{"id":103654,"date":"2026-09-11T07:25:09","date_gmt":"2026-09-10T23:25:09","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/103654.html"},"modified":"2026-09-11T07:25:09","modified_gmt":"2026-09-10T23:25:09","slug":"%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e4%b8%8e%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%ef%bc%9a%e4%bb%8e%e5%8e%9f%e7%90%86%e5%88%b0%e5%ae%9e%e8%b7%b5","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/103654.html","title":{"rendered":"\u795e\u7ecf\u7f51\u7edc\u4e0e\u6df1\u5ea6\u5b66\u4e60\uff1a\u4ece\u539f\u7406\u5230\u5b9e\u8df5"},"content":{"rendered":"<h3>1. \u5f15\u8a00<\/h3>\n<p>\u6df1\u5ea6\u5b66\u4e60\u662f\u673a\u5668\u5b66\u4e60\u7684\u4e00\u4e2a\u91cd\u8981\u5206\u652f&#xff0c;\u5176\u6838\u5fc3\u601d\u60f3\u662f\u901a\u8fc7\u6784\u5efa\u591a\u5c42\u795e\u7ecf\u7f51\u7edc&#xff0c;\u8ba9\u8ba1\u7b97\u673a\u81ea\u52a8\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u7279\u5f81\u8868\u793a\u3002\u8fd1\u5e74\u6765&#xff0c;\u968f\u7740\u8ba1\u7b97\u80fd\u529b\u7684\u63d0\u5347\u548c\u5927\u6570\u636e\u7684\u79ef\u7d2f&#xff0c;\u6df1\u5ea6\u5b66\u4e60\u5728\u56fe\u50cf\u8bc6\u522b\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u8bed\u97f3\u8bc6\u522b\u7b49\u9886\u57df\u53d6\u5f97\u4e86\u7a81\u7834\u6027\u8fdb\u5c55\u3002<\/p>\n<p>\u672c\u6587\u5c06\u4ece\u795e\u7ecf\u7f51\u7edc\u7684\u57fa\u672c\u539f\u7406\u51fa\u53d1&#xff0c;\u9010\u6b65\u6df1\u5165\u5230\u6df1\u5ea6\u5b66\u4e60\u7684\u6838\u5fc3\u6982\u5ff5\u3001\u5e38\u89c1\u7f51\u7edc\u7ed3\u6784\u4ee5\u53ca\u5b9e\u9645\u5e94\u7528&#xff0c;\u5e2e\u52a9\u8bfb\u8005\u5efa\u7acb\u5b8c\u6574\u7684\u77e5\u8bc6\u4f53\u7cfb\u3002<\/p>\n<h3>2. \u795e\u7ecf\u7f51\u7edc\u57fa\u7840<\/h3>\n<h4>2.1 \u4ec0\u4e48\u662f\u795e\u7ecf\u7f51\u7edc<\/h4>\n<p>\u795e\u7ecf\u7f51\u7edc\u662f\u4e00\u79cd\u53d7\u751f\u7269\u795e\u7ecf\u7cfb\u7edf\u542f\u53d1\u7684\u8ba1\u7b97\u6a21\u578b&#xff0c;\u7531\u5927\u91cf\u76f8\u4e92\u8fde\u63a5\u7684\u795e\u7ecf\u5143&#xff08;\u8282\u70b9&#xff09;\u7ec4\u6210\u3002\u6bcf\u4e2a\u795e\u7ecf\u5143\u63a5\u6536\u8f93\u5165\u4fe1\u53f7&#xff0c;\u7ecf\u8fc7\u52a0\u6743\u6c42\u548c\u548c\u975e\u7ebf\u6027\u6fc0\u6d3b\u540e\u4ea7\u751f\u8f93\u51fa&#xff0c;\u4f20\u9012\u7ed9\u4e0b\u4e00\u5c42\u795e\u7ecf\u5143\u3002<\/p>\n<p>\u4e00\u4e2a\u5178\u578b\u7684\u795e\u7ecf\u7f51\u7edc\u5305\u542b\u4e09\u5c42\u7ed3\u6784&#xff1a;<\/p>\n<ul>\n<li>\u8f93\u5165\u5c42&#xff1a;\u63a5\u6536\u539f\u59cb\u6570\u636e\u7279\u5f81<\/li>\n<li>\u9690\u85cf\u5c42&#xff1a;\u5bf9\u6570\u636e\u8fdb\u884c\u52a0\u5de5\u548c\u7279\u5f81\u63d0\u53d6<\/li>\n<li>\u8f93\u51fa\u5c42&#xff1a;\u4ea7\u751f\u6700\u7ec8\u9884\u6d4b\u7ed3\u679c<\/li>\n<\/ul>\n<h4>2.2 \u795e\u7ecf\u5143\u7684\u5de5\u4f5c\u539f\u7406<\/h4>\n<p>\u5355\u4e2a\u795e\u7ecf\u5143\u7684\u8ba1\u7b97\u8fc7\u7a0b\u53ef\u4ee5\u7528\u4ee5\u4e0b\u516c\u5f0f\u8868\u793a&#xff1a;<\/p>\n<p>z &#061; w1*x1 &#043; w2*x2 &#043; &#8230; &#043; wn*xn &#043; b<br \/>\na &#061; f(z)<\/p>\n<p>\u5176\u4e2d&#xff0c;w \u662f\u6743\u91cd&#xff0c;x \u662f\u8f93\u5165\u7279\u5f81&#xff0c;b \u662f\u504f\u7f6e\u9879&#xff0c;f \u662f\u6fc0\u6d3b\u51fd\u6570&#xff0c;a \u662f\u795e\u7ecf\u5143\u7684\u8f93\u51fa\u3002<\/p>\n<h4>2.3 \u5e38\u89c1\u6fc0\u6d3b\u51fd\u6570<\/h4>\n<p>\u6fc0\u6d3b\u51fd\u6570\u4e3a\u795e\u7ecf\u7f51\u7edc\u5f15\u5165\u975e\u7ebf\u6027\u80fd\u529b&#xff0c;\u4f7f\u5176\u80fd\u591f\u62df\u5408\u590d\u6742\u7684\u51fd\u6570\u5173\u7cfb\u3002\u5e38\u7528\u7684\u6fc0\u6d3b\u51fd\u6570\u5305\u62ec&#xff1a;<\/p>\n<ul>\n<li>Sigmoid&#xff1a;\u5c06\u8f93\u51fa\u538b\u7f29\u5230 (0, 1) \u533a\u95f4&#xff0c;\u9002\u5408\u4e8c\u5206\u7c7b\u8f93\u51fa\u5c42<\/li>\n<li>Tanh&#xff1a;\u5c06\u8f93\u51fa\u538b\u7f29\u5230 (-1, 1) \u533a\u95f4&#xff0c;\u5747\u503c\u4e3a\u96f6<\/li>\n<li>ReLU&#xff1a;max(0, x)&#xff0c;\u8ba1\u7b97\u7b80\u5355&#xff0c;\u7f13\u89e3\u68af\u5ea6\u6d88\u5931\u95ee\u9898<\/li>\n<li>Softmax&#xff1a;\u5c06\u591a\u4e2a\u8f93\u51fa\u8f6c\u6362\u4e3a\u6982\u7387\u5206\u5e03&#xff0c;\u7528\u4e8e\u591a\u5206\u7c7b<\/li>\n<\/ul>\n<h3>3. \u524d\u5411\u4f20\u64ad\u4e0e\u53cd\u5411\u4f20\u64ad<\/h3>\n<h4>3.1 \u524d\u5411\u4f20\u64ad<\/h4>\n<p>\u524d\u5411\u4f20\u64ad\u662f\u6307\u6570\u636e\u4ece\u8f93\u5165\u5c42\u7ecf\u8fc7\u9690\u85cf\u5c42\u9010\u5c42\u8ba1\u7b97&#xff0c;\u6700\u7ec8\u5f97\u5230\u8f93\u51fa\u7ed3\u679c\u7684\u8fc7\u7a0b\u3002\u6bcf\u4e00\u5c42\u7684\u8ba1\u7b97\u53ef\u4ee5\u8868\u793a\u4e3a&#xff1a;<\/p>\n<p>z[l] &#061; W[l] * a[l-1] &#043; b[l]<br \/>\na[l] &#061; f(z[l])<\/p>\n<p>\u5176\u4e2d l \u8868\u793a\u7b2c\u51e0\u5c42&#xff0c;W \u548c b \u662f\u8be5\u5c42\u7684\u6743\u91cd\u548c\u504f\u7f6e\u3002<\/p>\n<h4>3.2 \u635f\u5931\u51fd\u6570<\/h4>\n<p>\u4e3a\u4e86\u8861\u91cf\u6a21\u578b\u9884\u6d4b\u4e0e\u771f\u5b9e\u6807\u7b7e\u4e4b\u95f4\u7684\u5dee\u8ddd&#xff0c;\u9700\u8981\u5b9a\u4e49\u635f\u5931\u51fd\u6570\u3002\u5e38\u89c1\u7684\u635f\u5931\u51fd\u6570\u5305\u62ec&#xff1a;<\/p>\n<ul>\n<li>\u5747\u65b9\u8bef\u5dee&#xff08;MSE&#xff09;&#xff1a;\u7528\u4e8e\u56de\u5f52\u4efb\u52a1<\/li>\n<li>\u4ea4\u53c9\u71b5\u635f\u5931&#xff1a;\u7528\u4e8e\u5206\u7c7b\u4efb\u52a1<\/li>\n<\/ul>\n<h4>3.3 \u53cd\u5411\u4f20\u64ad<\/h4>\n<p>\u53cd\u5411\u4f20\u64ad\u662f\u8bad\u7ec3\u795e\u7ecf\u7f51\u7edc\u7684\u6838\u5fc3\u7b97\u6cd5\u3002\u5b83\u5229\u7528\u94fe\u5f0f\u6cd5\u5219&#xff0c;\u4ece\u8f93\u51fa\u5c42\u5411\u8f93\u5165\u5c42\u9010\u5c42\u8ba1\u7b97\u635f\u5931\u51fd\u6570\u5bf9\u5404\u53c2\u6570\u7684\u68af\u5ea6&#xff0c;\u7136\u540e\u4f7f\u7528\u68af\u5ea6\u4e0b\u964d\u6cd5\u66f4\u65b0\u6743\u91cd&#xff1a;<\/p>\n<p>W &#061; W &#8211; learning_rate * dW<br \/>\nb &#061; b &#8211; learning_rate * db<\/p>\n<h3>4. \u6df1\u5ea6\u5b66\u4e60\u7684\u6838\u5fc3\u6982\u5ff5<\/h3>\n<h4>4.1 \u68af\u5ea6\u4e0b\u964d\u4e0e\u4f18\u5316\u5668<\/h4>\n<p>\u68af\u5ea6\u4e0b\u964d\u662f\u6df1\u5ea6\u5b66\u4e60\u4e2d\u6700\u57fa\u672c\u7684\u4f18\u5316\u65b9\u6cd5\u3002\u4e3a\u4e86\u63d0\u9ad8\u8bad\u7ec3\u6548\u7387\u548c\u7a33\u5b9a\u6027&#xff0c;\u7814\u7a76\u8005\u63d0\u51fa\u4e86\u591a\u79cd\u4f18\u5316\u5668&#xff1a;<\/p>\n<ul>\n<li>SGD&#xff08;\u968f\u673a\u68af\u5ea6\u4e0b\u964d&#xff09;&#xff1a;\u6bcf\u6b21\u7528\u4e00\u4e2a\u6837\u672c\u66f4\u65b0\u53c2\u6570<\/li>\n<li>Momentum&#xff1a;\u5f15\u5165\u52a8\u91cf&#xff0c;\u52a0\u901f\u6536\u655b<\/li>\n<li>Adam&#xff1a;\u7ed3\u5408\u52a8\u91cf\u548c\u81ea\u9002\u5e94\u5b66\u4e60\u7387&#xff0c;\u662f\u76ee\u524d\u6700\u5e38\u7528\u7684\u4f18\u5316\u5668<\/li>\n<\/ul>\n<h4>4.2 \u8fc7\u62df\u5408\u4e0e\u6b63\u5219\u5316<\/h4>\n<p>\u5f53\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u8868\u73b0\u5f88\u597d&#xff0c;\u4f46\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8868\u73b0\u8f83\u5dee\u65f6&#xff0c;\u79f0\u4e3a\u8fc7\u62df\u5408\u3002\u5e38\u7528\u7684\u7f13\u89e3\u65b9\u6cd5\u5305\u62ec&#xff1a;<\/p>\n<ul>\n<li>L1\/L2 \u6b63\u5219\u5316&#xff1a;\u5728\u635f\u5931\u51fd\u6570\u4e2d\u52a0\u5165\u6743\u91cd\u60e9\u7f5a\u9879<\/li>\n<li>Dropout&#xff1a;\u8bad\u7ec3\u65f6\u968f\u673a\u4e22\u5f03\u90e8\u5206\u795e\u7ecf\u5143<\/li>\n<li>\u6570\u636e\u589e\u5f3a&#xff1a;\u901a\u8fc7\u53d8\u6362\u6269\u5145\u8bad\u7ec3\u6570\u636e<\/li>\n<li>\u65e9\u505c\u6cd5&#xff1a;\u5728\u9a8c\u8bc1\u96c6\u6027\u80fd\u4e0d\u518d\u63d0\u5347\u65f6\u505c\u6b62\u8bad\u7ec3<\/li>\n<\/ul>\n<h4>4.3 \u6279\u5f52\u4e00\u5316<\/h4>\n<p>\u6279\u5f52\u4e00\u5316&#xff08;Batch Normalization&#xff09;\u901a\u8fc7\u5bf9\u6bcf\u4e00\u5c42\u7684\u8f93\u5165\u8fdb\u884c\u6807\u51c6\u5316&#xff0c;\u52a0\u901f\u8bad\u7ec3\u6536\u655b&#xff0c;\u540c\u65f6\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u7f13\u89e3\u68af\u5ea6\u6d88\u5931\u95ee\u9898\u3002<\/p>\n<h3>5. \u5e38\u89c1\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784<\/h3>\n<h4>5.1 \u5377\u79ef\u795e\u7ecf\u7f51\u7edc&#xff08;CNN&#xff09;<\/h4>\n<p>CNN \u4e3b\u8981\u7528\u4e8e\u5904\u7406\u56fe\u50cf\u6570\u636e&#xff0c;\u5176\u6838\u5fc3\u601d\u60f3\u662f\u5229\u7528\u5377\u79ef\u6838\u63d0\u53d6\u5c40\u90e8\u7279\u5f81\u3002\u5178\u578b\u7ed3\u6784\u5305\u62ec&#xff1a;<\/p>\n<ul>\n<li>\u5377\u79ef\u5c42&#xff1a;\u63d0\u53d6\u5c40\u90e8\u7279\u5f81<\/li>\n<li>\u6c60\u5316\u5c42&#xff1a;\u964d\u4f4e\u7279\u5f81\u7ef4\u5ea6<\/li>\n<li>\u5168\u8fde\u63a5\u5c42&#xff1a;\u8fdb\u884c\u5206\u7c7b\u6216\u56de\u5f52<\/li>\n<\/ul>\n<p>\u4e0b\u9762\u7ed9\u51fa\u4e00\u4e2a\u4f7f\u7528 PyTorch \u6784\u5efa\u7b80\u5355 CNN \u6a21\u578b\u7684\u5b8c\u6574\u793a\u4f8b&#xff0c;\u5305\u542b\u5377\u79ef\u5c42\u3001\u6c60\u5316\u5c42\u548c\u5168\u8fde\u63a5\u5c42&#xff0c;\u5e76\u9644\u4e0a\u8bad\u7ec3\u4e0e\u8bc4\u4f30\u7684\u7b80\u8981\u6d41\u7a0b&#xff1a;<\/p>\n<p><span class=\"token keyword\">import<\/span> torch<br \/>\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>nn <span class=\"token keyword\">as<\/span> nn<br \/>\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>optim <span class=\"token keyword\">as<\/span> optim<br \/>\n<span class=\"token keyword\">from<\/span> torch<span class=\"token punctuation\">.<\/span>utils<span class=\"token punctuation\">.<\/span>data <span class=\"token keyword\">import<\/span> DataLoader<br \/>\n<span class=\"token keyword\">from<\/span> torchvision <span class=\"token keyword\">import<\/span> datasets<span class=\"token punctuation\">,<\/span> transforms<\/p>\n<p><span class=\"token comment\"># 1. \u5b9a\u4e49 CNN \u6a21\u578b<\/span><br \/>\n<span class=\"token keyword\">class<\/span> <span class=\"token class-name\">SimpleCNN<\/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_classes<span class=\"token operator\">&#061;<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span>SimpleCNN<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\"># \u5377\u79ef\u5c42&#xff1a;3 \u4e2a\u8f93\u5165\u901a\u9053&#xff08;RGB&#xff09;&#xff0c;\u8f93\u51fa 16 \u4e2a\u7279\u5f81\u56fe&#xff0c;\u5377\u79ef\u6838 3&#215;3<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>conv1 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Conv2d<span class=\"token punctuation\">(<\/span>in_channels<span class=\"token operator\">&#061;<\/span><span class=\"token number\">3<\/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        <span class=\"token comment\"># \u6c60\u5316\u5c42&#xff1a;2&#215;2 \u6700\u5927\u6c60\u5316&#xff0c;\u964d\u4f4e\u7279\u5f81\u7ef4\u5ea6<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>pool <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>MaxPool2d<span class=\"token punctuation\">(<\/span>kernel_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> stride<span class=\"token operator\">&#061;<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token comment\"># \u5377\u79ef\u5c42&#xff1a;16 \u4e2a\u8f93\u5165\u901a\u9053&#xff0c;\u8f93\u51fa 32 \u4e2a\u7279\u5f81\u56fe&#xff0c;\u5377\u79ef\u6838 3&#215;3<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>conv2 <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Conv2d<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        <span class=\"token comment\"># \u5168\u8fde\u63a5\u5c42&#xff1a;\u5c06\u5c55\u5e73\u540e\u7684\u7279\u5f81\u6620\u5c04\u5230\u7c7b\u522b\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\">32<\/span> <span class=\"token operator\">*<\/span> <span class=\"token number\">8<\/span> <span class=\"token operator\">*<\/span> <span class=\"token number\">8<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">128<\/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\">128<\/span><span class=\"token punctuation\">,<\/span> num_classes<span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token comment\"># \u6fc0\u6d3b\u51fd\u6570<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>relu <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>ReLU<span class=\"token punctuation\">(<\/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        <span class=\"token comment\"># \u5377\u79ef -&gt; \u6fc0\u6d3b -&gt; \u6c60\u5316<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>pool<span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>relu<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><span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>pool<span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>relu<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><span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token comment\"># \u5c55\u5e73\u4e3a\u4e00\u7ef4\u5411\u91cf<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> x<span class=\"token punctuation\">.<\/span>view<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">.<\/span>size<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token comment\"># \u5168\u8fde\u63a5\u5c42 &#043; \u6fc0\u6d3b<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<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        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>fc2<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 comment\"># 2. \u6570\u636e\u51c6\u5907&#xff1a;\u4ee5 CIFAR-10 \u4e3a\u4f8b&#xff0c;\u505a\u5f52\u4e00\u5316\u5e76\u8f6c\u4e3a\u5f20\u91cf<\/span><br \/>\ntransform <span class=\"token operator\">&#061;<\/span> transforms<span class=\"token punctuation\">.<\/span>Compose<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span><br \/>\n    transforms<span class=\"token punctuation\">.<\/span>ToTensor<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    transforms<span class=\"token punctuation\">.<\/span>Normalize<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">0.5<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.5<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.5<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token number\">0.5<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.5<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.5<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntrain_dataset <span class=\"token operator\">&#061;<\/span> datasets<span class=\"token punctuation\">.<\/span>CIFAR10<span class=\"token punctuation\">(<\/span>root<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;.\/data&#039;<\/span><span class=\"token punctuation\">,<\/span> train<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">,<\/span> download<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">,<\/span> transform<span class=\"token operator\">&#061;<\/span>transform<span class=\"token punctuation\">)<\/span><br \/>\ntest_dataset <span class=\"token operator\">&#061;<\/span> datasets<span class=\"token punctuation\">.<\/span>CIFAR10<span class=\"token punctuation\">(<\/span>root<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;.\/data&#039;<\/span><span class=\"token punctuation\">,<\/span> train<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">False<\/span><span class=\"token punctuation\">,<\/span> download<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">,<\/span> transform<span class=\"token operator\">&#061;<\/span>transform<span class=\"token punctuation\">)<\/span><br \/>\ntrain_loader <span class=\"token operator\">&#061;<\/span> DataLoader<span class=\"token punctuation\">(<\/span>train_dataset<span class=\"token punctuation\">,<\/span> batch_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> shuffle<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntest_loader <span class=\"token operator\">&#061;<\/span> DataLoader<span class=\"token punctuation\">(<\/span>test_dataset<span class=\"token punctuation\">,<\/span> batch_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> shuffle<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">False<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># 3. \u521d\u59cb\u5316\u6a21\u578b\u3001\u635f\u5931\u51fd\u6570\u4e0e\u4f18\u5316\u5668<\/span><br \/>\nmodel <span class=\"token operator\">&#061;<\/span> SimpleCNN<span class=\"token punctuation\">(<\/span>num_classes<span class=\"token operator\">&#061;<\/span><span class=\"token number\">10<\/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>          <span class=\"token comment\"># \u5206\u7c7b\u4efb\u52a1\u4f7f\u7528\u4ea4\u53c9\u71b5\u635f\u5931<\/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\"># Adam \u4f18\u5316\u5668<\/span><\/p>\n<p><span class=\"token comment\"># 4. \u8bad\u7ec3\u6d41\u7a0b<\/span><br \/>\nnum_epochs <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">5<\/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    model<span class=\"token punctuation\">.<\/span>train<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>                          <span class=\"token comment\"># \u5207\u6362\u5230\u8bad\u7ec3\u6a21\u5f0f<\/span><br \/>\n    running_loss <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0.0<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> images<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> train_loader<span class=\"token punctuation\">:<\/span><br \/>\n        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        outputs <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>images<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> labels<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u8ba1\u7b97\u635f\u5931<\/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><br \/>\n        running_loss <span class=\"token operator\">&#043;&#061;<\/span> loss<span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/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;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>running_loss<span class=\"token operator\">\/<\/span><span class=\"token builtin\">len<\/span><span class=\"token punctuation\">(<\/span>train_loader<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># 5. \u8bc4\u4f30\u6d41\u7a0b<\/span><br \/>\nmodel<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">eval<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>                               <span class=\"token comment\"># \u5207\u6362\u5230\u8bc4\u4f30\u6a21\u5f0f<\/span><br \/>\ncorrect <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0<\/span><br \/>\ntotal <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0<\/span><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\"># \u8bc4\u4f30\u65f6\u4e0d\u8ba1\u7b97\u68af\u5ea6<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> images<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> test_loader<span class=\"token punctuation\">:<\/span><br \/>\n        outputs <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">)<\/span><br \/>\n        _<span class=\"token punctuation\">,<\/span> predicted <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">max<\/span><span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        total <span class=\"token operator\">&#043;&#061;<\/span> labels<span class=\"token punctuation\">.<\/span>size<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        correct <span class=\"token operator\">&#043;&#061;<\/span> <span class=\"token punctuation\">(<\/span>predicted <span class=\"token operator\">&#061;&#061;<\/span> labels<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">sum<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;Test Accuracy: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span><span class=\"token number\">100<\/span> <span class=\"token operator\">*<\/span> correct <span class=\"token operator\">\/<\/span> total<span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.2f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">%&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>\u4ee3\u7801\u8bf4\u660e&#xff1a;<\/p>\n<ul>\n<li>\u6a21\u578b\u5b9a\u4e49&#xff1a;SimpleCNN \u4f9d\u6b21\u5305\u542b\u4e24\u4e2a\u5377\u79ef\u5c42&#xff08;\u63d0\u53d6\u5c40\u90e8\u7279\u5f81&#xff09;\u3001\u4e24\u4e2a\u6c60\u5316\u5c42&#xff08;\u964d\u4f4e\u7279\u5f81\u7ef4\u5ea6&#xff09;\u548c\u4e24\u4e2a\u5168\u8fde\u63a5\u5c42&#xff08;\u8fdb\u884c\u5206\u7c7b&#xff09;&#xff0c;\u5e76\u5728\u6bcf\u5c42\u4e4b\u95f4\u4f7f\u7528 ReLU \u6fc0\u6d3b\u51fd\u6570\u5f15\u5165\u975e\u7ebf\u6027\u3002<\/li>\n<li>\u6570\u636e\u51c6\u5907&#xff1a;\u4f7f\u7528 torchvision \u52a0\u8f7d CIFAR-10 \u6570\u636e\u96c6&#xff0c;\u901a\u8fc7 DataLoader \u6309\u6279\u6b21\u8bfb\u53d6&#xff0c;\u5e76\u505a\u5f52\u4e00\u5316\u5904\u7406\u3002<\/li>\n<li>\u8bad\u7ec3\u6d41\u7a0b&#xff1a;\u6bcf\u4e2a epoch \u5185\u5bf9\u6bcf\u4e2a\u6279\u6b21\u6267\u884c\u300c\u524d\u5411\u4f20\u64ad \u2192 \u8ba1\u7b97\u635f\u5931 \u2192 \u53cd\u5411\u4f20\u64ad \u2192 \u66f4\u65b0\u53c2\u6570\u300d\u56db\u6b65&#xff0c;\u5e76\u6253\u5370\u5e73\u5747\u635f\u5931\u3002<\/li>\n<li>\u8bc4\u4f30\u6d41\u7a0b&#xff1a;\u5728\u6d4b\u8bd5\u96c6\u4e0a\u5173\u95ed\u68af\u5ea6\u8ba1\u7b97&#xff0c;\u7edf\u8ba1\u9884\u6d4b\u6b63\u786e\u7684\u6837\u672c\u6570&#xff0c;\u8ba1\u7b97\u5e76\u8f93\u51fa\u6a21\u578b\u51c6\u786e\u7387\u3002<\/li>\n<\/ul>\n<p>\u4e0b\u9762\u901a\u8fc7\u4e00\u4e2a\u5bf9\u6bd4\u8868\u683c&#xff0c;\u4ece\u6838\u5fc3\u601d\u60f3\u3001\u9002\u7528\u6570\u636e\u7c7b\u578b\u3001\u4e3b\u8981\u4f18\u52bf\u548c\u5178\u578b\u5e94\u7528\u573a\u666f\u56db\u4e2a\u7ef4\u5ea6&#xff0c;\u5bf9 CNN\u3001RNN\u3001Transformer \u4e09\u79cd\u4e3b\u6d41\u7f51\u7edc\u7ed3\u6784\u8fdb\u884c\u6a2a\u5411\u6bd4\u8f83&#xff1a;<\/p>\n<table>\n<tr>\u7f51\u7edc\u7ed3\u6784\u6838\u5fc3\u601d\u60f3\u9002\u7528\u6570\u636e\u7c7b\u578b\u4e3b\u8981\u4f18\u52bf\u5178\u578b\u5e94\u7528\u573a\u666f<\/tr>\n<tbody>\n<tr>\n<td>CNN<\/td>\n<td>\u5229\u7528\u5377\u79ef\u6838\u5728\u5c40\u90e8\u533a\u57df\u63d0\u53d6\u7279\u5f81&#xff0c;\u901a\u8fc7\u6c60\u5316\u964d\u4f4e\u7ef4\u5ea6&#xff0c;\u9010\u5c42\u62bd\u8c61\u51fa\u9ad8\u5c42\u8bed\u4e49<\/td>\n<td>\u56fe\u50cf\u3001\u89c6\u9891\u7b49\u5177\u6709\u7f51\u683c\u7ed3\u6784\u7684\u6570\u636e<\/td>\n<td>\u53c2\u6570\u5171\u4eab\u4e0e\u5c40\u90e8\u8fde\u63a5\u5927\u5e45\u51cf\u5c11\u53c2\u6570\u91cf&#xff1b;\u5bf9\u5e73\u79fb\u3001\u7f29\u653e\u7b49\u5c40\u90e8\u53d8\u6362\u5177\u6709\u4e00\u5b9a\u4e0d\u53d8\u6027&#xff1b;\u8bad\u7ec3\u6548\u7387\u9ad8<\/td>\n<td>\u56fe\u50cf\u5206\u7c7b\u3001\u76ee\u6807\u68c0\u6d4b\u3001\u56fe\u50cf\u5206\u5272\u3001\u4eba\u8138\u8bc6\u522b\u3001\u533b\u5b66\u5f71\u50cf\u5206\u6790<\/td>\n<\/tr>\n<tr>\n<td>RNN<\/td>\n<td>\u9690\u85cf\u5c42\u4e4b\u95f4\u5b58\u5728\u5faa\u73af\u8fde\u63a5&#xff0c;\u6309\u65f6\u95f4\u6b65\u9010\u6b65\u5904\u7406\u8f93\u5165&#xff0c;\u5c06\u5386\u53f2\u4fe1\u606f\u4fdd\u5b58\u5728\u9690\u72b6\u6001\u4e2d<\/td>\n<td>\u6587\u672c\u3001\u8bed\u97f3\u3001\u65f6\u95f4\u5e8f\u5217\u7b49\u5177\u6709\u987a\u5e8f\u4f9d\u8d56\u7684\u5e8f\u5217\u6570\u636e<\/td>\n<td>\u5929\u7136\u9002\u5408\u53d8\u957f\u5e8f\u5217\u8f93\u5165&#xff1b;\u80fd\u591f\u5efa\u6a21\u65f6\u95f4\u4e0a\u7684\u5148\u540e\u4f9d\u8d56\u5173\u7cfb&#xff1b;LSTM\/GRU \u901a\u8fc7\u95e8\u63a7\u7f13\u89e3\u68af\u5ea6\u6d88\u5931<\/td>\n<td>\u673a\u5668\u7ffb\u8bd1\u3001\u8bed\u97f3\u8bc6\u522b\u3001\u60c5\u611f\u5206\u6790\u3001\u80a1\u7968\u9884\u6d4b\u3001\u6587\u672c\u751f\u6210<\/td>\n<\/tr>\n<tr>\n<td>Transformer<\/td>\n<td>\u57fa\u4e8e\u81ea\u6ce8\u610f\u529b\u673a\u5236&#xff0c;\u76f4\u63a5\u8ba1\u7b97\u5e8f\u5217\u4e2d\u4efb\u610f\u4e24\u4e2a\u4f4d\u7f6e\u4e4b\u95f4\u7684\u76f8\u5173\u6027&#xff0c;\u53ef\u5e76\u884c\u5904\u7406\u6574\u4e2a\u5e8f\u5217<\/td>\n<td>\u6587\u672c\u3001\u8bed\u97f3\u3001\u56fe\u50cf\u3001\u591a\u6a21\u6001\u7b49\u5404\u7c7b\u6570\u636e&#xff0c;\u5c24\u5176\u9002\u5408\u957f\u5e8f\u5217<\/td>\n<td>\u5b8c\u5168\u5e76\u884c\u8ba1\u7b97&#xff0c;\u8bad\u7ec3\u901f\u5ea6\u5feb&#xff1b;\u901a\u8fc7\u6ce8\u610f\u529b\u6355\u6349\u957f\u8ddd\u79bb\u4f9d\u8d56&#xff0c;\u6548\u679c\u4f18\u4e8e RNN&#xff1b;\u53ef\u6269\u5c55\u6027\u5f3a&#xff0c;\u652f\u6491\u5927\u89c4\u6a21\u9884\u8bad\u7ec3<\/td>\n<td>\u5927\u8bed\u8a00\u6a21\u578b&#xff08;\u5982 GPT\u3001BERT&#xff09;\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6587\u672c\u6458\u8981\u3001\u591a\u6a21\u6001\u7406\u89e3\u3001\u4ee3\u7801\u751f\u6210<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u9009\u578b\u5efa\u8bae&#xff1a;\u4e09\u79cd\u7f51\u7edc\u7ed3\u6784\u5404\u6709\u4fa7\u91cd&#xff0c;\u5b9e\u9645\u9879\u76ee\u4e2d\u5e94\u6839\u636e\u6570\u636e\u5f62\u6001\u4e0e\u4efb\u52a1\u7279\u70b9\u8fdb\u884c\u9009\u62e9\u3002\u82e5\u5904\u7406\u7684\u662f\u56fe\u50cf\u3001\u89c6\u9891\u7b49\u7f51\u683c\u7ed3\u6784\u6570\u636e&#xff0c;CNN \u901a\u5e38\u662f\u9996\u9009&#xff0c;\u5176\u5c40\u90e8\u7279\u5f81\u63d0\u53d6\u80fd\u529b\u4e0e\u9ad8\u6548\u8bad\u7ec3\u7279\u6027\u5728\u89c6\u89c9\u4efb\u52a1\u4e2d\u8868\u73b0\u4f18\u5f02&#xff1b;\u82e5\u9762\u5bf9\u7684\u662f\u5177\u6709\u660e\u663e\u5148\u540e\u987a\u5e8f\u7684\u5e8f\u5217\u6570\u636e&#xff08;\u5982\u6587\u672c\u3001\u8bed\u97f3\u3001\u65f6\u95f4\u5e8f\u5217&#xff09;&#xff0c;RNN \u53ca\u5176\u53d8\u4f53 LSTM\u3001GRU \u80fd\u591f\u81ea\u7136\u5730\u5efa\u6a21\u65f6\u95f4\u4f9d\u8d56&#xff0c;\u9002\u5408\u4e2d\u5c0f\u89c4\u6a21\u5e8f\u5217\u4efb\u52a1&#xff1b;\u800c\u5f53\u5e8f\u5217\u8f83\u957f\u3001\u9700\u8981\u6355\u6349\u5168\u5c40\u4f9d\u8d56\u5173\u7cfb&#xff0c;\u6216\u5e0c\u671b\u5229\u7528\u5927\u89c4\u6a21\u9884\u8bad\u7ec3\u6a21\u578b\u65f6&#xff0c;Transformer \u51ed\u501f\u5e76\u884c\u8ba1\u7b97\u4e0e\u5f3a\u5927\u7684\u6ce8\u610f\u529b\u673a\u5236\u6210\u4e3a\u66f4\u4f18\u9009\u62e9\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f&#xff0c;\u4e09\u8005\u5e76\u975e\u4e92\u65a5\u2014\u2014\u73b0\u4ee3\u89c6\u89c9\u6a21\u578b\u5e38\u5c06 CNN \u4e0e Transformer \u7ed3\u5408&#xff08;\u5982 Vision Transformer \u4e2d\u7684\u5377\u79ef\u5d4c\u5165&#xff09;&#xff0c;\u591a\u6a21\u6001\u6a21\u578b\u4e5f\u5e38\u6df7\u5408\u4f7f\u7528\u591a\u79cd\u7ed3\u6784&#xff0c;\u5b9e\u8df5\u4e2d\u53ef\u6839\u636e\u5177\u4f53\u4efb\u52a1\u7075\u6d3b\u7ec4\u5408\u3002<\/p>\n<p>\u4e0b\u9762\u518d\u8865\u5145\u4e00\u6bb5\u4f7f\u7528 matplotlib \u7ed8\u5236\u8bad\u7ec3\u635f\u5931\u66f2\u7ebf\u548c\u6d4b\u8bd5\u51c6\u786e\u7387\u66f2\u7ebf\u7684\u4ee3\u7801&#xff0c;\u5e2e\u52a9\u76f4\u89c2\u89c2\u5bdf\u6a21\u578b\u7684\u6536\u655b\u8fc7\u7a0b\u4e0e\u6cdb\u5316\u8868\u73b0&#xff1a;<\/p>\n<p><span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt<\/p>\n<p><span class=\"token comment\"># \u5728\u8bad\u7ec3\u5faa\u73af\u4e2d\u8bb0\u5f55\u6bcf\u4e2a epoch \u7684\u5e73\u5747\u635f\u5931\u4e0e\u6d4b\u8bd5\u51c6\u786e\u7387<\/span><br \/>\ntrain_losses <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span>   <span class=\"token comment\"># \u8bb0\u5f55\u6bcf\u4e2a epoch \u7684\u5e73\u5747\u8bad\u7ec3\u635f\u5931<\/span><br \/>\ntest_accs <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span>      <span class=\"token comment\"># \u8bb0\u5f55\u6bcf\u4e2a epoch \u7684\u6d4b\u8bd5\u51c6\u786e\u7387<\/span><\/p>\n<p>num_epochs <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">5<\/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    model<span class=\"token punctuation\">.<\/span>train<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    running_loss <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0.0<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> images<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> train_loader<span class=\"token punctuation\">:<\/span><br \/>\n        optimizer<span class=\"token punctuation\">.<\/span>zero_grad<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>images<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> labels<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><br \/>\n        running_loss <span class=\"token operator\">&#043;&#061;<\/span> loss<span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    avg_loss <span class=\"token operator\">&#061;<\/span> running_loss <span class=\"token operator\">\/<\/span> <span class=\"token builtin\">len<\/span><span class=\"token punctuation\">(<\/span>train_loader<span class=\"token punctuation\">)<\/span><br \/>\n    train_losses<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>avg_loss<span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token comment\"># \u6bcf\u4e2a epoch \u7ed3\u675f\u540e\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8bc4\u4f30\u4e00\u6b21\u51c6\u786e\u7387<\/span><br \/>\n    model<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">eval<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    correct <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0<\/span><br \/>\n    total <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0<\/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        <span class=\"token keyword\">for<\/span> images<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> test_loader<span class=\"token punctuation\">:<\/span><br \/>\n            outputs <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">)<\/span><br \/>\n            _<span class=\"token punctuation\">,<\/span> predicted <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">max<\/span><span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n            total <span class=\"token operator\">&#043;&#061;<\/span> labels<span class=\"token punctuation\">.<\/span>size<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span><br \/>\n            correct <span class=\"token operator\">&#043;&#061;<\/span> <span class=\"token punctuation\">(<\/span>predicted <span class=\"token operator\">&#061;&#061;<\/span> labels<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">sum<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    test_acc <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">100<\/span> <span class=\"token operator\">*<\/span> correct <span class=\"token operator\">\/<\/span> total<br \/>\n    test_accs<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>test_acc<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;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>avg_loss<span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">, Test Acc: <\/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\">%&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u7ed8\u5236\u8bad\u7ec3\u635f\u5931\u66f2\u7ebf<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>figure<span class=\"token punctuation\">(<\/span>figsize<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">12<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">4<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>subplot<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>plot<span class=\"token punctuation\">(<\/span><span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> num_epochs <span class=\"token operator\">&#043;<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> train_losses<span class=\"token punctuation\">,<\/span> marker<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;o&#039;<\/span><span class=\"token punctuation\">,<\/span> color<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;tab:blue&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>xlabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;Epoch&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>ylabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;Training Loss&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>title<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;Training Loss Curve&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>grid<span class=\"token punctuation\">(<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u7ed8\u5236\u6d4b\u8bd5\u51c6\u786e\u7387\u66f2\u7ebf<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>subplot<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>plot<span class=\"token punctuation\">(<\/span><span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> num_epochs <span class=\"token operator\">&#043;<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> test_accs<span class=\"token punctuation\">,<\/span> marker<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;o&#039;<\/span><span class=\"token punctuation\">,<\/span> color<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;tab:orange&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>xlabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;Epoch&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>ylabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;Test Accuracy (%)&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>title<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;Test Accuracy Curve&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>grid<span class=\"token punctuation\">(<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>plt<span class=\"token punctuation\">.<\/span>tight_layout<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>show<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>\u5982\u4f55\u901a\u8fc7\u66f2\u7ebf\u5224\u65ad\u6a21\u578b\u6536\u655b\u60c5\u51b5\u4e0e\u8fc7\u62df\u5408\u8ff9\u8c61&#xff1a;<\/p>\n<ul>\n<li>\u6536\u655b\u60c5\u51b5&#xff1a;\u8bad\u7ec3\u635f\u5931\u66f2\u7ebf\u6574\u4f53\u5448\u4e0b\u964d\u8d8b\u52bf\u5e76\u9010\u6e10\u8d8b\u4e8e\u5e73\u7a33&#xff0c;\u8bf4\u660e\u6a21\u578b\u5728\u9010\u6b65\u5b66\u4e60\u6570\u636e\u7279\u5f81\u5e76\u63a5\u8fd1\u6536\u655b&#xff1b;\u82e5\u635f\u5931\u66f2\u7ebf\u5728\u67d0\u4e2a epoch \u540e\u51e0\u4e4e\u4e0d\u518d\u4e0b\u964d&#xff0c;\u8bf4\u660e\u6a21\u578b\u5df2\u57fa\u672c\u6536\u655b&#xff0c;\u7ee7\u7eed\u8bad\u7ec3\u6536\u76ca\u6709\u9650\u3002\u6d4b\u8bd5\u51c6\u786e\u7387\u66f2\u7ebf\u540c\u6b65\u4e0a\u5347\u5e76\u8d8b\u4e8e\u7a33\u5b9a&#xff0c;\u5219\u8fdb\u4e00\u6b65\u5370\u8bc1\u6a21\u578b\u6cdb\u5316\u80fd\u529b\u826f\u597d\u3002<\/li>\n<li>\u8fc7\u62df\u5408\u8ff9\u8c61&#xff1a;\u5f53\u8bad\u7ec3\u635f\u5931\u6301\u7eed\u4e0b\u964d&#xff0c;\u800c\u6d4b\u8bd5\u51c6\u786e\u7387\u5728\u8fbe\u5230\u5cf0\u503c\u540e\u5f00\u59cb\u56de\u843d\u3001\u6216\u8bad\u7ec3\u635f\u5931\u4e0e\u6d4b\u8bd5\u51c6\u786e\u7387\u4e4b\u95f4\u7684\u5dee\u8ddd\u4e0d\u65ad\u62c9\u5927\u65f6&#xff0c;\u8bf4\u660e\u6a21\u578b\u5f00\u59cb\u300c\u6b7b\u8bb0\u786c\u80cc\u300d\u8bad\u7ec3\u6570\u636e&#xff0c;\u6cdb\u5316\u80fd\u529b\u4e0b\u964d&#xff0c;\u5373\u51fa\u73b0\u8fc7\u62df\u5408\u3002\u6b64\u65f6\u5e94\u7ed3\u5408\u65e9\u505c\u6cd5\u3001Dropout\u3001\u6570\u636e\u589e\u5f3a\u6216 L1\/L2 \u6b63\u5219\u5316\u7b49\u624b\u6bb5\u52a0\u4ee5\u7f13\u89e3\u3002<\/li>\n<li>\u6b20\u62df\u5408\u8ff9\u8c61&#xff1a;\u82e5\u8bad\u7ec3\u635f\u5931\u59cb\u7ec8\u5c45\u9ad8\u4e0d\u4e0b\u3001\u4e0b\u964d\u7f13\u6162&#xff0c;\u6d4b\u8bd5\u51c6\u786e\u7387\u4e5f\u957f\u671f\u5904\u4e8e\u8f83\u4f4e\u6c34\u5e73&#xff0c;\u5219\u8bf4\u660e\u6a21\u578b\u5bb9\u91cf\u4e0d\u8db3\u6216\u8bad\u7ec3\u4e0d\u5145\u5206&#xff0c;\u53ef\u8003\u8651\u52a0\u6df1\u7f51\u7edc\u3001\u589e\u52a0\u8bad\u7ec3\u8f6e\u6570\u6216\u8c03\u6574\u5b66\u4e60\u7387\u3002<\/li>\n<\/ul>\n<h4>5.2 \u5faa\u73af\u795e\u7ecf\u7f51\u7edc&#xff08;RNN&#xff09;<\/h4>\n<p>RNN \u9002\u7528\u4e8e\u5904\u7406\u5e8f\u5217\u6570\u636e&#xff08;\u5982\u6587\u672c\u3001\u65f6\u95f4\u5e8f\u5217&#xff09;&#xff0c;\u5176\u7279\u70b9\u662f\u9690\u85cf\u5c42\u4e4b\u95f4\u5b58\u5728\u5faa\u73af\u8fde\u63a5&#xff0c;\u80fd\u591f\u8bb0\u5fc6\u5386\u53f2\u4fe1\u606f\u3002\u5e38\u89c1\u7684\u53d8\u4f53\u5305\u62ec LSTM \u548c GRU&#xff0c;\u5b83\u4eec\u901a\u8fc7\u95e8\u63a7\u673a\u5236\u89e3\u51b3\u957f\u5e8f\u5217\u4e2d\u7684\u68af\u5ea6\u6d88\u5931\u95ee\u9898\u3002<\/p>\n<h4>5.3 Transformer \u4e0e\u6ce8\u610f\u529b\u673a\u5236<\/h4>\n<p>Transformer \u57fa\u4e8e\u81ea\u6ce8\u610f\u529b\u673a\u5236&#xff0c;\u80fd\u591f\u5e76\u884c\u5904\u7406\u5e8f\u5217\u6570\u636e&#xff0c;\u5df2\u6210\u4e3a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u4e3b\u6d41\u67b6\u6784\u3002\u5176\u6838\u5fc3\u7ec4\u4ef6\u5305\u62ec&#xff1a;<\/p>\n<ul>\n<li>\u81ea\u6ce8\u610f\u529b\u5c42&#xff1a;\u8ba1\u7b97\u5e8f\u5217\u4e2d\u6bcf\u4e2a\u4f4d\u7f6e\u4e0e\u5176\u4ed6\u4f4d\u7f6e\u7684\u76f8\u5173\u6027<\/li>\n<li>\u591a\u5934\u6ce8\u610f\u529b&#xff1a;\u4ece\u591a\u4e2a\u5b50\u7a7a\u95f4\u6355\u6349\u4e0d\u540c\u7279\u5f81<\/li>\n<li>\u4f4d\u7f6e\u7f16\u7801&#xff1a;\u4e3a\u5e8f\u5217\u5f15\u5165\u4f4d\u7f6e\u4fe1\u606f<\/li>\n<\/ul>\n<p>\u4e0b\u9762\u901a\u8fc7\u4e00\u5f20 Mermaid \u6d41\u7a0b\u56fe&#xff0c;\u76f4\u89c2\u5c55\u793a Transformer \u7f16\u7801\u5668-\u89e3\u7801\u5668\u7684\u6574\u4f53\u67b6\u6784&#xff0c;\u4ee5\u53ca\u591a\u5934\u81ea\u6ce8\u610f\u529b\u3001\u524d\u9988\u7f51\u7edc\u3001\u6b8b\u5dee\u8fde\u63a5\u4e0e\u5c42\u5f52\u4e00\u5316\u3001\u4f4d\u7f6e\u7f16\u7801\u7b49\u6838\u5fc3\u7ec4\u4ef6\u7684\u5c42\u7ea7\u5173\u7cfb\u4e0e\u6570\u636e\u6d41\u5411&#xff1a;<\/p>\n<p>#mermaid-svg-sjgI0QfAbP2H5HY1{font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#mermaid-svg-sjgI0QfAbP2H5HY1 .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-sjgI0QfAbP2H5HY1 .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-sjgI0QfAbP2H5HY1 .error-icon{fill:#552222;}#mermaid-svg-sjgI0QfAbP2H5HY1 .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-sjgI0QfAbP2H5HY1 .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-sjgI0QfAbP2H5HY1 .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-sjgI0QfAbP2H5HY1 .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-sjgI0QfAbP2H5HY1 .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-sjgI0QfAbP2H5HY1 .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-sjgI0QfAbP2H5HY1 .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-sjgI0QfAbP2H5HY1 .marker{fill:#333333;stroke:#333333;}#mermaid-svg-sjgI0QfAbP2H5HY1 .marker.cross{stroke:#333333;}#mermaid-svg-sjgI0QfAbP2H5HY1 svg{font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-sjgI0QfAbP2H5HY1 p{margin:0;}#mermaid-svg-sjgI0QfAbP2H5HY1 .label{font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;color:#333;}#mermaid-svg-sjgI0QfAbP2H5HY1 .cluster-label text{fill:#333;}#mermaid-svg-sjgI0QfAbP2H5HY1 .cluster-label span{color:#333;}#mermaid-svg-sjgI0QfAbP2H5HY1 .cluster-label span p{background-color:transparent;}#mermaid-svg-sjgI0QfAbP2H5HY1 .label text,#mermaid-svg-sjgI0QfAbP2H5HY1 span{fill:#333;color:#333;}#mermaid-svg-sjgI0QfAbP2H5HY1 .node rect,#mermaid-svg-sjgI0QfAbP2H5HY1 .node circle,#mermaid-svg-sjgI0QfAbP2H5HY1 .node ellipse,#mermaid-svg-sjgI0QfAbP2H5HY1 .node polygon,#mermaid-svg-sjgI0QfAbP2H5HY1 .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid-svg-sjgI0QfAbP2H5HY1 .rough-node .label text,#mermaid-svg-sjgI0QfAbP2H5HY1 .node .label text,#mermaid-svg-sjgI0QfAbP2H5HY1 .image-shape .label,#mermaid-svg-sjgI0QfAbP2H5HY1 .icon-shape .label{text-anchor:middle;}#mermaid-svg-sjgI0QfAbP2H5HY1 .node .katex path{fill:#000;stroke:#000;stroke-width:1px;}#mermaid-svg-sjgI0QfAbP2H5HY1 .rough-node .label,#mermaid-svg-sjgI0QfAbP2H5HY1 .node .label,#mermaid-svg-sjgI0QfAbP2H5HY1 .image-shape .label,#mermaid-svg-sjgI0QfAbP2H5HY1 .icon-shape .label{text-align:center;}#mermaid-svg-sjgI0QfAbP2H5HY1 .node.clickable{cursor:pointer;}#mermaid-svg-sjgI0QfAbP2H5HY1 .root .anchor path{fill:#333333!important;stroke-width:0;stroke:#333333;}#mermaid-svg-sjgI0QfAbP2H5HY1 .arrowheadPath{fill:#333333;}#mermaid-svg-sjgI0QfAbP2H5HY1 .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid-svg-sjgI0QfAbP2H5HY1 .flowchart-link{stroke:#333333;fill:none;}#mermaid-svg-sjgI0QfAbP2H5HY1 .edgeLabel{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-sjgI0QfAbP2H5HY1 .edgeLabel p{background-color:rgba(232,232,232, 0.8);}#mermaid-svg-sjgI0QfAbP2H5HY1 .edgeLabel rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-sjgI0QfAbP2H5HY1 .labelBkg{background-color:rgba(232, 232, 232, 0.5);}#mermaid-svg-sjgI0QfAbP2H5HY1 .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid-svg-sjgI0QfAbP2H5HY1 .cluster text{fill:#333;}#mermaid-svg-sjgI0QfAbP2H5HY1 .cluster span{color:#333;}#mermaid-svg-sjgI0QfAbP2H5HY1 div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}#mermaid-svg-sjgI0QfAbP2H5HY1 .flowchartTitleText{text-anchor:middle;font-size:18px;fill:#333;}#mermaid-svg-sjgI0QfAbP2H5HY1 rect.text{fill:none;stroke-width:0;}#mermaid-svg-sjgI0QfAbP2H5HY1 .icon-shape,#mermaid-svg-sjgI0QfAbP2H5HY1 .image-shape{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-sjgI0QfAbP2H5HY1 .icon-shape p,#mermaid-svg-sjgI0QfAbP2H5HY1 .image-shape p{background-color:rgba(232,232,232, 0.8);padding:2px;}#mermaid-svg-sjgI0QfAbP2H5HY1 .icon-shape rect,#mermaid-svg-sjgI0QfAbP2H5HY1 .image-shape rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-sjgI0QfAbP2H5HY1 .label-icon{display:inline-block;height:1em;overflow:visible;vertical-align:-0.125em;}#mermaid-svg-sjgI0QfAbP2H5HY1 .node .label-icon path{fill:currentColor;stroke:revert;stroke-width:revert;}#mermaid-svg-sjgI0QfAbP2H5HY1 :root{&#8211;mermaid-font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;}<span class=\"nodeLabel\"><\/p>\n<p>\u89e3\u7801\u5668 (Decoder) \u00d7 N<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u7f16\u7801\u5668 (Encoder) \u00d7 N<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u8f93\u5165\u4fa7<\/p>\n<p><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u8f93\u5165\u5e8f\u5217 (Input Sequence)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u8bcd\u5d4c\u5165 (Embedding)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u4f4d\u7f6e\u7f16\u7801 (Positional Encoding)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u591a\u5934\u81ea\u6ce8\u610f\u529b (Multi-Head Self-Attention)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u6b8b\u5dee\u8fde\u63a5 &#043; \u5c42\u5f52\u4e00\u5316 (Add &amp; Norm)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u524d\u9988\u7f51\u7edc (Feed-Forward Network)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u6b8b\u5dee\u8fde\u63a5 &#043; \u5c42\u5f52\u4e00\u5316 (Add &amp; Norm)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u8f93\u51fa\u5e8f\u5217 (Output Sequence)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u8bcd\u5d4c\u5165 (Embedding)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u4f4d\u7f6e\u7f16\u7801 (Positional Encoding)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u63a9\u7801\u591a\u5934\u81ea\u6ce8\u610f\u529b (Masked Multi-Head Self-Attention)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u6b8b\u5dee\u8fde\u63a5 &#043; \u5c42\u5f52\u4e00\u5316 (Add &amp; Norm)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u4ea4\u53c9\u591a\u5934\u6ce8\u610f\u529b (Cross Multi-Head Attention)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u6b8b\u5dee\u8fde\u63a5 &#043; \u5c42\u5f52\u4e00\u5316 (Add &amp; Norm)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u524d\u9988\u7f51\u7edc (Feed-Forward Network)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u6b8b\u5dee\u8fde\u63a5 &#043; \u5c42\u5f52\u4e00\u5316 (Add &amp; Norm)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u7ebf\u6027\u5c42 (Linear)<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>Softmax \u8f93\u51fa\u6982\u7387 (Output Probabilities)<\/p>\n<p><\/span><\/p>\n<p>\u67b6\u6784\u8bf4\u660e&#xff1a;<\/p>\n<ul>\n<li>\u7f16\u7801\u5668&#xff08;Encoder&#xff09;&#xff1a;\u8f93\u5165\u5e8f\u5217\u5148\u7ecf\u8fc7\u8bcd\u5d4c\u5165\u4e0e\u4f4d\u7f6e\u7f16\u7801&#xff0c;\u518d\u9001\u5165 N \u5c42\u76f8\u540c\u7684\u7f16\u7801\u5668\u5757\u3002\u6bcf\u4e2a\u7f16\u7801\u5668\u5757\u7531\u300c\u591a\u5934\u81ea\u6ce8\u610f\u529b \u2192 \u6b8b\u5dee\u8fde\u63a5\u4e0e\u5c42\u5f52\u4e00\u5316 \u2192 \u524d\u9988\u7f51\u7edc \u2192 \u6b8b\u5dee\u8fde\u63a5\u4e0e\u5c42\u5f52\u4e00\u5316\u300d\u7ec4\u6210&#xff0c;\u7528\u4e8e\u6355\u6349\u8f93\u5165\u5e8f\u5217\u5185\u90e8\u5404\u4f4d\u7f6e\u4e4b\u95f4\u7684\u4f9d\u8d56\u5173\u7cfb\u3002<\/li>\n<li>\u89e3\u7801\u5668&#xff08;Decoder&#xff09;&#xff1a;\u8f93\u51fa\u5e8f\u5217\u540c\u6837\u7ecf\u8fc7\u5d4c\u5165\u4e0e\u4f4d\u7f6e\u7f16\u7801&#xff0c;\u5148\u901a\u8fc7\u5e26\u63a9\u7801\u7684\u591a\u5934\u81ea\u6ce8\u610f\u529b&#xff08;\u4fdd\u8bc1\u9884\u6d4b\u5f53\u524d\u4f4d\u7f6e\u65f6\u53ea\u80fd\u770b\u5230\u5de6\u4fa7\u4fe1\u606f&#xff09;&#xff0c;\u518d\u901a\u8fc7\u4ea4\u53c9\u591a\u5934\u6ce8\u610f\u529b\u878d\u5408\u7f16\u7801\u5668\u8f93\u51fa\u7684\u4e0a\u4e0b\u6587\u4fe1\u606f&#xff0c;\u6700\u540e\u7ecf\u524d\u9988\u7f51\u7edc\u4e0e\u6b8b\u5dee\u8fde\u63a5\u3001\u5c42\u5f52\u4e00\u5316\u5904\u7406\u3002<\/li>\n<li>\u6570\u636e\u6d41\u5411&#xff1a;\u7f16\u7801\u5668\u7684\u8f93\u51fa\u4f5c\u4e3a\u4ea4\u53c9\u6ce8\u610f\u529b\u7684 Key \u548c Value \u63d0\u4f9b\u7ed9\u89e3\u7801\u5668&#xff0c;\u89e3\u7801\u5668\u6700\u7ec8\u8f93\u51fa\u7ecf\u7ebf\u6027\u5c42\u4e0e Softmax \u5f97\u5230\u6bcf\u4e2a\u4f4d\u7f6e\u7684\u9884\u6d4b\u6982\u7387\u5206\u5e03\u3002\u6b8b\u5dee\u8fde\u63a5\u4e0e\u5c42\u5f52\u4e00\u5316\u8d2f\u7a7f\u6bcf\u4e2a\u5b50\u5c42&#xff0c;\u5e2e\u52a9\u7f13\u89e3\u6df1\u5c42\u7f51\u7edc\u7684\u68af\u5ea6\u6d88\u5931\u5e76\u52a0\u901f\u8bad\u7ec3\u6536\u655b\u3002<\/li>\n<\/ul>\n<h3>6. \u6df1\u5ea6\u5b66\u4e60\u5b9e\u8df5\u6d41\u7a0b<\/h3>\n<p>\u4e00\u4e2a\u5b8c\u6574\u7684\u6df1\u5ea6\u5b66\u4e60\u9879\u76ee\u901a\u5e38\u5305\u542b\u4ee5\u4e0b\u6b65\u9aa4&#xff1a;<\/p>\n<li>\u6570\u636e\u51c6\u5907&#xff1a;\u6536\u96c6\u6570\u636e\u3001\u6e05\u6d17\u6570\u636e\u3001\u5212\u5206\u8bad\u7ec3\u96c6\/\u9a8c\u8bc1\u96c6\/\u6d4b\u8bd5\u96c6<\/li>\n<li>\u6a21\u578b\u8bbe\u8ba1&#xff1a;\u9009\u62e9\u5408\u9002\u7684\u7f51\u7edc\u7ed3\u6784<\/li>\n<li>\u8bad\u7ec3\u6a21\u578b&#xff1a;\u8bbe\u7f6e\u8d85\u53c2\u6570&#xff08;\u5b66\u4e60\u7387\u3001\u6279\u5927\u5c0f\u3001\u8bad\u7ec3\u8f6e\u6570&#xff09;&#xff0c;\u8fed\u4ee3\u4f18\u5316<\/li>\n<li>\u8bc4\u4f30\u6a21\u578b&#xff1a;\u5728\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\u4e0a\u8bc4\u4f30\u6027\u80fd<\/li>\n<li>\u90e8\u7f72\u4e0a\u7ebf&#xff1a;\u5c06\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u96c6\u6210\u5230\u5b9e\u9645\u5e94\u7528\u4e2d<\/li>\n<h3>7. \u603b\u7ed3<\/h3>\n<p>\u795e\u7ecf\u7f51\u7edc\u4e0e\u6df1\u5ea6\u5b66\u4e60\u662f\u4e00\u4e2a\u5e9e\u5927\u800c\u5feb\u901f\u53d1\u5c55\u7684\u9886\u57df\u3002\u672c\u6587\u4ece\u795e\u7ecf\u5143\u7684\u57fa\u672c\u539f\u7406\u51fa\u53d1&#xff0c;\u4ecb\u7ecd\u4e86\u524d\u5411\u4f20\u64ad\u3001\u53cd\u5411\u4f20\u64ad\u3001\u5e38\u89c1\u7f51\u7edc\u7ed3\u6784\u4ee5\u53ca\u5b9e\u8df5\u6d41\u7a0b&#xff0c;\u4e3a\u8bfb\u8005\u642d\u5efa\u4e86\u4e00\u4e2a\u5b8c\u6574\u7684\u77e5\u8bc6\u6846\u67b6\u3002<\/p>\n<p>\u6df1\u5ea6\u5b66\u4e60\u7684\u6838\u5fc3\u5728\u4e8e\u300c\u6570\u636e &#043; \u6a21\u578b &#043; \u7b97\u529b\u300d\u4e09\u8005\u7684\u7ed3\u5408\u3002\u5efa\u8bae\u8bfb\u8005\u5728\u7406\u89e3\u7406\u8bba\u7684\u57fa\u7840\u4e0a&#xff0c;\u52a8\u624b\u5b9e\u73b0\u4e00\u4e9b\u7ecf\u5178\u6a21\u578b&#xff08;\u5982 LeNet\u3001ResNet\u3001Transformer&#xff09;&#xff0c;\u5728\u5b9e\u8df5\u4e2d\u52a0\u6df1\u5bf9\u6982\u5ff5\u7684\u7406\u89e3\u3002<\/p>\n<h3>8. \u53c2\u8003\u8d44\u6599<\/h3>\n<p>\u672c\u6587\u5728\u64b0\u5199\u8fc7\u7a0b\u4e2d\u53c2\u8003\u4e86\u4ee5\u4e0b\u7ecf\u5178\u8bba\u6587\u4e0e\u6559\u6750&#xff0c;\u4f9b\u8bfb\u8005\u8fdb\u4e00\u6b65\u6df1\u5165\u5b66\u4e60&#xff1a;<\/p>\n<ul>\n<li>\u300aDeep Learning\u300b&#xff08;Ian Goodfellow\u3001Yoshua Bengio\u3001Aaron Courville \u8457&#xff09;\u2014\u2014\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u7684\u6743\u5a01\u6559\u6750&#xff0c;\u7cfb\u7edf\u8bb2\u89e3\u4e86\u795e\u7ecf\u7f51\u7edc\u3001\u4f18\u5316\u7b97\u6cd5\u3001\u5377\u79ef\u7f51\u7edc\u3001\u5faa\u73af\u7f51\u7edc\u53ca\u751f\u6210\u6a21\u578b\u7b49\u6838\u5fc3\u5185\u5bb9&#xff0c;\u662f\u5165\u95e8\u4e0e\u8fdb\u9636\u7684\u5fc5\u8bfb\u4e66\u76ee\u3002<\/li>\n<li>LeCun\u3001Bengio\u3001Hinton \u7684\u6df1\u5ea6\u5b66\u4e60\u7efc\u8ff0&#xff08;\u201cDeep Learning\u201d&#xff0c;Nature, 2015&#xff09;\u2014\u2014\u7531\u4e09\u4f4d\u56fe\u7075\u5956\u5f97\u4e3b\u8054\u5408\u64b0\u5199&#xff0c;\u5bf9\u6df1\u5ea6\u5b66\u4e60\u7684\u53d1\u5c55\u5386\u7a0b\u3001\u6838\u5fc3\u601d\u60f3\u4e0e\u672a\u6765\u65b9\u5411\u8fdb\u884c\u4e86\u9ad8\u5c4b\u5efa\u74f4\u7684\u603b\u7ed3&#xff0c;\u662f\u7406\u89e3\u9886\u57df\u5168\u8c8c\u7684\u91cd\u8981\u6587\u732e\u3002<\/li>\n<li>\u201cImageNet Classification with Deep Convolutional Neural Networks\u201d&#xff08;Alex Krizhevsky \u7b49&#xff0c;2012&#xff09;\u2014\u2014\u63d0\u51fa AlexNet&#xff0c;\u5728 ImageNet \u7ade\u8d5b\u4e2d\u5927\u5e45\u5237\u65b0\u7eaa\u5f55&#xff0c;\u5f00\u542f\u4e86\u6df1\u5ea6\u5b66\u4e60\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u7684\u65b0\u7eaa\u5143\u3002<\/li>\n<li>\u201cDeep Residual Learning for Image Recognition\u201d&#xff08;Kaiming He \u7b49&#xff0c;2016&#xff09;\u2014\u2014\u63d0\u51fa ResNet \u4e0e\u6b8b\u5dee\u8fde\u63a5&#xff0c;\u6709\u6548\u89e3\u51b3\u4e86\u6df1\u5c42\u7f51\u7edc\u8bad\u7ec3\u4e2d\u7684\u9000\u5316\u95ee\u9898&#xff0c;\u662f\u6784\u5efa\u6781\u6df1\u7f51\u7edc\u7684\u5173\u952e\u7a81\u7834\u3002<\/li>\n<li>\u201cAttention Is All You Need\u201d&#xff08;Ashish Vaswani \u7b49&#xff0c;2017&#xff09;\u2014\u2014\u63d0\u51fa Transformer \u67b6\u6784&#xff0c;\u4ee5\u81ea\u6ce8\u610f\u529b\u673a\u5236\u53d6\u4ee3\u5faa\u73af\u7ed3\u6784&#xff0c;\u5960\u5b9a\u4e86\u73b0\u4ee3\u5927\u8bed\u8a00\u6a21\u578b\u7684\u57fa\u7840\u3002<\/li>\n<li>\u201cLong Short-Term Memory\u201d&#xff08;Sepp Hochreiter\u3001J\u00fcrgen Schmidhuber&#xff0c;1997&#xff09;\u2014\u2014\u63d0\u51fa LSTM \u7f51\u7edc&#xff0c;\u901a\u8fc7\u95e8\u63a7\u673a\u5236\u6709\u6548\u7f13\u89e3\u4e86\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u4e2d\u7684\u68af\u5ea6\u6d88\u5931\u95ee\u9898&#xff0c;\u662f\u5e8f\u5217\u5efa\u6a21\u7684\u91cd\u8981\u91cc\u7a0b\u7891\u3002<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>1. \u5f15\u8a00<br \/>\n\u6df1\u5ea6\u5b66\u4e60\u662f\u673a\u5668\u5b66\u4e60\u7684\u4e00\u4e2a\u91cd\u8981\u5206\u652f&#xff0c;\u5176\u6838\u5fc3\u601d\u60f3\u662f\u901a\u8fc7\u6784\u5efa\u591a\u5c42\u795e\u7ecf\u7f51\u7edc&#xff0c;\u8ba9\u8ba1\u7b97\u673a\u81ea\u52a8\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u7279\u5f81\u8868\u793a\u3002\u8fd1\u5e74\u6765&#xff0c;\u968f\u7740\u8ba1\u7b97\u80fd\u529b\u7684\u63d0\u5347\u548c\u5927\u6570\u636e\u7684\u79ef\u7d2f&#xff0c;\u6df1\u5ea6\u5b66\u4e60\u5728\u56fe\u50cf\u8bc6\u522b\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u8bed\u97f3\u8bc6\u522b\u7b49\u9886\u57df\u53d6\u5f97\u4e86\u7a81\u7834\u6027\u8fdb\u5c55\u3002<br \/>\n\u672c\u6587\u5c06\u4ece\u795e\u7ecf\u7f51\u7edc\u7684\u57fa\u672c\u539f\u7406\u51fa\u53d1&#xff0c;\u9010\u6b65\u6df1\u5165\u5230\u6df1\u5ea6\u5b66\u4e60\u7684\u6838\u5fc3\u6982\u5ff5\u3001\u5e38\u89c1\u7f51\u7edc\u7ed3\u6784\u4ee5\u53ca\u5b9e\u9645\u5e94\u7528&#xff0c;\u5e2e\u52a9\u8bfb\u8005\u5efa\u7acb\u5b8c\u6574\u7684\u77e5\u8bc6\u4f53\u7cfb\u3002<br \/>\n2. \u795e\u7ecf\u7f51\u7edc\u57fa\u7840<br \/>\n2.1 \u4ec0\u4e48\u662f\u795e\u7ecf\u7f51\u7edc<br \/>\n\u795e\u7ecf\u7f51\u7edc\u662f<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[587],"topic":[],"class_list":["post-103654","post","type-post","status-publish","format-standard","hentry","category-server","tag-587"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ 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