{"id":2178,"date":"2024-06-12T16:06:28","date_gmt":"2024-06-12T08:06:28","guid":{"rendered":"https:\/\/17aitech.com\/?p=2178"},"modified":"2024-10-08T15:20:25","modified_gmt":"2024-10-08T07:20:25","slug":"%e3%80%90%e8%af%be%e7%a8%8b%e6%80%bb%e7%bb%93%e3%80%91day9%ef%bc%88%e4%b8%8a%ef%bc%89%ef%bc%9a%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e5%9f%ba%e6%9c%ac%e6%b5%81%e7%a8%8b","status":"publish","type":"post","link":"https:\/\/17aitech.com\/?p=2178","title":{"rendered":"\u3010\u8bfe\u7a0b\u603b\u7ed3\u3011Day8\uff08\u4e0a\uff09\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u672c\u6d41\u7a0b"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_78 ez-toc-wrap-left-text counter-hierarchy ez-toc-counter ez-toc-light-blue ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">\u6587\u7ae0\u76ee\u5f55<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs 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href=\"https:\/\/17aitech.com\/?p=2178\/#%E6%A8%A1%E5%9E%8B_model\" >\u6a21\u578b model<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E8%AE%AD%E7%BB%83%E6%B5%81%E7%A8%8B\" >\u8bad\u7ec3\u6d41\u7a0b<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E9%A2%84%E6%B5%8B%E6%B5%81%E7%A8%8B\" >\u9884\u6d4b\u6d41\u7a0b<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%A1%B9%E7%9B%AE%E6%B5%81%E7%A8%8B\" >\u6df1\u5ea6\u5b66\u4e60\u9879\u76ee\u6d41\u7a0b<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E6%89%B9%E9%87%8F%E5%8C%96%E6%89%93%E5%8C%85%E6%95%B0%E6%8D%AE\" >\u6279\u91cf\u5316\u6253\u5305\u6570\u636e<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E8%83%8C%E6%99%AF\" >\u80cc\u666f<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%8E%9F%E7%90%86\" >\u539f\u7406<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%AE%9E%E7%8E%B0\" >\u5b9e\u73b0<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E6%9E%84%E5%BB%BA%E6%A8%A1%E5%9E%8B\" >\u6784\u5efa\u6a21\u578b<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%AE%9A%E4%B9%89\" >\u5b9a\u4e49<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%B8%B8%E8%A7%81%E6%96%B9%E5%BC%8F\" >\u5e38\u89c1\u65b9\u5f0f<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E7%AD%B9%E5%A4%87%E8%AE%AD%E7%BB%83\" >\u7b79\u5907\u8bad\u7ec3<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%AE%9A%E4%B9%89%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0\" >\u5b9a\u4e49\u635f\u5931\u51fd\u6570<\/a><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%B8%B8%E8%A7%81%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0\" >\u5e38\u89c1\u635f\u5931\u51fd\u6570<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%AE%9A%E4%B9%89%E4%BC%98%E5%8C%96%E5%99%A8\" >\u5b9a\u4e49\u4f18\u5316\u5668<\/a><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%B8%B8%E8%A7%81%E4%BC%98%E5%8C%96%E7%AE%97%E6%B3%95\" >\u5e38\u89c1\u4f18\u5316\u7b97\u6cd5<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%AE%9A%E4%B9%89%E8%AE%AD%E7%BB%83%E6%AC%A1%E6%95%B0%EF%BC%88Epochs%EF%BC%89\" >\u5b9a\u4e49\u8bad\u7ec3\u6b21\u6570\uff08Epochs\uff09<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%AE%9A%E4%B9%89%E5%AD%A6%E4%B9%A0%E7%8E%87%EF%BC%88Learning_Rate%EF%BC%89\" >\u5b9a\u4e49\u5b66\u4e60\u7387\uff08Learning Rate\uff09<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E8%AE%AD%E7%BB%83%E6%A8%A1%E5%9E%8B\" >\u8bad\u7ec3\u6a21\u578b<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E6%8E%A8%E7%90%86%E6%A8%A1%E5%9E%8B\" >\u63a8\u7406\u6a21\u578b<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%9B%9E%E5%BD%92%E9%97%AE%E9%A2%98%EF%BC%9A%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%AE%9E%E7%8E%B0%E6%88%BF%E4%BB%B7%E9%A2%84%E6%B5%8B%E6%A1%88%E4%BE%8B\" >\u56de\u5f52\u95ee\u9898\uff1a\u6df1\u5ea6\u5b66\u4e60\u5b9e\u73b0\u623f\u4ef7\u9884\u6d4b\u6848\u4f8b<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/17aitech.com\/?p=2178\/#1_%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86\" >1. \u6570\u636e\u9884\u5904\u7406<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/17aitech.com\/?p=2178\/#11_%E6%95%B0%E6%8D%AE%E8%AF%BB%E5%8F%96\" >1.1 \u6570\u636e\u8bfb\u53d6<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/17aitech.com\/?p=2178\/#12_%E6%95%B0%E6%8D%AE%E5%88%87%E5%88%86\" >1.2 \u6570\u636e\u5207\u5206<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/17aitech.com\/?p=2178\/#13_%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86%E8%A7%84%E8%8C%83%E5%8C%96\" >1.3 \u6570\u636e\u9884\u5904\u7406(\u89c4\u8303\u5316)<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/17aitech.com\/?p=2178\/#2_%E6%89%B9%E9%87%8F%E5%8C%96%E6%89%93%E5%8C%85%E6%95%B0%E6%8D%AE\" >2. \u6279\u91cf\u5316\u6253\u5305\u6570\u636e<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/17aitech.com\/?p=2178\/#3_%E6%A8%A1%E5%9E%8B%E6%90%AD%E5%BB%BA\" >3. \u6a21\u578b\u642d\u5efa<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/17aitech.com\/?p=2178\/#4_%E7%AD%B9%E5%A4%87%E8%AE%AD%E7%BB%83\" >4. \u7b79\u5907\u8bad\u7ec3<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"https:\/\/17aitech.com\/?p=2178\/#5_%E8%AE%AD%E7%BB%83%E6%A8%A1%E5%9E%8B\" >5. \u8bad\u7ec3\u6a21\u578b<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-33\" href=\"https:\/\/17aitech.com\/?p=2178\/#51_%E5%AE%9A%E4%B9%89%E7%9B%91%E6%8E%A7%E6%8C%87%E6%A0%87%E5%92%8C%E6%96%B9%E6%B3%95\" >5.1 \u5b9a\u4e49\u76d1\u63a7\u6307\u6807\u548c\u65b9\u6cd5<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-34\" href=\"https:\/\/17aitech.com\/?p=2178\/#52_%E5%AE%9E%E7%8E%B0%E8%AE%AD%E7%BB%83%E8%BF%87%E7%A8%8B\" >5.2 \u5b9e\u73b0\u8bad\u7ec3\u8fc7\u7a0b<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-35\" href=\"https:\/\/17aitech.com\/?p=2178\/#53_%E5%BC%80%E5%A7%8B%E8%AE%AD%E7%BB%83\" >5.3 \u5f00\u59cb\u8bad\u7ec3<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-36\" href=\"https:\/\/17aitech.com\/?p=2178\/#54_%E5%9B%BE%E5%BD%A2%E5%8C%96%E7%9B%91%E6%8E%A7%E6%95%B0%E6%8D%AE\" >5.4 \u56fe\u5f62\u5316\u76d1\u63a7\u6570\u636e<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-37\" href=\"https:\/\/17aitech.com\/?p=2178\/#55_%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0\" >5.5 \u6fc0\u6d3b\u51fd\u6570<\/a><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-38\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95\" >\u4f7f\u7528\u65b9\u6cd5<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-39\" href=\"https:\/\/17aitech.com\/?p=2178\/#56_%E5%A4%9A%E5%B1%82%E6%84%9F%E7%9F%A5%E6%9C%BA\" >5.6 \u591a\u5c42\u611f\u77e5\u673a<\/a><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-40\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%AE%9A%E4%B9%89-2\" >\u5b9a\u4e49<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-41\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%AE%9E%E7%8E%B0%E6%96%B9%E6%B3%95%EF%BC%9A\" >\u5b9e\u73b0\u65b9\u6cd5\uff1a<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-42\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0_vs_%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0\" >\u6df1\u5ea6\u5b66\u4e60 vs \u673a\u5668\u5b66\u4e60<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-43\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%88%86%E7%B1%BB%E9%97%AE%E9%A2%98%EF%BC%9A%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%AE%9E%E7%8E%B0%E9%B8%A2%E5%B0%BE%E8%8A%B1%E5%88%86%E7%B1%BB%E6%A1%88%E4%BE%8B\" >\u5206\u7c7b\u95ee\u9898\uff1a\u6df1\u5ea6\u5b66\u4e60\u5b9e\u73b0\u9e22\u5c3e\u82b1\u5206\u7c7b\u6848\u4f8b<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-44\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E6%B5%81%E7%A8%8B%E5%9B%9E%E9%A1%BE%E4%B8%8E%E5%AF%B9%E6%AF%94\" >\u6d41\u7a0b\u56de\u987e\u4e0e\u5bf9\u6bd4<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-45\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E4%BB%A3%E7%A0%81%E5%AE%9E%E7%8E%B0\" >\u4ee3\u7801\u5b9e\u73b0<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-46\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E8%A1%A5%E5%85%85%E7%9F%A5%E8%AF%86\" >\u8865\u5145\u77e5\u8bc6<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-47\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E6%A6%82%E7%8E%87%E6%A8%A1%E6%8B%9F\" >\u6982\u7387\u6a21\u62df<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-48\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%B8%B8%E8%A7%81%E6%96%B9%E6%B3%95\" >\u5e38\u89c1\u65b9\u6cd5<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-49\" href=\"https:\/\/17aitech.com\/?p=2178\/#one-hot%E7%BC%96%E7%A0%81\" >one-hot\u7f16\u7801<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-50\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E4%BA%A4%E5%8F%89%E7%86%B5\" >\u4ea4\u53c9\u71b5<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-51\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0\" >\u6fc0\u6d3b\u51fd\u6570<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-52\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%86%85%E5%AE%B9%E5%B0%8F%E7%BB%93\" >\u5185\u5bb9\u5c0f\u7ed3<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-53\" href=\"https:\/\/17aitech.com\/?p=2178\/#%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99%EF%BC%9A\" >\u53c2\u8003\u8d44\u6599\uff1a<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"%E5%89%8D%E8%A8%80\"><\/span>\u524d\u8a00<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u5728\u4e0a\u4e00\u7bc7\u8bfe\u7a0b<a href=\"https:\/\/17aitech.com\/?p=2149\">\u300a\u3010\u8bfe\u7a0b\u603b\u7ed3\u3011Day7\uff1a\u6df1\u5ea6\u5b66\u4e60\u6982\u8ff0\u300b<\/a>\u4e2d\uff0c\u6211\u4eec\u4e86\u89e3\u5230\uff1a<\/p>\n<ul>\n<li>\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u2192\u672c\u8d28\u4e0a\u662f\u56fa\u5b9aw\u548cb\u53c2\u6570\u7684\u8fc7\u7a0b\uff1b<\/li>\n<li>\u8ba9\u6a21\u578b\u66f4\u597d\u2192\u672c\u8d28\u4e0a\u5c31\u662f\u8ba9\u6a21\u578b\u7684\u635f\u5931\u503closs\u53d8\u5c0f\uff1b<\/li>\n<li>\u8ba9loss\u53d8\u5c0f\u2192\u672c\u8d28\u4e0a\u5c31\u662f\u6c42loss\u51fd\u6570\u7684\u6700\u5c0f\u503c\uff1b<\/li>\n<\/ul>\n<p>\u672c\u7bc7\u6587\u7ae0\uff0c\u6211\u4eec\u5c06\u7ee7\u7eed\u6df1\u5165\u4e86\u89e3\u6df1\u5ea6\u5b66\u4e60\u7684\u9879\u76ee\u6d41\u7a0b\uff0c\u5305\u62ec\uff1a\u6279\u91cf\u5316\u6253\u5305\u6570\u636e\u3001\u6a21\u578b\u5b9a\u4e49\u3001\u635f\u5931\u51fd\u6570\u3001\u4f18\u5316\u5668\u4ee5\u53ca\u8bad\u7ec3\u6a21\u578b\u7b49\u5185\u5bb9\u3002<\/p>\n<h2><span class=\"ez-toc-section\" id=\"%E6%B1%82%E5%87%BD%E6%95%B0%E6%9C%80%E5%B0%8F%E5%80%BC%E5%9B%9E%E9%A1%BE\"><\/span>\u6c42\u51fd\u6570\u6700\u5c0f\u503c\u56de\u987e<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u4ee5$y=2x^2$\uff0c\u6211\u4eec\u56de\u987e\u4f7f\u7528pytorch\u6846\u67b6\u6c42\u51fd\u6570\u6700\u5c0f\u503c\uff0c\u5176\u8fc7\u7a0b\u5927\u81f4\u5982\u4e0b\uff1a<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/\u6c42\u6700\u5c0f\u503c\u6d41\u7a0b.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/\u6c42\u6700\u5c0f\u503c\u6d41\u7a0b.png\" alt=\"\" \/><\/a><\/p>\n<blockquote>\n<p>\u5907\u6ce8\uff1a\u4ee3\u7801\u4e0d\u518d\u91cd\u590d\u8d58\u8ff0\uff0c\u56de\u987e\u4ee3\u7801\u8bf7\u89c1<a href=\"https:\/\/17aitech.com\/?p=2149#toc-14\">\u4f7f\u7528pytorch\u6c42\u51fd\u6570\u6700\u5c0f\u503c<\/a><\/p>\n<\/blockquote>\n<h2><span class=\"ez-toc-section\" id=\"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%9F%BA%E6%9C%AC%E6%B5%81%E7%A8%8B\"><\/span>\u6df1\u5ea6\u5b66\u4e60\u57fa\u672c\u6d41\u7a0b<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u7531\u4e8e\u8bad\u7ec3\u7684\u672c\u8d28\uff1a<strong>\u6c42loss\u51fd\u6570\u7684\u6700\u5c0f\u503c<\/strong>\uff1b\u6240\u4ee5\uff0c\u6211\u4eec\u7c7b\u6bd4\u6c42$y=2x^2$\u6700\u5c0f\u503c\u7684\u8fc7\u7a0b\uff0c\u6765\u770b\u4e00\u4e0b\u7ebf\u6027\u56de\u5f52\u8bad\u7ec3\uff08\u4e5f\u5c31\u662f\u6c42loss\u6700\u5c0f\u503c\uff09\u7684\u8fc7\u7a0b\uff0c\u5176\u6d41\u7a0b\u5982\u4e0b\uff1a<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/\u6c42loss\u6700\u5c0f\u503c\u6d41\u7a0b.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/\u6c42loss\u6700\u5c0f\u503c\u6d41\u7a0b.png\" alt=\"\" \/><\/a><\/p>\n<p>\u76f8\u6bd4\u4e8e\u6c42$y=2x^2$\u6700\u5c0f\u503c\u7684\u8fc7\u7a0b\uff0c\u6211\u4eec\u6709\u5982\u4e0b\u8c03\u6574\uff1a<\/p>\n<ul>\n<li>\u66ff\u6362\uff1a\u968f\u673a\u9009\u62e9\u51fa\u751f\u70b9\u2192\u51c6\u5907\u8bad\u7ec3\u6570\u636e(\u672c\u4f8b\u4e2d\u8bad\u7ec3\u6570\u636e\u5148\u6a21\u62df\u751f\u6210)<\/li>\n<li>\u66ff\u6362\uff1a\u5b9a\u4e49\u539f\u59cb\u51fd\u6570\u2192\u5b9a\u4e49\u6a21\u578b<\/li>\n<li>\u589e\u52a0\uff1a\u521d\u59cb\u5316\u4f18\u5316\u5668\uff08\u8bad\u7ec3\u65f6\u8981\u5b9a\u4e49\u635f\u5931\u51fd\u6570\uff0c\u6211\u4eec\u5e38\u7528MSE\uff09<\/li>\n<li>\u66ff\u6362\uff1a\u8f93\u51fa\u6700\u5c0f\u503c\u4f4d\u7f6e\u2192\u8f93\u51fa\u8bad\u7ec3\u540e\u7684\u6a21\u578b\u6743\u91cd\u548c\u504f\u7f6e<\/li>\n<\/ul>\n<p>\u5177\u4f53\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<pre><code class=\"language-python\">import torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n# \u751f\u6210\u6a21\u62df\u6570\u636e\ntorch.manual_seed(42)\nx_train = torch.randn(100, 13)  # 100\u4e2a\u6837\u672c\uff0c\u6bcf\u4e2a\u6837\u672c\u670913\u4e2a\u7279\u5f81\ny_train = torch.randn(100, 1)   # \u6bcf\u4e2a\u6837\u672c\u5bf9\u5e94\u4e00\u4e2a\u8f93\u51fa\u503c\n\n# \u5b9a\u4e49\u6a21\u578b\nmodel = nn.Linear(13, 1)  # \u8f93\u5165\u7279\u5f81\u6570\u4e3a13\uff0c\u8f93\u51fa\u7279\u5f81\u6570\u4e3a1\n\n# \u521d\u59cb\u5316\u4f18\u5316\u5668\ncriterion = nn.MSELoss()  # \u5747\u65b9\u8bef\u5dee\u635f\u5931\noptimizer = optim.SGD(model.parameters(), lr=0.01)  # \u968f\u673a\u68af\u5ea6\u4e0b\u964d\u4f18\u5316\u5668\n\n# \u8fed\u4ee3\u6b21\u6570\u548c\u5b66\u4e60\u7387\nepochs = 1000\nlearning_rate = 1e-2\n\n# \u4f7f\u7528 PyTorch \u8fdb\u884c\u68af\u5ea6\u4e0b\u964d\nfor _ in range(epochs):\n    optimizer.zero_grad()  # \u68af\u5ea6\u6e05\u96f6\n\n    outputs = model(x_train)  # \u6b63\u5411\u4f20\u64ad\n    loss = criterion(outputs, y_train)  # \u8ba1\u7b97\u635f\u5931\n    loss.backward()  # \u53cd\u5411\u4f20\u64ad\n    optimizer.step()  # \u66f4\u65b0\u53c2\u6570\n\n# \u8f93\u51fa\u6700\u7ec8\u6a21\u578b\u53c2\u6570\nprint(&quot;\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u6743\u91cd\uff1a&quot;, model.weight)\nprint(&quot;\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u504f\u7f6e\uff1a&quot;, model.bias)<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612121430580.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612121430580.png\" alt=\"\" \/><\/a><\/p>\n<p>\u81f3\u6b64\uff0c\u6211\u4eec\u5b8c\u6210\u4e86\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u8bad\u7ec3\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53c2\u7167\u4e0a\u9762\u4ee3\u7801\uff0c\u518d\u6df1\u5165\u7406\u89e3\u4e00\u4e0b\u76f8\u5173\u7684\u57fa\u7840\u7406\u8bba\u3002<\/p>\n<h3><span class=\"ez-toc-section\" id=\"%E6%A8%A1%E5%9E%8B_model\"><\/span>\u6a21\u578b model<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li>\n<p><strong>\u524d\u5411\u4f20\u64ad\u7684\u5b9a\u4e49<\/strong>\uff1a\u628a\u7279\u5f81 X \u5e26\u5165\u6a21\u578b model \uff0c\u5f97\u5230\u9884\u6d4b\u7ed3\u679c y_pred <\/p>\n<ul>\n<li><strong>\u8bad\u7ec3\u65f6<\/strong>\uff1a\u81ea\u52a8\u5728\u5e95\u5c42\u6784\u5efa\u8ba1\u7b97\u56fe(\u628a\u6b63\u5411\u4f20\u64ad\u7684\u6d41\u7a0b\u8bb0\u5f55\u4e0b\u6765\uff0c\u65b9\u4fbf\u8fdb\u884c\u540e\u7eed\u7684\u5206\u5e03\u6c42\u5bfc\/\u94fe\u5f0f\u6c42\u5bfc\u3002<\/li>\n<\/ul>\n<blockquote>\n<p>\u4f8b\u5982\uff1a\u5728\u5bfc\u6570\u4e2d\uff0c\u5bf9\u4e8e\u4e00\u4e2a\u590d\u5408\u51fd\u6570h( g( f(x)))\uff0c\u6211\u4eec\u9700\u8981\u8fdb\u884c\u94fe\u5f0f\u6c42\u5bfc\uff0c\u5373h( g( f(x))) = f&#8217; <em> g&#8217; <\/em> h&#8217;<\/p>\n<\/blockquote>\n<ul>\n<li><strong>\u63a8\u7406\u65f6<\/strong>\uff1a\u76f4\u63a5\u8c03\u7528\u6b63\u5411\u4f20\u64ad\u5373\u53ef\uff0c\u4e0d\u9700\u8981\u6784\u5efa\u8ba1\u7b97\u56fe<\/li>\n<\/ul>\n<\/li>\n<li>\n<p><strong>\u53cd\u5411\u4f20\u64ad\u7684\u5b9a\u4e49<\/strong>\uff1a\u672c\u8d28\u662f\u8ba1\u7b97\u6bcf\u4e2a\u53c2\u6570\u7684\u68af\u5ea6\uff0c\u662f\u901a\u8fc7\u635f\u5931\u51fd\u6570\u53d1\u8d77\u7684<\/p>\n<\/li>\n<li>\n<p><strong>\u6a21\u578b\u7684\u4f5c\u7528<\/strong>\uff1a\u53ea\u8d1f\u8d23\u524d\u5411\u4f20\u64ad forward\uff0c\u4e0d\u8d1f\u8d23\u540e\u5411\u4f20\u64ad backward<\/p>\n<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"%E8%AE%AD%E7%BB%83%E6%B5%81%E7%A8%8B\"><\/span>\u8bad\u7ec3\u6d41\u7a0b<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ol>\n<li>\u4ece\u8bad\u7ec3\u96c6\u4e2d\uff0c\u53d6\u4e00\u6279 <code>batch<\/code> \u6837\u672c<code>(x, y)<\/code><\/li>\n<li>\u628a\u6837\u672c\u7279\u5f81 <code>X<\/code> \u9001\u5165\u6a21\u578b <code>model<\/code>\uff0c\u5f97\u5230\u9884\u6d4b\u7ed3\u679c <code>y_pred<\/code><\/li>\n<li>\u8ba1\u7b97\u635f\u5931\u51fd\u6570 <code>loss = f(y_pred, y)<\/code> \uff0c\u8ba1\u7b97\u5f53\u524d\u7684\u8bef\u5dee <code>loss<\/code><\/li>\n<li>\u901a\u8fc7<code>loss<\/code> \uff0c \u53cd\u5411\u4f20\u64ad\uff0c\u8ba1\u7b97\u6bcf\u4e2a\u53c2\u6570<code>(w, b)<\/code>\u7684\u68af\u5ea6<\/li>\n<li>\u5229\u7528\u4f18\u5316\u5668 <code>optimizer<\/code> \uff0c\u901a\u8fc7\u68af\u5ea6\u4e0b\u964d\u6cd5\uff0c\u66f4\u65b0\u53c2\u6570<\/li>\n<li>\u5229\u7528\u4f18\u5316\u5668 <code>optimizer<\/code> \u6e05\u7a7a\u53c2\u6570\u7684\u68af\u5ea6<\/li>\n<li>\u91cd\u590d<code>1-6<\/code> \u76f4\u81f3\u8fed\u4ee3\u7ed3\u675f(\u5404\u9879\u6307\u6807\u6ee1\u8db3\u8981\u6c42\u6216\u662f\u8bef\u5dee\u5f88\u5c0f)<\/li>\n<\/ol>\n<h3><span class=\"ez-toc-section\" id=\"%E9%A2%84%E6%B5%8B%E6%B5%81%E7%A8%8B\"><\/span>\u9884\u6d4b\u6d41\u7a0b<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ol>\n<li>\u62ff\u5230\u5f85\u6d4b\u6837\u672c <code>X<\/code>\uff08\u63a8\u7406\u65f6\uff0c\u6ca1\u6709\u6807\u7b7e\uff0c\u53ea\u6709\u7279\u5f81\uff09<\/li>\n<li>\u628a\u6837\u672c\u7279\u5f81 <code>X<\/code>  \u9001\u5165\u6a21\u578b <code>model<\/code> \uff0c\u5f97\u5230\u9884\u6d4b\u7ed3\u679c <code>y_pred<\/code><\/li>\n<li>\u6839\u636e <code>y_pred<\/code> \u89e3\u6790\u5e76\u8fd4\u56de\u9884\u6d4b\u7ed3\u679c\u5373\u53ef<\/li>\n<\/ol>\n<h2><span class=\"ez-toc-section\" id=\"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%A1%B9%E7%9B%AE%E6%B5%81%E7%A8%8B\"><\/span>\u6df1\u5ea6\u5b66\u4e60\u9879\u76ee\u6d41\u7a0b<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u901a\u8fc7\u4e0a\u8ff0\u5185\u5bb9\u68b3\u7406\uff0c\u6211\u4eec\u5df2\u7ecf\u4e86\u89e3\u6df1\u5ea6\u5b66\u4e60\u7684\u4e00\u4e2a\u57fa\u672c\u6d41\u7a0b\uff0c\u5305\u62ec\uff1a\u5b9a\u4e49\u6a21\u578b\u3001\u8bad\u7ec3\u3001\u9884\u6d4b\u3002<\/p>\n<p>\u4f46\u662f\u5728\u5b9e\u9645\u5de5\u7a0b\u4f7f\u7528\u4e2d\uff0c\u7531\u4e8e\u8bad\u7ec3\u6570\u636e\u6bd4\u8f83\u5e9e\u5927\uff0c\u6240\u4ee5\u6211\u4eec\u8fd8\u9700\u8981\u4e00\u4e9b\u989d\u5916\u7684\u6b65\u9aa4\uff0c\u4f8b\u5982\uff1a\u589e\u52a0\u6279\u91cf\u5316\u6253\u5305\u6d41\u7a0b\u3002<\/p>\n<p>\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u6df1\u5ea6\u5b66\u4e60\u7684\u6574\u4f53\u6d41\u7a0b\uff0c\u6211\u4eec\u4ecd\u7136\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u4e2d\u4f7f\u7528\u7684<a href=\"https:\/\/17aitech.com\/?p=2040#toc-3\">\u300a\u6ce2\u58eb\u987f\u623f\u4ef7\u9884\u6d4b\u300b<\/a>\u6848\u4f8b\uff0c\u6765\u770b\u4e00\u4e0b\u6df1\u5ea6\u5b66\u4e60\u4e0b\u5e94\u8be5\u5982\u4f55\u5b9e\u73b0\u3002<\/p>\n<h3><span class=\"ez-toc-section\" id=\"%E6%89%B9%E9%87%8F%E5%8C%96%E6%89%93%E5%8C%85%E6%95%B0%E6%8D%AE\"><\/span>\u6279\u91cf\u5316\u6253\u5305\u6570\u636e<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><span class=\"ez-toc-section\" id=\"%E8%83%8C%E6%99%AF\"><\/span>\u80cc\u666f<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>\u5728\u5b9e\u9645\u7684\u5de5\u7a0b\u4e2d\uff0c\u7531\u4e8e\u6df1\u5ea6\u5b66\u4e60\u662f\u8981\u8fdb\u884c\u5927\u6570\u636e\u91cf\u7684\u8bad\u7ec3\uff0c\u6240\u4ee5\u6211\u4eec\u9700\u8981\u57fa\u4e8e\u4ee5\u4e0b\u539f\u56e0\u8fdb\u884c\u6279\u91cf\u5316\u6253\u5305\u6570\u636e\u3002<\/p>\n<ul>\n<li><strong>\u63d0\u9ad8\u8bad\u7ec3\u6548\u7387<\/strong>\uff1a\u901a\u8fc7\u6279\u91cf\u5316\u5904\u7406\u6570\u636e\uff0c\u53ef\u4ee5\u5145\u5206\u5229\u7528GPU\u7684\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\uff0c\u52a0\u5feb\u6a21\u578b\u8bad\u7ec3\u901f\u5ea6\u3002<\/li>\n<li><strong>\u7a33\u5b9a\u6a21\u578b\u8bad\u7ec3<\/strong>\uff1a\u6279\u91cf\u5316\u5904\u7406\u53ef\u4ee5\u964d\u4f4e\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u65b9\u5dee\uff0c\u4f7f\u6a21\u578b\u66f4\u52a0\u7a33\u5b9a\u3002<\/li>\n<li><strong>\u51cf\u5c11\u5185\u5b58\u6d88\u8017<\/strong>\uff1a\u6279\u91cf\u5316\u5904\u7406\u53ef\u4ee5\u51cf\u5c11\u5728\u6bcf\u4e2a\u8fed\u4ee3\u4e2d\u9700\u8981\u5b58\u50a8\u7684\u6570\u636e\u91cf\uff0c\u8282\u7701\u5185\u5b58\u6d88\u8017\u3002<\/li>\n<\/ul>\n<h4><span class=\"ez-toc-section\" id=\"%E5%8E%9F%E7%90%86\"><\/span>\u539f\u7406<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<ul>\n<li>\n<p>\u4f7f\u7528\u751f\u6210\u5668\u6765\u6253\u5305\u6570\u636e<\/p>\n<blockquote>\n<p>\u751f\u6210\u5668\u8bb0\u5f55\u4e86\u4e00\u4e2a\u89c4\u5219\uff0c\u6bcf\u6b21\u8c03\u7528\u751f\u6210\u5668\u5c31\u4f1a\u8fd4\u56de\u4e00\u4e2a\u6279\u6b21\u6570\u636e\u3002<\/p>\n<\/blockquote>\n<\/li>\n<\/ul>\n<h4><span class=\"ez-toc-section\" id=\"%E5%AE%9E%E7%8E%B0\"><\/span>\u5b9e\u73b0<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<ol>\n<li>\u5148\u81ea\u5b9a\u4e49<code>dataset<\/code><\/li>\n<li>\u518d\u5b9a\u4e49<code>dataloader<\/code><\/li>\n<\/ol>\n<pre><code class=\"language-python\">from torch.utils.data import DataLoader, TensorDataset\n# \u6279\u91cf\u5316\u6253\u5305\u6570\u636e\u793a\u4f8b\u4ee3\u7801\n# \u521b\u5efa\u6570\u636e\u96c6\u548c\u6570\u636e\u52a0\u8f7d\u5668\ndataset = TensorDataset(x_tensor, y_tensor)\ndata_loader = DataLoader(dataset, batch_size=16, shuffle=True)<\/code><\/pre>\n<h3><span class=\"ez-toc-section\" id=\"%E6%9E%84%E5%BB%BA%E6%A8%A1%E5%9E%8B\"><\/span>\u6784\u5efa\u6a21\u578b<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><span class=\"ez-toc-section\" id=\"%E5%AE%9A%E4%B9%89\"><\/span>\u5b9a\u4e49<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>\u5728\u6df1\u5ea6\u5b66\u4e60\u4e2d\u6784\u5efa\u6a21\u578b\u662f\u6307\u8bbe\u8ba1\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\uff0c\u786e\u5b9a\u7f51\u7edc\u7684\u5c42\u6570\u3001\u6bcf\u5c42\u7684\u795e\u7ecf\u5143\u6570\u91cf\u3001\u6fc0\u6d3b\u51fd\u6570\u7b49\u53c2\u6570\uff0c\u4ee5\u5b9e\u73b0\u7279\u5b9a\u7684\u5b66\u4e60\u4efb\u52a1\u3002<\/p>\n<h4><span class=\"ez-toc-section\" id=\"%E5%B8%B8%E8%A7%81%E6%96%B9%E5%BC%8F\"><\/span>\u5e38\u89c1\u65b9\u5f0f<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<ul>\n<li><strong>Sequential\u6a21\u578b<\/strong>\uff1aSequential\u6a21\u578b\u662f\u4e00\u79cd\u7b80\u5355\u7684\u7ebf\u6027\u5806\u53e0\u6a21\u578b\uff0c\u5c42\u6309\u987a\u5e8f\u4f9d\u6b21\u5806\u53e0\u5728\u4e00\u8d77\uff0c\u9002\u7528\u4e8e\u987a\u5e8f\u5904\u7406\u7684\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u3002<\/li>\n<li><strong>Class\u5b50\u7c7b\u5316\u6a21\u578b<\/strong>\uff1a\u901a\u8fc7\u7ee7\u627f\u6846\u67b6\u63d0\u4f9b\u7684\u6a21\u578b\u57fa\u7c7b\uff0c\u7528\u6237\u53ef\u4ee5\u81ea\u5b9a\u4e49\u6a21\u578b\u7684\u7ed3\u6784\u548c\u8ba1\u7b97\u903b\u8f91\uff0c\u5b9e\u73b0\u66f4\u52a0\u7075\u6d3b\u548c\u5b9a\u5236\u5316\u7684\u6a21\u578b\u6784\u5efa\u3002<\/li>\n<\/ul>\n<blockquote>\n<p>\u9664\u4e0a\u8ff0\u65b9\u5f0f\u4e4b\u5916\uff0c\u8fd8\u6709<strong>\u8fc1\u79fb\u5b66\u4e60<\/strong>\u3001<strong>\u6a21\u578b\u7ec4\u5408<\/strong>\u3001<strong>\u6a21\u578b\u96c6\u6210<\/strong>\u3001<strong>\u81ea\u52a8\u673a\u5668\u5b66\u4e60\uff08AutoML\uff09<\/strong>\u3001<strong>\u8d85\u7f51\u7edc\uff08Hypernetwork\uff09<\/strong>\u7b49\u65b9\u5f0f\uff0c\u7531\u4e8e\u4e0d\u662f\u672c\u7ae0\u5185\u5bb9\u91cd\u70b9\uff0c\u6682\u4e0d\u5c55\u5f00\u3002<\/p>\n<\/blockquote>\n<h3><span class=\"ez-toc-section\" id=\"%E7%AD%B9%E5%A4%87%E8%AE%AD%E7%BB%83\"><\/span>\u7b79\u5907\u8bad\u7ec3<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><span class=\"ez-toc-section\" id=\"%E5%AE%9A%E4%B9%89%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0\"><\/span>\u5b9a\u4e49\u635f\u5931\u51fd\u6570<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<ul>\n<li>\u76ee\u7684\uff1a\u635f\u5931\u51fd\u6570\u7528\u4e8e\u8861\u91cf\u6a21\u578b\u9884\u6d4b\u7ed3\u679c\u4e0e\u771f\u5b9e\u6807\u7b7e\u4e4b\u95f4\u7684\u5dee\u5f02\uff0c\u662f\u4f18\u5316\u7b97\u6cd5\u7684\u76ee\u6807\u51fd\u6570\uff0c\u5e2e\u52a9\u6a21\u578b\u5b66\u4e60\u6b63\u786e\u7684\u53c2\u6570\u3002<\/li>\n<\/ul>\n<h5><span class=\"ez-toc-section\" id=\"%E5%B8%B8%E8%A7%81%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0\"><\/span>\u5e38\u89c1\u635f\u5931\u51fd\u6570<span class=\"ez-toc-section-end\"><\/span><\/h5>\n<ul>\n<li>\u5747\u65b9\u8bef\u5dee\u635f\u5931(Mean Squared Error, MSE)<\/li>\n<li>\u4ea4\u53c9\u71b5\u635f\u5931(Cross Entropy Loss)<\/li>\n<\/ul>\n<h4><span class=\"ez-toc-section\" id=\"%E5%AE%9A%E4%B9%89%E4%BC%98%E5%8C%96%E5%99%A8\"><\/span>\u5b9a\u4e49\u4f18\u5316\u5668<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<ul>\n<li>\u76ee\u7684\uff1a\u4f18\u5316\u5668\u7528\u4e8e\u66f4\u65b0\u6a21\u578b\u53c2\u6570\uff0c\u901a\u8fc7\u6700\u5c0f\u5316\u635f\u5931\u51fd\u6570\u6765\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\uff0c\u8c03\u6574\u6a21\u578b\u53c2\u6570\u4f7f\u5f97\u635f\u5931\u51fd\u6570\u8fbe\u5230\u6700\u5c0f\u503c\u3002<\/li>\n<\/ul>\n<h5><span class=\"ez-toc-section\" id=\"%E5%B8%B8%E8%A7%81%E4%BC%98%E5%8C%96%E7%AE%97%E6%B3%95\"><\/span>\u5e38\u89c1\u4f18\u5316\u7b97\u6cd5<span class=\"ez-toc-section-end\"><\/span><\/h5>\n<ul>\n<li>\u968f\u673a\u68af\u5ea6\u4e0b\u964d(SGD)<\/li>\n<li>Adam<\/li>\n<li>Adagrad\u7b49<\/li>\n<\/ul>\n<h4><span class=\"ez-toc-section\" id=\"%E5%AE%9A%E4%B9%89%E8%AE%AD%E7%BB%83%E6%AC%A1%E6%95%B0%EF%BC%88Epochs%EF%BC%89\"><\/span>\u5b9a\u4e49\u8bad\u7ec3\u6b21\u6570\uff08Epochs\uff09<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<ul>\n<li>\n<p>\u5b9a\u4e49\uff1a<strong>\u8bad\u7ec3\u6b21\u6570<\/strong>\u6307\u7684\u662f\u5c06\u6574\u4e2a\u8bad\u7ec3\u6570\u636e\u96c6\u5728\u6a21\u578b\u4e0a\u53cd\u590d\u8bad\u7ec3\u7684\u6b21\u6570\uff0c\u6bcf\u6b21\u5b8c\u6574\u5730\u904d\u5386\u6574\u4e2a\u6570\u636e\u96c6\u79f0\u4e3a\u4e00\u4e2a\u8bad\u7ec3\u5468\u671f\uff08Epoch\uff09\u3002<\/p>\n<\/li>\n<li>\n<p>\u4f5c\u7528\uff1a\u901a\u8fc7\u589e\u52a0\u8bad\u7ec3\u6b21\u6570\uff0c\u6a21\u578b\u53ef\u4ee5\u66f4\u597d\u5730\u5b66\u4e60\u6570\u636e\u96c6\u4e2d\u7684\u6a21\u5f0f\u548c\u7279\u5f81\uff0c\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u51cf\u5c11\u8fc7\u62df\u5408\u7684\u98ce\u9669\u3002<\/p>\n<\/li>\n<\/ul>\n<h4><span class=\"ez-toc-section\" id=\"%E5%AE%9A%E4%B9%89%E5%AD%A6%E4%B9%A0%E7%8E%87%EF%BC%88Learning_Rate%EF%BC%89\"><\/span>\u5b9a\u4e49\u5b66\u4e60\u7387\uff08Learning Rate\uff09<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<ul>\n<li>\u5b9a\u4e49\uff1a<strong>\u5b66\u4e60\u7387<\/strong>\u662f\u4f18\u5316\u7b97\u6cd5\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u8d85\u53c2\u6570\uff0c\u63a7\u5236\u6a21\u578b\u53c2\u6570\u5728\u6bcf\u6b21\u8fed\u4ee3\u4e2d\u66f4\u65b0\u7684\u6b65\u957f\u5927\u5c0f\uff0c\u5373\u53c2\u6570\u6cbf\u7740\u68af\u5ea6\u65b9\u5411\u66f4\u65b0\u7684\u5e45\u5ea6\u3002<\/li>\n<li>\u76ee\u7684\uff1a\u5b66\u4e60\u7387\u7684\u9009\u62e9\u5f71\u54cd\u6a21\u578b\u8bad\u7ec3\u7684\u901f\u5ea6\u548c\u6027\u80fd\uff0c\u5408\u9002\u7684\u5b66\u4e60\u7387\u80fd\u591f\u4f7f\u6a21\u578b\u66f4\u5feb\u5730\u6536\u655b\u5230\u6700\u4f18\u89e3\uff0c\u800c\u8fc7\u5927\u6216\u8fc7\u5c0f\u7684\u5b66\u4e60\u7387\u53ef\u80fd\u5bfc\u81f4\u8bad\u7ec3\u4e0d\u7a33\u5b9a\u6216\u9677\u5165\u5c40\u90e8\u6700\u4f18\u89e3\u3002<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"%E8%AE%AD%E7%BB%83%E6%A8%A1%E5%9E%8B\"><\/span>\u8bad\u7ec3\u6a21\u578b<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li>\n<p>\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u9700\u8981\u76d1\u63a7\u6a21\u578b\u6307\u6807\uff0c\u5982\uff1a\u51c6\u786e\u7387\u3001\u635f\u5931\u503c\u7b49<\/p>\n<\/li>\n<li>\n<p>\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u9700\u8981\u4fdd\u5b58\u6a21\u578b\u53c2\u6570\uff0c\u65b9\u4fbf\u540e\u7eed\u63a8\u7406<\/p>\n<\/li>\n<li>\n<p>\u907f\u514d\u8fc7\u62df\u5408<\/p>\n<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"%E6%8E%A8%E7%90%86%E6%A8%A1%E5%9E%8B\"><\/span>\u63a8\u7406\u6a21\u578b<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u8bad\u7ec3\u597d\u6a21\u578b\u4e4b\u540e\uff0c\u76f4\u63a5\u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u63a8\u7406\u5373\u53ef\u3002<\/p>\n<h2><span class=\"ez-toc-section\" id=\"%E5%9B%9E%E5%BD%92%E9%97%AE%E9%A2%98%EF%BC%9A%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%AE%9E%E7%8E%B0%E6%88%BF%E4%BB%B7%E9%A2%84%E6%B5%8B%E6%A1%88%E4%BE%8B\"><\/span>\u56de\u5f52\u95ee\u9898\uff1a\u6df1\u5ea6\u5b66\u4e60\u5b9e\u73b0\u623f\u4ef7\u9884\u6d4b\u6848\u4f8b<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"1_%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86\"><\/span>1. \u6570\u636e\u9884\u5904\u7406<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><span class=\"ez-toc-section\" id=\"11_%E6%95%B0%E6%8D%AE%E8%AF%BB%E5%8F%96\"><\/span>1.1 \u6570\u636e\u8bfb\u53d6<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<pre><code class=\"language-python\">file_name = &#039;.\/housing.data&#039;\n\n# \u539f\u59cb\u6570\u636e\u8bfb\u53d6\nX = []\ny = []\nwith open(file=file_name, mode=&#039;r&#039;, encoding=&#039;utf8&#039;) as f:\n#     f.readline()\n    for  line in f:\n        line = line.strip()\n        if line:\n            sample = [float(ele) for ele in line.split(&quot; &quot;) if ele]\n            X.append(sample[:-1])\n            y.append(sample[-1])\n<\/code><\/pre>\n<h4><span class=\"ez-toc-section\" id=\"12_%E6%95%B0%E6%8D%AE%E5%88%87%E5%88%86\"><\/span>1.2 \u6570\u636e\u5207\u5206<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<pre><code class=\"language-python\">from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, \n                                                    test_size=0.2,\n                                                    random_state=0)<\/code><\/pre>\n<h4><span class=\"ez-toc-section\" id=\"13_%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86%E8%A7%84%E8%8C%83%E5%8C%96\"><\/span>1.3 \u6570\u636e\u9884\u5904\u7406(\u89c4\u8303\u5316)<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<pre><code class=\"language-python\"># \u89c4\u8303\u5316\nimport numpy as np\n\n# \u8f6cnumpy\u6570\u7ec4\nX_train = np.array(X_train)\nX_test = np.array(X_test)\n\n# \u63d0\u53d6\u53c2\u6570\n_mean = X_train.mean(axis=0)\n_std = X_train.std(axis=0) + 1e-9  # \u4e3a\u4e86\u907f\u514d\u9664\u96f6\uff0c\u6b64\u5904\u52a0\u4e0a\u4e00\u4e2a\u975e\u5e38\u5c0f\u7684\u6570\n\n# \u6267\u884c\u89c4\u8303\u5316\u5904\u7406\nX_train = (X_train - _mean) \/ _std\nX_test = (X_test - _mean) \/ _std<\/code><\/pre>\n<h3><span class=\"ez-toc-section\" id=\"2_%E6%89%B9%E9%87%8F%E5%8C%96%E6%89%93%E5%8C%85%E6%95%B0%E6%8D%AE\"><\/span>2. \u6279\u91cf\u5316\u6253\u5305\u6570\u636e<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u901a\u8fc7\u5b9a\u4e49\u4e00\u4e2a\u7ee7\u627fDataset\u7684\u6570\u636e\u96c6\u7c7b\uff0c\u65b9\u4fbf\u6570\u636e\u7684\u5e38\u89c1\u64cd\u4f5c\uff0c\u5982\uff1alen()\u3001index()\u7b49\u3002<\/p>\n<pre><code class=\"language-python\">import torch\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\n\n# 1. \u7ee7\u627fDataset \u81ea\u5b9a\u4e49\u4e00\u4e2a\u6570\u636e\u96c6\u7c7b\nclass HouseDataset(Dataset):\n    &quot;&quot;&quot;\n        \u81ea\u5b9a\u4e49\u4e00\u4e2a\u623f\u4ef7\u6570\u636e\u96c6\n\n    &quot;&quot;&quot;\n    def __init__(self, X, y):\n        &quot;&quot;&quot;\n            \u63a5\u53d7\u53c2\u6570\uff0c\u5b9a\u4e49\u9759\u6001\u5c5e\u6027\n        &quot;&quot;&quot;\n        self.X = X\n        self.y = y\n\n    def __len__(self):\n        &quot;&quot;&quot;\n            \u8fd4\u56de\u6570\u636e\u96c6\u6837\u672c\u7684\u4e2a\u6570\n        &quot;&quot;&quot;\n        return len(self.X)\n\n    def __getitem__(self, idx):\n        &quot;&quot;&quot;\n            \u901a\u8fc7\u7d22\u5f15\uff0c\u8bfb\u53d6\u7b2cidx\u4e2a\u6837\u672c\n        &quot;&quot;&quot;\n        x = self.X[idx]\n        y = self.y[idx]\n\n        # \u8f6c\u5f20\u91cf\n        x = torch.tensor(data=x, dtype=torch.float32)\n        y = torch.tensor(data=[y], dtype=torch.float32)\n        return x, y<\/code><\/pre>\n<blockquote>\n<p>\u6b64\u5904<strong>len<\/strong>\u548c<strong>getitem<\/strong>\u65b9\u6cd5\u662fPython\u7684<a href=\"https:\/\/17aitech.com\/?p=150\">\u9b54\u6cd5\u65b9\u6cd5<\/a>\uff0c\u4ed6\u4eec\u662f\u56de\u8c03(<strong>callback<\/strong>)\u51fd\u6570\uff0c\u4e0d\u9700\u8981\u7528\u6237\u81ea\u5df1\u8c03\u7528\u800c\u7531\u7cfb\u7edf\u8c03\u7528\u3002\u5373\uff1a<\/p>\n<ul>\n<li>\u544a\u8bc9\u7cfb\u7edf\u6211\u5b9a\u4e49\u7684\u6570\u636e\u96c6\u5728<code>\u53d6\u957f\u5ea6<\/code>\u548c<code>\u6309index\u53d6\u5143\u7d20<\/code>\u8c03\u7528\u54ea\u4e2a\u65b9\u6cd5\uff1b<\/li>\n<li>\u5f53\u7cfb\u7edf\u89e6\u53d1\u8fd9\u4e24\u79cd\u60c5\u51b5(<code>\u53d6\u957f\u5ea6<\/code>\u548c<code>\u6309index\u53d6\u5143\u7d20<\/code>)\u65f6\uff0c\u4f1a\u81ea\u52a8\u8c03\u7528\u7c7b\u91cc\u7684<strong>len<\/strong>\u548c<strong>getitem<\/strong>\u65b9\u6cd5\u3002<\/li>\n<\/ul>\n<\/blockquote>\n<pre><code class=\"language-py\"># \u8bad\u7ec3\u96c6\u52a0\u8f7d\u5668\nhouse_train_dataset = HouseDataset(X=X_train, y=y_train)\nhouse_train_dataloader = DataLoader(dataset=house_train_dataset, \n                                    batch_size=12,\n                                    shuffle=True)\n\n# \u6d4b\u8bd5\u96c6\u52a0\u8f7d\u5668\nhouse_test_dataset = HouseDataset(X=X_test, y=y_test)\nhouse_test_dataloader = DataLoader(dataset=house_test_dataset, \n                                    batch_size=32,\n                                    shuffle=True)\n<\/code><\/pre>\n<p>\u901a\u8fc7\u4e0b\u9762\u7684\u6d4b\u8bd5\u4ee3\u7801\uff0c\u53ef\u4ee5\u67e5\u770b\u4e0a\u8ff0\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u7684\u6570\u636e\u5f62\u72b6<\/p>\n<pre><code class=\"language-python\"># \u6d4b\u8bd5\u4ee3\u7801\nfor X, y in house_train_dataset:\n    print(X)\n    print(X.shape)\n    print(y)\n    print(y.shape)\n    break<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612141510319.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612141510319.png\" alt=\"\" \/><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"3_%E6%A8%A1%E5%9E%8B%E6%90%AD%E5%BB%BA\"><\/span>3. \u6a21\u578b\u642d\u5efa<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u6a21\u578b\u7684\u6784\u5efa\u65b9\u6cd5\u6709\u4ee5\u4e0b\u4e09\u79cd\u5199\u6cd5\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u4f7f\u7528\u7b2c\u4e09\u79cdclass\u7684\u65b9\u6cd5\uff0c\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u81ea\u5b9a\u4e49\u6a21\u578b\u7684\u7ed3\u6784\u548c\u8ba1\u7b97\u903b\u8f91\uff0c\u5b9e\u73b0\u66f4\u52a0\u7075\u6d3b\u548c\u5b9a\u5236\u5316\u7684\u6a21\u578b\u3002<\/p>\n<pre><code class=\"language-python\">from torch import nn\n\n# # \u642d\u5efa\u65b9\u6cd51\uff1a\n# model = nn.Linear(in_features=13, out_features=1)\n\n# # \u642d\u5efa\u65b9\u6cd52\uff1a\n# model = nn.Sequential(\n#     nn.Linear(in_features=13, out_features=1)\n# )\n\n# \u642d\u5efa\u65b9\u6cd53\uff1a\nclass Model(nn.Module):\n    &quot;&quot;&quot;\n        \u81ea\u5b9a\u4e49\u4e00\u4e2a\u7c7b\n            - \u5fc5\u987b\u7ee7\u627f nn.Module\n    &quot;&quot;&quot;\n    def __init__(self, n_features=13):\n        &quot;&quot;&quot;\n            \u63a5\u6536\u8d85\u53c2\n            \u5b9a\u4e49\u5904\u7406\u7684\u5c42\n        &quot;&quot;&quot;\n        # \u5148\u521d\u59cb\u5316\u7236\u7c7b\n        super(Model, self).__init__()\n\n        # \u5b9a\u4e49\u4e00\u4e2a\u7ebf\u6027\u5c42(\u505a\u4e00\u6b21\u77e9\u9635\u53d8\u6362)\n        self.linear = nn.Linear(in_features=n_features, out_features=1)\n\n    def forward(self, x):\n        &quot;&quot;&quot;\n            \u6a21\u578b\u524d\u5411\u4f20\u64ad\u903b\u8f91\n        &quot;&quot;&quot;\n        x = self.linear(x)\n        return x<\/code><\/pre>\n<p>\u5b9e\u4f8b\u5316\u6a21\u578b<\/p>\n<pre><code class=\"language-pyt\">model = Model(n_features=13)<\/code><\/pre>\n<p>\u901a\u8fc7\u4e00\u4e0b\u6d4b\u8bd5\u4ee3\u7801\uff0c\u53ef\u4ee5\u770b\u5230\u6a21\u578b\u7684\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<pre><code class=\"language-python\"># \u6d4b\u8bd5\u4ee3\u7801\nfor X, y in house_train_dataloader:\n    y_pred = model(X)\n    print(y_pred)\n    print(y)\n    break<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612141855681.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612141855681.png\" alt=\"\" \/><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"4_%E7%AD%B9%E5%A4%87%E8%AE%AD%E7%BB%83\"><\/span>4. \u7b79\u5907\u8bad\u7ec3<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u8fd9\u4e00\u6b65\u9aa4\uff0c\u6211\u4eec\u5c06\u5b9a\u4e49\u6a21\u578b(\u5b9e\u4f8b\u5316\u4e0a\u8ff0\u81ea\u5b9a\u4e49\u6a21\u578b)\u3001\u5b9a\u4e49\u635f\u5931\u51fd\u6570\u3001\u5b9a\u4e49\u4f18\u5316\u5668\u3001\u5b9a\u4e49\u8bad\u7ec3\u6b21\u6570\u548c\u5b9a\u4e49\u5b66\u4e60\u7387\u3002<\/p>\n<pre><code class=\"language-python\"># \u5b9a\u4e49\u6a21\u578b\nmodel = Model(n_features=13)\n\n# \u5b9a\u4e49\u8bad\u7ec3\u7684\u8f6e\u6b21\nepochs = 100\n\n# \u5b9a\u4e49\u5b66\u4e60\u7387\nlearing_rate = 1e-3\n\n# \u5b9a\u4e49\u635f\u5931\u51fd\u6570\nloss_fn = nn.MSELoss()\n\n# \u5b9a\u4e49\u4f18\u5316\u5668\noptimizer = torch.optim.SGD(params=model.parameters(), \n                            lr=learing_rate)<\/code><\/pre>\n<h3><span class=\"ez-toc-section\" id=\"5_%E8%AE%AD%E7%BB%83%E6%A8%A1%E5%9E%8B\"><\/span>5. \u8bad\u7ec3\u6a21\u578b<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><span class=\"ez-toc-section\" id=\"51_%E5%AE%9A%E4%B9%89%E7%9B%91%E6%8E%A7%E6%8C%87%E6%A0%87%E5%92%8C%E6%96%B9%E6%B3%95\"><\/span>5.1 \u5b9a\u4e49\u76d1\u63a7\u6307\u6807\u548c\u65b9\u6cd5<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>\u5728\u8bad\u7ec3\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u8bad\u7ec3\u8fc7\u7a0b\u7684\u91cd\u8981\u6307\u6807\u52a0\u4ee5\u76d1\u63a7\uff0c\u4ee5\u907f\u514d\u8bad\u7ec3\u6709\u95ee\u9898\u3002<\/p>\n<pre><code class=\"language-python\"># \u5b9a\u4e49\u67e5\u770b\u635f\u5931\u51fd\u6570\u7684\u65b9\u6cd5\uff0c\u7528\u4e8e\u76d1\u63a7\u8bad\u7ec3\u8fc7\u7a0b\ndef get_loss(dataloader):\n    # \u6a21\u578b\u8bbe\u7f6e\u4e3a\u8bc4\u4f30\u6a21\u5f0f\n    # (BatchNorm LayNorm Dropout\u5c42\uff0c\u5728train\u6a21\u5f0f\u548ceval\u6a21\u5f0f\u4e0b\uff0c\u884c\u4e3a\u662f\u4e0d\u4e00\u6837)\n    model.eval()\n\n    # \u6536\u96c6\u6bcf\u4e2a\u6279\u91cf\u7684\u635f\u5931\n    losses = []\n\n    # \u6784\u5efa\u4e00\u4e2a\u65e0\u68af\u5ea6\u7684\u73af\u5883(\u5e95\u5c42\u4e0d\u4f1a\u9ed8\u8ba4\u81ea\u52a8\u521b\u5efa\u8ba1\u7b97\u56fe\uff0c\u8282\u7ea6\u8d44\u6e90)\n    with torch.no_grad():\n        for X, y in dataloader:\n            y_pred = model(X)\n            loss = loss_fn(y_pred, y)\n            losses.append(loss.item())\n\n    # \u8ba1\u7b97\u6bcf\u4e2a\u6279\u91cf\u635f\u5931\u7684\u5e73\u5747\u503c\n    final_loss = sum(losses) \/ len(losses)\n\n    # \u4fdd\u7559\u5c0f\u6570\u70b9\u540e5\u4f4d\n    final_loss = round(final_loss, ndigits=5)\n\n    return final_loss<\/code><\/pre>\n<h4><span class=\"ez-toc-section\" id=\"52_%E5%AE%9E%E7%8E%B0%E8%AE%AD%E7%BB%83%E8%BF%87%E7%A8%8B\"><\/span>5.2 \u5b9e\u73b0\u8bad\u7ec3\u8fc7\u7a0b<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<pre><code class=\"language-python\">def train():\n    # \u8bb0\u5f55\u8bad\u7ec3\u8fc7\u7a0b\n    train_losses = []\n    test_losses = []\n\n    # \u6bcf\u4e00\u8f6e\u6b21\n    for epoch in range(epochs):\n        #\u6a21\u578b\u8bbe\u4e3a\u8bad\u7ec3\u6a21\u5f0f\n        model.train()\n\n        # \u6bcf\u4e00\u6279\u91cf\n        for X, y in house_train_dataloader:\n            # 1. \u6b63\u5411\u4f20\u64ad\n            y_pred = model(X)\n\n            # 2. \u635f\u5931\u8ba1\u7b97\n            loss = loss_fn(y_pred, y)\n\n            # 3. \u53cd\u5411\u4f20\u64ad\n            loss.backward()\n\n            # 4. \u4f18\u5316\u4e00\u6b65\n            optimizer.step()\n\n            # 5. \u6e05\u7a7a\u68af\u5ea6\n            optimizer.zero_grad()\n\n        # \u8ba1\u7b97\u6a21\u578b\u5f53\u524d\u7684\u635f\u5931\u60c5\u51b5\n        train_loss = get_loss(dataloader=house_train_dataloader)\n        test_loss = get_loss(dataloader=house_test_dataloader)\n\n        train_losses.append(train_loss)\n        test_losses.append(test_loss)\n\n        print(f&quot;\u5f53\u524d\u662f\u7b2c{epoch+1}\u8f6e\uff0c\u8bad\u7ec3\u96c6\u635f\u5931\u4e3a\uff1a{train_loss}, \u6d4b\u8bd5\u96c6\u635f\u5931\u4e3a\uff1a{test_loss}&quot;)\n\n    return train_losses, test_losses<\/code><\/pre>\n<h4><span class=\"ez-toc-section\" id=\"53_%E5%BC%80%E5%A7%8B%E8%AE%AD%E7%BB%83\"><\/span>5.3 \u5f00\u59cb\u8bad\u7ec3<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>\u81f3\u6b64\uff0c\u6211\u4eec\u5df2\u7ecf\u5b8c\u6210\u6574\u4f53\u8bad\u7ec3\u524d\u7684\u91cd\u8981\u6b65\u9aa4\u5b9e\u73b0\uff0c\u63a5\u4e0b\u6765\u5c31\u53ef\u4ee5\uff1a\u8bad\u7ec3\uff0c\u542f\u52a8\uff01<\/p>\n<pre><code class=\"language-python\">train_losses, test_losses = train()<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612143024116.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612143024116.png\" alt=\"\" \/><\/a><\/p>\n<h4><span class=\"ez-toc-section\" id=\"54_%E5%9B%BE%E5%BD%A2%E5%8C%96%E7%9B%91%E6%8E%A7%E6%95%B0%E6%8D%AE\"><\/span>5.4 \u56fe\u5f62\u5316\u76d1\u63a7\u6570\u636e<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>\u4e3a\u4e86\u65b9\u4fbf\u67e5\u770b\u8bad\u7ec3\u8fc7\u7a0b\u53d8\u5316\u60c5\u51b5\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib\u7ed8\u5236\u635f\u5931\u51fd\u6570\u7684\u53d8\u5316\u66f2\u7ebf\u3002<\/p>\n<pre><code class=\"language-python\"> from matplotlib import pyplot as plt\n\nplt.plot(train_losses, c=&quot;blue&quot;, label=&quot;train_loss&quot;)\nplt.plot(test_losses, c=&quot;red&quot;, label=&quot;test_loss&quot;)\nplt.title(&quot;The Losses&quot;)\nplt.xlabel(xlabel=&quot;epoches&quot;)\nplt.ylabel(ylabel=&quot;loss&quot;)\nplt.legend()<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612144518941.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612144518941.png\" alt=\"\" \/><\/a><\/p>\n<p>\u7531\u4e0a\u56fe\u53ef\u4ee5\u770b\u5230<\/p>\n<ul>\n<li>\u5728\u4e00\u5f00\u59cb\u7684\u8bad\u7ec3\u4e2d\uff0c\u635f\u5931\u503c\u5feb\u901f\u4e0b\u964d\uff1b\u5f53\u8bad\u7ec3\u6b21\u6570\u523040\u6b21\u5de6\u53f3\u540e\uff0c\u635f\u5931\u503c\u5df2\u7ecf\u4e0b\u964d\u4e0d\u660e\u663e\u3002<\/li>\n<\/ul>\n<p>\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u52a0\u5165\u6fc0\u6d3b\u51fd\u6570\uff0c\u5f15\u5165\u975e\u7ebf\u6027\u56e0\u7d20\u6765\u4f18\u5316\u6a21\u578b\u3002<\/p>\n<h4><span class=\"ez-toc-section\" id=\"55_%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0\"><\/span>5.5 \u6fc0\u6d3b\u51fd\u6570<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<ul>\n<li>\n<p>\u5b9a\u4e49\uff1a\u6fc0\u6d3b\u51fd\u6570\u662f\u795e\u7ecf\u7f51\u7edc\u4e2d\u7684\u4e00\u79cd\u975e\u7ebf\u6027\u51fd\u6570\uff0c\u901a\u5e38\u5e94\u7528\u5728\u795e\u7ecf\u5143\u7684\u8f93\u51fa\u4e0a\uff0c\u5c06\u8f93\u5165\u4fe1\u53f7\u8f6c\u6362\u4e3a\u8f93\u51fa\u4fe1\u53f7\u3002\u6fc0\u6d3b\u51fd\u6570\u5f15\u5165\u4e86\u975e\u7ebf\u6027\u56e0\u7d20\uff0c\u4f7f\u795e\u7ecf\u7f51\u7edc\u53ef\u4ee5\u5b66\u4e60\u548c\u8868\u8fbe\u590d\u6742\u7684\u975e\u7ebf\u6027\u5173\u7cfb\u3002<\/p>\n<blockquote>\n<p>\u4e3e\u4e2a\u4f8b\u5b50\uff1a<\/p>\n<ul>\n<li>\n<p>\u60f3\u8c61\u4e00\u4e0b\uff0c\u795e\u7ecf\u7f51\u7edc\u5c31\u50cf\u662f\u4e00\u4e2a\u590d\u6742\u7684\u62fc\u56fe\u6e38\u620f\uff0c\u6bcf\u4e2a\u795e\u7ecf\u5143\u5c31\u50cf\u662f\u62fc\u56fe\u4e2d\u7684\u4e00\u4e2a\u5c0f\u5757\u3002\u5f53\u6211\u4eec\u53ea\u4f7f\u7528\u7ebf\u6027\u51fd\u6570\uff08\u6bd4\u5982\u76f4\u7ebf\uff09\u4f5c\u4e3a\u6fc0\u6d3b\u51fd\u6570\u65f6\uff0c\u5c31\u597d\u6bd4\u6bcf\u4e2a\u5c0f\u5757\u90fd\u662f\u76f4\u7ebf\uff0c\u65e0\u6cd5\u62fc\u51fa\u590d\u6742\u7684\u56fe\u6848\uff0c\u53ea\u80fd\u8868\u8fbe\u7b80\u5355\u7684\u7ebf\u6027\u5173\u7cfb\u3002<\/p>\n<\/li>\n<li>\n<p>\u4f46\u662f\uff0c\u5f53\u6211\u4eec\u5f15\u5165\u975e\u7ebf\u6027\u7684\u6fc0\u6d3b\u51fd\u6570\u65f6\uff0c\u5c31\u597d\u6bd4\u5728\u6bcf\u4e2a\u5c0f\u5757\u4e0a\u52a0\u5165\u4e86\u5404\u79cd\u5f62\u72b6\u548c\u66f2\u7ebf\uff0c\u4f7f\u5f97\u6bcf\u4e2a\u5c0f\u5757\u53ef\u4ee5\u8868\u8fbe\u66f4\u52a0\u590d\u6742\u7684\u5f62\u72b6\u548c\u5173\u7cfb\u3002\u8fd9\u6837\uff0c\u5f53\u6211\u4eec\u628a\u8bb8\u591a\u8fd9\u6837\u7684\u5c0f\u5757\uff08\u795e\u7ecf\u5143\uff09\u7ec4\u5408\u5728\u4e00\u8d77\u65f6\uff0c\u5c31\u53ef\u4ee5\u62fc\u51fa\u66f4\u52a0\u590d\u6742\u548c\u591a\u6837\u7684\u56fe\u6848\uff08\u975e\u7ebf\u6027\u5173\u7cfb\uff09\uff0c\u4ece\u800c\u8ba9\u795e\u7ecf\u7f51\u7edc\u80fd\u591f\u5b66\u4e60\u548c\u8868\u8fbe\u66f4\u52a0\u590d\u6742\u7684\u6a21\u5f0f\u548c\u7279\u5f81\u3002<\/p>\n<\/li>\n<li>\n<p>\u56e0\u6b64\uff0c\u6fc0\u6d3b\u51fd\u6570\u7684\u4f5c\u7528\u5c31\u662f\u4e3a\u795e\u7ecf\u7f51\u7edc\u5f15\u5165\u4e86\u8fd9\u79cd\u975e\u7ebf\u6027\u56e0\u7d20\uff0c\u4f7f\u5f97\u795e\u7ecf\u7f51\u7edc\u53ef\u4ee5\u66f4\u597d\u5730\u5b66\u4e60\u548c\u8868\u8fbe\u590d\u6742\u7684\u975e\u7ebf\u6027\u5173\u7cfb\uff0c\u5c31\u50cf\u5728\u62fc\u56fe\u6e38\u620f\u4e2d\u52a0\u5165\u4e86\u5404\u79cd\u5f62\u72b6\u548c\u66f2\u7ebf\uff0c\u8ba9\u6211\u4eec\u80fd\u591f\u62fc\u51fa\u66f4\u52a0\u4e30\u5bcc\u591a\u5f69\u7684\u56fe\u6848\u4e00\u6837\u3002<\/p>\n<\/li>\n<\/ul>\n<\/blockquote>\n<\/li>\n<\/ul>\n<blockquote>\n<p>\u5e38\u89c1\u6fc0\u6d3b\u51fd\u6570\u6709\uff1aSigmoid\u51fd\u6570\u3001Tanh\u51fd\u6570\u3001ReLU\u51fd\u6570\u3001Softmax\u51fd\u6570\uff0c\u76f8\u5173\u5185\u5bb9\u5728\u8865\u5145\u77e5\u8bc6\u6bb5\u843d\u5c55\u5f00\u3002<\/p>\n<\/blockquote>\n<h5><span class=\"ez-toc-section\" id=\"%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95\"><\/span>\u4f7f\u7528\u65b9\u6cd5<span class=\"ez-toc-section-end\"><\/span><\/h5>\n<p>\u5728\u81ea\u5b9a\u4e49\u6a21\u578b\u7c7b\u7684forward\u51fd\u6570\u4e2d\uff0c\u589e\u52a0<\/p>\n<pre><code class=\"language-python\">class Model(nn.Module):\n    &quot;&quot;&quot;\n        \u81ea\u5b9a\u4e49\u4e00\u4e2a\u7c7b\n            - \u5fc5\u987b\u7ee7\u627f nn.Module\n    &quot;&quot;&quot;\n    def __init__(self, n_features=13):\n        &quot;&quot;&quot;\n            \u63a5\u6536\u8d85\u53c2\n            \u5b9a\u4e49\u5904\u7406\u7684\u5c42\n        &quot;&quot;&quot;\n        # \u5148\u521d\u59cb\u5316\u7236\u7c7b\n        super(Model, self).__init__()\n\n        # \u5b9a\u4e49\u4e00\u4e2a\u7ebf\u6027\u5c42(\u505a\u4e00\u6b21\u77e9\u9635\u53d8\u6362)\n        self.linear = nn.Linear(in_features=n_features, out_features=1)\n\n    def forward(self, x):\n        &quot;&quot;&quot;\n            \u6a21\u578b\u524d\u5411\u4f20\u64ad\u903b\u8f91\n        &quot;&quot;&quot;\n        x = self.linear(x)\n        x = nn.ReLU()(x)  # \u6dfb\u52a0ReLU\u6fc0\u6d3b\u51fd\u6570\n        return x<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612151301913.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612151301913.png\" alt=\"\" \/><\/a><\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612151312930.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612151312930.png\" alt=\"\" \/><\/a><\/p>\n<p>\u5bf9\u6bd4\u52a0\u5165\u6fc0\u6d3b\u51fd\u6570\u524d\u540e\u7684\u56fe\u5f62\uff0c\u5176\u8bad\u7ec3\u96c6\u7684\u635f\u5931\u4ecd\u7136\u572819\u5de6\u53f3\uff0c\u53ea\u662f\u4e0b\u964d\u8fc7\u7a0b\u4e0d\u5982\u4e4b\u524d\u90a3\u4e48\u5feb\u4e86\u3002<\/p>\n<h4><span class=\"ez-toc-section\" id=\"56_%E5%A4%9A%E5%B1%82%E6%84%9F%E7%9F%A5%E6%9C%BA\"><\/span>5.6 \u591a\u5c42\u611f\u77e5\u673a<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<h5><span class=\"ez-toc-section\" id=\"%E5%AE%9A%E4%B9%89-2\"><\/span>\u5b9a\u4e49<span class=\"ez-toc-section-end\"><\/span><\/h5>\n<p>\u7531\u4e8e\u65e0\u6cd5\u6a21\u62df\u8bf8\u5982\u5f02\u6216\u4ee5\u53ca\u5176\u4ed6\u590d\u6742\u51fd\u6570\u7684\u529f\u80fd\uff0c\u4f7f\u5f97\u5355\u5c42\u611f\u77e5\u673a\u7684\u5e94\u7528\u8f83\u4e3a\u5355\u4e00\u3002\u4e00\u4e2a\u7b80\u5355\u7684\u60f3\u6cd5\u662f\uff0c\u5982\u679c\u80fd\u5728\u611f\u77e5\u673a\u6a21\u578b\u4e2d\u589e\u52a0\u82e5\u5e72\u9690\u85cf\u5c42\uff0c\u589e\u5f3a\u795e\u7ecf\u7f51\u7edc\u7684\u975e\u7ebf\u6027\u8868\u8fbe\u80fd\u529b\uff0c\u5c31\u4f1a\u8ba9\u795e\u7ecf\u7f51\u7edc\u5177\u6709\u66f4\u5f3a\u62df\u5408\u80fd\u529b\u3002<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612151825074.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612151825074.png\" alt=\"\" \/><\/a><\/p>\n<blockquote>\n<p>\u5927\u8111\u662f\u4e00\u4e2a\u591a\u5c42\u611f\u77e5\u673a\uff0c\u6bcf\u4e00\u5c42\u90fd\u5728\u5b66\u4e60\u4e0d\u540c\u7ea7\u522b\u7684\u7279\u5f81\u3002<\/p>\n<ul>\n<li><strong>\u8f93\u5165\u5c42<\/strong>\uff1a\u8f93\u5165\u5c42\u5c31\u50cf\u4f60\u7684\u773c\u775b\uff0c\u5b83\u63a5\u6536\u5230\u52a8\u7269\u7684\u5404\u79cd\u7279\u5f81\u4fe1\u606f\uff0c\u6bd4\u5982\u989c\u8272\u3001\u5927\u5c0f\u7b49\u3002<\/li>\n<li><strong>\u9690\u85cf\u5c42<\/strong>\uff1a\u9690\u85cf\u5c42\u5c31\u50cf\u4f60\u7684\u5927\u8111\u76ae\u5c42\uff0c\u5b83\u5904\u7406\u8f93\u5165\u7684\u7279\u5f81\u4fe1\u606f\uff0c\u5e76\u5c1d\u8bd5\u4ece\u4e2d\u63d0\u53d6\u51fa\u66f4\u52a0\u62bd\u8c61\u548c\u590d\u6742\u7684\u7279\u5f81\uff0c\u6bd4\u5982\u52a8\u7269\u7684\u8f6e\u5ed3\u3001\u7eb9\u7406\u7b49\u3002<\/li>\n<li><strong>\u8f93\u51fa\u5c42<\/strong>\uff1a\u8f93\u51fa\u5c42\u5c31\u50cf\u4f60\u7684\u5634\u5df4\uff0c\u5b83\u6839\u636e\u9690\u85cf\u5c42\u63d0\u53d6\u7684\u7279\u5f81\u4fe1\u606f\u505a\u51fa\u5224\u65ad\uff0c\u6bd4\u5982\u5224\u65ad\u8f93\u5165\u7684\u52a8\u7269\u662f\u72d7\u8fd8\u662f\u732b\u3002<\/li>\n<\/ul>\n<p>\u901a\u8fc7\u591a\u6b21\u5b66\u4e60\u548c\u8bad\u7ec3\uff0c\u4f60\u7684\u5927\u8111\uff08\u591a\u5c42\u611f\u77e5\u673a\uff09\u4f1a\u9010\u6e10\u8c03\u6574\u9690\u85cf\u5c42\u4e2d\u7684\u795e\u7ecf\u5143\uff08\u795e\u7ecf\u5143\u5c31\u50cf\u5927\u8111\u4e2d\u7684\u795e\u7ecf\u5143\uff09\u7684\u8fde\u63a5\u6743\u91cd\uff0c\u4ece\u800c\u66f4\u597d\u5730\u8bc6\u522b\u4e0d\u540c\u7c7b\u578b\u7684\u52a8\u7269\u3002<\/p>\n<\/blockquote>\n<h5><span class=\"ez-toc-section\" id=\"%E5%AE%9E%E7%8E%B0%E6%96%B9%E6%B3%95%EF%BC%9A\"><\/span>\u5b9e\u73b0\u65b9\u6cd5\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h5>\n<p>\u4fee\u6539\u81ea\u5b9a\u4e49\u6a21\u578b\u7684\u521d\u59cb\u5316\u65b9\u6cd5\uff0c\u52a0\u5165\u4e00\u4e2a\u65b0\u7684\u7ebf\u6027\u5c42<\/p>\n<pre><code class=\"language-python\">class Model(nn.Module):\n    &quot;&quot;&quot;\n        \u81ea\u5b9a\u4e49\u4e00\u4e2a\u7c7b\n            - \u5fc5\u987b\u7ee7\u627f nn.Module\n    &quot;&quot;&quot;\n    def __init__(self, n_features=13):\n        &quot;&quot;&quot;\n            \u63a5\u6536\u8d85\u53c2\n            \u5b9a\u4e49\u5904\u7406\u7684\u5c42\n        &quot;&quot;&quot;\n        # \u5148\u521d\u59cb\u5316\u7236\u7c7b\n        super(Model, self).__init__()\n\n        # \u5b9a\u4e49\u4e24\u4e2a\u7ebf\u6027\u5c42     \n        self.linear1 = nn.Linear(in_features=n_features, out_features=8)\n        self.linear2 = nn.Linear(in_features=8, out_features=1)\n\n    def forward(self, x):\n        &quot;&quot;&quot;\n            \u6a21\u578b\u524d\u5411\u4f20\u64ad\u903b\u8f91\n        &quot;&quot;&quot;\n        x = self.linear1(x)\n        x = torch.relu(x)  # \u6dfb\u52a0ReLU\u6fc0\u6d3b\u51fd\u6570\n        x = self.linear2(x)\n\n        return x<\/code><\/pre>\n<p>\u91cd\u65b0\u6267\u884c\u8bad\u7ec3\u8fc7\u7a0b\u5e76\u7ed8\u5236\u635f\u5931\u503c\u56fe\u5f62\uff1a<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612152515449.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612152515449.png\" alt=\"\" \/><\/a><\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612152523636.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612152523636.png\" alt=\"\" \/><\/a><\/p>\n<p>\u7531\u4e0a\u56fe\u53ef\u4ee5\u770b\u5230\uff0c\u591a\u5c42\u611f\u77e5\u673a+\u5f15\u5165\u6fc0\u6d3b\u51fd\u6570\uff0c\u635f\u5931\u503c\u5df2\u7ecf\u964d\u52308\u5de6\u53f3\uff1b\u5bf9\u6bd4\u4e4b\u524d\u672a\u4f18\u5316\u7684\u6a21\u578b\uff0c\u6a21\u578b\u7684\u635f\u5931\u503c\u53d8\u5c0f\uff0c\u9884\u6d4b\u51c6\u786e\u6027\u5f97\u5230\u63d0\u5347\u3002<\/p>\n<h3><span class=\"ez-toc-section\" id=\"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0_vs_%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0\"><\/span>\u6df1\u5ea6\u5b66\u4e60 vs \u673a\u5668\u5b66\u4e60<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u81f3\u6b64\uff0c\u6211\u4eec\u501f\u52a9\u623f\u4ef7\u9884\u6d4b\u7684\u793a\u4f8b\uff0c\u5df2\u7ecf\u5b8c\u6574\u5730\u5b9e\u73b0\u4e86\u6df1\u5ea6\u5b66\u4e60\u7684\u6574\u4f53\u6d41\u7a0b\uff1b\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5bf9\u6bd4\u4e00\u4e0b\u6df1\u5ea6\u5b66\u4e60\u548c\u673a\u5668\u5b66\u4e60\uff0c\u5728\u540c\u4e00\u95ee\u9898\uff1a\u623f\u4ef7\u9884\u6d4b\u95ee\u9898\u4e0a\u7684\u7ed3\u679c\u3002<\/p>\n<pre><code class=\"language-python\">from sklearn.tree import DecisionTreeRegressor\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.svm import SVR\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error\nimport matplotlib.pyplot as plt\n\nfile_name = &#039;.\/housing.data&#039;\n\n# \u539f\u59cb\u6570\u636e\u8bfb\u53d6\nX = []\ny = []\nwith open(file=file_name, mode=&#039;r&#039;, encoding=&#039;utf8&#039;) as f:\n#     f.readline()\n    for  line in f:\n        line = line.strip()\n        if line:\n            sample = [float(ele) for ele in line.split(&quot; &quot;) if ele]\n            X.append(sample[:-1])\n            y.append(sample[-1])\n\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, \n                                                    test_size=0.2,\n                                                    random_state=0)\n\n# \u89c4\u8303\u5316\nimport numpy as np\n\n# \u8f6cnumpy\u6570\u7ec4\nX_train = np.array(X_train)\nX_test = np.array(X_test)\n\n# \u63d0\u53d6\u53c2\u6570\n_mean = X_train.mean(axis=0)\n_std = X_train.std(axis=0) + 1e-9\n\n# \u6267\u884c\u89c4\u8303\u5316\u5904\u7406\nX_train = (X_train - _mean) \/ _std\nX_test = (X_test - _mean) \/ _std    \n\n# \u51b3\u7b56\u6811\ndt_model = DecisionTreeRegressor()\ndt_model.fit(X_train, y_train)\ndt_pred = dt_model.predict(X_test)\ndt_loss = mean_squared_error(y_test, dt_pred)\n\n# KNN\nknn_model = KNeighborsRegressor()\nknn_model.fit(X_train, y_train)\nknn_pred = knn_model.predict(X_test)\nknn_loss = mean_squared_error(y_test, knn_pred)\n\n# \u652f\u6301\u5411\u91cf\u673a\nsvm_model = SVR()\nsvm_model.fit(X_train, y_train)\nsvm_pred = svm_model.predict(X_test)\nsvm_loss = mean_squared_error(y_test, svm_pred)\n\n# \u968f\u673a\u68ee\u6797\nrf_model = RandomForestRegressor()\nrf_model.fit(X_train, y_train)\nrf_pred = rf_model.predict(X_test)\nrf_loss = mean_squared_error(y_test, rf_pred)\n\nmodels = [&#039;Decision Tree&#039;, &#039;KNN&#039;, &#039;SVM&#039;, &#039;Random Forest&#039;]\nloss_values = [dt_loss, knn_loss, svm_loss, rf_loss]\n\nplt.figure(figsize=(10, 6))\nbars = plt.bar(models, loss_values, color=&#039;skyblue&#039;)\nplt.xlabel(&#039;Models&#039;)\nplt.ylabel(&#039;Mean Squared Error&#039;)\nplt.title(&#039;Comparison of Mean Squared Error for Different Models&#039;)\n\n# \u6dfb\u52a0\u6570\u636e\u6807\u7b7e\nfor bar, loss in zip(bars, loss_values):\n    plt.text(bar.get_x() + bar.get_width() \/ 2 - 0.1, bar.get_height() + 0.001, f&#039;{loss:.4f}&#039;, ha=&#039;center&#039;, color=&#039;black&#039;, fontsize=10)\n\nplt.show()<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612153520368.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240612153520368.png\" alt=\"\" \/><\/a><\/p>\n<p>\u901a\u8fc7\u5bf9\u6bd4\u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u7684<a href=\"https:\/\/17aitech.com\/?p=2040#toc-4\">\u5e73\u5747\u5e73\u65b9\u504f\u5dee\uff08MSE\uff09<\/a>\uff0c\u6df1\u5ea6\u5b66\u4e60\u662f\u660e\u663e\u4f18\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u3002<\/p>\n<h2><span class=\"ez-toc-section\" id=\"%E5%88%86%E7%B1%BB%E9%97%AE%E9%A2%98%EF%BC%9A%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%AE%9E%E7%8E%B0%E9%B8%A2%E5%B0%BE%E8%8A%B1%E5%88%86%E7%B1%BB%E6%A1%88%E4%BE%8B\"><\/span>\u5206\u7c7b\u95ee\u9898\uff1a\u6df1\u5ea6\u5b66\u4e60\u5b9e\u73b0\u9e22\u5c3e\u82b1\u5206\u7c7b\u6848\u4f8b<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u4e0a\u8ff0\u6848\u4f8b\u4e2d\uff0c\u6211\u4eec\u4ee5\u623f\u4ef7\u9884\u6d4b\u4e3a\u4f8b\uff0c\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u5b9e\u73b0\u4e86\u7ebf\u6027\u56de\u5f52\u7684\u8bad\u7ec3\u548c\u9884\u6d4b\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4ee5\u300a\u9e22\u5c3e\u82b1\u5206\u7c7b\u300b\u4e3a\u4f8b\uff0c\u5b66\u4e60\u4e86\u89e3\u6df1\u5ea6\u5b66\u4e60\u5982\u4f55\u5b9e\u73b0\u5206\u7c7b\u95ee\u9898\u3002<\/p>\n<h3><span class=\"ez-toc-section\" id=\"%E6%B5%81%E7%A8%8B%E5%9B%9E%E9%A1%BE%E4%B8%8E%E5%AF%B9%E6%AF%94\"><\/span>\u6d41\u7a0b\u56de\u987e\u4e0e\u5bf9\u6bd4<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u6df1\u5ea6\u5b66\u4e60\u5b9e\u73b0\u5206\u7c7b\u95ee\u9898\u4e0e\u6df1\u5ea6\u5b66\u4e60\u5b9e\u73b0\u56de\u5f52\u95ee\u9898\u6d41\u7a0b\u57fa\u672c\u4e00\u81f4\uff0c\u5bf9\u6bd4\u4e0d\u540c\u70b9\u5982\u4e0b\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>\u6d41\u7a0b\u6b65\u9aa4<\/th>\n<th>\u56de\u5f52\u95ee\u9898<\/th>\n<th>\u5206\u7c7b\u95ee\u9898<\/th>\n<th>\u8bf4\u660e<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>1.\u6570\u636e\u9884\u5904\u7406<\/td>\n<td>\\<\/td>\n<td>\\<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>1.1 \u6570\u636e\u8bfb\u53d6<\/td>\n<td>\u2714<\/td>\n<td>\u2714<\/td>\n<td>\u8bfb\u53d6\u6570\u636e\u6e90\u4e0d\u540c<\/td>\n<\/tr>\n<tr>\n<td>1.2\u6570\u636e\u5207\u5206<\/td>\n<td>\u2714<\/td>\n<td>\u2714<\/td>\n<td>\u76f8\u540c<\/td>\n<\/tr>\n<tr>\n<td>1.3\u6570\u636e\u9884\u5904\u7406<\/td>\n<td>\u2714<\/td>\n<td>\u2714<\/td>\n<td>\u76f8\u540c<\/td>\n<\/tr>\n<tr>\n<td>2. \u6279\u91cf\u5316\u6570\u636e\u6253\u5305<\/td>\n<td>\u2714<\/td>\n<td>\u2b55\ufe0f<\/td>\n<td>\u6807\u7b7e\u6570\u636e\u7c7b\u578b\u4e0d\u540c\uff1a<br \/>1.\u56de\u5f52\u95ee\u9898\u6807\u7b7e\u6570\u636e\u7c7b\u578b\u4e3atorch.float32<br \/>2.\u5206\u7c7b\u95ee\u9898\u9700\u8981\u5c06\u6807\u7b7ey\u5b9a\u4e49\u7c7b\u578b\u6539\u4e3atorch.long<\/td>\n<\/tr>\n<tr>\n<td>3. \u6a21\u578b\u642d\u5efa<\/td>\n<td>\u2714<\/td>\n<td>\u2b55\ufe0f<\/td>\n<td>\u8d85\u53c2\u5b9a\u4e49\u4e0d\u540c\uff1a<br \/>1.\u56de\u5f52\u95ee\u9898\u9884\u6d4b\u7ed3\u679c\u662f1\u4e2a\uff0cout_features=1<br \/>2.\u5206\u7c7b\u95ee\u9898\u9884\u6d4b\u7ed3\u679c\u6709N\u7c7b\uff0cout_features=N<\/td>\n<\/tr>\n<tr>\n<td>4. \u7b79\u5907\u8bad\u7ec3<\/td>\n<td>\u2714<\/td>\n<td>\u2b55\ufe0f<\/td>\n<td>\u635f\u5931\u4e0d\u540c\uff1a<br \/>1.\u56de\u5f52\u95ee\u9898\u4f7f\u7528\u7684\u662fMSE<br \/>2.\u5206\u7c7b\u95ee\u9898\u4f7f\u7528\u7684\u662f\u4ea4\u53c9\u71b5<\/td>\n<\/tr>\n<tr>\n<td>5.\u8bad\u7ec3\u6a21\u578b<\/td>\n<td>\\<\/td>\n<td>\\<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>5.1\u5b9a\u4e49\u76d1\u63a7\u6307\u6807\u548c\u65b9\u6cd5<\/td>\n<td>\u2714<\/td>\n<td>\u2b55\ufe0f<\/td>\n<td>\u76d1\u63a7\u6307\u6807\u4e0d\u540c\uff1a<br \/>1.\u56de\u5f52\u95ee\u9898\u76d1\u63a7MSE<br \/>2.\u5206\u7c7b\u95ee\u9898\u76d1\u63a7\u51c6\u786e\u7387<\/td>\n<\/tr>\n<tr>\n<td>5.2\u5b9e\u73b0\u8bad\u7ec3\u8fc7\u7a0b<\/td>\n<td>\u2714<\/td>\n<td>\u2714<\/td>\n<td>\u76f8\u540c<\/td>\n<\/tr>\n<tr>\n<td>5.3\u5f00\u59cb\u8bad\u7ec3<\/td>\n<td>\u2714<\/td>\n<td>\u2714<\/td>\n<td>\u76f8\u540c<\/td>\n<\/tr>\n<tr>\n<td>5.4\u56fe\u5f62\u5316\u76d1\u63a7\u6570\u636e<\/td>\n<td>\u2714<\/td>\n<td>\u2714<\/td>\n<td>\u76f8\u540c<\/td>\n<\/tr>\n<tr>\n<td>5.5\u6fc0\u6d3b\u51fd\u6570<\/td>\n<td>\u2714<\/td>\n<td>\u2714<\/td>\n<td>\u76f8\u540c<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3><span class=\"ez-toc-section\" id=\"%E4%BB%A3%E7%A0%81%E5%AE%9E%E7%8E%B0\"><\/span>\u4ee3\u7801\u5b9e\u73b0<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<pre><code class=\"language-python\">import numpy as np\nimport torch\nfrom torch import nn\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\nfrom matplotlib import pyplot as plt\n\n# 1. \u7ee7\u627fDataset \u81ea\u5b9a\u4e49\u4e00\u4e2a\u6570\u636e\u96c6\u7c7b\nclass IrisDataset(Dataset):\n    &quot;&quot;&quot;\n        \u81ea\u5b9a\u4e49\u4e00\u4e2a\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\n\n    &quot;&quot;&quot;\n    def __init__(self, X, y):\n        &quot;&quot;&quot;\n            \u63a5\u53d7\u53c2\u6570\uff0c\u5b9a\u4e49\u9759\u6001\u5c5e\u6027\n        &quot;&quot;&quot;\n        self.X = X\n        self.y = y\n\n    def __len__(self):\n        &quot;&quot;&quot;\n            \u8fd4\u56de\u6570\u636e\u96c6\u6837\u672c\u7684\u4e2a\u6570\n        &quot;&quot;&quot;\n        return len(self.X)\n\n    def __getitem__(self, idx):\n        &quot;&quot;&quot;\n            \u901a\u8fc7\u7d22\u5f15\uff0c\u8bfb\u53d6\u7b2cidx\u4e2a\u6837\u672c\n        &quot;&quot;&quot;\n        x = self.X[idx]\n        y = self.y[idx]\n\n        # \u8f6c\u5f20\u91cf\n        x = torch.tensor(data=x, dtype=torch.float32)\n\n        # \u7d22\u5f15\u53f7\uff0clong\u7c7b\u578b\n        y = torch.tensor(data=y, dtype=torch.long)\n        return x, y\n\n# \u642d\u5efa\u65b9\u6cd53\uff1a\nclass Model(nn.Module):\n    &quot;&quot;&quot;\n        \u81ea\u5b9a\u4e49\u4e00\u4e2a\u7c7b\n            - \u5fc5\u987b\u7ee7\u627f nn.Module\n    &quot;&quot;&quot;\n    def __init__(self, n_features=4, n_classes=3):\n        &quot;&quot;&quot;\n            \u63a5\u6536\u8d85\u53c2\n            \u5b9a\u4e49\u5904\u7406\u7684\u5c42\n        &quot;&quot;&quot;\n        # \u5148\u521d\u59cb\u5316\u7236\u7c7b\n        super(Model, self).__init__()\n\n        # \u5b9a\u4e49\u4e00\u4e2a\u7ebf\u6027\u5c42(\u505a\u4e00\u6b21\u77e9\u9635\u53d8\u6362)\n        self.linear = nn.Linear(in_features=n_features, out_features=n_classes)\n\n    def forward(self, x):\n        &quot;&quot;&quot;\n            \u6a21\u578b\u524d\u5411\u4f20\u64ad\u903b\u8f91\n        &quot;&quot;&quot;\n        x = self.linear(x)\n        return x\n\ndef get_acc(dataloader):\n    # \u6a21\u578b\u8bbe\u7f6e\u4e3a\u8bc4\u4f30\u6a21\u5f0f\n    # (BatchNorm LayNorm Dropout\u5c42\uff0c\u5728train\u6a21\u5f0f\u548ceval\u6a21\u5f0f\u4e0b\uff0c\u884c\u4e3a\u662f\u4e0d\u4e00\u6837)\n    model.eval()\n\n    # \u6536\u96c6\u6bcf\u4e2a\u6279\u91cf\u7684\u635f\u5931\n    accs = []\n\n    # \u6784\u5efa\u4e00\u4e2a\u65e0\u68af\u5ea6\u7684\u73af\u5883(\u5e95\u5c42\u4e0d\u4f1a\u9ed8\u8ba4\u81ea\u52a8\u521b\u5efa\u8ba1\u7b97\u56fe\uff0c\u8282\u7ea6\u8d44\u6e90)\n    with torch.no_grad():\n        for X, y in dataloader:\n            y_pred = model(X)\n            y_pred = y_pred.argmax(dim=-1)\n            acc = (y_pred == y).to(dtype=torch.float32).mean().item()\n            accs.append(acc)\n\n    # \u8ba1\u7b97\u6bcf\u4e2a\u6279\u91cf\u635f\u5931\u7684\u5e73\u5747\u503c\n    final_acc = sum(accs) \/ len(accs)\n\n    # \u4fdd\u7559\u5c0f\u6570\u70b9\u540e5\u4f4d\n    final_acc = round(final_acc, ndigits=5)\n\n    return final_acc\n\ndef train():\n    # \u8bb0\u5f55\u8bad\u7ec3\u8fc7\u7a0b\n    train_accs = []\n    test_accs = []\n\n    # \u6bcf\u4e00\u8f6e\u6b21\n    for epoch in range(epochs):\n        #\u6a21\u578b\u8bbe\u4e3a\u8bad\u7ec3\u6a21\u5f0f\n        model.train()\n\n        # \u6bcf\u4e00\u6279\u91cf\n        for X, y in iris_train_dataloader:\n            # 1. \u6b63\u5411\u4f20\u64ad\n            y_pred = model(X)\n\n            # 2. \u635f\u5931\u8ba1\u7b97\n            loss = loss_fn(y_pred, y)\n\n            # 3. \u53cd\u5411\u4f20\u64ad\n            loss.backward()\n\n            # 4. \u4f18\u5316\u4e00\u6b65\n            optimizer.step()\n\n            # 5. \u6e05\u7a7a\u68af\u5ea6\n            optimizer.zero_grad()\n\n        # \u8ba1\u7b97\u6a21\u578b\u5f53\u524d\u7684\u635f\u5931\u60c5\u51b5\n        train_acc= get_acc(dataloader=iris_train_dataloader)\n        test_acc = get_acc(dataloader=iris_test_dataloader)\n\n        train_accs.append(train_acc)\n        test_accs.append(test_acc)\n\n        print(f&quot;\u5f53\u524d\u662f\u7b2c{epoch+1}\u8f6e\uff0c\u8bad\u7ec3\u96c6\u51c6\u786e\u7387\u4e3a\uff1a{train_acc}, \u6d4b\u8bd5\u96c6\u51c6\u786e\u7387\u4e3a\uff1a{test_acc}&quot;)\n\n    return train_accs, test_accs\n\nX,y = load_iris(return_X_y=True)\nX_train, X_test, y_train, y_test = train_test_split(X, y, \n                                                    test_size=0.2,\n                                                    random_state=0)\n\n# \u8f6cnumpy\u6570\u7ec4\nX_train = np.array(X_train)\nX_test = np.array(X_test)\n\n# \u63d0\u53d6\u53c2\u6570\n_mean = X_train.mean(axis=0)\n_std = X_train.std(axis=0) + 1e-9\n\n# \u6267\u884c\u89c4\u8303\u5316\u5904\u7406\nX_train = (X_train - _mean) \/ _std\nX_test = (X_test - _mean) \/ _std\n\n# \u8bad\u7ec3\u96c6\u52a0\u8f7d\u5668\niris_train_dataset = IrisDataset(X=X_train, y=y_train)\niris_train_dataloader = DataLoader(dataset=iris_train_dataset, \n                                    batch_size=4,\n                                    shuffle=True)\n\n# \u6d4b\u8bd5\u96c6\u52a0\u8f7d\u5668\niris_test_dataset = IrisDataset(X=X_test, y=y_test)\niris_test_dataloader = DataLoader(dataset=iris_test_dataset, \n                                    batch_size=8,\n                                    shuffle=True)\n\n# \u5b9a\u4e49\u6a21\u578b\nmodel = Model(n_features=4)\n\n# \u5b9a\u4e49\u8bad\u7ec3\u7684\u8f6e\u6b21\nepochs = 50\n\n# \u5b9a\u4e49\u5b66\u4e60\u7387\nlearing_rate = 1e-3\n\n# \u5b9a\u4e49\u635f\u5931\u51fd\u6570:\u5206\u7c7b\u95ee\u9898\u4f7f\u7528\u4ea4\u53c9\u71b5\nloss_fn = nn.CrossEntropyLoss()\n\n# \u5b9a\u4e49\u4f18\u5316\u5668\noptimizer = torch.optim.SGD(params=model.parameters(), \n                            lr=learing_rate)\n\ntrain_accs, test_accs = train()\n\nplt.plot(train_accs, c=&quot;blue&quot;, label=&quot;train_acc&quot;)\nplt.plot(test_accs, c=&quot;red&quot;, label=&quot;test_acc&quot;)\nplt.title(&quot;The Accs&quot;)\nplt.xlabel(xlabel=&quot;epoches&quot;)\nplt.ylabel(ylabel=&quot;acc&quot;)\nplt.legend()<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240613003459876.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240613003459876.png\" alt=\"\" \/><\/a><\/p>\n<h2><span class=\"ez-toc-section\" id=\"%E8%A1%A5%E5%85%85%E7%9F%A5%E8%AF%86\"><\/span>\u8865\u5145\u77e5\u8bc6<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"%E6%A6%82%E7%8E%87%E6%A8%A1%E6%8B%9F\"><\/span>\u6982\u7387\u6a21\u62df<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u7531\u4e8e\u5206\u7c7b\u95ee\u9898\uff0c\u6a21\u578b\u662f\u4e0d\u4f1a\u76f4\u63a5\u8f93\u51fa\u9884\u6d4b\u7c7b\u522b\uff0c\u800c\u662f\u8f93\u51fa\u6bcf\u4e2a\u7c7b\u522b\u7684\u6570\u5b57(\u6216\u8005\u53eb\u6743\u91cd)\uff0c\u6b64\u65f6\u6211\u4eec\u9700\u8981\u901a\u8fc7\u6a21\u62df\u6982\u7387\u5c06\u6bcf\u4e2a\u7c7b\u522b\u7684\u6570\u5b57\u8f6c\u6362\u4e3a[0, 1]\u4e4b\u95f4\u7684\u6982\u7387\u3002<\/p>\n<h4><span class=\"ez-toc-section\" id=\"%E5%B8%B8%E8%A7%81%E6%96%B9%E6%B3%95\"><\/span>\u5e38\u89c1\u65b9\u6cd5<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<ul>\n<li>\u4e8c\u5206\u7c7b\u6982\u7387\u6a21\u62df\uff1a<strong>Sigmoid\u51fd\u6570<\/strong>\uff0c\u53d6\u503c\u8303\u56f4\u5728(0, 1)\u3002<\/li>\n<\/ul>\n<pre><code class=\"language-python\">  def sigmod(x):\n      &quot;&quot;&quot;\n          sigmoid\n              - (0, 1) \n              - \u5355\u8c03\u9012\u589e\u51fd\u6570\n              - x\u4e3a0\u65f6\uff0c\u6a21\u62df\u6982\u7387\u4e3a0.5\n              - x &lt; 0\u65f6\uff0c\u6a21\u62df\u6982\u7387&lt;0.5\n              - x &gt; 0\u65f6\uff0c\u6a21\u62df\u6982\u7387&gt;0.5\n      &quot;&quot;&quot;\n      return 1 \/ (1 + np.exp(-x))\n\n  x = np.linspace(start=-5, stop=5, num=30)\n\n  plt.plot(x, sigmod(x))\n  plt.grid()<\/code><\/pre>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240613095636422.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240613095636422.png\" alt=\"\" \/><\/a><\/p>\n<ul>\n<li>\u591a\u5206\u7c7b\u6982\u7387\u6a21\u62df\uff1a<strong>Softmax\u51fd\u6570<\/strong>\uff1a<\/li>\n<\/ul>\n<pre><code class=\"language-python\">  import numpy as np\n\n  def softmax(logits):\n      &quot;&quot;&quot;\n          softmax:\n              - \u539f\u6765\u6bd4\u8f83\u5927\u7684\u6570\uff0c\u6a21\u62df\u6982\u7387\u4e5f\u6bd4\u8f83\u5927\n      &quot;&quot;&quot;\n\n      # \u8f6c\u5316\u4e3anp\u6570\u7ec4\n      logits = np.array(logits)\n      # \u8f6c\u5316\u4e3a\u6b63\u6570\n      logits = np.exp(logits)\n      # \u6a21\u62df\u6982\u7387\n      return logits \/logits.sum()\n\n  # \u6a21\u578b\u8f93\u51fa\u7684\u90fd\u662f\u539f\u59cb\u6570\u636e\n  logits = [0.6 , -3.6, 18.9 ]\n\n  logtis = np.array(logits)\n\n  softmax(logits)<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240613100135850.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240613100135850.png\" alt=\"\" \/><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"one-hot%E7%BC%96%E7%A0%81\"><\/span>one-hot\u7f16\u7801<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u5728<a href=\"https:\/\/17aitech.com\/?p=2070#toc-79\">\u300a\u3010\u8bfe\u7a0b\u603b\u7ed3\u3011Day6(\u4e0a)\uff1a\u673a\u5668\u5b66\u4e60\u9879\u76ee\u5b9e\u6218\u2013\u5916\u5356\u70b9\u8bc4\u60c5\u611f\u5206\u6790\u9884\u6d4b\u300b<\/a>\u4e2d\uff0c\u6211\u4eec\u66fe\u63a5\u89e6\u8fc7one-hot\u7f16\u7801\u3002\u672c\u6b21\u6211\u4eec\u518d\u505a\u4e0b\u56de\u987e\uff1a<\/p>\n<ul>\n<li>\n<p>\u8fd9\u662f\u4e00\u79cd\u72b6\u6001\u7f16\u7801\uff0c\u9002\u5408\u4e8e\u79bb\u6563\u91cf\u5185\u6db5<\/p>\n<\/li>\n<li>\n<p>\u7279\u5f81\u7f16\u7801\u65f6\uff1a<\/p>\n<blockquote>\n<p>\u4ee5\u6c7d\u8f66\u7684\u884c\u9a76\u72b6\u6001\u4e3e\u4f8b\uff1a<\/p>\n<ul>\n<li>\u4e09\u4e2a\u72b6\u6001\uff1a\u5de6\u8f6c0\uff0c\u53f3\u8f6c1\uff0c\u76f4\u884c2<\/li>\n<li>\u5de6\u8f6c\uff1a[1, 0, 0]<\/li>\n<li>\u53f3\u8f6c\uff1a[0, 1, 0]<\/li>\n<li>\u76f4\u884c\uff1a[0, 0, 1]<\/li>\n<\/ul>\n<\/blockquote>\n<\/li>\n<li>\n<p>\u6807\u7b7e\u7f16\u7801\u65f6\uff1a<\/p>\n<blockquote>\n<p>\u4ee5\u9e22\u5c3e\u82b1\u7c7b\u522b\u4e3e\u4f8b\uff1a<\/p>\n<ul>\n<li>\u4e09\u79cd\u82b1\uff1a\u7b2c\u4e00\u79cd0\uff0c\u7b2c\u4e8c\u79cd1\uff0c\u7b2c\u4e09\u79cd2<\/li>\n<li>\u7b2c\u4e00\u79cd\uff1a[1, 0, 0]<\/li>\n<li>\u7b2c\u4e8c\u79cd\uff1a[0, 1, 0]<\/li>\n<li>\u7b2c\u4e09\u79cd\uff1a[0, 0, 1]<\/li>\n<\/ul>\n<\/blockquote>\n<\/li>\n<li>\n<p>\u7279\u70b9\uff1a<\/p>\n<ul>\n<li>\u4e92\u76f8\u5782\u76f4\u7684\u5411\u91cf\uff0c\u6ca1\u6709\u9c9c\u8273\u7684\u8fdc\u8fd1\u5173\u7cfb<\/li>\n<li>\u90fd\u662f\u6bd4\u8f83\u957f\u7684\u5411\u91cf\uff0c\u8ddf\u7c7b\u522b\u6570\u91cf\u4e00\u81f4<\/li>\n<li>\u6bcf\u4e2a\u5411\u91cf\u53ea\u67091\u4f4d\u662f1\uff0c\u5176\u4f59\u90fd\u662f0(\u8fd9\u79cd\u72b6\u6001\u88ab\u79f0\u4e3a<strong>\u9ad8\u5ea6\u7a00\u758fsparse<\/strong>)<\/li>\n<li>\u4ece\u8ba1\u7b97\u548c\u5b58\u50a8\u4e0a\u6765\u8bf4\uff0c\u90fd\u6bd4\u8f83\u6d6a\u8d39\u6027\u80fd<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>\u795e\u7ecf\u7f51\u7edc\u5728\u9884\u6d4b\u591a\u7c7b\u522b\u5206\u7c7b\u4efb\u52a1\u65f6\uff1an_classes\u4e2a\u7c7b\u522b\u7684\u9884\u6d4b\u95ee\u9898\uff0c\u6a21\u578b\u4f1a\u8f93\u51fan_classes\u4e2a\u6982\u7387\u3002<\/p>\n<blockquote>\n<p>\u4ee5\u9e22\u5c3e\u82b1\u4e3a\u4f8b\uff0c\u795e\u7ecf\u7f51\u7edc\u4e0d\u4f1a\u76f4\u63a5\u8f93\u51fa\u9884\u6d4b\u7684\u7ed3\u679c\u662f\u54ea\u4e2a\u82b1\u7684\u7c7b\u522b\uff0c\u800c\u662f\u7ed9\u51fa\u4e09\u79cd\u82b1\u5404\u81ea\u7684\u6982\u7387\uff0c\u53ea\u4e0d\u8fc7\u6700\u9ad8\u6982\u7387\u7684\u54ea\u79cd\u82b1\u53ef\u80fd\u5c31\u662f\u6211\u4eec\u9700\u8981\u7684\u9884\u6d4b\u7ed3\u679c\u7c7b\u522b\u3002<\/p>\n<\/blockquote>\n<h3><span class=\"ez-toc-section\" id=\"%E4%BA%A4%E5%8F%89%E7%86%B5\"><\/span>\u4ea4\u53c9\u71b5<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u4f5c\u7528\uff1a\u4ece\u5206\u5e03\u7684\u89d2\u5ea6\u6765\u8861\u91cf\u4e24\u4e2a\u6982\u7387\u7684\u8fdc\u8fd1\u7a0b\u5ea6<\/p>\n<p>\u8ba1\u7b97\u8fc7\u7a0b\uff1a<\/p>\n<pre><code class=\"language-python\"># \u4ee5\u9e22\u5c3e\u82b1\u5206\u7c7b\u4e3e\u4f8b\uff1a\n# \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u771f\u5b9e\u7ed3\u679cy_true\uff0c\u7ed3\u679c\u662f\u7b2c2\u7c7b\uff0c\u7d22\u5f15\u53f7\u662f1\n# \u771f\u5b9e\u7ed3\u679c\uff1ay_true: \u7b2c2\u7c7b\uff0c\u7d22\u5f15\u53f7\u4e3a1\n# \u9884\u6d4b\u7ed3\u679c1\uff1ay_pred1: [12.5, -0.5, 2.7]\n# \u9884\u6d4b\u7ed3\u679c2\uff1ay_pred2: [-12.5, 6.4, 2.7]\n\n# \u7b2c\u4e00\u6b65\uff1a\u4f7f\u7528one-hot\u5bf9\u7c7b\u522b\u8fdb\u884c\u7f16\u7801\ny_true = np.array( [0, 1, 0])\n\n# \u7b2c\u4e8c\u6b65\uff1a\u8fdb\u884c\u7b2c\u4e00\u6b21\u9884\u6d4b\n# 1\u3001\u5bf9\u9884\u6d4b\u7ed3\u679c\u4f7f\u7528softmax\u8fdb\u884c\u6982\u7387\u6a21\u62df\ny_pred1 = np.array([12.5, -0.5, 2.7])\ny_pred1 = softmax(y_pred1) \n\n# \u6267\u884c\u7ed3\u679c\uff1a\n# y_pred1 \n# array([9.99942291e-01, 2.26019897e-06, 5.54483994e-05])\n\n# 2\u3001\u8ba1\u7b97\u4ea4\u53c9\u71b5\u635f\u5931 y_true @ y_pred1\nloss1 = -(0 * np.log(9.99942291e-01) + 1 * np.log(2.26019897e-06) + 0 * np.log(5.54483994e-05))\n# \u6267\u884c\u7ed3\u679c\n# loss1 \n# 13.00005770873235\n\n# \u7b2c\u4e09\u6b65\uff1a\u8fdb\u884c\u7b2c\u4e8c\u6b21\u9884\u6d4b\ny_pred2 = np.array( [-12.5, 6.4, 2.7])\ny_pred2 = softmax(y_pred2) \n\n# \u6267\u884c\u7ed3\u679c\uff1a\n# y_pred2 \n# array([6.04265198e-09, 9.75872973e-01, 2.41270213e-02])\n\nloss1 = -(np.log(9.75872973e-01))\n# \u6267\u884c\u7ed3\u679c\uff1a\n# loss1\n# 0.024422851654124902<\/code><\/pre>\n<p>\u901a\u8fc7\u4e0a\u9762\u8ba1\u7b97\u53ef\u4ee5\u770b\u5230\uff1a<\/p>\n<ul>\n<li>loss1\u4ea4\u53c9\u635f\u5931\u71b5\u6700\u4f4e\u65f6\uff0c\u4e5f\u5c31\u662f\u9884\u6d4b\u7ed3\u679c[-12.5, 6.4, 2.7]\u4e0e\u771f\u5b9e\u7ed3\u679c(\u7b2c2\u7c7b)\u76f8\u5339\u914d\uff0c\u7531\u6b64\u5f97\u5230\u4ea4\u53c9\u71b5\u8d8a\u4f4e\uff0c\u9884\u6d4b\u7ed3\u679c\u4e0e\u771f\u5b9e\u503c\u5dee\u5f02\u8d8a\u5c0f\u3002<\/li>\n<li>\u4ea4\u53c9\u71b5\u76f8\u6bd4MSE\uff0c\u4ea4\u53c9\u71b5\u8ba1\u7b97\u4ee3\u7801\u8981\u5c0f\u3002\n<ul>\n<li>\u5728\u8ba1\u7b97\u4ea4\u53c9\u71b5\u65f6\uff0c\u8ba1\u7b97\u65b9\u6cd5\u662f\u8fdb\u884c\u7b2ci\u4e2a\u7c7b\u522bone-hot\u7f16\u7801\u4e0e\u7b2ci\u4e2a\u7c7b\u522b\u6982\u7387\u76f8\u4e58\uff0c\u6700\u540e\u518d\u76f8\u52a0\uff1b<\/li>\n<li>\u8868\u9762\u4e0a\u770b\u6c42\u4e86\u591a\u4e2a\u71b5\uff0c\u5b9e\u9645\u4e0a\u53ea\u6c421\u4e2a\u71b5(\u56e0\u4e3aone-hot\u7f16\u7801\u4e3a0\u7684\u4f4d\u7f6e\u5b9e\u9645\u6ca1\u6709\u53c2\u4e0e\u8ba1\u7b97)\uff1b<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0\"><\/span>\u6fc0\u6d3b\u51fd\u6570<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li>\n<p><strong>Sigmoid \u51fd\u6570<\/strong><\/p>\n<ul>\n<li>\n<p><strong>\u516c\u5f0f<\/strong>\uff1a<code class=\"katex-inline\">{sigmoid}(x) = \\frac{1}{1 + e^{-x}}<\/code><\/p>\n<\/li>\n<li>\n<p>\u7279\u70b9\uff1a\u5c06\u8f93\u5165\u503c\u538b\u7f29\u5230 0 \u5230 1 \u4e4b\u95f4\uff0c\u5e38\u7528\u4e8e\u8f93\u51fa\u5c42\u7684\u4e8c\u5206\u7c7b\u95ee\u9898\uff0c\u4f46\u5bb9\u6613\u51fa\u73b0\u68af\u5ea6\u6d88\u5931\u95ee\u9898.<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<pre><code class=\"language-python\">    import numpy as np\n    import matplotlib.pyplot as plt\n\n    x = np.linspace(-5, 5, 100)\n    sigmoid = 1 \/ (1 + np.exp(-x))\n\n    plt.plot(x, sigmoid, label=&#039;Sigmoid Function&#039;)\n    plt.xlabel(&#039;x&#039;)\n    plt.ylabel(&#039;sigmoid(x)&#039;)\n    plt.legend()\n    plt.show()<\/code><\/pre>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240613104404133.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240613104404133.png\" alt=\"\" \/><\/a><\/p>\n<ul>\n<li>\n<p><strong>Tanh \u51fd\u6570<\/strong><\/p>\n<ul>\n<li>\n<p><strong>\u516c\u5f0f<\/strong>\uff1a<code class=\"katex-inline\">{tanh}(x) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}<\/code><\/p>\n<\/li>\n<li>\n<p>\u7279\u70b9\uff1a\u5c06\u8f93\u5165\u503c\u538b\u7f29\u5230 -1 \u5230 1 \u4e4b\u95f4\uff0c\u89e3\u51b3\u4e86 Sigmoid \u51fd\u6570\u7684\u96f6\u4e2d\u5fc3\u95ee\u9898\uff0c\u4f46\u4ecd\u5b58\u5728\u68af\u5ea6\u6d88\u5931\u95ee\u9898\u3002<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<pre><code class=\"language-python\">    import numpy as np\n    import matplotlib.pyplot as plt\n\n    # \u751f\u6210 x \u503c\n    x = np.linspace(-5, 5, 100)\n\n    # \u8ba1\u7b97\u53cc\u66f2\u6b63\u5207\u51fd\u6570\u7684 y \u503c\n    y = np.tanh(x)\n\n    # \u7ed8\u5236\u53cc\u66f2\u6b63\u5207\u51fd\u6570\u56fe\u50cf\n    plt.figure(figsize=(8, 6))\n    plt.plot(x, y, label=&#039;tanh(x)&#039;)\n    plt.xlabel(&#039;x&#039;)\n    plt.ylabel(&#039;tanh(x)&#039;)\n    plt.title(&#039;Plot of Hyperbolic Tangent Function&#039;)\n    plt.grid(True)\n    plt.legend()\n    plt.show()<\/code><\/pre>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240613104459361.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240613104459361.png\" alt=\"\" \/><\/a><\/p>\n<ul>\n<li>\n<p><strong>ReLU \u51fd\u6570\uff08Rectified Linear Unit\uff09<\/strong><\/p>\n<ul>\n<li>\n<p>\u516c\u5f0f\uff1a<code class=\"katex-inline\">{ReLU}(x) = \\max(0, x)<\/code><\/p>\n<\/li>\n<li>\n<p>\u7279\u70b9\uff1a\u7b80\u5355\u4e14\u9ad8\u6548\uff0c\u89e3\u51b3\u4e86\u68af\u5ea6\u6d88\u5931\u95ee\u9898\uff0c\u4f46\u53ef\u80fd\u5bfc\u81f4\u795e\u7ecf\u5143\u6b7b\u4ea1\u95ee\u9898\uff08\u8f93\u51fa\u6052\u4e3a 0\uff09<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<pre><code class=\"language-python\">    import numpy as np\n    import matplotlib.pyplot as plt\n\n    # \u5b9a\u4e49 ReLU \u51fd\u6570\n    def relu(x):\n        return np.maximum(0, x)\n\n    # \u751f\u6210 x \u503c\n    x = np.linspace(-5, 5, 100)\n\n    # \u8ba1\u7b97 ReLU \u51fd\u6570\u7684 y \u503c\n    y = relu(x)\n\n    # \u7ed8\u5236 ReLU \u51fd\u6570\u56fe\u50cf\n    plt.figure(figsize=(8, 6))\n    plt.plot(x, y, label=&#039;ReLU(x)&#039;)\n    plt.xlabel(&#039;x&#039;)\n    plt.ylabel(&#039;ReLU(x)&#039;)\n    plt.title(&#039;Plot of Rectified Linear Unit (ReLU) Function&#039;)\n    plt.grid(True)\n    plt.legend()\n    plt.show()<\/code><\/pre>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240613104531127.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/06\/image-20240613104531127.png\" alt=\"\" \/><\/a><\/p>\n<h2><span class=\"ez-toc-section\" id=\"%E5%86%85%E5%AE%B9%E5%B0%8F%E7%BB%93\"><\/span>\u5185\u5bb9\u5c0f\u7ed3<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>\n<p>\u6a21\u578b\u8bad\u7ec3\u672c\u8d28\uff1a\u56fa\u5b9aw\u548cb\u53c2\u6570\u7684\u8fc7\u7a0b<\/p>\n<ul>\n<li>\u8ba9\u6a21\u578b\u66f4\u597d\u2192\u672c\u8d28\u4e0a\u5c31\u662f\u8ba9\u6a21\u578b\u7684\u635f\u5931\u503closs\u53d8\u5c0f\uff1b<\/li>\n<li>\u8ba9loss\u53d8\u5c0f\u2192\u672c\u8d28\u4e0a\u5c31\u662f\u6c42loss\u51fd\u6570\u7684\u6700\u5c0f\u503c\uff1b<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u6df1\u5ea6\u5b66\u4e60\u7684\u6574\u4f53\u9879\u76ee\u8def\u7a0b<\/p>\n<ul>\n<li>\u7b2c\u4e00\u6b65\uff1a\u8bfb\u53d6\u6570\u636e\u3001\u5207\u5206\u6570\u636e\u3001\u6570\u636e\u89c4\u8303\u5316<\/li>\n<li>\u7b2c\u4e8c\u6b65\uff1a\u6279\u91cf\u5316\u6253\u5305\u6570\u636e<\/li>\n<li>\u7b2c\u4e09\u6b65\uff1a\u6784\u5efa\u6a21\u578b<\/li>\n<li>\u7b2c\u56db\u6b65\uff1a\u7b79\u5907\u8bad\u7ec3\uff0c\u5b9a\u4e49\u635f\u5931\u51fd\u6570\u3001\u5b9a\u4e49\u4f18\u5316\u5668\u3001\u5b9a\u4e49\u8bad\u7ec3\u6b21\u6570\u3001\u5b9a\u4e49\u5b66\u4e60\u7387<\/li>\n<li>\u7b2c\u4e94\u6b65\uff1a\u8bad\u7ec3\u6a21\u578b\uff0c\u5728\u8bad\u7ec3\u7684\u8fc7\u7a0b\u4e2d\u9700\u8981\u5bf9\u635f\u5931\u503c\u8fdb\u884c\u76d1\u63a7<\/li>\n<li>\u7b2c\u516d\u6b65\uff1a\u63a8\u7406\u6a21\u578b\uff0c\u5373\u76f4\u63a5\u5c06\u5f85\u9884\u6d4b\u6837\u672c\u6570\u636e\u4ee3\u5165\u6a21\u578b\u6c42\u9884\u6d4b\u503cy_pred\u5373\u53ef<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u7b2c\u4e09\u6b65\u6784\u5efa\u6a21\u578b\u65f6<\/p>\n<ul>\n<li>\u4e00\u822c\u5e38\u7528\u81ea\u5b9a\u4e49class\u7c7b\u7684\u65b9\u6cd5\uff0c\u8fd9\u79cd\u65b9\u6cd5\u66f4\u52a0\u7075\u6d3b<\/li>\n<li>\u4e3a\u4e86\u964d\u4f4e\u6a21\u578b\u7684\u635f\u5931\u503c\uff0c\u53ef\u4ee5\u91c7\u7528\u52a0\u5165\u6fc0\u6d3b\u51fd\u6570\u4ee5\u53ca\u591a\u5c42\u611f\u77e5\u673a\u7684\u65b9\u5f0f<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u7b2c\u4e94\u6b65\u5b9e\u73b0\u8bad\u7ec3\u6a21\u578b\u65f6<\/p>\n<ul>\n<li>\u57fa\u672c\u7684\u4e94\u6b65\u6cd5\uff1a1.\u6b63\u5411\u4f20\u64ad\u21922.\u635f\u5931\u8ba1\u7b97\u21923.\u53cd\u5411\u4f20\u64ad\u21924.\u4f18\u5316\u4e00\u6b65\u21925.\u6e05\u7a7a\u68af\u5ea6<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u8fdb\u884c\u5206\u7c7b\u95ee\u9898\u65f6\uff0c\u6d41\u7a0b\u4e0e\u56de\u5f52\u95ee\u9898\u57fa\u672c\u4e00\u81f4\uff0c\u4e0d\u540c\u7684\u5730\u65b9\u5728\u4e8e<\/p>\n<ul>\n<li>\u635f\u5931\u51fd\u6570\u8ba1\u7b97\u65b9\u6cd5\u4e0d\u540c\uff1a\u56de\u5f52\u95ee\u9898\u4f7f\u7528\u7684\u662fMSE\uff0c\u5206\u7c7b\u95ee\u9898\u4f7f\u7528\u7684\u662f\u4ea4\u53c9\u71b5\u3002<\/li>\n<li>\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u76d1\u63a7\u7684\u6307\u6807\u4e0d\u540c\uff0c\u56de\u5f52\u95ee\u9898\u76d1\u63a7MSE\uff0c\u5206\u7c7b\u95ee\u9898\u76d1\u63a7\u7684\u662f\u51c6\u786e\u7387\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u795e\u7ecf\u7f51\u7edc\u5728\u5904\u7406\u5206\u7c7b\u95ee\u9898\u65f6\uff0c\u8f93\u51fa\u7684\u4e0d\u662f\u67d0\u4e2a\u7c7b\u522b\uff0c\u800c\u662f\u4e0d\u540c\u7c7b\u522b\u7684\u6982\u7387\u3002<\/p>\n<ul>\n<li>\u5728\u8f93\u51fa\u6982\u7387\u65f6\uff0c\u9700\u8981\u8fdb\u884c\u6982\u7387\u6a21\u62df<\/li>\n<li>\u4e8c\u5206\u7c7b\u95ee\u9898\u6982\u7387\u6a21\u62df\u65f6\uff0c\u4f7f\u7528Sigmoid\u51fd\u6570\uff1b\u591a\u5206\u7c7b\u6982\u7387\u6a21\u62df\u65f6\uff0c\u4f7f\u7528\u7684\u662fsoftmax\u51fd\u6570<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99%EF%BC%9A\"><\/span>\u53c2\u8003\u8d44\u6599\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><a href=\"https:\/\/paddlepedia.readthedocs.io\/en\/latest\/tutorials\/deep_learning\/basic_concepts\/multilayer_perceptron.html\">\u591a\u5c42\u611f\u77e5\u673a<\/a><\/p>\n<p><a href=\"https:\/\/baike.baidu.com\/item\/ReLU%20%E5%87%BD%E6%95%B0\/22689567?fr=ge_ala\">\u767e\u5ea6\u767e\u79d1\uff1aReLU\u51fd\u6570<\/a><\/p>\n<p><a href=\"https:\/\/baike.baidu.com\/item\/Sigmoid%E5%87%BD%E6%95%B0\/7981407?fr=ge_ala\">\u767e\u5ea6\u767e\u79d1\uff1aSigmoid\u51fd\u6570<\/a><\/p>\n<p align=\"center\">\u6b22\u8fce\u5173\u6ce8\u516c\u4f17\u53f7\u4ee5\u83b7\u5f97\u6700\u65b0\u7684\u6587\u7ae0\u548c\u65b0\u95fb<\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/09\/\u626b\u7801_\u641c\u7d22\u8054\u5408\u4f20\u64ad\u6837\u5f0f-\u767d\u8272\u7248.bmp\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img 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[&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2198,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"aside","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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