Pytorch_Basics.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Ahy3-8ra3u_"
      },
      "source": [
        "\n",
        "#   <font color='green'>**Basics of PyTorch**</font>\n",
        "\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0uaBucgnlbch"
      },
      "source": [
        "## <font color = 'blue'> **Outline**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### 1. Installation\n",
        "### 2. Tensors\n",
        "### 3. Datasets and Dataloaders\n",
        "### 4. Transforms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AHFOi51Ej9t3"
      },
      "source": [
        "## <font color = 'red'> **Installation of PyTorch** </font>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdLAkDVCaehL",
        "outputId": "4d68b937-7a7c-4d0f-f8b9-a943128de823"
      },
      "outputs": [],
      "source": [
        "# !pip install torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "iziUUoUlaj_E"
      },
      "outputs": [],
      "source": [
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "HAjzYLKAbJng",
        "outputId": "01ccdc1e-fdf4-431a-d925-0241c37174b6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'1.9.0'"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'11.1'"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.version.cuda"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2021 NVIDIA Corporation\n",
            "Built on Wed_Jul_14_19:47:52_Pacific_Daylight_Time_2021\n",
            "Cuda compilation tools, release 11.4, V11.4.100\n",
            "Build cuda_11.4.r11.4/compiler.30188945_0\n"
          ]
        }
      ],
      "source": [
        "!nvcc --version"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYYY3xcrgboQ"
      },
      "source": [
        "# <font color='red'> **Tensors** </font>\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Tensors are a specialized data structure that are very similar to arrays and matrices. In PyTorch, we use tensors to encode the inputs and outputs of a model, as well as the model’s parameters.Tensors are similar to NumPy’s ndarrays, except that tensors can run on GPUs or other hardware accelerators.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UgG3cTTFhViB"
      },
      "source": [
        "###  <font color='blue'>**Initializing a Tensor**</font>\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### <font color = 'red'> 1. Directly from data </font>\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### Tensors can be created directly from data. The data type is automatically inferred."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jH5WI31UhN0M"
      },
      "outputs": [],
      "source": [
        "data = [[1, 2],[3, 4]]\n",
        "x_data = torch.tensor(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oCOV_MTlO_Z4",
        "outputId": "257d5f05-eb8a-442f-8274-0611d434f566"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_data.dtype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O3GvCQYbbNJ3"
      },
      "outputs": [],
      "source": [
        "x_d = torch.tensor(((2, 4, 6), (8, 10, 12)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8u-wTtRfRUD",
        "outputId": "67b40256-8f1c-4da8-a1c3-9dbe08d9494f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[ 2,  4,  6],\n",
              "        [ 8, 10, 12]])"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_d"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kENh09bIjJol"
      },
      "source": [
        "### <font color = 'red'> 2. From a NumPy array </font>\n",
        "\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1PtoX-KZbM5c"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0htSoNNXhwQw"
      },
      "outputs": [],
      "source": [
        "# https://pytorch.org/docs/stable/generated/torch.from_numpy.html\n",
        "\n",
        "np_array = np.array(data)\n",
        "x_np = torch.from_numpy(np_array)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfmUpMihj2P-"
      },
      "source": [
        "### <font color = 'red'> 3. From another Tensor </font>\n",
        "\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZcELG9cjjt2s",
        "outputId": "adabdac6-a309-4cf9-aa22-09c2b2f39a36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Ones Tensor: \n",
            " tensor([[1, 1],\n",
            "        [1, 1]]) \n",
            "\n",
            "Random Tensor: \n",
            " tensor([[0.6618, 0.9668],\n",
            "        [0.6131, 0.0685]]) \n",
            "\n"
          ]
        }
      ],
      "source": [
        "# https://pytorch.org/docs/stable/generated/torch.ones_like.html\n",
        "\n",
        "x_ones = torch.ones_like(x_data) # retains the properties of x_data\n",
        "print(f\"Ones Tensor: \\n {x_ones} \\n\")\n",
        "\n",
        "# https://pytorch.org/docs/stable/generated/torch.rand_like.html\n",
        "x_rand = torch.rand_like(x_data, dtype=torch.float) # overrides the datatype of x_data\n",
        "print(f\"Random Tensor: \\n {x_rand} \\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAEVLESflXWZ"
      },
      "source": [
        "### <font color = 'red'> 4. With random or constant values </font>\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "### shape is a tuple of tensor dimensions. In the functions below, it determines the dimensionality of the output tensor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4U8A9H1ljCu",
        "outputId": "9dd95ca0-2076-444a-ccce-48adf7a53981"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random Tensor: \n",
            " tensor([[0.4648, 0.7190, 0.5359],\n",
            "        [0.5784, 0.8263, 0.0139]]) \n",
            "\n",
            "Ones Tensor: \n",
            " tensor([[1., 1., 1.],\n",
            "        [1., 1., 1.]]) \n",
            "\n",
            "Zeros Tensor: \n",
            " tensor([[0., 0., 0.],\n",
            "        [0., 0., 0.]])\n"
          ]
        }
      ],
      "source": [
        "shape = (2,3,)\n",
        "rand_tensor = torch.rand(shape)\n",
        "ones_tensor = torch.ones(shape)\n",
        "zeros_tensor = torch.zeros(shape)\n",
        "\n",
        "print(f\"Random Tensor: \\n {rand_tensor} \\n\")\n",
        "print(f\"Ones Tensor: \\n {ones_tensor} \\n\")\n",
        "print(f\"Zeros Tensor: \\n {zeros_tensor}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "88kMW04pl4U8"
      },
      "source": [
        "### <font color = 'blue'> **Attributes of a Tensor** </font>\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Tensor attributes describe their shape, datatype, and the device on which they are stored."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j5nCcMmslwBa",
        "outputId": "52cdfa5c-10c8-422b-d04d-f01eaefe4980"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of tensor: torch.Size([3, 4])\n",
            "Datatype of tensor: torch.float32\n",
            "Device tensor is stored on: cpu\n",
            "Device: cuda:0\n"
          ]
        }
      ],
      "source": [
        "tensor = torch.rand(3,4)\n",
        "\n",
        "print(f\"Shape of tensor: {tensor.shape}\")\n",
        "print(f\"Datatype of tensor: {tensor.dtype}\")\n",
        "print(f\"Device tensor is stored on: {tensor.device}\")\n",
        "\n",
        "tensor = tensor.to('cuda')\n",
        "print(f'Device: {tensor.device}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSyGEvW6mLVH",
        "outputId": "5be941c0-086e-4ca5-e56c-d806aabfefdb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cpu\n",
            "cuda:0\n"
          ]
        }
      ],
      "source": [
        "# We move our tensor to the GPU if available\n",
        "t = torch.tensor([1,2,3])\n",
        "print(t.device)\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    t = t.to(\"cuda\")\n",
        "\n",
        "print(t.device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4LmTFPVkdbS8",
        "outputId": "58e572c4-863c-415b-d7dd-50bc14e75208"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The device is :  cuda\n"
          ]
        }
      ],
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "print(\"The device is : \", device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZIyD2q-mjeN"
      },
      "source": [
        "### <font color = 'blue'> **Operations on Tensors** </font>\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Operations, including arithmetic, linear algebra, matrix manipulation (transposing, indexing, slicing), sampling and more are comprehensively described https://pytorch.org/docs/stable/torch.html.\n",
        "\n",
        "### Each of these operations can be run on the GPU (at typically higher speeds than on a CPU). If you’re using Colab, allocate a GPU by going to Runtime > Change runtime type > GPU.\n",
        "\n",
        "### By default, tensors are created on the CPU. We need to explicitly move tensors to the GPU using .to method (after checking for GPU availability)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P76-hGVvn5Du"
      },
      "source": [
        "### <font color = 'red'> 1. Standard numpy-like indexing and slicing </font>\n",
        "\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HtyRo3VtnxwB",
        "outputId": "bf2c6aaf-aab4-4abe-c931-e2a3bdceb8ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "First row: tensor([1., 1., 1., 1.])\n",
            "First column: tensor([1., 1., 1., 1.])\n",
            "Last column: tensor([1., 1., 1., 1.])\n",
            "tensor([[1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.]])\n"
          ]
        }
      ],
      "source": [
        "tensor = torch.ones(4, 4)\n",
        "print(f\"First row: {tensor[0]}\")\n",
        "print(f\"First column: {tensor[:, 0]}\")\n",
        "print(f\"Last column: {tensor[..., -1]}\")\n",
        "tensor[:,1] = 0\n",
        "print(tensor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-hLk1x00ofFQ"
      },
      "source": [
        "### <font color = 'red'> 2. Joining tensors </font>\n",
        "\n",
        "\n",
        "---\n",
        "###  You can use torch.cat to concatenate a sequence of tensors along a given dimension. https://pytorch.org/docs/stable/generated/torch.cat.html\n",
        "### See also torch.stack (https://pytorch.org/docs/stable/generated/torch.stack.html), another tensor joining op that is subtly different from torch.cat."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rAdD2vL0oEm-",
        "outputId": "07b87b52-3eec-4769-d707-aedb794faf72"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.]])\n"
          ]
        }
      ],
      "source": [
        "t1 = torch.cat([tensor, tensor, tensor], dim=1)\n",
        "print(t1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bebM2_fvkrnX"
      },
      "outputs": [],
      "source": [
        "t1 = torch.tensor([1,2])\n",
        "t2 = torch.tensor((3,4))\n",
        "\n",
        "t3 = torch.stack([t1, t2], dim=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FO91UeHBlieU",
        "outputId": "ef48b988-9fc9-4ea6-b824-a026b1abdeac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[1, 2],\n",
            "        [3, 4]])\n"
          ]
        }
      ],
      "source": [
        "print(t3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLiCzbJzpDcp"
      },
      "source": [
        "### <font color = 'red'> 3. Arithmetic operations </font>\n",
        "\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1g4HYTMZp7uy",
        "outputId": "56aefd8e-8e04-4f0f-ab8b-a2cd832c0930"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[2., 2., 2.],\n",
            "        [2., 2., 2.],\n",
            "        [2., 2., 2.]])\n",
            "tensor([[2., 2., 2.],\n",
            "        [2., 2., 2.],\n",
            "        [2., 2., 2.]])\n",
            "tensor([[2., 2., 2.],\n",
            "        [2., 2., 2.],\n",
            "        [2., 2., 2.]])\n"
          ]
        }
      ],
      "source": [
        "# Addition\n",
        "ta1 = torch.ones(3, 3)\n",
        "\n",
        "a1 = ta1 + ta1\n",
        "print(a1)\n",
        "\n",
        "a2 = ta1.add(ta1)\n",
        "print(a2)\n",
        "\n",
        "p = torch.zeros(3,3)\n",
        "a3 = torch.add(ta1, ta1, out=p)\n",
        "print(a3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9BxS49QloWBT",
        "outputId": "be1ffef2-17f2-4df0-e500-1209b1bc2124"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.]])\n"
          ]
        }
      ],
      "source": [
        "print(tensor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ph6npO6loztl",
        "outputId": "810648b0-66f3-4bc5-f8c3-5f53d8d71b28"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[3., 3., 3., 3.],\n",
            "        [3., 3., 3., 3.],\n",
            "        [3., 3., 3., 3.],\n",
            "        [3., 3., 3., 3.]])\n",
            "tensor([[3., 3., 3., 3.],\n",
            "        [3., 3., 3., 3.],\n",
            "        [3., 3., 3., 3.],\n",
            "        [3., 3., 3., 3.]])\n",
            "tensor([[3., 3., 3., 3.],\n",
            "        [3., 3., 3., 3.],\n",
            "        [3., 3., 3., 3.],\n",
            "        [3., 3., 3., 3.]])\n",
            "tensor([[1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.]])\n",
            "tensor([[1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.]])\n",
            "tensor([[1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.]])\n"
          ]
        }
      ],
      "source": [
        "# Multiplication\n",
        "\n",
        "# This computes the matrix multiplication between two tensors. y1, y2, y3 will have the same value\n",
        "# ``tensor.T`` returns the transpose of a tensor\n",
        "y1 = tensor @ tensor.T\n",
        "\n",
        "print(y1)\n",
        "# https://pytorch.org/docs/stable/generated/torch.matmul.html\n",
        "y2 = tensor.matmul(tensor.T)\n",
        "print(y2)\n",
        "\n",
        "y3 = torch.rand_like(y1)\n",
        "torch.matmul(tensor, tensor.T, out=y3)\n",
        "print(y3)\n",
        "\n",
        "# This computes the element-wise product. z1, z2, z3 will have the same value\n",
        "z1 = tensor * tensor\n",
        "print(z1)\n",
        "\n",
        "z2 = tensor.mul(tensor)\n",
        "print(z2)\n",
        "\n",
        "z3 = torch.rand_like(tensor)\n",
        "torch.mul(tensor, tensor, out=z3)\n",
        "print(z3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Y9dxrUqpZ71"
      },
      "source": [
        "### <font color = 'red'> 4. Single-element tensors </font>\n",
        "### If you have a one-element tensor, for example by aggregating all values of a tensor into one value, you can convert it to a Python numerical value using item():"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m5zmte04pNYm",
        "outputId": "adc9446a-d353-4a21-ce4d-e3b86c455cea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor(12.) 12.0 <class 'float'>\n"
          ]
        }
      ],
      "source": [
        "agg = tensor.sum()\n",
        "\n",
        "# https://pytorch.org/docs/stable/generated/torch.Tensor.item.html\n",
        "\n",
        "agg_item = agg.item()\n",
        "print(agg , agg_item, type(agg_item))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CB8jNwkKp6p7"
      },
      "source": [
        "###  <font color = 'red'> 5. In-place operations </font>\n",
        "### Operations that store the result into the operand are called in-place."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dapQwTqfqJ6B",
        "outputId": "1e6093e4-a12e-4d03-fa7f-e83f8da8625e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.]]) \n",
            "\n",
            "tensor([[6., 5., 6., 6.],\n",
            "        [6., 5., 6., 6.],\n",
            "        [6., 5., 6., 6.],\n",
            "        [6., 5., 6., 6.]])\n"
          ]
        }
      ],
      "source": [
        "print(f\"{tensor} \\n\")\n",
        "tensor.add_(5)\n",
        "print(tensor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_Se3wpQKIZi"
      },
      "source": [
        "# <font color = 'red'> **Datasets and Dataloaders** </font>\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RmElGI7sddPh"
      },
      "source": [
        "### PyTorch provides two data primitives:\n",
        "### 1. torch.utils.data.DataLoader\n",
        "### 2. torch.utils.data.Dataset\n",
        "### Which allow us to use pre-loaded datasets as well as your own data. Dataset stores the samples and their corresponding labels, and DataLoader wraps an iterable around the Dataset to enable easy access to the samples.\n",
        "\n",
        "### PyTorch domain libraries provide a number of pre-loaded datasets (such as FashionMNIST) that subclass torch.utils.data.Dataset and implement functions specific to the particular data. They can be used to prototype and benchmark your model. You can find them here:\n",
        "### Image data - https://pytorch.org/vision/stable/datasets.html\n",
        "### Text data - https://pytorch.org/text/stable/datasets.html\n",
        "### Audio data - https://pytorch.org/audio/stable/datasets.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T7pvM1eNNmFF"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XvKIaSrLfUSI"
      },
      "source": [
        "### <font color = 'red'> 1. Loading Dataset </font>\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wwqe87W4ZcDF"
      },
      "outputs": [],
      "source": [
        "train_data = datasets.FashionMNIST(root = 'data', train = True, download = True, transform = ToTensor())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "POHm1GQSan-Z"
      },
      "outputs": [],
      "source": [
        "test_data = datasets.FashionMNIST(root = 'data', train = False, download = True, transform = ToTensor())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_mPXsFSTfk2g"
      },
      "source": [
        "### <font color = 'red'> 2. Iterating and Visualizing the Dataset </font>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gn9Sad6J3lbd"
      },
      "outputs": [],
      "source": [
        "labels_map = {\n",
        "    0: \"T-Shirt\",\n",
        "    1: \"Trouser\",\n",
        "    2: \"Pullover\",\n",
        "    3: \"Dress\",\n",
        "    4: \"Coat\",\n",
        "    5: \"Sandal\",\n",
        "    6: \"Shirt\",\n",
        "    7: \"Sneaker\",\n",
        "    8: \"Bag\",\n",
        "    9: \"Ankle Boot\",\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ak0cSlvn2LOd",
        "outputId": "d6b115d2-85cf-457c-bfda-a27d1f1b728e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0039, 0.0000, 0.0000, 0.0510,\n",
            "          0.2863, 0.0000, 0.0000, 0.0039, 0.0157, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0039, 0.0039, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0118, 0.0000, 0.1412, 0.5333,\n",
            "          0.4980, 0.2431, 0.2118, 0.0000, 0.0000, 0.0000, 0.0039, 0.0118,\n",
            "          0.0157, 0.0000, 0.0000, 0.0118],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0235, 0.0000, 0.4000, 0.8000,\n",
            "          0.6902, 0.5255, 0.5647, 0.4824, 0.0902, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0471, 0.0392, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.6078, 0.9255,\n",
            "          0.8118, 0.6980, 0.4196, 0.6118, 0.6314, 0.4275, 0.2510, 0.0902,\n",
            "          0.3020, 0.5098, 0.2824, 0.0588],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0039, 0.0000, 0.2706, 0.8118, 0.8745,\n",
            "          0.8549, 0.8471, 0.8471, 0.6392, 0.4980, 0.4745, 0.4784, 0.5725,\n",
            "          0.5529, 0.3451, 0.6745, 0.2588],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0039, 0.0039, 0.0039, 0.0000, 0.7843, 0.9098, 0.9098,\n",
            "          0.9137, 0.8980, 0.8745, 0.8745, 0.8431, 0.8353, 0.6431, 0.4980,\n",
            "          0.4824, 0.7686, 0.8980, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.7176, 0.8824, 0.8471,\n",
            "          0.8745, 0.8941, 0.9216, 0.8902, 0.8784, 0.8706, 0.8784, 0.8667,\n",
            "          0.8745, 0.9608, 0.6784, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.7569, 0.8941, 0.8549,\n",
            "          0.8353, 0.7765, 0.7059, 0.8314, 0.8235, 0.8275, 0.8353, 0.8745,\n",
            "          0.8627, 0.9529, 0.7922, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0039, 0.0118, 0.0000, 0.0471, 0.8588, 0.8627, 0.8314,\n",
            "          0.8549, 0.7529, 0.6627, 0.8902, 0.8157, 0.8549, 0.8784, 0.8314,\n",
            "          0.8863, 0.7725, 0.8196, 0.2039],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0235, 0.0000, 0.3882, 0.9569, 0.8706, 0.8627,\n",
            "          0.8549, 0.7961, 0.7765, 0.8667, 0.8431, 0.8353, 0.8706, 0.8627,\n",
            "          0.9608, 0.4667, 0.6549, 0.2196],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0157, 0.0000, 0.0000, 0.2157, 0.9255, 0.8941, 0.9020,\n",
            "          0.8941, 0.9412, 0.9098, 0.8353, 0.8549, 0.8745, 0.9176, 0.8510,\n",
            "          0.8510, 0.8196, 0.3608, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0039, 0.0157, 0.0235, 0.0275, 0.0078, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.9294, 0.8863, 0.8510, 0.8745,\n",
            "          0.8706, 0.8588, 0.8706, 0.8667, 0.8471, 0.8745, 0.8980, 0.8431,\n",
            "          0.8549, 1.0000, 0.3020, 0.0000],\n",
            "         [0.0000, 0.0118, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.2431, 0.5686, 0.8000, 0.8941, 0.8118, 0.8353, 0.8667,\n",
            "          0.8549, 0.8157, 0.8275, 0.8549, 0.8784, 0.8745, 0.8588, 0.8431,\n",
            "          0.8784, 0.9569, 0.6235, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0706, 0.1725, 0.3216, 0.4196,\n",
            "          0.7412, 0.8941, 0.8627, 0.8706, 0.8510, 0.8863, 0.7843, 0.8039,\n",
            "          0.8275, 0.9020, 0.8784, 0.9176, 0.6902, 0.7373, 0.9804, 0.9725,\n",
            "          0.9137, 0.9333, 0.8431, 0.0000],\n",
            "         [0.0000, 0.2235, 0.7333, 0.8157, 0.8784, 0.8667, 0.8784, 0.8157,\n",
            "          0.8000, 0.8392, 0.8157, 0.8196, 0.7843, 0.6235, 0.9608, 0.7569,\n",
            "          0.8078, 0.8745, 1.0000, 1.0000, 0.8667, 0.9176, 0.8667, 0.8275,\n",
            "          0.8627, 0.9098, 0.9647, 0.0000],\n",
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            "          0.8706, 0.8941, 0.8824, 0.0000],\n",
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            "          0.4157, 0.4588, 0.6588, 0.8588, 0.8667, 0.8431, 0.8510, 0.8745,\n",
            "          0.8745, 0.8784, 0.8980, 0.1137],\n",
            "         [0.2941, 0.8000, 0.8314, 0.8000, 0.7569, 0.8039, 0.8275, 0.8824,\n",
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            "          0.7098, 0.8039, 0.8078, 0.4510],\n",
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            "          0.7608, 0.7490, 0.7647, 0.7490, 0.7765, 0.7529, 0.6902, 0.6118,\n",
            "          0.6549, 0.6941, 0.8235, 0.3608],\n",
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            "          0.8000, 0.8196, 0.8235, 0.8235, 0.8275, 0.7373, 0.7373, 0.7608,\n",
            "          0.7529, 0.8471, 0.6667, 0.0000],\n",
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            "          0.9373, 0.9490, 0.9647, 0.9529, 0.9569, 0.8667, 0.8627, 0.7569,\n",
            "          0.7490, 0.7020, 0.7137, 0.7137, 0.7098, 0.6902, 0.6510, 0.6588,\n",
            "          0.3882, 0.2275, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1569,\n",
            "          0.2392, 0.1725, 0.2824, 0.1608, 0.1373, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000],\n",
            "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
            "          0.0000, 0.0000, 0.0000, 0.0000]]])\n",
            " - - - - - - - - - \n",
            "9\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f4b348313d0>"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "im, lab = train_data[0]\n",
        "print(im)\n",
        "print(\" - - - - - - - - - \")\n",
        "print(lab)\n",
        "\n",
        "plt.imshow(im.squeeze(), cmap=\"gray\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "iZp5FjAAfy6D",
        "outputId": "e13a3f26-f987-4a7d-eed0-9e1f0f639e6b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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DKuVopYwowO6D91EChd8xItpxxQfz/W4CqPI7MEQJNz6Zxq+AI+W/U3V3CvGrpkQrTfn3i3YBiZI/0DP53+MoKSfaDcbzK/VE14Po2L5vRsliixcvrpT9SmqStHz58pbn2JNxJwkAQAGDJAAABQySAAAUtBmT3GeffbI6v3tFtHr8uHHjKuXx48dnbfxz8SjG873vfS+r+8xnPlMpR3FLv9NBFKP07++f/0txnM7HgqLP7+Ok0bP83/zmN5Vy3Zikj0HWiUlG8d5Zs2ZVygsWLMjaYLshQ4ZUytF36mOCdRbjiPpdnbro/X2b6P07aqcSdD7/XW3YsCFr43//o+uRjzdGfafOIgR1HHzwwVkdMUkAAHZRDJIAABQwSAIAUMAgCQBAQZuZBW9/+9uzumOPPbZSfuaZZ7I2a9eurZSjSc2PPfZYpRwljkSBY//+0WRaXxftquCTGvyE29LrfHJE9P4+mD5ixIiszerVqyvlr33ta1mbKJju66IkjygJyPNJJtEuLLui9u5m4ZOo/K4gUvt2zYm+42jCv/8dinZkiBYv8Ejc6T38dxX9rvv+HPUBfx2N+nzUD+v0J3/sI444Imtz2223tTxOT8adJAAABQySAAAUMEgCAFDQZhDl8ssvz+pmz55dKR9zzDFZmwMOOKBS3nvvvbM2Pu5SZxKslD9Pj56bP/XUU5VytGC0P87GjRuzNtEkWB9vjSbm+njj2LFjszZz5syplGfMmJG1iRYBeOCBByrlaMGDNWvWVMrz58/P2jz99NOV8pNPPpm1ueqqq7K6vmrw4MGVctTvfF2dDQKi+GMUU/bHjvqdjw9FcUtfF/3eRTErf+w6C2tj5/h+UCfeGMXFo1imF73O942or/j333///Vu+V2/DnSQAAAUMkgAAFDBIAgBQwCAJAEDBS579PG/evDbLaJ/ePuG2t6iTgBIlzvjXRQtk+CSc6Ng+4aZOApCUJ19E7++TcqKFA/yx/Y45UpwMhq5XJ3HH10ULsPh+ECV9RYk7PoErauPfP1pko7fjThIAgAIGSQAAChgkAQAoeOkrMgO7uGixex/XiWI/dRab9nGe6L2iyd/+/aLFL/yC/NE5+vfba6+9sjZRnkEUx0Ln8jHAOn0uWgjCHydaFCCKX/v3ixbH8K+L4pZDhgyplNevX5+16cm4kwQAoIBBEgCAAgZJAAAKGCQBACggcQd9Sp0ElBEjRmR1dXb48EkTUYKEFyXpRMkP/ryjJI5Wr5Hyc4x2qIkSd9gFpOv5vjFq1Kisje+XUeKOP06d5J7odRs2bGjZJjrOhAkTKmUSdwAA2EUwSAIAUMAgCQBAAYMkAAAFJO4Ajk80kPKEBL/jhiTtsccelXKU3OITHaKVTiI+cSZ6Xd1j7ShacSdCok7X8yvVRHyf27p1a9YmWvnJi5LMfJ+P+sDgwYMr5WgHmcmTJ1fKjz76aMvz6Um4kwQAoIBBEgCAAgZJAAAKiEkCzpgxY7I6H7Ops2tCHdFiAlEMqc7u8j4+FfHHnjJlSsvXoHsMGDCgUl6zZk3WxvfLOjt8RG2ivlsnJvn888+3bDNx4sSsrjfhThIAgAIGSQAAChgkAQAoYJAEAKCAxB3AGTlyZFbnExt8woKUJ9NEiTQ+cSZqEyVR1EnK8VauXJnVjR8/vlKOdnZAz+D7Sp0dbOokgkX9K0rmqbPDiN8NZ/PmzVmb0aNHxyfbS3AnCQBAAYMkAAAFDJIAABQQk0SfEsVVvGgyv4/jRDuwexs3bszq+vXrVyn3798/axPFnvyE8Ciu9Nxzz1XKfjK6lMdSfUwJPcdhhx1WKUf90n/Ha9euzdr4uqh/RYsA+PeL4vCtzkfKP0dvw50kAAAFDJIAABQwSAIAUMAgCQBAAYk7gHP44YdndXV2Uhg2bFilHE2s9ok70XGiZB6/S73f7T06VpSUM3z48Ep53333zdqgZ7jlllsq5REjRmRtfJ87+uijszZDhw6tlPfaa6+Wx5HyRLClS5dmbUaNGlUpb926NWtz/vnnZ3W9CXeSAAAUMEgCAFDAIAkAQIG1NbnazFrPvMYuK6WUB8y6QHf3u+OPPz6re9WrXlUpR7ut+9hPNLHax3WmTp2atYkWIXjiiScq5WgxAR8P+u1vf5u18THIp59+Omsze/bsrK4rdUe/6+4+114+fj1t2rSsjV/0PGqzatWqrO7zn/98pfzf//3fWZvFixdXyldeeWX5ZHuwtvocd5IAABQwSAIAUMAgCQBAAYMkAAAFbSbuAADQl3EnCQBAAYMkAAAFDJIAABQwSAIAUMAgCQBAAYMkAAAFDJIAABQwSAIAUMAgCQBAAYMkAAAFfXKQNLP5ZrbFzDaa2Roz+7GZTe7u80Lv0uw/2/57cYc+tdHM3tnd54e+zczeYWZzm/1xiZn9xMxm7eQxbzezszrqHHuDPjlINp2cUhokabykZZK+0s3ng14mpTRo23+SnlazTzX/+962dma2W/edZc85B3QdM7tA0pckfVbSWElTJP1fSad042n1Sn15kJQkpZS2SrpB0nRJMrOTzOw+M1tvZgvN7FM7tjezM81sgZmtMrNLmnelx3bDqaOHMrOjzewZM/uYmS2V9G0z29PMvmRmi5v/fcnM9my2f6+Z3emOkcxs3+b/P9HM5pnZBjNbZGYf2aHdn5rZ/Wa21sx+bWaH7fBv85vn8KCkTQyUfYOZDZV0maS/SSndmFLalFJ6PqV0S0rpoy364nAz+y8zW9F8yvZfZjap+W+XSzpK0lebd6df7b5P2XX6/CBpZgMkvU3SXc2qTZLOlDRM0kmSzjWzU5ttp6vx19g71bgDHSppYteeMXqJcZJGSNpL0vslXSzpNZJmSHqFpJmSPlHzWN+S9IGU0mBJh0j6pSSZ2eGS/l3SBySNlPQNSTdvu+A1naFGPx6WUvrjzn0k9BJHSuonaXbh39vqiy+T9G01+u0USVskfVWSUkoXS/ofSec1n5ac10nn36P05UHyJjNbK2mdpOMkfV6SUkq3p5QeSim9mFJ6UNJ1kl7ffM2fS7olpXRnSuk5SZ+UxF5jiLwo6dKU0rMppS1q/GF1WUppeUpphaRPS3p3zWM9L2m6mQ1JKa1JKf1vs/79kr6RUvptSumFlNJ3JD2rxgVwm39NKS1sngP6hpGSVrbxR1GxL6aUVqWUfphS2pxS2iDpcm2//vVJfXmQPDWlNEyNv7jOk3SHmY0zsyPM7Lbm44Z1ks6RNKr5mgmSFm47QEpps6RVXXze6B1WNB/lbzNB0oIdyguadXX8maQTJS0wszvM7Mhm/V6SLmw+al3b/KNvsjvuQqGvWSVpVBuP14t90cwGmNk3miGl9ZJ+JWmYmb28U8+4B+vLg6QkqfkX+I2SXpA0S9K1km6WNDmlNFTSlZKs2XyJpEnbXmtm/dX4qw3w/BOGxWoMattMadZJjUf8A7b9g5mNqxwopXtSSqdIGiPpJkk/aP7TQkmXp5SG7fDfgJTSdW2cB3Z9v1HjicKphX9vqy9eKOkASUeklIZI+pNm/bZrYJ/rT31+kLSGUyQNl/SIpMGSVqeUtprZTEnv2KH5DZJONrPXmtkekj6l7Z0HaMt1kj5hZqPNbJQaj+qvaf7bA5IONrMZZtZPjX4lSTKzPczsnWY2NKX0vKT1ajzKlaRvSjqn+fTDzGxgM/FscJd9KvQ4KaV1avSvr5nZqc27w93N7AQzu0Jt98XBasQh15rZCEmXusMvkzStaz5Jz9CXB8lbzGyjGhedyyW9J6X0O0l/LekyM9ugRufZ9le7mv9+vqTr1bir3ChpuRp/tQFt+QdJcyU9KOkhSf/brFNK6fdqZCPeKulxSXe6175b0vzm469z1IgpKaU0V9LZaiRWrJH0hKT3dvLnQC+QUvqipAvUSMhZocZTh/PUeBJR7ItqTBvpL2mlGsmMP3WH/rKkP29mvv5rp36IHsJS6nN3zx3GzAZJWitpv5TSU918OgCADtaX7yTbxcxObj6+GCjpC2r8JTa/e88KANAZGCRfulPUCHIvlrSfpLcnbscBYJfE41YAAAq4kwQAoKDNtRzNrFfeZr785dV5ry+88ELL15x22mlZ3ZQpU7K6J598slK+5ZZbWh77ZS/L/xZ58cUXg5Y9S0qpW6a39MR+98Y3vrFSPumkk7I2q1ZV15WI+t2ee+5ZKW/cuDFrM3/+/KzuoIMOqpQ3bdqUtfFPhcaNG5e1ueqqqypl3597gu7odz2xz3WUSZMmVcozZszI2qxZsyarGzBgQKV8zz33ZG3Wrl27U+fWU7TV57iTBACggEESAIACBkkAAAoYJAEAKGhzCkhvCGb7JB0pT5g49th8T+T3vOc9lfKSJUuyNlFSw/77718pR8kRf/M3f1MpR0Hx3Xar5kz98Y89b6s/Ene2e+ihhyrlQw45pMveS5IOPfTQSjnqLz6JYtSoUVmbr3zlK5XyBz/4wXacYecicafBrPpjqDNd7x//8R+zunPPPbdS9sljUr3rz7PP5qtvfvGLX2z5/l5PTGQkcQcAgHZgkAQAoIBBEgCAgjYXE+iJ6sTy3vKWt1TKxx13XNbm3e9+d4ecTzQx1z+nf9/73pe18eft4w9SvRgEusaiRYsq5SgWvXLlykp5jz32yNr4791P2Jak0aNHZ3V1Jv37hQmimCR9qveo813dfffdlXJ0PfKLXETHjeKUPrcjet0nPvGJSnnmzJlZG79QS3fHH18q7iQBAChgkAQAoIBBEgCAAgZJAAAKenTiTrRQgE982H333bM2s2bNqpTPP//8lu8VBa59klDk/vvvz+p++tOfVsqf/vSnszaXXnpppRwleUSTd9E9/I4wzz//fNZm4MCBlXLUf+pM2o7a+CSKqN8PHjy45TlOnz695fujZzj55JMr5Ztvvjlr468Ry5Yty9r4a1u0O80DDzyQ1fnEryFDhmRt1q9fXyn7pElJWrBgQaX8V3/1V1mbW2+9NavrKbiTBACggEESAIACBkkAAAp6XUzSP0+/5JJLsjbR83XPx482b96ctYlign7Sf7QIwA9+8INK+YYbbsjaDBs2rFKOdviu8/nRNer83H0sMZp87ftLtNhz1Ke86Nj+HP3iAlIcV0LXeuc735nVfe5zn8vqRo4cWSn7RQGk/BpVJ7ciajN27NiszveVqM/5/rt06dKsjb/W/fjHP87aPPXUU1nd3//931fKs2fPztp0Be4kAQAoYJAEAKCAQRIAgAIGSQAACrotcafOrtt1Jl73798/q7vxxhtbvm7r1q0t3z/i20WLADz33HOVst9BQson1PqdQ6R4MjqJOz2X/26iBByfjBW18X1TyvtZtFBAnd+XOklB6Fxnn312Vhft2OJ3lYmuB/369auUo+tDnUVJpk6dmtVt2LChUo4SwfyiFlFS0JYtWyrldevWZW323XffrO7v/u7vKmUSdwAA6GEYJAEAKGCQBACgoNtikj62F02qrrODdbSLu38GHumo2F503t5DDz2U1b3pTW9q+boo7oTu4Rd78BOkpTyW6BeskOrt9u7jTFIej4rilv5Y0SLofkFqdL1p06ZldVGczn9/Uf6DF13XfKw66jsDBgxoeU7R4iY+xh3FTX2b6DirV6/O6iZMmFApR/HOrtgEgjtJAAAKGCQBAChgkAQAoIBBEgCAgh69C0jEB7Pbu6tBncUM6qjzumi38EGDBrV8XZS45IPeLC7QNZYvX14pz5gxI2vjd2mos2vCpEmTWraRpGeeeaZSrrMoQLTQxpIlS1q+Dh1r7733rpSjhQOiXYD8dxwlqfi+EvWLKJnGW7NmTcs2UcKNv0ZFSZP+ddFxoiRFnxw3fvz4rM38+fOjU+1Q3EkCAFDAIAkAQAGDJAAABQySAAAU9JjEnWh1kChQ7VddGDduXLvezwe825sAUyeBIvocfjUWH9yXuiYojXoef/zxSvm0007L2viVTaLEK5+oc/XVV2dtDjrooKzu8MMPr5R9IpGU97MoQeKpp57K6tC5Zs6cWSlHiTRRkpdfYSe6jvjXRcf2/TBauSe6/vlrcp1EtDqfLfq9iBJ3/MpTr3rVq7I2JO4AANCNGCQBAChgkAQAoGCnY5J1YnLRrgb+GXidXdWl/Nn18OHDszYnnHBCpfyTn/wka9PexQNanU8kirf6nQAmTpyYtYmet9fZGaXOziR1joPtFi1aVClH/b7OxGofD7rxxhuzNscdd1xWd8QRR1TK7e2/USwTneuQQw6plOvmP9T5jv2xomP7PlcnbhnVRTFu/37Rtcdf26PjRHX+80cLePzwhz/M6joad5IAABQwSAIAUMAgCQBAAYMkAAAFO524Uye4HCUwtNfkyZMr5SiB4uSTT66Uo8SdOglHUZs6E2O9k046qWWbKVOmZHVz5sxp+f4RknI63oIFC1q28ckHdZLRtm7dmtXV2ZHhueeey+rqfO9+UQR0voMPPrhSjq4rUd2AAQMq5ag/1dkppL07B/mFW6IkRd8Po4UK/DlGOyCtW7eu5fn4n2NX4U4SAIACBkkAAAoYJAEAKOiUBc79ZP6Pf/zjWZuPfexjlfKvf/3rWsf+t3/7t0o5Wkxg9OjRLY9T57l8nfhfnTZ+4QBJWr9+faX84Q9/OGszduzYrO7QQw+tlAcPHpy18Qsqf+hDH8ra/OhHPwrPFbH77ruvZZv2xIKjGGEUn/bqLr7hPfDAA+16Hdpv6tSplXKdhcKlPCYZxa99XRQTrBNbjF7n+1h03v7Y0e+AP7b/XFJ+PYyOFS240hW4kwQAoIBBEgCAAgZJAAAKGCQBACjolMQdnzgza9asrM1NN93U5mskad68eVnd5s2bK+VoUvXrXve6Svnmm2/O2nzwgx+slNu7w7V/L0m68sorK+X+/ftnbepMwr3wwguzutWrV1fKQ4cOzdr4APsf/vCHrA1emmeeeaZlG5+QsHHjxpavWblyZVZXZ6eOaNeEgQMHtnzd0qVLW7ZBx/LXtjqLAkTqJAlGOw75a2adXZmiuk2bNmVtfD+Mjl3nvKOdSfw10i8k01W4kwQAoIBBEgCAAgZJAAAKOiUmedppp1XKS5Ysydr4Ca1RzCd63ahRoyrl6Fm2X1D98MMPz9o89thjlXIUt6uzs3wUA3jwwQcr5WgSrK+LFiZetmxZVucXD4gmGPv4RrSYwcMPP5zVoaxOXMXHZ6JYtBfFLaN+70WTtn1frHPO6Hz+mhX9rkfXES/6zussqu+vkVGb6Nh+gYPoWuvPO4qV1+mH0WIK/pyGDRvW8jidgTtJAAAKGCQBAChgkAQAoIBBEgCAgk5J3PGJB9FOFX5iarQyfDQ52u/6Ea1o74PHUQKOT66JJllHCxX44PWKFSuyNj6YHX1+H5SOguJ+hf1INAl5//33r5R//vOftzwOXppo8rX/vqJ+V4dfMCISJX/4pDIWDugZ/AT7qF/UWUwg6nP+uhFdD/37R0k6UZ1PuIkWCvDnHSXuROfk1UncqZMI1xm4kwQAoIBBEgCAAgZJAAAKGCQBACjolMSdK664olL2K/BIeaJOtAvHoEGDsjofqK6zCkW0w4YPJo8bN65lGylfIWXMmDFZm5kzZ1bKUQKQX5k/Ckr7NlIevI8+m9+FJFqVBzsn2hEh+i7aI0rQ8OqsrFJn5xJ0veiaFSXu+etP1MaLEmf8+9XpX5HoeuiTFKPP5pN7ovePzjtKTusO3EkCAFDAIAkAQAGDJAAABTsdkzz11FOzOj95PYr3LV68uOWxhwwZktX559vRCvM+bhgtVDBixIhKOZq4H8V9fJw0ipv6ycLr1q3L2viFEqL337BhQ1bnP3/0/rfffntWh461fv36rM7HJNsbo6wzsTyafO0ne69Zs6Zd74+uF/WVVatWVcpR3M6L+o6vq9Mmqov6nD+n6Di+TbQAS7TDR5TL0R24kwQAoIBBEgCAAgZJAAAKGCQBACjY6cSdKVOmZHWXX355pbxs2bKsjZ/g7nf3kOLJ9H5CaxTcrRPwjZJi6vCTZessZhAlAPnJ6NHnj+p8MkY0MTcKgqNjRf3HJ4P5idZ1tXfSuE+a8Als6Hx1vvMo2TC6Rqxdu7ZSnjBhQtYmWtTC85Pyo2tWtMOHbxe9l08cjJJ7fFLSypUrszZRkmZ0np7/PWjvQglt4U4SAIACBkkAAAoYJAEAKNjpmOTcuXOzOr8j+oc//OGszcKFC1seO4qp+DhdFL/xk/mjncD95P0obhrxcYHBgwdnbXxcIpo86xd09/EHSRo/fnxW55/vR4sOH3TQQVkdOlb0c68TQ6kjilnV4d+fmGTX84uE1BXF0urkVvjXRbHq9sZJ/bW1Tqw8+hw+ThltuBD9PtXhr7/RdXRncScJAEABgyQAAAUMkgAAFDBIAgBQsNOJO1FyyQ9+8INK+eyzz87avPrVr66Uox0LogmmPgjcv3//rI0PDA8dOjRr4wPF0QT8KAjtJ+bW2VE7SlLybaLPHyUB+J9JFPCePn16VoeOFU3+bm/CjVcnYSN6L1/XU3ZR6EuiSfl1RDsVjR07tlKOEhC9aDK/F+3UEV3rfLvos/lknqjP+Wud/1xS/Pmj3zHPX/9J3AEAoAsxSAIAUMAgCQBAwU7HJKPJ9N4111yT1b3xjW+slB9//PGsTTQ52y8mEMUS/aK70S7yfoJ/FP+LYpn+GXy00LX/LNHCwH5h+BkzZmRt/IIHUv7MP5qYO3r06KwOHSv6Tn0Mp85O8pE6sZhogrh//45a3AD1RXkUXhQTrDOZP8o/qBOD9G2i+GOdvhq9lz9W9Nl8f/YLokjtj+dH18iOxp0kAAAFDJIAABQwSAIAUMAgCQBAwU4n7tTZPWPx4sVZnU8qGDNmTNbmpptuyuqOPPLISnncuHFZG5/c4hN5pHxibJTkEAWq/YRev5uHlAfco6Qcv4t9tMJ+nQSO6Byjnb/RsaLEHZ/8EH2nmzdvbnnsOrt3RMf271+n/6BjRUkpXpSkUjeZp5XoOF6UpBNdR/w1sc751Pkc0bW2znlH2rt4w0vBnSQAAAUMkgAAFDBIAgBQwCAJAEDBTifuzJkzJ6t705veVCnfddddWRuf+BCtAv+hD30oq/MrzESr6fjAdBSU9okP0Qr70QoXfoWJOivl1FmFwq+UL9Xb6SHaBeXOO+/M6tCxopWO/PdcZ4WSSLSKk1cncSfqU+hc0ffiRdeDqK/4uuh64L/zKCnHX9ui40TXEd9Xo4Sb9qwqFX3+9iYuRauidTTuJAEAKGCQBACggEESAICCnY5JRjFBb+bMmVnd0qVLK+Vo54oFCxZkdX6ybhQD8M+3o+fd/nVRrCjaGaTOBG3/LD06tq+rG7/yx44m0/76179ueY7YOVG8uk4svDO1d0I2Ok5H7sDjv886OQqROjkSdSb4R/25PedYZ+GC0nl6dXah2lncSQIAUMAgCQBAAYMkAAAFDJIAABTsdOJO5Oc//3mlfNFFF2VtfMLJ7rvvnrWJdvjwE2OjgK9Pyqmzm0eUkBPV+SB0dN5+EncUzPbnXTfJw+9w4suStGjRolrHQvtFE/X9d9jViTT+/dkFpOvtvffeWZ1feCK6HkSJYP7aEvW5OkmKXt3dRepck3xyYXRsf5xoAYI6CZjRtW7ixIktz3FncScJAEABgyQAAAUMkgAAFOx0TLLOwrRXXHFF1ub9739/pRzFFhcvXpzVDRkypFKOnpv7Z9fR834/wXbkyJFZm/YuAlDnWX6dWEIUU/IL+kbvFS2+jY61cePGrM734aj/RHGV9qgTV9q8eXOHvBfqO+qoo7K6OrHhKN7of4/r5E1E17o6sfKoP9WJb9a5HnqrVq3K6qLrv/9s0ed/3eteVyl/5zvfafn+LxV3kgAAFDBIAgBQwCAJAEABgyQAAAVdkrgT2WeffSrlFStWZG02bdqU1fmECZ/II+XJEdOmTcvazJ07t1J+9NFHszY+uUfKd42vuwiBV2eF/WjS7bp16yrlu+66K2vz5JNPtnx/7By/G40kjRgxolKOdnuP6toj2m1i0KBBlXKUjIbOFV3H/PcSqdOmvfz1OEruae/CF/510aIA7eWv9QMGDMja+EVhOgN3kgAAFDBIAgBQwCAJAECBtRU/NLOWwcW6u0x7d9xxR6U8fPjwrE0Ub/Si5+tr166tlP1i6pJ06KGHtjx2X5dS6pat7uv0u+42ffr0rO6CCy6olKPY9PXXX18p33777bXe7/TTT6+Up0yZkrXx8aFrr702axMt0NHTdEe/68w+N2vWrEp5/fr1WZuVK1dmdX6BgWOPPTZrM2HChEo5yqN44xvfWClH8c9o4Yk6C574c4xihMuWLauUZ8+e3bKNlI8JUYz9hhtuyOrao60+x50kAAAFDJIAABQwSAIAUMAgCQBAQZuJOwAA9GXcSQIAUMAgCQBAAYMkAAAFDJIAABQwSAIAUMAgCQBAAYMkAAAFDJIAABQwSAIAUMAgCQBAQZ8cJM1svpltMbONZrbGzH5sZpO7+7zQuzT7z7b/XtyhT200s3d29/mhbzOzd5jZ3GZ/XGJmPzGzWa1f2eYxbzezszrqHHuDPjlINp2cUhokabykZZK+0s3ng14mpTRo23+SnlazTzX/+962dma2W/edZc85B3QdM7tA0pckfVbSWElTJP1fSad042n1Sn15kJQkpZS2SrpB0nRJMrOTzOw+M1tvZgvN7FM7tjezM81sgZmtMrNLmnel+Zbh6LPM7Ggze8bMPmZmSyV928z2NLMvmdni5n9fMrM9m+3fa2Z3umMkM9u3+f9PNLN5ZrbBzBaZ2Ud2aPenZna/ma01s1+b2WE7/Nv85jk8KGkTA2XfYGZDJV0m6W9SSjemlDallJ5PKd2SUvpoi7443Mz+y8xWNJ+y/ZeZTWr+2+WSjpL01ebd6Ve771N2nT4/SJrZAElvk3RXs2qTpDMlDZN0kqRzzezUZtvpavw19k417kCHSprYtWeMXmKcpBGS9pL0fkkXS3qNpBmSXiFppqRP1DzWtyR9IKU0WNIhkn4pSWZ2uKR/l/QBSSMlfUPSzdsueE1nqNGPh6WU/rhzHwm9xJGS+kmaXfj3tvriyyR9W41+O0XSFklflaSU0sWS/kfSec2nJed10vn3KH15kLzJzNZKWifpOEmfl6SU0u0ppYdSSi+mlB6UdJ2k1zdf8+eSbkkp3ZlSek7SJyWx1xgiL0q6NKX0bEppixp/WF2WUlqeUloh6dOS3l3zWM9Lmm5mQ1JKa1JK/9usf7+kb6SUfptSeiGl9B1Jz6pxAdzmX1NKC5vngL5hpKSVbfxRVOyLKaVVKaUfppQ2p5Q2SLpc269/fVJfHiRPTSkNU+MvrvMk3WFm48zsCDO7rfm4YZ2kcySNar5mgqSF2w6QUtosaVUXnzd6hxXNR/nbTJC0YIfygmZdHX8m6URJC8zsDjM7slm/l6QLm49a1zb/6JvsjrtQ6GtWSRrVxuP1Yl80swFm9o1mSGm9pF9JGmZmL+/UM+7B+vIgKUlq/gV+o6QXJM2SdK2kmyVNTikNlXSlJGs2XyJp0rbXmll/Nf5qAzz/hGGxGoPaNlOadVLjEf+Abf9gZuMqB0rpnpTSKZLGSLpJ0g+a/7RQ0uUppWE7/DcgpXRdG+eBXd9v1HiicGrh39vqixdKOkDSESmlIZL+pFm/7RrY5/pTnx8kreEUScMlPSJpsKTVKaWtZjZT0jt2aH6DpJPN7LVmtoekT2l75wHacp2kT5jZaDMbpcaj+mua//aApIPNbIaZ9VOjX0mSzGwPM3unmQ1NKT0vab0aj3Il6ZuSzmk+/TAzG9hMPBvcZZ8KPU5KaZ0a/etrZnZq8+5wdzM7wcyuUNt9cbAacci1ZjZC0qXu8MskTeuaT9Iz9OVB8hYz26jGRedySe9JKf1O0l9LuszMNqjRebb91a7mv58v6Xo17io3Slquxl9tQFv+QdJcSQ9KekjS/zbrlFL6vRrZiLdKelzSne6175Y0v/n46xw1YkpKKc2VdLYaiRVrJD0h6b2d/DnQC6SUvijpAjUSclao8dThPDWeRBT7ohrTRvpLWqlGMuNP3aG/LOnPm5mv/9qpH6KHsJT63N1zhzGzQZLWStovpfRUN58OAKCD9eU7yXYxs5Objy8GSvqCGn+Jze/eswIAdAYGyZfuFDWC3Isl7Sfp7YnbcQDYJfG4FQCAAu4kAQAoYJAEAKCgzQWPzYxnsX1YSqlb5oD2hn73mc98JqsbOHBgpfziiy9mbfbcc89KeY899sjavPDCC1mdD4u87GX537cjRoyolBcvXpy1+du//dusrqfpjn7XG/pcHcOHD8/qvvWtb1XKc+bMydrsu+++Wd3YsWMr5TPPPDNrs3Hjxpd6ij1SW32OO0kAAAoYJAEAKGCQBACggEESAICCNudJ9tZgtk9qiBIoWr1Gkl7/+nwbtUMPPbTlsTZt2lQp33jjjVmbNWvWtDxOdyNxZzufELFkyZKsjU/KaW+/i/hjRb+3vs2zz+ZLCo8aNaplm+62qyfu7L777lnd888/365jvfzl1R2sLrrooqzN3nvvXSn7hBxJeuSRR7K6448/vlK+5pprsjZf/OIX65xmhVm9r7cr5/CTuAMAQDswSAIAUMAgCQBAQZuLCfREdeKN/jl91OaAAw6olE8++eSsze9+97us7j/+4z8q5fHjx2dtJkyYUCl/5CMfydrMnj27Up47d27WBj3Hhg0bKuXVq1dnbRYsWFAp77Zb/utVZ1GA6HWbN2+ulKO4jj/WPvvsk7WpEydFfe3Jf6gbfzz44IMr5aOOOiprc+CBB1bK0UIU/tpyyy23ZG0OOuigrO7Vr351pez7oCR95StfqZR/+9vfZm1++tPqlpQrV67M2kR8H++udca5kwQAoIBBEgCAAgZJAAAKGCQBACjYJRcT8PzuDJL03ve+t1L++te/nrXpzCSHyy+/vFK++uqrszaPPfZYp71/HSwmsN306dMr5X/5l3/J2jzwwAOVctTvfPJDlLgT9TufkOEXLpCkP/7xj5XyjBkzsjaf+9znKuVf/OIXWZvu1psXE/BJg1L+3fmkQUk64YQTsrpBgwa1eRwp709DhgzJ2vjFC6Lkmre97W1Z3YoVKyrl+++/P2vjd56pszjG1q1bs7ro+uvV/V1pDxYTAACgHRgkAQAoYJAEAKCgUxYTqPNcuisnNb/1rW/N6n7yk59Uyh15PnUmGH/2s5+tlN/ylrdkbbo7Jontpk6dWikPGDAgazNu3LhKOZrw7xcK6N+/f9YmyhMYPHhwpbx+/fqsjY9PRTFRfxzsHP8dR3FDH6eM4o9RP9i4cWPL93/uuecq5ahf+EXszznnnKyNjy1K+YLm0YID/hyjz+9/RmPGjMna/Nmf/VlW98Mf/jCr6w7cSQIAUMAgCQBAAYMkAAAFDJIAABR0SuJOZybl1EmK8QkL++23X9bmu9/9bsv36sgdxL1+/fpVyh/+8IezNtFu4dGEXnQ+3+98woSUJyhEfbNOG7/jSPT+UaLHpk2bKuW1a9dmbaLXof389xIlrvzJn/xJpRx9B1u2bMnq/IIRURt/jYqSvvw5/fKXv8zaREaOHFkpR0lBPhHNX9ei10W7gETJPF6d36fO2CmEO0kAAAoYJAEAKGCQBACgoFNikp2pTrzTxwCWLVvWrveK4gtetHCCnzwcnfPee+9dKUeT04899tis7h3veEelfNFFF7U8R+w8H5/ZY489sjY+9hItNu1fFy04EMWs/KLQ0ULa/v2i+Ex0Tmi/OteI/fffv1KOYotRP/AL1kdt/HccXWt8bLPOIgVSvZjkqlWrKmW/cIGUL7IRXTOjWOaBBx5YKT/66KNZG2KSAAB0IwZJAAAKGCQBAChgkAQAoKDXJe74AO8pp5yStZkwYUKlfOKJJ2Ztvva1r7V8rzpJQlGbOq+79957K+Urr7wyazN27Nisbvz48S2PjY7nd4mPdlePEis833+jRQmiJC7/Op8wIeUTy33iR3QcdKwoKcX/zi5ZsiRrU+d7iY7tE4fam7gSJYL5xSmiXUAOOeSQSnn16tVZm9tuu61Sjvp8dM2sk7jTFbtJcScJAEABgyQAAAUMkgAAFDBIAgBQ0G2JO36nDh8kjtpI0umnn96yjQ/wHnrooS3PZ9KkSVldtJqGD3BHAe/hw4dXytGKP7NmzaqUL7nkkqzNxRdfnNXdfffdlXK0gopf0SPaucQH3B9++OGsDbbzux1Eq5b4xJ0ouccn10RJOhH/fj6RSJI2b97cZvmlvB/ax68uI+UJVNHvrF8VR8qTs6I2dVb38texKKErSgry19bf//73WRvfL6dMmZK18clEo0ePztpEiWg9ZXUo7iQBAChgkAQAoIBBEgCAgm6LSd50002V8vz587M2Z599dlb3n//5n5VyFDf0dbfeemvWxq9wH01wrROTrCPa1cF/jr322itr4xcckKT777+/UvYxLimPQd5www1ZGx/TOv7447M22M7v3hFN2vZ1UZzH97OoP/n4p5R/pxs2bMja+HhUdBx0rqlTp2Z1Pm43efLkrM1hhx2W1f37v/97pTxs2LCsjf/Oo+uYv0ZEsfI6k/KjvJF77rmnzbKU993Xv/71WZs1a9ZkdU888USlPGbMmKzN8uXL45PtQNxJAgBQwCAJAEABgyQAAAUMkgAAFOx0ZL9O4sj06dOzNmeddVal/A//8A9Zm2nTpmV1ixcvrpSjCad1kmt8Uk6UpNPeHT58gNsnfURmzpyZ1UUT1n3iTrRQwBlnnFEp+8UFJOkDH/hApRwFxbGd7x9RH/Ntou9vxIgRlXL0+xNN9q6zCIBPyIh+N6JzQseJJsr7xD2fNChJxx57bFZ34403tnw/n4AX7cLRXj4JKFrMwCccRolD/voXJQCtWLEiqxs6dGilTOIOAAA9DIMkAAAFDJIAABTsdEwyiokdcMABlfKdd96ZtZkwYUKl/L73vS9rM2rUqKzOP99vz+T+6HXtPU7Ex5min5GfaO4XRZfiRaz9saPzfuihhyrl6667Lmvzpje9qVKeN29e1gbb+cXCoxi2/07rTOaPFiWIFu1/9tlnK+WoT/kF1qNj++OgYx1++OFZXZ38B59rIEn9+vWrlKNFCPzmCVGM218zo74T8f3Zn49Ur4+PHz++UvaxRinfuEHKx4jodV2BO0kAAAoYJAEAKGCQBACggEESAICCnU7cef/735/VfexjH6uUv/rVr2Zt/MTnaKJonRXt60zqrqPujh91ju0nz0Yr3PugtF8kQZJWrlzZ8r2iHUaefvrpSjmaVP7MM8+0PDa2q/O910nc8bt3RMlp0Y4w9913X8v394sQRO/fkZPNkYu+T7+bRXRdixaQ8JP3o9076uwqU6dfRouk+NdF1xrfn6PkHr9QwL777pu1GTduXFbnP9uBBx6YtZkzZ05W19G4kwQAoIBBEgCAAgZJAAAKXnJM8jWveU2lfM4552Rt/GLd0eLdr3vd6yrle++9N2sTxdKi5/Kejx/Ved4eTdyvE6esE6uKJu/65+tR/DFaGN2/fxTf8HGB9evXZ2187OQVr3hF1gbb+fhQexef8ItGPPnkk1mbsWPHZnU+Zh+9zvfz6Bx9DAkdyy86IUlLly6tlF/5yldmbfxCEJK0bt26lm38dSy6ZvgYZNQmukb6xSj8e9Xlr5H+c0n5wv9SnlsRxS27AneSAAAUMEgCAFDAIAkAQAGDJAAABS85ceeSSy6plKPdO1atWlUpn3nmmVmbq666qlKOgslRMo0Xva492puIESXO+F0copX5/cr4fucOKU4K8slMUVKQ/5lEbfz7H3/88VkbbLd27dpKOZpY7ftCtOPGoYceWinPnj07a+MTFiTprLPOqpQXLlyYtamTsBbtCo+OE02m999L9PsY1Q0YMKBSjhZceeqpp1qeU3SN8qJrVJ3X+eSeYcOGZW3qJPxEiyn41/nkua7CnSQAAAUMkgAAFDBIAgBQwCAJAEBBm4k70W4EfkeP+++/v+WbREkxfhWYOkkqkrRly5ZKuU5wuY66O374uihxyCdHRMfxP9toV45ly5ZldVHCSKtzjALnfmeSgw8+uOVx+zK/IlK0k4JP2oh2f/HJNI899ljWJkqu8ceO2vi+6BM/pDwBCTvHJ7xECTA+4WTjxo1ZmyhxxSd5RW18ws/QoUOzNv6aGV3rotV8fFJOxLcZPXp01sav8hT97kR1foyYNGlSy9dFP6OdxZ0kAAAFDJIAABQwSAIAUNBmTHLBggW16lo54IADsjofr4nij1G80T9Pr7MIQJ3YYp027X1dNKnbx6IeeeSRrE20mIJfBGDevHlZm+hn6fmfm49/oKrO7jM+PhJ9D36ydfT9RXxcK+pTPh4T/W5Eu82g/fz3WSeOV/c78HHCKEfBxyDrTMqvc45SHCf0fNx7xYoVLY8TLbgQXev89T/K//C76kTvv7O4kwQAoIBBEgCAAgZJAAAKGCQBAChoMzI7cuTIrM7v8FGHD65K0rp16yrlaJJ8FKj1yRBRco9PaqiTABQl4LSXP+8oUO13cRgxYkTWZtq0aS2Pfe+992ZtpkyZUilHk9p94tAXvvCFrM3HP/7xrK6v8pO2o10bfL+Lvne/M0h0HD/5WsqTL6KkCv/+0SISJO50rMGDB1fKUVKM/66iNtEOF75d1C/8jkObN2/O2vjFBKKkr6iv1kmK9IsnRO8/efLkSjlaOGXUqFFZne+/0bGjxRs6GneSAAAUMEgCAFDAIAkAQEGbMUn/LFnKF1aOJlm/4Q1vqJSjeKOPbR5yyCH5yQVxlzoTTOu08THIugul+9dFz+39sXzcQMoXOY7iBA899FBW5+OU0YR1/+w+en8fE7744ouzNsQkt/OTtKM+7X8XojbR4tZetHi5j09FcX7fN6OJ5Z2xAHRf5n//fPxPymNr0fWozjUyil/XyaXwE/6ja110rfXHjj6bj2X6mLuUX3+ixdSjmKg/p+gco59lR+NOEgCAAgZJAAAKGCQBAChgkAQAoKDNxB2/M7SUB5ijxB2f8HH33XdnbXwwO9pROwpK+/eP2vhjR0kxdRJwInV2AfGiY/ufWxSAjhJu/GTwKIHDJ35E7+9/jvvtt1/WBttFSROenxAeTeaPEhS86HfKJ9y0N9ECHcvvAhLxiTJ+AQIpTqjyv+tRf/KJMlEb31ei94quEb4uOm//exElID3xxBOV8sEHH5y1qbMIQ91rdEfjThIAgAIGSQAAChgkAQAoaDMmGS2GPGbMmEo5Wjzbx8nuuOOOrI2PgUWL3k6aNCmr88+l60zmj57B13lOHz0nr7MIQRQXaPX+0eeIjuMXCohimb7u8ccfz9ocdthhlXIUB0NZnUnMUfyxTpwwijf6/hlN2vai+BA6ls93iPIf/O9W1C+i/rR27dpKOcpR8NeN6Jrl1V3M3F/roj7nXxd9Dj9GLFq0KGvjN2WQ8s9b9xrZ0biTBACggEESAIACBkkAAAoYJAEAKGgzcSfascAn00SBU794wF577ZW18ckkxxxzTNYmOrYPjNeZ4FpnEmqUUFFnEYIocafObgw+wB6tjB8lcPhdB5YtW5a1mTBhQqU8c+bMrM2cOXMq5WgXFmznv686k6+jvhHt8OFFO7DXScjwWEyg8/nkqOia4XfhiL6XRx55JKubPn16pRz1C9/n/IIWkShxJrpG+STNKLkvOidv6tSplfKvfvWrrM3ee++d1fkEoyjhKLpudjTuJAEAKGCQBACggEESAIACBkkAAAraTNyJgtA+4ebzn/981ubee++tlKOVc7785S9Xym9+85uzNm9961uzOr9ST7Q7w+67714p11nBJFqdJAoU11mZxiccLV++vOVropUqosQPv+rFxIkTszZLly6tlB988MGszdVXX10pf/rTn255jn1ZnZVV6iT31Ek0iBI7/Pfu+7iU95eoDTqW370o+p31K5BFSTL+91GSTj/99Jav80mCK1asyNrUWaUsWk3Hr4LmE5AkaeTIkZVy9Hvhf0ZRklKUJOr7b3TedRKVdhZ3kgAAFDBIAgBQwCAJAEBBmzHJiI8BXnXVVVmb9evXV8pz587N2owdO7ZSPuuss7I2F1xwQctj11m93r9Gylemj+JAq1atyuoeffTRSnnBggUt37/OOfpySZ1V9/2E9REjRmRtbrjhhko5iv9iOx+fjmIh/ruoEzdsr6hP+feL4vXoWD5OF8X2fBx69erVWZsNGzZkdb6PRdcIf92qs3BKFDeM8jbqxLT9NTLK4/DX2jqLxETtonjrqFGjWp7jzuJOEgCAAgZJAAAKGCQBAChgkAQAoOAlJ+54jz32WMs2K1euzOq++93vVsp+4qokfetb38rq6iS4+An/fucMKU8ciiazRkHhm266qVKOdjjxyTz3339/1sZPDK6z44gkHXbYYZVyFAT3wftbb701a+OD6b/5zW+yNthu4MCBlXKUMOUTZaLJ1+1N3ImSrzz/vUfJGOhYPrkmSgD0v8d+lx5Juu2221rWRQtR1FnAwreJrhkRf97t2YmmrmHDhmV1PikoSooaN25cZ53S/487SQAAChgkAQAoYJAEAKCgzaBF3UmfrXzuc5/L6t71rndVyrfcckutY/l4YxQD8IuVR5N3fZw0iltOnjw5q/vkJz9ZKfvYnpRPqI0WRfc/27o/1+uvv75SjmJj/v2iScGDBg2qlOfNm1fr/fsqH+vxC/1L9WI2Uey7joceeqhSjuKd/tidGUNCg1+4I8pR2Lx5c4e8V53vs07ORnuu4Z3tiSeeyOp83kiUt9IVC2ZwJwkAQAGDJAAABQySAAAUMEgCAFDQZuJOnQBvneSe//mf/8naXHvttZXyZz/72azNkUcemdX5Sd0+SUfKE1WixQx8ws26deuyNlEQ/Gc/+1mlHO0wgl2Pn9h81FFHZW1+8YtfVMpR36y724t3yCGHVMoPP/xw1sb/bixfvrxd74X6/LXt6KOPztr45MK6SYpetJhAVNceHZXkFR2nTpLiddddl9VdcskllfLEiROzNldfffVLPcWXjDtJAAAKGCQBAChgkAQAoGCnV0CuE7eMJoHWeZY+Z86cdp0T0NHuvffeSvnCCy/M2vjd5aMJ/7/61a/a9f6f+MQnKuXx48dnbXzs3cfP0fH8Zgbf//73szZ+oZK777671rHrxPJ6w4IRdc5x0aJFWZ1fYCBalCFaqKWjcScJAEABgyQAAAUMkgAAFDBIAgBQYL0h8AsAQHfgThIAgAIGSQAAChgkAQAoYJAEAKCAQRIAgAIGSQAACv4f+Sat+l8mvZUAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 576x576 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "figure = plt.figure(figsize=(8, 8))\n",
        "cols, rows = 3, 3\n",
        "for i in range(1, cols * rows + 1):\n",
        "    sample_idx = torch.randint(len(train_data), size=(1,)).item()\n",
        "    img, label = train_data[sample_idx]\n",
        "    figure.add_subplot(rows, cols, i)\n",
        "    plt.title(labels_map[label])\n",
        "    plt.axis(\"off\")\n",
        "    plt.imshow(img.squeeze(), cmap=\"gray\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-eN-7oigczc"
      },
      "source": [
        "### <font color = 'red'> 3. Preparing your data with DataLoaders </font>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ahQyUa7GbBsy"
      },
      "outputs": [],
      "source": [
        "batch_size = 64\n",
        "train_dataloader = DataLoader(train_data, batch_size=batch_size)\n",
        "test_dataloader = DataLoader(test_data, batch_size = batch_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q0uHsAh5gvQ-"
      },
      "source": [
        "### <font color = 'red'> 4. Iterate through the DataLoader </font>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "GUjegpLKg3bd",
        "outputId": "20ee10eb-54ac-4e03-c94c-6bb0d89ef830"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Feature batch shape: torch.Size([64, 1, 28, 28])\n",
            "Labels batch shape: torch.Size([64])\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Label: 0\n"
          ]
        }
      ],
      "source": [
        "# Display image and label.\n",
        "train_features, train_labels = next(iter(train_dataloader))\n",
        "print(f\"Feature batch shape: {train_features.size()}\")\n",
        "print(f\"Labels batch shape: {train_labels.size()}\")\n",
        "img = train_features[1].squeeze()\n",
        "label = train_labels[1]\n",
        "plt.imshow(img, cmap=\"gray\")\n",
        "plt.show()\n",
        "print(f\"Label: {label}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08hFRwSqb83H",
        "outputId": "62a93857-4915-4fc1-bf4a-38eb968b78bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total training btaches are :  938\n"
          ]
        }
      ],
      "source": [
        "train_batch = 0\n",
        "for X, y in train_dataloader:\n",
        "  #print('Shape of X is :', X.shape)\n",
        "  #print('Shape of y is :', y.shape)\n",
        "  train_batch = train_batch + 1\n",
        "\n",
        "print(\"Total training btaches are : \", train_batch)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tWHnuh5PdFs7",
        "outputId": "b1e5d0ab-cb82-4106-c511-8b95f69060c6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total testing btaches are :  157\n"
          ]
        }
      ],
      "source": [
        "test_batch = 0\n",
        "for X, y in test_dataloader:\n",
        "  #print('Shape of X is :', X.shape)\n",
        "  #print('Shape of y is :', y.shape)\n",
        "  test_batch = test_batch + 1\n",
        "\n",
        "print(\"Total testing btaches are : \", test_batch)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gu7cJCxahOFG"
      },
      "source": [
        "## Note: For detailed reading of Dataset and Dataloader:\n",
        "### https://pytorch.org/docs/stable/data.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9EyrS3d6iYbo"
      },
      "source": [
        "## <font color='red'> **Transform** </font>\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejwT7UnpilaR"
      },
      "source": [
        "### Data does not always come in its final processed form that is required for training machine learning algorithms. We use transforms to perform some manipulation of the data and make it suitable for training.\n",
        "\n",
        "### All TorchVision datasets have two parameters -transform to modify the features and target_transform to modify the labels.\n",
        "\n",
        "### The FashionMNIST features are in PIL Image format, and the labels are integers. For training, we need the features as normalized tensors, and the labels as one-hot encoded tensors. To make these transformations, we use ToTensor and Lambda."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x0O5MtmD2jjh"
      },
      "outputs": [],
      "source": [
        "# Lambda transforms apply any user-defined lambda function.\n",
        "# Here, we define a function to turn the integer into a one-hot encoded tensor.\n",
        "# It first creates a zero tensor of size 10 (the number of labels in our dataset)\n",
        "# and calls scatter_ which assigns a value=1 on the index as given by the label y."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G1J8Goq6i96E"
      },
      "outputs": [],
      "source": [
        "# https://pytorch.org/docs/stable/generated/torch.Tensor.scatter_.html\n",
        "\n",
        "from torchvision.transforms import ToTensor, Lambda\n",
        "\n",
        "ds = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=ToTensor(),\n",
        "    target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1))\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RLTZdTQ4jsCU"
      },
      "source": [
        "# **Thank You!**"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.15"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}