tensors_in_pytorch.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t19_gq09KjvE",
        "outputId": "3a3c0a7a-d863-4a81-e081-22be273c9086"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.5.1+cu121\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "print(torch.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if torch.cuda.is_available():\n",
        "    print(\"GPU is available!\")\n",
        "    print(f\"Using GPU: {torch.cuda.get_device_name(0)}\")\n",
        "else:\n",
        "    print(\"GPU not available. Using CPU.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8o-1cUl4M2x",
        "outputId": "cc5d3abc-d3f6-4568-9590-07a5bd95c4ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU is available!\n",
            "Using GPU: Tesla T4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating a Tensor"
      ],
      "metadata": {
        "id": "BFcs4wpz4wlA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# using empty\n",
        "a = torch.empty(2,3)"
      ],
      "metadata": {
        "id": "AYzOQK7n4nNq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check type\n",
        "type(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSOoTAZY5FR1",
        "outputId": "d85de7cd-fa8e-4e79-8a8e-e3adaa86fc5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Tensor"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using zeros\n",
        "torch.zeros(2,3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxVQPOVN47Dt",
        "outputId": "bc713dac-9dce-47a7-c099-bfdc83a2cf23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0., 0., 0.],\n",
              "        [0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using ones\n",
        "torch.ones(2,3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U3-KPOlX48mT",
        "outputId": "c5355d7e-4975-4cd1-e1a0-e52e6ee271d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1., 1., 1.],\n",
              "        [1., 1., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using rand\n",
        "torch.rand(2,3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_j_mRGK4-DA",
        "outputId": "557aafe1-d51f-4fbb-ecf1-37a0e5f881e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.1192, 0.4828, 0.9105],\n",
              "        [0.4266, 0.4203, 0.2719]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# use of seed\n",
        "torch.rand(2,3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lCAf-HRw5SiA",
        "outputId": "7716f89f-7e5b-420f-d142-911d659de81f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0064, 0.4451, 0.1580],\n",
              "        [0.8119, 0.4629, 0.3227]])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# manual_seed\n",
        "torch.manual_seed(100)\n",
        "torch.rand(2,3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvAqsoFG5UMr",
        "outputId": "78421041-53ed-4ed9-f44b-3c3d25f5eb49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.1117, 0.8158, 0.2626],\n",
              "        [0.4839, 0.6765, 0.7539]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(100)\n",
        "torch.rand(2,3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96Akgtgs5Ulp",
        "outputId": "dbe6ae12-a145-4a22-c1ef-362bced7534d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.1117, 0.8158, 0.2626],\n",
              "        [0.4839, 0.6765, 0.7539]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using tensor\n",
        "torch.tensor([[1,2,3],[4,5,6]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-YLLqcGB4_4J",
        "outputId": "5ecd81d0-ba0f-4dee-f3c3-7c1960ac3430"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 2, 3],\n",
              "        [4, 5, 6]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# other ways\n",
        "\n",
        "# arange\n",
        "print(\"using arange ->\", torch.arange(0,10,2))\n",
        "\n",
        "# using linspace\n",
        "print(\"using linspace ->\", torch.linspace(0,10,10))\n",
        "\n",
        "# using eye\n",
        "print(\"using eye ->\", torch.eye(5))\n",
        "\n",
        "# using full\n",
        "print(\"using full ->\", torch.full((3, 3), 5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLj35yxr6Qgv",
        "outputId": "963b286d-055d-4a20-9b79-6d58bfe66127"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using arange -> tensor([0, 2, 4, 6, 8])\n",
            "using linspace -> tensor([ 0.0000,  1.1111,  2.2222,  3.3333,  4.4444,  5.5556,  6.6667,  7.7778,\n",
            "         8.8889, 10.0000])\n",
            "using eye -> tensor([[1., 0., 0., 0., 0.],\n",
            "        [0., 1., 0., 0., 0.],\n",
            "        [0., 0., 1., 0., 0.],\n",
            "        [0., 0., 0., 1., 0.],\n",
            "        [0., 0., 0., 0., 1.]])\n",
            "using full -> tensor([[5, 5, 5],\n",
            "        [5, 5, 5],\n",
            "        [5, 5, 5]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tensor Shapes"
      ],
      "metadata": {
        "id": "0ejaUWSkinz0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.tensor([[1,2,3],[4,5,6]])\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDrr5mQ86R4K",
        "outputId": "a8e5bd35-eb3c-4a0c-b652-e21dfa26e9b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 2, 3],\n",
              "        [4, 5, 6]])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGZhrLablBq-",
        "outputId": "a7a0c599-a75e-4a34-b032-c7d6fe5e37e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.empty_like(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NEQW9QhmlKXA",
        "outputId": "514e0fa0-09c6-4373-e0ce-9418d9c22f2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[     134842408414608,       96429401449888,     3503044046096754],\n",
              "        [-2738188572259385344,  7020378893157883663,  8389754676633367105]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.zeros_like(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PyyKSNHTlXMU",
        "outputId": "b4e7644b-d630-4e07-df5f-6821107c968d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0, 0, 0],\n",
              "        [0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.ones_like(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtKV7Wxalb3l",
        "outputId": "57ce0f49-40aa-4f38-ced2-90847cc65cfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 1, 1],\n",
              "        [1, 1, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.rand_like(x, dtype=torch.float32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WS_uk8ileyH",
        "outputId": "da2e905a-b62d-4f5d-e45f-6b570ba287e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.4076, 0.6037, 0.9820],\n",
              "        [0.0117, 0.3891, 0.5056]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tensor Data Types"
      ],
      "metadata": {
        "id": "nYM1fR5BmxIf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find data type\n",
        "x.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jy-rBsKSliAe",
        "outputId": "587f779c-48ec-4146-c282-e824c5163ac1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# assign data type\n",
        "torch.tensor([1.0,2.0,3.0], dtype=torch.int32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6T2gmrujnaMA",
        "outputId": "38ba3cd1-b85f-43e0-ba9d-9b84cf5e12dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 2, 3], dtype=torch.int32)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.tensor([1,2,3], dtype=torch.float64)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wZ8lMLsInffd",
        "outputId": "0e2841a9-a4a7-4093-fd82-9fbf9a7b5d9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1., 2., 3.], dtype=torch.float64)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using to()\n",
        "x.to(torch.float32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WTAB-h_ing07",
        "outputId": "d2abbc1d-28db-4fda-fdd9-e5646ae4482a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1., 2., 3.],\n",
              "        [4., 5., 6.]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "| **Data Type**             | **Dtype**         | **Description**                                                                                                                                                                |\n",
        "|---------------------------|-------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
        "| **32-bit Floating Point** | `torch.float32`   | Standard floating-point type used for most deep learning tasks. Provides a balance between precision and memory usage.                                                         |\n",
        "| **64-bit Floating Point** | `torch.float64`   | Double-precision floating point. Useful for high-precision numerical tasks but uses more memory.                                                                               |\n",
        "| **16-bit Floating Point** | `torch.float16`   | Half-precision floating point. Commonly used in mixed-precision training to reduce memory and computational overhead on modern GPUs.                                            |\n",
        "| **BFloat16**              | `torch.bfloat16`  | Brain floating-point format with reduced precision compared to `float16`. Used in mixed-precision training, especially on TPUs.                                                |\n",
        "| **8-bit Floating Point**  | `torch.float8`    | Ultra-low-precision floating point. Used for experimental applications and extreme memory-constrained environments (less common).                                               |\n",
        "| **8-bit Integer**         | `torch.int8`      | 8-bit signed integer. Used for quantized models to save memory and computation in inference.                                                                                   |\n",
        "| **16-bit Integer**        | `torch.int16`     | 16-bit signed integer. Useful for special numerical tasks requiring intermediate precision.                                                                                    |\n",
        "| **32-bit Integer**        | `torch.int32`     | Standard signed integer type. Commonly used for indexing and general-purpose numerical tasks.                                                                                  |\n",
        "| **64-bit Integer**        | `torch.int64`     | Long integer type. Often used for large indexing arrays or for tasks involving large numbers.                                                                                  |\n",
        "| **8-bit Unsigned Integer**| `torch.uint8`     | 8-bit unsigned integer. Commonly used for image data (e.g., pixel values between 0 and 255).                                                                                    |\n",
        "| **Boolean**               | `torch.bool`      | Boolean type, stores `True` or `False` values. Often used for masks in logical operations.                                                                                      |\n",
        "| **Complex 64**            | `torch.complex64` | Complex number type with 32-bit real and 32-bit imaginary parts. Used for scientific and signal processing tasks.                                                               |\n",
        "| **Complex 128**           | `torch.complex128`| Complex number type with 64-bit real and 64-bit imaginary parts. Offers higher precision but uses more memory.                                                                 |\n",
        "| **Quantized Integer**     | `torch.qint8`     | Quantized signed 8-bit integer. Used in quantized models for efficient inference.                                                                                              |\n",
        "| **Quantized Unsigned Integer** | `torch.quint8` | Quantized unsigned 8-bit integer. Often used for quantized tensors in image-related tasks.                                                                                     |\n"
      ],
      "metadata": {
        "id": "dLm25jhknmuQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mathematical operations"
      ],
      "metadata": {
        "id": "baPoka3lqpEQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Scalar operation"
      ],
      "metadata": {
        "id": "wre7rSdlqsOB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.rand(2,2)\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CS69nYyanqYD",
        "outputId": "e52cb850-57ba-41d2-bb8f-96c70386362a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.9594, 0.1334],\n",
              "        [0.8591, 0.6639]])"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# addition\n",
        "x + 2\n",
        "# substraction\n",
        "x - 2\n",
        "# multiplication\n",
        "x * 3\n",
        "# division\n",
        "x / 3\n",
        "# int division\n",
        "(x * 100)//3\n",
        "# mod\n",
        "((x * 100)//3)%2\n",
        "# power\n",
        "x**2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ztrwQq-cqyz9",
        "outputId": "8282534a-0a9e-4e66-b9af-dc606428be32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.9205, 0.0178],\n",
              "        [0.7380, 0.4408]])"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Element wise operation"
      ],
      "metadata": {
        "id": "y2n84o2ZrGcY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = torch.rand(2,3)\n",
        "b = torch.rand(2,3)\n",
        "\n",
        "print(a)\n",
        "print(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PcnbNatwq1_3",
        "outputId": "52897250-04d2-4d09-930b-10d01b59bdb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.0554, 0.7498, 0.3761],\n",
            "        [0.6721, 0.5572, 0.2686]])\n",
            "tensor([[0.3466, 0.1875, 0.7433],\n",
            "        [0.6713, 0.9844, 0.0096]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# add\n",
        "a + b\n",
        "# sub\n",
        "a - b\n",
        "# multiply\n",
        "a * b\n",
        "# division\n",
        "a / b\n",
        "# power\n",
        "a ** b\n",
        "# mod\n",
        "a % b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qye9acabrUae",
        "outputId": "6ad12653-577a-4fd4-f3d6-cfe2adaa14af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0554, 0.1872, 0.3761],\n",
              "        [0.0008, 0.5572, 0.0091]])"
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = torch.tensor([1, -2, 3, -4])"
      ],
      "metadata": {
        "id": "ftIq3-30rnhR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# abs\n",
        "torch.abs(c)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GHWZWPxWr4rG",
        "outputId": "2fb31653-439b-4add-9a43-78177adebb15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 2, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# negative\n",
        "torch.neg(c)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kROGMVNtrtfT",
        "outputId": "c71b6037-82b1-4544-e904-25f2e90bd8ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([-1,  2, -3,  4])"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = torch.tensor([1.9, 2.3, 3.7, 4.4])"
      ],
      "metadata": {
        "id": "U2RLWzlaryKh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# round\n",
        "torch.round(d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g4OrL7w9sAWF",
        "outputId": "1c38c9de-1f42-496a-c03d-b2962d45b833"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([2., 2., 4., 4.])"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ceil\n",
        "torch.ceil(d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1fPsOYOAsEE6",
        "outputId": "c9d36cdd-4329-43f1-ec67-af4a2b5cde0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([2., 3., 4., 5.])"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# floor\n",
        "torch.floor(d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ilT0yf1TsE6G",
        "outputId": "82af02a5-3012-40de-e172-28f00005713c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1., 2., 3., 4.])"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# clamp\n",
        "torch.clamp(d, min=2, max=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZzVkY4xgsFvP",
        "outputId": "39ea96e1-2a11-46ed-a756-dee74f120e87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([2.0000, 2.3000, 3.0000, 3.0000])"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Reduction operation"
      ],
      "metadata": {
        "id": "4vWfG_ZmsN0J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "e = torch.randint(size=(2,3), low=0, high=10, dtype=torch.float32)\n",
        "e"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQBlGL52sGae",
        "outputId": "975ccb38-2c5c-471a-909c-7468ef6e1bd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1., 2., 9.],\n",
              "        [7., 6., 5.]])"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sum\n",
        "torch.sum(e)\n",
        "# sum along columns\n",
        "torch.sum(e, dim=0)\n",
        "# sum along rows\n",
        "torch.sum(e, dim=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9o8zMdUosWSs",
        "outputId": "5d02d73c-81d1-4a65-b206-13a1a3edd4e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([18, 13])"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mean\n",
        "torch.mean(e)\n",
        "# mean along col\n",
        "torch.mean(e, dim=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Am6Hrdssl90",
        "outputId": "5097aa40-e353-4bc9-cabc-3270b9512c80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([4., 4., 7.])"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# median\n",
        "torch.median(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIXESOkIs8M8",
        "outputId": "b9ec3c4f-54f0-404f-9c7b-2d07deeb4909"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(5.)"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# max and min\n",
        "torch.max(e)\n",
        "torch.min(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RIs7y6cysnFT",
        "outputId": "64b872e1-fc5e-4239-d040-a360138265f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(1.)"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# product\n",
        "torch.prod(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIP4CUwasw4Q",
        "outputId": "a0638721-4ba6-41d9-d805-5cf6bb4189c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(3780.)"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# standard deviation\n",
        "torch.std(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BAQIjNjwsyLF",
        "outputId": "4c34a64d-25f0-4cb9-820b-b31eed16168e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(3.0332)"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# variance\n",
        "torch.var(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78S1Ggv_s2nC",
        "outputId": "c455e21e-d7d3-480d-bcbf-1995e77554d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(9.2000)"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# argmax\n",
        "torch.argmax(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOtGIsd9s3UZ",
        "outputId": "22e92510-d602-4436-8f89-287d5f8524a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(2)"
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# argmin\n",
        "torch.argmin(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J23C1bwes6TL",
        "outputId": "ae4c9df1-2745-4e20-d0f6-0d9176d05a53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0)"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Matrix operations"
      ],
      "metadata": {
        "id": "dw_h9fvMtLXw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f = torch.randint(size=(2,3), low=0, high=10)\n",
        "g = torch.randint(size=(3,2), low=0, high=10)\n",
        "\n",
        "print(f)\n",
        "print(g)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a54MqBBQs7BJ",
        "outputId": "a712ad9d-3f40-4f83-c7c3-12adb6eef0d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[3, 7, 8],\n",
            "        [7, 9, 9]])\n",
            "tensor([[1, 1],\n",
            "        [4, 2],\n",
            "        [4, 7]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# matrix multiplcation\n",
        "torch.matmul(f, g)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BqQ7sNBctWfS",
        "outputId": "b50e177c-2907-49ef-8b95-726a1c6f7873"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[63, 73],\n",
              "        [79, 88]])"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vector1 = torch.tensor([1, 2])\n",
        "vector2 = torch.tensor([3, 4])\n",
        "\n",
        "# dot product\n",
        "torch.dot(vector1, vector2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CopN1Oe5tZrD",
        "outputId": "64358f9f-f9ca-4ef2-b905-9cae273e3432"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(11)"
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# transpose\n",
        "torch.transpose(f, 0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Imz3Vq6ta4y",
        "outputId": "7a7ed03c-357e-4f51-c762-e619f9c15c61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[3, 7],\n",
              "        [7, 9],\n",
              "        [8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h = torch.randint(size=(3,3), low=0, high=10, dtype=torch.float32)\n",
        "h"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yEfuS9ZKtdSi",
        "outputId": "1aae67bb-44ac-4cff-f618-7daf4873b248"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[8., 2., 2.],\n",
              "        [4., 8., 8.],\n",
              "        [5., 0., 4.]])"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determinant\n",
        "torch.det(h)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PqfUsUvttpXZ",
        "outputId": "8907e944-d660-41ff-e590-324ad1c842b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(224.)"
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# inverse\n",
        "torch.inverse(h)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0khPormts14",
        "outputId": "e83bda9d-a427-4100-9dc6-ba2f8f15c167"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.1429, -0.0357,  0.0000],\n",
              "        [ 0.1071,  0.0982, -0.2500],\n",
              "        [-0.1786,  0.0446,  0.2500]])"
            ]
          },
          "metadata": {},
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Comparison operations"
      ],
      "metadata": {
        "id": "-lEkBmv5t1nB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "i = torch.randint(size=(2,3), low=0, high=10)\n",
        "j = torch.randint(size=(2,3), low=0, high=10)\n",
        "\n",
        "print(i)\n",
        "print(j)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G89Keg-xtxUr",
        "outputId": "d5f258bd-ca39-4232-9973-e50d6512effe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[5, 0, 1],\n",
            "        [1, 1, 3]])\n",
            "tensor([[2, 6, 3],\n",
            "        [5, 2, 5]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# greater than\n",
        "i > j\n",
        "# less than\n",
        "i < j\n",
        "# equal to\n",
        "i == j\n",
        "# not equal to\n",
        "i != j\n",
        "# greater than equal to\n",
        "\n",
        "# less than equal to"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5v--5uiuBWW",
        "outputId": "7080ac46-e2b8-4191-ef0c-2c695856f3b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[True, True, True],\n",
              "        [True, True, True]])"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6. Special functions"
      ],
      "metadata": {
        "id": "jd9JzlO1uaf3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k = torch.randint(size=(2,3), low=0, high=10, dtype=torch.float32)\n",
        "k"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKU6tQpyuY0M",
        "outputId": "5c5892ad-d4a4-480c-90a7-1f6b59f1d540"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0., 6., 4.],\n",
              "        [4., 8., 6.]])"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# log\n",
        "torch.log(k)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gl1dhdVuDOE",
        "outputId": "38f7fdfa-5c4e-4d4d-e514-d9c1cb23a6a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1.6094, 1.3863, 0.0000],\n",
              "        [1.9459, 0.0000, 2.0794]])"
            ]
          },
          "metadata": {},
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# exp\n",
        "torch.exp(k)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQSpBdkbujck",
        "outputId": "35eb847f-c808-476d-8a95-c6ca362d7ce1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1.4841e+02, 5.4598e+01, 2.7183e+00],\n",
              "        [1.0966e+03, 2.7183e+00, 2.9810e+03]])"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sqrt\n",
        "torch.sqrt(k)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gTDfXdbVukh0",
        "outputId": "f49657ae-1d50-48bc-a190-2802d2522b18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2.2361, 2.0000, 1.0000],\n",
              "        [2.6458, 1.0000, 2.8284]])"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sigmoid\n",
        "torch.sigmoid(k)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-rbMIVPunhr",
        "outputId": "61f1c546-8836-4cdd-ae92-d56f3808e443"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.9933, 0.9820, 0.7311],\n",
              "        [0.9991, 0.7311, 0.9997]])"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# softmax\n",
        "torch.softmax(k, dim=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X74chVfQusqf",
        "outputId": "c9a28553-881d-4413-97c8-b57e90d90dfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0180, 0.1192, 0.1192],\n",
              "        [0.9820, 0.8808, 0.8808]])"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# relu\n",
        "torch.relu(k)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JnLG2JbyutvC",
        "outputId": "f6153aa2-11da-48f9-941a-fe227494c376"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0., 6., 4.],\n",
              "        [4., 8., 6.]])"
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inplace Operations"
      ],
      "metadata": {
        "id": "JQ7im-Mz9Mu9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m = torch.rand(2,3)\n",
        "n = torch.rand(2,3)\n",
        "\n",
        "print(m)\n",
        "print(n)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mKVMTBq7uufZ",
        "outputId": "53367600-9531-4068-b174-fbec96ac5169"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.2018, 0.1089, 0.7653],\n",
            "        [0.6473, 0.3401, 0.6406]])\n",
            "tensor([[0.4749, 0.1632, 0.6222],\n",
            "        [0.5379, 0.2758, 0.7180]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m.add_(n)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HmN2o2Ge_j8h",
        "outputId": "981dc934-7feb-493e-ff08-521bf8b7a3c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.6767, 0.2721, 1.3875],\n",
              "        [1.1851, 0.6159, 1.3585]])"
            ]
          },
          "metadata": {},
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1k8bPLhgAN64",
        "outputId": "71bafa6b-0270-4d5c-95bd-7c58dd9fc9dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.6767, 0.2721, 1.3875],\n",
              "        [1.1851, 0.6159, 1.3585]])"
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_pBAPcGAkHW",
        "outputId": "995ed910-5316-4cfc-ae19-024278c7cce8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.4749, 0.1632, 0.6222],\n",
              "        [0.5379, 0.2758, 0.7180]])"
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.relu(m)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8OhePrDbAmwh",
        "outputId": "a4fb53da-2743-4000-883c-230fb70afc68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.6767, 0.2721, 1.3875],\n",
              "        [1.1851, 0.6159, 1.3585]])"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m.relu_()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_s6WQ0pAuTG",
        "outputId": "33aa41d9-ecb5-4154-fb9c-ded55fd7c9c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.6767, 0.2721, 1.3875],\n",
              "        [1.1851, 0.6159, 1.3585]])"
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sucQ4iPYAyqn",
        "outputId": "25f0b84f-a7e1-435b-81e4-051021f32adf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.6767, 0.2721, 1.3875],\n",
              "        [1.1851, 0.6159, 1.3585]])"
            ]
          },
          "metadata": {},
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Copying a Tensor"
      ],
      "metadata": {
        "id": "7-SgjcqnCN9P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = torch.rand(2,3)\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8SCsW7kA0jt",
        "outputId": "ba1b7a07-f2b0-420a-c34e-eaf4fc97a0ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.5153, 0.9985, 0.6783],\n",
              "        [0.2776, 0.6227, 0.2982]])"
            ]
          },
          "metadata": {},
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = a"
      ],
      "metadata": {
        "id": "3Z0IUCnpCsCv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YNvboxaECjm",
        "outputId": "c65857ca-8cbf-4ad5-ecfc-97d62b842035"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.5153, 0.9985, 0.6783],\n",
              "        [0.2776, 0.6227, 0.2982]])"
            ]
          },
          "metadata": {},
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a[0][0] = 0"
      ],
      "metadata": {
        "id": "waV6eAuUEDzq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U7u-UDXpEIgJ",
        "outputId": "e21eabdc-ae1b-489c-9d2c-f3bb35c4ab0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0000, 0.9985, 0.6783],\n",
              "        [0.2776, 0.6227, 0.2982]])"
            ]
          },
          "metadata": {},
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-4tihkNkEJW3",
        "outputId": "e9f91340-df5b-4c2d-f82f-4e916f9dbfe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0000, 0.9985, 0.6783],\n",
              "        [0.2776, 0.6227, 0.2982]])"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m8D9GAChEL7E",
        "outputId": "95dbaef3-5cad-4c18-b1d3-28a957b97a80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "134838567488544"
            ]
          },
          "metadata": {},
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vLh7o5KUEVJS",
        "outputId": "c1417c23-5de3-4e63-d973-97c30c877e05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "134838567488544"
            ]
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = a.clone()"
      ],
      "metadata": {
        "id": "KWDEt8h8EW2i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ndhb6dktEcov",
        "outputId": "a360800c-a260-4b80-e56d-23c326b8c005"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0000, 0.9985, 0.6783],\n",
              "        [0.2776, 0.6227, 0.2982]])"
            ]
          },
          "metadata": {},
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M1MgVSoXEd2E",
        "outputId": "e793b894-43de-47e5-de73-dc0796ddd87c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0000, 0.9985, 0.6783],\n",
              "        [0.2776, 0.6227, 0.2982]])"
            ]
          },
          "metadata": {},
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a[0][0] = 10"
      ],
      "metadata": {
        "id": "LGxrfDtTEeVI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KoGwR1k2Eg_1",
        "outputId": "8e058949-7b18-49a9-9bd2-5522359cad6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[10.0000,  0.9985,  0.6783],\n",
              "        [ 0.2776,  0.6227,  0.2982]])"
            ]
          },
          "metadata": {},
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "id": "hnmU7qDXEh_R",
        "outputId": "c9e3e770-05c5-4a58-ca42-a75e64890351",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0000, 0.9985, 0.6783],\n",
              "        [0.2776, 0.6227, 0.2982]])"
            ]
          },
          "metadata": {},
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id(a)"
      ],
      "metadata": {
        "id": "5cy76FS5EjIz",
        "outputId": "a8bbafb3-d9ff-41f9-be9d-5f039251ddd7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "134838567488544"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id(b)"
      ],
      "metadata": {
        "id": "jmBkTNQ8EmP1",
        "outputId": "1fbb56ec-0e73-4ebe-ed8a-3982c06a79db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "134838569581552"
            ]
          },
          "metadata": {},
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W1SwQS8jEnW2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}