sign-language-mnist-cnn-accuracy-100.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/jarredpriester/sign-language-mnist-cnn-accuracy-100?scriptVersionId=105421648\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"markdown","id":"fe8f162b","metadata":{"papermill":{"duration":0.013451,"end_time":"2022-09-11T15:49:27.108586","exception":false,"start_time":"2022-09-11T15:49:27.095135","status":"completed"},"tags":[]},"source":["# Sign Language Alphabet Detection and Classification\n","\n","### Jarred Priester\n","### 9/11/22"]},{"cell_type":"markdown","id":"1d293ce1","metadata":{"papermill":{"duration":0.011132,"end_time":"2022-09-11T15:49:27.131706","exception":false,"start_time":"2022-09-11T15:49:27.120574","status":"completed"},"tags":[]},"source":["1. Introduction\n","    - 1.1 Overview\n","    - 1.2 About the Data\n","    - 1.3 Purpose of this Project\n","2. Downloading the Data\n","    - 2.1 Downloading the Libraries\n","    - 2.2 Downloading the Training Images\n","    - 2.3 Downloading the Test Images\n","    - 2.4 Checking for Missing Data\n","3. Processing the Images\n","    - 3.1 Creating our X and our Y\n","    - 3.2 Scaling Our Images\n","    - 3.3 Reshaping Our Images\n","    - 3.4 Distribution of Labels\n","4. Viewing the Images\n","    - 4.1 Training Images\n","    - 4.2 Test Images\n","5. CNN Model\n","    - 5.1 Spliting Our Training Data\n","    - 5.2 Creating Our Model\n","    - 5.3 Training Our Model\n","6. Results\n","    - 6.1 Classification Report\n","    - 6.2 Brief Thoughts on Results\n","7. Conclusion\n","    - 7.1 Recap\n","    - 7.2 Future Work"]},{"cell_type":"markdown","id":"2455cbbf","metadata":{"papermill":{"duration":0.010994,"end_time":"2022-09-11T15:49:27.154178","exception":false,"start_time":"2022-09-11T15:49:27.143184","status":"completed"},"tags":[]},"source":["# 1. Introduction"]},{"cell_type":"markdown","id":"07d566d9","metadata":{"papermill":{"duration":0.011013,"end_time":"2022-09-11T15:49:27.178645","exception":false,"start_time":"2022-09-11T15:49:27.167632","status":"completed"},"tags":[]},"source":["### 1.1 Overview"]},{"cell_type":"markdown","id":"9f38d3f6","metadata":{"papermill":{"duration":0.010993,"end_time":"2022-09-11T15:49:27.201095","exception":false,"start_time":"2022-09-11T15:49:27.190102","status":"completed"},"tags":[]},"source":["Artifical Intelligence is a tool that has the potential to make our lives easier and, in some cases, remove obstacles. For example, artifical intelligence could be used to take video feeds of people using sign language and translating the signs into words. This could help remove communication obstacles with sign lanuage users with their coworkers and personal relationships.\n","\n","Such an application would consist of computer vision and natural language processing, but the first step would be to accurately detect and classify sign language signs using computer vision.\n","\n","In this notebook we will be applying a convolutional neural network to classify a dataset of American Sign Language alphabet images. "]},{"cell_type":"markdown","id":"0eb944f2","metadata":{"papermill":{"duration":0.011146,"end_time":"2022-09-11T15:49:27.223708","exception":false,"start_time":"2022-09-11T15:49:27.212562","status":"completed"},"tags":[]},"source":["### 1.2 About the Data"]},{"cell_type":"markdown","id":"da9f0cd2","metadata":{"papermill":{"duration":0.011502,"end_time":"2022-09-11T15:49:27.247287","exception":false,"start_time":"2022-09-11T15:49:27.235785","status":"completed"},"tags":[]},"source":["The dataset consist of 27,455 training images and 7,172 test images. Each image is grayscaled and has a 28 x 28 pixel structure. Each images includes the label of which letter in the alphabet the image represents. There are 24 letters in this dataset, 'j' and 'z' are not included because they both involve motion."]},{"cell_type":"markdown","id":"40e10698","metadata":{"papermill":{"duration":0.011926,"end_time":"2022-09-11T15:49:27.271408","exception":false,"start_time":"2022-09-11T15:49:27.259482","status":"completed"},"tags":[]},"source":["### 1.3 Purpose of this Project"]},{"cell_type":"markdown","id":"25a3b06c","metadata":{"papermill":{"duration":0.011231,"end_time":"2022-09-11T15:49:27.294345","exception":false,"start_time":"2022-09-11T15:49:27.283114","status":"completed"},"tags":[]},"source":["Our task will be to create a CNN model that can accuractly predict the alphabet letter for each image. I am setting out to achive an accuracy of at least 98%."]},{"cell_type":"markdown","id":"e6a7b485","metadata":{"papermill":{"duration":0.011204,"end_time":"2022-09-11T15:49:27.317178","exception":false,"start_time":"2022-09-11T15:49:27.305974","status":"completed"},"tags":[]},"source":["# 2. Downloading the Data"]},{"cell_type":"markdown","id":"5236d9be","metadata":{"papermill":{"duration":0.011804,"end_time":"2022-09-11T15:49:27.340661","exception":false,"start_time":"2022-09-11T15:49:27.328857","status":"completed"},"tags":[]},"source":["### 2.1 Downloading the Libraries"]},{"cell_type":"code","execution_count":1,"id":"b78550a8","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:27.367Z","iopub.status.busy":"2022-09-11T15:49:27.36646Z","iopub.status.idle":"2022-09-11T15:49:36.677223Z","shell.execute_reply":"2022-09-11T15:49:36.675841Z"},"papermill":{"duration":9.327762,"end_time":"2022-09-11T15:49:36.680226","exception":false,"start_time":"2022-09-11T15:49:27.352464","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/input/sign-language-mnist/sign_mnist_test.csv\n","/kaggle/input/sign-language-mnist/amer_sign2.png\n","/kaggle/input/sign-language-mnist/amer_sign3.png\n","/kaggle/input/sign-language-mnist/sign_mnist_train.csv\n","/kaggle/input/sign-language-mnist/american_sign_language.PNG\n","/kaggle/input/sign-language-mnist/sign_mnist_test/sign_mnist_test.csv\n","/kaggle/input/sign-language-mnist/sign_mnist_train/sign_mnist_train.csv\n"]}],"source":["#importing libraries\n","import numpy as np \n","import pandas as pd\n","import random as rd\n","\n","#data visualization\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","import seaborn as sns\n","import plotly.express as px\n","from PIL import Image\n","\n","#for the CNN model\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import classification_report\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras import layers\n","from tensorflow.keras.layers.experimental import preprocessing\n","from keras.preprocessing.image import ImageDataGenerator\n","\n","#setting seed for reproducability\n","from numpy.random import seed\n","seed(10)\n","tf.random.set_seed(20)\n","\n","#for viewing filenames\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))"]},{"cell_type":"markdown","id":"c89d5aaf","metadata":{"papermill":{"duration":0.012222,"end_time":"2022-09-11T15:49:36.704472","exception":false,"start_time":"2022-09-11T15:49:36.69225","status":"completed"},"tags":[]},"source":["### 2.2 Downloading the Training Images"]},{"cell_type":"code","execution_count":2,"id":"2f6a23c2","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:36.733136Z","iopub.status.busy":"2022-09-11T15:49:36.731519Z","iopub.status.idle":"2022-09-11T15:49:40.422731Z","shell.execute_reply":"2022-09-11T15:49:40.42182Z"},"papermill":{"duration":3.709006,"end_time":"2022-09-11T15:49:40.426237","exception":false,"start_time":"2022-09-11T15:49:36.717231","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>label</th>\n","      <th>pixel1</th>\n","      <th>pixel2</th>\n","      <th>pixel3</th>\n","      <th>pixel4</th>\n","      <th>pixel5</th>\n","      <th>pixel6</th>\n","      <th>pixel7</th>\n","      <th>pixel8</th>\n","      <th>pixel9</th>\n","      <th>...</th>\n","      <th>pixel775</th>\n","      <th>pixel776</th>\n","      <th>pixel777</th>\n","      <th>pixel778</th>\n","      <th>pixel779</th>\n","      <th>pixel780</th>\n","      <th>pixel781</th>\n","      <th>pixel782</th>\n","      <th>pixel783</th>\n","      <th>pixel784</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3</td>\n","      <td>107</td>\n","      <td>118</td>\n","      <td>127</td>\n","      <td>134</td>\n","      <td>139</td>\n","      <td>143</td>\n","      <td>146</td>\n","      <td>150</td>\n","      <td>153</td>\n","      <td>...</td>\n","      <td>207</td>\n","      <td>207</td>\n","      <td>207</td>\n","      <td>207</td>\n","      <td>206</td>\n","      <td>206</td>\n","      <td>206</td>\n","      <td>204</td>\n","      <td>203</td>\n","      <td>202</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>6</td>\n","      <td>155</td>\n","      <td>157</td>\n","      <td>156</td>\n","      <td>156</td>\n","      <td>156</td>\n","      <td>157</td>\n","      <td>156</td>\n","      <td>158</td>\n","      <td>158</td>\n","      <td>...</td>\n","      <td>69</td>\n","      <td>149</td>\n","      <td>128</td>\n","      <td>87</td>\n","      <td>94</td>\n","      <td>163</td>\n","      <td>175</td>\n","      <td>103</td>\n","      <td>135</td>\n","      <td>149</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>187</td>\n","      <td>188</td>\n","      <td>188</td>\n","      <td>187</td>\n","      <td>187</td>\n","      <td>186</td>\n","      <td>187</td>\n","      <td>188</td>\n","      <td>187</td>\n","      <td>...</td>\n","      <td>202</td>\n","      <td>201</td>\n","      <td>200</td>\n","      <td>199</td>\n","      <td>198</td>\n","      <td>199</td>\n","      <td>198</td>\n","      <td>195</td>\n","      <td>194</td>\n","      <td>195</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2</td>\n","      <td>211</td>\n","      <td>211</td>\n","      <td>212</td>\n","      <td>212</td>\n","      <td>211</td>\n","      <td>210</td>\n","      <td>211</td>\n","      <td>210</td>\n","      <td>210</td>\n","      <td>...</td>\n","      <td>235</td>\n","      <td>234</td>\n","      <td>233</td>\n","      <td>231</td>\n","      <td>230</td>\n","      <td>226</td>\n","      <td>225</td>\n","      <td>222</td>\n","      <td>229</td>\n","      <td>163</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>13</td>\n","      <td>164</td>\n","      <td>167</td>\n","      <td>170</td>\n","      <td>172</td>\n","      <td>176</td>\n","      <td>179</td>\n","      <td>180</td>\n","      <td>184</td>\n","      <td>185</td>\n","      <td>...</td>\n","      <td>92</td>\n","      <td>105</td>\n","      <td>105</td>\n","      <td>108</td>\n","      <td>133</td>\n","      <td>163</td>\n","      <td>157</td>\n","      <td>163</td>\n","      <td>164</td>\n","      <td>179</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 785 columns</p>\n","</div>"],"text/plain":["   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n","0      3     107     118     127     134     139     143     146     150   \n","1      6     155     157     156     156     156     157     156     158   \n","2      2     187     188     188     187     187     186     187     188   \n","3      2     211     211     212     212     211     210     211     210   \n","4     13     164     167     170     172     176     179     180     184   \n","\n","   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n","0     153  ...       207       207       207       207       206       206   \n","1     158  ...        69       149       128        87        94       163   \n","2     187  ...       202       201       200       199       198       199   \n","3     210  ...       235       234       233       231       230       226   \n","4     185  ...        92       105       105       108       133       163   \n","\n","   pixel781  pixel782  pixel783  pixel784  \n","0       206       204       203       202  \n","1       175       103       135       149  \n","2       198       195       194       195  \n","3       225       222       229       163  \n","4       157       163       164       179  \n","\n","[5 rows x 785 columns]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["#downloading the training data\n","train = pd.read_csv(\"../input/sign-language-mnist/sign_mnist_train.csv\")\n","train.head()"]},{"cell_type":"markdown","id":"a55088da","metadata":{"papermill":{"duration":0.01174,"end_time":"2022-09-11T15:49:40.456095","exception":false,"start_time":"2022-09-11T15:49:40.444355","status":"completed"},"tags":[]},"source":["### 2.3 Downloading the Test Images"]},{"cell_type":"code","execution_count":3,"id":"cdb45a54","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:40.481406Z","iopub.status.busy":"2022-09-11T15:49:40.480971Z","iopub.status.idle":"2022-09-11T15:49:41.44751Z","shell.execute_reply":"2022-09-11T15:49:41.445918Z"},"papermill":{"duration":0.982546,"end_time":"2022-09-11T15:49:41.450629","exception":false,"start_time":"2022-09-11T15:49:40.468083","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>label</th>\n","      <th>pixel1</th>\n","      <th>pixel2</th>\n","      <th>pixel3</th>\n","      <th>pixel4</th>\n","      <th>pixel5</th>\n","      <th>pixel6</th>\n","      <th>pixel7</th>\n","      <th>pixel8</th>\n","      <th>pixel9</th>\n","      <th>...</th>\n","      <th>pixel775</th>\n","      <th>pixel776</th>\n","      <th>pixel777</th>\n","      <th>pixel778</th>\n","      <th>pixel779</th>\n","      <th>pixel780</th>\n","      <th>pixel781</th>\n","      <th>pixel782</th>\n","      <th>pixel783</th>\n","      <th>pixel784</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>6</td>\n","      <td>149</td>\n","      <td>149</td>\n","      <td>150</td>\n","      <td>150</td>\n","      <td>150</td>\n","      <td>151</td>\n","      <td>151</td>\n","      <td>150</td>\n","      <td>151</td>\n","      <td>...</td>\n","      <td>138</td>\n","      <td>148</td>\n","      <td>127</td>\n","      <td>89</td>\n","      <td>82</td>\n","      <td>96</td>\n","      <td>106</td>\n","      <td>112</td>\n","      <td>120</td>\n","      <td>107</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>5</td>\n","      <td>126</td>\n","      <td>128</td>\n","      <td>131</td>\n","      <td>132</td>\n","      <td>133</td>\n","      <td>134</td>\n","      <td>135</td>\n","      <td>135</td>\n","      <td>136</td>\n","      <td>...</td>\n","      <td>47</td>\n","      <td>104</td>\n","      <td>194</td>\n","      <td>183</td>\n","      <td>186</td>\n","      <td>184</td>\n","      <td>184</td>\n","      <td>184</td>\n","      <td>182</td>\n","      <td>180</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>10</td>\n","      <td>85</td>\n","      <td>88</td>\n","      <td>92</td>\n","      <td>96</td>\n","      <td>105</td>\n","      <td>123</td>\n","      <td>135</td>\n","      <td>143</td>\n","      <td>147</td>\n","      <td>...</td>\n","      <td>68</td>\n","      <td>166</td>\n","      <td>242</td>\n","      <td>227</td>\n","      <td>230</td>\n","      <td>227</td>\n","      <td>226</td>\n","      <td>225</td>\n","      <td>224</td>\n","      <td>222</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>203</td>\n","      <td>205</td>\n","      <td>207</td>\n","      <td>206</td>\n","      <td>207</td>\n","      <td>209</td>\n","      <td>210</td>\n","      <td>209</td>\n","      <td>210</td>\n","      <td>...</td>\n","      <td>154</td>\n","      <td>248</td>\n","      <td>247</td>\n","      <td>248</td>\n","      <td>253</td>\n","      <td>236</td>\n","      <td>230</td>\n","      <td>240</td>\n","      <td>253</td>\n","      <td>255</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>3</td>\n","      <td>188</td>\n","      <td>191</td>\n","      <td>193</td>\n","      <td>195</td>\n","      <td>199</td>\n","      <td>201</td>\n","      <td>202</td>\n","      <td>203</td>\n","      <td>203</td>\n","      <td>...</td>\n","      <td>26</td>\n","      <td>40</td>\n","      <td>64</td>\n","      <td>48</td>\n","      <td>29</td>\n","      <td>46</td>\n","      <td>49</td>\n","      <td>46</td>\n","      <td>46</td>\n","      <td>53</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 785 columns</p>\n","</div>"],"text/plain":["   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n","0      6     149     149     150     150     150     151     151     150   \n","1      5     126     128     131     132     133     134     135     135   \n","2     10      85      88      92      96     105     123     135     143   \n","3      0     203     205     207     206     207     209     210     209   \n","4      3     188     191     193     195     199     201     202     203   \n","\n","   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n","0     151  ...       138       148       127        89        82        96   \n","1     136  ...        47       104       194       183       186       184   \n","2     147  ...        68       166       242       227       230       227   \n","3     210  ...       154       248       247       248       253       236   \n","4     203  ...        26        40        64        48        29        46   \n","\n","   pixel781  pixel782  pixel783  pixel784  \n","0       106       112       120       107  \n","1       184       184       182       180  \n","2       226       225       224       222  \n","3       230       240       253       255  \n","4        49        46        46        53  \n","\n","[5 rows x 785 columns]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["#downloading the test data\n","test = pd.read_csv(\"../input/sign-language-mnist/sign_mnist_test.csv\")\n","test.head()"]},{"cell_type":"markdown","id":"38f65432","metadata":{"papermill":{"duration":0.011627,"end_time":"2022-09-11T15:49:41.47449","exception":false,"start_time":"2022-09-11T15:49:41.462863","status":"completed"},"tags":[]},"source":["### 2.4 Checking for Missing Data"]},{"cell_type":"code","execution_count":4,"id":"7848e44b","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:41.50033Z","iopub.status.busy":"2022-09-11T15:49:41.499876Z","iopub.status.idle":"2022-09-11T15:49:41.548216Z","shell.execute_reply":"2022-09-11T15:49:41.546396Z"},"papermill":{"duration":0.064817,"end_time":"2022-09-11T15:49:41.551336","exception":false,"start_time":"2022-09-11T15:49:41.486519","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["0\n","0\n"]}],"source":["#summing the number of na in the training set for each column\n","print(sum(train.isna().sum()))\n","\n","#summing the number of na in the test set for each column\n","print(sum(test.isna().sum()))"]},{"cell_type":"code","execution_count":5,"id":"8a9c685d","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:41.578466Z","iopub.status.busy":"2022-09-11T15:49:41.577045Z","iopub.status.idle":"2022-09-11T15:49:41.630858Z","shell.execute_reply":"2022-09-11T15:49:41.629467Z"},"papermill":{"duration":0.071078,"end_time":"2022-09-11T15:49:41.634493","exception":false,"start_time":"2022-09-11T15:49:41.563415","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["0\n","0\n"]}],"source":["#summing the number of null values in the training set for each column\n","print(sum(train.isnull().sum()))\n","\n","#summing the number of null values in the test set for each column\n","print(sum(test.isnull().sum()))"]},{"cell_type":"markdown","id":"e2e6c601","metadata":{"papermill":{"duration":0.012298,"end_time":"2022-09-11T15:49:41.659658","exception":false,"start_time":"2022-09-11T15:49:41.64736","status":"completed"},"tags":[]},"source":["# 3. Processing the Images"]},{"cell_type":"markdown","id":"43ff7589","metadata":{"papermill":{"duration":0.012116,"end_time":"2022-09-11T15:49:41.684562","exception":false,"start_time":"2022-09-11T15:49:41.672446","status":"completed"},"tags":[]},"source":["### 3.1 Creating our X and our Y"]},{"cell_type":"markdown","id":"c08d5822","metadata":{"papermill":{"duration":0.013626,"end_time":"2022-09-11T15:49:41.711293","exception":false,"start_time":"2022-09-11T15:49:41.697667","status":"completed"},"tags":[]},"source":["The first column in the dataset is the labels. We will first separate the label and pixel data."]},{"cell_type":"code","execution_count":6,"id":"5053f09b","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:41.74043Z","iopub.status.busy":"2022-09-11T15:49:41.739841Z","iopub.status.idle":"2022-09-11T15:49:41.856612Z","shell.execute_reply":"2022-09-11T15:49:41.854993Z"},"papermill":{"duration":0.13526,"end_time":"2022-09-11T15:49:41.859688","exception":false,"start_time":"2022-09-11T15:49:41.724428","status":"completed"},"tags":[]},"outputs":[],"source":["#creating our Y for the training data\n","Y_train = train[\"label\"]\n","\n","#creating our X for the training data\n","X_train = train.drop(labels = [\"label\"],axis = 1) "]},{"cell_type":"markdown","id":"79cc1b34","metadata":{"papermill":{"duration":0.012014,"end_time":"2022-09-11T15:49:41.88413","exception":false,"start_time":"2022-09-11T15:49:41.872116","status":"completed"},"tags":[]},"source":["Now we will do the same for the test data."]},{"cell_type":"code","execution_count":7,"id":"2fe53d52","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:41.911112Z","iopub.status.busy":"2022-09-11T15:49:41.910584Z","iopub.status.idle":"2022-09-11T15:49:41.945286Z","shell.execute_reply":"2022-09-11T15:49:41.943571Z"},"papermill":{"duration":0.051946,"end_time":"2022-09-11T15:49:41.94838","exception":false,"start_time":"2022-09-11T15:49:41.896434","status":"completed"},"tags":[]},"outputs":[],"source":["#creating our Y for the test data\n","Y_test = test[\"label\"]\n","\n","#creating our X for the training data\n","X_test = test.drop(labels = [\"label\"],axis = 1) "]},{"cell_type":"markdown","id":"8aee134b","metadata":{"papermill":{"duration":0.011775,"end_time":"2022-09-11T15:49:41.972493","exception":false,"start_time":"2022-09-11T15:49:41.960718","status":"completed"},"tags":[]},"source":["### 3.2 Scaling Our Images"]},{"cell_type":"markdown","id":"6c7bf6b3","metadata":{"papermill":{"duration":0.011709,"end_time":"2022-09-11T15:49:41.996278","exception":false,"start_time":"2022-09-11T15:49:41.984569","status":"completed"},"tags":[]},"source":["Next, we need to scale the data from 0-255 to 0-1. This will make things easier to work with the neural network because it will allow the nn to converge faster."]},{"cell_type":"code","execution_count":8,"id":"ec3aed9e","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:42.023979Z","iopub.status.busy":"2022-09-11T15:49:42.0227Z","iopub.status.idle":"2022-09-11T15:49:42.099572Z","shell.execute_reply":"2022-09-11T15:49:42.098245Z"},"papermill":{"duration":0.094351,"end_time":"2022-09-11T15:49:42.102732","exception":false,"start_time":"2022-09-11T15:49:42.008381","status":"completed"},"tags":[]},"outputs":[],"source":["#converting the range of the pixel data from 0-255 to 0-1\n","X_train = X_train / 255.0\n","\n","X_test = X_test / 255.0"]},{"cell_type":"markdown","id":"fa0c7fd6","metadata":{"papermill":{"duration":0.013065,"end_time":"2022-09-11T15:49:42.128647","exception":false,"start_time":"2022-09-11T15:49:42.115582","status":"completed"},"tags":[]},"source":["### 3.3 Reshaping Our Images"]},{"cell_type":"markdown","id":"25c13463","metadata":{"papermill":{"duration":0.012508,"end_time":"2022-09-11T15:49:42.154331","exception":false,"start_time":"2022-09-11T15:49:42.141823","status":"completed"},"tags":[]},"source":["The images will need to be reshaped in order feed into our model. Our images will be 28x28 and, since we will be using grayscale, the color channel will be 1."]},{"cell_type":"code","execution_count":9,"id":"e540bf15","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:42.184582Z","iopub.status.busy":"2022-09-11T15:49:42.183594Z","iopub.status.idle":"2022-09-11T15:49:42.19266Z","shell.execute_reply":"2022-09-11T15:49:42.190379Z"},"papermill":{"duration":0.026555,"end_time":"2022-09-11T15:49:42.195333","exception":false,"start_time":"2022-09-11T15:49:42.168778","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["(27455, 28, 28, 1)\n","(7172, 28, 28, 1)\n"]}],"source":["X_train = X_train.values.reshape(-1,28,28,1)\n","X_test = X_test.values.reshape(-1,28,28,1)\n","print(X_train.shape)\n","print(X_test.shape)"]},{"cell_type":"markdown","id":"93826e5b","metadata":{"papermill":{"duration":0.012872,"end_time":"2022-09-11T15:49:42.22148","exception":false,"start_time":"2022-09-11T15:49:42.208608","status":"completed"},"tags":[]},"source":["### 3.4 Distribution of Labels"]},{"cell_type":"code","execution_count":10,"id":"c0b3d5a6","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:42.250334Z","iopub.status.busy":"2022-09-11T15:49:42.248913Z","iopub.status.idle":"2022-09-11T15:49:43.524584Z","shell.execute_reply":"2022-09-11T15:49:43.522935Z"},"papermill":{"duration":1.29274,"end_time":"2022-09-11T15:49:43.527252","exception":false,"start_time":"2022-09-11T15:49:42.234512","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["        <script type=\"text/javascript\">\n","        window.PlotlyConfig = {MathJaxConfig: 'local'};\n","        if (window.MathJax && window.MathJax.Hub && 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height=500)\n","fig.show()"]},{"cell_type":"code","execution_count":11,"id":"bf4259d2","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:43.556957Z","iopub.status.busy":"2022-09-11T15:49:43.556455Z","iopub.status.idle":"2022-09-11T15:49:43.719971Z","shell.execute_reply":"2022-09-11T15:49:43.718329Z"},"papermill":{"duration":0.181696,"end_time":"2022-09-11T15:49:43.722755","exception":false,"start_time":"2022-09-11T15:49:43.541059","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>                            <div id=\"c660db58-6726-41c0-a07b-40ce35ba756d\" class=\"plotly-graph-div\" style=\"height:500px; width:700px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"c660db58-6726-41c0-a07b-40ce35ba756d\")) {                    Plotly.newPlot(                        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of Labels in the Test Set\"},\"barmode\":\"relative\",\"height\":500,\"width\":700},                        {\"responsive\": true}                    ).then(function(){\n","                            \n","var gd = document.getElementById('c660db58-6726-41c0-a07b-40ce35ba756d');\n","var x = new MutationObserver(function (mutations, observer) {{\n","        var display = window.getComputedStyle(gd).display;\n","        if (!display || display === 'none') {{\n","            console.log([gd, 'removed!']);\n","            Plotly.purge(gd);\n","            observer.disconnect();\n","        }}\n","}});\n","\n","// Listen for the removal of the full notebook cells\n","var notebookContainer = gd.closest('#notebook-container');\n","if (notebookContainer) {{\n","    x.observe(notebookContainer, {childList: true});\n","}}\n","\n","// Listen for the clearing of the current output cell\n","var outputEl = gd.closest('.output');\n","if (outputEl) {{\n","    x.observe(outputEl, {childList: 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It shouldn't prevent us from coming up with an accurate prediction but it is something to keep an eye on.\n","\n","The label 9 and 25 columns are empty because they represent j and z and are not in the dataset."]},{"cell_type":"markdown","id":"13d5adba","metadata":{"papermill":{"duration":0.014273,"end_time":"2022-09-11T15:49:43.781855","exception":false,"start_time":"2022-09-11T15:49:43.767582","status":"completed"},"tags":[]},"source":["# 4. Viewing the Images"]},{"cell_type":"markdown","id":"a8f1825c","metadata":{"papermill":{"duration":0.015282,"end_time":"2022-09-11T15:49:43.812034","exception":false,"start_time":"2022-09-11T15:49:43.796752","status":"completed"},"tags":[]},"source":["### 4.1 Training Images"]},{"cell_type":"code","execution_count":12,"id":"659005ab","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:43.843568Z","iopub.status.busy":"2022-09-11T15:49:43.843086Z","iopub.status.idle":"2022-09-11T15:49:45.125301Z","shell.execute_reply":"2022-09-11T15:49:45.124328Z"},"papermill":{"duration":1.302274,"end_time":"2022-09-11T15:49:45.128943","exception":false,"start_time":"2022-09-11T15:49:43.826669","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAyQAAAJBCAYAAAC3RHFZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAADQd0lEQVR4nOy9efRdVZnm/+wAEiBknueQBBISAoQIIrOAyqgllnOpZVNV7VDVXXbbVS7tElutWt2rrOqf5VRlY2kpWiKKAwWizIPMc+aBDGSCJECAMJPz++Obu3n2m7vf7Hu+95sTwvNZi8V7v2ffc/e95z37nJP97OcNVVVBCCGEEEIIIZqgX9MdEEIIIYQQQrx+0QOJEEIIIYQQojH0QCKEEEIIIYRoDD2QCCGEEEIIIRpDDyRCCCGEEEKIxtADiRBCCCGEEKIx9EAihBBCCCGEaIy9/oEkhPDDEMKGEMJTIYSlIYQLm+6TELsihPC+EMKiEMK2EMKKEMJJTfdJiHaEEPYPIVwcQlgdQng6hHB/COGspvslRA7lrHgtEEL4VAjh7hDCCyGE79HfD9/x9yd2/HdNCOHwBrvaFfb6BxIAfwdgclVVAwGcD+DLIYRjGu6TEFlCCGcC+N8A/hjAwQBOBvBwo50SIs++AB4BcAqAQQA+D+DSEMLkJjslhINyVrwWWA/gywC+2+bv7wYwFMBwAL8C8O+7t2vdZ9+mO9DXVFW1gF/u+G8qgHua6ZEQu+SLAP5XVVW373i9rsnOCOFRVdU2ABfRn64IIawEcAyAVU30SQgP5ax4LVBV1c8BIIQwD8B4+vuTAJ7csS0AeAXAtN3fw+7yepghQQjhmyGEZwEsBrABwJUNd0mItoQQ9gEwD8CIEMLyEMLaEMLXQwgHNN03IUoIIYwCcCiABbtqK8SegHJWvBYJITwJ4HkA/wTgb5vtTe95XTyQVFX1CfRIX04C8HMALzTbIyGyjAKwH3qmY08CcBSAo9EjKRBijyaEsB+ASwB8v6qqxU33R4hdoZwVr1WqqhqMHsnhpwDc12xves/r4oEEAKqqeqWqqlvQM+318ab7I0SG53b8/5+qqtpQVdVmAP8A4OwG+yTELgkh9APwAwAvoucCKcQejXJWvNbZIT/8NoB/CyGMbLo/veF180BC7IueNSRC7HFUVfUEgLXoWesU/9xQd4QoYoeO+WL0zPBdUFXVSw13SQgX5azYi+gH4EAA45ruSG/Yqx9IQggjd9inDggh7BNCeBuA9wO4tum+CeHwrwD+fEf+DgHwlwCuaLhPQnh8C8BMAOdVVfXcrhoLsQegnBV7NCGEfUMI/QHsA2CfEEL/HX87M4Rw9I772oHoUVE8AWBRox3uJaGq9t5/fA0hjABwGYAj0fPwtRrA16qq+k6jHRPCYYem+f8D8AH0LFi7FMD/qKrq+UY7JkQbQgiT0ONM9AKAl2nTn1VVdUkjnRLCQTkrXguEEC4C8AXz5y+ix3zhS+hZgvAcgDsBfLaqqgd3awe7zF79QCKEEEIIIYTYs9mrJVtCCCGEEEKIPRs9kAghhBBCCCEaQw8kQgghhBBCiMbQA4kQQgghhBCiMfRAIoQQQgghhGiMfTtp3L9//+rggw8GAOyzzz7JNn7dr1+/tjEA9NQi8uPebMu12759e8d9apKW+9nGjRvx5JNP7hmdeg1y8MEHVyNGjACw5xxbptTlzmtXxymvk/3lzqOnn346abfffvvFePPmzZurqhrRcccEBg0aVI0ePbrpbtTKq925P+bxxx9PXj/11FMx5muTvW5xbm/btk05W5MhQ4ZUY8eObbobCZxvduzvxrhb5z1en1566dW6jBs3biz6rGeffVY5W5NBgwZVo0aNAlB+H+ndi5b8vTfbeot3Xff+Xucc8L7HQw89lM3Zjh5IDj74YLzzne8EAAwZMiTZNnDgwBgfdNBBMe7fv3/S7g1veEOM+QaG/2637btv2k2+qPA2r91zz71a94j7Z9vZCxZjH2Ry8MHwDibf3Fla7/vYxz5W9JmiPSNGjMBXvvIVADsf29ITLXc8vVyxx5Zz55VXXmkbe/uwfeXXdfZh+8evX3755WQb9/2FF16I8XXXXZe0aw3uAHDxxRevznZKuIwePRrf+ta3APhjTsn4AaT5a/dXmmMepe1sXrXw8rf0cy699NLk9dVXXx1jvlbxdQoA9t9//xjfeuutytmajB07Fj/5yU96tY9cznrH3d74cD5z7N1keWNprp3tU+k4y/vnexwAWL9+fYz/7u/+Ltt3fn333XcrZ2syatQo/NM//ROAne8d+TVf5+0x43Y8ttp2jL1v8P4Bn8k9GNkcyz0M2PE39x3t+73xOddf795o0qRJ2Zzt6IEkhBAHcO/mnztZ+uN7sxZ2W+4hxLbjhyG+QB1zzDFJu4kTJ6Id3gW/9EnWa1dys7En/qv+a4kQQhwcSn/L0gdPD++E5H7YdpxzPBB4F0Dv4l36MMWfZc/t3EX+kEMOSdoddthhMb744osh6tPKwbrjR+kNXekDjzcWci69+OKLbWNg5390auHlm/d37vvkyZOTbUOHDo0xXwf4X6EB4PnnVWu029icyuWYvUHKXfO93NvVZ7ew50DuH4jqXm95f6X/CMkPwwDw2GOPxZj/4cfe3OYe7EXntMYuT/GTu7e1r72x1Lvhz23zruXe5/L+nnjiiRgvXrw4acdj35vf/OYY20mE0nOR29U+j2q9SwghhBBCCCG6gB5IhBBCCCGEEI2hBxIhhBBCCCFEY3S8hqSlTytdBOStDeH3WM2Zp0cr1adxP1ifuXXr1qRdzoGr7lqCugtRS/chygkh7JSrJe9hPGeUUkoXw+faWT0p6569hcneNoY/y+Yoa5ZZX9py3GvXTtQnhFB0/pfq1kux+8iN4/Z8YsOQBx54IMZ333130m7evHkx5vy16zpOPPHE7DaG+zR16tRkGy9kZ620XTOiNSTdgcdZ75rPOeYt/OX3eAvNSzXtpWvrPLzPym3zFgjbbWvWrIkxj7Pbtm1L2nV6PRPtCSHEY1C6Vs+7F/XWTpc6dXnXfM4xXp9n1yINHjw4xryGhO+BAeDWW2+NMY+fdj1e7p4dSPO5E5OfHLrrFUIIIYQQQjSGHkiEEEIIIYQQjdHx3F+nNmmeFKuu7W9OzmCnk3g6/sknn2zbP7u/vrbZLbVQ68siYq8nQgjRbrR0er8udeR4deuQ5PLUk3bl9g2kcit7HrFdK7ez+9i8eXPbzxKd0zq+ni10u/Yt6sgMrYSGpQBcaHDlypVJu/vuuy/GK1asiPHDDz+ctGPJANeCevbZZ5N2bCddWiBy0KBByWuWvHCROVvM08odRD1Yzr2rdu1iD29s9urqcOydR969hrctB597VnLIY6uVuLJki21/bd+feeaZon6IXdM6HvY3zpWT8Np5SxVy7/Ha2rFpy5YtMV60aFF2/2zhy/ln7w14TF+wYEF239wPtvYHgEmTJsW4rlU3oxkSIYQQQgghRGPogUQIIYQQQgjRGLVdtkqnNu0UF8sCeJtX+d1zLODPsq4/PAXK05x2er/OtKxHnaloO8WlCu3do/VbWklKzl3NUtqu1GWiNMdKq7YynlMSO3N4UgLrsvHQQw/FmOU6tt0NN9zQtk+ic3LH13NhKdmXlYkMHDgwxrZCL0sErrzyyhhzPgDApk2bYszyKyvF4nOAJbS2T//8z/8c47/+67+OsZUw8P4GDBiQbJs+fXqM77jjjhjb6vGjRo2C6A6tPPOqope6W+UkVXZ/njSk9BrK0m6Wsdg+cp5aKSyPnwcccECMbV6y/JXd6QBg3bp1MWZJI8u3AP86I8rx3AxLq6KXvMdu844fbxs5cmSyjcdTvp9dvnx50o4lr5zbdpzlsXDZsmUxZhkWkEpyf//73yfbPvOZz8SYryW1HWprvUsIIYQQQgghuoAeSIQQQgghhBCNoQcSIYQQQgghRGPUXkNSavvraek8vHUoOR01a9iA1OKR38OVLC111m50Upk9p38t0TKKzuGc9Sqker9zrl0nOsmcha9nR8lrXqxmmdt5FdJ5G2uRhw8fnrTj9QPz589Ptl111VUxPuaYY2I8YsSIpJ13XonO6LRSeyl2HQbr3a1t88033xxjruq7evXqpF2ucrStMJ2r0m2/B68vKa0EbM9frgr/4x//uO3+gJ3XDIj6lORjHdtfi2dpnbNU99arcDte4wGkYyaPpVu3bk3a8b0Ga/P5/AJS21ReJwKk4+fatWtjbHO0rqWq2JmcvXru/tOz/fVyOzdGWh5//PEY2/GTx8yxY8fG2I7HvG6EY9637RNbBdu+siXw0qVLk22PPPJIjOfMmRNje09Seq3SDIkQQgghhBCiMfRAIoQQQgghhGiMjiVbrWmj0mrnpVKYUntg+1k8JWqtTNn2lyUpdho1Z+vmya1KpTt1Lfo0LdsdQgjZ6dI61azrViPlfOY89aZ2rUUpw5/NkoNhw4Yl7Q488MAYs83k+vXrk3YLFy6M8eLFi5Nts2fPjjFLDmz/5s6dm+2vKCeEkM3H0nGH23F+WMtzzrcrrrgi2cYWjyznsjak/JrzzUpSuL+8zdpbnn322TG2Vu5M7jsCqZzw3e9+d4zZ3hJIK8urAnZ9WBrbbQv7uu/nnLD5wdIsvg488cQTSbvRo0fHmO2trfyFrVY3btwYYyu3uv3222Ns7VVZcs79tb9nqd23KMeT/tW5hy219rWvV61aFeN77rknaceSPr4m2/telnqxtMvmEecs26QvWbIkacfXCLsPlmaV2iN7aIZECCGEEEII0Rh6IBFCCCGEEEI0RkeSLabUZau0ArttVzpNy9OcdnqUp54mTpwYY1uRmD+r1A2h29gpZdE9OnV/KaUTOV7OtcNOt/LrXPVfIJVLcQVXK6fhatssxdqwYUPSjqUx55xzTrKNq1mza4fNWdtHUZ9WPtrcLXHms7DEw0qgWCKwcuXKZBvLtPj8sLIvzlnObZZvAcB/+k//KcY8Hlt3tpy80ZOqeJJfrpZ9/vnnJ+34PPqf//N/Zvcvdk3OsYjhY1h6PDs57nw8+X3eucJy1SuvvDLZxlIWdtZ69NFHk3Y5OSLnof2s+++/P9mWk0XaexIeg0V9PNfYnPzIHovcdd2TgFkpK8ue2AXR3qc+8MADMWZptpVR5SS01mGR5YiMlWLza3se8bWg9Hzz0AyJEEIIIYQQojH0QCKEEEIIIYRoDD2QCCGEEEIIIRqj4wUTLQ1d6RoST0vnWat5ldp5G2s0rWaZdXZDhgyJMVcCttuY0srAndjwse6+1LZT1Idtf0uPk/3t69gsemuiPF08azLZmtLmLFtL8lopXk8CpOs6OM9nzZqVtOO1BbbKam5di6WuxbXI49lCexbAPM54OmK2f7bHPZf33rjFttDvec97km0zZsyIMeuevc/1csqr4s5rpxYtWhTjE044IWlXV+ss8tSxUAfK15fw/u16ilzJgeXLlyftePzkdR22Ojbb9Ho27zfccEOM2WrV9o/Xidj98b0Mrx+wv0VT61z3RnJr9XJ5ZHO7tMQFX4ftWmc+vnzPyuujgXS845IWvEYOSNdB3XnnnTHmPAdS62Dvc3lNil3/krt39n4nD43GQgghhBBCiMbQA4kQQgghhBCiMWpXardTXDn5lZ1Kz01xeVNmdlvOLth+1imnnBJjngK98cYbk3YTJkyI8XHHHRdjz4rX+1yeivWmW71p6dZ3lnSr93QqyyiVbHnHxuYOV0n3LACffvrpGLPNpJXaPPbYYzFmmcExxxyTtGPLXv5ca+1nZTNMrjq9JFp9A8sM6/7GnJtsq2srTLPVL8tYgJ3lK+32DaS5+Ud/9EcxnjlzZtKO5YScR3Ulkt55zftgCY21GL7vvvuKPkvsmpJrVWnVa46ttIklpDYHWJ7IspZ77703affQQw/FmPPS5h5/NrezFudso87yWpbdAum4a3+LESNGxPiMM85o+x4AuOKKKyC6Q2t8Lb3H9Np5lcq5nbWMzkmivaroq1evjvGhhx6atGN54j//8z/H+KijjkranXTSSW33t2zZsqQdb5s8eXKyjSXm3bgf0AyJEEIIIYQQojH0QCKEEEIIIYRoDD2QCCGEEEIIIRqj4zUkLd2j1T+Wau7q2P56ejzG6jVZc3fJJZfEmHX1AHDPPffEeO7cuTFm3T+QaqzZMs37LTw9NGsErZWfp+kX5XDO7qpdC3vMclp1247z5Yknnki2sTUf5ynrnIF0bcikSZNibG1/2U7yzDPPjLG17OM+eTapjP1e/NvwPrx2onfkfsvc3+2xYAtRzr2HH344acf6ZWsfyZaOnG92bQl/Fq+JsuuocuO9p9vnbVZT7eUbrxV54xvfGOPrr78+abdixYrsPkRn9NX5b/ODxy67JuiZZ56JMWv1bd9Y+75p06YY25zltaeHHHJIjDmnbB+PPvroGNvrOueb7RNfF6ZNmxZjXksIAKtWrYrxrbfeClEPvjfw1jDXwa759NZe8hqNoUOHZvfJ9rs8Ho8bNy5pZ+8BWnAuA+naab7v4PVQQLpeas6cOdltfA7U/f00QyKEEEIIIYRoDD2QCCGEEEIIIRqjdqX20imu0oruXjs7/TVmzJgY81SYtbRkaQHbqdrqqbw/tjyzld+5IvaRRx6JOuSm7nLT0pLB9J7cb5iTYtn2PI3PlXbZMhVI7Rmt/IXzj2NbWZ3lhNzO5gdbWvNUv5UZlspkGCsX5H2UVtEWvSP325ZKtliSwvlhqwTb1wyPf3a6nzn44INjPHLkyBh7tumlFY49qYMn52IpAdtirlu3Lmk3derU7GeLenh2zDn5p30fy1Ps/lgGZaWsbHnKsb034M/mXLH3BixpfO973xvjk08+OWnH9xr8HnteTpw4McbWznfBggUxZgkNV4sH8tWxRX1KrcY9eT5jbaE5J2zeb9myJcYsQeT32M/m+wa+TwDScZutfs8///xsOz7HeDkCkMpwrWUxfzZfBzottRDfV+tdQgghhBBCCNEF9EAihBBCCCGEaIyOJVst7FSV54qVe58n2eJpIlvNmqdfeaqUp76AtDorTyexqwaQOlrYasUMu3GdfvrpMWaZgsVKCXIyLTuNJ6lW92j95p4rFB8Xeyx4G0/F2uPOMgM73crnxLBhw2LsTb+z/MrKFnl6n6dv7XQrS224Tza/+LV1hil15xLdp7SytR1zOcc4P1gKAqSSFytX4bGLZXxWvjV9+vQY8/lhJSm5sc9zz/Jkrfw++/1z0kJbrdhKJkU9QghFMmM+LuzOBqTjJ+/DOg+xg6GVHHI+s/yapba2H3a8Y9797nfHmB04rQQsJ4e18ldPzsUyS76XYecwIK2wLfoeT16a22avkzwW2nsDzhHOUzt+8hjH+7AuWzw+s0TwXe96V9KOzx2Wn9v7aJZlWZfGRYsWxfjEE0+MsSfX9dAMiRBCCCGEEKIx9EAihBBCCCGEaAw9kAghhBBCCCEao/YaEs/2lzWZpWtNrE6UbVOtZpm1prx/q4/m16z5tNo81syxrs5WdGe98S9+8YsY/+mf/mnSztMS5rTT0ub3DaxtLrX2s+04j3j9kbWF5rVO1n6X4XUjdh82N1t4+v6bb745xlbjesIJJ8SYdZ2eztvqP/l8Zr1rqQWi6AyuINxuWwvOU7b2BdK1HFx511pEsj7dszz14PHZy4E6Vr8efP2wOZurGmx/J1vpW9Sn9Tt7x5bzg6/jFrbit+04Z+0aDX7t2V3z+hXOlc985jNJu3nz5sWYr/82z3Pnpf0teJu9zvA6V/4eXPkd2NnqWNSnZN1T6fpo3mbXJXljGo+fvIbEfhavFeH7WbuGZPjw4THmauzWipjPI85Fu4aE+2SvCXYdWI7SNdGaIRFCCCGEEEI0hh5IhBBCCCGEEI3RkWTLs/bjKapcdWgL29zZirk8bcTWYkAqm2EJgp0+mjx5cox52slaPbLlGVsKss0fkE6pLl26NMZW6sDSHW+qjn+nnPWl7H97T0nVUP79rc0iSwbWrl3b9u9Ams/2eLIUi6udck7Zvo4ePTrGno0gfxb3wVJXUpWzUPVsWEV3sOd/zvrR5lGuqq+VErI0xJ4nfKw9eRTLnryxv1TWkjtfSyu/29d8bbK/k608LOoRQsget1zO2vFjxIgRbdvxNdlus3a+q1atijGPzwMHDkzasYzxnHPOiTFblwKpNMYbP3ns88ZB73rO8l2WZY0fPz5pZ38PUZ/c8cgda2+cKa1ObstY8NKAFStWxNgeZ+4Tj+NWtsj3rZw7W7duTdrxfU4utq95KYXdf6kk10MzJEIIIYQQQojG0AOJEEIIIYQQojFqu2xZctM11m2At3H1aesuxFPpDz74YLLt9ttvj/HChQtjbGVfXNGUZQV2mp6ndll+ZafWeKqY5WHWvaC0ujJPWZdO94nOYMci77iwrMVK/9ithd1frITEkyPwdPy2bdtivGbNmmyfvKl5rpjKuTNr1qyknZ3ObVHqKgKkEgRvilqSre6Rkwzw3zn/WO4CpNPsLGuxLluc9/b45SSlVqLK459X9ZopndL3ZAB8jnlVjbkdX3MAVWrvJq3f3B4nzh0ej1iSamHJkj22fL22jkCcmxyzzBtIx/ELLrggxjYfOJ9LK3bnnDTtNgtfZ1haaPtu5Wei93iuaaWyLE+u6jlw8b0Bj+M2t/mcOPbYY2P8xje+MWnHEjDOKeuQxdcCdqGz5wC/b+zYscm2YcOGoR2eu6yH7oKFEEIIIYQQjaEHEiGEEEIIIURj6IFECCGEEEII0RgdryFpaeO8Cuzt2rfIaaOtvo3tVZcsWZJsW7x4cYx5fcnq1auTdmeeeWbb/VsNNOs1ed2I1frxd2RLYfudchbIQN66Mqfflu1v72n9hvZYsDaZbfSszp7XKbG+0lqI8v6spn/o0KEx5nyxek3WiXI/bHXe2267LcYXXnhhjK0VMecVa1VtO8/SknOWtaA2l1WpvTt4FqqcY7wuzq6NYN052z3a3Oax0Kt6zbE9ztdee22Mecy1Wvfc2OetDeG1MHZNFZ8Tdht/L14vwOM2AEybNq1tn0R9vOM5cuTIGNu1enys+fhZq2rW1lsrU34fr9WzVqannXZajDknNm7cmLQrXRNVYnkM5K1bLfw72bH/lltuKeqT8OH1pe22tYtLya3dbLc/Hrv5voHzF0jPF14bYtdncM7xPuzaaba+5nPKW1c3b9685DXfU+T6AJSvL9UMiRBCCCGEEKIx9EAihBBCCCGEaIyOK7W3pmLslAxPQ/G0ZGl1Uzvt1L9//xhba7EhQ4a0/dwJEyYk7Xj6lq0v7TQvT3+NGzcuxnZKleUOs2fPjrGVSzB17C2Bcps0sWty0+l8PFlO4tn5sj2elf5xXq1bty7Zdvjhh8d4+PDhMebcA9Lc5hywFYlZgvCNb3wjxuedd17S7rDDDosxy8YmTpyYtLPWmoxnZ8hYCZvoPfb3ZikAH08rwcvJmTh/gbwlK5BO3bN0xUpteP///u//HuNPf/rT2f3x51q5FUtvPetrzlkrM2DpDUvKbJ+stbuoT2ucsOMnV3fma6Vtx/KSXGxfcyV1+5rPAR5XAeD3v/99jOfPnx9jK+njvPIsXzmf+TvavFy2bFmM7ZjO34ulhDyGA8D3vve9bD9EOVVVZe+z+O+ejXPOGt3eO/LYyve2QHoPwOeEHZu4He/PLnfgfvA5YPONzxXvvobtud/ylregBM/22EMzJEIIIYQQQojG0AOJEEIIIYQQojFqu2yVYqfE+P08tWSnwrxK6OxEwNITO71/1113xZinrqy0a9KkSTHm6TQ7VZyrWGllafxd7DZ+7TkWtfYhl63eEUKIchPrVsLHl6dHrfyltDIuT6naKVt26uL9WykB5zNPnS5fvjxpx5XaWdJozwHOq1tvvTXGLBsDUpmBle5wPvPUK0sxgJ0dnER9WuOEnY7nsZBz1uZ2bjre5jZjP4slUd6UO+c95xjLWgFg/PjxMX7sscfa9tXCLi62f5zr7Bhj+zRjxowYe2O16B2tccM67/Brzz2LX3NsHbI8GQrnOueLHat4///n//yfGH/1q19N2nG+8Lho3bf4GsGSw5tvvjlpx9d8K0VftGhRjPk7c/4CwDvf+c4Y/+xnP4PoW0rvwXgs8WRU3jIG3uZJuzi2SxD4/pjPD3tvwNdrzjcrMzzrrLNizPItIM1n/p3sfb9ctoQQQgghhBB7PHogEUIIIYQQQjSGHkiEEEIIIYQQjdHxGpKSCuKsN7Z6OX4f6zA9m1trf8Y6Nl7/YXX2rJ9j21VbRZv1n1yx0mru+H1c/bcTi7Ocls77nUR9qqqK2l9recrrlFgvbzXLrPVlPbTVQLNu1K57Yr0mx1YDvXLlyrb7ePDBB5N2fL7weib7Hfl9rDW1GmjON5u/OWvDa665Jml3wgknQHSH1u9sLRinTJkSY84Bq/vl9VGcb3Z/nLNe5WjWM1ttc84W+vvf/37S7tBDD40x26ty/tp98FqTJUuWJO34u5xyyinJtjlz5rTtn9aM9A0hhJgXdr0Gr2PjPLXjLI9dvM2uTeMx0+Yzr5EaNGhQjG2feExfv359jK+66qqk3R/90R+17Z9ds8T3HosXL46xPVe4AvuCBQuSbWzj/ba3vS3Gdn2YrZYtdh/evZ13z8bXW3uvx/vk+xCv2nvuPUC6humRRx6JsbeGhM9LuwbsmGOOibF3n+6tiS69n9UMiRBCCCGEEKIx9EAihBBCCCGEaIyOK7W3ppvs1BVPQ5VWZWS5gJ165al1W806Z6Fmp2VZ2jV9+vQYWwkYyxu4T7bdxz/+8bbb7Pf17Ixzv02unaRbveOll16KU/J2GjE3JWorUeesKu3+SqdlOZ+tDStLtviz7LTsqFGjYszyhlWrViXt+H3vec97Ymzthvm3sBIJrjp/0003xZitjIFUFinqs3379jiFbi2n+bhx7ljJVq4quh1nOT9sjrGshW2irRwxV0HYjnUsX+Hzw9pWssxgxYoVyPEnf/InMWa7bCAdx3O21ba/oj6vvPJKlDTZnOVxkscqKxHMXYet/T5Lp7xyASw9sdIpfh/bUf/mN79J2nE/+HtY+SB/L/7cmTNnJu0eeOCBGNtz8cILL2z7ufY7WktZUY8QQvb+ypMw14H3YY8fj+Mc2+swj+l8HbBj/8aNG2PMlupWHs7jLn/u0UcfnbSbOnUqcnT7/lQzJEIIIYQQQojG0AOJEEIIIYQQojE6kmxVVRWnLT1nKd5m2/FUfenUo5XG8FQny2TsVDFLCViOYKdAebqVp4PZYQMApk2b1rZ/nbhq5apZerIvUZ/t27dn3YNYrnHwwQfH2LpMsIyKZUp2SvXRRx+NsZVsTZgwIcYs91u7dm3SjqdOOQestGTu3Lkx5sq97CwDpA5G/B2taxfLHdl1BgDuuOOOGPM5cOyxxybt7JSwqE9rPLBSpJyzlp2255znbd6Ya53XBg8eHGM+J+z5wXKY0krwa9asibGVbHEOn3322TG2kkBuZ78X/06ehFZ0h3Xr1uF//I//AQA4/vjjk20nnXRSjPn6anOW84q32fzgY2hlhgxf/73rq1cd+7e//W2MWapo5dws+2KJmR3f77rrrhh/5jOfyfaX81n3Bn1H69h7Eus62Os1j63WXY5znc8P64rFYxrnot3fpk2bdrlvIL2P5v1xZXa7rZS6v59mSIQQQgghhBCNoQcSIYQQQgghRGPogUQIIYQQQgjRGLVtf+3aEH7tWQDntGVW55+zWrX7YGtKW0WS+8HaZmsjyNXZ2U7tuOOOS9qxpSDrOL3PtdpEfh+vjcnZUcr2t3eEEOJxs2s+WC/MvzPb3ALAfffdF+NFixbF2Fbr5WNr13KwHp9zm9edAKl2mLXT733ve5N2n/rUp9ruz54rvB5k2bJlbd8DpOcE6/uBVMfP58fNN9+ctLvzzjshes/+++8frRbtug7WAXM+2+PO4x1rjO1aC9Y6W118zjbVjkmc96V2rVyx+u1vf3vSjm0nWVdvtfPdsOz1Kg+Lcl5++eVYUfyyyy5Ltl177bUxPuyww2LMY6KFr4d2bRqPz3ZM52s0H1tv7ZSXR5z3fK9hr+u8fz4X77///qQdryU88sgjk208JvP+7Rpa0X08C3/GWzvNOWDXXeTGSLsPXhNl+8D3FLx/O/bzPvgewq7Z4nbnnXdejGfNmoVScvfzde9bNUMihBBCCCGEaAw9kAghhBBCCCEaoyPJFvDqFI1nk8axJ1ni6dDSKtpA3u7RThPxdC7v35u6+shHPhJjW82aKbU1K63UbmlN10my1Xtax2Do0KHJ3zk3WTplpUdcOZrzzU69su20lWxxjrFE0OY9v/7c5z4X43POOSdpx/aRLCmz+xs3blzbPk2aNClpx9IuKyVgS8tf//rXMb799tuTdsrV7tHKWTtW5aqzW0tHnqpn+0hP/mElNCzh2n///WNs855tJlmmZfvOEtg//MM/jDFLYWwfefz0ZMJ1rVGVs92jddysXIVz0ZN1sjyPx2Zrf5qzawfyUhvet4X3x5Ivuz/ObSt55VxnC/V3vetdSbu3vOUt2X7kctHmtr2nEr3H+029Mha5Y2blrzymWWtpzhc+1jZneXzmvLSyb87Fxx57LMb2POJ7gDPOOCPbP6avLac1QyKEEEIIIYRoDD2QCCGEEEIIIRpDDyRCCCGEEEKIxuh4DUkLT3vL26zmjDWarJGz1mWsU/bsHlnXyfpqANi4cWOMWRdn9f2f/OQnY/zmN7+57fcAUn2fp6Vju0FP28y6RdlP9g1VVUX9pj0WvMZo7dq1MbaaTM43z9qPc8xanq5YsSLGrCcdMWJE0u4v/uIvYnzyySfH+KabbkracR9HjRrVtq9AqmVlG9eJEydm+7dw4cJk2x133BHjxYsXx3j48OFJu77Wl75e2L59e9QB2zUfrMfnnLVW1byNbX9LLU7tZ/PYaq1W+bMPPfTQGL///e9P2vE2xuZN6VjoabsZb92ext3uUFVV1lqXjy8fC7sOY/ny5TFmjTyPW3Yf7frRwltrwn3lfthc5LznfcyePTtpd+yxx8aYbavtGMn3OfZc5O/l3UOI7uDlbG59mncseLy0x5bXkNr1Kjzu8v2xzVm+32hZbNsYSNf08TZ7f8zrRkaPHh1jew54ZTxK1+CVtlOmCyGEEEIIIRpDDyRCCCGEEEKIxqhdqd2S+7u10ZsyZUqMWVZgq16zzMDalfH7uIorT4tZWLryl3/5l8k2tjktlaJ12/a3rmxBlGOnNjl3Hn744RizLS+QTu/z8bOSLW5npTY8JXrqqafG+MILL0zasb0q92/16tVJu+uvvz7GJ554YoytRIBt/9iW2Fr7XnXVVTG2FdiZnK0msLMEQ9Rj+/bt8bd8/PHHk21sQc1T81ayxe3YEthKCe34zLB9JI+tVtLIEpVPfepTMba26Z4MtwRvTPS2eWO1ZIbdoaqqeAysXIVf52z/gVRKvXLlyhhbC1U+nnY84nzmMZjPFSC1UD3kkENizGMkkN6vnHTSSTGeOnVq0o7vf1iWZa2vGc9CVtf/vuepp57CNddcA2Bni11+zbEdL/k4cU7YnOVx3I7BufsGex5xXrH8yt4f830Ov8eOx3xPwvca9pzi16XlPurKDDVDIoQQQgghhGgMPZAIIYQQQgghGqO2y5adkslN0XCVZyCdsmWHIXbYAIAlS5bE2JPaWHcuhqfT3va2t8X4qKOOStp5LgJMTs7VyfRqblrW7ru1TZWEe8f27dujKxDnDZDKtFgiYKdUeeqUY1v5/fDDD4+x58LC0oRrr702acfnAUttbB5wBfYbbrghxizfAoA3vvGNMWaZwsc+9rGkHX9ne37w97z77rtjbM+96dOnQ/Qezll2CgRSuSo7blkZFUv1OI/sdDyPd7w/IM11zp0PfvCDSbtzzz03xl5F95zbUjdkU6WyLM85UfSO1u9sj3vO2c3+nStWs0zGSrE5h61zEMPS20984hPJNh7/WOZqpTv8Wfy97NjHfeTrupW4eLlY6honOVd3eP7557Fo0SIAvhuqB0us2O3SwtdXO6bz+GzvdRmWgbE82o7bnKc5NzkA+PnPfx7jnNMXkOYwj+9AKnFkNzy7D64K76HRWAghhBBCCNEYeiARQgghhBBCNIYeSIQQQgghhBCNUXsNiQdr3VgXCqR2vqzb46rUQKqJtutLWNPmaf14/6ytt7rOnGVxKbn1H7vat7WGFd3nxRdfjJbSrL8HUmtU1mHaNSRsj/fRj340xmx3CgCHHXZYjFnDD6Q6UV6TsWrVqqQd66pZa/qWt7wlacd2knyusH0vkK5raellgZ1/C9Z/sr0lkNoFLliwIMZz5sxJ2lnLYVEPriBsNcVsj875sWbNmqQd5xvr4u14yXlk15ecddZZMX7rW98aY16/1OpvC68SfG7dSKkm3rNG99aGlF4jRH2qqorrKLy1Q97fc5biNj9KjyefOzNmzEjaHXrooTFmS1avwrZXPT23NtT21bP2zVVqt/bI1113HUTv6dev306/bTtK15PwOjtvrLL3F7zWidd52L7xPnk9rF1HxTnL77Hro3LrVex5yNbXfI/j9bfu2meNxkIIIYQQQojG0AOJEEIIIYQQojFCh5a1mwCs3mVD0U0mVVU1YtfNRDuUs42hvK2JcrYxlLM1Uc42hnK2JsrZxsjmbEcPJEIIIYQQQgjRTSTZEkIIIYQQQjSGHkiEEEIIIYQQjaEHEiGEEEIIIURj7FUPJCGET4UQ7g4hvBBC+J7ZdmAI4ZshhM0hhK0hhJsa6qYQLiGEySGEK0MIT4QQNoYQvh5C6JOaQUJ0Sghh/xDCxSGE1SGEp0MI94cQztqx7U0hhN+FEB4PIWwKIfw0hDCm6T4LYQkh/DCEsCGE8FQIYWkI4cKm+ySEx96es3vVAwmA9QC+DOC7bbb9C4ChAGbu+P9f7sZ+CdEJ3wTwGIAxAI4CcAqATzTZISGIfQE8gp68HATg8wAuDSFMBjAEPWPtZACTADwN4F8b6aUQPn8HYHJVVQMBnA/gyyGEYxrukxAee3XO7lX/6lpV1c8BIIQwD8D41t9DCDPQc/DGV1XVKmF8z+7voRBFTAHw9aqqngewMYTwGwCzGu6TEACAqqq2AbiI/nRFCGElgGOqqvoZtw0hfB3Ajbuxe0IUUVXVAn6547+p0L2B2EPZ23N2b5shyXEsevymv7hDsvVQCOGCpjslRIb/C+B9O2SG4wCcBeA3zXZJiPaEEEYBOBTAgjabT878XYjG2SHjfhbAYgAbAFzZcJeEcNmbc/b18kAyHsBsAFsBjAXwKQDfDyHMbLRXQrTnJvTMiDwFYC2AuwH8oskOCdGOEMJ+AC4B8P2qqhabbXMA/A2AzzTRNyF2RVVVnwBwMICTAPwcwAvN9kgIn705Z18vDyTPAXgJwJerqnqxqqobAVwP4K3NdkuIlBBCP/TMhvwcwEEAhqNHl/+/m+yXEJYdufoDAC+i5x95eNs0AFcB+C9VVd3cQPeEKKKqqleqqroFPf9w+fGm+yPErthbc/b18kDyYJu/qUS92BMZCmAietaQvFBV1Rb0LAo+u9luCfEqIYQA4GIAowBcUFXVS7RtEoBrAHypqqofNNRFITplX/To8YV4rbBX5exe9UASQtg3hNAfwD4A9gkh9N9hl3oTgDUAPrujzQkATgNwdYPdFWInqqraDGAlgI/vyNXBAD6C9g/VQjTFt9DjWHheVVXPtf64Y83Tdeh5oP52U50TwiOEMDKE8L4QwoAQwj4hhLcBeD+Aa5vumxDteD3kbKiqvWeiIIRwEYAvmD9/saqqi0IIswD8PwBz0LPA/XNVVV2+m7soxC4JIRyFnoXtRwJ4BT03eH9eVdWjDXZLCABxBmQVerTLL9OmPwMwDT0OXNv4PVVVDdhN3RNil4QQRgC4DD1jbD/03BN8raqq7zTaMSEyvB5ydq96IBFCCCGEEEK8ttirJFtCCCGEEEKI1xZ6IBFCCCGEEEI0hh5IhBBCCCGEEI2hBxIhhBBCCCFEY+zbSeMBAwZUw4YNAwD065c+y/TY0u+M/Tu/zsV1t+X6sKttpZ9bSun72FAg955HHnkEjz/+eL2OCBx00EHV0KFDO3pP3eNeuk/PSCLXrm6fumFaUWcfa9eu3VxV1Yhef/jrkP79+1cDBvSYUm3ZssVui/H+++8fY2885m223X777dfxPrzxmNs9++yz2b7vs88+yNGXY7rHokWLlLM1Ofjgg6vhw4cDaO563QndNvPp9jjL3+vRR1Nzxeeeiy7bePnll5WzNRk8eHA1duzYXu2j9Lrebep8Vl/0L7dP77OWLVuWzdmOHkiGDRuGz372swDSiwsA7Lvvq7viixJf8ID0IsrbbDu+YPF7Ovks3sb7sxfD3AWVP8e2y31Ou/3n2L59e3Yfrddve9vbivYl2jN06FD81//6X9tus7957u+5dp1cDDknXnnllY7bleaUxfsshgcQzkv72m7L8d//+39fXdRQ7MSAAQNw/vnnAwC+//3vJ9umTZsW4ylTpsTYjpH8+sADD0z2zYwY8ep14eCDD0628fh3wAEHxNjmIo+7Bx10UIzvv//+pN2MGTNiPGjQoBjb84g/147BuXbeucj99drNmzdPOVuT4cOH46KLLgKwcy7mrtH22PJx8h6ivW2l5MaxujdtL7/8ctu/2/3x59ptuQeSf/qnf0ra3XfffTHevHmzcrYmY8eOxSWXXLLLdt6YwflXem30jruXf7lrdOl7cjlqsd/De+jK3RvYffD73vrWt2ZzVpItIYQQQgghRGN0NEMSQoj/imGfGnNP93Wnbz2JQG4Wo1RGZsl9ln0azP1LW91/vfb+pacvZEOvR/r16xf/hdj+C0HpMeRZBjsLl9uf9y93uX8JBNJ/WSht51H6L4i8Pzurwv0onUkR9amqCs8//zyAnX9Tnu0oHSNfeumlGI8ZMyZpxzMmq1atSrbxjEYuB4B0tqPVbwBYsGBB0m7ChAkx5pkZe16yJIXPNzu70w15regO3r0BX6M9pULuemjHMG/WrJTcuOj96/DTTz8d4yeeeCJpN27cuLb7sGMp991+Fuc6n4srVqxI2vFspegbcvcG3myEdw/hXTdLKb1P5Zzjdt55w+1efPHFonb2tXf/U3y/UtRKCCGEEEIIIfoAPZAIIYQQQgghGkMPJEIIIYQQQojG6FiM2dKJlVo/1m3naU1z2npPR836TM9ly/t7nXUjpRr+bjg5ifa0jlXpMaurUfaOdWkecJ6y9t9SqsnMtbN/59/GWyfD+lTbv7prqUTKyy+/jCeffBLAzud/qX48p1WfOXNm0m7NmjUxXrZsWbLt8MMPj7F3bIcMGRLjBx98MMa27yNHjmzbJ7vv+fPnx5i1+hdccEHSjvNPubfn0I11o56DWulavVI4F70+sQvd5ZdfnrQ766yzYsx57unx7RjM33nt2rUx5jVVQLr+6pFHHsnuX/jwuifvetjtUhDe+opSV8xSy2yvdMDjjz8eY84xa4XsuXPVWafttitqJYQQQgghhBB9gB5IhBBCCCGEEI3RNclWTjrlWfuVyrK8AoU8JVVahdjapOWs0TxpV92Ks7m2uc+SZKt39OvXD294wxuy21rwtGmuvW3XiVUzT8Wy7Ob222/P9mnevHkxfuGFF5J2pbKyUslWKZ68QXSHV155BU899RQAf0zzZAA8nvAxs3I8ziMuatiuTy2s/S7vY/PmzTEePHhw0o4ti1kGYMfj9evXx5jlL3aM9CSNOYmApF19RysP6l7zc1a/nZQEKMW7b2D4WsAFH23urVu3LsYTJ07MtmO80gksy+JzAJDtbzdpHQPveuoVP8xt64aU0FJ6zc4VV7ZjP8u0brjhhhi/9a1vTdqNHj267Xss3j22bH+FEEIIIYQQezx6IBFCCCGEEEI0Ru1K7d50qyclKJV2lUqivKkgni7lqVev8mvOXaETvKmr0t9JdIcQQjy+3m9ceixKc9uDK1tv2bIl2fbQQw/F+LTTTouxN1XsfS6/j2Uype+x8LlStxqr8HnllVdiXvTv3z/ZxrIRb6ziqXo+Tvx+i5UF5qQ2VrL1zDPPxJhdsWxV+JyT22OPPZa0e/bZZ2N81FFHxdg7Byyl54foDiGEIjl3qRSr9DpcKsGz7kU5mbZ1FOJ2fH5w1Xa7zXMIYzwpOjsdsYsdkJ4fonfkjk8uT63sKecW6C0L8D7LqwSfG8fsuMj74NjmG/eXx3B2XgSASZMmxfj555/P7oPPMdunYjeuolZCCCGEEEII0QfogUQIIYQQQgjRGHogEUIIIYQQQjRGbdtfz/K0VGef04za17baKVdMnTJlSozZLhJIbSdZh2krErN+ju32rP0rr0mpa0tYxzpY9I5WLnlrh0r16KXWfnYb5zDnr9Wkbtiwoe3+bC7mziNvHUdpO68yK2M1qVpD0h1efvnlWEXXWvGWHsOctt5q6Tn/7HoVhscnmx88fm7dujXGtuJvzk7bVptmm1Peh13jUsdCVvQdufWl/DpXjd1u68b6Um8ffO5wXtlc4fOPx3Cby7k+eWtc7HoBPq+GDRsWY2v7e/PNN2f3KcoJIcTj7d3D2feUtLOUXhtL7wO9tSa8D/5c+55t27a13Wbvt0vPxbr9ZTRSCyGEEEIIIRpDDyRCCCGEEEKIxqht+1sqRfIqCHvT6jzFM3z48GTbIYcc0vazpk6duusvgZ1lEKtXr47xggULYnzEEUck7bgCK/fPShhy39G+j5Fkq28IIcTpdU8iUEqpzaRX+bV0+pb34e2vVCJQKvexn1VqF1wq9RI+27dvjzIoK9colbzmttlKu94x420DBw6MsZUZPvroo233byu1cz/Y6tdKaI8++ui2n2X7avvBSLK1e2H5iyeN9WSyOQle6T0EkF5f2ZrXXnfnzJkT482bN8f4l7/8ZdJu3rx5MWY5tydv9PqX6yuQjsksCbP3IZdeeml2n6Izcvddde7HSq19S/fh5Udp/zgXDzzwwOxnccySctuPUolkJ9sYjdRCCCGEEEKIxtADiRBCCCGEEKIxOnbZauG5bJVWKuV9WCkBV/ktlWJ5sLuMrbLKrgIsJfjJT36StOOpU57KtW4y7NrhVewscQuRlKt3dKNSe+l0a6lMhHPRumqxG1zO4cXbf2n1dE+yZbfl5GJy1eobtm/fHiviei5bHrl29ljyazsG8+tchXhgZ+euFpMnT05e83dhmQy7dAHA9OnTY8zOhp1ILj05G6PxtXu0fstSVyxPilXq7GPHxfHjx8f4sMMOi/H999+ftLvxxhtjPHfu3BhbGeAtt9wS49GjR8f4iSeeSNrl7nm83LPnDe+Dt7FEHQA+9KEPxfiHP/xhdv9i1+RctjzpVLv3Wzqp1J7bXx1pt30fS/+szJDHdN7mLUHw8NxlS9EMiRBCCCGEEKIx9EAihBBCCCGEaAw9kAghhBBCCCEao+M1JCUVLL21EaxHY50krxkBUv1nXXj/rFnmdSJAquNnzZ3Vzt19990xXrhwYYyPO+64pN1JJ52U7RNrC0vW2kjj3Du8aqy5tRGexa5Hqb6fLVStzp4tUDl/rWVfTtdpq1nn+uTZvda19pW9avdojROevai3Zol1xJwrAwYMSNqxlbm16WV9Ph/3TZs2Je3Wrl0b46eeeirGdm0dr0lZtGhRjFn3D6T2qry+xLP59daXdEPbLHZNbpzNrQ2paxPK12hbMf3xxx+P8caNG2PMeQ4Ad955Z4zZ9n/GjBlJu9ZaLgC44YYbYsw5D6RWqaVVqe0YnnuftWH1xgRRD68Ce6ntLWOPpbePnJ2vzQ8e//jewK7B4/VNvO7JrhHcunVr2/3ZtU11bKxLc9uiOwghhBBCCCFEY+iBRAghhBBCCNEYtSVbnk2aZ7mYs1PrhkTLWgCuWrUqxizTYrmA7RPbTNq+Dxs2LMY8Tfa73/0uabdmzZoYf/CDH0y25eRAkrv0Ha3jWNfON/cea8vnSUNy9nt2HyyN4SlaK5NhyQHv+4wzzkja8fQr57O1k922bVu2T/yav6NnNyh6R+t3tvIM7/dnWH7F77F2pQcccECMrRyP5S8seeUxEgC2bNkS4ze96U1t9w0ADz74YIx5bH7Xu97V9jsA6Tlgv2+usjdQLg3SuNsdWBprj1NOhmKPWS63rdyDrdGtBJHzlC1xrXUuywKvu+66GB999NFJO94/21EvXrw4acfjJ+e9ldOUwnlvZbiebFZ0Ris3vftZz6qaKa2kbq+TfP3mbc8++2zSjnN76NChMWaZLABcc801Mebr/Mknn5y04/dxKQwrEcxd/4H0e3JelsrjLRqNhRBCCCGEEI2hBxIhhBBCCCFEY+iBRAghhBBCCNEYHa0hCSFkNXcldsBAqmm3NpO9Zf369cnrpUuXxpg1y1abx3B/999//2RbTs9sNXcLFiyI8Y9//ONk28c+9rEY85oXqyvM2bqKzujXr1/HNomeTtSz5fPex9vYStLqg9maj9c9sUYZSO2CL7vsshgPHz48affmN785xrfddluMrS573rx52T6xNpRzlvMcSHXZoj79+vWLY4+3hoSPoV0T9Pa3vz3Gd9xxR4ztMeN9cF4Cqa6Yc9Hm2Pnnnx9jtm9fsWJF0o7XkLBuetq0aUk7XqPC46w9v3J2snabh2yAu0en16xSnblnaX3jjTcm28aNGxdjvr+4/fbbk3Yf/ehHYzx16tQY/8M//EPS7sQTT4zxqaeeGmO7duWee+6JMa/vO+KII5J21nqV4Xsj/o68XgBIywx873vfy+5P+Hj3s3XgPLXWuZzbDz/8cLKNX/N12K7Vu+uuu2LMa0oPPfTQpB2/5mvyzTffnLTj+1buu70vL7U9zq0P39X7GM2QCCGEEEIIIRpDDyRCCCGEEEKIxujY9rfTKS5PxlK34ihbV3LMEi0glQywxZmVv7C95bHHHtv2PUAq2WLJgZU68JTXvffem2xjScO5554bYzs9J7pHzqq6VGbYDfkcy544d4488sikHecLy/0+/OEPJ+2OP/74GPP07aWXXpq0mzJlSoz5e3zzm99M2vE5weeAhc9ZlikAwNy5c7PvE+WEEOJYY6uT53LWtmO7UbZUZ+tIIK1mbW16Bw4cGGOe3p80aVLSjmUyTz75ZNt9A8CyZctizPaq/DlAKqn1qtF78knZ/u5+Wr+zPRYsV+HYOy7emMvj57p165JtbMfL0hWWCwKp9T/b+T/66KNJuyuvvDLGc+bMibEtU8CSxt/+9rcxtuM7fy+vejVLfmw1epaYid5Rch/r2fnnbG+t/NBKuBjOue985zsx/oM/+IOkHY+TS5YsibGVLX7yk5+MMUv/bOkAXsbA7fge1W6rO16qUrsQQgghhBBij0cPJEIIIYQQQojGqC3Z8qZUvSmubkyRc2VgnqK11VO5HU/t3nfffUm7WbNmtf0cb5rJqzjLEi7rPHTDDTfEmB03zjrrrKRdyzVHLjC9J5ezuRz2Krp705de3vN0Lru32el45qqrrooxu2UBqayK8/dnP/tZ0o6nZdkZZsKECUm7r371qzE+6qijkm1cfZsdldiFSXSPEEKUxlkpFsOyACsvZRkqH08rt7rppptibN0H2f2FP8vmTk4Oa6vC83jHki1PrsrnlHf+etcZT9oluoPnWJSTz3nSWO848XWZZVRAKnnl+4FBgwYl7S6//PIYc24feOCBSTt27nzggQdifMYZZyTt2H3Qq0pdKtnKXXMAf0wQndH6nUudNT34uNjlCHxPOGTIkGTb9OnTY8xSQisf5BzmfaxduzZpx7JZ7jtLE4E0r1hyaKXYLA+3ucd53xWnsl7vQQghhBBCCCFqogcSIYQQQgghRGPogUQIIYQQQgjRGLXXkHgWjJ7u16tmWQprL9n+zNqacXVW1pbavrNGnjVxtlI7a0O9Kuvczq4RmD9/fozZevVrX/ta0u7CCy+E6D0hhJiDpdpbb22Ip+3NnQNAmgeePeAJJ5wQY7Zave6665J2rOscPXp0jEeOHIkc3F9bMZg/11YG5vVXXBHcakZtBVpRj379+kULXk+P7m3jfGNNMecekK4bGTVqVLKN1+Cxpbo97vw+1k6vXLkyacf5zO/xqhqXWm6WWnXL5rfvKa3mXGrVbI8Zj0G2Yjpr9flay2uqgHTdEsfW+prPF87fsWPHJu1yeWqvOfxZXs7yWF1a0V50RgghOx54duMMHyc+1lu3bk3acZ5yHgHpceeSEatXr07asY015/kLL7yQtOOx+vDDD2/7fiAdq/k7LliwIGnHr9/97ncn2/hege+J7W9m75VyaHQWQgghhBBCNIYeSIQQQgghhBCN0SdzfzxdY63teLrRVjgvhe3Q2FrSSrZYQsLTnnbKk60qeUrVShhY+sDfy8ogWC6xZs2aZNtDDz0UY57G46k10T246nWpXKPUAtCTI9p95KbZzz777OQ1T/WyZa+1Ebz66qtjzLauVjbF1V1ZgsiVvAHgzDPPjLG182WrarYHtOfHbbfdBtF7QgjxWJXmrB1n2ZqXxzeezrftrPyF85uPu80xlvSxHMFKCVjSwGOknc6vY9lrf6dcW9mo9x3dlMNxPtuxk7dt2LAh2ca57sG5yPIra5992mmnxZgthm1u56Q7nrzKy0VPGiy6R8kShJL3A6lltC1BwddUT1bN12i2nAbSsZXlURaW2q5YsSLGU6ZMSdrxuM1SMWt9zZXgeRkEkEq4+Jyo+3tqhkQIIYQQQgjRGHogEUIIIYQQQjRGbcmWlQh4jie5dnbaM4dtx5UpWXpip7FmzJgRY57+Gj9+fNKOp3k915lly5bF2Pu+PO17/fXXJ9tYpsZTcHPnzoXoG1rTh6VVmu00e+mUeWmFXq64ypVZAeDKK6+MMUsE7b4PO+ywGLPT3ObNm5N27IJx//33x9hWd+UqrtYhhPvB54d1C7HfRdSDXbZKp749Jx4eF231dJYC2mrW7N7G+7NyP94n547ND5bDsszVc7/zqrF7Tkylv5PYvXjXzZxUz7bjfLHX6BEjRsSYJSn23iCX91Y6fe6557b5FjuPnzwucmzvXbxrUM51tNRBTnROK+e8cYF/b3uMWGLF8uhDDjkkaXfLLbfE2I6LvASBc9ZWVme4H3ZM5/PjjjvuiLHN2XHjxrXd98aNG5PXfF7asdo+B7SoO85qhkQIIYQQQgjRGHogEUIIIYQQQjSGHkiEEEIIIYQQjdHRGpIQQsdVQq3ekV+zpthWjuaKqWxdBqRaONbwjRkzJmnH9mrDhw9vu28g1ZeedNJJ2T6xPpr1fda++D/+4z9ibDX9XGWW+2ttXUV38Kqxci7zOhGvUnvJ3+3+7GdxvlmbStYzs5bTVr3mqqucz5MnT872kSsX22rs/Jpt/oBUp837t7aEfL498sgjEPXo169fMq4xrNnlmG10gfR48rg1ceLEpB3bps6cOTPZxjplzjGrWebXHNvxk8c+D686O8PXEq/qd27foru0fnNvPRP//t4x83IgZx9t4XZW+86fzdfh8847L2nH5xGPaXa9HF/n+bz0riVWf59bq+CttRG9o2Stg2c9zrnNNv1skw6ka5vseqaFCxfGmPPoLW95S9KOx2C2Ebb3n5zP3D9rRcxW/1yewlpfe2tIcmu97O+aW2tiUWYLIYQQQgghGkMPJEIIIYQQQojG6Nj2Nzct264N4Fum8vSXlVEx3vQoW/jaqSuWD7DExe6Pp6646jVLr4C0giVXhb/sssuSdjy1y9bDdpu1hmNKp7jErsnZJOYqq5dKtjzse3ialitiT58+PWnH1YBz0kQAuPfee2PM1pSzZ89O2vHUMU8pz5s3L2nHr3/wgx8k27iSMct/7rvvvqTdWWedFeN/+Zd/gahHv3794ljj5SKPs3b8ZGtJlrVYyS2/thKBq6++OsZsZcqWvUBqbcp9Ykmu/SxPkpPb5lVj74bMUnSHur9x7prn2dzabSx5tZbADN8r8L2BvSazJStLVK2FKl/XeQy3Va9ZDmN/J75X4u9lfxdVbu8+dlzk39yTIvFY6NkDc+7YnGU5F8tcbXkKllUx9t6A857HZivF4j5yH7jSO5D+FjafS++hStHoLIQQQgghhGgMPZAIIYQQQgghGqNrkq3SaXbPZSMHy6OAdEqKp7Hs/lgaw1NX1mHoIx/5SIxZksIyBQtXYLeVradOnRpjKyPj15MmTcruXxWFuwO7bNWVYuWmyL392fewbIbdhmxu85Qox0OGDEnaLVu2LMZf//rXY2xdYthVifPUymluvvnmbJ9YinXrrbfGmJ0+AODtb397jCXZqk8IIeaLzaPSnOXjy+PdPffck7Q74ogjYsyVhlv9aLc/657Fuc3tbI6xrIxlBlaSkruWlDppee+T3KXvsXIVPhYsjbHHPSf1tvvja7mV2vDYyts8dyAeI+3+eMxk+Za95rOMnKu92+/oSbF5m5enknN3B3aNtWOJJ9PKtWMnV5uzLNm2Ume+trMTlt3H4MGDY3ziiSfG+Bvf+EbS7sorr4wxj/1WbpVzzPVkad7Sim7IYTVDIoQQQgghhGgMPZAIIYQQQgghGkMPJEIIIYQQQojG6LhSe28rW5Zq87hKta0MzNa81113XYzteg22MnvyySdj/L73vS9pxxpoXndiNdVsm/rMM8/EmCtUA6nG1Vbl5O/MdoNi95Orzu5pIb1tnOs2tznHeB9Wx8m5xFXRWb8MpJroYcOGtf07ADz88MMxPv3002P8rne9K2nH60Z4zQiQald/9atfxdjaalrLYVEP1jZbcusr7DjDOnPWAFvrR7aW5PHNvo/Xg1htM2uTeZtda8LjMZ8PPF4C+XWG3ppDzxLYaye6T931j5yzfPxsbvNra3nK7+OxcPTo0Uk7Pg+4nc1ZtkDl+xBrjcprV9jq396TeJXa+XfTOpHdi3csvDIWpdd1vpfke0cgHXfXrVsXY3vNnzt3box5LSBbvAPpOMv3yhwD6T02rw2xVeb5nLBjv2ffnmvnodFZCCGEEEII0Rh6IBFCCCGEEEI0Rm3b39K/W3i6laeWLDfeeGOMrU0vT1FxbPfH07ksSfEs9XjqykpSuBorT0HZad5t27bF2Kske+qpp2b7IbpHzvaX4SlWz9Lag/dhq1nnJCRWYsXWgSzFsucAywcmTJgQ49NOOy1pN23atBizVaCVQfLUs52yXblyZYwXLFgQ4yOPPDJpZ6eORX1auWRzLyeHtbInPr5sSWqPkZWUMLx/lhJaW2geT62EhmHZTM4qGCiX9XqV2kuvR7JX7x6539KTsubwql7zPYTdH0tjeFy0OcYSbt5mx0UeC3PnFJDKa/k+hO8FdkXu3LbXEuVs9+j0ftZKsXjs49he1zmH7T5YEs0W1Lfffnu23fz582N89tlnJ+3Yfp+lifYasWLFihhzjtnxnbd58tpu5KVmSIQQQgghhBCNoQcSIYQQQgghRGN0JNmqqipOS3kyFm/KnaeuWIaycOHCpB1Xov7ABz6QbHvooYfa7oMdsmwfefqWp6os3D92zgBSORdPxVoXEA+W4XCfRN/jVVb3phtzblydwC4evA/r5MY5x++xuZ1rN3Xq1KQdS7Z46tU6eKxduzbGVrLF5xvLFj/3uc8l7awsQtQjhJDNs1I5Ex9PZsqUKclrrjjdv3//ZBvLuXiMs3IE3ua5A/H46Tkx5qRYXjVl+3txP0p/M9EdvHG2FE9WzeMYS/+AVJLIY5zNWe4Tux7ZvrI0i9s9/vjjSbszzzyzbZ+sZMvLv9y5U1dCLMopdc+093o8ZnKOWekqS6dsxXS+bvL77FjNSwjOO++8GFuXV85Tll89/fTTSTs+j/j+217Hud3YsWOTbVZOmaN03FVmCyGEEEIIIRpDDyRCCCGEEEKIxtADiRBCCCGEEKIxOrb9bekcrd6RX3NstaCss2M9utVknnvuuTG2lqfclnXJVpvHurVDDjkkxuvXr0/aXXnllTE++eSTs31iDR7HVi/IukKvAijrEa0WVnQH1uPXrcCea2dz26vUysfa2jjm9sE5YSsNs20l66bt5+bOS34/kJ4TvK4ASNdcsXb1/PPPT9pZjaroPVZ7m9OZ2/Ufa9asifH06dNjbNfFsbbZrlPiHOGx0Fqj8nnAuW3PKR7HWXvsrQ3x1pp4uuTSdSNaU9I9WnlgdeWlayM4j3JrgIA0D2w+87WYx0I73vF4zOtEbAX23Lhox/DZs2fHmMsAePdJltLq7N76GlFOCCHmlh2rcmuJr7766qQdr6+84IILYmzvCTmP7BqNZ599Nsa8buSkk05K2vE9bC5/gTSHedy2dr7cR+6DvT/m8hSc50B6zefz0uZyac5qhkQIIYQQQgjRGHogEUIIIYQQQjRGR5Itlr+UTrPbdlZS0mL8+PHZ/dlK6PyaJVtW9sSfxdIEjgHgK1/5Sox//vOfx3j16tVJu0WLFsWYJRKeLZ+duspNKYu+o8RCtY6Vole11SMncQHS6VyWN1ppAstrWLLFld6B9Dtu3LgxxqtWrUrasU2stcVmW8GLLroo2/dcNXrRGSGEjn9La2vKU/WcAzZH161bF2M7VnEu8v68PPcqErPFtSfJYbxrSaklsPKy7wkhxOuvPe6cm7zN5ixv4+urlZDy+2wuspSFc5HHPiC1tGYpi23Hn83SmLlz5ybtjjjiiBiz1a+VkZdKtjzZu+zVuwOXsShdgmCvjbfddluMWb7F1rsWe/w4T9mq2t6nco4NGTIkxpzLQCqj4n1YKSXntpezH/nIR2JsbY95bPWuC6VyRM2QCCGEEEIIIRpDDyRCCCGEEEKIxtADiRBCCCGEEKIxOl7EkLNJy+mAS/XBVo/OOlGrfeM1JKxhs9o8brd48eIY81oQINVHz58/P8b2O/J6ldL1B56urs66BdE5uXVPud+/jgWw3b/Vrefy1LMAthprhnN7xIgRMbbnCttd8joRa+23fPnyGFud7MUXXxzjiRMnxthqu7Umqu8ptcTlPCi1Y7Zr9Xi8GzRoUIztWj22jOTYaps5n0vPFY7t+eatISlFtr/dYdu2bbj99tsB+OtGObbHk9dXci6yFSqQaum5dACQH0/tdZgtqFlnv2HDhqQdfxc+j+yavl/+8pcxZp19J/cG3roR0bfYNQ68honX2S1dujRpx+Mij7l8PQXS9XN2/OT7AV6n9MADDyTtfvGLX8SY143yehIgLRHAeepZG/N59Fd/9VdJO/4sey3J5beX2x66IxZCCCGEEEI0hh5IhBBCCCGEEI3RJxqL0iq5ufcA6fS+lWLxa57atdO1LMVasmRJjG01Vp4qZotTK0Hh/fMUlJXJcP9sxc6VK1fGmKfkjjzySIjm4KnHUsmWV0GYpSt2n5wTNhe5CjZPe7Itn30fSxqsHJHlAzwta6UOLFU899xzk23vfOc72/ZPEq2+o5VbnszQy1N+H+eOHY/Y4pGn5oFUtsCygIcffjhpx7JAju24yPC54skW61JXdinq8fzzz0dZtCet8+B2LF055phjsu/xLHBZGjNmzJhkG+cpS3KsbJHlXDzesfwVANasWRNjznuvYrU9P3Lnix1neQwWvaM1TtrjxHJVvk977LHHknacs5xv1n6fbfZZ9sx9sPvw4Py1ldoXLlwYYz4/7DnA99hnnXVWjE877bSkXa4aO5CXYtlzXpXahRBCCCGEEHs8eiARQgghhBBCNEbXNBe9dTwpdeMCUscCdvrhaSwgrbrK0hV2RgBS2YInieB9sMzATvPy1Kud4uJpMlUQ3r14Tg913NDs/vi4WweqQw89NMYsobHTrZxL3I6lfra/nM/3339/0o6lYzzdfM899yTt+Bz4yle+kmzjvPdccuQM0z1yuVo6tvI4wzkwZ86cpB1P6b/wwgvJNq4azP2x0i6WN3B+2L7y+MztrFSl9DtyvpW6Poq+YZ999onjUN3rGh/PyZMnx3jmzJlJOx5bOUeBVDrF/eAcBdLxlKXdtjo2S2NZzs33ILbvLIu05zGfb9ZFMSfRtbJelpiJ7uA5ULEEyrvme7DjoFftnLdZOWJOIm3/zvvgz7UOYfy+b3zjGzG236m0Gjvvz/5OnmMooxkSIYQQQgghRGPogUQIIYQQQgjRGHogEUIIIYQQQjRG7TUknkbX0+rn3me1aaxHs1Wl2YaMtWlWp8a6eLZhszaTrJHzqqVay8zc3z3bP7bSHDt2bNv9ie7SOt6lOvO6ayN4/cewYcOSbQMGDIgx65ftWhP+bN5m+8QWfpxTbC8IpOfKpk2bYmzPqb/7u7+L8SGHHJJsYw2z1j3tmXjHhccjrlANpOs6nnnmmew+OBft+Mn5x7HVQLPVNI/vtqJ7jk7WGeaQzW/f4+nq+XpojwWPn4cffniMeey07awFLq+v4HUjNsc473l/djzmvOK1et49Dm+z5wpvs+cbV4nn8dl+R8/qWHRG63jYe0deS8RV160tb27ssuuZOde9nMiVltjVNobXDPLn2rzhtVm8Pso7f71zlu8v7PXI3m9k91/USgghhBBCCCH6AD2QCCGEEEIIIRojeFM/OzUOYROA1X3XHdGGSVVVjWi6E69VlLONobytiXK2MZSzNVHONoZytibK2cbI5mxHDyRCCCGEEEII0U0k2RJCCCGEEEI0hh5IhBBCCCGEEI2xVz2QhBA+FUK4O4TwQgjhe2bbe0IIi0IIT4cQFoYQ3tlML4VI2UXenh5CWBxCeDaEcH0IYVJD3RQisoucPTCE8M0QwuYQwtYQwk0NdVMIAEAIYf8QwsUhhNU77gHuDyGctWPbm0IIvwshPB5C2BRC+GkIYcyu9ilEX+Pl7Y7te9X9wV71QAJgPYAvA/gu/zGEMA7ADwF8GsBAAJ8B8KMQwsid9iDE7ieXt8MB/BzA/wQwFMDdAH6y23snxM60zdkd/At68nXmjv//5W7slxDt2BfAIwBOATAIwOcBXBpCmAxgCHpydjKASQCeBvCvjfRSiJRs3u6N9wd75aL2EMKXAYyvquqjO14fB+DXVVWNpDabAJxfVdVtzfRSiJQ2efunAD5aVdWbd7w+CMBmAEdXVbW4sY4KsYM2OTsDwJ07/vaU914hmiSE8CCAL1ZV9TPz97kAbqyq6uBmeiZEnlbeAhiGvez+YG+bIclxN4BFIYTzQwj77JBrvQDgwWa7JYTLLAAPtF5UVbUNwIodfxdiT+RY9FhpfnGHZOuhEMIFTXdKCCaEMArAoQAWtNl8cubvQjSKydu97v5g36Y7sDuoquqVEMK/AfgRgP4AXgTwhzsOoBB7KgMAbDJ/2wpA/3In9lTGA5gN4GcAxgI4HsB/hBAWVlW1qNGeCQEghLAfgEsAfN/+S3IIYQ6AvwHwjib6JkQOm7chhL3u/uB1MUMSQjgDwP8BcCqAN6BHj/f/QghHNdgtIXbFM+hZ88QMRI/GWYg9kecAvATgy1VVvVhV1Y0Argfw1ma7JQQQQugH4Afo+UfJT5lt0wBcBeC/VFV1cwPdE6Itmbzd6+4PXhcPJACOAnBTVVV3V1W1vaqquwDcAeCMZrslhMsCAEe2XuzQiE6F5ARiz6WdDHbvW6goXnOEEAKAiwGMAnBBVVUv0bZJAK4B8KWqqn7QUBeF2Aknb/e6+4O96oEkhLBvCKE/gH0A7BNC6B9C2BfAXQBOas2IhBCOBnAStIZE7AE4eXs5gNkhhAt2bP8bAA++Vhesib0HJ2dvArAGwGd3tDkBwGkArm6wu0IAwLfQ4/x2XlVVz7X+uMOF8zoAX6+q6ttNdU6IDG3zFnvh/cFe5bIVQrgIwBfMn79YVdVFIYRPAfiv6HnK3ATgG1VVfXX39lCIndlF3p4B4OvosaO8Az2uGqt2bw+FSNlFzs4C8P8AzEHPAvfPVVV1+W7uohCRHTMgq9BjZvMybfozANMAXAQgWVNaVdWA3dQ9Idri5W1VVZfsbfcHe9UDiRBCCCGEEOK1xV4l2RJCCCGEEEK8ttADiRBCCCGEEKIx9EAihBBCCCGEaAw9kAghhBBCCCEaQw8kQgghhBBCiMbYt5PGBxxwQDVwYE9hyJ5aLa/Cr3MxAPTrV/YM5O2j5D2vdVruZ1u2bMEzzzyz93yx3UwIIdrI7bfffsm2ffbZh9t5+2gb21zm/XFs23q5XfpZufeU9r2TfdRh9erVm6uqGtHVnb5O2H///asBA3ocR7382HffV4fv0nyzeVSai97f+3LcrXMd8PBcJZcuXaqcrcl+++1X9e/fH0Calzu2xXjLli0xtu0OOOCAGJeOfXYf/Jrf5+U99++FF15I2r30UqydiIMOOqjt++3rbtyvlO5j8eLFytmaDBo0qBo9evQu23XjOPXl2LV9+/bkNee6N96VOuza/Zfsz9v3ww8/nM3Zjh5IBg4ciA9+8IMAdj7B3/CGN8R4//33j7G9CeR23g2hd7HNDVa2T7mLcl1yP7Ldd+mB8XjllVcAAF/5yldqvV/sjB18Bg0aFGMvVziHc3lu99d6cG/BFzN+n83t3P757/Z93D/vPMpdrO37Sm84vdz+kz/5k9XZjcJlwIABeNvb3gZg5/GzddMHAEOHDo3xkCFDknYHHnhgjDl3bB55Y3XuAdveBHIulT7ke2Mk78/L7TrYz2qNswBw+umnK2dr0r9/fxx11FEAgOHDhyfbxowZE+NLLrkkxoMHD07azZkzJ8Z83DnngTT/7GfxOcEPOK0H/Bacp3xdWLVqVdJu7dq1MT7++ONjbMfPXH+93C79xwZvrD7uuOOUszUZPXo0vvnNb7bdlhvTvLHPwzuejHfzz9s4tg/RnPfPP/98dn889nmf++KLL8b45Zdfzrbjfdh2/Pp973tfNmc7eiBhvJkP3uY9TCQd2be8K7mLnt23/ewcpU+AuQch+36vT6Wf1dr/3jTr0zT2ZiyH96/NHqW5WPqvad7Ddum/BObOgdLzd1fvY1TTqHvkzv/SCyU/aPA2ew7wa7uP3P5L/+HHXij5osTtnn322aTdc8+9WoyYP9fewPI/AJTm5e6c3Xk9UVVVvLbZh2M+7nyDxA/NQJpvuTwHgOnTp8f48ccfT7blHma98ZP/8WjTpk1Ju0cffbTt/vhmzu5/8+bNMT744IOTdvydS9UjdVUmYte0fkvvoa/0H71L25X0B9j5XpFzlrddfnlac5bPjze96U0xfuaZZ5J2dnzO4Y39PIPYle9f1EoIIYQQQggh+gA9kAghhBBCCCEaQw8kQgghhBBCiMboaA1JCCFqwayuM7d41tPserqy0oVEngY4t17D9p21od4aD9Yic7u6mk7vs6Rt7j6la4ospXmU00AD+ePpaZtL13LwPuy5klvP5S1+L8UzcxC9I6dtzi00twvS+XjyNru4lxfg2nzmxYy8zoP/DuQXxrO+2L7mXLHtnnzyyRjzGgF7Hk6dOjXGrJsG0u+SG7cBjbN9gTX04GPIx9rmYm5d0dy5c5N2vPj94osvzn72sGHDYmyPO68/4nPlqaeeyu6PFwvzOie7/1tuuSXGU6ZMSdq9+c1vjrFdcFy6zlA5232866F3fa2zhqTTdcTtXnPuPP3000m7K6+8Msbr16+PsV2Dx+tLvMXvjF2snhvvvfsaD82QCCGEEEIIIRpDDyRCCCGEEEKIxuhYp9GaVqxjYwaU26nVsRf1bCvZa3zjxo1JO57WGjVqVMd970SqkntfXXtgUU6p9K+uzaJnVVk6tcvHfeTIkTG2XvyLFy+O8cSJE7N9ysnIOjnfXo+FSZskhJD9LXMyLW/sO/TQQ2M8duzYpB3bkD7yyCPJNpavsESFbU2B1NqUbYTtd9i2bVuMt27dGmMr2WJ7Sn6PlQvccccdMbbn28yZM2PcjbpQwofl3NZamo8n//7W9pdzmG11bb0nrxYC79+TD7I1MY+51oJ60qRJbfvkfUc+b6xMhqlb0FY53B1CCEX3s7nlCN77Su2B68I5a62lr7/++hjzOWZtf4844ogYc500K8nlfPP6bmXDjGx/hRBCCCGEEHs8eiARQgghhBBCNEbtSu2W3BRjaUVob1W+naLktjz1aqVYs2fPjjHLX1gGYN+3atWqGJ999tlJuw0bNrTtA093ATtPeeXwKtprWrb7dNsNzR6z0rz33K54G0sTxo8fn7S7//77Y/zEE0/EmCWHQH661XMck6tL8+TcDHMuWza3eUyaMGFCjK38haUntvo0OwRxVd8tW7Yk7Xiqnp2NrPzl4YcfjjG7ulj5C8vD2EHGfkfexz333JNs4+/M1wg7rmqc7Q4hhJhbVl7KrmmcszYXcxJme4z4fVZCk7v2em51jz32WIztWD9ixIi2+7PXfJY7cp9Y8gWkY7p3/fD+rvG4e+TcDHMyrVL3qLqVyhmvUjuPmdYZjj+Lx/AHHnggabd69eoYz5s3L8aeJNLmHl8z+H323C52FitqJYQQQgghhBB9gB5IhBBCCCGEEI2hBxIhhBBCCCFEY3S8hqSlBfP0j14l9VL7M89qjG0smU2bNiWvWdvM/eUqrUBqCczrRHjdCZBq9e+9994Yc8VgINWdeutJPOtg6US7j2ex662pqGP76x0/7xzgnGX9vK1qzOfAfffdF2O71iSnWe5El5z7/rKm7jtyxyNXNdjmL2/jNRm2Ii9Xn7a6+Jze346LbDvNebpkyZKkHdtOsh2lXWvC61U8PTPnnx37+TWP73Z/WkPSHfr16xePvbX/5Osm55HVmeewVdG9tZd8fHMWwHafvCaKcw9Itfq8D5s3fN/A+WbHbW/tFOOtr9W9QffIrSEptenPvafUHtjDW0PiXXt5bOX1c/ZelMtd8Lovb01KKXXvDTRDIoQQQgghhGgMPZAIIYQQQgghGqNjyVbJFJdnf+ptY3gqduDAgck2lkRde+21MX700UeTdscdd1zb93CVYCCd5p07d26M7bQbt+OpsBtvvDFpd9ZZZ8XYTtnytJm12WRa31/Ts93DO54edWSG3j686XjOe6+C8AknnBDjpUuXxthasg4fPrzt/kptiS2lVVtF35Cz+rXHgscWPtb2HOCpeiuhsZV9W7ClLgAccsghMeZzyspfuFo25zPbwgKprIXzzZ6vniwgJ9exuS3ZYXfYb7/9opTP/qZ8fFkW6Mm+eZuVbHEesOTQbuP92WrWnNssKbM5xucH94nltEAq2Tr66KPbvse+9u6NvPFY427fk7P69aRY3njsyblLZaP8PpZl8TUeSHOW23lLELh/VkrI57M9t3PjpyeP91BmCyGEEEIIIRpDDyRCCCGEEEKIxuhIshVCiNNG3pSUV0GYp7x4Sv+YY45J2vHUKbsBWIYOHRrjzZs3J9tYzjVnzpwYswsGkE4jjxs3LsY83QWk34slEWvWrEnasYTm+OOPT7axZMuTxkhK0D1aOdcNtxIvt3NVtL3P8qRTzOOPP568PvLII2M8bdq0GC9btixpN3bs2Bhz7lnXGe+3KJVZyrGo+5S6v3hjCW+z0/Es07LHj6UygwcPjrGVCPA+2d3KSr54PGXJiyfF8uQC3F/7vbi/fM2xMlnJX7rDPvvsE90r7W/M8jzOAW8s4eNp3YFysiwgL/ez13Lexv21TnMsc+Ft1kGOZeAsYfSckmzfc3Iuybb7hhBC9t6gjhTLa1e6PMGDz4lt27bF+LHHHkvacc5xzvIyA2BneXe7z7H781xjS13APDQaCyGEEEIIIRpDDyRCCCGEEEKIxtADiRBCCCGEEKIxOrb97VTP6FVSZZ3vEUcckbRbvnx5jK0mlXVsbO07c+bMpN26detizOtJ2H4SyFe2tDp71sLaysC5z7WUapalx+8O/fr1y1bE5d+4jk7X04l2Q2vK5461RuXcnDx5cozvuOOOpB1rqtkO0OaXp1mWzn73EkKIv7mnM/fWLDG8Vo9zAPCtx9lSld9n38PjKeuSW2sKWrD1OvfXVo/n/npVubkfdhuP6Z7lpugOIYQ4JtnrK69F4vVH3jjjjZG8D7s2hNct8foom2OsweectZ/Fa/D4e6xduzZpx+tSeV2rtb72bH+99SWib6lbgZ0pXcfXyTaG75e5xIW1oOZ10Hw/a88BrsjOaz68exe7NqRO6QS3XVErIYQQQgghhOgD9EAihBBCCCGEaIzakq3SSpS2HU/n8jSnrRLMlVWt1djtt98e43e/+90xtrZmvM/FixfH+LrrrkvasfyK5T1WRsb2vhs3boyxN41lt3Uqf9HUbe8IIWQlW3xsvKlYhqfgrVzAk4bwtGyuCuyu9sGwXGXgwIExtvnG1VgnTZoUY3tOedPG3A9PSqhc7R65cTZnO+1Vc2Z5iq1YzflizxMexziPvOrYnIv8uUAqM+T+WWlsLhftb1Fq+8v79yRqoj79+vWLsj4rIWGpk80dJifPs1KTYcOGxZjHNAB46KGHYsz3Glbyyn1k+ZWtZs2yw1WrVsV4xYoVSbuTTz45xqXyn1LJryS0fQNLY73yFN71OnfMvIrupdhrOeccnxMTJkxI2rGci88jK9flJQiM7bt3jWD4OmC/b7G0q6iVEEIIIYQQQvQBeiARQgghhBBCNIYeSIQQQgghhBCN0fEakhw5baSnf2StGutMgVQDPGbMmGTb5ZdfHmNeN3LkkUdmP2vWrFkxfuyxx5J211xzTYy/8Y1vxPiYY45BDtZHW80sa5Y9bb6nK2z9btLl944QQlxL5K3XsO/JwfplT49v9Zrjx4+PMeeb1UBPnTo1xqx3tzr7nH32gAEDknaPPPJIjKdPn972/YB/zvJr/o7Kzb6jtT7C0zaznteOM9yOtcL2uPMxtDn2+OOPx5j7wfkGpHpmXpvE606Ancf4FlaXzDnM+WbtZHkbW60C6XWB92+/v+zVu0MIIY55vL4SSH9zHhc9G2e29rXnAI9pdn0JrxVZuXJljA866KCkHe+TywWccMIJSTu+V1i6dGmMbS7zuM156q0lKLWGt/vQuNs9Wr9t6b2BpY6luLdvHqvsOs9bbrml7T5sbm/evDnGbINt7zW4PIW3npa32fEzt6bEu7/w0AyJEEIIIYQQojH0QCKEEEIIIYRojNqSrdLpKU/+wbGtbs7TRFYSxfz617+OsZ0CPvroo2PMFsOHHXZY0u7hhx+O8YIFC2J81113Je24IrYHT0uX2r9pGrZv4ArC7ba18KqRsvXjRz/60Rj/5Cc/SdrxlL6VBbIFNU/FsoU1kEoBOU+tpTVLBnh/bLsKpJbW3rRsqe2vN/Uq+Uv38cZPT9bBx5OlMHYq3bO7ZqmXtZZkWJrFchrOPSDNU7bPtnmTk1h51pFcURtIzwPev2cdLOrDtr9cARpIc5blJVaCxzl2xhlnxPi2225L2t1xxx0x3rBhQ7KNbYU536yEdsqUKTE+9NBDY2zzga1+lyxZEmObbywx4+9lrz05a2P7OmfvLbqHZ/vLeLKs3PhZKsezsGWvLYVx7rnnxpjPAZZyAel4zPfOdvzk84PHenuvwWO1JVfh3f4usv0VQgghhBBC7PHogUQIIYQQQgjRGF1z2eKpzpwrj23HMipb9fr3v/99jJcvX55sY2eNrVu3xviqq65K2rHUhp00rPxgxowZMeZp2QcffDBpN2LEiBjz1LB1W2KXGM8lx/42ovuEEOJvbn/vnHzQTstyvt10000xtpWoOQ9sZWCuBsz5ZqWKy5YtizG7xNgpUHY62rJlS4ztNC/LdVhKYaVdnnSlVDKgfO4euUrtnAc5iYd9H2+z0++c61ZCw/IazjfrnsWyFpZscV4CqYyR3V+sQ1aplJJz1lbYzsm+7O+pnO0O/fr1i9dEK9nyqkUzvI3zlOVVQOr+Zl0Fc8fa5seoUaNizHlk+86v169fH+O3v/3tSTuWZvGY60mxbS7y7+Rdj0Tfk5NY1XVNLf0sHiMXL16ctDvqqKNi7I1b7AR6xRVXtP07kMq5+B7HOmd5rnHcj5x8q93rHJohEUIIIYQQQjSGHkiEEEIIIYQQjaEHEiGEEEIIIURjdLyGpKUFs7rGnE2d1brxWhF+j7XYZd2atQzj114V4gceeCDGxx9/fIytrRlX0ebq7HaNAMNafWvtx3prz7YzZ83Z7n2iHlyp3SO3BgpI84DzdMyYMUk7tn60WkvWR/P7rK6TNdFc0f2nP/1p0o5zndeu8DonILUi5rVYtiIxa7ZLrVG93BbdodQ+srSdXSfiadU5r3jNB68Tsa957RTbqQPAokWLYjx79uy2n2P7y/bWPNZbeL0gkLfjtLlsx11Rn9Zxs2uMePz19Pi85ogtSceNG5e0mzZtWoxtjt14440x5rVNNsd4TQn3yVZ+t2tKWhxyyCHJax6PPcve0nVfntW6bIC7R872N3csvGNWOh57sNW/vf/8wQ9+EOOTTz45xlOnTk3a8XpVHj9Xr16dtOM1UdzOK1Vh7+dz39m28+zlk/0VtRJCCCGEEEKIPkAPJEIIIYQQQojG6Fiy1ZqKsdP7ualYOyXOdntsL8b2vUAqEZg4cWKyjafueVrW2vmecsopMWaLMyvh4elb/ixbmZ2n0Ng21U7zsiTM+508JCXoDl6ldpZv8O/tWa16ts2cV1ZCwhbXLO2ysJ00T6NyZVYglXOxHNHa+fJ353PlpJNOStqV2lN67VT1uvt40lhP/pHbZqfO+ZjZfGa5yubNm2PM1tRAKtNiuY5tx/3gvLTWrTn5pJUw8LhrbeP5e3o5q3G2e7SOm7U8Z/vonG21hd9jx2MeZ608jNuyjMrmIksXWeZq7w1Y/sKyW5ZlWzwZpCfnKrWhl2SrO3iV2nsrjfXsce14x6Um7r///hjzvbJtx9f5SZMmJe04Xzhnf/vb3ybt+Nxhu2Fr++vZ+ZZKsUrt1ZXZQgghhBBCiMbQA4kQQgghhBCiMWq7bHnTrTw9Y52v2O2CZSzsnAEA8+fPjzFXZgXSKSV2xWKJFgDMmjUrxuwwYKUl3Cee4rJTpfxdPAkYTwF77lncj9w0rJyLeke/fv2yLlu539aTHuWq6bY+q4WVl5x66qkx5jzatGlT0o4lg5yXVorFVVxZisUuHUAqZeHP8qrWe84wufeI7pL7bXPSWO+YlVYati5WLKPl8XPjxo3Z97HrkZVKWqejHNxfHo/tecyvrQyS5WYbNmxo+3dgZymPqEdVVVEGZX/jnLOmHWf5uHN1dnZnA1JXNysvYcehRx99NMb2HoLHTJarWAkK75/7Vyr188ZIz6XQk/9o3N29eNfGnGSpVNoFpPl82WWXxfi9731v0o6d3fg9VtrFYya3Ywk4kI7bfG9gz6lSiSC/z973S7IlhBBCCCGE2OPRA4kQQgghhBCiMfRAIoQQQgghhGiMjtaQsE2a1YSVWn6yzozfY3VrrAm22mauKs32pccee2zSjq2Def8rV65M2vF6EGs/zOS0/3Pnzk3ase2vtQTO2fl5elpRH7b9tbrGUnL6R6vlZV2xrcDO1Yb52N59991JO15rwmuRxo8fn7TjtSZcndiuNeG84urHVgPNev9SjbJsfvser2quZ8ec0z3bY2Z1xQxbo/I6DDse85oBPlfYTh1Ix1a2nLR9YMtXxp6/rJ3mqsMA8K1vfSvGvP7FWtJ61d9FObyGhK+nADBmzJgYc17aMSh3zbPr8datWxdja/XPtune+MTXZV5fYsf6oUOHtn0P34MAqc17zrYa8NcScFs+73Uv0Pd4a/C8tXpMqdU4X4eBNHf4+r9w4cKk3bx582LMtv+333570o7XUbElMJ83QHpPzOOnveZ46w5zNtt1UaYLIYQQQgghGkMPJEIIIYQQQojG6EiyVVVVnJbJVb8G/Gkttn5kSYCdtme5iq3GyhKBGTNmZD+Xp1VZLmCngNmqktvZKeVcxe45c+Yk7bwpLoandu30sqz9+p5Sq+WcZMseZ84rnnq1+2RZlbUr5anYj33sYzG2+XHnnXfGmPPcVnTPWVDb6VXvfC61R5aEq3u0fsu6UoKcNIQrAQPAkUceGWOWmgJ521QrOeCxkOUCW7ZsSdotWLAgxmwdfPjhhyftOBf5nOLrBZDatX77299Otq1ZsybGLMuyeW+/i6jH9u3b4zXcXl/Z9te7rrGNM0uvbGVrlr9a23Qe73hstTIUzgnOUyv9y+WHvSbkLIHtNcKTWcr6f/dTUqmdc6fUvtbi3ROyTS/ff95yyy1Ju1tvvTXGfE/MSxOA9P7iqKOOijFLV4H0vLJjdQ77O+Xsr73f091/USshhBBCCCGE6AP0QCKEEEIIIYRojNqV2i059yjbnqeJ2LXCVuFlaRc7vADpNBS7CFjHFJ7iZ5mXdQHh6arNmzejBHaQsdWxeZrXTtXlHDgkf+k7WsfASj5yrhCesxFPUdrc5mPGMgUglTFwfkyfPj1px84a7AjkuSHxZ1mJActu2L3ItuN92GnY3PksKUHfU1qB3R4Lbrd8+fIYW8nWmWeeGWNbzZpf8/hp3VrYUY4lDbYKOufz5MmTY2ylYnz+8ZhuHQvXrl0bY668beHz3J7zdkwQ9di+fXuUjtjxgyUpjG3HMq3BgwfH2Dpw8ljFOQAAxx13XIw5J+y9Ad9vsPTP9smTXzF8LvI54LnfWXJyzE72ITojJ9nK/ca2Heemd1z4fTYXlyxZEmMec/l8ANJ7CD6nTjzxxKTd+9///rafa+9Tedzle2DrIMe56C0t4HHbjrOSbAkhhBBCCCH2ePRAIoQQQgghhGgMPZAIIYQQQgghGqN2pXZPL8c6M8/2jvW7tqovr/OwVmuHHHJI2/1Z3Rrr7nl/dg0Ja/qsfo7hfvA6Fq60affnaed4f7L97XusPjhn4WfziLWRfJys/px1nVbTztamrOO3lqfMvffeG2OuzA6kun3eZr8jWwKyBtXq8b31TLlK31r31DdUVZW1U6wzLrCt+emnn55s42PLWnogtZZkG9177rknacd6fz537Ho8/k6sZ+ZK3kC6ZpDz1NrJ8poUaw3L34V/M7sWS+Nsd3j55Zd3sudvYY9NC3tt5PGY17ixlS+QrmEaO3Zsso0t+FmPb+9DeE0Uf5ZdP8j5zHbUdl0L992rsp7T3Nu23jpc0RzesSit6G63cQ6zpTWv/wRSO19eY/3e9743acfrUnkdn73m56zhvbVSpeOlbH+FEEIIIYQQrzn0QCKEEEIIIYRojI5tf3PkphitLIZlLjztZKeTeBrVsxHkKVWucgmkkgOe3reVLUursXJ/3/zmN8fYSne8Ka+c5CVnIStJQe9p/ZZ1JUU5aZfdH9tRstwKSKUELNmyspb58+fHmOUlU6ZMSdrxdD/b+VoLVT4/PGki56zN55ycS3aUu59SKQePmTNnzoyxtX7kytS2ki9bqvI2KzVhCSzLqmyO8Xk0Y8aMGFvLd84/Hpt5DAfSau+2T3yNYHmO7RPLcER9XnnllSiRsuOAlTe1sL89HxvOFXs9ZTke2/wC6T0AS7ZsfuRKDli7ds4dzqmcDA3Il0Cw/SiVxtSVv4hySu3VS/dh7xm8ffD123sPtzv66KNjbMd0HqtZ9sXSXSC9Px4+fHiMc+cr4JexyMmMO0GZLYQQQgghhGgMPZAIIYQQQgghGqNjyVZOlpGTItlpHJ6O5+lR63zF07l2upW3sfzKSrZYDsNSAusGwp/N38+6usybNy/GPMVlK28y9vfKOWvZKb7WazkX9Z6c5KpOe85n246PtZ3avOyyy2L8pje9KcbWXe4tb3lLjEeNGhVje37k8shKCVj2xZIXK3/JOW5YJMvavXguhd4xYxnKG9/4xhhbuSrLsh555JFkG8sHeSy1LnS5XLS5wuM257b9jvw+3rcd31l2Y6VYLK9l2ZcnRxT1eemll2KO2OOZq/RsJVu5azRLrwDfEYgd2jh3ctXibZ+8sY/d5Ky0iz/LOy9zTlp2W65qu+ge7BrrkXNQa+2jHd5xt+MnS6Q4x6y7HI9ds2bNyvaJXehYvmXvxfm+dfTo0dm+e/K1nDzRtrN9zKEZEiGEEEIIIURj6IFECCGEEEII0Rh6IBFCCCGEEEI0Rtdsf3NY/SPr5zxdGetLuUKl3Qfr4KzGmLWn3M5W6+X9eXpB1mLzNmtbydg1IPy9+LNya0ikce4dVVXF42u1m/Z1C0+zy+/x1kdZOOdY337EEUck7XK65y1btiTtOK+4v0OHDk3acd5zzto1JLk+APlK7aLvKFlD5q1Z4urnnEdccR1IbX+XLFmSbFuxYkV2/wznfW4tIZCuu5s2bVqM7bqWnM20XUvAa0isdTu/j/tncztn+S46g21/DzjggGQbX/O8SuU8RnJe2vVBvP7Ilguw1/YWNn95nNy0aVPyPXJ4a1dK13Z55PT49jzSPcHupbQ6u/cefm3XHLO1NOPZ77IFMK8ZsfvjtVi5e0wgrRbfyZrb3Pow717IQzMkQgghhBBCiMbQA4kQQgghhBCiMTqSbG3fvj1OkdqpTZay8BSrNyXuVY7m99kpYJ5i5X1YGQrvk2NbGZOrW7P1pZWKsS3hQw89FGM7pZqzwfS22WnY1jY7JS06I4QQJXV2GjFnT+3Z+TKdVNBlWR9XTD3ppJOSdsOGDYsxy7RsHvBU6aBBg2JsZRAsc+H32OlglvXY719q2Se6w3777Yfx48cD2HkMYnhMY9tGIJVsrVq1Ksa2GvuCBQtifMcddyTbWErFuWPH/px9uW3HfeQ8t+clnyv8Ha0kNyfP2dU2JifbFJ3x8ssvx+uyvV4zngSRx6ClS5fGePLkyUm7CRMmxNjeX/Brju29Aed2TrJtX7PU1loWcw7zuWLHzlLZl9i9eBbMJX/fVTt+bcfFnEzQs6r27gs5F/n+mO+bLZMmTYqxJ9m223J9t9+/VAamM0AIIYQQQgjRGHogEUIIIYQQQjSGHkiEEEIIIYQQjdGROPy5557DAw88AMDX0vE2a1fKmnm2gbSaOLumhNmwYUOMWaNp9Zo53fOBBx6YtGO9Me/D6pB/9rOftd2fh6dJLYFt20TnHHDAATj88MMB7KwXZ60l641L11BYDTDvz2qWeR+c6zbH2I7S2qHm9jdixIgYW2tU/s68dmrlypVJu6uvvjrGnmWfl7+yBO4O++67b1xjYX9Tzis+FmyFCqRj5Pz582PMa98A4N57742xHat5LQDnutVbc4556+IYPt+sbfqAAQNizOfH008/nbTzPitnqW7P2U7HY9GeV155JdqNWuvxnDWqHY9ZM7927doYT5kyJWnH+fLoo48m2zgPePy07dgalfvhWcPzWlNLbuzzLHs9K+3SdqJ35O5jeVzoxtoePp42j9iKnXObr+tAau/r3X/yuhEe0+05wN9r+vTpMbbngGdjza+7YaGuGRIhhBBCCCFEY+iBRAghhBBCCNEYHUm2QgjRLtROkfMUElvi2Sketp2cPXt2jK0Vr2fZx9u40rWd+ucprpw8B0jtfHm6yu6Pv7NXsZvxpmx5m50ma03Tqipr73jppZd2mqpskbP6tTnL8qicFARIpVjWVpf3wVICK6EZOXJkjDnvbTVXPsf43LESP857fg9bDwPA8uXLY2y/v1c1mJH8pTuEELLnPx9DHp+snS8fz3vuuSfGbGsOAJs3b46xlYbwZ/O54tlWelITth/mfnCVYAAYPHhw233Yz+V888ZJ5WXfs3379igVGTduXLLNsw1leIxk2YmtbM1jkN3G13bOCXvN5/GZpWK2HUvHuZ13rngSFy9Pc/uwyB64OUrtgS2csyxJBdKlC3z9tnJuvr9YtmxZjNmO2m7buHFjjFesWJG0YykkW2nb+22PbueiMlsIIYQQQgjRGHogEUIIIYQQQjRGx5Kt1lSllW6wlICnXq2jFU8nMXaqlCVMVv7C074sW7ByFZa58DSUnQrjCsAsC7DTUfydecrWTt968obc9LV1cmpNDUqy1Tv69euXlRnmXHq86s1epV2vQnFuavM3v/lN8vrEE0+MMeev/axcteLHHnss+7mci7YCOJ9j9lzMVeKWq1bfEELYqRI0b2vBU/i2yjpvY6cVe2xzDoNA/rh75GReQFopmPP+4x//eNKOJQgsZ7DXgTquLvZ7aHztDlVVRXmolTrbdi08uUdO4mqxbpycO9wPdhEC0vsBdvSy42yu8rtXgZ1jm1+eW13ufZJo9Q1VVWWv9Tx2cRvvnpBzwu4359QJpG5aLC9np0Qgda9bv359dn9838DOhLZS+//6X/8rxpyX3v2PJSdhr+suq0wXQgghhBBCNIYeSIQQQgghhBCNoQcSIYQQQgghRGN0tIakqqoi3Tjr76wmk9d5eBVI2a7U2qRxNWrWy1mtKb9mTTWvGQF2rgDcwqv+67XzNJ85Da2sKfuO1vGxuetpfRlP91sKH19ea8KWrAAwc+bMGPM6EbvuifXRrK23WlVei8B94PMB8M9Ffp+3Pkp6/O5QVVX8bT3tsQcfG7Y1t3nEeBalpecAa/o9LfJvf/vbGJ911lnJNq5kzDbYdm1Cbty2lFaPF93BW/NZujaC895er9mu1K5RZWteL9e5j7x/a3nK4yffT9jrdem1xLOXV57uflrHY3fef3nrS3ictX1ie3++ftvr8OjRo2N89913x3jatGlJuwsuuCDGfA7YvPTWhvB36caaUs2QCCGEEEIIIRpDDyRCCCGEEEKIxgidTLOEEDYBWL3LhqKbTKqqasSum4l2KGcbQ3lbE+VsYyhna6KcbQzlbE2Us42RzdmOHkiEEEIIIYQQoptIsiWEEEIIIYRoDD2QCCGEEEIIIRpDDyRCCCGEEEKIxtirHkhCCJ8KIdwdQnghhPA9s+3AEMI3QwibQwhbQwg3NdRNISK5nA0hfDCE8Az992wIoQohHNNgd4UA4Obt4Tv+/sSO/64JIRzeYFeFAKCcFa89dnFPe2EIYfmO+4PfhBDGNtTNrrFXPZAAWA/gywC+22bbvwAYCmDmjv//5W7slxA52uZsVVWXVFU1oPUfgE8AeBjAvQ30UQhLbqxdD+Dd6BljhwP4FYB/371dE6ItylnxWqNtzoYQTgXwtwDegZ68XQngx7u5b12nXhngPZSqqn4OACGEeQDGt/4eQpgB4HwA46uqapV2v2fnPQixe8nlbBs+AuDfKtniiT2AXN5WVfUkgCd3bAsAXgEwbec9CLF7Uc6K1xrO/cG5AH5aVdWCHdu/BGBdCGFqVVUrdn9Pu8PeNkOS41j0+E1/cYdk66EQwgVNd0qIEkIIkwCcDODfmu6LECWEEJ4E8DyAf0LPv+QJsUejnBWvMUKbeHYTHekWr5cHkvHoOVBbAYwF8CkA3w8hzGy0V0KU8WEAN1dVtbLpjghRQlVVgwEMQs9Ye1+zvRFi1yhnxWuI3wB4TwhhTgjhAAB/A6ACcGCz3eodr5cHkucAvATgy1VVvVhV1Y0Argfw1ma7JUQRHwbw/aY7IUQnVFW1DcC3AfxbCGFk0/0RYlcoZ8VrgaqqrgHwBQA/A7Bqx39PA1jbXK96z+vlgeTBNn+TFl/s8YQQTkDPrN5lTfdFiBr0Q8+/2o1ruiNCFKKcFXs8VVV9o6qq6VVVjULPg8m+AOY33K1esVc9kIQQ9g0h9AewD4B9Qgj9Qwj7ArgJwBoAn93R5gQApwG4usHuCuHlbIuPAPhZVVVPN9NDIXYml7chhDNDCEeHEPYJIQwE8A8AngCwqNEOi9c9ylnxWsPJ2f4hhNmhh4nocZH9/6qqeqLZHveOveqBBMDn0SPP+msAH9oRf76qqpfQY492NnrWkXwHwIerqlrcVEeF2EHbnAWAHQPReyC5ltjzyOXtYPTYT24FsALAVABvr6rq+Wa6KUREOStea+Rytj+AHwF4BsCdAG4D8D8b6mPXCHIRFUIIIYQQQjTF3jZDIoQQQgghhHgNoQcSIYQQQgghRGPogUQIIYQQQgjRGHogEUIIIYQQQjTGvrtu8ipveMMbqgMP3HUhyBBerWi/zz77JNv69etX1I63efvPxbvaVvJZdd5Tt12Oxx57DE899VTvdvI6pn///tWAAQMA7HwsOBdzeelt60a+7Qk51hf7W758+eaqqkZ0oTuvO4YOHVqNG9e+BEK3j7UHf5ZnfpLrk31PnXF2+/bt2Xa8/0625XjwwQeVszUZOHBgNXJkTx3BumNf6XU9955dtS3dx2uJ+fPnK2drMnDgwGrUqFFtt+3OnOhLY6lu7Lvb+1i5cmU2Zzt6IDnwwANx0kknAfBv2vbd99Xdtm4GeR8t+vfvH+ODDjooafeGN7whxt5DzX777dc2tv3gbd7+crHdnzdg8v7tZzElg+6nP/3p7PvFrhkwYADOP/98ADsfC87NAw44IMb7779/0o63cV56+eHlIvfDtss9pNvPyr3HtuMburr7qMP555+/utc7eZ0ybtw4XH755QB2Phbdfjj24M/iPPLaMfY9ub7zuWF54YUXsp/j5XadvB87dqxytiYjR47E3//93wPY+Xjmrod27Mtdo3nM9fbX7rNbeDnA+/AebEupO37W+axp06YpZ2syatQo/MM//AOAnY977l6v9B9ZOuGVV17p1fvtOMt99Mbt0s/lfXg56n0Wb/vABz6QzVlJtoQQQgghhBCN0dEMSSnev86VymS8f7HN/ctx3X8l9GZFctSVm5XS2sdreTp5TyCEEI+P/dczfs3/Cmf/RY7/5Y7fY/fnzerx69xsif0s+z2YUuljTrpi/3WkVLImdg+tY1A6w9qNY+Ttw8uP3L+0ebMWTz75ZIw3b96ctBs/fnyMR4x4dWb/xRdfTNpxP+y20muE6B65nOXxjo9F3THS24e3rV1fAeDll1+OsR1/WdHBs3UW/izvX4pL70l43FatuL6jZDyooyzwZi08vNmN0nGsdHY411/vcz28mSTvnEg+q6iVEEIIIYQQQvQBeiARQgghhBBCNIYeSIQQQgghhBCN0fEakpY2rHTNRzfsT62GrdQ+snRtSE7fZ9cI9PY7dkKrT9KP9o4QQjyOnqtLbj1JJ+3quGx5GmjPfSi3NuS5555L2g0aNKjtezx9tXe+aT1J38Prnuz6DM8RqA7dcF7jvBo7dmzR5/L5ce+99ybbfvWrX8V4ypQpMR49enTSbubMmTG2Nsl2TUmO3jrciFdp5Ys3fnhr8HIOXHZ/ufG4XdscPC4uXrw4xrfddlvS7thjj43xnDlzYvzMM88k7Tjf2L3Rauc537y+1nG1E53B9walazQ918NSvLV1nB92zdL69etjPHjw4BgPHz48u79Sx61Sp87SvLTtvHuZZB9FrYQQQgghhBCiD9ADiRBCCCGEEKIxOpZsldjRdlum1e3iMwMHDky2vfTSSzH2pqe86WamE/nZrpBNZffw7Ci9gl25425zgN/nFfPyLC0Zz8KSc5OrzX7nO99J2k2cODHGp5xySoyt5MArwpg7Z72pXdEdrGSztxLOunKP0iKMW7dujTHLYixDhw6NsZVi8Xf80Y9+FOMTTzwxafcf//EfMf7yl7+cbOPipl5hL+/8E+Ww/MWz6ffGmVy7Tqx9+TVf8+2YzjKXCRMmxHjlypVJu1//+tcx5py13H777TF+6qmnYvyhD30oaVdaZM/LS8m4u0frd65bniJHqRTWtuUxk8dSAHjggQdizDn24Q9/OGmXk/WWFjXk+2G7v1KZYd0Co5ohEUIIIYQQQjSGHkiEEEIIIYQQjdEnldqZug5ZpRIBnpblaXoglcaMHDkyxlOnTk3aLVu2LMY8FbZly5ak3eOPPx7jYcOGxXjMmDFJO+6HNy0rt4y+J4QQp+s99yzP1SXnnuW1szmb20fdCuyc9/fcc0+MH3rooaTdkCFDYnzAAQfE2Kt6bcl9L+tQVOqkIcqxx71U8urtI4fN55zjy/PPP5+043xesGBB9nPnzZsXY66A7Y3bLAOwrjPr1q2L8cKFC5NtLE/kqvAac/sGdoYrlWJ5kldvLC2Vc/H7PPe3SZMmxfjII49MtrF85Rvf+EaMZ8+enbRj2dfvf//7GF9++eVJuz/+4z+OMeel13eLJFvdwcvZ3O/vtSutsm7hHGb3LJvbPGbyNZ/vS4FUAsv98GTfnrvnyy+/HOO6bmSq1C6EEEIIIYTY49EDiRBCCCGEEKIx9EAihBBCCCGEaIzaldo9Std/lLazn8maNtZy8roOINWxsYb02WefTdrlKqta27VHH300xqxZtjbCbLXK+lQg1fR7lS1F92hpIj3bX15f4lUQ5thq33mbXa/Crz29ZqmO+KCDDoox5+ny5cuTdn/4h3/Ydt/2t7C2mExOGyo9ft9Tqm323ucdJ95m12iwnvm5556L8Q033JC0O+KII2LMWvrNmzcn7TZu3Nh23/weIB0/+/fvH2O79o+tWzds2JBsy31/+1tIj989Wr+tN856lue5caZ0nYh9zcfW2pxzPrPm3uYiryHhtaa33HJL0u6ss86KMVd0v/vuu5N2733ve2Nsx1zPNpVRKYDu0cqluiUocmOrt27Prr3kMY5zgO9zLZy/dk1fnftK7168ztrQTtbQJO06/iQhhBBCCCGE6BJ6IBFCCCGEEEI0Rtd8Onl6tNvT4HaKa/LkyTHm6r1WImAlNS3sdBLbqfHUv7X9ZZkMW63Zz920aVOMrR0lywymTJnSNubP0vRs72DbX6+qb6lEwLOcZFmWnY7PVYL3JAc5+1O77bDDDosxnxtAei5y/7xK8t75m+uf6C45KUEdO0rv77n8AFIpa66aMJBaTbMklaVcQDrOPvLIIzG24zS/Znnt+vXrk3YsMeNzAEi/l6SFfQ9XarfjQqnkMzcee7JWO87mJIjWDn3RokUxfte73hVjK/vmfGb59fz585N2t956a4xZsmXt1dmqetq0acm2bdu2tf0ellIbb7FrWrnpjZ/evUHuWtmJZInHXZZi2ftKfs3t7GdxH3mbbZcb++39dp18k+2vEEIIIYQQ4jWHHkiEEEIIIYQQjdE1zUVOIuDJPzz5C7tb2Oqpc+fOjTFPidqpf54m4qqo1pXg6aefjvGaNWtibF1nRowYEePHHnsMJVinhNWrV7f9rAcffDBpd9RRRwFIp+ZE54QQYm56UiyOPYcsjj0JmOdixeeK527lwfJBdokZP3580m7lypUxZumDJ5colVxKTth3tI6BJ9UrnUr3HAt5/9aJaOTIkTHmvJ8xY0bSjnPsxz/+cYy5YjWQOgxxn66++uqk3b333htj/r5XXnll0u6EE06I8Tvf+c5kG4/pvA8rR1AOd4/cb5kbd+zYl3M69Ny4LHzNPuCAA2LM11oAePjhh2P8ox/9KMbvf//7s/vmc4X3DaQOXCx1tFW0WcJtq73ztV5y2L6HK7V7v7eXbzl5VCcyUf5sdsy0SwZ47D788MNjzFJYIJX+cf/s2Mf3ELy/K664ImnHLmBW0sh9yv0WgFy2hBBCCCGEEK8B9EAihBBCCCGEaAw9kAghhBBCCCEao2OhYqdrRUptK739ceVewLdDZXidB9tH2krtXIF97dq1MWYNNQA89dRTbT/HrjlgvZxXbZP1rnZNyjXXXAMg1UKLerSOh9WJ8mvWM5euDfGsfUvXq9StEMsaZt5m7U/vu+++GHPee1aaFv4u3noEq1EV9aiqKv6W3nHxfm/OTR5n7Do7Xn/07//+78m2iy++OMYf+MAHYjxr1qykHY/BrEX+q7/6q6QdV2p/xzveEWO77olthY877rgY23UAvNbkzjvvTLbx+hLW8ZfaI4vOCCEUjbM5a18gfw/hrZWy194xY8YU7YM/67e//W2MWX8PpHnvrTPgc+CJJ56IMa+FBfzree764Vl1i/qwVXVpBXaP0grpNhd5TTOP6TyW2tc8bts1JHw/y/cJ9n6FP4vzntdDAek9RenvYn/P0nsDzZAIIYQQQgghGkMPJEIIIYQQQojG6JNK7buzMi5/lq2KmpNp2QqYLAXgdlaixVOxOUmE7ZOd5uWpPG8aS9Oy3YGt/eyUZU5K4NlMllr7epItr1J7riqsnQJm6Q3n0ZQpU5J2N954Y4y5SvD06dOTdpzDpTIyi6wqu0fu/Oe/56oJA6lUhN/jyVA3bdqUbGOL8m9/+9sx/vrXv56042rWLK8988wzk3Y//OEPY3zzzTfHmOVVAPDpT386xmy1ztXdgfS7fO1rX8tuGzduXIytjbokW92jlYPe2Me/t/3teVzk91jZE29ji10gP47ZcZGv81wS4Gc/+1nS7k1velOMBw4cGONDDz00aceSa7bPtvcGVjLJeBXBGd0bdB/PBt8jNx6zVS4AHHzwwdl9s2SL85Jtee0+eR9sFQykElW+5tvlAxs2bIgx3xOPGjUqacfyMO/678nUZPsrhBBCCCGE2OPRA4kQQgghhBCiMfRAIoQQQgghhGiMrom+62hxPXvcyZMnx9jaQrJmnjV8VlvJr1lnZy12WUvH+7ZrSFgbyrppuxaEdaJW/9rUWpvXM63ctDlauoYktzbE7s+ztMzpg73zhjWZVivN+2Brv6FDhybtWIe6dOnSGB911FFJO15/VaqfFX2DZ0fJlNpM8roOOx6tWrUq+Vxm5syZMf75z38e47vvvjtpx7nEFpR2DQmvSWHd9JVXXpm0e+973xvjOXPmxNjqsjlneR0AAHzxi1+M8T/+4z/GmM8VYOffQ9SD1+p5OettY/0857MdjwYPHhzju+66K9n2ox/9KMZsGX3OOeck7Th3uE+8tglIrbA//vGPx9iOxw899FCM+bpic9a+j+H3caw1I31HK7dsjuXWQ9pxlsdgvifkNSNAek9o1xXxujYeF+19Kq8Nefjhh9u+x+7Py8WxY8fGmM8V/juQWmvbXOTfo+76EkZ3xEIIIYQQQojG0AOJEEIIIYQQojE6lmx1On1YWqnd7pelJ151XZ7+spaObNPL019cyRJIq1Sy/MpWdOf9Dxs2LMadWPuW2nZqmrZ75Owoc3a+nhSLY1sl2LN75ra5GEglNAsXLoyxta087bTTYsy5zfIcAJg2bVqMuQJrJ3KzXHV2Wab2Hbnf1rNNZXh8YnnK73//+6QdS0hslWu2OWUJAkugAODHP/5xjCdNmhRjKxE4+eSTY8wSMLaVBFIZzumnn962r0Cap/b8YIvgyy+/PMZ/8id/krTbsmULRHfIyV84T1n+auVznGMsNbX74+v12rVrk218PL/3ve/FePbs2Um7qVOntn3PEUcckbS79tprY/yhD30oxta+l3OY7xMGDRqUtOPx2OLZwYvuE0KIY4i95vH1m8dBKz3i48RyVXvfx7a6nL9AKrnivOccBdLz5YorroixHbfZRn3ChAlt9w3kLag9eZUnWSutxu6hGRIhhBBCCCFEY+iBRAghhBBCCNEYtV22rKSodIoxJzmwU2bsaGW3sQSBXVKs0wpXYGeZFrsV2M/iaTdbuZinjnmazfaPpQq8PyCVgT399NMxrvt7Ch92LPLcs5i6ldpzLimWJUuWxNhWRWVnI5YjXnXVVUm7e+65J8Ys+7LORlyl+rbbbosx5zxQLtliStuJ+pT+plYexdP7ixcvjjG7swDA4YcfHmObiywfZCctlmgBwD//8z/H+GMf+1iMeZwGgJNOOinG9913X4ytTOav//qvY8zngHViZOy5zI4369evz75PjnLdo5Wr3rjIY4aVlzLLly+PsZW48D6swxDn8Pz582N86aWXJu0uuuiiGPP4yeMvANx6660x/sY3vhHjCy64IGnHuc7f1zoqsWzWyshY8uLJtzTOdo/c+c9LBtjVzeYi38+xk6u9/uekXUB6rPl9drzje0TOlZEjRybtjj/++Bjz/bGVW+WkWfa67jlp8Tbvfr4UzZAIIYQQQgghGkMPJEIIIYQQQojG0AOJEEIIIYQQojG6Vqm9tza1VnPGVdKtzo/1c6whtWs+2BKQNfPWzpdtU/l78BoUADj66KNjzNpQ1isDqdWf7Tt/T17zojUkfQNXEPbWhuSqttvXdasQc06wveVvf/vbpN173vOetu8/77zzktesV924cWOML7nkkqQdazz5PbYKLNumWu2/crE5PHtmHgdzFo5AWnn3kEMOSbbxGHnYYYcl23ifp5xySoytbv+Tn/xkjNlumK2pgVSr/6UvfSnGd9xxR9Iut2aLK70DwIoVK2K8bt26ZBt/rxNPPDHGdbXNwoctVD0bfLvWieFrPo9pixYtStodeeSRMeY1ckCaI3xd5gruAPCRj3wkxjweW109f9bVV18dY7suifs+YsSIGNt8+/73vx/js88+O9nG51tpBWxRH743sPdffCz49+c1GUA6BvO6PZvnOet8IL0u8za7zpPvR3n98YIFC5J2uUrtpZXUvWtO6X1+3ecBZboQQgghhBCiMfRAIoQQQgghhGiMjiRbVVXFqZ1SK7rS6Ub7fpZVWes8hmVPK1euTLZxdUyWGVh7YJ52u/POO7OfxZIGrnppKwjz/mzfSyowA6/+bpLL9J7cb8l2exzbnM1Z4npTm9aGlNuy1SpbAAPplP7cuXNjbCu6v/GNb4wxW1rb/T300ENoh5VBcJ+sZEuSgd1P7rzPVcP1LB1ZZjB8+PCkHctc2UoaSKtK89Q/5x6QVkX/xCc+EePLLrssaceyQJY0WHkDj/1PPPFE2xhIpQ7WzpglNTxue5WWRX3YXt3+pizf4G08bgGpJS5L7uxYxZIUKwtkq1SusG0ruv/0pz+N8R/90R/F2MqvuQI7V8621bF5/OT7C3v9523XXXddsu2ss86KsfJy99D6nb3fm+VRtmRETlrn2f6zvA9I85nvG+xYzdJbvtew13iWdvHYZ22ES6/rXuX23D6893joTkMIIYQQQgjRGHogEUIIIYQQQjRGxy5brelXKx3IuQjYdjyVw+3s1A9P29tpMp7i52lf6xzE+7AyLYY/23MbYIkAT9Fa1xl+bd1v+Dt71bFbU3eauu0dLCXwplHte3LwMbP742PIjhsA8OCDD8aYc8xOo/7ud7+L8aGHHhpjrrIOAPfff3+M3/Wud8X4LW95S9Ju4cKFMeapXStv9KZvlYO7n9ZvnhsXLN4UOTtf2Zxled7SpUuTbexiNWnSpBhbucrf/u3fxvgLX/hCjFmKAKROc7zt5JNPTtpt2bIlxjz2W8kWj+/W/YbPU5aKlUpoRee0fkvrsMPXa3YO4rwE0lzn42QrW3P19GOPPTbZxvnNki1bFf7yyy+P8Tvf+c4YsywGSO8p2D3Lnm883rO8lnMZSCt9s7uXRXnZ9/C9QbttLfi+LyeZBdJ7zA0bNiTbOD84L4F0XOT8sG6f3I7h/gGp9HbmzJkxLr3G2++Yuz+2eO3sd8nuo6iVEEIIIYQQQvQBeiARQgghhBBCNIYeSIQQQgghhBCNUXsNiWft51VpzGkjreaMdaNWQ8pVXLlCr9VrssbYs2TjNR+s07N2lMcff3yMWSdqLQVZG2q/L2sEef/2N2v1UZar3aNUl+vZ/uaqSLd7zXBOcJ6yZSqQrilhXbxdR8X2wOeee26MZ8+enbRjfT5rS+16Kz4nPHtk0fds2bIF3/3udwHsPFbxuMOxHT/+9E//NMasX7a6ddbxT58+PdnG9pS81sSuDWG7Vtb0W30/a6c5thWJ+bNYz8zjOZBqtu36El4zwOsC7HVGud09cuueeFzkY2i19Gyxy+MdXzOB1OLZ2j0PHTo0xpxH48ePT9rxPm+++eYYT548OWn35je/ue33uO+++5CD29l84/ON9y2aoZWr9trN114en+w9IR9fHo/s2lAeg+waDX4fnx/Wfp/HWe6fbbd48WK0w37H3H163Srr/L3sGFBqA6y7XSGEEEIIIURj6IFECCGEEEII0RgdS7ZKYMlHqUzGTunw9K2FJVJcldJaOrL8ivdvp654Go4lDTNmzEjanXjiiTFmC1UrOWCrXzt1x1NtLMeQNKvv8X5jz86XqZPbQGoTfcstt2TbsaUqy0nOOOOMpN0vf/nLGC9YsCDGH/7wh5N2Rx99dIxZQsOWrkCap7YqPH9P5Wnfs23bNtx5550A/KlvzlNrM8nyUj7udn883nFFYiCVerHlKctigFQKyNXdS7Fj/+bNm2PMlpZWKsbjrt0Hywf+7//9vzG2uV1qBS7K8cZF/v3tdZiP4UEHHRRjazN9yCGHxPjee+9NtrHNKUvCrC00j605GSQADB48OMZsH71kyZKkHe+fr/9WRs5V4TWWNksIIZurfD0cOXJkjG3OcjseF60UlO9NrQSRlwzwNi5pAaRW/SyntRIw3gfLr0rLGdSVW+Wq1neCzgghhBBCCCFEY+iBRAghhBBCCNEYteeri1fNm6mb3BSSnXbi6VGengKA1atXt41HjRqV7SNPqVqnBN7GU1fWBYOn7nga2boa2P4yOYmA/Z1afSr9nUWe1jG1U5Z8LDyHLN7GU7H2PZ4DF0/ds6uGdZphOSLLVfh8AIALL7wwxmeeeWaMresM92Pu3LkxvuOOO5J2LNexVeZz1Wk9pz1Rn379+mHAgAEAdp7659+Yj62Vq15zzTUxtm5XDEu2eHyzn837t05dLEdkCc38+fOTdieddFKMeVyzslZ2jVm/fn2MrTOclXDl9sG5bj/LOneJeoQQYj56YylLsWy7Vs4D6bXcyq28ayKPXbNmzYoxS7vt60MPPTTGLLcCUjkNu33adrlctBKwU089Ndd10QA5aRFf89ihzcrz+brJY44dj/mabx0zOXf4foDlh0B6v8Jj2vLly5N2PM7yva69jtuxsIW9rnuSbT4Xc3G79+XQDIkQQgghhBCiMfRAIoQQQgghhGgMPZAIIYQQQgghGqN2pXbv76xNs1qy3LoRq8mcNGlSjK0unteNsA51woQJSTtey8FWaFaXzbpW1kdb62FuN27cuBjbCsdcxdWuJ2GdLOtd7e/U0s3m9PuijBBCVr/If/d0kjlNtD0X+H32eLIlINtWWm0zV/K9/fbbY8xaegB473vfG2O2pmRrQCBd38TaVaud53PWs0KVVeXuoXXeeznGx8yuReIxiI87j2FAmqecRxYex2zu8HjKa+vYDhhIbdRZW882v0CqqWbN9saNG7Pt7DoD1vvz+WvPZdZp33TTTRD1ydmK5ixF7bWNr4d8nbT3Bl4Fds517/5i4sSJMebrvF1fyucY56Jd07dp06YYP/HEEzG2Nti2v6I5eN2ThdeAePdgnBN8j/nII48k7fie1d4v8ho8PofsuiS+N+Cxevbs2Um7t73tbW37aq/dda7l9rfgfXhrx1SpXQghhBBCCLHHowcSIYQQQgghRGN0JNmqqipOjdvpHp5qysm3gHTKh2M7VcpT7uvWrUu2cVXiyZMnx3jIkCFJu+HDh7ftB1uwAelUm1eJm6ed+LPmzJmTtONpY2tVyf3gSpz2d2q9tlIE0RlVVRXJ3rptWWv3l6tQbGUyPMXPU7lHHXVU0u7EE0+MMcsFrPyFZS4LFy6MsT1/WdLgyYQ6qU4v6tOSzXkVyPlYWEkK58Evf/nLGLPUD0jHPjsGc27yZ7FkBshXqbZT+GxVyWOmlWKx3IHHUmulya/ZnhVIJWxstXrKKack7Viu84//+I8Q9WnlSKltqJWk8HWZ88hKrAcNGhRjvk8AUrkpS735PgFIx1nOcytH5P2x3JEluEBq0c7n3gUXXACx59LKR5uzPKbx/aaVM3M7jq009rjjjosxL0cAUtlXbuyz/eD32LGvVObP38Wrsu7Jrfiz+H32HkKSLSGEEEIIIcQejx5IhBBCCCGEEI3RkWRr+/btcbrekzPlZFm2HU/92yrBvG3ZsmXJNp6qZ1mWnRZiyQBP7fKUL5BOHfNUm5UI8NQYS6xshfg3vvGNMb7llluSbdZ9oQW7igCvTllbhzHROTmZUW4asXR60bbj1560ieV+7NYGpLnI54Ct+Mu5zXlpp3nZ8YVjrk4MpPln854/S9XYdw+tcdPLMZayWGkny1B+9atfxfiYY45J2nH+2X3wWOhVVufXLHmxOZsby+x3ZOmOJ8vi1/azPv/5z8f4iCOOiLG9HtnzRdSnZNzkHLPSaR5bWLJlZTJ8/bZyLh7HRowYEWMrq+b7Ad6/PQe4EjdLZqwUmyVb7BZ29tlnI4fNRd4Hy3Ct/MdzQRT1sNdrzj8egzy3Ns4V7xrKxxlIlySwjN9KWRctWhRjzkW+BwZS6S1/rs03Pt/qumfmnLVUqV0IIYQQQgjxmkMPJEIIIYQQQojG0AOJEEIIIYQQojE6EiPuv//+sUKp1dyxlox1ndaWjzV4rGnjirlAWsHaao+5YiXv37PIZG2op3Vlram1JVy+fHmM+ftbHSvr5ayVJleT57UEVt/Xen3vvfdm+yp2DVdq9yqV8vH0Ko7m3g+keWDXWvDx5f1b7Ttr+teuXRtjq3Vn/TzrSbds2ZK04wrbrPXnc8j2w+Z96e8hukcrt+xYxfpxzjGbb9yOc/ub3/xm0u5LX/pSjO1xZj0z5xWvRQJSC0oeZ61WmnOWx1IeEwHg4IMPjjFr6e0aEs77D3/4w8k2XjfCa7E8q1nRN+TW07HlNJCu//DW4/GaCnvt5fOA13LY84Pfx+tBrPaf9f18T8LjKgCsX78+xueff36M7ZoDvpf5/e9/n2zjdbRjxoyJsT0v7T5FfVq5VXqNs/dpueumN67YbZw7vF76gQceSNrNnz8/xrx25c/+7M+SdraUQAvPzpe3la6htfB5Wnedk2ZIhBBCCCGEEI2hBxIhhBBCCCFEY3Q0r7LPPvvE6XRviountewUqJ3qbGHt9liq5FUrX7p0aYztVFOuerydvmUpAFfEttVdef889WqrY7PMwMJyBJ56tlOBrT6pMnbvyU3Llsq0cla33v5sLnKO5ar/AqnMgGUytoIwT/uyrMDmIn8uS1e4QrXtkyWXg/bvsgTuDiwz9HKMf297LFiexzm2ZMmSpB3bkp9zzjnJNpYSrFq1KsZ2TOe84s+1EitrY9mCx0QgHe9ZTmOlYvydP/axj7XdN+Db0NvzStSn5FrFUikrZ+Zj40leBg4cGGN7b8DyQR7TrHyQx1mWjnHOA8Dq1atjzJb99ho/fvz4GL/97W+P8Ve/+tWkHcuyrEydrxkc25IAduwW9WnlrM1dzh2WYtkxjccnzlmbH5xjnFNAKtO67777Yrxw4cKkHZ87X//612M8a9aspF3uOuyVxfBkWp5lb+599veU7a8QQgghhBBij0cPJEIIIYQQQojG0AOJEEIIIYQQojE6WkNSVVWiF2ZKLcRY88ntrrjiiqQda+5Y7wmkOmXWjFrtHH+2t4aE27Flmv1cXlPC7aw1L9tlWt0zW/2yfZ/Vwrb6WKq9E7vG00myftlbC+EdD36f1f4//fTTbfthLRy5H6yxttpV1kSzNt9qr/k84hybNm1a+y8B/zt2w9pP+FRVFY9VqZ2vBx93u17jtttui/F5552X3QePuTa3+TW389a15MZcAFiwYEGMOZ/tWHrqqafG2Orsef/8mw0dOjRpZ9dcid7j5SWPLXac4Ws5Hz/PatWuZ+LzhcdWOy7mrr28TgRIrX55H/Z6zes6rrnmmhizNTUArFixIsaDBg1Kth1yyCExnjRpUoytlfsvfvELiN4TQohjl83FXA7b6zDD66PtejleU8KW50Bq58vrS2yOnXzyyTHmdSOl14G695LeWsUc9pzVGhIhhBBCCCHEHo8eSIQQQgghhBCN0bHmojX9beUvuSkZ+/eczMNOnXtTu7a6dad4Fm9sKWjbsYSL33P88ccn7d70pjfFmKttA+lUHksdrP1kazq3btVM0UMIIdrl2aq+/JpzytpR8vQ+t7NyKz6e9rM4l1iaYGGLQT72bO0LpNICtqq0kkreB/d9ypQpSTvPWjs3Zavc7HvqWivz+zi2uc25w7JCABgxYkSMWa5y4IEHJu147GYpoe17rsK0ld2wzIWn/q116zve8Y62+wNS2SzLuex5OXbs2Ow+RHcotSHl8YmPE0v9gDT/PMkWj2l2XGQpK+eVlUdxOz4/WFIFpPIrvuazzAYAbrjhhhhfcsklybYPfvCDMWb51tVXX520Y2tYsXuxYyRf8/m6btux1MuOn5zfnL92rOZxlu8TrAy3VH6eu357Fd3r7K8TNEMihBBCCCGEaAw9kAghhBBCCCEao7ZNjpVela7EL5UceO3qVC/n/dkpYK5kzLIC2weeCuMpOCsD4Gm8OXPmJNsWLVoUY57isq5HPFUs6vP888/Hiqd2qpTleXzcrbSE38exrbLO26yskHOC32flWyzpY5mWzQeuuM35bJ3hOE85x4488sikHee2/f6c33XOPVGf0rHUHjPOCT62djqepaJWZsCOVCzfsmM/55h1DmIef/zxGLPUxp4DLLXhfdtzyo6tDH9n/l5W3mDduUR9WrllXdg4X3ibV82Zt9nrsCebZWmWJ9liRyTOS3sOsLPbmDFjYvzpT386accS2l//+tcxtvIqduo86aSTkm3f/va3Y8y/hb1uyXmzO1RVFccJLxc5/6xEMCe3sm5cLI1duXJlss26B7aw4yzvg+VbNj9yjrJ1JVXePkr3WdpOmS2EEEIIIYRoDD2QCCGEEEIIIRpDDyRCCCGEEEKIxuhoDUkIIWrrPB2jpxdjrZ5X0b2360S8fdgqkqyVZk2qbWfXnrSw2mavoiu3nTBhQox5PQPwat+lF+0d27Ztw5133tl2Gx9f1mt6VVt5PYXVeNq1RAxr6z/3uc/F2OrnuaIr61Btn1jLyutG7P5YK815/oMf/CBpx9pum8+8f45tO2/9gOiM1vH2xlIe7zy7Z0+Pz8edcwUAhgwZEmOupm511PzZvD+7nonXQXFu28/l/rIl68SJE5N2fP7ZPvE6LdZos11muz6KerzyyivxONpjwddU/r2tzj5nL8rVq4F0jLTnBx9fziu73o+v5WwzbdeQ8Bor7vvtt9+etBs3blyMuWo7r/UDgGOOOSbGc+fOTbb9+Mc/jvF3v/vdGH/yk59M2p177rkx/ta3vgXRt3hjMOcwj312rRqv87D3kXxt53sIe/+Ze49ds5V7n72HyN3PeniWwLzN9kFrSIQQQgghhBB7PHogEUIIIYQQQjRGKLXhBYAQwiYAq3fZUHSTSVVVjdh1M9EO5WxjKG9ropxtDOVsTZSzjaGcrYlytjGyOdvRA4kQQgghhBBCdBNJtoQQQgghhBCNoQcSIYQQQgghRGPsVQ8kIYRPhRDuDiG8EEL4ntn2nhDCohDC0yGEhSGEdzbTSyFeJYSwfwjh4hDC6h25eX8I4awd294QQrgshLAqhFCFEE5ttrdC9LCLsfbAEMI3QwibQwhbQwg3NdRNIQDscpw9fEcuP7Hjv2tCCIc33WchLCGEH4YQNoQQngohLA0hXNh0n7rJXvVAAmA9gC8D+C7/MYQwDsAPAXwawEAAnwHwoxDCyN3eQyFS9gXwCIBTAAwC8HkAl4YQJu/YfguADwHY2EjvhGhP27F2B/8CYCiAmTv+/5e7sV9CtMMbZ9cDeDd6cnU4gF8B+PdmuimEy98BmFxV1UAA5wP4cgjhmF285zVDR4UR93Sqqvo5AIQQ5gEYT5vGA3iyqqqrdrz+jxDCNgBTATy2e3spxKtUVbUNwEX0pytCCCsBHFNV1SoA/xcAQgj5KklC7GZyY20IYQZ6LpTjq6pqVaa7Z/f3UIhX2cU4+zMATwJA6KnK+QqAabu7j0LsiqqqFvDLHf9NxV4yxu5tMyQ57gawKIRwfghhnx1yrRcAPNhst4RICSGMAnAogAW7aivEHsix6LHS/OIOydZDIYQLmu6UEEy7cTaE8CSA5wH8E4C/baZnQvjskMM+C2AxgA0Army4S11jr5ohyVFV1SshhH8D8CMA/QG8COAPd/yriRB7BCGE/QBcAuD7VVUtbro/QtRgPIDZAH4GYCyA49EzI72wqqpFjfZMCOTH2aqqBocQDgLwEag+hdhDqarqEyGEP0fP2Hoqev5xfa/gdTFDEkI4A8D/Qc/BewN6dKT/L4RwVIPdEiISQugH4AfoeVj+VMPdEaIuzwF4CcCXq6p6saqqGwFcD+CtzXZLiF2Pszv+kfLbAP5Na0zFnkpVVa9UVXULev4B6ONN96dbvC4eSAAcBeCmqqrurqpqe1VVdwG4A8AZzXZLiKhbvhjAKAAXVFX1UsNdEqIu7WSwqr4rGqeDcbYfgAMBjNtdfROiJvuiZw3JXsFe9UASQtg3hNAfwD4A9gkh9A8h7AvgLgAntWZEQghHAzgJWkMi9gy+hR5HovOqqnqON+ywq+y/4+UbduR02O09FIJwxtqbAKwB8NkdbU4AcBqAqxvsrhBAZpwNIZwZQjh6x/rSgQD+AcATACQxFHsMIYSRIYT3hRAG7MjVtwF4P4Brm+5btwhVtff841UI4SIAXzB//mJVVReFED4F4L+i519HNgH4RlVVX929PRQiJYQwCcAq9OhAX6ZNf1ZV1SUhhFUAJpm3TdnhwCVEI+xirJ0F4P8BmIMeLf7nqqq6fDd3UYiIN86iR771JfTIX54DcCeAz1ZVpX+wFHsMIYQRAC4DcCR6JhNWA/haVVXfabRjXWSveiARQgghhBBCvLbYqyRbQgghhBBCiNcWeiARQgghhBBCNIYeSIQQQgghhBCNoQcSIYQQQgghRGPogUQIIYQQQgjRGPt20njw4MHV2LFje/WB3SihUMcZrPQ9e0q71uvHHnsMTz31lOpO1OSAAw6oBg4cCADo1y99/uZctNty7XKx9x6vbWm7Xb2vpB3nWF+XMlm+fPnmqqpG9OmH7KX079+/OvjggwHsfJw4T738rZOndXO2G9u6+Z5O9sHnxIoVK5SzNdlvv/2q/v17yiXZXMzl7H777Zdtx/E+++yTtPNy1hvHS/ZReh0o3dZJ/tbJ9UWLFilnazJgwIBq2LBhAHbOsVzOWureD/SW3HV9V9ty9PY+tZP9eeNsRw8kY8eOxSWXXAKg/g/c7QcSjrdv355t9/LLL6ME3ofdH/PKK6+0jXfVp9w+7T5a/f30pz+9ix4Lj4EDB+IDH/gAAGD//fdPtrUuoHabNzi94Q1viLF3QS292NoLoH1frt2+++6b3cbwd+Hc895TelH3OOecc1b3eievUw4++GC84x3vAAAccMAByTbOWc4V247z2bu48j5s7nGOcR55N4heXnp5n9sff5b3wORdAL3P4nPine98p3K2Jv3798e8efMA7DzOcm5y/o4Ykd6TtP7hCAAOPPDAGA8YMCBpx/vn8RhIc5hz0eZHLu9t33MPRqU3sNwH287mpd1nCcccc4xytibDhg3DX//1XwPoGXMZzjk+LvZ48mtv7PNyog68f3tPyfvn+17vXtQbP7198DaO7f0sv++CCy7I5mxHv0wIoehmxbsAlj6JlV5s+IuWfpb9sXKDjm2Xe4+3f2/g4na5i62KcveOEEI8QevOkOQGGu8hwQ46zz33agH2rVu3xnjSJFvz8FV4/96FjbH5xu8rfSCxg043HlBEZ7R+c+8mnI+t9y/F3r82e2N1bh/eQ413TuX2UToL5I2FXo561w+Nr90hhJD9LXMzs3ZMyz2IdkIu1w866KCkHY/H/JBkv8OLL74YY+8fAPhmzLsOeOdRaa6L3UudcaeTmbveXl/t+xcsWBBjVjQNGTIkace57T2c1Ol73fzVnYYQQgghhBCiMfRAIoQQQgghhGgMPZAIIYQQQgghGqPj1TUlmrFurCHxqKuFb+EtKmItqNWx8pqPUhcQbzE9v8/+Lt1Y+CR6jkVL+1uq5/UWrnkL3JiRI0cmr1944YUYX3HFFTG2izJ5TQnnr6ep9ta15BaksW7avk9rSJont4YkN+54JgreYksvn+s4enmUrnvqS3evblx/RHtax9dbO+SNY7lF4945YPOex9mpU6fG2LqDLlq0KMa8gHnw4MHZdpdeemmMW6YTLSZPnhxj1ubXdb9jNP72HbmczR2bUqOOuiY0pXjnwK233hpjXjfy4Q9/OGmXy1PPzMnrh3eNKB13lelCCCGEEEKIxtADiRBCCCGEEKIxOrb9bU2lerKOOsWF7JROrn6C3YdX86N0aiw37VRaQ8R+jmdFXNIHfi37v97TOh6eJa5nscuUyg9YOgCkU/qjRo2K8QMPPJC0O/TQQ2Psyf1y1pK2Txs2bIjxtddeG+M/+IM/SNqxD3upzNJ+VidTvSIPW6h6Nr1eLvJrb2wurYvg5Rgf91IrYk+SU1rAs440WHLEvsfLI8/aNyf5sOOxJ1XkMXPcuHExXrVqVdJu/vz5MZ4+fXqM2Q7Yvn7iiSdifOONNybtZs2aFWOWwljqFHXsRr0SsTNcxsKTq+Zq29h2uTHXvq5zXwrklyrY8hQsA1+2bFm2Xe4+p7Rmn+2TVzKj+D64+JOFEEIIIYQQosvogUQIIYQQQgjRGB1bObWmXqw7UE5G5U1LehIPT45QOnXFU6e2sirD8pqXXnopxnY6+MADD4zx888/H2M79cW/zbPPPpv9XG9qTJKt7pGr1F7qkJGTrnhOGtu2bUu2cc5OmDAhxvfee2/SjnNx//33t1+l7WcxLL0CgM2bN8d4+fLlMX788ceTdkOHDo2xlbvk8tT7PUXvyJ33ntSpzn69fLbjX24fnKeeZIvH1lLXmVKJj83ZnDTGk/+K3tH6Le1vWir9yx1rzwHJHk+uyM7X/C1btiTteFxkJy2+xgPAMcccE+PRo0fHmKWwQD63vWrsnswy9x7RXUpctrx7g5w7ZycuW97yhFw7xnOwevTRR2PM96xAep/K13h7b9GJhKu36A5CCCGEEEII0Rh6IBFCCCGEEEI0hh5IhBBCCCGEEI3R8RqSlo6tdG1IqebO4q01YW0zrxuxdnv3339/jO+8884Yn3POOUm7QYMGxfiGG26IMWtLAeCCCy6I8YwZM2K8YsWKpB1r81ibD6RrBPg72r6ronB3CCFk86xOLnrVSD0tPGubTzzxxBhbLfJjjz0WY640bM+BnJ7Urjt58sknY8znDVdw3RW536bUFlvUp67OvtSeOmfZC6TaYa5gbdutWbMmxqylt+v2eO0Uf65n+d6NCvG8f1mm9g1cEsCzquYxyMtZzzo/d/0HgJEjR7Z939atW5N2bL1++eWXx5iv8UA6bk+aNCnGvAYFSO8Bpk2bFmN7Xc9Vo7f9VZ7uHnJrSEpt00tLB3gW5b097vYc4HWk/Lm2FAHntnft5u9Sdz1JsdVxrb0LIYQQQgghRBfQA4kQQgghhBCiMbpWqZ3xpiUZnsax0/b8mmUAQCoRGDFiRIx5CgpILXe5yuqXv/zlpN1f/MVfxJhlMldddVXS7l//9V9jfO6558bYVtt+8MEHY/z2t7892cZyMZ5GtlN8kmx1j1auetOojFfR3ZN2eTakDMulrKxl3bp1Meaq7Z4MgrdZO26WbPH5weeN3Z83LcvfSza/fUfO9ru0UjvnAU/p23ZHHHFEjK0MpX///jFmO1Qrf+Fx9pFHHokx5zKQjq1ve9vbYmzPFZa5lErPPLxzUZaq3aP1W5ZWavckW6U2qVaGwpItzsvhw4cn7ZYuXRrjiRMnxviNb3xj0o7l3E8//XSMvTEyZ3Nst3n2yF5Fd9EduFK7JZeLdcdjT7KVk4fZe+zcNmv1P378+BjzWLpp06ak3dixY2PM1wj7uZ4lcKlM20oXc+iOQgghhBBCCNEYeiARQgghhBBCNEbHLlutaSNv6qa31YSB1EnDTjXdfvvtMWZJyqxZs5J2LBFYuXJljFlWAAB33XVXjE8//fQYH3nkkUk7dkC69NJLY2zlLyyX+Na3vpVsO/7442PMMgg79dxC07W9p5WznhSrtBprqeOGncrMTVlaydbGjRuL9sfwlK2txsoV41m2wHIcoHxattQdSdSHpQReLnruLCxz5bH0uOOOS9rxGLlkyZJk2zPPPBPjZcuWxdhWveax9amnnmrbPwC4/vrrY7x27doYv+9970vaDRw4MMY8LnpyyVI8GYToHTnJFuPJXz3pacn+gPRazDJtlmUB6bX3He94R4ztmMbjKUu22CkRSKWP3M66HnqStRyeG5foDp58sFRm6Em2Sh0RS3PCkww+99xzMeZctOM25ybLG73ruPe53hKM0rFbo7EQQgghhBCiMfRAIoQQQgghhGgMPZAIIYQQQgghGqO2KNfqGFkjVmqz6FWA5G12jcacOXNizNrm3/zmN0k7ttxlizO28gPSKu6sqR4zZkzSjt/36KOPxtjqXbm/d999d7Lttttui/H5558f49waEtF7SrTNHqU6UW+dB68h4XUjdg0Ja/B5f7bvuW3WapXXpPAaEl5XsKvPytmwir6jlbOezSSPkbZaL1dFnz17doy5QjWQ6o2t7e/y5ctjzOOdXdPH23jtyoABA7J94jUkf//3f5+0Yxt2HoPt+qjSNUteziqfu4+3xsHT0nM+8/WQ1xQBeTtqAHj88cdjzGOcXTPHn8U6e16TCqR5ymtSbJ/q5KJ3bnv70xqS7pEbZxnP6r70mHnrRErXkORsf6+55pqkHVtaT548Oca8ntT7LK8P9h7Hu29gSnNWo7EQQgghhBCiMfRAIoQQQgghhGiMjiu1dzrF7Um7GCshYQmXV2GSrXi5SisA/OhHP4oxW/sNGzYsaceV31esWBHjKVOmJO1YFjF48OAYWykBTwHb3ytnxynpQN/AOetNkTNeRXdvepXtdzkHAOCWW26J8SGHHBJjawvJrzlXrOSA2/FUrLW0ZktWa6/KSNayZ5GTEuSsfu0YNHfu3BhzPrPsBAAeeOCBGLNEC0htf1lKaGUt/NncPysj4zxlKQHLbgHga1/7Woy/8pWvxNjKG1kuYH+n3DZZU/cNIYTsb5v7/b1rI1ebZok2kOaizTHOWb5G2/GYc9jLbZZc89h61FFHJe1Y1uOVPfAsZPl9kmz1PZ69eq46u9fOO37dkGwxQ4YMifFhhx2WbGMJLUu2Fy5cmN0fLzuw5xT3yd6nM7nyAIBvF5x8VlErIYQQQgghhOgD9EAihBBCCCGEaAw9kAghhBBCCCEao2Pb3xIL1Zy1r93GsdWcHXTQQTF+8MEHs5/F2jdeTwKk+ujjjz8+xlaP/+yzz8b44YcfbrtvAFi1alWM2faPtaoWu0aANdy8/9zvKb1o9+iGZZ+nkT/00ENjvHXr1mQb6+dvv/32tjGQ5j3b7VoLVc4d3rfVQLO2mbWm9nzj72Vtu7WGZPfTOgaezpw197NmzUrascaYx5zVq1cn7ThPOd/sNh4jOQbSNSScp3a9Co+7PC6OHj06acda5x/84Acx/i//5b8k7dji1Vv3xdjx1LOoF53R+m3tWOpp8Bkek4444ogYWztqHuPsNXro0KExZutga6vPWnhem7R+/fqkHefwli1bsn1n+2G20va+r/2dStbgiL7B+415bCk9nnY8yq2d3tU+Gc5ZHnNnzJiRtOP1Unw/y+v2gPTe4+ijj44x5y9QvobGWydS+h11pyGEEEIIIYRoDD2QCCGEEEIIIRqjdqV2O9Wdk2l5tneMlTbxdPz999+fbOMpqdNPPz3GCxYsSNrx1BPb+bLNL5DapHE1azsdzBXdP/vZz8bYVn5nqZiVc3Hl15x8TfQNda1tWUrAshM7xf6rX/0qxizfAlJZAE+9Whu9xYsXx5jz3MpaWErAVpecX0BaHZtz0cpzPHK2hFb2JWlX92jllp3q5+PGspbjjjsuaccSFc4Pa3/K7az9Lkuz+NjaKf1cblsJbU7aauU0PAb/7ne/i/Gxxx6btJs3b162T14Ve0Y2wN0jZ6/OvzFfU1lyCACzZ8+OMecRX5+BVBpiP4vbck7wOWD7xPcGVmq7YcOGGLNMxraz8sRc//hc8fLSs6rWvUL3aOVsqU2vHY/rjB/2Opk7nvb+k+WEv/zlL2M8YsSIpB1Ls/n6f8YZZyTt/uZv/ibGn/zkJ2N8wgknJO3YOth+f74H4G32/kKSLSGEEEIIIcQejx5IhBBCCCGEEI3RcaX21rRMqctWaXVsK11hSZitisoSLp4q9ape83SXrfzOLkU8PWWnVNmxgOUMXDkeSJ1mRo0alWzjfvD+7VRY6/trerb3lPyGOSctIJ2W5CrrVmrC8hIrZ2K5CeefldpMnTo1xldffXWMbQV2rgrP0oelS5cm7c4555wY8znmyQU8KVapY46oD1cQLs1FK3/hHGPZCY9vALBo0aK27YA0XzwXtpEjR8aYHZHstD3nLO/PymlYFsnj57/9278l7WbOnBljW8Wd81tVr3cvnmSLsTl78sknt23HshMgPQesrIUliZ40lp26eMy0kkZ21jrxxBNjbKWxn//852PM8lqbl2eeeWaM7X0NX0/4fsCTx4vuYK+Hpb9x7nroOXrafeccuGzuMHwOXHnllck2HndZZshjveWiiy6K8Re+8IVkG0tjrYtnTsJtv7+9p8ihOwohhBBCCCFEY+iBRAghhBBCCNEYeiARQgghhBBCNEbHtr85bXNOc+dZnHlVctlK11qorly5Msa8JsOu5WBNNK8nse3mz5/ftr92jQD39+abb44xV7kEUttKa9d62223xdirNFyquRM+IYSoYfaqXjO2HWvaeS3HunXrknasMV6+fHmy7ec//3mMx48fH2PWZwJpTvB7vvOd7yTt2O6adaK2qvHhhx8eY84p+929czGnDbU5qqrX3YHX6lltM69PY3h9G5CurVu1alWMeewEUqtfqw/mvOd1HpxvQKrjZwvVgQMHJu14XOSx2doB89jPecrjNABcddVVMf7IRz6SbOPv71VJFt0hhFCku+cxw+YHrxfivLTjMVvuWrtdbst5accmtqTmfLZrSHh/fD7YqtR87vCaVB7rAeCyyy6LsWcNz/0ttYkVnZOz/W3XxsYl++20LV+/v/e97yXtOF947aldE33XXXfFeNasWTHmvASAadOmxZjXSn3pS19K2vG9hy1xwdbanj1yKZohEUIIIYQQQjSGHkiEEEIIIYQQjdEnc9lssedZiLIcwdr38RTSkiVLkm0sH+BtLBcAUvkLT4faqr4MT8Xa6db/9t/+W9v3WHtLnoq234undln+M2PGjKSdnV4Tuw9vipytS60V7/XXXx9jm4sPPPBAjLnC9p//+Z8n7Xiqk+V+d999d9LuwQcfbNs/lhUAaRXX0srAnmUhI1lh37B9+/Y4RtnjydP2LNOyubhmzZoYr169OsZ2ep9zkXMbAB566KEYH3TQQTG2ki2WTnGusIQRSHOMxzcr2RoyZEiMeWwdOnRo0u73v/99jN/97ncn2+y4K/qeEglsTgIFpDI+vk7aSu2cw5zntq29fjOcV97Yx7nOn2Xt/HkfLA+3vwnnvb1vYDlmqW21qA/bq3vwNdkei9Jj47XjscqTD15zzTUxvuCCC2LM0isAuO6662LM34/vh4F0TGdp93333Ze042uLXYLA99J1yn1YNEMihBBCCCGEaAw9kAghhBBCCCEao2PJVmsqxnN68KZAeZsn7WK3Aet2VSoV4am2O+64I7s/niplB66PfexjSbsxY8bEmCu1W3kOS8q4nX391FNPxdhWklUV7O7R+i09KVKpywbnnq3WyzJDK0MZN25cjHla1lZZ5ZybPXt2jBcvXpy0Y7kOyxG5ejeQyiK4MnInMoCc1Ms6aVjnGVGP/fffPx7H448/PtnGUpZ77703xla6wtPs7ER43nnnJe3e+ta3xvgnP/lJso0lUuxsZI87t2NZgJUI8LjIuWJzkSUM7IZkJS5cdd5+/8MOOyzGLCvoxF1OlMMuW56shcdSe83j6zKPs9b9ja+3VoLIjnK/+MUvYnzqqacm7Viiwv2z4za7Jd55550xZpcjIJW/8He014ipU6fGmF3ngPz4LFetvqGqqo5lx157Hhe9SuUHHnhgso3HVs6XN73pTUm7W265JcZ8Pztx4sSkHUtv//Vf/zXGViLJzoQ51zkglX3b75+7h687ruquVwghhBBCCNEYeiARQgghhBBCNIYeSIQQQgghhBCN0dEaEtaJllYPLa3obpk0aVKMrYaU12GwhpTtIoFU28w6YqsTnTt3boxPO+20GE+ZMiVpx5/F+miruWOLV1vNm383tlDL2SNLP9o9Su1s7d9ZG8qWjlaXzHpjW6mdNZqsI7a5wzp51oZaW+ilS5fGmHWnnMtAqvG0GvxSZDu5exkxYgT+83/+zwCA+++/P9nGOmJeV8TrRIDUHvgd73hHjE8//fSkHeviWcMOpJpjzh1eswSkWnjen7VrP/roo2PM59jkyZOTdjwu8roTa//K1wVuB6TnKfdda/P6jtY4Ya9ZPH7ydc6uRcrpzu3aNB4zbU7wuioeM6dPn560Yx0/98Ou1bProFrY3D7mmGNizGugbIkB7pO1plae7jn05e9v855z7tZbb42xtfMdNmxYjG+66abs/nktK9+HWNt0/o68BsqO71zGonTNTd37VmW9EEIIIYQQojH0QCKEEEIIIYRojI5tf1vTPFbGwVM0pRIPnv6x07K8D55+B1IpAbfzpoAnTJgQ41mzZiXteLqVp3bt9+D+PvHEEzG207r82loCsx0lx7Yqp2Qy3SGEkP0t+XhyG5uLnH8sJ7FyPD6GXJEXSKdbeR88HQqkU6cshWEJDpDmM1c4tucAT8vyOepVbS9Flql9w7PPPosHH3wQwM5T88uWLYsx5wqPYQBw4YUXxpjHKrZCBdLxadGiRck2zhfOUyvt4lznPLVyGu47j8EzZ85M2rH1OttYW/kay26svIHzO3cOtHsteo/3G/OxsHJVbsdyJishYcm1laGwhT/fJzz00ENJO5a1sGW0tRFmGTiP23b85HOAx3pr+3/UUUchhx27W3j3WmL34sm+PZkXH1t7f8F5yhbAP/3pT5N2LPdj2197f8w5y/eYPK56/eukGn2uJIJ9T+n9hmZIhBBCCCGEEI2hBxIhhBBCCCFEY9Su1G5X2/OUjzelyFM53jQO748rTwKpfIDduOwUMO+fq6KPHz8+acduWjz9ZWU3vA+uJG9dwDwXkD/+4z+OMU9F874BTcvuDnJTrLmpcyCdbmXnLCDNFztlyY5ZufwF0oruLFuwVX35fezSwdWOLaUywNLpVVVq7xu2bt2KK6+8EsDOYxpLk9g96/rrr0/acdV1Hp/uueee7OdaaQzDMi079vNx55xluQCQOoHNnj07xoMGDUrasSRn1KhRMbbnygknnBBjlqXZPvK5Y3NU42z3yP2WPO54EmvOP85ZO07bCu8MSwYffvjhGLMsC0gdrTg/WAoLpLnIeWodsliaxdJdKyljCaJ1PfSuO6JvyLlGlUqxcthzgfdh7ytZ+s2SQ3sd5nweOXJkjO395xFHHBFjznseLy18P2slYCwN9sjJt4Dyew/NkAghhBBCCCEaQw8kQgghhBBCiMbQA4kQQgghhBCiMTqu1J7T0+U0Yp79l2eFxq+tZpT7wJZpbLcHpNV72aKUdZxAqttjDb5d18HaP45Zfwekemb7e7Hum/Xh9jtKj98dqqrqWCdaat9n13WwrZ7VYTJsV2o1y7m1IdZqldeheH0v1b96++jr6qwipaqqODZ46yvmz58fY67wC6RjF6/BY0tIIB1nbM6yFp51ynZs4jGd89SOadz3VatWxdhbk8K5aK8lfL6xpTAA/OY3v4nxySefHGM79ntrrkQ5IYR4/ntjDueKXYfBebp27dpk3/azWvBYCqTWvJwv1lafr/ljxoyJMeeo7ROfH1bfz/chbB3Ma0bt/vn+BEi/F8eyV+87Wrnq5axnZ9tuX+32562v4Pzj8YnXjABpvvC9B68Zsa85z3k9HgA88sgjMb7zzjtj/Ad/8AdJuxEjRsR448aNyTbvHp6R7a8QQgghhBBij0cPJEIIIYQQQojGqG37W7eSeO59drqHJQLWJo2nodiSzMoR2EaQP9faW/JnP/fcczG2tq4sxeKpfn4PkFY/Pv7445NtXG2Tp3ZzVoaSwfQOT2bI8NSjJ1nibTaPWD5gc4KPL7ezFdhZFsA5ZmWBPAXKud1kvtQdE8TO5H5LlkSxTOT8889P2t18880x5tyx4yxXBrbVrDnX+fywkgMrh2lhq1Rz3t97770xPvLII5N2f/7nfx5jHoM9GaSVtbC04Nprr40x2w0DO9sqi95j84PziMcna3vLYyaPfZ4UxFrl5nLWlg5guR/LZOyYzudYTpr4/7d3/6xRNX0YxyetjZUSUPP4D5UgFjZ2UUQs1E4sDCiWFtr4AsTG0rfhi/AViLWNKIJgVLiJIohtnmoP37nMDGc3G899w/dTTdzJ5rg7e/Yc5prflFJfr1y+fHlo37hxo+rHa5mxO9rr32uRSHRe6/FnRvpyZ/Xt7e2hzXF0/fr1qt+pU6eGNuNgPOeWUp9bOZ4zssXPaR47PxN7LZVcijMkkiRJkibkDYkkSZKkySy8NejYaEhGD1oVe7Ifp5Dyb3FalVOquStqq4JKTreyH9vfv3+v+nHXdU6fZT9Gu27dulU91pqW7U1za2/mfS17leE4fZkxAFZ54/gopY4Wtnb/LeXPsdTCccr/Xx7TMmIArfiQ1V/2R69iEd9PRlIyNsWfGYXJfr3o6V59/fp1VL8XL15UPzPadfHixaGd50hWmsuduFlljFW28tzveXb58nzBmEcv1sn3hlG6PM+0Ylml1Odq/h5j3qXU1xC9nd8ZO+Tfzd2x19bWhvajR4+GdsbNWp/fPF6O9d7xaTl6lSRbkcP8vV41rt55hu87++W1Ac/drFCXMUNWMOQu8Hnu45KB48ePD+319fWqX+tao5R2nD3H/dhKnZ6NJUmSJE3GGxJJkiRJk/GGRJIkSdJkFi7728P83NhsXuYp+XPu8sjf446V2a9VjjIzqcwbs5xa7tTOfD/XCHDHy1LqzOfNmzebxzQm32/Z372bjcHMdfK17Y3T1mNcF1JKneXM9UzEMZvH9OXLl6HdW0/SyhtnnrQ1fuYZV621Iq572j+zcdF7TflYrqFg9p3Z4d44Z464lDqL/Dc9f/58aL9+/Xpo53mW5YwzH3337t2hzXUo+R2Ru4Vrf/H1z3UdfA85tntrSFLrXJXnav5t5t3HlhjOa4MHDx7s2i+fr1e6mpZRQlXL1xuLvfeM512ukSulPle31omUUm9rwbWsrevcUup1gbl9xocPH4b25ubm0M51qFw73duBvrcb++jyyKN6SZIkSdI+8IZEkiRJ0mTmimytrKw0y/ZxmpJ9eqVBe+XUWM43/ybLPbI8YK/EMKe1WO4sn48xgNwdmz8zssWpr1LquMCJEyeqx7g7Jo83p7ssqbo8s7G5jNc4pz2J0/EZf2EJ1F4c4devX0ObpSUzasN+1Cp1Xcri8T/+XqtEoZZr9jrn+96Kg3z69KnqNzamxc/E6upq9dhUka1Dhw4N7TNnzgztY8eOVf34/89zf+t1csz+fTx/8L3g+1xK/V3+8+fPoc1IS/bLqB7HM68hMh7WioTnOOLPnz9/HtpXr16t+l26dGnX4+tFAnMs8m8Z2ZrW2DK1Y3HcZ6yaO7Izss2IVin19cW3b9+GNiNVpdTjntebb9++rfpxaQEjh3ltkcspaJHIWo8jXZIkSdJkvCGRJEmSNBlvSCRJkiRNZu6yv7NsM/NnpfyZvZzJ3Horg579WKaPazxKqXP8zNVlab+DBw8ObZbp29raqvox38fMXZZdZaY/n4OePHkytDPTz9eNr4XlfffPmLK/HIu9vCPXEWVpX46/zFDyvWZGM9cptUpQZ/lIlv1jjrpXtnLRjCcf641T1z0tz+y1zNeb5w+OAa5RKqUeE8yxZz6Y44XPV0qdYf7x48foY98r/l1+ZnNst75zSqn//70xaz5//7Xew9OnT1f9eL7jWMxxyefI95bXAzwf5fUKn5OfD67/KKX+vGxsbAzte/fuVf24DsUx9d8xZm0D+/TWKff+vVcel2vjWNo8n4Njk4+9e/eu6sex/vHjx6H95s2bqt/Tp0+H9tra2tDmOpY89jym1rVH9nMNiSRJkqR/PW9IJEmSJE1maTu1t+JHvSkutrO0GEujMXpVSj3lxXZO6fM4WqWCS6ljMpyizR0wOc3Lkph37typ+l24cGFoZ0m21uuX07y96I3m05oubE1F9soxMoKXJScZF8i/yfHMmNb79++rfq3xxx1X8/l5HLnLKvXK9/WmVPl7i+xor/nNxmCeL/hecHf23DmaY5hjNiNbfM+yNCrPu38zsnXkyJGhzc9NHsPYXewpx6gxw+XL73y+xoy15q7lLGXK96/3fBnF4ueDn538HLV2us6SrPfv3x/a165d2/X385ha5Xt3O14aG9s23r0cOzs7w3VWXm+1rlMzbsVxwPd6nnLP/JnbBeSSAf4tjqPe9Sw/U3kdffv27aHN5Qi9OPfYKFb+e28Xd3KGRJIkSdJkvCGRJEmSNJm5I1uz6cic1mnFtHr9OM2ZUzzsx50n8/k51ZZVttiPUZbcqZ1RAE7f5q7c3KmVU2bPnj2r+vE5epG13pSylTqWZ0x8oxdnao3TnLbnc2R1Ne48zPGRU8U8VsZrDhw40PxbHGPc2TqPfazelDL1Kolob2bng16Uk9VQcjqej3EK//Hjx1W/V69eDe2MCGQk8W85f/780ObY7sXXxsZYHKP7L98LnicYnc5zJL+H+VgvQpLf0Txncvz2voc5jh4+fFj1W19f3/WYehUbx0Zjx15DaX/s7OwMUaKMFPFnvmdjK/3ld2PvmpiRK15T5HUvqw/yc5TLAnh9y/bJkyerfocPHx7avCaZpwLn2OqkY3l2liRJkjQZb0gkSZIkTcYbEkmSJEmTmXsNyRi9nZ1bmfbsx6xeLwfPUpWZo2a27vfv30M784LMnTJvnZlqltnc3Nwc2ufOnav6cbf3Xh6fGVfLT+6f2Xjs5UTH/Dufq5Q/S/txzGaJUpbVY14zc5dc68Tcaa9cJH8nx2IrC5vGllBd9Dk0n9n5IMcYd/JlPpiZ4lLqcxfPR1tbW1W/69evD+2XL19Wj2Xfv+Xo0aO7/vs8ufrWWsX8nrGE6vLM3p9cQ8GMPHelznKlXA/SKsWfcp0Tn5OP5fvM57xy5crQPnv2bNWP5dZZprhXGr63ToQ/j12rp/0z707ti5bO7/Xj2mSuIVldXW0+B69tcy0Wz/3b29tDe2Njo+rH75bemO0de+uxRcur+wmQJEmSNBlvSCRJkiRNZmWeqNDKyso/pZRP+3c42sX/dnZ2Dk19EP9VjtnJOG4X5JidjGN2QY7ZyThmF+SYnUxzzM51QyJJkiRJy2RkS5IkSdJkvCGRJEmSNBlvSCRJkiRNxhsSSZIkSZPxhkSSJEnSZLwhkSRJkjQZb0gkSZIkTcYbEkmSJEmT8YZEkiRJ0mT+D7WNJrG/vtRKAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1080x720 with 25 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#creating a 5x5 grid of the first 25 photos in the training images\n","fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(15, 10),\n","                        subplot_kw={'xticks': [], 'yticks': []})\n","\n","for i in range(25):\n","    plt.subplot(5,5,i+1)\n","    plt.imshow(X_train[i], cmap='gray')\n","    plt.title(Y_train[i])\n","plt.show()"]},{"cell_type":"markdown","id":"d79a4762","metadata":{"papermill":{"duration":0.015162,"end_time":"2022-09-11T15:49:45.16036","exception":false,"start_time":"2022-09-11T15:49:45.145198","status":"completed"},"tags":[]},"source":["### 4.2 Test Images"]},{"cell_type":"code","execution_count":13,"id":"716d044e","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:45.195079Z","iopub.status.busy":"2022-09-11T15:49:45.194431Z","iopub.status.idle":"2022-09-11T15:49:46.213979Z","shell.execute_reply":"2022-09-11T15:49:46.212805Z"},"papermill":{"duration":1.040451,"end_time":"2022-09-11T15:49:46.217325","exception":false,"start_time":"2022-09-11T15:49:45.176874","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 1080x720 with 25 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#creating a 5x5 grid of the first 25 photos in the test images\n","fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(15, 10),\n","                        subplot_kw={'xticks': [], 'yticks': []})\n","\n","for i in range(25):\n","    plt.subplot(5,5,i+1)\n","    plt.imshow(X_test[i], cmap='gray')\n","    plt.title(Y_test[i])\n","plt.show()"]},{"cell_type":"markdown","id":"a30a2408","metadata":{"papermill":{"duration":0.016729,"end_time":"2022-09-11T15:49:46.251241","exception":false,"start_time":"2022-09-11T15:49:46.234512","status":"completed"},"tags":[]},"source":["# 5. CNN Model"]},{"cell_type":"markdown","id":"f4d6be08","metadata":{"papermill":{"duration":0.016073,"end_time":"2022-09-11T15:49:46.283808","exception":false,"start_time":"2022-09-11T15:49:46.267735","status":"completed"},"tags":[]},"source":["### 5.1 Spliting Our Training Data"]},{"cell_type":"markdown","id":"318195ca","metadata":{"papermill":{"duration":0.015909,"end_time":"2022-09-11T15:49:46.316385","exception":false,"start_time":"2022-09-11T15:49:46.300476","status":"completed"},"tags":[]},"source":["We will take the images in the Train set and we will split them into training and validation sets. While training the model, we will use the new train set to train the model and the validation set to validate the results.\n","\n","Once the model has been trained, we will take the pixel data from the test set and predict the labels for each test image. \n","\n","Then we will evaluate the model's performance by looking at a classification report."]},{"cell_type":"code","execution_count":14,"id":"33c0e686","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:46.352389Z","iopub.status.busy":"2022-09-11T15:49:46.35185Z","iopub.status.idle":"2022-09-11T15:49:46.788377Z","shell.execute_reply":"2022-09-11T15:49:46.786987Z"},"papermill":{"duration":0.45863,"end_time":"2022-09-11T15:49:46.791695","exception":false,"start_time":"2022-09-11T15:49:46.333065","status":"completed"},"tags":[]},"outputs":[],"source":["#spliting training images into the images we will use for training the model and validating the model\n","X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.3, random_state=7)"]},{"cell_type":"code","execution_count":15,"id":"7899e29b","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:46.827942Z","iopub.status.busy":"2022-09-11T15:49:46.82747Z","iopub.status.idle":"2022-09-11T15:49:46.83586Z","shell.execute_reply":"2022-09-11T15:49:46.834057Z"},"papermill":{"duration":0.030089,"end_time":"2022-09-11T15:49:46.838653","exception":false,"start_time":"2022-09-11T15:49:46.808564","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["(19218, 28, 28, 1)\n","(19218,)\n","(8237, 28, 28, 1)\n","(8237,)\n","(7172, 28, 28, 1)\n","(7172,)\n"]}],"source":["#showing the shapes of our train, validate, and test images\n","print(X_train.shape)\n","print(Y_train.shape)\n","print(X_val.shape)\n","print(Y_val.shape)\n","print(X_test.shape)\n","print(Y_test.shape)"]},{"cell_type":"markdown","id":"88bf3523","metadata":{"papermill":{"duration":0.015821,"end_time":"2022-09-11T15:49:46.87031","exception":false,"start_time":"2022-09-11T15:49:46.854489","status":"completed"},"tags":[]},"source":["### 5.2 Creating Our Model"]},{"cell_type":"code","execution_count":16,"id":"073347b6","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:46.906529Z","iopub.status.busy":"2022-09-11T15:49:46.90599Z","iopub.status.idle":"2022-09-11T15:49:47.032576Z","shell.execute_reply":"2022-09-11T15:49:47.031224Z"},"papermill":{"duration":0.148375,"end_time":"2022-09-11T15:49:47.035974","exception":false,"start_time":"2022-09-11T15:49:46.887599","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["2022-09-11 15:49:46.998246: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"]}],"source":["#creating our CNN model\n","model = keras.Sequential([\n","    \n","    layers.BatchNormalization(),\n","    layers.Conv2D(filters=32, kernel_size=(5,5), activation=\"relu\", padding='same',\n","                  input_shape=[28, 28, 1]),\n","    layers.MaxPool2D(),\n","    layers.Dropout(.25),\n","    \n","    layers.BatchNormalization(),\n","    layers.Conv2D(filters=32, kernel_size=(3,3), activation=\"relu\", padding='same'),\n","    layers.MaxPool2D(),\n","    layers.Dropout(.25),\n","    \n","    layers.BatchNormalization(),\n","    layers.Conv2D(filters=64, kernel_size=(3,3), activation=\"relu\", padding='same'),\n","    layers.MaxPool2D(),\n","    layers.Dropout(.25),\n","\n","    layers.BatchNormalization(),\n","    layers.Conv2D(filters=128, kernel_size=(3,3), activation=\"relu\", padding='same'),\n","    layers.MaxPool2D(),\n","    layers.Dropout(.25),\n","    \n","    layers.Flatten(),\n","    layers.Dropout(.25),\n","    layers.Dense(units=64, activation=\"relu\"),\n","    layers.Dense(units=26, activation=\"softmax\"),\n","])"]},{"cell_type":"code","execution_count":17,"id":"d10e893d","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:47.071544Z","iopub.status.busy":"2022-09-11T15:49:47.071135Z","iopub.status.idle":"2022-09-11T15:49:47.098836Z","shell.execute_reply":"2022-09-11T15:49:47.097504Z"},"papermill":{"duration":0.048422,"end_time":"2022-09-11T15:49:47.101845","exception":false,"start_time":"2022-09-11T15:49:47.053423","status":"completed"},"tags":[]},"outputs":[],"source":["#compiling the model\n","model.compile(\n","    optimizer=tf.keras.optimizers.Adam(epsilon=0.01),\n","    loss='sparse_categorical_crossentropy',\n","    metrics=['accuracy']\n",")"]},{"cell_type":"markdown","id":"4e8e06aa","metadata":{"papermill":{"duration":0.015745,"end_time":"2022-09-11T15:49:47.133627","exception":false,"start_time":"2022-09-11T15:49:47.117882","status":"completed"},"tags":[]},"source":["### 5.3 Training Our Model"]},{"cell_type":"code","execution_count":18,"id":"1c47dfaa","metadata":{"execution":{"iopub.execute_input":"2022-09-11T15:49:47.167736Z","iopub.status.busy":"2022-09-11T15:49:47.167235Z","iopub.status.idle":"2022-09-11T16:01:10.687806Z","shell.execute_reply":"2022-09-11T16:01:10.686322Z"},"papermill":{"duration":683.541669,"end_time":"2022-09-11T16:01:10.691273","exception":false,"start_time":"2022-09-11T15:49:47.149604","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["2022-09-11 15:49:47.397925: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"]},{"name":"stdout","output_type":"stream","text":["Epoch 1/50\n","151/151 - 15s - loss: 3.2532 - accuracy: 0.0767 - val_loss: 3.3040 - val_accuracy: 0.0481\n","Epoch 2/50\n","151/151 - 14s - loss: 2.6072 - accuracy: 0.1970 - val_loss: 2.8903 - val_accuracy: 0.1203\n","Epoch 3/50\n","151/151 - 13s - loss: 1.7095 - accuracy: 0.4275 - val_loss: 1.2360 - val_accuracy: 0.6377\n","Epoch 4/50\n","151/151 - 14s - loss: 1.0832 - accuracy: 0.6206 - val_loss: 0.5860 - val_accuracy: 0.8333\n","Epoch 5/50\n","151/151 - 13s - loss: 0.7632 - accuracy: 0.7261 - val_loss: 0.3372 - val_accuracy: 0.9194\n","Epoch 6/50\n","151/151 - 13s - loss: 0.5830 - accuracy: 0.7898 - val_loss: 0.2369 - val_accuracy: 0.9497\n","Epoch 7/50\n","151/151 - 14s - loss: 0.4512 - accuracy: 0.8380 - val_loss: 0.1594 - val_accuracy: 0.9659\n","Epoch 8/50\n","151/151 - 13s - loss: 0.3628 - accuracy: 0.8750 - val_loss: 0.1091 - val_accuracy: 0.9846\n","Epoch 9/50\n","151/151 - 14s - loss: 0.3070 - accuracy: 0.8951 - val_loss: 0.0964 - val_accuracy: 0.9798\n","Epoch 10/50\n","151/151 - 13s - loss: 0.2558 - accuracy: 0.9112 - val_loss: 0.0672 - val_accuracy: 0.9865\n","Epoch 11/50\n","151/151 - 14s - loss: 0.2118 - accuracy: 0.9296 - val_loss: 0.0353 - val_accuracy: 0.9964\n","Epoch 12/50\n","151/151 - 13s - loss: 0.1826 - accuracy: 0.9399 - val_loss: 0.0278 - val_accuracy: 0.9977\n","Epoch 13/50\n","151/151 - 14s - loss: 0.1503 - accuracy: 0.9511 - val_loss: 0.0149 - val_accuracy: 0.9993\n","Epoch 14/50\n","151/151 - 13s - loss: 0.1299 - accuracy: 0.9574 - val_loss: 0.0127 - val_accuracy: 0.9996\n","Epoch 15/50\n","151/151 - 13s - loss: 0.1189 - accuracy: 0.9594 - val_loss: 0.0093 - val_accuracy: 0.9996\n","Epoch 16/50\n","151/151 - 14s - loss: 0.1053 - accuracy: 0.9663 - val_loss: 0.0057 - val_accuracy: 0.9999\n","Epoch 17/50\n","151/151 - 13s - loss: 0.0923 - accuracy: 0.9705 - val_loss: 0.0044 - val_accuracy: 1.0000\n","Epoch 18/50\n","151/151 - 14s - loss: 0.0874 - accuracy: 0.9708 - val_loss: 0.0054 - val_accuracy: 0.9998\n","Epoch 19/50\n","151/151 - 13s - loss: 0.0794 - accuracy: 0.9755 - val_loss: 0.0036 - val_accuracy: 0.9998\n","Epoch 20/50\n","151/151 - 13s - loss: 0.0700 - accuracy: 0.9776 - val_loss: 0.0025 - val_accuracy: 0.9999\n","Epoch 21/50\n","151/151 - 14s - loss: 0.0651 - accuracy: 0.9787 - val_loss: 0.0016 - val_accuracy: 1.0000\n","Epoch 22/50\n","151/151 - 13s - loss: 0.0647 - accuracy: 0.9792 - val_loss: 0.0021 - val_accuracy: 0.9999\n","Epoch 23/50\n","151/151 - 14s - loss: 0.0565 - accuracy: 0.9807 - val_loss: 0.0017 - val_accuracy: 0.9999\n","Epoch 24/50\n","151/151 - 13s - loss: 0.0515 - accuracy: 0.9835 - val_loss: 0.0010 - val_accuracy: 1.0000\n","Epoch 25/50\n","151/151 - 14s - loss: 0.0496 - accuracy: 0.9843 - val_loss: 0.0010 - val_accuracy: 1.0000\n","Epoch 26/50\n","151/151 - 13s - loss: 0.0440 - accuracy: 0.9862 - val_loss: 8.8474e-04 - val_accuracy: 1.0000\n","Epoch 27/50\n","151/151 - 14s - loss: 0.0485 - accuracy: 0.9843 - val_loss: 5.5789e-04 - val_accuracy: 1.0000\n","Epoch 28/50\n","151/151 - 13s - loss: 0.0441 - accuracy: 0.9861 - val_loss: 5.9105e-04 - val_accuracy: 1.0000\n","Epoch 29/50\n","151/151 - 13s - loss: 0.0408 - accuracy: 0.9874 - val_loss: 6.2765e-04 - val_accuracy: 1.0000\n","Epoch 30/50\n","151/151 - 14s - loss: 0.0346 - accuracy: 0.9894 - val_loss: 6.7275e-04 - val_accuracy: 1.0000\n","Epoch 31/50\n","151/151 - 13s - loss: 0.0368 - accuracy: 0.9878 - val_loss: 3.7091e-04 - val_accuracy: 1.0000\n","Epoch 32/50\n","151/151 - 14s - loss: 0.0375 - accuracy: 0.9889 - val_loss: 3.2553e-04 - val_accuracy: 1.0000\n","Epoch 33/50\n","151/151 - 13s - loss: 0.0307 - accuracy: 0.9899 - val_loss: 3.2828e-04 - val_accuracy: 1.0000\n","Epoch 34/50\n","151/151 - 13s - loss: 0.0323 - accuracy: 0.9890 - val_loss: 2.3914e-04 - val_accuracy: 1.0000\n","Epoch 35/50\n","151/151 - 14s - loss: 0.0308 - accuracy: 0.9904 - val_loss: 2.5175e-04 - val_accuracy: 1.0000\n","Epoch 36/50\n","151/151 - 13s - loss: 0.0291 - accuracy: 0.9903 - val_loss: 3.4058e-04 - val_accuracy: 1.0000\n","Epoch 37/50\n","151/151 - 14s - loss: 0.0300 - accuracy: 0.9907 - val_loss: 2.5124e-04 - val_accuracy: 1.0000\n","Epoch 38/50\n","151/151 - 13s - loss: 0.0258 - accuracy: 0.9920 - val_loss: 1.5429e-04 - val_accuracy: 1.0000\n","Epoch 39/50\n","151/151 - 14s - loss: 0.0268 - accuracy: 0.9917 - val_loss: 1.5330e-04 - val_accuracy: 1.0000\n","Epoch 40/50\n","151/151 - 13s - loss: 0.0234 - accuracy: 0.9927 - val_loss: 1.9206e-04 - val_accuracy: 1.0000\n","Epoch 41/50\n","151/151 - 13s - loss: 0.0250 - accuracy: 0.9924 - val_loss: 1.1999e-04 - val_accuracy: 1.0000\n","Epoch 42/50\n","151/151 - 14s - loss: 0.0224 - accuracy: 0.9943 - val_loss: 1.1043e-04 - val_accuracy: 1.0000\n","Epoch 43/50\n","151/151 - 13s - loss: 0.0244 - accuracy: 0.9920 - val_loss: 1.5704e-04 - val_accuracy: 1.0000\n","Epoch 44/50\n","151/151 - 14s - loss: 0.0247 - accuracy: 0.9925 - val_loss: 1.9120e-04 - val_accuracy: 1.0000\n","Epoch 45/50\n","151/151 - 13s - loss: 0.0233 - accuracy: 0.9926 - val_loss: 1.1384e-04 - val_accuracy: 1.0000\n","Epoch 46/50\n","151/151 - 14s - loss: 0.0223 - accuracy: 0.9923 - val_loss: 2.2684e-04 - val_accuracy: 1.0000\n","Epoch 47/50\n","151/151 - 13s - loss: 0.0222 - accuracy: 0.9935 - val_loss: 7.7024e-05 - val_accuracy: 1.0000\n","Epoch 48/50\n","151/151 - 14s - loss: 0.0199 - accuracy: 0.9934 - val_loss: 1.0577e-04 - val_accuracy: 1.0000\n","Epoch 49/50\n","151/151 - 13s - loss: 0.0202 - accuracy: 0.9933 - val_loss: 7.8356e-05 - val_accuracy: 1.0000\n","Epoch 50/50\n","151/151 - 13s - loss: 0.0187 - accuracy: 0.9934 - val_loss: 8.5359e-05 - val_accuracy: 1.0000\n"]}],"source":["#Training the model\n","history = model.fit(\n","    x = X_train,\n","    y = Y_train,\n","    validation_data= (X_val,Y_val),\n","    batch_size = 128,\n","    epochs=50,\n","    verbose=2,\n",")"]},{"cell_type":"code","execution_count":19,"id":"59dc22bf","metadata":{"execution":{"iopub.execute_input":"2022-09-11T16:01:10.743482Z","iopub.status.busy":"2022-09-11T16:01:10.742582Z","iopub.status.idle":"2022-09-11T16:01:11.160049Z","shell.execute_reply":"2022-09-11T16:01:11.158972Z"},"papermill":{"duration":0.446506,"end_time":"2022-09-11T16:01:11.162708","exception":false,"start_time":"2022-09-11T16:01:10.716202","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["#Viewing the training results\n","history_frame = pd.DataFrame(history.history)\n","history_frame.loc[:, ['loss', 'val_loss']].plot()\n","history_frame.loc[:, ['accuracy', 'val_accuracy']].plot();"]},{"cell_type":"markdown","id":"217a1946","metadata":{"papermill":{"duration":0.023269,"end_time":"2022-09-11T16:01:11.210499","exception":false,"start_time":"2022-09-11T16:01:11.18723","status":"completed"},"tags":[]},"source":["# 6. Results"]},{"cell_type":"markdown","id":"23cdbd4c","metadata":{"papermill":{"duration":0.024026,"end_time":"2022-09-11T16:01:11.258745","exception":false,"start_time":"2022-09-11T16:01:11.234719","status":"completed"},"tags":[]},"source":["### 6.1 Classification Report"]},{"cell_type":"code","execution_count":20,"id":"df735eea","metadata":{"execution":{"iopub.execute_input":"2022-09-11T16:01:11.308586Z","iopub.status.busy":"2022-09-11T16:01:11.307286Z","iopub.status.idle":"2022-09-11T16:01:13.50792Z","shell.execute_reply":"2022-09-11T16:01:13.505134Z"},"papermill":{"duration":2.229396,"end_time":"2022-09-11T16:01:13.511517","exception":false,"start_time":"2022-09-11T16:01:11.282121","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00       331\n","           1       1.00      1.00      1.00       432\n","           2       1.00      1.00      1.00       310\n","           3       1.00      1.00      1.00       245\n","           4       0.99      1.00      1.00       498\n","           5       1.00      1.00      1.00       247\n","           6       0.95      1.00      0.97       348\n","           7       1.00      0.95      0.98       436\n","           8       1.00      1.00      1.00       288\n","          10       0.99      1.00      1.00       331\n","          11       1.00      1.00      1.00       209\n","          12       1.00      1.00      1.00       394\n","          13       1.00      1.00      1.00       291\n","          14       1.00      1.00      1.00       246\n","          15       1.00      1.00      1.00       347\n","          16       1.00      1.00      1.00       164\n","          17       0.99      0.97      0.98       144\n","          18       1.00      0.99      0.99       246\n","          19       1.00      1.00      1.00       248\n","          20       1.00      0.99      1.00       266\n","          21       0.99      1.00      0.99       346\n","          22       1.00      1.00      1.00       206\n","          23       1.00      1.00      1.00       267\n","          24       1.00      1.00      1.00       332\n","\n","    accuracy                           1.00      7172\n","   macro avg       1.00      1.00      1.00      7172\n","weighted avg       1.00      1.00      1.00      7172\n","\n"]}],"source":["#creating our predictions using the test pixel values\n","predictions = model.predict(X_test)\n","predictions = np.argmax(predictions,axis = 1)\n","\n","#creating a report that show how our predictions compare with actual values\n","print(classification_report(Y_test, predictions))"]},{"cell_type":"markdown","id":"d3c80815","metadata":{"papermill":{"duration":0.023494,"end_time":"2022-09-11T16:01:13.559639","exception":false,"start_time":"2022-09-11T16:01:13.536145","status":"completed"},"tags":[]},"source":["### 6.2 Brief Thoughts on Results"]},{"cell_type":"markdown","id":"37568aae","metadata":{"papermill":{"duration":0.024966,"end_time":"2022-09-11T16:01:13.608355","exception":false,"start_time":"2022-09-11T16:01:13.583389","status":"completed"},"tags":[]},"source":["We were able to get an accuracy of 100% according to the classification report. \n","The biggest take away for me was that the BatchNormalization and Dropout layers really helped with increasing the accuracy. The Dropout layers seemed to really smooth out the training results as well."]},{"cell_type":"markdown","id":"3e472bf5","metadata":{"papermill":{"duration":0.023534,"end_time":"2022-09-11T16:01:13.656757","exception":false,"start_time":"2022-09-11T16:01:13.633223","status":"completed"},"tags":[]},"source":["# 7. Conclusion"]},{"cell_type":"markdown","id":"6a46db4f","metadata":{"papermill":{"duration":0.023191,"end_time":"2022-09-11T16:01:13.703706","exception":false,"start_time":"2022-09-11T16:01:13.680515","status":"completed"},"tags":[]},"source":["### 7.1 Recap"]},{"cell_type":"markdown","id":"efd0b117","metadata":{"papermill":{"duration":0.023531,"end_time":"2022-09-11T16:01:13.750842","exception":false,"start_time":"2022-09-11T16:01:13.727311","status":"completed"},"tags":[]},"source":["We were able to take images of the American Sign Lanuage alphabet and use a CNN model to learn the alphabet and make predictions on new images. When training our CNN model, the training results were 99% accuracy but the validation results had an accuracy of 100%. When applying the CNN model on our test images we got the result of 100% accuracy."]},{"cell_type":"markdown","id":"4ceb3feb","metadata":{"papermill":{"duration":0.023287,"end_time":"2022-09-11T16:01:13.797546","exception":false,"start_time":"2022-09-11T16:01:13.774259","status":"completed"},"tags":[]},"source":["### 7.2 Future Work"]},{"cell_type":"markdown","id":"40bbea4e","metadata":{"papermill":{"duration":0.025082,"end_time":"2022-09-11T16:01:13.84632","exception":false,"start_time":"2022-09-11T16:01:13.821238","status":"completed"},"tags":[]},"source":["The next step would be to apply this model to live video feed to detect the American Sign Language alphabet within the video and translate each sign on the screen in real time."]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"},"papermill":{"default_parameters":{},"duration":720.534898,"end_time":"2022-09-11T16:01:16.845747","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2022-09-11T15:49:16.310849","version":"2.3.4"}},"nbformat":4,"nbformat_minor":5}