Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Training loss and accuracy over number of Epochs\n",
    "\n",
    "                                                                            Code Written By : Dasari Mohana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>loss</th>\n",
       "      <th>acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.518767</td>\n",
       "      <td>0.141574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3.167011</td>\n",
       "      <td>0.163365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2.948693</td>\n",
       "      <td>0.208929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.617472</td>\n",
       "      <td>0.279911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2.348704</td>\n",
       "      <td>0.340681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2.138005</td>\n",
       "      <td>0.389816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2.003399</td>\n",
       "      <td>0.423410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1.909276</td>\n",
       "      <td>0.447824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1.841778</td>\n",
       "      <td>0.464118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1.764172</td>\n",
       "      <td>0.485296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1.700561</td>\n",
       "      <td>0.502651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1.640164</td>\n",
       "      <td>0.513867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>1.582906</td>\n",
       "      <td>0.523661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>1.529423</td>\n",
       "      <td>0.535128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>1.494608</td>\n",
       "      <td>0.542076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1.457570</td>\n",
       "      <td>0.546680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>1.425620</td>\n",
       "      <td>0.553990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1.397976</td>\n",
       "      <td>0.559626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>1.367130</td>\n",
       "      <td>0.566797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1.344677</td>\n",
       "      <td>0.573800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>1.313548</td>\n",
       "      <td>0.582366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1.293113</td>\n",
       "      <td>0.584794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1.264488</td>\n",
       "      <td>0.589872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1.238285</td>\n",
       "      <td>0.598242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1.220307</td>\n",
       "      <td>0.602790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1.197404</td>\n",
       "      <td>0.608064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>1.172865</td>\n",
       "      <td>0.617997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>1.157766</td>\n",
       "      <td>0.621122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1.142872</td>\n",
       "      <td>0.626702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>1.115034</td>\n",
       "      <td>0.635575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>1.099478</td>\n",
       "      <td>0.638756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1.075674</td>\n",
       "      <td>0.648493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>1.055952</td>\n",
       "      <td>0.651758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>1.028288</td>\n",
       "      <td>0.664927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>1.012214</td>\n",
       "      <td>0.667690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>0.989278</td>\n",
       "      <td>0.676311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>0.957720</td>\n",
       "      <td>0.687026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>0.928484</td>\n",
       "      <td>0.695703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>0.907364</td>\n",
       "      <td>0.702065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>0.881311</td>\n",
       "      <td>0.712277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>0.861582</td>\n",
       "      <td>0.721122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>0.836730</td>\n",
       "      <td>0.727567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>0.812460</td>\n",
       "      <td>0.734040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>0.786593</td>\n",
       "      <td>0.742327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>0.770886</td>\n",
       "      <td>0.750391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>0.759511</td>\n",
       "      <td>0.753739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>0.735362</td>\n",
       "      <td>0.761217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>0.711351</td>\n",
       "      <td>0.769671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>0.692834</td>\n",
       "      <td>0.773298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>0.674972</td>\n",
       "      <td>0.781055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    epoch      loss       acc\n",
       "0       1  3.518767  0.141574\n",
       "1       2  3.167011  0.163365\n",
       "2       3  2.948693  0.208929\n",
       "3       4  2.617472  0.279911\n",
       "4       5  2.348704  0.340681\n",
       "5       6  2.138005  0.389816\n",
       "6       7  2.003399  0.423410\n",
       "7       8  1.909276  0.447824\n",
       "8       9  1.841778  0.464118\n",
       "9      10  1.764172  0.485296\n",
       "10     11  1.700561  0.502651\n",
       "11     12  1.640164  0.513867\n",
       "12     13  1.582906  0.523661\n",
       "13     14  1.529423  0.535128\n",
       "14     15  1.494608  0.542076\n",
       "15     16  1.457570  0.546680\n",
       "16     17  1.425620  0.553990\n",
       "17     18  1.397976  0.559626\n",
       "18     19  1.367130  0.566797\n",
       "19     20  1.344677  0.573800\n",
       "20     21  1.313548  0.582366\n",
       "21     22  1.293113  0.584794\n",
       "22     23  1.264488  0.589872\n",
       "23     24  1.238285  0.598242\n",
       "24     25  1.220307  0.602790\n",
       "25     26  1.197404  0.608064\n",
       "26     27  1.172865  0.617997\n",
       "27     28  1.157766  0.621122\n",
       "28     29  1.142872  0.626702\n",
       "29     30  1.115034  0.635575\n",
       "30     31  1.099478  0.638756\n",
       "31     32  1.075674  0.648493\n",
       "32     33  1.055952  0.651758\n",
       "33     34  1.028288  0.664927\n",
       "34     35  1.012214  0.667690\n",
       "35     36  0.989278  0.676311\n",
       "36     37  0.957720  0.687026\n",
       "37     38  0.928484  0.695703\n",
       "38     39  0.907364  0.702065\n",
       "39     40  0.881311  0.712277\n",
       "40     41  0.861582  0.721122\n",
       "41     42  0.836730  0.727567\n",
       "42     43  0.812460  0.734040\n",
       "43     44  0.786593  0.742327\n",
       "44     45  0.770886  0.750391\n",
       "45     46  0.759511  0.753739\n",
       "46     47  0.735362  0.761217\n",
       "47     48  0.711351  0.769671\n",
       "48     49  0.692834  0.773298\n",
       "49     50  0.674972  0.781055"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('logs/training_log.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot(df['epoch'], df['loss'],c='r')\n",
    "plt.title('Training Loss over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Training Loss')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Phm22KbqyuuRwpCRJ9WD4cDj+eBg1CnbeGS6/HL7znaKrqmv2hEmSVMsmTYKTToJNN4X33oPbb8+9YAawwhnCJEmqRSlBv36w5pp5pfuf/ASefz7PBYsoujrhcKQkSbXn6afh9NPzdkM9esB998FGGxVdlWZjT5gkSbVi+HDYc8+8z+OwYXDZZfnZAFaRDGGSJFW7IUNgxx1hk03g0Ufh17/Oa3+ddBK0bVt0dZoDhyMlSapGKeXhxt/+Nq9036ULXHhh3mi7Y8eiq1MTGMIkSaomM2fCvffm8DViBHTrBn/6Exx9dF54VVXD4UhJkqrBjBn5bsfu3WGvvWDiRLjmGnj1VTj5ZANYFTKESZJUyaZNg7598yr3Bx2Uw9hf/wovvph7vxZeuOgK1UwOR0qSVIm++AKuuy5vqj1+PGywAdxxB+y9N7SxD6UWGMIkSaokkyfDVVfBH/6QV7jfYgu4+mrYaScXWa0xhjBJkirBxIl5Xa9LL4WPP4btt4fbboOttzZ81ShDmCRJRfrsMzj//BzApkyBPfaAs86CjTcuujKVmSFMkqSiPPwwHHUUvPYaHHAAnHkmrLde0VWplTizT5Kk1jZ5cl5Uddtt84r2Q4bk5ScMYHXFECZJUmsaOBDWWSdPtv/Zz2DMGNhqq6KrUgEMYZIktYZJk/K6Xj/4ASy+ODz+eL4DcvHFi65MBTGESZJUbgMGwPe/D9dfD2ecAaNGwWabFV2VCubEfEmSyqTd5Mlw+OFw4405hN11F/TsWXRZqhCGMEmSymHgQHoecUQehjz77PxYZJGiq1IFcThSkqSWNGMGnHMO7LQT0zt1guHD4bzzDGD6FnvCJElqKe+/DwcfDA8+CEceycj992frDTcsuipVKHvCJElqCY8+Cuuvn+96vO466NuXmYsuWnRVqmCGMEmSFkRKcNFF0Ls3tG8Pw4bBf/930VWpCjgcKUlSc338MRxxBNxzD+y7b+4B69Sp6KpUJQxhkiQ1x8iRsN9+MH48XHopnHwyRBRdlaqIw5GSJM2PlODKK2HzzWH69DwX7Cc/MYBpvpU1hEXEThHxYkS8EhG/bOT90yNidOnxbETMiIily1mTJEnNNmoU7LUXnHgibLddPt5006KrUpUqWwiLiLbAFcDOwNrAQRGxdsNrUkoXp5TWTymtD/wP8EhKaWK5apIkqVkefRR23hk23BAGD4b/+z+47z5YZpmiK1MVK2dP2MbAKymlcSmlaUA/YM+5XH8QcGsZ65EkqelSyns+brUVbL11ngN2wQXw5pt5/8c2zujRgomUUnm+OGJfYKeU0tGl40OBTVJKJzVy7eLABGD1xnrCIuJY4FiArl27btSvX79m1TRlyhQ6dOjQrM+q/GyfymXbVDbbp4XNmMGyjzzCyrfcQodXX2Vq166MP+AA3tl55/le98u2qWyt0T69e/cemVLq0dh75bw7srEZinNKfLsDj89pKDKl1AfoA9CjR4/Uq1evZhU0ePBgmvtZlZ/tU7lsm8pm+7SQadPgppvgwgvh5ZdhzTXhhhtY9OCDWWOhhVijGV9p21S2otunnCFsArBig+NuwNtzuPZAHIqUJBXl7rvzHY7jx8NGG8Hf/54n4DvkqDIq59+uJ4E1ImLViFiYHLTumf2iiFgC2Ab4RxlrkSTp2957D/bfH/beG5ZeGh54AJ58En74QwOYyq5sPWEppekRcRLwANAW6JtSGhsRx5fev6p06d7AwJTSZ+WqRZKkb0gJbrwRTj0VPv88T7g/7TRYaKGiK1MdKeuK+SmlAcCA2c5dNdvxDcAN5axDkqT/eP11OO44GDgQttgCrr02z/+SWpl9rZKk+jBzJlx2GayzDjzxBFx+OQwZYgBTYdw7UpJU+55/Ho4+OoevnXaCq66ClVcuuirVOXvCJEm166uv4PzzYf314YUX8hIUAwYYwFQR7AmTJNWmd9+FffeFxx+HAw6AP/0JunQpuirpPwxhkqTa8+9/52UmPvkEbr0VDjyw6Iqkb3E4UpJUW667Lu/1uMgiMHSoAUwVyxAmSaoN06bBCSfkCfi9esGIEbDeekVXJc2RIUySVP3eeQd69853Pf7iF3ny/dJLF12VNFfOCZMkVbdhw2CfffL8r3798iR8qQrYEyZJql7XXgvbbJPnfz3xhAFMVcUQJkmqPrPmfx1zTA5hTz4J3bsXXZU0XwxhkqTqMnYsbLVVnv91+ul5/tcyyxRdlTTfDGGSpOrw5Zdw7rmwwQbw6qvwt7/BRRdBO6c3qzr5N1eSVPmGDs1LTzz3HBx0EFx6KSy7bNFVSQvEnjBJUuWaMgVOOQW22AImT4b77oNbbjGAqSbYEyZJqkz33w/HHQfjx8OJJ8LvfgcdOxZdldRi7AmTJFWWDz+EQw+FnXeGxReHRx+Fyy83gKnmGMIkSZVh8mS46SZYa6286Or//i+MHp2HIqUa5HCkJKn1TZ0KTz+d1/ea9Xj+eUgJNt44L8K67rpFVymVlSFMklReKcEzz3wzcD3zDHz1VX6/Sxfo2RP23x822QR22AHati22ZqkVGMIkSeXz5Zd5K6F//CMfL7EE9OgBP/95Dl49e0K3bhBRbJ1SAQxhkqTymDo1b6w9YACcfz7stx+sthq0cTqyBIYwSVI5fPEF7LUXDBoE11yTF1qV9A2GMElSy/r8c9hjD3joIejbF444ouiKpIpkCJMktZwpU2D33WHIELjxRjjkkKIrkiqWIUyS1DImT4ZddoEnnoC//jXv8ShpjgxhkqQF9+mnsNNOMHx4Xmh1v/2KrkiqeIYwSdKC+eQT+MEP4Kmn4G9/gx/+sOiKpKpgCJMkNd/EibDjjnn1+zvugD33LLoiqWoYwiRJzfPRR7D99vDcc3DnnbDbbkVXJFUVQ5gkqWlSgtdey3c+PvIIPPBA7gn7xz/yfDBJ88UQJklqXErwwgs5dM0KXm+9ld9bemnYems49dT8LGm+GcIkSV/74gu4/vq80OqQIfDBB/n8csvBNtvkwLXNNrDWWm4/JC0gQ5gkKRs7Fg48EJ59FlZeGXbeOYeurbeG1Vd3k22phRnCJKnepQRXX52HFpdYAu6/Py85Iams7EuWpHo2cSLsuy+ccEIeZhwzxgAmtRJDmCTVq0cfhfXXh3vvhd//HgYMgK5di65KqhuGMEmqN9Onw7nnQq9esMgiea/Hn//cifZSK3NOmCTVkzffhEMOyb1ghx0Gl18OHTsWXZVUlwxhklQv7rwTjj4avvoK/vpX+NGPiq5IqmuGMEmqVZMnw9Chuddr1oKrPXvCrbfCaqsVXZ1U9wxhklQr3n8fHnssh65HH4XRo2HGjDzXa4MN4Lzz4Be/gIUXLrpSSRjCJKl6pQR33cX3rrsOjjsOXnopn190Udh0UzjzTNhyS9hsM+d9SRXIECZJ1Wj8eDjmGHjgATp37JjvdDz66By6NtrI3i6pChjCJKmapATXXpuXlJg5Ey67jMfXXpte225bdGWS5pOLwkhStXjjjbya/bHHQo8e8PTTcNJJru8lVSn/y5WkSjdzJlx1FayzTr7b8cor4V//gu98p+jKJC0AhyMlqZK99lqe6/XQQ7D99nkocuWVi65KUguwJ0ySKtHMmXDFFbDuuvDkk9CnDwwcaACTaoghTJIqSUp5ra9tt83zvbbcEp59Nt8JGVF0dZJakMORklQJpkyBm2+GP/85T7hfaim47jo48kjDl1SjDGGSVKQXXsjB6y9/gU8/hfXXh2uugYMOgvbti65OUhkZwiSptU2fDvfck+d8PfRQXlh1v/3gxz/OK93b8yXVBUOYJLWGlGDcOLjlFrj6anjrLVhpJbjgAjjqKOjSpegKJbUyQ5gklcP06XkD7ccfzxPtH3sM3n03v7fjjrkXbLfdoG3bQsuUVBxDmCS1hMmTYdiwHLYefzy//uyz/N7KK8N22+U7HbffHlZfvdhaJVUEQ5gkLYgXXoDjjsvha+bMvIXQeuvluxq33BK22AK6dSu6SkkVyBAmSc11441wwgmw+OJw5pmw1VZ5Yn2nTkVXJqkKGMIkaX5NmZIXUv3LX2DrrfNk+xVWKLoqSVXGFfMlaX48/TT07Jl7wc45Bx580AAmqVnsCZOkpkgp79/405/CkkvCv/6VtxaSpGayJ0yS5mXSJDjwQDj++Dz8OGaMAUzSAjOESdLcjBgBG24If/87/O538M9/urCqpBZhCJOkxsycCZdeCptvDl99BY88Ar/8ZV6CQpJagHPCJKmhd96B66+H667L2wztsUc+XnrpoiuTVGP8v3SSNH063Hcf7LknrLginHVW3texXz+4+24DmKSysCdMUv16/XXo2zc/3noLunaF007LG2qvsUbR1UmqcYYwSfVl2jS45x645hoYNCif+8EP4E9/gt13h4UWKrY+SXWjrCEsInYCLgXaAtemlP6vkWt6AZcACwEfppS2KWdNkurQ9Onw8MNw221w553w8cd52PGcc+C//zsPPUpSKytbCIuItsAVwA7ABODJiLgnpfRcg2uWBP4M7JRSejMivO9bUsuYMQOGDIG//S0vL/HBB9ChQ573dfDBuferbduiq5RUx8rZE7Yx8EpKaRxARPQD9gSea3DNwcCdKaU3AVJK75exHkm1buZMeOKJ3ON1xx3w7rt5c+3dd4cDDoCddoLFFiu6SkkCIFJK5fniiH3JPVxHl44PBTZJKZ3U4JpLyMOQ3wc6ApemlG5s5LuOBY4F6Nq160b9+vVrVk1TpkyhQ4cOzfqsys/2qVyV3jbtJk9mpVtuocuDD7LoBx8wY+GFmbjJJrzfuzcfbbopM2s8eFV6+9Qz26aytUb79O7de2RKqUdj75WzJywaOTd74msHbARsBywGDI2IYSmll77xoZT6AH0AevTokXr16tWsggYPHkxzP6vys30qV8W2TUp5GYmf/hQ+/BB23RUOOIC2e+zBsh07smzR9bWSim0f2TYVruj2KWcImwCs2OC4G/B2I9d8mFL6DPgsIoYA3YGXkKS5efVVOPFEGDgQevaE+++HDTYouipJarJyLtb6JLBGRKwaEQsDBwL3zHbNP4CtIqJdRCwObAI8X8aaJFW7adPyHo7rrANDh8Jll+VnA5ikKlO2nrCU0vSIOAl4gLxERd+U0tiIOL70/lUppecj4n7gaWAmeRmLZ8tVk6Qq9/jjcNxxMHYs7LNP3ttxhRWKrkqSmqWs64SllAYAA2Y7d9VsxxcDF5ezDklV7uOP8+bZffrkNb3uuSff8ShJVcy9IyVVrpTg1lthzTXzhto//3nuBTOASaoBblskqfK8/TbcdBPccAO88IIT7yXVJEOYpMrw5Zdw771w/fU5cM2cCVtskYPYIYe4ur2kmmMIk1SsUaNy8Lr5Zpg4MU+0P+MMOOII+O53i65OksrGECapdaUEb72VN9Lu2xfGjIGFF4a99oIjj4QddrDXS1JdMIRJKp+pU/NE+qefzo8xY/LzRx/l9zfaCC6/HA46CJZeuthaJamVGcIktYyU4KGHYPjwrwPXSy/BjBn5/cUWg3XXhb33hvXWg1698rEk1SlDmKQFN24cHH88DBqUj1dZJQetffaB7t3z69VWc5hRkhowhElqvunT86r1//u/0K5d3kLo0ENhiSWKrkySKp4hTFLzjBoFxxwDI0fmxVP//Gfo1q3oqiSparhivqT58/nneQmJnj1hwgT429/gH/8wgEnSfLInTFLTPfQQHHssvPoqHHUUXHwxLLVU0VVJUlWyJ0zSPLX79FP47/+G7bbLJx58EK691gAmSQvAnjBJczZ9Otx4Ixufdhp8+in88pdwzjl5uQlJ0gIxhEn6tpkz4Y478l2PL73E1DXXZOGHHoL11y+6MkmqGQ5HSvpaSjBgQF7J/oADYKGF4M47eerPfzaASVILM4RJyoYMga22gl13zUOPN92UV73fe2+IKLo6Sao5hjCp3o0YAT/4AWyzTV75/sor4fnn4ZBDXOFeksrIECbVq+eey9sK9eyZF1y9+OK89MTxx8PCCxddnSTVPCfmS/Vm/Hj41a/gL3+B9u3h3HPh1FOhU6eiK5OkumIIk+rFRx/B734Hl1+eJ+CfcgqceSZ07lx0ZZJUlwxhUq377LO8yfaFF8LkyXDYYfDrX8PKKxddmSTVNUOYVKu++gquuy4HrnffzZtsX3ABrLNO0ZVJkjCESbUnJbj9djj7bHj5Zdhii3y85ZZFVyZJasC7I6VaMW0a3HprvtvxgAPyHY733AOPPmoAk6QKZE+YVO3eegv69IGrr4b33oPVVoMbbnCdL0mqcIYwqRqllHu4Lr8c7rwz7/W4yy5w0kmw447Qxk5uSap0hjCpmnz2Gdx8cw5fzzwDSy0FP/0pnHBC7gGTJFUNQ5hUDd54Ay65BK6/HiZNyptpX3stHHQQLL540dVJkprBECZVsokT87ISl12Whxz32w9+/GPYfHM31ZakKmcIkyrR1Kl5yPH883PP1+GHw29+AyuuWHRlkqQW4uxdqZLMnAk33QTf+x6cfjpsthmMHp2HIQ1gklRTDGFSpRg0CDbaKG8rtOyy8OCDMGAArLde0ZVJksrAECYVbfTovKzEjjvmocdbboHhw2HbbYuuTJJURs4Jk4rw5ZcwcGAeerzjjrzUxP/7f3mpiUUWKbo6SVIrMIRJrWX6dHj4YejXLy+w+sknsPTScMYZ+bHkkkVXKElqRYYwqZxmzoTHH8/B6/bb4YMPoGNH2HtvOPBA2H57WGihoquUJBXAECaVw4gReTPt227LezsuthjsvnsOXjvvDIsuWnSFkqSCGcKklvTEE3D22XnYcaGFcuC6+OIcwDp0KLo6SVIFMYRJLWHUqBy+BgyALl3yJPvDD88T7iVJaoQhTFoQzz8P55zz9R2Ov/sdnHwytG9fdGWSpApnCJOaY9w4+PWv4a9/zRton3MOnHqqdzhKkprMECbNjwkT4Le/heuug3bt4Gc/y8tLdO5cdGWSpCpjCJOaIiW46CL41a/yshPHHQdnnQXLL190ZZKkKmUIk+Zl2jQ49lj4y19g333z3Y6rrFJ0VZKkKmcIk+Zm4kTYZx8YPDjPAfvf/4WIoquSJNUAQ5g0J6++CrvsAq+/DjffDAcfXHRFkqQaYgiTGvP447Dnnvn1gw/CllsWW48kqea0KboAqeLceitsuy0sswwMG2YAkySVhSFMmiUlOO+8POy42WYwdCisvnrRVUmSapTDkRLAl1/mOyBvvBEOOwyuuQYWXrjoqiRJNcyeMOmjj2DHHXMAO+88uOEGA5gkqezsCVN9e+opOOAAGD8ebrkFDjqo6IokSXXCnjDVp5Tgkktg003hiy/goYcMYJKkVmVPmOrPBx/AkUdC//6wxx7Qt2++E1KSpFZkT5jqy8MPQ/fuMGgQXHYZ3H23AUySVAhDmOrD9Ol5y6HttoNOneDf/4aTTnILIklSYRyOVO1788289tfjj+dhyMsug/bti65KklTn5hnCImLplNLE1ihGanF33glHHQUzZnj3oySpojRlOPLfEXF7ROwS4diNqsTUqXDiibDPPnnV+1GjDGCSpIrSlBD2XaAPcCjwSkRcEBHfLW9Z0gJ47z3o3RuuvBJOOy0PQ662WtFVSZL0DfMMYSkblFI6CDgaOBwYHhGPRMRmZa9Qmh9PPw0bbwxjxsAdd8DFF7v6vSSpIjVlTtgywCHknrD3gJOBe4D1gduBVctYn9R099yTJ+AvuSQ89hhsuGHRFUmSNEdNGY4cCnQC9kop7ZpSujOlND2lNAK4qrzlSU2QUu7x2msvWGstGD7cACZJqnhNWaLieyml1NgbKaULW7geaf58+SUcf3zedHv//eH662HxxYuuSpKkeWpKT9jAiFhy1kFELBURD5SvJKmJPvgAdtghB7Bf/Qr69TOASZKqRlN6wpZNKX0y6yCl9HFEdClfSVITjB0Lu+8Ob78Nt94KBx5YdEWSJM2XpvSEzYiIlWYdRMTKQKPDk1Kr+Oc/YbPN4Isv4JFHDGCSpKrUlBB2FvBYRNwUETcBQ4D/acqXR8ROEfFiRLwSEb9s5P1eETEpIkaXHufMX/mqO9ddB7vtltf9Gj4cNtmk6IokSWqWeQ5HppTuj4gNgU2BAE5NKX04r89FRFvgCmAHYALwZETck1J6brZLH00p7Tb/pavuPPFEnoS/ww7w97+7/6Mkqao1pScMYAbwPjAJWDsitm7CZzYGXkkpjUspTQP6AXs2r0zVvfffh/32g5VXzhPwDWCSpCoXc1h94usLIo4GTgG6AaPJPWJDU0rbzuNz+wI7pZSOLh0fCmySUjqpwTW9gL+Te8reBk5LKY1t5LuOBY4F6Nq160b9+vVr2p9uNlOmTKFDhw7N+qzKb47tM2MG3U8/nU5jxzLqiiuYsvrqrV9cnfO/ncpm+1Qu26aytUb79O7de2RKqUdj7zXl7shTgJ7AsJRS74hYE/h1Ez7X2Gbfsye+p4CVU0pTImIX4G5gjW99KKU+5P0r6dGjR+rVq1cTfvy3DR48mOZ+VuU3x/Y566y8Aff119PjiCNauyzhfzuVzvapXLZNZSu6fZoyHDk1pTQVICIWSSm9AHyvCZ+bAKzY4LgbubfrP1JKn6aUppReDwAWiojOTapc9eHee+GCC+Doo8EAJkmqIU3pCZtQWqz1bmBQRHzMbGFqDp4E1oiIVYG3gAOBgxteEBHLAe+llFJEbEwOhR81vXzVtHHj4LDD8hZEl11WdDWSJLWoptwduXfp5bkR8TCwBHB/Ez43PSJOAh4A2gJ9U0pjI+L40vtXAfsCJ0TEdOAL4MA5bZGkOvPFF7Dvvvn1HXfAoosWW48kSS1sriEsItoAT6eU1gFIKT0yP19eGmIcMNu5qxq8vhy4fH6+U3Xi5JPzPLB774VVVy26GkmSWtxc54SllGYCYxqumC+VXd++eVHWs87KC7NKklSDmjInbHlgbEQMBz6bdTKltEfZqlL9GjUKfvxj2G47+HVTbsKVJKk6NSWE+S+hWkW7yZPhqKNgmWXgllugbduiS5IkqWyaMjF/vuaBSc0ycyZr/t//wZtvwpAh0KVL0RVJklRW8wxhETGZrxdZXRhYCPgspdSpnIWpzlx0EZ2feAIuvRQ226zoaiRJKrum9IR1bHgcEXuR94WUWsaoUXD22bzfqxddTj656GokSWoVTd3A+z9SSncDc903Umqy6dPhmGOgc2de+tnPIBrb7UqSpNrTlOHIHzY4bAP04Nt7QErNc9llMHIk3HYb0zt2nPf1kiTViKbcHbl7g9fTgdeBPctSjerLG2/A2WfDrrvCfvvBI94DIkmqH02ZE3ZkaxSiOpNSXg8sAq64wmFISVLdmeecsIj4S2kD71nHS0VE37JWpdp3++3Qvz/89rew8spFVyNJUqtrysT89VJKn8w6SCl9DGxQtopU+z7+GH7yE+jRI+8RKUlSHWrKnLA2EbFUKXwREUs38XNS4844Az78EP75T1fFlyTVraaEqT8AT0TEHeS7IvcHzi9rVapdQ4bANdfA6afDBnaoSpLqV1Mm5t8YESPIa4MF8MOU0nNlr0y158sv4dhjYdVV4Ve/KroaSZIK1ZR1wjYFxqaULi8dd4yITVJK/y57daotv/sdvPgi3H8/tG9fdDWSJBWqKRPzrwSmNDj+rHROarrnn4cLLoAf/Qh+8IOiq5EkqXBNCWGRUvrPCvkppZk4MV/zY+bMPAzZsSP88Y9FVyNJUkVoSggbFxE/iYiFSo9TgHHlLkw15Npr4bHH4A9/gC5diq5GkqSK0JQQdjywOfAWMAHYBDimnEWphrzzDvziF9C7Nxx+eNHVSJJUMZpyd+T7wIGzjiNiMWA34PYy1qVaccopMHUqXH21WxNJktRAU3rCiIi2EbFzRNwIvAYcUN6yVBNuvz0/zjkH1lij6GokSaooc+0Ji4itgYOBXYHhwBbAd1JKn7dCbapmY8bAEUfAJpvAaacVXY0kSRVnjiEsIiYAb5KXozg9pTQ5Il4zgGme3n8f9tgDlloK7roLFl646IokSao4cxuO/DuwAnnocfeIaE/etkias2nTYJ99chC7+25YfvmiK5IkqSLNMYSllE4BVgH+CPQGXgKWjYj9I6JD65SnqpIS/PjHeTmK66+HHj2KrkiSpIo114n5KXsopXQMOZAdDOwFvF72ylR9Lr88rwl25plw4IHzvl6SpDrW5JXvU0pfAfcC95aWqZC+9q9/wamnwp57wnnnFV2NJEkVr0lLVMwupfRFSxeiKvbyy7D//rDWWnDTTdCmWX+tJEmqK/5rqQUzaVLu/WrTBu65J+8PKUmS5smNuNV8M2bAwQfnnrBBg2DVVYuuSJKkqjHPEBYR9/LtpSkmASOAq1NKU8tRmKrAmWfCgAFw5ZXQq1fR1UiSVFWaMhw5DpgCXFN6fAq8B3y3dKx6dNNNcNFFcMIJcPzxRVcjSVLVacpw5AYppa0bHN8bEUNSSltHxNhyFaYK9u9/wzHH5N6vSy8tuhpJkqpSU3rClo2IlWYdlF53Lh1OK0tVqlxffQWHHZZXwr/9dlhooaIrkiSpKjWlJ+znwGMR8SoQwKrAiaVtjP5SzuJUga65Bl56Ce69Fzp3nvf1kiSpUfMMYSmlARGxBrAmOYS90GAy/iVlrE2V5tNP4dxzYZttYNddi65GkqSq1tQlKjYib1vUDlgvIkgp3Vi2qlSZLr4YPvggP0cUXY0kSVWtKUtU3ASsBowGZpROJ8AQVk/eegv+8Ie8J2TPnkVXI0lS1WtKT1gPYO2U0uxrhame/OpXMH06XHBB0ZVIklQTmnJ35LPAcuUuRBXs2Wfh+uvhpJNcFV+SpBbSlJ6wzsBzETEc+HLWyZTSHmWrSpXljDOgUyc4++yiK5EkqWY0JYSdW+4iVMEeeihvTXTRRbD00kVXI0lSzWjKEhWPtEYhqkAzZ8Jpp8FKK8HJJxddjSRJNWWOISwiHkspbRkRk/nmBt4BpJRSp7JXp2LdeiuMGpX3iVx00aKrkSSppswxhKWUtiw9d2y9clQxpk6FM8+EDTaAgw8uuhpJkmpOkxZrjYi2QNeG16eU3ixXUaoAl18Ob74JfftCm6bcRCtJkuZHUxZrPRn4FfAeMLN0OgHrlbEuFWniRDj/fNh5Z9huu6KrkSSpJjWlJ+wU4HsppY/KXYwqxPnnw6RJcOGFRVciSVLNaso403hgUrkLUYV47bU8FHnEEbDuukVXI0lSzWpKT9g4YHBE9Oebi7X+sWxVqThnnQVt28JvflN0JZIk1bSmhLA3S4+FSw/VqhEj8rIUZ54J3boVXY0kSTWtKYu1/ro1ClHBUoLTT4fOnfM2RZIkqazmtljrJSmln0bEvXxzsVbAvSNrzvDhMHgwXHJJ3idSkiSV1dx6wm4qPf++NQpRwfr0gfbt4cgji65EkqS6MLcV80eWnt07stZNmgT9+uWV8e0FkySpVTRlsdY1gN8BawP/2UAwpfSdMtal1nTLLfD553DssUVXIklS3WjKOmHXA1cC04HewI18PVSpapcSXH01rL8+9OhRdDWSJNWNpoSwxVJKDwKRUnojpXQusG15y1KrGTECxozJvWARRVcjSVLdaMo6YVMjog3wckScBLwFdClvWWo1ffrA4ovn+WCSJKnVNKUn7KfA4sBPgI2AQ4DDy1iTWsunn+bFWQ86CJZYouhqJEmqK3PtCYuItsD+KaXTgSmA6xfUkltugc8+c0K+JEkFmGNPWES0SynNADaKcLJQzZk1Ib97d+jZs+hqJEmqO3PrCRsObAiMAv4REbcDn816M6V0Z5lrUzmNHAmjR8MVVzghX5KkAjRlYv7SwEfkOyITEKVnQ1g169MHFlsMfvSjoiuRJKkuzS2EdYmInwHP8nX4muVbe0mqikyenOeDHXigE/IlSSrI3EJYW6AD3wxfsxjCqtmttzohX5Kkgs0thL2TUvpNq1Wi1tOnD6y7LmyySdGVSJJUt+a2TtgCz9aOiJ0i4sWIeCUifjmX63pGxIyI2HdBf6bmYeTI/HCFfEmSCjW3ELbdgnxxaY2xK4CdyZt/HxQRa8/huguBBxbk56mJrrkGFl0UDjmk6EokSaprcwxhKaWJC/jdGwOvpJTGpZSmAf2APRu57mTg78D7C/jzNC9TpsDNN8MBB8CSSxZdjSRJda0pS1Q01wrA+AbHE4BvTEKKiBWAvcnLX8xxxdCIOBY4FqBr164MHjy4WQVNmTKl2Z+tBcv378/3pkzhqZ49+bQCfw/13j6VzLapbLZP5bJtKlvR7VPOENaUuyovAc5IKc2Y26L8KaU+QB+AHj16pF69ejWroMGDB9Pcz9aEX/wC1lmHDU88sSLng9V9+1Qw26ay2T6Vy7apbEW3TzlD2ARgxQbH3YC3Z7umB9CvFMA6A7tExPSU0t1lrKs+jRoFTz4Jf/pTRQYwSZLqTTlD2JPAGhGxKvAWcCBwcMMLUkqrznodETcA9xnAyqRPHyfkS5JUQcoWwlJK0yPiJPJdj22BvimlsRFxfOn9q8r1szWbWRPy998fllqq6GokSRLl7QkjpTQAGDDbuUbDV0rpiHLWUtduuy1vVeQK+ZIkVYy5rROmWtGnD6y9Nmy+edGVSJKkEkNYrRszBoYPd4V8SZIqjCGs1t12G7RtC4ceWnQlkiSpAUNYrRs0CDbbDJZeuuhKJElSA4awWvbhh3mz7h13LLoSSZI0G0NYLXvwQUgJdtih6EokSdJsDGG1bODAvFF3jx5FVyJJkmZjCKtVKeUQtt120K6sy8FJkqRmMITVqhdfhAkTnA8mSVKFMoTVqoED87PzwSRJqkiGsFo1cCCsvjqsuuq8r5UkSa3OEFaLvvwSHn7YoUhJkiqYIawWDR0Kn39uCJMkqYIZwmrRoEF5q6LevYuuRJIkzYEhrBYNHJi3KurUqehKJEnSHBjCas2srYq8K1KSpIpmCKs1s7Yqcj6YJEkVzRBWawYNcqsiSZKqgCGslrhVkSRJVcMQVktefBHGj3c+mCRJVcAQVktmbVXkfDBJkiqeIayWuFWRJElVwxBWK6ZNg8GD7QWTJKlKGMJqxdCh8NlnhjBJkqqEIaxWDByYtyrq1avoSiRJUhMYwmrFwIGw6aawxBJFVyJJkprAEFYLPvoob1XkUKQkSVXDEFYL3KpIkqSqYwirBQMHulWRJElVxhBW7WZtVbTttm5VJElSFTGEVbtZWxU5FClJUlUxhFW7QYPysyFMkqSqYgirdm5VJElSVTKEVbNp0+Dhh+0FkySpChnCqtmsrYp22KHoSiRJ0nwyhFWzWVsV9e5ddCWSJGk+GcKq2aBBblUkSVKVMoRVq48+ghEjnA8mSVKVMoRVK7cqkiSpqhnCqtXAgXkY0q2KJEmqSoawajV4MPTq5VZFkiRVKUNYNfrgA3j1Vdh886IrkSRJzWQIq0b//nd+3myzYuuQJEnNZgirRkOH5mHIjTYquhJJktRMhrBqNGwYdO8Oiy9edCWSJKmZDGHVZsYMGD48L9IqSZKqliGs2owdC1OmGMIkSapyhrBqM3RofnZSviRJVc0QVm2GDYPOneE73ym6EkmStAAMYdVm2LDcCxZRdCWSJGkBGMKqycSJ8MILzgeTJKkGGMKqyfDh+dn5YJIkVT1DWDUZOhTatIGePYuuRJIkLSBDWDUZNgzWXRc6dCi6EkmStIAMYdVi5sy8Z6TzwSRJqgmGsGrxwgswaZIhTJKkGmEIqxbDhuVnJ+VLklQTDGHVYuhQWGopWGONoiuRJEktwBBWLYYNy0ORbWwySZJqgf+iV4NJk/LG3c4HkySpZhjCqsGTT0JKzgeTJKmGGMKqwdChea/IjTcuuhJJktRCDGHVYNgwWHttWGKJoiuRJEktxBBW6VL6elK+JEmqGYawSvfyyzBxoiFMkqQaYwirdC7SKklSTTKEVbqhQ6FTJ1hrraIrkSRJLcgQVumGDYNNNnGRVkmSaoz/sleyKVPg6aedDyZJUg0qawiLiJ0i4sWIeCUiftnI+3tGxNMRMToiRkTEluWsp+qMGAEzZzofTJKkGtSuXF8cEW2BK4AdgAnAkxFxT0rpuQaXPQjck1JKEbEe8DdgzXLVVHWGDs3PLtIqSVLNKWdP2MbAKymlcSmlaUA/YM+GF6SUpqSUUumwPZDQ14YNg+9+F5ZZpuhKJElSCytbTxiwAjC+wfEEYJPZL4qIvYHfAV2AXRv7oog4FjgWoGvXrgwePLhZBU2ZMqXZn211KbH5o48yceONeaFaal5AVdU+dca2qWy2T+WybSpb0e1TzhAWjZz7Vk9XSuku4K6I2Bo4D9i+kWv6AH0AevTokXr16tWsggYPHkxzP9vqxo2Djz9mub32YrlqqXkBVVX71BnbprLZPpXLtqlsRbdPOYcjJwArNjjuBrw9p4tTSkOA1SKicxlrqh4u0ipJUk0rZwh7ElgjIlaNiIWBA4F7Gl4QEatHRJRebwgsDHxUxpqqx9Ch0L49fP/7RVciSZLKoGzDkSml6RFxEvAA0Bbom1IaGxHHl96/CtgHOCwivgK+AA5oMFG/vg0blu+KbFfOEWNJklSUsv4Ln1IaAAyY7dxVDV5fCFxYzhqq0hdfwOjRcPrpRVciSZLKxBXzK9HIkTB9uvPBJEmqYYawSjRrUv4m31rRQ5Ik1QhDWCUaOhS+8x3o0qXoSiRJUpkYwipNSjmEORQpSVJNM4RVmvHj4Z13YNNNi65EkiSVkSGs0rhIqyRJdcEQVmmGDoXFFoP11iu6EkmSVEaGsEozbBj06AELLVR0JZIkqYwMYZXkyy/hqaecDyZJUh0whFWSZ56BadPydkWSJKmmGcIqyejR+XnDDQstQ5IklZ8hrJKMGgWdOsEqqxRdiSRJKjNDWCUZPRq6d4c2NoskSbXOf+0rxcyZMGYMbLBB0ZVIkqRWYAirFK+8Ap99BuuvX3QlkiSpFRjCKsWsSfmGMEmS6oIhrFKMHp0XaP3+94uuRJIktQJDWKUYNQrWXhsWXrjoSiRJUiswhFWK0aMdipQkqY4YwirBu+/mhyFMkqS6YQirBLMm5bs8hSRJdcMQVglmhbDu3QstQ5IktR5DWCUYPTpvVbTkkgUXIkmSWoshrBKMGuVQpCRJdcYQVrQpU+Dll52UL0lSnTGEFe2ZZyAlQ5gkSXXGEFY074yUJKkuGcKKNmoULL00dOtWdCWSJKkVGcKKNmul/IiiK5EkSa3IEFak6dPznDCHIiVJqjuGsCK9+CJMneqkfEmS6pAhrEizJuUbwiRJqjuGsCKNHg2LLAJrrll0JZIkqZUZwoo0ahSsuy60a1d0JZIkqZUZwoqS0td3RkqSpLpjCCvKW2/BRx95Z6QkSXXKEFaUUaPysz1hkiTVJUNYUUaPzgu0rrde0ZVIkqQCGMKKMno0rLEGdOhQdCWSJKkAhrCijBrlUKQkSXXMEFaETz6B114zhEmSVMcMYUV4+un8bAiTJKluGcKKMGu7IpenkCSpbhnCijBqFHTtCsstV3QlkiSpIIawIrhSviRJdc8Q1tqmTYOxYx2KlCSpzhnCWttzz8FXX9kTJklSnTOEtbZZk/INYZIk1TVDWGsbPRrat4fVVy+6EkmSVCBDWGsbNSrvF9m2bdGVSJKkAhnCWlNK3hkpSZIAQ1jrev11+PRT74yUJEmGsFY1alR+tidMkqS6ZwhrTaNH57lg66xTdCWSJKlghrDWNHo0rLkmLLZY0ZVIkqSCGcJa06hRDkVKkiTAENZ6PvwQJkwwhEmSJMAQ1nrGjMnP3hkpSZIwhLWeWXdGdu9ebB2SJKkiGMJay+jR0K0bdO5cdCWSJKkCGMJay+jRDkVKkqT/MIS1hi++gBdecFK+JEn6D0NYa3j2WZgxwxAmSZL+wxDWGtyuSJIkzcYQ1hoefhi6dIFVVy26EkmSVCEMYeU2fTrcfz/ssgtEFF2NJEmqEIawchs6FD75BHbdtehKJElSBTGEldt990G7drDjjkVXIkmSKkhZQ1hE7BQRL0bEKxHxy0be/1FEPF16PBERtbecfP/+sPXW0KlT0ZVIkqQKUrYQFhFtgSuAnYG1gYMiYu3ZLnsN2CaltB5wHtCnXPUU4o03YOxYhyIlSdK3lLMnbGPglZTSuJTSNKAfsGfDC1JKT6SUPi4dDgO6lbGe1te/f37ebbdi65AkSRWnXRm/ewVgfIPjCcAmc7n+KOCfjb0REccCxwJ07dqVwYMHN6ugKVOmNPuzzbHujTey2AorMPztt+Htt1vt51ar1m4fNZ1tU9lsn8pl21S2otunnCGssfUYUqMXRvQmh7AtG3s/pdSH0lBljx49Uq9evZpV0ODBg2nuZ+fb55/DmDFw3HGt9zOrXKu2j+aLbVPZbJ/KZdtUtqLbp5whbAKwYoPjbsC3uoMiYj3gWmDnlNJHZayndT30EEyd6nwwSZLUqHLOCXsSWCMiVo2IhYEDgXsaXhARKwF3AoemlF4qYy2tr39/6NAh3xkpSZI0m7L1hKWUpkfEScADQFugb0ppbEQcX3r/KuAcYBngz5FXk5+eUupRrppaTUp5fbAddoBFFim6GkmSVIHKORxJSmkAMGC2c1c1eH00cHQ5ayjEM8/AhAlw7rlFVyJJkiqUK+aXw6ylKXbZpdg6JElSxTKElUP//rDRRrD88kVXIkmSKpQhrKV99FHetNu7IiVJ0lwYwlra/ffDzJmGMEmSNFeGsJbWvz906QI9qv8mT0mSVD6GsJY0fXruCdtlF2jjr1aSJM2ZSaElDR0KH3/sUKQkSZonQ1hL6t8f2rXLi7RKkiTNhSGsJfXvD1ttBUssUXQlkiSpwhnCWsobb8Czz8JuuxVdiSRJqgKGsJYya5V854NJkqQmMIS1lP79YbXV4LvfLboSSZJUBQxhLeHzz+Ghh3IvWETR1UiSpCpgCGsJDz8MU6c6H0ySJDWZIawl3HcftG8PW29ddCWSJKlKGMIWVEp5PtgOO8AiixRdjSRJqhKGsAX17LMwfrxDkZIkab4YwhbUrKUpdtml2DokSVJVMYQtqPvugw03hOWXL7oSSZJURQxhC+Kjj/Km3S7QKkmS5pMhbEE88ADMnOl8MEmSNN8MYQuif3/o0gV69Ci6EkmSVGUMYc310ktwxx2w997Qxl+jJEmaP6aH5kgJTjwRFlsMzj236GokSVIVald0AVWpXz948EG44gpYbrmiq5EkSVXInrD59ckn8LOfQc+ecNxxRVcjSZKqlD1h8+vss+H99/Ok/LZti65GkiRVKXvC5seIEfDnP8NJJ+UFWiVJkprJENZUM2bA8cfnOWDnnVd0NZIkqco5HNlUV14JI0fmSfmdOhVdjSRJqnL2hDXFO+/AWWfBjjvC/vsXXY0kSaoBhrCm+NnP4Msv85IUEUVXI0mSaoAhbF4GDcpDkGeeCauvXnQ1kiSpRhjC5mbq1Lwy/hprwBlnFF2NJEmqIU7Mn5sLL4RXXsm9YYssUnQ1kiSphtgTNicvvwwXXAAHHQTbb190NZIkqcYYwhqTEvz4x7DoovDHPxZdjSRJqkEORzbmttvyEOTll7tBtyRJKgt7wmY3aRKceir06JFXyJckSSoDQ9jsxo3Lk/CvvNINuiVJUtk4HDm7DTbIk/IXWqjoSiRJUg2zJ6wxBjBJklRmhjBJkqQCGMIkSZIKYAiTJEkqgCFMkiSpAIYwSZKkAhjCJEmSCmAIkyRJKoAhTJIkqQCGMEmSpAIYwiRJkgpgCJMkSSqAIUySJKkAhjBJkqQCGMIkSZIKYAiTJEkqgCFMkiSpAIYwSZKkAhjCJEmSCmAIkyRJKoAhTJIkqQCGMEmSpAIYwiRJkgoQKaWia5gvEfEB8EYzP94Z+LAFy1HLsn0ql21T2WyfymXbVLbWaJ+VU0rLNvZG1YWwBRERI1JKPYquQ42zfSqXbVPZbJ/KZdtUtqLbx+FISZKkAhjCJEmSClBvIaxP0QVormyfymXbVDbbp3LZNpWt0PapqzlhkiRJlaLeesIkSZIqgiFMkiSpAHUTwiJip4h4MSJeiYhfFl1PPYuIvhHxfkQ82+Dc0hExKCJeLj0vVWSN9SwiVoyIhyPi+YgYGxGnlM7bRgWLiEUjYnhEjCm1za9L522bChERbSNiVETcVzq2bSpERLweEc9ExOiIGFE6V2j71EUIi4i2wBXAzsDawEERsXaxVdW1G4CdZjv3S+DBlNIawIOlYxVjOvDzlNJawKbAj0v/vdhGxfsS2Dal1B1YH9gpIjbFtqkkpwDPNzi2bSpL75TS+g3WBiu0feoihAEbA6+klMallKYB/YA9C66pbqWUhgATZzu9J/CX0uu/AHu1Zk36WkrpnZTSU6XXk8n/oKyAbVS4lE0pHS5UeiRsm4oQEd2AXYFrG5y2bSpboe1TLyFsBWB8g+MJpXOqHF1TSu9ADgFAl4LrERARqwAbAP/GNqoIpeGu0cD7wKCUkm1TOS4BfgHMbHDOtqkcCRgYESMj4tjSuULbp11r/rACRSPnXJtDmouI6AD8HfhpSunTiMb+M1JrSynNANaPiCWBuyJinYJLEhARuwHvp5RGRkSvgstR47ZIKb0dEV2AQRHxQtEF1UtP2ARgxQbH3YC3C6pFjXsvIpYHKD2/X3A9dS0iFiIHsJtTSneWTttGFSSl9AkwmDy/0rYp3hbAHhHxOnnKy7YR8Vdsm4qRUnq79Pw+cBd5qlKh7VMvIexJYI2IWDUiFgYOBO4puCZ90z3A4aXXhwP/KLCWuha5y+s64PmU0h8bvGUbFSwili31gBERiwHbAy9g2xQupfQ/KaVuKaVVyP/GPJRSOgTbpiJERPuI6DjrNbAj8CwFt0/drJgfEbuQx+vbAn1TSucXW1H9iohbgV5AZ+A94FfA3cDfgJWAN4H9UkqzT95XK4iILYFHgWf4em7LmeR5YbZRgSJiPfLk4bbk/xP9t5TSbyJiGWybilEajjwtpbSbbVMZIuI75N4vyFOxbkkpnV90+9RNCJMkSaok9TIcKUmSVFEMYZIkSQUwhEmSJBXAECZJklQAQ5gkSVIBDGGSql5EzIiI0Q0eLbYJb0SsEhHPttT3SdIs9bJtkaTa9kVKaf2ii5Ck+WFPmKSaFRGvR8SFETG89Fi9dH7liHgwIp4uPa9UOt81Iu6KiDGlx+alr2obEddExNiIGFharZ6I+ElEPFf6nn4F/TElVSlDmKRasNhsw5EHNHjv05TSxsDl5F0zKL2+MaW0HnAz8KfS+T8Bj6SUugMbAmNL59cArkgpfR/4BNindP6XwAal7zm+PH80SbXKFfMlVb2ImJJS6tDI+deBbVNK40qbkr+bUlomIj4Elk8pfVU6/05KqXNEfAB0Syl92eA7VgEGpZTWKB2fASyUUvptRNwPTCFvu3V3SmlKmf+okmqIPWGSal2aw+s5XdOYLxu8nsHX82l3Ba4ANgJGRoTzbCU1mSFMUq07oMHz0NLrJ4ADS69/BDxWev0gcAJARLSNiE5z+tKIaAOsmFJ6GPgFsCTwrd44SZoT/1+bpFqwWESMbnB8f0pp1jIVi0TEv8n/p/Og0rmfAH0j4nTgA+DI0vlTgD4RcRS5x+sE4J05/My2wF8jYgkggP+XUvqkhf48kuqAc8Ik1azSnLAeKaUPi65FkmbncKQkSVIB7AmTJEkqgD1hkiRJBTCESZIkFcAQJkmSVABDmCRJUgEMYZIkSQX4/04xSxtqUNlAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot(df['epoch'], df['acc'],c='r')\n",
    "plt.title('Training Accuracy over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Training Accuracy')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Conclusion\n",
    "As number of epochs increases the training loss decreases and Accuracy increases"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}