Recurrent+Neural+Network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('Google_Stock_Price_Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/3/2012</td>\n",
       "      <td>325.25</td>\n",
       "      <td>332.83</td>\n",
       "      <td>324.97</td>\n",
       "      <td>663.59</td>\n",
       "      <td>7,380,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2012</td>\n",
       "      <td>331.27</td>\n",
       "      <td>333.87</td>\n",
       "      <td>329.08</td>\n",
       "      <td>666.45</td>\n",
       "      <td>5,749,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/5/2012</td>\n",
       "      <td>329.83</td>\n",
       "      <td>330.75</td>\n",
       "      <td>326.89</td>\n",
       "      <td>657.21</td>\n",
       "      <td>6,590,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/6/2012</td>\n",
       "      <td>328.34</td>\n",
       "      <td>328.77</td>\n",
       "      <td>323.68</td>\n",
       "      <td>648.24</td>\n",
       "      <td>5,405,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/9/2012</td>\n",
       "      <td>322.04</td>\n",
       "      <td>322.29</td>\n",
       "      <td>309.46</td>\n",
       "      <td>620.76</td>\n",
       "      <td>11,688,800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date    Open    High     Low   Close      Volume\n",
       "0  1/3/2012  325.25  332.83  324.97  663.59   7,380,500\n",
       "1  1/4/2012  331.27  333.87  329.08  666.45   5,749,400\n",
       "2  1/5/2012  329.83  330.75  326.89  657.21   6,590,300\n",
       "3  1/6/2012  328.34  328.77  323.68  648.24   5,405,900\n",
       "4  1/9/2012  322.04  322.29  309.46  620.76  11,688,800"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_set=train.iloc[:,1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 325.25],\n",
       "       [ 331.27],\n",
       "       [ 329.83],\n",
       "       ..., \n",
       "       [ 793.7 ],\n",
       "       [ 783.33],\n",
       "       [ 782.75]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sc = MinMaxScaler(feature_range = (0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1258, 1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train=[]\n",
    "y_train=[]\n",
    "for i in range(60,1258):\n",
    "    X_train.append(training_set_scaled[i-60:i,0])\n",
    "    y_train.append(training_set_scaled[i,0])\n",
    "X_train, y_train = np.array(X_train), np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#reshape it to (batche_size(#size of inputs),timesteps,input_dimension)\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1198, 60, 1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regressor=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regressor.add(LSTM(units=100,return_sequences=True,input_shape=(X_train.shape[1], 1)))\n",
    "regressor.add(Dropout(0.4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regressor.add(LSTM(units=100,return_sequences=True))\n",
    "regressor.add(Dropout(0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regressor.add(LSTM(units=100,return_sequences=True))\n",
    "regressor.add(Dropout(0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regressor.add(LSTM(units=100))\n",
    "regressor.add(Dropout(0.4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regressor.add(Dense(units=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regressor.compile(optimizer='adam',loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1198/1198 [==============================] - 26s 21ms/step - loss: 0.0490\n",
      "Epoch 2/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0101\n",
      "Epoch 3/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0080\n",
      "Epoch 4/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0071\n",
      "Epoch 5/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0070\n",
      "Epoch 6/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0064\n",
      "Epoch 7/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0055\n",
      "Epoch 8/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0053\n",
      "Epoch 9/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0051\n",
      "Epoch 10/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0053\n",
      "Epoch 11/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0055\n",
      "Epoch 12/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0050\n",
      "Epoch 13/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0052\n",
      "Epoch 14/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0047\n",
      "Epoch 15/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0050\n",
      "Epoch 16/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0053\n",
      "Epoch 17/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0046\n",
      "Epoch 18/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0045\n",
      "Epoch 19/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0041\n",
      "Epoch 20/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0040\n",
      "Epoch 21/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0045\n",
      "Epoch 22/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0038\n",
      "Epoch 23/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0037\n",
      "Epoch 24/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0041\n",
      "Epoch 25/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0037\n",
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      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0037\n",
      "Epoch 27/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0038\n",
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      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0034\n",
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      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0054\n",
      "Epoch 30/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0038\n",
      "Epoch 31/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0036\n",
      "Epoch 32/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0039\n",
      "Epoch 33/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0033\n",
      "Epoch 34/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0036\n",
      "Epoch 35/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0035\n",
      "Epoch 36/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0030\n",
      "Epoch 37/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0031\n",
      "Epoch 38/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0036\n",
      "Epoch 39/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0035\n",
      "Epoch 40/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0034\n",
      "Epoch 41/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0037\n",
      "Epoch 42/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0030\n",
      "Epoch 43/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0031\n",
      "Epoch 44/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0028\n",
      "Epoch 45/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0031\n",
      "Epoch 46/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0027\n",
      "Epoch 47/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0028\n",
      "Epoch 48/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0032\n",
      "Epoch 49/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0026\n",
      "Epoch 50/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0030\n",
      "Epoch 51/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0027\n",
      "Epoch 52/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0025\n",
      "Epoch 53/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0028\n",
      "Epoch 54/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0026\n",
      "Epoch 55/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0029\n",
      "Epoch 56/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0030\n",
      "Epoch 57/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0026\n",
      "Epoch 58/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0028\n",
      "Epoch 59/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0028\n",
      "Epoch 60/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0027\n",
      "Epoch 61/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0029\n",
      "Epoch 62/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0028\n",
      "Epoch 63/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0029\n",
      "Epoch 64/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0025\n",
      "Epoch 65/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0023\n",
      "Epoch 66/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0024\n",
      "Epoch 67/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0026\n",
      "Epoch 68/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0027\n",
      "Epoch 69/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0024\n",
      "Epoch 70/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0025\n",
      "Epoch 71/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0023\n",
      "Epoch 72/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0022\n",
      "Epoch 73/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0024\n",
      "Epoch 74/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0022\n",
      "Epoch 75/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0026\n",
      "Epoch 76/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0025\n",
      "Epoch 77/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0025\n",
      "Epoch 78/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0023\n",
      "Epoch 79/100\n",
      "1198/1198 [==============================] - 19s 16ms/step - loss: 0.0021\n",
      "Epoch 80/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0021\n",
      "Epoch 81/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0024\n",
      "Epoch 82/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0022\n",
      "Epoch 83/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0017\n",
      "Epoch 84/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0022\n",
      "Epoch 85/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0021\n",
      "Epoch 86/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0020\n",
      "Epoch 87/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0022\n",
      "Epoch 88/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0026\n",
      "Epoch 89/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0019\n",
      "Epoch 90/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0020\n",
      "Epoch 91/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0021\n",
      "Epoch 92/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0022\n",
      "Epoch 93/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0022\n",
      "Epoch 94/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0019\n",
      "Epoch 95/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0023\n",
      "Epoch 96/100\n",
      "1198/1198 [==============================] - 20s 16ms/step - loss: 0.0025\n",
      "Epoch 97/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0021\n",
      "Epoch 98/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0020\n",
      "Epoch 99/100\n",
      "1198/1198 [==============================] - 19s 16ms/step - loss: 0.0021\n",
      "Epoch 100/100\n",
      "1198/1198 [==============================] - 20s 17ms/step - loss: 0.0018\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x3effd83f0d30>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train,y_train,epochs=100,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = pd.read_csv('Google_Stock_Price_Test.csv')\n",
    "real_stock_price = test.iloc[:, 1:2].values\n",
    "total_data=pd.concat([train['Open'],test['Open']],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inputs=total_data[len(train)-len(test)-60:].values\n",
    "inputs=inputs.reshape(-1,1)\n",
    "inputs = sc.transform(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test=[]\n",
    "for i in range(60,80):\n",
    "    X_test.append(inputs[i-60:i,0])\n",
    "X_test=np.array(X_test)\n",
    "X_test.shape\n",
    "X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted_stock_price=regressor.predict(X_test)\n",
    "predicted_stock_price=sc.inverse_transform(predicted_stock_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tIiIiUWnpUrjqKjj5ZJg2DUqXDrqi6HfVVf4DiFpDgqNwLSIiIlFnzRpo3hyK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\nEEIE1n7WtDXwQe7xscA9McYVwDshhJlAX+CfBap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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3eff928e9d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualization\n",
    "plt.rcParams[\"figure.figsize\"] = (12,8)\n",
    "plt.plot(predicted_stock_price,color='red',label='Predicted Stock Price')\n",
    "plt.plot(real_stock_price,color='blue',label='Real Stock Price')\n",
    "plt.title('Google Stock Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}