Fashion MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.14.0\n"
     ]
    }
   ],
   "source": [
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from keras.datasets import mnist\n",
    " \n",
    "# # Load pre-shuffled MNIST data into train and test sets\n",
    "# (X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# print (X_train.shape)\n",
    "# # (60000, 28, 28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 1us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 2s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a39c8c2b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a317d0780>"
      ]
     },
     "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": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/anthony/.local/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation=tf.nn.relu),\n",
    "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.2803 - acc: 0.8956\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 2s 31us/sample - loss: 0.2672 - acc: 0.9005\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 2s 30us/sample - loss: 0.2575 - acc: 0.9045\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 2s 30us/sample - loss: 0.2475 - acc: 0.9066\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 2s 30us/sample - loss: 0.2387 - acc: 0.9103\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f5a2de7f7f0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 16us/sample - loss: 0.3417 - acc: 0.8815\n",
      "Test accuracy: 0.8815\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.1119021e-07, 1.3615339e-11, 8.4647428e-10, 7.3386484e-09,\n",
       "       7.9924824e-08, 3.1954634e-03, 2.8529794e-07, 2.0971717e-01,\n",
       "       1.7340334e-06, 7.8708500e-01], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array[i], 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[i], true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a2c3574a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a2c357b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eb1955ZWd2r79Tig3811NZ8IV2VX6pImIiIiIoBNjERERERGgEaRSdOjQIZSDmTx5crRu+vTpIZ44cWLhNuwwpk2R6N69e9TOLnfp0iVaZ9MYbLrEypUro3azZ88OsR0utbPWQTxUW1lZGeJRo0ZF7QYNGhTixx57LMRp2Zpyw0h2KLh///4h7ty5c9SuKi1EM99JXUvTEYo+Y2lZNztrY/v27aN1NZ1Nz0pTmorYVI9yw7f3339/iG1fBZg2bVqI0/65evXqGu2HSGN3zDHHRMs2Xc/2o3KzRJZjj1PlZrHcme8DkZ2hK8YiIiIiIujEWEREREQEaASpFC1atAizv1111VWF7exMUi+++GK0zqY3vPDCCyGeP39+1O7VV18Nsa3kAPEQjh2OTYdIbTrGwQcfHOKTTz45ajd+/PgQ26Gncs4666wQv/3229G6Hj16hDhNkbDpI3ZIO50paMSIEbXaH5GaSvvJ+++/X7KdrUIBccpQ+nm1aRd2yLbccGvR7HZQnGZRbojWfoekaVB333134bbTlBGRpmrgwIHRsj3+2P6b9vm5c+eGeMiQIYXbt6mQ5fqNUgBld9EVYxERERERdGIsIiIiIgLoxFhEREREBGgEOcY1ZWdxO+mkk6J1dvmyyy7bbftU1x544IHd8jqaQaj+XHnllYXrrr322t24J7tXmh9clLebljGzeYnpNmo6e55dLprdLl0ul4tsSzlOmjQpxFU5+qWkr2VnxRRpTmxesS11aEueQs1zjO2stjanv1u3blE75RjL7qIzJBERERERdGIsIiIiIgI0oVQKEWm8bMkliGexs6UWv/a1r0XtHn/88RCn6Qc1TfkpSp+o6Sxa6eusXbs2xMcff3yIzzzzzKjdf/3Xf4U4TftIZ64UaUqKypcCnHPOOSG+7bbbQpymT9nZatNyplY642WpfYAPp1aI1BddMRYRERERQSfGIiIiIiKAToxFRERERADlGItIHUinWLc5t+WmfO3Vq1eI33zzzWidLfFUbtrmmirKm0zzo21Jud69e4e4Z8+ehdtO85QXLFiw0/sp0tDK5RifffbZIf79738f4tatW0ft7rnnnhB/5zvfKXwtW4atXLnFtJyjSH3RFWMREREREXRiLCIiIiICKJVCROrAscceGy3bGePatm0b4nT2uDfeeKN+d6wO2Zm8ADp16hTitDzbEUccsVv2SaQ+lCtneMYZZ4TYllBL+0BNyy0edNBBIZ4+fXqI7fcGwOLFi2u0PZFdpSvGIiIiIiLoxFhEREREBFAqhYjUgTR1wM5iZ+9Wr+nwamOUVtSwQ8dbtmyJ1nXo0GG37JNIfUhnciwycODAEE+ePDlat3HjxhC/8MILIT7mmGOidrYqxfvvvx/itE+tWLGiRvsksqua7lFKRERERKQO6cRYRERERASdGIuIiIiIAMoxFpE6MGDAgGh5zJgxIbZll8rl3m7bti1atnmOdiau+mZfy+7DsGHDonYf+9jHQrxmzZpo3dFHH11PeydS/9JZ54pccsklIR45cmS07rzzzgtxmldsXXjhhSFeu3ZtiDt27Bi1++hHP1qjfRLZVbpiLCIiIiKCToxFRERERABwu3OIsuQOOLccWNCgO7HnGei971VXG9N72OD0fjYPeh+bF72fzYPex+ajRu9lg58Yi4iIiIg0BkqlEBERERFBJ8YiIiIiIoBOjEVEREREgBqeGDvHOc7hnWPkjluDc8x3jp4lHn+vNjtX2/ZltnOxc/QvWHenc1Tk/+Y7R0X+eGvn+K1zTHeOSuc4Pn+8jXP83TlmOMdlZjs3O8eYUq+Rr/+Ec1yVPFbpHLfX4me4vsTj33GOr9dkGzvTvsx2BjnHBWb5YOf43a5uV+qWc2zPP9sznOMu52i/g/a/c45P5fHTzjF29+wpOMc/OsdrzvFB+rrO8f+cY45zzHaO08zjp+ePzXGOK83jf3KOV53jB+axbzvH2WVef4xz3JLHFzvH8vx397pzXFL0PPP89/L/BznHjNr99LXjHL2c4+/1+RpSf5yjhznuLHGOd81y64bevyrOca1zLHSONcnjbZ3j7rzfTXKOfc26b+WPz3KOk/PH+jjH8/n30MdN2wedo2+Z1/+6c1zgHDeZvrjJ/K7OqY+fuzac43rnKC7ULE1OTa8Ynw9MBM7bUcNG6mIofWLsPRO85xDvOQS4B7g3X3VJvv5g4BTgJ86xF3AaMBUYBXwBwDlGA3t5zytl9uE/gRuqFpxjf7Lf/zjnKJ71oPEaBNUnxt4zHdjbfkFKo7Ap/3wfBGwB/qWhd6iKc7RIHpoBfBJ4Nml3ANl3z4HA6cANztEif/7/AGcABwDnO8cBzjEKwHtGAR91ji7O0Q84wnvuL7NL3wB+ZZbvzL8Xjgd+4Bx9dvJHrVPO0dJ7lgOLnePYht4fqT3vWWmOOzcBP6ta9p4tAM7h8mPObuFcyQm/7geOKvH4F4Al3jOMrA/+MN/GKLI+fADwMeDG/Gf4J+AW4FiyYyH5Se1k71lSsD+tgAvJ+uG/5L+rs4DZ5nf1lxr8DPUm/w76FfD/dufrSv3aYadzjo5kH+Z/xpwYO8fx+RWlu/O/DP/kHC55bjuXXV390NUW5/gP53gpv6rzX2Ve/yfOMc05nnCOXvljhzjH5Py5f3GObkWP51e/xgJ/yv/CbFfwOg44F8IV3AOAJwC8ZxmwJt/OVqAd8ayB34P4anCy7RHAZu9ZYR6+APgj8ChZZ69q+7Rz/Mg5pjjHG87xoel+nONj+V/pPZPHh+a/76nO8ZwrvsI/2jmedI43q96b/Ev4x/lf9NOdY0K5x4FryU46Kpzjq/ljD9J0/3jaEzwHDEuvaOZXZb5T7onOcX7+/s9wjh/lj33ROa4zbS52LjuxdI5P55/hCuf4ddVJsHO85xzfdY4XgWh6OO+Z6T2zS7z82cAd3rPZe+YBc4Aj8n9zvGdufjJxR952K9AuPyC3BrYD36V8H+0EjPKeynRd3v/fAga6ZMQl/30MKrPdtq565OkV5zghf/xF5zjQtHvaOQ5zjg7OcWv+3fiKy69w57/bu5zjQbLvDID7yE44pJlwjmH5Z+omYBrQL+9LVX3vB3m7ls5cxXWO81z1aMd5edtK53jKtP9p3idfdY7P54+f7ByPO8cd8OELO94zCUqeuJ4N/D6P/wxhFOds4Hbv2eI9bwFvA4dRfdxsA2x32Unvl4Gflvl1nAK85D3bd/A7m+wc1zjHs8AX8+Pg0/nP+ajLR4ud4w7nONM8r2qEZx+XXc2uyH/PR+aPn5lv+xXnuL3q3MFlV/i/5RwvAGfl31mDnKN7uf2UpqMmf41+Avi797wBrHKOQ826McDlZCeRQyC6etGR7ETpNu/5X7tB5zgVGE52YDsEOMw5xpV47Q7ANO85FHgGuDp//A/AFfkVoenlHveeu4GXgX/K/8LcVPBzfhRY6j1v5suVwNn5F8pgss69D/AY0Bd4EbjOOc4CpnrPooLtkv9epiWPTQDuJDsRPz9Z19J7jiD73V5tV7jsr+wrgfHJiTbAzcBXvOcw4OuYK9SJUWR/zR8NXJV/cXyS7L0YDZwM/NhlV9mKHr8SeC7/nf4s3+7L8OETeWl4LruScgZZv6jtc/sDPwJOJPssHO4cnwDuJvt8VJkA3Omy0ZAJwLH5VZ7tVJ/AdQBmeM+R3jOxhrswAHjHLC/MHyv5uPfMJDsgTyM7aA8D3A5GdMZC6fQH5xhC9v02p4b7a30JwsjT+cDvnaMt2Un8ufn2+wH9vWcq8E3gSe85HDiBrL9VjSgdDVzkPSfmy+pvzdMBwG+8ZwzggGvIPgtjgGPtyV2Bq4GTvGc0hFSDLwDL8uPK4cCXXPXo3lHAf+af0ZoKfS//o3SDc3SluK/+H3Am8BDwHeArwK1ljseQHTen1nB/OnjPOO/5FdkV+Bvz84D7KH/yDfAZ4N78u+oQ4DWXpXd8HTghfx9m5ftcZZ33HGOuWFeQ/KEvTVdNhh3OB36ex3fky1UneVO8ZyGAy3JzB0E42N0PXOc9fyqxzVPzf1UHqo5kJ8rPJu0+IDt5hKxj3escXYCu3vNM/vjvgbuKHq/Bz2d/TpvveyuwP9nBZwHwArDNe7aRpxDkf/U+ApzlHD8F9gX+4D0PJNvuByyvWnCOw4Hl3rPAORYCtzpHN+9ZnTepSueYCtHVqBPIDuCnes86+wIuu7J/TP67qNKm4Ge9P/9C2pRfUTgC+AjZX/rbgaXO8QzZF2jR4+tKbHcZBSkr0mDa5X0TsivGv6H279HhwNP58D3O8SdgnPfc5xxzneMo4E1gP+B5spPBw4CX8s9iO7LPBmQnyffU8vVdicc8pf+w9wDec3l4cnaV9VLn+CbZH3iPpX+sk/TR3ATn+AiwGbjUe1a5UntS3kfI0zO8Z5ZzLABGkJ2wP0Z2EnMu1d9Vp5J9n1RdlW4L4QTmMe9ZZbat/tY8veU9L+XxkWR/KK0AcI7bgHFQNr/8eeAPznEX1ceSU4H9nQsjel3IjrkAk7zn7VruY1GfLPl4fmwbD1l+NVnqwafyq9xdyc4VpiTP60eJq9gF7jDxWLKfF7LzgG/t4LlTyNKzOgD3ec+rznE62R8ok/I+3xp42jznzmQb6ovNSNkT4/wDfCJwkHN4oAXgnctyhMgOGFW2J9t7HjjDOW7znnQWEQf80Ht+Xcv9rZfZSPKraZ8kO5hnL5SdAH/VtHkBwtXkKpeRdbyjyfI3JwCT4EMnxpvIvoiqnA+MdI75+XJn4B/IcrCg+vea/k7nkl25GkF2wm7tBazJ/+rdkfT3WPSFRpnHS2kLZa8AyO63Kf1MOMc24pPKtjvYRrnPwJ1kJ3azgL94j3dZWtLvvS+Zd/f+joZGS1hINlpTZW8IIzRFj2c7nqUivEx2pfog7znXOZ51jj95z0bTdBMf/j3c6T1fTh6rk9+d97zrHCtdlpM5AbjUtP+HNKUkH97dkGxG/a15su9zUd/7IFlnP4eXkJ1QnwlU5p8xB1zmfZYeGDae3RyXfq5qoqpPLnHZzYIdvGdtfqGnbJ8k+2Pwe8CngclkfyjfRTYiaZXqk0U2QEiJTFUd70LfzS9q7QXgPY+5LMXpY8AdLkvt/AD4m/d8ttzrGeqLzciOUik+RXYFdKD3DPKefYB5ZFdBduQqYCWlh/MfAT6XX+XEOQY4R++C/ftUHl8ATPSetcBqV517eyHwTNHjebwe6FRmX08GZlVd/c73qX3VEKZznEJ2tfh1s74b2RfPH4D2ZB3JU7ojzyQbzsVleY//SJbPOMh7BpHlZaXpFKUsIDuB/4Mz+YkA+RXkec7xj/nrOJfdFFjK2S7LfexBdmPRS2RX6ye47KamXmRXJaaUebzU73QEBcPR0qgsBXq77M74NrDDodkXgeOco6fLcoXPp7pv3UuWbnU+1VdRniC7GtQbwDm6O8fAXdjfB4DzXFYRZjDZla4pZJ/b4c4xOD84n4f5ozQ/+P0b8GOyPlp1gKzKPbZCH92B+ZClk+VpZYN30P5Z8jQSl91rsC+Ek947yG5E6pLfvArZd+NXqg7wrkylG9Tf9gSTgRPyvtqS7DP+jPd8QHa8G54fU2x1hiHeMxn4NrCaLJXhEeCyfBs4x36u4H6bGnoAuCiPz6U67/0BsptgWzvHUGAgJh3CZfe99MzTqKqOmx9AyX2paZ8M8otwL1F93nAh1SPR86m++PUPEPrYIGBxfqHuD2QpKxOBk/J1OEdH58rui/piM7KjE+PzIb7rk+yvuwtKtC3lcqCtMzfoAHjPo8BtZMMU08lyFUuduG4ADnSOqWRXrr+bP34RWe7dq2Q5QTt6/HcQyr2U6oDnwYfKpvUGpjnHTOAKsg5mXQVck3fER8iGb6bDh4ZoIeuYY/KD3TjgXe95N1l/QJ5rWFZ+JemfyFImhiar/wn4Z+eoBF6DwtJUU4C/kX3pfi/Pj/4L8CpZbvWTZDlnS8o8/iqwzWU3eFRdWT8h3640Yt6zlaxvvAj8lexqb7n2i8mGPp8i+xxM83l1h3yI9HVgYNVQaP4H5LeAR/O++Bjs+LPtsrKQC8lGYP7mHI/k23uNLPXgdbIh5C95z/Z8VOfLZP1vJvDnvG2VL5Fdud7FfIQ/AAAgAElEQVRI9nl1+ffN897H5ae8ZxbQxbmyf0BD9v3XPU9P+SLwxg7a3wC0yF/3TuBi78OI0N1k3z1/Nu2/B7QCXnXZDZLfK7Nt9bdmLr9YcxXZMH4FWRWHqvf8CrL+8ARUX9QBfpZ/3qYDj3vPDODXZCOeFfnn6kZqkErpshTB+UBnl5Vtq0pLuJnsxsA5ZH3wG/n+VpLl9c4kyye+LD+Jr/J9qlMbbiO7uv0CxOcIuYeA43a0jyV8Efhy/t3zSeDf88dvBMY7xxSyNMmqkatTyK6sv0J2H8b/5N95lwB359t5noKTdJfdMzCA7DtGmgHnfb1kJ0jCOX4BPOg9jzf0vtSH/MrjM8BH8hMWkSYl/wNvvfchpalRc9ld+GebexNEmhXneAC43HvmNvS+FHGO88mu0n+/ofdF6oZmvtt9fgDlJ1do4vYFrtRJsTRhNxLfN9Fo5WlNP9VJsTRzV9D4b2rzwC8aeiek7uiKsYiIiIgIumIsIiIiIgLoxFhEREREBNCJsYiIiIgIULOZ7+pVz549/aBBg3b7627bFt8jtnx59aRXLVq0CPFeexX/7WDblWPzuFu2jH/lnTpVV4dyrvbTau2M+fPns2LFijp7sZ19DysrYVvBrXotW8LooirMEpk6deoK732vutpeQ/XJcjZsqK6n/8EHH0Tr0uUitl2rVq1C3LFjx13cu7qxJ7yPe5Lm+n7Onl0994w9ZqXHL3vca926dcnHAbZu3Rricsdb+7zhw4cXtqtrjeF91LGybtT0vWzwE+NBgwbx8svpJG71z54IA/z619WT8HXt2jXE7doV10Dv0qV6Mrv0S2H79urJvbZs2RLi3r3jeUyOP/74ENsvj/o0duzYOt3ezr6H5f4O2LYNGuBj0SQ55xbU5fZq+n7aE830gFZ0U+/O/vE3adKkEG/cuDFaZ/uX7XepzZurC0706lX93Thu3Lid2qe61lDvo9SP5vp+2mOWvTjUpk2bqN37778fYnsiaB8HWLp0aYjthaK0L9vlhx56qHY7vQsaw/uoY2XdqOl72eAnxg3lrrvuipavueaaEHfr1i3E/frF8xLMmzcvxAMGDAjxiBEjonYzZ84Mcdu21ZPhnXxyPOul/VK48MJ0DhGRxsue5JarblPuZHj9+vUhfvLJJ6N106ZNC/HDDz8c4v32269w+++9916IV65cGbXr0aNHiO3B+fvfj8uPfvzjHw/xWWedFeJ999234KcQab7WrVsXLb/2WvUcOvYPzNSmTdUzJL/11lshtsdDiP+obt++uqKp/YN3R68lUpeUYywiIiIigk6MRUREREQAnRiLiIiIiAB7cI5xevOdvTmg3J2xffv2DbG9GSDNZ1y7dm2IO3fuHOJ33303ajdy5Mia7bBII1Mux7gor/jmm2+Olu0d7ml1Cds3JkyYEOKKioqonb3px1abSXOR7Y09HTp0CHH6XbBgQfX9GV/96ldLPgfg2muvDXH//o191lqRnZPeLGf7tj0GpjeP22V73056U53NYbbH3rRyQ7kb4UXqkq4Yi4iIiIigE2MREREREWAPTqVIUx9sKRhbWqZ79+5RO1teyg7NrlmzJmpnh5aLhooADj744NrstkijYT/j5Uqy3XDDDSFetWpVtG7w4MEhtpNuQDzkaut/H3fccVG7e++9N8Q21Skd2rV9z/Y7WwoO4skDbK1ym2IB8K1vfSvEt956KyLN0T333BMt22Pn3nvvHeI0RcKmRtl0pzRlypZ1s6lQNh0RYNGiRSGeOnVqiA877LDyP4BILemKsYiIiIgIOjEWEREREQH24FSKgQMHRsuVlZUhttNc2hjiO9PtUG06jGSHdFevXh3icnfeizQl5VIp3nnnnZLxkCFDonZ2prqU7Wt2hsihQ4dG7ezym2++GeI0DerII48M8bPPPhvitKKEvQvfTj+d3hW/ZMmSEP/xj3+M1tlZLGuaciLSGN1yyy3Rsp0N1qY42T4K0LJl9emF/Q6ws9tBfIy1s+LZ5wMsW7YsxFOmTAmxUimkrumKsYiIiIgIOjEWEREREQF0YiwiIiIiAuzBOcZprp8t32RzG9MZvWwpt3K5wyNGjCj5uml+ZJpHJdJUlJshcs6cOSG2OYS2HBNAx44dQ7x58+Zonc3bt+3S0ohnnHFGiCdOnBjiNCfYvraN7f0AABs2bAixLc+4ZcuWqJ0tQfXKK69E62yOsfKKpSmzs1MCjB07NsS21NrWrVujdvaYaPtv2o9sX7TlEW0M8feNLd0mUtd0xVhEREREBJ0Yi4iIiIgAe3AqRToMvM8++4T4gAMOCHE6DHrXXXeF2M7i9dprr0Xtxo0bF2JbTmbAgAFROzuslJaxEWmqbH+wJZjSdAmbqpR+/u1QrE3HsDNJQlw+6tRTTy35nHR52LBhJfcB4jJsdpjXlnFL2fJRIk3d4sWLQ5yWIrUl2mwJtfSYasuZ2nJt9vsA4jQLm46Rpl3Z59k0JpG6pivGIiIiIiLoxFhEREREBNiDUyn233//aPmJJ54ouS4dsjnwwANDfMQRR4T4C1/4QtRu3333DfHee+8d4m7dukXt0jvnRZqDhQsXhrhz584hTlMprD59+kTLdtY5O6zaqlWrqJ1N27DVZWzVGIhnuLN3tadVLuwMXrZiRZouNXjw4BD36NEjWmdTpOyQskhTYNOJyqX42TSk9Fi2YsWKENtKFjNmzIja2dkvbVpFmsJRNEOeSF3TFWMREREREXRiLCIiIiIC6MRYRERERATYg3OMbf4ixLPd2fyqNCfYsnmPae6kLTVl86HSme5sCSiVoJGmyublpmwOYZrPO2rUqBCnucNpjmGVtAyb7Td2++kMWzYf0paFsqWp0u3ZbaT7bqUzX7766qshtvmVIk3BG2+8EeK0X9pjpZWWNrX9ys4YO2bMmKidnVlv4MCBIU5z8+2xU8dKqU+6YiwiIiIigk6MRURERESAPTiVIh0OsqkVdgYfW9YJ4vSJQw45JMTpMNKmTZtCbIdj0+HhdJhKpCmaO3dutGzLLtl0oQ0bNkTtbL+xM0lCnO5Qbta5ohny0j5pZ+my69Jt29e13wv2Z4I4fSpNkZo3b16IlUohTc2sWbNCnJZrs33Y9rc01ahXr14lt33UUUdFyxUVFSG2/TJNT7TrVAJR6pOuGIuIiIiIoBNjERERERFAJ8YiIiIiIsAenGOcTl9p84rTXELLrkvLzlg2N9G+VlpmRjnG0hy888470bItUZiWMrMWLFgQ4kGDBkXrbB6hzc23ef4AnTp1CrHtT3bb6X7YnOB0eln7WrZ0Y3pfgn2ttB/bElQiTc2cOXNC3KVLl2idvWfGfu7T+3Euvvjiktv+3Oc+Fy3fdNNNIS73XWHzmdOSjSJ1SVeMRURERETQibGIiDRhffuCc6X/9e3b0HsnIk3NHptKkQ7F2CEhWxYmLflUlGaRpmbYkk92CDZ9XQ0JSXOQDqPa1KTOnTuHOC3BtH79+pLPgThlwvaTNJXCPs9uPx2WtSkXq1evDnGaSmFLLdp9X758edTODjGnr1VZWYnsHmUmXSy7ToqtW7cuxOmxzR4T7XHOxgCXX355yW0ffvjhhdsrKr0IcRqijptSn3TFWEREREQEnRiLiIiIiAB7cCpFz549o+Wi4Rx7By58eNi1ih2mBfDel3zOgAEDonbp8LFIU/Tee+9Fy7aiRLdu3UKcVoo4++yzC7dh+6RNdUrTMeyyHc5NZ6MrmkkvTZey/XXkyJEhvv/++6N2tu+mVSlsOoZIU2P7Tpo+aPuL/Zz3TRK6hwwZUqPXssdie+zt3r171G7lypUlX1ekrumsTEREREQEnRiLiIiIiAA6MRYRERERAfbgHON+/fpFyzaX2OYH2xns4MMlaaqkJaRsiTZb8qnczD4iTZXN2YW4xFOa62sdcMABIX7uueeidUWlEdO8/DVr1oTY5jOn7WwesN0n299TI0aMCHGa12ifl85ouXbt2sJtijR2PXr0CHF6bLPsfQGnn376Tr2WzU22ZdjS+4BWrVoVYh1HpT7pirGIiIiICDoxFhEREREB9uBUivbt2xcu2yHcdMjGDudYNnUC4hJSdpjVDlGJNGV2iDVNMdq+fXuIbcpBWtasf//+JdulbEpTmpqxYcOGENv+lZZhs8u2nFzK7vuwYcNK7kPaLv357RCzjYvSQ0QaE/s5tbNEQtzv58yZE+Kf/OQnhduzx9E0xWnw4MEhXrhwYYh79eoVtbP9zbYTqWu6YiwiIiIigk6MRURERESAPTiVwt79CnEqhB32Se/ITYd3qgwfPjxatnewF824JdKUrVixIsRpGoRNW7BDoGkqhe1faV+zKRO2akyajmDToGxfS6tI9O7dO8S2/6f7btfZVI9ys1TaKhwQ//xLliwJsU3NEGmsbPpfesyyqUG279gKMyn7HZD2owMPPDDE8+bNC3E6m+zy5ctDbKvPiNQ1XTEWEREREUEnxiIiIiIigE6MRURERESAPTjHOGXzG21JtjTvsSi3Kc2veuedd0K8bt26EKe5iCJNlZ1xLu0nbdu2Ldlu3333jdrZPEJbdg2gT58+JbefllC0OcE2HzLNMbbtbP5yWmpt/fr1Ibb5lHZ/0u3ZHEqIcy+XLVsWYuUYS1Nw8MEHh/jFF1+M1tk+Zu+tsTPYpcrl548fPz7Ev/zlL0Oclke0ufrdu3cv3J7IrtIVYxERERERdGIsIiIiIgIolSJYuXJliO3w0MMPPxy1u/TSS0s+/9BDD42Wp0yZEuIBAwaEOB0GFmmqbEmytISaLfc0e/bsEI8cOTJqZ5+XzmhnlZtlzu6Hfd00bckOAdvtpTPk2VQqW8bRDi9DnHKRpljZbdp0DJGmYMKECSH+7W9/G62z/dSmCT755JNRu1NPPTXE5Wa1tN8J++yzT4jT9Au7Ddv3ROqarhiLiIiIiKATYxERERERQKkUwTPPPBPiOXPmhDhNpfjjH/9Y8vkHHXRQtGyHY6+//voQjx49Omp32GGH1X5nRRoBm36UpkHYihBr164Ncfr5t7NZ2WFZiNMRbPrE5s2bo3Z25ju7H+lQrN0nm9KUzsZnq028/fbbIR46dGjU7oUXXii5bYiHh9OfS6Sxs30g7R82Nci2S4+NNpWiXJpUz549Q2wrTyxYsKDwdW3VG5G6phNjEZE9wJVXXlm47tprr92NeyIi0ngplUJEREREBJ0Yi4iIiIgAe3AqRVo+xpZvsjnGtnQbFOc2pTlUNq/Slm5LZwgTaaqmTZsW4jTH1i4vXbo0xGlZs5dffjnENlcY4hxhG6ezzLVu3TrEtn+l7eyyLetmY4j7cmVlZYg7d+4ctbPl4NKf387aZX/GT33qU4g0JWm5QftZt8dDe5zbWbak4tSpU6N19j6DtL+J1CVdMRYRERERQSfGIiIiIiLAHpxKkc52tWXLlhDbYZp0mLWIfT7Ewz42rSKdtUukqbKzwtkhUIB33303xHaWqrRcm01V6Nq1a7TOpiNYaRqULd9m0yVsKSmIZ9mz6RdpO/vdMH/+/BCfddZZUbt//ud/DvG5554brbNpIf369fvwDyHSRBx77LHR8m233Rbi7t27h9j2qZ01aNCgEK9evTpaV9TPReqarhiLiIiIiKATYxERERERYA9OpUjZYSA7U5UdLi4nnR3I3tlu0yf69u27s7so0qh89rOfLVxn72SfO3duiNPZ4+69994QpxUr7DbsTHVpysWKFStCbFOa0vQOW7HCxukMeb179w7x5MmTQ3zppZdG7eysfTZNAzQzlzQfX/7yl6Plu+++O8S276xZsyZqZ/v9kCFDavRanTp1CrFNwYL4OyD9rhCpS7piLCIiIiKCToxFRERERACdGIuIiIiIAMoxDuwsVjY3saa5gmmpGltSyuZG1UVJG5HGzubcjho1KsRp3uDKlStDbEs/QXFuflrGzW7D9ru0r9l8SFv6qVyftK9VUVERrRs/fnzh80SaiwEDBkTLNsff3geQliy1M+HVNMe4aBZLiPts+loidUlXjEVERERE0ImxiIiIiAigVIpgyZIlIbaz6tg0iHLSck122NZuz6ZsiDQX6Wx0tt/YmeUmTpwYtbNlDVN29ji7vTlz5kTtioZpbZ9Ot2HTpdKSjLaP2mHkZ599NmpnUynSnz+dWVOkKbGf5/SzfMopp4T4nnvuCXGaknT//feH+LzzzqvR69rj6KJFiwr3qabHZZGdoSvGIiIiIiLoxFhEREREBNCJsYiIiIgIoBzjoE+fPiFetmxZiG1+ZDnpFJVFpaHsdLMizUWah1jUb2bPnh0t29JPtp9AnH9snzd48OConc0Rfvfddwu3Z/MSN23aFOI0P9jmSto4zVm20p+/XI6mSGNXdI8AxLn1dnro9P6ZhQsX1vp1u3TpEuK0JJs9xq5atarW2xapKV0xFhERERFBJ8YiIiIiIoBSKYIzzjgjxC+//HKIa5pK0alTp2jZDgnZ0lADBw7c2V0UaTJsiULbhxYsWBC1s+kOI0aMiNbZ540cOTLE6Qx5r7/+eoht2oKdOQ/i1AzbX21fhXgI1+5fOuOeXdemTZtonVIppCmzqYCpj3zkIyG25QzXrFkTtbOpR5WVlSEePXp04bY7d+4c4rS/tWrVKsQ2BUukrumKsYiIiIgIOjEWEREREQGUShG0bds2xDb1oaapFCl717sdEtp77713ansiTUlR+sAPfvCDaPnHP/5xiB9++OFonR2atZUo0tnybF+zVV9Wr14dtVu3bl3JdWm1CTtM27NnzxB/+ctfjtql6RNWuaFokcaupuk/++67b4grKiqidTb14bHHHgtxuVSK9evXh9j269TSpUtrtH8iO0Pf3iIiIiIi6MRYRERERATQibGIiIiICKAc4+Azn/lMiCdOnBhiW8atNs4666ySjx988ME7tT2RpqQoxzadHeuqq64q3Mbbb78dYluSLc0vtLnDdsaulM15tLHNkwQ49thjQ9yxY8fC7Yns6b75zW+GuG/fvtE628eOO+64Gm1vwoQJIbaz0UKc+3/SSSfVaj9FakNXjEVERERE0ImxiIiIiAgAzs7Q1CA74NxyYMEOG0pdGui971VXG9N72OD0fjYPeh+bF72fzYPex+ajRu9lg58Yi4iIiIg0BkqlEBERERFBJ8YiIiIiIoBOjEVEREREgDo+MXaOHs5Rkf9b4hzvmuXWdflau8I5rnWOhc6xJnm8rXPc7RxznGOSc+xr1n0rf3yWc5ycP9bHOZ53jhnO8XHT9kHniIs6xq/zdee4wDluyn83rzvHJvO7Oqc+fu7acI7rneOYht4PaVyc45vO8ZpzvJp/Vo/MH5/vHD1LtD/LOa4s2Nbx5T5j+fqK/PWeyR9r6xxTnKMyf/y/TPs/5fv1A/PYt53j7DKvMcY5bsnjPs7x13zbrzvHQ2Y//lrw/Fuc44CCdZc7R3uz/LhzdCvaF9lz6FgZ2upYKY2P975e/oH/Dvivl3jcgd+rvl63xOu1LPHY0eD3Br8mefxfwV+fx58G/6c8HgV+GvjW4IeCfxP8XuC/Bv6z4LuAfy5vew74b5bZn1bgK8G3MI8NA19Rm5+hnn9nLcDvB/7B3fm6+te4/+X9ZhL4NvlyT/D983g++J612FbLou+IfH1X8K+D3zdf7p3/78B3zONW4F8Ef1TeR6v663N5n+y3o88w+LvAj87jX4P/N7NuVP7/8eD/WsvfVYv0dwL+onLfDfq3Z/7TsbJwf3Ss1L8G+bdbUimcY1j+l+JNwDSgn3N82jmm54//IG/X0v5l6hznmas55+VtK53jKdP+p/kVpFed4/P54yfnV2fuAF5J98d7JgFLSuzq2cDv8/jPwGnm8du9Z4v3vAW8DRwGbAXaAW2A7c7RCvgy8NMyv45TgJe8Z/sOfmeTneMa53gW+KJzDHWOp/Of81Hn6J+3u8M5zjTPey//f5/8L/SK/PdcdWXvzHzbrzjH7c7RLn98Sf6X/gvAWd4zGxjkHN3L7afsUfoBK7xnM4D3rPCeRWb9V5xjWv55GwngHBc7x/V5/Lu8vz4F3An8C/DV/DP60eS1LgDu9Z6389dalv/vvc8+40Cr/J8n74vOsRfQGtgOfBconFrPOToBo7yn0vx8C6vWe8+rpnnH/ArZrPzKtMu38bRzjM3j95zju87xIvBNoD/wVNX3FfAAcH7R/ojoWBnRsVIaxO7MMT4A+I33jAEccA1wAjAGONZ+YAtcDZzkPaMhDJ98AVjmPUcAhwNfctVDOkcB/+k9tZmDeQDwDoD3bAE2OEdX+3huYf7Y/wFnAg8B3wG+AtzqPZvKvMaxwNQa7k8H7xnnPb8CbgJu9J5RwH2U/0IB+AzZicUhwCHAay4bsvo6cEL+PszK97nKOu85xnv+ki9XAEfXcF+l+XsU2Mc53nCOG5wjned1hfccCtxI9jkrZQRwsvf8A9ln+mfec4j3PFeiXbf8ADfVOcKc7c7RwjkqgGXAY97zovfMJDsITyM7UA8DnPcfPtgbY4EZZvl/gN84x1MuSxnpb9aNAS4n+x4bQtaPUx2AGd5zpPd8F1hE1tdOAPCe1UAb5+hRZp9EdKzM6FgpDWJ3nhi/5T0v5fGRwJP5FaetwG3AuB08/3ngD/lfulX7fSrw2fwg+SLQFRier5tUdbWpFlyJx3zR496z2nvGe89YYDrZX80PuCzv8G7nOKLE8/oBy2u4P3eYeCzZAR+yv9R39PuaAvyLc1wFHJhfZfsI2ZfupPx3NgEYZJ5zZ7KNZRCdHMgeLP8MHUZ2kF0O3OkcF5sm9+b/TyX+XFl37egKUK5l/lofI+tX33aOEfl+bM8PYnsDRzjHQfnjl+cn2T8BvgdclZ/g/tk5LinxGlFf9J5HyE56/xcYCbziHFXF4Kd4z0Lv+YDsIFjq59sO3LODn0t9SnZEx8qMjpXSIFruxtfaYOJSnQfgg2RdWxNfQvYlcSZQ6Ryj8raXec8TdiMuS/i3r1dTC4F9gCUuuwGig/esdS48XmVviIaQIfsr/XvAp4HJZAfIuyC7+cDYlPxc5WwAqBq2TVTNzLKN/MsvH57aC8B7HnOOE8hOLO5w2U1KHwB/857Plns9o22+vyJAdlIKPA087RzTgYuA3+WrN+f/b6f4u6Wm/XIh2RXoDWRXo54FRgNvmH1Z4xxPA6djrvy67Ga7l8mu4B7kPec6x7PO8Sfv2Whe40N90XtWkZ183OayG+7GASvNz1bu53u/Bif96lOyIzpWZnSslAbRUOXaJgMnuOzO3JbAecAz+dWY1c4xPM8VtHecDvGeycC3gdVkwzOPAJfl28A59qvKA9pJD5Ad6AHOJRs6rnr8fOdo7RxDgYGYIR6X5VP29J6JQHuyTvUBlNyXmWTDvDXmPR54CfhU/tCFwLN5PJ/syhrAP0DIfRwELPaeXwN/IBuGmwiclK/DOTo6V3ZfRhAPNcseLO9fw81Dh7BrU5uuBzoVrLsf+GieG9me7EA/0zl65UO25H39ZLJhzqp9bAX8G/Bjsr5YdVCsyj22or7oHCfmr1WVfzwUan0lrfDnyw/afcn6rEhN6FhZCzpWSl3YnVeMA+9ZmA9bPE324XzQe/6Wr74C+DvZAel1smR9gJ85x+C8/aPeM8M5ZgL7AhUu+ztxGRSXZqriHD8l68yd879wb/Kea4Cbgf9zjjnACrIvIbyn0jnuI+uo28j+8v7AbPL7wH/k8W1kQ8r/DnyjxMs/BPxmR/tYwheBW53j22Q3Q1ycP34jcJ9znAE8DOGK1SnAvznHVmAd8GnvWZwPKd+d/5XvyX7fc9IXc462ZF+or6brZI/VEfhVfmK6jexz84Vd2N6DZJ/Fs4Gv2Dxj75npHH8n+/x9ANyS9/lRwO+dowXZye6fvY9KqX0J+L33bHSOVwGXX9l+yPu45JT3zHKOLs7RyXvWkx00r3cuXFm6xXteco7jd/Lnuxl42DkW53nGhwGTvWfbTm5P9jA6VupYKbufy0qOyO7kHA8Al3vP3IbelyLOcT7ZlYfvN/S+iNQX5/gqsN777I7+en6tXwAPpMPZIlKajpXSEDTzXcO4gsafqO+BXzT0TojUsxuJ84fr0wydFIvUio6VstvpirGIiIiICLpiLCIiIiIC6MRYRERERATQibGIiIiICNBA5dqsnj17+kGDBjX0bkS2b6+u0d+iRYto3ebN1ffpbNtWXXXJubiuuF1u125XykXWvfnz57NixYqiwvG11hjfwz3J1KlTV3jve+24Zc009vdz5cqV0fKGDdW19u09E2nfbdu2eq6Anj171tPe7bw97X1s7vR+Ng96H2OVlbCtoOBky5YwevTu3Z/aqOl72eAnxoMGDeLll1/eLa9lD5rpiay1evXqEHfr1i1a99Zbb4V4xYoVIU4Pwm3atAnxwQfXZgr6+jd27Ng63d7ufA93Rt++sHRp6XV9+sCSJbt3f+qac25XJtn4kN35fn7wQXWJ0/RG4LRPVfnDH/4QLU+aNCnE9o/VtO+OHDkyxJ/73OcK96mm3xNFz6nN85LnNNn3sSE11v6t97N50PsYK/fVtm0bNOYfrabvpVIppNkrOmjuaJ2INH7q3yJSlxr8inF9sikREF+BSq/w2Cu8W7duDXGaBrFpU/V06F27di35HIBWrVqF+JJLLgnxddddV6N9F9kT7LVXzf42f/XV6kmlLrroomjd0UcfXXJ7tg8C/OxnPyu5jfTKtL3aW9OrxztzhVhERBofXTEWEREREUEnxiIiIiIigE6MRURERESAZp5jXHRXO8Cdd94ZLV911VUhtvmMd911V9TuP/7jP0L8yiuvhPjxxx+P2p188skhvuyyy0K8Lalz0rJl9VuwM3fDizQXs2bNipaXmjunevfuHeIXX3wxanf11VeHeO3atSFO7w+45ZZbQvzss8+GeOLEiVG7K664IsStW7eu0XpTGMUAACAASURBVL6LiEjzoCvGIiIiIiLoxFhEREREBGjmqRTl2BQGgP79+4f4W9/6VojHjx8ftfv73/8e4nnz5hVu/4YbbghxTWe5UfqENHdTp06Nlu+7774QL1q0KFp37LHHhnjNmjUh7t69e9Ruv/32C/GyZctCnKZSjDZTMm3ZsiXEnTt3jtrZkorHHXdciPfff/+oXWOcPU9ERHaNrhiLiIiIiKATYxERERERoImmUqSz1tkUBDtEOm3atKidHY59//33o3Vz5swJ8YwZM0L80EMPRe3sbHf9+vUL8RtvvFG4v7Nnzw7x5s2bo3U2hcPOntenT5+oXU1nCBNpbGyVh5NOOilaZ9MRbEoEwEEHHRTi+fPnh/iPf/xj1O6www4L8YgRI0Kc9rUHHnggxKeddlqI0xSJyZMnh9hWm7GPA3ziE58I8fDhwxERkaZPZ1siIiIiIujEWEREREQE0ImxiIiIiAjQRHOMy5U1e/3110P80ksvRetsDqPNRQQ45JBDQvzuu++G+L333ova2fJSY8aMCfGKFSuidps2bQpxhw4dQrxy5cqo3ZtvvhliO8tWq1atonYqDSVNyfTp00Nsc3t/9KMfRe1sKcO0hOKQIUNKtlu9enXU7rOf/WyI586dG+KNGzdG7SoqKkJ85JFHFrazef8DBgwo+XyAn/70pyG+8cYbERGRpk9XjEVERERE0ImxiIiIiAjQRFMpyrHDrMOGDYvW2bSIXr16RevWrVsX4h49eoQ4TWF4+eWXQzxlypQQ29JSAMuXLw/x+vXrQ9ytW7eonX0tW5LNpmKINDV2hjs7W+Stt94atbv//vtDbPsCxGXUZs2aFeIHH3wwamf7ri3rtnTp0qidTVuy5RBtOUWI0zHsLHsHHHBA1O5jH/sYIiLSvOiKsYiIiIgIOjEWEREREQGaSSqFTZGwaQt2ZjqI744/+OCDo3XpTHhVOnbsGC3bmfVsukNaRWL79u0htlU02rdvH7Wzy/bu+PROeZGm5Mknnwzx4MGDQ2yrvwB06dIlxGlfs+lJCxYsCHHar0888cQQv/XWWyG2M0lCXCnDpkilKRc2zSLdhrVw4cIQp1VpVEVGRKRp0hVjERERERF0YiwiIiIiAujEWEREREQEaCY5xmvWrAnx5s2bQ9y3b9+onc0ltOXUIJ6drkWLFiFu27Zt1K5z584htnnF3vuonS09ZfMoP/jgg6idXbb5y2luo/252rRpg0hjZkuovfPOOyEeO3Zs1M7mC6d5/l27dg2xLcOY5vMPHz48xGvXrg1xms9vy7LZexHs60Dc54877rgQ33PPPVE7W/4tndFSOcYiIk2TrhiLiIiIiKATYxERERERoBmmUrRu3TrE6RCpnXXOpiak62xahJ2NDuLh3nbt2oU4Tbmw7WxZt3R416Z3bNu2rXDf7dB0OmufSGNTlAbx0EMPRe3sZzmd7dGmQtkZ7WycLtsZ8uysdRDPaPf5z38+xIsWLYraVVRUhPiZZ54J8QsvvBC1s305/T4REZGmSVeMRURERETQibGIiIiICKATYxERERERoJnkGNvcRJtjbMuupe3SKVxtrqPNK7bTOadatqz+9dkpoCEuw2bLq9nnQJybnK4raifS2B122GEhvuiii0Kc5unavN9Vq1ZF6xYvXhxim6dsp4CH+B4DW4Yt7ZO2pJqdztmWXYN4Onb7PZGWmrN51Gk+s4iINE26YiwiIiIigk6MRURERESAZpJKYUujlZu1zpZGs0OzEA+L2iFXOxsdxOkO9rVsCgfE6Rg2rcLOnAdxOagxY8aEOE3hSGfWE2lMpk+fHi3ffvvtIT7//PNDnM78aEsU2hkiATp27FhyXdony80YaRXNRpmmMNm+a/v16aefHrVbsmRJiJ966qlo3YUXXli4HyKNTTpzo01xsqlFAG+//XaIDzrooBDffPPNUTvbB/r37x/itJ/bUqlW+l2Rlk4tYo+V5VIhRYroirGIiIiICDoxFhEREREBmkkqhR1KtbNRpekHdvY4O6sWxHe22+GXdPjGDrvaoZ50yKZVq1YhtsPFqbvvvjvEI0aMCLEdeoI4XUSksdmwYUO0bNMMfve734U4nfnu6quvDrH9/AP06dMnxDZF4t13343aHX300SG2/bV3795RO1s5Yvjw4YXtbJrVOeecE+KZM2dG7SorK0N86KGHRuuUSiH1oSilrihlIK3MYtP/nnzyyRD/6le/itq99dZbIU77tk0vGjp0aIhtqiLAcccdF+Lrr78+xI8//njU7oEHHgjxUUcdFeJyqRP2mJ+mMSp9QnaVrhiLiIiIiKATYxERERERQCfGIiIiIiJAM8kx3rx5c4ht6Zc0H2vWrFkhTku52dnp7Ax5aY6WZdel+VA2/9iWnUr95S9/CfG///u/hzjNm0pn+xJpTA444IBo+Yc//GGITz311BDbGSYB7rnnnhCnZZz23nvvENv+ddttt0XthgwZEmKbG2lnzgN47rnnQmy/J955552onZ09zxo/fny0fMIJJ4Q4/flF6lNNS5mls79OmzYtxD//+c9DvN9++0XtJkyYEGI7iyXEpU3tPQOTJk2K2v3v//5viDt16hRie+8AxHn8gwcPDvGVV14ZtTvrrLNCnB4fReqSrhiLiIiIiKATYxERERERoJmkUtjyLHZmOZtiATB//vwQ26GdtK0tjWbLrkE8ZGXjdMjKKldCzpaNs2WoRo0aFbVLh85EGpM333wzWn7jjTdCbPvGsmXLona2lGGatmRTmuw20tSH1157LcQ2XSrt/7bv2fJvdiYvgFWrVoX4wAMPDHE6BGx/5ldffTVal/ZfkbpQdawrd7wpx6ZF2NnubCnD2rjoootKxql58+aF+JprronWVVRUhNimDNp0rHQb/fr1C7HtrxD38/Q7xR5Hi74PAE488cQSP4XsKXTFWEREREQEnRiLiIiIiABNNJUinQXODpfY6hJ2prvUxo0bo+UOHTqE2M5ul6ZSpEMuVdLZ7Wx6h72DNp21a9GiRSFeuHBh4f4qlUIaszSVwlZ9sX3mz3/+c9Tu2muvDbFNW4D47nf7+bepSQAXXHBBiF955ZWS+wDxUOwZZ5wRYjtzHsRDs1/96ldLbhvi75D0e8LOpGl/DpGdtWXLlnCMsKlKEPeJdu3ahTitVnH55ZeH2KYavfDCC1E7+/lNj7e2P9tj75QpU6J2dvZLm+I4cuTIqN0pp5wSYjsjpa1KA3DfffeF2FaYSdMibV9Mj5v2OG3X2Z8X4PDDD0f2XLpiLCIiIiKCToxFRERERACdGIuIiIiIAE00x7jcbHQ2lynNw7JsHhbEucl2++mMc7ZMjs1RKjfznc1rGjBgQNTOloBK8zQtm7Oc/vw7W7pHpK5MnTo1Wrbln2xZqNmzZ0ftbD7/k08+Ga2zs3HZfvjMM89E7caMGRNi2+fTvEG7H+PGjQtxOmOXvSdg3333DXGaY2z78ooVK6J1y5cvD7FyjKUutGjRIsyimub92pKD9l6V9Lh08MEHh/g3v/lN4WvZ/ON0ljl7P07v3r1DfO6550bt7Cx2trzazrr00ktDbO8fst8h8OH7fSxboi0tnWqpz+7ZdMVYRERERASdGIuIiIiIAE00lSJlh4ts2Zpp06YVPidNpSiaZSstw1SUtpAO59h9KjdkUzU0Bh8eZraKUjPK7ZPI7pKWPDvqqKNCPGPGjBB/5CMfidp169YtxNOnT4/WbdmyJcRFM1ZBnFpk+79NZ0jb2T6UlmC0Q8e2f6blH+2w9Pr166N1dohZpC60aNEiDPGPHz++gfemYdmUSZG6pivGIiIiIiLoxFhEREREBGiiqRTp0KcdZrXVG+wMVql0tpwNGzaE2A7hphUg7DBrubtfbXqDTdNIUzN69OgR4qJZ9aDmqRkiDaGioiJaHjZsWMl1aVWWxYsXhzidFdLeyW5TFewd+BDfhW9nt0tnkrQz1S1durRwe7ZPjhgxIsT2ewHimbkWLFgQrVu9enWIu3TpgoiINA26YiwiIiIigk6MRUREREQAnRiLiIiIiABNNMc4zfWzOca2bJrN7U2leX9LliwJsc3nTWe+szMC2XY2txnifGG7f2mZGbsfaa6jZX8uuz2RxuCvf/1rtGzz4H/xi1+E+LTTTovaHXbYYSFOZ+k69NBDQ/zOO++E+IgjjojaHXjggSG2fSPt4/aegNGjR4c4vRfBlpCzJd++9rWvRe1secU0P/ob3/hGiAcNGoSIiDQNumIsIiIiIoJOjEVEREREgCaaSpGWK0vTGKrYUlAAw4cPL3yOLa9m0xbSGe3ssi3lVq6EWjpEbO2///4hnjVrVmE7pVJIY/bf//3f0bKdCc+mIw0dOjRqt2bNmhCn5Qrbtm0b4qoZvwD69u0btbMl4GzfWLRoUdTOzlxn+/8+++wTtXv//fdDbNO2Pv/5z0ft7Cx+aZ9MZ/gTEZGmQVeMRURERETQibGIiIiICNDMUynSKg92pqp0G7bahE2RSCtbFM2ylw6l2nXlqmN07Nix5OumM+7ZVI9yM+6JNIS5c+dGyzYNwn6W99tvv6jdE088EeJ77703Wjdt2rQQ27SI3/3ud1E7O8ucrV4xc+bMqJ1NkbDbS2ftW7lyZYhPPfXUENsKFRDPnpdWyrEpIr169UJERJoGXTEWEREREUEnxiIiIiIigE6MRURERESAJppjnErLPFVJc3uHDRsWYpuzC9CmTZsQ2/zgtJ1dl+YVWunzinTo0KHk/m7cuDFqZ8u1lXtdkYawYcOGaNnm49p47NixUTs7u50tpwhxybPKysoQ2/xlgPPOOy/Er732WsltQ5zrfMEFFxTuk50J7/TTTy+5bYjL0KU/f7n7CkREpPHSFWMREREREXRiLCIiIiICNNFUClt2CYrTFubPnx8tH3PMMf+fvTuPt7Kq+///+jDIKAKCgKSiKKIi4jzkWIJWDlnet3dWTnWXeZvDXZq/u9TUTMu0r1pqWmZajuWAaTmjZqAoMioKKCiCKCI4MIms3x/XOovPtdh7czgc4HB4Px8PHnz2da197Wvvs9e+1l7rs9dK8euvv17a51fJa9euXYq7dOlSKufTNvzQbD6Fmi9XLdUjf6x58+ZVPDYsvwKfSFPy4Ycflm77adMmT56c4vbt25fKPfTQQynO3/O+Tr399tsp3n777auehz/+jjvuWNrnp5TzK+ltsskmpXJ+Gjb/ueCnVoTydJD5888/o0REZN2gHmMREREREdQwFhEREREB1DAWEREREQHW0RzjPJ+32vKzeZ6fn5YpXxJ6gw02qHgMP3UTlPMM/TLQ+XRNPuewRYtl3z/yc/JTSvXs2TPFPkcTykvp1spZFlkb8nzevfbaK8Wvvvpqilu3bl0q98EHH6TY10Eo59wPHz48xd26dSuVe/TRR1Psp1DbaqutSuWeffbZFA8ePDjFeV3zv03o169fig844IBSuZdeeinFnTp1Ku3r27cvIiKy7lGPsYiIiIgIahiLiIiIiADraCqFX30uvz1jxowU5yvEHX300av3xJyNN964XuV8eocfBn788cdL5fxQdZ62IbK2bb755qXbjz32WIr9tGY+rQhg7NixKd50001L+/zqjz69oWvXrlXPw6dZ5avP+ds+1SlfZdKnVviUK786JpSndevdu3dpXz7No4iIrBvUYywiIiIighrGIiIiIiLAOppKMW3atNJt/+v1uXPnpvjcc89dY+fUGE4//fQUb7nllqV9fuUvPxsGaNhW1r58Voqrr746xc8991zV+x133HEpHjFiRGmfX9HSpxnlaUpTpkxJsZ/1Ik+R8Ld9SkeecuXrU//+/VPs0z7y23369Cnty9O9RERk3aAeYxERERER1DAWEREREQHUMBYRERERAdbRHGO/+hyUV4LzK1AdeOCB9T6mn5ZpbeUHfvWrX01xvgqYX41PpKlp1ar8UfKVr3wlxX5Fx9yAAQMqxrmTTjopxbvuumtpn6//fsq3PO+3V69eKd5+++2rljv88MMrnkP+uD5PebPNNivtU46xiMi6ST3GIiIiIiKsoz3GIiKybjvnnHOq7rv00kvX4JmIiCxjPoVgrZyA2bvAtBUWlMa0RQihe2MdTH/DtU5/z+ZBf8fmRX/P5kF/x+ajXn/Ltd4wFhERERFpCpRjLCIiIiKCGsYiIiIiIoAaxiIiIiIiQBNsGJvR04zbzZhixktmPGhGvwYcp7MZp9TYf6MZ75gxPtve1YxHzJgU/+8St5sZV5kx2YyxZuwSt29rxgtmjDFj77itlRmPmtG+xuP/PzP2j3FrMy6NjznejOfM+MLKPud4rDP848bz6NKQY4nkzPixGRNiHRhtxp5x+1QzulUof4QZFacfMONAM/apsq+LGffEx3nOjAFu3+mxnkww4wy3/Rex/M1u2zfNOL3G8+llxt9j3N6Mv5gxLh7/X2Z0NKNP/jnh7n+hGQdX2XeCGZu627ebsU21c5H1W7W61QjHHWbGbg0pY8ap8ZoXfP2udj2M+46P17JJZhwft7Ux45+xXp3iyl5vxs41zuvLZpwX423jeY4242Uzrl+5V6LqYxxY9xlQnzJmHGbGBY3x2NI0NamGsRkG3AMMC4G+IbA98H9AjwYcrjNUbxgDNwGHVth+DvBYCGwDPBZvA3wB2Cb++w5wbdz+3VjmaOCHcdv3gFtCYH6lBzajK7BXCDwVN10E9AIGhMAA4HBgwxU8v2rOgFKD/BZqvw4i9RK/+B0G7BICA4GDgTdr3ScEhobAcnNvmdEKOBAqN4wp6v3o+DjHAVfG+w0A/hvYA9gJOMyMbczYCNgnlm9pxo5mtANOAK6pcYr/C9wQ49OBWSGwY6yH3wI+qXrP4vmdFwKPVnh+LeNjb+o2XwucXet4sn5qSN1aQ56hOJd8FoWK18N4bTsf2JOijp4fO2YOAV4ABsbymLET0CIEXqzx+GezrP5eBfw6BAaFwHbA1av87BrmAeCIWh1fsm5rUg1j4CDgkxC4rm5DCIwOgafjN9TL4jfOcWYcAxB7dB4zY1TcfmS866VA3/jt8rL8gWKjdE6FczgS+FOM/wR82W2/OQRCCIwAOpvRi+LC2Y6iMfqJGZ0pGrY3U93RwD/j+benuNB/PwQWxXObFQJ3xv1fcz1Yv6g7gBnXmvF87GG4IG47jeJC/IQZT8SiQ4Gv1TgXkfrqBcx279PZITDD7f++q4f9IfWa/ibGN5lxRXxv3gGcDJwZ6+h+2WNtT/HFlBCYCPQxowewHTAiBOaHwBLgSeAoYCmwQfxy3Y6iXp4FXBVCzcbtV4l1MT6/t+p2hMArdc+VorF9Q6xvD8dGd91zOjrGU804z4x/UdS53YC/xOfXDngaODh+KRDxqtat+J4aGa8B18f3eF0v7y+sGFF5ta4OmdEujk6MNeMOivpA3LfcdaOWEHgxBKZW2FXtengI8EgIzAmB94FHKDqg6q6T/r1/ERS9wZVYMVK8KARmu9douju3cbFcHzOejp89oyyOQsVe3mFm/NWMiVaMBtW9dofGbf8CvuIecw8z/m3Gi/H/bSu8JgEYRvFFRpqhptYwHkDxrbKSrwCDKHqJDgYuixVxIXBUCOxC0bC+PL75zwGmxG+XZ63EOfQIgZkA8f9N4vbelL/BT4/bfkvR63Qd8HOKin5xrDzVfNY9z62BN0Lgg7yQFcOwvwA+F5/77mapof7jENiN4hv4AWYMDIGrgBnAQSFwUHwO7wNtzNh4JV4DkUoeBjaLF+FrzDgg2z871sNrWTZ6kusHHBwCX6WoM3U9QE9n5cYQL1hm7AFsAXwGGA/sb8bG8UvlF4HNQuBD4G/Ai8DrwDxg9xC4r9qTMWNL4H3X+L0R+JEZw834mZXTHrYBfhsCOwBzKRrUlSwMgX1D4M/A88DX4/NbEAJLgckUn2EiXq269ZsQ2D2OYrSj3CBrFQJ7UIwUnh+3fQ+YH3ueLwb8WubLXTcaeL7VrofVtj8C9ASeBX5pxhHAC9kX69xngVHu9q+Bx834hxlnxk4ogHeAwfGz5xiKnuU6O1O8NtsDWwGfNaMtxSjR4cB+8bzqTAT2D4GdKa7lP69ybs/H+0oz1NQaxrXsC9wWAp+GwCyKnqLdAQN+bsZY4FGKStiQ1IsVsQrbQgi8EQIHhsDewHyKHtuJZtxixh1WOT+6F/BuPR5zd4q0kndj79hfoMhLBv7TjFEUDYEdKCp+Ne9QHtIVWWkh8BHFRfY7FO/fO8w4wRW5O/7/AtCnymHuCoFP6/FwlwJdzBgNfJ/ifb4kBF6m+LL4CEVP7xhgSTy/X8ZG6A+IvVFmfNuMO834SYXHKNXDEBhNcfG8DOgKjDRju7j79bh/Rc/vjhU8L9VFWc4K6tZBZjxrxjiKTpId3F0r1bn9gT/H444FxrryK3PdqKXi9bDa9hBYEgLHxgbnXRSN1cvjCNJfY0M5l9fPP1KMGN1FkYY1wow2QGvghvj63JU9p+dCYHr8Ujqa4jXqT1GfJ8UOrD+78hsBd1nxm4JfU36tPdXjZqypNYwnUP5261WqcABfB7oDu4bAIGAW0HYVzmFW7Ikm/v9O3D4d2MyV+wws9233YuBc4DSKRuz5LPsW7y1w5zgZ2NysYk5xxecce7p+CHw+9go8QO3n3DY+psgqiV9Mh4XA+cCplHtO63peP6X6cvMf1/NxPgiBE2OdPo6ijr8e9/0hBHYJgf0p0qEm+fvash/zvAocFwL/CQyw5X/45uth3eN+FAJ3h8ApFBfML2bPbVWfn+qiVFSpbsXezWuAo0NgR4qeTv+erVbnlhuxbMB1o5Zq18P6XCdPoUhT3BtYTNHLW+mLa6X6OSMEbgyBIym+EA8AzqS47u9Ekb60gbtLtXpbbUT3IuAJ91ufaq+P6nEz1tQaxo9TDPv/d90GM3aPw0pPAceY0dKM7hTfip+j+Ib3Tgh8YsZBFEOuAB/SsB+wDYXil7Tx//vc9uOsyHXeC5hXl3IRz/MA4K0QmESRb7yUoiJWStB/mSKFglD8QO8PwFVmRYW24pfy36AYdjrAjG5W/JjnaxQ95Z0oLsDzYt6ln8Gi9LxjWklPqJgnJlJvVvwq3DcuB7FqS5tWraNWzCpTd4H7NvBUXbqRWZHeZMbmFOkWt2V3r8tdbA20jNuWsnxdfBXX82vGZ23ZLDQbUPQ8Nfbz60fRASCS1KhbdQ2z2WZ0pPh9yoo8RdFhVPdj1bp0iVrXjZVV7Xr4EDDEilllugBD4jbi+XShSAW5mWXXyUDlBmi6Tsb7HmpG6xj3BDam+E3ARsDM2Cv8TZbV+WomAlua0Tfe9r/B2YhlvzM4ocYx+kHlmWpk3dekGsZxWOMoYLAV07VNAH5K8Y3zHoohoTEUDeizQ+Btip7Z3cyKfD6KNz0h8B7wjBU/WFjux3dm3AYMB7Y1Y7oZ34q7Lo2PPwkYHG8DPAi8RtHDewOUppwxim+8F8VN18f7/Q34VYWn+gDFUFCdn1AMGb0Uh3DuBd6NHzT/H/BEfN6jQuC+EBhDMRQ2gSIv8hl3rOuBf9iyH9/tSvFjpSUVzkNkZXQE/mTFNIpjKRqOP12F490PHGWVf3y3HTDBjIkUF3A/5drfzHgp3v9/Yh49UEzvBIyMPUtzgeFxiDXEepOEwMfAFLN08e0LPBnLv0iRR/i3VXh+NwHXxefXLjZGFvgv1CJRxboV38M3AOMorgsj63Gsa4GO8ThnU3QgsYLrRkVmnGbGdIqe37Fm/D7uqng9DIE5FNfBkfHfhXFbnfOAn8Vr/UMUPbzjWDYzjPcUsHO8vkLRyB5vxph437NiG+Aa4HgzRlA0WGuO2oTAQoqUlQes+PGd//L7S+ASM56hdgP7IIrruDRDFkKt34jJ6hIr5GHxg291Ps6VwNAQil/4i8gyZhxFkYZVaSi3sR/rTOCDEPjD6n4skeYgXr/uDxWmRFxb4hfcW0Pg82v7XGT1aFI9xuuZHwCbr4HHGa9GsUhlIXAPay7NaC7LpoIUkRX7OZXTEdemzSmu39JMqcdYRERERAT1GIuIiIiIAGoYi4iIiIgAahiLiIiIiADVJ6lfY7p16xb69Omztk9jvTJ16lRmz55dbcGUlaa/4TJjxsCSKhPjtWoFO62GxYBfeOGF2SGE7o11vKb493zzzWWrzC5YUJ5Xv2vXrileunRpis3Kb/H330+zutGjx7LFMTfaaKNGO89VsT78Hdcn+ns2D/o7lq2Na1xjqe/fcq03jPv06cPzzz+/tk9jvbLbbrs16vH0N1zGanzdWLIEVsfLZGarsgjFcpri3/P005dNYzxu3LjSvm9+85sp/uijj1LcqlX54+3uu+9OsT/eYYcdVq9z8I1ugBYtGnfAbX34O65P9PdsHvR3LFsb17jGUt+/5VpvGIuIAAwbNqx0+5prrklxmzZtUjxnzpxSudNOOy3FLVsum5O/ffvyLE977bVXiu+8884UDx06tFTu0ksvTbHvjW7shrCIiDQ9+qQXEREREUENYxERERERQA1jERERERFAOcYisga98sorpdu/+MUvUvzqq6+W9g0cODDFL7/8corbtWtXKtetW7cUz549O8UDBgwolfOzUvgf5vn8ZYAzzjgjxVtvvXWKTz755FK5TTbZBBERaV7UYywiIiIighrGIiIiIiKAUilEpBF8+umnpdt+2rRrr702xSNGjCiV69ChQ4r32GOP0r6OHTumeOHChSmeOHFiqZxPrfDpDfk5jRw5MsXf+ta3UtylS5dSuQ8++CDFM2fOTPF3v/vdUrnrrrsuxX7BECjPeaxp3kRE1h36xBYRkTWuZ89isYBK/3r2XNtnJyLrKzWMRURkjZs1q2H7RERWJ6VSiMgq86kTOb+Ec8+sK9DfL1/C2c8iccQRR6T4pZdeKpXz6Q6XX355ii+88MJSuSFDhlR8XJ+mAeUV8zp16pTifEnoW2+9dg3qawAAIABJREFUNcVnnnlmaZ/SJ0RE1k369BYRERERQQ1jERERERFADWMREREREUA5xiKyGvj8YJ/D271796rllixZUtq34YYbpvjdd99N8YEHHlgqN8v9UuvOO+9M8ZZbblkq179//xR//PHHKV68eHGp3CeffJJiPxVcnh89ffr0FNeark5ERNYd6jEWEREREUENYxERERERQKkUIrIavP766xW351OjLVq0KMV5+oFf+e6NN95IsV+ZDqBXr14p9ukTb7/9dqnc1KlTU+zTNPJV68wsxT5F4sMPPyyV889l3rx5pX1du3ZFRETWPeoxFhERERFBDWMREREREUCpFCKyGrz11lsp9ikHeXqDn+khT5F4+eWXUzx37twU+5XuoDxzhC/34osvlsp169YtxX6GijfffLNUzqdPfPTRRxXPNTdx4sTS7X322adqWRERabrUYywiIiIighrGIiIiIiKAGsYiIiIiIoByjJMQQsW4RYtV/+7w1FNPpXj//fdf5ePVl1/dC6BDhw5r7LFl/eZzjNu0aZPi/D3pV7vbeOONS/umTZuWYr9CXtu2bUvl/PE32WSTFG+33Xalcq1bt654jHwKuX79+qX40UcfTbGfPg7KOcsTJkwo7VOOsUhl/voK5d8MbLrppinOPyuuuOKKFJ966qkpzq9rG2ywQdXH9r8f0OqUUo16jEVEREREUMNYRERERARQKkXiV7vycS2nnXZaiv3KXAD77bdfih977LEU+5W5ADbbbLN6PZYfcm7Vqvqf7bLLLkvxXXfdVdr3+OOPA7B06dJ6PaZIQ/n0BD/l2eTJk0vlFixYkOI+ffqU9vnUCp8G8d5775XK+TSL+fPnpzhfqW6rrbaqeLx8SNWvYjd8+PAUDxgwoFRuyJAhKc6fl8j6Jk+R8NfR1157LcVnnHFGqdzJJ5+c4lGjRqX49NNPL5W74447UvzAAw+k+NZbby2VO+yww1KcT+3Yvn37FH/nO99JcZ7GlT8XWb+ox1hEREREBDWMRUREREQANYxFRERERIBmnmOc59I2JI/Y50YB7L777ik+9thjU7zLLruUyvm8RZ+/9P3vf79U7t57763XedTKK77llltSfPvtt6fY53bCsmVr8+mpRBqbX97ZT7uUvyd9zn2+r2/fvin2U7I999xzpXLvvvtuirfffvuqx/vkk09S7HObfd5hfk5/+MMfUvzjH/+4VM7nM+dTS4msb2pdU31+/9ChQ6uWu/vuu1M8ePDg0j4/JeKiRYtSnP9O58knn0xxPrWjV+uaKus39RiLiIiIiKCGsYiIiIgIsA6lUvjpU/Ihm2r7aq1at3jx4tLtt99+O8U777xzivOpZX70ox+leODAgSmeOnVqqZwfWvUrcPmVtAC6dOmS4v/7v/9L8Ze//OVSOT+91L/+9a/SvmuuuaZiuZ122qlUrnfv3suVEVkdfH3waRD51Ghf//rXU3zppZeW9vn3qa/LPk0DytO3vfPOOykeM2ZMqZyvr351LD8VIpSnefNTyOUpFz5VQ9M7iVRXN1UowJQpU0r7Nt988xTfdNNNKc5XrvRph361u7w94Kdo23fffUv7/GPff//9Kf7GN75RKudXyJP1j3qMRURERERQw1hEREREBFiHUilq/eK12r6nn3666n3OP//80u26NAMo/xI9n9li+vTpKc5/He/5X737YdYvfelLpXIbbbRRiq+99toU33jjjaVyG264YYpnz55d2ueHovbee+8UP/vss6VydUPQGiaS1c0PZ3br1i3Fc+fOLZXz9WSbbbYp7fMpDnUzqsDyaVC+DvkUjhkzZpTKffazn614n2nTppXK+brmZ6XJV9Lzv3jPZ8DwM1bkKRgiDVUtZcdfA32Z/PqVpzJV4+uen82l1jF8ehLAJZdckmJfj/KZInr27Jni3/3udyn2M0BBuR597nOfS3HXrl1L5XyqoZ+xBsrpGX/7299SnKdSaMaK9Zt6jEVEREREUMNYRERERARQw1hEREREBFiHcoxrmTx5cop9DuNtt91WKufzFM8999zSPj+9mp+6LV/Ryude+VypPG/X53b5leb8ij0A//Ef/5HiI444IsWvvPJKqZyfZiZf6efggw9Osc+dvOOOO0rl6nLA6rvqn0h95Xm//rafai3Pt/W38zxdX5e32GKLituhPEWbP4afdhHK9dCX88eG8nRwHTt2THGey+hz/X2eJJQ/Q/yqXyKroj6f3bXK1CdHGco5tvXNt/VTrUE533/HHXdMcX5N9SvD9urVK8X+9zwAp5xySopnzZqV4v79+5fK+ethp06dSvtOOumkFPvPjT//+c+lcnnOsaxf1GMsIiIiIoIaxiIiIiIiQBNIpVi0aBGTJk0C4Pbbby/t22STTVLsh0HzaZP8dDJ+uPOggw4qlfPTv+RTrfnhXj/8kk9N41Mm5syZk2I/dJqfo5+SKk+l8Pv8sO22225bKudX8PGr5eXn4VcH8kNUABMmTADKr6VIY/DpTFBe7c7Xz3nz5pXK+aHTfMjWpy21a9eu6jH8yne+zr/66qulcvk0h3Xy9A5f5/05+Gnc8tv+HGD5zyiRxrCyKyzWd3q2nH/fX3fddaV9L774Yor9VIwnnHBCqZyfUu3WW29N8UsvvVQq5z8f9tlnn6rn9Nvf/jbFZ555ZsXzgfL120/RCOWpTX38/PPPV31cWf+ox1hEREREBDWMRURERESAJpBK8c4776QV38aMGVPa54djvXzI1c/E4Fe6yYdcfWpGhw4dSvtef/31FI8fPz7F+S9j/S/ifRpEnp5QbXW5/Dn5IeLddtstxSNHjiyV+81vfpNin/YBsMMOO6TY/7o4L7f11ltXPAeRVZXPFFEtlWLgwIGlcn42h7yu+dQiP4tE/lj+Pe+PV5eiVek8/JC0n4UCysPI3bt3T3Fen6qlQcHynz0ijWFlZxTKr0M+tcKn3eV1z6cn5LPFHH/88Sl+8sknU+xXlYPyanf+upxfK/11uRb/3P2MEvlz9KtO5jNlDBkyJMW+zvq0CoA33nijXuckzZN6jEVEREREUMNYRERERARQw1hEREREBGgCOcZdunTh6KOPBpZfWerNN99M8fvvv5/ifCqkGTNmpNjnG/uVd/J9PqcYyqvx+BzmPK/QH8NP8+RX9oHytFF+Kqe77767VO7hhx+mPvxz9jlUOZ87XbfSXZ26XLH6rmQkUl8+5w+qT7WWrxDn837z3MMePXqk2E9zmL9/fbnHH388xfm0UH4FOj/lYf64/nx9TmZen3zOo38eUM4/FmlstaZt86uu1pqubfTo0SnO60Dr1q1TfNZZZ5X2+RUl/fXm5ZdfLpXz+fk+Zzk/d7/q3Mknn1z1fD1f36ZNm1ba169fvxTnv2m45557UvzNb34zxYMGDSqVGzduXL3OQ5on9RiLiIiIiKCGsYiIiIgI0ARSKdq1a5emG9tiiy1K+/yqWF4+PYsfBvJTxOTDu//4xz9SnK/S44dc/Ipx+fDpqjr88MNLt//5z3+meKeddkpxnsLhh8TyqaH80JRPCZk5c2apXF0KRj7sK7Kq8lXl/Kpw/v225ZZblsr54dd8WiifPuFTMHyKFZTTFnw6lk+JgPKwr9/n0z6g+nSGeb3x5fLhYa0uKatD3fus2nSgUE41yqcinDJlSop9CkKeCujTkH70ox+V9t15550Vj7/ZZpuVyvnr6BNPPJFivwItlK/TPhXKr5yX89fKWbNmlfYdc8wxKc6vt1/4whdSfOyxx6Y4T89U/V2/qcdYRERERAQ1jEVEREREgCaQStGyZcs000M+7PPYY4+l2A93+l/MAnTu3DnFAwYMSHE+e8Opp56aYv8LdYDFixen2A8L50Msnh/CzX+F7odW/dBW7969S+X8cOzTTz+dYj9UBOVh3PyXxn5YzT/nfGjaD22JNKb8/d+2bduK+7p161Yq54dR/YwvUE4Z8qvd5bNS+PQhn3IxZ86cUjk/PPr222+n2H9+QPU6n6dc+Nv5OfnPE5HGUjcTSn1nFspTfO67774Uv/LKKynOUwf8jBV+JVgoz7LkV7QbOnRoqdwZZ5yR4mHDhqX4ggsuKJXzdfGiiy5KcZ5K4VeTrLVanj9ezp+T52fNgOVns5D1i3qMRURERERQw1hEREREBFDDWEREREQEaAI5xl4+3Ut+u87kyZNLt30O46RJk1Ls8xKhPAWaz6+C8tRQnTp1SnGez+xXu/I5kPmqfT4n2Od55flPfnUg/1h+9aL8GH4VwJyfJis/p759+wLLT2Ml0tj8+9/n4uZ5uhMmTEhxPkWhv+3rsq+DUF7Fzj9uXnf9+97n9uc5+z4/2NfX/HcJXp7zWWt1SpGG+Pjjjxk+fDgA1113XWmf/z1JrZVb/T5/rcinNvV59/m0nyNGjEixnwLVX0NzPvff5wrnfP7ynnvuWdrnr/ODBw9Osa//ALfffnuKTz/99NK+bbbZJsW77LJLivPV86688sqq5yjNn3qMRURERERQw1hEREREBGhiqRT1tfXWW9erXL6aj4isHnl6g09j8ClHfqU7gH322SfF/fv3L+3zaQw+3cFPEQXl4WE/dWG+OphPs/DDufmUVn61S5/SlK9858/JT08Hy6eMiKyqdu3apWnEvv3tb5f2+TrhU+3yaRT9bT9FW17Ov7d/8pOflPb5OuHTDvPpQP0UaD414wc/+EGpnE8nrJVycfHFF6d4+vTpKc5XyPX1Od/n06n8Spj5Z4/q7/pNPcYiIiIiIqhhLCIiIiICrKOpFCLStOTpCD69wadZ5DOlfO9730vxa6+9Vto3atSoFPvh1nHjxpXKvfTSSxWPn6dS+GFan+oxY8aMUrnjjjsuxXvttVeK86Hd/Dy8fDYAkVXVokWLNPy/3377reWzWfP8DBgiq5M+vUVEREREUMNYRERERARQw1hEREREBFCOsYg0gny6Ns/n+u67775Vy+Ury1Vbae6AAw6oegw/lVS+Eteqrvjo85yh9nPOV64UEZF1g3qMRURERERQw1hEREREBFAqhYg0gjZt2pRuV0sz8NOk5fLp1fzqW346uFopDH6atIamTlR7rA033LDq+eWpE4sXL27QY4uIyNqlHmMREREREdQwFhEREREB1DAWEREREQGUYywijWD27Nml25988kmKfS6uXyp6Zfhc33z56Vo5xw3h84X9uec5xn46uHxfrVxqERFputRjLCIiIiKCGsYiIiIiIoBSKUSkEeRTrflUgiVLlqS4V69eq/xY9U2dqJVyUWv6t2qpFPn0bz5dxD9HWD61QkRE1g3qMRYRERERQT3GIiKyEs4555yq+y699NI1eCYiIo1PDWMRWWV+xTmADz/8MMVz585NcZ5y4eWrx/k0hoaolXLRkJks8hk1/HPJZ6Ho0KHDSh9fRETWPqVSiIiIiIighrGIiIiICKCGsYiIiIgIoBxjEWkEJ554Yun2Cy+8kGKfY7zrrrtWPUZDV8VrbHm+dJ18qjl/Oz/3zp07N/6JiYjIatc0rkQiIiINoFkyRKQxKZVCRERERASwfHWoNX4CZu8C09bqSax/tgghdG+sg+lvuNbp79k86O/YvOjv2Tzo79h81OtvudYbxiIiIiIiTYFSKUREREREUMNYRERERARQw1hEREREBGjEhrEZPzZjghljzRhtxp6Ndex4/APN+HsjHu9GM94xY3y2vasZj5gxKf7fJW43M64yY3J8jrvE7dua8YIZY8zYO25rZcajZrSv8fj/z4z9Y3yYGS/GY7xkxncb63m6x/toFe//aN1rIesvM3qacbsZU+J79UEz+jXgOJ3NOKXG/jPj58l4M24zo23c/jkzRsXtfzIrppw046ux/NNmbBy39TXj9hqPYWY8bkan1fnczOhuxj9X9jiyflld11AzhpmxW0PKmHFqvOYFM7q57RWvh3Hf8fH6OcmM4+O2Nmb8M9ZbXzeuN2PnGuf1ZTPOi/G28TxHm/GyGdev3CtR9TFW2LbwZeL1+oLGeGxpmhqlYRwbhIcBu4TAQOBg4M3GOHZjqLt4Zm4CDq2w/RzgsRDYBngs3gb4ArBN/Pcd4Nq4/buxzNHAD+O27wG3hMD8KufTFdgrBJ4yozVwPXB4COwE7AwMW5nntzrFD8AWwC1QvSEjzZ8ZBtwDDAuBviGwPfB/QI8GHK4zVd5PZvQGTgN2C4EBQEvgv+L78E/Af8Xt06C48AI/APYCbgaOjdt+Bpxb4xy+CIwJgQ9W53MLgXeBmWZ8tgHHkvVAE76GPkNxLvksChWvh/Hadj6wJ7AHcH7sUDkEeAEYGMtjxk5AixB4scbjnw1cE+OrgF+HwKAQ2A64epWfXcM8ABxRq+NL1m2N1WPcC5gdAosAQmB2CMwAMGOqGRfEXp5xZvSP2ztY0Ws7MvaWHhm394m9PqPiv33yBzNj93ifrWoc5wQz7jLjfuDh/Bgh8BQwp8JzOZLi4kv8/8tu+80hEEJgBNDZjF7AJ0A7oD3wiRmdgcMpLtDVHA2pB2lDioVW3ovntSgEXonP4ab4rfzfZrxmxtHuNTgrPuex/turGfda0YM9waz4AMpeu25mDDfjS9WOE/8GL5txDTAK2AwYCnytxnOS5u8g4JMQuK5uQwiMDoGn4xeoy2KP0DgzjgEwo6MZj7n6f2S866VA39j7c1mFx2oFtItfatsDM4CNgUUh8Gos8wjw1RgvBdqwrB7uB8wMgUk1ns/XgfvW0HO7Nz6eSCW1rqHnxc/o8bGH1eL2YWb8woznzHg1vucxo50VIx9jzbiD4vpE3HetGc/H68MKez1D4MUQmFphV7Xr4SHAIyEwJwTep6ijh7LsOuk7qS6Coje4EitGaxaFwGz3Gk135zYulqvYZoi9vMPM+KsZE834i3vtDo3b/gV8xT3mHvF6+2L8f9sKr0mg6Lw6rNZrJ+uwEN/Zq/IPQkcIoyG8CuEaCAe4fVMhfD/Gp0D4fYx/DuEbMe4c79sBQnsIbeP2bSA8H+MDIfwdwj4QXoCw+QqOcwKE6RC61jjvPhDGZ9vmZrffj///HcK+bvtjEHaDsDmEYRCGQxgI4Qr//Ks87p8gHO5u/x7COxBug/B1CC3i9psg3AWhBYTtIUyO24dAuB6CxX1/h7B/3Nc1/t8OwngIG8fbH0HoAeFZCINrHSe+Lksh7JWd96S64+nf+vcPwmkQfl1l31chPAKhZXyfvQGhF4RWEDrFMt0gTI7vt+XqXna80+N79l0If4nbDMI0CLvF21dCGBfjwfFz4X4IG0F4CEKXFTyfaRA2XBPPDULvunPVP/3L/1H7GtrVxbfUXTvidefyGH8RwqMx/l8IN8Z4IIQlrs7UXR9axvsPdMfarcb5TYXQzd2udj38IYSfuO3nxm2tINwK4UUIx0I4AsL5K3hNTqx7fu72PAj/gHAmhM5xe602wzwIn4nXt+EQ9oXQFsKbsaxBuBPC3+N9OkFoFeODIfzNHevv7ly+DuHqtf2+0b/V869ReoxD4CNgV4ohkneBO8w4wRW5O/7/AtAnxkOAc8wYTfHtqy2wOdAauMGMccBdwPbuONuxLO3gjRUcB+I318Z4jlB808yEEHgjBA4Mgb2B+cCmwEQzbjHjDquco9iL4nWKB+HbwOeB5yjSMW50Ze8NgaUh8BLLhnWHxH8vUvTo9qcY0gI4zYwxwAiKnt667a0pUkPODoFH6nGcaaHoCfDeic9PJLcvcFsIfBoCs4Angd0p6s3PzRgLPAr0ZgXpCVYMvR4JbEnxfutgxjdCIAD/BfzajOeAD4ElACHwSAjsGgKHU4zyPAhsG3uLbrDKw55dQ+DDNfTcVHekqhVcQw8y49l4TfwcsIO7a6Vr6/7An+NxxwJjXfn/NGMUxWf+DpSvryuj4vWw2vYQWBICx4bAzhTX9TOAy824ItbRIyrcL79O/pGiDXAXcCAwwow21G4zPBcC00NgKTCa4jXqD7weApPiZ8qfXfmNgLus+O3Rrym/1p7qczNWKfe2QULgU4qG6bD4Bj2eIo8XKIaHgE/dYxrw1RDTBuqY8VNgFhT5R8BCt3smRcN3Z4qh1VrH2RP4uAFPZZYZvUJgZhwaeidun07R0KzzGXcOdS4GfkKRH/kXYCpFvlU+hLogPo8kFMNC48y4BXgd0ofiIlfM3P+XhMDv/DHMOJAiH2zvEJhvlr4oQNGAeIFiqOvJFRynD5Vfu7bx3GX9NAGWpfNkKl0QoXjvdwd2DYFPzJhK9t6v4GCKC9e7AGbcDewD/DkEhkMaMh4C5S+esQF8PMX7/GGKBvax8TxuyB5niRkt4kVzdT831R2pqdI11Iofj15DkW//Zrw++vdYpWsrFI3UEjO2pOh42T0E3jfjJlZcF6updj2cTtFo9duHZfc9hSJNcW9gMXAMMJwiXc9bQNFQTUKRXnIjcGNsvA6gSF2s1mbw10//GlVb2ewi4IkQOCpeB/Nzr6P63Iw11o/vtjVLPY0Ag1jxkocPAd93OT91v0zdiCI3cCnwTYof3tSZC3yJopfmwBUcp6GGsuwHPcezLAdxKHBczDfcC5gXAjPr7mTGAcBbochpbE+R8/hpjHMvA1vH+3V0zwXq/9qdZEbHeIzeZmxC8dq9HxvF/Sl+jFQnACcB/c3SDwqrHWc58fXtCRXzzWT98DjQxoz/rttgRb7/AcBTwDFmtDSjO0Wv1XMU78l3YsPxIGCLeNcPKfLrK3kD2MuM9vF993mKOkPd+zP2FP0IluUER2cDV4aQchoDRV2sVA9fAbZaQ8+tH5RnwBGpU+MaWtdwnR0/p6t9efOeInbGmDGA4gdvAJ0oOjzmmdGD4gd0DVXtevgQMMSMLnHkZ0jcRjyfLhS5uTez7DoZqNxAT9fJeN9DrfixOmb0pPjNwVvUbjNUMhHY0oy+8bb/7cxG8ZhAadQ7p/rcjDXWj+86An+yYoqjsRRDGT9dwX0uohgCGRu/+V0Ut19D8U15BMWbr9RzGYcyDwd+G3uFqx2nJjNuo/iWuq0Z0834Vtx1KTDYjEnA4HgbiqHZ14DJFD1PfsoZo+gprnvs6+P9/gb8qsLDP8Cyb9UGnG3GKzEd5AJqV0hC4GHgVmB47Fn4K8WF+J9Aq/g3uAjKqRCxR+K/KIbmTqlxnEp2BUaEUAxdy/onDjseRVE/ppgxgaKez6CY0WEsMIaikXl2CLxNMXKymxnPU1ysJ8ZjvQc8Y8UPii7LHudZivfiKGAcxedU3dRMZ5nxcnys+0Pg8br7mbEpRc9a3ZfZyynqwPEU7/Ncqodr4LkdFB9PpJKK19AQmEtxvRlH8QPOkfU41rVAx3icsym+xBECYyhSKCZQ9Lo+s6IDmXGaGdMpen7HmvH7uKvi9TCmLl4Uz3MkcGGWznge8LNY3x4CdovPLR/NgaKBv3NdpxdFI3u8FamCDwFnxXpYs82QC4GFFCkrD1jx4zvfEfVL4BIznqF2A1v1uRmzIpFc1rRYIQ+LH3xNnhlXAkND4LG1fS4ijSGmSt0cAoPXwGM9BRwZil/qi0g9xOvO/SHw6No+lzqxt/3WEPj82j4XWT208t3a8wOW/UhwXTBejWJpTuLQ7w0WF/hYXWL6xRVqFIustJ9TOQ1qbdqc4votzZR6jEVEREREUI+xiIiIiAighrGIiIiICKCGsYiIiIgI0IgLfDRUt27dQp8+fdb2aaxXpk6dyuzZs6stWrDSmsXfcMwYWFJlJrpWrWCnndbs+ayEF154YXYIoXtjHa8p/j0XLVo2T3+bNm1W+XgLFiybm79du3arfLzG0Jz+jutwdWo0zenvWR+zZ88u3V5S5Q3QokW5P26DDTZIcefOnRv/xFbR+vZ3bM7q+7dc6w3jPn368Pzzz6/t01iv7Lbbbo16vGbxN7Qa3xOWLIEm/PzMbEULwqyUpvL3/PTTT1M8derUFPft27dC6dr3B2jZctm0pOPGjUvxgAEDSuWs1nthNWpOf8d1uDo1mub096yPG24oT0U8d+6ymUh9I7ljx46lcp/5zGdSfNRRR62ms2u49e3v2JzV92+pVAoREREREZpAj7GISCWffPJJit98880U1+ox9tNP+h7i3IwZM1K84447NvQURZq0fDrWaqMheTnfw9u6devSPj8S06rVsiZEnuJU7bHy7T6t6dBDD03xP/7xj4r3z8/Pn4NIY1CPsYiIiIgIahiLiIiIiABqGIuIiIiIAMoxFpEmqm3btin+/e9/n+J8SqdBgwaluNaMEvfdd1+Kr7zyyhQfcsghq3SeIk1VrRzjpUuXpjifQi3PK/ZOPfXUFPu84l69epXK+WnYFi5cmOLFixeXym244YYpHj16dNXH9Xxeca3ZZ0QaQj3GIiIiIiKoYSwiIiIiAiiVQkSaKD9d29NPP53ikSNHlsoNHDgwxSeeeGKKL7zwwlI5P5ybL+oh0hzlKRK+TtVKl3jwwQdT/Ktf/aq0b8qUKSnu2rVrivM0pt69e6fYT4+Ypz74+/nUjzw146yzzkrxGWeckWKlTjSenj1h1qzq+3v0gLffXnPns7aox1hERERkPVerUVyf/c2FGsYiIiIiIiiVQkSaKD/U27NnzxT7Va8AJk6cmOL/+Z//SbGf1QKgS5cuKe7evXujnadIU+VnnoDq6RNf+9rXSrfvvPPOFHfs2LG0r3379in2aRAfffRRqdzMmTMrPpZf6Q6gXbt2KfZpFosWLSqV+/GPf5ziyy67LMVXX311qdzRRx+d4vyzQqvkSX2ox1hEREREBDWMRUREREQANYxFRERERADlGIvIOsDnIb711lulfX7lLL8qnl+VC8rTtXXo0KGxT1FknfLEE0+k+N577y3t22KLLVLsp3iD5fN26+Qr2k2dOjXF22+/fYrz3OG5c+em2P8uIP+NgK+z/pxOOumkUjm/EubWW29d2ueng6u1Sqas39RjLCIiIiKCGsYiIiIiIoBxReUIAAAZq0lEQVRSKURkHeCHYv3KW1B9Cqp8u0+l8Kty5TTcKs1FvvKd97vf/S7F+epxPl0iX6nO1w8/HVxe3/xtv/JdnuJUrb757fk5+WPnz/HMM89M8f3331/ap/os9aEeYxERERER1DAWEREREQGUSiEiTUQ+dOqHPf0v0vPVq6oNxfbo0aNU7r333qv6WCLrA/++/9e//pViv5odlGd9yNMP/DF8uTxFwqdn+JSLjz/+uFTOzzjjj12rjvq0ik6dOpX2PfXUUykeN25cad+OO+5Y9ZgiddRjLCIiIiKCGsYiIiIiIoAaxiIiIiIigHKMRaSJqDWV0uTJk1Ncawoqv6rWhx9+WNq38cYbp3jatGkNOg+Rddkdd9yR4jlz5qQ4z9P1OcF5fdhoo41SPH/+/BTnK+T5ad78bwT8saFcZ/1qd7Vym2tt97cvv/zy0r6bbrqp4jFEPPUYi4iIiIighrGIiIiICKBUioquueaaFI8fP77qvlq0epZI43niiSdSvPnmm5f2+VWw8mFaz9fDiRMnNuLZiawb/v3vf6fYT6eWp0F4G2ywQen2ggULKt4vX/nOT6nWuXPnqsf310qffpGnTFW7pvrHgfLzevrpp6s+rkg16jEWEREREUENYxERERERQA1jERERERGgieUY+9wlKC8VWatcngNVjc89yv39739P8YwZM1K8ySablModd9xxKb744otTvNlmm5XKVcsr9jlUK3N+IuubSZMmpbh79+4pzpee9fxUUnkd9LdnzpzZGKcosk4ZNWpUin0Obz7lmb+m5vVo4cKFKfbTq+W5vtXqW368atfvxYsXVy3nHys/d//5kC91LVIf6jEWEREREUENYxERERERoImlUvg0BYBTTz01xQcccECKq6VYrAo/Ddsee+yR4nyY5zOf+UyK/SpCecrFUUcdleINN9wwxXm6hE+tqLayz4poOjhpjvywrx9Wzd/v1aaM8kO+UB46nj59eqOdp8i6YsqUKSn216L82uOnPcynTWvValmzoVZKgy/nj5FPDZenYFR73Grl8vRE/7gfffRRxfuI1KIeYxERERER1DAWEREREQGaQCrF0qVL+fjjj4HlhzeHDh2a4vnz56d4wIABpXJdu3ZNsf8Var4K1htvvJHiP/7xj6V9PXv2THG3bt1SfP/995fKHXnkkSmeO3duih988MFSOb+y1lZbbZXiwYMHl8ptscUWrKx86KjasJdmuZB12bPPPpti/77O3/9+CLfWL+19CkavXr1SPHny5FK5rbfeuoFnLNK0zZo1K8X+Olff9AYo1zFfp/IUJ38Mf43Ky/nj+XL5Snr+HOubPjh16tTS7Q8++CDFnTp1qtcxZP2jHmMREREREdQwFhEREREB1DAWEREREQGaQI7xggULGD9+fMV9dbnHAH/5y19SPHDgwFI5P6Waj/PcwXHjxqU4X1Vnv/32S7GfJuqQQw4plfM5zP6xDj300FK5d955J8WvvvpqiocPH14qt91226V4hx12SPFuu+1WKudX/spzh5VLLM3RhAkTUuzzEPMpFP2UTD73sNZKXD5f8b333iuVU46xNFc+795fN/Lroa8reU5/rbxiz+cL+3xm/3uh/LY/p/w3Qp4/p1rlcq+88kqKd99993rfT9Yv6jEWEREREUENYxERERERoAmkUnz66adp2rM5c+aU9vkVbObNm5fie+65p1SuS5cuKfZDpH7FOYC99947xf369Svt80O1fjq42bNnl8r5YR8/TVx+7j7lYvPNN68YQ3n6mKeffjrFI0eOrHq8zp07l/b5Kd/8Cnz9+/cvlWvTpg0i6wo/1ZJPn8hTJPxt/5mRDw9Xu8+kSZNK+/bcc8+VPleRpuitt96qus+nQTR01dVa/DF9ukNef/01O1/trhp/nzy1qtZzef3111OsVAqpRj3GIiIiIiKoYSwiIiIiAjSBVIoWLVrQoUMHoDx7A8CJJ56Y4j59+qQ4T1tYuHBhin2aQdu2bauWGzt2bNVz6tixY4p9CgOUh2fffvvtFOfDOX5VHX8fnzoB5V/r+tSMnD93P+MFwIwZMyqe789+9rNSuW984xtAecU+kabKr1S57bbbpjhfpcvzw8M+rQKqrxDpZ6sRaU78LAy15DM71DeloRY/w4Sf+SWfRclfp/151Donn46RX3trzVIxc+bMFZ22iHqMRURERERADWMREREREUANYxERERERoAnkGM+dO5ehQ4cC0KtXr9I+nzvrc3O32mqrUjk/BZrPPfL3B1i0aFGK89V88nOq46eJA2jdunWK/dRotXKMvTxnuUePHhXPKZ9qyudh5bnT/rXxzzlfleiKK64AYNasWRXPTWRtyuukz6v3+YW1pmHz+YX5+9/Xf5/n6H8rINKcvPbaa/Uql+fj+ynP8nrk62Ktcp6fKjSv577O1vdxfZyXq5Vj/O6771bdJ1JHPcYiIiIiIqhhLCIiIiICNIFUikWLFjF58mQA+vbtW9rnV6AbP358iqdPn14qV226slpDKvk+Pzzr43yYxg8J+WGZfFW5du3apdinX+T8ynr+nD788MNSOZ/eke/z08v54ed8Ra+6Y9QaihZZW6ZNm1Z1n6/XH3/8cWmfr1/VhmXz2z4dyU8LJ9Kc5FN7VpNf53y6Qz69Wn1VW/kur5f+sX2cp1z4a6xPpcinb6x1vc2nehWpRD3GIiIiIiKoYSwiIiIiAjSBVIoWLVqkYdIRI0aU9vkhfz/0macCzJ8/P8V+Ngi/qhzARx99lOJas1L4oaP817r+th/OyWel8PzQjk97gPJQl38e+Qp5PkUiHzry5+Rn5fD3AbjgggsAOP/886ueq8jaMnHixKr7ag2d+rrny+V13A/h+jrz1ltvrfzJiqwDpkyZUnWfryt5usSCBQtSXCs1oRafPrHpppum2K+CB+XrVK2VK/11v0uXLlWP5883P4ZmpZD6UI+xiIiIiAhqGIuIiIiIAGoYi4iIiIgATSDHePPNN+fqq69Osde1a9cU+2nN8hxjn2Po83TzqVk23HDDFPtcXCjnW/m8pHxaN5975fOh8hxjf47Vjl1rn3/uAJ07d05xvvKdL7vtttumePDgwVRy1VVXVdwusjbVN9c3r7terWmhfG6yr7v59IcizYW/bkL5GuPrQ36d8+XyeuT5fXk5f22bOXNm1ceqdp/8WulXoT3ooINS/MADD5TK+c+HPD86z0cWqUQ9xiIiIiIiqGEsIiIiIgI0gVSKli1bpqlXfv7zn6/lsxGRtSVPaajvsK8fOvX78tW8PD9kWys1Q2Rdltcpn1rg0w632GKLUjmfdvjss8+W9vXu3TvFixYtSnGt+lZrn+frr6+jUJ5u1fNTt0E5XSJPx6g1TatIHfUYi4iIiIighrGIiIiICNAEUilERGD5WSn8sK8fYs2HQ6sN0+ZDsf62P54fDoZy2kZDV/0SaQryVIp27dql2M/aNGjQoFI5n4KQr0jrZ5+olSLhy9VKV/LHqBbnx/PpE/369SuVe/TRR1Ocr35ba0YMkTrqMRYRERERQQ1jERERERFADWMREREREUA5xiLSRHzwwQel223atElxrdW3WrZsWbFcnqPoc4zz/GPP51726NGjxhmLNG15Pn61nHm/khzAhAkTqh6zVt3xfP3z07/5aeKgYdMlbrzxxinO84h9jnF+rrU+R0TqqMdYRERERAQ1jEVEREREAKVSiEgTka9sVd+p0vzwqI99ikWt4/np2QDmzp2bYqVSyLrMpyNB9ZXfjjzyyNLt0aNHVz1mtVUo87QFv8/Xy8WLF5fK+fv5cvk0it4GG2yQ4v3337+075JLLklxnk7VqVOnqscUqaMeYxERERER1DAWEREREQHUMBYRERERAZRjLCJNxMKFC0u3O3TokGKfG5nnSfpcRj/1U55f6XOOfZ7jlltuWfM8RNZVPhc317FjxxTnU559/PHHKc7zdH19q+8Sy35p6jwX2dfnWktCez5XOK/n/jMgP79qOdYinnqMRURERERQw1hEREREBFAqhYg0Ec8880zptl8ty2vXrl3V237oOJ+ezQ/N+mmh8tSJV155JcU77bTTik5bpMny6UhQnhKxVsqQrzt5qkK1FSTz6RF9ffMpDXmKhL/tj9eqVbl50rZt2xT7VTLzFTO9/Nz9inki1ajHWEREREQE9RiLiIiIrNPOOeecqvsuvfTSNXgm6z41jEWkSTj55JNLt/0KVn4WCf8Ld4CZM2emuGvXrinOV7TzaRY+TWP+/Pmlcl26dFmZ0xZpsh588MHS7dmzZ6d4wYIFVe83efLkeh2/1mwxPl3Jp0XkqRQ+BcPPKOHvnxs7dmyKzz333KqPK9IQSqUQEREREUENYxERERERQA1jERERERFAOcYi0kRceOGFpds77rhjil966aUU57mR/fr1S/GgQYNSnOcOt2/fPsV+Sravfe1rDTxjkXVLvsJdNT4f30+TBuWp3Hyc5/T7XF9/jFq5yF5ezv8uoH///lXPXWRVqWEsIiIiIo1iXZ8hQ6kUIiIiIiKAre2pTczsXWDaWj2J9c8WIYTujXUw/Q3XOv09mwf9HZsX/T2bB/0dm496/S3XesNYRERERKQpUCqFiIiIiAhqGIuIiIiIAGoYi4iIiIgAq6thbPZjzCZgNhaz0Zjt2UjHHYbZbg0qY3YqZpMxC5h1c9sNs6vivrGY7eL2HY/ZpPjv+LitDWb/xGw8Zqe4stdjtnON8/oyZufFeNt4nqMxexmz6+v3AqyE+rxWte//K8w+14hnJE2cGZ+aMdqMCWaMMeN/zdbMl2cz+psx3IxFZvww23eoGa+YMdmMc9z2Lc141oxJZtxhxgZx+/fNGG/Gg27bvmZcUePx25nxpBktzehjxgIzXjTjZTOeM+P41fXc4+P/ygzVN2kQM3qacbsZU8x4Kb73+634nssdp7MZp9TYf3qsWxPMOMNt72rGI7EuPmJGl7j9q7Hs02ZsHLf1NeP2Go9hZjxuRqfV+dzM6G7GP1f2ONL8Nf5Fz2xv4DBgF0IYCBwMvNnoj7PynqE4l/zXoF8Aton/vgNcC4BZV+B8YE9gD+B8zLoAhwAvAANjeTDbCWhBCC/WePyzgWtifBXwa0IYRAjbAVev4nNrXGYtKc6p+mSE0hwtCIFBIbADMBj4IkUdKDFbLfOfzwFOA36VPVZL4LcU9XR74GtmbB93/wL4dQhsA7wPfCtu/zZF/XwROMQMA84FLqrx+CcBd4dA3aoCU0Jg5xDYDvgv4EwzTszv1IivheqbNEh8f98DDAuBviGwPfB/QI8GHK4zVG4YmzEA+G+K6+FOwGFmbBN3nwM8FuviYyx7L/8A2Au4GTg2bvsZRX2s5ovAmBD4YHU+txB4F5hpxmcbcCxpxlZHb1AvYDYhLAIghNmEMAMAs/MwGxl7W6/HzOL2YZj9ArPnMHsVs/3i9naY3R57cu8A2qVHMbsWs+djz/QFKzyrEF4khKkV9hwJ3EwIgRBGAJ0x60XRAH6EEOYQwvvAI8ChwCfxPPwF8SLgvKqPbdYPWEQIs91rNN2d27hY7gTM7o490pMw+6U7xhDMhmM2CrO7MOsYt1d+TZfdrwVmf8LsZys4ztR4rH8B/0EI04CNMetZ9XlJsxUC71B88Ts19uCcYMZdZtwPPAxgxllmjDRjrBkXxG0dzHgg9jiPN+OYuP3S2Nsz1qzc+K17vBAYSVG/vD2AySHwWggsBm4HjowXzM8Bf43l/gR82d2vNdA+Hu+bwIMh8H6Np/x14L4qr8VrwP9SNNwx46dmXG/Gw8DNsZf5MvdafDeW62XGU7EXfrwZ+8WyN8Xb48w4Mz7GNGBjM1TfZGUdBHwSAtfVbQiB0SHwdKy7l7n3W1197GjGY2aMituPjHe9FOgb37OXZY+zHTAiBOaHwBLgSeCouO9IijoI5bq4FGhDrItm7AfMDIFJNZ6Pr4ur+7ndGx9PJFkdPT8PA+dh9irwKHAHITwZ9/2GEIp1X81uoehZvj+dSwh7YFbXS3Uw8D1gPiEMxGwgMMo9zo8JYU7s3XwMs4GEMLYB59ubco/29Lit2va7KC60zwK/xOwI4IXU+K/ss9m5/xp4HLN/U7xefySEuXHfIGBnYBHwCmZXAwuAnwAHE8LHmP2I4kJ9ISt6TeEvwHhCuDimkFQ7DsBCQtjXneeoeO5/q/HcpJkKgdesSKXYJG7aGxgYAnPMGEIxyrIHYMBQM/YHugMzQuBLAGZsZEZXigto/xAIZnReidOoVA/3BDYG5sYLdN323jH+FTACmEAxUnQvxZfaiqxIt9gqBKbWOI9RgF+Hdldg3xBYYMZ3gHkhsLsZbYBnYqP5K8BDIXBx7PluT1G/e4fAgPjYnbPHUH2TlTWAYhSzkq9QvOd2AroBI814CngXOCr2ynYDRpgxlKKnd0AIDKpwrPHAxTElYgFFz+7zcV+PEJgJEAIzzdJnxgXAQ8AM4BvAnRQjMLV8Foovl2vguT1P0YMtkjR+j3EIH1FcNL5D8Qa9A7MT4t6DMHsWs3EUvT07uHveHf9/AegT4/2BP8fjjgV8w/c/MRtFMVy6A6Th1ZVlFbaFqttDWEIIxxLCzhSN5DOAyzG7ArO/xoZyrhfFaxGPEv5I8e37LuBAYARmbeLexwhhHiEsBF4CtqAYitoeeAaz0cDxcTvUfk1/R12juFDrOAB3ZOf9DrBphecj6w9fDx4JgTkxHhL/vciyRuM2wDjgYDN+YcZ+ITAP+ABYCPzejK8A8xv4+HWq108gBG6JaRDfoPjidxXwBTP+asavbfm86W7AXGrLH29oCCyI8RDgODNGU3xh3pjitRgJnGjGT4EdQ+BD4DVgKzOuNuNQitemjuqbNLZ9gdtC4NMQmEXRy7s7xfv552aMpejA6s0K0hNC4GWK9KVHgH8CYyB9Ma12n0dCYNcQOJyiF/lBYNtYF28wo32Fu3WNdWVNPDfVOVnO6vlhTQifEsIwQjgfOBX4KmZtKXJsjyaEHYEbgLbuXovi/59S7slefgUSsy2BHwKfj3nMD2THWhnTgc3c7c9QfLuttt07hWLYaG9gMXAMRY9sbsFy5xfCDEK4kRCOpPhwGRD3LHKl6l4Lo0jrGBT/bU8I36rHa/pvioZz3bbKx1nm4+y828Zzl/WQGVtRvAffiZv8+8OAS2JO8qAQ2DoE/hACr1J8MR4HXGLGebFXdw+KntAvw0r94KVaPZwNdHY5vsvVTzM2BXYPgfso6uUxFPXr89ljLF8/l7cz8LK7nb8W33evxZYh8HAIPEXx5f4t4BYzjovpHDsBw4D/AX7vjqP6Jg0xgaLOVVLpCyQU6QPdgV1jD+os6nENjXV8lxDYn+J3AXUpEbPM6AVFChHLPjOI29pTdMRcA1xCkdP/ApXTGJa4L6+r+7mpzslyVseP77bFbBu3ZRDFD97q3pizY17r0fU42lPUVRyzARQ/qAHoRHFhmodZD4of5jTUUOA4itkp9gLmEcJMiuGfIZh1iT+6GxK3Ec+nC0Xaws0UQ6RLKRrxlSrgy8DW7r6HYtY6xj0pepjeqnGOI4DPYrZ1vE/7mLe8otf0DxTf0O/CrFWN41TTj2L4TNYzZnQHrgN+E0KFL6dFXTjJjI6xfG8zNomN0fkh8GeKlIZdYpmNQuBBihGWSsO01YwEtrFiBooNKIZhh8ZzeoJl7/njWT5H+CKW/cinHUX9XArlXqrYWG1pVvniaUaf+Fyq/Uj2IeB7ZrSO5fvFXOstgHdC4AaKurhLHNptEQJ/i+e2izuO6ps0xONAGzP+u26DGbubcQDFNfSYmNveneKL2nPARhTvzU/MOIhlI4cfAhtWe6C6FAkzNqdIZbgt7hoKaeaWSnXxbODKENJvdCrWxegVYKs19NxU52Q5qyPHuCNwNWadKXpCJwPfIYS5mN1A0ZM0leKCtyLXAn/EbCwwmuJNDyGMwexFim+Tr1HkEdZmdhpF5ewJjMXsQUL4NkXD8YvxPOdD/OV5kb98kTvPCwlhjjviecDPCCFg9hBF7884WPYjAecpinQLo1iDewhwJWYL4/6zCOFtrMoX4BDejekot7mUi58QwqsrfE1DuAKzjYBbKL5kLH8ceLXC69WaojH//HL7pLlqF9MBWlPU3Vug8hRnIfCwGdsBw+Pb9iOKHMKt4f9v745dowqCAIx/g/4n6ewUGzstrOUKS9NYiRZBiGJnISKIYrDUGBAECaSQIIIEBMEuaKNgYWWagAiCjRDIWMycnIcmIOQSzu/X3XHv7bsHy87um53H7Qi2qY1vF6jB6FkHngG14WxUbzpbpya921GloI50nuAlKvg8BDzK5EMfdhV4GsENKqVjceR8R/s6h5ViFql+skHlPY57ST2aXevPMxG8oyaf34H7mSz95b49pNK/3kZtCvxCrYyfBOYj2Or7M0s91l0aWRG71tdrf9M/6bz9AXAvqpzhD2o8mKPGnhNU2kMCVzLZjOAJsBrBOjW2fuxzfY3gTQTvgReZzI81t9I5xlvAxZENrbeA5QjOA5+Bs8MDerJ8PJPr/dUdapHmG79vmB16TvWdTxP4b6e6PemXqDhNey5iAVglc23X3x4EEQOq5N5OZXWkqdCB9OVMzu1T+wPgWOaOZaykqdepGI8zOT2Btl4DZ3apWKP/jG++m5yb/Pmx0UF1mJrZS1OvV5ZfdfWI/WB/k6iqFsCD6Bd87JVOv7hrUKxxrhhLkiRJuGIsSZIkAQbGkiRJEmBgLEmSJAEGxpIkSRJgYCxJkiQB8BPyr9T7ZddYFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a2c353320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted label, and the true label\n",
    "# Color correct predictions in blue, 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, test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions, test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset\n",
    "img = test_images[0]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.1119018e-07 1.3615390e-11 8.4647261e-10 7.3386621e-09 7.9924511e-08\n",
      "  3.1954630e-03 2.8529684e-07 2.0971720e-01 1.7340315e-06 7.8708494e-01]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a2c357278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(0, predictions_single, test_labels)\n",
    "plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "prediction_result = np.argmax(predictions_single[0])\n",
    "print(prediction_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
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