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A\u73ed4\u4f4d\u540c\u5b66\uff0c\u6bcf\u4f4d\u540c\u5b663\u79d1\u6210\u7ee9\nb = torch.rand(4, 3) # B\u73ed4\u4f4d\u540c\u5b66\uff0c\u6bcf\u4f4d\u540c\u5b663\u79d1\u6210\u7ee9\nc = torch.stack((a, b), dim=0) # \u7406\u89e3\uff1a\u5e74\u7ea7\u6240\u6709\u540c\u5b66\u76843\u79d1\u6210\u7ee9\uff08\u5047\u8bbe\u5e74\u7ea7\u53ea\u6709A\u73ed\u548cB\u73ed\u4e24\u4e2a\u73ed\uff0c\u6bcf\u4e2a\u73ed\u53ea\u6709\u56db\u540d\u540c\u5b66\uff09\nprint(c.shape) # torch.Size([2, 4, 3])\nd = torch.concat((a, b), dim=1) # \u7406\u89e3\uff1aa\u662fA\u73ed4\u4f4d\u540c\u5b663\u79d1\u6210\u7ee9\uff0cb\u662f\u8fd94\u540d\u540c\u5b66\u5176\u4ed63\u95e8\u8bfe\u7684\u6210\u7ee9\uff0c\u62fc\u63a5\u540e\u4ee3\u8868\u8fd94\u540d\u540c\u5b66\u76846\u79d1\u6210\u7ee9\nprint(d.shape) # torch.Size([4, 6])\n"))),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("p",{parentName:"li"},"list\u548ctensor\u4e58\u6cd5\u4e0d\u540c\u4e4b\u5904"),(0,a.kt)("p",{parentName:"li"},"list\u7684*\u4e58\u6cd5\u662f\u590d\u5236\u5143\u7d20\uff0c\u6539\u53d8list\u7684shape"),(0,a.kt)("p",{parentName:"li"},"tensor\u7684*\u4e58\u6cd5\u662f\u5bf9tensor\u4e2d\u7684\u5143\u7d20\u8fdb\u884c\u70b9\u4e58\u8ba1\u7b97"),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-python"},"a = torch.tensor([[3, 3, 3, 3]])\nb = [3] # list\u7684*\u4e58\u662f\u590d\u5236\u5143\u7d20\u8fdb\u884c\u6269\u5c55\nprint(a * 3) # tensor([[9, 9, 9, 9]])\nprint(b * 3) # [3, 3, 3]\n"))),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("p",{parentName:"li"},"\u6700\u5927\u503c / \u6700\u5c0f\u503c\u7d22\u5f15\uff1aargmax / argmin"),(0,a.kt)("p",{parentName:"li"},"\u9700\u8981\u901a\u8fc7\u53c2\u6570dim\u6307\u5b9a\u64cd\u4f5c\u7684\u7ef4\u5ea6\uff0cdim\u7684\u7406\u89e3"),(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("p",{parentName:"li"},"\u5b98\u65b9\u89e3\u91ca\uff1aThe dimension to reduce")),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("p",{parentName:"li"},"\u4ee5\u4e8c\u7ef4\u5f20\u91cf\u4e3e\u4f8b\uff0cdim=1\u5373\u5728\u6bcf\u4e00\u884c\u4e2d\u9009\u51fa\u4e00\u4e2a\u6700\u5927\u503c / \u6700\u5c0f\u503c\u5143\u7d20\u7684\u7d22\u5f15\uff0c\u7d22\u5f15\u7684shape\u5e94\u4e3a","[dim0, 1]","\uff0c\u5373reduce\u4e86dim=1\u7684\u7ef4\u5ea6"))),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-python"},'# \u6700\u5927\u503c\u6700\u5c0f\u503c\u7d22\u5f15\na = torch.tensor([[0.1, 0.9, 0.3], [0.9, 0.8, 0.99], [0.1, 0.7, 0.8], [0.88, 0.1, 0.2]]) # [4, 3]\nprint("argmax output: ", a.argmax(dim=0), a.argmax(dim=1)) # argmax output: tensor([1, 0, 1]) tensor([1, 2, 2, 0])\n'))),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("p",{parentName:"li"},"Python zip\u51fd\u6570"),(0,a.kt)("p",{parentName:"li"},"zip\u51fd\u6570\u53ef\u4ee5\u7406\u89e3\u4e3a\u538b\u7f29\uff0c\u5c06\u8f93\u5165\u7684\u4e24\u4e2a\u8fed\u4ee3\u5668\u7684",(0,a.kt)("strong",{parentName:"p"},"\u6700\u5916\u5c42"),"\u5bf9\u5e94\u5143\u7d20\u538b\u7f29\u4e3a\u4e00\u4e2a\u65b0\u7684\u5143\u7d20"),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-python"},"a = torch.tensor([1, 2, 3])\nb = torch.tensor([4, 5, 6])\nc = zip(a, b)\nfor i in c:\n print(i)\n'''\n(tensor(1), tensor(4))\n(tensor(2), tensor(5))\n(tensor(3), tensor(6))\n'''\na = torch.tensor([[1, 2, 3], [3, 2, 1]])\nb = torch.tensor([[4, 5, 6], [6, 5, 4]])\nc = zip(a, b)\nfor i in c:\n print(i)\n'''\n(tensor([1, 2, 3]), tensor([4, 5, 6]))\n(tensor([3, 2, 1]), tensor([6, 5, 4]))\n'''\n")))))}u.isMDXComponent=!0}}]); \ No newline at end of file diff --git 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A\u73ed4\u4f4d\u540c\u5b66\uff0c\u6bcf\u4f4d\u540c\u5b663\u79d1\u6210\u7ee9\nb = torch.rand(4, 3) # B\u73ed4\u4f4d\u540c\u5b66\uff0c\u6bcf\u4f4d\u540c\u5b663\u79d1\u6210\u7ee9\nc = torch.stack((a, b), dim=0) # \u7406\u89e3\uff1a\u5e74\u7ea7\u6240\u6709\u540c\u5b66\u76843\u79d1\u6210\u7ee9\uff08\u5047\u8bbe\u5e74\u7ea7\u53ea\u6709A\u73ed\u548cB\u73ed\u4e24\u4e2a\u73ed\uff0c\u6bcf\u4e2a\u73ed\u53ea\u6709\u56db\u540d\u540c\u5b66\uff09\nprint(c.shape) # torch.Size([2, 4, 3])\nd = torch.concat((a, b), dim=1) # \u7406\u89e3\uff1aa\u662fA\u73ed4\u4f4d\u540c\u5b663\u79d1\u6210\u7ee9\uff0cb\u662f\u8fd94\u540d\u540c\u5b66\u5176\u4ed63\u95e8\u8bfe\u7684\u6210\u7ee9\uff0c\u62fc\u63a5\u540e\u4ee3\u8868\u8fd94\u540d\u540c\u5b66\u76846\u79d1\u6210\u7ee9\nprint(d.shape) # torch.Size([4, 6])\n"))),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("p",{parentName:"li"},"list\u548ctensor\u4e58\u6cd5\u4e0d\u540c\u4e4b\u5904"),(0,a.kt)("p",{parentName:"li"},"list\u7684*\u4e58\u6cd5\u662f\u590d\u5236\u5143\u7d20\uff0c\u6539\u53d8list\u7684shape"),(0,a.kt)("p",{parentName:"li"},"tensor\u7684*\u4e58\u6cd5\u662f\u5bf9tensor\u4e2d\u7684\u5143\u7d20\u8fdb\u884c\u70b9\u4e58\u8ba1\u7b97"),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-python"},"a = torch.tensor([[3, 3, 3, 3]])\nb = [3] # list\u7684*\u4e58\u662f\u590d\u5236\u5143\u7d20\u8fdb\u884c\u6269\u5c55\nprint(a * 3) # tensor([[9, 9, 9, 9]])\nprint(b * 3) # [3, 3, 3]\n"))),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("p",{parentName:"li"},"\u6700\u5927\u503c / \u6700\u5c0f\u503c\u7d22\u5f15\uff1aargmax / 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tensor([1, 2, 2, 0])\n'))),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("p",{parentName:"li"},"Python zip\u51fd\u6570"),(0,a.kt)("p",{parentName:"li"},"zip\u51fd\u6570\u53ef\u4ee5\u7406\u89e3\u4e3a\u538b\u7f29\uff0c\u5c06\u8f93\u5165\u7684\u4e24\u4e2a\u8fed\u4ee3\u5668\u7684",(0,a.kt)("strong",{parentName:"p"},"\u6700\u5916\u5c42"),"\u5bf9\u5e94\u5143\u7d20\u538b\u7f29\u4e3a\u4e00\u4e2a\u65b0\u7684\u5143\u7d20"),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-python"},"a = torch.tensor([1, 2, 3])\nb = torch.tensor([4, 5, 6])\nc = zip(a, b)\nfor i in c:\n print(i)\n'''\n(tensor(1), tensor(4))\n(tensor(2), tensor(5))\n(tensor(3), tensor(6))\n'''\na = torch.tensor([[1, 2, 3], [3, 2, 1]])\nb = torch.tensor([[4, 5, 6], [6, 5, 4]])\nc = zip(a, b)\nfor i in c:\n print(i)\n'''\n(tensor([1, 2, 3]), tensor([4, 5, 6]))\n(tensor([3, 2, 1]), tensor([6, 5, 4]))\n'''\n")))))}u.isMDXComponent=!0}}]); \ No newline at end of file diff --git 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