{"id":70563,"date":"2026-02-02T02:50:04","date_gmt":"2026-02-01T18:50:04","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/70563.html"},"modified":"2026-02-02T02:50:04","modified_gmt":"2026-02-01T18:50:04","slug":"%e3%80%8a%e9%80%89%e6%8b%a9pytorch%e7%9a%84n%e4%b8%aa%e7%90%86%e7%94%b1%ef%bc%9a%e4%b8%80%e4%bb%bd%e6%9d%a5%e8%87%aa%e7%89%b9%e6%80%a7%e3%80%81%e7%94%9f%e6%80%81%e4%b8%8e%e7%a4%be%e5%8c%ba%e7%9a%84","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/70563.html","title":{"rendered":"\u300a\u9009\u62e9PyTorch\u7684N\u4e2a\u7406\u7531\uff1a\u4e00\u4efd\u6765\u81ea\u7279\u6027\u3001\u751f\u6001\u4e0e\u793e\u533a\u7684\u5168\u9762\u8bc4\u4f30\u300b"},"content":{"rendered":"<h2 id=\"%E6%9C%AC%E7%AF%87%E6%8A%80%E6%9C%AF%E5%8D%9A%E6%96%87%E6%91%98%E8%A6%81%20%F0%9F%8C%9F\" style=\"text-align:center\">\u672c\u7bc7\u6280\u672f\u535a\u6587\u6458\u8981 &#x1f31f;<\/h2>\n<ul>\n<li>PyTorch \u662f\u4e00\u4e2a\u4ee5\u4e24\u5927\u6838\u5fc3\u7279\u5f81\u8457\u79f0\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u3002\u5176\u9996\u8981\u7279\u6027\u662f\u52a8\u6001\u8ba1\u7b97\u56fe&#xff0c;\u5b83\u5141\u8bb8\u5728\u8fd0\u884c\u65f6\u5b9a\u4e49\u548c\u4fee\u6539\u8ba1\u7b97\u6d41\u7a0b&#xff0c;\u4e3a\u6a21\u578b\u5f00\u53d1\u4e0e\u8c03\u8bd5\u5e26\u6765\u4e86\u6781\u5927\u7684\u7075\u6d3b\u6027\u3002<\/li>\n<li>\u5176\u6b21&#xff0c;PyTorch \u63d0\u4f9b\u4e86\u7c7b\u4f3c NumPy \u7684\u5f20\u91cf\u64cd\u4f5c&#xff0c;\u5e76\u96c6\u6210\u4e86\u81ea\u52a8\u6c42\u5bfc\u7cfb\u7edf&#xff0c;\u4f7f\u5f97\u68af\u5ea6\u8ba1\u7b97\u548c\u53cd\u5411\u4f20\u64ad\u8fc7\u7a0b\u7b80\u6d01\u9ad8\u6548\u3002\u5728\u6a21\u578b\u6784\u5efa\u65b9\u9762&#xff0c;\u5176 torch.nn\u6a21\u5757\u8ba9\u795e\u7ecf\u7f51\u7edc\u7684\u5b9a\u4e49\u4e0e\u8bad\u7ec3\u6d41\u7a0b\u5341\u5206\u76f4\u89c2\u3002<\/li>\n<li>\u540c\u65f6&#xff0c;PyTorch \u5177\u5907\u5f3a\u5927\u7684 GPU \u52a0\u901f\u80fd\u529b&#xff0c;\u80fd\u591f\u663e\u8457\u63d0\u5347\u5927\u89c4\u6a21\u8ba1\u7b97\u4efb\u52a1\u7684\u6548\u7387\u3002\u8be5\u6846\u67b6\u62e5\u6709\u6d3b\u8dc3\u7684\u793e\u533a\u548c\u4e30\u5bcc\u7684\u751f\u6001\u7cfb\u7edf&#xff0c;\u63d0\u4f9b\u4e86\u4ece\u8ba1\u7b97\u673a\u89c6\u89c9\u5230\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7684\u5168\u5957\u5de5\u5177\u94fe\u3002<\/li>\n<li>\u5728\u4e0e TensorFlow \u7684\u5bf9\u6bd4\u4e2d&#xff0c;PyTorch \u56e0\u5176\u52a8\u6001\u56fe\u548c Python \u4f18\u5148\u7684\u8bbe\u8ba1&#xff0c;\u5728\u7814\u7a76\u9886\u57df\u548c\u539f\u578b\u5f00\u53d1\u4e2d\u66f4\u53d7\u9752\u7750&#xff1b;\u800c\u4e0e NumPy \u7684\u7d27\u5bc6\u517c\u5bb9\u6027\u5219\u964d\u4f4e\u4e86\u5b66\u4e60\u95e8\u69db\u3002\u81ea 2016 \u5e74\u5f00\u6e90\u4ee5\u6765&#xff0c;PyTorch \u7ecf\u5386\u4e86\u5feb\u901f\u8fed\u4ee3\u4e0e\u53d1\u5c55&#xff0c;\u73b0\u5df2\u6574\u5408\u4e86\u9488\u5bf9\u751f\u4ea7\u73af\u5883\u7684 TorchScript \u7b49\u7279\u6027&#xff0c;\u6301\u7eed\u5de9\u56fa\u5176\u4f5c\u4e3a\u5b66\u672f\u754c\u548c\u5de5\u4e1a\u754c\u4e3b\u6d41\u6846\u67b6\u4e4b\u4e00\u7684\u5730\u4f4d\u3002<\/li>\n<\/ul>\n<h2 id=\"%E5%BC%95%E8%A8%80%20%F0%9F%93%98\">\u5f15\u8a00 &#x1f4d8;<\/h2>\n<ul>\n<li><span style=\"color:#38d8f0\">\u5728\u8fd9\u4e2a\u53d8\u5e7b\u83ab\u6d4b\u3001\u5feb\u901f\u53d1\u5c55\u7684\u6280\u672f\u65f6\u4ee3&#xff0c;\u4e0e\u65f6\u4ff1\u8fdb\u662f\u6bcf\u4e2aIT\u5de5\u7a0b\u5e08\u7684\u5fc5\u4fee\u8bfe\u3002<\/span><\/li>\n<li><span style=\"color:#38d8f0\">\u6211\u662f\u76db\u900f\u4fa7\u89c6\u653b\u57ce\u72ee&#xff0c;\u4e00\u540d\u4ec0\u4e48\u90fd\u4f1a\u4e00\u4e22\u4e22\u7684\u7f51\u7edc\u5b89\u5168\u5de5\u7a0b\u5e08&#xff0c;\u4e5f\u662f\u4f17\u591a\u6280\u672f\u793e\u533a\u7684\u6d3b\u8dc3\u6210\u5458\u4ee5\u53ca\u591a\u5bb6\u5927\u5382\u5b98\u65b9\u8ba4\u53ef\u4eba\u5458&#xff0c;\u5e0c\u671b\u80fd\u591f\u4e0e\u5404\u4f4d\u5728\u6b64\u5171\u540c\u6210\u957f\u3002<\/span><\/li>\n<\/ul>\n<h2 id=\"\"><\/h2>\n<p style=\"text-align:center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"438\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260201184956-697fa054d3347.gif\" width=\"1200\" \/><\/p>\n<\/p>\n<h2 id=\"%E4%B8%8A%E8%8A%82%E5%9B%9E%E9%A1%BE\" style=\"text-align:center\">\u4e0a\u8282\u56de\u987e<\/h2>\n<p id=\"main-toc\">\u76ee\u5f55<\/p>\n<p id=\"%E6%9C%AC%E7%AF%87%E6%8A%80%E6%9C%AF%E5%8D%9A%E6%96%87%E6%91%98%E8%A6%81%20%F0%9F%8C%9F-toc\" style=\"margin-left:0px\">\u672c\u7bc7\u6280\u672f\u535a\u6587\u6458\u8981 &#x1f31f;<\/p>\n<p id=\"%E5%BC%95%E8%A8%80%20%F0%9F%93%98-toc\" style=\"margin-left:0px\">\u5f15\u8a00 &#x1f4d8;<\/p>\n<p id=\"-toc\" style=\"margin-left:0px\">\n<p id=\"%E4%B8%8A%E8%8A%82%E5%9B%9E%E9%A1%BE-toc\" style=\"margin-left:0px\">\u4e0a\u8282\u56de\u987e<\/p>\n<p id=\"-toc\" style=\"margin-left:0px\">\n<p id=\"PyTorch%20%E7%AE%80%E4%BB%8B-toc\" style=\"margin-left:0px\">PyTorch \u7b80\u4ecb<\/p>\n<p id=\"1.PyTorch%20%E4%B8%BB%E8%A6%81%E6%9C%89%E4%B8%A4%E5%A4%A7%E7%89%B9%E5%BE%81%EF%BC%9A-toc\" style=\"margin-left:0px\">1.PyTorch \u4e3b\u8981\u6709\u4e24\u5927\u7279\u5f81&#xff1a;<\/p>\n<p id=\"1.1%E7%89%B9%E5%BE%81%E4%B8%80-toc\" style=\"margin-left:40px\">1.1\u7279\u5f81\u4e00<\/p>\n<p id=\"1.2%E7%89%B9%E5%BE%81%E4%BA%8C-toc\" style=\"margin-left:40px\">1.2\u7279\u5f81\u4e8c<\/p>\n<p id=\"2.PyTorch%20%E7%89%B9%E6%80%A7-toc\" style=\"margin-left:0px\">2.PyTorch \u7279\u6027<\/p>\n<p id=\"2.1%E5%8A%A8%E6%80%81%E8%AE%A1%E7%AE%97%E5%9B%BE%EF%BC%88Dynamic%20Computation%20Graph%EF%BC%89-toc\" style=\"margin-left:40px\">2.1\u52a8\u6001\u8ba1\u7b97\u56fe&#xff08;Dynamic Computation Graph&#xff09;<\/p>\n<p id=\"2.1.1%E5%8A%A8%E6%80%81%E8%AE%A1%E7%AE%97%E5%9B%BE%E7%9A%84%E4%BC%98%E7%82%B9-toc\" style=\"margin-left:80px\">2.1.1\u52a8\u6001\u8ba1\u7b97\u56fe\u7684\u4f18\u70b9<\/p>\n<p id=\"2.2%E5%BC%A0%E9%87%8F%EF%BC%88Tensor%EF%BC%89%E4%B8%8E%E8%87%AA%E5%8A%A8%E6%B1%82%E5%AF%BC%EF%BC%88Autograd%EF%BC%89-toc\" style=\"margin-left:40px\">2.2\u5f20\u91cf&#xff08;Tensor&#xff09;\u4e0e\u81ea\u52a8\u6c42\u5bfc&#xff08;Autograd&#xff09;<\/p>\n<p id=\"2.2.1%E5%BC%A0%E9%87%8F%EF%BC%88Tensor%EF%BC%89-toc\" style=\"margin-left:80px\">2.2.1\u5f20\u91cf&#xff08;Tensor&#xff09;<\/p>\n<p id=\"2.2.2%E8%87%AA%E5%8A%A8%E6%B1%82%E5%AF%BC%EF%BC%88Autograd%EF%BC%89-toc\" style=\"margin-left:80px\">2.2.2\u81ea\u52a8\u6c42\u5bfc&#xff08;Autograd&#xff09;<\/p>\n<p id=\"2.3%E6%A8%A1%E5%9E%8B%E5%AE%9A%E4%B9%89%E4%B8%8E%E8%AE%AD%E7%BB%83-toc\" style=\"margin-left:40px\">2.3\u6a21\u578b\u5b9a\u4e49\u4e0e\u8bad\u7ec3<\/p>\n<p id=\"2.3.1%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%A8%A1%E5%9D%97%EF%BC%88torch.nn%EF%BC%89%EF%BC%9A-toc\" style=\"margin-left:80px\">2.3.1\u795e\u7ecf\u7f51\u7edc\u6a21\u5757&#xff08;torch.nn&#xff09;&#xff1a;<\/p>\n<p id=\"3.GPU%20%E5%8A%A0%E9%80%9F-toc\" style=\"margin-left:0px\">3.GPU \u52a0\u901f<\/p>\n<p id=\"3.1GPU%20%E6%94%AF%E6%8C%81%EF%BC%9A-toc\" style=\"margin-left:40px\">3.1GPU \u652f\u6301&#xff1a;<\/p>\n<p id=\"4.%E7%94%9F%E6%80%81%E7%B3%BB%E7%BB%9F%E4%B8%8E%E7%A4%BE%E5%8C%BA%E6%94%AF%E6%8C%81-toc\" style=\"margin-left:0px\">4.\u751f\u6001\u7cfb\u7edf\u4e0e\u793e\u533a\u652f\u6301<\/p>\n<p id=\"5.%E4%B8%8E%E5%85%B6%E4%BB%96%E6%A1%86%E6%9E%B6%E7%9A%84%E5%AF%B9%E6%AF%94-toc\" style=\"margin-left:0px\">5.\u4e0e\u5176\u4ed6\u6846\u67b6\u7684\u5bf9\u6bd4<\/p>\n<p id=\"TensorFlow%20vs%20PyTorch-toc\" style=\"margin-left:80px\">TensorFlow vs PyTorch<\/p>\n<p id=\"5.1TensorFlow%20vs%20PyTorch%E5%85%B7%E4%BD%93%E7%89%B9%E6%80%A7%E8%A1%A8%E6%A0%BC%E5%AF%B9%E6%AF%94%E5%A6%82%E4%B8%8B%EF%BC%9A-toc\" style=\"margin-left:40px\">5.1TensorFlow vs PyTorch\u5177\u4f53\u7279\u6027\u8868\u683c\u5bf9\u6bd4\u5982\u4e0b&#xff1a;<\/p>\n<p id=\"5.2PyTorch%20vs%20NumPy%E5%85%B7%E4%BD%93%E7%89%B9%E6%80%A7%E8%A1%A8%E6%A0%BC%E5%AF%B9%E6%AF%94%E5%A6%82%E4%B8%8B%EF%BC%9A-toc\" style=\"margin-left:80px\">5.2PyTorch vs NumPy\u5177\u4f53\u7279\u6027\u8868\u683c\u5bf9\u6bd4\u5982\u4e0b&#xff1a;<\/p>\n<p id=\"6.PyTorch%20%E7%9A%84%E5%8E%86%E5%8F%B2%E4%B8%8E%E5%8F%91%E5%B1%95-toc\" style=\"margin-left:0px\">6.PyTorch \u7684\u5386\u53f2\u4e0e\u53d1\u5c55<\/p>\n<p id=\"%E6%AC%A2%E8%BF%8E%E5%90%84%E4%BD%8D%E5%BD%A6%E7%A5%96%E4%B8%8E%E7%83%AD%E5%B7%B4%E7%95%85%E6%B8%B8%E6%9C%AC%E4%BA%BA%E4%B8%93%E6%A0%8F%E4%B8%8E%E5%8D%9A%E5%AE%A2-toc\" style=\"margin-left:0px\">\u6b22\u8fce\u5404\u4f4d\u5f66\u7956\u4e0e\u70ed\u5df4\u7545\u6e38\u672c\u4eba\u4e13\u680f\u4e0e\u6280\u672f\u535a\u5ba2<\/p>\n<p id=\"%E4%BD%A0%E7%9A%84%E4%B8%89%E8%BF%9E%E6%98%AF%E6%88%91%E6%9C%80%E5%A4%A7%E7%9A%84%E5%8A%A8%E5%8A%9B-toc\" style=\"margin-left:0px\">\u4f60\u7684\u4e09\u8fde\u662f\u6211\u6700\u5927\u7684\u52a8\u529b<\/p>\n<p id=\"%E4%BB%A5%E4%B8%8B%E5%9B%BE%E7%89%87%E4%BB%85%E4%BB%A3%E8%A1%A8%E4%B8%93%E6%A0%8F%E7%89%B9%E8%89%B2%20%5B%E7%82%B9%E5%87%BB%E7%AE%AD%E5%A4%B4%E6%8C%87%E5%90%91%E7%9A%84%E4%B8%93%E6%A0%8F%E5%90%8D%E5%8D%B3%E5%8F%AF%E9%97%AA%E7%8E%B0%5D-toc\" style=\"margin-left:40px\">\u70b9\u51fb\u27a1\ufe0f\u6307\u5411\u7684\u4e13\u680f\u540d\u5373\u53ef\u95ea\u73b0<\/p>\n<hr id=\"hr-toc\" \/>\n<h2 id=\"\"><\/h2>\n<p style=\"text-align:center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"1080\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260201184956-697fa054e734c.gif\" width=\"1200\" \/><\/p>\n<h2 id=\"PyTorch%20%E7%AE%80%E4%BB%8B\" style=\"background-color:transparent\">PyTorch \u7b80\u4ecb<\/h2>\n<ul>\n<li>PyTorch \u662f\u4e00\u4e2a\u5f00\u6e90\u7684 Python \u673a\u5668\u5b66\u4e60\u5e93&#xff0c;\u57fa\u4e8e Torch \u5e93&#xff0c;\u5e95\u5c42\u7531 C&#043;&#043; \u5b9e\u73b0&#xff0c;\u5e94\u7528\u4e8e\u4eba\u5de5\u667a\u80fd\u9886\u57df&#xff0c;\u5982\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3002<\/li>\n<li>PyTorch \u6700\u521d\u7531 Meta Platforms \u7684\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u56e2\u961f\u5f00\u53d1&#xff0c;\u73b0\u5728\u5c5e \u4e8eLinux \u57fa\u91d1\u4f1a\u7684\u4e00\u90e8\u5206\u3002<\/li>\n<li>\u8bb8\u591a\u6df1\u5ea6\u5b66\u4e60\u8f6f\u4ef6\u90fd\u662f\u57fa\u4e8e PyTorch \u6784\u5efa\u7684&#xff0c;\u5305\u62ec\u7279\u65af\u62c9\u81ea\u52a8\u9a7e\u9a76\u3001Uber \u7684 Pyro\u3001Hugging Face \u7684 Transformers\u3001 PyTorch Lightning \u548c Catalyst\u3002<\/li>\n<\/ul>\n<h2 id=\"1.PyTorch%20%E4%B8%BB%E8%A6%81%E6%9C%89%E4%B8%A4%E5%A4%A7%E7%89%B9%E5%BE%81%EF%BC%9A\" style=\"background-color:transparent\">1.PyTorch \u4e3b\u8981\u6709\u4e24\u5927\u7279\u5f81&#xff1a;<\/h2>\n<h3 id=\"1.1%E7%89%B9%E5%BE%81%E4%B8%80\" style=\"background-color:transparent\">1.1\u7279\u5f81\u4e00<\/h3>\n<ul>\n<li>\u7c7b\u4f3c\u4e8e NumPy \u7684\u5f20\u91cf\u8ba1\u7b97&#xff0c;\u80fd\u5728 GPU \u6216 MPS \u7b49\u786c\u4ef6\u52a0\u901f\u5668\u4e0a\u52a0\u901f\u3002<\/li>\n<li>\u57fa\u4e8e\u5e26\u81ea\u52a8\u5fae\u5206\u7cfb\u7edf\u7684\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u3002<\/li>\n<\/ul>\n<h3 id=\"1.2%E7%89%B9%E5%BE%81%E4%BA%8C\">1.2\u7279\u5f81\u4e8c<\/h3>\n<ul>\n<li>PyTorch \u5305\u62ec torch.autograd\u3001torch.nn\u3001torch.optim \u7b49\u5b50\u6a21\u5757\u3002<\/li>\n<li>PyTorch \u5305\u542b\u591a\u79cd\u635f\u5931\u51fd\u6570&#xff0c;\u5305\u62ec MSE&#xff08;\u5747\u65b9\u8bef\u5dee &#061; L2 \u8303\u6570&#xff09;\u3001\u4ea4\u53c9\u71b5\u635f\u5931\u548c\u8d1f\u71b5\u4f3c\u7136\u635f\u5931&#xff08;\u5bf9\u5206\u7c7b\u5668\u6709\u7528&#xff09;\u7b49\u3002<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"625\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260201184957-697fa0550a0f5.png\" width=\"1280\" \/><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"927\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260201184957-697fa0554ec2d.png\" width=\"1301\" \/><\/p>\n<h2 id=\"2.PyTorch%20%E7%89%B9%E6%80%A7\" style=\"background-color:transparent\">2.PyTorch \u7279\u6027<\/h2>\n<ul>\n<li>\n<p>\u52a8\u6001\u8ba1\u7b97\u56fe&#xff08;Dynamic Computation Graphs&#xff09;&#xff1a; PyTorch \u7684\u8ba1\u7b97\u56fe\u662f\u52a8\u6001\u7684&#xff0c;\u8fd9\u610f\u5473\u7740\u5b83\u4eec\u5728\u8fd0\u884c\u65f6\u6784\u5efa&#xff0c;\u5e76\u4e14\u53ef\u4ee5\u968f\u65f6\u6539\u53d8\u3002\u8fd9\u4e3a\u5b9e\u9a8c\u548c\u8c03\u8bd5\u63d0\u4f9b\u4e86\u6781\u5927\u7684\u7075\u6d3b\u6027&#xff0c;\u56e0\u4e3a\u5f00\u53d1\u8005\u53ef\u4ee5\u9010\u884c\u6267\u884c\u4ee3\u7801&#xff0c;\u67e5\u770b\u4e2d\u95f4\u7ed3\u679c\u3002<\/p>\n<\/li>\n<li>\n<p>\u81ea\u52a8\u5fae\u5206&#xff08;Automatic Differentiation&#xff09;&#xff1a; PyTorch \u7684\u81ea\u52a8\u5fae\u5206\u7cfb\u7edf\u5141\u8bb8\u5f00\u53d1\u8005\u8f7b\u677e\u5730\u8ba1\u7b97\u68af\u5ea6&#xff0c;\u8fd9\u5bf9\u4e8e\u8bad\u7ec3\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u81f3\u5173\u91cd\u8981\u3002\u5b83\u901a\u8fc7\u53cd\u5411\u4f20\u64ad\u7b97\u6cd5\u81ea\u52a8\u8ba1\u7b97\u51fa\u635f\u5931\u51fd\u6570\u5bf9\u6a21\u578b\u53c2\u6570\u7684\u68af\u5ea6\u3002<\/p>\n<\/li>\n<li>\n<p>\u5f20\u91cf\u8ba1\u7b97&#xff08;Tensor Computation&#xff09;&#xff1a; PyTorch \u63d0\u4f9b\u4e86\u7c7b\u4f3c\u4e8e NumPy \u7684\u5f20\u91cf\u64cd\u4f5c&#xff0c;\u8fd9\u4e9b\u64cd\u4f5c\u53ef\u4ee5\u5728 CPU \u548c GPU \u4e0a\u6267\u884c&#xff0c;\u4ece\u800c\u52a0\u901f\u8ba1\u7b97\u8fc7\u7a0b\u3002\u5f20\u91cf\u662f PyTorch \u4e2d\u7684\u57fa\u672c\u6570\u636e\u7ed3\u6784&#xff0c;\u7528\u4e8e\u5b58\u50a8\u548c\u64cd\u4f5c\u6570\u636e\u3002<\/p>\n<\/li>\n<li>\n<p>\u4e30\u5bcc\u7684 API&#xff1a; PyTorch \u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u9884\u5b9a\u4e49\u5c42\u3001\u635f\u5931\u51fd\u6570\u548c\u4f18\u5316\u7b97\u6cd5&#xff0c;\u8fd9\u4e9b\u90fd\u662f\u6784\u5efa\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u5e38\u7528\u7ec4\u4ef6\u3002<\/p>\n<\/li>\n<li>\n<p>\u591a\u8bed\u8a00\u652f\u6301&#xff1a; PyTorch \u867d\u7136\u4ee5 Python \u4e3a\u4e3b\u8981\u63a5\u53e3&#xff0c;\u4f46\u4e5f\u63d0\u4f9b\u4e86 C&#043;&#043; \u63a5\u53e3&#xff0c;\u5141\u8bb8\u66f4\u5e95\u5c42\u7684\u96c6\u6210\u548c\u63a7\u5236\u3002<\/p>\n<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"928\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260201184958-697fa056892d5.png\" width=\"932\" \/><\/p>\n<h3 id=\"2.1%E5%8A%A8%E6%80%81%E8%AE%A1%E7%AE%97%E5%9B%BE%EF%BC%88Dynamic%20Computation%20Graph%EF%BC%89\">2.1\u52a8\u6001\u8ba1\u7b97\u56fe&#xff08;Dynamic Computation Graph&#xff09;<\/h3>\n<ul>\n<li>\n<p>PyTorch \u6700\u663e\u8457\u7684\u7279\u70b9\u4e4b\u4e00\u662f\u5176\u52a8\u6001\u8ba1\u7b97\u56fe\u7684\u673a\u5236\u3002<\/p>\n<\/li>\n<li>\n<p>\u4e0e TensorFlow \u7684\u9759\u6001\u8ba1\u7b97\u56fe&#xff08;graph&#xff09;\u4e0d\u540c&#xff0c;PyTorch \u5728\u6267\u884c\u65f6\u6784\u5efa\u8ba1\u7b97\u56fe&#xff0c;\u8fd9\u610f\u5473\u7740\u5728\u6bcf\u6b21\u8ba1\u7b97\u65f6&#xff0c;\u56fe\u90fd\u4f1a\u6839\u636e\u8f93\u5165\u6570\u636e\u7684\u5f62\u72b6\u81ea\u52a8\u53d8\u5316\u3002<\/p>\n<\/li>\n<\/ul>\n<h4 id=\"2.1.1%E5%8A%A8%E6%80%81%E8%AE%A1%E7%AE%97%E5%9B%BE%E7%9A%84%E4%BC%98%E7%82%B9\">2.1.1\u52a8\u6001\u8ba1\u7b97\u56fe\u7684\u4f18\u70b9<\/h4>\n<ul>\n<li>\u66f4\u52a0\u7075\u6d3b&#xff0c;\u7279\u522b\u9002\u7528\u4e8e\u9700\u8981\u6761\u4ef6\u5224\u65ad\u6216\u9012\u5f52\u7684\u573a\u666f\u3002<\/li>\n<li>\u65b9\u4fbf\u8c03\u8bd5\u548c\u4fee\u6539&#xff0c;\u80fd\u591f\u76f4\u63a5\u67e5\u770b\u4e2d\u95f4\u7ed3\u679c\u3002<\/li>\n<li>\u66f4\u63a5\u8fd1 Python \u7f16\u7a0b\u7684\u98ce\u683c&#xff0c;\u6613\u4e8e\u4e0a\u624b\u3002<\/li>\n<\/ul>\n<h3 id=\"2.2%E5%BC%A0%E9%87%8F%EF%BC%88Tensor%EF%BC%89%E4%B8%8E%E8%87%AA%E5%8A%A8%E6%B1%82%E5%AF%BC%EF%BC%88Autograd%EF%BC%89\" style=\"background-color:transparent\">2.2\u5f20\u91cf&#xff08;Tensor&#xff09;\u4e0e\u81ea\u52a8\u6c42\u5bfc&#xff08;Autograd&#xff09;<\/h3>\n<ul>\n<li>PyTorch \u4e2d\u7684\u6838\u5fc3\u6570\u636e\u7ed3\u6784\u662f\u00a0\u5f20\u91cf&#xff08;Tensor&#xff09;&#xff0c;\u5b83\u662f\u4e00\u4e2a\u591a\u7ef4\u77e9\u9635&#xff0c;\u53ef\u4ee5\u5728 CPU \u6216 GPU \u4e0a\u9ad8\u6548\u5730\u8fdb\u884c\u8ba1\u7b97\u3002\u5f20\u91cf\u7684\u64cd\u4f5c\u652f\u6301\u81ea\u52a8\u6c42\u5bfc&#xff08;Autograd&#xff09;\u673a\u5236&#xff0c;\u4f7f\u5f97\u5728\u53cd\u5411\u4f20\u64ad\u8fc7\u7a0b\u4e2d\u81ea\u52a8\u8ba1\u7b97\u68af\u5ea6&#xff0c;\u8fd9\u5bf9\u4e8e\u6df1\u5ea6\u5b66\u4e60\u4e2d\u7684\u68af\u5ea6\u4e0b\u964d\u4f18\u5316\u7b97\u6cd5\u81f3\u5173\u91cd\u8981\u3002<\/li>\n<\/ul>\n<h4 id=\"2.2.1%E5%BC%A0%E9%87%8F%EF%BC%88Tensor%EF%BC%89\">2.2.1\u5f20\u91cf&#xff08;Tensor&#xff09;<\/h4>\n<ul>\n<li>\u652f\u6301\u5728 CPU \u548c GPU \u4e4b\u95f4\u8fdb\u884c\u5207\u6362\u3002<\/li>\n<li>\u63d0\u4f9b\u4e86\u7c7b\u4f3c NumPy \u7684\u63a5\u53e3&#xff0c;\u652f\u6301\u5143\u7d20\u7ea7\u8fd0\u7b97\u3002<\/li>\n<li>\u652f\u6301\u81ea\u52a8\u6c42\u5bfc&#xff0c;\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u68af\u5ea6\u8ba1\u7b97\u3002<\/li>\n<\/ul>\n<h4 id=\"2.2.2%E8%87%AA%E5%8A%A8%E6%B1%82%E5%AF%BC%EF%BC%88Autograd%EF%BC%89\">2.2.2\u81ea\u52a8\u6c42\u5bfc&#xff08;Autograd&#xff09;<\/h4>\n<ul>\n<li>PyTorch \u5185\u7f6e\u7684\u81ea\u52a8\u6c42\u5bfc\u5f15\u64ce&#xff0c;\u80fd\u591f\u81ea\u52a8\u8ffd\u8e2a\u6240\u6709\u5f20\u91cf\u7684\u64cd\u4f5c&#xff0c;\u5e76\u5728\u53cd\u5411\u4f20\u64ad\u65f6\u8ba1\u7b97\u68af\u5ea6\u3002<\/li>\n<li>\u901a\u8fc7\u00a0requires_grad\u00a0\u5c5e\u6027&#xff0c;\u53ef\u4ee5\u6307\u5b9a\u5f20\u91cf\u9700\u8981\u8ba1\u7b97\u68af\u5ea6\u3002<\/li>\n<li>\u652f\u6301\u9ad8\u6548\u7684\u53cd\u5411\u4f20\u64ad&#xff0c;\u9002\u7528\u4e8e\u795e\u7ecf\u7f51\u7edc\u7684\u8bad\u7ec3\u3002<\/li>\n<\/ul>\n<p><img decoding=\"async\" alt=\"\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260201184959-697fa0573b06f.gif\" \/><\/p>\n<h3 id=\"2.3%E6%A8%A1%E5%9E%8B%E5%AE%9A%E4%B9%89%E4%B8%8E%E8%AE%AD%E7%BB%83\">2.3\u6a21\u578b\u5b9a\u4e49\u4e0e\u8bad\u7ec3<\/h3>\n<ul>\n<li>PyTorch \u63d0\u4f9b\u4e86\u00a0torch.nn\u00a0\u6a21\u5757&#xff0c;\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u7ee7\u627f\u00a0nn.Module\u00a0\u7c7b\u6765\u5b9a\u4e49\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u3002\u4f7f\u7528\u00a0forward\u00a0\u51fd\u6570\u6307\u5b9a\u524d\u5411\u4f20\u64ad&#xff0c;\u81ea\u52a8\u53cd\u5411\u4f20\u64ad&#xff08;\u901a\u8fc7\u00a0autograd&#xff09;\u548c\u68af\u5ea6\u8ba1\u7b97\u4e5f\u7531 PyTorch \u5185\u90e8\u5904\u7406\u3002<\/li>\n<\/ul>\n<h4 id=\"2.3.1%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%A8%A1%E5%9D%97%EF%BC%88torch.nn%EF%BC%89%EF%BC%9A\">2.3.1\u795e\u7ecf\u7f51\u7edc\u6a21\u5757&#xff08;torch.nn&#xff09;&#xff1a;<\/h4>\n<ul>\n<li>\u63d0\u4f9b\u4e86\u5e38\u7528\u7684\u5c42&#xff08;\u5982\u7ebf\u6027\u5c42\u3001\u5377\u79ef\u5c42\u3001\u6c60\u5316\u5c42\u7b49&#xff09;\u3002<\/li>\n<li>\u652f\u6301\u5b9a\u4e49\u590d\u6742\u7684\u795e\u7ecf\u7f51\u7edc\u67b6\u6784&#xff08;\u5305\u62ec\u591a\u8f93\u5165\u3001\u591a\u8f93\u51fa\u7684\u7f51\u7edc&#xff09;\u3002<\/li>\n<li>\u517c\u5bb9\u4e0e\u4f18\u5316\u5668&#xff08;\u5982\u00a0torch.optim&#xff09;\u4e00\u8d77\u4f7f\u7528\u3002<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"909\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260201184959-697fa057c450a.png\" width=\"1148\" \/><\/p>\n<h2 id=\"3.GPU%20%E5%8A%A0%E9%80%9F\">3.GPU \u52a0\u901f<\/h2>\n<ul>\n<li>PyTorch \u5b8c\u5168\u652f\u6301\u5728 GPU \u4e0a\u8fd0\u884c&#xff0c;\u4ee5\u52a0\u901f\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u8bad\u7ec3\u3002\u901a\u8fc7\u7b80\u5355\u7684\u00a0.to(device)\u00a0\u65b9\u6cd5&#xff0c;\u7528\u6237\u53ef\u4ee5\u5c06\u6a21\u578b\u548c\u5f20\u91cf\u8f6c\u79fb\u5230 GPU \u4e0a\u8fdb\u884c\u8ba1\u7b97\u3002PyTorch \u652f\u6301\u591a GPU \u8bad\u7ec3&#xff0c;\u80fd\u591f\u5229\u7528 NVIDIA CUDA \u6280\u672f\u663e\u8457\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u3002<\/li>\n<\/ul>\n<h3 id=\"3.1GPU%20%E6%94%AF%E6%8C%81%EF%BC%9A\">3.1GPU \u652f\u6301&#xff1a;<\/h3>\n<ul>\n<li>\u81ea\u52a8\u9009\u62e9 GPU \u6216 CPU\u3002<\/li>\n<li>\u652f\u6301\u901a\u8fc7 CUDA \u52a0\u901f\u8fd0\u7b97\u3002<\/li>\n<li>\u652f\u6301\u591a GPU \u5e76\u884c\u8ba1\u7b97&#xff08;DataParallel\u00a0\u6216\u00a0torch.distributed&#xff09;\u3002<\/li>\n<\/ul>\n<h2 id=\"4.%E7%94%9F%E6%80%81%E7%B3%BB%E7%BB%9F%E4%B8%8E%E7%A4%BE%E5%8C%BA%E6%94%AF%E6%8C%81\">4.\u751f\u6001\u7cfb\u7edf\u4e0e\u793e\u533a\u652f\u6301<\/h2>\n<p>PyTorch \u4f5c\u4e3a\u4e00\u4e2a\u5f00\u6e90\u9879\u76ee&#xff0c;\u62e5\u6709\u4e00\u4e2a\u5e9e\u5927\u7684\u793e\u533a\u548c\u751f\u6001\u7cfb\u7edf\u3002\u5b83\u4e0d\u4ec5\u5728\u5b66\u672f\u754c\u5f97\u5230\u4e86\u5e7f\u6cdb\u7684\u5e94\u7528&#xff0c;\u4e5f\u5728\u5de5\u4e1a\u754c&#xff0c;\u7279\u522b\u662f\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7b49\u9886\u57df\u4e2d\u5f97\u5230\u4e86\u5e7f\u6cdb\u90e8\u7f72\u3002PyTorch \u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u4e0e\u6df1\u5ea6\u5b66\u4e60\u76f8\u5173\u7684\u5de5\u5177\u548c\u5e93&#xff0c;\u5982&#xff1a;<\/p>\n<ul>\n<li>torchvision&#xff1a;\u7528\u4e8e\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u7684\u6570\u636e\u96c6\u548c\u6a21\u578b\u3002<\/li>\n<li>torchtext&#xff1a;\u7528\u4e8e\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u7684\u6570\u636e\u96c6\u548c\u9884\u5904\u7406\u5de5\u5177\u3002<\/li>\n<li>torchaudio&#xff1a;\u7528\u4e8e\u97f3\u9891\u5904\u7406\u7684\u5de5\u5177\u5305\u3002<\/li>\n<li>PyTorch Lightning&#xff1a;\u4e00\u79cd\u7b80\u5316 PyTorch \u4ee3\u7801\u7684\u9ad8\u5c42\u5e93&#xff0c;\u4e13\u6ce8\u4e8e\u7814\u7a76\u548c\u5b9e\u9a8c\u7684\u5feb\u901f\u8fed\u4ee3\u3002<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"783\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260201185000-697fa058cc94d.png\" width=\"1200\" \/><\/p>\n<h2 id=\"5.%E4%B8%8E%E5%85%B6%E4%BB%96%E6%A1%86%E6%9E%B6%E7%9A%84%E5%AF%B9%E6%AF%94\">5.\u4e0e\u5176\u4ed6\u6846\u67b6\u7684\u5bf9\u6bd4<\/h2>\n<p>PyTorch \u7531\u4e8e\u5176\u7075\u6d3b\u6027\u3001\u6613\u7528\u6027\u548c\u793e\u533a\u652f\u6301&#xff0c;\u5df2\u7ecf\u6210\u4e3a\u5f88\u591a\u6df1\u5ea6\u5b66\u4e60\u7814\u7a76\u8005\u548c\u5f00\u53d1\u8005\u7684\u9996\u9009\u6846\u67b6\u3002<\/p>\n<h4 id=\"TensorFlow%20vs%20PyTorch\" style=\"background-color:transparent\">TensorFlow vs PyTorch<\/h4>\n<ul>\n<li>PyTorch \u7684\u52a8\u6001\u8ba1\u7b97\u56fe\u4f7f\u5f97\u5b83\u66f4\u52a0\u7075\u6d3b&#xff0c;\u9002\u5408\u5feb\u901f\u5b9e\u9a8c\u548c\u7814\u7a76&#xff1b;\u800c TensorFlow \u7684\u9759\u6001\u8ba1\u7b97\u56fe\u5728\u751f\u4ea7\u73af\u5883\u4e2d\u66f4\u5177\u4f18\u5316\u7a7a\u95f4\u3002<\/li>\n<li>PyTorch \u5728\u8c03\u8bd5\u65f6\u66f4\u52a0\u65b9\u4fbf&#xff0c;TensorFlow \u5219\u5728\u90e8\u7f72\u4e0a\u66f4\u52a0\u6210\u719f&#xff0c;\u652f\u6301\u66f4\u5e7f\u6cdb\u7684\u786c\u4ef6\u548c\u5e73\u53f0\u3002<\/li>\n<li>\u8fd1\u5e74\u6765&#xff0c;TensorFlow \u4e5f\u5f15\u5165\u4e86\u52a8\u6001\u56fe&#xff08;\u5982 TensorFlow 2.x&#xff09;&#xff0c;\u4f7f\u5f97\u4e24\u8005\u5728\u529f\u80fd\u4e0a\u8d8b\u4e8e\u63a5\u8fd1\u3002<\/li>\n<li>\u5176\u4ed6\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6&#xff0c;\u5982 Keras\u3001Caffe \u7b49\u4e5f\u6709\u4e00\u5b9a\u5e94\u7528&#xff0c;\u4f46 PyTorch \u7531\u4e8e\u5176\u7075\u6d3b\u6027\u3001\u6613\u7528\u6027\u548c\u793e\u533a\u652f\u6301&#xff0c;\u5df2\u7ecf\u6210\u4e3a\u5f88\u591a\u6df1\u5ea6\u5b66\u4e60\u7814\u7a76\u8005\u548c\u5f00\u53d1\u8005\u7684\u9996\u9009\u6846\u67b6\u3002<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"808\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260201185001-697fa05958cb1.png\" width=\"1121\" \/><\/p>\n<h3 id=\"5.1TensorFlow%20vs%20PyTorch%E5%85%B7%E4%BD%93%E7%89%B9%E6%80%A7%E8%A1%A8%E6%A0%BC%E5%AF%B9%E6%AF%94%E5%A6%82%E4%B8%8B%EF%BC%9A\">5.1TensorFlow vs PyTorch\u5177\u4f53\u7279\u6027\u8868\u683c\u5bf9\u6bd4\u5982\u4e0b&#xff1a;<\/h3>\n<table>\n<tr>\u7279\u6027TensorFlowPyTorch<\/tr>\n<tbody>\n<tr>\n<td>\u5f00\u53d1\u516c\u53f8<\/td>\n<td>Google<\/td>\n<td>Facebook (FAIR)<\/td>\n<\/tr>\n<tr>\n<td>\u8ba1\u7b97\u56fe\u7c7b\u578b<\/td>\n<td>\u9759\u6001\u8ba1\u7b97\u56fe&#xff08;\u5b9a\u4e49\u540e\u518d\u6267\u884c&#xff09;<\/td>\n<td>\u52a8\u6001\u8ba1\u7b97\u56fe&#xff08;\u5b9a\u4e49\u5373\u6267\u884c&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u7075\u6d3b\u6027<\/td>\n<td>\u4f4e&#xff08;\u8ba1\u7b97\u56fe\u5728\u7f16\u8bd1\u65f6\u6784\u5efa&#xff0c;\u4e0d\u6613\u4fee\u6539&#xff09;<\/td>\n<td>\u9ad8&#xff08;\u8ba1\u7b97\u56fe\u5728\u6267\u884c\u65f6\u52a8\u6001\u521b\u5efa&#xff0c;\u6613\u4e8e\u4fee\u6539\u548c\u8c03\u8bd5&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u8c03\u8bd5<\/td>\n<td>\u8f83\u96be&#xff08;\u9700\u8981\u4f7f\u7528\u00a0tf.debugging\u00a0\u6216\u5916\u90e8\u5de5\u5177\u8c03\u8bd5&#xff09;<\/td>\n<td>\u5bb9\u6613&#xff08;\u53ef\u4ee5\u76f4\u63a5\u5728 Python \u4e2d\u8fdb\u884c\u8c03\u8bd5&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u6613\u7528\u6027<\/td>\n<td>\u4f4e&#xff08;\u8f83\u590d\u6742&#xff0c;API \u8f83\u591a&#xff0c;\u5b66\u4e60\u66f2\u7ebf\u8f83\u9661\u5ced&#xff09;<\/td>\n<td>\u9ad8&#xff08;API \u7b80\u6d01&#xff0c;\u8bed\u6cd5\u66f4\u52a0\u63a5\u8fd1 Python&#xff0c;\u5bb9\u6613\u4e0a\u624b&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u90e8\u7f72<\/td>\n<td>\u5f3a&#xff08;\u652f\u6301\u5e7f\u6cdb\u7684\u786c\u4ef6&#xff0c;\u5982 TensorFlow Lite\u3001TensorFlow.js&#xff09;<\/td>\n<td>\u8f83\u5f31&#xff08;\u90e8\u7f72\u5de5\u5177\u548c\u5e73\u53f0\u76f8\u5bf9\u8f83\u5c11&#xff0c;\u867d\u7136\u6709 TensorFlow \u652f\u6301&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u793e\u533a\u652f\u6301<\/td>\n<td>\u5f88\u5f3a&#xff08;\u6210\u719f\u4e14\u5e9e\u5927\u7684\u793e\u533a&#xff0c;\u5e7f\u6cdb\u7684\u6559\u7a0b\u548c\u6587\u6863&#xff09;<\/td>\n<td>\u5f88\u5f3a&#xff08;\u793e\u533a\u6d3b\u8dc3&#xff0c;\u7279\u522b\u662f\u5728\u5b66\u672f\u754c&#xff0c;\u5feb\u901f\u53d1\u5c55\u7684\u751f\u6001&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u6a21\u578b\u8bad\u7ec3<\/td>\n<td>\u652f\u6301\u5206\u5e03\u5f0f\u8bad\u7ec3&#xff0c;\u652f\u6301\u591a\u79cd\u8bbe\u5907&#xff08;\u5982 CPU\u3001GPU\u3001TPU&#xff09;<\/td>\n<td>\u652f\u6301\u5206\u5e03\u5f0f\u8bad\u7ec3&#xff0c;\u652f\u6301\u591a GPU\u3001CPU \u548c TPU<\/td>\n<\/tr>\n<tr>\n<td>API \u5c42\u7ea7<\/td>\n<td>\u9ad8\u7ea7API&#xff1a;Keras&#xff1b;\u4f4e\u7ea7API&#xff1a;TensorFlow Core<\/td>\n<td>\u9ad8\u7ea7API&#xff1a;TorchVision\u3001TorchText \u7b49&#xff1b;\u4f4e\u7ea7API&#xff1a;Torch<\/td>\n<\/tr>\n<tr>\n<td>\u6027\u80fd<\/td>\n<td>\u9ad8&#xff08;\u4f18\u5316\u65b9\u9762\u6210\u719f&#xff0c;\u9002\u5408\u751f\u4ea7\u73af\u5883&#xff09;<\/td>\n<td>\u9ad8&#xff08;\u9002\u5408\u7814\u7a76\u548c\u539f\u578b\u5f00\u53d1&#xff0c;\u751f\u4ea7\u6027\u80fd\u4e5f\u5728\u63d0\u5347&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u81ea\u52a8\u6c42\u5bfc<\/td>\n<td>\u901a\u8fc7\u00a0tf.GradientTape\u00a0\u5b9e\u73b0\u52a8\u6001\u6c42\u5bfc&#xff08;\u8f83\u590d\u6742&#xff09;<\/td>\n<td>\u901a\u8fc7\u00a0autograd\u00a0\u52a8\u6001\u6c42\u5bfc&#xff08;\u66f4\u7b80\u6d01\u548c\u76f4\u89c2&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u8c03\u4f18\u4e0e\u53ef\u6269\u5c55\u6027<\/td>\n<td>\u5f3a&#xff08;\u652f\u6301\u5728\u591a\u5e73\u53f0\u4e0a\u8fd0\u884c&#xff0c;\u5982 TensorFlow Serving \u7b49&#xff09;<\/td>\n<td>\u8f83\u5f31&#xff08;\u867d\u7136\u5728\u5b66\u672f\u548c\u5b9e\u9a8c\u73af\u5883\u4e2d\u8868\u73b0\u4f18\u8d8a&#xff0c;\u4f46\u751f\u4ea7\u73af\u5883\u652f\u6301\u76f8\u5bf9\u8f83\u5c11&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u6846\u67b6\u7075\u6d3b\u6027<\/td>\n<td>\u8f83\u4f4e&#xff08;TensorFlow 2.x \u5f15\u5165\u4e86\u52a8\u6001\u56fe\u7279\u6027&#xff0c;\u4f46\u4ecd\u4e0d\u5b8c\u5168\u7075\u6d3b&#xff09;<\/td>\n<td>\u9ad8&#xff08;\u52a8\u6001\u56fe\u5e26\u6765\u66f4\u9ad8\u7684\u7075\u6d3b\u6027&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u652f\u6301\u591a\u79cd\u8bed\u8a00<\/td>\n<td>\u652f\u6301\u591a\u79cd\u8bed\u8a00&#xff08;Python, C&#043;&#043;, Java, JavaScript, etc.&#xff09;<\/td>\n<td>\u4e3b\u8981\u652f\u6301 Python&#xff08;\u4f46\u4e5f\u6709 C&#043;&#043; API&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u517c\u5bb9\u6027\u4e0e\u8fc1\u79fb<\/td>\n<td>TensorFlow 2.x \u4e0e\u65e7\u7248\u672c\u517c\u5bb9\u6027\u8f83\u597d<\/td>\n<td>\u4e0e TensorFlow \u517c\u5bb9\u6027<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"810\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260201185002-697fa05a644ea.png\" width=\"882\" \/><\/p>\n<h4 id=\"5.2PyTorch%20vs%20NumPy%E5%85%B7%E4%BD%93%E7%89%B9%E6%80%A7%E8%A1%A8%E6%A0%BC%E5%AF%B9%E6%AF%94%E5%A6%82%E4%B8%8B%EF%BC%9A\">5.2PyTorch vs NumPy\u5177\u4f53\u7279\u6027\u8868\u683c\u5bf9\u6bd4\u5982\u4e0b&#xff1a;<\/h4>\n<table>\n<tr>\u7279\u6027PyTorchNumPy<\/tr>\n<tbody>\n<tr>\n<td>\u76ee\u6807<\/td>\n<td>\u6df1\u5ea6\u5b66\u4e60\u4e13\u7528<\/td>\n<td>\u901a\u7528\u79d1\u5b66\u8ba1\u7b97<\/td>\n<\/tr>\n<tr>\n<td>GPU \u652f\u6301<\/td>\n<td>\u539f\u751f\u652f\u6301 CUDA<\/td>\n<td>\u4e0d\u76f4\u63a5\u652f\u6301<\/td>\n<\/tr>\n<tr>\n<td>\u81ea\u52a8\u5fae\u5206<\/td>\n<td>\u5185\u7f6e\u81ea\u52a8\u6c42\u5bfc<\/td>\n<td>\u9700\u8981\u624b\u52a8\u8ba1\u7b97\u68af\u5ea6<\/td>\n<\/tr>\n<tr>\n<td>\u795e\u7ecf\u7f51\u7edc<\/td>\n<td>\u4e30\u5bcc\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u5757<\/td>\n<td>\u9700\u8981\u4ece\u96f6\u5b9e\u73b0<\/td>\n<\/tr>\n<tr>\n<td>\u5b66\u4e60\u6210\u672c<\/td>\n<td>\u76f8\u5bf9\u8f83\u9ad8<\/td>\n<td>\u76f8\u5bf9\u8f83\u4f4e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"822\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260201185002-697fa05aefc20.png\" width=\"1447\" \/><\/p>\n<h2 id=\"6.PyTorch%20%E7%9A%84%E5%8E%86%E5%8F%B2%E4%B8%8E%E5%8F%91%E5%B1%95\" style=\"background-color:transparent\">6.PyTorch \u7684\u5386\u53f2\u4e0e\u53d1\u5c55<\/h2>\n<p>PyTorch \u7684\u524d\u8eab\u662f Torch&#xff0c;\u8fd9\u662f\u4e00\u4e2a\u57fa\u4e8e Lua \u8bed\u8a00\u7684\u79d1\u5b66\u8ba1\u7b97\u6846\u67b6\u3002\u968f\u7740 Python \u5728\u673a\u5668\u5b66\u4e60\u9886\u57df\u7684\u5174\u8d77&#xff0c;Facebook \u56e2\u961f\u51b3\u5b9a\u5c06 Torch \u7684\u6838\u5fc3\u601d\u60f3\u79fb\u690d\u5230 Python \u4e0a&#xff0c;\u4ece\u800c\u8bde\u751f\u4e86 PyTorch\u3002<\/p>\n<ul>\n<li>2016\u5e74&#xff1a;Facebook \u53d1\u5e03 PyTorch 0.1 \u7248\u672c<\/li>\n<li>2017\u5e74&#xff1a;PyTorch 0.2 \u5f15\u5165\u5206\u5e03\u5f0f\u8bad\u7ec3\u652f\u6301<\/li>\n<li>2018\u5e74&#xff1a;PyTorch 1.0 \u53d1\u5e03&#xff0c;\u589e\u52a0\u4e86\u751f\u4ea7\u90e8\u7f72\u80fd\u529b<\/li>\n<li>2019\u5e74&#xff1a;PyTorch 1.3 \u5f15\u5165\u79fb\u52a8\u7aef\u652f\u6301<\/li>\n<li>2020\u5e74&#xff1a;PyTorch 1.6 \u589e\u52a0\u4e86\u81ea\u52a8\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3<\/li>\n<li>2021\u5e74&#xff1a;PyTorch 1.9 \u5f15\u5165 TorchScript \u548c C&#043;&#043; \u524d\u7aef<\/li>\n<li>2022\u5e74&#xff1a;PyTorch 1.12 \u4f18\u5316\u4e86\u6027\u80fd\u548c\u7a33\u5b9a\u6027<\/li>\n<li>2023\u5e74&#xff1a;PyTorch 2.0 \u53d1\u5e03&#xff0c;\u5f15\u5165\u7f16\u8bd1\u6a21\u5f0f\u5927\u5e45\u63d0\u5347\u6027\u80fd<\/li>\n<\/ul>\n<\/p>\n<h2 id=\"%E6%AC%A2%E8%BF%8E%E5%90%84%E4%BD%8D%E5%BD%A6%E7%A5%96%E4%B8%8E%E7%83%AD%E5%B7%B4%E7%95%85%E6%B8%B8%E6%9C%AC%E4%BA%BA%E4%B8%93%E6%A0%8F%E4%B8%8E%E5%8D%9A%E5%AE%A2\" style=\"text-align:center\">\u6b22\u8fce\u5404\u4f4d\u5f66\u7956\u4e0e\u70ed\u5df4\u7545\u6e38\u672c\u4eba\u4e13\u680f\u4e0e\u6280\u672f\u535a\u5ba2<\/h2>\n<h2 id=\"%E4%BD%A0%E7%9A%84%E4%B8%89%E8%BF%9E%E6%98%AF%E6%88%91%E6%9C%80%E5%A4%A7%E7%9A%84%E5%8A%A8%E5%8A%9B\" style=\"text-align:center\"><span style=\"color:#fe2c24\">\u4f60\u7684\u4e09\u8fde\u662f\u6211\u6700\u5927\u7684\u52a8\u529b<\/span><\/h2>\n<h3 id=\"%E4%BB%A5%E4%B8%8B%E5%9B%BE%E7%89%87%E4%BB%85%E4%BB%A3%E8%A1%A8%E4%B8%93%E6%A0%8F%E7%89%B9%E8%89%B2%20%5B%E7%82%B9%E5%87%BB%E7%AE%AD%E5%A4%B4%E6%8C%87%E5%90%91%E7%9A%84%E4%B8%93%E6%A0%8F%E5%90%8D%E5%8D%B3%E5%8F%AF%E9%97%AA%E7%8E%B0%5D\" style=\"background-color:transparent;text-align:center\"><span style=\"color:#fe2c24\">\u70b9\u51fb<\/span>\u27a1\ufe0f<span style=\"color:#fe2c24\">\u6307\u5411\u7684\u4e13\u680f\u540d\u5373\u53ef\u95ea\u73b0<\/span><\/h3>\n<p style=\"text-align:center\">\u27a1\ufe0f\u8ba1\u7b97\u673a\u7ec4\u6210\u539f\u7406<\/p>\n<p style=\"text-align:center\">\u27a1\ufe0f\u64cd\u4f5c\u7cfb\u7edf<\/p>\n<p id=\"%E2%9E%A1%EF%B8%8F%E7%BD%91%E7%BB%9C%E7%A9%BA%E9%97%B4%E5%AE%89%E5%85%A8%E2%80%94%E2%80%94%E5%85%A8%E6%A0%88%E5%89%8D%E6%B2%BF%E6%8A%80%E6%9C%AF%E6%8C%81%E7%BB%AD%E6%B7%B1%E5%85%A5%E5%AD%A6%E4%B9%A0%C2%A0\" style=\"text-align:center\">\u27a1\ufe0f\u6e17\u900f\u7ec8\u6781\u4e4b\u7ea2\u961f\u653b\u51fb\u884c\u52a8\u00a0<\/p>\n<p 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