1_digits_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a532c9a0-6e0d-4fae-ab4e-04cd7e12f79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "923cab6a-8ef0-40a1-aa22-34a072091dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.9.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bbc07359-3ff1-4892-976c-c78a9c564168",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30eedb78-3824-4538-b954-751ee3feb596",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c220a04e-4d3f-41bb-b2dc-836c55de2683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19710b45-7366-4a9b-a15a-e23afa95c678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a338a31e-2284-4ff9-becb-c9ddf8b7eb3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x28e11cd00>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaMAAAGkCAYAAACckEpMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAZ6UlEQVR4nO3df2xU553v8c9gYGLQMCuX2DNTHK+VBbWKEb0FCvjyw7DBwqugECdakmi7RmpR0hgk5ETZUu4V3q6Es0iwSOuGqmmWggoCXYkQJNgQ54JNEKFrkKN4aYqciwnOxq6Lb+IxhowDPPuHl8lObCBnmPHXM36/pKMwM+fhPDw6ypvDzBz7nHNOAAAYGmc9AQAAiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5ogRAMBcRsXo1VdfVXFxsR544AHNnj1b7777rvWURlRtba18Pl/CFgqFrKc1Ik6ePKmVK1cqEonI5/Pp0KFDCa8751RbW6tIJKLc3FyVlZXp/PnzNpNNo3utw5o1a4acI/Pnz7eZbBrV1dVp7ty5CgQCys/P16pVq3ThwoWEfcbCOfFN1iFTzomMidGBAwe0YcMGbdq0SS0tLVq0aJEqKip0+fJl66mNqEceeUSdnZ3xrbW11XpKI6K/v1+zZs1SfX39sK9v3bpV27dvV319vZqbmxUKhbR8+XL19fWN8EzT617rIEkrVqxIOEeOHj06gjMcGU1NTaqurtaZM2fU0NCgGzduqLy8XP39/fF9xsI58U3WQcqQc8JliB/84Afu+eefT3juO9/5jvvpT39qNKORt3nzZjdr1izraZiT5N54443441u3brlQKOReeeWV+HNffPGFCwaD7pe//KXBDEfG19fBOeeqqqrc448/bjIfS93d3U6Sa2pqcs6N3XPi6+vgXOacExlxZTQwMKBz586pvLw84fny8nKdPn3aaFY22traFIlEVFxcrKeffloXL160npK59vZ2dXV1JZwffr9fS5YsGXPnhyQ1NjYqPz9fM2bM0Nq1a9Xd3W09pbTr7e2VJOXl5Ukau+fE19fhtkw4JzIiRleuXNHNmzdVUFCQ8HxBQYG6urqMZjXy5s2bpz179ujYsWN67bXX1NXVpdLSUvX09FhPzdTtc2Csnx+SVFFRob179+r48ePatm2bmpubtWzZMsViMeuppY1zTjU1NVq4cKFKSkokjc1zYrh1kDLnnBhvPQEvfD5fwmPn3JDnsllFRUX81zNnztSCBQv08MMPa/fu3aqpqTGc2egw1s8PSVq9enX81yUlJZozZ46Kiop05MgRVVZWGs4sfdatW6cPPvhAp06dGvLaWDon7rQOmXJOZMSV0dSpU5WTkzPkbzTd3d1D/uYzlkyePFkzZ85UW1ub9VRM3f5EIefHUOFwWEVFRVl7jqxfv16HDx/WiRMnNG3atPjzY+2cuNM6DGe0nhMZEaOJEydq9uzZamhoSHi+oaFBpaWlRrOyF4vF9OGHHyocDltPxVRxcbFCoVDC+TEwMKCmpqYxfX5IUk9Pjzo6OrLuHHHOad26dTp48KCOHz+u4uLihNfHyjlxr3UYzqg9Jww/POHJ/v373YQJE9zrr7/ufv/737sNGza4yZMnu0uXLllPbcS8+OKLrrGx0V28eNGdOXPGPfbYYy4QCIyJNejr63MtLS2upaXFSXLbt293LS0t7uOPP3bOOffKK6+4YDDoDh486FpbW90zzzzjwuGwi0ajxjNPrbutQ19fn3vxxRfd6dOnXXt7uztx4oRbsGCB+/a3v5116/CTn/zEBYNB19jY6Do7O+PbtWvX4vuMhXPiXuuQSedExsTIOed+8YtfuKKiIjdx4kT3/e9/P+Hji2PB6tWrXTgcdhMmTHCRSMRVVla68+fPW09rRJw4ccJJGrJVVVU55wY/yrt582YXCoWc3+93ixcvdq2trbaTToO7rcO1a9dceXm5e/DBB92ECRPcQw895Kqqqtzly5etp51yw62BJLdr1674PmPhnLjXOmTSOeFzzrmRuw4DAGCojHjPCACQ3YgRAMAcMQIAmCNGAABzxAgAYI4YAQDMZVSMYrGYamtrR90N/iywFoNYh0Gsw1dYi0GZtg4Z9T2jaDSqYDCo3t5eTZkyxXo6pliLQazDINbhK6zFoExbh4y6MgIAZCdiBAAwN+p+ntGtW7f06aefKhAIDPm5I9FoNOG/YxlrMYh1GMQ6fIW1GDQa1sE5p76+PkUiEY0bd/drn1H3ntEnn3yiwsJC62kAAFKko6Pjnj9nadRdGQUCAUnSQv2VxmuC8WwAAMm6oS91Skfj/1+/m1EXo9v/NDdeEzTeR4wAIGP917+7fZMf9Z62DzC8+uqrKi4u1gMPPKDZs2fr3XffTdehAAAZLi0xOnDggDZs2KBNmzappaVFixYtUkVFhS5fvpyOwwEAMlxaYrR9+3b96Ec/0o9//GN997vf1Y4dO1RYWKidO3em43AAgAyX8hgNDAzo3LlzKi8vT3i+vLxcp0+fHrJ/LBZTNBpN2AAAY0vKY3TlyhXdvHlTBQUFCc8XFBSoq6tryP51dXUKBoPxjY91A8DYk7YPMHz90xPOuWE/UbFx40b19vbGt46OjnRNCQAwSqX8o91Tp05VTk7OkKug7u7uIVdLkuT3++X3+1M9DQBABkn5ldHEiRM1e/ZsNTQ0JDzf0NCg0tLSVB8OAJAF0vKl15qaGv3whz/UnDlztGDBAv3qV7/S5cuX9fzzz6fjcACADJeWGK1evVo9PT36+c9/rs7OTpWUlOjo0aMqKipKx+EAABlu1N0o9fYPhCrT49wOCAAy2A33pRr15jf6AX/8PCMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5ogRAMAcMQIAmCNGAABzxAgAYI4YAQDMESMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5ogRAMAcMQIAmCNGAABzxAgAYI4YAQDMESMAgDliBAAwR4wAAOaIEQDA3HjrCQBAMvqfmud5zD9u3el5zD/89d96HiNJ7uy/JzVurOLKCABgjhgBAMylPEa1tbXy+XwJWygUSvVhAABZJC3vGT3yyCN655134o9zcnLScRgAQJZIS4zGjx/P1RAA4BtLy3tGbW1tikQiKi4u1tNPP62LFy/ecd9YLKZoNJqwAQDGlpTHaN68edqzZ4+OHTum1157TV1dXSotLVVPT8+w+9fV1SkYDMa3wsLCVE8JADDKpTxGFRUVevLJJzVz5kw9+uijOnLkiCRp9+7dw+6/ceNG9fb2xreOjo5UTwkAMMql/UuvkydP1syZM9XW1jbs636/X36/P93TAACMYmn/nlEsFtOHH36ocDic7kMBADJUymP00ksvqampSe3t7frd736np556StFoVFVVVak+FAAgS6T8n+k++eQTPfPMM7py5YoefPBBzZ8/X2fOnFFRUVGqDwUAyBIpj9H+/ftT/VsCALIcd+0eIdcf/4H3Md9K7s4Vef/yXlLjgEzSPcf7uwz/cGllGmaCVOBGqQAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOW6UOkI+Xey9+5Me/jy5g/1LcsMAE+OSuyGwe+i65zF/mf8Hz2P+r6/U8xh4x5URAMAcMQIAmCNGAABzxAgAYI4YAQDMESMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGCOG6WOkL9/7P94HvOPH5anYSbA6JLzcFFS4/6wxPsdgb/3b3/jeUykudXzGHjHlREAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5ogRAMAcMQIAmCNGAABzxAgAYI4YAQDMcdfuETLBd8N6CsCoNP7X10bsWNf/35QROxa84coIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADDHjVKTcGvh9zyPWfTAqdRPBMgCfz65Z8SOVfjOzRE7FrzhyggAYI4YAQDMeY7RyZMntXLlSkUiEfl8Ph06dCjhdeecamtrFYlElJubq7KyMp0/fz5V8wUAZCHPMerv79esWbNUX18/7Otbt27V9u3bVV9fr+bmZoVCIS1fvlx9fX33PVkAQHby/AGGiooKVVRUDPuac047duzQpk2bVFlZKUnavXu3CgoKtG/fPj333HP3N1sAQFZK6XtG7e3t6urqUnl5efw5v9+vJUuW6PTp08OOicViikajCRsAYGxJaYy6urokSQUFBQnPFxQUxF/7urq6OgWDwfhWWFiYyikBADJAWj5N5/P5Eh4754Y8d9vGjRvV29sb3zo6OtIxJQDAKJbSL72GQiFJg1dI4XA4/nx3d/eQq6Xb/H6//H5/KqcBAMgwKb0yKi4uVigUUkNDQ/y5gYEBNTU1qbS0NJWHAgBkEc9XRlevXtVHH30Uf9ze3q73339feXl5euihh7RhwwZt2bJF06dP1/Tp07VlyxZNmjRJzz77bEonDgDIHp5jdPbsWS1dujT+uKamRpJUVVWl3/zmN3r55Zd1/fp1vfDCC/rss880b948vf322woEAqmbNQAgq3iOUVlZmZxzd3zd5/OptrZWtbW19zOvUe3jx3I9j8nPmZSGmQCjy/g/f8jzmKfyDqdhJsPLbf/M8xhurToyuDcdAMAcMQIAmCNGAABzxAgAYI4YAQDMESMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGAupT9cb6wY/xd9I3KcL/7wZyNyHCBVOnZM9jzmf/pvJXWs16PTvA/6PJrUsZB+XBkBAMwRIwCAOWIEADBHjAAA5ogRAMAcMQIAmCNGAABzxAgAYI4YAQDMESMAgDliBAAwR4wAAOaIEQDAHHftHsXyzyZ3N2Nkr5yp30pq3B+fnOF5TN5ff+J5TNOM1z2PkR5IYoy08xerPI/J/+PppI6F9OPKCABgjhgBAMwRIwCAOWIEADBHjAAA5ogRAMAcMQIAmCNGAABzxAgAYI4YAQDMESMAgDliBAAwx41SR7Hrecn9XWFyiueRarcW/Q/PY1yOz/OYjkf9nscMRL70PEaSxk286XnM24v+2fOYCd6XQZLUddP7Wvzvi094HvP/b3m/ue+kcd7XTpIKftfneYxL6kgYCVwZAQDMESMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmuFFqEmJfTPA85lYSt2jc9bN/8jxGkg6v+15S40bK333r157HjJP3O4RedwOex3x6M7mbdtb/qczzmEff2eB5zJ+1TPQ8RpLCb//R8xjfx594HvOnD3M9jynISe7mtK65NalxGJ24MgIAmCNGAABznmN08uRJrVy5UpFIRD6fT4cOHUp4fc2aNfL5fAnb/PnzUzVfAEAW8hyj/v5+zZo1S/X19XfcZ8WKFers7IxvR48eva9JAgCym+cPMFRUVKiiouKu+/j9foVCoaQnBQAYW9LynlFjY6Py8/M1Y8YMrV27Vt3d3XfcNxaLKRqNJmwAgLEl5TGqqKjQ3r17dfz4cW3btk3Nzc1atmyZYrHYsPvX1dUpGAzGt8LCwlRPCQAwyqX8e0arV6+O/7qkpERz5sxRUVGRjhw5osrKyiH7b9y4UTU1NfHH0WiUIAHAGJP2L72Gw2EVFRWpra1t2Nf9fr/8fn+6pwEAGMXS/j2jnp4edXR0KBwOp/tQAIAM5fnK6OrVq/roo4/ij9vb2/X+++8rLy9PeXl5qq2t1ZNPPqlwOKxLly7pZz/7maZOnaonnngipRMHAGQPzzE6e/asli5dGn98+/2eqqoq7dy5U62trdqzZ48+//xzhcNhLV26VAcOHFAgEEjdrAEAWcVzjMrKyuTcnW/6eezYsfuaEABg7OGu3Un4i79p8Tzmkbp1nscUzv0Pz2MywYnuGZ7H/Olfp3ke863z3u8GPfGtZs9jBnk/1gydTfJY3iVzL/L/+LtSz2Pm+t/zPGb/1W97HoPsw41SAQDmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5ogRAMAcMQIAmCNGAABz3Ch1hBRv9H4DSXwlrMvWUxhzJi3+04gc53+deDKpcTP0bymeCSxxZQQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5ogRAMAcMQIAmONGqQBMFb3prKeAUYArIwCAOWIEADBHjAAA5ogRAMAcMQIAmCNGAABzxAgAYI4YAQDMESMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADA33noCALJHjs/7328/mzEhqWOF/jWpYRiluDICAJgjRgAAc55iVFdXp7lz5yoQCCg/P1+rVq3ShQsXEvZxzqm2tlaRSES5ubkqKyvT+fPnUzppAEB28RSjpqYmVVdX68yZM2poaNCNGzdUXl6u/v7++D5bt27V9u3bVV9fr+bmZoVCIS1fvlx9fX0pnzwAIDt4+gDDW2+9lfB4165dys/P17lz57R48WI557Rjxw5t2rRJlZWVkqTdu3eroKBA+/bt03PPPTfk94zFYorFYvHH0Wg0mT8HACCD3dd7Rr29vZKkvLw8SVJ7e7u6urpUXl4e38fv92vJkiU6ffr0sL9HXV2dgsFgfCssLLyfKQEAMlDSMXLOqaamRgsXLlRJSYkkqaurS5JUUFCQsG9BQUH8ta/buHGjent741tHR0eyUwIAZKikv2e0bt06ffDBBzp16tSQ13w+X8Jj59yQ527z+/3y+/3JTgMAkAWSujJav369Dh8+rBMnTmjatGnx50OhkCQNuQrq7u4ecrUEAMBtnmLknNO6det08OBBHT9+XMXFxQmvFxcXKxQKqaGhIf7cwMCAmpqaVFpampoZAwCyjqd/pquurta+ffv05ptvKhAIxK+AgsGgcnNz5fP5tGHDBm3ZskXTp0/X9OnTtWXLFk2aNEnPPvtsWv4AAIDM5ylGO3fulCSVlZUlPL9r1y6tWbNGkvTyyy/r+vXreuGFF/TZZ59p3rx5evvttxUIBFIyYQBA9vEUI+fcPffx+Xyqra1VbW1tsnMCkKFuulveB3FTMojTAAAwChAjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5pL+Sa8AkArX5l6zngJGAa6MAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5ogRAMAcMQIAmCNGAABzxAgAYI67dgNImRwff79FcjhzAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5ogRAMAcMQIAmCNGAABz3CgVwLBi7zzoeczN791Kw0wwFnBlBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCY8znnnPUk/rtoNKpgMKgyPa7xvgnW0wEAJOmG+1KNelO9vb2aMmXKXfflyggAYI4YAQDMeYpRXV2d5s6dq0AgoPz8fK1atUoXLlxI2GfNmjXy+XwJ2/z581M6aQBAdvEUo6amJlVXV+vMmTNqaGjQjRs3VF5erv7+/oT9VqxYoc7Ozvh29OjRlE4aAJBdPP2k17feeivh8a5du5Sfn69z585p8eLF8ef9fr9CoVBqZggAyHr39Z5Rb2+vJCkvLy/h+cbGRuXn52vGjBlau3aturu77/h7xGIxRaPRhA0AMLYkHSPnnGpqarRw4UKVlJTEn6+oqNDevXt1/Phxbdu2Tc3NzVq2bJlisdiwv09dXZ2CwWB8KywsTHZKAIAMlfT3jKqrq3XkyBGdOnVK06ZNu+N+nZ2dKioq0v79+1VZWTnk9VgslhCqaDSqwsJCvmcEABnOy/eMPL1ndNv69et1+PBhnTx58q4hkqRwOKyioiK1tbUN+7rf75ff709mGgCALOEpRs45rV+/Xm+88YYaGxtVXFx8zzE9PT3q6OhQOBxOepIAgOzm6T2j6upq/fa3v9W+ffsUCATU1dWlrq4uXb9+XZJ09epVvfTSS3rvvfd06dIlNTY2auXKlZo6daqeeOKJtPwBAACZz9OV0c6dOyVJZWVlCc/v2rVLa9asUU5OjlpbW7Vnzx59/vnnCofDWrp0qQ4cOKBAIJCySQMAsovnf6a7m9zcXB07duy+JgQAGHu4Nx0AwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5ogRAMAcMQIAmCNGAABzxAgAYI4YAQDMESMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwNx46wl8nXNOknRDX0rOeDIAgKTd0JeSvvr/+t2Muhj19fVJkk7pqPFMAACp0NfXp2AweNd9fO6bJGsE3bp1S59++qkCgYB8Pl/Ca9FoVIWFhero6NCUKVOMZjg6sBaDWIdBrMNXWItBo2EdnHPq6+tTJBLRuHF3f1do1F0ZjRs3TtOmTbvrPlOmTBnTJ9l/x1oMYh0GsQ5fYS0GWa/Dva6IbuMDDAAAc8QIAGAuo2Lk9/u1efNm+f1+66mYYy0GsQ6DWIevsBaDMm0dRt0HGAAAY09GXRkBALITMQIAmCNGAABzxAgAYI4YAQDMESMAgDliBAAwR4wAAOb+E/tGGZDbWV8uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_train[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ec0de38-f59a-402a-81cb-34b3054e6ade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e5eab96-75ac-437c-a8ef-b6106392f9ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling\n",
    "\n",
    "X_train = X_train/255\n",
    "X_test = X_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "88b781c6-4606-4c15-ab6b-4928397831bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Flattening\n",
    "\n",
    "X_train_flattened = X_train.reshape(len(X_train),28*28)\n",
    "X_test_flattened = X_test.reshape(len(X_test),28*28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7d8ddd97-a94f-4b83-a80a-3b1f4e824b54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cc627d63-a4d6-447d-a840-18cf3ed40580",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "  44/1875 [..............................] - ETA: 6s - loss: 1.8706 - accuracy: 0.4247"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-09 18:46:43.621834: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.4732 - accuracy: 0.8756\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3040 - accuracy: 0.9148\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2834 - accuracy: 0.9209\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2732 - accuracy: 0.9235\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2665 - accuracy: 0.9257\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2982f1c40>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(10, input_shape=(784,), activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(\n",
    "    optimizer=\"adam\",\n",
    "    loss=\"sparse_categorical_crossentropy\",\n",
    "    metrics=[\"accuracy\"]\n",
    ")\n",
    "\n",
    "model.fit(X_train_flattened, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4dc0b528-4fa5-4413-9917-1ab41801b55a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 29/313 [=>............................] - ETA: 1s - loss: 0.2619 - accuracy: 0.9235"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-09 19:41:41.114581: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step - loss: 0.2651 - accuracy: 0.9275\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.2650902569293976, 0.9275000691413879]"
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "model.evaluate(X_test_flattened, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6d3c4494-011a-4e11-9391-422874c049b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_test[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0ca26f2b-a941-49ab-a502-58e2732751e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([4.0448949e-01, 5.9664692e-03, 9.9950111e-01, 3.4805378e-01,\n",
       "       9.1848595e-10, 8.2555884e-01, 9.3639797e-01, 9.1240438e-13,\n",
       "       1.6297898e-01, 1.7102337e-09], dtype=float32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = model.predict(X_test_flattened)\n",
    "y_predicted[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "22cf185f-871a-4117-80ba-dcd25a9b991f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(y_predicted[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "081af25b-31f3-4b05-9899-0e469b81c7f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 2, 1, 0, 4]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted_labels = [np.argmax(i) for i in y_predicted]\n",
    "y_predicted_labels[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3b96135f-c4fd-4b63-a503-737e610c0a93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, 0, 4], dtype=uint8)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3e86ebfc-dd53-4bf1-81be-e1718b99672f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[ 959,    0,    2,    2,    0,    5,    8,    2,    2,    0],\n",
       "       [   0, 1109,    3,    2,    0,    1,    4,    2,   14,    0],\n",
       "       [   3,    8,  936,   11,    9,    5,   12,    8,   37,    3],\n",
       "       [   1,    0,   28,  910,    1,   30,    2,   10,   21,    7],\n",
       "       [   1,    1,    3,    1,  922,    0,   12,    3,   11,   28],\n",
       "       [   8,    3,    3,   23,    8,  792,   18,    2,   29,    6],\n",
       "       [   9,    3,    6,    1,    7,   11,  917,    2,    2,    0],\n",
       "       [   1,    5,   26,    5,    8,    1,    0,  942,    4,   36],\n",
       "       [   5,    7,    7,   17,    9,   24,   10,    8,  881,    6],\n",
       "       [   9,    7,    2,    9,   36,   10,    0,   20,    9,  907]],\n",
       "      dtype=int32)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm =  tf.math.confusion_matrix(labels=y_test, predictions=y_predicted_labels)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "db66720c-ed22-47ba-a20e-df5c6863e8a7",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "  Downloading seaborn-0.12.2-py3-none-any.whl (293 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m293.3/293.3 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: pandas>=0.25 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from seaborn) (1.5.2)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from seaborn) (3.6.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.17 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from seaborn) (1.24.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.3.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.25.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.5)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from pandas>=0.25->seaborn) (2022.7)\n",
      "Requirement already satisfied: six>=1.5 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.15.0)\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.12.2\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "40aa4400-b7e2-40a6-a9f8-8cff2d2b5fca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(cm, annot=True, fmt=\"d\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Truth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4014bcda-14e5-4d25-872b-e3f73de2a945",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-09 20:01:12.960553: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2751 - accuracy: 0.9215\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1229 - accuracy: 0.9634\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0872 - accuracy: 0.9736\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0663 - accuracy: 0.9800\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0531 - accuracy: 0.9838\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x29af98340>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " model = keras.Sequential([\n",
    "    keras.layers.Dense(100, input_shape=(784,), activation='relu'),\n",
    "     keras.layers.Dense(10, activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(\n",
    "    optimizer=\"adam\",\n",
    "    loss=\"sparse_categorical_crossentropy\",\n",
    "    metrics=[\"accuracy\"]\n",
    ")\n",
    "\n",
    "model.fit(X_train_flattened, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3600c823-d3a7-4467-96f3-1d1261d7269f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 39/313 [==>...........................] - ETA: 1s - loss: 0.0989 - accuracy: 0.9712"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-09 20:02:24.264434: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 4ms/step - loss: 0.0761 - accuracy: 0.9757\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07608883827924728, 0.9757000207901001]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c8c62209-ec46-431e-ac9b-acfc6cbcb4ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 89/313 [=======>......................] - ETA: 0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-09 20:06:10.143612: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "y_predicted = model.predict(X_test_flattened)\n",
    "y_predicted_labels = [np.argmax(i) for i in y_predicted]\n",
    "cm = tf.math.confusion_matrix(labels=y_test, predictions=y_predicted_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "026d110f-8fd2-420c-be83-55e8de8cbd36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MS2G+j8bzE+tkey/zpl/I9l5yY0WDiIiIiEiTjJe31WecDE5ERERERJJjRYOIiIiISJOKFQ0psKJBRERERESSY0WDiIiIiEgT52hIghUNIiIiIiKSHCsaRERERESaOEdDEqxoEBERERGR5FjRICIiIiLSxDkakmBFg4iIiIiIJMeKBhERERGRJs7RkAQrGkREREREJDlWNIiIiIiINHGOhiRY0SAiIiIiIslxoCGhIYP742bkKaQ9vY3ToQfQuNGHoiO9s3MXL2P4hGlo0bk3ajRqh6MnQrSeD/zrb3w1ZjIat++JGo3a4fqN2/m2seOP/fhixAR4temGGo3a4WlqWr51RkyYjtbd+qFui85o3rkXvvluHhIePdZZv+QwccII5GTFYMH8GaKj6NTECSNwKuRPJD+OxMPoS9i1czUqV64gOpZsivLx/T4Mrd+Dv+qH82GBSEq8jqTE6wg+sQcftW0hOpbONWnshd0B6/DgXhhysmLQuXNb0ZF0onFjLwT8vhb37p5DVma0Vj9NTEwwe9a3OB92BMlJN3Dv7jmsWb0YLi5OAhPrlqEd3yQ/DjQk0r17ZyxcMB3+c5ai3odtERx8Bvv2boKbm6voaO/k+fMMfFCxPL4dO+zVz2dkoE7NavAb8uVrt5GRkYnGXvUwqN9nr13nw7oeWPDdJOzb8gsWzZqCqJhYjJky673zi1LP0wMDfXvjUvhV0VF0rmmTBli5cj0aNemEj9p/DhNjExz4czMsLMxFR9O5on58/1eG2O+YmFhMnuwPL+/28PJuj+N//Y3fd61BtWqVRUfTKUtLC4SHX8Uovymio+jUP/3085ua7zkLC3PUrlMDs2cvhleDj9Cj51eoVKk8ft+1RkBS3TPE47tA8vLkW/SYQqVSqUSHkJqJWSnZ3zMkeC/OX7iCESMnqdsuh/+FPXsOYvKUObJkeP7wpCTbqdGoHZb4T0Wrpg3zPRcTG4+2n36BnWuXo8prvs0+cz4cA0ZORMjBHbC2Kv7G9zp+MhSjJn2H83/tganJf5syZO7a5D+97n1ZWlrg7JlDGDnyW3w7aRQuXrqKceOnCckigr29LeIeXkaLlt1wMvi06Dg6VRiObxEMtd8vS4i7gonfzMTadVtFR5FFTlYMun06AHv2HJL9vY0UCtneKyszGp92931jPz09PXAq5E9UqPghoqIe6ixLnoA/xQrD8Z2TFSPL+/wXz/9cLNt7mXfwk+295MaKhgRMTU1Rt24tBB4J0moPDAyCd4N6glIVfilPU7Hv8HHUrln1Pw8yRFq2dDYO7D+Ko8ekGeAVNTY21gCApOQnYoPomKEe34bab01GRkbo0aMzLC0tEHo6THQcEsDGxgp5eXl48uSp6CiS4vH9DlR58i16rFD/dRcVFYVp06ZhzZrCXba0t7eFiYkJEuITtdoTEhLh5OwoKFXhtXDFamzZtRfPMzLhUb0KfpxX9OY29OjRGXXq1EAD7w6iowgzf940BAefRkREpOgoOmWox7eh9hsAatSoguATe1CsmBJpac/wafeBuHbtpuhYJDOlUolZMydh69bdSH3FnMOizJCPb5JXoa5oJCUlYf369W9cJzMzE0+fPtVaRJ0N9vL7KhQKYVkKsy97fYoda5dj1aJZMDI2wqTv5xep/6fSpV2xaMF36P/FKGRmZoqOI8TSJbNQs0ZV9O47XHQU2Rjq8W2I/Y6MvA3P+j5o1LgTfl61AWtWL0bVqpVExyIZmZiY4LdNP8LIyAgjR30rOo7OGOLx/c44R0MSQisae/bseePzd+7cees2/P39MWOG9jfiCqPiUBhbv1e2gkhMTEJOTg6cnB202h0c7JAQ/0i2HEVFyRI2KFnCBuXKlEb5cm5o/XE/XIq4jto1qoqO9k7q1q0JJycHnAk9oG4zMTFBkyYNMHzYF7Ao7o48Pf7BsXjR9+jU0QctWnVDTEys6Dg6Z6jHt6H2GwCys7Nx+/Y9AEDY+XDU86yNkSMGYtjwiWKDkSxMTEywZfNPKFeuDHza9tC7agZg2Mc3yUvoQKNr165vHT0r3jIxbNKkSRg7dqxWW0m7KpLke1fZ2dk4fz4crVs1xR9/HFS3t27dFHv3yj+Zrij5Z9dnZWWLDVIAx44Fw6NOS622X39ZiMjI25g3/0e9HmQsWTwTXbt8hFZtuuPevSjRcWRhqMe3ofb7VRQKBZRKM9ExSAb/DDIqViyHNj49kJT0RHQkneDx/Q70fO6EXIQONFxcXPDjjz+ia9eur3z+4sWL8PT0fOM2lEollEqlVtvbBie6sGjJL1i/dgnCwi4h9HQYBvn2QRm3Uvh51UbZs/wX6enP8SD63ytqxDyMx/Ubt2FjbQUXZ0ekPE1FbFwCEhJf3PPi7oNoAIC9XUnY29kCABIfJyHxcbJ6Ozdv34OlhTlcnB1hY22Fy1cjcflqJOrWqg5r6+KIjonD8l83wq2UC2rXkHdw+D7S0p7lm5eQ/iwdjx8n6/V8hWVLZ+Pzz7qi2ycDkJqaBienF9+EpaSkIiMjQ3A63Srqx/d/ZYj9nvn9Nzh48Biioh/Cyqo4evbogmbNvNGhY2/R0XTK0tICFSu6qx+7lysDD4/qSEpK1unVluRmaWmBihXKqR+XK+cGj1rVkJT8BA8fxmPb1p9Ru3ZNfPxxfxgbG6t/ziUlPUF2dtH5QuxdGOLxTfITOtDw9PTE+fPnXzvQKErnCu7YsQd2tiUxZfIYuLg44kpEJDp17osHDwrvpds0Xbl+EwNG/ntawNxlqwAAXdq1xqwp43D8ZCimzF6ofv7raS8ufTd0QG8M9+0DANi2ez9WrvlNvU7/4V8DAGZ+OxZdO7SBUmmGI0Eh+HH1JjzPyICDnS0aeXli3nffwMyM3xYWdkOH9AcAHDu6S6t9gO8YbNi4XUQk2RT14/u/MsR+OzraY93apXBxcURKSiouX76GDh1748hR/b66XD1PDxw9slP9eMH86QCA9Ru2w3fgGEGppOfp6YEjgTvUj+fPmw4A2LBhO76fuRCdOr24gd+5c4Far2vdpjtOnDglW045GOLxXSB6fHaCnITeR+PkyZN49uwZPvroo1c+/+zZM5w7dw7NmjUr0HZF3EejMJDqPhpFjaj7aBARkbTkvI9GYSLiPhqFQaG+j0aAfPcKMv/4G9neS25CKxpNmrz5D0RLS8sCDzKIiIiIiN4L52hIolBf3paIiIiIiIqmQn3DPiIiIiIi2XGOhiRY0SAiIiIiIsmxokFEREREpIkVDUmwokFERERERJJjRYOIiIiISJOBXnJYaqxoEBERERGR5FjRICIiIiLSxDkakmBFg4iIiIiIJMeBBhERERERSY6nThERERERaeKpU5JgRYOIiIiIiCTHigYRERERkSYVKxpSYEWDiIiIiIgkx4oGEREREZEmztGQBCsaREREREQkOVY0iIiIiIg0qVSiE+gFVjSIiIiIiEhyrGgQEREREWniHA1JsKJBRERERESSY0WDiIiIiEgTKxqS4EBDj5i7NhEdQYi0EwtFRxCieNOxoiMQERERvRYHGkREREREmnhncElwjgYREREREUmOFQ0iIiIiIg2qPN5HQwqsaBARERERkeRY0SAiIiIi0sSrTkmCFQ0iIiIiIpIcBxpERERERCQ5njpFRERERKSJl7eVBCsaREREREQkOVY0iIiIiIg08fK2kmBFg4iIiIiIJMeKBhERERGRJl7eVhKsaBARERERkeRY0SAiIiIi0sSKhiRY0SAiIiIiIsmxokFEREREpEnFq05JgRUNIiIiIiKSHCsaRERERESaOEdDEqxoEBERERGR5FjRICIiIiLSxDuDS4IVDQk0aeyF3QHr8OBeGHKyYtC5c1vRkYSYOGEEcrJisGD+DNFR3lnY9XsYuWgTWo+eB4/+/8OxsGtaz6tUKqwMOIbWo+fhw4Hfwdd/DW5FJ2itk5WdA/+Nf6LZ8DnwGvQ9Ri36DfFJKernz167C4/+/3vlcuVOjCz9lMLECSNwKuRPJD+OxMPoS9i1czUqV64gOpbOGWq//zFkcH/cjDyFtKe3cTr0ABo3+lB0JJ0y1P09+Kt+OB8WiKTE60hKvI7gE3vwUdsWomNJrnFjLwT8vhb37p5DVmZ0vt/XXbu0w759m/AwJhxZmdHwqFVNUFJ5GNrxTfLjQEMClpYWCA+/ilF+U0RHEaaepwcG+vbGpfCroqMUyPPMLHzg5oxv+nZ45fNr9wdj48FT+KZvB/w2fTDsbIpjyLz1ePY8U73O3N8O4FjYNfwwrDvWTRmI9MwsjFz0G3L///zO2pXccHTJ11pLt2aecLUvgerurrL0UwpNmzTAypXr0ahJJ3zU/nOYGJvgwJ+bYWFhLjqaThlqvwGge/fOWLhgOvznLEW9D9siOPgM9u3dBDe3ovO5LShD3d8xMbGYPNkfXt7t4eXdHsf/+hu/71qDatUqi44mqX9+X/v5TX3t86dCzmHyFH+Zk8nPEI/vAlHlybfoMYVKpX/X7zIxKyXsvXOyYtDt0wHYs+eQsAxys7S0wNkzhzBy5Lf4dtIoXLx0FePGT5Pt/dNOLJRkOx79/4dFoz5HS8+qAF5UM1qPnofebb0xoEMTAC+qFy1HzcXoHm3QvUV9pKZnoPmIHzBrcDd85FUTAJCQ/BRtxyzA8nF90KhmpXzvk52TC58x8/FZay8M7tL8P+ct3nTsf36tFOztbRH38DJatOyGk8GnhWaRkyH1OyR4L85fuIIRIyep2y6H/4U9ew5i8pQ5ApPJx5D298sS4q5g4jczsXbdVtne00ihkO29sjKj8Wl331f+vi5btjRu3ghF/fo+snyBlifgT7HCcHznZBXeqn76vAGyvZfF12tkey+5saJB723Z0tk4sP8ojh47KTqKpGIeJSMxJQ3eNSqq28xMTeD5QTlcuhkFALh67yFycnPRUGMdx5LWqFjaUb3Oy4IuXMeT1HR0aVxHtx3QMRsbawBAUvITsUFkZij9NjU1Rd26tRB4JEirPTAwCN4N6glKJT9D2d+ajIyM0KNHZ1haWiD0dJjoOKQDPL7fQZ5KvkWPCZ8M/vz5c4SFhcHW1hbVqmmfC5mRkYHt27ejX79+r319ZmYmMjMztdpUKhUUMn4rYsh69OiMOnVqoIH3q089KsoSU9IAAHbWllrtdtaWePj4CQDgcUoaTE2MYW2pfVqFrXVx9etfFnDiPBrWrAhnOxvpQ8to/rxpCA4+jYiISNFRZGUo/ba3t4WJiQkS4hO12hMSEuHk7CgolfwMZX8DQI0aVRB8Yg+KFVMiLe0ZPu0+ENeu3RQdi3SAxzfJRWhF48aNG6hatSqaNm2KmjVronnz5oiNjVU/n5KSgi+//PKN2/D394eNjY3WospL1XV0AlC6tCsWLfgO/b8YlW+wp09eHrSqXtGWnwqvWiU+KQUhl2/h46Z1JcsnwtIls1CzRlX07jtcdBRZGWK/Xz67VqFQ5GvTV4a2vyMjb8Ozvg8aNe6En1dtwJrVi1G1av7TP0l/GPLx/TaqvDzZFn0mdKAxceJE1KxZEwkJCYiMjIS1tTUaNWqEBw8evPM2Jk2ahJSUFK1FYWSlw9T0j7p1a8LJyQFnQg8gI/0+MtLvo1mzhhg5YgAy0u/DyKhon5lnb1McAPJVJpKePoOd9Yvn7GyKIzsnF0+fPX/tOpp2n7wAm+IWaFanio5S697iRd+jU0cftPbpjpiY2Le/QE8YWr8TE5OQk5MDJ2cHrXYHBzskxD8SlEo+hra/ASA7Oxu3b99D2PlwTJ4yB+HhVzFyxEDRsUgHDP34JvkI/UswJCQEs2fPhr29PSpWrIg9e/agXbt2aNKkCe7cufNO21AqlbC2ttZaeNqUPI4dC4ZHnZbwrO+jXs6eu4jNWwLgWd8HeUV8lF7KoSTsbYoj9MotdVt2Tg7CIu/Bo5IbAKBaOVeYGBvj1JXb6nUePUnFregE9Tr/UKlU+OPkBXRq5AFTE2N5OiGxJYtn4uOu7dCmbQ/cu/fqOSj6yBD7nZ2djfPnw9G6VVOt9tatm+JU6DlBqeRhiPv7VRQKBZRKM9ExSAcM+fgmeQmdo/H8+XOYmGhH+PHHH2FkZIRmzZph8+bNgpIVjKWlBSpWdFc/di9XBh4e1ZGUlIyoqIcCk+lWWtqzfOctpz9Lx+PHyUXmfOb0jEw8iE9SP455lIzr92NhU9wcLnYl0LutN1bvO4kyTnYo42yH1XtPoJiZKdo3qAUAsLIoho+b1sWCrQdRorg5rItbYOHWg6jk5oQG1bWvvX/m6h3EPErGx808Ze2jVJYtnY3PP+uKbp8MQGpqGpycXnwTlpKSioyMDMHpdMdQ+w0Ai5b8gvVrlyAs7BJCT4dhkG8flHErhZ9XbRQdTWcMdX/P/P4bHDx4DFHRD2FlVRw9e3RBs2be6NCxt+hokrK0tEDFCuXUj8uVc4NHrWpISn6CqKiHKFmyBMq4ucLF1RkA1PdQiYt/hHg9+6bfEI/vAtHzSdpyETrQqFKlCs6dO4eqVatqtS9btgwqlQqdO3cWlKxg6nl64OiRnerHC+ZPBwCs37AdvgPHCEpF7yLi7kMMnLNW/Xj+loMAgM6Na+P7Qd3wZfvGyMzKxuwN+/A0PQM1y5fCyq/7wdJcqX7N170+grGxEb7+cTsys3PwYTV3fO/XDcYvnToWcOI8ald0Q3lX7VJ1UTF0SH8AwLGju7TaB/iOwYaN20VEkoWh9hsAduzYAzvbkpgyeQxcXBxxJSISnTr3xYMHhfeSlO/LUPe3o6M91q1dChcXR6SkpOLy5Wvo0LE3jhzVr6sJenp64EjgDvXj+fOmAwA2bNiOgYPGomPHNlj96yL187/9thIA8P33C/H9TGkupV5YGOLxTfITeh8Nf39/nDx5Evv373/l88OGDcNPP/1U4FNwRN5Hg+Qn1X00ihrR99EgIpKanPfRKExE3EejMCjM99F4NrOPbO9lOWWTbO8lN6FzNCZNmvTaQQYArFixosif509EREREZIiE30eDiIiIiKhQ4RwNSRTt648SEREREVGhxIoGEREREZEmnrovCVY0iIiIiIhIcqxoEBERERFp4hwNSbCiQUREREREkuNAg4iIiIhIkypPvqUAcnJyMGXKFLi7u8Pc3Bzly5fHd999p3U7CJVKhenTp8PV1RXm5uZo3rw5IiIitLaTmZmJkSNHwt7eHpaWlujcuTOio6Ml+a/TxIEGEREREVER8MMPP+Cnn37C8uXLce3aNcydOxfz5s3DsmXL1OvMnTsXCxcuxPLly3H27Fk4OzujTZs2SE1NVa/j5+eHgIAAbN26FcHBwUhLS0PHjh2Rm5sraV7O0SAiIiIi0lRI52icOnUKXbp0QYcOHQAA5cqVw5YtW3Du3DkAL6oZixcvxuTJk9GtWzcAwPr16+Hk5ITNmzdj8ODBSElJwerVq7Fx40a0bt0aALBp0ya4ubnhyJEjaNu2rWR5WdEgIiIiIhIkMzMTT58+1VoyMzNfuW7jxo1x9OhR3LhxAwBw6dIlBAcHo3379gCAu3fvIi4uDj4+PurXKJVKNGvWDCEhIQCAsLAwZGdna63j6uqKGjVqqNeRCgcaREREREQaVHl5si3+/v6wsbHRWvz9/V+Za+LEifj8889RpUoVmJqaok6dOvDz88Pnn38OAIiLiwMAODk5ab3OyclJ/VxcXBzMzMxQsmTJ164jFZ46RUREREQkyKRJkzB27FitNqVS+cp1t23bhk2bNmHz5s2oXr06Ll68CD8/P7i6uqJ///7q9RQKhdbrVCpVvraXvcs6BcWBBhERERGRJhnnaCiVytcOLF729ddf45tvvsFnn30GAKhZsybu378Pf39/9O/fH87OzgBeVC1cXFzUr0tISFBXOZydnZGVlYXk5GStqkZCQgIaNmwoVbcA8NQpIiIiIqIiIT09HUZG2n++Gxsbqy9v6+7uDmdnZwQGBqqfz8rKQlBQkHoQ4enpCVNTU611YmNjceXKFckHGqxoEBEREREVAZ06dcKsWbNQpkwZVK9eHRcuXMDChQsxYMAAAC9OmfLz88Ps2bNRqVIlVKpUCbNnz4aFhQV69eoFALCxsYGvry/GjRsHOzs72NraYvz48ahZs6b6KlRS4UCDiIiIiEhTIb287bJlyzB16lQMGzYMCQkJcHV1xeDBg/G///1Pvc6ECRPw/PlzDBs2DMnJyfDy8sLhw4dhZWWlXmfRokUwMTFBjx498Pz5c7Rq1Qrr1q2DsbGxpHkVKpWqcP5PvgcTs1KiI5CM0k4sFB1BiOJNx759JSKiIsRI4omoRUWe/v0p9k5ysmJER3ittK8/lu29is8LkO295MaKBhERERGRJlWe6AR6gZPBiYiIiIhIcqxoEBERERFpKqRzNIoaDjSoyLMy0LkKqYe/Fx1BCCufqaIjEOmcYc5UMNy5CkT6igMNIiIiIiINKlY0JME5GkREREREJDlWNIiIiIiINLGiIQlWNIiIiIiISHKsaBARERERacrjfTSkwIoGERERERFJjhUNIiIiIiJNnKMhCVY0iIiIiIhIcqxoEBERERFpYkVDEqxoEBERERGR5FjRICIiIiLSoFKxoiEFVjSIiIiIiEhyrGgQEREREWniHA1JsKJBRERERESS40CDiIiIiIgkx1OniIiIiIg08dQpSbCiQUREREREkmNFg4iIiIhIg4oVDUmwokFERERERJJjRYOIiIiISBMrGpJgRYOIiIiIiCTHgYaEhgzuj5uRp5D29DZOhx5A40Yfio4kC0Pr99SpY5GdFaO1RD24IDrWe3uWkYm5246g3aSV8BqxAP1+2Igr92LVz6dnZMF/SyB8Jv4IrxEL8PG0X7A9KH+/L92OwaCFW9Bg5EI09lsM3wWbkZGVLWdXdMLQPucvmzhhBHKyYrBg/gzRUXSqSWMv7A5Yhwf3wpCTFYPOnduKjiQbV1dnrF+3FHGxV5Dy5BbOnT2MunVqio4lK0P5nL/MUPv9RnkyLnqMp05JpHv3zli4YDpGjPwWIafOYtDAvti3dxNqejRHVNRD0fF0xlD7fSXiOj766DP149zcXIFppDFjw0HcevgIM7/sCIcSxfHn6QgMWbQVu6YPhFNJK8zbcRTnIh9g1oBOcLWzwamrd+G/5TAcbIqjRe1KAF4MMoYv3Y4B7bwx8bPWMDU2xo3oBBgpFIJ7934M9XP+j3qeHhjo2xuXwq+KjqJzlpYWCA+/inXrt2Hn9l9Fx5FNiRI2CPprN4KCQtCpUx8kPEpE+fLl8CTlqehosjGkz7kmQ+03yYMVDYmMGT0Ia9ZuxZq1W3D9+i2MGz8NUdEPMWRwP9HRdMpQ+52bk4v4+EfqJTExSXSk95KRlY2jFyLh90kLeFZ2QxnHkhjaqTFc7Utgx/9XLcLvPEQn7xqo/0EZlLK3wadNa6NyaUdcvf9v1WP+jqP4vKUnBnzUABVdHVDWyRZtPKvAzLRof6dhqJ9z4MUf3hs2LMeQoRPwJPmJ6Dg6d/DQcfxv2lzs3n1AdBRZff31MERHP8TAQWNx9txF3L8fjePHg3Hnzn3R0WRhaJ/zfxhqv9+FKk8l26LPONCQgKmpKerWrYXAI0Fa7YGBQfBuUE9QKt0z1H4DQMWK7rh/Lww3Ik9h06YVcHcvIzrSe8nNy0NungpKE2Ot9mKmJrhwOxoAUKdiafx16Rbik1OhUqlwNvI+7scno2H18gCApKfPcPluLGytLNHvh41oOX4ZfOdvxoVb0bL3R0qG/DkHgGVLZ+PA/qM4euyk6CikQx07+iAsLBxbtvyMmOhLOHvmEHwH9BIdSzaG+jk31H6TfIR/zXjt2jWEhobC29sbVapUwfXr17FkyRJkZmaiT58+aNmy5Rtfn5mZiczMTK02lUoFhYynatjb28LExAQJ8Yla7QkJiXBydpQth9wMtd9nzlzAlwNG4+bNO3B0dMC3k0bhRNAf8KjdEklJyaLj/SeWxZSoVd4Vq/aHwN3FDnbWljh45hou33uIMo62AICJPVtjxsaDaPvNCpgYGUFhpMC0vh+hTsXSAIDoxCcAgJ/2BWPMJy1Qxc0Je0Ov4KtFW7HzfwNQ1slWVPfei6F+zgGgR4/OqFOnBhp4dxAdhXSsvHsZDB7cF4uX/IIffliK+vXqYNGi75CZlYVNm3aKjqdThvo5N9R+vzM9rzTIRehA4+DBg+jSpQuKFy+O9PR0BAQEoF+/fvDw8IBKpULbtm1x6NChNw42/P39MWOG9uQlhVFxKIytdR0/H5VK+0OpUCjytekjQ+v3oUPHNR5dR2joOUReD0G/vt2xeMkqYbne16wBHTF9/QH4TFwBYyMFqpRxRrv61XA9Kh4AsPnYOVy++xBLhn0CFztrnL8ZhdmbA2FvUxwNqpZD3v/v80+a1EbXRrUAAFXKOOHM9fv4I+QyRn3cTFjfpGBon/PSpV2xaMF3aNehV74vc0j/GBkZISwsHFOnzgEAXLwYgWrVKmPwV/30eqBhqJ9zQ+03yU/oQOO7777D119/jZkzZ2Lr1q3o1asXhg4dilmzZgEAJk+ejDlz5rxxoDFp0iSMHTtWq62kXRWd5n5ZYmIScnJy4OTsoNXu4GCHhPhHsmaRk6H2+2Xp6c9x5cp1VKzoLjrKe3FzKInV43vheWYW0jKy4GBTHBNW/QFXOxtkZGVj2e4TWDi0G5rWrAAAqFzaEZFRCdhw+AwaVC0HB5viAIAKLvZa23V3tkNsUtGdUGqon/O6dWvCyckBZ0L/natgYmKCJk0aYPiwL2BR3B15eXp+uRQDEhubgGvXbmi1Xb9+Cx9/3F5QInkY6ufcUPtdIAbefakInaMRERGBL774AgDQo0cPpKam4pNPPlE///nnnyM8PPyN21AqlbC2ttZa5DxtCgCys7Nx/nw4WrdqqtXeunVTnAo9J2sWORlqv19mZmaGKlUqITYuXnQUSZgrzeBgUxxPn2Ug5OpdNPeohJzcPOTk5sHopUPLyEihrmS42tnAoURx3It/rLXO/YQkuNjKX2GUiqF+zo8dC4ZHnZbwrO+jXs6eu4jNWwLgWd+Hf4TomZBTZ1G5cgWttkqVyuPBgxhBieRhqJ9zQ+03yU/4HI1/GBkZoVixYihRooS6zcrKCikpKeJCFcCiJb9g/dolCAu7hNDTYRjk2wdl3Erh51UbRUfTKUPs9w9zpmLfn4GIioqBo4M9Jn07GtbWxbFx4w7R0d5LSMQdqFRAOWdbPEhIxqJdf6Gcky26NKoJU2NjeFZ2w6Jdf0FpagpXO2ucuxGFfaERGNf9RcVRoVCgf5sP8dPeYFQu7YgP3Jyw99Rl3ItLwvzBXcV27j0Z4uc8Le0ZIiIitdrSn6Xj8ePkfO36xNLSQqs66V6uDDw8qiMpKVmvL2W8dMkvOHHiD0ycOBI7d+5F/fq1MXBgbwwdNkF0NJ0y1M+5ofa7IPT9alByETrQKFeuHG7duoWKFSsCAE6dOoUyZf69ek9UVBRcXFxExSuQHTv2wM62JKZMHgMXF0dciYhEp8599f7bIEPsd6nSLti08UfY29vi0aPHOH3mPBo36VTk+5z6PBPLAk4g/kkqbCyKoVXdDzCia1OYGr+4EtUPAztjaUAQvl2zF0+fZcDF1hojujRB96a11dvo07o+snJyMX/HMaQ8y0Dl0g74ya8n3BxKCuqVNAzxc26o6nl64OiRf+ckLJg/HQCwfsN2+A4cIyiV7p0Lu4RPuw/ErJnfYMpkP9y9F4Vx46Zhy5YA0dGIqAhTqATOZvzpp5/g5uaGDh1efcWDyZMnIz4+Hr/+WrCbJpmYlZIiHhURRftWcP/d08Pfi44ghJXPVNERiHTOUH+u8Ttkw5KTVXi/rEn+pLls71Vy11+yvZfchFY0hgwZ8sbn/5kUTkRERERERQtv2EdERERERJIrNJPBiYiIiIgKA04GlwYrGkREREREJDlWNIiIiIiINPFWIpJgRYOIiIiIiCTHigYRERERkQYVKxqSYEWDiIiIiIgkx4oGEREREZEmVjQkwYoGERERERFJjhUNIiIiIiINnKMhDVY0iIiIiIhIcqxoEBERERFpYkVDEqxoEBERERGR5FjRICIiIiLSwDka0mBFg4iIiIiIJMeKBhERERGRBlY0pMGKBhERERERSY4VDSIiIiIiDaxoSIMVDSIiIiIikhwrGkREREREmlQK0Qn0AgcaVOSpRAcQxMpnqugIQqTumyw6ghBWHWeJjiCEof6qN9Sfa4bKUD/npP946hQREREREUmOFQ0iIiIiIg2cDC4NVjSIiIiIiEhyrGgQEREREWlQ5XHmjBRY0SAiIiIiIsmxokFEREREpIFzNKTBigYREREREUmOFQ0iIiIiIg0q3rBPEqxoEBERERGR5FjRICIiIiLSwDka0mBFg4iIiIiIJMeKBhERERGRBt5HQxqsaBARERERkeRY0SAiIiIi0qBSiU6gH1jRICIiIiIiybGiQURERESkgXM0pMGKBhERERERSY4VDSIiIiIiDaxoSIMVDSIiIiIikhwHGkREREREJDmeOkVEREREpIGXt5UGKxoSaNLYC7sD1uHBvTDkZMWgc+e2oiPJasjg/rgZeQppT2/jdOgBNG70oehIOsX9rV/7+1lGFubuCkK7/62B19jl6LdwO67cj9Na505cEkb/vAeNv16JhuNXoO+CbYhNegoAiHn8FLVHLnnlcvjCTRFdkpS+7e+3mTp1LLKzYrSWqAcXRMfSuYkTRuBUyJ9IfhyJh9GXsGvnalSuXEF0LJ0z1J/nhvo5J/mxoiEBS0sLhIdfxbr127Bz+6+i48iqe/fOWLhgOkaM/BYhp85i0MC+2Ld3E2p6NEdU1EPR8XSC+1u/9veMzUdwK/YxZvZrCwcbS/x59jqGLA/Arsl94VSiOKIePcGXi3agq3d1DG3fAMXNlbgTlwSl6Ysfn84li+PIrIFa29z19xWsOxKGxtXKiuiSZPRxf7+LKxHX8dFHn6kf5+bmCkwjj6ZNGmDlyvU4F3YRJiYm+H7GRBz4czNqejRHevpz0fF0xpB/nhvi57wgOBlcGoVuoKFSqaBQFK2de/DQcRw8dFx0DCHGjB6ENWu3Ys3aLQCAceOnwcenGYYM7ofJU+YITqcb3N/6s78zsnJw9NItLBrUCZ4VSwEAhrZvgOPht7EjOBwjOjbE8n2n0Lh6OYzp2lj9utL2Nup/GxsZwd7aUmu7x8Jvo23dSrBQmsnTER3Rt/39rnJzchEf/0h0DFl16NRH67HvoDGIe3gZnnVr4WTwaUGpdM+Qf54b4uec5FfoTp1SKpW4du2a6Bj0DkxNTVG3bi0EHgnSag8MDIJ3g3qCUpGu6OP+zs3LQ26eCkpTY632YqYmuHD7IfLyVDgZcRdlHUtg6I8BaDFpFfrM34pjl26/dptXH8QjMvoRunpX13V8ndLH/f2uKlZ0x/17YbgReQqbNq2Au3sZ0ZFkZ2NjDQBISn4iNgjpDD/nb6ZSKWRb9JmwisbYsWNf2Z6bm4s5c+bAzs4OALBw4cI3biczMxOZmZlabUWxKlIU2dvbwsTEBAnxiVrtCQmJcHJ2FJSKdEUf97dlMTPUcnfBqoNn4O5sCzsrCxwMu4HL9+NQxqEEktLSkZ6ZjTWB5zC8gzdGd2mMkKv3MG71Pvwy8hPUq1Q63zYDTkWgvLMtapd3FdAj6ejj/n4XZ85cwJcDRuPmzTtwdHTAt5NG4UTQH/Co3RJJScmi48lm/rxpCA4+jYiISNFRSAf4OSe5CBtoLF68GB4eHihRooRWu0qlwrVr12BpaflOgwV/f3/MmDFDq01hVBwKY2sp49IbqF66NINCocjXRvpD3/b3rL4+mL75CHymrIaxkQJVSjuinecHuB79CHn/36/mNcujb8u6AIAqpR1w6W4sdgZfzjfQyMjKwYGwSHzV1kv2fuiKvu3vtzmkdRrNdYSGnkPk9RD069sdi5esEpZLTkuXzELNGlXRrMXHoqOQjvBz/naqPNEJ9IOwgcasWbPwyy+/YMGCBWjZsqW63dTUFOvWrUO1atXeaTuTJk3KVx0paVdF0qz0aomJScjJyYGTs4NWu4ODHRJ43qfe0df97eZQAqtHf4rnmdlIy8iCg40lJqzZD1dba5S0NIeJkREqONtpvcbd2RYXbuefDH3k4k1kZOWg44dF/2eQvu7vgkpPf44rV66jYkV30VFksXjR9+jU0QctWnVDTEys6DgkE0P7nJN8hM3RmDRpErZt24ahQ4di/PjxyM7O/k/bUSqVsLa21lp42pQ8srOzcf58OFq3aqrV3rp1U5wKPScoFemKvu9vc6UpHGws8TQ9AyHX76N5rfIwNTFGtbJOuJegfSrB/YQncLG1yreNgFMRaF6zPGytLOSKrTP6vr/flZmZGapUqYTYuHjRUXRuyeKZ+LhrO7Rp2wP37kWJjkMyMqTP+bvKUylkWwoqJiYGffr0gZ2dHSwsLFC7dm2EhYWpn1epVJg+fTpcXV1hbm6O5s2bIyIiQmsbmZmZGDlyJOzt7WFpaYnOnTsjOjr6vf/fXiZ0Mnj9+vURFhaGR48eoV69erh8+XKRHCRYWlrAw6M6PDxeTP50L1cGHh7V4eZWtM/RfheLlvwC3wGf44v+PVGlSkUsmDcdZdxK4edVG0VH0xnub/3a3yHX7uPvq/cQk5iCU9fvY+DSXSjnWBJdGryoqn7Rqi4Onb+BXX9fwYNHT7A16BJOXLmDnk1qaW3nwaMnOH87Bh8X8UngmvRxf7/ND3OmokmTBihXzg0f1q+DbdtWwdq6ODZu3CE6mk4tWzobvXt1Q99+I5CamgYnJwc4OTmgWLFioqPplKH+PDfUz7k+SE5ORqNGjWBqaooDBw7g6tWrWLBggdZUhLlz52LhwoVYvnw5zp49C2dnZ7Rp0wapqanqdfz8/BAQEICtW7ciODgYaWlp6Nixo+SXOVaoCsnJtlu3boWfnx8ePXqEy5cvv/OpU69iYlZKwmRv16ypN44e2Zmvff2G7fAdOEbWLCIMGdwf48cNhYuLI65ERGL8+Ol6fTlE7m+x+zt132RJt3fo/A0s2xuC+CdpsLFQopVHRYzo1BBW5kr1OrtPRWB14FkkPElDWceSGNq+AVrU0r6Z2dI9f+PPs9dxYMYAGBlJ/4WJVcdZkm/zXYje33J/9bRp0wo0aewFe3tbPHr0GKfPnMf06fNw7Zq8N1+U+xdzTlbMK9sH+I7Bho3bZU4jn8Ly89xQP+fZr/ncFQaRVdrJ9l4fXD/wzut+8803+Pvvv3Hy5MlXPq9SqeDq6go/Pz9MnDgRwIvqhZOTE3744QcMHjwYKSkpcHBwwMaNG9GzZ08AwMOHD+Hm5ob9+/ejbVvpblxZaAYaABAdHY2wsDC0bt0alpaWb3/Ba8g90CAi+Ug90CgqRA00RCt6NW5pFJpfzCQLQ/2cc6DxQrlLu/NdQVWpVEKpVOZbt1q1amjbti2io6MRFBSEUqVKYdiwYRg0aBAA4M6dO6hQoQLOnz+POnXqqF/XpUsXlChRAuvXr8exY8fQqlUrJCUloWTJkup1PDw80LVr13wXWXofheo+GqVLl0aXLl3ea5BBRERERPQ+VHkK2RZ/f3/Y2NhoLf7+/q/MdefOHaxcuRKVKlXCoUOHMGTIEIwaNQobNmwAAMTFxQEAnJyctF7n5OSkfi4uLg5mZmZag4yX15FKobszOBERERGRoXjVFVRfVc0AgLy8PNSrVw+zZ88GANSpUwcRERFYuXIl+vXrp17v5TnP73KPOV3ch65QVTSIiIiIiERTqeRbXnUF1dcNNFxcXPLNY65atSoePHgAAHB2dgaAfJWJhIQEdZXD2dkZWVlZSE5Ofu06UuFAg4iIiIioCGjUqBEiIyO12m7cuIGyZcsCANzd3eHs7IzAwED181lZWQgKCkLDhg0BAJ6enjA1NdVaJzY2FleuXFGvIxWeOkVEREREpEGVVzin6I8ZMwYNGzbE7Nmz0aNHD5w5cwarVq3CqlUv7uiuUCjg5+eH2bNno1KlSqhUqRJmz54NCwsL9OrVCwBgY2MDX19fjBs3DnZ2drC1tcX48eNRs2ZNtG7dWtK8/2mgkZeXh1u3biEhIQF5edr3aG/atOlrXkVERERERP9V/fr1ERAQgEmTJuG7776Du7s7Fi9ejN69e6vXmTBhAp4/f45hw4YhOTkZXl5eOHz4MKys/r3R7KJFi2BiYoIePXrg+fPnaNWqFdatWwdjY2NJ8xb48rahoaHo1asX7t+/j5dfqlAoJL/Rx3/By9sS6S9e3tawFM7vFHWPl7c1LIb6OS/Ml7e9Ur6jbO9V484+2d5LbgWuaAwZMgT16tXDn3/+CRcXlyJ5J28iIiIiItKtAg80bt68iZ07d6JixYq6yENERERERHqgwFed8vLywq1bt3SRhYiIiIhIOJVKIduiz96pohEeHq7+98iRIzFu3DjExcWhZs2aMDU11Vq3Vq1a0iYkIiIiIqIi550GGrVr14ZCodCa/D1gwAD1v/95rrBMBiciIiIi+q8Kdqkkep13GmjcvXtX1zmIiIiIiEiPvNNA45+7DQLAiRMn0LBhQ5iYaL80JycHISEhWusSERERERU1eXo+d0IuBZ4M3qJFCyQlJeVrT0lJQYsWLSQJRURERERERVuBL2/7z1yMlz1+/BiWlpaShCIiIiIiEkXfrwYll3ceaHTr1g3Ai4nfX3zxBZRKpfq53NxchIeHo2HDhtInJCIiIiKiIuedBxo2NjYAXlQ0rKysYG5urn7OzMwMDRo0wKBBg6RPSEREREQkI151ShrvPNBYu3YtAKBcuXIYP348T5MiIiIiIqLXKvAcjWnTpukiBxERERFRocCrTkmjwAMNd3f3V04G/8edO3feKxARERERERV9BR5o+Pn5aT3Ozs7GhQsXcPDgQXz99ddS5SKitzDU71qsOs4SHUGIp4s+Fh1BCJsxAaIjkIwM9ecapwMUPrzqlDQKPNAYPXr0K9t//PFHnDt37r0DERERERFR0VfgG/a9Trt27bBr1y6pNkdEREREJESeSiHbos8kG2js3LkTtra2Um2OiIiIiIiKsAKfOlWnTh2tyeAqlQpxcXF49OgRVqxYIWk4IiIiIiK5cd6MNAo80OjatavWYyMjIzg4OKB58+aoUqWKVLmIiIiIiKgIK9BAIycnB+XKlUPbtm3h7Oysq0xERERERFTEFWigYWJigqFDh+LatWu6ykNEREREJJS+T9KWS4Eng3t5eeHChQu6yEJERERERHqiwHM0hg0bhnHjxiE6Ohqenp6wtLTUer5WrVqShSMiIiIikhtv2CeNdx5oDBgwAIsXL0bPnj0BAKNGjVI/p1AooFKpoFAokJubK31KIiIiIiIqUt55oLF+/XrMmTMHd+/e1WUeIiIiIiKh8kQH0BPvPNBQqV5cUbhs2bI6C0NERERERPqhQHM0NG/UR0RERESkj1Tg37xSKNBAo3Llym8dbCQlJb1XICIiIiIiKvoKNNCYMWMGbGxsdJWFiIiIiEi4PJXoBPqhQAONzz77DI6OjrrKQkREREREeuKdBxqcn0FEREREhiCPczQk8c53Bv/nqlNERERERERv884Vjbw8XlGYiIiIiPQfrzoljXeuaBAREREREb0rDjQkMHHCCJwK+RPJjyPxMPoSdu1cjcqVK4iOJZshg/vjZuQppD29jdOhB9C40YeiI+lUk8Ze2B2wDg/uhSEnKwadO7cVHUk2rq7OWL9uKeJiryDlyS2cO3sYdevUFB1Lp/Rxf+fk5eHHU7fQYd1JNPjxKDquC8bPp28j76VTZO8kpWH03gto8tNxNFp5DP22nUFs6nMAwMOnz1FnaeArl8Cb8SK6JRl+zvXjc/4ubt4IRXZWTL5l6ZJZoqPplKHu74LIk3HRZwW66hS9WtMmDbBy5XqcC7sIExMTfD9jIg78uRk1PZojPf256Hg61b17ZyxcMB0jRn6LkFNnMWhgX+zbuwk1PZojKuqh6Hg6YWlpgfDwq1i3fht2bv9VdBzZlChhg6C/diMoKASdOvVBwqNElC9fDk9SnoqOplP6uL/Xhd3DzsvR+K5NdVSwK46I+KeYfiQCVkpT9KpdBgAQ9SQdA3aeQ9dqrhjqVQHFlSa4m/QMSmNjAIBT8WII9G2qtd1dV6Kx/vx9NCprJ3ufpMLPuf58zt+Fd8P2MP7/zzQAVK9eBYcObsXOXfsEptI9Q93fJD8ONCTQoVMfrce+g8Yg7uFleNathZPBpwWlkseY0YOwZu1WrFm7BQAwbvw0+Pg0w5DB/TB5yhzB6XTj4KHjOHjouOgYsvv662GIjn6IgYPGqtvu348WmEge+ri/w2NT0Ky8A5q4OwAAXK3NcfBGHK7G//vH9PJTt9C4rD38GldWt5W2sVD/29hIAXtLpdZ2j99+BJ9KTrAwK7q/Wvg5NyyJido3GZ7w9QjcunUXJ06cEpRIHoa6vwuCczSkwVOndMDGxhoAkJT8RGwQHTM1NUXdurUQeCRIqz0wMAjeDeoJSkW60rGjD8LCwrFly8+Iib6Es2cOwXdAL9Gx6D+o7VoCZ6KScD/5GQAg8lEqLj58gkblXlQi8lQqBN9LRJmSFhi2+zxa/vIX+m47jeO3E167zasJTxGZmIqu1UvJ0gdd4efccJmamqJXr25Yt36b6ChEeoMDDR2YP28agoNPIyIiUnQUnbK3t4WJiQkS4hO12hMSEuHkzBs76pvy7mUweHBf3Lp1Fx069sKqVRuxaNF36NPnU9HRqIC+9CyHjyo74+ONIai//Ag+3xKKXrXLoN0HLgCApPQspGfnYu25u2hY1g4ru3qiRQVHjPvzEs5FJ71ym7sjYuBe0hK1XUrI2BPp8XNuuLp0+QglSlhjw4btoqNQIcA5GtIoVPXt5ORkrF+/Hjdv3oSLiwv69+8PNze3N74mMzMTmZmZWm0qlUrYDQaXLpmFmjWqolmLj4W8vwgv32NFoVDwvit6yMjICGFh4Zg69cUpcRcvRqBatcoY/FU/bNq0U3A6KohDN+OxPzIWsz+qiQq2loh8lIr5J2/AobgSnau6qieFNy/viD51ygIAPnCwwqXYJ9h5JRr1SttqbS8jJxcHIuMw6EN32fsiNX7ODdeXX3yGg4eOIza2aF/MgKgwEVrRcHV1xePHjwEAd+/eRbVq1fDDDz/g5s2b+Pnnn1GzZk1cv379jdvw9/eHjY2N1qLKS5Ujfj6LF32PTh190NqnO2JiYoVkkFNiYhJycnLg5Oyg1e7gYIeE+EeCUpGuxMYm4Nq1G1pt16/fgpubq6BE9F8tDr6BLz3d8VFlZ1Syt0LHqq7oXbsM1p67CwAoaW4GEyMFyttaar2uvG1xxKVm5NvekZvxyMjJRccqRf+zwM+5YSpTphRatWqCNWs2i45CpFeEDjTi4uKQm5sLAPj2229RpUoV3L59G4cPH8atW7fQpEkTTJ069Y3bmDRpElJSUrQWhZGVHPG1LFk8Ex93bYc2bXvg3r0o2d9fhOzsbJw/H47WrbSvPNO6dVOcCj0nKBXpSsips/ku21ypUnk8eBAjKBH9Vxk5eXi56GukUCDv/wuRpsZGqOZojfvJ6Vrr3E9+Bhcr83zb2331IZq5O8DWwkxXkWXDz7lh6t+/JxISErF//1HRUaiQ4KlT0ig0p06dPn0av/76KywsXlzVRKlUYsqUKfj00zefF6tUKqFUal/5RO7TppYtnY3PP+uKbp8MQGpqGpycXnzDn5KSioyM/N/+6ZNFS37B+rVLEBZ2CaGnwzDItw/KuJXCz6s2io6mM5aWFqhY8d9TRNzLlYGHR3UkJSXr7SV9AWDpkl9w4sQfmDhxJHbu3Iv69Wtj4MDeGDpsguhoOqWP+7upuz1Wn70LF6tiqGBXHNcfpWLThftaE7n7e5bDxAPhqFuqBOqVtkXI/UScuJuIXz7x1NrWgyfpOB+TjGWd68jdDZ3g5/wFfficvyuFQoH+/Xpi46Yd6i8/9Z0h72+Sl0Il8GR6IyMjxMfHw8HBAaVKlcLhw4dRvXp19fP37t1DlSpVCvzHuomZvFc9ycl69TddA3zHYMNG/Z9UNmRwf4wfNxQuLo64EhGJ8eOn6/VlfZs19cbRI/nP1V6/YTt8B46RLYeIWUjt27fGrJnfoGJFd9y9F4Uli1dhtcynGsj9A6uw7O+ni6Sb9/UsKwcrQm/j2O0EJKdnwcFSiY8+cMZXH5aHqfG/he7dETFYc+4uEtIyUbakBYZ4VUCLCtoXelgWchN/Xo/F/i+bwEgHX/LYjAmQfJtvw8/5vwzh51rr1k1xYP8WVKveBDdv3hGQwHD39+v+fioM/nT6XLb36hC/Rbb3kpvwgUaNGjVgYmKCmzdvYsOGDfj4439/mZ44cQK9evVCdHTBrmEu90CDSARDvcK3oV5mQMqBRlEiYqBRGBjq55w/1wwLBxov6PNAQ+ipU9OmTdN6/M9pU//Yu3cvmjRpImckIiIiIjJweYY66pVYoRpovGzevHkyJSEiIiIiIikVmsngRERERESFQZ7BnsgnLd4ZnIiIiIiIJMeKBhERERGRBkOdoC81VjSIiIiIiEhyrGgQEREREWnQ9zt2y4UVDSIiIiIikhwrGkREREREGvIUvOqUFFjRICIiIiIiybGiQURERESkgVedkgYrGkREREREJDlWNIiIiIiINPCqU9JgRYOIiIiIiCTHgQYREREREUmOp04REREREWnI49VtJcGKBhERERERSY4VDSIiIiIiDXlgSUMKrGgQEREREZHkWNEgIiIiItLAG/ZJgxUNIiIiIiKSHCsaREREREQaeNUpaXCgQVREGWpZ11B/9luPCRAdQYjUbSNFRxDCqucy0RGEMNSfa0T6igMNIiIiIiINeaID6AnO0SAiIiIiIsmxokFEREREpIGn8UmDFQ0iIiIiIpIcKxpERERERBp41SlpsKJBRERERESSY0WDiIiIiEgDrzolDVY0iIiIiIhIcqxoEBERERFpYEVDGqxoEBERERGR5FjRICIiIiLSoOJVpyTBigYREREREUmOAw0iIiIiIpIcT50iIiIiItLAyeDSYEWDiIiIiIgkx4oGEREREZEGVjSkwYoGERERERFJjgMNIiIiIiINKhmX/8rf3x8KhQJ+fn7/5lapMH36dLi6usLc3BzNmzdHRESE1usyMzMxcuRI2Nvbw9LSEp07d0Z0dPR7JHk9DjSIiIiIiIqQs2fPYtWqVahVq5ZW+9y5c7Fw4UIsX74cZ8+ehbOzM9q0aYPU1FT1On5+fggICMDWrVsRHByMtLQ0dOzYEbm5uZLn5ECDiIiIiEhDnkK+paDS0tLQu3dv/PLLLyhZsqS6XaVSYfHixZg8eTK6deuGGjVqYP369UhPT8fmzZsBACkpKVi9ejUWLFiA1q1bo06dOti0aRMuX76MI0eOSPXfp8aBhgQmThiBUyF/IvlxJB5GX8KunatRuXIF0bF0bvBX/XA+LBBJideRlHgdwSf24KO2LUTH0jlD3d9NGnthd8A6PLgXhpysGHTu3FZ0JFncvBGK7KyYfMvSJbNER9MpfT2+n2VmY+7e02g3Zzu8pmxAvxX7cCXqkfr5lYEX0HXB72gwdSOaTP8Ng389iMsPHuXbzqX7CRi06gAaTN2IxtN/g+/PB5CRnSNnVyRlqMe3vn7O39WQwf1xM/IU0p7exunQA2jc6EPRkQxSZmYmnj59qrVkZma+dv3hw4ejQ4cOaN26tVb73bt3ERcXBx8fH3WbUqlEs2bNEBISAgAICwtDdna21jqurq6oUaOGeh0pcaAhgaZNGmDlyvVo1KQTPmr/OUyMTXDgz82wsDAXHU2nYmJiMXmyP7y828PLuz2O//U3ft+1BtWqVRYdTacMdX9bWlogPPwqRvlNER1FVt4N26O0W2310vajzwAAO3ftE5xMt/T1+J6xKxihNx9iZo+m2OHXFd6VSmHIr4cQn/IMAFDWwRrfdG6AnX5dsXZoe7iWKI6hqw8hKS1DvY1L9xMwfM1heFcuhU0jOuK3ER3xWcOqMFL8h68mCwlDPb719XP+Lrp374yFC6bDf85S1PuwLYKDz2Df3k1wc3MVHa1QyJNx8ff3h42Njdbi7+//ylxbt27F+fPnX/l8XFwcAMDJyUmr3cnJSf1cXFwczMzMtCohL68jJV7eVgIdOvXReuw7aAziHl6GZ91aOBl8WlAq3dv3Z6DW46n/+wGDv+oLrw/r4urVG4JS6Z6h7u+Dh47j4KHjomPILjExSevxhK9H4Natuzhx4pSgRPLQx+M7IzsHR6/cx6J+reBZ3hkAMLRNHRy/+gA7Qq9jRFtPtK+tXZ0c1/FDBJy7iZtxSfCq+OIPsPn7zuDzRtUwoPm/50aXtbeRryM6YKjHtz5+zt/VmNGDsGbtVqxZuwUAMG78NPj4NMOQwf0wecocwekMy6RJkzB27FitNqVSmW+9qKgojB49GocPH0axYsVeuz3FS196qFSqfG0ve5d1/gtWNHTAxsYaAJCU/ERsEBkZGRmhR4/OsLS0QOjpMNFxZGWI+9tQmZqaolevbli3fpvoKLLSl+M7N0+F3DwVlCbGWu3FTI1x4V5CvvWzc3Kx60wkihczQ2UXWwBAUtpzXI56BFvLYui3Yh9aztwC35/348K9eFn6QLqjL5/zd2Fqaoq6dWsh8EiQVntgYBC8G9QTlKpwkbOioVQqYW1trbW8aqARFhaGhIQEeHp6wsTEBCYmJggKCsLSpUthYmKirmS8XJlISEhQP+fs7IysrCwkJye/dh0psaKhA/PnTUNw8GlERESKjqJzNWpUQfCJPShWTIm0tGf4tPtAXLt2U3QsWRnS/jZ0Xbp8hBIlrLFhw3bRUWShb8e3pdIUtco4YNXRS3B3LAG74sVw8NJdXI56hDJ21ur1TlyLwsQtfyEjOwf2Vhb4ydcHJS1ffHsYnfTiyi0/Hb2IMe3ro4qLLfaev4WvfjmInWO6FvnKhiHSt8/5u7C3t4WJiQkS4hO12hMSEuHk7CgoFb1Nq1atcPnyZa22L7/8ElWqVMHEiRNRvnx5ODs7IzAwEHXq1AEAZGVlISgoCD/88AMAwNPTE6ampggMDESPHj0AALGxsbhy5Qrmzp0reWahA40LFy6gRIkScHd3BwBs2rQJK1euxIMHD1C2bFmMGDECn3322Ru3kZmZmW/CjK7KP+9i6ZJZqFmjKpq1+FjI+8stMvI2POv7oISNNbp1a481qxejZetP9P6H9D8MbX8bui+/+AwHDx1HbKxhfHutj8f3rJ5NMX1nMHxmb4OxkQJVXO3QzqM8rj98rF6nfgVnbBvVBU/SM/D7mRuYsPkvbBreEbbFzZGnenHV+08+/ABd61UCAFQpZYczt2Pxx7mbGPURvw0uavTxc/6uVCrtuzgoFIp8bYaqMP4vWFlZoUaNGlptlpaWsLOzU7f7+flh9uzZqFSpEipVqoTZs2fDwsICvXr1AgDY2NjA19cX48aNg52dHWxtbTF+/HjUrFkz3+RyKQgdaPj6+mLBggVwd3fHr7/+ilGjRmHQoEHo27cvIiMjMWjQIKSnp2PAgAGv3Ya/vz9mzJih1aYwKg6FsfVrXqE7ixd9j04dfdCiVTfExMTK/v4iZGdn4/btewCAsPPhqOdZGyNHDMSw4RPFBpOBIe5vQ1amTCm0atUE3XsMFB1FNvp4fLvZWWP14PZ4npWNtIxsOFhbYMLm43AtaaVex9zMFGXsTVEG1qhVxhGd5u1EwNmb8G1RCw5WFgCACk4ltLbr7miD2CfP5OwKSUQfP+dvk5iYhJycHDg5O2i1OzjYISE+/1XWqOiYMGECnj9/jmHDhiE5ORleXl44fPgwrKz+/Rm3aNEimJiYoEePHnj+/DlatWqFdevWwdjY+A1b/m+EDjQiIyNRocKLiXcrVqzA4sWL8dVXX6mfr1+/PmbNmvXGgcarJtCUtKuim8BvsGTxTHTt8hFatemOe/eiZH//wkKhUECpNBMdQ+e4vw1P//49kZCQiP37j4qOIow+Hd/mZqYwNzPF0/RMhNx4CL92b65EZOW8uJGVa8nicLC2wL1HKVrP33/0FI0+KK2zvCQfffqcv052djbOnw9H61ZN8ccfB9XtrVs3xd69hwQmKzz+y/0tRPjrr7+0HisUCkyfPh3Tp09/7WuKFSuGZcuWYdmyZboNB8EDDXNzczx69AhlypRBTEwMvLy8tJ738vLC3bt337gNpVKZb8KM3KdNLVs6G59/1hXdPhmA1NQ0ODm9+IYgJSUVGRkZb3l10TXz+29w8OAxREU/hJVVcfTs0QXNmnmjQ8feoqPplKHub0tLC1Ss6K5+7F6uDDw8qiMpKRlRUQ8FJtM9hUKB/v16YuOmHTq5c2phpK/Hd8iNGKhUKpRzsMGDx0+xaP85lHOwRpd6lfA8Kxu/HAtH82pusLeyQEp6Brafuo74lHS0qVUOwP9/FprWwE+BF1DZxRYf/P8cjXuPUjC/T9G9/4KhHt/6+jl/F4uW/IL1a5cgLOwSQk+HYZBvH5RxK4WfV20UHY30iNCBRrt27bBy5Ur8+uuvaNasGXbu3AkPDw/189u3b0fFihUFJnw3Q4f0BwAcO7pLq32A7xhs2Ki/k0YdHe2xbu1SuLg4IiUlFZcvX0OHjr1x5OhJ0dF0ylD3dz1PDxw9slP9eMH86QCA9Ru2w3fgGEGp5NGqVROULVsa69YZztWm9PX4Ts3IwrKDYYhPeQYbCyVa1SiLEW09YWpshLy8PNx79ATjNt3Ck2cZKGGhRPXS9lgzuB0qOv17zfk+jasjKycX8/edRkp6Fiq7lMRPA9vCzU7+U3alYqjHt75+zt/Fjh17YGdbElMmj4GLiyOuRESiU+e+ePAgRnS0QiFPdAA9oVAJnPXz8OFDNGrUCGXKlEG9evWwcuVKeHp6omrVqoiMjERoaCgCAgLQvn37Am3XxKyUjhITkWhFpJotucI4MVEOqdtGio4ghFVP3Z/SQCRaTlbhHdTMKdvn7StJ5Jv7m2R7L7kJvY+Gq6srLly4AG9vbxw8eBAqlQpnzpzB4cOHUbp0afz9998FHmQQEREREZF4wu+jUaJECcyZMwdz5vAulEREREQknqFWkaXGO4MTEREREZHkhFc0iIiIiIgKkzzWNCTBigYREREREUmOFQ0iIiIiIg28vK00WNEgIiIiIiLJsaJBRERERKSBMzSkwYoGERERERFJjhUNIiIiIiINnKMhDVY0iIiIiIhIcqxoEBERERFpyFOITqAfWNEgIiIiIiLJsaJBRERERKSBdwaXBisaREREREQkOVY0iIiIiIg0sJ4hDVY0iIiIiIhIcqxoEBERERFp4H00pMGKBhERERERSY4VDSIiIiIiDbzqlDRY0SAiIiIiIslxoEFERERERJLjqVN6RCE6gCAKhWH2PE9lmGVdw+y14bLquUx0BCHSjs8VHUEIqxYTREcQgj/XCh/uE2mwokFERERERJJjRYOIiIiISAMvbysNVjSIiIiIiEhyrGgQEREREWng5W2lwYoGERERERFJjhUNIiIiIiINrGdIgxUNIiIiIiKSHCsaREREREQaeNUpabCiQUREREREkmNFg4iIiIhIg4qzNCTBigYREREREUmOFQ0iIiIiIg2coyENVjSIiIiIiEhyrGgQEREREWngncGlwYoGERERERFJjhUNIiIiIiINrGdIgxUNIiIiIiKSHAcaREREREQkOZ46RURERESkgZPBpcGKBhERERERSY4DDQk0aeyF3QHr8OBeGHKyYtC5c1vRkWQxdepYZGfFaC1RDy6IjiW5xo29EPD7Wty7ew5ZmdH59u/UKWNxOfwvJCfdQHzcFRw4sAX169cRlFZ3Jk4YgVMhfyL5cSQeRl/Crp2rUblyBdGxdM5Qj+9/DBncHzcjTyHt6W2cDj2Axo0+FB1JFvrW72fPMzF38yF89PUSfDh4NvrNWoMrd2PUzz9OScPU1X+g9ZiF8BoyG0MX/ob78Y/Vz8ckPoHHgO9euRw+e1VElyRhKL/H3mbihBHIyYrBgvkzREcpNPJkXPQZBxoSsLS0QHj4VYzymyI6iuyuRFxHabfa6qVO3VaiI0nun/3r5zf1lc/fvHkHo/2moK5na7Ro0Q3370Vj/5+/wd7eVuakutW0SQOsXLkejZp0wkftP4eJsQkO/LkZFhbmoqPplCEf3927d8bCBdPhP2cp6n3YFsHBZ7Bv7ya4ubmKjqZT+tjv6ev24tTVO5g1sCt2fjcE3tXLY/D8TYhPfgqVSgW/5dsQ/SgZi0f1xLZpX8HFzgaD529CemYWAMDZ1hpHF43VWoZ2bQZzpSka16wouHfvxxB+j71JPU8PDPTtjUvhRXfASIUX52hI4OCh4zh46LjoGELk5uQiPv6R6Bg6dejQcRx6w/7dum231uOvJ8zAgAGfo2bNqjh+/G8dp5NPh059tB77DhqDuIeX4Vm3Fk4GnxaUSvcM+fgeM3oQ1qzdijVrtwAAxo2fBh+fZhgyuB8mT5kjOJ3u6Fu/M7KycTTsGhaP7AnPD8oCAIZ2bY7jFyKx4/g5dGzogfDbMdj1/RBULOUIAJjctz1ajF6Ag6evoFvTujA2MoK9TXGt7R47H4m29avDopiZ7H2SkiH8HnsdS0sLbNiwHEOGTsC3k0aJjlOoqDhHQxKsaNB7qVjRHffvheFG5Cls2rQC7u5lREcSytTUFAMH9saTJykI1/Nvh2xsrAEASclPxAYhnTA1NUXdurUQeCRIqz0wMAjeDeoJSqV7+tjv3Nw85OapoDTV/m5RaWaCCzejkJ2T8+KxxvPGRkYwNTHGhZtRr9zm1XsPEfkgDh83LfqniRry77FlS2fjwP6jOHrspOgopKc40KD/7MyZC/hywGh06NgbQ4ZOgLOTA04E/QFb25Kio8mufftWSHocidSntzFq5CC0a98Ljx8ni46lU/PnTUNw8GlERESKjkI6YG9vCxMTEyTEJ2q1JyQkwsnZUVAq3dPHfluaK+FRoTRW7T2JhORU5OblYd+pcFy+E4NHT9JQztkernY2WLrzGJ4+e47snFys/jMYiSlpePQk9ZXbDDh5EeVd7FG7opvMvZGWIf8e69GjM+rUqYFvp/iLjlIocY6GNISeOjVy5Ej06NEDTZo0+c/byMzMRGZmplabSqWCQqF433j0FtqnE11HaOg5RF4PQb++3bF4ySphuUT4668Q1P+wLezsbOE7oBc2b16Jxo074dGjx29/cRG0dMks1KxRFc1afCw6CumYSqV9+oBCocjXpo/0rd+zBnXFtDV70GbcIhgbKVClrAvaedXE9fuxMDUxxoLh3TF97V40GTkPxkYKeFUr/9q5FxlZ2TgQehmDOjWVuRfSM9TfY6VLu2LRgu/QrkOvfH9DEUlJ6EDjxx9/xIoVK1ChQgX4+vqif//+cHZ2LtA2/P39MWOG9lUSFEbFoTC2ljIqvYP09Oe4cuU6KlZ0Fx1Fdunpz3H79j3cvn0PZ86cR0TESXz5xWeYO+9H0dEkt3jR9+jU0QctWnVDTEys6DikI4mJScjJyYGTs4NWu4ODHRL0+Hx2fe23m6Mt1nzzBdIzs/DseSYcSljh65U7UcqhBACgWjlXbJ8xGKnpGcjOyYWttSV6f/8rqpfLPwE+8Nw1PM/KRqeGtWTuhe4Zyu+xunVrwsnJAWdCD6jbTExM0KRJAwwf9gUsirsjL0/fv2t/M87RkIbwU6cOHz6M9u3bY/78+ShTpgy6dOmCffv2vfMHfNKkSUhJSdFaFEZWOk5Nr2JmZoYqVSohNi5edBThFAoFlEql6BiSW7J4Jj7u2g5t2vbAvXuvPneb9EN2djbOnw9H61ba31q3bt0Up0LPCUqle/rebwulGRxKWOHps+c4deU2mtf+QOt5K4tisLW2xP34x7h6LxbN63yQbxu7T15A89ofwNbaUq7YsjGU32PHjgXDo05LeNb3US9nz13E5i0B8KzvY/CDDJKO8KtO1axZE61atcK8efMQEBCANWvWoGvXrnBycsIXX3yBL7/8EhUrvv7SeUqlMt8fdHKfNmVpaaH17Yd7uTLw8KiOpKRkREU9lDWLnH6YMxX7/gxEVFQMHB3sMenb0bC2Lo6NG3eIjiYpS0sLVKxQTv24XDk3eNSqhqTkJ3j8OBmTvhmFvfsCERcXD1vbkhgyuD9Kl3LGrl37xIXWgWVLZ+Pzz7qi2ycDkJqaBienF9/4pqSkIiMjQ3A63THU4xsAFi35BevXLkFY2CWEng7DIN8+KONWCj+v2ig6mk7pY7//vnILUAFlne0QlZCERduPoKyzHbo0rg0AOHz2KkpaWcDF1gY3YxIwd/NBtKj7ARrW0L5XzoP4JITduI8f/XoJ6IX0DOX32MvS0p7lm1+X/iwdjx8nc97d/+NQSxrCBxr/MDU1RY8ePdCjRw88ePAAa9aswbp16zBnzhzk5uaKjvdG9Tw9cPTITvXjBfOnAwDWb9gO34FjBKXSvVKlXbBp44+wt7fFo0ePcfrMeTRu0gkPHsS8/cVFiKenB44E/vtLZ/686QCADRu2Y/iISfjgg4ro06c77O1L4vHjZISFXUKLlp/g6rUbghLrxtAh/QEAx47u0mof4DsGGzZuFxFJFoZ6fAPAjh17YGdbElMmj4GLiyOuRESiU+e+eneMv0wf+52Wnomlu44hPvkpbCzN0cqzKkZ2awFTE2MAwKMnqZi/9TAeP02DQwkrdPSuhcGd88/B2B18AY4lrOFdXT9u1mkov8eIRFGoBM5uMzIyQlxcHBwdX30lD5VKhSNHjqBNmzYF2q6JWSkp4hU5hjr93VAn/ucV4YmpRPRmacfnio4ghFWLCaIjCGGoP81zsgrvgK5v2W6yvdfG+7/L9l5yEzpHo2zZsjA2Nn7t8wqFosCDDCIiIiIiEk/oqVN3794V+fZERERERPkYapVJasKvOkVERERERPqn0EwGJyIiIiIqDPJY05AEKxpERERERCQ5VjSIiIiIiDTwzuDSYEWDiIiIiIgkx4EGERERERFJjqdOERERERFpyBMdQE+wokFERERERJJjRYOIiIiISAMvbysNVjSIiIiIiEhyrGgQEREREWng5W2lwYoGERERERFJjhUNIiIiIiINvOqUNFjRICIiIiIiybGiQURERESkQaXiHA0psKJBRERERESSY0WDiIiIiEgD76MhDVY0iIiIiIhIcqxoEBERERFp4FWnpMGKBhERERERSY4VDT1iqGcTGuqVIRSiAwhimHubDE3xFhNERxAi9fdxoiMIYdVtgegI9BLeGVwarGgQEREREZHkWNEgIiIiItLAq05JgxUNIiIiIiKSHAcaREREREQkOZ46RURERESkwVAvNCM1VjSIiIiIiEhyHGgQEREREWnIk3EpCH9/f9SvXx9WVlZwdHRE165dERkZqbWOSqXC9OnT4erqCnNzczRv3hwRERFa62RmZmLkyJGwt7eHpaUlOnfujOjo6AKmeTsONIiIiIiIioCgoCAMHz4coaGhCAwMRE5ODnx8fPDs2TP1OnPnzsXChQuxfPlynD17Fs7OzmjTpg1SU1PV6/j5+SEgIABbt25FcHAw0tLS0LFjR+Tm5kqaV6HSw5PQTMxKiY5ApHO8YR8R6RvesM+w5GTFiI7wWj5uH8n2XoejDv7n1z569AiOjo4ICgpC06ZNoVKp4OrqCj8/P0ycOBHAi+qFk5MTfvjhBwwePBgpKSlwcHDAxo0b0bNnTwDAw4cP4ebmhv3796Nt27aS9AtgRYOIiIiISJjMzEw8ffpUa8nMzHyn16akpAAAbG1tAQB3795FXFwcfHx81OsolUo0a9YMISEhAICwsDBkZ2drrePq6ooaNWqo15EKBxpERERERBryoJJt8ff3h42Njdbi7+//1owqlQpjx45F48aNUaNGDQBAXFwcAMDJyUlrXScnJ/VzcXFxMDMzQ8mSJV+7jlR4eVsiIiIiIkEmTZqEsWPHarUplcq3vm7EiBEIDw9HcHBwvucUCu0TrFUqVb62l73LOgXFgQYRERERkQY5pzArlcp3GlhoGjlyJPbs2YMTJ06gdOnS6nZnZ2cAL6oWLi4u6vaEhAR1lcPZ2RlZWVlITk7WqmokJCSgYcOG79OVfHjqFBERERFREaBSqTBixAj8/vvvOHbsGNzd3bWed3d3h7OzMwIDA9VtWVlZCAoKUg8iPD09YWpqqrVObGwsrly5IvlAgxUNIiIiIiINeYX0GofDhw/H5s2b8ccff8DKyko9p8LGxgbm5uZQKBTw8/PD7NmzUalSJVSqVAmzZ8+GhYUFevXqpV7X19cX48aNg52dHWxtbTF+/HjUrFkTrVu3ljQvBxpEREREREXAypUrAQDNmzfXal+7di2++OILAMCECRPw/PlzDBs2DMnJyfDy8sLhw4dhZWWlXn/RokUwMTFBjx498Pz5c7Rq1Qrr1q2DsbGxpHl5Hw2iIor30SAifcP7aBiWwnwfjealpf1m/03+ij4i23vJjXM0iIiIiIhIcjx1ioiIiIhIQ57+nfAjBCsaREREREQkOQ40JDD4q344HxaIpMTrSEq8juATe/BR2xaiY8lmyOD+uBl5CmlPb+N06AE0bvSh6EiymjhhBHKyYrBg/gzRUXRq6tSxyM6K0VqiHlwQHUvnDPX4btLYC7sD1uHBvTDkZMWgc+e2oiPJwlD398QJI3Aq5E8kP47Ew+hL2LVzNSpXriA61nt7lpGFubv/RruZv8Fr4q/ot3Q3rjxIUD8/dctx1B73s9bSd0mA1jZ2nroK3xV70OjbNag97mc8fZ4pdzckp6/7W0oqGRd9xoGGBGJiYjF5sj+8vNvDy7s9jv/1N37ftQbVqlUWHU3nunfvjIULpsN/zlLU+7AtgoPPYN/eTXBzcxUdTRb1PD0w0Lc3LoVfFR1FFlcirqO0W231UqduK9GRdM5Qj29LSwuEh1/FKL8poqPIylD3d9MmDbBy5Xo0atIJH7X/HCbGJjjw52ZYWJiLjvZeZmwPQuiNGMz8vAV2fN0d3h+UxpCf/0R8yjP1Oo2quOHItL7qZfmgdlrbyMjOQaMP3ODbqo7c8XVGX/c3FT6coyGBfX8Gaj2e+r8fMPirvvD6sC6uXr0hKJU8xowehDVrt2LN2i0AgHHjp8HHpxmGDO6HyVPmCE6nW5aWFtiwYTmGDJ2AbyeNEh1HFrk5uYiPfyQ6hqwM9fg+eOg4Dh46LjqG7Ax1f3fo1Efrse+gMYh7eBmedWvhZPBpQaneT0Z2Do5evotFX7aFZ4UXX34NbVsPx6/cw46QCIxo96L6bmpsDHtri9dup0/TWgCAs7ce6j60TPRxf1PhxIqGxIyMjNCjR2dYWlog9HSY6Dg6ZWpqirp1ayHwSJBWe2BgELwb1BOUSj7Lls7Ggf1HcfTYSdFRZFOxojvu3wvDjchT2LRpBdzdy4iOJCtDOr7JsPe3jY01ACAp+YnYIO8hNzcPuXkqKE207wtQzNQYF+7GqR+fu/0QLaatR2f/rZixPQhJqc/ljiqcPuxvqeVBJduiz1jRkEiNGlUQfGIPihVTIi3tGT7tPhDXrt0UHUun7O1tYWJigoT4RK32hIREODk7Ckoljx49OqNOnRpo4N1BdBTZnDlzAV8OGI2bN+/A0dEB304ahRNBf8CjdkskJSWLjqdThnh8GzLub2D+vGkIDj6NiIhI0VH+M8tiZqhV1gmrjpyHu1NJ2FmZ4+CFW7j8IAFl7G0AAI2ruKGNR3m4lrRCTNJT/HjwHAb9tBdbxnwCMxNpb1xWmOnD/qbCSfhAY9myZTh37hw6dOiAHj16YOPGjfD390deXh66deuG7777DiYmr4+ZmZmJzEztiVkqlQoKhby3M4uMvA3P+j4oYWONbt3aY83qxWjZ+hOD+OX08j0fFQpFvjZ9Urq0KxYt+A7tOvTK99nTZ4e0TqO5jtDQc4i8HoJ+fbtj8ZJVwnLJwZCPb0Nk6Pt76ZJZqFmjKpq1+Fh0lPc2q1cLTN8WBJ/vNsHYSIEqpezRrk5FXI958QVZ2zoV1etWdLFFNTcHtJu5GSev3kerWuVFxZaVPu1vKel7pUEuQgca33//PebNmwcfHx+MHj0ad+/exbx58zBmzBgYGRlh0aJFMDU1xYwZr7+aj7+/f77nFUbFoTC21nV8LdnZ2bh9+x4AIOx8OOp51sbIEQMxbPhEWXPIKTExCTk5OXBydtBqd3CwQ4Ien8dft25NODk54EzoAXWbiYkJmjRpgOHDvoBFcXfk5eUJTCiP9PTnuHLlOipWdBcdRecM8fg2ZIa8vxcv+h6dOvqgRatuiImJFR3nvbnZ22D18M54npmNtMwsOFhbYsKGQLjavvpvBAdrS7iULI4HiU9lTiqGvu1vKnyEDjTWrVuHdevWoVu3brh06RI8PT2xfv169O7dGwBQpUoVTJgw4Y0DjUmTJmHs2LFabSXtqug097tQKBRQKs1Ex9Cp7OxsnD8fjtatmuKPPw6q21u3boq9ew8JTKZbx44Fw6NOS622X39ZiMjI25g3/0eDGGQAgJmZGapUqYTgvw1v4qAhHN/0L0PZ30sWz0TXLh+hVZvuuHcvSnQcSZkrTWGuNMXT9EyEREbDr6PXK9d78iwD8U+evXFyuL7Q5/0tBX0+M0NOQgcasbGxqFfvxaRhDw8PGBkZoXbt2urn69ati4cP33yVB6VSCaVSqdUm92lTM7//BgcPHkNU9ENYWRVHzx5d0KyZNzp07C1rDhEWLfkF69cuQVjYJYSeDsMg3z4o41YKP6/aKDqazqSlPct3Hmv6s3Q8fpys1+e3/jBnKvb9GYioqBg4Othj0rejYW1dHBs37hAdTacM9fi2tLTQqla5lysDD4/qSEpKRlSU/lx952WGur+XLZ2Nzz/rim6fDEBqahqcnF5UqlNSUpGRkSE43X8Xcj0KKqhQzqEEHiQ+xaJ9oSjnWAJdPvwA6ZnZ+OnQObSq5Q57a0s8TErFsv1nUMKyGFrWKKfeRuLTdCSmpiMqMQUAcCs2CRZKU7iULA4bi2KCevZ+9HV/U+EjdKDh7OyMq1evokyZMrh58yZyc3Nx9epVVK9eHQAQEREBR8fCP6nY0dEe69YuhYuLI1JSUnH58jV06NgbR47q/9WIduzYAzvbkpgyeQxcXBxxJSISnTr3xYMHMaKjkcRKlXbBpo0/wt7eFo8ePcbpM+fRuEknvd/Xhnp81/P0wNEjO9WPF8yfDgBYv2E7fAeOEZRK9wx1fw8d0h8AcOzoLq32Ab5jsGHjdhGRJJGakYVl+88g/kkabCyKoVUtd4xoVx+mxsbIzVPhZmwS9obdQOrzLDhYW6BeBVfM7dsalsX+rWDtOHUVPx/+96pjA37cAwCY0bM5unz4gex9koK+7m8pcY6GNBQqgbWhKVOmYNWqVejSpQuOHj2Kzz77DL/99hsmTZoEhUKBWbNm4dNPP8XChQsLtF0Ts1I6SkxUeMhbtys8+KOfSH+l/j5OdAQhrLotEB1BiJyswvtF1YeuzWR7rzMPg96+UhEltKIxY8YMmJubIzQ0FIMHD8bEiRNRq1YtTJgwAenp6ejUqRO+//57kRGJiIiIyMCo+LWWJIRWNHSFFQ0yBKxoEJG+YUXDsBTmikZ916ayvdfZhydkey+5Cb+PBhERERFRYaKH38MLYSQ6ABERERER6R9WNIiIiIiINPCqU9JgRYOIiIiIiCTHigYRERERkQbO0ZAGKxpERERERCQ5VjSIiIiIiDRwjoY0WNEgIiIiIiLJsaJBRERERKSBdwaXBisaREREREQkOQ40iIiIiIhIcjx1ioiIiIhIQx4vbysJVjSIiIiIiEhyrGgQEREREWngZHBpsKJBRERERESSY0WDiIiIiEgD52hIgxUNIiIiIiKSHCsaREREREQaOEdDGqxoEBERERGR5FjRICIiIiLSwDka0uBAg4iKFGMjwyzE5ubliY5AMlKIDiCIdbcFoiMIkbpvsugIRDrBgQYRERERkQbO0ZCGYX41SEREREREOsWKBhERERGRBs7RkAYrGkREREREJDlWNIiIiIiINHCOhjRY0SAiIiIiIsmxokFEREREpEGl4iXFpcCKBhERERERSY4DDSIiIiIikhxPnSIiIiIi0pDHyeCSYEWDiIiIiIgkx4oGEREREZEGFW/YJwlWNIiIiIiISHKsaBARERERaeAcDWmwokFERERERJJjRYOIiIiISAPnaEiDFQ0iIiIiIpIcKxpERERERBryWNGQBCsaREREREQkOVY0iIiIiIg0qHjVKUmwoiGBJo29sDtgHR7cC0NOVgw6d24rOpIQEyeMQE5WDBbMnyE6iiyGDO6Pm5GnkPb0Nk6HHkDjRh+KjqRzrq7OWL9uKeJiryDlyS2cO3sYdevUFB1LUo0be+H3XWtw9845ZGZEoXMn7eP5l18WIjMjSms5EfSHoLTy4fGt38f3zRuhyM6KybcsXTJLdDSd08efa88ysjB3VxDa/W8NvMYuR7+F23HlfpzWOnfikjD65z1o/PVKNBy/An0XbENs0lOtdS7djcWgpbvQYNyPaDxhJXyX7ERGVo6cXaEijhUNCVhaWiA8/CrWrd+Gndt/FR1HiHqeHhjo2xuXwq+KjiKL7t07Y+GC6Rgx8luEnDqLQQP7Yt/eTajp0RxRUQ9Fx9OJEiVsEPTXbgQFhaBTpz5IeJSI8uXL4UnK07e/uAixtDBH+OVrWL9hO7Zv++WV6xw6dByDvhqnfpyVlS1XPCF4fOv/8e3dsD2MjY3Vj6tXr4JDB7di5659AlPpnr7+XJux+QhuxT7GzH5t4WBjiT/PXseQ5QHYNbkvnEoUR9SjJ/hy0Q509a6Ooe0boLi5EnfikqA0/ffPwkt3YzF8xW4MaFMPE7s3h6mxEW7EJMJIIbBjMuJVp6TBgYYEDh46joOHjouOIYylpQU2bFiOIUMn4NtJo0THkcWY0YOwZu1WrFm7BQAwbvw0+Pg0w5DB/TB5yhzB6XTj66+HITr6IQYOGqtuu38/WmAi3Th0+C8cOvzXG9fJzMxCfPwjeQIJxuPbMI7vxMQkrccTvh6BW7fu4sSJU4ISyUMff65lZOXg6KVbWDSoEzwrlgIADG3fAMfDb2NHcDhGdGyI5ftOoXH1chjTtbH6daXtbbS2M//3E/i8WW0M8KmvbivrWFKeTpDeEHrqVGxsLP73v/+hZcuWqFq1KmrUqIFOnTph9erVyM3NFRmNCmDZ0tk4sP8ojh47KTqKLExNTVG3bi0EHgnSag8MDIJ3g3qCUulex44+CAsLx5YtPyMm+hLOnjkE3wG9RMcSomnTBoh6cAFXLgdhxYof4OBgJzqSzvD4fkHfj29Npqam6NWrG9at3yY6is7p48+13Lw85OapoDQ11movZmqCC7cfIi9PhZMRd1HWsQSG/hiAFpNWoc/8rTh26bZ63aTUdFy+FwdbK3P0W7gdLb9dBd8lO3Hhdozc3REmDyrZFn0mbKBx7tw5VK1aFXv37kVGRgZu3LiBunXrwtLSEuPHj0eTJk2QmpoqKh69ox49OqNOnRr4doq/6Ciysbe3hYmJCRLiE7XaExIS4eTsKCiV7pV3L4PBg/vi1q276NCxF1at2ohFi75Dnz6fio4mq0OHjuOLL0ah7UefYeLE71HP0wOHDm6DmZmZ6GiS4/H9L30/vjV16fIRSpSwxoYN20VH0Tl9/LlmWcwMtdxdsOrgGSSkpCE3Lw9/nr2Oy/fjkPj0GZLS0pGemY01gefQsGpZrBz+MVrWqoBxq/fh3M0X1ZzoxBQAwE/7T6Nbw+pYMbQrqpR2wFfLA3A/IVlk96iIEXbqlJ+fH8aMGYNp06YBADZt2oTly5cjNDQUycnJaNmyJaZMmYIlS5a8cTuZmZnIzMzUalOpVFAoDOQkQoFKl3bFogXfoV2HXvn2gSF4+fxNhUKh1+d0GhkZISwsHFOnvjh15OLFCFSrVhmDv+qHTZt2Ck4nn50796r/ffVqJMLOh+PmjVNo164l/vjjoMBk0uLxbVjHt6Yvv/gMBw8dR2xsvOgoOqevP9dm9fXB9M1H4DNlNYyNFKhS2hHtPD/A9ehH6vtDNK9ZHn1b1gUAVCntgEt3Y7Ez+DLqVSqtXueTRjXQtUH1F+u4OeLMjSj8EXoVozo3EtMxGRnK8a5rwioa58+fR9++fdWPe/XqhfPnzyM+Ph4lS5bE3LlzsXPn2w9yf39/2NjYaC2qPFZC5FC3bk04OTngTOgBZKTfR0b6fTRr1hAjRwxARvp9GBnp50XNEhOTkJOTAydnB612Bwc7JOjxefuxsQm4du2GVtv167fg5uYqKFHhEBeXgAcPYlCxorvoKJLi8W1Yx/c/ypQphVatmmDNms2io8hCX3+uuTmUwOrRn+LU/GE4+J0vfvv6M+Tk5sHV1holLc1hYmSECs7ap3y6O9siNvnF308O1pYAgAouL63j9O86RO9C2G8KR0dHxMbGqh/Hx8cjJycH1tbWAIBKlSohKSnpdS9XmzRpElJSUrQWhZGVznLTv44dC4ZHnZbwrO+jXs6eu4jNWwLgWd8HeXl5oiPqRHZ2Ns6fD0frVk212lu3bopToecEpdK9kFNnUblyBa22SpXK48EDwzln91VsbUugdGkXxMUliI4iKR7fhnV8/6N//55ISEjE/v1HRUeRhb7/XDNXmsLBxhJP0zMQcv0+mtcqD1MTY1Qr64R7L50CdT/hCVxsX/z95GpnDQcbS9yLf2mdR0/gUtIw/sbKU6lkW/SZsFOnunbtiiFDhmDevHlQKpX4/vvv0axZM5ibmwMAIiMjUapUqbduR6lUQqlUarXJfdqUpaWF1reZ7uXKwMOjOpKSkvX2UogAkJb2DBERkVpt6c/S8fhxcr52fbNoyS9Yv3YJwsIuIfR0GAb59kEZt1L4edVG0dF0ZumSX3DixB+YOHEkdu7ci/r1a2PgwN4YOmyC6GiSsrS0QIUK5dSPy5VzQ61a1ZCc/ARJSU8wdcpYBOzej7i4BJQtWxrfzZiIxMRkvTptCuDxbWjHN/Did2f/fj2xcdMOg7kgi77+XAu5dh8qlQrlHEviQeITLNodjHKOJdGlQTUAwBet6mLC2gOoW6EU6lcujZCr93Hiyh38OuoTAP//WWjliZ/2h6JyKXt8UNoBe09fw734JMwf0F5k16iIETbQmDlzJmJjY9GpUyfk5ubC29sbmzZtUj+vUCjg7180JiDW8/TA0SP/nua1YP50AMD6DdvhO3CMoFSkSzt27IGdbUlMmTwGLi6OuBIRiU6d++rNt2Cvci7sEj7tPhCzZn6DKZP9cPdeFMaNm4YtWwJER5OUp2ctBB7eoX48b96LeWQbNu7AyJHfokaNKujd+xOUKGGN2LgEBAWdQp++w5CW9kxUZJKYIR7fANCqVROULVsa69bp/9Wm/qGvP9dSn2di2d4QxD9Jg42FEq08KmJEp4Yw/f97pbT0qIgpPVtideBZzN31F8o6lsR83w6oU+HfL3j7tKiDrOwczP/9BFLSM1C5lAN+Gv4x3BxKCOoVFUUKleDZLhkZGcjJyUHx4sUl26aJ2dsrIURFnaFe7kBf5wa8Ta6enqpEr2aox7eherpvsugIQpj7DBMd4bVKFq8o23slp92S7b3kJvyGfcWKFRMdgYiIiIiIJCZ8oEFEREREVJjo+4305GKY5yAQEREREZFOsaJBRERERKSBN+yTBisaREREREQkOVY0iIiIiIg06PuN9OTCigYREREREUmOFQ0iIiIiIg0qXnVKEqxoEBERERGR5FjRICIiIiLSwDka0mBFg4iIiIiIJMeKBhERERGRBt5HQxqsaBARERERkeRY0SAiIiIi0sCrTkmDFQ0iIiIiIpIcKxpERERERBo4R0MarGgQEREREZHkONAgIiIiIipCVqxYAXd3dxQrVgyenp44efKk6EivxIEGEREREZEGlUol21JQ27Ztg5+fHyZPnowLFy6gSZMmaNeuHR48eKCD/4n3w4EGEREREVERsXDhQvj6+mLgwIGoWrUqFi9eDDc3N6xcuVJ0tHw40CAiIiIi0qCScSmIrKwshIWFwcfHR6vdx8cHISEhBe2mzvGqU0REREREgmRmZiIzM1OrTalUQqlU5ls3MTERubm5cHJy0mp3cnJCXFycTnP+JyqSTEZGhmratGmqjIwM0VFkxX6z34aA/Wa/DQH7zX6T/KZNm5av0DFt2rRXrhsTE6MCoAoJCdFqnzlzpuqDDz6QIW3BKFQqXihYKk+fPoWNjQ1SUlJgbW0tOo5s2G/22xCw3+y3IWC/2W+SX0EqGllZWbCwsMCOHTvw8ccfq9tHjx6NixcvIigoSOd5C4JzNIiIiIiIBFEqlbC2ttZaXjXIAAAzMzN4enoiMDBQqz0wMBANGzaUI26BcI4GEREREVERMXbsWPTt2xf16tWDt7c3Vq1ahQcPHmDIkCGio+XDgQYRERERURHRs2dPPH78GN999x1iY2NRo0YN7N+/H2XLlhUdLR8ONCSkVCoxbdq015a79BX7zX4bAvab/TYE7Df7TUXDsGHDMGzYMNEx3oqTwYmIiIiISHKcDE5ERERERJLjQIOIiIiIiCTHgQYREREREUmOAw0iIiIiIpIcBxoSWrFiBdzd3VGsWDF4enri5MmToiPp1IkTJ9CpUye4urpCoVBg9+7doiPJwt/fH/Xr14eVlRUcHR3RtWtXREZGio6lcytXrkStWrXUNxPy9vbGgQMHRMeSnb+/PxQKBfz8/ERH0anp06dDoVBoLc7OzqJjySImJgZ9+vSBnZ0dLCwsULt2bYSFhYmOpVPlypXLt78VCgWGDx8uOppO5eTkYMqUKXB3d4e5uTnKly+P7777Dnl5eaKj6Vxqair8/PxQtmxZmJubo2HDhjh79qzoWKRnONCQyLZt2+Dn54fJkyfjwoULaNKkCdq1a4cHDx6IjqYzz549g4eHB5YvXy46iqyCgoIwfPhwhIaGIjAwEDk5OfDx8cGzZ89ER9Op0qVLY86cOTh37hzOnTuHli1bokuXLoiIiBAdTTZnz57FqlWrUKtWLdFRZFG9enXExsaql8uXL4uOpHPJyclo1KgRTE1NceDAAVy9ehULFixAiRIlREfTqbNnz2rt63/uOty9e3fByXTrhx9+wE8//YTly5fj2rVrmDt3LubNm4dly5aJjqZzAwcORGBgIDZu3IjLly/Dx8cHrVu3RkxMjOhopEd4eVuJeHl5oW7duli5cqW6rWrVqujatSv8/f0FJpOHQqFAQEAAunbtKjqK7B49egRHR0cEBQWhadOmouPIytbWFvPmzYOvr6/oKDqXlpaGunXrYsWKFZg5cyZq166NxYsXi46lM9OnT8fu3btx8eJF0VFk9c033+Dvv//W+4r02/j5+WHfvn24efMmFAqF6Dg607FjRzg5OWH16tXqtk8++QQWFhbYuHGjwGS69fz5c1hZWeGPP/5Ahw4d1O21a9dGx44dMXPmTIHpSJ+woiGBrKwshIWFwcfHR6vdx8cHISEhglKRXFJSUgC8+KPbUOTm5mLr1q149uwZvL29RceRxfDhw9GhQwe0bt1adBTZ3Lx5E66urnB3d8dnn32GO3fuiI6kc3v27EG9evXQvXt3ODo6ok6dOvjll19Ex5JVVlYWNm3ahAEDBuj1IAMAGjdujKNHj+LGjRsAgEuXLiE4OBjt27cXnEy3cnJykJubi2LFimm1m5ubIzg4WFAq0ke8M7gEEhMTkZubCycnJ612JycnxMXFCUpFclCpVBg7diwaN26MGjVqiI6jc5cvX4a3tzcyMjJQvHhxBAQEoFq1aqJj6dzWrVtx/vx5gzp/2cvLCxs2bEDlypURHx+PmTNnomHDhoiIiICdnZ3oeDpz584drFy5EmPHjsW3336LM2fOYNSoUVAqlejXr5/oeLLYvXs3njx5gi+++EJ0FJ2bOHEiUlJSUKVKFRgbGyM3NxezZs3C559/LjqaTllZWcHb2xvff/89qlatCicnJ2zZsgWnT59GpUqVRMcjPcKBhoRe/uZHpVLp/bdBhm7EiBEIDw83mG+APvjgA1y8eBFPnjzBrl270L9/fwQFBen1YCMqKgqjR4/G4cOH8337p8/atWun/nfNmjXh7e2NChUqYP369Rg7dqzAZLqVl5eHevXqYfbs2QCAOnXqICIiAitXrjSYgcbq1avRrl07uLq6io6ic9u2bcOmTZuwefNmVK9eHRcvXoSfnx9cXV3Rv39/0fF0auPGjRgwYABKlSoFY2Nj1K1bF7169cL58+dFRyM9woGGBOzt7WFsbJyvepGQkJCvykH6Y+TIkdizZw9OnDiB0qVLi44jCzMzM1SsWBEAUK9ePZw9exZLlizBzz//LDiZ7oSFhSEhIQGenp7qttzcXJw4cQLLly9HZmYmjI2NBSaUh6WlJWrWrImbN2+KjqJTLi4u+QbOVatWxa5duwQlktf9+/dx5MgR/P7776KjyOLrr7/GN998g88++wzAi0H1/fv34e/vr/cDjQoVKiAoKAjPnj3D06dP4eLigp49e8Ld3V10NNIjnKMhATMzM3h6/l979x8Tdf3Acfx58uPuPBAHk1/NIy9/gOgMZTXIX0l/2IrJ/EOarM4AnU43VlrMKGkLVDa08EcONI5FsWLqdDYmZURra4zZoDFBGQpaW4V/lPxQicGnP5y3yPymfj/HFb4e2/1xd5/P5/3iM8buxfv9+dwi7106bvviiy9ITU31UyrxFcMw2LJlC8ePH6ehoeGh/qNsGAZDQ0P+juFTaWlptLW10dra6n0kJyeTlZVFa2vrQ1EyAIaGhujo6CAmJsbfUXzqqaeeuuN21Z2dncTFxfkp0fjyeDxERkaOuUB4Irt+/TqTJo39KBQQEPBQ3N72NofDQUxMDL/++iv19fWsWrXK35FkAtGMhkleffVVXnzxRZKTk0lJSaGiooIrV66wceNGf0fzmYGBAbq6urzPu7u7aW1tJTw8HKfT6cdkvrV582Zqamo4efIkoaGh3pmssLAw7Ha7n9P5zhtvvMGzzz7L9OnT6e/v55NPPqGxsZHTp0/7O5pPhYaG3nH9jcPhICIiYkJfl7Nt2zbS09NxOp309vZSVFREX1/fhP8v7yuvvEJqaio7d+5kzZo1NDc3U1FRQUVFhb+j+dzo6Cgejwe3201g4MPx8SA9PZ3i4mKcTieJiYm0tLSwd+9esrOz/R3N5+rr6zEMgzlz5tDV1cVrr73GnDlzePnll/0dTSYSQ0xz8OBBIy4uzggODjYWLlxofP311/6O5FNfffWVAdzxcLvd/o7mU3/3MwOGx+PxdzSfys7O9v5+T5s2zUhLSzM+//xzf8fyi2XLlhl5eXn+juFTmZmZRkxMjBEUFGTExsYaq1evNs6dO+fvWOPi1KlTxrx58wyr1WrEx8cbFRUV/o40Lurr6w3AuHDhgr+jjJu+vj4jLy/PcDqdhs1mM1wul1FQUGAMDQ35O5rPffrpp4bL5TKCg4ON6OhoY/PmzcZvv/3m71gyweh7NERERERExHS6RkNEREREREynoiEiIiIiIqZT0RAREREREdOpaIiIiIiIiOlUNERERERExHQqGiIiIiIiYjoVDRERERERMZ2KhojIv8zbb7/N448/7n2+bt06MjIyxj1HT08PFouF1tbWcR9bRET++1Q0RETu0bp167BYLFgsFoKCgnC5XGzbto3BwUGfjltWVkZVVdU9batyICIi/xaB/g4gIvJfsnLlSjweD8PDw3zzzTfk5uYyODjIoUOHxmw3PDxMUFCQKWOGhYWZchwREZHxpBkNEZH7YLVaiY6OZvr06axdu5asrCxOnDjhXe5UWVmJy+XCarViGAbXrl1jw4YNREZGMmXKFFasWMH3338/5pi7d+8mKiqK0NBQcnJyuHnz5pj3/7p0anR0lJKSEmbOnInVasXpdFJcXAzAjBkzAEhKSsJisbB8+XLvfh6Ph4SEBGw2G/Hx8bz//vtjxmlubiYpKQmbzUZycjItLS0mnjkREXnYaEZDROT/YLfbGR4eBqCrq4va2lqOHTtGQEAAAM899xzh4eHU1dURFhZGeXk5aWlpdHZ2Eh4eTm1tLYWFhRw8eJAlS5ZQXV3Nvn37cLlcdx1z+/btHD58mHfffZfFixfz008/cf78eeBWWXjiiSc4c+YMiYmJBAcHA3D48GEKCws5cOAASUlJtLS0sH79ehwOB263m8HBQZ5//nlWrFjBRx99RHd3N3l5eT4+eyIiMpGpaIiIPKDm5mZqampIS0sD4Pfff6e6uppp06YB0NDQQFtbG729vVitVgBKS0s5ceIER48eZcOGDbz33ntkZ2eTm5sLQFFREWfOnLljVuO2/v5+ysrKOHDgAG63G4DHHnuMxYsXA3jHjoiIIDo62rvfO++8w549e1i9ejVwa+ajvb2d8vJy3G43H3/8MSMjI1RWVjJ58mQSExP58ccf2bRpk9mnTUREHhJaOiUich8+++wzQkJCsNlspKSksHTpUvbv3w9AXFyc94M+wHfffcfAwAARERGEhIR4H93d3Vy8eBGAjo4OUlJSxozx1+d/1tHRwdDQkLfc3IurV6/yww8/kJOTMyZHUVHRmBwLFixg8uTJ95RDRETkn2hGQ0TkPjz99NMcOnSIoKAgYmNjx1zw7XA4xmw7OjpKTEwMjY2Ndxxn6tSpDzS+3W6/731GR0eBW8unnnzyyTHv3V7iZRjGA+URERG5GxUNEZH74HA4mDlz5j1tu3DhQn7++WcCAwN59NFH/3abhIQEmpqaeOmll7yvNTU13fWYs2bNwm638+WXX3qXW/3Z7WsyRkZGvK9FRUXxyCOPcOnSJbKysv72uHPnzqW6upobN254y8z/yiEiIvJPtHRKRMRHnnnmGVJSUsjIyKC+vp6enh6+/fZb3nzzTc6ePQtAXl4elZWVVFZW0tnZSWFhIefOnbvrMW02G/n5+bz++ut8+OGHXLx4kaamJj744AMAIiMjsdvtnD59ml9++YVr164Bt74EcNeuXZSVldHZ2UlbWxsej4e9e/cCsHbtWiZNmkROTg7t7e3U1dVRWlrq4zMkIiITmYqGiIiPWCwW6urqWLp0KdnZ2cyePZsXXniBnp4eoqKiAMjMzGTHjh3k5+ezaNEiLl++/I8XYL/11lts3bqVHTt2kJCQQGZmJr29vQAEBgayb98+ysvLiY2NZdWqVQDk5uZy5MgRqqqqmD9/PsuWLaOqqsp7O9yQkBBOnTpFe3s7SUlJFBQUUFJS4sOzIyIiE53F0MJcERERERExmWY0RERERETEdCoaIiIiIiJiOhUNERERERExnYqGiIiIiIiYTkVDRERERERMp6IhIiIiIiKmU9EQERERERHTqWiIiIiIiIjpVDRERERERMR0KhoiIiIiImI6FQ0RERERETGdioaIiIiIiJjuD4cs/XUg7XEtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(cm, annot=True, fmt=\"d\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Truth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5d0236a9-e5ff-4a9b-980c-a707fb1709e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "  26/1875 [..............................] - ETA: 7s - loss: 1.6917 - accuracy: 0.5433"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-09 20:09:55.692082: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2755 - accuracy: 0.9220\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1222 - accuracy: 0.9647\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0850 - accuracy: 0.9744\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0652 - accuracy: 0.9803\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0520 - accuracy: 0.9838\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2db4e9c70>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " model = keras.Sequential([\n",
    "     keras.layers.Flatten(input_shape=(28,28)),\n",
    "     keras.layers.Dense(100, activation='relu'),\n",
    "     keras.layers.Dense(10, activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(\n",
    "    optimizer=\"adam\",\n",
    "    loss=\"sparse_categorical_crossentropy\",\n",
    "    metrics=[\"accuracy\"]\n",
    ")\n",
    "\n",
    "model.fit(X_train, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81a9783c-7ab7-49ca-8dd1-5c05123b1cff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}