{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "019601fb-6048-a735-282c-7433fa4a3eb1"
      },
      "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": "2b698a47-d137-c4a0-70e5-bf153a8b2faa"
      },
      "outputs": [],
      "source": [
        "import keras, theano"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "8e6e1d6a-be53-8ba0-f292-3c7632eb3d42"
      },
      "outputs": [],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from matplotlib import image as mpimg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "add9d20f-7e17-a8b8-a606-e9b48337e9ed"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "a2bb7750-c57f-3403-370c-ec991d86f1f1"
      },
      "outputs": [],
      "source": [
        "from glob import glob"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "a20070c1-30f7-4017-4ffd-34c89651e437"
      },
      "outputs": [],
      "source": [
        "from random import choice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "73ec5f5f-d5d8-a196-4457-3e6e3045325e"
      },
      "outputs": [],
      "source": [
        "files = glob('../input/train/c9/*.*')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "f6736631-6d64-a0dc-c4e6-e1d86b733142"
      },
      "outputs": [],
      "source": [
        "plt.imshow(mpimg.imread(choice(files)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "7c341049-ea7c-1b19-4a86-98c61b75a456"
      },
      "outputs": [],
      "source": [
        "plt.imshow?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "da08bb8e-f557-cba7-48be-57ef97152eee"
      },
      "outputs": [],
      "source": ""
    }
  ],
  "metadata": {
    "_change_revision": 0,
    "_is_fork": false,
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "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": "3.5.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}