{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: Romell D.Z.(@romellfudi) Software Engineer, Business Intelligence Analist and Data Scientist\n",
      "\n",
      "Last updated: 2023-09-15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "from IPython.display import clear_output \n",
    "clear_output()\n",
    "%watermark -a \"Romell D.Z.(@romellfudi) Software Engineer, Business Intelligence Analist and Data Scientist\" -iv  -u -d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QDThxrNK079q"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (18,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset from KAGGLE DATA BASE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": "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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "M6nn2GVv079u",
    "outputId": "13543a94-a712-4510-cbda-bc46a862035d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-c101eaf1-3701-4e6b-8606-e57b54d1a165\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-c101eaf1-3701-4e6b-8606-e57b54d1a165\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving kaggle.json to kaggle.json\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import zipfile\n",
    "import functools\n",
    "\n",
    "# Upload the API token.\n",
    "def get_kaggle_credentials():\n",
    "    token_dir = os.path.join(os.path.expanduser(\"~\"),\".kaggle\")\n",
    "    token_file = os.path.join(token_dir, \"kaggle.json\")\n",
    "    if not os.path.isdir(token_dir):\n",
    "        os.mkdir(token_dir)\n",
    "    try:\n",
    "        with open(token_file,'r') as f:\n",
    "            pass\n",
    "    except IOError as no_file:\n",
    "        try:\n",
    "            from google.colab import files\n",
    "        except ImportError:\n",
    "            raise no_file\n",
    "        uploaded = files.upload()\n",
    "\n",
    "        if \"kaggle.json\" not in uploaded:\n",
    "            raise ValueError(\"You need an API key! see: \"\n",
    "                           \"https://github.com/Kaggle/kaggle-api#api-credentials\")\n",
    "        with open(token_file, \"wb\") as f:\n",
    "            f.write(uploaded[\"kaggle.json\"])\n",
    "        os.chmod(token_file, 600)\n",
    "\n",
    "get_kaggle_credentials()\n",
    "\n",
    "import kaggle\n",
    "\n",
    "# Download data from Kaggle and unzip the files of interest. \n",
    "def load_data_from_zip(competition, file):\n",
    "    with zipfile.ZipFile(os.path.join(competition, file), \"r\") as zip_ref:\n",
    "        unzipped_file = zip_ref.namelist()[0]\n",
    "        zip_ref.extractall(competition)\n",
    "\n",
    "def load_data_from_7z(competition, file):\n",
    "    os.system( '7z x %s ' % os.path.join(competition, file) )\n",
    "\n",
    "def get_data(competition):\n",
    "    kaggle.api.competition_download_files(competition, competition)\n",
    "    load_data_from_zip(competition, 'train.zip')\n",
    "    load_data_from_zip(competition, 'train.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S6BN6XwH079x"
   },
   "outputs": [],
   "source": [
    "competition = 'dogs-vs-cats'\n",
    "get_data(competition)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LOAD ALL PICS DOG & CATS FROM ANIMALS TRAIN FOLDER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QJEhTIZN0793"
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "import os\n",
    "import shutil\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "YFfGgiGM0795",
    "outputId": "f6b7eaac-02a2-4f7e-d1f5-dad702c51c7e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25000, 25000)"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = glob.glob('%s/train/*'%competition)\n",
    "\n",
    "cat_files = [fn for fn in files if 'cat' in fn]\n",
    "dog_files = [fn for fn in files if 'dog' in fn]\n",
    "len(cat_files), len(dog_files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## select 1500 pics for train sample, 500 validation sample, 500 for test sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "NCq7b688079_",
    "outputId": "b2f270ed-19d4-4a8d-a11a-b63c990ed61a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cat datasets: (1500,) (500,) (500,)\n",
      "Dog datasets: (1500,) (500,) (500,)\n"
     ]
    }
   ],
   "source": [
    "cat_train = np.random.choice(cat_files, size=1500, replace=False)\n",
    "dog_train = np.random.choice(dog_files, size=1500, replace=False)\n",
    "cat_files = list(set(cat_files) - set(cat_train))\n",
    "dog_files = list(set(dog_files) - set(dog_train))\n",
    "\n",
    "cat_val = np.random.choice(cat_files, size=500, replace=False)\n",
    "dog_val = np.random.choice(dog_files, size=500, replace=False)\n",
    "cat_files = list(set(cat_files) - set(cat_val))\n",
    "dog_files = list(set(dog_files) - set(dog_val))\n",
    "\n",
    "cat_test = np.random.choice(cat_files, size=500, replace=False)\n",
    "dog_test = np.random.choice(dog_files, size=500, replace=False)\n",
    "\n",
    "print('Cat datasets:', cat_train.shape, cat_val.shape, cat_test.shape)\n",
    "print('Dog datasets:', dog_train.shape, dog_val.shape, dog_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## puts this samples into news folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "99wOn0-Q07-E"
   },
   "outputs": [],
   "source": [
    "train_dir = 'training_data'\n",
    "val_dir = 'validation_data'\n",
    "test_dir = 'test_data'\n",
    "\n",
    "train_files = np.concatenate([cat_train, dog_train])\n",
    "validate_files = np.concatenate([cat_val, dog_val])\n",
    "test_files = np.concatenate([cat_test, dog_test])\n",
    "\n",
    "os.mkdir(train_dir) if not os.path.isdir(train_dir) else None\n",
    "os.mkdir(val_dir) if not os.path.isdir(val_dir) else None\n",
    "os.mkdir(test_dir) if not os.path.isdir(test_dir) else None\n",
    "\n",
    "for fn in train_files:\n",
    "    shutil.copy(fn, train_dir)\n",
    "\n",
    "for fn in validate_files:\n",
    "    shutil.copy(fn, val_dir)\n",
    "    \n",
    "for fn in test_files:\n",
    "    shutil.copy(fn, test_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "pPont9gw07-G",
    "outputId": "4026d80b-f7f7-4634-c7a0-c20131d44a77"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array, array_to_img\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9OFTOwmN07-N",
    "outputId": "241a9532-8c38-4ead-9f88-f6a1e8770c57"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/'"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.sep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load each folder on RAM MEMORY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "d2lyi46A07-R",
    "outputId": "44cbf106-7011-4a1f-ee1a-ae5d2ea7b511"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset shape: (2905, 150, 150, 3) \tValidation dataset shape: (989, 150, 150, 3)\n"
     ]
    }
   ],
   "source": [
    "IMG_DIM = (150, 150)\n",
    "\n",
    "train_files = glob.glob('training_data/*')\n",
    "train_imgs = [img_to_array(load_img(img, target_size=IMG_DIM)) for img in train_files]\n",
    "train_imgs = np.array(train_imgs)\n",
    "train_labels = [fn.split(os.sep)[1].split('.')[0].strip() for fn in train_files]\n",
    "\n",
    "validation_files = glob.glob('validation_data/*')\n",
    "validation_imgs = [img_to_array(load_img(img, target_size=IMG_DIM)) for img in validation_files]\n",
    "validation_imgs = np.array(validation_imgs)\n",
    "validation_labels = [fn.split(os.sep)[1].split('.')[0].strip() for fn in validation_files]\n",
    "\n",
    "print('Train dataset shape:', train_imgs.shape, \n",
    "      '\\tValidation dataset shape:', validation_imgs.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Allways need scale(normalization): generating a better performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 184
    },
    "colab_type": "code",
    "id": "k2yb5iNr07-V",
    "outputId": "d3e11d68-6fea-410a-bb54-d8a7dc240078"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 150, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=150x150 at 0x7F0732AD8B70>"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_imgs_scaled = train_imgs.astype('float32')\n",
    "validation_imgs_scaled  = validation_imgs.astype('float32')\n",
    "train_imgs_scaled /= 255\n",
    "validation_imgs_scaled /= 255\n",
    "\n",
    "print(train_imgs[0].shape)\n",
    "array_to_img(train_imgs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding LABEL prectitions using One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "J5wGTSXo07-Z",
    "outputId": "f7ea4227-edb8-4ada-c649-f6fdb49a00e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dog', 'cat', 'cat', 'dog', 'dog', 'dog', 'cat', 'cat', 'dog', 'cat'] [1 0 0 1 1 1 0 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "batch_size = 30\n",
    "num_classes = 2\n",
    "epochs = 30\n",
    "input_shape = (150, 150, 3)\n",
    "\n",
    "# encode text category labels\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "le.fit(train_labels)\n",
    "train_labels_enc = le.transform(train_labels)\n",
    "validation_labels_enc = le.transform(validation_labels)\n",
    "\n",
    "print(train_labels[1495:1505], train_labels_enc[1495:1505])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Model 1 using Convolution Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "colab_type": "code",
    "id": "uHLFCXBA07-d",
    "outputId": "143d33f1-4a5a-406a-88dc-22c0e480e4ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 148, 148, 16)      448       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 74, 74, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 72, 72, 64)        9280      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 36992)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               18940416  \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 19,024,513\n",
      "Trainable params: 19,024,513\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "from keras.models import Sequential\n",
    "from keras import optimizers\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(16, kernel_size=(3, 3), activation='relu', \n",
    "                 input_shape=input_shape))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1125
    },
    "colab_type": "code",
    "id": "QFl3dSP607-h",
    "outputId": "97c04e6d-972b-45f3-a2ad-1628f8493614"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 2916 samples, validate on 993 samples\n",
      "Epoch 1/30\n",
      "2916/2916 [==============================] - 11s 4ms/step - loss: 0.8053 - acc: 0.5628 - val_loss: 1.0819 - val_acc: 0.5065\n",
      "Epoch 2/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.6463 - acc: 0.6488 - val_loss: 0.6257 - val_acc: 0.6526\n",
      "Epoch 3/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.5672 - acc: 0.7109 - val_loss: 0.7008 - val_acc: 0.5720\n",
      "Epoch 4/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.5064 - acc: 0.7507 - val_loss: 0.5800 - val_acc: 0.6858\n",
      "Epoch 5/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.4324 - acc: 0.7946 - val_loss: 0.5795 - val_acc: 0.6818\n",
      "Epoch 6/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.3468 - acc: 0.8474 - val_loss: 0.6050 - val_acc: 0.7059\n",
      "Epoch 7/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.2647 - acc: 0.8858 - val_loss: 1.4185 - val_acc: 0.6203\n",
      "Epoch 8/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.1952 - acc: 0.9194 - val_loss: 0.7608 - val_acc: 0.7432\n",
      "Epoch 9/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.1479 - acc: 0.9462 - val_loss: 1.0801 - val_acc: 0.6002\n",
      "Epoch 10/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0964 - acc: 0.9684 - val_loss: 1.2078 - val_acc: 0.7271\n",
      "Epoch 11/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0785 - acc: 0.9736 - val_loss: 1.3216 - val_acc: 0.7160\n",
      "Epoch 12/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0802 - acc: 0.9774 - val_loss: 1.3947 - val_acc: 0.7382\n",
      "Epoch 13/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0657 - acc: 0.9825 - val_loss: 1.4594 - val_acc: 0.7281\n",
      "Epoch 14/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0732 - acc: 0.9870 - val_loss: 1.6747 - val_acc: 0.7170\n",
      "Epoch 15/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0465 - acc: 0.9887 - val_loss: 1.5646 - val_acc: 0.6898\n",
      "Epoch 16/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.1323 - acc: 0.9808 - val_loss: 1.6134 - val_acc: 0.7231\n",
      "Epoch 17/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0601 - acc: 0.9877 - val_loss: 1.8608 - val_acc: 0.7271\n",
      "Epoch 18/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0510 - acc: 0.9873 - val_loss: 1.9094 - val_acc: 0.7120\n",
      "Epoch 19/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0446 - acc: 0.9911 - val_loss: 2.4751 - val_acc: 0.7019\n",
      "Epoch 20/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0528 - acc: 0.9894 - val_loss: 2.1112 - val_acc: 0.7170\n",
      "Epoch 21/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0506 - acc: 0.9870 - val_loss: 1.9705 - val_acc: 0.7452\n",
      "Epoch 22/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0395 - acc: 0.9928 - val_loss: 2.3017 - val_acc: 0.7059\n",
      "Epoch 23/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0494 - acc: 0.9907 - val_loss: 2.2039 - val_acc: 0.7382\n",
      "Epoch 24/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0156 - acc: 0.9966 - val_loss: 2.1927 - val_acc: 0.7291\n",
      "Epoch 25/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0839 - acc: 0.9877 - val_loss: 2.2331 - val_acc: 0.7231\n",
      "Epoch 26/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0048 - acc: 0.9990 - val_loss: 2.4191 - val_acc: 0.7341\n",
      "Epoch 27/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0352 - acc: 0.9911 - val_loss: 2.2420 - val_acc: 0.7221\n",
      "Epoch 28/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0512 - acc: 0.9904 - val_loss: 2.2553 - val_acc: 0.7301\n",
      "Epoch 29/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0347 - acc: 0.9928 - val_loss: 2.4888 - val_acc: 0.7160\n",
      "Epoch 30/30\n",
      "2916/2916 [==============================] - 7s 2ms/step - loss: 0.0089 - acc: 0.9976 - val_loss: 2.5108 - val_acc: 0.7402\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x=train_imgs_scaled, y=train_labels_enc,\n",
    "                    validation_data=(validation_imgs_scaled, validation_labels_enc),\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1 Basic Convolution Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 308
    },
    "colab_type": "code",
    "id": "21zDtDVw07-l",
    "outputId": "29bfd25f-a4ff-4f43-e1dd-fcdf3b57721b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MYDIu6PxDa+0nIW3+iAvE/tjLcF6OKy1wOPB73ERrBwHfNsb4gTtxAbKXgL8aYy4EPsaV8RiJy17OAm6y1i7rZrx/AA72zrXMGDPf234ULgD3LvCnSF+8tfZaY0wZLpj5R+AXxpg3gSIgBzjU67cWOM9ae1+kfYe4C/gesH8vjg2Mc4sx5le4oOKI3vbTy3O/Z4z5Oe66vmSMeQP3ng/FXfehwCe0BoS393wrvPrYtwNPGmPe9s43EpiNq1/8LPDXaJyvlx7ABdrHAl8YY17F1Z0+EtgMHIHLxs4GXjbGPGmtvak3J7LWLve+/PgX8Kwx5iXcZJ7TcVn0VcC53hczWGtXe79j9wH3GWMuwX2hk4n7PI/GBap/0pvxiIiIiAxmyngWERERiZ1v4YLCgX8/wk2c1oALdO1hrb0t9ABr7RO4Ce+W4DKVzwBSgLOstdcATwIPeX2cBGRaaxcC+wL/wCUanIwrRzALFyw+zVrbIRu6PWttHTAHV5e5EDgVNzHhVuCXwBHW2tqeXAAvIDgFuA4X4Jzlje14YANwAzC5l0FnvKzcS+lZiY5w/oML2MectfbPuKDl08BuuMzr43D1hK8CDrLWVkTxfHfhvrz4H+69OR/3hcPnuM/eKdbapmidrxfjq8QF3Z/DTfr3bVxZmjuAWV4N8Utwn5/9gF2383z/xr3+Z3HlXi4ECnDB5b29ciyh7R/22j2AC45fCJyCK/tyA7CXtXb99oxJREREZDDy+f399cSciIiIiIiIiIiIiOyIlPEsIiIiIiIiIiIiIlGlwLOIiIiIiIiIiIiIRJUCzyIiIiIiIiIiIiISVQo8i4iIiIiIiIiIiEhUKfAsIiIiIiIiIiIiIlGlwLOIiIiIiIiIiIiIRJUCzyIiIiIiIiIiIiISVQo8i4iIiIiIiIiIiEhUKfAsIiIiIiIiIiIiIlGlwLOIiIiIiIiIiIiIRJUCzyIiIiIiIiIiIiISVQo8i4iIiIiIiIiIiEhUKfAsIiIiIiIiIiIiIlGlwLOIiIiIiIiIiIiIRJUCzyIiIiIiIiIiIiISVQo8i4iIiIiIiIiIiEhUKfAsIiIiIiIiIiIiIlGlwLOIiIiIiIiIiIiIRJUCzyIiIiIiIiIiIiISVQo8i4iIiIiIiIiIiEhUKfAsIiIiIiIiIiIiIlGlwLOIiIiIiIiIiIiIRJUCzyIiIiIiIiIiIiISVQo8i4iIiIiIiIiIiEhUKfAsIiIiIiIiIiIiIlGlwLOIiIiIiIiIiIiIRJUCzyIiIiIiIiIiIiISVQo8i4iIiIiIiIiIiEhUJfT3AEREBhNjzGXArd7q7621v+rP8YiIiIiISP8yxvi9Hw+w1n7Qr4MRERlAlPEsItIzF4X8fJ4xJr7fRiIiIiIiIiIiMkAp8CwiEiFjzL7AHsByYBEwCji+XwclIiIiIiIiIjIAKfAsIhK5QLbzY94/gO/101hERERERERERAYsn9/v776ViMhOzhiTCWwEMoBdgUpgLeAHxlprizo57gDgSuBgIBfYAswHrrfWFva2vTHmOuBa4F5r7flh+umw3xhTAKwGqoE84A7gOOAja+3RIcfOBn4IHAAMB5qBNcAzuLrW28KcLx64GDgHmAokAyuBfwO3WWsbjTEGWAa0AOOstRs6uWZfA5OBc6y1D4ZrIyIiIiIyUPS2xrMxJhf4CXACUADE4/7meA34i7V2eZhjTgV+AOwFDAHKcPf4jwDzrLX1IW3jgPOB84BpQCbu74vlwH3AXdZaBYVEpM8o41lEJDJn4YLOH1prl1pr1wMv424OvxvuAGPMpcAC4ETgfeAuoBC4APjKGLPH9rTfTjfigtuP4m5sA2M4H3gTOA0XOL4LeALIAX4OvGeMyWg37hTgFdyki/nA/3AZ4UOBW4CXjDEJ1loLvIP7f8+54QZljNkHF3Su8M4rIiIiIrLDMcZ8A/gU+AXu74zncffm1bgnLT81xhze7pif4e61DwbeA24HXgUmAH8Dnm83B82twJ24coGveu3fBfbEJaHc3UcvT0QEgIT+HoCIyCARKLNxZ8i2O4FjgAuMMb8PzRYwxkwFbgbqgYOstZ+F7LseuAZ4GJc93eP22ykJOAqYbq0tDzlPHPA7wAf8xFr7t5B9acAn3vl/APw1pL+rgcOA14HjrbV13jGZuBviw3CZHH/EXbNZuMyL34cZ25ne8lFrbe32vlARERERkQHqXmAM8F/g29bapsAOY8xvgV8B9xtjJlpr640xicCvcU8P7mOtXRrSPhOXFHMEbg6aZ4wx+bgnEsuBqdbaTSHtR+AC0OcZY/5mrV3cx69VRHZSyngWEemGMWZPYAZQg3uELeAZ3KNqk3DB1VCX4r7cuz80iOz5A7AEKDPGjO9l++2RiAvslrfbngz8DLiKtgF2rLU1wAPe6sGB7caYBOASb/X/AkFn75hK4DfAYtyjg+AyoSuBKcaYg0LPYYzxAWd4q8q+EBEREZEdkveU3wFAI3BZaNDZcx1QgpvMfK63LRdXKqMsNOgMwfvubwF745JBwN1/+4BVoUFnr/1mXMm96biyGyIifUIZzyIi3fu+t3zMu6kDwFrbYIy5H/gxbpLB10OOCQSiF7TvzMvknd5uc0/bb683OjnPA2HaBgRqMmeHbNsTV1uuHlgYps//4rI4AuvVxphHcBnk38VlWgTsD4xzzez7kb0MEREREZFBZ7a3XGit3dJ+pzc/ylu48ncH4MprlODqOQ8zxtyCmwOmNOSYQlyZvoCVQBOwlzHmV8At1tqqkPYKOItIn1PGs4hIF7wSE9/2Vu8M0+QOb3mKMWZIyPYCbxl2Ar0wetp+exWH22iMSTbGXGGMedsYs94YU2+M8XsTptzlNfOFHFLgLTdZa1siPHfgOp7hXd+AQJmNeyLsR0RERERkMJrgLVd30WaNtxwH4GVFn4fLkv4RUGSMedMYc7WXQd2GtbYYuBx37/5bYLMx5kVjzFVemT8RkT6nwLOISNe+BWR5P//Gu7kL/gPm4TIJUoDvhByX4i0bIzxPT9tvr6r2G4wxybiJBm/C1WFej6srfbP375Uw/fR43NbaD4EvcY8KnuadOw44HWjGzbAtIiIiIrKjCiRfdDWnSaCEXWpgg7X2WdxEgffgJuM+BLgB+NgY84Ux5ojQDqy1/8I9Vfg47j57DvBn3MTl7xlj9tr+lyIi0jmV2hAR6dpFIT8f0k3bC4FbvJ+rcQHrIZ03b6On7buT2ItjLgYOArYBh1hrPw/daYy5ADcpYahqb9nTcd+Jm3n7HFyg+WAgH3jRWruxh32JiIiIiAwmgXvotC7aBPa1SRjx6jt/10vc2Ac4FveE5m7Ai8aYA6y1H4e0/xA43Zuc8MCQ9gcAbxtjplpr10fhNYmIdKCMZxGRThhjdsfdkDUBI621vnD/cDWPa4Dpxph9vcNXecvREZ6up+0DZS3iO9lfEGE/oQKB9fvbB509E8NsC4x7iDEmNcz+ztwPNACHGWOG01rORJMKioiIiMiOLnAPHe7+mnb7wpbjsNa2WGs/stbeAOyKm6slAZdMEq59o7X2LWvtLwCDe9IxAzi/58MXEYmMAs8iIp0LZDvP92Z+DstaWwE86a1+z1sGJu87un17Y0ycMWaDMabJGHNwL9sHJjkcGaZ9Mq0TlvRE4CmYsjB9pgHnequhNZ6/wE10EgccGea4Y71xfx263ZtE5WnvnOfgSm6UedtERERERHZkb3rLmcaYEe13GmNSgEND2xpjJhpjLjDG7Na+vbW2GXjUWx3ttd/NGHOJMaZDYou1tgZ4KrS9iEhfUOBZRCQM72YvULM5kizce7zlmcaYdOA2XN3jk40xx7ZrewUwCjdhyHvetp62X+ItDzXGTA4Zdzyublt2BGNub6m3PMbrJ9DnEOC/tE5wMiawz7vJneet/j50gkUvWH0NLiv7gTDnC0wyeC2uVMfD1tr6XoxbRERERGTQ8J4ufB2XhPGPdvfePuBPQA5uXpRXvV1H4e6f/22MyQztzzvmDG/1M295Du4+/WavzEZo+0TglHbtRUSizuf3+/t7DCIiA44x5hxcOYgtwChrbZeT53k11tYAY4ELrbV3GWN+APwLVxbjJWAdMB1XvqMKmGOtfS+kj4jbezennwLTcDWZ5wPluEkBh+EmCPwjcK+19nzvmAJaH9XLt9ZuavcaxuKCz+m4m9x3gaG4LOyPge/iHguMB54DHrPW3ucF6Z8HDgeKvbGAuzke5fVzlLW2zeQp7a4ZwL7W2o+6us4iIiIiIgONMSYQWHkU2NRF07estU96x4wH3gLGAyuB93FPFs7AlcIoBo601i7x2ifj7rMPw933v+6dKx03gaABVgD7W2tLjTHDcE9VTgM24zKnS3EJKrNx9+Af4uZ2UfKHiPQJZTyLiIQXKLPxYHdBZ3A11nCT5IFXbsNa+2/cpHnP4m4gL8TVXr4P2Ds06NzT9l6m8dG44Hg9rlTFicAnuBvPHk/QZ61d5/X5hnfes4EpwG+BY621hcDPccH4w3E3yVhr63AzZF8JFAKnAmcCW4FfAke0Dzp7x7XQmk3+pYLOIiIiIjLIfQv3tGJn/4KTlVtr1+ImB/wD7n7+m7j7aD/wV2CPQNDZa18PHAP8GPgKl3Dyfe+4GuB6YIa1ttRrX+q1uRrYgCuL931grrf+E+BQBZ1FpC8p41lERPqNMeZPwE+BH1pr/9nf4xERERERERGR6FDgWURE+oX3+N9qoBkYY62t7uchiYiIiIiIiEiUqNSGiIjEnDEmFXgQyAT+qqCziIiIiIiIyI4lob8HICIiOw9jzLm4WnNH4epIv4ubBFFEREREREREdiDKeBYRkVg6ADdxYwbwT9ykhd1O3igiIiIiIiIig4tqPIuIiIiIiIiIiIhIVCnjWURERERERERERESiSoFnEREREREREREREYkqBZ5FREREREREREREJKoUeBYRERERERERERGRqFLgWURERERERERERESiSoFnEREREREREREREYmqhP4ewI6opKTS39m+vLzMQJuYjWdnpusdO7rWsaNrHTu61rGl6x07sbzWeXmZvj4/iUgf0D39wKHrHTu61rGjax07utaxpesdO4Phnl4ZzyIiIiIiIiIiIiISVQo8i4iIiIiIiIiIiEhUKfAsIiIiIiIiIiIiIlGlwLOIiIiIiIiIiIiIRJUCzyIiIiIiIiIiIiISVQo8i4iIiIiIiIiIiEhUKfAsIiIiIiIiIiIiIlGlwLOIiIiIiIiIiIiIRJUCzyIiIiIiIiIiIiISVQo8i4iIiIiIiIiIiEhUKfAsIiIiIiIiIiIiIlGV0N8D2B7GmCTgRuAq4G1r7aE9OPZA4GpgfyAVWA7cDtxqrfVHf7QiIiIiIiIiIiIiO4dBG3g2xhjgIWAK4OvhsYcD84F1wHXAVuAk4BZgEnBlNMcqIiIiIiIiIiIisjMZlIFnY8wQ4BPga2AGsKyHXcwD6oCDrbVF3rb7jTFPAZcbY+621n4etQGLiIiIiEgbxpg84BrgFGAEUA68A/zGWvtJBMd395TiEGtt+XYPVERERER6ZVAGnoEk4D7gx9baOpf8HBljzH6AAe4ICToH3IrLfD4HUOBZRERERKQPGGOGA4uAYcC/cPfeU4DLgTnGmIOstZ9G0NVXwLWd7KuOxlhFREREpHcGZeDZWrsZuKSXh+/rLd8Ps+9Db7lfL/sWEREREZHu3QiMAb5prX0isNEY8xHwFPBL4IwI+imx1j7eN0MUERERke0xKAPP26nAW65vv8NaW2mMKQcmbs8J8vIyo9JGokfXO3Z0rWNH1zp2dK1jq7fX2+/386kt4cUP1pAQH8f+u49k5q4jSU3eGW93IqPPtvSjjcDDwJPttr8I+IHpMR+RiIiIyADW1NLEpupiNlQVsaGqiLJlZYxMz+WoUUeSFJ/Y38MLa2f8SyzwF1ZNJ/urQ9qIiIjIANfS4ueDL4p47LXlrFi/Lbh9wWcbSEqIY5+pI5i1x6idPghdXdvIyg3lrFhXzpqiCoZmpXDWnF1ITozv76HJTshae10nuzJxE4dX9KQ/Y4wPSLPWqrzGTuSFF57ld7+7nl/96lqOO+6E/h6OiIhI1FQ0VLKhsogEa1CXAAAgAElEQVQN1UWsryxiY3URm6qLafY3d2gb35zEMQVH9MMou7fz/vXVh0pKKjvdF8gs6qqNRI+ud+zoWseOrnXs6FqH19DYzMqNFdjCMpavK2dbdQMFIzM5aFo+u4wfQpzP16t+e3q9m5pbWLh0M8+/v5ai0vDfJzc0tfD+kiLeX1JEYkIc0yYOY+Yuw9lj8jBSknbc26C6hiYKN1expqiCNZsqWb2pks1bO16jurpGvnnIpD4di7KqpYcu9pYPRtg+1xhzH3AqkG6MqcQr1WGt3dAXA5S27rzz39x99+0Rtd1zz7259db/RO3ce+89g9/85g9Mnbpb1PoEmDVrBuPGjeehh/4X1X5FRETaa2ppYnNNCesrN7KhuigYbK5sqIq4jzhfXB+OcPvsuH9xdS6QPZHeyf4MephhISIisiOrb2hmxYZt2HVl2MJyVhdV0NTsb9OmqLSG97/czLCsZA7cPZ+DpuczPCe1T8bT2NTMO4uLmP9hIVu21XXY7/NBfFwcTc0t7Y5r4ZPlJXyyvCQYhJ6xSx57TMod1JnQ9Y3NrNtcxepNFazdVMmaTZUUbanG3/2hHa6RSH8yxhwLXIObdPBfER62K/AJbnLwBOAE4FzgUGPM3tbaLb0dj8rnRea0005mzz13b7PtH//4BytWrODGG28kM7P1Gg0dOnS7rln7Y/PyMpk2bUqv++tKfHzcTv3+7syvPdZ0rWNH1zq2dL3Dq6irZMHahawuW8fa8vWsr9xEc0vHLObuxPniGJ05gr1GTeObu80hJSG5D0a7/QbvX1m9t8pbjmm/wxiTDWTjbl5FREQGJL/fj6+XWcWRqK1vcoHmwnJsYRlrNlXS3BJJGBNKK+p59r01PPveGqaMzWHWtHxm7JIXlezi2vom3vpsIy8tLGRbdUOH/fFxPg6aNpJj9xtPVnoSn6/YwkfLilmyausOE4RubGpmXXE1azZVsKaokjWbKtiwpRp/ZG9PUHZGEjOmjuDY/cf3zUBFesgYcy5wB7AGOMFa2/GXvKNjcZMLLgrZ9rgxZh3wf8BPcJMUSh+aPHkykydPbrPtwQddwvqhhx5KXl5efwxLRERkwGloauDq1/5CUVVxj47LSEpnfM5oxueMoSBnDOOyRzMmO3/A1nUONXD/suo773nLg4A72+072Fu+E7vhiIiIdK6puYU1myqxhWXYdeWs3FBBY1MLWemJZKYlkZ2eRGZaIllpSWSlJ5GVlkRmeut6RmoiCfFdP3pVU9fI8vXbWF5Yjl1XxtpNVbREGMnMy0khJyOZFeu3dciwXb6unOXrynnwleXMMHnMmp7PN8bm9LgUR1VtI68tWs+rH6+juq6pw/6khDhm7zmKY/Ydx9CslOD2/Xcbyf67jaS2vonPV27ho6UDNwjd4vdTWdNIeWU95VWBfw1s85Zl3raK6oYeB5nTUxIoyM+iYGQmBSPd0kzKxefzqYyMDAjGmKuBG4CPgeOttRH9NWatfbGTXfNwgecj2Y7As8rn9V5Dg/tvdWlpFZDSYX+gNvPVV9+AtUuZP/955s49icsuuwKApUu/5MEH7+Wzzz6hqqqK4cOHM336dM4993uMG1fQoZ/QGs+nnXYC8fHx3HnnA/zznzfx7rsLqKjYxujRYzjvvO9x9NHHRPQamptbIn5/X355Pk888RgrV66gubmJkSPzOfTQI/jOd75Lamrr0z8bNqzn/vvvZtGijykt3UJaWioTJ07mjDPOZtas2cF2W7eW8uCD9/Luu+9QUrKZpKRkxo0bz8knf5Njj50b0Zh6S5/t2NG17t7Gqk3UNNUyMXv8dpUS0LWOLV3vzr1e+HaXQWcfPkak5TE6I58xGaMYlTGSMZmjyE7Kapt41AzbttaRl+cCz7G41r3NYN/hA8/GmF2AemvtagBr7WfGmE+A040x11hr13vtfMCPgUbg3n4bsIiI9FiL30/ptjqKSmsoKq2mqLSamvpm0lMSSE9JJCM1kfSUBLf0/mWkJJAeQVA21hqbWlhdVBEMNK/YsI2Gxo7lELZW1LO1oj6iPtNTEkKC0klkeYHq6rom7Loy1m2uiqgsA8CIoWmYsTmYcTmYsTnBQG/ptjre+3IT7y4porists0x9Y3NvPvFJt79YhO52SnMmpbPgbuPJLebUhzlVfW8vHAdb3y2gfqGjo+fpSbHc8Q+Yzhyxliy0pI67Sc1OYH9dx3J/ru2BqE/XlbC4pWl3Qahzdgc0lISSIyPIzEhjoT4OBIS4kgMWSYmBPb5SEyId8uQ9okJ7jNWUR0IIDe4wHJl688V1Q0RZ5V3JTU5wQswZwaDzbnZKR0y5PsyY16kJ4wxNwFXAM8AZ1lrO5sAvCdKAD+QFYW+pA+99torVFZWcOWVVzFmzDgAvv7a8sMffp/s7BzOOed8hg7NpaxsM/fddx/vvPMO9977CCNGjOyy35YWPz/96RUMG5bL979/KRUV23joofu58cZrGD++APcnYnTcc88d3HHHbey22zQuuuhi0tLS+Pzzz7jvvrtYvPgzbrnlNuLi4qiqquKSSy6koaGBM844izFjxlFZWcH8+c/xy1/+hN/+9s/Mnn0ozc3NXHHFJWzYsJ7TTz+LiRMnUVdXx+uvv8pvf3sdVVVVnH76mVEbv8hAtbjkS/69xIVmjik4ghMmzunnEYlsn7qmel5a+0ZwPSEugQlZ47wAcz5jMvIZmT5iUGQx98SgDDwbY3bF1XMLlWeMOS1k/QXvxnUpYIHQu4tLgTeAt72b3XLgTOBw4Gpr7co+G7yIiPRaY1MLm8tqXIB5SzVFW91y09YaGpp6V6s2JSm+NTid6gWnU1xwekRuBlnpScT5W8jJSGZIRhKpyQlRDdo1NDazamMFy7yJ+lZudBnN0VRd10R1XVOnE/B1ZVRuejDQPGVsDjkZ4WuHDctO4YQDC5h7wHi+Xr+Nd5cUsXBZcYeA8ZZtdTz1zmqeemc1u4zLYdb0fPaZMpzkpPjWNuW1zP+wkAWLi8LWIM5MS+TomWM5bK8xpKX07Famp0HoL1Zv7VH/sZScFM/4EYEgcyYTRmaRNyS115M7isSal+l8BXA3cJG1NuICh8aYacCBwHxrbWG73d8AfED77TFVWdPAkwtWU7Sluj+H0a383HROnT2RjNTY/6H71VdLePTRp0hPzwhuW7VqJVOn7saFF/6AvfbaB3BZVrm5uVx77bXMn/8c55//vS77LSrawP77H8hPfvLz4LahQ4dx443XsmDBm1ELPJeUFHP33bczceIkbr31PyQmums4d+7JpKSk8uSTj/HGG69yxBFHs2jRR2zdWsqll17B2Wd/J9jH3Lkn8etf/4z169cBsHLl16xevYpTTz2dSy75UbDdiSeewg03XE1JyeaojF1koFuw4YPgz6+vW8Cc8YfvcAE52bm8tf5dqhpb7wmOLziKowsO68cRxcagDDwDZwDXttu2K/BYyPoEXI24Dqy1HxpjZuMe6bsBSMYFqC+w1t4d9dGKiHSjpcVPTX0T1bWNVNU2Ul3nLWub2q7XNVHf2ExiQjy7jM1mxi7DyR/W2VypsdPU3EJLi5+4OB/xcb7tDszW1DV5mcs1weXG0mpKymt7XGagO3UNzdQ1NFNa0XGSunCSEuLIyUgmJyOJnMxk7+dksjOSWrdnJHdaoqG+oZkVG1395OWFZawKM1FfZ4ZkJmPG5pCdkURFdSOVNQ1U1DRQUd1AZU1jVDJmx+RlBLOZp4zL6TKTOByfz8eUsS5IffaRU1i0vJh3FhexrLC8Q9tlheUsKyzngaTlzNxlOIfsM5b3lhTx5qL1YUt9DMlM5pj9xjF7j1EkJ8Z32N9TnQWhl6wqjXrwv7cyUhODn6nA5yx/WBoFI7MYOTSNuDgFmWVwMsYcBlwPPAl8z1rb5S9d+6cYgd2B23Cl89pHIQPlNZ6I3oh77skFq3nz0w39OYSI2HXuv8/nzjExP/eMGfu1CToDzJlzHHPmHBdcr6mppqLCz+jRowHYtKkoor7PPPPbbdanTt0NgNLSXs832cG7775Nc3Mzxx9/YjDoHHD88Sfy5JOP8d57CzjiiKOJj3f/3/rqqyU0NzcH15OTk/nzn28OHhcf7+4fli+31NXVkZLinizy+Xxce+2NURu7yEDm9/tZW7EuuN7Q3MBXpcvYc/i0fhyVSO/VNNbySuFbwfXMxAwOGXtQP44odgZl4Nlaex1wXYRtw/5FZq39GDgu3D4RkWjaVlXP2s1VrCuuZFtVA1V1bQPK1bWN1NQ1RVzqIGDpmq08uWA1Y/LSmbnL8JgGoVv8ftZuqmTJylIWrypl9caKNuP3QTAIHRfnI87n67AeH+fDF9gWWPdBWVU926oimVOqc6nJ8WSlJXnB/KaI6xVHoqGpheLyWorLa7tsl5wUH8ySzslIJi0lgbWbK1lTFPlEfcOyUoJBYDMuh7yc1E6D+n6/+/Kioro1EB0ISlfUNFJZ7QWpaxqpqG6gtr4JHzB2RAZm7JBgRnM0M96Sk+I5cPd8Dtw9ny3lta7cxpIitmxrG+Sva2hmweIiFiwOH0wYPiSV4/Yfz4G7j+yz0ijtg9CLV5ayyBZTtLWGpqYWmppbaGxqobHZH/x5e88XGlDOyUzyPi/JwS8wsjOSSEzY/gC7yAD1F2/5KnCqMWGDni+ElN5o/xTjY8AFwIXGmFzgBSAeOBVX2/lV4Pa+GbpES37+qA7b/H4/Tz75OM888ySFhWtpaGhbVqq5ufvE+Pj4eEaOzG+zLSnJfZHa1NRxroDeWrt2LQATJ07qsG/cODd5a2GhS7zfd9/92X336bz55uucfvqJzJo1m332mcnMmfuRltZ6/zZp0mQOOeQw3nrrDU47bS4HHTSbffbZl3333Z+cnJyojV1kICupLaW6qe2Tep+WLFHgWQat19ctoLap9e/HowsOIzm+Zwk+g9WgDDyLiAxELS1+NpfVULi5isLiStZtrqKwuIqK6u0LonZnfUk160tW93kQuqaukS/XlLF4xRaWrCqloqax07Z+oLnFH5UM3K5kZyQxalg6+cPSyA9Z5mQkBQO0fr+f2vpmL+Dv/gWC/20zzJtCMs1792VAqPqGZjZvrWHz1sjLW+TlpASDwGZsTrc1kEP5fD5XIiQlMaL33gVO/TELbObmpHLSrAmccFABX68r553FRXxki8PWrw4Yk5fO8QcUMHOX4THN7E1NTmC/XUew364jOm3j97vPtwtGt9DUYemn0QtQB54IyExLdFny6cltSouI7KT29pb/7KJNV08wNhljTgB+iAtAHwO0AMuBnwI3W2ujF2HshVMOnoAP2DjAS22Myk3nlNkT++XcoQHXgDvuuI17772TgoIJXHbZFYwZM5a8vGxWrFjBDTfcEFG/8fHxwYzivlRb6/4fn5LS8f/XycmuNFVdnQs0JCUl8fe//5Onnvof8+c/yxNPPMYTTzxGUlIyJ554MpdeekUwOH799b/nhRee5bnnnuaFF57l+eefIT4+niOOOJof//hnZGb2boInkcFiTUXHSklLtnxFQ3Ojym3IoFPVUM0b6xYE13OSszl41P79OKLYUuBZRKQX6hub2VBSTeHmSgqLq1i3uZJ1JVVdBtG2R2CSvPTURIZkpbC+pIriMAHN9kHoGbsMZ2Yvg9B+v58NJdUsXlXK4pWlrFi/LaqZw5Hy+WB4TmqbwHJ+bhr5Q9Mjqu/r8/lIS0lwbXsQyG1p8ZOakcK2qnpWryuj3MvELq+qbzMxXFlVfa+zXzubqC8WAhPexVqcz4cZNwQzbghnHzWFj20x7y7ZxPJ1raU4Jo3K4vgDC9hj0rABOwmez+cjId5HQnwckX+qRCSgs6cSe9LeWluHy5z+S8cj+l9mWhLf6YfyFYNZU1MTjz32CJmZWdx66+3BDN+8vEwaGvr2i/zeSE1NA6C2tuNTUIGAc2hwPTU1lbPOOoezzjqH4uLNfPDBezzxxGM8/vij1NXV8YtfXA1AQkICJ554CieeeArl5eUsXPg+Tz/9BC+/PJ/S0lJuvnleDF6dSP8JLbMRUN/cwNKtlj3ydu+HEYn03quFb1HX3Pr0zjEFh5O4E32BosCziEg3KmoaXPayF2Qu3FzJpq01vao1nJIUH5y8LiM1gfRUF0wOTm6X4k1ul+rWM1ITSUtOaJPtmZeXid/v56MlG/loWTEfLyvuULoAWoPQT/UgCF3X0MTSNWXBYHNZZX2nbQNys1OYPmkYQzKTaWnx0+J32c4tgX9eZmiHn8OsN7f4SU9NbJPBPGJIWr8ESePifGSlJ5GVnkRKF6d3GdVNlAWC0pVeYLpNkLqBytoGcrNTI5qob2eRmpzAwdNHcfD0URSX1VBYWsu4EZnkZSQO2ICziIj0nW3byqmpqWavvfbpUFbi448/7qdRda6gYAIAK1euYN9922avrV7typGPH18Q9tjhw0dw4omncMwxx3P66Sfy5puvBwPPoXJycjj66GM58sg5XHTReSxatJCqqioyMjLC9CqyY1gTJvAM8EnxYgWeZVDZVl/Jm+vfDa4PSxnCAfkz+3FEsafAs4hIiMamFgo3V7JyYwWrNm5j1caKsEHd7vhw2azjRmQwbkQm44ZnMHZ4BtlRCjT6fD4m5GcxIT+L0w+dxJpNlb0KQo8cmsbmsloWryxlycot2HXl3U50Fx/nw4zLYfrEYUybNIyRQ9N26iChy6hOJC0lkdG5/T/R42A1fEgau01xpS1KSir7eTQiItIfsrNziI+PZ/PmTfj9/uD9hbWWZ555BoD6+u6/FI+VWbNmc9NNf+b555/htNO+1WaCwWeecXNbHnroEQDcddd/mD//OW6//b42QfWEhASSkpLxexkNzzzzJHfffTt///s/g4FtgLi4OFJTU4mPjycurn+eWhKJhaaWJtZXhp+Y9YstS2lsbtypskVlcHt57es0trSWqDxuwlEkxO1codid69WKiITw+/2UbKtj1QYXYF65sYLCzZFP/BaQmBDHmLwMxo/IYKwXZB6TlxGzGq7bE4TOSE2kqrbzWs0BQzKTmTZxGNMnDWPq+CGkJut/HyIiIhJdCQkJzJ59GG+88So33HA1++9/IOvXr+Oppx7nL3/5C9///vdZtGghL7zwLLNmze7TsdTU1PDGG6+G3ZeRkcnMmfsxbFguF110CfPm3cLll/+AI488hsTERBYtWshrr73CIYccxoEHzgJgn3325f777+YHPzifuXNPZsSIkdTW1vDWW69TVLSBiy66BIC9957BvHk3c/nlF3PSSacyduw4GhoaWLjwAz777BNOOOFk0tLS+vS1i/SnDVVFNPlbJxGdlD2BldvcEwR1zfUs3bqc6Xm79dfwJEYKK9bz+NfPkJmUwZnmVDKTBt9THmV15byz4YPg+oi0PGaO2KsfR9Q/FDkQkZ1GTV0TqzdVsGpjhQs2F1VQ2cUEeeFkpCYy3stiHjsig3HDMxkxNJX4AZJ50tMgdGdB5zifj8mjs5g2aRjTJ+UyJi99p85qFhERkdi46qpfkJSUyMKFH/LeewswZiq33norM2bM4LvfvYiHHrqfefNuYfr0Pft0HFu2lHD11b8Iu2/y5Cncc89DAJx99rkMHz6C//73YW677R80N7cwduxYLr30cs444+zgMXvssSf/+Md/eOih+3j88YfZtm0bSUnJTJo0mV//+nqOOeZ4AMaMGcttt93N/fffzfPPP0NZ2Vbi4+MZP34Cl1/+E775zTP69HWL9Lf2ZTZOnHQMN3/6b1r8bk6VT4qXKPC8g6tqrGbe53dR2VgFQE1THT/a83vE+QbG39yRmr/mtTZfohw/4Sji43a+CcZ9/n6YKGpHV1JS2elFzcvLDLSJ2Xh2ZrresTPQrnVzSwtFW2pYuXEbKzdWsHpjBRu3VNOT/+KlpyQwcVQ2E0dlMSE/k7HDM8nJSOr3AGxvrrXf7+82CJ2ZlhjMat5twlDSU/QI20D7XO/odL1jJ5bXOi8vU99ayaCke/qBQ9c7dnStY0fXOrx7v3qEhZs+ASAlPpk/z76eeZ/fxdKty71tKfzh4GtI7EG5Al3r2Nre633fV4/y4aZFbbadPuUkDh1z0HaPLVZKakq54cM/B78wGZU+kl/ue2XUg+eD4Z5eGc8iMqjVNzRTtLWaotIaikqrKdpSw8bSaorLantUMiM+zseY4RlMGpXFxFFZTBqVzfAhqf0eZI6WcJnQHy8rpqS8llG56ewxOZfxIzOJ20Fer4iIiIiIDD5rQzKex2WNJc4Xx17DpwUDz3XNdSzbupxpubv21xClD31ZajsEnQGeWvECuw6dwvC0vH4YVc+9sOaVYNAZYO7EOYMuYztaFHgWkQHP7/dTWdtI0RYXYN5YWs0mL9BcWtG7CWaGZiUzMT+LiaOymTQ6i/EjMklK3DkeewkNQouIiIiIiAwENY01bK4pCa4XZI0FYI/c3XnE92QwkPdp8RIFnndAdU11PLzsf2H3NbY0ct9X/+X/7XPJgA/gFlVv5qNNnwbXx2WOYfpO/HlV4FlEBpStFXWsL6kKZjBvLK2haEs11XVNve4zKTGOgpFZwWzmiaOyGZKZHMVRi4iIiIiIyPZYW7m+zXog8JyRlM6UnEksK/sagMVbvqSxpalH5TZk4Htm1YuU1ZcH1w/In0lxTQkrt60BYHXFWl4rfJujxh/aPwOM0POrX8EfUuTzhIlzdpgnqXtDv6Ui0m/qG5pZs6mCVUUVrNrglmWVvctgDsjNTiF/WDr5w9LIH5bGhPwsRuelD5jJ/0RERERERKSjNdvaTixYkDUu+PNew6cFA8+1TXXYrV+ze+7UmI5P+s7K8jW8vf794Hp2UhanTp5LdWMNv1v4NxpaGgF4btVL7DrMMDojv7+G2qV1lRv5tHhxcH1SdgFTh07pxxH1PwWeRSQmWvx+Nm+tYdXGClZurGDVxm2sL66mpRcTnCbE+xgxJM0LLqeTn5tG/tB0Rg5LI3knKZchIiIiIiKyI1lTURj8OSc5m+zk1tKAe+TtzqPLn2pTbkOB5x1DY3MjDy57vE2W8LfMKaQlppKWmMopk+fy6PInAWjyN3P/V49y1YwfkjAAM96fX/1Sm/WdPdsZFHgWkT5SVdvIKi/A7JYV1NT3rFxGanK8CywPTSM/12UxjxqWTm5OijKYRUREREREdhB+v7/NxIKh2c4AmUkZfCNnIrZsBQCfb/mSs1qaBmTwUXrmxTWvsbmmOLi+9/Dp7JG3W3D94NH783nJF8GM93VVG3lxzevMnXh0zMfaldXbClmyZWlwfZch3+AbQyb144gGBv2GikhUrNtcyeIVW/jcFrNq4zY2l9X26PistEQmjspm4ig36d2o3HRyMpJ2+m8HRUREREREdnRb68qobKwKrgfqO4faa/i0YOC5tqkWW7aC3YbtErMxSvStr9zIy4VvBtfTE9I4fcpJbdr4fD7OmXo6v134N2qb6gB4ae3rTMudyvgwn5P+8tyqttnOcyfO6aeRDCwKPIvIdmnx+3nk1a95ddH67ht7EuJ9jBuRycRRWUzygs252SkKMouIiIiIiOyE1lS0r+/cMaC4Z940HrVPBUsyfFq8RIHnQay5pZkHlz0WLJ8C8M1vnEBWUmaHtkNScjj9Gydx39JHAWjxt3DfV4/yi5lXkBifGLMxd+brspXBjGyAablTmZA9rosjdh4KPItIrzU1t3DXC0v54MvNXbbLzU5h0uhsJuZnMXF0FuOGZ5KYoFIZIiIiIiIi0ra+sw8fYzPHdGgTKLexvHwlAItLvqTZnEp8nOb5GYxeX7eAwsoNwfVdhxr2Hbl3p+33Hbk3n5V8weItXwKwqaaYZ1e/xKmT5/b5WLvi9/t5dtXLbbYdP0HZzgEKPItIrzQ2NXPb01/y6ddb2mxPSYpnQn5Wm2zmrPSkfhqliIiIiIiIDHSh9Z3z00eQkpActt1ew6cHA8/VTTXYshXsOszEZIwSPcU1JTy/ujVYmxSfxJnm1C6fgvb5fJy1y6ms/HA11Y01ALxeuIDpubsxOWdCn4+5M8u2fs3KbauD63sNn87YzFH9Np6BRimHItJjdQ1N3PTY4jZB57g4H1eeuRe3Xjmbn561F988ZBJ7fiNXQWcRERERERHpVHNLc5vM13BlNgL2yNsdH63ByU+LF/fp2CT6WvwtPLTsfzS2NAW3nTTpWIalDun22KykTM40pwbX/fi5/6tHqWuq75OxdsdlO7fWdvbhY+6Eo/plLAOVAs8i0iPVdY389dHPWLq2LLgtIT6OX503kyNmjiMuTnWaRUREREREJDIbqzfT2NIYXC/I6rw2bnZyZpvs1s9LvqS5pblPxyfR9e7GhXxdviq4PjG7gNmjD4j4+L2HT2fGiD2D61vqtvLUyheiOsZILdnyFWsrW7P1Z47ci5HpI/plLAOVAs8iErFt1Q386aFPWbmhIrgtOTGeK0+fzn675/fjyERERERERGQwCq3vDDC+i4xncKUMAqqbaoKlN2TgK6sr56kVrUHiBF88397lNOJ8PQtPnjHlZLJDJiFcsOF9lpYuj9o4I9Hib+G5kHIhcb44jitQtnN7CjyLSERKt9Xxhwc/YV1xVXBbWnICV525J7sWDO3HkYmIiIjIjqypqQljDFdccWlw23/+M49Zs2bw+eefdXv8Rx99yKxZM7jnnjuiOqZZs2a0GZOI9E5ofeekuETyu8kY3VPlNgYlv9/PI/ZJ6prrgtuOnXAUI9OH97iv9MQ0zt7ltDbbHlj2GDWNtds9zkh9WryYDVVF/5+9+46Pqkz/Pv6Z9F4gDQIk1AMGAgSkKx1EEbH3urrFLb/VLb/d9dnyrPt7tv12V3dd3dV1UdS1YUcUBEFEpEivh5ZQAiShpPeZef6Y5CRDKslkJgnf9+uVV7zuc+bcV44JmVxzz3Vb8cReY4kP6+m1+bsKFZ5FpEWnz5Xy21e2kO68taoAACAASURBVHOu1BqLCg/iv+/MYGBytA8zExERERFf+tGP/qumALyt2fMcDgc33HANM2ZMIj8/v93zzpo1l8cf/x2pqantvlZrvPnmaxw6dNCK/f39efzx3/HAAw95Zf7G1BbfV69e6bMcRDyh/ornflF98Pfzb/b86OAoBkSnWnFXbLdR7ajG4XT4Og2v2pK7g91n91lxckQvZveb2ubrDY8bxqRe46w4v6KAJQffb1eOrWV32Pkw8xMrDrD5c1XqTK/M3dWo8CwizTqeW8zvXt7CucK6Zv09o4L56Z0Z9E2I8GFmIiIiIuJr113n2uTpww+b/2N/06YN5ObmMHXqDGJiYto974ABA5k+fRbR0e2/VksqKsp56qm/cPhwXeHZZrMxffosRo4c3eHzi3Rn5dXlnC7JteKW2mzUyqjXbqO4qsStZ3Bn99Xpbfzo81/xi/W/Y8/Z/b5OxyuKK0t488B7VmzDxl1Db27xRYaW3DB4Pj1C6jYl3Hh6Czvydrfrmq2xOWcbOaV5Vjw5eYJbHlJHhWcRadLh7AJ+/8pWCkvrNnpI7BHGT+4cQ2KPMB9mJiIiIiKdwcSJU4iPT2D16pWUlpY0ed7Spa6Cw4IF13srNY85cMDEbu9aqylFuopjRSdw4rTi5jYWrG9UwnC3uKu026h2VPPGgfeotFdyviKfZ3Ys4pOja3A6nS0/uAtbcvB9iqvqfkfM6jeVflF92n3d0IAQ7h52s9vYq/vfpqiyuIlHtF+1o5plmXXvNAn0C2RuyvQOm6+rC/B1AiLSOe3NOsff3tpFRVXdk+y+CRE8eusoosODfJiZiIiIiHQW/v7+zJ9/HYsWPcfKlSsaLSwXFOTzxRdrSUlJZfToMdZ4YWEhr776Ep9++gm5uTmEhobRp09fbr31TmbObH6DpmeffZrFi//N3//+L0aOHAW4+i4vWvQcH320lPPnz5GYmMTChTcycOCgRq+xceOXvP76K+zdu4eKinLi4xMYM2YcX/vaN4iLiwPg17/+OStWfATA44//gscf/wV///u/SEsbzrRpExgzZhxPPvl0va+pgBdffJ7PP/+MvLxcgoODGTzY4NZb72TKlCut8z744F1+//vf8Ktf/Q+BgUG8+OLzHD2aSWBgEOPHT+TRR3/s8dXcOTmnWbToOTZt2sC5c2cJCwsnLW04d999P+npo6zznE4nS5e+y3vvvUN29gkqKsqJi4tnypSp3Hffg0RG1m3o9dlnn/Lmm69x9GgWJSXF9OjRk8svn8D99z9IQkLzfXpFALLq9XcGSG3liueY4GgGRKdypCALgO15u7llyMJ2r6DtaHvO7qekuq6FpRMn7x5exonik9w59CaC/Lvf39q7z+xjc05dO6b40J5c3d9zm/ANiR3EtD6TWXPiCwCKqop5zXyHB4ffhc1ma+HRF+/LU19xtvycFU/tM4no4CiPz9NdqPAsIg1sO5DHM+/tptpe96rrwOQovn/zSMJDAn2YmYiIiIh0NvPnX8eLLz7P0qXvNVp4Xr58GVVVVVx77UJrzOl08oMffBfT3McNN9zCZZelUVpawocfvs8vf/lTCgryueGGmxtcqzlPPfUXlix5nYyMsdxzz/1UVFSwcuVyvvxyfYNz161by89+9kNSU/vz9a8/TEREBIcOHWDJktf56quNvPTSG4SEhHDzzbcRFhbGu+++xU033cbIkaOa7CtdVlbGww8/xPHjR7n22oUMG5ZGUVEhH320lJ/85FF++tNfcM01C9wes379OrZu/Yobb7yVnj17sm7dWlatWoHdbuc3v/n9RX39zTlzJo+vf/1eSkpKuO66Gxk0aDBnzpzhvffe4rvf/Qb/+79PcvnlEwD4xz/+wRNPPMGECZNYsOB6goKCMM19vPXW6+zcuZ3nnnsRgBUrPuLXv/45w4en87WvfZ2wsAiyso6wZMnrbN68gZdffpOQkBCPfQ3SPdUvPEcFRRIb3PoXXDIS0q3Cc3FVCYfyMzF6NP5CU2ex6fTWRse/ytlOTmkeXx9xT7dq2VBWXc5r5jtuY64Cu2frCtcNnMfecya5pWcA2J63iy052xmb5Nl2SFX2Kj7OWmXFIf7BzO43zaNzdDcqPIuImy/3nOb5pftw1Hurz2WpsXznhhGEBOmfDBEREZHWKK4s4YPM5eTU613aGSWGJ3DtgLlEBIa3/RqJSUyYMIn169dx5MhhBgwY6Hb8ww/fJygoiHnz5ltjubk5REVFc8cd9/DNb37HGp85cy7XXjubJUteu6jC8/nz53jnnSX07duPP//5KQICXM9bFy68ifvuu73B+ceOZTF8eDqPPfYrkpNdb/eeM2ceTie8+upLrFv3GbNmzWXYsDRrU8Fhwy5j+vRZgGt19YXeeOM/ZGUd4eGHv8cdd9xjjc+fv5Dbb7+Bv//9SebMmUdgYF3B5fPP1/Dyy2+SmJgEwFVXXcOtty7kiy/WUl1dbX0d7fX88//k7Nmz/PrXv2PGjFnW+KxZc7jzzpv429/+wuLFrwOwdOlSIiOj+MMfnsDPz9Wdc+7cqxkwYCCff/4Zubk5JCQk8sknHwPwhz88QVRU3Wq/ESPSeeutNzh+/CiDBxseyV+6r6P1Cs8pUX0vaoXqqPjhbpvJbcvb1akLz6VVpew+U7e5XlxID85V5FubDB4vyuYPm//GgyPuZlBMf1+l6VHvH/6I8xV1G8pOSZ7A4NiBzTyibYL8g7hn2K38acvTVuuW1w+8y6DYAcQER3tsnnUnN5JfUWDF0/teQURQ239/XgpURRIRy+qtJ3h5xQHqd5caPTiOb16XRmBA537LkoiIiEhn8kHmctZlb/B1Gi2q3ZDrduOGdl1nwYIbWL9+HR9++B7f/e6j1vi+fXs4fPgQs2df5dY6IjExiT/96a9WXFFRQWVlJQA9e8Zx+vSpi5p/27at2O12pk2b6VasDQ4O5uqrr+Wf//y72/l33HGPVRx2Op2UlpbgcDjp3TsZgFOnLm5+gLVr1+Dn58eCBe73MiIigqlTp/Puu2+xe/dOt3Yj06fPsorOAH5+fhjGUE6dOklhYQE9evS86Dwaz201MTExTJs2w228V6/ejB49lo0b13P69Cni4yPx9/entLSEw4cPMXjwEOvc+fMXMn9+3ap1f3/X3we7du1g8uQrrPGJE6cwceIUj+Qt3Vt+RYFbEa+1/Z1rxYbEMCA6hSMFRwHXKtdbhlyHn61zbme2JXcn1c66VpYLB11DeGAo/9r9MiVVrvYbRVXF/HXbs9wy5DqmJE/wVaoecSg/k7XZX1pxTHA0Cwde3WHz9Y9OYXbKNFYcXQ1AaXUZr+xfwsPpD3ik5UaFvZLlWZ9acVhAKDP6XtHMIwRUeBaRGh9+mcVbn7nvBDwxLYkHrhmKv1/n/MUtIiIiIp3DxImTSUhIZPnyZXzzm9+1VvXWbip43XUNC9v79u1h0aLn2L17F4WFBW7HaouarXXy5AkA+vZtWLhKSWm4crCqqoqXX36BTz75mFOnTlJVVeV23G5vuKK5JceOZREfn0BERESDY/36pQBw/Pgxt8JzbaG7vqCgYKDxVdVtkZ+fT0FBAaNGZVgrmC/MbePG9Rw/fpQRI4bwrW99i0cffZQHH7ybMWPGMW7ceMaNm8CAAe4rSW+//R42b97IT37yKOnpoxg/fiJjx45j2LC0DumrKt1PW/s71zc6foRVeC6qLOZQfiZDOmBFrSfUb7MRGhDK8LhhBPoF8OOx3+PZXS+SXex6wcvutPOq+TbHi09y8+AFBPh1vdJdpb2KV/a/6TZ2m3E9oQEd237n6v6z2X1mHydLTgOw96zJ+lObmNx7fLuv/dmJLyiqqtu0cFa/qYQFhrb7ut1d1/vuFRGPcjqdvPXZEZZtOOo2Pj0jmTtnD8FPTxpFRERELtq1/ediw8bpkhxfp9KspPBE5g+Y0+7r+Pv7c801C1i06DnWrfuM6dNnUVFRzqpVK0hJSWXUqAy38w8ePMC3v/0QNpuNm266jREj0gkLc71d+de//jnnz59rbJomVVRUABAc3LCoERwc3GDsN7/5JatWrWD48HTuuOMeEhMT8fcPYPPmjbz00qKLmhtcz6nLysrcVi/XV1tMLi8va3S8I5WVuVZSNtVvufb+lJWVAzBv3jxCQ2N47bWX2bBhPZs2uVYsDho0hEce+bG1mePIkaN4/vmX+c9/FvPFF2vZsWMbzz77NL17J/Od7zzClVdO6+CvTLq6rIJjbnFKVJ+LvsbohHTeOrTUirfl7uyUhee80rNWP2pw9acOrCkox4X24NGMh3lp3xtsz9tlnbMuewOninN4aMTdRAY1fEGrM1uy50Or3zLA2MRRjIi7rMPnDfQL4J7LbuMPX/3VamHy1sEPGBo7mJ6hPZp8nMPpoLCyiPPlBdZK/PMV+eS7xXUvkEYEhjO1z+QO/3q6AxWeRS5hDqeTVz45wOqt2W7j10xM4YYrB2ilgoiIiEgbRQSFc5vRcKO97mz+/OtYvPjfLF36PtOnz2LNmk8pLi7m/vsfanDu22+/QWVlJY899iu33s/QtpW+tQXcysqKBsdqC6+1cnNzWLVqBamp/XnyyWfcCtOZmUcufHir2Gw2QkNDKS0tbfR4bcE5LCysTddvj9BQ15xlZWWNHm8st7S04Tz++O+oqqpi9+6dfPrpSt5//20eeeTbvPzyG9ZK7dTU/vzsZ7/Ebrezf/8+1q5dzdtvv8ljj/2Ip5/+FyNGjOzgr066sqzCusJzYlgCoQEXv3o0NiSG/lH9yKy51va83dzcCdttbMpx31RwXJL7i3EhAcE8OPwuPs76lKWZy63xwwWZ/H7zX/lG+r30jWz4DonOKPP8cd7f/4kVhweGcdPgBc08wrP6Rvbm6tTZ1n2ssFfy0r43WDBwnquAXJ5vFZJrC8sFlYVWobo15qZMJySg41847A5UeBa5RDmdTl5Ytp91u9z71900bSBXT0jxUVYiIiIi0lUlJiYxfvwkNm5cT35+PsuXf0RQUHCDwjLAyZMnARg7dpzbeFZWJvn55y+61UZSUlLNdbMbHDty5LBbXNu/ecSIkQ1WQ+/Yse2i5q0vNbU/+/fvo7Cw0G2zPXB9XdB424+OFhMTQ0xMLFlZR3A4HA3abWRmunJLTW2YW2BgIKNHj2H06DEkJyfz1FNP8OWXX3Djjbe4nefv709a2nDS0oZz2WVpPPbYj1m7do0Kz9Ikh9PBsaITVtyWNhu1RiekW4XnwsoiDudnMTh2QLtz9BSn0+nWZqNnSA8GRqc2OM9mszGv/0ySI5J4ce9rlNtdL6Sdr8jnT1ue5q5hNzM2cZS30m4Tu8POM5sWuxVxbx58nddXbM9JmcauM3s5WuRq53Iw/wh/2vL3Fh7VOglhcUxJnuiRa10KOtdLQCLiNW99dsSt6GwD7p4zREVnEREREWmzBQuux2638/77b7NlyyamTZtBVFR0g/N69HC95fnUqZPWWEVFOU8++b9ERERgt9svauXzqFEZ2Gw21q5dg91et3lXRUU5y5cva3FugE2bNrBly6aax9WtnK4t1NZuftiU6dNn4XQ6ef/9t93Gz58/z2efrSYhIZHLLhve6q/Jk6ZPn0VBQQGrV690Gz96NIvt27eQljaCuLh4Tp48yfz583n++X82uEZ4uKtwFBQURHl5OQ89dA+//e2vG5xX2zKlts+3SGNOl+RSYa/7mWpP4XlU/Ai3eFvezjZfqyNkFh7jTNlZKx6XNLrZdxenx6fxw7HfIT60bnPRKkcVi/b8h/cOf3RRK3O9Kb+igLcOfUBWft0LCsN7DvVJsdzfz597LrvVamfSpmvY/OkZEsvA6FTGJIxkZr8ruXXI9fwg49sE+evft9bSimeRS9CnW0+49XT2s9n42vxhTExrvCediIiIiEhr1G4y+MILz2O32xvdVBBg5szZfPLJx/zud49z++13U1VVxQcfvMOoUWOIjIzi008/4dlnn2bWrDmMHz+6xXnj4xO46qpr+OijpfzoR9/nyiunUVlZwYoVH5OSkkp2dl0hpE+fvgwZMpSvvtrEk0/+iaFDh2Ga+/jkk+X87Ge/4r//+xFWr15Famp/Zs6cQ69evQFYsuQ1SkqKGTlyNIMHGw1yuPHGW1mx4mOee+4Z8vJyGTr0Ms6fP8cHH7xLaWkJjz32q4teyd0ae/bsbvJYevooevaM44EHHuKLL9by29/+mgMHTFJT+5OTc5p3332LoKAgHnnkRwD07t2bxMREXnjhX+TknCY9fRRBQcEcO5bFkiWvERcXz9Sp0wkJCWHwYIP333+HwsJCJkyYRFhYGKdOneTtt98kLCycq6++1uNfq3QfDTcWbLgxaGv1DI0lJaovR2uuuT13FzcNXtBp2m3UX+0MDdtsNKZXeCI/Hvtd/r3nP+w7d8AaX3F0NdnFp7g/7fY2tSbxtPyKArbn7mZr7g6OFBzFidM6FuIfzG3GDT5r4ZkUnsD1g+bzxoF3GxwL8AsgJjia2OBoYmo+YkNi6sZCookIDO8030NdmQrPIpeYbQfyeOWTA25j984zVHQWERERkXbz9/dn/vzr+Pe/nyU1tT8jRzZeNJ4yZSqPPPIj3nzzdZ544o8kJiZx7bXXc9ttd7J//1727dvDkiWv06NHj1YVngF++MOfEhUVzapVK9i27SsSEhJZsOB6xo2bwPr166zzbDYb//M/f+Avf/kjH3/8IcuXL2PkyFH89a//oH//AVxzzQJWrVrBP//5d6ZNm8no0WO46qprWLNmFS+88Dz//d+PNVp4Dg4O5m9/+yeLFj3L559/xnvvvU1oaBhpacP5yU9+YW3K52mvvfZyk8d+//u/MHnyFcTG9uDZZ1/gX//6B8uXL+P8+XNERkYxevQY7r//QQYMGGQ95plnnuEvf/kbq1evZM2aT6msrCAuLp6ZM+dw771fs1aw//CHPyU1dQDLly/jmWf+RllZKbGxPRg9egz33vs1+vRp+wpW6f7q93cO8Augd0T7/h7NSEi3Cs8FlUUcKTjKoBjvt7a5ULWjmq05O6y4f1Q/EsLiW/XYsMAwvpV+P+8d+YhVx9Za43vO7uePXz3FN0bcS2J4gsdzbklBRRHb83axNXcHh/Oz3IrN9V03cB6xITFezs7d1D6T6B2eSG7pGaKCI4kJjiE2OJrwwDDtaeUlNqez8W8Qabu8vKImb2p8fGTtOV7L51Km++3uUHYBf3x1G1XVdW/NWTilPwumtP8Xsu619+hee4/utXfpfnuPN+91fHykntVLl6Tn9J2H7rf36F57z6V+r3+76QlOFLva3fSPSuGHY7/druudKTvHL7/8nRVP6zOZm4dcB/j2Xu/I282zuxZb8a1DFnJln0kXfZ1Np7fyyv4lVDvqWhCF+Idwf9rtDI8b5pFcm1NQUcSOvF1szd3JofzMJovNAJHBEVw/bC7jYsepuNvBusJzeq14FrlEnD5Xyl+X7HQrOl85shfXTk71XVIiIiIiIiJySam0V3Ky5LQVt6e/c6240B70i+xjbVi4LXcXNw6+1uetEuq32fC3+ZOR2LYNN8clZZAYFs+zuxaTX1EAQLm9nH/sfIH5A+YwKn4EUUERhAaEeqzYW1hZZLXRaKnYHB4Yxqj4EWQkpDNp8Ej8/fwv2RdVxJ0KzyKXgIKSSv78+naKy6qssfSBPbl7rqFXIEVERERERMRrjhVlu22Q54nCM7jabdQWngsqC8ksOMbAmFSPXLstSqpK2XVmnxWn9RxKRGB4m6+XEtWXH4/9Hv/avZgjBa49m5w4+eDIcj44shyAAJs/kUGRRAVFEhUcQVRQpBVHBrli10cEwf7BDeoBRZXFbM/bzdbcnRw8f7gVxebhjE5IZ0jMQPz9XD3saz+LgArPIt1eeWU1T7y5gzMF5dZYSlIk37wuDX8/NcoXERERERER76nf3xkgNbrtGwvWNzphBO8eXmbF2/J2+rTwvDV3B3an3Ypbs6lgS6KDI/ne6G/whvkO609tbnC82mnnfEU+5yvyoYUFx0F+gfWK1JGUVZe3WGwOCwhlVPxwMhJGMiR2oIrM0iIVnkW6MbvDwT/e28PR03W/ceKiQ/j+zSMJCdKPv4iIiIiIiHhX7SaAABGB4fQM6eGR68aF9qRvZDLHi7IBV7uNGwbN98i126J+m43QgFCP9WIO9AvgjqE30ScymXcOLaWqXt/ni1HpqOJs+TnOlp9r9rywgFBGxg8nIyEdI3aQis1yUVR5EummnE4nLy032Xn4rDUWERrIo7eOIjo8yIeZiYiIiIiIyKUqq17hOSWqr0fbP2bEp1uF5/yKArIKj5OYEO2x67dWXulZqx0GuNqABPp5rgRns9mY2mcSYxNHcazoBEWVxRRWFrk+Koopqv3vyiJKqkqbXcXcmNCAUEbGp1nF5gAP5i6XFn3niHRTH3yRxdodp6w4MMCP792UTlKPMB9mJSIiIiIiIpeqwsoizpWft+IUD/V3rjU6IZ33jnxkxdtydzJ+0HCPztEam3K2usWeaLPRmPDAMIb1GNLsOXaHneKqkppCtKtAXVRRRGFVEYUVRVbRuspRzaCY/mQkpDO0x2AVm8Uj9F0k0g19vuMk767LtGKbDb65II1Byd5/pVdEREREREQE3NtsAKRGeaa/c634sJ70jejN8eKTgKvdhtPp9Oiq6pY4nU63Nhs9Q3owMDrVa/NfyN/Pn+jgKKKDo3yWg1y6tLOYSDez68hZXvzYdBu7c/YQRg+J91FGIiIiIiIiIu5tNgBSovp4fI5RCenWf5+vyOfQuSyPz9GczMJjnCmra3k5LinDq4Vvkc5EhWeRbiTrdCFPv7Mbh7Ouf9PVE1KYkeH5X+YiIiIiIiIiFyOr4Jj13/GhPYkIDPf4HBkJI9ziL49vbeLMjlF/tTPAuKTRXp1fpDNR4Vmkm8jLL+OJN3dSUWW3xiakJXLD1AE+zEpEREREREQEHE4HR4tOWLGn+zvXSgiLJzmilxVvPL4Vp/PiNtdrqypHNVtzdlhx/6h+JITp3cdy6VLhWaQbKC6r4s9v7KCwpNIaG5YSywNXD8NPb+kRERERERERH8srPUNZdZkVe7q/c32j4+vabeSVnuPwuaMdNld9e87up6S61Io7alNBka5ChWeRLq6yys6TS3aQc67ul1uf+Ai+ff0IAvz1Iy4iIiIiIiK+d2F/59QOWvEMDdttbDjhnXYb9dts+Nv8yUgc6ZV5RTorVaVEujCHw8mzH+zlcHahNdYjKphHbhlJWEiADzMTERERERERqVO/8Oxv86dPRO8OmysxPIHe4UlW/KUX2m2UVJWy+8w+K07rObRDeliLdCUqPIt0UU6nk1dXHmTrgTxrLDQ4gEduHklsZLAPMxMRERERERFxl1VYt7FgckQvAv0DO3S+jIR67TZKznK8KLtD59uauwO7s27PJbXZEFHhWaTL+njjMVZtrduYIcDfxvduHEFyfIQPsxIRERERERFxV2WvIrv4lBV3ZH/nWqMvaLexNXdnh85Xv81GaEAow+OGdeh8Il2BCs8iXdCGPad5c81ht7EH51+G0S/WRxmJiIiIiIiINO5E8Um31cAd2d+5VlJ4Ir3CE614a+4OqhzVHTJXXulZjhTUbWA4JiGdQD+1vxRR4Vmki8k8VcjzH+5zG7tl+iDGDUts4hEiIiIiIiIivuPNjQXrG12v3cbZ8vO8vO+NDun1vCnHffPCcUljPD6HSFekwrNIF1JRaefZD/Zid9T9opw1tg9zx3nnl7aIiIiIiIjIxarf3zk0IJT4sDivzDu59zhCA0Os+Kuc7SzL/MSjczidTrc2G3EhPRgQneLROUS6KhWeRbqQ11cfIudcqRWnD+zJbTMGY7PZfJiViIiIiIiISNOO1lvxnBLZBz+bd8pRMcHR/GDS193mW5a1ko2ntnhsjszCo5wpO2vFlydl6G90kRoqPIt0EdsPnWHNtrpdeKPCg3jgmmH4+ekXmoiIiIiIiHROxVUl5NUrzKZGd/zGgvWlJw3jwTG3uY29sn8JB88f8cj1N56+sM3GaI9cV6Q7UOFZpAsoKKlk0TL3vs4PXD2UqLAgH2UkIiIiIiIi0rKjhSfcYm/1d65v1sArmNVvqhXbnXae27WY3NK8dl23ylHN1pwdVtw/qh8JYfHtuqZId6LCs0gn53Q6WbRsH0WlVdbY9Ixk0gd6pyeWiIiIiIiISFvV7+8MkOKDwjPAdQPnMSp+uBWXVJfy9I5/U1xV0uZr7jm7n9LqMisel5TRrhxFuhsVnkU6uTXbT7LzcN3bknr1DOOW6YN8mJGIiIiIiIhI69Tv79wjJJaooEif5OFn8+Pey24jJbKu8J1XdpZndy6mylHdpmvW31TQ3+ZPRuLIducp0p2o8CzSiZ06W8Lrqw5asb+fja9fm0ZwoL8PsxIRERERERFpmdPpdFvx7Is2G/UF+QfxjfT7iA2OscYOF2Tyyr4lOJ3Oi7pWSVUpu8/UtcQc3nMoEYHhHstVpDtQ4Vmkk6q2O3j2/b1UVjussYVX9CclyTevDouIiIiIiIhcjLPl5yipKrViX7XZqC86OJKHRz5AiH+wNbY5ZyvLslZe1HW25u7A7rRbsdpsiDSkwrNIJ/XeukyO5hRZ8ZC+Mcwbn+LDjERERERERERaL6vAvb9zalQ/H2XirndEEl8bfhd+trqy2LLMT9xaZ7Sk/rlhAaGkxQ3zaI4i3UGArxNoK8MwegC/BBYCvYAzwDLg56ZpnmrF428DvgvUNuDZAfzWNM2lHZOxSOsdOJ7Psi+PWnFosD8Pzh+Gn5/Nh1mJiIiIiIiItF5WUV1/Zz+bH/0ik32YjbvLehrcMmQhr5lvW2Ov7HuTHiGxDIrp3+xjc0vPcKSg7m/2jIR0Av26bIlNpMN0yZ8KwzBCgTXAUOAp4CtgMPBDYIZhGGNM0zzfzON/hatoMreRtAAAIABJREFUvR/4BVAAPAx8YBjGHaZpvtqhX4BIM0rLq3nug73U7y511xyDuOhQn+UkIiIi4mmGYcTjei5+PZAI5APrgMdN02zVkjPDMCYBPwcmAKHAAeA54CnTNC+uWaeIiHhcVkFd4bl3eBJB/kE+zKahK5InkFd6hlXH1wJQ7bTz7K4X+eGYb5MQFt/k4zZfsDJ6XNKYDs1TpKvqqq02vg+MAL5vmuajpmn+xzTN/wvcDfTH9eSzUYZh9AUeA7KBcaZp/tk0zeeBK4E9wF8Nwwjr8K9ApAmvfHKAs4XlVjz+skQmpiX5MCMRERERzzIMIwHYCnwNeL3m8z+BmcA6wzBGt+IaM4DVuBag/Ap4CFfh+a/AXzokcRERaTW7w87x4mwr7gz9nRuzcNDVjIxLs+KSqlKe2bGI4qqSRs93Op1sytlmxXEhPRgQrbaYIo3pqoXne4AS4PkLxt8DTgB3GYbRVE+C2bhWev/bNE2rga5pmiXAE0AcMM/jGYu0wqZ9OXy557QV94gK5q45Q3yYkYiIiEiH+A3QB7jLNM0fmKa52DTN/4NrIUko8NNWXONpoBy4wjTNJ03TfMk0zZtw/U3wPcMwRjb/cBER6UjZxaeodlRbcWfp73whP5sf96bdTr/IPtZYbtkZntu1mKp6+dfKLDzKmbKzVnx5UgY2m9piijSmyxWeDcOIwtViY6tpmhX1j9W8nW4TEI9r5XNjetV8PtLIse01n8d5IFWRi3KusJzFH5tWbAMevOYywkMCfZeUiIiISMc4CbwKvHPB+MeAE0hv7sGGYYwHDOCNRvZ3eQrXU6m7PJOqiIi0RVbhcbc4tZOueAYI9g/im+n3ERscY40dys/kP/uX4HS6d27a2KDNRoZXchTpirpij+fa9y+caOJ47ZapA2i8uFxQ87mxZj1lF8zRJvHxkR45Rzyns99vh8PJE0t2UlpR92rqDdMHccXYzvmKcHM6+73uTnSvvUf32rt0v71H91p8xTTNXzVxKBJX0biwhUvULhT5spFjG2s+j7/4zERExFOyCo9Z/x3sH0RSeIIPs2lZdHAU3xp5P3/e8jTldtc6x02nt5IQGse8/rMAqHJUszVnh/WY/lEpJITF+SRfka6gKxaea/9CKm3ieMkF511ofc3nm4A/XnDstprPEW1LTaRt3v3sMDsPnbHiAb2jufOqoT7MSERERMQnvlnz+ZUWzkut+dxgMYppmkWGYeTjWojSZlpM0vnofnuP7rX3dOd7faKkrr/zoJ6pJCZE+zCb1v+7/mjIQ/zu86dxOB0ALM1cwYDEZKakjGPTie2UVpdZ588cPLFb/z9sD90X7+nM97orFp7bxTTNrYZhLAfmGoaxCFd/OYA7az4cQEVTj2+NvLyiJo/VfjM0d454Tle438dyili8bK8VBwb4cf/VQ8k/39RrK51TV7jX3YXutffoXnuX7rf3ePNed+YnwtK5GIYxD/gFsAV4poXTW7MYRd98IiI+UlpZRnZR3f5Fg3qk+i6ZizSqVxoPZNzCv7a8Zo09vekl4sJ6sDZrozXm7+fPxL5jfJGiSJfRFQvPtW+7C2/ieMQF5zXmNmARcF/NB7h21b4Z+Ao4164MRVqpssrOcx/sxe6o6xl1y/RBJMc19e0tIiIi0v0YhnEP8C8gC7jWNM1K32akxSSdie639+hee093v9f7zx10ixMCk3z2tbblXo+OzmBG32w+Pf45ANWOan7/+TOUV9etUxzeYyjlhU7K6Z7/D9uqu39vdyZdYTFJl9tcEMjEteFInyaO1/ZnPtjEcUzTzDdN83qgNzAJGGia5hig9gnuPg/lKtKsJWsOk32mxIqHD+jBjIxkH2YkIiIi4l2GYfwceBHYAUxpZLPAxrRmMUpLfaJFRKSDdKWNBZty/aBrSI9Ls+KSqlLsTrsVa1NBkZZ1ucKzaZolwE4gwzCMkPrHDMPwx1VIPm6a5rHGHn/BtU6ZpvmlaZq1mxDOq/m80pM5izRm95GzrNxS15YwIjSQB64ehs1m82FWIiIiIt5jGMYTwK+B94GppmnmtvKhtc/fGyxGMQwjGoimmYUoIiLSsepvLBgTHE1MsG/7O7eFn82P+9Jup19kw8VhYQGhpMUN80FWIl1Llys813geCAO+ccH4XUACrrfpAWAYxlDDMPrXiyMMw9hvGMYawzBs9cbjgUeAL0zT3Nmh2cslr6i0kuc/dF9Yf9+8ocREBPsoIxERERHvqlnp/F+4WuDdYJrmxWxwUbth+ORGjl1R83ldO9ITEZE2cjqdboXnlC642rlWsH8Q30i/r0HhPCMhnUC/rti9VsS7umrh+R/ARuB/DcP4s2EYdxiG8T8147uA/6137j7go9rANM1i4BNgKrDUMIx7DcP4r5rrhdCwmC3iUU6nkxc/NikoqWtdeOXIXmQMifdhViIiIiLeYxjGdOD/Au8AD5qmaW/hfLfFJKZpbqdmjxbDMPrUO8+GazFJFa72HSIi4mXnK/Ipqiy24q7YZqO+mOBoHh75AMH+QQAE2Py5Inmij7MS6Rq65MszpmlWGYYxB/gVcCPwHSAX10rnX7ZitcT3gZPAvbiK1cXAKuAx0zQPd1TeIgDrdp5i64E8K06IDeW2mYN9mJGIiIiI19UuFFkJ3GAYRmPnLKv3vH4fYAJD6x1/GFgNrK1p2ZGPaxPxGcDP9bxeRMQ3ukN/5wslR/TiZ+MeZfPprQyOHUifyN6+TkmkS+iShWcA0zQLgUdrPpo7r0HD3JoVFb+t+RDxmtzzpfxnZV27QT+bjYeuvYyQoC77oygiIiLSFrU7Mv29mXP6A1lNHTRNc6NhGFfi6hH9ayAYV4H6AdM0F3koTxERuUj122zYsNEvskE7/i4pLrQH8/rP8nUaIl2Kql0iXmJ3OHjug71UVNW9k3TB5FQG9u56myyIiIiItEdji0Pacr5pml8BV3skKRER8Yij9VY8J4UnEBIQ4sNsRMSXumqPZ5Eu5+ONxzh8stCKByZHcc2kFB9mJCIiIiIiIuI5doedY4UnrDg1qp8PsxERX1PhWcQLcvPLeP+LLCsODvLnofmX4e+nH0ERERERERHpHk6X5lLpqLLilG7Q31lE2k5VL5EO5nQ6eWXFAaqqHdbYLdMGkhAb5sOsRERERERERDwrq+CYW6wVzyKXNhWeRTrYFjOPXUfOWvGA3lFMHZ3sw4xEREREREREPMvhdHAg/7AVB/oF0js80YcZiYivaXNBkQ5UVlHNf1YesGKbDe6Za+Bnu6j9dEREREREREQ6paLKYjac+op12Rs4U37OGu8XmYy/n78PMxMRX1PhWaQDvft5JvnFlVY8e2xf+iVG+jAjERERERERkfZxOp0czD/CuuwNbM/bjd1pb3DOkNiBPshMRDoTFZ5FOsjR00Ws3HLcimMjg7luSn8fZiQiIiIiIiLSdiVVpWw89RXrTm4kpzSv0XNs2BiVMIIZfa/0cnYi0tmo8CzSARwOJ4uXmziddWN3zBpMaLB+5ERERERERKTrcDqdZBYe5fPsDWzN3Um1o7rR86KDIpnUexyTeo+jR0isl7MUkc5IVTCRDvDZjpNkniq04vSBPckYEu/DjERERERERERar6y6jE2nt7EuewMnS043ed6wHkOYkjyBET2HqaeziLhR4VnEwwpKKlmypt5OvgF+3Dl7CDZtKCgiIiIiIiKdmNPp5FjRCdZlb+CrnO1UOqoaPS8iMJyJvS5ncu/xxIf19HKWItJVqPAs4mGvf3qQsoq6tx4tmJxKfEyoDzMSERERERERaVqVo9rq3Xy8KLvJ84bEDGRK8nhGxg8nwE8lJRFpnv6VEPGgfVnn2LAnx4p79Qxj7rh+PsxIREREREREpGl2h51ndvwb8/yhRo+HB4QxvtcYpvQeT2J4gpezE5GuTIVnEQ+pqnaweMUBt7F75hoE+Pv5KCMRERERERGR5m3K2dZo0XlgdCpTkicwOn4Egf6BPshMRLo6FZ5FPOSjjUfJOVdqxZOHJ2H0006+IiIiIiIi0jk5nA5WZH1qxQF+AUzuPZ4pvcfTOyLJh5mJSHegwrOIB+ScL2Xp+qNWHB4SwM0zBvkwIxEREREREZHmbc3ZQW7ZGSu+MnkiNw6+1ocZiUh3oh4AIu3kdDp5ecUBqu0Oa+zm6YOICgvyYVYiIiIiIiIiTXM4HXx81H2186x+U32YkYh0Nyo8i7TT5v257Mk8Z8WDkqOZkt7LhxmJiIiIiIiING9H3h5OleRY8eTe44gOjvJhRiLS3ajwLNIOpeXVvLrqoBX72WzcM9fAz2bzYVYiIiIiIiIiTXM6nXyctcqK/W3+zO43zXcJiUi3pMKzSDu88/kRCoorrXjOuL70SYjwYUYiIiIiIiIizdt9dh8nik9a8YReY4gNifFhRiLSHanwLNJGmacK+XTrCSvuERXMgsmpvktIREREREREpAVOp5OP6q129rP5MSdlug8zEpHuSoVnkTZwOJwsXm7idNaN3TlrCCFBAb5LSkRERERERKQF+88f5GjhcSu+PHE0caE9fZiRiHRXKjyLtMHqbdkcPV1kxaMGxTF6SLwPMxIRERERERFp2UeZdaudbdiYq9XOItJBVHgWuUj5xRW8vfawFQcF+nHH7ME+zEhERERERESkZQfPH+ZwQaYVZySkkxie4MOMRKQ7U+FZ5CK9tuogZRV2K75uSn/iokN9mJGIiIiIiIhIyz7O+tQtvip1po8yEZFLgQrPIhdhd+ZZNu3LteLk+HBmj+3rw4xEREREREREWpZZcJT95w9a8cj44fSOSPJhRiLS3anwLNJKlVV2Xl5+wG3s7jkGAf76MRIREREREZHO7aOsVW7xVakzfJSJiFwqVDETaaVlG46Sm19mxVek92JI3xgfZiQiIiIiIiLSsmNFJ9hzdr8VD+85lH6RfXyYkYhcClR4FmmF0+dKWbbhqBVHhAZy8/RBPsxIREREREREpHXU21lEfEGFZ5EWOJ1OXlpuUm13WmO3TB9ERGigD7MSERERERERaVl28Sl25O224qGxg+kfneLDjETkUhHgyYsZhhEJzAcygETgSdM0t9QcG2Ca5hFPzifiDRv35rDv6HkrHtI3hskjtAGDiIiIiIiIdH7LtdpZRHzEYyueDcO4CcgEXgZ+ANwJJNccCwf2GIbxe0/NJ+INdoeDtz6re73E38/G3XOGYLPZfJiViIiIiIiISMtySnLZmrvTigdG92dw7AAfZiQilxKPFJ4Nw5gAvAZEAouBX1xwSjiwE/ihYRi3e2JOEW/YYuZxtrDcimdf3pfk+AgfZiQiIiIiIiLSOsuPrsZJXdvIef212llEvMdTK57/G6gAxpqmeT/wEmAtCTVNMxeYCWQD3/DQnCIdyul0snzTMSsO8PfjqnH9fJiRiIiIiIiISOucKTvL5pxtVpwa1Y+hsYN9mJGIXGo8VXieBLxqmuaupk4wTbMYWAKM9NCcIh3q4IkCMk8VWfGk4YlEhQf5MCMRERERERGR1llxdDUOp8OK56XOVNtIEfEqTxWeY4CsVpyXB4R5aE6RDlV/tTPAnMu12llEREREREQ6v3Pl59lwaosV943oTVrPoT7MSEQuRZ4qPJ8BhrTivBFArofmFOkwOedK2X7wjBWnD+xJ77hwH2YkIiIiIiIi0jorj32G3Wm34qu02llEfMBThec1wE2GYUxs6gTDMBYCN9ecK9KprfjqeL3tF2DO5X19louIiIiIiIhIaxVUFPLFyU1W3Cs8kfT4NB9mJCKXqgAPXef/AQuBNYZhLAFqexRcYxjG5cAsYBxQDvzWQ3OKdIjisiq+2HnKivsmRDAsJdaHGYmIiIiIiIi0zspjn1HtqLbiq1Jm4Gfz1LpDEZHW80jh2TTNPYZhzAcWA7eDtVj0QaD2vRzZwL2mae71xJwiHWXNtmwqq+s2YJg7rq/ekiQiIiIiIiLYHa72Ff5+/j7OpHFFlcWsy95gxQlhcWQkjvRhRiJyKfPUimdM01xtGMYAYB4wAUjAVYA+DWwElpumWeWp+UQ6QlW1g1VbTlhxTEQQ44Yl+jAjERERERER6QzWn9zMa+bbRAVFcu9ltzE4doCvU2rg0+OfU+moK73M1WpnEfEhjxWeAWoKy+/XfIh0ORv35lBQUmnFM8f0IcBfv6RFREREREQuZWfLzvH6gXewO+2cr8jnqR3/4oG0OxgZP9zXqVlKqkpZe2K9FfcMieXyxNE+zEhELnUeKTwbhnFRlTnTNB0tnyXiXU6nkxWbj1lxcKA/00Yn+zAjERERERER6QzeP/KxW9/kakc1z+16iTuG3sik3uN8mFmdNSe+oNxeYcVzUqZ32pYgInJp8NSK54tpoeH04LwiHrM36zwn8kqseEp6L8JDAn2YkYiIiIiIiPhaZsExvsrZ3mDciZNX9i+huLKE2SnTfLo3UFl1OauPr7PimOBoxvca67N8REQAPNVDwNaKDwdwEjjloTlFPGr5prrVzjZg9uV9fZeMiIiIiIiI+JzT6eTtQx+4jQ2NHewWv3fkI94+tBSH03dv7l57Yj1l1WVWPLvfNAL9tOZPRHzLU4XnwGY++gDXAduApaZpqponnc6JvGJ2Z56z4owh8STEhPowIxEREREREfG1bXm7OFJw1IrT49L4zqgHmd1vmtt5nx7/nMV738DusHs5Q6iwV/Lp8c+tODIootO0/xCRS5tHXv4yTbO5f1lPAicNw1gNbDMM48emaf7BE/OKeMqKzcfd4rnj+vkoExEREREREekMqhzVvHdomRX72fxYOOhqbDYbCwddTURQOO8c+tA6vjlnKyXVJTw4/G6C/YO8lue67A0UV9W1jZzVbypB/mobKSK+56kVzy0yTbMYeBf4mrfmFGmNguIKNuw5bcUDe0cxqE+0DzMSERERERERX1t7Yj1nyuveGXtl8kQSw+KteFa/qdwz7Fb8bHWllb1nTf627VlKqkq9kmOlvYqVxz6z4vDAMK5InuiVuUVEWuK1wnONMkBLSaVTWbU1m2q704q12llEREREROTSVlxVwkdZq6w4NCCUef1nNThvfK8xfGPEvQT61a0wziw8xp+3PsP58vwOz/PLU5sprCyy4hl9r/TqamsRkeZ4rfBsGEYwsAAoaelcEW+pqLKzZlu2FcdFhzB6SJwPMxIRERERERFf+yhzpdtmfVelziAiMLzRc4fHDeN7ox8iNKBun6DTJTn8acvTnC7J7ZD8nE4nWYXHWHF0tTUWGhDK1D6TOmQ+EZG28EiPZ8Mwnm3hlBhgGtATeMcTc4p4wvrdpykuq7Li2WP74u/n7TcCiIiIiIiISGeRU5rH2uwvrTgupAdT+0xu9jEDolN5NONbPLX9XxRUFgJwviKfP299modHPkBqlGfeWVteXc7mnO2sy97AieKTbsem95lMaECIR+YREfEEjxSegQdbed5e4PsemlOkXRxOp9umgqHBAUxJ7+XDjERERERERMTX3j20DIfTYcXXDbqaQL+Wyye9I5L4wZhv89SO58gtPQNASVUpT257lq8Pv4dhPYe0OafjRdmsy97A5pxtVNgrGxwPDwxjWt8pbb6+iEhH8FTh+aFmjjmBcuAIsNE0TWcz54p4zY5DZ8g5V7fhw7RRvQkN9tSPhIiIiEjXZRjGRCADSAReNU1zX814pGmaRc0+WESkCzt4/jA7z+yx4gHRqYyOH9Hqx/cMjeXRjId5ese/OVZ0AoBKeyXP7FzEPcNuYWzS6FZfq9JeyZacHXx+cgNHC483ed7A6P7cZlxPeGBYq68tIuINHqmymab5vCeuI+JNyzfV/eL297Mxc0wfH2YjIiIi4nuGYUwCFgGD6g1/BewzDCMIyDQM4+emaT7jkwRFRDqQw+ngrUNL3cZuGDQfm812UdeJDIrgv0Z/ned2vcT+8wcBsDvtvLD3NYqrSpnWt/m2HSeLT7Pu5EY2nd5CWXV5o+eEBoQyPimDKckT6BWeeFH5iYh4i5Z3yiUp81QhB47X7TB8+bAEekSpF5aIiIhcugzDGAp8AoQCnwEm7u9sjAcKgKcMw8g0TfNj72cpItJxNp/exvGius3nxyaOon9023ozhwSE8M2R9/PS3tfZkrsDACdO3jz4HsVVxdwXd6NbQbvKXsW2vF2sy97A4YKsJq+bGtWPKckTGJOQTpB/UJtyExHxljYVng3DWNGOOZ2mac5tx+NF2q1+b2eAuZd7ZqMHERERkS7s/wBBwDzTNJcbhpECfL32oGma2YZhTAZ2Ad8DVHgWkW6j0l7J+0fq/lkL8AtgwYB57bpmoF8A96XdTkRQOJ+dWG+Nf5S1iiq/Ch4cczs5pXl8kb2RDae/oqSqtNHrBPsHcXlSBlN6T6BvZO925SQi4k1tXfE8qx1zqsez+NTZgnI278u14qH9YkhJivRhRiIiIiKdwnTgDdM0lzd1gmmapw3DeBO42XtpiYh0vFXHPie/osCKp/eZQs/Q2HZf18/mx82DryMyMIKlmXVr+FYeWcfOnH3klpxt8rF9I3ozJXkCYxNHERKgd+iKSNfT1sLzYI9mIeJFK7ccx+Gse/1jzjitdhYREREB4oB9rTjvOBDdwbmIiHhNQUURK46ttuKIwHDmpk732PVtNhvz+s8iIiic1813cdasx2us6BzkF8jYxFFMSZ5Av8g+F91fWkSkM2lT4dk0zcNteZxhGAaunbHb9HiR9iqrqGbtjpNWnNQjjPSBPX2YkYiIiEinkQ/0bcV5A4BzHZyLiIjXfJi5nEp7pRVf038OoQGhHp/niuSJhAeG8+KeV6l22t2O9Q5PYkryBMYlje6QuUVEfMHbmwveiatPXJKX5xUB4PMdJymrqPsFP2dcX/z0CrKIiIgIwJfALYZh/NE0zUONnWAYxlhcz+nV31lEuoXs4lOsP7nZipPCEpjce1yHzZeRkE54QBj/ObCEosoSRsalcUXyBPpHpWh1s4h0Ox4rPBuGEYrrSWg60FjzoVhgHlDtqTlFLobd4eCTr05YcURoIJPS9BqIiIiISI0/ANcAmwzDeAaofZvY5YZhxOPa5+UGwA/4o29SFBHxrHcOfWi1vgC4ftA1+Pv5d+icRo9BPDX/cWzYOHOmuEPnEhHxJY8UnmueiK4DBgG1L9E5L/hvauKnPDGnyMXaYuZxtrDcimdkJBMU2LFPKERERES6CtM01xuGcR/wD+Cn1D2H/1nNZxtQAnzLNM0vvZ+hiIhn7Tlrsu/cASseGjuYtJ5DvTK3n83PK/OIiPiSp1Y8/wrXhoPbgA8BO/BL4DngKHAVrl5wd5qm+ZknJjQMo0fNHAuBXsAZYBnwc9M0T7Xi8XcB3wRGAkHAMWAp8BvTNJveVla6JKfTyfJNx6w4wN+PGRl9fJiRiIiISOdjmuYrhmEsx/VOxvFAAq4C9GlgI/CaaZpnPDmnYRhBwG+AHwJrTdOc1srHOVs4JdY0zfx2pici3ZTdYeftQ0ut2IaN6wddo3YXIiIe5KnC8zxgi2malwMYhpGCqyj8oWma7wP/zzCM/wM8YxjGZNM0z7dnspq2HmuAobhWUH+Fq/D9Q2CGYRhjmpvDMIz/h2sVxyZcKziKgUnAd4H5NY8vbE+O0rkcPFFA5qkiK540PJGo8CAfZiQiIiLSOdUUlp/0xlw1m4//BxhC3bslL8ZeXH93NKakrXmJSPe3/tRmTpfkWPGEXmPpE9nbhxmJiHQ/nio8JwMvNTJuPXk0TfM3hmHMx7U6+r/aOd/3gRHAt03TfLp20DCMHcA7wM+BRxt7YM1K6R8BWcCVpmlW1BxaZBjGGeAnwP146cm2eEf91c4Asy/v56NMRERERATAMIxYYCtwEBgL7G/DZfJM01zi0cREpNsrqy7nwyMrrDjIL5D5A+b4MCMRke7JU4XnalztNWrVri6IuuC8j4F7aX/h+Z6aOZ6/YPw94ARwl2EYPzBNs7G33/XD9XVvqld0rrUWV+E5tZ35SSdyMq+Y7Qfr3hE6YkBPkuPCfZiRiIiISOdjGMaxls+yOE3TTGnnlEHAYuAR0zTLXYufRUQ63idH11BUVbep3+yUacQER/swIxGR7slT3ewPA3MNw6hd4XwWVyF6wgXnBeLqE9dmhmFE4WqxsfXCwnFNoXkTEA/0b+ISmUAFrtYcF0qt+by7PTlK5/Le2sPUfwVi7ri+PstFREREpBPrcxEf7X5CZZpmjmma3zJNs7zls5tnGIbNMAytLBCRFp0rP8+nx9dacXRQFDP7TfVhRiIi3ZenVjy/DfwC2GAYxtdN09xhGMY24AHDMNbg2rQvDbgP14rk9qhdWdHUdWpXagwAjlx40DTNAsMwHgd+YxjG34AngCJgHPAYsB14pT0JxsdHeuQcab/CkkpWbj5uxf17R3Hl2H7aMKID6Xvbe3SvvUf32rt0v71H91ou0NjCjFqJwBhc71xcBPzOKxm1LM4wjMXADUC4YRhFwLvAT03TzG7PhfWcvvPR/fae7nyvX92whCpHtRXfOWohfZJ6+iyf7nyvOxvda+/S/faeznyvPVV4/gMwC9cGfbX/Yv8OWAK8Vu88G64CdXvU3s3SJo6XXHBeA6Zp/o9hGDnA34Dv1Du0FLjHE6supHP4+MssKqvqusAsnDpIRWcRERGRRpimebiZw4eB9YZhvAFsA04B//ZKYs27DFef6Ltw/W1zLa62fNMMw8io2ShRRASAQ2ezWHd0kxX3j+nLlanjfZiRiEj35pHCs2mapYZhXAnMAHbVjL1tGMaDwE9xrVI+CbwA/NYTc7aHYRjfAv4KrABeBfKA8cCPgWWGYcwzTTO/rdfPyytq8ljtqxDNnSOeUVXtYOm6ukXvMRFBDOsTpXvfQfS97T26196je+1dut/e48173ZlXYMjFM00zxzCMJbg2+/Z14Xkers0Ft9QbW2IYxnFc72T8Aa6/RdpEz+k7D909GxvUAAAgAElEQVRv7+nO99rpdPL81tfdxhb0n8fZMyVNPKJjded73dnoXnuX7rf3dIXn9J5a8Yxpmg5g5QVj/8bzT0gLaz431cMt4oLz3BiuXUv+CqwyTfOaeoeWG4axA9db836GqwgtXdjGvTmcL6prAz5zTB8C/D3V1lxERETkknUWGOjrJEzT/LiJQ0/jKjzPoh2FZxHpXnbk7eZwQZYVj4gbxpDYQb5LSETkEtDmwrNhGAeBZ4AXTNM857mUWpQJOHFtatKY2h7QB5s4PgPX1/12I8c+qrn29PYkKL7ncDpZsbluY/bgQH+mjU72YUYiIiIi3cYsoMrXSTQjD9dz+ihfJyIinUO1o5p3Dy+zYj+bHwsHXtPMI0RExBPas+J5IPBHXJv0vfH/2bvv+KjuK+H/nxn1hgrqEqCGvvTewYAx3QVccC/YsZ1kU5603SdPdrO7v9+WJ5tkN68UO+4t7rjiAgbTe0f0ryhCvTdQ15Tnj5FGMyog0GhG5bxfL7/glrn3eBjEnXPPPQf4q9b6gGvC6prWulYpdQKYopTyd+zHrJTywtZnOldrndPFIVorpf072eaHrQ91Z9tEP3L4XAl5pW2PTM2bEEeQv48HIxJCCCGE6NuUUr+6zi5hwHJsQ8M3935EXVNKjcd23b+hk+v+kdiu6bv6PiCEGGR25u+jtL7cvnxLwixig6I9GJEQQgwOPUk8LwOeBe7CNsDjMaXUcWxV0O9oretdEF9XXsXWLuO7wB8d1j8KRAP/0rpCKTUKaNRaZ7Ws2tvy6wNKqT9rra0Or1/Tbh/RD5nMFj7Z0dbb2dvLwNLpwzwYkRBCCCFEv/Dv2CqFu9I6obkE+EXvh9Omk2v6ccAL2L4XPN1u99b2Gp094SiEGEQqG6r4Kmsz+wsP29cFePuzMmmJB6MSQojB46YTz1rrzcBmpVQUsBbbBd9k4EXgd0qpt4AXtNZnXRFoOy8AjwC/V0qNAA5jq7z4Gbbhhr932PcsoIFRLXHvVUqtw5Zk3t1SrV0KTAd+ABQD/9ELMQs32ZlRQElV232PFXOSiQoL8GBEQgghhBD9wn/SdeLZCjQAl4CvtNY9nmKjlBoDjGm3OkopdZ/D8tda6zraXdMD64CngO8opSKBrwEv4B5srUC+BV7uaYxCiP6ptrmOTdnb2JG3h2aLyWnbshGLCPbtamSUEEIIV+rxcEGtdSm2lhu/U0rNx1YFfQ/wI+CHSqkd2KqgP9Vam7o+0g2ds1kptRT4V+Be4IfYKi9eAf6l5eL0Wh4CdmJLmP8H4AsUYBuE+G9a63xXxCncr6HJxPrdWfblAD9vHlicTlN9kwejEkIIIYTo+7TW/+TmU96Pw5OKLcZgSyq3SgYut3+h1tqklLoT2/eAp7C1ALEAmcDfA3901XcPIUT/0WRuZnvebjZlb6fe1PEh7IlR47h12DwPRCaEEIOTwWq91tN0N0cpFYat/cYz2CqRrdgqiV8BXtZa57r8pH1IaenVLt/UqKiQ1n3cFs9gsn53Fp85JJ4fWT6KB5coeb/dQD7b7iPvtfvIe+1e8n67jzvf66ioEMP19xKi75Fr+r5D3m/36a/vtdliZn/RYb7O+paqxuoO2+ODYlmVuoKxQ0dhMPSNf5b663vdH8l77V7yfrtPf7im73HFc2e01lXYejD/SSk1E1vv5XuBfwR+ia3CWAiXulLbxIaDbTNkhgT5smp+qgcjEkIIIYTou5RSL/Xg5Vat9XddFowQQtwEq9VKRtlp1l/cSHFdSYftEf7h3JG8lOmxkzEajB6IUAghBrdeSTy3cxAIaPnvcWy914RwuS/2XqaxyWxfXjU3iQA/d3zEhRBCCCH6pfZD+W6EFdugbyEGnarGaprMzUQHRno6lEHtfOUlPr/4NVlXcjpsC/IJZHnSbdySMBsfo3wnFEIIT+m1n8AtQ/+eAZ4EYrFNwT6Lrd+zEC5VUlnH9mNtrbmjwwO4ZWK8ByMSQgghhOjzlng6ACH6mzPlmuczXsOKlQfS72Z+4mxPhzTo5NcU8vnFDZwuP9dhm6/Rh0XD57N4+HwCvGXAvBBCeJpLE89KKS9gNbaE82LACDQDHwHPa613uPJ8QrT6dFcWZktbG757F6Ti7SWPUgkhhBBCdEVrveVmXqeUigWCXRyOEP3Cjry9WLF979iau1MSz25UXl/Bl1mbOFR0zP5n0MpoMDI3fiYrkm4j1G+IhyIUQgjRnksSz0qpFGzJ5rVANLbq5jzgJWzDBItdcR4hOpNddJUDZ9o+YslxIUxTUR6MSAghhBBiQHsaeBYY7ulAhHA3xz7CpfXlNJga8Pf292BEA19NUy0bs7ewK28fJqu5w/Yp0RO4M2UZ0YHyHVAIIfqam048K6W8gXuwXXQuxFbdbAW+BZ4HvtBaW1wQoxDX9NH2C07L9y1M6zOTioUQQggh+hul1GJgAtBZNi0ceALb/BYhBhWTxUR5Q6XTuoLaIlJCkzwT0ADXbDGxJWcnm7O30WBu7LA9PTyN1akrGDFkmAeiE0II0R09qXjOByKxVTdXAG8Af9VaX3RBXEJ0y+msCk5fbrv4G5cSwegR4R6MSAghhBCif1JKBQEbgLnX2dUArOv9iIToW8rrK7BYnWur8q4WSuK5F5wtz+TDzM8oqS/rsG1YcDyrUlcyKmKkFBwJIUQf15PEcxRwCFt18wda6wbXhCRE91isVj7a3nafwwDctyDVcwEJIYQQQvRv/wTMAwqBLUADtrYanwOlwCJsVdA/BT7xUIxCeExnSdD8mgIPRDJwVTZU8fH5LzhWerLDtkj/CO5MWcaUmIkYDTLPRwgh+oOeJJ6naa2PuiwSIW7QwbPFZBdftS/PGhvL8JgQD0YkhBBCCNGv3QNkAlO01nVKqRHYEs+va63XK6V8gD8DPwDWAx2brQoxgBXXlXZYl19T5IFIBh6zxcy2vN18lbWZJnOT07ZA7wBuT17KvISZeBtdMqZKCCGEm9z0T21JOgtPMpktfLLjkn3Z28vA3bckezAiIYQQQoh+bzjwP1rrunbrDQBa62al1A+B48A/Av/s5viE8KiSuk4qnmsLsVgtUoHbA+crL/FB5qcU1hZ32DY7bjqrUlcQ4hvsgciEEEL0lNwuFP3S9mP5lFW3dXdZNCWRyDCZcSOEEEII0UOOSef6ll/tGR+ttUkp9TnwAJJ4FoNMSScVz03mJsrqK4gOjPRARP3blaarfHrhKw4WdaxpSwiO40F1t/TPFkKIfk4Sz6LfqW80sX7PZftygJ8Xt88e4bmAhBBCCCEGhhycBwuWA1ZgEvCOw/pmINGNcQnRJ5TWl3e6Pr+mUBLPN8BitbArfz9fXNpIvcl5VJS/lx93pCxjfsJsvIxeHopQCCGEq8jzQKLf+eZgDjX1zfblFTNHEBLo68GIhBBCCCEGhK+AZUqpj5RSaVprM3AGeFopNQNAKRUDPAh0fCZeiAGswdRIVWN1p9tkwGD3ZVXn8NvDf+bDzM86JJ2nxUzin2f9PbcOmydJZyGEGCCk4ln0K9U1jXxzMNe+HBrsy5JpwzwYkRBCCCHEgPHvwB3A3cCLwAVswwRfAPYppcqACGzFK3/0VJBCeEJX1c4AeTWFboykf6pprmX9xY3sLTiIFavTtpjAaB5IX42KSPNQdEIIIXqLSxLPSqmxWuvTrjiWENeyfu9lGpvbBqivmpeMn6/cDRdCCCGE6CmtdYVSagq2iuazLeteUkolAj8FogAT8DfgnzwWqBAe0L6/c5hfqL0COl8Sz12yWC3sLzzMZxe/prbZeW6pr9GHFcmLWTTsFryNUhMnhBADkat+up9USh0F3gLe01p3nLogRA8VV9Sx83jbY2wxEYHcMiHOgxEJIYQQQgwsWusa4JV26/5ZKfVvQDRQrLU2eSQ4ITyopK7MaXly9Hi25e4GoKKhkrrmegJ9ZNi5o9yrBXygPyXrSnaHbROjxnHfyDuJ8A/3QGRCCCHcxVWJZw1MASYDv1dKfYMtCf251rrJRecQg9wnOy9htrQ9lnXfghS8jNKmXAghhBDiZimltgDPYbtuN3e1n9a6Gch3W2BC9DEl9W21VSG+waSFJtsTz2Creh4ZnuKJ0PqcJnMz6y9uYHveng5tNSL9I1iTvopxkaM9FJ0QQgh3cknWTms9GpgI/AbIBm4H3geKlVIvKqXmueI8YvDKKrzCoXMl9uWU+CFMSY/yYERCCCGEEAPCrcA6IEcp9a9KqQRPByREX+RY8RwdEEVCcLzTdmm3YVNvauD5jFfZlrfbKensbfRmZdJi/nHmzyXpLIQQg4jLGilprU8CJ4F/VEpNxtYbbg3wDLZJ2Jex9YN7W2t9wVXnFQOf1Wpl3Tbnj8yahakYDAYPRSSEEEIIMWB8D9v1+lTg18CvlFJfAH/VWn/r0ciE6COsVivFDj2eYwIjGRoQjp+XL41m2wO+kni2DRB8/vhrZF/NdVo/JkKxJn0V0YGRHopMCCGEp/RKB3+t9THgGPC/lVKzgUeB+7BdzP5aKbUbeB74WHrEies5nVXBuZwq+/KE1KGo4dILTAghhBCip7TWLwEvKaUmAs8CDwN3A6uVUueBF4E3tNaVHgxTCI+qba6j3lRvX44OjMJoMBIfFGfvXzzYE8/VjVf4y/FXKKgtsq/z9/Lj0dH3MylqnBQNCSHEINWrDXKVUt5ABBAKBACGlv/mAu8C55RSi3ozBtG/WaxW1m2/aF82APctTPVcQEIIIYQQA5DWOkNr/QMgHngS2AekA78H8pRSrymlZngyRiE8xbG/M2Cv3E0IaRt0XlBbhMVqcWtcfUV5fSV/OPpXp6RzkHcgP578LJOjx0vSWQghBrFeSTwrpaYqpf4MFAHrsVVOmIGXgDlAJPBPQBSwUSm1pDfiEP3fgTPF5JbU2JfnjI8lMSrYgxEJIYQQQgxcWut6rfWbWut5wBjgj0AdsBbYp5Q6rJR6SikV4Mk4hXCnYof+zmCreAZIDG5LPDdbmp36QA8WxbUl/M/R5ymtL7evC/UN4SdTvseIIcM8GJkQQoi+wGWtNpRS0dhaaqwFxmIrTrUA3wJvAJ9qrRscXvJ/lVKfYqum+C9gs6tiEQNDs8nCpzsv2Ze9vYysnieTooUQQggh3EFrfQ74mVLql9jabzwKLAFexlYJHeHB8IRwmxKH/s4GDEQGDAXoZMBgAbFB0W6NzZPyrhbw5+MvU9Nca1831D+cH016lqjAoR6MTAghRF/hksSzUmo9sKzleAbgPPAm8JbWOq+r12mtzymlXgd+6Io4xMCy/Vg+ZdVt9yoWT01kaKi/ByMSQgghhBh8tNZNwAdKqYPA94GfYGulJ8SgUOpQyRzhH46P0fY1Oj4oFgMGrFgByKspZGrMJI/E6G6XqrN5PuM1p97XMYHR/GjS04T7h3kwMiGEEH2Jqyqe7wCuAOuA17XWe2/gtZnAxevuJQaVugYTX+y9bF8O8PNm5ewRngtICCGEEGIQUkoFYmub9zQwvWV1JbYnGoUYFErq2xLPrf2dAfy9/YgMiLC3mRgsAwbPVZznxZNv0mRusq9LDI7nh5OeJsRX2iIKIYRo46rE82PAJ1rr+uvu2Y7W+gXgBRfFIQaIjQezqalvti/fPnsEwQE+HoxICCGEEGLwUEpNBZ4BHgKCsT3VeAh4HvigXQs9IQYsi9Xi1LvZMfEMtnYbgynxfLLsDK+cehuTxWRflxI6gu9PeIpAH2n9LoQQwplLEs9a63cAlFIPAmVa628dtyul/hdQrrV+2xXnEwNbVU0jmw7m2pfDQ/xYPDXRgxEJIYQQQgx8Sqlg4BFsCefJ2JLN9cDrwPNa66MeDE8Ij6huvEKzpa0gJjogyml7YnAcx0tPAlDVWE1Ncy3BPkFujdFdDhcd482zH2CxWuzrRoWP5NkJT+Dn5evByIQQQvRVrurx7AN8AqwE/gPbQEFHC4G7lFIPAHdrrU0I0YX1u7NoMrVdzKyal4yvj5cHIxJCCCGEGLiUUjOAZ4H7gSBsCedM4K/AG1rrag+GJ4RHFTsMFoTOKp7jnJYLagpJD0/r9bjcbXf+ft7Xn9r7WQNMiBzLU2MfxsdLnkwVQgjROVe12vgBcDuwGfiyk+3/CViBVcAvgN+46LxigCksr2VnRtsjanFDA5k7PtaDEQkhhBBCDFxKqePAeGzJZhPwKbbq5q0eDUyIPsKxzQZAdKBzxXP7xHPeAEw8f5uzg08vfOW0bnrMZB4bfT9eRikQEkII0TVXJZ6/B+zTWi/rbKPW+hBwj1JqD/A4kngWnbBarby7OROLte0u+n0LUvEyGj0YlRBCCCHEgDYBKABeBl7WWhd4OB4h+pSS+raKZ2+DFxH+YU7bI/zDCfD2p95ka3uef3Xg9Hm2Wq18lbWZDZedH2ielzCLB9JXYzTI9zQhhBDX5qrEcxLwbjf2+wb4lYvOKQaYo5mlnL5caV9OHxbGpJGR13iFEEIIIYToofuAz7XWZk8HIkRf5FjxHBkY2SHZajAYiA+K42J1FgD5NQPj3o3VauXjC1+wLXe30/olwxeyKnUFBoPBQ5EJIYToT1yVeL4KBHZjvzCg1kXnFANIY7OZ97ecty8bDQYeWZIuFzRCiAGjwdSAr5evVAcJIfoUrfUnno5BiL6sxKHHc0xA50UxiSFtiefC2mLMFnO/bkFhsVh499xH7C085LT+zpTlLBtxq3xHE0II0W2u+va7H3hYKTW0qx2UUunY2mwc6mofMXh9tS+b8iuN9uVFUxIYFh3swYiEEMI1LFYL7+lP+PnOf+Y3h/7I1aYaT4ckhBBCiG4wWUyUN7Q9kdm+v3Mrxz7PJqu5w0DC/sRkNvHH/a91SDqvGbmK5UmLJOkshBDihriq4vm3wDbgtFLqTSADqAT8gCjgVuAuIAD4nYvOKQaI4so6Nh7Iti8PCfRh9S3JHoyo77tQlcX5yotMjZnY5QWwEKJv+OLSN+zO3w9Afk0hH5//grVjH/JwVEIIIYS4nvL6CixWi305OrCLiufgeKflvJoC4oP734D0JnMzv9vzFscKT9nXGTDwyOg1zI6b5sHIhBBC9FcuSTxrrXcppdYCfwX+HrC228UA1APf01pvccU5xcDx3rfnMZkdBgouTCPQ38eDEfVtxbUl/PHYi1isFvYVHuLXs/4eH6Or7iEJIVzpQOERNmVvc1p3qPgYc+JnkB6e6qGohBBCCNEdJfVlTstdFXzEBcViwIC15Wtwfk3/GzDYbG7mxRNvcK6yrf2hl8GLtWMfYkr0BA9GJoQQoj9zWaNJrfXbQBrwU+A9YAuwGXgb+F9Aqtb6ZVedTwwMxy+UceJiuX05NX4Ic8b3v+oAd8ooO22vvChvqCTv6sAYYCLEQHOhKot3z33U6bYPMj/DbJE5XsJzSmrLqaqv9nQYQgjRpzkOFoSuK559vXycktL9LfFstph57fS7TklnH6M3353whCSdhRBC9IhLyyS11sXAH7varpQaDcRqrbd1tY8YPJpNZt77NtO+bAAeXaowSt+wa2qfaC6qLSY5dLiHohFCdKasvoKXT76FydqWXA71HUJ10xXA9vd2W95uFg9f4KkQxSBltVr5KmsTGy9vxWgw8N0Jaxk7dJSnwxJCiD7JcbCgv5cfIT5dz6BJDI6juK4EsLXa6C8sVgt/O7uOE2Wn7ev8vP34/vgnGRme4sHIhBBCDAQuq3jupoeB9918TtFHbTiQQ2lVg315weQERsSGeDCi/iG3Jt9pubC22EOR9F1Wq5V9hYd568wH7C04SJO52dMhiUGk3tTACydep6a51r5uQuRY/mH6j/D38rev+zprM1WNnqk4NVvMbLq8jffOfczxkpOYLCaPxCHc76usTWy4vAUrVsxWC8dKTno6JNEHKaUSOlm3SCn1C6XUg0opX0/EJYS7OVY8RwdGXnOwnuOAwatNNVxputqrsbmC1Wrlw8zPOVR81L7Ox+jNP8z7niSdhRBCuITLKp6VUkHAY8AEwL+TXcKBZUCjq84p+q+yqnq+2tc2UDA4wId75svFzfU0mBo6PPJXWCeJZ0cWq4V1mZ+zM38fAAeKjvDZha+ZEz+DWxJmMTQgwsMRioHM9qjqO043hBKC43hizIP4e/txR8pSPjq/HoBGcxOfnP+Sp8Y94vY4P8z8jN0FBwDYXXCAYJ8gZsROYXbc9H45DEl0z4asb9lw2XnUxqSocR6KRvRFSikvbG3ylmO7dm9d/xrwhMOux5VSC7TWNW4OUQi3cuzxfL2B3o6JZ7C12xgS0beLaj6/uIFdLdfMAEaDkZ/OeZrxMaMoLe37iXMhhBB9n0sSz0qpaGAPkIKtYwLYBgw6/p6W5RdccU7Rv32w9QLNprYJ0fcsSCE4QAYKXk9eJ/3iimpLPBBJ32S2mPnb2XVOVRsAtaY6Nuds59ucHYyPHMOCxDmo8LRrVq0IcTM+vfAVZ8q1fXmIbwjfn/Ak/t5+AMxPmM2+wkP23o9HSjKYUzGDUREj3Rbj0ZIT9qRzq5rmWrbm7mJr7i5GhAxjdvw0pkZPItAnwG1xid616fI2vsza5LRu7eQ1jAsf7aGIRB/1Y+ABYKtSyqi1tiilFgNrgXzgt8BU4HFsc13+zVOBCtHbGkyNTk8mRQd03t+5VWeJ59ER6b0Smyt8c3krm3O225cNGHh89ANMS5jouaCEEEIMOK6qeP7/gFTgMPAlYMZ2IfoCcBlYAYwEHtJa73LROUU/dSqrnCOZbf3SRsSGMH9CvAcj6j86GyRY0VBJg6nRntgarJrNzbx6+h1Olp3pch8rVk6UneZE2Wlig2JYkDCHGbFTBv17J1xjV/5+tuXtti+3DuUJ9w+zr/MyevFA+t38z9Hn7es+zPycX834Cd5Gl45d6FRZfQXvnO184GGr7Ku5ZOtcPj7/BZOixjM7bjojw1MwGtzdnevmlNVXcLDoCL5evsyOm06QT6CnQ/K4b3N28PmlDU7rHp14DyvTF0lFm2jvYSADWKq1bq0QWIutiOQJrfVWAKXUMOAeJPEsBrDS+nKn5etVPIf5hRLkHUitqQ6AvKt9d8Dg9rw9rL+00Wndg+pupsdO9lBEQgghBipXfctdDhzSWs8EUEqNwHYhulFrvR74rVLql8DLSqm5WuvyaxxLDGAms4V3Np93Wvfo0nSMRqk87Y7cq/mdri+uK2HEkGFujqbvaDA18OKJN8msumhfZzQYeUjdQ5OlmZ15eyl2GA4DtuFuH2R+yvpLG5gVN435CXO6nFQuxPWcqzjPh5mfOa17bPT9JA3pOPgzNSyJWbHT2F90GLD9/d2au4ulI27t1Rhb24A0mNt667d+7h2rsFs1W0wcKj7GoeJjDPUPZ1bcNGbFTSPCP7z9ofuE/JpCNmdv50hJBharLV+2IWsLi4bN49Zhtwza6u1tubv59MJXTuvuSlnOXaOWeCgi0celAC84JJ0BlgJ5rUnnFnuwVTwLMWCVtLt2vN51osFgICE4zn49mt9HBwzuLzzMuszPndbdnXY78xJmeSgiIYQQA5mrEs/xwJudrLeXR2mtf6OUugv4V+BHLjqv6Gc2H8qluKLOvjxvQhyp8aEejKh/aT9YsFVBbfGgTTzXNNfy/PHXyL6aa1/nbfTm6XGPMj5yDGBrb6ArLrA9bw+ny89htXf/sQ2C25a7m+25exgzVLEgcS6jI0b2m+pO4XnFtSW8cupte7ITYGXyEqbGTOryNavTVpJRdpp6Uz1g6707PWayU3W0q31x6Ruyr7T9PRkRMox7R96Bt9GbhYlzya3JZ1/BYQ4VH7PH1aq8oZKvsjbzdda3qPA0ZsdPZ2LkWHy8PNsiyWq1crH6Mpuyt3G6/FyH7Q3mBr6+/C3b8vawePh8FibOxd+7szEUA9POvL32nuKtViYvYVnSIg9FJPqBYMA+GVUpNR6IBF5rt18DLpwVI0Rf1H6uSncKFBJC2hLPRXUlNFtM+LjhiabuOl5ykrfPrnNatyLpNhYPX+ChiIQQQgx0rvpX0AQOmZy2C9Yh7fbbhG0wiSSeB6GKKw2s33PZvhzo5819C1I9F1A/02wxOQ0sc1TUxfqBrqqxmr8cf8XpffHz8uV7E54kPbzts2U0GBk9NJ3RQ9Mpqy9nZ94+9hYeckquWbFyuvwcp8vPER0QyfzEOcyKm0bAIEpSiRtX21zHCyfecPosTY2eyMqkxdd8XYhvMHelLOODlirpJkszH53/gmfGP9YrcZ4p1059HP29/Hlq3MP29h4Gg4HhIYkMV4nck3Y7GWWn2VdwCF15welGjRUr5yrPc67yPIHeAUyPnczsuOkMC0nolbi7YrFaOF1+jk3Z27hUnX3d/etN9Xxx6Ru25u5iyfCFzE+cg5+XrxsitVWan6+6REFtEWMjFDFB0W457578A/bPV6vlIxZd97MpBr0SQDks34/tGn9Du/2SgFKEGMBK6ts+4iG+wQR4X//JmYTgtvaBFquFotoShoX0jZaCZ8o1r51+1+nf9YWJc7k9eakHoxJCCDHQuSrxfBFYrpT6t5ZH88qx9XmeBbzlsJ8P4J5vXKLP+XDbBRqbzfblu+enMCTIPV/8B4LCmiKnikpHgzHxXFZfzp+OvUx5Q4V9XZB3ID+Y9J1rVn9HBgzlnpF3cHvKUg4VHWVH3l4Kaouc9impL+Oj8+v54tJGZsZOY0HibGKDYnrt/0X0T2aLmVdOve008T5pyHAeHX1/twZXzkuYxd6Cg+S2PIp7vPQkZ8o1Y4aq67zyxlQ3XuHNM+87rXt41D1EBgztdH8fLx+mxUxiWswkyusrOVB0mP2FhylvqHTar85Uz468vezI20tCcBwTI8eSHp5GUujwXqvuMlvMHC4+zqac7V3+3EsLS2bJ8IVUNFTxTfZWp8FQtc11fHbxa7bk7GRp0q3Mi5+Fby9UbVusFi5UZXGkJIPjJSepabbdj//U8CuoguIAACAASURBVBULE+eyMnlJr97U2ld4mPf0J07rlgxfyB0py2Soqrie3cAapdRebNfyP8N2Xf916w5KqQRs/Z23eSRCIdzEseI5OuDa/Z1bJXYYMFjQJxLPF6qyeOnkW5itbd/FZsVN496Rd8q/C0IIIXqVq74ZfoSthcZBpdTTWuvjSqkjwFNKqV3YBg6OBZ4E8lx0TtGPnM2u5ODZEvtyYlQwCyd7/iKsP2nfZiMqYKh96ElXldDuVNNU67YKwoKaIv5y/GWqm9qGYoX6hvDDSc8QHxzbrWP4efkyL2EWc+Nncr7qEjvy9pBRetqpCqTR3MTO/L3szN/L6Ih0lo5YyMiwVLlAb8dsMVPVWE2o3xC3DMjrC6xWKx9kfkZm5QX7unC/MJ4d/0S3E5lGg5EH1N38/shz9nXrMj/nVzN/5rLErcVq4c0z79sTnwBz4mZcsw2Io6EB4axMXsLypNs4X3mJfYWHOF56kmaLyWm//JpC8msK+fryt/gYfUgJHUF6eBrp4amMCEnEy+jVo/+PRnMTewsOsiVnJ5WNVZ3uMz5yDEtHLCQlNMm+bnbcNHYXHOCb7K1cbaqxr7/aXMPH57/g2+wdLEtaxJz4GT1+zy1WC1nVORwpyeBYyQmuNHUc2mexWtiau4vDxce5O+12psdMdvnPk4NFR3nn7Dqnn2WLht3CqtQV8rNLdMe/A3cArRNQDcCPtdb1AEqpVOAAEAo81+kRhBgArFar03yQmG7OAYkNisFoMNqLRdrPT/CEnKt5/DXjdZotzfZ1k6PG88io+6S1nBBCiF7nqgzB74DbgFuAiJZ1vwE+Ad522M8A/NlF5xT9hMls4d3NmU7rHl2ajpdRLnRuRN7VtgElPkZvJkWNtz86X95QSaO5yW2J3/b2Fx7m3e0fY8TAzLhpLB62gKjAzqspe+rylRyeP/6afWI4QKR/BD+a/EyXFZzXYjAYSA9PJT08lYqGSnbl72dPwQFqm+uc9jtbkcnZikxGDBnG0uELmRA1dtBdrFusFsrqKyisLaKwtpiCGtuvxXWlmK1mwvxCWZO+iklR4zwdaq/blrebPQUH7Mu+Xr58b8JaQv1Cbug4yaEjmBM3g72FBwFbtf2WnB0sT7rNJXFuzt6OdkiOxwZGsyb9rhs+jtFgREWkoSLSqGtezZGS4+wrOOzUW71Vs6UZXXnBfl4/L19Sw5JJD7P9PRsWktDtvzs1zbUtVdV7OvydbI1resxkloxYSFwnTyX4ePlw67B5zI2fwc78fWzO3u6UhK9uusKHmZ+xOXs7y5MWMTtu+g0lya1WKzlX8zhSnMHRkhNdJsXbu9J0lTfPvM/u/P3cn76aRBdVwx0uPs5bZz5wSjovSJzDPWl3SNJZdIvW+rRSajLwFBAGfKW1dpxOWYStxd7TWuvtHghRCLeoba5zaqMVHdi9imcfozexgdH2p+k8nXgurC3mL8dfcRosPCZCsXbsQ4PuOlYIIYRnuCTxrLWuV0otBBYAp1rWfaaUWgv8H2x94PKBN4D/csU5Rf+x9Wg++WVtX/Rnj40hfVjvDdAaqHKvtlU8xwfFkdDuUb7i2hKGD0l0d1gAbLi8BbPFjBnYnb+fPfkHmBI9gSUjFrq092tm5QVeOPEGjeYm+7q4oBh+NOkZQv3at5S/cRH+4axKXcHKpMUcLslgR94ep/cdIPtKLi+f+hsxgVEsHr6QGbGTB1yVr9VqpaKhqi3B3PJrUW1xh0pXR1WN1bx88i0mRY1jTfoqwvwG5uDQU2Vn+eT8l/ZlAwbWjnnoppOHq1JXcLz0JHUtX3A3Xt7K9JjJDA2IuM4rr+1S9WW+zNpkX/YxevPUuEfw7eENqkCfAG5JmM0tCbPJrylkf+FhTpSdoazlCYz2Gs1NnCnXnCnXAAR4+5MWlmK74ROWSnxwbIcvv5UNVWzJ3cme/AM0OVRotfI1+jA3fiaLht9ChH/4dWP29fJl8fAFzIufxY68PXybs8P+fgNUNlbxnv6ETdnbWZG8mBkxk7tMQFutVvJqCjlaksHR4gzKHNr9dCYlNImp0RMJ9Ang84sbnFp/XKy+zG8O/ZH5ibO5I3kZgT7X7x/alWMlJ3nzzPtOSed58TNZM3KVJJ3FDdFaXwB+1cW2WqVUnNba2tl2IQYKxzZa0L3Bgq3ig2OdEs9Wq9UjP4fL6iv487GXnW7cpoUl88z4xwbctasQQoi+y2X/4rRcgG5vt+4tnHs8i0GmuqaRz3dfsi/7+3qx5tY0D0bUP1msFqeKicSQ+A7VfYW1xR5JPNc113dIOFmxcqQkgyMlGS5rUXGi9DSvnn4Hk0Pic8SQYfzdxKcI9gm66eN2xsfLh9lx05gVO5WsK9lsydnZoQ1HcV0p75xbx1dZm1g07Bbmxs/Avx8OIqxuvGJLLNe0JpltCeYGc+NNH/N46Sl05QVWp65kTvyMAVVRU1BTxOvtBvOsSl3BxKixN33MYN8g7kpdwfstPXmbLc18fP4Lnp3wxE0fs665jtdOvevUF/7ekXd1uGHVUwnBcdw78k7uHXknFQ2VZFZetP/XVfVvvamBk2VnOFl2BoAgn0BGtlRDj7Ymsz1rH7uyD3ba0z7IO5AFw+ayIHHOTf299/f2Y1nSIuYnzmZb7m625OxyqgIrb6jg7bMfsunyVlYkL2ZazCT757egpoijLT/XHPt+dmZEyDCmxExgavREwv3bbrROjBrHxstb2JKz095n04qVHXl7OVKcwarUlcyKm3rDf2cySk/z2ul3nN6zOXHTeUDdLUlncVOUUgla6/x26xYBU4BcpdSnWuumzl8tRP9XUuc8OzMqoPuJ58TgeA4XHwdsT+5UN11x+834qsZq/nTsJaqbrtjXDQ9J5HsTnuzxDWghhBDiRrgk8ayU2gi825JoFsJu3faL1De2DbFYNS+ZsGA/D0bUP5XUlTpV/Q0LSSA6MAoDBnsCzFN9nvNrCq653alFxYhbmRA55oaTKgeLjvK3sx86JVXSw9P47vjHezXZazAYSAlNImV8EsW1JXybs4MDRUedBrNUNVbzyYUv2XB5CwsS57AwcS4hvsG9FpOrNJgaefHkm049im/UEN8Q4oJiiA+KpcnSzN6Cg/bPY72pgff0JxwsOsbDo+4lNqj/z5W92lTDX0+87pSUnxU3jcXDF/T42HPjZ7Cv4JC9dUVG2WlOlZ1lXOToGz6W1WrlnXMfOSV+J0eNZ178zB7HeS0R/uHMipvGrLhpWK1WyuoryKy6YE9Ed9bzGGyPMx8vPcnx0pOQ2ekuhPuFcdvw+cyJn+GSlkIB3gGsTF7CwsS5bMnZyba83U5PUpTUl/Hmmff55vJWJkSN5VTZ2Q5DSNtLDI5navREpsRM6LLtj5+XL6tSVzArbhofZa7nTIW2b6tpruWdc+vYU3CA+9NXXXNIqqNTZWd59dTbTj8fZ8ZO5aFR9w6omz7CPZRSXtja5C0Hwh3WvwY43g07rpRaoLWuQYgByPEGowEDUTfQzq39Td78mkK3Jp5rmmr5c7sB3HFBMfxg0nd6dbCtEEII0RlXVTxPAva46FhigDifV8XeU21f1OOGBnLbVM+0gujvctq1exgWEo+vlw+RAREeHzCY16533SOj1nCo6CiZVRed1mdfyeXlk28RExjFkuELmd7NFhU78/byQeZnTusmRI7lqbEP49PNIW6uEBMUzSOj13B7ylK25uxid8F+p0RVvaneXsk4J346tw2b3+NWCb3p4/NfdDvpHOQdSFywLcEcFxRDXFAsccExHSpOZ8dN591zHzkl6C5WZ/F/D/6B5Um3sWTEwn77aGezuZmXTr5JRUOlfV1qaDIPqXtcUlFqGzS4mt8d/os9eb8u83NUeNoNf8535e/neOkp+3KEfzgPj7rPrZWvBoOBqMChRAUOZW78TPuQJlsS+gLnqy459VruSmxQDEuGL2BazKRe+ewE+gRyZ+pybh12C9/m7GBH3h6nm3xFdSUUZZd0+frYoBimRtsqm2Nu4OZKTGAUfzfxKU6UneHj8+spd/hcXb6Sw+8O/4U58TO4K3X5NSu7z5RrXj75ltPNsGkxk3h09BpJOoub9WPgAWCrUsqotbYopRYDa7G1zfstMBV4HPgp8G+eClSI3uRY8RzhH3ZD/xYnBDu33sq/WsjYoaNcFtu11JvqeS7jFYrq2v7tigwYyo8mPePyJwSFEEKI7nDVt7gXgSeUUi9rra9dEiQGBYvFyjub2g0UXJKOt5d8Eb4ZjoMFjQYj8UG2Soq4oFh74rnIU4lnh9gCfQKYHTeNOfHTyarOYXPOdk500qLi7XPr+PI6LSqsVivfZG/ji0sbndbPiJ3Co6PW3NAAMFcK8wvlnpF3sCxpETvz9rE9b7dTAq3Z0syOvL3syt/P1OhJLBmxwOXtDXrqWMlJ+zA7R/5efrakclAM8cFtSeYhvsHdSlomhw7nl9P/F5tzdrDh8rf2tigmq5kvszZxpCSDR0bdR3LoCJf/P/UmWwXxx1yqzravi/SP4Nnxj7s0GTpiyDDmxs9gd8vQwrKGCjbnbGdl8pJuHyO/ppCPL3xhXzYajDw19uEe9Q52BYPBQGxQNLFB0cxPnI3FaqGwttheDX2+6iL1praWF8lDhrN0xK2MixztlgRqsG8Qq9NWsmj4LWzO3s7O/H1ObX0cRQdEMiVmIlOjJxIfHHvT5zQYDEyMGsvoiHQ2Z29jU852+zmtWNlTcIDjJSe5M3UZc+NndngfzlWc56WTb2JySDpPjp7A46MfkKSz6ImHgQxgqda6tYx+LWAFntBabwVQSg0D7kESz2KAcuzx3N3Bgq1C/UII8QnmarPtgYC86zwd6CpN5ib+mvG6U8FKmF8oP3bRLBQhhBDiZrjqG/MeIAY4qZTaARwDKgBzZztrrV9y0XlFH7X9eD45JW1PX04fFc3opL5b/dnX5TpcsMYERuHbUnURGxTNibLTAJQ3VNJkbnJ73zbHVhsjwhLtCcrk0OE8O/5xilpaVBzsokXFxpYWFQscWlRYrVY+u/g13+bscDrXgsQ53Dfyrj6RVAnyCWRF8m3cNvwW9hUeZkvODqeqRYvVwqHioxwqPsq4oaNYMuJW0sKSPRixTWVDFe+e+8hp3cPqXsYMVYT5hfa4KtbL6MXypEVMjh7Pe+c+5nxVW4/3wtpi/vvI88xPnMNdKcv6TU/sT89u5FDxUfuyv5c/35v4JMG+rq8cuit1BcdKT9oHAW3K3saM2Cldtm5w1Ghu4tVTzn3Q70xZ1icT/UaDkYRg25DUW4fNw2K1kHe1gFqvK8SHxDDEHOGR3sRDfEO4d+Sd3DZ8Pt9c3sbeggOYrGaG+oczJXoiU2Mmkhgc79LYfL18uD1lKTPjpvLR+S/sva8Bak11vK8/ZU/BQe5PX01Ky5/l+cqLvHDiDadhnxOjxvHkmIc8dlNODBgpwAsOSWeApUBea9K5xR5sFc+ih5rNzeTWFJAYHCe9d/sIi9Xi1GrjRgYLtkoIjuNc5XkApzktvaXZYuKlk29xsfqyfV2wTxA/mvRMn34CTwghxMDnqsTzRmyVEAZs1Q93d7GfoWU/STwPYFfqmvhkR1uyydfHyAOLZKDgzbJareQ6VC4MC0mw/95xwKAVK0V1JQwPcV87E5PF5NTiIyms47ljg6J5dPQabk9ewtbcXewuOECTQ4uKOlM9Gy5v4duWFhWLht3Cpuxt7ClwrshdnnQbdyQv7XODsny9fFmQOId58TM5WnKCTdnbOvSCPVV+jlPl50gJHcHypNvc9rhlexarhbfOfkidqd6+7tZh85ib4PrevzGBUfx48rPsKzjEpxe/slez2gap7eFE6WkeUKsZHznG5ed2pQN5x3j/5Hr7sgED3xn3SIfhnq4S5BPI6tSVvNNyc6DZYmJd5nq+P/HJ6752XebnFDs8Wjs6It0l/afdwWgwMnxIIlFRIQCUlnbeD9pdwvxCeUCt5vaUJdQ01RITGNXrP3siA4byvQlrOVV2lnXn1zsNbc29ms9/H3mOWbHTmBA1ljfOvEezQ0uQ8ZGjeWrsw5J0Fq4QDNgf41FKjQcigdfa7deAC4eUD1Zmi5k/HH2B7Ku5DPUP51czftpvbsoOZNWNV5x+xkYH3FjFM0BCSFviubiulCZzs71wpDe8ffZDzla0PW0a4O3PDyc9PSBmbAghhOjfXHXB+BY4PEsvBrVPdlykrtGh4m5OEhFD5CL6ZlU0VFLvkCgc5tA3rn3yq6jWvYnn4rpSp8e8O0s8twr3D+PekXeyPOm2a7ao2JG3t8Nr7067vc8n0LyMXkyPncy0mEmcLj/HpuztXKzOctrnUnU2z2e8xl0py1mWtMjtMW7J2enU1zkhOI5VKSt67XxGg5G5CTMZFzmadZmfc6z0pH1bZWMVL5x4g6nRE7kv/S6G+Ib0Whw3o9liYtPlrXyTs81p/X3pdzFmqOrVc8+Km8begoNkXckB4FT5WU6Wnblmkv5Q0TH2FR6yL4f4BvP4GGm50FPBPkFu74k5LnI0KjyNLbm72Hh5i1PyY3/RYfYXHXbaf8xQxXfGPdZv+6eLPqcEcPwhdz+2a/wN7fZLAkoRPXKl6ap9qGx5QyXHSk4yO366h6MSxXXOH+2bqXhOdLhet2KlsLao20Njb9TlKzkcLj5uX/Y1+vD9CU85FasIIYQQnuKSbyla67WuOI7o/y4VXGFXRtvjZDHhASydPtyDEfV/ue36wiU6XETGBEZhwGDvoezuAYOO/Z0BksKHQedtUe0cW1TsLTzElpydTgPbHBkw8PCoe5kTP8NVIfc6g8HAuMjRjIsczaXqy2zK3u706DzA+ksbCfcPY0bsFLfFlXM1jy8ufWNf9jF6s3bMQ24Z0BjqN4Snxz9GRulpPsz8jKrGavu2IyUZnK3I5J60O5gVN61PVLRfqMrivXMfOw3mAZifMJuFiXN7/fxGg5H71Wp+e+jPHQYNdvYYdkldGe/pj+3LBgysHfNQn0vmi+7z8fJhedIiZsRO5pPzXzrdtHE0Knwkz457HB9JOgvX2Q2sUUrtxdYy72dAOfB16w5KqQRsTzhu6/QIottCfIPxNfrYh5rqyguSeO4DHNtswI33eAY6zPfIrynstcTzvkLnG5LPjH+c1LCkXjmXEEIIcaOkFEq4jMVq5Z3N2qn0/eEl6fh4y8esJxzbbIBzBYWvl69T3za3J54dkuJeBiOJQ7o/aMvXy5eFiXP511n/wBNjHiQ+yPm1XgYvnhr3SL9KOreXEprE9yas5R9n/IzpMZOdtr19dh2ZlRfdEkejuYk3Tr/n1GN7ddrtPRqMdjMmRo3ln2b+nPkJs53W15nqefvcOv50/OUOX/bcqd5Uz3v6E/5w9K8dks4To8Zx38i73BbL8JBEbnF4n8obKtmU3THH02wx8drpd2h0aF+zZMRCRkWMdEucondF+Ifz9PjH+OGkp4lpl/hID0vluxOecMvNIzGo/Du2hPPz2IaHBwD/R2tdD6CUSsU2fDAUeM5TQQ4U3kZv0sJS7Mu68gJWqzxE6mkl9W0Vz94GLyL8w274GDGBUXgZ2tof9Vaf5yZzM0ccqp2Th4zo9SezhBBCiBvhkhIZpdSzN7K/DBccmHafKCSrsK0v5+SRkYxPuf5ALHFteQ6J50j/CAJ9Apy2xwVF23uBFrk98dx2EZ0wJK4lAdJwQ8fwMnoxI3YK02Mmc7r8HLvy91FnauCO5KWoiIHRGzw+OJa1Yx8ixDeYrbm7ADBbzbx08i1+MfXviO2lfsGtPjn/hdNjo2OHjmJBwpxePWdXArz9eUDdzbSYybx77iOnBG9m5QX+8+D/sCJpMQsS57i1z2VG6Sk+0J9R3XTFaX2QbyBPTLqPMUFj3V6NfWfKUo6WZNhb0mzO3s6M2KlOj/yuv7jB6eZU8pAR3JG81K1xit43OiKdX834Kdvz9nC85CTDhySyKnWlDCITLqe1Pq2Umgw8BYQBX2mtv3LYpQjbs01Pa623eyDEAUdFpHGmQgO21huFtcVuvzEsnDneBI8MjLyptlXeRm9ig6LtCefeSjyfKD1ln6MBMDtuWq+cRwghhLhZrno28wW61+NZhgsOUDX1zXy0va1608fbyIO3ScWdK+Q6tLNI7KRXW1xQLCfLzgJQVl/R68NLWlmtVvIdYrtWf+fucGxRMVDdnXY7FQ2VHC89BdgqbJ/LeI1fTP0hoX690xYho/Q0uwsO2JdDfIJ5bPT9Hm9pkRqWxC9n/IRN2dv45vJWezV2s8XE+ksb2ZS9nVlxU5mfOKdDpacrVTVWsy7zc/ufiaNpMZP47qyHCPUf4pFhd4E+gdyddjt/O/shACarmXWZn/N3E5/CYDBwsuyM/UYGQIB3AE+OfUgGzA1Q3kZvFg9f0Of73Yv+T2t9AfhVF9tqlVJxWmspy3URFe58vawrL0ji2cNKHG7WxwTceH/nVonB8faEc15NIVar1eXXX/uLjth/72P0YUrMBJceXwghhOgpdwwXjAEmANHYEs4yiGQA2nggh5r6tgFIK2eNICos4BqvEN1xpemqUwVmZ0NCYgPbplVbsVJcV8qwkPgO+7laVWM1taY6+3JSuPuGGvZXRoORJ8Y8RPWxF+2D4yoaKnnhxOv8ZMr38HNx9WJVYzXvnFvntO6xMfcT4hvs0vPcLB+jN7cnL2FK9ATePfcRl6qz7dsazA1sz9vD9rw9jIlQLEicw5ihymXD8ixWC3sLDvLZxa+dKoUAwv3CeFDdzbjI0YT6e7ZP8ozYKewpOMil6ssAnKnQZJSdJmnIMHtCutUjo+5zar0jhBA9oZSKwnYNHwlYsF3DH9NaV1/zheKGJATHEuwTZH+65VzFeW4dNs/DUQ1eZouZcofZI1E3MViwlWOf53pTPZWNVUT4h/coPkeVDVWcqzhvX54UNY4Ab/n+JYQQom9xy3BBpZQX8FPgJ4Bnnu8WvcZktrD7ZNvjY5Gh/qyYKQMFXSG33fC+zhLKce3aNBTVFrsl8ZzXbuhhTyueBwtfLx++O2Etvz/ynL1FSs7VPF4//Q7Pjn/CpYnVv535kNrmtpsDCxLnMnboKJcc35XigmL46ZTvszv/AF9e+sbphgbYkq1nKjSRAUNZkDCbWXHTO7ScuRHFtSW8c+5jLlZnOa03YGBB4hzuTFnm1jYf12I0GHkgfTW/OfRH+6DBjzLXMzQg3OnP9paE2UyOHu+pMIUQA4hSSgF/BhZhe1rRkVkp9QnwU6117/QOGGSMBiPp4akcLTkBwIWqS5gtZnl6xUPKGiqwWC325WgXJZ7B1m7DlYnnA0VH7dcGALOkzYYQQog+yC1j0LXWZuD3SqmFwH8BD7njvMI9Tl2q4Ept22Cr26Ym4usjF8uukNdhsGAnFc9B0Rgw2C883TVgMO+q8/fNEZJ47rYQ32D+buJT/Pfh5+xJ1pNlZ/no/HrWjFzlkscwt+Xu5lxlWxVMfFAsq1NX9vi4vcVoMDI/cTaz4qZxuPgY2/P2dOiHWFZfzscXvuSLrE3MiJ3CgoQ5N/Q4ssliYnP2djZe3oLJYdAi2N6fh0fdR3Jo37tplhgSz8LEuWzL2w1AZWMVlY1V9u3xQbHck3aHp8ITQgwgSqkRwC5sVc5XsA0SLMU2kDwKmATcD8xQSk3XWpe76Ly+2AYb/gLYqbVeeAOvnQP8GpiFbRhiJvAy8Jf+0hJkVPhIe+K5wdxI9tVcUkKTPBvUIOXYZgMgOuDm2305DgQH27Xz+MgxN308R1arlf2Fh+zL4X5hpIenuuTYQgghhCu5JfHs4CDwA1ccSCkVAfwLsBqIA8qAr4FfX6sCQym1Fnj9OoffcSMXvIOdY7Wzl9HA7LEDqy9dVnUOX2VtItgnmAfVardWQjoODQv1Dem0D7Cvly8R/uGUN1QA7hsw6FjxHOYXSohf32jf0F/EBEbx3Qlr+dPxlzBZTADsyNvLUP8Ibhs+v0fHzr1awPqLG+zL3kZv1o59yC29v3vK18uHOfEzmB03nYvVl9met4eM0lNO1UdN5iZ25+9nd/5+0sPTWJg4h/GRY65ZLZ5Vnc075z7qcGPG2+jNiqTFLBm+oE9Xl92esoTDJce52lTjtN7X6MN3xj3SL/5shRD9wq+AocDPsSVumx03KqX8sSWH/3/gfwP/0NMTtlRYvwuk07HC+nqvXQRsAHKBfwUqgFXAn4BUbE9b9nkqol2f54oLknj2EMfBggDRPZgzEewbRKjvEHvbvPx2Twv2xMXqy5TWt933mRU31WVPzQkhhBCu5O7EcxS2Cdk9opQKALYDo4C/AIeBkdguhBcppaZqrSu7ePk2YE0X2xKBPwCnexrjYHGltomMC20XaBNShzIkyLV9aj3pQlUWzx1/hSaL7XvXEN9g7hnpvsrC3JprDxZsFRcUbU88u63i2TG24N5v7TEQpYYl8cSYB3n11Nv2dZ9e+IoI//CbbpvQZG7ijdPvOlX0rk5d2eFxz77OYDCQFpZMWlgyVY3V7GpJNLf2wGyVWXmBzMoLhPuFMT9xNnPiZxDsE2Tf3mBqYP2lb9iZt9fpcVSAkWEpPDTq3l4dXugqAd4B3JN2B2+eed9p/f3pq4lt125HCCF6YAnwudb6D51t1Fo3AP/eUmW8ih4mnpVS4cBR4DwwDTh3g4d4HmgAbnEoPPmbUuoz4MdKqde11hk9idEdIgMiGOofYb+WO1d5nhXJiz0c1eDkWPHs7+XHkB7OxUgIiaO6vDXx7LruNAcKDzstz4yVNhtCCCH6JrcknpVSQ4AVwJPYKhJ66ifAeOAHWuvnHc6TAXyK7XG7n3X2Qq11NpDd2baWi9Ry4J9dEOOgsO90EWZLWzJn3oT+ldy6lkvV2Tyf8ao96QyQUXrKbYnnelO9vQcwwLBrJHfjgmI5VW77rlZaX06zuRmfXqyArDc1OMWW6Iae0gPVlOgJlKeu5LOLylcMVgAAIABJREFUXwO2AZFvnnmPUL8hpISOuOHjfXrhK4rqSuzLYyIUCxPnuixeTwjzC+XOlGUsT7qNo8UZ7MjbS/ZV539KKhur+PziBr7O2sy0mMksSJxLVWMVH+jPnNpSAAR4+3N32u3Mjpver6qDpsdMZl/BITKrLgIwLWaS9HMUQrhaPPBmN/Y7DCx0wfl8sQ0p/6nWusFW/Nw9SqmZgAJe6eRpx79gS4w/iq1dSJ83KiKNPQUHAdvTdo3mJpcPHRbX51jxHB0Y2eP2Z4nB8Zwp14DtGt0Vf66N5iaOlLR9rNPCkokKHNqjYwohhBC9xSWJZ6WU+fp7AbbH5151wSkfB2o7OdbnQB7wqFLq5zfS100pdTe2C9SnXdWvbqCzWq3sPtF2nT8kyJfxKQPjoufylRyeO/4qjeYmp/VlDRWU1JX1aNBId+V1GCzYdcVzbFC0/fdWrJTUl/VqhWtBTZHTcn+rpu1rFg9fQHlDJbvy9wHQbDHx4ok3+PnUH9zQZ+1k2Rl2thwDINgniEdH3++SntF9gY/Rm5lxU5kZN5Ws6hx25O3haMkJzA7V3c0WE/sKD7HPoe+ho8lR41mTvopQvyHuCttlDAYD353wBN/m7MTf249bE+cNmD9bIUSf0QhEdGO/IMDU05NprYuB79/ky2e0/Lqvk20HWn6deZPHdjsV3pZ4NlvNXKjKYuzQ7ifihWuU1Dsmnnv+RJTjNbIVKwU1hSTfRGGBo+MlJ52+o8yKm96j4wkhhBC9yVUVz9f75tsAXAJew9bK4qa1VE+PAnZprRsdt2mtrUqpg8A9QHLLObtzTD/gj9h6UL/Wk/gGk8tFV8kva3vsfc64WLy9+k/1YFdyrubxl+Ov0mBu6HT72YpMtySec9v1gbt2qw3nR+0La4p6NRmc1z42abXRIwaDgTUj76KyodJeuV7TXMvzGa/yi6k/JNg36DpHgOrGq7x9dp3TukdHr+m0L/hAkBw6nOTQ4dyddgd7CmxtOKqbrna5f5hfKA+kr2ZC1Fg3Rul6/t7+3JGy1NNhCCEGrtPA7UqpX2qt6zvbQSkVCNwJnHJrZB0ltfya136D1vqqUqoKSOnJCaKirv9vaHf26Y45IZN47fS79uWchmwWRslTLe256v3uTENzA1WN1fblpMiEHp9vvG+aUxPHK4ZKoqLG9eiYR04ds//ez9uPpaNn4+/j+hk0vfleC2fyXruPvNfuJe+3+/Tl99oliWettTuzja23iDtcZLbIafk1hW4mnoFngGHAY66Yfu3Oi1RPWrfD+e29a0Fan/3/6m5clyvzeC7jVepNbd+1RkelkVNdQG1THQCXai5xX9SyXonTUemltnYJQT4BjBo2vMvqxpCwFNtDry2qqerVP4uyyw7977z9GD28rXKjr34G+oN/iPgu/7Ltf8iqtLWRKK0v57Wzf+PXt/6k0+Fxre+1xWrh5Z1vOPU/Xpo2n0Wj+02h102LIoS0xHt4xHwXB/KPsfH8DnTZRft2AwaWpN3CwxNWE+gTcPPnkc+1W8n77T7yXot23gBeAPYrpf4LWzVxCbYik2hgLvD32Ab3/ZeHYmzV+uGt62J7rcM+fd4Q/xCSwhK5XPX/2LvzOLnKMu3jv1PV+74v6TXr6U4CJCFggLCKKALiiKMioMjqitu4vOMGI7M4g44KoiLggqOOKw4MKg4oIrKFJaSTzsne3en0vnf1XlXvH9V96lQv6a2qeru+n/f9pJ+qU3WeVMfh9N33ue7AjzhVTbONu5b5auxtCVmvSs2b4siZK0zNI9YVw/DoIOmx7+9cNfe2srf5gL0+q3hbRIrOIiIi4RLt4YLhMJOLTOdxJzXa7fxZ4C+WZT01z72tGIPDXv7ySvDCySzLpCR/yVzbT6q2s54vP/UNeoeCxTszew2fPfdDfPvFh3iu7mUAqpotRrwjxLgj+z+fseIjQHlmyUlvqU+ITSA3KYuWvsBQmuNd4RteMpmajuD3viyjeEnl5C5mCbEJfPbcD/G5//t3Wke/l1bbEe55/gd87Kwbp/ycf3/wz7zauM9eF6UVcN1pV0Vlz4tFjDuGc0rP4JzSMzjSXsuTR5+hd9DDpRsuxMxZu9DbExFZKr4HnA9cDTw0xTEG8KBlWeGIz1vUWlqmvpNm7Jc2JztmttamrrELk8c6j3OkvoHUeQ63Wy4i8XmPt78pdAxQwkhKWM5XmJxPbU89AIdaauf1no8d/UvIekvmqWH/TKLxWUuAPuvo0WcdXfq8oyean/VcG1bCVjkzTTMHuBN4cfyFqGma3xv98jOWZbWH65xhcj1QBHwkXG8Y7YvUhfDc3kY8A8Fovx2VeYvy7zTTz7vR08TXX/4uPcO99mPlaaXcvOl6ejuHWZu8hucIFJ4HRgZ54XAV6zMjV8wa8g5T3x3MUc6Pz5/275CXmGcXnms66iP2/fD6vNR01Tv2FvjeL5d/2wvPxa2br+drL99L/0gg7uW5upe530jl79ZdBoT+u67vbeDHu39jvzrGcHOd+S66OwYJRHWuPKlkcmXp6BBQ//z+TerfdXTp846epXCRKtE3euffNaZp/orATJXTgVzAT6Dz+UUCw/x+v3C7tHWP/jlVHlWK45glwcxazxN1wcLigY5DnJ6/ZQF3tLI4BwsCYYvWK0pZZReeT/Q24PP75tS04fP7eL4heItjTkIW6zLmlSYjIiISceEaLphD4EK0FOiY5JAcAoP7LjZNc8foIJG5mslFpvO46dwEtAGPzmNPK87TjqGCcTEuzqzMP8nRi1tTXwvfeOW+kKJzaWoRHzrtRhJjAreuVWZtCHnNvvYDES08N3ga8fl99vpkgwXHFCTnsXc0H7ilv41h3wixrvB3ZTf3tzLiC/7SQfnO4bcqpYCbN7+Hb+1+wB6c93+1T5GdkMV5xWfZxw15h/nB3p+GfD/esvZSSlL1PRERkbmzLOvXwK8Xeh/TGMt8Kx7/hGma6UA6jHYNLBHrMlbjNtz2f/stFZ6jqrk/GLWRGpdCYszcI7qcnHNXBryDtA90kJM4+4HshzqP0jYQ/FF7R+F2DRkWEZFFL1z3x3+RQNH5i8Bdkzx/DfBxAjnKX5rnuY4S6LqYcJE5aixs9uB0b2SaZjmwHfhfy7KG57mvFaO1s5/9NcGLntPNPBLjl2JqC7T0tfHNV+6j2zGUrDhlFR/ecnNIHmxmQgYFScGct+r2A0TSWFfEmJkUnguTgsV/n99Hc1/LSY6eu+M944ceqsgZCWbWOq6peHvIYz8/8DB7WoORGr89/BgnPMHO+IrM9VxYsjNqexQRkZXJNM2tpmnetsDb+Nvon+dM8ty5o3/+NUp7CYt4dxyr00vt9f72Qwu4m5WnxdHxnJcYvkHixeMGfh/vnVsk3nOObmcDgzMLTp/XvkRERKIhXIXntwG/sizrny3Lahv/pGVZfZZlfYNA58QV8zmRZVke4DVgm2maIZMUTNN0A2cDdZZl1U72+nHGJsQ9OZ89rTTPVDXinMB47qmFUx67mLX1t/ONV74bMr16VXIBH9lyM8mxSROOr8wOdj3X9dTTM9Q74ZhwOe4oPMe6YslPyp32NYUpoV3njZ753FgwteO9wcKzgUFhckFEziPwusLTuWz1G+y1Hz8PVv0XR9preKWhij8ff8Z+Ljk2ies2vkN52yIiEg0XAf8ZzROapllhmubqsbVlWa8S6Gj+e9M0ix3HGQQaXoaBH0Zzj+FQkbne/rptoJ3W/gk/WkmEOKM28mZw7T1TReMKz/XjmjhmYmBkgFeaX7PXGzLXkp2YOe+9iYiIRFq42lRzgVdncNyrwFvCcL4HgG8CtwLfcDx+LYGJ23ZXtWmaFcCgZVlHJ3mfsV8TV4VhTyuCz+/nmT3B39LnpCewoTRjAXc0N+0DHXzjle/SMdhpP1aQlMdtW28hJW7yFJfKLJM/1QUbZ/a3H+SMgq0R2V+do7hblFI4o2KisyMboMHTHPZ9QWjHc35SLnHu2IicRwIuLb+Ytv4OnmsMdLkM+Yb5t6fvxe/3hxx3TcXfkxGfvhBbFBERmRPTNDcCG8c9nGuapvOWn8csy+oDqgELqHA890HgT8BfTNP8OtAJvItAYfwLlmUdjtjmI8TMWsejRx+311b7IXKKZh/LILPTO+zBMxKcXR+ufGeApNgkMuMz7J876ufQ8fxy8x6GfMEbdHcUbg/b/kRERCIpXIXnZgIxGtPZQCBPeb6+QyC+4y7TNMuAXcAm4BPAHkLjPia7SHXuB+BYGPa0Ilg1HbR2DdjrnacW4lpi2WKdg11845X7QjLS8pJyuG3rLSedHL4+YzUxrhg7T7e6/UBECs9en5cTjgvSmcRsACTEJIRc1DZEoOPZ7/eHdDwrZiPyDMPg3RVX0TnYxf6OQIJQ50BohP3OVa/jtNxNC7E9ERGR+XgHE2P4NgK/cKxXM8W1umVZz5umeR7wT6P/P57Atf8NlmV9P+y7jYKy1BIS3PEMeAMDgvd3HOScotct8K6Wv4mDBcPX8QxQnFpoX6PPJWrjuYYX7a8T3Alsyd0ctr2JiIhEUrgKz38ArjNN86eWZf1lsgNM07wWeDfws/mezLKsYdM0LwFuB64CPkyg+H0/8KXRroiZGLs/KfIj3ZeJpx3dzgZwzualFbPRNdjNN175bshtizmJ2Xx0662kx6ed9LVx7jjWpa+2i3/V7Qfw+/1hH+rR1NfCsGNYXMkshvcVJufbF7WRiNroHuqhd9hjrzVYMDrcLjc3nXItX3vp2yGZzhDoOn/b+nklGImIiCwIy7JuJ3A9P5NjJ73gsixrF/Dm8O1qYbldbtZlrKGqrRqAAx2H8fl9itKKsPGzUcKZ8QxQlFzIntbA97RtoJ3+kQF7iPn0e2vlcNcxe316/qnEuePCuj8REZFICVfh+Q7g7cCfTNN8iUCkRgeBroNc4HygkECB945wnNCyrG4CHc6fmOa4KauClmWdFo69rBR9AyO8ZAUvyjaWZ5KdPrMLpsWge6iHb75yX0hHQ3ZCFh/beuuMIwoqszfYhefuoR7qexvC3vVbN26w4GzevyA5j33tFgDN/a2M+EaIcYVv8KOz2xlUeI6mxJhEPnjaDfzHrnvoGgp0PLsNN9dvupp4/fAhIiKybFRkrbcLz73DHup7GynRXWYR5fz5wMAgNzG88SZF475/J3obWZtRPqPXPu8YKgiK2RARkaUlLL86tyyrDjiLwICP7cBNwKeA24CrgVXAbuCipZi1JgEvVDcxPOKz1ztPjcwFcPdQD/vaLBo8TXh93rC8Z++Qh7tf+R6NfcHc48z4DD669RYyE2aeUV2ZtSFkXd1+ICz7c6rrDRaeXYaLVbMY3ucc9Ofz+ybcNjhfx8cNQylKXVod70tdZkIGH9pyI4UpecTHxHNt5d9Tmlo8/QtFRERkyTAz14WsrdGmB4kcZ8dzVkIGsWGeYTJhwGDvzAYM+vw+nmt8yV7nJeWwOq0srHsTERGJpLC1QlqWVQ2cYZrmacAZBIb8+YAmYJdlWXvCdS5ZGE+/FozZSIqPYduG8N6CBoEojDuf/yp9I/1AoKMzPymXwuR8CpMLKEzJZ1VyPjmJ2TO+5bB30MPdr34vJKIgIz6dj227lezErFntb1VyAelxaXbHaXX7Ad5QdsGs3mM6zuJuYXL+rC58C5NDBww29jWzKmXmhevpOIehpMWlkhaXGrb3lpkpSink62++HcMwaGlRSpCIiMhyU5icT1pcKt1Dgf/OW+2HuLj0/AXe1fLW3B9s1gh3vjNAbmI2ca5Ye0DgTHOerY5DdA522esdBdvDHvMnIiISSeG7B3+UZVm7CXQ3yzJS39LL0YbgQLPXbconNsYd9vO83PyaXXQG8Pq9nPA0jhaNg/+sYl0xFCTlUZhSQGFyPquSA39mJmSEFKQ9Q33c+dQ3QyIi0uNS+ejWW8iZwy10hmFQmbWB5xoDt7wd7jzKoHcobFEHPr+POkfhebZRFgXJ+SHrht5GyDs1LHuD0KgNxWwsHP3AISIi4WKa5t/m8DLd8hRBhmFgZq7jxaZXADjUeSTs8WkSNP4uwbyk8DfXuAwXq1IKOdZdC4Q2c5zMc46YDQOD1xWeHva9iYiIRFLYrl5M08wB7gRetCzrgXHPfW/0y89YltUernNK9Px1T+jF0bmnRubnjZruuhkdN+wboa73BHXjblOLd8cFOqOTA53Ru9urONJRaz+fGpfCbVtvnVcnQ2XWervwPOL3crDjMJtzKuf8fk5t/R0MeAfsdUlq0axenxiTQEZ8ut0Z0eCIFpmvQe9QyEV5uLOtRUREZEHsmOPr/GHdhYRwFp6HfMMc7aplfeaaBd7V8tQ12M3waCcyQF5i+DueIXDX2ljh+URvw7RDI/uG+9ndUmWvK7M2zHgujYiIyGIRlsLzaNH5RaCUwFDB8XKAK4GLTdPcYVlWUzjOK9Ex4vXxbFUwpqI4N5my/MhELNT0BAvPhcn5VGStp6G3iQZPI11D08cKDHqHONZda1/UOaXEJnPbllsoGBdHMVsVWRswMPCP/rxV3X4gbIVnZ74zzL7wDIHPzS48e8L3P7UTvQ323xkmZtWJiIjIknThQm9AJjKzJuY8q/AcGU2OfGeITMczQLHj2nnIN0xLfxv5J2mGeal5N8O+EXu9Q93OIiKyBIWr4/mLBIrOXwS+M8nz1wA3A18FvgR8MEznlSjYc7iN7r5gF8DOU1dF5Fb/vuH+kI7aU3I2cuXaS+21Z7iPBk8TJ3obafAEitEnPI14hvumfe/kmCRu23pLWPKOU+KSKUktorbnOBDeAYMThvfNobhbmJxv76m5rwWvz4vbNf9YlOPjussVtSEiIrL0WZb11ELvQSbKSsgkLynHvjbe336Iy9e8cYF3tTy19IcO445ExjNA0bhr5/rehpMWnp0xG4kxiZyasyki+xIREYmkcBWe3wb8yrKsf57sScuy+oBvmKZ5DnAFKjwvKc6hgm6XwY5N+Sc5eu7GCrljytJKQtbJsUmsy1jNuozV9mN+v5+e4d6QYnSgON1kR1akxafwgVNvCGuH7sasDfZ+m/paaOvvIDsxc97vW9cT7HjOS8whMSZh1u/h7Oj2+X209LdOyH6eC+cQlFhXbMS6QUREREQEzMz1duG5pqeO/pGBOV0bysk5G19iDDdZCRkROc/4Bpj6nhNsm2IWS6OnKeQOzu35W2Y1cFxERGSxCFfhORd4dQbHvQq8JUznlCjo6h3ktcNt9nrLuhzSksIzSG+88fnO5eMKz5MxDIO0uFTSslKpyFpvP+73++kc7MKXMERJeiF9Xd6w7rUy2+T3NU/a6/3tBzin6HXzfl9n1MZcM5QLxxWZT3iawlJ4rnd0YxelFJ40k05ERERE5qcicx1P1z8LBJoJDnUe4ZScjQu8q+Wn2RG1kZOYHbFr3MSYBLITsmgbCIw8On6SAYPPNbwUsj6rcHtE9iQiIhJp4fqvajMwfZUQNgBt0x4li8aze5vw+YO5vjsjNFQQQgvP6XGp8xqeYRgGmQkZVOSuJTkuKRzbC7E6rZQEd7y93heGuI2uwW56hnrtdUnK7POdAQqSQovMjWHIefb5fSHTt4uV7ywiIiISURsy12IQjLez2g8t4G6WL2fHc6RiNsY4r6Hrpyg8e31enm8MFp4Lk/MpTS2O6L5EREQiJVyF5z8A15mmed5UB5imeS3wbuCJMJ1TIszv9/P0a8Eu1/SUODavyYrY+WocURulM+h2Xkhul5sNmcGhL1bHQby++XVVO2M2YG6DBQGSYhNJj0uz142e5nntC6Clr5Uhx7Tv8Rl1IiIiIhJeSbFJIdeD+zsOLuBulievz0vraAcyRG6w4Bhn9F/HYCd9k8yqqW4/QLdjqPqOwu0Rma8jIiISDeGK2rgDeDvwJ9M0XyIQqdEBxBOI4TgfKAR6Ro+VJeDIiW4a2oIXQ2dvLsDtisytZ12D3XQOdtnrstTFXXgGqMzawGutewHoHxmgpqeONenlc36/unGDBecatQGBzoiuoW4AGsLQ8Tz+VsD57E1EREREZqYia709V6TB00TXYDfp8WnTvEpmqnWgHZ/fZ68jXnhOnThgcH3m2pDHnEMFXYaLM/K3RXRPIiIikRSWKqJlWXXAWcDLwHbgJuBTwG3A1cAqYDdwkWVZh8NxTom8v+4JLTbuPCU6MRsws3znhbYxe0PIurptfnEbxx35zhnx6aTGpcz5vZw5z019LfPuxj7eGyyKGxisSi44ydEiIiIiEg6m4w47AKtDcRvh5Mx3BshLjF7UBkxs7ugd9rCndZ+93phlkh6fGtE9iYiIRFK4Op6xLKsaOMM0zdOAM4A8wAc0Abssy9oTrnNJ5A0Oe3l+X7BTdl1xOoXZyRE73/jCc2na4s8xy0nMJjcxm5b+QGx5dfsBLltzyZzfzxm1UTLPjuKC5Dz7a6/fS0t/W8hjs+UsPOcmZZMQE3+So0VEREQkHNaklxPjimHENwIEcp7PLFAHbLg4850h8hnPWQmZJLjjGfAOAhNznnc1vcqIP9gwoqGCIiKy1IWt8DzGsqzdBLqbJzBNMwV4j2VZ94b7vBJeL1stDAwFL3oi2e0MofnOOYnZJMeGfyBgJFRmbaBldNr4se46+ob7SJrD3vuG+2gb6LDXxXMcLDimcFxHcqOnaV6F53pHDIjynUVERESiI84dy9r0crvT2eo4hN/vV+ZvmDg7nhPc8aTN447DmXAZLlalFHKk6xgA9b2hUXvOmI3k2CQ251RGdD8iIiKRFpnA3nFM09xmmuZ9wAng7micU+bHOVQwLtbFGRVzL1pOx+/3U9sdLDwvhZiNMZVZwbgNP372z/H2x+PjLjrn2/FcOK7I3DCPAYM9Q710OQacFKvwLCIiIhI1zriNjsFOmvtbT3K0zIaz4zkvKScqBX1n3MYJT5MdiVff2xByB+QZ+VuJcYW9T0xERCSqIlZ4Nk0z0TTNG03TfAF4kUDucxzwy0idU8KjubOf/bWd9voMM4/E+Mhd9LT2t+MZCQ4xLEtd/DEbYzZkrsVlBP9nVN1mzel9ah0XmUDIBPO5SIpNIj0umAfX4Gmc83uNL4qPz6YTERERkcipyFofsrbaDy7QTpYfZxE/0jEbY4oc19IjvhF7D85uZ4AdhWdEZT8iIiKRFPbCs2mam03TvJtAd/N9BIYNWgSGDRZblvXOcJ9Twutv44cKnhrhmI3u2pB16RLqeE6ISWBterm9rm4/iN/vn/X7HHdEWSTHJJEZnzHvvRU4Bgw29s2949m5N4DieXZji4iIiMjMlaQWkRiTaK81YDA8Br1DdA522eu8xJyonHd8bF19zwm8Pi8vNL7sOKZw3ndAioiILAZhaWM1TTMOeAfwfuAswAB6Rv/8hYrNS4fP5+evjsJzXmYiG0rmXwQ9GWe+s4Ex727faKvM2sDBziNA4PbHpr7mkKLvTNQ5uoqLU1eF5Ta/guR8+weTJk8zXp8Xt8s96/dxdjynxCaTHpc2772JiIiICPT2D7O/poO1Relkpk4+vNlluNiQuZbdLVUAWB2H8fl9IXfdyey1jBssmJsUncLzqpQCDAz8BJpV6j2NxLbF0TvssY85S93OIiKyTMzrasU0zfWmaX4VqAd+CJwNPAO8Dxj7Fa1nipfLIlRd00F796C9PueUwohnndV019lfr0opIN4dF9HzhVtl9oaQ9b72A7N6/ZB3iCZHBnO4Cu+FjuL3iN9L60D7nN7HOW27OCU8RXERERGRlc7n8/PlH77IvQ9X8cUHnudE69Q/NlU4cp77R/pDsoBlbsZnZedHKWoj3h1HbmK2vT7eeyIkZsNluNievyUqexEREYm0OReeTdN8AtgPfBwYAv4DqLAs6zzLsn5oWZYKzkuQs9vZAM7ZXBDR83l93pAL56WU7zymOGUVKbHJ9rq6bXaF5/reBrvjAaAkTMP7Csd1XTd6mmb9HkPeYZoc076LUpXvLCIiIhIOnb2DtHQOAOAZGOHbD1cxOOSd9FjngEEAq11xG/PV7LjGBciNUtQGhOY8H+uqpaqt2l6fkrOR1LiUqO1FREQkkubT8XwhcAR4C1BiWdZnLcuaXcVNFhXPwDAvWcELsE1rsshKS4joORv7mhnyDdvrpZTvPMZluEKGvhzsPMKwd/gkrwhVNyFDOTwdzwXJeSHrhjkUnhs8jfj8PntdHKaiuIiIiMhKl5WWQGVZpr2ub/Xw0OPWpPNC8pJyyYhPt9f7OzRgcL6aHVEbqbEpJMUmnuTo8HLmPPeN9Idcb59VuD1q+xAREYm0+RSee4C1wI+BB0zTvCAsO5IF8/y+Jka8wYuenadEvrvVGbMBUJa29DqeATZmmfbXw75hDncdm/FrnR3fce448sKUL5cSmxzSLTGXwrMz3xlUeBYREREJp5su30haUqy9/ltVI399rWHCcYZhhHQ9H+46xtAsGh1kImfHc7iuv2eqeIq7CFNjU0J+rhAREVnq5lN4LiQwTPAo8F7gCdM0D5mm+TnTNJfWdDgBCLnITU6IYev6yOecOQvPMa4YipKXZpRDRdb4nGdrxq893hssPBenFIZ1UExhUjBuo9GRIz1Tx3uC/yZiXDFRy74TERERWQkyU+O55S2bcE7Q+PEfD1DX3DvhWOcddiO+EY7MotFBJnJ2POdF+RrXGbXhdGbBtjkNAxcREVms5lzhsiyrz7Ks+yzL2gqcA/wXgYGCXwaOmab5uzDtUaKgrrmXY4099nrHpgJiYyI/Kbum57j9dUnKqiV7oZUenxpyATnTnGevz8uJ3kZ7XZwS3t/ZFKYEC89Nfc0ht/HNRL2j43lVcv6S/f6IiIiILFYby7O4cudqez084uPe3+yhf3Ak5LgJOc8dynmeq95hD56RPnsd7Y7nzPgMEmMmRnvsUMyGiIgsM2GpLFqW9axlWe8BioHPADXAG0efvtI0zf/+TX9qAAAgAElEQVQwTVP3DC1i42/pi0bMxrB3mPre4HmXYr6zU6Wj6/mEp5HOwa5pX9PgaWLEHxwiUxKmfOcxBY6O52HfCK397TN+rc/vC/n+KGZDREREJDIuP7ucTeXBvOemjn5++Pv9IXnP6fFpFDiGR2vA4Nw5u50h+h3PhmFQPK7ruTS1mFUpkR3sLiIiEm1hbWm1LKvdsqz/sCxrHXAp8AiQBnwS2Gea5p9N07wmnOeU+Rvx+nh2b7DrtjQvhbKC1Iif93jviZAO3LLUpZnvPKZyXNxGdfv0Q1/qxmUol6SGt7hbOI8Bg239HQx4B+11kQrPIiIiIhHhchncfMUmMlLi7MdeqG7mT6/UhxxX4eh6ru05Tt9wHzJ7znxngLzE6HY8w8S4DQ0VFBGR5ShiWQqWZf3BsqwrgdXAPwNNwHnAjyJ1Tpm99oEOvrPrvxnIrAYjUATeeWp0cpZruo+HrMuXeMfz2ozVxLmCw2H2t08ft3HcMVjQbbgpdHSxhENhcmjXROMsCs8TBguGuSguIiIiIkFpyXG8/8rNuIxg4vPPnjjI0YZue+3Mefbj50DnkajucblwdjwbGOQmZkd9D+sz1thfx7njOD1/S9T3ICIiEmkRD/G1LOu4ZVlfAEqBdwFPRfqcMnM/s35DtecVYosPEVNwlBi3wY5N0bnFq6YnOFgwwZ1AbpSz1cIt1hXD+sy19np/+8FpM5XrHIXnVcn5xLhiwrqnlLhkUmKT7XXDLAYM1o8rPBfp1j8RERGRiNpQksFV5wcLkiNeP99+uArPwDAA6zLWhAyitmZwh51M5Ox4zkrIINYde5KjI+PU3E1cUnYhGzLWctPma0mOTYr6HkRERCIt8tPjRlmWNWJZ1s8ty7ooWueUk/P6vCHZcO7sBraszyUlMToXXjXdwcJzaVpxyEX0UuWM2+gd9oQUlsfz+X0hXcXFYc53HuPsom70NJ7kyFDOveUkZE06AEVEREREwuuNryvltLXBDtzWrgEe/N9q/H4/iTEJlKUG7xLc36HC81w09wc7nqOd7zzGZbi4cu2lfHTbrWzKrliQPYiIiETa0q/0yZy1DbQz4g9Oy3Yl9XJaZXR+094/0k+To9NgqcdsjJmY8zx13EZrfxuD3iF7Hakoi5DCc1/ztF3YY473OAYLKmZDREREJCpchsGNl28kOy3efuyVg608/mKgacPMCuY8N/e10jHQGfU9LmU+v48WR9RG7gLkO4uIiKwUKjyvYI2TxC4MJ828I3Y+artDO4GX+mDBMflJuWTGZ9jrfW1TF57Hd0OXRqjj2Tn9fNg3Qlt/x7Sv8Qz30TEY/CFm/PATEREREYmclMRY3v/WzbhdwbznX/75MIeOd4UMGATY33Fo/MvlJLoGuxnyDdvrvCUe9yciIrKYqfC8gh1pnxgDsa99f1TO7cx3BihbJh3PhmGwMTvY9Xy0u4b+kYFJj63rCUZZGBisSo5McXf8wMLGvukHDI7Pdy5OUceziIiISDStXZXOOy4KFpm9Pj/f/m0VObGFIQOtndF5Mj3nYEFYuKgNERGRlUCF5xXsYOvEwvOBjkMMOeIfIsWZ75wal0JGfHrEzxktlVmm/bXP7+NAx+FJj3NmKOcl5ZAQEz/pcfM1vvDc4Jm+8Hy8Z1zhWVEbIiIiIlF38enFnG4GC6MdPYP84LGDrM1YbT9mdRzE7/cvxPaWpOb+lpB1vjqeRUREIkaF5xWsqW+SqA3fCFYUbter6T5uf12eVoJhGCc5emkxM9dhEPz7TJbz7Pf7Q6I2ItlRnBKbHDIle0aF595gvnNSTGJIfIiIiIiIRIdhGLzv0kryMoJDnvccacPfHRw+2D3UM6Pru5kY9A7xzInn+XPdMyGzSJYTZ8ez23CTlZC5gLsRERFZ3lR4XqG8Xh/9TD6IpKotsnEb3UM9IfnBzsncy0FSbCLlaaX2urrNmnBM52AXvcMee10SoXxnCPzAEjJgcEaF52DHc1FK4bL6xYCIiIjIUpKUEMMH3rqZGHfwR7fdu0OvzcLROFLVWs2dz3+Vn+z/Fb84+Fu+/vJ36Bvum/f7LjbNjgHnuYnZuAz9SCwiIhIp+q/sClVVXw9u76TP7W3dH9Hb9ZwxGwClyyTf2anSkfPcOtA+IUvu+LgM5UgWniF0wGCjpxmf3zflscO+kZCuGcVsiIiIiCyssoJU3n3xenvt86TCSJy9tjoOzvm9Owe7uH/PQ3z7te/TPhAcQl3bc5y7X72fvuH+Ob/3YuS8Lle+s4iISGSp8LxCvVRzNGRdnlJuf90x2MkJT2PEzj2+8FyWWhyxcy2UjVkbQtbj4zZqe0LztSNd3HV2PA/5hmkfmLzbHSYWpjVYUERERGThnb9lFTs2jl3TGYx0ZdnPHew4gtc3eVPJVHx+H38+/gxffu4uXmnZM+kxgeLz95ZN8dnr89I60G6v85TvLCIiElEqPK9Qh1uPh6wvXXNhyLqqtTpi53bmO+ckZJESlxyxcy2UsrQSkmKCWXzjC8/O4X2Z8RmkxEb2MyhMCh0weLK4jfHd2Co8i4iIiCw8wzB4z5tMCrMDszt8jpznAe8gNT3Hp3rpBHU9J7hr17f4xYHfMuAdDJ4Dgx0F20Pmg9T2HOeeZdL53DrQHtJgocKziIhIZKnwvAL1D47QNtRmr13+WDZlV5ARn24/FqmcZ7/fT01PsOO5bBnGbAC4DBdmVvB2yAMdhxjxjdhr52DBSMdsQGjUBpx8wGC9oyjuNtwUJOdFbF8iIiIiMnMJcYG857gYV0jhGcBqnz5uY2BkkF8ffJR/3/XNkGtyCMz1+OTpH+S6je/go1tvDSk+1/TULYviszPfGSAvUVEbIiIikaTC8wq0v6YDEnrsdVZcNoZhsDmn0n7saFdNyPC7cGkb6MDjGFJSmrb8YjbGVDoKz4PeIY521QDQO+wJGa4YjQzltLgUkmOCPzycrPDs7HguSM4jxhUT0b2JiIiIyMwV56Zw3RtN/INJ+AaDd9hVtR44yatgT+s+7nz+qzxR95eQrt9YVyxvXftmPrP9NlanlwGBIvRtW26ZWHzefT/9I0u3+Dx+7ooynkVERCJLhecVaM+xNlyJwaJyeUag8Lk5u8J+zI+f6raTX7zORU13bci6LHV5djwDbMwyQ9b7RuM2nDEbAKVR6Hg2DCOkc7nR0zzpcX6/n+O9DfZaMRsiIiIii885pxSy89RCfF3Brudj3bV4hgYmHNsx0Ml9e37Ed177QUjzA8Cm7Aq+8LpP8oayC3C73CHPFaeumlh87q7j7leXbvHZ2fEc744jLS5lAXcjIiKy/KnwvAJV1TZgxAzb6+K0AgDMzHXEOrpbq9rCn/PszHc2MKISM7FQMhMyKEgKFnvHcp7rxg8WjFJx1zlgsKGvKaTTZUz7QGfIDxLR6MYWERERkdm75g0byMBxrWb4eOivz9pLn9/Hn+r+ypefv4vdLVUhr02PS+XGzdfygVPfR3ZiFlOxi88xy6P43NwfjBvMS8rFMIwF3I2IiMjyp8LzCtPc2U/7YOgtZmOdsHHuONZnrrUf39dmzXo69nScWXKFyfkkxMSH9f0Xm8rsDfbXdT319Az1hhSeU2KTQ7K1I8mZ8zzkHaJjoGvCMRMHCxZGfF8iIiIiMnvxsW5uvvC8kMdebqjmZ08c5HBHLf+x6x5+efB/GPQO2c8bGJxXdDZf2PEPbMs7dUaF1+LUVdy2dWLx+Z5XH1hyxWdnx3NeogYLioiIRJoKzyvM3qPtGImh2c0FScGC5CnZwZznvpF+jo6LxpgPn99HraPoupzzncdUjovb2N9+MKS4W5JaFLVOi8IJAwYbJxwzvvBcpKgNERERkUVrXX4emTHBnGJ3eit/anqcr718D7U9x0OODQwP/BDvNN9KYkzi+Lc6qeLUVXxkXPH5WHct31pCxedB7xCdg8HGC+U7i4iIRJ4KzytM1ZE2XIm99jrWFUN2Yqa93uQoPAPsbdsftnM3epoZcnRclKct33znMeszVocM53u1ZU/IUJNoZiiPLzw39k3Mea535DtnxmeEZPqJiIiIyOKztTA4p8WV1EtMQQ04+hpiXbH83brLRocHls75PCWpq/jI1ptDis9H7eLzxGzpxaZlwmBBdTyLiIhEmgrPK8iI18f+2g6MhGDhOS8pF5cR/GeQnZgZUqCsag1fznNNd13IejkPFhwT545jXfpqe727ZS9+/Pa6JIoZymlxqSHdLQ2epgnHOAcfKt9ZREREZPGryFo/5XPezlzcBy6k0HvKhOGBc1GSWsRHtt5MkuOaMlB8vn/RF5+b+0MLz/nqeBYREYk4FZ5XkKMN3fQPenE5ojbGd8ECbHZ0PZ/wNNI+0BGW89c4bveLMdysSikIy/suds6cZ2fRGYjqcEXDMChMDg47HF947hvup22g3V4r31lERERk8Vubvhq3EVpU9g/FM3hwC0MHttHR7uKr//0q33+smr6B4SneZeZKUou4bestkxSfF3fnszPfGSBXGc8iIiIRp8LzClJ1pB3cwxhxg/ZjznznMZtzQuM2qlrDE7dR48iLLkpdFRJBsZxVZm2Y9PEEdzw5idlR3YvzFw2Nnib8/mAh3BmzAdGNARERERGRuUmIiefcoh1AYHjg+cXncP3q95M2XIozc+Pp1xr4/P3P8+qh1ineaeYm73yuWdTFZ2fcXWpsCkmxs8u5FhERkdlbGZU/AWDvsfaQfGeAAkcH7JjVaaUkxSTSNzooZG9bNecVnzWvcw/7RqjvDQ6zWwn5zmNWJReQHpdK11BPyONFKYUhMSfRUOAoPI8NWMlMyAAmDhZU1IaIiIjI0vD29W/hzIJtpMenkRGfDsDmsjz++8lDPP1asLmgs3eIb/7yNc7alM/VF28gJTF2zucsTS3mI1tv5u5Xvmf/3HC0u4Z7dz/Ah067kYSYhPn9pebJ6/NywtPEse5aarrr2NO6z35O+c4iIiLRocLzCtHbP8zRhm5c2Z6QxwsnKTy7XW4qszbwUvNuAKyOQwx5h4hzx835/PW9J/D6vfZ6JeQ7jzEMg4qsDTzf+FLI49GM2RgzPlrlhKfJLjw7O54T3AlkJWQiIiIiIoufYRiUjWvsSEqI5X1vruTMynx+8Lv9tHUHO5Gf3dvE3qPtXHuJyfaKiT8PzNRY8fmbr3yP/tHi85GuGr4V5eKz3++nbaCDmu5ajnXXcay7jrqeeoZ9k0eL5KrwLCIiEhUqPK8Q1TUd+P1gODqeXYZrymyzzTmVduF52DfCgY7DEyI4ZqOm+3jIuiyteM7vtRRtnKTwXLwICs+NniY2ZZtAaMdzUUpB1LuxRURERCT8Nq3O4p9uPJNfPXWYJ1+utx/v7hvm3oer2G7mcs0lJunJc2syKU0t5rYtN/PNV6NXfPYM93Gsu46a0W7mY9119A57pn/hqO15W8K+JxEREZlIhecVoupIG0BI1EZeYs6U0603ZpkYGPYwvKq2/fMsPNfZXye448lbYVOkK7I2hHyeACULkKGcHpdGgjuBAW+g42VswKDX56XBEYWimA0RERGR5SMxPoZrLzE5oyKP7/9uP80d/fZzu6wWqms6ePcbNrBjYz6GYZzknSZXmjZV8flB3rz6YozRrGkDg8DbG6NfT/Z4oIPb+Zo2YjnSUUvViYPUdNfR0t82q/3FuWIpTSumLK2ErbmnsDq9bNZ/RxEREZk9FZ5XAL/fz95j7QAYicFOgMnyncekxCWzOr2UI101AFS1VuPf8NY5XYhCaOG5NLV4xXXTpsQlU5JaRG1PoPM7xnBP6D6OBsMwKEzO52h34PvaOFp4buxrZsQRhaLBgiIiIiLLj1mayR03nMlv/nKEP75YZ7dEeAZG+N4j+3hhXxPveVMFmanxs37v0rRiPrLlJu5+9X5H8fkY97x6fxj/BtMzMFiVUkB5WgllaSWUp5VSkJQ3ZcONiIiIRI4KzytAY3sf7d2D4BrBFR/sbiiYpvC5KbvSLjx3DHZywtNIUUrhrM8/MDJAU1+LvR6fP7dSnFmwzS48b8qpXLCL38LkPLvw3OBpxu/3h+Q7gwrPIiIiIstVfKybd71+PWdU5PHgY9U0tPXZz+0+3Mbn73+Od160np2nFuKaZdNJWVrJhOJzpGUlZI4WmANF5pLUIuLnMZtGREREwkeF5xWg6shot3NCaO5ZQdLJB4lszq7gkSO/t9d7W/fPqfBc21MfEjFRusLyncecX3w2LsNFz1AP5xWfvWD7cP7CYcA7QOdgF8d7gvnOLsO1IN3YIiIiIhI9a4vSuf19Z/A/zxzjd8/V4vMHrtf7B7384Hf7eeKl41y5czVb1+fM6q7HseLzvbsfnFXu8kwkxiRQllpCeXop5WkllKaWkB6fGtZziIiISPio8LwCjMVsuBLHFZ6nKS4WpRSSEZ9O52AXAFVt1VxSfuGsz++M2QAoS12ZHc8uw8X5C1hwHjNxwGBzyGDB/KRcYt2x0d6WiIiIiERZbIybq85fy3Yzjwf+t5rjLcF5MHXNvdzz6z2U5qdw5c7VbFk38wJ0WVoJt5/1GRo9Tfj8fvz48fv9YLej+O1Ctx8/gf/nPC54pM/vJyM9kVWp+bgHEldcZJ+IiMhSpsLzMjc84mN/bQcAhmOwoIFB/jQD/gzDYHN2BX898TwQGBDiGe4jOTZpVntwFp5TY1PISsiY1eslvMYXnhs8jSGFZ8VsiIiIiKwsZQWpfPH67Tz2XA2P/u0YI97g3Yq1Tb3c/as9lBWkcuXO1Zy2NntGBejEmISwDfHLzQ10NbcM9oTl/URERCQ69OviZe7Q8U6Ghn0AuBKChefshEziZtDVujmn0v7aj599bdas91AzmmsMUJZWPOcBhRIeGfHpJLiDA2OqOw7iGQ5m+xWnqvAsIiIistLEuF285ZzV/MstOzjvtFW4XaHX7DWNPXzzl69x54928drhNrszWURERGQqKjwvc1WjMRsAhiNqoyD55PnOYzZkriPGFWyMr2qrntX5e4Z6aR/osNelK3Sw4GJiGEZIzMr+9oMhz6vjWURERGTlyklP5PpLK/iXW3Zw7iQDBo829PD1X+zmnx96iaojKkCLiIjI1BS1scztHR0siOHDlRDsap0u33lMvDuODRlr2dce6HSubjuAz++bcbbaxHznlTlYcLEpSM7jWHctAD6/L+S5uQyQFBEREZkL0zSzgC8BbwUKgVbgMeALlmU1TPPa6SqemZZldYZloytQbkYi73tzJZedXc6jzxzjb1WNdi4zwJET3Xzt57tZW5TGW3euYWN5pu5sFBERkRAqPC9jXZ4hapsD8RpGggeM4IViQdLMOp4BNuVU2IVnz0gfR7tqWZtRPqPXTig8q+N5URif8zwmPS6N1LiUKO9GREREViLTNBOBPwMVwD3ALmA98A/ARaZpnm5ZVsfU7wDAPgKF68l4pnhcZiEvI5EbLqvksrPLAgXovY04m5wP13fz1f9+lfXF6Vy5czWVZSpAi4iISIAKz8vYvqOOmI2E0OvumXY8A2zOruQX/NZeV7VVz7zw7Mh3zk7IVFFzkZiq8Kx8ZxEREYmijwGnAB+yLOvesQdN09wN/Ab4AvCJad6jxbKsX0ZuizImPzOJGy/fyGVnl/PIM0d5bl9TSAH64PEu7vrZq2woyeCtO1dTUZa5cJsVERGRRWHJFp7nc1ve6Ovjgc8C1wIlo6//X+BzlmW1Rmrf0VTlKDzHJI8vPOfO+H1yErMoSM6n0dMUeN/Waq5ce+m0r/P7/SEdz8p3XjymLDwr31lERESi5z0EupIfGPf4b4HjwLWmaX7SsiyFCC8iBVlJ3HzFJi4/u5xHnjnG8/uacH6DDtR18u8/fYWK0gyu3Lkas1QFaBERkZVqSQ4XdNyW9wHgV8D1wHeBdwLPmKZ50qsb0zRjCBSZPw88CtwE/BK4EXjKNM24SO09Wnx+P3sdgwXTsgbtrzPi00mMSZzV+23OrrC/PuFpDBkYOJX2gQ56h4MF73IVnheNzPgM4t0T/5kr31lERESiwTTNNAIRGy9bljXofG600PwCkAusnuH7GaZpJod9ozKlwuxkbnnLJv7pptdxZmUe48M19td28pWfvMI3frGb1s7+BdmjiIiILKwlWXgmeFvexyzL+oRlWT+xLOsO4DoCF6dfmOb17wdeD9xoWdbHLcv6sWVZHwNuBxKB10Vu69FxvLmXbs+QvXYnBQvAs8l3HuMsPAPsbds/7WucMRugwYKLiWEYFCRN7HpW1IaIiIhESdnon8eneL529M8107xPjmmaPwJ6gF7TNLtN0/yRaZpF4dikTK8oJ5n3X7mZf7rxTLZXTPw5Y/fhNj5///P877PHGPH6Jr6BiIiILFtLNWpjvrflfQg4CDzkfNCyrDuBO8O81wXh7HYGPx5/cKB3QfLsC89r0stJjEmkfyTQrVDVup9zi8466WucMRsGBiWpuv5fTAqT86npCX6P4txx5CZmL+COREREZAVJHf2zb4rnPeOOm8pG4GUC8XkxwBUEfla4wDTNbfOJ0MvNne7UMztmpcjNTWXLxkKONXTz08f387fXgumHQyM+fvXUEV7Y38wHrjqNU9bmzPkcEh36rKNHn3X06LOOLn3e0bOYP+sl1/E839vyTNMsHn3942OFadM0E0zTXFajl6uOBAvP6ZnDeP1eez2XwrPb5WZj1gZ7bXUcYsg7fNLXOAvP+cl5JMQkzPq8Ejnj/x0UJRfiMpbc/0kQERGRletS4AzLst5jWdbDlmX90rKs9wL/TGCGyycXdnsrU3lhGv/vvWfynx87n/UlGSHP1TX18o/3PsN//vRlOnsGp3gHERERWS6WYsfzbG7LOzLJ82OZEYdN0/wo8PHR9xw0TfP3wD9YlnUoXJtdCIPDXg4eD3Y4F5eGfhCTRSzMxKbsCl5q3g3AsG+YAx2H2JxTOemxPr+PWkfURnmq8p0Xm/EDBotSle8sIiIiUdM9+udUucwp446bwLKs30/x1L3A54CLgf83p90BLS09Uz431ll0smNWuvQEN5+5eitPvVrPL586Qv/giP3ck7vqeL6qgasuWMt5p63CZZy8B0ifd/Tos44efdbRo886uvR5R080P+u5dlUvxcLzfG/Lyxr9871AHIGOiCYCmc8fBs4yTXOLZVkNU7x+Wgt9W96u6iZGvMGUkfxVfo60BZ/fXLqGtITZn/+81NN5qPrn+EfnVh/uO8yFuWdOeuzxrgYGvcGM6U2r1i1o6/9ivu1goZySvA5eC643Fa4Ny+ekzzp69FlHjz7r6NLnHT36rGUBHQX8wFRDQMaaTQ7O4b1bRt87bQ6vlTByuQwu3FbMNjOPnz95kGf3NtnPeQZG+NHvLZ55rYHr3mhSmq//eyQiIrLcrMT76uNG/8wHzrUs63uWZf2PZVkfJTCUMI8lflveKwea7a8NA0jotdep8SlzKjoDpCWksi67PHieE1X4/ZPHaB9qPxayXptVNulxsnBykrJ447rzASjLKObcssl/iSAiIiISbpZleQj8CnybaZoheWymabqBs4E6y7JqJ3u9aZqnmKZ5q2mapZM8vR4wCN4JKQssPTmOm6/YxKfetYWCrKSQ5w6f6OaffrCLnz1xMKQrWkRERJa+pdjxPN/b8saqsP9jWVbbuOceINABfcGcd8fC35b34t5G++vS/FROdO+113kJufM6d0X6Bg62HQWgpa+d144dYlVKwYTjquqDaSVuw03SSPqC3GahWzxO7i2ll3FR4QUkxyTR1TEIzD1rT5919Oizjh591tGlzzt6lsJtebIiPAB8E7gV+Ibj8WsJNIN8aewB0zQrgEHLso6OPrQZ+M7oe9w07n3H4jV+HYE9yzxUlmdxxw1n8vsXann0b8cYHvEB4PP7efzFOl7c38zVr1/P6WYuxjTxGyIiIrL4LcXC83xvyzs2+qd7kudaWeK35bV3D9DQFkwh2VSeyd/6gre0Fc5hsKDTpuxKHjnyB3td1VY9aeHZOViwOGUVsa6l+E9tZUiJnep3OCIiIiIR9R3gGuAu0zTLgF3AJuATwB7gLsex1YBFcF7LL4AbgBtN08wBHiNwff82AtnO/wd8Lwp/B5ml2BgXV5xdzus25vNfjx9gjyMTsKNnkHsfruKUNdlcc8kG8jISF3CnIiIiMl9LLmpjvrflAfuALmDLJM+VELgtb6rBhYve3qPtIevyktiQrOWC5LkNFhxTnFJIRny6va5q3T/hmBHfCPW9J+x1WdpUvyMQERERkZXKsqxh4BLgbuAq4AcE5rDcD1xgWdZUM12wLGsEuAL4FLCBQOf0V4Hc0cfePHqMLFJ5GYl87O9P5YNv3UxGSlzIc3uOtPGF+5/nEUdXtIiIiCw9S7UNdc635VmWNWSa5k+AD5imeYVlWY84Xv/h0T+djy0pVY7Cc3ysm/i0/pDnC+bZ8WwYBpuyTZ458QIAR7tr8Az3kRwbzGqr721gxO+116VpJfM6p4iIiIgsT5ZldRPocP7ENMdNyF2wLGuAQFf0XRNfIUuBYRhsr8hj0+osfvvXo/xxVx1jI2SGR3z85i9HeLaqkY+8cwunrstd2M2KiIjIrC3VwvN8bsuDQGH6jcAvTNP8NwLxGxcB1wGvjr7/kuPz+dl3LFh4rijNoGWgJeSYgqT5FZ4BNmdX2oVnn99HdZvF9oKt9vM13aEN42Wp6ngWEREREZHJJcbH8K7Xr+fszQX86A8WR04Ex/U0tvfxuW//jYu2l/C2natJSliqP8KKiIisPEsuagPmd1ve6OtbgB3AD4FbgPuA84Gvjb6+/yQvX7SONfbgGQjeUbh5TTaNnmC+c4I7PiQmY67MrPXEODKbq9pC4zac+c7x7rh5d1mLiIiIiMjyV5qfyj9edzrveZNJUnxogfnJXXV86cEXOFDXuUC7ExERkdlasr8uns9teaOPtxCI6rg1/LtbGHuPtvdCf4gAACAASURBVIWsN63OYvfRZnudn5wXlunQ8e441mesobr9AAD72i18fh8uI/B7jJqeYOG5NLXYflxERERERORkXIbBBVuK2LY+l5//6RB/q2q0n2vrHuArP3mZy84q5y3nlBPj1s8ZIiIii5n+S72MOAcL5qQnkJeRQKMnWHguTJrfYEGnzdmV9tee4T6OdQdmOQ6MDIScs1SDBUVEREREZJbSkuO46fKNfOrqreRkJNqP+/3w6N+O8a8/fpmmjpPe6CoiIiILTIXnZaJ/cITDjiy0Tauz8Iz04RkJXoyFM/Jic05FyHpPazUAdT31+PHbj5enlYbtnCIiIiIisrJUlmVy9ycv4NwtRSGPH23o5vYHX+Tp107g9/uneLWIiIgsJBWel4n9NR14fcELrs2rs2hw5DtDeAvPOYnZIYMK947mPNf0aLCgiIiIiIiET0pSHJ+69nRuuryShDi3/fjgsJfvP7afbz9cRW//8ALuUERERCajwvMyUeWI2TCMQGeAM/ICoCCMURsAmxxdz/W9DXQMdIYMFkyJTSYrITOs5xQRERERkZXHMAzO3lzI7TecydqitJDndlktfOnBF6g+1j7Fq0VERGQhqPC8TDjzndesSiMpIZbGvmDHc6wrhuzE8BaBnTnPAFVt+6npDnY8l6YVh2WYoYiIiIiICEBeRiKfvWYbV+5cjcvxs0ZHzyB3/exVfv6nQ4x4fQu4QxERERmjwvMy0NzRR3Nnv73evDobIKTjOS8pF5cR3m/32vRyEmMS7PXzDS/RNhAsgJenloT1fCIiIiIiIm6Xiyt3ruaz124jJz3484gf+P3ztdz5o12caPUs3AZFREQEUOF5WXB2O0NgsCBAoyPjuTA5vDEbAG6Xm8qsDfb6aHdNyPNlaSo8i4iIiIhIZKwrSueOG87knM0FIY/XNvXyTz94kT+9Uq/BgyIiIgtIhedlwJnvnBQfw+rCVPqG++ka6rEfdw4CDKfxcRtOKjyLiIiIiEgkJcbHcOPlG3n/lZtIio+xHx8a8fHQHyzu/tUeuj1DC7hDERGRlUuF5yVuxOujuqbDXleWZ+J2uWjsGzdYMAIdzwAbs00MJuY4Z8ZnkBqXEpFzioiIiIiIOJ1Zmc8dN5yJWZIR8virh1r54oMvsOdI2wLtTEREZOVS4XmJO3Kim4Ehr70OxmyMLzxHpuM5NS5l0s7mcnU7i4iIiIhIFGWnJ/Cpq7dy1flrcLuCzTHdniH+8+e7+ckfDzA07D3JO4iIiEg4qfC8xFWNy3feXD4x39lluMhNzI7YHiaL21DMhoiIiIiIRJvLZXDZWeV87j2nk5+VFPLc/710nC//cBc1jT1TvFpERETCSYXnJc45WDA/K4mcjESAkKiN3MQcYlwxE14bLptzKiY8VpZWHLHziYiIiIiInEx5QRq3X38G529ZFfJ4fauHO3+0i0eeOYrX51ug3YmIiKwMKjwvYb39wxxr6LbXm0djNiC047kwQjEbY4pTVpEel2avDQxKUlV4FhERERGRhRMf5+a9b6rgw287hZTEWPtxr8/Pb54+yr889BINbZ4F3KGIiMjypsLzErbvWDt+x3os33nQO0T7QKf9eEFSZAvPhmFwWu5me12SWkRiTEJEzykiIiIiIjIT2zbkcscNZ3LKmtD4waMNPdz+/Rd5/IVafH7/FK8WERGRuVLheQlzxmy4XQYVpYEJzk19zfgdJemC5PyI7+WKNZdwSs5G1qSX8S7z7yJ+PhERERERkZnKTI3nY39/KtdfWkF8nNt+fHjEx8+ePMR//OQVWjr7F3CHIiIiy0/kgn8lovx+f8hgwfXF6STEBb6djZ7mkGMLIhy1AZAUm8T7T70+4ucRERERERGZC8MwOO+0VVSWZfLg/1Zj1QXvErXqOvnigy/wrovWcd5pqzAMYwF3KiIisjyo43mJamjro6Nn0F5vCsl3DhaeDQzyk3KjujcREREREZHFKjcjkU+9eytXv349sTHBH4kHh7z88PcWX//FayE/a4mIiMjcqPC8RDm7nQE2rw7mlTX2BQvPWQmZxLnjorYvERFZHB577BF27tzOY489stBbERERWXRchsEbzijh9vedwerCtJDn9hxp44sPPM9zexvxK/tZRERkzhS1sUQ5851Tk2IpyU+x142eJvvrwijEbIiIyOQeeOC7fP/735vRsVu2bOOee+4L27m3bdvOl7/8b1RWbgrbe4538OAB3ve+dwNwxx3/yutf/4aInUtERCQSCrOT+cfrtvHYc7X8z1+P4vUFCs2egRHue2QfLx1o4bo3mqQlqZlHRERktlR4XoJGvD6s2g57vak8C9doBtmIb4SW/jb7uXwVnkVEFsxFF72BNWvWhjz2wAP3cezYET7zmc+TkhL8pWFGRmZYz11QUEhBQWFY33O83/721wC4XC4effRhFZ5FRGRJcrtcXHF2Oaetzeb+R/dxvMVjP/eS1cLBuk7ee2kFW9crwlBERGQ2VHhegrw+P0MjPnt9yppgzEZzXys+f/C5wqT8qO5NRESCVq9ew+rVa0Ie+9Wvfg7A2WfvJDs7ZyG2FRb9/f388Y+/o7x8DVlZWeza9QInTtSzalXRQm9NRERkTkrzU/nCe8/gf545ymPP1TCWstHdN8zdv9rDOZsLuPriDSQl6MdoERGRmVDG8xIUH+vmyp2rSYhzs21DLmduDHY1O/OdAQrU8SwisqSMZTP/4Q+P8c1vfpVLL72Ib33rG/bz1dV7+fznP83ll1/MBRfs4KqrLucLX/gstbXHJn0fZ8bz299+Be9851vp7e3lK1+5k7e85Y1ccMEOrrnm7Tz++O9ntc8nnvgDHo+HCy98Pa9//SX4/X4effS3Ux5fXb2XT3/647z5za/noovO5uab38NTTz054bjXXnvtpMeNjIywc+d2PvrRD0547R/+8Bg7d27nBz+4337sAx+4kYsuOpv6+uO8//03cNFFZ1NTcwwAn8/Hr3/9C2688TouvngnF1+8k+uuewcPPfR9BgcnDpWqq6vlS1/6R6644hIuvPAsrr32HTzyyMP4fD5GRka44opLuPzyNzAyMjLJ3+tVdu7czle+cudJP1cREVlYsTEurjp/Lf/v2tPJz0wMee6Zqka++ODz7D3WPsWrRURExEmF5yXqyp2r+dbHz+PDbzsFtyv4bXTmO4MKzyIiS9UTT/yR6up9fOxj/8AFF7wegIMHLT784VvYt28v1157Pf/4j7dz+eVXsmvXC9xyy/U0NTVO+74+n59Pfeqj9PT0cMstH+SWWz5IV1cXd975RSxr/4z399vf/hrDMHjTmy7jooveQFxcPI899gher3fCsbt3v8L7338DTU2N3HLLB/jEJz5NbGwcn/vcp3n44V/ax+3atYurr7562uPm4q67/pXTTtvKZz/7RTIzA7Em3/723Xzta18hOzuHj3/803ziE59hzZp1fPe73+LOO78U8vra2hpuvPE69u2r4rrr3senP/05iouL+cpX7uS73/0WMTExvOlNl9HZ2cGzz/51wvmffPKPAFx66eXz+nuIiEh0rCtK5/YbzuTi04tDHm/vHuSrP3uVHz9u0T848ReNIiIiEqR7hJYwYzTX2anRE+x4To9LIzEmccIxIiILradviN88fZSGVs/0By+gNcUZXHtp5YKce9++Pfz3fz9McnIwB/rIkcNUVm7ixhtvZevW0+3HMzOzuOuuf+V3v3uU66+/6aTv29BQz44dZ/PJT37GfiwrK5s77/wSTz/9Z0yzYtq9HTxoUV29jy1btlFUFPiB/Nxzz+eJJx7n2Wf/ys6d54cc/7WvfYWUlBTuuec+UlNTAXjDGy7l3e++iu9+914uu+xKYmNjueOOO6Y9brL/9k1naGiIoqJiPvCBj4Q83tbWyo4dZ/OVr3zNft83v/kK6uuP86c//R+tra3k5ATiUL71ra8zNDTI179+r/13ftObLuPWW9/Hz372Y97xjqu5/PIr+elPH+Kxxx7l3HMvsM/j8/n485+fpLi4lFNP3TLr/YuIyMKIj3X/f/buO06q6v7/+GtmtrNsg22wwFKWAwgCUlTACopS1GCNSYwmmhhLNMauUX/R5JtiikaMJYoaoyiISgdBEBAFkSIIXMoudQsLbGF7mfn9MbPDDlvYxiy7vJ+Pxz4uc++5M2cOF+aznzn3c7j5sr4MTenMm/O3cST/+N0wn68/yLdWNtde1JtRgxK8a+6IiIjIcUo8tzPVS20kdlB9ZxE5PX28Mo3lGw62djdOytqfC8D1F/U6ScuWN3z4uT5JZ4Dx4ycwfvwE7+OiokIqK50kJnYBIDMzo0HPfdNNP/J53L//WYA7EdsQVYsKTpgw2btv4sSrWLp0MbNnf+KTeE5LS2X37l1MmDDZm0wGCA4O5q9//Sfl5RXedjt27GDKlCn1tmuqSy4ZV2PfU0896/1zRUUFJSUluFwuunXrzvbtW8nMzKBz584UFBSwZs1XDBo02Jt0BvcXwE899SzHjuUTFtaBTp06M2jQYL76ahW5ublERUUB7jIbhw9n84tf1CwPIiIip7/+yTH8v5+dy/TPd7Lqu+OftXmFZbw5fxufrz/AzeP60icpshV7KSIicvpR4rkdcbqcZBVlex/Hq8yGiEibVZVMrs7lcvHxxzOZPftj9u3bS1mZbx3i2spcnMjhcJCQkOizLygoCKDW2sQnci8quJDQ0DCfZO7w4SOJi4tnzZrVZGcfIjbW/RmUmroboNZFB3v16uP9c1W7pKSkets1pI+1qW08Dx8+zBtvvMLXX6/myJHDOJ1On+NV47lv3x4qKipqfQ9JSd18Hk+adDWbN29i8eIF3HDDDwF3mQ273c4VV0xsUt9FRKT1hYUE8LMJ/TmnbyzvLNxObkGZ99iezGP88d1vOW9APNdd3JuYiJBW7KmIiMjpQ4nnduRw8VEqnMd/IU9U4llETlM/uKAnNiD9dC+10S2KH1/Rn9KimgvNnWphYR1q7PvPf17h7bffIDm5J3fffR9JSd0IDAxkz540/v73PzfoeR0OBw6Ho8n9WrLEvajg6NEX1Jghfe65o5gz52PmzZvtLflRlRwPCKg/5KhqFxgY2OS+1efE8SwuLubuu2/n4MEDXHbZFYwZcxFRUVHYbDbef/+/fPXVl962VQsNNqRvl156GS+88DcWLpzLDTf8EKfTyRdffO5NzIuISNs2pE9n+v3iPOZ/vZeFa/ZTUXn8S8uvt2axfmc2E87twfhzuxMc2PTPWxERkfZAied2JKtamQ2AhDAlnkXk9NQxLIifjDet3Y2Tio11l3zIboXE84kqKiqYMWM6HTtG8NJLr3vLOACUl5f7rR9VZTa+/HIlX365stY28+bN5qc//Tk2m42oKPdCfgUFBfU+b1W7Y8eONblvVQnihli5cjkHDx5gwoTJPP6470KCs2bN8HkcHR3T4L6FhoYyduzlzJnzMXv37uHw4WyOHDnCvfc+0OC+iYjI6S0kKIApF/bmgrO7MGPZLtZZx+86LSt38smqNFZ+l871l/RhRL+4Jq1PICIi0h4o8dyOZBRm+TxOUI1nEZF2Iy8vl6KiQoYOHeaTdAbYuHG9X/qwY8d2tm/fSrdu3eusVzx9+v/4/vvNfPPNGkaOPM9b4iItbXeNtlu2bGbfvj2MGXOht93OnTvrbdexYwQ2m43y8rIa7fbv39fg95Ke7q4xPnz4SJ/95eXlbN26xWdfXFw8drudPXtSazxPauoutm/fxjnnjCAhIQGAyZOvZs6cj1m6dDHZ2YcID+/os9igiIi0D7FRodz1g0FY+3J4b8lO9h86/iXrkfxSXvn0e5Z+667/3COhYz3PJCIi0j4p8dyOZBYen/HcITCMjkHh9bQWEZG2JDIyCofDQVZWJi6Xyzt7avfuXSxevABo3Izfpqia7XzDDTfXulgfgMMRwOOPP8js2R8zcuR5JCf3pFu37qxd+zVZWZnEx7uTsxUVFfz5z8+SlZXFuHHjSU7uSXJyMqtWraq3nc1mIyamE2lpqZSWlhIcHAy4Z1QvXDivwe8lJqYTABkZ6T77p017neLiYgDKykoACAsLY9iwEXzzzRq2bt3CgAEDve1feukFvvnma2bNOv7aAwYMpFev3ixdupijR48ybtzl3n6KiEj7Y7pH8/StI1jxXTqzvkiloPj4nUg7D+Tx+7e+YczZiUy5qDeRHYJasaciIiL+ZW/tDkjLqZ54TgjTbGcRkfYkICCACy+8hPT0g/z+979j0aL5vPHGq9x335088siTOBwOvv12LfPnzyE/P6/FX7+oqIjPPltEeHjHehfJGz36AhISElm16gtyco4C8MADD+N0Orn33l/y0UcfMHfupzzwwD2kpaVy1133ehc3fOqpp3C5XCdtd8EFF3PsWD6PPfYg8+fP4YMP/sftt9/CiBHnNvj9jBo1hpCQEN577x3ee+8dFiyYy2OPPcjatV9z2213ADBjxgesWfMVAHfffT9hYR145JEHvO2ffPJh1q79iptvvsW7mGKVSZOuZu/ePRw7ls+VV05u+ECLiEibZLfbuHhIV/70y/O4fEQ3HPbj5TVcwMrvMnjs1a9YsGYv5RXOup9IRESkHVHiuZ1wuVxkFh0vtZGghQVFRNqdBx98lPHjr2TdurX8/e9/ZtOmDfzhD88zcuR53HbbHZSXV/Dyyy+Sn5/f4q+9ZMkiiooKmTz5GkJDQ+ts53A4+MEPrqOiooIFC+YCMGLEefzrX6+SlNSN11//N3/7258oKCjgj398nmuuuc577ujRo3nnnXdO2u6uu37NddfdxN69aTz//J+YM+dTfvKTW5k06eoGv5/OnWP5y1/+SbduPXjzzdd4/fV/07lzLP/4x1SuuGIiAweezbp1a5g92z3Lu0+fFF555U0GDRrMu+++zZ///Bx79qTx6KNPcued99R4/vHjJ2C320lO7slZZw2scVxERNqnsJBAbhqbwu9/PpKze3fyOVZSVsmMZbv53Rtr2LjzMC6Xq5V6KSIi4h82fdi1vOzsY3UOqnehquymL55Um5ySXJ5c/Ufv4+tSruKSbmNa9DXaolM13lKTxtp/NNb+o7H2r/Y03ps2beDuu+/gN795mGuvvaG1u1ODP8c6NrajVtWSNqk1YnqpXVse7+92H2H60p1kHi2qceys5GhuGptC19jTp0RiWx7rtkZj7T8aa//SePtPW4jpVeO5naheZgMgIUwznkVERFpDRUUF//73v4iOjmHCBJXZEBE5k53duxMDkqP5fP1BPl2VRnFphffY93tyePrNbxjRP44LB3fBdI/CbtN3dSIi0n4o8dxOZFQrswEqtSEiIuJvu3fvYseO7cybN5stW77jmWf+UG9ZEhEROTMEOOxcPqIb550Vzycr0/hi40Gqbjx2ulys2ZrFmq1ZxEWFcsHgRMYMSiQyXIvSiohI26fEcztRfcZziCOYqODIVuyNiIjImWflyuW88carxMXF8/DDTzBu3PjW7pKIiJxGIsKCuGW84ZKhXXl/yQ6278v1OX4ot5iPvkjl4xVpDO7TiYuGdGFgz07Y7ZoFLSIibZMSz+1EZuHxGc/xHeKw6RYtERERv7r11tu59dbbW7sbIiJymusWF85DPxzKpl1H+Hz9Ab5PO0r1guJOl4sNOw+zYedhojsGc8HZiYw5O5HOkbqLRkRE2hYlntsBl8vlM+M5MSy+FXsjIiIiIiIi9bHZbAxJ6cyQlM4czi1m5XcZrNqcQc6xUp92OcdKmf3lHuZ8uYezesZw4eAuDEnpTIDD3ko9FxERaTglntuBgvJCCiuOr5Ks+s4iIiIiIiJtQ+eoUH5wYS+uHtOTzalHWLEpnU27juB0HZ8H7QK2pB1lS9pRIsICGTUokQsHdyEhJqz1Oi4iInISSjy3A9XLbIASzyIiIiIiIm2N3W5jcJ/ODO7TmdyCUr7cnMGKTelk55b4tMsvKmfhmn0sXLOPvt2iuGhwF4aZWIICHa3UcxERkdop8dwOZFQrswGQoFIbIiIiIiIibVZUeDATz0/myvN6YO3N4YtN6azfkU1Fpcun3Y79uezYn8v/Pgtg1KAExp6TRLxmQYuIyGlCied2ILPoeOI50B5Ap9DoVuyNiIiIiIiItAS7zUb/5Bj6J8dQUFzO6i2ZrNiUTvrhQp92RaUVLFl3gCXrDjCoVyfGDktiYK8Y7Fp0XkREWpESz+1A9VIbcWGx2G1aaEJERERERKQ9CQ8N5PIR3bhseBK70/NZsTGdtduzKCt3+rTbnHqEzalHiI8O5dJhSYwZlEhosH71b68OZBewN/MYA3vGEBke3NrdERHxoU+fdiCzWqmNxA4qsyEiIiIiItJe2Ww2+nSNpE/XSH44LoWvt2axbP1BDmQX+LTLyinm/SU7mbUilTEDE7l0WFcSO3VopV5LS6uodPLxilQWrtmHCwgOcnD16J6MG55EgEOT0UTk9KDEcxtXXFFMXlm+93FCmBYWFBEREREROROEBgdwydCuXDykCzv257Lk2wNs2HEYp+t4LejSskqWrj/A0vUHOKtnDOOGJTGodyeV4WjDMo4U8trsrezNOubdV1pWyYfLdrFqcwY/uqwv/XuoBKeItD4lntu4zBMXFtSMZxERERERkTOKzWbDdI/GdI/maH4JyzYc5IuN6RQUl/u0+z7tKN+nHSUuKpRLz+nKmLMTCQsJbKVeS2O5XC6+2JTO9CU7Katw1tom/XAhf31/AyP7x3HjpSlEd1T5DRFpPbr/oo3LqJF41oxnEZEzSUVFBWPGDOe+++7y7nvttZcZM2Y4mzZtPOn533yzhjFjhvPWW/85pX0SERER/4iJCOHai3rz/F2juG1CP7rHh9docyi3mOmf7+K3U1fz30UWB09YrFBOP8eKynhp1mbeWWj5JJ1DgwO4fEQ3QoMdPu3XbjvE469/zYI1e6morD1JLSJyqmnGcxuXWXR8YUG7zU5saKdW7I2IiFT30EP38dVXXzJ16usMHjy0znZOp5PrrptMbm4Os2bNJyoqqlmvO27ceFJS+pKcnNys52moGTOmM3ToMPr0SQHA4XDw7LN/Iibm9PlMevjh37B69Up6907h7bffb+3uiIiInHJBgQ4uOLsLYwYlsutgHku/PcC67dm+ZTjKK1m24SDLNhxkQHI0Uy5JYfiAhFbstdTm+7Sj/GfeVvIKynz29+0WxR2TBtApMoQrz+3Oh8t289X3md7jpWWVzFi2m1XfuctvDEiO8XfXReQMp8RzG1e91EZsaGcC7PorFRE5XVx99RS++upL5s2bXW/iee3arzl0KIvLLrui2UlngF69etOrV+9mP09DlJaW8NJL/+Dxx5/2Jp5tNhuXXDLOL6/fEFlZmaxZsxq73c7u3TvZunULAwYMbO1uiYiI+IXNZiMlKYqUpChyLi31lOE4yLEi3zIcW/fksHXaWjqEBhIfHUp8dBgJMaHEx4SREBNGfHQYwUGOOl5FToXyCicffbGbxd/s99nvsNu4ekxPJpzXA7vdXas7MjyYOyYP4KIhXXh38Q6fxSYzjhTx/PSNjOgXx42X9iEmIsSv70NEzlzKUrZx1RPPiSqzISJyWjn//DHExsaxbNkS7r//QcLCal9Jfu7cTwG46qof+LN7LWLHDovKysrW7ka95s79lMrKSqZMuZ5Zs2YwZ84nSjyLiMgZKbpjMFMu7MXkUT1Yu+0QS789wJ7MYz5tCovLSS0uJzU9v9bz46ND3YloT0I6ISaMTpEhBDhUybMlHTxcyGuzv2f/oQKf/XHRofxi8ln06hJR63l9u0Xx9G3DWbb+IB+vTKW49Hic9s32Q3y3+whXjU7mshHd9Hcmp0RxaQUh+pJKPJR4bsPKKss4WpLjfZwQpsSziMjpxOFwMGnS1Uyb9jpLliyuNbGcl5fLl1+uoEePZIYOHebdn5+fz/vv/5fPP/+MQ4eyCA0NIympGzfe+CPGjr2s3td97bWXeeedN5k69T8MHjwEcNddnjbtdRYsmEtOzlHi4xO45ppr6d27T63PsWbNV3zwwf/YuvV7SktLiI2NY9iwkfz857+kc+fOAPz+979j8eIFADz77FM8++xTTJ36H846ayAXX3wew4aN5IUXXq72nvJ4++03WLnyC7KzDxEcHExKiuHGG3/EmDEXetvNmfMJf/7zczzzzB8IDAzi7bffYO/eNAIDgzj33PN54IGHiYxs2MzwyspK5s2bTWhoGHfeeQ9ffPE5S5Ys5t57HyAsLKxG+7KyMv7732ksXbqYzMwMwsM7cvHFl3LbbXcQHR3TqHb//ve/+N//3uaVV6YxcOAgn9e57rrJHD16hM8/Xw3A11+v5sEHf81dd91HWVkpM2dO59xzz+d3v3sWgLS0VN59dxrr1q0lLy+P6OgYUlIMt9/+S/r27efz3E6nk5kzP2D+/Dns27eXkJAQzj9/FD//+Z106dKV6dPf5aWX/slTTz3F5ZdfVWMM7r33l3z33UY++mgunTvHNmicRUSkbQkMcDB6UCKjBiaQmp7P0m8P8M32Q1Q6XfWel3OslJxjpWzfl+uz32G30TkqlITo4zOkI8ODcDrB6XJR6XTickKl04XT5cLpdLn/fOJj1/F9VcftNhtRHYPpFBHi/okMoUNIADab7VQOUatxuVws23CQDz7fRfkJCwiOOTuRm8elEBJUfyrHYbczbng3RvSPZ+ayXXy5pVr5jfJKZizfzarNGdx8WV/OUvkNaSH7so7x4bJdbN2TQ3JiBD8c24eUpObfzSltmxLPbVhWUTYujgcGCR3iW7E3IiJSm0mTrubtt99g7txPa008L1o0n/LyciZPvsa7z+Vy8dvf3otlbWPKlBsYMOAsiooKmTdvNk8//Rh5eblMmXJ9o/rx0kv/YObMDzjnnOHccsttlJaWsmTJIr76anWNtqtWreDxxx8k8axBRwAAIABJREFUObknv/jFXYSHh7Nr1w5mzvyAdevW8N//fkhISAjXX38TYWFhfPLJR1x33U0MHjykzrrSxcXF3HXXHezfv5fJk6+hf/+zOHYsnwUL5vLoow/w2GNPceutP/I5Z/XqVaxfv45rr72RTp06sWrVCpYuXUxlZSXPPffnBr3vr75axaFDWUyceBVhYR247LIrmT79XZYsWVTj78PlcvHII79hw4ZvufbaGzGmH2lpqXz44XusX7+OV1+dRocO4Q1u1xQbN67n0KEs7r77fhISEgHIyEjnrrtux+Gwc+ONPyYhIYGsrExmzJjOnXf+jNdee9tb5gTgL3/5A3PnfsqkSVdz880/ITMzg/fff5d169by+uvvMH78RF59dSqzZs2qkXg+cuQwmzZtYMSI85R0FhE5A9hsNnp3jaR310huuLQPW/fnsTcjn73peWTmFJNfWHbyJ8GdUM46WkTW0SLYfeQU9xqCgxx0jgghxpOI7hQRTKfIEDpHhBITEUxUeLC3BEVjuVwuysqdFJVWUFRSTnFpJUWl5RSVVFBUWkFxaQUdw4Lo3SWCxM4dsLdgAjy/qIy35m9n467DPvvDggP46ZX9GNGvcZPNIjsE8fNJA7jQU36j+uzpjCNF/G36Rob3i+Mmld+QZsgrKGXWilRWfZfhzVDtycjnT++uZ+ywJK69qLfK9JzBlHhuwzIKs3weJ6jUhoi0EQVlhcxJW0RWtXJBp6PkTl25aVDNGaGNER+fwHnnjWL16lWkpu6uUXt53rzZBAUFceWVk7z7Dh3KIiIikptvvoU777zHu3/s2PFMnnwZM2dOb1TiOSfnKB9/PJNu3brz97+/RECA++P/mmuu49Zbf1ij/b59exg48GyeeOIZunZNAuDyy6/E5YL33/8vq1Z9wbhx4+nf/yx27doJQP/+A7x1nSsqKmo854cfvseePancddevufnmW7z7J026hh/+cApTp77AzTdfT1BQkPfYypXLeffdGcTHuxc5uuKKidx44zV8+eUKKioqvO+jPrNnfwzAhAlXebaTmT79XebM+bhG4vnzz5fwzTdr+OUv7+EnP7nVuz8mphMvvPA8c+d+yo03/qjB7Zpi3bo1fPjhbO+scnDPdk5J6cu1197ARRdd6t3fo0cyjz32ILNnz+KBBx4BYOvWLcyd+ylXXz2Fhx563Nu2V6/ePProb3nvvf9y//0Pcumll7Jo0aIa1+Ty5UtxOp1MmDC5Sf0XEZG2Kyo8mKsvdH8mZGe7y28UlVSQleNOKGd6frKOFpOZU0RpWeuV2iotq+Tg4UIOHi6s9bjDbiO6YzCdIz3J6YgQoiOCqax0UVRS7kkqV3i3xaW+fz7ZzO8qocEOeiVGeJP3vbpE0CEksEnvaUvqEf4zb1uNZH+/7lHcPmlAsxLDKUlRPHXrcJZvSGfWilSKS4/Hauu2H+K73Ye5anRPLlf5DWmEsvJKFn+zn3lf7631/wMXsOTbA2zcdZjbruxHf82uPyMp8dyGVU/Y2LARH6aZSSLSNsxJW8Sqg1+3djdOamduKgDX9GheEu6qq6awevUq5s37lHvvfcC7f9u279m9exeXXXaFT+mI+PgE/va3F72PS0tLKStz/xLSqVNnMjMzGvX6Gzasp7KykosvHuuTrA0ODmbChMm8+upUn/Y333yLNznscrkoKirE6XTRpUtXADIyGvf6ACtWLMdut3PVVVN89oeHh3PRRZfwyScfsXHjRkaOHOk9dskl47xJZwC73Y4x/cjISCc/P4+YmE71vmZWViZff72apKRu3pIjvXr1pn//AWzbtpWdO3eQktLX237p0sUAXHHFBJ/nmThxMn379qNLly6NatcUAwee7ZN0Bhg1agyjRo3xPi4qKqKystI7NtWvh6q+jR/v27fzzhvNSy+9Rmys+0vq6667jkWLFrFgwVzuvvs+b7vPP19Cx44RXHDBRU1+DyIi0n6EhQTQMzGCnom+9YRdLhd5hWXehHTW0WL3NqeIQznFDU7c1sVus2G323DYbVRUOhv9fJVOF4fzSjicV9KsfpxMcWkl3+/J4fs9x0tgJnYKo3eXSHp3dSeku5xkVnR5RSUzl6fy2bqaCwj+4MJeXDGye5Nnb/s+n52xw5IY3i+Omct38eXm4+U3ysqdzFy+m5XfZXDh2YmEhwUSHur70yEksEX6IW2fy+Vi7bZDzFy+myP5Nf+NJcWFc6Da7PrDeSX8dfpGLhrShesv7kNYiFKRZxL9bbdhGUXHE88xIdEEOYLqaS0iIq3l/PNHExcXz6JF87nzznsJDHTPhKlaVPDqq6fUOGfbtu+ZNu11tmzZTH5+ns8xh6Nxt6qlpx8AoFu37jWO9ejRs8a+8vJy3n33LT77bCEZGemUl/uuel9ZWXNG88ns27eH2Ng4wsNrlqDo3r0HAHv27PFJPFcluqsLCgoGap9VfaK5cz/F6XT6zCYH9+znbdu2MmfOx96ZwgCpqbsJDAz0JmerhIV18CauG9OuKRITa09aL168gBkz3ictLZWSEt8Av6Li+AyT1NTdAHTpkuTTJiAggCFDzvE+HjNmDImJiSxePJ8777wHh8PB4cPZbN68iauvvtZn5rmIiMiJbDYbUeHukhame7TPsUqnk8N5JRSVVOCwH08g2+027DbfP3uPVfuzzYZP/Wany0V+YRlH8ks4klfi3R7NL+Ww53H1GbytLeNIERlHili12f3FcGiwg56JEZ5ktDshXTUrem9GPn96ex0Hsn1nbsfHhPHLqwaQnFD7AoLNEdkhiJ9PHMBFg7vy7mKLfdUShFlHi5ixfHet59lwfxERHhroTkyHBNaaoK76CQsJJCTIQXCQo0XLkZxMpdNJUUkFhSUVFJa4S6TY9+dRUlpBUWGp+zpz2Aiw273XnMNhw2Gz4XDYfa7ZqseOEx6HBQecsUn43el5TF+6k90Hay4+mtgpjJvGpnDpucl8tTmDqTM2kldtBv8XG9P5bvcRfnpFP87uXf8EktNRUUkFBSXlRIcHERig0iENpcRzG5ZZbcZzospsiEgbMrnneGzYyDyhZNDppmenJG4cNJmS/ObN2nE4HEyceBXTpr3OqlVfcMkl4ygtLWHp0sX06JHskxAE2LlzB3fffQc2m43rrruJQYPOJiysA+Be0C8n52ijXr+0tBSA4OCat2gGBwfX2Pfcc0+zdOliBg48m5tvvoX4+HgcjgC++WYN//3vtEa9NrhnRRQXF/vMXq6uKplcVFRU6/6mqKys9Cb2Bw0azIEDx2cR9e8/AIfDweLFC7n77vu841JWVkpAwMlvj21ou6ao+nuu7qOPPuQf//gLiYlduP32O+nRoyfBwcEcPXqEZ555wqdt1d91YGD9IZ7dbmfKlClMnTqVtWu/4vzzx7BsWVWZjUn1nisiIlIfh91OfHTNBXybyl4tyd27S2StbYpKKtwJ6RrJ6RIO55eQV+BbviIo0E5ocABhwQGEhQQQFhxIWEjACfs8j0OO7wsNDiA40EFWThG7D+az+2Aeuw7m1Tuzuri0kq17cth64qzopCjWfJ9ZYwHBCwcn8sOxfU95Tdw+SZE8desIlm04yMcrUik6SfLeBZ5kbgVZOcUNfh0b7prcocEBhAQ5CAkKIDTYs/U8Dgl2EOJpExrkaedpb7N5Xre43JNQLqewxF1/23e/+1iJH0rA2GwQERZERIcgIjvUvY0MDyYsJKBJiffyCifHiso4VlTOsWLPtqjcs8/3cUCAnX7dohnYK4Z+3aNPybVzNL+Emct38/XWmr+/hYcGcs0FPbloSBccdneplvMHJZIYFcz0pTt9ZtfnHCvlnzM2MWpgAjeNTSE89NTE1E1RUenk6LFSsnOLOZxbTHZuCdm5xd6fwhL3vxEbENUxmLioUGKjQ4mPDiU2KpS46FDiokIJa2K5nfZKiec2qsJZQXbx8QUH4pV4FpE2JDyoAzeZmgvtnW5iYzsCUMKxZj/XpElX8847bzJ37mwuuWQcy5d/TkFBAbfddkeNtrNmfUhZWRlPPPFMjdm6DZnpe6KqBG5ZWWmNY8XFvsneQ4eyWLp0McnJPXnhhX/7JKbT0lIb/drgnrUUGhpaI7FcpaTE/ctLhw41k65NtXr1SrKz3V/Q/vrXd9bapqDgGMuWLeWKKyYCEBUVzaFDWSetH93QdvWpShA3xPTp7xIQEMCLL77iMyN6584dNdpGR7tnnRUUFPiUb6nNlClTePnll/nss0Wcf/4Yli5dTM+evejf/6wG901EROR0EBYSQFhION3ial/ct7zCSX5hGYGBdsKCA5pdxzg5IYLkhAjGDnPfYZRXWEbqwTx2peeRejCftMx8ysqddZ5fNSu6ug4hAdx6ZT+GGf/9bm+32xg7LIkR/eKYtSKVNVuzKC1v2cStCygpq/RLQthfXC7333leYRn7T9LWYbcR0SGIiLAgIsN9t5VO1/GkcmEZx4rLvUnlxo7XwexClq4/QIDDRt9uUQzs2YmBvWLo2rmDzx0EjVVSVsH8r/exaO2+Gl+SOOw2xg1PYvKo5FqTrR1CAvn5xAGM7B/P2wu3czT/ePy7eksmW9KO8pPLDcOMf8rGulwuCksqfJLJ1ZPLR/NLcbpOPuHIhTuBnnOsFGt/bo3jHUICiKuWjI6Nciek46LDiAoPatbfR1ukxHMblV18BKfr+D/6xLD4VuyNiIicTHx8AueeO4o1a1aTm5vLokULCAoKrpFYBkhPTwdg+PCRPvv37EkjNzen0aU2EhISPM97sMaxqtIMVarqNw8aNLjGbOhNmzY06nWrS07uyfbt28jPzyciwve20T170gDo3bt3bac2SdWigrfddkeNBR0BMjMzmTr1n8yZ84k38ZyYmMiOHdvZsyeNPn1SvG3ds9M/IyEhkXPOGd7gdlVJ6fJy31lWBQUF5OQcbVA5C6fTSWZmBt2796hRhmPTpvU12ickuNukpe32LgxZZeHCeXTsGMHo0RcAkJSUxPDhI1m1agV79qSxZct33HXXfTWeU0REpK0LDLDTKbLpi/OdTGSHIIb2jWVoX3cCraLSycHsQnYdzCM1PY/dB/M5lFv3LOH+PaK5fdIAojs2/W6v5ojoEMStV/bjp1cYiksrKSgpp6ConILicgqLyzlW7PvnwmL3bNvCEve2orLuJPuZrtLp8iYp8cPNnhWVLu8M+w+XQXTHYAb2jGFQr04MSI5u8Gxcp8vFl5szmLUitcYdAwDn9I3l+kt6N+juhkG9OvHsz89lxvLdLN9w/PeR/MIypn68mRH94vjRZX2J6NBypd5cLheHcovZvjcHa38u6dmFZOcVU1x66r8AKSypIC3jGGkZNScvBQXYiY1yJ6OT4sLp1SWCXl0iiAhrv2XulHhuozJOuD09QTOeRUROe1dd9QNWr17J7Nmz+PbbtYwdezkRETVvGY2Jca/4nJGR7q0jXFpawgsvPE94eDgFBQWNmm07ZMg52Gw2VqxYzq233u5NXJeWlrBo0fw6X7u6tWu/5ttv13rOOz5bwe65na5q8cO6XHLJOLZt28rs2bP48Y9v9e7Pycnhiy+WERcXz+DBgxv0fk4mMzOTNWu+Ij4+wef9nmjBgjls2rSBvXv30KNHMmPGXMQXXyxj7txPuP/+h7ztli1byh//+P/41a/u5Zxzhje4XadO7kUCt2/fxtChw7ztZs6c3uD3YrfbiYqK5siRI5SVlXmT1RkZ6XzyyUeA70z2Cy64iA8++B9z5nzCmDHHFwjctGkDzz33NNdcc5038QzumfjffLOGP//5WRwOR40FE0VONWNMDPA0cA2QCBwG5gO/syzrpCuZGmNGAb8DzgNCgR3A68BLlmU1r06SiEgTBTjs9EjoSI+Ejt5Z0fmFZexOzyM13V2iIy3zGHabjUmjejB+ZHe/1kGui81m88weDyAuKrRB57hcLsrKnRR4ktNVP8WlFRSXVVBSWundlpRVUFzm3lbfX1xWQQMmmnoFOGyEhQTSISSADp6t93Gou2zK8WOBJHWJJCwkgOzsAiqd7sUqK50uKitdOF3ubaXTSYXThdN5/HHNdk7KK10cK3LPds4vPL7NLyxr9qKaDWGzQcfQQDqGBdExLJDwsCCO5peQlp5Pba+ec6yUld9lsPK7DOw2G726RngT0T0SOtZ63Vn7cpi+dBd7s2omTrvHh3PTpSn06xFd41h9QoMDuGW8YUS/ON5asI3s3OPlab7Zfohte3P40WV9Gdk/rskzgrM9iebt+3LYvi/XnexvBrvNRkxEsCdRHEJsVCjhoYEcyS/lUE4R2bnFHMo5XoKjIcoqnBw8XMjBw4Vs3HW8ikHnyBB3Ejoxgp5dIugR35GgwPZRR1qJ5zYqq1p9Z1DiWUSkLahaZPCtt96gsrKy1kUFAcaOvYzPPlvIn/70LD/84U8oLy9nzpyPGTJkGB07RvD555/x2msvM27c5fTq1eekrxsbG8cVV0xkwYK5PPTQ/Vx44cWUlZWyePFCevRI5uDBA962SUnd6Nu3H+vWreWFF/5Gv379saxtfPbZIh5//BkeeeQ3LFu2lOTknowde7l3Fu7MmdMpLCxg8OChpKSYGn249tobWbx4Ia+//m+ysw/Rr98AcnKOMmfOJxQVFfLEE880eiZ3XebO/QSn08mUKdfX+5xTptzA88//H3PmfMI999zP5Zdfybx5s/noI3epkyFDzmHfvr188MF7dO/egylTbgBocLvzzx9NQEAAb7/9HyoqyuncOZZNmzawZctm+vbtx549DStdMnbsZcyc+QFPPvkIl146jkOHspg58wMeeugx/u//nmXbtu/59NNZjB59AUOGnMPll1/J4sULePLJRxgz5kKys7P58MP3iIqK5tZbf+7z3BdccDGRkZFs3vwdo0ZdQExM21voRdouY0wosBzoB7wErANSgAeBS40xwyzLyqnn/EuBBcB+4BngKHA18CLQG7j/FHZfRKRRIjoEMTQllqEp7lnRnTqF4wKOHimo/8TTnM1mI9iziGBTZ5VXJa+rJ6aLSyspKa3ABd4kcpgnsRwUYG9UcrKqfJ6z7NQtQul0uSgqqXAnogtKySsqI7+gZoI6r7CM/KIyb6LdYbcRHhZIx1B3Itn94/5zRFiQz+OOYUF11owuKC5n656jbE49wpbUoz6L+lXv464Deew6kMcnK9MIDw1kYM8YBvaK4ayenSgtq2DGst18uyO7xrmRHYKYclEvRg9MbNbCiv17RPP7n53LrBWpLFm335ssLygu59XZ37N2WxY/vtw0aPb/kbwST5I5h+17czmSX3et9bp0CAnwzkDu7EkuV/3EdAxuUFmeopJyDnmS0NnVt7nF5OSX1vqFwIkO55VwOK+EtdvcuT6H3UZSrHtGdM9E96zohE5hp8UXVI2lxHMbVX3Gc2RQBKEBDfs2UkREWo/D4WDSpKt5883XSE7uyeDBQ2ttN2bMRfzmNw8xY8YH/POffyU+PoHJk3/ATTf9iO3bt7Jt2/fMnPkBMTExDUo8Azz44GNERESydOliNmxYR1xcPFdd9QNGjjyP1atXedvZbDb+8Ie/8I9//JWFC+exaNF8Bg8ewosvvkLPnr2YOPEqli5dzKuvTuXii8cydOgwrrhiIsuXL+Wtt97gkUeeqDXxHBwczL/+9SrTpr3GypVf8OmnswgNDeOsswby6KNPMXjwkKYN6gmqFhUMCQlh8uT664iPHz+BV155iYUL5/LLX95NYGAgzz//Im+//QZLly5mwYK5hId3ZPz4CfziF78iNNT9WetwOBrULjGxC88//yKvvjqVd96ZRlBQIMOHj+SFF17myScfafB7+sUv7qaiopJVq75gw4Zv6d27D4899hTnnz+aI0cO88orU3nllZfo06cvnTvH8vjjT9OnTwrz589h9eqVBAeHcO655/OrX91L586+NfSCgoIYO/ZyZs2awcSJkxs52iLNdj8wCLjbsqyXq3YaYzYBH+OeyfxAPee/DJQAF1SbHf1fY8wnwK+NMdMsy9p0arouItI8zUnetTfVk9e1Lx95+rPbbISHBhIeGkjXzvWvW+J0uigoLsfhsBEWHNAiNX/DQwMZ2T+ekf3jcblc7D9UwJa0o2xJPcLOA3m1zsYuKC7n661Z3gUD7TZbjRrHgQF2xo/szoTzuhMS1DIpxOAgBz8cl8KIfnG8OX8bmUeP1zvfsPMw1r5cbhqbwuhBCT5jk3OslO17c9i2LwdrX47PrOl6Xy/Q4U7cxoS5E8yRId5ZzC2xEGBYSCDJCYEkJ0TUOFZeUUl2bgmHcovJznEno7Nzi8k4Ulhv/yudLvZmHWNv1jGWecqThAY7SE6I8Jbn6JUY4f1S5XRmczXmfgZpkOzsY3UOatVFkZ3dvIWq/rj2HxwscMfX/aJTuHdozcWppOXGW05OY+0/Gmv/0Vj7l8bbf6qP9T33/IL09IN8+OGnTV4s8SSvpd+spVbGmG1AN6CTZVml1fbbgH1AMBBfW8kMY8y5wNfAfyzLuuOEY+OAz4DnLct66MRzG8ofMb00jMbbfzTW/qOx9p8zfayLSyvYvi+HLanuGdGH8xqWsD1vQDzXXtS70TPZGzPe5RWVfLpqDwvW7K1RbmVgzxjOOyueHfvzsPblkJVTd5306oIC7aR0jaRfj2j6dY+mR0LHZi8oeioUFJeTmp5PWkY+qen5pKbnNapsB0BsdCjnD0xk/PAkQoNP7dzipsb0bXbGc3PqwRljTpZtj7Ysq+bSlKcJp8tJVtHxWx/iVWZDREREmmDFiuVs3Liee+/9zSlJOovUxRgTgbvExsrqSWcAy7Jcxpi1wBSgJ1BbXZqq1Ve/quXYGs/23BbqroiISJsVGhzgLfPicrnIyin2luSw9uVQVuG7OGTvLhHcNDaF3l1P/fzzwAAH113cm2Emlmnzt3Egu9B7bEvaUbakHW3Ac9jp0zWSft2j6Ncjmp6JEadlovlE4aGBnN27E2f3dpe6q1oQMS3dk4jOyGdf1jEqKutOYWbnFDN7ZSoBNpg0KtlPPW+cNvkbRnPrwXlsxZ24rk1hHftPC8UVJVQ4j38LkqjEs4iIiDSQ0+lk/vz57Nixg2nTppGS0pdrr72xtbslZ54enu2BOo7v82x7UXviObmu8y3LOmaMyfWc22QNuX21Ldzi2p5ovP1HY+0/Gmv/0Vi7xcVFMMjEA1BWXsn3qUf4dvshDuUUccHgrowZ0qVFyn80ZrxjYzsydEAiM5bu4MMlO+pdqDHAYadfcjRn9+7MoD6dMT2iCQxoHwvxxcVFMLBvvPdxeYWTtPQ8duzL8f4czK6ZsgwPDz5tr+82mXim+fXgALIty5p56rp46oQFhNIjoht78/cTGhDCkNhBrd0lERERaSMqKip4+OGHsdvtjBx5Hr/97WOa7Sytoeq3o6I6jhee0K4p55+ev4GJiIicJoICHQw1cQw1rT+hMTDAzs3j+3H+oERe/GADuw7kARDgsGF6xDCwdyfO7tMZ0yOG4MD2kWg+mcAAO327R9O3e7R337GiMnbuz2XHvhz2pOcTHxPG1Rf2bsVe1q+t/pZxC+5g8o0T9n+Ke9bDj40xv62tHlx7YLPZ+PWQO9idt5fuHbvSMSi8tbskIiIibURQUBBbtmwBztxahyINUd+/jzO9Xqi/abz9R2PtPxpr/9FY+1dzxzs80M4jNw9l294cHDYbvbpG+iSa83Pr+s75zNEtJpRuMaHEXuZe1N0f13ZTZ1Sf/kVPTlCtHtz62urBAWuBWNz14BryfDZjTP1Ljp6GQgJCOKuTUdJZRERERNqifM+2rjg8/IR2TTm/rnNFRETkNOaw2xnYsxP9k8+c2c3tVZtLPNO4enD16WyMeQc4BhQYY/KNMe8YY7q2RCdFRERERKROaYALSKrjeFXMv7OO41V1n2ucb4yJBCLrOVdERERE/KAtltpobj24KgOA9cCPcY/DZNwlPC42xpxjWdbhpnZQC5GcfjTe/qOx9h+Ntf9orP1L4+0/GmtpLZZlFRpjvgPOMcaEWJZVUnXMGOMARgH7LcvaV8dTrPZsR1Oz/N4Fnu2qluyziIiIiDROW5zx3BKuBEZYlnWLZVmfWJY107KsnwJ/ALoBv23d7omIiIiItHtvAGHAL0/Y/2MgDvhP1Q5jTD9jjLeUnmVZG3FPIrneGJNUrZ0N+A1QDrx96rouIiIiIifTFmc8N7ceHJZlLazj0MvAE8A44LEm9Q4tRHI60Xj7j8bafzTW/qOx9i+Nt//4c6w1q1rq8QrwI+B5Y0wPYB1wFvAAsBl4vlrbbYCFe62XKncBy4AVxph/ArnATcClwO8sy9p9yt+BiIiIiNSpLc54bm49uPpke547ognnioiIiIhIA1mWVQ5cDvwLuBZ4C/gp7pnOF1uWVe+y9ZZlrQEuBLYDvwdeBRKAn1mW9dyp67mIiIiINESbm/Hc3HpwxphBnjYLammTAtg4vkChiIiIiIicIpZl5eOe4fzASdrZ6ti/DphwCromIiIiIs3UFmc8QzPqwQEDcd/W91Qtz1tVXmNWy3VVRERERERERERE5MzS5mY8ezSnHtwM4GfAz40xnYH5gAOYgru28xLgdT+8BxEREREREREREZF2qU3OeG5OPTjLsiqAycBDQF/gReBvQKxn3wRPGxERERERERERERFpgrY647lZ9eA8daGfx3dmtIiIiIiIiIiIiIi0gDY541lERERERERERERETl9KPIuIiIiIiIiIiIhIi1LiWURERERERERERERalBLPIiIiIiIiIiIiItKilHgWERERERERERERkRalxLOIiIiIiIiIiIiItCiby+Vq7T6IiIiIiIiIiIiISDuiGc8iIiIiIiIiIiIi0qKUeBYRERERERERERGRFqXEs4iIiIiIiIiIiIi0KCWeRURERERERERERKRFKfEsIiIiIiIiIiJnYl7QAAAOyklEQVQiIi1KiWcRERERERERERERaVFKPIuIiIiIiIiIiIhIi1LiWURERERERERERERalBLPIiIiIiIiIiIiItKilHgWERERERERERERkRalxLOIiIiIiIiIiIiItCglnkVERERERERERESkRSnxLCIiIiIiIiIiIiItSolnEREREREREREREWlRAa3dgTOJMSYGeBq4BkgEDgPzgd9ZlpXRmn1rT4wxbwE/rafJbyzL+qefutOuGGOCgOeAB4EVlmVdXEubUOAx4CagB5APfI77Ot/hv962fScbb2PMM7j/T6nLC5Zl3X/KOtgOGGNigaeAHwDxQC6wCnjWsqz1J7TVtd1MDR1vXdstwxgzCHgYGAN0wX3Nrgb+aFnWmmrtdG2LNJDief9RTH/qKKb3H8Xz/qGY3n8Uz/tXe4jnlXj2E89FsBzoB7wErANScH8AXWqMGWZZVk7r9bBdugvIrmX/Rn93pD0wxhjgPaAvYKujjQ34FBgHTAP+H+7/HB8EvjLGjLQsa7d/ety2NWS8q3kG+L6W/TtbuFvtijEmDvgW6AT8G9iEe7x/DYw3xoy2LGuDp62u7WZqzHhX8wy6tpvEGHM+sAT3LwNTgf1Af+Ae4EpjzMWWZa3WtS3ScIrnW41i+hakmN5/FM/7h2J6/1E871/tJZ5X4tl/7gcGAXdblvVy1U5jzCbgY+B3wAOt1Lf2aoFlWXtauxPtgTEmGliP+8NhOLC9jqY3AZcBf7Us6+Fq5y/F/cvZX4Epp7a3bV8jxrvKF5ZlLT/V/WqHngOSgGsty5pVtdMY8w3wCe5vjG/w7Na13XyNGe8qurab7hXcv+SOrv5ZaIxZizvueAS4Gl3bIo2heL51KKZvIYrp/UfxvF8ppvcfxfP+1S7iedV49p9bgELgjRP2fwocAH7s+ZZC5HQUBLwDnGdZllVPu1s82xer7/TccrMamGSMiTo1XWxXGjre0jzpwPu4P7SrWwi4gLOr7dO13XyNGW9pBmOMHXgbuK+WZM1nnm13z1bXtkjDKZ6Xtk4xvf8onvcfxfT+o3jeT9pTPK8Zz35gjInAfUveSsuySqsfsyzL5fm2YgrQE0hthS62a8aYEKDCsqyK1u5LW2VZVhbwqwY0HQnstyzrQC3H1gCjgXNw1xqSOjRivH14ashhWVZZi3eqHbIs65k6DnXE/c1yfrV9urabqZHj7UPXduNYluUE/l7H4X6e7Xeera5tkQZQPN/6FNM3n2J6/1E87z+K6f1H8bz/tKd4XjOe/aOHZ1vbRQCwz7Pt5Ye+nEnuNsakAcVAqTHma2PMhNbuVHtljOkIxKDrvDXcYIz5HijFfa1vNsb8pLU71Ybd6dn+D3Rt+4HPeJ9A13YLMMZEGWOSjDE34Z6ZmQY8o2tbpFEUz7cexfR+pM+GVqOYp+UppvcfxfOnWFuO55V49o+Onm1RHccLT2gnLWM88EdgIvAE7sVf5nr+oUrL03Xeeq7EXf/pSuA+IBJ4xxjzSKv2qg0yxlyJe5Xmb3EvmAG6tk+ZOsa7Ol3bLSMH92Ik7wGLgBGWZaWha1ukMfTvpfUopvcvXeutQzFPC1JM7z+K5/2mzcbzKrUh7dHfcNcdWl7tVsj5xpjZuFe//psx5kPPrQsibdm7wNfAV5Zl5Xn2LTTGTMe9gMnTxphXLcvKbbUetiHGmFuA/wB7gMm6DezUOsl469puWZcAHYChwF3ApcaY63HX6RMROV0pppczgWKeFqaY3n8Uz/tVm43nlXj2j6o6Nx3qOB5+QjtpBsuyNgOba9m/1RizHPdqn/2B7/3ctfZO17mfWZa1C9hVy/5DxpiZwB246znN83ff2hpjzO+A3+Ne9XeiZVmHqh3Wtd3CTjLeurZbWLWVxOcZY94F1uOeLTHcs1/XtsjJ6bPAzxTTtxpd636kmKdlKab3H8Xz/tWW43mV2vCPNNwrfCbVcbyqZtxO/3TnjJbl2Ua0ai/aIcuyCoBsdJ2fLnStN5Ax5p+4g6bZwEW1BE26tlvQyca7AXRtN4NnVeyluG9Vj0fXtkhDKZ4/veiz4BRR3HNa0XXeCIrp/UfxfOtqa/G8Es9+YFlWIe7VJs/xrMbsZYxxAKNwr0C5r7bzpeGMMRHGmB8ZY66oq4lnu99ffTrDrAaSjDHdazl2Ae5FYdb7t0vtkzEm0Bhzo+f2mlqbeLb6f6Uenm/q7wOmAVMsy6qrNpau7RbQkPHWtd18xpj+xpj9xpg362gS5dkGoGtbpEEUz/uXYvpWp88GP1DM03IU0/uP4nn/aE/xvBLP/vMGEAb88oT9PwbicNfFkeYrA6YCbxljOlc/YIwZB4wA1lqWVdeKn9I8b3i2v6m+0xhzETAMmO75plmaybKscuD/4V6YIaX6MWPMAOAa3Cvbrm2F7rUJxphLcI/hx8DtlmVV1tNc13YzNXS8dW23iJ1ACHC9MaZn9QPGmN64b2vMBnaga1ukMRTP+49i+talzwY/UMzTMhTT+4/ieb9qN/G8zeVytdZrn1GMMYHAStx/6f/CXQfnLOAB3BfUefV8KyeNYIz5KfAW7lsiXwEycRdg/xVQAlxsWdbGVutgG+T5cBhQbdcMYCvwdLV98y3LKjLGfARMAd4EPsd9a8eDuFdTHWFZVqZ/et12NXS8gfOBhcBR3L+cpeH+9vheIBi4xrKshf7oc1tkjPkW9/8N9wB13R42v+r/Zl3bzdOY8TbGjEXXdrMYY24C/gccwT2GqUBP3OMfC/zMsqxpnra6tkUaQPG8fymmb3mK6f1H8bz/KKb3H8Xz/tVe4nklnv3IGBMBPANcCyTi/of6MfC0ZVlHW7Fr7Y7nm7jHgJG4i6xnAouBP1iWldqafWuLjDHP4Bsk1aanZVl7jDFBwKO4Z/8kAznAIuAJy7J0O2QDNHK8zwGeBC4EInF/KH0B/J9+GaufMaYhH4A9PTW00LXdPE0Yb13bzWSMOR94BPeMiCjci4p8A/zdsqzF1drp2hZpIMXz/qWYvmUppvcfxfP+o5jefxTP+197iOeVeBYRERERERERERGRFqUazyIiIiIiIiIiIiLSopR4FhEREREREREREZEWpcSziIiIiIiIiIiIiLQoJZ5FREREREREREREpEUp8SwiIiIiIiIiIiIiLUqJZxERERERERERERFpUUo8i4iIiIiIiIiIiEiLUuJZRERERERERERERFqUEs8iIiIiIiIiIiIi0qKUeBYRERERERERERGRFqXEs4iIiIiIiIiIiIi0KCWeRUSkQYwxzxhjXMaYW1u7LyIiIiIi0niK6UXEnwJauwMiImcCY8zFwLJGnHKJZVnLT01vRERERESksRTTi4g0jhLPIiL+tQ/4VwPa7T7VHRERERERkSZRTC8i0gBKPIuI+FeGZVnPt3YnRERERESkyRTTi4g0gBLPIiKnMWPMHqAHkAD8GPiF5/ExYBHwqGVZB044JxJ4CJgCJAMuIBWYCfzNsqyCE9oHAL8GbgFSACewBnjGsqxVdfRrIPBnYBQQAmwFnrUs65NmvmURERERkXZFMb2InKm0uKCISNvwHPAb3IHpc8A24EfASmNMVFUjT4C6GngCyANeBF4CCoFngOXGmNBq7e3AHOBvwFHg/4D/AEM8z31TLX1JAZYD6cBfgLnAOcBHxphzW+oNi4iIiIi0M4rpReSMohnPIiJtw0RgiGVZhwCMMX/AHVxOBO4C/uhp9ywwAHjbsqxbq042xtiAT4CrgN/iDnTxnHsF8I5lWT+t1n4qsBl41RjzqWVZxdX68gBwZfWFUowxrwO3AzfjnlkhIiIiIiK+FNOLyBlFiWcREf8KNsYkn6RNvmVZR0/Y93JVgApgWZbLGPMS7iD1CuCPnkD0x54mT1U/2dP+/3AHqTdzPEi93bP96wntdxljHgKiPT/Vg9RFtazOvdDzXD1O8t5ERERERNo6xfQiIg2gxLOIiH8NAdJO0uYF4P4T9n1dS7vtnm0/z7Y37oDygGVZ+2ppvx6oBIwxJgR3nbhBQDnuem4+LMt6qY7+fVvLvhzPNrKOc0RERERE2gvF9CIiDaDEs4iIf+0AHjlJm9217DtUy76qwLCqHlxsPW2xLKvMGJMHxOAOZu2en6OWZTlP0qfaXre6qvNtjXgeEREREZG2SDG9iEgDKPEsIuJfOU1cJbq2ILJqgdgSz9bl2dYXKFadU/35gpvQHxERERGRM5ViehGRBrCfvImIiJwGOteyr2pWRJZnWzUrIq62JzDGBAMdgQrcMxyOeP7c0XNMREREREROHcX0InJGUeJZRKRtGFnLvrM8272ebRruwLOrMaa2BUFGAg7gO8uyyizLKsO9yjXAJSc2NsY8aoz5xBgzuHldFxERERERFNOLyBlGiWcRkbbhbmNMTNUDz2rX93gezgP3KtfAG559z1Q/2RjjAJ70PHyz2qG3PdsHPG2q2ncHHgUuBXa1zFsQERERETmjKaYXkTOKajyLiPhXojHmwQa022lZ1qfVHn8ObDTGzOb/t3e/Kl4FYQCGX5vJS9CgMHgRm0TZKNgWg9Eq/rkBi2J2EazegGDSZhEE+04z2l2TLGv4ubDNXRlcWJ8HTvvCOW14mTNT36pb1Vabi01eH5t7Wm1X98YYV6uP1cXqRpvbt99Xr47N71a3q5vVpzHGuza/+92tLlU7c84fp/5KAAA4v6zpAU5AeAb4ty5XL04w9/b3c+R59aW6X12rvldvqsdzzv2joTnn/hhjq3pS3aketTnzba96UL2ccx4cm/85xtiuHlY7bXZEHFSfq2dzzg9/+Z0AAHBeWdMDnMCFw8PDP08BcCbGGF+rK9X1Oefe2b4NAABwWtb0wP/KGc8AAAAAACwlPAMAAAAAsJTwDAAAAADAUsIzAAAAAABLuVwQAAAAAICl7HgGAAAAAGAp4RkAAAAAgKWEZwAAAAAAlhKeAQAAAABYSngGAAAAAGAp4RkAAAAAgKWEZwAAAAAAlhKeAQAAAABYSngGAAAAAGAp4RkAAAAAgKWEZwAAAAAAlhKeAQAAAABYSngGAAAAAGCpXwWjJXoCza1BAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 291,
       "width": 719
      },
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "t = f.suptitle('Basic CNN Performance', fontsize=12)\n",
    "f.subplots_adjust(top=0.85, wspace=0.3)\n",
    "\n",
    "epoch_list = list(range(1,31))\n",
    "ax1.plot(epoch_list, history.history['acc'], label='Train Accuracy')\n",
    "ax1.plot(epoch_list, history.history['val_acc'], label='Validation Accuracy')\n",
    "ax1.set_xticks(np.arange(0, 31, 5))\n",
    "ax1.set_ylabel('Accuracy Value')\n",
    "ax1.set_xlabel('Epoch')\n",
    "ax1.set_title('Accuracy')\n",
    "l1 = ax1.legend(loc=\"best\")\n",
    "\n",
    "ax2.plot(epoch_list, history.history['loss'], label='Train Loss')\n",
    "ax2.plot(epoch_list, history.history['val_loss'], label='Validation Loss')\n",
    "ax2.set_xticks(np.arange(0, 31, 5))\n",
    "ax2.set_ylabel('Loss Value')\n",
    "ax2.set_xlabel('Epoch')\n",
    "ax2.set_title('Loss')\n",
    "l2 = ax2.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TmxOTtSi07-q"
   },
   "outputs": [],
   "source": [
    "model.save('cats_dogs_basic_without_dropout.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 2 CNN with Dropout layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1125
    },
    "colab_type": "code",
    "id": "7tMI35P607-v",
    "outputId": "59adff26-de38-4198-c3f8-6fe8ba1051df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "Train on 2916 samples, validate on 993 samples\n",
      "Epoch 1/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.7244 - acc: 0.5041 - val_loss: 0.6920 - val_acc: 0.4935\n",
      "Epoch 2/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.6939 - acc: 0.5487 - val_loss: 2.2433 - val_acc: 0.4935\n",
      "Epoch 3/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.6711 - acc: 0.6337 - val_loss: 0.6448 - val_acc: 0.6576\n",
      "Epoch 4/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.5867 - acc: 0.6996 - val_loss: 0.5880 - val_acc: 0.6697\n",
      "Epoch 5/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.5274 - acc: 0.7548 - val_loss: 0.5787 - val_acc: 0.7412\n",
      "Epoch 6/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.4785 - acc: 0.7781 - val_loss: 0.5449 - val_acc: 0.7200\n",
      "Epoch 7/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.4296 - acc: 0.8018 - val_loss: 0.6493 - val_acc: 0.7422\n",
      "Epoch 8/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.3924 - acc: 0.8278 - val_loss: 1.0365 - val_acc: 0.6425\n",
      "Epoch 9/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.3200 - acc: 0.8680 - val_loss: 0.5699 - val_acc: 0.7744\n",
      "Epoch 10/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.2542 - acc: 0.8968 - val_loss: 0.8832 - val_acc: 0.7623\n",
      "Epoch 11/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.2122 - acc: 0.9170 - val_loss: 0.9782 - val_acc: 0.7714\n",
      "Epoch 12/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.1736 - acc: 0.9345 - val_loss: 0.8127 - val_acc: 0.7754\n",
      "Epoch 13/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.1603 - acc: 0.9427 - val_loss: 1.3601 - val_acc: 0.6878\n",
      "Epoch 14/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.1363 - acc: 0.9510 - val_loss: 0.9988 - val_acc: 0.7835\n",
      "Epoch 15/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.1130 - acc: 0.9575 - val_loss: 0.7704 - val_acc: 0.7764\n",
      "Epoch 16/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.1233 - acc: 0.9606 - val_loss: 1.5219 - val_acc: 0.7372\n",
      "Epoch 17/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0854 - acc: 0.9743 - val_loss: 1.2230 - val_acc: 0.7774\n",
      "Epoch 18/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0819 - acc: 0.9681 - val_loss: 1.3888 - val_acc: 0.7734\n",
      "Epoch 19/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0863 - acc: 0.9726 - val_loss: 1.5275 - val_acc: 0.7704\n",
      "Epoch 20/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0686 - acc: 0.9774 - val_loss: 1.3179 - val_acc: 0.7704\n",
      "Epoch 21/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0730 - acc: 0.9784 - val_loss: 1.2313 - val_acc: 0.7855\n",
      "Epoch 22/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0847 - acc: 0.9787 - val_loss: 1.3993 - val_acc: 0.7744\n",
      "Epoch 23/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0691 - acc: 0.9781 - val_loss: 1.7341 - val_acc: 0.7553\n",
      "Epoch 24/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0755 - acc: 0.9750 - val_loss: 1.4530 - val_acc: 0.7724\n",
      "Epoch 25/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0819 - acc: 0.9805 - val_loss: 0.8672 - val_acc: 0.6858\n",
      "Epoch 26/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0729 - acc: 0.9777 - val_loss: 1.3318 - val_acc: 0.7825\n",
      "Epoch 27/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0871 - acc: 0.9801 - val_loss: 1.3081 - val_acc: 0.7976\n",
      "Epoch 28/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0606 - acc: 0.9794 - val_loss: 1.7220 - val_acc: 0.7764\n",
      "Epoch 29/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0615 - acc: 0.9849 - val_loss: 1.9543 - val_acc: 0.7895\n",
      "Epoch 30/30\n",
      "2916/2916 [==============================] - 6s 2ms/step - loss: 0.0861 - acc: 0.9777 - val_loss: 1.1088 - val_acc: 0.7885\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(16, kernel_size=(3, 3), activation='relu', \n",
    "                 input_shape=input_shape))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])\n",
    "              \n",
    "              \n",
    "history = model.fit(x=train_imgs_scaled, y=train_labels_enc,\n",
    "                    validation_data=(validation_imgs_scaled, validation_labels_enc),\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-XvX5SOd07-y"
   },
   "outputs": [],
   "source": [
    "model.save('cats_dogs_basic_cnn.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Data Augmentation Generator to reduce VARIANCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S4FCRyEu07-1"
   },
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1./255, zoom_range=0.3, rotation_range=50,\n",
    "                                   width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, \n",
    "                                   horizontal_flip=True, fill_mode='nearest')\n",
    "\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 242
    },
    "colab_type": "code",
    "id": "83u4bxEi07-4",
    "outputId": "c8db3ca4-929b-4ce1-d6dd-09ab9cbe4b2c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels: ['dog', 'dog', 'dog', 'dog', 'dog']\n"
     ]
    },
    {
     "data": {
      "image/png": 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GSVEAAAAAAAAAAAAAo/b/pXithI2UfkYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1152x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 188,
       "width": 930
      },
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_id = 1991\n",
    "dog_generator = train_datagen.flow(train_imgs[img_id:img_id+1], train_labels[img_id:img_id+1],\n",
    "                                   batch_size=1)\n",
    "dog = [next(dog_generator) for i in range(0,5)]\n",
    "fig, ax = plt.subplots(1,5, figsize=(15, 6))\n",
    "print('Labels:', [item[1][0] for item in dog])\n",
    "l = [ax[i].imshow(dog[i][0][0]) for i in range(0,5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 232
    },
    "colab_type": "code",
    "id": "cgHHv7FV07-6",
    "outputId": "dcd1e79e-f454-4044-8f58-a61e96817368"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels: ['cat', 'cat', 'cat', 'cat', 'cat']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 178,
       "width": 875
      },
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_id = 2595\n",
    "cat_generator = train_datagen.flow(train_imgs[img_id:img_id+1], train_labels[img_id:img_id+1],\n",
    "                                   batch_size=1)\n",
    "cat = [next(cat_generator) for i in range(0,5)]\n",
    "fig, ax = plt.subplots(1,5, figsize=(16, 6))\n",
    "print('Labels:', [item[1][0] for item in cat])\n",
    "l = [ax[i].imshow(cat[i][0][0]) for i in range(0,5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 3 CNN with Dropout layers using Data Augmentation Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3437
    },
    "colab_type": "code",
    "id": "aSas0HnG07-8",
    "outputId": "fefbc1be-d69a-4009-9720-6036623e4e19"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "100/100 [==============================] - 20s 196ms/step - loss: 0.6923 - acc: 0.5127 - val_loss: 0.6859 - val_acc: 0.5509\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 18s 177ms/step - loss: 0.6846 - acc: 0.5630 - val_loss: 0.6694 - val_acc: 0.6113\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 18s 182ms/step - loss: 0.6763 - acc: 0.5726 - val_loss: 0.6758 - val_acc: 0.5498\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 18s 178ms/step - loss: 0.6653 - acc: 0.5980 - val_loss: 0.6707 - val_acc: 0.5730\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 18s 178ms/step - loss: 0.6601 - acc: 0.6007 - val_loss: 0.6347 - val_acc: 0.6334\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 18s 184ms/step - loss: 0.6484 - acc: 0.6180 - val_loss: 0.6515 - val_acc: 0.6093\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 18s 177ms/step - loss: 0.6363 - acc: 0.6313 - val_loss: 0.6082 - val_acc: 0.6606\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 18s 179ms/step - loss: 0.6221 - acc: 0.6453 - val_loss: 0.6152 - val_acc: 0.6445\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 18s 176ms/step - loss: 0.6225 - acc: 0.6417 - val_loss: 0.5944 - val_acc: 0.6777\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 18s 177ms/step - loss: 0.6144 - acc: 0.6427 - val_loss: 0.5976 - val_acc: 0.6757\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 18s 183ms/step - loss: 0.5940 - acc: 0.6780 - val_loss: 0.6131 - val_acc: 0.6707\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 18s 176ms/step - loss: 0.6013 - acc: 0.6747 - val_loss: 0.5833 - val_acc: 0.6767\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.5892 - acc: 0.6883 - val_loss: 0.5646 - val_acc: 0.7059\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 18s 176ms/step - loss: 0.5842 - acc: 0.6840 - val_loss: 0.5730 - val_acc: 0.6949\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 17s 168ms/step - loss: 0.5721 - acc: 0.6923 - val_loss: 0.5623 - val_acc: 0.7200\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 17s 171ms/step - loss: 0.5678 - acc: 0.7017 - val_loss: 0.5557 - val_acc: 0.7090\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 17s 167ms/step - loss: 0.5738 - acc: 0.6913 - val_loss: 0.5445 - val_acc: 0.7291\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 17s 172ms/step - loss: 0.5626 - acc: 0.7003 - val_loss: 0.5563 - val_acc: 0.7039\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 18s 180ms/step - loss: 0.5592 - acc: 0.7067 - val_loss: 0.5347 - val_acc: 0.7311\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 18s 177ms/step - loss: 0.5570 - acc: 0.7053 - val_loss: 0.5297 - val_acc: 0.7281\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 18s 178ms/step - loss: 0.5591 - acc: 0.7114 - val_loss: 0.5534 - val_acc: 0.7170\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 18s 185ms/step - loss: 0.5487 - acc: 0.7110 - val_loss: 0.5627 - val_acc: 0.7331\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 18s 178ms/step - loss: 0.5370 - acc: 0.7307 - val_loss: 0.5016 - val_acc: 0.7613\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 18s 180ms/step - loss: 0.5458 - acc: 0.7103 - val_loss: 0.5289 - val_acc: 0.7341\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 18s 178ms/step - loss: 0.5311 - acc: 0.7273 - val_loss: 0.5153 - val_acc: 0.7472\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 18s 179ms/step - loss: 0.5365 - acc: 0.7277 - val_loss: 0.5237 - val_acc: 0.7392\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 18s 181ms/step - loss: 0.5315 - acc: 0.7357 - val_loss: 0.4984 - val_acc: 0.7513\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.5200 - acc: 0.7370 - val_loss: 0.5322 - val_acc: 0.7301\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 18s 179ms/step - loss: 0.5137 - acc: 0.7493 - val_loss: 0.5275 - val_acc: 0.7321\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 17s 172ms/step - loss: 0.5140 - acc: 0.7407 - val_loss: 0.5193 - val_acc: 0.7563\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.5278 - acc: 0.7377 - val_loss: 0.4939 - val_acc: 0.7583\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 18s 182ms/step - loss: 0.5080 - acc: 0.7420 - val_loss: 0.5204 - val_acc: 0.7452\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.4832 - acc: 0.7633 - val_loss: 0.4919 - val_acc: 0.7543\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.5161 - acc: 0.7417 - val_loss: 0.4981 - val_acc: 0.7553\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 18s 180ms/step - loss: 0.4996 - acc: 0.7547 - val_loss: 0.5086 - val_acc: 0.7613\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.5128 - acc: 0.7467 - val_loss: 0.4941 - val_acc: 0.7654\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 18s 177ms/step - loss: 0.4828 - acc: 0.7657 - val_loss: 0.4676 - val_acc: 0.7754\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 17s 172ms/step - loss: 0.4890 - acc: 0.7633 - val_loss: 0.5727 - val_acc: 0.7503\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.5085 - acc: 0.7490 - val_loss: 0.4880 - val_acc: 0.7664\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 18s 178ms/step - loss: 0.4924 - acc: 0.7576 - val_loss: 0.4923 - val_acc: 0.7825\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 17s 171ms/step - loss: 0.4982 - acc: 0.7584 - val_loss: 0.4768 - val_acc: 0.7654\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 17s 168ms/step - loss: 0.4935 - acc: 0.7557 - val_loss: 0.4820 - val_acc: 0.7714\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.4796 - acc: 0.7680 - val_loss: 0.5440 - val_acc: 0.7513\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 17s 168ms/step - loss: 0.4809 - acc: 0.7610 - val_loss: 0.4793 - val_acc: 0.7774\n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 17s 170ms/step - loss: 0.4855 - acc: 0.7643 - val_loss: 0.4844 - val_acc: 0.7654\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 17s 167ms/step - loss: 0.4816 - acc: 0.7620 - val_loss: 0.4696 - val_acc: 0.7795\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.4725 - acc: 0.7780 - val_loss: 0.5151 - val_acc: 0.7482\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 17s 171ms/step - loss: 0.4729 - acc: 0.7700 - val_loss: 0.4790 - val_acc: 0.7875\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 17s 166ms/step - loss: 0.4676 - acc: 0.7727 - val_loss: 0.5276 - val_acc: 0.7462\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 17s 169ms/step - loss: 0.4595 - acc: 0.7857 - val_loss: 0.4976 - val_acc: 0.7633\n",
      "Epoch 51/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.4613 - acc: 0.7797 - val_loss: 0.5057 - val_acc: 0.7664\n",
      "Epoch 52/100\n",
      "100/100 [==============================] - 17s 168ms/step - loss: 0.4600 - acc: 0.7737 - val_loss: 0.4777 - val_acc: 0.7895\n",
      "Epoch 53/100\n",
      "100/100 [==============================] - 17s 171ms/step - loss: 0.4610 - acc: 0.7810 - val_loss: 0.4700 - val_acc: 0.7825\n",
      "Epoch 54/100\n",
      "100/100 [==============================] - 17s 168ms/step - loss: 0.4667 - acc: 0.7807 - val_loss: 0.4948 - val_acc: 0.7513\n",
      "Epoch 55/100\n",
      "100/100 [==============================] - 17s 170ms/step - loss: 0.4599 - acc: 0.7833 - val_loss: 0.4625 - val_acc: 0.8026\n",
      "Epoch 56/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.4520 - acc: 0.7823 - val_loss: 0.4653 - val_acc: 0.7845\n",
      "Epoch 57/100\n",
      "100/100 [==============================] - 17s 169ms/step - loss: 0.4549 - acc: 0.7790 - val_loss: 0.4282 - val_acc: 0.8107\n",
      "Epoch 58/100\n",
      "100/100 [==============================] - 17s 171ms/step - loss: 0.4467 - acc: 0.7860 - val_loss: 0.4469 - val_acc: 0.7855\n",
      "Epoch 59/100\n",
      "100/100 [==============================] - 18s 181ms/step - loss: 0.4495 - acc: 0.7903 - val_loss: 0.4630 - val_acc: 0.7996\n",
      "Epoch 60/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.4641 - acc: 0.7804 - val_loss: 0.4453 - val_acc: 0.7966\n",
      "Epoch 61/100\n",
      "100/100 [==============================] - 18s 176ms/step - loss: 0.4356 - acc: 0.7927 - val_loss: 0.4599 - val_acc: 0.8016\n",
      "Epoch 62/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.4552 - acc: 0.7890 - val_loss: 0.4813 - val_acc: 0.7915\n",
      "Epoch 63/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.4442 - acc: 0.7886 - val_loss: 0.4543 - val_acc: 0.7966\n",
      "Epoch 64/100\n",
      "100/100 [==============================] - 18s 179ms/step - loss: 0.4464 - acc: 0.7860 - val_loss: 0.5102 - val_acc: 0.7603\n",
      "Epoch 65/100\n",
      "100/100 [==============================] - 18s 177ms/step - loss: 0.4315 - acc: 0.8037 - val_loss: 0.4873 - val_acc: 0.7845\n",
      "Epoch 66/100\n",
      "100/100 [==============================] - 18s 176ms/step - loss: 0.4402 - acc: 0.7937 - val_loss: 0.4894 - val_acc: 0.7855\n",
      "Epoch 67/100\n",
      "100/100 [==============================] - 18s 181ms/step - loss: 0.4387 - acc: 0.7943 - val_loss: 0.4418 - val_acc: 0.8036\n",
      "Epoch 68/100\n",
      "100/100 [==============================] - 18s 176ms/step - loss: 0.4264 - acc: 0.7984 - val_loss: 0.6016 - val_acc: 0.7432\n",
      "Epoch 69/100\n",
      "100/100 [==============================] - 18s 178ms/step - loss: 0.4313 - acc: 0.8037 - val_loss: 0.4373 - val_acc: 0.8127\n",
      "Epoch 70/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.4128 - acc: 0.8140 - val_loss: 0.4353 - val_acc: 0.8066\n",
      "Epoch 71/100\n",
      "100/100 [==============================] - 18s 177ms/step - loss: 0.4292 - acc: 0.7963 - val_loss: 0.4196 - val_acc: 0.8298\n",
      "Epoch 72/100\n",
      "100/100 [==============================] - 18s 181ms/step - loss: 0.4369 - acc: 0.7957 - val_loss: 0.4697 - val_acc: 0.7724\n",
      "Epoch 73/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.4162 - acc: 0.8040 - val_loss: 0.4838 - val_acc: 0.8056\n",
      "Epoch 74/100\n",
      "100/100 [==============================] - 18s 178ms/step - loss: 0.4433 - acc: 0.7927 - val_loss: 0.4586 - val_acc: 0.7875\n",
      "Epoch 75/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.4180 - acc: 0.8160 - val_loss: 0.4418 - val_acc: 0.8167\n",
      "Epoch 76/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.4108 - acc: 0.8203 - val_loss: 0.4585 - val_acc: 0.8077\n",
      "Epoch 77/100\n",
      "100/100 [==============================] - 17s 172ms/step - loss: 0.4132 - acc: 0.8023 - val_loss: 0.5029 - val_acc: 0.7835\n",
      "Epoch 78/100\n",
      "100/100 [==============================] - 17s 169ms/step - loss: 0.4155 - acc: 0.8147 - val_loss: 0.4134 - val_acc: 0.8187\n",
      "Epoch 79/100\n",
      "100/100 [==============================] - 17s 168ms/step - loss: 0.3923 - acc: 0.8337 - val_loss: 0.4650 - val_acc: 0.8077\n",
      "Epoch 80/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.4183 - acc: 0.8010 - val_loss: 0.4633 - val_acc: 0.8157\n",
      "Epoch 81/100\n",
      "100/100 [==============================] - 17s 166ms/step - loss: 0.4097 - acc: 0.8077 - val_loss: 0.3891 - val_acc: 0.8359\n",
      "Epoch 82/100\n",
      "100/100 [==============================] - 17s 172ms/step - loss: 0.3982 - acc: 0.8167 - val_loss: 0.4860 - val_acc: 0.8006\n",
      "Epoch 83/100\n",
      "100/100 [==============================] - 17s 170ms/step - loss: 0.3938 - acc: 0.8213 - val_loss: 0.4282 - val_acc: 0.8278\n",
      "Epoch 84/100\n",
      "100/100 [==============================] - 17s 167ms/step - loss: 0.4204 - acc: 0.8123 - val_loss: 0.4275 - val_acc: 0.8218\n",
      "Epoch 85/100\n",
      "100/100 [==============================] - 17s 172ms/step - loss: 0.4058 - acc: 0.8180 - val_loss: 0.4317 - val_acc: 0.8097\n",
      "Epoch 86/100\n",
      "100/100 [==============================] - 17s 168ms/step - loss: 0.4059 - acc: 0.8140 - val_loss: 0.3900 - val_acc: 0.8379\n",
      "Epoch 87/100\n",
      "100/100 [==============================] - 17s 169ms/step - loss: 0.4025 - acc: 0.8190 - val_loss: 0.4313 - val_acc: 0.8167\n",
      "Epoch 88/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.4202 - acc: 0.8000 - val_loss: 0.4001 - val_acc: 0.8288\n",
      "Epoch 89/100\n",
      "100/100 [==============================] - 17s 168ms/step - loss: 0.3995 - acc: 0.8147 - val_loss: 0.3921 - val_acc: 0.8429\n",
      "Epoch 90/100\n",
      "100/100 [==============================] - 17s 166ms/step - loss: 0.4037 - acc: 0.8220 - val_loss: 0.4734 - val_acc: 0.8157\n",
      "Epoch 91/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.3812 - acc: 0.8213 - val_loss: 0.4083 - val_acc: 0.8298\n",
      "Epoch 92/100\n",
      "100/100 [==============================] - 17s 167ms/step - loss: 0.3973 - acc: 0.8217 - val_loss: 0.4022 - val_acc: 0.8308\n",
      "Epoch 93/100\n",
      "100/100 [==============================] - 17s 170ms/step - loss: 0.3892 - acc: 0.8257 - val_loss: 0.4414 - val_acc: 0.8228\n",
      "Epoch 94/100\n",
      "100/100 [==============================] - 17s 170ms/step - loss: 0.3926 - acc: 0.8203 - val_loss: 0.4197 - val_acc: 0.8308\n",
      "Epoch 95/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.3957 - acc: 0.8320 - val_loss: 0.4067 - val_acc: 0.8258\n",
      "Epoch 96/100\n",
      "100/100 [==============================] - 18s 179ms/step - loss: 0.3876 - acc: 0.8263 - val_loss: 0.6104 - val_acc: 0.7875\n",
      "Epoch 97/100\n",
      "100/100 [==============================] - 18s 177ms/step - loss: 0.3987 - acc: 0.8170 - val_loss: 0.4011 - val_acc: 0.8499\n",
      "Epoch 98/100\n",
      "100/100 [==============================] - 18s 176ms/step - loss: 0.3852 - acc: 0.8263 - val_loss: 0.4080 - val_acc: 0.8177\n",
      "Epoch 99/100\n",
      "100/100 [==============================] - 18s 180ms/step - loss: 0.3923 - acc: 0.8260 - val_loss: 0.4669 - val_acc: 0.8207\n",
      "Epoch 100/100\n",
      "100/100 [==============================] - 18s 177ms/step - loss: 0.3814 - acc: 0.8283 - val_loss: 0.4099 - val_acc: 0.8359\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow(train_imgs, train_labels_enc, batch_size=30)\n",
    "val_generator = val_datagen.flow(validation_imgs, validation_labels_enc, batch_size=20)\n",
    "input_shape = (150, 150, 3)\n",
    "\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "from keras.models import Sequential\n",
    "from keras import optimizers\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(16, kernel_size=(3, 3), activation='relu', \n",
    "                 input_shape=input_shape))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-4),\n",
    "              metrics=['accuracy'])\n",
    "              \n",
    "history = model.fit_generator(train_generator, steps_per_epoch=100, epochs=100,\n",
    "                              validation_data=val_generator, validation_steps=50, \n",
    "                              verbose=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s3wyFr4707-_"
   },
   "outputs": [],
   "source": [
    "model.save('cats_dogs_cnn_img_aug.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 4 Using a VGG16 pre-trained model, adding new outout layers (Transfer Learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 723
    },
    "colab_type": "code",
    "id": "Mva8j62707_C",
    "outputId": "4cdf2776-7b70-45c7-a924-fa3b3c66cdc5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58892288/58889256 [==============================] - 2s 0us/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Layer Type</th>\n",
       "      <th>Layer Name</th>\n",
       "      <th>Layer Trainable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt;keras.engine.input_layer.InputLayer object at 0x7f3a8df32f60&gt;</td>\n",
       "      <td>input_1</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8dde2780&gt;</td>\n",
       "      <td>block1_conv1</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8df32278&gt;</td>\n",
       "      <td>block1_conv2</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&lt;keras.layers.pooling.MaxPooling2D object at 0x7f3aa0092b00&gt;</td>\n",
       "      <td>block1_pool</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8ddce6d8&gt;</td>\n",
       "      <td>block2_conv1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a88fdecf8&gt;</td>\n",
       "      <td>block2_conv2</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>&lt;keras.layers.pooling.MaxPooling2D object at 0x7f3a8d02b630&gt;</td>\n",
       "      <td>block2_pool</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8d02b518&gt;</td>\n",
       "      <td>block3_conv1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cfdc2b0&gt;</td>\n",
       "      <td>block3_conv2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cfee908&gt;</td>\n",
       "      <td>block3_conv3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>&lt;keras.layers.pooling.MaxPooling2D object at 0x7f3a8cf8ad30&gt;</td>\n",
       "      <td>block3_pool</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cf8ac88&gt;</td>\n",
       "      <td>block4_conv1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cfb7ba8&gt;</td>\n",
       "      <td>block4_conv2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cf62240&gt;</td>\n",
       "      <td>block4_conv3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>&lt;keras.layers.pooling.MaxPooling2D object at 0x7f3a8cf7cbe0&gt;</td>\n",
       "      <td>block4_pool</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cf7c6a0&gt;</td>\n",
       "      <td>block5_conv1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cf2a400&gt;</td>\n",
       "      <td>block5_conv2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cebfa58&gt;</td>\n",
       "      <td>block5_conv3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>&lt;keras.layers.pooling.MaxPooling2D object at 0x7f3a8ceed6d8&gt;</td>\n",
       "      <td>block5_pool</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>&lt;keras.layers.core.Flatten object at 0x7f3a8ce4cd68&gt;</td>\n",
       "      <td>flatten_4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                        Layer Type  \\\n",
       "0   <keras.engine.input_layer.InputLayer object at 0x7f3a8df32f60>   \n",
       "1   <keras.layers.convolutional.Conv2D object at 0x7f3a8dde2780>     \n",
       "2   <keras.layers.convolutional.Conv2D object at 0x7f3a8df32278>     \n",
       "3   <keras.layers.pooling.MaxPooling2D object at 0x7f3aa0092b00>     \n",
       "4   <keras.layers.convolutional.Conv2D object at 0x7f3a8ddce6d8>     \n",
       "5   <keras.layers.convolutional.Conv2D object at 0x7f3a88fdecf8>     \n",
       "6   <keras.layers.pooling.MaxPooling2D object at 0x7f3a8d02b630>     \n",
       "7   <keras.layers.convolutional.Conv2D object at 0x7f3a8d02b518>     \n",
       "8   <keras.layers.convolutional.Conv2D object at 0x7f3a8cfdc2b0>     \n",
       "9   <keras.layers.convolutional.Conv2D object at 0x7f3a8cfee908>     \n",
       "10  <keras.layers.pooling.MaxPooling2D object at 0x7f3a8cf8ad30>     \n",
       "11  <keras.layers.convolutional.Conv2D object at 0x7f3a8cf8ac88>     \n",
       "12  <keras.layers.convolutional.Conv2D object at 0x7f3a8cfb7ba8>     \n",
       "13  <keras.layers.convolutional.Conv2D object at 0x7f3a8cf62240>     \n",
       "14  <keras.layers.pooling.MaxPooling2D object at 0x7f3a8cf7cbe0>     \n",
       "15  <keras.layers.convolutional.Conv2D object at 0x7f3a8cf7c6a0>     \n",
       "16  <keras.layers.convolutional.Conv2D object at 0x7f3a8cf2a400>     \n",
       "17  <keras.layers.convolutional.Conv2D object at 0x7f3a8cebfa58>     \n",
       "18  <keras.layers.pooling.MaxPooling2D object at 0x7f3a8ceed6d8>     \n",
       "19  <keras.layers.core.Flatten object at 0x7f3a8ce4cd68>             \n",
       "\n",
       "      Layer Name  Layer Trainable  \n",
       "0   input_1       False            \n",
       "1   block1_conv1  False            \n",
       "2   block1_conv2  False            \n",
       "3   block1_pool   False            \n",
       "4   block2_conv1  False            \n",
       "5   block2_conv2  False            \n",
       "6   block2_pool   False            \n",
       "7   block3_conv1  False            \n",
       "8   block3_conv2  False            \n",
       "9   block3_conv3  False            \n",
       "10  block3_pool   False            \n",
       "11  block4_conv1  False            \n",
       "12  block4_conv2  False            \n",
       "13  block4_conv3  False            \n",
       "14  block4_pool   False            \n",
       "15  block5_conv1  False            \n",
       "16  block5_conv2  False            \n",
       "17  block5_conv3  False            \n",
       "18  block5_pool   False            \n",
       "19  flatten_4     False            "
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.applications import vgg16\n",
    "from keras.models import Model\n",
    "import keras\n",
    "\n",
    "vgg = vgg16.VGG16(include_top=False, weights='imagenet', \n",
    "                                     input_shape=input_shape)\n",
    "\n",
    "output = vgg.layers[-1].output\n",
    "output = keras.layers.Flatten()(output)\n",
    "vgg_model = Model(vgg.input, output)\n",
    "\n",
    "vgg_model.trainable = False\n",
    "for layer in vgg_model.layers:\n",
    "    layer.trainable = False\n",
    "    \n",
    "import pandas as pd\n",
    "pd.set_option('max_colwidth', -1)\n",
    "layers = [(layer, layer.name, layer.trainable) for layer in vgg_model.layers]\n",
    "pd.DataFrame(layers, columns=['Layer Type', 'Layer Name', 'Layer Trainable']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing how VVG6 working"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 303
    },
    "colab_type": "code",
    "id": "vlQyYmEC07_E",
    "outputId": "164ebcef-52a5-4185-bd52-50f1250442cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 4, 4, 512)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3a8cd934a8>"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 252,
       "width": 258
      },
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bottleneck_feature_example = vgg.predict(train_imgs_scaled[0:1])\n",
    "print(bottleneck_feature_example.shape)\n",
    "plt.imshow(bottleneck_feature_example[0][:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "mnmmIzOs07_H",
    "outputId": "9ffd242b-da9c-4da5-96f4-708455b7a61c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Bottleneck Features: (2916, 8192) \tValidation Bottleneck Features: (993, 8192)\n"
     ]
    }
   ],
   "source": [
    "def get_bottleneck_features(model, input_imgs):\n",
    "    features = model.predict(input_imgs, verbose=0)\n",
    "    return features\n",
    "    \n",
    "train_features_vgg = get_bottleneck_features(vgg_model, train_imgs_scaled)\n",
    "validation_features_vgg = get_bottleneck_features(vgg_model, validation_imgs_scaled)\n",
    "\n",
    "print('Train Bottleneck Features:', train_features_vgg.shape, \n",
    "      '\\tValidation Bottleneck Features:', validation_features_vgg.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 4 Basic Transfer Lerning Neural networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "E_bvL_wp07_K",
    "outputId": "0ca13d49-3565-4f2f-d8ed-200cd4ce5d44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_9 (Dense)              (None, 512)               4194816   \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 4,457,985\n",
      "Trainable params: 4,457,985\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, InputLayer\n",
    "from keras.models import Sequential\n",
    "from keras import optimizers\n",
    "\n",
    "input_shape = vgg_model.output_shape[1]\n",
    "\n",
    "model = Sequential()\n",
    "model.add(InputLayer(input_shape=(input_shape,)))\n",
    "model.add(Dense(512, activation='relu', input_dim=input_shape))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-4),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1074
    },
    "colab_type": "code",
    "id": "90QTpN5O07_O",
    "outputId": "225da787-08d3-4a84-970d-db75725da222"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2916 samples, validate on 993 samples\n",
      "Epoch 1/30\n",
      "2916/2916 [==============================] - 2s 539us/step - loss: 0.4275 - acc: 0.7966 - val_loss: 0.3040 - val_acc: 0.8550\n",
      "Epoch 2/30\n",
      "2916/2916 [==============================] - 1s 316us/step - loss: 0.2872 - acc: 0.8759 - val_loss: 0.2484 - val_acc: 0.8902\n",
      "Epoch 3/30\n",
      "2916/2916 [==============================] - 1s 308us/step - loss: 0.2483 - acc: 0.8930 - val_loss: 0.2259 - val_acc: 0.9013\n",
      "Epoch 4/30\n",
      "2916/2916 [==============================] - 1s 308us/step - loss: 0.2095 - acc: 0.9122 - val_loss: 0.2150 - val_acc: 0.9114\n",
      "Epoch 5/30\n",
      "2916/2916 [==============================] - 1s 312us/step - loss: 0.1696 - acc: 0.9270 - val_loss: 0.2263 - val_acc: 0.9094\n",
      "Epoch 6/30\n",
      "2916/2916 [==============================] - 1s 308us/step - loss: 0.1481 - acc: 0.9400 - val_loss: 0.2607 - val_acc: 0.8983\n",
      "Epoch 7/30\n",
      "2916/2916 [==============================] - 1s 308us/step - loss: 0.1180 - acc: 0.9540 - val_loss: 0.2187 - val_acc: 0.9164\n",
      "Epoch 8/30\n",
      "2916/2916 [==============================] - 1s 308us/step - loss: 0.0959 - acc: 0.9612 - val_loss: 0.3365 - val_acc: 0.8802\n",
      "Epoch 9/30\n",
      "2916/2916 [==============================] - 1s 305us/step - loss: 0.0786 - acc: 0.9691 - val_loss: 0.2733 - val_acc: 0.9094\n",
      "Epoch 10/30\n",
      "2916/2916 [==============================] - 1s 309us/step - loss: 0.0623 - acc: 0.9753 - val_loss: 0.5602 - val_acc: 0.8580\n",
      "Epoch 11/30\n",
      "2916/2916 [==============================] - 1s 310us/step - loss: 0.0491 - acc: 0.9829 - val_loss: 0.3511 - val_acc: 0.8963\n",
      "Epoch 12/30\n",
      "2916/2916 [==============================] - 1s 308us/step - loss: 0.0375 - acc: 0.9842 - val_loss: 0.3783 - val_acc: 0.8973\n",
      "Epoch 13/30\n",
      "2916/2916 [==============================] - 1s 312us/step - loss: 0.0326 - acc: 0.9890 - val_loss: 0.3156 - val_acc: 0.9114\n",
      "Epoch 14/30\n",
      "2916/2916 [==============================] - 1s 308us/step - loss: 0.0244 - acc: 0.9931 - val_loss: 0.4533 - val_acc: 0.8953\n",
      "Epoch 15/30\n",
      "2916/2916 [==============================] - 1s 310us/step - loss: 0.0292 - acc: 0.9914 - val_loss: 0.3982 - val_acc: 0.9074\n",
      "Epoch 16/30\n",
      "2916/2916 [==============================] - 1s 308us/step - loss: 0.0188 - acc: 0.9945 - val_loss: 0.4182 - val_acc: 0.9074\n",
      "Epoch 17/30\n",
      "2916/2916 [==============================] - 1s 306us/step - loss: 0.0182 - acc: 0.9914 - val_loss: 0.4084 - val_acc: 0.9114\n",
      "Epoch 18/30\n",
      "2916/2916 [==============================] - 1s 307us/step - loss: 0.0104 - acc: 0.9966 - val_loss: 1.6013 - val_acc: 0.7946\n",
      "Epoch 19/30\n",
      "2916/2916 [==============================] - 1s 310us/step - loss: 0.0120 - acc: 0.9973 - val_loss: 0.4396 - val_acc: 0.9053\n",
      "Epoch 20/30\n",
      "2916/2916 [==============================] - 1s 308us/step - loss: 0.0176 - acc: 0.9949 - val_loss: 0.4491 - val_acc: 0.9114\n",
      "Epoch 21/30\n",
      "2916/2916 [==============================] - 1s 310us/step - loss: 0.0059 - acc: 0.9986 - val_loss: 0.4922 - val_acc: 0.9144\n",
      "Epoch 22/30\n",
      "2916/2916 [==============================] - 1s 310us/step - loss: 0.0172 - acc: 0.9955 - val_loss: 0.4816 - val_acc: 0.9154\n",
      "Epoch 23/30\n",
      "2916/2916 [==============================] - 1s 311us/step - loss: 0.0053 - acc: 0.9976 - val_loss: 0.4900 - val_acc: 0.9194\n",
      "Epoch 24/30\n",
      "2916/2916 [==============================] - 1s 305us/step - loss: 0.0045 - acc: 0.9986 - val_loss: 0.5613 - val_acc: 0.9053\n",
      "Epoch 25/30\n",
      "2916/2916 [==============================] - 1s 305us/step - loss: 0.0148 - acc: 0.9959 - val_loss: 0.5541 - val_acc: 0.9104\n",
      "Epoch 26/30\n",
      "2916/2916 [==============================] - 1s 309us/step - loss: 0.0070 - acc: 0.9983 - val_loss: 0.5563 - val_acc: 0.9104\n",
      "Epoch 27/30\n",
      "2916/2916 [==============================] - 1s 307us/step - loss: 0.0029 - acc: 0.9993 - val_loss: 0.5653 - val_acc: 0.9063\n",
      "Epoch 28/30\n",
      "2916/2916 [==============================] - 1s 310us/step - loss: 0.0032 - acc: 0.9990 - val_loss: 0.6226 - val_acc: 0.9104\n",
      "Epoch 29/30\n",
      "2916/2916 [==============================] - 1s 309us/step - loss: 0.0011 - acc: 0.9997 - val_loss: 0.6483 - val_acc: 0.9084\n",
      "Epoch 30/30\n",
      "2916/2916 [==============================] - 1s 309us/step - loss: 0.0017 - acc: 0.9990 - val_loss: 0.6809 - val_acc: 0.9124\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x=train_features_vgg, y=train_labels_enc,\n",
    "                    validation_data=(validation_features_vgg, validation_labels_enc),\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vPcE8rjR07_Q"
   },
   "outputs": [],
   "source": [
    "model.save('cats_dogs_tlearn_basic_cnn.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Augmentation with Re-scaling (Normalization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JDI0lkDp07_T"
   },
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1./255, zoom_range=0.3, rotation_range=50,\n",
    "                                   width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, \n",
    "                                   horizontal_flip=True, fill_mode='nearest')\n",
    "\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow(train_imgs, train_labels_enc, batch_size=30)\n",
    "val_generator = val_datagen.flow(validation_imgs, validation_labels_enc, batch_size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3437
    },
    "colab_type": "code",
    "id": "mfNfZWoT07_W",
    "outputId": "b63c104d-0e9a-4101-ea52-f3a400d0c18b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "100/100 [==============================] - 30s 300ms/step - loss: 0.6549 - acc: 0.6000 - val_loss: 0.4954 - val_acc: 0.8207\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 25s 255ms/step - loss: 0.5680 - acc: 0.7017 - val_loss: 0.3932 - val_acc: 0.8560\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 25s 252ms/step - loss: 0.5345 - acc: 0.7200 - val_loss: 0.3489 - val_acc: 0.8681\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 26s 258ms/step - loss: 0.4851 - acc: 0.7600 - val_loss: 0.3066 - val_acc: 0.8852\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 26s 261ms/step - loss: 0.4564 - acc: 0.7810 - val_loss: 0.2904 - val_acc: 0.8862\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 27s 267ms/step - loss: 0.4411 - acc: 0.7840 - val_loss: 0.2830 - val_acc: 0.8812\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 26s 261ms/step - loss: 0.4128 - acc: 0.8047 - val_loss: 0.2633 - val_acc: 0.8973\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 26s 258ms/step - loss: 0.4227 - acc: 0.8057 - val_loss: 0.2555 - val_acc: 0.8963\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 26s 260ms/step - loss: 0.3949 - acc: 0.8103 - val_loss: 0.2491 - val_acc: 0.8943\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 26s 260ms/step - loss: 0.3971 - acc: 0.8170 - val_loss: 0.2798 - val_acc: 0.8771\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 27s 266ms/step - loss: 0.4083 - acc: 0.8067 - val_loss: 0.2431 - val_acc: 0.9003\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 26s 260ms/step - loss: 0.3866 - acc: 0.8230 - val_loss: 0.2569 - val_acc: 0.8993\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3893 - acc: 0.8144 - val_loss: 0.2323 - val_acc: 0.9074\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3871 - acc: 0.8180 - val_loss: 0.2339 - val_acc: 0.9043\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 26s 260ms/step - loss: 0.3876 - acc: 0.8253 - val_loss: 0.2414 - val_acc: 0.8953\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 26s 260ms/step - loss: 0.3751 - acc: 0.8287 - val_loss: 0.2248 - val_acc: 0.9124\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 26s 256ms/step - loss: 0.3623 - acc: 0.8270 - val_loss: 0.2225 - val_acc: 0.9094\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 26s 263ms/step - loss: 0.3671 - acc: 0.8270 - val_loss: 0.2214 - val_acc: 0.9104\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3612 - acc: 0.8437 - val_loss: 0.2396 - val_acc: 0.9023\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3651 - acc: 0.8287 - val_loss: 0.2275 - val_acc: 0.9114\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3798 - acc: 0.8230 - val_loss: 0.2251 - val_acc: 0.9114\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 26s 257ms/step - loss: 0.3355 - acc: 0.8463 - val_loss: 0.2133 - val_acc: 0.9124\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 26s 265ms/step - loss: 0.3667 - acc: 0.8277 - val_loss: 0.2297 - val_acc: 0.9053\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 26s 256ms/step - loss: 0.3449 - acc: 0.8420 - val_loss: 0.2100 - val_acc: 0.9174\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 26s 256ms/step - loss: 0.3506 - acc: 0.8433 - val_loss: 0.2096 - val_acc: 0.9184\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3460 - acc: 0.8490 - val_loss: 0.2090 - val_acc: 0.9184\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 26s 263ms/step - loss: 0.3458 - acc: 0.8407 - val_loss: 0.2093 - val_acc: 0.9215\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 26s 264ms/step - loss: 0.3396 - acc: 0.8470 - val_loss: 0.2275 - val_acc: 0.9084\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 26s 263ms/step - loss: 0.3524 - acc: 0.8483 - val_loss: 0.2070 - val_acc: 0.9194\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3411 - acc: 0.8410 - val_loss: 0.2055 - val_acc: 0.9225\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 26s 260ms/step - loss: 0.3437 - acc: 0.8500 - val_loss: 0.2079 - val_acc: 0.9265\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 26s 258ms/step - loss: 0.3298 - acc: 0.8543 - val_loss: 0.2134 - val_acc: 0.9174\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3462 - acc: 0.8447 - val_loss: 0.2256 - val_acc: 0.9134\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 26s 258ms/step - loss: 0.3183 - acc: 0.8597 - val_loss: 0.2074 - val_acc: 0.9215\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 26s 264ms/step - loss: 0.3352 - acc: 0.8500 - val_loss: 0.2148 - val_acc: 0.9204\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3195 - acc: 0.8543 - val_loss: 0.2033 - val_acc: 0.9265\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3314 - acc: 0.8510 - val_loss: 0.2046 - val_acc: 0.9194\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3325 - acc: 0.8497 - val_loss: 0.2160 - val_acc: 0.9174\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 26s 260ms/step - loss: 0.3430 - acc: 0.8470 - val_loss: 0.2257 - val_acc: 0.9074\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 26s 258ms/step - loss: 0.3294 - acc: 0.8503 - val_loss: 0.2195 - val_acc: 0.9144\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 26s 262ms/step - loss: 0.3359 - acc: 0.8473 - val_loss: 0.2095 - val_acc: 0.9235\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 26s 260ms/step - loss: 0.3335 - acc: 0.8480 - val_loss: 0.2087 - val_acc: 0.9235\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3197 - acc: 0.8600 - val_loss: 0.2078 - val_acc: 0.9215\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 26s 260ms/step - loss: 0.3117 - acc: 0.8530 - val_loss: 0.2193 - val_acc: 0.9164\n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 26s 260ms/step - loss: 0.3115 - acc: 0.8590 - val_loss: 0.2028 - val_acc: 0.9235\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 26s 258ms/step - loss: 0.3356 - acc: 0.8507 - val_loss: 0.2074 - val_acc: 0.9225\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 26s 265ms/step - loss: 0.3286 - acc: 0.8503 - val_loss: 0.1995 - val_acc: 0.9245\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3124 - acc: 0.8600 - val_loss: 0.2280 - val_acc: 0.9063\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 26s 256ms/step - loss: 0.3026 - acc: 0.8693 - val_loss: 0.2054 - val_acc: 0.9265\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 26s 256ms/step - loss: 0.3224 - acc: 0.8553 - val_loss: 0.2164 - val_acc: 0.9164\n",
      "Epoch 51/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3241 - acc: 0.8553 - val_loss: 0.1999 - val_acc: 0.9285\n",
      "Epoch 52/100\n",
      "100/100 [==============================] - 26s 258ms/step - loss: 0.3202 - acc: 0.8597 - val_loss: 0.2101 - val_acc: 0.9245\n",
      "Epoch 53/100\n",
      "100/100 [==============================] - 26s 262ms/step - loss: 0.3250 - acc: 0.8563 - val_loss: 0.2292 - val_acc: 0.9084\n",
      "Epoch 54/100\n",
      "100/100 [==============================] - 26s 258ms/step - loss: 0.3142 - acc: 0.8573 - val_loss: 0.2005 - val_acc: 0.9285\n",
      "Epoch 55/100\n",
      "100/100 [==============================] - 26s 258ms/step - loss: 0.3120 - acc: 0.8567 - val_loss: 0.2027 - val_acc: 0.9275\n",
      "Epoch 56/100\n",
      "100/100 [==============================] - 26s 257ms/step - loss: 0.3416 - acc: 0.8530 - val_loss: 0.2124 - val_acc: 0.9174\n",
      "Epoch 57/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3094 - acc: 0.8600 - val_loss: 0.2025 - val_acc: 0.9285\n",
      "Epoch 58/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.3000 - acc: 0.8667 - val_loss: 0.2128 - val_acc: 0.9225\n",
      "Epoch 59/100\n",
      "100/100 [==============================] - 26s 262ms/step - loss: 0.3085 - acc: 0.8663 - val_loss: 0.2109 - val_acc: 0.9194\n",
      "Epoch 60/100\n",
      "100/100 [==============================] - 25s 250ms/step - loss: 0.3012 - acc: 0.8710 - val_loss: 0.2086 - val_acc: 0.9265\n",
      "Epoch 61/100\n",
      "100/100 [==============================] - 25s 249ms/step - loss: 0.3066 - acc: 0.8583 - val_loss: 0.2112 - val_acc: 0.9194\n",
      "Epoch 62/100\n",
      "100/100 [==============================] - 25s 249ms/step - loss: 0.3063 - acc: 0.8627 - val_loss: 0.2072 - val_acc: 0.9255\n",
      "Epoch 63/100\n",
      "100/100 [==============================] - 25s 250ms/step - loss: 0.3196 - acc: 0.8630 - val_loss: 0.2164 - val_acc: 0.9174\n",
      "Epoch 64/100\n",
      "100/100 [==============================] - 25s 252ms/step - loss: 0.2919 - acc: 0.8760 - val_loss: 0.2025 - val_acc: 0.9265\n",
      "Epoch 65/100\n",
      "100/100 [==============================] - 25s 254ms/step - loss: 0.3087 - acc: 0.8677 - val_loss: 0.2104 - val_acc: 0.9204\n",
      "Epoch 66/100\n",
      "100/100 [==============================] - 25s 248ms/step - loss: 0.2962 - acc: 0.8773 - val_loss: 0.2094 - val_acc: 0.9295\n",
      "Epoch 67/100\n",
      "100/100 [==============================] - 25s 248ms/step - loss: 0.3073 - acc: 0.8627 - val_loss: 0.2082 - val_acc: 0.9265\n",
      "Epoch 68/100\n",
      "100/100 [==============================] - 25s 249ms/step - loss: 0.3032 - acc: 0.8650 - val_loss: 0.2000 - val_acc: 0.9305\n",
      "Epoch 69/100\n",
      "100/100 [==============================] - 25s 248ms/step - loss: 0.2962 - acc: 0.8683 - val_loss: 0.2018 - val_acc: 0.9285\n",
      "Epoch 70/100\n",
      "100/100 [==============================] - 25s 250ms/step - loss: 0.3092 - acc: 0.8597 - val_loss: 0.2018 - val_acc: 0.9315\n",
      "Epoch 71/100\n",
      "100/100 [==============================] - 26s 257ms/step - loss: 0.2826 - acc: 0.8740 - val_loss: 0.2003 - val_acc: 0.9285\n",
      "Epoch 72/100\n",
      "100/100 [==============================] - 25s 250ms/step - loss: 0.2883 - acc: 0.8757 - val_loss: 0.2037 - val_acc: 0.9335\n",
      "Epoch 73/100\n",
      "100/100 [==============================] - 25s 248ms/step - loss: 0.3179 - acc: 0.8550 - val_loss: 0.2168 - val_acc: 0.9184\n",
      "Epoch 74/100\n",
      "100/100 [==============================] - 25s 253ms/step - loss: 0.3176 - acc: 0.8597 - val_loss: 0.2014 - val_acc: 0.9295\n",
      "Epoch 75/100\n",
      "100/100 [==============================] - 26s 256ms/step - loss: 0.2895 - acc: 0.8713 - val_loss: 0.2221 - val_acc: 0.9184\n",
      "Epoch 76/100\n",
      "100/100 [==============================] - 25s 254ms/step - loss: 0.2851 - acc: 0.8770 - val_loss: 0.2016 - val_acc: 0.9255\n",
      "Epoch 77/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.2956 - acc: 0.8700 - val_loss: 0.2058 - val_acc: 0.9245\n",
      "Epoch 78/100\n",
      "100/100 [==============================] - 25s 255ms/step - loss: 0.2926 - acc: 0.8723 - val_loss: 0.2049 - val_acc: 0.9275\n",
      "Epoch 79/100\n",
      "100/100 [==============================] - 26s 256ms/step - loss: 0.2975 - acc: 0.8657 - val_loss: 0.2058 - val_acc: 0.9275\n",
      "Epoch 80/100\n",
      "100/100 [==============================] - 25s 255ms/step - loss: 0.2960 - acc: 0.8720 - val_loss: 0.2138 - val_acc: 0.9265\n",
      "Epoch 81/100\n",
      "100/100 [==============================] - 25s 255ms/step - loss: 0.2955 - acc: 0.8767 - val_loss: 0.2098 - val_acc: 0.9265\n",
      "Epoch 82/100\n",
      "100/100 [==============================] - 26s 255ms/step - loss: 0.2866 - acc: 0.8707 - val_loss: 0.2202 - val_acc: 0.9174\n",
      "Epoch 83/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.2773 - acc: 0.8817 - val_loss: 0.2091 - val_acc: 0.9265\n",
      "Epoch 84/100\n",
      "100/100 [==============================] - 26s 256ms/step - loss: 0.2917 - acc: 0.8727 - val_loss: 0.2002 - val_acc: 0.9245\n",
      "Epoch 85/100\n",
      "100/100 [==============================] - 26s 256ms/step - loss: 0.2936 - acc: 0.8677 - val_loss: 0.2106 - val_acc: 0.9265\n",
      "Epoch 86/100\n",
      "100/100 [==============================] - 26s 255ms/step - loss: 0.3082 - acc: 0.8617 - val_loss: 0.1996 - val_acc: 0.9275\n",
      "Epoch 87/100\n",
      "100/100 [==============================] - 26s 256ms/step - loss: 0.2828 - acc: 0.8743 - val_loss: 0.2031 - val_acc: 0.9235\n",
      "Epoch 88/100\n",
      "100/100 [==============================] - 26s 258ms/step - loss: 0.2799 - acc: 0.8773 - val_loss: 0.1999 - val_acc: 0.9325\n",
      "Epoch 89/100\n",
      "100/100 [==============================] - 26s 262ms/step - loss: 0.2803 - acc: 0.8773 - val_loss: 0.2098 - val_acc: 0.9285\n",
      "Epoch 90/100\n",
      "100/100 [==============================] - 26s 257ms/step - loss: 0.3028 - acc: 0.8687 - val_loss: 0.2029 - val_acc: 0.9215\n",
      "Epoch 91/100\n",
      "100/100 [==============================] - 26s 256ms/step - loss: 0.2837 - acc: 0.8857 - val_loss: 0.2022 - val_acc: 0.9235\n",
      "Epoch 92/100\n",
      "100/100 [==============================] - 26s 257ms/step - loss: 0.2839 - acc: 0.8753 - val_loss: 0.2082 - val_acc: 0.9345\n",
      "Epoch 93/100\n",
      "100/100 [==============================] - 26s 257ms/step - loss: 0.2715 - acc: 0.8830 - val_loss: 0.2050 - val_acc: 0.9255\n",
      "Epoch 94/100\n",
      "100/100 [==============================] - 26s 257ms/step - loss: 0.2956 - acc: 0.8723 - val_loss: 0.2302 - val_acc: 0.9104\n",
      "Epoch 95/100\n",
      "100/100 [==============================] - 26s 259ms/step - loss: 0.2745 - acc: 0.8803 - val_loss: 0.2098 - val_acc: 0.9275\n",
      "Epoch 96/100\n",
      "100/100 [==============================] - 26s 261ms/step - loss: 0.2764 - acc: 0.8764 - val_loss: 0.2124 - val_acc: 0.9265\n",
      "Epoch 97/100\n",
      "100/100 [==============================] - 26s 257ms/step - loss: 0.2828 - acc: 0.8790 - val_loss: 0.2156 - val_acc: 0.9255\n",
      "Epoch 98/100\n",
      "100/100 [==============================] - 26s 256ms/step - loss: 0.2903 - acc: 0.8753 - val_loss: 0.2224 - val_acc: 0.9204\n",
      "Epoch 99/100\n",
      "100/100 [==============================] - 26s 255ms/step - loss: 0.2771 - acc: 0.8823 - val_loss: 0.2046 - val_acc: 0.9275\n",
      "Epoch 100/100\n",
      "100/100 [==============================] - 26s 257ms/step - loss: 0.2711 - acc: 0.8874 - val_loss: 0.2150 - val_acc: 0.9255\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, InputLayer\n",
    "from keras.models import Sequential\n",
    "from keras import optimizers\n",
    "\n",
    "model = Sequential()\n",
    "model.add(vgg_model)\n",
    "model.add(Dense(512, activation='relu', input_dim=input_shape))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=2e-5),\n",
    "              metrics=['accuracy'])\n",
    "              \n",
    "history = model.fit_generator(train_generator, steps_per_epoch=100, epochs=100,\n",
    "                              validation_data=val_generator, validation_steps=50, \n",
    "                              verbose=1)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1xjspHpl07_Y"
   },
   "outputs": [],
   "source": [
    "model.save('cats_dogs_tlearn_img_aug_cnn.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a Pre-trrainer CNN & clear all weights at 4th and 5th block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 669
    },
    "colab_type": "code",
    "id": "0SyaOYDh07_c",
    "outputId": "29fcce9d-b721-4717-c472-3e6dbf9c191f"
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Layer Type</th>\n",
       "      <th>Layer Name</th>\n",
       "      <th>Layer Trainable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt;keras.engine.input_layer.InputLayer object at 0x7f3a8df32f60&gt;</td>\n",
       "      <td>input_1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8dde2780&gt;</td>\n",
       "      <td>block1_conv1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8df32278&gt;</td>\n",
       "      <td>block1_conv2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&lt;keras.layers.pooling.MaxPooling2D object at 0x7f3aa0092b00&gt;</td>\n",
       "      <td>block1_pool</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8ddce6d8&gt;</td>\n",
       "      <td>block2_conv1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a88fdecf8&gt;</td>\n",
       "      <td>block2_conv2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>&lt;keras.layers.pooling.MaxPooling2D object at 0x7f3a8d02b630&gt;</td>\n",
       "      <td>block2_pool</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8d02b518&gt;</td>\n",
       "      <td>block3_conv1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cfdc2b0&gt;</td>\n",
       "      <td>block3_conv2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cfee908&gt;</td>\n",
       "      <td>block3_conv3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>&lt;keras.layers.pooling.MaxPooling2D object at 0x7f3a8cf8ad30&gt;</td>\n",
       "      <td>block3_pool</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cf8ac88&gt;</td>\n",
       "      <td>block4_conv1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cfb7ba8&gt;</td>\n",
       "      <td>block4_conv2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cf62240&gt;</td>\n",
       "      <td>block4_conv3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>&lt;keras.layers.pooling.MaxPooling2D object at 0x7f3a8cf7cbe0&gt;</td>\n",
       "      <td>block4_pool</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cf7c6a0&gt;</td>\n",
       "      <td>block5_conv1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cf2a400&gt;</td>\n",
       "      <td>block5_conv2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>&lt;keras.layers.convolutional.Conv2D object at 0x7f3a8cebfa58&gt;</td>\n",
       "      <td>block5_conv3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>&lt;keras.layers.pooling.MaxPooling2D object at 0x7f3a8ceed6d8&gt;</td>\n",
       "      <td>block5_pool</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>&lt;keras.layers.core.Flatten object at 0x7f3a8ce4cd68&gt;</td>\n",
       "      <td>flatten_4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        Layer Type  \\\n",
       "0   <keras.engine.input_layer.InputLayer object at 0x7f3a8df32f60>   \n",
       "1   <keras.layers.convolutional.Conv2D object at 0x7f3a8dde2780>     \n",
       "2   <keras.layers.convolutional.Conv2D object at 0x7f3a8df32278>     \n",
       "3   <keras.layers.pooling.MaxPooling2D object at 0x7f3aa0092b00>     \n",
       "4   <keras.layers.convolutional.Conv2D object at 0x7f3a8ddce6d8>     \n",
       "5   <keras.layers.convolutional.Conv2D object at 0x7f3a88fdecf8>     \n",
       "6   <keras.layers.pooling.MaxPooling2D object at 0x7f3a8d02b630>     \n",
       "7   <keras.layers.convolutional.Conv2D object at 0x7f3a8d02b518>     \n",
       "8   <keras.layers.convolutional.Conv2D object at 0x7f3a8cfdc2b0>     \n",
       "9   <keras.layers.convolutional.Conv2D object at 0x7f3a8cfee908>     \n",
       "10  <keras.layers.pooling.MaxPooling2D object at 0x7f3a8cf8ad30>     \n",
       "11  <keras.layers.convolutional.Conv2D object at 0x7f3a8cf8ac88>     \n",
       "12  <keras.layers.convolutional.Conv2D object at 0x7f3a8cfb7ba8>     \n",
       "13  <keras.layers.convolutional.Conv2D object at 0x7f3a8cf62240>     \n",
       "14  <keras.layers.pooling.MaxPooling2D object at 0x7f3a8cf7cbe0>     \n",
       "15  <keras.layers.convolutional.Conv2D object at 0x7f3a8cf7c6a0>     \n",
       "16  <keras.layers.convolutional.Conv2D object at 0x7f3a8cf2a400>     \n",
       "17  <keras.layers.convolutional.Conv2D object at 0x7f3a8cebfa58>     \n",
       "18  <keras.layers.pooling.MaxPooling2D object at 0x7f3a8ceed6d8>     \n",
       "19  <keras.layers.core.Flatten object at 0x7f3a8ce4cd68>             \n",
       "\n",
       "      Layer Name  Layer Trainable  \n",
       "0   input_1       False            \n",
       "1   block1_conv1  False            \n",
       "2   block1_conv2  False            \n",
       "3   block1_pool   False            \n",
       "4   block2_conv1  False            \n",
       "5   block2_conv2  False            \n",
       "6   block2_pool   False            \n",
       "7   block3_conv1  False            \n",
       "8   block3_conv2  False            \n",
       "9   block3_conv3  False            \n",
       "10  block3_pool   False            \n",
       "11  block4_conv1  True             \n",
       "12  block4_conv2  True             \n",
       "13  block4_conv3  True             \n",
       "14  block4_pool   True             \n",
       "15  block5_conv1  True             \n",
       "16  block5_conv2  True             \n",
       "17  block5_conv3  True             \n",
       "18  block5_pool   True             \n",
       "19  flatten_4     True             "
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgg_model.trainable = True\n",
    "\n",
    "set_trainable = False\n",
    "for layer in vgg_model.layers:\n",
    "    if layer.name in ['block5_conv1', 'block4_conv1']:\n",
    "        set_trainable = True\n",
    "    if set_trainable:\n",
    "        layer.trainable = True\n",
    "    else:\n",
    "        layer.trainable = False\n",
    "        \n",
    "layers = [(layer, layer.name, layer.trainable) for layer in vgg_model.layers]\n",
    "pd.DataFrame(layers, columns=['Layer Type', 'Layer Name', 'Layer Trainable'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 5: Tranfer Lerning CNN, DataAugmentation, clean weighs at 4th & 5th layers blocks (fine tunning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3437
    },
    "colab_type": "code",
    "id": "4J2nYSkj07_e",
    "outputId": "c0ec7f33-9e28-43c7-814c-70d2704394c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "100/100 [==============================] - 34s 341ms/step - loss: 0.5933 - acc: 0.6693 - val_loss: 0.3330 - val_acc: 0.8550\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.4064 - acc: 0.8137 - val_loss: 0.2034 - val_acc: 0.9114\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.3370 - acc: 0.8497 - val_loss: 0.1853 - val_acc: 0.9215\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.2852 - acc: 0.8687 - val_loss: 0.1426 - val_acc: 0.9376\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.2730 - acc: 0.8843 - val_loss: 0.1340 - val_acc: 0.9416\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.2422 - acc: 0.8943 - val_loss: 0.1400 - val_acc: 0.9396\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.2297 - acc: 0.9077 - val_loss: 0.1432 - val_acc: 0.9406\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.2116 - acc: 0.9107 - val_loss: 0.1378 - val_acc: 0.9386\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.1939 - acc: 0.9190 - val_loss: 0.1113 - val_acc: 0.9537\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.1704 - acc: 0.9337 - val_loss: 0.1130 - val_acc: 0.9577\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.1783 - acc: 0.9243 - val_loss: 0.1208 - val_acc: 0.9406\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 32s 318ms/step - loss: 0.1462 - acc: 0.9377 - val_loss: 0.1165 - val_acc: 0.9557\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.1626 - acc: 0.9350 - val_loss: 0.0973 - val_acc: 0.9637\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.1523 - acc: 0.9400 - val_loss: 0.1293 - val_acc: 0.9476\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.1411 - acc: 0.9400 - val_loss: 0.0948 - val_acc: 0.9668\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.1328 - acc: 0.9470 - val_loss: 0.1188 - val_acc: 0.9507\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.1241 - acc: 0.9520 - val_loss: 0.0931 - val_acc: 0.9668\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.1181 - acc: 0.9483 - val_loss: 0.0862 - val_acc: 0.9688\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.1173 - acc: 0.9540 - val_loss: 0.0967 - val_acc: 0.9658\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.1176 - acc: 0.9537 - val_loss: 0.0933 - val_acc: 0.9718\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.1039 - acc: 0.9573 - val_loss: 0.1188 - val_acc: 0.9627\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.0990 - acc: 0.9580 - val_loss: 0.1191 - val_acc: 0.9587\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.1020 - acc: 0.9627 - val_loss: 0.1326 - val_acc: 0.9567\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0838 - acc: 0.9690 - val_loss: 0.0937 - val_acc: 0.9738\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.0852 - acc: 0.9627 - val_loss: 0.1325 - val_acc: 0.9607\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.0854 - acc: 0.9667 - val_loss: 0.1981 - val_acc: 0.9456\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.0737 - acc: 0.9687 - val_loss: 0.1150 - val_acc: 0.9688\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0750 - acc: 0.9737 - val_loss: 0.1081 - val_acc: 0.9708\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.0804 - acc: 0.9697 - val_loss: 0.0981 - val_acc: 0.9698\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.0866 - acc: 0.9660 - val_loss: 0.1052 - val_acc: 0.9718\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.0709 - acc: 0.9723 - val_loss: 0.1118 - val_acc: 0.9698\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.0662 - acc: 0.9723 - val_loss: 0.1295 - val_acc: 0.9658\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0710 - acc: 0.9720 - val_loss: 0.1157 - val_acc: 0.9688\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.0624 - acc: 0.9733 - val_loss: 0.1366 - val_acc: 0.9637\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0664 - acc: 0.9707 - val_loss: 0.1182 - val_acc: 0.9637\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.0619 - acc: 0.9810 - val_loss: 0.1106 - val_acc: 0.9708\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0475 - acc: 0.9833 - val_loss: 0.1228 - val_acc: 0.9718\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0642 - acc: 0.9780 - val_loss: 0.1519 - val_acc: 0.9577\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0604 - acc: 0.9753 - val_loss: 0.1180 - val_acc: 0.9718\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0603 - acc: 0.9820 - val_loss: 0.1325 - val_acc: 0.9698\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.0608 - acc: 0.9773 - val_loss: 0.1073 - val_acc: 0.9738\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0426 - acc: 0.9863 - val_loss: 0.1553 - val_acc: 0.9658\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0492 - acc: 0.9810 - val_loss: 0.1288 - val_acc: 0.9708\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0454 - acc: 0.9807 - val_loss: 0.2012 - val_acc: 0.9577\n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0590 - acc: 0.9807 - val_loss: 0.1550 - val_acc: 0.9648\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0455 - acc: 0.9840 - val_loss: 0.1295 - val_acc: 0.9728\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0474 - acc: 0.9843 - val_loss: 0.1577 - val_acc: 0.9627\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0416 - acc: 0.9840 - val_loss: 0.1411 - val_acc: 0.9678\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 31s 314ms/step - loss: 0.0344 - acc: 0.9870 - val_loss: 0.1308 - val_acc: 0.9748\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0423 - acc: 0.9847 - val_loss: 0.1662 - val_acc: 0.9668\n",
      "Epoch 51/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0402 - acc: 0.9850 - val_loss: 0.1562 - val_acc: 0.9708\n",
      "Epoch 52/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0434 - acc: 0.9847 - val_loss: 0.1887 - val_acc: 0.9567\n",
      "Epoch 53/100\n",
      "100/100 [==============================] - 32s 317ms/step - loss: 0.0456 - acc: 0.9840 - val_loss: 0.1759 - val_acc: 0.9637\n",
      "Epoch 54/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0345 - acc: 0.9887 - val_loss: 0.2266 - val_acc: 0.9496\n",
      "Epoch 55/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0323 - acc: 0.9877 - val_loss: 0.1827 - val_acc: 0.9688\n",
      "Epoch 56/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0386 - acc: 0.9867 - val_loss: 0.1480 - val_acc: 0.9708\n",
      "Epoch 57/100\n",
      "100/100 [==============================] - 31s 315ms/step - loss: 0.0389 - acc: 0.9860 - val_loss: 0.1858 - val_acc: 0.9658\n",
      "Epoch 58/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0371 - acc: 0.9880 - val_loss: 0.1928 - val_acc: 0.9648\n",
      "Epoch 59/100\n",
      "100/100 [==============================] - 31s 315ms/step - loss: 0.0344 - acc: 0.9867 - val_loss: 0.1438 - val_acc: 0.9718\n",
      "Epoch 60/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0388 - acc: 0.9847 - val_loss: 0.1735 - val_acc: 0.9688\n",
      "Epoch 61/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0355 - acc: 0.9890 - val_loss: 0.1567 - val_acc: 0.9748\n",
      "Epoch 62/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0376 - acc: 0.9853 - val_loss: 0.1692 - val_acc: 0.9648\n",
      "Epoch 63/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0292 - acc: 0.9897 - val_loss: 0.1776 - val_acc: 0.9658\n",
      "Epoch 64/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0354 - acc: 0.9887 - val_loss: 0.1807 - val_acc: 0.9658\n",
      "Epoch 65/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0323 - acc: 0.9870 - val_loss: 0.2022 - val_acc: 0.9658\n",
      "Epoch 66/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0391 - acc: 0.9850 - val_loss: 0.1842 - val_acc: 0.9648\n",
      "Epoch 67/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0290 - acc: 0.9900 - val_loss: 0.1705 - val_acc: 0.9708\n",
      "Epoch 68/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0340 - acc: 0.9877 - val_loss: 0.1627 - val_acc: 0.9708\n",
      "Epoch 69/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0265 - acc: 0.9890 - val_loss: 0.1717 - val_acc: 0.9708\n",
      "Epoch 70/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0311 - acc: 0.9900 - val_loss: 0.4104 - val_acc: 0.9366\n",
      "Epoch 71/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0310 - acc: 0.9900 - val_loss: 0.2189 - val_acc: 0.9627\n",
      "Epoch 72/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0348 - acc: 0.9897 - val_loss: 0.1395 - val_acc: 0.9698\n",
      "Epoch 73/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0296 - acc: 0.9897 - val_loss: 0.1766 - val_acc: 0.9658\n",
      "Epoch 74/100\n",
      "100/100 [==============================] - 31s 315ms/step - loss: 0.0265 - acc: 0.9903 - val_loss: 0.2309 - val_acc: 0.9577\n",
      "Epoch 75/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0349 - acc: 0.9863 - val_loss: 0.1571 - val_acc: 0.9688\n",
      "Epoch 76/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0227 - acc: 0.9917 - val_loss: 0.2125 - val_acc: 0.9648\n",
      "Epoch 77/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0311 - acc: 0.9893 - val_loss: 0.2300 - val_acc: 0.9658\n",
      "Epoch 78/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0431 - acc: 0.9883 - val_loss: 0.1675 - val_acc: 0.9708\n",
      "Epoch 79/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0294 - acc: 0.9920 - val_loss: 0.1909 - val_acc: 0.9668\n",
      "Epoch 80/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0258 - acc: 0.9927 - val_loss: 0.1996 - val_acc: 0.9668\n",
      "Epoch 81/100\n",
      "100/100 [==============================] - 31s 315ms/step - loss: 0.0280 - acc: 0.9937 - val_loss: 0.1779 - val_acc: 0.9728\n",
      "Epoch 82/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0264 - acc: 0.9907 - val_loss: 0.1750 - val_acc: 0.9658\n",
      "Epoch 83/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0332 - acc: 0.9897 - val_loss: 0.1785 - val_acc: 0.9678\n",
      "Epoch 84/100\n",
      "100/100 [==============================] - 31s 315ms/step - loss: 0.0211 - acc: 0.9947 - val_loss: 0.1914 - val_acc: 0.9698\n",
      "Epoch 85/100\n",
      "100/100 [==============================] - 32s 316ms/step - loss: 0.0320 - acc: 0.9893 - val_loss: 0.1750 - val_acc: 0.9718\n",
      "Epoch 86/100\n",
      "100/100 [==============================] - 31s 315ms/step - loss: 0.0245 - acc: 0.9907 - val_loss: 0.1977 - val_acc: 0.9668\n",
      "Epoch 87/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0238 - acc: 0.9923 - val_loss: 0.1635 - val_acc: 0.9728\n",
      "Epoch 88/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0159 - acc: 0.9937 - val_loss: 0.1901 - val_acc: 0.9688\n",
      "Epoch 89/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0232 - acc: 0.9923 - val_loss: 0.1985 - val_acc: 0.9658\n",
      "Epoch 90/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0172 - acc: 0.9947 - val_loss: 0.2372 - val_acc: 0.9678\n",
      "Epoch 91/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0340 - acc: 0.9893 - val_loss: 0.1622 - val_acc: 0.9698\n",
      "Epoch 92/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0228 - acc: 0.9917 - val_loss: 0.2475 - val_acc: 0.9688\n",
      "Epoch 93/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0325 - acc: 0.9923 - val_loss: 0.1918 - val_acc: 0.9678\n",
      "Epoch 94/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0329 - acc: 0.9893 - val_loss: 0.2077 - val_acc: 0.9637\n",
      "Epoch 95/100\n",
      "100/100 [==============================] - 31s 315ms/step - loss: 0.0210 - acc: 0.9927 - val_loss: 0.3841 - val_acc: 0.9547\n",
      "Epoch 96/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0216 - acc: 0.9933 - val_loss: 0.2069 - val_acc: 0.9738\n",
      "Epoch 97/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0349 - acc: 0.9920 - val_loss: 0.2394 - val_acc: 0.9617\n",
      "Epoch 98/100\n",
      "100/100 [==============================] - 31s 314ms/step - loss: 0.0207 - acc: 0.9950 - val_loss: 0.2262 - val_acc: 0.9658\n",
      "Epoch 99/100\n",
      "100/100 [==============================] - 32s 315ms/step - loss: 0.0236 - acc: 0.9910 - val_loss: 0.2090 - val_acc: 0.9688\n",
      "Epoch 100/100\n",
      "100/100 [==============================] - 32s 322ms/step - loss: 0.0158 - acc: 0.9950 - val_loss: 0.3115 - val_acc: 0.9637\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1./255, zoom_range=0.3, rotation_range=50,\n",
    "                                   width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, \n",
    "                                   horizontal_flip=True, fill_mode='nearest')\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "train_generator = train_datagen.flow(train_imgs, train_labels_enc, batch_size=30)\n",
    "val_generator = val_datagen.flow(validation_imgs, validation_labels_enc, batch_size=20)\n",
    "\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, InputLayer\n",
    "from keras.models import Sequential\n",
    "from keras import optimizers\n",
    "\n",
    "model = Sequential()\n",
    "model.add(vgg_model)\n",
    "model.add(Dense(512, activation='relu', input_dim=input_shape))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-5),\n",
    "              metrics=['accuracy'])\n",
    "              \n",
    "history = model.fit_generator(train_generator, steps_per_epoch=100, epochs=100,\n",
    "                              validation_data=val_generator, validation_steps=50, \n",
    "                              verbose=1)              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vAO9p_BP07_f"
   },
   "outputs": [],
   "source": [
    "model.save('cats_dogs_tlearn_finetune_img_aug_cnn.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmarking between this 5 models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lanKrTRG07_h"
   },
   "outputs": [],
   "source": [
    "# load dependencies\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.preprocessing.image import load_img, img_to_array, array_to_img\n",
    "from keras.models import load_model\n",
    "import model_evaluation_utils as meu\n",
    "%matplotlib inline\n",
    "\n",
    "# load saved models\n",
    "basic_cnn = load_model('cats_dogs_basic_cnn.h5')\n",
    "img_aug_cnn = load_model('cats_dogs_cnn_img_aug.h5')\n",
    "tl_cnn = load_model('cats_dogs_tlearn_basic_cnn.h5')\n",
    "tl_img_aug_cnn = load_model('cats_dogs_tlearn_img_aug_cnn.h5')\n",
    "tl_img_aug_finetune_cnn = load_model('cats_dogs_tlearn_finetune_img_aug_cnn.h5')\n",
    "\n",
    "# load other configurations\n",
    "IMG_DIM = (150, 150)\n",
    "input_shape = (150, 150, 3)\n",
    "num2class_label_transformer = lambda l: ['cat' if x == 0 else 'dog' for x in l]\n",
    "class2num_label_transformer = lambda l: [0 if x == 'cat' else 1 for x in l]\n",
    "\n",
    "# load VGG model for bottleneck features\n",
    "from keras.applications import vgg16\n",
    "from keras.models import Model\n",
    "import keras\n",
    "\n",
    "vgg = vgg16.VGG16(include_top=False, weights='imagenet', \n",
    "                  input_shape=input_shape)\n",
    "output = vgg.layers[-1].output\n",
    "output = keras.layers.Flatten()(output)\n",
    "vgg_model = Model(vgg.input, output)\n",
    "vgg_model.trainable = False\n",
    "\n",
    "def get_bottleneck_features(model, input_imgs):\n",
    "    features = model.predict(input_imgs, verbose=0)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "-6BCop6N07_l",
    "outputId": "ff8c80cd-773c-4433-c572-c6fda9d42d9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test dataset shape: (990, 150, 150, 3)\n",
      "['dog', 'dog', 'cat', 'dog', 'dog'] [1, 1, 0, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "IMG_DIM = (150, 150)\n",
    "\n",
    "test_files = glob.glob('test_data/*')\n",
    "test_imgs = [img_to_array(load_img(img, target_size=IMG_DIM)) for img in test_files]\n",
    "test_imgs = np.array(test_imgs)\n",
    "test_labels = [fn.split('/')[1].split('.')[0].strip() for fn in test_files]\n",
    "\n",
    "test_imgs_scaled = test_imgs.astype('float32')\n",
    "test_imgs_scaled /= 255\n",
    "test_labels_enc = class2num_label_transformer(test_labels)\n",
    "\n",
    "print('Test dataset shape:', test_imgs.shape)\n",
    "print(test_labels[0:5], test_labels_enc[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UmJDYMdx07_m"
   },
   "source": [
    "## Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "17vUzUk507_n",
    "outputId": "7d32d004-15f1-4861-da48-1dafa9594baf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance metrics:\n",
      "------------------------------\n",
      "Accuracy: 0.7826\n",
      "Precision: 0.7832\n",
      "Recall: 0.7826\n",
      "F1 Score: 0.7826\n",
      "\n",
      "Model Classification report:\n",
      "------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         dog       0.80      0.77      0.78       502\n",
      "         cat       0.77      0.80      0.78       487\n",
      "\n",
      "   micro avg       0.78      0.78      0.78       989\n",
      "   macro avg       0.78      0.78      0.78       989\n",
      "weighted avg       0.78      0.78      0.78       989\n",
      "\n",
      "\n",
      "Prediction Confusion Matrix:\n",
      "------------------------------\n",
      "            Predicted:     \n",
      "                   dog  cat\n",
      "Actual: dog  385        117\n",
      "        cat  98         389\n"
     ]
    }
   ],
   "source": [
    "predictions = basic_cnn.predict_classes(test_imgs_scaled, verbose=0)\n",
    "predictions = num2class_label_transformer(predictions)\n",
    "meu.display_model_performance_metrics(true_labels=test_labels, predicted_labels=predictions, \n",
    "                                      classes=list(set(test_labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J1O6AmYl07_x"
   },
   "source": [
    "## Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "VQEZGlCS07_y",
    "outputId": "91681eea-bb5c-4761-e654-e04cb3723b22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance metrics:\n",
      "------------------------------\n",
      "Accuracy: 0.8342\n",
      "Precision: 0.8343\n",
      "Recall: 0.8342\n",
      "F1 Score: 0.8342\n",
      "\n",
      "Model Classification report:\n",
      "------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         dog       0.84      0.83      0.84       502\n",
      "         cat       0.83      0.84      0.83       487\n",
      "\n",
      "   micro avg       0.83      0.83      0.83       989\n",
      "   macro avg       0.83      0.83      0.83       989\n",
      "weighted avg       0.83      0.83      0.83       989\n",
      "\n",
      "\n",
      "Prediction Confusion Matrix:\n",
      "------------------------------\n",
      "            Predicted:     \n",
      "                   dog  cat\n",
      "Actual: dog  416        86 \n",
      "        cat  78         409\n"
     ]
    }
   ],
   "source": [
    "predictions = img_aug_cnn.predict_classes(test_imgs_scaled, verbose=0)\n",
    "predictions = num2class_label_transformer(predictions)\n",
    "meu.display_model_performance_metrics(true_labels=test_labels, predicted_labels=predictions, \n",
    "                                      classes=list(set(test_labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BPukgWUj07_1"
   },
   "source": [
    "## Model 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "6Ez2MqOZ07_5",
    "outputId": "4972440d-e89b-45a6-9ead-16ea40f63cdd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance metrics:\n",
      "------------------------------\n",
      "Accuracy: 0.9181\n",
      "Precision: 0.9181\n",
      "Recall: 0.9181\n",
      "F1 Score: 0.9181\n",
      "\n",
      "Model Classification report:\n",
      "------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         dog       0.92      0.92      0.92       502\n",
      "         cat       0.92      0.92      0.92       487\n",
      "\n",
      "   micro avg       0.92      0.92      0.92       989\n",
      "   macro avg       0.92      0.92      0.92       989\n",
      "weighted avg       0.92      0.92      0.92       989\n",
      "\n",
      "\n",
      "Prediction Confusion Matrix:\n",
      "------------------------------\n",
      "            Predicted:     \n",
      "                   dog  cat\n",
      "Actual: dog  462        40 \n",
      "        cat  41         446\n"
     ]
    }
   ],
   "source": [
    "test_bottleneck_features = get_bottleneck_features(vgg_model, test_imgs_scaled)\n",
    "\n",
    "predictions = tl_cnn.predict_classes(test_bottleneck_features, verbose=0)\n",
    "predictions = num2class_label_transformer(predictions)\n",
    "meu.display_model_performance_metrics(true_labels=test_labels, predicted_labels=predictions, \n",
    "                                      classes=list(set(test_labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DZ3Cwn9d07_9"
   },
   "source": [
    "## Model 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "hNvRHdaQ07_-",
    "outputId": "25b4a996-a145-4c31-dc6a-fce9d852162a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance metrics:\n",
      "------------------------------\n",
      "Accuracy: 0.9141\n",
      "Precision: 0.9156\n",
      "Recall: 0.9141\n",
      "F1 Score: 0.9139\n",
      "\n",
      "Model Classification report:\n",
      "------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         dog       0.89      0.95      0.92       502\n",
      "         cat       0.94      0.88      0.91       487\n",
      "\n",
      "   micro avg       0.91      0.91      0.91       989\n",
      "   macro avg       0.92      0.91      0.91       989\n",
      "weighted avg       0.92      0.91      0.91       989\n",
      "\n",
      "\n",
      "Prediction Confusion Matrix:\n",
      "------------------------------\n",
      "            Predicted:     \n",
      "                   dog  cat\n",
      "Actual: dog  475        27 \n",
      "        cat  58         429\n"
     ]
    }
   ],
   "source": [
    "predictions = tl_img_aug_cnn.predict_classes(test_imgs_scaled, verbose=0)\n",
    "predictions = num2class_label_transformer(predictions)\n",
    "meu.display_model_performance_metrics(true_labels=test_labels, predicted_labels=predictions, \n",
    "                                      classes=list(set(test_labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b4dnhYkI07__"
   },
   "source": [
    "## Model 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "BwOp8BP108AA",
    "outputId": "2c6acb54-d8ba-45eb-aed0-2b173c19d7b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance metrics:\n",
      "------------------------------\n",
      "Accuracy: 0.9838\n",
      "Precision: 0.9838\n",
      "Recall: 0.9838\n",
      "F1 Score: 0.9838\n",
      "\n",
      "Model Classification report:\n",
      "------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         cat       0.98      0.99      0.98       496\n",
      "         dog       0.99      0.98      0.98       494\n",
      "\n",
      "   micro avg       0.98      0.98      0.98       990\n",
      "   macro avg       0.98      0.98      0.98       990\n",
      "weighted avg       0.98      0.98      0.98       990\n",
      "\n",
      "\n",
      "Prediction Confusion Matrix:\n",
      "------------------------------\n",
      "            Predicted:     \n",
      "                   cat  dog\n",
      "Actual: cat        489    7\n",
      "        dog          9  485\n"
     ]
    }
   ],
   "source": [
    "predictions = tl_img_aug_finetune_cnn.predict_classes(test_imgs_scaled, verbose=0)\n",
    "predictions = num2class_label_transformer(predictions)\n",
    "meu.display_model_performance_metrics(true_labels=test_labels, predicted_labels=predictions, \n",
    "                                      classes=list(set(test_labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Worst Model - Model 5 - Basic CNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "colab_type": "code",
    "id": "vsMuI7NdiOw1",
    "outputId": "c7adb0e8-3165-4ffb-a703-26c32ce1a4b7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "meu.plot_model_roc_curve(basic_cnn, test_imgs_scaled, \n",
    "                         true_labels=test_labels_enc, \n",
    "                         class_names=[0, 1]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best model - Model 5 - Transfer Learning with Fine-tuning & Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "colab_type": "code",
    "id": "qq5n9zNT08AB",
    "outputId": "b991a3f5-f84a-46fc-e241-ba1855d5c268"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# best model - transfer learning with fine-tuning & image augmentation \n",
    "meu.plot_model_roc_curve(tl_img_aug_finetune_cnn, test_imgs_scaled, \n",
    "                         true_labels=test_labels_enc, \n",
    "                         class_names=[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hi9ihoAY08AI"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Cat Dogs.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}