lab_1_tensor_manipulation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "lab-1-tensor-manipulation.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m6oCCLHmF9dU"
      },
      "source": [
        "1D array with NumPy\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vAjEHQxKFVLl"
      },
      "source": [
        "import numpy as np\n",
        "import torch"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZCpO3ULjFxjS",
        "outputId": "9213f3e2-10ca-444e-d3ff-d724ef57139c"
      },
      "source": [
        "t = np.array([0., 1., 2., 3., 4., 5.,6.])\n",
        "print(t)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 1. 2. 3. 4. 5. 6.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZUrd8AQGGJ0",
        "outputId": "bead8447-d0f8-4d80-c321-59f32b67e5a8"
      },
      "source": [
        "print('Rank of t: ', t.ndim)\n",
        "print('Shape of t: ', t.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rank of t:  1\n",
            "Shape of t:  (7,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7D9yQBzDGPYP",
        "outputId": "9d2ffda8-8d75-4e6f-9ad0-dee576797502"
      },
      "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": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBCFpWsEHYSO",
        "outputId": "c84799ee-01b2-465a-d84b-dbe833ab47f9"
      },
      "source": [
        "t = np.array([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.], [10., 11., 12.]])\n",
        "print(t)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1.  2.  3.]\n",
            " [ 4.  5.  6.]\n",
            " [ 7.  8.  9.]\n",
            " [10. 11. 12.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nc8d2gBVHjYx",
        "outputId": "eea6ac40-e32b-4a0b-b570-88d06f151674"
      },
      "source": [
        "print('Rank of t: ', t.ndim)\n",
        "print('Shape of t: ', t.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rank of t:  2\n",
            "Shape of t:  (4, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2CcUPOwEHxrR",
        "outputId": "7b6701fb-c6e0-4223-b18f-4092f7ecc376"
      },
      "source": [
        "t = torch.FloatTensor([0., 1., 2., 3., 4., 5., 6.])\n",
        "print(t)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([0., 1., 2., 3., 4., 5., 6.])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMCUOvUnIIJT",
        "outputId": "3ea4fe3e-c709-4238-ac08-ca40fcbdd8f5"
      },
      "source": [
        "print(t[4:-2])\n",
        "print(t.dim())\n",
        "print(t.shape)\n",
        "print(t.size())\n",
        "print(t[0], t[1], t[-1])\n",
        "print(t[2:5], t[4:-1])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([4.])\n",
            "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QZJoarmJjSG",
        "outputId": "2aeae69d-cefd-4e42-cbc5-fae7ac3f6b42"
      },
      "source": [
        "print()\n",
        "print('-------------')\n",
        "print('Mul vs Matmul')\n",
        "print('-------------')\n",
        "m1 = torch.FloatTensor([[1, 2], [3, 4]])\n",
        "m2 = torch.FloatTensor([[1], [2]])\n",
        "print('Shape of Matrix 1: ', m1.shape) # 2 x 2\n",
        "print('Shape of Matrix 2: ', m2.shape) # 2 x 1\n",
        "print(m1.matmul(m2)) # 2 x 1\n",
        "\n",
        "m1 = torch.FloatTensor([[1, 2], [3, 4]])\n",
        "m2 = torch.FloatTensor([[1], [2]])\n",
        "print('Shape of Matrix 1: ', m1.shape) # 2 x 2\n",
        "print('Shape of Matrix 2: ', m2.shape) # 2 x 1\n",
        "print(m1 * m2) # 2 x 2\n",
        "print(m1.mul(m2))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "-------------\n",
            "Mul vs Matmul\n",
            "-------------\n",
            "Shape of Matrix 1:  torch.Size([2, 2])\n",
            "Shape of Matrix 2:  torch.Size([2, 1])\n",
            "tensor([[ 5.],\n",
            "        [11.]])\n",
            "Shape of Matrix 1:  torch.Size([2, 2])\n",
            "Shape of Matrix 2:  torch.Size([2, 1])\n",
            "tensor([[1., 2.],\n",
            "        [6., 8.]])\n",
            "tensor([[1., 2.],\n",
            "        [6., 8.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUyl_WWJbzBb",
        "outputId": "8bcc4ff2-bbae-4766-d9dd-e1e8d70a7c6e"
      },
      "source": [
        "# Same shape \n",
        "m1 = torch.FloatTensor([[3, 3]])\n",
        "m2 = torch.FloatTensor([[2, 2]])\n",
        "print(m1 + m2)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[5., 5.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0kXSpLFcFhM",
        "outputId": "c27a3888-4d85-431e-f694-3d78a102b9d3"
      },
      "source": [
        "# Vector + scalar\n",
        "m1 = torch.FloatTensor([[1, 2]])\n",
        "m2 = torch.FloatTensor([3]) # 3 -> [[3, 3]]\n",
        "print(m1 + m2)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[4., 5.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_b0lszycab-",
        "outputId": "bb814a2d-5a71-4115-c2df-8f024071562f"
      },
      "source": [
        "m1 = torch.FloatTensor([[1, 2]])\n",
        "m2 = torch.FloatTensor([[3], [4]])\n",
        "print(m1 + m2)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[4., 5.],\n",
            "        [5., 6.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fop0y6MmcwJJ",
        "outputId": "c727a19c-0c38-46e2-833c-c9da8829bb52"
      },
      "source": [
        "t = torch.FloatTensor([1, 2])\n",
        "print(t.mean())"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(1.5000)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M62kTCVkc4N1",
        "outputId": "5bac4a3f-c14c-45d5-ee72-ae9be71d52e7"
      },
      "source": [
        "# Can't use mean() on integers\n",
        "t = torch.LongTensor([1, 2])\n",
        "try:\n",
        "    print(t.mean())\n",
        "except Exception as e:\n",
        "    print(e)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean(): input dtype should be either floating point or complex dtypes. Got Long instead.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-uQHq8XdNaO",
        "outputId": "0c7c9b1f-c1d9-401a-81c5-8dfa379e5553"
      },
      "source": [
        "t = torch.FloatTensor([[1, 2], [3, 4]])\n",
        "print(t)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[1., 2.],\n",
            "        [3., 4.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Fd12Qwddbh5",
        "outputId": "e3555079-199b-4566-afbf-e332fba18bf9"
      },
      "source": [
        "print(t.mean())\n",
        "print(t.mean(dim=0))\n",
        "print(t.mean(dim=1))\n",
        "print(t.ndim)\n",
        "print(t.mean(dim=-2))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(2.5000)\n",
            "tensor([2., 3.])\n",
            "tensor([1.5000, 3.5000])\n",
            "2\n",
            "tensor([2., 3.])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDU86fo0eDJD",
        "outputId": "901886ba-fae7-4271-e355-45f13de20dee"
      },
      "source": [
        "t = torch.FloatTensor([[1,2], [3,4]])\n",
        "print(t)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[1., 2.],\n",
            "        [3., 4.]])\n"
          ]
        }
      ]
    }
  ]
}