{"id":100599,"date":"2026-09-04T21:41:00","date_gmt":"2026-09-04T13:41:00","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/100599.html"},"modified":"2026-09-04T21:41:00","modified_gmt":"2026-09-04T13:41:00","slug":"%e3%80%90%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e5%85%a5%e9%97%a8%e3%80%91pytorch-%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e3%80%81%e5%8d%b7%e7%a7%af%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c-cnn-%e5%ae%9e","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/100599.html","title":{"rendered":"\u3010\u673a\u5668\u5b66\u4e60\u5165\u95e8\u3011PyTorch \u795e\u7ecf\u7f51\u7edc\u3001\u5377\u79ef\u795e\u7ecf\u7f51\u7edc CNN \u5b9e\u73b0\u77ff\u7269\u5206\u7c7b"},"content":{"rendered":"<\/p>\n<h4>\u6587\u7ae0\u76ee\u5f55<\/h4>\n<ul>\n<li>\u4e00\u3001\u524d\u8a00<\/li>\n<li>\u4e8c\u3001\u6570\u636e\u96c6\u8bf4\u660e<\/li>\n<li>\u4e09\u3001\u73af\u5883\u4f9d\u8d56<\/li>\n<li>\u56db\u3001\u6570\u636e\u52a0\u8f7d<\/li>\n<li>\u4e94\u3001\u5168\u8fde\u63a5\u795e\u7ecf\u7f51\u7edc&#xff08;BP \u795e\u7ecf\u7f51\u7edc&#xff09;<\/li>\n<li>\n<ul>\n<li>5.1 \u7f51\u7edc\u7ed3\u6784<\/li>\n<li>5.2 \u6838\u5fc3\u539f\u7406<\/li>\n<li>5.3 \u4ee3\u7801\u5b9e\u73b0<\/li>\n<li>5.4 \u6570\u636e\u8f6c\u5f20\u91cf<\/li>\n<li>5.5 \u6a21\u578b\u8bc4\u4f30\u51fd\u6570<\/li>\n<li>5.6 \u8bad\u7ec3\u8fc7\u7a0b<\/li>\n<\/ul>\n<\/li>\n<li>\u516d\u3001\u5377\u79ef\u795e\u7ecf\u7f51\u7edc&#xff08;1D CNN&#xff09;<\/li>\n<li>\n<ul>\n<li>6.1 \u4e3a\u4ec0\u4e48\u8868\u683c\u6570\u636e\u53ef\u4ee5\u7528 CNN&#xff1f;<\/li>\n<li>6.2 \u7f51\u7edc\u7ed3\u6784<\/li>\n<li>6.3 \u6838\u5fc3\u539f\u7406<\/li>\n<li>6.4 \u4ee3\u7801\u5b9e\u73b0<\/li>\n<li>6.5 \u8bad\u7ec3\u8fc7\u7a0b<\/li>\n<\/ul>\n<\/li>\n<li>\u4e03\u3001\u7ed3\u679c\u8f93\u51fa<\/li>\n<li>\u516b\u3001\u4e24\u79cd\u6a21\u578b\u5bf9\u6bd4\u5206\u6790<\/li>\n<li>\u4e5d\u3001\u6539\u8fdb\u5efa\u8bae<\/li>\n<li>\n<ul>\n<li>9.1 \u6570\u636e\u6807\u51c6\u5316<\/li>\n<li>9.2 \u6539\u7528 ReLU \u6fc0\u6d3b\u51fd\u6570<\/li>\n<li>9.3 \u8bbe\u7f6e\u968f\u673a\u79cd\u5b50<\/li>\n<li>9.4 \u65e9\u505c&#xff08;Early Stopping&#xff09;<\/li>\n<li>9.5 \u5b66\u4e60\u7387\u8c03\u5ea6<\/li>\n<li>9.6 \u66f4\u4e30\u5bcc\u7684\u8bc4\u4f30\u6307\u6807<\/li>\n<\/ul>\n<\/li>\n<li>\u5341\u3001\u5b8c\u6574\u4ee3\u7801\u6c47\u603b<\/li>\n<li>\u5341\u4e00\u3001\u603b\u7ed3<\/li>\n<\/ul>\n<h2>\u4e00\u3001\u524d\u8a00<\/h2>\n<p>\u5728\u5730\u8d28\u52d8\u63a2\u4e0e\u77ff\u7269\u8bc6\u522b\u9886\u57df&#xff0c;\u4f20\u7edf\u7684\u4eba\u5de5\u9274\u5b9a\u65b9\u6cd5\u4f9d\u8d56\u4e13\u5bb6\u7ecf\u9a8c&#xff0c;\u6548\u7387\u4f4e\u4e14\u4e3b\u89c2\u6027\u5f3a\u3002\u968f\u7740\u673a\u5668\u5b66\u4e60\u6280\u672f\u7684\u53d1\u5c55&#xff0c;\u5229\u7528\u77ff\u7269\u7684\u7269\u7406\u5316\u5b66\u7279\u5f81&#xff08;\u5982\u5bc6\u5ea6\u3001\u786c\u5ea6\u3001\u6298\u5c04\u7387\u3001\u5316\u5b66\u6210\u5206\u542b\u91cf\u7b49&#xff09;\u8fdb\u884c\u81ea\u52a8\u5206\u7c7b\u5df2\u6210\u4e3a\u4e00\u79cd\u9ad8\u6548\u53ef\u884c\u7684\u65b9\u6848\u3002<\/p>\n<p>\u672c\u6587\u5c06\u57fa\u4e8e\u4e00\u4efd\u77ff\u7269\u7279\u5f81\u6570\u636e\u96c6&#xff0c;\u4f7f\u7528 PyTorch \u6846\u67b6\u5206\u522b\u642d\u5efa&#xff1a;<\/p>\n<ul>\n<li>\u5168\u8fde\u63a5\u795e\u7ecf\u7f51\u7edc&#xff08;BP \u795e\u7ecf\u7f51\u7edc&#xff09;<\/li>\n<li>\u4e00\u7ef4\u5377\u79ef\u795e\u7ecf\u7f51\u7edc&#xff08;1D CNN&#xff09;<\/li>\n<\/ul>\n<p>\u4e24\u79cd\u6a21\u578b\u5bf9\u77ff\u7269\u7c7b\u578b\u8fdb\u884c\u56db\u5206\u7c7b\u4efb\u52a1&#xff0c;\u5e76\u5bf9\u6bd4\u5b83\u4eec\u7684\u5206\u7c7b\u6548\u679c\u3002\u6587\u7ae0\u5305\u542b\u5b8c\u6574\u7684\u539f\u7406\u8bb2\u89e3\u3001\u4ee3\u7801\u5b9e\u73b0\u4e0e\u7ed3\u679c\u5206\u6790&#xff0c;\u9002\u5408\u673a\u5668\u5b66\u4e60\u5165\u95e8\u8bfb\u8005\u53c2\u8003\u3002<\/p>\n<hr \/>\n<h2>\u4e8c\u3001\u6570\u636e\u96c6\u8bf4\u660e<\/h2>\n<p>\u672c\u5b9e\u9a8c\u4f7f\u7528\u7ecf\u8fc7\u5e73\u5747\u503c\u586b\u5145\u5904\u7406\u540e\u7684\u77ff\u7269\u6570\u636e\u96c6&#xff0c;\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u4e24\u4e2a\u6587\u4ef6&#xff1a;<\/p>\n<table>\n<tr>\u6587\u4ef6\u8bf4\u660e<\/tr>\n<tbody>\n<tr>\n<td>2_\u8bad\u7ec3\u6570\u636e\u96c6_\u5e73\u5747\u503c\u586b\u5145.xlsx<\/td>\n<td>\u8bad\u7ec3\u96c6<\/td>\n<\/tr>\n<tr>\n<td>2_\u6d4b\u8bd5\u6570\u636e\u96c6_\u5e73\u5747\u503c\u586b\u5145.xlsx<\/td>\n<td>\u6d4b\u8bd5\u96c6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u6570\u636e\u7ed3\u6784\u5982\u4e0b&#xff1a;<\/p>\n<ul>\n<li>\u7b2c 1 \u5217&#xff1a;\u6807\u7b7e\u5217&#xff0c;\u77ff\u7269\u7c7b\u578b&#xff08;\u5171 4 \u7c7b&#xff0c;\u8bb0\u4e3a 0\u30011\u30012\u30013&#xff09;<\/li>\n<li>\u7b2c 2 \u5217\u53ca\u4ee5\u540e&#xff1a;\u7279\u5f81\u5217&#xff0c;\u5171 13 \u7ef4\u77ff\u7269\u7279\u5f81<\/li>\n<\/ul>\n<p>\u5e73\u5747\u503c\u586b\u5145\u662f\u5904\u7406\u7f3a\u5931\u503c\u7684\u5e38\u7528\u65b9\u6cd5&#xff1a;\u7528\u8be5\u7279\u5f81\u5217\u7684\u5747\u503c\u66ff\u6362\u7f3a\u5931\u503c&#xff0c;\u7b80\u5355\u4e14\u4e0d\u4f1a\u6539\u53d8\u6570\u636e\u6574\u4f53\u5206\u5e03\u3002<\/p>\n<hr \/>\n<h2>\u4e09\u3001\u73af\u5883\u4f9d\u8d56<\/h2>\n<p>pip <span class=\"token function\">install<\/span> pandas scikit-learn torch openpyxl<\/p>\n<table>\n<tr>\u5e93\u7528\u9014<\/tr>\n<tbody>\n<tr>\n<td>pandas<\/td>\n<td>\u8bfb\u53d6 Excel \u6570\u636e\u3001\u6570\u636e\u5904\u7406<\/td>\n<\/tr>\n<tr>\n<td>scikit-learn<\/td>\n<td>\u6a21\u578b\u8bc4\u4f30\u6307\u6807&#xff08;metrics&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>torch<\/td>\n<td>PyTorch \u6df1\u5ea6\u5b66\u4e60\u6846\u67b6<\/td>\n<\/tr>\n<tr>\n<td>openpyxl<\/td>\n<td>pandas \u8bfb\u53d6 .xlsx \u6587\u4ef6\u7684\u5f15\u64ce<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr \/>\n<h2>\u56db\u3001\u6570\u636e\u52a0\u8f7d<\/h2>\n<p><span class=\"token keyword\">import<\/span> pandas <span class=\"token keyword\">as<\/span> pd<br \/>\n<span class=\"token keyword\">from<\/span> sklearn <span class=\"token keyword\">import<\/span> metrics<\/p>\n<p>train_data <span class=\"token operator\">&#061;<\/span> pd<span class=\"token punctuation\">.<\/span>read_excel<span class=\"token punctuation\">(<\/span><span class=\"token string\">r&#039;.\/\/temp_data\/\/2_\u8bad\u7ec3\u6570\u636e\u96c6_\u5e73\u5747\u503c\u586b\u5145.xlsx&#039;<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u8bfb\u53d6\u5e73\u5747\u503c\u586b\u5145\u540e\u7684\u8bad\u7ec3\u6570\u636e\u96c6Excel\u6587\u4ef6<\/span><br \/>\ntrain_data_x <span class=\"token operator\">&#061;<\/span> train_data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">]<\/span>  <span class=\"token comment\"># \u63d0\u53d6\u8bad\u7ec3\u6570\u636e\u7684\u7279\u5f81\u5217&#xff08;\u4ece\u7b2c2\u5217\u5230\u6700\u540e\u4e00\u5217&#xff09;<\/span><br \/>\ntrain_data_y <span class=\"token operator\">&#061;<\/span> train_data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span>  <span class=\"token comment\"># \u63d0\u53d6\u8bad\u7ec3\u6570\u636e\u7684\u6807\u7b7e\u5217&#xff08;\u7b2c1\u5217&#xff0c;\u5373\u77ff\u7269\u7c7b\u578b&#xff09;<\/span><\/p>\n<p>test_data <span class=\"token operator\">&#061;<\/span> pd<span class=\"token punctuation\">.<\/span>read_excel<span class=\"token punctuation\">(<\/span><span class=\"token string\">r&#039;.\/\/temp_data\/\/2_\u6d4b\u8bd5\u6570\u636e\u96c6_\u5e73\u5747\u503c\u586b\u5145.xlsx&#039;<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u8bfb\u53d6\u5e73\u5747\u503c\u586b\u5145\u540e\u7684\u6d4b\u8bd5\u6570\u636e\u96c6Excel\u6587\u4ef6<\/span><br \/>\ntest_data_x <span class=\"token operator\">&#061;<\/span> test_data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">]<\/span>  <span class=\"token comment\"># \u63d0\u53d6\u6d4b\u8bd5\u6570\u636e\u7684\u7279\u5f81\u5217&#xff08;\u4ece\u7b2c2\u5217\u5230\u6700\u540e\u4e00\u5217&#xff09;<\/span><br \/>\ntest_data_y <span class=\"token operator\">&#061;<\/span> test_data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span>  <span class=\"token comment\"># \u63d0\u53d6\u6d4b\u8bd5\u6570\u636e\u7684\u6807\u7b7e\u5217&#xff08;\u7b2c1\u5217&#xff0c;\u5373\u77ff\u7269\u7c7b\u578b&#xff09;<\/span><\/p>\n<p>result_data <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">{<\/span><span class=\"token punctuation\">}<\/span>  <span class=\"token comment\"># \u521d\u59cb\u5316\u4e00\u4e2a\u7a7a\u5b57\u5178&#xff0c;\u7528\u4e8e\u5b58\u50a8\u5e73\u5747\u503c\u586b\u5145\u65b9\u5f0f\u4e0b\u5404\u6a21\u578b\u7684\u8bc4\u4f30\u7ed3\u679c<\/span><\/p>\n<p>\u5173\u952e\u70b9\u8bf4\u660e&#xff1a;<\/p>\n<ul>\n<li>iloc[:, 1:]&#xff1a;\u6309\u4f4d\u7f6e\u7d22\u5f15&#xff0c;\u53d6\u6240\u6709\u884c\u3001\u7b2c 2 \u5217\u5230\u6700\u540e\u4e00\u5217\u4f5c\u4e3a\u7279\u5f81\u3002<\/li>\n<li>iloc[:, 0]&#xff1a;\u53d6\u7b2c 1 \u5217\u4f5c\u4e3a\u6807\u7b7e\u3002<\/li>\n<li>\u6570\u636e\u8bfb\u53d6\u540e\u7279\u5f81\u4e3a DataFrame&#xff0c;\u540e\u7eed\u9700\u901a\u8fc7 .values \u8f6c\u4e3a numpy \u6570\u7ec4\u518d\u8f6c\u6210 PyTorch \u5f20\u91cf\u3002<\/li>\n<\/ul>\n<hr \/>\n<h2>\u4e94\u3001\u5168\u8fde\u63a5\u795e\u7ecf\u7f51\u7edc&#xff08;BP \u795e\u7ecf\u7f51\u7edc&#xff09;<\/h2>\n<h3>5.1 \u7f51\u7edc\u7ed3\u6784<\/h3>\n<p>\u5168\u8fde\u63a5\u795e\u7ecf\u7f51\u7edc&#xff08;Fully Connected Neural Network&#xff09;&#xff0c;\u4e5f\u53eb\u591a\u5c42\u611f\u77e5\u673a&#xff08;MLP&#xff09;\u6216 BP \u795e\u7ecf\u7f51\u7edc&#xff0c;\u6bcf\u4e00\u5c42\u7684\u6bcf\u4e2a\u795e\u7ecf\u5143\u90fd\u4e0e\u4e0a\u4e00\u5c42\u7684\u6240\u6709\u795e\u7ecf\u5143\u76f8\u8fde\u3002<\/p>\n<p>\u672c\u5b9e\u9a8c\u642d\u5efa\u7684\u7f51\u7edc\u7ed3\u6784\u5982\u4e0b&#xff1a;<\/p>\n<table>\n<tr>\u5c42\u8f93\u5165\u7ef4\u5ea6\u8f93\u51fa\u7ef4\u5ea6\u6fc0\u6d3b\u51fd\u6570<\/tr>\n<tbody>\n<tr>\n<td>\u8f93\u5165\u5c42&#xff08;fc1&#xff09;<\/td>\n<td>13<\/td>\n<td>32<\/td>\n<td>Sigmoid<\/td>\n<\/tr>\n<tr>\n<td>\u9690\u85cf\u5c42&#xff08;fc2&#xff09;<\/td>\n<td>32<\/td>\n<td>64<\/td>\n<td>Sigmoid<\/td>\n<\/tr>\n<tr>\n<td>\u8f93\u51fa\u5c42&#xff08;fc3&#xff09;<\/td>\n<td>64<\/td>\n<td>4<\/td>\n<td>\u65e0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<ul>\n<li>\u8f93\u5165\u7ef4\u5ea6 13 \u5bf9\u5e94 13 \u4e2a\u77ff\u7269\u7279\u5f81\u3002<\/li>\n<li>\u8f93\u51fa\u7ef4\u5ea6 4 \u5bf9\u5e94 4 \u79cd\u77ff\u7269\u7c7b\u522b\u3002<\/li>\n<li>\u8f93\u51fa\u5c42\u4e0d\u4f7f\u7528\u6fc0\u6d3b\u51fd\u6570&#xff0c;\u56e0\u4e3a CrossEntropyLoss \u5185\u90e8\u5df2\u5305\u542b Softmax\u3002<\/li>\n<\/ul>\n<h3>5.2 \u6838\u5fc3\u539f\u7406<\/h3>\n<p>Sigmoid \u6fc0\u6d3b\u51fd\u6570&#xff1a;<\/p>\n<p><span class=\"katex--display\"><span class=\"katex-display\"><span class=\"katex\"><span class=\"katex-mathml\"><\/p>\n<p>         \u03c3<\/p>\n<p>         (<\/p>\n<p>         x<\/p>\n<p>         )<\/p>\n<p>         &#061;<\/p>\n<p>          1<\/p>\n<p>           1<\/p>\n<p>           &#043;<\/p>\n<p>            e<\/p>\n<p>             \u2212<\/p>\n<p>             x<\/p>\n<p>        \\\\sigma(x) &#061; \\\\frac{1}{1 &#043; e^{-x}}<\/p>\n<p>     <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0359em\">\u03c3<\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\">x<\/span><span class=\"mclose\">)<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 2.0908em;vertical-align: -0.7693em\"><\/span><span class=\"mord\"><span class=\"mopen nulldelimiter\"><\/span><span class=\"mfrac\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 1.3214em\"><span class=\"\" style=\"top: -2.314em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"mord\"><span class=\"mord\">1<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">&#043;<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">e<\/span><span class=\"msupsub\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.6973em\"><span class=\"\" style=\"top: -2.989em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mtight\">\u2212<\/span><span class=\"mord mathnormal mtight\">x<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"\" style=\"top: -3.23em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"frac-line\" style=\"border-bottom-width: 0.04em\"><\/span><\/span><span class=\"\" style=\"top: -3.677em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"mord\"><span class=\"mord\">1<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.7693em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><span class=\"mclose nulldelimiter\"><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>\u5c06\u8f93\u5165\u538b\u7f29\u5230 (0, 1) \u533a\u95f4&#xff0c;\u5386\u53f2\u4e0a\u5e38\u7528\u4e8e\u4e8c\u5206\u7c7b\u548c\u9690\u85cf\u5c42\u3002\u4f46 Sigmoid \u5b58\u5728\u68af\u5ea6\u6d88\u5931\u95ee\u9898&#xff1a;\u5f53\u8f93\u5165\u7edd\u5bf9\u503c\u8f83\u5927\u65f6&#xff0c;\u5bfc\u6570\u8d8b\u8fd1\u4e8e 0&#xff0c;\u6df1\u5c42\u7f51\u7edc\u8bad\u7ec3\u56f0\u96be\u3002\u56e0\u6b64\u73b0\u4ee3\u7f51\u7edc\u66f4\u5e38\u7528 ReLU\u3002<\/p>\n<p>\u4ea4\u53c9\u71b5\u635f\u5931&#xff08;CrossEntropyLoss&#xff09;&#xff1a;<\/p>\n<p><span class=\"katex--display\"><span class=\"katex-display\"><span class=\"katex\"><span class=\"katex-mathml\"><\/p>\n<p>         L<\/p>\n<p>         o<\/p>\n<p>         s<\/p>\n<p>         s<\/p>\n<p>         &#061;<\/p>\n<p>         \u2212<\/p>\n<p>          \u2211<\/p>\n<p>           i<\/p>\n<p>           &#061;<\/p>\n<p>           1<\/p>\n<p>          C<\/p>\n<p>          y<\/p>\n<p>          i<\/p>\n<p>         log<\/p>\n<p>         \u2061<\/p>\n<p>         (<\/p>\n<p>           y<\/p>\n<p>           ^<\/p>\n<p>          i<\/p>\n<p>         )<\/p>\n<p>        Loss &#061; -\\\\sum_{i&#061;1}^{C} y_i \\\\log(\\\\hat{y}_i)<\/p>\n<p>     <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6833em\"><\/span><span class=\"mord mathnormal\">L<\/span><span class=\"mord mathnormal\">oss<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 3.106em;vertical-align: -1.2777em\"><\/span><span class=\"mord\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.1667em\"><\/span><span class=\"mop op-limits\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 1.8283em\"><span class=\"\" style=\"top: -1.8723em;margin-left: 0em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\">i<\/span><span class=\"mrel mtight\">&#061;<\/span><span class=\"mord mtight\">1<\/span><\/span><\/span><\/span><span class=\"\" style=\"top: -3.05em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"\"><span class=\"mop op-symbol large-op\">\u2211<\/span><\/span><\/span><span class=\"\" style=\"top: -4.3em;margin-left: 0em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0715em\">C<\/span><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 1.2777em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.1667em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.0359em\">y<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em\"><span class=\"\" style=\"top: -2.55em;margin-left: -0.0359em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">i<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.1667em\"><\/span><span class=\"mop\">lo<span style=\"margin-right: 0.0139em\">g<\/span><\/span><span class=\"mopen\">(<\/span><span class=\"mord\"><span class=\"mord accent\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.6944em\"><span class=\"\" style=\"top: -3em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0359em\">y<\/span><\/span><span class=\"\" style=\"top: -3em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"accent-body\" style=\"left: -0.1944em\"><span class=\"mord\">^<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.1944em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em\"><span class=\"\" style=\"top: -2.55em;margin-left: -0.0359em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">i<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mclose\">)<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>\u5176\u4e2d <span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"><\/p>\n<p>         y<\/p>\n<p>         i<\/p>\n<p>       y_i<\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em;vertical-align: -0.1944em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.0359em\">y<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em\"><span class=\"\" style=\"top: -2.55em;margin-left: -0.0359em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">i<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u662f\u771f\u5b9e\u6807\u7b7e\u7684 one-hot \u7f16\u7801&#xff0c;<span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"><\/p>\n<p>          y<\/p>\n<p>          ^<\/p>\n<p>         i<\/p>\n<p>       \\\\hat{y}_i<\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.8889em;vertical-align: -0.1944em\"><\/span><span class=\"mord\"><span class=\"mord accent\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.6944em\"><span class=\"\" style=\"top: -3em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0359em\">y<\/span><\/span><span class=\"\" style=\"top: -3em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"accent-body\" style=\"left: -0.1944em\"><span class=\"mord\">^<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.1944em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em\"><span class=\"\" style=\"top: -2.55em;margin-left: -0.0359em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">i<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u662f\u6a21\u578b\u9884\u6d4b\u7684\u6982\u7387\u3002\u4ea4\u53c9\u71b5\u635f\u5931\u662f\u591a\u5206\u7c7b\u4efb\u52a1\u7684\u6807\u51c6\u9009\u62e9\u3002<\/p>\n<p>Adam \u4f18\u5316\u5668&#xff1a;\u7ed3\u5408\u4e86\u52a8\u91cf&#xff08;Momentum&#xff09;\u548c\u81ea\u9002\u5e94\u5b66\u4e60\u7387&#xff08;RMSprop&#xff09;\u7684\u4f18\u70b9&#xff0c;\u662f\u76ee\u524d\u6700\u5e38\u7528\u7684\u4f18\u5316\u5668\u4e4b\u4e00\u3002<\/p>\n<h3>5.3 \u4ee3\u7801\u5b9e\u73b0<\/h3>\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<\/p>\n<p><span class=\"token keyword\">class<\/span> <span class=\"token class-name\">Net<\/span><span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Module<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>  <span class=\"token comment\"># \u5b9a\u4e49\u795e\u7ecf\u7f51\u7edc\u7c7b&#xff0c;\u7ee7\u627f\u81eann.Module<\/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>  <span class=\"token comment\"># \u6784\u9020\u51fd\u6570&#xff0c;\u7528\u4e8e\u521d\u59cb\u5316\u7f51\u7edc\u7ed3\u6784<\/span><br \/>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span>Net<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>  <span class=\"token comment\"># \u8c03\u7528\u7236\u7c7bnn.Module\u7684\u6784\u9020\u51fd\u6570<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>fc1 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span><span class=\"token number\">13<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">32<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u5b9a\u4e49\u7b2c\u4e00\u4e2a\u5168\u8fde\u63a5\u5c42&#xff0c;\u8f93\u5165\u7ef4\u5ea613&#xff0c;\u8f93\u51fa\u7ef4\u5ea632<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>fc2 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Linear<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>  <span class=\"token comment\"># \u5b9a\u4e49\u7b2c\u4e8c\u4e2a\u5168\u8fde\u63a5\u5c42&#xff0c;\u8f93\u5165\u7ef4\u5ea632&#xff0c;\u8f93\u51fa\u7ef4\u5ea664<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>fc3 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">4<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u5b9a\u4e49\u7b2c\u4e09\u4e2a\u5168\u8fde\u63a5\u5c42&#xff08;\u8f93\u51fa\u5c42&#xff09;&#xff0c;\u8f93\u5165\u7ef4\u5ea664&#xff0c;\u8f93\u51fa\u7ef4\u5ea64&#xff08;\u5bf9\u5e944\u4e2a\u7c7b\u522b&#xff09;<\/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>  <span class=\"token comment\"># \u5b9a\u4e49\u524d\u5411\u4f20\u64ad\u51fd\u6570<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>sigmoid<span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>fc1<span class=\"token punctuation\">.<\/span>forward<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u7b2c\u4e00\u5c42\u5168\u8fde\u63a5\u540e\u4f7f\u7528sigmoid\u6fc0\u6d3b\u51fd\u6570<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>sigmoid<span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>fc2<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u7b2c\u4e8c\u5c42\u5168\u8fde\u63a5\u540e\u4f7f\u7528sigmoid\u6fc0\u6d3b\u51fd\u6570<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>fc3<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u8f93\u51fa\u5c42\u5168\u8fde\u63a5&#xff0c;\u4e0d\u4f7f\u7528\u6fc0\u6d3b\u51fd\u6570&#xff08;CrossEntropyLoss\u5185\u90e8\u4f1a\u505asoftmax&#xff09;<\/span><br \/>\n        <span class=\"token keyword\">return<\/span> x  <span class=\"token comment\"># \u8fd4\u56de\u6700\u7ec8\u8f93\u51fa<\/span><\/p>\n<h3>5.4 \u6570\u636e\u8f6c\u5f20\u91cf<\/h3>\n<p>X_train <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>train_data_x<span class=\"token punctuation\">.<\/span>values<span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">&#061;<\/span>torch<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><br \/>\nY_train <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>train_data_y<span class=\"token punctuation\">.<\/span>values<span class=\"token punctuation\">)<\/span><br \/>\nX_test  <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>test_data_x<span class=\"token punctuation\">.<\/span>values<span class=\"token punctuation\">,<\/span>  dtype<span class=\"token operator\">&#061;<\/span>torch<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><br \/>\nY_test  <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>test_data_y<span class=\"token punctuation\">.<\/span>values<span class=\"token punctuation\">)<\/span><\/p>\n<ul>\n<li>\u7279\u5f81\u7528 float32&#xff0c;\u4e0e\u7f51\u7edc\u53c2\u6570\u9ed8\u8ba4\u7c7b\u578b\u4e00\u81f4\u3002<\/li>\n<li>\u6807\u7b7e\u76f4\u63a5\u8f6c\u5f20\u91cf&#xff0c;CrossEntropyLoss \u8981\u6c42\u6807\u7b7e\u4e3a LongTensor&#xff08;\u6574\u578b&#xff09;&#xff0c;pandas \u8bfb\u53d6\u7684\u6570\u503c\u9ed8\u8ba4\u4f1a\u8f6c\u4e3a\u5bf9\u5e94\u6574\u578b\u3002<\/li>\n<\/ul>\n<h3>5.5 \u6a21\u578b\u8bc4\u4f30\u51fd\u6570<\/h3>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">evaluate_model<\/span><span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">,<\/span> X_data<span class=\"token punctuation\">,<\/span> Y_data<span class=\"token punctuation\">,<\/span> train_or_test<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token triple-quoted-string string\">&#034;&#034;&#034;\u8bc4\u4f30\u6a21\u578b\u5728\u7ed9\u5b9a\u6570\u636e\u96c6\u4e0a\u7684\u51c6\u786e\u7387&#034;&#034;&#034;<\/span><br \/>\n    size <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">len<\/span><span class=\"token punctuation\">(<\/span>X_data<span class=\"token punctuation\">)<\/span><br \/>\n    <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>                          <span class=\"token comment\"># \u5173\u95ed\u68af\u5ea6&#xff0c;\u8282\u7701\u5185\u5b58<\/span><br \/>\n        predictions <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>X_data<span class=\"token punctuation\">)<\/span>                <span class=\"token comment\"># \u524d\u5411\u4f20\u64ad\u5f97\u5230 logits<\/span><br \/>\n        correct <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">(<\/span>predictions<span class=\"token punctuation\">.<\/span>argmax<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&#061;&#061;<\/span> Y_data<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">type<\/span><span class=\"token punctuation\">(<\/span>torch<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">float<\/span><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><br \/>\n        correct <span class=\"token operator\">\/&#061;<\/span> size<br \/>\n        <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>train_or_test<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">: \\\\t Accuracy: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">100<\/span> <span class=\"token operator\">*<\/span> correct<span class=\"token punctuation\">)<\/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><br \/>\n    <span class=\"token keyword\">return<\/span> correct<\/p>\n<ul>\n<li>torch.no_grad()&#xff1a;\u8bc4\u4f30\u9636\u6bb5\u4e0d\u9700\u8981\u8ba1\u7b97\u68af\u5ea6&#xff0c;\u53ef\u5927\u5e45\u51cf\u5c11\u663e\u5b58\u5360\u7528\u3002<\/li>\n<li>argmax(1)&#xff1a;\u5728\u7ef4\u5ea6 1&#xff08;\u7c7b\u522b\u7ef4\u5ea6&#xff09;\u4e0a\u53d6\u6700\u5927\u503c\u7d22\u5f15&#xff0c;\u5373\u9884\u6d4b\u7c7b\u522b\u3002<\/li>\n<li>.item()&#xff1a;\u5c06\u5355\u5143\u7d20 PyTorch \u5f20\u91cf\u8f6c\u4e3a Python \u6807\u91cf\u3002<\/li>\n<\/ul>\n<h3>5.6 \u8bad\u7ec3\u8fc7\u7a0b<\/h3>\n<p>model <span class=\"token operator\">&#061;<\/span> Net<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\ncriterion <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>CrossEntropyLoss<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>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\">0.0001<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>epochs <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">15000<\/span><br \/>\naccs <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><\/p>\n<p><span class=\"token keyword\">for<\/span> epoch <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span>epochs<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    outputs <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">)<\/span>              <span class=\"token comment\"># \u524d\u5411\u4f20\u64ad<\/span><br \/>\n    loss <span class=\"token operator\">&#061;<\/span> criterion<span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">,<\/span> Y_train<span class=\"token punctuation\">)<\/span>    <span class=\"token comment\"># \u8ba1\u7b97\u635f\u5931<\/span><br \/>\n    optimizer<span class=\"token punctuation\">.<\/span>zero_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>                  <span class=\"token comment\"># \u6e05\u7a7a\u68af\u5ea6<\/span><br \/>\n    loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>                        <span class=\"token comment\"># \u53cd\u5411\u4f20\u64ad<\/span><br \/>\n    optimizer<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>                       <span class=\"token comment\"># \u66f4\u65b0\u53c2\u6570<\/span><\/p>\n<p>    <span class=\"token keyword\">if<\/span> <span class=\"token punctuation\">(<\/span>epoch <span class=\"token operator\">&#043;<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">%<\/span> <span class=\"token number\">100<\/span> <span class=\"token operator\">&#061;&#061;<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#039;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>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\">&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n        train_acc <span class=\"token operator\">&#061;<\/span> evaluate_model<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">,<\/span> X_train<span class=\"token punctuation\">,<\/span> Y_train<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;train&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        test_acc  <span class=\"token operator\">&#061;<\/span> evaluate_model<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">,<\/span> X_test<span class=\"token punctuation\">,<\/span>  Y_test<span class=\"token punctuation\">,<\/span>  <span class=\"token string\">&#039;test&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        accs<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>test_acc <span class=\"token operator\">*<\/span> <span class=\"token number\">100<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>net_result <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">{<\/span><span class=\"token punctuation\">}<\/span><br \/>\nnet_result<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;acc&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">max<\/span><span class=\"token punctuation\">(<\/span>accs<span class=\"token punctuation\">)<\/span>          <span class=\"token comment\"># \u8bb0\u5f55\u6700\u4f73\u6d4b\u8bd5\u51c6\u786e\u7387<\/span><br \/>\nresult_data<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;net&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;<\/span> net_result<\/p>\n<p>\u8bad\u7ec3\u4e94\u6b65\u66f2&#xff08;\u6bcf\u6b21\u8fed\u4ee3\u5fc5\u505a&#xff09;&#xff1a;<\/p>\n<li>\u524d\u5411\u4f20\u64ad&#xff1a;outputs &#061; model(X_train)<\/li>\n<li>\u8ba1\u7b97\u635f\u5931&#xff1a;loss &#061; criterion(outputs, Y_train)<\/li>\n<li>\u6e05\u7a7a\u68af\u5ea6&#xff1a;optimizer.zero_grad()&#xff08;\u5fc5\u987b\u5728\u53cd\u5411\u4f20\u64ad\u524d&#xff09;<\/li>\n<li>\u53cd\u5411\u4f20\u64ad&#xff1a;loss.backward()<\/li>\n<li>\u66f4\u65b0\u53c2\u6570&#xff1a;optimizer.step()<\/li>\n<p>\u5171\u8bad\u7ec3 15000 \u8f6e&#xff0c;\u6bcf 100 \u8f6e\u8bc4\u4f30\u4e00\u6b21&#xff0c;\u6700\u7ec8\u53d6\u6240\u6709\u8bc4\u4f30\u70b9\u4e2d\u7684\u6700\u5927\u6d4b\u8bd5\u51c6\u786e\u7387\u4f5c\u4e3a\u6a21\u578b\u7ed3\u679c\u3002<\/p>\n<hr \/>\n<h2>\u516d\u3001\u5377\u79ef\u795e\u7ecf\u7f51\u7edc&#xff08;1D CNN&#xff09;<\/h2>\n<h3>6.1 \u4e3a\u4ec0\u4e48\u8868\u683c\u6570\u636e\u53ef\u4ee5\u7528 CNN&#xff1f;<\/h3>\n<p>\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u901a\u5e38\u7528\u4e8e\u56fe\u50cf&#xff08;2D CNN&#xff09;\u548c\u6587\u672c\/\u5e8f\u5217&#xff08;1D CNN&#xff09;\u3002\u5bf9\u4e8e\u8868\u683c\u6570\u636e&#xff0c;\u6211\u4eec\u53ef\u4ee5\u5c06 13 \u7ef4\u7279\u5f81\u770b\u4f5c\u4e00\u4e2a\u957f\u5ea6\u4e3a 13 \u7684\u4e00\u7ef4\u5e8f\u5217&#xff0c;\u4f7f\u7528 1D \u5377\u79ef\u63d0\u53d6\u7279\u5f81\u4e4b\u95f4\u7684\u5c40\u90e8\u5173\u8054\u6a21\u5f0f\u3002<\/p>\n<p>\u4f8b\u5982&#xff0c;\u77ff\u7269\u7684&#034;\u5bc6\u5ea6&#034;\u548c&#034;\u786c\u5ea6&#034;\u53ef\u80fd\u5b58\u5728\u5c40\u90e8\u76f8\u5173\u6027&#xff0c;\u5377\u79ef\u6838\u53ef\u4ee5\u6355\u6349\u8fd9\u79cd\u76f8\u90bb\u7279\u5f81\u95f4\u7684\u7ec4\u5408\u89c4\u5f8b\u3002<\/p>\n<h3>6.2 \u7f51\u7edc\u7ed3\u6784<\/h3>\n<table>\n<tr>\u5c42\u8f93\u5165\u901a\u9053\u8f93\u51fa\u901a\u9053\u5377\u79ef\u6838\u586b\u5145\u8bf4\u660e<\/tr>\n<tbody>\n<tr>\n<td>Conv1<\/td>\n<td>1<\/td>\n<td>16<\/td>\n<td>3<\/td>\n<td>1<\/td>\n<td>\u7b2c\u4e00\u5c42\u5377\u79ef<\/td>\n<\/tr>\n<tr>\n<td>Conv2<\/td>\n<td>16<\/td>\n<td>32<\/td>\n<td>3<\/td>\n<td>1<\/td>\n<td>\u7b2c\u4e8c\u5c42\u5377\u79ef<\/td>\n<\/tr>\n<tr>\n<td>Conv3<\/td>\n<td>32<\/td>\n<td>64<\/td>\n<td>3<\/td>\n<td>1<\/td>\n<td>\u7b2c\u4e09\u5c42\u5377\u79ef<\/td>\n<\/tr>\n<tr>\n<td>GAP<\/td>\n<td>&#8211;<\/td>\n<td>&#8211;<\/td>\n<td>&#8211;<\/td>\n<td>&#8211;<\/td>\n<td>\u5168\u5c40\u5e73\u5747\u6c60\u5316<\/td>\n<\/tr>\n<tr>\n<td>FC<\/td>\n<td>64<\/td>\n<td>4<\/td>\n<td>&#8211;<\/td>\n<td>&#8211;<\/td>\n<td>\u5168\u8fde\u63a5\u8f93\u51fa\u5c42<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<ul>\n<li>kernel_size&#061;3, padding&#061;1&#xff1a;\u5377\u79ef\u540e\u5e8f\u5217\u957f\u5ea6\u4e0d\u53d8&#xff08;<span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\">\n<p>          L<\/p>\n<p>           o<\/p>\n<p>           u<\/p>\n<p>           t<\/p>\n<p>         &#061;<\/p>\n<p>          L<\/p>\n<p>           i<\/p>\n<p>           n<\/p>\n<p>         &#043;<\/p>\n<p>         2<\/p>\n<p>         p<\/p>\n<p>         \u2212<\/p>\n<p>         k<\/p>\n<p>         &#043;<\/p>\n<p>         1<\/p>\n<p>         &#061;<\/p>\n<p>          L<\/p>\n<p>           i<\/p>\n<p>           n<\/p>\n<p>        L_{out} &#061; L_{in} &#043; 2p &#8211; k &#043; 1 &#061; L_{in}<\/p>\n<p>     <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.8333em;vertical-align: -0.15em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">L<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.2806em\"><span class=\"\" style=\"top: -2.55em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\">o<\/span><span class=\"mord mathnormal mtight\">u<\/span><span class=\"mord mathnormal mtight\">t<\/span><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.8333em;vertical-align: -0.15em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">L<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em\"><span class=\"\" style=\"top: -2.55em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\">in<\/span><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">&#043;<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.8389em;vertical-align: -0.1944em\"><\/span><span class=\"mord\">2<\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.7778em;vertical-align: -0.0833em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0315em\">k<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">&#043;<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.6444em\"><\/span><span class=\"mord\">1<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.8333em;vertical-align: -0.15em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">L<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em\"><span class=\"\" style=\"top: -2.55em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\">in<\/span><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>&#xff09;\u3002<\/li>\n<li>\u5168\u5c40\u5e73\u5747\u6c60\u5316&#xff08;Global Average Pooling&#xff09;&#xff1a;\u5728\u7279\u5f81\u7ef4\u5ea6\u4e0a\u6c42\u5747\u503c&#xff0c;\u5c06 (batch, 64, 13) \u538b\u7f29\u4e3a (batch, 64)&#xff0c;\u53c2\u6570\u91cf\u8fdc\u5c0f\u4e8e\u5c55\u5e73\u540e\u63a5\u5168\u8fde\u63a5\u3002<\/li>\n<\/ul>\n<h3>6.3 \u6838\u5fc3\u539f\u7406<\/h3>\n<p>\u4e00\u7ef4\u5377\u79ef\u8fd0\u7b97&#xff1a;<\/p>\n<p><span class=\"katex--display\"><span class=\"katex-display\"><span class=\"katex\"><span class=\"katex-mathml\"><\/p>\n<p>         (<\/p>\n<p>         f<\/p>\n<p>         \u2217<\/p>\n<p>         g<\/p>\n<p>         )<\/p>\n<p>         [<\/p>\n<p>         n<\/p>\n<p>         ]<\/p>\n<p>         &#061;<\/p>\n<p>          \u2211<\/p>\n<p>          k<\/p>\n<p>         f<\/p>\n<p>         [<\/p>\n<p>         k<\/p>\n<p>         ]<\/p>\n<p>         \u22c5<\/p>\n<p>         g<\/p>\n<p>         [<\/p>\n<p>         n<\/p>\n<p>         \u2212<\/p>\n<p>         k<\/p>\n<p>         ]<\/p>\n<p>        (f * g)[n] &#061; \\\\sum_{k} f[k] \\\\cdot g[n &#8211; k]<\/p>\n<p>     <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.1076em\">f<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2217<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0359em\">g<\/span><span class=\"mclose\">)<\/span><span class=\"mopen\">[<\/span><span class=\"mord mathnormal\">n<\/span><span class=\"mclose\">]<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 2.3521em;vertical-align: -1.3021em\"><\/span><span class=\"mop op-limits\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 1.05em\"><span class=\"\" style=\"top: -1.8479em;margin-left: 0em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0315em\">k<\/span><\/span><\/span><\/span><span class=\"\" style=\"top: -3.05em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"\"><span class=\"mop op-symbol large-op\">\u2211<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 1.3021em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.1667em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.1076em\">f<\/span><span class=\"mopen\">[<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0315em\">k<\/span><span class=\"mclose\">]<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u22c5<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0359em\">g<\/span><span class=\"mopen\">[<\/span><span class=\"mord mathnormal\">n<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0315em\">k<\/span><span class=\"mclose\">]<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>\u5377\u79ef\u6838\u5728\u7279\u5f81\u5e8f\u5217\u4e0a\u6ed1\u52a8&#xff0c;\u63d0\u53d6\u5c40\u90e8\u6a21\u5f0f\u3002\u591a\u5c42\u5377\u79ef\u5806\u53e0\u53ef\u4ee5\u63d0\u53d6\u4ece\u4f4e\u7ea7\u5230\u9ad8\u7ea7\u7684\u7279\u5f81\u7ec4\u5408\u3002<\/p>\n<p>\u5168\u5c40\u5e73\u5747\u6c60\u5316&#xff08;GAP&#xff09;&#xff1a;\u5bf9\u6bcf\u4e2a\u901a\u9053\u7684\u6574\u4e2a\u7279\u5f81\u56fe\u53d6\u5e73\u5747\u503c&#xff0c;\u76f8\u6bd4\u5168\u8fde\u63a5\u5c55\u5e73&#xff1a;<\/p>\n<ul>\n<li>\u5927\u5e45\u51cf\u5c11\u53c2\u6570\u91cf&#xff0c;\u964d\u4f4e\u8fc7\u62df\u5408\u98ce\u9669<\/li>\n<li>\u5bf9\u8f93\u5165\u957f\u5ea6\u53d8\u5316\u66f4\u9c81\u68d2<\/li>\n<li>\u589e\u5f3a\u6a21\u578b\u5bf9\u7a7a\u95f4\u4f4d\u7f6e\u7684\u4e0d\u53d8\u6027<\/li>\n<\/ul>\n<h3>6.4 \u4ee3\u7801\u5b9e\u73b0<\/h3>\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\">import<\/span> torch<span class=\"token punctuation\">.<\/span>optim <span class=\"token keyword\">as<\/span> optim<\/p>\n<p><span class=\"token keyword\">class<\/span> <span class=\"token class-name\">ConvNet<\/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> num_features<span class=\"token punctuation\">,<\/span> hidden_size<span class=\"token punctuation\">,<\/span> num_classes<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span>ConvNet<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        <span class=\"token comment\"># \u4e09\u5c421D\u5377\u79ef&#xff0c;\u901a\u9053\u6570\u9010\u6b65\u589e\u52a0&#xff1a;1 -&gt; 16 -&gt; 32 -&gt; 64<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>conv1 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Conv1d<span class=\"token punctuation\">(<\/span>in_channels<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span>  out_channels<span class=\"token operator\">&#061;<\/span><span class=\"token number\">16<\/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> padding<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>conv2 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Conv1d<span class=\"token punctuation\">(<\/span>in_channels<span class=\"token operator\">&#061;<\/span><span class=\"token number\">16<\/span><span class=\"token punctuation\">,<\/span> out_channels<span class=\"token operator\">&#061;<\/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> padding<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>conv3 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Conv1d<span class=\"token punctuation\">(<\/span>in_channels<span class=\"token operator\">&#061;<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">,<\/span> out_channels<span class=\"token operator\">&#061;<\/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> padding<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>activation <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Sigmoid<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u6ce8\u610f&#xff1a;\u6b64\u5904\u5b9e\u9645\u4e3aSigmoid\u6fc0\u6d3b<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>fc <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> num_classes<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 comment\"># x \u5f62\u72b6: (batch_size, 13) -&gt; \u589e\u52a0\u901a\u9053\u7ef4\u5ea6 -&gt; (batch_size, 1, 13)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> x<span class=\"token punctuation\">.<\/span>unsqueeze<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>conv1<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span>    <span class=\"token comment\"># (batch, 16, 13)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>activation <span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>conv2<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span>    <span class=\"token comment\"># (batch, 32, 13)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>activation <span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>conv3<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span>    <span class=\"token comment\"># (batch, 64, 13)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>activation <span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> x<span class=\"token punctuation\">.<\/span>mean<span class=\"token punctuation\">(<\/span>dim<span class=\"token operator\">&#061;<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span>    <span class=\"token comment\"># \u5168\u5c40\u5e73\u5747\u6c60\u5316 -&gt; (batch, 64)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>fc<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span>       <span class=\"token comment\"># (batch, 4)<\/span><br \/>\n        <span class=\"token keyword\">return<\/span> x<\/p>\n<p>unsqueeze(1) \u662f\u5173\u952e\u64cd\u4f5c&#xff1a;Conv1d \u8981\u6c42\u8f93\u5165\u5f62\u72b6\u4e3a (batch, in_channels, length)&#xff0c;\u800c\u539f\u59cb\u6570\u636e\u4e3a (batch, 13)&#xff0c;\u9700\u8981\u5728\u7b2c 1 \u7ef4\u63d2\u5165\u901a\u9053\u7ef4\u5ea6\u3002<\/p>\n<h3>6.5 \u8bad\u7ec3\u8fc7\u7a0b<\/h3>\n<p>X_train <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>train_data_x<span class=\"token punctuation\">.<\/span>values<span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">&#061;<\/span>torch<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u5c06\u8bad\u7ec3\u96c6\u7279\u5f81\u8f6c\u6362\u4e3aPyTorch\u5f20\u91cf&#xff0c;\u6570\u636e\u7c7b\u578b\u4e3afloat32&#xff0c;\u5f62\u72b6\u4e3a(\u6837\u672c\u6570, 13)<\/span><br \/>\nY_train <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>train_data_y<span class=\"token punctuation\">.<\/span>values<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u5c06\u8bad\u7ec3\u96c6\u6807\u7b7e\u8f6c\u6362\u4e3aPyTorch\u5f20\u91cf<\/span><br \/>\nX_test <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>test_data_x<span class=\"token punctuation\">.<\/span>values<span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">&#061;<\/span>torch<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u5c06\u6d4b\u8bd5\u96c6\u7279\u5f81\u8f6c\u6362\u4e3aPyTorch\u5f20\u91cf&#xff0c;\u6570\u636e\u7c7b\u578b\u4e3afloat32&#xff0c;\u5f62\u72b6\u4e3a(\u6837\u672c\u6570, 13)<\/span><br \/>\nY_test <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>test_data_y<span class=\"token punctuation\">.<\/span>values<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u5c06\u6d4b\u8bd5\u96c6\u6807\u7b7e\u8f6c\u6362\u4e3aPyTorch\u5f20\u91cf<\/span><\/p>\n<p>hidden_size <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">10<\/span>  <span class=\"token comment\"># \u5b9a\u4e49\u9690\u85cf\u5c42\u5927\u5c0f&#xff08;\u6b64\u53c2\u6570\u5728ConvNet\u4e2d\u672a\u76f4\u63a5\u4f7f\u7528&#xff0c;\u4ec5\u4f5c\u4e3a\u63a5\u53e3\u53c2\u6570&#xff09;<\/span><br \/>\nnum_classes <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">4<\/span>  <span class=\"token comment\"># \u5b9a\u4e49\u7c7b\u522b\u6570\u91cf&#xff08;\u77ff\u7269\u7c7b\u578bA\u3001B\u3001C\u3001D\u51714\u7c7b&#xff09;<\/span><br \/>\nmodel <span class=\"token operator\">&#061;<\/span> ConvNet<span class=\"token punctuation\">(<\/span><span class=\"token number\">13<\/span><span class=\"token punctuation\">,<\/span> hidden_size<span class=\"token punctuation\">,<\/span> num_classes<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u5b9e\u4f8b\u5316\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u6a21\u578b&#xff0c;\u8f93\u5165\u7279\u5f81\u7ef4\u5ea6\u4e3a13&#xff0c;\u9690\u85cf\u5c42\u5927\u5c0f\u4e3a10&#xff0c;\u8f93\u51fa\u7c7b\u522b\u4e3a4<\/span><br \/>\ncriterion <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 comment\"># \u5b9a\u4e49\u635f\u5931\u51fd\u6570\u4e3a\u4ea4\u53c9\u71b5\u635f\u5931&#xff08;\u9002\u7528\u4e8e\u591a\u5206\u7c7b\u95ee\u9898&#xff09;<\/span><br \/>\noptimizer <span class=\"token operator\">&#061;<\/span> optim<span class=\"token punctuation\">.<\/span>Adam<span class=\"token punctuation\">(<\/span>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>  <span class=\"token comment\"># \u5b9a\u4e49\u4f18\u5316\u5668\u4e3aAdam&#xff0c;\u5b66\u4e60\u7387\u4e3a0.001<\/span><\/p>\n<p>num_epochs <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">15000<\/span>  <span class=\"token comment\"># \u8bbe\u7f6e\u8bad\u7ec3\u8fed\u4ee3\u6b21\u6570&#xff08;\u8f6e\u6570&#xff09;<\/span><br \/>\naccs <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span>  <span class=\"token comment\"># \u521d\u59cb\u5316\u7a7a\u5217\u8868&#xff0c;\u7528\u4e8e\u5b58\u50a8\u6bcf\u4e2a\u8bc4\u4f30\u70b9\u7684\u6d4b\u8bd5\u51c6\u786e\u7387<\/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>  <span class=\"token comment\"># \u5faa\u73af\u8bad\u7ec3num_epochs\u6b21<\/span><br \/>\n    outputs <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u524d\u5411\u4f20\u64ad&#xff1a;\u5c06\u8bad\u7ec3\u6570\u636e\u8f93\u5165\u6a21\u578b&#xff0c;\u5f97\u5230\u9884\u6d4b\u8f93\u51fa&#xff08;\u6bcf\u4e2a\u7c7b\u522b\u7684logits&#xff09;<\/span><br \/>\n    loss <span class=\"token operator\">&#061;<\/span> criterion<span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">,<\/span> Y_train<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u8ba1\u7b97\u635f\u5931\u503c&#xff1a;\u4f7f\u7528\u4ea4\u53c9\u71b5\u635f\u5931\u51fd\u6570\u6bd4\u8f83\u9884\u6d4b\u503c\u548c\u771f\u5b9e\u6807\u7b7e<\/span><br \/>\n    optimizer<span class=\"token punctuation\">.<\/span>zero_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u6e05\u7a7a\u68af\u5ea6\u7f13\u5b58&#xff08;\u9632\u6b62\u68af\u5ea6\u7d2f\u52a0&#xff09;<\/span><br \/>\n    loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u53cd\u5411\u4f20\u64ad&#xff1a;\u8ba1\u7b97\u635f\u5931\u5173\u4e8e\u6a21\u578b\u53c2\u6570\u7684\u68af\u5ea6<\/span><br \/>\n    optimizer<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u66f4\u65b0\u6a21\u578b\u53c2\u6570&#xff1a;\u4f7f\u7528Adam\u4f18\u5316\u5668\u6839\u636e\u68af\u5ea6\u66f4\u65b0\u6743\u91cd<\/span><\/p>\n<p>    <span class=\"token keyword\">if<\/span> <span class=\"token punctuation\">(<\/span>epoch <span class=\"token operator\">&#043;<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">%<\/span> <span class=\"token number\">100<\/span> <span class=\"token operator\">&#061;&#061;<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">:<\/span>  <span class=\"token comment\"># \u6bcf100\u8f6e\u6253\u5370\u4e00\u6b21\u8bad\u7ec3\u8fdb\u5ea6\u548c\u8bc4\u4f30\u7ed3\u679c<\/span><br \/>\n        <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#039;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\">&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u6253\u5370\u5f53\u524d\u8f6e\u6b21\u548c\u635f\u5931\u503c<\/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>  <span class=\"token comment\"># \u7981\u7528\u68af\u5ea6\u8ba1\u7b97&#xff08;\u8bc4\u4f30\u65f6\u4e0d\u9700\u8981\u53cd\u5411\u4f20\u64ad&#xff0c;\u8282\u7701\u5185\u5b58\u548c\u8ba1\u7b97\u8d44\u6e90&#xff09;<\/span><br \/>\n            <span class=\"token comment\"># \u8bc4\u4f30\u8bad\u7ec3\u96c6\u51c6\u786e\u7387<\/span><br \/>\n            predictions <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u5c06\u8bad\u7ec3\u6570\u636e\u8f93\u5165\u6a21\u578b&#xff0c;\u5f97\u5230\u9884\u6d4b\u8f93\u51fa<\/span><br \/>\n            predicted_classes <span class=\"token operator\">&#061;<\/span> predictions<span class=\"token punctuation\">.<\/span>argmax<span class=\"token punctuation\">(<\/span>dim<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u83b7\u53d6\u6bcf\u884c\u6700\u5927\u503c\u7684\u7d22\u5f15&#xff08;\u9884\u6d4b\u7c7b\u522b&#xff09;&#xff0c;\u5f62\u72b6\u4e3a(batch_size,)<\/span><br \/>\n            accuracy <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">(<\/span>predicted_classes <span class=\"token operator\">&#061;&#061;<\/span> Y_train<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">float<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>mean<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u8ba1\u7b97\u8bad\u7ec3\u96c6\u51c6\u786e\u7387&#xff1a;\u6bd4\u8f83\u9884\u6d4b\u7c7b\u522b\u4e0e\u771f\u5b9e\u6807\u7b7e&#xff0c;\u6c42\u5747\u503c<\/span><br \/>\n            <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#039;Train Accuracy: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>accuracy<span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <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\">%&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u6253\u5370\u8bad\u7ec3\u96c6\u51c6\u786e\u7387&#xff08;\u767e\u5206\u6bd4\u5f62\u5f0f&#xff0c;\u4fdd\u75592\u4f4d\u5c0f\u6570&#xff09;<\/span><\/p>\n<p>            <span class=\"token comment\"># \u8bc4\u4f30\u6d4b\u8bd5\u96c6\u51c6\u786e\u7387<\/span><br \/>\n            predictions <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u5c06\u6d4b\u8bd5\u6570\u636e\u8f93\u5165\u6a21\u578b&#xff0c;\u5f97\u5230\u9884\u6d4b\u8f93\u51fa<\/span><br \/>\n            predicted_classes <span class=\"token operator\">&#061;<\/span> predictions<span class=\"token punctuation\">.<\/span>argmax<span class=\"token punctuation\">(<\/span>dim<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u83b7\u53d6\u6bcf\u884c\u6700\u5927\u503c\u7684\u7d22\u5f15&#xff08;\u9884\u6d4b\u7c7b\u522b&#xff09;<\/span><br \/>\n            accuracy <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">(<\/span>predicted_classes <span class=\"token operator\">&#061;&#061;<\/span> Y_test<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">float<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>mean<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u8ba1\u7b97\u6d4b\u8bd5\u96c6\u51c6\u786e\u7387&#xff1a;\u6bd4\u8f83\u9884\u6d4b\u7c7b\u522b\u4e0e\u771f\u5b9e\u6807\u7b7e&#xff0c;\u6c42\u5747\u503c<\/span><br \/>\n            <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#039;Test Accuracy: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>accuracy<span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <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\">%&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u6253\u5370\u6d4b\u8bd5\u96c6\u51c6\u786e\u7387&#xff08;\u767e\u5206\u6bd4\u5f62\u5f0f&#xff0c;\u4fdd\u75592\u4f4d\u5c0f\u6570&#xff09;<\/span><br \/>\n            accs<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>accuracy <span class=\"token operator\">*<\/span> <span class=\"token number\">100<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u5c06\u6d4b\u8bd5\u51c6\u786e\u7387&#xff08;\u8f6c\u6362\u4e3a\u767e\u5206\u6bd4&#xff09;\u5b58\u5165\u5217\u8868<\/span><\/p>\n<p>cnn_result <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">{<\/span><span class=\"token punctuation\">}<\/span>  <span class=\"token comment\"># \u521d\u59cb\u5316\u4e00\u4e2a\u7a7a\u5b57\u5178&#xff0c;\u7528\u4e8e\u5b58\u50a8CNN\u6a21\u578b\u7684\u8bc4\u4f30\u7ed3\u679c<\/span><br \/>\ncnn_result<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;acc&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">max<\/span><span class=\"token punctuation\">(<\/span>accs<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u4ece\u6240\u6709\u8bb0\u5f55\u7684\u6d4b\u8bd5\u51c6\u786e\u7387\u4e2d\u53d6\u6700\u5927\u503c&#xff08;\u8f6c\u6362\u4e3aPython\u6807\u91cf&#xff09;&#xff0c;\u4f5c\u4e3a\u6a21\u578b\u7684\u6700\u4f73\u6d4b\u8bd5\u51c6\u786e\u7387<\/span><br \/>\nresult_data<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;cnn&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;<\/span> cnn_result  <span class=\"token comment\"># \u5c06CNN\u7684\u7ed3\u679c\u5b58\u5165\u603b\u7ed3\u679c\u5b57\u5178<\/span><\/p>\n<p><span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>result_data<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u6253\u5370\u6240\u6709\u6a21\u578b\u7684\u8bc4\u4f30\u7ed3\u679c<\/span><\/p>\n<p>CNN \u7684\u8bad\u7ec3\u6d41\u7a0b\u4e0e\u5168\u8fde\u63a5\u7f51\u7edc\u5b8c\u5168\u4e00\u81f4&#xff0c;\u533a\u522b\u4ec5\u5728\u4e8e\u7f51\u7edc\u7ed3\u6784\u5b9a\u4e49\u548c\u5b66\u4e60\u7387\u8bbe\u7f6e&#xff08;CNN \u7528 lr&#061;0.001&#xff0c;\u5168\u8fde\u63a5\u7528 lr&#061;0.0001&#xff09;\u3002<\/p>\n<hr \/>\n<h2>\u4e03\u3001\u7ed3\u679c\u8f93\u51fa<\/h2>\n<p>\u8bad\u7ec3\u5b8c\u6210\u540e&#xff0c;result_data \u5b57\u5178\u4e2d\u5b58\u50a8\u4e86\u4e24\u4e2a\u6a21\u578b\u7684\u6700\u4f73\u6d4b\u8bd5\u51c6\u786e\u7387&#xff1a;<\/p>\n<p><span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>result_data<span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token comment\"># \u8f93\u51fa\u793a\u4f8b&#xff08;\u5b9e\u9645\u503c\u53d6\u51b3\u4e8e\u6570\u636e\u548c\u968f\u673a\u521d\u59cb\u5316&#xff09;&#xff1a;<\/span><br \/>\n<span class=\"token comment\"># {&#039;net&#039;: {&#039;acc&#039;: 92.5}, &#039;cnn&#039;: {&#039;acc&#039;: 95.0}}<\/span><\/p>\n<table>\n<tr>\u6a21\u578b\u6700\u4f73\u6d4b\u8bd5\u51c6\u786e\u7387<\/tr>\n<tbody>\n<tr>\n<td>\u5168\u8fde\u63a5\u795e\u7ecf\u7f51\u7edc&#xff08;Net&#xff09;<\/td>\n<td>result_data[&#039;net&#039;][&#039;acc&#039;]<\/td>\n<\/tr>\n<tr>\n<td>\u5377\u79ef\u795e\u7ecf\u7f51\u7edc&#xff08;CNN&#xff09;<\/td>\n<td>result_data[&#039;cnn&#039;][&#039;acc&#039;]<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u7531\u4e8e\u4ee3\u7801\u672a\u8bbe\u7f6e\u968f\u673a\u79cd\u5b50&#xff08;torch.manual_seed&#xff09;&#xff0c;\u6bcf\u6b21\u8fd0\u884c\u7ed3\u679c\u4f1a\u6709\u6ce2\u52a8\u3002\u5982\u9700\u53ef\u590d\u73b0&#xff0c;\u5efa\u8bae\u5728\u8bad\u7ec3\u524d\u8bbe\u7f6e&#xff1a;<\/p>\n<p> torch<span class=\"token punctuation\">.<\/span>manual_seed<span class=\"token punctuation\">(<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<hr \/>\n<h2>\u516b\u3001\u4e24\u79cd\u6a21\u578b\u5bf9\u6bd4\u5206\u6790<\/h2>\n<table>\n<tr>\u5bf9\u6bd4\u7ef4\u5ea6\u5168\u8fde\u63a5\u795e\u7ecf\u7f51\u7edc1D \u5377\u79ef\u795e\u7ecf\u7f51\u7edc<\/tr>\n<tbody>\n<tr>\n<td>\u53c2\u6570\u91cf<\/td>\n<td>\u8f83\u591a&#xff08;\u6bcf\u5c42\u5168\u8fde\u63a5&#xff09;<\/td>\n<td>\u8f83\u5c11&#xff08;\u5377\u79ef\u6743\u91cd\u5171\u4eab &#043; GAP&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u7279\u5f81\u63d0\u53d6\u65b9\u5f0f<\/td>\n<td>\u5168\u5c40\u7ec4\u5408&#xff0c;\u6bcf\u4e2a\u7279\u5f81\u72ec\u7acb\u6743\u91cd<\/td>\n<td>\u5c40\u90e8\u6a21\u5f0f\u63d0\u53d6&#xff0c;\u6355\u6349\u76f8\u90bb\u7279\u5f81\u5173\u8054<\/td>\n<\/tr>\n<tr>\n<td>\u8fc7\u62df\u5408\u98ce\u9669<\/td>\n<td>\u8f83\u9ad8<\/td>\n<td>\u8f83\u4f4e&#xff08;\u6743\u91cd\u5171\u4eab\u3001GAP&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u5bf9\u7279\u5f81\u987a\u5e8f\u654f\u611f\u5ea6<\/td>\n<td>\u4e0d\u654f\u611f&#xff08;\u6362\u5217\u4e0d\u5f71\u54cd&#xff09;<\/td>\n<td>\u654f\u611f&#xff08;\u5377\u79ef\u4f9d\u8d56\u76f8\u90bb\u5173\u7cfb&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u8bad\u7ec3\u7a33\u5b9a\u6027<\/td>\n<td>Sigmoid \u6613\u68af\u5ea6\u6d88\u5931<\/td>\n<td>\u540c\u6837\u53d7 Sigmoid \u5f71\u54cd<\/td>\n<\/tr>\n<tr>\n<td>\u9002\u7528\u573a\u666f<\/td>\n<td>\u901a\u7528\u8868\u683c\u5206\u7c7b<\/td>\n<td>\u6709\u5e8f\u7279\u5f81\/\u5e8f\u5217\u578b\u6570\u636e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u5bf9\u4e8e\u77ff\u7269\u8fd9\u79cd\u7279\u5f81\u95f4\u6709\u7269\u7406\u5316\u5b66\u5173\u8054\u7684\u6570\u636e&#xff0c;1D CNN \u5f80\u5f80\u80fd\u5b66\u5230\u66f4\u6709\u610f\u4e49\u7684\u5c40\u90e8\u7ec4\u5408\u6a21\u5f0f&#xff0c;\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8868\u73b0\u53ef\u80fd\u66f4\u4f18\u3002<\/p>\n<hr \/>\n<h2>\u4e5d\u3001\u6539\u8fdb\u5efa\u8bae<\/h2>\n<p>\u539f\u4ee3\u7801\u53ef\u4ece\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\u4f18\u5316&#xff0c;\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u5206\u7c7b\u6548\u679c&#xff1a;<\/p>\n<h3>9.1 \u6570\u636e\u6807\u51c6\u5316<\/h3>\n<p><span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>preprocessing <span class=\"token keyword\">import<\/span> StandardScaler<br \/>\nscaler <span class=\"token operator\">&#061;<\/span> StandardScaler<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntrain_data_x <span class=\"token operator\">&#061;<\/span> scaler<span class=\"token punctuation\">.<\/span>fit_transform<span class=\"token punctuation\">(<\/span>train_data_x<span class=\"token punctuation\">)<\/span><br \/>\ntest_data_x  <span class=\"token operator\">&#061;<\/span> scaler<span class=\"token punctuation\">.<\/span>transform<span class=\"token punctuation\">(<\/span>test_data_x<span class=\"token punctuation\">)<\/span>   <span class=\"token comment\"># \u6ce8\u610f&#xff1a;\u6d4b\u8bd5\u96c6\u53ea\u7528transform<\/span><\/p>\n<p>\u4e0d\u540c\u77ff\u7269\u7279\u5f81\u91cf\u7eb2\u5dee\u5f02\u5927&#xff0c;\u6807\u51c6\u5316\u540e\u53ef\u52a0\u901f\u6536\u655b\u3001\u63d0\u5347\u7cbe\u5ea6\u3002<\/p>\n<h3>9.2 \u6539\u7528 ReLU \u6fc0\u6d3b\u51fd\u6570<\/h3>\n<p>Sigmoid \u5728\u6df1\u5c42\u7f51\u7edc\u4e2d\u5bb9\u6613\u68af\u5ea6\u6d88\u5931&#xff0c;\u5efa\u8bae\u66ff\u6362\u4e3a ReLU&#xff1a;<\/p>\n<p>x <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>relu<span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>fc1<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token comment\"># \u6216\u5728CNN\u4e2d&#xff1a;self.activation &#061; nn.ReLU()<\/span><\/p>\n<p>ReLU \u8ba1\u7b97\u7b80\u5355\u3001\u7f13\u89e3\u68af\u5ea6\u6d88\u5931&#xff0c;\u662f\u76ee\u524d\u9690\u85cf\u5c42\u7684\u4e3b\u6d41\u9009\u62e9\u3002<\/p>\n<h3>9.3 \u8bbe\u7f6e\u968f\u673a\u79cd\u5b50<\/h3>\n<p>torch<span class=\"token punctuation\">.<\/span>manual_seed<span class=\"token punctuation\">(<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntorch<span class=\"token punctuation\">.<\/span>cuda<span class=\"token punctuation\">.<\/span>manual_seed_all<span class=\"token punctuation\">(<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>\u4fdd\u8bc1\u5b9e\u9a8c\u53ef\u590d\u73b0\u3002<\/p>\n<h3>9.4 \u65e9\u505c&#xff08;Early Stopping&#xff09;<\/h3>\n<p>\u8bb0\u5f55\u9a8c\u8bc1\u96c6\u635f\u5931&#xff0c;\u5f53\u8fde\u7eed\u591a\u8f6e\u4e0d\u518d\u4e0b\u964d\u65f6\u505c\u6b62\u8bad\u7ec3&#xff0c;\u9632\u6b62\u8fc7\u62df\u5408&#xff1a;<\/p>\n<p>best_loss <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">float<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;inf&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\npatience <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">500<\/span><br \/>\ncounter <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0<\/span><br \/>\n<span class=\"token comment\"># \u8bad\u7ec3\u4e2d\u6bcf\u8f6e\u68c0\u67e5\u9a8c\u8bc1\u96c6\u635f\u5931&#xff0c;\u82e5\u4e0d\u4e0b\u964d\u5219 counter&#043;&#043;&#xff0c;\u8fbe\u5230 patience \u5219 break<\/span><\/p>\n<h3>9.5 \u5b66\u4e60\u7387\u8c03\u5ea6<\/h3>\n<p>scheduler <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>optim<span class=\"token punctuation\">.<\/span>lr_scheduler<span class=\"token punctuation\">.<\/span>StepLR<span class=\"token punctuation\">(<\/span>optimizer<span class=\"token punctuation\">,<\/span> step_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">3000<\/span><span class=\"token punctuation\">,<\/span> gamma<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.5<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token comment\"># \u6bcf3000\u8f6e\u5b66\u4e60\u7387\u51cf\u534a<\/span><\/p>\n<p>\u8bad\u7ec3\u540e\u671f\u964d\u4f4e\u5b66\u4e60\u7387\u6709\u52a9\u4e8e\u7cbe\u7ec6\u6536\u655b\u3002<\/p>\n<h3>9.6 \u66f4\u4e30\u5bcc\u7684\u8bc4\u4f30\u6307\u6807<\/h3>\n<p>\u9664\u51c6\u786e\u7387\u5916&#xff0c;\u5efa\u8bae\u8865\u5145&#xff1a;<\/p>\n<p><span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>metrics <span class=\"token keyword\">import<\/span> classification_report<span class=\"token punctuation\">,<\/span> confusion_matrix<br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>classification_report<span class=\"token punctuation\">(<\/span>Y_test<span class=\"token punctuation\">.<\/span>numpy<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> predicted_classes<span class=\"token punctuation\">.<\/span>numpy<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\">print<\/span><span class=\"token punctuation\">(<\/span>confusion_matrix<span class=\"token punctuation\">(<\/span>Y_test<span class=\"token punctuation\">.<\/span>numpy<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> predicted_classes<span class=\"token punctuation\">.<\/span>numpy<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>\u53ef\u67e5\u770b\u6bcf\u4e00\u7c7b\u7684\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u3001F1 \u503c\u548c\u6df7\u6dc6\u77e9\u9635&#xff0c;\u66f4\u5168\u9762\u8bc4\u4f30\u6a21\u578b\u3002<\/p>\n<hr \/>\n<h2>\u5341\u3001\u5b8c\u6574\u4ee3\u7801\u6c47\u603b<\/h2>\n<p><span class=\"token comment\"># -*- coding: utf-8 -*-<\/span><br \/>\n<span class=\"token triple-quoted-string string\">&#034;&#034;&#034;<br \/>\nPyTorch \u5b9e\u73b0\u77ff\u7269\u5206\u7c7b&#xff1a;\u5168\u8fde\u63a5\u795e\u7ecf\u7f51\u7edc &#043; 1D\u5377\u79ef\u795e\u7ecf\u7f51\u7edc<br \/>\n\u6570\u636e\u96c6&#xff1a;\u5e73\u5747\u503c\u586b\u5145\u540e\u7684\u77ff\u7269\u7279\u5f81\u6570\u636e\u96c6&#xff08;13\u7ef4\u7279\u5f81&#xff0c;4\u5206\u7c7b&#xff09;<br \/>\n&#034;&#034;&#034;<\/span><\/p>\n<p><span class=\"token keyword\">import<\/span> pandas <span class=\"token keyword\">as<\/span> pd<br \/>\n<span class=\"token keyword\">from<\/span> sklearn <span class=\"token keyword\">import<\/span> metrics<br \/>\n<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\">import<\/span> torch<span class=\"token punctuation\">.<\/span>optim <span class=\"token keyword\">as<\/span> optim<\/p>\n<p><span class=\"token triple-quoted-string string\">&#034;&#034;&#034;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061; 1. \u6570\u636e\u52a0\u8f7d &#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#034;&#034;&#034;<\/span><br \/>\ntrain_data <span class=\"token operator\">&#061;<\/span> pd<span class=\"token punctuation\">.<\/span>read_excel<span class=\"token punctuation\">(<\/span><span class=\"token string\">r&#039;.\/\/temp_data\/\/2_\u8bad\u7ec3\u6570\u636e\u96c6_\u5e73\u5747\u503c\u586b\u5145.xlsx&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntrain_data_x <span class=\"token operator\">&#061;<\/span> train_data<span class=\"token punctuation\">.<\/span>iloc<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><br \/>\ntrain_data_y <span class=\"token operator\">&#061;<\/span> train_data<span class=\"token punctuation\">.<\/span>iloc<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><\/p>\n<p>test_data <span class=\"token operator\">&#061;<\/span> pd<span class=\"token punctuation\">.<\/span>read_excel<span class=\"token punctuation\">(<\/span><span class=\"token string\">r&#039;.\/\/temp_data\/\/2_\u6d4b\u8bd5\u6570\u636e\u96c6_\u5e73\u5747\u503c\u586b\u5145.xlsx&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntest_data_x <span class=\"token operator\">&#061;<\/span> test_data<span class=\"token punctuation\">.<\/span>iloc<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><br \/>\ntest_data_y <span class=\"token operator\">&#061;<\/span> test_data<span class=\"token punctuation\">.<\/span>iloc<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><\/p>\n<p>result_data <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">{<\/span><span class=\"token punctuation\">}<\/span><\/p>\n<p><span class=\"token comment\"># \u8f6c\u5f20\u91cf<\/span><br \/>\nX_train <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>train_data_x<span class=\"token punctuation\">.<\/span>values<span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">&#061;<\/span>torch<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><br \/>\nY_train <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>train_data_y<span class=\"token punctuation\">.<\/span>values<span class=\"token punctuation\">)<\/span><br \/>\nX_test  <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>test_data_x<span class=\"token punctuation\">.<\/span>values<span class=\"token punctuation\">,<\/span>  dtype<span class=\"token operator\">&#061;<\/span>torch<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><br \/>\nY_test  <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>test_data_y<span class=\"token punctuation\">.<\/span>values<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token triple-quoted-string string\">&#034;&#034;&#034;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061; 2. \u5168\u8fde\u63a5\u795e\u7ecf\u7f51\u7edc &#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#034;&#034;&#034;<\/span><br \/>\n<span class=\"token keyword\">class<\/span> <span class=\"token class-name\">Net<\/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>Net<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>fc1 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span><span class=\"token number\">13<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">32<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>fc2 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Linear<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><br \/>\n        self<span class=\"token punctuation\">.<\/span>fc3 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">4<\/span><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        x <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>sigmoid<span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>fc1<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>sigmoid<span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>fc2<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>fc3<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token keyword\">return<\/span> x<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">evaluate_model<\/span><span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">,<\/span> X_data<span class=\"token punctuation\">,<\/span> Y_data<span class=\"token punctuation\">,<\/span> train_or_test<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    size <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">len<\/span><span class=\"token punctuation\">(<\/span>X_data<span class=\"token punctuation\">)<\/span><br \/>\n    <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        predictions <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>X_data<span class=\"token punctuation\">)<\/span><br \/>\n        correct <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">(<\/span>predictions<span class=\"token punctuation\">.<\/span>argmax<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&#061;&#061;<\/span> Y_data<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">type<\/span><span class=\"token punctuation\">(<\/span>torch<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">float<\/span><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><br \/>\n        correct <span class=\"token operator\">\/&#061;<\/span> size<br \/>\n        <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>train_or_test<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">: \\\\t Accuracy: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">100<\/span> <span class=\"token operator\">*<\/span> correct<span class=\"token punctuation\">)<\/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><br \/>\n    <span class=\"token keyword\">return<\/span> correct<\/p>\n<p>model <span class=\"token operator\">&#061;<\/span> Net<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\ncriterion <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>CrossEntropyLoss<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>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\">0.0001<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>epochs <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">15000<\/span><br \/>\naccs <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><br \/>\n<span class=\"token keyword\">for<\/span> epoch <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span>epochs<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    outputs <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">)<\/span><br \/>\n    loss <span class=\"token operator\">&#061;<\/span> criterion<span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">,<\/span> Y_train<span class=\"token punctuation\">)<\/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\">if<\/span> <span class=\"token punctuation\">(<\/span>epoch <span class=\"token operator\">&#043;<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">%<\/span> <span class=\"token number\">100<\/span> <span class=\"token operator\">&#061;&#061;<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#039;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>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\">&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n        evaluate_model<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">,<\/span> X_train<span class=\"token punctuation\">,<\/span> Y_train<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;train&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        test_acc <span class=\"token operator\">&#061;<\/span> evaluate_model<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">,<\/span> X_test<span class=\"token punctuation\">,<\/span> Y_test<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;test&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        accs<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>test_acc <span class=\"token operator\">*<\/span> <span class=\"token number\">100<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>result_data<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;net&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">{<\/span><span class=\"token string\">&#039;acc&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token builtin\">max<\/span><span class=\"token punctuation\">(<\/span>accs<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">}<\/span><\/p>\n<p><span class=\"token triple-quoted-string string\">&#034;&#034;&#034;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061; 3. 1D\u5377\u79ef\u795e\u7ecf\u7f51\u7edc &#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#034;&#034;&#034;<\/span><br \/>\n<span class=\"token keyword\">class<\/span> <span class=\"token class-name\">ConvNet<\/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> num_features<span class=\"token punctuation\">,<\/span> hidden_size<span class=\"token punctuation\">,<\/span> num_classes<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span>ConvNet<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>conv1 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Conv1d<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">16<\/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> padding<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>conv2 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Conv1d<span class=\"token punctuation\">(<\/span><span class=\"token number\">16<\/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> padding<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>conv3 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Conv1d<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> padding<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>activation <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Sigmoid<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>fc <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> num_classes<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        x <span class=\"token operator\">&#061;<\/span> x<span class=\"token punctuation\">.<\/span>unsqueeze<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>activation<span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>conv1<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>activation<span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>conv2<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>activation<span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>conv3<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> x<span class=\"token punctuation\">.<\/span>mean<span class=\"token punctuation\">(<\/span>dim<span class=\"token operator\">&#061;<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>fc<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token keyword\">return<\/span> x<\/p>\n<p>model <span class=\"token operator\">&#061;<\/span> ConvNet<span class=\"token punctuation\">(<\/span><span class=\"token number\">13<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">10<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">4<\/span><span class=\"token punctuation\">)<\/span><br \/>\ncriterion <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>CrossEntropyLoss<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\noptimizer <span class=\"token operator\">&#061;<\/span> optim<span class=\"token punctuation\">.<\/span>Adam<span class=\"token punctuation\">(<\/span>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>num_epochs <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">15000<\/span><br \/>\naccs <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><br \/>\n<span class=\"token keyword\">for<\/span> epoch <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span>num_epochs<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    outputs <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">)<\/span><br \/>\n    loss <span class=\"token operator\">&#061;<\/span> criterion<span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">,<\/span> Y_train<span class=\"token punctuation\">)<\/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\">if<\/span> <span class=\"token punctuation\">(<\/span>epoch <span class=\"token operator\">&#043;<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">%<\/span> <span class=\"token number\">100<\/span> <span class=\"token operator\">&#061;&#061;<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#039;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\">&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n        <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            train_pred <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>argmax<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            train_acc <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">(<\/span>train_pred <span class=\"token operator\">&#061;&#061;<\/span> Y_train<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">float<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>mean<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> <span class=\"token operator\">*<\/span> <span class=\"token number\">100<\/span><br \/>\n            <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#039;Train Accuracy: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>train_acc<span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.2f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">%&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>            test_pred <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>argmax<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            test_acc <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">(<\/span>test_pred <span class=\"token operator\">&#061;&#061;<\/span> Y_test<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">float<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>mean<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> <span class=\"token operator\">*<\/span> <span class=\"token number\">100<\/span><br \/>\n            <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#039;Test Accuracy: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>test_acc<span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.2f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">%&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n            accs<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>test_acc<span class=\"token punctuation\">)<\/span><\/p>\n<p>result_data<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;cnn&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">{<\/span><span class=\"token string\">&#039;acc&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token builtin\">max<\/span><span class=\"token punctuation\">(<\/span>accs<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">}<\/span><\/p>\n<p><span class=\"token triple-quoted-string string\">&#034;&#034;&#034;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061; 4. \u7ed3\u679c\u8f93\u51fa &#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#061;&#034;&#034;&#034;<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;&#061;&#034;<\/span> <span class=\"token operator\">*<\/span> <span class=\"token number\">50<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u5404\u6a21\u578b\u6700\u4f73\u6d4b\u8bd5\u51c6\u786e\u7387&#xff1a;&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u5168\u8fde\u63a5\u795e\u7ecf\u7f51\u7edc: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>result_data<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;net&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;acc&#039;<\/span><span class=\"token punctuation\">]<\/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><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u5377\u79ef\u795e\u7ecf\u7f51\u7edc:   <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>result_data<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;cnn&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;acc&#039;<\/span><span class=\"token punctuation\">]<\/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><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;&#061;&#034;<\/span> <span class=\"token operator\">*<\/span> <span class=\"token number\">50<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<hr \/>\n<h2>\u5341\u4e00\u3001\u603b\u7ed3<\/h2>\n<p>\u672c\u6587\u57fa\u4e8e\u77ff\u7269\u7279\u5f81\u6570\u636e\u96c6&#xff0c;\u4f7f\u7528 PyTorch \u5b9e\u73b0\u4e86\u4e24\u79cd\u6df1\u5ea6\u5b66\u4e60\u5206\u7c7b\u6a21\u578b&#xff1a;<\/p>\n<li>\u5168\u8fde\u63a5\u795e\u7ecf\u7f51\u7edc&#xff1a;\u7ed3\u6784\u7b80\u5355&#xff0c;\u901a\u8fc7\u4e09\u5c42\u5168\u8fde\u63a5\u5c42\u5b66\u4e60\u7279\u5f81\u7684\u5168\u5c40\u7ec4\u5408\u3002<\/li>\n<li>1D \u5377\u79ef\u795e\u7ecf\u7f51\u7edc&#xff1a;\u5c06 13 \u7ef4\u7279\u5f81\u89c6\u4e3a\u5e8f\u5217&#xff0c;\u901a\u8fc7\u4e09\u5c42\u5377\u79ef\u63d0\u53d6\u5c40\u90e8\u6a21\u5f0f&#xff0c;\u914d\u5408\u5168\u5c40\u5e73\u5747\u6c60\u5316\u51cf\u5c11\u53c2\u6570\u91cf\u3002<\/li>\n<p>\u4e24\u8005\u5747\u4f7f\u7528\u4ea4\u53c9\u71b5\u635f\u5931\u548c Adam \u4f18\u5316\u5668&#xff0c;\u8bad\u7ec3 15000 \u8f6e\u540e\u53d6\u6700\u4f73\u6d4b\u8bd5\u51c6\u786e\u7387\u8fdb\u884c\u5bf9\u6bd4\u3002<\/p>\n<p>\u901a\u8fc7\u672c\u6587\u7684\u5b66\u4e60&#xff0c;\u4f60\u53ef\u4ee5\u638c\u63e1&#xff1a;<\/p>\n<ul>\n<li>PyTorch \u81ea\u5b9a\u4e49\u7f51\u7edc\u7684\u6807\u51c6\u5199\u6cd5&#xff08;\u7ee7\u627f nn.Module\u3001\u5b9e\u73b0 forward&#xff09;<\/li>\n<li>\u8bad\u7ec3\u4e94\u6b65\u66f2&#xff1a;\u524d\u5411\u4f20\u64ad \u2192 \u8ba1\u7b97\u635f\u5931 \u2192 \u6e05\u7a7a\u68af\u5ea6 \u2192 \u53cd\u5411\u4f20\u64ad \u2192 \u66f4\u65b0\u53c2\u6570<\/li>\n<li>torch.no_grad() \u5728\u8bc4\u4f30\u9636\u6bb5\u7684\u4f7f\u7528<\/li>\n<li>1D CNN \u5904\u7406\u8868\u683c\u6570\u636e\u7684\u65b9\u6cd5\u4e0e unsqueeze \u7ef4\u5ea6\u53d8\u6362<\/li>\n<li>\u5168\u5c40\u5e73\u5747\u6c60\u5316\u7684\u539f\u7406\u4e0e\u4f18\u52bf<\/li>\n<\/ul>\n<p>\u5efa\u8bae\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\u7ed3\u5408\u6570\u636e\u6807\u51c6\u5316\u3001ReLU \u6fc0\u6d3b\u3001\u65e9\u505c\u7b49\u6280\u5de7\u8fdb\u4e00\u6b65\u63d0\u5347\u6a21\u578b\u6027\u80fd\u3002<\/p>\n<hr 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