{"id":921,"date":"2025-04-19T18:00:10","date_gmt":"2025-04-19T10:00:10","guid":{"rendered":"https:\/\/www.wunen.com\/index.php\/2025\/04\/19\/%e3%80%90%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e3%80%91%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e4%b8%8e%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%9c%a8%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bd%e9%a2%86%e5%9f%9f%e7%9a%84\/"},"modified":"2025-04-19T18:00:10","modified_gmt":"2025-04-19T10:00:10","slug":"%e3%80%90%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e3%80%91%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e4%b8%8e%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%9c%a8%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bd%e9%a2%86%e5%9f%9f%e7%9a%84","status":"publish","type":"post","link":"http:\/\/www.wunen.com\/index.php\/2025\/04\/19\/%e3%80%90%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e3%80%91%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e4%b8%8e%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%9c%a8%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bd%e9%a2%86%e5%9f%9f%e7%9a%84\/","title":{"rendered":"\u3010\u673a\u5668\u5b66\u4e60\u3011\u673a\u5668\u5b66\u4e60\u4e0e\u5927\u6a21\u578b\u5728\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u878d\u5408\u5e94\u7528\u4e0e\u6027\u80fd\u4f18\u5316\u65b0\u63a2\u7d22"},"content":{"rendered":"<div class=\"article_content clearfix\" id=\"article_content\">\n <link href=\"https:\/\/csdnimg.cn\/release\/blogv2\/dist\/mdeditor\/css\/editerView\/kdoc_html_views-1a98987dfd.css\" rel=\"stylesheet\"\/>\n <link href=\"https:\/\/csdnimg.cn\/release\/blogv2\/dist\/mdeditor\/css\/editerView\/ck_htmledit_views-704d5b9767.css\" rel=\"stylesheet\"\/>\n<div class=\"markdown_views prism-tomorrow-night\" id=\"content_views\">\n  <svg style=\"display: none;\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n   <path d=\"M5,0 0,2.5 5,5z\" id=\"raphael-marker-block\" stroke-linecap=\"round\" style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0);\">\n   <\/path>\n  <\/svg><\/p>\n<div class=\"toc\">\n<h4>\n    \u6587\u7ae0\u76ee\u5f55<br \/>\n   <\/h4>\n<ul>\n<li>\n<ul>\n<li>\n       <a href=\"#_1\" rel=\"nofollow\"><br \/>\n        \u5f15\u8a00<br \/>\n       <\/a>\n      <\/li>\n<li>\n       <a href=\"#_7\" rel=\"nofollow\"><br \/>\n        \u673a\u5668\u5b66\u4e60\u4e0e\u5927\u6a21\u578b\u7684\u57fa\u672c\u6982\u5ff5<br \/>\n       <\/a>\n      <\/li>\n<li>\n<ul>\n<li>\n         <a href=\"#_9\" rel=\"nofollow\"><br \/>\n          \u673a\u5668\u5b66\u4e60\u6982\u8ff0<br \/>\n         <\/a>\n        <\/li>\n<li>\n<ul>\n<li>\n           <a href=\"#_13\" rel=\"nofollow\"><br \/>\n            \u76d1\u7763\u5b66\u4e60<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#_17\" rel=\"nofollow\"><br \/>\n            \u65e0\u76d1\u7763\u5b66\u4e60<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#_21\" rel=\"nofollow\"><br \/>\n            \u5f3a\u5316\u5b66\u4e60<br \/>\n           <\/a>\n          <\/li>\n<\/ul>\n<\/li>\n<li>\n         <a href=\"#_25\" rel=\"nofollow\"><br \/>\n          \u5927\u6a21\u578b\u6982\u8ff0<br \/>\n         <\/a>\n        <\/li>\n<li>\n<ul>\n<li>\n           <a href=\"#GPT3_29\" rel=\"nofollow\"><br \/>\n            GPT-3<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#BERT_33\" rel=\"nofollow\"><br \/>\n            BERT<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#ResNet_37\" rel=\"nofollow\"><br \/>\n            ResNet<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#Transformer_41\" rel=\"nofollow\"><br \/>\n            Transformer<br \/>\n           <\/a>\n          <\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n       <a href=\"#_45\" rel=\"nofollow\"><br \/>\n        \u673a\u5668\u5b66\u4e60\u4e0e\u5927\u6a21\u578b\u7684\u878d\u5408\u5e94\u7528<br \/>\n       <\/a>\n      <\/li>\n<li>\n<ul>\n<li>\n         <a href=\"#_47\" rel=\"nofollow\"><br \/>\n          \u81ea\u7136\u8bed\u8a00\u5904\u7406<br \/>\n         <\/a>\n        <\/li>\n<li>\n<ul>\n<li>\n           <a href=\"#_51\" rel=\"nofollow\"><br \/>\n            \u6587\u672c\u751f\u6210<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#_73\" rel=\"nofollow\"><br \/>\n            \u6587\u672c\u5206\u7c7b<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#_90\" rel=\"nofollow\"><br \/>\n            \u673a\u5668\u7ffb\u8bd1<br \/>\n           <\/a>\n          <\/li>\n<\/ul>\n<\/li>\n<li>\n         <a href=\"#_113\" rel=\"nofollow\"><br \/>\n          \u56fe\u50cf\u8bc6\u522b<br \/>\n         <\/a>\n        <\/li>\n<li>\n<ul>\n<li>\n           <a href=\"#_117\" rel=\"nofollow\"><br \/>\n            \u81ea\u52a8\u9a7e\u9a76<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#_152\" rel=\"nofollow\"><br \/>\n            \u533b\u5b66\u5f71\u50cf\u5206\u6790<br \/>\n           <\/a>\n          <\/li>\n<\/ul>\n<\/li>\n<li>\n         <a href=\"#_174\" rel=\"nofollow\"><br \/>\n          \u8bed\u97f3\u8bc6\u522b<br \/>\n         <\/a>\n        <\/li>\n<li>\n<ul>\n<li>\n           <a href=\"#_179\" rel=\"nofollow\"><br \/>\n            \u667a\u80fd\u52a9\u624b<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#_203\" rel=\"nofollow\"><br \/>\n            \u8bed\u97f3\u8f6c\u6587\u5b57<br \/>\n           <\/a>\n          <\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n       <a href=\"#_231\" rel=\"nofollow\"><br \/>\n        \u5927\u6a21\u578b\u6027\u80fd\u4f18\u5316\u7684\u65b0\u63a2\u7d22<br \/>\n       <\/a>\n      <\/li>\n<li>\n<ul>\n<li>\n         <a href=\"#_233\" rel=\"nofollow\"><br \/>\n          \u6a21\u578b\u538b\u7f29<br \/>\n         <\/a>\n        <\/li>\n<li>\n<ul>\n<li>\n           <a href=\"#_237\" rel=\"nofollow\"><br \/>\n            \u6743\u91cd\u526a\u679d<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#_256\" rel=\"nofollow\"><br \/>\n            \u91cf\u5316<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#_274\" rel=\"nofollow\"><br \/>\n            \u77e5\u8bc6\u84b8\u998f<br \/>\n           <\/a>\n          <\/li>\n<\/ul>\n<\/li>\n<li>\n         <a href=\"#_309\" rel=\"nofollow\"><br \/>\n          \u5206\u5e03\u5f0f\u8bad\u7ec3<br \/>\n         <\/a>\n        <\/li>\n<li>\n<ul>\n<li>\n           <a href=\"#_313\" rel=\"nofollow\"><br \/>\n            \u6570\u636e\u5e76\u884c<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#_339\" rel=\"nofollow\"><br \/>\n            \u6a21\u578b\u5e76\u884c<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#_377\" rel=\"nofollow\"><br \/>\n            \u5f02\u6b65\u8bad\u7ec3<br \/>\n           <\/a>\n          <\/li>\n<\/ul>\n<\/li>\n<li>\n         <a href=\"#_419\" rel=\"nofollow\"><br \/>\n          \u9ad8\u6548\u63a8\u7406<br \/>\n         <\/a>\n        <\/li>\n<li>\n<ul>\n<li>\n           <a href=\"#_423\" rel=\"nofollow\"><br \/>\n            \u6a21\u578b\u88c1\u526a<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#_449\" rel=\"nofollow\"><br \/>\n            \u7f13\u5b58\u673a\u5236<br \/>\n           <\/a>\n          <\/li>\n<li>\n           <a href=\"#_480\" rel=\"nofollow\"><br \/>\n            \u4e13\u7528\u786c\u4ef6<br \/>\n           <\/a>\n          <\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n       <a href=\"#_498\" rel=\"nofollow\"><br \/>\n        \u672a\u6765\u5c55\u671b<br \/>\n       <\/a>\n      <\/li>\n<li>\n<ul>\n<li>\n         <a href=\"#_500\" rel=\"nofollow\"><br \/>\n          \u8de8\u9886\u57df\u5e94\u7528<br \/>\n         <\/a>\n        <\/li>\n<li>\n         <a href=\"#_504\" rel=\"nofollow\"><br \/>\n          \u667a\u80fd\u5316\u7cfb\u7edf<br \/>\n         <\/a>\n        <\/li>\n<li>\n         <a href=\"#_508\" rel=\"nofollow\"><br \/>\n          \u4eba\u5de5\u667a\u80fd\u4f26\u7406<br \/>\n         <\/a>\n        <\/li>\n<li>\n         <a href=\"#_512\" rel=\"nofollow\"><br \/>\n          \u6280\u672f\u521b\u65b0<br \/>\n         <\/a>\n        <\/li>\n<\/ul>\n<\/li>\n<li>\n       <a href=\"#_516\" rel=\"nofollow\"><br \/>\n        \u7ed3\u8bba<br \/>\n       <\/a>\n      <\/li>\n<\/ul>\n<\/li>\n<\/ul><\/div>\n<\/p>\n<h3>\n   <a id=\"_1\"><br \/>\n   <\/a><br \/>\n   \u5f15\u8a00<br \/>\n  <\/h3>\n<p>\n   \u968f\u7740\u8ba1\u7b97\u80fd\u529b\u7684\u4e0d\u65ad\u63d0\u5347\u548c\u6570\u636e\u89c4\u6a21\u7684\u7206\u70b8\u6027\u589e\u957f\uff0c\u673a\u5668\u5b66\u4e60\u548c\u5927\u6a21\u578b\u5728\u4eba\u5de5\u667a\u80fd\uff08AI\uff09\u9886\u57df\u7684\u5e94\u7528\u53d8\u5f97\u8d8a\u6765\u8d8a\u5e7f\u6cdb\u548c\u6df1\u5165\u3002\u5c24\u5176\u662f\u5927\u89c4\u6a21\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u5982\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\uff08\u5982GPT-3\u3001BERT\u7b49\uff09\uff0c\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u56fe\u50cf\u8bc6\u522b\u3001\u8bed\u97f3\u8bc6\u522b\u7b49\u65b9\u9762\u5c55\u73b0\u4e86\u5353\u8d8a\u7684\u6027\u80fd\u3002\u7136\u800c\uff0c\u5982\u4f55\u6709\u6548\u5730\u878d\u5408\u673a\u5668\u5b66\u4e60\u4e0e\u5927\u6a21\u578b\uff0c\u63d0\u5347\u5176\u5e94\u7528\u6027\u80fd\uff0c\u4ecd\u7136\u662f\u5f53\u524d\u7814\u7a76\u548c\u5e94\u7528\u4e2d\u7684\u91cd\u8981\u8bfe\u9898\u3002\u672c\u6587\u5c06\u63a2\u8ba8\u673a\u5668\u5b66\u4e60\u4e0e\u5927\u6a21\u578b\u5728\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u878d\u5408\u5e94\u7528\uff0c\u5e76\u91cd\u70b9\u8ba8\u8bba\u6027\u80fd\u4f18\u5316\u7684\u65b0\u65b9\u6cd5\u548c\u65b0\u63a2\u7d22\u3002<br \/>\n   \n  <\/p>\n<h3>\n   <a id=\"_7\"><br \/>\n   <\/a><br \/>\n   \u673a\u5668\u5b66\u4e60\u4e0e\u5927\u6a21\u578b\u7684\u57fa\u672c\u6982\u5ff5<br \/>\n  <\/h3>\n<h4>\n   <a id=\"_9\"><br \/>\n   <\/a><br \/>\n   \u673a\u5668\u5b66\u4e60\u6982\u8ff0<br \/>\n  <\/h4>\n<p>\n   \u673a\u5668\u5b66\u4e60\u662f\u4e00\u79cd\u901a\u8fc7\u6570\u636e\u8bad\u7ec3\u6a21\u578b\uff0c\u5e76\u5229\u7528\u6a21\u578b\u5bf9\u65b0\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u548c\u51b3\u7b56\u7684\u6280\u672f\u3002\u5176\u57fa\u672c\u601d\u60f3\u662f\u8ba9\u8ba1\u7b97\u673a\u901a\u8fc7\u6837\u672c\u6570\u636e\u5b66\u4e60\u89c4\u5f8b\uff0c\u800c\u4e0d\u662f\u901a\u8fc7\u660e\u786e\u7684\u7f16\u7a0b\u6307\u4ee4\u3002\u6839\u636e\u5b66\u4e60\u7684\u7c7b\u578b\uff0c\u673a\u5668\u5b66\u4e60\u53ef\u4ee5\u5206\u4e3a\u76d1\u7763\u5b66\u4e60\u3001\u65e0\u76d1\u7763\u5b66\u4e60\u548c\u5f3a\u5316\u5b66\u4e60\u3002\n  <\/p>\n<h5>\n   <a id=\"_13\"><br \/>\n   <\/a><br \/>\n   \u76d1\u7763\u5b66\u4e60<br \/>\n  <\/h5>\n<p>\n   \u76d1\u7763\u5b66\u4e60\u662f\u901a\u8fc7\u5e26\u6807\u7b7e\u7684\u6570\u636e\u96c6\u8bad\u7ec3\u6a21\u578b\uff0c\u4f7f\u5176\u80fd\u591f\u5bf9\u65b0\u6570\u636e\u8fdb\u884c\u5206\u7c7b\u6216\u56de\u5f52\u9884\u6d4b\u3002\u5e38\u89c1\u7684\u7b97\u6cd5\u5305\u62ec\u7ebf\u6027\u56de\u5f52\u3001\u903b\u8f91\u56de\u5f52\u3001\u652f\u6301\u5411\u91cf\u673a\u3001\u51b3\u7b56\u6811\u548c\u795e\u7ecf\u7f51\u7edc\u7b49\u3002\n  <\/p>\n<h5>\n   <a id=\"_17\"><br \/>\n   <\/a><br \/>\n   \u65e0\u76d1\u7763\u5b66\u4e60<br \/>\n  <\/h5>\n<p>\n   \u65e0\u76d1\u7763\u5b66\u4e60\u662f\u5728\u6ca1\u6709\u6807\u7b7e\u7684\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u8bad\u7ec3\uff0c\u4e3b\u8981\u7528\u4e8e\u6570\u636e\u805a\u7c7b\u548c\u964d\u7ef4\u3002\u5e38\u89c1\u7684\u7b97\u6cd5\u5305\u62ecK-means\u805a\u7c7b\u3001\u5c42\u6b21\u805a\u7c7b\u548c\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u7b49\u3002\n  <\/p>\n<h5>\n   <a id=\"_21\"><br \/>\n   <\/a><br \/>\n   \u5f3a\u5316\u5b66\u4e60<br \/>\n  <\/h5>\n<p>\n   \u5f3a\u5316\u5b66\u4e60\u662f\u4e00\u79cd\u901a\u8fc7\u4e0e\u73af\u5883\u4ea4\u4e92\u5b66\u4e60\u6700\u4f18\u884c\u4e3a\u7b56\u7565\u7684\u6280\u672f\u3002\u667a\u80fd\u4f53\u901a\u8fc7\u8bd5\u9519\u6cd5\u5728\u73af\u5883\u4e2d\u5b66\u4e60\uff0c\u4ee5\u6700\u5927\u5316\u7d2f\u79ef\u5956\u52b1\u3002\u5e38\u89c1\u7684\u7b97\u6cd5\u5305\u62ecQ-learning\u3001\u6df1\u5ea6Q\u7f51\u7edc\uff08DQN\uff09\u548c\u7b56\u7565\u68af\u5ea6\u65b9\u6cd5\u7b49\u3002\n  <\/p>\n<h4>\n   <a id=\"_25\"><br \/>\n   <\/a><br \/>\n   \u5927\u6a21\u578b\u6982\u8ff0<br \/>\n  <\/h4>\n<p>\n   \u5927\u6a21\u578b\u662f\u6307\u5177\u6709\u5927\u91cf\u53c2\u6570\u548c\u5c42\u6570\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u901a\u5e38\u901a\u8fc7\u5927\u89c4\u6a21\u6570\u636e\u96c6\u8fdb\u884c\u8bad\u7ec3\u3002\u5927\u6a21\u578b\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u56fe\u50cf\u8bc6\u522b\u548c\u8bed\u97f3\u8bc6\u522b\u7b49\u4efb\u52a1\u4e2d\u8868\u73b0\u51fa\u8272\u3002\u5176\u4ee3\u8868\u6027\u6a21\u578b\u5305\u62ecGPT-3\u3001BERT\u3001ResNet\u548cTransformer\u7b49\u3002\n  <\/p>\n<h5>\n   <a id=\"GPT3_29\"><br \/>\n   <\/a><br \/>\n   GPT-3<br \/>\n  <\/h5>\n<p>\n   GPT-3\uff08Generative Pre-trained Transformer 3\uff09\u662fOpenAI\u5f00\u53d1\u7684\u4e00\u79cd\u8bed\u8a00\u6a21\u578b\uff0c\u5177\u67091750\u4ebf\u4e2a\u53c2\u6570\u3002\u5b83\u901a\u8fc7\u5927\u91cf\u7684\u6587\u672c\u6570\u636e\u8fdb\u884c\u9884\u8bad\u7ec3\uff0c\u80fd\u591f\u751f\u6210\u9ad8\u8d28\u91cf\u7684\u81ea\u7136\u8bed\u8a00\u6587\u672c\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6587\u672c\u751f\u6210\u3001\u7ffb\u8bd1\u548c\u5bf9\u8bdd\u7cfb\u7edf\u7b49\u9886\u57df\u3002\n  <\/p>\n<h5>\n   <a id=\"BERT_33\"><br \/>\n   <\/a><br \/>\n   BERT<br \/>\n  <\/h5>\n<p>\n   BERT\uff08Bidirectional Encoder Representations from Transformers\uff09\u662fGoogle\u5f00\u53d1\u7684\u4e00\u79cd\u8bed\u8a00\u6a21\u578b\uff0c\u901a\u8fc7\u53cc\u5411Transformer\u67b6\u6784\u8fdb\u884c\u9884\u8bad\u7ec3\u3002BERT\u5728\u5404\u79cd\u81ea\u7136\u8bed\u8a00\u7406\u89e3\u4efb\u52a1\u4e2d\u8868\u73b0\u4f18\u5f02\uff0c\u5982\u95ee\u7b54\u7cfb\u7edf\u548c\u60c5\u611f\u5206\u6790\u7b49\u3002\n  <\/p>\n<h5>\n   <a id=\"ResNet_37\"><br \/>\n   <\/a><br \/>\n   ResNet<br \/>\n  <\/h5>\n<p>\n   ResNet\uff08Residual Network\uff09\u662fMicrosoft\u63d0\u51fa\u7684\u4e00\u79cd\u6df1\u5ea6\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff0c\u901a\u8fc7\u6b8b\u5dee\u8fde\u63a5\u89e3\u51b3\u4e86\u6df1\u5c42\u795e\u7ecf\u7f51\u7edc\u8bad\u7ec3\u4e2d\u7684\u68af\u5ea6\u6d88\u5931\u95ee\u9898\u3002ResNet\u5728\u56fe\u50cf\u8bc6\u522b\u4efb\u52a1\u4e2d\u53d6\u5f97\u4e86\u663e\u8457\u6210\u679c\u3002\n  <\/p>\n<h5>\n   <a id=\"Transformer_41\"><br \/>\n   <\/a><br \/>\n   Transformer<br \/>\n  <\/h5>\n<p>\n   Transformer\u662fGoogle\u63d0\u51fa\u7684\u4e00\u79cd\u57fa\u4e8e\u6ce8\u610f\u529b\u673a\u5236\u7684\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u3002\u4e0e\u4f20\u7edf\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u548c\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u4e0d\u540c\uff0cTransformer\u80fd\u591f\u66f4\u9ad8\u6548\u5730\u5904\u7406\u957f\u8ddd\u79bb\u4f9d\u8d56\u5173\u7cfb\u3002<br \/>\n   \n  <\/p>\n<h3>\n   <a id=\"_45\"><br \/>\n   <\/a><br \/>\n   \u673a\u5668\u5b66\u4e60\u4e0e\u5927\u6a21\u578b\u7684\u878d\u5408\u5e94\u7528<br \/>\n  <\/h3>\n<h4>\n   <a id=\"_47\"><br \/>\n   <\/a><br \/>\n   \u81ea\u7136\u8bed\u8a00\u5904\u7406<br \/>\n  <\/h4>\n<p>\n   \u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u662f\u673a\u5668\u5b66\u4e60\u4e0e\u5927\u6a21\u578b\u5e94\u7528\u6700\u5e7f\u6cdb\u7684\u9886\u57df\u4e4b\u4e00\u3002\u5927\u6a21\u578b\u5728\u6587\u672c\u751f\u6210\u3001\u6587\u672c\u5206\u7c7b\u3001\u60c5\u611f\u5206\u6790\u3001\u673a\u5668\u7ffb\u8bd1\u7b49\u4efb\u52a1\u4e2d\u8868\u73b0\u51fa\u8272\u3002\n  <\/p>\n<h5>\n   <a id=\"_51\"><br \/>\n   <\/a><br \/>\n   \u6587\u672c\u751f\u6210<br \/>\n  <\/h5>\n<p>\n   GPT-3\u7b49\u5927\u6a21\u578b\u5728\u6587\u672c\u751f\u6210\u4efb\u52a1\u4e2d\u5c55\u73b0\u4e86\u5f3a\u5927\u7684\u80fd\u529b\u3002\u901a\u8fc7\u9884\u8bad\u7ec3\uff0c\u5927\u6a21\u578b\u80fd\u591f\u7406\u89e3\u4e0a\u4e0b\u6587\uff0c\u751f\u6210\u8fde\u8d2f\u4e14\u6709\u610f\u4e49\u7684\u6587\u672c\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5bf9\u8bdd\u7cfb\u7edf\u3001\u5185\u5bb9\u521b\u4f5c\u548c\u81ea\u52a8\u6458\u8981\u7b49\u573a\u666f\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> openai\n\nopenai<span class=\"token punctuation\">.<\/span>api_key <span class=\"token operator\">=<\/span> <span class=\"token string\">'YOUR_API_KEY'<\/span>\n\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">generate_text<\/span><span class=\"token punctuation\">(<\/span>prompt<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    response <span class=\"token operator\">=<\/span> openai<span class=\"token punctuation\">.<\/span>Completion<span class=\"token punctuation\">.<\/span>create<span class=\"token punctuation\">(<\/span>\n        engine<span class=\"token operator\">=<\/span><span class=\"token string\">\"davinci-codex\"<\/span><span class=\"token punctuation\">,<\/span>\n        prompt<span class=\"token operator\">=<\/span>prompt<span class=\"token punctuation\">,<\/span>\n        max_tokens<span class=\"token operator\">=<\/span><span class=\"token number\">100<\/span>\n    <span class=\"token punctuation\">)<\/span>\n    <span class=\"token keyword\">return<\/span> response<span class=\"token punctuation\">.<\/span>choices<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>text<span class=\"token punctuation\">.<\/span>strip<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n\nprompt <span class=\"token operator\">=<\/span> <span class=\"token string\">\"Explain the significance of machine learning in modern technology.\"<\/span>\ngenerated_text <span class=\"token operator\">=<\/span> generate_text<span class=\"token punctuation\">(<\/span>prompt<span class=\"token punctuation\">)<\/span>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>generated_text<span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<h5>\n   <a id=\"_73\"><br \/>\n   <\/a><br \/>\n   \u6587\u672c\u5206\u7c7b<br \/>\n  <\/h5>\n<p>\n   BERT\u7b49\u5927\u6a21\u578b\u901a\u8fc7\u9884\u8bad\u7ec3\u548c\u5fae\u8c03\uff0c\u5728\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d\u53d6\u5f97\u4e86\u663e\u8457\u6210\u679c\u3002\u901a\u8fc7\u5bf9\u6587\u672c\u8fdb\u884c\u8bed\u4e49\u7406\u89e3\uff0c\u5927\u6a21\u578b\u80fd\u591f\u51c6\u786e\u5730\u5bf9\u6587\u672c\u8fdb\u884c\u5206\u7c7b\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5783\u573e\u90ae\u4ef6\u68c0\u6d4b\u3001\u60c5\u611f\u5206\u6790\u548c\u4e3b\u9898\u8bc6\u522b\u7b49\u9886\u57df\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">from<\/span> transformers <span class=\"token keyword\">import<\/span> BertTokenizer<span class=\"token punctuation\">,<\/span> BertForSequenceClassification\n<span class=\"token keyword\">from<\/span> transformers <span class=\"token keyword\">import<\/span> TextClassificationPipeline\n\ntokenizer <span class=\"token operator\">=<\/span> BertTokenizer<span class=\"token punctuation\">.<\/span>from_pretrained<span class=\"token punctuation\">(<\/span><span class=\"token string\">'bert-base-uncased'<\/span><span class=\"token punctuation\">)<\/span>\nmodel <span class=\"token operator\">=<\/span> BertForSequenceClassification<span class=\"token punctuation\">.<\/span>from_pretrained<span class=\"token punctuation\">(<\/span><span class=\"token string\">'bert-base-uncased'<\/span><span class=\"token punctuation\">)<\/span>\n\npipeline <span class=\"token operator\">=<\/span> TextClassificationPipeline<span class=\"token punctuation\">(<\/span>model<span class=\"token operator\">=<\/span>model<span class=\"token punctuation\">,<\/span> tokenizer<span class=\"token operator\">=<\/span>tokenizer<span class=\"token punctuation\">)<\/span>\nresult <span class=\"token operator\">=<\/span> pipeline<span class=\"token punctuation\">(<\/span><span class=\"token string\">\"I love this product, it's amazing!\"<\/span><span class=\"token punctuation\">)<\/span>\n\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>result<span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<h5>\n   <a id=\"_90\"><br \/>\n   <\/a><br \/>\n   \u673a\u5668\u7ffb\u8bd1<br \/>\n  <\/h5>\n<p>\n   Transformer\u67b6\u6784\u5728\u673a\u5668\u7ffb\u8bd1\u4efb\u52a1\u4e2d\u8868\u73b0\u51fa\u8272\u3002\u901a\u8fc7\u5927\u89c4\u6a21\u53cc\u8bed\u6570\u636e\u96c6\u7684\u8bad\u7ec3\uff0cTransformer\u80fd\u591f\u5b9e\u73b0\u9ad8\u8d28\u91cf\u7684\u6587\u672c\u7ffb\u8bd1\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u8de8\u8bed\u8a00\u4fe1\u606f\u4ea4\u6d41\u548c\u5168\u7403\u5316\u4e1a\u52a1\u62d3\u5c55\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">from<\/span> transformers <span class=\"token keyword\">import<\/span> MarianMTModel<span class=\"token punctuation\">,<\/span> MarianTokenizer\n\nmodel_name <span class=\"token operator\">=<\/span> <span class=\"token string\">'Helsinki-NLP\/opus-mt-en-de'<\/span>\ntokenizer <span class=\"token operator\">=<\/span> MarianTokenizer<span class=\"token punctuation\">.<\/span>from_pretrained<span class=\"token punctuation\">(<\/span>model_name<span class=\"token punctuation\">)<\/span>\nmodel <span class=\"token operator\">=<\/span> MarianMTModel<span class=\"token punctuation\">.<\/span>from_pretrained<span class=\"token punctuation\">(<\/span>model_name<span class=\"token punctuation\">)<\/span>\n\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">translate<\/span><span class=\"token punctuation\">(<\/span>text<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    inputs <span class=\"token operator\">=<\/span> tokenizer<span class=\"token punctuation\">(<\/span>text<span class=\"token punctuation\">,<\/span> return_tensors<span class=\"token operator\">=<\/span><span class=\"token string\">\"pt\"<\/span><span class=\"token punctuation\">,<\/span> padding<span class=\"token operator\">=<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span>\n    translated <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">.<\/span>generate<span class=\"token punctuation\">(<\/span><span class=\"token operator\">**<\/span>inputs<span class=\"token punctuation\">)<\/span>\n    translated_text <span class=\"token operator\">=<\/span> tokenizer<span class=\"token punctuation\">.<\/span>batch_decode<span class=\"token punctuation\">(<\/span>translated<span class=\"token punctuation\">,<\/span> skip_special_tokens<span class=\"token operator\">=<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span>\n    <span class=\"token keyword\">return<\/span> translated_text<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span>\n\ntext <span class=\"token operator\">=<\/span> <span class=\"token string\">\"Machine learning is transforming the way we approach problems in various fields.\"<\/span>\ntranslated_text <span class=\"token operator\">=<\/span> translate<span class=\"token punctuation\">(<\/span>text<span class=\"token punctuation\">)<\/span>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>translated_text<span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<\/p>\n<h4>\n   <a id=\"_113\"><br \/>\n   <\/a><br \/>\n   \u56fe\u50cf\u8bc6\u522b<br \/>\n  <\/h4>\n<p>\n   \u5927\u6a21\u578b\u5728\u56fe\u50cf\u8bc6\u522b\u9886\u57df\u540c\u6837\u53d6\u5f97\u4e86\u663e\u8457\u6210\u679c\u3002\u901a\u8fc7\u6df1\u5ea6\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08\u5982ResNet\uff09\uff0c\u5927\u6a21\u578b\u80fd\u591f\u51c6\u786e\u5730\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u7269\u4f53\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u81ea\u52a8\u9a7e\u9a76\u3001\u5b89\u9632\u76d1\u63a7\u548c\u533b\u5b66\u5f71\u50cf\u5206\u6790\u7b49\u9886\u57df\u3002\n  <\/p>\n<h5>\n   <a id=\"_117\"><br \/>\n   <\/a><br \/>\n   \u81ea\u52a8\u9a7e\u9a76<br \/>\n  <\/h5>\n<p>\n   \u5728\u81ea\u52a8\u9a7e\u9a76\u9886\u57df\uff0c\u56fe\u50cf\u8bc6\u522b\u6280\u672f\u7528\u4e8e\u68c0\u6d4b\u9053\u8def\u4e0a\u7684\u884c\u4eba\u3001\u8f66\u8f86\u548c\u4ea4\u901a\u6807\u5fd7\u7b49\u3002\u5927\u6a21\u578b\u901a\u8fc7\u5927\u91cf\u7684\u56fe\u50cf\u6570\u636e\u8fdb\u884c\u8bad\u7ec3\uff0c\u80fd\u591f\u5b9e\u73b0\u9ad8\u7cbe\u5ea6\u7684\u76ee\u6807\u68c0\u6d4b\u548c\u5206\u7c7b\uff0c\u63d0\u9ad8\u81ea\u52a8\u9a7e\u9a76\u7cfb\u7edf\u7684\u5b89\u5168\u6027\u548c\u53ef\u9760\u6027\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> torch\n<span class=\"token keyword\">import<\/span> torchvision<span class=\"token punctuation\">.<\/span>models <span class=\"token keyword\">as<\/span> models\n<span class=\"token keyword\">import<\/span> torchvision<span class=\"token punctuation\">.<\/span>transforms <span class=\"token keyword\">as<\/span> transforms\n<span class=\"token keyword\">from<\/span> PIL <span class=\"token keyword\">import<\/span> Image\n\nmodel <span class=\"token operator\">=<\/span> models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span>pretrained<span class=\"token operator\">=<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span>\nmodel<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">eval<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">predict_image<\/span><span class=\"token punctuation\">(<\/span>image_path<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    img <span class=\"token operator\">=<\/span> Image<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">open<\/span><span class=\"token punctuation\">(<\/span>image_path<span class=\"token punctuation\">)<\/span>\n    preprocess <span class=\"token operator\">=<\/span> transforms<span class=\"token punctuation\">.<\/span>Compose<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span>\n        transforms<span class=\"token punctuation\">.<\/span>Resize<span class=\"token punctuation\">(<\/span><span class=\"token number\">256<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span>\n        transforms<span class=\"token punctuation\">.<\/span>CenterCrop<span class=\"token punctuation\">(<\/span><span class=\"token number\">224<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span>\n        transforms<span class=\"token punctuation\">.<\/span>ToTensor<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span>\n        transforms<span class=\"token punctuation\">.<\/span>Normalize<span class=\"token punctuation\">(<\/span>mean<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">0.485<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.456<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.406<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> std<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">0.229<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.224<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.225<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span>\n    <span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\n    img_tensor <span class=\"token operator\">=<\/span> preprocess<span class=\"token punctuation\">(<\/span>img<span class=\"token punctuation\">)<\/span>\n    img_tensor <span class=\"token operator\">=<\/span> img_tensor<span class=\"token punctuation\">.<\/span>unsqueeze<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span>\n\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>\n        output <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">(<\/span>img_tensor<span class=\"token punctuation\">)<\/span>\n    \n    _<span class=\"token punctuation\">,<\/span> predicted <span class=\"token operator\">=<\/span> torch<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">max<\/span><span class=\"token punctuation\">(<\/span>output<span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span>\n    <span class=\"token keyword\">return<\/span> predicted<span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n\nimage_path <span class=\"token operator\">=<\/span> <span class=\"token string\">'path\/to\/your\/image.jpg'<\/span>\npredicted_class <span class=\"token operator\">=<\/span> predict_image<span class=\"token punctuation\">(<\/span>image_path<span class=\"token punctuation\">)<\/span>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>predicted_class<span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<h5>\n   <a id=\"_152\"><br \/>\n   <\/a><br \/>\n   \u533b\u5b66\u5f71\u50cf\u5206\u6790<br \/>\n  <\/h5>\n<p>\n   \u5728\u533b\u5b66\u5f71\u50cf\u5206\u6790\u9886\u57df\uff0c\u56fe\u50cf\u8bc6\u522b\u6280\u672f\u7528\u4e8e\u68c0\u6d4b\u548c\u8bca\u65ad\u75be\u75c5\u3002\u5927\u6a21\u578b\u901a\u8fc7\u5927\u91cf\u7684\u533b\u5b66\u5f71\u50cf\u6570\u636e\u8fdb\u884c\u8bad\u7ec3\uff0c\u80fd\u591f\u51c6\u786e\u5730\u8bc6\u522b\u75c5\u53d8\u533a\u57df\uff0c\u8f85\u52a9\u533b\u751f\u8fdb\u884c\u8bca\u65ad\u548c\u6cbb\u7597\uff0c\u63d0\u9ad8\u533b\u7597\u670d\u52a1\u7684\u8d28\u91cf\u548c\u6548\u7387\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">from<\/span> fastai<span class=\"token punctuation\">.<\/span>vision<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">all<\/span> <span class=\"token keyword\">import<\/span> <span class=\"token operator\">*<\/span>\n\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">load_learner<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    learner <span class=\"token operator\">=<\/span> load_learner<span class=\"token punctuation\">(<\/span><span class=\"token string\">'path\/to\/your\/exported\/learner.pkl'<\/span><span class=\"token punctuation\">)<\/span>\n    <span class=\"token keyword\">return<\/span> learner\n\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">predict_image<\/span><span class=\"token punctuation\">(<\/span>image_path<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    learner <span class=\"token operator\">=<\/span> load_learner<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n    img <span class=\"token operator\">=<\/span> PILImage<span class=\"token punctuation\">.<\/span>create<span class=\"token punctuation\">(<\/span>image_path<span class=\"token punctuation\">)<\/span>\n    pred<span class=\"token punctuation\">,<\/span> pred_idx<span class=\"token punctuation\">,<\/span> probs <span class=\"token operator\">=<\/span> learner<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>img<span class=\"token punctuation\">)<\/span>\n    <span class=\"token keyword\">return<\/span> pred<span class=\"token punctuation\">,<\/span> probs<span class=\"token punctuation\">[<\/span>pred_idx<span class=\"token punctuation\">]<\/span>\n\nimage_path <span class=\"token operator\">=<\/span> <span class=\"token string\">'path\/to\/your\/image.jpg'<\/span>\npredicted_class<span class=\"token punctuation\">,<\/span> probability <span class=\"token operator\">=<\/span> predict_image<span class=\"token punctuation\">(<\/span>image_path<span class=\"token punctuation\">)<\/span>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f'Predicted class: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<!-- --><\/span>predicted_class<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">, Probability: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<!-- --><\/span>probability<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">'<\/span><\/span><span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<h4>\n   <a id=\"_174\"><br \/>\n   <\/a><br \/>\n   \u8bed\u97f3\u8bc6\u522b<br \/>\n  <\/h4>\n<p>\n   \u8bed\u97f3\u8bc6\u522b\u662f\u5927\u6a21\u578b\u5728\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u91cd\u8981\u5e94\u7528\u4e4b\u4e00\u3002\u901a\u8fc7\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u548c\u5927\u89c4\u6a21\u8bed\u97f3\u6570\u636e\u96c6\u7684\u8bad\u7ec3\uff0c\u5927\u6a21\u578b\u80fd\u591f\u5b9e\u73b0\u9ad8\u7cbe\u5ea6\u7684\u8bed\u97f3\u8bc6\u522b\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u667a\u80fd\u52a9\u624b\u3001\u8bed\u97f3\u63a7\u5236\u548c\u8bed\u97f3\u8f6c\u6587\u5b57\u7b49\u573a\u666f\u3002<br \/>\n   \n  <\/p>\n<h5>\n   <a id=\"_179\"><br \/>\n   <\/a><br \/>\n   \u667a\u80fd\u52a9\u624b<br \/>\n  <\/h5>\n<p>\n   \u667a\u80fd\u52a9\u624b\u5982Siri\u3001Alexa\u548cGoogle Assistant\u7b49\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5bb6\u5ead\u548c\u529e\u516c\u573a\u666f\u3002\u5927\u6a21\u578b\u901a\u8fc7\u8bed\u97f3\u8bc6\u522b\u6280\u672f\uff0c\u5b9e\u73b0\u8bed\u97f3\u6307\u4ee4\u7684\u7406\u89e3\u548c\u54cd\u5e94\uff0c\u63d0\u9ad8\u7528\u6237\u7684\u4ea4\u4e92\u4f53\u9a8c\u548c\u5de5\u4f5c\u6548\u7387\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> speech_recognition <span class=\"token keyword\">as<\/span> sr\n\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">recognize_speech<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    recognizer <span class=\"token operator\">=<\/span> sr<span class=\"token punctuation\">.<\/span>Recognizer<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n    <span class=\"token keyword\">with<\/span> sr<span class=\"token punctuation\">.<\/span>Microphone<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">as<\/span> source<span class=\"token punctuation\">:<\/span>\n        <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">\"Say something:\"<\/span><span class=\"token punctuation\">)<\/span>\n        audio <span class=\"token operator\">=<\/span> recognizer<span class=\"token punctuation\">.<\/span>listen<span class=\"token punctuation\">(<\/span>source<span class=\"token punctuation\">)<\/span>\n\n    <span class=\"token keyword\">try<\/span><span class=\"token punctuation\">:<\/span>\n        text <span class=\"token operator\">=<\/span> recognizer<span class=\"token punctuation\">.<\/span>recognize_google<span class=\"token punctuation\">(<\/span>audio<span class=\"token punctuation\">)<\/span>\n        <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">\"You said: \"<\/span> <span class=\"token operator\">+<\/span> text<span class=\"token punctuation\">)<\/span>\n    <span class=\"token keyword\">except<\/span> sr<span class=\"token punctuation\">.<\/span>UnknownValueError<span class=\"token punctuation\">:<\/span>\n        <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">\"Google Speech Recognition could not understand audio\"<\/span><span class=\"token punctuation\">)<\/span>\n    <span class=\"token keyword\">except<\/span> sr<span class=\"token punctuation\">.<\/span>RequestError <span class=\"token keyword\">as<\/span> e<span class=\"token punctuation\">:<\/span>\n        <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">\"Could not request results from Google Speech Recognition service; {0}\"<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">format<\/span><span class=\"token punctuation\">(<\/span>e<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>\n\nrecognize_speech<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<h5>\n   <a id=\"_203\"><br \/>\n   <\/a><br \/>\n   \u8bed\u97f3\u8f6c\u6587\u5b57<br \/>\n  <\/h5>\n<p>\n   \u8bed\u97f3\u8f6c\u6587\u5b57\u6280\u672f\u7528\u4e8e\u5c06\u8bed\u97f3\u4fe1\u606f\u8f6c\u6362\u4e3a\u6587\u672c\u4fe1\u606f\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u4f1a\u8bae\u8bb0\u5f55\u3001\u5ba2\u6237\u670d\u52a1\u548c\u8bed\u97f3\u641c\u7d22\n  <\/p>\n<p>\n   \u7b49\u573a\u666f\u3002\u5927\u6a21\u578b\u901a\u8fc7\u8bed\u97f3\u8bc6\u522b\u6280\u672f\uff0c\u5b9e\u73b0\u9ad8\u7cbe\u5ea6\u7684\u8bed\u97f3\u8f6c\u6587\u5b57\uff0c\u63d0\u9ad8\u4fe1\u606f\u5904\u7406\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">from<\/span> transformers <span class=\"token keyword\">import<\/span> Wav2Vec2ForCTC<span class=\"token punctuation\">,<\/span> Wav2Vec2Tokenizer\n<span class=\"token keyword\">import<\/span> torch\n<span class=\"token keyword\">import<\/span> librosa\n\ntokenizer <span class=\"token operator\">=<\/span> Wav2Vec2Tokenizer<span class=\"token punctuation\">.<\/span>from_pretrained<span class=\"token punctuation\">(<\/span><span class=\"token string\">\"facebook\/wav2vec2-base-960h\"<\/span><span class=\"token punctuation\">)<\/span>\nmodel <span class=\"token operator\">=<\/span> Wav2Vec2ForCTC<span class=\"token punctuation\">.<\/span>from_pretrained<span class=\"token punctuation\">(<\/span><span class=\"token string\">\"facebook\/wav2vec2-base-960h\"<\/span><span class=\"token punctuation\">)<\/span>\n\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">speech_to_text<\/span><span class=\"token punctuation\">(<\/span>audio_path<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    speech<span class=\"token punctuation\">,<\/span> _ <span class=\"token operator\">=<\/span> librosa<span class=\"token punctuation\">.<\/span>load<span class=\"token punctuation\">(<\/span>audio_path<span class=\"token punctuation\">,<\/span> sr<span class=\"token operator\">=<\/span><span class=\"token number\">16000<\/span><span class=\"token punctuation\">)<\/span>\n    input_values <span class=\"token operator\">=<\/span> tokenizer<span class=\"token punctuation\">(<\/span>speech<span class=\"token punctuation\">,<\/span> return_tensors<span class=\"token operator\">=<\/span><span class=\"token string\">\"pt\"<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>input_values\n    logits <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">(<\/span>input_values<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>logits\n    predicted_ids <span class=\"token operator\">=<\/span> torch<span class=\"token punctuation\">.<\/span>argmax<span class=\"token punctuation\">(<\/span>logits<span class=\"token punctuation\">,<\/span> dim<span class=\"token operator\">=<\/span><span class=\"token operator\">-<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span>\n    transcription <span class=\"token operator\">=<\/span> tokenizer<span class=\"token punctuation\">.<\/span>batch_decode<span class=\"token punctuation\">(<\/span>predicted_ids<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span>\n    <span class=\"token keyword\">return<\/span> transcription\n\naudio_path <span class=\"token operator\">=<\/span> <span class=\"token string\">'path\/to\/your\/audio.wav'<\/span>\ntranscription <span class=\"token operator\">=<\/span> speech_to_text<span class=\"token punctuation\">(<\/span>audio_path<span class=\"token punctuation\">)<\/span>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>transcription<span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<\/p>\n<h3>\n   <a id=\"_231\"><br \/>\n   <\/a><br \/>\n   \u5927\u6a21\u578b\u6027\u80fd\u4f18\u5316\u7684\u65b0\u63a2\u7d22<br \/>\n  <\/h3>\n<h4>\n   <a id=\"_233\"><br \/>\n   <\/a><br \/>\n   \u6a21\u578b\u538b\u7f29<br \/>\n  <\/h4>\n<p>\n   \u5927\u6a21\u578b\u867d\u7136\u5728\u6027\u80fd\u4e0a\u8868\u73b0\u51fa\u8272\uff0c\u4f46\u5176\u5de8\u5927\u7684\u53c2\u6570\u91cf\u548c\u8ba1\u7b97\u590d\u6742\u5ea6\u5e26\u6765\u4e86\u663e\u8457\u7684\u5b58\u50a8\u548c\u8ba1\u7b97\u6210\u672c\u3002\u4e3a\u4e86\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u66f4\u9ad8\u6548\u5730\u90e8\u7f72\u5927\u6a21\u578b\uff0c\u6a21\u578b\u538b\u7f29\u6280\u672f\u6210\u4e3a\u4e86\u91cd\u8981\u7684\u7814\u7a76\u65b9\u5411\u3002\n  <\/p>\n<h5>\n   <a id=\"_237\"><br \/>\n   <\/a><br \/>\n   \u6743\u91cd\u526a\u679d<br \/>\n  <\/h5>\n<p>\n   \u6743\u91cd\u526a\u679d\u662f\u4e00\u79cd\u901a\u8fc7\u5220\u9664\u7f51\u7edc\u4e2d\u4e0d\u91cd\u8981\u7684\u6743\u91cd\uff0c\u51cf\u5c11\u6a21\u578b\u53c2\u6570\u91cf\u548c\u8ba1\u7b97\u91cf\u7684\u6280\u672f\u3002\u901a\u8fc7\u526a\u679d\u540e\u7684\u6a21\u578b\u5728\u63a8\u7406\u9636\u6bb5\u80fd\u591f\u5927\u5e45\u5ea6\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\uff0c\u540c\u65f6\u4fdd\u6301\u8f83\u9ad8\u7684\u51c6\u786e\u5ea6\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> torch\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>utils<span class=\"token punctuation\">.<\/span>prune <span class=\"token keyword\">as<\/span> prune\n\nmodel <span class=\"token operator\">=<\/span> models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span>pretrained<span class=\"token operator\">=<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span>\nparameters_to_prune <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">(<\/span>module<span class=\"token punctuation\">,<\/span> <span class=\"token string\">'weight'<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">for<\/span> module <span class=\"token keyword\">in<\/span> model<span class=\"token punctuation\">.<\/span>modules<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">if<\/span> <span class=\"token builtin\">isinstance<\/span><span class=\"token punctuation\">(<\/span>module<span class=\"token punctuation\">,<\/span> torch<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>Conv2d<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">]<\/span>\n\n<span class=\"token keyword\">for<\/span> module<span class=\"token punctuation\">,<\/span> param <span class=\"token keyword\">in<\/span> parameters_to_prune<span class=\"token punctuation\">:<\/span>\n    prune<span class=\"token punctuation\">.<\/span>l1_unstructured<span class=\"token punctuation\">(<\/span>module<span class=\"token punctuation\">,<\/span> name<span class=\"token operator\">=<\/span>param<span class=\"token punctuation\">,<\/span> amount<span class=\"token operator\">=<\/span><span class=\"token number\">0.2<\/span><span class=\"token punctuation\">)<\/span>\n\n<span class=\"token comment\"># Remove pruning reparameterization to enable inference<\/span>\n<span class=\"token keyword\">for<\/span> module<span class=\"token punctuation\">,<\/span> param <span class=\"token keyword\">in<\/span> parameters_to_prune<span class=\"token punctuation\">:<\/span>\n    prune<span class=\"token punctuation\">.<\/span>remove<span class=\"token punctuation\">(<\/span>module<span class=\"token punctuation\">,<\/span> param<span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<h5>\n   <a id=\"_256\"><br \/>\n   <\/a><br \/>\n   \u91cf\u5316<br \/>\n  <\/h5>\n<p>\n   \u91cf\u5316\u662f\u5c06\u6a21\u578b\u53c2\u6570\u4ece\u9ad8\u7cbe\u5ea6\uff08\u598232\u4f4d\u6d6e\u70b9\u6570\uff09\u8f6c\u6362\u4e3a\u4f4e\u7cbe\u5ea6\uff08\u59828\u4f4d\u6574\u6570\uff09\u7684\u6280\u672f\u3002\u91cf\u5316\u540e\u7684\u6a21\u578b\u80fd\u591f\u5927\u5e45\u5ea6\u51cf\u5c11\u5b58\u50a8\u7a7a\u95f4\u548c\u8ba1\u7b97\u590d\u6742\u5ea6\uff0c\u540c\u65f6\u4fdd\u6301\u8f83\u9ad8\u7684\u6027\u80fd\u3002\n  <\/p>\n<pre><code class=\"prism language-python\">model <span class=\"token operator\">=<\/span> models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span>pretrained<span class=\"token operator\">=<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span>\nmodel<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">eval<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n\nquantized_model <span class=\"token operator\">=<\/span> torch<span class=\"token punctuation\">.<\/span>quantization<span class=\"token punctuation\">.<\/span>quantize_dynamic<span class=\"token punctuation\">(<\/span>\n    model<span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">{<!-- --><\/span>torch<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">}<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">=<\/span>torch<span class=\"token punctuation\">.<\/span>qint8\n<span class=\"token punctuation\">)<\/span>\n\n<span class=\"token comment\"># Test the quantized model<\/span>\ninput_tensor <span class=\"token operator\">=<\/span> torch<span class=\"token punctuation\">.<\/span>randn<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">)<\/span>\noutput <span class=\"token operator\">=<\/span> quantized_model<span class=\"token punctuation\">(<\/span>input_tensor<span class=\"token punctuation\">)<\/span>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>output<span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<h5>\n   <a id=\"_274\"><br \/>\n   <\/a><br \/>\n   \u77e5\u8bc6\u84b8\u998f<br \/>\n  <\/h5>\n<p>\n   \u77e5\u8bc6\u84b8\u998f\u662f\u4e00\u79cd\u901a\u8fc7\u8bad\u7ec3\u4e00\u4e2a\u8f83\u5c0f\u7684\u5b66\u751f\u6a21\u578b\u6765\u6a21\u4eff\u5927\u6a21\u578b\u7684\u884c\u4e3a\uff0c\u4ece\u800c\u8fbe\u5230\u6a21\u578b\u538b\u7f29\u7684\u6280\u672f\u3002\u5b66\u751f\u6a21\u578b\u5728\u63a8\u7406\u9636\u6bb5\u80fd\u591f\u66f4\u9ad8\u6548\u5730\u8fd0\u884c\uff0c\u540c\u65f6\u4fdd\u6301\u63a5\u8fd1\u5927\u6a21\u578b\u7684\u6027\u80fd\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>functional <span class=\"token keyword\">as<\/span> F\n\n<span class=\"token comment\"># Define teacher and student models<\/span>\nteacher_model <span class=\"token operator\">=<\/span> models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span>pretrained<span class=\"token operator\">=<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span>\nstudent_model <span class=\"token operator\">=<\/span> models<span class=\"token punctuation\">.<\/span>resnet18<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n\n<span class=\"token comment\"># Define a knowledge distillation loss function<\/span>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">distillation_loss<\/span><span class=\"token punctuation\">(<\/span>student_outputs<span class=\"token punctuation\">,<\/span> teacher_outputs<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">,<\/span> alpha<span class=\"token operator\">=<\/span><span class=\"token number\">0.5<\/span><span class=\"token punctuation\">,<\/span> temperature<span class=\"token operator\">=<\/span><span class=\"token number\">2.0<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    loss_ce <span class=\"token operator\">=<\/span> F<span class=\"token punctuation\">.<\/span>cross_entropy<span class=\"token punctuation\">(<\/span>student_outputs<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">)<\/span>\n    loss_kd <span class=\"token operator\">=<\/span> F<span class=\"token punctuation\">.<\/span>kl_div<span class=\"token punctuation\">(<\/span>\n        F<span class=\"token punctuation\">.<\/span>log_softmax<span class=\"token punctuation\">(<\/span>student_outputs <span class=\"token operator\">\/<\/span> temperature<span class=\"token punctuation\">,<\/span> dim<span class=\"token operator\">=<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span>\n        F<span class=\"token punctuation\">.<\/span>softmax<span class=\"token punctuation\">(<\/span>teacher_outputs <span class=\"token operator\">\/<\/span> temperature<span class=\"token punctuation\">,<\/span> dim<span class=\"token operator\">=<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span>\n        reduction<span class=\"token operator\">=<\/span><span class=\"token string\">'batchmean'<\/span>\n    <span class=\"token punctuation\">)<\/span> <span class=\"token operator\">*<\/span> <span class=\"token punctuation\">(<\/span>temperature <span class=\"token operator\">**<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span>\n    <span class=\"token keyword\">return<\/span> alpha <span class=\"token operator\">*<\/span> loss_ce <span class=\"token operator\">+<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span> <span class=\"token operator\">-<\/span> alpha<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">*<\/span> loss_kd\n\n<span class=\"token comment\"># Example training loop<\/span>\noptimizer <span class=\"token operator\">=<\/span> torch<span class=\"token punctuation\">.<\/span>optim<span class=\"token punctuation\">.<\/span>SGD<span class=\"token punctuation\">(<\/span>student_model<span class=\"token punctuation\">.<\/span>parameters<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> lr<span class=\"token operator\">=<\/span><span class=\"token number\">0.01<\/span><span class=\"token punctuation\">,<\/span> momentum<span class=\"token operator\">=<\/span><span class=\"token number\">0.9<\/span><span class=\"token punctuation\">)<\/span>\n\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><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    <span class=\"token keyword\">for<\/span> inputs<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> dataloader<span class=\"token punctuation\">:<\/span>\n        optimizer<span class=\"token punctuation\">.<\/span>zero_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n        student_outputs <span class=\"token operator\">=<\/span> student_model<span class=\"token punctuation\">(<\/span>inputs<span class=\"token punctuation\">)<\/span>\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>\n            teacher_outputs <span class=\"token operator\">=<\/span> teacher_model<span class=\"token punctuation\">(<\/span>inputs<span class=\"token punctuation\">)<\/span>\n        loss <span class=\"token operator\">=<\/span> distillation_loss<span class=\"token punctuation\">(<\/span>student_outputs<span class=\"token punctuation\">,<\/span> teacher_outputs<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">)<\/span>\n        loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n        optimizer<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<h4>\n   <a id=\"_309\"><br \/>\n   <\/a><br \/>\n   \u5206\u5e03\u5f0f\u8bad\u7ec3<br \/>\n  <\/h4>\n<p>\n   \u5927\u6a21\u578b\u7684\u8bad\u7ec3\u901a\u5e38\u9700\u8981\u5927\u91cf\u7684\u8ba1\u7b97\u8d44\u6e90\u548c\u65f6\u95f4\u3002\u4e3a\u4e86\u52a0\u901f\u8bad\u7ec3\u8fc7\u7a0b\uff0c\u5206\u5e03\u5f0f\u8bad\u7ec3\u6280\u672f\u6210\u4e3a\u4e86\u91cd\u8981\u7684\u7814\u7a76\u65b9\u5411\u3002\n  <\/p>\n<h5>\n   <a id=\"_313\"><br \/>\n   <\/a><br \/>\n   \u6570\u636e\u5e76\u884c<br \/>\n  <\/h5>\n<p>\n   \u6570\u636e\u5e76\u884c\u662f\u4e00\u79cd\u5c06\u5927\u89c4\u6a21\u6570\u636e\u96c6\u5206\u5272\u6210\u591a\u4e2a\u5c0f\u6279\u6b21\uff0c\u5e76\u5728\u591a\u4e2a\u8ba1\u7b97\u8282\u70b9\u4e0a\u5e76\u884c\u8bad\u7ec3\u6a21\u578b\u7684\u6280\u672f\u3002\u6570\u636e\u5e76\u884c\u80fd\u591f\u663e\u8457\u52a0\u901f\u5927\u6a21\u578b\u7684\u8bad\u7ec3\u8fc7\u7a0b\uff0c\u540c\u65f6\u63d0\u9ad8\u8ba1\u7b97\u8d44\u6e90\u7684\u5229\u7528\u7387\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> torch\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>distributed <span class=\"token keyword\">as<\/span> dist\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>nn <span class=\"token keyword\">as<\/span> nn\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>optim <span class=\"token keyword\">as<\/span> optim\n<span class=\"token keyword\">from<\/span> torch<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>parallel <span class=\"token keyword\">import<\/span> DistributedDataParallel <span class=\"token keyword\">as<\/span> DDP\n\ndist<span class=\"token punctuation\">.<\/span>init_process_group<span class=\"token punctuation\">(<\/span>backend<span class=\"token operator\">=<\/span><span class=\"token string\">'nccl'<\/span><span class=\"token punctuation\">)<\/span>\nmodel <span class=\"token operator\">=<\/span> models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>cuda<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\nddp_model <span class=\"token operator\">=<\/span> DDP<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">)<\/span>\noptimizer <span class=\"token operator\">=<\/span> optim<span class=\"token punctuation\">.<\/span>SGD<span class=\"token punctuation\">(<\/span>ddp_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\">=<\/span><span class=\"token number\">0.01<\/span><span class=\"token punctuation\">)<\/span>\n\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><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    <span class=\"token keyword\">for<\/span> inputs<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> dataloader<span class=\"token punctuation\">:<\/span>\n        inputs<span class=\"token punctuation\">,<\/span> labels <span class=\"token operator\">=<\/span> inputs<span class=\"token punctuation\">.<\/span>cuda<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">.<\/span>cuda<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n        optimizer<span class=\"token punctuation\">.<\/span>zero_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n        outputs <span class=\"token operator\">=<\/span> ddp_model<span class=\"token punctuation\">(<\/span>inputs<span class=\"token punctuation\">)<\/span>\n        loss <span class=\"token operator\">=<\/span> F<span class=\"token punctuation\">.<\/span>cross_entropy<span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">)<\/span>\n        loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n        optimizer<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<h5>\n   <a id=\"_339\"><br \/>\n   <\/a><br \/>\n   \u6a21\u578b\u5e76\u884c<br \/>\n  <\/h5>\n<p>\n   \u6a21\u578b\u5e76\u884c\u662f\u4e00\u79cd\u5c06\u5927\u6a21\u578b\u5206\u5272\u6210\u591a\u4e2a\u5b50\u6a21\u578b\uff0c\u5e76\u5728\u591a\u4e2a\u8ba1\u7b97\u8282\u70b9\u4e0a\u5e76\u884c\u8bad\u7ec3\u7684\u6280\u672f\u3002\u6a21\u578b\u5e76\u884c\u80fd\u591f\u89e3\u51b3\u5355\u4e2a\u8ba1\u7b97\u8282\u70b9\u5185\u5b58\u4e0d\u8db3\u7684\u95ee\u9898\uff0c\u63d0\u9ad8\u5927\u6a21\u578b\u7684\u8bad\u7ec3\u6548\u7387\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> torch\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>nn <span class=\"token keyword\">as<\/span> nn\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>optim <span class=\"token keyword\">as<\/span> optim\n<span class=\"token keyword\">from<\/span> torch<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>parallel <span class=\"token keyword\">import<\/span> DistributedDataParallel <span class=\"token keyword\">as<\/span> DDP\n\n<span class=\"token keyword\">class<\/span> <span class=\"token class-name\">ModelParallelResNet50<\/span><span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Module<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\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>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span>ModelParallelResNet50<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>\n        self<span class=\"token punctuation\">.<\/span>model <span class=\"token operator\">=<\/span> nn<span class=\"token punctuation\">.<\/span>Sequential<span class=\"token punctuation\">(<\/span>\n            models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>layer1<span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span><span class=\"token string\">'cuda:0'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span>\n            models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>layer2<span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span><span class=\"token string\">'cuda:1'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span>\n            models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>layer3<span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span><span class=\"token string\">'cuda:2'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span>\n            models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>layer4<span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span><span class=\"token string\">'cuda:3'<\/span><span class=\"token punctuation\">)<\/span>\n        <span class=\"token punctuation\">)<\/span>\n\n    <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>\n        <span class=\"token keyword\">for<\/span> layer <span class=\"token keyword\">in<\/span> self<span class=\"token punctuation\">.<\/span>model<span class=\"token punctuation\">:<\/span>\n            x <span class=\"token operator\">=<\/span> layer<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span>\n        <span class=\"token keyword\">return<\/span> x\n\nmodel <span class=\"token operator\">=<\/span> ModelParallelResNet50<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\noptimizer <span class=\"token operator\">=<\/span> optim<span class=\"token punctuation\">.<\/span>SGD<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\">=<\/span><span class=\"token number\">0.01<\/span><span class=\"token punctuation\">)<\/span>\n\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><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    <span class=\"token keyword\">for<\/span> inputs<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> dataloader<span class=\"token punctuation\">:<\/span>\n        inputs<span class=\"token punctuation\">,<\/span> labels <span class=\"token operator\">=<\/span> inputs<span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span><span class=\"token string\">'cuda:0'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span><span class=\"token string\">'cuda:3'<\/span><span class=\"token punctuation\">)<\/span>\n        optimizer<span class=\"token punctuation\">.<\/span>zero_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n        outputs <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">(<\/span>inputs<span class=\"token punctuation\">)<\/span>\n        loss <span class=\"token operator\">=<\/span> F<span class=\"token punctuation\">.<\/span>cross_entropy<span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">)<\/span>\n        loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n        optimizer<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<h5>\n   <a id=\"_377\"><br \/>\n   <\/a><br \/>\n   \u5f02\u6b65\u8bad\u7ec3<br \/>\n  <\/h5>\n<p>\n   \u5f02\u6b65\u8bad\u7ec3\u662f\u4e00\u79cd\u5141\u8bb8\u4e0d\u540c\u8ba1\u7b97\u8282\u70b9\u5728\u4e0d\u540c\u6b65\u7684\u60c5\u51b5\u4e0b\u8fdb\u884c\u6a21\u578b\u66f4\u65b0\u7684\u6280\u672f\u3002\u5f02\u6b65\u8bad\u7ec3\u80fd\u591f\u63d0\u9ad8\u8ba1\u7b97\u8d44\u6e90\u7684\u5229\u7528\u6548\u7387\uff0c\u52a0\u901f\u5927\u6a21\u578b\u7684\u8bad\u7ec3\u8fc7\u7a0b\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> torch\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>distributed <span class=\"token keyword\">as<\/span> dist\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>nn <span class=\"token keyword\">as<\/span> nn\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>optim <span class=\"token keyword\">as<\/span> optim\n<span class=\"token keyword\">from<\/span> torch<span class=\"token punctuation\">.<\/span>multiprocessing <span class=\"token keyword\">import<\/span> Process\n\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">train<\/span><span class=\"token punctuation\">(<\/span>rank<span class=\"token punctuation\">,<\/span> world_size<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    dist<span class=\"token punctuation\">.<\/span>init_process_group<span class=\"token punctuation\">(<\/span>backend<span class=\"token operator\">=<\/span><span class=\"token string\">'gloo'<\/span><span class=\"token punctuation\">,<\/span> rank<span class=\"token operator\">=<\/span>rank<span class=\"token punctuation\">,<\/span> world_size<span class=\"token operator\">=<\/span>world_size<span class=\"token punctuation\">)<\/span>\n    model <span class=\"token operator\">=<\/span> models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span>rank<span class=\"token punctuation\">)<\/span>\n    ddp_model <span class=\"token operator\">=<\/span> nn<span class=\"token punctuation\">.<\/span>parallel<span class=\"token punctuation\">.<\/span>DistributedDataParallel<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">,<\/span> device_ids<span class=\"token operator\">=<\/span><span class=\"token punctuation\">[<\/span>rank<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\n    optimizer <span class=\"token operator\">=<\/span> optim<span class=\"token punctuation\">.<\/span>SGD<span class=\"token punctuation\">(<\/span>ddp_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\">=<\/span><span class=\"token number\">0.01<\/span><span class=\"token punctuation\">)<\/span>\n\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><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n        <span class=\"token keyword\">for<\/span> inputs<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> dataloader<span class=\"token punctuation\">:<\/span>\n            inputs<span class=\"token punctuation\">,<\/span> labels <span class=\"token operator\">=<\/span> inputs<span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span>rank<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span>rank<span class=\"token punctuation\">)<\/span>\n            optimizer<span class=\"token punctuation\">.<\/span>zero_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n            outputs <span class=\"token operator\">=<\/span> ddp_model<span class=\"token punctuation\">(<\/span>inputs<span class=\"token punctuation\">)<\/span>\n            loss <span class=\"token operator\">=<\/span> F<span class=\"token punctuation\">.<\/span>cross_entropy<span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">)<\/span>\n            loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n            optimizer<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">main<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    world_size <span class=\"token operator\">=<\/span> <span class=\"token number\">4<\/span>\n    processes <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span>\n    <span class=\"token keyword\">for<\/span> rank <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span>world_size<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n        p <span class=\"token operator\">=<\/span> Process<span class=\"token punctuation\">(<\/span>target<span class=\"token operator\">=<\/span>train<span class=\"token punctuation\">,<\/span> args<span class=\"token operator\">=<\/span><span class=\"token punctuation\">(<\/span>rank<span class=\"token punctuation\">,<\/span> world_size<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>\n        p<span class=\"token punctuation\">.<\/span>start<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n        processes<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>p<span class=\"token punctuation\">)<\/span>\n\n    <span class=\"token keyword\">for<\/span> p <span class=\"token keyword\">in<\/span> processes<span class=\"token punctuation\">:<\/span>\n        p<span class=\"token punctuation\">.<\/span>join<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n\n<span class=\"token keyword\">if<\/span> __name__ <span class=\"token operator\">==<\/span> <span class=\"token string\">\"__main__\"<\/span><span class=\"token punctuation\">:<\/span>\n    main<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<\/p>\n<h4>\n   <a id=\"_419\"><br \/>\n   <\/a><br \/>\n   \u9ad8\u6548\u63a8\u7406<br \/>\n  <\/h4>\n<p>\n   \u5927\u6a21\u578b\u5728\u63a8\u7406\u9636\u6bb5\u540c\u6837\u9762\u4e34\u8ba1\u7b97\u590d\u6742\u5ea6\u9ad8\u7684\u95ee\u9898\u3002\u4e3a\u4e86\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u66f4\u9ad8\u6548\u5730\u8fdb\u884c\u63a8\u7406\uff0c\u9ad8\u6548\u63a8\u7406\u6280\u672f\u6210\u4e3a\u4e86\u91cd\u8981\u7684\u7814\u7a76\u65b9\u5411\u3002\n  <\/p>\n<h5>\n   <a id=\"_423\"><br \/>\n   <\/a><br \/>\n   \u6a21\u578b\u88c1\u526a<br \/>\n  <\/h5>\n<p>\n   \u6a21\u578b\u88c1\u526a\u662f\u4e00\u79cd\u5728\u63a8\u7406\u9636\u6bb5\uff0c\u6839\u636e\u8f93\u5165\u6570\u636e\u7684\u7279\u6027\u52a8\u6001\u88c1\u526a\u6a21\u578b\u7ed3\u6784\u7684\u6280\u672f\u3002\u6a21\u578b\u88c1\u526a\u80fd\u591f\u51cf\u5c11\u8ba1\u7b97\u91cf\uff0c\u63d0\u9ad8\u63a8\u7406\u901f\u5ea6\uff0c\u540c\u65f6\u4fdd\u6301\u8f83\u9ad8\u7684\u6027\u80fd\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> torch\n\n<span class=\"token keyword\">class<\/span> <span class=\"token class-name\">DynamicPruningModel<\/span><span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Module<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    <span class=\"token keyword\">def<\/span> <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> model<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span>DynamicPruningModel<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>\n        self<span class=\"token punctuation\">.<\/span>model <span class=\"token operator\">=<\/span> model\n\n    <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>\n        <span class=\"token comment\"># Example of dynamic pruning logic<\/span>\n        <span class=\"token keyword\">if<\/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 operator\">&gt;<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">:<\/span>\n            self<span class=\"token punctuation\">.<\/span>model<span class=\"token punctuation\">.<\/span>layer4<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>conv1 <span class=\"token operator\">=<\/span> nn<span class=\"token punctuation\">.<\/span>Identity<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n        <span class=\"token keyword\">return<\/span> self<span class=\"token punctuation\">.<\/span>model<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span>\n\nmodel <span class=\"token operator\">=<\/span> models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span>pretrained<span class=\"token operator\">=<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span>\npruned_model <span class=\"token operator\">=<\/span> DynamicPruningModel<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">)<\/span>\n\ninput_tensor <span class=\"token operator\">=<\/span> torch<span class=\"token punctuation\">.<\/span>randn<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">)<\/span>\noutput <span class=\"token operator\">=<\/span> pruned_model<span class=\"token punctuation\">(<\/span>input_tensor<span class=\"token punctuation\">)<\/span>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>output<span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<h5>\n   <a id=\"_449\"><br \/>\n   <\/a><br \/>\n   \u7f13\u5b58\u673a\u5236<br \/>\n  <\/h5>\n<p>\n   \u7f13\u5b58\u673a\u5236\u662f\u4e00\u79cd\u901a\u8fc7\u7f13\u5b58\u63a8\u7406\u8fc7\u7a0b\u4e2d\u7684\u4e2d\u95f4\u7ed3\u679c\uff0c\u51cf\u5c11\u91cd\u590d\u8ba1\u7b97\u7684\u6280\u672f\u3002\u7f13\u5b58\u673a\u5236\u80fd\u591f\u663e\u8457\u63d0\u9ad8\u5927\u6a21\u578b\u7684\u63a8\u7406\u6548\u7387\uff0c\u7279\u522b\u662f\u5728\u5b9e\u65f6\u5e94\u7528\u573a\u666f\u4e2d\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> torch\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>nn <span class=\"token keyword\">as<\/span> nn\n\n<span class=\"token keyword\">class<\/span> <span class=\"token class-name\">CachedModel<\/span><span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Module<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n    <span class=\"token keyword\">def<\/span> <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> model<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span>CachedModel<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>\n        self<span class=\"token punctuation\">.<\/span>model <span class=\"token operator\">=<\/span> model\n        self<span class=\"token punctuation\">.<\/span>cache <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">{<!-- --><\/span><span class=\"token punctuation\">}<\/span>\n\n    <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>\n        <span class=\"token keyword\">if<\/span> x <span class=\"token keyword\">in<\/span> self<span class=\"token punctuation\">.<\/span>cache<span class=\"token punctuation\">:<\/span>\n            <span class=\"token keyword\">return<\/span> self<span class=\"token punctuation\">.<\/span>cache<span class=\"token punctuation\">[<\/span>x<span class=\"token punctuation\">]<\/span>\n        output <span class=\"token operator\">=<\/span> self<span class=\"token punctuation\">.<\/span>model<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span>\n        self\n\n<span class=\"token punctuation\">.<\/span>cache<span class=\"token punctuation\">[<\/span>x<span class=\"token punctuation\">]<\/span> <span class=\"token operator\">=<\/span> output\n        <span class=\"token keyword\">return<\/span> output\n\nmodel <span class=\"token operator\">=<\/span> models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span>pretrained<span class=\"token operator\">=<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span>\ncached_model <span class=\"token operator\">=<\/span> CachedModel<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">)<\/span>\n\ninput_tensor <span class=\"token operator\">=<\/span> torch<span class=\"token punctuation\">.<\/span>randn<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">)<\/span>\noutput <span class=\"token operator\">=<\/span> cached_model<span class=\"token punctuation\">(<\/span>input_tensor<span class=\"token punctuation\">)<\/span>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>output<span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<h5>\n   <a id=\"_480\"><br \/>\n   <\/a><br \/>\n   \u4e13\u7528\u786c\u4ef6<br \/>\n  <\/h5>\n<p>\n   \u4e13\u7528\u786c\u4ef6\u5982GPU\u3001TPU\u548cFPGA\u7b49\uff0c\u80fd\u591f\u4e3a\u5927\u6a21\u578b\u7684\u63a8\u7406\u63d0\u4f9b\u9ad8\u6548\u7684\u8ba1\u7b97\u652f\u6301\u3002\u901a\u8fc7\u5229\u7528\u4e13\u7528\u786c\u4ef6\u7684\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\uff0c\u5927\u6a21\u578b\u80fd\u591f\u5728\u63a8\u7406\u9636\u6bb5\u663e\u8457\u63d0\u9ad8\u6027\u80fd\u3002\n  <\/p>\n<pre><code class=\"prism language-python\"><span class=\"token keyword\">import<\/span> torch\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>nn <span class=\"token keyword\">as<\/span> nn\n\n<span class=\"token comment\"># Check if a GPU is available<\/span>\ndevice <span class=\"token operator\">=<\/span> torch<span class=\"token punctuation\">.<\/span>device<span class=\"token punctuation\">(<\/span><span class=\"token string\">'cuda'<\/span> <span class=\"token keyword\">if<\/span> torch<span class=\"token punctuation\">.<\/span>cuda<span class=\"token punctuation\">.<\/span>is_available<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">else<\/span> <span class=\"token string\">'cpu'<\/span><span class=\"token punctuation\">)<\/span>\nmodel <span class=\"token operator\">=<\/span> models<span class=\"token punctuation\">.<\/span>resnet50<span class=\"token punctuation\">(<\/span>pretrained<span class=\"token operator\">=<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span>device<span class=\"token punctuation\">)<\/span>\n\ninput_tensor <span class=\"token operator\">=<\/span> torch<span class=\"token punctuation\">.<\/span>randn<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span>device<span class=\"token punctuation\">)<\/span>\noutput <span class=\"token operator\">=<\/span> model<span class=\"token punctuation\">(<\/span>input_tensor<span class=\"token punctuation\">)<\/span>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>output<span class=\"token punctuation\">)<\/span>\n<\/code><\/pre>\n<\/p>\n<h3>\n   <a id=\"_498\"><br \/>\n   <\/a><br \/>\n   \u672a\u6765\u5c55\u671b<br \/>\n  <\/h3>\n<h4>\n   <a id=\"_500\"><br \/>\n   <\/a><br \/>\n   \u8de8\u9886\u57df\u5e94\u7528<br \/>\n  <\/h4>\n<p>\n   \u968f\u7740\u5927\u6a21\u578b\u7684\u4e0d\u65ad\u53d1\u5c55\u548c\u4f18\u5316\uff0c\u673a\u5668\u5b66\u4e60\u548c\u5927\u6a21\u578b\u7684\u878d\u5408\u5e94\u7528\u5c06\u5728\u66f4\u591a\u9886\u57df\u5f97\u5230\u62d3\u5c55\u3002\u672a\u6765\uff0c\u5927\u6a21\u578b\u5c06\u5728\u533b\u7597\u3001\u91d1\u878d\u3001\u6559\u80b2\u3001\u80fd\u6e90\u7b49\u9886\u57df\u53d1\u6325\u66f4\u5927\u7684\u4f5c\u7528\uff0c\u4e3a\u5404\u884c\u5404\u4e1a\u5e26\u6765\u6df1\u8fdc\u7684\u5f71\u54cd\u548c\u53d8\u9769\u3002\n  <\/p>\n<h4>\n   <a id=\"_504\"><br \/>\n   <\/a><br \/>\n   \u667a\u80fd\u5316\u7cfb\u7edf<br \/>\n  <\/h4>\n<p>\n   \u672a\u6765\u7684\u667a\u80fd\u5316\u7cfb\u7edf\u5c06\u66f4\u52a0\u4f9d\u8d56\u4e8e\u5927\u6a21\u578b\u7684\u652f\u6301\u3002\u901a\u8fc7\u5c06\u5927\u6a21\u578b\u5e94\u7528\u4e8e\u667a\u80fd\u5236\u9020\u3001\u667a\u80fd\u4ea4\u901a\u548c\u667a\u6167\u57ce\u5e02\u7b49\u9886\u57df\uff0c\u53ef\u4ee5\u5b9e\u73b0\u66f4\u52a0\u9ad8\u6548\u3001\u667a\u80fd\u548c\u81ea\u52a8\u5316\u7684\u7cfb\u7edf\uff0c\u63d0\u9ad8\u751f\u4ea7\u6548\u7387\u548c\u751f\u6d3b\u8d28\u91cf\u3002\n  <\/p>\n<h4>\n   <a id=\"_508\"><br \/>\n   <\/a><br \/>\n   \u4eba\u5de5\u667a\u80fd\u4f26\u7406<br \/>\n  <\/h4>\n<p>\n   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