LAB01_1_Tensor_manipulation(1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LAB01-1 Tensor manipulation(1).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNrv3GFSwPtjmH3Z1xXbhIg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mieseung/Deep-learning-zero-to-all/blob/main/LAB01_1_Tensor_manipulation(1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iNwQf_swzln7"
      },
      "source": [
        "# 1D Array with numpy with numpy\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xYHpRahypr6"
      },
      "source": [
        "import numpy as np\n",
        "import torch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdV6ByyHy7At",
        "outputId": "42473f4f-71bf-445a-b181-426cc1a31b0e"
      },
      "source": [
        "t = np.array([0., 1., 2., 3., 4., 5., 6.])\n",
        "print(t)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0. 1. 2. 3. 4. 5. 6.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GkbcHVCvzA9Z",
        "outputId": "656e64b5-2945-4ae7-ca07-8e6737ef2362"
      },
      "source": [
        "print('Rank of t: ', t.ndim)\n",
        "print('Shape of t: ', t.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rank of t:  1\n",
            "Shape of t:  (7,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WpMEvLh5zKAQ",
        "outputId": "07d8e871-59b9-45d2-d449-9ed3bb7bd80c"
      },
      "source": [
        "print('t[0] t[1] t[-1] = ', t[0], t[1], t[-1] ) # element\n",
        "print('t[2:5] t[4:-1] = ', t[2:5], t[4:-1]) # slicing\n",
        "print('t[:2] t[3:] = ', t[:2], t[3:]) # slicing"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "t[0] t[1] t[-1] =  0.0 1.0 6.0\n",
            "t[2:5] t[4:-1] =  [2. 3. 4.] [4. 5.]\n",
            "t[:2] t[3:] =  [0. 1.] [3. 4. 5. 6.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4bnI02Cz0Ki"
      },
      "source": [
        "# 1D Array with pytorch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oH3Vm4TZz4GD",
        "outputId": "470ce71f-2c9c-4ecc-eb05-fc878cffdbcd"
      },
      "source": [
        "t = torch.FloatTensor([0., 1., 2., 3., 4., 5., 6.])\n",
        "print(t)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([0., 1., 2., 3., 4., 5., 6.])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "at5Wv9QNz9sc",
        "outputId": "248a935e-a6b0-4545-cabf-b1ea90a51cf8"
      },
      "source": [
        "print(t.dim()) # rank\n",
        "print(t.shape) # shape\n",
        "print(t.size()) # shape\n",
        "print(t[0], t[1], t[-1]) # element\n",
        "print(t[2:5], t[4:-1]) # slicing\n",
        "print(t[:2], t[3:]) # slicing"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "torch.Size([7])\n",
            "torch.Size([7])\n",
            "tensor(0.) tensor(1.) tensor(6.)\n",
            "tensor([2., 3., 4.]) tensor([4., 5.])\n",
            "tensor([0., 1.]) tensor([3., 4., 5., 6.])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6_OFJ27a07Za"
      },
      "source": [
        "# Broadcasting\n",
        "텐서간의 크기가 달라도 연산이 가능하게끔 해줌.\n",
        "사용자 입장에서 주의해서 사용해야함. 실수를 의도와 다르게 연산이 되면 결과값이 잘못 나올 수 있음."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lG7_OZ3m0-cq",
        "outputId": "e23dbfcc-56e1-4549-f8ba-7e7160b8ef7c"
      },
      "source": [
        "# Same shape\n",
        "m1 = torch.FloatTensor([[3, 3]])\n",
        "m2 = torch.FloatTensor([[2, 2]])\n",
        "print(m1 + m2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[5., 5.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWYkHq3f1fPk",
        "outputId": "f565aa48-9f10-44f6-c76f-64c3957645ab"
      },
      "source": [
        "# Vector + shape\n",
        "m1 = torch.FloatTensor([[1, 2]])\n",
        "m2 = torch.FloatTensor([3])\n",
        "print(m1 + m2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[4., 5.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kx5BDcU2Kav",
        "outputId": "89f8a55c-256d-43b1-90da-fad3b0c77161"
      },
      "source": [
        "# 2x1 vector + 1x2 vector\n",
        "m1 = torch.FloatTensor([[1, 2]])\n",
        "m2 = torch.FloatTensor([[3], [4]])\n",
        "print(m1 + m2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[4., 5.],\n",
            "        [5., 6.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6oHMozoT2mYr"
      },
      "source": [
        "# Multiplication vs Matrix Multiplication"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXeIGAT02tA2",
        "outputId": "a1573ca2-d54d-4aa5-b934-aa518e1edec0"
      },
      "source": [
        "# matrix multiplication\n",
        "m1 = torch.FloatTensor([[1, 2], [3, 4]])\n",
        "m2 = torch.FloatTensor([[1], [2]])\n",
        "print(m1.matmul(m2))\n",
        "\n",
        "# multiplication\n",
        "m1 = torch.FloatTensor([[1, 2], [3, 4]]) # 2 * 2\n",
        "m2 = torch.FloatTensor([[1], [2]]) # 2 * 1\n",
        "print(m1 * m2) # 2 * 2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[ 5.],\n",
            "        [11.]])\n",
            "tensor([[1., 2.],\n",
            "        [6., 8.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E7VObKqs31kx"
      },
      "source": [
        "# Mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g1-vO_yc33xm",
        "outputId": "1534b1f7-eaa5-426c-fbe9-bd7669bf5c91"
      },
      "source": [
        "t = torch.FloatTensor([1, 2])\n",
        "print(t.mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(1.5000)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tw21IvCk38lk",
        "outputId": "5117f6a0-fae9-4d04-e066-1ca06e209a75"
      },
      "source": [
        "# Can't use mean() on integers\n",
        "t = torch.LongTensor([1, 2])\n",
        "try:\n",
        "  print(t.mean())\n",
        "except Exception as exc:\n",
        "  print(exc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Can only calculate the mean of floating types. Got Long instead.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9HYQflH4H0Z",
        "outputId": "5e0672a5-dc60-4869-ac89-facd68b282b6"
      },
      "source": [
        "t = torch.FloatTensor([[1, 2], [3, 4]])\n",
        "print(t)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[1., 2.],\n",
            "        [3., 4.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m2og4rYU4MFD",
        "outputId": "f47fa272-abe2-451b-f3f9-a2b4ba7d36c9"
      },
      "source": [
        "print(t.mean())\n",
        "print(t.mean(dim=0)) # columnwise mean\n",
        "print(t.mean(dim=1)) # rowwise mean\n",
        "print(t.mean(dim=-1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(2.5000)\n",
            "tensor([2., 3.])\n",
            "tensor([1.5000, 3.5000])\n",
            "tensor([1.5000, 3.5000])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1mVVo3G4l2L"
      },
      "source": [
        "# Sum"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7j58BEvG4nXC",
        "outputId": "5ff7ab41-6761-4007-9712-bff74c3ae817"
      },
      "source": [
        "t = torch.FloatTensor([[1, 2], [3, 4]])\n",
        "print(t.sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(10.)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3TFbSVfC4qGH",
        "outputId": "f25e2de4-55eb-48b8-f1c3-2d6c7981bec2"
      },
      "source": [
        "print(t.sum())\n",
        "print(t.sum(dim=0)) # columnwise mean\n",
        "print(t.sum(dim=1)) # rowwise mean\n",
        "print(t.sum(dim=-1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(10.)\n",
            "tensor([4., 6.])\n",
            "tensor([3., 7.])\n",
            "tensor([3., 7.])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXZjUmZu49aR"
      },
      "source": [
        "# Max and Argmax\n",
        "Argmax : max인 index 값을 return"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQomferY48u0",
        "outputId": "e4517fbf-0166-41b3-dd9f-a3bd42a6554b"
      },
      "source": [
        "t = torch.FloatTensor([[1, 2], [3, 4]])\n",
        "print(t)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[1., 2.],\n",
            "        [3., 4.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3M9bXhEW5I6X",
        "outputId": "f0d0c8de-8861-4466-d33b-096fc48a8929"
      },
      "source": [
        "print(t.max())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(4.)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAShgs0_5MDD",
        "outputId": "9079e6b3-a542-4ea0-8bbf-975b6f941f09"
      },
      "source": [
        "print(t.max(dim = 0)) # returns two values when dimension is specified : max and argmax\n",
        "print('Max : ', t.max(dim=0)[0])\n",
        "print('ArgMax : ', t.max(dim=0)[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.return_types.max(\n",
            "values=tensor([3., 4.]),\n",
            "indices=tensor([1, 1]))\n",
            "Max :  tensor([3., 4.])\n",
            "ArgMax :  tensor([1, 1])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2xJPqX75fjO",
        "outputId": "8e7b0806-7a48-4df8-d196-b417c678ff87"
      },
      "source": [
        "print(t.max(dim=1))\n",
        "print(t.max(dim=-1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.return_types.max(\n",
            "values=tensor([2., 4.]),\n",
            "indices=tensor([1, 1]))\n",
            "torch.return_types.max(\n",
            "values=tensor([2., 4.]),\n",
            "indices=tensor([1, 1]))\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}