U47768_CH04_tensorBasic.ipynb

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      "name": "python"
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      "cell_type": "markdown",
      "metadata": {
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        "<a href=\"https://colab.research.google.com/github/submouse9903/uos-deepLearning/blob/main/U47768_CH04_tensorBasic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ruRMjnBcSwv-"
      },
      "outputs": [],
      "source": [
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "z = torch.zeros(5, 3)\n",
        "print(z)\n",
        "print(z.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3sCPhTQT7I9",
        "outputId": "ff43dd2b-0c48-4ff0-bf16-e942df8a6efd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0., 0., 0.],\n",
            "        [0., 0., 0.],\n",
            "        [0., 0., 0.],\n",
            "        [0., 0., 0.],\n",
            "        [0., 0., 0.]])\n",
            "torch.float32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "i = torch.ones((5, 3), dtype=torch.int16)\n",
        "print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NlEApPN0T7zj",
        "outputId": "bd581304-68c8-473b-b434-f0c2959c20c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[1, 1, 1],\n",
            "        [1, 1, 1],\n",
            "        [1, 1, 1],\n",
            "        [1, 1, 1],\n",
            "        [1, 1, 1]], dtype=torch.int16)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ds0pS390UHpp",
        "outputId": "9bca55c3-dc69-4ef4-b317-3ac81162c057"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Tensor"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(1729)\n",
        "r1 = torch.rand(2, 2)\n",
        "print('A random tensor:')\n",
        "print(r1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBQh7fRRUK0R",
        "outputId": "2dd29ac1-74cd-4a72-8488-7331ac80a26f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A random tensor:\n",
            "tensor([[0.3126, 0.3791],\n",
            "        [0.3087, 0.0736]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r1.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FEkEyReQUXWH",
        "outputId": "1b58ec2e-0d2e-450f-d7f7-40148cb61f12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.float32"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ones = torch.ones(2, 3)\n",
        "print(ones)\n",
        "\n",
        "twos = torch.ones(2, 3) * 2 \n",
        "print(twos)\n",
        "\n",
        "threes = ones + twos       # addition allowed because shapes are similar\n",
        "print(threes)              # tensors are added element-wise\n",
        "print(threes.shape)        # this has the same dimensions as input tensors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "A2u2wzzqUb-u",
        "outputId": "9f33a1d5-e4e8-4274-d1be-23750143242d"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[1., 1., 1.],\n",
            "        [1., 1., 1.]])\n",
            "tensor([[2., 2., 2.],\n",
            "        [2., 2., 2.]])\n",
            "tensor([[3., 3., 3.],\n",
            "        [3., 3., 3.]])\n",
            "torch.Size([2, 3])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.empty(2, 2, 3)\n",
        "print(x.shape)\n",
        "print(x)\n",
        "\n",
        "empty_like_x = torch.empty_like(x)\n",
        "print(empty_like_x.shape)\n",
        "print(empty_like_x)\n",
        "\n",
        "zeros_like_x = torch.zeros_like(x)\n",
        "print(zeros_like_x.shape)\n",
        "print(zeros_like_x)\n",
        "\n",
        "ones_like_x = torch.ones_like(x)\n",
        "print(ones_like_x.shape)\n",
        "print(ones_like_x)\n",
        "\n",
        "rand_like_x = torch.rand_like(x)\n",
        "print(rand_like_x.shape)\n",
        "print(rand_like_x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82WvFF0sUvw8",
        "outputId": "d30305ab-b5c1-4bd1-8893-e320568a46eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 2, 3])\n",
            "tensor([[[4.0845e-35, 0.0000e+00, 4.9231e-38],\n",
            "         [0.0000e+00, 4.4842e-44, 0.0000e+00]],\n",
            "\n",
            "        [[8.9683e-44, 0.0000e+00, 4.9231e-38],\n",
            "         [0.0000e+00, 2.1707e-18, 4.5447e+30]]])\n",
            "torch.Size([2, 2, 3])\n",
            "tensor([[[-1.2569e-23,  4.5859e-41, -1.2569e-23],\n",
            "         [ 4.5859e-41,  4.4842e-44,  0.0000e+00]],\n",
            "\n",
            "        [[ 1.1210e-43,  0.0000e+00,  4.9207e-38],\n",
            "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00]]])\n",
            "torch.Size([2, 2, 3])\n",
            "tensor([[[0., 0., 0.],\n",
            "         [0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0.],\n",
            "         [0., 0., 0.]]])\n",
            "torch.Size([2, 2, 3])\n",
            "tensor([[[1., 1., 1.],\n",
            "         [1., 1., 1.]],\n",
            "\n",
            "        [[1., 1., 1.],\n",
            "         [1., 1., 1.]]])\n",
            "torch.Size([2, 2, 3])\n",
            "tensor([[[0.4216, 0.0691, 0.2332],\n",
            "         [0.4047, 0.2162, 0.9927]],\n",
            "\n",
            "        [[0.4128, 0.5938, 0.6128],\n",
            "         [0.1519, 0.0453, 0.5035]]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "empty_like_x = torch.empty_like(x)\n",
        "print(empty_like_x.shape)\n",
        "print(empty_like_x)\n",
        "\n",
        "zeros_like_x = torch.zeros_like(x)\n",
        "print(zeros_like_x.shape)\n",
        "print(zeros_like_x)\n",
        "\n",
        "ones_like_x = torch.ones_like(x)\n",
        "print(ones_like_x.shape)\n",
        "print(ones_like_x)\n",
        "\n",
        "rand_like_x = torch.rand_like(x)\n",
        "print(rand_like_x.shape)\n",
        "print(rand_like_x)"
      ],
      "metadata": {
        "id": "12Q46zTWMcFs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.rand(2,3)\n",
        "y = torch.rand(3,4)"
      ],
      "metadata": {
        "id": "nPMzSlqeVDsZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x@y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPXGS1RAYJXl",
        "outputId": "b5471dc5-9ca4-4855-f0ed-1c9b734de681"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.2283, 0.3617, 0.2450, 0.2374],\n",
              "        [1.1728, 0.9648, 1.4286, 1.0833]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(x>0.5) | (x<0.3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jXQzrZLxYNHs",
        "outputId": "ff25408a-ac2a-434c-af21-bb947d331e3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ True,  True, False],\n",
              "        [ True,  True,  True]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = torch.ones(2, 2)\n",
        "b = a\n",
        "\n",
        "a[0][1] = 561  # we change a...\n",
        "print(b)       # ...and b is also altered"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zuqPJsIiYWUD",
        "outputId": "bc0a3d24-f5a5-4fa3-c693-2fcb1e229199"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[  1., 561.],\n",
            "        [  1.,   1.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = torch.ones(2, 2)\n",
        "b = a.clone()\n",
        "\n",
        "assert b is not a      # different objects in memory...\n",
        "print(torch.eq(a, b))  # ...but still with the same contents!\n",
        "\n",
        "a[0][1] = 561          # a changes...\n",
        "print(b)               # ...but b is still all ones"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kWhanzYJZBEZ",
        "outputId": "40815d91-e591-46f1-fff6-b6cdb40ec3e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[True, True],\n",
            "        [True, True]])\n",
            "tensor([[1., 1.],\n",
            "        [1., 1.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = torch.rand(2, 2, requires_grad=True) # turn on autograd\n",
        "print(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4QVJRVTlZHGI",
        "outputId": "11b6fc25-6be4-4f0f-a27d-db4f0e0f3c83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.9038, 0.5741],\n",
            "        [0.7026, 0.3463]], requires_grad=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = a.clone()\n",
        "print(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FKLyAfQPZcQH",
        "outputId": "e9ec644b-7bec-439d-ded2-5bdeb1c2c0d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.9038, 0.5741],\n",
            "        [0.7026, 0.3463]], grad_fn=<CloneBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = a.detach().clone()\n",
        "print(c)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXdK4_7VZfR1",
        "outputId": "cc2c40c0-dbf0-4f8d-f50e-b99b8b44b44c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.9038, 0.5741],\n",
            "        [0.7026, 0.3463]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(a)"
      ],
      "metadata": {
        "id": "TKgYny--aEmg",
        "outputId": "37f21b7b-3cab-40df-b056-43299de18498",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.9038, 0.5741],\n",
            "        [0.7026, 0.3463]], requires_grad=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### - numpy 객체의 텐서 변환\n",
        "\n",
        "- shape: tensor의 크기 변환은 함수 작성시 종종 필요한 부분이다. \n",
        "- data type: 데이터의 형이 만지 않을 때 오류가 종종 발생한다. 데이터의 형을 맞추는 것이 필요하다.\n"
      ],
      "metadata": {
        "id": "vZ7AtJOtPMgL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch \n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "a = np.array([10,5,3])\n",
        "type(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eccO-opWMCsz",
        "outputId": "02314881-5002-488e-ad25-8ea4ca0c04ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.Tensor(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oINZQwH-MdYj",
        "outputId": "8491293c-0ad2-4af1-e4b4-f088b95c0b23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([10.,  5.,  3.])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 1차원 텐서의 생성"
      ],
      "metadata": {
        "id": "bKvJ0gcRRazS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = torch.tensor(a)\n",
        "print(\"sum of a:\", a.sum())\n",
        "print(\"dtype of a:\", a.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3nIONoiMl_2",
        "outputId": "d3fbdb9b-5784-43c1-951c-c5414190c665"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sum of a: tensor(18)\n",
            "dtype of a: torch.int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-7e6f1e7344e6>:1: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  a = torch.tensor(a)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a[0] = 1\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4eQCnwllM1nv",
        "outputId": "0ab6d57a-93b6-4e25-c065-e8485a59cffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 5, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "텐서의 형 변환\n",
        "- "
      ],
      "metadata": {
        "id": "NyxGtmuGRGz1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 10행 5열인 행렬 3개를 생성한다. 3차원 텐서를 이용하며 차원은 (관측치의 개수, 행의 개수, 열의 개수)로 한다. \n",
        "- 다음을 해결한다. \n",
        "\n",
        "  1) a를 마지막 차원이 1안 4D tensor로 변환한다. \n",
        "\n",
        "  2) a를 데이터의 형식을 (행의 개수, 관측치의 개수, 열의 개수)로 바꾼후 b로 저장한다.\n",
        "\n",
        "  3) 4D tensor 인 b의 마지막 차원을 줄여 3D tensor 로 만든다. \n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "8Fpexk12zeg7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = torch.rand(size = (3, 10, 5))\n",
        "print(\"shape:\", a.shape)\n",
        "# 1) \n",
        "b = a.unsqueeze(3)\n",
        "print(\"shape:\", b.shape)\n",
        "# 2) \n",
        "print(\"shape:\", a.permute(1,0,2).shape)\n",
        "# 3) \n",
        "b = b.squeeze(3)\n",
        "print(\"shape:\", b.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nDDoq9vYzCBq",
        "outputId": "2114bc90-ab57-416b-99a1-3089d737522c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: torch.Size([3, 10, 5])\n",
            "shape: torch.Size([3, 10, 5, 1])\n",
            "shape: torch.Size([10, 3, 5])\n",
            "shape: torch.Size([3, 10, 5])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 데이터의 형의 결정\n",
        "  - numpy에서 입력값의 생성시\n",
        "  - tensor 생성시\n",
        "  - tensor dtype의 변환"
      ],
      "metadata": {
        "id": "53FwQiVc1kho"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1) \n",
        "a1 = np.array([1,2,3])\n",
        "print(\"\", a1.dtype)\n",
        "a1 = a1.astype('float32')\n",
        "print(\"\", a1.dtype)\n",
        "a1 = torch.tensor(a1)\n",
        "print(\"\", a1.dtype)\n",
        "\n",
        "#2-1) \n",
        "a1 = np.array([1,2,3])\n",
        "a1 = torch.tensor(a1, dtype = torch.float32)\n",
        "print(\"\", a1.dtype)\n",
        "\n",
        "#2-2) \n",
        "a1 = np.array([1,2,3])\n",
        "a1 = torch.FloatTensor(a1)\n",
        "print(\"\", a1.dtype)\n",
        "\n",
        "#3)\n",
        "a1 = np.array([1,2,3])\n",
        "a1 = torch.tensor(a1)\n",
        "print(\"\", a1.dtype)\n",
        "a1 = a1.to(torch.float)\n",
        "print(\"\", a1.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCUG_Azk2Vzf",
        "outputId": "3796aec3-35ba-46da-b0f6-9a81fe73b181"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " torch.float32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# you can see an error\n",
        "embedding = nn.Embedding(5,2,0)\n",
        "x = torch.FloatTensor([2,1,1,3])\n",
        "x = x.unsqueeze(1)\n",
        "print(\"\",x.shape)  \n",
        "embedding(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "QxyGUHEh2u5r",
        "outputId": "e1935b93-4042-4f37-a53f-528454dc5a14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " torch.Size([4, 1])\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-46-0e483d302aa4>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0membedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/sparse.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m         return F.embedding(\n\u001b[0m\u001b[1;32m    163\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadding_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_norm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m             self.norm_type, self.scale_grad_by_freq, self.sparse)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36membedding\u001b[0;34m(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)\u001b[0m\n\u001b[1;32m   2208\u001b[0m         \u001b[0;31m# remove once script supports set_grad_enabled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2209\u001b[0m         \u001b[0m_no_grad_embedding_renorm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_norm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2210\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale_grad_by_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msparse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.FloatTensor instead (while checking arguments for embedding)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.LongTensor([2,1,1,3])\n",
        "x = x.unsqueeze(1)\n",
        "print(\"\",x.shape)  \n",
        "embedding(x).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OUjtO53P3cYM",
        "outputId": "64388aef-e5ed-48c4-dd16-60945bdcc318"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([4, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### - 참고문헌: Torch Tensor\n",
        "\n"
      ],
      "metadata": {
        "id": "b-A_UB4gHH_N"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0QGKjcUzsp74"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}