Modularbeit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modularbeit Neuronale Netze für betriebswirtschafltiche Anwendungen\n",
    "\n",
    "von Nicholas Aiken (40214918) und Christian Kallmaier (39573418)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aufbau\n",
    "\n",
    "1. Vorbereitungen\n",
    "2. Vergleich mehererer neuronaler Netze für die Klassifikation von Graustufen-Bildern\n",
    "    1. Standard Multi-Layer Perzeptron\n",
    "        1. mit 3 Schichten (ein Hidden-Layer)\n",
    "            1. Dropout vs. kein Dropout\n",
    "            2. Vergleich mehrerer Aktivierungsfunktionen\n",
    "            3. Vergleich mehrerer Fehlerfunktionen\n",
    "            4. Vergleich mehererer Optimizer\n",
    "            5. Variation von Batchsize und Epochs\n",
    "        2. tiefes Netz mit mehreren Hidden-Layers\n",
    "            1. Dropout vs. kein Dropout\n",
    "            2. Vergleich mehrerer Aktivierungsfunktionen\n",
    "            3. Vergleich mehrerer Fehlerfunktionen\n",
    "            4. Vergleich mehererer Optimizer\n",
    "            5. Variation von Batchsize und Epochs\n",
    "    2. Convolutional Neuronal Network\n",
    "        1. Variation verschiedener Convolution-Layer\n",
    "        2. Dropout vs. kein Dropout\n",
    "        3. Vergleich mehrerer Aktivierungsfunktionen\n",
    "        4. Vergleich mehrerer Fehlerfunktionen\n",
    "        5. Vergleich mehererer Optimizer\n",
    "        6. Variation von Batchsize und Epochs\n",
    "    3. Convolutional Neuronal Network mit Autoencoder\n",
    "3. Anwendung der Ergebnisse auf die Klassifikation von RGB-Farbbildern\n",
    "    1. MLP\n",
    "    2. CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Vorbereitungen\n",
    "\n",
    "Zunächst müssen alle notwendigen Pakete und Bibliotheken importiert werden:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-24 21:36:15.152508: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "import scipy as sc\n",
    "import math as ma\n",
    "from scipy import linalg, optimize, constants, interpolate, special, stats\n",
    "from math import exp, pow, sqrt, log\n",
    "\n",
    "import seaborn as sns #spezielle Graphikdarstellungen\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.stats.api as sms\n",
    "\n",
    "#den oberen Teil kennt man schon vom letzten Mal...\n",
    "#Theano kommt neu dazu - \n",
    "#Library für effiziente Berechnungen mit großen Matrizen -> DeepLearning!\n",
    "import theano \n",
    "import tensorflow\n",
    "\n",
    " \n",
    "#Jetzt noch scikit-learn:\n",
    "#hier stecken viele Funktionalitäten drin, die man gut brauchen kann: \n",
    "#Fehlerfunktionen, Standard-Modelltypen, Preprocessing-Algorithmen, Daten... \n",
    "\n",
    "import sklearn as sl \n",
    "from sklearn import model_selection, metrics, datasets\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import confusion_matrix\n",
    "#Import wichtiger Keras Funktionalitäten\n",
    "\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "from keras.utils import to_categorical\n",
    "from keras.optimizers import SGD #Stochastic Gradient Descent\n",
    "from keras.utils import plot_model\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anschließend werden die notwendigen Daten als .pickle Files geladen und soweit notwendig vorverarbeitet.\n",
    "Im Rahmen dieser Arbeit nutzen wir die Datensätze data1.pickle und data6.pickle.\n",
    "\n",
    "data1.pickle enthält die vorverarbeiteten Daten zur Klassifikation deutscher Verkehrsschilder in Farbe, data6.pickle enthält die Daten in Graustufen. Alle Daten sind bereits mit dem Faktor 255 auf einen Bereich zwischen 0 und 1 normalisiert. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# prepare gray scale data\n",
    "data_gray_file = open('./archive/data6.pickle', 'rb')\n",
    "data_gray = pickle.load(data_gray_file, encoding='latin1')\n",
    "data_gray_file.close()\n",
    "\n",
    "# Preparing y_train and y_validation for use in Keras\n",
    "data_gray['y_train'] = to_categorical(data_gray['y_train'], num_classes=43)\n",
    "data_gray['y_validation'] = to_categorical(data_gray['y_validation'], num_classes=43)\n",
    "\n",
    "\n",
    "# prepare color data\n",
    "data_color_file = open('./archive/data1.pickle', 'rb')\n",
    "data_color = pickle.load(data_color_file, encoding='latin1')\n",
    "data_color_file.close()\n",
    "\n",
    "# Preparing y_train and y_validation for use in Keras\n",
    "data_color['y_train'] = to_categorical(data_color['y_train'], num_classes=43)\n",
    "data_color['y_validation'] = to_categorical(data_color['y_validation'], num_classes=43)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shape der Daten\n",
    "\n",
    "Um einen Überblick über die Datensätze zu bekommen, geben wir die Shape der einzelnen Datensätze an und plotten Beispiele der enthaltenen Bilder:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Graustufen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train: (86989, 43)\n",
      "y_validation: (4410, 43)\n",
      "y_test: (12630,)\n",
      "labels: 43\n",
      "x_train: (86989, 1, 32, 32)\n",
      "x_validation: (4410, 1, 32, 32)\n",
      "x_test: (12630, 1, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "# Showing shapes of loaded data\n",
    "for i, j in data_gray.items():\n",
    "    if i == 'labels':\n",
    "        print(i + ':', len(j))\n",
    "    else: \n",
    "        print(i + ':', j.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Farbe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train: (86989, 3, 32, 32)\n",
      "y_test: (12630,)\n",
      "x_test: (12630, 3, 32, 32)\n",
      "x_validation: (4410, 3, 32, 32)\n",
      "labels: 43\n",
      "y_train: (86989, 43)\n",
      "y_validation: (4410, 43)\n"
     ]
    }
   ],
   "source": [
    "# Showing shapes of loaded data\n",
    "for i, j in data_color.items():\n",
    "    if i == 'labels':\n",
    "        print(i + ':', len(j))\n",
    "    else: \n",
    "        print(i + ':', j.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um die Daten sinnvoll verarbeiten zu können, verschieben wir die Grau-/Farb-Kanäle ans Ende der Vektoren und führen One-Hot-Encoding für die Labels aus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train_gray.shape: (86989, 32, 32, 1)\n",
      "x_train_color.shape: (86989, 32, 32, 3)\n",
      "x_test_gray.shape: (12630, 32, 32, 1)\n",
      "x_test_color.shape: (12630, 32, 32, 3)\n",
      "y_train_gray.shape: (86989, 43)\n",
      "y_train_color.shape: (86989, 43)\n",
      "y_test_gray.shape: (12630, 43)\n",
      "y_test_color.shape: (12630, 43)\n"
     ]
    }
   ],
   "source": [
    "# Transpose data to have channel at the end\n",
    "x_train_gray = data_gray['x_train'].transpose(0, 2, 3, 1)\n",
    "x_train_color = data_color['x_train'].transpose(0, 2, 3, 1)\n",
    "\n",
    "x_test_gray = data_gray['x_test'].transpose(0, 2, 3, 1)\n",
    "x_test_color = data_color['x_test'].transpose(0, 2, 3, 1)\n",
    "\n",
    "x_validation_gray = data_gray['x_validation'].transpose(0, 2, 3, 1)\n",
    "x_validation_color = data_color['x_validation'].transpose(0, 2, 3, 1)\n",
    "\n",
    "# one hot encoding\n",
    "#y_train_gray = to_categorical(data_gray['y_train'], num_classes=43)\n",
    "y_train_gray = data_gray['y_train']\n",
    "y_train_color = data_color['y_train']\n",
    "y_test_gray = to_categorical(data_gray['y_test'], num_classes=43)\n",
    "#y_test_gray = data_gray['y_test']\n",
    "y_test_color = to_categorical(data_color['y_test'], num_classes=43)\n",
    "\n",
    "print('x_train_gray.shape: ' + str(x_train_gray.shape))\n",
    "print('x_train_color.shape: ' + str(x_train_color.shape))\n",
    "print('x_test_gray.shape: ' + str(x_test_gray.shape))\n",
    "print('x_test_color.shape: ' + str(x_test_color.shape))\n",
    "\n",
    "\n",
    "print('y_train_gray.shape: ' + str(y_train_gray.shape))\n",
    "print('y_train_color.shape: ' + str(y_train_color.shape))\n",
    "print('y_test_gray.shape: ' + str(y_test_gray.shape))\n",
    "print('y_test_color.shape: ' + str(y_test_color.shape))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Beispielbilder plotten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/vzjoelcVAl10D7LiMCyt2EOSgs3+3BUu/7887W32H/3f+Ax+7q8+wdq98fP/kJezs//I6rYNLd+LWZYR3wFQjr7RK4qiKMoAow96RVEURRlg+lZ1H4bhspqy0verPLMdcwcJItHOqy6d4+p0dJVrgduQdK/LUO0otT9M9+qLqLYkIhoC16Og4Obj1VBpKrLjQUantOvHKLycqAmq/EImtZI0gAUzByv3rlPvOk4UhhQEAWXSLdL2jn5HxE08qBWUiasgMVwhul6ALpkVGukmmIwaInJd0rqWl9EbdHTPLtZu+A6vdsxCrkLtLszkZdPlWe8aw6N+jLGX/zTh7VAog5BPQpkDVCEBJMq1kP9V10RV3XOMMctrgMx2CXO5rc7X0z9/35/n5dfe9PN59cIMaxdC5Lr3CNX9X93rM77t3t/Iy//nf3EPa7e44F3X/uO//ztW153z50OXz6K5tzx7qalIZ8gDjTKHWNHS13VF9Mrfnfdmrou/P5OXP/Lf/kvW7sqMN3tcPPs73nsos9m9PfpGryiKoigDjD7oFUVRFGWA6V/VfWCW1c1ShVShJ0a1M+6yDaR6EhLNJJarQbosWQ2UnfxNBKp7WQNNY4gaNTTUYO1G4VhG6AqgV6EBpgxCoKUQ5cwlfCQR7NjGcSz3Acfs1FURsaTq3vT8+1anHkUUBAGl4nd0y3r1tIyaF8JFwIh3USBVi76cFXaq9474VRf6/1Hw/nAdkawGxljfucOPYydX3WdghhqeGGV1S1fHfXfXuFp/BPSftuZ3/C85LuSYOAU9DZY/6L93Bp4gWWWkMKGiX21boardihgKVpLKCPmEedq3awermx5/T17+5a98hDcnbIkpyPzJc1zujv8/f5qXn4Coi++84y7W7om/emdevjbfZnWvnvB9mMUlP47rTrolHz6+WNUlroey2Wzby+Hrv57Kyx+6ep612/8e74Xwy5/8z6wuilbuG8OjVVahb/SKoiiKMsDog15RFEVRBhh90CuKoijKANO3Nnoyy/+MdKGDsnSbYK42YJe0hkf/Qte4RNi/E7AhZhVp2RzYrFxF5L06uNCNNHl0sRrYOUPiAzFDvmyFi0Y39bapDnwBKzL7dcA0Kzz7KIUpCXFvQ8FXEMvSbrfyGTXRM4IwoDAImBwQEXUs7q3osrrSxHZiztk1tuX3Btr2x0T0u0bq6zpgyyQiihvefl/fuzcvT4nojJ23/pSX7xyf4P1PbM/Lyewcq6t1fNayeNgLeVTjSxGa7DMRkZFFKCuJBkhEVAvL9zqsBgC8bvPtgDI2vp3CMCS5p6EO0RXfcfc/YXVvXvMZ2dpzYHuvcF28lvLr8f/4hbftT3zvRF7+P/33/zVrd++O/Xn50//iz1jd7JyX5V+9+lpedm2eKY/fbOWud7HYG+LgOIV1t7AHALOSiqoU+v/NtB/vz1/9FWv3jvv/q7w8vOdOVpe9Nb/SmdroFUVRFEUhfdAriqIoykDTt6r75ch4YdGly5S7LqCiyMFvmFSovhM4zoR2ybHkF66kzA+l6hqjnNXrXnW/lIgEH6HvpBZIVxRQT0oVEowR1frOSRW/L3ci3n8DvjfOcdUvv6KG3on/K0TLZh1nHGVixkJ0a5MJbzASIrglyYQ0JR50RMTV0yNwrhGIQEdElCz46FxGuLWN7PLq+nBid16emeXqz6U574b3jgm+jDS2bcvLi5cu8nMvehVtDDdfPeTmhTaYNqy8SWVypRWkmS+C+ySOuBklSVc/o3Yn5H/4xv+FhoeHC3+fnvLX7ee/5S6Tf/fjc3mZmVUqIsbJhE+XEt/2f/z5pbzcjHn0u4/9y7/Ky+/fcQer+z9+1Luk/d/aXn7+8dQ/8GF0YR0W48DIeO/dzd1G43d6175/+Iff5uVsgZunMPmXTKaEZ5vv+KO3/nCFtdv75/P+vOEIq5tbWDaPZJmq7hVFURRFIX3QK4qiKMpAow96RVEURRlg+tZGHxizEgaz2hJfCro4CBt9Bm5JaSG8LJ7JVwbCPSdgewV4nbPeBtQC145OR4T5DL0NKDK8Lm17F7p2i9d1upC9riL5FtYJ7yjmmcXdFMt/+zlhL17N/KUZwDiZs+Rc0XOnFqFMCptx4u1tKGtGhBENQdQCsTkEQ+eOwN6QIOGufAm4uDUnuD22ude7L9nGWF4e4l54lIDdv53yvSejTS/X9W08XGp3fjYvN8BWaoZ5eOg23LNG3r+YmcyW76PBuyYUIazdyrFzKrvI4Qc/SGNjY4W/z7b9bP6X//cpXrfgs7A55vpYMbdCdvEyvgWJDL/1szdZuyT+T3n5n33sw6zuvp0Tefmxj/7TvPz/WuD7S6be+LUfhnBRC8Fmv3ech04//OG/yMvdut+H8su/+0+snWv7m0XuRcBvncKXvnR1lrWbnfM2+8aIuIdWLoUMjV6FvtEriqIoygCjD3pFURRFGWD6VnUfBYbCwBSyuhGqg6THG6hJUG1qpdqaRb8TfUAZPSOG6lzVimrYVHTSAZ1Ka8lHiqrXuQsRqm6sUHN1wDeu3eHqpS5ElcLofUGFK4ecAzx3DCpfJyL0VTnOuRVzg9MMYAybWSJHFEnX0Mqf1b3dRgvyCfaYWsj7H6t5dfoQ9JcuzbN2UewHMrp7L6sLx31Uuystr/I/PzXF2l05712qaIm7W71r8u68PDwxweraF72rUNr1bknhEHcBJOOXpm7WYVUW7G1ovijkXYT1oC7c6zK7PAfr0H5uaVB2u0LdnXVQNe7lU7o7kkMZl+6l/jiBK/knkVzxf/zp7/xnmtzc85f/4kN5+f59Xs7mH/0ga/fvEj/eK//4e1YXgOmh0eCq+3dM+ON/fvjdefmPv/ktazf7J7g35PMLvlsGJqmrS9y8NnNtxo/JcdNDsLLermfd1Td6RVEURRlg9EGvKIqiKANM36ruQxNQaIJCkhUZ/Y3BtPq4M5erT1hOA9FdBFHoYlC1jw9xNU4NPtjtclWW6/jdxF3UmWdClQvqelOIlAYfk4HB8HuiOkz8bMMu5cZ4NF/Yiil1oNYnEaHPrKhDje66ZwTOUOAMkYh2yLfMc1Uy7rTHKZeR29DEUw+4KWisBqrMRa8KzLpcLThyx6683NzFo4t1nJfzi9d8VLuZa1dZO2p5r5B0iC8jb131qvzJPftZXR12dCfTfqexaXP1fL3mvQEWC/YLNL3hPcTnqgETuUOYzVbVyuvZubwVaLfbVKvVCn+fb4MpsSVMhHj/V60l7MiU1uFaKC/PHxf9uY7/5NesLmv45DrvOXx/Xr7/Th7hbuG//md5+Qct7jHSPf+HvFxrCJkJIQkZRDVN5MJbZfAsiWzZEc+GmRlvs1ia56axVW+GQjKdCvSNXlEURVEGGH3QK4qiKMoAow96RVEURRlg+tZGT2b5n7RC8DhY5dmRmKudaIWm5qZwuwnBljc8Au5KIvtbDOcKZZQnsL+mUGeFQTAwXSiLTGfoNiT7r8qqx2pwM4K0q4FtH6bAWrlXAMYR8X0KFEWrHyodw1YkyzJyzlEY8tsrrnsbunV8X0cHoimGsNlERmTEjRgT9SarCsANM1nyGerqw7zdyB3ebp6N8Cho03P+c+m8d8tryGsP98nYKO+fZUqs8ftraJffH9CeuZaXMZoYEdEQRN6bEfsZLLyfWMi+J/e5xHDfRPKdZnX+12Hn3Aq88O/+J2o2mywrJhGRAVmudfewOtxTIjM2lrHWiITy6mTwl9/N8PX03/7IZ6n772I/3jsP/lPW7qF3eZlffPQhVvd3/97LYa3J718LX7QD0SazhO8vKbrUefj38e2iSGRv7Pj+kw6/N1Zdo+VaXYW+0SuKoijKAKMPekVRFEUZYPpWdW9tRsYQWalyLnFPIBIqbvBJC0UylhqoQ0OhWmxAco06REaqkUx+AP03udolakBULxhlJH5WBVCXCR86AypJa1NR58eMijLh/cbU//IXHVNzsjxBfFYDUFfV6zwSVTdbdVGyRHSJlGVS5yhwjiIxlyHMZU1cLDTxYKIWed1GQGXeFCrtbNG75Bhw/xnds5u1q2/bmZe7wryAkRH3TIz7cRjebi7x6sSRkRFWNzzqXeMiEb0s2uZV8rVxnxgku7TA2sWpV102hLvXEkTGc+y+EQ5cMN9WzFW66l5HCvL4J/4PNDY2VrBozC/6mVp8+TeszoBbp3MQhbFgFsHVqsLkWKmRhoRkouEbV73M/E8/+vu8/N82edTFvX9+X17+0Pt2sjrTeiAvj9grrA5tFAkkZMq6QnUP46r6Kvi4Gmnw+yuz3n3VisiQazOOcPSNXlEURVEGGH3QK4qiKMoAow96RVEURRlg+tZG72xGzhA56V7EXMZEnendLhBZfvA4kK4Q4CqWgrtSHMu9AhACN5GuUmCjwcxwkcxm5utCMY4QvnckvqdhGaLgc8LNLWDuRcJ9D9y0DNo8iYeENLBHol7jtq5wJQRrpnFEGc45cs5RN+XzUoMsXzLxFIZeTkG2QvFbfLw25M/TFlnEINTt0DZvJx8SYW4N7LWIxf21d9S7Hs0abyek2TnWbhhsirGwoY+P+Qx40m3INv35Gtu8jb59bZqPEb9LnctdG+6jDO41uVIEGLK0OcTqnF3JvKiyyxhqNmmo2SzYltvWX496zFPKjUz4ELOdy2DXlsZ2dlhhaS5Zx+WxzIDXhfvm9Wkvuz/4//6MtfvfN7z873jXe1jdR/5y0vdvuRthBvs8Oh04t8jmx10HxdoNazKu8RNj3M11CPaEySyVq2uyMWt3a17XG/2xY8fo/vvvp9HRUdq9ezc99thjdObMGdam3W7TkSNHaMeOHTQyMkKPP/44TU9Pl/SoKLcHlV1ls6Kyq9wo63rQv/LKK3TkyBF69dVX6Uc/+hElSUIf/ehHaRF2+37pS1+iH/zgB/TCCy/QK6+8QufPn6dPfOITN33girIeVHaVzYrKrnKjrEt1/8Mf/pAdf+9736Pdu3fTqVOn6EMf+hDNzs7SX//1X9Nzzz1HH/nIR4iI6Pjx4/Te976XXn31VXrwwQfXcTa3/K9S/SM/0jtiXME9jSByXcbrkiWvXkW1e1eoIOsQeUkkr6MFiGrUBdXgcIO7GjWb3n0vEJGiZMY9BLWtzmAUNaH+x+iA0kKR+rouRGXDSGPLHwT1v5iDVZPFZlDd307ZNcYsq+iE7KYQTQtd7ZaPoQwq57EGjzoXQcSyrN1mdTG4g47s9mrHYGSctbOBl10ZoczBb/9L815db1Ourh0e9erauQ6/AUbZofiesf9uzW1+XK3RbaydnfWZ84Zr3Jw0D36qWVLuQhpF/ns2t23nlcHKtRBq137k9q67y8hldqHtr8HIEtcU3HfXgbz8t3846/uw1zm3sL7VY+4W+Y59O/JyOMKz0mVgQnWJ/wbTYo3/2ak38vJHdnL3uqGd/r5xZpjVdeDeWwKPN5fKtbrcLGFgZkcaYMaa4OcKnH8OiSnIg5xK818VN7QZb3Z2Oc3k9u3LN9GpU6coSRJ65JFH8jb33nsvTU5O0smTJ3v20el0aG5ujv1TlFuNyq6yWVHZVdbLdT/orbX01FNP0cMPP0z33bccgGBqaopqtRpNTEywtnv27KGpqame/Rw7dozGx8fzfwcOHOjZTlFuFiq7ymZFZVe5Hq77QX/kyBE6ffo0Pf/88zc0gGeeeYZmZ2fzf+fOnbuh/hTl7VDZVTYrKrvK9XBd7nVHjx6ll156if72b/+W9u/3mbD27t1L3W6XZmZm2K/L6elp2rt3b8++6vU61YX7DBFRljoiZ4uucWDbLFpCSrK6STskRsrNeC9dCL2JWbK6htuhI8xQJ7IIda1vm4DNO7M8lKGFn1l18WVSsPsYMX4DcxLCuGSoX3S9k3b0FtgmAxivEzZ6x+zM3CbcWQkDaTdR9rrbIbsru0soEBKK9nA5ZRiSeCj2tvaxUGSNW/TXwIgMeM0d3t7Y2OVtjVa4RaJHZirGeHXBh6INFrw6944Jbg9N6/748lUeKrS95O35tSG+x8CE4No34l3ecOxERJ15n9kuFNnBmjXfZxL4+XGZcA1F9zqxPyZbGeJas631A7dDdsuwsFdnZJS7gr37oNcG/C//+b/k5WxGhJBFN+E12pdj0e4vdvnr+JH/5gFWZ8b8Pg9n/bVvJbwT6/waHw2JDHWwFsqdRwncOEtt3wdlFXsRZBhsWKMnd/nxhjXex1JrJi+L7Ty5nX89kruuN3rnHB09epRefPFFevnll+nuu+9m9QcPHqQ4junEiRP5386cOUNnz56lw4cPr+dUinJTUdlVNisqu8qNsq43+iNHjtBzzz1H3//+92l0dDS3/4yPj1Oz2aTx8XH67Gc/S08//TRt376dxsbG6Mknn6TDhw9f185PRblZqOwqmxWVXeVGWdeD/jvf+Q4REX34wx9mfz9+/Dh9+tOfJiKir3/96xQEAT3++OPU6XTo0UcfpW9/+9vrHthqNrdQRpODsoyMxP0Nyl2+DKh1pGkAPRlcBtnlROS6BNWwYhgpZqXDzwRCxZ9An6LOsIhfvC6Gtsy9znDVJYEaClVSREQZqDkDZubgXfDZ53PQ6Wwe1f3tlN3lWTNkRLrCFGQmEhPdAP3caOzdGEMRXS8FNXZ9hKvFh3buysvBkM8oZ4XiDrWJiciaeG1uNi83YXUYHZ1g7ZK6V7svLgqTTtu7BmVdXheCSylFkB0SMuUREUXDXj2cLlxldcPQRRvMHF0h4wZ8FuUcrJqyZNbIfuT2ym5vmiAz//SeCVY3dN+78vKJv7s3L//hVbHjH+a6qHaG7I1QuSRc1/6X034vwfax11jdwYe99qIGGRTrw+IxF3n1v5XR9eD+SoUZMzFe1tqLXsYLqnv4nBHfdARMBbsmvflrYe4C76Ljs4HWYm52Mivusetxr1vXg95V5w8kIqJGo0HPPvssPfvss+vpWlFuKSq7ymZFZVe5UTSpjaIoiqIMMP2b1GYlMYgVO84JkrHIHY34yxcVJka0CxyquIUSCZoGEEEsE30kcCx/ceMh613o+NMujENEtcOd9kb8HKuDfQHV+kao1gNUPYlzm4AZKeDv0lRSvlN2ta1UT2114iCgIAgoEhcuADOL0OrTEKqxQcbTVos3hIvQhF3GRETx0AS0ghMIjwsH16uLu4eJKM686nJi3KvnG2L3fAz3xnZhQkhgzJnl5qTAolxDYpzhEdauAd8tXZzndeAZ04B5S1MeQgzvhtYCn8e0tbTcpmrH9BZG6hDQOWm4xteZ+w/46/8v/3cP5eW//uOfWLuF8+DCJwOesiXElDWjy21f9+J/+j2re/UNv8s/bnqPgqjOH3M1sEk1GvxGHG16s1lTeCUEQ968NP2P3sRlE2EyhVHHwvT87ru9d0kYeJmcvsDnahzmOGyKZGLRipyvQ3evb/SKoiiKMsDog15RFEVRBhh90CuKoijKANO3NvrMWSJHFEjfNfhpYkQdmvN5IjtpEPJ2OWm/Dw3Ukbf/hSGfKgNRnqSFGvuEhEdkpIsbZJALQj6OCNytoojbHuswBzFGCnTycuJ+BqoAK4WNHr+n4XWtzrI9dzO4191OIjIUkKGhGr9utdDPXxzyuobxtkE37+3ktsNty5j1Lu3wiHGLl71LjgE3OdxrQkRkQJaNyDC2G0SmCXsFjNgrEAX+3KNCdpOmd22KRORJA2N25G2b0tzYHPauR52I2yhtx9vsGzCP3ZhHEezC+nDtGnfR684vh8azmyDzYj+AlycIuOyOxf4aP/Zf3ZOX3/jdh1m7l//mP+TlRERT5OsT2ujLF65rLX7tZs7jNa7IIGdwX1O5+3ZgRNo4kLUMZEuufzHcD++Z5FEEd+7w9/mbf/z7vJwuXmPtRrZ7+Q/Euhus3L/r8K7TN3pFURRFGWT0Qa8oiqIoA0z/qu5p2b0uFGqR0JWrzMu1NdL9DaLaCZc0VOtE4AMl1dY1UKc7J6Jugb4egusJFxIiQncr4W8VQ1SvUJ4bxhiDGjYQ2Q8czF2VCsyVHogpNb3VXOpcxwkMUWiIGpFw64n89WgIU1AAGvQuuusI+Uc3z4WrXN1nZrxKG90kjfTPxOtY8C71f1gAGQ+EqSFA9b8wLaEcdiOuTsdEM2hCCIXs2iU/B06YvBxEC6wlfuIaMVfxo8y3FkW+9dU5tqq674VcLVK2eAlVMhy+b7e/Bp97/C9Zu6Tt5fOn/+EVVte5OuMPKk2BoHYX65ErOXJSPc++i3A7hraZnAUWpRLWYJF559137c7LB/YNsbpz536Zlxcuv5WXh6QrH5jbjLg3wvxZsfb3dH2jVxRFUZQBRh/0iqIoijLA6INeURRFUQaYvrXRr4bALdoQy+LLcky5SYl521kZGhbs5iGEkI2FnUS65SEp2P26qbc1pqKPOthlGrUaq6uF6DYnXADR/gp7DIycD2hnZZhewj4A0QdzPynMo2P/V5bpJCkFQUDdjM9LANcjC+ScwfVueLkwwjaO9ks57ZaFPGYVsqEvixDTeMiyJhbuE9wDwLtnMZtlSGW2P8BAM/HOAafLRIhRBy6BIYSwjev8HsL5lntUVt0KjbqGrgkW1lu6nQE1uLwP3TXB6qJP/rO8PDrMM7K98h9/lpdnznrbtUm4+2fVUsMkDezwBe9qaBjId90KmQzgnh0Z8XPwnnvewdqNNvyYz/7xP7O61vyMPwDXTnkPofutC+QemBX3unW8pusbvaIoiqIMMPqgVxRFUZQBpm9V98u6O0e21GmiOjKQq2hZ1QfT+DMto8gMh0ndhPovwkx0mIVIhP+KwgzquJtPwFRIYpBML1s+C6gmlSoqpr2tmkmmuecDyVa+t0bG4ywlGZnAUtzmKucUosR1Yz6XIVzTACLqmVioSatUkix7I157DjP9SHdK0N07yK7oOjzLXQDjDYUbIaoapWSwbJQghKk0IeCXExnAHESsdOiGKr5oXPPjigLuvhR1lr+betetjS5cnkLCv5LAms2QX7eH3rU9L+/87x9ide9/l3dJ+/f/8fW8/Jv/9Ves3SK44UmTDlvUUD0vhbDcGkkRjHl4nLvG3fMOn73unrt95DorXKP/8bQf89LMJVaHQ+T5Q3kfKa6pqcgAubKuu4LNrBx9o1cURVGUAUYf9IqiKIoywPSt6t7AvzLWutlbNlvrLnGuuhe7dlFNKgaZYVvcxSwjy1VEKCvoVJGyKjkQvlW2tBOmkpc5hILy34LZyq5RVd1LQjIUUOK42j1te/V31OVzhpHhMIFSIfoXar7FtKdZ7+sgIyuiKSgU+m408YRs5z7vE6PhBQ2RdAZ2DMsN/8xphpWFaQzls5DXChKK4M56GUESVPfx0DZWN7OSECXTpDY9kZetAzvQO1GNrocYRO19+0ZZ3Z3/zZ/n5X/+Fz4xzqu/eD9r97M33sjLv/vHc6wuuexV3J2FpbycifUexIIaw/we3bPLq+vfuZePcXLPjrz8wft25uWlyXtZu2f/r9/PywtTv2Z1GdyjeOfJ+6QLYmnaS6xubMW0lxZsEuXoG72iKIqiDDD6oFcURVGUAUYf9IqiKIoywPStjX7VvU5G7mLhgKrM2K53mYiYPdwVjIhyDCUnY/ZAYb8H9yIcvnShwAhiRtRxVzaZYQ/LVbsY8IuWZ2kq+4j8mPxEtjJ3aqLn1OIaBUFAjeYI+/tiy2fvSpI2q4vI+ywFYHsr2OgxYpwwL2cWP+f/Xhd2+OGad0+ry8xYEPUMA+0l0s6PciEEIIVBtkR0wC4MGu+9QNxDOCrpXlq2b6Qu3PAmMDtenbtKBSuZ7lwgfcWUXqALcZgJt7brSF8pPzIChvO/vMu7sd13YIy1+9/+1V15+erVDqtbuuLvqcuzl/PyP7w5xdr9/u9fy8ujxPv4i3d5+f/oR/4Fq9tzp4+AV2828/Iv+a1MDjI2yv1gFqI6GriJZIa6DO63pMtPENplmz1GiHw79I1eURRFUQYYfdAriqIoygDTt6p7Y5ZVdlaEYWLKdKnCAzUJJnEpuNOxTgrhxXqOp9AMTl1Ug/d2mzNGqufXZoYomAbK1PVSTbTWEIBrxIkPpWm6clpNaoMEYUBBENDw0DCvgOu/tCDmMvEuNA5kXlqu8MLJecdAZE1Qz4+KhElDgb/tgy7X/1tQE2YQkasm1POoPg9F1MgaRMqrC1esRZDdFqiAnfiimGxKukc5XBLgY3UTs3boVliQUCv+ryzjEiKXkBNzuQRx3DqNpvzUTQXvjJqIrjc5PtKzTEREd/uiJa9m3/YGV///5mev5OWJce5C9x4wFUy+ex+rCyL/vVkwQJkzCu4VK+Qa3TnZM0S6ysHnmjX+Pa+ZFhERpUaYUCrQN3pFURRFGWD0Qa8oiqIoA4w+6BVFURRlgOlbG/1bb14iYwwFwjUIQ3ZGIR8+C6kJ9rkqFyVpNw8hE1GSePtiHPFQiTxkKa/DPplpU+wpYPsIpBUR6gIZOrfUJi6zfEH/a7XJr8PevuoepTZ6gXFExlGjLm8vHyo27XLbdTf1bj7oniazuqFACfMlNWJvVx2Pff8Naf6DkJpp0jszFhFRBHJtIm6zRVtjIrL0WXRZirn7UgT7BSK4H7rya0L/qfAjzMBlD92+bJdn2Evf8m5VNl5gdTMzc8t/V99Qhp2ZJmuXiLbtZ38PYJ6iVIYNvo4NP7cYXGlH6nx93rF3V17eNn4HqwtrbFPVms5VyLCK8iqeL8y2j1XS/RncXGP5DFx5Frl1bDDRN3pFURRFGWD67o1+9e1Q/l/W96rDn0slKZJXPod14pcUviGw8hoD0xD/IcjihYg3EwySkImfXCl7G5fBbtb2Rs81BmtEzin7VSs1I9XXaqux+v1X5WbVK2EVfAu2IhG6LZW7ck1P1W5fDJ6Tyi6gzsnd9CWasELipoox4huO7J99rmTs8rjQv+29Bsh27FxZ7/le/b/K7vL3n5tf1nyk4RyrX5z3mpn5FteODM9h2+tLeHMrWZifZ8fdtvcsadd5wpjFJS/oc3N8DoIQvESw/0UuO0m3lZezrHwNQJGUa0UCmrYwFJqqFW+Y1f+vRXb77kE/v3JRuolmldpszM/P0/j4+Ns3HFBWZXf6wgUiIrrw1lu39fyXofyn23rmPuXa7Jqbquwuy+6df/7ABo9EWS9rkV3j+uynrLWWzp8/T845mpycpHPnztHY2Njbf3DAmZubowMHDvTlfDjnaH5+nvbt21eZ1nbQUdntjcpu/6Oy25tBkd2+e6MPgoD279+fq03Gxsb6boI3kn6dj638NrSKym41/TofKrsqu29Hv87HWmV36/6EVRRFUZQtgD7oFUVRFGWA6dsHfb1ep69+9atUr9ffvvEWQOdj86DXiqPzsXnQa8UZlPnou814iqIoiqLcPPr2jV5RFEVRlBtHH/SKoiiKMsDog15RFEVRBhh90CuKoijKANOXD/pnn32W7rrrLmo0GvTAAw/Qz3/+840e0m3h2LFjdP/999Po6Cjt3r2bHnvsMTpz5gxr02636ciRI7Rjxw4aGRmhxx9/nKanpzdoxIpEZVdldzOzFeV3S8iu6zOef/55V6vV3He/+133q1/9yn3uc59zExMTbnp6eqOHdst59NFH3fHjx93p06fdL37xC/exj33MTU5OuoWFhbzNE0884Q4cOOBOnDjhXnvtNffggw+6hx56aANHrayisquyu5nZqvK7FWS37x70hw4dckeOHMmPsyxz+/btc8eOHdvAUW0MFy9edETkXnnlFeecczMzMy6OY/fCCy/kbX796187InInT57cqGEqK6jselR2Nx8qv8sMouz2leq+2+3SqVOn6JFHHsn/FgQBPfLII3Ty5MkNHNnGMDu7nH1r+/btRER06tQpSpKEzc+9995Lk5OTW3J++gmVXY7K7uZC5dcziLLbVw/6y5cvU5ZltGfPHvb3PXv20NTU1AaNamOw1tJTTz1FDz/8MN13331ERDQ1NUW1Wo0mJiZY2604P/2Gyq5HZXfzofK7zKDKbt9lr1OWOXLkCJ0+fZp++tOfbvRQFGVdqOwqm5VBld2+eqPfuXMnhWFY2M04PT1Ne/fu3aBR3X6OHj1KL730Ev34xz+m/fv353/fu3cvdbtdmpmZYe232vz0Iyq7y6jsbk5UfgdbdvvqQV+r1ejgwYN04sSJ/G/WWjpx4gQdPnx4A0d2e3DO0dGjR+nFF1+kl19+me6++25Wf/DgQYrjmM3PmTNn6OzZs1tifvoZlV2V3c3MVpbfLSG7t2qX37e+9S135513unq97g4dOuR+9rOfrelzzz//vKvX6+573/uee/31193nP/95NzEx4aampm7VUPuGL3zhC258fNz95Cc/cRcuXMj/LS0t5W2eeOIJNzk56V5++WX32muvucOHD7vDhw9v4KgHD5Xd9aOy2x9cr+w6t3XldyvI7i150N+oP+Y3v/lNNzk56Wq1mjt06JB79dVXb8Uw+w4i6vnv+PHjeZtWq+W++MUvum3btrmhoSH38Y9/3F24cGHjBj1gqOxeHyq7G8/N8IPfivK7FWT3lqSpfeCBB+j++++nb33rW0S0rAI6cOAAPfnkk/TlL3+58rPWWjp//jyNjo6SMeZmD025BTjnaH5+nvbt20dB0FfWoHWjsru1UNmlvK3K7uZiPbJ703fdr/pjPvPMM/nf1uOPef78eTpw4MDNHpZyGzh37hzbxLLZUNnduqjsquxuVtYiuzf9QV/lj/nGG28U2nc6Hep0OvnxLVAwVBKFITuO49jXRX56sixj7bpJ4tvBZ4iI2O9h+D6h+KVc9buZN+VzEsKvN7PGlxA5r9bisYF2fFT4uflWq/Ico6OjaxtMn3KzZNeYkIwxtHs335FrrZehLLO8M+ePLcoMcflshLW8HAflEjQ6viMv7xTjqNWH8vLszBVWt9ReyMtRo5mXg3qTtWunfrxLnUVWF4O8pp2E1bkk9eXOfF5OWnOsXTfx82otn6su3IsdKPM7lCiz5WuJWRmjc46stSq7KzK3/55JCoKAXSciohjWnEiIXQTrB65NgZBPC4dO7AM3xl+rAMsBl38K/FrbFWuyTb2s1XEcoVjT4Nyid4rhiWhCsbga37rT9fNjxdqawKEVY4ygDseViacBWx+E/K+KdWYt/fb3f1yT7G64H/2xY8foa1/72oadX6qp8Lis/LZ1az1X5biq6nxlxVrPEI8V0b8pKa+PrabyK5NdYwwZY3qo0/xdXvg9i8cWFz3eR1CxkCIh/ICNIv5DNIbjKORLQAjHWBeIPkJ4rIap6AO/Z8i/qMugDr5LJuYqQFla6z1KJNpRKcW2KrtEy/IVhAG5jF8PfICL5yZ7geEPevkw9+Xrf9D7Y/mQNg5kEtpVPejlAzCCTgsPeugzAznO5EsUHEqpCkse9D0ksrTKuPL7oYyb/qBfrz/mM888Q08//XR+PDc3RwcOHKAwCMgYw96wiYhi+MlVE3V4jO3iuMba1Wv+WC6kDhapJPW/2ubmF1i7mTn/BoJv/kREDn6B4S86ubZXXR6UHXkd8RgXe3nBU/hVaMXbzfUoTkJx42ZW/nzY3Nws2V190KepeMfESbdyccBjB38Xb7PWy6RcSAOQqLTrtS9Ls/OsXbcJMm55H2Pju/JyfXjYtxMCk3Vm/IHhmp6Y/JtVW/4IqHnNgAH5STP+BmnhnhLrGs112r4/mEf5Rl+14K7eKs45mp2doc3OTVt3HVHgim+puKaFMb+mch3OEesR9uHEqwfKroULnkrNV+ZlKxQ/dGvwlDZwDwXiXLiOReKHRCMGban4HP4wTeFcacplF3/oyh/jAa6ZqLmTPypQeIXW2cp1ZQ3c9N0n6/XHrNfrNDY2xv4pykagsqtsVlR2lSpuier+6aefpk996lP0wQ9+kA4dOkTf+MY3aHFxkT7zmc/citMpyk1DZVfZrKjsKmXckgf9Jz/5Sbp06RJ95StfoampKfrABz5AP/zhDwsbRRSl31DZVTYrKrtKGbfEj/5GmJubo/Hxcbr3nf+EbSjKYTs6nKha44aaig082CXaDecX+c7i2VnYJVyxKQrtKbIZ2x9QcRlwowoRUQibOKTdnJ0b7ZfCni5315fj2y2126wmETtKZ2dnt7QKcFV2a3GNjDE0MbGD1VftAme3oSvf+4Cb1KKA20brUDcU1/Py8DC/JvHwSF4O68OsrjHq2wa1Ruk4uqm3+2fZJVZnuv5eWZzhMpKCOd/Brm+bcDu/BYt71/Kd+/Pdbl7u4LSJu5mJvLiHVu+3ZV/kWZXdFdm95+7lXfdyt3gA8lmP+LqMNvoAbM1GrE2pw70n4nrY3l4n0hqNtvc4KN8wyPYxid1RMeyej8UzpgaHkdzEB522YP1sJXL3P+zLEnsMDNtkh4MUu+5hPuS6sbpvIcssnfnH369Jdjd3hAhFURRFUSrRB72iKIqiDDAb7kdfhjEBmR7RYND9rdLvDD8jVfyVn3FQVa4iR7cJqYbCOtSQF1yn4XOVrnbimGvhy9XBGGikoKrvHS+n8uwFv+31e3lsCczKv0C6pDG3SOHWiWpN9DV3vYNlEBGlmTDpQJ8JBA9pLc2wdqnzKvOaUNFSAG6pFvyGhZihO5TJuAkhbYHqsstV8i5BtaYfoyXpRujbtcUY8Xs75m/Px4iH0r3Um8P6ynK54QTkKCRH0iLIfLdNhZoZK6SLHpTlmsmuT8W6aIJy11Psgt1DlstPaPyxNEOk8D2jwnswmnx9XUPMRxfWyczyOgwuZNAvXy7PcOpMuvmt9Llm6yvpG72iKIqiDDT6oFcURVGUAUYf9IqiKIoywPStjd6t/Le+z3hYFOFCjPm1GTeqbPQYOjER4TuZ1wS4aDhpJ6w6OYuIKtyGsKrSZavsgIP2t6oZ32rxwK+XYCV8c2G+LNrey+NjV+FYeFzh1sNCh/q/F0J5YujZLu+jDck6akPenVJGIkU7J1nuetpd8OGiLSSnISIyYG/HvSaJOEEHkuZ0RB2OeK1bTQJxLVbP3V/OxRtPHAUUhkExTj1GlpfrEbrD4X4K6dFYkTyLhSuGcrMmQijHGPKb949SksANkIpzZWDbz2Qd7EsRpn2WQCzA0OYFz83ysOSsP/iikdwUAUb6TCY16+F1/nboG72iKIqiDDD6oFcURVGUAaZvVfdroyLta6UOmoXTquxzFZnlDlX5MolbBOoazHjkTLlLSaXqttwDkA9/jd+lpPHbslaTx5bHhERGKsyJDLrQFcw4FVHDAOaSaYTqHvPAQ/Qv4/htbnBgHR7t0IIZqtUBFbzwr2Ppch2PXEfgQhdJlTmo5NFNriv0pB2Yn26FCphcuSrXwBctms1UlnvhjFn+J6Q3goh30oyTwvVGVzsZjJCdR6jMuQkGsroZrqeuR7CeCndfvDci0G+7WEToA7V+JMZouM1UVPo+u2BAyjJpGuvtok3EzU54V9ZEuxijtwpdfbryvbO15ignfaNXFEVRlIFGH/SKoiiKMsBsOtX92hPXQLmgCl3rrntQT4b8N1EMEZVcwvuvOUzQgNHpeB8Wd+5bGeWpyrxAa6urBHfar23vstyJq1RTUOnBPEtVcpnqsjKqo1B3YwQtCxG5ElOuhg1l4o7UJ4yhlKv1kQB2YRvDI+PFoT/uCjVvAur6DMafyMQdOAcyahj8QUXy5mKtJWN6LStwIYVMhrAm4zopl92UeVwImQQ1NCaTMTL8pkO1Pn98BRA1L4RvMD7cZO1G6j6p01yXe0xdmffJmqx4D7Zw9yUOzRXlSW2M6AMT/eB6ILXwXF0v1oCVOTDrcBnR20RRFEVRBhh90CuKoijKAKMPekVRFEUZYPrWRp9HFquyUZJ00ehts7juaG/wQWmfjmM/dZk0ZSbe7oNRyDAz2HKf3g4Tym8Gh9JNS9ptb5y17VmQ0QGVEowjMsXsWtWg/R77ks2gnajM0K0TOpFueAn0HwmZrMfevh6DfMbCiIhR+DJhb21DVrqsyyPjpdDWos1WRgpkR8LOidtXqqYY5mM9V2IrEzhHgXPMHk3E7euxuFZ1WAsN2M2ljbsL62Kactv4UOxlrVH3Mii869gaH4rNG43Qn3ukVs/LB0YnWLvRqJGXOwd2srrfXLmcl+fn5llda8kv9CaFfQSWfxf2rZ1c13FvFNr8WTMKLO5lEfu3Vo7lHqAqdOVWFEVRlAFGH/SKoiiKMsD0r+p+5V8x3FW525lhLmPlrFnxjW4jIjIeRl5CVzsiruYyoP4saNxBJROJr8kTd1S5YvX++/L5yiM0XQ9lrl6aF0TglqW3KuGQVLqVWUWKwQ4rkmSgKQhUqCbi7m8hHNfjBqurgcqzBglFQnle59WVSYer510H3QPFEpOgixWq1oWMV5kvykKuFdzwepdZm95/3roEAVEQFN0/UX0sXI0DMOugB7EUfwNuczGFpXVR4M+FKngiombk5Wm0zt3mGkEMZd//aIcLxgi4eMaX5ljdn4X+fNkEP3dn1PdzYeZqXr7a4v3PO4wuye90kwh3wdVzieMU1PIyiuOq2cxKv9MK9I1eURRFUQYYfdAriqIoygCjD3pFURRFGWD61kZfSlVGpJLKavt0eXhc/JT8RRSgm5N0jYhqeTkGeyi6ExERJQlk/RKdoN2rKgsUDrL4Ldf6vdcYEli4WK26HDrnKjOubTWCoLdraEVeLN604pqi25N0+QzAvhjWhvJyvTHM2tUb3i7faEgbvZfXEK53EJYbwG3C3Yvqiz7r3dLiDB8/2DPZ3SDcrVxQeaP3/nPFRwLpHliZLnLrkmSWLBFlYn7w8kdiLuMYs7r5zyUivCxeoFjIUwy29wasnxMxt5Nvq4HsiksYgv27BnZ+11li7dqwP0AsyRSAq1wqBK1W92PZDfuyakPb+Tgw09883wOQZpAREl1NpfBCiF1TyPS3su7S2tE3ekVRFEUZYPRBryiKoigDTN+q7t3qPxH6CrP/rFV3UfAMKtOTFo7LXflshpnChLMUqFqi2KuhiHs5UeZ6RwlbHjNkzqsYf1WUPPzexWbX4W4n3WWqT7B1MYbIGLJVrnDiuNT9SzREdT2aiIiIorpX1zeHtvlyc4y1G4JsXpHw62RJszC7VsV9krqE1QSRPzYhV72GUBewe0j0DipUKeNlMl8d5VKYnVYOVWo5ZuU/6e6L2eBqYkGqYxRPzC4nXSZh6ZaWoFGIrrcD1szdIZfxYRCUeiQijdZ92/rwaF4e2cZV643hCT8m8a67tORV67MzM6zuyqVLedkseXPAaMjHYYb8Ql+DcRARvQkmqvbSYl4OxXMusJgBT7qYrw5eI+MpiqIoikL6oFcURVGUgaZvVfd5aDwRkQ531hdUdaW7cat28FaoBaGYCdU67tqVUaQwyUMDdjFnYrjd1KuaMssz41RFtWOJEdaoxq9U1a95A77wDFgZh6NiZKetTGBCMsYUdnozqnZ9Y3QxeUEgCQ2q6omImk2vJmyO+J32Q00eQSwClWcgdrcb5uIBZgJx7TEpSZbyyHhdSGpjhcuIATVnDDuoZeTJJPF9Zhk3DTiDMo9jJxINyytVZ9+TZXNp8Q0QPTDk3KEshLAbvRFzWyWaOIcCHhlvW93LwrbIm3tGRQzJkbof2c5dXCW/7R135uW977wvL+/Y/09Yu+a2HXk5Frv6lxa9Sn7uKt8x/4c3zuXl3//Dqbx8afqXrF3anfHjrXGPlxFQ5Xdg3tIl7hmACbECJ6/G8gWQHgNV6Bu9oiiKogww+qBXFEVRlAFGH/SKoiiKMsD0rY3eOfc2Nmdas2dcsfO1dYmufVnGozwlSTcvS1tsHdw8gsj/lpKuHDWISiYjIyVd338hap7pbXuUzhY3xeON2e/LI+Oty2A06BhDZIoOaQzxE5t7ylRlqAPXnTp33WkM+eM6ZJ4zpsva2Qzs36lwrwNbuYEIYlbswkiTVl5ut7l9sZv4/Sap5Z8LQhg/uN7ZkEfoI+PdnFxbyBba6OHPTrrQVV2BvA811iOODDljCmuJBXntijnD7SYB2OFj4XZWg70h24RtfAdkqRsFGRyqcTv/9j3evn7HO9/F6va97y/ycuMdd/s+JvbwcQx5d1MnNk5lwXxeHh/nn/uz3Xfl5eHtE3n5f/3PYv/KW7/Ly0mX3xvDMAeLsd87M2v4Hq0sxQ1i/NkTrNyLBbfuCvSNXlEURVEGGH3QK4qiKMoA07eq+zw0XkW0q8qocJVqu6rzQjIEUDumiVR/+jpMXENEFIEayoCqUg4X6+IaV8Pgd0tT7l5UNieFxAhrNWVUzUdFHbrXKZ6AlqdNul0yhNsZap2Zh6dIXBOCy1Kjzt3mhjBBjcEIXAusHZoJiiLj1fURuPLJCJU286r7JOXqSUxmYoQbVQCuU1HsxxuKuQpAeZwl0vUU7tE1Cp9MeHUdcSG3BNZlRNYVIuM5mLHMSFMiyigkQhIXZ6zhTZrbpNsZNG1C2LyhsW2sXbxjf16erfGIj7br5eLC67/yfY/8ibUbHfbq/zDk99Cl6Zm8PLfA75uAruXlO7f7PibvfZi16yx5uV6c/gdWNw73ZVL3c9CKuItqB54vgeH33ur0qHudoiiKoihEpA96RVEURRlo9EGvKIqiKANM39ronVl28yiGtaz6EBYxhGzF7xlp5GNeDRDmM+F2cgOfi0Juow/ABcoYP8Vph9v5Ox1vlzHii4VhuVse2uIDMO4Gol1mq4w412Ol7D3Gt3WD3GIEQdBb5kz5PKENFN0nZQY8tHnX6mJvCNg2O+Ce2V5cZO1QKlwisiZC/wHY6I0Ilesy33+ayT0k2AfPPhbWvE202fA2ylg6dGXeLt9p82XKwfmYe510+8I1QC4cq3Z+lV1GYIiWb2uRTQ2uaSEkN6xVKLuhcDuuwVwPiT0fI9C2BiHE6zt2sHbpxK68/GaXX7u5M7/PyxeueTe5rMXlc8eol8Ht49xFFV3qLi7yvSEz82/m5SXIPPfOHfeydnv27c3L167wvQjZkg+rG9T8vbHQ4O6lCwTu21ZmNl0Ngbt2I72+0SuKoijKAKMPekVRFEUZYPpXdZ9HxhNqooqjMr2+dA3iPfA+MCtdlnjVvU15dCLUXoUiAlQI6k/sPRTDTUG9Krpg2cJC8XPMoC+WLW+Hbi9ZJtyjmMry+pyNVlVzqv3sjZEudBVtA3ZJMTuhVJOCbAXydzrarrxaT0Z1xPtBWndQzgOIqFdQ14Iq31kRkS70Ksm4wVWXtYbPuFeLfbvQ8THWwI3QiPcRHAszKaw9UJhSwthwncIwpEi4dKGrZSokGUW0SnWPjpZBIauhb1tv+nPt2rOLtRu/+52+HPLoehfbXl4bO8HV7twV1m5oGExZscgaR/74wD5+7nfW3pGX09mZvLzUnWbtxnb5bzo6cQerA2stdQJ/ENaFearr+3DSNLay4Fa67wr0jV5RFEVRBhh90CuKoijKANO3qntjzIqKTobugt3oIqj/de0jF3pnm/Xeae+E6tsYHvGL9Qk6xDTx6hmZhMA6338gVKi46z4UatMM5gTVsMZKVTH8jhPjdayP69O9r5o5dNe9wIREJiDrpMysTbeMGs/MSLU17HAWqtHSKJIuKz12os5CMg0LHiOhMBMEDsfBZSuChDpxje+6jyJQSTK1u4i2BsmgjPye7P1kbTuPnTT6rX4flV1GPTIUhYZCIaoRLiVCJltg4sSd4HHBlujlwhaut5eTxpA374wO88h175rcl5fbzZ2s7s0WyMyli3m5HnPvlKQ9k5ddIrxCGt4cMBILmUnRTOrHFYY8qh01/PnqY9y8EF7FRGl+/Tc1cX/BBYht7/u6MvqrQN/oFUVRFGWA0Qe9oiiKogww+qBXFEVRlAGmf230zpFxMtaVjH5VmYYOmslUTL1t3EQ8Gh7LWFdwc/JT1xaud7bdgnbeJpkKGz0O31reR4CZ7WJuR8pw/NZHb0pTaa/EMYvfdGqbvHWY5a0khcCEePmlyyQ2c6XNuMQXMs/1Tksno9plEA3PWpGVEe2BaIt15SOJYrGMwP3WTVqsKoFMjA3I8ihdsZyIecfpvddBujNWoftLehOZ5X9OLq0G1xyZadA3TjNf17XcLczWIaKn2DOUgGtcAutnLRRueDXoY/t2Vtd9y/eRtb3dfEjY0OsT/nPtdILVOXCvi2t8jEOhb3t1aSYvtzrcRa8O+7mGhmRkSHANbfv7cEhs+TLDkAG1I+6NlSlINTKeoiiKoihE+qBXFEVRlIGmb1X3SZqStbaoFizolNZAhXZOus1xdzivTg8j4UJU97+ROkJ1v9T1x7W6T1ZQawhXi8yrdbKucL3DUHkR/1yMKlUotxxXk3aTcjMHc81AtVwhxw8mBrm+CHrKMpbZangdty75Sifdx8A8Y1MhMzGq7uFzwmSEURKtuJ8s/PYPIZZZEInETRG43kVSde/7SMUYDfiRZjCuVMgdmtCkee161O3SnWt1jovGwa1Np5NRFrpClE1MkFW4VqCux+U0E+sFqvUL1wPOZ2H97MwvsGbdrl/jZoXKfBHDzqW+3BRr955dPunMpRZfu6evvpWXL1/lyaC2171LHX5nNDsQEaWQ8ObaVT7+TopRHWFcHZ5AJ2uD67U0+a5MVkXA1wL6Rq8oiqIoA4w+6BVFURRlgNEHvaIoiqIMMOuy0R87doz+5m/+ht544w1qNpv00EMP0b/5N/+G3vOe9+Rt2u02/et//a/p+eefp06nQ48++ih9+9vfpj179qxrYEmSUBYEFInwhWjLvG7XGMxQl3IXEMwoZ8B+V6vJMJ/gQiRsKCkYqtAFInDSVcTb6J1wL0rQJirCj9bgODJ+D0AsbDYuAPtTkopKsMvj2OUYB8Qufztl14dvXjsoy4a5Mgk7J9iukzZ3GwoDCK8J7bKCjQ/OS+L+gjCltbrPPNdoDrF2Me6dKaZNzIs24/JkoQ5drNKU2zm7cB9a4afomHspjF1MOdqBjbDFbyb3utspu4udlMIwYCFviYgCWHPkvgZml4c5FysOdVK084t9IyALS0v+2i9c4pnn5i9O5eVrlsvk3IwvO+f7GBniMn7Hjh15eWaWr/+/PfN6Xh6e4ee+dzeE2IXvGXSE7M7M5+XOErfzoyt2AvNYuE+g/67ci7PyTMlulXvdK6+8QkeOHKFXX32VfvSjH1GSJPTRj36UFhf9l/nSl75EP/jBD+iFF16gV155hc6fP0+f+MQn1nMaRbnpqOwqmxWVXeVGWdcb/Q9/+EN2/L3vfY92795Np06dog996EM0OztLf/3Xf03PPfccfeQjHyEiouPHj9N73/teevXVV+nBBx+8eSNXlHWgsqtsVlR2lRvlhtzrZmdniYho+0qEolOnTlGSJPTII4/kbe69916anJykkydPrkvgut2EgiCgIa6d4aq2NWvdhOoD3Xq6XMGE7naYsUu6+YXg/hZHvP8EshKxbHuG9xFAn4Hh6iVUykg1GqoocRxRwN3wcByBE5H30ARCOEahUut/zeZ1cStl1zm3IqfCDIIejSKKG1PdY2ZBmfEQ3D/b7XlWR85ff0v+2ksXOkPeZBTG0m3O9zE0NAplfiOGYUkEPSJycN9IlSRm7CI0L2RSdd+BOnGPsqiXvihdtqpiaLoebTYLt1J2MxssZ14U5hI0C0lDEJoqcXkOhIwvQKTRhZSbnbY3xvwY4NQX3ppi7eq//bUvBzyz3ZjxEe9czcvMjm283faJkbx8h8iu+Bfv/Yu83PrDb1idARmN4YumbS6f81f9fdlanGF1Cfk+0KPaSTdXjEQpogjalbW84DFawXU/6K219NRTT9HDDz9M9913HxERTU1NUa1Wo4mJCdZ2z549NDU11aMXok6nQx3wf5ybm7veISnKmlDZVTYrKrvK9XDdu+6PHDlCp0+fpueff/6GBnDs2DEaHx/P/x04cOCG+lOUt0NlV9msqOwq18N1PeiPHj1KL730Ev34xz+m/fv353/fu3cvdbtdmpmZYe2np6dp79691ItnnnmGZmdn83/nzp27niEpyppQ2VU2Kyq7yvWyLtW9c46efPJJevHFF+knP/kJ3X333az+4MGDFMcxnThxgh5//HEiIjpz5gydPXuWDh8+3LPPer1O9Xq98PckTZhLhwdtGYX0Xb2rZMjGBOyXwr0OzUohZJCLwgZrx+qEEb1hemeeC4QdJu2iDZ2PsR57u0xNGBhDaIyZoxp1YW+1fsxLS8KNEEOMMpcnfjKWKa/EFWkzuCjdTtldpaf45uOpqCO8HrzOgp2w3ZovrcMNAU7s/0C7fNTgcl1reJe6JrjU1UIZ5haz45WH0a0JO23qvBx2nVcdpwkPZ9qFkKBJKkPg+nK2HkMl9rGJ3Otuu+y6HvkBcc6FTCa4vwTd64RczIHxfVbuyQB3uwastXMLPDTs7395Oi//Gdj1iYjefd9B3399Z16e2Maz3OH3vqfO+7hj5M/zcmsf/5F05ezvff9n/5SXp65dZO3mrk3n5Y7Yi9CCPSoL8OxpdXk79BsNxZrsQpHqbg2s60F/5MgReu655+j73/8+jY6O5vaf8fFxajabND4+Tp/97Gfp6aefpu3bt9PY2Bg9+eSTdPjwYd35qWwoKrvKZkVlV7lR1vWg/853vkNERB/+8IfZ348fP06f/vSniYjo61//OgVBQI8//jgL3KAoG4nKrrJZUdlVbhTj+kx3NTc3R+Pj4zQ2OkrGGHrHvjtK2xaCjzF1Pboo8a/YbXk1YWeRZxdyEL1pqDncs0xELBpYR0bGg8xhEehvhRaTRaGT0cuwrREZzBy6vsB3k5n4LEZhSrhqCLOKoTtUKlylkooUSaui45yjhXabZmdnaWxsrLT9oLMqu9u376IgCApZ13iktusDXeWCUKjkIzATgckorHEVbbPp3YtGhkdYXb3hXZHQdVOadAxzw5Tf05cDMQdJxwd5WVyYycsL8zOsXWvJ36MYJY+IyMJ9U8xK1xsZGS9dcfVyzlGro7K7Krvve/c9FIYBm+NlIOqiEF5sauBAmp0wgOKOBpfdu0e35eU9dV8ezrjJMSCvym+OcBX2vQ++Py/vfd/D/lx77mPtxrftysvthH/P+UW/Tl586zyrOw/udm/+zkfQu3D+t7wPMKnNJVz+r4DZeK7jnz2LIoIeymtI0py68v8so1++8bs1ya7GulcURVGUAUYf9IqiKIoywNxQZLxbSZIky8lBZAXTf4vdiKieg6IVwf8x4QfuPifiOxxRW5kmfPdn1vEn4MologRU6wnqMaWKH4/XaIZYPja964SaFKOqFTScTBVbrkiu8HFYd+KWrUYhUhtLXCP1n73nUlrWDEuEIdOGwOfgJ3zgZPS7AMpc/clMRiA/MpGJNXBumXQG7jcZ8a615NWaS4uzebnd5rvuE0jCVGE9YlTJY0ETnZuo+spyueHEtZDCMCzIHUYnNFImYRc4emBIkymaHGfaXC7+SD5gTxB4eW0Gwjxl/SNr8RqXmdf+fz/Ny+On/K74sR13sXbDYxO+vzr3Oplb8H0uzvIgQteu+N31My2vau9G/D6cAfm/mvCnw1UwSc0vedW9E8+oAFX3wpyarazK2TrCluobvaIoiqIMMPqgVxRFUZQBRh/0iqIoijLA9L2N3gq7NpXY0Imo1K4tbZlJx9uHrLDRY6ctcHnoGPmbyB9nws7H7Jls/HJPQcXvLGYsFdmLLLjswXgtiSxfzAxfPkbmKFUZsq2krr88NDcc51ampMK+vlYHOyfbgVubke57KOdgAs0Cfpu3Qa5DcelszUfDi8D2Kl2lCKLaJWmLVaWQLKXb4XXtNkS862JkPCG76B4rTo3fG3fxVHkKS/t9toki491OrM3IGFfImohRSuWeCWdhPwVuGXLCtgwuvZno5Grm7dXGernojPKodtsbEPEOXPKIiCzs8zj3lren2zenWTuC+0G6CrJRhbzSwn6BLmS9WxK2cnSpm23zvV1zC/57djEyqrzNYb3m1nsiuyLzhWdjBfpGryiKoigDjD7oFUVRFGWA6VvVfbriblBQ21GFWxjXG+XFLJHKD08oknWUqVet+E2ErTLLx8HUXKD+cYafK83AzSksV9Fa4qr7FJOehOUqNcsi5UlXL+qJVXejG8cY/w+5CVNr0B4jzTEgAOgORQl3ZTItUCeKWyOJfNsoALcp8V0CkM9MqO47ba+eTLrcBQpdSpkqt+D/WTFZmC2oIjJelbudV42qvCNZmhE5R1aoo4OgQpUMbnMsGqRQ3bNLJRYgVF1fA5X2wtwMazfe8XJ9h+XRSvdvH8/LzaYvy3wxKBXSQw0TfLWEy9scRGhcAnfrWXGCeTBdLbT4vZFhEjV2v4rIqOhBLXLYrJpXVXWvKIqiKAoR6YNeURRFUQYafdAriqIoygDTtzb6VboihGAdM3FJ9yVjelYFATdyxBD20ICbBBFRCDYaA7+DUmHL6YJ9MUmkfQUaw6ldyM/VBTNq0c0Hwo9KdxYYV1CxZ4F9TNgy2V6EqtCs2E4j3q4Rt+y+WOX+KQGZwetd6ILtQxFdMJdJ2OMhXEiz0NseTcRDjNaHwL0OXErlOCzIP+5JISLKUm/L7HQWRZ2/n60rl11XEXwZP1cdhbm8D3Wv600tjCgMQ+pYLjNdtCEXFiTMyghrsJhzDJ0bigs3FPlHUb0Gbp0iRHMGfqMXFrjr2rXuNd8ugTXSDLF2AbjXVYb1FjZw6/yctECOW8J9G+3tsXDLNvA9ca9VIgKpVy4VK2NcTwhyfaNXFEVRlAFGH/SKoiiKMsD0vepeZperM1V7RfY6UMEHNa6ejEEdJNWOAWZfYtH1uBonAzePVKhuUFsTxBBBT7jyOTh32hGqG/D7kB5E+K1DqKz61Wau041oLZpN1X5yrLUrmvhy01IB19tEUjTb9FaTFvvDcoWqVYTkipv+/mrGXl6tkE9+xOW6E2GfIqMiRrwDFXwh0x+UqxSUhciB7FzQh/Rezc+gwos4m5EzjpzjrmW4xhVjhMJfMHKpK7+mEu7mVm5ySUGeWikf41VwG+2C67K1XMWP30CadTEaZEPITB1dDNHDU8guc02UboToGm3QvMDHwe5zIbyr92y503gRfaNXFEVRlAFGH/SKoiiKMsD0veq+2+FRvUaGcAdlRbQ3VCGFIrQQHlfoBbFKqvhNiupPPo4ITQMRqO5ldDFQ5dccr0vaPrqS3BnMA0yVq2GZvlLqLkuiKslzcU8GVXOuCxl1C/5QiANnUVW3/uQ3Eqb8LFxTMPeEXAEYhl4uwgDHJExLoVflZpbfo5jwpgoUwcJ8VEyBqZhH2bKsodVd9z3ppimFLihEjGPGGLmUwBziKinzgKGGOxPrj4OkRhlEXYxF1hlc4uQYUzR3YrQ+maAn8uapMCxX3UtzpzVZzzqpWsdzd4V5ASNAosrfirUbp64uPGOieHn8WbZ25b2+0SuKoijKAKMPekVRFEUZYPRBryiKoigDTN/b6BMRGQ8pWNdKbNJVbkjSDoNt0fYSClteBLZME3BbScAi14ELnfguAbgvRcJWlKLNRtroXZmNco3uVhUUXMAqAuOpdbM3zjoi4yqjtkmXHLSbs6iOxdBdpX2ya8c+xzuJwf2tLk5QI28rRctgKmz0YeTlv93hNnnDoqrJ/SU3N7xilY2dTZXcL+HURt+LbmYpdMUslngko9oZcFHj0fAKk54Xregjwb1GYGsPxd6oEg/qwrhw/CYU+6ugLhZ1MUSuI8vX9QTk2oJ9PBVhU1OoK7iNluw9CcR84LBWbfJ526Cx0pfa6BVFURRFIX3QK4qiKMpA0/+qexEZjwJ0f+AY0ztCU8HPg31GqKFQdY8VQsWHQe6M4WNEdWAMDevCzSODaFPS2w3PZivUiyzHSYWaVyaYYFH/4ORSlWlL2mGdqj+vBxHVEU0kUJZOkCjJRZe0MpcfoRaEKFyRiMgVQq8BxL+LhIqfuTkJ61qAalMRlQ+jpVVG9kMXOuF6ymS5d7FHd2WmEpVdJL/HpSswXHCp7g5hTUaXt1QkxkE1fFiRgAvlOBMRSdHNORJjRLfmCNyajZBxXMbqIlopRspLRf9dSGSTQiIzOUaMolr1Jo1iLd38wtAbzqycqxWTQmZVda8oiqIoCumDXlEURVEGGn3QK4qiKMoAswls9MJ1Larn5aKNviTka4XLWKW7EnPlEHZOmDlneHYktNlEYPPhThJECRiL2l0eRjRN/HGWCiMo2GbQDtYV7hY2K3dzUm4dCwszZIwp7v9g4YTLbaDMeCdtpZj9KizcAT3LQchDaKLLalfIVqcLLqVoUxVRpAnClDrpA7hGL08Dllkn3jnY7VvloofFEhe64pnVva6MLAjIBQHVhFtbDLIWCRs9YVtY04yQ8RAzg4rzsoyccBQa6RoHWemKu1TwZFRGFEBWxoL3nj93ImLsdsFtjpnl5R4w5uUqd9n4PiOY0zAWj2LjnxZd4ZadZUsr/1cbvaIoiqIo1Idv9IUELuIYf8Xczjd6JxMXsKAIYmcxHGOCg8KudYs7i2Xe7vKd8LiD2FVuu7+9bytb/e1oPW+JFZe08rLxhEbrG9cqKIfyrSBjgUDwjZuDAX5S2QeT/4pgT2zH/Nplt7TmBt7oVXaXv/+qbMh3xaDMG4mIaaAyW74bvSLHUOG9N++j4tW/4I3EjvEblGvFjOHfFDUGVfdG4buxhnjfiHVd6LT8oMSMGzxX73GsXqu1yG7fPejn5+fZcbvdYse/fv2Xt3M4N8zVqxs9gtvH/Pw8jY+Pb/QwNoxV2XXOrfvBsQ4tnKc8aGQli4vX8vLF6evrY9BQ2V2W3TfPTm3wSJT1shbZNa7Pfspaa+n8+fPknKPJyUk6d+4cjY2NbfSwNpy5uTk6cOBAX86Hc47m5+dp3759hXS+WwmV3d6o7PY/Kru9GRTZ7bs3+iAIaP/+/TQ3N0dERGNjY303wRtJv87HVn4bWkVlt5p+nQ+VXZXdt6Nf52Otsrt1f8IqiqIoyhZAH/SKoiiKMsD07YO+Xq/TV7/6VarX62/feAug87F50GvF0fnYPOi14gzKfPTdZjxFURRFUW4efftGryiKoijKjaMPekVRFEUZYPRBryiKoigDjD7oFUVRFGWA6csH/bPPPkt33XUXNRoNeuCBB+jnP//5Rg/ptnDs2DG6//77aXR0lHbv3k2PPfYYnTlzhrVpt9t05MgR2rFjB42MjNDjjz9O09Max7RfUNlV2d3MbEX53RKy6/qM559/3tVqNffd737X/epXv3Kf+9zn3MTEhJuent7ood1yHn30UXf8+HF3+vRp94tf/MJ97GMfc5OTk25hYSFv88QTT7gDBw64EydOuNdee809+OCD7qGHHtrAUSurqOyq7G5mtqr8bgXZ7bsH/aFDh9yRI0fy4yzL3L59+9yxY8c2cFQbw8WLFx0RuVdeecU559zMzIyL49i98MILeZtf//rXjojcyZMnN2qYygoqux6V3c2Hyu8ygyi7faW673a7dOrUKXrkkUfyvwVBQI888gidPHlyA0e2MczOzhIR0fbt24mI6NSpU5QkCZufe++9lyYnJ7fk/PQTKrscld3NhcqvZxBlt68e9JcvX6Ysy2jPnj3s73v27KGpqa2VPtFaS0899RQ9/PDDdN999xER0dTUFNVqNZqYmGBtt+L89Bsqux6V3c2Hyu8ygyq7fZe9TlnmyJEjdPr0afrpT3+60UNRlHWhsqtsVgZVdvvqjX7nzp0UhmFhN+P09DTt3bt3g0Z1+zl69Ci99NJL9OMf/5j279+f/33v3r3U7XZpZmaGtd9q89OPqOwuo7K7OVH5HWzZ7asHfa1Wo4MHD9KJEyfyv1lr6cSJE3T48OENHNntwTlHR48epRdffJFefvlluvvuu1n9wYMHKY5jNj9nzpyhs2fPbon56WdUdlV2NzNbWX63hOzeql1+3/rWt9ydd97p6vW6O3TokPvZz362ps89//zzrl6vu+9973vu9ddfd5///OfdxMSEm5qaulVD7Ru+8IUvuPHxcfeTn/zEXbhwIf+3tLSUt3niiSfc5OSke/nll91rr73mDh8+7A4fPryBox48VHbXj8puf3C9suvc1pXfrSC7t+RBf6P+mN/85jfd5OSkq9Vq7tChQ+7VV1+9FcPsO4io57/jx4/nbVqtlvviF7/otm3b5oaGhtzHP/5xd+HChY0b9IChsnt9qOxuPDfDD34ryu9WkN1bkqb2gQceoPvvv5++9a1vEdGyCujAgQP05JNP0pe//OXKz1pr6fz58zQ6OkrGmJs9NOUW4Jyj+fl52rdvHwVBX1mD1o3K7tZCZZfytiq7m4v1yO5N33W/6o/5zDPP5H+r8sfsdDrU6XTy47feeove97733exhKbeBc+fOsU0smw2V3a2Lyq7K7mZlLbJ70x/0Vf6Yb7zxRqH9sWPH6Gtf+9qa+3/PPQfychTyXzFxGOblMAqgHLJ2AflfrHHUYHW1euz7r/vpieIaa9fJvCKk0+2yuvZCy9ctzedlZy1rB8OgJMlYVTf1x0vtDq9LUl/udnr+fblPf3zz9TZFRkdHb/1JbiG3WnZRPifGxlhdEJieZXnhHAhNGPPbNyqR+SCOebsY2on+Q/LHAXl5DcVLXhT4dmnK5XqpnfjyEr83FuHhsgB17S6X3duNyu4yf3XowxRFEc00muzvp3/zel62c7OsLrJ+rUI5YXJMRCG8dTYafN2tN4f954xvtyPk8jl2YIcf02LC6i7+41t5uZm087LJ+NpqQMbls6Ee+XulMTTM6ijyz4Bh4++9Oyb4XM2M+OPTv/0j72NhMS8GmZf5gMR9CFMn7/P60DgRLWthpi/8aU2yu+F+9M888ww9/fTT+fHc3BwdOOAf5lJYavClw4IgweIG6qdQOBegaiqQK1iIU+LLzvILgYugdXyhsxYuIIwxFD9MUPCbTS74mfWfG81E/3A+Z3GB5O0MzIeQdep2/CLbgR8SScoX3HbX30zdhN9Y7c7ysVve67HlVH5vJ7uSGG9Yw+XJ4gPX4oOeX7gM5DAR14PNPxYNX8wiePDHYhGBNZbLbsRlN4jgHjL8h0Tc9AvizuYQq7uDfQ775LKz2PIL9UKrI+q6vdsttVm79fy4Vdldlt10xUSdZXwdMHCtpJrYwHrERNAImYHPBWK+DdSxdSvi7TowLCffm6BPdm5xrxk4NkLumBw4+Tlfl8KDueXEGLtiYKwPX8Y5kIp3nKtQzGO4MubVsa9Fdm/6g369/pj1ep3q9frNHoairBuVXWWzorKrVHHTd59sZX9MZXOjsqtsVlR2lSpuier+6aefpk996lP0wQ9+kA4dOkTf+MY3aHFxkT7zmc/citMpyk1DZVfZrKjsKmXckgf9Jz/5Sbp06RJ95StfoampKfrABz5AP/zhDwsbRdZCo843waFde812NWGjQUNJIozXFjbBdcE2LrYDUGa9fVRuxstSX4d2TiM2XKBno5VGdLCryn0KaLZFG5Mz0nYGNlYxV82GV9s1YY7lvgfc/OUK41/+f5ZZev23f6BB4GbKriSCzXhFr1Y03uFfhdINLr6Te0NKjNLG8Hb4OZRVIrlh1PUoFY+DgO8BQLt/JPbAhDAHFKLdl7eL4bg5zO38o+N+81EN+ovFHpg2bApcWJJ2/mV7vrWWzl+6SoPAzZDdjqtR5mLqdPk1RVEzcj0tSEfJ3x3axoVco9iBvFrL5QK3EFmxgTko7V/sXWL7CMrXu0IdTEIK5+qIfU0p3A/GVsxBxX4Gw/ZB8Dq3sn+isLm7glu2Ge/o0aN09OjRW9W9otwyVHaVzYrKrtKLzR0hQlEURVGUSjbcve7tqAkfYOYaJ9QdqP10BlXOHOamLOpSUGWiqlqqSdCFLku5CompVEF16Ryfbta/9JdGFa1wAUG3DFOhDs6y3mrYIqiiFe4mFZ/Lz3c7nPQHgDqYSKSqOpMqvhWkmhSPpcsnyhOadKT7J16uzAr1J1NXopqR32sRc2XlY07BV74rZMPCmDOLbllCdQ/q/7jG14AQjusNP6dyrWhA7IuJbdzXedv48jjSLBsY1f3NIAvqZIKYkkyqksHVuOJ2Z+ZIuR45vPZc7kKUBeZpyq+ps/44dFxlzuUQZVfcQ2yNk+ZfuG9ImC8cmJqY2VW0S+EZJTX3+FyqXJKxUrh2r5o2zNpV9/pGryiKoigDjD7oFUVRFGWA0Qe9oiiKogwwfW+jl+51zHIhzCsY6hPtlWkmbTRQFkYUizZ6tMNYaecsd41De74ldAesGEeVq6B0Z0HbrEObT7m9Sdp5+OnKz83c6wohIXv1pZSB7nVS7nBqo9DbIQMjXHcy5lvJARF1TGZ4Q4v2UXHxUMrjml8ehuoiJwS4/zgh/234Mm2RfyHLcO9JOWjLT7rSBRDGDPtQXE3MaQxrgAx1vbJvIZNurVucWlSjKIqpGYp9ESBDWeHC4X4odCcWsgU27q4I6511IDY9c3/mZ5q/6mPF24D3EYPc4eV2ImQv+5SQ/wS/mwxlG/jBpHB/XV3krpsd63OdhGKyHDyjDNu/JeajwpbfTpbdueUzqQp9o1cURVGUAUYf9IqiKIoywPS96j4SaQSZy0bBPwGzumG6zQqVuXQBYWpxPJVUtYI7iMxsxzIUVahawX1DZhjDyEjWctUl+57wW01q7h20Kyj1pWvi6mfUVe6m0RBuYcNNH40wERHpmBsjqt2FapFH1+N6zQRUeVWXEXu0QoVqILpcvQbRE8U40rm5vByKlKY8uqJ0AfQE8F1kymn83nINwDBtXYh+h2UiojD24x8aEu5iK+6x2TrUn1uBgBIKiCiQURdTH/2zmL0OyhWuy4jMkpkydztQ8QvTj4G03JkYRwTrMKZfrozjJ64/mlfTREQ8ZX36zy22FolTLv8Bqu7Rfa8QvRV6KHFRLfHI7Ym+0SuKoijKAKMPekVRFEUZYPpedY8RsohElC+R8AC1364iClNVXCSmykP1v4zcBarvSIwRNitTGJRHp6vVh/04ghFW1wK1UTedZXVJAskVsvJd8RnMVSTUXK7koEobJKPkrc6J0233PRkd5de0OeSTs2QL86zOgIyyea7wpCiYWUquo7w8+DkjwtrVIZpcHXf+Li7xUaQg10K9ipHsZJKkFO5ZU6GexKiXMoqghfO5DD1c5M5liN7X4uN3K+aw9exc3hpkRBSQIemNgOZCse6iBwb+Xa4mbCe5mHfYnY4yY6SJK/W78wOhuzYQodSw/oV6vuooBFk2fF3HZwA4EFBWEV2vzERaRCbQQdOziIxauUr3Rt/oFUVRFGWA0Qe9oiiKogww+qBXFEVRlAGm7230deGihK41QcDdbtCGwi3jMjIY2uFl9i5fF4ONb1hkxhqKfKSwSLjGxfDzKYIZNiI6VxT7PqzhkcdS+N4iORLNtb2dambR25S6He4OIs/HwWhWFUZ66fYBOPF/hSNtlEtL3k4ss9W5kkuAWRKJuLtRmgoXqJJgeMWIhr6yFvPIk0PgyhahPKUVmcKEm59xXnbjgC8xWQD29Qqxwzor3VexT4MupMLOCd+z2xE2+pV1RGZY2+rEgaMocIUHA8pyccZMz3bFVuW26xAzcmIXMjOo9TJpxL1RtvFoPTuIHPTpiEe8Y3sRQAYjcOMkIrL4HCp4zWFWPRhZYSMNc4JlVeHKONaRvE7f6BVFURRlkNEHvaIoiqIMMH2rujfGkDGGapF0cfCqD+kyhjoa1GpI1zjsIxR9xNB2tDmal8cDruKM216tY7tCxQNnz9pe1dTJuKtIBu1CYRoIm16FOrSdq4bCunfTiuLxvByICE1zMzP+oCKyH5WofIt/UDXnepBTmaBbWMVc4rWR6nlU+Uu1M48a6c8uI5lFkZflZo3LVpyBahTU9VGNyz+qINOCGQLNa6yKHWNQvkLkSVbmtqsQI+VBxLZEqHlZEinhzqVmp94kiSXnLGWFR0P5OyFTR1eq+GHdlVUo13BNjVgz0W1Omgmkida3K6cqEqjsn6nKIVpp4V6Gtdw4Po+uNHRg+TiEdylFK/1rUhtFURRFUYhIH/SKoiiKMtDog15RFEVRBpi+tdHX4mjF/YAbKEIMayndaco6E3aYCOwkTbEHYFvNu7kNG2+XzGbbrF3W8tm7Usvd2jDDWA3KDcd/V1mwIRrH+0jgfJ22+J5DE3l5fMzbgw7s28/avTnsQ7C+eekiq0vBRY/ZqcQYmQtIyQyrh1Jv4phbIjG8srQbol0br4eVoUIBGb6Wu9uU2+iHGt4uX5fhoRNvewzgXhjduZOfHEKRzl2dYVXoDif3wOBRit9NuFvxOZChnaEO+w4q1oOC+xIpPbi6MEdhGNFSJiYI3c5K9jtICnuj2IeEfR3kjjJcCyvC11a4rpV/iMtWdcjv8rC0bFWUYXoxe2ko1gDmegdhbsVAUJYL91DuVr52x0F9o1cURVGUAUYf9IqiKIoywPSt6j6OQjLGsOxURMRzKlWq4zDzHG9WD/3vm9GoyeqGMq8Kt/Mz/rytFh8feBsN75hgdaM7dvtzNX2GulC4Mjnn1WHGctPAwpWreXnmGs901m57VVFn4bw/b8RVSPvv2JuXF4Uq7sol33/WBTW+cNngKmVWRWYlM1+mGcAY4YpraFW4NxkZz5a4pEnZxYSNUrXIAm3Bb/h6g8t4EzttcbkzGA1y2468PLRzN2uXLl7Ly+Ecl8808XdpJDKARXDvJSiTgTQZ+bLNeAS0xPXO9OdkGDI2eXKulttWBH7cklybmaYgCCkVcx6DaTFxMrNd7/dFKZ/YyolIiw7dOrF/scbjJZamAaZar8iUV9bu7eB9wt9FOzRDSJF08ODg88NV/CxJq8gkuGoakxEjq9A3ekVRFEUZYPRBryiKoigDTP+q7uOYAmPICd2aZQlYhAop650YoSbUgrjTvi60ztnigj/o+EhzjWEeuW7iHV6VueOuu1nd6G6/+7025KPrhQ2uus9gJ2va4aaBpZlZP97pa6zu4psX8vLcud/n5dlzb/JxgJp0cuIdrC7p+rqrM35Hvkl4lD9U5csIUKtVmW67Z0QrZqdUJuRgR0Lhx/XupVTNNO6ujyHRxkjMb3PT9glepOqysW27/9wuv9M+rIvkUtabA2KReKoD0SCDSER8hN31xvj5KSaXgd3JQo3sQK4D1p/YuV8RRXB1JjWpDafbbi3LkUgYwyQoFN5DFo/BBCXtIhjVznIzI36O76wvv2sKUe1Qtc4i9BWU6+X9V6jyLdt1z7JGlX7GSdmFRGwmgOeBPK1BkylfR7KVPjUynqIoiqIoRKQPekVRFEUZaPRBryiKoigDTN/a6GtxvGwrknY3KEv3AjRzRHDUENGJRsBGH7S5TTqFiHdxzfdR37uDtauB65qZ2MbqshEfka4F5w6Fm0QEWbiieJTVjQ2N+fFvu4Ofu+nrLsD4Fy/+ibVbnLqUl5vRCKvbMeFtrEsdnw2vW4jyhDY3bkhadRFTKycnjqI8+yLC9ztwylx3CpZl60rrYsia1QS7fJTwqIsE46gNj7Gq0Z1ezmtNHxlPvhKY0J+rNjzE6sIFvwcgFW5UmHkuML5O7vMoixRIxCMAOhZSbG0ZAYmIjEyrpxDRchS2IAjIkbTDw/wFfD29Lnu12ANwPVejMisda1cIoVfaS1Uiz7IYoZUuekLuAohEaQK8l4WMV2SAXI24aW3VDHBU2hVFURRlgNEHvaIoiqIMMH2ruq/XllX3zpVHP5JJLPCoFvqjkVqNtauB+rMjIt6xZAI7vEr+Ssyji1287KOBbbNXWF2z6/tvgbtatz3H2m0b9lHz9u3cx+ri2NctLPExmqZXm47v82r9ziLvP215t7zu7GVWN7zTf25iyKteL6N7IRFZcIORka7CcP3JFbYCcb1BgTEUhPz2cs7PpYziFqDbjanQ3cNUh0KF2gQ3twa65HRF0qWmv94ju3bxPka9CUneX4gD81fU4Kp7dLfLulJF68ccg1nLZtyslTLXofI1IMvKTUvcHFKiGlX3OkYURRQEARkRqQ0vhwzIlkGEQ4z2SSmXO0cyoh7U4Tpf4iZHVFyDeP+sYel4iZ1KJlPq2awwrtLzimPpHs5V+XhvlH+vwhgN//9a0Dd6RVEURRlg9EGvKIqiKAOMPugVRVEUZYDpWxt9HMUUBkHBEGEqfppgVQ2zcIkwnMG8D21rOzx71+jYRF7eMflP8vJZx8PXXrzgs7+ljvcRO2+/vzjn7eZJa5a12z3s++hem+FjJO/adGWO281rgbd1ba97m+rwdu6Gd+1Nb9tvzy2xuqFRby8egvlB9yciIuZt52RI12X7k2av4xizLKfWiqxTYACUoVcjsP+hS2Oxby/lQ40Gq2tgiM4OhKENufyP7PB2+eY4d+s0Ybl9lAH7A6I63wMTgptfIGz02CXLZCdkiJlDC7Z3KBPa4fmp0CVMutdlK+GyC2FUtzjBinudJARXsECsyVHkj7MU9l0UbPn+fghkWGZ2gBtRRFY3vF7Svdr0tvPLrSYYElfu60D5LJjk12gTt1R+D+Ec8Kx0FSGxZehxt/7wzfpGryiKoigDjD7oFUVRFGWA6VvVfRRFFAZBIfpdAFmOCu4FoO6IQf3UDPjXtJBVLBLqj+0QGezA3T4r3bUrXAU5cwXUpIaraJc6fowLbXAnMhOsXSvx7m9zHe4alyz4qHaUcde++a7/nrUxf66xXTx638KCNw10oExEFEPEshpkOqOQTyrOsXQJW82qtJ4sSluBVddQIwTUWq9OdyRU2kx1X64jbNS9un5YqDXN0iIc+OLwzp2s3cgOn6HOBVx224k398SxP1cgbWaoXa1xE0IEJgWzKNxXQYUemnIVqqmIUca1mujaJbMrlsulW1lHVHXPWY6MFxbmzlS5WoKcG3ApTRMuWyb2bphhja/JZa6cdj2uu2X3jZRdlrxOuu/hui4+tsahhNDQSRFMvJnXpv5+dTKKJrqQijEGK9dGs9cpiqIoikJE+qBXFEVRlIGmb1X3zllyrqieQC2G3B0awC5ejBomNS4YhSsQ6s+xbT4aXjDidyRHXa6GCiMfaS4UqvthSIZzz+SEr0j57vy3zr/l+w95Ypyk69U694zzOtf148rafhzbdg+zdtOwe7vV4cl7sq73DAiHYB7lT7+SZCvLf7D8/woRLassg6CoEmbeCQVVna9LIdJYGPEd7cNsRztXi6NppQ7eI2O79rB2qMmcWeAeHTiqkRF/bvl1UpkoBwhRdS/kycJ3i2qY4KZCNSzWAKbOZZ4MpV0UVfRViUi2MG7lX+X8VF0rtm298EHfTqzdlgkKJnspj3Yo+2fXmF3v8t35RTNZ+W73spQ3lcafon0Zyv47ByLKZQB1MhrgquV5PRZTfaNXFEVRlAFGH/SKoiiKMsDog15RFEVRBpi+tdGTccv/pI0PDHGmkNnIg5mxpPEOIwrVRGY7U/dTsgQfa8voRODmVyPhijI349tBdjw3xM/l4Mu1Ux55r516O2cW8OxgQ7H/bmi/HBvhbnghfDdr+KVO0dUr9WOMZIapinROq7ajKnewrUiapis2Zyl3/lpFcSg+hW49Xi4aEb9uMWYES7nc1UfH8zLa5eU+lPlFv/9jcYn3MVTzMtRd8Ps6ki7f49FKvHvg8BjfG1KH7Hj1BpfrxQWI0Gj9dwsL7nUeGV3MlbjUyUyXXHblnohiE8Xb6AtzbtBmLMBMcewzohlkcnM2YXWuLOSpTP5WtXUA26FcSFdBNibRCTwrCpHnmAtguY0eXUjJCrmz6FZb4RoKUUhl9rrV+15Ge6xC3+gVRVEUZYDRB72iKIqiDDD9q7p3jsi5ggsdqpSqHBe4SkYkYwEVkgl5/xnUtUDN0umICF/G18kkJCGoSiPj3aFaIsFHHZONCDVsEIM6dGSC1bWv+eQ4w7Effyfl6jBU2QZCzdOGyHgdUMtKV8FAHCPZippLRi/c6libkTOmoBbE6GKhcKeJ4FrVAi8XkbimlPjjsM4j0tXGx/JyBt1fvXaFtVsCtbs1IroeHqMGMpUJejBhDB9iAC6AcUOYk5a8rKXo5irmKoD7MJPJS+BON1XunyzhjVCNZhoZrxeG3PK/ikhtEnSBM7heizUnBZdhJxNklWih5VlN5fXq7RpXjH6H8iPOBwMpaPXXcC4ioqAiml8Ggo7JoDL5TZkHqbRfqOpeURRFURRAH/SKoiiKMsDog15RFEVRBpi+tdEbY8gYUwiN6cDGUXCvwzo0NUrvBGgXCNeLebDFhx0fHtSkS6xdFHk3p0S4SrUgGxyGMrRtnqFuBGyUbnGe1TWNt7+6Nt8fYDNv54zAZa/d4fZcC7b3WFiYMBxrG+Y4KYTAxZjD/Fq028vn0+x1EkM9nJCY/TuMYlZXBze6WgY2dBG6OIDsYE0I10xEFDS93GURhNfM+EWN4YaQ90ZQg+xyuI8g4fdaBm5+S+1FVmfqfhw25t+TwIaLbk8yOx63c4oQuOXRTfk4mBlVZBJ0aqPvxfLWKFfYM8EyvskPmd72anSnIyJyINeU8f1KzG6OrnEyBC6Vr/8lwy3a+Utbijrx7AmYrKx1HLwuhEyqEZTl98T+rZUbCUTmyzWwrjf6Y8eO0f3330+jo6O0e/dueuyxx+jMmTOsTbvdpiNHjtCOHTtoZGSEHn/8cZqenl73wBTlZqKyq2xWVHaVG2VdD/pXXnmFjhw5Qq+++ir96Ec/oiRJ6KMf/SgtQgCOL33pS/SDH/yAXnjhBXrllVfo/Pnz9IlPfOKmD1xR1oPKrrJZUdlVbhTjbkB3denSJdq9eze98sor9KEPfYhmZ2dp165d9Nxzz9G/+lf/ioiI3njjDXrve99LJ0+epAcffPBt+5ybm6Px8XG6954DFIZBDzcPGLyoq4O+aTdE69rV5NG5lq7M+APhvjR+1/68HN7zzrw8s8TbZQtenZ4J9eRliCiWQnawIcvV8yN1iJQmXfSsH/NwbRevy/z33DEB3y3hat4/nvmNH8cSz1Jmd/kMeJfB9HBtjrfL2v57O8d/F87MLn8fax1dujxDs7OzNDY2RpuBWym7Y8PDy2Yn4bqJrqKjoyOsbgjdaea9+6TU0g3v9hHvRvdwuUCXyRDU52km3C5BDd/u8IyKJvYDycANr90Sbpbw1aKaiN6HGfZE/+0rl/y4QLYyEb1vCVS7rUS49tHaYK65Yplrr5hEnHPUSVKV3RXZfcf+uygIgh5vgKBKLkS8Q3OMn+dURFPstvz6Z7rl2Q9ZAryC3n2NKnOm/q9iPY+/3m5zhWdU7wB6y4dwXwY1H0EyLbgoo2uoMCGsuItba+nq1ek1ye4NbcabnV1ekLZv305ERKdOnaIkSeiRRx7J29x77700OTlJJ0+e7NlHp9Ohubk59k9RbjUqu8pmRWVXWS/X/aC31tJTTz1FDz/8MN13331ERDQ1NUW1Wo0mJiZY2z179tDU1FTPfo4dO0bj4+P5vwMHDlzvkBRlTajsKpsVlV3lerjuB/2RI0fo9OnT9Pzzz9/QAJ555hmanZ3N/507d+6G+lOUt0NlV9msqOwq18N1udcdPXqUXnrpJfrbv/1b2r/f27T37t1L3W6XZmZm2K/L6elp2rt3b8++6vU61ev1wt9XHZSk/YMfcrtGAnaZxa63De4Y5mE4h0a8fXrxCt+ZOnflov/cXv/dtsXcphrumsjLS8IWe2XprbxswW46JOwo47t9n23herd01dvK09YMqxsL/PexXW8PnbvIQ512Fr1LYCyyiGUBhF8EW2wgDHDW9ba/ERElKyF9N5OL0u2Q3RLvOgpBTiIRvtK2wZYN4ZDrY+Os3eiu3Xm5NsTP3ZmHPSBwozTqXP6TxJ8rFNnxArCAL0HmxSzhXyiuQZjnWPSBpvFCbFv/OWv9OEzI+8BsdqEMgw1ueWuWPDGMVfdSld3eyPCqFuzETlwPw0KMg+uy2HcRgtuxFSG/0fWu8u2TebjJtar3tay6wgX3beijwsNQ/F24EcInQ+F6HUPYW4IQ6JEIRd0F12ubif0x+Rq99qyh63qjd87R0aNH6cUXX6SXX36Z7r77blZ/8OBBiuOYTpw4kf/tzJkzdPbsWTp8+PB6TqUoNxWVXWWzorKr3CjreqM/cuQIPffcc/T973+fRkdHc/vP+Pg4NZtNGh8fp89+9rP09NNP0/bt22lsbIyefPJJOnz48Jp2firKrUJlV9msqOwqN8q6HvTf+c53iIjowx/+MPv78ePH6dOf/jQREX3961+nIAjo8ccfp06nQ48++ih9+9vfXvfAgsBREDiKY6F0AFVLlol8QhjhLQMXIqF2HK971X0tmGV1C1eu+jFM+40s2+58F2vXmPBRyToii9gYuPYFYEKIQ+6GZ8Dtb2aWq91N6I+zazwqXwxqtM6MH//CpYusnSXvvhQMT/C60J/bdctV9xi9yQoVVTfZPKr72ym7q/MRCtUlRr8LRMQ7B8dxzavaGyNcdW+gz1S4nQWhv3atBZBrw29zA8c1YdJpsMyL/t5bXBBueKi6j0QGPOvHlQjZQBNSCqYlmR0P+zQVDnVcxVyhhpWq+02Uve52ym6OzGqIJpiCKQUzecJnRPTHENYWl4hrCjJDtjyDHIt4V7h0va+lNP+yTxQy2/Uuy/5Zxj4hn2Hs1fNRXZh8IzBfMBc6mSXUH2dWuMeujHk92evW9aBfy03RaDTo2WefpWeffXY9XSvKLUVlV9msqOwqN4omtVEURVGUAaZvk9rEYUhhGBSSK6B6Uu6YZCGJYHfvolDxj9S8qr0+zBODJNe8ivLqH37r2wX8V3UDTjW2Zz+rGxv36lYTTviyUKF24XfWSMRVPNsCb15oBVyt765d9mO8fCEvL7ausXa1cd9nIDwP2JTAbvpYqJszUEtlInpfmm4e1f3tJAyWIzrWxI72GKN1iahhuNM+sT5q2OI1fk2TxNeFYte0g139hqnT+fVBjWpUCN3li3VQs2di52+H7QoWCUpA9VoTZq0I5sSC6r7VEsl7yI8/DvkYU6a/XZtaVu7ILtuhvdUxJiBjgh5KcFBVF6K4lfQlFu+o5lXaNhVeQJmXa0MYjVPsaK/YFV8qCfJaw3NDfhNUycvd9Ex1j48a+F5ERFHDr7WRkH8CUyjeJ1ZEr0TzlxEq+tXEPuuRYH2jVxRFUZQBRh/0iqIoijLA6INeURRFUQaYvrXRkwuIXJDbI3IqXC8MRuSCtEeLCXdPazf81x4fH2V1tuttJcmSj5p36Q+/Ye06kKFubG6R1U3s3ZeX60PeRiPdkCLrB7w0z92XgiveLp9ceovVXX3zbF6em/U23PGx7axdNuzd/BaF+9ISROyz4NoRiHRR6GVjhMtNZ2Wu1NwpWImMJ90/0wBkV7r1oDCDO026xCMmtiALYdFtCOyo4AIVChti3PQ2xES412HbACLeFWzaaP9OuY0+hghojRrvPwBbZNz090a3zbOZYfS7QLrN2d4udVU2W7nHQOmNo2U7dbXnWrlLGrN/Z9L9y38uqnPXO7I+k5vt+PUaXfeWj6HPwsIDsoD3RoXsFrOjVmS9w3sKIt6FDb7/ycT+2Il3aYvZ/cDF0FoZiRD3IohIhCtjrvAaLKBv9IqiKIoywOiDXlEURVEGmL5V3ZtgRRUv9RPo1iAT3oA7B2pCOil33bna8urPeJS7tTW2+6Qhcc2r+1qL3M3pwlmf7enKlXlWN/wnr1qPY6+iqonkHwTR+5I2d6/otHxks/mFGV4HLlZ1SNDTrfMoam3n1UtLHW5e6IBKDCMsFdwZMQmJiAClKvveOOuIDFEq3M7QtSxscHU6CizOq5WuTKjSy+T1ALUjyJZpcbV40PKyEJjeakEiYmazsCaiOoJcx8LNL2j4cbXrIuIdmoYq1ME4H/I+j0CFmuL8VCTAUlldG44cuUIMzGW3u1Xk2yHWoaq6oBYnNLvyutqQV92ncJ9kXS67NoG1XLikcbU7qr7LzWQFBzr4Lka4GhsYVwQyL5P8pLC2ZuLZYzA5EKzPgQwAi8mBgt7mBXWvUxRFURSFiPRBryiKoigDjT7oFUVRFGWA6VsbvTOGXK9QjK7cdhHg7xaok14eCx1w8Qmvsro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      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot first few images\n",
    "for i in range(9):\n",
    "        plt.subplot(330+1+i)\n",
    "        plt.imshow(x_train_color[i])\n",
    "\n",
    "plt.show()\n",
    "plt.savefig('sample_images_color.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot first few images\n",
    "for i in range(9):\n",
    "        plt.subplot(330+1+i)\n",
    "        plt.imshow(x_train_gray[i], cmap=mpl.cm.Greys)\n",
    "\n",
    "plt.show()\n",
    "plt.savefig('sample_images_gray.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utilities\n",
    "\n",
    "Für mehr Komfort werden im Folgenden einige Funktionen definiert, die die Arbeit mit Daten, Plots und Modellen erleichtern."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotConfusionMatrix(y_true, y_pred):\n",
    "    y_pred1 = np.argmax(y_pred, axis=1)\n",
    "    y_true1=np.argmax(y_true, axis=1)\n",
    "\n",
    "    cm = confusion_matrix(y_true=y_true1, y_pred=y_pred1)\n",
    "\n",
    "    plt.figure(figsize=(14,12))\n",
    "    sns.heatmap(cm, cmap=mpl.cm.Blues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Vergleich mehrerer neuronaler Netze zur Klassifikation von Graustufen-Bildern\n",
    "\n",
    "\n",
    "### 2.1. Standard Multilayer Perzeptron"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zunächst testen wir normale Standard-MLPs in verschiedenen Ausprägungen. Wir variieren verschiedene Metaparameter der Modelle, wie z.B. die Fehler- und Aktivierungsfunktionen, die Breite und Tiefe des Netzes und inkludieren Dropout in verschiedenen Stärken."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.1. Vergleich verschiedener Dropout-Stärken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer3_mlp = Sequential()\n",
    "layer3_mlp.add(Dense(256, activation='relu', input_dim=(32*32*1)))\n",
    "layer3_mlp.add(Dense(43, activation='softmax'))\n",
    "\n",
    "plot_model(layer3_mlp, to_file='3lmlp.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.2. Vergleich mehrerer Aktivierungsfunktionen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1.2.1 Aktivationfunction - sigmoid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.3. Vergleich mehrerer Optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.4 Variation von Batchsize und Anzahl der Epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create function to compile and fit a model with different batch sizes and plot the results\n",
    "def fitAndPlotBat(x_train, y_train, x_test, y_test, batch_size):\n",
    "    # define model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(784, input_dim=(32*32*1), activation='relu'))\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dense(43, activation='softmax'))\n",
    "\n",
    "    # compile\n",
    "    model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "\n",
    "    # fit model\n",
    "    history = model.fit(\n",
    "        x=x_train,\n",
    "        y=y_train,\n",
    "        epochs=50,\n",
    "        batch_size=batch_size,\n",
    "        verbose=1,\n",
    "        validation_data=(x_test, y_test)\n",
    "    )\n",
    "\n",
    "    plt.plot(history.history['accuracy'], label='train_acc')\n",
    "    plt.plot(history.history['val_accuracy'], label='test_acc')\n",
    "    plt.title('batch= ' + str(batch_size), pad=-40 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(86989, 1, 32, 32)\n",
      "Epoch 1/30\n",
      "1360/1360 [==============================] - 27s 20ms/step - loss: 1.1547 - accuracy: 0.7224 - val_loss: 0.7531 - val_accuracy: 0.8086\n",
      "Epoch 2/30\n",
      "1360/1360 [==============================] - 27s 20ms/step - loss: 0.4481 - accuracy: 0.8933 - val_loss: 0.7600 - val_accuracy: 0.8013\n",
      "Epoch 3/30\n",
      "1360/1360 [==============================] - 27s 20ms/step - loss: 0.2998 - accuracy: 0.9246 - val_loss: 0.7651 - val_accuracy: 0.8160\n",
      "Epoch 4/30\n",
      "1360/1360 [==============================] - 27s 20ms/step - loss: 0.2255 - accuracy: 0.9399 - val_loss: 0.6190 - val_accuracy: 0.8640\n",
      "Epoch 5/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.1804 - accuracy: 0.9516 - val_loss: 0.7003 - val_accuracy: 0.8451\n",
      "Epoch 6/30\n",
      "1360/1360 [==============================] - 25s 19ms/step - loss: 0.1533 - accuracy: 0.9579 - val_loss: 0.7953 - val_accuracy: 0.8507\n",
      "Epoch 7/30\n",
      "1360/1360 [==============================] - 25s 18ms/step - loss: 0.1278 - accuracy: 0.9639 - val_loss: 0.6709 - val_accuracy: 0.8742\n",
      "Epoch 8/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.1091 - accuracy: 0.9686 - val_loss: 0.8909 - val_accuracy: 0.8370\n",
      "Epoch 9/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0977 - accuracy: 0.9719 - val_loss: 0.8588 - val_accuracy: 0.8368\n",
      "Epoch 10/30\n",
      "1360/1360 [==============================] - 25s 19ms/step - loss: 0.0887 - accuracy: 0.9734 - val_loss: 0.7966 - val_accuracy: 0.8596\n",
      "Epoch 11/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0819 - accuracy: 0.9748 - val_loss: 0.9042 - val_accuracy: 0.8579\n",
      "Epoch 12/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0738 - accuracy: 0.9777 - val_loss: 0.8142 - val_accuracy: 0.8561\n",
      "Epoch 13/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0693 - accuracy: 0.9786 - val_loss: 0.7793 - val_accuracy: 0.8852\n",
      "Epoch 14/30\n",
      "1360/1360 [==============================] - 25s 19ms/step - loss: 0.0667 - accuracy: 0.9796 - val_loss: 0.9729 - val_accuracy: 0.8661\n",
      "Epoch 15/30\n",
      "1360/1360 [==============================] - 25s 18ms/step - loss: 0.0610 - accuracy: 0.9813 - val_loss: 0.8497 - val_accuracy: 0.8679\n",
      "Epoch 16/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0580 - accuracy: 0.9819 - val_loss: 0.8906 - val_accuracy: 0.8796\n",
      "Epoch 17/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0587 - accuracy: 0.9822 - val_loss: 0.9275 - val_accuracy: 0.8711\n",
      "Epoch 18/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0531 - accuracy: 0.9834 - val_loss: 1.0020 - val_accuracy: 0.8660\n",
      "Epoch 19/30\n",
      "1360/1360 [==============================] - 27s 20ms/step - loss: 0.0505 - accuracy: 0.9843 - val_loss: 0.9656 - val_accuracy: 0.8711\n",
      "Epoch 20/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0473 - accuracy: 0.9853 - val_loss: 0.9729 - val_accuracy: 0.8694\n",
      "Epoch 21/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0444 - accuracy: 0.9863 - val_loss: 0.9499 - val_accuracy: 0.8835\n",
      "Epoch 22/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0461 - accuracy: 0.9858 - val_loss: 1.0594 - val_accuracy: 0.8739\n",
      "Epoch 23/30\n",
      "1360/1360 [==============================] - 25s 19ms/step - loss: 0.0439 - accuracy: 0.9867 - val_loss: 1.1423 - val_accuracy: 0.8729\n",
      "Epoch 24/30\n",
      "1360/1360 [==============================] - 25s 19ms/step - loss: 0.0446 - accuracy: 0.9865 - val_loss: 1.3847 - val_accuracy: 0.8288\n",
      "Epoch 25/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0356 - accuracy: 0.9890 - val_loss: 1.0929 - val_accuracy: 0.8831\n",
      "Epoch 26/30\n",
      "1360/1360 [==============================] - 27s 20ms/step - loss: 0.0422 - accuracy: 0.9871 - val_loss: 1.0052 - val_accuracy: 0.8838\n",
      "Epoch 27/30\n",
      "1360/1360 [==============================] - 27s 20ms/step - loss: 0.0368 - accuracy: 0.9883 - val_loss: 1.2222 - val_accuracy: 0.8654\n",
      "Epoch 28/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0472 - accuracy: 0.9868 - val_loss: 1.1781 - val_accuracy: 0.8735\n",
      "Epoch 29/30\n",
      "1360/1360 [==============================] - 25s 19ms/step - loss: 0.0339 - accuracy: 0.9895 - val_loss: 1.0577 - val_accuracy: 0.8752\n",
      "Epoch 30/30\n",
      "1360/1360 [==============================] - 26s 19ms/step - loss: 0.0359 - accuracy: 0.9893 - val_loss: 1.1445 - val_accuracy: 0.8751\n",
      "Epoch 1/30\n",
      "680/680 [==============================] - 14s 20ms/step - loss: 1.3179 - accuracy: 0.6944 - val_loss: 0.8777 - val_accuracy: 0.7711\n",
      "Epoch 2/30\n",
      "680/680 [==============================] - 11s 17ms/step - loss: 0.5726 - accuracy: 0.8782 - val_loss: 0.7321 - val_accuracy: 0.8160\n",
      "Epoch 3/30\n",
      "680/680 [==============================] - 11s 17ms/step - loss: 0.4051 - accuracy: 0.9093 - val_loss: 0.7173 - val_accuracy: 0.8227\n",
      "Epoch 4/30\n",
      "680/680 [==============================] - 11s 17ms/step - loss: 0.3078 - accuracy: 0.9285 - val_loss: 0.7053 - val_accuracy: 0.8419\n",
      "Epoch 5/30\n",
      "680/680 [==============================] - 11s 17ms/step - loss: 0.2430 - accuracy: 0.9423 - val_loss: 0.7428 - val_accuracy: 0.8317\n",
      "Epoch 6/30\n",
      "680/680 [==============================] - 11s 17ms/step - loss: 0.1935 - accuracy: 0.9533 - val_loss: 0.6825 - val_accuracy: 0.8505\n",
      "Epoch 7/30\n",
      "680/680 [==============================] - 13s 20ms/step - loss: 0.1626 - accuracy: 0.9588 - val_loss: 0.8102 - val_accuracy: 0.8288\n",
      "Epoch 8/30\n",
      "680/680 [==============================] - 13s 20ms/step - loss: 0.1308 - accuracy: 0.9665 - val_loss: 0.7921 - val_accuracy: 0.8360\n",
      "Epoch 9/30\n",
      "680/680 [==============================] - 12s 17ms/step - loss: 0.1166 - accuracy: 0.9697 - val_loss: 0.6834 - val_accuracy: 0.8621\n",
      "Epoch 10/30\n",
      "680/680 [==============================] - 11s 17ms/step - loss: 0.0977 - accuracy: 0.9736 - val_loss: 0.7408 - val_accuracy: 0.8709\n",
      "Epoch 11/30\n",
      "680/680 [==============================] - 13s 18ms/step - loss: 0.0929 - accuracy: 0.9746 - val_loss: 0.8380 - val_accuracy: 0.8518\n",
      "Epoch 12/30\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0755 - accuracy: 0.9784 - val_loss: 0.7597 - val_accuracy: 0.8748\n",
      "Epoch 13/30\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0673 - accuracy: 0.9809 - val_loss: 0.8411 - val_accuracy: 0.8733\n",
      "Epoch 14/30\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0649 - accuracy: 0.9802 - val_loss: 0.8502 - val_accuracy: 0.8631\n",
      "Epoch 15/30\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0597 - accuracy: 0.9823 - val_loss: 0.7281 - val_accuracy: 0.8832\n",
      "Epoch 16/30\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0480 - accuracy: 0.9858 - val_loss: 0.7736 - val_accuracy: 0.8747\n",
      "Epoch 17/30\n",
      "680/680 [==============================] - 13s 18ms/step - loss: 0.0506 - accuracy: 0.9844 - val_loss: 0.7224 - val_accuracy: 0.8795\n",
      "Epoch 18/30\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0492 - accuracy: 0.9852 - val_loss: 0.7814 - val_accuracy: 0.8750\n",
      "Epoch 19/30\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0517 - accuracy: 0.9839 - val_loss: 0.8948 - val_accuracy: 0.8743\n",
      "Epoch 20/30\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0395 - accuracy: 0.9881 - val_loss: 0.8376 - val_accuracy: 0.8801\n",
      "Epoch 21/30\n",
      "680/680 [==============================] - 12s 17ms/step - loss: 0.0405 - accuracy: 0.9874 - val_loss: 1.0116 - val_accuracy: 0.8546\n",
      "Epoch 22/30\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0429 - accuracy: 0.9867 - val_loss: 1.0717 - val_accuracy: 0.8554\n",
      "Epoch 23/30\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0352 - accuracy: 0.9892 - val_loss: 1.0193 - val_accuracy: 0.8626\n",
      "Epoch 24/30\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0372 - accuracy: 0.9883 - val_loss: 0.9146 - val_accuracy: 0.8732\n",
      "Epoch 25/30\n",
      "680/680 [==============================] - 13s 18ms/step - loss: 0.0375 - accuracy: 0.9886 - val_loss: 1.0803 - val_accuracy: 0.8609\n",
      "Epoch 26/30\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0325 - accuracy: 0.9894 - val_loss: 0.9672 - val_accuracy: 0.8745\n",
      "Epoch 27/30\n",
      "680/680 [==============================] - 13s 18ms/step - loss: 0.0312 - accuracy: 0.9902 - val_loss: 1.0444 - val_accuracy: 0.8606\n",
      "Epoch 28/30\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0377 - accuracy: 0.9886 - val_loss: 1.0494 - val_accuracy: 0.8768\n",
      "Epoch 29/30\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0270 - accuracy: 0.9918 - val_loss: 0.9946 - val_accuracy: 0.8888\n",
      "Epoch 30/30\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0360 - accuracy: 0.9892 - val_loss: 1.0794 - val_accuracy: 0.8784\n",
      "Epoch 1/30\n",
      "340/340 [==============================] - 9s 24ms/step - loss: 1.5316 - accuracy: 0.6415 - val_loss: 0.8691 - val_accuracy: 0.7743\n",
      "Epoch 2/30\n",
      "340/340 [==============================] - 8s 25ms/step - loss: 0.6625 - accuracy: 0.8634 - val_loss: 0.6883 - val_accuracy: 0.8315\n",
      "Epoch 3/30\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.4834 - accuracy: 0.8984 - val_loss: 0.6261 - val_accuracy: 0.8467\n",
      "Epoch 4/30\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.3837 - accuracy: 0.9172 - val_loss: 0.6648 - val_accuracy: 0.8394\n",
      "Epoch 5/30\n",
      "340/340 [==============================] - 7s 21ms/step - loss: 0.3075 - accuracy: 0.9326 - val_loss: 0.5876 - val_accuracy: 0.8607\n",
      "Epoch 6/30\n",
      "340/340 [==============================] - 7s 21ms/step - loss: 0.2547 - accuracy: 0.9431 - val_loss: 0.5848 - val_accuracy: 0.8620\n",
      "Epoch 7/30\n",
      "340/340 [==============================] - 7s 21ms/step - loss: 0.2158 - accuracy: 0.9515 - val_loss: 0.5977 - val_accuracy: 0.8666\n",
      "Epoch 8/30\n",
      "340/340 [==============================] - 8s 22ms/step - loss: 0.1828 - accuracy: 0.9583 - val_loss: 0.6215 - val_accuracy: 0.8615\n",
      "Epoch 9/30\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.1525 - accuracy: 0.9648 - val_loss: 0.5892 - val_accuracy: 0.8709\n",
      "Epoch 10/30\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.1341 - accuracy: 0.9684 - val_loss: 0.6043 - val_accuracy: 0.8713\n",
      "Epoch 11/30\n",
      "340/340 [==============================] - 8s 22ms/step - loss: 0.1183 - accuracy: 0.9712 - val_loss: 0.6279 - val_accuracy: 0.8705\n",
      "Epoch 12/30\n",
      "340/340 [==============================] - 7s 21ms/step - loss: 0.1008 - accuracy: 0.9757 - val_loss: 0.6274 - val_accuracy: 0.8726\n",
      "Epoch 13/30\n",
      "340/340 [==============================] - 7s 22ms/step - loss: 0.0871 - accuracy: 0.9779 - val_loss: 0.6344 - val_accuracy: 0.8712\n",
      "Epoch 14/30\n",
      "340/340 [==============================] - 6s 17ms/step - loss: 0.0765 - accuracy: 0.9807 - val_loss: 0.6446 - val_accuracy: 0.8708\n",
      "Epoch 15/30\n",
      "340/340 [==============================] - 6s 19ms/step - loss: 0.0731 - accuracy: 0.9803 - val_loss: 0.6440 - val_accuracy: 0.8770\n",
      "Epoch 16/30\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.0592 - accuracy: 0.9849 - val_loss: 0.6878 - val_accuracy: 0.8689\n",
      "Epoch 17/30\n",
      "340/340 [==============================] - 8s 22ms/step - loss: 0.0573 - accuracy: 0.9847 - val_loss: 0.7343 - val_accuracy: 0.8671\n",
      "Epoch 18/30\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.0459 - accuracy: 0.9879 - val_loss: 0.7189 - val_accuracy: 0.8745\n",
      "Epoch 19/30\n",
      "340/340 [==============================] - 7s 21ms/step - loss: 0.0492 - accuracy: 0.9861 - val_loss: 0.6931 - val_accuracy: 0.8746\n",
      "Epoch 20/30\n",
      "340/340 [==============================] - 8s 22ms/step - loss: 0.0465 - accuracy: 0.9872 - val_loss: 0.7800 - val_accuracy: 0.8728\n",
      "Epoch 21/30\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.0452 - accuracy: 0.9872 - val_loss: 0.6342 - val_accuracy: 0.8920\n",
      "Epoch 22/30\n",
      "340/340 [==============================] - 8s 24ms/step - loss: 0.0413 - accuracy: 0.9876 - val_loss: 0.7305 - val_accuracy: 0.8832\n",
      "Epoch 23/30\n",
      "340/340 [==============================] - 7s 21ms/step - loss: 0.0333 - accuracy: 0.9905 - val_loss: 0.7222 - val_accuracy: 0.8849\n",
      "Epoch 24/30\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.0334 - accuracy: 0.9902 - val_loss: 0.7281 - val_accuracy: 0.8774\n",
      "Epoch 25/30\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.0243 - accuracy: 0.9932 - val_loss: 0.7901 - val_accuracy: 0.8795\n",
      "Epoch 26/30\n",
      "340/340 [==============================] - 7s 21ms/step - loss: 0.0345 - accuracy: 0.9900 - val_loss: 0.8173 - val_accuracy: 0.8773\n",
      "Epoch 27/30\n",
      "340/340 [==============================] - 8s 22ms/step - loss: 0.0243 - accuracy: 0.9927 - val_loss: 0.7542 - val_accuracy: 0.8918\n",
      "Epoch 28/30\n",
      "340/340 [==============================] - 8s 24ms/step - loss: 0.0255 - accuracy: 0.9922 - val_loss: 0.8363 - val_accuracy: 0.8774\n",
      "Epoch 29/30\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.0285 - accuracy: 0.9911 - val_loss: 0.9364 - val_accuracy: 0.8675\n",
      "Epoch 30/30\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.0298 - accuracy: 0.9908 - val_loss: 0.9701 - val_accuracy: 0.8680\n",
      "Epoch 1/30\n",
      "170/170 [==============================] - 5s 28ms/step - loss: 1.7921 - accuracy: 0.5857 - val_loss: 1.0625 - val_accuracy: 0.7219\n",
      "Epoch 2/30\n",
      "170/170 [==============================] - 4s 26ms/step - loss: 0.8002 - accuracy: 0.8387 - val_loss: 0.8105 - val_accuracy: 0.7911\n",
      "Epoch 3/30\n",
      "170/170 [==============================] - 5s 27ms/step - loss: 0.6005 - accuracy: 0.8819 - val_loss: 0.7704 - val_accuracy: 0.8073\n",
      "Epoch 4/30\n",
      "170/170 [==============================] - 5s 27ms/step - loss: 0.4958 - accuracy: 0.9010 - val_loss: 0.7133 - val_accuracy: 0.8273\n",
      "Epoch 5/30\n",
      "170/170 [==============================] - 4s 26ms/step - loss: 0.4089 - accuracy: 0.9177 - val_loss: 0.6385 - val_accuracy: 0.8436\n",
      "Epoch 6/30\n",
      "170/170 [==============================] - 5s 28ms/step - loss: 0.3425 - accuracy: 0.9307 - val_loss: 0.6404 - val_accuracy: 0.8502\n",
      "Epoch 7/30\n",
      "170/170 [==============================] - 5s 29ms/step - loss: 0.2952 - accuracy: 0.9398 - val_loss: 0.6255 - val_accuracy: 0.8602\n",
      "Epoch 8/30\n",
      "170/170 [==============================] - 5s 28ms/step - loss: 0.2575 - accuracy: 0.9472 - val_loss: 0.5968 - val_accuracy: 0.8637\n",
      "Epoch 9/30\n",
      "170/170 [==============================] - 5s 28ms/step - loss: 0.2230 - accuracy: 0.9529 - val_loss: 0.6388 - val_accuracy: 0.8549\n",
      "Epoch 10/30\n",
      "170/170 [==============================] - 5s 29ms/step - loss: 0.1974 - accuracy: 0.9575 - val_loss: 0.5983 - val_accuracy: 0.8678\n",
      "Epoch 11/30\n",
      "170/170 [==============================] - 5s 27ms/step - loss: 0.1685 - accuracy: 0.9643 - val_loss: 0.6037 - val_accuracy: 0.8651\n",
      "Epoch 12/30\n",
      "170/170 [==============================] - 5s 28ms/step - loss: 0.1486 - accuracy: 0.9681 - val_loss: 0.6317 - val_accuracy: 0.8653\n",
      "Epoch 13/30\n",
      "170/170 [==============================] - 5s 31ms/step - loss: 0.1342 - accuracy: 0.9703 - val_loss: 0.6316 - val_accuracy: 0.8600\n",
      "Epoch 14/30\n",
      "170/170 [==============================] - 4s 26ms/step - loss: 0.1177 - accuracy: 0.9744 - val_loss: 0.5949 - val_accuracy: 0.8728\n",
      "Epoch 15/30\n",
      "170/170 [==============================] - 4s 26ms/step - loss: 0.1028 - accuracy: 0.9780 - val_loss: 0.5883 - val_accuracy: 0.8750\n",
      "Epoch 16/30\n",
      "170/170 [==============================] - 4s 26ms/step - loss: 0.0897 - accuracy: 0.9805 - val_loss: 0.6564 - val_accuracy: 0.8632\n",
      "Epoch 17/30\n",
      "170/170 [==============================] - 5s 27ms/step - loss: 0.0802 - accuracy: 0.9824 - val_loss: 0.6674 - val_accuracy: 0.8680\n",
      "Epoch 18/30\n",
      "170/170 [==============================] - 5s 28ms/step - loss: 0.0703 - accuracy: 0.9844 - val_loss: 0.6784 - val_accuracy: 0.8674\n",
      "Epoch 19/30\n",
      "170/170 [==============================] - 5s 27ms/step - loss: 0.0648 - accuracy: 0.9852 - val_loss: 0.5915 - val_accuracy: 0.8841\n",
      "Epoch 20/30\n",
      "170/170 [==============================] - 5s 27ms/step - loss: 0.0548 - accuracy: 0.9876 - val_loss: 0.6699 - val_accuracy: 0.8729\n",
      "Epoch 21/30\n",
      "170/170 [==============================] - 5s 27ms/step - loss: 0.0497 - accuracy: 0.9891 - val_loss: 0.6755 - val_accuracy: 0.8709\n",
      "Epoch 22/30\n",
      "170/170 [==============================] - 5s 29ms/step - loss: 0.0433 - accuracy: 0.9907 - val_loss: 0.7140 - val_accuracy: 0.8664\n",
      "Epoch 23/30\n",
      "170/170 [==============================] - 5s 28ms/step - loss: 0.0404 - accuracy: 0.9915 - val_loss: 0.6982 - val_accuracy: 0.8779\n",
      "Epoch 24/30\n",
      "170/170 [==============================] - 5s 30ms/step - loss: 0.0432 - accuracy: 0.9891 - val_loss: 0.7675 - val_accuracy: 0.8644\n",
      "Epoch 25/30\n",
      "170/170 [==============================] - 5s 28ms/step - loss: 0.0358 - accuracy: 0.9919 - val_loss: 0.6658 - val_accuracy: 0.8807\n",
      "Epoch 26/30\n",
      "170/170 [==============================] - 5s 29ms/step - loss: 0.0291 - accuracy: 0.9933 - val_loss: 0.7001 - val_accuracy: 0.8762\n",
      "Epoch 27/30\n",
      "170/170 [==============================] - 5s 29ms/step - loss: 0.0351 - accuracy: 0.9912 - val_loss: 0.7676 - val_accuracy: 0.8698\n",
      "Epoch 28/30\n",
      "170/170 [==============================] - 4s 26ms/step - loss: 0.0275 - accuracy: 0.9935 - val_loss: 0.7655 - val_accuracy: 0.8721\n",
      "Epoch 29/30\n",
      "170/170 [==============================] - 4s 26ms/step - loss: 0.0290 - accuracy: 0.9924 - val_loss: 0.7429 - val_accuracy: 0.8816\n",
      "Epoch 30/30\n",
      "170/170 [==============================] - 5s 28ms/step - loss: 0.0199 - accuracy: 0.9957 - val_loss: 0.7689 - val_accuracy: 0.8751\n",
      "Epoch 1/30\n",
      "85/85 [==============================] - 3s 33ms/step - loss: 2.3516 - accuracy: 0.4464 - val_loss: 1.4305 - val_accuracy: 0.6382\n",
      "Epoch 2/30\n",
      "85/85 [==============================] - 3s 31ms/step - loss: 1.1183 - accuracy: 0.7704 - val_loss: 0.9727 - val_accuracy: 0.7663\n",
      "Epoch 3/30\n",
      "85/85 [==============================] - 3s 31ms/step - loss: 0.8210 - accuracy: 0.8423 - val_loss: 0.8373 - val_accuracy: 0.7936\n",
      "Epoch 4/30\n",
      "85/85 [==============================] - 3s 33ms/step - loss: 0.6851 - accuracy: 0.8715 - val_loss: 0.7917 - val_accuracy: 0.8043\n",
      "Epoch 5/30\n",
      "85/85 [==============================] - 3s 31ms/step - loss: 0.5963 - accuracy: 0.8889 - val_loss: 0.7506 - val_accuracy: 0.8158\n",
      "Epoch 6/30\n",
      "85/85 [==============================] - 3s 33ms/step - loss: 0.5327 - accuracy: 0.9001 - val_loss: 0.7351 - val_accuracy: 0.8174\n",
      "Epoch 7/30\n",
      "85/85 [==============================] - 3s 32ms/step - loss: 0.4794 - accuracy: 0.9099 - val_loss: 0.6940 - val_accuracy: 0.8348\n",
      "Epoch 8/30\n",
      "85/85 [==============================] - 3s 33ms/step - loss: 0.4384 - accuracy: 0.9161 - val_loss: 0.6653 - val_accuracy: 0.8432\n",
      "Epoch 9/30\n",
      "85/85 [==============================] - 3s 35ms/step - loss: 0.3936 - accuracy: 0.9239 - val_loss: 0.6952 - val_accuracy: 0.8378\n",
      "Epoch 10/30\n",
      "85/85 [==============================] - 3s 32ms/step - loss: 0.3560 - accuracy: 0.9311 - val_loss: 0.6853 - val_accuracy: 0.8435\n",
      "Epoch 11/30\n",
      "85/85 [==============================] - 3s 33ms/step - loss: 0.3274 - accuracy: 0.9355 - val_loss: 0.6703 - val_accuracy: 0.8447\n",
      "Epoch 12/30\n",
      "85/85 [==============================] - 3s 33ms/step - loss: 0.2983 - accuracy: 0.9406 - val_loss: 0.6704 - val_accuracy: 0.8481\n",
      "Epoch 13/30\n",
      "85/85 [==============================] - 3s 33ms/step - loss: 0.2747 - accuracy: 0.9451 - val_loss: 0.6506 - val_accuracy: 0.8522\n",
      "Epoch 14/30\n",
      "85/85 [==============================] - 3s 33ms/step - loss: 0.2507 - accuracy: 0.9499 - val_loss: 0.6602 - val_accuracy: 0.8596\n",
      "Epoch 15/30\n",
      "85/85 [==============================] - 3s 33ms/step - loss: 0.2305 - accuracy: 0.9536 - val_loss: 0.6601 - val_accuracy: 0.8531\n",
      "Epoch 16/30\n",
      "85/85 [==============================] - 3s 31ms/step - loss: 0.2124 - accuracy: 0.9569 - val_loss: 0.6296 - val_accuracy: 0.8563\n",
      "Epoch 17/30\n",
      "85/85 [==============================] - 3s 33ms/step - loss: 0.1933 - accuracy: 0.9617 - val_loss: 0.6320 - val_accuracy: 0.8631\n",
      "Epoch 18/30\n",
      "85/85 [==============================] - 3s 33ms/step - loss: 0.1810 - accuracy: 0.9633 - val_loss: 0.6180 - val_accuracy: 0.8678\n",
      "Epoch 19/30\n",
      "85/85 [==============================] - 2s 27ms/step - loss: 0.1714 - accuracy: 0.9649 - val_loss: 0.6312 - val_accuracy: 0.8697\n",
      "Epoch 20/30\n",
      "85/85 [==============================] - 3s 33ms/step - loss: 0.1553 - accuracy: 0.9688 - val_loss: 0.6402 - val_accuracy: 0.8622\n",
      "Epoch 21/30\n",
      "85/85 [==============================] - 3s 31ms/step - loss: 0.1427 - accuracy: 0.9710 - val_loss: 0.6404 - val_accuracy: 0.8677\n",
      "Epoch 22/30\n",
      "85/85 [==============================] - 3s 34ms/step - loss: 0.1316 - accuracy: 0.9730 - val_loss: 0.6287 - val_accuracy: 0.8743\n",
      "Epoch 23/30\n",
      "85/85 [==============================] - 3s 31ms/step - loss: 0.1250 - accuracy: 0.9744 - val_loss: 0.6091 - val_accuracy: 0.8749\n",
      "Epoch 24/30\n",
      "85/85 [==============================] - 3s 32ms/step - loss: 0.1129 - accuracy: 0.9774 - val_loss: 0.6251 - val_accuracy: 0.8713\n",
      "Epoch 25/30\n",
      "85/85 [==============================] - 3s 32ms/step - loss: 0.1025 - accuracy: 0.9799 - val_loss: 0.6398 - val_accuracy: 0.8679\n",
      "Epoch 26/30\n",
      "85/85 [==============================] - 3s 31ms/step - loss: 0.0957 - accuracy: 0.9810 - val_loss: 0.6487 - val_accuracy: 0.8689\n",
      "Epoch 27/30\n",
      "85/85 [==============================] - 3s 34ms/step - loss: 0.0898 - accuracy: 0.9822 - val_loss: 0.6436 - val_accuracy: 0.8739\n",
      "Epoch 28/30\n",
      "85/85 [==============================] - 3s 30ms/step - loss: 0.0847 - accuracy: 0.9829 - val_loss: 0.6366 - val_accuracy: 0.8738\n",
      "Epoch 29/30\n",
      "85/85 [==============================] - 3s 31ms/step - loss: 0.0767 - accuracy: 0.9845 - val_loss: 0.6317 - val_accuracy: 0.8760\n",
      "Epoch 30/30\n",
      "85/85 [==============================] - 3s 31ms/step - loss: 0.0719 - accuracy: 0.9859 - val_loss: 0.6362 - val_accuracy: 0.8768\n",
      "Epoch 1/30\n",
      "43/43 [==============================] - 2s 45ms/step - loss: 2.7754 - accuracy: 0.3313 - val_loss: 1.9679 - val_accuracy: 0.5045\n",
      "Epoch 2/30\n",
      "43/43 [==============================] - 2s 43ms/step - loss: 1.4732 - accuracy: 0.6830 - val_loss: 1.2425 - val_accuracy: 0.6897\n",
      "Epoch 3/30\n",
      "43/43 [==============================] - 2s 44ms/step - loss: 1.0266 - accuracy: 0.7930 - val_loss: 0.9943 - val_accuracy: 0.7565\n",
      "Epoch 4/30\n",
      "43/43 [==============================] - 2s 42ms/step - loss: 0.8342 - accuracy: 0.8374 - val_loss: 0.8944 - val_accuracy: 0.7670\n",
      "Epoch 5/30\n",
      "43/43 [==============================] - 2s 42ms/step - loss: 0.7278 - accuracy: 0.8602 - val_loss: 0.7814 - val_accuracy: 0.8074\n",
      "Epoch 6/30\n",
      "43/43 [==============================] - 2s 46ms/step - loss: 0.6531 - accuracy: 0.8766 - val_loss: 0.7666 - val_accuracy: 0.8144\n",
      "Epoch 7/30\n",
      "43/43 [==============================] - 2s 44ms/step - loss: 0.5946 - accuracy: 0.8885 - val_loss: 0.7466 - val_accuracy: 0.8212\n",
      "Epoch 8/30\n",
      "43/43 [==============================] - 2s 44ms/step - loss: 0.5568 - accuracy: 0.8932 - val_loss: 0.7315 - val_accuracy: 0.8206\n",
      "Epoch 9/30\n",
      "43/43 [==============================] - 2s 46ms/step - loss: 0.5144 - accuracy: 0.9017 - val_loss: 0.6807 - val_accuracy: 0.8406\n",
      "Epoch 10/30\n",
      "43/43 [==============================] - 2s 44ms/step - loss: 0.4768 - accuracy: 0.9096 - val_loss: 0.6994 - val_accuracy: 0.8312\n",
      "Epoch 11/30\n",
      "43/43 [==============================] - 2s 43ms/step - loss: 0.4460 - accuracy: 0.9152 - val_loss: 0.6519 - val_accuracy: 0.8496\n",
      "Epoch 12/30\n",
      "43/43 [==============================] - 2s 45ms/step - loss: 0.4216 - accuracy: 0.9203 - val_loss: 0.6343 - val_accuracy: 0.8470\n",
      "Epoch 13/30\n",
      "43/43 [==============================] - 2s 46ms/step - loss: 0.3943 - accuracy: 0.9244 - val_loss: 0.6660 - val_accuracy: 0.8493\n",
      "Epoch 14/30\n",
      "43/43 [==============================] - 2s 45ms/step - loss: 0.3814 - accuracy: 0.9256 - val_loss: 0.6400 - val_accuracy: 0.8478\n",
      "Epoch 15/30\n",
      "43/43 [==============================] - 2s 41ms/step - loss: 0.3583 - accuracy: 0.9307 - val_loss: 0.6350 - val_accuracy: 0.8532\n",
      "Epoch 16/30\n",
      "43/43 [==============================] - 2s 45ms/step - loss: 0.3318 - accuracy: 0.9364 - val_loss: 0.6198 - val_accuracy: 0.8593\n",
      "Epoch 17/30\n",
      "43/43 [==============================] - 2s 47ms/step - loss: 0.3173 - accuracy: 0.9386 - val_loss: 0.6357 - val_accuracy: 0.8546\n",
      "Epoch 18/30\n",
      "43/43 [==============================] - 2s 45ms/step - loss: 0.3001 - accuracy: 0.9418 - val_loss: 0.6566 - val_accuracy: 0.8462\n",
      "Epoch 19/30\n",
      "43/43 [==============================] - 2s 45ms/step - loss: 0.2826 - accuracy: 0.9457 - val_loss: 0.6324 - val_accuracy: 0.8557\n",
      "Epoch 20/30\n",
      "43/43 [==============================] - 2s 42ms/step - loss: 0.2734 - accuracy: 0.9456 - val_loss: 0.6325 - val_accuracy: 0.8561\n",
      "Epoch 21/30\n",
      "43/43 [==============================] - 2s 45ms/step - loss: 0.2580 - accuracy: 0.9494 - val_loss: 0.6160 - val_accuracy: 0.8610\n",
      "Epoch 22/30\n",
      "43/43 [==============================] - 2s 45ms/step - loss: 0.2447 - accuracy: 0.9521 - val_loss: 0.6441 - val_accuracy: 0.8603\n",
      "Epoch 23/30\n",
      "43/43 [==============================] - 2s 45ms/step - loss: 0.2324 - accuracy: 0.9544 - val_loss: 0.6060 - val_accuracy: 0.8629\n",
      "Epoch 24/30\n",
      "43/43 [==============================] - 2s 47ms/step - loss: 0.2198 - accuracy: 0.9570 - val_loss: 0.6197 - val_accuracy: 0.8632\n",
      "Epoch 25/30\n",
      "43/43 [==============================] - 2s 44ms/step - loss: 0.2110 - accuracy: 0.9587 - val_loss: 0.6461 - val_accuracy: 0.8598\n",
      "Epoch 26/30\n",
      "43/43 [==============================] - 2s 44ms/step - loss: 0.2015 - accuracy: 0.9600 - val_loss: 0.6312 - val_accuracy: 0.8571\n",
      "Epoch 27/30\n",
      "43/43 [==============================] - 2s 44ms/step - loss: 0.1919 - accuracy: 0.9622 - val_loss: 0.6356 - val_accuracy: 0.8591\n",
      "Epoch 28/30\n",
      "43/43 [==============================] - 2s 45ms/step - loss: 0.1846 - accuracy: 0.9635 - val_loss: 0.6258 - val_accuracy: 0.8661\n",
      "Epoch 29/30\n",
      "43/43 [==============================] - 2s 41ms/step - loss: 0.1716 - accuracy: 0.9666 - val_loss: 0.6158 - val_accuracy: 0.8706\n",
      "Epoch 30/30\n",
      "43/43 [==============================] - 2s 46ms/step - loss: 0.1653 - accuracy: 0.9675 - val_loss: 0.6635 - val_accuracy: 0.8587\n"
     ]
    },
    {
     "data": {
      "image/png": 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/+Ly9wqXtla4jlrxJdyZoQFZKh8Fh315J6pWcyKhBAIgyQkUAYfN4fdpV1dAaHO4PhIcu1TQefnW7dGeChuSmanBOqgb7b4cRHgIAAPRoXp+h3QcbtLm8XlsO1GvL/vpgiHjwGFdGDtYqzEnRwOwUZTLVGADijlARwGEZhqG9NU3aWFarDWV12lhWpw376rStol4eb8fDDi0WqbB3sgblpJjhYU6qBuekaHBuKnVmAAAAejCP16edlQ3asr9eW/ab9bKPVu5GkvpkOINh4aDsVA3MSdFgVkYGgC6PUBGAJKm2yaNNZXXaUFanDWW1ZoBYVqe6ppYOz09KtGlwbtvgMFWDc1NUlJUiZyIrLAMAAPRU7hazVvbm8np/gFivzfvrtL3CddgLz/YEqwbnpGqof7bKoJzWUYesjAwA3RP/9QZOMB6vT9srXFq/zwwOA+Hh4QpeJ1gtGpyTquH5aRqen6YR/tuCjCRZuXIMAADQozV5vPp8T43+s6NKa0urtWV/vXZWNcjr6zg8TLbbNCQ3tbVetn+/MJNyNwDQ0xAqAj1UTYNHWyvqtf2AS9sqAnVrzK3Z2/H0kz4ZTn9wmB4MDwfnpMqeYI1x6wEAABAPVa5mrd55UJ/sqNInOw/q8901HfYd05wJGtpmob0huakampemPulOLjwDwAmCUBHoxpo8XpVWNZhhYTBANFfMq3I1H/Z5qY6E0JGHeWaQmJGcGMPWAwAAIJ4Mw9DOygb9Z0eVVu88qP/sqNLWA6525+WkOXTKgN6aOKC3TuqTrqG5qcpJc1ArGwBOcISKQDdQ5WrWF3trWkcbVri0vaJeuw82yuh45okkKT+9TdHrnFQNzE7W0Nw09eudRCcQANDjLV68WI8++qjKyso0btw4PfbYY5o0aVKH53o8Hi1YsEBPP/209uzZo+HDh+uRRx7RhRdeGONWA9Hj8fr0xd5acxTijoP6ZOdBVdS72503JDdVpxb11ikDMnVKUW/1z0ym7wgAaIdQEeiCqlzNWrW9Uh9vq9LH2yq1oazusOemORKCweHA7NQ2+ylKcfB/cQDAien555/XnDlztGTJEhUXF2vRokWaOnWqNm7cqNzc3Hbn33PPPXrmmWf0u9/9TiNGjNCbb76pyy+/XB999JEmTJgQh08AHL/GZq9W7zyoldsrzZqIu6rV5Amdymy3WTW2X4YmFvXWqQMyNXFAb/VOscepxQCA7sRiGEca59Q91NbWKiMjQzU1NUpPT493c4CwHXQ1a+V2M0A8XIg4yD/acFBOigZlt44+zE61c+UYAHoA+jORVVxcrFNPPVW/+c1vJEk+n0+FhYW64447NHfu3HbnFxQU6O6779btt98ePHbFFVcoKSlJzzzzTIfv4Xa75Xa3jvKqra1VYWEhf4aIG5e7Rat3HtTH2yq1cnuVPttd3W415oykRJ0yoLdOKTJHIY7pmyFnoi1OLQYAdCXh9kcZxgTEQXVD2xCxShvKattNYx6Wl6rJg7J02qAsTRqYqaxUR3waCwBAN9Pc3KzVq1dr3rx5wWNWq1VTpkzRihUrOnyO2+2W0+kMOZaUlKQPP/zwsO+zYMECPfDAA5FpNNAJ9e4WfbKjSh9vq9LK7ZX6fHeNWg5ZlblPhlPFAzN16sBMTSrK1OCcVBZSAQBEBKEiEAM1DR6tbDOdeX0HIeLQ3FSdNihLkwebIWI2ISIAAJ1SUVEhr9ervLy8kON5eXnasGFDh8+ZOnWqFi5cqLPPPluDBw9WSUmJXn75ZXm93sO+z7x58zRnzpzg/cBIRSBaaps8+mRHlVb6+5Tr9tbKe0iI2LdXkooHZeq0gebF6cJMamkDAKKDUBGIgsZmr1btqNK/t1To31sq9L997UPEIbmpOm1QpiYPytakgZnKSSNEBAAgXn71q1/p1ltv1YgRI2SxWDR48GDNmjVLS5cuPexzHA6HHA7+/kZ0GIah/XVufb67Jjid+Yu9NTokQ1RhZpJOG5il4kFZKh6YqcLM5Pg0GABwwiFUBCLA6zP0xd4afbC5Qh9urtDqnQfV7A0tgj04JyU4ErF4YBYhIgAAUZKdnS2bzaby8vKQ4+Xl5crPz+/wOTk5OXrllVfU1NSkyspKFRQUaO7cuRo0aFAsmowTlMfr056DjdpZ1aDSSpd2VDZoZ2WDSqtcKq1qaLeoiiQVZSWreGCWThucqeKBWSrolRSHlgMAQKgIdFppZYM+3FKhD7cc0EdbK1Xd4Al5vCDDqTOHZuuMIdmaPDhLuWnOw7wSAACIJLvdrokTJ6qkpEQzZsyQZC7UUlJSotmzZx/xuU6nU3379pXH49Ff//pXXXXVVTFoMXqyhuYW7WwTFpq3DdpR6dLe6qZ205fbslqkouwUM0QcZIaI+Rn0KQEAXQOhInCMqhua9dHWSn2w2ZzSXFrVEPJ4miNBpw3O0ln+IHFQdgr1awAAiJM5c+bopptu0imnnKJJkyZp0aJFcrlcmjVrliTpxhtvVN++fbVgwQJJ0sqVK7Vnzx6NHz9ee/bs0f333y+fz6cf/OAH8fwY6IZKKxv098/26r2NB7S90qUDde4jnu9IsGpAVrL6Z6ZoQFayfz9ZRVkp6ts7SYk2a4xaDgBAeAgVgcNwt3i1eudBfbi5Qh9uqdDne2pC6iImWC06uX9vnTEkW2cOzda4fhlKoNMHAECXMHPmTB04cEDz589XWVmZxo8fr2XLlgUXbyktLZXV2vr3dlNTk+655x5t27ZNqampuuiii/SnP/1JvXr1itMnQHeyv65Jr3+2T6+u3au1u6rbPZ6RlOgPDFM0IDNZ/bOSNSDTvJ+b5mA1ZgBAt2QxjEOXj+h+amtrlZGRoZqaGqWnp8e7OeiGDMPQ7oON+u+uaq0trdbaXQe1bm+tmltC69gMzU3VmUOzdeaQbBUPylKqg1weABAZ9Ge6P/4MTyw1DR4t+2KfXvt0r1ZsrQwuoGK1SJMHZ+mSsQUaVZCuAZkpykhOjG9jAQA4BuH2ZUhEcEKqafTos92BANHcKl3N7c7LSXPozCFmiHjGkGxq2AAAAJzAGpu9emd9uV771Jze3HZhvvGFvXTpuAJdMraPctPpMwIAej5CRfR4Hq9PG8vqQkYhbj3gandeos2ik/qka3xhr+A2kLqIAAAAJzSP16cPNh/Qa2v36q3/lauh2Rt8bFheqi4b31fTxxaof1ZyHFsJAEDsESqixzlQ59bK7ZXBUYjr9taoyeNrd17/zOTWALF/L43sky5noi0OLQYAAEBX4vMZWrWjSq+u3at/rtun6gZP8LF+vZN06bgCXTq+QCPymeYOADhxESqiR6isd2vZF2X6x6f7tHJ7a02bgDRngsYX9tIEf4A4rl8vZaU64tNYAAAAdEn7ahr11L936NW1e1VW2xQ8np3q0CVj++jS8QWaUNiLmSwAAIhQEd1YdUOz3vyiTP/4bJ8+2lopb5skcWSfdJ08oJfGF/bW+MJeGpSdwqp6AAAA6NDOSpeWvLdVL63eLY/X7FOmORM0bXS+Lh3XV6cNylSCzXqUVwEA4MRCqIhupbbJo7e/KNc/PturDzZXqKVNkDi6b7ouGVugi8f0UWEmNW0AAABwZJvK6/T4u1v02qd7gzNdJg3M1NfOGKgvjciRI4HSOAAAHE7UQsXFixfr0UcfVVlZmcaNG6fHHntMkyZN6vBcj8ejBQsW6Omnn9aePXs0fPhwPfLII7rwwguj1Tx0I/XuFpWsL9ffP92n9zeFrrI3Ij9N08eZQWJRdkocWwkAAIDu4vPdNfrNu5v15hflwWPnDMvR7POG6NSizDi2DADQI7Q0SzW7pIM7zM1ikYZNk9L7xLtlERWVUPH555/XnDlztGTJEhUXF2vRokWaOnWqNm7cqNzc3Hbn33PPPXrmmWf0u9/9TiNGjNCbb76pyy+/XB999JEmTJgQjSaii2tobtG/NuzX65/t07827Je7pTVIHJqbao5IHNtHQ3JT49hKAAAAdCertldp8btb9N6mA8FjF47K1+1fGqIx/TLi2DIAQLdiGFJDZWtoeHC7/3aneVu7RzIOXTB2jlR0pjTmq9JJl0rJ3f8ilsUwDOPop4WnuLhYp556qn7zm99Iknw+nwoLC3XHHXdo7ty57c4vKCjQ3Xffrdtvvz147IorrlBSUpKeeeaZo75fbW2tMjIyVFNTo/R0VmDrrtwtXr274YD+8dlelazfr0aPN/jYwOwUXTK2jy4ZW6Dh+WlxbCUAANFBf6b748+wazIMQ+9vrtDif23Rqh1VkiSb1aJLxxXoW+cO1tA8+pYAgA74vFLVNqlqe2t4WL2zdb+5/sjPT0iSeheZW0OltHtV62PWRGnI+dLor0rDp0mOrjFgKty+TMRHKjY3N2v16tWaN29e8JjVatWUKVO0YsWKDp/jdrvldDpDjiUlJenDDz887Plutzt4v7a2NgItR7ys31er5/+zS6+s3aPqBk/weGFmki4ZW6BLxvbRyD7prLIHAACAY+bzGXp7fbkWv7tFn+2ukSQl2iz66sRCffOcweqfRQ1uAICfz2cGiHv/27rt+1TyuI7wJIuUXtAaHLbdeg2QUnPNac8BB3dKX7wsff5XqfxzadMyc0tMloZdaI5gHDJFSnBE85NGVMRDxYqKCnm9XuXl5YUcz8vL04YNGzp8ztSpU7Vw4UKdffbZGjx4sEpKSvTyyy/L6/V2eP6CBQv0wAMPRLrpiKGaRo9e+3SvXvxkV7CTJ0n56U5NH2eOSBzbL4MgEQAAAGFp8fr0+uf7tPjdLdpUbo4icSZadc2k/rrt7EHqk5EU5xYCQJh8Xqmx2hzt1lApNVb596vM0XKGr81mHHLr33TIfcMnGWrdd2ZIaXlSWh8p1X+bli8l9Q4NxnoCwzBHHLYNEPd+Krlr2p+bmCxlDpZ6D2gTGg4072cUSonO9s85nN4DpDPvNLf9G6R1L0mfv2ROnf7iZXNzZkgnTTdHMA48W7J27QXDusTqz7/61a906623asSIEbJYLBo8eLBmzZqlpUuXdnj+vHnzNGfOnOD92tpaFRYWxqq56CSfz9DK7VV64ZNdeuPzfcE6iYk2i748Mk9XnVKos4bmyGbtYf/BAgAAQNQ1t/j08prdeuK9rdpZ2SBJSnMk6IbJA/S1MwcqO7X7jPwA0MV4mszprlVbzamwLU2SLdGcwmpLNIOf4H6CuQUf998/9HGLVWqqNoPBkLCwqv2xxmqZCWAc2OxSar4ZMHYUOqblm/tdNXw0DKlun7RnTWiI2FjV/twEp5Q/ViqYYG59T5ayhkQn2MsdIZ13j/Slu6W9a8zRi1+8bLb1v8+YW0quNOpycwRjv1O75Pcb8VAxOztbNptN5eXlIcfLy8uVn5/f4XNycnL0yiuvqKmpSZWVlSooKNDcuXM1aNCgDs93OBxyOOgUdBdlNU16afUuvfDJbpVWNQSPD8tL1VWnFOryCX2VRScPAAAAnfTJjird9dJn2l5hTlPrnZyor50xUDeeXqSMpMQ4tw6IMsOQavdK+/8nla8zp1im5ZujqTIHmrcp2V0ykOhSmhvMEWNV21q3Sn+IWLtHcQv12nJmSEmZUnKWf8uUHGmSxWb++VqsrbcK3G+7He4cixlc1peboVad/7axSvI2SzWl5nYkNrsZNqZkt2mfv43Jhx7LMkNIWycjKcOQPA1SU63UVCO5/beBzV1rfp4DG8wAsb68/WtYE6X80a0BYsEEKWeEGfzGksUi9Z1obhc8JO38yBzB+L9XJdd+adX/mVuv/tLoK6QxV0p5o2LbxiOIeKhot9s1ceJElZSUaMaMGZLMhVpKSko0e/bsIz7X6XSqb9++8ng8+utf/6qrrroq0s1DjDS3+FSyvlwvfLJL7206IJ//v7+pjgRNH1egmacWahzTmwEAAHAcmjxe/fzNjXry39tlGFJ2qkPfOGeQrpnUXymOLjEpC4gsd70ZlJSvk8q/kMr9QWJT9ZGfZ0/1h4xFoWFj5kApvV/nw53uxl0fGhwGQsOqbVLd3iM/15EuZQ4yvzN7ijkl2euRfB7J2yL5Wvz7HvOx4L7/sUPPNbxmSBgM2TL9AVzmIcciEMJ1VovbHzSWtW71Ze3vN1T6w8dd5nZMLFJSr44DyARnaFAY3G8TIvpajv1zWGxS7kipYHxrgJg3quvVLrTapIFnmdu0R6Vt70qfvyhteEOqLpU+/KW04nHpri2Ss2ssCBeVX+ScOXN000036ZRTTtGkSZO0aNEiuVwuzZo1S5J04403qm/fvlqwYIEkaeXKldqzZ4/Gjx+vPXv26P7775fP59MPfvCDaDQPUbS5vE7P/2eX/vbfPap0NQePTxqYqatOKdRFY/KVbD9B/sICAABA1KzeWaW7XvxM2/yjE786sZ/uvWQkIxPRM/i85nTb8i/8mz9EPLhDHY6Ys9ik7KFmUNJ7oBkEHdzROsquud5cGKL88/bPtSaYo6AODRtzRpghWrwGgrS4pdIV0pYSafd/zCnHPq9Z/8/nNUO54K0v9H7wnEPO9XmO/J7ODLN+XtZgf4DYZkvOOvFGeyY4zN9Gr/5HPq+l2fzN1ZdLrorWqduH2xoPSjLM28aDUuWWzrXPYjPDNWeGGfo6M9rsp5u/5b4nS3mjJXs3W5wrwS4Nm2puzQ3Spn+aU6QTk7pMoChFKVScOXOmDhw4oPnz56usrEzjx4/XsmXLgou3lJaWymq1Bs9vamrSPffco23btik1NVUXXXSR/vSnP6lXr17RaB4irMXr08tr9ujZVaVau6s6eDw3zaErJvbTVacUamB2SvwaCAAAgB6jyePVL97aqN9/aI5OzEt3aMFXxui8EXlHfzLQ1fh8ZuhXscncyr8wpzHvX29O7+xIap4ZHuaNknL9tznDDz/qytNkjnI6uN0MGdveHtxhjjALjNzb2sF7DThDKjrT3LKHRTdYq9pmhohb3pG2v3/47+B4JGcdEhgObh2BmJwZ+fc7ESTYpV6F5nYsvC1mmHi40LGlqX1Q2DYsDOzbU06MoNeebE59Hn2FOfW7C7EYRhdrUSfU1tYqIyNDNTU1Sk/vOontiWDV9irNf3WdNpTVSZISrBadNyJXM08t1DnDcpRgsx7lFQAAgER/pifgzzD6Vu88qLte+lTbDpijE684uZ/mXzJSGcmMTkQX1+I2p9pWbJIqNksVG1v3DxecJTil3JNag8PAlpIduXb5vGY9xo4Cx/0bJK879PyUnDYh41lmmHk8oU6zS9rxoRkibnnHDBXbSs2TBp8vDTrHvxCIv3ag1Wbuh9xaj37cnmJOuQXQoXD7MsxDRaccqHNrwT/X6+U1eyRJvZIT9f/OHqyvTuynnLQuVpcAAAAA3VqTx6uFb2/S7z/YJp9hzohZ8JUxOv8kRid2Kz6v9PlLUtlnkrNXm9pxmaE15SJd58wwzPCqqbp1umXjQXMhB3ed+X6JyWbgZE/x7yebdQjb7h/LAg6NB82g8MDG0ADx4A5zKm5HrAnmaLnsoWbdt7yR5nTNzEHRWXU25L1trSPMBp4d+pinSdrzibTj39KOD8wpyK4D0v9eMTfJrH9XdIY0wD+SMWeEZD3CwBLDMEdhBkLE0hXmSMlgexKk/pOlIedLQ6aY38OJMBIN6KYIFRGWFq9Pz3y8U794a5Pq3C2yWKSrT+2vH0wdrt4p9ng3DwAAAD3MmtKDuuvFT7XVPzrxKxP66r7poxid2J0YhrRpmfTOA9KB9Uc/354mJfduv1BFYBGLQPgYmELZLiz0B4Zt7x+tlt6xsCaaAWOiP3wM7iebAVzFRjN0OxxHujl9OHuYlDOsdb93UexXnD0Wic7Wac/6oTnacs/q1pBx1yqpocJcpfZ/r5rPSco0Q8ais8wRjbkjzUU1ti33B4kl7RdE6dXfDBCHTDGDTUdarD8pgE5i+jOO2Sc7qnTvq19o/b5aSdKYvhl6aMZojS/sFd+GAQDQA9Cf6f74M4ysJo9Xv3xnk373vjk6MSfNoQWXj9GUkYxO7FZKV0rv3GeOSJPMEYpjr5I8jYfUVKuSGqsOP5ovEqyJ5hTatpsj1QzLPA3maMZml3/ff9/jCm+VWUlK79saGGYPNacIZw8zp/L2pFF3Lc3S3jXm9OUdH0q7Vrafyu3MMFdcNrytxxKcZugYCBKzBves7wXoxpj+jIirqHfrp//coJdW75YkZSQl6q6pw3XNpP6yWfmPPwAAACJr7a5qff/FT7Vlf70k6fIJfXXf9JHqlczMmG5j/wap5EFp4+vm/YQk6bRvSGd89/A17Xw+yV1jBoyBoLGh0gwb294PBJC2Q0JCZ6/2oWFSm2OJyZ0Lr1qazXCx2WWGjR5XaOjY3GC2JWuIGSKeKCPtEuxS/9PM7ezvm9/TvrXmKMYd/5ZKP5aaasxzc0aYtRGHnC8NON1cwRZAt0eoiMPy+gz9eeVO/fzNjaptMq/OzTylUD+cNkKZTHUGAABAhDV5vFr0zmb99v2twdGJD18+Rl9mdGL3UbNHWr5AWvtnc9ShxSpNuEE6d66UXnDk51qtrQFg1uDYtPdYJNjNLal3vFvStSXYpcJJ5nbW9ySvx1zJOinz2FcFBtCtECqiQ6t3HtT8V9fpi73mVOdRBel6aMZondyfv0gBACcYn89c/dJijfziAQCCPvWPTtzsH504Y3yB7r90FKMTj4dhmAujfPhLcyTd4PPMkWL9JpkBUCQ1HjTfZ+X/SS1N5rERl0jnzzen/+LEY0uU+oyLdysARBGhIkJU1rv1yLINeuETc6pzujNBd00drmuLBzDVGQDQNYVMl+tgqpynwayX5W3237rNKVqB25amwz/mdbfW0jrr+9L598b3swI9kLvFHJ34f++ZoxOzUx36yeWjNXVUfryb1r3t+1T65w9baxlK5tTUDxeaKxkXnWUGjIPPM1cZ7mxNO0+jtOq30ge/aJ3q2v906csPmCPWAAA9FqEiJJlTnZ9dVaqfv7lRNY3mymhXTuynH04boexURmUA6AF8XnMKTunHUvVOc8VIn8cMjAL7Xo//9hgfS+ptrliYUSj1GmBO7ckoNI85T8BFGgzDXPWyaru/ILv/H6gWyzHsq/3xFrc/HKxqLeQfcj8QIB4MLQAfLV539N8DOMEYhqHvvfCp/vHZPknSpeMK9MClo9SbUjud56qU/vWQtPopSYZZR/CsOVJ6P2nrv8ytoULa9E9zk8y/w4acb9a8G3iWubjG0fi80qd/kd59WKrdYx7LHSlNuV8aegELbwDACYBQEVq7q1r3vrJOn+8xryyO7JOuh2aM0sQBmXFuGQAcB3edtPsTcyXC0o/N/ea6yL7HwR3S3v92/Jgzwx849jdvexW2CSD7m4Fkd/4HV1ONWYR//xfS/vX+7X9myBcv9jQpubeUnGVuSZlScqZkT5FsDnOqX8itQ7LZD7k9wnkUlQci7umPdugfn+1TgtWiX18zQReN6RPvJnVf3hZp9R+kf/1Yaqo2j42+Qvryg1JGP/P++GvM0d1ln7UGjIGLbZ8sNTeLzRxhONg/irFgvGS1tb6PYUgb/ymVPCAd2GAeS+8nnXe3NHZm6LkAgB7NYhiGEe9GHK9wl7yGyecz9OPX1+sPH22XYUhpzgR9/4Lhuq64vxJs1ng3DwDCU72rNUDc9bFU/oVZIL4te5rU7xRzJEWCXbImmvV+rAn+20TJlmDeBo918FjguKtCqi6VanaZ/yCr3mXeb6w6envtqW1CxkLzH3wZgdt+Umq++X7x5mmUKjZJ5f8zQ8NAgFi7+zBPsJifI8EhyTD/8Sl/V+Ow++r4uDWhNSBMzgwNCtseS84yQ9puXu+Q/kz3x59heNaUHtTM/1shj9fQ/EtG6mtnDox3k7qvHR+aU53L15n380ZL034mFZ1x9Oe6683nby2RtpRIVVtDH0/qLQ061wwZ0/tI7z1q/j0beOys70mn3iolOiP6kQAAsRduX6YL/GsF8eDzGZr38ud6/pNdkqQrTu6nudNGKCete/+DDMAxMgypuV5qrDZHMzRWmyPPgvttjiU6pQFnmtOhjrZqY6x4W6Tyz6XSlWaQuGtl69SrtjL6S/2LpUL/ljcqNiMo3PX+oLG0dQve3yW59pvf/35/UNcRi838vg8NG9vud3aKtWGY07lbmlo3T5NZe/Dg9tZRh/vXS1Xb2oezAel9pdyT/NtI8zZ7uGRP7ly7AJwwKuvduv3Pa+TxGrp4TB/NOqMo3k3qnmp2S2/dK33xsnnf2Us67x5p4qxjvzDlSJWGX2hukjkKf+u/zIBx+/tmiYkv/mZuAQlJ0mnflM74jpTUK4IfCADQnRAqnoB8PkM/+psZKFot0i9njtdl4/vGu1kAIqGpRjqwSarYaIZBjQcPCQmrW8PCcGrQrfmjeZs1RBp4trkVnSWlZEf8I7RjGGYgV/a5tHetOTpi92rJ4wo9z2KT8sdI/U8zA8T+p8UvBHWktoZtHfE0mv8QrN7pDxz3mPdrdks1pVLtXrNmY80uc9OKjl/HmdEaMjp7+QNCd/uwMHi8sfXxwwWFHUnqLeWOOiRAHGEeB4AweX2Gvvv8Wu2radKg7BT99IoxsnTnchDx4GmSVjwmfbDQvCAki3TKLOm8e81R3Mejd5F0ytfMzeuR9qw2A8at/5IqNkujZkjnzu06FxoBAHHD9OcTjM9n6O5X1ukvq0oJFIHuyjDMabcHNpjh4YFN/v1NUt2+8F7LZjfDKGeGOdLA2av1NnDMdUDa/oG5YuShQVTuqNaQccDpxz9awesxP8e+z8wQscx/G6gN1ZYjQyo8VSo8zRyNWHCyGeb1BD6vVF/uDxl3tQkc29xvPBi590tIMqcOJziljL6hIw9zR0mpud27/mM3QX+m++PP8NgsfHuTfl2yWUmJNr1y+xkanp8W7yZ1H4YhbXxDWjbPvDAlSf0nS9MekfqMi2/bAADdHtOfcViGYejeV1sDxYVXESgCXZphmOHRgY3+8HBj6/6RAqW0PlLOcClrqDmS0JkRGhYm9Wo9lph07GFRY7W08yNzKtT29/0LdPi3lU9IFqvUZ7w5TXrg2eY/cuwph389d51Utq5NePiZOd3W29z+XGuClDPCHInY71RzFGLOSZK1h9Z/tfqnPqcXmMXyO+KuazPCcZfkrjVDwcCW6Ay9n+Aw/7wTHK0hYmKSGSwTGAKIkeUb9+uxf22WJD38ldEEiuE4sElaNtesfShJaQXSBQ+Zi7Hw33EAQBwQKp4gDMPQ/Fe/0J9XlspikX5+5TjNmECgCHQZhmFOKSpdYdYH3P8/8x8Ph07xDbJIvQeYQVv2MDNEzBkhZQ81A8NoSOoljbjI3CSp/oC04wNz2/6+VLlF2rvG3P79K3NBk36n+KdKn2lO1QqEh2Wfm9OzO2JPM8PDtlvuSd1+EY6Ic6SZU5BzR8S7JQBwTPZUN+q7z6+VYUjXFffX5RP6xbtJ3UNTrfTeI9LKJWZpDJtdmjzbXCClp4zQBwB0S4SKJwDDMHT/a1/oTx/vlMUiPfrVcfrKyXTigLhqaTbDtdIV5mrFpSukhsr251kTpazB/uBwhD88HG7WNkxMin2720rNkUZ/xdwkc9RcIGDc/r45eq50hbm990jHr5FW0Boc9hlr3vYq6rkjEAHgBOVu8epbf16j6gaPxvbL0PzpI+PdpMgyDHOhq71rzXIhe9eaF9skc7S9NUGyJZp/r9v899vtJx5yboI5cn3DG+YCX5I0bJo09Sdm3wAAgDgjVOzhDMPQA3//n55eYQaKP7tirL46kUARPUCLW9r9ibloh7OXuTBH7kmxWdm3M5pqpd3/aQ0Qd39iLprRVoJT6nuKObW3YLy5im7mQPMfFt1BRl9p3NXmFvjH1XZ/yFi6QrKnhoaHeWPMYBIA0OP95PX1+nRXtTKSErX42pPlSOiif18fi44CxH1rzUXQoiVriHThT6WhX47eewAAECZCxR7MMAw9+I//6amPdkiSHvnKWF15SmF8GwV0lrfF7LBvf88Mqko/bh/K2dOkviebAWPhJHPqbbxWp63dZwaeO/0j9crXtV/kJKm3WXcwsPUZJyXY49PeSLNYpMxB5jbxpni3BgAQR6+u3aM/rjAXFfnlzHEqzEyOc4vCYBhmuY624eG+TzsOEG12KW+0eWGwz3gpb5Q52tDXYi5E5vP4b71t9lsOebzFvA0eazHr6469uuf0EQAAPQahYg9lGIZ+/Pp6/eHfOyRJP/3KGF11KoEiuhGfzwziAtNpd/xbaq4LPSclVyo6w1y0ZPdq8/Ht75lbQPZwM2AsnCT1m2ROI47U1Fqvx1xtuXavVLvHvC3/wgwRD+5of36vAWZ4OMAfImYNZZovAKBH21xep7l//VySNPtLQ3TeiLw4t+gIDMP8+3vvmjajED+V3B0FiA4zNAwEiAUTzBkT3WV2AQAAEUCo2AMZhqGH31ivJz/cLkl6+PIxunpS/zi3CjiKwEIl29/zh4gfSo1Voec4e5kLfgw8x1z8I2d462qHPq+5cvDuVdIu/1a11VwpuWKj9N8/+V8jw1w9uLDYvO07UXKmt2+Pp6lNYLhXqt3dZt8fINbvl2Qc5gNZpPzRUv/TzenM/U8zRxoAAHCCqHe36BvPrFajx6szhmTpzi8Pi3eTQtXuMwPEPWukvf81t0P7HpIZIOaP9oeH481bAkQAAAgVexrDMPTTf27Q7z4wA8WfXD5a1xYTKKKLOrijdVGP7e9L9eWhj9tTpQGnmwHiwLPNKUWHq5lotZkd/vzR0ilfM4+5Ksw6hoGQcc9qc7rSlnfMTZIsVil3pPnaTTWtgWFDxbF9BmuiGRam9zVvMweaAWK/U6O3CjMAAF2cYRia+9fPtPWAS/npTv3q6gmyWS3xa1BDlT84XCPt8d/W7Wt/XnAK8wQzQCyYYC6URoAIAEA7hIo9iGEYemTZRv3f+9skSQ/NGK3rigfEuVU44TUelCq3mfWIqraZowertkmVW9uPBkhwmiMIB55ljkYsmHB8nfiUbGn4NHOTzOnK5eukXf+Rdq00RzVWl5rHyte1f36CszUsDN4WhB5LzmIKMwAAh/jjip36x2f7lGC1aPF1E5Sd6ojdm7vrzbqHwVGIazouS2KxSjknSX0nSAUnm/2OvNHULgQA4BgRKvYQhmHo0Tc3asl7WyVJD142SjecRqCIGGmoag0NK7eGBoiNBw//PGuCudpxYCRiv1OlRGf02mlL9I88mCAV32YeqyszRzFWbJSSs0PDw6TerdOrAQDAMVlTelA/fv1/kqR5F52kiQMyY/PGK/9P+mSpdGCjOixPkjnIDA/7nmze9hkr2VNi0zYAAHogQsUewDAM/eKtTXp8uRko3j99pG6cXBTfRqFn8TSZ04Jrdkk1u83RfW1DxKbqIz8/NV/KGmxODc4c3LoqcNYQyR7nFSDT8qWRl8a3DQCAqFi8eLEeffRRlZWVady4cXrsscc0adKkw56/aNEiPfHEEyotLVV2dra++tWvasGCBXI6o3jBq4epcjVr9p/XyOM1dNGYfH3tjKLYvPHqp6R//qD1fnrf1guJff2jEJN6x6YtAACcIAgVe4BfvrNZv3l3iyRp/iUjdfMZA+PcInQrhmHWHgwEhsGtzX3X/qO/Tloff2A40B8gDmq9zygAAECMPf/885ozZ46WLFmi4uJiLVq0SFOnTtXGjRuVm5vb7vxnn31Wc+fO1dKlS3X66adr06ZNuvnmm2WxWLRw4cI4fILux+sz9J3n/qu9NU0alJ2iR64YK0ssRvxvWy69/j1z/4zvSKd9y7xoCAAAoopQsZtb9M4m/bpksyTpnotP0tfOJFA8Ifm8kqfRvzW03rY0tbnfKDW7zMVQDg0QW5qO/h6JyVJGoZTRz9yCow0HS72LCA4BAF3KwoULdeutt2rWrFmSpCVLluj111/X0qVLNXfu3Hbnf/TRRzrjjDN07bXXSpKKiop0zTXXaOXKlTFtd3f265LN+mBzhZyJVj1+/clKc8ZgcZOKzdILN0q+FmnMldKUByhdAgBAjBAqdmO/emezFr3TGijectagOLeohzAMqWKTuRqxPcUs2J0zIvZFu9315uIhe9eaxcZrdpnBYEhQ6L/1Nh//+6XmS73ahIYZh+xTXxAA0E00Nzdr9erVmjdvXvCY1WrVlClTtGLFig6fc/rpp+uZZ57RqlWrNGnSJG3btk1vvPGGbrjhhsO+j9vtltvtDt6vra2N3IfoZpZv3K9f/8vslz58+Zj/3969x0dZn/n/f89MMhMSkkDOIQQiohzkpImJUWutRvHwpaXHeKhQVvGnJf2p2e7WVIFFW7LbfovsbrG0rqzdWpTqamsLRW0sdl1RLBQB5SCoHJSZJERyJJlk5v7+MZkJkQQJmfu+c3g9H495ZHLf92QubqePx6fXfK7r0uSsJPPftKVO+vXXpdZ6aWyh9MWfslYBAMBCJBUHqae3HNIjf9onSfr+DZNJKPaXvzmURHzv5dCj/lD3884YKW2SlDUtlGTMmiZlTpdGpkfn/VvrJe/OrgTi0bdDic2emox/lpgRUuyI0M7C2BGhwSfh5zEjpJEZn0oYjg0NJYmxcCojAAAmqq2tVSAQUGZmZrfjmZmZ2rNnT4+vueWWW1RbW6vLL79chmGoo6NDd911l77//e/3+j6VlZVatmxZVGMfjD46fkL3rtsuw5BuKRqnr1w01vw37fBL674pffKBNGqcdNNac4e9AQCAU5BUHISCQSMylOWeq8/TnVeca3NEg5BhSMf2dyYRX5IO/m/33X4ujzS+WAq0h3YLttZL1e+EHlrXdd3IzO5JxswLpLTzQlOGe9NSJ3l3hBKH4SRi3YGer03MlrJnSdkzO4eaJHQmCj+dOOz86fJITmcUbhAAAMPLpk2btHz5cj366KMqKirS/v37dc899+jhhx/W4sWLe3xNRUWFysvLI783NDQoNzfXqpAHBH9HUN/+9TYdb2nX9JxkLfk/U81/U8OQ/nBfaP3mTpRuXhe9L3oBAMAZI6k4CL22v1aH6lqUGBejuz5PQvGM+VukD18LJRH3vyx98mH386PGSeddK028Rjrnc109Ag0j1HfQt0vy7pJ8O0M/694P9Sds8kkHqrr+jssjpU+SsqaHEo6jxkm1e7sSiMcP9hxfcm4oeZg9SxozS8qaISVm9nwtAADoVVpamlwul3w+X7fjPp9PWVk9D/BYvHixbrvtNt1xxx2SpOnTp6u5uVl33nmnHnjgATl7+NLO4/HI4xneO/1/uP5dvX34uJJHxOrRWy9SXKzL/Df935XS9iclh1P6+hNSpgWJTAAAcAqSioPQ2jdDpblfvWisRrgtWLgNZscOhHYj7n85lFA8eSCJM1bKuyyURDzv2tAOw5768DgcoV6Do3KlSdd3HW9rkqp3dyUZfbsk3zuSvym0E9G7o/e4Rud17UAcM0vKmiklpEbpHw0AwPDmdruVn5+vqqoqzZ07V5IUDAZVVVWlsrKyHl/T0tJySuLQ5QqtswzjLNqRDAPbDx/XLzeHvix9pHSmclPizX/Td1+Q/vRPoefX/Yt0Xon57wkAAHpEUnGQqW5o1cu7Q9+631I0zuZoBqiPtko7fhPakVj3fvdzybnSxJJQEvGcKyTPyLN/H89IKffi0CMsGJSOf9iVZPTuCg1YSTuvK4mYPSM09AQAAJimvLxc8+fPV0FBgQoLC7Vy5Uo1NzdHpkHPmzdPOTk5qqyslCTNmTNHK1as0IUXXhgpf168eLHmzJkTSS6iuz/uPCpJunFGtq6abEF1xcd/k567M/T84oVS0Z3mvycAAOgVScVB5jd/PaxA0FDB+NE6PzPR7nAGlqaa0DfX25/sOuaMkcYVh5KI510TmuJs5lRAp1NKmRB6TP2iee8DAABOq7S0VDU1NVqyZIm8Xq9mzZqljRs3Roa3HDp0qNvOxAcffFAOh0MPPvigPvroI6Wnp2vOnDn64Q9/aNc/YUAzDEMvvRv6ovv6aT2XlEdVw8fSUzdLHSekc6+Wrvtn898TAACclsMYAvUcDQ0NSk5OVn19vZKSkuwOxzSBoKErfvRnfXT8hFZ8Y6Y1k/UGg2BA+usa6ZWHQwNVJGna16QL5krnfF6KG7qfCQDA0DFc1jND2XD6b7i/ulElK/6iWJdD2xZfo8S40wyp6y9/s7TmulBrmfTJ0u0vSXHJ5r0fAADDVF/XMuxUHET+8l6NPjp+QskjYnXD9Gy7wxkYDr8lbfj70AAUKTTc5MYV3UuSAQAAEFXhXYqXnptmbkIxGAyVPHt3SPFp0i3rSCgCADBAkFQcRE4e0GLJZL2BrLk2VOr8t1+Ffo9Llq5aLBX8neQc5vcGAADAZC+9E0oqXjPV5F6KVf8k7fmD5HJLN60NDbsDAAADAknFQcJb36pX9lRLkm4pyrU5GhsFA9LWJ6Sqh6TW46Fjs26VSpZJI9PtjAwAAGBYqG5o1fbDxyWZnFTc9ivpf/819PxLq6RxRea9FwAA6DOSioPEurdCA1oK81I0MWOYDmj5aKu0/u9Dk/8kKXO6dOP/lcZdYm9cAAAAw8ifdoe+6J6ZO0qZSXHmvMkH/yP94d7Q8yv+UZrxDXPeBwAAnDWSioNAIGho3Vuh0udbisbZHI0NWuqkqmXS1l9KMiRPknTVg1LB7ZKLjzAAAICVXnrXK0m61qxdiscOSL+5TQp2SBd8RfrC9815HwAA0C9kZAaBTXur9XF9q0bHx+q6aVl2h2OdYFD623+Feiee+CR0bObN0jUPSSMzbA0NAABgOGpq69Dr+49JMimp2FInrf1GaO2XUyDNfVRyOKL/PgAAoN9IKg4Cw3JAy0fbpA3fDZU8S1LGBaFS5/GX2hsXAADAMPbq3hr5A0HlpcZrYsbI6P7xQLv0m3nSsf1Scm5oMEvsiOi+BwAAiBqSigPcx8dP6M97Q31rbh4Opc8tddIrD0t//U9JhuRODJW8FN5JqTMAAIDNXu4sfb5maqYc0dxBaBjS+nLpw/+R3COlm5+WEk2eLA0AAPqFLM0A9/RbhxU0pEsmpOjc9Ch/G2ymQIfkb5TaPv1oOP2xj7dLJ+pCf2P6N6RrH5YSh1HJNwAAwADVHgjqlT2hL7uvvSDK67PNP5W2/ZfkcEpfWyNlTYvu3wcAAFFHUnEA6wgETxrQMt7maHrhb5beeV7asU5q+LgrOdjecvZ/M31KqNQ57/LoxQkAAIB+2fJBnRpaO5Sa4NZF40b3/w92+KWD/yvt+YP01uOhY7OXS+fP7v/fBgAApiOpOIC9sqdavoY2pSS4NfuCAVb+4d0pbX1C2vGb0E7D3sTESZ7Ekx5Jn/r9U8fj06QJn5dcsZb9UwAAAPDZXn7XJ0m6ekqGXM6zLH1urZfee1nau0F6709SW33XuYLbpaK7ohApAACwAknFAWztltAuxa/nj5UnZgAMaPE3S7ueCyUTP/pr1/HRedJF86Vxl3RPErpHSjFuu6IFAABAlBiGoZfeCfdT7GPpc/0Rae8fpT3rpQ9fk4LtXecSMqRJ10mT50jnXcOkZwAABhGSigPU4boWvbqvRpJ0c6HNA1q8O0ODU3Y+07Ur0RkjTf4/Uv63pHM+LzmdtoYIAAAA87zzcYM+rm9VXKxTl09MO/3FhiH5dkl7Nkh710tH3+5+Pu18adIN0uQbpZwC1pEAAAxSJBUHqHVvHZZhSJdNTFVeWoL1AfibpV3/3bkrcWvX8dHnSPnzpVm3SiMzrI8LAAAAlguXPl9xXrpGuHuooAm0SwdfD5U1790gHT900kmHlFskTb5BmnSjlDbRmqABAICpSCoOQO2BoNb99bAk6ZZCiwe0HN3R1SvR3xg65oyVpnTuSsy7gm+TAQAAhpmXOpOK10z9VJ/v/VXS209L770Y6pcYFhMnnXtVaEfi+ddJI9MtjBYAAFiBpOIAVLXbp5rGNqWNdJ+6cDNDW5P0znOhEuePt3UdH31OKJE461YWggAAAMPU4boW7T7aIKdDunrKSWvT9/4k/fqrXb/Hp0rnXx/akTjhC5I73vpgAQCAZUzbcrZq1Srl5eUpLi5ORUVF2rJly2mvX7lypSZNmqQRI0YoNzdX9913n1pbW80Kb0D79ZudA1oKcuWOMek/kWFIh96Ufn+P9JPJ0gvfCSUUnbHSBV+W5r0gfWebdPm9JBQBAACGsXDpc0FeilISOofwGYb0ysOh55NukBZslL77njR3VahXIglFAACGPFN2Kq5bt07l5eVavXq1ioqKtHLlSs2ePVt79+5VRsapffjWrl2r+++/X2vWrNGll16qffv26Vvf+pYcDodWrFhhRogD1qFjLfqf92olSTdfbMKAlk8OSjvWSW8/JdW933U8ZUJoV+LMW0giAgAAICKcVLz25AqaPeulo9sl90jpiz+VElLtCQ4AANjGlKTiihUrtHDhQi1YsECStHr1aq1fv15r1qzR/ffff8r1r7/+ui677DLdcsstkqS8vDzdfPPNevPNN80Ib0B76q3QLsXPnZemcalR+oa3tUHa/YK0/Snp4Gtdx2MTpKlflGbdIo2/nF6JAAAA6OZ4i19bPqyTdFI/xWBQ+vPy0POiu0goAgAwTEU9qej3+7V161ZVVFREjjmdTpWUlGjz5s09vubSSy/Vk08+qS1btqiwsFDvv/++NmzYoNtuu63H69va2tTW1hb5vaGhIbr/CJv4O4J6pnNAy61F/dylGAxI728KNc7e/Xup40TnCYd0zhXSzJulKXMkz8j+vQ8AAACGrFf2VCsQNDQpM1HjUxNCB9/9rVT9juRJki4tszU+AABgn6gnFWtraxUIBJSZ2X3ASGZmpvbs2dPja2655RbV1tbq8ssvl2EY6ujo0F133aXvf//7PV5fWVmpZcuWRTt02738rk+1TX6lJ3q6N8Hui+o90ttrQ9ObG492HU89T5p1szT9G9Ko3OgEDAAAgCEtUvp8QXiXYkDa9M+h58WLpBGjbYoMAADYbUBMf960aZOWL1+uRx99VEVFRdq/f7/uuecePfzww1q8ePEp11dUVKi8vDzye0NDg3JzB3+ibO2Wg5Kk0oJcxbr6UIrcXCvt+m9p+9pQb5uwuFHS9K+F+iTmXCQ5HFGNFwAAAENXa3tAr+6rkXRS6fPOZ6XavaF15iV32xccAACwXdSTimlpaXK5XPL5fN2O+3w+ZWVl9fiaxYsX67bbbtMdd9whSZo+fbqam5t155136oEHHpDzU73+PB6PPB5PtEO31Ye1zfrf/cfkcEg3FZ5BgjQYkPZuCPVJfO9FKdgROu6Mkc6bLc28STp/thQztO4TAAAArPH6gVq1+APKSorT9JxkKdAhvdq5S/Gy/1+KS7Y3QAAAYKuoJxXdbrfy8/NVVVWluXPnSpKCwaCqqqpUVtZzz5WWlpZTEocul0uSZBhGtEMckJ7aEhrQ8vnz0zV29GcMaPG3SL+ZJ+1/uetY9qzQwJVpX5US0swLFAAAAMNCuPT5mqmZcjgc0ttPSXXvS/FpUuH/Z3N0AADAbqaUP5eXl2v+/PkqKChQYWGhVq5cqebm5sg06Hnz5iknJ0eVlZWSpDlz5mjFihW68MILI+XPixcv1pw5cyLJxaGsrSOgZ7YekSTdUvgZA1paG6S1pdKh16XYeKlwYWjoSsYUCyIFAADAcBAMGnr53WpJnaXPHX7p1R+FTl5+L8P+AACAOUnF0tJS1dTUaMmSJfJ6vZo1a5Y2btwYGd5y6NChbjsTH3zwQTkcDj344IP66KOPlJ6erjlz5uiHP/yhGeENOC++41Nds19ZSXG6anJG7xe21ElPfkX6+G+haXu3PiONu8S6QAEAADAs/O3wcdU2tSnRE6NLJqRK25+Q6g9JIzOlgtvtDg8AAAwApg1qKSsr67XcedOmTd2DiInR0qVLtXTpUrPCGdDWvhka0PKNi3MV09uAlkav9F9zpZrdUnyqdNvzUvZM64IEAADAsPHSu15J0pWTM+Q2/NJf/m/oxOf+XnJ/RqseAAAwLAyI6c/D2YGaJr3xfp2cDummi3sZ0PLJh9J/fSn0M3GMNO+3UvokC6MEAADAcHJyP0Vt+6XU8JGUlCNdNN/myAAAwEDRy7Y4WOWpN0MDWr4wKUNjRo049YKafdKa60MJxdF50t/9kYQiAAAATLO/uknv1zQr1uXQlRMSpP/5SejEFd+VYuPsDQ4AAAwY7FS0UWt7QM9u6xzQUtTDgJajb0u/+rLUckxKnyzd9lspKdvaIAEAADCshHcpXjIhVUk7/0tq8kmjxkmzvmlzZAAAYCAhqWijjbu8Ot7SrjHJcbpy0qcGtBx6Q/r1N6S2eil7lvTN56SEVFviBAAAwPDxcmc/xRsmJUmvPRI6eMU/SjFuG6MCAAADDUlFG63tLH0uvXicXE5H14kDr0hP3yq1t0jjLpVuWSfFJdkUJQAAAIaL6sZW/e3wcUnS/znxQqhiJmWCNPNmewMDAAADDklFm7zna9SWD+vkcjpUevKAlt2/l579OynglyaWSN/4FRP2AAAAYImq3dUyDOmSMTFK3Ppo6ODn75dc/N8GAADQHYNabLJ2S2iX4lWTM5SV3Nnw+u110m/mhxKKU74o3fQUCUUAAABYJtxP8b7EP0mtx6W0SdL0r9kbFAAAGJBIKtqgtT2g/97aOaClsHNAy1v/IT1/p2QEpFm3Sl/7T/rWAAAAwDLNbR16bX+tktWkgqNPhQ5eeb/kdNkbGAAAGJBIKtpg/Y6jamjtUM6oEbri/HTpf1ZI6/8+dLLoLumLP6XEBAAAAJb6y74a+TuC+vvEl+XyN0qZ06Spc+0OCwAADFBkrmwQLn2+qWCsXK88JL22InTiin+QvvCA5HCc5tUAAABA9L38rk+j1aDSwPrQgSsrJCd7EAAAQM9IKlpsf3WTth78RDFOQ7c3/kx6e03oRMky6fJ7bY0NAAAAw1N7IKiqPdW6O+YP8gRbpOyZ0uQb7Q4LAAAMYCQVLbbH2yCXAnos6QnFv10lySHd+BPp4tvtDg0AAADD1Fsf1sl9okbz414KHaB6BgAAfAaSihY71tCin8b+m77Q+pbkcElfXi3N+IbdYQEAAGAYe+kdn+6OeUEj5JdyCqTzrrU7JAAAMMDRJMViiUc26XrXW+pwxEqlvyKhCAAAAFsZhqHt77yjW11VoQNXsUsRAAB8NpKKFks9tlWStCfjRvrUAAAAmGjVqlXKy8tTXFycioqKtGXLll6vvfLKK+VwOE553Hjj0F+v7T7aqK82r5PH0a5AbrE04Qt2hwQAAAYBkooWy27YIUlqzMi3ORIAAICha926dSovL9fSpUu1bds2zZw5U7Nnz1Z1dXWP1z/33HM6evRo5LFr1y65XC59/etftzhy623e9jeVuv4sSXJd/SC7FAEAwBkhqWilDr/Gt+2TJLWPudjmYAAAAIauFStWaOHChVqwYIGmTp2q1atXKz4+XmvWrOnx+pSUFGVlZUUeL7/8suLj44dFUnHszp/K7QjIl3aJlHe53eEAAIBBgqSilbw75ZFfdcZIJWSfb3c0AAAAQ5Lf79fWrVtVUlISOeZ0OlVSUqLNmzef0d94/PHHddNNNykhIaHXa9ra2tTQ0NDtMdgc/eAdXd36J0lS3DUP2hwNAAAYTEgqWsg4/IYkaVvwPKUlxtkcDQAAwNBUW1urQCCgzMzMbsczMzPl9Xo/8/VbtmzRrl27dMcdd5z2usrKSiUnJ0ceubm5/YrbDs0vLVeMI6i/uQuUPOlzdocDAAAGEZKKFuo4+KYkaVvwfKWN9NgcDQAAAHry+OOPa/r06SosLDztdRUVFaqvr488Dh8+bFGEUVKzTxOObpAkfTjjXntjAQAAg06M3QEMG4Yhx+FQUnGXc7ISPNx6AAAAM6Slpcnlcsnn83U77vP5lJWVddrXNjc36+mnn9ZDDz30me/j8Xjk8QzeL4r9VcvlVlAvB/J14SVX2R0OAAAYZNipaJX6I4pp9qrDcOrjkVPsjgYAAGDIcrvdys/PV1VVVeRYMBhUVVWViouLT/vaZ555Rm1tbfrmN79pdpj28r2r2D2/lST9d/I85aX13jsSAACgJ2yXs8qRLZKkd4w8JSYm2RwMAADA0FZeXq758+eroKBAhYWFWrlypZqbm7VgwQJJ0rx585STk6PKyspur3v88cc1d+5cpaam2hG2dTZVyiFD6wOFOnf6JXZHAwAABiGSilY5HEoqbgueRz9FAAAAk5WWlqqmpkZLliyR1+vVrFmztHHjxsjwlkOHDsnp7F60s3fvXr322mt66aWX7AjZOoF2GftelEPSqo65Wj719CXhAAAAPSGpaJXD4SEt5yltpNvmYAAAAIa+srIylZWV9Xhu06ZNpxybNGmSDMMwOaoBoPY9OQJtajRGqG7keZqRk2x3RAAAYBCip6IV/M3S0R2SpK1MfgYAAICdfLskSXuMXF09NVtOp8PmgAAAwGBEUtEKH/9NMgL6xJWmj5VKUhEAAAD28e6UJO0Ojtc1UzNtDgYAAAxWJBWt0NlPcXfMFEkOkooAAACwTdAb2qm42xinqWMYIAgAAM4OSUUrhIe0GOdLEj0VAQAAYBsjvFPRGK/UBL7sBgAAZ4ekotkMIzKk5fW2CZKktEQWbwAAALBBo0+ulhoFDIdq4yfKRT9FAABwlkgqmu3YAelEnQyXR2+15UoS5c8AAACwhy+0S/FDI0vJSZQ+AwCAs0dS0WyduxT9mTPVrhi5XU4lxcXYHBQAAACGpUg/xfHKoHoGAAD0A0lFs3UmFY+nXiRJSh3plsNBmQkAAABs4AslFd8NjlNGYpzNwQAAgMGMpKLZjrwlSfImzZBE6TMAAABsdPJOxSTWpQAA4OyRVDTTieNS9W5J0vtxUyUx+RkAAAA2aW+VavdJkt4NUv4MAAD6h6SimT76qyRDGn2OPmofKYmdigAAALBJzW7JCKjBkSSfRiudpCIAAOgHkopmOrwl9DO3SLVNfklSGos3AAAA2KGz9HmfY7wkh9LpqQgAAPqBpKKZOoe0KLdQtU1tktipCAAAAJt0DmnZ2Z4rSZQ/AwCAfiGpaJZgQDry19Dz3KKTkor0VAQAAIANOncq7gqMkyTKnwEAQL+QVDRL9W7J3yS5E6WMKV3lz+xUBAAAgNUMQ/LtlCTtNsYpeUSs4mJdNgcFAAAGM5KKZgmXPo8tkJwuyp8BAABgn/ojUmu9gs5YvWeMpfQZAAD0G0lFs0SGtBSqPRDU8ZZ2SZQ/AwAAwAbe0C7FhpHnqF0xykgiqQgAAPqHpKJZThrScqyz9NnldGh0PElFAAAAWKxzSItvxHmSpHSqZwAAQD+RVDRDU7X0yQeSHFJOQaT0OSXBLafTYW9sAAAAGH46dyoejJ0gScpIirMzGgAAMASQVDRDuPQ5Y4o0YhT9FAEAAGCvzp2K+zRekuipCAAA+o2kohmOdPVTlHTS5GdKnwEAAGCxtiap7gNJ0nb/WElSOklFAADQTyQVzRAZ0lIkSZGdivSuAQAAgOWq35VkSInZer9lhCQpI5HyZwAA0D8kFaOtwy99tC30fGznTsXGUFIxlZ2KAAAAsJp3R+hn5jRVd65Lmf4MAAD6i6RitHl3SIE2aUSKlHquJNFTEQAAAPbxhvoptqdPVVNbhyTKnwEAQP+RVIy2w2+GfuYWSY7QpOeunoos3gAAAGCxziEtxxMnSZLiYp1K9MTYGREAABgCTEsqrlq1Snl5eYqLi1NRUZG2bNnS67VXXnmlHA7HKY8bb7zRrPDMc7j7kBbppJ2KfCMMAAAAKwWDku9dSdLREedJCvVTdHR++Q0AAHC2TEkqrlu3TuXl5Vq6dKm2bdummTNnavbs2aquru7x+ueee05Hjx6NPHbt2iWXy6Wvf/3rZoRnHsPovlOxE9OfAQAAYItPPpDam6WYOB1xZEuSMviiGwAARIEpScUVK1Zo4cKFWrBggaZOnarVq1crPj5ea9as6fH6lJQUZWVlRR4vv/yy4uPjB19Ssf6I1HhUcsZIYy6UJAWChuqamf4MAAAAG4SHtGRMla851E+RIS0AACAaop5U9Pv92rp1q0pKSrrexOlUSUmJNm/efEZ/4/HHH9dNN92khISEHs+3tbWpoaGh22NACO9SzJouueMlSZ+0+BU0QodTEtipCAAAAAt1DmlR1kmTnxPjbAwIAAAMFVFPKtbW1ioQCCgzM7Pb8czMTHm93s98/ZYtW7Rr1y7dcccdvV5TWVmp5OTkyCM3N7ffcUdFpJ/iyaXPocXb6PhYxbiYiwMAAAALdQ5pUeZ0VTd0Vs9Q/gwAAKJgwGW5Hn/8cU2fPl2FhYW9XlNRUaH6+vrI4/DhwxZGeBqRfoonDWlpZPIzAAAAbNJtp2KrJJKKAAAgOmKi/QfT0tLkcrnk8/m6Hff5fMrKyjrta5ubm/X000/roYceOu11Ho9HHs8AWwz5myXvztDzHnYqklQEAACApVrqpIYjoeeZF6im8W1JDGoBAADREfWdim63W/n5+aqqqoocCwaDqqqqUnFx8Wlf+8wzz6itrU3f/OY3ox2W+T7+m2QEpKQcKXls5HAkqcjiDQAAAFbyvRP6OWqcFJesGnoqAgCAKIr6TkVJKi8v1/z581VQUKDCwkKtXLlSzc3NWrBggSRp3rx5ysnJUWVlZbfXPf7445o7d65SU1PNCMtc4dLnsRd3O1zbFC5/ZkgLAAAALBSuosmaofZAUMeaQ+tSpj8DAIBoMCWpWFpaqpqaGi1ZskRer1ezZs3Sxo0bI8NbDh06JKez+ybJvXv36rXXXtNLL71kRkjm62FIi0T5MwAAAGwSGdIyLbImjXE6lBLPl90AAKD/TEkqSlJZWZnKysp6PLdp06ZTjk2aNEmGYZgVjrkM46QhLT0nFdNJKgIAAMBKkZ2K0yKTn9NGeuR0OmwMCgAADBUDbvrzoHRsv3TiEykmTsqa3u1UV09FvhEGAACARQLtUs2e0PPMaaru7KfI5GcAABAtJBWjIVz6POYiKaZ78rC2MdS7JjWBBRwAAAAsUvueFPBL7kRp1HhVN7ZKYvIzAACIHpKK0RApfS7sdtgwDB1rZvozAAAALBbpp3iB5HR2TX5mSAsAAIgSkorREBnS0j2pWH+iXe2BUJ/I1ATKnwEAAGAR747Qz87WPF3lz3F2RQQAAIYYkor9deK4VLM79Hxs96RiuJ9iYlyM4mJdFgcGAACAYcvbuVMxa5okRQa1UP4MAACihaRifx35a+hnygRpZHq3U7VNoX6KTH4GAACw3qpVq5SXl6e4uDgVFRVpy5Ytp73++PHjWrRokbKzs+XxeHT++edrw4YNFkUbZZHy59BOxRp6KgIAgCiLsTuAQe9IuPS56JRTkcnPJBUBAAAstW7dOpWXl2v16tUqKirSypUrNXv2bO3du1cZGRmnXO/3+3XNNdcoIyNDzz77rHJycnTw4EGNGjXK+uD7q9EnNddIDqeUMUWSmP4MAACijqRif/UypEWSahvDQ1ropwgAAGClFStWaOHChVqwYIEkafXq1Vq/fr3WrFmj+++//5Tr16xZo7q6Or3++uuKjY2VJOXl5VkZcvT4doZ+ppwrueMVDBonDWqhpyIAAIgOyp/7IxjoKn/ucadiqPyZnYoAAADW8fv92rp1q0pKSiLHnE6nSkpKtHnz5h5f88ILL6i4uFiLFi1SZmampk2bpuXLlysQCPT6Pm1tbWpoaOj2GBA+1U/x+Il2dQRDwwNpywMAAKKFpGJ/VL8r+Zskd6KUPvmU0+Hy59QEFm8AAABWqa2tVSAQUGZmZrfjmZmZ8nq9Pb7m/fff17PPPqtAIKANGzZo8eLF+slPfqIf/OAHvb5PZWWlkpOTI4/c3Nyo/jvOmrdzp2Jk8nOon+Lo+Fi5Y1j+AwCA6GBV0R/h0uexBZLz1OnOkZ6KlD8DAAAMaMFgUBkZGfrFL36h/Px8lZaW6oEHHtDq1at7fU1FRYXq6+sjj8OHD1sY8Wl8akhL1+RnSp8BAED00FOxPw73PqRFkmoofwYAALBcWlqaXC6XfD5ft+M+n09ZWVk9viY7O1uxsbFyubq+KJ4yZYq8Xq/8fr/c7lO/JPZ4PPJ4Btg6r71Vqn0v9Lyz/Lk60k9xgMUKAAAGNXYq9kckqXjqkBZJOsb0ZwAAAMu53W7l5+erqqoqciwYDKqqqkrFxcU9vuayyy7T/v37FQwGI8f27dun7OzsHhOKA1bNbskISCNSpMRsSV3lz/RTBAAA0URS8Ww1VUuffCDJESp//hTDMCLlzyzgAAAArFVeXq7HHntMv/zlL7V7927dfffdam5ujkyDnjdvnioqKiLX33333aqrq9M999yjffv2af369Vq+fLkWLVpk1z/h7Jw8pMXhkNRV/pzOTkUAABBFlD+frfAuxYwpUlzyKaeb/QG1toe+6aanIgAAgLVKS0tVU1OjJUuWyOv1atasWdq4cWNkeMuhQ4fkdHZ9v56bm6sXX3xR9913n2bMmKGcnBzdc889+t73vmfXP+HshIe0dPZTlKSaJnoqAgCA6COpeLbCQ1p6KX2u7exdE+92Kd7NbQYAALBaWVmZysrKejy3adOmU44VFxfrjTfeMDkqk4WHtGSdlFSMDGphpyIAAIgeyp/P1mcMaQmXPqeOZJciAAAALGAY3cufO4V7KpJUBAAA0URS8Wx0+KWP/xZ6/hlJRYa0AAAAwBL1h6W2eskZK6VNihzumv5M+TMAAIgekopnw7tDCrRJ8alSyoQeL6lp8ksiqQgAAACLhHcppk+SYkLVMk1tHWrxBySxUxEAAEQXScWzEemnWBSZqvdpx9ipCAAAACuF+ylmnlT63BAqfY53u5Tgoc83AACIHpKKZyOcVBx7ca+XhMuf0+mpCAAAACt4d4R+duunyJAWAABgDpKKfWUYnzmkRZJqGzvLn1nAAQAAwAreHiY/R5KK9FMEAADRRVKxr+oPS41HJWeMNObCXi9jUAsAAAAs09YoffJB6HlmV1IxvFMxPYk1KQAAiC6Sin0V3qWYNUNyx/d6GUlFAAAAWMb3buhnYraUkBo5XN0Y6qlI+TMAAIg2kop9dQalz5JU2zn9OZWeigAAADCbb2fo50lDWiSppoHyZwAAYA6Sin0Vmfxc2Oslre0BNbV1SGKnIgAAACwQ6afYPakYKX9mpyIAAIgykop94W+WvJ3fAp8mqRhuiO12OZUUF2NFZAAAABjOvD3vVKT8GQAAmIWkYl98tE0yAlJSjpQ8ttfLjjV3Tn4e6ZbD4bAqOgAAAAxHwYBU3dlTMWtGt1OR6c8MagEAAFFGUrEvzqD0WZJqOxdvaXwjDAAAALPVfSC1t0gxI6TUcyOH/R1BfdLSLomeigAAIPpIKvbFkbdCPz9zSAuTnwEAAGCR8JCWjCmS0xU5XNO5Jo11OTQ6PtaOyAAAwBBGUvFMGcaZ71SMJBWZ/AwAAACT9TakpSHUTzF9pIeWPAAAIOpIKp6ptkbp3Kuk1IlS5vTTXlrbFOqpmMpORQAAAJjN15lU/NQalcnPAADATIwmPlNxSdLX1pzRpTWUPwMAAMAq4cnPn96pGEkq0k8RAABEHzsVTRAZ1EL5MwAAAMzUUic1fBR6nnlBt1NMfgYAAGYiqWiCY82h8ud0dioCAADATOHS51HjpbjkbqdqGkM9FTMofwYAACYgqWiCyKAWFnAAAAAwU2RIy6k9v6sbOncqUv4MAABMQFIxytoDQR1vaZdET0UAAACYLDKkZdopp8I9FdmpCAAAzEBSMcqOdU5+djkdGjUi1uZoAAAAMKR5d4R+ZvWUVOwsf6anIgAAMAFJxSgLlz6nJrjldDpsjgYAAABDVqBdqtkbev6pnYqBoKHazi+709mpCAAATEBSMcpqwklFSp8BAABgptp9UsAveZJCg1pO8kmLX4GgIYeDljwAAMAcJBWjrLazd03aSLfNkQAAAGBICw9pybxAcnZf1oeHtKTEuxXrYskPAACijxVGlEXKTPhGGAAAAGby7Qz97HFIS6ifIqXPAADALCQVo+xYZ/lzGgs4AAAAmCm8U7HHIS2dk5+T4qyMCAAADCMkFaMsPKiF8mcAAACYxjAkb3in4vRTTteEk4p80Q0AAExCUjHKwuXPNMQGAACAaZp8Ukut5HBKGVNOOV3dQPkzAAAwF0nFKOvaqcgCDgAAACYJlz6nTpTc8aecrmanIgAAMBlJxSgLJxVTKX8GAACAWU4zpEU6ufyZnooAAMAcJBWjKBA0VNfM9GcAAACY7DRDWqSTB7WwJgUAAOYgqRhFdc1+BQ3J4ZBSEtipCAAAAJP4OpOKPQxpMQxD1Y2hnoqUPwMAALOQVIyiY82hb4RHx7sV4+LWAgAAwATtJ6TafaHnPexUbGzrUGt7UBKDWgAAgHlMy3ytWrVKeXl5iouLU1FRkbZs2XLa648fP65FixYpOztbHo9H559/vjZs2GBWeKaobQxPfmaXIgAAAExSvVsygtKIFCkx+9TTDaEvukd6YhTvjrE6OgAAMEyYsspYt26dysvLtXr1ahUVFWnlypWaPXu29u7dq4yMjFOu9/v9uuaaa5SRkaFnn31WOTk5OnjwoEaNGmVGeKZh8jMAAABM5zupn6LDccppSp8BAIAVTEkqrlixQgsXLtSCBQskSatXr9b69eu1Zs0a3X///adcv2bNGtXV1en1119XbGysJCkvL8+M0ExFUhEAAACmiwxpmdHj6fDkZ0qfAQCAmaJe/uz3+7V161aVlJR0vYnTqZKSEm3evLnH17zwwgsqLi7WokWLlJmZqWnTpmn58uUKBAI9Xt/W1qaGhoZuj4GghqQiAAAAzBYZ0tLz5OeayOTnOKsiAgAAw1DUk4q1tbUKBALKzMzsdjwzM1Ner7fH17z//vt69tlnFQgEtGHDBi1evFg/+clP9IMf/KDH6ysrK5WcnBx55ObmRvufcVbCPRVT6akIAAAAMxjGSTsVe04qVoeTiuxUBAAAJhoQI4qDwaAyMjL0i1/8Qvn5+SotLdUDDzyg1atX93h9RUWF6uvrI4/Dhw9bHHHPwuXP6exUBAAAgBmOH5La6iVnrJQ2qcdLqhvoqQgAAMwX9Z6KaWlpcrlc8vl83Y77fD5lZWX1+Jrs7GzFxsbK5XJFjk2ZMkVer1d+v19ud/edfx6PRx7PwFskRXoqJrJTEQAAACYIlz6nT5Jiel5zRnYqJg289TIAABg6or5T0e12Kz8/X1VVVZFjwWBQVVVVKi4u7vE1l112mfbv369gMBg5tm/fPmVnZ5+SUBzIjjWFyp/pqQgAAGC/VatWKS8vT3FxcSoqKtKWLVt6vfaJJ56Qw+Ho9oiLG4A9Cb2n76codSUV00cOwPgBAMCQYUr5c3l5uR577DH98pe/1O7du3X33Xerubk5Mg163rx5qqioiFx/9913q66uTvfcc4/27dun9evXa/ny5Vq0aJEZ4ZnCMAwda2ZQCwAAwECwbt06lZeXa+nSpdq2bZtmzpyp2bNnq7q6utfXJCUl6ejRo5HHwYMHLYz4DIV3KmZN7/WSGnYqAgAAC0S9/FmSSktLVVNToyVLlsjr9WrWrFnauHFjZHjLoUOH5HR25TNzc3P14osv6r777tOMGTOUk5Oje+65R9/73vfMCM8U9Sfa1R4wJDGoBQAAwG4rVqzQwoULI19qr169WuvXr9eaNWt0//339/gah8PRa7uenrS1tamtrS3ye0NDQ/+CPhNf/Q+pZo+UkNHj6db2gOpPtEuipyIAADCXKUlFSSorK1NZWVmP5zZt2nTKseLiYr3xxhtmhWO6cD/FpLgYeWJcn3E1AAAAzOL3+7V169ZulTFOp1MlJSXavHlzr69ramrS+PHjFQwGddFFF2n58uW64IILer2+srJSy5Yti2rsnynGI2XP7PV0eJeiO8ap5BGxVkUFAACGoQEx/XkoqGns7KfIN8IAAAC2qq2tVSAQiFTJhGVmZsrr9fb4mkmTJmnNmjX63e9+pyeffFLBYFCXXnqpjhw50uv7VFRUqL6+PvI4fPhwVP8dZ6Orn6JHDofD5mgAAMBQZtpOxeEmMvk5gaQiAADAYFNcXNxtqOCll16qKVOm6Oc//7kefvjhHl/j8Xjk8QystV9NY6sk+ikCAADzsVMxSiJJxUT6KQIAANgpLS1NLpdLPp+v23Gfz3fGPRNjY2N14YUXav/+/WaEaJqTdyoCAACYiaRilBxr6ix/ZgEHAABgK7fbrfz8fFVVVUWOBYNBVVVVdduNeDqBQEA7d+5Udna2WWGagsnPAADAKpQ/R0lkpyJJRQAAANuVl5dr/vz5KigoUGFhoVauXKnm5ubINOh58+YpJydHlZWVkqSHHnpIl1xyiSZOnKjjx4/rxz/+sQ4ePKg77rjDzn9Gn1U3dCYVE+NsjgQAAAx1JBWjhKQiAADAwFFaWqqamhotWbJEXq9Xs2bN0saNGyPDWw4dOiSns6to55NPPtHChQvl9Xo1evRo5efn6/XXX9fUqVPt+ieclepwT0WGBwIAAJORVIySmkj5Mz0VAQAABoKysjKVlZX1eG7Tpk3dfn/kkUf0yCOPWBCVuaopfwYAABahp2KU1DaGB7WwgAMAAIA9IklFyp8BAIDJSCpGgWEYXeXPCSQVAQAAYL1A0NCxzjVpOl90AwAAk5FUjIKmtg61dQQlSWmJlD8DAADAesea2xQ0JIdDSk1gTQoAAMxFUjEKjnX2U4x3uxTvpk0lAAAArBee/Jya4FGMi2U+AAAwF6uNKGDyMwAAAOxWE+mnyJoUAACYj6RiFHQlFSkzAQAAgD2qG1slMfkZAABYg6RiFNR0lj+zUxEAAAB2CZc/s1MRAABYgaRiFNR2lpqksYADAACATaoj5c9xNkcCAACGA5KKUUBPRQAAANgtXP6czhfdAADAAiQVo4CeigAAALAbg1oAAICVSCpGQS09FQEAAGCzSPkzg1oAAIAFSCpGwTHKnwEAAGAjwzDoqQgAACxFUjEKunYqUv4MAAAA6zWc6JC/IyiJnooAAMAaJBX7qbU9oKa2DklMfwYAAIA9wkNakuJiFBfrsjkaAAAwHJBU7KdwQ2x3jFOJnhibowEAAMBwFC59ZpciAACwCknFfgpPfk4f6ZHD4bA5GgAAAAxHNfRTBAAAFiOp2E/hfoqp9FMEAACATcLlz0x+BgAAViGp2E+1TH4GAACAzaobwjsVWZMCAABrkFTsp2ORpCI7FQEAAGCPasqfAQCAxUgq9lO4/JmdigAAALAL5c8AAMBqJBX7qYbyZwAAANgsMv2ZNSkAALAIScV+qu1cwKXRvwYAAAA2iUx/ZqciAACwCEnFfqqlpyIAAABs1NoeUGNrhyQpnZ6KAADAIiQV+yncU5FSEwAAANghPPnZE+NUUlyMzdEAAIDhgqRiP/g7gqo/0S5JSiWpCAAAABucPKTF4XDYHA0AABguSCr2w7Hm0LfCLqdDo0bE2hwNAAAAhqPwkJYMSp8BAICFSCr2w7HO0ufUBLecTr4VBgAAgPWqGzp3KjI4EAAAWIikYj/URIa0sIADAACAPcJr0nSSigAAwEIkFfuhtrPUJI0FHAAAAGwSHtTCTkUAAGAlkor9EJ78nDbSbXMkAAAAGK7oqQgAAOxAUrEfasOlJpQ/AwAAwCbhpGJ6EmtSAABgHZKK/VBLT0UAAADYrKaRQS0AAMB6JBX7IZxUTKX8GQAAADboCAR1rDnUkofyZwAAYCWSiv1wLNJTkW+FAQAAYL1jzX4ZhuR0SCkJfNENAACsQ1KxHyh/BgAAgJ3Ck5/TRnrkcjpsjgYAAAwnJBXPUiBoqK6z1CQtkW+FAQAAYL3qcD9FhrQAAACLkVQ8S3XNfgUNyeGQUuJJKgIAAMB64cnP9FMEAABWI6l4lsKlzynxbsW4uI0AAACwXrj8mcnPAADAamTDzhL9FAEAAGC3SPkzSUUAAGAxkopnKZxUTB1J6TMAAADsES5/TiepCAAALEZS8SzVNnYOaWGnIgAAwIC0atUq5eXlKS4uTkVFRdqyZcsZve7pp5+Ww+HQ3LlzzQ0wCmoiSUV6KgIAAGuRVDxLtc2UPwMAAAxU69atU3l5uZYuXapt27Zp5syZmj17tqqrq0/7ug8//FDf/e539bnPfc6iSPsnnFRk+jMAALAaScWzFNmpmEj5MwAAwECzYsUKLVy4UAsWLNDUqVO1evVqxcfHa82aNb2+JhAI6NZbb9WyZcs0YcIEC6M9O4ZhdCUVKX8GAAAWMy2p2JdykyeeeEIOh6PbIy5uYJdwMKgFAABgYPL7/dq6datKSkoix5xOp0pKSrR58+ZeX/fQQw8pIyNDt99++xm9T1tbmxoaGro9rHS8pV3+QFASPRUBAID1TEkqnk25SVJSko4ePRp5HDx40IzQoiacVEwnqQgAADCg1NbWKhAIKDMzs9vxzMxMeb3eHl/z2muv6fHHH9djjz12xu9TWVmp5OTkyCM3N7dfcfdVeEjLqPhYeWJclr43AACAKUnFsyk3cTgcysrKijw+vQgcaNipCAAAMDQ0Njbqtttu02OPPaa0tLQzfl1FRYXq6+sjj8OHD5sY5amqG1slUfoMAADsERPtPxguN6moqIgcO5Nyk6amJo0fP17BYFAXXXSRli9frgsuuKDHa9va2tTW1hb53epSk2DQ0LEmeioCAAAMRGlpaXK5XPL5fN2O+3w+ZWVlnXL9gQMH9OGHH2rOnDmRY8FgqKw4JiZGe/fu1bnnnnvK6zwejzwe+xJ6XZOfSSoCAADrRX2n4tmUm0yaNElr1qzR7373Oz355JMKBoO69NJLdeTIkR6vt7vUpP5EuzqChiQpJYGkIgAAwEDidruVn5+vqqqqyLFgMKiqqioVFxefcv3kyZO1c+dObd++PfL44he/qC984Qvavn275WvNM1UdGdIysHuRAwCAoSnqOxXPRnFxcbcF3qWXXqopU6bo5z//uR5++OFTrq+oqFB5eXnk94aGBksXe8eaQwu4pLgY+tcAAAAMQOXl5Zo/f74KCgpUWFiolStXqrm5WQsWLJAkzZs3Tzk5OaqsrFRcXJymTZvW7fWjRo2SpFOODyTVDUx+BgAA9ol6UrGv5SY9iY2N1YUXXqj9+/f3eN7+UpNw6TMLOAAAgIGotLRUNTU1WrJkibxer2bNmqWNGzdGqmkOHTokp9OU9uKWCfdUpPwZAADYIepJxZPLTebOnSupq9ykrKzsjP5GIBDQzp07dcMNN0Q7vKhgSAsAAMDAV1ZW1uv6c9OmTad97RNPPBH9gKIsUv6cRPkzAACwninlz30pN5Gkhx56SJdccokmTpyo48eP68c//rEOHjyoO+64w4zw+i2cVEwnqQgAAACb1DRS/gwAAOxjSlKxr+Umn3zyiRYuXCiv16vRo0crPz9fr7/+uqZOnWpGeP3WtVORIS0AAACwB9OfAQCAnUwb1NKXcpNHHnlEjzzyiFmhRF1tuKciOxUBAABggxZ/h5raOiSxUxEAANhjcHentkl4p2IqSUUAAADYIDz5eUSsSyM9pu0TAAAA6BVJxbNQ2xzeqUj5MwAAAKzXNaTFI4fDYXM0AABgOCKpeBZqOxdxaZSaAAAAwAbVja2SKH0GAAD2IanYR4ZhMP0ZAAAAtgqXP2ckxtkcCQAAGK5IKvZRU1uH2jqCkhjUAgAAAHvUNDH5GQAA2IukYh/VNoX6KSa4XRrhdtkcDQAAAIaj8E5FkooAAMAuJBX7KFz6TD9FAAAA2IWeigAAwG4kFfsoMqSF0mcAAADYpCYy/ZmeigAAwB4kFfsovFMxNcFtcyQAAAAYrqrDSUV2KgIAAJuQVOyjcE9Fyp8BAABgB39HUHXNoTUpSUUAAGAXkop9FOmpSPkzAAAAbHCsObQejXE6NDqe6hkAAGAPkop9FE4qpo9kAQcAAADrhSc/p430yOl02BwNAAAYrkgq9lGk/JmdigAAALBBpJ9iEutRAABgH5KKfRQpf6Z/DQAAAGxQ3dgqiX6KAADAXiQV+6i2kZ6KAAAAsE+4/Dk9Mc7mSAAAwHBGUrEPTvgDavYHJElp9FQEAACADSLlz+xUBAAANiKp2Afh0md3jFMjPTE2RwMAAIDhqCZc/kxPRQAAYCOSin3QNfnZI4eDSXsAAACwXk1j15oUAADALiQV+6Br8jOlzwAAALBH1/RneioCAAD7kFTsg8jkZ74VBgAAgA2CQSOyU5GeigAAwE4kFfuAyc8AAACw0yctfnUEDUmsSQEAgL1IKvZBZKdiIuXPAAAAsF649DklwS13DEt5AABgH1YifdDVU5FvhQEAAGC9akqfAQDAAEFSsQ9qOncqppJUBAAAgA0ik59JKgIAAJuRVOyDY5FBLZQ/AwAAwHrVja2SSCoCAAD7kVTsg3D5czo7FQEAAGCD6oZw+XOczZEAAIDhLsbuAAaLQNDQuJR41TS20VMRAAAAtkiKi9H41HiNHT3C7lAAAMAwR1LxDLmcDv3+O5fbHQYAAACGsfJrJ6n82kl2hwEAAED5MwAAAAAAAIC+IakIAAAAAAAAoE9IKgIAAAAAAADoE5KKAAAAAAAAAPqEpCIAAAAAAACAPiGpCAAAAAAAAKBPSCoCAAAAAAAA6BOSigAAAAAAAAD6hKQiAAAAAAAAgD4hqQgAAAAAAACgT0gqAgAAYEhatWqV8vLyFBcXp6KiIm3ZsqXXa5977jkVFBRo1KhRSkhI0KxZs/SrX/3KwmgBAAAGF5KKAAAAGHLWrVun8vJyLV26VNu2bdPMmTM1e/ZsVVdX93h9SkqKHnjgAW3evFk7duzQggULtGDBAr344osWRw4AADA4OAzDMOwOor8aGhqUnJys+vp6JSUl2R0OAABAn7Geia6ioiJdfPHF+ulPfypJCgaDys3N1Xe+8x3df//9Z/Q3LrroIt144416+OGHz+h6/hsCAIDBrK9rmRgLYjJdOC/a0NBgcyQAAABnJ7yOGQLf99rO7/dr69atqqioiBxzOp0qKSnR5s2bP/P1hmHolVde0d69e/Uv//I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      "text/plain": [
       "<Figure size 1600x1600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Try different batch sizes\n",
    "batch_sizes = [64, 128, 256, 512, 1024, 2048]\n",
    "plt.figure(figsize=(16, 16))\n",
    "\n",
    "print(data['x_train'].shape)\n",
    "\n",
    "x_train1 = data['x_train'].reshape(data['x_train'].shape[0], 1*32*32).astype('float32')\n",
    "x_test1 = data['x_test'].reshape(data['x_test'].shape[0], 1*32*32).astype('float32')\n",
    "x_valid1 = data['x_validation'].reshape(data['x_validation'].shape[0], 1*32*32).astype('float32')\n",
    "\n",
    "\n",
    "for i in range(len(batch_sizes)):\n",
    "    #determine plot no\n",
    "    plot_no = 320 + (i+1)\n",
    "    plt.subplot(plot_no)\n",
    "\n",
    "    #use function from above to fit and plot\n",
    "    fitAndPlotBat(x_train=x_train1, \n",
    "        y_train=data['y_train'],\n",
    "        x_test=x_test1,\n",
    "        y_test=y_testing,\n",
    "        batch_size=batch_sizes[i]\n",
    "    )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(86989, 1, 32, 32)\n",
      "Epoch 1/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 1.2235 - accuracy: 0.7055 - val_loss: 0.8450 - val_accuracy: 0.7835\n",
      "Epoch 2/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.5005 - accuracy: 0.8837 - val_loss: 0.6922 - val_accuracy: 0.8284\n",
      "Epoch 3/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.3399 - accuracy: 0.9173 - val_loss: 0.6467 - val_accuracy: 0.8519\n",
      "Epoch 4/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.2537 - accuracy: 0.9359 - val_loss: 0.6953 - val_accuracy: 0.8447\n",
      "Epoch 5/50\n",
      "1360/1360 [==============================] - 21s 16ms/step - loss: 0.2073 - accuracy: 0.9455 - val_loss: 0.6475 - val_accuracy: 0.8708\n",
      "Epoch 6/50\n",
      "1360/1360 [==============================] - 21s 16ms/step - loss: 0.1674 - accuracy: 0.9549 - val_loss: 0.6605 - val_accuracy: 0.8734\n",
      "Epoch 7/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.1420 - accuracy: 0.9612 - val_loss: 0.6947 - val_accuracy: 0.8672\n",
      "Epoch 8/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.1239 - accuracy: 0.9650 - val_loss: 0.7512 - val_accuracy: 0.8404\n",
      "Epoch 9/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.1099 - accuracy: 0.9688 - val_loss: 0.6810 - val_accuracy: 0.8713\n",
      "Epoch 10/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0984 - accuracy: 0.9716 - val_loss: 0.7171 - val_accuracy: 0.8667\n",
      "Epoch 11/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0902 - accuracy: 0.9729 - val_loss: 0.8013 - val_accuracy: 0.8545\n",
      "Epoch 12/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0818 - accuracy: 0.9758 - val_loss: 0.9706 - val_accuracy: 0.8308\n",
      "Epoch 13/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.0728 - accuracy: 0.9782 - val_loss: 0.8175 - val_accuracy: 0.8644\n",
      "Epoch 14/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0701 - accuracy: 0.9792 - val_loss: 1.0777 - val_accuracy: 0.8224\n",
      "Epoch 15/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0675 - accuracy: 0.9794 - val_loss: 1.0652 - val_accuracy: 0.8565\n",
      "Epoch 16/50\n",
      "1360/1360 [==============================] - 24s 18ms/step - loss: 0.0624 - accuracy: 0.9811 - val_loss: 0.8865 - val_accuracy: 0.8839\n",
      "Epoch 17/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0618 - accuracy: 0.9814 - val_loss: 0.8052 - val_accuracy: 0.8777\n",
      "Epoch 18/50\n",
      "1360/1360 [==============================] - 24s 18ms/step - loss: 0.0557 - accuracy: 0.9832 - val_loss: 0.9835 - val_accuracy: 0.8683\n",
      "Epoch 19/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.0529 - accuracy: 0.9832 - val_loss: 0.8917 - val_accuracy: 0.8791\n",
      "Epoch 20/50\n",
      "1360/1360 [==============================] - 21s 16ms/step - loss: 0.0567 - accuracy: 0.9826 - val_loss: 0.8640 - val_accuracy: 0.8854\n",
      "Epoch 21/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0461 - accuracy: 0.9854 - val_loss: 0.9988 - val_accuracy: 0.8824\n",
      "Epoch 22/50\n",
      "1360/1360 [==============================] - 24s 18ms/step - loss: 0.0475 - accuracy: 0.9851 - val_loss: 0.9605 - val_accuracy: 0.8785\n",
      "Epoch 23/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0457 - accuracy: 0.9861 - val_loss: 0.9886 - val_accuracy: 0.8869\n",
      "Epoch 24/50\n",
      "1360/1360 [==============================] - 24s 17ms/step - loss: 0.0524 - accuracy: 0.9841 - val_loss: 1.0246 - val_accuracy: 0.8850\n",
      "Epoch 25/50\n",
      "1360/1360 [==============================] - 24s 18ms/step - loss: 0.0368 - accuracy: 0.9887 - val_loss: 1.0045 - val_accuracy: 0.8816\n",
      "Epoch 26/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0499 - accuracy: 0.9852 - val_loss: 1.1074 - val_accuracy: 0.8781\n",
      "Epoch 27/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.0413 - accuracy: 0.9874 - val_loss: 1.2683 - val_accuracy: 0.8610\n",
      "Epoch 28/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.0447 - accuracy: 0.9867 - val_loss: 1.2909 - val_accuracy: 0.8678\n",
      "Epoch 29/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.0348 - accuracy: 0.9895 - val_loss: 1.0280 - val_accuracy: 0.8821\n",
      "Epoch 30/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0370 - accuracy: 0.9885 - val_loss: 1.0559 - val_accuracy: 0.8801\n",
      "Epoch 31/50\n",
      "1360/1360 [==============================] - 24s 18ms/step - loss: 0.0359 - accuracy: 0.9890 - val_loss: 1.1469 - val_accuracy: 0.8742\n",
      "Epoch 32/50\n",
      "1360/1360 [==============================] - 24s 17ms/step - loss: 0.0452 - accuracy: 0.9865 - val_loss: 1.2264 - val_accuracy: 0.8783\n",
      "Epoch 33/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0348 - accuracy: 0.9898 - val_loss: 1.1490 - val_accuracy: 0.8866\n",
      "Epoch 34/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0322 - accuracy: 0.9900 - val_loss: 1.2652 - val_accuracy: 0.8827\n",
      "Epoch 35/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.0399 - accuracy: 0.9882 - val_loss: 1.2112 - val_accuracy: 0.8787\n",
      "Epoch 36/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0351 - accuracy: 0.9895 - val_loss: 1.2202 - val_accuracy: 0.8746\n",
      "Epoch 37/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0351 - accuracy: 0.9896 - val_loss: 1.1565 - val_accuracy: 0.8832\n",
      "Epoch 38/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0321 - accuracy: 0.9905 - val_loss: 1.6991 - val_accuracy: 0.8609\n",
      "Epoch 39/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0355 - accuracy: 0.9891 - val_loss: 1.2437 - val_accuracy: 0.8838\n",
      "Epoch 40/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0323 - accuracy: 0.9910 - val_loss: 1.2272 - val_accuracy: 0.8865\n",
      "Epoch 41/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.0379 - accuracy: 0.9891 - val_loss: 1.6351 - val_accuracy: 0.8553\n",
      "Epoch 42/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0281 - accuracy: 0.9917 - val_loss: 1.2266 - val_accuracy: 0.8826\n",
      "Epoch 43/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0332 - accuracy: 0.9903 - val_loss: 1.3329 - val_accuracy: 0.8866\n",
      "Epoch 44/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0361 - accuracy: 0.9896 - val_loss: 1.3210 - val_accuracy: 0.8808\n",
      "Epoch 45/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0365 - accuracy: 0.9903 - val_loss: 1.2860 - val_accuracy: 0.8807\n",
      "Epoch 46/50\n",
      "1360/1360 [==============================] - 22s 16ms/step - loss: 0.0278 - accuracy: 0.9926 - val_loss: 1.4379 - val_accuracy: 0.8719\n",
      "Epoch 47/50\n",
      "1360/1360 [==============================] - 23s 17ms/step - loss: 0.0275 - accuracy: 0.9923 - val_loss: 1.6940 - val_accuracy: 0.8529\n",
      "Epoch 48/50\n",
      "1360/1360 [==============================] - 24s 17ms/step - loss: 0.0369 - accuracy: 0.9901 - val_loss: 1.3316 - val_accuracy: 0.8870\n",
      "Epoch 49/50\n",
      "1360/1360 [==============================] - 24s 17ms/step - loss: 0.0290 - accuracy: 0.9919 - val_loss: 1.3530 - val_accuracy: 0.8926\n",
      "Epoch 50/50\n",
      "1360/1360 [==============================] - 24s 17ms/step - loss: 0.0352 - accuracy: 0.9907 - val_loss: 1.3224 - val_accuracy: 0.8839\n",
      "Epoch 1/50\n",
      "680/680 [==============================] - 13s 18ms/step - loss: 1.3745 - accuracy: 0.6779 - val_loss: 0.8074 - val_accuracy: 0.7948\n",
      "Epoch 2/50\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.5920 - accuracy: 0.8742 - val_loss: 0.6877 - val_accuracy: 0.8327\n",
      "Epoch 3/50\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.4029 - accuracy: 0.9078 - val_loss: 0.6370 - val_accuracy: 0.8441\n",
      "Epoch 4/50\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.3004 - accuracy: 0.9289 - val_loss: 0.7127 - val_accuracy: 0.8277\n",
      "Epoch 5/50\n",
      "680/680 [==============================] - 12s 17ms/step - loss: 0.2342 - accuracy: 0.9429 - val_loss: 0.6665 - val_accuracy: 0.8490\n",
      "Epoch 6/50\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.1906 - accuracy: 0.9523 - val_loss: 0.5963 - val_accuracy: 0.8683\n",
      "Epoch 7/50\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.1574 - accuracy: 0.9599 - val_loss: 0.6476 - val_accuracy: 0.8576\n",
      "Epoch 8/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.1306 - accuracy: 0.9659 - val_loss: 0.6266 - val_accuracy: 0.8682\n",
      "Epoch 9/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.1137 - accuracy: 0.9701 - val_loss: 0.6732 - val_accuracy: 0.8651\n",
      "Epoch 10/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0988 - accuracy: 0.9725 - val_loss: 0.6898 - val_accuracy: 0.8635\n",
      "Epoch 11/50\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0894 - accuracy: 0.9748 - val_loss: 0.6776 - val_accuracy: 0.8676\n",
      "Epoch 12/50\n",
      "680/680 [==============================] - 12s 17ms/step - loss: 0.0735 - accuracy: 0.9798 - val_loss: 0.7148 - val_accuracy: 0.8667\n",
      "Epoch 13/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0699 - accuracy: 0.9795 - val_loss: 0.7225 - val_accuracy: 0.8773\n",
      "Epoch 14/50\n",
      "680/680 [==============================] - 12s 17ms/step - loss: 0.0595 - accuracy: 0.9820 - val_loss: 0.6450 - val_accuracy: 0.8911\n",
      "Epoch 15/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0606 - accuracy: 0.9821 - val_loss: 0.8980 - val_accuracy: 0.8551\n",
      "Epoch 16/50\n",
      "680/680 [==============================] - 12s 17ms/step - loss: 0.0573 - accuracy: 0.9822 - val_loss: 0.7930 - val_accuracy: 0.8769\n",
      "Epoch 17/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0471 - accuracy: 0.9857 - val_loss: 0.7388 - val_accuracy: 0.8892\n",
      "Epoch 18/50\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0476 - accuracy: 0.9854 - val_loss: 0.7747 - val_accuracy: 0.8859\n",
      "Epoch 19/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0423 - accuracy: 0.9870 - val_loss: 0.8012 - val_accuracy: 0.8770\n",
      "Epoch 20/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0428 - accuracy: 0.9867 - val_loss: 0.8619 - val_accuracy: 0.8752\n",
      "Epoch 21/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0366 - accuracy: 0.9886 - val_loss: 1.0642 - val_accuracy: 0.8542\n",
      "Epoch 22/50\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0412 - accuracy: 0.9870 - val_loss: 0.8812 - val_accuracy: 0.8770\n",
      "Epoch 23/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0394 - accuracy: 0.9877 - val_loss: 0.9255 - val_accuracy: 0.8781\n",
      "Epoch 24/50\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0345 - accuracy: 0.9889 - val_loss: 1.0447 - val_accuracy: 0.8704\n",
      "Epoch 25/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0340 - accuracy: 0.9891 - val_loss: 0.9525 - val_accuracy: 0.8812\n",
      "Epoch 26/50\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0335 - accuracy: 0.9893 - val_loss: 0.9078 - val_accuracy: 0.8773\n",
      "Epoch 27/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0387 - accuracy: 0.9882 - val_loss: 0.9046 - val_accuracy: 0.8794\n",
      "Epoch 28/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0248 - accuracy: 0.9920 - val_loss: 1.0004 - val_accuracy: 0.8840\n",
      "Epoch 29/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0355 - accuracy: 0.9889 - val_loss: 1.0360 - val_accuracy: 0.8666\n",
      "Epoch 30/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0328 - accuracy: 0.9901 - val_loss: 1.0683 - val_accuracy: 0.8838\n",
      "Epoch 31/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0277 - accuracy: 0.9915 - val_loss: 0.9041 - val_accuracy: 0.8934\n",
      "Epoch 32/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0305 - accuracy: 0.9902 - val_loss: 0.9982 - val_accuracy: 0.8812\n",
      "Epoch 33/50\n",
      "680/680 [==============================] - 11s 17ms/step - loss: 0.0256 - accuracy: 0.9920 - val_loss: 1.0447 - val_accuracy: 0.8796\n",
      "Epoch 34/50\n",
      "680/680 [==============================] - 12s 17ms/step - loss: 0.0257 - accuracy: 0.9921 - val_loss: 1.3880 - val_accuracy: 0.8494\n",
      "Epoch 35/50\n",
      "680/680 [==============================] - 11s 17ms/step - loss: 0.0289 - accuracy: 0.9908 - val_loss: 1.2292 - val_accuracy: 0.8828\n",
      "Epoch 36/50\n",
      "680/680 [==============================] - 12s 17ms/step - loss: 0.0284 - accuracy: 0.9911 - val_loss: 1.0287 - val_accuracy: 0.8880\n",
      "Epoch 37/50\n",
      "680/680 [==============================] - 12s 17ms/step - loss: 0.0272 - accuracy: 0.9915 - val_loss: 0.9805 - val_accuracy: 0.8926\n",
      "Epoch 38/50\n",
      "680/680 [==============================] - 11s 17ms/step - loss: 0.0271 - accuracy: 0.9916 - val_loss: 1.1589 - val_accuracy: 0.8668\n",
      "Epoch 39/50\n",
      "680/680 [==============================] - 11s 17ms/step - loss: 0.0244 - accuracy: 0.9923 - val_loss: 1.1079 - val_accuracy: 0.8795\n",
      "Epoch 40/50\n",
      "680/680 [==============================] - 11s 17ms/step - loss: 0.0270 - accuracy: 0.9916 - val_loss: 1.1973 - val_accuracy: 0.8781\n",
      "Epoch 41/50\n",
      "680/680 [==============================] - 11s 17ms/step - loss: 0.0281 - accuracy: 0.9914 - val_loss: 1.1694 - val_accuracy: 0.8792\n",
      "Epoch 42/50\n",
      "680/680 [==============================] - 11s 16ms/step - loss: 0.0229 - accuracy: 0.9932 - val_loss: 1.2071 - val_accuracy: 0.8753\n",
      "Epoch 43/50\n",
      "680/680 [==============================] - 11s 16ms/step - loss: 0.0235 - accuracy: 0.9929 - val_loss: 1.2768 - val_accuracy: 0.8703\n",
      "Epoch 44/50\n",
      "680/680 [==============================] - 13s 18ms/step - loss: 0.0204 - accuracy: 0.9939 - val_loss: 1.2172 - val_accuracy: 0.8730\n",
      "Epoch 45/50\n",
      "680/680 [==============================] - 12s 18ms/step - loss: 0.0291 - accuracy: 0.9910 - val_loss: 1.1409 - val_accuracy: 0.8793\n",
      "Epoch 46/50\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0239 - accuracy: 0.9931 - val_loss: 1.2690 - val_accuracy: 0.8812\n",
      "Epoch 47/50\n",
      "680/680 [==============================] - 12s 17ms/step - loss: 0.0168 - accuracy: 0.9950 - val_loss: 1.4409 - val_accuracy: 0.8683\n",
      "Epoch 48/50\n",
      "680/680 [==============================] - 13s 18ms/step - loss: 0.0213 - accuracy: 0.9936 - val_loss: 1.4405 - val_accuracy: 0.8686\n",
      "Epoch 49/50\n",
      "680/680 [==============================] - 13s 20ms/step - loss: 0.0261 - accuracy: 0.9924 - val_loss: 1.1688 - val_accuracy: 0.8880\n",
      "Epoch 50/50\n",
      "680/680 [==============================] - 13s 19ms/step - loss: 0.0224 - accuracy: 0.9939 - val_loss: 1.1923 - val_accuracy: 0.8838\n",
      "Epoch 1/50\n",
      "340/340 [==============================] - 9s 25ms/step - loss: 1.6381 - accuracy: 0.6154 - val_loss: 0.9375 - val_accuracy: 0.7533\n",
      "Epoch 2/50\n",
      "340/340 [==============================] - 8s 24ms/step - loss: 0.7291 - accuracy: 0.8511 - val_loss: 0.7978 - val_accuracy: 0.7868\n",
      "Epoch 3/50\n",
      "340/340 [==============================] - 8s 24ms/step - loss: 0.5375 - accuracy: 0.8893 - val_loss: 0.6836 - val_accuracy: 0.8309\n",
      "Epoch 4/50\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.4214 - accuracy: 0.9122 - val_loss: 0.6509 - val_accuracy: 0.8328\n",
      "Epoch 5/50\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.3370 - accuracy: 0.9278 - val_loss: 0.6311 - val_accuracy: 0.8527\n",
      "Epoch 6/50\n",
      "340/340 [==============================] - 7s 21ms/step - loss: 0.2754 - accuracy: 0.9396 - val_loss: 0.6222 - val_accuracy: 0.8593\n",
      "Epoch 7/50\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.2271 - accuracy: 0.9497 - val_loss: 0.6315 - val_accuracy: 0.8577\n",
      "Epoch 8/50\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.1914 - accuracy: 0.9572 - val_loss: 0.5893 - val_accuracy: 0.8651\n",
      "Epoch 9/50\n",
      "340/340 [==============================] - 8s 24ms/step - loss: 0.1600 - accuracy: 0.9634 - val_loss: 0.5908 - val_accuracy: 0.8698\n",
      "Epoch 10/50\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.1342 - accuracy: 0.9689 - val_loss: 0.6597 - val_accuracy: 0.8625\n",
      "Epoch 11/50\n",
      "340/340 [==============================] - 8s 24ms/step - loss: 0.1194 - accuracy: 0.9718 - val_loss: 0.6005 - val_accuracy: 0.8751\n",
      "Epoch 12/50\n",
      "340/340 [==============================] - 8s 25ms/step - loss: 0.0959 - accuracy: 0.9774 - val_loss: 0.6382 - val_accuracy: 0.8670\n",
      "Epoch 13/50\n",
      "340/340 [==============================] - 8s 24ms/step - loss: 0.0860 - accuracy: 0.9790 - val_loss: 0.6193 - val_accuracy: 0.8732\n",
      "Epoch 14/50\n",
      "340/340 [==============================] - 8s 25ms/step - loss: 0.0748 - accuracy: 0.9813 - val_loss: 0.6732 - val_accuracy: 0.8705\n",
      "Epoch 15/50\n",
      "340/340 [==============================] - 7s 21ms/step - loss: 0.0688 - accuracy: 0.9824 - val_loss: 0.6619 - val_accuracy: 0.8783\n",
      "Epoch 16/50\n",
      "340/340 [==============================] - 8s 23ms/step - loss: 0.0627 - accuracy: 0.9838 - val_loss: 0.6473 - val_accuracy: 0.8756\n",
      "Epoch 17/50\n",
      "340/340 [==============================] - 8s 24ms/step - loss: 0.0566 - accuracy: 0.9850 - val_loss: 0.7342 - val_accuracy: 0.8765\n",
      "Epoch 18/50\n",
      "340/340 [==============================] - 21s 61ms/step - loss: 0.0511 - accuracy: 0.9864 - val_loss: 0.8361 - val_accuracy: 0.8471\n",
      "Epoch 19/50\n",
      "340/340 [==============================] - 9s 26ms/step - loss: 0.0481 - accuracy: 0.9872 - val_loss: 0.8517 - val_accuracy: 0.8504\n",
      "Epoch 20/50\n",
      "340/340 [==============================] - 116s 342ms/step - loss: 0.0409 - accuracy: 0.9886 - val_loss: 0.7107 - val_accuracy: 0.8846\n",
      "Epoch 21/50\n",
      "340/340 [==============================] - 8s 24ms/step - loss: 0.0328 - accuracy: 0.9913 - val_loss: 0.7451 - val_accuracy: 0.8709\n",
      "Epoch 22/50\n",
      "340/340 [==============================] - 6s 16ms/step - loss: 0.0433 - accuracy: 0.9875 - val_loss: 0.7444 - val_accuracy: 0.8796\n",
      "Epoch 23/50\n",
      "340/340 [==============================] - 6s 17ms/step - loss: 0.0424 - accuracy: 0.9881 - val_loss: 0.7310 - val_accuracy: 0.8832\n",
      "Epoch 24/50\n",
      "340/340 [==============================] - 5s 16ms/step - loss: 0.0262 - accuracy: 0.9928 - val_loss: 0.7633 - val_accuracy: 0.8861\n",
      "Epoch 25/50\n",
      "340/340 [==============================] - 5s 15ms/step - loss: 0.0391 - accuracy: 0.9888 - val_loss: 0.7448 - val_accuracy: 0.8881\n",
      "Epoch 26/50\n",
      "340/340 [==============================] - 5s 16ms/step - loss: 0.0237 - accuracy: 0.9933 - val_loss: 0.7277 - val_accuracy: 0.8946\n",
      "Epoch 27/50\n",
      "340/340 [==============================] - 6s 16ms/step - loss: 0.0218 - accuracy: 0.9936 - val_loss: 0.8414 - val_accuracy: 0.8800\n",
      "Epoch 28/50\n",
      "340/340 [==============================] - 5s 16ms/step - loss: 0.0294 - accuracy: 0.9910 - val_loss: 0.7542 - val_accuracy: 0.8880\n",
      "Epoch 29/50\n",
      "340/340 [==============================] - 6s 18ms/step - loss: 0.0281 - accuracy: 0.9917 - val_loss: 0.8033 - val_accuracy: 0.8875\n",
      "Epoch 30/50\n",
      "340/340 [==============================] - 6s 16ms/step - loss: 0.0243 - accuracy: 0.9925 - val_loss: 0.8655 - val_accuracy: 0.8776\n",
      "Epoch 31/50\n",
      "340/340 [==============================] - 5s 15ms/step - loss: 0.0254 - accuracy: 0.9926 - val_loss: 0.8250 - val_accuracy: 0.8801\n",
      "Epoch 32/50\n",
      "340/340 [==============================] - 5s 15ms/step - loss: 0.0264 - accuracy: 0.9924 - val_loss: 1.0945 - val_accuracy: 0.8572\n",
      "Epoch 33/50\n",
      "340/340 [==============================] - 5s 15ms/step - loss: 0.0263 - accuracy: 0.9921 - val_loss: 0.9468 - val_accuracy: 0.8740\n",
      "Epoch 34/50\n",
      "340/340 [==============================] - 5s 15ms/step - loss: 0.0334 - accuracy: 0.9895 - val_loss: 0.9447 - val_accuracy: 0.8838\n",
      "Epoch 35/50\n",
      "340/340 [==============================] - 5s 15ms/step - loss: 0.0169 - accuracy: 0.9949 - val_loss: 0.8845 - val_accuracy: 0.8952\n",
      "Epoch 36/50\n",
      "340/340 [==============================] - 4s 13ms/step - loss: 0.0157 - accuracy: 0.9954 - val_loss: 0.9669 - val_accuracy: 0.8752\n",
      "Epoch 37/50\n",
      "340/340 [==============================] - 4s 13ms/step - loss: 0.0268 - accuracy: 0.9917 - val_loss: 1.0391 - val_accuracy: 0.8659\n",
      "Epoch 38/50\n",
      "340/340 [==============================] - 4s 13ms/step - loss: 0.0203 - accuracy: 0.9937 - val_loss: 0.9388 - val_accuracy: 0.8876\n",
      "Epoch 39/50\n",
      "340/340 [==============================] - 4s 13ms/step - loss: 0.0246 - accuracy: 0.9921 - val_loss: 0.9459 - val_accuracy: 0.8812\n",
      "Epoch 40/50\n",
      "340/340 [==============================] - 5s 14ms/step - loss: 0.0214 - accuracy: 0.9933 - val_loss: 0.9765 - val_accuracy: 0.8843\n",
      "Epoch 41/50\n",
      "340/340 [==============================] - 4s 12ms/step - loss: 0.0202 - accuracy: 0.9938 - val_loss: 1.0043 - val_accuracy: 0.8841\n",
      "Epoch 42/50\n",
      "340/340 [==============================] - 4s 13ms/step - loss: 0.0179 - accuracy: 0.9949 - val_loss: 0.8657 - val_accuracy: 0.8946\n",
      "Epoch 43/50\n",
      "340/340 [==============================] - 4s 13ms/step - loss: 0.0173 - accuracy: 0.9947 - val_loss: 1.0086 - val_accuracy: 0.8832\n",
      "Epoch 44/50\n",
      "340/340 [==============================] - 4s 13ms/step - loss: 0.0260 - accuracy: 0.9920 - val_loss: 0.9892 - val_accuracy: 0.8867\n",
      "Epoch 45/50\n",
      "340/340 [==============================] - 4s 13ms/step - loss: 0.0158 - accuracy: 0.9951 - val_loss: 1.1763 - val_accuracy: 0.8592\n",
      "Epoch 46/50\n",
      "340/340 [==============================] - 5s 14ms/step - loss: 0.0216 - accuracy: 0.9935 - val_loss: 0.9688 - val_accuracy: 0.8923\n",
      "Epoch 47/50\n",
      "340/340 [==============================] - 5s 14ms/step - loss: 0.0159 - accuracy: 0.9951 - val_loss: 1.1122 - val_accuracy: 0.8779\n",
      "Epoch 48/50\n",
      "340/340 [==============================] - 4s 13ms/step - loss: 0.0156 - accuracy: 0.9951 - val_loss: 0.9939 - val_accuracy: 0.8936\n",
      "Epoch 49/50\n",
      "340/340 [==============================] - 93s 275ms/step - loss: 0.0156 - accuracy: 0.9953 - val_loss: 1.0909 - val_accuracy: 0.8725\n",
      "Epoch 50/50\n",
      "340/340 [==============================] - 5s 14ms/step - loss: 0.0245 - accuracy: 0.9924 - val_loss: 0.9667 - val_accuracy: 0.8930\n",
      "Epoch 1/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 1.9469 - accuracy: 0.5366 - val_loss: 1.1331 - val_accuracy: 0.7040\n",
      "Epoch 2/50\n",
      "170/170 [==============================] - 4s 24ms/step - loss: 0.8698 - accuracy: 0.8245 - val_loss: 0.8711 - val_accuracy: 0.7754\n",
      "Epoch 3/50\n",
      "170/170 [==============================] - 3s 18ms/step - loss: 0.6597 - accuracy: 0.8725 - val_loss: 0.7700 - val_accuracy: 0.8067\n",
      "Epoch 4/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.5496 - accuracy: 0.8929 - val_loss: 0.7181 - val_accuracy: 0.8245\n",
      "Epoch 5/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.4667 - accuracy: 0.9071 - val_loss: 0.6915 - val_accuracy: 0.8333\n",
      "Epoch 6/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.3944 - accuracy: 0.9212 - val_loss: 0.6779 - val_accuracy: 0.8416\n",
      "Epoch 7/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.3438 - accuracy: 0.9299 - val_loss: 0.6373 - val_accuracy: 0.8494\n",
      "Epoch 8/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.2928 - accuracy: 0.9405 - val_loss: 0.6124 - val_accuracy: 0.8553\n",
      "Epoch 9/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.2582 - accuracy: 0.9465 - val_loss: 0.6541 - val_accuracy: 0.8521\n",
      "Epoch 10/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.2284 - accuracy: 0.9519 - val_loss: 0.5886 - val_accuracy: 0.8697\n",
      "Epoch 11/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.2020 - accuracy: 0.9576 - val_loss: 0.6196 - val_accuracy: 0.8660\n",
      "Epoch 12/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.1805 - accuracy: 0.9615 - val_loss: 0.6147 - val_accuracy: 0.8665\n",
      "Epoch 13/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.1605 - accuracy: 0.9662 - val_loss: 0.5915 - val_accuracy: 0.8755\n",
      "Epoch 14/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.1402 - accuracy: 0.9705 - val_loss: 0.6117 - val_accuracy: 0.8719\n",
      "Epoch 15/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.1252 - accuracy: 0.9731 - val_loss: 0.6673 - val_accuracy: 0.8632\n",
      "Epoch 16/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.1113 - accuracy: 0.9763 - val_loss: 0.7017 - val_accuracy: 0.8583\n",
      "Epoch 17/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.1031 - accuracy: 0.9774 - val_loss: 0.6399 - val_accuracy: 0.8711\n",
      "Epoch 18/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.0900 - accuracy: 0.9809 - val_loss: 0.6604 - val_accuracy: 0.8735\n",
      "Epoch 19/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0821 - accuracy: 0.9821 - val_loss: 0.6463 - val_accuracy: 0.8785\n",
      "Epoch 20/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0812 - accuracy: 0.9806 - val_loss: 0.7288 - val_accuracy: 0.8624\n",
      "Epoch 21/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0654 - accuracy: 0.9857 - val_loss: 0.6732 - val_accuracy: 0.8781\n",
      "Epoch 22/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.0565 - accuracy: 0.9877 - val_loss: 0.6791 - val_accuracy: 0.8774\n",
      "Epoch 23/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.0535 - accuracy: 0.9881 - val_loss: 0.6627 - val_accuracy: 0.8799\n",
      "Epoch 24/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0457 - accuracy: 0.9900 - val_loss: 0.7496 - val_accuracy: 0.8762\n",
      "Epoch 25/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0422 - accuracy: 0.9908 - val_loss: 0.7341 - val_accuracy: 0.8701\n",
      "Epoch 26/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0450 - accuracy: 0.9891 - val_loss: 0.7262 - val_accuracy: 0.8770\n",
      "Epoch 27/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0452 - accuracy: 0.9889 - val_loss: 0.7447 - val_accuracy: 0.8767\n",
      "Epoch 28/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0361 - accuracy: 0.9915 - val_loss: 0.7124 - val_accuracy: 0.8841\n",
      "Epoch 29/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0245 - accuracy: 0.9951 - val_loss: 0.7884 - val_accuracy: 0.8806\n",
      "Epoch 30/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0250 - accuracy: 0.9947 - val_loss: 0.7172 - val_accuracy: 0.8873\n",
      "Epoch 31/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0306 - accuracy: 0.9923 - val_loss: 0.7262 - val_accuracy: 0.8830\n",
      "Epoch 32/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0255 - accuracy: 0.9941 - val_loss: 0.7515 - val_accuracy: 0.8812\n",
      "Epoch 33/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0258 - accuracy: 0.9936 - val_loss: 0.8046 - val_accuracy: 0.8679\n",
      "Epoch 34/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0309 - accuracy: 0.9916 - val_loss: 0.7643 - val_accuracy: 0.8838\n",
      "Epoch 35/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0185 - accuracy: 0.9958 - val_loss: 0.8022 - val_accuracy: 0.8840\n",
      "Epoch 36/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0233 - accuracy: 0.9940 - val_loss: 0.8435 - val_accuracy: 0.8736\n",
      "Epoch 37/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.0162 - accuracy: 0.9965 - val_loss: 0.8140 - val_accuracy: 0.8884\n",
      "Epoch 38/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0120 - accuracy: 0.9977 - val_loss: 0.8027 - val_accuracy: 0.8886\n",
      "Epoch 39/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0082 - accuracy: 0.9989 - val_loss: 0.8359 - val_accuracy: 0.8857\n",
      "Epoch 40/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0341 - accuracy: 0.9896 - val_loss: 0.9683 - val_accuracy: 0.8660\n",
      "Epoch 41/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0253 - accuracy: 0.9928 - val_loss: 0.9485 - val_accuracy: 0.8711\n",
      "Epoch 42/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0197 - accuracy: 0.9948 - val_loss: 0.8849 - val_accuracy: 0.8810\n",
      "Epoch 43/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0117 - accuracy: 0.9974 - val_loss: 0.8891 - val_accuracy: 0.8827\n",
      "Epoch 44/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0176 - accuracy: 0.9954 - val_loss: 1.0350 - val_accuracy: 0.8565\n",
      "Epoch 45/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0309 - accuracy: 0.9911 - val_loss: 0.9251 - val_accuracy: 0.8858\n",
      "Epoch 46/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0088 - accuracy: 0.9981 - val_loss: 0.8950 - val_accuracy: 0.8894\n",
      "Epoch 47/50\n",
      "170/170 [==============================] - 3s 16ms/step - loss: 0.0051 - accuracy: 0.9993 - val_loss: 0.8862 - val_accuracy: 0.8952\n",
      "Epoch 48/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0033 - accuracy: 0.9996 - val_loss: 0.8647 - val_accuracy: 0.8942\n",
      "Epoch 49/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0035 - accuracy: 0.9996 - val_loss: 0.8762 - val_accuracy: 0.8961\n",
      "Epoch 50/50\n",
      "170/170 [==============================] - 3s 17ms/step - loss: 0.0026 - accuracy: 0.9998 - val_loss: 0.8832 - val_accuracy: 0.8974\n",
      "Epoch 1/50\n",
      "85/85 [==============================] - 92s 1s/step - loss: 2.2688 - accuracy: 0.4662 - val_loss: 1.4121 - val_accuracy: 0.6333\n",
      "Epoch 2/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 1.0450 - accuracy: 0.7858 - val_loss: 0.9260 - val_accuracy: 0.7634\n",
      "Epoch 3/50\n",
      "85/85 [==============================] - 2s 20ms/step - loss: 0.7568 - accuracy: 0.8533 - val_loss: 0.8034 - val_accuracy: 0.7939\n",
      "Epoch 4/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.6371 - accuracy: 0.8772 - val_loss: 0.7552 - val_accuracy: 0.8025\n",
      "Epoch 5/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.5550 - accuracy: 0.8934 - val_loss: 0.6888 - val_accuracy: 0.8337\n",
      "Epoch 6/50\n",
      "85/85 [==============================] - 2s 29ms/step - loss: 0.4894 - accuracy: 0.9065 - val_loss: 0.6751 - val_accuracy: 0.8413\n",
      "Epoch 7/50\n",
      "85/85 [==============================] - 3s 31ms/step - loss: 0.4373 - accuracy: 0.9161 - val_loss: 0.6313 - val_accuracy: 0.8515\n",
      "Epoch 8/50\n",
      "85/85 [==============================] - 2s 26ms/step - loss: 0.3926 - accuracy: 0.9240 - val_loss: 0.6597 - val_accuracy: 0.8422\n",
      "Epoch 9/50\n",
      "85/85 [==============================] - 2s 21ms/step - loss: 0.3573 - accuracy: 0.9309 - val_loss: 0.6222 - val_accuracy: 0.8551\n",
      "Epoch 10/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.3271 - accuracy: 0.9362 - val_loss: 0.6526 - val_accuracy: 0.8474\n",
      "Epoch 11/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.2975 - accuracy: 0.9417 - val_loss: 0.6228 - val_accuracy: 0.8609\n",
      "Epoch 12/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.2740 - accuracy: 0.9453 - val_loss: 0.6632 - val_accuracy: 0.8414\n",
      "Epoch 13/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.2475 - accuracy: 0.9509 - val_loss: 0.5991 - val_accuracy: 0.8663\n",
      "Epoch 14/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.2317 - accuracy: 0.9538 - val_loss: 0.6415 - val_accuracy: 0.8524\n",
      "Epoch 15/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.2077 - accuracy: 0.9585 - val_loss: 0.5769 - val_accuracy: 0.8687\n",
      "Epoch 16/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.1940 - accuracy: 0.9615 - val_loss: 0.6202 - val_accuracy: 0.8633\n",
      "Epoch 17/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.1849 - accuracy: 0.9619 - val_loss: 0.6018 - val_accuracy: 0.8639\n",
      "Epoch 18/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.1685 - accuracy: 0.9652 - val_loss: 0.6120 - val_accuracy: 0.8633\n",
      "Epoch 19/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.1516 - accuracy: 0.9698 - val_loss: 0.6035 - val_accuracy: 0.8652\n",
      "Epoch 20/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.1390 - accuracy: 0.9722 - val_loss: 0.6068 - val_accuracy: 0.8689\n",
      "Epoch 21/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.1288 - accuracy: 0.9745 - val_loss: 0.5872 - val_accuracy: 0.8732\n",
      "Epoch 22/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.1167 - accuracy: 0.9768 - val_loss: 0.6045 - val_accuracy: 0.8694\n",
      "Epoch 23/50\n",
      "85/85 [==============================] - 2s 20ms/step - loss: 0.1123 - accuracy: 0.9771 - val_loss: 0.6103 - val_accuracy: 0.8678\n",
      "Epoch 24/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.0998 - accuracy: 0.9801 - val_loss: 0.6066 - val_accuracy: 0.8728\n",
      "Epoch 25/50\n",
      "85/85 [==============================] - 2s 20ms/step - loss: 0.0937 - accuracy: 0.9807 - val_loss: 0.6027 - val_accuracy: 0.8767\n",
      "Epoch 26/50\n",
      "85/85 [==============================] - 2s 21ms/step - loss: 0.0846 - accuracy: 0.9835 - val_loss: 0.6212 - val_accuracy: 0.8761\n",
      "Epoch 27/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.0749 - accuracy: 0.9855 - val_loss: 0.6184 - val_accuracy: 0.8762\n",
      "Epoch 28/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0721 - accuracy: 0.9855 - val_loss: 0.6164 - val_accuracy: 0.8775\n",
      "Epoch 29/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.0671 - accuracy: 0.9867 - val_loss: 0.6386 - val_accuracy: 0.8760\n",
      "Epoch 30/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.0629 - accuracy: 0.9876 - val_loss: 0.6393 - val_accuracy: 0.8736\n",
      "Epoch 31/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.0566 - accuracy: 0.9893 - val_loss: 0.6671 - val_accuracy: 0.8675\n",
      "Epoch 32/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.0525 - accuracy: 0.9902 - val_loss: 0.6537 - val_accuracy: 0.8748\n",
      "Epoch 33/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.0468 - accuracy: 0.9912 - val_loss: 0.6449 - val_accuracy: 0.8761\n",
      "Epoch 34/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0427 - accuracy: 0.9922 - val_loss: 0.7095 - val_accuracy: 0.8655\n",
      "Epoch 35/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.0421 - accuracy: 0.9917 - val_loss: 0.6521 - val_accuracy: 0.8830\n",
      "Epoch 36/50\n",
      "85/85 [==============================] - 2s 24ms/step - loss: 0.0366 - accuracy: 0.9936 - val_loss: 0.7122 - val_accuracy: 0.8736\n",
      "Epoch 37/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0377 - accuracy: 0.9926 - val_loss: 0.6526 - val_accuracy: 0.8806\n",
      "Epoch 38/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0314 - accuracy: 0.9947 - val_loss: 0.6390 - val_accuracy: 0.8817\n",
      "Epoch 39/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0296 - accuracy: 0.9950 - val_loss: 0.6673 - val_accuracy: 0.8828\n",
      "Epoch 40/50\n",
      "85/85 [==============================] - 2s 24ms/step - loss: 0.0268 - accuracy: 0.9955 - val_loss: 0.6857 - val_accuracy: 0.8811\n",
      "Epoch 41/50\n",
      "85/85 [==============================] - 2s 22ms/step - loss: 0.0254 - accuracy: 0.9959 - val_loss: 0.7257 - val_accuracy: 0.8789\n",
      "Epoch 42/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0236 - accuracy: 0.9960 - val_loss: 0.7035 - val_accuracy: 0.8800\n",
      "Epoch 43/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0210 - accuracy: 0.9967 - val_loss: 0.7397 - val_accuracy: 0.8758\n",
      "Epoch 44/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0222 - accuracy: 0.9960 - val_loss: 0.7151 - val_accuracy: 0.8808\n",
      "Epoch 45/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0191 - accuracy: 0.9969 - val_loss: 0.6864 - val_accuracy: 0.8879\n",
      "Epoch 46/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0223 - accuracy: 0.9955 - val_loss: 0.7302 - val_accuracy: 0.8767\n",
      "Epoch 47/50\n",
      "85/85 [==============================] - 2s 24ms/step - loss: 0.0233 - accuracy: 0.9951 - val_loss: 0.7665 - val_accuracy: 0.8785\n",
      "Epoch 48/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0147 - accuracy: 0.9979 - val_loss: 0.7498 - val_accuracy: 0.8804\n",
      "Epoch 49/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0142 - accuracy: 0.9978 - val_loss: 0.8207 - val_accuracy: 0.8666\n",
      "Epoch 50/50\n",
      "85/85 [==============================] - 2s 23ms/step - loss: 0.0140 - accuracy: 0.9979 - val_loss: 0.7458 - val_accuracy: 0.8812\n",
      "Epoch 1/50\n",
      "43/43 [==============================] - 2s 36ms/step - loss: 2.8158 - accuracy: 0.3328 - val_loss: 1.9773 - val_accuracy: 0.5317\n",
      "Epoch 2/50\n",
      "43/43 [==============================] - 1s 33ms/step - loss: 1.4940 - accuracy: 0.6679 - val_loss: 1.2819 - val_accuracy: 0.6789\n",
      "Epoch 3/50\n",
      "43/43 [==============================] - 1s 33ms/step - loss: 1.0519 - accuracy: 0.7824 - val_loss: 1.0308 - val_accuracy: 0.7528\n",
      "Epoch 4/50\n",
      "43/43 [==============================] - 2s 36ms/step - loss: 0.8471 - accuracy: 0.8367 - val_loss: 0.8955 - val_accuracy: 0.7726\n",
      "Epoch 5/50\n",
      "43/43 [==============================] - 2s 37ms/step - loss: 0.7347 - accuracy: 0.8625 - val_loss: 0.8294 - val_accuracy: 0.7982\n",
      "Epoch 6/50\n",
      "43/43 [==============================] - 1s 31ms/step - loss: 0.6564 - accuracy: 0.8792 - val_loss: 0.7924 - val_accuracy: 0.8019\n",
      "Epoch 7/50\n",
      "43/43 [==============================] - 1s 34ms/step - loss: 0.6034 - accuracy: 0.8879 - val_loss: 0.7404 - val_accuracy: 0.8231\n",
      "Epoch 8/50\n",
      "43/43 [==============================] - 1s 34ms/step - loss: 0.5567 - accuracy: 0.8966 - val_loss: 0.7366 - val_accuracy: 0.8263\n",
      "Epoch 9/50\n",
      "43/43 [==============================] - 1s 34ms/step - loss: 0.5170 - accuracy: 0.9034 - val_loss: 0.7415 - val_accuracy: 0.8218\n",
      "Epoch 10/50\n",
      "43/43 [==============================] - 2s 36ms/step - loss: 0.4829 - accuracy: 0.9087 - val_loss: 0.7189 - val_accuracy: 0.8348\n",
      "Epoch 11/50\n",
      "43/43 [==============================] - 2s 35ms/step - loss: 0.4499 - accuracy: 0.9142 - val_loss: 0.6769 - val_accuracy: 0.8404\n",
      "Epoch 12/50\n",
      "43/43 [==============================] - 1s 35ms/step - loss: 0.4184 - accuracy: 0.9205 - val_loss: 0.6677 - val_accuracy: 0.8447\n",
      "Epoch 13/50\n",
      "43/43 [==============================] - 2s 36ms/step - loss: 0.3936 - accuracy: 0.9248 - val_loss: 0.6584 - val_accuracy: 0.8444\n",
      "Epoch 14/50\n",
      "43/43 [==============================] - 2s 38ms/step - loss: 0.3660 - accuracy: 0.9300 - val_loss: 0.6441 - val_accuracy: 0.8512\n",
      "Epoch 15/50\n",
      "43/43 [==============================] - 2s 37ms/step - loss: 0.3507 - accuracy: 0.9324 - val_loss: 0.6376 - val_accuracy: 0.8580\n",
      "Epoch 16/50\n",
      "43/43 [==============================] - 2s 38ms/step - loss: 0.3278 - accuracy: 0.9367 - val_loss: 0.6302 - val_accuracy: 0.8596\n",
      "Epoch 17/50\n",
      "43/43 [==============================] - 2s 38ms/step - loss: 0.3072 - accuracy: 0.9411 - val_loss: 0.6591 - val_accuracy: 0.8528\n",
      "Epoch 18/50\n",
      "43/43 [==============================] - 2s 36ms/step - loss: 0.2964 - accuracy: 0.9419 - val_loss: 0.6505 - val_accuracy: 0.8543\n",
      "Epoch 19/50\n",
      "43/43 [==============================] - 2s 36ms/step - loss: 0.2801 - accuracy: 0.9454 - val_loss: 0.6359 - val_accuracy: 0.8510\n",
      "Epoch 20/50\n",
      "43/43 [==============================] - 2s 39ms/step - loss: 0.2644 - accuracy: 0.9483 - val_loss: 0.6302 - val_accuracy: 0.8606\n",
      "Epoch 21/50\n",
      "43/43 [==============================] - 2s 37ms/step - loss: 0.2531 - accuracy: 0.9506 - val_loss: 0.6273 - val_accuracy: 0.8622\n",
      "Epoch 22/50\n",
      "43/43 [==============================] - 2s 38ms/step - loss: 0.2379 - accuracy: 0.9544 - val_loss: 0.6504 - val_accuracy: 0.8540\n",
      "Epoch 23/50\n",
      "43/43 [==============================] - 2s 42ms/step - loss: 0.2280 - accuracy: 0.9552 - val_loss: 0.6386 - val_accuracy: 0.8619\n",
      "Epoch 24/50\n",
      "43/43 [==============================] - 2s 36ms/step - loss: 0.2155 - accuracy: 0.9584 - val_loss: 0.6411 - val_accuracy: 0.8587\n",
      "Epoch 25/50\n",
      "43/43 [==============================] - 2s 36ms/step - loss: 0.2077 - accuracy: 0.9590 - val_loss: 0.6472 - val_accuracy: 0.8580\n",
      "Epoch 26/50\n",
      "43/43 [==============================] - 2s 38ms/step - loss: 0.1988 - accuracy: 0.9605 - val_loss: 0.6296 - val_accuracy: 0.8656\n",
      "Epoch 27/50\n",
      "43/43 [==============================] - 2s 35ms/step - loss: 0.1870 - accuracy: 0.9634 - val_loss: 0.6113 - val_accuracy: 0.8669\n",
      "Epoch 28/50\n",
      "43/43 [==============================] - 2s 38ms/step - loss: 0.1799 - accuracy: 0.9648 - val_loss: 0.6277 - val_accuracy: 0.8605\n",
      "Epoch 29/50\n",
      "43/43 [==============================] - 2s 38ms/step - loss: 0.1705 - accuracy: 0.9670 - val_loss: 0.6352 - val_accuracy: 0.8681\n",
      "Epoch 30/50\n",
      "43/43 [==============================] - 2s 37ms/step - loss: 0.1638 - accuracy: 0.9678 - val_loss: 0.6397 - val_accuracy: 0.8700\n",
      "Epoch 31/50\n",
      "43/43 [==============================] - 2s 37ms/step - loss: 0.1541 - accuracy: 0.9701 - val_loss: 0.6349 - val_accuracy: 0.8666\n",
      "Epoch 32/50\n",
      "43/43 [==============================] - 2s 37ms/step - loss: 0.1535 - accuracy: 0.9699 - val_loss: 0.6630 - val_accuracy: 0.8622\n",
      "Epoch 33/50\n",
      "43/43 [==============================] - 2s 39ms/step - loss: 0.1429 - accuracy: 0.9717 - val_loss: 0.6609 - val_accuracy: 0.8618\n",
      "Epoch 34/50\n",
      "43/43 [==============================] - 2s 40ms/step - loss: 0.1400 - accuracy: 0.9722 - val_loss: 0.6107 - val_accuracy: 0.8738\n",
      "Epoch 35/50\n",
      "43/43 [==============================] - 2s 40ms/step - loss: 0.1302 - accuracy: 0.9748 - val_loss: 0.6398 - val_accuracy: 0.8702\n",
      "Epoch 36/50\n",
      "43/43 [==============================] - 91s 2s/step - loss: 0.1255 - accuracy: 0.9756 - val_loss: 0.6471 - val_accuracy: 0.8663\n",
      "Epoch 37/50\n",
      "43/43 [==============================] - 2s 45ms/step - loss: 0.1203 - accuracy: 0.9763 - val_loss: 0.6417 - val_accuracy: 0.8719\n",
      "Epoch 38/50\n",
      "43/43 [==============================] - 2s 38ms/step - loss: 0.1154 - accuracy: 0.9770 - val_loss: 0.6826 - val_accuracy: 0.8623\n",
      "Epoch 39/50\n",
      "43/43 [==============================] - 2s 40ms/step - loss: 0.1101 - accuracy: 0.9786 - val_loss: 0.6350 - val_accuracy: 0.8762\n",
      "Epoch 40/50\n",
      "43/43 [==============================] - 2s 38ms/step - loss: 0.1039 - accuracy: 0.9800 - val_loss: 0.6580 - val_accuracy: 0.8690\n",
      "Epoch 41/50\n",
      "43/43 [==============================] - 2s 37ms/step - loss: 0.1012 - accuracy: 0.9806 - val_loss: 0.6577 - val_accuracy: 0.8727\n",
      "Epoch 42/50\n",
      "43/43 [==============================] - 2s 37ms/step - loss: 0.0940 - accuracy: 0.9821 - val_loss: 0.6679 - val_accuracy: 0.8698\n",
      "Epoch 43/50\n",
      "43/43 [==============================] - 2s 56ms/step - loss: 0.0882 - accuracy: 0.9835 - val_loss: 0.6424 - val_accuracy: 0.8763\n",
      "Epoch 44/50\n",
      "43/43 [==============================] - 2s 35ms/step - loss: 0.0829 - accuracy: 0.9850 - val_loss: 0.6644 - val_accuracy: 0.8656\n",
      "Epoch 45/50\n",
      "43/43 [==============================] - 2s 36ms/step - loss: 0.0829 - accuracy: 0.9838 - val_loss: 0.6580 - val_accuracy: 0.8746\n",
      "Epoch 46/50\n",
      "43/43 [==============================] - 2s 38ms/step - loss: 0.0779 - accuracy: 0.9854 - val_loss: 0.6956 - val_accuracy: 0.8654\n",
      "Epoch 47/50\n",
      "43/43 [==============================] - 2s 37ms/step - loss: 0.0742 - accuracy: 0.9865 - val_loss: 0.6502 - val_accuracy: 0.8793\n",
      "Epoch 48/50\n",
      "43/43 [==============================] - 2s 37ms/step - loss: 0.0702 - accuracy: 0.9874 - val_loss: 0.6786 - val_accuracy: 0.8728\n",
      "Epoch 49/50\n",
      "43/43 [==============================] - 2s 37ms/step - loss: 0.0670 - accuracy: 0.9880 - val_loss: 0.6798 - val_accuracy: 0.8740\n",
      "Epoch 50/50\n",
      "43/43 [==============================] - 2s 37ms/step - loss: 0.0664 - accuracy: 0.9877 - val_loss: 0.6828 - val_accuracy: 0.8764\n"
     ]
    },
    {
     "data": {
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yZAnuv/9+zJ4923Kf9evX46OPPqpXTW8iIiKi1sTuOxSHDBmCzZs317ht06ZNGDJkCABAp9Ohf//+NY4xmUzYvHmz5RgiIiIiooayR01vgHW9iYiIqPWyOqBYVFSEgwcP4uDBgwAkheTgwYNISEgAIOnIs2bNshz/wAMP4Pz583jyySdx8uRJvPPOO1i9ejUee+wxyzHz58/H8uXL8cknn+DEiROYM2cOiouLLSvEREREREQNZY+a3gDrehMREVHrZXXK859//onRo0dbrs+fPx8AcM899+Djjz9GamqqJbgIAO3bt8f69evx2GOP4c0330RkZCQ++OADS3oJAEyfPh2ZmZl49tlnkZaWhj59+mDjxo2XTeqIiIiIiJzFggULLHNhACgoKGBQkYiIiFoFqwOKo0aNgqIodX69to55o0aNwoEDB674uHPnzsXcuXOtHQ4RERER0RXZo6Y3wLreRERE1HrZvSkLERERUW2K9AYcTsyDUVGg06jh6qKBq1YNV60aOq0arloNXF0qr2vUUKlUjh4yNVNDhgzBhg0batxWV03vm266CUBVTW8ueBMREZGiKEjKLcXF7BKE+LiibYAH3Fw0jh6WQzGgSERE1MIoioKzGUVQq1VoG+ABF43de7DVS1mFEfsv5mLnuSzsOpeNQ0n5MJrqznq4VJCXKwa298fgmEAMjglExxCvBgUZi/UGHEspwOGkPOgNJkQFeKBdgAfaBXrAz0Nn9eNR0ysqKsLZs2ct1801vQMCAtC2bVssWLAAycnJ+PTTTwFITe+3334bTz75JO6991789ttvWL16NdavX295jPnz5+Oee+7BNddcg4EDB+KNN95gTW8iIiInU6w3QK1SwV1nv2Ce0aQgPqsIR5MLcCwlH8dSCnAspQD5pRU1jgv1cUW7AE+0DfRAdKAH2gZ62m1OaTIpSMkvRV5JBXpE+Nr0sRuKAUUiIiIHMZkUnM8qwuGkfLho1OgV6Yu2AR4NCpLlFJdj+5lMbDudid9PZyGrSA8A0FQGFdsHeSImyBPtgz3RPsgTscFeCPF2teuuv3KDCYeT8rDzXDZ2nstC3MU8lBtNNY6J9HeHt5sL9AYj9BUmlBtN0FcYoTeYoDfUPDarSI8NR9Kw4Yg0yQjw1GFQ+wAMjgnEoJgAdArxhlpd8/XoDUacSC3E4aQ8HE7Kx+GkPJzNKEJdcUwfNy3aBcrE0DwhbBvgiXaBHgjzcbvs8ckxWNObiIjIeZlMspvvRFoBMgr1cNWoLVknrtrKjBSXape1Gmg1KuSVVCCjsAwZhXpkVjtlFJZVnutRUm6EVq3CNdH+GNkpBCM7BaNrG+8Gz2krjCacSivEsZR8SwDxRGohSiuMlx3rolEhyt8DmYV6FOoNSC/QI71Aj70Xci471sdNi6gAD0T4uSPczx0Rfu6I8K+6HOSlu2zMiqIgq6gc8VnFiM8qQnxWSeV5MS5kl6DcYEJMkCd+e3xUg16rramUKxVEbCYKCgrg6+uL/Px8+Pj4OHo4RERUTZHegMKyClQYFJQbTaioPJUbTJXXFVRUXnbXadA93Ach3m4OG29JuXlyUFbtJNczCvRILyyDRq1CbLAXOoR4oUPleUywJ7zdXOp8XEVRkJJfhsOJeTiYlIfDifk4kpyPIr2hxnG+7i7oGeGLnpG+6FV5HuHnftmEw2A04WBiHn4/LUHEw8n5qP6O7u6igUoFlJRfPhky89Rp0D7YE+0CPOHv6YIADx38PHQI8NTBz8MFAZ46+Hvo4O+pg6dOYxmD0aSgoLQC+aUVyCutQF5JOfLN10vkdC6zCPsu5Fz2/KE+rhgaG4QhsYEYEhOIqACPK37PKowK9AYjyipMuJhdjN3ns7H7fA72X8y9bKLn7+GCge0D0DvKD0m5pTiSlI+TaQWoMF4+1QnzcUPPSF94u2qRkFOCizklyCzU1zkWALhzUFu8NK3nFY9pDM5nmj/+DImopUjLL4Onq+aKc5vmpKCsApmFehhNCgxGBQaTCYZql6tuV2BSFIT6uKFtgAf8PVyaTcmVCqPJMj+rfiooM8BFrYKvuwt83V3gU3nu6+ECb1dto19ffmkFTqUV4mRaAU6kyvnptEIUX2EOamsh3q4Y2SkYozqHYFiHIPh61P57azRJFs/hpDwcSc7H4aR8HE8tQPkli9iAzKW7hfuge7gPeoT7olu4DzqFekOnVUNRFOSWVOBidrHMI7PllJBTjIvZJci4ypwSAHRaNcJ93RDh7w5fdxck5pQiPqv4ss8G1blo5DPIhkeG222R25q5DAOKRETNTGahHpuOp+O3k+nQG0xo4+uGNr7uCPdzQ5ivO8J93dDGzx1ervXfhK43GFGsN6Kk3IBgb1e4ahuXQlCsN+CX42n49kAKdpzJrHM3WF3a+LqhR0RVQK1XpB8CPK+cNqAoCjKL9IjPLJZVvexixGcWI7ekHCYFMCkKTIocZ1IUmExym1L5NYNJQVblamNDhfm4oUOIF2KDPdEhxAshPm44lSa74w4m5lt2DVbn7qJBjwgflBtMOJFaeNkOPkB24vWM8EWvSF+EeLti1/lsbD+ThcKymmPtEuaNkZ2DMbJjMPpH+0OnUSO9QI/zmUU4n1WM85my2nk+qxiJOSVW/VxcNCr4uutQbjCioKz+36MATx2GxARiSGwghsYGon2Qp00m5uUGE44k52H3+RzsPp+NPy9cHmA08/dwQa9IP/Su/F3qFemLEJ/Lg9Yl5QYk5pTWnBzmlCAhuxhJuaV4fGJnPDAyttFjrwvnM80ff4ZE1JwVllXgx8OpWP1nIg4k5MFFo8LgmECM7xaKcV1DEe7nbpfnNRhNSMotRXx2MS5W7sQqKTcgws8DUQHuiArwQJS/B0K8Xa8aRNEbjDiXUYxT6QU4mVaIU2mFOJ1WiJT8sgaNzVOnQVSAByL9K8fi7yHjqbzs6aqFyaSgwiSL5AajLJIbjErlIrpiWUwvKTeitNyI0gpj5WUDSsorL1fIPLyk3AiDsXKuWm2+ap7HWs5NslBfUFphCSI2JICnVqEqwOjuAm83LbRqNbRqFbQaFbSaystqNVw0KmjUKkspncScEpxMK0RyXmmtj63TqNEx1AsRfu4wmBRLNopkoVRmo1RUXS43mODn4YIgL1eE+Lgh2MsVIT6ul5y7IcTbFVlFemw7nYmtpzKx81wWyiqq5s8atQp9o/wwslMwBsUEIjW/FIcS83EkOQ9HkwtqnS/6uGnRI8IXPSJ80T3cB93DfdE+yBOaBgbtSsuNSMgpQVJuCVLySpGUV4qUvDKk5JUiObcU6YVlqCsSp1IBEX7uVdlFQZ6IDvJETJAXwv3coLVzKSMGFImIGighuwQf7DiPgtIK3NArHCM7BztF/bmL2cX4+Vgafj6WjriE3DrfgKrzdtNago2BnjqUVhhRpDegWG9Asb7ycrlcr76Dy81FjQHRARjeMQjDOgSjS9jlaaS1MRhN2HE2C+sOJOPnY+k13qxdNDL5cNFIsw2dRl3rbbkl5TibWVTr64vwc0evSAkwdm3jg/ySCpzPKrakBFzIKrniip41PHQahPm4IcTHFaE+bpWX3RDq44oQbzfoDUaczSjC2YwinMsswtmM4lqDhZfSqFXoEuaNXpF+6BPli95RfugQ7GWZGJQbTDidXojDSTLpOZyUj1NphTDUEfnzdXfB8I5BGNkpGCM6BSO0liBZXcoNJiTkSJAxMbcUeSXlyC0pR25JBXKL5TyvpBw5xeWXpR6beeo08PPQwdfdBX4eLtXOdQjzccWgmEB0Dq3f709jVRhNOJyUjz3x2TiWXIDIAHf0ipDgYaT/5Ts8rWU0yQcCexbf5nym+ePPkIiaG0VRsO9CLlbtS8SGI6mW+ZtKhcvmYz0ifDC+axjGdwu1OsW0rMKI1PwyXKgWNLyQXYwLWbJoV9dcpzqdRo0If3dE+rtbAnxhPm5Izi3FyXQJHsZnFddZn9nbVQsXrVqCYmoVNBoVXNRy3Rwo06hVUACk5pXWa5eZRq2yqh50U/By1VbbiaiFj5sLDCblsp2Lte3Ka6gIP3d0CfNGlzbe6Bzmg65h3mgf5Gn34Bcgv1v7LuRg26lMbD2dibMZRVc83kOnsWxc6BXlh14RvmgX2LCSQw1VYTQhLb8MyXmlSMkrRW5JBSL93RET5IkoBzd7YUCRiMhKF7OL8fZvZ7H2QHKNSUGQlyum9Q3Hrf2j0DnMu0GPXW4w4WhKPor1BvhXppMGeOqu+EahKAqOpRTgl8og4qn0whpf7x3lhwndQhHq44bUvFKk5JchNb8Uafmy8mXNDrLqXDSqy9JDg7x0GBobhGEdgzC8YxDa+FatTiuKgiPJ+fj2QDJ+OJSCrKJyy9faBXrgpj4RuKlvBNoHedZ7DMV6A46nFkhQLSkPh5PzcT6zuF73VauASH+PylU8WdEzr2arVSqoVYBapYKq8tx8G1SARqVCkLcEEK3Z3WmWX1KBs5lFOJdRZDlPKyhDhxAv9I70Q+8oP3QP97F6glBWYcTJtEL5XiTlI62gDP3b+WNkp2D0ivRr8MqpNUrLjZXBxnK4ajWW4KEzBNtbEs5nmj/+DImovswLVZJ2W7m7zVS1s02+rkgqrjkl16RY7lf9usGk1FhI9nG7ehprekEZ1sQl4es/kxCfVTXPig32xPQBUZjWNxKFZRXYdDwdm46nY/8lC9qR/u4Y1zUUE7qFom9bf2QV6ZFaOR9Nyat5nppfhpzi8lpGUcVVq0Z0oCeigzwQHegJT1ctknNLkZhbgsTcEqTkldU7cOfjpkWXMB90DvNGpzBvdAnzRqdQb/i6W5e+XVZhRFLlGJJySpCYW4rEHBlPYk7pZQ06qjMvnmvNi+caFdx0GnjoNPBw0cplF7nurjOfa+HuooFOq7bMWWUOK/NU8xxWUzmv1WpU8HFzqbHD0MdNW+8gXlmFsWZqdGkFiio3Ghir7bg0VP6OWS5XpoaH+7lZvs/Wfm/tKSm3BNtOZ2LbqUwcSMxDpL87ekf6WbJ9YoK9mmT+3FwxoEhEVE8Xsorx9paz+LZaIHFEp2B0CPbCdweTkV1t8tMr0he39o/E1N7hV+zaVVhWgbiEPOyLz8G+Czk4mJhX6w4vdxeN1KjzdLEEGv09dDCaFPx2MqNG+oBWLSknE7uHYly30BpBvdoU6w2WSV1qXhlySsrhodPAU6eFp6sWXq5aeLpqKs8rTzoNNGoVTqcXYcfZLOw4k4k98ZfXwIsJ9sTwDkHw89Dhh8MpNYJ9/h4umNI7HDf1jUDfKD+brfQVlFXgWHJBjV17/p46S9CwfZAnYoJlRa+x6dpEjsL5TPPHnyGR8yosq8DJtEIcTylAfFYxgrx0iA32QmyIF9oF2m7+YDJJCRbzzqPk3MrzvKrdSAVlFfXKNmkoD50GbXzdEO4nO/ja+LlXBhvdUFJuxJr9SdhyKsNS+sRTp8ENvcJx+4BI9GvrX+v8LatIj99OZOCX4+nYcTazRoppfbm7aNA2wMMSNIwO8rQEEUO9r9z4zGA0Ia2gDIk5VQG+pFwJVrbxdUPnMG90DvNGlzAfhPrYt+mcWUFZBUr0Rsm6qcy20VbudmwudReJLsWAIhE5rXKDCS4a695kyw0mpBeUIa2gDKn5ZUjLL0Vavh5pBTKJAIDekX7oE+WHvm396tUl90JWMd767SzWHawKJI7sFIx54zqiX1t/ALIVfcvJDHyzPwm/ncywpGLoNGqM7x6KW/tHYkTHYOQUl2PfhRzsjc/BnxdzcDyl4LLadAGeOgR7uVp2edXWJOJSbi5qjOwUjIndwzC2S2idxYXtqdxgwoGEXOw4m4XtZ7JwOCnvstfmqlVjfLdQTOsbgRGdnCNFnKg54nym+ePPkMj2FEWxat5oboJ2PKUAJ1ILcDylAMdTC5CQU1LnfdQqoG2AR2Ud5MpTiCdig73g5apFfmmFpRRIXkkFcivP80prlggx78irzzyvNua6dS5qNTSamnXrzIEqbWWKbtXtaqjVQH6pAan5pcgrqXvX3KWuaeeP2wdE4fqebeBpRXZGabkR289kYtPxdGw+mYGc4nLotGpL0DLc1x1hlTW9w6vV+vZ1bz4NTohaKwYUichpGIwm7LuQi19PpOPXE+m4mC2TOVetWk4umqrLWg1cXeSyS2U9vbT8shpptPUR4KlD38rgYt+2/ugV6WvpUBefVYy3fjuDdQeSLYGxUZ2DMW9sR/StDCTWJqtIj+8OpuDrPxNxMq0q/djLVVtr3b6oAHcMaBeAAe0DMCA6ALHBVY0oFEVBkd6A3OIK5JSUI7dYJqG5lZPRsgoTBscEYHjHYLjrnGu3XX5JBXadz8aOs5nIKizH2K4huK5HWIvpAEjkSJzPNH/8GRI1XEm5Aeczi6vVJ5bThexiKIrMHd0q541ulWmh1a+7atUoKKvAidTCOlNRw33d0LWNDzqEeCGzSI9zmcU4l1FksxrMZmoVLEG0cD93RPi5y7m/XPZzd5FmF5XBQ61GAoa2CLaVlhstacap+WWW0jhplbfpDSZM6B6K2/pHoUOIV6Ofz2hSUFBaAb9m1A2ZiOrGgCIROVRhWQV+P52FX0+k47eTGVesL1Jf5lXPMB9Z+Qz1dUMbH+lqXG404WBCHg4k5uJYcsFlXXJVKqBjiBfC/dzx++mqjsOjOwdj3rhO6BPlV+9xmGsbfrM/Cd8dTEZuSQVUKqBzqDcGtg/ANdEBGBDtf9WUZCKiS3E+0/zxZ0itldGkYM/5bPxyPB3FegNcXdTQaSTo56pVW87Nl3VaNcoqTJag4dmMojo7xTaEVq1ChxAvdAv3Qbc2curaxgf+npeXrFEUBZmFekv943OZxThXebl6Z2AfNy38PHTw93CBn4cOfh5SssbPwwV+7i7w99Shja8EDUO9XZukGQURka0xoEhElykpN+B4SgGOpRSgsKwqwFf9P8Cl/ww0ahW83bTwdtNaCv7KuVz30GksK5HJeaXYfEKKNu8+n10j1cPfwwVjuoRifLcQDIgOgEkB9AYj9AYT9BWmqssGE/QVcrncYIK/pwvCfCRlwr+eq556gxHHUwpwICEPBxLzcCAhF0m5NSeoY7qE4JGxHa0KJNb1XKfSCtEu0NOpChETUfPE+Uzzx58htSaKouBgYh6+P5SC9YdT69UR92r8PVzQIcTLknpsPnfRqFFWOUe80rmbiwZd23ijQ4iXTWoiFusNKKswwrdyRyERUUtnzVzG+jaWROT0ivQSPDySnI+jyfk4kpyPc5lFNi/+bA44urtoLLUMzWKCPTG+qzQQ6dfWv8k6ablqNejb1r9G+nJmoR4HE/NwLrMIQ2IC0buRgcTqz9Ur0jaPRURERNQcnEwrwA+HUvDDodQadQl93V1wXfcwtA30QHnlQnG5wYRyoxH6ChPKjZXXK7+mUasQE+wpAcTK4GGgl6sDX9nlzI3riIjocvzvSOTEcipr65UbTKgwykSswmCCvvK83Fh5u8GEvJIKHE+VIGJ8VnGtwcNQH1f0CPdFiM+lk7WqYF/1TYBGo4JCfQUKSg0oLKtAQZkBBaUVyC+tgMGkwGhSpCA1KqBWAf3b+WN8t1CM7RqK2ODG12SxlWBvV4zvForxCHX0UIiIiIianYTsEnx/KBnfH0rB6fQiy+0eOg3GdwvFlF7hGNEpGDotd/EREbUWDCgSOQGjScGF7OKqbnSpcp5e0PDUkTa+bugR4Yse4b7oGemDHhG+CPF2s8l4FUVBWYUJBWUVlkBjuwAPp1tVJiIiImoNknJLsOtcNnadz8aJ1EIYTSYoipSzURRFytpccl1RAJMiC8SWk6LAaJRzg0mBySTn1ek0aozsHIypvcMxtmsIPHT8SElE1Brxvz+RHZhMCipMpsqdhYplh6G+8rxIb8DJtEIJHqYU4FRaIUorjLU+lq+7ixSv1kgBaxeNCi6Wy1Udkd11GnQJ9UaPSF/0jPBFkB2DeyqVCu46Ddx1GoT62CZISURERET1k5Zfhl3nsyxBxMQc2zU0qY1aBQyNDcLU3uGY2COMtaOJiIgBRaL6UhQFOcXlSMkrQ0p+KVLzSpGSX4aUvFKkVp7nl1agwmiq0ZCkvtxc1OgcZu5E542ubXzQpY0PvFi3hYiIiKjFScwpwS/H06EoSmUH5KquyOYuyXIuC8lnM4qw63w2dp/Lxvms4hqPpVGr0CvSF0NiAtG/nT/cXTSAClBBZSlno4IsCqtU5suAWqWCRl110qpVUKtU0KrVUKthOffUsZYgERHVxHcFolqUG0w4lJSHP85mYf9F6RKcklcKvcHUoMfTqFXQaWR3oU6rgZuLGh1CvNCtjQ+6Vp7aB3k2WeMSIiIiInKMhOwSLN1yFmviki5LJ64vtQroHu6LIbGBGBIbiAHRAVyEJiKiJsV3HSJIDcNjKfnYeS4bO89lY198Tp0pyMHergj3dUO4nzva+Loj3M982Q0BnjpLerKL+VyjZqCQiIiIqJW7mF2Mt387i7UHkmGsDCQOah+AMF83S+djvcFYdbmyM7K+wgi9wYQQHzcMiZEA4sD2AUw7JiIih2JAkVolRVFwJqMIO89mYee5bOw+n42CMkONYwI9dRgcG4ghMYHoEOKFcF93hPq6wlWrcdCoiYiIiKi5uZBVjLe3nMW31QKJIzoFY97Yjujfzt/BoyMiImoYBhSp1agwmrA3Pge/HEvDL8fTkZpfVuPr3q5aDIoJxNDYQAztEIhOId5Qc2chERERETVAfFYx3vrtDL47mGIJJI7sFIx54zqiX1sGEomIqHljQJFatNJyI34/k4mfj6Vh84kM5JdWWL7m5qLGgOgADIkNxNDYIPQI94FWo3bgaImIiIioOTMYTTiRWoiP/ojHdweTYS6ROLpzMOaN64Q+UX4OHR8REZGtMKBILU5eSTk2n8jAz8fS8PuZTJRVVDVSCfDUYVzXEEzsHoZrOwTBzYXpy0RERETUMNlFehxIyENcQi7iEnJxOCkfJeVVdbjHdgnBI2M7ojcDiURE1MIwoEjNVoXRhKTcUlzIKkZ8VjEuZBfjZFoh9l/MtaSVAECEnzsmdg/DxO6huCY6gA1SiIiIiMhqBqMJJ9MKcSAhF3EJeTiQkIsL2SWXHeflqsXwjkF4cFQH9Iz0dcBIiYiI7I8BRXJ6eSXlOJyUj/hqgcMLWcVIzC2tETisrkuYNyZUBhG7tfGBSsUgIhERERHVX1mFEYcS87AnPgd743MQl5BbY/ehWYcQL/Rr64e+bf3Rr60/OoR4cQGbiIhaPAYUySkl5pTgl+Pp2HQ8Dfsu5NYZOHR30aBdoAfaB3kiOsgT7QM9MSgmAO0CPZt4xERERETUnBXrDYhLyMXe+BzsOZ+Dg4l5KDeaahzj7aZFnyg/9Gvrj37t/NEn0g++Hi4OGjEREZHjMKBITsFkUnAkOR+bjqfj1xPpOJlWWOPr7YM80SHESwKHgZ6IDvJATJAXQn1cufuQiIiIiKymKAr2xOdgy8kM7InPwdHkfBguWcQO9nbFoPYBGNQ+AAPbB6JjiBfU3H1IRETEgCI5jt5gxM5z2fi1MoiYXqC3fE2jVmFAtD/GdwvD+K6haBvo4cCREhEREVFLUVBWgbX7k/DZngSczSiq8bUIP3cMiqkKIEYHenDxmoiIqBYMKFKTO5dZhJW7LmJNXBIKywyW2z10GozsFIzx3UIxpksI/Dx0DhwlEREREbUkR5Pz8fmei1h3IAWlFVIL0UOnweSebXBth0AMiA5ApD8XsYmIiOqDAUVqEgajCZtPZmDlrovYcTbLcnuItyvGdQvF+G6hGBITCDcXjQNHSUREREQtSVmFERuOpGLl7os4kJBnub1TqBfuGtwO0/pGwNuNNRCJiIisxYAi2VV2kR5f7UvEF3sSkJxXCgBQqYCxXUJw95BoDO8QxDo0RERERGRTCdkl+HzPRaz+MxG5JRUAABeNChO7h+Huwe0wsH0AU5mJiIgagQFFsjlFUXAgMQ8rd13E+sOplu54/h4umD6gLe4c1BZRAUwnISIiIiLbyispx6u/nMIXexJg7q8S7uuGmYPa4vYBUQjxdnPsAImIiFoIBhTJZkrLjfjhcApW7rqII8n5ltt7R/pi1pBoXN+rDVOaiYiIiMjmTCYF3+xPwr83nkROcTkAYESnYNw9uB3GdAmBhhkxRERENsWAIjXa+cwifL4nAd/sT0J+qaSU6LRqTOkVjllD2qF3lJ9jB0hERERELdbR5Hw8+91RxFXWSOwY4oUXbuyBIbGBjh0YERFRC8aAIjWIucnKZ7svYvuZqiYrUQHuuHNQO9x+TRQCPNmlmYiIiIjsI7+0Aq/9cgqf7b4IkwJ46jR4dFwn/OXaaLho1I4eHhERUYvGgCJZJaOwDKv2JuKLvQlIzS8DIE1WxnQOwV2D22FEp2CmlBARERGR3ZhMCtbEJeHfP51EdmV685Te4fjn5K4I82WNRCIioqbAgCLVy58XcvDxzgvYeDQNhsoK1wGeOkwfEIWZA9lkhYiIiIjs73hKAZ797ij+vJgLAIgN9sSLN/bA0A5BDh4ZERFR68KAIl1Rsd6Af60/gS/3Jlhu69/OH3cPbodJPcPgqmWTFSIiIiKyrwqjCa/+fArLt5+HSQE8dBrMG9sRs69tD52W6c1ERERNjQFFqtP+i7mYv/ogLmaXAABu6x+Jv1wbje7hvg4eGRERERG1Fsl5pZj7RRwOVDZdub5XGyy8viva+Lo7dmBEREStGAOKdJkKown/23wGS7echUkBwn3d8OptvZlKQkRERERN6reT6Zi/+hDySirg7abFf2/tjet6hDl6WERERK0eA4pUw9mMQjy26hCOJOcDAKb1jcBzU7vD193FwSMjIiIiotaiwmjCq7+cwnvbzgMAekX6YunMfqzbTURE5CQYUCQA0i3vk10X8O+fTkJvMMHPwwUv3dQT1/dq4+ihEREREVErkppfioe/OGBpvPKXodFYMLkLa3cTERE5EQYUCan5pXji68PYcTYLADC8YxBeva03Qn3cHDwyIiIiImpNtp7KwGOrDiK3pALerlq8cmsvTO7JBW4iIiJnw4BiK/fDoRT889sjKCgzwM1Fjacnd8Xdg9tBpVI5emhERERE1EoYjCa8/utpLN1yDgDQI8IHS2f2Q7tATwePjIiIiGrDgGIrpSgKXvjxOFb8cQEA0DvSF0um90FssJdjB0ZERERErUp6QRke/vIA9sbnAADuHtwO/7y+K9xcmOJMRETkrNSOHgA1PUVRsPink1jxxwWoVMAjYzvimzlDGUwkIiKiZmPp0qWIjo6Gm5sbBg0ahL1799Z5bEVFBV544QXExsbCzc0NvXv3xsaNG2sc89xzz0GlUtU4denSxd4vo9U7mJiHyW9ux974HHi5avHWjL548aYeDCYSERE5Oe5QbIVe33Qa7/8uHfNentYTMwa2dfCIiIiIiOpv1apVmD9/Pt59910MGjQIb7zxBiZOnIhTp04hJCTksuMXLlyIzz77DMuXL0eXLl3w888/Y9q0adi5cyf69u1rOa579+749ddfLde1Wk6V7Wnn2Szc/+mfKC43okuYN965sx9iuMBNRETULDRohyJXhJuvpVvO4n+/nQUAPDelG4OJRERE1OwsWbIE999/P2bPno1u3brh3XffhYeHBz766KNaj1+5ciWefvppTJ48GTExMZgzZw4mT56M1157rcZxWq0WYWFhllNQUFBTvJxW6ZdjafjLx/tQXG7EtR0CsWbOUAYTiYiImhGrA4rmFeFFixYhLi4OvXv3xsSJE5GRkVHr8QsXLsR7772Ht956C8ePH8cDDzyAadOm4cCBAzWO6969O1JTUy2nHTt2NOwVUZ0+3BGP//58CgDw1KQu+Mu17R08IiIiIiLrlJeXY//+/Rg3bpzlNrVajXHjxmHXrl213kev18PNza3Gbe7u7pfNN8+cOYPw8HDExMTgzjvvREJCwhXHotfrUVBQUONEV7dmfxLmfB6HcoMJE7uH4qO/DICnK3eDEhERNSdWBxS5Itw8fbb7Il788TgA4NFxHfHAyFgHj4iIiIjIellZWTAajQgNDa1xe2hoKNLS0mq9z8SJE7FkyRKcOXMGJpMJmzZtwtq1a5Gammo5ZtCgQfj444+xceNGLFu2DPHx8Rg+fDgKCwvrHMvixYvh6+trOUVFRdnmRbZgK/6Ix/99fQhGk4Jb+0di6cx+cNWyXiIREVFzY1VA0VlWhLkabJ3VfyZi4bqjAIAHRsZi3tiODh4RERERUdN588030bFjR3Tp0gU6nQ5z587F7NmzoVZXTYUnTZqE2267Db169cLEiROxYcMG5OXlYfXq1XU+7oIFC5Cfn285JSYmNsXLaZYURcEbv57G8z/IAve917bHf27pBa2GPSKJiIiaI6vewZ1lRZirwfX33cFk/GPNYQDAX4ZG4x/XdYZKpXLwqIiIiIgaJigoCBqNBunp6TVuT09PR1hYWK33CQ4Oxrp161BcXIyLFy/i5MmT8PLyQkxMTJ3P4+fnh06dOuHs2bN1HuPq6gofH58aJ7qcyaTg+R+O441fzwAA5o/vhGdu6Aq1mnNSIiKi5sruS4L2WBHmanD9bDyaivmrD0FRgBkD22LRlG4MJhIREVGzptPp0L9/f2zevNlym8lkwubNmzFkyJAr3tfNzQ0REREwGAxYs2YNbrzxxjqPLSoqwrlz59CmTRubjb01MhhNePybQ/h45wUA0hTwkbEdOSclIiJq5qwKKDrLijBXg69uy8kMPPzlARhNCm7uF4GXburBiRsRERG1CPPnz8fy5cvxySef4MSJE5gzZw6Ki4sxe/ZsAMCsWbOwYMECy/F79uzB2rVrcf78eWzfvh3XXXcdTCYTnnzyScsxjz/+OLZt24YLFy5g586dmDZtGjQaDWbMmNHkr6+lKKswYs7ncVgblwyNWoXXp/dmU0AiIqIWwqqAIleEm4cdZ7Lw98/2o8Ko4IZebfCfW3oxpYSIiIhajOnTp+PVV1/Fs88+iz59+uDgwYPYuHGjpSxPQkJCjfI6ZWVlWLhwIbp164Zp06YhIiICO3bsgJ+fn+WYpKQkzJgxA507d8btt9+OwMBA7N69G8HBwU398lqEIr0Bs1fsw6bj6dBp1Xjvrv6Y1jfS0cMiIiIiG1EpiqJYc4dVq1bhnnvuwXvvvYeBAwfijTfewOrVq3Hy5EmEhoZi1qxZiIiIwOLFiwHIinBycjL69OmD5ORkPPfcc4iPj0dcXJxlEvf4449jypQpaNeuHVJSUrBo0SIcPHgQx48fr9ckrqCgAL6+vsjPz2/1uxX3xudg1kd7UFZhwoRuoVh6Zz+4sNg1ERGR0+N8pvnjz1CYTApmfbQXO85mwVOnwQf3DMCQ2EBHD4uIiIiuwpq5jNbaB58+fToyMzPx7LPPIi0tDX369LlsRbh6fUTzivD58+fh5eWFyZMnY+XKlbWuCGdnZyM4OBjDhg3jinADHEzMw70f70NZhQkjOwXjrZl9GUwkIiIioia1fPt57DibBXcXDb64fzB6R/k5ekhERERkY1bvUHRGXA0GjqcU4I73d6GgzIDBMQH4ePZAuLloHD0sIiIiqifOZ5o//gyBw0l5uPmdnTCYFLxyS09MH9DW0UMiIiKierJmLsPtay3A2YxC3P3hHhSUGdCvrR8+uGcAg4lERERE1KSK9QbM++ogDCYFk3qE4fZrohw9JCIiIrITBhSbuYvZxbjzgz3ILi5HjwgfrJg9EF6uVmeyExERERE1ygs/HEd8VjHa+Lph8c09oVKxKSAREVFLxYBiM5acV4qZy/cgvUCPTqFe+PTeQfB1d3H0sIiIiIioldlwJBWr/kyESgW8Pr0P/Dx0jh4SERER2REDis1URmEZ7vpgD5LzStE+yBOf3TcIAZ6cuBERERFR00rJK8VTaw4DAB4cFYvBMezoTERE1NIxoNgM5RSX464P9iA+qxgRfu74/L5BCPF2c/SwiIiIiKiVMZoUPLrqIArKDOgd5YdHx3Vy9JCIiIioCTCg2Mzkl1bg7g/34HR6EUJ9XPHl/YMR7ufu6GERERERUSu0bOtZ7I3PgadOg//d0QcuGn68ICIiag34jt+MFOkN+MuKvTiWUoBATx0+v28w2gZ6OHpYRERERNQKHUjIxeu/ngEAvHBjD7QL9HTwiIiIiKipMKDYTJRVGHHfJ/twICEPvu4uWPnXQegQ4uXoYRERERFRK1RYVoF5Xx2E0aRgSu9w3NwvwtFDIiIioibEgGIz8crGk9h9Pgderlp8eu9AdAv3cfSQiIiIiKiVWvT9MSTklCDCzx3/uqkHVCqVo4dERERETYgBxWYgo6AMn+9JAAC8NaMvekf5OXZARERERNRqfXcwGWvjkqFWAW/e0Qe+7i6OHhIRERE1MQYUm4H3fz+PcoMJ/dv5Y1TnYEcPh4iIiIhaqcScEiz89igA4OExHXFNdICDR0RERESOwICik8su0lt2Jz48pgPTSYiIiIjIIYwmBY+uOohCvQH92/nj4TEdHD0kIiIichAGFJ3cBzviUVphRK9IX4zsxN2JREREROQY3x9Kxv6LufB21eKN6X2g1fCjBBERUWvFWYATyyspx6c7LwAA5o7m7kQiIiIicgyTScE7W84BAB4YFYuoAA8Hj4iIiIgciQFFJ/bRHxdQXG5ElzBvjO8W6ujhEBEREVEr9cvxdJzJKIK3mxZ3D2nn6OEQERGRgzGg6KQKyiqw4o94AFLwmrsTiYiIiMgRFEXBO1vPAgDuGRINHzd2dSYiImrtGFB0Up/uvIDCMgM6hHhhUo8wRw+HiIiIiFqp7WeycDgpH+4uGsy+NtrRwyEiIiInwICiEyrWG/DhDtmdOHd0B6jV3J1IRERERI6xdIvsTpwxsC0CvVwdPBoiIiJyBgwoOqHPdl9EbkkFogM9cEOvNo4eDhERERG1Un9eyMGe+By4aFS4f0R7Rw+HiIiInAQDik6mtNyI5dvPAwAeHN0BWg1/RERERETkGObdibf0i0QbX3cHj4aIiIicBaNVTuarfQnIKipHpL87pvWNcPRwiIiIiKiVOpaSjy2nMqFWAQ+MjHX0cIiIiMiJMKDoRPQGI97bJrsT54yKhQt3JxIRERGRg7yz5RwA4IZe4YgO8nTwaIiIiMiZMGLlRL7+MwlpBWVo4+uGW/tHOno4RERERNRKncsswoajqQCAB0dzdyIRERHVxICik6gwmrBsq6wC/31EDFy1GgePiIiIiIhaq2Vbz0FRgHFdQ9ElzMfRwyEiIiInw4Cik/g2LhnJeaUI8nLFHQPbOno4RERERNRKJeWWYN2BZADAQ9ydSERERLVgQNEJGIwmLN0qHfT+PiIGbi7cnUhEREREjrH89/MwmBRc2yEQfdv6O3o4RERE5IQYUHQCPxxOwcXsEgR46nDnYO5OJCIiIiLHyCzU46t9iQCAh0Z1cPBoiIiIyFkxoOhgRpOCt3+T3Yl/HdYeHjqtg0dERERERK3VhzvioTeY0LetH4bEBjp6OEREROSkGFB0sJ+OpuJcZjF83V0wa0g7Rw+HiIiIiFqp/JIKfLb7IgDZnahSqRw8IiIiInJWDCg6kKJU7U6cfW00vN1cHDwiIiIiImqtPtl1AUV6A7qEeWNs1xBHD4eIiIicGAOKDnQ2owgn0wrhqlVj9tD2jh4OEREREbVSxXoDPvojHgDw4GjuTiQiIqIrY0DRgbafyQIADGwfAF8P7k4kIiIiIsf4cm8C8koqEB3oget7tnH0cIiIiMjJMaDoQNvPZAIAhncMcvBIiIiIiKi10huMeP/38wCAOaNioVFzdyIRERFdGQOKDqI3GLH7fA4AYFiHYAePhoiIiIhaqzX7k5FRqEcbXzdM6xvp6OEQERFRM8CAooPEXcxDaYURQV6u6BLm7ejhEBEREVEr9fX+RADAX4e1h07LjwdERER0dZwxOMiOs5LuPKxDINRMKyEiIiIiB8gq0uNgYh4A4IZe4Y4dDBERETUbDCg6iLkhy/COTHcmIiIiIsf47WQGFAXoEeGDMF83Rw+HiIiImgkGFB0gt7gcR5LzAQDD2JCFiIiIiBzktxMZAICxXUIdPBIiIiJqThhQdIA/zmVBUYDOod4I9eFKMBERERE1Pb3BiO1npAzPuK4MKBIREVH9MaDoADsq0525O5GIiIioYZYuXYro6Gi4ublh0KBB2Lt3b53HVlRU4IUXXkBsbCzc3NzQu3dvbNy4sVGP2RLsOZ+D4nIjQrxd0T3cx9HDISIiomaEAcUmpihKtfqJDCgSERERWWvVqlWYP38+Fi1ahLi4OPTu3RsTJ05ERkZGrccvXLgQ7733Ht566y0cP34cDzzwAKZNm4YDBw40+DFbgs0n0gEAY7uGsEkgERERWYUBxSYWn1WM5LxS6DRqDGof6OjhEBERETU7S5Yswf3334/Zs2ejW7duePfdd+Hh4YGPPvqo1uNXrlyJp59+GpMnT0ZMTAzmzJmDyZMn47XXXmvwYwKAXq9HQUFBjVNzoSgKfmX9RCIiImogBhSbmHl3Yv92/nDXaRw8GiIiIqLmpby8HPv378e4ceMst6nVaowbNw67du2q9T56vR5ubjXrVru7u2PHjh0NfkwAWLx4MXx9fS2nqKioxry0JnU6vQjJeaVw1apxbQdmzRAREZF1GFBsYpZ0506cuBERERFZKysrC0ajEaGhNXfVhYaGIi0trdb7TJw4EUuWLMGZM2dgMpmwadMmrF27FqmpqQ1+TABYsGAB8vPzLafExMRGvrqm82tluvO1HYK4yE1ERERWY0CxCVUYTdh9PhsAMLxDsINHQ0RERNQ6vPnmm+jYsSO6dOkCnU6HuXPnYvbs2VCrGzcVdnV1hY+PT41Tc2GunzimS4iDR0JERETNEQOKTehgYh6K9Ab4e7iwkx4RERFRAwQFBUGj0SA9Pb3G7enp6QgLC6v1PsHBwVi3bh2Ki4tx8eJFnDx5El5eXoiJiWnwYzZn2UV6HEjMAyANWYiIiIis1aCA4tKlSxEdHQ03NzcMGjQIe/furfPYiooKvPDCC4iNjYWbmxt69+6NjRs3Nuoxm6vtpzMBSGoJO+kRERERWU+n06F///7YvHmz5TaTyYTNmzdjyJAhV7yvm5sbIiIiYDAYsGbNGtx4442NfszmaMupTCgK0D3cB2183R09HCIiImqGrA4orlq1CvPnz8eiRYsQFxeH3r17Y+LEicjIyKj1+IULF+K9997DW2+9hePHj+OBBx7AtGnTcODAgQY/ZnO1/azUTxzRkenORERERA01f/58LF++HJ988glOnDiBOXPmoLi4GLNnzwYAzJo1CwsWLLAcv2fPHqxduxbnz5/H9u3bcd1118FkMuHJJ5+s92O2JOZ057FMdyYiIqIGsjqguGTJEtx///2YPXs2unXrhnfffRceHh746KOPaj1+5cqVePrppzF58mTExMRgzpw5mDx5Ml577bUGP2ZzlF9agUOVqSXDOrIhCxEREVFDTZ8+Ha+++iqeffZZ9OnTBwcPHsTGjRstTVUSEhIsDVcAoKysDAsXLkS3bt0wbdo0REREYMeOHfDz86v3Y7YU5QYTfq/MmhnbtWW9NiJqARQF2LUU+P4RQF/o6NEQ0RVorTm4vLwc+/fvr7Hiq1arMW7cOOzatavW++j1eri5udW4zd3dHTt27GjUY+r1esv1goICa16GQ+w6lwWTAsQGeyLcj6klRERERI0xd+5czJ07t9avbd26tcb1kSNH4vjx4416zJZiT3w2isuNCPZ2Rc8IX0cPh4ioiskE/PQksG+5XC/KAO74HFC3gk70FWXA4a+AP1cAYT2BG9929IiIrsqqHYpZWVkwGo2XrdSGhoYiLS2t1vtMnDgRS5YswZkzZ2AymbBp0yasXbvWsmrckMdcvHgxfH19LaeoqChrXoZDbD8j6c7Dme5MRERERA6y+YSUFBrTOYQ1vVsiYwWQHCe7vIiaE6MB+H5uZTBRBWh0wOmfgN9edPTI7KssH9i+BHizF/DDPCD1IHBgJZB62NEjI7oqu3d5fvPNN9GxY0d06dIFOp0Oc+fOxezZs6FWN/ypFyxYgPz8fMspMTHRhiO2j6qAItOdiYiIiKjpKYqCzScr6yeyu3PLYzIBX80Elo8GDq9y9GiI6s9QDqz5K3Dwc0ClBqa9C9z4jnxtx+vA4dWOHZ89FKQCvzwDLOkObH4eKEoHfCKA0B7y9QMrHTs+onqwKqoXFBQEjUaD9PT0Grenp6cjLCys1vsEBwdj3bp1KC4uxsWLF3Hy5El4eXkhJiamwY/p6uoKHx+fGidndjG7GAk5JdCqVRgUE+jo4RARERFRK3QmowiJOaXQadWs6d0S7V4KnPlFLh//zjFjSI4Dss855rmpeaooBVbdBRxfB6hdgNs+AXrfAfS6DRj2mBzz3Vwgab9Dh2kzmaeB7x4C3ugJ7PwfUF4IBHcFbnoXeOQgMP4FOe7wKvneEDkxqwKKOp0O/fv3x+bNmy23mUwmbN68GUOGDLnifd3c3BAREQGDwYA1a9bgxhtvbPRjNhfm3Yn92vnDy9WqspVERERERDZhTnceGhsIDx3npC1Kchzw6/NV1+N/l11fTWn/x7I78q1+wLJrga3/BtKPM/2a6qYvAr64HTjzM6B1A2Z8BXSbWvX1Mc8CnSYBRr3svi1IcdxYGytxH/DVncDSgcCBzwBTBdB2CDBjFTBnJ9BnBqDVATGjAd+2kgp94gdHj5qaitHg6BE0iNV5x/Pnz8fy5cvxySef4MSJE5gzZw6Ki4sxe/ZsAMCsWbNqNFjZs2cP1q5di/Pnz2P79u247rrrYDKZ8OSTT9b7MZu7HeZ05w5cCSYiIiIix9h8wpzuzO7OLUpZAfDNvRKg6DoF8AwGyouAxD1NN4YLfwDr/6/yigpIPwpsXQwsGwK81R/49TkgeT+Di1SlNA9YOU2C3zov4K41QMdxNY9Rq4FblgMh3YCiNAkqNrdde4n7gE9vBD4cB5z8EYACdL4euPcX4N6NQOfr5HWaqdVA3zvlctynDhkyNbHfXwVeDAQ+uwU49RNgMjp6RPVm9dLk9OnTkZmZiWeffRZpaWno06cPNm7caGmqkpCQUKM+YllZGRYuXIjz58/Dy8sLkydPxsqVK+Hn51fvx2zODEYT/jhXGVDsxIYsRERERNT0corLEZeQCwAY24X1E1sMRZFAXm484BsFTH0L+Okfki559leg/XD7jyEvAVh9N2AyAN1vBq5/TT4Un/gBOPcbkHNO6uDteB3wiZSgZ7epQNSg1tG9ly5XnCXBxLTDgJsvcNdaIPKa2o919QZmfAm8PxpIOSDpwrd8CKicvKlUygFgy8tVZQjULkCv6cC1jwDBna983z53yg7fC9ulhEBgrP3HS45RkAr8/l+5fPZXOfm1Ba65F+h7N+Dp3JvSVIrS/JeJCgoK4Ovri/z8fKerpxiXkIub39kJX3cXxD0zHhp20yMiIqJaOPN8hurHmX+Ga+OSMH/1IXRt44Of5jVBkImaxsEvgHVzAJUGmL0BaDtYGlisvR8I6wk8sMO+z68vAj6aKDsS2/QGZm8EdB7Vvl4oAZXj3wNnNgEVxVVf842SenmR/e07RnIuBamyYy/rFOARBMxaJ7+rV3Nhh9zPZADGPAOMeNzuQ22QtKMSSDy1Xq6rNECfmcCIJwD/dvV/nM9ukeDSsPnAuEX2GSs53o/zgT8/BCL6A+2GSjp8qSz+QeMKdJ8GDLxfvt5EQXRr5jJ27/Lc2m0/LbsTr+0QyGAiERGRs1AU2UFz6idHj4SoSZjrJ45jd+eWI+sssL4yqDJqgQQTASB2DAAVkHYEKEyz3/ObTBLMTD8KeIYAd3xRM5gIyO6yHrcAt38CPHlOjul1B+DqC+QnAp/cAJz+2X5jJOeSexFYcZ0EE73DJeW3PsFEAIgeBkx+VS7/9iJw4kf7jbMhMk4Cq+8B3r1Wgokqtfyuz90H3Pi2dcFEAOg3S84Pft5s6+vRVeTEA3GfyOXxLwAT/gXMPwHcuBRo00dqhx7+CvhgLPD+KCBupdOl/DOgaGc7zmYCAIZ1YLozERGRU8g8BXwyBfjyDjltfYV1vahFKzeYsO20zEnHMN25ZTDogW9my46/6OHA8PlVX/MMkt2CgKQc28u2V4AT3wMaHXDH54Bv5JWPd3EHulwP3PweMP840GEcUFECfDkD2P+J/cZJ1inJAbb9B3izN7DhyasfX18FKcCKSUDuBcA/Grj3JyCoo3WPcc1sYODf5PLav8luQEfLPgesuR94Z7B0qgYk9f/B3fK73tB05U6TZAdnUXpV2jS1LNtekR23sWMkYA7I/8m+dwF/3wbc9xvQe4bsVEw9CHw/F3itC/DzP2X3txNgQNGOCssqEJeQBwAY3tG5c9+JiIhavPJiaQyw7FqpS6TRye1bXwZ+WcigIrVY+y7koEhvQJCXK3pH+jl6OGQLmxZJ/Tn3AODm9y+vRdihsrnF2c32ef5j64Bt/5bLN7wORA207v6uXtLRt8+dgGIEfngE2LKY/4cdKSdedrwu6QZseUkCf3vfA+K32+bxf1kIFCQDgR2B2T9JULEhJi4G2o+UYPqXM6Qeo6P8+jzw9gDgyGoACtDlBunYfNuKq9dJvBqtTjo/A8CBlY0eKjmZjJNS6xYAxiys/ZjI/sC0d2XX4rjnpbZiWR5wdC2gdW+yoV4JA4p2tPt8DowmBdGBHogK8Lj6HYiIiMj2FEVSo5YOkqYApgqg03WShnTdK3LMrreBHx9tVp31iOrr18ruzmO6BEPNEjzN36mNwJ5lcvmmZYBP+OXHmAOK536z/f+11MOS6gwAgx+S3TQNoXGR1L4Rlbvgtv0b+P5hpnc2teT9kqr7Vj9g33LAUAqE9ar6Hdr4VON/hy78ARxdI2nAt35U++9sfWm0wG0fAwExQH4CsOpuwFDeuPE1RNYZYMcSCYh3nAj8bZvs1A3tbrvn6FuZ9nz6Z6k9SS3HlpcAxSRB6Iir1JH1DASGPQo8chCYuRqY+C/5O3ACzjGKFmr7GUktGd6R6c5EREQOkRMvHU/PVNbo8m0LTHoF6DJZrg9+ANB5yu6Y/R/LLsablskHXaIWQFEUS/3EMV1CHTwaarSClKpg3qA5QOfraj8u8hrA1QcozQFSDtqu8UlRJvDVTElVjh0jdb8aQ6UCxvwT8Gkj3aoPrJQUz9s+lv/Nrdm2/wJ/vAnEjpLAUoextuuKbTJJGu3O/wEX/6i6vcM4YOjDsgOwNBf4X1+pkRn3iXSdbdBzGeV9GAD63QO06dX48XsEyA7XD8YBCTuBf7cFgjsBwV2qnTrLLkh7dRI/tUHOY0YDd662z3MEdwKiBgOJu4FDXwDD/88+z9PalBcDGSfkdzv9mKTOZxyTOeI938vvlz2lHJRyEVABo/9Z//upNUCnifYaVYMwoGhHO87I9uthTHcmIiJqWga9fBDb/hpgKAPULsC1jwDDH7+8aUC/u+WD69r7gSNfy0Tz1hWAi5tjxk5kQ+cyi5CQUwKdRs0SPM2dySh140pzZAfZ+OfrPlbjAsSMBE78AJzbbJuAoqEcWH23NFMJiJWdZrbaJXPNvYBXGPDNvRLo+vgG2Ynj1Uo3Zux8G9jyL7l84gc5eYdLCmzfu2R3XkOU5UvH7Z1vSWMUAFBrgZ63SSCx+u46jwBg9NPAT08Cm1+UbrPu/tY/Z9wnQPoRwM1XujPbSnBnCTyv+asEP1MPyak6jSsQ1AkIqQwwth9pfXp+XU5tlPPOk23zeHXpN0sCinErgWsfA9QNSDJNPy47T/3by8+wiboFO4W8RCkPkXa0KoCYcx5ALeUVyo7I/9iZqxv2fa6v3yr/tnveBoR2s9/zNAEGFO0kKbcE57OKoVGrMCQ20NHDISIiaj3ObgY2PAHknJPr7UcAk1+Tlf669LgZ0HnJh+VTG4AvbpdupK5eTTNmIjv5tXJ34pDYQHi6curfrO1YIvVfXTxl0UPreuXjO4yTQNTZX4GRjWyuoSjAhv8DEnbJzscZXzUsuHQlXSYD9/wg/39T4oAPxwN3rWl4U4vmKu5T4JfKXUtDH5GmDYe+AgpTZJFs+2vSiKfvXUDXqZcvkpkZymXXVdKfQHIckPwnkHW66uuuPkD/vwCDHgB8I2p/jGvuBf78CMg8KY1arlts3WspzZVgJCA7sTxt/Lm4w1jg8bNA3kUZY+ZJqU2XeVJeq6FMgpnpR+R49b+Bh+Os77h8qZIcCfIBde8StpXuN8kOz9x44OIOmdNY48SPwKq7YAmgufrIzs3qp4D2cu4bJYsRBr3sFC5MAwpTJd26MLXqemEaUJwJhPUA+t4NdJ0izUScSVEm8PMCWSiujVeoBNBDuwOhPaUj/TezgbObpPzC6KftM66Lu+Q51Fpg1FP2eY4mxFmFnZh3J/aJ8oOPG9OmiOyivBj49gGp/RLWS+pPRPQFwvvZf6s61U9RprwpB3aQwutuPo4ekfPJvQCc3wqEdLPdqnl96QuBg18AGcflQ0tL+NB26ifp3AzIZHHiy0CPW+q3Gt9pAnDnN3L/+G3AZzfLKrW7n12HTGRPv1UGFMd2ZXfnZi1hjzQtAYDrXwWCOlz9PrFj5TxpnwR2GhMA3LtcAl1Qyc7EKy3QNEbUAOCvm+T/b2488OEE+T9sq5TtxjAZJYhyaWClIEXODWXAoL9LcKWhjq0Dfpgnl4c+LCnlKhUw7jlZ7DrwmSyaXdgupw1PyHtcv7sBN7/KwOF+CR6mHgaM+sufIyAG6D8b6H+P7Bq8Eo2LBBFXTgP2vi8BSGuajWxZLDtqg7sC1/y1/vezhkYr85fAWOkibmYyVgYaT0mAMW6lLDQeXwdcO69xz3lmk9S/C+kujTLsSecJ9LwV2L9CXoM1AcXM0/JZCYr8rMvyAX1B5Y69w5cfr1JLwLEsr36PH/+7nNx8Zbdd37uANn0cuwNSUeTv5JeFla9DJYHPUPOpu5zXtvt5ypvAt3+X7svh/WwfLFYUYHNlmYi+d7WIebdKUZp/K62CggL4+voiPz8fPj7O8WH1oS/isP5wKuaN7YjHxtvpDZfIWRRlypu5rVeqr6QkB/j8Npkw1ca/PRDRT94MIvpLvZbWXounqeVekAloznm5rvOSN8+Bf2sRb6ANZjQAiXuA0xslrSvzpNyu8waeONM0K7x5idK5cf+ngD5fbtO6yQ6WoY807/qBq++RDwtdpwI3vn31D0u1SfpTPsyW5QNhPYG71wGe9k8Vdcb5DFnH2X6GucXl6P+vTTApwI5/jEakP5sENksFKcAH44GCJKDn7dLVub4f2N8eKKmtt30iO50a4vw2eT9XjMD4F6V8hL0VZcg8L/Ug4OIBDLxfggAhXSWF9Wq7M21l+2uyw6swTXZsKfVoTjL4QenIqtVZ91xnfwW+uEMah/WbBUz5X+0/5/wkWQw88JkEzK7EzU/mwZHXyHl4v4alkX85QwKasWNl12h9fv/SjwPvDpPv2azvgJhR1j+vLe37QOp0hvcD/ralcY/19V+AY99KTcOxz9pkeFeUHAcsHy0p3I+fqt9nrrICYPkYIPsM0G4YMGsdYKwA8hJkjp57QYL2lssXJChuptEB3mGAd5tLzsPl3M1HmsUc+Fya45iF9pQAd8/bmn6DR9YZ4IdHZScnIHO4Kf+Tz4T1tf5xaU7k6iu/J7b8zHL2V+CzW+Tn+MiBuncGO5g1cxkGFO3AaFLQ/1+bkFdSgTVzhqB/O+6UohaqvBjYtEj+6aq1kn7RbSrQ+XrA246F3/OTgJU3ywTZzQ+4/jVZMU7eL2+45jTH6lRq2QHWeRLQ6476rexTw6UdlYBMUboUOHZxr6rVA5UUFB70gEwuW0Mdl5IcmUSc3ijnZflVX1Np5O/HqJfdcR3H228cSX9KN+Pj31d9KArsKDv5zJOvkO7A1P/Jh4/mxmQE/hsrO3Hu/QVoO6jhj5V2FFh5k/xvCeokH4Ya05WyHpxtPkPWc7af4bcHkvDYqkPoEuaNjY9amSZHzqEoE/h4sqRvBsQCf9tq3W7/jU8Du5dKWuKNb1v//CYTsHSgBCV63QFMe7fp3rf1RcDX98j7ZnUqjWQ+hHSVuV1oNzm3dQOO9GPAsqGXPLca8AyRgIpPeM1AS8YJYPc7clzkQOC2FYBvZP2eK2GPvOdUlEitwls+vPprMZlkl+KBz6TBg6LIAnpE/6pTQIxtfl7Z54ClgyTYOXP11RtDKArw6Y2y27/rFGD6Z40fQ2MVZQCvdZadhfMONzzt2VAucw19AfDXX2VXrb0pigRn048Ck/4LDPrblY83maSEy8kfJQD4922A11V2qZtMMm8vzZXf5/rWWjSZ5Od8YKUE3827YjU66WDc726g/Sj71iQ06IEdr8sCgLFcFiFGPy2Nq6yt82ooBz65QRb/Q3vIjum6ygpYQ1EkKJxyABj8EHDdy41/TDthQNHBDiflYerbf8DbVYsDz46HVmPHPx5q2Y6ukQ/IPW617z/hhkjcJ1vCawveQQW0HSw7hLreYNtUgMxTskpekAz4RAB3rZVCy9WV5so/6+Q4OaXESVpKdRHXAL3vALrfbPt6Lq3dhT9kJVufL8Gpu9bIxOT8FmD3MtmVZxbcVbrs9rzdNm/WzsSgl9d76icgaa9MYM3c/YEO42VC3mEs8Otz0mF4wP2SymZLRgNw8gdg1zsyDrP2I4EhD8k4VCrg8Cpg4wJJTYJKdpKOfUZqyjQXKQeA90fJbs9/xDd+p2XWWflAVJAE+LWToGJAe5sMtTbONp8h6znbz3DuF3H48XAqHhodiycmdrn6Hci5lOQAn0yV+m8+kcC9P1k/pzLviPEOB+Yftz64dOJHYNWdstv7sWNN/55grAAOr5ZF44wTUhOw+qJcdVp3oN1QCV7ZYk7x3UMSrOswXoIT3m0Az+ArByhOrge+nSNzIPcA4JblUsvyStKOACuul/t0GAfc8aX1uxsNegAq6+9njU3PSrOzgFjgwd1Xfq7j30tAS+MKzN0rwV5n8PENEoQd/0LD057Pb5W5gWcw8H+nm+4z2p73pEFOaA/ggR1X/lve9l9p6qPRAbM3Nl3JgJIc4Mg3wIFP5ffazK8tMOwxoM9dtv8dvfAH8OOjVfVBO4yXzSaNqZNZkAq8NwIozrB+V3hdTvwgtSxdPIF5h5y64RQDig72/u/n8PKGkxjfLRTLZzXDHR7kHDJOAu9U7q6JHg7c9I79a3TUh6Fc6krsWCIBEu9wWfH2aydBixM/yKSvujZ9ZOdi16lAUMeGP3fSn8Dnt0rAMKiTBBP9oup334JU4OIfMik9+2vV7iy1Fug4Eeg9Heh0XdOl0LRUJ9cDX8+W1cm2QyqLtvvVPCbrrKTbHvgcqCiW29z9pS7PgPsds/0/6U9Z1Rw233YrzT8+JoXMzUK6SwCx00QgckDNnQcn1wNfzZS/8XmHbbOboCxfau3sea8qFUWjkxSUwXMkDeRSxdnAz08Dh7+S6z4RMinrPKnx42kKO16X4GznycCML23zmHkJ8oE+Nx7oOAG4s47i3jbgbPMZsp4z/QwrjCb0e3ETCssMWPvgUPRr24RlSajxygpkx1ryftkRd+/GhqXeVZQCr0RLKuODu2VXX30pijRGSdrXdKmd9RlTYarU/s04IWm1GcelfIg5XXPiy7Jg1hhFmcDr3WU+89dN1tU4zomXnZWphwCogBFPSPOF2nYcZp8DPpoou+HbDpG5rbMusJYVAG/1lyDLhH9JjcfaVJTKrta8BHntYxY27TivZN+HwPr5jUt7/ukfwJ53pYzPjUttO74rKckBXusiv5P3b6k7jffMJikXAAWY+pakzztC6iGZhx5ZXbUI4BMJDH9Mdkw39jNXSY4EuQ+slOueIcCkf8tmEVvMoy/8AXwyRT4zTvqP1EdtKJNRdjtnngSGPy4L9k6MAUUHW7zhBN77/TzuG9YeC29o3m3AyYG2LJZmFmY6b2DSK0CfmY5LEU0/JrsSzStOPW8HJv/n8joe+Umyon3iByBhZ82dWSHdpGNc7zusW+U+86usdFaUyO7CmasbvrOwKEN2fx76SmrzmLn5SZpJ7xkycXTmVFxFkdot+gIJTgV3sW2aT0PErQR+eER+3p0mSarPleoBlubJyv/e92TSCUgaU7cbJeAVOaBpfgbntwJfzpTgZtRg4K8/N/4xywokraaiBBjzDNBr+pWD3/oi4D/tJU3jwT2X77q1VlGmrKwWpsh1j0Aphj7gvvqVIzj3mwREcy/I9W43ymTKO6xx47K3T6ZK2k1jJ36XKkyTwvfXL7HrirKzzWfIes70M4zPKsboV7fC3UWDo89PhEbtxO9pVFN5iewqTNgpu9z+sl7Sehvqs1tkMfVKQaDaXNwJrJgku8wePWLfcjaNZTJK45iN/5CdhPMONS5gsfXfwNbFMue8f7P1968oAzY+JY00AMkKuOXDmu8h+UnAR9cB+YmyyHfPj87fBOzAZ7Jz09UHeHh/7Wm05t1xPhHA3H3OVcO8KBN4rVNl2vMh63dOKgrwZm+pXTn9c8nEakpr7pOuxdfcC9zw+uVfzz4nabVl+XUf09QqSoH9nwB/vFGVMeYTITsW+94NuLjV/7EURV7j2U2yEaA4U27v/xdpXGTrev673pFO0Wqt/H22G9Kwxzm0Cvj2b7LTe95hp/87t2Yu42Q5lC1Dkd4AAPB0ZRNtaoRj38r5qAVSh6W8EPjuQeCrO+XNsCmZjJLi8P4oCSa6BwC3fSxpHLX94/aNlDTW2eslFWDKm5LCoXaRVeQNjwNLuskKX3ZtKdOXOPw18OV0Cc7EjpW0w8akKXuFSMDq79skeDPsMXljK8uTid9HE4C3+gHbl0ggwRmd+AH4ZrZ0Alw2FPh3W0nj+PU52e1WmN50Y1EU+V59P1cmaH3uknSjqzUXcfcDhs4FHjkok7Lo4bIKeGyt7IhYPkZ2lBrK7Tf2kxtkFde8UzJxtwTOG+vI1/L7GtRZdnVcbSetqxcQPUwun7FBQPPI1xJM9A6Xv7/HjgFj/ln/D4OxY4A5uyQdSKUBjn8nhf3/XCEfdI0V8nN3JhWlQMJuuWzrwu/eYcD0lU6dnkJ0qaIymY/6urswmNicGPSSYpywU4I2d69tXDARqOr2fGktwqv540057zPDuYOJgCyqXjNb3vcKU6VpSUNVlEkDDwAY8mDDHsPFDZjyBnDzcqnnFr8NeG84cHGXfL04C/j0JgkmBnYA7vrW6YMMAIDeMyXzSF8A/Pbi5V/PT5JADyBpxc4UTATkfbzdtXL5+HfW3z/zpAQTNa5A7Gjbjq0+zLsNj3wjteyr0xdJSm1Zvnx2vO6Vph9fbVzc5XPhIwel/qN3uJSu2vA48L8+wO53ZQ5Xl6JMeb3fPQS83gN4u78E64szZUPF7I0y17VHc9DBc2THo8kgu44b8rnQWAFsrayXeO285vF3bgXuULSDR786gHUHU/DPyV1x/4gYRw+HmqOME8A7gyU98Ymz0h33jzeBLS9LMWSPQPnH2XWK/ceScx5Y9yCQUDkB6nSddMtqyMSyNE/qtO19H8g+W3V7h3HAwL9XBh0vWefYvUzeNACpJXnTMvvUhzEXtj68SiYY5UVyu0ojr7nfLBmftYV97aE0TwpjF6VJwKogRQLOl/JtKzVTIgdIGk14X9vv+DOZgF/+WVWE/NpHZYWwoc+TdkTSSA5/XVXU2StMdtZdM9u2nXaPfAOs/ZsEMbtOkcD5qQ3yXNe/1vDHVRT50JB2BJi4uP4fRna/Kzsr2g2TYHxjfDBe6iXaYqde6mHZeZpy4PKvmRvKqLXyt6HWysKBWiu1Bqe+ZdeagzWc2yLpgQ2tE+YEnG0+Q9Zzpp/hrnPZmLF8N2KDPbH5/0Y5dCxUT8YK6VR/ar0Eoe5e17jmUmaZp4GlA2Re+Y8L9QvypB8Hlg0BoJKdaLbsdGpP5nmjfzQwd3/D5m3mXXg+kbKLrbFzv4yTwOpZ0pxOpZF6jCe+l5RQn0hJZ69vCR9nkLBb0rShksX5Nr2rvvbNvZIF1HYoMHuDc74XNybtefsSYPPzUqfvrm/sM74rMZlk00NuvHwm6jNTblcU2Whw7FtptPe3bYBPm6YfX30Y9JKmvP11qVENyFz/2nky11cUWVA5vxU4t1VqyFan0QFRg4Au10v2jT3rhgISqP1gHJB5Qj5P3fODdTW6//xIsn48g+X/ibMF2WvBHYoOVqSX2mzcoUgNdmydnMeOka3Rag0wfL686YX2AEqyZQXq2wckuGQPiiK7kZYNk2CizkuCAzO+avgqtbufBDce2ieNOjpdB0AlK+Zf3CYrTrvekZU1RQE2v1AVTBz0gKzy2utNQ60GYkZKrcrHT8ubdNshEmw6tV52SL7RA9j8otTGcaRfn5NgYmAH4O+/A09dlN1k5jopId0AqKRu3rFvpSbe8tHyO2PLHZfGCkmBNwcTJ7wEjH++cZPHsJ5Sj2b+cWD0QplgFKVJ6sySbsC6h2oWeW6o/R9L2ohilK6Vt35cFXg7tEomDw2VckDGqNFJan99dZog5wm7Gvd3nZ9U2XxFJXVLG6tNL+C+zRIcdb1kUqEYJfBbUSx/tyXZ8vMqSJIA/YrJQNaZxo+hPs5XfihoLZ3Dia6iuDJjxsutkc2JqGmYjPKeemq97H6a8aVtgomA1K/2bStlNS78Ub/77HxLzrtOaT7BRADod48svOdekMCWtRRF5qKAdNK1xUJySBfg/t+khrFilJ19qYcAjyDJumlOwURAGi/2uBWAAvz0VFXGwsWd8j1XqaVMk7O+F3edKmNMiasq7VJfpzfKeefrbD6selGrpWsyAMR9WnX7zv/JnF+tBW7/1HmDiYCUIhhwH/BInKRk+0bJ3PHnBcCSrsAr7aRMw863qoKJoT2lXMNda4B/XAT+8qPsHrR3MBGQLKI7Ppc5cMIu4BcraoJWlEoJAEBqJzaDYKK1GPGyg6oJHL+91EDH18l5t5tq3h7WUyYkWxfLjsVDXwLxv0sAxpbb7hUF+P7hqiK37a6VQJutOrSp1bLbr8M42QG570Opv5dzXt5MfvuXBDHMuyLHPCNpo001MdF5yopfn5nSVTruU/leF6YC21+VU/uRErzrcoN1tT8a6+LOqno8U96seu7QbnIyp0KUFUhgK2mfNBw5uwk4+aMEeSa+DPS5s3Hfz/JiWW0/+6ustt/0jnXBs6vxDAJGPiGrlce/k6BlShxw8DM5tRsmBdc7XWd9d72db8uuSkBWNie/Ko8RPUI6F+acq6xPM7thY9//sZx3uxHwCKj//QJigMCOQPYZCY51n9aw5zen8LQdYrsJpVojOy0H/k2K3psq5MOvsULSQC69XlEi6fiZJyWoOOu7xqfsXc35rXLuiBQkIidkLsHj5erg+rp0dSaT7AQ/ukZ2eU9fadvSDSoV0GGszB/O/lq1gFWX/CRppABI5kFzovMABj8oQbsdSySIZ808IX6bdJJ28ZTgpK24esnCeNshsliudZd09qAOtnuOpjT+eSmxk7BTAlndbpQOxIB839r0cuz4rsQrWMrMxP8uc6b6dnsuzgIS98rlTg4KKAKSdv7bv+RzUuZpSR/+9Tn52nX/loBvc6B1lTqPfe4CDn0hqfLmmuo+kUDsKCBmtHzmcnTJmcBYYNp7wFczJJMq4hqg121Xv9++D6UEkU9kwz9XODlGvOyguJwTOGqEjJPyIVztUntnVa2rpJR2miQr2bnxkuY38G/A+BdtE9za8boEE1UamTAMfsj6oE19BcQAE1+S9I/Dq4A978uW8oRdsnp4wxtAfxtO6KwV3FnGN3aRpMPGfSoNK+K3yck9ALj9E6D9CPuPxaCXIA0ggUNzzb3auPnIjsuYkXI97aik76QelPOja+R769/OujGU5cv3YPcymcBo3WUl9GofThpKq5M37J63SnB09zKZ/F3cIafADhJY7D3j6jUbFUU6lG9dLNevnQeMq7ajUq2Wic0v/5T0hP5/sT7oqi+UVGpA7m+tThOBXWeA0780PKBorr/a0PtfiUYLaLzqd+xf1kt9qPQjwMfXA3d/C4T3sf2YAOlOnXpYLrcfaZ/nIGpmLDW9dU4+3TfoZd4RPezK72vWKC+WxR2/drLI4My7QhRFyl0c+EzmPbd8IO8FtmYOKJ6rR4OR3ctkcSh6uJROaW4G3g/88T+ZT5/8EehmxW598+7EvnfavtaZSgUM+KssRqs1ti3j0tR8I6UG+daXpdNuYZpkZ7j5ykYAZ9ftJgkoHvu2/gHFM78AUGSDh2+kPUd3ZT5tgI4TgdM/SQPPc1sqa5jfKTv/mhutTubMfe6Uz3/e4RLAc7Ydrl0myy7D7a/KxptDX8j7l6Gs7nNjZR34Uf9ofFdrJ+XkM4zmqdlM4Mg5mXcndhh75YlM20HAAzvkTfzPD6UuYe5FaYbRmO3fJ36Q2iCApCsMvL/hj2UNnacEc/rPll10R9fIhKvj+KZ5/qvR6oDuN8kpL0Em/gc+k6DaD/OAh/ZaV0+jIbYvAbJOA54hUujaGmE9JG1119sSUDv3G/DOEAlOD7jv6gHj/CT5gLH/k6p6jV6VjSqiBjbo5VhFpZLniRoI5CfL7/ufK6QW54+PyUrtgPvl97W2CbqiSIrCrrflel27XvvMlFT7tMNA8n4g8hrrxnnkG0n/DexQVfTbGh0nyBjPbpIdK9YG8vMSJfAKlXUfoOzBMwi453tJW0mJkw7Md60BogbY/rnitwFQJN3f2RsHEDURS8aMs5fgMb8vaXTAzFVS7qUxKkqBL++QYAEAaN1kt1/nSbIYa8//EWUFsrMv7lOpb+wZLP8LPYMvuRxSdX3/x/KeBgA3viPzDHtoP0LSIbPPSumWuurbluZW7bSvb6DF2bj5ynxg+6uy66nrlPoFJ7LOVDZGU0mpHXtpKe9TQyuzmfITJcMIAEb/s3GNE5tK16nSFCTlgKQ91ycL69RPct55sj1HVj/9ZklA0ZzWH94XuH6J8wXhrKFxaZoNGo0x+mmZ0577TU71Ed5XdpW2UE4+w2iezF31WEORGsRcP/HSdOfauHoBNyyRbferZ8kk6JvZ0oG5IcGt1EPSpAKQHY9NFUysTqWSNxNnfkPxaytvKEMfke5kOedlQnXNvfZ7zoyTVV3zJr3SsE5mGi0w7FEJ1H7/sKSp/PSETEZufFtqLF0q5aB82Dv2rexWAKQRzNC5QM/bmzbd28w3QnbOjnhcgrq735Eg77Z/A3+8IbsVhzxU9XpMRgk6xn0i1697RbrN1cYjAOhxs6S4//mR9QFF84ewhuxuBCQVSuctnetSDli/M8Sc7txuqHQmdjSPAEl3/vw26aC98iZg5mogugHB1isxpzvHMN2ZyMwcUHTq+WhpblUnYWM58NWdspu5oSl7hnKZD8X/LrWfPQKlI+vpjZW1zx6V/+udJwGdr5csBFt8AE87IqltR76uauoGyP/y+rp+iXRTthc3X2lkcPEP2aUYUMdOpn0fymsI6S6laZqrwQ/K/CD1oLze+ryW3cvkvPOk5lU30lF0HrLA/U1lKmdwVykl0xxUT3s+tk7mx1di0FcFkByZ7mzWcUJVnXGPQOD2lY6Zk7c2ao18r0/+KNe1rrJopXWVrK0a1yvP3QPsl+nnBFruK3OgZrMiTM4n46Sk+9aV7lyXThOAGV9IEe+TP0oqtMlo3XMXpAJf3CG1z2LHSgMGujJXL2DEE3J56ytAeYl9nsdcW8lUIZOYxqayBnWQdNTJr8oHrsTdwLJrZQek0SC7+c5sAj6ZArw/Uj4gmVOfZq4GHtwtK6OOnri4ektB5ocPALeukG59hjJJ6Xr7GuDLGUD8dvl7iPtEUsmmvl13MNHMHBg+ugYoyan/eFIOyAcXja7hK5FaXVUNwDM/W39/e6Y7N5Sbj+xMbD9CPqR+douk59iKotRsyEJEAJpJk8Cdb0kpjeCuEvCpKJEFiJSD1j+W0QCs+aukJWrd5f1q3iFgzk5gzEIgoj8ARXZxb34BeGcQ8L++wM//lLqC+clVzSXqo6IMOPgl8MF44N1h8t5TXgQEdZI6Zn/fLl2ab/5A5lTD5gN975ZdkhH9ZXFS6y61+q57RVJh7c28+/NsHTtrKsqkPhgguxOb824nz0DJegGA31+7+vElObKYCEgwkuqn+zRZzFNrgcn/sU0Tm6Zi3rxhzg67kgvb5e/bKwxo08eOg6onjRYYvQDwby8BrubW2Kc5c/WSuvG975Df/86T5H9ruyFARD+pGR4YKxsgPINadDAR4A5FmzOZFBSXN4MJHDkn8xta7Bjr67bEjpFadqvukkCIxlWatdTnn1h5iRSZLUyR3We3rWheEwJH6j9bdvDlJcgkfPh82z/H/o+AxD0S/Lv+NdtM8NVq2YHaaSLww6Oyer/5eeDYWmmskXlSjlNpZMfekLn2q3/XWBqtjLH7NGlas+ttqXdpPgEy0b15uRx3NZEDpJtc+hHg0FfSjKQ+9lfugOw6pXHpPp0mAie+B07/LDth6ysvAUj+Ezbr7mxLrl7y4X7V3ZLO/cV0SZe3RZ2w3Hh57WoX2ZlJRACqL3A7aU3vogxgd2XwasxCmcd8fqvsoFs5DZj9k3THrQ+TSeoDn/heFnXu+LxqJ3RodzmNeEIWT0//JKmL57fJ/49db1eVw9B5ScmKoE5AcCc5D+ok9Z7N9a+yz8kO9oOfyw5LQN5juk6R3VnRw6x7nzYZZddLU+gwTpqVxG+T3ZyXlsg59IXsqvSNqt/7pbMbOhfYt1wyMi7uvPJ7xP6PJaAd1tN2tTxbA5VKShWU5Dh3Z+HaWJP2fKqyu3Onic4TIOr/l4bV6yayISf5a2g5SiqqdoU59Q7F8mKZTP35kXU7cMi+zOnODa2f0/k64NaPJAh06Atg/fyrr7abTMC6OfJm6h4AzPxK0mKofrQ6YPRCubzjDdv/PRWkAJuek8tjn7V9EWi/trJ77KZlgJufpG1lnpS02yFzZXfHLR84bzCxOpVKPkDO+BJ4aJ9MsjSVKQd3fFH/D0cqVVUntj8/qt+OFX2R7OQEGj+561BZNzT1oBQ5ry9LuvO1zlmfycVdPuR3vh4w6iW18fj3jX9cc7pz1EAJXBIRAKCo3MlTnrcvkZqz4f2ALtdL+uSMr+R6aQ7w6Y1SUuRqFAXY8H/A4a9k/nPbx1KHujY+bWQX+p1fA0+el509vWdI0FCtlR1IqQelDuJv/5L06XcGAy+FyW7G5WOAt/pJALI0VwJvYxYCjx2X520/3PpFv6YKJgJAWC+p3VheJAuV1ZmMsmMUkLIh9q4L3RR8wqU2MgD8/mrdxxkrgL3L5fLgB5v3zkxH0Lo2v2AiUJX2DFR9BquNolSWTIB1GWRErQADijZmXg1WqwA3Fyf69iqKFBretVS6br4SLQWrf3xMGjOc/sXRI6TMU9XSnRtR7LfbVODm9wGoJP1m44IrB0S2LpadkWoX+bAfENPw526tet4GhPYA9PlSw8+WNjwhTVAirrFf5zaVSibcD+0FBs0BJvwLmH9Muls31xSK4E7AlDeBx08Bjx6xfidcr9tlp0r2GUlzuZqja+QDWkCMpIY3hneoFHAGJPW8vizpzjc17vntSesqXdG73ywp/F//BTj8deMe8xzTnYlq49QlePISpaEcIItl5gCOuURCSDepDfbJjZKKXBdzw60/PwKgkvlPl+vrNwZXL5kzTXsXmLsP+GeaLEZN/xwYu0gCjRH9ZYFNMUlwM3m/PE/HCcCMVbLoNuIJ51zEqY1aXZX2fGm355M/ymt085PU7Jbi2kcl0HxuM5AcV/sxx9ZJlo5nCNDjlqYcHTlafdKe049J4xmtG9B+ZFOMiqjZcMIZRvNWVK0AtsrRq1vlJfJB+Mwv8qE072LNr/u1lZpiuReAL26TycPEl2UyR03PvDLWkHTnS/W8VYoHf/cgsGeZ7KIb9/zlK66HVwO//0cuT3mT6YINpVbLB6Ivbgf2vAcM/LvUzWisEz/IBF+tBab+z/67GLxDgUn/tu9zNLWGNK8BpD5jr9vlQ+q+D6/eJKixzVgu1XGi7Bo+8zPQrx4f7HIvVn3QdbZ050tpXGTXq9ZNdlKvvV++Zz1vtf6xTMaqTq4MKBLVYG4S6JQBxW2vSBOW6OGX/+16BEjtwRWTgJxzslNx9k+ym+hSW16uSlee+lbD/o+YaVxkMSq4U83bFUV2i2edBgqSZa5Un46wzqrDOODwKqkbOe45uU1RJMsCkHIoLWm3d0B7+b04vArYsQSY/lnNrysKsHupXB54f1VqO7UO1dOe6+p+frqyu3PMKNlJTUQWTrSFrmVwitXgQ6uAlTfLLsQvbgf2fSDBRI1O/hFOeElWYOcdBubsqiw8rJIutcuGSk0Ze8u9ALzaCXh/lKReW1MEu6Uyr4zZandR3zulYyAgHRS3XhIoStwLfDdXLl87T46nhus4AWg7VJqCbHul8Y9Xlg+sf1wuXztP6j9R0zI3Zzn5I1CYXvdxqYeBlDjZ5dvQZiyX6jRBzs9tlTpXV2NOd44e1jx2yqg1UuO1/2wACvDz09IMwFqph4CyPMDVR9IkiciiyFm7PGedAQ5+IZer706szjtUOsT7RMpO8ZXTquoVmu14vWpRdNJ/67f40hAqlaRzxoyU3fzNOZgIVO5QVEmJE/N724Ud8j6mdZNF0ZZm2GNyfuIHaYBYXcJuCSZpXKve96n1qJ72bJ5LXeoU052J6sKAoo05fPKWfhz49m+yrd+ol9ou19wL3PEl8GS8TM6GzpXVV5VKVlmuWywdX/3ayXbuT6dWplkW22+ch1YBRenyBv7lHcDy0dKAoLUGFjNPARnHG5/ufKkBf63q1rzt31KvCJAGBl/NlN+RLjcAY5+z3XO2VioVMG6RXD7wmXxgaoxfn5N0r4BYYMSTjR4eNUBYTyByoHS4PvBp3cfFmZux3FD7DpqGaNNXUq/KC6WY/NWY05273Wib528KajUw6T8SMChKBw5+dvX7XMrc3Tl6OBtJEV2i2FlrKG55GVCM0u04amDdx/lFAfd8L/8L049I92d9oXxtz/vyPgnILrtBf7P3qFsOzyCgTW+5fK6y27O5XEufO233PuZMQrrKfBeQXYrVmXe49p4u3xtqfbpPk/Pa0p4L0yszQAB0uq7JhkTUXDCgaGPFemnK4rAdiuamAG2HAg/ukdphN7wOdJl85fSF6GuBOTurVub2vg+8OwxI2FP3fRrjzM9y3n4E4OIhgcUvbgc+GCvp2a0tsGhJdx7d+HTnSw15sCqlZfPzUpT6izuki19YT2Dae87Tray5aztYPiApRumi2FAXd1XWgwIw5Q3Axc0mw6MGMP9P3P+JpNdeqrxYSgcAtu20p1YDHSubs1ytxm3uRdlZolI7f7rzpbQ64NpH5PKON6UwvjXMDVmY7kx0GYfPSWuTehg4tlYuj1l49eMDY4FZ66R8RdI+4MsZknnz0xPy9RFPVO0+o/rrME7Oz/4KpB2Vc5VaNh20VCMqsz6OfCOprYCcn1wvlwc/6JhxkeN1mSK//+a05+rO/AxAkdrW3mEOGR6RM2MUwcYcmvKsKNIYAJAaICFdrKvl5eolwce71gI+EVKY+aOJwC/PNCwVrS5FGVUrPTcvl9TroQ8DWne5/fNbgQ/Hy+SmtQQWzSti5sLAtjbsMWDkU3L5txeBjGOAV6gUFG9JdXKcwdhnAagkbcL8e24Ngx74YZ5c7nvX1Wv3kX11v0k+yOYn1t4g5di3gL4A8G8PRNv4Z9WxMu3ZvABTF/P/D2ft7nw1fe8GPIKA/ISqRbH6KC+RVDVAFmOIqIaqrJkm7CJ8Nb/9S8573AqE9ajffUK7S6MWnbfUBl//f3L74IeA0f+0zzhbOnNA8dxvkjoOyA73ltyYL7wvEDtWFn3NOzL3vAdAkTTwkK6OHB050pXSns3pzp2Y7kxUGwYUbcyhk7ekP6VWos6rcVuyO4yV3Yp97gSgADv/B7w/Umqt2IL5Q3mbPrLS4xUsXWUfPQwMmSuBxaR9wGe3SEDz3G8tO7CYeboq3bmLDdOdLzXqKel0B0iNnBlf2qZxCNUU2g3ofYdc/vV56+5bXgx89xCQdQrwDAbGN2KXI9mGi3vl/0JU7RqtztKM5R7b7/SNHS0NebLPAtnn6j6uOXR3vhKdBzDkIbm8fUntO0Frk7BLmjr4RACBHew3PqJmqMJoQrnBBMCJdigm7JYFEpUGGP20dfeN6A/MXCVzREDqr058yTZNsFqjyAGAqy9QmgMc/UZuG/qIY8fUFMy7FA9+IbUUD6yU64MfctyYyDmY057NcypANtSYS6t0ZrozUW0YULQxh9ZQNO/s6Dy58R2o3P2Am96R2oueIUDmSam5ZzI1epg4bV7pmVjzdq8QmRzOOyRpB1o3IHGPFOL+ZMrlxbhbCvPuotjRDe9IWx8qlaQ+T/8cuO9XmZyTfYxaIE2Q4rcB57bU7z4ZJ4DlY+TvWKWW3cIeAfYdJ9VP/9lyfuYXSS82Szsqix9qbVXQ0ZbcfIG2Q6qeuza5FyRFpzmmO1c34D55vdlnpGh+fVjSnUczqEB0CXPGDOAkNRQVBdhcuUjW9y5JZbZW9LUyf7l5uTSd4999w2m0QEy1XfXtRwARraCxVbuh8r5qLAc+uxkoLwKCOstmCmrdzGnPqQer0p7jfwcqSmThMqyXQ4dH5KwYULQxh6U8Gw1VKyo9b7Xd43aZDDy4Wzpo5iUASXsb93iG8qoAy6UBRTPvUGkUM+8QMOgB6bp2YTuw7qGWuVPRXD/RXunO1alU0jgirKf9n6s1828HXPNXufzrc1f+vVUUaeLy/mgJ3HuFAff8AHSd0iRDpXoI6gC0HwlAqWrAAlRd7nK9LIjYg/n/5Ok60p7N/z+ih9lvDE3Bzaeqs+j21+r3v571E4nqZF7gdtWq4aJxgun++S3AxR0ypxvZiEZjYT2AXrez9rMtmNOegaoMltZgeOUuxYJkOR88h8Fpqkx7Hi6XzZs9Tv8k552u4+8IUR34bmxjDtuheGE7UJwBuAdIHRBb8gys6jx8dG3jHithp3Qt9QyRLqZX4h0GTHoF+OvPstvr1Hpgz7uNe/6GKC+2XyAz87TUM7R3ujM1vRGPS/mB1IO1d40DAH0R8O0DkuZsKJW/3Qd2VNVxIecxoDJAHPepLIyUl0i3esC2zVgu1bEyoHjxD/l9uZS96682pcFzABdPIO1w7fUqqyvOkuMAIGak/cdG1MwUObKm96UUBdj8glwecB/gG+nY8ZDoPFk+N7QfYfvPDs6sw1gpuwTI6zeXqSEyl445tk7+b5kXczuzfiJRXRhQtDGH7VA01z/pdiOgcbH94/e4Wc6Pr6t/favamLuVdpxQ/9Xl8L7AhJfk8i/PNKzRRUOd/hl4ObyyaLMdmIMBMaPsm+5MTc8zSJoNAZLmdWn32vRjwPLRwOGvJMVizDPAnWtkhZScT+fJsnu0OBM4+aP87erzAb92QPtR9nveoI6Af7SkZ5l35JnlxLeMdGczjwDgmsr08u2vXnkhJ36bnIf2aN47M4nspNiRJXgudfJH+V+l8wKGz3f0aMjMKwT4v5PSjLE17b5SqYDxL0j21einpVYyESBzKXPa84nvZReri2fVzkUiugwDijZWrJdgm6euCZuyGPTA8cqaU7ZMd64uZjTg5gcUpQMXdzb8ceqqn3g1A++XFFBTBfD1X4DSvIaPwRoHPpNzc/dsWzOnKzbXZgp0ZUMeku61OeeqfpcUBdj/idRLzDoNeIcDf1kvOxqZwuW8NC5Av1ly+c+Pqpqx9Jtl35+bSlW1S/HSbs/mBYno4S0nED1kruxIT9wjuzLrwnRnArB06VJER0fDzc0NgwYNwt69Vy7L8sYbb6Bz585wd3dHVFQUHnvsMZSVlVm+/txzz0GlUtU4denSxd4vwy6KzPNRRwcUTcaqzs6DH5TFNnIeWlf7bERwdjEjgQWJ8vmCyMwzqCp4uKGyNEPsaMDFzXFjInJy/PRqYw5JeT6zSXbKeIcDbYfa5zm0Oqm9BwDHGpj2nHVWAitqF/nnbA2VCpj6tuwGyksAvp9r/3qKJmPVLpjUQ5LmaEtZZyrTnbVVKeXUsrh6AyOekMvbXgGKMoG19wM/PAIYyoAO4yXFuZ2d/m7JtvrfIyvXF7ZLwEulkeYC9tZpgpyf2VTz/15LXJDwaVP1Pf391dqPURTg3Fa5zIBiq7Vq1SrMnz8fixYtQlxcHHr37o2JEyciIyOj1uO/+OILPPXUU1i0aBFOnDiBDz/8EKtWrcLTT9fsNty9e3ekpqZaTjt27GiKl2NzVRkzTbjAXZsjX0t9YDc/YOhcx46FiOhqzHOqojQ578TuzkRXwoCijTkk5dmc7tzjZvvulOluTnv+XprAWMu8uyb6Wgm0WMvdD7jtYwlInvgB2Lvc+sewRsoBoCxfLhv1QPoR2z6+ORgQM5rdfFuya2YDfm2BwlTgf30quzhrpOP2zNVSo5SaB9/ImhPLzpOk1qu9tRsGuHjI75C5bmDOeUnJaSnpztVdO0/+Rs5vqb3ERc55ID9B3gsYjG+1lixZgvvvvx+zZ89Gt27d8O6778LDwwMfffRRrcfv3LkT1157LWbOnIno6GhMmDABM2bMuGxXo1arRVhYmOUUFHTlHXV6vR4FBQU1Ts7AYTW9qzOUA1telsvDHpNO7kREzsyc9gwAUFmfVUfUyjCgaGOWIthuTTSB0xcCpyo7UNkr3dms/UjAIxAoyQIu/G79/c3pzh0b8Y85oh8w4UW5/Ms/JehnL+Zu1GZJf9r28c3pii1pdxFdTusKjP6nXC4vAnwigNkb5MMVU5ybn2vurbrcf3bTPKeLW9VOPHMdWvOCRPsRLS+F0D9aurgCwO+vXf51c7pz1CBA59lUoyInUl5ejv3792PcuKoutWq1GuPGjcOuXbtqvc/QoUOxf/9+SwDx/Pnz2LBhAyZPrpkhcObMGYSHhyMmJgZ33nknEhISrjiWxYsXw9fX13KKiopq5KuzDaeooXjgUyDvIuAVCgz8m+PGQURUX9XTniP6s04z0VXw06yNFZc38QTu5AZJnQzsUNWxzF402qqdMNZ2ey4rqKq92NiVnkEPAF1ukCYFX/+lahehrZ37Tc59Kz8c2DKgmHUGSD/KdOfWoudtEnzqe5ekOLcd7OgRUUPFjpX/g12nWF+6oTE6mtOeK3d6t6TuzrUZ9hgAFXBqPZB+vObXzlcu9jDdudXKysqC0WhEaGhojdtDQ0ORlpZW631mzpyJF154AcOGDYOLiwtiY2MxatSoGinPgwYNwscff4yNGzdi2bJliI+Px/Dhw1FYWFjnWBYsWID8/HzLKTEx0TYvspEsGTM6BwUUDeXA9iVyecQTgM7DMeMgIrLW0IclM2TQA44eCZHTY0DRxsxNWZos5dmS7nxr03RoM3d7PvGDdTUFz/0GmAwS+AyMbdwYVCrgxrcljTT3AvD9I7avp6gvBJIq06CGPSbnSfts9/iWdOdRTHduDdQaYMobwI1L+fNu7tRqYPpKYPpn8nNtKuaAYtKfQOI+qeuq0khgsyUK7lz12nYsqbrdZATiK3fIN2VAl5q9rVu34uWXX8Y777yDuLg4rF27FuvXr8eLL75oOWbSpEm47bbb0KtXL0ycOBEbNmxAXl4eVq9eXefjurq6wsfHp8bJGZibsjRZxsyljqyWDqleYVUNrYiImoOO44F/pgK9bnP0SIicHgOKNtakNWuKs6t20dk73dms3bWAZwhQlleVdlYfZyrT9GxV2NbdH7h1hezwO74O2PeBbR7X7MIOCYD6RwM9bpHbcuPle24LLX13ERHZlm8EENoTgAL8+Kjc1hLTnasb8bicH10DZJ+Ty6kHZVe6q6/9d+WT0woKCoJGo0F6enqN29PT0xEWVntd02eeeQZ333037rvvPvTs2RPTpk3Dyy+/jMWLF8NkMtV6Hz8/P3Tq1Alnz561+WuwtyJ9BQAHpTybjMCON+TykIek9AcRERG1OAwo2lC5wYRyg0xKmyTF5Pg6CXq16Q0EdbT/8wGyI6fbjXK5vt2eTaZqAUUbFraNvAYY/4Jc/vlp2bFjK+b6ibFjpBlMUCe5nmyDtOess1Xpzl2ub/zjEVHrYO72nH5Uzlt6/dU2vaUTumIC/nhDbjP/b24/XMpwUKuk0+nQv39/bN682XKbyWTC5s2bMWTIkFrvU1JSAvUldWs1GtllrNSR5VBUVIRz586hTZs2Nhp506nKmHFAl+eTPwLZZ6Sz8zVNVGuWiIiImhwDijZkrlcDAJ5NMYE7ukbOezTR7kQzc9rzyfVARdnVj085ABRnAq4+QNvaJ/oNNvhBqUFoqadoo+6K5p2fMZUpdZED5NwWdRTNuxOZ7kxE1qje0EqlAbq00HTn6sy7FA9+CeQnVe2MZ/3EVm/+/PlYvnw5PvnkE5w4cQJz5sxBcXExZs+WANasWbOwYMECy/FTpkzBsmXL8NVXXyE+Ph6bNm3CM888gylTplgCi48//ji2bduGCxcuYOfOnZg2bRo0Gg1mzJjhkNfYGFZnzOQlAnGfAgZ9455YUapqJw78G+Dq3bjHIyIiIqfF5X0bMk/eXLVqaDV2jtXmJ1U1OTEH+JpK1GDAOxwoTJHAW5erNBUxd3eOHQNoXGw7FpVK6tK9NwLIOQ/8MA+49aPG1ZPMS5SVdZVaUgoB6fJ18HPb1FE0ByvZjIWIrBF5DeAeAJTmADEjAc9AR4/I/toOllIbF/8Atv0HSNwjt8ewfmJrN336dGRmZuLZZ59FWloa+vTpg40bN1oatSQkJNTYkbhw4UKoVCosXLgQycnJCA4OxpQpU/DSSy9ZjklKSsKMGTOQnZ2N4OBgDBs2DLt370ZwcHCTv77GsjRlqU9AMT8J+HCCzOtSDwPXv9rwJz6/RUoTsKEBERFRi8eAog2ZOzw3SUOWo2sBKEDboYBvpP2frzq1WlLtdr8jac/1DSjaMt25Oo8ACSKumCTjiRkF9L+n4Y9n7iAa0V/SnYGqHYrJ+yWFW93AgLG+CEisbPbChgJEZA21RhaQ9n0gHcNbi+H/JwHFuE/kuk9k45t7UYswd+5czJ07t9avbd26tcZ1rVaLRYsWYdGiRXU+3ldffWXL4TmUOaDoebUSPKV5wGe3SjARkP8vfWYCEf0a9sTm3Yn97mkdix5EREStGFOebai4KRuymLs7N1Uzlkt1r9wVeeonoKK07uMKUoC0wwBUUgvLXqIGAmMWyuXtr0rQr6Gq1080C+kmq+36Atm92FAXdwKmCulQ7d++4Y9DRK3ThJeAB/6oahbVGsSOAcL7Vrs+qnG70IlagXqlPBv0wKq7gMwT0o254wRI46fHpLGKtZL+BC5sB9QuwNDaA71ERETUcjCgaENFlQWw7R5QzDojDUjUWsd1CY68BvBtC5QXVTVcqY35axH9AS87pwwN/LvUacxLAC783rDHMJmq1eiqtoNQo636QNuYtOfqj80PxERkLRc3IKyHo0fRtFQqYPjjVdeZ7kx0VVVNWeqYk5pMwLoHJQCo8wbu/BqY+rZ0UE89CPz5kfVPat6d2Gt602fPEBERUZNrUEBx6dKliI6OhpubGwYNGoS9e/de8fg33ngDnTt3hru7O6KiovDYY4+hrKyqmcdzzz0HlUpV49SlS5eGDM2hqurV1LMhi6IAP/8T+P4RoCy//k90pHJ3Ysxox6WTqFRVHUaPXqHb82lzd+fr7D4k6Dyqdmwe+Kxhj5F2SOqT6bwlaFqd+XpjGrOwoQARkfU6TwbaDZNdVB3GOno0RE7PMid1qyOguPl5yXZRa4HpnwJtegHeocDYZyq//gJQmF7/J8w4AZxaD0AFDHu0UWMnIiKi5sHqgOKqVaswf/58LFq0CHFxcejduzcmTpyIjIyMWo//4osv8NRTT2HRokU4ceIEPvzwQ6xatQpPP/10jeO6d++O1NRUy2nHjh0Ne0UOVGRNAWwASI4Ddr0tdaGWjwEyTl79PopSLd35tgaO1EbMzWBO/yy1AS9VUVZVj7DThKYZk7mu2PHvgdJc6+9vbpjSfvjlDWQiGhlQLEwHMo4BUAHtRzbsMYiIWiO1Gpj1HTD/OODu7+jREDk1RVFQVG5Oea5lkXvvcuCPN+Ty1Ldrlni55l7JyNAXAD8/ffl967Kj8vG6TgGCOjZo3ERERNS8WB1QXLJkCe6//37Mnj0b3bp1w7vvvgsPDw989FHtqRE7d+7Etddei5kzZyI6OhoTJkzAjBkzLtvVqNVqERYWZjkFBQXVOQa9Xo+CgoIaJ2dgdQ1Fc4F5AMg+C3wwFjj+3ZXvk3pIjtW6Xb0Zir216SN1AA2lVY1Xqru4A6goAbzbAGG9mmZM4f2AkO6AUV+1k9MatdVPNDM3Zsk4VnsA9Writ8l5m14sVE5EZC2NVhrTENEVlZQboShy+bJF7hM/AhuekMtjFgJ9ZtT8uloD3PA6oFLLArZ5XnQluReBI1/L5eHzGzd4IiIiajasCiiWl5dj//79GDduXNUDqNUYN24cdu3aVet9hg4div3791sCiOfPn8eGDRsweXLNYNiZM2cQHh6OmJgY3HnnnUhISKhzHIsXL4avr6/lFBUVZc3LsJtia3Yo6ouAo2vk8m0fSypXeRGwehbw63N1F8M2T9g6XQe4ejd6zI2iUlXtUjz27eVfP/2znHea2HT1AlUqoN/dcvnASuvuW14MJOyWy7XV6PJpA/hEAIpJ6gtZi+nOREREZGfm+ahaBbi7VAvCJ+4F1vwVgAL0/0vN2qTVhfcFBtwnlzc8Ls1brmTnW4BilLlT9QZKRERE1KJZFVDMysqC0WhEaGhojdtDQ0ORlpZW631mzpyJF154AcOGDYOLiwtiY2MxatSoGinPgwYNwscff4yNGzdi2bJliI+Px/Dhw1FYWFjrYy5YsAD5+fmWU2JiojUvw26saspyfJ0EEANipLHKrO+AIZUd8Xa8Dnx2C1CSU/M+JlNV4M7R6c5m5m7PZzYBZdV2iipKVUCx48SmHVPP26XDYOohIPVw/e9n7sDs2xYIjK39GEsdRSsbsyhK1So/GwoQERGRnVg6POu0UJkXdLPOAl9MBwxlMi+b/NqVF3vHLAS8QiUr5o83r/BkGVULuNydSERE1KrYvcvz1q1b8fLLL+Odd95BXFwc1q5di/Xr1+PFF1+0HDNp0iTcdttt6NWrFyZOnIgNGzYgLy8Pq1evrvUxXV1d4ePjU+PkDIr0FQDqGVCMq5x89b1bJnQaLTDxJeCWDwEXD6k9+N5ICYqZJewCCpKlA1/H8XZ4BQ0Q2h0I7Cgpxqc2VN2eeQrIuwhoXIGYJq4X6BkIdLleLlvTnMWS7jyq7kl2Q+soZp0BClPk+9F2sHX3JSIiIqqn4ksXuIsygc9vkaZz4X2B21bIvPNK3HyBiS/L5d9fBbLP1X7c7mUSpIzoD0QPt9ErICIioubAqoBiUFAQNBoN0tNrdn1LT09HWFhYrfd55plncPfdd+O+++5Dz549MW3aNLz88stYvHgxTCZTrffx8/NDp06dcPbsWWuG53DmCdxVuzxnngISdwMqDdBnZs2v9bwV+OsmqU2YnwB8OAE49JV8zdyMpesUQOtq49E3UPW05+rdns9U7k5sPxzQeTb9uPpWpj0fXiXNYerD3JCltvqJZuY6ikl/wlKgqD7MzWnaDgZc3Ot/PyIiIiIrWHYoumqknMsXtwO5FwD/aGDm6vrPy3rcImVajHqpu3jpvKcsH9j3gVweNr/pytsQERGRU7AqoKjT6dC/f39s3rzZcpvJZMLmzZsxZMiQWu9TUlICtbrm02g0EnBT6gjIFBUV4dy5c2jTpo01w3O4ovo2ZYn7VM47TQS8awnEhvUA/rYF6DhBVn2//Tuw/nHg2Dr5es9bbDdoWzCnPZ/7raqzsqV+4nWOGVPsaKl3WJYHnFp/9eMLUoHME7hqB+Y2vQG1FihKk92i9WWunxjLdGciIiKyH0tNb50GWHMfkBIHuAcAd64BvELq/0AqlaRGa3TAuc1Srqe6fR9KN+jgLkBnBzcKJCIioiZndcrz/PnzsXz5cnzyySc4ceIE5syZg+LiYsyePRsAMGvWLCxYsMBy/JQpU7Bs2TJ89dVXiI+Px6ZNm/DMM89gypQplsDi448/jm3btuHChQvYuXMnpk2bBo1GgxkzZtQ6BmdVr6YshvKqHYf9ZtV9nLs/MGMVMPIpub5vuaSqeIYA0SNsNGIbCekChHST+oMnfpSgorm5SccJjhmTutruz7h6NGcx7yAM7wt4BNR9nM5D0ryB+tdRNBqA+O1ymQ1ZiIiIyI6Ky2U+2lMTL+VoNK7AzFVAUAfrHyyoAzDsMbm8cUFVveyKUmD3O3L52kcBtd2rKBEREZGTqUexv5qmT5+OzMxMPPvss0hLS0OfPn2wceNGS6OWhISEGjsSFy5cCJVKhYULFyI5ORnBwcGYMmUKXnrpJcsxSUlJmDFjBrKzsxEcHIxhw4Zh9+7dCA4OtsFLbDrF1Ypg1+n0T0BJFuAVBnS4Sh1EtRoYvQAI7wOs/ZusAve4+ep1bxyh+81AxnHg2FpJ6VWMQHBXwL+d48bU507g9//K7sC8BMCvbd3HWuon1mMHYeQAqW2Z9CfQfdrVj0/eD5QXSpA47P/Zu/P4KOrD/+Pv3U12cyckIQkhgXAjN4JEvFChIloUaxVvRMXWiq3m20NaBbX1S49vKVqx+FPwrIp3bbV4RFFRBAVRUe77SkhCzk2ym+zO749JNkSCZEOyk+P1fDzmsbOzM7OfzQT2k/d8jhHNKjoAAEBLlFeb9dGTa+vG4e4/Scoc1/ITnpEjffWCVLxTWjFfOn++OUa1u8CcyG74j1uh1AAAoKNpUTI1e/ZszZ49u8nXVqxY0fgNwsI0b948zZs375jne/7551tSjHanWV2e61vLjbqq+cHgoCnSzSvMGZ7H3nBihWwrw34kvf8HaccHkurG0BkY4tmdvyuxjzlA+K6PpPXPSmff2fR+fn9DC8XvGz+xXs+x5phBzZ2Ypb67c58JZstJAACANlJ/g3uo5wtzw4n2jgiPkC78P+mZS6XVi80A8ZMHzddOu01yhJ/Y+QEAQIdE/4RW1DApyzGCwtJ90rZ3zfXR1wR38qR+0lm//P7uuFZK6me2vjN85jg7kvWBotTQrfyLf5rBYVMOfWPeZQ+PljKacQe/fmKWg+slX83x968PFOnuDAAA2pjbUyuXvOpb+bW5oTXqH/0nmb0yDL/09I/Mnh9RycHXZwEAQKdBoNiKAmMoRhwjUFz/rCTDbDWX1C90BQuV+tmeJSkioXnhXFs7aarkijdnzN75QdP71M/unHWGFOY8/jmT+pmfr7Zayt/w/ft6yqV9a8x1JmQBAABtrMLj0xj7FoUbXik2XUoe0DonnjxfcsaaE95J0qm3mGNLAwCALolAsZUYhhEYBDva1US3Vr9f+qKuu/Poa0NYshA6cjzB/pPax1iP4ZENY/t88UzT+wQzfqJkznqYMdZcP163592fSP5aqVuWuQAAALQht6dWZ9jrbnj2Pdust7SGuB7Sub8z152x0ik3tc55AQBAh0Sg2EqqanzyG+Z6k12ed35gdg9xxUtDLgpt4UKlW5aUmW2un/RDS4vSSH13nI3/NmegPlJNlRn6Sc0bP7Fefbfn4830XB9W0t0ZAACEQIW3VqcfGSi2pnE3my0Vr/inFJnQuucGAAAdCoFiK6mfkMVukyLDm2ihuO4p83HEZWaruc7qx0uly56QhkyzuiQN0kdLqcMkn0f6+qXGr+1ZZW6PTZeSBzb/nD2b2UIxMH4i3Z0BAEAIVB7WcNtOc73vhNY9t90hjf9Z658XAAB0OASKraSiuq67szNMtu92Lak8LG36j7leP0lIZxWfYXZ9bq3uNa3BZmvoZl4f7NarHz+x37nBlbnnyebj4e3m9W1KeZ5UsFGSTepzVlBFBgAAaIk+FetktxkqjxsgxaZZXRwAANBJESi2kvoZnqOb6u781TLJ55V6jDQXhN6IyyWHU8r7Sjr4ZcP27SvMx2AnTIlKlJLqBjnfv7bpfepbJ/YY2X5n5wYAAJ3K4Mp1kqSytNMsLgkAAOjMCBRbSX2X56MmZDGMhlZxnXUylo4gKlEafKG5Xj85S8UhKf9rc71PC7ruBCZmOcY4ivWBIrM7AwCAEBlZs16SVJV5prUFAQAAnRqBYitx1wWKR03Isn+ddOhbKSxCGn6ZBSVDQP3kLF+9INVUNwR+aSOkmO7Bn+/7AkXDOGL8xLODPzcAAECwincr0zioWsMuo/fpVpcGAAB0YgSKrcTtrW+h+J1Acd2T5uOQi5kNz2p9z5HiMqTqEnNMy8D4iS1sQVg/Mcv+tZLf3/i1gs1S+UEzSM48tcVFBgAAaLadH0iS1hv9FRmbYG1ZAABAp0ag2EoaujwfESh6KqQNL5vrnX0ylo7A7pBGXWWur3tK2v6+ud7v3JadL3WoFBYpVZdKRdsav1bfOrHXeCk8omXnBwAACIK/rm7zsX/Y0b1mAAAAWhGBYitpssvzt69J3gopsa9Et5P2YfTV5uPOD6SKvBNrQegIl9JHmev7P2/8Gt2dAQBAKPn90s4PJUkrfcOanigQAACglRAotpKKulmeGwWK6542H0dfK9lsFpQKR+mWJfU5q+F579NPrAVhU+Mo+mqkXSvNdSZkAQAAoXDoG9krC+U2XNroGKhwB9V8AADQdqhptBL3d7s8F2yW9n4q2Y7oZov2YfQR3c9PNPDLOMV83HdEC8X9ayVvuRSZKKUOP7HzAwAANEdd74jV/pPkcjHcCgAAaFsEiq2kocuzw9zwRV3rxIGTpdg0i0qFJp30QzPsk03q/4MTO1f9xCz530het7lePzZj3wmSnX9iAAAgBHaYE7J87Ke7MwAAaHvUNlpJo0lZar3S+ufMF0Zfa2Gp0KTwSOn6/0gVh6SUwSd2rvieUmy6VH5AOrBeyjr9iPET6e4MAABCoNYr7f5YkrSSQBEAAIQAzadaSaNAcetbUmWhFJMmDTjP4pKhSalDW298w4wx5uP+z6XqsobxFJmQBQAAhMK+z6SaSnlcSdpsZDb0mAEAAGgjBIqtpNEsz9vfMzcOu1RycIe40wuMo/iZ2TrA8End+kjdeltbLgAA0DXU9Y7ISzpVko0WigAAoM1R22gl9bM8R7vCpANfmBszT7GwRAiZQKC4Vorraa4zuzMAAAiVukBxT4JZJyFQBAAAbY0Wiq2kvoVibFitlLfB3Jg+2sISIWR6jDJn8y4/IG142dxGd2cAABAK1aXS/rWSpO0x5mRxMU4CRQAA0LYIFFtJfaDYzb1d8tdIkd2kBLq8dgnOKHNMRklyF0iySVlnWlokAADQReyqG24lqb/ybN0lSTERBIoAAKBtESi2kvpJWeIPH9E60WazsEQIqYyxDevpo6WoROvKAgAAuo667s7qe3bgBjddngEAQFsjUGwFtT6/PLV+SVJ00dfmxh6jrCsQQi/jiPEy6e4MAABCpT5Q7DMhcIObWZ4BAEBbI1BsBe66CVkkyZn/pbnC+Ildy5GBIhOyAACAUCg7IBVulmST+pwZCBRpoQgAANoatY1WUOFtmJDFVrDR3Eig2LUk9pMyxkneCikz2+rSAACArmDHB+Zj+mgpslugy3MMgSIAAGhj1DZaQX3lbbRzn+SvlaKSpfgMi0uFkLLbpZvesboUAACgKzli/ESpoU4azSzPAACgjdHluRXUdy8Z5dhpbmBCFgAAALQlwzgqUKTLMwAACBUCxVZQUW1W3obZdpgb6O4MAACAtlSwWarIk8IiAsOt1I/rTZdnAADQ1ggUW0F995JB/u3mBgJFAACANrVo0SJlZWUpIiJC2dnZWrNmzffuv3DhQg0aNEiRkZHKzMzUHXfcoerq6hM6p6XqWyf2Gi+FR0hqqJPGRBAoAgCAtkWg2AoqPLWKkEeZtXvMDQSKAAAAbWbZsmXKycnRvHnztG7dOo0cOVKTJ0/WoUOHmtz/2Wef1Z133ql58+Zp48aNWrJkiZYtW6bf/va3LT6n5b7T3dkwDLm99V2eHdaUCQAAdBkEiq3A7anVENtu2eWXYtKkuB5WFwkAAKDTWrBggWbNmqWZM2dqyJAhWrx4saKiorR06dIm9//kk090+umn66qrrlJWVpbOO+88XXnllY1aIAZ7TknyeDwqKytrtISEr1batdJcrwsUq2p88hvmJro8AwCAtkag2ArcXp9G2Bk/EQAAoK15vV6tXbtWkyZNCmyz2+2aNGmSVq1a1eQxp512mtauXRsIEHfs2KE333xTF1xwQYvPKUnz589XfHx8YMnMzGyNj3h8B9ZJ3nIpspuUNkJSw5jedpsUGU4LRQAA0LYIFFtBhadWwwkUAQAA2lxhYaF8Pp9SU1MbbU9NTVVeXl6Tx1x11VW67777dMYZZyg8PFz9+vXT2WefHejy3JJzStKcOXNUWloaWPbu3XuCn66Z6rs795kg2c3qfGCGZ2eYbDZbaMoBAAC6LALFVuD21GqEbaf5hEARAACgXVmxYoX+93//Vw8//LDWrVunV155RW+88YZ+//vfn9B5XS6X4uLiGi0h8Z3xE6WGGZ6j6e4MAABCgBpHK6ipLFM/2wHzSfooS8sCAADQmSUnJ8vhcCg/P7/R9vz8fKWlpTV5zN13361rr71WN910kyRp+PDhcrvduvnmm/W73/2uRee0jKdC2ls39uMRgWKghSITsgAAgBCghWIrSCrfJLvNkDsiTYpJsbo4AAAAnZbT6dSYMWOUm5sb2Ob3+5Wbm6vx48c3eUxlZaXs9sbVXofDDN4Mw2jROS2zZ5Xkr5ESekuJfQKb3XWBIhOyAACAUKDG0Qp6uDdKkkq7DVO0xWUBAADo7HJycjRjxgyNHTtW48aN08KFC+V2uzVz5kxJ0nXXXaeePXtq/vz5kqSpU6dqwYIFGj16tLKzs7Vt2zbdfffdmjp1aiBYPN45240mujtLkttbFyhGUL0HAABtjxpHK8is3ixJcicNt7gkAAAAnd/06dNVUFCguXPnKi8vT6NGjdLy5csDk6rs2bOnUYvEu+66SzabTXfddZf279+v7t27a+rUqbr//vubfc52IypJSux7VKB45KQsAAAAbc1mGIZhdSFOVFlZmeLj41VaWhq6wbCPsO++k5ThP6BvJj6hoWdeEvL3BwAAHZ/V9RmcuJBeQ8OQjpjN+f99uF3/++Ym/Wh0Ty2YPqpt3xsAAHRKwdRlGEPxRFWVKMNfNyFLj1GWFgUAAABdxBFhoiRVVNdPykILRQAA0PYIFE/UwS8lSXv83RUR393iwgAAAKArqvD4JBEoAgCA0CBQPEHGgS8kSV8ZfZlVDwAAAJZomOXZYXFJAABAV0CgeIJ8+9ZJkr729+WOMAAAACxR4aXLMwAACB0CxRNkO9jQQjEqnDvCAAAACL36FooEigAAIBQIFE9E5WE5SvdIknaF95fdbjvOAQAAAEDrqw8UYwkUAQBACBAonoi68RN3+lPld33/dNoAAABAW2FSFgAAEEotChQXLVqkrKwsRUREKDs7W2vWrPne/RcuXKhBgwYpMjJSmZmZuuOOO1RdXX1C52wX6gLFrw3GTwQAAIB16PIMAABCKehAcdmyZcrJydG8efO0bt06jRw5UpMnT9ahQ4ea3P/ZZ5/VnXfeqXnz5mnjxo1asmSJli1bpt/+9rctPme7UT/Ds58ZngEAAGCdisAsz9RJAQBA2ws6UFywYIFmzZqlmTNnasiQIVq8eLGioqK0dOnSJvf/5JNPdPrpp+uqq65SVlaWzjvvPF155ZWNWiAGe85248B6SXUzPDupvAEAAMAaFYEWikwSCAAA2l5QgaLX69XatWs1adKkhhPY7Zo0aZJWrVrV5DGnnXaa1q5dGwgQd+zYoTfffFMXXHBBi8/p8XhUVlbWaAm5ikNS2T4ZsukbozfdSwAAAGCJGp9f3lq/JFooAgCA0AiqxlFYWCifz6fU1NRG21NTU7Vp06Ymj7nqqqtUWFioM844Q4ZhqLa2Vj/96U8DXZ5bcs758+fr3nvvDabora+udWJJVJYqqqMUw91gAAAAWKB+/ESJMRQBAEBotPkszytWrND//u//6uGHH9a6dev0yiuv6I033tDvf//7Fp9zzpw5Ki0tDSx79+5txRI3U934iXnRgyVReQMAAIA16rs7u8LsCne0efUeAAAguBaKycnJcjgcys/Pb7Q9Pz9faWlpTR5z991369prr9VNN90kSRo+fLjcbrduvvlm/e53v2vROV0ul1wuVzBFb311geKeiEGS6F4CAAAAa7g9PknURwEAQOgEdQvT6XRqzJgxys3NDWzz+/3Kzc3V+PHjmzymsrJSdnvjt3E4zO7BhmG06JztQl2guNM5QBItFAEAAGCNhglZqI8CAIDQCLrWkZOToxkzZmjs2LEaN26cFi5cKLfbrZkzZ0qSrrvuOvXs2VPz58+XJE2dOlULFizQ6NGjlZ2drW3btunuu+/W1KlTA8Hi8c7Z7pQdlCryJJtdW+19JRVTgQMAAIAl3ASKAAAgxIKudUyfPl0FBQWaO3eu8vLyNGrUKC1fvjwwqcqePXsatUi86667ZLPZdNddd2n//v3q3r27pk6dqvvvv7/Z52x3Dq43H7sPVnFNuCQplgocAAAALFDfQpFJAgEAQKi0KAWbPXu2Zs+e3eRrK1asaPwGYWGaN2+e5s2b1+Jztjt13Z2VPloV+dwRBgAAgHXo8gwAAEKNaeBa4ohAsaGLCXeEAQAAEHp0eQYAAKFGoBgsw2gyUGRWPQAAAFghUB91Uh8FAAChQaAYrLL9krtAsodJqUNV4fFJ4o4wAAAArFFfH42JoD4KAABCg0AxWPWtE1NOksIjVeGpkUQLRQAAAFiDLs8AACDUCBSDdUR351qfX9U1fklU4AAAAGANN7M8AwCAECNQDNaR4yd6fYHNTMoCAAAAKzDLMwAACDUCxWAcY0KWcIdNrjACRQAAAIReBZMEAgCAECNQDEbJbqmqWHI4pZQhjFcDAAAAywXqpMzyDAAAQoRAMRj1rRNTh0phLu4GAwAAwHJ0eQYAAKFGoBiMI7o7S5LbY46hSKAIAAAAq1AnBQAAoUagGIz6QLHHKEncDQYAAID1ArM8R1AnBQAAoUGg2Fx+v3TgS3M90EKRQBEAAADWMQxDbm99nZRJAgEAQGiQhDVXTaU0dJqU/42UcpIkBSpvMVTeAAAAYIGqGp/8hrlOl2cAABAq1DqayxUjXfRgo00VzKgHAAAAC9XXR+02KTKcm9wAACA06PJ8Aiqq6fIMAAAA6wTqo84w2Ww2i0sDAAC6CgLFExAYAJtAEQAAABaon+GZG9wAACCUCBRPQAUVOAAAAFgoMAQPY3oDAIAQIlA8AQ0tFKnAAQAAIPToMQMAAKxAoHgC6md5poUiAAAArFBfH42JoD4KAABCh0DxBFRwRxgAAAAWCnR5dlIfBQAAoUOgeALoYgIAAAArUR8FAABWIFA8AcyqBwAAACsxSSAAALACgeIJaJhVjwocAAAAQq+imvooAAAIPQLFFjIMgy4mAAAAFlm0aJGysrIUERGh7OxsrVmz5pj7nn322bLZbEctF154YWCf66+//qjXzz///FB8lBPSUB91WFwSAADQlZCEtZCn1q9avyFJiqYCBwAAEDLLli1TTk6OFi9erOzsbC1cuFCTJ0/W5s2blZKSctT+r7zyirxeb+B5UVGRRo4cqcsuu6zRfueff74ef/zxwHOXy9V2H6KVVHhpoQgAAEKPmkcL1Xd3lphVDwAAIJQWLFigWbNmaebMmZKkxYsX64033tDSpUt15513HrV/YmJio+fPP/+8oqKijgoUXS6X0tLSml0Oj8cjj8cTeF5WVhbMx2gVbobgAQAAFqDLcwvVV96inA7Z7TaLSwMAANA1eL1erV27VpMmTQpss9vtmjRpklatWtWscyxZskRXXHGFoqOjG21fsWKFUlJSNGjQIN1yyy0qKir63vPMnz9f8fHxgSUzMzP4D3SC6uuksQSKAAAghAgUW4gJWQAAAEKvsLBQPp9PqampjbanpqYqLy/vuMevWbNGGzZs0E033dRo+/nnn6+nnnpKubm5+tOf/qQPPvhAU6ZMkc/nO+a55syZo9LS0sCyd+/eln2oE8AszwAAwArUPFrIXVd5Y0IWAACAjmPJkiUaPny4xo0b12j7FVdcEVgfPny4RowYoX79+mnFihWaOHFik+dyuVyWj7NIl2cAAGAFWii2UEPljQlZAAAAQiU5OVkOh0P5+fmNtufn5x93/EO3263nn39eN95443Hfp2/fvkpOTta2bdtOqLxtrWGWZwJFAAAQOgSKLVRB5Q0AACDknE6nxowZo9zc3MA2v9+v3NxcjR8//nuPffHFF+XxeHTNNdcc93327dunoqIi9ejR44TL3JbKuckNAAAsQKDYQtwNBgAAsEZOTo4effRRPfnkk9q4caNuueUWud3uwKzP1113nebMmXPUcUuWLNG0adOUlJTUaHtFRYV+9atf6dNPP9WuXbuUm5uriy++WP3799fkyZND8plaosbnl7fWL4k6KQAACC1qHi3EpCwAAADWmD59ugoKCjR37lzl5eVp1KhRWr58eWCilj179shub3zffPPmzVq5cqXefvvto87ncDj01Vdf6cknn1RJSYnS09N13nnn6fe//73lYyR+n/ob3BJ1UgAAEFrUPFrIzYx6AAAAlpk9e7Zmz57d5GsrVqw4atugQYNkGEaT+0dGRuqtt95qzeKFRP0NbmeYXeEOOh4BAIDQoebRQm4vXZ4BAABgnfob3LHURwEAQIgRKLZQoMuzkwocAAAAQo8heAAAgFUIFFuoopoZ9QAAAGAdN4EiAACwCIFiCzHLMwAAAKzUUB/lBjcAAAgtAsUWoosJAAAArFROfRQAAFiEQLGFmJQFAAAAVqLLMwAAsAqBYgvVz6pHBQ4AAABWCHR5ZpJAAAAQYgSKLVTBGIoAAACwUEXdDe6YCOqjAAAgtAgUW4hJWQAAAGAlujwDAACrECi2gN9vqNJb3+WZWfUAAAAQeszyDAAArEKg2AL1E7JI3BEGAACANSpooQgAACxCoNgC9ROyhNltcoXxIwQAAEDo1d/kZggeAAAQai1KwxYtWqSsrCxFREQoOztba9asOea+Z599tmw221HLhRdeGNjn+uuvP+r1888/vyVFC4kj7wbbbDaLSwMAAICuqKK6rk7KLM8AACDEgq59LFu2TDk5OVq8eLGys7O1cOFCTZ48WZs3b1ZKSspR+7/yyivyer2B50VFRRo5cqQuu+yyRvudf/75evzxxwPPXS5XsEULGWZ4BgAAgNXo8gwAAKwSdAvFBQsWaNasWZo5c6aGDBmixYsXKyoqSkuXLm1y/8TERKWlpQWWd955R1FRUUcFii6Xq9F+3bp1a9knCoGGGfUYABsAAADWqB+Gh5vcAAAg1IIKFL1er9auXatJkyY1nMBu16RJk7Rq1apmnWPJkiW64oorFB0d3Wj7ihUrlJKSokGDBumWW25RUVHRMc/h8XhUVlbWaAkl7gYDAADAaoFZniOokwIAgNAKKlAsLCyUz+dTampqo+2pqanKy8s77vFr1qzRhg0bdNNNNzXafv755+upp55Sbm6u/vSnP+mDDz7QlClT5PP5mjzP/PnzFR8fH1gyMzOD+RgnzE2XZwAAAFjIMIzApCz0mgEAAKEW0kRsyZIlGj58uMaNG9do+xVXXBFYHz58uEaMGKF+/fppxYoVmjhx4lHnmTNnjnJycgLPy8rKQhoqEigCAADASlU1PvkNc506KQAACLWgWigmJyfL4XAoPz+/0fb8/HylpaV977Fut1vPP/+8brzxxuO+T9++fZWcnKxt27Y1+brL5VJcXFyjJZQq6sarocszAAAArFA/BI/dJkWG00IRAACEVlCBotPp1JgxY5SbmxvY5vf7lZubq/Hjx3/vsS+++KI8Ho+uueaa477Pvn37VFRUpB49egRTvJChhSIAAACsVD8hS7QzTDabzeLSAACAriboWZ5zcnL06KOP6sknn9TGjRt1yy23yO12a+bMmZKk6667TnPmzDnquCVLlmjatGlKSkpqtL2iokK/+tWv9Omnn2rXrl3Kzc3VxRdfrP79+2vy5Mkt/Fhtq4JZngEAAGChimomCQQAANYJugYyffp0FRQUaO7cucrLy9OoUaO0fPnywEQte/bskd3eOKfcvHmzVq5cqbfffvuo8zkcDn311Vd68sknVVJSovT0dJ133nn6/e9/L5fL1cKP1bbczPIMAAAAC3GDGwAAWKlFidjs2bM1e/bsJl9bsWLFUdsGDRokwzCa3D8yMlJvvfVWS4phmfoZ9ejyDAAAACswBA8AALBS0F2eccSkLE4qcAAAAAi9wA3uCOqjAAAg9AgUW6CiukYSXZ4BAABgjUCXZ25wAwAACxAotkD9rHp0MQEAAIAV6PIMAACsRKDYAgyCDQAAACsFhuAhUAQAABYgUGwBJmUBAACAldyBG9zURwEAQOgRKLZAoIsJg2ADAADAAhXV9Te46TEDAABCj0AxSJ5an2p8hiTuCAMAAMAaFV5aKAIAAOsQKAapfkIWiVn1AAAAYA26PAMAACsRKAapvvIWGe6Qw26zuDQAAADoiurrpLEEigAAwAIEikGq4G4wAAAALMYszwAAwEoEikEKTMjCANgAAACwCF2eAQCAlQgUg0QLRQAAAFit4SY3dVIAABB6BIpBIlAEAACA1RrqpPSaAQAAoUegGCTuBgMAAMBKNT6/PLV+SdRJAQCANQgUg8QA2AAAALBS/Q1uiTopAACwBoFikJiUBQAAAFaq7+7sDLMr3EF1HgAAhB41kCDR5RkAAABWctf1mImlPgoAACxCoBgkJmUBAACw3qJFi5SVlaWIiAhlZ2drzZo1x9z37LPPls1mO2q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      "text/plain": [
       "<Figure size 1600x1600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Try different batch sizes\n",
    "batch_sizes = [64, 128, 256, 512, 1024, 2048]\n",
    "plt.figure(figsize=(16, 16))\n",
    "\n",
    "print(data['x_train'].shape)\n",
    "\n",
    "x_train1 = data['x_train'].reshape(data['x_train'].shape[0], 1*32*32).astype('float32')\n",
    "x_test1 = data['x_test'].reshape(data['x_test'].shape[0], 1*32*32).astype('float32')\n",
    "x_valid1 = data['x_validation'].reshape(data['x_validation'].shape[0], 1*32*32).astype('float32')\n",
    "\n",
    "\n",
    "for i in range(len(batch_sizes)):\n",
    "    #determine plot no\n",
    "    plot_no = 320 + (i+1)\n",
    "    plt.subplot(plot_no)\n",
    "\n",
    "    #use function from above to fit and plot\n",
    "    fitAndPlotBat(x_train=x_train1, \n",
    "        y_train=data['y_train'],\n",
    "        x_test=x_test1,\n",
    "        y_test=y_testing,\n",
    "        batch_size=batch_sizes[i]\n",
    "    )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2. Tiefes MLP (DNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1. Vergleich verschiedener Dropout-Stärken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2. Vergleich mehrerer Aktivierungsfunktionen\n",
    "\n",
    "(ReLU, Leaky ReLU, Sigmoid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.3. Vergleich mehrerer Optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.4. Variation von Batchsize und Anzahl Epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3. Convolutional Neural Netowrks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.1. Vergleich und Variation verschiedener Convolution-Layer und ihrer Metaparameter \n",
    "\n",
    "Filtersitze etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First try\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=3, padding='same', activation='relu', input_shape=(32, 32, 1)))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(300, activation='relu'))\n",
    "model.add(Dense(43, activation='softmax'))\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "\n",
    "hist = model.fit(x_train_gray, data_gray['y_train'], \n",
    "        epochs=1,\n",
    "        batch_size=2048,\n",
    "        verbose=1,\n",
    "        validation_data=(x_test_gray, y_test_gray),  \n",
    ")\n",
    "\n",
    "\n",
    "y_pred = model.predict(x_test_gray)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.2. Vergleich verschiedener Dropout-Stärken\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.3. Vergleich verschiedener Aktivierungsfunktionen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.4. Vergleich mehrerer Optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.5. Variation von Batchsize und Anzahl Epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4. Convolutional Neural Networks mit Autoencoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Anwendung der Ergebnisse auf die Klassifikation von RGB-Farbbildern"
   ]
  }
 ],
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