cnn.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python385jvsc74a57bd0b3ba2566441a7c06988d0923437866b63cedc61552a5af99d1f4fb67d367b25f",
   "display_name": "Python 3.8.5 64-bit (conda)"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import datetime\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from keras.layers import Flatten, Dense, GlobalAveragePooling2D, Input, BatchNormalization, Dropout\n",
    "from keras.models import Model, load_model\n",
    "from keras.optimizers import Adam\n",
    "from keras.preprocessing import image_dataset_from_directory\n",
    "from keras.applications.resnet50 import ResNet50, preprocess_input\n",
    "from keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint, CSVLogger, ReduceLROnPlateau\n",
    "from cv2 import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%load_ext tensorboard\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_size = (112,112)\n",
    "batch_size = 32\n",
    "base_learning_rate = 1e-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Found 40200 files belonging to 8 classes.\n",
      "Found 3450 files belonging to 8 classes.\n",
      "Found 2220 files belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "trainData = image_dataset_from_directory(\n",
    "    './dataset/train/',\n",
    "    color_mode='rgb',\n",
    "    label_mode='categorical',\n",
    "    batch_size=batch_size,\n",
    "    image_size=img_size,\n",
    "    seed=1337\n",
    ")\n",
    "\n",
    "validData = image_dataset_from_directory(\n",
    "    './dataset/val/',\n",
    "    color_mode='rgb',\n",
    "    label_mode='categorical',\n",
    "    batch_size=batch_size,\n",
    "    image_size=img_size,\n",
    "    seed=1337\n",
    ")\n",
    "\n",
    "testData = image_dataset_from_directory(\n",
    "    './dataset/test/',\n",
    "    color_mode='rgb',\n",
    "    label_mode='categorical',\n",
    "    batch_size=batch_size,\n",
    "    image_size=img_size,\n",
    "    seed=1337\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = list(trainData.class_names)\n",
    "num_classes = len(class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "AUTOTUNE = tf.data.AUTOTUNE\n",
    "\n",
    "trainData = trainData.prefetch(buffer_size=AUTOTUNE)\n",
    "validData = validData.prefetch(buffer_size=AUTOTUNE)\n",
    "testData = testData.prefetch(buffer_size=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    resnet50 = ResNet50(include_top=False, weights='imagenet', input_shape=(img_size + (3,)))\n",
    "    \n",
    "    \n",
    "    inputs = Input(name='input_layer', shape=(img_size + (3,)))\n",
    "    x = preprocess_input(inputs)\n",
    "    x = resnet50(x)\n",
    "    x = GlobalAveragePooling2D(name='avg_pool')(x)\n",
    "    x = Dense(256, activation='relu')(x)\n",
    "    x = Dense(64, activation='relu')(x)\n",
    "    x = Dropout(0.5)(x)\n",
    "    outputs = Dense(num_classes, activation='softmax', name='predictions')(x)\n",
    "    \n",
    "    model = Model(inputs, outputs)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"model\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_layer (InputLayer)     [(None, 112, 112, 3)]     0         \n_________________________________________________________________\ntf.__operators__.getitem (Sl (None, 112, 112, 3)       0         \n_________________________________________________________________\ntf.nn.bias_add (TFOpLambda)  (None, 112, 112, 3)       0         \n_________________________________________________________________\nresnet50 (Functional)        (None, 4, 4, 2048)        23587712  \n_________________________________________________________________\navg_pool (GlobalAveragePooli (None, 2048)              0         \n_________________________________________________________________\ndense (Dense)                (None, 256)               524544    \n_________________________________________________________________\ndense_1 (Dense)              (None, 64)                16448     \n_________________________________________________________________\ndropout (Dropout)            (None, 64)                0         \n_________________________________________________________________\npredictions (Dense)          (None, 8)                 520       \n=================================================================\nTotal params: 24,129,224\nTrainable params: 24,076,104\nNon-trainable params: 53,120\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "from keras.utils.vis_utils import plot_model\n",
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "  optimizer= Adam(learning_rate=base_learning_rate),\n",
    "  loss='categorical_crossentropy',\n",
    "  metrics= [tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), tf.keras.metrics.CategoricalAccuracy()]\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_dir = \"./log/tensorboard/\"+ datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "tensorboard = TensorBoard(\n",
    "    log_dir= log_dir, \n",
    "    histogram_freq=1,\n",
    "    write_grads=False\n",
    "    )\n",
    "\n",
    "earlystopping = EarlyStopping(\n",
    "    monitor=\"val_accuracy\",\n",
    "    min_delta=1e-7,\n",
    "    patience=30,\n",
    "    restore_best_weights=True,\n",
    "    verbose=1\n",
    "    )\n",
    "\n",
    "modelcheckpoints = ModelCheckpoint(\n",
    "    './log/checkpoints/{val_loss:.4f}-{val_accuracy:.4f}.h5',\n",
    "    monitor=\"val_accuracy\",\n",
    "    save_best_only=True,\n",
    "    verbose=1\n",
    "    )\n",
    "\n",
    "csvlogger = CSVLogger(\n",
    "    './log/log.csv'\n",
    "    )\n",
    "\n",
    "reducelr = ReduceLROnPlateau(\n",
    "    monitor=\"val_loss\",\n",
    "    factor=0.1,\n",
    "    patience=3,\n",
    "    min_lr=0.5e-6,\n",
    "    verbose=1\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "1257/1257 [==============================] - 933s 738ms/step - loss: 0.0235 - precision: 0.9965 - recall: 0.9959 - categorical_accuracy: 0.9962 - val_loss: 0.3557 - val_precision: 0.9498 - val_recall: 0.9435 - val_categorical_accuracy: 0.9449\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    trainData,\n",
    "    epochs=1,\n",
    "    validation_data=validData\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x288 with 2 Axes>",
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UOLKSYslKtl/ZybEMzEhgRHYSSbHtfx5S73KzblcVK4sqqG5w2cyQNzuTlRxLXHTzp9VV9S7W7KxgZVEFK3fY29U7K5vK83yagqqUOBJjnVTUuSiraWjKlrlC8IbL6RAGZSTY0rx+iQzNsmV6A9MTcDgEt3vfgNHtMdS73OyurLeBqjcY2eENVHdX1hMb5SApLoqk2GiSY6O839usW1N2LD66KYhNjY8mJS4al8dQXe+yXw0uqurdTfczEmPIT08gPz2enJS4gCWPHo+htLqBneV1FJXXUl7b2HSMSt+x691U1bvonxLHzBGZHDw0k/TEmE6fQ7fHUFHbSFltI1EOIS8tvikbEgxfUJydHPjnCsTjMR16LWMMq3dW8sXGUjISY2wpa2ocOSlx+2VEexsRWWyMKYj0OHqLrl4ffdn72GgnUUH+Dur1USmlIiPQNTJsGT9jzIXtPG+AHwR4bi7QJ2s5ZsyYsc8aP//61794+eWXAdi2bRvr1q0jM3PfMqihQ4cyefJkAKZNm8bmzZu7a7i9WqPbw0tfF3LPvPVs21PLhLwUZgzNZHdlPbur6lm/q5LdVfX7ZYwGpMYxIieZkdlJ9isnmUa3hxVFNmhbUVTO+uKqNgOx1PhoclJiaXB52LKnpqmELy0hmnG5KVx6yGBGZCfZzJk3e5YaH7iM0hibsSuraaCyzoXbY/CY5mye79Zj/JVr2vK92CgHeenxRDt79xv+lhwOaQreD8pP7ZbXdDqE9MSYTgePSd4MZGd0JOgD22VwbG4KY3NTOvV6SjXx/m2yl+7wZG71+qiUUuEVyVLPbtfWJ4/dJTGxuTTro48+4v3332fBggUkJCRw9NFH+10DKDY2tul7p9NJba3/eUS9WU2Diy2lNWwprfY2kKihrLaxuXSwrrl8sKbBTX56PFMGpTNlUBpTBqYzJje5KaBpcHl48etC7p23nsK9tUzMT+X3p43n2DHZ+wVWxhjKaxsprqxnc0l101ytdcWVPPll6X6lkFnJsYzLTeHYMdmMG5DC+AGppMRF2dK8ijqKK+oprrQlm8WVdQjC2VPzGZebwrgBKd6yuo6/aRKRLgUMSinVlvaujx6PYXlROf1T4shOiQvLGPT6qJRS4aXvIsMsOTmZykr/LdbLy8tJT08nISGB1atX88UXX3Tz6EJjd2U9yXFR+5Q3BlJZ1+jNmtmyx80l1WzZU8Puyvp9tktLiCYjMaapdLBfUoItJYyLIj7GycbdVXy2voSXvfOlYqMcHJSXyrgBKXywqpjtZbVMGpjGH86YwNGjswIGWyJCWkIMaQkxjMpJ5oQW7308HsP2slrW7qrE4RDGD0ghO9n/G57MpFjNqiil+iyHQxAR3CGcHnIgXB+VUqon0cAvzDIzM5k5cyYTJkwgPj6enJycpudmz57NAw88wMSJExk9ejSHHHJIBEfacUVltdz25ireXLYDsM0i8tPjyUu3TSry0+LplxTLptJqVmy3JZKbS2ua9u+XFMvwrESOGZ3F4MzEpsYRgzITSI2Pbvf1jTEUldexZGsZ32zdyzfbynh24TbGDUjhtrMmcNSowAFfMBwOYWBGAgMzEjp9DKWU6iuc3uZRodKXr49KKdUTha25SyS0N3ldhcbKlSv5qDiGuz9Yj8cYrpg5lIQYp22/X1bD9r22G2JDi4V+B2bEMz43lQl5tjxy/ICUsJQLGWO0c6BSBwBt7tIxobg+rtlZSXy0k0GZ+mFYIPqeQynVE3R7cxfVN1XW2flwd7y9iePH5fC7U8f5zYh5PIbdVfUUV9QzKCOB1IT2M3ihoEGfUkqFh8NBSEs9lVJKdS8N/A5gLreHmgY31Q0uaurd1Ls8RDuF2CgnMdG2C2Ss00FMlAOPgR3edvnGwKNXTOeY0dkBj+3wLn6dE6YmAEop1ReJyCPAqUCxMWaCn+cFuAs4GbsU0uXGmK+7Y2yhLvVUSinVvTTwOwAYb9v/BpeHepeHmnoX1Q1u6l1uwGbJ4qOdpMRF0eD2UNPooqx2/4W9HSL0T4kjKiWWcW0EfUoppTrtv9g1bh8P8PxJwEjv18HA/d7bsHM6BFfj/tcGpZRSvYMGfn1Mg8tNea2LBreHRpeHBpeHBrcHT4vyHKdDSIyJIj0hmoTYKBKinfutD+YxNlD0BYtuj4eMxBhiopyUajmlUkqFhTHmExEZ0sYmZwCPe9fC/UJE0kQk1xizI9xjc4a4q6dSSqnupYFfH9Ho9rC7sp7S6gaMMThFiI6yZZpJcVHERDmI8ZZtxkY52p0L5xAhLtoZ1BINSimluk0esK3F/ULvY/sFfiJyNXA1wKBBg7r8wg6HlnoqpVRvpoFfL+fyeCiprKekqgFjID0xmuzkWKKd7Qd3Simleh1/f9j9RmPGmIeAh8B29ezqCzsdgscY7Z6slFK9lCPSA1D7SkpKAqCoqIhzzz3X7zZHH300X321kOLKOtbsrKS4sp6UuGhG5SSRn55ATJSTu+66i5qa5jXzTj75ZMrKyrrjR1BKKRU+hcDAFvfzgaLueGGHN9iLVNYv2Otj62UrWrvzzjv1+qiUOiBp4NdDDRgwgBdeeGGfx4wx1DS4aHB52LKnmp3ldSTERDEyO4lBmQnEtijLbH1hmzt3Lmlpad01fKWUUuHxGvAdsQ4Byrtjfh/YjB+wz5zxSPB3fewIvT4qpQ5UGviF2U033cR9993XdP/3v/89//d//8esWbOYOnUqBx10EK+++up++23evJkJEybg8Rh27a3g9LPPY8z4gzjrnPOprK4h2ulgeFYSd/z2Zxxx2CGMHz+eW265BYB//etfFBUVccwxx3DMMccAMGTIEEpKSgD4xz/+wYQJE5gwYQJ33nln0+uNHTuW733ve4wfP54TTjiB2traMJ8dpZRSLYnI08ACYLSIFIrId0XkGhG5xrvJXGAjsB74N3Bdd43NF/iFKuPX1esjQG1tLXPmzGHixIlccMEF+1y3rr32WgoKCvT6qJRSXgfWHL+3boady0J7zP4HwUm3B3x6zpw53HjjjVx3nb02P/fcc7z99tv8+Mc/JiUlhZKSEg455BBOP/30feZMVNe7aHR7WLmjgv8+eA/OmFje/fRLtq1fzdEzDyY/PYHE2Chuu+02MjIycLvdzJo1i6VLl3L99dfzj3/8g3nz5tGvX799xrN48WIeffRRvvzyS4wxHHzwwRx11FGkp6ezbt06nn76af79739z/vnn8+KLL3LJJZeE9nwppZQKyBhzYTvPG+AHIX/hIK6PSR4Pwxo9xMQ4IZg5fmG6PrZ0//33k5CQwNKlS1m6dClTp05tek6vj0optS/N+IXZlClTKC4upqioiG+//Zb09HRyc3P51a9+xcSJEznuuOPYvn07u3btatqnqKyWrXtq8BhIT4hm9Tdf8oPvXcHgzEQOP3gaEydObNr2ueeeY+rUqUyZMoUVK1awcuXKNsfz2WefcdZZZ5GYmEhSUhJnn302n376KQBDhw5l8uTJAEybNo3NmzeH/HwopZTqpbzBlwlRqWdnro+tffLJJ00B2MSJE/X6qJRSbTiwMn5tfPIYTueeey4vvPACO3fuZM6cOTz55JPs3r2bxYsXEx0dzZAhQ6irq6Om3oUxUFJVT0ZiDLFRDvLSE4hyOnA69o/RN23axN/+9jcWLlxIeno6l19+OXV1dW2Opa0LdmxsbNP3TqdTS1mUUupAEcT10dXoZuOuSgamJ5CeGBOSlw32+tgWf9lAvT4qpdT+NOPXDebMmcMzzzzDCy+8wLnnnkt5eTnZ2dlER0czb948tmzZwu6KejbsrsIAw/olkpMS17T/kUceyZNPPgnA8uXLWbp0KQAVFRUkJiaSmprKrl27eOutt5r2SU5OprKycr+xHHnkkbzyyivU1NRQXV3Nyy+/zBFHHBHeE6CUUqrXa5rjF8LmLsFcH9ui10ellAregZXxi5Dx48dTWVlJXl4eubm5XHzxxZx22mkUFBQwYeIkho0cRUl1PRPyY3AIJMVFU9Ji/2uvvZYrrriCiRMnMnnyZGbMmAHApEmTmDJlCuPHj2fYsGHMnDmzaZ+rr76ak046idzcXObNm9f0+NSpU7n88subjnHVVVcxZcoULVtRSinVJkeIm7tA29fHyZMnM2bMmDb31+ujUkoFT0JVq+/34CKzgbsAJ/CwMeb2Vs+nA48Aw4E64EpjzHLvc5uBSsANuIwxBe29XkFBgWm9fs+qVasYO3Zs13+YMCipqmdHeR1OEfLT40mJj470kILSk8+p6gU8Hlj+Igw/BhL7tb+9Un6IyOJgrgvKCtX1cfn2cjKTYshNjQ/l8PoMvT4qpXqCQNfIsJV6iogTuBc4CRgHXCgi41pt9itgiTFmIvAdbJDY0jHGmMl97eJujKGorJaislqSY6MYlZPUa4I+pbrs/VvgpavgtR9FeiRKqQ5yOCRiC7grpZTqmnDO8ZsBrDfGbDTGNADPAGe02mYc8AGAMWY1MEREcsI4pojzeAxb99RQUlVPv6RYBmfa5i1KHRC+eADm/wsyR8KaubD+g0iPSCnVAU7RwE8ppXqrcEYcecC2FvcLvY+19C1wNoCIzAAGA/ne5wzwrogsFpGrA72IiFwtIotEZNHu3bv9bhPOctaOcLk9bCyppry2kQGp8QxIiw+4NlFP1VPOpeqFVr4Kb98MY06F738C6UPh7V+CuzHSI1PqgNXRv+lOzfgFpNdHpVRPF87Az19E0/qv4u1AuogsAX4EfAO4vM/NNMZMxZaK/kBEjvT3IsaYh4wxBcaYgqysrP2ej4uLo7S0NOJ/kOsb3azfXUVdo5vBmQn0S45tf6cexhhDaWkpcXFx7W+sVEtb5sOL34P86XDOwxCTACfeBiVrYOHDXT++MbDmbdizsevHUsFpqI70CFQXdeb66BDQuG9/en1USvUG4ezqWQgMbHE/HyhquYExpgK4AkBs6muT9wtjTJH3tlhEXsaWjn7S0UHk5+dTWFhIoGxgd2hweSitqgcgMymWogrHvieiF4mLiyM/P7/9DVXPY0zTAszdqng1PD0H0gbBRc9CtLcpxOiTYdgxMO/PcNB5nW/0snM5zP05bJ0PGcPgms8gJjF04w81twvqynp3Y5uv/wdv3AiH/gCO+7+O/V5t/RJyJzb/HqiI6cz1cU91A41uD42lGuC0ptdHpVRPF87AbyEwUkSGAtuBOcBFLTcQkTSgxjsH8CrgE2NMhYgkAg5jTKX3+xOAWzsziOjoaIYOHdqFH6NrPli1i+ue/Zrc1Dj+e8UMhvTrwW9IVd9kDMz7Eyx5Eq7+CJKyu++1K4rgiXPAGQuXvAAJGc3PicDs2+H+w+DDP8Jpd3bs2LVl8NGf4at/Q1wqzLwBPr8L3v8/OPmOUP4UofXaD21X05PugGmXBx80NdZB0TfgjLEZ0+gEG+DGJEJUXPcE9cbAp3+HD/8AyQPs+Y5NhiN/Htz+y1+Cl66Gg79vM74qojpzfbz5xaV8uLqUr359XJhGpZRSKlzCFvgZY1wi8kPgHexyDo8YY1aIyDXe5x8AxgKPi4gbWAl817t7DvCyd/5bFPCUMebtcI01XPZUN/CT575lZE4Sj195MBmJMZEekjrQNNbBqz+A5S/Y+xs/honndc9r15XDk+fZ7Nblb0L6kP23yR4DM66GLx+AgittJqg9Hg98+xS8dwvUlNr9jv2NDSob6+CrB2HsqTDUb3X4/nxlbm0FTsZAVTGUrLVfpevtbeZImP3n4IOuTZ/At0/boOmNG2HbV3DK320g15bNn8Pr19vX9UsgbSBc+AzkjA9uLB3l8dg5ml89CAedD2fcA69db4P22BQbzLVl0aPwxo9h0CHBB4qqx0mOi6KyztX+hkoppXqcsC7gboyZC8xt9dgDLb5fAIz0s99GYFI4x9Yd7nh7NdX1Lv55/mQN+lT3qy6FZy+GrQtg1u/gs7tgy+ehC/zKC+28vbpyG7jEJEJ0YnM2aucy2L0aLnoOBkwOfJyjb4Jlz9mg4vI32w6iipbA3J9B4ULInwGXvLjvsY+7Bda/Z4Pda+fbbFRbavfC0xfC1i+842+RRfN9X18BJevsrU90AiT3h/Xv20Br6qXtny9XA7z5UxsAXzsfPv8XfPwX2LkUzn8cMofvv09duQ1wFz9qS2XP+Y/9mRqqobEGGmqgocp+/80TNtC+6n1IGdD+eDrCVQ8vfx9WvAyH/hCO/wM4HHDGvfb13/oFxCTBlIv339cY+Owf8MGtMPIEOO+x9gNd1WMlx0VT2+im0e0hWjtSK6VUrxLWwO9A9s3WvTyzcBtXHzmMkTntvPlUKlhF38CKV2DcGTBgSuAgqXQDPHkulG+H8/4L48+yc6u2fB66sbz9SzueEbOaA5HqkubvjQfOvN8+35b4dJuxe+PHNrCYcPb+25QX2szSt8/YuXFn3g8T59jgo6WYRDjzAXh0Nrz7Gzit9dKgLdTsgf+dCcWr7Fw1Y6Cx2htMVXu/r7YBzcQLoN9I+5U5ElK8DYofP90GrEMOh4x2Subm/8tmCS9+wY7zmF/aZjcvXQUPHW1/prGnNm+/6nV482dQXWyDrWN+1fbcxbGnw6MnwZPnw5VvtR/0Amz/2gZlWWNgxPGQXwAO577b1FXYDxA2fWIDvpnXNz/njIJzH4GnLrAlrLFJ9nfTxxj777DgHjuP88z7walrlvZmyXH2bUNlnUs/0FRKqV5GA78wcHsMv3t1BTkpsVw/a7+EpuqJjLFfrQOJtrgbbale9W5IzLJfSVkQl+Y/IDMGGmtt6WNtGSRkQnIHlq3cssAGcw1V8PmdkDUWJl9kg5KWx9kyH565CMQBl78BA2fYxwcfBuvegarddpxdseFDWPWaDdhCUbY39TJY9Ai8+1sYNbs5I1RXDp/9E764356/mdfD4T+B+LTAxxp0sA2U5v8Lxp4GI/zMRarZA4+fYTOSFzwBo07s3LjPvB/un2mzYVe8tX/Q5LN3M3zyVxucjTy++fGRx9mlLZ77jg2uZt5gS1/fvtkGfjkHwYVPQ97U9seSOxHOf8wGfs9fbss+2wqyVrwCL18DUTGweq4dX3w6DD/WBoEjjrPB+5PnQvFKOOtBmDRn/+NExcKcJ+F/Z8EL34ULE+3P5XbBGzfYTOSMq2H2Xzr2/0v1SClx9neqsq5RAz+llOplNPALg6e/2sqy7eXcfeEUkmL1FPdoxsDKV2zAUV9pgyPfV/9JNqPRUsUOW9637l3Y+NG+5X8+jujmINAZY4OX2jIb8LkbmreLioNT/2mDt/Zs/Nh2xkzJs4HK1gWw5Cl477fw/u/tm/TJF9lM2+s3QNpguPj5fbNQQw63t1vn75uV6ShXA8z9he2gedj17W8fDIfTNjt59CQbsB3xUzsn7OPb7Ty+iRfYIDNtUHDHO+bX9t/o1R/BdQv2DRRr9thM3e61MOepfQOxjkobCKf8DV76ng1Qj/zZ/tsYY8+XOO18wP2OMQiufMcGe5/fZUtAnTEw6xY47Ecdy5CNOM7+Tr1+vS0rPe2u/T+EMAY++RvM+6Mtl53zlP093zDP/m6vf982nwE7d8/jgguftcFcIDGJtqT3sdPg2Uvgwqdg4X9g9Rtw1E1w9C8j01FWhVzLjJ9SSqneRaOSECutquev76zhsOGZnDoxN9LDUW0pXm3nJm36GPofBMOOtgHVGu+01OhEmz0afJgt/1v/np23BrY5x/gzbWYkY5jN+vm+qoptyWN1sQ30UvJs4BGXarOBvu8XPQqvXAvbvrTZkOgA7dHXvw/PXGwXPL/sNduVM3sMFFxh554tecqWQD5/md1+yBFwwf9s9qal3El2btrmz7sW+H1xH5SusyWLUSFcj3LwYTD+bBtALX0O9mywP8sJf7BlrR0RHWezcQ8fZwOqs7xTi6tLbaavxBf0haAz4UHnwZq3bIfREcftP59x9Rs203rCHyE1QKv3qFgbsA08xG57zK/9z/kLxrTLoGyL7b6ZPtgG0T6NdbYkc9nztkHL6Xc3/95NONt+eTx23uH692DXCjj0R5A/rf3XjU+DS1+2wfv/zrKPzb4dDrm2cz+H6pGSvRm/irrGCI9EKaVUR2ngF2J3vL2G6noXt54xHukrn3BX7LDzgI66qXevPeZTXwkf3W47ScYkwsl/s50hfWV6lbtsVmzz57Zs8sM/2mzNoEPhuN/bYC9nfNczGGPPsFmXz/5p58qd//j+nS/XvGXLALNGw6WvQmLmvs/3G2kbmhz7G5uBLNsCky+x5XutOaNt2eeW+Z0fc/l2+PgOuwZfVzJlgZzwB1j3nh3rRc/ZZiCdPc95U23Q88kdtsRy4ME201eyzmak/JWAdoaI7cy59Qu7VMH3P25eo66+Ct66GbLHw8HXtH+sSRfYr6469rdQttU2VEkdZBv6VBXbEuDChfb5I37q/9w6HDZ4bashTyCJ/eDSV2xznckXwcTzu/iDqJ7Gl/GrqNWMn1JK9TYa+IXQ11v38uyibXz/yGGMyO5DDV0++Sss+g9U7oDz/9d7S7aMsZmOd38LVbtsJ8ZZt+wfzCbn2GYo471Zi9q9NvCLSwnteJxRNpDMn2HnWj14FJz9bxh1gn1+5avwwpU2G3nJS/uugdeaw9l+ExWAwYfDvNvsz9Q6IxiMd38Dxu2/ZDEUUvPhx8ttY5JA8+U64sifw9q3bPlrYpbNIl74dHDnqiMSMuDMe22m6/3fw0l/sY9//BeoKIRz/9O9TU1EbMfNih3w6nV2Xuinf7eZ6PMf71rGtz2pefCdV8J3fBVRLef4KaWU6l10pn2I2IYuy+mfEseP+lJDl6rdduHv5AG22cSyFyI9os5xu+Cp8+1crJRcuOoDW+YWTAYzPj30QV9LY06G738EqQPhqfNshnHpc/D8FTBgKnzn1baDvo4YfBhgbHaqozZ9AitegsN/7H9NvlCJTwtN0Ac283nmAzbQ3bPBNjwJddDnM/xYm9X78gHb/GbXSlsWO+VSu3Zdd4uKhTlP2BLhN260c/WufDu8QZ/q83SOn1JK9V6a8QuRp77ayvLtFdxzUR9r6LLw3+Cqg0tfsos1z/2ZbRKSEsb5izV7bGOISRcE38yjPR/+wTb7OOE2OOS6ntddMGMYXPWebd//yV/tY4MPh4ueCa4tf7DypoEz1i7rMPqk4PdzN8Lcn9umMTNvCN14ukP/CXDJC3ZeZUfnCnbUcb+3TVJeuc5mL2NT4Phbw/uabYlPtz/7lw/aJStCvb6fOuBo4KeUUr1XD3v32zuVVtXz17dXM3NEJqcc1IMburjqYfvi4LdvqIGv/m3nc2WPtc0yXPW2bM6Y8Ixx9Ztw3yF27tvHd4TmmGvfscsfTLsCDvthzwv6fKLjbbngGffZsV78fGiDPrCNPPIL7PzFjvjyQbv0wezbm+ev9SbDjg5/0Af23Jz9kG3yU7jQBn2hytZ2VtogOPE2DfpUSEQ5HSTEOLXUUymleqEe+g64d/n7e2upaXDzf6f38IYub/wE/n0srHk7uO2XPAm1e5pb9vcbYRuJrHvHPhdKNXvgxats84nEbBh6FKx8zXYh7IqybXaNtf4H2aClN5hyMZx2Z/NadqE2+DDY8a1tchOMyp22Gc7IEzqWJTxQDZhsO3ROvQwmXxzp0SgVcslxUdrVUymleiEN/LrI4zHMXbaD0ycN6NkNXTZ/DkuesGvMvfmT9t/0u10w/27In77v/KQZ37cliG//EsoLQzO2la/BvTNgxctw9K/gex/C4TdCfbkNMjvL1QAvXGF/lvMeC7xcwoFm8GG2Qcu2L4Pb/r3fgbveBs49+YONnmTqd+D0f/Xc7LJSXZAcF62lnkop1Qvpu5IuWrWzgrKaRg4f2YOXOXA1wBs/tiVfl7wAFUXwwR/a3mfVa3ZpgMOu3/fNvsNhyxE9btuyvSsln9Ul8Pzl8Nyltgzt6o/h6JtsQ46hR0FSjm1y0lnv/96W251xd+fXROuLBh4MjqjglnUoXAxLn7W/B3oOlVLYjJ8Gfkop1fto4NdFCzaUAnDo8Mx2toyg+XdByRo4+e92rtOM78FXD8G2hf63Nwbm/8s2HBlzyv7Ppw+BE/9o141b9J/Ojalsm53Lt+oNuwbdVR/YJhw+DidMOMc2ZKnd2/Hjr3oDvrgXZlzdvCyDsmISIXdycIHfp3+zDUIO/3HYh6WU6h1S4qJ1jp9SSvVCGvh10YINpQztl0huag9teLFnI3zyN9vC3bc+3Kzf2Qzbaz+y2cDWNn9mFxQ/7EeB2+pPu8K2r3/3d7BnU8fGZIx97YYauHqeXWvN3xpnE88Hd4Ndz64j9myyXRUHTIET/tixfQ8Ugw+zjX4aawNvU7wK1sy15b2xSd03NqVUj6YZP6WU6p008OsCl9vDV5v29NxsnzHw5k/tvL7Zf2l+PDYZTvkH7F4Fn9+1/36f3wUJ/WDShYGPLWLXwXM4bcmnxxP8uBb/FzbOgxNutU1XAsmdDJkjYenzwR/bVW/LRwU47792LTO1vyGH26C6cFHgbT67E6IT4eDvd9uwlFI9X3JcNBUa+CmlVK+jgV8XLC+qoLLexaHDemjgt/xFu5D0rN/uv+7e6Nm2BPKTO2D32ubHd62E9e/ZN/vtte1PzYeT/mLXhHv/d8GNqWwrvPsbGHokTLuy7W1FbNZvy2e2NDQY7/4WdiyxSyKEc5Hx3m7gwYDYfzt/9m6BZc/DtMsjvxyBUgcYEZktImtEZL2I3Ozn+VQReV1EvhWRFSJyRXeOL0W7eiqlVK+kgV8XzN9QAsAhPTHwqy2znTdzJ8P0q/xvM/svNrh7/YbmjN38uyE6IfA+rU260M6jm383fHF/29saYxeBN8abLQzi1++g8+zt8hfa33brF/DVg3DwtTD21Pa3P5DFp9k5lYECv/l3gzjsot9KqW4jIk7gXuAkYBxwoYiMa7XZD4CVxphJwNHA30UkprvGmBwXRYPLQ73L3V0vqZRSKgQ08OuCBRtKGZWTRFZyDywn/PAPUFNi14MLNE8vOQdOuA22zoevH4Py7TbLM+WS4LM8IrbN/5hTbaC54pXA2379WHOJZ7DZuIyhkD+j/XJPt8uWtaYOtBlO1b7Bh9sGP63neVYVwzf/g0lzIDUvMmNT6sA1A1hvjNlojGkAngHOaLWNAZLFLhybBOwBuq32MjnOzsnWeX5KKdW7hDXwC6JcJV1EXhaRpSLylYhMCHbfSGtweVi0eS+HDe/mZRzcjbD1S1j+EpSs8z+3rnAxLPyPzcQNmNL28aZcAkOOsGu1fXCrXd+to1kehxPOediWD750tV0zsLWybfDOb+xrtVfi2drE86F4BexcHnibhf+GXcth9p9t10rVvsGHgavWNvJp6Yv77VzJmTdGZFhKHeDygJa17YXex1q6BxgLFAHLgBuMMftdDETkahFZJCKLdu/eHbIBpsRHAS0CP2Pa/vuslFKqRwhb4BdkucqvgCXGmInAd4C7OrBvRH1bWEZtozv8jV2Msd0Vv7gfnroA/jIUHjnBLkx+TwHcPhAeOclm27591m77xg2Q3B+O+XX7xxeB0+6yjT6WPgPjzuzc3LjoeLjwaUgfDM9caMfR8md47UdgPHDGPR1f1Hr8WXbduWUB1vSr3Akf3gYjjrOZRxWcwYfZ25blnnXlsPBhGHc69BsRmXEpdWATP4+1XjD1RGAJMACYDNwjIin77WTMQ8aYAmNMQVZWVsgGmBzry/h55/mteQsemGk/dFRKKdVjhTPjF0y5yjjgAwBjzGpgiIjkBLlvRM1fX4oIHDI0TIHfjqXw4vfg76Ptendv3wwla+Ggc+G8x+Dqj+D0e2w5nrsBFj0CL19tt925zDZdidvvfYB/mcNtkOiIhpk3dH7MCRlwyYsQFQ9PnGsXiofOlXi2lNgPhs+CZS/4z3C++xt7Dk66Y9/F5lXbEvtB1ph9A79Fj0B9BRz+k8iNS6kDWyEwsMX9fGxmr6UrgJeMtR7YBIzppvGRHGczfhW13ozfqtft7Y4l3TUEpZRSnRAVxmP7K1c5uNU23wJnA5+JyAxgMPYiF8y+gC1lAa4GGDRoUEgGHoz5G0oYPyCF1AQ/6891VdlW+N9Z4HHBiFl20fWhR9lsWksDpgCX2u/dLrtIe9ESu9/Y0zv2mjOv79jcvkDSBsHFz8OjJ8OT58HZD3W+xLOliefDi+/YIGXoEc2Pb/rEzks86iYbwKqOGXyYnT/pdoGnERbcZ9dnHDA50iNT6kC1EBgpIkOB7cAc4KJW22wFZgGfej8sHQ1s7K4BNs/xawSPG9a9Y5/Yvbq7hqCUUqoTwhn4BVOucjtwl4gswc5T+AY7QT2Yfe2DxjwEPARQUFDgd5tQq2t0883WMi47bHD7G3dUfRU8faGdy/e9D6DfyOD2c0ZBznj71VmhatufOxEu+B88eS48eBQ4YzpX4tnS6JPsmnLLnmsO/NyN8ObPIG0wHP7j0Iz9QDN4ps3y7VpmF3SvLtZsn1IRZIxxicgPgXcAJ/CIMWaFiFzjff4B4A/Af0VkGfZ6eZMxpqS7xujL+FXWuezfjZpS2wW4ZYm/UkqpHiecgV+75SrGmApsyQre7mSbvF8J7e0bSYu37KXB7Ql9YxePB17+PhSvhItfCD7o64mGH2PX0nvlGjjxj11fUy8mEcaeBitehZP+CtFx8MV9Nst54bPtrzmo/PPN89v4sQ0A86fbxd2VUhFjjJkLzG312AMtvi8CTujucfmkxNuMX0Vdo53fJ04Yc7JdUkcppVSPFc45fk3lKt71heYAr7XcQETSWqw9dBXwiTcYbHffSFqwoRSnQ5g+NMQLW3/0Z1j9hl1iYcSs0B47EiZdADdtgYIulHi2NPE8qC+Hde/apSc++guMPtkuRq86J2UApA+Fz/4JZVtstk/nSSql2pAU2yLjt/Zt+wHSwEOgejdUl0Z4dEoppQIJW+BnjHEBvnKVVcBzvnIVX8kKth31ChFZje3geUNb+4ZrrB01f0MJE/NTmy5+IbH8JfjkDjvP7pBrQ3fcSAu2wUwwhh4NiVm23POdX9mlJ2b/OXTHP1ANmQl1ZbbRyygNopVSbXM6hKTYKKR8q61QGTUbsr29ZXZruadSSvVU4Sz1DKZcZQHgt57R3749QVW9i28Ly7nmqGGhO2jREnjlOvuJ6Sn/0IxLIM4omHAufPWQDfqO+U3XS0iVXcj9myfsPMmuzMNUSh0wkuOiGFzysb0z+iSIirPfF6/ScnGllOqhwhr49UULN+/B7TEcOixE8/sqd8EzF9nW+hc8AVGxoTluXzXxPPjyfsgYbjuRqq6bcA7EJMCY0yI9EqVUL5EcF8Wo8vmQOcJ2VDYGYlO0s6dSSvVgGvh10IINpcQ4HUwbnN71gzXWwbMXQ+1euPIdSArdArt91oCpcMRPYfQpGiSHSlQMjOtRy2QqpXq4rJhGRpUvgcnftw+I2HLxYg38lFKqp9LAr4MWbChl8qA04mOcXT/Y+7+HwoVw/uN2CQTVPhGY9btIj0IppQ5oh/It0TTaMk+f7DGw+s3IDUoppVSbdEJPB5TXNLK8qJzDhmd2/WCVu2z7/CmXarZFKaVUr1JQv5BKEmHgwc0PZo21a/pV7Y7cwJRSSgWkgV8HfLGpFGMIzfp9Xz4A7gZdeFwppVTv4vEwoeYLPpMp4Ixuflw7eyqlVI+mgV8HLNhQSly0g0kDU7t2oLoKWPgfm+nLHB6awSmllFLdYftiklx7ea9xMsaY5sezxtpbneenlFI9kgZ+HbBgQynTh2QQG9XF+X2LH7ULkR9+Y0jGpZRSSnWbtW/jwcn7ronUuzzNjyf3h9hUzfgppVQPpYFfkEqq6lmzq5JDhnVxfp+rHhbcB8OOhgFTQjI2pZRSqtusfZvi9ClUkERFXWPz4yK23FMzfkop1SNp4BekLzaWAnS9scu3z0DVTph5Y9cHpZRSSnWnsq2wazklA44GoLLOte/zWWNsxq9lCahSSqkeQQO/IM3fUEpSbBQH5XVhfp/HDZ/fBbmTbcZPKaWU6k3WvgNA1eDjAKiobdz3+eyxdm3aau3sqZRSPY0GfkFasrWMqYPTiXJ24ZSteh32bLCdPEVCNzillFKqO6x9GzKG48weDQTI+AEU6zw/pZTqaTTwC1J5bSPZybGdP4Ax8PmdkDEcxp4WsnEppZRS3aK+CjZ9AqNmkxwXBfgJ/LK9nT136zw/pZTqaTTwC1JlXSNJsVGdP8Cmj6HoG5h5PTi62BVUKaWU6m4bP7Lrz46eTXKcXb+vsq5VqWdSDsSlacZPKaV6oC5EMgcOYwxV9a6mTzg75bM77QVx4pyQjUsppZTqNmvfsss1DDqUFG+8t1/GT8Rm/TTjp5RSPY5m/IJQ2+jGY+h8xq/oG9g4Dw65DqLjQjs4pZRSKtw8Hlj7LoyYBc5oEmOiEGHf5Rx8ssbYjJ929lRKqR5FA78gVHk/0UzqbMbv87vsp6QFV4ZwVEoppVQ3KfoGqoth9EkAOBxCUmzU/hk/sBm/ujKo2tW9Y1QqXPRDDNVHaOAXhMp6b+DXmYxf6QZY+SpMvxLiUkI8MqWUUqob1O21mbwRxzU9lBIXHTjjBzrPT/UNL3wXXro60qNQKiR0jl8QfBm/Ts3x++wf4IiGg68N8aiUUkqpbjLiuH2CPrDXxIAZP7Dz/IYf0w2DUyqMti+GxppIj0KpkAhrxk9EZovIGhFZLyI3+3k+VUReF5FvRWSFiFzR4rnNIrJMRJaIyKJwjrM9vgtbUmx0x3YsXAzfPAnTr4LknDCMTCmllIoMG/j5yfglZkF8hmb8VO/n8UDFdlu2XLU70qNRqsvCFviJiBO4FzgJGAdcKCLjWm32A2ClMWYScDTwdxGJafH8McaYycaYgnCNMxhV9fbC1qFST48b3rgRkvvD0fvFvEoppVSvlhIX7T/jp509VV9RU2KXMAEoXhHZsSgVAkEFfiLyooicIiIdCRRnAOuNMRuNMQ3AM8AZrbYxQLKICJAE7AH8XEUiq7IzpZ4LH4adS+HEP+ncPqWUUn1OclyU/zl+4O3suVqbYqjerXxb8/e7VkZuHEqFSLCB3P3ARcA6EbldRMYEsU8e0OJ/DIXex1q6BxgLFAHLgBuMMR7vcwZ4V0QWi0jAWbUicrWILBKRRbt3hycNX1XfwcCvcid8+EcYfiyMPyssY1JKKaUiKTlQxg9sxq++HCp3dO+glAql8sLm73dpxk/1fkEFfsaY940xFwNTgc3AeyIyX0SuEJFAE9/E36Fa3T8RWAIMACYD94iILz020xgzFVsq+gMROTLA2B4yxhQYYwqysrKC+XE6zNfcJTHYUs93fgWuejj5b7bkRSmllOpjfM1djL+snnb2VH2BL/DLnQy7lkd0KEqFQtClmyKSCVwOXAV8A9yFDQTfC7BLITCwxf18bGavpSuAl4y1HtgEjAEwxhR5b4uBl7GloxFRVe8iLtpBtDOI07XhQ1j+IhzxE8gcHv7BKaWUUhGQHBeN22OobXTv/2TLzp5K9Vbl2yE6EYYcbn+X3T1uNpJSHRLsHL+XgE+BBOA0Y8zpxphnjTE/ws7N82chMFJEhnobtswBXmu1zVZglvc1coDRwEYRSRSRZO/jicAJQMQ+aqmsdwXX0dNVD2/+DDKGwcwbwz4upZRSKlJS4m0VjN9yz8R+kNBPM36qdyvfBqn5kDMBXHWwZ2OkR6RUlwTbreQeY8yH/p4I1HHTGOMSkR8C7wBO4BFjzAoRucb7/APAH4D/isgybGnoTcaYEhEZBrxse74QBTxljHm7Iz9YKFXVuYKb3/f5XbBnA1zyEkTHhX9gSimlVIQkx9kPRCtqG8lJ8XPN086eqrcrL4TUPMgZb+/vWg5ZoyI7JqW6INjAb6yIfG2MKQMQkXTgQmPMfW3tZIyZC8xt9dgDLb4vwmbzWu+3EZgU5NjCrqre1f5SDns2wid/s81cRszqnoEppZTqc0RkNnY6hRN42Bhzu59tjgbuBKKBEmPMUd04RKC54VlFoAYvWWNg6bO2s6fOd1e9UXkh9J8AWaNBnFC8Ejg70qNSqtOCneP3PV/QB2CM2Qt8Lywj6oGq6toJ/IyBuT8HZ7RdvkEppZTqhGDWwBWRNOA+4HRjzHjgvO4eJ0BKnK/UM8CSDtljoL7CLoCtVG/jqofqYkgdCFGx0G+kdvZUvV6wgZ/Du9Ye0HRhimlj+z6lst5FUlulnqteg/XvwzG/hpQB3TcwpZRSfU0wa+BehG2MthWamqB1O1+pZ8AlHbK8DV6KtdxT9UK+DyxS8+1tznjt7Kl6vWADv3eA50RklogcCzwNRGzOXXerqm8kua2M36f/sBe4GQGXG1RKKaWCEcwauKOAdBH5yLvW7Xf8HSjc69wmx7XR3AVadPbUBi+qF/It5ZDi/e+XMx7KtkJdeeTGpFQXBRv43QR8CFwL/AD4APhFuAbV01TVtZHxq6+Cnctg7KngDHbKpFJKKeVXMGvgRgHTgFOw6+H+VkT26zgR7nVuU3zNXQKVeiZkQGK2ZvxU7+QL/JoyfhPsrXaqVb1YUJGKMcYD3O/9OqAYY6hsa45f0ddg3DDw4O4dmFJKqb4omDVwC7ENXaqBahH5BNsQbW33DNFKiHHidEjgOX5g5/m1zvgZA9u+gq8ft9Mk5jwJ+X4bhCsVOf4yfmDLPQcdEpkxKdVFwa7jN1JEXhCRlSKy0fcV7sH1BPUuDy6PCZzx2/alvdWLllJKqa4LZg3cV4EjRCRKRBKAg4FuT0OICEmxUYFLPcFOg9i9xgZ71SUw/264dwY8cgKseNk2z1jzVvcNWqlglRfajLVvea6UPIhNhV0rIzuunsLjtv+f6yoiPRLVAcHWJj4K3AL8EzgGuAL/5Sh9ju+CFnCO37avbMvq+PRuHJVSSqm+KJg1cI0xq0TkbWAp4MEu+RCRrhPJce0EftljoKEKnjofNswDTyPkz4DT77bLH/33FChc2H0DVipYvjX8fES8DV60sycAW7+Ad38DMUlQcEWkR6OCFGzgF2+M+UBExBizBfi9iHyKDQb7tKp6b+DnncuwD4/HBn7jTu/mUSmllOrpROQG7AenlcDDwBTgZmPMu23t194auN77fwX+GtIBd0JyXHTbpZ65k+1t4SLbAG3qpc1NXwDyp8O3z9rsgcMZ1rEq1SHlhfsv1p4zHr59RtemhOYAuFgzoL1JsM1d6kTEAawTkR+KyFlAdhjH1WNUeT/J9DvHr3Qd1JXp/D6llFL+XGmMqQBOALKw1TL7Lcbem6XERQVewB0gbypcuwB+uhpm/2nfoA9s4NdQactBleopjPFm/Abu+3jOePv7WrY1MuPqSXxLW2jpa68SbOB3I5AAXI/tJHYJcFmYxtSjVNbbTzL9zvHzze/TwE8ppdT+fCmBk4FHjTHf0semSSTHRVNR20bGDyBnnF0A25/86fZWyz1VT1JXBo3VzR09fXydPbXcs/kc7FpuA2XVK7Qb+HkXaz/fGFNljCk0xlxhjDnHGPNFN4wv4trM+G370s7tyxzRzaNSSinVCywWkXexgd87IpKMnZPXZ6S0N8evPRnD7HVUAz/Vk7Tu6OmTPcbeHuiBn8djl7WISbZBcuWOSI9IBandwM8Y4wamiRyYxczNc/z8BX5f2WzfgXlqlFJKte27wM3AdGNMDRCNLffsM2xzl3Yyfm0RsVm/wkWhG5RSXdW0hl+rUs/YZEgf0lzmeKAq22wzomNPs/cP9EC4Fwm21PMb4FURuVREzvZ9hXNgPYUv8Nsv41ezB0rWwsAZERiVUkqpXuBQYI0xpkxELgF+A5RHeEwhlRwXTVW9C9OVUq/86bB7NdT1qVOjerPWi7e3lDNBG5r4Ar2DzvXeP8AD4V4k2MAvAygFjgVO836dGq5B9SS+Epb95vj5ylJ0fp9SSin/7gdqRGQS8AtgC/B4ZIcUWslxUXgMVDe4O3+Q/ALAwPavQzYupbqkvBCcMZCYtf9zOeOhdD001nb/uHqKXSsAsQvZp+Rrg5deJKjlHIwxfao0pSOq6l3EOB3ERrVqM73tSxAnDJgamYEppZTq6VzGGCMiZwB3GWP+IyJ9qjFaSrxd6qiittH/XPhg5E0DxJZ7Dj8mdINTqrPKC+38Poef/EjOeDAem6UeMKX7x9YT7Fpu5+fGJOrahr1MUH+lReRRYL86DmPMlSEfUQ9TVecK0NHzK8idCDEJ3T8opZRSvUGliPwSuBQ4wtsszc+isL2Xb/57lxq8xKVC1mht8KJ6jvJC/2WesG9nzwM28FtpAz6wXXs3fAiuBoiKiey4VLuCLfV8A3jT+/UBkAJUhWtQPUlVvWv/TzHdjbB9sZZ5KqWUassFQD12Pb+dQB49YNH1UEqOs3Fslxq8gC33LFyobeFVz9BW4Jc+BKLiD9wsV0M17NnYHADnTABPo13bWvV4QQV+xpgXW3w9CZwPTAjv0HqGyjo/5Su7lkNjjTZ2UUopFZA32HsSSBWRU4E6Y0yfm+MHXcz4gW3wUrvHvqFUKpLcLrs8QaDAz+GE7LEHbkOT4tWAac74ZY+ztzrPr1cINuPX2khgUHsbichsEVkjIutF5GY/z6eKyOsi8q2IrBCRK4Ldt7tU+iv13PaVvdWMn1JKqQBE5HzgK+A87AemX4rIuZEdVWileK+PFV3O+PkWctdlHVSEVe0E4w4c+EHzvLYDMUPtC3hzvAFfv5HgiD5wA+FeJqjAT0QqRaTC9wW8DtzUzj5O4F7gJGAccKGIjGu12Q+AlcaYScDRwN9FJCbIfbtFVb2L5NYZv21f2km/bf1RUEopdaD7NXYNv8uMMd8BZgC/jfCYQspX6lnR1Yxf1hiISdJ5firymhZvbyvwmwA1pVBV3D1j6kl2rYDoREgbYu87o+0c3Y4scbH+fV2+JUKCLfVMNsaktPgaZYx5sZ3dZgDrjTEbjTENwDPAGa0PDSR7F4dPAvYAriD37RZV9a79F2/f9pWWeSqllGqPwxjT8p1hKZ2vtOmRUkI1x8/hhLypGvipyGtrDT8fX5njgZjlKl5ps30tO552pLPnnk3wxDnw+b/CMz7VpmAzfmeJSGqL+2kicmY7u+UB21rcL/Q+1tI9wFigCFgG3GCM8QS5r28sV4vIIhFZtHv37mB+nA7Zr6tn+XYo36Zlnkoppdrztoi8IyKXi8jl2AZpcyM8ppCKi3YQ5ZCuz/EDW+65azk01HT9WKG28jXY+HGkR6G6Q1Pg5/dtp9UU+B1gDV6Msf9HfT+/T/Y4qNgOtXvbP8aGD/a9Vd0q2E8ebzHGNOVkjTFlwC3t7CN+HmtdDH0isAQYAEwG7hGRlCD39Y3lIWNMgTGmICvLz0KbXVRZ7yIptkX37ULf/D7N+CmllArMGPNz4CFgIjAJeMgY0+Y0id5GREiOi+p6xg9s4OdxwY5vu36sUJv7c3jvd5EeheoO5YUQlwaxyYG3SciA5NzuD/yMAVd9975mS5U7bHCX06q/Y9MSF0GUe67/0N4WLYHq0pAOT7Uv2MDP33btrQFYCAxscT8fm9lr6QrgJWOtBzYBY4LcN/TWvb/PL229y02Dy7NvqefWL20b3/4Twz4cpZRSvZu3G/ZPjDE/Nsa8HOnxhENyXHRoMn55Bfa2p5V7Vu60DT92LoX6ykiPRoVbW0s5tBSJhcsXPQJ/Gwl7t3Tv6/r4ft7sVm03fI1e2pvn526ETZ9430Mb2Dgv5ENUbQs28FskIv8QkeEiMkxE/gksbmefhcBIERkqIjHAHOC1VttsBWYBiEgOMBrYGOS+odVYC6/9EJ67FOoqAKiudwPsu5zDti/tPARnn1qDVymlVIi0bojW4qvS2yCtT7EZvxAEfklZdo00X2VNT1G0xN4aT88LSlXodSTw273aBjPdZeWrtinK3J9FpqNo646ePsm5EJ/e/pzHwoXQUAmH/9hmVTdEOPBzN9pgenWfqsBvU7CB34+ABuBZ4DmgFtuRMyBjjAv4IfAOsAp4zhizQkSuEZFrvJv9AThMRJZhF4a/yRhTEmjfjv1oHRQdD+c8bCedvvYjMIYq74WsKfBrqLGf+GmZp1JKqQD8NETzfSUbY1IiPb5QS4mLpqI2RG9+86fDtjAt5G4MvHcLbPiwY/vtWAIIiAO2LAj9uFTPUhFs4OdbuHx9+McE9j3o1gW22+i6d20Q2N12rbSvH5++7+MikD2+/VLP9R+AOGH4sTDsaPt/MVJLYmz8GB44At74MbxwJezdHJlxdLNgu3pWG2Nu9s2lM8b8yhhTHcR+c70dQIcbY27zPvaAMeYB7/dFxpgTjDEHGWMmGGOeaGvfsBtyOMz6Hax8Bb58gMp6eyFrau5S9I2df6CNXZRSSikghBk/gPwZtqyyYntojtfSt0/D53fClw91bL8d39q1ynIm2Dfe4VBbBoWL4dtn4MM/wvOXw7t9auWP3qG+ys5hCybwa1q4vJvKPbd8Du4GOO1OyJ0Eb93U/Usi7Fqxf2MXn5zxttTT4wm8/4YPIL8A4tNs8FdZBLvXhGWoAZVtg+cug8dPh8YaOP1u21V47s8PiHUZ25unB4CIvAec523qgoikA88YY04M49giY+YNtpzz3d/AiSMAmtfx2/alvc3XjJ9SSikFvjl+ocr4tZjnF8q1cqt2wzu/8h77K/sGT/z1kfOjaIn9YDghAxY/Bq4GiIrp+phWz4XP77IZo5qS5sfFAXGpNgA59AeQ3L/rr6WC4/vAoa01/Hz6jQJHlC1vPOjc8I4LbLYsKs7+Lp52F/z7WPjgVjjl7+F/bbC/9yVrYFSAt/4546ChCsq32pLt1qpL7f+lo39p7w8/1t5u+ACyx4RjxPtqrIP5d8OnfwcMHPNrOOxHtuKvvtL+fVj1Oow7PfxjiaBgSz37+YI+AGPMXiA7LCOKNBE4835IzWf4Rz8kg4rmjN+2ryBzBCRmRnaMSimlVA8R0oxfzgT75rZwUWiO5/POL20255Dr7MLbezYGt19Vsc1KDJgMgw4FV62d8tFVlbvg5e9D1S4YczIcfyvMeQp+sBB+vQu+4y3j62hZquqacu9KYsF86BAVA/1Gw85uWstvwwcw+DAbqAyYAjO+Dwv/Y0uju0PJWlv1FjDj5+vsGSADunEeYJoDvrSBNnjujt/xDfPgvoNh3h9h5PHww4Vw1C/suQR7LvsfZLOofbyBU7CBn0dEBvnuiMgQAiyv0CfEp8H5jxNdv4c7o+8lKVrsp4PbvtQyT6WUUqqFlLgoqhpceDwheFsQFQO5k0PbRGXde7DseTjiJzDlEvtYsMf3LS2RO8kGfgBb5nd9TO/9zjaVu/gFW2o28wYYcwpkjbLnIOcgSMy2WR7Vfcq9Gb9gs80Dp9vy33CvPVm2zQZew2c1P3bsryFlALx+Q/c0mPEFdIECvyxv1i7QPL8NH9qGLnlTmx8bfixs/txm48KlcBE8dT44Y+wHKhf8D9IG7buNMwpOvdMuVzHvT+EbSw8QbOD3a+AzEfmfiPwP+Bj4ZfiG1QPkTuKrsb/kSOcycr65C0o3QO0ebeyilFJKtZAcF40xdt3bkMgvsCVhroauH6u+Ct74ic0sHPFT++Y0JtlW8ATD19Gz/0RIzoGMYbD1i66NafPnsPQZmHk99BvhfxuHw74p3vAheNzBH9sYG1D2VcbAx39t/ncJtfJCW2qbnBvc9uPPsuWN694Nz3h8fIudj2gR+MUmw8l/heIVsODe8L4+2NdxxtjKN39ikyB9qP/OnsbY3+VhR9v5dD7Dj7VZ9G1d/D8VSOVOePYS++955Tv29QPJL4CCK+HLB3rmWqIhEmxzl7eBAmANtrPnT7GdPfu0Jf1O53nXkSR88Q/4+Hb7oGb8lFJKqSYp8XY6ROjm+U0Hdz3sWtb1Y837k51zdNq/ICrWvunMnxb8khE7ltg3unHeZqyDDrUZnrYaWLTF3Whb8acOtIFoW0bMsh8471gS/PEXPgx/G21LSfuiwoW2XO+DW8Nz/PJCGyQ4g2qBAUOOsJnZFS+FZzw+Gz6E5AHNWTWfMafAmFPho9vD35Vy1wrIGt32cma+Bi+tFa+y2bSWgSvA4JngiA5PZttVD89eahvgzHnKztFtz6zfQUI/eP3Gjn3g0osEFfiJyFXY5RZ+6v36H/D78A2rZ6hqcPF7z5V2wuqy5yE21dZzK6WUUgqwGT8ghJ09p9vbrs7z274Yvrwfpl0Bgw9tcfwZ9k1sfVX7x9jxrS3z9Bl0qA3GStd1bkxfPWTfGM++HWIS2952+LGAwPoOzIH65gmoL4f5/+rc+Hq6hQ/b2w0fQtnW0B+/fFvHmgo5nDD+TFj7TvjmhrldsPEjGHGs/4ZEJ/3FjuPNn4a3K+WuFc3z+ALJGW+bFbXOOvsylr75fT6xSTDokNCv52eM/YCl8Cvbt6N/O+P2iU+D2X+Goq/t+n59ULClnjcA04EtxphjgCnA7rCNqoeoqnMRHZeInP8/iE2xv5yOYE+ZUkop1fdlJccCsH1viAqBUvNsdqMr8/zcjfDaDTYbc/z/7fvcwBl2Mfair9s+RnWpDQRyJzc/1pV5fhU7YN6fYcTxNlPTnsR+NujcEGQ2pHSDzQ7Gpto3rdUl7e7Sq1SXwIqXYfTJ9v43T4b+NYJdvL2lCeeAqw7WvBX68YD9Pa0r33d+X0up+XDsb2H9++HLPNbssRm7QPP7fLLH2f9bu1fv+/iGD23ixN+5HX6sze6HMku98GH4+nE44mc2MO+ICefYktAPbu2TmfNgo5g6Y0wdgIjEGmNWA30+9VVZ77KLt2cOh+/Ng9P76CdoSimlVCdNGJBKlENYtGVv6A6aX9C1wG/BPfbN5Ml/tUsjtJQ3zd62N89vxzf2dsDk5scyh0NiVufm+b33W1vCetJfgl9KYsQsO85g1mvzvek/71Gbcfnivo6PsSf7+nG7jt2sW2yw8M0ToS3H83igoqjjgV/+DLv8w/IXQzeWltZ/YOcdtjU/bcb37AcUb91s57WFWnuNXXx8z7ds8NJYaz8oaV3m6ePLAm78qEtDbLL5c3j7Zhh5ol2yoaNE4JR/2FJR3xIwfUiwgV+hiKQBrwDvicirQFG4BtVTVNV5Az+wE7B1LR2llFJqH/ExTibkpbJ4y57QHTR/up2zVNWJ4qLSDXbO05hT/a/JlZABmSPbDyxbNnbxEfHO8+tgxm/TJ3bKyMwbbfAYrBHHgXHDxo/b33b5yzDwEPsGe9wZdqH62hAG45HkccOiR+2cuuwxMPU7UFEY2hLBmhIbmAezhl9LDgdMOMsGaOE43xs+gAFT256j5nDCGfdCQ7XtYBlMGXNH+AK/7HYCv4xhdjmWlks6bPncZkQDZSz7T7Tz6oLNbLelbBs89x3bZOacf3e+Si9zuO0CvPyFPtdZN9jmLmcZY8qMMb8Hfgv8BzgzjOPqEarqXSTHBTnBVymllAoBEZktImtEZL2I3NzGdtNFxC0i3bB6dNsKBqfzbWE59a4QZWB88/w2dvCNvbvRtrd3xthsXyADZ9jAr605UTu+tW8g49P2fXzQoXZ+ma/1fzBjevNntoX8ET8Jbh+f/Om2C2l7b4qLV9uuixPOtveP/Dk0VMKXD3bs9Xqqde/ZJj3Tr7L3R58MCZnw9WOhe42OrOHX2vizwdMIq94I3XjABpLbF+8/N86f/hPgvP/CzmXwwpV2bmCo7Fpug7OkdpbwdjhtA5riFoHfhnngjLVrEPrdxwHDj7HbdbZpEtglNZ692GaFL3x6/0x/Rx3+Y9vYae7PbPavj+hwKGyM+dgY85oxJgR9lnu2qvoWGT+llFIqzETECdwLnASMAy4UkXEBtvsL8E73jtC/giHpNLg8LN8eREliMPKmQtZYG8Rt/jy4fVwN8PzlsPlT26AhZUDgbfOnt7+Q+44l+5Z5+vgaxWxdENy4vrgfStbASXc0LxgdLGc0DDvKZh3aClJXvASIzfSBDQJGn2LLPesq2n+d8u3wwR/sXKzuWBOuoxY+DEn9m+dGRsXApAthzdzOZYX9KS+0t50J/AZMsR8ShLrcc+PHds5coDLJ1kadAKf8Hda9A2/9PHTNXnatsGWcwZQo50zYN+O3/gP7fyYmIfA+w4+F6uJ9A8aOMMb+rdixFM55GPqN7NxxWoqKtf9n92yE+Xd3/Xg9hHYqaUNVnYukuDba1iqllFKhNQNYb4zZ6P2A9RngDD/b/Qh4ESjuzsEFMm2wLUNbtDlEpW5RsXax5dSB8OS57Qd/rnp4/jJY/YbtmOlbqD0Q35q8gco9a/bYrF7Lxi4+OQdBTFJw8/zKt9uy01GzYfRJ7W/vz4hZNhtVEqCTqDGw/CUYcvi+U1KO/JmdG7jw320fv3YvPHEOfPo3+N9Z8NcR8PK1sHpueBfWDtaejbZxybTL911KYOp3wOOCb58Ozet0dPH2lkRsU5BNH4cuEAWb6Y1NhbyC4PcpuNKWFC96BD6/q+tj8LjtcgztdfT0yRkH1buhqtie092rApd5+gw7xt5u6EAH25YWPgzLnrNz+kad2Llj+DNiFow9DT75my0j7QM08GtDpWb8lFJKda88oOU7jELvY01EJA84C3igrQOJyNUiskhEFu3eHd5G3FnJsQzJTAhtg5fkHLjs9faDv8Y6u17Xmrlw8t/gkGuDGHA7C7n7FnBuuZSDjzPKZgyDyfi9+xs7R++kv7S/bSC+N82Byj13LbfLS/jKPH3ypto5ggu8c7/8aayDZy62Lfgvet6udzb6JFjzJjxzIfx1uM2irng5cuuaLXrENjeZdtm+j2eNtnMav348NJmt8kKIToD49M7tP+Ecm51b+UrXxwL2Z1r/IQw7Mvh1BX1m3WLH8/4tXc9C7t1sF1lvr7GLT1ODlxXNgVx7GcuUXNsRtDPz6bYthLd/aT9caW9tzM448c/2to80etHArw1VdTrHTymlVLfyV0vV+l3tncBNxpg234kbYx4yxhQYYwqysrJCNb6Apg3OYPGWvZhQriXWXvDXWGfn9ax7x3bim/G94I7b3kLuvkXT/QV+YOf57VoBtWWBX2PLfFuCOfMGSB8S3Lj8SR9s5xqtf9//88tfAnHCWD+J4SN/YUta/a1J5vHAK9fY5htnPWDLBMecYr//2Xq45CU46DzY/JkN/v5zwr7dGrtDY63t3jn2VP+lu1O/Y4PeznRZbc23hl+wHVdbyxlny5OXh2hJhZK1toFNe9kyfxwOOOM++3v68jWdW37EZ9dye5uzX8W5f74GMMUrbeCX1N8Gde0Zfqz9MKWhJvixVZfYTH/KAPt7G44l19IGwpE/hVWvdT4j2YNo4BeAy+2httGtGT+llFLdqRAY2OJ+Pvt30S4AnhGRzcC5wH0icma3jK4N04eks6e6gY0lAbJLndUU/OXvG/w11tqs1Pr34bS7YPp3O3bcthZyL1oCaYMDd1IcfChgAmcMPW546yZIybNld1014jj7c7cuvTTGZnSGHQ2JmfvvN+hgGHokfP6v/RfVfvc3NpN3/B/goFb9gaJibJbmtDvhp2vgrIdg7yZ48Ej48Lbua3ax4mVbiupr6tLa+DNt5vbrx7v+Wp1Zw6+1CefYjq/BNv5piy/7Fez8vtai42wGN20QPH1h4FLh9uxaYTOuWWOC2z4py66fuXOZbc40PMDC860NP9Y2Zgk2SPW44cXv2uDvgv91PlMbjMOutx1L5/7CziXuxTTwC6C63n6QqoGfUkqpbrQQGCkiQ0UkBpgDvNZyA2PMUGPMEGPMEOAF4DpjzCvdPtJWCobYN16LQzXPr6XkHLjsjebgb9378NQFthPg6ffY+V8d1dZC7ju+DZztAzvnyhEVeFmHb56AnUvh+FvbbmoRrOGzbLld69cr+hrKtuxf5tnSkb+wjTNaBkfz74Ev7oWDr4HDftT2azucMOkC+MFC+zqf3AEPHA5bgmxu0xULH4Z+o+wyDv7EJNqgdcXLwa112JaK7SEI/Lz/DqEo99zwgV12JG1Q54+RkAEXv2B/Vx8/08433fhx4NJff3atsBnnjjQmyhkHq163QXuwgevgw2z3z2CXdfjoz3btv1P+3vb/1VDwNXopXdfr18fUwC+Aijrb1SpJSz2VUkp1E2OMC/ghtlvnKuA5Y8wKEblGRK6J7OjaNqxfEmkJ0SwK5Xp+Le0T/J1j18Y78z6YemnnjpfvbZjROmtXW2azW/46evrEJNjGL/5KDOvK4YNb7fyzCed0bmytDZlpl6hoPQdq+UvgiG7udul338Ntyd/nd9lM3bIX4N1f2w6gJ/4p+NLGxEw4+yG45EWbeXx0Nrzxk+C6hnbG9q/tUgbTr2p7jFO/Y4PiZS90/rVc9VC1y5YUd0XmcBuEdHVeXWOdzfAGs4xDezKGwsXP2SDwo9vh8dPh9kHw72PhnV/D6jdt1iyQXcuDn9/nkzMBGqoAaXvh+Zai423wF0w55dp34JO/wpRLO///v6NGHm+XEfn4jtBkdCNEA78Aqurt+ifJmvFTSinVjYwxc40xo4wxw40xt3kfe8AYs18zF2PM5caYLrzjDR2HQygYnB66zp7++IK/USfZtu2TL+r8seLTbTapdWfPpsYuk9vef/ChNjBpXX758R12Xt1Jt3d+vlhrMYk2eGsZ+Hk8NtM1YlbbZW4idl2/iu3w5k/glWth0GG2fNPh7PhYRhwH1y2AQ66zcwfvPRhKN3T8OO1Z+B+IToRJc9rebsAU22m1vXLP4tWBO25WeN/Ip+T5f74jJpxjfy/2bOr8MbYusMFsZ8s8W8ubBtd8CjdtthnAw663HyR89RA8c5Ft4nP7YHjwKHjuMnjvFlj0KKx91zZ36Wjg55vTlzsJEvsFv9/wY2H36rYDq72b4aXv2YXf21qrMxxm/9k2a3r3N937uiEU1sCvvUVoReTnIrLE+7XcuxBthve5zSKyzPvconCO0x9f4KcZP6WUUio40wZnsLGkmtKqMM4BS86Bi57Zf15aZ+RP338h96bGLpPb3nfQoXZOUtE3zY+VrIMvH7DLSQyY0vXxtTTiONsa3/emuPArG7AEk1Ucfqx98//NE3au0oVP2TlgnRWbZN8EX/W+zey89YvQrRkHdjmN5S/AxPPbX4hbxGb9diyx67i1tnut7fp638Fw73RY8cr+23RlDb/Wxp9lb1d0ocnLhg9sYDbk8K6Pp6X4NJu5Ou4WuPJtuHkbXPE2nPBH+/8psZ/N8C24F964EZ46z+7Xf2LHXscXKHY0cPVlON/8KXzxgM3+lW9v/t3ydfAFOP/xjq+L2VXpQ+zC7iteshUHvVDYopoWi9Aej52svlBEXjPGNLWEMsb8Ffird/vTgB8bY1rWiBxjjGkj/xw+VXXejJ+u46eUUkoFpWme35a9nDC+fztb9wD502HJk3atuMzh9rEd39qSP3/NUloaeIi93Tq/eVH3d34NUfEw63ehH+uIWfDeb+2b4amX2jLPqLjg1gcUsWWdH90Op98dukYY+QVw1E22dHTt251fq7C1JU+Bqy74hj0Tz7NZmG/+B7neLFDZNvvzfvuUXabhiJ/ac/f8ZbD2IrvERlyK3bYra/i1ljYIBh5s/306u7zA+g9h0CE20xtO0XH2d9f3++vjcUPlDptdq91rP3ToiP4HwVE3778ER3tyxtt18zZ9Amvfan48Jtkuyi5i585e+KwtYY2EmTfY38+5P4drPtt3bclQqK+E2OTQHrOFcKazmhahBRAR3yK0gXoBXwiEaBXOrqv0Zfy01FMppZQKykF5qcQ4HSzqLYGfbyH3bV81B35FS4JrFpGYCf1GN8/zW/eeXVbi+D9AUnbox5o9DpJzbRfTyRfZBiIjjw/+TeKgQ+A7r4R+XAd/35ZZvn2zXYg7mExieaFdIqK6xGZtouL2vd32lQ2s+x8U3Bji0+2cxaXP2jLGBffCov8AAgdfC0f8xGazjv6lLcX99G+w5TM460E7r8yX8QtFqSfYLOxbv4Dda+x6gx1RsQOKV8Bx/xeasXSGw2mD4M4Gwg4nHPPLju8nAhc8YTN8VcVQssaew5K19nbvJtswafTszo0rFKLj7YcGT8+xv2eH3xia47obYd5tsPR5W5YbqKNwF4Wz1LPdRWh9RCQBmA20nA1rgHdFZLGIXB3oRcK1QG1zxk8DP6WUUioYcdFODspPZdHmMDV4CTXfQu6+9fzqymHPhrYbu7Q0+FDY+qUtQXv7l5Ax3HbKDAcR291z40c2I1K1K3TNY7rCGW3fCO/dDAvubn97V72dR1a8ymYMM4Z53+QK1JXZuXHxaXDUzzs2jqnfsf9+d02Crx6EiRfA9V/D7D81zzNzRsOxv7bljeKAR0+G939vM76JWV0rf21p3Jn2+B1p8tJYZ3+XPr7d3g/V/L7eSMSWdA890q7NefJf4bLX4MZlNuMWaaNm23nG798Cr9/Q9Y6yezfDI7Phs3/CiGNtF9EwCWdUE8witD6nAZ+3KvOcaYwpEpFs4D0RWW2M2a+g1hjzEPAQQEFBQcgKzKvqvV09NeOnlFJKBa1gcDqPfL6JukY3cdGdaB7SnXwLuW/zNnjZuczetje/z2fQYbD4v7ZpSuk6W4IWFROOkVojjoUlT9g3nNGJMPLE8L1WRww/xpboffoPmHRh25mid34F2xfZOVrj/Cw631lDDocRx9v5h8f82pYGBjLoYFum9/Yv7ZttCO2czOQcO56P/wLfPAn9RthGQpkjm793RNnM5rYv7e2OJXbOKNi5bjkTQjceFVoicN6jdkmJ+XfbLqOn/rNzpc7LXoA3fgwInPto20uzhEA4o5pgFqH1mUOrMk9jTJH3tlhEXsaWjnbbTMqqOhcikBDTwy9aSimlVA9SMCSDBz/ZyNLCcmYMDU+5Ukjlz7Clf/WVtswTOhD4eef5LXnSZuNGhTkQG3YMIHYe4oRzQrNGYKiccJstd333N3Def/1v8+2zdm2+w34U2qAP7JvxSzrQ4DY2Gc64x2ZvXr8+9GvBnXqnnedXus42/VnyNDRU7r+dM9YGnQdfY3+f8mfYRdBVzxYdb8tOx50Jr/7Qln5OOMeu9xdMJ9OGarsg/JIn7L/5OQ9D+uCwDzucgV/TIrTAdmxwt1/fZRFJBY4CLmnxWCLgMMZUer8/Abg1jGPdT2W9i6TYKCRUrZiVUkqpA8C0wbZxyKIte3pH4OdbyH371zbrkpIX/BvvtEF2+8qdtstluN8zJGTY7pzbF8H48GYGOix9MMy80ZYqFnwXhrZadH3nclsWN/hwmPX7SIzQv7Gnhidgzxy+b7mqMbY8t2StDQRd9ba5UO7EsJb2qTDLmwpXf2TXyfzkDtgwz5Y+H3Re4L8HO5bCC1dC6Xo44mdw9M2hbxITQNgCP2OMS0R8i9A6gUd8i9B6n/etR3QW8K4xprrF7jnAy96gKwp4yhjzdrjG6k9VnUvX8FNKKaU6KCMxhmFZiSwO53p+oeRbyL1woc2kdSTzI2K7WrobOt7Eo7PGnwUVRR3vtNgdDr/Rdjx86yb4/ifg9L6Pqi2D5y61SzOc+0jz4z1Fd7zpFoHk/vZr6JHhfz3VfaJibJA/9jR47Yd2ncFP/2HLjlszxnYmTci08xa7+XchrP/zjDFzgbmtHnug1f3/Av9t9dhGIMQ5946pqnfpGn5KKaVUJ0wfnMHbK3bi8Rgcjh5eOeNbyH3jRzYTM6GD6wN2tGV9Vx36A1sW2NOCJ7DlbyfeZoO8Rf+xHT+NgVeug7KtcPmbdv6bUn1R9hi48h1bzrz2HQK2Npk0x2a921syJgx64F+NnqHKW+qplFJKqY6ZNiSdZxdtY8PuKkbmhG9NqpDJn2Hn2kDwHT0jRaRnBn0+Y0+DYUfb1vQTzrFr6615E2bf3jwnUqm+yuG0H3gc/P1Ij8SvcC7n0KtV1rlI0sXblVJKqQ4raJrn10vKPQdOb/4+2MYuyj8R2+CioRqevRQ+uNXORwzXMhdKqaBp4BdAZV2jzvFTSimlOmFov0QyE2NY2FvW88v3Bn5J/bUUMRSyRttAb+t8W0Z7+t3hb3yjlGqXRjYBaKmnUkop1TkiwrTB6SzuLRm/rDEQm9Lzyzx7k6NusvP7pn/Xf5MLpVS308gmgKo6be6ilFJKdVbBkHTeXbmL4so6spPjIj2ctjmcdkHm5AGRHknfEZcCs/8U6VEopVrQUk8/3B5DdYObZA38lFJKqU4pGGLX8Os1yzqMOA5yxkV6FEopFTYa+PlR3eAC0FJPpZRSqpMmDEglNsrRexq8KKVUH6eBnx9VdTbw04yfUkop1TkxUQ4m5adp4KeUUj2EBn5+VNX7Mn66nINSSinVWQVD0lmxvZzaBnekh6KUUgc8Dfz8qPRm/LS5i1JKKdV5BUPScXkMn68vifRQlFLqgKeBnx/NGT8N/JRSSqnOmjmiH/np8fzjvbV4PCbSw1FKqQOaBn5+6Bw/pZRSqutio5z8/MTRrNxRwcvfbI/0cJRS6oCmgZ8fVfWNgGb8lFJKqa46beIAJuan8rd311DXqHP9lFIqUjTw80Pn+CmllFKh4XAIvzp5LDvK6/jPZ5siPRyllDpgaeDnh2+OX2KMBn5KKaVUVx0yLJPjxuZw/0cbKK2qj/RwlFLqgKSBnx9VdS4SY5w4HRLpoSillFJ9ws0njaG20c2/PlgX6aEopdQBSQM/PyrrXFrmqZRSSoXQiOwk5kwfyJNfbmXj7qpID0cppQ44Gvj5UVXv0sYuSimlVIjdeNwoYqMc/OXt1ZEeilJKHXDCGviJyGwRWSMi60XkZj/P/1xElni/louIW0Qygtk3nCrrXSTFRXfnSyqllFJ9XlZyLNccNZx3Vuxi4eY9kR6OUkodUMIW+ImIE7gXOAkYB1woIuNabmOM+asxZrIxZjLwS+BjY8yeYPYNp6q6RlK01FMppVQEBPGh6cUistT7NV9EJkVinJ111RHDyEmJ5U9zV2GMLuqulFLdJZwZvxnAemPMRmNMA/AMcEYb218IPN3JfUNKSz2VUkpFQpAffG4CjjLGTAT+ADzUvaPsmvgYJz89fjTfbC1j7rKdkR6OUkodMMIZ+OUB21rcL/Q+th8RSQBmAy92Yt+rRWSRiCzavXt3lwcNtqunBn5KKaUioN0PPo0x840xe713vwDyu3mMXXbOtHzG9E/mL2+vpsHlifRwlFLqgBDOwM/fWgiBajpOAz43xvgK/oPe1xjzkDGmwBhTkJWV1Ylh7s/O8dPATymlVLcL+oNPr+8Cb/l7IhwfjIaK0yH88uSxbN1Tw+9eXa4ln0op1Q3CGfgVAgNb3M8HigJsO4fmMs+O7htSxhiq6l0ka8ZPKaVU9wv6g08ROQYb+N3k7/lwfDAaSkeNyuKHx4zgmYXbuO1Nne+nlFLhFs7oZiEwUkSGAtuxwd1FrTcSkVTgKOCSju4bDjUNboxBM35KKaUiIagPPkVkIvAwcJIxprSbxhZyPz1hFFX1Lh7+bBPJcdHccNzISA9JKaX6rLBFN8YYl4j8EHgHcAKPGGNWiMg13ucf8G56FvCuMaa6vX3DNdaWqupdACTF6nIOSimlul27H3yKyCDgJeBSY8za7h9i6IgIvzt1HJV1Lv75/lqS4qL47uFDIz0spZTqk8Ka1jLGzAXmtnrsgVb3/wv8N5h9u0NlnTfw04yfUkqpbhbkh6a/AzKB+0QEwGWMKYjUmLvK4RD+cs5B1DS4+MMbK0mKdXLB9EGRHpZSSvU5Gt204sv46Rw/pZRSkdDeh6bGmKuAq7p7XOEU5XRw55zJVD++mJtfWkZCTBSnTRoQ6WEppVSfEs7mLr1SlWb8lFJKqW4XG+XkwUumMX1wBj9+dgkfrt4V6SEppVSfooFfK5V1jQC6jp9SSinVzeJjnDx8eQFjc1O45omveWNptzT0VkqpA4IGfq1UNjV30cBPKaWU6m4pcdE8duUMxuam8MOnvuHHzy6hvLYx0sNSSqleTwO/Vnylnsla6qmUUkpFREZiDC9ccyg3zBrJa98WcdKdnzB/fUmkh6WUUr2aBn6tVGnGTymllIq4aKeDHx8/ihevPYy4aCcXPfwlt76+krpGd6SHppRSvZIGfq1U1buIj3YS5dRTo5RSSkXa5IFpvHn9EXzn0ME88vkmTrv7M5ZvL4/0sJRSqtfR6KaVyjqXdvRUSimlepD4GCe3njGBx66cQXltI2fe+zm3vr6S4sq6SA9NKaV6DQ38Wqmqd+kafkoppVQPdNSoLN658UjOnprHYws2c+Qd8/jjGyvZXVkf6aEppVSPp4FfK1V1jZrxU0oppXqo9MQY7jh3Eh/85ChOPiiXRz7fxJF3zOPPc1dRWqUBoFJKBaKBXytV9S5t7KKUUkr1cEP6JfKP8yfz3k+O4sTxOfz7040cccc8bn9rNXurGyI9PKWU6nE08Gulsk4DP6WUUqq3GJ6VxJ1zpvDuj4/iuLE5PPjJBo68Yx73zltPTYMr0sNTSqkeQwO/VqrqtbmLUkop1duMyE7iXxdO4Z0bj+TgYZn89Z01HP3Xj3jyyy00uj2RHp5SSkWcBn6taHMXpZRSqvcalZPMw5cV8MI1hzIoI4Ffv7ycE//5CXOX7cAYE+nhKaVUxGiE04IxhipdzkEppZTq9QqGZPD8NYfy/qpi7nh7Ndc9+TWT8lM5r2AgY/onM6p/Milx0ZEeplJKdRuNcFqoa/Tg8hiSYvVCoJRSSvV2IsLx43I4dkw2L31dyJ3vr+M3ryxvej4vLZ4x/ZMZ3T+ZMbkpjMtNYWi/RJwOCer4dY1uVu+spH9KHP1T48L1YyilVEho4NdCZX0jgGb8lFJKqT7E6RDOKxjIudPy2V5Wy5qdlaz2fq3ZWcHHa3fj8tgy0IQYJ+NyU5iQl8r4AfZ2RHYSbo9h1Y4Klm8vZ9n2cpZtr2DtrkrcHkN8tJM/njmBc6blR/gnVUqpwMIa4YjIbOAuwAk8bIy53c82RwN3AtFAiTHmKO/jm4FKwA24jDEF4RwrQFWd7f6lc/yUUkqpvkdEyE9PID89gVljc5oer3e52VBczUpvYLeiqJznFm2jpsENQEyUA7fH4PYGhxmJMUzIS+XYMVmMzU3h8QVb+Onz3/L5hhL+cMYEEvV9hFKqBwrbXyYRcQL3AscDhcBCEXnNGLOyxTZpwH3AbGPMVhHJbnWYY4wxJeEaY2tV9d7ATzN+Siml1AEjNsrJuAEpjBuQwrnerJ3bY9hcWu0NBCuIdgoH5aVyUH4aA1LjEGkuB509vj//+nA9d3+4jiXbyrj3oqmMzU2J1I+jlFJ+hTPCmQGsN8ZsBBCRZ4AzgJUttrkIeMkYsxXAGFMcxvG0y5fx03X8lFJKqQOb0yEMz0pieFYSZ0zOa3PbKKeDnxw/ikOGZnDDs0s4497PueW0cVw0Y9A+ASKAxxdQFlVQ1+Bm3IAURuYkERvlDOePQ6PbQ3FlPXlp8WF9HaVUzxXOCCcP2NbifiFwcKttRgHRIvIRkAzcZYx53PucAd4VEQM8aIx5KIxjBaDSm/HTOX5KKaWU6qjDRvTjrRuO4MfPLuHXLy9n/vpSrj16OGt3VbJ8ewXLi8pZWVTRVGHkE+0URmYnMyEvhfEDUpmQl8LY3BQSYrr+fmRTSTXPLtzGC4sLKa2u59cnj+W7hw/dLyBVSvV94Yxw/P1Fab2AThQwDZgFxAMLROQLY8xaYKYxpshb/vmeiKw2xnyy34uIXA1cDTBo0KAuDbh5jp929VRKKaVUx/VLiuWxK2bwwCcb+Pu7a3lz2Q4A4qIdjMtN4eypeUwYkMr4PBvYrSyyAeHy7eW8v6qY5xYVAhDlECbmp3Lo8EwOHdaPaYPTiY8JLitY1+jmnRU7efqrrXyxcQ9Oh3DsmGw8HsMf31xF4d5afnvquKC7lyql+oZwBn6FwMAW9/OBIj/blBhjqoFqEfkEmASsNcYUgS3/FJGXsaWj+wV+3kzgQwAFBQVdWpm1SjN+SimllOoih0O47ugRHDUqi3W7qhg/IIVhWUl+A62h/RI5ZWIuYNcT3llRx4rtFSzeupcvNpbywMcbuXfeBqKdwuSBaRw6LJNxA1IBQ6PbNpxpdHvsrcewobiKV5Zsp6ymkYEZ8fz8xNGcOy2fnJQ4PB7Dn+au4uHPNrG9rJZ/zZkSdDDpj8dj2FFRR25KHA4NIpXq8cIZ4SwERorIUGA7MAc7p6+lV4F7RCQKiMGWgv5TRBIBhzGm0vv9CcCtYRwr0Bz4JcaGt85eKaWUUn3f+AGpjB+QGvT2IkJuajy5qfEcN852Ha2qd7Fw8x6+2FDKgo2l3DNvPZ42PuaOdgonju/PnOmDOGx45j4BmcMh/ObUceSlx3PrGyuZ8+8v+M9lBfRLig1qfMYYNpZUM399CfO94ymraSQ7OZZZY3M4YXwOhw3PDGq+osdjNFhUqpuFLfAzxrhE5IfAO9jlHB4xxqwQkWu8zz9gjFklIm8DSwEPdsmH5SIyDHjZW38eBTxljHk7XGP1qaxzERPlCPsEa6WUUkqpYCTFRnHM6GyOGW0bn1fUNbKlpAanQ4hyClEOIdrpaLqfFBvV7tzAK2YOZUBaPDc88w1n3fc5/71iBsOzkvbbrtHtYX1xFcsKy1mwsZT5G0rYVVEPQF5aPMePzWHcgBQWbd7La0u28/RXW0mMcXLU6CyOH5fDUaOyqa53sbGkmk27q9hUUs2m0ho2lVSxfW8t2clxjMlNZnT/ZMb2T2F0/2SGZyURE+UI/YkMwJdlTYqNIjlOp/qovk2M6VJ1ZI9SUFBgFi1a1On9f/PKMt5atpPFvz0+hKNSSikVaiKyuDvWd+0runp9VH3TN1v3ctVji3Abw11zphDtFFbtqGRlUQWrdlSwvriKBrcHgMzEGA4dnsnMEf04bHgmgzIS9mkQU+9yM39DKe+t3MX7K3dRXFm/3+slxUYxLCuRof0SyUuLZ2d5Hat2VrKhxetEebupjsxJYmR2MiOy7fdDMhPbDQgbXB4cYrusBlJcWceywnK+LSxnWWEZy7aXU1LVQGyUg1MOymXOjEFMH5Le5eY3bo+husGFAA4RREDw3gpEOxwdzni63B7qXB7tPh9CNQ0uKutcZCXF9qkMdKBrpP7mtFBV59L5fUoppZQ6IEwZlM5L1x3G5Y8u5LJHvmp6vF9SLOMGpHDEqH6My01h/IAUhmcltRkMxUY5mzKTfzxjAt8WlrFgYymZiTEM7ZfE0H6J9EuK8XuMRreHTSXVrN5ZyeodFazZWcnSwnLeXLYDX37C6RAGZyQwPDsJpwiV9Y1U1rm8X41U1LlocNngMcbpID7GSXy0k4QYZ9P3hXtr2VlRB4BDYGR2MseMzmZCXiprd1Xy6pIiXvpmO8OyEpkzfSDnTM0nM0AZrMvtoaSqge1ltRTurWHbnhoK99ayba+9LSqrpdEdOLkSG+VgRHYSo3OSGdU/mVE5SYzKSSYvLR4Roby2kVU7Klp8VbJ2VyUNbg9TBqZx3Lgcjh+bw4jstv9dWiuraWDD7mo27q5qui2tbuDIkVmcOWUAgzMTgz6WP7UNbtYVV7JxdzUjspMYl5vSIwMqj8fw/OJt3P7WavbWNBIX7WBQRgKDMhIZnJnA4MwEBmUkkJUcS4zTQbTTQXSUg2inNN2Pj3b2yJ+tLZrxa+Gqxxayo7yON68/IoSjUkopFWqa8esYzfiptuytbuDtFTvJS4tnbG4KWcnBzfkLt9oGNxtLqlhfXMW6XfZ2w+4qAJLjbGmm7zYlLoqk2CgMUNPgprbBRU2Dm5pGN3UNbmoa3GSnxDIxP42J+amMH7D/chk1DS7eXLqDZxZuY/GWvUQ7hRPG9WfcgBR2VdSxs7zO3lbUsbuyfr+5lv2SYshPT2BgRgID0+PJSIzBGDAYPIam742x53xtcRXrdlWyo7yu6Ri25DRqn8cyE2MYm5vCmP7JJMQ4mbdmN8u2lwMwODOB48bmcNzYHKYNTqe8tpEd5bXsKLfjLSqvZWd5Hdv31rKxpJo91Q1Nx412CoMyEkiKi2ZpYRnGwJRBaZw1JY9TDsoNGPSCDXx3lNexZmclq3ZUsHpnJat2VrC5pHqf85KeEM1hw/sxc0Q/Dh/Rj0GZCU3PGWMo3FvLkm1lLC0s49tt5awoKic5LpoR2UlN2d6R2cmMzE4iPTEGYwzltY1s2+MNuL2B9va9tQzPTuKiGYMY0q/t4HX1zgp+8/JyFm3Zy/Qh6ZxyUC6Fe2vZsqeGraU1bNlTTV2jp81jAKTERXHU6GxmjcnmqFFZpCfGBNy2pKqeBRtKmb+hlD3V9cwaY+fEpiUE3qcrAl0jNfBr4fwHFyDAs98/NHSDUkopFXIa+HWMBn5KdczaXZU8u3AbL31dyN6aRlLiouifGkdOShz9U+Kavh+QFsfA9ATy0uM7ve5ieW0j63ZVsmZXJet2VVFe28ionGTG5iYzzhuIt87q7Siv5YNVxby/ahfz15c2lcq2Fu0U+qfGkZsaz7B+iQzPSmJYViLDspIYmB7fVBZbVFbLa98W8co321m9s5Ioh3DUqCxOnNCfepeHojKbxdzuzWburKjbJ8AblJHA2NxkxvRPYWxuMkP6JbJ6RyWfrS/hs3UlTZnWgRnxHDw0k5KqepYWljcFojFRDsYPSOGgvFSq6l2sL7aBfk2Du+k1MhJjaHB59lsHMyUuitzUeDbsrsLlMRwxsh+XHjKYY8dk71P2W13v4s731/LI55tJiYvilyeP5dyp+ftl7Ywx7K6sZ8ueGkqrGnB5PDS6PTS6DA1u+32Dy85/nbemmJKqBhwCBYMzOHasDQRzUuP4auMePt9QwoINpazeWQlAsjewLyqvI8ohHDo8k1MOyuWE8f3JaCNw7CgN/IJw8l2fMiAtjocvmx7CUSmllAq1vhz4ichs4C5sY7SHjTG3t3pevM+fDNQAlxtjvm7rmBr4KdU5jd43+p0N6rpDdb2LT9ftZkVRBVnJsfRPsYFeblocGQkxHS5HXLWjgleWbOfVb4qaArYoh5CbFseA1Hjy0uPJS7NfI3Nsc5625h36usF+7g0CF27eQ3ZyHJMGpjIxP43JA9MYlZO83xxO33Ih63ZVNmV7Y6Oc5KfHMzAjgfz0ePLTE0iNt015iivqeGbhNp7+ais7yuvITY3johmDuGDGQL7eUsb/vb6CHeV1zJk+kJtmj2kzQxcsj8ewdHs5H6zaxQerilm5o2Kf52OjHEwfktE0P3bCgBScDmHZ9nLmLtvJ3GU72LrHNms6ZFgGJx+Uy1lT8rr8+6aBXxCOvGMeUwelceecKSEclVJKqVDrq4GfiDiBtcDx2LVuFwIXGmNWttjmZOBH2MDvYOAuY8zBbR1XAz+lVEe5PYb1xVWkxkeTlRzrdx3Knsjl9vDB6mKe+GILn64rwSHgMTCmfzK3nTWBaYMzwvbaRWW1fLi6mNKqBg4elsGUQWltrhZgjGFFUQVvLd/B3GU7KSqr5evfHk9iFxv4aHOXIBwxsh9jclMiPQyllFIHrhnAemPMRgAReQY4A1jZYpszgMeN/eT2CxFJE5FcY8yO7h+uUqqvcjqE0f2TIz2MDotyOjhxfH9OHN+fTSXVvLB4GzkpNvvXVsfXUBiQFs8lhwwOensRYUJeKhPyUvnZCaPZXlbb5aCvLRr4tXDbWQdFeghKKaUObHnAthb3C7FZvfa2yQP2CfxE5GrgaoBBgwaFfKBKKdXTDe2XyM9PHBPpYQRFRMhPT2h/wy7ovhUylVJKKdUef7VUredkBLMNxpiHjDEFxpiCrKyskAxOKaVU76WBn1JKKdVzFAIDW9zPB4o6sY1SSim1Dw38lFJKqZ5jITBSRIaKSAwwB3it1TavAd8R6xCgXOf3KaWUao/O8VNKKaV6CGOMS0R+CLyDXc7hEWPMChG5xvv8A8BcbEfP9djlHK6I1HiVUkr1Hhr4KaWUUj2IMWYuNrhr+dgDLb43wA+6e1xKKaV6Ny31VEoppZRSSqk+TgM/pZRSSimllOrjNPBTSimllFJKqT5O7FSBvkFEdgNbuniYfkBJCIbT1+h5CUzPTWB6bvzT8xJYsOdmsDFGF6cLkl4fw07PTWB6bvzT8xKYnhv/OnJe/F4j+1TgFwoissgYUxDpcfQ0el4C03MTmJ4b//S8BKbnpufSf5vA9NwEpufGPz0vgem58S8U50VLPZVSSimllFKqj9PATymllFJKKaX6OA389vdQpAfQQ+l5CUzPTWB6bvzT8xKYnpueS/9tAtNzE5ieG//0vASm58a/Lp8XneOnlFJKKaWUUn2cZvyUUkoppZRSqo/TwE8ppZRSSiml+jgN/LxEZLaIrBGR9SJyc6THE0ki8oiIFIvI8haPZYjIeyKyznubHskxRoKIDBSReSKySkRWiMgN3sf13IjEichXIvKt99z8n/fxA/7cAIiIU0S+EZE3vPf1vAAisllElonIEhFZ5H1Mz00PpNfIZnqN9E+vkYHpNbJteo30LxzXSA38sL9wwL3AScA44EIRGRfZUUXUf4HZrR67GfjAGDMS+MB7/0DjAn5qjBkLHAL8wPt7oucG6oFjjTGTgMnAbBE5BD03PjcAq1rc1/PS7BhjzOQWaxPpuelh9Bq5n/+i10h/9BoZmF4j26bXyMBCeo3UwM+aAaw3xmw0xjQAzwBnRHhMEWOM+QTY0+rhM4DHvN8/BpzZnWPqCYwxO4wxX3u/r8T+kcpDzw3GqvLejfZ+GfTcICL5wCnAwy0ePuDPSxv03PQ8eo1sQa+R/uk1MjC9Rgam18gO69K50cDPygO2tbhf6H1MNcsxxuwA+8cdyI7weCJKRIYAU4Av0XMDNJVqLAGKgfeMMXpurDuBXwCeFo/pebEM8K6ILBaRq72P6bnpefQa2T79vW1Br5H702tkQHei18hAQn6NjArxAHsr8fOYrnOh/BKRJOBF4EZjTIWIv1+fA48xxg1MFpE04GURmRDhIUWciJwKFBtjFovI0REeTk800xhTJCLZwHsisjrSA1J+6TVSBU2vkf7pNXJ/eo1sV8ivkZrxswqBgS3u5wNFERpLT7VLRHIBvLfFER5PRIhINPaC9qQx5iXvw3puWjDGlAEfYefAHOjnZiZwuohsxpbHHSsiT6DnBQBjTJH3thh4GVtSqOem59FrZPv09xa9RgZDr5H70GtkG8JxjdTAz1oIjBSRoSISA8wBXovwmHqa14DLvN9fBrwawbFEhNiPLf8DrDLG/KPFU3puRLK8n2IiIvHAccBqDvBzY4z5pTEm3xgzBPt35UNjzCUc4OcFQEQSRSTZ9z1wArAcPTc9kV4j23fA/97qNTIwvUb6p9fIwMJ1jRRjtFoDQEROxtYZO4FHjDG3RXZEkSMiTwNHA/2AXcAtwCvAc8AgYCtwnjGm9eT2Pk1EDgc+BZbRXIv+K+wchgP93EzETjJ2Yj9Qes4Yc6uIZHKAnxsfbxnLz4wxp+p5AREZhv0EE+y0g6eMMbfpuemZ9BrZTK+R/uk1MjC9RrZPr5H7Ctc1UgM/pZRSSimllOrjtNRTKaWUUkoppfo4DfyUUkoppZRSqo/TwE8ppZRSSiml+jgN/JRSSimllFKqj9PATymllFJKKaX6OA38lOrjRORoEXkj0uNQSimlehq9RqoDiQZ+SimllFJKKdXHaeCnVA8hIpeIyFciskREHhQRp4hUicjfReRrEflARLK8204WkS9EZKmIvCwi6d7HR4jI+yLyrXef4d7DJ4nICyKyWkSeFBGJ2A+qlFJKdZBeI5XqOg38lOoBRGQscAEw0xgzGXADFwOJwNfGmKnAx8At3l0eB24yxkwElrV4/EngXmPMJOAwYIf38SnAjcA4YBgwM8w/klJKKRUSeo1UKjSiIj0ApRQAs4BpwELvB43xQDHgAZ71bvME8JKIpAJpxpiPvY8/BjwvIslAnjHmZQBjTB2A93hfGWMKvfeXAEOAz8L+UymllFJdp9dIpUJAAz+legYBHjPG/HKfB0V+22o7084xAqlv8b0b/b+vlFKq99BrpFIhoKWeSvUMHwDnikg2gIhkiMhg7P/Rc73bXAR8ZowpB/aKyBHexy8FPjbGVACFInKm9xixIpLQnT+EUkopFQZ6jVQqBPQTDaV6AGPMShH5DfCuiDiARuAHQDUwXkQWA+XYOQ4AlwEPeC9aG4ErvI9fCjwoIrd6j3FeN/4YSimlVMjpNVKp0BBj2sqKK6UiSUSqjDFJkR6HUkop1dPoNVKpjtFST6WUUkoppZTq4zTjp5RSSimllFJ9nGb8lFJKKaWUUqqP08BPKaWUUkoppfo4DfyUUkoppZRSqo/TwE8ppZRSSiml+jgN/JRSSimllFKqj/t/RL7GMspa6BAAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 0 Axes>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 4))\n",
    "plt.subplot(121),\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()\n",
    "plt.savefig(\"cnn_plot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('./log/50epoch.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('./log/50epoch.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%tensorboard --logdir log2/tensorboard --host localhost --port 8888"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "70/70 [==============================] - 49s 600ms/step - loss: 4.5830 - precision: 0.4817 - recall: 0.4626 - categorical_accuracy: 0.4698\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_prec, test_rec, test_accuracy = model.evaluate(testData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}