{
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      "metadata": {
        "_cell_guid": "ca583c83-4d7a-d8ae-f6cd-e3a57aa8d52e"
      },
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      "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": "6caafa96-5c9e-52bf-fcef-5f6285c6843a"
      },
      "outputs": [],
      "source": [
        "# create the training & test sets, skipping the header row with [1:]\n",
        "train = pd.read_csv(\"../input/train.csv\")\n",
        "#train.head()\n",
        "\n",
        "test_images = (pd.read_csv(\"../input/test.csv\").values).astype('float32')\n",
        "train_images = (train.ix[:,1:].values).astype('float32')\n",
        "train_labels = train.ix[:,0].values.astype('int32')\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "8cbb3bca-3c60-343c-a8f7-7f8538864b45"
      },
      "outputs": [],
      "source": [
        "train_images.shape"
      ]
    }
  ],
  "metadata": {
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