Tensor_Basics.ipynb

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      "cell_type": "markdown",
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/smlra-kjsce/Pytorch-101-2023/blob/main/Tensor_Basics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09KDbjVNdszt"
      },
      "source": [
        "# Tensor Basics\n",
        "This section covers:\n",
        "* Converting NumPy arrays to PyTorch tensors\n",
        "* Creating tensors from scratch\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75WG2NmFZ1CR"
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      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5orRvE0Z_m3"
      },
      "source": [
        "## Perform standard imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQCaYAzOdszu"
      },
      "source": [
        "import torch\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "xWrCMndPdszw",
        "outputId": "ea57788c-80b8-4ea6-f625-626efb1afd72"
      },
      "source": [
        "torch.__version__"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.13.1+cu116'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UPNP7dETdszx"
      },
      "source": [
        "## Converting NumPy arrays to PyTorch tensors\n",
        "A <a href='https://pytorch.org/docs/stable/tensors.html'><strong><tt>torch.Tensor</tt></strong></a> is a multi-dimensional matrix containing elements of a single data type.<br>\n",
        "Calculations between tensors can only happen if the tensors share the same dtype.<br>\n",
        "In some cases tensors are used as a replacement for NumPy to use the power of GPUs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B9fTZ_Uudszx",
        "outputId": "6b37d216-f768-4759-9c82-d5ddbb5457a2"
      },
      "source": [
        "arr = np.array([1,2,3,4,5])\n",
        "print(arr)\n",
        "print(arr.dtype)\n",
        "print(type(arr))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3 4 5]\n",
            "int64\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4zqZyWFdszy",
        "outputId": "9f1a1aa9-2063-44f5-dad5-b57b3aef1ba6"
      },
      "source": [
        "x = torch.from_numpy(arr)\n",
        "# Equivalent to x = torch.as_tensor(arr)\n",
        "\n",
        "print(x)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1, 2, 3, 4, 5])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "um8QCoQjdszy",
        "outputId": "91628f40-b9ce-4d11-8dfb-4c6f0995a322"
      },
      "source": [
        "# Print the type of data held by the tensor\n",
        "print(x.dtype)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7a3vQpHdszy",
        "outputId": "e954b9f7-bf23-42bd-da9d-ba9c4118785d"
      },
      "source": [
        "# Print the tensor object type\n",
        "print(type(x))\n",
        "print(x.type()) # this is more specific!"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'torch.Tensor'>\n",
            "torch.LongTensor\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "krBO-ah-dszy",
        "outputId": "3b841bb5-4a43-4d0b-d879-bbc70da91cc7"
      },
      "source": [
        "arr2 = np.arange(0.,12.).reshape(4,3)\n",
        "print(arr2)\n",
        "print()\n",
        "print(arr2.dtype)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.  1.  2.]\n",
            " [ 3.  4.  5.]\n",
            " [ 6.  7.  8.]\n",
            " [ 9. 10. 11.]]\n",
            "\n",
            "float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ILE6sQjVdszz",
        "outputId": "e7f9c52c-8234-49e2-c798-92c2e5b7f3f4"
      },
      "source": [
        "x2 = torch.from_numpy(arr2)\n",
        "print(x2)\n",
        "print()\n",
        "print(x2.type())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[ 0.,  1.,  2.],\n",
            "        [ 3.,  4.,  5.],\n",
            "        [ 6.,  7.,  8.],\n",
            "        [ 9., 10., 11.]], dtype=torch.float64)\n",
            "\n",
            "torch.DoubleTensor\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uaVv7xBtdszz"
      },
      "source": [
        "<h2><a href='https://pytorch.org/docs/stable/tensors.html'>Tensor Datatypes</a></h2>\n",
        "<table style=\"display: inline-block\">\n",
        "<tr><th>TYPE</th><th>NAME</th><th>EQUIVALENT</th><th>TENSOR TYPE</th></tr>\n",
        "<tr><td>32-bit integer (signed)</td><td>torch.int32</td><td>torch.int</td><td>IntTensor</td></tr>\n",
        "<tr><td>64-bit integer (signed)</td><td>torch.int64</td><td>torch.long</td><td>LongTensor</td></tr>\n",
        "<tr><td>16-bit integer (signed)</td><td>torch.int16</td><td>torch.short</td><td>ShortTensor</td></tr>\n",
        "<tr><td>32-bit floating point</td><td>torch.float32</td><td>torch.float</td><td>FloatTensor</td></tr>\n",
        "<tr><td>64-bit floating point</td><td>torch.float64</td><td>torch.double</td><td>DoubleTensor</td></tr>\n",
        "<tr><td>16-bit floating point</td><td>torch.float16</td><td>torch.half</td><td>HalfTensor</td></tr>\n",
        "<tr><td>8-bit integer (signed)</td><td>torch.int8</td><td></td><td>CharTensor</td></tr>\n",
        "<tr><td>8-bit integer (unsigned)</td><td>torch.uint8</td><td></td><td>ByteTensor</td></tr></table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bkTm1qESdszz"
      },
      "source": [
        "Here <tt>torch.DoubleTensor</tt> refers to 64-bit floating point data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGfDmPxAdszz"
      },
      "source": [
        "## Copying vs. sharing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qx8sWe3ydsz0"
      },
      "source": [
        "<a href='https://pytorch.org/docs/stable/torch.html#torch.from_numpy'><strong><tt>torch.from_numpy()</tt></strong></a><br>\n",
        "<a href='https://pytorch.org/docs/stable/torch.html#torch.as_tensor'><strong><tt>torch.as_tensor()</tt></strong></a><br>\n",
        "<a href='https://pytorch.org/docs/stable/torch.html#torch.tensor'><strong><tt>torch.tensor()</tt></strong></a><br>\n",
        "\n",
        "There are a number of different functions available for <a href='https://pytorch.org/docs/stable/torch.html#creation-ops'>creating tensors</a>. When using <a href='https://pytorch.org/docs/stable/torch.html#torch.from_numpy'><strong><tt>torch.from_numpy()</tt></strong></a> and <a href='https://pytorch.org/docs/stable/torch.html#torch.as_tensor'><strong><tt>torch.as_tensor()</tt></strong></a>, the PyTorch tensor and the source NumPy array share the same memory. This means that changes to one affect the other. However, the <a href='https://pytorch.org/docs/stable/torch.html#torch.tensor'><strong><tt>torch.tensor()</tt></strong></a> function always makes a copy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N9XPwpA_dsz0",
        "outputId": "e4cddf9c-af70-4f2e-df4d-09a376f3dc51"
      },
      "source": [
        "# Using torch.from_numpy()\n",
        "arr = np.arange(0,5)\n",
        "t = torch.from_numpy(arr)\n",
        "print(t)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([0, 1, 2, 3, 4])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vvA1t6Ykdsz0",
        "outputId": "ab83f99a-c399-4834-fe77-283c4fd3e898"
      },
      "source": [
        "arr[2]=77\n",
        "print(t)\n",
        "t[1] = 10\n",
        "print(arr)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([ 0,  1, 77,  3,  4])\n",
            "[ 0 10 77  3  4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5rG9R69Udsz0",
        "outputId": "302e1107-ba19-4cad-881d-2dcaf4a9486b"
      },
      "source": [
        "# Using torch.tensor()\n",
        "arr = np.arange(0,5)\n",
        "t = torch.tensor(arr)\n",
        "print(t)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([0, 1, 2, 3, 4])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yvB8240ndsz0",
        "outputId": "46d03ce8-ee13-4ab7-f3cc-4a9d816cdcbf"
      },
      "source": [
        "arr[2]=77\n",
        "print(t)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([0, 1, 2, 3, 4])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l3O2w2pvdsz1"
      },
      "source": [
        "## Class constructors\n",
        "<a href='https://pytorch.org/docs/stable/tensors.html'><strong><tt>torch.Tensor()</tt></strong></a><br>\n",
        "<a href='https://pytorch.org/docs/stable/tensors.html'><strong><tt>torch.FloatTensor()</tt></strong></a><br>\n",
        "<a href='https://pytorch.org/docs/stable/tensors.html'><strong><tt>torch.LongTensor()</tt></strong></a>, etc.<br>\n",
        "\n",
        "There's a subtle difference between using the factory function <font color=orange><tt>torch.tensor(data)</tt></font> and the class constructor <font color=orange><tt>torch.Tensor(data)</tt></font>.<br>\n",
        "The factory function determines the dtype from the incoming data, or from a passed-in dtype argument.<br>\n",
        "The class constructor <tt>torch.Tensor()</tt>is simply an alias for <tt>torch.FloatTensor(data)</tt>. Consider the following:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaYdvbrydsz1"
      },
      "source": [
        "data = np.array([1,2,3])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DwSbs_EFdsz1",
        "outputId": "9d6ebc8f-8143-4080-e814-89c71cc39dcf"
      },
      "source": [
        "a = torch.Tensor(data)  # Equivalent to cc = torch.FloatTensor(data)\n",
        "print(a, a.type())"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1., 2., 3.]) torch.FloatTensor\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zjY0pvFMdsz1",
        "outputId": "66e81014-e4ff-493e-dec8-b8c8ec8582dd"
      },
      "source": [
        "b = torch.tensor(data)\n",
        "print(b, b.type())"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1, 2, 3]) torch.LongTensor\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpYRoyDQdsz1",
        "outputId": "10e4eddf-a809-4f44-d92d-b5692dbc9b89"
      },
      "source": [
        "c = torch.tensor(data, dtype=torch.int)\n",
        "print(c, c.type())"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1, 2, 3], dtype=torch.int32) torch.IntTensor\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTb9lAAIdsz2"
      },
      "source": [
        "## Creating tensors from scratch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iVCKhWJgdsz2"
      },
      "source": [
        "### Initialized tensors with <tt>.zeros()</tt> and <tt>.ones()</tt>\n",
        "<a href='https://pytorch.org/docs/stable/torch.html#torch.zeros'><strong><tt>torch.zeros(size)</tt></strong></a><br>\n",
        "<a href='https://pytorch.org/docs/stable/torch.html#torch.ones'><strong><tt>torch.ones(size)</tt></strong></a><br>\n",
        "It's a good idea to pass in the intended dtype."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gz5PQm7hdsz2",
        "outputId": "525fc186-9ea6-42ca-d9ad-b000284b88ab"
      },
      "source": [
        "x = torch.zeros(4, 3, dtype=torch.int64)\n",
        "print(x)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0, 0, 0],\n",
            "        [0, 0, 0],\n",
            "        [0, 0, 0],\n",
            "        [0, 0, 0]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.ones(4, 3, dtype=torch.int64)\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QHjCJkOp18Ae",
        "outputId": "e727da50-bfb2-47a7-d012-316146827e4a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[1, 1, 1],\n",
            "        [1, 1, 1],\n",
            "        [1, 1, 1],\n",
            "        [1, 1, 1]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LprQVmsfdsz2"
      },
      "source": [
        "### Tensors from ranges\n",
        "<a href='https://pytorch.org/docs/stable/torch.html#torch.arange'><strong><tt>torch.arange(start,end,step)</tt></strong></a><br>\n",
        "<a href='https://pytorch.org/docs/stable/torch.html#torch.linspace'><strong><tt>torch.linspace(start,end,steps)</tt></strong></a><br>\n",
        "Note that with <tt>.arange()</tt>, <tt>end</tt> is exclusive, while with <tt>linspace()</tt>, <tt>end</tt> is inclusive."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "trpyr_0Wdsz2",
        "outputId": "5a3e1c69-30da-41aa-940a-8c18caa27f74"
      },
      "source": [
        "x = torch.arange(0,18,2) # [0,18) [start,stop,step]\n",
        "print(x)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([ 0,  2,  4,  6,  8, 10, 12, 14, 16])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_SPujTrdsz2",
        "outputId": "a8bda2be-d86e-43e2-ff38-80573ead7692"
      },
      "source": [
        "x = torch.linspace(0,18,12)\n",
        "print(x)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([ 0.0000,  1.6364,  3.2727,  4.9091,  6.5455,  8.1818,  9.8182, 11.4545,\n",
            "        13.0909, 14.7273, 16.3636, 18.0000])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLPboPrkdsz3"
      },
      "source": [
        "### Changing the dtype of existing tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pw8dMV4Cdsz3",
        "outputId": "073c11e4-0d8b-410e-f703-0ef1086320a1"
      },
      "source": [
        "print('Old:', x.type())\n",
        "\n",
        "x = x.type(torch.int64)\n",
        "\n",
        "print('New:', x.type())\n",
        "print(x)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Old: torch.FloatTensor\n",
            "New: torch.LongTensor\n",
            "tensor([ 0,  1,  3,  4,  6,  8,  9, 11, 13, 14, 16, 18])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qNcn9Gfedsz3"
      },
      "source": [
        "### Random number tensors\n",
        "<a href='https://pytorch.org/docs/stable/torch.html#torch.rand'><strong><tt>torch.rand(size)</tt></strong></a> returns random samples from a uniform distribution over [0, 1)<br>\n",
        "<a href='https://pytorch.org/docs/stable/torch.html#torch.randn'><strong><tt>torch.randn(size)</tt></strong></a> returns samples from the \"standard normal\" distribution [σ = 1] [μ=0\n",
        "]<br>\n",
        "&nbsp;&nbsp;&nbsp;&nbsp;Unlike <tt>rand</tt> which is uniform, values closer to zero are more likely to appear.<br>\n",
        "<a href='https://pytorch.org/docs/stable/torch.html#torch.randint'><strong><tt>torch.randint(low,high,size)</tt></strong></a> returns random integers from low (inclusive) to high (exclusive)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EN4wAbLhdsz3",
        "outputId": "fc1e9583-ba8c-4fba-8d29-d25fe1f0ee0f"
      },
      "source": [
        "x = torch.rand(4, 3)\n",
        "print(x)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.7283, 0.2324, 0.8404],\n",
            "        [0.0990, 0.5903, 0.3747],\n",
            "        [0.5832, 0.3576, 0.3326],\n",
            "        [0.1682, 0.7969, 0.2899]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pn_YaEi3dsz3",
        "outputId": "6900602e-46e1-43a9-d6f9-15505f5061fb"
      },
      "source": [
        "x = torch.randn(4, 3)\n",
        "print(x)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[ 2.1118,  0.0788,  0.3945],\n",
            "        [-0.0558, -0.8005, -0.4475],\n",
            "        [-0.4503, -1.4099,  0.6201],\n",
            "        [ 0.3506, -0.4905,  0.9646]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JT-74aFddsz3",
        "outputId": "465abb6b-d8f4-4012-e0dd-1fe4a32d5495"
      },
      "source": [
        "x = torch.randint(0,5,(4,3))\n",
        "print(x)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[3, 4, 1],\n",
            "        [0, 0, 1],\n",
            "        [1, 1, 3],\n",
            "        [3, 0, 4]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDCTg3wldsz4"
      },
      "source": [
        "### Setting the random seed\n",
        "<a href='https://pytorch.org/docs/stable/torch.html#torch.manual_seed'><strong><tt>torch.manual_seed(int)</tt></strong></a> is used to obtain reproducible results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.rand(2, 3)\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JGatkcbm3Pp_",
        "outputId": "b6e9d1a7-1b80-4c8a-cf46-dc502fcaf3e3"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.4440, 0.9492, 0.5047],\n",
            "        [0.3330, 0.8693, 0.1494]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ygkrDQ-zdsz4",
        "outputId": "4c94eaaf-18c1-412f-f86b-1a0a988d65eb"
      },
      "source": [
        "torch.manual_seed(10)\n",
        "x = torch.rand(2, 3)\n",
        "print(x)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.4581, 0.4829, 0.3125],\n",
            "        [0.6150, 0.2139, 0.4118]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GwhnNw3Fdsz4"
      },
      "source": [
        "## Tensor attributes\n",
        "Besides <tt>dtype</tt>, we can look at other <a href='https://pytorch.org/docs/stable/tensor_attributes.html'>tensor attributes</a> like <tt>shape</tt>, <tt>device</tt> and <tt>layout</tt>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Apphpg51dsz4",
        "outputId": "d4a11ed5-ef6d-4c60-b99d-dc1b498c46d5"
      },
      "source": [
        "x.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mm3ZbnQfdsz4",
        "outputId": "f400fff6-8328-4674-ede2-b3ccff7f9565"
      },
      "source": [
        "x.size()  # equivalent to x.shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFQ9bmGwrceO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2119346b-f6c2-47a9-fe01-3bc1c6f92ff6"
      },
      "source": [
        "x.device"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    }
  ]
}