Fashon_MNIST_CNN_using_Keras_10_Runs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> A notebook for the classification of the MNIST fashion dataset.\n",
    "\n",
    "I used Sequential Convolutional Neural Networks (CNN) model using Keras and tensorflow as a backend.\n",
    "\n",
    "I re-Run the model **10 times** for taking averages. \n",
    "\n",
    "\n",
    "\n",
    "Result : |Training Accuracy | Dev(Validation) Accuracy| Testing Accuracy |\n",
    "---------------------------------------------------------------------|\n",
    "         | 93.196 %          | 91.864 %               | 92.159 %          |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout, Activation, Flatten\n",
    "from keras.layers import Convolution2D, MaxPooling2D\n",
    "from keras.utils import np_utils\n",
    "from keras import backend as K\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tf'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.backend.image_dim_ordering()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size     = 64\n",
    "nb_classes     = 10\n",
    "nb_ephochs     = 50\n",
    "img_rows, img_cols = 28,28\n",
    "nb_filters     = 32\n",
    "pool_size      = 2\n",
    "kernel_size    = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if K.image_dim_ordering() == 'th':\n",
    "    input_shape = (1,img_rows, img_cols)\n",
    "else:\n",
    "    input_shape = (img_rows,img_cols,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data_train = pd.read_csv('input_data/fashion-mnist_train.csv')\n",
    "data_test = pd.read_csv('input_data/fashion-mnist_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>label</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>pixel9</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "      <th>pixel784</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0      2       0       0       0       0       0       0       0       0   \n",
       "1      9       0       0       0       0       0       0       0       0   \n",
       "2      6       0       0       0       0       0       0       0       5   \n",
       "3      0       0       0       0       1       2       0       0       0   \n",
       "4      3       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel9    ...     pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0    ...            0         0         0         0         0   \n",
       "1       0    ...            0         0         0         0         0   \n",
       "2       0    ...            0         0         0        30        43   \n",
       "3       0    ...            3         0         0         0         0   \n",
       "4       0    ...            0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  pixel784  \n",
       "0         0         0         0         0         0  \n",
       "1         0         0         0         0         0  \n",
       "2         0         0         0         0         0  \n",
       "3         1         0         0         0         0  \n",
       "4         0         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>label</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>pixel9</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "      <th>pixel784</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>87</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>53</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>137</td>\n",
       "      <td>126</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>133</td>\n",
       "      <td>224</td>\n",
       "      <td>222</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0      0       0       0       0       0       0       0       0       9   \n",
       "1      1       0       0       0       0       0       0       0       0   \n",
       "2      2       0       0       0       0       0       0      14      53   \n",
       "3      2       0       0       0       0       0       0       0       0   \n",
       "4      3       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel9    ...     pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       8    ...          103        87        56         0         0   \n",
       "1       0    ...           34         0         0         0         0   \n",
       "2      99    ...            0         0         0         0        63   \n",
       "3       0    ...          137       126       140         0       133   \n",
       "4       0    ...            0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  pixel784  \n",
       "0         0         0         0         0         0  \n",
       "1         0         0         0         0         0  \n",
       "2        53        31         0         0         0  \n",
       "3       224       222        56         0         0  \n",
       "4         0         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clothes = {0: 'T-shirt/top',\n",
    "           1: 'Trouser',\n",
    "           2: 'Pullover',\n",
    "           3: 'Dress',\n",
    "           4: 'Coat',\n",
    "           5: 'Sandal',\n",
    "           6: 'Shirt',\n",
    "           7: 'Sneaker',\n",
    "           8: 'Bag',\n",
    "           9: 'Ankle boot'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAJoCAYAAAB7txeYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xm8XGWV7//vkiFknhOSEAgBpA1jkEllEBUFGxQuaoO0\nBhURbEGU65WLbYO2V7itiK33/tQgSLpBBBEVWuDKoCDIFAYBiUAMCZkHMoeEcf3+qJ3unfOsnbPP\nqapzatf5vF+vvJKzzh6eqlq168muvfYydxcAAACAmjf19gAAAACAVsIEGQAAAMhhggwAAADkMEEG\nAAAAcpggAwAAADlMkAEAAIAcJsgVYWZuZruXWG5Stuy2PTEu9D1mdpqZ3buV399qZtN6ckxA3taO\nl2WPpcF6W817oFnM7F4zO63gd5PNbH0PD6lPYIJcJzM7zMz+aGZrzGylmd1nZgf19riAenU3t939\nWHefsZXtMtFAKWb2ezNbZWb9ensszWJm7zSzBb09DjSWma3P/XnDzDbmfj61Uftx9znuPqiTsYQT\nbDM7wszuMbNts/84TmrUuNoBZxnrYGZDJP2HpLMkXS9pe0mHS3q5N8cF1KtZuc03Gygr+7A+XNIa\nSR+Q9PPeHA/QFflJq5nNlXS6u9/Rk2Mws85Ogr5f0i09MZYq4gxyfd4sSe5+rbu/7u4b3f237v6E\nme1mZneZ2YtmtsLMrjGzYZtXNLO5ZvbfzeyJ7AzddWa2Q+73XzKzxWa2yMw+md+pmf2tmT1mZmvN\nbL6ZXdRjjxh9RWFub17AzL6dnd173syOzcV/b2anZ/8+LTvzfJmZrZR0naQfSnpbdiZldQ8/LlTH\nxyU9IOkqSVtcsmNmV5nZ/zWz35jZOjN70Mx2izaSfRMy38yOCn7XL8vjF8xsqZn90Mz6b2VMZmbf\nz47ZfzGzd+d+Md7Mbsq+bZltZp/usJ/vZsfzRdm/+5nZQEm3ShqfO7s4vkvPEtqCmQ0ws59mc4bV\nZvaQmY3KLbJr9o3eOjO7zcxGZOvtbmae2869ZvbPZna/pA2SrpX0Nkk/zPLru7ltbp4g35P9/Ods\nmZOybZ2Z5fKLZvYrMxuXxTefcT47O/6vMLNLSkzIK6WtHkwveFbS62Y2w8yONbPhud+ZpIsljZf0\nFkkTJV3UYf2PSDpG0q6S9pV0miSZ2TGS/rukoyXtIek9HdbboNqHxzBJfyvpLDM7oWGPCth6bkvS\nIZKekTRK0r9IusLMrGBbh0iaI2mMpL+XdKak+919kLsPK1gH+Lika7I/7zOzsR1+f4qkr0kaLmm2\npP/VcQNm9j7VJggnufvvgn38b9X+M7i/pN0lTZD0T1sZ0+ZcHiXpQkk3bp6oZPtZoNox/0OSvpmb\nQH9F0qHZfvaTdLCkf3T3DZKOlbQoez8McvdFW9k/2tcnJA2QtJOkkZI+K2lT7vcfVe0/imMlDZT0\nxa1s62OSPilpiKRTJd0v6cwsv86VJDPbSdKw7KTHEdl6e2XL/MLM3ivp66rl8gRJi1R7L+Z9UNIB\nkg7Mlvt4Nx53y2KCXAd3XyvpMEku6XJJy7MzCGPdfba73+7uL7v7cknfkXRkh018z90XuftKSTer\ndvCUahPnn7j7U9kB9KIO+/29uz/p7m9kyX1tsG2g27aW29ki89z9cnd/XdIMSeNUO3BHFrn79939\nNXff2PTBo/LM7DBJu0i63t0fkfRX1SYIeTe6+0Pu/ppqH9z7d/j9hyVNl/R+d38o2IdJ+rSkL7j7\nSndfJ+mbkk7eytCWSfquu7/q7tep9p/EvzWziaq9X77s7pvc/XFJP1ZtoiLVJilfd/dl2efB13K/\nAyTpVdX+47V79q3dTHfPF99d4e7PuftLql1u1DHf865091lZnr5WsMzfqvbtRZFTJf3Y3R93902S\nzpd0ZDax3uwSd1/l7nMlfU+1/7S2DSbIdcqS8DR330nS3qqdPfiumY0xs5+Z2UIzWyvpatWSP29J\n7t8vSdp8zdJ4SfNzv5uXX8nMDjGz35nZcjNbo9oZuY7bBupSlNvZr5fklnsp+2dRocj8gjhQZJqk\n37r7iuznn6rDZRYqPn5udq5qE+wnC/YxWrUzdo9kX2mvlnRbFi+y0N099/M81d4X4yVtnmTnfzch\n+/d4bXkc37we+iAz28a2LOIbr9qlRHdIuj6bN1xiW9ZsdJbveWWOuZ1df7xFzmYnTVbpv3K6437a\nLqeZIDeQu/9FtSTfW7XLK1zSvu4+RLWvlou+gu5osWqXZGy2c4ff/1TSTZImuvtQ1a7pLLttoMs6\n5HaXV+/kZ+A/ZdcAf0S1s1VLzGyJpC9I2s/M9uvCpj4s6QQzO7fg9yskbVTta+Vh2Z+hndwRYEKH\nS4l2Vu2r50WSRpjZ4A6/W5j9e5FqZ8Q7rifxfuhzsjPEg3J/Frn7K+5+kbu/RbVvI05U7Sxut3ax\ntZ+tdleYd6g2IY+WlzrkbJbbw/VfOS2l85S2ujyICXIdzOxvzOy8zV85ZF+znaJaYclgSeslrTaz\nCZK+1IVNXy/pNDObYmYDVLvWLW+wamcrNpnZwUq/egTq0klu12uppJ3MbPsGbAvt5wRJr0uaotrX\nyPurVsfxB3XtGsdFkt4t6Rwz+2zHX7r7G6pdPnSZmY2RJDObkF23XGRMtr3tzOzD2bhucff5kv4o\n6WIz28HM9pX0Kf3XNZvXSvpHMxudFV79k2rfKkq198NIMxvahceGNmNm7zKzvbNCt7WqXXLxeoM2\nv1TS5NzPR0p6NLuEU9mlci92WOZaSZ8ys32zCfXFkv7g7vlbEv4PMxtmZjtLOke1Iuy2wQS5PutU\nK9p40Mw2qDZ5eErSeapdY3aAarco+o2kG8tu1N1vVe2r7LtUKz65q8Min5X0dTNbp9qB9vr6HgaQ\n2Fpu1+suSX+WtMTMVnS2MPqcaarVYLzg7ks2/5H0fySdal24VaC7v6DaJPnLlt1ZpYMvq3aMfSC7\nFO4OSXtuZZMPqlY4vUK1osAPufuL2e9OkTRJtYn5LyVd6O63Z7/7hqSZkp6Q9KSkR7PY5m9nrpU0\nJ7vUo62+pkZp41WbJ6xV7fh4h2p50QjflXRKll/fUXx5xYWSfpot89/c/TbVivR+qdq32jsrPaN9\ns6THJT2WLXdVg8bbEmzLy6kAAADQrszsWUnHufuz3Vx/W9XOcO+aFei1Jc4gAwAA9AFW67dwRXcn\nx30JZ5ABAABQSl85g8wEGQAAAMjhEgsAAAAgp3Q1cCRrifyvkrZRrePKJZ0sz+lqdJu7N+Vez13J\n42bk8DbbbNPoTTbc66+ndxsaOHBgEhs6NL1T1dKlS0ttr9Weh2iM9WqFHM6W51iMbmtGHpPD3bfl\nrbmLY9EVA331KoIyOdztSyzMbBtJz0o6WrX+8w9LOsXdn97KOn3zlUBDNOmg3KU8bkYODx8+vNvr\nNuPgFh1YV61alcTe8Y53JLFjjjkmiV166aVJbM2aNUksmlwXjSfS6Odi9erVDd2e1Bo5nK3DsRjd\n1ug8Jofr079//yQWnXB49dVXk9jLL7/clDG1ujI5XM8lFgdLmu3uc9z9FUk/k/TBOrYH9AbyGFVH\nDqPqyGG0nHomyBO0ZR/uBdqyR7ckyczOMLOZZjazjn0BzdJpHpPDaHEci1F15DBaTj3XIEenp5Ov\nPNx9uqTpEl+JoCV1msfkMFocx2JUHTmMllPPBHmBpIm5n3dSrcUmUCV9No/f9Kb4C6SVK1cmsYkT\nJyaxr3zlK0nsW9/6VhI744wzkti//Mu/JLG+WizSAH02h5slupY+uuZ+9913T2Jf/epXw21OmzYt\nifXr1y+JRdfnb9iwIYlF15OuXbs2iU2aNCkcz1133ZXE7rvvvnDZHtBncjg67r7xxhul13/ppZdK\nLbd+/foktt122yWx6FrlIUOGlB5PvY+nldVzicXDkvYws13NbHtJJ0u6qTHDAnoMeYyqI4dRdeQw\nWk63zyC7+2tm9jlJ/0+127Jc6e5/btjIgB5AHqPqyGFUHTmMVlTXfZDd/RZJtzRoLECvII9RdeQw\nqo4cRquhkx4AAACQU9cZZADVULarUpGTTjopid10U3qJYNRQZNdddy21j9deey2Mb7ttepgq2zwE\n7a+o2LRsodCsWbOS2N/8zd8ksej98sorrySxk08+OdzPxo0bk1jUpKFsV7TocUfjGTFiRDiegw8+\nOIkdf/zx4bJonK4UsEXFolGh3aZNm5LYzJnpnfCmTp2axKJGVZMnTw7HM2fOnCTWLgV5Ec4gAwAA\nADlMkAEAAIAcJsgAAABADhNkAAAAIMd6snsVrSFRD3fv9cqsZuTwsGHDGrq96D0dxYqKK6IOTNH6\nUTFS1LXruOOOS2KHH354Eit6HsoWgURFS9G4yxb4rV69utRyXdEKOSy117F4++23D+NRwdqhhx6a\nxK6++uokFhXULV++PIlFHe5GjhwZjmfUqFFJLCpAvfnmm5PYHnvsEW6zo/322y+JRd36JGnZsmVJ\n7AMf+EASmz17dhJrhTyuag6PGTMmiU2fPj1c9n3ve18SiwryIlHB9IABA5JYVPT3+uuvh9u87rrr\nktg555yTxKrQFbVMDnMGGQAAAMhhggwAAADkMEEGAAAAcpggAwAAADlMkAEAAIAc7mKBymjXyul6\n7mJR9o4VUeyll14Ktzlu3Lgk9vd///dJ7Fvf+lYS+/nPf57Eoqr8n/zkJ0msqCVuUQvqjhrdfnrN\nmjUN3Z7UGjks9d1j8UMPPZTEojuvrF27NolFd8uI7kIxaNCgcN/RXTUGDx6cxObPn5/EovdqdNyI\n2gYX3Y0lWj9qT3zUUUclsVbI41bL4aFDhyaxW265JYlF7Z779+8fbnPlypVJLDoeRnfwiZYreywt\nuvNJdIyOjpM33XRTEjv99NOTWNHdMnoCd7EAAAAAuogJMgAAAJDDBBkAAADISS+g6gIzmytpnaTX\nJb3m7gc2YlBATyKPUXXkMKqOHEarqatIL0voA919RcnlW+qiejTPGWecEcajApRbb7211DabVRjS\nlTxuRg5HxR1llS3Ii4ozilriRusvXLgwiU2ePDmJRe1No0KMbbbZJolFRUxFy/aEqFCrXq2Qw9ny\nlTwWR7nQlUKfqDV01HI5ardeNo+jwj0pLqTaYYcdkljU+jd6T0ZFg9Fy0WOW4gLBG2+8MYlFx/Jm\n5HHVc/iaa65JYh/96EeT2NKlS5NYUYFxFC87Z4vyNVo3Wi7KQUl69dVXSy274447JrHLL788iRXN\nE3oCRXoAAABAF9U7QXZJvzWzR8ws/K+AmZ1hZjPNLL1/DNAatprH5DAqgGMxqo4cRkup6xpkSe9w\n90VmNkbS7Wb2F3e/J7+Au0+XNF1qva9EgMxW85gcRgVwLEbVkcNoKXWdQXb3RdnfyyT9UtLBjRgU\n0JPIY1QdOYyqI4fRarp9BtnMBkp6k7uvy/79Xklfb9jIKioqxHjjjTeS2JQpU5LYtGnTklh0AX1U\ncLV48eJwPFFXtKhoY+PGjUls4sSJSWzChAlJLOrydMghh4TjiTo1lS3Sa4ZWyONGd7OMCjuiHCwq\nJoqWHT9+fBKLcmbgwIFJLCpkisYYFa9I8Xsq2mY07r6gFXK4p0S5UG+RXrTNqJNY9D6NCpS60tEx\nGns0nqjTWrRu2W5/Ulyk21udzdohh++6664k9oEPfCCJdeV4X/b1iHKu7LrRsbSo4160n6hwLypy\n/c1vflNqPK2knkssxkr6ZfaEbSvpp+5+W0NGBfQc8hhVRw6j6shhtJxuT5DdfY6k/Ro4FqDHkceo\nOnIYVUcOoxVxmzcAAAAghwkyAAAAkFPvbd7QQdkL8KOCpN/97ndJLLoAfvjw4UmsqOtX1CVqzJgx\npfZ9+OGHJ7GoKOWGG25IYqeffno4nrPOOiuMo7m6UhgSFfWU7c738ssvJ7ERI0aUWrcrRXaNLmxE\n+xk9enSp2IIFC5JYVGwaFc9FRXFFRXpRp8ho/ajDXfQ5EI0nWi4q8JOkFSvShnVnnnlmErv44ou3\n+HnJkiXh9vq6qEts2WLiom6hZY9zUc5Fx/HouNuVwswoN6Mi/ciDDz5Yej+tgjPIAAAAQA4TZAAA\nACCHCTIAAACQwwQZAAAAyKFIr8HKXlT/4osvJrHbbmut+6L/5S9/6fa6RYUqUYe9H/3oR0msr3ZF\na4TouS9bZCdJgwYNSmJRgVG0n6jYJNp31IWvSNkuUWX3jb7huOOOK7XcqlWrklhUpFdW1IVPinMx\nKnqaNWtWqf0MHTo0ie2www5JLHrvSnFB36hRo5LYRRddtMXPX/va10qNr69ZuHBhqeXKHp+l+j4H\ny3Z0jPZdlMN33313EjvggAOS2Lp165JYFYs7OYMMAAAA5DBBBgAAAHKYIAMAAAA5TJABAACAHIr0\neknUBSnqfFNWUcFVtJ+urN9dF1xwQRiPujp997vfTWLnnHNOQ8fT10UFbFHXO0kaOXJkEouKRVav\nXp3EonyLYlFHxqJcjYpIyhagoL10pWipqNCoo7IFedExcv369UmsqKtpVFT3/PPPJ7H77rsviZ1y\nyilJLCqEihR10ossXrw4ie21117d3l5fEnXSiwoho2NftFxXlC1kjpSdI0jSHnvskcSiwtDoMVYR\nZ5ABAACAHCbIAAAAQA4TZAAAACCHCTIAAACQ02lVmJldKek4Scvcfe8sNkLSdZImSZor6SPunrYj\nQqGo2KSo41Gj99No0UX6hx56aLjsQQcdlMQeeeSRJNax495TTz3VzdHV9PU8jgoxigozly5dmsS2\n3377JBYV/kXbjGJRAQld77aur+ewVL7wSJKmTJlSarmyxdHReyBat6j4NVq2bBFXdIzdtGlTEove\nk0UFYGU7T3Zcrp4C2XbO4ag4c8iQIUlszZo1SawrRXrR6xa9JlG+RfOBKFY0nujxREWut99+e7h+\n1ZQ5g3yVpGM6xM6XdKe77yHpzuxnoJVdJfIY1XaVyGFU21Uih1ERnU6Q3f0eSSs7hD8oaUb27xmS\nTmjwuICGIo9RdeQwqo4cRpV098a7Y919sSS5+2IzG1O0oJmdIemMbu4HaKZSeUwOo4VxLEbVkcNo\nSU1vFOLu0yVNlyQz4yJDVA45jHZAHqPqyGH0pO7exWKpmY2TpOzvZY0bEtBjyGNUHTmMqiOH0ZK6\newb5JknTJF2S/f3rho0IvSKqgi17V4FTTz01id19993hsjvvvHMS++hHP5rEjj/++C1+fu6550qN\npYv6dB5v3LgxjE+bNi2JPfDAA0nsT3/6UxIbN25cEhs/fnwSi+5sEd09Q4qrsWk1/Z/6dA5LxS2l\nzz777CT2wgsvJLEFCxYkseHDhyex6E4S0TEyuruEJA0ePDiJRXeXie4UEN0ZI2pzPWDAgFL7kOK7\nF0R36hgxYsQWP0d3UahT2+ZwdPelqF1z0bE4uqNJlHNlP6vL3u2iaHvR3S2i9W+77bZS42l1nZ5B\nNrNrJd0vaU8zW2Bmn1ItkY82s+ckHZ39DLQs8hhVRw6j6shhVEmnZ5Dd/ZSCX727wWMBmoY8RtWR\nw6g6chhVQic9AAAAIIcJMgAAAJDT9Nu8oXfVU3wXmTRpUhI799xzk9h3vvOdcP2ozeb111+fxJ54\n4oktfv79739fboAIRYU1RcU7v/rVr5LYm9/85iQWtf2dM2dOEjvggAOSWFQYVdTetH///kmsbKFK\nPcUrqIbPfvazpZddtiy9QUJUMLpkyZIkNmjQoCQWva+eeeaZcN977713Ett9991LrT979uwkFhXz\nRYpaX0ctgkeOHJnEHnvssVLbQ+rOO+9MYlEeFKnnuFT22FemvfjWln3llVeSWFScWEWcQQYAAABy\nmCADAAAAOUyQAQAAgBwmyAAAAEAORXptrp4L9SPDhg1LYlGXu+XLl4frH3jggUnsjjvuSGIdu2NR\nRFWf6PWNOntJ0rx585JYVJA3derUJLZu3boktmHDhiQ2c+bMJBZ1KpMan8NoPVHBaNTpLXLBBReE\n8ahrXtR1L8q71atXJ7Eot6PjUvRekaT58+cnsTFjxiSxo446KolFz8/KlStLjadjJ7zNVqxYkcSi\nIr2f/OQnW/z84osvhttD6umnny61XFHBdD3HtLIF+l0pbo62GRW5Llq0qMwQWx5nkAEAAIAcJsgA\nAABADhNkAAAAIIcJMgAAAJBDkV4fVE9BzKZNm5LYLrvsksTe8573hOtfeumlSeyEE05IYnffffcW\nP0fdelBeVHQRFSxJcfFeVGB02GGHJbGoM9i1115bZoiFRXpR0VP0eCjkrK6yx59TTjkliY0aNSpc\ndtasWUks6soYdXAcOnRoElu4cGESW7t2bRKLCpmluCAv2nd0rIuen6gTXvQeisYoSdtum378r1+/\nPomVff8iFXUWjXTl2FVPd9x6uo0WiTorUqQHAAAAtCEmyAAAAEAOE2QAAAAgp9MJspldaWbLzOyp\nXOwiM1toZo9nf97f3GEC9SGPUXXkMKqOHEaVlCnSu0rS/5H0bx3il7n7txs+IjRd2YKY+++/P4kd\neuihSezGG29MYgcccEC4zcWLFyexxx57LInNmDFji5+jzlZddJX6cB5HhZlRkY4UF4FEsYcffjiJ\nPfnkk0ks6sw3fPjwJBYVgGILV6kP57AkXXbZZUks6pgnSdtvv30Si459ZYtAo8K9rryvovWj8UTF\ns1Ex3zbbbJPEouPksmXLwvFEx+jbbrstXLaBrlIfyuGoo2yUb61WYFxUuBfle9QptV2O5Z2eQXb3\neySlPS2BCiGPUXXkMKqOHEaV1HMN8ufM7InsK5P0dBBQDeQxqo4cRtWRw2g53Z0g/0DSbpL2l7RY\nUnpz24yZnWFmM81sZjf3BTRLqTwmh9HCOBaj6shhtKRuTZDdfam7v+7ub0i6XNLBW1l2ursf6O4H\ndneQQDOUzWNyGK2KYzGqjhxGq+pWJz0zG+fum6utTpT01NaWbwVR4URUIFG2gK1IPV1umrGPQYMG\nJbEf/OAHSWzcuHFJLLr4fuedd05iUYeof//3fw/HE4m6W334wx/e4ucbbrih9PbKqmIed1eUM6+9\n9lq4bPQeiLrrRYUYc+fOTWJRsVRXcrhs0WBf1Eo5HBXwRMoeY7/97bRma+zYsUls9uzZ4fplu29G\ny7300ktJLCquit5DRfuNOo6NHDkyiUUFedF7KOqk9+yzzyaxF198MRxP1DXv1ltvDZdtplbK4UaL\nPkOjPCoSFWJGx8noPdWMbqPRe7wrj6dqOp0gm9m1kt4paZSZLZB0oaR3mtn+klzSXEmfaeIYgbqR\nx6g6chhVRw6jSjqdILv7KUH4iiaMBWga8hhVRw6j6shhVAmd9AAAAIAcJsgAAABATreK9OrR8SLx\negvYouK7qHCiqCCp1ZV9fCeeeGK4/iWXXJLE7rrrriT2nve8pxujqzniiCOS2FNPxXUWkyZNSmJR\nkd+jjz66xc9R0Qxi9b6nokKOAQMGlIpFHfKi165sQVdPoeivOcoW5H36059OYuedd14Si44rUQGp\nFBe7RQVra9euTWJRHkdFbdH2Jk6cGI6naJwdDRkypNRy3/zmN5PYZz6TXr67ww47hOtv3Lgxid1y\nyy2l9o1y1qxZk8Siz++i42HZ41I9x696Py+iQsJ20VqfUgAAAEAvY4IMAAAA5DBBBgAAAHKYIAMA\nAAA5TJABAACAnB6/i0XHisl6WzOXvTvFYYcdlsSiKucHH3yw9L4j9VSElr1jxd57753EPvaxj4Xb\n/OxnP5vE7rzzzm6MrljUpjpqcS3Fd6yIHmO9r0NfEeVbVBFddjkpvvNAlJtRS91ouajKOVquK62m\nG63eFvNV1PF5jdoZR+2Ri5R9Di+++OIkdv755yexxYsXJ7F+/folsWjcUtwKPRpj9DkQtc9dsmRJ\nqX3ssssu4Xiiu1hErYhHjBiRxKZOnZrEHn/88SR24YUXJrGi1tfR3TuK2naje+q9i0XZ+UTZY2TZ\n9tNdORZzFwsAAACgj2CCDAAAAOQwQQYAAABymCADAAAAOT1epNdRvUV606ZNS2JRa83tttsuie2/\n//5JLLrg/I9//GPp8dQjung/Kna79tprk9gpp5wSbrOo5XNH9bwOUaHJihUrwmVXrlyZxEaOHFlq\nP+i+6PUtKqqKlo2KSKL1o/dP9N6LiqDQszq+v7tSkFfWJz7xiSR2+umnJ7E5c+YksahF+bBhw5JY\nUZFetGxUkBfForbSUcHVHnvskcSiIjspPk5Gbam/8Y1vJLGoIC/y17/+NYkNHDgwXHb+/Pmltonu\nK3sTgXrbPTe6kLkr26NIDwAAAOgjmCADAAAAOUyQAQAAgJxOJ8hmNtHMfmdms8zsz2b2+Sw+wsxu\nN7Pnsr+HN3+4QNeRw2gH5DGqjhxGlZQp0ntN0nnu/qiZDZb0iJndLuk0SXe6+yVmdr6k8yV9ubON\ndbz4u94OVgMGDEhiv/nNb5LYCy+8kMS+8pWvJLEf/OAHSezSSy9NYv/2b/9WdoilnXDCCUns6KOP\nTmKf/vSnk1hRMV7Z4rt6igRGjx6dxA488MBw2aibVFTE1WANzeHeUva1jN5TUZFdUaFcVOQa7bts\noV00xqgDWVE3KfynpuZxVLT8uc99LolFhWVS3JktKkz7wx/+kMT22muvJBYV30W5WXTsirruTZo0\nKYlFRXVPPvlkEosKpidPnpzEioodo/UffvjhJPbVr341XL+MefPmJbG3vvWt4bJRwXQPaItjcVlR\nvpYteO6Ksh3y6vkMKdKni/TcfbG7P5r9e52kWZImSPqgpBnZYjMkpbM7oAWQw2gH5DGqjhxGlXTp\nNm9mNknSVEkPShrr7oulWtKb2ZiCdc6QdEZ9wwQagxxGOyCPUXXkMFpd6QmymQ2S9AtJ57r72rL3\nyXP36ZKmZ9uo72Z/QB3IYbQD8hhVRw6jCkpd9Gdm26mWzNe4+41ZeKmZjct+P07SsuYMEagfOYx2\nQB6j6shhVEWnZ5Ct9l+7KyTNcvfv5H51k6Rpki7J/v51mR12txgsKq6QpGOOOSaJHXDAAaViUbHJ\njBkzktj8rs8pAAAgAElEQVRBBx2UxKLuS1Lc5S7yq1/9KomNGZN+q3TiiScmsaVLlyaxbbeNX8qy\nnXzq8dhjjyWxM888M1x2wYIFSeyf/umfGj6mvEbncKOVLZqsp5Ayyo+iHC5boFF2jFHRUlSYWfT4\nunB2qdRy9eyjNzUyjwcOHKj99ttvi9h9992XLBd1lCvKjyFDhiSx6LWPOuRFy0VFelGsqFg0KgSN\nCtOi9aNi4re85S1JbMcdd0xir7zySjie6Pn5wAc+EC7bXVFx4cEHHxwuGxWuN1urH4sbrX///qWW\n68rxp9EFefUe+9q5uLrMJRbvkPQxSU+a2eZ+lxeolsjXm9mnJL0g6cPNGSJQN3IY7YA8RtWRw6iM\nTifI7n6vpKL/Yry7scMBGo8cRjsgj1F15DCqpH3PjQMAAADdwAQZAAAAyOnSfZDrNW7cuKQLXNRp\n5tFHH01in/zkJ8NtRp2MDj/88CQWFSk988wzSWzs2LFJbNddd01ixx13XDieI444IoktXrw4iUWF\nIW9/+9vDbXYUPZZ6i/HKXtAfOemkk5JY9Jgl6a9//Wup9Z9++uktfl6yZEmpsVRR2dcuKo6KuhiV\nfd2Kiomi4r1GF8pFRXpRUZUUP+5GF5u0c6FJZPTo0frMZz6zRSx6j61bty6JDRw4MNxmVHwXdY8b\nPrxcF+HodV+zZk0SKyo2jcZTtkBw/PjxSWzkyJGl1i3qDHr33XcnsbLHtbLd16KiyqjgUJJefPHF\nUvvuWBhZ9HwjFeVMlB9FnwHR6172mF/2WNyVY2k0nnbOh771qQAAAAB0ggkyAAAAkMMEGQAAAMhh\nggwAAADk9GiR3vLly/XDH/5wi1jUIW/t2rVJ7KKLLgq3GRVJREUJ0YXk0cXpUUFedAH9/Pnzw/FE\nF+UvWrQoiX39618P1+8ouli+Gd3x6ulC9tRTTyWxvfbaK1x26tSpSWznnXdOYv/wD/+wxc/tXAgw\nYcKEJFa26KJscVlU2Pncc8+Fy0aFqjvttFMSKyqq62jDhg1JLCr+ivJAit/jkUZ30ps3b163t9fq\nNm3apGeffXaLWNTVbdWqVUkseu2kuOgrOk5G3eyiYr7oWNqvX78kFnXXk+LPgQEDBiSxqMNdVEgV\nHYOi3I6660nSP/7jP4bxMvsu6hbY0RVXXJHE3vzmN4fLPv/886W2Wc/7qq+Ljru92bWz7L6LumX2\ntVzgDDIAAACQwwQZAAAAyGGCDAAAAOQwQQYAAAByrCcvujazvnWFNxrK3XuvuiHTjByOimiiIomy\nsbJdt4o6okWdmqLC0KhwqH///kksOsZEhXdli/6kxne+i56fuXPnNnQfUmvksCQNGjTI99lnny1i\n999/f7JcVDC9cePGcJtRQVJU0BfFVq9encSi1yQqvCsqWo6K96JiwGjcZbuVRQV5d911VzieY489\nNoy3krLFia2Qx1WYT+y7775JLOoUHBV7dkXZbnhli7+7Mi+M3n9jxowpvX5vKZPDnEEGAAAAcpgg\nAwAAADlMkAEAAIAcJsgAAABATqcTZDObaGa/M7NZZvZnM/t8Fr/IzBaa2ePZn/c3f7hA15HDaAfk\nMaqOHEaVlGk1/Zqk89z9UTMbLOkRM7s9+91l7v7t5g0PaIiWzuFXXnmloduLqpejqvwDDzwwXH/W\nrFlJbNmyZUksqv5fv359Ettzzz2TWFQZH7Usl+K7EUR3OChbyd3oO2D0oIbl8YYNG/TAAw9sETvq\nqKOS5b74xS8msYMOOijcZtQaOorVo+xdW4qUzZGyfv/73yexrtytouxdI8qKnoui5ye6+0BRi+EG\nauljcaNFx67o9ShqJV42t6O8jrZZNteL7mLRZsfTTnU6QXb3xZIWZ/9eZ2azJE1o9sCARiGH0Q7I\nY1QdOYwq6dLU38wmSZoq6cEs9Dkze8LMrjSz4QXrnGFmM81sZl0jBRqAHEY7II9RdeQwWl3pCbKZ\nDZL0C0nnuvtaST+QtJuk/VX7H+Gl0XruPt3dD3T3+PtcoIeQw2gH5DGqjhxGFZSaIJvZdqol8zXu\nfqMkuftSd3/d3d+QdLmkg5s3TKA+5DDaAXmMqiOHURWdXoNstauyr5A0y92/k4uPy64nkqQTJcUV\nNkAva/Ucjord6lHUerej5557LoxHbX+jwqGoOCNqIT179uwkFrUMjgpapPKFWWULSMoWMbaaZudx\nVHAWxYpEz/Xw4ek35VHRZtRufezYsUls/PjxdY0nep2j1tfz589PYrfddlsSi3K7K+opyIuUbUdf\npKhYrFFa/VjcaFG+RsefqFizyMCBA5NY9BqXLdKLlisqHI/Wj/Zdts11qyvzyfwOSR+T9KSZPZ7F\nLpB0ipntL8klzZX0maaMEKgfOYx2QB6j6shhVEaZu1jcKym6N8gtjR8O0HjkMNoBeYyqI4dRJe17\nAzsAAACgG5ggAwAAADnWkxdOm1n1rtJGy3D37re8apBm5PCkSZOSWNlim7IFElHhXlTsIUmjRo1K\nYvPmzUtiUcFT2TGOGzcuia1ZsyYcz4YNG5JYVNQSHcvKFulF6y5cuDAcTz1aIYcljsWoTyvkcRVy\nOCo0vfHGG5PYgAEDwvU3btyYxPr165fEogLnaLnoeBgV5BUVa0bF1VGR6z777BOu30rK5DBnkAEA\nAIAcJsgAAABADhNkAAAAIIcJMgAAAJDT00V6yyVtrvYZJWlFj+28uXgszbeLu4/u7UGQw5XQqo+l\nJXJY2iKPW/W56g4eS89oiTxu0xyW2uvxtOpjKZXDPTpB3mLHZjPd/cBe2XmD8Vj6pnZ6rngsfVM7\nPVc8lr6p3Z6rdno8VX8sXGIBAAAA5DBBBgAAAHJ6c4I8vRf33Wg8lr6pnZ4rHkvf1E7PFY+lb2q3\n56qdHk+lH0uvXYMMAAAAtCIusQAAAAByenyCbGbHmNkzZjbbzM7v6f3Xw8yuNLNlZvZULjbCzG43\ns+eyv4f35hjLMrOJZvY7M5tlZn82s89n8Uo+np5U5RyWyGPUVDmPyWFI5HCraNcc7tEJspltI+n/\nSjpW0hRJp5jZlJ4cQ52uknRMh9j5ku509z0k3Zn9XAWvSTrP3d8i6VBJ/5C9FlV9PD2iDXJYIo/7\nvDbI46tEDvdp5HBLacsc7ukzyAdLmu3uc9z9FUk/k/TBHh5Dt7n7PZJWdgh/UNKM7N8zJJ3Qo4Pq\nJndf7O6PZv9eJ2mWpAmq6OPpQZXOYYk8hqSK5zE5DJHDLaNdc7inJ8gTJM3P/bwgi1XZWHdfLNWS\nRNKYXh5Pl5nZJElTJT2oNng8TdaOOSy1wetOHndJO+Zx5V9zcrhLyOEW1E453NMTZAti3EajF5nZ\nIEm/kHSuu6/t7fFUADncgsjjLiOPWww53GXkcItptxzu6QnyAkkTcz/vJGlRD4+h0Zaa2ThJyv5e\n1svjKc3MtlMtma9x9xuzcGUfTw9pxxyWKvy6k8fd0o55XNnXnBzuFnK4hbRjDvf0BPlhSXuY2a5m\ntr2kkyXd1MNjaLSbJE3L/j1N0q97cSylmZlJukLSLHf/Tu5XlXw8Pagdc1iq6OtOHndbO+ZxJV9z\ncrjbyOEW0bY57O49+kfS+yU9K+mvkr7S0/uvc+zXSlos6VXV/vf6KUkjVavOfC77e0Rvj7PkYzlM\nta+jnpD0ePbn/VV9PD383FU2h7Pxk8f8qXQek8P8yZ47crgF/rRrDtNJDwAAAMihkx4AAACQwwQZ\nAAAAyGGCDAAAAOQwQQYAAABymCADAAAAOUyQAQAAgBwmyAAAAEAOE2QAAAAghwkyAAAAkMMEGQAA\nAMhhggwAAADkMEEGAAAAcpggAwAAADlMkAEAAIAcJsgAAABADhNkAAAAIIcJMgAAAJDDBBkAAADI\nYYIMAAAA5DBBBgAAAHKYIAMAAAA5TJABAACAHCbIAAAAQA4TZAAAACCHCTIAAACQwwQZAAAAyGGC\nDAAAAOQwQQYAAABymCADAAAAOUyQAQAAgBwmyAAAAEAOE2QAAAAghwkyAAAAkMMEGQAAAMhhggwA\nAADkMEEGAAAAcpggAwAAADlMkAEAAIAcJsg9zMxOM7N7t/L7W81sWk+OCWg2M5trZu/p7XEAQNWY\n2b1mdlrB7yab2foeHlKfwAS5SczsMDP7o5mtMbOVZnafmR3U2Xrufqy7z9jKdrc6wQY6093cBKog\n+8/YRjNbZ2ars1w/08z4vEOPMbP1uT9vZDm5+edTG7Ufd5/j7oM6GUs4wTazI8zsHjPb1szczCY1\nalztYNveHkA7MrMhkv5D0lmSrpe0vaTDJb1c53Z5vVCXZuVmTzGzbd39td4eB1re8e5+h5kNlXSk\npH+VdIikT3Rc0My2cffXe3qAaG/5SauZzZV0urvf0ZNjKPGfwvdLuqUnxlJF/I+6Od4sSe5+rbu/\n7u4b3f237v7E5gXM7NtmtsrMnjezY3Px35vZ6dm/T8vO7l1mZislXSfph5Lelv0vdHUPPy5UX2Fu\nbv52Yiu5OdTMrjCzxWa20My+YWbbZL/bzczuMrMXzWyFmV1jZsOiAZjZ32TbPjn7ebyZ/cLMlmfx\nc3LLXmRmN5jZ1Wa2VtJpzXxy0F7cfY273yTp7yRNM7O9zewqM/uBmd1iZhskHWVm/bK8f8HMlprZ\nD82svySZ2Sgz+4/sbPRKM/vD5omHmX05ey+sM7NnzOzdvfhwUWFmNsDMfpodQ1eb2UNmNiq3yK7Z\ntyHrzOw2MxuRrbe7mXluO/ea2T+b2f2SNki6VtLbJP0wmzd8N7fNzRPke7Kf/5wtc1K2rTPNbHY2\npl+Z2bgsvvmM89nZMXuFmV3Sbt/StNWDaSHPSnrdzGaY2bFmNrzD7w+R9IykUZL+RdIVZmYF2zpE\n0hxJYyT9vaQzJd3v7oPcPZyAAFtRT27OkPSapN0lTZX0XkmnZ78zSRdLGi/pLZImSrqo487N7ABJ\nv5V0trv/LDug3izpT5ImSHq3pHPN7H251T4o6QZJwyRd0/2Hjr7K3R+StEC1b0sk6aOS/pekwZLu\nlfS/VfvP4/6q5fcESf+ULXtetu5oSWMlXSDJzWxPSZ+TdJC7D5b0Pklze+DhoD19QtIASTtJGinp\ns5I25X7/UUnTVMvBgZK+uJVtfUzSJyUNkXSqpPslnZnNG86VJDPbSdKw7MTdEdl6e2XL/MLM3ivp\n65I+pNr7YZHS4+8HJR0g6cBsuY9343G3LCbITeDuayUdJsklXS5puZndZGZjs0Xmufvl2dd6MySN\nUy3pI4vc/fvu/pq7b2z64NHWupub2e+PlXSuu29w92WSLpN0crbd2e5+u7u/7O7LJX1Hta+28w6X\ndJOkae7+H1nsIEmj3f3r7v6Ku8/JxnVybr373f1X7v4G7wHUYZGkEdm/f+3u97n7G6pdXvRpSV9w\n95Xuvk7SN/VfOfiqau+DXdz9VXf/g7u7pNcl9ZM0xcy2c/e57v7XHn1EaCevqnZiYvfs272Z7p4v\nvrvC3Z9z95ck/Vy1/8wVudLdZ2X5WnRJ2t9KunUr2zhV0o/d/XF33yTpfElHZhPrzS5x91XuPlfS\n9ySdsvWHWC1MkJskS87T3H0nSXurdmZt81cbS3LLvZT9s+gi+/nNGyX6om7m5i6StpO0OPv6b7Wk\nH6n2zYbMbIyZ/Sz7unmtpKtVO9jnnSnpj+7+u1xsF0njN28z2+4F2vI/jLwH0AgTJK3M/p3PqdGq\nnbl7JJeDt2VxSfqWpNmSfmtmc8zsfKn2n0JJ56r2TcmyLP/HN/9hoOrMbBvbsohvvKSrJN0h6frs\nOHqJbVl3tCT375dUPGeQyh0zO7v+eLykeZt/yE6urFLtfRTtZ162TttggtwD3P0vqiX/3t1ZvZOf\ngW7rQm7OV+1M2yh3H5b9GeLue2W/v1i13NzX3YeodjlQx8uGzpS0s5ld1mG7z+e2OczdB7v7+/PD\n7N6jA2qsdpeWCapdTiFtmVMrJG1U7evlzTk4dHORlbuvc/fz3H2ypOMlfXHztcbu/lN3P0y1/+i5\napdqAFuVnSEelPuzKPsG7SJ3f4tq3/KdqNpZ3G7tYms/m1k/Se9QbUIeLS/VvnHZJbfOYEnDJS3M\nLTMx9++ds3XaBhPkJsiKkM7b/FWEmU1U7auHBxqw+aWSdjKz7RuwLfQx3c1Nd1+s2rXDl5rZEDN7\nk9UK8zZfRjFY0npJq81sgqQvBZtZJ+kYSUeY2SVZ7CFJa7Nip/7ZmZW9jdvOoQGyXD1O0s8kXe3u\nT3ZcJrvM4nJJl5nZ5m9EJmy+Dt7MjssKoUzSWtUurXjdzPY0s3dlk41Nqk2yuRsGuiXLpb2zuoy1\nql1y0ah8Wippcu7nIyU96u4bpNqEXdKLHZa5VtKnzGzfLMcvlvQHd1+QW+Z/mNkwM9tZ0jmq3Uig\nbTBBbo51qhU7PWi1KukHJD2lWrFHve6S9GdJS8xsRQO2h76lntz8uGq3hXtata/ablDt2kxJ+ppq\nxRprJP1G0o3RBtx9taSjJR1rZv+cHZiPV+16uudVO5v3Y0lDu/PggMzNZrZOtW8ovqLaNfHJLd5y\nvqzaZRQPZJcI3SFpz+x3e2Q/r1et2On/c/ffq3b98SWq5ewS1S43uqDhjwR9xXjVjptrVfuMv0O1\nSWojfFfSKdklRN9RfHnFhZJ+mi3z39z9NtWK9H4pabFqZ4g7ntG+WdLjkh7LlruqQeNtCVarNQAA\nAEC7M7NnJR3n7s92c/1tVTvDvWtWoNeWOIMMAADQB5jZDqrdEaNbk+O+hDPIAAAAKKWvnEFmggwA\nAADkcIkFAAAAkLNt54sUM7NjJP2rpG1U67hySSfLt/3p6n79+iWx7bcvd0e2HXbYIYlFHaiXLVvW\n9YG1AXcvasddl67kcV/IYTRPK+RwtnyfzONddtklia1evTqJDRqU9mBYvHhxEnvjjTcaM7CKaUYe\nk8OpgQMHJrFtt02nbW96U3quM5o7RPm6zTbblFpOklatWhXGq6hMDnf7Egsz20bSs6rdsmmBpIcl\nneLuT29lnbZP6De/+c1JbMKECUksSso999wziUWT68suuyyJFYneJFW9rKZJB+Uu5XFfyGE0Tyvk\ncLZO2+dxNGn40Y9+lMRuvvnmJHbYYYclsW984xtJbO3ataXHU/ZYXIVjdqPzuKdzOMoNqfX+w/P2\nt789iY0a1bFBaXxyLZpjbNq0KYkNGzYsiW3YsCEcz/XXXx/Gq6hMDtdzicXBkma7+xx3f0W1G7F/\nsI7tAb2BPEbVkcOoOnIYLaeeCfIEbdmHe4G27NEtSTKzM8xsppnNrGNfQLN0msfkMFocx2JUHTmM\nllPPNcjR6enkKw93ny5putQ3vtZD5XSax+QwWhzHYlQdOYyWU88EeYGkibmfd5K0qL7hNE90zVEz\nrjc68sgjk1h0Uf2UKVOS2Fve8pYkNmvWrLrGU8+1a1W4Fq4BKpXHQKDyOVzPseY973lPGP/whz+c\nxF5//fUkNnjw4CQWFVt/8YtfTGK33XZbEnvggQfC8XAs3qoezeGiz/5GP89vfetbk1iUl9G1xlJc\npDd58uQktn79+iQWzXmi4tMhQ4Yksaeeeiocz+mnn57EnnvuuST261//OondfvvtSazscxu9Ll1Z\nv7vqucTiYUl7mNmuZra9pJMl3dSYYQE9hjxG1ZHDqDpyGC2n22eQ3f01M/ucpP+n2m1ZrnT3Pzds\nZEAPII9RdeQwqo4cRiuq6z7I7n6LpFsaNBagV5DHqDpyGFVHDqPV0EkPAAAAyOl2o5Bu7awCVafR\nhe1R4d273vWucP3o+YwahXzyk59MYvPmzUtiZ599dhLbbbfdktjSpUvD8Tz88MNJbPbs2eGyra5Z\nXci6ogo5jNbVCjksVSOPjzjiiCR20kknJbEBAwaE6y9ZsiSJRQV548aNS2JRt9I5c+YksUMOOSSJ\nPfPMM+F4pk+fnsQWLlyYxKIGD1FxYW9qhTzuzRw+66yzklhUaDdmzJgk9tprryWxosYcGzduTGJj\nx45NYtG8Y926dUksaka2fPnyJLZoUVwf2b9//yQWvf+iBmdRIeFNN6WXmf/kJz8J991ozW4UAgAA\nALQdJsgAAABADhNkAAAAIIcJMgAAAJDTZ4r0Jk2alMTe9ra3JbGJEycmsahwb8WKFeF+oiKQ/fff\nP4lF3Wui4rl777231HhGjBgRjmfkyJFJLCoCiYpFom44a9asCffTE/p6YQiqrxVyWGq9PI46i557\n7rlJLDruRkVGUlwMFe3nlVdeSWJR567nn38+iUXH4h122CEcT1SsfeGFFyax6PG0WuFeK+RxT+Xw\n//yf/zOJHX300Ukset02bdqUxKLOukWd/aL8evnll0stF+VMtO6rr76axKL5iRS/L8rmYfS4x48f\nn8SuvvrqJHb55ZeX2kdXUKQHAAAAdBETZAAAACCHCTIAAACQwwQZAAAAyGGCDAAAAOS05V0sTj75\n5CR2+OGHJ7EFCxYksagSNaryjCqfJWnYsGFJbOrUqeGyHd16661JbJ999klijz76aBKLWkBKcdXq\ndtttl8QGDhyYxIYOHZrEfv7znyexqKVrM/Slymm0p1bIYan18vjUU09NYu9973uTWHSsWbVqVbjN\nqAVu1Lr3xRdfLDNEjR49OolFdx+I7iggSTvttFMSi+5SdM0115TaZtGdD3pCK+RxM3J4+PDhSWzG\njBlJbPXq1dF4klh054ZozlXvHUnKzuPKjjG6A0yR6G4Z0X7Kiu4Cc+KJJ3Z7e0W4iwUAAADQRUyQ\nAQAAgBwmyAAAAEBOevFJF5jZXEnrJL0u6TV3P7ARgwJ6EnmMqiOHUXXkMFpNXRPkzFHuHvdd7gG7\n7757EjvwwPR99eCDDyaxqDAtKmCLLmKPlpPii/f/+Mc/JrGopXVUdBGNOyrYKGoBHRUYRhfBRy0x\no+KVI488Moldd9114b4rplfzGGiAyuZwVIw8aNCgJBYV3hUVTP/pT39KYocddlgSi47vK1euLBWL\nCu+i7RXF3/WudyWxqEivNwvyeliv5nCUH9E8Ifq8jYrVomK3qKCuK0Vtjb6xQlcK8iJRgWE0R4ne\np1FRZPS+/9CHPhTu+4YbbigzxG7jEgsAAAAgp94Jskv6rZk9YmZnNGJAQC8gj1F15DCqjhxGS6n3\nEot3uPsiMxsj6XYz+4u735NfIEt0kh2tbKt5TA6jAjgWo+rIYbSUus4gu/ui7O9lkn4p6eBgmenu\nfiAX3KNVdZbH5DBaHcdiVB05jFbT7TPIZjZQ0pvcfV327/dK+nrDRlbSXnvtlcSiC7+HDBmSxKKi\niagrU9nuM1JcvBcVxUVd/DZs2JDEBg8enMS6UrARdcOLLpaPivSi5aLnMXq+i7pbtZpWyWOgu9oh\nh0eMGJHEomNSVPwTFUdJ0jHHHJPEFi5cmMSi4/OOO+6YxEaNGpXEok54xx9/fDiepUuXhnG0Tg4f\nfHAyJw8/b6PCvejzOxLNJ4oK78oW5JUt/Cu7XFeKBqP3ZFS4t/322yexkSNHJrHoeXz3u98d7rvZ\nRXr1XGIxVtIvsydyW0k/dffbGjIqoOeQx6g6chhVRw6j5XR7guzucyTt18CxAD2OPEbVkcOoOnIY\nrYjbvAEAAAA5TJABAACAnEZ00utVUUe69evXJ7G99947iT3++ONJbNKkSUksKq54+eWXS44w7lQT\nXeTfv3//JBZdAB91x4uWk6S1a9eWWnbdunVJrF+/fkksem7HjRuXxKpSpIfeEXVLioqypPo7PTXa\npz71qSR2xRVX9MJI2kd07IuKf6Oi7Ntvvz3c5nPPPZfEos6rUZFe9NkQ5ec73/nOJLbzzjuH44mO\n2y+99FISiwqXoq6maLyoC290/InyNXqNotzqirJFevUW2pVVVBDbUfScRe/naB4UzUX233//Uvtt\nNM4gAwAAADlMkAEAAIAcJsgAAABADhNkAAAAIKfyRXqTJ09OYn/961+TWNSJJepSd8sttySx8ePH\nJ7GiC+CjC8yjwraoc1RUPBd15osKBIu660Ud/+bNm5fEPvKRjySxhx56qNR+utLZD+0tKsSIulNN\nmTIliUXdlyTpe9/7Xv0Dyxk2bFgSi/L/vPPOC9efNWtWErv55puT2LJly7oxur4pOvZFBTyLFi1K\nYkWFQ4cddlgSiwr3IoceemgSW7FiRRJbuXJlEhswYEC4zahgK+ouFnXxo0ivsaLPVSn+/I5eo+g1\n3nfffZPY888/n8SiYs2izryNLlCut3Avep9GsWhOEM15ynb1Xb58eTie6KYKc+fODZftDs4gAwAA\nADlMkAEAAIAcJsgAAABADhNkAAAAIKfyRXrRxeDRRfVRN7y/+7u/S2JPPPFEEosuJC/qXFe2o020\nfrRc2a55GzduDMcTdbSLugWdddZZSez+++8vte8hQ4aE+0b7iIpIouLV6L339NNPJ7HofTt27Nhw\n3+9973uTWNRtbPTo0Uks6rw2derUJHbQQQclsSuvvDIczxe+8IUkdtRRRyWxX//61+H6fV1UrBZ1\nVoyKkR955JEkFnXylKQFCxYksai4KiqE+stf/pLEomNfVHj0wgsvhOOJ3i9RkV+U22is97///WE8\nKgyNPv+j3IwKxjZs2JDE5s+fn8SiznxFomLmsoVyRfOWjoqK+aKC2Oj5iZ7HqDg6eq9E78ehQ4eG\n4zn++OOT2Pe///1w2e7gDDIAAACQwwQZAAAAyGGCDAAAAOQwQQYAAAByOi3SM7MrJR0naZm7753F\nRki6TtIkSXMlfcTd02qwHhAVp0UFH4899lgSe9/73lcqdtNNNyWxoq5f0b6jzjBR8V3UxScqgIsu\nvo+KBqS4W+Ds2bOT2CWXXJLEdtpppyQWdaIq6krUSlo9j3tDUfemKLf322+/JPbss88msd122y2J\nLVy4MIk988wzSezhhx8Ox/PWt741iUWd73bZZZckFr1/1qxZk8ROO+20JHbdddeF49l7772TWPS+\n72HgpZ4AACAASURBVFioUlRIW1a75HB0XImev5deeimJRTly9tlnh/uJOt9FRVNRgdSYMWOSWFRQ\nFx1fZ86cGY7nkEMOSWJR0WBUzNcuWiWHo4JeKX6No9zceeedk1jUMTeKRQVw/fr1C8cTFcAVHbfL\nKNtJrytFepGo0C5670XPY1TUHc1ZpMZ2zYuUOYN8laRjOsTOl3Snu+8h6c7sZ6CVXSXyGNV2lchh\nVNtVIodREZ1OkN39Hkkd/1v1QUkzsn/PkHRCg8cFNBR5jKojh1F15DCqpLvn6se6+2JJcvfFZpZ+\nH5UxszMkndHN/QDNVCqPyWG0MI7FqDpyGC2p6Y1C3H26pOmSZGbe7P0BjUYOox2Qx6g6chg9qbt3\nsVhqZuMkKft7WeOGBPQY8hhVRw6j6shhtKTunkG+SdI0SZdkfze9p2pRlWdUGVm20vLcc89NYuef\nn9YHRC0So3aRUlwFG1X2R5WoUUVzdLeLqMI0urOFFFdoR22358yZk8SiltRRFfmECRPCfVdAj+dx\nPaLX3b37J1GKWtpGdyVZvHhxEovuGvHiiy8msT322COJRflaVL0ftQz++Mc/nsQmTpxYaj9R5XP0\nHo/aVBeJXpuOdzgoutNMnSqVw1L8OkXHqahtedSatuiOBEuWLCm1n+guQ9HrGeV2dEeTq6++OhxP\n9Lj33HPPJFb0eNpYj+fwj3/849Lx6LN68uTJSSy660q0va60gI7uKBQdn6N5QrTN6POi7HypSPR4\nohbs55xzThKLWkjfcccdSazormHN1ukZZDO7VtL9kvY0swVm9inVEvloM3tO0tHZz0DLIo9RdeQw\nqo4cRpV0egbZ3U8p+NW7GzwWoGnIY1QdOYyqI4dRJXTSAwAAAHKYIAMAAAA5Tb/NW6MUtTPu2M5V\nktauXZvEooKkVavSbpZR0V90sXvUNlSK29OuXr06iUUX+UftGaMCkujC9iLRRflRMWBUYBA9Z9EY\no2KavqRs8VxR+86yhXZltxkVA02ZMiWJ/eEPfwj3s+uuuyaxqG1v1EY1ej9GY4xaQBeJ8jCKRQWy\nUavWqLAqeo8XFb5GjzFqhdpxueh40xdFuRgd56JjceSJJ54I41HL2qgYM2o9HhUZRUWp0fvinnvu\nCccT5d3UqVOTWFeKQ9F80Wfes88+W2rdqHgu+vztSgFv2SK/Rhd1S+WLAaPj+wMPPFDXvnsDZ5AB\nAACAHCbIAAAAQA4TZAAAACCHCTIAAACQU5kivaibjRQXlw0fPjyJRYVyUXFGdBH7xo0bk1hRYdru\nu++exJYtSztnRkVGUXFhdLF7dPF9VEggxV1yotisWbOS2Oc///lwm2UUdQYqKnyqsnqK7IpEr1FU\n3BHl0Q477JDEogKxqEBIiruVjRo1Kly2o7JdmaKCuqLcKJsz0fMTvX+iwr2o8C56HqW4o9q4ceOS\n2Lp167b4uahIs68p03VQkpYvX57EDj300CS24447hvuJPgc6viZF+45ez6iQ6u1vf3sS+9jHPhaO\nJypOjD5H2vEYWRVRbkafZVFnt+jYV3a5qOC5K+Mpu+96i/SieUa076joNvqsio6l0faKxt3s9wpn\nkAEAAIAcJsgAAABADhNkAAAAIIcJMgAAAJBTmSK9MWPGhPGo21LUBWn06NFJLLpAPBJdIL548eJw\n2aiwJ+p8F12UX7YjXTSeoovVyxbpRYVZ0UX+gwYNSmJlH7PUd7qJRcUV/fr1C5eNisvKPs/R6x4V\nSESFaUVFaFFxU5RzUbFIVMQRjTEqui0qxI1Ez2VRt82OynaYirpGSfHzG4neP4jzJiq2jvIzKvQp\n6kI2d+7cJBYV30Vd0aJcLOrY11H0WSPFxYTRsTgqKEfP6Mpna5nlos/06LO/qAgtOs41+rhS9tgu\nle+kt2jRoiRW9vgejafe4sLu4gwyAAAAkMMEGQAAAMhhggwAAADkMEEGAAAAcjq9atrMrpR0nKRl\n7r53FrtI0qclbW51dIG739KsQUrFRXpRwUxUKBR11Fq4cGESiy6Aj4p/oiIqKS4iiYouJkyYkMSi\nC9uLCoU6KroAvuzjiTz++ONJLOqoVrbrm9R7RXrNzOOo+CbKt6Juh0UFEWXWjwrOyhbzFY0nKnqK\nikXKdnSKxhNtr6gLXxSPHk9UsFu2w1S0blFRZfS4oyLXjoVn9XZ9apVjcb2iroX9+/dPYtFxM8rN\naF0pfp3Hjh2bxKLj4dKlS5PYsGHDSo0xWk6Kj33R59r48eOTWHQ8iTqvtroq5nDZot4oFuVRUXF0\nJCquLlvMX7brXVc6fJZ9LsoWcEeiMZadBzVamU/mqyQdE8Qvc/f9sz8tk8xAgatEHqParhI5jGq7\nSuQwKqLTCbK73yNpZQ+MBWga8hhVRw6j6shhVEk91yB/zsyeMLMrzSy9cWrGzM4ws5lmNrOOfQHN\n0mkek8NocRyLUXXkMFpOdyfIP5C0m6T9JS2WdGnRgu4+3d0PdPcDu7kvoFlK5TE5jBbGsRhVRw6j\nJXWrk567/+eV52Z2uaT/aNiICkSFC9lYklh0QXd0YXtUmBOJLhp/+umnw2WnTJmSxKKxP//880ks\nKviIijO60vkmWrZskd5DDz2UxKIuUdG4yxae9abu5PG2226bdGo85JBDkuWiAsei4ozo9ainm1DZ\n4rmi4oyyhW1lO+RF44mKrYo61JXN4agIZOPGjUkseizRMaOoMCQqQI3epx0fT71FepHeOBbXa/Dg\nwUksem6i7npRbkavsRQXsa1bty6JRR33osK/JUuWJLFdd901iRW9r6LOrWU7nR500EFJ7M477wz3\nUzWtnsNdKWLrqGyxWtFncrTv6L0SLVd23F0piosKl8se16owJ+ioWyM2s3y/zhMlPdWY4QA9hzxG\n1ZHDqDpyGK2qzG3erpX0TkmjzGyBpAslvdPM9pfkkuZK+kwTxwjUjTxG1ZHDqDpyGFXS6QTZ3U8J\nwlc0YSxA05DHqDpyGFVHDqNKqndRCAAAANBE3SrS6w1RcZhUvpAjKs6IisvKXnBe1C1p3rx5Saxs\nV7XoIvaooCiKFXXSiwqfynbiiZaLOlFFRS5FXciqbptttklez+g5njhxYhKL8k2KCyKi17hoPB2V\n7ZpXVPRXdt9llS0QLOq0FI2zbIFt1IkqGk9UJFP0noqKwqIuaWvWrCkzxD7n0UcfTWL77LNPEote\np6jzXFG+Dh+e3i1s9913T2LRezUqwo6K9KIciToFSnFxZ5RLUb5HRd1oLWU/v7tSOF/2sz7ad9mi\n7uh9FhVRF+07OsZGRa5lbw7QSjiDDAAAAOQwQQYAAABymCADAAAAOUyQAQAAgBwmyAAAAEBOZe5i\nUVQZHFWzDxo0KIlFFciR6A4A0V0Kito4RpWaS5cuTWKTJ09OYtEYoyrWqGK1qOI+UrYFZdQSdty4\ncUksqsRu17tYmFny2KK7FUR3NSgSPVfRHR3KVkSXXbcrov1EORfdkaPe1qjR3VSi8USV19F4ouXK\ntqRG/UaNGpXEomPNggULSm0vauEsxa9pdIydPXt2Ettjjz2SWNQ+PvpcKmp9Hd3FZrfddktiixYt\nSmJz5swJt4nmq+duENHdt6J8LTr2RcfO6PO/nrtYROsWjSdaNhpj2TsFtTrOIAMAAAA5TJABAACA\nHCbIAAAAQA4TZAAAACCnMkV6RYUPkREjRiSxp556qtS6UYFTdMF6dLG6FBf0RRenR62q67kAvqhN\nb7TsDjvsEC7b0WOPPZbETjjhhCQWtZWMiqPawaZNm/TMM89sEdt///2T5aLXt6jVdFTEGRXARUUX\nZdtKR7EoN6TyRRvRGKNcj4oQyxbPSXFRS9RivmxRSr3KFrn21HiqJjoWR4VyUaHrhAkTktjatWvD\n/axfvz6JRbkYFUxHBXnR+2Lu3LlJbOzYseF45s+fn8Siz7Wyx2e0lrKtprtSoByJlm21Y03Zwuyy\n6/YWziADAAAAOUyQAQAAgBwmyAAAAEBOpxNkM5toZr8zs1lm9mcz+3wWH2Fmt5vZc9nfw5s/XKDr\nyGG0A/IYVUcOo0rKFOm9Juk8d3/UzAZLesTMbpd0mqQ73f0SMztf0vmSvtysgUZFOVJcaBR1anr0\n0UdL7Se62D3q5FXUZeuVV15JYlHRYFSYFXVkiwo+omKmok56UdHgmDFjwmU7ioppos5R0UX1Xens\n1wMamsMdC95mzpyZLDNgwIAkNnLkyHB7URexoiLQjqLnvmysXtE2o8KoKFZUkNdoZTtMdaXIpeyy\nHYtSGlBI0xLH4nrde++9pWJf+tKXktiUKVOSWFGRXnTcjY6HZQvtooK6ZcuWlVpXil//o446qtRy\nbaRyOdzootyuFOSVLZiO9l22GLArBXVlx1O2A2Cr63TE7r7Y3R/N/r1O0ixJEyR9UNKMbLEZktLb\nGwAtgBxGOyCPUXXkMKqkS6f5zGySpKmSHpQ01t0XS7WkN7PwtKSZnSHpjPqGCTQGOYx2QB6j6shh\ntLrSE2QzGyTpF5LOdfe1XfjaYbqk6dk22vq7I7Q2chjtgDxG1ZHDqIJSF4WY2XaqJfM17n5jFl5q\nZuOy34+TlF6MBbQIchjtgDxG1ZHDqIpOzyBb7b92V0ia5e7fyf3qJknTJF2S/f3rpowws2HDhjAe\nFUREnYgeeeSRJDZ8eFooGxWXRQV5UXevon0PGjQoiUUX1UcFJFEBV1SYGBUHFo0nKjqMRI87eixR\nMUzUNaq39EYOR0WlRYWmaJ7e7MrU6GKrVjkW95QhQ4Yksei4u3z58tLbjDpKRsev559/PomtW7cu\niUX59cILL4T7jorH+5p2zuGyRWhdOS7UU2hXdnvR53y9BXX1dNJrJWUusXiHpI9JetLMNvffvEC1\nRL7ezD4l6QVJH27OEIG6kcNoB+Qxqo4cRmV0OkF293slFU39393Y4QCNRw6jHZDHqDpyGFVSvRvT\nAQAAAE3EBBkAAADIaal2Z1sTFUhI0r777pvEoo50kfHjxyex6OLyqCva4sWLw21GXcOiYpNdd901\niY0ePTqJRY8l6mZXdOF/FI8eY1SwuGrVqiQWdY7acccdk9izzz4bjgcAyoiOpVFB0dChQ8P1o8Ll\nKBYd36Pi5uhY2r9//yRWVATdr1+/UvuOCtKjwuyibq7oHa+++moSi3Jmu+22S2JdeS3LFvmV/eyP\nbkzQlflE2cK/6HG3Os4gAwAAADlMkAEAAIAcJsgAAABADhNkAAAAIKcyRXovv/xyGI86zc2bN6/U\nNseMGZPEoovYowvOi7oiRQUW0TajosNFixYlsSlTppQaT1QAIkmrV69OYtGF9v9/e3cfZVdd33v8\n870IFEgImYSEPBKBWEBAWI0g4q110aJwkYd1rUrFQoXSq1jF2rvMtVrF1ZZeq6j19tqFBcFiUdZS\nl1yLVotYBCXkAYRAeAhJCHkOJCGRhEd/949zsrozv88mv3k6c/aZ92utWTPznb3P/u1zvnvPb87s\n7/5OmjQpi7kivV27dtnt9OeKSgCglCv+cb8HXAdSyRfLlT5maSGV20ZdgdOKFSuyWF0H1NLHRPdw\n+eHyzXWpq+uE5wro3LKlRZwuj9x46vKtdB+d0g6+3YR3kAEAAIAKJsgAAABABRNkAAAAoIIJMgAA\nAFDRmCK9mTNn2vjOnTuLl+3PFfi99NJLWcxdsO62K/kOTO7i9KlTp2Yx14mn9IL+uovqXbHJQIoE\n+nP78rrXvS6LPfjgg0WPBwBOafGPO2dLvvjOFTO5c/aWLVuymDs/u6Jst5zkO6DWLdtf6XOB4TeU\nAskNGzZksUMOOaR4ffe72hXKuWK+0pwZyP6VzhMc1zXS6aaCVN5BBgAAACqYIAMAAAAVTJABAACA\nCibIAAAAQMVeJ8gRMSsibo+IZRHxYER8uB3/dESsjYj72h9njfxwgYEjh9ELyGM0HTmMJim5i8VL\nkj6aUloSEeMlLY6IH7d/9oWU0udGbnj/yd1xQpKOPPLILLZ+/fqixzz44IOzmKsGdVWjv/rVr+xj\nuqpkdyeJGTNmZLEDDjggiw3kjhWO2x/XlnrVqlVFj/ee97wni7nq1Keffrro8TqkK3IYGKIxlcfu\n7jju7kGbNm2y60+YMCGLuXO5uwvGM888UzJE9fX1ZbFDDz3ULrtt27aix3SGcveALjOmcniodx8p\nnU+4vHY542Iu/+vyrbR1tuPuFlO6jdGy1wlySmm9pPXtr3dExDJJ+ewO6FLkMHoBeYymI4fRJAO6\nBjki5kg6SdKCduiDEXF/RFwXERNr1rksIhZFxKIhjRQYBuQwegF5jKYjh9HtiifIETFO0rclXZFS\n2i7pK5KOlHSiWn8Rft6tl1K6JqU0L6U0bxjGCwwaOYxeQB6j6chhNEHRBDki9lUrmb+RUvqOJKWU\nNqaUXk4p/VrSVyWdPHLDBIaGHEYvII/RdOQwmmKv1yBH64rpayUtSyldXYlPa19PJEnnS1o6MkNs\n+fKXvzygeImf/OQnWez888/PYq51dV0Rx3PPPZfFpkyZksXmzJmTxUoLSNxF8a5YRPIFfYsXL85i\npUUgu3btKop1k27JYWAoeiWPS1vdv/vd785i//RP/5TFzj77bLud+++/P4u5AiBX9HTCCSdksXHj\nxmUxV6D8wx/+0I7nqquusvES3dR+dyh6JYdL7bfffkXL1RWwuXx1MddC3RX9uzmGyy13TNRtx92w\nwN0cwI2n25XcxeI0Se+V9EBE3NeOfVzSBRFxoqQkaZWkPxmREQJDRw6jF5DHaDpyGI1RcheLOyW5\n+27cOvzDAYYfOYxeQB6j6chhNAmd9AAAAIAKJsgAAABARck1yD1r3bp1WeyDH/xgFjv99NOzmLvY\nXZK2b9+exdwF77fddlsWcxe2lxbPrVy5sji+du3aosd0Srvc9EpRCYDh5c5p7rziziGXXnppFjvu\nuOPsdo455pgs5grtXOe7Bx54IIu5or+hnEul8oLF0ucH3cX9TneF/Fu3brXru6I4lx9ujuHyo3Q+\nMZBOem4u5Lr1bt68uWjb3ZTXvIMMAAAAVDBBBgAAACqYIAMAAAAVTJABAACAiujkBdERsVnSE+1v\nJ0t6qmMbH1nsy8g7PKWUV9N0GDncCN26L12Rw9Ieedytz9VgsC+d0RV53KM5LPXW/nTrvhTlcEcn\nyHtsOGJRSmneqGx8mLEvY1MvPVfsy9jUS88V+zI29dpz1Uv70/R94RILAAAAoIIJMgAAAFAxmhPk\na0Zx28ONfRmbeum5Yl/Gpl56rtiXsanXnqte2p9G78uoXYMMAAAAdCMusQAAAAAqmCADAAAAFR2f\nIEfE2yLikYhYHhHzO739oYiI6yJiU0QsrcT6IuLHEfFY+/PE0RxjqYiYFRG3R8SyiHgwIj7cjjdy\nfzqpyTkskcdoaXIek8OQyOFu0as53NEJckTsI+kfJJ0p6VhJF0TEsZ0cwxBdL+lt/WLzJd2WUpor\n6bb2903wkqSPppSOkfQGSZe3X4um7k9H9EAOS+TxmNcDeXy9yOExjRzuKj2Zw51+B/lkSctTSitS\nSi9I+qakczs8hkFLKd0haUu/8LmSbmh/fYOk8zo6qEFKKa1PKS1pf71D0jJJM9TQ/emgRuewRB5D\nUsPzmByGyOGu0as53OkJ8gxJT1a+X9OONdnUlNJ6qZUkkqaM8ngGLCLmSDpJ0gL1wP6MsF7MYakH\nXnfyeEB6MY8b/5qTwwNCDnehXsrhTk+Qw8S4z9woiohxkr4t6YqU0vbRHk8DkMNdiDweMPK4y5DD\nA0YOd5ley+FOT5DXSJpV+X6mpHUdHsNw2xgR0ySp/XnTKI+nWETsq1YyfyOl9J12uLH70yG9mMNS\ng1938nhQejGPG/uak8ODQg53kV7M4U5PkBdKmhsRr46I/SS9W9ItHR7DcLtF0kXtry+S9L1RHEux\niAhJ10pallK6uvKjRu5PB/ViDksNfd3J40HrxTxu5GtODg8aOdwlejaHU0od/ZB0lqRHJT0u6S86\nvf0hjv0mSeslvajWX6+XSJqkVnXmY+3PfaM9zsJ9eZNa/466X9J97Y+zmro/HX7uGpvD7fGTx3w0\nOo/JYT7azx053AUfvZrDtJoGAAAAKuikBwAAAFQwQQYAAAAqmCADAAAAFUyQAQAAgAomyAAAAEAF\nE2QAAACgggkyAAAAUMEEGQAAAKhgggwAAABUMEEGAAAAKpggAwAAABVMkAEAAIAKJsgAAABABRNk\nAAAAoIIJMgAAAFDBBBkAAACoYIIMAAAAVDBBBgAAACqYIAMAAAAVTJABAACACibIAAAAQAUTZAAA\nAKCCCTIAAABQwQQZAAAAqGCCDAAAAFQwQQYAAAAqmCADAAAAFUyQAQAAgAomyAAAAEAFE2QAAACg\nggkyAAAAUMEEGQAAAKhgggwAAABUMEEGAAAAKpggAwAAABVMkAEAAIAKJsgAAABABRNkAHuIiBQR\nRw30Z3t5zIsj4s6hjw4YHnvLyYj4QURc1MkxAQNBDo8sJsgDFBG/qnz8OiJ2Vb5/z2iPD9gtIn4a\nEVsjYv/RHstIiYjfiYg1oz0OdK+IeFNE/DwinomILRFxV0S8fm/rpZTOTCnd8AqPyx996AhyeHQw\nQR6glNK43R+SVkt6eyX2jf7LR8SrOj/K7hsDOisi5kj6r5KSpHNGdTDAKImIgyV9X9KXJfVJmiHp\nSknPD/FxOaeiI8jh0cMEeZhFxF9FxLci4qaI2CHpwoj4jYj4+4hYHxFrI+LqiNivvfylEfHTyvqv\nav8be077+7MjYllE7IiINRHxkcqy50TELyNiW0TcGRHHVX62JiL+Z0Q8IGlnh3Yf3eMPJd0t6XpJ\ne/yLLSKuj4h/iIh/befVgog40j1I+52LJyPiLeZn+0fE5yJidURsjIh/jIgDXmFMERFfbr8L8nBE\nnF75wfSIuKX97sjyiPjjftv5YkSsa398sR07SNIPJE2v/Bdn+oCeJfS610hSSummlNLLKaVdKaUf\npZTu371AO4e3RsTKiDizEv9pRFza/vri9rt2X4iILZK+JekfJZ3azrttHd4vjB3k8Chhgjwyzpf0\nL5ImqJWEfylpnqQTJJ0k6TRJ/6vwsb4m6ZKU0vj2+v8hSe1/r3xV0qWSJkm6TtL3dk+8294t6cz2\nODC2/KGkb7Q/3hoRU/v9/AK13oWYKGm5pL/u/wAR8VZJN0n67yml2802/rdaJ+8TJR2l1jsbf/kK\nYzpF0gpJkyV9StJ3IqKv/bObJK2RNF3SOyT9TWUC/ReS3tDezusknSzpEymlZ9XK73WV/+Kse4Xt\nY+x5VNLLEXFDRJwZERP7/fwUSY+olZOflXRtRETNY+3O3ymSLpT0PyT9op13h4zM8AFyeLQwQR4Z\nd6aU/l9K6dcppV2S3iPp0ymlzSmlTZI+I+m9hY/1oqRjI2J8SmlLSmlJO36ZpP+bUlrY/qvyuna8\nel3Sl1JKa9pjwBgREW+SdLikm1NKiyU9LukP+i32nZTSPSmll9SaRJ/Y7+e/L+kaSWellO4x2whJ\nfyzpI+283CHpb9T6o6zOJklfTCm9mFL6llon9f8WEbMkvUnSx1JKz6WU7pP0T/rPY+Q9kj6TUtqU\nUtqs1sS+9PjBGJZS2q5WbiW13lDY3P5Pxe4/GJ9IKX01pfSypBskTZPU/4/J3dallL6cUnqJcyo6\nhRwePUyQR8aT/b6fJumJyvdPqPVuW4nz1bqGdHX73yWntOOHS/pY+/KKbe1/j0zr97j9x4Gx4SJJ\nP0opPdX+/l/U7zILSRsqX++UNK7fz69Qa4L9QM02DpV0oKTFlfz7YTteZ21KKVW+f0Ktd4ynS9o9\nya7+bHcuT1d+/HApBYqklJallC5OKc2UdJxaufPF9o83VJbbfSla/2NhN86nGBXk8OhggjwyUr/v\n16s1od1ttqS17a+fVWuisdthezxQSgtSSueo9S+R70v6ZvtHT0q6MqV0SOXjwJTSza8wDvS49jXA\n75T05ojYEBEbJH1E0usi4nUDeKjfl3ReRFxR8/OnJO2S9NpK/k1oF6/WmdHvX3+zJa1rf/RFxPh+\nP9t9jKxTfvzsvpSCHEexlNLDal2Xf9xeFrWr7+V7YMSRw53DBLkzbpL0lxExOSIOlfRJSTe2f/ZL\nSSdExPHtyc2ndq8UEQdExB9ExMEppRcl7ZD0cvvH10i6PCJeHy3jIuLt7cIljF3nqZUjx6p12cSJ\nko6R9DO1rksutU7S6ZI+FBEf6P/DlNKv1fp33xciYookRcSM9nXLdaa0H2/fiPj99rhuTSk9Kenn\nkq6KVkHrCZIuUevSD6l1/HwiIg6NiMlqXee8+/jZKGlSRHCdPTIRcXREfDQiZra/n6XW9fd3D8PD\nb5Q0s1/dBzCsyOHRwwS5M65UayL8gKT7JS2QdJUkpZQeUuvazZ+qdU3mHf3WvUjSExGxXa1Jw3vb\n6y2Q9H5JX5G0Va0L+S8c4f1A97tI0tdSSqtTSht2f0j6P5LeEwO4tU9KabVak+SP7a6E7udjahX4\n3d3Oz3+X9Juv8JALJM1V693nv5b0jpTS0+2fXSBpjloT8+9K+lRK6cftn/2VpEVqHTsPSFrSju1+\nN+UmSSval3pw6QWqdqhVmLQgIp5Va1KxVNJHh+GxfyLpQUkbIuKpvS0MDBI5PEpiz0sCAQAAgLGN\nd5ABAACACibIAAAAQAUTZAAAAKCCCTIAAABQMaQJckS8LSIeiYjlETF/uAYFdBJ5jKYjh9F05DC6\nzaDvYhER+6h1a7Hfk7RG0kJJF7RvW1a3Ts/cMuOoo46y8QkT8tuxPv/881nsv/yX/G+TodxR5Ne/\n/nXxsvvtl9/y8N577x30tjslpVTXX37QBprHvZTD6LxuyOH2OoPO43322cfGDz744Cy2a1fezdad\n59xj7tlTpuXFF1/MYu5cKkkvv/xyFnOP6cbjlnvVq/I7JLp1687jLv7CCy9kMfdclG7nN37jvscX\nVAAAIABJREFUN7LYSy+9ZMfjtl1quPN4LM0nJk+enMUmTZpkl123bl0W27Fjh1ly8FyuT5/u75Z5\n0EF5m4Wnn366KNZtSnK4+J6oxsmSlqeUVkhSRHxT0rmSahO6l1x99dU2fvbZZ2exlStXZrEDDjgg\ni7kTupv4upj7RST55J89e3YWc4k/RozpPEZP6GgOjx8/3sbf+ta8R8zSpUuzmJuYHXLIIVnMTXzd\nhMFNzCVp27ZtWcy9OfDcc88VLdfX15fF3OSzbkLq4qtWrcpi7rlw67pxH3300Vnsqaf87W1Xr15t\n46Oka8/D7neoU/oG13nnnZfFLrroIrvslVdemcX+/d//vWg7pVyuX3755XbZU089NYt97Wtfy2Jf\n//rXhz6wLjCUSyxmaM++3mvasT1ExGURsSgiFg1hW8BI2Wsek8PocpyL0XTkMLrOUN5Bdn9WZX9C\npZSuUastcmP/JYKettc8JofR5TgXo+nIYXSdoUyQ10iaVfl+plptYseEM844w8afffbZLOYup9h3\n332zmLv2zF12UXoNn+Sv2XPjcdfX1f2rsMeM6TxGT+hoDs+ZM8fG3b+i3bLunDZjRvZmoTZt2pTF\nNmzYkMXqzlMHHnhgFnOXY7hLOdxybtyuvqTukgZnypQpWcyN+7DDDivaztSpU7NY3fPTZZdYNOo8\n7H4Hz5o1K4vdddddWcxdJlR3iaS7VMFd3/vTn/40i7nj5+STT85i7rIJl9eS9Oijj2axK664Iot9\n8pOfzGLuEqwVK1bY7XSLoVxisVDS3Ih4dUTsJ+ndkm4ZnmEBHUMeo+nIYTQdOYyuM+h3kFNKL0XE\nByX9m6R9JF2XUnpw2EYGdAB5jKYjh9F05DC60VAusVBK6VZJtw7TWIBRQR6j6chhNB05jG5DJz0A\nAACgYkjvII8V1157bRZz96CUpM2bN2cxd/N2p/Q+yK5AoK4Qo+7i//7OPffcLPbtb3+7aF0AY4cr\nLJN88ZA7923fvj2LuXsju+Xc+bCu4YVb1hX5uQI4dw/lNWvW2O2Ujsfdb9YVR7vYr371qyzmCq5c\nEVWdMVyYPSCl9zf+0Y9+lMVczjzzzDNZrK7ZjbuP+Ny5c7PY7/7u72YxNxd5zWtek8VcnwZ3nNRx\ncyF3c4Cf/OQnWayu4Ldb8A4yAAAAUMEEGQAAAKhgggwAAABUMEEGAAAAKijS68cVUrzvfe/LYnUF\nG/vvv38Wc13u3IX/pQV5jlt3II4//vgsRpEegP5c1ztJ2rp1axZzBTx9fX1ZbPbs2VnsoYceymKl\nhW6SL5ZzsZ07dxZtpzRWV8DtuEJE91y4osHJkydnsUWLFmWxuufHFVu6Lm/IXXLJJVmstNDUvR51\nv+ddR7slS5ZksUMOOSSLuQLZX/ziF3Y7/Y0bN87G3ThdB02Xr6578IUXXpjFbrzxxpIhdgTvIAMA\nAAAVTJABAACACibIAAAAQAUTZAAAAKCCCTIAAABQwV0s+nGVqK5tYh3XLtpVebrtlLb+dHfFcLE6\nrjJ20qRJxesDo+2UU07JYgsWLBiFkfQ2d8cJV40ulVezu9bOxx57bBb74Q9/mMVctb5rw1zHVee7\nMR511FFF2169enUWq7uLhWsn7O4+5MbjHHzwwVnMPRd1ra/d+tzFoszFF1+cxdw84aCDDiparq7F\nt7srlrs7hXuN3VzE5X/pXbbqHtM54IADspjL9Q984ANZjLtYAAAAAF2KCTIAAABQwQQZAAAAqGCC\nDAAAAFQMqUgvIlZJ2iHpZUkvpZTmDcegus3GjRuzmLsIXfJFEqUFdO4CeBdzxR51hTMu7i7Ady1T\nx4qxksdNdeCBB2axv//7v89i7rg49dRTs5griHEFZu7xJH+suMLX/sdZadv4wRipHHbPfd25Yteu\nXVnMFay5QiH3mK6o5/DDD89iK1eutONxr5/bnxUrVhRt+4QTTshirqitrk2v47bjxui4NsauqHLV\nqlV2fdeqejR163n4uOOOy2KuUM6dA9w8wRXpufNPHXcecble2tLajaeuPXnpOEu37YoYP/zhD2ex\nL33pS0XbHW7DcReLt6SUnhqGxwFGE3mMpiOH0XTkMLoGl1gAAAAAFUOdICdJP4qIxRFxmVsgIi6L\niEURsWiI2wJGyivmMTmMBuBcjKYjh9FVhnqJxWkppXURMUXSjyPi4ZTSHdUFUkrXSLpGkiJi5C7C\nAwbvFfOYHEYDcC5G05HD6CpDmiCnlNa1P2+KiO9KOlnSHa+8VvPMmDEjiz355JN22WeffbboMceP\nH5/FXMGGK3JxhXsD6XzjuvNs2rTJrj8WjJU87gRXxDHU4rRPf/rTRdtx3SBdAdbs2bOzmBtjXdco\nV4w22kYqh133OFegVMcVD7mY244r9HHnrrqCIteRzhXAuW2786EreHZdAdesWWPHU/pclBZcuf1z\n3fHqXq+6DnujpVvPw29/+9uz2MSJE7OYez3cazmQ39+lBXlOaRdedy6tO/eVnt9dvro8dMfZ97//\nfbvt0TDoSywi4qCIGL/7a0lnSFo6XAMDOoE8RtORw2g6chjdaCjvIE+V9N32XxSvkvQvKaUfDsuo\ngM4hj9F05DCajhxG1xn0BDmltELS64ZxLEDHkcdoOnIYTUcOoxtxmzcAAACgYjgahfSUadOmZTF3\nYXpdMZ7rnFO6vusq4y6qdxffu244kr/Y3o1n9erVdn2MPUMptCtd1x1nn/nMZ+xjvva1r81i7jjb\nunVrFnPHz89+9rMsduuttxYtJ/nCP9dRrReUFpFJ/rxUWpB38sknZzFXULdly5YsVlek57jCNNd9\nbvny5VnMnbOnTJmSxVx+SL4I243nqafyPhmuGNBxRd11z89Anrex7Kqrrspi8+fPL1rXddZ1eVDX\nmdcVBJf+TnfHnltuIF1EXdzlXOlx786bjz/+uN32aOAdZAAAAKCCCTIAAABQwQQZAAAAqGCCDAAA\nAFRwlX4/b3zjG4e0vit8cB2YXFcZd6G+KwwpvUi/Lu5iO3bssOsDA+EKUJyjjz46i5133nl22bvu\nuiuLuWPAFTK5AkHXxfIDH/hAFvvTP/1TO56dO3dmMVds0r+4x+1Ht3NFZHUd2Nzz4s6Hrtvbz3/+\n8yy2ffv2LDZhwgS7bcedY11+uoIiV3Do1h03blzRcpK03377ZTHXSey3f/u3Bz3GgRRVlh6ryJ16\n6qlZ7N/+7d+ymPvdv2HDhizmckMqL8gv7a7nuHNk3XzCHc9ujK4gzxUcXnbZZSVDHDW8gwwAAABU\nMEEGAAAAKpggAwAAABVMkAEAAIAKivT6mTx5chZzF7G7IiHJd5VxF7y7ApK/+7u/y2Kf+MQnspjr\nzlN3Uf3+++9v4/3dfffdRcuh95V2zRuKefPmZTFXsCT5bmUu5rpBTpw4MYu5Y8V1L3Od3CTfnc8d\nZ/fff/8e37silSZ65plnbNwV67iCPHeO3bZtWxZzRWTuua/jCp/cY7qYK0ZyhVDuPF7Xoc4t656z\nd7zjHVnsiSeeyGILFy602ylVWuQ31ov53PnioYceymKnnXZaFlu6dGkWc+ekusJXN88oLcgrLex0\n6uYTbv3SLo9/9Ed/lMVcXncT3kEGAAAAKpggAwAAABVMkAEAAIAKJsgAAABAxV6L9CLiOklnS9qU\nUjquHeuT9C1JcyStkvTOlNLWkRtm59QV5vRXd7G765xz6KGHZjFXkPTJT34yi7kiPXcBfV0hhRuP\ns2rVqqLlmmqs5XE3cV3z3ve+92Wxum5S06dPz2Kuy1pfX18Wc50oS4tXnn/+eTse1/HSFQiuXbvW\nrj9Yo5HD7rlyhch13Pn0iCOOyGKu6NIVI7uiv7qiOLdtVwxVuo+lXfiGyj0Xjus+WNrNUPJjd8fg\nQF7vvWniebi005w7B7jOvK6j5i9/+Uu77U7k3EAKX91zcfjhh2ex0hzudiXP9PWS3tYvNl/SbSml\nuZJua38PdLPrRR6j2a4XOYxmu17kMBpirxPklNIdkrb0C58r6Yb21zdIOm+YxwUMK/IYTUcOo+nI\nYTTJYO+DPDWltF6SUkrrI6L2/fSIuEzSZYPcDjCSivKYHEYX41yMpiOH0ZVGvFFISukaSddIUkSM\nfAcCYJiRw+gF5DGajhxGJw32au+NETFNktqffQssoLuRx2g6chhNRw6jKw32HeRbJF0k6W/bn783\nbCMaZdOmTctirnKzrvLTVb67NrQ333zzIEbX4qqzXcW35CuYO9FKuCF6No/7c1XXA8kDl0fuzg+u\nne63vvWtLLZkyZIstnHjRrvtyy+/PIu5sU+YMCGLuQr8p59+Oott3ZoXzde1hnb7WNrSfQSMaA67\n17jurgbueXGxOXPmZLG6Kv7+DjrooCxW1/rabbv0Dibubg7ujihTp06123bq2gn351pIH3/88VnM\ntSEuPSYlf4eEcePGZbHhvItFjZ49D7tW066tet2dRkrP0aXLuXmLOybqHm/8+PFZbN26dUXbbqK9\nvoMcETdJ+oWk34yINRFxiVqJ/HsR8Zik32t/D3Qt8hhNRw6j6chhNMle30FOKV1Q86PTh3kswIgh\nj9F05DCajhxGk9BJDwAAAKhgggwAAABUjPht3prGtTIdiNJina985StFy5W2uaxrNe3GU1fQh941\nkCK90kImxxXUuRasM2fOzGLz5s2zj/m1r30ti73mNa/JYq6luyu0c/nvirLqWl+7QrHSAqymOeSQ\nQ7LYQPLGtQl3RV//8R//kcVc4ZJbt67AyeV8abFpactlV9RWVxTncsQt+9WvfjWL3XLLLVnMFVy5\nbUyePNmOx7VrRxmX665o0hlI4bzL4aG0mnZjdLn+4osv2vVdfrljoJTbl7q5zGjgHWQAAACgggky\nAAAAUMEEGQAAAKhgggwAAABUUKTXj+uy5S6Ur7uI3RXwOI8//njRcps25V03XeFdXYGAK+TYvHlz\n0bbh9c+HJnQmdIUPLq/rlnXOPPPMLHbVVVdlsQULFmSxuXPnZrG64qYpU6ZkMVcs4jpU7dixI4u5\n48d1iHKPJ0kTJ07MYlu2bLHLNp0rsqsrinPFOpMmTcpi7nXesGFDFuvr68ti7lgbSNGSy223Pzt3\n7sxipQVOdcePi7vnd8WKFVnsnnvuyWKnn57fOtgVOx522GF2PE5pMS7KuNwcSNfNunN0yXKlBf5O\n3e80dwy4Qt6hdm7tFryDDAAAAFQwQQYAAAAqmCADAAAAFUyQAQAAgAqK9Pp56qmnipar67J1wAEH\nZDFXYFHq9ttvz2Jnn3128fquAMUV/mHwhlr40AlD7Vjk8vrSSy/NYjfeeGMWe+tb35rF1q5dm8Vm\nzJhht33WWWdlsZUrV2Yxd+y6Aj+3L88//3wWcwWukj+mjjzySLts07ncdh3lJF+s47jXyXU3dNs5\n8MADs1hpBzOpvKDPHRsu5goO64oY3fpuPG79m2++OYvNnz8/i7lOsHXFr45bv65YFXvn5gnu90Dd\nudgdf6W/bxyXb/vuu28WqyvWdDcnKD0fLl++vGg8dNIDAAAAuhQTZAAAAKCCCTIAAABQwQQZAAAA\nqNhrkV5EXCfpbEmbUkrHtWOflvTHkna3ZPt4SunWkRpkJ5UWfNQVYriLzg8//PBBj+ehhx7KYuef\nf37x+m48zz777KDH01TDmcfd1BGotIhjIIUPZ5xxRhZzBXSzZ8/OYq74zhVlTZgwIYutW7fOjsd1\nt3Rd21wBlytQcp0kH3744Sz2hje8wY7HPZd1BYbDaTTOxS7X6wp4XHdDx3UddEV67jUeSEGeKz5y\n69edy/tzxWpu3brHqytu7M8Vyi1atCiL3XLLLVnMHQN1XV+duuLz4dIr84nSQrmZM2dmsVWrVhVv\np7RrrisodvnmlnP7UpfDu3btymJuf44//vgsVlqk101KRne9pLeZ+BdSSie2P7o6mQGRx2i+60UO\no9muFzmMhtjrBDmldIek/E9+oEHIYzQdOYymI4fRJEN5f/uDEXF/RFwXERPrFoqIyyJiUUTk/yMC\nRt9e85gcRpfjXIymI4fRdQY7Qf6KpCMlnShpvaTP1y2YUrompTQvpTRvkNsCRkpRHpPD6GKci9F0\n5DC60qA66aWUNu7+OiK+Kun7wzaiUea6bLmL2Ou6bLmioF/+8peDHo8rmhhIEZYb59atWwc9nl4y\n2DwuKdAYSOGDW9YVR7nijIF0ZervLW95i41/7nOfy2LuuHB5dMQRR2QxVxT66KOPZrHXv/71djyu\nqM5xhX8LFizIYnfeeWcWe/Ob35zF6jrDuf0+6KCDSoY47EbjXFxXpLd9+/Ys5nLWFW26x6w7x5Zy\n65c+ZmnxkMuRunXd7wZ3rPb19WUxVyB43333ZbGBFDa616u0YHE4NXE+UZofs2bNymIuB+sKv93r\nUVqk55QWF7oCV8kX07rHLO0sWncu6RaDegc5IqZVvj1f0tLhGQ7QOeQxmo4cRtORw+hWJbd5u0nS\n70iaHBFrJH1K0u9ExImSkqRVkv5kBMcIDBl5jKYjh9F05DCaZK8T5JTSBSZ87QiMBRgx5DGajhxG\n05HDaJLuvkszAAAA0GGdvyK/y9V18+qv7iJ9d7H84sWLBz2eNWvWZDF3Qf9AOtJs2LBh0ONB/vyX\nFk2WdhobKjeeP/uzP8ti73rXu+z67hhwRXpvfOMbs5jreue667lCpPXr19vx7NixI4u5zncLFy4s\nesyjjjoqi7niwrriJvfaumLa/kWDrsCl27l9rXteXGGPOy+tWLGiaDt1hUIl60r+XOw6k7mOY644\nyhXkHXbYYUXLSb4obufOnXbZkvG4fHJd+OqMdNe8XlZa7OY6bLpjou6YcvlaqrTLqovVHXulY3fH\nhTOQDq+jgXeQAQAAgAomyAAAAEAFE2QAAACgggkyAAAAUMEEGQAAAKjgLhb93HvvvUNa31VOl7Zd\ndNwdJ1zV6EDuqlF6pw6UqWsT2t/JJ59s467q3bWWdS2b3/nOd2axc845J4utWrUqi9WN+8UXX8xi\n//qv/5rFXKWyq7qeO3duFnPV++6OLXXLujsP/NZv/VYWO/HEE7OYq96fMmVKFnOvgeSfN3ec9b/z\nh3v9up1roe3yQ/KvvWt97O6+4O7S4O7I4NpU192NwZ0T3bKu3a0bT10+9Ofu0CL5nHUxxz2PpVyL\na4m7WAxF3THQ38SJE7PYQO7c4M4rpXeicPlfeseluvmEi7vz4dSpU+36TcM7yAAAAEAFE2QAAACg\nggkyAAAAUMEEGQAAAKigSK+fpUuXFi3nLp6XfAFdaRGX4wop3IX2da0q3bKuYAuDN2fOnCz2pS99\nqXj9adOmZTHX6tMV6rjcuvHGG7OYy1dXmCZJp512WhZzLZs3btyYxdy4t27dmsVc6+q6Y8oVMboW\nrpMnT85i7vhxY3RtgN3+1XEFXM8888we39cdo91s5cqVWcy9dpI0adKkLLZkyZIs5p6H/m25JV88\n5wr86oriXEFRaUGS2/aWLVuymCuAqzuuNm3aVLS+K0p143aFhE7dceWUtr4e60oL7UpbLtfNEVzh\nqzsG3Prud7/LBVdwWNeyvHQuU3cMNA3vIAMAAAAVTJABAACACibIAAAAQMVeJ8gRMSsibo+IZRHx\nYER8uB3vi4gfR8Rj7c/5HbGBLkAOoxeQx2g6chhNUnKV/0uSPppSWhIR4yUtjogfS7pY0m0ppb+N\niPmS5kv62MgNdfTs2rUrix144IF22eeffz6LHXvssYPetivEcBff13HFJq47X48b0Rz+7Gc/m8Ve\n+9rXZrF77rnHrn///fdnMVcM4Qo+HnjggSz26le/Oou5YqCHHnrIjsfl3CWXXJLFVq9encVcYduO\nHTuymCtqqysAcQUxrmjQdeJz++KKxFzhWV0RlCtgcd0p3XljiDp+Lr777ruH42H2MG/evCx26KGH\nZjF3Lh1I9zeXN6XFVW650m3XdSFz3PnZxV544YUs5oq16o7pLjKm5hN1XQz7q/ud7nKutPi0tIjT\nnXddB82BqCvkbZq9HskppfUppSXtr3dIWiZphqRzJd3QXuwGSeeN1CCBoSCH0QvIYzQdOYwmGdBt\n3iJijqSTJC2QNDWltF5qJX1E2Pt6RMRlki4b2jCB4UEOoxeQx2g6chjdrniCHBHjJH1b0hUppe2l\n/+ZPKV0j6Zr2Ywz+hsDAEJHD6AXkMZqOHEYTFF0sFRH7qpXM30gpfacd3hgR09o/nyYpvws60CXI\nYfQC8hhNRw6jKfb6DnK0/rS7VtKylNLVlR/dIukiSX/b/vy9ERlhF1i0aFEWO/744+2yriBp7ty5\ng9626yLmLtKvKwxxBR+us1kvG84cHj9+vE455ZQ9YrNmzcqWc0VkdTnz3HPPZTHX3cgV6hxxxBFZ\nzBWQukKMujxYvHhxFps4MS8qd8VurkudKzRxY3TbqOOOM1eUUtoZzHUprHt+3HPp1h9uvXIuduc0\nxz3PpQVKdVx+uuPPHWuuU6Nbrm48riua444Xdx6vKxTvZr2Sw6Xc+ce9W15XoOzyvXR9l2+l+VpX\nZFfaSW/8+PFFy3W7kkssTpP0XkkPRMR97djH1UrkmyPiEkmrJf3+yAwRGDJyGL2APEbTkcNojL1O\nkFNKd0qqu0Do9OEdDjD8yGH0AvIYTUcOo0nopAcAAABUMEEGAAAAKgZ0H+Sx6utf/3oW+8IXvmCX\ndcUUQzFt2rSi5epuk+O6LbnObSjzwgsvaOXKlXvEnnzyyWy5KVPy23hu3rzZPqYrtnGFOq4I7aij\njspipV2VXLc/yefMs88+m8VclyjXZc4dE5MmTcpiDz74oB2PK3SZPn16FnOd61xRlhuP22fXya1O\nr3SO6gSXN64YqbTAcvv27Tbuct69Tm47rpiptGDarSuV77cr+HSFhG5dd96oG09psfdw/04bS9xr\n6X5X1z3HLmf22WefLOZeY3f+cudnV4w81I6VA+n22814BxkAAACoYIIMAAAAVDBBBgAAACqYIAMA\nAAAVFOkV+Od//ucsduWVV9plXVHQU089lcXcxfuuUKi0S1TdRfVbtmwZ9GMi9/zzz+vxxx/fI/bO\nd74zW+7888/PYuecc459TFdo5zoRuQILV+DnXt++vr4sVleE5roluc51rujwnnvuyWLf/OY3s9jd\nd9+dxeo6173pTW/KYj/72c+y2MaNG7OYOy5chyn3nD322GN2PK6DYF0hFHLu+du0Ke8s7M6brpud\nK2CTpNWrVxetv2LFCrt+f0uXLs1iroD74Ycftuu787vbtstFV8TlCupcUVddbrrt8LtheLnzpns9\nXEGp5M9VrtDUFSi7Quht27ZlMXeOdMdo3Xgc1wm2iXgHGQAAAKhgggwAAABUMEEGAAAAKpggAwAA\nABUU6RVwxUyPPPKIXdZ1J3PFFK4T2KOPPlq0riuiqrvI3xWlYOR997vfLYrVcUWcLrdckd5hhx2W\nxVwnvKefftpu28XrCo864c4778xirnDPFQ2648cdK64IyhW+SP58sGHDBrsscuvXr89ixx13XBa7\n+OKLs9gRRxyRxerOca5DnlvWFSnNnDkzi7mOfaUFfpK0Zs2aLOYKCV1BrdvOwoULhzQed2xQpFem\ntGOhy8GDDjqo6PEkX7jsOjqWFuO73yuusNMVyErSxIkTs5gryHPdZZuId5ABAACACibIAAAAQAUT\nZAAAAKCCCTIAAABQsdcJckTMiojbI2JZRDwYER9uxz8dEWsj4r72x1kjP1xg4Mhh9ALyGE1HDqNJ\nSu5i8ZKkj6aUlkTEeEmLI+LH7Z99IaX0uZEbXudFRBZzd43o3254t5NOOimLuWrSI488Mou5u1i4\n9rkDaVV5xx132PgY07gcdm1pFyxYMAoj6U533XXXaA9hNDQuj51jjjkmi33oQx/KYq6y392lwbWp\nlny7dndXAXcHE3fHCndMujbXdXckcHfGmDJlShZzd9o444wzstjs2bOz2A9+8IMsVve7we13BzQu\nh92cwN0BxHF3eDj22GOz2DPPPGPXd7/r3d11XC64ttAuF9wxMWHCBDueffbZJ4u51teld0Pp9jup\n7HWCnFJaL2l9++sdEbFM0oyRHhgwXMhh9ALyGE1HDqNJBnQNckTMkXSSpN1vZX0wIu6PiOsiIr9B\nXmudyyJiUUQsGtJIgWFADqMXkMdoOnIY3a54ghwR4yR9W9IVKaXtkr4i6UhJJ6r1F+Hn3XoppWtS\nSvNSSvOGYbzAoJHD6AXkMZqOHEYTFE2QI2JftZL5Gyml70hSSmljSunllNKvJX1V0skjN0xgaMhh\n9ALyGE1HDqMp9noNcrSuUL9W0rKU0tWV+LT29USSdL6kpSMzxM4qLdL73ve+Z9d/17velcUOPvjg\nLOZa5boCC3cRu2sX6WKSdO+999p4yXa66WL5oRhrOYze1Ct5fNNNN2UxV4D653/+51ls3rz8jUN3\nfpV8sZs7z23bti2LuXa+hx56aBZzraJdMZ5UXnC1fPnyLOaeMxdzRqkYz2piDrvf/67dvPP5z+dv\nhLtcf/vb327XP/roo7PYm9/85iy2cuXKLOYK/1zrapeX69ats+NZvHhxFlu0KL/axcUc99x2k5K7\nWJwm6b2SHoiI+9qxj0u6ICJOlJQkrZL0JyMyQmDoyGH0AvIYTUcOozFK7mJxp6T8bVXp1uEfDjD8\nyGH0AvIYTUcOo0nopAcAAABUMEEGAAAAKqKTF0lHRHdfkS3fKebll18uXv+RRx4pir3//e/PYmvX\nrs1ifX19WewjH/lIFqsrVJk/f34Wc4UhTZBScv+a66gm5DC6VzfksNRbeVxXFOcK+qZNm5bFpk+f\nnsVcJzFXCLVw4cIs5s7ZkrRly5Ysduedd9plu1035HG35XBpgf9QXXjhhVnsxhtvHPTjuTlP3bh7\npXBfKsth3kEGAAAAKpggAwAAABVMkAEAAIAKJsgAAABARaeL9DZLeqL97WRJT3Vs4yOn9lvwAAAD\nKUlEQVSLfRl5h6eU8lZWHUYON0K37ktX5LC0Rx5363M1GOxLZ3RFHvdoDku9tT/dui9FOdzRCfIe\nG45YlFLKy4wbiH0Zm3rpuWJfxqZeeq7Yl7Gp156rXtqfpu8Ll1gAAAAAFUyQAQAAgIrRnCBfM4rb\nHm7sy9jUS88V+zI29dJzxb6MTb32XPXS/jR6X0btGmQAAACgG3GJBQAAAFDBBBkAAACo6PgEOSLe\nFhGPRMTyiJjf6e0PRURcFxGbImJpJdYXET+OiMfanyeO5hhLRcSsiLg9IpZFxIMR8eF2vJH700lN\nzmGJPEZLk/OYHIZEDneLXs3hjk6QI2IfSf8g6UxJx0q6ICKO7eQYhuh6SW/rF5sv6baU0lxJt7W/\nb4KXJH00pXSMpDdIurz9WjR1fzqiB3JYIo/HvB7I4+tFDo9p5HBX6ckc7vQ7yCdLWp5SWpFSekHS\nNyWd2+ExDFpK6Q5JW/qFz5V0Q/vrGySd19FBDVJKaX1KaUn76x2SlkmaoYbuTwc1Oocl8hiSGp7H\n5DBEDneNXs3hTk+QZ0h6svL9mnasyaamlNZLrSSRNGWUxzNgETFH0kmSFqgH9meE9WIOSz3wupPH\nA9KLedz415wcHhByuAv1Ug53eoIcJsZ95kZRRIyT9G1JV6SUto/2eBqAHO5C5PGAkcddhhweMHK4\ny/RaDnd6grxG0qzK9zMlrevwGIbbxoiYJkntz5tGeTzFImJftZL5Gyml77TDjd2fDunFHJYa/LqT\nx4PSi3nc2NecHB4UcriL9GIOd3qCvFDS3Ih4dUTsJ+ndkm7p8BiG2y2SLmp/fZGk743iWIpFREi6\nVtKylNLVlR81cn86qBdzWGro604eD1ov5nEjX3NyeNDI4S7RszmcUuroh6SzJD0q6XFJf9Hp7Q9x\n7DdJWi/pRbX+er1E0iS1qjMfa3/uG+1xFu7Lm9T6d9T9ku5rf5zV1P3p8HPX2Bxuj5885qPReUwO\n89F+7sjhLvjo1Rym1TQAAABQQSc9AAAAoIIJMgAAAFDBBBkAAACoYIIMAAAAVDBBBgAAACqYIAMA\nAAAVTJABAACAiv8PxeIxJCXV0YEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ffbbeb9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 4,figsize=(10,10))\n",
    "for row in axes:\n",
    "    for axe in row:\n",
    "        index = np.random.randint(10000)\n",
    "        img = data_train.iloc[index, 1:].values.reshape((28, 28))\n",
    "        obj = data_train.iloc[index, 0]\n",
    "        axe.imshow(img, cmap='gray')\n",
    "        axe.set_title(clothes[obj])\n",
    "        plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = data_train.iloc[:,1:].values\n",
    "y = data_train.iloc[:,0].values\n",
    "y = to_categorical(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test = data_test.iloc[:,1:].values\n",
    "y_test = data_test.iloc[:,0]\n",
    "y_test  = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reshaping Data +  Normalization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48000, 784)\n",
      "(12000, 784)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_val.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
    "X_val = X_val.reshape(X_val.shape[0], img_rows, img_cols, 1)\n",
    "X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
    "\n",
    "\n",
    "X_train = X_train.astype('float32')\n",
    "X_val = X_val.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "X_train = X_train  / 255\n",
    "X_val   = X_val   / 255\n",
    "X_test  = X_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48000, 28, 28, 1)\n",
      "(12000, 28, 28, 1)\n",
      "(10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_val.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 1.0, 1.0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.max(),X_val.max(), X_test.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0, 0.0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.min(),X_val.min(), X_test.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Architecture\n",
    "\n",
    "## Now we need to follow the following steps:\n",
    "    \n",
    "    1. Convolution \n",
    "    2. Max Pooling\n",
    "    3. Convolution\n",
    "    4. Max pooling\n",
    "    5. Dropout\n",
    "    6  Flattening\n",
    "    7. Full Connection (Dense)\n",
    "    8. Dropout\n",
    "    9. Full Connection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_architecture(nb_filters,kernel_size,input_shape):\n",
    "    \n",
    "    model = Sequential()\n",
    "    model.add(Convolution2D(filters= nb_filters,kernel_size=(kernel_size,kernel_size),input_shape = input_shape,activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(pool_size,pool_size)))\n",
    "\n",
    "    model.add(Convolution2D(filters=nb_filters, kernel_size=(kernel_size,kernel_size),activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(pool_size,pool_size)))\n",
    "\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Flatten())\n",
    "\n",
    "    model.add(Dense(output_dim = 128 , activation=\"relu\"))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(output_dim = nb_classes, activation='softmax'))\n",
    "    model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
    "    return model\n",
    "#model.compile(loss = keras.losses.categorical_crossentropy, optimizer= keras.optimizers.adam(), metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 11, 11, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 32)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 5, 5, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 800)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               102528    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 113,386\n",
      "Trainable params: 113,386\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agebresi\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=128)`\n",
      "  del sys.path[0]\n",
      "C:\\Users\\agebresi\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"softmax\", units=10)`\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "model = model_architecture(nb_filters,kernel_size,input_shape)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########    Run 1    ######## \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agebresi\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=128)`\n",
      "  del sys.path[0]\n",
      "C:\\Users\\agebresi\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"softmax\", units=10)`\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.6667 - acc: 0.7591 - val_loss: 0.4146 - val_acc: 0.8460\n",
      "Epoch 2/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.4517 - acc: 0.8351 - val_loss: 0.3557 - val_acc: 0.8742\n",
      "Epoch 3/50\n",
      "48000/48000 [==============================] - 27s - loss: 0.4002 - acc: 0.8554 - val_loss: 0.3229 - val_acc: 0.8848\n",
      "Epoch 4/50\n",
      "48000/48000 [==============================] - 27s - loss: 0.3706 - acc: 0.8650 - val_loss: 0.3042 - val_acc: 0.8915\n",
      "Epoch 5/50\n",
      "48000/48000 [==============================] - 27s - loss: 0.3475 - acc: 0.8736 - val_loss: 0.3012 - val_acc: 0.8898\n",
      "Epoch 6/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.3313 - acc: 0.8795 - val_loss: 0.2893 - val_acc: 0.8912\n",
      "Epoch 7/50\n",
      "48000/48000 [==============================] - 27s - loss: 0.3195 - acc: 0.8827 - val_loss: 0.2807 - val_acc: 0.8966\n",
      "Epoch 8/50\n",
      "48000/48000 [==============================] - 27s - loss: 0.3055 - acc: 0.8891 - val_loss: 0.2644 - val_acc: 0.9005\n",
      "Epoch 9/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2926 - acc: 0.8932 - val_loss: 0.2590 - val_acc: 0.9026\n",
      "Epoch 10/50\n",
      "48000/48000 [==============================] - 27s - loss: 0.2887 - acc: 0.8933 - val_loss: 0.2569 - val_acc: 0.9081\n",
      "Epoch 11/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2749 - acc: 0.8981 - val_loss: 0.2530 - val_acc: 0.9048\n",
      "Epoch 12/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2716 - acc: 0.8992 - val_loss: 0.2561 - val_acc: 0.9066\n",
      "Epoch 13/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2643 - acc: 0.9009 - val_loss: 0.2522 - val_acc: 0.9073\n",
      "Epoch 14/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2577 - acc: 0.9047 - val_loss: 0.2431 - val_acc: 0.9117\n",
      "Epoch 15/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2552 - acc: 0.9052 - val_loss: 0.2452 - val_acc: 0.9120\n",
      "Epoch 16/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2473 - acc: 0.9095 - val_loss: 0.2511 - val_acc: 0.9097\n",
      "Epoch 17/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2414 - acc: 0.9089 - val_loss: 0.2503 - val_acc: 0.9086\n",
      "Epoch 18/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2401 - acc: 0.9102 - val_loss: 0.2411 - val_acc: 0.9128\n",
      "Epoch 19/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2384 - acc: 0.9113 - val_loss: 0.2368 - val_acc: 0.9148\n",
      "Epoch 20/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2316 - acc: 0.9125 - val_loss: 0.2404 - val_acc: 0.9142\n",
      "Epoch 21/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2297 - acc: 0.9135 - val_loss: 0.2376 - val_acc: 0.9157\n",
      "Epoch 22/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2277 - acc: 0.9139 - val_loss: 0.2483 - val_acc: 0.9108\n",
      "Epoch 23/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2239 - acc: 0.9155 - val_loss: 0.2430 - val_acc: 0.9131\n",
      "Epoch 24/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2235 - acc: 0.9147 - val_loss: 0.2496 - val_acc: 0.9099\n",
      "Epoch 25/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2208 - acc: 0.9171 - val_loss: 0.2428 - val_acc: 0.9167\n",
      "Epoch 26/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2138 - acc: 0.9196 - val_loss: 0.2473 - val_acc: 0.9110\n",
      "Epoch 27/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2094 - acc: 0.9206 - val_loss: 0.2393 - val_acc: 0.9143\n",
      "Epoch 28/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2098 - acc: 0.9206 - val_loss: 0.2379 - val_acc: 0.9165\n",
      "Epoch 29/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2092 - acc: 0.9212 - val_loss: 0.2363 - val_acc: 0.9163\n",
      "Epoch 30/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2065 - acc: 0.9226 - val_loss: 0.2448 - val_acc: 0.9145\n",
      "Epoch 31/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2027 - acc: 0.9227 - val_loss: 0.2626 - val_acc: 0.9068\n",
      "Epoch 32/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.1994 - acc: 0.9237 - val_loss: 0.2473 - val_acc: 0.9127\n",
      "Epoch 33/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2016 - acc: 0.9233 - val_loss: 0.2411 - val_acc: 0.9159\n",
      "Epoch 34/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.2001 - acc: 0.9238 - val_loss: 0.2429 - val_acc: 0.9183\n",
      "Epoch 35/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.1975 - acc: 0.9252 - val_loss: 0.2369 - val_acc: 0.9155\n",
      "Epoch 36/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.1971 - acc: 0.9245 - val_loss: 0.2343 - val_acc: 0.9168\n",
      "Epoch 37/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.1934 - acc: 0.9261 - val_loss: 0.2415 - val_acc: 0.9153\n",
      "Epoch 38/50\n",
      "48000/48000 [==============================] - 30s - loss: 0.1930 - acc: 0.9257 - val_loss: 0.2428 - val_acc: 0.9175\n",
      "Epoch 39/50\n",
      "48000/48000 [==============================] - 30s - loss: 0.1964 - acc: 0.9252 - val_loss: 0.2423 - val_acc: 0.9186\n",
      "Epoch 40/50\n",
      "48000/48000 [==============================] - 31s - loss: 0.1906 - acc: 0.9265 - val_loss: 0.2498 - val_acc: 0.9164\n",
      "Epoch 41/50\n",
      "48000/48000 [==============================] - 31s - loss: 0.1893 - acc: 0.9281 - val_loss: 0.2467 - val_acc: 0.9149\n",
      "Epoch 42/50\n",
      "48000/48000 [==============================] - 31s - loss: 0.1888 - acc: 0.9278 - val_loss: 0.2452 - val_acc: 0.9159\n",
      "Epoch 43/50\n",
      "48000/48000 [==============================] - 31s - loss: 0.1841 - acc: 0.9294 - val_loss: 0.2536 - val_acc: 0.9139\n",
      "Epoch 44/50\n",
      "48000/48000 [==============================] - 30s - loss: 0.1827 - acc: 0.9298 - val_loss: 0.2474 - val_acc: 0.9166\n",
      "Epoch 45/50\n",
      "48000/48000 [==============================] - 30s - loss: 0.1840 - acc: 0.9297 - val_loss: 0.2390 - val_acc: 0.9166\n",
      "Epoch 46/50\n",
      "48000/48000 [==============================] - 30s - loss: 0.1820 - acc: 0.9310 - val_loss: 0.2443 - val_acc: 0.9136\n",
      "Epoch 47/50\n",
      "48000/48000 [==============================] - 31s - loss: 0.1800 - acc: 0.9303 - val_loss: 0.2391 - val_acc: 0.9173\n",
      "Epoch 48/50\n",
      "48000/48000 [==============================] - 29s - loss: 0.1827 - acc: 0.9293 - val_loss: 0.2453 - val_acc: 0.9164\n",
      "Epoch 49/50\n",
      "48000/48000 [==============================] - 28s - loss: 0.1791 - acc: 0.9307 - val_loss: 0.2423 - val_acc: 0.9178\n",
      "Epoch 50/50\n",
      "48000/48000 [==============================] - 29s - loss: 0.1744 - acc: 0.9335 - val_loss: 0.2452 - val_acc: 0.9183\n",
      " 9792/10000 [============================>.] - ETA: 0s########    Run 2    ######## \n",
      "\n",
      "########    Run 3    ######## \n",
      "\n",
      "########    Run 4    ######## \n",
      "\n",
      "########    Run 5    ######## \n",
      "\n",
      "########    Run 6    ######## \n",
      "\n",
      "########    Run 7    ######## \n",
      "\n",
      "########    Run 8    ######## \n",
      "\n",
      "########    Run 9    ######## \n",
      "\n",
      "########    Run 10    ######## \n",
      "\n"
     ]
    }
   ],
   "source": [
    "training_time    = {}\n",
    "training_history = {}\n",
    "testing_history  = {}\n",
    "\n",
    "tranining_acc   = 0.0\n",
    "validation_acc  = 0.0\n",
    "testing_acc     = 0.0\n",
    "for i in range(1,11):\n",
    "    print('########    Run ' + str(i) + '    ######## \\n')\n",
    "    \n",
    "    start_time = time.time()\n",
    "    if (i == 1):\n",
    "        verbose = 1\n",
    "    else:\n",
    "        verbose = 0\n",
    "    model = model_architecture(nb_filters,kernel_size,input_shape)\n",
    "    training_history['run_' + str(i)] = model.fit(X_train,y_train,batch_size= batch_size, epochs = nb_ephochs, verbose=verbose, validation_data=(X_val, y_val))\n",
    "    training_time['run_' + str(i)] = time.time() - start_time\n",
    "    tranining_acc  =  tranining_acc  + training_history['run_' + str(i)].history['acc'][-1]\n",
    "    validation_acc =  validation_acc + training_history['run_'  + str(i)].history['val_acc'][-1]\n",
    "    \n",
    "    score = model.evaluate(X_test, y_test, verbose=verbose)\n",
    "    testing_history['loss run_' + str(i)] = score[0]\n",
    "    testing_history['acc run_'  + str(i)] = score[1]*100\n",
    "    testing_acc = testing_acc + score[1]\n",
    "    \n",
    "    model.save('model_run' + str(i) + '.h5')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols_train_acc = []\n",
    "cols_val_acc   = []\n",
    "cols_test_acc  = []\n",
    "accuracy_cols  = []\n",
    "\n",
    "for i in range(1,11):\n",
    "    col_train =  'training_acc_' + 'run_' + str(i) \n",
    "    cols_train_acc.append(col_train)\n",
    "    \n",
    "    col_val = 'val_acc_' + 'run_' + str(i)  \n",
    "    cols_val_acc.append(col_val)\n",
    "    \n",
    "accuracy_cols  = cols_train_acc + cols_val_acc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>training_acc_run_1</th>\n",
       "      <th>training_acc_run_2</th>\n",
       "      <th>training_acc_run_3</th>\n",
       "      <th>training_acc_run_4</th>\n",
       "      <th>training_acc_run_5</th>\n",
       "      <th>training_acc_run_6</th>\n",
       "      <th>training_acc_run_7</th>\n",
       "      <th>training_acc_run_8</th>\n",
       "      <th>training_acc_run_9</th>\n",
       "      <th>training_acc_run_10</th>\n",
       "      <th>val_acc_run_1</th>\n",
       "      <th>val_acc_run_2</th>\n",
       "      <th>val_acc_run_3</th>\n",
       "      <th>val_acc_run_4</th>\n",
       "      <th>val_acc_run_5</th>\n",
       "      <th>val_acc_run_6</th>\n",
       "      <th>val_acc_run_7</th>\n",
       "      <th>val_acc_run_8</th>\n",
       "      <th>val_acc_run_9</th>\n",
       "      <th>val_acc_run_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.91</td>\n",
       "      <td>74.54</td>\n",
       "      <td>74.28</td>\n",
       "      <td>74.40</td>\n",
       "      <td>75.65</td>\n",
       "      <td>74.65</td>\n",
       "      <td>74.97</td>\n",
       "      <td>74.16</td>\n",
       "      <td>74.25</td>\n",
       "      <td>75.46</td>\n",
       "      <td>84.60</td>\n",
       "      <td>84.48</td>\n",
       "      <td>84.98</td>\n",
       "      <td>84.40</td>\n",
       "      <td>85.19</td>\n",
       "      <td>84.87</td>\n",
       "      <td>84.28</td>\n",
       "      <td>83.91</td>\n",
       "      <td>83.53</td>\n",
       "      <td>84.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83.51</td>\n",
       "      <td>82.93</td>\n",
       "      <td>83.26</td>\n",
       "      <td>82.80</td>\n",
       "      <td>83.95</td>\n",
       "      <td>82.68</td>\n",
       "      <td>83.25</td>\n",
       "      <td>82.69</td>\n",
       "      <td>82.67</td>\n",
       "      <td>83.36</td>\n",
       "      <td>87.42</td>\n",
       "      <td>86.47</td>\n",
       "      <td>87.01</td>\n",
       "      <td>86.93</td>\n",
       "      <td>87.71</td>\n",
       "      <td>86.09</td>\n",
       "      <td>86.92</td>\n",
       "      <td>86.36</td>\n",
       "      <td>86.22</td>\n",
       "      <td>86.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85.54</td>\n",
       "      <td>84.93</td>\n",
       "      <td>85.14</td>\n",
       "      <td>84.75</td>\n",
       "      <td>85.67</td>\n",
       "      <td>85.00</td>\n",
       "      <td>85.33</td>\n",
       "      <td>84.95</td>\n",
       "      <td>85.00</td>\n",
       "      <td>85.31</td>\n",
       "      <td>88.48</td>\n",
       "      <td>87.74</td>\n",
       "      <td>87.82</td>\n",
       "      <td>87.58</td>\n",
       "      <td>88.44</td>\n",
       "      <td>88.53</td>\n",
       "      <td>87.87</td>\n",
       "      <td>87.78</td>\n",
       "      <td>88.09</td>\n",
       "      <td>87.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86.50</td>\n",
       "      <td>86.13</td>\n",
       "      <td>86.35</td>\n",
       "      <td>85.99</td>\n",
       "      <td>86.98</td>\n",
       "      <td>86.36</td>\n",
       "      <td>86.79</td>\n",
       "      <td>86.38</td>\n",
       "      <td>86.26</td>\n",
       "      <td>86.39</td>\n",
       "      <td>89.15</td>\n",
       "      <td>88.85</td>\n",
       "      <td>88.85</td>\n",
       "      <td>88.61</td>\n",
       "      <td>88.90</td>\n",
       "      <td>88.89</td>\n",
       "      <td>89.08</td>\n",
       "      <td>88.86</td>\n",
       "      <td>88.77</td>\n",
       "      <td>88.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87.36</td>\n",
       "      <td>87.02</td>\n",
       "      <td>87.27</td>\n",
       "      <td>86.90</td>\n",
       "      <td>87.67</td>\n",
       "      <td>87.23</td>\n",
       "      <td>87.82</td>\n",
       "      <td>86.93</td>\n",
       "      <td>87.05</td>\n",
       "      <td>87.21</td>\n",
       "      <td>88.98</td>\n",
       "      <td>89.18</td>\n",
       "      <td>89.03</td>\n",
       "      <td>88.44</td>\n",
       "      <td>88.92</td>\n",
       "      <td>89.40</td>\n",
       "      <td>89.65</td>\n",
       "      <td>89.30</td>\n",
       "      <td>89.22</td>\n",
       "      <td>88.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   training_acc_run_1  training_acc_run_2  training_acc_run_3  \\\n",
       "0               75.91               74.54               74.28   \n",
       "1               83.51               82.93               83.26   \n",
       "2               85.54               84.93               85.14   \n",
       "3               86.50               86.13               86.35   \n",
       "4               87.36               87.02               87.27   \n",
       "\n",
       "   training_acc_run_4  training_acc_run_5  training_acc_run_6  \\\n",
       "0               74.40               75.65               74.65   \n",
       "1               82.80               83.95               82.68   \n",
       "2               84.75               85.67               85.00   \n",
       "3               85.99               86.98               86.36   \n",
       "4               86.90               87.67               87.23   \n",
       "\n",
       "   training_acc_run_7  training_acc_run_8  training_acc_run_9  \\\n",
       "0               74.97               74.16               74.25   \n",
       "1               83.25               82.69               82.67   \n",
       "2               85.33               84.95               85.00   \n",
       "3               86.79               86.38               86.26   \n",
       "4               87.82               86.93               87.05   \n",
       "\n",
       "   training_acc_run_10  val_acc_run_1  val_acc_run_2  val_acc_run_3  \\\n",
       "0                75.46          84.60          84.48          84.98   \n",
       "1                83.36          87.42          86.47          87.01   \n",
       "2                85.31          88.48          87.74          87.82   \n",
       "3                86.39          89.15          88.85          88.85   \n",
       "4                87.21          88.98          89.18          89.03   \n",
       "\n",
       "   val_acc_run_4  val_acc_run_5  val_acc_run_6  val_acc_run_7  val_acc_run_8  \\\n",
       "0          84.40          85.19          84.87          84.28          83.91   \n",
       "1          86.93          87.71          86.09          86.92          86.36   \n",
       "2          87.58          88.44          88.53          87.87          87.78   \n",
       "3          88.61          88.90          88.89          89.08          88.86   \n",
       "4          88.44          88.92          89.40          89.65          89.30   \n",
       "\n",
       "   val_acc_run_9  val_acc_run_10  \n",
       "0          83.53           84.53  \n",
       "1          86.22           86.72  \n",
       "2          88.09           87.87  \n",
       "3          88.77           88.76  \n",
       "4          89.22           88.93  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_val_acc = pd.DataFrame(columns=accuracy_cols)\n",
    "\n",
    "for i in range(1,11):\n",
    "    train_val_acc['training_acc_' + 'run_' + str(i)]  = training_history['run_' + str(i)].history['acc'] \n",
    "    train_val_acc['val_acc_'      + 'run_' + str(i)]  = training_history['run_' + str(i)].history['val_acc']\n",
    "train_val_acc = train_val_acc.applymap(lambda x:  x * 100).applymap(lambda x: '%.2f' % x)\n",
    "train_val_acc = train_val_acc.applymap(lambda x: pd.to_numeric(x))\n",
    "train_val_acc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Training Accuracy</th>\n",
       "      <th>Validation Accuracy</th>\n",
       "      <th>Testing Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93.35</td>\n",
       "      <td>91.83</td>\n",
       "      <td>92.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93.01</td>\n",
       "      <td>92.12</td>\n",
       "      <td>92.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93.22</td>\n",
       "      <td>91.64</td>\n",
       "      <td>91.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92.89</td>\n",
       "      <td>91.43</td>\n",
       "      <td>91.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>93.33</td>\n",
       "      <td>92.13</td>\n",
       "      <td>92.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>92.92</td>\n",
       "      <td>91.76</td>\n",
       "      <td>92.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>93.31</td>\n",
       "      <td>92.04</td>\n",
       "      <td>92.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>93.19</td>\n",
       "      <td>92.03</td>\n",
       "      <td>92.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>93.41</td>\n",
       "      <td>91.77</td>\n",
       "      <td>92.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>93.33</td>\n",
       "      <td>91.89</td>\n",
       "      <td>92.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Training Accuracy  Validation Accuracy  Testing Accuracy\n",
       "1               93.35                91.83             92.31\n",
       "2               93.01                92.12             92.45\n",
       "3               93.22                91.64             91.77\n",
       "4               92.89                91.43             91.92\n",
       "5               93.33                92.13             92.08\n",
       "6               92.92                91.76             92.26\n",
       "7               93.31                92.04             92.13\n",
       "8               93.19                92.03             92.17\n",
       "9               93.41                91.77             92.36\n",
       "10              93.33                91.89             92.14"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model_final_acc = pd.DataFrame(columns=['Training Accuracy', 'Validation Accuracy', 'Testing Accuracy'],index=[i for i in range(1,11)])\n",
    "\n",
    "model_final_acc['Training Accuracy']   = train_val_acc.iloc[49:,:10].T.values\n",
    "model_final_acc['Validation Accuracy'] = train_val_acc.iloc[49:,10:].T.values\n",
    "\n",
    "y_test = []\n",
    "for i in range(1,11):\n",
    "    test = testing_history['acc run_'  + str(i)]\n",
    "    y_test.append(test)\n",
    "model_final_acc['Testing Accuracy']    = y_test\n",
    "model_final_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average time 21.796345467964805 mints\n"
     ]
    }
   ],
   "source": [
    "t = 0.0\n",
    "for i in range(1,11):\n",
    "    t = t + training_time['run_' + str(i)]\n",
    "avg_time = t/10\n",
    "avg_time = avg_time / 60\n",
    "print('Average time {} mints'.format(avg_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Training Accuracy</th>\n",
       "      <th>Validation Accuracy</th>\n",
       "      <th>Testing Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93.35</td>\n",
       "      <td>91.83</td>\n",
       "      <td>92.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93.01</td>\n",
       "      <td>92.12</td>\n",
       "      <td>92.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93.22</td>\n",
       "      <td>91.64</td>\n",
       "      <td>91.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92.89</td>\n",
       "      <td>91.43</td>\n",
       "      <td>91.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>93.33</td>\n",
       "      <td>92.13</td>\n",
       "      <td>92.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>92.92</td>\n",
       "      <td>91.76</td>\n",
       "      <td>92.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>93.31</td>\n",
       "      <td>92.04</td>\n",
       "      <td>92.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>93.19</td>\n",
       "      <td>92.03</td>\n",
       "      <td>92.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>93.41</td>\n",
       "      <td>91.77</td>\n",
       "      <td>92.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>93.33</td>\n",
       "      <td>91.89</td>\n",
       "      <td>92.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Training Accuracy  Validation Accuracy  Testing Accuracy\n",
       "1               93.35                91.83             92.31\n",
       "2               93.01                92.12             92.45\n",
       "3               93.22                91.64             91.77\n",
       "4               92.89                91.43             91.92\n",
       "5               93.33                92.13             92.08\n",
       "6               92.92                91.76             92.26\n",
       "7               93.31                92.04             92.13\n",
       "8               93.19                92.03             92.17\n",
       "9               93.41                91.77             92.36\n",
       "10              93.33                91.89             92.14"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_final_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_final_acc.to_csv('Final_Accuracy.csv')\n",
    "train_val_acc.to_csv('Training_Validation_accuracies_each_run.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols_train_loss = []\n",
    "cols_val_loss   = []\n",
    "loss_cols  = []\n",
    "\n",
    "for i in range(1,11):\n",
    "    col_train =  'training_loss_' + 'run_' + str(i) \n",
    "    cols_train_loss.append(col_train)\n",
    "    \n",
    "    col_val = 'val_loss_' + 'run_' + str(i)  \n",
    "    cols_val_loss.append(col_val)\n",
    "    \n",
    "loss_cols  = cols_train_loss + cols_val_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>training_loss_run_1</th>\n",
       "      <th>training_loss_run_2</th>\n",
       "      <th>training_loss_run_3</th>\n",
       "      <th>training_loss_run_4</th>\n",
       "      <th>training_loss_run_5</th>\n",
       "      <th>training_loss_run_6</th>\n",
       "      <th>training_loss_run_7</th>\n",
       "      <th>training_loss_run_8</th>\n",
       "      <th>training_loss_run_9</th>\n",
       "      <th>training_loss_run_10</th>\n",
       "      <th>val_loss_run_1</th>\n",
       "      <th>val_loss_run_2</th>\n",
       "      <th>val_loss_run_3</th>\n",
       "      <th>val_loss_run_4</th>\n",
       "      <th>val_loss_run_5</th>\n",
       "      <th>val_loss_run_6</th>\n",
       "      <th>val_loss_run_7</th>\n",
       "      <th>val_loss_run_8</th>\n",
       "      <th>val_loss_run_9</th>\n",
       "      <th>val_loss_run_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   training_loss_run_1  training_loss_run_2  training_loss_run_3  \\\n",
       "0                 0.67                 0.69                 0.70   \n",
       "1                 0.45                 0.47                 0.46   \n",
       "2                 0.40                 0.42                 0.41   \n",
       "3                 0.37                 0.38                 0.37   \n",
       "4                 0.35                 0.36                 0.35   \n",
       "\n",
       "   training_loss_run_4  training_loss_run_5  training_loss_run_6  \\\n",
       "0                 0.70                 0.67                 0.70   \n",
       "1                 0.47                 0.45                 0.47   \n",
       "2                 0.42                 0.40                 0.41   \n",
       "3                 0.38                 0.36                 0.38   \n",
       "4                 0.36                 0.34                 0.35   \n",
       "\n",
       "   training_loss_run_7  training_loss_run_8  training_loss_run_9  \\\n",
       "0                 0.68                 0.71                 0.71   \n",
       "1                 0.46                 0.47                 0.48   \n",
       "2                 0.40                 0.41                 0.42   \n",
       "3                 0.37                 0.38                 0.38   \n",
       "4                 0.34                 0.35                 0.36   \n",
       "\n",
       "   training_loss_run_10  val_loss_run_1  val_loss_run_2  val_loss_run_3  \\\n",
       "0                  0.67            0.41            0.44            0.41   \n",
       "1                  0.46            0.36            0.37            0.35   \n",
       "2                  0.40            0.32            0.34            0.33   \n",
       "3                  0.37            0.30            0.31            0.31   \n",
       "4                  0.35            0.30            0.30            0.30   \n",
       "\n",
       "   val_loss_run_4  val_loss_run_5  val_loss_run_6  val_loss_run_7  \\\n",
       "0            0.44            0.41            0.42            0.44   \n",
       "1            0.36            0.34            0.38            0.37   \n",
       "2            0.34            0.32            0.32            0.33   \n",
       "3            0.32            0.30            0.31            0.30   \n",
       "4            0.31            0.29            0.29            0.28   \n",
       "\n",
       "   val_loss_run_8  val_loss_run_9  val_loss_run_10  \n",
       "0            0.45            0.44             0.42  \n",
       "1            0.37            0.38             0.37  \n",
       "2            0.33            0.33             0.34  \n",
       "3            0.31            0.32             0.31  \n",
       "4            0.29            0.29             0.30  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_val_loss = pd.DataFrame(columns=loss_cols)\n",
    "\n",
    "for i in range(1,11):\n",
    "    train_val_loss['training_loss_' + 'run_' + str(i)]  = training_history['run_' + str(i)].history['loss'] \n",
    "    train_val_loss['val_loss_'      + 'run_' + str(i)]  = training_history['run_' + str(i)].history['val_loss']\n",
    "train_val_loss = train_val_loss.applymap(lambda x: '%.2f' % x)\n",
    "train_val_loss = train_val_loss.applymap(lambda x: pd.to_numeric(x))\n",
    "train_val_loss.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_val_loss.to_csv('Training_Validation_losses_each_run.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1fca379780>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJkAAALJCAYAAAAXlPLmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJkiRJGtq/AcE827LjRF4uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fca7788d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=2,figsize=(20,12))\n",
    "\n",
    "axes[0,0].plot(train_val_acc.iloc[:,:10])\n",
    "axes[0,0].set_xlabel('No of Epoch')\n",
    "axes[0,0].set_ylabel('Training Accuracy')\n",
    "axes[0,0].set_title('Training Accuracies for 10 different runs')\n",
    "\n",
    "axes[0,1].plot(train_val_acc.iloc[:,10:])\n",
    "axes[0,1].set_ylabel('Validation Accuracy')\n",
    "axes[0,1].set_title('Validation Accuracies for 10 different runs')\n",
    "\n",
    "axes[1,0].plot(train_val_acc.iloc[:,:10].mean(axis = 1),'b')\n",
    "axes[1,0].plot(train_val_acc.iloc[:,10:].mean(axis = 1),'r')\n",
    "axes[1,0].legend(['Avg_Train_acc', 'Avg_val_acc'])\n",
    "axes[1,0].set_xlabel('No of Epoch')\n",
    "axes[1,0].set_ylabel('Accuracy')\n",
    "axes[1,0].set_title('Average Training & Validation accuracies')\n",
    "\n",
    "axes[1,1].plot(train_val_loss.iloc[:,:10].mean(axis=1),'b')\n",
    "axes[1,1].plot(train_val_loss.iloc[:,10:].mean(axis = 1),'r')\n",
    "axes[1,1].legend(['Avg_Train_loss', 'Avg_val_loss'])\n",
    "axes[1,1].set_xlabel('No of Epoch')\n",
    "axes[1,1].set_ylabel('Loss')\n",
    "axes[1,1].set_title('Average Training & Validation losses')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fc050def0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8AAAAHjCAYAAADscXYqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fc0518f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(model_final_acc)\n",
    "plt.xlabel('Run')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(model_final_acc.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Result: Average Model Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg_tranining_acc 93.196 %\n",
      "Avg_validation_acc 91.864 %\n",
      "Avg_testing_acc 92.159 %\n"
     ]
    }
   ],
   "source": [
    "Avg_tranining_acc, Avg_validation_acc, Avg_testing_acc  = model_final_acc.mean()\n",
    "\n",
    "print('Avg_tranining_acc {0:.3f} %'.format(Avg_tranining_acc))\n",
    "print('Avg_validation_acc {0:.3f} %'.format(Avg_validation_acc))\n",
    "print('Avg_testing_acc {0:.3f} %'.format(Avg_testing_acc))\n"
   ]
  }
 ],
 "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": "3.6.1"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}