Mnist_dataset.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import torch\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data\n",
        "from torch.autograd import Variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "k-CkLPVbLDqw"
      },
      "outputs": [],
      "source": [
        "train=  pd.read_csv('/content/sample_data/mnist_train_small.csv')\n",
        "test=  pd.read_csv('/content/sample_data/mnist_test.csv')"
      ]
    },
    {
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        "train.drop(train.columns[0],axis=1,inplace=True)"
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      "metadata": {
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        "train = train.values"
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      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(train,y_train , random_state=42,  test_size=0.25,  shuffle=True)"
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    {
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      "execution_count": 9,
      "metadata": {
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      "source": [
        "BATCH_SIZE = 32\n",
        "\n",
        "torch_X_train = torch.from_numpy(X_train).type(torch.LongTensor)\n",
        "torch_y_train = torch.from_numpy(y_train).type(torch.LongTensor) # data type is long\n",
        "\n",
        "# create feature and targets tensor for test set.\n",
        "torch_X_test = torch.from_numpy(X_test).type(torch.LongTensor)\n",
        "torch_y_test = torch.from_numpy(y_test).type(torch.LongTensor) # data type is long\n",
        "\n",
        "# Pytorch train and test sets\n",
        "train = torch.utils.data.TensorDataset(torch_X_train,torch_y_train)\n",
        "test = torch.utils.data.TensorDataset(torch_X_test,torch_y_test)\n",
        "\n",
        "# data loader\n",
        "train_loader = torch.utils.data.DataLoader(train, batch_size = BATCH_SIZE, shuffle = False)\n",
        "test_loader = torch.utils.data.DataLoader(test, batch_size = BATCH_SIZE, shuffle = False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x,y = train[0]\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8RHPziYQQDDA",
        "outputId": "d5af46f3-1b03-4811-b232-cb81dd087118"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
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              "          0,   0,   0,   0,   0,   0,  17,  47, 213, 253, 253, 253, 253, 254,\n",
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              "          0,   0,   0,   0,   0,  26,  59,   0,   0,   0,   0,   0,   0,   5,\n",
              "         68, 229, 236,  60,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  32, 139,\n",
              "        253, 253, 228, 185, 123,  26,   0,   0,   0,   0,   0,   0,   0,   0,\n",
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              "          0,   0,   0,   0,   0,   0,   0,   0,  11, 194, 253, 253, 253, 254,\n",
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              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,  24, 253, 253,  56,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,  89, 223, 254,  76,   0,\n",
              "          0,   0,   0,  64, 203, 254, 122,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0, 162, 253, 173,   8,   0,\n",
              "          0,   0, 133, 204, 254, 224,  56,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0, 130, 253, 197,  80,  70,\n",
              "         70, 102, 241, 253, 206,  25,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   9, 155, 253, 253, 254,\n",
              "        253, 253, 177, 109,  21,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   7,  65, 243, 254,\n",
              "        232, 137,  25,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "zv0NLQNBLHDK",
        "outputId": "15b8758b-3725-4e6d-ab59-77e8efaf6889"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x78d92083b610>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.imshow(x.reshape(28,28))\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNAjFyzHfstBhh2TK4MjBV/",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}