AutoDiff & dataloader_Creation.ipynb

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        "\n",
        "\n",
        "**Automatic differentiation**"
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        "id": "NSAL87AhcaDB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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            "text/plain": [
              "                    Name             Sport     Nationality  Age  Wt kg     Ht\n",
              "0         ABBAS Muhammad     Alpine Skiing        Pakistan   23   55.0  168.0\n",
              "1          ABBOTT Jeremy    Figure Skating   United States   24    NaN  175.0\n",
              "2           ABLAEV Enver  Freestyle Skiing         Ukraine   30   68.0  169.0\n",
              "3     ABRAMASHVILI Iason     Alpine Skiing         Georgia   21   82.0  176.0\n",
              "4       ABRAMENKO Evgeny          Biathlon         Belarus   22   81.0  180.0\n",
              "...                  ...               ...             ...  ...    ...    ...\n",
              "2601         ZUEV Stepan     Alpine Skiing          Russia   21   90.0  189.0\n",
              "2602   ZURBRIGGEN Silvan     Alpine Skiing     Switzerland   28   94.0  185.0\n",
              "2603   ZUZULOVA Veronika     Alpine Skiing        Slovakia   25   72.0  172.0\n",
              "2604  ZVARICOVA Veronika          Biathlon  Czech Republic   21   62.0  176.0\n",
              "2605  ZVEJNIEKS Kristaps     Alpine Skiing          Latvia   17   87.0  185.0\n",
              "\n",
              "[2606 rows x 6 columns]"
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              "      <th>Name</th>\n",
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              "      <td>ABBAS Muhammad</td>\n",
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              "      <th>1</th>\n",
              "      <td>ABBOTT Jeremy</td>\n",
              "      <td>Figure Skating</td>\n",
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              "      <th>3</th>\n",
              "      <td>ABRAMASHVILI Iason</td>\n",
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              "      <th>2601</th>\n",
              "      <td>ZUEV Stepan</td>\n",
              "      <td>Alpine Skiing</td>\n",
              "      <td>Russia</td>\n",
              "      <td>21</td>\n",
              "      <td>90.0</td>\n",
              "      <td>189.0</td>\n",
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              "    <tr>\n",
              "      <th>2602</th>\n",
              "      <td>ZURBRIGGEN Silvan</td>\n",
              "      <td>Alpine Skiing</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>28</td>\n",
              "      <td>94.0</td>\n",
              "      <td>185.0</td>\n",
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              "    <tr>\n",
              "      <th>2603</th>\n",
              "      <td>ZUZULOVA Veronika</td>\n",
              "      <td>Alpine Skiing</td>\n",
              "      <td>Slovakia</td>\n",
              "      <td>25</td>\n",
              "      <td>72.0</td>\n",
              "      <td>172.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2604</th>\n",
              "      <td>ZVARICOVA Veronika</td>\n",
              "      <td>Biathlon</td>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>21</td>\n",
              "      <td>62.0</td>\n",
              "      <td>176.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2605</th>\n",
              "      <td>ZVEJNIEKS Kristaps</td>\n",
              "      <td>Alpine Skiing</td>\n",
              "      <td>Latvia</td>\n",
              "      <td>17</td>\n",
              "      <td>87.0</td>\n",
              "      <td>185.0</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "data = pd.read_csv('sampledata.csv',encoding=\"ISO-8859-1\")\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "9ng0rqcLKkXu"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "def describe_tensor(tensor, Name= '', Age=''):\n",
        "  print('name: ', Name)\n",
        "  print('age', 20)\n",
        "  print('data : ', tensor.data)\n",
        "  print('requires_grad : ', tensor.requires_grad)\n",
        "  print('grad: ', tensor.grad)\n",
        "  print('grad_fn: ', tensor.grad_fn)\n",
        "  print('is_leaf: ', tensor.is_leaf)\n",
        "  print(torch.tensor(2))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a tensor x\n",
        "x = torch.tensor(1.0)\n",
        "describe_tensor(x, Name='ABBOTT Jeremy')"
      ],
      "metadata": {
        "id": "1E0j6KSCZ4OO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a14a391-a3aa-4cea-ce09-0e213fc488d6"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "name:  ABBOTT Jeremy\n",
            "age 20\n",
            "data :  tensor(1.)\n",
            "requires_grad :  False\n",
            "grad:  None\n",
            "grad_fn:  None\n",
            "is_leaf:  True\n",
            "tensor(2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a tensor y\n",
        "y = torch.tensor(2.0)\n",
        "describe_tensor(y, Name ='ABLAEV Enver')"
      ],
      "metadata": {
        "id": "zKj7-n-6Z67Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9756614-ceec-4600-f43c-3faf334763db"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "name:  ABLAEV Enver\n",
            "age 20\n",
            "data :  tensor(2.)\n",
            "requires_grad :  False\n",
            "grad:  None\n",
            "grad_fn:  None\n",
            "is_leaf:  True\n",
            "tensor(2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create z as the multiplicative outcome of x * y\n",
        "z = x * y\n",
        "describe_tensor(z, Name='ZUEV Stepan')\n",
        "z.type"
      ],
      "metadata": {
        "id": "2xyD2kPnbaA-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e9d8eb8-d4ca-412e-99ea-7e8fe9fe74ec"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "name:  ZUEV Stepan\n",
            "age 20\n",
            "data :  tensor(2.)\n",
            "requires_grad :  False\n",
            "grad:  None\n",
            "grad_fn:  None\n",
            "is_leaf:  True\n",
            "tensor(2)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function Tensor.type>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make x track gradients\n",
        "x = torch.tensor(1.0, requires_grad=True)\n",
        "z = x * y\n",
        "describe_tensor(z)"
      ],
      "metadata": {
        "id": "EBQM2GAKbsJY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fc0a3c0-1209-4179-fae0-6a56d4bad0be"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "name:  \n",
            "age 20\n",
            "data :  tensor(2.)\n",
            "requires_grad :  True\n",
            "grad:  None\n",
            "grad_fn:  <MulBackward0 object at 0x7f634021e6a0>\n",
            "is_leaf:  False\n",
            "tensor(2)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-8deacd0b3767>:7: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:480.)\n",
            "  print('grad: ', tensor.grad)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# call .backward() now on z\n",
        "z.backward()\n",
        "\n",
        "# Now describe each tensor x, y and z\n",
        "describe_tensor(x, 'x')\n",
        "describe_tensor(y, 'y')\n",
        "describe_tensor(z, 'z')"
      ],
      "metadata": {
        "id": "6iA_ND1ZbzmY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e2b935a-824f-4e96-b5fe-01ea4e369e91"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "name:  x\n",
            "age 20\n",
            "data :  tensor(1.)\n",
            "requires_grad :  True\n",
            "grad:  tensor(2.)\n",
            "grad_fn:  None\n",
            "is_leaf:  True\n",
            "tensor(2)\n",
            "name:  y\n",
            "age 20\n",
            "data :  tensor(2.)\n",
            "requires_grad :  False\n",
            "grad:  None\n",
            "grad_fn:  None\n",
            "is_leaf:  True\n",
            "tensor(2)\n",
            "name:  z\n",
            "age 20\n",
            "data :  tensor(2.)\n",
            "requires_grad :  True\n",
            "grad:  None\n",
            "grad_fn:  <MulBackward0 object at 0x7f62c74b4550>\n",
            "is_leaf:  False\n",
            "tensor(2)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-8deacd0b3767>:7: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:480.)\n",
            "  print('grad: ', tensor.grad)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xqe53vTscDp7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.ones(3, requires_grad=True)\n",
        "y = 3 * torch.ones(3)\n",
        "z = x * y\n",
        "describe_tensor(x, 'x')\n",
        "describe_tensor(y, 'y')\n",
        "describe_tensor(z, 'z')\n",
        "# Backprop with the explicit gradient\n",
        "z.backward(torch.ones(3))\n",
        "print('AFTER BACKWARD:')\n",
        "describe_tensor(x, 'x') # now this should show a grad value of [3, 3, 3]"
      ],
      "metadata": {
        "id": "pJYrnuSLcD0l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b322afa-ac40-4a90-855b-186db9c6455a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "name:  x\n",
            "age 20\n",
            "data :  tensor([1., 1., 1.])\n",
            "requires_grad :  True\n",
            "grad:  None\n",
            "grad_fn:  None\n",
            "is_leaf:  True\n",
            "tensor(2)\n",
            "name:  y\n",
            "age 20\n",
            "data :  tensor([3., 3., 3.])\n",
            "requires_grad :  False\n",
            "grad:  None\n",
            "grad_fn:  None\n",
            "is_leaf:  True\n",
            "tensor(2)\n",
            "name:  z\n",
            "age 20\n",
            "data :  tensor([3., 3., 3.])\n",
            "requires_grad :  True\n",
            "grad:  None\n",
            "grad_fn:  <MulBackward0 object at 0x7f62c74b4c40>\n",
            "is_leaf:  False\n",
            "tensor(2)\n",
            "AFTER BACKWARD:\n",
            "name:  x\n",
            "age 20\n",
            "data :  tensor([1., 1., 1.])\n",
            "requires_grad :  True\n",
            "grad:  tensor([3., 3., 3.])\n",
            "grad_fn:  None\n",
            "is_leaf:  True\n",
            "tensor(2)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-8deacd0b3767>:7: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:480.)\n",
            "  print('grad: ', tensor.grad)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eH_bxrRIdzIQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Dataset creation**"
      ],
      "metadata": {
        "id": "LRGoHoWZdr0T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch  \n",
        "import numpy as np  \n",
        "import matplotlib.pyplot as plt  \n",
        "from sklearn import datasets  "
      ],
      "metadata": {
        "id": "dQJKXiBLdwKw"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create a dataset by using make_blobs()\n",
        "no_of_points=50\n",
        "datasets.make_blobs()  "
      ],
      "metadata": {
        "id": "2JqfIjVBeFzo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa404c28-10ba-4eeb-f5c9-070add4b5c78"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[  1.51222507,  11.20404835],\n",
              "        [ -1.71999602,  -2.85887577],\n",
              "        [ -9.78984744,   0.03248528],\n",
              "        [  2.14256305,   9.09864109],\n",
              "        [ -2.64228183,  -3.26118604],\n",
              "        [  0.5642818 ,   9.11575158],\n",
              "        [  2.45900045,  10.25627788],\n",
              "        [  0.06422168,  -3.07646073],\n",
              "        [ -9.47730686,  -0.26690987],\n",
              "        [ -7.08093613,   2.42798222],\n",
              "        [ -7.7573169 ,  -1.16182295],\n",
              "        [ -7.70699179,  -0.05124118],\n",
              "        [ -0.05615395,   9.3088615 ],\n",
              "        [  3.63202578,  11.56172199],\n",
              "        [ -6.08585635,   0.64815502],\n",
              "        [  2.09880349,   9.26215564],\n",
              "        [ -1.64739784,  -3.12284713],\n",
              "        [ -8.01427941,  -1.77456852],\n",
              "        [ -7.5436547 ,  -2.49818178],\n",
              "        [ -7.41500915,   0.05625307],\n",
              "        [ -0.87312105,  -3.56388119],\n",
              "        [  1.55989346,  10.03036672],\n",
              "        [ -7.59344858,   0.56800865],\n",
              "        [ -1.03698723,  -2.60887481],\n",
              "        [  2.86703376,   9.8380161 ],\n",
              "        [ -8.42004061,  -2.03631738],\n",
              "        [  2.51409597,  10.61390109],\n",
              "        [ -3.98186408,  -2.76408046],\n",
              "        [  0.59925597,  10.46900731],\n",
              "        [ -2.07108982,  -2.85971208],\n",
              "        [  1.14008577,   9.44093503],\n",
              "        [ -8.19536426,   1.44209543],\n",
              "        [  1.25829864,  10.29077945],\n",
              "        [ -2.33421787,  -3.82055224],\n",
              "        [ -9.69491013,  -1.04960263],\n",
              "        [ -2.56924108,  -2.53468009],\n",
              "        [ -7.97968161,   0.40998767],\n",
              "        [ -4.01306936,  -3.60990057],\n",
              "        [ -0.70700028,  -3.68581201],\n",
              "        [  2.62660113,   9.00387786],\n",
              "        [  3.03382984,   9.11774535],\n",
              "        [  2.6548617 ,   8.36552726],\n",
              "        [ -2.74345034,  -4.70368428],\n",
              "        [ -1.19437266,  -4.20248761],\n",
              "        [ -7.55016723,   0.78772715],\n",
              "        [  1.59360254,  10.89848238],\n",
              "        [ -8.16772429,   0.19825453],\n",
              "        [ -1.5806599 ,  -1.93140792],\n",
              "        [ -8.07674131,   0.59824078],\n",
              "        [ -3.22010971,  -3.3782405 ],\n",
              "        [ -1.19076931,  -1.95855541],\n",
              "        [ -2.71496916,  -4.70124467],\n",
              "        [ -9.22850903,   0.0892288 ],\n",
              "        [ -3.86347846,  -2.35940228],\n",
              "        [ -8.98427368,  -1.83496987],\n",
              "        [ -7.79856926,  -1.44767526],\n",
              "        [ -2.7004856 ,  -0.53160315],\n",
              "        [ -8.69439116,   0.1122698 ],\n",
              "        [ -4.56971306,  -2.24194813],\n",
              "        [ -2.00921041,  -3.78553569],\n",
              "        [ -2.9272087 ,  -2.72870528],\n",
              "        [ -8.56479224,   0.15222896],\n",
              "        [  0.47486336,   9.06459911],\n",
              "        [ -9.29871506,  -1.52927092],\n",
              "        [  2.22113638,   9.28440404],\n",
              "        [ -3.77888099,  -2.84151086],\n",
              "        [ -1.10445188,  -3.7196501 ],\n",
              "        [  3.38297886,  10.63401776],\n",
              "        [ -2.0004098 ,  -4.74744909],\n",
              "        [  2.03210696,   7.67734122],\n",
              "        [ -7.0611119 ,  -0.93628237],\n",
              "        [-10.20306564,  -0.67062375],\n",
              "        [ -2.03425977,  -1.97253482],\n",
              "        [  2.14743676,  10.56750455],\n",
              "        [  1.09280656,  10.78920865],\n",
              "        [  1.18813577,  10.88428969],\n",
              "        [ -8.76498197,   0.20377382],\n",
              "        [ -8.94486803,  -0.18137719],\n",
              "        [  3.02597791,   9.92578433],\n",
              "        [  1.58307854,   9.14713876],\n",
              "        [ -8.50577651,  -2.32749302],\n",
              "        [ -8.74628989,  -0.58181591],\n",
              "        [ -1.57409745,  -2.37502898],\n",
              "        [  1.40321454,   9.98119132],\n",
              "        [ -1.92060886,  -3.43044505],\n",
              "        [ -3.29807439,  -4.61831185],\n",
              "        [ -7.70800561,   0.85774597],\n",
              "        [  1.61320458,   9.34769567],\n",
              "        [ -1.31976945,  -2.66699543],\n",
              "        [  2.97992044,  12.04529268],\n",
              "        [ -1.465766  ,  -2.31375092],\n",
              "        [ -9.20918482,  -1.72690381],\n",
              "        [  1.72069305,  10.62542674],\n",
              "        [ -9.18218458,   0.09256007],\n",
              "        [ -8.25756369,   0.69473572],\n",
              "        [ -9.18414853,  -1.21653343],\n",
              "        [  2.06125506,   9.8675461 ],\n",
              "        [  1.88004481,   9.67542591],\n",
              "        [ -0.9588738 ,  -4.11267669],\n",
              "        [  1.56837459,   7.99077506]]),\n",
              " array([1, 2, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 1, 0, 1, 2, 0, 0, 0, 2, 1,\n",
              "        0, 2, 1, 0, 1, 2, 1, 2, 1, 0, 1, 2, 0, 2, 0, 2, 2, 1, 1, 1, 2, 2,\n",
              "        0, 1, 0, 2, 0, 2, 2, 2, 0, 2, 0, 0, 2, 0, 2, 2, 2, 0, 1, 0, 1, 2,\n",
              "        2, 1, 2, 1, 0, 0, 2, 1, 1, 1, 0, 0, 1, 1, 0, 0, 2, 1, 2, 2, 0, 1,\n",
              "        2, 1, 2, 0, 1, 0, 0, 0, 1, 1, 2, 1]))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a nested list\n",
        "centers=[[-0.5,0.5],[0.5,-0.5]]  \n",
        "centers"
      ],
      "metadata": {
        "id": "LiyHO0P3d0Oy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cb6e129-db20-4d27-fe2b-6c0cbe050aa6"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[-0.5, 0.5], [0.5, -0.5]]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x,y=datasets.make_blobs()  \n",
        "x,y=datasets.make_blobs(n_samples=no_of_points,random_state=123,centers=centers,cluster_std=0.4)  \n"
      ],
      "metadata": {
        "id": "Lof7Jo_De68y"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "id": "u_n-rHBmfJsO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c4f01ab-f0cc-4ade-c43b-bc47393bd399"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.09655585  0.2444392 ]\n",
            " [ 0.6257088  -1.03050618]\n",
            " [-0.67759278  0.32625949]\n",
            " [ 1.06691962 -0.17710539]\n",
            " [-0.93425224  0.89893818]\n",
            " [-0.36456438  0.4952678 ]\n",
            " [ 0.33865358 -0.55041183]\n",
            " [-0.60224775 -0.61943564]\n",
            " [-0.65635992  0.72952234]\n",
            " [ 1.09825765 -0.07224293]\n",
            " [-0.3868086  -0.10251789]\n",
            " [ 0.16499331 -1.1423851 ]\n",
            " [ 1.164381   -0.17707673]\n",
            " [ 0.96488162 -0.93888122]\n",
            " [-1.00155227  0.2448994 ]\n",
            " [ 0.38237203  1.37471444]\n",
            " [-0.82214661 -0.1910678 ]\n",
            " [ 1.33484534 -0.43422351]\n",
            " [-0.85181454  0.61345093]\n",
            " [ 0.0063745   0.15330384]\n",
            " [-1.20861324  0.22004911]\n",
            " [ 0.19091651 -0.18205493]\n",
            " [-0.01763413 -0.91551528]\n",
            " [ 0.37409674 -0.93436096]\n",
            " [-0.55602749  0.15529804]\n",
            " [ 0.96008222 -1.00694082]\n",
            " [-0.13715792 -0.07147228]\n",
            " [ 0.02067954 -0.42019037]\n",
            " [-0.20505257  1.09629281]\n",
            " [ 0.20701521 -0.98500925]\n",
            " [ 0.57241405 -0.02885522]\n",
            " [ 0.85628256  0.20195447]\n",
            " [ 1.19748489 -0.81922509]\n",
            " [-0.87433355  0.97033162]\n",
            " [ 0.06617284 -1.04538862]\n",
            " [ 0.3659957  -0.08755422]\n",
            " [-0.09837844  0.65447456]\n",
            " [ 0.51187329 -0.07227361]\n",
            " [ 1.00209495 -0.77554759]\n",
            " [ 0.68737565 -0.83246199]\n",
            " [-0.49886163  0.77528908]\n",
            " [-0.7314401   1.16057461]\n",
            " [-0.34924014 -0.08410916]\n",
            " [ 0.65176024 -0.65167057]\n",
            " [-0.77155446  0.46211641]\n",
            " [ 0.45694611  0.66516486]\n",
            " [-0.1085056   1.39525734]\n",
            " [-0.12901503  0.43054573]\n",
            " [-1.4706717   0.32843495]\n",
            " [ 0.51819603 -0.59323682]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "id": "9Gt7LVaTffex",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89203ed8-42d0-4509-a02d-2f366899cb38"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 0 1 0 0 1 0 0 1 0 1 1 1 0 0 0 1 0 0 0 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 0\n",
            " 1 1 1 0 0 1 1 0 0 0 0 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot and visualize it using plt.scatter() function.\n",
        "plt.scatter(x[y==0,0],x[y==0,1])  \n"
      ],
      "metadata": {
        "id": "yVoZbKgyflJr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "c056cade-d89e-4679-f323-fd0104d7d350"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f62c5506610>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xdata=torch.Tensor(x)\n",
        "xdata\n",
        "ydata=torch.Tensor(y)\n",
        "ydata  \n"
      ],
      "metadata": {
        "id": "0i6hBfLKf1sA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db95ff41-7001-4973-bc0f-d9cecd0d218f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0., 1., 0., 1., 0., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0., 0., 0., 1.,\n",
              "        0., 0., 0., 1., 1., 1., 0., 1., 0., 1., 0., 1., 1., 1., 1., 0., 1., 1.,\n",
              "        0., 1., 1., 1., 0., 0., 1., 1., 0., 0., 0., 0., 0., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x[y==0,0], x[y==0,1])\n",
        "\n",
        "plt.scatter(x[y==1,0], x[y==1,1])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "XjjCimGVgsJ-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "a2c770ef-a4d0-4575-ce02-03e0a8773401"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BnrlwFtPhNgF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data loader**\n"
      ],
      "metadata": {
        "id": "JiBtjbT-i4e3"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "psTSoYSuhN3e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "training_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=ToTensor()\n",
        ")\n",
        "\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor()\n",
        ")"
      ],
      "metadata": {
        "id": "blMW9iLqhOEE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471,
          "referenced_widgets": [
            "a40dd33a03e34ce1a002b4b23cacb986",
            "512425bccb3345a48d7437f1eda9ae10",
            "bfd6d421d9da43ebbf0fa871d25d3f24",
            "484107679e334fdf88b26db7ac31ebdf",
            "e2c29993252c4fc4b3dfdeaab57a0698",
            "49f88fae4a86490d805f5bd58d642c19",
            "b5b4805543294fac8d4caa9a4cb666f5",
            "1817ae43b70f4d0097b4edb757899c4d",
            "6d105c1333084017875271efde75ceb1",
            "1ba7bdc96e58402a865157850a54df9d",
            "0095266e0e094f2a92dc4f474a3040bf",
            "15ff7d689fd942e7833b9e069f8cc71c",
            "16bdfee6777d4a84b9b32a9572710760",
            "2a7d3b8067454f469e3c1526e78e2d31",
            "0c866251a7f04ad581e5981d946343d5",
            "d41adc3887834d4292ecc312e84e0e1a",
            "ec9572d754aa4cfa9ee77eb61e8d2d4d",
            "7e4b2d9e141449289c8710dcf17c8333",
            "f6e0ffb668034c169090f962c1bfb345",
            "c34813a3a11e45469c7c7543ae5b9499",
            "3d363fa249c348bb88db5741d1e6dd17",
            "0d533ceda12d4d4e86243624e9b2a1d8",
            "2552331eff8b490cbb269cd3942d7eef",
            "49c68f407cd04bf8b3c54e4496659bcc",
            "df0e3478836f46ca8dd07e8cf9642d42",
            "c90c67733f64440993967712da1ff354",
            "3f30fffcb7ee4e3c8b7f0fa3d03b1408",
            "592a3565b4cd443caf6eccebe958e2d7",
            "93802ac78dab4fee90fabdc7cb63c53c",
            "3012cab65f3c4460a9de375918206cba",
            "c5948005e7ee4d4196e014c887c5d95b",
            "2c35a50b283941f2b6c9b4d878145a7b",
            "45fdf1debe68481bba2a80ffe7c32546",
            "a948871447754b96a247cceaa40d4b32",
            "9977d614121f4bcbb6b4f361f13bf3e4",
            "9b334f1cea634321b11cb99a58c7eb7b",
            "877cfecb2e82479dbce3fa25c85a01ab",
            "8840890e8a66428788fa3fef065630d7",
            "ec6bdb7ed8144615aee4acc599f70545",
            "01e02988565a476fa2137325a6105cc4",
            "80fbc1d15fa24cce8a94d7dd7fa396d3",
            "20af2524e8144344aab58239c09206ed",
            "d3a66b88f12e44a9964caabe424770ba",
            "671063b01ecb42a59264a69bdcdd43c3"
          ]
        },
        "outputId": "c4d0aa2e-d701-4df7-e925-28b707608373"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/26421880 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a40dd33a03e34ce1a002b4b23cacb986"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/29515 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15ff7d689fd942e7833b9e069f8cc71c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4422102 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2552331eff8b490cbb269cd3942d7eef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5148 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a948871447754b96a247cceaa40d4b32"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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: \"Boot\",\n",
        "}\n",
        "figure = plt.figure(figsize=(7, 7))\n",
        "cols, rows = 4, 4\n",
        "for i in range(1, cols * rows + 1):\n",
        "    sample_idx = torch.randint(len(training_data), size=(1,)).item()\n",
        "    img, label = training_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()"
      ],
      "metadata": {
        "id": "H64upLHNhj4v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "aa9a00c2-9a7a-4ae5-81cf-32f9cbe68f13"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Preparing your data for training with DataLoaders\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(training_data, batch_size=64, shuffle=True)\n",
        "test_dataloader = DataLoader(test_data, batch_size=64, shuffle=True)\n",
        "\n",
        "# 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[0].squeeze()\n",
        "label = train_labels[0]\n",
        "plt.imshow(img, cmap=\"gray\")\n",
        "plt.show()\n",
        "print(f\"Label: {label}\")"
      ],
      "metadata": {
        "id": "Uc82QUUviTvV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "0f8e5355-dbb2-4395-8c0c-04ca44b3d5b8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature batch shape: torch.Size([64, 1, 28, 28])\n",
            "Labels batch shape: torch.Size([64])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: 3\n"
          ]
        }
      ]
    }
  ]
}