NN_using_Keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aKhoCKCr_nuo"
   },
   "source": [
    "# Neural Network in Keras -- a Case Study of the Fashion MNIST Dataset\n",
    "\n",
    "This code is based the (more extensive) Keras classification tutorial at (https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/classification.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 1644,
     "status": "ok",
     "timestamp": 1616675684197,
     "user": {
      "displayName": "Xianjun Geng",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjp2zNPbIYU0DaDSSzFs4-oEwNRuLg3LhJILAgh3RY=s64",
      "userId": "03603659341670711497"
     },
     "user_tz": 300
    },
    "id": "CDf7WYohrsZF"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yR0EdgrLCaWR"
   },
   "source": [
    "## Import the Fashion MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2390,
     "status": "ok",
     "timestamp": 1616675687226,
     "user": {
      "displayName": "Xianjun Geng",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjp2zNPbIYU0DaDSSzFs4-oEwNRuLg3LhJILAgh3RY=s64",
      "userId": "03603659341670711497"
     },
     "user_tz": 300
    },
    "id": "7MqDQO0KCaWS",
    "outputId": "a2102931-972e-43b9-9be2-30a610387fd7"
   },
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t9FDsUlxCaWW"
   },
   "source": [
    "The [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset contains 70,000 grayscale images -- 60,000 for training and 10,000 for testing. Each image is of size 28 pixels by 28 pixels, and shows a piece of clothes -- see the example below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 369,
     "status": "ok",
     "timestamp": 1616675689389,
     "user": {
      "displayName": "Xianjun Geng",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjp2zNPbIYU0DaDSSzFs4-oEwNRuLg3LhJILAgh3RY=s64",
      "userId": "03603659341670711497"
     },
     "user_tz": 300
    },
    "id": "3qx5wDuTqPlV",
    "outputId": "9901f016-979d-483e-a420-a28b7e7c9b6c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (10000, 28, 28))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape, test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 680,
     "status": "ok",
     "timestamp": 1616675690684,
     "user": {
      "displayName": "Xianjun Geng",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjp2zNPbIYU0DaDSSzFs4-oEwNRuLg3LhJILAgh3RY=s64",
      "userId": "03603659341670711497"
     },
     "user_tz": 300
    },
    "id": "ZQA6_5SqrJe2",
    "outputId": "590399d8-4663-46e9-ce27-42121e29ed34"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0], cmap=plt.cm.binary)\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5ltHHXRhqOCJ"
   },
   "source": [
    "Each image is represented by a 28x28 NumPy array, with pixel values ranging from 0 to 255. For example, the image above is presented as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "2r_sMFZ6q7y3",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0\n",
      "    0   1   4   0   0   0   0   1   1   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62\n",
      "   54   0   0   0   1   3   4   0   0   3]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134\n",
      "  144 123  23   0   0   0   0  12  10   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178\n",
      "  107 156 161 109  64  23  77 130  72  15]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216\n",
      "  216 163 127 121 122 146 141  88 172  66]\n",
      " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229\n",
      "  223 223 215 213 164 127 123 196 229   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228\n",
      "  235 227 224 222 224 221 223 245 173   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198\n",
      "  180 212 210 211 213 223 220 243 202   0]\n",
      " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192\n",
      "  169 227 208 218 224 212 226 197 209  52]\n",
      " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203\n",
      "  198 221 215 213 222 220 245 119 167  56]\n",
      " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240\n",
      "  232 213 218 223 234 217 217 209  92   0]\n",
      " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219\n",
      "  222 221 216 223 229 215 218 255  77   0]\n",
      " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208\n",
      "  211 218 224 223 219 215 224 244 159   0]\n",
      " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230\n",
      "  224 234 176 188 250 248 233 238 215   0]\n",
      " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223\n",
      "  255 255 221 234 221 211 220 232 246   0]\n",
      " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221\n",
      "  188 154 191 210 204 209 222 228 225   0]\n",
      " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117\n",
      "  168 219 221 215 217 223 223 224 229  29]\n",
      " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245\n",
      "  239 223 218 212 209 222 220 221 230  67]\n",
      " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216\n",
      "  199 206 186 181 177 172 181 205 206 115]\n",
      " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191\n",
      "  195 191 198 192 176 156 167 177 210  92]\n",
      " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209\n",
      "  210 210 211 188 188 194 192 216 170   0]\n",
      " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179\n",
      "  182 182 181 176 166 168  99  58   0   0]\n",
      " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]]\n"
     ]
    }
   ],
   "source": [
    "print(train_images[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I6aAIA9d3zzH"
   },
   "source": [
    "These clothes belong to 10 categories/classes (so this is a 10-class prediction problem rather than a binary prediction problem). These 10 classes are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 382,
     "status": "ok",
     "timestamp": 1616675693795,
     "user": {
      "displayName": "Xianjun Geng",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjp2zNPbIYU0DaDSSzFs4-oEwNRuLg3LhJILAgh3RY=s64",
      "userId": "03603659341670711497"
     },
     "user_tz": 300
    },
    "id": "IjnLH5S2CaWx"
   },
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 302,
     "status": "ok",
     "timestamp": 1616675694936,
     "user": {
      "displayName": "Xianjun Geng",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjp2zNPbIYU0DaDSSzFs4-oEwNRuLg3LhJILAgh3RY=s64",
      "userId": "03603659341670711497"
     },
     "user_tz": 300
    },
    "id": "nFBB1wLK4hyL",
    "outputId": "e28d0c05-9bfc-44cf-a563-a2626e9383d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image above has a target/label value of 9.\n",
      "Therefore, it is an image of Ankle boot.\n"
     ]
    }
   ],
   "source": [
    "print(f\"The image above has a target/label value of {train_labels[0]}.\")\n",
    "print(f\"Therefore, it is an image of {class_names[train_labels[0]]}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ee638AlnCaWz"
   },
   "source": [
    "Let's plot a few more for fun:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 589
    },
    "executionInfo": {
     "elapsed": 1450,
     "status": "ok",
     "timestamp": 1616675697998,
     "user": {
      "displayName": "Xianjun Geng",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjp2zNPbIYU0DaDSSzFs4-oEwNRuLg3LhJILAgh3RY=s64",
      "userId": "03603659341670711497"
     },
     "user_tz": 300
    },
    "id": "oZTImqg_CaW1",
    "outputId": "72a73943-f315-4cfd-ed77-3162f58cbe29"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAI8CAYAAAAazRqkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAACnYElEQVR4nO2dd7xcVdX+n0VReiAFCCGF0EkICQm9FxGQIgIiSpNXsbwqqD8QUXgVCyKigAooqAgYpUYBgYQWWogkgZBCCZACIUAICSU0Kfv3x8zdefbKnJ25N7fMvef5fj75ZJ05e86cOfvsPeeuZ621LYQAIYQQQoiuzgodfQJCCCGEEO2BHnqEEEIIUQr00COEEEKIUqCHHiGEEEKUAj30CCGEEKIU6KFHCCGEEKVgpeY07tmzZxgwYEAbnYqoxezZs7FgwQJr7eM2Sl++++670X7uueeivc466yTtVltttWibWU3bH2/RokXR/vjHP560W3/99aO94oorNve0W8ykSZMWhBB6tfZxO6o/P/jgg2R7wYIF0e7Ro0e0V1555eX+rLfffjva3M9Aer/4e6Kt6Apj87333ov24sWLk32vvfZatHmMcL8C6dgsGn8A8Oabb0Z7hRWW/L3dvXv3pF2vXq0+POqiLcZmo8yzbcn7778f7dYY561Bri+b9dAzYMAATJw4sXXOStTFiBEj2uS4rdGXXOOppT80TzzxRLS/8Y1vRPuzn/1s0m7YsGHR/tjHPhbtlVZKb+Hp06dHe9SoUdEeOHBg0u60006L9tprr93Ms245ZjanLY7bUWNz/vz5yfYVV1wR7eOOOy7a/JDZUiZPnhztJ598Mtl3+OGHR7u9Jt5GHpv1MmvWrGjfe++9yb5//etf0eYHk2OPPTZpt+2220ab++WGG25I2t15553RXn311aN9zDHHJO1OOumkus69tWmLsVmG38x58+ZFe4MNNujAM1lCri8lbwkhhBCiFDTL0yPKR86bU+TdefTRR5Pta665Jtr+rz92m7N7/YwzzkjaLVy4sM4zXsJmm20W7cceeyzZd84550SbvRCf/OQnk3bf/e53o7311ls3+xy6ItxPN910U7LvyiuvjPY//vGPaHvJgr117JnxEgvLL88//3y0P/3pTyft+D468sgjs+dfNm677bZo/+Y3v0n2rbrqqtH+73//m+xbZZVVoj179uxof+5zn0vavfzyy9FmKcd7YXv37h3tbt26Rfv6669P2l1wwQXR3nfffaN90UUXQRSz9957R9tLiz179oz2ZZddFu16pTf25gDAXnvtFe133nkn2v369UvajR49Otrs3etI5OkRQgghRCnQQ48QQgghSoEeeoQQQghRChTTI7LksrLeeOONaHOmjo+f4bigNdZYI9nHMQWcduzTyDk1+vXXX482p8v69+XOffvtt482p9mOGzcuaTd27Nho77rrrsm+q6++uvD4XRnuQ47NAIBf/OIX0f7Zz34WbZ9txXEgHLfjM+nWXHPNaHN8x4EHHpi087FAZefZZ5+N9siRI6Pt49I4HuOjjz5K9nFaed++faO91lprFX4ujzk/hvl9HMflY3922mmnaM+dOzfaHF8HAOeff37heZQR7j8uHQEAL7zwQrT5HvDz8RFHHBFtnt8+/PDDpB3He/GY5bIEQOPE8TDy9AghhBCiFOihRwghhBCloEvJWyyjAMXyhnfBPfDAA9E+4IAD6jo+u/u8e7Ze/Pky7VVVdnk47LDDos3VlNdbb72kHX8X7yYtqobs2/G14oqwvl3Re3KwxMZuWyA99/vvvz/Zx4UVt9xyy7o+q6vB0hSQurr/93//N9q//e1vk3ZcITsnbw0fPjzaX/ziF6PNKdRAx1XxbVRY+sldG5ZEfJVrHps8x2200UZJO5Y4+Rh+DvP3Sq1jA2mFX06pnjZtWtLulltuifZBBx1U89hlggtIctFJIJ0zufzHSy+9lLTjccphClOmTEnacSgC95ev1t2IyNMjhBBCiFKghx4hhBBClIIuJW/57AN2zz7zzDPRvvzyy5N2LG9wtLmXOjjjJydpsaziz4n35Y6Rk206ikmTJiXbLGlxxU+/CCXD2SJAmlWQyyTha8XXhjNMPFxh1q/HxFlBG264Yc3P8fjP4vuorJkkfB2BNGukf//+0fbXh/v9lVdeibavEMv3FR/b32P1Spll4YQTTog2V2H2UhdL0V72L1rDjKtpA2n/MT7Ly2daFsHH50VPeZwCkrQ8G2+8cbTHjx+f7OPfQr/4chE8Fr20z2ts8bzNiwI3KvL0CCGEEKIU6KFHCCGEEKVADz1CCCGEKAVdKqYnlw599913R/uOO+5I2nG1UU6r9PrkmDFjov3lL3852rkU7aKUbCCtIuvjRerVv9uTe+65J9nma8Wpqv67cHyO15N/+ctfRptXYeY+AdJVfrmdj/3hOASO6fEVex955JFo8+rNPuaB0zH99+IV48sa05O7v1999dXCfRyrw6vc+zHHsT+5atudocRDe8Lxh1zh+F//+lfSbocddoi2j5PivuB0aB/Tw2OG4yB9X/JY4jT3+fPnF3yLNF6Eq32LpeGyGX5e5PHBcau+L31qehM+vpVj6Lhfc9W6GwV5eoQQQghRCvTQI4QQQohS0KXkLe+qYyZMmBBtX82VXYFs77fffkm7Rx99NNqnnXZatEeMGJG04wXdfKXehx9+uOY57bzzzkm7Jpd0I6WuX3/99ck2yw183XzaN7u5/QKVLBOyfOjT40888cRo/+EPf4j2oEGDknYss/G1W3fddZN23/72t6N98cUXR5tdtf54fvE8XkRzxowZ0d5ss81QFnJV0Pn+8PcxpyK35LO8nJUrk1B2vvWtb0X7ggsuSPZxWQEv7fL9znJ7TsLgfvDH4305SYQXFOYK+Z1BOulIcqU3ePyx7M+hAgAwbNiwaPP19uUCvHzWhJ/fGxF5eoQQQghRCvTQI4QQQohS0OnlrZzLm7O0Jk6cGG3vJn3rrbeizTIF2wCw3XbbRXuTTTaJts8MGjduXLRvvPHGZB+7HTnD4rLLLkvaNUl1jVThkhegA9IMK3afFi0sCKSua88nP/nJaK+xxhrJPl7c81e/+lW0edFTALj55pujze50dtsCafYW94m/3pyx5bO3+Ps/9NBD0S6TvOXvfe57zvjw8hZfS96Xq6xcJEMDSy+WWXb43uf7+8EHH0za/eAHPyg8BktanBXpq6pzRXvuS9+OMzeL5BG/7+CDDy5sJ1JYqvLVtHlcsezs23G4AEuQvr9YxuIxn+vXRkGeHiGEEEKUAj30CCGEEKIU6KFHCCGEEKWgU8T0tHQF5TPPPDPaL774YmE7juPIrUb7wAMPRJtjhHws0bbbbhvtTTfdNNnHx//d734X7ZkzZybtmqr9+lWs25upU6dG26egFqUk+/gN1va5sqtn+vTp0fbXnvuP4xD8vcEaNe/jmBsPa+Fc+RnIVwHmWIb77rsv2scff3zhZ3U1cquds+21/pa049gU366RSjs0Aj5luQmfojxw4MBoz5o1K9nHMVk8D/nYNm7H/eLj8ng19lxf9uvXr+a5izw8P/uyLFtssUW0ub/8/OlLdjSRixHi+yFXNqZRkKdHCCGEEKVADz1CCCGEKAWdQt5q6WKC66yzTrRZHmFZAkhT7ti959Nx2S3Iko0/P5bBOH0dSN2CL7/8crT333//gm/RsZx77rnR9imoXLE1l/bN1827SVkm5AUqFy5cmLTjfuHr5o/Hn8WVR30F4GuuuSbaixYtira/N/h9fh+fk68gXRa8NMFpziw55WSr3KKlRWPfy5+iZXA/+PmOZQueI73kzuOMx19O6sj1ua+eLuqDF+71FC0Qmksx57HnZWze5nHOv7mNijw9QgghhCgFeugRQgghRCnQQ48QQgghSkGniOlpKRxbkosv4FgN1kV79OiRtOM0QNa7fdpfrhQ7v4917blz59b+Eh0Mr/7OsTQA8Mwzz0Sbl5fwMT2ctu/TXXfYYYdo8/Xw7Xib+8+nWBalOPuUZl6KhJeN4CVJ/Gf5ft5ggw2i/elPfxplJBcTwNfc92duPBbBcQQ+psffm2IJfH19P/Tp0yfaU6ZMKXwfX29/DF4ChPf5pUF4nuXYnwULFiTt/IreTfi4kqK0fJFe3+bAcTxs+xgsvvY8L/olnhoReXqEEEIIUQr00COEEEKIUtAp/INeVmC3K7vdfMolV9dl96xPpeSUS27HKdlAKuGw9OXlHD6er0r6xhtvRHvrrbeOtpdVmlK5O3qV9a9//es1bSBN9X766aejfckllyTtxo4dG21fkZmvwdprrx1tvoZAy1bvzVX6Zfcv9+uQIUOSdiNHjmz253Z1uN+9bMjXnN3jLV19meUSlje8+57HCcsqLXXzl4UBAwZE2/clj0Hu8/79+yftWOrgshM+fZnb8Rzs53fJVstPvWVefLui8evb8Xjmff43sxGRp0cIIYQQpUAPPUIIIYQoBZ3Cj+hda+yGZXmLq+wCaRVmXozNZ1TxMVhmeu6555J2XP2XK5R6dyxnFPnP4kyF//3f/4325MmTk3ZNrvyWLrbaHrD7evvtt4+2z6y5++67o+37kq8jX3ufqeEzRprw16doITz+HCDtS5ZDOFtN1Ib71/d1S93qTeSkbMZLMd26dYu2JK364QrauSrJRdmTQHH2lpe3eMFRH4rAeGlbNJ96fzd8O553c9mv3M9sz58/v1nn2RHI0yOEEEKIUqCHHiGEEEKUAj30CCGEEKIUdIqYHh/fUbR67+DBg5NtjjfgOBuvT7KWzZqkjw3gdGs+J18VmGNTvK7dt2/faHM69Kmnnpq023HHHQE0Vgqg13/5e3Of+HgNXpU5d+1z8SBFqZQtpShWhNPmPTlduzXOqbPA39Vfk/b6XB+jJYopiocD0rgNjnsE0jGdWz2bxwy/x8czrrfeetHm+J5GmuO6Ci2N6SlKRc/F/nB8JK9a0KjI0yOEEEKIUqCHHiGEEEKUglaTt9j9lVtMkNuxW6xeF2yOAw44INnmasi82F0uJZJdvF5W49TMIokNSM83t9AiL/DHKbeNipdwuP+YjTfeONnmRejqlSrrrRRaL7kq3EyuH/y9nEvx7crkJK1canNrvifXF7kFNstI7npwhXiuugykcyZXWvbwnMmVsbnSOVA81n1f+lIhTahSc/3k5K3cIspFx6i3bIzkLSGEEEKIBkEPPUIIIYQoBS32F+aycFrbDXnfffcl2zfccEO0H3jggWhzdVEgXRSUsz28q47Pl4/hvyMfg6Uuf7xcNgLLKtzuxhtvTNodfPDBhcdoFIoWfmW3OJBm0fF1A1KJjLPBvNu1KJOg3gq+uQUq+RhllayaQ+7eL+onf125n+rNAMu523mbx5iqM+clPpamBg0alOzr169ftHm8+Gv68ssvR5slLL8wKb+PZbXevXsn7V544YXC8xXFzJgxI9pevq938d/c3FrUjn8/ecWBRkWeHiGEEEKUAj30CCGEEKIU6KFHCCGEEKWgxcE39cY+LFy4MNmeN29etFmD5NeBNMaF2wFpjAjrkz6WhtMsN9hgg2h7TZpjSVif9itIs67Nq3G/+eabSbv7778/2l5P55RojmcZP348OhtFqeP+O+cqF+eqfha1aw1Nms+JY0py8Q9lqrqcI3eN6y0tUG/F2Ja8v960d5HOVb7UBMfk8JzJFdaBdP577bXXou1jLDnex8/3DM/BXCF/3XXXTdqpNEHKE088Ee0NN9ww2cfXnn/HPDwX5sYYt+PfyZdeeilpN27cuGjzb2ZHojtFCCGEEKVADz1CCCGEKAUtlrceeuihZPuss86KNi8mx+5OoLj6ql/okeUz705ldxq74HyqNLvTrrnmmmhvt912STtOn2Q3bq66JFdTXrx4cbKPXYtecmPXIi9M2hkqWbYUdmX7fi5KV87JJi3Bv5+lRd7nK0aLpWmNRUbrlTWL5DLfT3xO6sNi6ef5559P2j3++OPRHjhwYLKPKzRzqMAmm2yStON5bObMmdH2i5TyPJuDK+nzosynnHJK0k6SVspdd90VbS8t8/2QkwXrlaeLFib198Yll1wSbclbQgghhBDtiB56hBBCCFEKmi1vNbmRTz755OR1ljByC24WVSvmasdAKlV52YrhRe3mzJmT7Dv99NNrHoNdbkBaEZTlrb333jtpx9kNTz/9dLT9YnwsnXhXO7sF+Tr5zITOQL3ZTLlMP64cyvdKTt7KuWCL9vkKpSyR5mQTRtlbFXKVlotkq1xGVe66tiRrj+cEXuy2TBRJP6NHj062t9pqq2j7aul87Xhu7dOnT9LuySefjDbfDz6DiEMC1ltvvWj7+ZNlMa7OzHMuAGy66aYQS+AMYL8qAs9r9WZl5eCxyPeNz3jm7K1GQZ4eIYQQQpQCPfQIIYQQohTooUcIIYQQpaBZMT0LFizAX//6VwBLx89wuiOnMPpqxV6/bcLHUrAu77Vh1pTfeeedaLNODADHH398tP/5z39G269gPmvWrJrnPmnSpKTdPffcE+2iipRAGp/kY0kY1l19u6bU0tz7OwtFFbSBNAYgl0pZFHfD8VO+HfeRjxvxmncTvsSCWBquYO77syhewL++vPFRvv/4eD42RSyB42oAYMiQIdH2fclzj4+5ZIri4HJjmGMnfRo9xxIVxRUBiunxcNkTXy6g3lT03JxZBN83/HsMpBWa+R7yv5ntiTw9QgghhCgFeugRQgghRClolry18sorx9RqLzmxjMWuq379+hW2Yze5r9bZvXv3aPPCd/4Y7Cb1C4mydHLYYYdFe+utt07asVuQ5TfvguNqwiyr+LRdXtzNy1NFadne/d+0yGrOrdxZqHdx2pa4YItkKn+MnLzCfends0XvKTO59NeWuMfrJdfXRRW2RSrfc3kOIJUCuRIykPYzj+HcGMmVKymay/zCpCyJcCgDV/oXacVsIL0+vgQKX/uiVRGAdMzWW0KEj73ffvsl7a699tpoc7hIR1ZnlqdHCCGEEKVADz1CCCGEKAXNlreaZC3vuuzbt2+0OQPKuyRZIurVq1dNG0hdq94tyvvYPesX/mRXe48ePaLNi+wBqVuX5TgfAc+fxefr3e7savf72DXMbtxu3bol7SZPngwgXaC0s1Jvlc965ZB65YtcNV/ex677rnC925pcRmGRezxXTbkl+HuFxxzPPyLNjvLzNs+lvl95vuN5jMMSPCy5+LmvaFHYjTbaKGnHlZf5PZzRCwALFy6MNodDlIVHH320cF/udyc3LrnP+X7IVV7nsffUU08l7bj/nnjiiWhL3hJCCCGEaGP00COEEEKIUqCHHiGEEEKUgmbF9Ky22moYOnQogDQFHAD+8pe/RHuDDTaINq9MDqRp5RyD4/Vk1iC9hsx6MB/PVwZl3ZHTIn3aJmucrF3643E8UlGKvm/HNpCms7MWymmlwJLq0r7icCPRkpTklsZ2FMXx5OKFcinrRavd1xt/VGZ4rOYqXbd26jj3mY8x4HHy7LPPRnvYsGGteg6dEZ7H/PjjedHHs/G8y/OWv/Y8f/K86ONKeJ7k1dNHjBiRtLvvvvuizXO1n485fqiMMT233HJLst2zZ89o+98N7jPuLx8Hy2OWr7dvx5WyuZ85TtV/7tSpU2t8i/ZHnh4hhBBClAI99AghhBCiFDRL3mLOOOOMZLtJ9gKAX/3qV9H2sg2nerP046tyshvWp6wXpT7mqu7mUjNZSssdj+F9/tzZxctplUDqWmRXIC/8BwDHHHMMAOCCCy4oPIeOpt4Kyuwaz1VzZXxqbZG04d31/n1F58fnzserVy4rM/PmzSvcx/1RlL4O1F+5uWgRWj822cXObn6RVpn3cx/Px9OmTUv28Vjlkhr+GHztcyELHIrAC59+6lOfStrx7wIfw1cgLlrotCywjAukvzteZioq3+Lb3XzzzdE+6KCDor3qqqsm7VgK9ZW8i9pNnz69sF17Ik+PEEIIIUqBHnqEEEIIUQr00COEEEKIUtDsmJ4mjd1r9AceeGBN++67707acSwQr27uS4yzZu/jLDiVMpciyyvNctyAXyGetWbWJ+tNX+aYFSCN8fExJ5/4xCeiveWWW0a7I8tytyf+enA8Dfefb8fbRXEe/hiMjxspSp1Xyvqy4fHiy0nwdeZr6ful3jgqTr3ldr7fOZaEl5IR6VJA/r7n+I7XXnst2cfXm8uQ+FgdXq5n9dVXL/ysInxMCB+P7yc+NgC8+OKL0d58883r+qyuBMfcAMDYsWOj7ccbj5fcUjtF8Tm5pZZy7Xiu2HrrrQs/tz2Rp0cIIYQQpUAPPUIIIYQoBc2Wt4pSgovYe++9k+3x48fXbPfkk08m2+yS9audz507N9r9+/ePtpeZfDVo0brUm8LNrnFeQRlI3aF8b/n7jF3qvM+fA2/XuzI0o5T1ZbP99ttHe8aMGck+lkjYte1h9zv3U73XmKUNIL0nyih15OBV5315DZ8GzvCK2zy3+lRxnqs5Bd6vds/t2Pap10WlCfy9wSnaZeTLX/5ysn3SSSdF28tbLGP6itpM0e+7LwPB45zvjTfeeCNpx9snn3xy4ee2J/L0CCGEEKIU6KFHCCGEEKWgxRWZW5stttgiu80MHjy4rU9HtCLsCvUL17HsxJVjvczEmSD1SlW5hUQ5g48rz3pXe9E5AM2XersKLJEcd9xxyb577rkn2gsWLIi2lzpYIsktqsv9xv05YMCApB3L6F7CKTssKW+00UbJPpawPHy/c8aPly0583TkyJHR9jLYPvvsU/PYflzxfMF9OXDgwKTdXnvtVXjuZYSrXPsK/4xfIJuZP39+zdd95Wa+b3iMeslx9OjR0eZQlI6knLO2EEIIIUqHHnqEEEIIUQr00COEEEKIUtAwMT2i81HvKuvbbrtttAcNGpTs4xWVc7E6rPtz1dDc6ulF6fBAGkfCMQScju0pawyPh6+xj+844IADar5n4cKFyTbHCHA1dt+f66+/fk273nR4lRkALr744mj7irk8ro466qhkH8e3cTzG888/n7TjOKERI0bUdU6HH3544b4jjzyyrmOIFK547FPW77///mg/8cQT0fYrJuyyyy41j/2Nb3wj2ebYH75veDWGRkWzuBBCCCFKgR56hBBCCFEKrGiBxpqNzV4BMKftTkfUoH8IodeymzUP9WWHof7sOqgvuxat3p/qyw6jsC+b9dAjhBBCCNFZkbwlhBBCiFKghx4hhBBClIKGeOgxs8PMLJhZ8doTafvZZtazxuuLa7XPHKdZ7TPHOcHMNlh2y66PmfUws8nVfy+Z2Qu0/bHM+waY2bSCfWeb2b4F+5a69mZ2tJn9wMz2NLOda71PLBv1Zbkxsw+rfT3dzB4zs++YWUP8ZpQdjc2W0yh1eo4G8ACAzwH4UceeSos4AcA0APM6+Dw6nBDCqwCGAoCZ/QjA4hDCr5bzmGfVet3MVkTta78/gIsAHAxgMYBxy/P5ZUV9WXreCSEMBQAzWxfASADdAPwfNzKzlUIIHyz9dtFWaGy2nA5/ajezNQDsAuB/UHnoaXp9TzMba2bXm9mTZvY3c5XGzGxVM7vdzL5c47inmtkEM5tiZj/OfP75ZvaImd1lZr2qrw01s/HV944ys3WKXjezIwCMAPC36lP2qq1yYbowZjbIzB6uXq8pZrZpddeKZnZZ9S/LMU3X0syuqF7nJi/fWWb2ACoPy8m1r94jQwEsBPBVAN+u7tvNzPpX+3lK9f9+dPxLzex+M5thZge18yXptKgvy0EIYT6AkwB8wyqcYGbXmdnNAMaY2epm9ufqnPuomR0K1L4/qm3/bRXv0TQzOyr74aJFaGzWpsMfegB8GsDtIYQZABaa2ba0bxiAUwBsBWAgKg9HTawB4GYAI0MIl/EBzWw/AJsC2B6VjhluZrvX+OzVATwSQtgWwL1Y8hfMlQC+F0IYAmBq7vUQwvUAJgL4QghhaAjhHYhl8VUAF1b/ihwBYG719U0B/D6EMAjAawCKyra+G0LYNYRwNZa+9sMAPBZCmAXgUgC/qe67H8DvAFxZ7b+/ofJXShMDAOwB4FMALjWz4pK/glFfloQQwkxUfjPWrb60E4DjQwh7A/gBgLtDCNsB2AvAeWa2OmrfH/sDmBdC2CaEMBjA7e37TUqDxmYNGuGh52gA/6ja/6huN/FwCGFuCOEjAJNRuWBN/AvAX0IIV9Y45n7Vf48CeATAFqh0tOcjANdU7asB7Gpm3QCsHUK4t/r6XwHsXvR6vV9SJDwE4Awz+x4q9RSaHhRnhRAmV+1JSPubuabgdaAyod5WsG8nVFz0AHAVgF1p37UhhI9CCE8DmInKPSOWjfqyXLC3/Y4QQtP6IvsBON3MJgMYC2AVAP1Q+/6YCmBfMzvXzHYLIbwO0RZobNagQx96zKwHgL0BXG5mswGcCuCoqusMAN6j5h8ijUF6EMAB1DY5NIBzqk+eQ0MIm4QQ/lTHKaloURtglUD1piC7ESGEkQAOAfAOgNFmtne1aa6/mbcyH7cfgDF1nloosGttC6gvy4yZDUSlL5sWXuK+MwCH05zbL4TwRK37o+rVH47Kw885ZlYzlkQ0D43N+uhoT88RqLjB+ocQBoQQ+gKYhfTJsIizALwK4OIa+0YDONEq8UIwsz5WCcTzrFA9BwD4PIAHqn91LDKz3aqvHwvg3qLXq/abANas45xLSQhhFE2GE6uT58wQwkUAbgIwZDkOH6991Ru3UjXIL9lXZRyWxI19AZXg+SaONLMVzGxjVKTUp5bjnLos6styYpV4x0sB/C7Urmg7GsA3m/4INbNh1f+Xuj+skgX0dlU2+RWAbWscTzQTjc366OiHnqMBjHKv3YDKA0g9nAJgFTP7Jb8YQhiDinvtITObCuB61H4oeQvAIDObhIrH6ezq68ejoklPQSUmaFmvX4GKPqlA5vo4CsC0qit8C1RipVrKFahee1T+qrmT9t0MoOmvn90AfAvAF6v9dyyAk6ntU6g8xN4G4KshhHeX45zKhPqy67Jq9XpPR6UvxgAoSgr5CYCVAUyxSkr0T6qv17o/tgbwcPW1HwD4aZt9g3KjsVkDLUMhugxmdjmAy0MI45v5visA3FINShcNgPpSiMaks4/NRqnTI8RyE0L4Ukefg2gd1JdCNCadfWzK0yOEEEKIUtDRMT1CCCGEEO2CHnqEEEIIUQr00COEEEKIUqCHHiGEEEKUgmZlb/Xs2TMMGDCgjU6lmA8+SBfwfeONN6K9YMGCaK+44opJu1VWWbKsxworLHm+88d7660lhSdXX331aPfp0ydpx8doL2bPno0FCxbUqjq9XHRUX5adSZMmLQgh9Grt4zZif7755pvR/vjHP57s+9jHPlbXMd57b0nx2Lfffjva66yzznKe3fKjsdm1aIuxqb7sGHJ92ayHngEDBmDixInN+nCfHVZ71Yg88+fPT7bvvvvuaF922ZK1Rtdee+2k3ZZbbhltnnQXLVqUtHvooYeiveOOO0b75z//edJu1VXrqzvI37kl35cZMWLEcr2/iJb0pVh+zGxOWxy3NfqzKJOzpffwvffeG+2NN9442bfhhhvWdYxZs2ZFm7/fkUce2aJzak00NrsWbTE21ZcdQ64v26ROT70/+uylufDCC5N9d965pODju++mRRvZG/Pf//432hMmTEja3XjjjTU/d+WVV0622aPzn//8J9o777xz0q579+7R3mOPPaL9zW9+M2nXCH+FCtFceNzmvJpz586N9p///Odk3/nnnx9t9si2BnxOxx57bLLv3HPPjfbJJ5+Mevjoo48Kjy+E6JpolAshhBCiFOihRwghhBClQA89QgghhCgF7b721rPPPhvtgw46KNrrr79+0o6Dkn0MDmdpcYCyDyxcvHjxMt8DpHFBr7zySrR9lhdnktxxxx3RfvDBB5N2X/nKV6L9mc98BkI0IvXGtAwbNizZfvrpp6PNYwIAVltttWjzmPZxeRz3xmP9xRdfTNq988470eZEAn+8//f//l+0OQFhn332SdqNHDky2v778vVQfE8xPuC96Lrl4jlzyx+1JHB+3LhxyTbHYz711FPR3myzzZb7s7oyrZ3MUC/HHHNMtL/zne8k+7bddtto83zjf8frRSNbCCGEEKVADz1CCCGEKAVtIm/lXGHf//73o927d+9o+zRvlpb88VZaaclpszuO5SwgdX+xzXIWkBYnZCmNPwdIix2yS9cf7/e//32099tvv2TfGmusASE6inrT0nfaaadoT5s2Ldm33nrrRdvf+zxWeZ8fSy+99FK0WdLytbC4iCFLWjwW/TbPHX//+9+Tdlzg8J///Geyj69Ha9baKhP1XquWXNOxY8cm21OnTo02S64AcMYZZ0Sb+3LMmDFJu5ZKJI1Ivfdsrh1vc7t66+29//77yTb/nnJ/HXHEEUm7GTNmRNv/jvM4bY2xKE+PEEIIIUqBHnqEEEIIUQraPHvLZ2OwW3uttdaKtneLsTucXdJAKkd9+OGH0fZrb/E2u6595gcfn9vlssZYpvKudj6/m266Kdn3+c9/HkJ0FDn38KhRo6I9fvz4aPft2zdpx9KuH7d8/CIbSMc+u859RlmRHOfHMB+fx22/fv2SdqNHj472bbfdluw74IADCs+3DNQrYfjX/bxbxJVXXhltXu7n/vvvT9pddNFF0d5ggw2i/dhjjyXtOBOLM3wA4IILLoj20KFD6zq/zk6RNJVrx7+fHh6LPpOZZWhu538z77vvvmgfdthh0fZr722xxRbR5vAQjz9+S5CnRwghhBClQA89QgghhCgFeugRQgghRClo85ieRYsWJdsc08NasK/synE2XjPmVNiiNFMg1RpZx/T6JJPTRTnOiCs39+zZs/D8eLV4QDE9ov3Jxb0xXD2c7+k333wzaZerls4xPrkxx/vqrX6ca1c0D/iUej73Aw88MNnH8YdcTdqfu0+/F0t44oknou2vG6ecT5w4MdoLFy5M2h1//PHR3mOPPaLt43b4GGwDaczIM888E+1NNtkke/5dhXpj0nLzAe/LxdLw2Hv++eeTfTzG1lxzzWj7WKLzzz8/2n369En2tXb5CHl6hBBCCFEK9NAjhBBCiFLQ5n7aKVOmJNvs8mSpy6eq8rZPCec0xo033jjaAwYMSNrx4oecYrf66qsn7dh1xzIbV5AEgJtvvrnm8V577bWkHVeU5PR1ITqCIhf2oYcemmyz9MMlGWbPnl3YzktORW7wXGpsS/Cfy25v/r5+XuE5wc8rLL987nOfq3m8rky90oEvIcKLfbIs2K1bt6TdiSeeGO3f/OY30fZyBi84OX/+/MLz4zTnRx55JNnHC0JzP5dF3qp3MWHPyy+/HG2WHV999dWk3aRJk2q+x0ua3bt3jzbfG6+//nrSzi8W3pbI0yOEEEKIUqCHHiGEEEKUgjaXt9hNDAC77bZbtP/2t79F2y9qyAvGsRszh3e7vvPOOzVtLzlxdVeWvnym1TnnnBPt7bbbLtos0wGpC33mzJl1nbsQ7c1DDz1UuM9nUzI5V3muCjOTqxhbD/UulOjPlbPLfFXnCRMmRJvnrbJUZ/YSJF87vga5hZ15HvcLhP7hD3+I9u233x7tT37yk4XntO666xbuY+mLZRQAeOGFF6L95z//Odq77LJL0m7w4MGFx+/M5Pry2WefjfYpp5yStONQDc62mj59etKOQ0wef/zxaO+5555JO5YueU7xC73mMqrrpV4JXZ4eIYQQQpQCPfQIIYQQohTooUcIIYQQpaDNY3pOO+20ZJu1xb322ivaw4YNS9q98cYb0fYxPazZ82rNPXr0SNoVVY71Gj0fj1PpfJwRpztyPBKn9/rz8Npl2Wnp6r9F8QUtrZbLKZ31pnN6OD6EP7ezxIBw2QUgrV6cu47ch7mKzHyMnN6eSzEvul9yaeR8T/i0dI4r8KUrRo4cGW2uEFsWcmUAGH/fcB/dfffd0T7mmGOSdpdeeunynmICp1Hz7wUADB8+PNpcndnHqvlU7K5CroIyl3m54oorkn3+N7S59OrVK9nmuDmOnzrqqKOSdhwjlJv7eV9uxYQc8vQIIYQQohTooUcIIYQQpaDN5S2fjnjXXXdF+4Ybboj2mDFjkna86NzFF1+c7GMJiheT86mURTIIu+CB1P3JrjTvnuUUvl/84hfR9hLWOuusE+0bb7wx2cfVS32aZRmoV/rxrsui99Xr0vT30E9/+tNoz5s3r65jeHIu5EblscceizYvmgukFXTZLc3jw+/z8lHR4qZetuJ9uTT3osUGc4sL8z3h2/ECyH7cln0h0XrHJs+DALD77rvXtD1cNoTvm3pLG/h2vEAsz7lAGvZwwAEH1HwPAMyZM6fws8uAl7N4HPFYrneu45AVIP2N5z669957k3bf+973ol3vIqieeqVKeXqEEEIIUQr00COEEEKIUqCHHiGEEEKUgjYXsU8//fT0A0k35zS1LbfcMml30003Rfvss88uPD5rjV6jL4ob8Np9UbyPX66CU+B32GGHaPPqsUCqa/pVfcsYx5OjSLOvN76C04wBYPLkydG+7rrrou1jTzi18uijj4723//+97o+F0hTvH/5y19G+4c//GHdx2hv+F73cTYMx8f5VGbuM18ygPfx8X1sDccL8PFzKes5Pb+onU9/5fnCf6+5c+cWHl8UU29fMryvpavYc0yaLxtSdB/6uM+yx3HlYidzcTw87vkaHnfccUk7noP5szgWF0jjvXxJBIaXvPjf//3fZB8veZFDnh4hhBBClAI99AghhBCiFLS5b++www5LtjllfdKkSdHmtEIAOOSQQ6LNq+kCQL9+/aLNrlWfis4us1xFWHbP8Qrp3r335ptvRptTHX/zm98k7XifX2mYK0/7KtRdlVzaaVG66tNPP51ss5uUVwf3pQ4GDhwY7Q033DDaPs129uzZ0b711luLTj3LP/7xj2j/5z//adEx2ptHHnkk2izPAcUp4T5lnd3PXgIucon7fi6qsO0lJx63uUrcRePbv85zgq8eyxIJ9ydL2WJpiuQp/zrfN7n5ODdfMHzv/fWvf032HXTQQdH+/Oc/H20vg+WklDLQ0urxRVXs+boDaZo6r+DOJQWA9Lmgb9++yT7/DNEEl58A0lAHXjHBI0+PEEIIIUqBHnqEEEIIUQraXN564oknkm2Wjzjraccdd0zaPfjgg9GeOnVqso9dcrkMgaJKr7lFL4syEfz5sst06NChSbuNNtoo2t5Vt/nmmxd+diOSW5iT5REvgTA5Fyq7PM8444xoX3PNNUk7Xhyyd+/e0d5+++2Tdixxvv3229H2i9a+8MIL0T7zzDMLz4+lVX9O3/nOd6L95JNPRptlWyBd/LCj4XvfjwOWI+qtwOqPwe/jys1e6iiSrXJjk/H3FC8kyZWlfbYOy2L+O/IxLrjggmg3J6Ov0am30nlbk8uwK2rn4WrCPlRg4sSJ0f7KV74S7WeffTZpt/POOy/7ZLsY9cqHubmi3vuGf/84PGThwoVJu4MPPrjwGOutt160ecz66s/8u5BDnh4hhBBClAI99AghhBCiFOihRwghhBCloM1jeryGyvrt888/H21f1TiXOs5ph6w1+uqaRfE5uZWcOQ7Efy7Hd/D5+bgBjhfhmBUAeOmll6LN6dWNRE7LZXJxPAynI/Kqu0CaZsjVqgcNGpS04759/fXXo/3GG28k7TgFleOAWOMH0vuN0xvPO++8wuNtvfXWyT6OAeH4FZ8e30j4lF2maFVl3898T+TiMZhc7F295NLoeZzx+PZp+VxV3Z8TH5P7syvRUTE8OeqtyMzV1gFgm222iTZXVQeAW265JdqjR4+Otr8ffMxlGWjJPVCUor4sHnvssWgPGTIk2n61ey7/4ef0s846K9r8W/uJT3yiReckT48QQgghSoEeeoQQQghRCtpc3vLyCC/8yJKFlwRYZvKuNXZLs3vdf1ZRurVvV7RInneF8r6ePXuiCE7H85Vj582bF+1GlbfY/Vmv6/miiy6K9iWXXJLse/nll6Pt3cmDBw+ONt8P/J7c+eWkSu5XX33Xu1Cb8Cmso0aNKjyPn/70p9H+/e9/H+3+/fsn7a6++urCY7Q3P//5z6Pt5VveZunOp5dyqnC9KeatAY91L2/xfcrn7qu0s7zHcwyQStb//Oc/o90oad5dCe7L3Bxz7rnnRtvfh1/96lejfdVVVyX7+B498MADo82V2IH6JfqyUJTO7n/Hihbz9mOFFwHn3/jmzBs/+9nPos2/wUceeWTdx2Dk6RFCCCFEKdBDjxBCCCFKQZvLWz5Dokh+4IXJgHRhwJy8lXM111uRucit7116/LlcJZIlOyB1/fljcFXKRoEXoQSAO+64I9pPPfVUtH1GC0t1/L04QwZIF/7kzCsgvd5+H8PSA1/TnFTJ0oa/hzgri/vPLxzKVT794pp9+vSJ9mabbRZtL5tcdtllaBRmzpwZbXY9A2lfsLTr5Tr+fu0pbzG5Mcz3ope3ctXcWXIZMGBAzfeI1oHnSC85/ehHP4o2j/V11103aceZoJtuummyj/ud56nOKGfxvc73bG7s+fmupdlXRe8vGhMjRoxItrlqMmfR5fBhJTwueS7KhZjkkKdHCCGEEKVADz1CCCGEKAV66BFCCCFEKWjzmB4Pa7SsC/qKzD4uooiiGCH/WayFei2ft+td/ZfjIXKp8rkq0R3J/Pnz8bvf/Q4AcOONNyb7OJ4qVwWXdXOufuyvB1fR9H3EsTocC+Rjofhe4dgi/1kcl8L9wN/JH4M1ZF6hG0jvBx93xnEkfPxGi9viCuF8nl4TL6pG7vusqNI5UJzy6tOSvW5fBB+fj5FLjeXYMH/PcvyW7yceq88991xd59co+Hml3lITrf3Z3C++j3msP/HEE9E+9dRTk3YcH8dV+88///ykXS7Wiqs3cxzbTjvtVPietiZX+iC38nlLSoi0NrmYoM985jPR5qrLAPCXv/yl5nv8bzAf38/9HEs5bNiwZZ/sMpCnRwghhBClQA89QgghhCgFbS5v1Zvu6aUD7+JiiqoreympKLU9d058DO8y5s9imcCnaLPE4mmUhQx79OiBY489FgCw3XbbJfsefPDBaE+bNi3ac+bMSdqxPLBo0aJo+zRhvqbercmLuC5YsCDaOUmF3eb+s4rSOP1CmyzHsQTi3cd8r/jSBHwe7Lr3qeCf+tSnov3LX/6y5vm1Jffff3/N13OSE8tb/ntzZVwvHxW54ustLdFS+Jpz3/r7iKVWP8fw92yNBVLbk5zskUttbo1rXxQSwGMCSGXWX//619Hee++9k3ZcNuK6665r0Tnx98qdU3uSqx7fkn548sknk+0///nP0faSoa9I30ROZuLfKj8H/PCHP4z2K6+8Em0fKlFETi7LlajZeOONC99Xb/kMeXqEEEIIUQr00COEEEKIUtDu2Vv1wq4177otqlCZc0nn3IdFC456meK1116LNstbvhooZw54939HVbCtRdO58KKfALDDDjvUbO9lu1mzZkX7mWeeibavsMoVUb28V9SX3sXJCwjywnX8OpBKjZyJ5SVIdnPnXN4s+eT6jjOhWF4BOr6ir19YtAl/fxdVe+X7HkjlgpykXDSu/DafX+4a8+f6a1okx/nvzjKsl6/9d+kqtPb9l8tCyslsXGl5gw02iPaUKVOSdtdcc81ynmF677Fs3t4VmUMIUYLPVY/ne4+lIwC4/PLLo+2znBmej//1r38l+7iyftE5+HPkccRZdEAqO956662F58S/k1wFPyer8RgF0vtr1113LfwsyVtCCCGEEIQeeoQQQghRCvTQI4QQQohS0OYiNsdfAGnKaC4Gh7VAr8uzbpxLfSuqeOm1v6L0+Fw8Dp97v379knYTJ06Mto+baJSKzCuuuGKMc/Grh7/44ovRzumk3bt3j/aee+4ZbR+3UxRTAhTHafh7g49ZlL4OpCns/B6+74A0zTK3Kjefu79PuIIx3+c+NsSvUt7e7LHHHjVf97EeRTEGvi/4muTigvj4/trxNmv9/voXpUP74/E55SpG8/E7qrptW5CLs+GYrJdffjlpx2Odx3COemOE/u///i/Z5nuK43hGjRpV1/FyZUxyle85pqe9MbPs/FeLRx55JNnmPsvNkbwKPZcCAYCbb7452gcffHD2fGtx9NFHJ9v7779/tHNp5Dy26+Wll15KtjlGcuedd2728Tzy9AghhBCiFOihRwghhBCloE3kLZYcclUo11prrcJjsBs6l0rKx8+5xutNhc1JZ0Xu+gEDBiTt+Dxy7vVGwadY++0iWILMyQYsLfm096Lr4WXAokVhc+/j/vIya58+faLN94Z3oee+V9F9468fp+d2BP/+979rvu7lW95m+W+99dYrbOfHVdG9768dy2JFkhiQXuNcO+63XGXloj6rtd2ZyElOjz/+eLR96jHPwX6R55ZUL+aqy+PGjUv2sdxcVCU8R06OzbXtyMVjFy9ejPvuu6/meRxxxBHR5nuWJUcPl+HwqxiwlOTnoJNPPjnaOXmLOfTQQ6M9ffr0ZJ9PiW9NeMFgoP77UCnrQgghhBCEHnqEEEIIUQraRN7KLe7J7m+WGDy56qtFbk3v3irK2PLvL6oc6z+XZTbO+PEVmXPyViNVZF5e2J2ai9L3bljRvtx+++01X/eyMUtOfH9fcsklSbsvfOEL0fbyJC/syve+l9J4X26sF73HZwjyNrvHfeYaL5rrq3QX4TOevNzXFjTNE/VmSuWyt1oj46VevvzlL0d7xowZyb5bbrlluY6dq8zv4XvFL8zZnrz33nuYOXMmAOArX/lKsu/MM8+MNo8blgj9Ps4E81Ilvy+3aOdpp50W7S996UtJu+9973vRvueee6K97777Ju18JfzWxMt7PjShiHrHijw9QgghhCgFeugRQgghRCnQQ48QQgghSkGbV2T2Ohtri7lU3nqrqhaltNZ6XxP1rhKc04w5bmDQoEHJvtzK710ppkd0DrhMAOvjPkW5aLwcdthhyfa3vvWtaI8cOTLZx7FACxcujHbv3r0Lz4nxcRs8NjmewVfY5vftsMMO0eZUXQC49957ax671mc3cdNNNyXbHLfSVjR3ZfRce55zDjzwwGQfx4Gcfvrpyb7Pf/7zdX322WefHW2OHzvllFOSdltvvXVdx2sN+HfBr9rdnvTo0QMnnHACAOCPf/xjso9LCfA5+nHIK6vzfc+VtgGgZ8+e0fYxb3wPnHfeeTVtAOjVq1e0OU7zxz/+MYrg37hcGYF68d+r3ti7ej9bnh4hhBBClAI99AghhBCiFLS7vMVuttxCjJw+yy43IHXR56qoFi2amFvolM/Pu+CLFrDMpd7788stmidEW8BjkOWnet3Gnl/84hc17Rze3c7nwWPOzxe8zWnvuWru9ZKrJs0VcnmxRqDt5a0333wTY8eOBbB0qj/Pfbzgr6/Ay/Mnfxe2AeCZZ56J9vnnn5/s4zRlXsxyzJgxSbsLL7ww2rxoab33RkvJSXo8x/tFcTsKX7l//Pjx0eZFq/0iylwygb8Xp7ID6e9V7tpwCZHctWFZLSdNNleKBZb+bWUpzVdkLioR4ecUf28XIU+PEEIIIUqBHnqEEEIIUQr00COEEEKIUtAmMT1Fyz94cuWlWfPz2h2nrr766qvR9mX1600/Z1gz9XEDb731VrS5VLbXEvncfQyP12uFaGv+9Kc/RfvGG2+MNt/PQOunnjJ+jNSrv7c2HFfBK8kDaYwTzzm77LJLW59Wwn//+1/Mnj0bAOL/TcyfPz/aHBfFcyKQxm3wPNi3b9+k3THHHBPtIUOGJPvuvPPOaPOK6VOnTk3a7brrrtHmuCAfj8TzYlvH2XCMyCc/+ck2/ax6+f73v59s//3vf482Lynhf6v4d5J/k/w15Nga/7vD8Wp8fB/fyveUL0fBLO9ckfs99r/3RTE9udjcHPL0CCGEEKIU6KFHCCGEEKWgTeQtrobpXZz1Sk5HHHFEtN94441kH6ew82fl0te5XW41dnbVebmsW7du0R4xYkThZ7Gr2Z8Tn4cQ7QHLNrzKuF99m8dZvdV4c+TKRPB2LuW1aJ93qfN2LgV+//33j/bll1+e7OMyFJ/61KeizStPtwdcxbdeWOYHgLlz50abK2Pz60B6rfjeAFJJi+8NX9WZ7xUvnzHtmTrO8tavf/3raPPK5u2NT/vma8+VrM8666yk3YQJE6Ltfwtbm9122y3ae+21V5t9Tk4S4/sOKF65oSWp8oA8PUIIIYQoCXroEUIIIUQpaBN565133ol2zq3tFxZjfKR7Z4Ldbv77576zEG1NrvIrZ254GYThrC9fCZhhF3ZrZ4PlYAnZS9RDhw4t3Mfy1je+8Y22Obk2okePHtntssFZep2hL1l2ZdszY8aMaE+aNCnZN2XKlGjzQrJAKnHy75NfTeDSSy+t+bk+JGR5x3NO6jzttNOS7c0337xmOx86Uy/y9AghhBCiFOihRwghhBClQA89QgghhCgFbRLTw6v/brbZZsk+TmncYYcdCo+RS2dvaapae8EpnLNmzUr2DR8+vL1PR4gIj6vzzjsv2cfjtnfv3oXHaJRVq4vIzQ9c7oLTmoH0e7VnDJJoW37yk5909Cm0Gvx76n9bjz766Db73Nb+zc0db999963rGLkSNTk0soUQQghRCvTQI4QQQohSYPUuxAkAZvYKgDnLbChak/4hhF7LbtY81Jcdhvqz66C+7Fq0en+qLzuMwr5s1kOPEEIIIURnRfKWEEIIIUqBHnqEEEIIUQoa9qHHzD40s8lmNs3MrjOz1ZbRfqyZjajas82sZ/ucqagHM/uBmU03synVfi2uV9D8Y+9pZre01vFEHo3NrktbjFPu/+VpI5qP+nNp2qROTyvxTghhKACY2d8AfBXArzv0jCrnYqjEQn20zMYCAGBmOwE4CMC2IYT3qj96LVs4pZUxs5VCCB909Hl0MjQ2uyCNPE5F81F/1qZhPT2O+wFs4v+iN7PfmdkJuTea2Xeqf5FOM7NTqq+da2ZfpzY/MrPvVu1TzWxC9cn4x9XXBpjZE2Z2MYBHAPSt8VGimN4AFoQQ3gOAEMKCEMK86l/9PzazR8xsqpltAQBmtrqZ/bnaD4+a2aHV1weY2f3V9o+Y2c7+g8xsu+p7BprZcDO718wmmdloM+tdbTPWzH5uZvcCOLn9LkOXRGOz61A0Ts+qXvdpZvbH6sNl0zg618weNrMZZrZb9fVVzewf1X66BkCsAmlml5jZxKr34ccd8SVLhPqzBg3/0GNmKwE4AMDUFrx3OIAvAtgBwI4AvmxmwwD8A8BR1PSzAK4zs/0AbApgewBDAQw3s92rbTYHcGUIYVgIQSmIzWMMgL7VgXSxme1B+xaEELYFcAmA/1d97QcA7g4hbAdgLwDnmdnqAOYD+ES1/VEALuIPqT4EXQrgUADPA/gtgCNCCMMB/BnAz6j52iGEPUII57f2ly0LGptdjqJx+rsQwnYhhMGo/OAdRO9ZKYSwPYBTAPxf9bWvAXg7hDAElTHHZeh/EEIYAWAIgD3MbEgbfp+yo/6sQSM/9KxqZpMBTATwHIA/teAYuwIYFUJ4K4SwGMCNAHYLITwKYF0z28DMtgGwKITwHID9qv8eReWvxi1QmWgBYE4IYfxyfaOSUr32wwGcBOAVANeQF+DG6v+TAAyo2vsBOL3a/2MBrAKgH4CVAVxmZlMBXAdgK/qYLQH8EcDB1b7cHMBgAHdUj/NDABtS+2ta6/uVEI3NLkhmnO5lZv+pjru9AQyit9Uav7sDuLp6zCkAplD7z5rZI6j04yCkY1i0IurP2nSKmJ4mzOwDpA9qqyzjGLkFQ64HcASA9VH567Kp/TkhhD+4zx0A4K1ln7IoIoTwISoPMGOrg+346q73qv9/iCX3owE4PITwFB/DzH4E4GUA26ByH7xLu19E5X4YBmBe9RjTQwg7FZyS+rPlaGx2UWqM06+g8lf8iBDC89UxyH1ba/wCwFIF4MxsI1S8uduFEBaZ2RVY9n0ilgP159I0sqenFnMAbGVmHzezbgD2WUb7+wB82sxWq8ojh6ESgwBUJtPPoTK5Xl99bTSAE81sDQAwsz5mtm5rf4myYWabm9mm9NJQ5KuUjgbwTdKah1Vf7wbgxWqg6rEAeMW51wB8CsDPzWxPAE8B6GWVYD6Y2cpmxn/RiNZFY7OTUzBOm/7wWFC99kfUcaj7AHyheszBqPzIAsBaqDygvm5m66EijYo2Qv1Zm0b29CxF9cn0WlTca0+j4lLLtX+k+vT5cPWly6vuc4QQppvZmgBeCCG8WH1tjJltCeCh6u/tYgDHoPLUK1rOGgB+a2ZrA/gAwDOouFwPKmj/EwAXAJhSffCZXW17MYAbzOxIAPfA/YUfQnjZzA4GcBuAE1EZ0BdVf4RXqh5zeit+L1FFY7NLUDROX0Mlbms2gAl1HOcSAH8xsykAJqPaxyGEx8zsUVTG4EwAD7bq2QuP+rMGWoZCCCGEEKWgs8lbQgghhBAtQg89QgghhCgFeugRQgghRCnQQ48QQgghSoEeeoQQQghRCvTQI4QQQohS0Kw6PT179gwDBgxokxP56KN0YeQXXngh2m+9lRZc7dGjR7R79erVJucDAIsWLUq2FyxYEO211lor2uutt16bncPs2bOxYMGCXPXaFtGWfdnWvPvukkLMb7zxRrJvxRWX1CtcYYUlz/RrrLFG0m7llVduo7PLM2nSpAUhhFa/aTtzf3ZWNDa7Fm0xNtWXHUOuL5v10DNgwABMnDixdc7K4R9szjzzzGiPGzcu2XfcccdF++tf/zraiuuuuy7Zvvzyy6N9wAFLik+ecsopbXYOI0aMaJPjtmVftjVPPbVkdYrbb7892de9e/dor7LKkoroO++cLsjep0+f5T4PrnFVLZi3TMysTRbE7Mz92VnR2OxatMXYVF92DLm+lLwlhBBCiFLQoctQfPWrX432vffem+xjucvLR+wFuuiii6Ldt2/fpN2mmy5ZdqRbt27RXrhwYdKOPUn//e9/o+2lk969e0f7kksuifbNN9+ctLvsssuiPXDgQIj6qNdz8rWvfS3aDz/8cLLvgw8+iPZ7772HIr70pS9F+7HHHov222+/nbTbfffdo33++ecn+1ZdddVof/jhktUQWGITQgjROMjTI4QQQohSoIceIYQQQpQCPfQIIYQQohS0e0zP3XffHe1Zs2ZFe9iwYUk7jqfx6ezbbLNNtF955ZVoP/vss0k7zgjjTIspU6Yk7VZaacll6NmzZ+E5zZ8/P9obbbRRtF977bWk3Xe/+91ojxo1CqI+6o3peemll6K9zjrrJPs4JutjH/tYtH0fXX311dHmFHifyj59+vRo830CpPFk/Lkc6yOEEKJxkKdHCCGEEKVADz1CCCGEKAXtLm/dcccd0eZKlT69mGWG999/P9nHEhRLDiyPAGkaMcsUXn7gar1rrrlmtLkqNACsttpqNT9rww03TNqxNPfAAw8k+3bddVeI2rCMydWUgVQ+eu6556K9+uqrJ+04ZZ3lTV+RmWUxlllZEgPSfv72t79deO7+fIUQQjQemqmFEEIIUQr00COEEEKIUtDu8ta8efOizYt25uQtlql8W5YjvITBkgjjK+ayHMUVeVnO8sdnOcOfH2ceSd7Kw/KRz9JjOOuPZSuWI3PH8PcCH4PvJy+lDhkypOZ7gDSLbP311y88B0lfQgjRGGg2FkIIIUQp0EOPEEIIIUqBHnqEEEIIUQraPKbHxzdw/AyvfM42kFbJ9XDcBcfTLF68OGnH6csc++PjNvgc+T3+3Pl9q6yySuH5cUzPjBkzCtuJ9Fr5dHFmwoQJ0eb4mbXXXjtp99RTT9U8to/P4kreDMeZAcChhx4a7TFjxiT7hg8fXvOcfOkEIYQQjYE8PUIIIYQoBXroEUIIIUQpaHN5i6vdAqlk9M4770TbywpcMdfLUW+++Wa0uSKzT0tmmYHlMi8/cHo8y1u+HcslnIbspRPGV3UWKfUuMnrPPffUfN3LW5/4xCeiPXPmzMJjs7w1dOjQaE+ePDlpx/fU4Ycfnuzr379/zXPyJRFE/cyePTvZnjt3brRV7kEIsbzI0yOEEEKIUqCHHiGEEEKUgjaXt1588cVk++Mf/3i0WSLyUhJLB77iMVfh5ff57C2Wrfiz+HUglc94MVIvU3B2Ue/evaPtK/XyefTo0SPZx7JKr169UHa4b1mq9LBUxVWzx48fn7Tr3r17tPne8NmBe+65Z7RZQjn66KOTdj//+c8Lz6leaU7kue6666J95plnJvv233//aLOUOXjw4DY9p6uvvjram222WbJv++23b9PPFkK0HfL0CCGEEKIU6KFHCCGEEKVADz1CCCGEKAVtHtPz6quvJtscC/P6669H+7777kvafeELX4j2BhtskOzjOCFeIZvjcYDiCr8+doTbccq6b7fuuutGm2NJ/CraW265ZbS5AjUAPPnkk9FWTE9xevf999+fbM+fPz/aHM/h769FixZFm8se+ArMXEH5mWeeiTb3nWg+XJKCx4Uv3fCtb32r5r6BAwcm7aZMmRLtk046Kdrjxo2r63x8nN+f//znaC9YsCDZxyU01lhjjWj7+aerkivRkeOiiy6K9rbbbhttni+BdM7kuW/IkCFJuz59+tT1ufVyzjnnRHvQoEHJvkMOOaRVP0s0PvL0CCGEEKIU6KFHCCGEEKWgzeUtLytwNWWusuvbTZo0Kdq77757so9d3pzG6uUsdrVzmrqv3MySFldu9qnonEbPVZj/85//JO34GBtuuGGy77HHHov2brvthrJT5ELnlGEgdb1zf/mSACxxFlXa9u2YI488Mtn+zne+E+1f//rXheeu9PUKRYutLly4MNnmhWEHDBgQ7ZwkwnOEvz/22muvaN9yyy3RHjVqVNKOJSw//o4//vhot3VKfCPiS4MUlZC48847k+3Pfe5z0WbZyl97rnbO8+fFF1+ctGOJc7vttos2L/ALpFK0r+R91113RXvOnDnR5v4HJG/Vix/XfA9wf2288caF72uUeVGeHiGEEEKUAj30CCGEEKIU6KFHCCGEEKWgzWN6vvSlLyXbvAr2a6+9Fm1OewTS1FJO8waAVVZZJdocx+NjdThllpea8PokH4O1Zo4/AoCHH3442lw638d6cArupZdemuzjZTjKiI8bKEpZHzNmTLLNsTt8fXlJCiDt56KSBcDSqe5NHHvssYXnd+ihhyb7/vWvf0W7UfTq1oLj4fx3y33Xov7ceuutk21eLmT69OnR5jIDQBrHwX32zW9+M2nHsXPbbLNNtL/73e8m7ThWh8tneIpiyICll7HpTHC/Aukc6WN4nnjiiWjzfMfLtgDArbfeGm3uP3+d+vXrV/Oz/BIxvP38889He8KECUk7jh/y5/7Zz3422lziZMaMGeiqtEb8DC/3c/bZZ0eb4+4A4N577432wQcfHG2OgVye8yjid7/7XbSHDh2a7Nt1113rOoY8PUIIIYQoBXroEUIIIUQpaHN5y8Np3zfeeGNhO3ZD++q87MouSpH1sFvXu3hZcllrrbWi7SUQbsfu+Z/+9Kd1nYPIuzu5FIFPQd1oo42izVW4WeoEgL59+0abXbW+yquvot0E358A8OCDD0abq4R3BXJSR9H1aS3OO++8aO+zzz7RZskQSCsjszyy3nrrJe3Y7b3HHnss9/nxfdoZ5Cw/D/I220XyIwDcfvvtyfZvfvObaH/jG9+Itq+aXSQZvfzyy8k2X1OWpVdfffWkHd+XXFrC3698b/hSE3z/skTGFduBpaW6RqToN645sjPL/iwn33TTTUk7lgKZqVOnJtuc6s/X1P9Wt6QsC5erAYCvf/3rNc/j05/+dNJO8pYQQgghBKGHHiGEEEKUgjaXt7xrrkhm8i5kzvZgNyaQuvH4GD7LgiP6c+56fh8fmzO5gNRNmsNnKDE593IZyPUDZ2z5+4Gz3thV6/ucF5hkGcwvGsnVffmznnvuuaTdmWeeWXi+J5xwQrSvuOKKwnbtRdNYy7m5eTzm+uKll16K9lVXXZXsu+2226J99913N/s8AWCHHXaINmfa8LGBdAwXyR5Aml2Uk7d4bPKCx0B673Dl3nnz5iXtmjKUfOZgR+LnWe5bvm5cCRsANt9882j/+Mc/TvZxBi1Xp2epGQCOOeaYZp8vZ+6OHj062ceVm1mi9jIYV//1Ff1ZWuN+8vNKe8hbTX2TW9A1N2ZbkgHl57Ezzjgj2nw/sGQMpFlaHMKx5pprJu1YFuNVEXwVbl6tgDNwfT9whrY/91122SXaHPYwbdo0tAR5eoQQQghRCvTQI4QQQohSoIceIYQQQpSCNo/p8Xokx7TkYgp8HA/DlXZ5RXNflZP1+6I4IH8efDyvIecq/BYdr6tV6m0J3A8+ponjbrgqt6+2ybEIXHnb94nXnpvo2bNnsv3ss8/WPD8uWQCksTo+nX3s2LHR5pW9DzrooJrn0F74+7vee/CUU06JNlcf99eEU1Q5nRRYesXsevjDH/4Q7b///e/JPr7GrOf7aul//etfo82xd1wBHkhjON54441kH8eH8Vzi4w823XRTAGkMUHtRVHXXz6Xcf9xfnNoPAHvvvXe0//3vfyf7+Hpz3A7HT3mKrqGH40COOuqoZB9vc9zG73//+6TdHXfcEW2O8wPSOCyeL3zF7/agqZ/qHYd+/PJ9tmDBgmj72JeFCxdG++mnn072cSkPrljO8VNAOhfyWPbXbd9996157n4+5vHG49KvnsAxm1xpG0hjsg488MBo+5IIHHeWQ54eIYQQQpQCPfQIIYQQohS0e0Vmhl1p3hXK7kq/j93N7PrzaawsVfF7vPuQj8+pqt5Vt9lmm9X4FkvTGgu/dSVyafpczZrdn+z+BlL3bJHUBSwtSdZzTnw/eJmA7ymW4oC0GjQvuuhlk89//vN1ndPy0lw3umfQoEHR/tvf/hbtJjmniU022STaPkX19NNPj7ZPhy2Cxya73oHUxc7Xn9NYAWDYsGHR5nIXfqHE7bffvubxPDwn+Mrs6667LoD677WW0HRP1lt195JLLkm2WZrift1zzz2TdiwR+X0PPPBAtFlWyM2DfH65FO1650iWvH3pAP798HInj0GeS3zYhC9l0Zb4352iNG2WqYC0tAJLPV7KZ2nRX/utttoq2vfdd1+0OY0cSCudN93nwNJzGq+KwHiJicczlynwY4d/x30pCC6RwIvRsoQLpNJfDnl6hBBCCFEK9NAjhBBCiFLQofJWjhdeeCHaPnuCZSvGu9aKFgr0EkaRlJbL8uKodO/qq3cR1K5K7rp5ODuK3dC++jVnELF88cwzzyTtOFOFpQ2faVPvIpIsd3p3Mme+tCRrqTUJIUSpz7uH2SWckxK+/OUvR5uzqLzscdZZZ0V7xx13TPZxdV0+nu/P8ePHR5ur7vqxPWTIkGhvt9120fbucZaqOMtu4sSJSTs+D3a3A6mEyvewr9rbJPW0pXTd3AVf/RzEch/LHl6q5IWd/ffcdttta+7jTBtPvRXnc9eO76HLLrss2vvvv3/Sjhc69dmZXE2f739/fm0tby1cuBBXX301gFT6BYATTzwx2pyx5LMlWYLi7+mlOq5K7TOgWDLjzFh/P/B8x4vM+t+0osr3fjUCv8BrE/Pnz0+2WZryczN/1iOPPBJtvyh1vcjTI4QQQohSoIceIYQQQpQCPfQIIYQQohR0aExPTtd96KGHou01Pk5TZu3da82sT/I+r+tyO44V8Ct4czvWJL2ezufUlVdVr7c6LHPzzTcn2xwrwDE9fK2BNGWS01N9ijPfG3PmzIm215r5s/h8c1VkBw4cmGz/6U9/Kmzb3rz33nuxyrRftZr7KbdSOccIcGyNT0vndr6sw0knnRRtjiPwFXP5fVtssUXyPRiO45gwYUK0+/TpgyI4xXe33XZL9k2ZMiXa++yzT7KP70Ue+7wSObDkfmmkchQ+fbcolsJXseWyC77iOKeIcwXzHHzdXnzxxWQf9wvHbPpYTP7cG264Idq+BAJXCfYxXvybwfeaj3fLjffWYK211sIBBxxQ87O4z+pdMZzjCv0cOWvWrGj7z+Jxxe/zx+B5kvuS+86/j+dP/1vN455jlXx/8ZySG1f8O+7v5UmTJhW+j5GnRwghhBClQA89QgghhCgFHSpv5WQQTkXOyVEsZ3h5qygVPSc5sVuf0x798bgqMKd2Ao3l9m5LWvI9Od0ZSNPKOX3Spzhzv3CqIleNBdJqsXx/3XPPPUk7vh9Y5vEyTNE55MhVom0rVlhhhegiZrkISK8JV4H1qbHsLuZ0Wp/Wym70k08+Odn36U9/Oto8LnILDPLiiF5imTp1arRZkvQyGB+f+9AvvMjHuP/++5N9LJWyDOgrATdVqm0raWTx4sXxvr7xxhuTfb179442fxc/V7FkxPetlzQ5HfiJJ55I9vF9zOn8t99+e9KuaJFRL1sVyche6uD7l9/j54THH3882n7c8jZLLj5V+n/+53/QlphZ/PzPfe5zyT6/vbzwd/a/rTxe+Hr4uapojvO/mXwMtjvyt89X5S5Cnh4hhBBClAI99AghhBCiFLS7vFW0uKPPlOLqkl62yi1qxxRJX94tzccoWogSSN14LG95mltNtSuQW7STs24mT56c7OPKodzOLzjKi87xgpfepckVOzkjYNddd03acUVgvk98NhLfa1zZNUdHuHhXWGGFKF1wZgyQZlFxFlz37t2Tdpzxw/3iZQWu6MoLJQKppMXSFGfaAGkWClfF9VISu9s508jLW7zN96KvTMvZKb4/X3rppWjnFm9skpLaapyvuuqqsVKy70ve5oVQeaFIIJXB+Br6hSO5Eq6/pix98TXgRYKBVKLm7Cg/pzN8PH99+b7hPvL9xeMsJ0vzYpv+eh533HGF72sNVlxxxSgj+2vP23xfeimJf69y7Rg/B3Hf8jjyx/C/eU34Pir63fWv8/HY9vca3yu578XH8JI5L5Cao3y/zkIIIYQoJXroEUIIIUQp0EOPEEIIIUpBu8f0FGmBXu/klWV9miGn2nJMh68G6avwNuG1Zj4nfo/XRfl9fnVvhrX+jkhfbk2KNFkg/Z65+Ibvfe970WY9GUivB+/z2junqXM7Xy2X9XtOwebqzEC6ujSncXs9mWN8fFxKI8GxA74veLzkKphznA2PP79CPacK+3uCxyqnuvsxVxSD42O5OH2ZY5M4ZgVI+5C/l48d4LgQH9PEsS9c/ZePDSyJFWurausrrrhivA5HHXVUXe/xcx1/F04d933J197PwXzvc8yMn8N4tXo+nl/BnMct3w++SjIfj9vlVt/2fcH3PKfz++r5/h5oS3yJCL8t2gd5eoQQQghRCvTQI4QQQohS0DDylk+LZVdrLv2O09Z8O3bJFqW++vdxtWd29wNp6mCR6xdI3bDe/d+IC5D6PuHvw9+z3hTd8847L9nm9PA99tgj2Tdu3Lho87Xx6ans5ubz84saeim0icsvv7zwnDiN3ruc+bN8+nMjYWaxr/y14/IK3J9+UUpeVJDT/XNpqB6+XixHcWo0kI5hlqj9sfl4ubRk7je+T/39wfOMr2LMshjPCZyi74/fKPh5hascs11vWq8QXZXGG71CCCGEEG2AHnqEEEIIUQo6dMFRxmdI1Fs5NiczsSSSk7f4GJw54LMF+H18PJYFAKBnz57RzlWMbhS8LOirEjfhM0S4Gu9vf/vbaP/mN79J2u20007R5qq3ALDzzjtHm6sp+0rLRdJDTmq46aabon3wwQcn+2699daa7/HH4/7LVWTmdh2dofeZz3wm2WbJiBfg9H3B0uDMmTOj7ReE5HvfVzfna8TjjytqA2kmHMvIXqbhLC1+T70Sk79n+Tv68c2SW05qFUJ0XuTpEUIIIUQp0EOPEEIIIUqBHnqEEEIIUQoaJqaH01uBVF/3cQMcQ8OVY71+z7EVHNfgq8Nyei7H9PiUdT4Gf5aPjeCYns7I9ddfH+0vfvGL0fbXjWM7GB8DMX369GgPHz482TdlypRob7zxxtGeNm1a0q6oMqu/9qNGjYq2j+Nhiqp1e/ge8hVmGb43Gq0sAce/cAVrX826K5KLERJClA95eoQQQghRCvTQI4QQQohS0DAVmWfNmpVs+3RShheaGzhwYLT94oIMS2J+4UhO0eZjc3VmIE2bZjnDp1cznSFl3VetPfXUU6PN0iLLgDm8dMT98tBDDyX7dtxxx2hzmrT/LE415gUUDzvssKTdpz/96brOsSgt38shLA35xTCZztDPQghRduTpEUIIIUQp0EOPEEIIIUqBHnqEEEIIUQoaJmXdx1Lwkg+52BqO/eEV14E09oNT4n1JfP++JnxsCp8jL3mRW3YgtyJ1o8DLNQDptVp//fWjzdcTSK8Pp6/778xxMT72ZcKECdHecMMNoz1ixIikHS9RMXv27GjfeOONKIJjifieAZZeWqGJonsBANZbb73CfUIIIRofeXqEEEIIUQr00COEEEKIUtAw8pZPIWYpyUsO6667brRZOvESBr+Pj+dXbX/77bejzbKHl2KKZCy/ajtT72rQHclxxx2XbF977bXRfuKJJ6LN6fxAccXrXNr3qquumuzj9z377LPR5hR1IK2Ufc899yz9JWrgK3kzRSUR/Hu4EnQuZZ+lvtznCiGE6Dga/xdZCCGEEKIV0EOPEEIIIUpBw/jhZ8yYkWyznOGliEWLFtW0vQz26quvRvuNN96I9jPPPJO0e/nll6M9efLkaO+0005JO5Z3WPoqqu7bWfCS01133RXtuXPnRvuKK65I2v373/+ONmdX5TKg6sUvZnrrrbdGe88991zu42+66aY1X+f7Dkgrfg8aNKjweI22yKgQQoilkadHCCGEEKVADz1CCCGEKAV66BFCCCFEKWj3mJ6iFG5fgXfBggXR5hR1IE1N79WrV7R9XMW8efNq2sOHD0/aceXeOXPmRNunqK+22mrR5tgfrlrs6Qwp6zm4SvIPf/jDZJ/fbsLHZ/Hq6RyDBaTlAzh+pijmprXgleS32267aPt7jc+vR48ehcdTmroQQjQ+nfsXWQghhBCiTvTQI4QQQohSYL7qcLax2SsA5iyzoWhN+ocQei27WfNQX3YY6s+ug/qya9Hq/am+7DAK+7JZDz1CCCGEEJ0VyVtCCCGEKAV66BFCCCFEKejwhx4z62Fmk6v/XjKzF2i7cH0HMxtgZtMK9p1tZvsW7DvBzDZwrx1tZj8wsz3NbOfl+0blxswOM7NgZlvU2X62mfWs8friWu0zx2lW+8xxlro/RJ7q2JluZlOq43aHVjjmWDMbsbxtRPNQX3Z+2qIP6dh7mtktrXW8jqDDi4uEEF4FMBQAzOxHABaHEH61nMc8q9brZrYigBMATAMwj3btD+AiAAcDWAxg3PJ8fsk5GsADAD4H4Ecdeyot4gQsfX+IAsxsJwAHAdg2hPBe9QG2cy9GV1LUl52fRu5DM1sphPBBR59Hh3t66sHMBpnZw9Wn1ilm1lS5bkUzu6z6VDvGzFattr/CzI6o2rPN7CwzewCVH+QRAP5WPdaqVqlAOBTAQgBfBfDt6r7dzKy/md1V/cy7zKwfHf9SM7vfzGaY2UHtfEkaEjNbA8AuAP4HlYeeptf3rP4ld72ZPWlmfzNX+bHaF7eb2ZdrHPdUM5tQ7YcfZz7/fDN7pNpXvaqvDTWz8dX3jjKzdYper94zyf3RKhema9MbwIIQwnsAEEJYEEKYVx1zE8xsmpn9sam/q/fBudXxPMPMdqu+vqqZ/aPaH9cAiNfezC4xs4nVcV7Y/2K5UV92for6cLaZ/bg6P061qifezFY3sz9X+/dRMzu0+vqA6u/bI9V/SykgZrZd9T0DzWy4md1rZpPMbLSZ9a62GWtmPzezewGc3H6XIUMIoWH+oeIZ+H81Xv8tgC9U7Y+hMogGAPgAwNDq69cCOKZqXwHgiKo9G8BpdKyxAEbQ9rYArqz1+QBuBnB81T4RwD/p+Lej8tC4KYC5AFbp6OvX0f8AHAPgT1V7HCp/bQDAngBeB7Bh9Zo9BGBX6p8BAO4EcBwda3H1//0A/BGAVd97C4Dda3x2oHvkLAC/q9pTAOxRtc8GcMEyXk/uD/1bZp+vAWAygBkALqZr2p3aXAXgYLq+51ftAwHcWbW/A+DPVXtIdWyP4GMBWLH6/iHqK/Wl/jWrD2cD+GbV/jqAy6v2z7Hkd3Pt6vtWB7Aaqr9pqPzGTazae1bn4J0BTALQD8DKqMz3vaptjqL+Hwvg4o6+LvyvU3h6UPmRPMPMvodK/v071ddnhRAmV+1JqPx41uKazLH3B3Bbwb6dAIys2lcB2JX2XRtC+CiE8DSAmQDqimHp4hwN4B9V+x/V7SYeDiHMDSF8hMqgHED7/gXgLyGEK2scc7/qv0cBPILKda61RsVHWNLPVwPY1cy6AVg7hHBv9fW/Ati96PV6v6RYQghhMYDhAE4C8AqAa8zsBAB7mdl/zGwqgL0BDKK33Vj9n8fs7qj0G0IIU1B5KG3is2b2CCr3wCAAW7XJlyk56svOT6YPgdp9tR+A081sMioPKKtgyYPMZdU+vw5pP22Jyh+iB4cQngOwOYDBAO6oHueHqPyB20Tu97fd6fCYnlqY2WEA/q+6+aUQwkgz+w+ATwEYbWZfQuVB4z1624cgN6rjrczH7Qfg8DpPLRTYtbZLhZn1QGVCHGxmAZW/5IKZnVZt4vuK770HARxgZiND9c8DPjSAc0IIf2jmKZW6P9qTEMKHqEyYY6uT5FdQ+Qt/RAjheavE6q1Cb2m6F/x9sFSfmdlGAP4fgO1CCIvM7Ap3LNGKqC87PzX68Pjqrlp9ZQAODyE8xceo9vPLALZBxcP+Lu1+EZV+G4ZK7KMBmB5C2KnglHK/v+1OQ3p6QgijQghDq/8mmtlAADNDCBcBuAmVQdhS3gSwJgBU/+JfKVSCqZN9VcZhSWzKF1AJ0G3iSDNbwcw2BjAQQHLTlJAjUJEJ+4cQBoQQ+gKYhdQ7VsRZAF5FxR3rGQ3gRKvEC8HM+pjZujXarVA9BwD4PIAHQgivA1jUFGsA4FgA9xa9XrX9PSAymNnmtiTGDqjExzWNhQXVfjtiqTcuzX2ojDGY2WAsGeNroTJpvm5m6wE4oDXOWyyN+rLzU9CHuYrQowF8k+K0hlVf7wbgxapn/lhU/oht4jVUHBA/N7M9UblHelkliBpmtrKZsTewoWhIT08NjgJwjJm9D+AlVGIw1mrhsa4AcKmZvQPgfFRiSZq4GcD11WCubwL4FoA/m9mpqLgKv0htn0Llh3I9AF8NIfCTcBk5GsAv3Gs3oPIAUo978xRUrvUvQwhN3iGEEMaY2ZYAHqqOy8WoxA7Nd+9/C8AgM5uESvzQUdXXj0elv1dDxTv4xWW8fgWW3B87kZQqarMGgN+a2dqoxG48g4pr/TUAU1GJJZhQx3EuAfAXM5uCivz5MACEEB4zs0cBTEelnx5s1bMXjPqy81PUh0XJNj8BcAGAKdUHn9nVthcDuMHMjgRwD5y3JoTwspkdjEpoyImoPAxf1ORIqB5zeit+r1aj1MtQmNnlqAR0jW/m+64AcEsI4fo2OTEhhBBCtDqdxdPTJoQQvtTR5yCEEEKI9qHUnh4hhBBClIeGDGQWQgghhGht9NAjhBBCiFKghx4hhBBClAI99AghhBCiFDQre6tnz55hwIABbXQqohazZ8/GggULbNktm0dH9eVbb6XFOV999dVor7TSkttxxRVXTNoZrU/6wQfFC/V+7GNLFhR+++23C9/z/vvvR3vzzTdf1mm3GpMmTVoQQujV2sdtxLHJ1zzXn52VrjA2OZHlv//9b7LvnXeWlKhaffXVo73yyisv9+fyZ/HnAEC3bt2W+/gtoS3GZqOMy48++ijafL39tV9ttdWizWOU50sgvQdWXbXx1mXO9WWzHnoGDBiAiRMnts5ZiboYMWJEmxy3o/pywoS0ttmVVy5ZbqtHjx7RXnPNtCgyPxAtWLAg2v7Hs1+/ftGePHlytOfPT2sZvvLKK9G+55576jn1VsHMctVRW0wjjk1+oPU/ZNyfbYnPTuXtFVZYPkd3R49N/iHz3yW3j+GHj+eeey7ZN336ktpyO+ywQ7TXX3/9ZZ7bspgzZ8kwePzxx5N9+++/f7TrfTjm7wu0rG/bYmy25bhszndevHhxtLlf2QaAIUOWLHbw8Y9/PNovvvhi0m699daL9jbbbFP4uTze2vMPnVxflrpOj2h/xo4dm2xPmzYt2jwoZs2albTjQcsPPeuss07Sjn9c11577Wj37NkzaTd79uy6z1mk8EQ2evToZN+1114bbX6YfPnll5N27767pID5V7/61Wg/+uijSTue2J944olob7FFur7v5ZdfHm2euP1Ey9v+gaizeZ/4fOv9AfzKV76SbL/33pIl8fhHDkj77MILL6z5uUDqBRg2bFi0vReBH3T5Qcf/gXP77bdH+7XXXov2IYcckrQ7/PAlSya29KGvM5P7Xk89la6K9Oabb0Z7xowZ0Z4yZUrSjudPnlu5H4B0/PI4Gjp0aNKuEcdU17wbhBBCCCEceugRQgghRCnQQ48QQgghSoFiekS74rO3Ntpoo2gvXLgw2n379k3asUbP2VYck+DbcUxP9+7dk3b8Po7vaYRMi0aAA00/+9nPJvu4D19//fVkH8cZ8DXn7B9/fI7z8rFcDAcOc4wCAHzuc5+LNscbnHTSSUm7008/Pdo+3qCjgi5bSr1B2d///vejvWjRomTfBhtsEG2fvcVjkPvZB7Xytf/a174W7Z122ilpx8Gv/Lk+3o5jhDibiOPFgDTw+tvf/nayr4zLKz377LPRnjt3brKvf//+0eb+8/Mn9xHPhT77kpNOON7HB223VbD/8iBPjxBCCCFKgR56hBBCCFEKJG+JdoXTJYG0Xg6npXsZjLfXXXfdaOeKDrIE4t3d/L777rsv2pK3KpxwwgnR9pIIp7J62YplFpaIfGkBljW5BME+++yTtFtrrbWi/cYbb0R7jTXWSNoVSVO33npr0u6mm26K9rhx45J9nUHSYnJp2TNnzow2l4XwsjHLG/778zH79OlT8z1AKjNdd9110WZpCkhlLO7XDz/8sPBz2WZJDACmTp1aeAyWY3ifl2m6EiwzsUwFpOUINtxww2hfddVVSbtRo0ZF+8ADD4z2vvvum7Tbcssta36WLwXCZQsapYihPD1CCCGEKAV66BFCCCFEKZC8JdoVljKAVILKZQVxJhC7q71sxcdgd713ybO85eWbsnLZZZdFm6vx+uwavv65rCHuG792D6+Lxm5vL2tyv+VkCt5eZZVVot2rV7r8DktkN9xwQ7KPK/x2BnJLedx1113R5j7i6w6k1yq3ph2P0969eyf7WKK++eabo+2r87J8zbKHv4d4XSeW8PxY53vq/vvvT/btueeehe/rzPD1YAkTSK8vL8EDpLImS5XPPPNM0o7XLuRsvnnz5iXtWBpmeZMzyIBUSjv66KNrvt7eyNMjhBBCiFKghx4hhBBClAI99AghhBCiFJQmpodTKS+99NJk36BBg6LNKbOHHnpo259YyfCxOhwfwNo+r8IMpHE3HIfgKdLvffost/OfVVYuvvjiaPP18enADMdf+PcxuerHjI9T4c/meAPfjlNyOTbFrz7OsT8+XbezxfTk4Huar7WPmeJr6q8Vw9fNV27ma8+lBHLtOB7Hx/Tw+Ob5gittA+k9xWn5QBrTk4t96mxwHA/H0gDpHLfJJpsk+3g19e233z7a66+/ftKOU845TorfAwAPP/xwtDleaO+9907a8X3z4IMPRnuzzTZL2g0bNgzthTw9QgghhCgFeugRQgghRCnoOn6/ZTB+/Pho+8UKJ0yYEO3f/va30T755JOTdhdccEGzP9e7k3/6059Gm9OC//CHPyTtvGzQmeG0Y04ZBlJpkV3tXg7haqMvvPBCtDlNE0grvbK716ddcxVRv4CiSKUOL1Nwf+Zkw1w6O/dvURVnIJUmeJ9Pr+bzZXnEV4Hldr56LKfl+uq/nQ1OHeZr6EsHcOq4l415PHIf5aqb82f5dix1cDsvP/H9xZ/L5+qPz2nzXRmeB7kyvd/nx9F+++0XbZ4jucSAb8fSspetuM+4/3nRaCCt2M73np9zN91002j7auutjTw9QgghhCgFeugRQgghRCno9PJWvYvJceR4t27dkn0sd3HU/4UXXpi0O/bYY6M9fPjwws9iNyMfDwBeffXVaHN11OOPPz5pt8ceexQev7PBLs8111wz2ccVc9lF7SUVvlbsuvUu71122SXa7Br39wa78rtSxdbmcOKJJybbfC35ej///PNJO3aP++wPztDhPswtZlnvIpBFi0h6WJZ56aWXkn1cEdzfi/fee2+0uXpsZ8DLViwRsKTM1wZIpWK/GCmPEZYFc5Wb/bhlWLaqt885Y8tLJ3y+vjpxV4LHJV9fLwuylOTnRZ5b+Zr2798/acd9yxlbXMUZAKZPnx7tografjuXVTl37txob7HFFmhL5OkRQgghRCnQQ48QQgghSoEeeoQQQghRCjp9TI+PFWBYA541a1a0vWbIWjPHK/iqliNGjIj2EUccEe1+/fol7X79619He6ONNkr2cQwEa+09evQo+BadH66m7GMKOLaD4xJ8O47h4GqzPrWYq5QOGDAg2j51mfu5K5UHaA7f/OY3k+0xY8ZEm6+/jw/gfvIlGTjOgOM2cuOU9+UqN3M/cfwCkMafcBq9r9TL38V/1n333RftzhbT41OAOSaLx5gv8cBz5Oabb57s4zGXq9DNx+dYjXqrcPvxx2P1kUceibbvc74POY6yq8FxaEWlGYA0Vqd79+7JPv6N4zHgr9vll19e8xg+No7hucLHlvF8wPeon9+5fItieoQQQgghWgE99AghhBCiFHR6eStX9XXkyJHRXnvttaPt0+XYBccp5b7aLLt/b7vttmh7F/+WW24ZbU7hBdIF9NgFzSl7ADB48GB0Fdjt6l3UDLtGvRueKyqz25z7FUhdvlxx18uH3Oe5NNuujF/kj+9BXnzTpwoPHDgw2n7RQx4jPDa9K74o7Znd8EA6Bvk9/j5iqZjd8htuuGHSjvd9+9vfTvZtt912Nc+pM8AyEFB8T/OcAxRXUwaKFwX1c25Ouixql0tZL6rc7KUYDhXw45vHPsvcnRGeP9n2KwvwXOj7mfuMf5P8b9y//vWvaHO5FX8N+Xcsl4rOUhrLW0OHDk3a5eSz1kaeHiGEEEKUAj30CCGEEKIU6KFHCCGEEKWg08f05PjZz34WbV56wq/0XbQyMOunfh+XQPeaNpe39+m+rFezZs6rwAPA/vvvj64CXx+fOs6wHuyXCuE0dWadddZJtrn8Pq/c62NPuG/9cgQCuOGGGwr3ff7zn4+2X92aY3I4jsfHgRQtH+Pb8ZjLxZ/wfcWxSbfffnvBt+hacMqvh2M4fPwhl27IpRvz2PSp50Vp6rm4HU5T98fj8+Bz90tNcPyYP8bkyZOj3dljejh+huc3H9PD+3xKuI+Va8L/Pu27777R5t84347HNs+luc/l+CHfjo/h+7LemLF6kadHCCGEEKVADz1CCCGEKAWdUt5i9xe7vrjqMpCmwXF6o5et2I2bc7NxO3bP+/RQXw2z6Bjsyn/ooYcK39PZ4euYKzHA+7w71qewN+GrZj/22GPRZnnLp2ayy7jeFZ9FhaJxAKQyU65UQVF1Xt8XLJ3kJBY+j9wq4EXHBvKVoRudZ599NtlmiYilCF9+YLPNNou2H5tF1zF33fg9RX3sz8/fQyzT8D7fjj/Xn9NTTz1V+NmNjk8353AMloX87x2PMV/Ko+je9r9dLPUXjT2geLz5e4hlMa4s7dux7MplY4C0XElrIE+PEEIIIUqBHnqEEEIIUQo6hbzlI8c5op9ddWeffXbSrlevXtHmLAXvqsu5zRl26bF71mf/8D6fEcHfhd24Y8eOLfzczg73kc+6YdmJpRGfFVSU9cXueQB48MEHo81ufZY3gbQ6qHebizw++7GIogwtoHhxWT9eclk+DB8/V/WbyUmtnY158+Yl2ywt5ir18lzq5awiia/e8VLv9fVV61ly4exMf2/wvO3lb78Aa2fCX3e+t1kG8uPQX8ci6pWjcpm2fL15XPr5fcaMGdHmrErflzxmfXVmyVtCCCGEEC1ADz1CCCGEKAV66BFCCCFEKWjYmB7WCXPa4s033xztK664ItnH6cysf3rdsSgFPteO40W8lsq6eW4Fb9arn3nmmWTf6NGjlzrvroDXq1lf5mvq4wt8CmYTW221VeFnceqjjwfheK/Olp7c0XDasx+bRfECPo6u3nRo3ubYBh9XwrE/9cY2dCV8KrqPmWgiF1Pn4WvP1zsXW8X7/NzH/cdj3Zen4PGYi8/i7+irE/sYp86E7zvuo6Jq1UC60rxP+y4qK+DHG19vHtu+L3m85UpEcAwSz7m+4n7RSvJtgTw9QgghhCgFeugRQgghRCloNXmL3ZpFtofd315iyEkO55xzTrR/8pOfRHuLLbZI2rHbjd2zuRTJ3PkWLXjoXYTsxvWpukVSGrt7gSWVhX2KaWck5/IuWqzOp1IWLQq63XbbJdvcF9xfvh+KFsITy4Yrq3IpCCBNeWVXuZejihap9BTJn35c8HlwKYiy4Mt68JgrqooLpH1UbyVr31/8WdzPfk5juJ0f6zxH1LtIpZ9XOnMZCn9v83fha+8lTZ7Tcn2U++3ibT6+lxn5N5TP1193/ixORfcL5LI0J3lLCCGEEKIV0EOPEEIIIUpBq8lbrb1Y30033RTt0047LdnHi8lts8020c5Vl2SXt3fjcjt2x+Ukt1wmSU46KVqo1GfBNLkWO7Obtolc5gdnIyxatKiwXVGWVlFWF5DeDznXvbK3KhRJrx52gXsJgxdy5b7xbvQiGTnnHs/JpLydk1Xq/Y6dAZ/1xLBEwJLW0KFDk3bcR15yKKp8n5NEOKunKIMMSOc7Pzb5e6233nrR9hILf6/c4tB8Hnx+jYqXIPne5vGRk+VzFdB5XvSSIZMb55xVzMfz45JlK/6d9fcQH//5558vPKfWQJ4eIYQQQpQCPfQIIYQQohTooUcIIYQQpaDNKzL7ypB33nlntCdPnhztW265JWk3bdq0aPuVtDlNmbVKn7bJemUuFZ0pSkv3sL7stXXWU/0x+Jz4s7z+3dSus8cdAPk+4hV0eWVkf0379u1b89g+lb2oUmiurEBO1xZLUxRjAKSxJNwXuZRqPoYfBzx+uM98f/L90pVWT8/BMXAevqZF8RdAPu6G2+auab1za1GqtI8D4fHIFX19DAuv4O1jlfiY8+fPj3afPn3qOteOxPcJfxf+zn4MrL/++tHm308gjWnNpYQX9bOfI7kCNq8sMHHixKQdV17m+CwfP8b3kI9pam3KMTsIIYQQovTooUcIIYQQpaDF8tbYsWOT7bPPPjvanHLGrkUA2GCDDaK9ePHiaPt0xN122y3aXuJhdx/vy7ng+D2+HVdzZdeidx9ymmWuoiyngXr3f1ElUr4WALDTTjsBAP7+97+jK/HKK68k20UyoXd58+KxOdiNy8fzJQHYxVvGCr61qDedO7c4II8tlrf8/c3Hz5VlKJKb/efyPl+ptuhzOzuvvfZatP314PmJK+b2798/acdjxEvxfIychFVUMdjj06iL3sNjn9PmBw8enLTj3xk/p/M5sUTWGfBp9UVlTjgd3O/zVZ2L5jh/bfh685j1C1/z9ebfu1mzZiXtuNTI9ttvH+3bb789abf11ltH299rTz75ZLT9qgstQZ4eIYQQQpQCPfQIIYQQohQ0S956//33Y9T11772tWQfu7s4I4dtIHWhcmS3d0/mFjtj2AWby9DJwTITf5Z3u7KLkGUwzjry5+EXN2W3Y05+2X333QEUL7TZmeB+8Fk8c+fOjXYum81n8BXBLl92//vr2NoVxMsESyQsIQNpZVW+rr4/eV9RJheQzhe5CsR879S7cGZnJyfZF80zn/zkJ5N2U6ZMibaXVXgey1U35+Pze3xf8vv4eF6a4/Pg77jpppsm7a699tpoe/m0KAOsM+DnSJ4/+VrvuuuuSbui3zGgWEL2kiaPy9w44uPzPOv7iOFnAS/NcX/5+bi1s7nk6RFCCCFEKdBDjxBCCCFKgR56hBBCCFEKmhXT88orr+Diiy8GsHRKMcfn1FvxkVPFve7KOqbfx5ofa5K+miTHyfDxcumdXPXTf0dOkXzppZeizZUwAaB3797R9tolx5bwObEuCizRTLt6ddkivd2nLXbv3r2u42244YbRfuKJJ6LtVwlmvbozrLzcHhTFcPi+4HgRHxPA1zKXil6UAu3HHI8R7jMfr5eLOan3HDpbbFeuYjx/N27nYww51sqPsXpjeji+g9v5GCzft034OZKPwXOuj2HhVGkfM8bxlz7dutHx8Vn8XXgey8Vg5eDfP/7d9p/NsUX8Ww0AL7zwQs3PHThwYGG7Xr16RdvHYPG94avv52J6W0LX/kUVQgghhKiihx4hhBBClIJmyVtmFl2lXpZgWYjdbl5KYtclS0Q5V7OXJthFy8fz7r2itEgvGbEblt1x3i265557RvsnP/lJtEePHp204++Sq67JLr62XmStUfB9xFIJ31P+uvGidjnWXXfdaHMlTy8f8nZnWISwI/EyFd/ffizVKzPlFoNlivZ5aYfvna5Q5qEecjIjz5k8v+XkLZ6PgXTMsdThK17zmON9XqbhfuGFqJ977rmkHctWPEd6+ZHPlyv6Aun39yngjY7/LeSxwjKTr7LMY8DLvzyOihZl9tu5BX65HfeXlzS5Aj9LWFydGUjvZV++pbXHszw9QgghhCgFeugRQgghRClolrzVu3dvnHnmmQCWXjjy7rvvjja7HX10OLvJ2D3n3bMsR+UWwmPbtyuSvti16tt95zvfifYpp5yCerjqqquSbc7e8m5Bdi+za7kos6GrkXO7sovTZwt4V3kRnAnC7/H3Bl/vXBaMyGc7ermkKNvKU1S510sY3I6P5z+3JRV4O3v2Ft/DXnJ6/fXXo51b2Ji/c64yctGil0D6W8CS8o477pi0K5LBvHzKVb753H2WLG/7hSiffvrpwvNtdPwcydeH5SO/2sHEiRPrOj6PHX/teRzx+PChHiwf+nuK4d94ljE333zzpN19991X8/yApUMTlhd5eoQQQghRCvTQI4QQQohSoIceIYQQQpSCFgczXHTRRck2x6dccMEF0b7yyiuTdpwSvmjRomj7qoucpubjOTiljT/Xp8vxZ/F7fvjDHybtzjjjDCwPvFIxkGqXXp/luBWuUNm0en0TTTp0UeXazgTHCvg0S/5+nFq6wQYbtOizBgwYEG3W8n3ZA0YxPRWK7rXmrFJdtGK6j5cpSm3PrbLO5GIReIx1ZTiWIhdXwdf3P//5T7KP40Lmzp2b7ONrysf3fcJ9wcfzY52Pwe/xFZmnTZsWbU6bv+OOO5J2PN/7mCaOC/Fza2fGp3MzPMflUtG5//zvU1FMni8hwnM1jzcfw8uxmfxbzWnuQL56u4/xWV7k6RFCCCFEKdBDjxBCCCFKQYv9+j4Vm91fp556ak3bw2nujzzySLKPXZxz5sxJ9nEKG7v7vBvsG9/4RrRPP/30wvMoIlfhmfnFL36RbHN16tziceziGz58eM1jd7Y02lqwW9O7U1mCYne1d3/WC6fF8rXz15E/15+TSOH0Z6D+FHO2vXRWtMird8uzK54/N+cO94tPdlXmz58f7U022STZx3Mkp4D7tG+Wnv38yRIG95fvyyL5OjfWeZ8vT8FyKks2PvWcP+upp55K9vF909nnUJ4X+/XrF22fRv74449H21eoLpKd/XjjfdznPjyAJcOiFRL8Mfh75EIKcqsYtAby9AghhBCiFOihRwghhBClQA89QgghhCgFLY7pKYpvaQ577713TbtRqPc7Hn/88W18Jp0bjrEoiuUAUt2Z46Jy7bxez9pzTmvmOIJcOnuZqDdlPXf9i8ZMbiX1nGbPcRy5+6golqgrUxQPB6T3/oIFC6Lt+4tjIn2KOY+LXOkMjh/aaKONCtsVjW/fX1zKg+8nf365+CH+/p2tJAXHYAHA888/H+2hQ4dG28e6zp49O9rbbLNNso/HGF8Pf+35OnLZEL90E7fjvvRxRryPY9D8fcjn5Je4au2YS3l6hBBCCFEK9NAjhBBCiFLQufx+otPDFVY97ArNVR5ll6x3fXJ1V3aZetmF3auSt/J4eavelHAu15CTsDht1vcF93Wun7h/2S3f2VdSz8FV7L0kwpXJueSAlw64SrKXlLktX19fPZ9lJpbZOOXdw+fr2/FncX9xpXsglTi93MnzTE5ya0QGDx6cbPP5c8VjLzkdeuih0fZVyXkc8LzoxwfLgjx+fdkKXjGB5wc/H/M8zjKrLz/wmc98Jtr+Xs6FRLQEeXqEEEIIUQr00COEEEKIUiB5S7Q57CbnCH4gXaCQK7vmpIycvFVUAdTLGizR5BZrLBNF0o+/PuwSZ5c1AMybNy/a7Ir3WSJ8DJa3vAzJshjfO/54LAFwNXfOLALy8mpnY9CgQdH20hQvgvyzn/0s2j6TiSUSHotAKjs9/fTT0b7pppuSdiylcf/NmDEjacfXnvt8v/32S9px33L/+fNjyWXixInJPq7ovssuu6Az4StU++0m/CoGTG6RztwCwtx/LDP5eZaPwfO2p2iRWS9VckVxls7aAnl6hBBCCFEK9NAjhBBCiFKghx4hhBBClALF9Ig2h1f8Pfjgg5N9rO1379492nvttVfh8XKVsnkVadaJfWwHV33l2IgyU1S5dv/990+2R48eHW2uAgukMT6s9fu4II4X4PRV37cce8UxQn61cE6bHjhwYLRzMTydPX2dU5u/973vJfseeOCBaB9yyCHR5jTklnLmmWcu9zFaA47pOfnkk5N9u+66a7Q7W0XmHDxf+rgdjoP0cTZFJUB8OjiPNz6ev4Ycp8lzqY8X4ngkPoeiOCVg6Xi91lj9ITleqx5NCCGEEKJB0UOPEEIIIUqB5RaSW6qx2SsA5iyzoWhN+ocQei27WfNQX3YY6s+ug/qya9Hq/am+7DAK+7JZDz1CCCGEEJ0VyVtCCCGEKAV66BFCCCFEKWiIhx4zO8zMgpltUWf72WbWs8brzVpPoLntM8c5wcw2WHbLcmNmPcxscvXfS2b2Am0vfy6taFVa2l9mNsDMphXsO9vM9i3Yt9Q4MrOjzewHZranme28fN9ItJRqH0w3synV/t8hMw8fYmanFxxH/djBmNn6ZvYPM3vWzB43s1vNbLNmHmNtM/t6W51jW9IoBQyOBvAAgM8B+FHHnkqLOAHANADzltGu1IQQXgUwFADM7EcAFocQftW038xWCiF8UPvdrY+ZrRhC+HDZLcvJsvqrhcc8q9brZrYiao+j/QFcBOBgAIsBjFuezxfNx8x2AnAQgG1DCO9VH3QKH3pDCDcBuMm/bmYrAdgT6scOwyrFqUYB+GsI4XPV14YCWA/AjMxbPWsD+DqAi1v5FNucDvf0mNkaAHYB8D+oPPQ0vb6nmY01s+vN7Ekz+5u5amJmtqqZ3W5mX65x3FPNbEL1L5MfZz7/fDN7xMzuMrNe1deGmtn46ntHmdk6Ra+b2REARgD4W/UvoNpVoERNzOwKM/u1md0D4NzMtR9rZiOqdk8zm121B5nZw9VrP8XMNq2+fgy9/ofqjyrMbHHV2/AfADt1yJfuQhRdfwArmtllVe/AmKZxUe3vI6r2bDM7y8weQOUPn2QcVcf7UAALAXwVwLer+3Yzs/7VMTul+n8/Ov6lZna/mc0ws4Pa+ZJ0RXoDWBBCeA8AQggLQghND6bfrM6fU63qqa967H5XtXl8XwPXjx3wXcrOXgDeDyFc2vRCCGEygAfM7Dwzm1bty6OAyu9zdXw19fGh1bf9AsDG1X48r92/xXLQ4Q89AD4N4PYQwgwAC81sW9o3DMApALYCMBCVh6Mm1gBwM4CRIYTL+IBmth+ATQFsj8qkOdzMdq/x2asDeCSEsC2AewH8X/X1KwF8L4QwBMDU3OshhOsBTATwhRDC0BDCOxDNZTMA+4YQvovia1/EVwFcGEIYisqP5lwz2xLAUQB2qb7+IYAvVNuvDmBaCGGHEMIDNY4nmsdS17/6+qYAfh9CGATgNQCHF7z/3RDCriGEq7H0OBoG4LEQwiwAlwL4TXXf/QB+B+DK6n3yN1S8QU0MALAHgE8BuNTMVoFYHsYA6Ft9iLzYzPagfQuq8+clAP5fwfubxvfhWLofRfsyGMCkGq9/BpXfym0A7AvgPDPrDeBdAIdV+3gvAOdX/xg5HcCz1X48tV3OvJVohIeeowH8o2r/o7rdxMMhhLkhhI8ATEZlMmviXwD+EkK4ssYx96v+exTAIwC2QGUS9nyEyl8fAHA1gF3NrBuAtUMI91Zf/yuA3Yter/dLiizXhRA+bOE1fgjAGWb2PVRqM7wDYB8AwwFMMLPJ1e2mtQk+BHBDa3+BElPr+gPArOpfkEBlkh1Q8P5rCl4HKtLWbQX7dgIwsmpfBWBX2ndtCOGjEMLTAGaiMv5FCwkhLEZlPJ0E4BUA15jZCdXdN1b/z/XxdZKRG55dAfw9hPBhCOFlVJwA2wEwAD83sykA7gTQBxUprNPSoTE9ZtYDwN4ABptZALAigGBmp1WbvEfNP0R6vg8COMDMRoaliw0ZgHNCCH9o5impaFHH8Naym+ADLHlIj3+5hxBGVqWqTwEYbWZfQqX//xpC+H6N47yrCbjlmNlhWOJ9+1LB9Z+Jpcdukeyb6/v9UOwh8oQCu9a2aCbVMTMWwFgzmwrg+Oqupn728zNTz/gW7cN0AEfUeL1oIbovAOgFYHgI4f1qWEGn9px2tKfnCFRc1P1DCANCCH0BzEL6V1sRZwF4FbUDqUYDONEq8UIwsz5mtm6NditgyQ3weQAPhBBeB7CI9OZjAdxb9HrVfhPAmnWcs8iwjGs8G5W/NgEatGY2EMDMEMJFqARPDgFwF4AjmvrczLqbWf+2/wZdnxDCqKpLe2gIYWLB9W8pcRxVvX4rVYOpk31VxmFJDOAXUEmEaOJIM1vBzDZGxcP31HKcU+kxs80pVguoyCAtrTKsubJjuRvAx43iYM1sOwCLABxlZitaJbZ1dwAPA+gGYH71gWcvAE3zaKftx45+6DkalUhy5gZUHkDq4RQAq5jZL/nFEMIYVFzfD1X/KrketTvoLQCDzGwSKh6ns6uvH4+KpjkFlQG+rNevQCV2QIHMy0/RNf4VgK+Z2TgAnCZ7FIBpVRlrC1Qeoh8H8EMAY6rHuQOVYEzR+ix1/ZfjWFegOo4AHIKKO72JmwEcRgGw3wLwxWr/HguAl9l+CpWH5dsAfDWEkC45LZrLGgD+apX05imoxFj+qIXH8v0o2pGqKnIYgE9YJWV9Oip9ORLAFACPofJgdFoI4SVU4uVGmNlEVP64eLJ6nFcBPFgNfO5UgcxahkII0XCY2eUALg8hjG/m+64AcEs1wUAIIRIapU6PEEJEQghf6uhzEEJ0PeTpEUIIIUQp6OiYHiGEEEKIdkEPPUIIIYQoBXroEUIIIUQp0EOPEEIIIUpBs7K3evbsGQYMGNBGp1LMm2++mWy/996SYq89e/b0zVuNV155JdleddUlJXjWWGONNvtcZvbs2ViwYEFRtcwW0559+dFHH0V7hRUa4zmbA/jNWv3yFjJp0qQFIYRerX3cjhqb9fL+++8n26+99lq0P/xwSYFsn1ix5ppLymu115irl64wNsUS2mJsNkpfLly4MNpvvPFGtD/44IOkHY8/HpcrrZQ+KvBYXH/99VvtPFuLXF8266FnwIABmDhx4nKdTEt+bO65555ke+bMmdH+n//5n+U6nxwXX5wWex4yZEmx2V13rado9PIzYsSINjlua/RlvbzzzpI1WPnBsSPhwe4HdFtiZi2tZJulLfuzORmeRWP6hRdeSLZvueWWaC9atCja/uFor732inZuzBXNK/7cW/MBtyuMTbGEthibjdKXI0eOjPZdd90V7QULFiTtePzxw5F3Luyyy5K1v089tfHWG831ZWP82S2EEEII0cY0THFC/msPAA4//PDCfSuvvHK0p0yZEm12xwGplMISC7v6PC+99FK058+fX3i8VVZZsubaww8/XHg8kXp3/vvf/yb7+Hr36dMn2jnvAnuO3n333cJ9r776arS7d++etOvfX0txtQY5zwl7c/74xz8m+7g/evVa4oXmcQqk3tYZM2ZE+8QTT6z7PJiOkjWFaA3qDRVYZ511ku3XX3892t26dYu2l6beemvJ2rCrr756tJ999tmk3ZgxY6J95plnRtvPx0yjjD15eoQQQghRCvTQI4QQQohSoIceIYQQQpSCdo/pKdLyvv3tbyfbTz75ZLQ33XTTZN+KK64Y7QkTJkS7b9++STtOdT/ggAOi/dBDDyXtOOZk8eLF0eZ0Wf+5Tz/9dLSvuOKKpN0JJ5wAUZuvfOUryfbtt98e7bXXXjvaPqbn4x//eLQ5w8DHgPD9xf3v282bN68ZZ11u/Jjla+n3jRo1KtpXXnlltH1WFscjcBxBjx49knYbb7xxtO++++5oDx8+PGm3zTbb1Dy/RimRIERrkLufn3nmmWj7+Y7HC5eLWG+99QqPzzGyHMMKpDGRs2fPjvb3v//9pN0555wTbZ4r/Pm15zjVjCCEEEKIUqCHHiGEEEKUgg5NWWcX11NPPZXsY/eZr4zMKa7sguOUViBNuRs7dmxhu6LidN7lxunWvXv3jja78ADJWzmmTZuWbBdV8+Sq2wDw4osvRpslSJ96vtZaa0WbXbKNUhSxM+KlxpwrmtPUuWQA9x8AbLTRRtHmNNd77703acdlDFiSvOiii5J2l1xySbQ/9rGPRbsj3ejLQ9M1b8/U3lwhx1y6Mc/BfH19u5YUkGyUNOf2pN6CmrNmzUq2OXWc50EgLQ7KhVm5xAeQ/sa9/fbb0fahI3wMTo+/7bbbknacHn/66adH24/D9pSkO8cMIIQQQgixnOihRwghhBCloEPlre9973vR9nIGu6g5cwdIs6hYtvCuOl47hCUR7z7k7dVWWy3avsIzu+H5HFhGA4Abbrgh2lxZWqQVmIG0Mi9fRy97sXt24MCB0fayFd83bD/44IMtPGPRHFlhiy22iDZXTvfjoKi6Oa+1BaTudq7M7mVSrjibq/DcWeStoms+derUaPP15fkNaNm6YLl+zu3jubAlx2/p53ZVct+ZK5HfcccdyT5eH8uvlfXyyy9Hm8M5/IKjLCfzGpf+/uLfQp63/aLAXIl9/Pjx0f7nP/+ZtCtaPcHvaw06xwwghBBCCLGc6KFHCCGEEKVADz1CCCGEKAXtHtPDeh1XRmZNHkh1eR/Tw3A8jo+t8fEjtc4BADbYYIOax/MxQvw+1jR9u9///vfRVkxPil9lneMBOK6L43GAtHIov8dr0kWxIl4nnzNnTrS14nrr8cQTT0R74cKF0d5kk02SdtOnT482xwH52D5Om+Ux56ulc/xeLqanM6RAf/TRR/F7X3vttcm+m266KdpDhgyJto97uO+++6Ldr1+/aHM1XiC9br7yPZcK4Wvq4WPyXO3PiWMk+dhciR1I+yw393P/+XmF5wW+p3z5E46RaVTuueeeaD/wwAPR9v3F143jvYD0t5HnVj8GuIr9LrvsUvN1AJg7d260OUbIj0uet3lu+MlPfpK043R7pawLIYQQQrQCeugRQgghRClod3mLXVfsqjvuuOOSdryQaM79yS5TX1mZ06E53ZWrKfv38eKH3s3G7nU+nk+z9S7pssPXbf78+ck+dr2zbOUXqGT3LKepe/e3T61swi9kydV9JW9VYOmH7Zy7+U9/+lOyveGGG0Z70KBB0fYyE49Bdp17uZJd+1tttVXhOXEK7He/+91oe5k0t1hqo/D666/j5ptvBgBMnjw52ffTn/402vfff3+0eeFeIJV2hw4dGm1fxZdlEL8QM6c9c8rzggULknZc5oNlMF40GkjHILfjNHwgHd889/uxzhIeV/8G0u/M8inP70C6cHSjctVVV0Wbf6u8pMf4e5uvHc+z/pry7ynfG74swRe/+MVoP//889H2qx2wPM2Vm1nqam/k6RFCCCFEKdBDjxBCCCFKQYdWZGauvPLKZJuznu66665kH7suOXMqt4gZu1a9648lEZZivFzGmQ7f//73o/2d73wHohjO4vHXlF2ePkOAKcriYDc+kPYRf5av8OyzBUU6LooWkQSAu+++O9qTJk1K9rE0wdffH4MXROS+YEkaAA4++OCa+zh7xG+ffPLJ0b7wwguTdnwe9S7s2N6svPLKMaPUywoTJ06M9sMPPxxtXtjRb7MMtMceeyTtuNK5n4P333//aM+ePTva/pyOOuqoaLN8zdIGkM4DvM9LHTvvvHO0ed720gmHGPh5he8vzthiSRBIZZpGhaV+Hpd+Dtt4442jnZtLGS8n8zZ/lh8bLF3ye1gGBdKwBJbLWBJrb+TpEUIIIUQp0EOPEEIIIUqBHnqEEEIIUQo6NKaHY2685s8rlbOeDADbbbddtFnH9NVcWbNnfTJXpZV5/PHHk23WSTlNU+RhLd+viu5T05vwK9wzuaq6vI8/y1fr9mm3IiW3cva4ceOi7ctJcOwVx4sMHjw4affUU0/V3OdLDnAcAKdQ+9RrToHnuC6+94A0LsjPA/WuFt7WvPvuu/H68DUE0lgIvm7PPvts0o7nzClTpkTbl9fgqvW+ajangfPq2VxmwsMlAvr27Zvs4/mUv5evaM9wRd+mNP5a+/z99cwzz0Sby5/4WJfcZzcKPFfx76SPn+GVBXwMJMfd8H3uf/uKfid96Qe+D3mfr8jMldc333zzaPvrzqUDfKXp1kaeHiGEEEKUAj30CCGEEKIUtLu8VVTp1csZ7IJjtzaQusCLqsgCxdVXvVubP5uP4dtJ0mp9uESAXySPYemSXbW+T7j/cguT5qqZlpV6F+Nk+YhtD0siLEUAwHPPPRdtTl/2n8uufU5R9nI4nwf3ra9ovPfee0e7UeWtlVZaKcpwvoI5l15gSct/F35f0XuAtJL1iBEjkn0sYWyzzTbR5pIFQCo1br311tFmWQlIU9HHjh0bbS+RPvLII9HmPvG/ESzh+YVEWT7h4/vfiCJ5vZEoSj/3cxhLlf43kyWoXOgAhwQUpa/747HtZSue33ls8+tAKndK3hJCCCGEaAX00COEEEKIUqCHHiGEEEKUgnaP6SmKFcjFEBQtQQCkmqxPWeclCorS13PH86XNi2jUcvaNAmvPPhaDrzHHgHjNl3V5Tn3kUvxAWn6e+8F/bqPEbzQSHBfC18fHS3AMzoABA5J9rM1vtNFG0fbxHdw3L774YrQ5JgRI40p4SQIfo8WpsRzD4lfw5pieRh2nH374YVwNnK8hAOy2227R5pXVfSzFlltuGW0eEz7N+ZRTTom2j9XheCpeCmiXXXYpPCfu/wMPPDBp99hjj0Wbl544+uijk3ZFy19wXBEAjB8/Ptq+NAGz1VZbRZtXXAeWjjVrRLi8A69O73/vGP+bxG35N86PAZ4nc3GPPP6K4ij98YtKwwDpON1zzz0L27UG8vQIIYQQohTooUcIIYQQpaBhVlnPuZp9KjOnyLGbLZfyzK4672ZjiYVd/EpRbx24xICv7MnkUsxZ4uQ+8is5swzG94OXt3ISZ1kpcj/fdNNNyTa72FlqBNKxxC51lhiANKWa7w8vU/AYZLnap/E2yUFAKudwGq+nXvm6vfnggw+iDMWSHpCm4HOavp/7eAVuvgYsMQHAPvvsU3gMllV+9atfRdvPi1dddVW0Wd7yK5izbHHPPfdE299DLNVdf/310X7ttdeSdlxB2svh8+bNq3k8fx/Wuxp5e+LHAI8Prrrs5S2e03g8AOn14fHhrxsfg+dMPx8zLJd5SYyPwb/x/vd+0qRJhcdvbeTpEUIIIUQp0EOPEEIIIUpBh/p3660A62F3KLtxvduVXXIsieSqP/O+bt261X1Oohh2oXpJgd2fOXmLK4yyi9dTVGHVf66XxUTxGPTZWzxuubIukPZn//79o+2lCZZceJFCn23FciWfn5cAeKzy4rJ+AVOWBHJZoR3JaquthuHDhwNIKyYDqaTDi6zee++9STuWDzlDy2dvnXvuudH21+O8886LNmfEXXjhhUk7zvJi+fqhhx5K2h188MHR/ta3vhVtfw/xvcEZW14G4wVIOcsPSBcgZcnFy3s77rgjGg2uVg4Uryzg4bnPS5U8t+ZkXR6/udUJit7j4c/KZW/579yWyNMjhBBCiFKghx4hhBBClAI99AghhBCiFHToKustrYjKaYasVXrNkPVl1vY5hgAoXrXba5W8yvM666xT+LmNWum1o6h3RXPWoXN9ydeeVwVui3MqE0VVqqdNm5Zsb7vtttH2cSAzZsyINvfZhhtumLTjMcJxG1yV29O3b99oz507N9nHcWP8PfwYfvrpp6PNcR+NxAorrBDjkm677bZk36BBg6LNlYxfffXVpB1v83UbOXJk0o7T3ufMmZPs43iXjTfeONrHHnts0u7GG2+MNsd+8H0CpKuxc2wVz6tAem/w9xg2bFjSjvf5YxxwwAHR/stf/hJtn6KdizPpKHzcFc+LuQrHuZRwHgcct+rjW4uuhz8eX0c+P56bgTQ+i0sH+OPlSpm0NvL0CCGEEKIU6KFHCCGEEKWgYRYc9Slx7I7705/+lOxjlxyntPpF9/gYbPuUPU71Y3nLV3P9/ve/H+1LL7205rHF0nB/5RbJ43vDy0/sQmVJxae282exzOFT2XPnIVK5wEtO7H73KeYsVXGa88yZM5N27Ebn8gF+AUhOl2d5xKeic78/+eST0fZjkxc+bVR56913343VkL1ExN/n8ccfjzYv+gmk9/uDDz4Y7SFDhiTtuDovLwIKAP369Yv21VdfHW2u1AykqejcLw888EDSjsfw0KFDo+0laq74zfPxv//976TdZpttFu1vf/vbyT6WWfne8L8/XiZtBHyJiFw1ZKZIBgOK50U/PuoNzeDfUD62LxvDMlgutIVLz7Q1+rUWQgghRCnQQ48QQgghSkHDrLiXc6vdddddyXZRBWUPu9Y4OtxLHSytsc2VXYH2XRStK8F95GVMdnmyq9XLT5wVwLJJTgbLZWYUVW4WFfi6coYPAOy3337R5sq/QNpvnLHFMjSQSmTPPPNMtH12DVf75QrPXsrm+YMXlfRZTbkFSBuFVVZZBZtuuimApb8n3/tcoZgX/QTSa7DllltG+6c//WnSbqeddoq2vza33nprtFly8dWPWdLiRWH/9re/Je0OPfTQmp/lq/Gy5Pbiiy9G+5BDDkna8b02atSoZN8OO+wQ7abq1sDSFa5ZImsUfCYa9znjM6W4Xb1Zan4+5t/W3G8y7+Nj+Hl7++23jzZXUffztq/Y3pbI0yOEEEKIUqCHHiGEEEKUAj30CCGEEKIUdIqYHl+hkttyvIhPRWcdkzVEX0WWj5fTNP3KtUWwxql09hR/Dfka87XyKcl9+vSJNq807bVhPsZbb71VeB71poGWlRtuuCHaPmWdr7m/xv/5z3+izdWEfTuOC+FSENdcc03SjtOZOabOp7juu+++0eaK7S+88ELSjuOCGpUQQow586noHKtxzz33RHvixIlJuw022CDaHGczcODApJ1PP2d4bO69997R9jFeHO/Dc+vWW2+dtOP4Do5V8nEgHMfF8ztXlgbS6to+pofP6bDDDou2jwvy6eGNgI/j4uvDfdKtW7ekHaf6+37lVHL+ffKxPkUxlrkKz/yb6c+9KTYNSO8bH3PUnvOxfpGFEEIIUQr00COEEEKIUtCh8la9i49y2iKQyljsJvMp5kWVOL3kxOdRVLkSSN1zkrDqp8g9C6R9yWUFvLuT3fXrrrtutL1swvIZ95+X1ZSynoerJHt5ixcg7d27d7Lv0UcfjTb3ta/UypILp976fmJ3OY9N75bntHeu6uwlFpZEGpX3338/znmcvg2kcw2XAfDfk9935ZVXRtuHCnTv3j3avjIyV3LmscTp4ECa9s399c1vfjNpx/JkbiFRlpxmz54d7bvvvjtpx4uK+srVnALNc7WXyBpxwVEeG0B63/O8uMUWWyTtevToEW0fHsBSWK5CddHvmv+NK5K+/LzK8wNXQ/elZnLHqDespF70ay2EEEKIUqCHHiGEEEKUgk4hb3kJo8hV57O3ij7Lw5+dOw92+XP2iK+MKVJY3splC3Bf+uycNddcM9osb3lXaNE95eUy7kuxNHx9fIYcS8q8uCeQyiC5McdjldvlKnbnxiZn/LCE4TONvNu/EVlxxRWjPOUXxORKxiNGjIg2y78A8Oyzz9bcN2DAgKQdy0c+q3WvvfaKNt8DXlbhSrssl3kpjY/BUsycOXOSdnwMlip91V6W37g6NQAceOCB0ebFR/k+AYBPfepTaDT8fc5zHO/zVc6LqiQD6XjLhWbkVjhgihbw9r/V3M98f3GGJZBKevPmzUv2tXbGpTw9QgghhCgFeugRQgghRCnQQ48QQgghSkHDVGTOwdV4gVQPZD3Ra6EcD8C2j+/g9+ViCFhbZR1bMT15+Jr6GJyiSpw+9sLHIjThU3o53qSoCilQv3ZdVlhX33nnnZN9nEI6derUZB/3b25sMkXjFEj7jW1fToI/l9OhOU0aSGMOfPyBL3nRkTTFTPhqxQ899FC0Of3e398c/8IVif04GjduXLR92jtv83lcdtllSTu+H3r27BltP4b333//aHM80rnnnpu0mz59erS//OUvR3ubbbZJ2p1zzjnR9mVN+DeC46K4QjCwdMxXI+BjU7lved7y5SJ4Ls2VBuGx4sdR0efmUtbZ9hWZ+bdxyy23jDZXawfScgl+lXnF9AghhBBCtAA99AghhBCiFDRMyrqH3XjeZVaUiuxdermU5Xo+17v++HzZnbrxxhvXdWyxtKzE/cIudO/i9QslNsHprUDqUvcpnSIPlwng6+jHKadD+xTglpCTtxh2t/sqrSxT8HzBC5ECwJgxY6Lt5ZdGkbdWXnnlmKrtqySzRMDjxadzc8r2HnvsEW2umA0AO+20U7T9GOOyBfxZXiLj1HS+pl6a40rLXNV70KBBSTtOc+Zjz5o1K2nH866X9/h+4N8BX12cP6tR4Mr0QHr+fE192AfLnf4YRRWUvWxV9Fm5xbf5GLlKy3zf+DAHPoYvV9LayNMjhBBCiFKghx4hhBBClIIOlbdyGR2chZOr4stuzXoXj8u1433e9cef5SU3UQy7Qr3MWFSl08tbRdKDl7DYvc6u1pw7VVRg+YFd50899VTSjvvQZ5BwhWaunO4pqoJeb5aIz7ziSsV8Dr169Urascv+8ccfT/Zx9d+O5N13343X/B//+Eeyj6src5VyzpoCgJEjR0ab5UifocWSka/+vN9++0WbZTHOjgOWloya8Fk4vCgsy0qcrQWkY53bTZ48OWk3ZcqUaPssTr4/eC7xC86OHz++5rl3JH7u4/HBVa394ql8fbwsyr9dud/d3HkwPLfy/O4/11dernU+ntaQzHNo5hdCCCFEKdBDjxBCCCFKgR56hBBCCFEKGrYic66aa1FaeS72h8lVZM5pnxxTwKvCijxcGdn3CafF8vXmeAWguHJoLqaEdX3/uTm9uqxwrMbzzz8fbZ/KzFVtR40alezjGC0ep7k4Am7ntX5+H6dl+zIRfE587/gYA44/qDcGsL1ZYYUV4nfguBogjXXktG+/QvoOO+xQcx+PNyBN7fZlALiaNcfO5Vaq52vvU9F53vUVlBlOU+dV4H06dL9+/aLt44w4ZZtTpX26vV+dvRHwqf4MXwPf57wvN7/xXOp/C3lMcLvcageMH29Fx8vFdubur9ZAnh4hhBBClAI99AghhBCiFDSsj5/dXd5Vxy7eetPvmHrfk3N/+xTJet9XdjbaaKNkm1PJuQxAUQVmj69Kyumv3M/+HpI8uTScss5yBssNQNpP3p2dq+TM5FJWGXaJ83tOOOGEpN1BBx0U7U984hPRZgnEU2+V9vbmo48+irKTT7nn8XLnnXdGe9iwYUm77bffPtqczn7//fcn7bisgJe+OOWcFy31i7g+99xz0eYQAE6vB1Lpi+VTL9Pwd+T70Kc/szTlyyPwgpb77LNPtDnlG0jls0bBl2Ng2ZH3cZkGoP6K4vVWQC8qK5E7hpdI+R7isez7nOVI/n1vC+TpEUIIIUQp0EOPEEIIIUqBHnqEEEIIUQoaNqaH8fofr8LakuUEvI7JWiOn/fkUSf4sX/adaUmcUVeGS9371FJeJZ1Tknfeeee6ju1jNrjPWBv28QCNqOV3NBwXwdfVa+zcT/661ru8xLrrrhvtefPmRTu3rAiPud/85jdJux/84AfR3mabbaK9ySabJO04DqatV3NuKaussgq22morAEvHd3Bs2pFHHhltP1fxEhtc1sGXeOBrdcsttyT7OJ6I47p8POPgwYOjzctG+KVf+D7iWDx/TvxZPDf7e4Pjgvh+AtLV6Hl5Db9S+1FHHYVGw/8+cSwUx0/5PueYHr80CI+/ovIfQBo3V7Qye63tJnw/cEkE7pN6V5JvC+TpEUIIIUQp0EOPEEIIIUpBp5C32P3tyVX7LaLeND3vkmfXMn9uc45fRji11Kesr7/++tGeOXNmtIcOHVrXsYcMGZJsr7POOtFmuca7gj/5yU/Wdfwywano7Jb2q2WzLOTlRXa/swzmrz+nDi9cuDDaXv7kz+bx593jRenLfoV4Tm2vN8W3vVl11VXjauh+VfS25Ljjjmu3zxL1w/IWy0++KvmYMWOi7aVbDhHhUg1+XDL1hmnkKi3znL7HHntE25cQ4ff5sgKtjTw9QgghhCgFeugRQgghRCnoUHmrXvcZZwQAS1eibMIvVMbbHBHuo8OLFmfz1WZzrkBG2VspLCmw3RqwyxQAxo4dG+1cloJYGnaBc9VdzrADgA033DDaI0eOLDzeY489Fm0vUbOMxQtTHnzwwUk7HnO5xSw5S4vf85nPfCZpx+cxfPjwwnMXoqPwVY3nzJkTbZa3fKgAS/a+8jb/lvExfGX0ogVCc1nSvM/LapyFy4sC+4xQlrgXLFhQ+FmtgTw9QgghhCgFeugRQgghRCnQQ48QQgghSkGniOnxK2lzFVhOHfexB5zWypVNvWbKOibrk5xyC6Q6ZG6VdZHCKYg+1bhe+NpzDJaPxyqK4/HxWJwi6St+lxWOj7rgggui7cfLeeedV9fxuNov2zn8auEtge8BP3fwHMGrsQvRKPi4R64izjE4vvrx1772tZp2I3LIIYck2zw/H3744W362fL0CCGEEKIU6KFHCCGEEKXAmlM92MxeATBnmQ1Fa9I/hNBr2c2ah/qyw1B/dh3Ul12LVu9P9WWHUdiXzXroEUIIIYTorEjeEkIIIUQp0EOPEEIIIUpBp3voMbMPzWyymU03s8fM7Dtm1um+Rxkxsx7VvptsZi+Z2Qu03bJcdtGwmNn6ZvYPM3vWzB43s1vNbLNmHmNtM/t6W52jqB+aex8zs0fMbOdlv0s0GmUfl50upsfMFocQ1qja6wIYCeDBEML/uXYrhRA+qHUM0fGY2Y8ALA4h/Ipea9c+M7MVQwj1LagmmoVVinCNA/DXEMKl1deGAlgzhHB/7r3uOAMA3BJCGNwW5ynqx829nwRwRghhj2W8TTQQGped0NPDhBDmAzgJwDeswglmdp2Z3QxgjJmtbmZ/NrMJZvaomR0KAGY2yMwerv7VMsXMNq22/Xf1r5hpZnZUh365kmBmV5jZr83sHgDnmtlQMxtf7ZdRZrZOtd1YMxtRtXua2eyqvVRfVl8/hl7/g5mtWH19sZmdbWb/AbBTh3zpcrAXgPebJlYACCFMBvCAmZ1XHWNTm8aZma1hZndVPQhTm8YqgF8A2Ljaj/VVRRTtwVoAFgHZvoOZnWlmT5rZHWb2dzP7fx12xgLQuOzYisytQQhhZlXeaipPuROAISGEhWb2cwB3hxBONLO1ATxsZncC+CqAC0MIf6vKKisCOBDAvBDCpwDAzLq1+5cpL5sB2DeE8KGZTQHwzRDCvWZ2NoD/A3BK5r1L9aWZbQngKAC7hBDeN7OLAXwBwJUAVgcwLYRwVlt+IYHBACbVeP0zAIYC2AZATwATzOw+AK8AOCyE8IaZ9QQw3sxuAnA6gMEhhKHtctYix6pmNhnAKgB6A9i7+vq7qN13wwEcDmAYKr81j6D2PSHaj9KPy07/0FOF17O4I4TQtE79fgAOob8uVgHQD8BDAH5gZhsCuDGE8LSZTQXwKzM7FxW3Xd2uPrHcXFd94OkGYO0Qwr3V1/8K4LplvLdWX+6DyoQ7oeLNxaoA5lfbfwjghlb/BqJedgXw96qs+LKZ3QtgOwC3Afi5me0O4CMAfQCs13GnKWrwTtOPnJntBOBKMxuMyvxbq+92BfCvEMI71ffc3CFnLeqhNOOy0z/0mNlAVH7Imn7U3uLdAA4PITzl3vZEVd74FIDRZvalEMLdZjYcFY/POWY2JoRwdlufvwCQ9lkRH2CJHLtK04shhJG+L1Hp97+GEL5f4zjvKo6nXZgO4IgarxctuPcFAL0ADK9652aD+lk0FiGEh6p/+fdCZc6s1Xf1La4o2pPSj8tOHdNjZr0AXArgd6F2RPZoAN+06p/7Zjas+v9AADNDCBcBuAnAEDPbAMDbIYSrAfwKwLbt8R3EEkIIrwNYZGa7VV86FkCT12c2Kt4bgAZtrb4EcBeAI6wS6A4z625m/dv+GwjibgAfN7MvN71gZtuhEgdylJmtWB2/uwN4GEA3APOrE+teAJr6600Aa7bvqYtlYWZboBIW8CqK++4BAAeb2SpmtgYqf5iIjqX047IzenqadOWVUfnr/yoAvy5o+xMAFwCYUn3wmQ3gIFTiPY4xs/cBvATgbFRceeeZ2UcA3gfQ2MvUdl2OB3Cpma0GYCaAL1Zf/xWAa83sWFQGbhNL9WU1nuuHqASzr4BKf/4vVA6+3QghBDM7DMAFZnY6KnEfs1GJz1oDwGMAAoDTQggvmdnfANxsZhMBTAbwZPU4r5rZg2Y2DcBtIYRT2/3LiCaa5l6g4hk4vipLF/XdhGr8x2OojL2JAF5v97MWEY3LTpiyLoQQonNgZmuEEBZX/4i5D8BJIYRHOvq8RHnpjJ4eIYQQnYM/mtlWqMSB/FUPPKKjkadHCCGEEKWgUwcyCyGEEELUix56hBBCCFEK9NAjhBBCiFKghx4hhBBClAI99AghhBCiFOihRwghhBCl4P8DZojOzUdubqYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ES6uQoLKCaWr"
   },
   "source": [
    "## Normalize the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wz7l27Lz9S1P"
   },
   "source": [
    "Modern NN prefers (and often requires) input data being normalized/standardized -- a topic related to how optimizers work and how regularization works. For bounded data such as this one where values range between 0 and 255, we need to normalize it so that it is within the range of 0 to 1. For this dataset, it means dividing the values by 255. It's important that the *training set* and the *testing set* be preprocessed in the same way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 731,
     "status": "ok",
     "timestamp": 1616675700948,
     "user": {
      "displayName": "Xianjun Geng",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjp2zNPbIYU0DaDSSzFs4-oEwNRuLg3LhJILAgh3RY=s64",
      "userId": "03603659341670711497"
     },
     "user_tz": 300
    },
    "id": "bW5WzIPlCaWv"
   },
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "59veuiEZCaW4"
   },
   "source": [
    "## Build the model\n",
    "\n",
    "Two steps: 1. Set up the layers. 2. Compile the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gxg1XGm0eOBy"
   },
   "source": [
    "### Set up the layers\n",
    "\n",
    "Our goal today is to have a basic feel of how to build a NN model using Keras. We will gradually learn more of the Keras package and related programming details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 5741,
     "status": "ok",
     "timestamp": 1616675707613,
     "user": {
      "displayName": "Xianjun Geng",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjp2zNPbIYU0DaDSSzFs4-oEwNRuLg3LhJILAgh3RY=s64",
      "userId": "03603659341670711497"
     },
     "user_tz": 300
    },
    "id": "9ODch-OFCaW4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-09 19:08:53.666634: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "#three layers,flatten to 784 length long string\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)), #flatten to 784 length long string,menas 784 outputs, \n",
    "    #and 784 outputs are weighted sum together and feed in each neuro at the dense layer\n",
    "    keras.layers.Dense(128, activation='relu'), #128 neuors in the dense layer\n",
    "    keras.layers.Dense(10, activation='softmax') #For categorical prediction with J possible outcomes: softmax\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gut8A_7rCaW6"
   },
   "source": [
    "The first layer in this network, `tf.keras.layers.Flatten`, transforms the format of the images from a two-dimensional array (of shape (28, 28)) to a one-dimensional array (of shape (784)). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.\n",
    "\n",
    "After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely connected, or fully connected, neural layers. The first `Dense` layer has 128 nodes (or neurons). The second `Dense` layer has 10 nodes. Notice that these two dense layers use different activation functions (why?)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101770"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#q(d+l)+(q+l)\n",
    "128*(784+10)+(128+10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1374,
     "status": "ok",
     "timestamp": 1616675707614,
     "user": {
      "displayName": "Xianjun Geng",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjp2zNPbIYU0DaDSSzFs4-oEwNRuLg3LhJILAgh3RY=s64",
      "userId": "03603659341670711497"
     },
     "user_tz": 300
    },
    "id": "A_2kqvGH5KkL",
    "outputId": "2dbe8e3d-bc5a-41db-a106-e7b2b0b75627"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "executionInfo": {
     "elapsed": 506,
     "status": "ok",
     "timestamp": 1616675873852,
     "user": {
      "displayName": "Xianjun Geng",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjp2zNPbIYU0DaDSSzFs4-oEwNRuLg3LhJILAgh3RY=s64",
      "userId": "03603659341670711497"
     },
     "user_tz": 300
    },
    "id": "_XKP9Ye4YaWg",
    "outputId": "77c4c561-d11e-44fe-cbfb-81e9a5ec1a61"
   },
   "outputs": [],
   "source": [
    "# keras.utils.plot_model(model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BLWq2JMQx6DD"
   },
   "source": [
    "### Compile the model\n",
    "\n",
    "The model's *compile* step allows us to specify the *loss function*, the *optimizer*, and the *metrics*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "Lhan11blCaW7"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              #if there are two types(binary prediction),replace SparseCatrgoricalCrossentropy with BinaryCrossentropy\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qKF6uW-BCaW-"
   },
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "xvwvpA64CaW_"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-09 20:09:55.954070: 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 1/10\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.4974 - accuracy: 0.8237\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3755 - accuracy: 0.8627\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3394 - accuracy: 0.8767\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3153 - accuracy: 0.8847\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2962 - accuracy: 0.8914\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2809 - accuracy: 0.8962\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2680 - accuracy: 0.9006\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2598 - accuracy: 0.9042\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2464 - accuracy: 0.9075\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2394 - accuracy: 0.9101\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1533d1240>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10) #epochs=10,random set(non reason),a epcho means a full circle goes from input to weight update"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W3ZVOhugCaXA"
   },
   "source": [
    "As the model trains, the loss and accuracy metrics (of every epoch) are displayed. This model reaches an accuracy of about 93.0% on the training dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wCpr6DGyE28h"
   },
   "source": [
    "## Evaluate accuracy\n",
    "\n",
    "Next, compare how the model performs on the test dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "VflXLEeECaXC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.3444 - accuracy: 0.8831\n",
      "\n",
      "Test accuracy: 0.8831\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "\n",
    "print(f'\\nTest accuracy: {test_acc:.4}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yWfgsmVXCaXG"
   },
   "source": [
    "The accuracy on the test dataset, 88.86% (you might get a slightly different value due to randomness in keras training), is only slightly less than the accuracy on the training dataset. Therefore, overfitting is not a significant concern in this model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v-PyD1SYE28q"
   },
   "source": [
    "## Make predictions\n",
    "\n",
    "Now we can use the NN we trained to make predictions. Let's just use the test dataset to practice prediction. Recall that we have 10,000 images in the test dataset. We use `keras.Sequential.predict()` for prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "Gl91RPhdCaXI"
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape #we have ten choices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because this is a 10-class prediction problem (i.e., the target variable has 10 possible values), each prediction is a Numpy array with 10 elements. For example, this is the prediction of the first image in the test dataset:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we are 99.39% sure that predictions[0] belongs to type 9:ankle boot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "3DmJEUinCaXK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.6331880e-07, 1.2903433e-11, 2.9291565e-09, 2.2724708e-12,\n",
       "       7.9676807e-09, 8.7250381e-05, 6.3862835e-09, 5.9436494e-03,\n",
       "       4.6763368e-08, 9.9396873e-01], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0] #the probability that the picture belongs to this item"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In above, the first number represents the probability that this image belongs to class 0 (i.e., is a 'T-shirt/top'), the second number represents the probability that this image belongs to class 1 (i.e., is a 'Trouser'), and so on.\n",
    "\n",
    "Therefore, the point prediction is just the class value that receives the highest probability:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 2, 1, ..., 8, 1, 5])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_predictions = predictions.argmax(1)\n",
    "point_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are we doing a good job predicting what's in the first image?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "KG4mVsgk9GE-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our trained NN model predicts that the first test image is a(an) Ankle boot.\n",
      "The ground truth is a(an) Ankle boot.\n",
      "Therefore, the prediction of this first image is correct.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Our trained NN model predicts that the first test image is a(an) {class_names[point_predictions[0]]}.\")\n",
    "print(f\"The ground truth is a(an) {class_names[test_labels[0]]}.\")\n",
    "\n",
    "verdict = 'correct' if point_predictions[0]==test_labels[0] else 'incorrect'\n",
    "print(f\"Therefore, the prediction of this first image is {verdict}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.8847\n"
     ]
    }
   ],
   "source": [
    "# We already got test accuracy earlier using `evaluate()`. \n",
    "# Here we can get the same number using the familiar `accuracy_score()`.\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(test_labels, point_predictions)\n",
    "print(f'Test accuracy: {accuracy:.4}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kXKwv8cn-l8A"
   },
   "source": [
    "(**Not required for our course**) Below are codes for more visually-appealing predictions for the first 15 records:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "DvYmmrpIy6Y1"
   },
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array, true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "hQlnbqaw2Qu_"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "NN_using_Keras.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}