Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "resnet = pd.read_csv(\"Results/ResNet50_without_dropout_without_conv_without_pool_basemap32.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>acc</th>\n",
       "      <th>loss</th>\n",
       "      <th>lr</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>val_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.408131</td>\n",
       "      <td>1.635986</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.4192</td>\n",
       "      <td>1.640235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.556472</td>\n",
       "      <td>1.240068</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.5689</td>\n",
       "      <td>1.188344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.645626</td>\n",
       "      <td>0.997266</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6059</td>\n",
       "      <td>1.181285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.703060</td>\n",
       "      <td>0.845248</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6809</td>\n",
       "      <td>1.003683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.742170</td>\n",
       "      <td>0.734876</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7156</td>\n",
       "      <td>0.836653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.772869</td>\n",
       "      <td>0.654869</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6774</td>\n",
       "      <td>1.011903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.793396</td>\n",
       "      <td>0.592657</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7372</td>\n",
       "      <td>0.846899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.812560</td>\n",
       "      <td>0.540628</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7675</td>\n",
       "      <td>0.786487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.827119</td>\n",
       "      <td>0.500079</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7855</td>\n",
       "      <td>0.664445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.838413</td>\n",
       "      <td>0.465444</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8065</td>\n",
       "      <td>0.576569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.850048</td>\n",
       "      <td>0.432495</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7924</td>\n",
       "      <td>0.643681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.860802</td>\n",
       "      <td>0.401018</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8301</td>\n",
       "      <td>0.504650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.867731</td>\n",
       "      <td>0.377405</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8351</td>\n",
       "      <td>0.488669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.877984</td>\n",
       "      <td>0.353112</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8356</td>\n",
       "      <td>0.499134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.881168</td>\n",
       "      <td>0.342405</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8349</td>\n",
       "      <td>0.492916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.893203</td>\n",
       "      <td>0.307631</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8194</td>\n",
       "      <td>0.588103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.898350</td>\n",
       "      <td>0.293149</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8037</td>\n",
       "      <td>0.638502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.931172</td>\n",
       "      <td>0.197506</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8977</td>\n",
       "      <td>0.310693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.941305</td>\n",
       "      <td>0.167983</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8930</td>\n",
       "      <td>0.334530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.943528</td>\n",
       "      <td>0.158351</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.9011</td>\n",
       "      <td>0.304022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.949395</td>\n",
       "      <td>0.146374</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.9024</td>\n",
       "      <td>0.309921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.951979</td>\n",
       "      <td>0.136372</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.9046</td>\n",
       "      <td>0.313014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.955964</td>\n",
       "      <td>0.126466</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.9047</td>\n",
       "      <td>0.307453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.959128</td>\n",
       "      <td>0.118373</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.9031</td>\n",
       "      <td>0.319699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.968680</td>\n",
       "      <td>0.089137</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.9158</td>\n",
       "      <td>0.286985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.973386</td>\n",
       "      <td>0.077189</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.9134</td>\n",
       "      <td>0.294525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.974668</td>\n",
       "      <td>0.072526</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.9142</td>\n",
       "      <td>0.307032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.976810</td>\n",
       "      <td>0.067450</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.9140</td>\n",
       "      <td>0.304069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0.978012</td>\n",
       "      <td>0.062624</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.9172</td>\n",
       "      <td>0.303868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.980555</td>\n",
       "      <td>0.056889</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9161</td>\n",
       "      <td>0.299674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>0.981957</td>\n",
       "      <td>0.053339</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>0.299648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>0.982638</td>\n",
       "      <td>0.051165</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9170</td>\n",
       "      <td>0.304532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>0.982558</td>\n",
       "      <td>0.050139</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.9184</td>\n",
       "      <td>0.303603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>0.983959</td>\n",
       "      <td>0.046767</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.9174</td>\n",
       "      <td>0.304745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>0.983499</td>\n",
       "      <td>0.046455</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.9175</td>\n",
       "      <td>0.306619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>0.985121</td>\n",
       "      <td>0.044473</td>\n",
       "      <td>3.162280e-06</td>\n",
       "      <td>0.9173</td>\n",
       "      <td>0.306440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>0.985361</td>\n",
       "      <td>0.044471</td>\n",
       "      <td>3.162280e-06</td>\n",
       "      <td>0.9182</td>\n",
       "      <td>0.307334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>0.985101</td>\n",
       "      <td>0.044314</td>\n",
       "      <td>3.162280e-06</td>\n",
       "      <td>0.9174</td>\n",
       "      <td>0.306571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>0.985081</td>\n",
       "      <td>0.045034</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.9172</td>\n",
       "      <td>0.306420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>0.984961</td>\n",
       "      <td>0.043426</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.9186</td>\n",
       "      <td>0.306715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>0.985361</td>\n",
       "      <td>0.044367</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.9177</td>\n",
       "      <td>0.306921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>0.984060</td>\n",
       "      <td>0.046409</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9178</td>\n",
       "      <td>0.306818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>0.984260</td>\n",
       "      <td>0.045056</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9182</td>\n",
       "      <td>0.306535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>0.985842</td>\n",
       "      <td>0.043616</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9180</td>\n",
       "      <td>0.306996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>0.985141</td>\n",
       "      <td>0.043168</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9181</td>\n",
       "      <td>0.307390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>0.984520</td>\n",
       "      <td>0.043565</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9180</td>\n",
       "      <td>0.307535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>0.985401</td>\n",
       "      <td>0.044013</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9180</td>\n",
       "      <td>0.306970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>0.984500</td>\n",
       "      <td>0.045635</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9181</td>\n",
       "      <td>0.307307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>0.984881</td>\n",
       "      <td>0.045153</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9183</td>\n",
       "      <td>0.306254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>0.985461</td>\n",
       "      <td>0.042169</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9180</td>\n",
       "      <td>0.307199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>0.984380</td>\n",
       "      <td>0.044932</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9181</td>\n",
       "      <td>0.307396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>0.985622</td>\n",
       "      <td>0.043398</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9181</td>\n",
       "      <td>0.306654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>52</td>\n",
       "      <td>0.985181</td>\n",
       "      <td>0.044814</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9181</td>\n",
       "      <td>0.306613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>0.984340</td>\n",
       "      <td>0.045965</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9185</td>\n",
       "      <td>0.306509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54</td>\n",
       "      <td>0.985562</td>\n",
       "      <td>0.043724</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9185</td>\n",
       "      <td>0.307370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>0.985201</td>\n",
       "      <td>0.042783</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9184</td>\n",
       "      <td>0.306958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    epoch       acc      loss            lr  val_acc  val_loss\n",
       "0       0  0.408131  1.635986  1.000000e-03   0.4192  1.640235\n",
       "1       1  0.556472  1.240068  1.000000e-03   0.5689  1.188344\n",
       "2       2  0.645626  0.997266  1.000000e-03   0.6059  1.181285\n",
       "3       3  0.703060  0.845248  1.000000e-03   0.6809  1.003683\n",
       "4       4  0.742170  0.734876  1.000000e-03   0.7156  0.836653\n",
       "5       5  0.772869  0.654869  1.000000e-03   0.6774  1.011903\n",
       "6       6  0.793396  0.592657  1.000000e-03   0.7372  0.846899\n",
       "7       7  0.812560  0.540628  1.000000e-03   0.7675  0.786487\n",
       "8       8  0.827119  0.500079  1.000000e-03   0.7855  0.664445\n",
       "9       9  0.838413  0.465444  1.000000e-03   0.8065  0.576569\n",
       "10     10  0.850048  0.432495  1.000000e-03   0.7924  0.643681\n",
       "11     11  0.860802  0.401018  1.000000e-03   0.8301  0.504650\n",
       "12     12  0.867731  0.377405  1.000000e-03   0.8351  0.488669\n",
       "13     13  0.877984  0.353112  1.000000e-03   0.8356  0.499134\n",
       "14     14  0.881168  0.342405  1.000000e-03   0.8349  0.492916\n",
       "15     15  0.893203  0.307631  1.000000e-03   0.8194  0.588103\n",
       "16     16  0.898350  0.293149  1.000000e-03   0.8037  0.638502\n",
       "17     17  0.931172  0.197506  3.162280e-04   0.8977  0.310693\n",
       "18     18  0.941305  0.167983  3.162280e-04   0.8930  0.334530\n",
       "19     19  0.943528  0.158351  3.162280e-04   0.9011  0.304022\n",
       "20     20  0.949395  0.146374  3.162280e-04   0.9024  0.309921\n",
       "21     21  0.951979  0.136372  3.162280e-04   0.9046  0.313014\n",
       "22     22  0.955964  0.126466  3.162280e-04   0.9047  0.307453\n",
       "23     23  0.959128  0.118373  3.162280e-04   0.9031  0.319699\n",
       "24     24  0.968680  0.089137  1.000000e-04   0.9158  0.286985\n",
       "25     25  0.973386  0.077189  1.000000e-04   0.9134  0.294525\n",
       "26     26  0.974668  0.072526  1.000000e-04   0.9142  0.307032\n",
       "27     27  0.976810  0.067450  1.000000e-04   0.9140  0.304069\n",
       "28     28  0.978012  0.062624  1.000000e-04   0.9172  0.303868\n",
       "29     29  0.980555  0.056889  3.162280e-05   0.9161  0.299674\n",
       "30     30  0.981957  0.053339  3.162280e-05   0.9167  0.299648\n",
       "31     31  0.982638  0.051165  3.162280e-05   0.9170  0.304532\n",
       "32     32  0.982558  0.050139  1.000000e-05   0.9184  0.303603\n",
       "33     33  0.983959  0.046767  1.000000e-05   0.9174  0.304745\n",
       "34     34  0.983499  0.046455  1.000000e-05   0.9175  0.306619\n",
       "35     35  0.985121  0.044473  3.162280e-06   0.9173  0.306440\n",
       "36     36  0.985361  0.044471  3.162280e-06   0.9182  0.307334\n",
       "37     37  0.985101  0.044314  3.162280e-06   0.9174  0.306571\n",
       "38     38  0.985081  0.045034  1.000000e-06   0.9172  0.306420\n",
       "39     39  0.984961  0.043426  1.000000e-06   0.9186  0.306715\n",
       "40     40  0.985361  0.044367  1.000000e-06   0.9177  0.306921\n",
       "41     41  0.984060  0.046409  5.000000e-07   0.9178  0.306818\n",
       "42     42  0.984260  0.045056  5.000000e-07   0.9182  0.306535\n",
       "43     43  0.985842  0.043616  5.000000e-07   0.9180  0.306996\n",
       "44     44  0.985141  0.043168  5.000000e-07   0.9181  0.307390\n",
       "45     45  0.984520  0.043565  5.000000e-07   0.9180  0.307535\n",
       "46     46  0.985401  0.044013  5.000000e-07   0.9180  0.306970\n",
       "47     47  0.984500  0.045635  5.000000e-07   0.9181  0.307307\n",
       "48     48  0.984881  0.045153  5.000000e-07   0.9183  0.306254\n",
       "49     49  0.985461  0.042169  5.000000e-07   0.9180  0.307199\n",
       "50     50  0.984380  0.044932  5.000000e-07   0.9181  0.307396\n",
       "51     51  0.985622  0.043398  5.000000e-07   0.9181  0.306654\n",
       "52     52  0.985181  0.044814  5.000000e-07   0.9181  0.306613\n",
       "53     53  0.984340  0.045965  5.000000e-07   0.9185  0.306509\n",
       "54     54  0.985562  0.043724  5.000000e-07   0.9185  0.307370\n",
       "55     55  0.985201  0.042783  5.000000e-07   0.9184  0.306958"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd7e61b9090>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmYHGW59/HvLwlhCYQkhAAS2WQRVCARQ5ADDgYh5GUz\nIBIFAeUFFQ54jiJ48DoEFQF9UQHxKKvsHESBqMjOKAiBQNjkZGOLIYREIJyQmIQkc79/PDVM92zp\n6emanu75fa6rru6qrq66CyZ197PU8ygiMDMza9av2gGYmVnv4sRgZmZFnBjMzKyIE4OZmRVxYjAz\nsyJODGZmViTXxCDpKkkLJT3XyT6XSJoj6RlJu+cZj5mZrV3eJYZrgAM7+lDSQcCHImIH4GTglznH\nY2Zma5FrYoiIR4DFnexyGHBdtu/jwMaSNsszJjMz61y12xi2BOYVrM/PtpmZWZVUOzGYmVkvM6DK\n558PfLBgfWS2rQ1JHtTJzKwMEaGu7N8TJQZlS3umAF8CkDQWeCciFnZ0oIio2+Wcc86pegy+Pl+b\nr6/+lnLkWmKQdBPQAGwi6e/AOcBAICLi8oi4S9IESS8Cy4AT8ozHzMzWLtfEEBFfKGGfU/OMwczM\nusaNz71EQ0NDtUPIVT1fXz1fG/j6+iKVWwfV0yRFrcRqZtZbSCJ6YeOzmZnVECcGMzMr4sRgZmZF\nnBjMzKyIE4OZmRVxYjAzsyJODGZmVsSJwczMijgxmJlZEScGMzMr4sRgZmZFnBjMzKyIE4OZmRVx\nYjAzsyJODGZmVsSJwczMijgxmJlZEScGMzMr4sRgZmZFnBjMzKyIE4OZmRVxYjAzsyJODGZmVmRA\ntQMw622amuDtt2HjjWGddbr+/QhYswaWLIE33mhZFi6ERYtg/fVh6NCWZdgwGDQofa+pqeW1qQn6\n9YP+/dMyYEB6hXTsd96B//3flgVg8OC0bLRRy+uAAS3fbX5dsSLFUxjfokUwcGC67iFD0uvGG7fE\nVhhXBKxaBStXFi+rVrWNt3//dB3N3y08hpQ+69ev5b2Ultb/TVufv6kp/XduXlavbnkvpf93Awem\n1+al8BjNS1esWZOu8733Wq75vfc6Pk7hNTVf54QJMG5c187b0xQR1Y6hJJKiVmK1nvHHP8ITT8CH\nPww77ww77ZRuul21aBE8/ng61uOPw7Rp6QaydClssAFssknLIsE//5mWZcvS6/Ll6eawalXL0q9f\nuilvvnnxsummaf/Fi4uXZcvav0FGFN/wVq9OMQ8e3PYGDilhvPtuem1+v3p122Osu277sa1alZJM\nYdJZtqztDbz5xrvuusXLOuukm2Tz+Zpfm5paEkTrm3/rZNHRTbb1f5/m94WJr3lpTlyrVhX/v2n+\nXntxlKJfv3SdAwe2XPPAgS0Ju7X2ktnee8PYsaWfs7skERFduEonBqtBEXDBBfCLX8Cxx8Ls2TBz\nJrz0EmyxBeywQ7ohtb5JrljR9pezlG5cY8bAnnum1zFjYLPN0j/iJUvgrbdSCeKtt9K5Bw1KCaN5\nWX/9tr9M+7mS1nqJXpkYJI0HfkZqz7gqIi5s9flWwNXApsBbwDER8Xo7x3FiMFauhJNPhuefhylT\nYMstWz5bvRpefhlefDHdqJurU5qrVNZbr/hX7OrV6eY/fLhv5Fa/el1ikNQPmA2MA14HpgFHR8TM\ngn1uBaZExA2SGoAvR8SX2jmWE0Mf9+abMHFiqtK54Yb0y93MOldOYsi78XkMMCci5gJIugU4DJhZ\nsM8uwL8BRESjpDtzjsmqaOXKVOUzZw68/npL/XNhXezQoakk0LwMGQKzZsHBB8MRR8D55/sXvlme\n8k4MWwLzCtZfIyWLQs8AE4FLJU0ENpQ0NCIW5xybVVhzVc6iRS3LwoVpKUwGW20FO+4II0emuv7W\njYnPPw/z58Nrr6XXNWvSfj/9KXz5y9W+SrP61xu6q54B/FzS8cBfgPnAmqpGZF0SAbffDt/5TuoB\n8oEPpMbbESPSsssu6df+DjvANtt0vQvokiXpuMOH5xK+mbWSd2KYD2xVsD4y2/a+iFgAHAEgaRBw\nREQsae9gkydPfv99Q0MDDQ0NlY3W2vjFL+Cee+Dww9PNfdNNiz9/+GH49rdTF8xLLoEDDuha979S\nDB5c2eOZ1bPGxkYaGxu7dYy8G5/7A7NIjc8LgCeASRExo2CfTYC3IyIk/QBYHRGT2zmWG5+rYMIE\n2H57WLAA7r0Xdt89JYlRo+Cii+Bvf4Mf/AAmTXK9v1lv1Ot6JcH73VUvpqW76gWSzgWmRcQfJB0B\nnA80kaqSTomIVe0cx4mhCrbbLpUYdtghlQoeeADuuAOmToUTT4SvfS095GNmvVOvTAyV4sTQ85Yv\nT11DlyxJjb9mVnvKSQwu/FuHZs2CD33IScGsr3FisA7NmJHGITKzvsWJwTo0Y0YanM7M+hYnBuuQ\nE4NZ3+TEYB1yYjDrm9wrydq1enUakfTtt8ub48DMegf3SrKKeeWVNLeBk4JZ3+PEYO1yNZJZ3+XE\nYO1yV1WzvsuJwdrlEoNZ3+XEYO1yYjDru9wrydqISLOmvfIKDBtW7WjMrDvcK8kqYsECWG89JwWz\nvsqJwdpwNZJZ3+bEYG24R5JZ3+bEYG24xGDWtzkxWBtODGZ9mxODteHEYNa3OTFYkXfegaVLYeTI\nakdiZtXiSRtryMKF8LvfwQsvwIoVaU7mFSvSsnJl6mK64YbFy9ixcPDBpZ9j5szU8Kwu9Xo2s3ri\nxNDLNSeD3/wGpk+HCRNgzz1hgw3SyKfrrZdeBw5MyWHp0pbl3Xfhq1+FH/4QvvSl0s7nHklmttbE\nIKl/RKzpiWCsxfLlMHEiPPZYSgannQYHHtj1YbCPPBLGjYN114XPf37t+7t9wcxKKTHMkfRb4JqI\n+J+8A7Lkt7+FVavSU8jdmRNh553h7rvhgANSqeKzn+18/xkz4MtfLv98Zlb7Sml83g2YDVwpaaqk\nkyQNzjmuPu/yy+GUUyozUc6uu8If/wgnnwx33dX5vi4xmFmXBtGT9CngJmAIcBvw/Yh4MafYWp+7\nzwyiN2NGqv6ZOxfWWadyx506FQ49FG6+OR2/tRUrYOhQWLKksuc1s+rJZRA9Sf0lHSrpduBnwEXA\ndsDvgbX8/rRyXHEFnHBC5W/OY8emKqpJk+Dhh9t+Pns2bLutk4JZX1dSGwPwEPDjiHi0YPttkvbN\nJ6y+a8UKuP56eOKJfI6/zz5w001wxBGpeukTn2j5bOZMVyOZWWmJYdeIWNreBxFxWoXj6fN+9zsY\nPTr9cs/L/vvDVVfBIYfAvfemNghwV1UzS0ppfL5M0pDmFUlDJV2dY0x92uWXw0kn5X+eQw6BSy6B\n8eNh1qy0zQ3PZgalJYZdI+Kd5pWIWAyMKvUEksZLmilptqQz2/n8g5IelDRd0jOSDir12PVm9uxU\nnXPIIT1zvqOOSg+/feYzabY2JwYzg9ISQz9JQ5tXJA2jxCemJfUDfg4cCHwEmCSpdWXFd4H/jojR\nwCTgF6Ucuxbdf3+q458/v/3Pr7gCjj8+PW/QU44/Hs46K/VSmjPHVUlmVtoN/iLgMUm/AQQcCZxX\n4vHHAHMiYi6ApFuAw4CZBfs0Ac3PRQwBOrht1rY334Tjjku/zvfZJyWJ7bZr+XzlSrj2Wnj00Y6P\nkZevfx2WLYMrr4RBg3r+/GbWu6y1xBAR1wFHAAuBN4CJEXF9icffEphXsP5atq3QucCxkuYBfwD+\ntcRj14wI+MpX4AtfgF//Gr79bdh33zQYXrM77kiNwNtvX50YzzgDnnuuOuc2s96lpCqhiHhB0j+A\n9QAkbRURf69QDJNIw238VNJY4AZStVMbkydPfv99Q0MDDQ0NFQohX7/6FcyblwbCgzSw3eDBqfrm\n979PXUYvvzw9mVxN665b3fObWfc1NjbS2NjYrWOs9clnSYeSqpM+ACwCtgZmRES7N+9W3x0LTI6I\n8dn6WUBExIUF+/wNODAi5mfrLwF7RsSbrY5Vk08+z5iRSgcPP9y2/v73v08lifPPh+98JyUP35zN\nrJJyefIZ+D4wFpgdEdsC44CpJR5/GrC9pK0lDQSOBqa02mcusD+ApJ2BdVsnhVq1cmV6yvi889pv\n1D3kELjlFjj99NT+4KRgZr1BKSWGJyNiD0nPAqMioknSsxGxW0knkMYDF5OS0FURcYGkc4FpEfGH\nLBlcAWxIaog+IyIeaOc4NVdi+Na34OWX0zAUnU188+KLMGJEql4yM6ukckoMpSSG+4HDgfOB4aTq\npE9ExCfLDbQctZYY7rsvDV/9zDOwySbVjsbM+qq8EsMgYDnpF/8XgY2BGyPirXIDLUctJYa5c2Gv\nveCGG+DTn652NGbWl1U8MUjqD9wfEft1N7juqpXEsHQp7L13enDs3/6t2tGYWV9X8cbnbErPJkkb\ndyuyPqKpCY45JnU//cY3qh2NmVl5SnmOYSnwvKT7gGXNGz2yaltnnw2LF8Ott3be2Gxm1puVkhh+\nly3WieuvTwnh8cd7dqwjM7NK69LUntXUm9sYHn0UDj8cHnoIPrLWx/7MzHpOOW0May0xSHoFaHNH\njojt2tm9z5k3D448Mo2B5KRgZvWglKqkPQrerwd8DhiWTzi157vfTc8rTJhQ7UjMzCqjrKokSU9F\nxMdziKezc/a6qqQXX4SxY9PrkCFr39/MrKflVZU0umC1H6kEUdKorPXuvPPg1FOdFMysvpQ6UU+z\n1cArwFH5hFM7Xn4ZpkxJpQUzs3riXkllOvFE2GIL+P73qx2JmVnHchl2W9IPJQ0pWB8q6QflBFgv\nXn0Vbr/dQ16YWX0qZT6GgyLineaViFgM9Ok+OD/8YZqFbZj7ZplZHSqljaG/pHUjYiWApPWBPjul\nzNy5cNttMGdOtSMxM8tHKYnhRuABSddk6ycA1+YXUu92wQVw0kmeY8HM6ldJjc/ZLGz7Z6v3RcQ9\nuUbVfgxVb3yeNw922w1mzYJNN61qKGZmJclrop5tgQURsSJbXx/YLCJeLTfQcvSGxHDKKTBoEPzo\nR1UNw8ysZHklhieBT0bEe9n6QOCvEfGJsiMtQ7UTw4oVqZTw4ouw2WZVC8PMrEty6a4KDGhOCgDZ\n+z43sPTUqbDLLk4KZlb/SkkM/5B0aPOKpMOAN/MLqXdqbIT9qj7BqZlZ/kpJDF8F/kPS3yXNA84E\nTs43rN6nsREaGqodhZlZ/koeEkPShgARsVTSZhGxMNfI2p6/am0MK1bA8OGwYAFstFFVQjAzK0te\nbQzNBgCfl/QA8HSXIqtxU6fCRz/qpGBmfUOnD7hlXVMPA74AjAI2Ag4H/pJ/aL3HQw+5GsnM+o4O\nSwySbgJmA58BLgW2ARZHRGNENPVMePlbvXrt+7jh2cz6ks6qknYBFgMzgBkRsYZ25n6uZU8/DVtv\nDcuXd7zP8uXw1FOw9949F5eZWTV1mBgiYnfShDwbAfdLegTYSFLd9OT/2c/g7bfToHgdmToVPvYx\n2HDDnovLzKyaOm18joiZEXFORHwYOJ00eN40SY+WegJJ4yXNlDRb0pntfP4TSU9Lmi5plqS3u3wV\nZVi4MM3AdumlcMUVHe/nbqpm1td0eQY3SQL2iYi1NkBL6kdqpxgHvA5MA46OiJkd7H8qsHtEnNjO\nZxXtrvq978H8+SkxbLUV/PnPsNNObffbd184+2w48MCKndrMrMfk3V0VgEhK7ZU0BpgTEXMjYhVw\nC6mXU0cmATd3Naaueu89+OUv4bTTYOBAOO44uPLKtvv9858wfbrbF8ysb+lyYuiiLYF5BeuvZdva\nkLQVqefTgznHxK23wkc+khZI8zdfd11KGIWmToVdd3X7gpn1LaVM1NNTjgZu66y+aPLkye+/b2ho\noKGMyv8IuPhi+M//bNm2ww6w886pzeHII1u2u33BzGpNY2MjjY2N3TpGKcNurwscQfo1/34iiYjv\nrfXg0lhgckSMz9bPSl+NC9vZdzrw9YiY2sGxKtLG8NhjcOyxMHs29CsoL914I1x/Pdx9d8u2ffeF\n734XDjig26c1M6uKvNoY7iS1C6wGlhUspZgGbC9p62weh6OBKa13kvRhYEhHSaGSLr4YTj21OCkA\nTJwITz4Jr76a1pvbFz75ybwjMjPrXUqpShrZ/Iu/qyJiTdbT6F5SEroqImZIOheYFhF/yHb9PKlh\nOlevvQb33gu/+lXbz9ZfH77wBbj66tRj6bHH0jSebl8ws76mlKqky4FLI+L5ngmpwzi6XZV09tmw\nZEnqotqe556DCRNg7lyYPBmamuC887p1SjOzqiqnKqmUEsO/AMdLegVYCYjUTrBrGTFWzfLl6UG2\nRx7peJ9dd4Utt0ztDI2NxQ3UZmZ9RSmJ4aDco+gBN90Ee+wBO+7Y+X4nngiXXJLGUXL7gpn1RSU9\n+SxpN2CfbPXhiHg216jaj6FbVUn7758anQ8/vPP93n0XttgitS/89a9ln87MrFfIpVeSpNOBG4ER\n2XKDpH8tL8TqiEgjpO6119r33Wij9CT0QXVRTjIz67pSGp+fA/aKiGXZ+iDgsZ5uY+hOieGll9KD\navPmrXVXANasAaltl1Yzs1qTV+OzgDUF62uybTXjqafg4x8vff/+/fOLxcystyslMVwDPC7p9mz9\ncOCq/EKqvCef7FpiMDPry9ZaWRIRPwFOAN7OlhMi4md5B1ZJXS0xmJn1ZR22MUgaHBFLJA1r7/OI\n6JEJdQriKauNIQKGDYOZM2Gzupl7zsysNJVuY7gJOBh4iuK5npWtb9flCKvg5ZfTsBZOCmZmpekw\nMUTEwdnrtj0XTuW5GsnMrGtKeY7hgVK29VZueDYz65oOE4Ok9bL2heGShkoali3b0MEsbL2RSwxm\nZl3TWRvDycA3gA+Q2hmaGy+WAD/POa6KiEhzKjgxmJmVrpQnn/81IjoYqLrnlNMrqatPPJuZ1Ztc\nnnyOiEslfRTYBVivYPt1XQ+xZ7kaycys69aaGCSdAzSQEsNdpGG4HwF6fWJww7OZWdeVMkzckcA4\n4I2IOAHYDdg416gqxCUGM7OuKyUxLI+IJmC1pMHAIuCD+YbVfW54NjMrTymD6D0paQhwBal30lLg\nsVyjqgA/8WxmVp5SGp+/nr39paS7gcER8Vy+YXWfq5HMzMrTYWKQNLqzzyJiej4hVYYbns3MytNZ\nieGi7HU9YA/gWdJDbrsCTwIlTJRZPU89Bd/8ZrWjMDOrPR02PkfEfhGxH7AAGB0Re0TEx4FRwPye\nCrAcbng2MytfKb2SdoqI55tXIuJvwM75hdR9bng2MytfKb2SnpN0JXBDtv5FoFc3Prvh2cysfKUk\nhhOArwGnZ+t/Af4rt4gqwA3PZmblW+sger1FVwbRGzcuNTxPmJBzUGZmvVw5g+h1Nh/Drdnr85Ke\na710IajxkmZKmi3pzA72OUrSC9m5bmhvn1K54dnMrHs6q0pqrjo6uNyDS+pHmrthHPA6ME3SnREx\ns2Cf7YEzgb0iYomk4eWeD9zwbGbWXZ3N+bwge53bjeOPAeY0H0PSLcBhwMyCff4vcFlELMnO92Y3\nzsddd8G++3bnCGZmfVtnTz6/C7RXqS8gImJwCcffEiicJuc1UrIotGN2vkdIVVvnRsQ9JRy7jQj4\n5S/hssvK+baZmUHnJYaNejCG7YF9ga2Av0j6aHMJotDkyZPff9/Q0EBDQ0PR5488AmvWwKc+lWe4\nZma9V2NjI42Njd06Rsm9kiSNoHgGt7+X8J2xwOSIGJ+tn5W+GhcW7PNfwNSIuDZbvx84MyKeanWs\ntfZKOuYY2GMP+MY3SrokM7O6V9FeSQUHPVTSHOAV4M/Aq8CfSjz+NGB7SVtLGggcDUxptc8dwH7Z\nuYYDOwAvl3j89735Jvzxj/ClL3X1m2ZmVqiUITG+D4wFZkfEtqQeRlNLOXhErAFOBe4FXgBuiYgZ\nks6VdHC2zz3AW5JeAB4AvhURi7t6Ib/+NRx2GAwb1tVvmplZobVWJUl6MiL2kPQsMCoimiQ9GxG7\n9UyI78fRYVVSUxPstBNcdx3s1avHfDUz61nlVCWVMiTGO5I2JA2FcaOkRcCycgLMy4MPwgYbwNix\n1Y7EzKz2lVJiGASsIHVT/SKwMXBjRLyVf3hFcXRYYvjc5+DTn4avfa0nIzIz6/3KKTF0mBgkXQbc\nFBF/rURw3dVRYliwAHbZBebOhcGlPFlhZtaHVLpX0mzg/0l6VdKPJI3qXnj5uPpqOOooJwUzs0op\npSppa1I306OB9YGbgZsjYnb+4RXF0abEsGYNbLcd3HEHjOqVacvMrLpyeY4hIuZGxIURMQqYBBwO\nzCgzxoq6++40WJ6TgplZ5ZTygNsASYdIupH0YNssYGLukZXg8svhq1+tdhRmZvWls8bnz5BKCBOA\nJ4BbgDsjoipdVdurStp88zSN55ZbViMiM7Per9K9kh4EbgJ+W86TyJXWOjGsXg3rrw/Ll8OAUp7G\nMDPrgyr6gFtEfLr7IeVn4UIYPtxJwcys0koZK6lXev11+MAHqh2FmVn9qdnEsGABbLFFtaMwM6s/\nNZsYXGIwM8tHzSaGBQucGMzM8lCzieH1112VZGaWh5pODC4xmJlVXs0mBlclmZnlo2YTg6uSzMzy\nsdbRVXuLwief/dSzmVlpchldtTdauBA23dRJwcwsDzWZGFyNZGaWn5pNDG54NjPLR00mBvdIMjPL\nT00mBlclmZnlp2YTg0sMZmb5qMnE4JFVzczyU5OJwSUGM7P81GRicOOzmVl+ck8MksZLmilptqQz\n2/n8OEmLJE3Pli93drxVq+Ctt2DEiPxiNjPry3J9dlhSP+DnwDjgdWCapDsjYmarXW+JiNNKOWbz\nU8/9+1c4WDMzA/IvMYwB5kTE3IhYBdwCHNbOfiWP4+FqJDOzfOWdGLYE5hWsv5Zta22ipGck3Spp\nZGcH9DMMZmb56g2Nz1OAbSJid+B+4NrOdnaPJDOzfOU9Pul8YKuC9ZHZtvdFxOKC1SuBH3V0sMmT\nJ/PQQyBBY2MDDQ0NlYzVzKzmNTY20tjY2K1j5Dofg6T+wCxS4/MC4AlgUkTMKNhn84h4I3v/WeCM\niPhkO8eKiODEE2HMGDjppNzCNjOrG+XMx5BriSEi1kg6FbiXVG11VUTMkHQuMC0i/gCcJulQYBXw\nNnB8Z8d0VZKZWb5qbga3UaPgqqtg9OhqR2Rm1vv1iRnc3CvJzCxfNVVieO+9YIMNYMUKP+BmZlaK\nui8x+KlnM7P81VRicMOzmVn+nBjMzKxITSUGT9BjZpa/mkoMLjGYmeWvphKDR1Y1M8tfTSUGP8Ng\nZpa/mksMLjGYmeWrphKDq5LMzPJXU08+DxgQfurZzKwL6v7J5xEjnBTMzPJWU4nB1UhmZvmrqcTg\nHklmZvmrqcTgEoOZWf5qKjG4xGBmlr+aSgwuMZiZ5c+JwczMitRUYnBVkplZ/moqMbjEYGaWv5p6\n8nn16vADbmZmXVD3Tz47KZiZ5a+mEoOZmeXPicHMzIo4MZiZWREnBjMzK+LEYGZmRXJPDJLGS5op\nabakMzvZ7whJTZJG5x2TmZl1LNfEIKkf8HPgQOAjwCRJH25nvw2B04CpecbTmzU2NlY7hFzV8/XV\n87WBr68vyrvEMAaYExFzI2IVcAtwWDv7fR+4AFiZczy9Vr3/cdbz9dXztYGvry/KOzFsCcwrWH8t\n2/Y+SaOAkRHxp5xjMTOzEgyo5sklCfgJcFzh5iqFY2Zm5DxWkqSxwOSIGJ+tnwVERFyYrQ8GXgSW\nkhLC5sBbwKERMb3VsWpjUCczs16mq2Ml5Z0Y+gOzgHHAAuAJYFJEzOhg/4eAf4+Ip3MLyszMOpVr\nG0NErAFOBe4FXgBuiYgZks6VdHB7X8FVSWZmVVUzw26bmVnPqIknn0t9SK5WSLpK0kJJzxVsGyrp\nXkmzJN0jaeNqxlguSSMlPSjpBUnPSzot214v17eupMclPZ1d3znZ9m0kTc3+Rm+WVNWOHd0hqZ+k\n6ZKmZOt1c20Akl6V9Gz2//CJbFu9/H1uLOk3kmZk/wb3LOfaen1iKPUhuRpzDel6Cp0F3B8ROwEP\nAt/p8agqYzWpnegjwF7AKdn/r7q4vohYCewXEaOA3YGDJO0JXAhcFBE7Au8AX6limN11OvA/Bev1\ndG0ATUBDRIyKiDHZtrr4+wQuBu6KiJ2B3YCZlHNtEdGrF2As8KeC9bOAM6sdVwWua2vguYL1mcBm\n2fvNgZnVjrFC13kHsH89Xh+wAfAk6UHORUC/bPtY4O5qx1fmNY0E7gMagCnZtn/Uw7UVXOMrwCat\nttX83ycwGHipne1dvrZeX2KghIfk6sSIiFgIEBFvACOqHE+3SdqG9Kt6KukPsy6uL6tqeRp4g3QT\nfQl4JyKasl1eA2p1hvKfAmeQOoIgaRNgcZ1cW7MA7pE0TdKJ2bZ6+PvcFnhT0jVZVeDlkjagjGur\nhcTQV9V0r4Bs/KvbgNMjYiltr6dmry8imiJVJY0klRZqvWoTAEn/B1gYEc9Q3Duw3noK7h0RewAT\nSFWd+1Aff58DgNHAZRExGlhGqmHp8rXVQmKYD2xVsD4y21ZvFkraDEDS5qSqiZqUNU7eBlwfEXdm\nm+vm+ppFxBKgkdSWMiRrD4Pa/RvdGzhU0svAzcCnSXXWG9fBtb0vIhZkr/8gVXWOoT7+Pl8D5kXE\nk9n6b0mJosvXVguJYRqwvaStJQ0EjgamVDmmShDFv8SmAMdn748D7mz9hRpyNfA/EXFxwba6uD5J\nw5t7dUhaH/gMqaH2IeBz2W41eX0R8R8RsVVEbEf6d/ZgRBxDHVxbM0kbZKVZJA0CDgCepw7+PrPq\nonmSdsw2jSM9P9bla6uJ5xgkjSf9cukHXBURF1Q5pG6RdBOpcW8TYCFwDumXy2+ADwJzgaMi4p1q\nxVguSXsDfyH9Y4ts+Q/SU++3UvvX9zHgWtLfYj/gvyPiPEnbkkYPHgo8DRwTaUThmiTpU8A3I+LQ\nerq27FpuJ/1dDgBujIgLJA2jPv4+dwOuBNYBXgZOAPrTxWuricRgZmY9pxaqkszMrAc5MZiZWREn\nBjMzK+J/9ZVeAAAC8UlEQVTEYGZmRZwYzMysiBODmZkVcWKwuiCpSdKPC9a/Kek/K3DcgZLuy8ae\n+Vyrz66R9HL22dOSHunu+Vod/yFJoyt5TLNS1PS46mYFVgITJZ0fEW9X8LijSfOUd3SD/mZE3F7B\n85lVnUsMVi9WA5cD/976g2w4lQckPZP9+h/Zzj5DJd2eTeDyqKSPStoUuB74RFYq2Lad87b5NyTp\nHEnXZceZVTCCJ5J+nE3w86ykowq2nynpuazk8cOCwx2VTQw0M3uqHEm7ZNumZ9f0oa78hzJbG5cY\nrF4EcBnwvKQLW312KXBNRNwg6YRs/bOt9jkXmB4Rn5W0H2kAwFHZTf2bEXFoB+f9saSzSeNe/S0i\njs22fwzYE9gIeFrSH4BPArtGxMckjQCmSfozMAo4BPhERKyUNKTg+P0jYk9JBwGTSWMzfRX4WUQ0\nz6bWvyv/oczWxonB6kZELJV0LWkGsuUFH+1FSyK4HvhRO1//F2BidpyHJA1rHmxtLb4VEb9rZ/ud\nEfEe8JakB0lJ4l9Io5YSEYskNZJG9vwUKXGtzD4rHMem+dhPkSZ3AngMODsr+dweES+WEKdZyVyV\nZPXmYtLUk4MKtpUyHn3rbd2dg6DweCJNJ9maOoil0MrsdQ3ZD7mIuJlUwlgB3CWpoVuRmrXixGD1\nQgARsZg0kmThvMSPApOy98cAD7fz/Yezz8hutP/IJhgq6bztOCzr0bQJqUQwLTvH57MZ4DYF9iGN\nOnsfcEI2jDeShnZ2LknbRsQrEXEpaQjlXUuI06xkrkqyelH4y/si4JSCbacB10j6Fmn+4hPa+f65\nwNWSniXNfHVcief9UUEbQ5CqhgCeI03iswnwvWxKxdsljQWeJZUgzoiIRaRpJncDnpS0ErgL+C4d\nl3SOknQssApYAJxXYqxmJfGw22YVJukc4N2I+Em1YzErh6uSzMysiEsMZmZWxCUGMzMr4sRgZmZF\nnBjMzKyIE4OZmRVxYjAzsyJODGZmVuT/Aw+wiK4NwdcvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e61f2cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resnet[\"epoch\"],resnet[\"val_acc\"])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"Resnet.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd7e615c910>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e6014850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resnet[\"epoch\"],resnet[\"val_loss\"])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"ResNet_loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "WRN = pd.read_csv(\"Results/WRN_16-8-with_dropout.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>acc</th>\n",
       "      <th>loss</th>\n",
       "      <th>lr</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>val_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.524568</td>\n",
       "      <td>1.313850</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.4658</td>\n",
       "      <td>1.885848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.690684</td>\n",
       "      <td>0.876134</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6499</td>\n",
       "      <td>1.058078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.761475</td>\n",
       "      <td>0.685405</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7117</td>\n",
       "      <td>0.881397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.799463</td>\n",
       "      <td>0.576138</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7570</td>\n",
       "      <td>0.760727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.825997</td>\n",
       "      <td>0.507323</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7731</td>\n",
       "      <td>0.716314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.843700</td>\n",
       "      <td>0.450848</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7694</td>\n",
       "      <td>0.691402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.860081</td>\n",
       "      <td>0.403641</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8163</td>\n",
       "      <td>0.566250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.875260</td>\n",
       "      <td>0.363825</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8335</td>\n",
       "      <td>0.507307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.886575</td>\n",
       "      <td>0.329925</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8630</td>\n",
       "      <td>0.385045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.894345</td>\n",
       "      <td>0.301796</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8506</td>\n",
       "      <td>0.455567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.904177</td>\n",
       "      <td>0.278365</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8545</td>\n",
       "      <td>0.455590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.913269</td>\n",
       "      <td>0.252337</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8591</td>\n",
       "      <td>0.433379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.940163</td>\n",
       "      <td>0.171021</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.9096</td>\n",
       "      <td>0.269077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.948334</td>\n",
       "      <td>0.147657</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.9153</td>\n",
       "      <td>0.262807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.954041</td>\n",
       "      <td>0.135660</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.9141</td>\n",
       "      <td>0.270488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.955663</td>\n",
       "      <td>0.126505</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.9211</td>\n",
       "      <td>0.247748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.959969</td>\n",
       "      <td>0.114977</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.9082</td>\n",
       "      <td>0.295237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.960850</td>\n",
       "      <td>0.108484</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.9160</td>\n",
       "      <td>0.283489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.964515</td>\n",
       "      <td>0.100784</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.9195</td>\n",
       "      <td>0.267011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.973947</td>\n",
       "      <td>0.075764</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.9259</td>\n",
       "      <td>0.244216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.977391</td>\n",
       "      <td>0.066745</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.9277</td>\n",
       "      <td>0.247052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.978613</td>\n",
       "      <td>0.063646</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.9280</td>\n",
       "      <td>0.247374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.979894</td>\n",
       "      <td>0.060327</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.9284</td>\n",
       "      <td>0.242004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.979794</td>\n",
       "      <td>0.058096</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.9286</td>\n",
       "      <td>0.250068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.981997</td>\n",
       "      <td>0.054019</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.9265</td>\n",
       "      <td>0.249419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.982137</td>\n",
       "      <td>0.051942</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.9287</td>\n",
       "      <td>0.257471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.984200</td>\n",
       "      <td>0.045258</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9288</td>\n",
       "      <td>0.248716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.986343</td>\n",
       "      <td>0.042143</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9309</td>\n",
       "      <td>0.245448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0.986783</td>\n",
       "      <td>0.040821</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.9309</td>\n",
       "      <td>0.247169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.986182</td>\n",
       "      <td>0.041046</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.9299</td>\n",
       "      <td>0.248736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>0.987544</td>\n",
       "      <td>0.038715</td>\n",
       "      <td>3.162280e-06</td>\n",
       "      <td>0.9312</td>\n",
       "      <td>0.247854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>0.987905</td>\n",
       "      <td>0.038220</td>\n",
       "      <td>3.162280e-06</td>\n",
       "      <td>0.9308</td>\n",
       "      <td>0.248301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>0.987704</td>\n",
       "      <td>0.037470</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.9309</td>\n",
       "      <td>0.248494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>0.987124</td>\n",
       "      <td>0.038580</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.9311</td>\n",
       "      <td>0.249142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>0.987384</td>\n",
       "      <td>0.038816</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9308</td>\n",
       "      <td>0.249397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>0.987945</td>\n",
       "      <td>0.038841</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9306</td>\n",
       "      <td>0.249144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>0.987764</td>\n",
       "      <td>0.038213</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9306</td>\n",
       "      <td>0.248610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>0.987884</td>\n",
       "      <td>0.038043</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9307</td>\n",
       "      <td>0.248363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>0.988225</td>\n",
       "      <td>0.036896</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9312</td>\n",
       "      <td>0.247927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>0.987704</td>\n",
       "      <td>0.038795</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9317</td>\n",
       "      <td>0.248796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>0.987524</td>\n",
       "      <td>0.038544</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9313</td>\n",
       "      <td>0.249070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>0.987464</td>\n",
       "      <td>0.038098</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9307</td>\n",
       "      <td>0.248280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>0.987764</td>\n",
       "      <td>0.038195</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9311</td>\n",
       "      <td>0.248301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>0.988625</td>\n",
       "      <td>0.036893</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9308</td>\n",
       "      <td>0.247745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>0.987484</td>\n",
       "      <td>0.038655</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9311</td>\n",
       "      <td>0.248027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>0.987684</td>\n",
       "      <td>0.038032</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9310</td>\n",
       "      <td>0.249073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>0.987644</td>\n",
       "      <td>0.038439</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9310</td>\n",
       "      <td>0.248582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>0.987103</td>\n",
       "      <td>0.039707</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9308</td>\n",
       "      <td>0.248342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>0.987164</td>\n",
       "      <td>0.038362</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9305</td>\n",
       "      <td>0.249202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>0.987484</td>\n",
       "      <td>0.037340</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9310</td>\n",
       "      <td>0.248241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>0.988265</td>\n",
       "      <td>0.037848</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9313</td>\n",
       "      <td>0.248376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>0.987464</td>\n",
       "      <td>0.037754</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9311</td>\n",
       "      <td>0.247330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>52</td>\n",
       "      <td>0.987744</td>\n",
       "      <td>0.036983</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9309</td>\n",
       "      <td>0.247664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>0.987744</td>\n",
       "      <td>0.039067</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9309</td>\n",
       "      <td>0.248574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    epoch       acc      loss            lr  val_acc  val_loss\n",
       "0       0  0.524568  1.313850  1.000000e-03   0.4658  1.885848\n",
       "1       1  0.690684  0.876134  1.000000e-03   0.6499  1.058078\n",
       "2       2  0.761475  0.685405  1.000000e-03   0.7117  0.881397\n",
       "3       3  0.799463  0.576138  1.000000e-03   0.7570  0.760727\n",
       "4       4  0.825997  0.507323  1.000000e-03   0.7731  0.716314\n",
       "5       5  0.843700  0.450848  1.000000e-03   0.7694  0.691402\n",
       "6       6  0.860081  0.403641  1.000000e-03   0.8163  0.566250\n",
       "7       7  0.875260  0.363825  1.000000e-03   0.8335  0.507307\n",
       "8       8  0.886575  0.329925  1.000000e-03   0.8630  0.385045\n",
       "9       9  0.894345  0.301796  1.000000e-03   0.8506  0.455567\n",
       "10     10  0.904177  0.278365  1.000000e-03   0.8545  0.455590\n",
       "11     11  0.913269  0.252337  1.000000e-03   0.8591  0.433379\n",
       "12     12  0.940163  0.171021  3.162280e-04   0.9096  0.269077\n",
       "13     13  0.948334  0.147657  3.162280e-04   0.9153  0.262807\n",
       "14     14  0.954041  0.135660  3.162280e-04   0.9141  0.270488\n",
       "15     15  0.955663  0.126505  3.162280e-04   0.9211  0.247748\n",
       "16     16  0.959969  0.114977  3.162280e-04   0.9082  0.295237\n",
       "17     17  0.960850  0.108484  3.162280e-04   0.9160  0.283489\n",
       "18     18  0.964515  0.100784  3.162280e-04   0.9195  0.267011\n",
       "19     19  0.973947  0.075764  1.000000e-04   0.9259  0.244216\n",
       "20     20  0.977391  0.066745  1.000000e-04   0.9277  0.247052\n",
       "21     21  0.978613  0.063646  1.000000e-04   0.9280  0.247374\n",
       "22     22  0.979894  0.060327  1.000000e-04   0.9284  0.242004\n",
       "23     23  0.979794  0.058096  1.000000e-04   0.9286  0.250068\n",
       "24     24  0.981997  0.054019  1.000000e-04   0.9265  0.249419\n",
       "25     25  0.982137  0.051942  1.000000e-04   0.9287  0.257471\n",
       "26     26  0.984200  0.045258  3.162280e-05   0.9288  0.248716\n",
       "27     27  0.986343  0.042143  3.162280e-05   0.9309  0.245448\n",
       "28     28  0.986783  0.040821  1.000000e-05   0.9309  0.247169\n",
       "29     29  0.986182  0.041046  1.000000e-05   0.9299  0.248736\n",
       "30     30  0.987544  0.038715  3.162280e-06   0.9312  0.247854\n",
       "31     31  0.987905  0.038220  3.162280e-06   0.9308  0.248301\n",
       "32     32  0.987704  0.037470  1.000000e-06   0.9309  0.248494\n",
       "33     33  0.987124  0.038580  1.000000e-06   0.9311  0.249142\n",
       "34     34  0.987384  0.038816  5.000000e-07   0.9308  0.249397\n",
       "35     35  0.987945  0.038841  5.000000e-07   0.9306  0.249144\n",
       "36     36  0.987764  0.038213  5.000000e-07   0.9306  0.248610\n",
       "37     37  0.987884  0.038043  5.000000e-07   0.9307  0.248363\n",
       "38     38  0.988225  0.036896  5.000000e-07   0.9312  0.247927\n",
       "39     39  0.987704  0.038795  5.000000e-07   0.9317  0.248796\n",
       "40     40  0.987524  0.038544  5.000000e-07   0.9313  0.249070\n",
       "41     41  0.987464  0.038098  5.000000e-07   0.9307  0.248280\n",
       "42     42  0.987764  0.038195  5.000000e-07   0.9311  0.248301\n",
       "43     43  0.988625  0.036893  5.000000e-07   0.9308  0.247745\n",
       "44     44  0.987484  0.038655  5.000000e-07   0.9311  0.248027\n",
       "45     45  0.987684  0.038032  5.000000e-07   0.9310  0.249073\n",
       "46     46  0.987644  0.038439  5.000000e-07   0.9310  0.248582\n",
       "47     47  0.987103  0.039707  5.000000e-07   0.9308  0.248342\n",
       "48     48  0.987164  0.038362  5.000000e-07   0.9305  0.249202\n",
       "49     49  0.987484  0.037340  5.000000e-07   0.9310  0.248241\n",
       "50     50  0.988265  0.037848  5.000000e-07   0.9313  0.248376\n",
       "51     51  0.987464  0.037754  5.000000e-07   0.9311  0.247330\n",
       "52     52  0.987744  0.036983  5.000000e-07   0.9309  0.247664\n",
       "53     53  0.987744  0.039067  5.000000e-07   0.9309  0.248574"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WRN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e61a3c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(WRN[\"epoch\"],WRN[\"val_acc\"])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"WRN.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmZmVcWIwM7MyTgxmZlbm/wMfuQMxgGIfWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e5f12790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(WRN[\"epoch\"],WRN[\"val_loss\"])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"WRN_loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd7e62fe5d0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e65a6710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resnet[\"epoch\"],resnet[\"val_acc\"],'r-',WRN['epoch'],WRN['val_acc'])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"comparison\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd7e5e91250>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e5f12ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resnet[\"epoch\"],resnet[\"val_loss\"],'r-',WRN['epoch'],WRN['val_loss'])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"Loss_Comparison\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "resnet2 = pd.read_csv(\"Results/ResNet50_without_dropout_without_conv_without_pool.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>acc</th>\n",
       "      <th>loss</th>\n",
       "      <th>lr</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>val_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.367778</td>\n",
       "      <td>1.736815</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.4313</td>\n",
       "      <td>1.621649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.507930</td>\n",
       "      <td>1.354666</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.5579</td>\n",
       "      <td>1.228859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.587252</td>\n",
       "      <td>1.152685</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6012</td>\n",
       "      <td>1.173388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.643944</td>\n",
       "      <td>1.004709</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6043</td>\n",
       "      <td>1.192108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.695170</td>\n",
       "      <td>0.870828</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6898</td>\n",
       "      <td>0.944721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.725609</td>\n",
       "      <td>0.784936</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7228</td>\n",
       "      <td>0.799339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.750561</td>\n",
       "      <td>0.710334</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6980</td>\n",
       "      <td>0.942628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.774111</td>\n",
       "      <td>0.651445</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7709</td>\n",
       "      <td>0.678664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.789270</td>\n",
       "      <td>0.602286</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7382</td>\n",
       "      <td>0.767273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.805030</td>\n",
       "      <td>0.559959</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7396</td>\n",
       "      <td>0.786805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.816946</td>\n",
       "      <td>0.525476</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7868</td>\n",
       "      <td>0.625241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.826498</td>\n",
       "      <td>0.496637</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7894</td>\n",
       "      <td>0.624058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.837512</td>\n",
       "      <td>0.467720</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7839</td>\n",
       "      <td>0.661859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.848066</td>\n",
       "      <td>0.437183</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8008</td>\n",
       "      <td>0.588105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.855555</td>\n",
       "      <td>0.415924</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7901</td>\n",
       "      <td>0.648566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.863625</td>\n",
       "      <td>0.394672</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8020</td>\n",
       "      <td>0.587924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.866910</td>\n",
       "      <td>0.380215</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8435</td>\n",
       "      <td>0.455627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.875200</td>\n",
       "      <td>0.357599</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8259</td>\n",
       "      <td>0.534413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.879726</td>\n",
       "      <td>0.347086</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8377</td>\n",
       "      <td>0.502355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.884873</td>\n",
       "      <td>0.330364</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8420</td>\n",
       "      <td>0.472881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.915372</td>\n",
       "      <td>0.244800</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8825</td>\n",
       "      <td>0.355655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.922801</td>\n",
       "      <td>0.221812</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8806</td>\n",
       "      <td>0.358673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.925545</td>\n",
       "      <td>0.208578</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8858</td>\n",
       "      <td>0.350070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.929550</td>\n",
       "      <td>0.199890</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8855</td>\n",
       "      <td>0.352079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.932454</td>\n",
       "      <td>0.189566</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8803</td>\n",
       "      <td>0.382121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.936018</td>\n",
       "      <td>0.183098</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8874</td>\n",
       "      <td>0.363022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.946952</td>\n",
       "      <td>0.150018</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8943</td>\n",
       "      <td>0.343977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.949916</td>\n",
       "      <td>0.143057</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8971</td>\n",
       "      <td>0.338289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0.951698</td>\n",
       "      <td>0.136450</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8962</td>\n",
       "      <td>0.346081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.953040</td>\n",
       "      <td>0.135140</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8969</td>\n",
       "      <td>0.339411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>0.954662</td>\n",
       "      <td>0.128461</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8969</td>\n",
       "      <td>0.347714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>0.959528</td>\n",
       "      <td>0.116172</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.8998</td>\n",
       "      <td>0.342484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>0.958527</td>\n",
       "      <td>0.118715</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.8981</td>\n",
       "      <td>0.342934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>0.960089</td>\n",
       "      <td>0.115092</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.8990</td>\n",
       "      <td>0.342814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>0.960269</td>\n",
       "      <td>0.112704</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.8998</td>\n",
       "      <td>0.343239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>0.960710</td>\n",
       "      <td>0.112639</td>\n",
       "      <td>3.162280e-06</td>\n",
       "      <td>0.8992</td>\n",
       "      <td>0.343307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>0.962712</td>\n",
       "      <td>0.110514</td>\n",
       "      <td>3.162280e-06</td>\n",
       "      <td>0.8996</td>\n",
       "      <td>0.343515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>0.959789</td>\n",
       "      <td>0.111800</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.8993</td>\n",
       "      <td>0.342863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>0.960109</td>\n",
       "      <td>0.113232</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.8997</td>\n",
       "      <td>0.342623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>0.961911</td>\n",
       "      <td>0.111438</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8996</td>\n",
       "      <td>0.342629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>0.961391</td>\n",
       "      <td>0.109876</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9001</td>\n",
       "      <td>0.342996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>0.960970</td>\n",
       "      <td>0.112551</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.342946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>0.961791</td>\n",
       "      <td>0.111214</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8997</td>\n",
       "      <td>0.342808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>0.961150</td>\n",
       "      <td>0.111745</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.342532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>0.962692</td>\n",
       "      <td>0.108372</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8994</td>\n",
       "      <td>0.342597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>0.961411</td>\n",
       "      <td>0.109893</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8994</td>\n",
       "      <td>0.342836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>0.961250</td>\n",
       "      <td>0.111460</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8995</td>\n",
       "      <td>0.342619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>0.961831</td>\n",
       "      <td>0.109397</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8997</td>\n",
       "      <td>0.342518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>0.960950</td>\n",
       "      <td>0.111466</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8997</td>\n",
       "      <td>0.342551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>0.962031</td>\n",
       "      <td>0.109676</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.343232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>0.961531</td>\n",
       "      <td>0.110924</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8993</td>\n",
       "      <td>0.343417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>0.960870</td>\n",
       "      <td>0.110522</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8994</td>\n",
       "      <td>0.342679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>52</td>\n",
       "      <td>0.961290</td>\n",
       "      <td>0.112310</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8998</td>\n",
       "      <td>0.343297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>0.961691</td>\n",
       "      <td>0.109174</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9002</td>\n",
       "      <td>0.343088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54</td>\n",
       "      <td>0.959508</td>\n",
       "      <td>0.113086</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8999</td>\n",
       "      <td>0.343248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>0.960550</td>\n",
       "      <td>0.109979</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9001</td>\n",
       "      <td>0.343180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>56</td>\n",
       "      <td>0.962252</td>\n",
       "      <td>0.109475</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8997</td>\n",
       "      <td>0.343604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>57</td>\n",
       "      <td>0.961891</td>\n",
       "      <td>0.109320</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8996</td>\n",
       "      <td>0.343341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>0.962772</td>\n",
       "      <td>0.108430</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8998</td>\n",
       "      <td>0.342640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    epoch       acc      loss            lr  val_acc  val_loss\n",
       "0       0  0.367778  1.736815  1.000000e-03   0.4313  1.621649\n",
       "1       1  0.507930  1.354666  1.000000e-03   0.5579  1.228859\n",
       "2       2  0.587252  1.152685  1.000000e-03   0.6012  1.173388\n",
       "3       3  0.643944  1.004709  1.000000e-03   0.6043  1.192108\n",
       "4       4  0.695170  0.870828  1.000000e-03   0.6898  0.944721\n",
       "5       5  0.725609  0.784936  1.000000e-03   0.7228  0.799339\n",
       "6       6  0.750561  0.710334  1.000000e-03   0.6980  0.942628\n",
       "7       7  0.774111  0.651445  1.000000e-03   0.7709  0.678664\n",
       "8       8  0.789270  0.602286  1.000000e-03   0.7382  0.767273\n",
       "9       9  0.805030  0.559959  1.000000e-03   0.7396  0.786805\n",
       "10     10  0.816946  0.525476  1.000000e-03   0.7868  0.625241\n",
       "11     11  0.826498  0.496637  1.000000e-03   0.7894  0.624058\n",
       "12     12  0.837512  0.467720  1.000000e-03   0.7839  0.661859\n",
       "13     13  0.848066  0.437183  1.000000e-03   0.8008  0.588105\n",
       "14     14  0.855555  0.415924  1.000000e-03   0.7901  0.648566\n",
       "15     15  0.863625  0.394672  1.000000e-03   0.8020  0.587924\n",
       "16     16  0.866910  0.380215  1.000000e-03   0.8435  0.455627\n",
       "17     17  0.875200  0.357599  1.000000e-03   0.8259  0.534413\n",
       "18     18  0.879726  0.347086  1.000000e-03   0.8377  0.502355\n",
       "19     19  0.884873  0.330364  1.000000e-03   0.8420  0.472881\n",
       "20     20  0.915372  0.244800  3.162280e-04   0.8825  0.355655\n",
       "21     21  0.922801  0.221812  3.162280e-04   0.8806  0.358673\n",
       "22     22  0.925545  0.208578  3.162280e-04   0.8858  0.350070\n",
       "23     23  0.929550  0.199890  3.162280e-04   0.8855  0.352079\n",
       "24     24  0.932454  0.189566  3.162280e-04   0.8803  0.382121\n",
       "25     25  0.936018  0.183098  3.162280e-04   0.8874  0.363022\n",
       "26     26  0.946952  0.150018  1.000000e-04   0.8943  0.343977\n",
       "27     27  0.949916  0.143057  1.000000e-04   0.8971  0.338289\n",
       "28     28  0.951698  0.136450  1.000000e-04   0.8962  0.346081\n",
       "29     29  0.953040  0.135140  1.000000e-04   0.8969  0.339411\n",
       "30     30  0.954662  0.128461  1.000000e-04   0.8969  0.347714\n",
       "31     31  0.959528  0.116172  3.162280e-05   0.8998  0.342484\n",
       "32     32  0.958527  0.118715  3.162280e-05   0.8981  0.342934\n",
       "33     33  0.960089  0.115092  1.000000e-05   0.8990  0.342814\n",
       "34     34  0.960269  0.112704  1.000000e-05   0.8998  0.343239\n",
       "35     35  0.960710  0.112639  3.162280e-06   0.8992  0.343307\n",
       "36     36  0.962712  0.110514  3.162280e-06   0.8996  0.343515\n",
       "37     37  0.959789  0.111800  1.000000e-06   0.8993  0.342863\n",
       "38     38  0.960109  0.113232  1.000000e-06   0.8997  0.342623\n",
       "39     39  0.961911  0.111438  5.000000e-07   0.8996  0.342629\n",
       "40     40  0.961391  0.109876  5.000000e-07   0.9001  0.342996\n",
       "41     41  0.960970  0.112551  5.000000e-07   0.9000  0.342946\n",
       "42     42  0.961791  0.111214  5.000000e-07   0.8997  0.342808\n",
       "43     43  0.961150  0.111745  5.000000e-07   0.9000  0.342532\n",
       "44     44  0.962692  0.108372  5.000000e-07   0.8994  0.342597\n",
       "45     45  0.961411  0.109893  5.000000e-07   0.8994  0.342836\n",
       "46     46  0.961250  0.111460  5.000000e-07   0.8995  0.342619\n",
       "47     47  0.961831  0.109397  5.000000e-07   0.8997  0.342518\n",
       "48     48  0.960950  0.111466  5.000000e-07   0.8997  0.342551\n",
       "49     49  0.962031  0.109676  5.000000e-07   0.9000  0.343232\n",
       "50     50  0.961531  0.110924  5.000000e-07   0.8993  0.343417\n",
       "51     51  0.960870  0.110522  5.000000e-07   0.8994  0.342679\n",
       "52     52  0.961290  0.112310  5.000000e-07   0.8998  0.343297\n",
       "53     53  0.961691  0.109174  5.000000e-07   0.9002  0.343088\n",
       "54     54  0.959508  0.113086  5.000000e-07   0.8999  0.343248\n",
       "55     55  0.960550  0.109979  5.000000e-07   0.9001  0.343180\n",
       "56     56  0.962252  0.109475  5.000000e-07   0.8997  0.343604\n",
       "57     57  0.961891  0.109320  5.000000e-07   0.8996  0.343341\n",
       "58     58  0.962772  0.108430  5.000000e-07   0.8998  0.342640"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resnet2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd7e5e86e90>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIpJEiUFERJIoMYiISJL/D/+e4WijXzcMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e65e9110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resnet2[\"epoch\"],resnet2[\"val_acc\"])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"resnet2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd7e6108550>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e5e43bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resnet2[\"epoch\"],resnet2[\"val_loss\"])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"resnet2_loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e5d187d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resnet[\"epoch\"],resnet[\"val_acc\"],'r-',resnet2['epoch'],resnet2['val_acc'])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"Resnet_basemap32vs16_accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd7e5d5b610>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxCAiIgn+P9hhGTHQ8AkQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e627c450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resnet[\"epoch\"],resnet[\"val_loss\"],'r-',resnet2['epoch'],resnet2['val_loss'])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8VPXZ9/HPxRJAQHbQyCoIooiCG7jGHVHUamutr7ZW\ne2tt66231j5au4C1tXWttuVp3eqjtS51t1bFjdQi4o2KQlkEWYUgyiYIsiS5nj9+Z8jMZDKZhExm\n4ft+vY4z58yZM9cJca78dnN3REREYlrkOgAREckvSgwiIpJAiUFERBIoMYiISAIlBhERSaDEICIi\nCbKaGMzsPjNbZWYz63h9dzN7zszeN7NZZvadbMYjIiL1y3aJ4X7glDSv/xCY7e4HAccBt5lZqyzH\nJCIiaWQ1Mbj7FGBdulOAjtHzjsAad6/MZkwiIpJerv86/yPwnJlVAB2Ar+c4HhGRXV6uG59PAWa4\neykwAphoZh1yHJOIyC4t1yWGC4HfALj7QjNbDOwLvJN8oplpUicRkUZwd2vI+c1RYrBoS2UpcCKA\nmfUCBgOL6rqQuxftNn78+JzHoPvT/e1q97Yr3F9jZLXEYGYPA2VANzNbBowHSgB397uBXwH/L647\n6/9x97XZjElERNLLamJw9/PreX0l6buziohIM8t147NEysrKch1CVun+Clcx3xsU//01hjW2Dqq5\nmZkXSqwiIvnCzPA8bHwWEZECosQgIiIJlBhERCSBEoOIiCRQYhARkQRKDCIikkCJQUREEigxiIhI\nAiUGERFJoMQgIiIJlBhERCSBEoOIiCRQYhARkQRKDCIikkCJQUREEigxiIhIAiUGERFJkNXEYGb3\nmdkqM5uZ5pwyM5thZv8xs8nZjEdEROqX1aU9zewo4AvgQXcfnuL1TsBU4GR3X2Fm3d19dR3X0tKe\nIiINlHdLe7r7FGBdmlPOB5509xXR+SmTwg7btjVdcCIiklKu2xgGA13NbLKZTTezb6U9+9NPmycq\nEZFdWKs8+PyRwPFAe+AtM3vL3T9KdfLPJ9xAy957AlBWVkZZWVlzxSkiUhDKy8spLy/fqWtktY0B\nwMz6Af+oo43hGqCtu18f7d8LvOjuT6Y415c98Dp9vn1cVuMVESkmedfGELFoS+VZ4Cgza2lmuwGH\nA3PrulDFgk1ZCE9EROJltSrJzB4GyoBuZrYMGA+UAO7ud7v7PDObBMwEqoC73X1OXderWLo9m+GK\niAhZTgzufn4G59wK3JrJ9SpWqLuqiEi25bpXUoNUrGqZ6xBERIpeYSWGdW1zHYKISNErrMSwoWOu\nQxARKXqFlRi2dMl1CCIiRa+wEkNlT6iuznUYIiJFraASw0Y6smXFmlyHISJS1AoqMezZajUrZ6/N\ndRgiIkWtoBJD6W7rqPhwY67DEBEparmeRK9B9tr9CyoWafSziEg2FVRiKO26hRXLch2FiEhxK6yq\npF5VVKzMdRQiIsWtsBJD7xZUrC7JdRgiIkWtsBJD/xIq1u+W6zBERIpaYbUxDO5Axabdcx2GiEhR\nK6zEsF9nKrZqWgwRkWwqqKqkTgO7U+kt2bhB6zKIiGRLQSUGa78bpbaSlQu+yHUoIiJFq6ASA0Bp\nmzVUzP0812GIiBStwksM7TdQsWBTrsMQESlaWU0MZnafma0ys5n1nHeomW03s7Pru2Zp581ULN7a\ndEGKiEiCbJcY7gdOSXeCmbUAfgtMyuSCpT22U7FCjc8iItmS1cTg7lOAdfWc9t/AE8CnmVyzdI9q\nKla13NnQRESkDjltYzCzUuAsd/8TYJm8p7RvKyrWtsluYCIiu7BcD3C7A7gmbj9tcpgwYQJrPlzA\nrNVvUl5+OmVlZVkNTkSk0JSXl1NeXr5T1zD37NbXm1k/4B/uPjzFa4tiT4HuwCbgEnd/LsW57u58\n8dIUepx2KJsr22AZlTFERHZdZoa7N+jbsjlKDEYdJQF333vHSWb3ExJIraQQr8OAHpSwjfXr29BF\ns2OIiDS5rCYGM3sYKAO6mdkyYDxQAri73510emZFl549KaWCioohSgwiIlmQ1cTg7uc34NyLMjqx\nc2dK/X0qlgxg//21NoOISFMruJHPmFHabi0V8zbkOhIRkaJUeIkBKO24kYpFW3IdhohIUao3MZhZ\n3o0mK+26lYpl23MdhohIUcqkxLDAzG4xs/2yHk2GSntVUlGR6yhERIpTJonhQGA+cK+ZTTOzS8ws\np+trlpYaFZ+p4VlEJBvqTQzuvtHd73H3IwijlMcDK83sATMblPUIUyjtX0LF+na5+GgRkaKXURuD\nmZ1hZk8TprC4Ddgb+AfwQpbjS2nPQe1ZuWl3qqtz8ekiIsUtk3EMC4DJwC3uPjXu+BNmdkx2wkqv\nbe/udGyxmTVrdqdHj1xEICJSvDJJDMPdPeUiy+5+eRPHk5levSht+QkVFUoMIiJNLZPG54lm1jm2\nY2ZdzOwvWYypfr16UVq9ghUrchqFiEhRyiQxDHf39bEdd18HjMheSBno1o3SyqVUfFyV0zBERIpR\nJomhhZntmK7OzLqS63UcWrVir3ZrqVi4OadhiIgUo0y+4G8D3jKzxwnTZ38V+HVWo8pAaafNzFy8\nFeiY61BERIpKvYnB3R80s3eB46JDZ7v7nOyGVb/S7tt4aXl2FxkSEdkVZVQl5O6zzewzoC2AmfV1\n92VZjawepXs6FQsKcg5AEZG8lskAtzPMbAGwGPgXsAR4Mctx1au0T0sq1rTJdRgiIkUnkz+5bwBG\nAfPdfQBwAjAtq1FloFf/dnz2RTsqK3MdiYhIcckkMWx39zWE3kkt3H0ycEiW46pX69IedCv5gk8/\nzXUkIiLFJZM2hvVm1gF4A/ibmX0KbMpuWBno1YvSks+oqOhEaWmugxERKR6ZlBjOBDYDVwIvAQuB\ncZlc3MzuM7NVZjazjtfPN7MPom2KmR2QaeD07MnBrWfx0EMZv0NERDJg7nV3+YxWb3vV3Y+r86R0\nFzc7CvgCeNDdh6d4fRQw190/N7MxwAR3H1XHtTwh1mXLWDtqLAe1+g933QWnntqYCEVEipuZ4e7W\nkPekLTG4exVQbWadGhOQu08B1qV5fZq7fx7tTgP2yvjiPXvSdfV8/vqgc9FF8MknjYlQRESSZdLG\n8AUwy8xeIa5tIQszq/4XDekG27YttGvHsQeu57/+qwsXXAAvvggtNLRBRGSnZJIYnoq2rDGz44AL\ngaPSnTdhwoQdz8vKyijr1QtWreIXv+jCMcfAHXfAVVelfu/27SFptGzZdHGLiOSb8vJyysvLd+oa\nadsYmoKZ9QP+kaqNIXp9OPAkMMbdF6a5jteK9eij4eCDYcwYFnc6iMPO6MXLLxsj4uZ+XbcO/u//\nhTvvhJ/+FK64ogluSkSkQDR5G0N00cVmtih5a0hc0Zbq2n0JSeFb6ZJCnW64AczgllsYcOZw7vzy\nEr5x5FI2/fYPrFgBV18NgwbBRx/BhRfCjBkN/gQRkV1OvSUGM+sWt9sW+BrQ1d1/Ue/FzR4GyoBu\nwCpgPFACuLvfbWb3AGcDSwnJY7u7H1bHtWqXGJKtWsUF36xixmtrWd55fy64wLjqKujTB958M1Qz\nvf12fVGLiBSPxpQYGlWVZGbvuvvBDX7jTsgoMQAbN8KD+9/EeX8uo9vYw3ccX7sW+veHzz8PhQwR\nkV1BYxJDvY3PZjYybrcFYTqM3C7Uk0bHjvDDs1bAnH9DXGLo2hV22w1WrIDevXMYoIhInst0oZ6Y\nSsIsq+dmJ5wmMno0PP54rcNDh8LcuUoMIiLpZLJQT6NGPefUEUfAlVeCe0K90dChMGcOnHRSDmMT\nEclzmfRKutHMOsftdzGzX2U3rJ3Ut28YsLBkScLh/fYLJQYREalbJuOET3X39bEdd18HjM1eSE3A\nLFQnTZ2acDhWlSQiInXLJDG0NLMdS6WZWTsg/5dOO+IIeOuthEP77ReqkkREpG6ZJIa/Aa+Z2XfN\n7LvAK8AD2Q2rCRxxRK0Swx57hKkxVq/OUUwiIgUgo3EM0ZTYJ0a7r7j7pKxGlTqGjMYx7LB1a+ij\numoVdOiw4/Do0XDzzWE2DRGRYpetKTEGAOXufrW7Xw28YWb9GxdiM2rTBg46CKZPTzis6iQRkfQy\nqUp6HKiO26+KjuW/FNVJaoAWEUkvk8TQyt23xXai5yXZC6kJpeiZpBKDiEh6mSSGz8zsjNiOmZ0J\nFEbz7ejRMG0aVNcUeFRiEBFJL5PZVQcSeiaVEmZA/Rj4trt/lP3wEuJoWONzzIABYWm3ffcFQo7o\n2BFWroTdd2/iIEVE8kxWGp/dfaG7jwL2A4a6+xHAxkbG2PyS2hlatIAhQ2DevBzGJCKSxxqyQnIr\n4Otm9hpQOEvepBjopuokEZG6pZ1ELxrlfCZwPjAC6AicBbyR/dCayOjRYW3POJozSUSkbnWWGKLV\n1+YDJwF/APoD69y93N2r63pf3hk+HJYtC4s/R2KzrIqISG3pqpL2A9YBc4G57l4FNKL1N8datYJD\nD01Y01MlBhGRutWZGNz9IMKCPB2BV81sCtDRzHo1V3BNJmk8w8CBsHw5bNmSw5hERPJU2sZnd5/n\n7uPdfV/gCsLkedPNbGq698WY2X1mtsrMZqY55/dmtsDM3jezgxoUfaaSGqBbtw69WOfPz8qniYgU\ntIx7Jbn7u9FcSf2AazN82/3AKXW9aGanAgPdfR/ge8CfM42nQUaNClVJVVU7Dqk6SUQktYZ0VwXA\ng4x6Jbn7FEI7RV3OBB6Mzn0b6JSVqqpu3aC0FP7znx2H0jZAP/IIXJtp7hMRKS4NTgxNbC/CSOqY\nFdGxppc00C1tieHFF+GZZ7IShohIvks7jiHfTJgwYcfzsrIyysrKMn/zUUfBq6/C978PhBLDr39d\nx7lvvhm6uK5aBb0Kr61dRHZd5eXllJeX79Q1MpkrqQ1wDmEcw45E4u6/zOgDzPoB/3D34Sle+zMw\n2d0fi/bnAce6+6oU5zZurqSYBQvg+OPDF74ZX34Z1vHZuDH0aN3hk09CcWL0aPjOd+BrX2v8Z4qI\n5FhW5koCniW0BVQCm+K2jOOKtlSeA74NYGajgPWpkkKTGDQIKith6VIA2rULzQ6LFiWdN3VqSApl\nZfBG4QzwFhFpKplUJfV29zGNuXg0eroM6GZmy4DxhLUc3N3vdvcXzGysmX1ESDYXNuZzMgwmrOf5\nxhvQvz9Q0wA9eHDceVOnwpFHwjHHwCWXZC0cEZF8lUmJYaqZHdCYi7v7+e5e6u5t3L2vu9/v7ne5\n+91x51zm7oPc/UB3f68xn5Oxo4+Gf/97x27KBuipU0ND9ciRoTixdm1WQxIRyTeZJIajgHfN7EMz\nm2lms9INWMtrxxyTkBhqzbK6ZQvMnBmm0GjdOox/ePPN5o9TRCSHMqlKOjXrUTSXYcNCT6NPP4We\nPRk6FCZOjHv93XdDtmjfPuwfc0yoeho3LifhiojkQiYL9SwFOgPjoq1zdKzwtGwZqomiUsPQoWHB\nnh0rf775Zng9JpYYIh9+mLBKqIhIUao3MZjZFYSlPXtG20Nm9t/ZDixr4qqTOnWCPn1gRmzZoVjD\nc8xhh4XR0l98QXV1aKKYNq35QxYRaU6ZtDF8Fzjc3X/h7r8ARgEXZzesLEpqgD7lFHj5ZcC9puE5\npl270Aj91lu89x589pmWBBWR4pdJYjCgKm6/irrHJeS/Qw4JdUIbNgBw8skwaRLw0UfQti307p14\n/rHHwhtvMGlSePnDD5s/ZBGR5pRJYrgfeNvMJpjZBGAacF9Wo8qmkpLQ6yiaN+nYY0Ob88ZX306s\nRoqJ2hleegm++U0lBhEpfpk0Pt9OGHi2NtoudPc7sh1YVsVVJ7VvH5oS/vXMusRqpJjRo/n8nQW8\n/75z8cVaw0FEil+6NZ93jx67AkuAh6JtaXSscMVGQEdOOQUmvdM1dYmhY0de2/ObHLnfOg44IIx5\nq6xsxlhFRJpZuhLDw9Hju8A7cVtsv3CNHh26IkVre548agMvrzsMhtea5w+Al3Y7m1O6vUu7drDH\nHrBkSTPGKiLSzNKt+Xx69DjA3feO2wa4+97NF2IWdOgQ5sOYPh2A4RvfZH2rbixZXnu8nztM+mQ4\nYz5/DIAhQ9TOICLFLZNxDK9lcqzgxFUntXjrTU4esix0W00ybx54SRv2nfl3qKxUYhCRopeujaFt\n1JbQ3cy6mFnXaOtPtlZZa07x8yZNncrJp1jotppk0iQYM7YlNqA/zJjB4MFqgBaR4pauxPA9QnvC\nvtFjbHsW+GP2Q8uyo46Ct94K7QzTp3PSxf15/fXaDcsvvRQap2PdVlViEJFil66N4U53HwBcHde2\nMCCaHrvwE0O3bmEw24MPQr9+7DGkE/367Wh2AODLL8P0SSecgBKDiOwyMhnH8AczG2Zm55rZt2Nb\ncwSXdcccA7fcsqOb6o5R0JE33oADD4TOnaNzp0yhd2k1n3++Y+C0iEjRyaTxeTzwh2g7DrgZOCPL\ncTWPo48OU2FEA9t2zJsUmTQpqkaC0E+1Vy9aTH6NffYJS0iLiBSjTKbE+CpwAvCJu18IHAh0ympU\nzeXoo8NjlBiOPDJMprpuXTj80kswJn5R05tugksvZfDelapOEpGilUli+NLdq4HKaDT0p0Cf7IbV\nTPr0gcceg0GDgDBJ3lFHweuvw7JlYT2fkSPjzh83Do44giErXlNiEJGilUlieMfMOgP3EHolvQe8\nlekHmNkYM5tnZvPN7JoUr/cxs9fN7D0ze9/MmnfFuHPPBauZLDbWzjBpUnjesmXS+XfeyZD5z/Ph\nlM+aNUwRkeZi7p75yWEMw+7untGaz2bWAphPqIqqAKYD57n7vLhz7gLec/e7zGwo8ELUGyr5Wt6Q\nWBtrzhwYOxYOPhjOOAMuuKD2OdNvKeeSn/dkxtr+sNtuWY9JRKSxzAx3b9BSCekGuI1M3oCuQKvo\neSYOAxa4+1J33w48CpyZdE41sHv0vDOwoiE30NSGDoWqKvjnP0OJIZXBl5SxoGpv/Gc/b97gRESa\nQe3JgWrcFj22BQ4BPiAs0DOcMIne6Ayuvxfwcdz+ckKyiHc98LKZXQ7sBpyYwXWzxiwkhHffhT33\nTH1Op07QoUsJKx6aTO+vTk09XbeISIGqMzG4+3EAZvYUMNLdZ0X7w4AJTRjDN4D73f13ZjaKMLX3\n/qlOnDCh5mPLysooKytrwjBqXHwxnFhPehoytAUflt1B74suCjO1tmuXlVhERBqivLyc8vLynbpG\nvW0MZjbb3fev71gd7x0FTHD3MdH+tYC7+01x5/wHOMXdV0T7CwlrTK9OulaztDFk6pJL4KCD4Afl\n58LAgfCb39T7nlmzwvRMP/hBMwQoIkITtzHEmWlm95pZWbTdA2TU+ExobB5kZv3MrAQ4D3gu6Zyl\nRNVHUeNzm+SkkI+GDIkm07vtNrjrLti2rc5z33sPvvKVUEX14x/D6ry/OxHZlWWSGC4EZgNXRNuc\n6Fi93L0KuAx4ObrGo+4+18yuN7PTo9OuBi42s/eBvwEp+gHlnx1zJvXpA/vuGwY/JJk2DU47LfRu\nOu64sPpbNOWSiEjealB31VzKt6qk+fPDqOhFi4Dbbw/9XO+9d8frV14JTz0F114LF14YBs9BGDy9\nYgX8/ve5iVtEdi2NqUqqMzGY2d/d/VwzmwXUOsndU6+DmSX5lhi2b4eOHWH9emi7aikccgisXAmt\nWrF8eVgldOFC6NIl8X3TpsH3vgcffJCbuEVk19KYxJCuu+oV0ePpac7ZZbVuDf37hzn4hg3rBwMG\nwL/+BSecwO9/HwbGJScFCAPnFi+GtWuha9dmD1tEpF7p1mNYGT0uTbU1X4j5a0cDNMBXvwpPPMGG\nDXDffXDFFanf07o1jB6tdgYRyV/pRj5vNLMNKbaNZqbVCIDBg+MW7TnnHHj6ae67p5qTTgqliboc\ne2woXIiI5KN0A9w6NmcghWjIEJgyJdoZOJDte/bljlu28eTzbdO+r6wMLrss6+GJiDRKJt1VATCz\nnmbWN7ZlM6hCkbzM5xODr6N/y4855JD07zvkkLDQT2zdBxGRfJLJCm5nmNkCYDHwL2AJ8GKW4yoI\n8W0M7nDrf8Zw9Ze/gurqtO8rKYFRo+JKGyIieSSTEsMNwChgfjQd9gnAtKxGVSB69Ag5YPXq0Gaw\nqaotp5XOgLfqX67i2GNhJ6czERHJikwSw3Z3XwO0MLMW7j6ZMNvqLs+spgH6ttvgqqugxdfOgSee\nqPe9ZWVqgBaR/JTJJHqvAmcBvwG6E5b2PNTdm3Wu6Xwb4Bbz7W/DHnvAAw/AkiXQbtFsOPVUWLo0\nYWW4ZFu3QvfusHx5mMZbRCQbsjWJ3pnAl8CVwEvAQmBcw8MrTkOGwO9+F2ZMbdcO2G8/aN8epk9P\n+742beDQQ9XOICL5J904holmdqS7b3L3KnevdPcH3P33UdWSEBJDq1ZxU2mb7RjsVh9VJ4lIPkpX\nYpgP3GpmS8zsZjMb0VxBFZKTTw45oEePuIOxxFBP1ZcGuolIPsqkjaEfYR2F84B2wCPAI+4+P+0b\nm1i+tjGk5B5apR99NEyOVIctW0I7w8qVYUK+TFRXh/YJLRgnIpnIShtDNDfSTe4+grAM51nA3EbG\nuGswC7Po/elPaU9r2zYMdnvzzcwuu3VrKIyUldU7VEJEpNEyGeDWyszGmdnfCAPbPgTOznpkhe77\n3w8LMqxcmfa0TMczbNoE48ZBy5ZQVQWPP17/e6qqovUiREQaIF3j80lm9hdgOXAx8E9goLuf5+7P\nNleABatbNzj/fPjjH9OeVlYG/3p+A7z9dp3nrF8Pp5wCe+0FjzwCN98MP/1p2tVEAbj1Vhg2LEwN\nLiKSqXQL9bwOPAw86e45n9WnoNoYYhYuDHNfLF4MHTqkPOXLz76gRy/jk7HfpcPzj9Z6/bPPQlI4\n+ujQLbZFlMpPPRXGjoX//u/UHz13bnjPt78N774LkyfXvDelRYvg8svhH/9IO/5CRApLk7YxuPvx\n7n5vPiSFgjVwYCgS3Hdfnae0u/UGRnZZwpuTt4VGhDgrVoQ1oseOhTvuSPxiv+km+PWvYUOKCdCr\nquCii+CXv4RbbgmrzaVt7ti0Cb7yFfjnP+HTTxt2jyJSdDKeXbWxzGyMmc0zs/lmdk0d55xrZrPN\nbJaZPZTtmJrV1VeHP/UrK2u/Nm8e/OUvnP6DvozZ/BSt27emY8fQU6l3b9h//7Be9K9+VfuP+OHD\nQ0ni1ltrX/aOO0LD9qWXhjaJv/wFJkwIBZda3OHii+HAA2HkyDBiW0R2be6etY2QeD4C+gGtgfeB\nfZPOGQS8C+we7Xev41pesI4+2v2RRxKPVVe7n3SS++9+F3av/6VvveLH/vnn7p9+6r5smfvy5ekv\nu3Spe9eu7hUVNcc+/NC9Wzf3jz5KPPemm9xPOCF8bILbb3cfMcJ982b3s892f+yxxt2jiOSl6Luz\nQd/d2S4xHAYs8NDldTvwKGGKjXgXAxPdfUP07b86yzE1v6uvDnU68W0kTz0FFRXwwx8CYKeNpWTS\nP9h99zBYrk+f0NicTt++oURx/fVhP1aF9ItfhFqseFddFaqd7rkn7uDkyaFO6qmnwsCIfv1UYhCR\nrCeGvYCP4/aXR8fiDQaGmNkUM5tqZqdkOabmd/rpoR4/1i918+bwTT1xYlgEGmDECFi7to76nrpd\ndx08+WSY4fUPfwjtEKlWh2vVKlQp/fSnsGwZ4T/nnw8PPVSzDqkSg4iQZmnPZtSKUJ10DNAXeMPM\nhsVKEPEmTJiw43lZWRllZWXNFOJOatECfvSjUGo47ji48UY48sgwiCH+nFNPhRdfjJt4qX5du8KP\nfxyaCebMCUtB1NX7aNgwuOIK+N7FVbyw5hzsyivhxBNrTujfH159tXH3KCJ5oby8nPKdXeyloXVP\nDdkIC/y8FLd/LXBN0jl/Ai6I238VODjFtZq03q3Zffmle69e7s88ExoBUjUgPPqo+2mnNfjSmze7\n9+0bmgvqs22b+0E9lvvjo26t3eAwY4b7sGEN/nwRyV/kYRvDdGCQmfUzsxLCfEvPJZ3zDHAcgJl1\nB/YBim+8btu2oY7nnHPgmmtSNyCcfDK88UaYRKkB2rWD2bPhf/6n/nNbf7mBy7+4kce6fb92V6dY\nVVKhjRcRkSaV1cTg7lXAZcDLwGzgUXefa2bXm9np0TmTgDVmNht4Dbjai3XsxA9+EFqLr7gi9etd\nuoRuo42YcrVDhwzHpT34IKedsIVXpuyWPGwCOncOj+vXN/jzRaR41Du7ar4oyJHPjXHjjbBqFdx5\nZ9Nf2z0sJPSnP3HEdWVMmBAKKQkOOAD++lc46KCm/3wRaXbZWsFNmtPYsfDCC9m59uuvhxFvxx7L\nuHFh9ota+vdXzySRXZwSQ7458MDQtXXBgqa/9sSJYdyEGePGwXPPpWhO6NcvLF4tIrssJYZ8Y1bT\nbbUpLVsWxlF861tAmG6jZUuYNSvpPI1lENnlKTHko7Fjmz4x3HVXSArRLK9mpK5OSkoMq1Zlp/Ai\nIvlLiSEfnXgiTJkSRkg3ha1b4d57aw2cO+OMUJ2UIKmN4dJLw6Dsxx5rmlBEJP8pMeSjTp3CWtGT\nJzfN9R5/PLRdDBmScPjoo2H+fPjkk7iDcW0M770X1g965RW49tow/KKqqmlCEpH8pcSQr5qyneGP\nf9wxWV+8kpLQXfX55+MO9uwZGr83bWLCBPjJT2D0aJg+Hd55J9RyrV3bNGGJSH5SYshXY8eGhXOq\nq3fuOu+8E9adPv30lC+fcUZSO4MZ9O3L9OdXMWNGmIMJwhoRkyaF+ZYOPTRFo7WIFA0lhnw1bFio\n799nH/jtb5Pqe5IsXgwPPhimz54zJ3Ex6IkTQ9tCy5Yp33rqqaHG6ssv4w7278/4Wztw3XVhJo+Y\nVq3gttvghhvg+OPDR4pI8cmH2VUlFbMwIO2dd+Duu2HoUDjhBLjkkrB8W3k5vPZa2DZtCjO1btkS\nVoVbtiws6LDvvqERO023oq5dQ+Pya6/VFCreanscsxe14+mLUr/n/PPDAOlzzw3vmzixziWtRaQA\naUqMQrGP3mmpAAAQ5klEQVRhAzz8cOh2unBhSAQnnBB6MO2/f+JESdu2hXPmzYM2bUK1VBq33RYa\noe+6K+yfPGgRXxs0g4tfOift+zZtgssvD7nnscc0i4ZIPmrMlBhKDIXIPcMZ8zIzf35YJuLjj+HN\nN+GCczby4XHfp/VjmS2//fDDYV7ACRNCrVUThiYiO0lzJe0qmvibd/Bg6NgxdE8dPx5+/t2VtP44\n85nPzz8fpk4NK8TVNXGsiBQOlRgECKvAvftuaJ6Y98rHtDry8LAmdQOsWQN77w0rVqjNQSRfqMQg\njTZuXOidNH48tOpbGr7lay3YkF63bnDUUXXM2ioiBUOJQQA44gj4zW/gG98gdG0tLQ2NDg309a/D\no482fXwi0nyUGAQIYxSuvTY8Ao2eZfXMM0NPWi0CJ1K4lBgktf79G7UuQ6dOYfDbM880eUQi0kyU\nGCS1nViX4etf12ysIoUs64nBzMaY2Twzm29m16Q57xwzqzazkdmOSTKwE4nh9NND99XVq5s4JhFp\nFllNDGbWAvgjcAqwP/ANM9s3xXkdgMuBadmMRxpgJxJDhw4wZkyYuklECk+2SwyHAQvcfam7bwce\nBc5Mcd4NwG+BhvWPlOzZybWfVZ0kUriynRj2AuL7PC6Pju1gZiOA3u7exGtZyk7p0ycMcKusbNTb\nTz01jKRONymsiOSnnM6uamYG3A5cEH+4rvMnTJiw43lZWRllZWXZCk3atIEePUJy6Nu3wW9v1y60\nNTzxBFx2WRbiE5GUysvLKS8v36lrZHVKDDMbBUxw9zHR/rWAu/tN0f7uwEfAF4SEsAewBjjD3d9L\nupamxGhuRxwBN90U1gBthOefD2//97+bOK6IeyjQtG6dneuLFIN8nBJjOjDIzPqZWQlwHrBj+Xl3\n3+DuPd19b3cfQGh8HpecFCRHdrKd4eSTw7pBjRhAXa+KCigrg169wjQea9Y0/WeI7KqymhjcvQq4\nDHgZmA086u5zzex6M0u11qSTpipJmln//o3umQRhTemzzoLHH2+6kABefhkOPhhOOgneeiskiX32\ngR/9qMHz/olIClkfx+DuL7n7EHffx91/Gx0b7+7Ppzj3eJUW8shOdFmNOe+8puudVFkJP/sZXHQR\nPPJIeD5kCNxzD8ycGZbHHjYMLr0UFmU+a7iIJNG021K3F1+E3/0u/IneSJWVYT6+738/rPi2Zk3Y\nVq8OC80ddBAcfnjY9t+/zqWpqagIE/yVlMBDD4UqpFQ++wzuvBP+/OcwluLaa0OyENlVaQU3aVpz\n5oS6oPnzd+oyDz8MH3wA3buHqbm7dQvPW7YMa0C8/XbYKirgkENCZ6iNGxO31avDmhHXXVd38oj3\n+efwpz/BHXfAqFHwk5+E5JMJd9i8Gdq3r//cTz6BWbPCPQ0YAF26ZPYZIs1FiUGa1qZN4Rt80yZo\nkf1ptdauhenTw5d6hw5hVbnY1rVr4750N28OK8vdcgv07BlKD/vsA4MG1Wxr1oQxF/Hb+vXQuTPs\nt18oyey/f3heWRlinD4d/vd/w49m+PBw/qJFIWkNGBC2nj1D9VZVVc1jVVWIy6xmIb7Y89jr8Vvs\nVz7+V9+9ZquurnluFmbHbdUq9NSKPTcL/3zxj/Gfn3zdWLyxmJP/t4uPOz7+2NaiRc0W/5nxn1HX\nfVRXJ14z1TWSH1Opqgr/VpWVsH17zfP4n1fseezn1rp1zc+tdeuaXm/xW1VV6tji7yX+HlP9zGI/\n4/iturr2NWNbfEyxx+99L/wRlQklBml6PXqEP4n32KPm2NatYfrUk04K39gFYPv2sJ71ggXw0Udh\niz3v2hVGjoQRI8LjyJHQu3cowcyZA7Nn12wtW8Khh8Jhh4XHvfdO/NJbuzYkiMWLQ7VWy5Y1W4sW\nNaWdVF+O8efGvycm/osw1Zd87Asn/otw+/bUX751/a8U+zKKfXZsi0lOVKm+EGOfEdti+8mJJPk+\nUiWQ+GvV9fmpEkT8F2rsyzR2T8k/u1gCiP3ctm8PW4sWNe+PbbGfRfx9xe4t1ZYqZqj73zn5ZxZL\ncPGxVVaGGYwHDkz9b5hMiUGa3iGHwMSJNfUwkybB5ZeHepZ16+Dvfw/fkAWqri8WkWKRj+MYpNDF\nxjIsWQJf+Qr84Adw++2hvuXWW+G000LiyGbSXr48lFC2b2/ySyspiNSmEoOkd9VVoQ5m4UL4n/+B\nq6+Gtm1rXv/oI/jqV2HoULj77tAgkKyyMrQex7ojxbbNm8NYiYEDwxZr7XUPrdXPPRe2xYvD3E1d\nu4YSSvfuzXLrIsWgMSWGnM6VJAXgmGNg5crwhdyvX+3XBw0Ko8wuvzxUKd14I6xaFSrw588P29Kl\nYWm37t1rtm7dQoKZPDkkl8WLQ+vywIGwbFmo0D3zzFAqOeqo8Kf9ddeFyv1nn4UDDsj8HpYvDwnm\nvfdCi3Ksa1Rsa9u2doti69ahXaV794Y1vLuHpLdkSWhwKClJ3Fq3DhXHW7aEtpqtW8PzLVvgiy9q\nb5WVtWODcHzbtppt+/ZQKR1rQY1vqYyvUIea58kV2sn7yS2pqVqbU7U0x7eoJrewJj/GN3jEf1ar\nVqHyPf4REuOMtYyXlIS5vWKPbdqEWOJ/PrEt+f5iDQTxrc+xra5/97p6DmzfnvjvEWuoiDXaxBoT\njj8evvOdzH+nckAlBmk6DzwADz4YWmQHDw7bPvuE/fhSRirV1bBiRSiZ9OgRugClquf5299CyeWe\ne0JX2lTcYe7cUP30zDPhmmPHhrmfNm6sGUwR27Zurd3auW1b6Iu6cSPsuSfstVcYkNGpU+24qqpC\nS/XSpSGptW0bkmi3brW/wLdtC19CsS+wtm1rHjt0SNzat6/pHpPcgpycbEpKQlyxL6T4VtRUX76x\n1u74L/fk5/HJKF1rc3JSiW+9TtXKmtyynfxFG/uZxroBxVrSkxNR7L2xn2ss0cYSQCxZxP+cUnWX\nim99jt9Sfd/U1a2qZcvEf4uSkvDvHN8FKZbM9tkn/C42EzU+y65h+nQ4+2y45JIw/LmiAt5/P2wz\nZoTBEVVVIXGcdVaYBLCxM+1t2RKuv2JF2DZurH2OWUge/fuHmWhTVaeJ5IgSg+w6Vq4MyWH27PDX\n9ogRYRh1bNt3X7Usi6DEILua7dtDVVCvXkoCInVQYhARkQQaxyAiIjtNiUFERBIoMYiISAIlBhER\nSaDEICIiCbKeGMxsjJnNM7P5ZnZNitevNLPZZva+mb1iZn2yHZOIiNQtq4nBzFoAfwROAfYHvmFm\n+yad9h5wsLsfBDwJ3JLNmPJVeXl5rkPIKt1f4Srme4Piv7/GyHaJ4TBggbsvdfftwKPAmfEnuPu/\n3H1LtDsN2CvLMeWlYv/l1P0VrmK+Nyj++2uMbCeGvYCP4/aXk/6L/7vAi1mNSERE0sqbabfN7JvA\nwcCxuY5FRGRXltUpMcxsFDDB3cdE+9cC7u43JZ13InAncIy7r6njWpoPQ0SkEfJqriQzawl8CJwA\nrAT+F/iGu8+NO2cE8DhwirsvzFowIiKSkay2Mbh7FXAZ8DIwG3jU3eea2fVmdnp02s1Ae+BxM5th\nZs9kMyYREUmvYGZXFRGR5lEQI5/rGyRXaMzsPjNbZWYz4451MbOXzexDM5tkZp1yGWNjmVlvM3s9\nGrQ4y8wuj44Xy/21MbO3o9LtLDMbHx3vb2bTot/RR8wsbzp2NIaZtTCz98zsuWi/aO7PzJaY2QfR\nv+H/RseK5fezk5k9bmZzo/8HD2/MveV9YshwkFyhuZ9wP/GuBV519yHA68BPmj2qplEJXOXu+wOj\ngR9G/15FcX/uvhU4zt1HAAcBp5rZ4cBNwG3uPhhYT+h6XciuAObE7RfT/VUDZe4+wt0Pi44Vxe8n\noRPPC+4+FDgQmEdj7s3d83oDRgEvxu1fC1yT67ia4L76ATPj9ucBvaLnewDzch1jE93nM8CJxXh/\nwG7AO4SBnJ8CLaLjo4CXch3fTtxXb+AVoAx4Ljr2WRHd32KgW9Kxgv/9BHYHFqY43uB7y/sSAw0f\nJFeoerr7KgB3/wTomeN4dpqZ9Sf8VT2N8ItZFPcXVbPMAD4hfIEuBNa7e3V0ynKgNFfxNYHfAT8G\nHMDMugHriuj+HJhkZtPN7L+iY8Xw+zkAWG1m90fVgHeb2W404t4KITHsqgq6V4CZdQCeAK5w9y+o\nfT8Fe3/uXu2hKqk3obRQ6FWbO5jZacAqd38fiO/7XkyLah/p7ocAYwlVnUdTHL+frYCRwER3Hwls\nItSwNPjeCiExrAD6xu33jo4Vm1Vm1gvAzPYgVE0UpKhh8gngr+7+bHS4aO4vxt03AOWEtpTOUXsY\nFPbv6JHAGWa2CHgEOJ5Qb92pSO4Pd18ZPX5GqOo8jOL4/VwOfOzu70T7TxISRYPvrRASw3RgkJn1\nM7MS4DzguRzH1BSMxL/CngO+Ez2/AHg2+Q0F5C/AHHe/M+5YUdyfmXWP9eows3bASYRG2snA16LT\nCvb+3P06d+/r7nsT/l973d2/SZHcn5ntFpVmMbP2wMnALIrg9zOqLvrYzAZHh04gjB9r8L0VxDgG\nMxtD+KulBXCfu/82xyHtFDN7mNCw1w1YBYwn/OXyONAHWAqc6+7rcxVjY5nZkcAbhP/ZPNquI4x6\n/zuFf38HAA8QfhdbAI+5+6/NbABh9uAuwAzgmx5mFC5YZnYs8CN3P6NY7i+6j6cJv5etgL+5+2/N\nrCvF8ft5IHAv0BpYBFwItKSB91YQiUFERJpPIVQliYhIM1JiEBGRBEoMIiKSQIlBREQSKDGIiEgC\nJQYREUmgxCBFwcyqzeyWuP0fmdkvmuC6JWb2SjT3zNeSXrvfzBZFr80wsyk7+3lJ159sZiOb8poi\nmSjYOdVFkmwFzjaz37j72ia87kjCOuV1fUH/yN2fbsLPE8k5lRikWFQCdwNXJb8QTafympm9H/31\n3zvFOV3M7OloAZepZjbMzHoAfwUOjUoFA1J8bq3/h8xsvJk9GF3nw7gZPDGzW6IFfj4ws3Pjjl9j\nZjOjkseNcZc7N1oYaF40qhwz2y869l50TwMb8oMSqY9KDFIsHJgIzDKzm5Je+wNwv7s/ZGYXRvtf\nSTrneuA9d/+KmR1HmABwRPSl/iN3P6OOz73FzH5KmPfqP+7+rej4AcDhQEdghpk9DxwBDHf3A8ys\nJzDdzP4FjADGAYe6+1Yz6xx3/ZbufriZnQpMIMzNdClwh7vHVlJr2ZAflEh9lBikaLj7F2b2AGH1\nsS/jXhpNTSL4K3BzircfBZwdXWeymXWNTbZWj6vd/akUx591923AGjN7nZAkjiLMWIq7f2pm5YSZ\nPY8lJK6t0Wvx89jErv0uYXEngLeAn0Yln6fd/aMM4hTJmKqSpNjcSVh2sn3csUzmo08+trPrD8Rf\nzwjLSSazOmKJtzV6rCL6Q87dHyGUMLYAL5hZ2U5FKpJEiUGKhQG4+zrCTJLxaxJPBb4RPf8m8O8U\n7/939BrRF+1n0QJDGX1uCmdGPZq6EUoE06PP+Hq0AlwP4GjCrLOvABdG03hjZl3SfZaZDXD3xe7+\nB8IUysMziFMkY6pKkmIR/5f3bcAP445dDtxvZlcT1i6+MMX7rwf+YmYfEFa+uiDDz705ro3BCVVD\nADMJi/h0A34ZLan4tJmNAj4glCB+7O6fEpaZPBB4x8y2Ai8AP6Puks65ZvYtYDuwEvh1hrGKZETT\nbos0MTMbD2x099tzHYtIY6gqSUREEqjEICIiCVRiEBGRBEoMIiKSQIlBREQSKDGIiEgCJQYREUmg\nxCAiIgn+P9hhGTHQ8AkQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e627c450>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfigure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e50f86d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(resnet['epoch'],resnet['val_acc'],label = 'ResNet')\n",
    "pylab.plot(WRN['epoch'],WRN['val_acc'],label = 'WRN')\n",
    "figure = pylab.legend(loc = 'upper right')\n",
    "pylab.xlabel(\"No of Epochs\")\n",
    "pylab.ylabel(\"Validation Accuracy\")\n",
    "pylab.savefig(\"ResNet_vs_WRN_val_acc\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e5292310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(resnet['epoch'],resnet['val_loss'],label = 'ResNet')\n",
    "pylab.plot(WRN['epoch'],WRN['val_loss'],label = 'WRN')\n",
    "figure = pylab.legend(loc = 'upper right')\n",
    "pylab.xlabel(\"No of Epochs\")\n",
    "pylab.ylabel(\"Validation Loss\")\n",
    "pylab.savefig(\"ResNet_vs_WRN_val_loss\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e5a4ef90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(resnet['epoch'],resnet['val_loss'],label = 'ResNet_Basemap32')\n",
    "pylab.plot(resnet2['epoch'],resnet2['val_loss'],label = 'ResNet_Basemap16')\n",
    "figure = pylab.legend(loc = 'upper right')\n",
    "pylab.savefig(\"ResNet_basmap32_vs_16_val_loss\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wNtKoruVno8hAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e534c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(resnet['epoch'],resnet['val_acc'],label = 'R-BM32')\n",
    "pylab.plot(resnet2['epoch'],resnet2['val_acc'],label = 'R-BM16')\n",
    "figure = pylab.legend(loc = 'upper right')\n",
    "pylab.xlabel(\"No of Epochs\")\n",
    "pylab.ylabel(\"Validation Accuracy\")\n",
    "pylab.savefig(\"ResNet_basmap32_vs_16_val_acc\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "WRN2 = pd.read_csv(\"Results/WRN_16-8-without_dropout.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>acc</th>\n",
       "      <th>loss</th>\n",
       "      <th>lr</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>val_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.518886</td>\n",
       "      <td>1.330050</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.5580</td>\n",
       "      <td>1.279791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.685718</td>\n",
       "      <td>0.891663</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.6691</td>\n",
       "      <td>0.964039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.754346</td>\n",
       "      <td>0.701922</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.6712</td>\n",
       "      <td>1.065284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.803168</td>\n",
       "      <td>0.574350</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.7520</td>\n",
       "      <td>0.733164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.831164</td>\n",
       "      <td>0.487628</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.8163</td>\n",
       "      <td>0.550438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.852131</td>\n",
       "      <td>0.431187</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.8107</td>\n",
       "      <td>0.565627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.870054</td>\n",
       "      <td>0.378490</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.8029</td>\n",
       "      <td>0.589544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.882670</td>\n",
       "      <td>0.342085</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.7837</td>\n",
       "      <td>0.646404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.921500</td>\n",
       "      <td>0.228787</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>0.8927</td>\n",
       "      <td>0.311762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.931272</td>\n",
       "      <td>0.200492</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>0.8934</td>\n",
       "      <td>0.324384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.936479</td>\n",
       "      <td>0.184987</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>0.8931</td>\n",
       "      <td>0.331688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.944329</td>\n",
       "      <td>0.163048</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>0.8902</td>\n",
       "      <td>0.359771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.957906</td>\n",
       "      <td>0.122611</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.9159</td>\n",
       "      <td>0.255709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.963413</td>\n",
       "      <td>0.107800</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.9158</td>\n",
       "      <td>0.256280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.966057</td>\n",
       "      <td>0.100279</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.9159</td>\n",
       "      <td>0.263670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.968299</td>\n",
       "      <td>0.094411</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.9152</td>\n",
       "      <td>0.257789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.974007</td>\n",
       "      <td>0.077968</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.9193</td>\n",
       "      <td>0.254821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.975328</td>\n",
       "      <td>0.075253</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.9189</td>\n",
       "      <td>0.255069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.977731</td>\n",
       "      <td>0.070618</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.9180</td>\n",
       "      <td>0.259524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.977471</td>\n",
       "      <td>0.069410</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.9193</td>\n",
       "      <td>0.262995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.978212</td>\n",
       "      <td>0.066367</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.9198</td>\n",
       "      <td>0.258389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.979634</td>\n",
       "      <td>0.064414</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.9194</td>\n",
       "      <td>0.258759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.980074</td>\n",
       "      <td>0.063544</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.9199</td>\n",
       "      <td>0.259261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.981436</td>\n",
       "      <td>0.060569</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.9199</td>\n",
       "      <td>0.259251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    epoch       acc      loss        lr  val_acc  val_loss\n",
       "0       0  0.518886  1.330050  0.001000   0.5580  1.279791\n",
       "1       1  0.685718  0.891663  0.001000   0.6691  0.964039\n",
       "2       2  0.754346  0.701922  0.001000   0.6712  1.065284\n",
       "3       3  0.803168  0.574350  0.001000   0.7520  0.733164\n",
       "4       4  0.831164  0.487628  0.001000   0.8163  0.550438\n",
       "5       5  0.852131  0.431187  0.001000   0.8107  0.565627\n",
       "6       6  0.870054  0.378490  0.001000   0.8029  0.589544\n",
       "7       7  0.882670  0.342085  0.001000   0.7837  0.646404\n",
       "8       8  0.921500  0.228787  0.000316   0.8927  0.311762\n",
       "9       9  0.931272  0.200492  0.000316   0.8934  0.324384\n",
       "10     10  0.936479  0.184987  0.000316   0.8931  0.331688\n",
       "11     11  0.944329  0.163048  0.000316   0.8902  0.359771\n",
       "12     12  0.957906  0.122611  0.000100   0.9159  0.255709\n",
       "13     13  0.963413  0.107800  0.000100   0.9158  0.256280\n",
       "14     14  0.966057  0.100279  0.000100   0.9159  0.263670\n",
       "15     15  0.968299  0.094411  0.000100   0.9152  0.257789\n",
       "16     16  0.974007  0.077968  0.000032   0.9193  0.254821\n",
       "17     17  0.975328  0.075253  0.000032   0.9189  0.255069\n",
       "18     18  0.977731  0.070618  0.000032   0.9180  0.259524\n",
       "19     19  0.977471  0.069410  0.000032   0.9193  0.262995\n",
       "20     20  0.978212  0.066367  0.000010   0.9198  0.258389\n",
       "21     21  0.979634  0.064414  0.000010   0.9194  0.258759\n",
       "22     22  0.980074  0.063544  0.000003   0.9199  0.259261\n",
       "23     23  0.981436  0.060569  0.000003   0.9199  0.259251"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WRN2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e538f410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(WRN['epoch'],WRN['val_acc'],label = 'WRN-dropout')\n",
    "pylab.plot(WRN2['epoch'],WRN2['val_acc'],label = 'WRN')\n",
    "pylab.xlabel(\"No of Epochs\")\n",
    "pylab.ylabel(\"Validation Accuracy\")\n",
    "figure = pylab.legend(loc = 'upper right')\n",
    "pylab.savefig(\"WRN-With_and_without_dropout_val_acc\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nf4J+vBaTcyVmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7e542ce90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(WRN['epoch'],WRN['val_loss'],label = 'WRN-dropout')\n",
    "pylab.plot(WRN2['epoch'],WRN2['val_loss'],label = 'WRN')\n",
    "pylab.xlabel(\"No of Epochs\")\n",
    "pylab.ylabel(\"Validation Loss\")\n",
    "figure = pylab.legend(loc = 'upper right')\n",
    "pylab.savefig(\"WRN-With_and_without_dropout_val_loss\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "simplecnn = pd.read_csv(\"Results/SimpleCNN_results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>acc</th>\n",
       "      <th>loss</th>\n",
       "      <th>lr</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>val_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.428097</td>\n",
       "      <td>1.900574</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.5606</td>\n",
       "      <td>1.288526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.553268</td>\n",
       "      <td>1.499652</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6501</td>\n",
       "      <td>1.192698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.631628</td>\n",
       "      <td>1.247160</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6712</td>\n",
       "      <td>1.219315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.661507</td>\n",
       "      <td>1.146009</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7053</td>\n",
       "      <td>0.955501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.685798</td>\n",
       "      <td>1.066759</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7261</td>\n",
       "      <td>0.878394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.716257</td>\n",
       "      <td>0.915044</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7614</td>\n",
       "      <td>0.784090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.744713</td>\n",
       "      <td>0.819049</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7811</td>\n",
       "      <td>0.722206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.758331</td>\n",
       "      <td>0.785266</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7958</td>\n",
       "      <td>0.697777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.770426</td>\n",
       "      <td>0.759932</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7989</td>\n",
       "      <td>0.688376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.781060</td>\n",
       "      <td>0.736625</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8053</td>\n",
       "      <td>0.670654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.788690</td>\n",
       "      <td>0.719270</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.661884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.797941</td>\n",
       "      <td>0.701568</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8126</td>\n",
       "      <td>0.654051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.802968</td>\n",
       "      <td>0.687342</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8195</td>\n",
       "      <td>0.645804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.809216</td>\n",
       "      <td>0.674824</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8242</td>\n",
       "      <td>0.640875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.666668</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8340</td>\n",
       "      <td>0.624846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.816565</td>\n",
       "      <td>0.662280</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8390</td>\n",
       "      <td>0.616924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.822973</td>\n",
       "      <td>0.647293</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8323</td>\n",
       "      <td>0.622235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.825216</td>\n",
       "      <td>0.647223</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8486</td>\n",
       "      <td>0.585529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.827099</td>\n",
       "      <td>0.635255</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8463</td>\n",
       "      <td>0.593070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.831284</td>\n",
       "      <td>0.631270</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8423</td>\n",
       "      <td>0.623087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.832205</td>\n",
       "      <td>0.626513</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.8491</td>\n",
       "      <td>0.587223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.855275</td>\n",
       "      <td>0.561212</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8730</td>\n",
       "      <td>0.526669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.862083</td>\n",
       "      <td>0.535914</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8723</td>\n",
       "      <td>0.523032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.865027</td>\n",
       "      <td>0.525710</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8785</td>\n",
       "      <td>0.504970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.869273</td>\n",
       "      <td>0.507973</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8792</td>\n",
       "      <td>0.505486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.868772</td>\n",
       "      <td>0.505717</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8788</td>\n",
       "      <td>0.485784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.873578</td>\n",
       "      <td>0.493465</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8814</td>\n",
       "      <td>0.481429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.873358</td>\n",
       "      <td>0.485642</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8747</td>\n",
       "      <td>0.496603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0.876202</td>\n",
       "      <td>0.480319</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8829</td>\n",
       "      <td>0.479176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.878084</td>\n",
       "      <td>0.474352</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8831</td>\n",
       "      <td>0.483439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>0.887536</td>\n",
       "      <td>0.428016</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8917</td>\n",
       "      <td>0.434316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>0.889839</td>\n",
       "      <td>0.421774</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8933</td>\n",
       "      <td>0.437028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>0.891081</td>\n",
       "      <td>0.415977</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8937</td>\n",
       "      <td>0.435522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>0.890740</td>\n",
       "      <td>0.417184</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8880</td>\n",
       "      <td>0.440556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>0.899672</td>\n",
       "      <td>0.388638</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8974</td>\n",
       "      <td>0.421839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>0.902435</td>\n",
       "      <td>0.380536</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8983</td>\n",
       "      <td>0.420492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>0.903296</td>\n",
       "      <td>0.377902</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8964</td>\n",
       "      <td>0.427578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>0.902455</td>\n",
       "      <td>0.375588</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8999</td>\n",
       "      <td>0.410594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>0.904858</td>\n",
       "      <td>0.374186</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8968</td>\n",
       "      <td>0.417562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>52</td>\n",
       "      <td>0.905860</td>\n",
       "      <td>0.369842</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8989</td>\n",
       "      <td>0.411032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>0.906661</td>\n",
       "      <td>0.368306</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8997</td>\n",
       "      <td>0.413553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54</td>\n",
       "      <td>0.908983</td>\n",
       "      <td>0.358739</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9010</td>\n",
       "      <td>0.408304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>0.909024</td>\n",
       "      <td>0.357534</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9012</td>\n",
       "      <td>0.406360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>56</td>\n",
       "      <td>0.909764</td>\n",
       "      <td>0.355194</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9015</td>\n",
       "      <td>0.406772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>57</td>\n",
       "      <td>0.911347</td>\n",
       "      <td>0.351265</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9017</td>\n",
       "      <td>0.405451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>0.909144</td>\n",
       "      <td>0.354292</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9016</td>\n",
       "      <td>0.402697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59</td>\n",
       "      <td>0.913369</td>\n",
       "      <td>0.348312</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9009</td>\n",
       "      <td>0.405795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>60</td>\n",
       "      <td>0.912848</td>\n",
       "      <td>0.347120</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9015</td>\n",
       "      <td>0.402651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>61</td>\n",
       "      <td>0.911787</td>\n",
       "      <td>0.351374</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9021</td>\n",
       "      <td>0.400047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>62</td>\n",
       "      <td>0.914711</td>\n",
       "      <td>0.342861</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9006</td>\n",
       "      <td>0.403758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>63</td>\n",
       "      <td>0.912848</td>\n",
       "      <td>0.346442</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9030</td>\n",
       "      <td>0.403409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>0.914130</td>\n",
       "      <td>0.344755</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.9028</td>\n",
       "      <td>0.403618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>0.913269</td>\n",
       "      <td>0.346210</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.9025</td>\n",
       "      <td>0.402140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>0.913629</td>\n",
       "      <td>0.343909</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.9021</td>\n",
       "      <td>0.401994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>0.913149</td>\n",
       "      <td>0.342666</td>\n",
       "      <td>3.162280e-06</td>\n",
       "      <td>0.9023</td>\n",
       "      <td>0.401403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68</td>\n",
       "      <td>0.914611</td>\n",
       "      <td>0.338006</td>\n",
       "      <td>3.162280e-06</td>\n",
       "      <td>0.9022</td>\n",
       "      <td>0.400754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>69</td>\n",
       "      <td>0.915352</td>\n",
       "      <td>0.342317</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.9026</td>\n",
       "      <td>0.400672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>70</td>\n",
       "      <td>0.914110</td>\n",
       "      <td>0.341282</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.9025</td>\n",
       "      <td>0.400034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>71</td>\n",
       "      <td>0.914911</td>\n",
       "      <td>0.338740</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9023</td>\n",
       "      <td>0.400656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>72</td>\n",
       "      <td>0.914190</td>\n",
       "      <td>0.337506</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.9025</td>\n",
       "      <td>0.400271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    epoch       acc      loss            lr  val_acc  val_loss\n",
       "0       0  0.428097  1.900574  1.000000e-03   0.5606  1.288526\n",
       "1       1  0.553268  1.499652  1.000000e-03   0.6501  1.192698\n",
       "2       2  0.631628  1.247160  1.000000e-03   0.6712  1.219315\n",
       "3       3  0.661507  1.146009  1.000000e-03   0.7053  0.955501\n",
       "4       4  0.685798  1.066759  1.000000e-03   0.7261  0.878394\n",
       "5       5  0.716257  0.915044  1.000000e-03   0.7614  0.784090\n",
       "6       6  0.744713  0.819049  1.000000e-03   0.7811  0.722206\n",
       "7       7  0.758331  0.785266  1.000000e-03   0.7958  0.697777\n",
       "8       8  0.770426  0.759932  1.000000e-03   0.7989  0.688376\n",
       "9       9  0.781060  0.736625  1.000000e-03   0.8053  0.670654\n",
       "10     10  0.788690  0.719270  1.000000e-03   0.8100  0.661884\n",
       "11     11  0.797941  0.701568  1.000000e-03   0.8126  0.654051\n",
       "12     12  0.802968  0.687342  1.000000e-03   0.8195  0.645804\n",
       "13     13  0.809216  0.674824  1.000000e-03   0.8242  0.640875\n",
       "14     14  0.812500  0.666668  1.000000e-03   0.8340  0.624846\n",
       "15     15  0.816565  0.662280  1.000000e-03   0.8390  0.616924\n",
       "16     16  0.822973  0.647293  1.000000e-03   0.8323  0.622235\n",
       "17     17  0.825216  0.647223  1.000000e-03   0.8486  0.585529\n",
       "18     18  0.827099  0.635255  1.000000e-03   0.8463  0.593070\n",
       "19     19  0.831284  0.631270  1.000000e-03   0.8423  0.623087\n",
       "20     20  0.832205  0.626513  1.000000e-03   0.8491  0.587223\n",
       "21     21  0.855275  0.561212  3.162280e-04   0.8730  0.526669\n",
       "22     22  0.862083  0.535914  3.162280e-04   0.8723  0.523032\n",
       "23     23  0.865027  0.525710  3.162280e-04   0.8785  0.504970\n",
       "24     24  0.869273  0.507973  3.162280e-04   0.8792  0.505486\n",
       "25     25  0.868772  0.505717  3.162280e-04   0.8788  0.485784\n",
       "26     26  0.873578  0.493465  3.162280e-04   0.8814  0.481429\n",
       "27     27  0.873358  0.485642  3.162280e-04   0.8747  0.496603\n",
       "28     28  0.876202  0.480319  3.162280e-04   0.8829  0.479176\n",
       "29     29  0.878084  0.474352  3.162280e-04   0.8831  0.483439\n",
       "..    ...       ...       ...           ...      ...       ...\n",
       "43     43  0.887536  0.428016  3.162280e-04   0.8917  0.434316\n",
       "44     44  0.889839  0.421774  3.162280e-04   0.8933  0.437028\n",
       "45     45  0.891081  0.415977  3.162280e-04   0.8937  0.435522\n",
       "46     46  0.890740  0.417184  3.162280e-04   0.8880  0.440556\n",
       "47     47  0.899672  0.388638  1.000000e-04   0.8974  0.421839\n",
       "48     48  0.902435  0.380536  1.000000e-04   0.8983  0.420492\n",
       "49     49  0.903296  0.377902  1.000000e-04   0.8964  0.427578\n",
       "50     50  0.902455  0.375588  1.000000e-04   0.8999  0.410594\n",
       "51     51  0.904858  0.374186  1.000000e-04   0.8968  0.417562\n",
       "52     52  0.905860  0.369842  1.000000e-04   0.8989  0.411032\n",
       "53     53  0.906661  0.368306  1.000000e-04   0.8997  0.413553\n",
       "54     54  0.908983  0.358739  3.162280e-05   0.9010  0.408304\n",
       "55     55  0.909024  0.357534  3.162280e-05   0.9012  0.406360\n",
       "56     56  0.909764  0.355194  3.162280e-05   0.9015  0.406772\n",
       "57     57  0.911347  0.351265  3.162280e-05   0.9017  0.405451\n",
       "58     58  0.909144  0.354292  3.162280e-05   0.9016  0.402697\n",
       "59     59  0.913369  0.348312  3.162280e-05   0.9009  0.405795\n",
       "60     60  0.912848  0.347120  3.162280e-05   0.9015  0.402651\n",
       "61     61  0.911787  0.351374  3.162280e-05   0.9021  0.400047\n",
       "62     62  0.914711  0.342861  3.162280e-05   0.9006  0.403758\n",
       "63     63  0.912848  0.346442  3.162280e-05   0.9030  0.403409\n",
       "64     64  0.914130  0.344755  3.162280e-05   0.9028  0.403618\n",
       "65     65  0.913269  0.346210  1.000000e-05   0.9025  0.402140\n",
       "66     66  0.913629  0.343909  1.000000e-05   0.9021  0.401994\n",
       "67     67  0.913149  0.342666  3.162280e-06   0.9023  0.401403\n",
       "68     68  0.914611  0.338006  3.162280e-06   0.9022  0.400754\n",
       "69     69  0.915352  0.342317  1.000000e-06   0.9026  0.400672\n",
       "70     70  0.914110  0.341282  1.000000e-06   0.9025  0.400034\n",
       "71     71  0.914911  0.338740  5.000000e-07   0.9023  0.400656\n",
       "72     72  0.914190  0.337506  5.000000e-07   0.9025  0.400271\n",
       "\n",
       "[73 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simplecnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7ff6dad92190>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEPCAYAAABRHfM8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nW0b3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6dade0490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(simplecnn[\"epoch\"],simplecnn[\"val_acc\"])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"simplecnn_acc.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7ff6dac98c90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbX8yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6dad006d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(simplecnn[\"epoch\"],simplecnn[\"val_loss\"])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"simplecnn_val_loss.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKIXNzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6da6db590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(WRN['epoch'],WRN['val_loss'],label = 'WRN-dropout')\n",
    "pylab.plot(simplecnn['epoch'],simplecnn['val_loss'],label = 'SimpleCNN')\n",
    "pylab.xlabel(\"No of Epochs\")\n",
    "pylab.ylabel(\"Validation Loss\")\n",
    "figure = pylab.legend(loc = 'upper right')\n",
    "pylab.savefig(\"WRN_and_SimpleCNN_val_loss\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6da514b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(WRN['epoch'],WRN['val_acc'],label = 'WRN')\n",
    "pylab.plot(simplecnn['epoch'],simplecnn['val_acc'],label = 'S-CNN')\n",
    "pylab.xlabel(\"No of Epochs\")\n",
    "pylab.ylabel(\"Validation Loss\")\n",
    "figure = pylab.legend(loc = 'upper right')\n",
    "pylab.savefig(\"WRN_and_SimpleCNN_val_acc\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWGIwxhjjwxKDMcYYH5YYjDHG+Pj/YXWGAyb2PXcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6da4638d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(resnet['epoch'],resnet['val_acc'],label = 'ResNet')\n",
    "pylab.plot(simplecnn['epoch'],simplecnn['val_acc'],label = 'S-CNN')\n",
    "pylab.xlabel(\"No of Epochs\")\n",
    "pylab.ylabel(\"Validation Loss\")\n",
    "figure = pylab.legend(loc = 'upper right')\n",
    "pylab.savefig(\"ResNet_and_SimpleCNN_val_acc\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6da3c0c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(resnet['epoch'],resnet['val_loss'],label = 'ResNet')\n",
    "pylab.plot(simplecnn['epoch'],simplecnn['val_loss'],label = 'S-CNN')\n",
    "pylab.xlabel(\"No of Epochs\")\n",
    "pylab.ylabel(\"Validation Loss\")\n",
    "figure = pylab.legend(loc = 'upper right')\n",
    "pylab.savefig(\"ResNet_and_SimpleCNN_val_loss\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vgg = pd.read_csv(\"Results/vgg16imagenetpretrained_upsampleimage_cifar10_data_argumentation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>acc</th>\n",
       "      <th>loss</th>\n",
       "      <th>lr</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>val_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.339629</td>\n",
       "      <td>1.693363</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.3817</td>\n",
       "      <td>1.550955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.434316</td>\n",
       "      <td>1.443026</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.4665</td>\n",
       "      <td>1.401919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.524051</td>\n",
       "      <td>1.254391</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.5672</td>\n",
       "      <td>1.173143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.603312</td>\n",
       "      <td>1.091959</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6367</td>\n",
       "      <td>1.033902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.642663</td>\n",
       "      <td>0.994602</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6775</td>\n",
       "      <td>0.911932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.671479</td>\n",
       "      <td>0.931370</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.6732</td>\n",
       "      <td>0.942200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.693868</td>\n",
       "      <td>0.869848</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7209</td>\n",
       "      <td>0.804449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.707806</td>\n",
       "      <td>0.838481</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7155</td>\n",
       "      <td>0.834796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.720702</td>\n",
       "      <td>0.804591</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7378</td>\n",
       "      <td>0.754999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.731096</td>\n",
       "      <td>0.784383</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7368</td>\n",
       "      <td>0.767846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.743372</td>\n",
       "      <td>0.753940</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7359</td>\n",
       "      <td>0.773621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.749700</td>\n",
       "      <td>0.743873</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7583</td>\n",
       "      <td>0.732452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.756849</td>\n",
       "      <td>0.716355</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7509</td>\n",
       "      <td>0.733586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.762776</td>\n",
       "      <td>0.698444</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7529</td>\n",
       "      <td>0.759714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.767042</td>\n",
       "      <td>0.692529</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.7469</td>\n",
       "      <td>0.758362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.815424</td>\n",
       "      <td>0.548257</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8052</td>\n",
       "      <td>0.587185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.829462</td>\n",
       "      <td>0.503849</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8164</td>\n",
       "      <td>0.559198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.835730</td>\n",
       "      <td>0.482571</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8149</td>\n",
       "      <td>0.554202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.841297</td>\n",
       "      <td>0.469128</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8116</td>\n",
       "      <td>0.575127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.848546</td>\n",
       "      <td>0.450038</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8141</td>\n",
       "      <td>0.565250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.850168</td>\n",
       "      <td>0.441613</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8214</td>\n",
       "      <td>0.549377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.854674</td>\n",
       "      <td>0.425162</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8300</td>\n",
       "      <td>0.513885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.859821</td>\n",
       "      <td>0.414874</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8281</td>\n",
       "      <td>0.535230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.862063</td>\n",
       "      <td>0.406118</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8280</td>\n",
       "      <td>0.529385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.865288</td>\n",
       "      <td>0.394248</td>\n",
       "      <td>3.162280e-04</td>\n",
       "      <td>0.8268</td>\n",
       "      <td>0.532586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.880567</td>\n",
       "      <td>0.345766</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8412</td>\n",
       "      <td>0.495981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.886274</td>\n",
       "      <td>0.334989</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8410</td>\n",
       "      <td>0.500673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.889719</td>\n",
       "      <td>0.318496</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8408</td>\n",
       "      <td>0.496158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0.893023</td>\n",
       "      <td>0.310567</td>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.8454</td>\n",
       "      <td>0.499505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.898530</td>\n",
       "      <td>0.298940</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.8442</td>\n",
       "      <td>0.498027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>0.900473</td>\n",
       "      <td>0.289600</td>\n",
       "      <td>3.162280e-05</td>\n",
       "      <td>0.8452</td>\n",
       "      <td>0.500979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>0.901834</td>\n",
       "      <td>0.284791</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.8439</td>\n",
       "      <td>0.497917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>0.901013</td>\n",
       "      <td>0.287835</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.8456</td>\n",
       "      <td>0.494548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>0.903236</td>\n",
       "      <td>0.280388</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.8445</td>\n",
       "      <td>0.496280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>0.903617</td>\n",
       "      <td>0.280983</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.8464</td>\n",
       "      <td>0.497969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>0.902015</td>\n",
       "      <td>0.282265</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.8452</td>\n",
       "      <td>0.499375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>0.903496</td>\n",
       "      <td>0.281016</td>\n",
       "      <td>3.162280e-06</td>\n",
       "      <td>0.8454</td>\n",
       "      <td>0.498833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>0.904378</td>\n",
       "      <td>0.276829</td>\n",
       "      <td>3.162280e-06</td>\n",
       "      <td>0.8451</td>\n",
       "      <td>0.498132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>0.904758</td>\n",
       "      <td>0.277113</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.8446</td>\n",
       "      <td>0.497834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>0.903657</td>\n",
       "      <td>0.277681</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.8454</td>\n",
       "      <td>0.497718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>0.904217</td>\n",
       "      <td>0.278367</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8455</td>\n",
       "      <td>0.497616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>0.904117</td>\n",
       "      <td>0.278353</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8455</td>\n",
       "      <td>0.497612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>0.904638</td>\n",
       "      <td>0.279105</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8450</td>\n",
       "      <td>0.497724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>0.904578</td>\n",
       "      <td>0.279349</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.8452</td>\n",
       "      <td>0.497759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    epoch       acc      loss            lr  val_acc  val_loss\n",
       "0       0  0.339629  1.693363  1.000000e-03   0.3817  1.550955\n",
       "1       1  0.434316  1.443026  1.000000e-03   0.4665  1.401919\n",
       "2       2  0.524051  1.254391  1.000000e-03   0.5672  1.173143\n",
       "3       3  0.603312  1.091959  1.000000e-03   0.6367  1.033902\n",
       "4       4  0.642663  0.994602  1.000000e-03   0.6775  0.911932\n",
       "5       5  0.671479  0.931370  1.000000e-03   0.6732  0.942200\n",
       "6       6  0.693868  0.869848  1.000000e-03   0.7209  0.804449\n",
       "7       7  0.707806  0.838481  1.000000e-03   0.7155  0.834796\n",
       "8       8  0.720702  0.804591  1.000000e-03   0.7378  0.754999\n",
       "9       9  0.731096  0.784383  1.000000e-03   0.7368  0.767846\n",
       "10     10  0.743372  0.753940  1.000000e-03   0.7359  0.773621\n",
       "11     11  0.749700  0.743873  1.000000e-03   0.7583  0.732452\n",
       "12     12  0.756849  0.716355  1.000000e-03   0.7509  0.733586\n",
       "13     13  0.762776  0.698444  1.000000e-03   0.7529  0.759714\n",
       "14     14  0.767042  0.692529  1.000000e-03   0.7469  0.758362\n",
       "15     15  0.815424  0.548257  3.162280e-04   0.8052  0.587185\n",
       "16     16  0.829462  0.503849  3.162280e-04   0.8164  0.559198\n",
       "17     17  0.835730  0.482571  3.162280e-04   0.8149  0.554202\n",
       "18     18  0.841297  0.469128  3.162280e-04   0.8116  0.575127\n",
       "19     19  0.848546  0.450038  3.162280e-04   0.8141  0.565250\n",
       "20     20  0.850168  0.441613  3.162280e-04   0.8214  0.549377\n",
       "21     21  0.854674  0.425162  3.162280e-04   0.8300  0.513885\n",
       "22     22  0.859821  0.414874  3.162280e-04   0.8281  0.535230\n",
       "23     23  0.862063  0.406118  3.162280e-04   0.8280  0.529385\n",
       "24     24  0.865288  0.394248  3.162280e-04   0.8268  0.532586\n",
       "25     25  0.880567  0.345766  1.000000e-04   0.8412  0.495981\n",
       "26     26  0.886274  0.334989  1.000000e-04   0.8410  0.500673\n",
       "27     27  0.889719  0.318496  1.000000e-04   0.8408  0.496158\n",
       "28     28  0.893023  0.310567  1.000000e-04   0.8454  0.499505\n",
       "29     29  0.898530  0.298940  3.162280e-05   0.8442  0.498027\n",
       "30     30  0.900473  0.289600  3.162280e-05   0.8452  0.500979\n",
       "31     31  0.901834  0.284791  1.000000e-05   0.8439  0.497917\n",
       "32     32  0.901013  0.287835  1.000000e-05   0.8456  0.494548\n",
       "33     33  0.903236  0.280388  1.000000e-05   0.8445  0.496280\n",
       "34     34  0.903617  0.280983  1.000000e-05   0.8464  0.497969\n",
       "35     35  0.902015  0.282265  1.000000e-05   0.8452  0.499375\n",
       "36     36  0.903496  0.281016  3.162280e-06   0.8454  0.498833\n",
       "37     37  0.904378  0.276829  3.162280e-06   0.8451  0.498132\n",
       "38     38  0.904758  0.277113  1.000000e-06   0.8446  0.497834\n",
       "39     39  0.903657  0.277681  1.000000e-06   0.8454  0.497718\n",
       "40     40  0.904217  0.278367  5.000000e-07   0.8455  0.497616\n",
       "41     41  0.904117  0.278353  5.000000e-07   0.8455  0.497612\n",
       "42     42  0.904638  0.279105  5.000000e-07   0.8450  0.497724\n",
       "43     43  0.904578  0.279349  5.000000e-07   0.8452  0.497759"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7ff6dab4bfd0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6da15e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(vgg[\"epoch\"],vgg[\"val_loss\"])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"vgg_loss.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7ff6da339b50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcFPWd//HXZ1BQbuVQhIAaEq/EMx4JmoyikUQRo4li\nEjXq+nOzQdxEjbjJ7wesMYnG5BdXEzdZj2BQkRzGCyJGGRUURE7lFhUFJ4qicinH8Nk/vtXSPfTM\n9PR0dVXPvJ+PRz26urq66tMF05+u72nujoiISEZV0gGIiEi6KDGIiEgOJQYREcmhxCAiIjmUGERE\nJIcSg4iI5Ig9MZjZEDNbYmbLzOyaPK/3N7N/mNl8M3vSzPaJOyYREWmYxdmPwcyqgGXAYOBNYBYw\n3N2XZO0zEXjI3cebWTVwsbtfEFtQIiLSqLjvGI4Blrv7SnffCkwAhtXb52BgKoC71+R5XUREyiju\nxNAXeCPr+apoW7Z5wFkAZnYW0NnM9og5LhERaUAaKp+vBqrNbDZwArAaqEs2JBGRtmuXmI+/Guif\n9bxftO1j7l4LnA1gZp2As919Xf0DmZkGdRIRKYK7W3P2j/uOYRYw0MwGmFl7YDjwUPYOZtbDzDJB\nXwvc2dDB3D1Vy+jRoxOPoRJiSmtcikkxtYW4ihFrYnD3OmAEMAVYCExw98VmNtbMTo92qwaWmtkS\noDdwfZwxiYhI4+IuSsLd/w4cUG/b6Kz1vwB/iTsOEREpTBoqnytWdXV10iHsJI0xQTrjUkyFUUyF\nS2tczRVrB7dSMjOvlFhFRNLCzPCUVT6LiEiFUWIQEZEcSgwiIpJDiUFERHIoMYiISA4lBhERyaHE\nICIiOZQYREQkhxKDiIjkUGIQEZEcSgwiIpJDiUFERHIoMYiISI7Y52MQkWRs3w6LF8P06fDSS9Cp\nE3TrFpbu3XMf6+pg/frcZcOG8Lh1a9hvjz12fuzWLez39tuwZk14zCxr1sCmTdC1646lW7cd6506\nwdq18M9/Qm1teMwstbWwZUtujNnrXbqEz1hXFz5nXV3uelUV7LJLWNq127GeWTp0gPbtw5K93r59\n+LybNoVl48Yd65s2wUcfgXs4j/vOS2OqqvKfs0MH2HXX8P7sz5D9eNJJcOKJ8f+fydCw2yIp4R6+\nlKqqdizNsWkTPP98SATTp8Nzz0GPHjBoEBx2WPhS++ADeP/98Ji93q5d+LLt0gU6d85d33XXsM97\n74X933tvx/r774f9evWC3r3Dkr2+++4huaxbl7t88EH40t1zT+jTB/beOyzZ6+3bNxzv+vVgFq5R\nu3ZhyaxXVYVruW3bjqWubsf61q0h6WzeHB6z1zdvDuft2DEsnTrlrnfoEI5vlrtktjWmri6cO995\nt2zZ8W9e/7O0awdf+hJ88YvF/b8qZthtJQaRFKithQsvhCef3PGLFPJ/SdR/zKyvXQuHHhoSwRe+\nEJa99072c0nyikkMKkoSSdgjj8Cll8Jll8GkSaGoA0JyyFes0FDxSa9e4Re6SEspMYgk5MMP4Yc/\nhIcfhj/9CY4/Pvf1TNHCLvorlTJTqySRBCxcCMccA2+9BXPn7pwURJKk3yIiWVasgFtvhSlToGfP\n3ArR7IrR3XbbueIxs7RvH1rOdOy4c4WkO9x2G4weDTfeCN/5TtOVliLlpsQgbZ47PPEE3HwzzJgB\nl1wCd98dWs9kN59ctGjH80yzxcyS3Xxxy5bQcmbr1p2bWW7aFFrGTJ8On/500p9cJD8lBmmzNm6E\nP/4Rbrkl/Oq/4gq4//7wS78UMgkiu5nlRx/BKaeEuwqRtFJzVWlT3EOZ/t13w/jxcMIJMHIkVFer\nSEdaJzVXFWnAqlVwzz3hDmHjRjj/fJg1C/bbL+nIRNJHdwySmA8+gJ/9DJ5+OvSA7dEjLD177ljv\n1QsOOAD22qv5v+jXr4e//jUkg7lz4eyzQ0IYNKj5vYpFKpV6PktF2LYNfv97GDsWTjsNLrggVPS+\n+y688054zCxvvw1LloTK3YMPhkMOCY+ZpX17eO21/Mvrr4fxZc4/H4YODS2JRNqaVCYGMxsC/JrQ\nZ+IOd7+h3uufAMYB3aN9rnX3yXmOo8RQ4dxDz96rroK+feGmm+Dwwwt775o1oVXQwoXhMbO+bVso\nDtp33/xL585xfRqRypC6xGBmVcAyYDDwJjALGO7uS7L2+R0wx91/Z2YHAZPcfaeSXyWGyjZ/Plx5\nJaxeHRLCV7+qyl6RcigmMcRd0noMsNzdV7r7VmACMKzePtuBrtF6d2B1zDFJmY0aBaeeCmedBQsW\nhOIjJQWR9Iq7VVJf4I2s56sIySLbWGCKmY0EOgInxxyTlNFbb8F//ze8+moYv19E0i8NbTPOA+5y\n908ApwHjE45HSugvf4HTT1dSEKkkcd8xrAb6Zz3vx85FRZcApwK4+wwz283Merr7O/UPNmbMmI/X\nq6urqa6uLnW8UmITJoQRREWkPGpqaqipqWnRMeKufG4HLCVUPtcCzwPnufvirH0eBSa6+7io8vlx\nd++X51iqfK4wq1aFmcNqazUEhEhSUlf57O51wAhgCrAQmODui81srJmdHu12FXCpmc0D7gEujDMm\nKZ+JE+HMM5UURCqNOrhJbI45Bq6/PgwaJyLJSN0dg7RdK1bAypWh57GIVBYlBonFxInw9a9rWkqR\nSqTEILGYMAGGD086ChEphhKDlNyiRWEAvEGDko5ERIqhxCAld//9cM45GtpapFLpT1dKyl3FSCKV\nTolBSmrePNi6FY4+OulIRKRYSgxSUvffH+4WNHqqSOVSBzcpGfcwac5DD8GhhyYdjYiAOrhJwmbO\nhI4d4bOfTToSEWkJJYY2ZNMm2LAhvuNPmADnnqtiJJFKp8TQRrzzDhxxRJhr+fzz4fHHoa6udMev\nqwu9nc89t3THFJFkKDG0AZs2wdChcPbZ8PLLocXQqFGw775w7bWwZEnD73WHdetg+XL48MOG93vm\nGdhrLzjwwJKHLyJlpsrnVm7bNvja12DPPeEPf8gt5nnpJRg3DsaPh098IszF/MEH8OabYQ6FzCNA\n796hN3N1dUgyp50GffrsONZ3vxsSzTXXlPHDiUiTiql8VmJoxdzhssvg9dfh4Ydh113z77dtG/zj\nH1BTAz17wj77hKVPn/DYpUvYb+1amDwZHnkEHnsMBg4M03Z+9athef75kBxEJD2UGCTH2LEhIdTU\nQOfOpT321q0wbVo4/sMPw957h+IkEUkXJQb52O23w89+Bs8+G8r+4+QeFo2NJJI+SgwChKKeSy+F\np5+GT30q6WhEJEnFJAZNo5IydXVw002hErhLl/xL585h6dQpPO62245K5Zkz4eKLQ3JQUhCRYigx\npIg7jBwJCxbAqaeG5LBqFaxfv2PZsCE8btwYlg0bYMuWkCQ6dQpNU++9N8y3LCJSDCWGFPnpT2H6\n9FAE1LVr4e+rq9uRJHbZJTQtFREplhJDStx1V6gwfvbZ5iUFgHbtwnua+z4RkXyUGFLg0UdDD+Sn\nnsrtNCYikgQlhoTNnAkXXRT6AhxwQNLRiIhorKTYLFsGb73V9D5nnhmKkY49tjxxiYg0Rf0YYvDR\nRzBgQGgtNGAAnHJKWE44AXbfPexTWwuDBsGPfxyal4qIxEET9aTEn/4Ehx8Oa9bAbbeFvgfXXRda\nC518MtxwQxhb6OKLlRREJH10xxCDY48NdwJDh+ZuX7cujFv0+ONhmIof/UiT2ohIvDQkRgrMmgXf\n+AasWBGakYqIJCmVRUlmNsTMlpjZMjPbabR+M/uVmc01szlmttTM1sYdU5x+85swN4GSgohUqljv\nGMysClgGDAbeBGYBw90975xhZjYCONzd/yXPa6m/Y3jnnTA+0fLlYV4DEZGkpfGO4RhgubuvdPet\nwARgWCP7nwfcF3NMsbnzThg2TElBRCpb3B3c+gJvZD1fRUgWOzGz/sC+wJMxxxSLujr47W9DiyQR\nkUqWpp7Pw4E/N1ZeNGbMmI/Xq6urqa6ujj+qAk2aFFoaHX100pGISFtWU1NDTU1Ni44Rdx3DccAY\ndx8SPR8FuLvfkGffOcC/ufuMBo6V6jqGIUPgm9+ECy5IOhIRkR1iqWMws05RJTJm9mkzO8PMGphW\nfiezgIFmNsDM2hPuCh7Kc44Dge4NJYW0W7YM5syBc85JOhIRkZYrpPL5aWA3M+sLTAHOB/5QyMHd\nvQ4YEb1vITDB3Reb2VgzOz1r13MJFdMV6bbb4JJLwkxqIiKVrsmiJDOb4+5HmtnlwO7ufqOZzXP3\nw8sT4sdxpLIoaeNG6N8/3DEMGJB0NCIiueJqrmpm9nngW8Cj0TZ134rccw8cf7ySgoi0HoUkhn8H\nrgUecPeFZrY/MDXesCqDe+jpPGJE0pGIiJROs1olRZXQnd19XXwhNXjushclbdwY5lDu0CH/69Om\nhbqFxYuhSuPUikgKxdUq6V4z62pmnYCXgEVmdnWxQVYKdxg8GLp3D6OljhwZio1efjm8BnDrrfC9\n7ykpiEjrUshX2sHRHcKZwGRgP0LLpFZtyhRYvz7MqXDTTaGC+W9/g5NOgl69wnwKjz0GF16YdKQi\nIqVVSKukhcDhwL3Are7+lJnNd/fDyhFgVhxlK0pyDxXKI0bAeeft/HptbZireddd4bTTyhKSiEhR\niilKKmRIjN8BrwHzgafNbABQ9jqGcpo6NYyU2lCHtT59wlzNIiKtUVFDYpjZLu6+LYZ4Gjtn2e4Y\nqqvDlJsa3kJEKl1clc/dosl0XoiWXwKdio4y5Z5+GlatCuMeiYi0RYVUPt8JrAfOiZZ1wF1xBpWk\n666Da68NzVRFRNqiQiqfdxr+orUOifHcc6GyedkyaN8+1lOJiJRFXENifGhmx2edZBDwYXODqwTX\nXQejRikpiEjbVkiByXeBcWbWDTBgLfCdOINKwgsvwIsvwgMPJB2JiEiyCm6VZGZdAZIYDiM6f6xF\nScOGwcknw+WXx3YKEZGyK6YoqcHEYGY/aOyN7v6r5pyopeJMDPPmhZ7MK1bA7rvHcgoRkUSUuoNb\nlxbGUzF+8hO46iolBRERiHnO51KK647hpZdCEdKKFdCp1fbOEJG2Kq5WSa3a9dfD97+vpCAiktGm\n7xjeeAOOOAJefRW6tJmCMxFpS3TH0EyTJsGQIUoKIiLZmuzHYGYdgLOBfbP3d/f/jC+s8pg0Cc49\nN+koRETSpZAhMf4OfADMBuoy2939l/GGtlMcJS1K2rwZeveGV16BHj1KdlgRkVSJaz6Gfu4+pMiY\nUuuZZ+Dgg5UURETqK6SO4Vkz+2zskZTZpEmhU5uIiOQqpChpETAQeBXYTBgvyd390PjDy4mjpEVJ\nBx0E48fDUUeV7JAiIqkTV1HSV4qMJ7VeeQXWrg1NVUVEJFeTRUnuvhLoDgyNlu7Rtoo1eTJ85StQ\n1aYb64qI5FfI1J5XAPcAvaNlvJlV9BikkyerfkFEpCGF1DEsAD7v7huj552A5wqtYzCzIcCvCUno\nDne/Ic8+5wCjge3AfHf/dp59SlLH8OGHsNdesHIl7LFHiw8nIpJqcdUxGFn9F6L1gk5iZlXArcBg\n4E1glpk96O5LsvYZCFxDSD7rzKxnocEX46mn4LDDlBRERBpSSGK4C5hpZpm5zc4E7ijw+McAyzN1\nEmY2ARgGLMna51LgN5kJgNz9nQKPXRQVI4mINK6QyudfARcRpvRcC1zk7r8u8Ph9gTeynq+KtmX7\nNHCAmU0zs2fN7NQCj12USZNCxbOIiOTX4B2DmXWNinb2BF6Llsxre7r72hLGMBD4ItAfeNrMPpNv\nCtExY8Z8vF5dXU11dXWzTrR8OWzcGIqSRERao5qaGmpqalp0jMam9nzE3U83s1eB7J0yHdz2b/Lg\nZscBYzJDapjZqOi9N2Ttcxsww93HRc//AVzj7rPrHavFlc//9V+wYAHcfnuLDiMiUjFKOuy2u58e\nPe7n7vtnLfsVkhQis4CBZjbAzNoDw4GH6u3zN+DE6AP0BD4FvNKcD1EoFSOJiDStkH4MTxSyLR93\nrwNGAFOAhcAEd19sZmPNLJN4HgPeNbOFwBPAVe7+XjM+Q0E2bYLp08M0niIi0rDGipJ2AzoCU4Fq\ndjRR7Qr83d0PLEeAWfG0qCjp0Ufhpptg6tQSBiUiknKl7sdwGfDvwD6EuRgyB15H6JtQUVSMJCJS\nmEJ6Pl/u7reUKZ7G4ij6jsEd9t8fHn4YPvOZEgcmIpJisfR8dvdbzOwzwMHAblnb725+iMlYuhTq\n6uCQQ5KOREQk/QqZ83k0oY7hYGASYRjuaUDFJIZMMZI1K2eKiLRNhQw8/XXCWEf/dPeLgMOAbrFG\nVWKarU1EpHCFJIYP3X07sM3MugJvA5+IN6zS2bABZs6EwYOTjkREpDIUMojeC2bWHfgfQuukDcBz\nsUZVQk88AcceC507Jx2JiEhlaLJVUs7OZvsCXd19QVwBNXLuololXX459O8PV18dQ1AiIilX0lZJ\nZnZkY6+5+5zmnCgpL7wAX/960lGIiFSOxno+Z/oI7wZ8DphP6OR2KPCCu3++LBHuiKfZdwzbtkG3\nblBbC127xhSYiEiKlXoQvRPd/USgFjjS3T/n7kcBRwCrWxZqeSxZAv36KSmIiDRHIa2SDnD3FzNP\n3P0l4KD4Qiqd2bPhyAYLxEREJJ9CWiUtMLPbgfHR828BZa98Lsbs2XDUUUlHISJSWQq5Y7iIMGT2\nFdGyKNqWekoMIiLN16zmqklqbuVzXV2oeF69OjyKiLRFpW6uOtHdzzGzF8md2hMAdz+0iBjLZskS\n6NNHSUFEpLkaq2O4Ino8vRyBlNqcOSpGEhEpRoOJwd1ro8eV5QundFS/ICJSnMaKktaTpwiJ0MnN\n3T3VvQNmz4ahQ5OOQkSk8rTKyue6OujeHV5/HfbYI+bARERSLJYZ3LIO3pvcGdxeb86JymnZMujd\nW0lBRKQYTfZjMLMzzGw58CrwFPAaMDnmuFpE9QsiIsUrpIPbdcBxwDJ3348wm9uMWKNqIbVIEhEp\nXiGJYau7vwtUmVmVu08ljLaaWrpjEBEpXiF1DO+bWWfgaeAeM3sb2BhvWMXbvh3mzoUjjkg6EhGR\nylTIHcMw4EPg+8DfgRVAahuCLl8OPXqERUREmq+xfgy/Ae519+lZm8fFH1LLqBhJRKRlGrtjWAbc\nZGavmdmNZlYRhTNKDCIiLdPYDG43R9N3fgl4F7jTzJaY2Wgz+3ShJzCzIdH7lpnZNXlev9DM3jaz\nOdFycVGfJKIWSSIiLdOsns/RXcOdwKHu3q6A/asIdx6DgTeBWcBwd1+Stc+FwFHuPrKJYzXZ83n7\n9tCpbcUK6NmzyY8jItLqlXTO56yD7mJmQ83sHkLHtqXAWQUe/xhgubuvdPetwARCZfZOpyk04Mas\nWBGGwlBSEBEpXoOJwcxOMbM7gVXApcCjwCfdfbi7P1jg8fsCb2Q9XxVtq+8sM5tnZhPNrF+Bx96J\n6hdERFqusX4M1wL3Ale6+3sxxvAQofXTVjP7P4SWT4Pz7ThmzJiP16urq6murs55XYlBRNq6mpoa\nampqWnSMWEdXNbPjgDHuPiR6PoowZPcNDexfBax19+55XmuyjuGkk+CHP4QhQ1oeu4hIaxBLHUML\nzQIGmtkAM2sPDCfcIXzMzPbOejoMWFTMidzVIklEpBQKHna7GO5eZ2YjgCmEJHSHuy82s7HALHd/\nBBhpZmcAW4G1wHeKOdcrr0DXrtCrV4mCFxFpo1rNRD0TJ8J998EDD5QxKBGRlEtjUVLZzJ4NRx6Z\ndBQiIpWvVSUG1S+IiLRcqyhKcg+jqS5eDHvtVebARERSrM0WJb32GnTsqKQgIlIKrSIxqBhJRKR0\nlBhERCRHq0kMapEkIlIaFV/57B46tb34IvTpk0BgIiIp1iYrn5ctg06dlBREREql4hPDtGlwwglJ\nRyEi0nq0isRw/PFJRyEi0npUfGJ45hndMYiIlFJFJ4baWli7Fg46KOlIRERaj4pODNOnw6BBUFXR\nn0JEJF0q+itVxUgiIqVX0YlBFc8iIqVXsR3c1q2DffaBd9+FDh0SDExEJMXaVAe3GTPC+EhKCiIi\npVWxiUHFSCIi8ajYxKCKZxGReFRkHcOWLWHGtlWroFu3hAMTEUmxNlPHMGcODByopCAiEoeKTAyq\nXxARiY8Sg4iI5Ki4Oobt26F3b5g/H/r2TToqEZF0axN1DEuXQteuSgoiInGpuMTwzDMqRhIRiVPF\nJQbN2CYiEq/YE4OZDTGzJWa2zMyuaWS/s81su5kd2djxVPEsIhKvWBODmVUBtwKnAocA55nZgXn2\n6wyMBGY0drzVq2H9ejhwpyOIiEipxH3HcAyw3N1XuvtWYAIwLM9+1wE/BzY3drBp08LEPNas+nUR\nEWmOuBNDX+CNrOerom0fM7MjgH7uPrmpg6niWUQkfolWPpuZAb8Crsze3ND+qngWEYnfLjEffzXQ\nP+t5v2hbRhdC3UNNlCT2Bh40szPcfU79gy1aNIZHHoHJk6G6uprq6uoYQxcRqTw1NTXU1NS06Bix\n9nw2s3bAUmAwUAs8D5zn7osb2H8q8AN3n5vnNT/xROfJJ2MLV0Sk1Uldz2d3rwNGAFOAhcAEd19s\nZmPN7PR8b6GRoiTVL4iIxK+ixkp67DHny19OOhIRkcpRzB1DRSWGdeucLl2SjkREpHK0+sRQKbGK\niKRF6uoYRESk8igxiIhIDiUGERHJocQgIiI5lBhERCSHEoOIiORQYhARkRxKDCIikkOJQUREcigx\niIhIDiUGERHJocQgIiI5lBhERCSHEoOIiORQYhARkRxKDCIikkOJQUREcigxiIhIDiUGERHJocQg\nIiI5lBhERCSHEoOIiORQYhARkRxKDCIikkOJQUREcigxiIhIjtgTg5kNMbMlZrbMzK7J8/plZrbA\nzOaa2dNmdmDcMYmISMNiTQxmVgXcCpwKHAKcl+eL/x53P9TdjwB+Afz/OGMqpZqamqRD2EkaY4J0\nxqWYCqOYCpfWuJor7juGY4Dl7r7S3bcCE4Bh2Tu4+4asp52B7THHVDJp/E+QxpggnXEppsIopsKl\nNa7m2iXm4/cF3sh6voqQLHKY2b8BPwB2BU6KOSYREWlEKiqf3f237j4QuAb4v0nHIyLSlpm7x3dw\ns+OAMe4+JHo+CnB3v6GB/Q14z92753ktvkBFRFoxd7fm7B93UdIsYKCZDQBqgeHAedk7mNlAd385\neno6sCzfgZr7wUREpDixJgZ3rzOzEcAUQrHVHe6+2MzGArPc/RFghJmdDGwB3gMujDMmERFpXKxF\nSSIiUnlSUfnclKY6ySXBzF4zs/lRx7znE4rhDjN7y8wWZG3bw8ymmNlSM3vMzLqlIKbRZrbKzOZE\ny5Ayx9TPzJ40s4Vm9qKZjYy2J3at8sR0ebQ96WvVwcxmRv+vXzSz0dH2fc1sRvQ3eJ+ZxV0MXUhM\nd5nZK9H2OWZ2aLliyoqtKjr3Q9HzxK5TvZjmZsX0h2ZfJ3dP9UJIXi8DAwjNWecBB6YgrleAPRKO\n4XjgcGBB1rYbgB9G69cAP09BTKOBHyR4nfYGDo/WOwNLgQOTvFaNxJTotYri6Rg9tgNmAMcC9wPf\niLbfBlyWgpjuAs5K+Fp9HxgPPBQ9T/Q6NRDTXcDXmnOMSrhjaLKTXEKMhO+43H0aoV4m2zBgXLQ+\nDjgzBTFBuF6JcPd/uvu8aH0DsBjoR4LXqoGY+kYvJ9rQwt03RasdCPWQDpwI/CXaPg74WsIxZTrC\nJnatzKwf8FXg9qzNJ5HgdWogJmjmd1UlJIZ8neT6NrBvOTnwmJnNMrNLkw4mS293fwvClw/QO+F4\nMr5nZvPM7PZyF29lM7N9CXc0M4C90nCtsmKaGW1K9FpliiKAfwKPAyuA990982W8CtgnyZjcfVb0\n0k+ia/VLM9u1nDERhu+5mvBdgJn1IDS3T+w61Y8pS7OuUyUkhrQa5O6fI2Tn75nZ8UkH1IA0tC74\nLfBJdz+c8If9qySCMLPOwJ+BK6Jf6fWvTdmvVZ6YEr9W7r7dw9hl/Qh37IkPbFk/JjM7GBjl7gcB\nRwM9CMWBZWFmpwFvRXd92XctSd7BNBRTs69TJSSG1UD/rOf9om2Jcvfa6HEN8AB5hvpIyFtmtheA\nme0NvJ1wPLj7Go8KO4H/IfwHLauoEvDPwB/d/cFoc6LXKl9MabhWGe6+DqgBPg90jwbFhAT/BrNi\nGpJ1t7eVUI5ezr/BQcAZZvYKcB+hCOlmoFuC12mnmMzs7mKuUyUkho87yZlZe0InuYeSDMjMOka/\n9DCzTsCXgZeSCofcXwcPAd+J1i8EHqz/hjLIiSn60s04i2Su1Z3AIne/OWtb0tdqp5iSvlZm1jNT\nfGVmuwOnAIuAqcA3ot3Keq0aiGlJ5lqZmRHqh8p2rdz9P9y9v7vvT/hOetLdv02C16mBmC4o5jqV\nvSlVc3kDneQSDmsv4AELw3TsQhg6fEq5gzCze4FqoIeZvU5o0fJz4E9mdjGwEjgnBTGdaGaHEyoM\nXwMuK3NMg4BvAS9G5dQO/AehVdLEJK5VIzF9M8lrBfQBxkW/equA+919kpktBiaY2XXAXOCOFMT0\nhJn1JPwImQf8axljasgokrtODbmnuddJHdxERCRHJRQliYhIGSkxiIhIDiUGERHJocQgIiI5lBhE\nRCSHEoOIiORQYpBWwcy2m9kvsp5faWb/rwTHbW9mj0fDFX+j3muZYZ/nREMaT2vp+eodf6qZHVnK\nY4oUIvUd3EQKtBk4y8x+5u5rS3jcIwnzlDf0BX2luz9QwvOJJE53DNJabAN+D/yg/gvRcCpPRKNL\nPh4NTVx/nz3M7AELky89a2afMbNewB+Bo6O7gv3ynHenvyELk+3cHR1nqZn9S9Zrv7Aw2cx8Mzsn\na/s1ZrYguvP4adbhzrEwSc2SqLc0ZnZwtG1O9Jk+2ZwLJdIU3TFIa+HAbwhDTNxQ77VbgLvcfbyZ\nXRQ9rz9O/lhgjrt/zcxOJAxsd0T0pX6lu5/RwHl/YWY/Igw38JK7nx9t/yxhMpkuwFwzewT4AnCo\nu3/WzHpvujHeAAAB0klEQVQDs8zsKeAIYChwtLtvNrPuWcdv5+7HmtlXgDGEcYL+Ffi1u2dmCGvX\nnAsl0hQlBmk13H2DmY0DrgA+zHrp8+xIBH8Ebszz9uMJg9bh7lPNbM/MQIlNuMrd/5pn+4PuvgV4\n18yeJCSJ4wmjXuLub5tZDWGkyy8REtfm6LX3s46TOfZswiyGAM8BP4rufB5w95cLiFOkYCpKktbm\nZuASoFPWtkLmXai/raXj6mcfz9gx41j9czQ1WNnm6LGO6Iecu99HuMP4CJhkZtUtilSkHiUGaS0M\nwN3fAyYSkkPGs8B50fq3gWfyvP+Z6DWiL9o10cQ5BZ03j2FRi6YehDuCWdE5zrUwG1kv4ATgecIs\naRdFQ0pjZns0di4z28/dX3X3WwjDOjc9ubtIM6goSVqL7F/evwS+l7VtJHCXmV0FrAEuyvP+scCd\nZjYf2EgYS78QN2bVMTg7JkFZQJhQpgfwn9HUoQ+Y2XHAfMIdxNXu/jZhitjDgBfMbDMwCfgxDd/p\nnGNm5wNbgVrg+gJjFSmIht0WKTEzGw2sd/dEpjAVaSkVJYmISA7dMYiISA7dMYiISA4lBhERyaHE\nICIiOZQYREQkhxKDiIjkUGIQEZEc/ws692YRu+XgEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6da238cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(vgg[\"epoch\"],vgg[\"val_acc\"])\n",
    "figure = plt.gcf()\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Validation loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figure.savefig(\"vgg_Accuracy.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6da361750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(vgg['epoch'],vgg['val_loss'],label = 'VGG')\n",
    "pylab.plot(simplecnn['epoch'],simplecnn['val_loss'],label = 'SimpleCNN')\n",
    "pylab.xlabel(\"No of Epochs\")\n",
    "pylab.ylabel(\"Validation Loss\")\n",
    "figure = pylab.legend(loc = 'upper right')\n",
    "pylab.savefig(\"VGG_and_SimpleCNN_val_loss\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6da2c0990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(vgg['epoch'],vgg['val_acc'],label = 'VGG')\n",
    "pylab.plot(simplecnn['epoch'],simplecnn['val_acc'],label = 'S-CNN')\n",
    "pylab.xlabel(\"No of Epochs\")\n",
    "pylab.ylabel(\"Validation Loss\")\n",
    "figure = pylab.legend(loc = 'upper right')\n",
    "pylab.savefig(\"VGG_and_SimpleCNN_val_acc\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}