transfer_learning_part_2_fine_tuning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y5mjdQGiFXPe",
    "outputId": "607b6500-0211-49a4-cc73-36835279ac9f",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Jun 17 21:22:24 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 516.40       Driver Version: 516.40       CUDA Version: 11.7     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA GeForce ... WDDM  | 00000000:01:00.0  On |                  N/A |\n",
      "| N/A   55C    P8    12W /  N/A |    544MiB /  6144MiB |      2%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|    0   N/A  N/A      5108    C+G   ...artMenuExperienceHost.exe    N/A      |\n",
      "|    0   N/A  N/A      6744    C+G   ...6bftszj\\TranslucentTB.exe    N/A      |\n",
      "|    0   N/A  N/A      7604    C+G   ...t\\Teams\\current\\Teams.exe    N/A      |\n",
      "|    0   N/A  N/A      9504    C+G   ...werToys.ColorPickerUI.exe    N/A      |\n",
      "|    0   N/A  N/A     10848    C+G   ...ows\\System32\\prevhost.exe    N/A      |\n",
      "|    0   N/A  N/A     11512    C+G   ...2txyewy\\TextInputHost.exe    N/A      |\n",
      "|    0   N/A  N/A     14952    C+G   C:\\Windows\\explorer.exe         N/A      |\n",
      "|    0   N/A  N/A     16768    C+G   ...n1h2txyewy\\SearchHost.exe    N/A      |\n",
      "|    0   N/A  N/A     16956    C+G   ...erver\\YourPhoneServer.exe    N/A      |\n",
      "|    0   N/A  N/A     17200    C+G   ...ekyb3d8bbwe\\onenoteim.exe    N/A      |\n",
      "|    0   N/A  N/A     18124    C+G   ...cw5n1h2txyewy\\LockApp.exe    N/A      |\n",
      "|    0   N/A  N/A     18736    C+G   ...y\\ShellExperienceHost.exe    N/A      |\n",
      "|    0   N/A  N/A     18876    C+G   ...8bbwe\\WindowsTerminal.exe    N/A      |\n",
      "|    0   N/A  N/A     20436    C+G   ...dows\\System32\\dllhost.exe    N/A      |\n",
      "|    0   N/A  N/A     24820    C+G   ...210.53\\msedgewebview2.exe    N/A      |\n",
      "|    0   N/A  N/A     25968    C+G   ...ekyb3d8bbwe\\HxOutlook.exe    N/A      |\n",
      "|    0   N/A  N/A     27180    C+G   ...me\\Application\\chrome.exe    N/A      |\n",
      "|    0   N/A  N/A     28644    C+G   ...4vj0pshhgkwm\\Telegram.exe    N/A      |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xvFtBgmBYIqz",
    "outputId": "bd0638cf-7639-4e3d-ed35-600801a6383a",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved under helper_functions.py\n"
     ]
    }
   ],
   "source": [
    "!python -m wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Hik1UGRMYo04",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from helper_functions import create_tensorboard_callback, plot_loss_curves, unzip_data, walk_through_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mvNkObEMZftP",
    "jp-MarkdownHeadingCollapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "# Download the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E37-lw5Uu7mq",
    "outputId": "ea226c5b-cd0f-43cd-ae45-e714fdfc63c2",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved under 10_food_classes_10_percent.zip\n"
     ]
    }
   ],
   "source": [
    "# Get 10% of the data\n",
    "!python -m wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_10_percent.zip\n",
    "\n",
    "unzip_data(\"10_food_classes_10_percent.zip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cXxak0CLvdeQ",
    "outputId": "0656be91-583a-446c-f29d-b28558d1049f",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 directories and 0 images in '10_food_classes_10_percent'.\n",
      "There are 10 directories and 0 images in '10_food_classes_10_percent\\test'.\n",
      "There are 0 directories and 250 images in '10_food_classes_10_percent\\test\\chicken_curry'.\n",
      "There are 0 directories and 250 images in '10_food_classes_10_percent\\test\\chicken_wings'.\n",
      "There are 0 directories and 250 images in '10_food_classes_10_percent\\test\\fried_rice'.\n",
      "There are 0 directories and 250 images in '10_food_classes_10_percent\\test\\grilled_salmon'.\n",
      "There are 0 directories and 250 images in '10_food_classes_10_percent\\test\\hamburger'.\n",
      "There are 0 directories and 250 images in '10_food_classes_10_percent\\test\\ice_cream'.\n",
      "There are 0 directories and 250 images in '10_food_classes_10_percent\\test\\pizza'.\n",
      "There are 0 directories and 250 images in '10_food_classes_10_percent\\test\\ramen'.\n",
      "There are 0 directories and 250 images in '10_food_classes_10_percent\\test\\steak'.\n",
      "There are 0 directories and 250 images in '10_food_classes_10_percent\\test\\sushi'.\n",
      "There are 10 directories and 0 images in '10_food_classes_10_percent\\train'.\n",
      "There are 0 directories and 75 images in '10_food_classes_10_percent\\train\\chicken_curry'.\n",
      "There are 0 directories and 75 images in '10_food_classes_10_percent\\train\\chicken_wings'.\n",
      "There are 0 directories and 75 images in '10_food_classes_10_percent\\train\\fried_rice'.\n",
      "There are 0 directories and 75 images in '10_food_classes_10_percent\\train\\grilled_salmon'.\n",
      "There are 0 directories and 75 images in '10_food_classes_10_percent\\train\\hamburger'.\n",
      "There are 0 directories and 75 images in '10_food_classes_10_percent\\train\\ice_cream'.\n",
      "There are 0 directories and 75 images in '10_food_classes_10_percent\\train\\pizza'.\n",
      "There are 0 directories and 75 images in '10_food_classes_10_percent\\train\\ramen'.\n",
      "There are 0 directories and 75 images in '10_food_classes_10_percent\\train\\steak'.\n",
      "There are 0 directories and 75 images in '10_food_classes_10_percent\\train\\sushi'.\n"
     ]
    }
   ],
   "source": [
    "# Check images and subdirectories\n",
    "walk_through_dir(\"10_food_classes_10_percent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "u9fgsK1rvk8J",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Create training and test directories paths\n",
    "train_dir = \"10_food_classes_10_percent/train\"\n",
    "test_dir = \"10_food_classes_10_percent/test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d2D5v5DmwNPR",
    "outputId": "4e04a944-7a14-437e-ef07-0999aab7eb62",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 750 files belonging to 10 classes.\n",
      "Found 2500 files belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "IMG_SIZE = (224, 224)\n",
    "BATCH_SIZE = 32\n",
    "train_data_10_precent = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    directory=train_dir,\n",
    "    image_size=IMG_SIZE,\n",
    "    label_mode=\"categorical\",\n",
    "    batch_size=BATCH_SIZE\n",
    ")\n",
    "\n",
    "test_data = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    directory=test_dir,\n",
    "    image_size=(224, 224),\n",
    "    label_mode=\"categorical\",\n",
    "    batch_size=BATCH_SIZE\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XEpZhX3axecr",
    "outputId": "8c41346a-c8f6-46be-8e97-f8c4f949f22a",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BatchDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 10), dtype=tf.float32, name=None))>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_10_precent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oMl6txZxxh4i",
    "outputId": "f4858c52-c41e-40b8-f51e-d1aa5093ce1e",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['chicken_curry',\n",
       " 'chicken_wings',\n",
       " 'fried_rice',\n",
       " 'grilled_salmon',\n",
       " 'hamburger',\n",
       " 'ice_cream',\n",
       " 'pizza',\n",
       " 'ramen',\n",
       " 'steak',\n",
       " 'sushi']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out class names of dataset\n",
    "train_data_10_precent.class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[[[177.64287   161.64287   136.64287  ]\n",
      "   [179.38264   163.38264   138.38264  ]\n",
      "   [177.43367   161.43367   136.43367  ]\n",
      "   ...\n",
      "   [106.658035   88.44377    76.576485 ]\n",
      "   [116.57643    99.719315   92.719315 ]\n",
      "   [ 83.948425   70.361664   66.12193  ]]\n",
      "\n",
      "  [[175.18878   158.18878   132.18878  ]\n",
      "   [175.21428   158.21428   132.21428  ]\n",
      "   [173.21428   156.        130.42856  ]\n",
      "   ...\n",
      "   [189.14284   170.92857   158.50005  ]\n",
      "   [201.03064   184.17352   176.3113   ]\n",
      "   [178.9023    165.9023    159.9023   ]]\n",
      "\n",
      "  [[168.86224   151.43367   123.005104 ]\n",
      "   [167.9847    150.55612   122.12755  ]\n",
      "   [168.61734   150.97449   124.7602   ]\n",
      "   ...\n",
      "   [190.19397   172.40823   157.9797   ]\n",
      "   [195.41336   181.41336   170.52562  ]\n",
      "   [191.67818   178.67818   170.46388  ]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[140.92342   103.28062    14.494884 ]\n",
      "   [137.35712   100.35712    12.3571205]\n",
      "   [133.99998    95.99998    11.42855  ]\n",
      "   ...\n",
      "   [143.78574    64.142944   21.785736 ]\n",
      "   [145.80104    67.80104    21.801043 ]\n",
      "   [144.64282    66.64282    19.07135  ]]\n",
      "\n",
      "  [[142.95407   105.95407    16.954073 ]\n",
      "   [139.14285   102.14285    14.142857 ]\n",
      "   [134.77042    96.7704     12.1989765]\n",
      "   ...\n",
      "   [143.49991    63.857117   21.499908 ]\n",
      "   [144.         66.         20.       ]\n",
      "   [143.64282    65.64282    17.642822 ]]\n",
      "\n",
      "  [[142.71432   105.71432    16.714321 ]\n",
      "   [138.50003   102.90313    14.096941 ]\n",
      "   [133.86226    97.43368    12.076538 ]\n",
      "   ...\n",
      "   [139.56627    60.137733   17.137733 ]\n",
      "   [142.14288    64.14288    18.142883 ]\n",
      "   [144.35718    66.35718    18.357178 ]]]\n",
      "\n",
      "\n",
      " [[[ 64.015305   47.015305   17.015305 ]\n",
      "   [ 54.59694    37.59694     9.596938 ]\n",
      "   [ 48.         32.352043    6.581633 ]\n",
      "   ...\n",
      "   [ 83.21942    73.78576    69.27561  ]\n",
      "   [ 81.50507    75.25001    73.7194   ]\n",
      "   [ 77.203926   74.51519    76.91828  ]]\n",
      "\n",
      "  [[ 62.596935   43.596935   11.596938 ]\n",
      "   [ 55.07143    37.938774    7.9387755]\n",
      "   [ 48.459183   32.887756    6.8877554]\n",
      "   ...\n",
      "   [ 81.14776    74.14776    67.71923  ]\n",
      "   [ 80.98977    77.785675   73.07141  ]\n",
      "   [ 76.04591    74.97449    73.188774 ]]\n",
      "\n",
      "  [[ 66.72449    46.57143    11.142857 ]\n",
      "   [ 71.         51.928574   18.89796  ]\n",
      "   [ 59.5        44.714287   16.142857 ]\n",
      "   ...\n",
      "   [ 93.35655    89.57084    80.66267  ]\n",
      "   [ 75.77045    72.81637    65.37249  ]\n",
      "   [ 78.79076    78.79076    70.36218  ]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[162.        162.        164.       ]\n",
      "   [162.        162.        164.       ]\n",
      "   [162.95409   163.        165.       ]\n",
      "   ...\n",
      "   [122.        118.42853   107.045906 ]\n",
      "   [124.15813   120.15813   108.15813  ]\n",
      "   [122.49991   116.92844   104.92844  ]]\n",
      "\n",
      "  [[161.07144   159.07144   162.07144  ]\n",
      "   [160.07144   160.07144   162.07144  ]\n",
      "   [161.07144   161.07144   163.07144  ]\n",
      "   ...\n",
      "   [121.58678   117.80104   107.22957  ]\n",
      "   [127.005104  120.005104  110.005104 ]\n",
      "   [124.785706  117.785706  107.785706 ]]\n",
      "\n",
      "  [[161.28564   159.28564   162.28564  ]\n",
      "   [160.64282   160.64282   162.64282  ]\n",
      "   [161.28564   161.28564   163.28564  ]\n",
      "   ...\n",
      "   [123.290794  119.290794  110.290794 ]\n",
      "   [125.714264  118.714264  110.714264 ]\n",
      "   [126.64282   119.64282   111.64282  ]]]\n",
      "\n",
      "\n",
      " [[[  9.571428   10.928572   11.857143 ]\n",
      "   [ 16.882654   16.954082   26.596941 ]\n",
      "   [ 17.57143    16.         38.07653  ]\n",
      "   ...\n",
      "   [140.3928    143.3928    150.3928   ]\n",
      "   [110.32158   111.39302   115.03581  ]\n",
      "   [142.17867   142.17867   141.46431  ]]\n",
      "\n",
      "  [[ 10.928572   10.928572   12.214286 ]\n",
      "   [ 16.         15.         22.785715 ]\n",
      "   [ 16.301022   15.285714   30.015305 ]\n",
      "   ...\n",
      "   [119.15783   122.15783   127.6986   ]\n",
      "   [110.40827   111.413376  115.25518  ]\n",
      "   [130.63277   130.63277   129.91841  ]]\n",
      "\n",
      "  [[ 10.933674    8.290816   11.505102 ]\n",
      "   [ 13.142858   10.5        15.285714 ]\n",
      "   [ 13.52551    11.357142   16.17347  ]\n",
      "   ...\n",
      "   [123.0556    126.0556    131.48413  ]\n",
      "   [ 95.45409    96.45409   100.3112   ]\n",
      "   [ 97.20421    97.20421    97.20421  ]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[232.30672   231.2357    241.09276  ]\n",
      "   [232.42926   230.92972   240.57251  ]\n",
      "   [229.85771   225.57243   234.38358  ]\n",
      "   ...\n",
      "   [130.95409    87.045906   50.       ]\n",
      "   [127.515335   83.515335   46.51534  ]\n",
      "   [126.367325   82.367325   45.367325 ]]\n",
      "\n",
      "  [[232.50514   254.00511   249.29082  ]\n",
      "   [235.28064   255.        250.34184  ]\n",
      "   [232.72966   253.47452   246.54596  ]\n",
      "   ...\n",
      "   [130.85712    86.85712    49.857117 ]\n",
      "   [131.93365    87.93366    50.933662 ]\n",
      "   [128.40302    82.40301    46.40301  ]]\n",
      "\n",
      "  [[241.22969   253.4438    254.77042  ]\n",
      "   [241.8113    254.33667   255.       ]\n",
      "   [243.80104   253.86726   254.86226  ]\n",
      "   ...\n",
      "   [128.21426    84.214264   47.214264 ]\n",
      "   [132.66833    86.66834    50.66834  ]\n",
      "   [129.57129    83.57129    47.57129  ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   ...\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]]\n",
      "\n",
      "  [[  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   ...\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]]\n",
      "\n",
      "  [[  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   ...\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   ...\n",
      "   [  0.          0.          2.       ]\n",
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      "   [  0.          0.          2.       ]]\n",
      "\n",
      "  [[  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   ...\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]]\n",
      "\n",
      "  [[  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   ...\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]\n",
      "   [  0.          0.          2.       ]]]\n",
      "\n",
      "\n",
      " [[[192.12755   183.55612   159.60204  ]\n",
      "   [187.54593   168.5204    144.59183  ]\n",
      "   [147.7653    117.89796    93.83673  ]\n",
      "   ...\n",
      "   [197.86226   223.86226   248.86226  ]\n",
      "   [198.        224.        249.       ]\n",
      "   [197.87244   223.87244   248.87244  ]]\n",
      "\n",
      "  [[199.31122   184.31122   158.00511  ]\n",
      "   [176.69388   151.20407   124.2755   ]\n",
      "   [110.5051     75.17857    47.44898  ]\n",
      "   ...\n",
      "   [196.80104   222.80104   247.80104  ]\n",
      "   [198.        224.        249.       ]\n",
      "   [198.        224.        249.       ]]\n",
      "\n",
      "  [[179.16837   154.10204   122.2551   ]\n",
      "   [150.38776   117.0153     85.44386  ]\n",
      "   [136.30101    94.22959    61.632652 ]\n",
      "   ...\n",
      "   [197.        223.        248.       ]\n",
      "   [196.9847    222.9847    247.9847   ]\n",
      "   [195.78572   221.78572   246.78572  ]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[172.7908    189.36227   209.57654  ]\n",
      "   [174.0561    190.62758   210.84184  ]\n",
      "   [175.83163   192.4031    212.61737  ]\n",
      "   ...\n",
      "   [142.55098   171.78574   210.16835  ]\n",
      "   [141.21426   170.42853   210.       ]\n",
      "   [141.49492   170.70918   210.28065  ]]\n",
      "\n",
      "  [[173.73979   190.73979   210.73979  ]\n",
      "   [174.79082   191.79082   211.79082  ]\n",
      "   [174.92856   191.92856   211.92856  ]\n",
      "   ...\n",
      "   [141.84187   171.05614   210.62761  ]\n",
      "   [142.0051    171.0051    211.0051   ]\n",
      "   [138.7857    167.7857    207.7857   ]]\n",
      "\n",
      "  [[175.07143   192.07143   212.07143  ]\n",
      "   [174.52548   191.52548   211.52548  ]\n",
      "   [172.42854   189.42854   209.42854  ]\n",
      "   ...\n",
      "   [140.1479    169.36217   208.93364  ]\n",
      "   [141.9795    170.9795    210.9795   ]\n",
      "   [143.12758   172.12758   212.12758  ]]]\n",
      "\n",
      "\n",
      " [[[150.0153    154.68367   153.03061  ]\n",
      "   [158.19897   155.4847    146.69897  ]\n",
      "   [113.86734   108.36224    90.87755  ]\n",
      "   ...\n",
      "   [121.35185   115.994644   80.637436 ]\n",
      "   [119.90825   107.83681    67.6429   ]\n",
      "   [130.76016   115.2755     71.688705 ]]\n",
      "\n",
      "  [[ 89.82143    90.89285    85.39796  ]\n",
      "   [106.07141   104.219376   92.37243  ]\n",
      "   [ 50.08162    46.913254   29.969378 ]\n",
      "   ...\n",
      "   [110.83145   107.64259    72.87216  ]\n",
      "   [113.88263   104.464226   65.448906 ]\n",
      "   [119.566635  107.4952     66.63799  ]]\n",
      "\n",
      "  [[ 41.071426   36.642857   25.42857  ]\n",
      "   [ 42.811222   38.38265    25.954077 ]\n",
      "   [ 26.811224   23.239798   11.071429 ]\n",
      "   ...\n",
      "   [111.81114   109.45393    76.23967  ]\n",
      "   [110.673386  105.703964   67.84681  ]\n",
      "   [105.34713    98.27063    58.48992  ]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[ 18.214264   15.214264   10.214264 ]\n",
      "   [ 16.84186    14.270388    9.270388 ]\n",
      "   [ 16.16835    15.260178   10.42855  ]\n",
      "   ...\n",
      "   [ 33.004913   16.550974   11.550974 ]\n",
      "   [ 27.270336   18.41322    13.341779 ]\n",
      "   [ 27.352211   21.495094   15.418564 ]]\n",
      "\n",
      "  [[ 20.188745   17.188745   10.188745 ]\n",
      "   [ 18.923443   15.923442   10.923442 ]\n",
      "   [ 16.785675   15.785675   10.785675 ]\n",
      "   ...\n",
      "   [ 50.51018    37.11227    30.882698 ]\n",
      "   [ 36.63787    23.494987   17.56643  ]\n",
      "   [ 45.117275   30.831509   27.90295  ]]\n",
      "\n",
      "  [[ 19.785854   14.785853    8.785853 ]\n",
      "   [ 17.50014    14.500139    7.500139 ]\n",
      "   [ 14.428711   13.428711    8.428711 ]\n",
      "   ...\n",
      "   [ 37.16327    24.80606    18.163269 ]\n",
      "   [ 45.388046   29.17874    24.58184  ]\n",
      "   [ 42.566345   22.851992   19.20917  ]]]], shape=(32, 224, 224, 3), dtype=float32) tf.Tensor(\n",
      "[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]], shape=(32, 10), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# See the example of batch data\n",
    "for images, labels in train_data_10_precent.take(1):\n",
    "    print(images, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "twM9mZlXyJFg",
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "## Model 0: Functional API (Baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6nnl18iVMiFR",
    "outputId": "9d955964-1d69-4c0d-f178-43544d77907f",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape after passing inputs through base model:  (None, 7, 7, 1280)\n",
      "Shape after GlobalAveragePooling2D:  (None, 1280)\n"
     ]
    }
   ],
   "source": [
    "# Create model using tf.keras.applications\n",
    "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n",
    "\n",
    "# Freeze the base model\n",
    "base_model.trainable = False\n",
    "\n",
    "#Create inputs into model\n",
    "inputs = tf.keras.layers.Input(shape=(224, 224,3), name=\"input_layer\")\n",
    "\n",
    "# if using models like ResNet50V2, we need to normalize inputs\n",
    "# x = tf.keras.layers.experimental.preprocessing.Normalization(1./255)(inputs)\n",
    "\n",
    "# Pass the inputs to the base model\n",
    "x = base_model(inputs)\n",
    "print(\"Shape after passing inputs through base model: \", x.shape)\n",
    "\n",
    "# Average pool the outputs of the base model\n",
    "x = tf.keras.layers.GlobalAveragePooling2D(name=\"global_average_pooling_layer\")(x)\n",
    "print(\"Shape after GlobalAveragePooling2D: \", x.shape)\n",
    "\n",
    "# Create the output activation layer\n",
    "outputs = tf.keras.layers.Dense(10, activation=\"softmax\", name=\"output_layer\")(x)\n",
    "\n",
    "# Combine the inputs with the outputs into a model\n",
    "model_0 = tf.keras.Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "xE_15zF9OvE7",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model_0.compile(\n",
    "    loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "    optimizer=tf.keras.optimizers.Adam(),\n",
    "    metrics=[\"accuracy\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4kq_vd3LPHhQ",
    "outputId": "85a78c35-7362-450e-e49d-f0aa4ebedb65",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving TensorBoard log files to: transfer_learning/10_percent_feature_extraction/20220616-203928\n",
      "Epoch 1/5\n",
      "24/24 [==============================] - 15s 184ms/step - loss: 1.8267 - accuracy: 0.4600 - val_loss: 1.2925 - val_accuracy: 0.7303\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - 3s 107ms/step - loss: 1.0698 - accuracy: 0.7653 - val_loss: 0.8872 - val_accuracy: 0.8207\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - 3s 107ms/step - loss: 0.7610 - accuracy: 0.8307 - val_loss: 0.7097 - val_accuracy: 0.8405\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - 3s 107ms/step - loss: 0.6310 - accuracy: 0.8547 - val_loss: 0.6397 - val_accuracy: 0.8438\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - 3s 107ms/step - loss: 0.5344 - accuracy: 0.8813 - val_loss: 0.5869 - val_accuracy: 0.8536\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "history_0 = model_0.fit(\n",
    "    train_data_10_precent,\n",
    "    epochs=5,\n",
    "    steps_per_epoch=len(train_data_10_precent),\n",
    "    validation_data=test_data,\n",
    "    validation_steps=int(0.25 * len(test_data)),\n",
    "    callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\", \n",
    "                                           experiment_name=\"10_percent_feature_extraction\")]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pjiorvv9P3ZC",
    "outputId": "9d668652-775b-45db-d84e-c1c986cd2ab9",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_layer (InputLayer)    [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " efficientnetb0 (Functional)  (None, None, None, 1280)  4049571  \n",
      "                                                                 \n",
      " global_average_pooling_laye  (None, 1280)             0         \n",
      " r (GlobalAveragePooling2D)                                      \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 10)                12810     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,062,381\n",
      "Trainable params: 12,810\n",
      "Non-trainable params: 4,049,571\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_0.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "id": "3PtYSaIvQkcJ",
    "outputId": "0c28c382-691b-44de-870d-990ea080d848",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss_curves(history_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "riqmXTccQudg",
    "outputId": "aade38de-228f-4522-bfb7-94bf073251fe",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 5s 57ms/step - loss: 0.5606 - accuracy: 0.8588\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5605648756027222, 0.8587999939918518]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_0.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y-T1AKxiQz2g",
    "outputId": "7cbdeae7-2ea3-402e-8c51-f954040a32ca",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"efficientnetb0\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, None, None,  0           []                               \n",
      "                                 3)]                                                              \n",
      "                                                                                                  \n",
      " rescaling (Rescaling)          (None, None, None,   0           ['input_1[0][0]']                \n",
      "                                3)                                                                \n",
      "                                                                                                  \n",
      " normalization (Normalization)  (None, None, None,   7           ['rescaling[0][0]']              \n",
      "                                3)                                                                \n",
      "                                                                                                  \n",
      " tf.math.truediv (TFOpLambda)   (None, None, None,   0           ['normalization[0][0]']          \n",
      "                                3)                                                                \n",
      "                                                                                                  \n",
      " stem_conv_pad (ZeroPadding2D)  (None, None, None,   0           ['tf.math.truediv[0][0]']        \n",
      "                                3)                                                                \n",
      "                                                                                                  \n",
      " stem_conv (Conv2D)             (None, None, None,   864         ['stem_conv_pad[0][0]']          \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " stem_bn (BatchNormalization)   (None, None, None,   128         ['stem_conv[0][0]']              \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " stem_activation (Activation)   (None, None, None,   0           ['stem_bn[0][0]']                \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " block1a_dwconv (DepthwiseConv2  (None, None, None,   288        ['stem_activation[0][0]']        \n",
      " D)                             32)                                                               \n",
      "                                                                                                  \n",
      " block1a_bn (BatchNormalization  (None, None, None,   128        ['block1a_dwconv[0][0]']         \n",
      " )                              32)                                                               \n",
      "                                                                                                  \n",
      " block1a_activation (Activation  (None, None, None,   0          ['block1a_bn[0][0]']             \n",
      " )                              32)                                                               \n",
      "                                                                                                  \n",
      " block1a_se_squeeze (GlobalAver  (None, 32)          0           ['block1a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block1a_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block1a_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block1a_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block1a_se_excite (Multiply)   (None, None, None,   0           ['block1a_activation[0][0]',     \n",
      "                                32)                               'block1a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block1a_project_conv (Conv2D)  (None, None, None,   512         ['block1a_se_excite[0][0]']      \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " block1a_project_bn (BatchNorma  (None, None, None,   64         ['block1a_project_conv[0][0]']   \n",
      " lization)                      16)                                                               \n",
      "                                                                                                  \n",
      " block2a_expand_conv (Conv2D)   (None, None, None,   1536        ['block1a_project_bn[0][0]']     \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " block2a_expand_bn (BatchNormal  (None, None, None,   384        ['block2a_expand_conv[0][0]']    \n",
      " ization)                       96)                                                               \n",
      "                                                                                                  \n",
      " block2a_expand_activation (Act  (None, None, None,   0          ['block2a_expand_bn[0][0]']      \n",
      " ivation)                       96)                                                               \n",
      "                                                                                                  \n",
      " block2a_dwconv_pad (ZeroPaddin  (None, None, None,   0          ['block2a_expand_activation[0][0]\n",
      " g2D)                           96)                              ']                               \n",
      "                                                                                                  \n",
      " block2a_dwconv (DepthwiseConv2  (None, None, None,   864        ['block2a_dwconv_pad[0][0]']     \n",
      " D)                             96)                                                               \n",
      "                                                                                                  \n",
      " block2a_bn (BatchNormalization  (None, None, None,   384        ['block2a_dwconv[0][0]']         \n",
      " )                              96)                                                               \n",
      "                                                                                                  \n",
      " block2a_activation (Activation  (None, None, None,   0          ['block2a_bn[0][0]']             \n",
      " )                              96)                                                               \n",
      "                                                                                                  \n",
      " block2a_se_squeeze (GlobalAver  (None, 96)          0           ['block2a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2a_se_reshape (Reshape)   (None, 1, 1, 96)     0           ['block2a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2a_se_reduce (Conv2D)     (None, 1, 1, 4)      388         ['block2a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2a_se_expand (Conv2D)     (None, 1, 1, 96)     480         ['block2a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2a_se_excite (Multiply)   (None, None, None,   0           ['block2a_activation[0][0]',     \n",
      "                                96)                               'block2a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2a_project_conv (Conv2D)  (None, None, None,   2304        ['block2a_se_excite[0][0]']      \n",
      "                                24)                                                               \n",
      "                                                                                                  \n",
      " block2a_project_bn (BatchNorma  (None, None, None,   96         ['block2a_project_conv[0][0]']   \n",
      " lization)                      24)                                                               \n",
      "                                                                                                  \n",
      " block2b_expand_conv (Conv2D)   (None, None, None,   3456        ['block2a_project_bn[0][0]']     \n",
      "                                144)                                                              \n",
      "                                                                                                  \n",
      " block2b_expand_bn (BatchNormal  (None, None, None,   576        ['block2b_expand_conv[0][0]']    \n",
      " ization)                       144)                                                              \n",
      "                                                                                                  \n",
      " block2b_expand_activation (Act  (None, None, None,   0          ['block2b_expand_bn[0][0]']      \n",
      " ivation)                       144)                                                              \n",
      "                                                                                                  \n",
      " block2b_dwconv (DepthwiseConv2  (None, None, None,   1296       ['block2b_expand_activation[0][0]\n",
      " D)                             144)                             ']                               \n",
      "                                                                                                  \n",
      " block2b_bn (BatchNormalization  (None, None, None,   576        ['block2b_dwconv[0][0]']         \n",
      " )                              144)                                                              \n",
      "                                                                                                  \n",
      " block2b_activation (Activation  (None, None, None,   0          ['block2b_bn[0][0]']             \n",
      " )                              144)                                                              \n",
      "                                                                                                  \n",
      " block2b_se_squeeze (GlobalAver  (None, 144)         0           ['block2b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2b_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2b_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2b_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2b_se_excite (Multiply)   (None, None, None,   0           ['block2b_activation[0][0]',     \n",
      "                                144)                              'block2b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2b_project_conv (Conv2D)  (None, None, None,   3456        ['block2b_se_excite[0][0]']      \n",
      "                                24)                                                               \n",
      "                                                                                                  \n",
      " block2b_project_bn (BatchNorma  (None, None, None,   96         ['block2b_project_conv[0][0]']   \n",
      " lization)                      24)                                                               \n",
      "                                                                                                  \n",
      " block2b_drop (Dropout)         (None, None, None,   0           ['block2b_project_bn[0][0]']     \n",
      "                                24)                                                               \n",
      "                                                                                                  \n",
      " block2b_add (Add)              (None, None, None,   0           ['block2b_drop[0][0]',           \n",
      "                                24)                               'block2a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3a_expand_conv (Conv2D)   (None, None, None,   3456        ['block2b_add[0][0]']            \n",
      "                                144)                                                              \n",
      "                                                                                                  \n",
      " block3a_expand_bn (BatchNormal  (None, None, None,   576        ['block3a_expand_conv[0][0]']    \n",
      " ization)                       144)                                                              \n",
      "                                                                                                  \n",
      " block3a_expand_activation (Act  (None, None, None,   0          ['block3a_expand_bn[0][0]']      \n",
      " ivation)                       144)                                                              \n",
      "                                                                                                  \n",
      " block3a_dwconv_pad (ZeroPaddin  (None, None, None,   0          ['block3a_expand_activation[0][0]\n",
      " g2D)                           144)                             ']                               \n",
      "                                                                                                  \n",
      " block3a_dwconv (DepthwiseConv2  (None, None, None,   3600       ['block3a_dwconv_pad[0][0]']     \n",
      " D)                             144)                                                              \n",
      "                                                                                                  \n",
      " block3a_bn (BatchNormalization  (None, None, None,   576        ['block3a_dwconv[0][0]']         \n",
      " )                              144)                                                              \n",
      "                                                                                                  \n",
      " block3a_activation (Activation  (None, None, None,   0          ['block3a_bn[0][0]']             \n",
      " )                              144)                                                              \n",
      "                                                                                                  \n",
      " block3a_se_squeeze (GlobalAver  (None, 144)         0           ['block3a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block3a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block3a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block3a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3a_se_excite (Multiply)   (None, None, None,   0           ['block3a_activation[0][0]',     \n",
      "                                144)                              'block3a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3a_project_conv (Conv2D)  (None, None, None,   5760        ['block3a_se_excite[0][0]']      \n",
      "                                40)                                                               \n",
      "                                                                                                  \n",
      " block3a_project_bn (BatchNorma  (None, None, None,   160        ['block3a_project_conv[0][0]']   \n",
      " lization)                      40)                                                               \n",
      "                                                                                                  \n",
      " block3b_expand_conv (Conv2D)   (None, None, None,   9600        ['block3a_project_bn[0][0]']     \n",
      "                                240)                                                              \n",
      "                                                                                                  \n",
      " block3b_expand_bn (BatchNormal  (None, None, None,   960        ['block3b_expand_conv[0][0]']    \n",
      " ization)                       240)                                                              \n",
      "                                                                                                  \n",
      " block3b_expand_activation (Act  (None, None, None,   0          ['block3b_expand_bn[0][0]']      \n",
      " ivation)                       240)                                                              \n",
      "                                                                                                  \n",
      " block3b_dwconv (DepthwiseConv2  (None, None, None,   6000       ['block3b_expand_activation[0][0]\n",
      " D)                             240)                             ']                               \n",
      "                                                                                                  \n",
      " block3b_bn (BatchNormalization  (None, None, None,   960        ['block3b_dwconv[0][0]']         \n",
      " )                              240)                                                              \n",
      "                                                                                                  \n",
      " block3b_activation (Activation  (None, None, None,   0          ['block3b_bn[0][0]']             \n",
      " )                              240)                                                              \n",
      "                                                                                                  \n",
      " block3b_se_squeeze (GlobalAver  (None, 240)         0           ['block3b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3b_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block3b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3b_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block3b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3b_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block3b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3b_se_excite (Multiply)   (None, None, None,   0           ['block3b_activation[0][0]',     \n",
      "                                240)                              'block3b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3b_project_conv (Conv2D)  (None, None, None,   9600        ['block3b_se_excite[0][0]']      \n",
      "                                40)                                                               \n",
      "                                                                                                  \n",
      " block3b_project_bn (BatchNorma  (None, None, None,   160        ['block3b_project_conv[0][0]']   \n",
      " lization)                      40)                                                               \n",
      "                                                                                                  \n",
      " block3b_drop (Dropout)         (None, None, None,   0           ['block3b_project_bn[0][0]']     \n",
      "                                40)                                                               \n",
      "                                                                                                  \n",
      " block3b_add (Add)              (None, None, None,   0           ['block3b_drop[0][0]',           \n",
      "                                40)                               'block3a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4a_expand_conv (Conv2D)   (None, None, None,   9600        ['block3b_add[0][0]']            \n",
      "                                240)                                                              \n",
      "                                                                                                  \n",
      " block4a_expand_bn (BatchNormal  (None, None, None,   960        ['block4a_expand_conv[0][0]']    \n",
      " ization)                       240)                                                              \n",
      "                                                                                                  \n",
      " block4a_expand_activation (Act  (None, None, None,   0          ['block4a_expand_bn[0][0]']      \n",
      " ivation)                       240)                                                              \n",
      "                                                                                                  \n",
      " block4a_dwconv_pad (ZeroPaddin  (None, None, None,   0          ['block4a_expand_activation[0][0]\n",
      " g2D)                           240)                             ']                               \n",
      "                                                                                                  \n",
      " block4a_dwconv (DepthwiseConv2  (None, None, None,   2160       ['block4a_dwconv_pad[0][0]']     \n",
      " D)                             240)                                                              \n",
      "                                                                                                  \n",
      " block4a_bn (BatchNormalization  (None, None, None,   960        ['block4a_dwconv[0][0]']         \n",
      " )                              240)                                                              \n",
      "                                                                                                  \n",
      " block4a_activation (Activation  (None, None, None,   0          ['block4a_bn[0][0]']             \n",
      " )                              240)                                                              \n",
      "                                                                                                  \n",
      " block4a_se_squeeze (GlobalAver  (None, 240)         0           ['block4a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4a_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block4a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4a_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block4a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4a_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block4a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_se_excite (Multiply)   (None, None, None,   0           ['block4a_activation[0][0]',     \n",
      "                                240)                              'block4a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_project_conv (Conv2D)  (None, None, None,   19200       ['block4a_se_excite[0][0]']      \n",
      "                                80)                                                               \n",
      "                                                                                                  \n",
      " block4a_project_bn (BatchNorma  (None, None, None,   320        ['block4a_project_conv[0][0]']   \n",
      " lization)                      80)                                                               \n",
      "                                                                                                  \n",
      " block4b_expand_conv (Conv2D)   (None, None, None,   38400       ['block4a_project_bn[0][0]']     \n",
      "                                480)                                                              \n",
      "                                                                                                  \n",
      " block4b_expand_bn (BatchNormal  (None, None, None,   1920       ['block4b_expand_conv[0][0]']    \n",
      " ization)                       480)                                                              \n",
      "                                                                                                  \n",
      " block4b_expand_activation (Act  (None, None, None,   0          ['block4b_expand_bn[0][0]']      \n",
      " ivation)                       480)                                                              \n",
      "                                                                                                  \n",
      " block4b_dwconv (DepthwiseConv2  (None, None, None,   4320       ['block4b_expand_activation[0][0]\n",
      " D)                             480)                             ']                               \n",
      "                                                                                                  \n",
      " block4b_bn (BatchNormalization  (None, None, None,   1920       ['block4b_dwconv[0][0]']         \n",
      " )                              480)                                                              \n",
      "                                                                                                  \n",
      " block4b_activation (Activation  (None, None, None,   0          ['block4b_bn[0][0]']             \n",
      " )                              480)                                                              \n",
      "                                                                                                  \n",
      " block4b_se_squeeze (GlobalAver  (None, 480)         0           ['block4b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4b_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_se_excite (Multiply)   (None, None, None,   0           ['block4b_activation[0][0]',     \n",
      "                                480)                              'block4b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_project_conv (Conv2D)  (None, None, None,   38400       ['block4b_se_excite[0][0]']      \n",
      "                                80)                                                               \n",
      "                                                                                                  \n",
      " block4b_project_bn (BatchNorma  (None, None, None,   320        ['block4b_project_conv[0][0]']   \n",
      " lization)                      80)                                                               \n",
      "                                                                                                  \n",
      " block4b_drop (Dropout)         (None, None, None,   0           ['block4b_project_bn[0][0]']     \n",
      "                                80)                                                               \n",
      "                                                                                                  \n",
      " block4b_add (Add)              (None, None, None,   0           ['block4b_drop[0][0]',           \n",
      "                                80)                               'block4a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_expand_conv (Conv2D)   (None, None, None,   38400       ['block4b_add[0][0]']            \n",
      "                                480)                                                              \n",
      "                                                                                                  \n",
      " block4c_expand_bn (BatchNormal  (None, None, None,   1920       ['block4c_expand_conv[0][0]']    \n",
      " ization)                       480)                                                              \n",
      "                                                                                                  \n",
      " block4c_expand_activation (Act  (None, None, None,   0          ['block4c_expand_bn[0][0]']      \n",
      " ivation)                       480)                                                              \n",
      "                                                                                                  \n",
      " block4c_dwconv (DepthwiseConv2  (None, None, None,   4320       ['block4c_expand_activation[0][0]\n",
      " D)                             480)                             ']                               \n",
      "                                                                                                  \n",
      " block4c_bn (BatchNormalization  (None, None, None,   1920       ['block4c_dwconv[0][0]']         \n",
      " )                              480)                                                              \n",
      "                                                                                                  \n",
      " block4c_activation (Activation  (None, None, None,   0          ['block4c_bn[0][0]']             \n",
      " )                              480)                                                              \n",
      "                                                                                                  \n",
      " block4c_se_squeeze (GlobalAver  (None, 480)         0           ['block4c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4c_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_se_excite (Multiply)   (None, None, None,   0           ['block4c_activation[0][0]',     \n",
      "                                480)                              'block4c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_project_conv (Conv2D)  (None, None, None,   38400       ['block4c_se_excite[0][0]']      \n",
      "                                80)                                                               \n",
      "                                                                                                  \n",
      " block4c_project_bn (BatchNorma  (None, None, None,   320        ['block4c_project_conv[0][0]']   \n",
      " lization)                      80)                                                               \n",
      "                                                                                                  \n",
      " block4c_drop (Dropout)         (None, None, None,   0           ['block4c_project_bn[0][0]']     \n",
      "                                80)                                                               \n",
      "                                                                                                  \n",
      " block4c_add (Add)              (None, None, None,   0           ['block4c_drop[0][0]',           \n",
      "                                80)                               'block4b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5a_expand_conv (Conv2D)   (None, None, None,   38400       ['block4c_add[0][0]']            \n",
      "                                480)                                                              \n",
      "                                                                                                  \n",
      " block5a_expand_bn (BatchNormal  (None, None, None,   1920       ['block5a_expand_conv[0][0]']    \n",
      " ization)                       480)                                                              \n",
      "                                                                                                  \n",
      " block5a_expand_activation (Act  (None, None, None,   0          ['block5a_expand_bn[0][0]']      \n",
      " ivation)                       480)                                                              \n",
      "                                                                                                  \n",
      " block5a_dwconv (DepthwiseConv2  (None, None, None,   12000      ['block5a_expand_activation[0][0]\n",
      " D)                             480)                             ']                               \n",
      "                                                                                                  \n",
      " block5a_bn (BatchNormalization  (None, None, None,   1920       ['block5a_dwconv[0][0]']         \n",
      " )                              480)                                                              \n",
      "                                                                                                  \n",
      " block5a_activation (Activation  (None, None, None,   0          ['block5a_bn[0][0]']             \n",
      " )                              480)                                                              \n",
      "                                                                                                  \n",
      " block5a_se_squeeze (GlobalAver  (None, 480)         0           ['block5a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5a_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block5a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5a_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block5a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5a_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block5a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_se_excite (Multiply)   (None, None, None,   0           ['block5a_activation[0][0]',     \n",
      "                                480)                              'block5a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_project_conv (Conv2D)  (None, None, None,   53760       ['block5a_se_excite[0][0]']      \n",
      "                                112)                                                              \n",
      "                                                                                                  \n",
      " block5a_project_bn (BatchNorma  (None, None, None,   448        ['block5a_project_conv[0][0]']   \n",
      " lization)                      112)                                                              \n",
      "                                                                                                  \n",
      " block5b_expand_conv (Conv2D)   (None, None, None,   75264       ['block5a_project_bn[0][0]']     \n",
      "                                672)                                                              \n",
      "                                                                                                  \n",
      " block5b_expand_bn (BatchNormal  (None, None, None,   2688       ['block5b_expand_conv[0][0]']    \n",
      " ization)                       672)                                                              \n",
      "                                                                                                  \n",
      " block5b_expand_activation (Act  (None, None, None,   0          ['block5b_expand_bn[0][0]']      \n",
      " ivation)                       672)                                                              \n",
      "                                                                                                  \n",
      " block5b_dwconv (DepthwiseConv2  (None, None, None,   16800      ['block5b_expand_activation[0][0]\n",
      " D)                             672)                             ']                               \n",
      "                                                                                                  \n",
      " block5b_bn (BatchNormalization  (None, None, None,   2688       ['block5b_dwconv[0][0]']         \n",
      " )                              672)                                                              \n",
      "                                                                                                  \n",
      " block5b_activation (Activation  (None, None, None,   0          ['block5b_bn[0][0]']             \n",
      " )                              672)                                                              \n",
      "                                                                                                  \n",
      " block5b_se_squeeze (GlobalAver  (None, 672)         0           ['block5b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_se_excite (Multiply)   (None, None, None,   0           ['block5b_activation[0][0]',     \n",
      "                                672)                              'block5b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_project_conv (Conv2D)  (None, None, None,   75264       ['block5b_se_excite[0][0]']      \n",
      "                                112)                                                              \n",
      "                                                                                                  \n",
      " block5b_project_bn (BatchNorma  (None, None, None,   448        ['block5b_project_conv[0][0]']   \n",
      " lization)                      112)                                                              \n",
      "                                                                                                  \n",
      " block5b_drop (Dropout)         (None, None, None,   0           ['block5b_project_bn[0][0]']     \n",
      "                                112)                                                              \n",
      "                                                                                                  \n",
      " block5b_add (Add)              (None, None, None,   0           ['block5b_drop[0][0]',           \n",
      "                                112)                              'block5a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_expand_conv (Conv2D)   (None, None, None,   75264       ['block5b_add[0][0]']            \n",
      "                                672)                                                              \n",
      "                                                                                                  \n",
      " block5c_expand_bn (BatchNormal  (None, None, None,   2688       ['block5c_expand_conv[0][0]']    \n",
      " ization)                       672)                                                              \n",
      "                                                                                                  \n",
      " block5c_expand_activation (Act  (None, None, None,   0          ['block5c_expand_bn[0][0]']      \n",
      " ivation)                       672)                                                              \n",
      "                                                                                                  \n",
      " block5c_dwconv (DepthwiseConv2  (None, None, None,   16800      ['block5c_expand_activation[0][0]\n",
      " D)                             672)                             ']                               \n",
      "                                                                                                  \n",
      " block5c_bn (BatchNormalization  (None, None, None,   2688       ['block5c_dwconv[0][0]']         \n",
      " )                              672)                                                              \n",
      "                                                                                                  \n",
      " block5c_activation (Activation  (None, None, None,   0          ['block5c_bn[0][0]']             \n",
      " )                              672)                                                              \n",
      "                                                                                                  \n",
      " block5c_se_squeeze (GlobalAver  (None, 672)         0           ['block5c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_se_excite (Multiply)   (None, None, None,   0           ['block5c_activation[0][0]',     \n",
      "                                672)                              'block5c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_project_conv (Conv2D)  (None, None, None,   75264       ['block5c_se_excite[0][0]']      \n",
      "                                112)                                                              \n",
      "                                                                                                  \n",
      " block5c_project_bn (BatchNorma  (None, None, None,   448        ['block5c_project_conv[0][0]']   \n",
      " lization)                      112)                                                              \n",
      "                                                                                                  \n",
      " block5c_drop (Dropout)         (None, None, None,   0           ['block5c_project_bn[0][0]']     \n",
      "                                112)                                                              \n",
      "                                                                                                  \n",
      " block5c_add (Add)              (None, None, None,   0           ['block5c_drop[0][0]',           \n",
      "                                112)                              'block5b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6a_expand_conv (Conv2D)   (None, None, None,   75264       ['block5c_add[0][0]']            \n",
      "                                672)                                                              \n",
      "                                                                                                  \n",
      " block6a_expand_bn (BatchNormal  (None, None, None,   2688       ['block6a_expand_conv[0][0]']    \n",
      " ization)                       672)                                                              \n",
      "                                                                                                  \n",
      " block6a_expand_activation (Act  (None, None, None,   0          ['block6a_expand_bn[0][0]']      \n",
      " ivation)                       672)                                                              \n",
      "                                                                                                  \n",
      " block6a_dwconv_pad (ZeroPaddin  (None, None, None,   0          ['block6a_expand_activation[0][0]\n",
      " g2D)                           672)                             ']                               \n",
      "                                                                                                  \n",
      " block6a_dwconv (DepthwiseConv2  (None, None, None,   16800      ['block6a_dwconv_pad[0][0]']     \n",
      " D)                             672)                                                              \n",
      "                                                                                                  \n",
      " block6a_bn (BatchNormalization  (None, None, None,   2688       ['block6a_dwconv[0][0]']         \n",
      " )                              672)                                                              \n",
      "                                                                                                  \n",
      " block6a_activation (Activation  (None, None, None,   0          ['block6a_bn[0][0]']             \n",
      " )                              672)                                                              \n",
      "                                                                                                  \n",
      " block6a_se_squeeze (GlobalAver  (None, 672)         0           ['block6a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block6a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block6a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block6a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_se_excite (Multiply)   (None, None, None,   0           ['block6a_activation[0][0]',     \n",
      "                                672)                              'block6a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_project_conv (Conv2D)  (None, None, None,   129024      ['block6a_se_excite[0][0]']      \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " block6a_project_bn (BatchNorma  (None, None, None,   768        ['block6a_project_conv[0][0]']   \n",
      " lization)                      192)                                                              \n",
      "                                                                                                  \n",
      " block6b_expand_conv (Conv2D)   (None, None, None,   221184      ['block6a_project_bn[0][0]']     \n",
      "                                1152)                                                             \n",
      "                                                                                                  \n",
      " block6b_expand_bn (BatchNormal  (None, None, None,   4608       ['block6b_expand_conv[0][0]']    \n",
      " ization)                       1152)                                                             \n",
      "                                                                                                  \n",
      " block6b_expand_activation (Act  (None, None, None,   0          ['block6b_expand_bn[0][0]']      \n",
      " ivation)                       1152)                                                             \n",
      "                                                                                                  \n",
      " block6b_dwconv (DepthwiseConv2  (None, None, None,   28800      ['block6b_expand_activation[0][0]\n",
      " D)                             1152)                            ']                               \n",
      "                                                                                                  \n",
      " block6b_bn (BatchNormalization  (None, None, None,   4608       ['block6b_dwconv[0][0]']         \n",
      " )                              1152)                                                             \n",
      "                                                                                                  \n",
      " block6b_activation (Activation  (None, None, None,   0          ['block6b_bn[0][0]']             \n",
      " )                              1152)                                                             \n",
      "                                                                                                  \n",
      " block6b_se_squeeze (GlobalAver  (None, 1152)        0           ['block6b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6b_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_se_excite (Multiply)   (None, None, None,   0           ['block6b_activation[0][0]',     \n",
      "                                1152)                             'block6b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_project_conv (Conv2D)  (None, None, None,   221184      ['block6b_se_excite[0][0]']      \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " block6b_project_bn (BatchNorma  (None, None, None,   768        ['block6b_project_conv[0][0]']   \n",
      " lization)                      192)                                                              \n",
      "                                                                                                  \n",
      " block6b_drop (Dropout)         (None, None, None,   0           ['block6b_project_bn[0][0]']     \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " block6b_add (Add)              (None, None, None,   0           ['block6b_drop[0][0]',           \n",
      "                                192)                              'block6a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_expand_conv (Conv2D)   (None, None, None,   221184      ['block6b_add[0][0]']            \n",
      "                                1152)                                                             \n",
      "                                                                                                  \n",
      " block6c_expand_bn (BatchNormal  (None, None, None,   4608       ['block6c_expand_conv[0][0]']    \n",
      " ization)                       1152)                                                             \n",
      "                                                                                                  \n",
      " block6c_expand_activation (Act  (None, None, None,   0          ['block6c_expand_bn[0][0]']      \n",
      " ivation)                       1152)                                                             \n",
      "                                                                                                  \n",
      " block6c_dwconv (DepthwiseConv2  (None, None, None,   28800      ['block6c_expand_activation[0][0]\n",
      " D)                             1152)                            ']                               \n",
      "                                                                                                  \n",
      " block6c_bn (BatchNormalization  (None, None, None,   4608       ['block6c_dwconv[0][0]']         \n",
      " )                              1152)                                                             \n",
      "                                                                                                  \n",
      " block6c_activation (Activation  (None, None, None,   0          ['block6c_bn[0][0]']             \n",
      " )                              1152)                                                             \n",
      "                                                                                                  \n",
      " block6c_se_squeeze (GlobalAver  (None, 1152)        0           ['block6c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6c_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_se_excite (Multiply)   (None, None, None,   0           ['block6c_activation[0][0]',     \n",
      "                                1152)                             'block6c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_project_conv (Conv2D)  (None, None, None,   221184      ['block6c_se_excite[0][0]']      \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " block6c_project_bn (BatchNorma  (None, None, None,   768        ['block6c_project_conv[0][0]']   \n",
      " lization)                      192)                                                              \n",
      "                                                                                                  \n",
      " block6c_drop (Dropout)         (None, None, None,   0           ['block6c_project_bn[0][0]']     \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " block6c_add (Add)              (None, None, None,   0           ['block6c_drop[0][0]',           \n",
      "                                192)                              'block6b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6d_expand_conv (Conv2D)   (None, None, None,   221184      ['block6c_add[0][0]']            \n",
      "                                1152)                                                             \n",
      "                                                                                                  \n",
      " block6d_expand_bn (BatchNormal  (None, None, None,   4608       ['block6d_expand_conv[0][0]']    \n",
      " ization)                       1152)                                                             \n",
      "                                                                                                  \n",
      " block6d_expand_activation (Act  (None, None, None,   0          ['block6d_expand_bn[0][0]']      \n",
      " ivation)                       1152)                                                             \n",
      "                                                                                                  \n",
      " block6d_dwconv (DepthwiseConv2  (None, None, None,   28800      ['block6d_expand_activation[0][0]\n",
      " D)                             1152)                            ']                               \n",
      "                                                                                                  \n",
      " block6d_bn (BatchNormalization  (None, None, None,   4608       ['block6d_dwconv[0][0]']         \n",
      " )                              1152)                                                             \n",
      "                                                                                                  \n",
      " block6d_activation (Activation  (None, None, None,   0          ['block6d_bn[0][0]']             \n",
      " )                              1152)                                                             \n",
      "                                                                                                  \n",
      " block6d_se_squeeze (GlobalAver  (None, 1152)        0           ['block6d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6d_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6d_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6d_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_se_excite (Multiply)   (None, None, None,   0           ['block6d_activation[0][0]',     \n",
      "                                1152)                             'block6d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_project_conv (Conv2D)  (None, None, None,   221184      ['block6d_se_excite[0][0]']      \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " block6d_project_bn (BatchNorma  (None, None, None,   768        ['block6d_project_conv[0][0]']   \n",
      " lization)                      192)                                                              \n",
      "                                                                                                  \n",
      " block6d_drop (Dropout)         (None, None, None,   0           ['block6d_project_bn[0][0]']     \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " block6d_add (Add)              (None, None, None,   0           ['block6d_drop[0][0]',           \n",
      "                                192)                              'block6c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block7a_expand_conv (Conv2D)   (None, None, None,   221184      ['block6d_add[0][0]']            \n",
      "                                1152)                                                             \n",
      "                                                                                                  \n",
      " block7a_expand_bn (BatchNormal  (None, None, None,   4608       ['block7a_expand_conv[0][0]']    \n",
      " ization)                       1152)                                                             \n",
      "                                                                                                  \n",
      " block7a_expand_activation (Act  (None, None, None,   0          ['block7a_expand_bn[0][0]']      \n",
      " ivation)                       1152)                                                             \n",
      "                                                                                                  \n",
      " block7a_dwconv (DepthwiseConv2  (None, None, None,   10368      ['block7a_expand_activation[0][0]\n",
      " D)                             1152)                            ']                               \n",
      "                                                                                                  \n",
      " block7a_bn (BatchNormalization  (None, None, None,   4608       ['block7a_dwconv[0][0]']         \n",
      " )                              1152)                                                             \n",
      "                                                                                                  \n",
      " block7a_activation (Activation  (None, None, None,   0          ['block7a_bn[0][0]']             \n",
      " )                              1152)                                                             \n",
      "                                                                                                  \n",
      " block7a_se_squeeze (GlobalAver  (None, 1152)        0           ['block7a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block7a_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block7a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block7a_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block7a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block7a_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block7a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block7a_se_excite (Multiply)   (None, None, None,   0           ['block7a_activation[0][0]',     \n",
      "                                1152)                             'block7a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block7a_project_conv (Conv2D)  (None, None, None,   368640      ['block7a_se_excite[0][0]']      \n",
      "                                320)                                                              \n",
      "                                                                                                  \n",
      " block7a_project_bn (BatchNorma  (None, None, None,   1280       ['block7a_project_conv[0][0]']   \n",
      " lization)                      320)                                                              \n",
      "                                                                                                  \n",
      " top_conv (Conv2D)              (None, None, None,   409600      ['block7a_project_bn[0][0]']     \n",
      "                                1280)                                                             \n",
      "                                                                                                  \n",
      " top_bn (BatchNormalization)    (None, None, None,   5120        ['top_conv[0][0]']               \n",
      "                                1280)                                                             \n",
      "                                                                                                  \n",
      " top_activation (Activation)    (None, None, None,   0           ['top_bn[0][0]']                 \n",
      "                                1280)                                                             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 4,049,571\n",
      "Trainable params: 0\n",
      "Non-trainable params: 4,049,571\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WNoa36_RRV9R",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Getting a Feature Vector from trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2nkmqeDyYm6s",
    "outputId": "7a5d6bd6-59fe-479d-ee79-198849b088c8",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random input tensor:\n",
      "[[[[ 0.3274685  -0.8426258   0.3194337 ]\n",
      "   [-1.4075519  -2.3880599  -1.0392479 ]\n",
      "   [-0.5573232   0.539707    1.6994323 ]\n",
      "   [ 0.28893656 -1.5066116  -0.2645474 ]]\n",
      "\n",
      "  [[-0.59722406 -1.9171132  -0.62044144]\n",
      "   [ 0.8504023  -0.40604794 -3.0258412 ]\n",
      "   [ 0.9058464   0.29855987 -0.22561555]\n",
      "   [-0.7616443  -1.8917141  -0.93847126]]\n",
      "\n",
      "  [[ 0.77852213 -0.47338897  0.97772694]\n",
      "   [ 0.24694404  0.20573747 -0.5256233 ]\n",
      "   [ 0.32410017  0.02545409 -0.10638497]\n",
      "   [-0.6369475   1.1603122   0.2507359 ]]\n",
      "\n",
      "  [[-0.41728503  0.4012578  -1.4145443 ]\n",
      "   [-0.5931857  -1.6617213   0.33567193]\n",
      "   [ 0.10815629  0.23479682 -0.56668764]\n",
      "   [-0.35819843  0.88698614  0.52744764]]]] \n",
      "\n",
      "2D global average pooled random tensor:\n",
      " [[-0.09368646 -0.45840448 -0.2885598 ]] \n",
      "\n",
      "Shape of input tensor: (1, 4, 4, 3)\n",
      "\n",
      "Shape of global average pooled random tensor: (1, 3)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define the input shape\n",
    "input_shape = (1,4,4,3)\n",
    "\n",
    "# Create a random tensor\n",
    "tf.random.set_seed(42)\n",
    "input_tensor = tf.random.normal(input_shape)\n",
    "print(f\"Random input tensor:\\n{input_tensor} \\n\")\n",
    "\n",
    "# Pass the random tensor through a global average pooling 2D layer\n",
    "global_average_pooled_tensor = tf.keras.layers.GlobalAveragePooling2D()(input_tensor)\n",
    "print(f\"2D global average pooled random tensor:\\n {global_average_pooled_tensor} \\n\")\n",
    "\n",
    "print(f\"Shape of input tensor: {input_tensor.shape}\\n\")\n",
    "print(f\"Shape of global average pooled random tensor: {global_average_pooled_tensor.shape}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sf2qi4-TZgOS",
    "outputId": "26025ffc-d02a-4017-e400-5feef8cdef5f",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[-0.09368646, -0.45840448, -0.2885598 ]], dtype=float32)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Raplicate GlobalAveragePooled2D layer\n",
    "tf.reduce_mean(input_tensor, axis=[1, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Runing a series of transfer learning experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "# Model 1: 1% of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved under 10_food_classes_1_percent (1).zip\n"
     ]
    }
   ],
   "source": [
    "# Download and unzip data - preprocessed from Food101\n",
    "!python -m wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_1_percent.zip\n",
    "unzip_data(\"10_food_classes_1_percent.zip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Creating train and test dirs\n",
    "train_dir_1_percent = \"10_food_classes_1_percent/train\"\n",
    "test_dir = \"10_food_classes_1_percent/test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 directories and 0 images in '10_food_classes_1_percent'.\n",
      "There are 10 directories and 0 images in '10_food_classes_1_percent\\test'.\n",
      "There are 0 directories and 250 images in '10_food_classes_1_percent\\test\\chicken_curry'.\n",
      "There are 0 directories and 250 images in '10_food_classes_1_percent\\test\\chicken_wings'.\n",
      "There are 0 directories and 250 images in '10_food_classes_1_percent\\test\\fried_rice'.\n",
      "There are 0 directories and 250 images in '10_food_classes_1_percent\\test\\grilled_salmon'.\n",
      "There are 0 directories and 250 images in '10_food_classes_1_percent\\test\\hamburger'.\n",
      "There are 0 directories and 250 images in '10_food_classes_1_percent\\test\\ice_cream'.\n",
      "There are 0 directories and 250 images in '10_food_classes_1_percent\\test\\pizza'.\n",
      "There are 0 directories and 250 images in '10_food_classes_1_percent\\test\\ramen'.\n",
      "There are 0 directories and 250 images in '10_food_classes_1_percent\\test\\steak'.\n",
      "There are 0 directories and 250 images in '10_food_classes_1_percent\\test\\sushi'.\n",
      "There are 10 directories and 0 images in '10_food_classes_1_percent\\train'.\n",
      "There are 0 directories and 7 images in '10_food_classes_1_percent\\train\\chicken_curry'.\n",
      "There are 0 directories and 7 images in '10_food_classes_1_percent\\train\\chicken_wings'.\n",
      "There are 0 directories and 7 images in '10_food_classes_1_percent\\train\\fried_rice'.\n",
      "There are 0 directories and 7 images in '10_food_classes_1_percent\\train\\grilled_salmon'.\n",
      "There are 0 directories and 7 images in '10_food_classes_1_percent\\train\\hamburger'.\n",
      "There are 0 directories and 7 images in '10_food_classes_1_percent\\train\\ice_cream'.\n",
      "There are 0 directories and 7 images in '10_food_classes_1_percent\\train\\pizza'.\n",
      "There are 0 directories and 7 images in '10_food_classes_1_percent\\train\\ramen'.\n",
      "There are 0 directories and 7 images in '10_food_classes_1_percent\\train\\steak'.\n",
      "There are 0 directories and 7 images in '10_food_classes_1_percent\\train\\sushi'.\n"
     ]
    }
   ],
   "source": [
    "# How many images we are working with\n",
    "walk_through_dir(\"10_food_classes_1_percent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Setup data loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 70 files belonging to 10 classes.\n",
      "Found 2500 files belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "IMG_SIZE = (224, 224)\n",
    "BATCH_SIZE = 32\n",
    "train_data_1_percent = tf.keras.preprocessing.image_dataset_from_directory(train_dir_1_percent,\n",
    "                                                                           label_mode=\"categorical\",\n",
    "                                                                           image_size=IMG_SIZE,\n",
    "                                                                           batch_size=BATCH_SIZE) # Default\n",
    "\n",
    "test_data = tf.keras.preprocessing.image_dataset_from_directory(test_dir,\n",
    "                                                                label_mode=\"categorical\",\n",
    "                                                                image_size=IMG_SIZE,\n",
    "                                                                batch_size=BATCH_SIZE)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Adding data augmentation right into the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers.experimental import preprocessing\n",
    "\n",
    "# Create data augmentation\n",
    "data_augmentation = keras.Sequential([\n",
    "    preprocessing.RandomFlip(\"horizontal\"),\n",
    "    preprocessing.RandomRotation(0.2),\n",
    "    preprocessing.RandomZoom(0.2),\n",
    "    preprocessing.RandomHeight(0.2),\n",
    "    preprocessing.RandomWidth(0.2),\n",
    "    # preprocessing.Rescale(1./255) # Keep for ResNet50V2\n",
    "], name=\"data_augmentation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Visualize data augmentation layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View a random image and comapare it to augmentaed image\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import os\n",
    "import random\n",
    "target_class = random.choice(train_data_1_percent.class_names)\n",
    "target_dir = \"10_food_classes_1_percent/train/\" + target_class\n",
    "\n",
    "random_image = random.choice(os.listdir(target_dir))\n",
    "random_image_path  = target_dir + \"/\" + random_image\n",
    "\n",
    "# Read in the random image\n",
    "img = mpimg.imread(random_image_path)\n",
    "plt.imshow(img)\n",
    "plt.title(f\"Original random image from class: {target_class}\")\n",
    "plt.axis(False);\n",
    "\n",
    "# Plot augmented image\n",
    "augmented_img = data_augmentation(img, training=True)\n",
    "plt.figure()\n",
    "plt.imshow(augmented_img/255.)\n",
    "plt.title(f\"Augmented random image form class: {target_class}\")\n",
    "plt.axis(False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Creating a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (512, 382, 3) for input KerasTensor(type_spec=TensorSpec(shape=(512, 382, 3), dtype=tf.uint8, name='random_flip_input'), name='random_flip_input', description=\"created by layer 'random_flip_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
      "Saving TensorBoard log files to: transfer_learning/1_percent_data_aug/20220615-222608\n",
      "Epoch 1/15\n",
      "WARNING:tensorflow:Model was constructed with shape (512, 382, 3) for input KerasTensor(type_spec=TensorSpec(shape=(512, 382, 3), dtype=tf.uint8, name='random_flip_input'), name='random_flip_input', description=\"created by layer 'random_flip_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
      "WARNING:tensorflow:Model was constructed with shape (512, 382, 3) for input KerasTensor(type_spec=TensorSpec(shape=(512, 382, 3), dtype=tf.uint8, name='random_flip_input'), name='random_flip_input', description=\"created by layer 'random_flip_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
      "3/3 [==============================] - ETA: 0s - loss: 2.4593 - accuracy: 0.0714WARNING:tensorflow:Model was constructed with shape (512, 382, 3) for input KerasTensor(type_spec=TensorSpec(shape=(512, 382, 3), dtype=tf.uint8, name='random_flip_input'), name='random_flip_input', description=\"created by layer 'random_flip_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
      "3/3 [==============================] - 7s 1s/step - loss: 2.4593 - accuracy: 0.0714 - val_loss: 2.2473 - val_accuracy: 0.1497\n",
      "Epoch 2/15\n",
      "3/3 [==============================] - 2s 821ms/step - loss: 2.2043 - accuracy: 0.1429 - val_loss: 2.1119 - val_accuracy: 0.2599\n",
      "Epoch 3/15\n",
      "3/3 [==============================] - 2s 938ms/step - loss: 1.9367 - accuracy: 0.3714 - val_loss: 2.0040 - val_accuracy: 0.3355\n",
      "Epoch 4/15\n",
      "3/3 [==============================] - 2s 818ms/step - loss: 1.8368 - accuracy: 0.5286 - val_loss: 1.8876 - val_accuracy: 0.4391\n",
      "Epoch 5/15\n",
      "3/3 [==============================] - 2s 873ms/step - loss: 1.6143 - accuracy: 0.6714 - val_loss: 1.8018 - val_accuracy: 0.4424\n",
      "Epoch 6/15\n",
      "3/3 [==============================] - 2s 831ms/step - loss: 1.4781 - accuracy: 0.7429 - val_loss: 1.7295 - val_accuracy: 0.4934\n",
      "Epoch 7/15\n",
      "3/3 [==============================] - 2s 794ms/step - loss: 1.3819 - accuracy: 0.8143 - val_loss: 1.6530 - val_accuracy: 0.5395\n",
      "Epoch 8/15\n",
      "3/3 [==============================] - 2s 800ms/step - loss: 1.2360 - accuracy: 0.8714 - val_loss: 1.5738 - val_accuracy: 0.5822\n",
      "Epoch 9/15\n",
      "3/3 [==============================] - 2s 791ms/step - loss: 1.1053 - accuracy: 0.8714 - val_loss: 1.5017 - val_accuracy: 0.6283\n",
      "Epoch 10/15\n",
      "3/3 [==============================] - 2s 916ms/step - loss: 1.0427 - accuracy: 0.9286 - val_loss: 1.4296 - val_accuracy: 0.6628\n",
      "Epoch 11/15\n",
      "3/3 [==============================] - 2s 833ms/step - loss: 0.9441 - accuracy: 0.9286 - val_loss: 1.4113 - val_accuracy: 0.6546\n",
      "Epoch 12/15\n",
      "3/3 [==============================] - 2s 733ms/step - loss: 0.8394 - accuracy: 0.9286 - val_loss: 1.3564 - val_accuracy: 0.6727\n",
      "Epoch 13/15\n",
      "3/3 [==============================] - 2s 789ms/step - loss: 0.7857 - accuracy: 0.9429 - val_loss: 1.3051 - val_accuracy: 0.6924\n",
      "Epoch 14/15\n",
      "3/3 [==============================] - 2s 736ms/step - loss: 0.7290 - accuracy: 0.9714 - val_loss: 1.2358 - val_accuracy: 0.7171\n",
      "Epoch 15/15\n",
      "3/3 [==============================] - 2s 736ms/step - loss: 0.6733 - accuracy: 0.9714 - val_loss: 1.2218 - val_accuracy: 0.7171\n"
     ]
    }
   ],
   "source": [
    "# Setup input shape and base model, freezing the base model layers\n",
    "input_shape = (224, 224, 3)\n",
    "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n",
    "base_model.trainable = False\n",
    "\n",
    "# Create input layer\n",
    "inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n",
    "\n",
    "# Add in data augmentation Sequential model as a layer\n",
    "x= data_augmentation(inputs)\n",
    "\n",
    "# Give base_model the inputs (after augmentation) and don't trainit\n",
    "x = base_model(x, training=False)\n",
    "\n",
    "# Pool output features of the base model\n",
    "x = layers.GlobalAveragePooling2D()(x)\n",
    "\n",
    "# Put a dense layer on as the output\n",
    "outputs = layers.Dense(10, activation=\"softmax\", name=\"output_layer\")(x)\n",
    "\n",
    "# Make a model using inputs and outputs\n",
    "model_1 = keras.Model(inputs, outputs)\n",
    "\n",
    "# Compile the model\n",
    "model_1.compile(loss=\"categorical_crossentropy\",\n",
    "               optimizer=tf.keras.optimizers.Adam(),\n",
    "               metrics=[\"accuracy\"])\n",
    "\n",
    "# Fit the model\n",
    "history_1_percent =  model_1.fit(train_data_1_percent,\n",
    "                                epochs=15,\n",
    "                                steps_per_epoch=len(train_data_1_percent),\n",
    "                                validation_data=test_data,\n",
    "                                validation_steps=int(0.25 * len(test_data)),\n",
    "                                # Track model training logs\n",
    "                                callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\",\n",
    "                                                                      experiment_name=\"1_percent_data_aug\")])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_layer (InputLayer)    [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " data_augmentation (Sequenti  (None, None, 3)          0         \n",
      " al)                                                             \n",
      "                                                                 \n",
      " efficientnetb0 (Functional)  (None, None, None, 1280)  4049571  \n",
      "                                                                 \n",
      " global_average_pooling2d_1   (None, 1280)             0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 10)                12810     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,062,381\n",
      "Trainable params: 12,810\n",
      "Non-trainable params: 4,049,571\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Check out model summary\n",
    "model_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 5s 57ms/step - loss: 1.2086 - accuracy: 0.7164\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.2086297273635864, 0.7164000272750854]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate on full datasets\n",
    "results_1_percent_data_aug = model_1.evaluate(test_data)\n",
    "results_1_percent_data_aug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How does the model with 1 percent data go with data augmentaion\n",
    "plot_loss_curves(history_1_percent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "# Model 2: 10% data with data augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "## Setting up data loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_10_percent_dir = \"10_food_classes_10_percent/train\"\n",
    "test_dir = \"10_food_classes_10_percent/test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 750 files belonging to 10 classes.\n",
      "Found 2500 files belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "IMG_SIZE = (224, 224)\n",
    "BATCH_SIZE = 32\n",
    "\n",
    "train_10_percent_data = tf.keras.preprocessing.image_dataset_from_directory(train_10_percent_dir,\n",
    "                                                                            label_mode=\"categorical\",\n",
    "                                                                            image_size=IMG_SIZE,\n",
    "                                                                            batch_size=BATCH_SIZE)\n",
    "\n",
    "test_data = keras.preprocessing.image_dataset_from_directory(directory=test_dir,\n",
    "                                                             label_mode=\"categorical\",\n",
    "                                                             image_size=IMG_SIZE,\n",
    "                                                             batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['chicken_curry',\n",
       " 'chicken_wings',\n",
       " 'fried_rice',\n",
       " 'grilled_salmon',\n",
       " 'hamburger',\n",
       " 'ice_cream',\n",
       " 'pizza',\n",
       " 'ramen',\n",
       " 'steak',\n",
       " 'sushi']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_10_percent_data.class_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "## Setup data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data_augmentation = keras.Sequential([\n",
    "    layers.RandomFlip(\"horizontal\"),\n",
    "    layers.RandomZoom(0.2),\n",
    "    layers.RandomHeight(0.2),\n",
    "    layers.RandomRotation(0.2),\n",
    "    layers.RandomWidth(0.2),\n",
    "    layers.RandomContrast(0.2)\n",
    "], name=\"data_augmentation\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import os\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Augmented image of sushi')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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T/pPXwJxwXjkdRnQkpokIFHM6qzB040OpdcXz0zPKssgzB4oF2v7A/ngo9yVc7AGgVJTLFUXr4XIcJ/QG98rP733mQ9MZQlEioAisCzTIUTpKQsIxeH+JuEDHe0Q9OQM5fL7NgGEgYocNxbRiMKsgmKKDIOqB0p6BwCnfNEdMWEHDaD3gvtCZepnXNzqjNGBcz2VFqQu/IgdIlF25i4Z+2olUiDpsOrKZiwMotaKWgr5v6J0/O8bE3OIkjggfg98359oydTXUkt+PI3B9Di1FBEbvRChKR0LBpI8QUjbZE8hNxMDoSoVlk73rmXxJlexLjXy5PpEgAMkLuJGM6H0aabq/0N9du8BmTJl+QQ8V8/sD/TDQMWY0hkGQXVAFdAbDjFDHAID5r4FECY3dTiSRoBG4yYbgO+yAVsz3B5FGqfTSAGznPZRSFXlORt5tPvA+uu5/aOMGQXUYEA0I3d8YdIYRKO64rCuWdcXtfsfeNsTocADFDE1r02Og9U4SSGkDPbiMy+fC62kF05dQFDClIvquaV26nLInh4KMJtC6cS0JB5W7GlOpiAGrhi4E4AZwEBIjuycRAMLQAGCdCGCIyHQnLzBkBB0DHuPgdXKr6N6YCvF++mkvmTtKrYhSwEY9U2qVQeQwTDp4JgbF7Ug1IbhdCkqtuF6vKO5oZnjcOzqaHABTHebjY+7J426TG2JQK1bQ+w5EYKkLjXF0uCuvF4lsiRrtRGLrmcxMdQY1Ot50SqG9e6D8jzTyy/Oz0hAaiG+OfXtgBNB7m1ELeZO6mBjBHZAXG8mwnlI9PyJEugS3NKAg9BWDOjI3Mi4i4dQ4orcVfQbQJZ829yRd0c9QZiQJMo5QCDLCSZCNIMkzygGLaSQG6/LCMSaxyDShz3QAoIh7u9/liQ1dbDYZ+o7r5Yp933G73xDR0bYH2r5htB29N/TOjTHiMPKS5BVys50js9KBjOKBDx3e5/YAN4qeiAzezBCd/y2KbjFirjP0bxjRVsL5g6gc82eGPqMUwWXT2p7TLD0by+el9XariDCYkWhjgI+5D0o6gzGIziJQapnPqQ2lTl6Ut+bOS3SYFpXLR+OHYHeMQCkkVGMYRmu8T9hk55M1d0/UqcpPoofAdPDJ7RxMHIQESeiOEfDCVMyC98A1Oz0rEYrFuZ7yrkwT7OxyfvrryyP55Ym2aoRQkEdrexeI4AMaimj0/PbBTeReyChuOEi7jOIGHLl7Ogbj75As8gnxi4lgCUPP0ghjEzKNcJUc8jUiyRVdQIgF10Mh/MSEYFn6Gs1gReQN0hnJQYBscpbcMqWYuX/C7n5mXA1737FvN7yzE7egXHzbH+htx75vhHLQJYfSI6GT+eAz3Tl5+oTAROBG56N8evRxoC0YSrG5100f5qeS5YT16cmMpaPcs7DkAIDWOopnmgY62Jn3HimXGdBVWYA7ilWUZYF5FXIq2mdGyG3aA2C0it6mo6EjnyGPzx9BiA9MMitO3888Ha5UU6RljIG2b7phOSEjik0DZ0BJYjkQfYinGBjRYUEn1Xo/kJL2WDobN3IM84kJFSUy7snoGYOKezlsKQIYPbMa1OIYo31l+9yXs+tlQRqdodDb+A4vBRENDkZWB/OhJOXyoiYxZDEJsdygM+eewdQOWJfRNQ18XlAypgbEUMad5Y4BuLP8NAZMbG5eQ2Y3aXwTsiLm1ywguD0mvEQ/+wfC4Znj6yHp4g7kIMOmkUsLoPx8jEBvQFE5CmMgVD8fvWPfGnofaK1Nr2/OMk6SQ4GEwLzXhMAZjSe7D0L2fB61Lspn01G4YKL4lBGoeo59An2RmenMZnbA3NasCn4SEnuRczuXk+b7yMGbg/XDgrJcsFyuLJuB0TtEzJkVIZIEGwHUMhFLXRd4rcq3XeTpkcdC63RWdY7eD+cllOLCyoFj/biPySOhGqJ37G2TtsLRO6slvQtRQDxQok0EhlCIiw+AOCBEorG0i4BHwMQ9kBRWsDIo5eD3emuA7qnLA6cdfdHrS408FEEZFDpKXWDFZ/QI5WkzAgK6LWXkxrposob8nYN1Tyib+azFsUGRJSP9e5w2TBI1HxoryZTDImkOM/+aHua06DMVOCJZACJcJPzguxwgYEYnGVvm6jCKIsZA7xTG5HtDmweqUgCGoXJPjK7cs6MJphOCArPuPzpGa9o4c3UZGXQv6VTNjySKkUesrZxASY4l4oMNHRFA0SZXJByR5UOfbnLAMHqglkonP3hdTHt3tN6RETUTzeRMLBytK2gUh/lC47bKP3CgFDlQ0x7wSZqZUE3pKryVAvOCPoJ8gEQqAKZIajpwO2L6ke8OfUoIshcRlvEB4ebmiEQpNjBsPoGZghgSccZMc2ASa2GCGa3pgaAQwGgdKAEHEacF06aGgVKqxDb9cDpy7LlXmNd/caX8y0to7nPjh9Glei0TIuVmH6EPzgidv698zfNnIdiWSxT8aYcjUiWWEDoNQwZrJeaGNK8HLJMK61BWaeXmYxCjipPowGM6KQNmWSQN2EqBIdD7mGbOh6a8EMf9d3RB7syB+R69NUVjQTJ39NFhnoIdzBwtJAiZsDCGcj7QwEdXlUPXcl5PnNPLZHgPUU8pckLaAxPZyDkFmI+eU6ssbxbVk4MXqM/N7HtgDJvPjgw482Hm5p3QthRBdOXrHmhjwEdgWQq8rjL2FeYFVhRYrCCGAVkiNWPerFQqr9+yjKkobuYoS5FRqSzqhhyVbiIEM3K7HXySO9FYS+cLrgHJ2Q5YKt+071XNcNC5h0Opsk0Ri+k5J3tulk55TOMc4LMffaAUh4drnzn6aCQXDeigihAWCiTZvp/E4nd/fUWdXGTWGFSDYfCqDRIbYEYcyGhY305PaFpgzGjCfDkX+YiIPYwPWe9DZly5o+rYJsWTGxehmWP0XQ/gBNO0abkZ5TQmMQX0iOkczpEtS1emeyxLRW9tws4x0nmcovphauitK5fOEuFQVBCBEwOjjenkMr2JyAh65H4IMvGRUNlzU2ujR59w27QeVAGGNCxB3qL3gyDMz0UcNX8QPh+yZQlXSn6etoKnxoUlRqo8RXoWg2NBjKHo0pWXH4k4U7aOwI4xGsIM1QxhDi8LSr2g1CoJAOF3ltUC4OZnKJzOIMapMiDE4tI9jD6mc00kmekAIo7963QkyBQzfKZaGEC4Yl1gPgs6GqV0Y8AmN2EziB3paq6fGB2h24T1fQj5lCTpBsIGXGMCRzLxroDVu9ad+4D7JmZQ+PpGnrvZdQNp0DjlfhZIkQAfqkgnDHnjIUOVsQ/mcgmjMwKHeQZ6bt4UZZgDXlEvK2qt8EJ4Zj0AL2i7USsNGm4g9IDHydtrIUTWzPpxohRtXgo5xnRiGAEvjqa8KTeNy2HwI5P8g0i2g7UvXoiA5ESAwQghODpGKC8UCpDogp6/i/2G6tjiuCP/YLqXfPHrkpDqGkspU1gBt5leZK0/9Q5jhEqQWaLMjZzpVcGIgSKIOiXL2XvgjmFNJdfc2AVZ+SCE3VFKgy8DVgxlWQGv8GWFVebXXtPgWKrL9M5m6UgQ2x3hmFxFOrkuB5ZpSjrho9Z/iHOgvQYT6Zs6DCECS+Z8xKzv57Ppetb5bFKZJp+p685HdH5OMZ0AnQodg6cDGoFoCfP5s6N3OLIPoMuZHI6FasyPhOvRmWudJXeZs54skhc9BmSnMzekB804d/y/GYBxlF6yHNPHEAHBjW5eUOqCZb1ivVyxrAus0DhaayybFINtJEIs2iRl3CuNtceMxO5F6QVr0ox8R8PMAVnpKABGkOO6abzUWvd5/RM661nO9N9MMFWkYhOBYjY32zTcnhH6iJ7JsJsIySQez3XnEPk2N6bZvIbzPeVzGIKFMEk3cXAlQqEn08hAnGnVkQbEkfrTKI15MJljE4pjM1BIwecW2PY70w4HrFZKdGsFigOFzTdWCqN4H+IHMgoC8DgZaVZWci/mjUN7CEI0+Vc73RlTCS8FtaZ8+5T6REzkFzHQmhBacZb9TlH6g+uZuTGdRSozcz9MTuf0u8UrDEyt0mlRsHNE8EycJ1MUaqoBDbxP6P7TX19u5AGgnYygA9GztLHPqCcuSdEzSw1ij/N3cWy2ocUXk/Jd8nD9kjvKsqCuF9T1MlViVGpWdG+KigXeN7R9PmnVUFNgDEWrCd6R5UBEwmOVhkS4Zc6WtVmEun2CkMl1nwlpGV0MudZmYN4kZ8Zof+SX8nRMp7pysnR8gmKA0qIhaOwZGhxnh2QGHKW8D9MAhOB6fiQwOZUxAilKJJo6NuGIMSN674fUzfLRWIGqjhONpFQ4U4hSFrgvQBRxiYalVkR0QvfisLrA6oKU3oYUcCP4fhY21YruqlDIAWdFgnlx7reD7PQTNzEbRCJZdFeawkBiANrGPT1SlZfCIKVsTBPb8dzdZ5NKilQQR0r3OaD1IXpKPuEE4106/+lB9cD42Mckn6fgTO+fDP0Xvb6iQUVeNDKvDESn9NEUOQIUbISNefPchtyQh3aY7zdGlkSyK+pDlldPAANArRXL5YLL9QmlkpiZunh3thyCXrC3hHQUlRAKHQYYet8qMjFJi7k4AdgI+Z5DhNN7KIIKoRCHExH0o93QTB5Y8JxyxtRx+wcR+QiCcRL9pOqJ7Lwh0YdUUhl/ThH683nfGIMePyF5ijP0LOamy81op+qCugFH5udxOJDUg5sMiQQVme8xOrLvy2Q0roaYUhaYLXRuAQwHCiobKgyABfPxsija8lo7DCP1AOlgRYq5S4Ks59OMnFFgwIOpASKoaXdF/7yHzGsjZckHzDehrW3vGGMHxkBXTT6dQzbCZDOMqCXE6McazSCXRqr7wKm0mf/Fse+ZmspBOAlUIE7O+XPPe8ITnPbTRxm5T6+OCG3AzlpdjFPb5EAPEQjaP2FZehMAtLmtkeWv7IPNjcsyEwBzmBfUdUFZFkQp7DvWZg9LdpLCCR8kIxYpjJIBjSFDL2U6EmYKMVVhA/oZGV/RgyHZeIKsSHh/lP34NUwxxQGVDyiez9rmYwi0bHqZ+yBm11N66/TOTHsO0dGMupFOhOIL5oJFvAGAkzZ8aMPToYRAktoj5ezc0rBV+z05lYiBAQMFabmlfMLi4kAfOwIGtwVuFWVZ1YbrIsOAEezHz/bIAGv3ESbWWOSmNA/sMgwkOWHS0qbgCQOwYQiRbJYPC8yRGTWh1IoNHgrvc+1673g8HlzftmNvDyC6YPOh2eBcga5nJN4mzqUzm87jg6qQtByyKKLD3mfOz/fnzw9knk3kdZbfAudAk/eJI1B+LPFWasG+9WmJIdIho9aIhLUfNk7kyhAtsq2TEY/X1kaTB5PowNntloMEmC8W1HXlZimVvytIyZVgKWRgINzhtoBc3IJSWT+cQgO9n7uzy0tk2RBkwrxuaapnNXriIVjR5jgppI5oykV3ywEUx9rYKSoe+RixTkLl3CxDxGEphjHKTC9GANHHzNcDMdV+rTWWrYybo5SKWkzO6mQcyHTqEPGQQCsTbiZbnVHSnMYXcJV7bP6X+8xRSnbXMYrDCqysCF8QGtJRSwHQ0TubKhCHLLiUCrcy0x/AUL3Cqqvs2DjkwUiqAVCawDXeG0lXqveUV+vZjxZTy5HI0UthjNQ+GmNge9zT/IDIARi5h4m2ugaGJDo1A6DymkHNJxM2H8pP+ieVNqGoJl5hbw2lFJTCdTQchPRRcuZ1Zp6OOCS8IxuJMkB+jJHnRY3BskhIt5ssstmpZjyA7KLtox3NHlqo3DSH6olRZRqTZWQ3TgRZLhOiW9br540cD6iHlEIBBHahAMdMHpFRKaZgJ6GVZ15jBT0OBjVhUSIMgEamcDrRQpbcDjpEkDf6vPlQ3p/SU5cGPoDJwFquVWG9kwMlTiTaAEhemQw7m1cEE83JRMdANUZCWypKTuMJbWAcebh2Dss1GcUhGJtrHImy7CB8cJQSXc+lVA7JGEJwYSx7llqxrOt08uYFe9tnhYEoxY57jaztkzSNfuTco3e2y7Y2y4m99zlxZWrp5Sx73w8DB1FOb7s2qoqcRidGErQjOicAueW9qivcgYJy5OmC/dnPIPprym0x0xscjke9GrNxEfNH9Wz4jVl+TbSUQSS/aphOJu1HH/6FdvylRt6Vb8K4edu+o7cdo+2wUC4muGnyYDkm59hEnCYju56LlM9kMo4I6XsLzBfUcoFFhaMC42BzD7iqB2GGPoDRCHbcHR2subvIrLZzlE9ujizL5CsEz8NsMvBAsKZtmbYczRAUeThaaxQyKAnJ/HaW1BT5c25HEjkT51hCcJpQtiUGMB9sTEMTazvzfkd20DXls/nzQ1FiOJCDBWJk371NA2U0SJGIEfpqA3op8zmyn/vDNc+y1pxcYoLO6qdflgXr5YK6rsr1g/xGbmVThcAH8/NEPAjs24Pr2ftBrBmvedghrjJ9LZs3TOuTqrfIOQLgknV9JiNnPRIoBTDghMzSiZkqNMZ0Z28ndJglQgBNzif79I81laMIwvEQvzB6qjzpgMbIhi85E2SJOs1fe0BpQqLrYw9/8esrSmiq+/aGfXtg2+5zTln6duYZpEzS01nGb8HAfHhEBdRt50YBcNp82sNM9riAKU7QN0d+P4gcoJINFzzQo6s+bdj70am27W1ujFRynUtHM6dJpxMxO+mmoCWjQKojtLo9jr+HnNw5R8w8qvejfJLO2eSwe8txPvVz9VpTxIjjmSpajsj3Z0tpjMEKiJ/gXm4YbbQ+uuampUEz6syykLqxkhDM3DmjUuDU3ZbuygCzgjEjDg2kVj2TJvTXh7Qn2rmQA8rcN5iGZF98yn4jAtU5LMLdBZPjWFdpE0Ib7JjLplltMWTUGbBoSKXWuVcz7ZzwZQSGpT7fgBgSUcV8dmdNgBuZ/Qm5VbqbXFPY6fnhmDzjB/Jl2oKpWRjBWXoTEQoNnMnCJPnSsX1tIx/7DvSGsW9ojxv2xw3oe4KTk1yUV1+cXqvtbeZuMdo0gJQoRkjRo1XIbjQMQtbM745pLdrYc1OTdBlDbXrZftmdO9xyesqhLe6tkUwziQuUo482JsxDJGCKuXj5PUtr1IL3E1yHctbWGyzKB1Aqf44GO5/x8SlKO0L3yOvF8fMhM8yw42pbnbmbgmBkekEDLO4TilIuWeRw2UjBSSMaagFGGZZmQgaTZJal552ONnUHR50/pais6JLs69i2B1JpyJZKaQsmZNUGVYlwZMo0kujNLB3IKTSMYgfRqUSMbaqKwtQCNECS4KG5hEOz5nIYZO1qbhk4BagsH4qk7YeE9sjfmPqNeS9CblqvdFwjxpyPGKmRGpiVF7Pj/bySmzA9l0R1HMBx0LaYe1L48eTsPsrIe9/Q24b98cooLphuqo+fW0WTgTWVOnITHjke641eMv/K/LiwoT5liaAz4Jgjn5s/v5ZCi6YDM20uevaCy4y8AL5g9KbOtJgPvCma51unyD+nt8INprJMmuVUNM3FpaGmgsz07+wKGwmn7MTAK282KAUYyt/1b0alZO8xDSIj3UBi+SzRANDwjAO+sWxUzOZAxj4GWh/wYUIikEPpp4mqWQIiSslmm2T22WKblOQxvXcMVjss69heJnrbt4dgfyAbcWb0l6FgyJgm/3BSF6Y3CCBUqLOeP6OKAwI9AiXK3HPQdbMfnw6qtQ6AXWKlcCBJbxw0aUhlpMpk4xjoEAMYaErFjvFZDAL8vNEb99FMO7I0BwWFkfnX7OE3KSeJIIu6OE+5/UyDBPOnFasSIycJ2CRuv+j1pUbe9ge2xw374y7p6NAD1iY5wTgAUycNRYSIECEkg5e6CMjygE1oOzTFBQEsXnAMHiT9UReV00ag7yfZYpyGEcgxJiA2z0myisBB9pS5FUUNKQ8ky6mhBUWz3cJQ7ORV5zInHHcUdQMlGMhnwVo3ADttWohBTTZ8Rr+iltTMv0yTYlgy7NIrQ1G/ZwkHufFA1ONEThGM4m5kpElEaYOnbNUoTEkRSi5eBqyk/uPgVomyxpj6cJirRt3hfiiwcoSxi+XvEhilcQbH3c6v/7QZBOm/Ekwnksi99PnFBjjYAVkiZXPQtm/MwRNCBZ9HEod8PyrxmIppckzv6v4bSPILwXRKqluSn00aCTm4EYHRdtX0bbYj0whzqAVQbKFjnUgELA9rj6WEVzc977uUo+Ji08mO+Ttf9PpSI9+2Gx6PVzbq98aeHpEwNg1Um+CUY/Oac263HoLY9T6nT9rxdQQ4CQRTbDDiaCEttaLWBaUukqk2eEbmOEEWN4yMVjPfl3TVIEitRdMMr7CcsFHQGjdWRjfmV8dCpwR2VgsE1XKOuOITWkveAtIxp75b95v+LTKu6eEiaTGlBMHe4dTbj9SzG9DajohGeDkAVjoYlcpJ3pqquyFxNqsBREa1mKD5QX4enAHTIq+CyghN0mV7ZykVOcDwA6iYyqwYcFswwIoMbVNGPhKat9m/Tkdv+j1JbufmOr8IhTGv1ZDz7NJw933Dtm3YNw1ULH5KH4lNsuSbveTIZyqS1FVy00fya64hHPmsT87mAwLTJ8OkqHOIvlx8lNc6A1lKgpObytmCfHsT6hO6y8pRXqODwqEvwetfGcl722hU4CZJz5r5cY5fTkiS43ZTIff57phsbpgbye2oICgMjxHTSAiBXHO4FhE5J6loQnk39L2f/Ipl8nv6WnrFocpBJwyUyAch6aK7arsiWrKhRMac3jOj6CEF5WSPEUdOf3R74YCgSmeGyJypLIMn2kMO08/aeZb2+PlpOHGUFQMaD1VOwhaoSYTtn6bmgghumjFAYgf8ucxzXY4xp+iGdcpLvYjLIOppGsBwNsau0UtjpDHya5mi0JgSsrDODxxOP+cM7Bqa4UgG/4DuqSqDcvnMquhAOtpjx+N2ZzpSCoChgYc+uQqAAz0GmlLPw7mPCLTRUMsyoXcgVXKY15BlVCjA5IAKljZzF/Kgikn4WhF/wO3u5cQrhE2OpKh1N5+9uU8F3jFBWQ5EnXlf9PpyI+8bpqRTkCUyD1WUNeWHfZyMVg/WRfwcA+JVmkGmq4d6h2kloV+SJhYHYkBQudQaSZzW2hQozNRNMHEOHcycUf87D54IsKGmt4Ft3+SQTS2Njl4rYnTmt/WYNApTz3QELAZZ34TUkfl6yED1AAAKalzdRohTjp1/TPlgXueHxFxedbLGbpqTOkkwNtPQ07Px5BhooI2GdIhVDTwatmiKbPq7GctpiWBgkqo6hydm3r3IoTCdOMFpBMYwOaIDTmaE8uw4NGPpyOhwaKSCuEnUpcCnKsLC4CXjpJCQ/PnoA6N17GLUc/BHH4nqAsMLhh8z68NZng3L9ORY6x6aA39qskrnPENXGvP0tfn9wxBdCChwQHEF6QNxylnMHZIgQDfI7EnvoQ8mn5ODRb7Yjr/UyN1MEzwABoOs6x2Jfxb7MxJk/lSyNxwnbfrJE2f+NyOH5+YWDNefOgbQOobr6CAZcuDc+GGIOKJFBGFq1/V01Vtl6idB/5HTQ9FzCCUw+ihfVw6c75fIxTEQCr2tH2lIMtwTQsoI0ZMtPmqpQMJY0HFGzDp+WNbOxXkoHz96ih2mvvEyu5+4tr13DEjCa340NRTOUvNaAVfjiGSciJwdfmive9O4JJf2ICG7opcJas6uukiGXL0BSmuYZindsRSkpGFLfVcKJtnphuismbvvaLVwGo0ZPJKQDHRFQAaBhrZvGlxBy6PUFMd1+QA0+59agYAPTnzJBhhtbUTvc1oMn1NoPgFUvnQUyyec9zJ0/BKv0Y1z61xpScJ15PskqkGWxLQnAFR3VAWVSZNYPo5MTfXr57zv6xj5ul4QQbUbykD0pqmmx4UwjdLkk7lrMdlBMpf4IN9ghMfcBEMb8wxR97bDlw5qhlkCC0GkmdeHMVfBQDTm2V11/N5JvvSROSnFDiPzQmhEkzZZ5ms0bsNQnmcGeM9RulSaZXXAMNBOq/sBMpnIJ4tyqslzFTCnrSp9iHHoj2OQ+BlKKwAdT2U42GckTHZlNEmGUcGQ5Rwo2rMGfxx3ddZbJx8CYI7bnnmlpSM+YPNsvoigwQMYfiCHMVjciB4itSCVG59vcU46ocM+9AuIg30fI06lNz2zPFXHjg0/eyIiNIdt5z0p/cuhmumExujYd6YI7gWt72yEWhZYVBGNNo1ujM7xV1qMFFOlIzwfHmGGiXjy7ylG4jXL2IFZ0cn57qmwhH6v6HnmUUgU0LQZsQcy2Ep+bB9JvF0uV9VZBY3FqKaSaIzMhyzTJRpDHBDywBy5SVLQkgRCfOCFBgYsBlrbUfs+VUVSh/J9QYPWJ2O0zj89j+rJ8gZnn5MxxZzaMrXPIw09pDvvs9faklAKAKOjlFBnkqaDEBALAuu6PoDVMf3yPK/KqAMPkVOWpJ4ckMfRnttH57AKQfveQgbnp8qE3t8OiM7D+poOFzhSh8mXzEkxekZZfVPqkHvFB+agjXQUvQ/kZEuXCjAdwWzJRaAWR+tZggzMnoZMnzKi59gjObzhecSUausj90poyKae/ch2ZUjPrr0QqesGegMdjEH1eSHI6LNqQ5Rk0/h7WbDUihyIcXABx/CMc4ALkajJosMO0iyhd1iI3DXUckzosZKdiYDLiQQkiPnc4I10fqFegUlghipIjo9n16lIcpSqSKbe5H3fNKEjH2yKOU45kuC560YxN9JRKklpZM46VxqI1nfWDQcNt1tTvi8vCZuS2jTWVJj1MSS93fT7HDeULYdD88fOUL24zxlk8CQ7ZKQyqDE2ntfVs+wDsbPH9JlztLCATnPpqAuJqkQ6JM9MSCRnu+Waya8oJ0/Bz0F0pvBEBwokPB+HdsHUycVcXOOOYsBLDoqIadTniBUInjsG8FmMnOMeqFVHEnlB0bVxgCKQ5NSslCDrtipZitg8pslAznFMjiEiUFC0H+RwREzlyaVzcJYRVptDbdCazuMp7lWdYgQGzmx4inFiXn+qAOuo0wHVqtl2iXqmwg+YhzZOFGXimrJ+7hOqH1ej0dGJoEJpJTRDbroN/Uf8QDqyMBMKzrjYQWCU6Ezv+3FGfsBK94rRGcxrQKNtRXRlfj5OUQ0QHIR+3+di51jehIgZoY6IxHdgg74MWFElp3h0SPmlDQHl6j0PJ0gGGnoggllHnZXOxmGakw11uumSpNrKWVyjM2XY9zbhY0I200ZPfmDbNqr3VLa6XFfkMP4iAqk15dtSY0EbZYQm5Shq5IjqdJq5MWqZt8GqQ8LmvBa1syZI4rDNbMldmJbkQIbMCfX4UmCED35fB+xpMoqnOUfCZZGnhlnWDLAN9hi8Idgg58xny+udY5FPRo8QEepEABMRuc/rrg5A9WPOWgtNcWHKMbJWnVLXyPtTsMVJKgpgl6GXDCjFUUT+tdYwh3PkOlmZ93/UrjXlyE1EJQDN6D9aUQ9DZ0vu3Hh8FiW7MoUERyjQqlSqe2UKJGf/Ba+vGMls2jwOYMDLQsF9NTRs0tvSAJN5TkHLXATQoCM3UzDuuzqQWH+t82J7HFGstYbiDWYVOeSR+SfFI+d5XVlHzuF6ycBH1tIVqZN0m0P5RtcJFNCQCZ8iH7eKER2ja2Ty3tiko885WG9Mw090YJ2ZOPukmyiKgdIcgcFzzuActdwlutgNQAHc1VUYk+zLKSK9D9QS6JVOoAiu1WKzBj+0IQ7VGhAZyRFAA48IdirNkgVOGCtKbObxeThDOOeuFSpfpmNDOk8hih7ZshqIREnZntzHFL9AWvLzvP3ocVyHmRCRYSllfm12v5VsICEcnsMdAiS7lEoV5/N2dRtSUsq9m79vc/yz6XkGLDoKfRB6tPm8p+ArY7emqdLYtW7SKpQcQxZccw7gLBRkWhrnwccAR5kuK1ks7aliFAycVetBiJ/X8xFGDuCU1GuaijvcKhannG/fB6jZcCQzmh9+bM6cnc2LzjQ+a4eQpJOZeojxJanR2o4BjQPylFzKq6rM0qcxn417zHp7djPFaIzIInR6a8rNBrIJZshgs8wz+kD0jr41jQeK+fW97XwYEdj3feZpYwz0nWq0dV1xe2yTPMkTOCCPXuh5QEY/RJp1oExfidktdswK5BAHLyIC97n5HZia/3mIHk0WY9gUdGDk3DhumCxt8odtPh8opfKi89w1dz/oBWWUR7fX2TEhCcIJU6Xx7jyr3aDylQaOEA2qIiF47+UwdIPy65Bx5kz2IA8xYbtRLZiVDndHiUrojiQaAzG2mRbM1K2Uw7F6YFiHh8NH5udMNfPM9zyT/NyRd7Keie6SmpolSih9zZ2fBKJ+k8+Sa1SEiF0NWUj0OFGv4aR7/Wmvr+gnl8GaiI8cezSUUxZDjUrP3ZT3JizxnOBKr0+xAE+anKINbZ5cvNmQMmg0JHt2Rd5OzzsWBAqaaqJdpGD0ht73+e8kyNKoaehNYo2YI3sBKqQY6YryoSSM4kAIvaH1ff5eHrPUVdfujchj33fsbef1d46orqUKYvOo4qIurFocS1F5Cx0jHAODuSKO1CdLVxlJAqa528ckEvOGBVUoSc09kbyD5MYj0HaNzZ5TXkCGI7rEHGo6MtHXzm6t0ImfhxEMOTvNFpBxD+W9YwAY2bob8zlkGjAbiyJ0CCNJUzc9m0FGh+2/OWefSCVGo6JiKI0ZcdTVzea58RCvMvN0cRkE5g3ZKJKTd2tdYd2QJ5dkoGWX2OmkWynNpqOKAzDnKKwkQlsfs5ycpOIsKSel4okKTnV1+BT5ANm5GczFITm0nDpgM8382kaeZzZxY2WcY2Y40QrkheS5J4OtumeSa5CQgqUb1Vp5hyypycsVN86LC+Dx2LDvG9x3LNHRMDAaO8y6YB2fZUNrG1rf0No+Dzborc/Wxb63ebQNiSIghljcJibYMtdR11ono9w7p6/MgRIBNko0ev+mQ+taG7jdHrg/7vpcbtZSKoqY27rsOZgU61JxWQqhl2rWprPLYZJ7gqeVHKwzm02qjgLOIQQx2JttGpGV6C03IY/J1bMcWY7Krrqu/DEhoNog83pg8ItQlwy7ZUkrAh8OP4aMnk6Ksto4HCNoPB2B1hl9YRJBtR2sPyglG3y2HQPuTGlqqRgYGDbo0AAcXXEDxeqxTwOTuEy1JoU+Imh1HFVrO2rl/IE+OuBHVeHcXDLtIjJI7HA5cOCYbDRLlBr08UEqJB4gh6qwNEjFWjYOJXKvtShg8kETwRR0y1RaTU09vguK+B6N/CAJRMIkjHMJWdJpWbKteT52vmyqpIpG7bpVZB8x5izpI6aEcpFsQOm9o+1Zfx2Hc4CQQQyM/kCPB/rYELGzSUMRvfVdZ42p5hosv8VIJNBVszdsW4d5g2NMNNDaUatNyWY0PuR937H3jk1R6rHteOwNjz0/j0xUj4D3hr0N4L4RsltgXQqerxc8P11QlwABWdeAShJwbo6+d3ip2rSsWBgfCMZg2pAR63Ipk0HPvTkHJ2iDAmrIcbpsDtQAWtNwTAk4GNUbRid8TpZ+18gpg6HikDDzpJcsM2l0ds/qQUgQopRKTpe95g1jsNxZzVBrwbIo7RcD7jZgVjicQXyXmUnvHml9Qn6HNiM1ApkGpJH2zjPp8vTUCJ8S2yGnV0vhsU0gj1T8mK6qjf9BM8kUeU34rH0cMuxgqpBpkiWKTcYTmO6SCVbMfH7aWX6GHYIqO7zHRxh5yZo3gYibjjKCWOdzW6lJEZaDAXXUkEv9g/RullNJ5OncwRNKdSdMu1HcUFvFtm3o0bFvZM7nrC4vWJaFxtd39L5h9I2CnbYBQUUaRse+K8LLcHP2FyZxl6o01q7zONkk2/Y25jllow2MnUa+tR33vWFvDXvv2CWpbD3kONhGi8FZXp7QfuyUy26ObR/YO/Dm2XG5LABySMY4bRAT0Ui9NWxgHyprKVL33tSH3ISO8jgg6LTQZL37dA65SWicjPrdnWPQCzecB7u69u2O+43pV57z5jC0gCofjpxGG9kEhJjwc8zDAIG+b7g/bri9vuL28oK2cz3cgaUUPL95IqpTj5NbwS7ZMB1QIFqf6V6eGJqNMQWV63Ri8pnq+ITGEYbWxf2Ezz3Qtx21FKyLTwfZEx6re26Kiqa4SKSyOh7DkCcLI+v6tdYZ0CJSFDa1cnwOSSKDXFaPMdHTtLEwBDg8czrUL8nHv9rIPeFbSIctbzhwyvfYTNB6I8z2NFZDWSpKPYi3rHfOPCTSMx2wMjd1gItZizOSi+X2dA7miLFzY/YdvT1oxBr1NAY16dv2wN5ooPu+ozcZyqk2DWB+rmmBye4P7PvAY9+xbTtz7tbRdvYi763h/th0/x3trK5S/pbTYLpGHrsbB1OOjr7tU8W1LCuWJUQIqVKhNfZCHf0MWNp8rYW09S5Y3NGaw2KH6TSTuQG1Ub06et+nkdPQB3huuwGtYQygeIdpqES0Da1teOwPlFrUIeiAZKYzqROMhaK7Zf3WBko99Sx4YN/veP/ZT+Hddz7Dvm106rVivaziXwyXy4XX5DHXNKJPZxYDGP3gdWatWDm2WzlpKLK6Q3TRuoaZKGUcEROZ7oNrl+3HMyBZjo/i1w71oGVGrc+AVJSE0ynFZsA9nEdq191VUZk2x+s55LH8d29d6lAHPAHAUcf/KCMH5F0itdRGeBwhrTOOZpEcOp8Gq7yrmNjbmgMZ+SdzlMyd5stYgR2DkalWlg84ZbWRaBAUGr0o/2+a6rmjbbt+Vi2H+4a259nfDcgBAXlvA8jmfwfQd46bHrqWPgZeXm643x9ovePRGratzTx833iSZzaEwDCH/8OPXnQHiGLCgV3NN0HSa993tH3D6IvmyBeOHtKTTIXcmAk4Zt4XMTjfzgLbtnFzRI46JhR3yUhzbdNTzBKgQfpu5c/7jl46PAqs8YebGbb9gbosJBIrw+wQsspjroaK5TT9NIAkWhltawEcHdF3jP2Bvj3QI7CbYd9WoDcO7Xj7FstS6WyUcyZ3w3o11Zc2OF+NiOVAP/M0VUvJrRzuaOh9m/0Gh4YjJ8A0bI9joIY7HcYUcJlP48u8P0DnFWcFqGyl+HFUVa0yUqSDZcB0pJaE2gLTeW5zok/v1OlnW61luyz3cdanvraRR+beM+/JXJgXwHponxpxjEOAQfibv6Y8JhxhhTPYpmXbJG3sJJtF0pV0ixhjn+w4AoTVrmklEs30rjp265yGsrdJwk2EoI3sJt0xd+a8bgvJeAPY9obXx4aX+wO3+wPbTta8ZRku+FDHyDzKYMmA8+kLGwxCL6/I6TQOyj/NGO1bp6ba1SU2dtaWS60kZozEy4gBX6oOQ6CTIgFlTCv2DfVCR+ieNdyD4AR4CAE3lnIjGUIKi7IFNyNfKhk9qKfHKtGKdzWNFAw75vmJsUOWepJQJaG5I/Yd1hsqBp4uFT7162SQ+77h9vIexQB/84QSFaHOMzPDoB9GzHq1s36slMCsTu1+NoVQPag9MXKIRiJSTo7JtmgXYbhtDQZHqcYDIMfAuizHMAihlRmoAlNjcWbd82QhgLxCfi7ncWafg0Q50wmrJ7ORCGQ1g6SxGzQUxHVs2Jjk49c28uz/PY4y0kaedXMyvdn0AT1fZP6n9yCxwaRrdvloKfgQDkKBRk5dbqRqFpDEklLaodJV7GMaeTaubCpdZYPDvrOhZvSjay3zxZGHxyF12YHRd7Te0Frg9fbA+5cb3r/e8HK7szav44jnSRqCaCbCkJ5Ngo90VEoPkQ4qSRiwzMKhk4Gt6cAKzcUr1eHS0qcbKSqbjKzPgs8eYlpHHxiPjaRYBRYzDHRQTOFIcM0Ow4LzWKs5wTXyiKQxCSEgKDempA5VJUEDACdySC1/jtGmPjubWOiA9/sNt/fv0Lc71hKobyqe15X3YT6JRSAw9jv2B2CxcHaez6uf+T3Vjxrz1MnxdNf9gdqOYSZJMh1pP+0rAGixi4dxFM/jnwJmHV4GRW2DRy3zPHQq51JmOkbM465lIVqxA3UdoQ84ypSmTpMj3XGQkEsE4Ap0Ka+NNqYCbmh++xxK8gWvLx/kqFposrJE6IoekYbKSz8buqNy5I6MlmygxATKU7I5PmFjMsBZo9YnTTLC3DiJNFTbPIthekfrR84cE5ZJa57Q1OgsUkJ55ONSwMXA3vkej8eG19sdr7c7tm3H47HhmC3G666mAxLyeiNFND619fNoXQAYHEXUI/vGB/ZEOvcdAUdrgaVWrGsFehKeWT8VOdTbzMHmoRZMlDlRNE/FlKOphevnep6EnxQv8dA/n+uYeI2OeswolJEIYWjbgyhqWcknaCprRvDUyGcnm1lH2x64vb7Dy2c/ifvrpxjtgVIarqsD13XuuXRAvcuR9RvFTpF6CscIDfosy9HBaIyhlBjoGZ/WngdimsRDVVp6qedCKj69KM9mCW/fmOKR7Xf0MgDPsV1AHkU1VXwiTdMwbCLSQ6bsEOE8ToddsBNH61Ynw4/M2w0wOLpz3BQ8Mv7iaIX67q+vmLuupvSMOMq3EpHN44AkfslxNnzQlTJYrzBnnTiUvx3dPOp1LhVmbFCxAXQpqXiQQ5sTMYbquzkZZYw21Wxte+Cxb2rdE9EyPyuQp0OipOjbpHGnWyf87ugxcH/c8fpyx/uXG15fb2xNHAesNaOgZy0FsCGPfkhnyWRns4ljDELpgeyQY+mHhFwAPdC3HX0E+tKwrgsCV9hVB9KHmjXMxa7bNPwcGtFjzJ8NBEzQf4yBXtgXXYpLwmvkSMClcKGbYhJVeCVTHl3RldfOyaVEE6MPdHNErRP6llJRHKg14B4IdPT2wL7dsD1ueH3/Kbb7dxDjDrOGUjpqPZ6HF1cPt2FZmMbQcbEU2kdgdMF1OKysGHChSfq9HsCuUO0BXJaVNXAzSUELIpqcV51tnkXDEWMYutR1296x7Q1X7Z+6LnToBswZXtoT7LtXWuo290EGhIT1HHRSUMwpZ87RVBnZAXCgiXoD9o5wtQ8LCWbuHpP7MeTYqq9t5KxX82JjHLK6Jngcc9MmLNclBkX3tWgCyZSl2iziH+hFC1KKRFjSFoOwdd83lb5Y74ZhEigkPrqidpcKrMt9it00Tv9wnOqKurumyZ+IQGs7tn3H1na8vn/By+sNt9cb2tZgI7BqpXLKZnXDUjkmqnigOCgKGcy/CeuOUk3vgb03pFpwdPDroLiE9XuWCZ/iGGAAY4uiBZtOkCTQEXMBB53iYJRhxGoYjzHLPeuyYikF4SQz85wwIKajoNwy9ehQ0w7TDwwmF4j8eVY1tk2svVGi69W1Dg88tjv2xw1te8Xj/or98QqLGxZrsMK9U+zcOFJgPmRwgbCgrFWko/VAGx1tNGzbQBuvuG8dr/eNazkC+8hrBtZS8ObpGc9PT7heL+glsFRNp0kCCzg1xwQCOm8PmXIOtOBZf2EjgSmRi0pqOfSDBKS4GO2TkRCcrvXI1S1bWbkHBwAbjpRzZ16PGBRrKa01pYp5KmsgYENy148xcoBOto+RcREp7jdFr8QMOXkEGKi1Uj0lIs1MctEJngRDsv8z8ktDSiJG6317YJeSrfeMpjHz4WwGaL1jb0PwvM8uJerEeRNEIpn7cGFt0LBa2/G4P3B73HG73fD6+oK27bDRUL0fum3jvbnRUC7rgqWaFGwBH5ylBvBSCcsNrXOA5LaRDW0d2LaB+z5w31hfh5dJdJbdsCwFtYn00eH0pThcm2BWMSKmhr6UCgdz16xlFy/oplHU7qjuWNZK9JWtp4Lw6XMZsTn696xoC7WNmniZELwfDrTosKUCYeh7YGsbHvcXtP0Vfbsh+iaCrcEkBKI6UuSnG4rTiUWw1l/cEcaSmcsYqzkuywWPR8Pt0dF6R/FDvfZoAyMMbW94eMFj23G73/H2zVs8Xa94ukITg8XP9AErnHlnBgwbOjEG0roTJeV0FqYxWaITGaMhi2yY8Rl0xqm81sdAKZLMas+7Kidc04K+dVYrpGaUAO5wIiXz+IDVtDdMjumjjDzhQ+avdoYqyfS5o5ZFwTMQDtTCKJ5NKaayGY05SxzTtrVpsvc8CTDKDZsieZJMU8Fk2sgheGU8VI8Oxea8ssyH3MjE99GpJusDfd/RHhvu9ztebzfcHxse2x1924BoWIrhui64risPEURgWRbVUB3rpaBWx1INhg5TbzgMUtcR/o1grr23FX0aeODlvgP2QHaljeCo3zu2eQhe2Jgws8bC6DDazNd627HrdJhlWVRvX2ZFhMMXqNhbvGBZCtpgzpeHK7j6n6Ho4mq9mueIR85hz0g+lMAZa+gYiPbAaIG7DcSgzLi3B3p7sMQFzgRYloIYDcW46cd0wMpJATmUIUI2NzozreRy1mXF0zPw/GbF823D7b7hsQVebjtebg8UuBjyB/Z9x2Pb8fx0xds3z3jz/ITL5TI16g4RuRgIZxdiRMCK0yGY4stEP5BuY6i0Jm4kDnFVGt+s2hgm1E+F6JwVFxByFamW8wL1c6xOOPLsPPaQCEE7z8H7oMHo6xm5T8POqD3HJ0XW+VS01015qShVhuCHmGC2cPoxh5vejMZfkIQUIXeXkfe+qwkiNfEhmMITLIY5ehhgFe6ERHl6BeE8HUj2KSMG2t7xuN/x+vIe2/2B19dXbFvDiMBiwHKtKIK167pgrQuWYigWKGVhxHRDKYa68L8AlW05ZopEETAVVTL03gLbHrg9OgkrcJPd28BjZ1lyax3+2FBqIZlUqd3uLUMuHzA37wO9swttrSuuV6KZNPTUrbfe0dHw2Km+uj1uej7q0iqUtJZapE0wzAk7RiOwZN0jSJ6EoW0cCbbUwjnnsaF4YPQNfX8A6KiuCanG+Jin4rpzXhvL/3aMqh7Q8+KeKoUOpXimZzxx181R14qn54rWn3G7N7zeGt6/PPB4NOx74PagruF2v+GxPfDYH2ij4VsGXC7rDDbZP44wHZbgE/FlVSjJVR7QUJgeapjiGGxOCXM1pPjkTY72WKEllc2yN51VyoEc5zS5HWfUT7XcGGNG8/Qls4r1JXb81UYerNceQFtMcl4IygeSRpJLgrcy9OPsKztJ9w7izsHqEoJOpPWdnnIcmvUhljyz6hGEsbTvha2Fkk8y2jRdZ+br/HpvOx63G17fv8f99RXbY8PoHUupqLVoxvsFtYIlqFpULmKOysYQhpc09BTTsISWBgKkJh5BtnjRuLx1H3pfYz5fBj574SSb+86W0G3veHm506ldVurMg+W0MajYyj9jMI0INHjvPE3UjtwvnXGPQanu6IiH1HdGkmlZKmpZUArltZzIQvFGPluTtNkNyDPfRm8YPXjEtTNiNxtENug8zFD1YZ4rT307XPPwxZcAiRYNXtM55Rn0SWIJlpqIVBNxC8A7g83zdcG33j5hRMH93vGTP/Ue797f8PJ6RxsDr/eHFJiU/S6axBsRiKZa/1DJUlWX5VqwFOoUYgz0fN6R+TmvZQwT6RtKQzQbj6wpESvVXMr5B4qVoz6veQGzHTW1B5YclOrxJb2Ffnbu948xckwOa+qC0wPNnlpgGqCJkMqcLbGNGT6YRHpIbaUeAuZ8toQxTSIWImD9DJIcAZCsb1AKiCGsB2qtswacgyT2fUPbN+b5txse91eMtqNg4HKpuK4r8+CF9eW6GGoRI+sGBJngA5nMO9BV76AmWVAZqeQjIx5gTh3SnC9LYNFnGRqPn4KudegssV1GqnbF9LTbtjEPFwRcljrZY6K9ht5IChpyaIIi5BjYhxz3oEzUzLC3BscDpRTcbsb3LDZVh4lQisko87is4FQdGOfg1cp1MwilIeapLZSoJqyUEtDyZ+kMVXSbjHS2h2qDUVCkYRPs5FLZrwDVDL4UPD0tABY8noLPUFr8l/vGlOxxx+1eSWheLkCQkIwxYHWZZTFEZzDpDo88WhusUSNLsFnhYNphp+ktOR+d5TYavam8SgQnp5ZCJUsHoKEfliOfVP4VlzW6ro8yRczjpD7GyLtgNAT52EHUVLdMelzaW3mnUil8yR7oY850fJDThMoUeW3U5x5nV2WLaLKuXirPj1a+20dXl45NiA+weZ9ESMwo0NtOo3ncsd3vGPsGDw79q+uCWgzXS0UpbIpgFDeUymOQSGQWQU42z4QcDi8hgGCWml55aER0TtNcV06z6TFQiwp8Ri6hliesFbDgfb1/DGxtoKPg/mDTT8LvMTRII+gsa82JOYy0pjJna/uE15latd7ncAwvhHy9A+YBs6G2Wp0qqqoJoTvXnGORgGqBUoDrWlB9oFbHda1Yik8jH4NHDuXZYxHc5q69kIcj5raGZRRMAowVmpybT/l4ckIaS5ZadRG/pRBqI8gLFBjePjkcT7guhs9eCt6/AI99x/54RbssGCv3K1WIQN93IEyyXWODTmOr8uhHb0aX5DTHecNMFQMJes00Untosk7Kcvl8AGict9pFkeXpmNHfjlYPBr8RdDiOyaMEcvrxRxp58QqMRvARbCXMOmy+JYX6Nh9inCA1dLMZgUik+IzohN9xIsmcGFcLhcw9wCg+fw6YX3cnA4zFEQM6vTSvLruPSOT11vC4vwKt4VILLguJrGKBWpj7uUN5aqgzitp5JRhyXH2SgKYcOR90nhqTHASyGqHuPc/JHgishXru4gGPCw3YDC02NruAZbHHTuSU8+1KrajaJKUwZ6vlOLwgRqBj5BARKb0o9fVSBDuhzcc6dYp2tpbS3RyOyOjf1etdLHBZDNeLYy0XrNdFf1de79yyrP7J0LV3cn3M2OraRp4CIy43A6gIXRNr1cGji+eIpMg1zGEKfNZkuwdGNK11wVoN5bnguj5hXYDLMvDyCux7x3Z/wWUpvOZ8lqDDHq2rQcWpOxkUYGXTCjmDI5iUmgM/+sGFRJHRiiRDNrK4EKH2Mq0fPbrIQMf8RkgDoVJoVxQnWQmkVuKj+8mTETyfTCIcBbhmdmYzh4x6yBNFYOZVBmOZQj8zzwSHmHcbgunJYhZJIzWWKBx5hCzEKFbliXmhZqYzyGKq2yw6BTMYaILsfd9RjVNZigQyNgkh46DKWpRrM8q5GM0cxhgiW5KYyuhDnoE3YVATDzSTXkcklyLNQDqNAqyrw2xBQ+DeA7dtYB8dj33MdYXe20/NGmUhhK81WfdAA2v+MMLBpdZZZuy9w2XoWTHJMiQU7XvrjHTbxqhjZMKKdyyFHMXzU8W3317x9u2Kp2vFUp0aiuzsQ27qtD+1nJZEdSRG8xCEokag3NyUiVJjAGAeGTSPEFYKkPuIvjSAchCGZOIJCYazPl7KFZfVcV0L3r081IOwo4+qmrbN53Kkjdzb/TTTf05szZxYHEUkZDe+SxaNuc6HUIqIRPV4kW9lTvNRHXx2vcVMEQeIlNJy8nVm9L+2kXPgwq7RR403lfYMTLY8ALkjPVYxlSmk8ODEy7SEgB0/b8xOkiMMCBoGYM5TNxGh2iU3o0m/XWsVIqD37E1ntQFavBOJAUwnVYoYZKchuIX8Fks8pR7D8U2RifBbp4sgSTAST5wnpoietwQpw8QhEO3QqIpnYwrFFfmAnxrw7bfAfQvs7Y59Y4kw5aoWMeXCE02NwLbtR9NPNKUURXXyNjcTN8dAqbpObZAxVNPvDa09WLZsu+CiYa2O50vF06Xi228v+OR5xdPF8fy0zLIW1z80dEI96toRYTTkyAhsJN3OM8qnzFnbN6edzhQ9eGLIWSDCVNEnxE0naBpjxdlrdMTegXVxuC1Yl4L1suDltWlPdsArSIZlKgFp1AdgrBzUXlFPnWt5cflcuWd5HVPWfTK/7FQ0c3RjtYjz8YVU5fQWr3NyTA5pzAAyy9raA1QTm9bjI4ycpSjVSsUoHgcY2PSi+YFIskSedwwplkrFAMtZx5QLfYgeYtdAwNZ23B/s+GJEJgwiqUE4ZYKRRUMtSCz2kwqPRk6jpmGb0ZUU51y1OqEyoaI7aOBz3havsRaVNZBdRXZyOMdaZTRMQikNKLkKQniWXXLzuwFWfE4Aua4F3366Yn8TeNw77o+Gm9h2HsLHnnVWKxzWhKwGWejs788e5YwSWUKcDm+zeW0ZLSJIWEbfYdHAyTUL6lLw9qniF3xywdvnK94+LVgXoJbAZfEZmYYNlEXjk/sx+kuQToan6TGWa3PwBSnrNDOgEJVFYDpE4EAd83fsvM40+hlrxI/kaSdenaq9UlA152CpO7adA0DHNECioyYFZS1Ohd3YUBYOKuHobE545aEccZB1g+gsSlBsZdmtls1eBxTPDkEGyZNytKQhA+FH/0bM+J33nmuTMu6PMHIk0YEUQ4yp9kkIBssApgguCCjAM/Nzdw7QozbZAfR5wUNtdJz+umN7bBoPJOMyRV0miEjByZT/maSuI2YTCY8/ogItj15GBNbqWFeRbJ6dRGIx1WxCmSZksENGpDRDHXmlHKds5FFNs2kvgYrWyFLHHkM1dcwWQiscUljhsDIwrOPN4vjkacHrvuHedrSuE2QSnXhyHdn/PE6bHYqWR05LkZAipiJfjjE+w2pHR/XA9VJwvVQ8Xy9483zF2+eCbz0vuCzKcSXjzZLacPVLyJkgB4fonocQGOwgoOzUhnsWjWRinm2rGbnYRKOWSpUCIwKjcDZarWyMyaO1AQYcOh067qI16z1Qh+P5ecWydbRmeOzAcB60sI+hfoOO969U6JWq2nxxXOMya9cuGqm3gRg8HPPp+ZkVJz2nARwNLNMgj9w7Im1MRHEfs410Bk5NDIKHSoBCPS4E+8Vo/SvY9Z6jjnNkLj1NIHuyj1b1Lr25yWsPPWhO1eQRS8mmDslkc7AfmfQdrW3YHw+0tjF6e0YpIEfmGIwqMpXHMmIz2sZ8f1YASABlE0txsB4+69NOFrZQ5cVhhur40QMKza3LSJO9v9l6OzAmAQJgSiJlaxMC8mHGdCJFDPSIPvNet4F1AZ5Ww5ur4+luWCs34DwyyBgtIrIhQqmDyKrUNVsaz4wgckKp14eQ0AhUA5YCrNXw9mnFt99e8Hwt+OTNM56fLni6GJZqcHQ4BjjkRX3zIvgI6HjDPtuFz30Ngq9aC372IEwe0mILFcKoz59GrpzWQARHpWObsDgnqOjDuOEzTwekN7fJE9FobTqBsvN39h5ojx1tCpYa7nemqe4UxQwzPLUd18uKdWEJubWBaNSHlErJsHkOmdBBEJGw44Deo3NijntB5O1PVuDDQBHZkmp22ouYodTOsPLrGDlCXTMzUiT41OTOafpH4s8xzFBOe/xMzqsqVjDypEy93xyZrGb+kACHN6DDDQHWyw0iwFhz3PvAtlHaeZZwhnKc6IPS1dcXoO0ol4qikhGCDqRUO7HqNnvBLTeuHlT2WbuV2b2USCLkoXNAgRZweulzvs75bboPCWY4WafDKlBqYFmAyxK4VsfNTocxMJDJibDiwEghhVaKEEwETg7EmMTJUJ0biMYpLdfFZdQX/MC3nvDJU8Wbp8oSWTHw7IDOKgDEL+DgBtwOVmWcc2xtZJgdzDiU2iHVk1o5GWtyMvCQrj5mlApknu6AFSGqY/1TJBOh9Chv2SXoUXpDLkbfNJAoXYCIhqWSpL0/7nh52UR+qrf/5YZmgef9gqfrBU+XBeuyAN3gVlW2Php4ig6gPFIjph2HsIjtrJyDj8nc56ipXC8KZMrsM4hcfyF0Kk4/UtY62tCQgJQkHiWtucGVo5rqlmxuJwseEBssXOyFtWL3YHNDQHBNByPoUIRsXx09p78mwRGcw61N0QF2X7Vjgmg/RfSIgbY9cH99j317YLGBWihRZYuo6Zw3IOvCuTkyMnluUEXLY/G18SKdzrxMKOQiJ9CwfhvTaTCCZwdUVS01EJW/6ovjein45LridR24V6KVRw/A6lFvDrbnsrxJ+Jt9x67nkFUJM4epvFeMpbC1Op5WxydvF7x5s+Jbb1f8wLee8HStqBaobnQKfmaHTeotnkZiQiFEUpjOm40mAZQDgQGQhDVmapEjjJhiKS+f6QjfLPuzSc5lakFLdRnwXHuDrkXVkAj0vCxjiXXm/w74CHiFZuEbwgy3bUeMDX2wdRlWOV/t5YbHvuP1dsObpwu+/clbfOsNTy4lEqQR57HQYVOJD5tpIAnXdHzJM6QBd+kL6Oxy9PYxaIVjs1370yec/+jDFaZFh95E0cQyfAHT8ACTTC+hWUIO3sSqHIaBscy8sTeWfcZo2NuGvW0kgBTNe8/NK1QwAlEEaQYhf1eJr4+GDvaFDw2M2O53vL6+wHvD+rROg64qifGsrTRUsdC6n4PEUc4PcQ8ypIzOCdcPWuRYu3yQE/7rTVOf7d61l1UyckNZ2Kt+LQXPS8HrumPrbKPseSKhHY6WeV6G6yTaTk7LKGoq5lhLYKkDlzXw9nnBJ29WfPJmxdPzgqdrwfPVUMuAR9I8ZKgJw20aaCoHauoEmq4Jx2YLrVNCUEE3zgaIcXzdWGLNwNBHV0Q7lZ2CjL2AwmSxw43nDxkd0ozO0pH3zp54k2Ylx+RxT5EYixIAOsoSuJjhTV+wbTlrYMc2AgMcONFuDY878Lg94ChYyoqnlY53ONO4tnMxCmJOLc4ydO4D056a5hWpnGOEdz/OlM8TaGyedGMf7C9k5eoLXl8xdz1LQEdTimWfkFY7Zl7oGkaXxkhjYlmhIIdKGAArRk1z7LiPHSM0zFDKtCHiLA8SMDiWkiQXRR0Ul+R89J1DBcQdMG0d2B8P3G+Ur66Lz9p4kXGbx8nQBe9D0z9nipN1/Tg2VkJgGXMgRxbp31JpFfe5qWMaohBCYZ7GQxk0S8xYfbDRsBTHZSl4vhY8PSped5aBut4rTvlr1bryrCxGgszR3RhFqgWeloKni+F6GXj77PjkE7Ll10vFshbB8R0ccRwwsAIRCIpORtbU1XrsJEJDjH+esX6M/jo2nimUJhWbtWZD7tmEiNBkV5sONpAGzj1XdLgjySkgPBGFnoXuf6Dre/lchnL94+cpYOGhG6lYvF4MP/CtK9alovgNr/eB18eOfWcVoweDz1JesPgKe1vxdOEeigCiD4Rz2EOmCqwEZVottHLKpN1yTDPt53yePIlnNdCIrHP1hIRStLn/vq6RmwtetHHAA+TJGzFZ7fSMfCKGOd43OJ20OodHeK06kxsUqnQQqn9wUKFOOhlDU1Azes7wBYBdUa01Rm3VsYeOVBr7jv3xwOvLCx73O6x3+MrccimOIsGLl5ywwuswc8pEReIjSPIwlcrPz72XAOowtlnHlwfIZoV8JQGXrwg6IxJVQiZKUao71qXgsjiFHF5gxuhXPSNq9gwIiQSPQHLj6anF+LMJy98+LXjzVPB8dTw/OdbV8bQugoAqdxommqEYLiYqO5wTv26RohFeCx0+98i5NAZVMOAGG31G0xzwYeUYOJIewYvWW+2tEy1NRvooY1ocTmKSo3o2yQWYGRxFVY38faCNFGEJ+RiogrOCxQssgKVsMOt43QJt6FSdFvjs/Q3FOV3X3OG1kMOyheSeJR60ScbBWG1J5Jv7+4NSYkpguWmQDXwhbVTR5GOW0CQP/mLe7avY9ZToYXrpfMCzu2y2dZ49Nw2cD4R/eIrKgnlAoWakc7pqQwwa9/Z4SLFlGiCBWYowpwPpOkb4sW9oKbWNAAcWNDxud7z/7DO8//RToG94c3VcLwuWCnjRQfXO6ABPKSaORoI0VDPMQXsB5FnmjFZDijY7fh6HmEKhdTqGfAjnmvCM7nNH6qFI8lmL0SkZGfBqwLDM7QNZ83Dl1wbHWouaSirWWnFZHM9Xx9snx3UJPF0q1tWlCSDZWItEPsYI23VkM4WDiRpIvpYUsOQ1R8jRuWxyHE5f69ZPbH6qt1rblQGGTg5VWFczy5yXrqaVFCadp5IyysVc/9yDx5lxmUolB6A8eAgei/SDUQgDBSxWOoClBt4+8WCKUgzlvuPlzhN3egf2PfD+5Y5aX1DWirpU1ChofUcp/JmSStlEW2N6pXmNlvPxcShFp2YApiOmi/ZNgVl2QtKuRois/CgjFwueXobjnw64HpqnFjjqn5AnmmeNixRKpn7fHti3DdF2jM6zznrb0bcN++2G/f6gus6oJEpteHo7lvV4oskIIoneQ91qhv3R8P6zF7z79B22xwPPFzafXK8Vl9XVWeZHTmtnFdGRK7oc2jz9BTbr4HMz2CHgSAiPANMG2Xc5qOFpxJmbDkWojFTslVf7ZlB0VN2wGFCt4+LScOutcohhcbD+Xx1Pl4plcazLguu64LIuWBfD05OjemPjjZqHvAbMB7ITTSGX6rm8TvUGUGRkM2rnWOK5P2ToeQba9G9JouWdz02chCeQvf5VTThZ9ouR1Qs/nMDJQNgEcvKnsMOY0+keF3n6/FRVau8MBpOsOuihwKyjLo63vlC66w4HA9KrhEmPveH97Yb1uuL6dME6Fux9Rx2u9+M0H2geQmZZs39dm8JgxzDJSKGMREC6rHlAqAnWm6ENIch8Ht/l9RUz3nIDHhErp1n4yaPnA89onyJ75lKczjHe8yH11tD2DdF5Ztm+P7Bvdzwer9i3O8a+a6yQEd70wPCBrodJaM8mlL011igHDeR+e+Ddp+/xnZ/6DPeXF9Q68HR9xpvnC9bFsK5aeKUcNNpkb2MaX0JQRA4vFPERKWpI9hcTpkd2g0xBjCHP/Z45ub6eQyl5DTGFO5ZM/CBvkJH8sjieF023EfGZZUoOT2DX3HWteL4uuKzU3i9FHXWFgxHdOVgjJ8IMaxRXIINoqEGIc9yFOeRwYxpbnnozS4ruVOLl/SEJvyyBHQhwxJjjsaehIlEgdQPqIJ9DF9yzIUSfzY15QqiGVLyl4i9/7AzfWQU4HE3yIXkGWvHCQZfKhy2k64nAWgw/8OYKi41ksUZoP7YNgYG6VLx5+4ynfsGIwmaTYbCoyMETyWeUcpidmU9Zs7lmM2i/uXLxoj4OK8fPwWVfMzU5HOnXM/I4yj4TDomqN3fB7JgwKkmAPNqW56WR4Wy32xTXhKbjR/AwgH2/o+0PRvn9oXKQw7xPjbMh5ZD5gAjnUiH3+vKKn/yJn8R3fuIncX95QYwNl5Ubfr04J7jIMEjaHkwt7zW1v0PROJDzrAlHj0gwUxNFktxm2VEVep80n67N5WZTD5COIgmg88D+wOFI3YDrUvD2uuA6GOVSaVWroy6E2rWy7HZZKmp1BHRoX81DDKXFp8CY95+KQekD1Dqi0zPpSSaPEIfjO4Qtxz7JVfiAObZ+BIA4GmLSQZrSOh7Dy7UK+ZlUF0e+fzqQ7M1PgrdnEFP5SmlDhPTg+t1QL72BjSh9cGbBLv4nWcDIPDd0znpKVoMimuu14Lkv2BGwR2DfGWzev97w6fsXXK4XrJcVe++8Ce9YvIi76vOADQCoasCiE5MNJQ8yQj0UadDMvYeQZMqvJ0/xsTn5IcXT9s+uoSKPk6SK8vaEbpT0+YQ+qVmO03ikGZV70zCHO7bHqyaW8nOS7Rzmk1RL/91GYHs88PL6gtf3L/js08/wnZ/6Dj579x6x71jrwFINlwvJq+JxwGU1pABHf3Ou0txUaQB+rAH3Zsx8Ojfh8X06wmHg6OizF1HkCwRySsh5yH5KTdOxHlLQmA0ifSSMwyFSseCUmcVRC7CubF0dAODZPnvU0HmUkFRSfgy/yAYWtzxxRcsl0c84wdj8/gdoTkZ6QsYzuljmwvpGtmMyxzzSlHMuGlrw/PvUrZsWMxEVMJFSphQ0ZJVWE1WZc2baON6rZxcbgDYGRocGXqRDV5R0oCzAaB3rteLbywV+MSwvBa+3HY/HjtYeeP/+PZ6uK56eLliWKwNe7zA0oiQQWpkN+Gy+ShwFpQ8cn53DVxPSZ9SeqaTuP2vv8bFGzlNHbeZMAGFM9sIeUJSwI0kaEiZAzBQjVUuh0pfwZpBh7a3x+JzOM6ox+IBiDA7xB0sQfVAZt+kYpPvtgU+/8x28++w97rc73r9/j8fjjgI2V1wvjksFqme1lqQSS16mDZcz44/QYfOa7UAkMU4LfqQvmWPNwxsEJREpNzx7WfEV+tzcq1EA6zTo/JO5afGCZQlcg1rq/F4RpC6F7LuqNYCmirJHyOCmFtepU1CEyvRCxptOhV+WsrAcRpG7qJzLi2GzT3yuS2BCZt3ydMyTlLNDU8C/81rmwX1a10iSkR9Fe1DlZiAjLN+/1pzIIq1DOslIJ8PP4r46oQMYx4GfkNkYNCb22Ge5NDCswzyweMEbK2rCAcxY6t33O7btjtY2IFaup1IQHirAk0od7EXomQoPTv4JEy8Am/l3jl4upXB4qdZsnhYMsfUfOzSCpe6TV7PD42R+lZK9Cb9OBjD/ld4GVSkrJaLUmHdsjw37gyd8Uummw65gGK1hINAaOHJ323B7PHC/73h5/4L3n73H7fWGiEDbdyAG6lKwLI7npwubUcwmmRQecGPZ7QO4OW08tMkOVVqeBDJG130AsDxE8GCC59CGOFBQrkHmqXP6hycpecCBPMyOc+vY+bBcynQGhtQNGAxiW+MwmBwhxLq2urCCo4LTyYbg8VDKMXqjijDloqf8I0/1OOd7eepHjCydiTBKIxEznS3IBpvvwzXifP3sfaCj5/c9I7uQYLLLTdA3FZTpR4ED2WUkHz3JYq250gUKVDCfrR0BFACN5gN2JlhGZL2a47GmxNqMcuC1AJ+sqIvj5fUOs44+duToas3CwVDTC6LBW8e6XjnsVPskVYRsxpKzc/IvM8orlUnf1SNQMnW2VKR+99dXjmROg07o1GPMjq7MwQGIGTwZ+XwPPqylVFhwkOK280yx6B1t27E9Hnjc74hGDS8nl5DF7aNj7zzJ4na/4f544PbYcHt94PZ6x3bfRb6N+UCqk2gqDtYq0zlByiPlpQe0y8iTpbi8+CNPBtRUAZVd1JGWxJThNOonI/rMKTGNhVNQTNfCHyq1oDeWi7iJdWSRBerCVtd95+ilHEzJrrwqg08xDA3VXQYT4EaLU0nKNBUUARuY+fEH+urpzwMlnVkoBRljduglcj4oMK4vS4xtvnd2lCXPQU0E0HFM4DVnNSeQTjWQgtTj1E7uv5Qbp0gJ4IGF80qSKJbTCMGtMQ3vCD7mhqMTOzFcTsohxOeAixwqmcYWMGu4XirMFxmZ47Ky5NZ6g3X+9/4gWQcUXK7PMC+4XJ+YernB1aQCYAaRgKMF4IO6+QitizQVbWvonsSkntPHGLl7UcNIlsi0eCMXiR4kYKAcN2ZHUdF4UzlparhdUG/Lw+8eeNxv2B4P7I2jlyE1WxJUrfMQw5fbDe9fXnF/PPB63/Dy/hWPe8PYtbEQiOioDiw16+JZiuEpGCyH0dtTbqhmgVn/h5wZgMnxAgCZoOwwOyLb2UEcOTZMJBuyJp4mcOR5yPfJwGny3pl7YsAL2G8+OEE132VsXX39io6NBBsH1qShmho4an4yYf7Cw/QwAvO8NOWueX46QGdS1GyT5cu85pjvl0jdaJAhSD4sl0EBQtvW1a6sOQFZdiSiYPqSz2WuvYEO9UTwwpPZP5AlPwu6HznTEzmYjirbvnkgoxx96jFgGgee+W9nZA8meDrWHjlYsRSijFod16cVxSvqwualPgZio8793WfvMcLw/PQWdbnAC49ZZskwWXIiNDjXYB5RVg99uplLNp7VLNAJnQLZd3t9qZHv6tlNKWcewjbLH/P/FQ0AlQFI+YcMr49Ad8N5UudoO+63V9xfX1krHzqKRsq3aCTpHo87Xm4PfPruBa+3O/oI3O4PPO48Q3q0rNsCiI665rz0gnUpU7zBNkwefpBGkDXU4lXKM+VDZqcBFBQguEYvxzju/EA5mIbOCIzTDC9uzGOQ34dpzCQtnTPFUZxHC3c6TULaKqTEXTaGizQiy5r9GbMNRM/L5rglyEgEwWeolmdTtDXXySIatzybTJSSWMTM14c4imz5LZq+kQFFFe6Zz49sSBpZ9hnTwQUOAx69I+efc6Aip5Za3kNAqUI/GfmxZ7P6E5YtqRQw5WCM7PAaUGQXwkkSLoOXSeSVSGzAJts+y3JmEz3lMdQGTth5PB7oY+A73/kOWgtcLk9YL1dcn57x/PwWb96+xbI8qceiH2Iz7YeUgHedVV5mfmHTMfBiOR7to+vkudGzXzk3aW4iTgnmAyimt4qjPtqzO6l37Aiy5DGwbzfc7y94fX3B/X5D23d5UP3ZG0bbse8PvN5ueH2943ZvuD94HNJj43DDfev8PEWWIqHLunLuGB8lXffs9MJgU0VG2RxigOO6iVwKspR1cHJJLk3AN6fTcARQ5srKOTOXR+b3R9nMklxKKI04QpcbbcBd8JeQ7XKpM3Le75uiuSK8YKWN3ATM6LgH1brLnlTkgIuYxGoIysd0QoiYYigPCpLY8jgmAuBxVYeh5hDLvJ6zucNsMvhHLd0n9J2v0NglANUrUu2ShBMy7/bAcSZeOf36gTZhxtZmpXLZ0OQo8zkH+HkB8MRTuP6dfRpsBc0ZdFZJ3naRd3SaDWYsk1kHbvcbXm93PB4btm3H8/Mn+OSTb+OTT76Np6dnXK5X1PXC8llI1KJUZQYN7TaX+Cn301HGpEIujyAbR87x017fw6mmYxq5nwTysGNelRtPlswzobORxcUk72LQW3Dq5+32gpf373B7fU9WXc0HJN0Gxz892D32ervhft+xqZH/8djJZLahs6aS2R7SCgMWDRgNyfTHsd/gcM7VimNDMjqJd9DDO89sy1zdpksTClRES0mtF55ZRpSY0spsHVTuLJSQ9VtTA0NIuTTGmJsunUuKdi6XddZUW+vYRx7dLJJNw/jzfK8+BmxQdeci35IQ8uw/DjD3RFCQEyyzdV1Lnq6Z7b1jDPUxm+bJH6ac5FVEbtA6U6Ix7ym1AVpAHGUxVu8yLeTP9iQiJf7gUqtdNwnPRCJ5fSfnrcxkMuZjBHo0xDjVwmVE7DQ8HVE0OACTTiZTuhwbbicbOYjW1jbsrfFE1MeGUio+KQXXy1VnuKvzsnWujxO6H3v1KBkeJ/9w/ntd6wxGAEUxHKZS5nyHr23kaUBZ22a+TPFF5qLuFQM5QN4n6TE3bzDC9sb5bdvjhvvrC+63FzQdeDDnuIeh7QO31zs+e/cZXl5fsO88EHBrgbazbzznwdGDJRHEqFvd53jlotY85sd9PojzUcuwVDipLJhiBChCGJ3P3M4RcxKK26Hr5ykzCZk0TTSOoQhHrDpIKlMb6Gm/T+gMMxqrIl+pzFtLcdSlYl0XacttaumngMSgaK5+/IyijlOHmBAaMOvliYn2bZ+R1rS5NW4BMPZDR5yYboCRTShnNpfE4QQyknK4plRdGizhQhTH6aqOcsr9CfvHjNwhBEGjlDNVGjVJMc1o42dThtsTncDVDJWqGz5zOiKbTiQiSLjVon+zCSfJ5pzWczxXknXJQdSlEI1AisbBFt7e2HFZFMmztXRq7rPdOQ5HlVA+jyCzZN5N+f3HGnnmC5B88SBpFL3mRiYwrmr2GB7ToBgpSGi0fcPjfsOrhjiMzrPOEIHolAi+vL7i3fsXvH+94/6gfLXtA9ve0ZtkkYr686gc8PMuq2aTPa14uq5Y10Wb2FC9YESjIxlATTGMbiv/5MGIOcsuCbGM4eyttuN7MtZ82ewQO5jfSddlaMahJMwNRtaX71U0+qgHDzb0sFlCL2ZYg006+97Re67FqUUx8twsP/LeyCiYe5JZPASzYYZhSRxa7lM9Z5vkWOvt1H9/lA4B5rUsCWlstMQgafw24bnQSbLvqdHFMZv9yJOOdCJRQub5RD4hBw5MZiIh/xjzfcLUxHEyYAhVdGkLstmjDT0NM6AU7HJqQwFsik+UonWdqQ5QiehmWNdKvtYXchI6DDNn2Ld9YyVoWY+RYdoe5DjUNCM0WUrBsqx48/YtANds/ENK/EHK87WMvLfZnEDRi5RZkR6H0DThKBBTU5udR9EH+k42fXvccXt9weN+E0yXhwVVcm1veH294f3rDffHDk7ecey9Yd/VjiomWNhT8JOE4GUt+OTtBZ+8ecLz06JZ266pG01OgQbENlPMyS0ZvbPIEtLoZ5SeY50UPZiDQw9dhzgqEjA6yyqRUSY3LmaemXl0ikQUyA/HcEozAOWwHahRsC4V7bLyoMauQQu6twn9Akwf8vzqwDx4cApSJt8gUi3TkdPPZBqBtDuaN0IkWYhE4773Q+SRpF0/ZL20W9dhjtqbTpHIlLba6XNiTMGVVnGuD5FkRkDxIqPr82I6VwAc0tjzcMLMpQ0WR+l36L529U7w+XAP8ow+OuJySg0iuZxcG+XTniOAgx2TozcMKT7nDMJlxWgdvmi8GI5y7fRzYDpiBqzXC57fvEFrHHNmnUMyeQ7aEWg+//py7foYsxsrn+IsAeXmyM2kaISgCogE2g62h1HRdnt9xevre6qCOoUr0ysPGvr9fsf9saEF4+wYmAsOsAuNY4J41llE49E9lYfdvX1e8fxU53lc006KIRuZ3VMookmiIzdVsqYAZPhjKKkr51IZEo1iBhB9EEklNYcr8rA/oUxS6+iM0n8V0aiTFlwLHLAWJhEKGFWL4fJ0meWdDW0elJcNFgGx5ADQkyRV7t0HCVmx1ofE9Pys5dyRLbhqPoIae6AppO5CDDI6y3HcYuRN91sYMTnsQKU23XfPvyNLsEf0zuEhAKYoib6LPNGsmUcaiCrsiuLZ9huA+i365BJ4fdy/fcQcBpFBqpSKZLvhrrIug4FPB8BSadXceCI/TMhuRofZ+o7WG3o0nju3bVjqwvq/V3bCqGyJlGBrWnJYIDwwYsd9e8XYOX0mT0Ud6tr8OCNPplUe31Uy0YrRaAa9nzvmJolIueqO0XeMtuNxv+N+ZxQfaheNQeY9RzU9NkKQLhKkD6rc+GAO8obXNrPEKe18uhRcLzzGCAYOOLBAGPPVERwmmay3adIMz+3yCdvO5a7cID2UviXEzkXSv+essjFw1IWTRTaUUo9GCP3e0Ca2qT0OHk3rmY8jk4QE/LovQzhz83pZGDnlqQJcP8u24Nk+y/zXOBhtPt/I+wKmqixVYzl1dSiXmTJe5EDNOnnYyCAw1+LoCqOT47UPA6fV+EEwTQGSnRBVlpSEeo6mmCPVyTkCZ+cUSgtSqZnz7/zU/sz50Xy2rbNmn0dr5TnjKfjpjaQXCxPkaHLcMuPT+IDwC8vRzxz77Tq1tNvA1jfUfYOVi/Yz73v0Tg5C75u98wMaKBIcpvL+/Wd4fX1FtQugkc556Gje79c28jzj7NwqeHQQMXdxT/EdDb5Fm6WfMdh8MtqG7XHD43GbZ43H6GxGGczFHztPnNz2XR1ChMRQ7TTrqllXznINRTbA9VLxtBYsCxVAhjFHLBGKKUd1OYgUgkC+StEkGVcD94H7UReeh+9lDpSiBFothioRE/KK0QWUZ+amTq87obtQRYxZ5+Ua83dnY4ZR2zZGYDS2N65Dk0rGRggX3CDM1yTQsUD4oJy02SzFJJeQZ22NiSDS5gyT3PR0ZiQoj8MHDTlZN5EO15JOIuFn79lNqLAxDYNk6RSQaJ8x/dGYrpPxp14+SUMXEZZ7M+8jnUC+RnAP6F0QWtcslyVZMdMSGKWwkaU/F+FGr9hiyE/INkZHnssXSkncAa+OUheW3izQklMyXVMEx3fp2nNtQjqEqUgcjc7ck7Ctytu7UsiPNHKmFYegYeaSWqjU3abR5bnNzrtW/OnY9wfud7LpMZifhB5qk2T15fWGz959hsdjm7nurvr5nO+FI/e3QUKqGLAYcCmBggaLimJVDQu68TwqxzUzS3VTyx09AkOQ23W8UOZ0ycweDSyKqSd4dNSZxcwilW42IXqPfkRrEZhJ5lhQOZU8hpszd5Y+3I2nbM5R0xnxi2M1Sj1b73jcNzXA0OHGGNoUjDJedUDBFDTJYXvOsxcktzyNZcz0JZVrOdh/ZKkwnc8plUnEMevTOJOTWWYF8pjfHJ5x6Pipjuwju/LscCjg72n64TQKkyF/0MGGg2voMRiEuhSPziYR8zKrBYgstUkZpxl8QxB+RKBFF3QnB2CuacTmkr4aEI4OaiYbeBBFzntrSgtgPptuKBBMMcxxzfNsdijaZyOSB1zDJ3Nc2DwX8GsbOZJwUz6otjgOeNRPDAU0jWweMTCaiIcYaiV9MA/fHnNgRIpftn3H7X7Huxcq2jhDvaM1zWAXSZJejyQgdGa3Y1kdawGuZWCtMfunlbl90K0T8r7sZhpzw53bGS1DuOUKMB2xpJuVZwFHDsi88Ph9CjFYXsl8kSEyJYk23wuhHDEfUnpu3W9BSn0y2ATmoNgArDrKUlGWCt8bBR3BrrPcuEupKq/5vKbj6KE0SkXDCOQxUOfGm55lVAwUHKRZSCI8hzXgLCrK+zyUdzTw1AyA9er8znScx9eYAqiYNjiIkc5XRhip9yZ6SIeRkuuhFGx2rAULXTHUXjog5GZyrlWG3WatPKHNcR35LA/HBsTsYIOxzLr1jtX5+Yp78EXXlQHGNNZKz9nl4LhejuwzT0fMs8xNB2cqdQhShh9l5EONEClWp9/ktMvQTTN/aoAVdfxoMCMGhqa+3G8vuL++YNvuGG1H38mstz5wuz/w7uUF796/x/2x0cC3hqZ+7BE6U/tw8jAkeQYNS+DABK+Hxx8GlBKANbHbqbASU56GesrxEiomCXSsw0CefAoD2miz73pGgMjpqISvlk5AjHLWVvnjQ9/PzZqn0cT0LzmBh5v4uPfM+wDAisMHUJeKy9MFrXXc7w9uuMYGkVLyRNBjw7LsEyTklgobuck6Z6mfyK9kqrlPB6xDkQjK4QF4ltEyuh7sNZfT0qcitfXM/WMKZRIdzCGS7vPAyHlKbXYv5jUJSvQ4HGQfiYhSm5AEamo++Giz1zygSlCwPGl6ll2Pf5z+kPswrafPe8p+dkgaTUFTn2ngkMgr9RYxZ/yXD2YzcMU4q28MsJxaKDUuGjBhOoACRocASw7nI+F6a9tkomdejnzoqrEqv6Uqp6G3DbCBGA331/d4fXmHl5d3eDxedWrmjrZt2PeGrTW8f7nhs3fv8P52w7bvaDtLbvS4YjrlZPLMNAtDAVVqReKXWnlgoTnFGByjlND00HXHCNaDp5OySQLRCPPYJShPSimqes8FZbP2GzPghE5+PRhr2PG+R5Q6fi9yU8QB+ROpmPI6gV2mE6fnmNcUMvb1smLfyNz2pq6pUjg7dQyM7uy8K4czS714R8zZbJla+OQnOorUi+eGm+ADYlQRK5NoY5ZYI5SLJ3KYCHtC+SQjJ3hKDkLOLwVFh0zYZkTNZ0hBlKJqpnTiTbpUaxESr0g0M05fz/UgV8R178qbmz5rGNeJr+OIYR7umBUVGu8sqYmc671j9x2PtqMOOl8vBbVWDfHwozVZzpIpYkGqJM/joI5efbUIyyF8lJGzeC8IlASH4BYjWeG/x5D34Qz0GJyUcbu9Z8ns8Yq+PThuZ+9oLfDYO969f8G79y94fb0fZ4vnzDaVt2Z7XRyboWijFQ8si+HpqeLpacFyqagL2dOZQ0JspYwKut7jjOmYUDOPu50dQXYqi8yBjzZ5iilNtWNCyjS+af2ZqwN5ymgWp86npR7VAjqdJGHyjDcGOG5yij94f6HDDL0Y1ktFawte+13vd4iVEgLnGGf3YwbfTG+GlG3BPDGRRqYwEcGDLUIbrCS1rnvNnxuhSBfT2Gl3ioCRDZ/6dTASkVUG5/olL6L1MxlUIpPsQssUbuCYWGomfbnQYB9xRHFzIYhEGtmcIvmxHHYH6/vdDulrpgGGI52aSAAxBTE23wfHsAvJjjPC50CINNgmnsAG+xSQQUZ7MI+HdigdTUMXx3QMUv2aRh5x6Na5vwugEcsgjwDzggEeaqgsAvftgcf9Fa8vr7i9vmJ/3NFaw7bt2LaG2+2B2+2Bz969w+12x/2+qXQGestiiJYwa0yZJ0+oFIsKjuq5XBxv317w5u2Kp6cVdZF3E3meTLgJ2iUM7Z2dU1N8MHK7pbZNFJ9Y9WzoQOaKOKJzCD9l1E+d9ESvAB0mjrx9NqWcDOBwCryWJLfO8ts0tq40g03hnHlWF8dyqbA7EDbUWBGEtBaohfPNPFg6Mnc14gTyNFheazolrUsEoursb+OkHjeDFx0fpPuzE+ycxqkUA1Bq5Edp0rVWSGPtzPsHvY+iU8x8FYDOupPzIESDiv4UIwnG9zEmc54HG6SyrMloZ4oAQfKudllo8kw27whtWaY603kd56gHyJ9ARl1qQVmoTed0CD3n4qjLgrosPIdA45jdxMJbmSiKElafPEcGiIToQBCmZ1nvo4x8dGRDJlvqjvqp0ChyiDwhYZsH2D/ud9xfX9G2HW1v2FvHvnfc7g+8f3nFy8srPv30HbZtZz1c5NqeOVUm4Xq6bo6msbZZNrtcHG/eXPCtT654+2bF5bJQdird2iTVZnksE8MTPA7MdkN9axomo+QBu+dM9dN10XHwlyhIORp4EBldYpbi0iEAmS4cn5cOKGd4mWutnZts9KNbjQoppis8uXXAPVCrYV0LsustzxfrwQ3hxQ9hTYBRrQN1WRDhklRmSnKqnpzyVy881WOk/FOM9DFFKJEM1IjBwxsCSG9Kg81HrJJbm5NxVf7SMIKjjg7s+06/WElJzq4CWi1PQ+k7G0uQoimlPGApb1jCc+XaEKs+36PLsIUChDwMZcL03JOc/c5DJI8tq5SxFk6ArWWqiNd10fnoNPAwB4yz8q0s2lI+nSjr4JZbTemz5MoBTfUpRzD5Lq+vkLX2acx6R3pd6/BSkWWlGANDo5bv91fc76+4vbxg3zYdntAZwe8bXl5e8e7dC15fX/HY9nkGeR+dZ01lrTbvYghuSm3lrpNBysD1WvHtbz3jk0+esF4KvPI6J9QWrCSj++EMeU5b6XMD5WY+DBGHQKLmKGcZqKBsBKStSKFIzM08c/Fy+k1ntOn9kCFmTXeqvJDXxwMO6Niy68g+yMvzwWNwC9cKjO5YL8s85QN2MlRzjJE5a8BiSIfus0YNbfiYUbASodSjpJgag/ORzEzXEmbnOsY83K+ITe9jzPnizMuzHswOwd7SsR7GzTXhvXPNP6yIMGXkuo259rSqEez4YipeCJnVUtp0rS2fM9QhKLZzHtqBDHJcu/n1EhNJZZ3bi6HWheeXF8eyLAAMe9ux1s7vLwsRcPB9dWwFHGUGFa9VlaFyNFbh3Amaar8kA7/49eU5Od/5YGURjJRpLGDJpfeG1jZsjzvut1e8vrzH435H3xOib7jfN7x//4J3717xervhsTcqxOIgWmiTOiJ4Xro6rJCwhZGzFmC9OC7Xwlx8LfLVQWgc3EQOTvCAhCGMyEOsZ0/1KZLxPaCQomrmkG0chhhZvz6EGqa+37MvIFHnMjTBxFxLPuHJ+M+aPmz+3V3k4dC65wNBOl7lpopHXgjZ17WgbYUHWAzqsMMMTFltdizZ/P/DYA8BhmtIhOleAjkFqO2dk2JlbOfZdry2TLVinpgy+9ZTQRcxofKU32ojs+GG0WrOksvV0VxtoooDuusYeWkqEoaP09om5yISFzEdNfdJTmjB3Hk5RTZHYuW45rPcdCiXN2O9nMclVVipKGXBCKDvDRZizC0PrkgykvPwS11hZWFQhU0mPQ8qCShRVu9BEo5HxvfFhv4VdXJlWHYwlqHo3XWWmYE1xb5v2Bv7wLf7neOd9oa2Mwd/eWF32cvrDduuQxEiyZ3QiRYa14vMT5Ub2wngjg6IPb9eSLiVQlLDkn3PnL2yPMHzqShL5KGImA+x9w7qGw5IboWzveYhBMpVS0YPQOQYxRxpGJlnp3OYTS2hctEHxpyw22Y0ht73EGaM6dmPSJ8lNclmFZnNCuWs7rheLiiouPkDj/uGHkk08ZzrESq/ROaVdIyA4GCtU8I8j0wKymnJTRC+VjHBfcxVOX2Ozzxy1nMtPYsMMw7onuv2AVyXM2VkLbMkOUtOkXJnmoDBEUM96EjuhZ/ROo83CgNGGywDChkaxnQW5D+OKb6H9LegjeSmWJcP9p8cDX0FKGthw0lJFLGjbwNLvaKHo7VA37OCE9MpwAvMK5ZlmfuueF4f5pDTOZ8viKoyy/xouB6nh5B5YGpr4WB9PAb6vmNrFLxs+wM9CL23bcfL6w2ffvoZXl9e8Xq74/54oOkmu4wMls38SrwgkY3yrZQ5plDAfaCUwHrhPPXiOl9alYBadGrFkDOSwwrYRA+p/Y1gTT9nusFyMMJRJjl8+4cGlx1Sns0s+tkQ+sl1P9RICf0OEc35QeUDnPoLy98nN5LIgSRLRqfTmmFoniDVbctlIcv82HXfZxKKa55jhQJZYcA01LzHqbNwdTG7Bk4anQuHTZiW52hOmWUviT0mEsNp9BJSv5CI8cjpp4McwXPFRRLS6mRgGSgmAaaqxEhWXY6sM+3LdlfLMlgP+Tc7VRy0tiM0ODL77TMl0HUiqM1wcKz2YkAFvBrCdVrQ1tD3AbeFKEZdaAfs4XouS8WyrljX9VBm5q4ZPPVmqBWXgTdQUIhdBzCifZyRfwAAgl5sqWV61dY27BvPM3vcb7jfb9g2DmV8bBvev77iO5++41z0xwNtp7qrn2SCiPToH6qc9JH65xDbyjOr3QOXS8HTlYf3Ef1lbkSxyjyyZ/430DJZnDfIkyghGGmlzLnjKe5gVLYpOURCx5BSKhstMl+NIxqP0++ehSW5aY58L93xObJhlomC9b9piJxLkAbOHC1ZZSsqERagLgVlL7CtyTFw0ufI8b1JRAoqmXpXZoS1DFEBy6O55tKlZFecybwfilP6nJ7KiGSn++ynKAbwYEDL7rU+JsymgyioBp67N5hfNxsTXdKAyee1PJ1Ghp9TXfgsTlyDVTqiiXDODUk58UhpWKSIiG3X09EVPodSnSedFBBhXhTJjT3/A0NSYTrOrnHjLHsRba7rgkUn37AqSaVeIiPKsE8Sc3ZbgcKZAOwvuIQmSCFGuxaqxeilOG318bhj2x543G7YHnfsW8OmHPyzd+/w+nqb0Jj1cG4uB05SwKOhIQJznngoBUqlETBQq7M2fq1YdRZYYs/QEImsxya7fhx+kEZm8Aiym6Ook+44uyvLYyYBQh9ZWcBUZSWs7qNP+W3JkocbTGOeDmmryMSM9rK13juPHMbMSz4gANNAMqocJ9ucmH3ltaYh/VYMPoydaivbFHvPPJQiIS+Gfe/waqiSwXaJY1y90F588iMM+sQ9luy9WOzDUJIBTznsQLSAC0Vk/n9WsWVvPv8eSFXl6FyrZOG5hwbIgB+qtt4De9uFEGKKlkZgfk4yyAlMRvA8szitL/X7h06DETXh/9CwSqOTLBQWeQW8BspSUNeKZV1Q68IBo/rcWguWylNkPRGBZiCUYtSkO3Xpox2KvemAI8ThMChZbsQhhJrr+TFGPjty0nsE1Pw+sN3veH19h9vrK7bHA9v+wOOxoW0Nj63hfr/jRTn4Y9tpDP3oDU6mLWvKhEKD3Tzy0DndNck2NpmAp3Q+r3j79gnrWtQ5mUIDqcgQGsyXJNq5oSZmVKFBFhRp72lXmWPHMcDPjg2ajL0XOxpaRPodm2acRDb6vAmtIbLvIO3ysz1zcJzTJTqH0dn1Bz+izYS+cdTdTVDDnNF87QG3gtvtgd66VFZ2EI6SluYhCybjNGPEKOq97xo/5W6zqwxzkylyjzMjnYMIc9CgZCuWSCURCqagCjL0WUbVz81SVpdTxcGuDzhG6Pc6jdc9T7wp89oSdW37jsmWRyKmDDKZZmW+5FMkA9es+GLwypFcXg1YHPWyYL2sms1PlWaX6rBaRa0V18sVT9crqwuypd53tG1nh1qpc00PNCmt5qmakM89kZ3BPt7ITQbmIrJidGyPhsfjjtfXV9xuL7jdbtjbjn3bsD12tJ3dUC/v3+N2vyNH7aROONQjzLZKemG2pfMH3ApLGeiHIco4LVgHvj4tePPmgsulYg5/CI0ZsgMdcPPQOcCO+mZGSNocDWqyy6fGg2wC6KcmDESq8GgMKUJwc9VfcWwSYOZfmV/l5p9HCSVAMQlkVHeftdH5TjhnMtMJBfq858xlYUCxrHmfDkXcK6XHQSo6B0ywtMXINAIY246BwLIsyCkqphNPUw8/WoqkKBDJTsHe+lzf3htr5C4BkDgLeM6XPykAk/swTkhpPUVBvKbWO5nqERROCZWwtmATtfWheXNmlHVY8jAkZkPdXa2Pk4Dk1ACVewN0smTO2abqxQAfsMqhiqVyLcplRVkXlLowdeoxHdxSF6yj4FIvuF6uKF65VzQgJaIgRsG6XA4Dz2ceIprN4XkSqtBJkV3CXXn5eYd9+PryEpqa+5GDCMywbw9sG3Pw28sLDbxtFLxsG7ZHx+3lhpd377E9NoxGFVEeAGjgKadjhCajGnIaiGnDkVXUzGlg1ppZOnOUErhcK7w6QpDKekeEg4TRke8zumAytmY4os3JetwryZ2M9kxhZ8qQwwHMM3oG0BpqWegE3FFrnfc2c0YcXUgA5oPkYFdxExk1pxPAqV/A8gpn80MK78wgIimZ9gMxpJTSDfDBQdFlLfDo1Lb3XUIZDVMIAJ3TXZPb6L2jKjIlcdnaUIOMn/4IBcURBeeopQhActQUCmVJLMU2kHPiM9EQjRllabRNJbLHngdiHt9vkSSfqx6uU0ZO5wTQaLMM7HAZdebgbodE1r0c4hZTCui0Fi+Geq1YLwvcDUtd4XWBlQVeVw062YHgaSqrVaxjwbpeUH1BtUqn2Af2rcG9YakdtijhSfIR2oDKy811XZ6oFhrNdgxZ/SgjD3T00ZAD+UfvMuQH2vZA3xvbQXvw761jezzw/t17PB4b+t6pslK+ZjK4iANuFLUEph1kjjlLdtJWBgLh3PzrWrBWTaiUtjjH8iBTsACAbNnTHDXEd12LCX90DE7efW6clBkyvxYxJSjbYp+5XJslLzLitGdNKdEGCxnsnMPuaT420wcgnQGAuVYq4zkngWih+DNp4Pq9UBuvQRHeA1GBeuGo5X1QKFPC4BiUCos8c+WNHHFtUJ0UvQvBVJ/KvkMMFfMgg6nhR/ILLja+cB/NUqWQlNK1DPIJSUOopgewa9RRU7nsaGABxT0hJzRyDzGPzipFlkcn0TeyLwIq7R05bqKjPMmlhw6OqAZfDfW6wldKVr04ar0AUQE4ohvQAR+cwLpYwcUXXJdnPD8943J9RllWTAeley8226dO6UsHTz/lKStZmUjlpJJQNhPBD4j3XV5fnpO3ffZ0AyLb9DUeaQSMPbBtDY994PW+4dN37/D+9QX3+4Z9yzPAlf+Fcgdhd9dNFVO9WIYP0+QNnEowYbBgKWmtrjO4JS5BGjDfW2Q05jnb4O+n8SZ0zKh3HhRBnuPI3Q/1FX835mIGy4giaPJY4uOc9pikyWG65/cJqK/xZBDKw2U8SUSdUYcpfcnNGoraqfGfDPdoyFnkVg0l6Ez34bDdEVE4a8yAPP99EmOjk6PwJLrAuu8YiGFi2QcgR6I7mne5bztThuywkj4bTmXjENKaadIpjTqmuQjSd04MTkc+B0xElr6AMp9fLp3GN9nRiDPX3jAdEexU0TEHXE6qOANKkaZ80fFSpaNcKqqkqW4FhgWjF7Q2gOgoKLjYilocl1JxXa54Wt/g6fktLusTii3TQRXnYZHn4DZ0hnmII+HAE60hL/poS9YBnAP28eOfetun4oqnm9C4h8bsjuAxRrf7A/fHjpfXV7y83nDbHmi9oxgfft+PpnytLnhuGbuOqpkeCrtr6J0MDazHB3+F+WUxLAvJtjE4Hjj19Tm073jRK2dkyPw+YVkaPCP9sUlJYGns0zhklymYOJ/LnTnlWbiS57gfgxQw6+H5/tOh4Mjbc1BhQmVPQweRwKxjnzep0hJGsiSX6LiQzSAg3LMCLGtBbZUseh4DHIAPwOpJuZbh1m0qrgaYxx8HFxwIgBOsDxUbdJ1Fz6T1MZ1RdqAdUB2nNMfF2cQk4LInOwOGlQJLzqWrdGmY1+5TMHKsez4nEq3qzmNdaj6TQctDXSvXvFDSXFdHvTisLmLPV1YxuqE9DPfXB+73BwoKntYnXJ8v+OTpCdf1gstyxeXyjHW5oJYFxReYJKy1sIHFk6zLikFk5UGnl+II1IkoLSiGzSOkPhquz3OaIpCa3sy7egy00VkTv99xe7nj9eUF/bEzBwzm0Q4gyvEwGwLDAXMeY2zaCA4ASXCY8nCJTLLjqhbHslA4wCF5JEUE8maemhv7rK3ObHGeTDnmr/D3UpAhjBuWM9kFSYOdXjj9O0NDQuUPFEmnHHXme7kBZwmOkTt0fwnVTKINjCNCfjC0ML0DclTTSU2WKAIARr7PQIQO0CvAsvAQRUThHLPB9W57nv9G4x6dSKMUciql5tilw7GRq/H579aP2vvQGvg4NATsDuMo51qY+7ZGLb9rFFMfJMWGNv6c4Ep3JQMUy39KEZKryDo/o73iX6oST44jQCM/UicimgAdoldDvboMvWC9LCi2YHRDbI62A4/3O96/e8H+aLiuT1jqgie/4upXPNUr1vUJ1+sbRd/CZ6vIvCwraiV5PIdtIib/E+D0XTjtrXpFHhsdMn4EeDpw+1gxjBRhnbsTXAJB4XJ84H5/YHu9AVvDKgMPEIqw/U4U1BgYRUx6iraCmyrh6wjj8UfqddyHjkkGHc1SL7heFyyLnwwcSKvNY3YCJEmyXRMSj2TvbxrmYYwh75mNIMrB09ADqsGfPjKhn6JwwvTzAMw8ajjZ3w/OdNfnZ7TPCCRaebL3jMxU6OXRVFCjxeyL1wbvOr/ufFrMnHo62PyxrsyP256dWphEVaZIkWRchAY9MmpCAyEhp9RaUz25TKSTMDjSJEfOzM8+8MLxXwMYljn1/LjpJEzvFwBPMekU2YRSMK47SbRIj22YQp1U6oUMx4Pjo3PyL9dYhKoOonAzlIWnnyzXgvW6ohYenOko8Kho964TdYH91rCMBc9Pb/F8fca3n7+Ft9e3uK4XXNcLSlnh4YT2OpzQjIGKxm4JJuSsfaYvbEYUsaoNR2Q3ZrAYvfHA0LZ9nJEPM8AJLVJp42Wg9oGBHcu6Y/EKG4ElAkshRVgWQx2BagXVXREbPEVldGytYe+Uvu7ZhYZsDo3ZwmeSJUpliFI4/WVZuDiIJNTk7WNosP8pHeaTlAOwOWWGJ1cCKTKRucm+DiMcuTmyZJWtmEjAr/iS+Z42D8svZf7MNFIZbQ5JCB2vlIcNzvPZtNHTkcwsZBJ71Ngnrju30QLk9LNUp+lgSMGKQUqrZcHYh4ydizbkzHrnfR+ciPrhR+ikFb6mFDQ5CX2NgxPkaBLx6NMjofe5scVIguUwy3TsiaR4ektHZhGW65KO9oywck3scBzupuAyktiYe4JHeoHa86WiLI71UnF5WlBq4R4PR4mKeBj8MVDugA/HZX2D9c0Fl/UJz0/PeH56wuVywWVdNFuvsLkqzvVvchR1qZpGCz5/6S34fTWySPnGaZDHAZ/kKzpi39D2B884+Bgj97qga6WytFRU1om4odgDCMNqjvV6xcULqhWs5rgWR7WCxQyLOUbbJZrZ8egD7x8PvN7v2K1gi8Ajz4TOqRcSeJAdxWRDl0seI2yH+8/tFYm/7fgrbBI2tAGbBpvH+2TEcHVnBTDfv/eh6Z5+IIrIPmZtXi34B2WwdBTpdBRNcjPHyPLRmFWohGznqTX8XXXinQYHzMg17z5lrsEaMXBS/PGnhthjt4JLMaAOjOqakc85fQNQg3WHF6BE5vjguHJA7Drmep45jzkFZa5D/jxz+5yyUiZqkaPVKStpsJoSCHdOQhkAB0gOStYSfWU9OgFATvbJI41meoMDRU3UZobQQAevjnqpKEvBeq007rowsoNnlpVg26iXik/eLnBbsCwXQu5SsdQFy7JgrZUOPsT4D5tjtwAGGC+FcljPEiowItEgFLE7hhVUjxmQxkRbfEY8cmlH3z8yki+XJ3Tc2VroBV6qEn1HK9SHo3c8ryvevn2LTy5XrKXSuN2xSnfsEUBr2O43bNuG948N9aXAYuDl8SDbagURjl0RgcE3TmQKUCo0ivZQl2nZwJJbmYbxgUZ8MJdDZMlERjVS7y6hodhK5t8HuTTRsDY12xSTrDukk5NDn84nneOBKgISnmRuGcdUj3P3FTAmqZVvkBs0ibDsze7aJMwqjlQjSbncWKa6uSnwLTrHvXVyK70HUU8iKbVf9q5ONBWtU1wUCDpGY85oMB0yyVNuJ6RC4Jiqk5WUwwnBXGIm8O8jjyQ6hi1mCvDBTHUhjxTdQPefxj+duh/qxfmIisMqdQxegLo6vEJty+wEK15Q4Fh8RQlHwYrl6YL6tGLxFXMiDT8FpVSJVHSrSnvoY+lo6LQqqqa+IJ0/wJxd308HxfPnyT1pBRChjrpBqN72bZK9X9/In57RAjBnkz3zCpO+tuN242in51Lwb3j7LfyCb30Ll2WF+UIlUChKxgB6w7qzxj7ev+AewG3bsQ3q2U3wdt8ZwbM8pOcOL1S71WKoNQUBNiHkuSwCGXaO2iVkkwmKqJqy0ACgU0tG7/Tsdhju3DDjyM2z+0x+A9meeGbcvRRN0LTc5xhKJ3JjUPM+5r1MplSOLctzON+romaX8xmIGTkzugegGWIpkJHTU2MDEcWAL4bSC2orqD3QH30ih4jAvu9TX92l8148BRpdCEQCpsqtRC2NUp3J+Op+ex5oOHRWfegcuszNBUGRyK1MZyBvx3UNIOfIlWrY9yRny3x/08kxwDhq6aoMhAVQDKiGWqRcq4ZlLVgvRY0iFYstWGyBNcdqK9Z6RfULii1YyqporfKl+WHEXqQNyLybzqA4GXUOH62TBM6+cpjNDjS3gjKNnKtYSqZq1GHEYMWLTS9HyPtaRv789hN4qRQj9JhJzmN/wcvjjve3F2yPB54X5SRvfgDXpycdhcPD9yAoPHpDGR3lfsPqBU+j47U1PDDwuN+BNjCP7oxkYU/ROoB1KVhX5jEJm3MAomxjwtRZapCXINShEwjPxn0ihsCpQwwsC83PtWztZK6URnbWD+f1TXg8gnBXIT1bIntI0pIoAkeb6RwiOGxebwjCuCXpptJcQk4Zdh4BPMYhFaWRndKYNDWlP3nZdSlY+oq9A49toDc6t4JDkJPOBXBCSqnuChwBNe9IXZbgIZ1r/t95D44AOsSiQxA7KxS6znCfKUjAVGlIg9CxXO6z7HagN+ktIrkR/k7I6N0GGa0C2OJk0BcJrNZl9nCvXlFQUFGxlBWLXVCwoNqKnKLKkco89CINfUbobMs+9dX7RFPilBj/QdFWEp2Y3JCdnD4wsG0ckZ7HToccvLlheoLv8vpSI6/rFRcY6sppGw7H9tjx7v17zml7/8K6+RIYBYi1YFwW1jEFvw1OVVtwRFTUFXUElm3Hcruhbg94aygxWCYQqYUcih8anI+QdLTM/u2exydxGyCZaUI5rhgZcjttVEyox8B8wE/1fkwxTRfp4Sfofv5vTgfFfCAycFdbgZACSamEdTjKPpGs/GEEhOsk0YoMPEnX4uoUi0zjiXpSCkmnldNqwMgAA0pM5JFOcFqvG8riKHvRUc+cx8cxUYMp0MjaMtl0UxMLZqefxiop1z1Uepk+ZUqSQqSBHmpYCsw5BekgMhWZnMRMTw4xTB8SqeNwyGxgacgzz9wcTQ1VXrWeRbfkA2UpsMVga4WvNM5iLohesfoVy1hx8Sf4KLCoGtGk1FWHIBzR2yeR5140eKQwYkfOJlRrqjguyMnNfojM9WJoztyAdNkIGEeN6X1sNWBcgfYA+sey6wiUdYH1ImMt6FvD9vKK10/fIVrDpZJwaABaMayXBeEFXi+M5mCZzUYAyuNHvaOhotQF63XF2jZ0C4y9oQ8DZ7YaOG6nUOlmOgBQuUlCaUzDHcKK0lZP4GGqHo0ZYRLTJuA1O9XpO2WCZMN5Emko783OqOScJokzAA7CL8iSGILNGnnMbYgsglh2eB54xz/HvHJDTt8MVBqh15mWULueuaBY8+C5cwCQc+CGHIilA8n1ArRhaIxZjlmWwhbLwQmvoUMQMoFnxNUM9aB4Zohkm8jCOKUUenah63BzWOE1khQ7pK0QW5+OKRFMn2iHcJVrlWXQHKyh1CzvT7CIzU50HFbZlDMsxOmwtLpcFpS1wi4VdamAOQwLFl+wouKCK1a/oPqK1S5St7GpPmZqxfVPIjjPLS9eoMZw5Hjt7Gdw1ffjXP4FRDKX/If4n+CgCP1c0UipRAsxOoVko6Nv948z8uLHbC8zYN83vLx7h5fPPkN/veEyDM/1gstyoZABQJQCrytQV4Rx8RRGgbZjtI6tDzzajjDDZb3gzROnt2w7c93W9SBxRB22OFKgzwd6aq3MTXtatFRIpZgmG2wS8tac9jl4bdluSM1wzKgyBtnqAOFhZFdZRuFxmq4qcuiQxGJeVaYXGMnQH/mq6aEfNXATkZUlJergzVQG0mfkvU/RDxixDUfrYQpxDjDHXBeDkDkAwALLwg3WmmOgag447ytmhIE4D06K7b2zychoONqffE/VPUln5CSgMZ8V35EG33uoAy4mv3KUNaF9APVAJKY9Up/IfSJE1XQ66Aj2yptBfd+M4mUpWC4L6rqirivc1Q7aHWtUrEYzL3HBUi4otqL4QYYl5wI5GrMc1UQH51VO2Ax5nhknt9ZjdpueS8qKAxo3luXHGIClcpKHdZbiWJZ6/Aw0nQcO+xJT/goxDM80GzGw7Q2P2x2ffvqTePf+UyA63lyf8KYuuKxXGrZVuC9YlieU5Qp4lTKSxrGHodkN3QxRKkq9oJZs/QNutzb16dDpKBEu5M1hezVnf0+vr40zUjYa6o8OIQn+nBdG06SohloZJ9l1Cglkz0kSYcLpAwfkyxXpDuglOJrHO0OQP2yyxHnqqcm4eYEqyJljYEgiqiiIELwXSTjZ6i6InscpHXB8tujqXj6IGWqIGTbYxSfkUAQHL9fKfPihRCgCJoUYx35V1MmOZ6TNTR0KCqeVsixhQkZ/ROJAogC2g/aRU5htbtouPoR+g8M5mriN1PmzIaUfxJpyrlD+bSVgi8MWRtK6rqjLgrVeUG3BYisufkEJx4KCJSNmWSlFxSJxFlC9wmuZgpxkyDHr36Z9A2R1ByDKq6VyzNNS2ZMusi7PfT+MPAEq0zEiBUqT//+k/VmTJUmOpQl+4EVE7lVVW3yJyK6koaF5mP//j3qmqSuX8HC3RVXvIiK8zAMAluvVlFXtNpHkGRbmZqp6RRhg4OCcgxDBJZLi1Uvruvb4R4Ic0ZW4vTdq2bnf31nLjR4qcYpMkjjNZ6blTFjOkGdCnpmWMzEtVDuYQaLy3tnZWqenzPL8AjkhZWPKK/TAvlXWvbBVmGJicw6z9KGL1nk1wzVEL/M2+nHoo8x7PPTehoptVfEHJOIdu72cwRV8nPXaJPqhafTb2O2d/P/sb4/EMKio+lqsIhDDK/RA73sx875oc1B3edVAit6bwbi9/RbxsVK3r48ffhyj0L/2J+vnB7fRkDT5tdKQ2JW+aXz5vTif3BOOtgylNKdj4Qv+eu3Wp2vQBnu+2lKq3t8n/F6h/Wk81g/ANAQd1fpWHf+8avKJlsz9oMcON1hUT9EtUAhdAbbkHPRJnXJiYkoTiYmZmdwyuWayJKaYB1klSkZQf/QgQVcM48iljbiG862TjbTS8f7clyOklFVbPk3EnPTmDopbPSLy/r58vbSfQD3Leqm5oCo0oDXKvqq89UeCPMSOlErvO0JB2EipMS2JXhZinAnLE2l5IswLPU+0mOkp03MmSLLrQOhdaDFBnpienphSRPY7sq2U241AYF03btud0nda2SldK4naq3qfx4aI+X95D9ecCqI3qC6gd0DDZ6p2s+NocVMwkIdbz0rhNjpuSwr+6+63sCHZ3aI9/g+gHFbCubmjueEMRZgnjBEEB7FErMeVfhBiamsHw87KOzgqi94ZBInRpz78PI8juNE/2M84RlxitM6m63FTa9SqBgy7uZxirUqgK/sL5XT7V9RWpFnlY6h5Z3AAWqsGdDrp7EgSOq4MI0327gmmjUB5nI8Py7Ag6phqXvcanJb8pBnJRVQ1NiUN8pSZ48SSFjITExOpGYoumUggxayltijhJYasiLj7GsRgDrf6DsZSSTC8JRJjPv68BFOuqbGEvk/v4Y8S/agIj1bLzSu1S9Lk1pze2hpl39m2ld5+kLuuvC5dZNgoxBQ5PZ15uu8EyaQ4I/MTMp1IeSamhdKEvQESdSNEPG442RN5npH+jMwTsk2025WAylifn89c7wt7McdXEzMQurqyJl9699CD+gHuINhuNtASuJldEc5fdwqp0zsP5NetkTVQHlRmmCFfa+akor/3WKq7eaAl+KETPsp8xgscoy/vmYMaVgxGIVFLdi8aRLjvux6y0fPr+4kPM/8REDGM7408TAA08p2ToeCQtygOXAGI3ua1NdWe9we3F3uGKTrazujZ8fTVGaW0ENB9eb5KyZOj6EhM/PZ+6OcbdDk+D4JJRro674iP1brNvI3X3vuw0XZKbcjRZt+z9c2JHGemuChy3hOpJ5Ikcsxjdh1jNrFKIEhSQMx6b8eEfPnisO1uGoS6YCETU7KdZZpop2ki5zzozQyukLEJH8wgvA1QUBRL0g+6hd6BRq8723qnbP9/kGFab5RWqBTt4XJmPj+x3CudRAozKZ9I08I0LaR8QsJE64nalGvui9qoQkiZaT4RgLZa91s2wpTJS+bptPDx6cx91Q2nzW7DIoUUOnOOZPMEr30nPGB6ffR8gJWbHVg3zXAp6ezEpZsDGbfbsdSqyjgDVFyrrqolLcfcAxs/xNZzBTOBUO75cdM7RTPGB192eVy4YH289RW1aCVylOJC76YCxJNOc48N/bxGGhq7xFsY7QPdW5IDCPS2whODJyBXotmLJ05BD78ZN+g00VuIfqD2PIB70f5s7yOx11oYyxyF/4EnoBXeUQX18bkc3FPlY2XkmOOjmyBOKz3spiN0JJnF8az/zHlW/UUVUs0kMsO/LSZSnIgeyMY407FXwHeV+bzeE3U0iSjY97Usr9tKo4pakoNkTn4JI7b0UdoZMCfXYf3sbb3E4SOolZ3+WlcVK9267Bv7dqfVH7zJq48ygC4BSYk0R+Zzo9SooMX8RJ4Wcp6JaUbyRA+JhjK+ErZLXAKBRAgTIRV62XXumDPUiVxmTqeFp6cTL7cT97tmpx4bO4FTikw5msKoKF5ggBSIjbcs7aElYR+HU217QzBEuR1LFC0n0p3A0FV556fpoLj2sVVzSDvxik2G2ERRd8u27fj3eoD0xXUPbA9c+znwEp6jTQghjFVDtanG3CuEcES79bR6XtQeycq9EeAykoHfSn6RDw43oooxOjFFEoFYIVjp3KraVvcmNj7TRFmL8cyJ9hwVSBMefdN01OfCC09eTrJB1G7ZP/9IKBw2TQ6wjr3d4VDdIaqVJ3amKZKnTJ6UPiok6t7pFQURc1d3F6OX5piJoo47MSZSyBa4NsIdFZRRnqMF/zhD9u+DVlI5297xoE6tXgEocNrt9n4IeAty7/O9Rck5j/XG8OdkWPuxmsxboR8KcgnZyq1EA0pR4kNIE102CJk0ncjLWcv1mAhR/a505Ys+DN2Mar8WAZRM0KtujSBmQpqJeWHOM8/zxLYsBDbauhNj5DSnYdwotn/q8OiC5k6G/jK8RbARhga93ZQwyvwDfNMo81GX33KdNtYFaVV5LOXrGMKO6uD9rvnT5hOO7/MoP/VfpxhxY4Nu/x2jP6eDief7t2sp9mc71YJczQeS2j410CV/GqwpGhj2CLxhPbuDXQZkDfagOfGkHJi62JKCzZ5ht93d+jNVm4S4wi+M+biX61o5qLMLLggEA2ODfU5vibzdCg8glDyq/uTh9hMxP3gLrjkRzG8gxUSURN91PdW2VtrWeMqJOCWWaWFKMzkkRdBFwbU4AlyGBbM+kmPbShAjuFhboNObA2wTnKNuiwyd2qoRrQ6v1haOBZk8gm4a4PM8k/M0gDbHW2op9F2diA+t/ZHw/1KQhzAR4oK0gLRGZ6eURikdFS0JIU7k6Uyal0Hzi3EihASts2+Fsu/QiwVLUKVZS3RV5hNihqyGeDlGnnKizglpO3vvlCkwn1S0n1JAYudPpZ2BH76hohrLSWAEtQa8jNtLwTDB2VQ8UEcdxfXv4IYHbmroCLIAuyHs3Qthu20Y5bqbRDBeEhhppB3JxBtj16IPQs+oKPSmbuPke8XS2MzqqeOMOcMegrDXR327CkpiCJRSKLXo5xnbRDGGXseTWc6B1jO1Fra94uO+Un2EyPhsbnns72VgC90rFDWCdIIQI+FUDVQDaB28dDDx8ZrqDwk4WP8dk60VSoE8T6QQkRZgF7a1mjFGIPTINE/MaSbHiXmaiWJy0IGc60LCMRYc6d9KeZl05p0iYNWkMdZSiqSgpXqKcSDrQWTo+5t0J7DhIKNTX71NyDkxTTM55z8nATuVQbT8L1HxhravbD8KvAXJpHRSimPdiKFTWmHfK/tutrBhIk8nDdQQLEN31vuN7XbndrlQy875aeG0zAZOJFqYkFiRpD2mtEq0bRJ7TEwpMaXA0gNlgeVsq4kjdosbalsrvetmFR44vWJ9dqTbaMpmi91pptCrHUwDQMw+1qoDK2sN7R6vWo4y2qNvMNVQw0PskHrvOoIBRmk5bnMY6PngmjN+DPtHGVO1dnoPHHvEu4F8WK/cjhK4d8autmblIsFEM7rJcy9tsNX0YAbcO02CmmRK0Nl5a1q91doo9jWsTAH8RrPU6CCmEZrEeejGDHSnnzp+zmBIcjcA1WmtRyA8dD36DoJWPikm8qQS0ZQnAom2C5ROuReowpIXptPCFGeelzOn+cycF7Ni0rK8Gf1aUfF0VHdirLagLjwqaFEUX9snQHQJhZuLxhQNUDXk3R+X+Fzd26VgE5/j97SF0CmAiIuB/q/VYEqZaTnx/FKhFfZ9/bEgl5aRWqEEeom0Uim7EWO2jSgLgpDiZHa0XcdglwuX11cub69cvn+n1cK//Ou/kH7+RUtLiYoo58lUs51WViQp1TVPMzndSTEyk0lTVzeYOSFx8LT0cPWmhqIhDqsqD8DgLLbeEEOED2KGyqZDY4y33JW2lN2qJy3ZfFw1CCEWrg9PynpnM/F3FN5KywFEeUBYkglj3nv0+Id7aLPv6Ui9thzan3sicA584+CReeAxEmHvSrBR9ypRu6BqywdMGYZVICO4mrniRP2avvd827vJzRulKkCmVYlQqapG85s3hDHi9JVE7vN3PFNQTkL488+MB/Yje9D+W8ziOelNFrMFXo+0XdiuG7J3Qg08nZ54ef7I09MLc57JNgdPabI2x6cMov9OBBHdK+5sNhHr1ZPz1Q0ToTsVjyklcoq2sDCOaUTHxUx6TqL4xhgZn19EzIU34tbeGtwPrEkv7+0999BI0wy9s+wb2/0Haa3NNzDunbYrwLJtG/d15b7e6BWut3eeywdS6Gy3je9fv/Hl99/5/vUP3l+/c7+8k1PkfM48n8/E07MJEiItBFowECNG28mcrORXBtAUAnKKLOeJPCXjOQd6C9TdFsCNMYP6itX2UCo7YGG3h5aV2rM6qKU3gs0gWx2rf0LQnlNESCFpgqiOoGtQSzgOosaXZv1h4ijqYdYfsrn/WgklB3uP3s00IwymlyCUWqlN2IvKCpOttD2mCoxAsEJdq5HeBmbg1Fw6ugTSNcwxDuKKpahxIFUPYCumQyfNEUmCrMK6FaWPmq2Ra9UfiSK91cO80RJuo40+V1skm+/3bkCJ8eXtczV9aKM60zGhItUxJXKajTrqAX7nfrkRe+Dj0weeT8+8nF84n56Y0nwISYIBWmI3qSVgRa/1PPqFEaNOdQYvv5uJogGsIjqSHGNJOzt+IYiY9LcHglmjEVTRJ9biansVyHkaHvXjnHR9Rinae23d9CFCbBPTfGY+/WCQ163Td6CKrnvdC9t9Zb1c2O939rrxx/QPpiiclpnb24Uv//kbv//jN16/f+N2u2j/Ni+8v35n+/lXns8vBiw1aEpB9H3gIZi5X4qErCBK6pF40v3juvDd2VJeCjq6bihreOzbXOpnIyMLmmZrkv1l1apoei27RoFvMfGDigZ7DMeD95VPEStxjV7bUJBxgFv0kdXHDR9k+Iz5GA28P7US1QUtIsYa6+MAYf2zlshaKnop0HtnN8WXLhtQgMzHPcMR9KHtMI4ax/xZMQZ3Vg1+myGDO117o4lyGHqo6ssXLQBMndbt+zVXlOFyXlVleQ+MjSrF3Bacu+7c+dYdX9HPnbMSTWJUXXfrwnYvbNed++VKK5U8zUx5YjmdmOaFlCZi0ls8WiCHkCwpGTutY3NuA9ZEjuANurghGrDmmgyx9+tkGbfv8kAd+vAQdUzXGeCi0Su0qouKBaQ84caYXk0igWhAadeXptWdyKh85/n0Y0Gu83UdI4Aun9/Xle1+Z7vdWK8bbBtpvfByPrNf79y+fKO/fydtV1JZqdKpu3C/XtjWu/YxMdgPbGQUq58dXokxEXMCXwQ3B+IUBmsQlHxwKHX6QKjBRklm5t/QJQLOPfYBVbde2nva0uqwKRozZivdBAbhxYNyCFRGMNuN1boqnmzm7PNrzEesNVW5iTgCLaOcE8/SjuzbCejdyvhuM2mneooMJPjYGIMlARmfk5Gcjt1uGDA3RCMjE+mkwicX7gveMWpxdNvsjuw7W230oFRSZesZWcmDW9RaqbeKeThofDTjJuBjKE0CwUvcAF1MORgO8CqEQMoZ13T3KtTS2W+V+9tKK40kgTnPnE9PnJazbi/Jk/bb1ioiPufWSjKK/oyPa4OTzchdLaejDw90fdwx6iKFlHxspv/ElBXktPVGQVTF6TyIw7JcW8KY0ujDhxCKQ8XnjMBuiUj5Bo2OrlBKef6xIA8pkQxgUGZYpW471AJlo9zeebu982X9Dh8/qUyvF04pIDkjvXIrha1W1nXlvq6aoVuj1d2WJypiK1bqSUqQhBaBJMQpkRZd69pDozQlP1grp06orRtxBRPmm2Fi52G5XNU5LnYDBg3Ave6D811rVSLIIB8cu796b9RexlhEL6Hj9nNRjPdQx7KGMG5UVYg9bGkxtZvTXg+Hm4MS2zuUovJPV+J1d6HgwfSytjGn1xjzRON9H+MmEIGxC50+yDUiDwnL+PN6w6LiCKtuUwjMkukJ25sGRGP6BRxO14MaFCQjCFQLZsFG4/Kng6vtuuv/1ZjCN4sqYy2SYrYxnVD3znq9s++Nfd0p90KOkXmZeXn6yMcPnzifnpXDEVQF5gsflI/uPbbq3ts4U9ZJYaM8K9E18Xa7hRvBRoea/AwZnybdWyZW9lui8HPp56xLMN6EEWiyXWzeyfi0wkaiWl1GolUBMQi64ahDTOTTD97kIafDhseApXW9U/eV0ApJGpNAjJ0gjXlOuvggBYhKmNj7HdJEzhOlFvayDy6yjpmsjBVRCd6cCfsEd92DnpKOJjqqaW6u+rLeURCoGvh6u3dDPps9mDDKv97kkK8aE6nbf1f3rRYotRAMkU4pDZMHQUUT2K3iva0fVJeeHrfo0YcONhyWcJxMYcFVfR4+OOtGJPF+1v5Px3UBV7C11sYON/C5vbobiRgYyZ9HW/wJqdV34csdSy0K/DwwyYKBXV0aEhohJuYs9K3TQqN0I8l41dFUMRWjIta6NBIFm4YxpAt0GM8PxyaDKRAdTQ+6cyyGROiRbS3UvbGvjftVpz00zSV5mnh5+siH5088nZ6Z8qzLBGNWsNcqHx/3BjPiAL25j2eo7UJ0NN+epXRoY1xlCHqItiJ60oohTXr7263uAhdNms2Sm3nimWHkNC+j5MfbFbRnV826uSaPC0KUG5I6ZQ8w/fBNHqEWJOkNeLvfuN+vbNsdqDydJ87TzMvLmel0Ik5KD5R5oU2ZkqKWTvOJD58+8vLyYmwhXd7euiKJIp1WE+QEJavoIwRSniDqphVCYKceo5nmcsM+5JRYLwnerwI2L+8hDH81B6C69bWOKDsBRW97rQzqsDXqR8NcqxlE9NHPKxNK95z3biuTxdh0PmYzwo62KdrPA/iGEETM5BFLgI5IR7v8+yhfo43ltNXoo8R7tKDyl+s9o/Yd/MklRnt3vceajSFrNy91G1eKGS0gKlHNUckfc87ILNz3YiaQ+vxD7/TSiVVJQr14kurGZ+haGicTpUg4klkywEoYZJScJnJaaKVR1krZK3WD7V6pW6fumqxSViDuvDzx/PTCNJ3I06ylrMSxslkT8sFY03A17AEscPPY0hJGXIluRu2mOYjBVhhryZzzot/jIbgxhp/71Sl92Mp347Q7z30UaGDovrfKRpqxRIGIUqCF8Rdizj8W5FoeooZx+0bdV12dVHdigPPpzMenZ57OZ51R2voYJlhiokigEEnTiU+fP/Hh80fmZdbDUDoQtGwxxLTHZCi7KnamKdOWQJojJdZB9FATu05pHaey6hnWX+vqWFD+s1UKYK2BgUq1jBe8lzbMB52o4DdfaboBxANZelenz65WSIyb3DhvgpEjnCGlt3GHYdvsJTOepLonJf1sUeKhv0YThZeFh7zxSAyupw7B5q4dqlT2h+/X8TLeNMpBDRp1pFhx8CnlrMEWjfFFB6mGZovq/xOErGaDoQVCiey1sZUykkwoeqBD6xBFJ3VNg7kWvXaDUTx714lBa3ozpmwCkWB9rmTaLuoRf6/Ue6VXYYoTcYYU9X1PWUdjeZrJ06zTmjQp4UoMsUZblZCcn26JvWkboUEeR5J1tMDxEEfUtcqzxDLNTPPTWJ+krVAArCcP3pboxRREmXrOZx+ecNGrBjt93X4f23dmGduFSIdJpxK1fijIp5zZ7zf2+431+k7dVuWc0zk9nfj4/IGX5YmU9IFGm0FGCcQ00Xug1UATsYeRCFnVPV2vOSgg0qFXvc1HoEdC1oQhOSK5I0VL84FIYZLDrqXpKBdRMsaxjdRnr/5XmwWcPsBarez3mnGAbtZ3G9fFtdXS3J9FAxFQjbU0c0lVJFqBJUW2g4itAjZmFQeV08EvR6IbHO0MmqwQGcGu7YMvk+zj78OxMvgAF7U18TU6ugJJefZ7KfjGT7D2oPJnm+XQmbKyyYJ0JHif3fX3yKQ5s7dGqro0o7aK1Gi4QieURm2Kk9Qdbee6g2lhWCFl0fI5xqxKMAtMqYm9F1rZiZKJOROnTM4LEMdxEGCeJmWLmWgqT7PiIA0bm5lFU9QSuhqe4z4DmuhtbJq8pHcCkozkHFIkJTOBmE/EPOsNbv6GDcY42Ksvzb56CGOMJnfu432JviD7Hgcv3mJbWXPdLhE6vSrbLowb/weCPKVIqxvb/ULZVtUbiz7Ip6cz5+cndc9Iyp12LW2QSEwTH0JGJHG5XrmvK+u2keZFS92cdPwUOmWr+oBStuybtIRJEZkwJ46iSG0wggogXdHYFrr5k5mDiphAJOgNdpTUA/JiIOjd6nbPoP1A6p3IEnqz76mJYN8rIRgxxm2lLIv01keWbXZ4Irr6Sfsoq5ofjC2cLFKtjKh+u4/bw5h36hWE+pK7CCWYl92xNilgLchAzY8SvY/01P2Oohvrr3vOcstiO2vdmG867xWjMysi3IP2lHMU3Y3XdrayD9P/1tTOa9sKtYj+nYaekZAGEhiCkGJGeqT3CE0NFerW6HujFyFHZYJNcSab7VieZrM5VpZZzrpkcDmdmPLCFKdxNrU68CZFS/woyhvQxYl1GIVGK421hbdKKsTB4JummWVZbC+f0ridg+GYjd/QQTQp6uvVr+n8hcNZ5uGC4ZiO6O/JmAS4MelICGjCSukHy/Xb+yvX9++U7U6gQlevsWmaOT89KdvIGGxg1EabyUrQcmiic2uV99uV6dtXCJHzSZjirFI8MrRKa4Um2nsHWx2DiSAw8EPdMqw3isp11sRoAW1BBp0e+qBs+tjMATufsbtlVGvd9lHrqfZjgAVLQwjJCSj69X0feu9YCR9xBdqBjj44mNhL3/YyevwghyS2lDYC35+j+9IpSKYsqeYl2sNnkXEjaPLyWbjEQOhOLz1otFYDEWMY/u3gpSI0qhI3ogJDIR4IvSPeugUkEacFSYtWW1EovbDXjX1XnXOpO+u+k+ZEr9pH924sya7nJsZkI85Ar1A3YbtXtnVnWxUETTEyTyeeT8+c5jPLvGivnifbXKIjqCnPNirTZJ/TBDEq8CbOXziMOmqt+u6sv00pkUI0yvbDzW2ai2RY0bKcmJeT9c5x9NI4ii4KrQV/bqPXPrQUfpEMINQecowJJwb5nNwdeB4FTn6T/1no8heD/P31C+vljbJe2dYb23antErsUdFfEfUDC84aMhWOWRkFyWTg/KFxvVz59vUrrTb2Dxun5WQPs9NroRsl1Htu9QsrULsudc+QJWif3ywrmiGEbk3VsnOQXrsoSm79S8D3a9tEtlV0VVFEwjG6EjGJrRsZBiEHrUxiFErZKbbSOcVpaKm17+6HV7zJ/8Q+k7/MQdbpCtDhf793WyFkdr7+d6wf1J7xQMOP6uOwCRI5gla15QcfvvVmghGVkXZt0KkjxBk3l0TR7xUdVIqo7x6jfGSYVyZ6C/SiCq4UZ1I6MeUTdd7Y9pW0b+x7oWzqC0BXCqfifDrK6hXjYVTWa2G7V1oJSFOF2JIXzucnXl4+2DLBRdlhJtbI08Q8LyYtlSOpS0QMRR8lsQ3pOrogojed8QcTu8Sg68B8YiHIqAJSnlhOTyzLWSm8hvF4P48HeD84+SP9WiJ57PGbiVsQY26aik8sMzRryYYFtpeMrUEzY5SUBlX7Lwf5fnunlZVaNrbblfvtyu16Zc+FmNXpsomQMBqfH7hedQYZA9OU6W2hlY33t3f++OeN9+9fOZ+ftKeJkSkImUpuG1I2KLty5ltV6mp14CnSUNCEXgkVxqzZymTneivgdGy26LYBxAkwDsCAqcZG73MEzOjSbJY5TcpGauYuGsy3q7XGvm20srOXOiiNg3NtL7TUNnpCVZqJcdFNYWaJyo0gtDzUz5eGHuYBi7AyL4RIKY0Qu9Fh7efv7qfmAJ3e+M1akmZJzBgo6mSaDFVXhjoNV41hbQNIUlS5o2X4ulf20onTzLTMKhhJs64XyiciG1l2ilRagl4CtWjCqHujF9WSb/fGeitQI3NcOJ2fmaJywedlZj6dOJ90PXAKQS+JoKVqzmoNHlNijDTRylCJMxaoYtqB3uwWBlBqr28YtYLSbk1BehpBnrMSawhRwTW7sendQDc9e6E/+P95OYBOVVylKLZ1xrtyN4YARuKPIQ/yS2/eepkFd1npdT0Ox48Eed3v9FaUrdSV0HK93djfLtzuG3utnE5ncp44nZ6Ycsd5zHqe9GGqm0mDtnG/XLh8L3w1H+sgwvNp5uNp4tMpM7WVUAuxNZaYuPeK2FhJcqLFTJBOKDJW7aqIQnvrGNUKiKY3VrBSR5Fx0RdvXnAqn+6GYHpydCWXhb2NNZxvHFJCkkoZQ8zkeWHfVd9b2m49dzjKWxsNRQPxfESmP6L13paRPeCjzVUH2BOUc9Ca7T7rPiqz99TbuFmdTppCUH+2UUUcXfigTHpxYYQT/9CPvbcklHkmXT+3dCR2EHXEq2Xn9duNt/edmBaeXj5wOp3VuCEHhAl6JDET0DXYpTb2tegobFVNRG+Q5cT5/IF5emKeTkMpFqN6pBFllNNKWgrmm5ZsPm0mjLg+wK21tNXyDSbdLqJumLVPF5VGaxx5J+bERGAC0e0oeToR0wxmQe67AXUsrMYT7rRzBLCMZI+1bQNAf/x/Xf/saPfkuHi8zaB3Qq+0ulO3O7XshDyP6dBfDvK+3+l1pzctUXvXjHu93bjeblxuV3KaeHp64pdffuXzp5+ZphkRTOjRWO831tuV9X6h7rrpoe0rtXRa00+eeaLPH6Er8yhGnZ9PbaJFl44GeoLSErE3Ekq71SmDILGbzLNCsYMcfPNGG/2y0EHquAHdDspv5z89866fo9TGfVVjyd4re9EdZu0B0V63zcCwQ4CiZZndhPay/P0XI6m4Ms43hfooJNqs2IO5t646eSsHdeR+UFJFbF2RZZDSCsUorI8uoI8uK8r0MC222f4GcSKLQApIEs10ZtKgyURJMb1Xc3rd2e4rIpEcOrF12KCg4GRKEyEYIrzvlPtGWaHsAiWRJLCcTzydnlimRYM7JmsfDKDzKYBoIOg4L9rUZmJKyjTTE+XUZBmjs8e+VcVI6jD0J3GRiWzULtnOQdNnF4Ju7p2WEzFlnGDkhh4SA2JzeKyaDDCetXC45LgWwP71+DoHXmAkKXtpzVs2q25plbbduV/fFXfoyr77oSBvRc0eAloe1FZw3/B9L7x9f6UD6+3GlCaeljPzpH1q3RU0ud2ubPcrZd+0fE5C7pEgjd4D8zTz6eMHPv/0iXMSUo20oLe/SZrZQ9GADcIUXTxh62ZCHWBTR+e0MQTtR6v32lbiGumkWr+UggoGalVfdsGcQaut0rW3UGvjvmkrUW0cF0To28b9puofHVHpLe3Iquu7gwFeflseq5StjTC98EgEHNxo7dW1b3Oj/lp1+6pSUu3rmI5eT6d9lqjIu68AHgh6b6Schsm/6zSUSmr0WAmIOK1XyEMjHUhZTLaq3e3plLndOik+c56emONCaJoAY0/K1Q/QeyXUSJZETF3bhUlJIefzmafzmcn6bLdldm57630IcRx5Dikq7Tkrgh5sgw1YL9z1JvfscBgxdrsRbeYtYviJHG2JWEZxSm3K5CnbiExHgGOddHCwzYLaan6xIlGZkI3uZovdfgYPZTlAVW+bAGsrzZSkN9q+0ctGXe9s1wuX91fm5cRcngj1By2ZUwp0Epu9YJdijnU5oqqkZZmgV9VhOwjVlP4XxOauVuom6UieyEkN7s+nE59fXvjwfCZRYYsU6Ux09UDvkRBXamg66omRSmCvOouWar5bzZBmK3t9LNVaf9gkqiV3zGLv2Ugq6A0sVuo5+6m3fsg2DZ3WPrdhehHTB3d80eCYYze96egKUKaY6L3Zdg91hunNbgPRG8eT0Z++n/3e4LY7wiI6OnESzKPQJ7hFsJUkCropc01VcwY4ifHD8b5cVWUh6jMiqBlnTCBJE2HyEWcwMCsmnp8zvZ8JfGBOCznOSoSRqOQcsf5WMonOFKBFbY9CSOScOZ0WdTRNadyEriHwXppxe2rgxKQ3ebAyXVdrm9OLPS/xoMI58lqZKBshGAVBxj+HWacRmmK0NjCZOs0rAkXOe6+jGmi1HpeFHMy9Q3qsdFnsMnGZLd6aeZWFVxfeRjZa2dhuF/bbhfvljftFx9qtVtI0s4y+/y8GeTS7o3meOZ1OzNM0CgjpnWU58eHDCy8vL5yWMznrGMpvkWkCXWfTtNTsigQ+nU+8PL8wzbMipcvMMiWk7lRDNyUFpjlBnaBf2eJGpRNCJSUhz0DYbXVrpbWqJJXAIIR0f4HNwS5bRC9+42JKNSuRArqJwv+e38BRS6/au46PzeGE1gevHXuh8sDLd9tecYEK2OweA0ibjU0ceuHoneXozRS1rwNbSL4muGuQNl/rJA9B3btVLNpbY2XosYZYwbgYBYLaHRtzUn3TklhfDpL0H3VfiVZ+a7BLnziFTEiZvi1ETjoai2LzXP06eivJqDSaOdUkWzgwzbO6qlhAhxF0RhE1cNLXUefJx2Ya3CpAUQLNQNgtI7gasdU6DDrE5tciKM+DIyhdaOT6gmBcANWfa401QFUDJWlNZ+StDbq0j25Hn4EDZPozjctHXMgU/d/audGX0oruIVivb7x/+8p2u1L3jd4a9ytISkj84TVJgRA6OSWenp74+OED18tt0DQ/vDzz+dNnnp9fWJYzp+VZrZlTVsZa04CqrVJqoofOaZn56aefeLZd5n6DNUOKO4tye7PQ90jchLirqQOp0UJRZBVdl+PBWncN8JDUy6s3tBSuug6o7g3M2UW8bC4FiZEIFPuzvoRQQhwB2Q19EwveySqTaiKGVs0fHnufI7BUZBBthu6IfjOgTSToPL815fyPZGzw2IPxxPBTsL5Y1y+ZiqwdXjnuQut8ljH77qZYChFJ5umtFnuGKCtxh9gJWcbmkZg1EaSstNaQAjErnTOliSBPzPnEHDP7LVHWAE0pr+oLb4nGEpm2DLqddkq6vM/BM1fU6QIBmIb/uVZg0jspKM9bfdbU9ikaJyMN15esjrVGUSaKAr/WEqlHuY4FUxCbmngNZzMVRR9V2ehipj46cQXtHgBLMXQHG6W2eLzPWpteHobzOLAbojPadNTXLSm5d75bSDUCu6h4aNtX9n1FbIpSa+F+uxJ/VKDiN2Sns8wTH15euF1v1FrYa2GeJ5ZlZp4XFeYb57jbjRYF8jxTaiXbFpS8zEynhfmkTpRCoOx6I2uflVBCckd2kNBI0TZhhkKPwWybzK0jBPYCW9k1uFLQBQXWz9SqL0Z7TiUSROMfE48FAcFuGDoHUBOsQOya/XNOlL1ogFovNW4GVzHYKJFgs2xLKoI6xPgsXA/TQx/tN7ydBMW59GseI0H7ER0FD4FudNVgIqKOBnX3diJqhSK9a3lu6jCJCVeW9ag9djS+epz0dpKEBrcHeAwaVFltjqbpRJInJD+Te+ZSmk7eqvasIoc1sS8z1ESkgZii2yUZj9yCS4yzrUCWVjxil44E64+zrsh2Q4axbDC4p5p+La+GtHe2pY7Gn0hRSKY01HaqDx5F70qPjkGJODHauuDg6kKbMHSvWDTAdf7mGLmRm3of77b3ThT3E9CbPLjHG0fr5oCpG1xqRaaVRjdas/4Pm+m7Z/5fDfLWKr7DSkTdXz59/EDrle9vb9Ra7OBWSilMWccqemtgyGpgOc1I6GybZq3bfSVPCpakaPbMIgqV92o6ZO3zJSUCsyrBY4G4U3tjShlBs1stMiTLXcRWFzV61ENaS7PPYJiH8b1RnQcOltXaNbAVVsVdPlQJZmSJ7ECOvppSMJKK6bxhUEC9Vy/Veuqgc1fswDqBJgT/uY7glIfD+XBENHjCgzQzyKC1BmuvWtdgliDqp26IOVYNxGyVRhAEJQOF5GMzvclDVEwmREhTGP7fy3I2o8ETOT2RONPaDASizdCbgVjBxqmAKrqs1NUSfSamrOV28JW9lmQH0UZGUsDKWWe2hZQGu7By9OvB4ltv/uPgD7PM3khBwb6UItl0BcMVJ1mvTcfdctRuyijErY333GwsW2s3Hrq2iB3/DHbm8KDEFG1WDVh+t7ysgW1lvSPweutrFernNAbVARACYcrM5xPL+fxjQe5Ak3K/GzknzueFrTxRa+W+7VyuF50PBuG+Q5XCNM/0lshBnS+nkLXkRVHot9dXs0+C8/lJ7XyyWdyWjS6VXitNIqSsh0cyRQpQaDLRg97WpRQ0ro24EIW96Sio26EDmyU7LdARS0QXJ8ohP/UZpwJGDXDPc9hq8S5Te73uEs86sq6W1JpUQjT+s8jgmw92mpXkx9pjfdkhWU9vpfagxhqY2E2PHJKtMY5OvxS6mFwzyFgIoRebBbp022rTBvFFRJl0MelYSqIQcyDnQEr6mfKkfADVKZzIeWGKTwROtDqx75FWfaOnAk3Rb1kJpmnXrKSeaWqKmKfpALKslGUkNwef5OEGVyQ9Z5VzuturA2shpjE2VTArDADMN/k0O4cxumwz2B447dmtsAO72KQ74KosQOwy8vGlF4DDF9ASmQbxsaSjtaKB7FWkYw7x+H6dZiuS+zgTrZrnYLdWwBSY2PnK08RyOjP/qGkEIdBL9e8JNFKKnJaFfd/Z9sL1+q7l3ByQ0pBUCbXRydrzGdiSp0iIM9dr4/Z24+39yu228svf/s6Hj5/Iw0TBCPg9Ua00oQdyAJFKb4XWIpVOSpXSNqR18pKQHWrVr6EmpEItjSp2w5jTaUBHV3STOooGZwCdl1svtBdlfSkmJrTQzSZoFNYG0JgkVxEYFRlGLVVrsU1eogdLmlURAWR4jOssNNpIS8+8bhDrfjXZLe/podlhCtHEC25T7SweI6vEnMet71z0Yw2u7toKCfIcNehjIKdAjgZIpWgeYmem/ESKi7r0thO1ZLabUO7QdqG3aFZG8SFYdRzmiyNASSPJdoMpQ9Hcc2zG5yg3Y3aM6rXnhWme9Rk0XW/lxKBg38976yAGklmiFNOmR0kMyvBAtsO45fXYB+cW4dnHwT9fl+zlNdg4VY6bWBei9NFW6Xlpf44tEaIx7PR7OH6iSylaL2q/ZomslkKt+6gkvM1MeWJeFvI0/ViQ+00+HnQIqjhKjSVvpHhh3bTxn0+ZPKtbRW126Ltvb0ykbH2edLZ94+vX71yvNzrqUBlenrXniUlZTtK4lwqx6ApZaYRWmVInSibsIFKo7U6TnVqt5qlCj526WtsgumrJPw/d5I9RkO7+HIxyTW/XRBf9M80CzIGQgXjbSEQrgH6QcowXLeaoqr05BzgjAukgdvTWRz+li/OMeGIvX40hGdJiwfeOwRgBJay302vFARtlejVqr1Z6KwAWghJCdLeFSkbTZH15DkwxMueJmCckZ9I8Meczc3pB+gJ1ouyJ9dbYbh1qIPQ0XEcdVRc7zEotPqSQMR07vp3K2W2ufDikJm3bRHuMPE3My2mMtLqYOMP3kUkghGPtsBs26qEw7rf/x/z6XBDUzN4MVy/au7Yo8P5Ny+MudvHov/PLXR7qbhFzNhCvHtSEw73hBoBq5bj+ngW9+cT17rvZNfnUalhQt7FtMrfaaSZP0/CS++tB3kxOadmrB0MkY2SeJpZp4r7eud9vTNdJyQLifYPQWqRUiP1w8GytcLtfdd1qh+9fv/Ly/EJOiXmZx8PaW2PbG52ks1M6SSpdOokJMkjo1L7TQ2MLkAzE8p6uy4ZIQUpRgk2E7ov7Ogw81cwlW1eJoViguNLLTpEGmHmpOYtMly/IGD9p7No4xMAxXVBnz9NK1jL4yz5LDYoCjzmts918FHNUDhG9MUsrViUYluC9XWiDhAGdFCAlDgLLw/+OSckteUpIFGWP5YVpeiKEGUlqaTSlM6GdaGtiv2fWW2dbC71FskmK6e704qMvS1522P0DBF8/ZJMAXzDgoJT74CuVVEv0ycQn3la1qriDTxOCEVaS9eoOyonNr/UB9pFEtOY6qMHeSgwb5aCY1OPPLYj56FkL1A5tgn0J3AGYB96DOvn4eFlL7SN3WMvWNPnq5lwF9GIwxxw059dSxmdRsUw2bOt41n85yMW5fTbEdzAoRh2hnE4n3q9X3q9XLm/vTFMmCUxRPdWLlbaF3bauVC6XG/u2knNiniZ6q7y/fmfOkSgfEQnsZed6fed+v3A+zyzLmSllM5RsNCqtANKZ2kYNlR40+GgV9mIEisgeNiQEyl501GS2zr177w05RFrtZvMY7DYGmmZ2p7hrnxTUTK9pFo5GkcSArWA9ve7H1qqi9ToshiVq0AZ5kIha/9XxXtzlr4yb4ZEY06WrlbWVo4Q+Ko8oAlQD2PTlxySEoIlAjRACEjrJ+vCQA5Ptzk55Ik1PzPMnYnyCPiE9I2Wm3CP398Z61VVZ9ECOE4KOsYYLLF1dT3x6AUfgSzBxj6rCtOd2hxZPitaDh8g86/QmZ6WtttbM777bTF0JKznlg7ziGIv9NClG3R9WrbQfyfBYde1uq1qd6w06FhwOivGBG4BiD/pp6wDq/HZu3RO6jeD8OzmM0J2NqW1eb429o645AkMrgXXrPtoVsYtMpb7zcmKa52G5/deDHCcfGGBgLhQhqjBgOZ04n8+8vr9zvVyVmrgsykuPnUKhlMa+V263O+t9o5XOKS/EJRPjRAyR/X7n+5cv9FKIaWJd77y+v1LbTp5+IqaJNM1EE3s0k2hKCyyoiimkSMyJvdxZZaW1YL10GyVqqyYEKZVWujmSWIkrEGNWXroBJjEFpPp+7GOtj+5z0yOEeNnXh+pIazhNQi41TeEghvQOwbzdCXF4sylY04/5a2BIFf1FS7SlB1FIQefdnmwQA/tCBml6SzugFnTSEKxn9p1yOSdbsZtIadZ/8hMpv5DDB+qeKRtsd2F775R7oO6amHypYApOXVVtdRQZ+ELvD62OHXBdUGHzYHM1VTDL9qYHZe0t88KynGzhQLZ3rvdaDHomo7VFaZpG1eUkEv+Gvfn7O/gJXu154lbATu93iWKtg62bHokKv4R5qPGsF28ucqajrr896hKQ1usAhzVGA7X7+Rjln06zjIKsitJGl2DkJUHMiLK3QJxmlvOTedjlMR7860EeArGbVLH2A8DomkGHzC9mtn3TD1aVmLJLtbXBUVHl1mkNUspqk5smazT1XWzrxvdv3wghsO4r9/Vu5b/dEqPXsj4mJiiB0quu5yGSyazpjRAjte6oDxfsmy5obLVSi1CjUGNTbnuFWrqOtjrm33bEabSbGeuztOT256Oa9Zg8uJ02adm8NTPt8z6u2xg1KC324Xb3frR1yx12grqN8iS4qKINwKZT7dfRvpby+bsUYgzkKZBs3XPKiWlS9FmZa9aTh0hKMzGeSHFBSERZ6PuJrU7s98B2LWy3Sl0DUiNiar8kiSSJSDJehK34sXJbbPg/zDrsFi67+gTMS9SfAfGL3HKuynjTsjAtrhHXPlWCYiZexQy+uogJVOyWG1MJtf0upbBvG73rSqMgKuHsNvry1kvHftBroZVCq7v2wSmZjbL76B3XugJvTrgBxD+/vkHnW/SRLbwlCX9KhJbnjz9vyZ0QiNPEfDpTN7Uyz8uJ+fxMnGaFetv/AOz93w3yFKNaqtVuIAY0K6NiaDo3ndSho5amoFcTNbzf1ZTB+9IpTcRTYsqLzlrjBMihrmpdk8G+DbFHjF4GRoRkIwwbHYREaZUUziyx0kNmDxOhKe1yLyspZVrbKXtWw4t9Z98qJVQFpHa9EUNUtL01tRgWv7G79tfSFRGPVnQFm00LjcnYYz62UuabIvgu8A/jTBia3yvO2RBrH/Ql+7Hoo5+mNVKOhKBVVcBYfUGDRqTZrFkLu5i10lASi5ByJGa1/M1pRlK033NwKRDiiRjOBJmJbaKuie02sV1huzXK1rU9ajDFRI5KJ0054RJfF2q4K9Ch7nJMQw92lmDbY1RiCsqqVMKMJl7I6G2dtIrLupjQXXEUOLcb2NoA/98KfnqLowrBhk5QqmZfvSWj7t3zhKRzaMVnWtnZtzvr7Qa96p6zGAx76YgSzkf75I5/Awt6+NijhbO/cbTw/cjk9v5dwYZgSy2E1nRLynJ+IoVIzhO9ddI8qZVankD0svmhINeH1qG6Gkl/v3eIsal4P00s08K27nojVmjlKD1TEqQJES2ppmlRix536kfXFnm2bbVaBaHlIwRK6XZDPZhAGAoeg0pbJS4E0Ragtju13tn3G6Xe2ferBv6+EeNOSYVSKi3reK5VPXD7XtVZv5nRo72L0EGSOXgag8umKECz5YEd9SQHd3bRtbgyynlg3Ni1Gq5voJuIUFrTrR1BEV/P/hLVAjogBs65FNKQ9NgdwyIlTYIehGmamOYT83ImppmQJkI89ma3HqEvxPCMMNPXTrtBuQTW9862ahArS85Qa9tZpyWiHm6JSas8p4A6vmHAlSPm9Ka0UyuDa62UWpXgErVH9qULagahFk9gRhk+XkMTsE87fDOJA2uAudBq0KVpUmGNKI4wz7OW+Pb3Wt1Yb+p+tN2vlO1O2TcCCujKNOnZ817aSj1BbBISjsB2hpp3BEH+tMACBF9T1f1rjLGctRAh/KmNoSb1ns8zTs3u4pzJxv9lRPd/N8i7/VRiq391XhmRXXW4KSXmPDHPJ9L1poGyFSCQuyhJBHP6DIKvhVVgRD9QjMk01LbLvBZC000SMWVqhb10chMbl4gBEw0JiWk6g5xBKsITSVZquNHiSo7vrNsbKSzMeaPUjW26s20r+76zb7v16e71VQge5EWzqgIo+gibJZtgt6wr3aIFHvThDdYbD32p9YYcyS+0MJ7xmO+aDRF0pTK62ilATibXlGMur+6g1kca7hDdWCFl0nRimk9M05lpOhHigqQnRJKxtwLUQC2R0hdoE+3a2a6V9argpptXOKknxmRrisIYS0nSBYExubf5aGhwXzVfLkAPpDlQW2NdV1viqLx6Bc8SMWhblw3ZDzGN53wEgvbMYpeR9tbdu3JNPqJy1EkWkq0bznEixTzWGAnK/y5bpyRVde3rnbKvRoRpqF2VJnuvJn1HercWTYFeBrBmmKIRpHRCpeCpWF9vIJrF2AGcORgZCKLPsjlb0ObiMWc9S6KJTH3+/+v//M/JMFZqB47Sq3cFuZJA2woxJ+Z5ZplP1NrYtmI3rB7oSjUwxdi8WhjQg7qXlKqrh6ZsntV7IMFR+hBozYwEm2U5BCQZIipIDGpTRKXUjVJXet8o4UrgjdZvdFZaW9mnK+t2ZduurOuNsu86AXCxyl6oZadbn1tqG0mO5DZC/jwaismHQU8cN5scWRmwubo+1lILrTYG5dOyeLLDqvLFYIe2ky2xeJCrO+exrgm6AVF6KHUt70JMZ1I4EzjR20SXJ6gvwEStkbLBvjX2tWqZ3DbaCnXv1NKBZHxtJdv4zq5jMYEbTqRjfbC4kAPzh9fxUbBJzSCroHPgUio+/VFL6EbMMM/quJrzZO2aNauDgWYjMBtXHu6mfZBFJERyCMR5Me+BQJQ0wC79fsrmjEEG3XUw8FozJF6Mau1LJL0uU1xF99cftl5/rtftzynKqj9hP25vT4hDJhx0QgERJ0e43aeuZtJDoN7xqtPovXBsdfmLQT76HDpEBc5areSkXtStwTR1ltNCfs+s24Vt3/SF96QjqhhsZZH2Mt3mm72rDZCLF0qtxBCYl9lYisoJrk3daJSME8cNIoBEMZKHimJCh7BvBDaQRmo3pV62G8hGYyOlKym/kfMrKWmvXktFth0JhRQLtQTd09YrqYUhG9Q+2zJo04pFMzJm2fwY4P7CGRnbb/VgM/OOv3FjhplaTfv446UnW+UcsgZ6jKa4isfaYeW12352WYjhhPQzdVuo24SEzDS9kKdPtBrZbp3tpjZMtdiyCTq99JFE1TK5W9nZdQpQGyS1T45GI02uCIvBw2yQSwRNwm7YoLwLJQW5Y08p7mar+Ms8nZin2ZYVaFLJMeJEhGq3f+zYqM2ANs136o47Zu79T7dsa2oQ2qwlq01NUYK56sakt3zZ1OREZ5vjWrbAFN0SAzqH76jNtNOLH7wFRm1h49KhRnTAzy5ObTlMB49+T21dLHV1tVBziazPV/VZiyWGHwlyz2rRX46aRtRWdTMGgd6EnDOlVW73u47cgq0/miYasJcCEomi7i3dGD3Bx1DK8QQ5xkcdYS87+164r3dO9Yk8K7NH2WTWm9qKGYI/kBn6Cr0iJCRk9rYAG4SNKDNTnJniRAozKV4p+04MCvjVulPKSi2rzuPb/mDBxOjHuo3AFCeQwZnWxfb64jxwAMvAOm5LXRjbL3iwnQ5C61UZY87/FnUzTWkiLYmQdFd6irYbTAL7Xtj3ndYVF2k1U9tMKZH13kYSen6aiU8nWg3cXq/sd112ID3Qe1Lk2gAgBTxhKJ7scGKLEOJQkMXB7tMlfG2U6H6bKkc7GCOOATClmGhJWWdYKxjjpOYRk1krx6xOuUnfO72zyQpSjEASDPRUAYrzusEmI3RNTGDc9GNzbG3VuODVPDNU5JPzREkr1QwhNLjjIO+IKCFJn0cYegT9HIq1uJ/co6W4J6HxDPw292rE2UqtD8FSa00vR/tH/fUeKkSz6vZk8peDPLtGtQsEDfDQFEhTgENLipQuw+b3cr0oQJMyJxsVAWrcl3dl6ZRCMmdNNeiLNMnQtZyiK0p9u9243TcawryclL5nhn0xp0EIcAZV6B36rjNwE42EruVmo9DqSuuRHBeIGZnP5HijTBv7vLNtK6WslHKjVC3vWy90fDlDs8Ct6I6rPmbZSODY0OH994R7rPemSLmuN66jpxvlvQWQl+mqE8gazJLJ+cS0vOi4S7ImMFNAtbayrlfWbWfbdtZ7Y9927vdCbzPTNHM6TaQWkV3de/b7Zoh5MxeXoLN8L6cNVGviN4Xxt4MMWy0doToI54SeY/7XLJAdSHJKqjO8Yuyk+ED7FUtq0f5JioBLjPQQdVbeVWCjHpfquBqsgnBhT7BkoJMbBXXpbk/tJb0GXHU2HErKkdBJeSblVc+QMAAuH3kh3TT5wTgPVmT0Plpb66LsGj4CUAiq1Xd5qVWm6tGue++6jVkDul2me+XRO3G0BSZjpVnM/+BNPs2TlqdWNvsWjmGtk6BE3ToxTRMpJ+7bxvvlRojZsoz2u2XbORl5JuXMsizMy8LclXsrQMhqb0xr7LVyu115f7/SerfVN5k8z2YYoOOPEH3pAfRWLFMaMNJsvINaAhWUexx6ZQoTkTtTLLReKG2nnDb27cbt/sZernR2YKe1ohVIb/S2EaSpgKBVzMpM+9Wo3wdRNFrRXXsZrdENSdZys+DmFcMh1EA73Vut5Jw5n8hpJudnpvwTKZyhzZRN2LfCdrvx/v6Nb98675eN+23lfq9s907ZhadzYvopM51OSIH1erfPUQcxRDdpNuUxuBDDRoFiBzOEOMpRsVsP7709gIMcyUt0SbMuAPTvoS1XjDpGC7FpFWY3QSGqliDYMoSYUeMG0fVYlmx1zBbGLT7gEL/NLMBVwWVEI+vj3fLrUTKqP67eukESzTb4lN2wEMc+xNh59uvHv6+TBGUy+vzcwTVnjnZjrTnNV6sKBkGqPSRYJZ9B78WmMTp58pFZCJEu2kIP8PFHgtzBB8Gtfk2FJbYj3JxL8jQxLTNpyrT3zvV6pSNcb6sG+L5T950pZ55enjnbksSn1oZPVxNVXcWuu8Hu9zuboZy9nqht53a/Mt1m8px1JGL9SymVUirrfWVbV8q60tqO9KYeZcZ2CyKkMNN7JaWFlJ5R88dKokAv7OlODE/s5aYlITutbzR2Ld3bhvRKbTu2s5MukGKz3nkmBi0va1vp7HR7Vr0W9n0zHf7OoE+Gw/hAunO99WvlsDDlhZxeiPKCtBNtX6i3zuX1na9f3vjtn6/88/c/uLxfWNdNQTNJzPnEOUdyn5AKPe6KqFvFF0b9qPP7AFZFdJt86M1qCIO1U9ov1lrZd63MHDcgBJq4qERG8IuJRdwRR2fzAQkPe9sQrY4k0EXtjLoEKzQ6SOWRCqriIK0CfA59BHI3nncbyjLs952k5MDpYTNlmEEXYrMKUxaFwFIiZJupe1vQO/Q4qocDexF8WYcnnmZecz5a9dQ5SDOP5BjTznfvd+yH94qjg5XyB3Ano2z4gSDft1WdQQkW7P4srRcSmCbh+eWZn8sv3O8rl8uNdX3ncrnS+1VBFfqwIir7UXpU89walE58PKF70FXEUpUrHyOtVW43bQda7yynM63Dvu7cbjdev7/y/v6uf681YujMU2JZMikHpnlmWk7HzRLcFklv3E4hxxVaJoc7MSkfAKnsbaO2XUGbulPbbgIR5YmH0MkxMaUzKS6EEKl9o/Ybtd5ptdBjIcU7pay0rBWEGwEGzDecAD3ReyaFM1M6M6UTkTO9JHrNlLtw/X7j9//8zn//7//Jb7/9J9+/f2Pb9iHdTEmYPwTmOJNN0dVLxW2YsL7Rg0SA0HVvW4hRt8qac89ArZROojZSfoBitORmZbX9vkgwa20FsgSUm/DgdkuDtlWaujRooJljjAdlEMVaaqm0tqFtgwtJ9EZv7WEc1bUHb9X2xfWOSwS8W+8PF5bLYodja9dENs0zOUWQhttCj1GdfkAF28zmyZ9qw4FLw3E8UD2uGSx2+23rr0NAUFqwcwu660WCjPFkTO7X5WQm9VUYIpy/GuSP812XUXqvgKidUUyRl5cnQoys95Xvr29cryv3+6ZSua6jX+VRq1FAzuoIorY7D4vah8ROb7x9X/WDBV1Lm6NQy871/Z1SKrf7jdbUEvry+sbb2ytfv37l9fWVWgs5Jc7nE58+v/Dx47MaUs6L3hII3aiovekYpVNZ1yvSNig6t31aFuIUKb1bQmrstiqpNg3UhvZuKsM9kcJkts4bra9UubH1d0q9Ie1ODhsEnW82azFCn5AWDKVeiHKit5kUzmQ5IyVTtsq+Fb5/eeUf//lP/o//7//Bv//7v3O5vmplYGSRbgsQcoxMUyBFSOLuOXYDdDNwCHHcYjlHI7uoc0t6GJd141/31kdiTjFqFTdNpLwoD79rZeKri3LOFhCdspdjIQYmxgiJLgXfGILhHq0WCwcbGwnsZcdpns5U02fvSzXakGXW3kbrELyqtjwgQZVvcQDKfZTrrZYRmDFGEPc58C/QPVrNo42RKLsZZI7LUB6rDr3Ags9au0+uDpS9oUnGwU8xvoUSv2S4HjvIqz+mIus/DLxhLyyg2eqY+xovW3x+K5yWiU+fXvj5p8+8v10oRUsxHZ9p77UsC6fTiWVWMGg+qTdcnuYHVpPe7m77rJhaJ9BI5jpa6sZ27+z7yr5X1vuNy9sbt/uNvaxcr2+s97tKE3vhdJqoz88EA7DSNON3N2KlPJ1aNq77levbyu1yoZxn9RE/f2SJk4oFmu4s15tC0fjaCl3Um3xOM6EHqhT2eqe0Fak39n2i3l8pNRFjRdWzlb2sBhQmBSBTJi1n5tMLgirAKIn7pXJ5v/P6/Ru//eM3/u3f/o3/+Pd/5+3tTdlxwamweiOlFJjmyLJkrUhMDZWSr+LVtmC2pZV50jW8Ij4OU0ai8wJEsh1chTqUnJJI84n5/KSgmCV/lX0m9VBPiU63m9iklGhvHOx7dA8aO1u1FLb1zn6/UXMd5fe+bdTWTLASB6rut3o1bryXtqqzsMTXjX7l2IdYyNo0KNiouLducv5HViMGCtrCy5Esjrl3EPXZt0i1lHAQoBxhxyoTHaPJcKfBbv3WVL0YNBi0vTG3H3eyGCi8pUudXvygQMWpiI/QRjcjwmiqKs+eIcDz8xM//fyZL1+/cb3d9K/slSDC6Xzi/PTE6Xy25XRq85yn2ZQ2xwKAsldqbUP0ryuEdmrZtCfqvirYs/6u5XKKLPPEPE/s6x2RzjwvnM8vnJYXkEwnEaJSA1vATPtBupZ4271wv9y5vd+o28aSZ1I+c345E9NC7ZGErliqpaiEURijP5WhFqgrzXvYKsTWSD0gbUF6RWqnth3qDpYQpSd6jzRZqEF13XThft358ttX/vM//oN//OMf/P7773z98oXr9Wo+fDbG6/5zRKb5xNPzByWUTPOYa+esrEOXfib7PbViklFVqcOsjHPgO8SDlZMp6GaUaTkT8zIYbf79tbTUmxBX6cVItzav96577vzgY7/sjX1buby9qqd4npRHsW/GJUjKHTeHV4yRV4q1f7WNQBQD8bQ1Objm2p4oaca9++jdNAvtMOo8IkHPvkbuwCUU6TdWogRcKKS7461X96A1YOxPAFl3cM6iayxcOHoLjTFD/p0sZf/+MIi0h/cjQZ6y9YiaZvSL2gPCMnzwzQ/ANE18+PDCLz//xNv7G5fLDbqiqctp4fx05nQ6Mc0TeZ4JQemK1EYrKrTQcQekkCDaC2uFVjZ6m8ahCxjhIAWYJ51dpkQIwv32xLbeyXni5eWFTx9/5un5E3k6gdgu9WQi/t5MQVTZOvTazPo3QO28f38HmYDM80fjDRtnWmchnR4YlsJKX1RjDXJk64IQSWGBBLUnetMtrr1kWt2JralElKQrg1um3gP3e6GWxtuX7/z7//l/8r//7/8ffv/9d263G9u2mrLP1vDq3EWB0DyznJ54efnEcnphWU6DYhuij71c1WflnzZ/g1NtyzwIIdIebjOnp4kkM2nQpBEt+FUJp7TX7iutYbR83rUPg0TjwHfdgAh0Wt25XzXYtRrTm1/92Req88JRRDqM9yGjN/8fFV4+GvTpgHT0vfuN2Aul6drlbktBonnc+PkfNjAjqPzrKf6gHyxQq7IWRwk+4trReRlBrKiCDIxNPNbwiUwbALfe2r4Jp9m7t6HgAOn+apD7nmTby9paQ3q1Csikl0GJAfTGNEU+fHjh519/4u3yToxf2e4bMSZO5xPLaWFeZgVjQhgWxb4orjfjR4swpQzd+tpWrUczzCGIWQ2Lei0aKWOeZuZppuyVdd1YlpmffvqVT59/4fT8omaE04KEPMgTYqBf2wt133VMEQJTtrXEFS6v7yCZWgPnpxe9XWqj7jv39Q4R3eaZDKgyWYOECZEC0olJVz9FEequIF5rndi7zmZTUrslJyBV0c9xX3n94xu//+MffP39d67v71o9GIvQLxW/rVIKLMvEy/MTT0/PuohymW0bDjizavSZpqjTFbqa3IKt+h0qsgck1w9osJm2yOF7bqWEHtAuIya6fw2rNAD15UsTU57Zwt0WQer3G/rv0v8E+LYQ6FnxkxEVzkFwNh1Hy9ItafidLFbx6mpsD19j8rVC2TdK2Z3HqCBaN859cP444/vo74fRn3vpjvh2nKZBbeX6KAX85xP/rGJtTB/GFDp+PbAwRBNGcIQPnYh4kB9+Qn8xyGOwJQUwssngJtuvG9U+tNrULMvCzz//TK2Np/OZy/uFKEFn4nMeHOx931jX1WiRZtsDo+zEkNWQszrO5ESa4nFLHM0SbhYoCVKcOJ83zucrp+XEx48/8fzhI8vTM2K3ju4Ai6r/Rfu4bV2535TimtNE8mBDd6Xd3i/UBtfrnWVZiDFS9sL1eqH1NrjWMSVcfdWb0FsiSNNbTSKECkH3iOvnVaVfzlrdiAT2bdflALVS15XL2xuv317Z1tW41t2mDWpwUG1RQozCNE+8fHzm119/4cOHF+Z5Vj+14IiuB6uVfqJElZiijYfc/CGY+EX/rPePrTU77NE0CtkC2z6zqNZAacDHeEskmrjDwT/t6ad5Zt5nttWCFzvlhkG7Tt6tiZ1v0IK5BPWmBhxOLvG2snvJq587eDA8lM6+9quVoi1Wa+biKnbZmEtQOH72aK6vwPi59Mi2kQj13aiyUaIJkqwnV7NNeyYoLtTNHMITqnsIjrYjWuLEKLNNRnBbVvnxmzxEE/N3G55054/rS6g28xQx0gONEBIfP+hN8OHlA+vtZn2SBmSpeivv+8622UIFQ2kFA8EM9RWBaU4spxPzadFAz0pVrWZCQTv6I+/P1GddZ80heEmovl8M4oyO9lprlH3jdrlyv97oTft4J6bQVERREcq28r4Xrm+vihrTqftOrY39duP2/jaM+GZbNkFrJl/FtPYmRjCwpraOIrjZ1vyoK2wIjR0l9Oz7NsaCjroq6KQaezVlVPXZ8/MzP//8M59//pnz87MtUYgD8LHmD8TkpsiD6CTie9T0dVnyHDekl5OWKEYCsLGO3VK6oFFZXGPcGo6A17FYJ0yBuRRKWQ0XqVa1VUWq3WpZsMulUUsxIoi2BcHOlVKsHw56P0pzr3hCOHpzX6mNJXoZAawXUfCbupuLTHCAzZ7/uI3ReBDnnhurrzezGRddHCI2+ntsu7v7C2jfbnCfnV2NvbEkcVQAR6uioWnylR81jXBGTxfUjL/JQBirN204P9fK9670v48fP/J8PlM25YSXUtlL4Xa/s20mL6w7t/udshdFYZsZI0hgmhRAe5FnPhpPeiwV4ACYpFjGtJnvvu/crle2+50YIm9vrzx9+EiPpkjyQ24001Ib2/3O7XqllF09zgyYi9EMI2qltM6+79RWqWVXWmhVB5aABu++r2pW2TvbfFKQ8WEzhyYkLcWjqHKuYW4kkrAptUlbqwJ0lr3VMw4VJ/kSCEFBsqx2ysvpxOeffuKXv/2d5w8fyNNiAgEb1YhPYMz7zQkgwYI5eqArSOfEjVEGdzC9pd3ijsbbbW6fXUkjDxVAtN7Xv4SI8rFR/GCeF2v1q0l/K60045g7dUQG6KQ+7jstq6EnnpDFGXeaTAYOIdodC1Cb6wkUIFO6hPfeDffmV0tlOZR+lgQe6m687AasXQk0swIPMXkkIyEPvERDORhgqhtvu83mu19W3XLPQOcxvETZdt2ATIMPDynqDwW53XRDyhdU8omodVAM2W5Q+9NeZomOSpIIk23erE1NGaZ5Yt12ki1T2PeV6+XO/W3jfl/NLUSdKKd54pf9Zz58/kBDE0uThrSitMcQyOIgh85d7/eV+10F/9f3ym//+R+UWvn008+8fPrM+fl5+H2rHZHqmn2unpNu6fBxU2+dvSjJZF3vlLJRI7qEzkFITfF0UWVdr4W6QU1CyLP2r3p16/LFEGg1jPFItNl0TJPeQMFvi/LQxol6zYnQo9DksC6eponT04lPnz/z069/4+nlI2k+E/NM57As1sMoY2FijMlmxQ83t+ht5FZKLsACZz1iweZ5stvqZx/nON3URSth3HT6VZzW2elU1TLUxUrngogKi3qGsioz0GYpuHNto0IX9rIRSxpCEA3og21m6ffAXVBNgzLlHAE//q5+EwuqgIFkjpo9gG7dnqdgpb/BZw8lN+PnATF9hc7kGaV4N+Zda744Q5+n7k3rw79dolYuIdp8XFHD4aTTO4w1XX81yJVQ45smIrqbwGmAyQzmUPDDHk6rldJ2Zcq1NsQb0Uz7FKQ4tlempCOc79/faK1za0qiqdumyrbbna1UvWGy+ggHkx9GE0yk1tk2na1eL+9s61355q3w/fvvXK9vfPnjNz799DO//u1fePn4kWma9KC2QtlXYhDivJCNhy9WItbWSRJVnI8aM1RR4UbBysdxfLuuZo7qojMlHTUatgzo36EfXHXfNOI+dr2p00sryo3vtSlxI0RSUOGOAzHdrvNpWvj80y/8+ve/8+nzz5yfPjIvzzbSUUKFl6P6Txxz6pQTxz5uk/HaDT9stP0dW/8XzVet9kZplVgKKcz6/bAeP6oneEq+wNC2u2C3cmujxQpmxRS6km4clwlEW/FchtLLKyWhDcWgLgX05RRhdCS0QlUvbA4UvePbc1z++efPz6hIDmKqJ4FwBLu3AoKV196++IQhDqam6ysEjFOvyHi1sxBMImxwo16mVc9ALTvZzC21tQ1WNemWoU4jIdZC/0CQi819m33QYT9sr0pLpIBv76imslLlj3mlRSUR1Fqt5FHhgdierPP5zNPTE88vF15e3/j2/Y37faVUvfU///QTLx9eWM4n5caj31PQPikSqEEoZed2u3K9XfTFC4g06n6j7HfW9crl8sq23vl1/RsvHz5qzywAjZSTVh9us9SdLKMvtFbvu6xcs+rFKaHNM7OhwbpML9oh1z610+1wGxPQ7Y7sYAYO2aI/b5d05jGiA++FG5CmzPPHj/zy69/56ee/8fzhI9PyRMqzltodXW9KN5ah+ZHDuF1iOMZvYgnHb2skGHmjc2wm1Zas1sq2F0IsxJTpNkYTgoprpok8RUppuru+Hbu89UN6Maq4gA0M7JkaiNVU015qUZqqL0KgK2OwFuq+QauQ8riRu/W8zsv3UVoMj8CxDHBrgPUOeBmxaHBfuuCCE8b/tsAzcoqYgs53vKUYRhvhkKB+jYjUhps9tAEp2WymC1Xgftfloimr6hLDyAYZJkRidMeZ//o///ObPGmf4SaHTpYAVc+4xxcCodsBHaCAjNbXs1+QQMrTOOCasYraSC0LHz584MOndy7XG7V1zk9P/Mv/9nd+/fvfyPMMwWaKhCPOrG+pdWddb6z3G92EKS4VbA3qXtmlc7t85/I2EaRzOr/obePUTnFgRXumYomp1gKtQC1086HTebpPGwy7YICd9lIV8GlgCKwZFspxYNoDwypifnel2O2D9dq6wz3lRDRBRJdAmmY+fP7M519+4enjZ05PH1hOOioUsYm0n1f6oBZLNL2zeNlpN1aIYyzkN5vuaerjHw8YL2t7q0rxLQlXT1leHOUneCtwPIcW0JvooQWIJs4YAF00lhpCLDv5gdGmRBfznnN7XVf8dQal1b+f/0DNSvnoM3zpalZqFlfq8NJtUwoj4J2AAj5Cs18PApCaTmqQJ51MGDDn7q7NEo3LREMwXoj4wbH8gXItSt01SLM671Tcv07bKmJHsiP2Pxrk0V8yCqSYzY1IpEf/sK7sqSNbuZl8bwYKGKUwGc+51UbtEILOe2OEeZrUWGBKvHx6IcTM84cPfP7pE8v5NCh9cBguxNGGaF9cqws+tBxW7CdQA0hT32sbr4/ACsbT1LMexrkupVH3Yp+tDGZdL/Vhj7n1fEBvxrX21s56Pb28q4FIih77goqy11EKRtEKIopZXXV92brULjMvM9M8sWOrb2Pm/PKRn//2L/z08995eXGyT3gAw2RQPGMM5Kx4gz8vP7B+aHWFsHmyBd0RplLsPm69PkpDA36atiyl7BwriPXQlrJT6v8ws/b+18rTIJi01ub3Bnz5KAy70WPK4+ce6rLebRW0/jy6wNFu56p/F5NjegLwd+OB1ixwovECnHNQuy1v4FgQoRfBQaAZBpI2Zosx6bZVEz/5hUeXI+H1jjtCOHCqXAdlLipDUv9unnS0TFRX90NHYE/GmIvJQ+NHgtx/OAlR+yUbV/kOK1fu6C4pey1u8WSIkQoa+ujxIBylvZkoavmp7qvznDjnxOnpmfOzEjkU8DPRftfA7ATtzxEzGQgM0C8GsrcWIrpYsAvTvHA6nTktJ+blRMozEtRnbuxD0FmPdkdOW+1NWWrGVAtdySAt1GEG4QFce8flgo4ua0o3U/9R2dgqpdY10w/zO2sBYiDVMGbN8zwxn2aKQA+R5fzCz7/+N/7bv/4/+Omnnzg/Pes2WcApkDGaelD0NjjAn6PdGDNf7xtjVHBq3MJiSLuNmkaQONLQoBa2bddxpQV5i1Etur2oFQ0Sn/R0K7f9XzoYx2NZbUF+eLgpcCgwQNNt23BRjLoSiUekVk0dEyB50Jlrj1VtQeL4GcZ/5LihgeEsI4au65dytNyxeUtcg7+uFU1tCvyNpCXOd7czkDLS7Uav+6hceqjkPJFmdZkl2OrrhxbSE7QblfxYkNdg39AymvUOx0OwobxtmtjW1eiWqi7yTNk6tNJYV12X1Fpj2zcdhbjWVgIhJ6o5oky2GaLjN4kGOCio07rOZyUGJglMy3QYO3ZbeYOtwWmARKb5zOn0gdP5hfn0pLdD7yQxs35DPhsYndBRTs/ANs4JfsgxUKmOf98fpxFi64VxHEH/XTMkOdjL8WBvVWmMHuQ9CNJ1n/bT+cynDx+ReKWHyM+//gv/z//X/5v/9t/+lZgSe1FATgOpWylciaKAVAoaOAp0W5th2zqwcjMYF1zXw4SD0w0K4ClUiRNAxo3bO7XsbJse/GmCVnUzjP5Bqwysx3rEL6q1OmLVn4+wtHKUAU5iySWY6MTXPyGRYtOPbsi+t42EqPsmg75XX2Ms452IaSH8lv9zJebzanH/Pgf1LAawS6uLnv+9Fh17xkiodvEYwcbR+BGLYlMNrwYNiJbW6GVHpJIm5VqEnBX8tPf2iHv4CPSREv+XglyFD6aXFVfMCHDwnntVU4iybWzrnfvtRgiRODlbTm/R1jttK/ZCMcQ32Ny1j4fpoKuCeB2RZsQHRy9NQGHcaU2bSj6ZF00MFZinZbzwWjutB1KaVVM+L4oNhGB6d+ilWalmKG7xPdAPSd4Q5oDQA7RmGzFdKQQDtMNm443m+ozRN3lW7wghm31R1Om40yx7rwRQuWjOPD89UVojL2dCzvzyt3/hX/+3/42ff/6JdV35/v2V2/2qX93FMtKJOZHiokBgetxNBhLSuPmbBELITPM89OLuM94M8NJgr/YPo+TuJusc63u9gjE7LNAphs+tm7myDAJHENsi43c3esb8ABtpR8blwkiyOeuNq0sUYK/9SCrd5vTBKaY+zzbcfAB8cbDXjijuuFliCJ4YH6O0H3iGHIIpcaI/jdiiVu3B+kU4/OA6ejl27LLUBRri7AmJtjN+0tseBjvRWwCtGLUIjPzXUf6/cGu1pC4HvXA8hK49a607ZV/Z1gvbeqGVDUkTtEwIiVa8x+K4LW2kkWzdam/qCuPoaq1mGVWrJplo5bmRRiAZYt0GWJZzNmPBhHT1p5vyrC9+L9oyJCtJQ9QyzswFhK6obesqWEH78GJGjClaX2lvx8c4h78WlH4ICbRAP4Aedyd1fTD4IcREGibl7bqBtHcoRtoQgZwiT+czcZp4Lg1JmaeXD7S68/r9K9u2sd5v7GW30tduYOmKWted3grL6Tz67qYxamw3HU1O88zHTz+BiFpRd/SGXu9s97sSPJom7W7zWWym3ywZbPs2bmLdPeh7vY9z1bvv2D4qAr/xscUKYqIZdcqV4ya0rx3QdkKNLRWA0ZFTHaV9TDr+tTeMRb+1AweyPjT249wfvXu0keKfsIR+EFjc1FPL+H5own0mDtaWyrjJ9fkwQFrFtfCfEB+3h5g0wIPe2P6zA4M558+2/ijwFs0gUYzv6zNE8cxdCmW9s6839vVGr6sKWCpI1z1mI0s5JZNu3GYZVrqtKTmvNPX/LrVyv9+RFJnPZ1K0gxUizVB8FZcY19jAPy/tokRVYi1nu6lUdx5D1J1YpSCpjVagmve28perPXSf6yrAhrfWqOVvo4GpgNxDzMGoFCO+qFB81GIvN0qg2RseixIMCPIhiy9KbJZwUk4sLIQGM8rdShGu79+5XtQwAgPzNF95+wBlD6oTuN/Yto2n52djmMXBcCPoKqVlOfPy4QMhRNZ1t34XRPZhqKg8by2b60OvWpoGmM60E7WoY27vaZTbbpX0J5BIrP6RA4AUC8o/3aBHHa23n8jwRwzGZvQqofWDZKKd0mPvrW2mJo42Fix48LmPnQacjjCD74+XYyoiokmk1WZLMu0sudORmWz4xpzHr+vjOQnOP9dzoHvlGnsvrG1jmZ+IOZofj5boivL/D0lCGNjHXw7yZkCG9nHa57m1joNSbd9p+x2pG6FXtTSq3YCqqHxsBKIy3HqtxCi0bqUGDHfK6D9wVZXQXgqxVEJSYoQuwQoPZbTLGO0FSTzMBMwqx4GvFHVLR9kL99t1EDYUqdcAb3U3N1Y1kQi4zZVlbx5fZFWBS1WRA2DOoYl5mpXBFkxIYwePVvydqFtpSrriN9jShpEo7FNZoIYQMfiAgCgVvhbjcRsBQ8IodzE3WEWNNWTKvnO7XgYANk2LTRmcoWhyyaZknpx1eYXQTZsvI1G5mEITtE8arI+2hNU9SZUdl6MF3HrY+2IPYkey9cb28d4jFRQYvbjfXh7UClZqWVx7Y9+LJTstfceedDsbjISi6sUOA1XHqxAHli24ne2nDrWK/cSkI9C4N7XErhXnL2gVd+j81SrqYTwoIMHfd9BNsCiv4bqulL2QP2TyMrM1KFUJZ/qKG6U7XqLncySQvx7kneg2FmbDq3JMFZjUstNLodcN6kboBWnG796tvDMQR+fJNr/q6nNda3vIjN6TCtnQxFbdRtdQSFHvL98FNtxO7cDoOIXBhPPbMaeoyqCugXu7vNNqORbbN7MMajv7viG9MWW3ptLbVRcLVJNEomV9LYbcdlxLnU1j7Uy2ZuWn/znv1RVwNADFEdKqm2D9JgIN8BCaeYjrzDWCzaar9t4PnmhO6cxBMQ71RQ9KA055BLw7huYp6jqgWtjvK/fbldyUvRaDJl6XYQb8Z29KsW1Gw6QPzMYXYHjZ08R4AFZu+j+OTrsizVVuzlozvcZDopPjVrc33mwS4cKdIIEYspqC2o3qF4GnCl93HOVwlfETpJeFJv8UjzHw4EH0ZkOQg+rqk4CCofAdq9a0gNdzYSaZNEsetjM+2b9H27i+V26XC9//+KLurJ8UB4nG6BvTAysN9Wd2M8r/Oo7/F+V6UoKCMbbGLV7rIG20stHKruUJujdMemXfbqqASkDw/dLqEtJ7MJGBghfOpXb1kr/FEBLSIjnOnJYnEF0m35pRH61M3/ed9++vXN4v7FshTAK2Mikg43MkE1FcL6+8vX5lWc6cT2dz5tSXWMtmmnLVtAsylFHNRlyOuOu45CDChOFrpqCeM9S6Xp+KeFfFMtQy2qYKPlbx/nCg2rr0T7m0ZuMs6t0WQqIEpRenlOgdtm0zFZ/1reIjlmjJwHTLtVL2Hb+5YtKr5n4X+K5mISC26nlXJWHZHZMeLU6tppd2VVYwVxjfRCsyzCmcpee/HgsQORIkvRNSYp4yIrYMsRSr+DSYwHvmDlbxPd5iR3nvZyiMjbzHggJtIVWM46M6rMH1ZKKJQUFFS2Q6j6O3nR4DrR4zc+ebq3pNLLmoBDhFxRCyseC0TXV6rbV0vXG7X/n62z/4+s/fyNPMdr8xnU7EbLTW6m2k0JsMYo82lT94k09pGgdBWqf0Qmt2/wTRuZ1oKa+9haLMwQKm1ZVhNYsuT/CSTh+yji6i+a0rR1x73Nq7zb8zKc2czk80UEfSbVdesiWZt+/f+Md//Cd//POftFp5eToTpOuNahVCEAXrStmN5rqzryv7euf89MS0TECjlI1WGlsIZFtPpCYNBrtZcAt6c1Rp48CmaTJrJf3M0Q6Jq99LY5hN0AUmqzrE5IiGAbiZX0ctjeflRCwr9X5lq7utjermyRZGQhAO/oKPi3RvWrYSWQ5U3CsTW7YgJki5t6qHXkRZfSL6Tt2owQ6/hIA40crxEPMF8Nu8Bx8d+ROQ8d/gAeJBFYzMohMWH+czUGMdN/mtrHvKbTmCKB9g33ajVGv7oGWy7uM7Np12kz5rgsT56A56K/JrlktHVSXiSrFu23dllN5uQuntXKvFNrFkYlS/vRwjORrrDw7xCdD2wuX1lX/+x3/wxz/+wfvrK+eXF+7vn/n4+ZNeUqJ6iFIbZVexUrDvreD2fx3H/3NnmJTHDz/MHGx1UOimdw2C7SE8SjA6koROpZU7wS68EGckTw9oMqauSWbSIOyt2RomvSFDmujYQsIQDe29UdYbdVvZ7xf++P2f/OM//p3ff/+dOWfEPNeXaaKFSK87Las5oY+D3CCy1Z37/QrozvBWigpv7OfwakH7BMaBrGZXFIOSTGLKuton5DEnd904vRvZpUPRtTchKGFnoLwOFLWDn32/q6nG8zSxLAtr3diK7ln36YSgvO4xzvN25k/CCDGiSjDLo4OzrtJhCAaoUjl6PPEANB2+u7SI9pA16BIM78+11PUVxFr6dvR2DGZI0cVuVrGE4z1rtzMinb2r/dJ4fhp+ByHKMJwejh3wXRh78/ZSh5CqNcUdeutDd9AMI+gG4vn3GGnIbme3Ou7dP7dZQ1c1/mgiI8mN/4gmpm4JRmJULMJAZz8PvTXFSS5X3r995+vv/+T1yx/c39/Y1401wOuX3/nw6SMv+fMws4gxIFUTRAXE5Nk/TIYRJyF0hp93aIEUA5UANruuzYgqrqW1m5ymbLHSKk0K0wLkRB88d5Mjpsy8LCynM7WrtHO939n3nRAjtVS2242YEvfrhev7K/f3V+6XV+6XN96+f+P+9pX7+zdWCcRekVbp80mDacrkWZOLs9DmabJSNdss1w0w9EGq+N9WGvdmiLWXq9owtlaVSZYSaZpJeVYUkYMs46U+VU9xM+ummHUsotJHDQSaIpG9QtkL729vvL2/M51nTh/OVFSD7+YcYssFemu6E74z1H0ibrd87IHnIbiTCYdCDEdJbZMUPdBGvLBnout7NDjERp/TLCzd+vKuZX7O09gCa9g7PlLzfOdnXZmQ1tn61zF9QsNmyv7M0d7USTzVKaK4ekvbsiIK7IqxKEdQ9a7PFh95eWzKCGLFMzwBKPC8riu17Hj/7RLRaPiLb4bxn8JbJAHKttPLThG4U23/reJat+ud69sbl9fv3N5eWa8Xynqj7RsA5X7j+5c/WJ6eyfPE6fnZqNfClLXKWPdCdW4FPxjk1RBMMXok1cAf5/kGdd+MMZrszbOg5UQraWprhFgRqvLLmzq++k6oiloKxZyZcibvZQA8vTfqtrKafnq9aIBfvn/h+vaV7Xalrjfm1DhPwtvbO9/aTt3u1GeVlOYyM7VKyLoEYMoTMWtZmfM0bqEQAmQxnCCO3iz0aOCcWRSZQCKY3jimTJpmQp6gYhpfR5itd+9OfW3je9VS6az6vIw1SCuE3mmlcr+98/s//5Mu8PzphXyakKRJy7nWranZowqI3EP98RZ399Q4uM/RAvzgiFsg+jmxRDFsivyWFuUY6F4yJ0bpP/69U9KZu04hFKhUEEzRZafEeuDW2m1RogZVcLkrfsO3MSrTl2G3fvXA1C8UDEhz4M4Drxm464i9o/P0rhbPVj5XI3UpH8RRfdVD7NtO731YPwVRL4WO03IZyJ30rm6yAFU9+tbbhfv7O2W908pOMaefsq5QCnVf6fuKGpYqVlJFuL298s//+HdCCPztX/+V88uTiouCkoCQRO+bUp3/690K/6v95DYSGQdBD4yKLPSFh5DIWQUqsWQaBwId7PYgCDEFai+U9UpHBRZR1EGjtcpedi7XqyLStY0VSdVAvf2ufev1+s7t/ZXb+3f9WvtK6DtLEj6/nJBWuVxvvL0qz/x8fma2Jm+SQBxIrpeYRykL6pPmyx/0FpGH7RQHuiygzLtwLOUbvHfnflcL7qqjw1p2U93peK21StvquB10RZKiwvt65365cru8a/s+q8FDJKv6KGoiKFU5rBySQAAANgZJREFU3EHcZtlvEnsDcpTSR/8rY/R2jKjspIq9M7G7SyJiz2foxb3kN6uokQRs84qSjTqJzr4X+l4ebnEzJAzWC1vwjnLTynYP5v4AZHkLofCARX5/JP/YZKaLchlaBZsAKACmibvXXbEXA/Z8HFxNFRdtdOg2T86Xp1vJnHRve7TRZ21HhLWuHvNKUy7s643L91de//iD7XrR6rYVei+KeTSd0jAca+yibJW63rl+/8Z/dthK4e//+q88f3gh5ISbdqaWVBn5w+W63TCG2OutIIkQXPibCBlF+mqlpTYE/To2Qm2MQlAlV4cuqkCLvR3mgrWy1UbbNjq2TqcVLZPLpmuJSmFdb1wvb6y3i47oWsH3UJMgnhY7SY3r9c7316/ct41PCKfTWW8aJz+0RqOwdz0wecqjxE1J9dujkjDVXMdGQRINhFIHm8P/W8tt7cmslzMk3gkqeuNoWRwxIo5NK1SureO86/sbl7c3HXdlJaE4UOM8d6qr8cxbTJQUGaNKHWOKAwQLEsaY6wDpjht9gGHWXzfTz4dwlPMHaGbNjRw3s96WDs4ciSPZsymu3DMgrdqzcOzCE0+z21ARbeUD9y6kFAdoqM9a+QlxfC8jq4gl5bpbG1MGUNYMC6plVyq2Bfe+b9RSEEzDb3rwEKOtbHq4wQ0d9zYJEbvMDJStTW9Vn8LoaVCcpRYoO1CQViy4jfg0Nt0qyNfRi3Rf79Tv39jKzv1+52//8i88f/pIPp1Mv2+qQ59K/dUgD4YSd1T9piWo/tjqjtrVmJBOSjN91n9X9s24zM0YX+2Y4QZ1Muu1U9emgn+JSMyUhx/Uxz6tqYZbeiO0QtvuUJR4I3ZQdINqQ6g8n052yN/4+u2Nb9+/klLi08ePtppXj2kzcUxKQcvoaqolgObsIusTj8kZiBpfKDdbtBePh8eZ3mwa7Jr9XYCjNz8Jb+wVTW+Vtm/KN+jKdb/drnz/8kWDfF0JPY+lkTknujSaWC8/aL+6SljcTfSxWgnRRlCum7eS2HpHnwZ0Y4J1s6ByGyg/B9icQNUClhSsNAebHAR1T3WQMoRAMEBNgiPRmNbfaaMGpNnXGaQeFDVWNbA6pRyJykp2UTKNA1nr/ca23pX3YOYltVTbq6fPvJedvWz4LnNPEjEGuklaY5hsnKtJOjqW4c8kOtHKsMN+9OxaE3Uwl6CQAjErd6P0pr5u9SBSDc82YSQyMTBUrcTu3Fvh923j8vrGy+fPvPz0E8vLB9K00IwD/0NB7iWSoFB4xfspLdFajxCaqb7M7cVmoPu26ojFYNOOHB/OynhBZ46NgNQEj+OnKOSoQvlei92KjWBstD4O8tFPS0cFH3mCGKkdvn79xu36zu36xtPzk7rCipsAHMqR3jq1N6TaOmZb+6PHXkX6TZ+8eXap/VSynt7iecxUg4HpiggzeuRYfcuGlmrUCrVosNdKbYX75cr76yvr9UrZdkLXA+zeCOLPMRhZ5wGlHj34kLu6XNjL8AiDzup7sTUog2EsXoIfgJ3jzpiqjvH7ytYzaq8E5VBbjywiw5hRmt1WfRRBYEEvfitaMA1woGuCFSK1+7JEw0yMalrKzvV+Y71ZcJeNYuwz3R5buF4ufP32lbIXZhtniTCcclJydp8mtB4VaG1iQZ4SImmc+7GTTI7+3lsPJU81HTWHSAiZ5enMtj7ZWq87rehn8lsbW/LpGFDvjuqDxCMB1PXOZW/cbyvX251PvxTOHz4S0sTQ8f/VII+2QN4fbGvVPJ+1X40xmt+o0e16sx1fepPtZVXiSuBAp62sVWfKpg+qQydSQ1JpXcpmohBIoXO3Pka6klRa1DVF3ju5MIQYdTOngKRo57Bzebvy+vqVp5ePTNNMj9r0BesFtUwqFoyqIy+xDKplCFFJJlEBp5jNJ95mwM0aSH1JB3HGV+T6csBIQERHc4NC26o2oUXLt/vtyuv3b1zf39jMYLKje8Xvy02XDGphqS/fPL+ClW10LbXjQx/O+PUDu/AxiP3XY02xg4pWerutk5X6retoyHePa3JpQ/zjybc31XvfrjeulwvbpmzIaVk42aKN3vU8aGGktFo3hBAb2/kedRn5rFFaR1rjdnnn9ftXXr9/5367qQGFJcy9bLy/v/PbP37jjz/+SS2FTy8f+OnTJ132sUz0Kdu7iEx9spI90lPUhR4SofvKYSOeeFti488uNrLEeunSLSkKIUzkGZ4/GisP4f76Rts2ffbNL9SjupFu051gJCIMhFQNtI5596pOyOvGJEo++uEgdyBFCSHmXmGC/YduyPAa3wXWaDHRaZS9U1vx84L2WFb2dyUl6DZIvVVKTUieFHHflcMbeqG2Qi0rQh82uUoseKRViolKGlMOfPpwZsqR19Mb+9a43945n06EhxtY+rGvWi/kzm5o+DRNWuLaE25U9W3zUdEDfbPWqjdzZyQOZ1iBjVYwG2bzwmtlh7IjZhhQ1pX3b9/49vs/bVOK2v/01livdy7pjSnZmuDgNtS6MlmCtjzdwCAs6HVhgpWPQdlpxEc5aByjRS+/HSnXsVU6bnX7OxEH7pSafKwjDqOz74ZeX97f+frlC3/8/jvv7+/UVllOCz/99BM///IL5/PZkrHtOUuCg9VYS4XNt2sfPRCt7Gz3K+/fv3F5e+V2vWhy7qJrpW1B4vXyzvvbK9++fmVbVy7fvvP961d+/fUXfvrpE/3pRM6Z3qsVmMlaxEhPKieutUIoNvN+3G/um0/laC+LKQ+sZOm9E/LE8vSiLV3p1LVw27axey0YGOxjag+W3gMSlBpeTc+fc2K2lWN5moCuIOKPAm8xZqbppLPqsGnpWMwo3jJHiBmkgTQkqoOkmmE3+l1Lni56uA/gBIbmshsw0YNVAJW97pRtBTkMELv5nY89ZQPxbJpRHWEdlV4n51nXCefM/b7Te2G9v5kFlAVuQxNM74aqq21Ss3U5Mk1GPyzs+11dZ1KymzyOGXHorkE3ZxArQXXUZoe0HeuK675C2ellI9QKpVBuN26v37l+/8Z2u+EMNemdJoHr+9W46ImTIftur6XfI5qQR4ZsV6swMUsivWnFQDj8VpdjxBaibi91gQtislOb0apCTvvyUvTruwKw2v6vGEVXS99uvH77xpfff+eP33/ndnmj07i+JbbrO3W/8/nzZz1ftZJiYpn1nWmSsavbk0Y1EQiNfd+4vr1yv16ppjdIUc+Qcgg6dP16nz9+5P31nfv1zvfLK29v71zN9POXX3/h48cXTmEh1Koo+76zW3sSRRMfhsT34FtmDpfZmI5lIM1UfVglR2sEdJ/cNM/kaTFmZKBXcG/BNsayynRsDYhBk3ZIOv5dJpbzE+eXDyzPSsc++Bz7jwV5qYVUFa0dLh1mNED3pQbRbJt16wXSDsDlQUByrLqxQ+KCZswgcmjKO72KZSd7waIBE+gGMhl7CBCjOjacGtp180uXYUqwLCdyntk2LfvX9UophRAS1/vG7Xandzifn3j58IGcZ1pMhqaKlv4mLS3bRu+VZFs/2/hM2HjRkxlIl+Erh83LW630faPvG6EW+r7Tdr3Fb6/fuHz9wn55h03lnSFEEwsrkfF2uSHxO8TIbApBJJCJpMmmIU1wR6kAylazJ9atN++WQIKVn77HU3o/GHOmu2+9I+rHPbQF3Q9lUHaXrllCVYndVG/v77x9+8r3r1+4vr1S9hvQabvwWlb29crX338jxGDKsc5pWXh6eiLnmZiSVimi33PbN3MUKsa/3yzxN9Nwe+JyFFzXXcUQKbs++3/+9htb2fny5Su77T5rtdA+6mJIcTITqNouBEJshGae+kUBT7FRY0yROU2kZAy+EIy/Xu3PonbeQcfFccpIcrmr9ePdt5pree5+ioSAxMTy/IHzxxfm5yddK3U6k6aZUcoLg2L7l4OcrmZ8ijC2A3CraumEAW1qW+9WP4bMxmBbRtXuyN00j20c9ne86JdGEjH/PUHMG656CfygWAuO2jiq2bU8CiK0YIvkadbr6S0agzDPeXh+7fudba98/fqdL1++Qhd++eVvTDGRXzIxKne77BsxTKM3XNc71/d3XW4YzFzQHFe9znSaagyRKBDtRdOaUmz3DfbNfr1S7nfub28a4O9vzE0R2Lbv9NDooetIKwjl3rkGBRj3VpA4MZfCqQc6kTRNCE6UMVWUPVOMwlptrKc01wO5loAadJoMeLwfKyVVkFJHYCsWEkYblrLSoNf7je1+1wD/8gfX79+o203nwyg7r5Wdfb3x9v2L4hr2bGOMfHjRlcun5cQ0zXZp6JKObfNxF4z0/lCqWvFBMmPFGAOnZeHvf/91eN7985+/UcrO29srpajW/v7rL3z+6SdKqSymNjxxTBVAte5ddCRcW4N9V+ZbfjAyqU1NQJvqL46Fjaok6+IgqP7s+uSMryZ+daGVakrMT888f/rM80+fmZ/OtsvPsZCO8wT6fx3j/yv7p8K2N3rLGkComXsbh8YF/DaO6QH1gtZSOCalfAaj47QRBZbFELWutcMCKk1sjj6jTp5qR2TOK44iHou9NKOGro6s0qnoXJaHfk660SI71F6ppVL2Qi13btdX7nd9YR8/fOD5+VnHMrWw3nT5QqtN563A5f0dEE6nJ9y80JVZpRRaKYSObgjFxCDbTt1X6n6nb6ve5nVju194//6N2/dXbu+vZFF/9Cp6gXezZrq1StlWeo/sEd7fAtd1I+aJPK/c1p35dOf09MT56ZmU5qMtstLCbZp8dh/Ew8QAUbOJdXKHz8fdHFFR30aVNvruEPTdFR8B1sr9euH71y/8/ts/+PrH71yvl+Gkq21ao8cw+lYn37RtY2+VgBFEqj57p4rW2sZoLAiD8+A7xrVE1zqvNl2+oLiF6uM/f/7MXnQ98Zcvf1DqzvvlnXW7c71deL9c+Pz5Jz58+Mj5SXfcnVpjsWeFZMVtGjoC7o2VHbixZVcsNg1uP+tN2XTSdSWX9vI6JUKMQ2HVUrA2VEG9QJxnludnnj595PTyQponxQy6t2KKzocQbCryA0FeW9FkYUSO6tbCvR3jFBG7xW0e2/zXiRgrKaUxznjsr9Tf2+7xgI0R3D3GJZeGpiqByID+B5buwy+cQaX8BAPhLLiH4MAYTTqCEWqCZUlMU1SA6OvvPJ2fWOYFASNydGTzNKmZdr3dVN76/s5pOfP0/Ezvnev7O9e3d6R1Pj6/kPKiANHtTi8brWzU9Q5FA37fbtxu79zuF9ZNt7hMpxO4nFeE0oWtdtZN6ZD7Dq0WYm2EeSfNM2mr3Led6Xbjvt4otfD04QOTmCNJ0/6vW5/tlV1t7WF2bp58DhbFsUn8MOng4HS7QEbjqukk0Iwpvn75J3/89htf//jdxlrFpg8gwcQ/e7GvaNxxEVO6Qdt2SrxToqB7c/W7O/5AV5q//vwCKRrxqo3RnEEuB88BTVYvL2fzxbtxubzTm0qX397f2baN9/c3Pn78zIePn3h+uvD84QOfPn3iRSIxK9mqVxW5uIhm34t6L2joDhDMN7P4FlUaynfYVqTprrtWq4lkrLW1uyukxHQ6sbw8Mz2dCVPWEXPMh7OwPhVbjvGD6Lpn2mq3wZDpGV7rXORampXp2qNoZao9ZIhKexURalMdtaA9UxsSIBkgkQMRtR2uHzFFJhF2C1LkMZO5ZY/L7XTFjAJAYfw5dHw5NLxd1Bssp8Dz08LtcmHbVr5++Z15nmm18PzywjQdN/UgBnU1vn99/c4yL7Smcs0vf/zB9y9fySHqjYz28Ne3C6E3pG607Y60je1+pZQ7nUaaMvnDC3I+g/u6906hc1k3yvVOFbXHutfGVgp13ZBp0nHUuZC3nbyurNvKXpXsMS1qWJlnFc9oH6ca9TQl80/zEeJxbLwN6vb7bqft+v8QdGtst6mCc/PLtvH92xf++Y//5Osfv3O7vEOruGtLjKoJaA32zTaYmvz28T+17LSSoGYwg4ZqjDnn/ouoMUML9nPR7FYTY9dVDfJqRp5NufO9FaYcmXLkHrr2yjq/4n6/UcrO+/uF3377jafnF15ePvDr3/7Ov/zLf+NTqZyfnunzDH1CmOi9KcHFLoBH3rw+L5iSjum21ijrnbquqn+o6r8wSLEKy+szTpHptDCdz5AyBK3oWu9KxjFyTvR+/EfL9WDGg4Kr0MCtkADmWcGR9X7XPtg2llRFYsiiyHDhmEeHgAlUtErzQPdexavwMYsUofVjvY3u1Oo2dtEqorVmpZuVQPi2DiODGKvIy1N1XFXF1pQzp9PCskys67tm+PdX5pzovfL88qx8aDlAJu+d9vXGtt6gK7329es37pd3dgLfp6S024aOylpF2gZtY72+cr+921gL5tNCnAOxq7iht0Zpjcu+0ejcrleuZefeGvfWuJXGtm6U642UL0zvF6sonljKTu3K8IomyMnTzDSf1Xf+6Znnl4356ZlpOTEtp2FC0bomZ10JZYIM+8xDndU9KevoRn31K73u3K9Xvn75na9f/uD97TvdFHO6QEDXHdXd8JK2a8nrhpBWN+x7IeyQUzJjimI6Ap2wlL0gQUgxU1pj652wCTFomxMEStURWrdbvNWOO8Tcble+f/uDbb0iVKOoqlknBtau6511vXNf77y+fef19Ttfv3zh73/7Oz/9/AsfP37kfH5iOZ1UXhwTUgs9ZttmKmMmTmuUfVWA9X5nv90om611sk8drF2JdLPjCmbiOBHShEikVLe4OiS6IUZyegS4fyTIc6aXOoANL9OiBDUXnBcdsUR7oChLJ4Sg8D+q8lIkvtBi0tscIzvYvEuMraaloX1vIxvAIUM8NlY6qUN5JL3pttPWmiL8+lVxUE9E6DYjdkfW1hsRRb6nFDktM9u6c1pmpqxIMb1S940g2l6Uqv2jDJxAy63L2zd9yG0jRaj7yuv3LyQa5+WEEnbUJqvvq+5le39DYiDPk96wSefc0UgVsTXS/U7fCrsEVuDeOvfaudXG3jt7r/RtI9xWpnTlcrvy/PLC876zbqv5tGXytCDhQkwT8/zKt+Uby/mJ0/MLzy8feXr5wHJ60sMqakTpc37wm1PBr+KjJNsqgyHp98uFb9++8vtv/+D1+1fqdjePPK0ElRCFHngzYvCKq3tyaY37/c79fmffNuZ5UmKKTysQK5EbpW2mo9ZzI6gVloKyzSS+iu1go6lSCu+v33j7/pX19q6MOLfamtXJtpiaMqWM0Ght53Z747d/bry/feOP3//Bzz//wi8//8Knj594fn5RG+vtRsozOc+I+cBJD7Syc982yv3K7fU72+WdZqPgZoB1w1ZUY+M64yT4ptuUJ6263KRDOpSm+s2guFaI//VV/j8N8nk5qXpq260xbvR6ZA213zUmlfd+0nCTgNa6AgimTmouKohh9PV/LtsZZUcIQQGOcWt74KvHWTUQyZHFVg3d91bC3THH3zd+thgaWxuERjSHkWXOtKezBkYUpKtZ5bauqh+2/UpOAulAisecExW6MU2JQqWUlbfLd3orLHmi1x3Knbrf2PaV0gtJVHvdQ0LShEyT7qA2iWiUTJw3iO+0WNikcyl37kXBnTI2kOiNuJbC++3G+f2N5+czp/PZEOoNtYmauF6uhPiqe9jzTJ4XpvnEcn7h+eWZlw8feXp+fpiUBO29U0cmxWe2+51WN+0p943L5Y3ff/sn375+4fX7d7bbRacx8QGIqsUqt4KTRErRU+u75/a9cL/deHt743q5ICHwt1//xvl8NgmpnRcb0zkTrndtZah6AThoq1Vsh66jrG29cru+Ib1wyomefJtPtwtDGZWHAKYwxcz5vNgsf+P7tz+I0pmjMEmHfVPpb0pEe6auMqSp807ZVu6XV7bblf12pW0roZcRN+hSc3WjCdmMNRIpZnIwkYxESlfEvuwVEeOktEatSQHB5x8I8mU5aYmU0hhdIGpoUGuj7L4sIdicOADROS1KvOiKpKakt3htCpJUB1HCn/CzAfxECbYXzAYM+g0GQ03BOYYbyqMnvLhzKMFuGw1AX14nIx90dHdaY54jnUxvnW27qrbYpJrLvPD0dOb89ESelfygY5Oo5npGa+2tUnpVl5JeuK83BNi3jdB2pK70/U7plZAzkhM9JiQvSM5INqGLrznqQj6d6Smz1s51L1z3wm5Ivh7orvvOeyO0RqmVfd+53W/Myzs5ZZZlYcqzJrqYSVFR2ph0pLgVBdqW05mPn9Ry6Pz0xOl0NsdbBebyrBzpbbvj7raX9ze+/v4b375+Yd93Y6lpsNSiwd2kK4hmiw1AnX6wyUwr+r/XVWmot9sNEP7t3/6N++3Gzz//wsvLs5XjjqIb99/2suHVgV0k6hvpCLf+TPu2MqVIfnnWamkw+zpbrWz7bg4/E/d15Xa/czrNfP70iecPHwgSaHvhvCyc5gytcr++sV0dGY92iyei6OKRVnYlcm03VRLWSrBFFcEUeTJwqEPKTOvGoVAuPqL3fUeTmnrdN9a2sdqqqH/95eNfD/I0qe+Zyyn3bbcs3h7Amj4YTy5rbM1KC1E+s3543YcdQ9Eipas6CbBJu0b5uH3Hf+yDD+qk/T9bxFgNJHIiRAuPihx3snSHE60aSlEzSbcGkl4I0jjNCgrd73ferq9cLiu1dnJKPL+88Ldff+Xj5488PZ3J80zM2ivtpbLdN7Z9Z7uvrPebEixMOLCXHcpK7IVEo4VAmCYkZXqaqEYtbdHYTUZTpUMzHvXaCvd913GX3RJOvlCVmQ8lFXjq604pDZEb72/v6r0WdfOmoJ8zhEgD9tJNLqv74uZ5YTmdmJZFgVRDf5dlIT3sRatl43J55Xp5oxXdTrtMkx5FkWFguLedTjf742a3piak3qDUSi2N+33ler0qUUqE6+XKvm28vr7y9HTmNM+cTgvn08I8TbY0spgphRlnxGMjrQOCjpkInafTMtDuFKJaaKXIXgv3daXWpoGeEss88fz8zE+fP/Pr3/7O6XRCGkwpMacMtbGvK3W70/dq6UsoBuQxGG9Nx8j2eY3yMohUerDVlEN5HI19u3F5/ULImTMwn5/JeWIrVqU0a0u6L/b4M3j5fzvIfXanC9UTKe2DWaNKn/agntGyXYkx6lqORC3fRZk7oapPm9CRZlRUA3QUGJeBNvqo7sEWZPzZGMQsijvRaa/oyM+N8kaJbiiwK+SUp1I1EfUGvRJpzEmMzCHMUyaFQNlW3veV6+3Gtt8QacQEOQWmnIBMbZV1XbndbtwvN7Z1Zd82BRcndWuZc7Zk1eiioGTrLg21DSpJb3KXMSrPeafQWGthrUVttOzFOuI9tNiGsIYg4xBJN1pmbWBtSbKZdy2F+7biLNxalABTtju3y+EFJ5Y0A6oKVLtrLYOVI76peMh7+fOZnBICbKB4zEML5Y4xpRZK2TThIoa4b5RiLUgXatC1yEpc+U5AOC0TL89PfPzwgfP5BBz8BO2lbZ95UyA4SGDOumpKH5EZXIpbYUUkJSY0IV+uV/Z9I+WJDy8fdDlmysxp4uPLJ3JSPYP0TmiNNV1YW2G/bfiFh2iginvzdysdu4Jn3ZnPNjrsVsGqu29DWqFtN66v9myqYkPT+Zluu+Obg9lyWJH/UJALGlfdkOwpT2oB1IUbdwqbZmXLSG6zg3SlBFqQSzQ0O2dyr+ybI93NRmtKYNAE2McIQhl2bnR4rJkluNd4QxdWtiFeUE22j3rshzf5pRhzqdsSiFQ0AMP5ZAGio7K5KIW2lhMhKSASQ2SaA7Vt3NcLKQmxJNZSuLxfuFwu3C831vudXpsetlZJIkxJP1ul45tCS+uWYFYmoEehRxk3YOuNy3rj+/Wdy3ql9aorgVqg2EonDVonrOjL8jFVjJF5mjgtC7qvPXE6PZGmiW3duK0r67pzv+/su5FPRKnMvmigW+LtrSrby11DU0QLjWqSGwUgtwKtrOSUieYg1FozLzdjaOETkmNVkrduKtM9RrfUoJJhtC0MIqxSwbj/t8uifHU7qEGEeDqRc2JKiRBmUgwskwKa7hk3vM9N/uuusvOy8PHjR0ApqNOsu/XmSdudIMH67Wh4QCNNC3tWghO1GPlJ2wMFlQx3wHgcXauYaECOTyu09avjeUvtUOD+9o2yb9xvF04vH4nzopeAPc9ulzE+i//LQe4bJh0osc0pHX1QLYhpwR1ES6YEAxWs2MhNvCrQiqDFYr300V8IDHWPjh+sX7HKIdgBkeCiCjERf6a0Mkgvrbmftem4g1kUyZ8lozlPtL0Q5hPTpH5dw0++VuYceXqamc+Lrjk2iq56xBfu6wU2Ydsr1+uV71+/cr1cWW8a5MsyU09nxBhcgHmXW0LssBdFlHPOWiafFpbzmTzN7LXw9dt3/vnlNy63d6Ax58hsPWTrjSgqUgHNZTEIMUfmWd1dn5+eeDqfh6osJrWM3rad17c3nT3XwLev33m/vLPXoki4pfiGBVtvpCQsWQ0wUwqkORGS3jyq+HL3no377WpJWA91KdUuACe+qB58mnQTrVddzYwftm3XBQhGS41RzFo6MeeJnJOKiWIkBR0jxRBIOXI6nww8jWNjSXQ2pQdccMPOqBXeg5IsT2oomlIehhkxJub5pIq+1selF9VveciOG135EKYsa+N2fiAUWcXV5VA9go7ualeXH+WBNXrZqLWz7yvbeud2fSfkmZCzGohaPHV029APBXlZN824BghUk+/V+jCesFuXUTJ6+TFShWaZcCh3cp4MATX7o+YrfIOWsx37vuD0LF8B3O1LannTrGrQHlGXCsQRRL7BpTe9zbvdolPWLZEtTxQUIPI5aqm6aFEiPD2fCGkiT1rKleLbUroSYBBoql+nVzUsKBtBhG3fjAm2s+4rAqzmTuKlWXULZTtIKWfrgzObed5dr+96iJ9P0GFaFqZ5UllvCEq0aHYLii6uzzmznGZynphMUtu6PkN3MX2RJ1LMzPMTHz++8N//7b/z9v7GXjQJtu78g8CcF86nM8/nM8s8W0mqWnalk4axb/52u7HeV3z+W2pjL2qS6NOQ8+nE0/nM+XxmmjVoJQj7vrOtK+uqSxNTSkw5WlBP/Mmfzs5Xjknn/KI72E+nmcd1vs6QA+gtjDbHpwfB1HeaVDTZnuaTmYtoBZhiHvPqwwnnMOkMtqmmFa0qAzbPrjaGcVMOa2btRrK1zdrGNEsA3f6cdKhtV3kjjbIqBkKIpuVXtVsw8HTdf1CFdrtcBssIdB5afYsI2ndgD957LbGspAvl7c9Z1HsWc8M9BcV2YzPpRo4DQJCBvDug5LxefdHKre+90i1rSlB7nua3eg8a4N0xg0OwIqKup703kIzbXebUqVO32buQp/9fe1fXIzlyHCOriuye2T3bMmzL///PGbAB2T5Lt9NNVqUfIiLZC+N0vnvTYSgc9ksz0yQrqzIjIyNul/68UG2o7g+wxn2/78A//gHvb3c8Ph4AWPMe54GVEx/Pb1iZeD4pt0vZImqub9tWQvz7vmPfd7bnWsPbfcft9g9oQd25MTbOE99v7BLUAmb25LQ4WhSQtnl0VqdqroU+eOr1xhOxtR/weP4BYwAfz4dKF6b9t23D/XbH/X7D/XbDaB7cOdglGKNsoYDAnD+AxgVMbedaZal1PskMfLvf8fb2JncY1vLRgi3L56NccsbQ6OnG751OS9NlDzeS0QcNDAadWT2kElCJJLma1vlcwy9QBw9HbYeesdlklHnq0THGjuiDJWiCeAWCQ0fgIdfsfJvCpaTH502mBUswZxF8b5roW1Cwizfng5Mr6YX7AaoIzSD7rbH9mgC+PT5+W5A/Pz7qVDTAkaopoEDywyQHYcpX/AVwwERZ1UoFpVLyTh334kIPGhAaKYWA9qZ58SLw4wKe5jzYexX9kd4jkulZgWx0nbBJQKh+c7DbCD7TumUN++L3fZx09iyHFD2tEj9cqdqqYRtf8eX9rhSYz+F5PHB4cOPKfDVZl2znbAP7ThLFGBu6NhRAWIfIKH2jiUPTwgyBlPZUm9M/w8YKrYaHnA1NbaBzLexpaaXEvjf8/d99xXl+4PbsaJ1I+v12x77f2BJqMusz1XkjcQQtOCc9Nj558eBbvzy/Z0qk0mlrGpOhwyv736Qhj94k23SgtYbbPspGayUdZxLQRBy3WpYiImCF03H4nODJmbYwasV2RGPNj0bNQk9Ndpd4uLzZEDaI4GXmmUsneeQWNoUUQ1RY00riRrE8KG2eR6tN1cAgV6jWPbK0DyCVZCzO7I+xoe032XAZoP6VQZ5arA7sy3UjasGgboiAlhv0lJ3l1BHZRxRkpAyuZngF/RsQYWYfkpjizsEDmGlWk4Km0X5EItchzrEQbZ3aKZXZTMlU6Q+WLF6r4TwE8GkDSEQtHuJibEyd55RCR8NLO7602fXadeJwAc41cV9bfQaaAbyUMhHowfacHVi1QtVtiErfnL1EF8Dm0shpDgItgbQ0Fzwrfg1L0PRwyINOS8kDQjOxbR1fv7zj2Ae2feCL++SNLLgCwxRt7knPtaSRdrmzlix0UAyhG3vRZ7OX3VoEuHiG8FDog3V468JcNnu9BYYil+/0un1LMEVriB4cXVSQeMQlK+h0z8J0oo8C3mwCAQWN8R+WclHvBSphI+IK7iXcoQXWua75bh2GRtL5+f9vuyvBZzUadQpckrIs69ehFwH0gX5/x+39K/a3d2Qk7vNnmDC/FORzvhAMMhFpTfLBkb0nVVlXqlZFMu1dJ5CUoOWwBVVd5vGQG+cphPtKTXJpDBK+Qb6RhBDnMTjIcbvhdrthbHfxqk+MP/83vv30FxzHUXxlAErF+XK+c2kPjinOsfHUzVk7MOvoqZKDn+04D34OSRiXL5rCpYUXnnGHhZENmdw4VordJZS/akFNEIVwi7UsyqCaUuncTKvkBsoEIq4FpJ2AUZtNvIJU8JtMRHmt3oN13kvJ1bOJxz9w3wfe3t7w5SvBp2UqpUU+tLlUtrEW3JmwJqAnCQMpEQXUOzE4mgj5oxvm4wCLtmaMZBtudBsjivapQLH+G0C/+AgNTClIQxTXHoMtWZkxdIlf0mlV70JcgzLbbNJCUAlS7UTflYZ1sMiKI2CsEd6QfLS6RMZ6rhTd+I87SlLW8b29LtPwlB7fpac+9/2O+5evuL3/oOwlcNt/60keMnXXojnzLJDNbRAK2J+1E+dK/nlN5EknznnSEph1N4PcQVKnQ6JuqEpNxejCxDkDeXJh7PuOaPy1xY2MommCCz/DmnTjoDMJOdS8Jy4K5DXM7wdtl5TzPGgduyao1UCjRJqDXKdUNHYBmvPCkC9c7wh0NEAKNGImCxdAo8UOwJ061TrqXb1vvVQPhzT39wGehtFqAtBmFBQcZMAzyGsf0AYmbkE07M7E0qQmZhRTNf/725tspIhsU14bwiKuVk2Yw2AgLKwZgCqKe3Pv2Kd/Vkrvto8tqHvfcEkUq8Z1K/ZK9fRrr4GUuahz7yxz9E7zxORbCHQsBUz3wE3F3KX/7rrc36fhMqGoHEC/N1KPldJPp9Fn6Jvz4KpUCzZoSP17OPBfEvRIMIOAvr5Sf/MWOsZ+w+39Hdvtjr4NPu+ICqNfHeSjD5xwHDIxeQhdPw+1wSQGEBXkSxa9R7mGnAdR58zUKa6Ay1UPvE5x1A9EALXgIReTR1K+dj8ObPsGJKeGaHHD8+56gzYO8DAMsw2KKHLM0eyxUG1VSjharqEsA02gW6o33YJdAdqeXGmergbJ/m5DL7miVH/2hFciRlz3GTx9yxBB95OaB4DooQbYWmtYnai5uwdQgCPxki3xidrphTP+E0cehcz3XdbLoq+iTjDIb9vnLNXkGlrV3lMtVg8z+bkLjVIVorS/C2wDP0dC/+5Sap5FX54rMbaOIX57AkV4acF3sW2SuUaq5Sb6qwhV9nyDQyouDobFMB3sXbMWvXVZDJOxmRllDbUyMaIjYmA9tGoXW2fAqkGSDK3l1GSmPkERyGq1WPTCWRm0BoF6pRHo28C47VQ07p38Aq2n9VsZb60Naq8JuLA97JEPPtDwh7kIAPRMm5jHiUMtJYJPq8gRPmIM6LnKXYtCgJnXjCxllwgwrbXQ1kSuE+fx7QI05oRlnuvUc4JgPy+nkS2hJoXAQBFy1A4xCHSe/pwM7qbAXmdidQDZ0MZlZLBSp6nTWVDs0qbxHtV04NrcwKWQ0WkzpsryOa7pJDPhbEnk05w3GsUzQCaxiQR6mmCySpfAWcpxclrtOOhc08dGgG+QBWb0FxlawFqQoBTSCH4GtTqQEAaTl9JPRGDFEujIlHqMrbzf57Seuua9kXg8uMC7Oxrnib5vVaZ0k4acegPIGi8OMfK0YYpM5QwCWm0GT3uj0Gj1zpXmR6CeLfEER0V6L+YmMwa2MXAENF2phCqjshe8fB/LQb3iWt0TZInqWDkzDAYiRSGEIcQYHGQK404XFv+rg3xNpRzrkgzS2cZxzbUIri3a0QTo/7QOoqOHRPeoJGlxfcE+qusshRxgr9Bz3wAXBe2NRXFdyifywDwoRZXOSbksMKFgWeAire6AaujJVN6+01P9/rWmUsYG5MQ25PslRJQbgvTIoRSvMT33IqUOnjTKLcingEa7Npq1siS0WhvkOOs2QmysEsuPy8kk2kAmF1bfRs25p1Br14ZzLnlWx5Vdgba7cy1RSk88n09aBUn5x228qq0d1L3VZsm/EVmpdaROQ7ennMy/po/Nn02p+9g4/tp7ZytOJpgOnj42QOqjAWJDz8cDrQVutxu9vsV1AFIUZ2Eo3qibzDCMTYTev5/nyyZ0acpDILMUh5Wlm2dA+2xGtzctZFa9T63PpZLiNeyumpu0bv7raP072+GL/cY/d3WTZhJ07dvG/+S9h+gVQ38tyn9R/gkirXjyy1S9QswXdc7XPGmPdNKDykEOuLecAJYWzGt6kXIBvQAJ2/lWAtIuMAdLmQMIelhssI9NaRAAvCjCpH8ftQhMp4TSuyllknpgkGUOmoTu2ZdlHZWV0iGa6LS2+2kV4ORjm5t8zWR7ly5iR4SsfqVa24zYcyEZnMoWiE72VvRegn5NAFS2RFeK24LKt0zXZ6W+9PyadBvVc4iAACtrh48C9xwQwMsorzfN7EoV1TbTIu5xpfR6qAA0uXhOEZbGRf5oAf2BLEoDWHpfaAy+qb65gd9XTzuSk1Cnr+KQeAeiAh3+d4GbXBNMxUttRu3NXAsrltqAC4AESSVT1wEqxp4sSZ1pp+8+tOSV3iPdgbpOXbPmPHsBpNZGq1igNiGdbaJ1tHG53Di2SQ76rQMqk4om7G2fkrjRaW3wSo4O1EV/4jQjrmxtdNNEkCT1c9UjTjdcEznYnMYyuSb1sYFzsw1MzRckAtFSXGGDG8oMpnfI64RENIrqqf3jEzzTWl3coPg16lU7ddN8e4Do+ArUIofS64vWi+8AG5+4BqTS5YG8vus00dc0Bf7rgoiGqv+5OOVJR8qdMhK6aq518NdJMspxPKtjAH00958N8gSuMqHLAbQrrc5BjjkBTdeORvuHK66a+UdTwGhjWGmGWOKcJ9DlmTcnR3/BoBig1viclyHH2AciNuw3koVsiRW+kcxK27VjcPTW60l/7wD3a0kFIdH7Xs9licDTE2Tjwa9Q63/RLfV8PnF+PLhu5gRegjXgASgvhahnbPBvrQm0dtXu+jkZleFXlARCWMEGoJNOLjCRtfvPH+V/HV1P2vmwGf99mwmLJgEG2c7jkN2wzA6volt1UbBuW5cWmx8/ZZ+MrHsvvM6DtU6mv/qSlbMeJ1LEiMU2F5o/Y9M45XUq8VlmWdXSYlior8UTF3dnDrpwyVCsH9LwvmyOa/OQfZL/pwq8TutcbIMFAIg7H41pO7OcVilXz4ZswACPoxaN/XGdshTTD5UZvJ99v9xcWog6qfRyzkMz7xftOLrkgZPtxpWrPusYG835+rXpeLgnpqbo+FZrM+De5V6y7bUEpqp74Ah6Ph7cXJ/POnxGcw8cwLK0NrOu0cjT3kXXrY18XRLdlSEpwEkJt8OMgswiIp7HqB54h3vgAi+QiyKaHTSXbNqBUoQWuqVmgcprnuVa680uoN594OXg0dr07/xjFfzRXoDWWmBM8ff7G/bbG8Z2kymj1ZRC2fZ3u8L/P8jXeQC5KNl0cpzQOzIWhebnPGRZ8w3H86GHiKuXnAtrvbRdELASzPWxpFH9sps5UJAcO4VAu1Da76F52ttcD5cnq4QXm2sjft85F44nTefmeVYb5NpYouyfFsw440Ioxc3QNJy+xs6aXuAcenvJVJRS+dTxRmnHjSmM4Dg5FjkHzSqiZ9FF20umUO9GM9SYuJ6brnkcYgKKBNICiE6IHARMafeT5P1ruvMywFhI9MpQuubiO4GEUr2Fgpx31+r+eHI19OF6nkSY3ndQS04lGphNMGhPDhrp/UfrlBlTir6NDaVClNezxEsWQjDMQBy3aFtq1WkfKpl7aMMemFXSmRDjFJgZ4mqir7osSCDWiZYEm5dm1+sdC3MpfAI+mRs8eeaNFQDsyZe5xFy72mcjKO6x395wu7+j7zT+WIsZ7atl089dfz3IlZJnEuSap3vmYF0+GfwPaXIx/XB93Sqg5zwKnQ6EtKazFuDKS46ZgeH+PLd6DwKYkBEBpqip8VLtiq03YC307x6xTzwI3T/rIVcy4M+ilHkmuTNMheX9nYDR8ytwQ++EPyDj8kBzi6s2vFp8Zg+qHgiU1PW5JkY0jMGTu4sNR+DnpfQAsxtuaHKOgU5TK4u0QHQvILafWhdQo9MyMDEBjODAKBckDS0ibaLITGXfbvCoLtP/Q6eMmG7KJEya8iSg035EB44TX77elE1NRAx8ef+irs0DP/74XzjPBzpSi91DOHQUReoUFE7kB+I63JqBDDGtk3ihUHdt1i3Eub+h950yx9ElKrmwMoDk+m3OQI39tKYNcmKdT5a0SED2wSwvW3Uj/E68FpCswbsA4uVN2Guq3vWr3sDG/yQN5U2D5VVQyeznD/JfkmRmUMD60GpncRxTqaCIIxSkW0Cd4OTZKiHn4pp5eZVJ7IF1rqmtOvMyvzPSQ6CGK5Zq9w7W13RnEaorh8xTABxrTp480aDPy/ae0z0venPAEzqhqB9UrauOVq0VG/u5DPFHd71e0ZjcsLTyOAXmCaloflQwiIK4qLuceDKDjt5aTleWNg3iGO1KRRNALA89we04LoionxU6LaJRYsudg+ItJIkkTJqySEJDdOK5JnAaGxBbTB5sIwwcCmxSG2nOEx8fP6FLcWXcbmhtw+1+x/vbF2zbwH/+6U/4j3//NxyPH9HS/OwOy2ivpUxJAogBAX1p6Cq12FeBt9GgAZaBsYmiq81p23fcbl/Qt1vRaxPA+Zw4nlMdCyCSAG0xJyYNOI/no8QsXwdRihSGrKxyzUt38KK/6DEZu6jsSn8UvrNASbAJDgcZc7oOcGa3vynIz/MJqvJTGtdtmlnGcE9pqc8iNTjVUGJe00KZqbl07m5LgygvuCK/Jigi4akpAygsP6zxppfqdQ2itLbAjWDv0an0nCfWOXE8npgCSRJTIIuEDaqVRuWV4VaFTvdLj0uhlgIAlZ4zueFncm8WKxBxOZU44nj/SwIC1C4Hrhq+JpX8dxDpxalZQHRYfc/CPoTEd04MAkoLgfqZLpKc6joz4c/TsJAWqzcQ6N7YKiKmQFdY4wScaWbWIZ26pL7ZPJ+Y88Dz8Q3n8YHnx8T5ofns2PD49oHzhwN//OO/4p/++V/wl7/8N/7n+aPqzFbpe1MJlQXmannr0Ge5nVc923h6b/uGfd8oH67Sh4JMnYaY+w37/Q2tb1REzcTaFuZt4jwWns+DqXGQGwAEZgQOneblNQ/UswKgroEzxKuadOJnYLkJRF3CEJbupUEa6wgkOo6ZiDOBUAmmQHcJ8nJi/Logn+cToZ7BeTwl4GhxhfNadIITLHwPgWgmB/DmGI7Wbzdyfk67Z3IhUexxvtTlMuOLEILplAYKVrWAFuvpFlCvO7EaH/5xHDhOOaVOsZLAl3NO9ouTCBsppxtFJYZmtTlwov6rgCjLK4VW2PTOWmk/N4dqyTVbEtEokhtaK5ZVU3oK8B5In/XUHOqk4FNhOmxRBG+OvDQkkkDfbkjhKqnSBsIOmvzNfYrkJIchV1Y/O9X3hzbWuQ70OTn3vt8x9jeOYQrgoqqrXWFpATXPD6zzgfP5k8QMJ+JodJIRa+zx7c84nj/hdr/LFFGqKhDIujrBx/q8XvzXZR1AjtKGNqKO/e2Gfd9K0YU2zQOIgdZ3tLEhGtuRghkwGtBGYrsFbqfLVGZQuRbaaFjbwHM0rIMbH1rW6W2PgPQuhO8BOKI/V241CzuxXFnKd0Bt3NYBzeznTFDjojmv0LP6+ZM8fqlo/7w+r8/rb/tqv/x/+bw+r8/rb/n6DPLP6/P6nV+fQf55fV6/8+szyD+vz+t3fn0G+ef1ef3Or88g/7w+r9/59b9kWTmUW4dTJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_class = random.choice(train_10_percent_data.class_names)\n",
    "class_dir = \"10_food_classes_10_percent/train/\" + random_class\n",
    "\n",
    "random_image = random.choice(os.listdir(class_dir))\n",
    "random_image_path = class_dir + \"/\" + random_image\n",
    "\n",
    "img  = mpimg.imread(random_image_path)\n",
    "\n",
    "plt.imshow(img/255.)\n",
    "plt.axis(False)\n",
    "plt.title(f\"Image of {random_class} before applying data augmentation\");\n",
    "\n",
    "augmented_img = data_augmentation(img, training=True)\n",
    "plt.figure()\n",
    "plt.imshow(augmented_img/255.)\n",
    "plt.axis(False)\n",
    "plt.title(f\"Augmented image of {random_class}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Create a model with functional API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "input_shape = (224, 224, 3)\n",
    "\n",
    "base_model = keras.applications.EfficientNetB0(include_top=False)\n",
    "base_model.trainable = False\n",
    "\n",
    "inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n",
    "x = data_augmentation(inputs)\n",
    "x = base_model(x, training=False)\n",
    "x = layers.GlobalAveragePooling2D()(x)\n",
    "outputs = layers.Dense(10, activation=\"softmax\", name=\"output_layer\")(x)\n",
    "\n",
    "model_2 = keras.Model(inputs, outputs)\n",
    "\n",
    "model_2.compile(loss=\"categorical_crossentropy\",\n",
    "                optimizer=keras.optimizers.Adam(),\n",
    "               metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Create Model Checkpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "checkpoint_path = \"10_percent_model_checkpoints_weights/checkpoints.ckpt\"\n",
    "\n",
    "model_checkpoint_callback = keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
    "                                                            save_weights_only=True,\n",
    "                                                            save_best_only=False,\n",
    "                                                           save_freq=\"epoch\",\n",
    "                                                           verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving TensorBoard log files to: transfer_learning/10_percent_data_aug/20220617-212250\n",
      "Epoch 1/5\n",
      "24/24 [==============================] - 17s 426ms/step - loss: 2.0393 - accuracy: 0.3027 - val_loss: 1.5381 - val_accuracy: 0.5674\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - 8s 310ms/step - loss: 1.4038 - accuracy: 0.6320 - val_loss: 1.0617 - val_accuracy: 0.7467\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - 7s 283ms/step - loss: 1.0596 - accuracy: 0.7427 - val_loss: 0.8668 - val_accuracy: 0.7714\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - 7s 281ms/step - loss: 0.9144 - accuracy: 0.7667 - val_loss: 0.7666 - val_accuracy: 0.7829\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - 7s 281ms/step - loss: 0.8252 - accuracy: 0.7853 - val_loss: 0.7053 - val_accuracy: 0.7878\n"
     ]
    }
   ],
   "source": [
    "initial_epochs = 5\n",
    "\n",
    "history_model_2 = model_2.fit(train_10_percent_data,\n",
    "           epochs=initial_epochs,\n",
    "           steps_per_epoch=len(train_10_percent_data),\n",
    "           validation_data=test_data,\n",
    "           validation_steps=int(0.25 * len(test_data)),\n",
    "            callbacks=[model_checkpoint_callback, create_tensorboard_callback(dir_name=\"transfer_learning\",\n",
    "                                                   experiment_name=\"10_percent_data_aug\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss_curves(history_model_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 5s 58ms/step - loss: 0.7141 - accuracy: 0.8032\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.7141461372375488, 0.8032000064849854]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_layer (InputLayer)    [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " data_augmentation (Sequenti  (None, 224, 224, 3)      0         \n",
      " al)                                                             \n",
      "                                                                 \n",
      " efficientnetb0 (Functional)  (None, None, None, 1280)  4049571  \n",
      "                                                                 \n",
      " global_average_pooling2d (G  (None, 1280)             0         \n",
      " lobalAveragePooling2D)                                          \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 10)                12810     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,062,381\n",
      "Trainable params: 12,810\n",
      "Non-trainable params: 4,049,571\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"data_augmentation\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " random_flip (RandomFlip)    (None, 224, 224, 3)       0         \n",
      "                                                                 \n",
      " random_zoom (RandomZoom)    (None, 224, 224, 3)       0         \n",
      "                                                                 \n",
      " random_height (RandomHeight  (None, 224, 224, 3)      0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " random_rotation (RandomRota  (None, 224, 224, 3)      0         \n",
      " tion)                                                           \n",
      "                                                                 \n",
      " random_width (RandomWidth)  (None, 224, 224, 3)       0         \n",
      "                                                                 \n",
      " random_contrast (RandomCont  (None, 224, 224, 3)      0         \n",
      " rast)                                                           \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 0\n",
      "Trainable params: 0\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "data_augmentation.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model_0' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [16]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# model 0 evaluation\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m result_10_pecent \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_0\u001b[49m\u001b[38;5;241m.\u001b[39mevaluate(test_data)\n\u001b[0;32m      3\u001b[0m result_10_pecent\n",
      "\u001b[1;31mNameError\u001b[0m: name 'model_0' is not defined"
     ]
    }
   ],
   "source": [
    "# model 0 evaluation\n",
    "result_10_pecent = model_0.evaluate(test_data)\n",
    "result_10_pecent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 5s 55ms/step - loss: 0.7141 - accuracy: 0.8032\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.7141463160514832, 0.8032000064849854]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model 2 evaluation\n",
    "result_10_percent_data_aug = model_2.evaluate(test_data)\n",
    "result_10_percent_data_aug"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Loading in the checkpointed weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x1a250a8fdc0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2.load_weights(checkpoint_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 5s 56ms/step - loss: 0.7141 - accuracy: 0.8032\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.7141460180282593, 0.8032000064849854]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate model 2 with loaded weights\n",
    "loaded_weights_model_results = model_2.evaluate(test_data)\n",
    "loaded_weights_model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_weights_model_results == result_10_percent_data_aug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check how close results are\n",
    "import numpy as np\n",
    "np.isclose(np.array(loaded_weights_model_results), np.array(result_10_percent_data_aug))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "source": [
    "# Model 3: Fine-tuning model with 10% data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<keras.engine.input_layer.InputLayer at 0x1a0eb927070>,\n",
       " <keras.engine.sequential.Sequential at 0x1a0eb9251b0>,\n",
       " <keras.engine.functional.Functional at 0x1a1dd563880>,\n",
       " <keras.layers.pooling.GlobalAveragePooling2D at 0x1a1dd4ef7c0>,\n",
       " <keras.layers.core.dense.Dense at 0x1a1de74c850>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# layers in our loaded model\n",
    "model_2.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<keras.engine.input_layer.InputLayer object at 0x000001A0EB927070> True\n",
      "<keras.engine.sequential.Sequential object at 0x000001A0EB9251B0> True\n",
      "<keras.engine.functional.Functional object at 0x000001A1DD563880> False\n",
      "<keras.layers.pooling.GlobalAveragePooling2D object at 0x000001A1DD4EF7C0> True\n",
      "<keras.layers.core.dense.Dense object at 0x000001A1DE74C850> True\n"
     ]
    }
   ],
   "source": [
    "# Are layers trainable\n",
    "for layer in model_2.layers:\n",
    "    print(layer, layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. 0 --> input_1 --> False\n",
      "No. 1 --> rescaling --> False\n",
      "No. 2 --> normalization --> False\n",
      "No. 3 --> stem_conv_pad --> False\n",
      "No. 4 --> stem_conv --> False\n",
      "No. 5 --> stem_bn --> False\n",
      "No. 6 --> stem_activation --> False\n",
      "No. 7 --> block1a_dwconv --> False\n",
      "No. 8 --> block1a_bn --> False\n",
      "No. 9 --> block1a_activation --> False\n",
      "No. 10 --> block1a_se_squeeze --> False\n",
      "No. 11 --> block1a_se_reshape --> False\n",
      "No. 12 --> block1a_se_reduce --> False\n",
      "No. 13 --> block1a_se_expand --> False\n",
      "No. 14 --> block1a_se_excite --> False\n",
      "No. 15 --> block1a_project_conv --> False\n",
      "No. 16 --> block1a_project_bn --> False\n",
      "No. 17 --> block2a_expand_conv --> False\n",
      "No. 18 --> block2a_expand_bn --> False\n",
      "No. 19 --> block2a_expand_activation --> False\n",
      "No. 20 --> block2a_dwconv_pad --> False\n",
      "No. 21 --> block2a_dwconv --> False\n",
      "No. 22 --> block2a_bn --> False\n",
      "No. 23 --> block2a_activation --> False\n",
      "No. 24 --> block2a_se_squeeze --> False\n",
      "No. 25 --> block2a_se_reshape --> False\n",
      "No. 26 --> block2a_se_reduce --> False\n",
      "No. 27 --> block2a_se_expand --> False\n",
      "No. 28 --> block2a_se_excite --> False\n",
      "No. 29 --> block2a_project_conv --> False\n",
      "No. 30 --> block2a_project_bn --> False\n",
      "No. 31 --> block2b_expand_conv --> False\n",
      "No. 32 --> block2b_expand_bn --> False\n",
      "No. 33 --> block2b_expand_activation --> False\n",
      "No. 34 --> block2b_dwconv --> False\n",
      "No. 35 --> block2b_bn --> False\n",
      "No. 36 --> block2b_activation --> False\n",
      "No. 37 --> block2b_se_squeeze --> False\n",
      "No. 38 --> block2b_se_reshape --> False\n",
      "No. 39 --> block2b_se_reduce --> False\n",
      "No. 40 --> block2b_se_expand --> False\n",
      "No. 41 --> block2b_se_excite --> False\n",
      "No. 42 --> block2b_project_conv --> False\n",
      "No. 43 --> block2b_project_bn --> False\n",
      "No. 44 --> block2b_drop --> False\n",
      "No. 45 --> block2b_add --> False\n",
      "No. 46 --> block3a_expand_conv --> False\n",
      "No. 47 --> block3a_expand_bn --> False\n",
      "No. 48 --> block3a_expand_activation --> False\n",
      "No. 49 --> block3a_dwconv_pad --> False\n",
      "No. 50 --> block3a_dwconv --> False\n",
      "No. 51 --> block3a_bn --> False\n",
      "No. 52 --> block3a_activation --> False\n",
      "No. 53 --> block3a_se_squeeze --> False\n",
      "No. 54 --> block3a_se_reshape --> False\n",
      "No. 55 --> block3a_se_reduce --> False\n",
      "No. 56 --> block3a_se_expand --> False\n",
      "No. 57 --> block3a_se_excite --> False\n",
      "No. 58 --> block3a_project_conv --> False\n",
      "No. 59 --> block3a_project_bn --> False\n",
      "No. 60 --> block3b_expand_conv --> False\n",
      "No. 61 --> block3b_expand_bn --> False\n",
      "No. 62 --> block3b_expand_activation --> False\n",
      "No. 63 --> block3b_dwconv --> False\n",
      "No. 64 --> block3b_bn --> False\n",
      "No. 65 --> block3b_activation --> False\n",
      "No. 66 --> block3b_se_squeeze --> False\n",
      "No. 67 --> block3b_se_reshape --> False\n",
      "No. 68 --> block3b_se_reduce --> False\n",
      "No. 69 --> block3b_se_expand --> False\n",
      "No. 70 --> block3b_se_excite --> False\n",
      "No. 71 --> block3b_project_conv --> False\n",
      "No. 72 --> block3b_project_bn --> False\n",
      "No. 73 --> block3b_drop --> False\n",
      "No. 74 --> block3b_add --> False\n",
      "No. 75 --> block4a_expand_conv --> False\n",
      "No. 76 --> block4a_expand_bn --> False\n",
      "No. 77 --> block4a_expand_activation --> False\n",
      "No. 78 --> block4a_dwconv_pad --> False\n",
      "No. 79 --> block4a_dwconv --> False\n",
      "No. 80 --> block4a_bn --> False\n",
      "No. 81 --> block4a_activation --> False\n",
      "No. 82 --> block4a_se_squeeze --> False\n",
      "No. 83 --> block4a_se_reshape --> False\n",
      "No. 84 --> block4a_se_reduce --> False\n",
      "No. 85 --> block4a_se_expand --> False\n",
      "No. 86 --> block4a_se_excite --> False\n",
      "No. 87 --> block4a_project_conv --> False\n",
      "No. 88 --> block4a_project_bn --> False\n",
      "No. 89 --> block4b_expand_conv --> False\n",
      "No. 90 --> block4b_expand_bn --> False\n",
      "No. 91 --> block4b_expand_activation --> False\n",
      "No. 92 --> block4b_dwconv --> False\n",
      "No. 93 --> block4b_bn --> False\n",
      "No. 94 --> block4b_activation --> False\n",
      "No. 95 --> block4b_se_squeeze --> False\n",
      "No. 96 --> block4b_se_reshape --> False\n",
      "No. 97 --> block4b_se_reduce --> False\n",
      "No. 98 --> block4b_se_expand --> False\n",
      "No. 99 --> block4b_se_excite --> False\n",
      "No. 100 --> block4b_project_conv --> False\n",
      "No. 101 --> block4b_project_bn --> False\n",
      "No. 102 --> block4b_drop --> False\n",
      "No. 103 --> block4b_add --> False\n",
      "No. 104 --> block4c_expand_conv --> False\n",
      "No. 105 --> block4c_expand_bn --> False\n",
      "No. 106 --> block4c_expand_activation --> False\n",
      "No. 107 --> block4c_dwconv --> False\n",
      "No. 108 --> block4c_bn --> False\n",
      "No. 109 --> block4c_activation --> False\n",
      "No. 110 --> block4c_se_squeeze --> False\n",
      "No. 111 --> block4c_se_reshape --> False\n",
      "No. 112 --> block4c_se_reduce --> False\n",
      "No. 113 --> block4c_se_expand --> False\n",
      "No. 114 --> block4c_se_excite --> False\n",
      "No. 115 --> block4c_project_conv --> False\n",
      "No. 116 --> block4c_project_bn --> False\n",
      "No. 117 --> block4c_drop --> False\n",
      "No. 118 --> block4c_add --> False\n",
      "No. 119 --> block5a_expand_conv --> False\n",
      "No. 120 --> block5a_expand_bn --> False\n",
      "No. 121 --> block5a_expand_activation --> False\n",
      "No. 122 --> block5a_dwconv --> False\n",
      "No. 123 --> block5a_bn --> False\n",
      "No. 124 --> block5a_activation --> False\n",
      "No. 125 --> block5a_se_squeeze --> False\n",
      "No. 126 --> block5a_se_reshape --> False\n",
      "No. 127 --> block5a_se_reduce --> False\n",
      "No. 128 --> block5a_se_expand --> False\n",
      "No. 129 --> block5a_se_excite --> False\n",
      "No. 130 --> block5a_project_conv --> False\n",
      "No. 131 --> block5a_project_bn --> False\n",
      "No. 132 --> block5b_expand_conv --> False\n",
      "No. 133 --> block5b_expand_bn --> False\n",
      "No. 134 --> block5b_expand_activation --> False\n",
      "No. 135 --> block5b_dwconv --> False\n",
      "No. 136 --> block5b_bn --> False\n",
      "No. 137 --> block5b_activation --> False\n",
      "No. 138 --> block5b_se_squeeze --> False\n",
      "No. 139 --> block5b_se_reshape --> False\n",
      "No. 140 --> block5b_se_reduce --> False\n",
      "No. 141 --> block5b_se_expand --> False\n",
      "No. 142 --> block5b_se_excite --> False\n",
      "No. 143 --> block5b_project_conv --> False\n",
      "No. 144 --> block5b_project_bn --> False\n",
      "No. 145 --> block5b_drop --> False\n",
      "No. 146 --> block5b_add --> False\n",
      "No. 147 --> block5c_expand_conv --> False\n",
      "No. 148 --> block5c_expand_bn --> False\n",
      "No. 149 --> block5c_expand_activation --> False\n",
      "No. 150 --> block5c_dwconv --> False\n",
      "No. 151 --> block5c_bn --> False\n",
      "No. 152 --> block5c_activation --> False\n",
      "No. 153 --> block5c_se_squeeze --> False\n",
      "No. 154 --> block5c_se_reshape --> False\n",
      "No. 155 --> block5c_se_reduce --> False\n",
      "No. 156 --> block5c_se_expand --> False\n",
      "No. 157 --> block5c_se_excite --> False\n",
      "No. 158 --> block5c_project_conv --> False\n",
      "No. 159 --> block5c_project_bn --> False\n",
      "No. 160 --> block5c_drop --> False\n",
      "No. 161 --> block5c_add --> False\n",
      "No. 162 --> block6a_expand_conv --> False\n",
      "No. 163 --> block6a_expand_bn --> False\n",
      "No. 164 --> block6a_expand_activation --> False\n",
      "No. 165 --> block6a_dwconv_pad --> False\n",
      "No. 166 --> block6a_dwconv --> False\n",
      "No. 167 --> block6a_bn --> False\n",
      "No. 168 --> block6a_activation --> False\n",
      "No. 169 --> block6a_se_squeeze --> False\n",
      "No. 170 --> block6a_se_reshape --> False\n",
      "No. 171 --> block6a_se_reduce --> False\n",
      "No. 172 --> block6a_se_expand --> False\n",
      "No. 173 --> block6a_se_excite --> False\n",
      "No. 174 --> block6a_project_conv --> False\n",
      "No. 175 --> block6a_project_bn --> False\n",
      "No. 176 --> block6b_expand_conv --> False\n",
      "No. 177 --> block6b_expand_bn --> False\n",
      "No. 178 --> block6b_expand_activation --> False\n",
      "No. 179 --> block6b_dwconv --> False\n",
      "No. 180 --> block6b_bn --> False\n",
      "No. 181 --> block6b_activation --> False\n",
      "No. 182 --> block6b_se_squeeze --> False\n",
      "No. 183 --> block6b_se_reshape --> False\n",
      "No. 184 --> block6b_se_reduce --> False\n",
      "No. 185 --> block6b_se_expand --> False\n",
      "No. 186 --> block6b_se_excite --> False\n",
      "No. 187 --> block6b_project_conv --> False\n",
      "No. 188 --> block6b_project_bn --> False\n",
      "No. 189 --> block6b_drop --> False\n",
      "No. 190 --> block6b_add --> False\n",
      "No. 191 --> block6c_expand_conv --> False\n",
      "No. 192 --> block6c_expand_bn --> False\n",
      "No. 193 --> block6c_expand_activation --> False\n",
      "No. 194 --> block6c_dwconv --> False\n",
      "No. 195 --> block6c_bn --> False\n",
      "No. 196 --> block6c_activation --> False\n",
      "No. 197 --> block6c_se_squeeze --> False\n",
      "No. 198 --> block6c_se_reshape --> False\n",
      "No. 199 --> block6c_se_reduce --> False\n",
      "No. 200 --> block6c_se_expand --> False\n",
      "No. 201 --> block6c_se_excite --> False\n",
      "No. 202 --> block6c_project_conv --> False\n",
      "No. 203 --> block6c_project_bn --> False\n",
      "No. 204 --> block6c_drop --> False\n",
      "No. 205 --> block6c_add --> False\n",
      "No. 206 --> block6d_expand_conv --> False\n",
      "No. 207 --> block6d_expand_bn --> False\n",
      "No. 208 --> block6d_expand_activation --> False\n",
      "No. 209 --> block6d_dwconv --> False\n",
      "No. 210 --> block6d_bn --> False\n",
      "No. 211 --> block6d_activation --> False\n",
      "No. 212 --> block6d_se_squeeze --> False\n",
      "No. 213 --> block6d_se_reshape --> False\n",
      "No. 214 --> block6d_se_reduce --> False\n",
      "No. 215 --> block6d_se_expand --> False\n",
      "No. 216 --> block6d_se_excite --> False\n",
      "No. 217 --> block6d_project_conv --> False\n",
      "No. 218 --> block6d_project_bn --> False\n",
      "No. 219 --> block6d_drop --> False\n",
      "No. 220 --> block6d_add --> False\n",
      "No. 221 --> block7a_expand_conv --> False\n",
      "No. 222 --> block7a_expand_bn --> False\n",
      "No. 223 --> block7a_expand_activation --> False\n",
      "No. 224 --> block7a_dwconv --> False\n",
      "No. 225 --> block7a_bn --> False\n",
      "No. 226 --> block7a_activation --> False\n",
      "No. 227 --> block7a_se_squeeze --> False\n",
      "No. 228 --> block7a_se_reshape --> False\n",
      "No. 229 --> block7a_se_reduce --> False\n",
      "No. 230 --> block7a_se_expand --> False\n",
      "No. 231 --> block7a_se_excite --> False\n",
      "No. 232 --> block7a_project_conv --> False\n",
      "No. 233 --> block7a_project_bn --> False\n",
      "No. 234 --> top_conv --> False\n",
      "No. 235 --> top_bn --> False\n",
      "No. 236 --> top_activation --> False\n"
     ]
    }
   ],
   "source": [
    "# What layers are in our base model (EfficientNetB0) and if they are trainable.\n",
    "for i, layer in enumerate(model_2.layers[2].layers):\n",
    "    print(f\"No. {i} --> {layer.name} --> {layer.trainable}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# how many trainable variables are in our base model?\n",
    "print(len(model_2.layers[2].trainable_variables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To begin fine tuning, lets start by setting last 10 layers of our base_model.trainable =True\n",
    "base_model.trainable = True\n",
    "\n",
    "# Freeze all layers except last 10\n",
    "for layer in base_model.layers[:-10]:\n",
    "    layer.trainable = False\n",
    "    \n",
    "# Recompile (Do this every time we make change in model)\n",
    "model_2.compile(loss=\"categorical_crossentropy\",\n",
    "               optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), # when fine tuning, lower the learning rate by 10x\n",
    "               metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 input_1 False\n",
      "1 rescaling False\n",
      "2 normalization False\n",
      "3 stem_conv_pad False\n",
      "4 stem_conv False\n",
      "5 stem_bn False\n",
      "6 stem_activation False\n",
      "7 block1a_dwconv False\n",
      "8 block1a_bn False\n",
      "9 block1a_activation False\n",
      "10 block1a_se_squeeze False\n",
      "11 block1a_se_reshape False\n",
      "12 block1a_se_reduce False\n",
      "13 block1a_se_expand False\n",
      "14 block1a_se_excite False\n",
      "15 block1a_project_conv False\n",
      "16 block1a_project_bn False\n",
      "17 block2a_expand_conv False\n",
      "18 block2a_expand_bn False\n",
      "19 block2a_expand_activation False\n",
      "20 block2a_dwconv_pad False\n",
      "21 block2a_dwconv False\n",
      "22 block2a_bn False\n",
      "23 block2a_activation False\n",
      "24 block2a_se_squeeze False\n",
      "25 block2a_se_reshape False\n",
      "26 block2a_se_reduce False\n",
      "27 block2a_se_expand False\n",
      "28 block2a_se_excite False\n",
      "29 block2a_project_conv False\n",
      "30 block2a_project_bn False\n",
      "31 block2b_expand_conv False\n",
      "32 block2b_expand_bn False\n",
      "33 block2b_expand_activation False\n",
      "34 block2b_dwconv False\n",
      "35 block2b_bn False\n",
      "36 block2b_activation False\n",
      "37 block2b_se_squeeze False\n",
      "38 block2b_se_reshape False\n",
      "39 block2b_se_reduce False\n",
      "40 block2b_se_expand False\n",
      "41 block2b_se_excite False\n",
      "42 block2b_project_conv False\n",
      "43 block2b_project_bn False\n",
      "44 block2b_drop False\n",
      "45 block2b_add False\n",
      "46 block3a_expand_conv False\n",
      "47 block3a_expand_bn False\n",
      "48 block3a_expand_activation False\n",
      "49 block3a_dwconv_pad False\n",
      "50 block3a_dwconv False\n",
      "51 block3a_bn False\n",
      "52 block3a_activation False\n",
      "53 block3a_se_squeeze False\n",
      "54 block3a_se_reshape False\n",
      "55 block3a_se_reduce False\n",
      "56 block3a_se_expand False\n",
      "57 block3a_se_excite False\n",
      "58 block3a_project_conv False\n",
      "59 block3a_project_bn False\n",
      "60 block3b_expand_conv False\n",
      "61 block3b_expand_bn False\n",
      "62 block3b_expand_activation False\n",
      "63 block3b_dwconv False\n",
      "64 block3b_bn False\n",
      "65 block3b_activation False\n",
      "66 block3b_se_squeeze False\n",
      "67 block3b_se_reshape False\n",
      "68 block3b_se_reduce False\n",
      "69 block3b_se_expand False\n",
      "70 block3b_se_excite False\n",
      "71 block3b_project_conv False\n",
      "72 block3b_project_bn False\n",
      "73 block3b_drop False\n",
      "74 block3b_add False\n",
      "75 block4a_expand_conv False\n",
      "76 block4a_expand_bn False\n",
      "77 block4a_expand_activation False\n",
      "78 block4a_dwconv_pad False\n",
      "79 block4a_dwconv False\n",
      "80 block4a_bn False\n",
      "81 block4a_activation False\n",
      "82 block4a_se_squeeze False\n",
      "83 block4a_se_reshape False\n",
      "84 block4a_se_reduce False\n",
      "85 block4a_se_expand False\n",
      "86 block4a_se_excite False\n",
      "87 block4a_project_conv False\n",
      "88 block4a_project_bn False\n",
      "89 block4b_expand_conv False\n",
      "90 block4b_expand_bn False\n",
      "91 block4b_expand_activation False\n",
      "92 block4b_dwconv False\n",
      "93 block4b_bn False\n",
      "94 block4b_activation False\n",
      "95 block4b_se_squeeze False\n",
      "96 block4b_se_reshape False\n",
      "97 block4b_se_reduce False\n",
      "98 block4b_se_expand False\n",
      "99 block4b_se_excite False\n",
      "100 block4b_project_conv False\n",
      "101 block4b_project_bn False\n",
      "102 block4b_drop False\n",
      "103 block4b_add False\n",
      "104 block4c_expand_conv False\n",
      "105 block4c_expand_bn False\n",
      "106 block4c_expand_activation False\n",
      "107 block4c_dwconv False\n",
      "108 block4c_bn False\n",
      "109 block4c_activation False\n",
      "110 block4c_se_squeeze False\n",
      "111 block4c_se_reshape False\n",
      "112 block4c_se_reduce False\n",
      "113 block4c_se_expand False\n",
      "114 block4c_se_excite False\n",
      "115 block4c_project_conv False\n",
      "116 block4c_project_bn False\n",
      "117 block4c_drop False\n",
      "118 block4c_add False\n",
      "119 block5a_expand_conv False\n",
      "120 block5a_expand_bn False\n",
      "121 block5a_expand_activation False\n",
      "122 block5a_dwconv False\n",
      "123 block5a_bn False\n",
      "124 block5a_activation False\n",
      "125 block5a_se_squeeze False\n",
      "126 block5a_se_reshape False\n",
      "127 block5a_se_reduce False\n",
      "128 block5a_se_expand False\n",
      "129 block5a_se_excite False\n",
      "130 block5a_project_conv False\n",
      "131 block5a_project_bn False\n",
      "132 block5b_expand_conv False\n",
      "133 block5b_expand_bn False\n",
      "134 block5b_expand_activation False\n",
      "135 block5b_dwconv False\n",
      "136 block5b_bn False\n",
      "137 block5b_activation False\n",
      "138 block5b_se_squeeze False\n",
      "139 block5b_se_reshape False\n",
      "140 block5b_se_reduce False\n",
      "141 block5b_se_expand False\n",
      "142 block5b_se_excite False\n",
      "143 block5b_project_conv False\n",
      "144 block5b_project_bn False\n",
      "145 block5b_drop False\n",
      "146 block5b_add False\n",
      "147 block5c_expand_conv False\n",
      "148 block5c_expand_bn False\n",
      "149 block5c_expand_activation False\n",
      "150 block5c_dwconv False\n",
      "151 block5c_bn False\n",
      "152 block5c_activation False\n",
      "153 block5c_se_squeeze False\n",
      "154 block5c_se_reshape False\n",
      "155 block5c_se_reduce False\n",
      "156 block5c_se_expand False\n",
      "157 block5c_se_excite False\n",
      "158 block5c_project_conv False\n",
      "159 block5c_project_bn False\n",
      "160 block5c_drop False\n",
      "161 block5c_add False\n",
      "162 block6a_expand_conv False\n",
      "163 block6a_expand_bn False\n",
      "164 block6a_expand_activation False\n",
      "165 block6a_dwconv_pad False\n",
      "166 block6a_dwconv False\n",
      "167 block6a_bn False\n",
      "168 block6a_activation False\n",
      "169 block6a_se_squeeze False\n",
      "170 block6a_se_reshape False\n",
      "171 block6a_se_reduce False\n",
      "172 block6a_se_expand False\n",
      "173 block6a_se_excite False\n",
      "174 block6a_project_conv False\n",
      "175 block6a_project_bn False\n",
      "176 block6b_expand_conv False\n",
      "177 block6b_expand_bn False\n",
      "178 block6b_expand_activation False\n",
      "179 block6b_dwconv False\n",
      "180 block6b_bn False\n",
      "181 block6b_activation False\n",
      "182 block6b_se_squeeze False\n",
      "183 block6b_se_reshape False\n",
      "184 block6b_se_reduce False\n",
      "185 block6b_se_expand False\n",
      "186 block6b_se_excite False\n",
      "187 block6b_project_conv False\n",
      "188 block6b_project_bn False\n",
      "189 block6b_drop False\n",
      "190 block6b_add False\n",
      "191 block6c_expand_conv False\n",
      "192 block6c_expand_bn False\n",
      "193 block6c_expand_activation False\n",
      "194 block6c_dwconv False\n",
      "195 block6c_bn False\n",
      "196 block6c_activation False\n",
      "197 block6c_se_squeeze False\n",
      "198 block6c_se_reshape False\n",
      "199 block6c_se_reduce False\n",
      "200 block6c_se_expand False\n",
      "201 block6c_se_excite False\n",
      "202 block6c_project_conv False\n",
      "203 block6c_project_bn False\n",
      "204 block6c_drop False\n",
      "205 block6c_add False\n",
      "206 block6d_expand_conv False\n",
      "207 block6d_expand_bn False\n",
      "208 block6d_expand_activation False\n",
      "209 block6d_dwconv False\n",
      "210 block6d_bn False\n",
      "211 block6d_activation False\n",
      "212 block6d_se_squeeze False\n",
      "213 block6d_se_reshape False\n",
      "214 block6d_se_reduce False\n",
      "215 block6d_se_expand False\n",
      "216 block6d_se_excite False\n",
      "217 block6d_project_conv False\n",
      "218 block6d_project_bn False\n",
      "219 block6d_drop False\n",
      "220 block6d_add False\n",
      "221 block7a_expand_conv False\n",
      "222 block7a_expand_bn False\n",
      "223 block7a_expand_activation False\n",
      "224 block7a_dwconv False\n",
      "225 block7a_bn False\n",
      "226 block7a_activation False\n",
      "227 block7a_se_squeeze True\n",
      "228 block7a_se_reshape True\n",
      "229 block7a_se_reduce True\n",
      "230 block7a_se_expand True\n",
      "231 block7a_se_excite True\n",
      "232 block7a_project_conv True\n",
      "233 block7a_project_bn True\n",
      "234 top_conv True\n",
      "235 top_bn True\n",
      "236 top_activation True\n"
     ]
    }
   ],
   "source": [
    "# Check which layers are tunable\n",
    "for layer_number, layer in enumerate(model_2.layers[2].layers):\n",
    "    print(layer_number, layer.name, layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    }
   ],
   "source": [
    "# Checking trainable variable after unfrezzein some layers\n",
    "print(len(model_2.trainable_variables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving TensorBoard log files to: transfer_learning/10_percent_fine_tune_last_10/20220617-212659\n",
      "Epoch 5/10\n",
      "24/24 [==============================] - 12s 325ms/step - loss: 0.6869 - accuracy: 0.8147 - val_loss: 0.5577 - val_accuracy: 0.8092\n",
      "Epoch 6/10\n",
      "24/24 [==============================] - 6s 240ms/step - loss: 0.5752 - accuracy: 0.8120 - val_loss: 0.5230 - val_accuracy: 0.8273\n",
      "Epoch 7/10\n",
      "24/24 [==============================] - 6s 245ms/step - loss: 0.5037 - accuracy: 0.8480 - val_loss: 0.5135 - val_accuracy: 0.8273\n",
      "Epoch 8/10\n",
      "24/24 [==============================] - 6s 254ms/step - loss: 0.4621 - accuracy: 0.8560 - val_loss: 0.5008 - val_accuracy: 0.8224\n",
      "Epoch 9/10\n",
      "24/24 [==============================] - 5s 216ms/step - loss: 0.4154 - accuracy: 0.8840 - val_loss: 0.4847 - val_accuracy: 0.8421\n",
      "Epoch 10/10\n",
      "24/24 [==============================] - 5s 224ms/step - loss: 0.3721 - accuracy: 0.8867 - val_loss: 0.4655 - val_accuracy: 0.8322\n"
     ]
    }
   ],
   "source": [
    "# Fine tune for another 5 epochs\n",
    "fine_tune_epochs = initial_epochs + 5\n",
    "\n",
    "# Refit the model\n",
    "history_fine_10_percent_data_aug = model_2.fit(train_10_percent_data,\n",
    "                                            epochs=fine_tune_epochs,\n",
    "                                            validation_data=test_data,\n",
    "                                            validation_steps=int(0.25 * len(test_data)),\n",
    "                                            initial_epoch=history_model_2.epoch[-1],\n",
    "                                            callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\", experiment_name=\"10_percent_fine_tune_last_10\")])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 5s 56ms/step - loss: 0.4859 - accuracy: 0.8372\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the fine-tuned model \n",
    "results_fine_tune_10_percent = model_2.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check out plot loss curves\n",
    "plot_loss_curves(history_fine_10_percent_data_aug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function to compate training histories\n",
    "def compare_histories(original_history, new_history, initial_epochs=5):\n",
    "    \"\"\"\n",
    "    Compares two TensorFlow History objects\n",
    "    \"\"\"\n",
    "    # Get original history measurements\n",
    "    acc = original_history.history[\"accuracy\"]\n",
    "    loss = original_history.history[\"loss\"]\n",
    "                                    \n",
    "    val_acc = original_history.history[\"val_accuracy\"]\n",
    "    val_loss = original_history.history[\"val_loss\"]\n",
    "    \n",
    "    # combine original history\n",
    "    total_acc = acc + new_history.history[\"accuracy\"]\n",
    "    total_loss = loss + new_history.history[\"loss\"]\n",
    "    \n",
    "    total_val_acc = val_acc + new_history.history[\"val_accuracy\"]\n",
    "    total_val_loss = val_loss + new_history.history[\"val_loss\"]\n",
    "    \n",
    "    # Make plot for accuracy\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.subplot(2, 1, 1)\n",
    "    plt.plot(total_acc, label=\"Training Accuracy\")\n",
    "    plt.plot(total_val_acc, label=\"Val Accuracy\")\n",
    "    plt.plot([initial_epochs - 1, initial_epochs - 1], plt.ylim(), label=\"Start Fine Tuning\")\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.title(\"Training and Validation Accuracy\")\n",
    "    \n",
    "    # Make plot for loss\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.subplot(2, 1, 1)\n",
    "    plt.plot(total_loss, label=\"Training Loss\")\n",
    "    plt.plot(total_val_loss, label=\"Val Loss\")\n",
    "    plt.plot([initial_epochs - 1, initial_epochs - 1], plt.ylim(), label=\"Start Fine Tuning\")\n",
    "    plt.legend(loc=\"upper right\")\n",
    "    plt.title(\"Training and Validation loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_histories(history_model_2, history_fine_10_percent_data_aug, initial_epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Model 4: Fine tuning existing model on 100% data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved under 10_food_classes_all_data.zip\n"
     ]
    }
   ],
   "source": [
    "# Download and unzip 10 classes of Food101 data with all images\n",
    "!python -m wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_all_data.zip\n",
    "\n",
    "unzip_data(\"10_food_classes_all_data.zip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print training and test dir\n",
    "train_dir_all_data = \"10_food_classes_all_data/train\"\n",
    "test_dir = \"10_food_classes_all_data/test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 directories and 0 images in '10_food_classes_all_data'.\n",
      "There are 10 directories and 0 images in '10_food_classes_all_data\\test'.\n",
      "There are 0 directories and 250 images in '10_food_classes_all_data\\test\\chicken_curry'.\n",
      "There are 0 directories and 250 images in '10_food_classes_all_data\\test\\chicken_wings'.\n",
      "There are 0 directories and 250 images in '10_food_classes_all_data\\test\\fried_rice'.\n",
      "There are 0 directories and 250 images in '10_food_classes_all_data\\test\\grilled_salmon'.\n",
      "There are 0 directories and 250 images in '10_food_classes_all_data\\test\\hamburger'.\n",
      "There are 0 directories and 250 images in '10_food_classes_all_data\\test\\ice_cream'.\n",
      "There are 0 directories and 250 images in '10_food_classes_all_data\\test\\pizza'.\n",
      "There are 0 directories and 250 images in '10_food_classes_all_data\\test\\ramen'.\n",
      "There are 0 directories and 250 images in '10_food_classes_all_data\\test\\steak'.\n",
      "There are 0 directories and 250 images in '10_food_classes_all_data\\test\\sushi'.\n",
      "There are 10 directories and 0 images in '10_food_classes_all_data\\train'.\n",
      "There are 0 directories and 750 images in '10_food_classes_all_data\\train\\chicken_curry'.\n",
      "There are 0 directories and 750 images in '10_food_classes_all_data\\train\\chicken_wings'.\n",
      "There are 0 directories and 750 images in '10_food_classes_all_data\\train\\fried_rice'.\n",
      "There are 0 directories and 750 images in '10_food_classes_all_data\\train\\grilled_salmon'.\n",
      "There are 0 directories and 750 images in '10_food_classes_all_data\\train\\hamburger'.\n",
      "There are 0 directories and 750 images in '10_food_classes_all_data\\train\\ice_cream'.\n",
      "There are 0 directories and 750 images in '10_food_classes_all_data\\train\\pizza'.\n",
      "There are 0 directories and 750 images in '10_food_classes_all_data\\train\\ramen'.\n",
      "There are 0 directories and 750 images in '10_food_classes_all_data\\train\\steak'.\n",
      "There are 0 directories and 750 images in '10_food_classes_all_data\\train\\sushi'.\n"
     ]
    }
   ],
   "source": [
    "# How many images are we working with\n",
    "walk_through_dir(\"10_food_classes_all_data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 7500 files belonging to 10 classes.\n",
      "Found 2500 files belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "# Setup data inputs\n",
    "import tensorflow as tf\n",
    "\n",
    "IMG_SIZE = (224, 224)\n",
    "\n",
    "train_data_10_classes_full = tf.keras.preprocessing.image_dataset_from_directory(train_dir_all_data,\n",
    "                                                                                image_size=IMG_SIZE,\n",
    "                                                                                label_mode=\"categorical\")\n",
    "\n",
    "test_data = tf.keras.preprocessing.image_dataset_from_directory(test_dir,\n",
    "                                                               image_size=IMG_SIZE,\n",
    "                                                               label_mode=\"categorical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 5s 58ms/step - loss: 0.4859 - accuracy: 0.8372\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.48592621088027954, 0.8371999859809875]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate model 2\n",
    "model_2.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.4859263300895691, 0.8371999859809875]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_fine_tune_10_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x1a0eb927280>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load checkpoint weights to remove fine tuned version\n",
    "model_2.load_weights(checkpoint_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 5s 58ms/step - loss: 0.7141 - accuracy: 0.8032\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.7141461372375488, 0.8032000064849854]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After removing fine tuning, accuracy is low and loss is high\n",
    "model_2.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7141463160514832, 0.8032000064849854]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare it to original evaluation of model_2\n",
    "result_10_percent_data_aug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 input_layer True\n",
      "1 data_augmentation True\n",
      "2 efficientnetb0 True\n",
      "3 global_average_pooling2d True\n",
      "4 output_layer True\n"
     ]
    }
   ],
   "source": [
    "# Check which layers are tunable in model-2\n",
    "\n",
    "for layer_number, layer in enumerate(model_2.layers):\n",
    "    print(layer_number, layer.name, layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 input_1 False\n",
      "1 rescaling False\n",
      "2 normalization False\n",
      "3 stem_conv_pad False\n",
      "4 stem_conv False\n",
      "5 stem_bn False\n",
      "6 stem_activation False\n",
      "7 block1a_dwconv False\n",
      "8 block1a_bn False\n",
      "9 block1a_activation False\n",
      "10 block1a_se_squeeze False\n",
      "11 block1a_se_reshape False\n",
      "12 block1a_se_reduce False\n",
      "13 block1a_se_expand False\n",
      "14 block1a_se_excite False\n",
      "15 block1a_project_conv False\n",
      "16 block1a_project_bn False\n",
      "17 block2a_expand_conv False\n",
      "18 block2a_expand_bn False\n",
      "19 block2a_expand_activation False\n",
      "20 block2a_dwconv_pad False\n",
      "21 block2a_dwconv False\n",
      "22 block2a_bn False\n",
      "23 block2a_activation False\n",
      "24 block2a_se_squeeze False\n",
      "25 block2a_se_reshape False\n",
      "26 block2a_se_reduce False\n",
      "27 block2a_se_expand False\n",
      "28 block2a_se_excite False\n",
      "29 block2a_project_conv False\n",
      "30 block2a_project_bn False\n",
      "31 block2b_expand_conv False\n",
      "32 block2b_expand_bn False\n",
      "33 block2b_expand_activation False\n",
      "34 block2b_dwconv False\n",
      "35 block2b_bn False\n",
      "36 block2b_activation False\n",
      "37 block2b_se_squeeze False\n",
      "38 block2b_se_reshape False\n",
      "39 block2b_se_reduce False\n",
      "40 block2b_se_expand False\n",
      "41 block2b_se_excite False\n",
      "42 block2b_project_conv False\n",
      "43 block2b_project_bn False\n",
      "44 block2b_drop False\n",
      "45 block2b_add False\n",
      "46 block3a_expand_conv False\n",
      "47 block3a_expand_bn False\n",
      "48 block3a_expand_activation False\n",
      "49 block3a_dwconv_pad False\n",
      "50 block3a_dwconv False\n",
      "51 block3a_bn False\n",
      "52 block3a_activation False\n",
      "53 block3a_se_squeeze False\n",
      "54 block3a_se_reshape False\n",
      "55 block3a_se_reduce False\n",
      "56 block3a_se_expand False\n",
      "57 block3a_se_excite False\n",
      "58 block3a_project_conv False\n",
      "59 block3a_project_bn False\n",
      "60 block3b_expand_conv False\n",
      "61 block3b_expand_bn False\n",
      "62 block3b_expand_activation False\n",
      "63 block3b_dwconv False\n",
      "64 block3b_bn False\n",
      "65 block3b_activation False\n",
      "66 block3b_se_squeeze False\n",
      "67 block3b_se_reshape False\n",
      "68 block3b_se_reduce False\n",
      "69 block3b_se_expand False\n",
      "70 block3b_se_excite False\n",
      "71 block3b_project_conv False\n",
      "72 block3b_project_bn False\n",
      "73 block3b_drop False\n",
      "74 block3b_add False\n",
      "75 block4a_expand_conv False\n",
      "76 block4a_expand_bn False\n",
      "77 block4a_expand_activation False\n",
      "78 block4a_dwconv_pad False\n",
      "79 block4a_dwconv False\n",
      "80 block4a_bn False\n",
      "81 block4a_activation False\n",
      "82 block4a_se_squeeze False\n",
      "83 block4a_se_reshape False\n",
      "84 block4a_se_reduce False\n",
      "85 block4a_se_expand False\n",
      "86 block4a_se_excite False\n",
      "87 block4a_project_conv False\n",
      "88 block4a_project_bn False\n",
      "89 block4b_expand_conv False\n",
      "90 block4b_expand_bn False\n",
      "91 block4b_expand_activation False\n",
      "92 block4b_dwconv False\n",
      "93 block4b_bn False\n",
      "94 block4b_activation False\n",
      "95 block4b_se_squeeze False\n",
      "96 block4b_se_reshape False\n",
      "97 block4b_se_reduce False\n",
      "98 block4b_se_expand False\n",
      "99 block4b_se_excite False\n",
      "100 block4b_project_conv False\n",
      "101 block4b_project_bn False\n",
      "102 block4b_drop False\n",
      "103 block4b_add False\n",
      "104 block4c_expand_conv False\n",
      "105 block4c_expand_bn False\n",
      "106 block4c_expand_activation False\n",
      "107 block4c_dwconv False\n",
      "108 block4c_bn False\n",
      "109 block4c_activation False\n",
      "110 block4c_se_squeeze False\n",
      "111 block4c_se_reshape False\n",
      "112 block4c_se_reduce False\n",
      "113 block4c_se_expand False\n",
      "114 block4c_se_excite False\n",
      "115 block4c_project_conv False\n",
      "116 block4c_project_bn False\n",
      "117 block4c_drop False\n",
      "118 block4c_add False\n",
      "119 block5a_expand_conv False\n",
      "120 block5a_expand_bn False\n",
      "121 block5a_expand_activation False\n",
      "122 block5a_dwconv False\n",
      "123 block5a_bn False\n",
      "124 block5a_activation False\n",
      "125 block5a_se_squeeze False\n",
      "126 block5a_se_reshape False\n",
      "127 block5a_se_reduce False\n",
      "128 block5a_se_expand False\n",
      "129 block5a_se_excite False\n",
      "130 block5a_project_conv False\n",
      "131 block5a_project_bn False\n",
      "132 block5b_expand_conv False\n",
      "133 block5b_expand_bn False\n",
      "134 block5b_expand_activation False\n",
      "135 block5b_dwconv False\n",
      "136 block5b_bn False\n",
      "137 block5b_activation False\n",
      "138 block5b_se_squeeze False\n",
      "139 block5b_se_reshape False\n",
      "140 block5b_se_reduce False\n",
      "141 block5b_se_expand False\n",
      "142 block5b_se_excite False\n",
      "143 block5b_project_conv False\n",
      "144 block5b_project_bn False\n",
      "145 block5b_drop False\n",
      "146 block5b_add False\n",
      "147 block5c_expand_conv False\n",
      "148 block5c_expand_bn False\n",
      "149 block5c_expand_activation False\n",
      "150 block5c_dwconv False\n",
      "151 block5c_bn False\n",
      "152 block5c_activation False\n",
      "153 block5c_se_squeeze False\n",
      "154 block5c_se_reshape False\n",
      "155 block5c_se_reduce False\n",
      "156 block5c_se_expand False\n",
      "157 block5c_se_excite False\n",
      "158 block5c_project_conv False\n",
      "159 block5c_project_bn False\n",
      "160 block5c_drop False\n",
      "161 block5c_add False\n",
      "162 block6a_expand_conv False\n",
      "163 block6a_expand_bn False\n",
      "164 block6a_expand_activation False\n",
      "165 block6a_dwconv_pad False\n",
      "166 block6a_dwconv False\n",
      "167 block6a_bn False\n",
      "168 block6a_activation False\n",
      "169 block6a_se_squeeze False\n",
      "170 block6a_se_reshape False\n",
      "171 block6a_se_reduce False\n",
      "172 block6a_se_expand False\n",
      "173 block6a_se_excite False\n",
      "174 block6a_project_conv False\n",
      "175 block6a_project_bn False\n",
      "176 block6b_expand_conv False\n",
      "177 block6b_expand_bn False\n",
      "178 block6b_expand_activation False\n",
      "179 block6b_dwconv False\n",
      "180 block6b_bn False\n",
      "181 block6b_activation False\n",
      "182 block6b_se_squeeze False\n",
      "183 block6b_se_reshape False\n",
      "184 block6b_se_reduce False\n",
      "185 block6b_se_expand False\n",
      "186 block6b_se_excite False\n",
      "187 block6b_project_conv False\n",
      "188 block6b_project_bn False\n",
      "189 block6b_drop False\n",
      "190 block6b_add False\n",
      "191 block6c_expand_conv False\n",
      "192 block6c_expand_bn False\n",
      "193 block6c_expand_activation False\n",
      "194 block6c_dwconv False\n",
      "195 block6c_bn False\n",
      "196 block6c_activation False\n",
      "197 block6c_se_squeeze False\n",
      "198 block6c_se_reshape False\n",
      "199 block6c_se_reduce False\n",
      "200 block6c_se_expand False\n",
      "201 block6c_se_excite False\n",
      "202 block6c_project_conv False\n",
      "203 block6c_project_bn False\n",
      "204 block6c_drop False\n",
      "205 block6c_add False\n",
      "206 block6d_expand_conv False\n",
      "207 block6d_expand_bn False\n",
      "208 block6d_expand_activation False\n",
      "209 block6d_dwconv False\n",
      "210 block6d_bn False\n",
      "211 block6d_activation False\n",
      "212 block6d_se_squeeze False\n",
      "213 block6d_se_reshape False\n",
      "214 block6d_se_reduce False\n",
      "215 block6d_se_expand False\n",
      "216 block6d_se_excite False\n",
      "217 block6d_project_conv False\n",
      "218 block6d_project_bn False\n",
      "219 block6d_drop False\n",
      "220 block6d_add False\n",
      "221 block7a_expand_conv False\n",
      "222 block7a_expand_bn False\n",
      "223 block7a_expand_activation False\n",
      "224 block7a_dwconv False\n",
      "225 block7a_bn False\n",
      "226 block7a_activation False\n",
      "227 block7a_se_squeeze True\n",
      "228 block7a_se_reshape True\n",
      "229 block7a_se_reduce True\n",
      "230 block7a_se_expand True\n",
      "231 block7a_se_excite True\n",
      "232 block7a_project_conv True\n",
      "233 block7a_project_bn True\n",
      "234 top_conv True\n",
      "235 top_bn True\n",
      "236 top_activation True\n"
     ]
    }
   ],
   "source": [
    "# Check the same for EfficientNetB0\n",
    "\n",
    "for layer_number, layer in enumerate(model_2.layers[2].layers):\n",
    "    print(layer_number, layer.name, layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model_2.compile(loss=\"categorical_crossentropy\",\n",
    "               optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),\n",
    "               metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving TensorBoard log files to: transfer_learning/full_10_classes_fine_tune_last_10/20220617-221001\n",
      "Epoch 5/10\n",
      "235/235 [==============================] - 26s 109ms/step - loss: 0.3904 - accuracy: 0.8715 - val_loss: 0.3279 - val_accuracy: 0.9079\n",
      "Epoch 6/10\n",
      "235/235 [==============================] - 25s 105ms/step - loss: 0.3728 - accuracy: 0.8744 - val_loss: 0.3531 - val_accuracy: 0.8964\n",
      "Epoch 7/10\n",
      "235/235 [==============================] - 25s 108ms/step - loss: 0.3534 - accuracy: 0.8849 - val_loss: 0.3278 - val_accuracy: 0.8964\n",
      "Epoch 8/10\n",
      "235/235 [==============================] - 26s 111ms/step - loss: 0.3315 - accuracy: 0.8972 - val_loss: 0.3127 - val_accuracy: 0.9030\n",
      "Epoch 9/10\n",
      "235/235 [==============================] - 25s 105ms/step - loss: 0.3176 - accuracy: 0.8971 - val_loss: 0.3237 - val_accuracy: 0.8980\n",
      "Epoch 10/10\n",
      "235/235 [==============================] - 24s 104ms/step - loss: 0.2944 - accuracy: 0.9061 - val_loss: 0.3335 - val_accuracy: 0.8947\n"
     ]
    }
   ],
   "source": [
    "# Continue to train and fit the model\n",
    "fine_tune_epochs = initial_epochs + 5\n",
    "\n",
    "history_fine_10_classes_full = model_2.fit(train_data_10_classes_full,\n",
    "                                         epochs=fine_tune_epochs,\n",
    "                                         validation_data=test_data,\n",
    "                                         validation_steps=int(0.25*len(test_data)),\n",
    "                                         initial_epoch=history_model_2.epoch[-1],\n",
    "                                         callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\",\n",
    "                                                                               experiment_name=\"full_10_classes_fine_tune_last_10\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "model_3 end: loss: 0.3721 - accuracy: 0.8867 - val_loss: 0.4655 - val_accuracy: 0.8322"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 5s 55ms/step - loss: 0.3216 - accuracy: 0.8972\n"
     ]
    }
   ],
   "source": [
    "# Evaluate on all the data\n",
    "result_fine_tune_full_data = model_2.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare fine tuning with all data vs 10% data\n",
    "compare_histories(original_history=history_fine_10_percent_data_aug,\n",
    "                 new_history=history_fine_10_classes_full,\n",
    "                 initial_epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# View our expermients data on TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "# View tensorboard logs of transfer learning modelling experments\n",
    "# Upload TensorBoard logs\n",
    "!tensorboard dev upload --logdir ./transfer_learning --name \"Transfer learning experiments\" --description \"Experments\" --one_shot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyMTPExneOQ42uG+gzzahI4I",
   "name": "transfer_learning_part_2_fine_tuning.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "tensorflow",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}