{
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "953bb715-1ddf-bf72-03c5-521a472bd60b"
      },
      "outputs": [],
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load in \n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
        "\n",
        "from subprocess import check_output\n",
        "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
        "\n",
        "# Any results you write to the current directory are saved as output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "a3212e28-7c81-f2bf-f45d-ab4e5d105d13"
      },
      "outputs": [],
      "source": [
        "testDF = pd.read_csv('../input/test.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "ef443a42-d724-32b9-b8f1-2da622df03f6"
      },
      "outputs": [],
      "source": [
        "X_test1 = testDF.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "304120af-8b3d-46c6-d68e-57c116ce01e6"
      },
      "outputs": [],
      "source": [
        "X_test = X_test1.reshape(28000,28,28)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "d7d15a52-9aff-e54c-d54f-9e292327d69e"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "9e26595f-17bc-0079-cf8b-e29a130f2f09"
      },
      "outputs": [],
      "source": [
        "plt.imshow(X_test[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "e51b3628-8a87-6bb5-bf03-e6a5051b35fd"
      },
      "outputs": [],
      "source": [
        "plt.imshow(X_test[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "fed14c3b-be0d-4852-d1f1-f1887b394d53"
      },
      "outputs": [],
      "source": [
        "plt.imshow(X_test[2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "a990f985-5950-a60c-632a-5dc9dedc1e18"
      },
      "outputs": [],
      "source": [
        "plt.imshow(X_test[3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "dd2cbfb9-db37-a3a5-f320-b74f30f9db24"
      },
      "outputs": [],
      "source": [
        "plt.imshow(X_test[4])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "915a0bd9-db63-e272-83eb-4822782aeecf"
      },
      "outputs": [],
      "source": [
        "plt.imshow(X_test[5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "94684904-9581-28dd-2e41-d4f52e85eaca"
      },
      "outputs": [],
      "source": ""
    }
  ],
  "metadata": {
    "_change_revision": 0,
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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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      },
      "file_extension": ".py",
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