{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "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\nimport numpy as np # linear algebra\nimport 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\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n\n# Any results you write to the current directory are saved as output."
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "import tensorflow as tf"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "# settings\nLEARNING_RATE = 1e-4\n# set to 20000 on local environment to get 0.99 accuracy\nTRAINING_ITERATIONS = 2500        \n    \nDROPOUT = 0.5\nBATCH_SIZE = 50\n\n# set to 0 to train on all available data\nVALIDATION_SIZE = 2000\n\n# image number to output\nIMAGE_TO_DISPLAY = 10"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "# read training data from CSV file \ndata = pd.read_csv('../input/train.csv')\n\nprint('data({0[0]},{0[1]})'.format(data.shape))\nprint (data.head())\ndata.head()"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "images = data.iloc[:,1:].values\nimages = images.astype(np.float)\n\n# convert from [0:255] => [0.0:1.0]\nimages = np.multiply(images, 1.0 / 255.0)\n\nprint('images({0[0]},{0[1]})'.format(images.shape))"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": ""
 }
],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}}, "nbformat": 4, "nbformat_minor": 0}