;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n            connection<span class=\"token punctuation\">.<\/span><span class=\"token function\">setRequestProperty<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;Content-Type&#034;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#034;application\/json&#034;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n            connection<span class=\"token punctuation\">.<\/span><span class=\"token function\">setRequestProperty<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;Authorization&#034;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#034;Bearer &#034;<\/span> <span class=\"token operator\">&#043;<\/span> <span class=\"token constant\">API_KEY<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n            connection<span class=\"token punctuation\">.<\/span><span class=\"token function\">setDoOutput<\/span><span class=\"token punctuation\">(<\/span><span class=\"token boolean\">true<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><\/p>\n<p>            <span class=\"token class-name\">JsonObject<\/span> requestBody <span class=\"token operator\">&#061;<\/span> <span class=\"token keyword\">new<\/span> <span class=\"token class-name\">JsonObject<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n            requestBody<span class=\"token punctuation\">.<\/span><span class=\"token function\">addProperty<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;prompt&#034;<\/span><span class=\"token punctuation\">,<\/span> prompt<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n            requestBody<span class=\"token punctuation\">.<\/span><span class=\"token function\">addProperty<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;max_tokens&#034;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">100<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><\/p>\n<p>            <span class=\"token keyword\">try<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token class-name\">OutputStream<\/span> os <span class=\"token operator\">&#061;<\/span> connection<span class=\"token punctuation\">.<\/span><span class=\"token function\">getOutputStream<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n                <span class=\"token keyword\">byte<\/span><span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span> input <span class=\"token operator\">&#061;<\/span> requestBody<span class=\"token punctuation\">.<\/span><span class=\"token function\">toString<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">getBytes<\/span><span class=\"token punctuation\">(<\/span><span class=\"token class-name\">StandardCharsets<\/span><span class=\"token punctuation\">.<\/span><span class=\"token constant\">UTF_8<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n                os<span class=\"token punctuation\">.<\/span><span class=\"token function\">write<\/span><span class=\"token punctuation\">(<\/span>input<span class=\"token punctuation\">,<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">,<\/span> input<span class=\"token punctuation\">.<\/span>length<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n            <span class=\"token punctuation\">}<\/span><\/p>\n<p>            <span class=\"token keyword\">try<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token class-name\">BufferedReader<\/span> br <span class=\"token operator\">&#061;<\/span> <span class=\"token keyword\">new<\/span> <span class=\"token class-name\">BufferedReader<\/span><span class=\"token punctuation\">(<\/span><br \/>\n                    <span class=\"token keyword\">new<\/span> <span class=\"token class-name\">InputStreamReader<\/span><span class=\"token punctuation\">(<\/span>connection<span class=\"token punctuation\">.<\/span><span class=\"token function\">getInputStream<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token class-name\">StandardCharsets<\/span><span class=\"token punctuation\">.<\/span><span class=\"token constant\">UTF_8<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n                <span class=\"token class-name\">StringBuilder<\/span> response <span class=\"token operator\">&#061;<\/span> <span class=\"token keyword\">new<\/span> <span class=\"token class-name\">StringBuilder<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n                <span class=\"token class-name\">String<\/span> responseLine<span class=\"token punctuation\">;<\/span><br \/>\n                <span class=\"token keyword\">while<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">(<\/span>responseLine <span class=\"token operator\">&#061;<\/span> br<span class=\"token punctuation\">.<\/span><span class=\"token function\">readLine<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">!&#061;<\/span> <span class=\"token keyword\">null<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n                    response<span class=\"token punctuation\">.<\/span><span class=\"token function\">append<\/span><span class=\"token punctuation\">(<\/span>responseLine<span class=\"token punctuation\">.<\/span><span class=\"token function\">trim<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n                <span class=\"token punctuation\">}<\/span><br \/>\n                <span class=\"token class-name\">JsonObject<\/span> jsonResponse <span class=\"token operator\">&#061;<\/span> <span class=\"token class-name\">JsonParser<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">parseString<\/span><span class=\"token punctuation\">(<\/span>response<span class=\"token punctuation\">.<\/span><span class=\"token function\">toString<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">getAsJsonObject<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n                <span class=\"token keyword\">return<\/span> jsonResponse<span class=\"token punctuation\">.<\/span><span class=\"token function\">getAsJsonArray<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;choices&#034;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">get<\/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 function\">getAsJsonObject<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">get<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;text&#034;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">getAsString<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n            <span class=\"token punctuation\">}<\/span><br \/>\n        <span class=\"token punctuation\">}<\/span> <span class=\"token keyword\">catch<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token class-name\">IOException<\/span> e<span class=\"token punctuation\">)<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n            e<span class=\"token punctuation\">.<\/span><span class=\"token function\">printStackTrace<\/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 keyword\">null<\/span><span class=\"token punctuation\">;<\/span><br \/>\n        <span class=\"token punctuation\">}<\/span><br \/>\n    <span class=\"token punctuation\">}<\/span><\/p>\n<p>    <span class=\"token keyword\">public<\/span> <span class=\"token keyword\">static<\/span> <span class=\"token keyword\">void<\/span> <span class=\"token function\">main<\/span><span class=\"token punctuation\">(<\/span><span class=\"token class-name\">String<\/span><span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span> args<span class=\"token punctuation\">)<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n        <span class=\"token class-name\">String<\/span> prompt <span class=\"token operator\">&#061;<\/span> <span class=\"token string\">&#034;\u5199\u4e00\u7bc7\u5173\u4e8e\u6625\u5929\u7684\u77ed\u6587&#034;<\/span><span class=\"token punctuation\">;<\/span><br \/>\n        <span class=\"token class-name\">String<\/span> generatedText <span class=\"token operator\">&#061;<\/span> <span class=\"token function\">generateText<\/span><span class=\"token punctuation\">(<\/span>prompt<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n        <span class=\"token class-name\">System<\/span><span class=\"token punctuation\">.<\/span>out<span class=\"token punctuation\">.<\/span><span class=\"token function\">println<\/span><span class=\"token punctuation\">(<\/span>generatedText<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n    <span class=\"token punctuation\">}<\/span><br \/>\n<span class=\"token punctuation\">}<\/span><\/p>\n<p>4.2 \u4f7f\u7528DeepSeek\u76f8\u5173SDK\u8fdb\u884c\u6587\u672c\u5904\u7406 \u5047\u8bbeDeepSeek\u63d0\u4f9b\u4e86Java SDK&#xff0c;\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u6587\u672c\u60c5\u611f\u5206\u6790\u793a\u4f8b\u4ee3\u7801&#xff1a;<\/p>\n<p> <span class=\"token keyword\">import<\/span> <span class=\"token import\"><span class=\"token namespace\">com<span class=\"token punctuation\">.<\/span>deepseek<span class=\"token punctuation\">.<\/span>sdk<span class=\"token punctuation\">.<\/span><\/span><span class=\"token class-name\">SentimentAnalyzer<\/span><\/span><span class=\"token punctuation\">;<\/span><br \/>\n<span class=\"token keyword\">import<\/span> <span class=\"token import\"><span class=\"token namespace\">com<span class=\"token punctuation\">.<\/span>deepseek<span class=\"token punctuation\">.<\/span>sdk<span class=\"token punctuation\">.<\/span><\/span><span class=\"token class-name\">SentimentResult<\/span><\/span><span class=\"token punctuation\">;<\/span><\/p>\n<p><span class=\"token keyword\">public<\/span> <span class=\"token keyword\">class<\/span> <span class=\"token class-name\">DeepSeekSentimentAnalysis<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n    <span class=\"token keyword\">public<\/span> <span class=\"token keyword\">static<\/span> <span class=\"token keyword\">void<\/span> <span class=\"token function\">main<\/span><span class=\"token punctuation\">(<\/span><span class=\"token class-name\">String<\/span><span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span> args<span class=\"token punctuation\">)<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n        <span class=\"token class-name\">String<\/span> text <span class=\"token operator\">&#061;<\/span> <span class=\"token string\">&#034;\u8fd9\u90e8\u7535\u5f71\u771f\u7684\u592a\u7cdf\u7cd5\u4e86&#xff0c;\u5267\u60c5\u6df7\u4e71&#xff0c;\u6f14\u5458\u6f14\u6280\u4e5f\u5f88\u5dee\u3002&#034;<\/span><span class=\"token punctuation\">;<\/span><br \/>\n        <span class=\"token class-name\">SentimentAnalyzer<\/span> analyzer <span class=\"token operator\">&#061;<\/span> <span class=\"token keyword\">new<\/span> <span class=\"token class-name\">SentimentAnalyzer<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n        <span class=\"token class-name\">SentimentResult<\/span> result <span class=\"token operator\">&#061;<\/span> analyzer<span class=\"token punctuation\">.<\/span><span class=\"token function\">analyze<\/span><span class=\"token punctuation\">(<\/span>text<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n        <span class=\"token class-name\">System<\/span><span class=\"token punctuation\">.<\/span>out<span class=\"token punctuation\">.<\/span><span class=\"token function\">println<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u60c5\u611f\u503e\u5411: &#034;<\/span> <span class=\"token operator\">&#043;<\/span> result<span class=\"token punctuation\">.<\/span><span class=\"token function\">getSentiment<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n        <span class=\"token class-name\">System<\/span><span class=\"token punctuation\">.<\/span>out<span class=\"token punctuation\">.<\/span><span class=\"token function\">println<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u7f6e\u4fe1\u5ea6: &#034;<\/span> <span class=\"token operator\">&#043;<\/span> result<span class=\"token punctuation\">.<\/span><span class=\"token function\">getConfidence<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span><br \/>\n    <span class=\"token punctuation\">}<\/span><\/p>\n<p>\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d&#xff0c;SentimentAnalyzer\u662fDeepSeek SDK\u63d0\u4f9b\u7684\u7528\u4e8e\u60c5\u611f\u5206\u6790\u7684\u7c7b&#xff0c;analyze\u65b9\u6cd5\u63a5\u53d7\u4e00\u6bb5\u6587\u672c\u5e76\u8fd4\u56de\u60c5\u611f\u5206\u6790\u7ed3\u679c&#xff0c;\u5305\u62ec\u60c5\u611f\u503e\u5411&#xff08;\u5982\u79ef\u6781\u3001\u6d88\u6781\u3001\u4e2d\u6027&#xff09;\u548c\u7f6e\u4fe1\u5ea6\u3002<\/p>\n<p>\u4e94\u3001\u6210\u672c\u4e0e\u53ef\u6269\u5c55\u6027\u8003\u91cf 5.1 \u6210\u672c\u5206\u6790 \u4f7f\u7528ChatGPT API\u9700\u8981\u6839\u636e\u4f7f\u7528\u7684\u8d44\u6e90\u91cf\u652f\u4ed8\u8d39\u7528\u3002\u4f8b\u5982&#xff0c;\u6587\u672c\u751f\u6210\u7684\u957f\u5ea6\u3001\u8c03\u7528\u7684\u9891\u7387\u7b49\u90fd\u4f1a\u5f71\u54cd\u6210\u672c\u3002\u5bf9\u4e8e\u5927\u89c4\u6a21\u7684\u5e94\u7528\u573a\u666f&#xff0c;\u5982\u4f01\u4e1a\u7ea7\u7684\u667a\u80fd\u5ba2\u670d\u7cfb\u7edf&#xff0c;\u6210\u672c\u53ef\u80fd\u4f1a\u6210\u4e3a\u4e00\u4e2a\u91cd\u8981\u7684\u8003\u91cf\u56e0\u7d20\u3002<\/p>\n<p>DeepSeek\u5728\u6210\u672c\u65b9\u9762\u53ef\u80fd\u5177\u6709\u4e00\u5b9a\u7684\u4f18\u52bf\u3002\u5982\u679c\u5176\u91c7\u7528\u4e86\u66f4\u9ad8\u6548\u7684\u6a21\u578b\u67b6\u6784\u548c\u8bad\u7ec3\u7b97\u6cd5&#xff0c;\u90a3\u4e48\u5728\u5904\u7406\u76f8\u540c\u89c4\u6a21\u7684\u4efb\u52a1\u65f6&#xff0c;\u53ef\u80fd\u4f1a\u6d88\u8017\u66f4\u5c11\u7684\u8ba1\u7b97\u8d44\u6e90&#xff0c;\u4ece\u800c\u964d\u4f4e\u6210\u672c\u3002\u6b64\u5916&#xff0c;DeepSeek\u53ef\u80fd\u4f1a\u63d0\u4f9b\u66f4\u7075\u6d3b\u7684\u6536\u8d39\u6a21\u5f0f&#xff0c;\u4ee5\u6ee1\u8db3\u4e0d\u540c\u7528\u6237\u7684\u9700\u6c42\u3002<\/p>\n<p>5.2 \u53ef\u6269\u5c55\u6027 \u968f\u7740\u4e1a\u52a1\u7684\u589e\u957f\u548c\u6570\u636e\u91cf\u7684\u589e\u52a0&#xff0c;\u6a21\u578b\u7684\u53ef\u6269\u5c55\u6027\u81f3\u5173\u91cd\u8981\u3002ChatGPT\u5728\u53ef\u6269\u5c55\u6027\u65b9\u9762\u5df2\u7ecf\u7ecf\u8fc7\u4e86\u5927\u89c4\u6a21\u5e94\u7528\u7684\u9a8c\u8bc1&#xff0c;OpenAI\u901a\u8fc7\u4e0d\u65ad\u4f18\u5316\u57fa\u7840\u8bbe\u65bd\u548c\u7b97\u6cd5&#xff0c;\u80fd\u591f\u652f\u6301\u5927\u91cf\u7528\u6237\u7684\u540c\u65f6\u8bf7\u6c42\u3002<\/p>\n<p>DeepSeek\u4e5f\u5728\u79ef\u6781\u63a2\u7d22\u53ef\u6269\u5c55\u6027\u7684\u89e3\u51b3\u65b9\u6848\u3002\u5b83\u53ef\u80fd\u91c7\u7528\u5206\u5e03\u5f0f\u8bad\u7ec3\u548c\u63a8\u7406\u6280\u672f&#xff0c;\u4ee5\u63d0\u9ad8\u6a21\u578b\u5728\u5927\u89c4\u6a21\u6570\u636e\u548c\u9ad8\u5e76\u53d1\u573a\u666f\u4e0b\u7684\u6027\u80fd\u3002\u540c\u65f6&#xff0c;\u901a\u8fc7\u5bf9\u6a21\u578b\u67b6\u6784\u7684\u4f18\u5316&#xff0c;\u4f7f\u5f97\u5728\u589e\u52a0\u8ba1\u7b97\u8d44\u6e90\u65f6\u80fd\u591f\u66f4\u6709\u6548\u5730\u63d0\u5347\u5904\u7406\u80fd\u529b\u3002<\/p>\n<p>\u516d\u3001\u5b89\u5168\u6027\u4e0e\u9690\u79c1\u4fdd\u62a4 6.1 ChatGPT\u7684\u5b89\u5168\u6027\u4e0e\u9690\u79c1 ChatGPT\u5728\u5b89\u5168\u6027\u548c\u9690\u79c1\u4fdd\u62a4\u65b9\u9762\u91c7\u53d6\u4e86\u4e00\u7cfb\u5217\u63aa\u65bd\u3002OpenAI\u5bf9\u7528\u6237\u8f93\u5165\u7684\u6570\u636e\u8fdb\u884c\u4e25\u683c\u7684\u5ba1\u67e5\u548c\u8fc7\u6ee4&#xff0c;\u4ee5\u9632\u6b62\u6076\u610f\u4f7f\u7528&#xff0c;\u5982\u751f\u6210\u6709\u5bb3\u4fe1\u606f\u3001\u4fb5\u72af\u4ed6\u4eba\u9690\u79c1\u7b49\u3002\u540c\u65f6&#xff0c;\u5728\u6570\u636e\u5b58\u50a8\u548c\u4f20\u8f93\u8fc7\u7a0b\u4e2d&#xff0c;\u91c7\u7528\u4e86\u52a0\u5bc6\u6280\u672f&#xff0c;\u4fdd\u969c\u7528\u6237\u6570\u636e\u7684\u5b89\u5168\u3002<\/p>\n<p>\u7136\u800c&#xff0c;\u7531\u4e8e\u5176\u57fa\u4e8e\u4e91\u670d\u52a1\u7684\u7279\u6027&#xff0c;\u7528\u6237\u53ef\u80fd\u4f1a\u62c5\u5fc3\u6570\u636e\u662f\u5426\u4f1a\u88ab\u4e0d\u5f53\u4f7f\u7528\u6216\u6cc4\u9732\u3002\u867d\u7136OpenAI\u627f\u8bfa\u9075\u5b88\u4e25\u683c\u7684\u9690\u79c1\u653f\u7b56&#xff0c;\u4f46\u5728\u5b9e\u9645\u5e94\u7528\u4e2d&#xff0c;\u4ecd\u7136\u9700\u8981\u7528\u6237\u8c28\u614e\u8bc4\u4f30\u98ce\u9669\u3002<\/p>\n<p>6.2 DeepSeek\u7684\u5b89\u5168\u6027\u4e0e\u9690\u79c1 DeepSeek\u540c\u6837\u91cd\u89c6\u5b89\u5168\u6027\u548c\u9690\u79c1\u4fdd\u62a4\u3002\u5b83\u53ef\u80fd\u91c7\u7528\u4e86\u66f4\u4e25\u683c\u7684\u6570\u636e\u8bbf\u95ee\u63a7\u5236\u673a\u5236&#xff0c;\u786e\u4fdd\u53ea\u6709\u7ecf\u8fc7\u6388\u6743\u7684\u4eba\u5458\u624d\u80fd\u8bbf\u95ee\u7528\u6237\u6570\u636e\u3002\u5728\u6570\u636e\u5904\u7406\u8fc7\u7a0b\u4e2d&#xff0c;\u91c7\u7528\u533f\u540d\u5316\u548c\u52a0\u5bc6\u6280\u672f&#xff0c;\u8fdb\u4e00\u6b65\u4fdd\u62a4\u7528\u6237\u7684\u9690\u79c1\u3002<\/p>\n<p>\u6b64\u5916&#xff0c;DeepSeek\u53ef\u80fd\u4f1a\u5728\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u91c7\u7528\u8054\u90a6\u5b66\u4e60\u7b49\u6280\u672f&#xff0c;\u4f7f\u5f97\u6570\u636e\u53ef\u4ee5\u5728\u672c\u5730\u8fdb\u884c\u8bad\u7ec3&#xff0c;\u800c\u65e0\u9700\u4e0a\u4f20\u5230\u4e2d\u592e\u670d\u52a1\u5668&#xff0c;\u4ece\u800c\u6700\u5927\u7a0b\u5ea6\u5730\u4fdd\u62a4\u7528\u6237\u6570\u636e\u7684\u9690\u79c1\u3002<\/p>\n<p>\u4e03\u3001\u672a\u6765\u53d1\u5c55\u8d8b\u52bf\u5c55\u671b 7.1 ChatGPT\u7684\u672a\u6765\u53d1\u5c55 ChatGPT\u672a\u6765\u53ef\u80fd\u4f1a\u5728\u6a21\u578b\u67b6\u6784\u4e0a\u8fdb\u4e00\u6b65\u521b\u65b0&#xff0c;\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u548c\u6548\u7387\u3002\u4f8b\u5982&#xff0c;\u63a2\u7d22\u66f4\u5148\u8fdb\u7684\u81ea\u6ce8\u610f\u529b\u673a\u5236\u53d8\u4f53&#xff0c;\u4ee5\u66f4\u597d\u5730\u5904\u7406\u8d85\u957f\u6587\u672c\u548c\u590d\u6742\u7684\u8bed\u8a00\u7ed3\u6784\u3002\u540c\u65f6&#xff0c;OpenAI\u53ef\u80fd\u4f1a\u52a0\u5f3a\u591a\u6a21\u6001\u80fd\u529b\u7684\u5f00\u53d1&#xff0c;\u4f7f\u5176\u4e0d\u4ec5\u80fd\u591f\u5904\u7406\u6587\u672c&#xff0c;\u8fd8\u80fd\u4e0e\u56fe\u50cf\u3001\u97f3\u9891\u7b49\u5176\u4ed6\u6a21\u6001\u7684\u6570\u636e\u8fdb\u884c\u4ea4\u4e92\u3002<\/p>\n<p>\u5728\u5e94\u7528\u65b9\u9762&#xff0c;ChatGPT\u53ef\u80fd\u4f1a\u6df1\u5165\u66f4\u591a\u7684\u5782\u76f4\u9886\u57df&#xff0c;\u5982\u91d1\u878d\u3001\u6cd5\u5f8b\u7b49&#xff0c;\u4e3a\u8fd9\u4e9b\u9886\u57df\u63d0\u4f9b\u66f4\u4e13\u4e1a\u3001\u7cbe\u51c6\u7684\u670d\u52a1\u3002\u901a\u8fc7\u4e0e\u884c\u4e1a\u4e13\u5bb6\u7684\u5408\u4f5c&#xff0c;\u5bf9\u6a21\u578b\u8fdb\u884c\u66f4\u6709\u9488\u5bf9\u6027\u7684\u8bad\u7ec3&#xff0c;\u4ee5\u6ee1\u8db3\u7279\u5b9a\u9886\u57df\u7684\u9700\u6c42\u3002<\/p>\n<p>7.2 DeepSeek\u7684\u672a\u6765\u53d1\u5c55 DeepSeek\u672a\u6765\u53ef\u80fd\u4f1a\u7ee7\u7eed\u4f18\u5316\u5176\u6a21\u578b\u67b6\u6784&#xff0c;\u5728\u4fdd\u6301\u9ad8\u6027\u80fd\u7684\u540c\u65f6&#xff0c;\u8fdb\u4e00\u6b65\u964d\u4f4e\u8ba1\u7b97\u6210\u672c\u3002\u5b83\u53ef\u80fd\u4f1a\u5728\u591a\u8bed\u8a00\u548c\u8de8\u6587\u5316\u7406\u89e3\u65b9\u9762\u53d6\u5f97\u66f4\u5927\u7684\u7a81\u7834&#xff0c;\u4e3a\u5168\u7403\u7528\u6237\u63d0\u4f9b\u66f4\u4f18\u8d28\u7684\u670d\u52a1\u3002<\/p>\n<p>\u5728\u5e94\u7528\u573a\u666f\u4e0a&#xff0c;DeepSeek\u53ef\u80fd\u4f1a\u4e13\u6ce8\u4e8e\u4e00\u4e9b\u7279\u5b9a\u7684\u7ec6\u5206\u9886\u57df&#xff0c;\u6253\u9020\u5177\u6709\u5dee\u5f02\u5316\u7ade\u4e89\u4f18\u52bf\u7684\u4ea7\u54c1\u3002\u4f8b\u5982&#xff0c;\u5728\u6559\u80b2\u9886\u57df&#xff0c;\u5f00\u53d1\u51fa\u66f4\u667a\u80fd\u7684\u5b66\u4e60\u8f85\u52a9\u5de5\u5177&#xff0c;\u5e2e\u52a9\u5b66\u751f\u63d0\u9ad8\u5b66\u4e60\u6548\u679c&#xff1b;\u5728\u79d1\u7814\u9886\u57df&#xff0c;\u534f\u52a9\u7814\u7a76\u4eba\u5458\u8fdb\u884c\u6587\u732e\u7efc\u8ff0\u548c\u6570\u636e\u5206\u6790\u3002<\/p>\n<p>\u516b\u3001\u7ed3\u8bba DeepSeek\u548cChatGPT\u90fd\u662fAI\u8bed\u8a00\u6a21\u578b\u9886\u57df\u7684\u6770\u51fa\u4ee3\u8868&#xff0c;\u5b83\u4eec\u5728\u6a21\u578b\u67b6\u6784\u3001\u6027\u80fd\u8868\u73b0\u3001\u5e94\u7528\u573a\u666f\u7b49\u65b9\u9762\u5404\u6709\u5343\u79cb\u3002ChatGPT\u51ed\u501f\u5176\u5e7f\u6cdb\u7684\u77e5\u540d\u5ea6\u548c\u5f3a\u5927\u7684\u8bed\u8a00\u4ea4\u4e92\u80fd\u529b&#xff0c;\u5728\u804a\u5929\u673a\u5668\u4eba\u548c\u901a\u7528\u5185\u5bb9\u521b\u4f5c\u7b49\u9886\u57df\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u800cDeepSeek\u5219\u901a\u8fc7\u5728\u6a21\u578b\u4f18\u5316\u3001\u591a\u8bed\u8a00\u652f\u6301\u548c\u7279\u5b9a\u9886\u57df\u5e94\u7528\u7b49\u65b9\u9762\u7684\u4f18\u52bf&#xff0c;\u5c55\u73b0\u51fa\u4e86\u5de8\u5927\u7684\u6f5c\u529b\u3002<\/p>\n<p>\u5728\u8fd9\u573a\u7ec8\u6781\u5bf9\u51b3\u4e2d&#xff0c;\u5f88\u96be\u7b80\u5355\u5730\u5224\u5b9a\u8c01\u662f\u7edd\u5bf9\u7684\u201cAI\u8bed\u8a00\u4e4b\u738b\u201d\u3002\u6700\u7ec8\u7684\u9009\u62e9\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u548c\u7528\u6237\u9700\u6c42\u3002\u5bf9\u4e8e\u6ce8\u91cd\u901a\u7528\u8bed\u8a00\u4ea4\u4e92\u548c\u5feb\u901f\u83b7\u53d6\u7075\u611f\u7684\u7528\u6237&#xff0c;ChatGPT\u53ef\u80fd\u662f\u66f4\u597d\u7684\u9009\u62e9&#xff1b;\u800c\u5bf9\u4e8e\u9700\u8981\u4e13\u4e1a\u9886\u57df\u652f\u6301\u3001\u591a\u8bed\u8a00\u5904\u7406\u548c\u4e2a\u6027\u5316\u5185\u5bb9\u751f\u6210\u7684\u7528\u6237&#xff0c;DeepSeek\u53ef\u80fd\u66f4\u80fd\u6ee1\u8db3\u5176\u9700\u6c42\u3002<\/p>\n<p>\u968f\u7740AI\u6280\u672f\u7684\u4e0d\u65ad\u53d1\u5c55&#xff0c;\u6211\u4eec\u6709\u7406\u7531\u76f8\u4fe1&#xff0c;DeepSeek\u548cChatGPT\u5c06\u7ee7\u7eed\u63a8\u52a8\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u8fdb\u6b65&#xff0c;\u4e3a\u6211\u4eec\u5e26\u6765\u66f4\u591a\u7684\u60ca\u559c\u548c\u4fbf\u5229\u3002\u65e0\u8bba\u662f\u5728\u667a\u80fd\u5ba2\u670d\u3001\u5185\u5bb9\u521b\u4f5c\u8fd8\u662f\u5176\u4ed6\u9886\u57df&#xff0c;\u5b83\u4eec\u90fd\u5c06\u53d1\u6325\u8d8a\u6765\u8d8a\u91cd\u8981\u7684\u4f5c\u7528&#xff0c;\u5171\u540c\u5851\u9020\u4e00\u4e2a\u66f4\u52a0\u667a\u80fd\u7684\u672a\u6765\u3002<\/p>\n<p>\u53ef\u4ee5\u770b\u5230&#xff0c;\u84dd\u8018\u5143\u751f\u4ee3\u667a\u7b97\u4e91\u5e73\u53f0\u63d0\u4f9b\u7684\u6ee1\u8840\u7248DeepSeek&#xff0c;\u5728\u6587\u672c\u751f\u6210\u8d28\u91cf\u8fd8\u662f\u975e\u5e38\u4e0d\u9519\u7684&#xff0c;\u5185\u5bb9\u4e5f\u5f88\u5168\u9762\u3002<\/p>\n<h3>3.5 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