fashion_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "686acb10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n"
     ]
    }
   ],
   "source": [
    "print('hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5945217",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6.0\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27a43ea5",
   "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 0us/step\n",
      "40960/29515 [=========================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 0s 0us/step\n",
      "26435584/26421880 [==============================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "16384/5148 [===============================================================================================] - 0s 0s/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",
      "4431872/4422102 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2db3d6c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "85a2e08b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c104215c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3d7d605",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5973cf69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CVpO32P2VW0yQ27FbrF4XbI4DDjgg2eZqyLzYXS4lkl28Xlbj1MwiiQ1Izze30CIv8Mcpt42Kl3C4/5iNN9442eZF6OqVKuutFFovuSrcTK4f/L2cS/HtyuQkrVxqc2u+J9cXuQU2y0juenCFeK66DKRzJlda9vCcyZWxudI5UDzWfV/6UiFNqFJz/eTkrdwiykXHqLdsjOQtIYQQQogGQQ89QgghhCgFLfYX5rJwWtsNef/99yfbN9xwQ7QffPDBaHN1USBdFJSzPbyrjs+Xj+G/Ix+DpS5/vFw2Assq3O7GG29M2h188MGFx2gUihZ+Zbc4kGbR8XUDUomMs8G827Uok6DeCr65BSr5GGWVrJpD7t4v6id/Xbmf6s0Ay7nbeZvHmKoz5yU+lqYGDRqU7OvXr1+0ebz4a/rSSy9FmyUsvzApv49ltd69eyftnn/++cLzFcXMmDEj2l6+r3fx39zcWtSOfz95xYFGRZ4eIYQQQpQCPfQIIYQQohTooUcIIYQQpaDFwTf1xj4sWrQo2Z4/f360WYPk14E0xoXbAWmMCOuTPpaG0yw32GCDaHtNmmNJWJ/2K0izrs2rcb/xxhtJuwceeCDaXk/nlGiOZxk3bhw6G0Wp4/475yoX56p+FrVrDU2az4ljSnLxD2Wqupwjd43rLS1Qb8XYlry/3rR3kc5VvtQEx+TwnMkV1oF0/nv11Vej7WMsOd7Hz/cMz8FcIX/ddddN2qk0QcoTTzwR7Q033DDZx9eef8c8PBfmxhi349/JF198MWk3duzYaPNvZkeiO0UIIYQQpUAPPUIIIYQoBS2Wtx5++OFk+6yzzoo2LybH7k6guPqqX+iR5TPvTmV3GrvgfKo0u9OuueaaaG+33XZJO06fZDdurrokV1NesmRJso9di15yY9ciL0zaGSpZthR2Zft+LkpXzskmLcG/n6VF3ucrRotlaY1FRuuVNYvkMt9PfE7qw2Lp57nnnkvaPf7449EeOHBgso8rNHOowCabbJK043ls1qxZ0faLlPI8m4Mr6fOizKecckrSTpJWyt133x1tLy3z/ZCTBeuVp4sWJvX3xiWXXBJtyVtCCCGEEO2IHnqEEEIIUQqaLW81uZFPPvnk5HWWMHILbhZVK+Zqx0AqVXnZiuFF7ebOnZvsO/3002seg11uQFoRlOWtvffeO2nH2Q1PP/10tP1ifCydeFc7uwX5OvnMhM5AvdlMuUw/rhzK90pO3sq5YIv2+QqlLJHmZBNG2VsVcpWWi2SrXEZV7rq2JGuP5wRe7LZMFEk/o0aNSra32mqraPtq6XzteG7t06dP0u7JJ5+MNt8PPoOIQwLWW2+9aPv5k2Uxrs7Mcy4AbLrpphBL4QxgvyoCz2v1ZmXl4LHI943PeObsrUZBnh4hhBBClAI99AghhBCiFOihRwghhBCloFkxPQsXLsRf//pXAMvGz3C6I6cw+mrFXr9twsdSsC7vtWHWlN9+++1os04MAMcff3y0//nPf0bbr2A+e/bsmuc+ceLEpN29994b7aKKlEAan+RjSRjWXX27ptTS3Ps7C0UVtIE0BiCXSlkUd8PxU74d95GPG/GadxO+xIJYFq5g7vuzKF7Av7688VG+//h4PjZFLIXjagBgyJAh0fZ9yXOPj7lkiuLgcmOYYyd9Gj3HEhXFFQGK6fFw2RNfLqDeVPTcnFkE3zf8ewykFZr5HvK/me2JPD1CCCGEKAV66BFCCCFEKWiWvLXyyivH1GovObGMxa6rfv36FbZjN7mv1tm9e/do88J3/hjsJvULibJ0cthhh0V76623TtqxW5DlN++C42rCLKv4tF1e3M3LU0Vp2d7937TIas6t3Fmod3Halrhgi2Qqf4ycvMJ96d2zRe8pM7n015a4x+sl19dFFbZFKt9zeQ4glQK5EjKQ9jOP4dwYyZUrKZrL/MKkLIlwKANX+hdpxWwgvT6+BApf+6JVEYB0zNZbQoSPvd9++yXtrr322mhzuEhHVmeWp0cIIYQQpUAPPUIIIYQoBc2Wt5pkLe+67Nu3b7Q5A8q7JFki6tWrV00bSF2r3i3K+9g96xf+ZFd7jx49os2L7AGpW5flOB8Bz5/F5+vd7uxq9/vYNcxu3G7duiXtJk+eDCBdoLSzUm+Vz3rlkHrli1w1X97HrvuucL3bmlxGYZF7PFdNuSX4e4XHHM8/Is2O8vM2z6W+X3m+43mMwxI8LLn4ua9oUdiNNtooaceVl/k9nNELAIsWLYo2h0OUhUcffbRwX+53Jzcuuc/5fshVXuex99RTTyXtuP+eeOKJaEveEkIIIYRoY/TQI4QQQohSoIceIYQQQpSCZsX0rLbaahg6dCiANAUcAP7yl79Ee4MNNog2r0wOpGnlHIPj9WTWIL2GzHowH89XBmXdkdMifdoma5ysXfrjcTxSUYq+b8c2kKazsxbKaaXA0urSvuJwI9GSlOSWxnYUxfHk4oVyKetFq93XG39UZnis5ipdt3bqOPeZjzHgcTJz5sxoDxs2rFXPoTPC85gffzwv+ng2nnd53vLXnudPnhd9XAnPk7x6+ogRI5J2999/f7R5rvbzMccPlTGm59Zbb022e/bsGW3/u8F9xv3l42B5zPL19u24Ujb3M8ep+s+dOnVqjW/R/sjTI4QQQohSoIceIYQQQpSCZslbzBlnnJFsN8leAPCrX/0q2l624VRvln58VU52w/qU9aLUx1zV3VxqJktpueMxvM+fO7t4Oa0SSF2L7Arkhf8A4JhjjgEAXHDBBYXn0NHUW0GZXeO5aq6MT60tkja8u96/r+j8+Nz5ePXKZWVm/vz5hfu4P4rS14H6KzcXLULrxya72NnNL9Iq837u4/l42rRpyT4eq1xSwx+Dr30uZIFDEXjh00996lNJO/5d4GP4CsRFC52WBZZxgfR3x8tMReVbfLtbbrkl2gcddFC0V1111aQdS6G+kndRu+nTpxe2a0/k6RFCCCFEKdBDjxBCCCFKgR56hBBCCFEKmh3T06Sxe43+wAMPrGnfc889STuOBeLVzX2JcdbsfZwFp1LmUmR5pVmOG/ArxLPWzPpkvenLHLMCpDE+PubkE5/4RLS33HLLaHdkWe72xF8Pjqfh/vPteLsozsMfg/FxI0Wp80pZ/2h4vPhyEnyd+Vr6fqk3jopTb7md73eOJeGlZES6FJC/7zm+49VXX0328fXmMiQ+VoeX61l99dULP6sIHxPCx+P7iY8NAC+88EK0N99887o+qyvBMTcAMGbMmGj78cbjJbfUTlF8Tm6ppVw7niu23nrrws9tT+TpEUIIIUQp0EOPEEIIIUpBs+WtopTgIvbee+9ke9y4cTXbPfnkk8k2u2T9aufz5s2Ldv/+/aPtZSZfDVq0LvWmcLNrnFdQBlJ3KN9b/j5jlzrv8+fA2/WuDM0oZf2j2X777aM9Y8aMZB9LJOza9rD7nfup3mvM0gaQ3hNllDpy8KrzvryGTwNneMVtnlt9qjjP1ZwC71e753Zs+9TrotIE/t7gFO0y8uUvfznZPumkk6Lt5S2WMX1Fbabo992XgeBxzvfG66+/nrTj7ZNPPrnwc9sTeXqEEEIIUQr00COEEEKIUtDiisytzRZbbJHdZgYPHtzWpyNaEXaF+oXrWHbiyrFeZuJMkHqlqtxCopzBx5Vnvau96ByA5ku9XQWWSI477rhk37333hvthQsXRttLHSyR5BbV5X7j/hwwYEDSjmV0L+GUHZaUN9poo2QfS1gevt8548fLlpx5OnLkyGh7GWyfffapeWw/rni+4L4cOHBg0m6vvfYqPPcywlWufYV/xi+QzSxYsKDm675yM983PEa95Dhq1KhocyhKR1LOWVsIIYQQpUMPPUIIIYQoBXroEUIIIUQpaJiYHtH5qHeV9W233TbagwYNSvbxisq5WB3W/blqaG719KJ0eCCNI+EYAk7H9pQ1hsfD19jHdxxwwAE137No0aJkm2MEuBq778/111+/pl1vOrzKDAAXX3xxtH3FXB5XRx11VLKP49s4HuO5555L2nGc0IgRI+o6p8MPP7xw35FHHlnXMUQKVzz2KesPPPBAtJ944olo+xUTdtlll5rH/sY3vpFsc+wP3ze8GkOjollcCCGEEKVADz1CCCGEKAVWtEBjzcZmLwOY23anI2rQP4TQ66ObNQ/1ZYeh/uw6qC+7Fq3en+rLDqOwL5v10COEEEII0VmRvCWEEEKIUqCHHiGEEEKUAj30CCGEEKIU6KFHCCGEEKVADz1CCCGEKAV66BFCCCFEKWiIhx4zO8zMgpltUWf7OWbWs8brS5r5uc1qnznOCWa2QWscq7NjZj3MbHL134tm9jxtfyzzvgFmNq1g39lmtm/BvmWuvZkdbWY/MLM9zWzn5ftG5UV9WW7M7INqX083s8fM7Dtm1hC/GWVHY7PlNMraW0cDeBDA5wD8qGNPpUWcAGAagPkdfB4dTgjhFQBDAcDMfgRgSQjhV8t5zLNqvW5mK6L2td8fwEUADgawBMDY5fn8sqK+LD1vhxCGAoCZrQtgJIBuAP6PG5nZSiGE99v/9MqLxmbL6fCndjNbA8AuAP4HlYeeptf3NLMxZna9mT1pZn8zt3qgma1qZneY2ZdrHPdUMxtvZlPM7MeZzz/fzCaZ2d1m1qv62lAzG1d9701mtk7R62Z2BIARAP5WfcpetVUuTBfGzAaZ2SPV6zXFzDat7lrRzC6r/mU5uulamtkV1evc5OU7y8weROVhObn21XtkKIBFAL4K4NvVfbuZWf9qP0+p/t+Pjn+pmT1gZjPM7KB2viSdFvVlOQghLABwEoBvWIUTzOw6M7sFwGgzW93M/lydcx81s0OB2vdHte2/reI9mmZmR2U/XLQIjc3adPhDD4BPA7gjhDADwCIz25b2DQNwCoCtAAxE5eGoiTUA3AJgZAjhMj6gme0HYFMA26PSMcPNbPcan706gEkhhG0B3Ielf8FcCeB7IYQhAKbmXg8hXA9gAoAvhBCGhhDebvYVKB9fBXBh9a/IEQDmVV/fFMDvQwiDALwKoGgp5ndCCLuGEK7Gstd+GIDHQgizAVwK4DfVfQ8A+B2AK6v99zdU/kppYgCAPQB8CsClZla8jLdg1JclIYQwC5XfjHWrL+0E4PgQwt4AfgDgnhDCdgD2AnCema2O2vfH/gDmhxC2CSEMBnBH+36T0qCxWYNGeOg5GsA/qvY/qttNPBJCmBdC+BDAZFQuWBP/AvCXEMKVNY65X/XfowAmAdgClY72fAjgmqp9NYBdzawbgLVDCPdVX/8rgN2LXq/3S4qEhwGcYWbfQ2WNlKYHxdkhhMlVeyLS/mauKXgdqEyotxfs2wkVFz0AXAVgV9p3bQjhwxDC0wBmoXLPiI9GfVku2Nt+ZwhhUdXeD8DpZjYZwBgAqwDoh9r3x1QA+5rZuWa2WwjhtXY7+3KhsVmDDn3oMbMeAPYGcLmZzQFwKoCjqq4zAHiXmn+ANAbpIQAHUNvk0ADOqT55Dg0hbBJC+FMdp6SFyNoAqwSqNwXZjQghjARwCIC3AYwys72rTXP9zbyZ+bj9AIyu89RCgV1rW0B9WWbMbCAqfbmg+hL3nQE4nObcfiGEJ2rdH1Wv/nBUHn7OMbOasSSieWhs1kdHe3qOQMUN1j+EMCCE0BfAbKRPhkWcBeAVABfX2DcKwIlWiReCmfWxSiCeZ4XqOQDA5wE8WP2rY7GZ7VZ9/VgA9xW9XrXfALBmHedcSkIIN9FkOKE6ec4KIVwE4GYAQ5bj8PHaV71xK1WD/JJ9VcZiadzYF1AJnm/iSDNbwcw2RkVKfWo5zqnLor4sJ1aJd7wUwO9C7VWqRwH4ZtMfoWY2rPr/MveHVbKA3qrKJr8CsG2N44lmorFZHx390HM0gJvcazeg8gBSD6cAWMXMfskvhhBGo+Jee9jMpgK4HrUfSt4EMMjMJqLicTq7+vrxqGjSU1CJCfqo169ARZ9UIHN9HAVgWtUVvgUqsVIt5QpUrz0qf9XcRftuAdD0189uAL4F4IvV/jsWwMnU9ilUHmJvB/DVEMI7y3FOZUJ92XVZtXq9p6PSF6MBFCWF/ATAygCmWCUl+ifV12vdH1sDeKT62g8A/LTNvkG50disgdV+aBei82FmlwO4PIQwrpnvuwLArdWgdNEAqC+FaEw6+9hslDo9Qiw3IYQvdfQ5iNZBfSlEY9LZx6Y8PUIIIYQoBc3y9PTs2TMMGDCgjU6lmPffT4t9vv7669FeuHBhtFdcccWk3SqrLC0BsMIKS8OX/PHefHNpkPrqq68e7T59+iTt+BjtxZw5c7Bw4cJaGWrLRUf1ZdmZOHHiwhBCr9Y+biP25xtvvBHtj3/848m+j32ssFJ+wrvvLk00eeutt6K9zjrrLOfZLT8am12Lthib6suOIdeXzXroGTBgACZMmNCsD/eepNoZ5nkWLFiQbN9zzz3RvuyypXUJ11577aTdlltuGW2edBcvXpy0e/jhh6O94447RvvnP/950m7VVeuLUebv3JLvy4wYMWK53l9ES/pSLD9mNrctjtsa/Vnk9W3pPXzfffdFe+ONN072bbjhhnUdY/bs2dHm73fkkUe26JxaE43NrkVbjE31ZceQ68uOzt4SQgghhGgX2iSQuV5PB0tTF154YbLvrruWZsS9806a1cYS1H//+99ojx8/Pml344031vzclVdeOdlmGes///lPtHfeOV04tnv37tHeY489ov3Nb34zadcIrnchmguP25yUO2/evGj/+c9/Tvadf/750WYZujXgczr22GOTfeeee260Tz75ZNTDhx9+WHh8IUTXRKNcCCGEEKVADz1CCCGEKAV66BFCCCFEKWj34oQzZ86M9kEHHRTt9ddfP2nHmVg+BodT0zkry2dTLFmy5CPfA6RxQS+//HK0fWo7p8/eeeed0X7ooYeSdl/5ylei/ZnPfAZCNCL1xrQMGzYs2X766aejzWMCAFZbbbVo85j2cXkc98Zj/YUXXkjavf3229Hm7El/vP/3//5ftDnrcp999knajRw5Mtr++/L1UHxPMT7Lr+i65eI5c/XhWpItOHbs2GSb4zGfemrp8k6bbbbZcn9WV6a1Mzjr5Zhjjon2d77znWTfttsuXZqN5xv/O14vGtlCCCGEKAV66BFCCCFEKWgTeSvnCvv+978f7d69e0fbp3mztOSPt9JKS0+b3XEsZwGp+4ttlrOAtCIzS2n8OUBa4Zlduv54v//976O93377JfvWWGMNCNFR1JuWvtNOO0V72rRpyb711lsv2v7e57HK+/xYevHFF6PNkpYvAMqVm1nS4rHot3nu+Pvf/56046rO//znP5N9fD1as8Bomaj3WrXkmo4ZMybZnjp1arRZcgWAM844I9rcl6NHj07atVQiaUTqvWdz7Xib29VbZPi9995Ltvn3lPvriCOOSNrNmDEj2v53nMdpa4xFeXqEEEIIUQr00COEEEKIUtDm2Vs+G4Pd2muttVa0vVuM3eHskgZSOeqDDz6Itl9wlLfZde0zP/j43C6XNcYylXe18/ndfPPNyb7Pf/7zEKKjyLmHb7rppmiPGzcu2n379k3asbTrxy0fv8gG0rHPrnOfUVYkx/kxzMfncduvX7+k3ahRo6J9++23J/sOOOCAwvMtA/VKGP51P+8WceWVV0ab1zh84IEHknYXXXRRtDfYYINoP/bYY0k7zsTiDB8AuOCCC6I9dOjQus6vs1MkTeXa8e+nh8eiz2RmGZrb+d/M+++/P9qHHXZYtP2Cw1tssUW0OTzE44/fEuTpEUIIIUQp0EOPEEIIIUqBHnqEEEIIUQraPKZn8eLFyTbH9LAW7Cu7cpyN14w5FbYozRRItUbWMb0+yeR0UY4z4srNPXv2LDw/Xi0eUEyPaH9ycW8MVw/ne/qNN95I2uWqpXOMT27M8b56qx/n2hXNAz6lns/9wAMPTPZx/CFXk/bn7tPvxVKeeOKJaPvrxinnEyZMiPaiRYuSdscff3y099hjj2j7uB0+BttAGjPyzDPPRHuTTTbJnn9Xod6YtNx8wPtysTQ89p577rlkH4+xNddcM9o+luj888+Pdp8+fZJ9rV0+Qp4eIYQQQpQCPfQIIYQQohS0uZ92ypQpyTa7PFnq8qmqvO1TwjmNceONN472gAEDkna8+CGn2K2++upJO3bdsczGFSQB4JZbbql5vFdffTVpxxUlOX1diI6gyIV96KGHJtss/XBJhjlz5hS285JTkRs8lxrbEvznstubv6+fV3hO8PMKyy+f+9znah6vK1OvdOBLiPBinywLduvWLWl34oknRvs3v/lNtL2cwQtOLliwoPD8OM150qRJyT5eEJr7uSzyVr2LCXteeumlaLPs+MorryTtJk6cWPM9XtLs3r17tPneeO2115J2frHwtkSeHiGEEEKUAj30CCGEEKIUtLm8xW5iANhtt92i/be//S3aflFDXjCO3Zg5vNv17bffrml7yYmru7L05TOtzjnnnGhvt9120WaZDkhd6LNmzarr3IVobx5++OHCfT6bksm5ynNVmJlcxdh6qHehRH+unF3mqzqPHz8+2jxvlaU6s5cg+drxNcgt7MzzuF8g9A9/+EO077jjjmh/8pOfLDynddddt3AfS18sowDA888/H+0///nP0d5ll12SdoMHDy48fmcm15czZ86M9imnnJK041ANzraaPn160o5DTB5//PFo77nnnkk7li55TvELveYyquulXgldnh4hhBBClAI99AghhBCiFOihRwghhBCloM1jek477bRkm7XFvfbaK9rDhg1L2r3++uvR9jE9rNnzas09evRI2hVVjvUaPR+PU+l8nBGnO3I8Eqf3+vPw2mXZaenqv0XxBS2tlsspnfWmc3o4PoQ/t7PEgHDZBSCtXpy7jtyHuYrMfIyc3p5LMS+6X3Jp5HxP+LR0jivwpStGjhwZba4QWxZyZQAYf99wH91zzz3RPuaYY5J2l1566fKeYgKnUfPvBQAMHz482lyd2ceq+VTsrkKugjKXebniiiuSff43tLn06tUr2ea4OY6fOuqoo5J2HCOUm/t5X27FhBzy9AghhBCiFOihRwghhBCloM3lLZ+OePfdd0f7hhtuiPbo0aOTdrzo3MUXX5zsYwmKF5PzqZRFMgi74IHU/cmuNO+e5RS+X/ziF9H2EtY666wT7RtvvDHZx9VLfZplGahX+vGuy6L31evS9PfQT3/602jPnz+/rmN4ci7kRuWxxx6LNi+aC6QVdNktzePD7/PyUdHipl624n25NPeixQZziwvzPeHb8QLIftyWfSHRescmz4MAsPvuu9e0PVw2hO+beksb+Ha8QCzPuUAa9nDAAQfUfA8AzJ07t/Czy4CXs3gc8Viud67jkBUg/Y3nPrrvvvuSdt/73veiXe8iqJ56pUp5eoQQQghRCvTQI4QQQohSoIceIYQQQpSCNhexTz/99PQDSTfnNLUtt9wyaXfzzTdH++yzzy48PmuNXqMvihvw2n1RvI9froJT4HfYYYdo8+qxQKpr+lV9yxjHk6NIs683voLTjAFg8uTJ0b7uuuui7WNPOLXy6KOPjvbf//73uj4XSFO8f/nLX0b7hz/8Yd3HaG/4XvdxNgzHx/lUZu4zXzKA9/HxfWwNxwvw8XMp6zk9v6idT3/l+cJ/r3nz5hUeXxRTb18yvK+lq9hzTJovG1J0H/q4z7LHceViJ3NxPDzu+Roed9xxSTueg/mzOBYXSOO9fEkEhpe8+N///d9kHy95kUOeHiGEEEKUAj30CCGEEKIUtLlv77DDDku2OWV94sSJ0ea0QgA45JBDos2r6QJAv379os2uVZ+Kzi6zXEVYds/xCunevffGG29Em1Mdf/Ob3yTteJ9faZgrT/sq1F2VXNppUbrq008/nWyzm5RXB/elDgYOHBjtDTfcMNo+zXbOnDnRvu2224pOPcs//vGPaP/nP/9p0THam0mTJkWb5TmgOCXcp6yz+9lLwEUucd/PRRW2veTE4zZXibtofPvXeU7w1WNZIuH+ZClbLEuRPOVf5/smNx/n5guG772//vWvyb6DDjoo2p///Oej7WWwnJRSBlpaPb6oij1fdyBNU+cV3LmkAJA+F/Tt2zfZ558hmuDyE0Aa6sArJnjk6RFCCCFEKdBDjxBCCCFKQZvLW0888USyzfIRZz3tuOOOSbuHHnoo2lOnTk32sUsulyFQVOk1t+hlUSaCP192mQ4dOjRpt9FGG0Xbu+o233zzws9uRHILc7I84iUQJudCZZfnGWecEe1rrrkmaceLQ/bu3Tva22+/fdKOJc633nor2n7R2ueffz7aZ555ZuH5sbTqz+k73/lOtJ988slos2wLpIsfdjR87/txwHJEvRVY/TH4fVy52UsdRbJVbmwy/p7ihSS5srTP1mFZzH9HPsYFF1wQ7eZk9DU69VY6b2tyGXZF7TxcTdiHCkyYMCHaX/nKV6I9c+bMpN3OO+/80SfbxahXPszNFfXeN/z7x+EhixYtStodfPDBhcdYb731os1j1ld/5t+FHPL0CCGEEKIU6KFHCCGEEKVADz1CCCGEKAVtHtPjNVTWb5977rlo+6rGudRxTjtkrdFX1yyKz8mt5MxxIP5zOb6Dz8/HDXC8CMesAMCLL74YbU6vbiRyWi6Ti+NhOB2RV90F0jRDrlY9aNCgpB337WuvvRbt119/PWnHKagcB8QaP5Deb5zeeN555xUeb+utt072cQwIx6/49PhGwqfsMkWrKvt+5nsiF4/B5GLv6iWXRs/jjMe3T8vnqur+nPiY3J9diY6K4clRb0VmrrYOANtss020uao6ANx6663RHjVqVLT9/eBjLstAS+6BohT1j+Kxxx6L9pAhQ6LtV7vn8h9+Tj/rrLOizb+1n/jEJ1p0TvL0CCGEEKIU6KFHCCGEEKWgzeUtL4/wwo8sWXhJgGUm71pjtzS71/1nFaVb+3ZFi+R5Vyjv69mzJ4rgdDxfOXb+/PnRblR5i92f9bqeL7roomhfcsklyb6XXnop2t6dPHjw4Gjz/cDvyZ1fTqrkfvXVd70LtQmfwnrTTTcVnsdPf/rTaP/+97+Pdv/+/ZN2V199deEx2puf//zn0fbyLW+zdOfTSzlVuN4U89aAx7qXt/g+5XP3VdpZ3uM5Bkgl63/+85/RbpQ0764E92Vujjn33HOj7e/Dr371q9G+6qqrkn18jx544IHR5krsQP0SfVkoSmf3v2NFi3n7scKLgPNvfHPmjZ/97GfR5t/gI488su5jMPL0CCGEEKIU6KFHCCGEEKWgzeUtnyFRJD/wwmRAujBgTt7KuZrrrchc5Nb3Lj3+XK4SyZIdkLr+/DG4KmWjwItQAsCdd94Z7aeeeiraPqOFpTr+XpwhA6QLf3LmFZBeb7+PYemBr2lOqmRpw99DnJXF/ecXDuUqn35xzT59+kR7s802i7aXTS677DI0CrNmzYo2u56BtC9Y2vVyHX+/9pS3mNwY5nvRy1u5au4suQwYMKDme0TrwHOkl5x+9KMfRZvH+rrrrpu040zQTTfdNNnH/c7zVGeUs/he53s2N/b8fNfS7Kui9xeNiREjRiTbXDWZs+hy+LASHpc8F+VCTHLI0yOEEEKIUqCHHiGEEEKUAj30CCGEEKIUtHlMj4c1WtYFfUVmHxdRRFGMkP8s1kK9ls/b9a7+y/EQuVT5XJXojmTBggX43e9+BwC48cYbk30cT5Wrgsu6OVc/9teDq2j6PuJYHY4F8rFQfK9wbJH/LI5L4X7g7+SPwRoyr9ANpPeDjzvjOBI+fqPFbXGFcD5Pr4kXVSP3fVZU6RwoTnn1aclety+Cj8/HyKXGcmyYv2c5fsv3E4/VZ599tq7zaxT8vFJvqYnW/mzuF9/HPNafeOKJaJ966qlJO46P46r9559/ftIuF2vF1Zs5jm2nnXYqfE9bkyt9kFv5vCUlRFqbXEzQZz7zmWhz1WUA+Mtf/lLzPf43mI/v536OpRw2bNhHn+xHIE+PEEIIIUqBHnqEEEIIUQraXN6qN93TSwfexcUUVVf2UlJRanvunPgY3mXMn8UygU/RZonF0ygLGfbo0QPHHnssAGC77bZL9j300EPRnjZtWrTnzp2btGN5YPHixdH2acJ8Tb1bkxdxXbhwYbRzkgq7zf1nFaVx+oU2WY5jCcS7j/le8aUJ+DzYde9TwT/1qU9F+5e//GXN82tLHnjggZqv5yQnlrf89+bKuF4+KnLF11taoqXwNee+9fcRS61+juHv2RoLpLYnOdkjl9rcGte+KCSAxwSQyqy//vWvo7333nsn7bhsxHXXXdeic+LvlTun9iRXPb4l/fDkk08m23/+85+j7SVDX5G+iZzMxL9Vfg744Q9/GO2XX3452j5UooicXJYrUbPxxhsXvq/e8hny9AghhBCiFOihRwghhBCloN2zt+qFXWvedVtUoTLnks65D4sWHPUyxauvvhptlrd8NVDOHPDu/46qYFuLpnPhRT8BYIcddqjZ3st2s2fPjvYzzzwTbV9hlSuienmvqC+9i5MXEOSF6/h1IJUaORPLS5Ds5s65vFnyyfUdZ0KxvAJ0fEVfv7BoE/7+Lqr2yvc9kMoFOUm5aFz5bT6/3DXmz/XXtEiO89+dZVgvX/vv0lVo7fsvl4WUk9m40vIGG2wQ7SlTpiTtrrnmmuU8w/TeY9m8vSsyhxCiBJ+rHs/3HktHAHD55ZdH22c5Mzwf/+tf/0r2cWX9onPw58jjiLPogFR2vO222wrPiX8nuQp+TlbjMQqk99euu+5a+FmSt4QQQgghCD30CCGEEKIU6KFHCCGEEKWgzUVsjr8A0pTRXAwOa4Fel2fdOJf6VlTx0mt/RenxuXgcPvd+/fol7SZMmBBtHzfRKBWZV1xxxRjn4lcPf+GFF6Kd00m7d+8e7T333DPaPm6nKKYEKI7T8PcGH7MofR1IU9j5PXzfAWmaZW5Vbj53f59wBWO+z31siF+lvL3ZY489ar7uYz2KYgx8X/A1ycUF8fH9teNt1vr99S9Kh/bH43PKVYzm43dUddu2IBdnwzFZL730UtKOxzqP4Rz1xgj93//9X7LN9xTH8dx00011HS9XxiRX+Z5jetobM8vOf7WYNGlSss19lpsjeRV6LgUCALfccku0Dz744Oz51uLoo49Otvfff/9o59LIeWzXy4svvphsc4zkzjvv3OzjeeTpEUIIIUQp0EOPEEIIIUpBm8hbLDnkqlCutdZahcdgN3QulZSPn3ON15sKm5POitz1AwYMSNrxeeTc642CT7H220WwBJmTDVha8mnvRdfDy4BFi8Lm3sf95WXWPn36RJvvDe9Cz32vovvGXz9Oz+0I/v3vf9d83cu3vM3y33rrrVfYzo+ronvfXzuWxYokMSC9xrl23G+5yspFfVZruzORk5wef/zxaPvUY56D/SLPLalezFWXx44dm+xjubmoSniOnByba9uRi8cuWbIE999/f83zOOKII6LN9yxLjh4uw+FXMWApyc9BJ598crRz8hZz6KGHRnv69OnJPp8S35rwgsFA/fehUtaFEEIIIQg99AghhBCiFLSJvJVb3JPd3ywxeHLVV4vcmt69VZSx5d9fVDnWfy7LbJzx4ysy5+StRqrIvLywOzUXpe/dsKJ9ueOOO2q+7mVjlpz4/r7kkkuSdl/4whei7eVJXtiV730vpfG+3Fgveo/PEORtdo/7zDVeNNdX6S7CZzx5ua8taJon6s2UymVvtUbGS718+ctfjvaMGTOSfbfeeutyHTtXmd/D94pfmLM9effddzFr1iwAwFe+8pVk35lnnhltHjcsEfp9nAnmpUp+X27RztNOOy3aX/rSl5J23/ve96J97733RnvfffdN2vlK+K2Jl/d8aEIR9Y4VeXqEEEIIUQr00COEEEKIUqCHHiGEEEKUgjavyOx1NtYWc6m89VZVLUpprfW+JupdJTinGXPcwKBBg5J9uZXfu1JMj+gccJkA1sd9inLReDnssMOS7W9961vRHjlyZLKPY4EWLVoU7d69exeeE+PjNnhscjyDr7DN79thhx2izam6AHDffffVPHatz27i5ptvTrY5bqWtaO7K6Ln2POcceOCByT6OAzn99NOTfZ///Ofr+uyzzz472hw/dsoppyTttt5667qO1xrw74Jftbs96dGjB0444QQAwB//+MdkH5cS4HP045BXVuf7nittA0DPnj2j7WPe+B4477zzatoA0KtXr2hznOaPf/xjFMG/cbkyAvXiv1e9sXf1frY8PUIIIYQoBXroEUIIIUQpaHd5i91suYUYOX2WXW5A6qLPVVEtWjQxt9Apn593wRctYJlLvffnl1s0T4i2gMcgy0/1uo09v/jFL2raOby7nc+Dx5yfL3ib095z1dzrJVdNmivk8mKNQNvLW2+88QbGjBkDYNlUf577eMFfX4GX50/+LmwDwDPPPBPt888/P9nHacq8mOXo0aOTdhdeeGG0edHSeu+NlpKT9HiO94vidhS+cv+4ceOizYtW+0WUuWQCfy9OZQfS36vcteESIrlrw7JaTppsrhQLLPvbylKar8hcVCLCzyn+3i5Cnh4hhBBClAI99AghhBCiFOihRwghhBCloE1ieoqWf/Dkykuz5ue1O05dfeWVV6Lty+rXm37OsGbq4wbefPPNaHOpbK8l8rn7GB6v1wrR1vzpT3+K9o033hhtvp+B1k89ZfwYqVd/b204roJXkgfSGCeec3bZZZe2Pq2E//73v5gzZw4AxP+bWLBgQbQ5LornRCCN2+B5sG/fvkm7Y445JtpDhgxJ9t11113R5hXTp06dmrTbddddo81xQT4eiefFto6z4RiRT37yk236WfXy/e9/P9n++9//Hm1eUsL/VvHvJP8m+WvIsTX+d4fj1fj4Pr6V7ylfjoJZ3rki93vsf++LYnpysbk55OkRQgghRCnQQ48QQgghSkGbyFtcDdO7OOuVnI444ohov/7668k+TmHnz8qlr3O73Grs7Krzclm3bt2iPWLEiMLPYlezPyc+DyHaA5ZteJVxv/o2j7N6q/HmyJWJ4O1cymvRPu9S5+1cCvz+++8f7csvvzzZx2UoPvWpT0WbV55uD7iKb72wzA8A8+bNizZXxubXgfRa8b0BpJIW3xu+qjPfK14+Y9ozdZzlrV//+tfR5pXN2xuf9s3XnitZn3XWWUm78ePHR9v/FrY2u+22W7T32muvNvucnCTG9x1QvHJDS1LlAXl6hBBCCFES9NAjhBBCiFLQJvLW22+/He2cW9svLMb4SPfOBLvd/PfPfWch2ppc5VfO3PAyCMNZX74SMMMu7NbOBsvBErKXqIcOHVq4j+Wtb3zjG21zcm1Ejx49sttlg7P0OkNfsuzKtmfGjBnRnjhxYrJvypQp0eaFZIFU4uTfJ7+awKWXXlrzc31IyPKO55zUedpppyXbm2++ec12PnSmXuTpEUIIIUQp0EOPEEIIIUqBHnqEEEIIUQraJKaHV//dbLPNkn2c0rjDDjsUHiOXzt7SVLX2glM4Z8+enewbPnx4e5+OEBEeV+edd16yj8dt7969C4/RKKtWF5GbH7jcBac1A+n3as8YJNG2/OQnP+noU2g1+PfU/7YeffTRbfa5rf2bmzvevvvuW9cxciVqcmhkCyGEEKIU6KFHCCGEEKXA6l2IEwDM7GUAcz+yoWhN+ocQen10s+ahvuww1J9dB/Vl16LV+1N92WEU9mWzHnqEEEIIITorkreEEEIIUQr00COEEEKIUqCHHiGEEEKUAj30CCGEEKIU6KFHCCGEEKVADz1CCCGEKAUN+9BjZh+Y2WQzm2Zm15nZah/RfoyZjajac8ysZ/ucqagHM/uBmU03synVfi1eg6T5x97TzG5treOJPBqbXZe2GKfc/8vTRjQf9eeytMnaW63E2yGEoQBgZn8D8FUAv+7QM6qci6FS3+jDjj6XzoKZ7QTgIADbhhDerf7ofayDTwsAYGYrhRDe7+jz6GRobHZBGnmciuaj/qxNw3p6HA8A2MT/RW9mvzOzE3JvNLPvVP8inWZmp1RfO9fMvk5tfmRm363ap5rZ+OqT8Y+rrw0wsyfM7GIAkwD0rfFRopjeABaGEN4FgBDCwhDC/Opf/T82s0lmNtXMtgAAM1vdzP5c7YdHzezQ6usDzOyBavtJZraz/yAz2676noFmNtzM7jOziWY2ysx6V9uMMbOfm9l9AE5uv8vQJdHY7DoUjdOzqtd9mpn9sfpw2TSOzjWzR8xshpntVn19VTP7R7WfrgEQV3Y1s0vMbELV+/DjjviSJUL9WYOGf+gxs5UAHABgagveOxzAFwHsAGBHAF82s2EA/gHgKGr6WQDXmdl+ADYFsD2AoQCGm9nu1TabA7gyhDAshKCy4s1jNIC+1YF0sZntQfsWhhC2BXAJgP9Xfe0HAO4JIWwHYC8A55nZ6gAWAPhEtf1RAC7iD6k+BF0K4FAAzwH4LYAjQgjDAfwZwM+o+dohhD1CCOe39pctCxqbXY6icfq7EMJ2IYTBqPzgHUTvWSmEsD2AUwD8X/W1rwF4K4QwBJUxN5za/yCEMALAEAB7mNmQNvw+ZUf9WYNGfuhZ1cwmA5gA4FkAf2rBMXYFcFMI4c0QwhIANwLYLYTwKIB1zWwDM9sGwOIQwrMA9qv+exSVvxq3QGWiBYC5IYRxy/WNSkr12g8HcBKAlwFcQ16AG6v/TwQwoGrvB+D0av+PAbAKgH4AVgZwmZlNBXAdgK3oY7YE8EcAB1f7cnMAgwHcWT3ODwFsSO2vaa3vV0I0NrsgmXG6l5n9pzru9gYwiN5Wa/zuDuDq6jGnAJhC7T9rZpNQ6cdBSMewaEXUn7XpFDE9TZjZ+0gf1Fb5iGNYZt/1AI4AsD4qf102tT8nhPAH97kDALz50acsigghfIDKA8yY6mA7vrrr3er/H2Dp/WgADg8hPMXHMLMfAXgJwDao3Afv0O4XULkfhgGYXz3G9BDCTgWnpP5sORqbXZQa4/QrqPwVPyKE8Fx1DHLf1hq/ALDMoo5mthEq3tztQgiLzewKfPR9IpYD9eeyNLKnpxZzAWxlZh83s24A9vmI9vcD+LSZrVaVRw5DJQYBqEymn0Nlcr2++tooACea2RoAYGZ9zGzd1v4SZcPMNjezTemlocivPDwKwDdJax5Wfb0bgBeqgarHAliR3vMqgE8B+LmZ7QngKQC9rBLMBzNb2cz4LxrRumhsdnIKxmnTHx4Lq9f+iDoOdT+AL1SPORiVH1kAWAuVB9TXzGw9VKRR0UaoP2vTyJ6eZag+mV6LinvtaVRcarn2k6pPn49UX7q86j5HCGG6ma0J4PkQwgvV10ab2ZYAHq7+3i4BcAwqT72i5awB4LdmtjaA9wE8g4rL9aCC9j8BcAGAKdUHnznVthcDuMHMjgRwL9xf+CGEl8zsYAC3AzgRlQF9UfVHeKXqMae34vcSVTQ2uwRF4/RVVOK25gAYX8dxLgHwFzObAmAyqn0cQnjMzB5FZQzOAvBQq5698Kg/a2AhLOO1EkIIIYTocnQ2eUsIIYQQokXooUcIIYQQpaBZMT09e/YMAwYMaJMT+fDDtIjq888/H+0330yTM3r06BHtXr16tcn5AMDixYuT7YULF0Z7rbXWivZ6663XZucwZ84cLFy4MJfp0iLasi/bmnfeWZq09frrryf7VlxxaWzzCissfaZfY401knYrr7xyG51dnokTJy4MIbT6TduZ+7OzorHZtWiLsam+7Bhyfdmsh54BAwZgwoQJrXNWDv9gc+aZZ0Z77Nixyb7jjjsu2l//+tfRVlx33XXJ9uWXXx7tAw5YGqh+yimntNk5jBjRNsuXtGVftjVPPbU0k/2OO+5I9nXv3j3aq6yyNHty553T4s19+vRZ7vPgeLhqcO1HYmZtUjyvM/dnZ0Vjs2vRFmNTfdkx5PqyQ7O3vvrVr0b7vvvuS/ax58d7UviB6KKLlhbl7ds3rUC/6aZLs/W6desW7UWLFiXt+KHqv//9b7S9F6F3797RvuSSS6J9yy23JO0uu+yyaA8cOBCiPup9iPja174W7UceeSTZ9/77S5fRevfdd1HEl770pWg/9thj0X7rrbeSdrvvvnu0zz8/Ld686qqxGjs++GBpEhF7m4QQQjQOiukRQgghRCnQQ48QQgghSoEeeoQQQghRCto9pueee+6J9uzZs6M9bNiwpB3H0/jMrm222SbaL7/8crRnzpyZtOPgaA46nDJlStJupZWWXoaePXsWntOCBQuivdFGG0X71VdfTdp997vfjfZNN90EUR/1xvS8+OKL0V5nnXWSfRyT9bGPfSzavo+uvvrqaHM2mM/qmj59aQFnvk+ANJ6MP5djfYQQQjQO8vQIIYQQohTooUcIIYQQpaDd5a0777wz2ly0yacXs8zw3nvvJftYgmLJwa8jxmnELFN4+YEL16255prR5gKJALDaaqvV/KwNN9wwacfS3IMPPpjs23XXXSFqwzImFxYEUvno2Wefjfbqq6+etOOUdZY3fXFClsVYZmVJDEj7+dvf/nbhufvzFUII0XhophZCCCFEKdBDjxBCCCFKQbvLW/Pnz482r1+Vk7dYpvJtWY7wEgZLIoyvmMtyFFfkZTnLH5/lDH9+nHkkeSsPy0c+S4/hrD+WrViOzB3D3wt8DL6fvJQ6ZMiQmu8B0iyy9ddfv/AcJH0JIURjoNlYCCGEEKVADz1CCCGEKAV66BFCCCFEKWjzmB4f38DxM7zyOdtAWiXXw3EXHE+zZMmSpB2nL3Psj4/b4HPk9/hz5/etssoqhefHMT0zZswobCfSa+XTxZnx48dHm+Nn1l577aTdU089VfPYPj6LK3kzHGcGAIceemi0R48enewbPnx4zXPypROEEEI0BvL0CCGEEKIU6KFHCCGEEKWgzeUtrnYLpJLR22+/HW0vK3DFXC9HvfHGG9Hmisw+LZllBpbLvPzA6fEsb/l2LJdwGrKXThhf1Vmk1LvI6L333lvzdS9vfeITn4j2rFmzCo/N8tbQoUOjPXny5KQd31OHH354sq9///41z8mXRBD1M2fOnGR73rx50Va5ByHE8iJPjxBCCCFKgR56hBBCCFEK2lzeeuGFF5Ltj3/849FmichLSSwd+IrHXIWX3+ezt1i24s/i14FUPuPFSL1MwdlFvXv3jrav1Mvn0aNHj2Qfyyq9evVC2eG+ZanSw1IVV80eN25c0q579+7R5nvDZwfuueee0WYJ5eijj07a/fznPy88p3qlOZHnuuuui/aZZ56Z7Nt///2jzVLm4MGD2/Scrr766mhvttlmyb7tt9++TT9bCNF2yNMjhBBCiFKghx4hhBBClAI99AghhBCiFLR5TM8rr7ySbHMszGuvvRbt+++/P2n3hS98IdobbLBBso/jhHiFbI7HAYor/PrYEW7HKeu+3brrrhttjiXxq2hvueWW0eYK1ADw5JNPRlsxPcXp3Q888ECyvWDBgmhzPIe/vxYvXhxtLnvgKzBzBeVnnnkm2tx3ovlwSQoeF750w7e+9a2a+wYOHJi0mzJlSrRPOumkaI8dO7au8/Fxfn/+85+jvXDhwmQfl9BYY401ou3nn65KrkRHjosuuija2267bbR5vgTSOZPnviFDhiTt+vTpU9fn1ss555wT7UGDBiX7DjnkkFb9LNH4yNMjhBBCiFKghx4hhBBClII2l7e8rMDVlLnKrm83ceLEaO++++7JPnZ5cxqrl7PY1c5p6r5yM0taXLnZp6JzGj1XYf7Pf/6TtONjbLjhhsm+xx57LNq77bYbyk6RC51ThoHU9c795UsCsMRZVGnbt2OOPPLIZPs73/lOtH/9618XnrvS1ysULba6aNGiZJsXhh0wYEC0c5IIzxH+/thrr72ifeutt0b7pptuStqxhOXH3/HHHx/ttk6Jb0R8aZCiEhJ33XVXsv25z30u2ixb+WvP1c55/rz44ouTdixxbrfddtHmBX6BVIr2lbzvvvvuaM+dOzfa3P+A5K168eOa7wHur4033rjwfY0yL8rTI4QQQohSoIceIYQQQpQCPfQIIYQQohS0eUzPl770pWSbV8F+9dVXo81pj0CaWspp3gCwyiqrRJvjeHysDqfM8lITXp/kY7DWzPFHAPDII49Em0vn+1gPTsG99NJLk328DEcZ8XEDRSnro0ePTrY5doevLy9JAaT9XFSyAFg21b2JY489tvD8Dj300GTfv/71r2g3il7dWnA8nP9uue9a1J9bb711ss3LhUyfPj3aXGYASOM4uM+++c1vJu04dm6bbbaJ9ne/+92kHcfqcPkMT1EMGbDsMjadCe5XIJ0jfQzPE088EW2e73jZFgC47bbbos39569Tv379an6WXyKGt5977rlojx8/PmnH8UP+3D/72c9Gm0uczJgxA12V1oif4eV+zj777Ghz3B0A3HfffdE++OCDo80xkMtzHkX87ne/i/bQoUOTfbvuumtdx5CnRwghhBClQA89QgghhCgFbS5veTjt+8Ybbyxsx25oX52XXdlFKbIedut6Fy9LLmuttVa0vQTC7dg9/9Of/rSucxB5dyeXIvApqBtttFG0uQo3S50A0Ldv32izq9ZXefVVtJvg+xMAHnrooWhzlfCuQE7qKLo+rcV5550X7X322SfaLBkCaWVklkfWW2+9pB27vffYY4/lPj++TzuDnOXnQd5mu0h+BIA77rgj2f7Nb34T7W984xvR9lWziySjl156Kdnma8qy9Oqrr5604/uSS0v4+5XvDV9qgu9flsi4YjuwrFTXiBT9xjVHdmbZn+Xkm2++OWnHUiAzderUZJtT/fma+t/qlpRl4XI1APD1r3+95nl8+tOfTtpJ3hJCCCGEIPTQI4QQQohS0ObylnfNFclM3oXM2R7sxgRSNx4fw2dZcER/zl3P7+NjcyYXkLpJc/gMJSbnXi4DuX7gjC1/P3DWG7tqfZ/zApMsg/lFI7m6L3/Ws88+m7Q788wzC8/3hBNOiPYVV1xR2K69aBprOTc3j8dcX7z44ovRvuqqq5J9t99+e7TvueeeZp8nAOywww7R5kwbPjaQjuEi2QNIs4ty8haPTV7wGEjvHa7cO3/+/KRdU4aSzxzsSPw8y33L140rYQPA5ptvHu0f//jHyT7OoOXq9Cw1A8AxxxzT7PPlzN1Ro0Yl+7hyM0vUXgbj6r++oj9La9xPfl5pD3mrqW9yC7rmxmxLMqD8PHbGGWdEm+8HloyBNEuLQzjWXHPNpB3LYrwqgq/CzasVcAau7wfO0Pbnvssuu0Sbwx6mTZuGliBPjxBCCCFKgR56hBBCCFEK9NAjhBBCiFLQ5jE9Xo/kmJZcTIGP42G40i6vaO6rcrJ+XxQH5M+Dj+c15FyF36LjdbVKvS2B+8HHNHHcDVfl9tU2ORaBK2/7PvHacxM9e/ZMtmfOnFnz/LhkAZDG6vh09jFjxkSbV/Y+6KCDap5De+Hv73rvwVNOOSXaXH3cXxNOUeV0UmDZFbPr4Q9/+EO0//73vyf7+Bqznu+rpf/1r3+NNsfecQV4II3heP3115N9HB/Gc4mPP9h0000BpDFA7UVR1V0/l3L/cX9xaj8A7L333tH+97//nezj681xOxw/5Sm6hh6OAznqqKOSfbzNcRu///3vk3Z33nlntDnOD0jjsHi+8BW/24Omfqp3HPrxy/fZwoULo+1jXxYtWhTtp59+OtnHpTy4YjnHTwHpXMhj2V+3fffdt+a5+/mYxxuPS796AsdscqVtII3JOvDAA6PtSyJw3FkOeXqEEEIIUQr00COEEEKIUtDuFZkZdqV5Vyi7K/0+djez68+nsbJUxe/x7kM+PqeqelfdZpttVuNbLEtrLPzWlcil6XM1a3Z/svsbSN2zRVIXsKwkWc858f3gZQK+p1iKA9Jq0LzoopdNPv/5z9d1TstLc93onkGDBkX7b3/7W7Sb5JwmNtlkk2j7FNXTTz892j4dtggem+x6B1IXO19/TmMFgGHDhkWby134hRK33377msfz8JzgK7Ovu+66AOq/11pC0z1Zb9XdSy65JNlmaYr7dc8990zasUTk9z344IPRZlkhNw/y+eVStOudI1ny9qUD+PfDy508Bnku8WETvpRFW+J/d4rStFmmAtLSCiz1eCmfpUV/7bfaaqto33///dHmNHIgrXTedJ8Dy85pvCoC4yUmHs9cpsCPHf4d96UguEQCL0bLEi6QSn855OkRQgghRCnQQ48QQgghSkGHyls5nn/++Wj77AmWrRjvWitaKNBLGEVSWi7Li6PSvauv3kVQuyq56+bh7Ch2Q/vq15xBxPLFM888k7TjTBWWNnymTb2LSLLc6d3JnPnSkqyl1iSEEKU+7x5ml3BOSvjyl78cbc6i8rLHWWedFe0dd9wx2cfVdfl4vj/HjRsXba6668f2kCFDor3ddttF27vHWariLLsJEyYk7fg82N0OpBIq38O+am+T1NOW0nVzF3z1cxDLfSx7eKmSF3b233PbbbetuY8zbTz1VpzPXTu+hy677LJo77///kk7XujUZ2dyNX2+//35tbW8tWjRIlx99dUAUukXAE488cRoc8aSz5ZkCYq/p5fquCq1z4BiyYwzY/39wPMdLzLrf9OKKt/71Qj8Aq9NLFiwINlmacrPzfxZkyZNirZflLpe5OkRQgghRCnQQ48QQgghSoEeeoQQQghRCjo0pien6z788MPR9hofpymz9u61ZtYneZ/Xdbkdxwr4Fby5HWuSXk/nc+rKq6rXWx2WueWWW5JtjhXgmB6+1kCaMsnpqT7Fme+NuXPnRttrzfxZfL65KrIDBw5Mtv/0pz8Vtm1v3n333Vhl2q9azf2UW6mcYwQ4tsanpXM7X9bhpJNOijbHEfiKufy+LbbYIvkeDMdxjB8/Ptp9+vRBEZziu9tuuyX7pkyZEu199tkn2cf3Io99XokcWHq/NFI5Cp++WxRL4avYctkFX3GcU8S5gnkOvm4vvPBCso/7hWM2fSwmf+4NN9wQbV8CgasE+xgv/s3ge83Hu+XGe2uw1lpr4YADDqj5Wdxn9a4YznGFfo6cPXt2tP1n8bji9/lj8DzJfcl959/H86f/reZxz7FKvr94TsmNK/4d9/fyxIkTC9/HyNMjhBBCiFKghx4hhBBClIIOlbdyMginIufkKJYzvLxVlIqek5zYrc9pj/54XBWYUzuBxnJ7tyUt+Z6c7gykaeWcPulTnLlfOFWRq8YCabVYvr/uvffepB3fDyzzeBmm6Bxy5CrRthUrrLBCdBGzXASk14SrwPrUWHYXczqtT2tlN/rJJ5+c7Pv0pz8dbR4XuQUGeXFEL7FMnTo12ixJehmMj8996Bde5GM88MADyT6WSlkG9JWAmyrVtpU0smTJknhf33jjjcm+3r17R5u/i5+rWDLi+9ZLmpwO/MQTTyT7+D7mdP477rgjaVe0yKiXrYpkZC918P3L7/FzwuOPPx5tP255myUXnyr9P//zP2hLzCx+/uc+97lkn99eXvg7+99WHi98PfxcVTTH+d9MPgbbHfnb56tyFyFPjxBCCCFKgR56hBBCCFEK2l3eKlrc0WdKcXVJL1vlFrVjiqQv75bmYxQtRAmkbjyWtzzNrabaFcgt2slZN5MnT072ceVQbucXHOVF53jBS+/S5IqdnBGw6667Ju24IjDfJz4bie81ruyaoyNcvCussEKULjgzBkizqDgLrnv37kk7zvjhfvGyAld05YUSgVTSYmmKM22ANAuFq+J6KYnd7Zxp5OUt3uZ70Vem5ewU358vvvhitHOLNzZJSW01zlddddVYKdn3JW/zQqi8UCSQymB8Df3CkVwJ119Tlr74GvAiwUAqUXN2lJ/TGT6ev75833Af+f7icZaTpXmxTX89jzvuuML3tQYrrrhilJH9tedtvi+9lMS/V7l2jJ+DuG95HPlj+N+8JnwfFf3u+tf5eGz7e43vldz34mN4yZwXSM1Rvl9nIYQQQpQSPfQIIYQQohTooUcIIYQQpaDdY3qKtECvd/LKsj7NkFNtOabDV4P0VXib8FoznxO/x+ui/D6/ujfDWn9HpC+3JkWaLJB+z1x8w/e+971os54MpNeD93ntndPUuZ2vlsv6Padgc3VmIF1dmtO4vZ7MMT4+LqWR4NgB3xc8XnIVzDnOhsefX6GeU4X9PcFjlVPd/ZgrisHxsVycvsyxSRyzAqR9yN/Lxw5wXIiPaeLYF67+y8cGlsaKtVW19RVXXDFeh6OOOqqu9/i5jr8Lp477vuRr7+dgvvc5ZsbPYbxaPR/Pr2DO45bvB18lmY/H7XKrb/u+4Hue0/l99Xx/D7QlvkSE3xbtgzw9QgghhCgFeugRQgghRCloGHnLp8WyqzWXfsdpa74du2SLUl/9+7jaM7v7gTR1sMj1C6RuWO/+b8QFSH2f8Pfh71lviu55552XbHN6+B577JHsGzt2bLT52vj0VHZz8/n5RQ29FNrE5ZdfXnhOnEbvXc78WT79uZEws9hX/tpxeQXuT78oJS8qyOn+uTRUD18vlqM4NRpIxzBL1P7YfLxcWjL3G9+n/v7gecZXMWZZjOcETtH3x28U/LzCVY7ZrjetV4iuSuONXiGEEEKINkAPPUIIIYQoBR264CjjMyTqrRybk5lYEsnJW3wMzhzw2QL8Pj4eywIA0LNnz2jnKkY3Cl4W9FWJm/AZIlyN97e//W20f/Ob3yTtdtppp2hz1VsA2HnnnaPN1ZR9peUi6SEnNdx8883RPvjgg5N9t912W833+ONx/+UqMnO7js7Q+8xnPpNss2TEC3D6vmBpcNasWdH2C0Lyve+rm/M14vHHFbWBNBOOZWQv03CWFr+nXonJ37P8Hf34ZsktJ7UKITov8vQIIYQQohTooUcIIYQQpUAPPUIIIYQoBQ0T08PprUCqr/u4AY6h4cqxXr/n2AqOa/DVYTk9l2N6fMo6H4M/y8dGcExPZ+T666+P9he/+MVo++vGsR2Mj4GYPn16tIcPH57smzJlSrQ33njjaE+bNi1pV1SZ1V/7m266Kdo+jocpqtbt4XvIV5hl+N5otLIEHP/CFax9NeuuSC5GSAhRPuTpEUIIIUQp0EOPEEIIIUpBw1Rknj17drLt00kZXmhu4MCB0faLCzIsifmFIzlFm4/N1ZmBNG2a5QyfXs10hpR1X7X21FNPjTZLiywD5vDSEffLww8/nOzbcccdo81p0v6zONWYF1A87LDDknaf/vSn6zrHorR8L4ewNOQXw2Q6Qz8LIUTZkadHCCGEEKVADz1CCCGEKAV66BFCCCFEKWiYlHUfS8FLPuRiazj2h1dcB9LYD06J9yXx/fua8LEpfI685EVu2YHcitSNAi/XAKTXav311482X08gvT6cvu6/M8fF+NiX8ePHR3vDDTeM9ogRI5J2vETFnDlzon3jjTeiCI4l4nsGWHZphSaK7gUAWG+99Qr3CSGEaHzk6RFCCCFEKdBDjxBCCCFKQcPIWz6FmKUkLzmsu+660WbpxEsY/D4+nl+1/a233oo2yx5eiimSsfyq7Uy9q0F3JMcdd1yyfe2110b7iSeeiDan8wPFFa9zad+rrrpqso/fN3PmzGhzijqQVsq+9957l/0SNfCVvJmikgj+PVwJOpeyz1Jf7nOFEEJ0HI3/iyyEEEII0QrooUcIIYQQpaBh/PAzZsxItlnO8FLE4sWLa9peBnvllVei/frrr0f7mWeeSdq99NJL0Z48eXK0d9ppp6QdyzssfRVV9+0seMnp7rvvjva8efOifcUVVyTt/v3vf0ebs6tyGVD14hczve2226K95557LvfxN91005qv830HpBW/Bw0aVHi8RltkVAghxLLI0yOEEEKIUqCHHiGEEEKUAj30CCGEEKIUtHtMT1EKt6/Au3DhwmhzijqQpqb36tUr2j6uYv78+TXt4cOHJ+24cu/cuXOj7VPUV1tttWhz7A9XLfZ0hpT1HFwl+Yc//GGyz2834eOzePV0jsEC0vIBHD9TFHPTWvBK8tttt120/b3G59ejR4/C4ylNXQghGp/O/YsshBBCCFEneugRQgghRCkwX3U429jsZQBzP7KhaE36hxB6fXSz5qG+7DDUn10H9WXXotX7U33ZYRT2ZbMeeoQQQgghOiuSt4QQQghRCvTQI4QQQohSoIceIYQQQpQCPfQIIYQQohTooUcIIYQQpUAPPUIIIYQoBR3+0GNmPcxscvXfi2b2PG1/LPO+AWY2rWDf2Wa2b8G+E8xsA/fa0Wb2AzPb08x2Xr5vVG7M7DAzC2a2RZ3t55hZzxqvL2nm5zarfeY4y9wfIk917Ew3synVcbtDKxxzjJmNWN42onmoLzs/bdGHdOw9zezW1jpeR9DhCwaFEF4BMBQAzOxHAJaEEH61nMc8q9brZrYigBMATAMwn3btD+AiAAcDWAJg7PJ8fsk5GsCDAD4H4Ecdeyot4gQse3+IAsxsJwAHAdg2hPBu9QG28I8V0bioLzs/jdyHZrZSCOH9jj6PDvf01IOZDTKzR6pPrVPMrGk1yhXN7LLqU+1oM1u12v4KMzuias8xs7PM7EFUfpBHAPhb9VirWmVV0aEAFgH4KoBvV/ftZmb9zezu6mfebWb96PiXmtkDZjbDzA5q50vSkJjZGgB2AfA/qDz0NL2+Z/UvuevN7Ekz+5u51VyrfXGHmX25xnFPNbPx1X74cebzzzezSdW+6lV9baiZjau+9yYzW6fo9eo9k9wfrXJhuja9ASwMIbwLACGEhSGE+dUxN97MppnZH5v6u3ofnFsdzzPMbLfq66ua2T+q/XENgHjtzewSM5tQHeeF/S+WG/Vl56eoD+eY2Y+r8+NUq3rizWx1M/tztX8fNbNDq68PqP6+Tar+W0YBMbPtqu8ZaGbDzew+M5toZqPMrHe1zRgz+7mZ3Qfg5Pa7DBlCCA3zDxXPwP+r8fpvAXyhan8MlUE0AMD7AIZWX78WwDFV+woAR1TtOQBOo2ONATCCtrcFcGWtzwdwC4Djq/aJAP5Jx78DlYfGTQHMA7BKR1+/jv4H4BgAf6raY1H5awMA9gTwGoANq9fsYQC7Uv8MAHAXgOPoWEuq/+8H4I8ArPreWwHsXuOzA90jZwH4XdWeAmCPqn02gAs+4vXk/tC/j+zzNQBMBjADwMV0TbtTm6sAHEzX9/yqfSCAu6r2dwD8uWoPqY7tEXwsACtW3z9EfaW+1L9m9eEcAN+s2l8HcHnV/jmW/m6uXX3f6gBWQ/U3DZXfuAlVe8/qHLwzgIkA+gFYGZX5vle1zVHU/2MAXNzR14X/dQpPDyo/kmeY2fdQWVPj7errs0MIk6v2RFR+PGtxTebY+wO4vWDfTgBGVu2rAOxK+64NIXwYQngawCwAdcWwdHGOBvCPqv2P6nYTj4QQ5oUQPkRlUA6gff8C8JcQwpU1jrlf9d+jACahcp03rdHuQyzt56sB7Gpm3QCsHUK4r/r6XwHsXvR6vV9SLCWEsATAcAAnAXgZwDVmdgKAvczsP2Y2FcDeAAbR226s/s9jdndU+g0hhCmoPJQ28Vkzm4TKPTAIwFZt8mVKjvqy85PpQ6B2X+0H4HQzm4zKA8oqWPogc1m1z69D2k9bovKH6MEhhGcBbA5gMIA7q8f5ISp/4DaR+/1tdzo8pqcWZnYYgP+rbn4phDDSzP4D4FMARpnZl1B50HiX3vYByI3qeDPzcfsBOLzOUwsFdq3tUmFmPVCZEAebWUDlL7lgZqdVm/i+4nvvIQAHmNnIUP3zgA8N4JwQwh+aeUql7o/2JITwASoT5pjqJPkVVP7CHxFCeM4qsXqr0Fua7gV/HyzTZ2a2EYD/B2C7EMJiM7vCHUu0IurLzk+NPjy+uqtWXxmAw0MIT/Exqv38EoBtUPGwv0O7X0Cl34ahEvtoAKaHEHYqOKXc72+705CenhDCTSGEodV/E8xsIIBZIYSLANyMyiBsKW8AWBMAqn/xrxQqwdTJvipjsTQ25QuoBOg2caSZrWBmGwMYCCC5aUrIEajIhP1DCANCCH0BzEbqHSviLACvoOKO9YwCcKJV4oVgZn3MbN0a7VaongMAfB7AgyGE1wAsboo1AHAsgPuKXq/a/h4QGcxsc1saYwdU4uOaxsLCar8dscwbl+V+VMYYzGwwlo7xtVCZNF8zs/UAHNAa5y2WRX3Z+Snow9wq76MAfJPitIZVX+8G4IWqZ/5YVP6IbeJVVBwQPzezPVG5R3pZJYgaZraymbE3sKFoSE9PDY4CcIyZvQfgRVRiMNZq4bGuAHCpmb0N4HxUYkmauAXA9dVgrm8C+BaAP5vZqai4Cr9IbZ9C5YdyPQBfDSHwk3AZORrAL9xrN6DyAFKPe/MUVK71L0MITd4hhBBGm9mWAB6ujsslqMQOLXDvfxPAIDObiEr80FHV149Hpb9XQ8U7+MWPeP0KLL0/diIpVdRmDQC/NbO1UYndeAYV1/qrAKaiEkswvo7jXALgL2Y2BRX58xEACCE8ZmaPApiOSj891KpnLxj1ZeenqA+Lkm1+AuACAFOqDz5zqm0vBnCDmR0J4F44b00I4SUzOxiV0JATUXkYvqjJkVA95vRW/F6thi2rJpQHM7sclYCucc183xUAbg0hXN8mJyaEEEKIVqezeHrahBDClzr6HIQQQgjRPpTa0yOEEEKI8tAsT0/Pnj3DgAED2uhURC3mzJmDhQsX2ke3bB4d1ZdvvpkG8r/yyivRXmmlpbfjiiuumLQzqmX4/vvFRT0/9rGlxUffeuutwve899570d58880/6rRbjYkTJy4MIfRq7eM24tjka57rz85KVxib/Efvf//732Tf228vDWdbffXVo73yyisv9+fyZ/HnAEC3bt2W+/gtoS3GZqOMyw8//DDafL39tV9ttdWizWOU50sgvQdWXbXxarjm+rJZDz0DBgzAhAkTWuesRF2MGNE2y9F0VF+OH5/GQV555dLSPD169Ij2mmumCVT8QLRw4cJo+x/Pfv36RXvy5MnRXrAgjXt++eWXo33vvffWc+qtgpnlMilaTCOOTX6g9T9k3J9tifdk8/YKKyxf8mpHj03+IfPfJbeP4YePZ599Ntk3ffrSONQddli6fNP666//kef2Ucydu3QYPP7448m+/fffP9r1Phzz9wVa1rdtMTbbclw25zsvWbJ0aULuV7YBYMiQpYnRH//4x6P9wgsvJO3WW2+9aG+zzTaFn8vjrT3/0Mn1ZUOmrAshhBBCtDalDmQW7c+YMWOS7WnTpkWb/xKYPXt20o7/UmFPzzrrrJO0Y4/C2muvHe2ePdOF3OfMmVP3OYsU/utt1KhRyb5rr7022uxBe+mll5J277yztMLDV7/61Wg/+uijSTv+a/aJJ56I9hZbpAXQL7/88mjzX6v+r0ve9l6gzia58fnW+1f/V77ylWT73XeX1gzlv+yBtM8uvPDCmp8LpNLHsGHDou2lE/busXfHe3XvuOOOaL/66qvRPuSQQ5J2hx++tKZsSz1dnZnc93rqqbRs3BtvvBHtGTNmRHvKlClJO54/eW7lfgDS8cvjaOjQoUm7RhxTXfNuEEIIIYRw6KFHCCGEEKVADz1CCCGEKAWK6RHtik9Z32ijjaK9aNGiaPft2zdpxxo9p5hzTIJvxzE93bt3T9rx+zi+pxHSSxsBzq757Gc/m+zjPnzttdeSfRxnwNecU5798TnOy8dyMZwtxTEKAPC5z30u2hxvcNJJJyXtTj/99Gj7eIOOyjRpKfVmon3/+9+P9uLFi5N9G2ywQbR9yjqPQe5nn8nD1/5rX/tatHfaKV1/kjN++HN9vB3HCHEKNceLAWm22be//e1kXxnrz82cOTPa8+bNS/b1798/2tx/fv7kPuK50Jec4ExbjvfxmWptleG4PMjTI4QQQohSoIceIYQQQpQCyVuiXeF0SSAtEshp6V4G4+1111032rlKyyyBeHc3v+/++++PtuStCieccEK0vSTCqaxetmKZhSUiX1qAZU0uQbDPPvsk7dZaa61ov/7669FeY401knZF0tRtt92WtLv55pujPXbs2GRfZ5C0mFxa9qxZs6LNZSG8bMzyhv/+fMw+ffrUfA+QykzXXXddtFmaAlIZi/v1gw8+KPxctlkSA4CpU6cWHoPlGN7nZZquBMtMLFMBaTmCDTfcMNpXXXVV0u6mm26K9oEHHhjtfffdN2m35ZZb1vwsXwqEyxY0SuVmeXqEEEIIUQr00COEEEKIUiB5S7QrLGUAqQSVywriTCB2V3vZio/B7nrvkmd5y8s3ZeWyyy6LNlfj9dk1fP1zWUPcN37BQl4Mlt3eXtbkfsvJFLy9yiqrRLtXr3TNQZbIbrjhhmQfV/jtDOTWL7v77rujzX3E1x1Ir1VuIV8ep7179072sUR9yy23RNtX52X5mmUPfw/xYpYs4fmxzvfUAw88kOzbc889C9/XmeHrwRImkF5fXncQSGVNliqfeeaZpB0v2MzZfPPnz0/asTTM8iZnkAGplHb00UfXfL29kadHCCGEEKVADz1CCCGEKAV66BFCCCFEKShNTA+nUl566aXJvkGDBkWbU2YPPfTQtj+xkuFjdTg+gLV9XoUZSONuOA7BU6Tf+/RZbuc/q6xcfPHF0ebr49OBGY6/8O9jctWPGR+nwp/N8Qa+HafkcmyKX32cY398um5ni+nJwfc0X2sfM8XX1F8rhq+br9zM155LCeTacTyOj+nh8c3zBVfaBtJ7itPygTSmJxf71NngOB6OpQHSOW6TTTZJ9vFq6ttvv320119//aQdp5xznBS/BwAeeeSRaHO80N5775204/vmoYceivZmm22WtBs2bBjaC3l6hBBCCFEK9NAjhBBCiFLQdfx+H8G4ceOi7RcrHD9+fLR/+9vfRvvkk09O2l1wwQXN/lzvTv7pT38abU4L/sMf/pC087JBZ4bTjjllGEilRXa1ezmEq40+//zz0eY0TSCt9MruXp92zVVE/QKKIpU6vEzB/ZmTDXPp7Ny/RVWcgVSa4H0+vZrPl+URXwWW2/nqsZyW66v/djY4dZivoS8dwKnjXjbm8ch9lKtuzp/l27HUwe28/MT3F38un6s/PqfNd2V4HuTK9H6fH0f77bdftHmO5BIDvh1Ly1624j7j/udFo4G0Yjvfe37O3XTTTaPtq623NvL0CCGEEKIU6KFHCCGEEKWg08tb9S4mx5Hj3bp1S/ax3MVR/xdeeGHS7thjj4328OHDCz+L3Yx8PAB45ZVXos3VUY8//vik3R577FF4/M4GuzzXXHPNZB9XzGUXtZdU+Fqx69a7vHfZZZdos2vc3xvsyu9KFVubw4knnphs87Xk6/3cc88l7dg97rM/OEOH+zC3mGW9i0AWLSLpYVnmxRdfTPZxRXB/L953333R5uqxnQEvW7FEwJIyXxsglYr9YqQ8RlgWzFVu9uOWYdmq3j7njC0vnfD5+urEXQkel3x9vSzIUpKfF3lu5Wvav3//pB33LWdscRVnAJg+fXq0iypo++1cVuW8efOivcUWW6AtkadHCCGEEKVADz1CCCGEKAV66BFCCCFEKej0MT0+VoBhDXj27NnR9poha80cr+CrWo4YMSLaRxxxRLT79euXtPv1r38d7Y022ijZxzEQrLX36NGj4Ft0friaso8p4NgOjkvw7TiGg6vN+tRirlI6YMCAaPvUZe7nrlQeoDl885vfTLZHjx4dbb7+Pj6A+8mXZOA4A47byI1T3per3Mz9xPELQBp/wmn0vlIvfxf/Wffff3+0O1tMj08B5pgsHmO+xAPPkZtvvnmyj8dcrkI3H59jNeqtwu3HH4/VSZMmRdv3Od+HHEfZ1eA4tKLSDEAaq9O9e/dkH//G8Rjw1+3yyy+veQwfG8fwXOFjy3g+4HvUz+9cvkUxPUIIIYQQrYAeeoQQQghRCjq9vJWr+jpy5Mhor7322tH26XLsguOUcl9tlt2/t99+e7S9i3/LLbeMNqfwAukCeuyC5pQ9ABg8eDC6Cux29S5qhl2j3g3PFZXZbc79CqQuX6646+VD7vNcmm1Xxi/yx/cgL77pU4UHDhwYbb/oIY8RHpveFV+U9sxueCAdg/wefx+xVMxu+Q033DBpx/u+/e1vJ/u22267mufUGWAZCCi+p3nOAYqrKQPFi4L6OTcnXRa1y6WsF1Vu9lIMhwr48c1jn2XuzgjPn2z7lQV4LvT9zH3Gv0n+N+5f//pXtLncir+G/DuWS0VnKY3lraFDhybtcvJZayNPjxBCCCFKgR56hBBCCFEK9NAjhBBCiFLQ6WN6cvzsZz+LNi894Vf6LloZmPVTv49LoHtNm8vb+3Rf1qtZM+dV4AFg//33R1eBr49PHWdYD/ZLhXCaOrPOOusk21x+n1fu9bEn3Ld+OQIB3HDDDYX7Pv/5z0fbr27NMTkcx+PjQIqWj/HteMzl4k/4vuLYpDvuuKPgW3QtOOXXwzEcPv6QSzfk0o15bPrU86I09VzcDqep++PxefC5+6UmOH7MH2Py5MnR7uwxPRw/w/Obj+nhfT4l3MfKNeF/n/bdd99o82+cb8djm+fS3Ody/JBvx8fwfVlvzFi9yNMjhBBCiFKghx4hhBBClIJOKW+x+4tdX1x1GUjT4Di90ctW7MbNudm4HbvnfXqor4ZZdAx25T/88MOF7+ns8HXMlRjgfd4d61PYm/BVsx977LFos7zlUzPZZVzvis+iQtE4AFKZKVeqoKg6r+8Llk5yEgufR24V8KJjA/nK0I3OzJkzk22WiFiK8OUHNttss2j7sVl0HXPXjd9T1Mf+/Pw9xDIN7/Pt+HP9OT311FOFn93o+HRzDsdgWcj/3vEY86U8iu5t/9vFUn/R2AOKx5u/h1gW48rSvh3Lrlw2BkjLlbQG8vQIIYQQohTooUcIIYQQpaBTyFs+cpwj+tlVd/bZZyftevXqFW3OUvCuupzbnGGXHrtnffYP7/MZEfxd2I07ZsyYws/t7HAf+awblp1YGvFZQUVZX+yeB4CHHnoo2uzWZ3kTSKuDere5yOOzH4soytACiheX9eMll+XD8PFzVb+ZnNTa2Zg/f36yzdJirlIvz6VeziqS+OodL/VeX1+1niUXzs709wbP217+9guwdib8ded7m2UgPw79dSyiXjkql2nL15vHpZ/fZ8yYEW3OqvR9yWPWV2eWvCWEEEII0QL00COEEEKIUqCHHiGEEEKUgoaN6WGdMKct3nLLLdG+4oorkn2czsz6p9cdi1Lgc+04XsRrqayb51bwZr36mWeeSfaNGjVqmfPuCni9mvVlvqY+vsCnYDax1VZbFX4Wpz76eBCO9+ps6ckdDac9+7FZFC/g4+jqTYfmbY5t8HElHPtTb2xDV8KnovuYiSZyMXUevvZ8vXOxVbzPz33cfzzWfXkKHo+5+Cz+jr46sY9x6kz4vuM+KqpWDaQrzfu076KyAn688fXmse37ksdbrkQExyDxnOsr7hetJN8WyNMjhBBCiFKghx4hhBBClIJWk7fYrVlke9j97SWGnORwzjnnRPsnP/lJtLfYYoukHbvd2D2bS5HMnW/RgofeRchuXJ+qWySlsbsXWFpZ2KeYdkZyLu+ixep8KmXRoqDbbbddss19wf3l+6FoITzx0XBlVS4FAaQpr+wq93JU0SKVniL5048LPg8uBVEWfFkPHnNFVXGBtI/qrWTt+4s/i/vZz2kMt/NjneeIehep9PNKZy5D4e9t/i587b2kyXNaro9yv128zcf3MiP/hvL5+uvOn8Wp6H6BXJbmJG8JIYQQQrQCeugRQgghRCloNXmrtRfru/nmm6N92mmnJft4Mbltttkm2rnqkuzy9m5cbsfuuJzklsskyUknRQuV+iyYJtdiZ3bTNpHL/OBshMWLFxe2K8rSKsrqAtL7Iee6V/ZWhSLp1cMucC9h8EKu3DfejV4kI+fc4zmZlLdzskq937Ez4LOeGJYIWNIaOnRo0o77yEsORZXvc5IIZ/UUZZAB6XznxyZ/r/XWWy/aXmLh75VbHJrPg8+vUfESJN/bPD5ysnyuAjrPi14yZHLjnLOK+Xh+XLJsxb+z/h7i4z/33HOF59QayNMjhBBCiFKghx4hhBBClAI99AghhBCiFLR5RWZfGfKuu+6K9uTJk6N96623Ju2mTZsWbb+SNqcps1bp0zZZr8ylojNFaeke1pe9ts56qj8GnxN/lte/m9p19rgDIN9HvIIur4zsr2nfvn1rHtunshdVCs2VFcjp2mJZimIMgDSWhPsil1LNx/DjgMcP95nvT75futLq6Tk4Bs7D17Qo/gLIx91w29w1rXduLUqV9nEgPB65oq+PYeEVvH2sEh9zwYIF0e7Tp09d59qR+D7h78Lf2Y+B9ddfP9r8+wmkMa25lPCifvZzJFfA5pUFJkyYkLTjysscn+Xjx/ge8jFNrU05ZgchhBBClB499AghhBCiFLRY3hozZkyyffbZZ0ebU87YtQgAG2ywQbSXLFkSbZ+OuNtuu0XbSzzs7uN9ORccv8e342qu7Fr07kNOs8xVlOU0UO/+L6pEytcCAHbaaScAwN///nd0JV5++eVku0gm9C5vXjw2B7tx+Xi+JAC7eMtYwbcW9aZz5xYH5LHF8pa/v/n4ubIMRXKz/1ze5yvVFn1uZ+fVV1+Ntr8ePD9xxdz+/fsn7XiMeCmej5GTsIoqBnt8GnXRe3jsc9r84MGDk3b8O+PndD4nlsg6Az6tvqjMCaeD+32+qnPRHOevDV9vHrN+4Wu+3vx7N3v27KQdlxrZfvvto33HHXck7bbeeuto+3vtySefjLZfdaElyNMjhBBCiFKghx4hhBBClIJmyVvvvfdejLr+2te+luxjdxdn5LANpC5Ujuz27sncYmcMu2BzGTo5WGbiz/JuV3YRsgzGWUf+PPzipux2zMkvu+++O4DihTY7E9wPPotn3rx50c5ls/kMviLY5cvuf38dW7uCeJlgiYQlZCCtrMrX1fcn7yvK5ALS+SJXgZjvnXoXzuzs5CT7onnmk5/8ZNJuypQp0fayCs9juermfHx+j+9Lfh8fz0tzfB78HTfddNOk3bXXXhttL58WZYB1BvwcyfMnX+tdd901aVf0OwYUS8he0uRxmRtHfHyeZ30fMfws4KU57i8/H7d2Npc8PUIIIYQoBXroEUIIIUQp0EOPEEIIIUpBs2J6Xn75ZVx88cUAlk0p5viceis+cqq4111Zx/T7WPNjTdJXk+Q4GT5eLr2Tq37678gpki+++GK0uRImAPTu3TvaXrvk2BI+J9ZFgaWaaVevLlukt/u0xe7du9d1vA033DDaTzzxRLT9KsGsV3eGlZfbg6IYDt8XHC/iYwL4WuZS0YtSoP2Y4zHCfebj9XIxJ/WeQ2eL7cpVjOfvxu18jCHHWvkxVm9MD8d3cDsfg+X7tgk/R/IxeM71MSycKu1jxjj+0qdbNzo+Pou/C89juRisHPz7x7/b/rM5toh/qwHg+eefr/m5AwcOLGzXq1evaPsYLL43fPX9XExvS+jav6hCCCGEEFX00COEEEKIUtAsecvMoqvUyxIsC7HbzUtJ7LpkiSjnavbSBLto+XjevVeUFuklI3bDsjvOu0X33HPPaP/kJz+J9qhRo5J2/F1y1TXZxdfWi6w1Cr6PWCrhe8pfN17ULse6664bba7k6eVD3u4MixB2JF6m4vvbj6V6ZabcYrBM0T4v7fC90xXKPNRDTmbkOZPnt5y8xfMxkI45ljp8xWsec7zPyzTcL7wQ9bPPPpu0Y9mK50gvP/L5ckVfIP3+PgW80fG/hTxWWGbyVZZ5DHj5l8dR0aLMfju3wC+34/7ykiZX4GcJi6szA+m97Mu3tPZ4lqdHCCGEEKVADz1CCCGEKAXNkrd69+6NM888E8CyC0fec8890Wa3o48OZzcZu+e8e5blqNxCeGz7dkXSF7tWfbvvfOc70T7llFNQD1dddVWyzdlb3i3I7mV2LRdlNnQ1cm5XdnH6bAHvKi+CM0H4Pf7e4Oudy4IR+WxHL5cUZVt5iir3egmD2/Hx/Oe2pAJvZ8/e4nvYS06vvfZatHMLG/N3zlVGLlr0Ekh/C1hS3nHHHZN2RTKYl0+5yjefu8+S5W2/EOXTTz9deL6Njp8j+fqwfORXO5gwYUJdx+ex4689jyMeHz7Ug+VDf08x/BvPMubmm2+etLv//vtrnh+wbGjC8iJPjxBCCCFKgR56hBBCCFEK9NAjhBBCiFLQ4mCGiy66KNnm+JQLLrgg2ldeeWXSjlPCFy9eHG1fdZHT1Hw8B6e08ef6dDn+LH7PD3/4w6TdGWecgeWBVyoGUu3S67Mct8IVKptWr2+iSYcuqlzbmeBYAZ9myd+PU0s32GCDFn3WgAEDos1avi97wCimp0LRvdacVaqLVkz38TJFqe25VdaZXCwCj7GuDMdS5OIq+Pr+5z//SfZxXMi8efOSfXxN+fi+T7gv+Hh+rPMx+D2+IvO0adOizWnzd955Z9KO53sf08RxIX5u7cz4dG6G57hcKjr3n/99KorJ8yVEeK7m8eZjeDk2k3+rOc0dyFdv9zE+y4s8PUIIIYQoBXroEUIIIUQpaLFf36dis/vr1FNPrWl7OM190qRJyT52cc6dOzfZxyls7O7zbrBvfOMb0T799NMLz6OIXIVn5he/+EWyzdWpc4vHsYtv+PDhNY/d2dJoa8FuTe9OZQmK3dXe/VkvnBbL185fR/5cf04ihdOfgfpTzNn20lnRIq/eLc+ueP7cnDvcLz7ZVVmwYEG0N9lkk2Qfz5GcAu7Tvll69vMnSxjcX74vi+Tr3Fjnfb48BcupLNn41HP+rKeeeirZx/dNZ59DeV7s169ftH0a+eOPPx5tX6G6SHb24433cZ/78ACWDItWSPDH4O+RCynIrWLQGsjTI4QQQohSoIceIYQQQpQCPfQIIYQQohS0OKanKL6lOey999417Uah3u94/PHHt/GZdG44xqIolgNIdWeOi8q183o9a885rZnjCHLp7GWi3pT13PUvGjO5ldRzmj3HceTuo6JYoq5MUTwckN77CxcujLbvL46J9CnmPC5ypTM4fmijjTYqbFc0vn1/cSkPvp/8+eXih/j7d7aSFByDBQDPPfdctIcOHRptH+s6Z86caG+zzTbJPh5jfD38tefryGVD/NJN3I770scZ8T6OQfP3IZ+TX+KqtWMu5ekRQgghRCnQQ48QQgghSkHn8vuJTg9XWPWwKzRXeZRdst71ydVd2WXqZRd2r0reyuPlrXpTwrlcQ07C4rRZ3xfc17l+4v5lt3xnX0k9B1ex95IIVybnkgNeOuAqyV5S5rZ8fX31fJaZWGbjlHcPn69vx5/F/cWV7oFU4vRyJ88zOcmtERk8eHCyzefPFY+95HTooYdG21cl53HA86IfHywL8vj1ZSt4xQSeH/x8zPM4y6y+/MBnPvOZaPt7ORcS0RLk6RFCCCFEKdBDjxBCCCFKgeQt0eawm5wj+IF0gUKu7JqTMnLyVlEFUC9rsESTW6yxTBRJP/76sEucXdYAMH/+/GizK95nifAxWN7yMiTLYnzv+OOxBMDV3DmzCMjLq52NQYMGRdtLU7wI8s9+9rNo+0wmlkh4LAKp7PT0009H++abb07asZTG/TdjxoykHV977vP99tsvacd9y/3nz48llwkTJiT7uKL7Lrvsgs6Er1Dtt5vwqxgwuUU6cwsIc/+xzOTnWT4Gz9ueokVmvVTJFcVZOmsL5OkRQgghRCnQQ48QQgghSoEeeoQQQghRChTTI9ocXvH34IMPTvaxtt+9e/do77XXXoXHy1XK5lWkWSf2sR1c9ZVjI8pMUeXa/fffP9keNWpUtLkKLJDG+LDW7+OCOF6A01d933LsFccI+dXCOW164MCB0c7F8HT29HVObf7e976X7HvwwQejfcghh0Sb05Bbyplnnrncx2gNOKbn5JNPTvbtuuuu0e5sFZlz8Hzp43Y4DtLH2RSVAPHp4Dze+Hj+GnKcJs+lPl6I45H4HIrilIBl4/VaY/WH5HitejQhhBBCiAZFDz1CCCGEKAWWW0humcZmLwOY+5ENRWvSP4TQ66ObNQ/1ZYeh/uw6qC+7Fq3en+rLDqOwL5v10COEEEII0VmRvCWEEEKIUqCHHiGEEEKUAj30CCGEEKIU6KFHCCGEEKVADz1CCCGEKAV66BFCCCFEKWiIhx4zO8zMgpltUWf7OWbWs8brS2q1zxynWe0zxznBzDZojWN1Zcysh5lNrv570cyep+3lr48vWpWW9peZDTCzaQX7zjazfQv2LTOOzOxoM/uBme1pZjsv3zcSLaXaB9PNbEq1/3fIzMOHmNnpBcdRP3YwZra+mf3DzGaa2eNmdpuZbdbMY6xtZl9vq3NsSxplUZKjATwI4HMAftSxp9IiTgAwDcD8Dj6PhiaE8AqAoQBgZj8CsCSE8Kum/Wa2Ugjh/drvbn3MbMUQwgft9XmdjY/qrxYe86xar5vZiqg9jvYHcBGAgwEsATB2eT5fNB8z2wnAQQC2DSG8W33QKXzoDSHcDODmGsdZCcCeUD92GFZZcO4mAH8NIXyu+tpQAOsBmNGMQ60N4OsALm7lU2xzOtzTY2ZrANgFwP+g8tDT9PqeZjbGzK43syfN7G/mVgg0s1XN7A4z+3KN455qZuOrf5n8OPP555vZJDO728x6VV8bambjqu+9yczWKXrdzI4AMALA36p/AdVe2U3UxMyuMLNfm9m9AM7NXPsxZjaiavc0szlVe5CZPVK99lPMbNPq68fQ63+o/qjCzJZUvQ3/AbBTh3zpLkTR9QewopldVvUOjG4aF9X+PqJqzzGzs8zsQVT+8EnGUXW8DwWwCMBXAXy7um83M+tfHbNTqv/3o+NfamYPmNkMMzuonS9JV6Q3gIUhhHcBIISwMITQ9GD6zer8OdWqnvqqx+53VZvH9zVw/dgB36Xs7AXgvRDCpU0vhBAmA3jQzM4zs2nVvjwKqPw+V8dXUx8fWn3bLwBsXO3H89r9WywHHf7QA+DTAO4IIcwAsMjMtqV9wwCcAmArAANReThqYg0AtwAYGUK4jA9oZvsB2BTA9qhMmsPNbPcan706gEkhhG0B3Afg/6qvXwngeyGEIQCm5l4PIVwPYAKAL4QQhoYQ3m72FRCbAdg3hPBdFF/7Ir4K4MIQwlBUfjTnmdmWAI4CsEv19Q8AfKHafnUA00IIO4QQHqxxPNE8lrn+1dc3BfD7EMIgAK8COLzg/e+EEHYNIVyNZcfRMACPhRBmA7gUwG+q+x4A8DsAV1bvk7+h4g1qYgCAPQB8CsClZrYKxPIwGkDf6kPkxWa2B+1bWJ0/LwHw/wre3zS+D8ey/Sjal8EAJtZ4/TOo/FZuA2BfAOeZWW8A7wA4rNrHewE4v/rHyOkAZlb78dR2OfNWohEeeo4G8I+q/Y/qdhOPhBDmhRA+BDAZlcmsiX8B+EsI4coax9yv+u9RAJMAbIHKJOz5EJW/PgDgagC7mlk3AGuHEO6rvv5XALsXvV7vlxRZrgshfNDCa/wwgDPM7HuorLfyNoB9AAwHMN7MJle3B1bbfwDghtb+AiWm1vUHgNnVvyCByiQ7oOD91xS8DlSkrdsL9u0EYGTVvgrArrTv2hDChyGEpwHMQmX8ixYSQliCyng6CcDLAK4xsxOqu2+s/p/r4+skIzc8uwL4ewjhgxDCS6g4AbYDYAB+bmZTANwFoA8qUlinpUNjesysB4C9AQw2swBgRQDBzE6rNnmXmn+A9HwfAnCAmY0Myy4gZgDOCSH8oZmnpIXIOoY362jzPpY+pMe/3EMII6tS1acAjDKzL6HS/38NIXy/xnHe0QTccszsMCz1vn2p4PrPwrJjt0j2zfX9fij2EHlCgV1rWzST6pgZA2CMmU0FcHx1V1M/+/mZqWd8i/ZhOoAjarxuNV4DKh7yXgCGhxDeq4YVdGrPaUd7eo5AxUXdP4QwIITQF8BspH+1FXEWgFdQO5BqFIATrRIvBDPrY2br1mi3ApbeAJ8H8GAI4TUAi0lvPhbAfUWvV+03AKxZxzmLDB9xjeeg8tcmQIPWzAYCmBVCuAiV4MkhAO4GcERTn5tZdzPr3/bfoOsTQrip6tIeGkKYUHD9W0ocR1Wv30rVYOpkX5WxWBoD+AVUEiGaONLMVjCzjVHx8D21HOdUesxsc4rVAioySEtXDtdc2bHcA+DjRnGwZrYdgMUAjjKzFa0S27o7gEcAdAOwoPrAsxeApnm00/ZjRz/0HI1KJDlzAyoPIPVwCoBVzOyX/GIIYTQqru+Hq3+VXI/aHfQmgEFmNhEVj9PZ1dePR0XTnILKAP+o169AJXZAgczLT9E1/hWAr5nZWACcJnsUgGlVGWsLVB6iHwfwQwCjq8e5E5VgTNH6LHP9l+NYV6A6jgAcgoo7vYlbABxGAbDfAvDFav8eC+BkavsUKg/LtwP4agjhneU4J1GJn/yrVdKbp6ASY/mjFh7L96NoR6qqyGEAPmGVlPXpqPTlSABTADyGyoPRaSGEF1GJlxthZhNQ+ePiyepxXgHwUDXwuVMFMtuyypAQQnQsZnY5gMtDCOOa+b4rANxaTTAQQoiERqnTI4QQkRDClzr6HIQQXQ95eoQQQghRCprl6enZs2cYMGBAG51KMW+88Uay/e67SxNDevZcpgp6q/Hyyy8n26uuujRcZ4011mizz2XmzJmDhQsXFkXWt5j27MsPP/ww2ius0NFhZBX4Yd+s1S9vIRMnTlwYQujV2sftqLFZL++9916y/eqrr0b7gw+WJtP5P8LWXHNpKF57jbl66QpjUyylLcZmo/TlokWLov36669H+/330wL4PP54XK60UvqowGNx/fXXb7XzbC1yfdmsh54BAwZgwoQJy3UyLfmxuffee5PtWbNmRft//ud/lut8clx8cZoYNmTI0sSUXXetJ8Fs+RkxYkSbHLc1+rJe3n57ab1GfnDsSHiw+wHdlphZS7NesrRlfzbHG1w0pp9//vlk+9Zbb4324sWLo+0fjvbaa69o58Zc0bziz701H3C7wtgUS2mLsdkofTly5Mho33333dFeuHBh0o7HHz8ceefCLrssrRN86qmNV5sw15cNE9PDEx8AHH744YX7Vl555WhPmTIl2vxkCqReBfY28FOv58UXX4z2ggULCo+3yipLSxU88sgjhccT6YPOf//732QfX+8+ffpEO/dDyw9R77zzTuG+V155Jdrdu3dP2vXvrwz21iD3EMEPNn/84x+TfdwfvXot/YOMxymQ/uExY8bSpYFOPPHEus+D6SgPnxCtQb1e83XWWSfZfu2116LdrVu3aHsvzZtvLi2ptPrqq0d75syZSbvRo0dH+8wzz4y2n4+ZRhl7jaE1CCGEEEK0MXroEUIIIUQp0EOPEEIIIUpBu8f0FGl53/72t5PtJ598MtqbbpquFbriiitGe/z48dHu27dv0o6zvg444IBoP/zww0k7jjlZsmRJtDlzxH/u008/He0rrrgiaXfCCSdA1OYrX/lKsn3HHXdEe+211462j+n5+Mc/Hm0OtvMxIHx/cf/7dvPnz2/GWZcbP2b5Wvp9N920tMD6lVcuLc7sA5Q5HoHjCHr06JG023jjjaN9zz33RHv48OFJu2222abm+TVKtqAQrUHufn7mmWei7ec7Hi+cObneeunaoXx8jpHlGFYgjYmcM2dOtL///XS5w3POOSfaPFf482vPcaoZQQghhBClQA89QgghhCgFHZqyzi6up55KF0Jm95kvEsgpruyC45RWIE25GzNmTGG7ojot3uXG6da9ey9dv5JdeIDkrRzTpk1LtosKW3EBSgB44YUXos0SpE89X2uttaLNLtlGqQ/UGfFSY84VzWnqXDKA+w8ANtpoo2hzmut9992XtOMyBixJXnTRRUm7Sy65JNof+9jHot2RbvTloemat2dqb66mUS7dmOdgvr6+XUtqKTVKmnN7Um9tqdmzZyfbnDrO8yCQ1sniGmVc4gNIf+PeeuutaPvQET4Gp8fffvvtSTtOjz/99NOj7cdhe0rSnWMGEEIIIYRYTvTQI4QQQohS0KHy1ve+971oezmDXdScuQOkWVQsW3hXHZfRZknEuw95e7XVVou2r/DMbng+B5bRAOCGG26INleWFmkFZiCtzMvX0cte7J4dOHBgtL1sxfcN2w899FALz1g0R1bYYostos2V0/04KKpuzstOAKm7nSuze5mUK87mKjx3Fnmr6JpPnTo12nx9eX4DWrZERq6fc/t4LmzJ8Vv6uV2V3HfmSuR33nlnso+XivDLRrz00kvR5nAOv/YWy8m83JO/v/i3kOdtvz4eV2IfN25ctP/5z38m7YpWT/D7WoPOMQMIIYQQQiwneugRQgghRCnQQ48QQgghSkG7x/SwXseVkVmTB1Jd3sf0MByP42NrfPxIrXMAgA022KDm8XyMEL+PNU3f7ve//320FdOT4ldZ53gAjuvieBwgrRzK7/GadFGsiNfJ586dG22tuN56PPHEE9FetGhRtDfZZJOk3fTp06PNcUA+to/TZnnM+WrpHL+Xi+npDCnQH374Yfze1157bbLv5ptvjvaQIUOi7eMe7r///mj369cv2lyNF0ivm698z6VC+Jp6+Jg8V/tz4hhJPjZXYgfSPsvN/dx/fl7heYHvKV/+hGNkGpV777032g8++GC0fX/xdeN4LyD9beS51Y8BrmK/yy671HwdAObNmxdtjhHy45LnbZ4bfvKTnyTtON1eKetCCCGEEK2AHnqEEEIIUQraXd5i1xW76o477rikHS8kmnN/ssvUV1bmdGhOd+Vqyv59vPihd7Oxe52P59NsvUu67PB1W7BgQbKPXe8sW/kFKtk9y2nq3v3tUyub8AtZcnVfyVsVWPphO+du/tOf/pRsb7jhhtEeNGhQtL3MxGOQXedermTX/lZbbVV4TpwC+93vfjfaXibNLZbaKLz22mu45ZZbAACTJ09O9v30pz+N9gMPPBBtXrgXSKXdoUOHRttX8WUZxC/EzGnPnPK8cOHCpB2X+WAZjBeNBtIxyO04DR9IxzfP/X6ss4TH1b+B9DuzfMrzO5AuHN2oXHXVVdHm3yov6TH+3uZrx/Osv6b8e8r3hi9L8MUvfjHazz33XLT9agcsT3PlZpa62ht5eoQQQghRCvTQI4QQQohS0KEVmZkrr7wy2easp7vvvjvZx65LzpzKLWLGrlXv+mNJhKUYL5dxpsP3v//9aH/nO9+BKIazePw1ZZenzxBgirI42I0PpH3En+UrPPtsQZGOi6JFJAHgnnvuifbEiROTfSxN8PX3x+AFEbkvWJIGgIMPPrjmPs4e8dsnn3xytC+88MKkHZ9HvQs7tjcrr7xyzCj1ssKECROi/cgjj0SbF3b02ywD7bHHHkk7rnTu5+D9998/2nPmzIm2P6ejjjoq2ixfs7QBpPMA7/NSx8477xxtnre9dMIhBn5e4fuLM7ZYEgRSmaZRYamfx6WfwzbeeONo5+ZSxsvJvM2f5ccGS5f8HpZBgTQsgeUylsTaG3l6hBBCCFEK9NAjhBBCiFKghx4hhBBClIIOjenhmBuv+fNK5awnA8B2220XbdYxfTVX1uxZn8xVaWUef/zxZJt1Uk7TFHlYy/erovvU9Cb8CvdMrqou7+PP8tW6fdqtSMmtnD127Nho+3ISHHvF8SKDBw9O2j311FM19/mSAxwHwCnUPvWaU+A5rovvPSCNC/LzQL2rhbc177zzTrw+fA2BNBaCr9vMmTOTdjxnTpkyJdq+vAZXrfdVszkNnFfP5jITHi4R0Ldv32Qfz6f8vXxFe4Yr+jal8dfa5++vZ555Jtpc/sTHuuQ+u1HguYp/J338DK8s4GMgOe6G73P/21f0O+lLP/B9yPt8RWauvL755ptH2193Lh3gK023NvL0CCGEEKIU6KFHCCGEEKWg3eWtokqvXs5gFxy7tYHUBV5URRYorr7q3dr82XwM306SVuvDJQL8InkMS5fsqvV9wv2XW5g0V820rNS7GCfLR2x7WBJhKQIAnn322Whz+rL/XHbtc4qyl8P5PLhvfUXjvffeO9qNKm+ttNJKUYbzFcy59AJLWv678PuK3gOklaxHjBiR7GMJY5tttok2lywAUqlx6623jjbLSkCaij5mzJhoe4l00qRJ0eY+8b8RLOH5hURZPuHj+9+IInm9kShKP/dzGEuV/jeTJahc6ACHBBSlr/vjse1lK57feWzz60Aqd0reEkIIIYRoBfTQI4QQQohSoIceIYQQQpSCdo/pKYoVyMUQFC1BAKSarE9Z5yUKitLXc8fzpc2LaNRy9o0Ca88+FoOvMceAeM2XdXlOfeRS/EBafp77wX9uo8RvNBIcF8LXx8dLcAzOgAEDkn2szW+00UbR9vEd3DcvvPBCtDkmBEjjSnhJAh+jxamxHMPiV/DmmJ5GHacffPBBXA2cryEA7LbbbtHmldV9LMWWW24ZbR4TPs35lFNOibaP1eF4Kl4KaJdddik8J+7/Aw88MGn32GOPRZuXnjj66KOTdkXLX3BcEQCMGzcu2r40AbPVVltFm1dcB5aNNWtEuLwDr07vf+8Y/5vEbfk3zo8BnidzcY88/oriKP3xi0rDAOk43XPPPQvbtQby9AghhBCiFOihRwghhBCloGFWWc+5mn0qM6fIsZstl/LMrjrvZmOJhV38SlFvHbjEgK/syeRSzFni5D7yKzmzDMb3g5e3chJnWSlyP998883JNrvYWWoE0rHELnWWGIA0pZrvDy9T8Bhkudqn8TbJQUAq53Aar6de+bq9ef/996MMxZIekKbgc5q+n/t4BW6+BiwxAcA+++xTeAyWVX71q19F28+LV111VbRZ3vIrmLNsce+990bb30Ms1V1//fXRfvXVV5N2XEHay+Hz58+veTx/H9a7Gnl74scAjw+uuuzlLZ7TeDwA6fXh8eGvGx+D50w/HzMsl3lJjI/Bv/H+937ixImFx29t5OkRQgghRCnQQ48QQgghSkGH+nfrrQDrYXcou3G925VdciyJ5Ko/875u3brVfU6iGHahekmB3Z85eYsrjLKL11NUYdV/rpfFRPEY9NlbPG65si6Q9mf//v2j7aUJllx4kUKfbcVyJZ+flwB4rPLisn4BU5YEclmhHclqq62G4cOHA0grJgOppMOLrN53331JO5YPOUPLZ2+de+650fbX47zzzos2Z8RdeOGFSTvO8mL5+uGHH07aHXzwwdH+1re+FW1/D/G9wRlbXgbjBUg5yw9IFyBlycXLezvuuCMaDa5WDhSvLODhuc9LlTy35mRdHr+51QmK3uPhz8plb/nv3JbI0yOEEEKIUqCHHiGEEEKUAj30CCGEEKIUdOgq6y2tiMpphqxVes2Q9WXW9jmGAChetdtrlbzK8zrrrFP4uY1a6bWjqHdFc9ahc33J155XBW6LcyoTRVWqp02blmxvu+220fZxIDNmzIg299mGG26YtOMxwnEbXJXb07dv32jPmzcv2cdxY/w9/Bh++umno81xH43ECiusEOOSbr/99mTfoEGDos2VjF955ZWkHW/zdRs5cmTSjtPe586dm+zjeJeNN9442scee2zS7sYbb4w2x37wfQKkq7FzbBXPq0B6b/D3GDZsWNKO9/ljHHDAAdH+y1/+Em2fop2LM+kofNwVz4u5Cse5lHAeBxy36uNbi66HPx5fRz4/npuBND6LSwf44+VKmbQ28vQIIYQQohTooUcIIYQQpaBhFhz1KXHsjvvTn/6U7GOXHKe0+kX3+Bhs+5Q9TvVjectXc/3+978f7UsvvbTmscWycH/lFsnje8PLT+xCZUnFp7bzZ7HM4VPZc+chUrnAS07sfvcp5ixVcZrzrFmzknbsRufyAX4BSE6XZ3nEp6Jzvz/55JPR9mOTFz5tVHnrnXfeidWQvUTE3+fxxx+PNi/6CaT3+0MPPRTtIUOGJO24Oi8vAgoA/fr1i/bVV18dba7UDKSp6NwvDz74YNKOx/DQoUOj7SVqrvjN8/G///3vpN1mm20W7W9/+9vJPpZZ+d7wvz9eJm0EfImIXDVkpkgGA4rnRT8+6g3N4N9QPrYvG8MyWC60hUvPtDX6tRZCCCFEKdBDjxBCCCFKQcOsuJdzq919993JdlEFZQ+71jg63EsdLK2xzZVdgfZdFK0rwX3kZUx2ebKr1ctPnBXAsklOBstlZhRVbhYV+Lpyhg8A7LffftHmyr9A2m+cscUyNJBKZM8880y0fXYNV/vlCs9eyub5gxeV9FlNuQVIG4VVVlkFm266KYBlvyff+1yhmBf9BNJrsOWWW0b7pz/9adJup512ira/Nrfddlu0WXLx1Y9Z0uJFYf/2t78l7Q499NCan+Wr8bLk9sILL0T7kEMOSdrxvXbTTTcl+3bYYYdoN1W3BpatcM0SWaPgM9G4zxmfKcXt6s1S8/Mx/7bmfpN5Hx/Dz9vbb799tLmKup+3fcX2tkSeHiGEEEKUAj30CCGEEKIU6KFHCCGEEKWgU8T0+AqV3JbjRXwqOuuYrCH6KrJ8vJym6VeuLYI1TqWzp/hryNeYr5VPSe7Tp0+0eaVprw3zMd58883C86g3DbSs3HDDDdH2Ket8zf01/s9//hNtribs23FcCJeCuOaaa5J2nM7MMXU+xXXfffeNNldsf/7555N2HBfUqIQQYsyZT0XnWI1777032hMmTEjabbDBBtHmOJuBAwcm7Xz6OcNjc++99462j/HieB+eW7feeuukHcd3cKySjwPhOC6e37myNJBW1/YxPXxOhx12WLR9XJBPD28EfBwXXx/uk27duiXtONXf9yunkvPvk4/1KYqxzFV45t9Mf+5NsWlAet/4mKP2nI/1iyyEEEKIUqCHHiGEEEKUgg6Vt+pdfJTTFoFUxmI3mU8xL6rE6SUnPo+iypVA6p6ThFU/Re5ZIO1LLivg3Z3srl933XWj7WUTls+4/7ysppT1PFwl2ctbvABp7969k32PPvpotLmvfaVWllw49db3E7vLeWx6tzynvXNVZy+xsCTSqLz33ntxzuP0bSCda7gMgP+e/L4rr7wy2j5UoHv37tH2lZG5kjOPJU4HB9K0b+6vb37zm0k7lidzC4my5DRnzpxo33PPPUk7XlTUV67mFGieq71E1ogLjvLYANL7nufFLbbYImnXo0ePaPvwAJbCchWqi37X/G9ckfTl51WeH7gaui81kztGvWEl9aJfayGEEEKUAj30CCGEEKIUdAp5y0sYRa46n71V9Fke/uzcebDLn7NHfGVMkcLyVi5bgPvSZ+esueaa0WZ5y7tCi+4pL5dxX4pl4evjM+RYUubFPYFUBsmNOR6r3C5XsTs3NjnjhyUMn2nk3f6NyIorrhjlKb8gJlcyHjFiRLRZ/gWAmTNn1tw3YMCApB3LRz6rda+99oo23wNeVuFKuyyXeSmNj8FSzNy5c5N2fAyWKn3VXpbfuDo1ABx44IHR5sVH+T4BgE996lNoNPx9znMc7/NVzouqJAPpeMuFZuRWOGCKFvD2v9Xcz3x/cYYlkEp68+fPT/a1dsalPD1CCCGEKAV66BFCCCFEKdBDjxBCCCFKQcNUZM7B1XiBVA9kPdFroRwPwLaP7+D35WIIWFtlHVsxPXn4mvoYnKJKnD72wsciNOFTejnepKgKKVC/dl1WWFffeeedk32cQjp16tRkH/dvbmwyReMUSPuNbV9Ogj+X06E5TRpIYw58/IEvedGRNMVM+GrFDz/8cLQ5/d7f3xz/whWJ/TgaO3ZstH3aO2/zeVx22WVJO74fevbsGW0/hvfff/9oczzSueeem7SbPn16tL/85S9He5tttknanXPOOdH2ZU34N4LjorhCMLBszFcj4GNTuW953vLlInguzZUG4bHix1HR5+ZS1tn2FZn5t3HLLbeMNldrB9JyCX6VecX0CCGEEEK0AD30CCGEEKIUNEzKuofdeN5lVpSK7F16uZTlej7Xu/74fNmduvHGG9d1bLGsrMT9wi507+L1CyU2wemtQOpS9ymdIg+XCeDr6Mcpp0P7FOCWkJO3GHa3+yqtLFPwfMELkQLA6NGjo+3ll0aRt1ZeeeWYqu2rJLNEwOPFp3NzyvYee+wRba6YDQA77bRTtP0Y47IF/FleIuPUdL6mXprjSstc1XvQoEFJO05z5mPPnj07acfzrpf3+H7g3wFfXZw/q1HgyvRAev58TX3YB8ud/hhFFZS9bFX0WbnFt/kYuUrLfN/4MAc+hi9X0trI0yOEEEKIUqCHHiGEEEKUgg6Vt3IZHZyFk6viy27NehePy7Xjfd71x5/lJTdRDLtCvcxYVKXTy1tF0oOXsNi9zq7WnDtVVGD5gV3nTz31VNKO+9BnkHCFZq6c7imqgl5vlojPvOJKxXwOvXr1Stqxy/7xxx9P9nH1347knXfeidf8H//4R7KPqytzlXLOmgKAkSNHRpvlSJ+hxZKRr/683377RZtlMc6OA5aVjJrwWTi8KCzLSpytBaRjndtNnjw5aTdlypRo+yxOvj94LvELzo4bN67muXckfu7j8cFVrf3iqXx9vCzKv125393ceTA8t/L87j/XV16udT6e1pDMc2jmF0IIIUQp0EOPEEIIIUqBHnqEEEIIUQoatiJzrpprUVp5LvaHyVVkzmmfHFPAq8KKPFwZ2fcJp8Xy9eZ4BaC4cmgupoR1ff+5Ob26rHCsxnPPPRdtn8rMVW1vuummZB/HaPE4zcURcDuv9fP7OC3bl4ngc+J7x8cYcPxBvTGA7c0KK6wQvwPH1QBprCOnffsV0nfYYYea+3i8AWlqty8DwNWsOXYut1I9X3ufis7zrq+gzHCaOq8C79Oh+/XrF20fZ8Qp25wq7dPt/ersjYBP9Wf4Gvg+5325+Y3nUv9byGOC2+VWO2D8eCs6Xi62M3d/tQby9AghhBCiFOihRwghhBCloGF9/Ozu8q46dvHWm37H1PuenPvbp0jW+76ys9FGGyXbnErOZQCKKjB7fFVSTn/lfvb3kOTJZeGUdZYzWG4A0n7y7uxcJWcml7LKsEuc33PCCSck7Q466KBof+ITn4g2SyCeequ0tzcffvhhlJ18yj2Pl7vuuivaw4YNS9ptv/320eZ09gceeCBpx2UFvPTFKee8aKlfxPXZZ5+NNocAcHo9kEpfLJ96mYa/I9+HPv2ZpSlfHoEXtNxnn32izSnfQCqfNQq+HAPLjryPyzQA9VcUr7cCelFZidwxvETK9xCPZd/nLEfy73tbIE+PEEIIIUqBHnqEEEIIUQr00COEEEKIUtCwMT2M1/94FdaWLCfgdUzWGjntz6dI8mf5su9MS+KMujJc6t6nlvIq6ZySvPPOO9d1bB+zwX3G2rCPB2hELb+j4bgIvq5eY+d+8te13uUl1l133WjPnz8/2rllRXjM/eY3v0na/eAHP4j2NttsE+1NNtkkacdxMG29mnNLWWWVVbDVVlsBWDa+g2PTjjzyyGj7uYqX2OCyDr7EA1+rW2+9NdnH8UQc1+XjGQcPHhxtXjbCL/3C9xHH4vlz4s/iudnfGxwXxPcTkK5Gz8tr+JXajzrqKDQa/veJY6E4fsr3Ocf0+KVBePwVlf8A0ri5opXZa2034fuBSyJwn9S7knxbIE+PEEIIIUqBHnqEEEIIUQo6hbzF7m9PrtpvEfWm6XmXPLuW+XObc/wywqmlPmV9/fXXj/asWbOiPXTo0LqOPWTIkGR7nXXWiTbLNd4V/MlPfrKu45cJTkVnt7RfLZtlIS8vsvudZTB//Tl1eNGiRdH28id/No8/7x4vSl/2K8Rzanu9Kb7tzaqrrhpXQ/erorclxx13XLt9lqgflrdYfvJVyUePHh1tL91yiAiXavDjkqk3TCNXaZnn9D322CPavoQIv8+XFWht5OkRQgghRCnQQ48QQgghSkGHylv1us84IwBYthJlE36hMt7miHAfHV60OJuvNptzBTLK3kphSYHt1oBdpgAwZsyYaOeyFMSysAucq+5yhh0AbLjhhtEeOXJk4fEee+yxaHuJmmUsXpjy4IMPTtrxmMstZslZWvyez3zmM0k7Po/hw4cXnrsQHYWvajx37txos7zlQwVYsveVt/m3jI/hK6MXLRCay5LmfV5W4yxcXhTYZ4SyxL1w4cLCz2oN5OkRQgghRCnQQ48QQgghSoEeeoQQQghRCjpFTI9fSZurwHLquI894LRWrmzqNVPWMVmf5JRbINUhc6usixROQfSpxvXC155jsHw8VlEcj4/H4hRJX/G7rHB81AUXXBBtP17OO++8uo7H1X7ZzuFXC28JfA/4uYPnCF6NXYhGwcc9chVxjsHx1Y+/9rWv1bQbkUMOOSTZ5vn58MMPb9PPlqdHCCGEEKVADz1CCCGEKAXWnOrBZvYygLkf2VC0Jv1DCL0+ulnzUF92GOrProP6smvR6v2pvuwwCvuyWQ89QgghhBCdFclbQgghhCgFeugRQgghRCnQQ48QQgghSoEeeoQQQghRCvTQI4QQQohSoIceIYQQQpSCTvfQY2YfmNlkM5tuZo+Z2XfMrNN9jzJiZj2qfTfZzF40s+dpu2XrU4iGxczWN7N/mNlMM3vczG4zs82aeYy1zezrbXWOon5o7n3MzCaZ2c4dfU6i+ZR9XHa6Oj1mtiSEsEbVXhfASAAPhRD+z7VbKYTwfq1jiI7HzH4EYEkI4Vf0Wrv2mZmtGEL44KNbiuZilYX1xgL4awjh0uprQwGsGUJ4oBnHGQDg1hDC4LY4T1E/bu79JIAzQgh7fMTbRAOhcdkJPT1MCGEBgJMAfMMqnGBm15nZLQBGm9nqZvZnMxtvZo+a2aEAYGaDzOyR6l8tU8xs02rbf1f/iplmZkd16JcrCWZ2hZn92szuBXCumQ01s3HVfrnJzNapthtjZiOqdk8zm1O1l+nL6uvH0Ot/MLMVq68vMbOzzew/AHbqkC9dDvYC8F7TxAoAIYTJAB40s/OqY2xq0zgzszXM7O6qB2Fq01gF8AsAG1f7sb6VTkV7sBaAxUC272BmZ5rZk2Z2p5n93cz+X4edsQA0Ljt2lfXWIIQwqypvNS05uxOAISGERWb2cwD3hBBONLO1ATxiZncB+CqAC0MIf6vKKisCOBDA/BDCpwDAzLq1+5cpL5sB2DeE8IGZTQHwzRDCfWZ2NoD/A3BK5r3L9KWZbQngKAC7hBDeM7OLAXwBwJUAVgcwLYRwVlt+IYHBACbWeP0zAIYC2AZATwDjzex+AC8DOCyE8LqZ9QQwzsxuBnA6gMEhhKHtctYix6pmNhnAKgB6A9i7+vo7qN13wwEcDmAYKr81k1D7nhDtR+nHZad/6KliZN8ZQlhUtfcDcAj9dbEKgH4AHgbwAzPbEMCNIYSnzWwqgF+Z2bmouO3qdvWJ5ea66gNPNwBrhxDuq77+VwDXfcR7a/XlPqhMuOMr3lysCmBBtf0HAG5o9W8g6mVXAH+vyoovmdl9ALYDcDuAn5vZ7gA+BNAHwHodd5qiBm83/ciZ2U4ArjSzwajMv7X6blcA/wohvF19zy0dctaiHkozLjv9Q4+ZDUTlh6zpR+1N3g3g8BDCU+5tT1TljU8BGGVmXwoh3GNmw1Hx+JxjZqNDCGe39fkLAGmfFfE+lsqxqzS9GEIY6fsSlX7/awjh+zWO847ieNqF6QCOqPG61XgNqHjiegEYXvXOzQH1s2gsQggPV//y74XKnFmr74r6WnQcpR+XnTqmx8x6AbgUwO9C7YjsUQC+adU/981sWPX/gQBmhRAuAnAzgCFmtgGAt0IIVwP4FYBt2+M7iKWEEF4DsNjMdqu+dCyAJq/PHFS8NwAN2lp9CeBuAEdYJdAdZtbdzPq3/TcQxD0APm5mX256wcy2QyUO5CgzW7E6fncH8AiAbgAWVCfWvQA09dcbANZs31MXH4WZbYFKWMArKO67BwEcbGarmNkaqPxhIjqW0o/LzujpadKVV0blr/+rAPy6oO1PAFwAYEr1wWcOgINQifc4xszeA/AigLNRceWdZ2YfAngPwNfa7iuIDMcDuNTMVgMwC8AXq6//CsC1ZnYsKgO3iWX6shrP9UNUgtlXQKU//xfA3Pb6EmUnhBDM7DAAF5jZ6ajEfcxBJT5rDQCPAQgATgshvGhmfwNwi5lNADAZwJPV47xiZg+Z2TQAt4cQTm33LyOaaJp7gYpn4PiqLF3Ud+Or8R+PoTL2JgB4rd3PWkQ0LjthyroQQojOgZmtEUJYUv0j5n4AJ4UQJnX0eYny0hk9PUIIIToHfzSzrVCJA/mrHnhERyNPjxBCCCFKQacOZBZCCCGEqBc99AghhBCiFOihRwghhBClQA89QgghhCgFeugRQgghRCnQQ48QQgghSsH/B2DAztNfZq5eAAAAAElFTkSuQmCC",
      "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": "code",
   "execution_count": 16,
   "id": "c23ac2eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a09c4d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "262c88ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4294 - accuracy: 0.8486\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4236 - accuracy: 0.8514\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4181 - accuracy: 0.8530\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4127 - accuracy: 0.8555\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4081 - accuracy: 0.8560\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4040 - accuracy: 0.8573\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3994 - accuracy: 0.8596\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3956 - accuracy: 0.8608\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3924 - accuracy: 0.8616\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3886 - accuracy: 0.8631\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1fbdc8aed60>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b13746eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 1s - loss: 0.4264 - accuracy: 0.8488\n",
      "\n",
      "Test accuracy: 0.848800003528595\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "54b406ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e06f105f",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bca7c186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.2068541e-07, 8.5539398e-09, 1.5911476e-06, 1.8165028e-06,\n",
       "       3.4645675e-06, 9.7793773e-02, 9.8987994e-06, 1.8201329e-01,\n",
       "       3.5563451e-03, 7.1661967e-01], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5ff5b811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0bf95966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e3efa42d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  true_label, img = 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",
    "  true_label = 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": 36,
   "id": "31b6f4bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1e9d1a66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4b78abe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 52,
   "id": "30db210f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[1]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "68d77212",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e8c076c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.1909504e-04 2.0461421e-06 9.2086673e-01 4.9100989e-05 1.8829182e-02\n",
      "  1.9098445e-11 5.9672460e-02 4.7325112e-19 1.6146184e-04 1.3185488e-13]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = probability_model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bbd6a536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(1, predictions_single[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "306b65c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions_single[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('readvice')",
   "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": "3.8.13"
  },
  "vscode": {
   "interpreter": {
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