7_统计属性.ipynb

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    "colab": {
      "name": "7.统计属性.ipynb",
      "version": "0.3.2",
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  "cells": [
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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mengcius/pytorch-learn/blob/master/7_%E7%BB%9F%E8%AE%A1%E5%B1%9E%E6%80%A7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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        "id": "9dQEp4EPSG02",
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      "source": [
        "## 7.统计属性\n",
        "\n",
        "norm 范数\n",
        "\n",
        "max/min/argmin/argmax 最大/小值及其位置\n",
        "\n",
        "mean/sum/prod 均值/和/乘积\n",
        "\n",
        "keepdim 输出保持维度\n",
        "\n",
        "topk/kthvalue 前几大/第几小的值及位置\n",
        "\n",
        "比较运算\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1xGR-p8zS1_i",
        "colab_type": "text"
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      "source": [
        "### norm 范数\n",
        "norm 指范数，区分以下几个概念：\n",
        "\n",
        "normalize 正则化、标准化，如batch_norm\n",
        "\n",
        "matrix norm 矩阵范数\n",
        "\n",
        "vector norm 向量范数\n",
        "\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xqyC6i-ElJzW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6jRLsF_SGiR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "14ef6456-49a8-4fc6-f44a-774f2b464524"
      },
      "source": [
        "a=torch.full([8],1) #向量\n",
        "b=a.view(2,4) #矩阵\n",
        "c=a.view(2,2,2) #矩阵\n",
        "b"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1., 1., 1., 1.],\n",
              "        [1., 1., 1., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjiFtVNmSDEA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "outputId": "4cd9fdf0-4eb9-4a60-a8ea-2cee4e2505a1"
      },
      "source": [
        "c"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[1., 1.],\n",
              "         [1., 1.]],\n",
              "\n",
              "        [[1., 1.],\n",
              "         [1., 1.]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omyJ-_MdmZX1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "a9d13d67-86c0-408c-bb56-6a8e344d4046"
      },
      "source": [
        "a.norm(1),b.norm(1),c.norm(1) #范数都为1，1的范数为所有元素绝对值之和"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(8.), tensor(8.), tensor(8.))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlaMMi4Xmfpn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "4b9462de-ece9-401b-a214-65a52f5b79b2"
      },
      "source": [
        "a.norm(2),b.norm(2),c.norm(2) #2范数，所有元素绝对值的平方之和，再开根号"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(2.8284), tensor(2.8284), tensor(2.8284))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UvWva6NAmk_a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "5b09f931-5561-49db-c9c0-48efcf21c77c"
      },
      "source": [
        "b.norm(1,dim=1),b.norm(2,dim=1) #对某维度做范数，第一个参数是批范数，第二参数是第几维度"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([4., 4.]), tensor([2., 2.]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3pdZ-nAms3x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "c8e01300-1371-4d0a-84ed-ea9355bae857"
      },
      "source": [
        "c.norm(1,dim=0) #消掉了第0维"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2., 2.],\n",
              "        [2., 2.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uhZ1OYEY3CHV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "a1fd1410-0899-49ba-cd39-6ea29cfa0b60"
      },
      "source": [
        "c.norm(1,dim=2)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2., 2.],\n",
              "        [2., 2.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hk-lD-kUm0al",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "afd87a35-a5ff-42aa-ab2d-749a9cd08816"
      },
      "source": [
        "c.norm(2,dim=0)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1.4142, 1.4142],\n",
              "        [1.4142, 1.4142]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5YUl2Zym_wg",
        "colab_type": "text"
      },
      "source": [
        "### max/min/argmax/argmin 最大/小值及其位置"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvVaGJIMm3EO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "b3651559-5672-4f46-cef3-f9ef545be62c"
      },
      "source": [
        "a=torch.arange(8).view(2,4).float()\n",
        "a"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0., 1., 2., 3.],\n",
              "        [4., 5., 6., 7.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vhdVq8inpKm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "1de37f40-215d-497b-92e9-a28d367a855f"
      },
      "source": [
        "a.max(),a.min() #最大值，最小值"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(7.), tensor(0.))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGrDypdZn4H9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "622fd4e6-9b38-4a84-f8d2-e90f86cb696f"
      },
      "source": [
        "a.argmax(),a.argmin() #最大值的位置，最小值的位置，这个位置是把tensor展开后的索引"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(7), tensor(0))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Atby8iYPpcv0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "b14d2e80-a5c0-4f17-a3ed-41d8b9eacc33"
      },
      "source": [
        "b=torch.rand(2,3,4)\n",
        "b"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[0.6167, 0.4856, 0.6627, 0.2452],\n",
              "         [0.9823, 0.9273, 0.1527, 0.8079],\n",
              "         [0.8636, 0.2244, 0.3796, 0.7682]],\n",
              "\n",
              "        [[0.7121, 0.3076, 0.9650, 0.1044],\n",
              "         [0.4546, 0.9258, 0.7367, 0.8671],\n",
              "         [0.6584, 0.0765, 0.2301, 0.4632]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhnhv_Lbpl0A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "67bdd18d-07f8-4f87-9ef8-caeccff4252c"
      },
      "source": [
        "b.max(),b.argmax()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(0.9823), tensor(4))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GDBguhETGBwa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "febbd819-8183-4c48-d226-63fd056b64f1"
      },
      "source": [
        "b.max(dim=1)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.return_types.max(values=tensor([[0.9823, 0.9273, 0.6627, 0.8079],\n",
              "        [0.7121, 0.9258, 0.9650, 0.8671]]), indices=tensor([[1, 1, 0, 1],\n",
              "        [0, 1, 0, 1]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HMIwZLappIJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "7ee42ecc-8334-4eb6-8c18-57e41e500748"
      },
      "source": [
        "b.argmax(dim=0) #某维度下的最大值的位置，在此维度下的对应部分做比较"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 1, 1, 0],\n",
              "        [1, 1, 1, 1],\n",
              "        [1, 0, 1, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u5uGfF5WtgU4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "0ed91e0c-6066-4889-8978-35c458ac453a"
      },
      "source": [
        "b.argmax(dim=1)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 1, 0, 1],\n",
              "        [0, 1, 0, 1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Z2WcT-9uBHj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "b0cb37cb-f76b-4ad5-d94e-463d87a68c20"
      },
      "source": [
        "b.argmax(dim=2)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2, 0, 0],\n",
              "        [2, 1, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UDfd-60OoVB-",
        "colab_type": "text"
      },
      "source": [
        "### mean/sum/prod 均值/和/乘积"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8fWZRuW6Wvi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "26c3e205-415b-47ad-e58e-c22d2fa21436"
      },
      "source": [
        "a=torch.arange(8).view(2,4).float()\n",
        "a"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0., 1., 2., 3.],\n",
              "        [4., 5., 6., 7.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "thWmVTBvntYY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "65a5a0d5-bd01-497d-83a1-2dd44e25bb19"
      },
      "source": [
        "a.mean(),a.sum() #均值，和"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(3.5000), tensor(28.))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0lu2oUgn1bM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "46a4ef79-e5ac-454a-a239-7a54034bd259"
      },
      "source": [
        "a.prod() #累乘，乘积，product"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0.)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cX7SZJo6RhN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "81572164-2518-4f9d-ff9f-6afac52f45c6"
      },
      "source": [
        "a.mean(dim=0),a.sum(dim=1),a.prod(dim=0) #某维度上"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([2., 3., 4., 5.]), tensor([ 6., 22.]), tensor([ 0.,  5., 12., 21.]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ry4cv0BuIYr",
        "colab_type": "text"
      },
      "source": [
        "### keepdim 输出保持维度\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Qr5yXcH-4co",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "7fa499c4-2fdb-48b1-c11c-de75c9f5a9b9"
      },
      "source": [
        "a=torch.randn(8).view(2,4).float() #比如它是神经网络的预测，想要找到2张图的预测值\n",
        "a"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-2.0132, -0.1581,  0.2038, -0.5052],\n",
              "        [ 0.4008,  0.5014, -1.5073,  0.7564]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DsrfS8Bxn9Bo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "32e0f1b1-01c3-449d-cd11-90bd421d23f8"
      },
      "source": [
        "a.max(dim=1) #max加维度，返回最大值和它的位置，argmax是它的一部分，"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.return_types.max(values=tensor([0.2038, 0.7564]), indices=tensor([2, 3]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVHO8Jef-xUr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "69544037-65a2-4647-f9f0-0e820dde2960"
      },
      "source": [
        "a.max(dim=1,keepdim=True) #keepdim使此dim=1以外的其他维度不变，即保持维度不变"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.return_types.max(values=tensor([[0.2038],\n",
              "        [0.7564]]), indices=tensor([[2],\n",
              "        [3]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4y7pCNeIIYd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "cb8b1458-1b34-4492-cede-2c06c5e91645"
      },
      "source": [
        "a.max(dim=0)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.return_types.max(values=tensor([0.4008, 0.5014, 0.2038, 0.7564]), indices=tensor([1, 1, 0, 1]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MHF_05JgILVD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "0c3fc784-bc39-4401-9ffd-7217825e64d1"
      },
      "source": [
        "a.max(dim=0,keepdim=True)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.return_types.max(values=tensor([[0.4008, 0.5014, 0.2038, 0.7564]]), indices=tensor([[1, 1, 0, 1]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-ovvQQDD8yn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "955da521-a481-402d-bb91-43123d2e528b"
      },
      "source": [
        "a.argmax(dim=1) #只返回位置"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([2, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWzWtjLu_M2Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "f8f1bbca-eb7f-462c-b69a-83651a025584"
      },
      "source": [
        "a.argmax(dim=1,keepdim=True) #还是2维，找到了两张图的判断类别的索引"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2],\n",
              "        [3]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSJtihsZIXe1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "177ce1a8-de8e-4125-a9fb-b1ba3c9be541"
      },
      "source": [
        "a.argmax(dim=0).shape"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7eo9XD-fIXTP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "05301490-57e8-4392-b89f-b980818c93db"
      },
      "source": [
        "a.argmax(dim=0,keepdim=True).shape #还是2维的，和以前一样"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wruc40tqKT0F",
        "colab_type": "text"
      },
      "source": [
        "### topk/kthvalue 前几大/第几小\n",
        "\n",
        "topk 取前k个最大的值及位置，用在取预测概率前k大的类别，和Top-5 ACC一样\n",
        "\n",
        "kthvalue 取第k小的值及位置，只能是小"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZEpB3rCKTgx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "9ae266f6-926e-47d7-9eb4-ec246d2d0b23"
      },
      "source": [
        "a=torch.arange(15).view(3,5)\n",
        "a"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0,  1,  2,  3,  4],\n",
              "        [ 5,  6,  7,  8,  9],\n",
              "        [10, 11, 12, 13, 14]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IxYd3KwoBIre",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "outputId": "f91337d5-a394-4fa1-ff8a-a44898b71eb8"
      },
      "source": [
        "a.topk(3,dim=1) #取此维度下的前3个最大的值，以及他们的在此维度上的索引，最大的在前面"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.return_types.topk(values=tensor([[ 4,  3,  2],\n",
              "        [ 9,  8,  7],\n",
              "        [14, 13, 12]]), indices=tensor([[4, 3, 2],\n",
              "        [4, 3, 2],\n",
              "        [4, 3, 2]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0MAHobBdOEhc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "outputId": "0a8043f0-223f-4b38-cac9-f9afbe3739c0"
      },
      "source": [
        "a.topk(3,dim=1,largest=False) #前3个最小的值，最小的在前面，此维度之外的其他维度不变"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.return_types.topk(values=tensor([[ 0,  1,  2],\n",
              "        [ 5,  6,  7],\n",
              "        [10, 11, 12]]), indices=tensor([[0, 1, 2],\n",
              "        [0, 1, 2],\n",
              "        [0, 1, 2]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9lbRlwOTPFJ5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "e01e0ad0-6e60-44ac-cfd6-51b18a173b6d"
      },
      "source": [
        "a.kthvalue(1,dim=1) #第1小的值及位置"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.return_types.kthvalue(values=tensor([ 0,  5, 10]), indices=tensor([0, 0, 0]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NihrE-YhRKLD",
        "colab_type": "text"
      },
      "source": [
        "### 比较运算\n",
        "\n",
        "==，!=，>，>=，<，<=  对tensor中每个元素做运算\n",
        "\n",
        "torch.eq(a, b) 比较元素是否相等\n",
        "\n",
        "torch.equal(a, b) 比较整个tensor是否相等\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9I9A0qhRJzy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a=torch.arange(8).view(2,4).float()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rxU2ibG2Qyef",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "52f1d775-2b4b-4f54-ce27-f83b31690595"
      },
      "source": [
        "a>0 #真为1，假为0"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0, 1, 1, 1],\n",
              "        [1, 1, 1, 1]], dtype=torch.uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKPhA6fJldHS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "d5c14750-d580-4338-b69b-6c2f2246e116"
      },
      "source": [
        "a!=1"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 0, 1, 1],\n",
              "        [1, 1, 1, 1]], dtype=torch.uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8F4vwMtclgt9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "25cb9d3a-1c00-4d58-f25c-0c070b05fd43"
      },
      "source": [
        "b=torch.randn(8).view(2,4)\n",
        "b.type()"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'torch.FloatTensor'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mi5O6Q3ImEBq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "822c25e3-7c20-42d1-898d-12a251bd415e"
      },
      "source": [
        "torch.eq(a,b) #比较元素是否相等"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0, 0, 0, 0],\n",
              "        [0, 0, 0, 0]], dtype=torch.uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yctxo-bBm7gP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "f3d1110a-82b4-44f7-ce22-7f9ca5f6cbfb"
      },
      "source": [
        "torch.equal(a,b) #比较整个tensor是否相等"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    }
  ]
}