{"id":205096,"date":"2025-05-29T13:15:40","date_gmt":"2025-05-29T05:15:40","guid":{"rendered":"https:\/\/server.hk\/cnblog\/205096\/"},"modified":"2025-05-29T13:15:40","modified_gmt":"2025-05-29T05:15:40","slug":"pytorch-%e4%b8%ad%e7%9a%84-eq-%e5%92%8c-ne","status":"publish","type":"post","link":"https:\/\/server.hk\/cnblog\/205096\/","title":{"rendered":"PyTorch \u4e2d\u7684 eq \u548c ne"},"content":{"rendered":"<p><b><\/b>     <\/p>\n<h1>PyTorch \u4e2d\u7684 eq \u548c ne<\/h1>\n<p>\u4f60\u5728\u5b66\u4e60<span style=\"color: #FF6600;, Helvetica, Arial, sans-serif;font-size: 14px;background-color: #FFFFFF\">\u6587\u7ae0<\/span>\u76f8\u5173\u7684\u77e5\u8bc6\u5417\uff1f\u672c\u6587<span style=\"color: #FF6600;, Helvetica, Arial, sans-serif;font-size: 14px;background-color: #FFFFFF\">\u300aPyTorch \u4e2d\u7684 eq \u548c ne\u300b<\/span>\uff0c\u4e3b\u8981\u4ecb\u7ecd\u7684\u5185\u5bb9\u5c31\u6d89\u53ca\u5230<span style=\"color: #FF6600;, Helvetica, Arial, sans-serif;font-size: 14px;background-color: #FFFFFF\"><\/span>\uff0c\u5982\u679c\u4f60\u60f3\u63d0\u5347\u81ea\u5df1\u7684\u5f00\u53d1\u80fd\u529b\uff0c\u5c31\u4e0d\u8981\u9519\u8fc7\u8fd9\u7bc7\u6587\u7ae0\uff0c\u5927\u5bb6\u8981\u77e5\u9053\u7f16\u7a0b\u7406\u8bba\u57fa\u7840\u548c\u5b9e\u6218\u64cd\u4f5c\u90fd\u662f\u4e0d\u53ef\u6216\u7f3a\u7684\u54e6\uff01<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.17golang.com\/uploads\/20241123\/1732347747674187637f48a.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 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 isclose() \u548c equal()\u3002<\/li>\n<\/ul>\n<p>eq() \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\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 d \u5f20\u91cf\u96f6\u4e2a\u6216\u591a\u4e2a\u5143\u7d20\uff0c\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n<p>*\u5907\u5fd8\u5f55\uff1a<\/p>\n<ul>\n<li> eq() \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\uff1a\u5f20\u91cf\u6216 int\u3001float\u3001complex \u6216 bool \u6807\u91cf\uff09\u3002<\/li>\n<li>torch \u5b58\u5728 out \u53c2\u6570\uff08\u53ef\u9009-\u9ed8\u8ba4\uff1a\u65e0-\u7c7b\u578b\uff1a\u5f20\u91cf\uff09\uff1a *\u5907\u6ce8\uff1a\n<ul>\n<li> \u5fc5\u987b\u4f7f\u7528 out=\u3002<\/li>\n<li> \u6211\u7684\u5e16\u5b50\u89e3\u91ca\u4e86\u8bba\u70b9\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\u7ed3\u679c\u662f\u5177\u6709\u66f4\u591a\u5143\u7d20\u7684\u66f4\u9ad8 d \u5f20\u91cf\u3002 <\/li>\n<\/ul>\n<pre>import torch\n\ntensor1 = torch.tensor([5, 0, 3])\ntensor2 = torch.tensor([7, 0, 3])\n\ntorch.eq(input=tensor1, other=tensor2)\ntensor1.eq(other=tensor2)\ntorch.eq(input=tensor2, other=tensor1)\n# tensor([false, true, true])\n\ntensor1 = torch.tensor(5)\ntensor2 = torch.tensor([[3, 5, 4],\n                        [6, 3, 5]])\ntorch.eq(input=tensor1, other=tensor2)\ntorch.eq(input=tensor2, other=tensor1)\n# tensor([[false, true, false],\n#         [false, false, true]])\n\ntorch.eq(input=tensor1, other=3)\n# tensor(false)\n\ntorch.eq(input=tensor2, other=3)\n# tensor([[true, false, false],\n#         [false, true, false]])\n\ntensor1 = torch.tensor([5, 0, 3])\ntensor2 = torch.tensor([[5, 5, 5],\n                        [0, 0, 0],\n                        [3, 3, 3]])\ntorch.eq(input=tensor1, other=tensor2)\ntorch.eq(input=tensor2, other=tensor1)\n# tensor([[true, false, false],\n#         [false, true, false], \n#         [false, false, true]])\n\ntorch.eq(input=tensor1, other=3)\n# tensor([false, false, true])\n\ntorch.eq(input=tensor2, other=3)\n# tensor([[false, false, false],\n#         [false, false, false],\n#         [true, true, true]])\n\ntensor1 = torch.tensor([5., 0., 3.])\ntensor2 = torch.tensor([[5., 5., 5.],\n                        [0., 0., 0.],\n                        [3., 3., 3.]])\ntorch.eq(input=tensor1, other=tensor2)\n# tensor([[true, false, false],\n#         [false, true, false], \n#         [false, false, true]])\n\ntorch.eq(input=tensor1, other=3.)\n# tensor([false, false, true])\n\ntensor1 = torch.tensor([5.+0.j, 0.+0.j, 3.+0.j])\ntensor2 = torch.tensor([[5.+0.j, 5.+0.j, 5.+0.j],\n                        [0.+0.j, 0.+0.j, 0.+0.j],\n                        [3.+0.j, 3.+0.j, 3.+.0j]])\ntorch.eq(input=tensor1, other=tensor2)\n# tensor([[true, false, false],\n#         [false, true, false],\n#         [false, false, true]])\n\ntorch.eq(input=tensor1, other=3.+0.j)\n# tensor([false, false, true])\n\ntensor1 = torch.tensor([true, false, true])\ntensor2 = torch.tensor([[true, false, true],\n                        [false, true, false],\n                        [true, false, true]])\ntorch.eq(input=tensor1, other=tensor2)\n# tensor([[true, true, true],\n#         [false, false, false],\n#         [true, true, true]])\n\ntorch.eq(input=tensor1, other=true)\n# tensor([true, false, true])\n<\/pre>\n<p>ne() \u53ef\u4ee5\u6309\u5143\u7d20\u68c0\u67e5\u7b2c\u4e00\u4e2a 0d \u6216\u66f4\u591a d \u5f20\u91cf\u7684\u96f6\u4e2a\u6216\u591a\u4e2a\u5143\u7d20\u662f\u5426\u4e0d\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 d \u5f20\u91cf\u96f6\u4e2a\u6216\u591a\u4e2a\u5143\u7d20\uff0c\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n<p>*\u5907\u5fd8\u5f55\uff1a<\/p>\n<ul>\n<li> ne() \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\uff1a\u5f20\u91cf\u6216 int\u3001float\u3001complex \u6216 bool \u6807\u91cf\uff09\u3002<\/li>\n<li>torch \u5b58\u5728 out \u53c2\u6570\uff08\u53ef\u9009-\u9ed8\u8ba4\uff1a\u65e0-\u7c7b\u578b\uff1a\u5f20\u91cf\uff09\uff1a *\u5907\u6ce8\uff1a\n<ul>\n<li> \u5fc5\u987b\u4f7f\u7528 out=\u3002<\/li>\n<li> \u6211\u7684\u5e16\u5b50\u89e3\u91ca\u4e86\u8bba\u70b9\u3002<\/li>\n<\/ul>\n<\/li>\n<li> not_equal() \u662f ne() \u7684\u522b\u540d\u3002 <\/li>\n<\/ul>\n<pre>import torch\n\ntensor1 = torch.tensor([5, 0, 3])\ntensor2 = torch.tensor([7, 0, 3])\n\ntorch.ne(input=tensor1, other=tensor2)\ntensor1.ne(other=tensor2)\ntorch.ne(input=tensor2, other=tensor1)\n# tensor([True, False, False])\n\ntensor1 = torch.tensor(5)\ntensor2 = torch.tensor([[3, 5, 4],\n                        [6, 3, 5]])\ntorch.ne(input=tensor1, other=tensor2)\ntorch.ne(input=tensor2, other=tensor1)\n# tensor([[True, False, True],\n#         [True, True, False]])\n\ntorch.ne(input=tensor1, other=3)\n# tensor(True)\n\ntorch.ne(input=tensor2, other=3)\n# tensor([[False, True, True],\n#         [True, False, True]])\n\ntensor1 = torch.tensor([5, 0, 3])\ntensor2 = torch.tensor([[5, 5, 5],\n                        [0, 0, 0],\n                        [3, 3, 3]])\ntorch.ne(input=tensor1, other=tensor2)\ntorch.ne(input=tensor2, other=tensor1)\n# tensor([[False, True, True],\n#         [True, False, True],\n#         [True, True, False]])\n\ntorch.ne(input=tensor1, other=3)\n# tensor([True, True, False])\n\ntorch.ne(input=tensor2, other=3)\n# tensor([[True, True, True],\n#         [True, True, True],\n#         [False, False, False]])\n\ntensor1 = torch.tensor([5., 0., 3.])\ntensor2 = torch.tensor([[5., 5., 5.],\n                        [0., 0., 0.],\n                        [3., 3., 3.]])\ntorch.ne(input=tensor1, other=tensor2)\n# tensor([[False, True, True],\n#         [True, False, True],\n#         [True, True, False]])\n\ntorch.ne(input=tensor1, other=3.)\n# tensor([True, True, False])\n\ntensor1 = torch.tensor([5.+0.j, 0.+0.j, 3.+0.j])\ntensor2 = torch.tensor([[5.+0.j, 5.+0.j, 5.+0.j],\n                        [0.+0.j, 0.+0.j, 0.+0.j],\n                        [3.+0.j, 3.+0.j, 3.+.0j]])\ntorch.ne(input=tensor1, other=tensor2)\n# tensor([[False, True, True],\n#         [True, False, True],\n#         [True, True, False]])\n\ntorch.ne(input=tensor1, other=3.+0.j)\n# tensor([True, True, False])\n\ntensor1 = torch.tensor([True, False, True])\ntensor2 = torch.tensor([[True, False, True],\n                        [False, True, False],\n                        [True, False, True]])\ntorch.ne(input=tensor1, other=tensor2)\n# tensor([[False, False, False],\n#         [True, True, True],\n#         [False, False, False]])\n\ntorch.ne(input=tensor1, other=True)\n# tensor([False, True, False])\n<\/pre>\n<p>\u672c\u7bc7\u5173\u4e8e\u300aPyTorch \u4e2d\u7684 eq \u548c ne\u300b\u7684\u4ecb\u7ecd\u5c31\u5230\u6b64\u7ed3\u675f\u5566\uff0c\u4f46\u662f\u5b66\u65e0\u6b62\u5883\uff0c\u60f3\u8981\u4e86\u89e3\u5b66\u4e60\u66f4\u591a\u5173\u4e8e\u6587\u7ae0\u7684\u76f8\u5173\u77e5\u8bc6\uff0c\u8bf7\u5173\u6ce8\u516c\u4f17\u53f7\uff01<\/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 eq \u548c &#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-205096","post","type-post","status-publish","format-standard","hentry","category-4925"],"_links":{"self":[{"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/posts\/205096","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=205096"}],"version-history":[{"count":0,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/posts\/205096\/revisions"}],"wp:attachment":[{"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/media?parent=205096"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/categories?post=205096"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/tags?post=205096"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}