{"id":204439,"date":"2025-05-29T15:23:46","date_gmt":"2025-05-29T07:23:46","guid":{"rendered":"https:\/\/server.hk\/cnblog\/204439\/"},"modified":"2025-05-29T15:23:46","modified_gmt":"2025-05-29T07:23:46","slug":"pytorch-%e4%b8%ad%e7%9a%84%e6%8e%a5%e8%bf%91%e5%92%8c%e7%9b%b8%e7%ad%89","status":"publish","type":"post","link":"https:\/\/server.hk\/cnblog\/204439\/","title":{"rendered":"PyTorch \u4e2d\u7684\u63a5\u8fd1\u548c\u76f8\u7b49"},"content":{"rendered":"<p><b><\/b>     <\/p>\n<h1>PyTorch \u4e2d\u7684\u63a5\u8fd1\u548c\u76f8\u7b49<\/h1>\n<p>\u5927\u5bb6\u597d\uff0c\u4eca\u5929\u672c\u4eba\u7ed9\u5927\u5bb6\u5e26\u6765\u6587\u7ae0<span style=\"color: #FF6600;, Helvetica, Arial, sans-serif;font-size: 14px;background-color: #FFFFFF\">\u300aPyTorch \u4e2d\u7684\u63a5\u8fd1\u548c\u76f8\u7b49\u300b<\/span>\uff0c\u6587\u4e2d\u5185\u5bb9\u4e3b\u8981\u6d89\u53ca\u5230<span style=\"color: #FF6600;, Helvetica, Arial, sans-serif;font-size: 14px;background-color: #FFFFFF\"><span style=\"color: #FF6600;, Helvetica, Arial, sans-serif;font-size: 14px;background-color: #FFFFFF\"><\/span><\/span>\uff0c\u5982\u679c\u4f60\u5bf9<span style=\"color: #FF6600;, Helvetica, Arial, sans-serif;font-size: 14px;background-color: #FFFFFF\">\u6587\u7ae0<\/span>\u65b9\u9762\u7684\u77e5\u8bc6\u70b9\u611f\u5174\u8da3\uff0c\u90a3\u5c31\u8bf7\u5404\u4f4d\u670b\u53cb\u7ee7\u7eed\u770b\u4e0b\u53bb\u5427~\u5e0c\u671b\u80fd\u771f\u6b63\u5e2e\u5230\u4f60\u4eec\uff0c\u8c22\u8c22\uff01<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.17golang.com\/uploads\/20241107\/1730974907672c94bb01087.jpg\" class=\"aligncenter\"><\/p>\n<p>\u8bf7\u6211\u559d\u676f\u5496\u5561<\/p>\n<p>*\u5907\u5fd8\u5f55\uff1a<\/p>\n<ul>\n<li> \u6211\u7684\u5e16\u5b50\u89e3\u91ca\u4e86 eq() \u548c ne()\u3002<\/li>\n<li> \u6211\u7684\u5e16\u5b50\u89e3\u91ca\u4e86 gt() \u548c lt()\u3002<\/li>\n<li> \u6211\u7684\u5e16\u5b50\u89e3\u91ca\u4e86 ge() \u548c le()\u3002<\/li>\n<li> \u6211\u7684\u5e16\u5b50\u89e3\u91ca\u4e86 torch.nan \u548c torch.inf\u3002<\/li>\n<\/ul>\n<p>isclose() \u53ef\u4ee5\u68c0\u67e5\u7b2c\u4e00\u4e2a 0d \u6216\u66f4\u591a d \u5f20\u91cf\u7684\u96f6\u4e2a\u6216\u591a\u4e2a\u5143\u7d20\u662f\u5426\u7b49\u4e8e\u6216\u63a5\u8fd1\u7b49\u4e8e\u7b2c\u4e8c\u4e2a 0d \u6216\u66f4\u591a d \u5f20\u91cf\u7684\u96f6\u4e2a\u6216\u591a\u4e2a\u5143\u7d20\uff0c\u5f97\u5230 0d \u6216\u66f4\u591a\u96f6\u4e2a\u6216\u591a\u4e2a\u5143\u7d20\u7684 d \u5f20\u91cf\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n<p>*\u5907\u5fd8\u5f55\uff1a<\/p>\n<ul>\n<li> isclose() \u53ef\u4ee5\u4e0e torch \u6216\u5f20\u91cf\u4e00\u8d77\u4f7f\u7528\u3002<\/li>\n<li>\u7b2c\u4e00\u4e2a\u53c2\u6570\uff08\u8f93\u5165\uff09\u4f7f\u7528 torch \u6216\u4f7f\u7528\u5f20\u91cf\uff08\u5fc5\u9700\u7c7b\u578b\uff1aint\u3001float\u3001complex \u6216 bool \u7684\u5f20\u91cf\uff09\u3002<\/li>\n<li>\u5e26\u6709 torch \u7684\u7b2c\u4e8c\u4e2a\u53c2\u6570\u6216\u5e26\u6709\u5f20\u91cf\u7684\u7b2c\u4e00\u4e2a\u53c2\u6570\u662f\u5176\u4ed6\uff08\u5fc5\u9700\u7c7b\u578b\uff1aint\u3001float\u3001complex \u6216 bool \u7684\u5f20\u91cf\uff09\u3002<\/li>\n<li>\u5e26\u6709 torch \u7684\u7b2c\u4e09\u4e2a\u53c2\u6570\u6216\u5e26\u6709\u5f20\u91cf\u7684\u7b2c\u4e8c\u4e2a\u53c2\u6570\u662f rtol(optional-default:1e-05-type:float)\u3002<\/li>\n<li>\u5e26\u6709 torch \u7684\u7b2c\u56db\u4e2a\u53c2\u6570\u6216\u5e26\u6709\u5f20\u91cf\u7684\u7b2c\u4e09\u4e2a\u53c2\u6570\u662f atol(optional-default:1e-08-type:float)\u3002<\/li>\n<li>\u5e26\u6709 torch \u7684\u7b2c\u4e94\u4e2a\u53c2\u6570\u6216\u5e26\u6709\u5f20\u91cf\u7684\u7b2c\u56db\u4e2a\u53c2\u6570\u662f equal_nan(optional-default:false-type:bool)\uff1a *\u5907\u6ce8\uff1a\n<ul>\n<li>\u5982\u679c\u4e3a true\uff0c\u5219 nan \u548c nan \u8fd4\u56de true\u3002<\/li>\n<li>\u57fa\u672c\u4e0a\uff0cnan \u548c nan \u8fd4\u56de false\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\u516c\u5f0f\u4e3a |\u8f93\u5165 &#8211; \u5176\u4ed6| &lt;= rtol x |\u5176\u4ed6| + \u963f\u6258\u5c14\u3002 <\/li>\n<\/ul>\n<pre>import torch\n\ntensor1 = torch.tensor([1.00001001, 1.00000996, 1.00000995, torch.nan])\ntensor2 = torch.tensor([1., 1., 1., torch.nan])\n\ntorch.isclose(input=tensor1, other=tensor2)\ntorch.isclose(input=tensor1, other=tensor2,\n              rtol=1e-05, atol=1e-08, equal_nan=false)\n            # 0.00001   # 0.00000001\ntensor1.isclose(other=tensor2)\ntorch.isclose(input=tensor2, other=tensor1)\n# tensor([false, false, true, false])\n\ntorch.isclose(input=tensor1, other=tensor2, equal_nan=true)\n# tensor([false, false, true, true])\n\ntensor1 = torch.tensor([[1.00001001, 1.00000996],\n                        [1.00000995, torch.nan]])\ntensor2 = torch.tensor([[1., 1.],\n                        [1., torch.nan]])\ntorch.isclose(input=tensor1, other=tensor2)\ntorch.isclose(input=tensor2, other=tensor1)\n# tensor([[false, false],\n#         [true, false]])\n\ntensor1 = torch.tensor([[[1.00001001],\n                         [1.00000996]],\n                        [[1.00000995],\n                         [torch.nan]]])\ntensor2 = torch.tensor([[[1.], [1.]],\n                        [[1.], [torch.nan]]])\ntorch.isclose(input=tensor1, other=tensor2)\ntorch.isclose(input=tensor2, other=tensor1)\n# tensor([[[false], [false]],\n#         [[true], [false]]])\n\ntensor1 = torch.tensor([[1.00001001, 1.00000996],\n                        [1.00000995, torch.nan]])\ntensor2 = torch.tensor([1., 1.])\n\ntorch.isclose(input=tensor1, other=tensor2)\ntorch.isclose(input=tensor2, other=tensor1)\n# tensor([[false, false],\n#         [true, false]])\n\ntensor1 = torch.tensor([[1.00001001, 1.00000996],\n                        [1.00000995, torch.nan]])\ntensor2 = torch.tensor(1.)\n\ntorch.isclose(input=tensor1, other=tensor2)\ntorch.isclose(input=tensor2, other=tensor1)\n# tensor([[false, false],\n#         [true, false]])\n\ntensor1 = torch.tensor([0, 1, 2])\ntensor2 = torch.tensor(1)\n\ntorch.isclose(input=tensor1, other=tensor2)\n# tensor([false, true, false])\n\ntensor1 = torch.tensor([0.+0.j, 1.+0.j, 2.+0.j])\ntensor2 = torch.tensor(1.+0.j)\n\ntorch.isclose(input=tensor1, other=tensor2)\n# tensor([false, true, false])\n\ntensor1 = torch.tensor([false, true, false])\ntensor2 = torch.tensor(true)\n\ntorch.isclose(input=tensor1, other=tensor2)\n# tensor([false, true, false])\n<\/pre>\n<p>equal() \u53ef\u4ee5\u68c0\u67e5\u4e24\u4e2a 0d \u6216\u66f4\u591a d \u5f20\u91cf\u662f\u5426\u5177\u6709\u76f8\u540c\u7684\u5927\u5c0f\u548c\u5143\u7d20\uff0c\u5f97\u5230\u5e03\u5c14\u503c\u7684\u6807\u91cf\uff0c\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n<p>*\u5907\u5fd8\u5f55\uff1a<\/p>\n<ul>\n<li> equal() \u53ef\u4ee5\u4e0e torch \u6216\u5f20\u91cf\u4e00\u8d77\u4f7f\u7528\u3002<\/li>\n<li>\u7b2c\u4e00\u4e2a\u53c2\u6570\uff08\u8f93\u5165\uff09\u4f7f\u7528 torch \u6216\u4f7f\u7528\u5f20\u91cf\uff08\u5fc5\u9700\u7c7b\u578b\uff1aint\u3001float\u3001complex \u6216 bool \u7684\u5f20\u91cf\uff09\u3002<\/li>\n<li>\u5e26\u6709 torch \u7684\u7b2c\u4e8c\u4e2a\u53c2\u6570\u6216\u5e26\u6709\u5f20\u91cf\u7684\u7b2c\u4e00\u4e2a\u53c2\u6570\u662f\u5176\u4ed6\uff08\u5fc5\u9700\u7c7b\u578b\uff1aint\u3001float\u3001complex \u6216 bool \u7684\u5f20\u91cf\uff09\u3002 <\/li>\n<\/ul>\n<pre>import torch\n\ntensor1 = torch.tensor([5, 9, 3])\ntensor2 = torch.tensor([5, 9, 3])\n\ntorch.equal(input=tensor1, other=tensor2)\ntensor1.equal(other=tensor2)\ntorch.equal(input=tensor2, other=tensor1)\n# True\n\ntensor1 = torch.tensor([5, 9, 3])\ntensor2 = torch.tensor([7, 9, 3])\n\ntorch.equal(input=tensor1, other=tensor2)\ntorch.equal(input=tensor2, other=tensor1)\n# False\n\ntensor1 = torch.tensor([5, 9, 3])\ntensor2 = torch.tensor([[5, 9, 3]])\n\ntorch.equal(input=tensor1, other=tensor2)\ntorch.equal(input=tensor2, other=tensor1)\n# False\n\ntensor1 = torch.tensor([5., 9., 3.])\ntensor2 = torch.tensor([5.+0.j, 9.+0.j, 3.+0.j])\n\ntorch.equal(input=tensor1, other=tensor2)\ntorch.equal(input=tensor2, other=tensor1)\n# True\n\ntensor1 = torch.tensor([1.+0.j, 0.+0.j, 1.+0.j])\ntensor2 = torch.tensor([True, False, True])\n\ntorch.equal(input=tensor1, other=tensor2)\ntorch.equal(input=tensor2, other=tensor1)\n# True\n\ntensor1 = torch.tensor([], dtype=torch.int64)\ntensor2 = torch.tensor([], dtype=torch.float32)\n\ntorch.equal(input=tensor1, other=tensor2)\ntorch.equal(input=tensor2, other=tensor1)\n# True\n<\/pre>\n<p>\u4eca\u5929\u5e26\u5927\u5bb6\u4e86\u89e3\u4e86\u7684\u76f8\u5173\u77e5\u8bc6\uff0c\u5e0c\u671b\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff1b\u5173\u4e8e\u6587\u7ae0\u7684\u6280\u672f\u77e5\u8bc6\u6211\u4eec\u4f1a\u4e00\u70b9\u70b9\u6df1\u5165\u4ecb\u7ecd\uff0c\u6b22\u8fce\u5927\u5bb6\u5173\u6ce8\u516c\u4f17\u53f7\uff0c\u4e00\u8d77\u5b66\u4e60\u7f16\u7a0b~<\/p>\n<p>      \u7248\u672c\u58f0\u660e \u672c\u6587\u8f6c\u8f7d\u4e8e\uff1adev.to \u5982\u6709\u4fb5\u72af\uff0c\u8bf7\u8054\u7cfb\u5220\u9664<\/p>\n","protected":false},"excerpt":{"rendered":"<p>PyTorch \u4e2d\u7684\u63a5\u8fd1\u548c\u76f8\u7b49 &#46;&#46;&#46;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4925],"tags":[],"class_list":["post-204439","post","type-post","status-publish","format-standard","hentry","category-4925"],"_links":{"self":[{"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/posts\/204439","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/comments?post=204439"}],"version-history":[{"count":0,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/posts\/204439\/revisions"}],"wp:attachment":[{"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/media?parent=204439"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/categories?post=204439"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/tags?post=204439"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}