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Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb 607 KiB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dWyPGNkCGhIX"
   },
   "source": [
    "# Part I : Create Your Own Dataset and Train it with ConvNets\n",
    "\n",
    "In this part of the notebook, you will set up your own dataset for image classification. Please specify \n",
    "under `queries` the image categories you are interested in. Under `limit` specify the number of images \n",
    "you want to download for each image category. \n",
    "\n",
    "You do not need to understand the class `simple_image_download`, just execute the cell after you have specified \n",
    "the download folder.\n"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8rckz3ZuGhIc",
    "outputId": "6f615f06-759a-4eea-839e-658155df8d36"
   },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
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      "Saved 2 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
      "ERROR - Could not save https://upload.wikimedia.org/wikipedia/commons/5/59/Marion_Cotillard_at_2019_Cannes.jpg - cannot identify image file <_io.BytesIO object at 0x7f1a0b4d6d70>\n",
      "Saved 1 images\n"
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     ]
   "source": [
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    "from selenium import webdriver\n",
    "from selenium.webdriver.firefox.options import Options\n",
    "from Image_crawling import Image_crawling\n",
    "\n",
    "# Specifiy the queries\n",
    "queries = [\"brad pitt\",\"johnny depp\", \"leonardo dicaprio\", \"robert de niro\", \"angelina jolie\", \"sandra bullock\", \"catherine deneuve\", \"marion cotillard\"]\n",
    "#queries = [\"Bart Simpson\",\"Homer Simpson\"]\n",
    "limit = 2\n",
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    "download_folder = \"./brandnew_images/train/\"\n",
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    "waittime = 0.1  # Time to wait between actions, depends on the number of pictures you want to crawl. More pictures means you need to wait longer for them to load. \n",
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    "# Set options\n",
    "options = webdriver.FirefoxOptions()\n",
    "options.add_argument('--headless')\n",
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    "# Create Driver\n",
    "driver = webdriver.Firefox(options=options, executable_path=\"/usr/bin/geckodriver\")\n",
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    "# create instance of crawler\n",
    "image_crawling = Image_crawling(driver, waittime=waittime)\n",
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    "# Find urls and download images\n",
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    "for query in queries:\n",
    "    # Craws image urls:\n",
    "    image_urls = image_crawling.fetch_image_urls(query, limit)\n",
    "      \n",
    "    # download images\n",
    "    image_crawling.download_image(download_folder + query)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CRHl9UX6GhIs"
   },
   "source": [
    "Please check carefully the downloaded images, there may be a lot of garbage! You definitely need to \n",
    "clean the data.\n",
    "\n",
    "In the following, you will apply data augmentation to your data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3SX21FtcGhIu"
   },
   "outputs": [],
   "source": [
    "# General imports\n",
    "import tensorflow as tf\n",
    "tf.compat.v1.enable_eager_execution(\n",
    "    config=None, device_policy=None, execution_mode=None\n",
    ")\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
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    "import os, datetime\n",
    "\n",
    "# Shortcuts to keras if (however from tensorflow)\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from tensorflow.keras.callbacks import TensorBoard \n",
    "\n",
    "# Shortcut for displaying images\n",
    "def plot_img(img):\n",
    "    plt.imshow(img, cmap='gray')\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()\n",
    "    \n",
    "# The target image size can be fixed here (quadratic)\n",
    "# the ImageDataGenerator() automatically scales the images accordingly (aspect ratio is changed)\n",
    "image_size = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rN_Mp1rmGhI1",
    "outputId": "6417b1f9-e7d4-4d56-a213-191f9d17524a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 480 images belonging to 8 classes.\n"
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     "data": {
      "image/png": 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\n",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
     "execution_count": 96,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the class names; this defines the ordering of the classes\n",
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    "class_names = [\"brad pitt\", \"johnny depp\", \"leonardo dicaprio\", \"robert de niro\",\n",
    "           \"angelina jolie\", \"sandra bullock\", \"catherine deneuve\", \"marion cotillard\"]\n",
    "\n",
    "\n",
    "# Class ImageDataGenerator() returns an iterator holding one batch of images\n",
    "# the constructor takes arguments defining the different image transformations\n",
    "# for augmentation purposes (rotation, x-/y-shift, intensity scaling - here 1./255 \n",
    "# to scale range to [0, 1], shear, zoom, flip, ... )\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rotation_range=10,\n",
    "        width_shift_range=0.2,\n",
    "        height_shift_range=0.2,\n",
    "        rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True,\n",
    "        fill_mode='nearest')\n",
    "\n",
    "\n",
    "dir_iter = train_datagen.flow_from_directory('./train/', \n",
    "                                         target_size=(image_size, image_size),\n",
    "                                         classes=class_names,\n",
    "                                         batch_size=25, class_mode='sparse', shuffle=False)\n",
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    "plot_img(dir_iter[0][0][1,...])\n",
    "dir_iter[0][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V2fYccc8GhJF"
   },
   "source": [
    "Before you continue, you need to split the downloaded images into a `train` folder and into a `validation` folder."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "colab_type": "raw",
    "id": "VamXG4FoGhJH"
   },
   "source": [
    "./\n",
    "├── train\n",
    "│   ├── brad pitt\n",
    "│   └── johnny deep\n",
    "|   ├── leonardo di caprio\n",
    "|   └── ...\n",
    "│       \n",
    "└── validation\n",
    "    ├── brad pitt\n",
    "    ├── johnny deep\n",
    "    ├── leonardo di caprio\n",
    "    └── ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9322su6vGhJJ"
   },
   "source": [
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    "If you want to use the example of this jupyter notebook, you can use the images provided in the ./train and ./validation folders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xPqJWgeAGhJL"
   },
   "source": [
    "## Define a ConvNet Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UuJV4JBKGhJO"
   },
   "outputs": [],
   "source": [
    "batch_size = 20\n",
    "num_train_images = 480\n",
    "num_valid_images = 80\n",
    "num_classes = 8\n",
    "\n",
    "model_scratch = Sequential()\n",
    "model_scratch.add(Conv2D(32, (3, 3), input_shape=(image_size, image_size, 3)))\n",
    "model_scratch.add(Activation('relu'))\n",
    "model_scratch.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model_scratch.add(Conv2D(32, (3, 3)))\n",
    "model_scratch.add(Activation('relu'))\n",
    "model_scratch.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model_scratch.add(Conv2D(64, (3, 3)))\n",
    "model_scratch.add(Activation('relu'))\n",
    "model_scratch.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "# this converts our 3D feature maps to 1D feature vectors\n",
    "model_scratch.add(Flatten())  \n",
    "model_scratch.add(Dense(64))\n",
    "model_scratch.add(Activation('relu'))\n",
    "model_scratch.add(Dropout(0.5))\n",
    "model_scratch.add(Dense(num_classes))\n",
    "model_scratch.add(Activation('softmax'))\n",
    "\n",
    "model_scratch.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JFdkIokMGhJT",
    "outputId": "63e7d032-4083-4fe0-d970-c10bf0c39a94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 480 images belonging to 8 classes.\n",
      "Found 80 images belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "# This is the augmentation configuration we will use for training\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True)\n",
    "\n",
    "# This is the augmentation configuration we will use for validation:\n",
    "# only rescaling\n",
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# This is a generator that will read pictures found in\n",
    "# subfolers of './train', and indefinitely generate\n",
    "# batches of augmented image data\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        './train',  # this is the target directory\n",
    "        target_size=(image_size, image_size),  # all images will be resized to 150x150\n",
    "        classes=class_names,\n",
    "        batch_size=batch_size)  \n",
    "\n",
    "# This is a similar generator, for validation data\n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "        './validation',\n",
    "        target_size = (image_size, image_size),\n",
    "        classes = class_names,\n",
    "        batch_size = batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cytHiQUTGhJb"
   },
   "outputs": [],
   "source": [
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Mirko Birbaumer committed
    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C7dCbyXPGhJg",
    "outputId": "98b4085e-ed6d-43e2-831f-aec32161583f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      " 4/24 [====>.........................] - ETA: 23s - loss: 2.2787 - accuracy: 0.0750"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/PIL/Image.py:952: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
      "  \"Palette images with Transparency expressed in bytes should be \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 31s 1s/step - loss: 2.1227 - accuracy: 0.0979 - val_loss: 2.0783 - val_accuracy: 0.1375\n",
      "Epoch 2/20\n",
      "24/24 [==============================] - 29s 1s/step - loss: 2.0774 - accuracy: 0.1479 - val_loss: 2.0719 - val_accuracy: 0.1625\n",
      "Epoch 3/20\n",
      "24/24 [==============================] - 29s 1s/step - loss: 2.0655 - accuracy: 0.1417 - val_loss: 2.0479 - val_accuracy: 0.1875\n",
      "Epoch 4/20\n",
      "24/24 [==============================] - 30s 1s/step - loss: 2.0295 - accuracy: 0.2104 - val_loss: 2.0195 - val_accuracy: 0.2625\n",
      "Epoch 5/20\n",
      "24/24 [==============================] - 30s 1s/step - loss: 1.9806 - accuracy: 0.2104 - val_loss: 1.9734 - val_accuracy: 0.2625\n",
      "Epoch 6/20\n",
      "24/24 [==============================] - 29s 1s/step - loss: 1.9266 - accuracy: 0.2688 - val_loss: 1.9223 - val_accuracy: 0.2625\n",
      "Epoch 7/20\n",
      "24/24 [==============================] - 29s 1s/step - loss: 1.8778 - accuracy: 0.2438 - val_loss: 1.8354 - val_accuracy: 0.3375\n",
      "Epoch 8/20\n",
      "24/24 [==============================] - 29s 1s/step - loss: 1.8005 - accuracy: 0.2562 - val_loss: 1.7621 - val_accuracy: 0.3625\n",
      "Epoch 9/20\n",
      "24/24 [==============================] - 30s 1s/step - loss: 1.7497 - accuracy: 0.3333 - val_loss: 1.6562 - val_accuracy: 0.4000\n",
      "Epoch 10/20\n",
      "24/24 [==============================] - 29s 1s/step - loss: 1.6707 - accuracy: 0.3333 - val_loss: 1.5198 - val_accuracy: 0.4625\n",
      "Epoch 11/20\n",
      "24/24 [==============================] - 29s 1s/step - loss: 1.6633 - accuracy: 0.3958 - val_loss: 1.5632 - val_accuracy: 0.4750\n",
      "Epoch 12/20\n",
      "24/24 [==============================] - 31s 1s/step - loss: 1.6404 - accuracy: 0.3729 - val_loss: 1.5778 - val_accuracy: 0.4125\n",
      "Epoch 13/20\n",
      "24/24 [==============================] - 30s 1s/step - loss: 1.5924 - accuracy: 0.4021 - val_loss: 1.5459 - val_accuracy: 0.4125\n",
      "Epoch 14/20\n",
      "24/24 [==============================] - 28s 1s/step - loss: 1.5209 - accuracy: 0.4292 - val_loss: 1.5800 - val_accuracy: 0.3750\n",
      "Epoch 15/20\n",
      "24/24 [==============================] - 29s 1s/step - loss: 1.4475 - accuracy: 0.4417 - val_loss: 1.5742 - val_accuracy: 0.4000\n",
      "Epoch 16/20\n",
      "24/24 [==============================] - 28s 1s/step - loss: 1.4813 - accuracy: 0.4187 - val_loss: 1.5788 - val_accuracy: 0.3875\n",
      "Epoch 17/20\n",
      "24/24 [==============================] - 29s 1s/step - loss: 1.4735 - accuracy: 0.4437 - val_loss: 1.4948 - val_accuracy: 0.4375\n",
      "Epoch 18/20\n",
      "24/24 [==============================] - 27s 1s/step - loss: 1.4049 - accuracy: 0.4563 - val_loss: 1.4764 - val_accuracy: 0.5000\n",
      "Epoch 19/20\n",
      "24/24 [==============================] - 29s 1s/step - loss: 1.3805 - accuracy: 0.4917 - val_loss: 1.4958 - val_accuracy: 0.4750\n",
      "Epoch 20/20\n",
      "24/24 [==============================] - 29s 1s/step - loss: 1.3101 - accuracy: 0.4667 - val_loss: 1.5749 - val_accuracy: 0.4375\n"
     ]
    }
   ],
   "source": [
    "history = model_scratch.fit(\n",
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    "    train_generator,\n",
    "    steps_per_epoch = num_train_images // batch_size,\n",
    "    epochs = 20,\n",
    "    validation_data = validation_generator,\n",
    "    validation_steps = num_valid_images // batch_size,\n",
    "    callbacks = [tensorboard_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wt_ONw5PGhJm",
    "outputId": "e75d8a73-da49-4dbe-ffcf-7cb316be39a2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='lower right')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tensorboard"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {},
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   "outputs": [],
   "source": [
    "# Load the TensorBoard notebook extension\n",
    "%load_ext tensorboard\n",
    "\n",
    "os.makedirs(logdir, exist_ok=True)\n",
    "%tensorboard --logdir logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y8oAT4oUGhJs"
   },
   "source": [
    "# Part II : Transfer Learning\n",
    "\n",
    "\n",
    "Having to train an image-classification model using very little data is a common situation,\n",
    "which you’ll likely encounter in practice if you ever do computer vision in a\n",
    "professional context. A “few” samples can mean anywhere from a few hundred to a\n",
    "few tens of thousands of images. As a practical example, we’ll focus on classifying\n",
    "560 images belongig to 8 actors. We’ll use 480 pictures for training, and 80 for validation.\n",
    "\n",
    "## 2.1 Feature Extraction with a Pretrained Model\n",
    "Feature extraction consists of using the representations learned by a previously\n",
    "trained model to extract interesting features from new samples. These features are\n",
    "then run through a new classifier, which is trained from scratch.\n",
    "As you saw previously, ConvNets used for image classification comprise two parts:\n",
    "they start with a series of pooling and convolution layers, and they end with a densely\n",
    "connected classifier. The first part is called the _convolutional base_ of the model. In the\n",
    "case of convnets, feature extraction consists of taking the convolutional base of a previously\n",
    "trained network, running the new data through it, and training a new classifier\n",
    "on top of the output.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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   "outputs": [],
   "source": [
    "# General imports\n",
    "import tensorflow as tf\n",
    "tf.compat.v1.enable_eager_execution(\n",
    "    config=None, device_policy=None, execution_mode=None\n",
    ")\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os, datetime\n",
    "\n",
    "# Shortcuts to keras if (however from tensorflow)\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from tensorflow.keras.callbacks import TensorBoard "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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8Sz/0i1CcjYgPZKt1be2OHg4XXJJqjXMKLrpK3XEy2eV2Tsb1q6qqHo/H0bsAVVVVTZN8a6xXF99JHqqPwt5V9ng8mqa5Q/L4+9evo3cBqqqq/vr58+hduCNZHyjtne+bpmkHr+P2iHztE2kHG3Afsj5wmH7oF8LYl4gP3JysDxzvFcIM87MXER/gRdYHzsLcHrZz5AC0yfrA6Qj9fMpAPsAgWR84LxP6mSbiA0yT9YELMKGfNhEfYCFZH7gMc3vwewf4iKwPXI/QfzcG8gHWkfWBCzOhP5uID7CRrA8kMKE/iYgPsBdZH8hhbs+lifgAu5P1gUBC/7X4HQF8iawPJDOh/8wM5AN8m6wP3IIJ/ech4gMUI+sDN2Juz4FEfIDymvkmAHEev1W/c387ibKv9gSq99sOzPrr58+jd4HLM66fbGF22fK5W2wCtJnWfMmOI/0qbnAlL4r35l6Z9e9fv1Y8e4jyu9Tf4uuRdtw/1VvEVcj6yfofrhIzjNl+Fa+KexHxGfPXz5/S6kKd6G+An9VkfTbxQU6eM1/Fe5496RPxWULcHzP9tnjTWE3WBxjgKt7lvD8ApyXr82eaweAHdn8Kcv/Z9zhoZz399kvWOdYGDvGN0D9RdBeqOAP5fOrvX7/++vlz4dB+f9ZKZ0JLZyVj890ntjWxiRWNNzZ47227WefBjSv05cA9iVP81+An/eCtM6YzQXuRwfbtcDPdxi07OJX+rXs2rrC/hktUXPu2RYqUdWZnn7fjaX/Oej+zrpjOPr2JwcavloONZ9e2fHPtBu2f1+2/if7I+vwxmDDan+ILP9HfzSbGDmfbrNgulLHjzTo75w/ViStOxGcXH43otyPvRLMVm1u4iXbjdoOxxrNrW7K5JT7a/84pCncj6zNq8LN89tN9ycd/p0076MBV9If5Nx7Dp604EZ99vULnbFLvZNPZpToNVpwJjAXiwcdno/NsvN6evz99i7gn8/X5ryVf3K9b7dhEBcjQ/irsown9023OUHEuuuWrpifunzazjk28eV2K8P7vpw322pP+drdviEsTuZjStLweOeojX9Tg/LZP6D9DxXW2biCfb9geQNtj2AXOCl4xfTbN99t/1AC+wbg+oxbelmcF0YFsnUkyTdMsOeaPrbj+TXV8/8ZXzd6TZ8X5wPueM/v+1dvBfD82ut95tvMCZxt8xJg9S+jKmVEml8+mCrGDy+lE9oUj/QdWXP9CYShg+dj27GT9T1c4uImxxWeDdXvBwbn4sw2280UBfXpzDjB4vz/ZglTb5/ZsNFZxZ5iex51Nh91ObO1P19lyKergslu+DVh+F9Edzb5FUMn6zGrPQ2j/vG5tnRt6VkPzFvqRyGkAGQZva9tRrOKq8ZMQFUcxY/e9ef3Qv6nO8iC+sOU7Hy//w1vtsf9+2p5e4WyDhXZ5i7gJHTqj+ncX2eUqvSU3LRnc9Mbtwgm1B9cLV9zgDfVVHCfRzrIfpdgVJwOz6+/vTH8GzuAkorFb8u+Sy1e/RdxN/Xw+j96Hg9V17VONk2iaJr4kVVxf/7rY8685w00qTv7jJP76+TO+4k7IfXgADrb6Dv0TTMQHoJL1Ac5j+1waA/kAtMn6AKfTv152+R/ZFfEBeJP1AU5qdm6PiA/ANFkf4OwGQ3/nKQDoc89NgAtzL3wAJhjXBzi7ibk67ogPwARZH+CklkzH//QqXgBuRdYHOJ1Pg/s37tAPQABZH+Astt9XR+gHoM1FXRzDBYXw1vxWVdXjt43rbK+nf/ceGPPXz59H7wLX8NfPn46WS9D730v5j/zBLb4eaVpK7hIUM314fyPiT9yUU8Ux69joViw7btzQisUHF2k/8uk6O+3LvHWdrfz969e3t8gu9PiUtm+ygSvaPeJPb6u/IYmfMQJcGTEj4jEvJJj5+nzXbIgR97mPAn/mdnnFmdBPh9C20IrTodlFNp5iHXWG9vevX6/BfqeIZ2ZcB+C7vjFXZzsT+umT2Dqc/xDAuH6g/mf2dLCYbb+lQfuv/LSbvR8f/DNA01vsLNtfHAobPGLHRvHPVnHvB6d3WMVlG5tN/hq4fT3yPhPoN26fJPQX7LcZW8+SHVu+rSWTy/uvrrPmsUU678b0620v0pln3162v8jYCpfs0qCFv8TZ9bSXckZ0cgZy0rQ/jDsf/xPt+3N5l69w+RbbDSaGNheu0DAkJzF7xA7m5vNXXGedKu6eJs4BXv+WNBtsM7iesa2v2NbC+LvXVxmzr7e9xXebsde+8E0e815w7O1d8sYu+QX1V8gJ6buj9Efdlozod0b4BlvOrnDhFmd99BLOMx2C22ofsZ183D8yL1dx/Vek4m6onfb6w88TKbCdaDtPLVlPPz1/tK33gsUmJi3ZzyU+epOXr/C9nv5XGet+QVyFrB+o80k88dk8+NTsB/nsh/32NNB/CZVhRc6tM2A/WALXqrhd1swlTMxX6Rgc4h1c8EvRsD8NZvlSy9e5/A1ZuImPLH+TP13t7H8Nz0cyX58/xqYBTN+vY7bBXnvS3+72DcFq22+qc62Kq4a+BCDedMT8aFbJ93LkCYecd3y9G9czdt4i1t+HrH93s9cUdh4Z/LCfbQAxOhH/06+brltxg1P5lfkNDV7TuUtwPNVVnseeP+z1Jq/4gmLCqX5BLCfr39rgB/bYWGPn2aZpBqcdjzX4iADBCW0PuBkVJ/Tf2eBdawLyX/s+8Ye/nL3e5Omgf8IvQ/gSE6BvYfrm2bMf0v2L8z5tsJ3J+hyl+a365PCeKLqYimtfmTDdyXByn8a+L8XEw0P2qex+ge/CRbY04Jx0zVEGr2Ht3/rjI7Of39/4gB98CZIEJfUj/tjlttWuRXfFiuskfqV6UedMcoM3gvyGpHvOrPjDBa//nvMYYCM9cqb3R/WSzNGeA9CfBNx5amydsw0WGpyZ0HkKvmdhxB9csFp2/KdWXH+YX+hP9U6E79vYVx/GxPddXyYW79/3fezvXu2ocNhtvwNje/Lpm9y5oVDnX7XsjV3yC+IqdMRp+p/cEx/Y/Xm3/WQzOLF4cN7wwo3OMhuYQ6yL+NUnRXeTihP6gw3+sap1yXvi715NtBlsFmbHN3n5JqreG7vkF9RvzAnVz+fz6H04WF3XciQn0TRNfEmereLaMfRUO5bktG/yTSpuYQ57XZn67f05s33vWnMTH71pf/38GV9xJ2SsBbij1XN1WMFVvFdhngYrODs6OR0ucCMi/rHM7Tmzmyc2g/orODm8BPfXB27BtR/n4ZqcM7vhTB6BdYu7HS1XJOsDyU47U5xK6D+fe+a2e77q7W54WnhRsj4QSMS/lsE7FB26R8AMQf8qZH0gh4h/df3rd/0eAbaQ9YEEomESc3sA9iLrAxdmID+b0A+wkawPXI+Ifzcm9AOsI+sDlyHiY0I/wEdkfeACBDvazO0BWEjWB87LQD7ThH6AabI+cDoiPp8yoR9gkKwPnIWIz3Ym9AO0yfrAwUR8dmduD8CLrA8cRgjj24R+4OZkfaA0A/mUZ0I/cE/18/k8eh8OVtf10bsAfwSXpFrjnIKLrlJ3nEx2uZ2TrM8+6tqxxLB21HCQ7EXF7cXxyRIqjusyh4cdGDdi0PvA8Bm5LxW3o/fBWde1I5ZBKo5Lk/WBnRko5YqEfqYZ2ueimvkmsIyRD6rfh8Hzt6N3J5mK+5L20etNBq7OuD6wJ/meGA5mIIBxfbYy7gUlqTgoScVxdbI+wFVJIVCSiuOKZH32pB8EADgPWZ9NhHsAgNOS9QEuw9k1lKTiCCDrszM9I5Sk4qAkFcflyPoAAJBJ1mc9wxtQkooD4FOyPgBAl7NrMsj67E//CCWpOChJxXEtsj4AAGSS9VnJwAaUpOKgJBVHDFkfAAAy1c/n8+h9IEFdO5agHBUHJak4rsu4PgAAZJL1AQAgk6wPAACZZH0AAMgk6wMAQCZZHwAAMsn6AACQSdYHAIBMsj4AAGSS9QEAIJOsDwAAmWR9AADIJOsDAECm+vl8Hr0PB6vr+uhdgD/iS1LFcSoqDkqKr7gT+nH0DpzC4/E4ehcur2kab+N2TXOLr9ocKtupuF2oOBZScbu4ScWdjTedfegEoSQVByWpOK5L1gcAgEyyPgAAZJL1AQAgk6wPAACZZH0AAMgk6wMAQCZZHwAAMsn6AACQSdYHAIBMsj4AAGSS9QEAIJOsDwAAmWR9AADIJOsDAEAmWR8AADLJ+gAAkOnH0TvAFzXNonO5x+OxcRNb1nC2DcFqKg5KUnGwhKyfrN9r6E3ge1QclKTiYAlZn010qVCSioOSVBwBzNcHAIBMxvWpqtb3nu/pj+/BjP6EyPY4R/sL0/5K+u2XrHOsDcRQcVDSLhU3uJ7+IrPrHGsDX+JQ449+1/Pu117Gmo0tMti+3dVOt+lsF8KoOChpl4qr5opOxXE2sj7/0O56+hc5LeyS3s0mRjJm26zYLlyOioOSdqm4arygVBwnJOszanC8YbZLWtJnddq0u124LRUHJa2ruCVtVBynYr4+f0z3X+v6qUdvXuPqVUEYFQclfaPiqqGiU3GciqzPlMELjA7pxY7aLpSk4qAkFccdyPqM6s87rHYarjA3EfpUHJSk4rgJJ5HMKNNnLbzvAcRTcVCSiiOeY4sDDE5t1NPBl6g4KEnFcSqOPGa8u6emado/r1tb/2bDY3co22VzcDkqDkpSccRzeDGq3Wf1/3TIjqutet+iDm5643bh5FQclKTiuIn6+XwevQ8Hq+tajXESTdPEl6SK4zxUHJR0h4o7IeP6AACQSdYHAIBMsj4AAGSS9QEAIJOsDwAAmWR9AADIJOsDAEAmWR8AADLJ+gAAkEnWBwCATLI+AABkkvVZr2mapllzCL2XWr2G7Q7cNKyzveK2rGQjFcflqDgyOA4oTe8DJak4KEnFcTY/jt4Bbu3xeBy9C3Avig5KUnEcztknAABkMq5/Uv0vAdtjA0uefTwenWbvNu8GgxtdvqHZxTsPdqYwjq1z46tbt/Pc3OzRMtHgQhX33skd+5N1O8/Nqbixjao4dmdc/4zeBfx4PDqd1+yznZUMtun3Sit2Y4X2eto/r9joxKvrN9hl5wk2e9QtOSxVXKXiWEbFzW5UxbEjh8XpdHqr6p9n6v36nziPX9JmbJGPNrSXvV5dfxylwM5zUdMVV31yWKq4fmPoUHEqjsJk/ZPq1O3YCEG78ez3fdOLLBkPWDhYsq91r67fQFfIhI8qrho6LFXc4HpgkIqb3qiKY0ey/l1k9wLTr675rdj+gIpTcZSk4lQcY1ybS1WNX8l0XYMXLekKOQkVByWpOO5M1r+vV7/QNE1S9/fSnw9aFf9aFjpUHJSk4uBF1r8MZfypvP6dkvIGAr/Ne8UWKu5T3isWEh9PZ/AynU4nOPjsxm0N9horNrT9nGSvVwdLLKm4sQZbtqXiuCcVp+IozBF2Xq/6f19w8+qn+nF/8Lu87VZsqLPI9KVCg8/u++raK+mvEDoGK64qVXQqjrtRcUs2OkHFsZBj4oz63cHgbXTb5b2xBxxcfMWGOns+exu1XTa6ZCUr9oT7mK646gtFp+K4MxX30UaXrETFMaF+Pp9H78PB6rpWIZxE0zTxJaniOA8VByXdoeJOyLg+AABkkvUBACCTrA8AAJlkfQAAyCTrAwBAJlkfAAAyyfoAAJBJ1gcAgEyyPgAAZJL1AQAgk6wPAACZZH0AAMgk61NVVdU0TdM4GKAQFQclqTjuzKEPAACZZH0AAMgk6wMAQKYfR+8A59KZ0fh4PKYb9NvMNljYBu5AxUFJKo4bMq7PH6/u6fF4vHulTofVbjDY5v3z7Eqm28AdqDgoScVxTw4+/uHdMY0NVLQfHxuomF3JdBu4DxUHJak4bkjW549Ol/T67+AQxdgig+ucXaqzIbgJFQclqTjuyXx9Vhrsth6PR/s2xmO9pC4PPqXioCQVRwxZnw8MXm/Uv9Tp/chshwhMUHFQkoojkqzPUoOd2tjIR+fZpmmWTIIE3lQclKTiSOVrJj4z24W1e8bBiYyzSwFvKg5KUnHkcfDxx9jdx1av4aMN6Qq5GxUHJak47smRxz/0pyFONBhr/H7q/UP/7mPmO0Kl4qAsFccN1c/n8+h9OFhd1yrw3Vv1L0LqN+s82+npBq9tGtzcdJt7apomviRVXKXiTkPF3YSKO4k7VNwJyfr6QU7kDv2giuM8VByUdIeKOyFzeAAAIJOsDwAAmWR9AADIJOsDAEAmWR8AADLJ+gAAkEnWBwCATLI+AABkkvUBACCTrA8AAJlkfQDg2ppmOM+MPX4qTdNcYj+5KMcWAHBhg0H5HaDPH6Mfj8fRu0CyH0fvAADAJp24/Mr318rQTdNca4e5irOf7AIAjDn/sP0Sr5Sf8Vo4G0cVAHBhhsNhgjk8AMAl9QfC24+8Z/J0fqhapwf9NbTPHCYG2j9aQ3vT/TbvR4zr8w2yPgAQoj0ZZnAS/+Aj7aXa8+b7ibyzktk1tJtNt3m39DUF+3IGCQDcwuO3qpW/J5J9x2Cyby84sYblW4F9yfoAwPV842Y7S66R7Wxx8L+zM3agGHN4AIB8GwP32KnFikn2puZTkkMNAGDKFW/YDy/G9QEARk0HfScAnJxxfQDgesqE7BUj+ubncCoORwDgqjYG67F7aC5f+eAi4j7n4VgEAG6n/8ewxm6p+fq5Y8ka4AxkfQDgjtphfV1M376GsRXCXlybCwBc0tjNK6dvgb/9qeXNll/Ra9oPX+LAAgAuLCYlG9TnG0LKAwC4oYx8HHO6wgk5tgCAawvIyhknLZyQ+foAwIVdPSU3TXP1l8CZXf48GADgugR9vkrWBwCATLI+AABkkvUBACCTrA8AAJlkfQAAyCTrAwBAJlkfAAAyyfoAAJBJ1gcAgEw/jt4B4ML+73//5/3zv/79nwP3BOIpN2AFWR/YhyACxSg3YCFZH9ifIALFKDdggqwPfJcgAsW8y02tAS+yPpxLOxnnyX51XFHqMZn6uoBP1c/n8+h9OFhd14/H4+i9gKqqqqZprlWSn+aJf/37PyqO87hWxa0ot8pnHGdyrYqLYVwf+DrTCaAMtQZ0yPrAV8gcUIxyA8bI+sBuBA4oRrkBS8j6wCYCBxSj3IBPuTbXdUucyB2uW1JxnIeKg5LuUHEn1By9AwAAwFfI+gAAkEnWBwCATLI+AABkkvUBACCTrA8AAJlkfQAAyCTrAwBAJlkfAAAyyfoAAJBJ1gcAgEyyPgAAZJL1AQAgk6wPAACZZH0AAMgk6wMAQCZZHwAAMsn6AACQSdYHAIBMsj4AAGSS9QEAIJOsDwAAmWR9AADIJOsDAEAmWR8AADLJ+gAAkEnWBwCATLI+AABkkvUBACCTrA8AAJlkfQAAyCTrAwBAJlkfAAAyyfoAAJBJ1gcAgEyyPgAAZJL1AQAgk6wPAACZZH0AAMgk6wMAQCZZHwAAMsn6AACQSdYHAIBMsj4AAGSS9QEAIJOsDwAAmWR9AADIJOsDAEAmWR8AADLJ+gAAkEnWBwCATLI+AABkkvUBACCTrA8AAJlkfQAAyCTrAwBAJlkfAAAyyfoAAJBJ1gcAgEyyPgAAZJL1AQAgk6wPAACZZH0AAMgk6wMAQCZZHwAAMsn6AACQSdYHAIBMsj4AAGSS9QEAIJOsDwAAmWR9AADIJOsDAEAmWR8AADLJ+gAAkEnWBwCATLI+AABkkvUBACCTrA8AAJlkfQAAyCTrAwBAJlkfAAAyyfoAAJBJ1gcAgEyyPgAAZJL1AQAgk6wPAACZZH0AAMgk6wMAQCZZHwAAMsn6AACQSdYHAIBMsj4AAGSS9QEAIJOsDwAAmWR9AADIJOsDAEAmWR8AADLJ+gAAkEnWBwCATLI+AABkkvUBACCTrA8AAJlkfQAAyCTrAwBAJlkfAAAyyfoAAJBJ1gcAgEyyPgAAZJL1AQAgk6wPAACZZH0AAMgk6wMAQCZZHwAAMsn6AACQSdYHAIBMsj4AAGSS9QEAIJOsDwAAmWR9AADIJOsDAEAmWR8AADLJ+gAAkEnWBwCATLI+AABkkvUBACCTrA8AAJlkfQAAyCTrAwBAJlkfAAAyyfoAAJBJ1gcAgEyyPgAAZJL1AQAgk6wPAACZZH0AAMgk6wMAQCZZHwAAMsn6AACQSdYHAIBMsj4AAGSS9QEAIJOsDwAAmWR9AADIJOsDAEAmWR8AADLJ+gAAkEnWBwCATLI+AABkkvUBACCTrA8AAJlkfQAAyCTrAwBAJlkfAAAyyfoAAJBJ1gcAgEyyPgAAZJL1AQAgk6wPAACZZH0AAMgk6wMAQCZZHwAAMsn6AACQSdYHAIBMsj4AAGSS9QEAIJOsDwAAmWR9AADIJOsDAEAmWR8AADLJ+gAAkEnWBwCATLI+AABkkvUBACCTrA8AAJlkfQAAyPTj6B04haZxzgPlqDgoScXBndXP5/PofQAAAPbnXJ991HV99C7Ajag4KEnFcV2yPjvQCUJJKg5KUnFcmqwPADBD4ueiZH12ox+EklQcALNkfQAAyCTrs5XBRShJxUFJKo6rk/UBrkoKgZJUHFck67Mn/SAAwHnI+mwi3AMAnJasD3AZzq6hJBVHAFmfnekZoSQVByWpOC5H1gcAgEyyPusZ3oCSVBwAn5L1AQC6nF2TQdZnf/pHKEnFQUkqjmuR9QEAIJOsz0oGNqAkFQclqThiyPoAAJCpfj6fR+8DCerasQTlqDgoScVxXcb1AQAgk6wPAACZZH0AAMgk6wMAQCZZHwAAMsn6AACQSdYHAIBMsj4AAGSS9QEAIJOsDwAAmWR9AADIJOsDAEAmWR8AADLJ+gAAkEnWBwCATPXz+Tx6HwAAgP0Z1wcAgEyyPgAAZJL1AQAgk6wPAACZZH0AAMgk6wMAQCZZHwAAMsn6AACQSdanqqqqruu6ro/dgQO3DuUdW3QqjrtRcdyWrM/xdIJQkoqDklQcx5L1AQAgk6wPAACZfhy9A5zR6wvH5/PZ+ebx+XyuazO48uqf32wONoY7UHFQ2GxBqThiGNdn1Ltj6vR9n7aZ0F6w/TPckIqDwmYLSsURQNZnSruTWt7GdUiwjoqDwmaLTsVxdbI+o5aMQHTaGLSA1VQcFDZbQSqOALI+S+njoCQVB4UpOiLJ+gAAkEnWBwCATLI+AABkkvXZZOz2ZBNtgNVUHJSk4ggg67PVu5vr93edO5TVdT3RJ04/C7yoOChJxXF1sj6bvP804LsLG7tDWf8vkgCfUnFQkoojQO2gZB1/7htKUnFQkoojhnF9AADIJOsDAEAmWR8AADKZrw8AAJmM6wMAQCZZHwAAMsn6AACQSdYHAIBMsj4AAGSS9QEAIJOsDwAAmWR9AADIJOsDAEAmWR8AADLJ+gAAkEnWBwCATLI+AABkkvUBACCTrA8AAJlkfQAAyCTrAwBAJlkfAAAyyfoAAJBJ1gcAgEyyPgAAZJL1AQAgk6wPAACZZH0AAMgk6wMAQCZZHwAAMsn6AACQSdYHAIBM/w9QVOE6+pp8PAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"./Images/feature_extraction.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why only reuse the convolutional base? Could we reuse the densely connected\n",
    "classifier as well? In general, doing so should be avoided. The reason is that the representations\n",
    "learned by the convolutional base are likely to be more generic and, therefore,\n",
    "more reusable: the feature maps of a ConvNet are presence maps of generic\n",
    "concepts over a picture, which are likely to be useful regardless of the computer vision\n",
    "problem at hand. But the representations learned by the classifier will necessarily be\n",
    "specific to the set of classes on which the model was trained—they will only contain\n",
    "information about the presence probability of this or that class in the entire picture.\n",
    "Additionally, representations found in densely connected layers no longer contain any information about where objects are located in the input image; these layers get rid of\n",
    "the notion of space, whereas the object location is still described by convolutional feature\n",
    "maps. For problems where object location matters, densely connected features\n",
    "are largely useless.\n",
    "\n",
    "\n",
    "Note that the level of generality (and therefore reusability) of the representations\n",
    "extracted by specific convolution layers depends on the depth of the layer in the\n",
    "model. Layers that come earlier in the model extract local, highly generic feature\n",
    "maps (such as visual edges, colors, and textures), whereas layers that are higher up\n",
    "extract more-abstract concepts (such as “cat ear” or “dog eye”). So if your new dataset\n",
    "differs a lot from the dataset on which the original model was trained, you may be better\n",
    "off using only the first few layers of the model to do feature extraction, rather than\n",
    "using the entire convolutional base.\n",
    "\n",
    "\n",
    "\n",
    "In this case, because the ImageNet class set does not contain images of actors, we’ll \n",
    "choose not to use the densely connected layers, in order to cover\n",
    "the more general case where the class set of the new problem doesn’t overlap the\n",
    "class set of the original model. Let’s put this into practice by using the convolutional\n",
    "base of the VGG16 network, trained on ImageNet, to extract interesting features\n",
    "from actors, and then train a classifier for the 8 actors on top of\n",
    "these features.\n",
    "\n",
    "The VGG16 model, among others, comes prepackaged with Keras. You can import\n",
    "it from the `keras.applications` module. Many other image-classification models (all\n",
    "pretrained on the ImageNet dataset) are available as part of `keras.applications`:\n",
    "\n",
    "\n",
    "-  Xception\n",
    "-  ResNet\n",
    "-  MobileNet\n",
    "-  EfficientNet\n",
    "-  DenseNet\n",
    "-  etc.\n",
    "\n",
    "Let's instantiate the VGG16 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4Luec7pbGhJv",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# The target image size can be fixed here (quadratic)\n",
    "# The ImageDataGenerator() automatically scales the images accordingly (aspect ratio is changed)\n",
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    "image_size = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eRes_n9BGhJ0"
   },
   "outputs": [],
   "source": [
    "conv_base = keras.applications.vgg16.VGG16(weights=\"imagenet\",\n",
    "                                           include_top=False,\n",
    "                                           input_shape=(image_size, image_size, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vEIWLeqSGhJ5"
   },
   "source": [
    "You pass three arguments to the constructor:\n",
    "\n",
    "- `weights` specifies the weight checkpoint from which to initialize the model.\n",
    "\n",
    "- `include_top` refers to including (or not) the densely connected classifier on\n",
    "top of the network. By default, this densely connected classifier corresponds to\n",
    "the 1'000 classes from ImageNet. Because we intend to use our own densely\n",
    "connected classifier (with 8 classes of actors), we don’t need to\n",
    "include it.\n",
    "- `input_shape` is the shape of the image tensors that we’ll feed to the network.\n",
    "This argument is purely optional: if we don’t pass it, the network will be able to\n",
    "process inputs of any size. Here we pass it so that we can visualize (in the following\n",
    "summary) how the size of the feature maps shrinks with each new convolution\n",
    "and pooling layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here’s the detail of the architecture of the VGG16 convolutional base. It’s similar to\n",
    "the simple convnets you’re already familiar with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M7Bk7t1MGhJ6"
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 150, 150, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 150, 150, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 75, 75, 64)        0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 75, 75, 128)       73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 75, 75, 128)       147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 37, 37, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 37, 37, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 37, 37, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 37, 37, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 18, 18, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 18, 18, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 9, 9, 512)         0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DBSrhVORGhKH"
   },
   "source": [
    "\n",
    "The final feature map (output volume) has shape $(5, 5, 512)$. That's the feature on top of which we will stick a densely connected classifier.\n",
    "\n",
    "At this point, there are two ways how we could proceed:\n",
    "\n",
    "- __Approach 1__: Run the convolutional base over our dataset, record its output to a NumPy array\n",
    "on disk, and then use this data as input to a standalone, densely connected classifier\n",
    "similar to those you saw in Block 4 of this course. This solution is fast and\n",
    "cheap to run, because it only requires running the convolutional base once for\n",
    "every input image, and the convolutional base is by far the most expensive part\n",
    "of the pipeline. But for the same reason, this technique won’t allow us to use\n",
    "data augmentation.\n",
    "\n",
    "- __Approach 2__: Extend the model we have (`conv_base`) by adding `Dense` layers on top, and run\n",
    "the whole thing from end to end on the input data. This will allow us to use\n",
    "data augmentation, because every input image goes through the convolutional\n",
    "base every time it’s seen by the model. But for the same reason, this technique is\n",
    "far more expensive than the first.\n",
    "We’ll cover both techniques. Let’s walk through the code required to set up the first\n",
    "one: recording the output of `conv_base` on our data and using these outputs as inputs\n",
    "to a new model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mlpIDmSCGhKI"
   },
   "source": [
    "### 1. Approach : Fast feature extraction without data augmentation\n",
    "We’ll start by extracting features as NumPy arrays by calling the `predict()` method of\n",
    "the `conv_base` model on our training, and validation datasets.\n",
    "Let’s iterate over our datasets to extract the VGG16 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 480 files belonging to 8 classes.\n",
      "Found 80 files belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.utils import image_dataset_from_directory\n",
    "train_dataset = image_dataset_from_directory(\n",
    "    './train',\n",
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    "    image_size=(150, 150),\n",
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    "    batch_size=32,\n",
    "    shuffle=False,\n",
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    "    label_mode=\"categorical\")\n",
    "validation_dataset = image_dataset_from_directory(\n",
    "    './validation',\n",
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    "    image_size=(150, 150),\n",
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    "    batch_size=32,\n",
    "    shuffle=False,\n",
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    "    label_mode=\"categorical\")"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def get_features_and_labels(dataset):\n",
    "    all_features = []\n",
    "    all_labels = []\n",
    "    for images, labels in dataset:\n",
    "        preprocessed_images = keras.applications.vgg16.preprocess_input(images)\n",
    "        features = conv_base.predict(preprocessed_images)\n",
    "        all_features.append(features)\n",
    "        all_labels.append(labels)\n",
    "    return np.concatenate(all_features), np.concatenate(all_labels)\n",
    "train_features, train_labels = get_features_and_labels(train_dataset)\n",
    "val_features, val_labels = get_features_and_labels(validation_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importantly, `predict()` only expects images, not labels, but our current dataset yields\n",
    "batches that contain both images and their labels. Moreover, the VGG16 model expects\n",
    "inputs that are preprocessed with the function `keras.applications.vgg16.preprocess_input`, which scales pixel values to an appropriate range.\n",
    "The extracted features are currently of shape `(samples, 5, 5, 512)`:"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {},
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   "outputs": [],
   "source": [
    "train_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the labels are now referring to the order of the folders"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {},
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   "outputs": [],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {},
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   "outputs": [],
   "source": [
    "print(val_features.shape)\n",
    "print(val_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
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    "inputs = keras.Input(shape=(4, 4, 512))\n",
    "# Note the use of the Flatten\n",
    "# layer before passing the\n",
    "# features to a Dense layer\n",
    "x = layers.Flatten()(inputs)\n",
    "x = layers.Dense(256)(x)\n",
    "x = layers.Dropout(0.7)(x)\n",
    "outputs = layers.Dense(8, activation=\"softmax\")(x)\n",
    "model = keras.Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {},
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   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {},
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   "outputs": [],
   "source": [
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "    optimizer=\"rmsprop\",\n",
    "    metrics=[\"accuracy\"])\n",
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    "\n",
    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(filepath=\"feature_extraction.keras\", save_best_only=True, monitor=\"val_loss\"),\n",
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    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "]\n",
    "\n",
    "history = model.fit(\n",
    "train_features, train_labels,\n",
    "epochs=30,\n",
    "validation_data=(val_features, val_labels),\n",
    "callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we’ll also use a `ModelCheckpoint` callback to save the model after each\n",
    "epoch. We’ll configure it with the path specifying where to save the file, as well as the\n",
    "arguments `save_best_only=True` and `monitor=\"val_loss\"`: they tell the callback to\n",
    "only save a new file (overwriting any previous one) when the current value of the\n",
    "`val_loss` metric is lower than at any previous time during training. This guarantees\n",
    "that your saved file will always contain the state of the model corresponding to its bestperforming\n",
    "training epoch, in terms of its performance on the validation data. As a\n",
    "result, we won’t have to retrain a new model for a lower number of epochs if we start\n",
    "overfitting: we can just reload our saved file."
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s look at the loss and accuracy curves during training:"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {},
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   "outputs": [],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='lower right')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.evaluate(validation_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reach a validation accuracy of about 46% — much worse than we achieved in the\n",
    "previous section with the small model trained from scratch. \n",
    "\n",
    "The learning curves indicate that we’re overfitting almost from the start—\n",
    "despite using dropout with a fairly large rate. That’s because this technique doesn’t\n",
    "use data augmentation, which is essential for preventing overfitting with small image\n",
    "datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the TensorBoard notebook extension\n",
    "%load_ext tensorboard\n",
    "\n",
    "%tensorboard --logdir logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DJT-DgHvGhKu"
   },
   "source": [
    "### 2. Approach : Feature Extraction with Data Augmentation\n",
    "\n",
    "\n",
    "Now let’s review the second technique we mentioned for doing feature extraction,\n",
    "which is much slower and more expensive, but which allows us to use data augmentation\n",
    "during training: creating a model that chains the `conv_base` with a new dense\n",
    "classifier, and training it end to end on the inputs.\n",
    "In order to do this, we will first freeze the convolutional base. Freezing a layer or set of\n",
    "layers means preventing their weights from being updated during training. If we don’t\n",
    "do this, the representations that were previously learned by the convolutional base will\n",
    "be modified during training. Because the Dense layers on top are randomly initialized,\n",
    "very large weight updates would be propagated through the network, effectively\n",
    "destroying the representations previously learned.\n",
    "\n",
    "In Keras, we freeze a layer or model by setting its trainable attribute to `False`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "50DF9pH1GhKw"
   },
   "source": [
    "#### Instantiating and freezing the VGG16 convolutional base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base = keras.applications.vgg16.VGG16(weights=\"imagenet\", include_top=False)\n",
    "conv_base.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting trainable to `False` empties the list of trainable weights of the layer or model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing the list of trainable weights before and after freezing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base.trainable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights before freezing the conv base: 26\n"
     ]
    }
   ],
   "source": [
    "print(\"This is the number of trainable weights before freezing the conv base:\", len(conv_base.trainable_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights after freezing the conv base: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"This is the number of trainable weights after freezing the conv base:\", len(conv_base.trainable_weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create a new model that chains together\n",
    "\n",
    "1. A data augmentation stage\n",
    "2. Our frozen convolutional base \n",
    "3. A dense classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding a data augmentation stage and a classifier to the convolutional base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_augmentation = keras.Sequential(\n",
    "[\n",
    "layers.RandomFlip(\"horizontal\"),\n",
    "layers.RandomRotation(0.1),\n",
    "layers.RandomZoom(0.2),\n",
    "]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(150, 150, 3))\n",
    "# Apply data augmentation\n",
    "x = data_augmentation(inputs)\n",
    "# Apply input value scaling\n",
    "x = keras.applications.vgg16.preprocess_input(x)\n",
    "x = conv_base(x)\n",
    "x = layers.Flatten()(x)\n",
    "x = layers.Dense(256)(x)\n",
    "x = layers.Dropout(0.5)(x)\n",
    "outputs = layers.Dense(8, activation=\"softmax\")(x)\n",
    "model = keras.Model(inputs, outputs)\n",
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "    optimizer=\"rmsprop\",\n",
    "    metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this setup, only the weights from the two Dense layers that we added will be\n",
    "trained. That’s a total of four weight tensors: two per layer (the main weight matrix\n",
    "and the bias vector). \n",
    "\n",
    "Note that in order for these changes to take effect, you must first\n",
    "compile the model. If you ever modify weight trainability after compilation, you\n",
    "should then recompile the model, or these changes will be ignored.\n",
    "\n",
    "Let’s train our model. Thanks to data augmentation, it will take much longer for\n",
    "the model to start overfitting, so we can train for more epochs — let’s do 50.\n",
    "\n",
    "__NOTE__ This technique is expensive enough that you should only attempt it if\n",
    "you have access to a GPU (such as the free GPU available in Colab) — it’s\n",
    "intractable on CPU. If you can’t run your code on GPU, then the previous\n",
    "technique is the way to go."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
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    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "\n",
    "\n",
    "callbacks = [\n",
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    "    keras.callbacks.ModelCheckpoint(filepath=\"feature_extraction_with_augmentation.keras\", save_best_only=True, monitor=\"val_loss\"),\n",
    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "15/15 [==============================] - 170s 11s/step - loss: 40.4872 - accuracy: 0.3104 - val_loss: 22.2523 - val_accuracy: 0.4375\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 163s 11s/step - loss: 16.3431 - accuracy: 0.5542 - val_loss: 23.0516 - val_accuracy: 0.4875\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 162s 11s/step - loss: 16.4505 - accuracy: 0.6062 - val_loss: 30.8938 - val_accuracy: 0.4125\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 165s 11s/step - loss: 14.6992 - accuracy: 0.6229 - val_loss: 21.7834 - val_accuracy: 0.5125\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 167s 11s/step - loss: 11.4793 - accuracy: 0.6729 - val_loss: 27.7946 - val_accuracy: 0.4500\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 168s 11s/step - loss: 12.4257 - accuracy: 0.6729 - val_loss: 25.0661 - val_accuracy: 0.4125\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 164s 11s/step - loss: 8.7416 - accuracy: 0.7250 - val_loss: 26.6285 - val_accuracy: 0.5125\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 162s 11s/step - loss: 8.3796 - accuracy: 0.7250 - val_loss: 24.7431 - val_accuracy: 0.4375\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 163s 11s/step - loss: 8.1311 - accuracy: 0.7417 - val_loss: 20.8759 - val_accuracy: 0.5000\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 161s 11s/step - loss: 8.5853 - accuracy: 0.7604 - val_loss: 27.0080 - val_accuracy: 0.5250\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 161s 11s/step - loss: 7.2803 - accuracy: 0.7750 - val_loss: 25.0368 - val_accuracy: 0.4875\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 161s 11s/step - loss: 8.3474 - accuracy: 0.7708 - val_loss: 24.2293 - val_accuracy: 0.5000\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 163s 11s/step - loss: 7.2649 - accuracy: 0.7937 - val_loss: 27.9647 - val_accuracy: 0.4875\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 161s 11s/step - loss: 5.6787 - accuracy: 0.8000 - val_loss: 25.7642 - val_accuracy: 0.5625\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 161s 11s/step - loss: 9.5175 - accuracy: 0.7750 - val_loss: 25.2030 - val_accuracy: 0.5875\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 159s 11s/step - loss: 5.8167 - accuracy: 0.8125 - val_loss: 27.7553 - val_accuracy: 0.4500\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 160s 11s/step - loss: 5.7371 - accuracy: 0.8375 - val_loss: 33.3951 - val_accuracy: 0.4875\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 162s 11s/step - loss: 5.5106 - accuracy: 0.8062 - val_loss: 24.9338 - val_accuracy: 0.5250\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 160s 11s/step - loss: 5.6374 - accuracy: 0.8208 - val_loss: 31.7434 - val_accuracy: 0.5375\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 181s 12s/step - loss: 5.2847 - accuracy: 0.8500 - val_loss: 27.8406 - val_accuracy: 0.5250\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 160s 11s/step - loss: 3.9255 - accuracy: 0.8604 - val_loss: 24.6560 - val_accuracy: 0.5500\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 162s 11s/step - loss: 4.5439 - accuracy: 0.8667 - val_loss: 28.7081 - val_accuracy: 0.4875\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 157s 10s/step - loss: 5.1945 - accuracy: 0.8333 - val_loss: 28.1471 - val_accuracy: 0.5250\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 157s 11s/step - loss: 5.4685 - accuracy: 0.8292 - val_loss: 27.2732 - val_accuracy: 0.4875\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 156s 10s/step - loss: 4.9645 - accuracy: 0.8458 - val_loss: 28.9958 - val_accuracy: 0.4625\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 158s 11s/step - loss: 4.2419 - accuracy: 0.8604 - val_loss: 31.0298 - val_accuracy: 0.5250\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 177s 12s/step - loss: 4.6601 - accuracy: 0.8667 - val_loss: 35.1135 - val_accuracy: 0.5000\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 158s 11s/step - loss: 4.3568 - accuracy: 0.8729 - val_loss: 35.3589 - val_accuracy: 0.4750\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 182s 12s/step - loss: 4.6585 - accuracy: 0.8479 - val_loss: 39.2265 - val_accuracy: 0.4500\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 165s 11s/step - loss: 4.8581 - accuracy: 0.8583 - val_loss: 30.4833 - val_accuracy: 0.5375\n",
      "Epoch 31/50\n",
      "15/15 [==============================] - 165s 11s/step - loss: 3.3673 - accuracy: 0.8833 - val_loss: 31.5158 - val_accuracy: 0.5125\n",
      "Epoch 32/50\n",
      "15/15 [==============================] - 160s 11s/step - loss: 5.6675 - accuracy: 0.8625 - val_loss: 29.5487 - val_accuracy: 0.5375\n",
      "Epoch 33/50\n",
      "15/15 [==============================] - 157s 10s/step - loss: 3.5243 - accuracy: 0.8771 - val_loss: 29.5125 - val_accuracy: 0.5500\n",
      "Epoch 34/50\n",
      "15/15 [==============================] - 158s 11s/step - loss: 3.1388 - accuracy: 0.8833 - val_loss: 31.3419 - val_accuracy: 0.5125\n",
      "Epoch 35/50\n",
      "15/15 [==============================] - 164s 11s/step - loss: 4.4508 - accuracy: 0.8771 - val_loss: 28.2114 - val_accuracy: 0.5375\n",
      "Epoch 36/50\n",
      "15/15 [==============================] - 159s 11s/step - loss: 3.7561 - accuracy: 0.8667 - val_loss: 27.7479 - val_accuracy: 0.5250\n",
      "Epoch 37/50\n",
      "15/15 [==============================] - 160s 11s/step - loss: 2.8535 - accuracy: 0.8958 - val_loss: 30.9036 - val_accuracy: 0.5125\n",
      "Epoch 38/50\n",
      "15/15 [==============================] - 158s 11s/step - loss: 2.7719 - accuracy: 0.8896 - val_loss: 28.7516 - val_accuracy: 0.5500\n",
      "Epoch 39/50\n",
      "15/15 [==============================] - 162s 11s/step - loss: 4.3758 - accuracy: 0.8562 - val_loss: 30.6686 - val_accuracy: 0.5500\n",
      "Epoch 40/50\n",
      "15/15 [==============================] - 182s 12s/step - loss: 3.4704 - accuracy: 0.8750 - val_loss: 26.9565 - val_accuracy: 0.5750\n",
      "Epoch 41/50\n",
      "15/15 [==============================] - 169s 11s/step - loss: 3.2676 - accuracy: 0.9042 - val_loss: 33.6229 - val_accuracy: 0.5000\n",
      "Epoch 42/50\n",
      "15/15 [==============================] - 167s 11s/step - loss: 3.6569 - accuracy: 0.8792 - val_loss: 30.1333 - val_accuracy: 0.5250\n",
      "Epoch 43/50\n",
      "15/15 [==============================] - 164s 11s/step - loss: 3.4009 - accuracy: 0.9062 - val_loss: 30.1136 - val_accuracy: 0.5625\n",
      "Epoch 44/50\n",
      "15/15 [==============================] - 157s 11s/step - loss: 3.0915 - accuracy: 0.9083 - val_loss: 33.6270 - val_accuracy: 0.5500\n",
      "Epoch 45/50\n",
      "15/15 [==============================] - 177s 12s/step - loss: 3.6787 - accuracy: 0.8562 - val_loss: 32.5366 - val_accuracy: 0.5625\n",
      "Epoch 46/50\n",
      "15/15 [==============================] - 160s 11s/step - loss: 2.6454 - accuracy: 0.9042 - val_loss: 35.2887 - val_accuracy: 0.5375\n",
      "Epoch 47/50\n",
      "15/15 [==============================] - 165s 11s/step - loss: 4.6315 - accuracy: 0.8771 - val_loss: 26.3066 - val_accuracy: 0.5750\n",
      "Epoch 48/50\n",
      "15/15 [==============================] - 179s 12s/step - loss: 3.1069 - accuracy: 0.9021 - val_loss: 27.8295 - val_accuracy: 0.6000\n",
      "Epoch 49/50\n",
      "15/15 [==============================] - 163s 11s/step - loss: 2.7718 - accuracy: 0.8854 - val_loss: 35.5000 - val_accuracy: 0.5125\n",
      "Epoch 50/50\n",
      "15/15 [==============================] - 159s 11s/step - loss: 3.1988 - accuracy: 0.8938 - val_loss: 33.8036 - val_accuracy: 0.5625\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "train_dataset,\n",
    "epochs=50,\n",
    "validation_data=validation_dataset,\n",
    "callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s plot the results again. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='lower right')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 23s 7s/step - loss: 33.8036 - accuracy: 0.5625\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[33.8035888671875, 0.5625]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(validation_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we reach a validation accuracy of over 56%. This is a strong improvement over the previous model."
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
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    "#### Tensorboard"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the TensorBoard notebook extension\n",
    "%load_ext tensorboard\n",
    "%tensorboard --logdir logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
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    "id": "FZYRLtbkGhLV"
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    "## Fine Tuning\n",
    "\n",
    "Another widely used technique for model reuse, complementary to feature extraction, is _fine-tuning_. \n",
    "Fine-tuning consists of unfreezing a few of the top layers of a frozen model base used\n",
    "for feature extraction, and jointly training both the newly added part of the model (in this case, the\n",
    "fully connected classifier) and these top layers. This is called _fine-tuning_ because it slightly \n",
    "adjusts the more abstract representations of the model being reused in order to make them more relevant for the problem at hand.\n",
    "\n",
    "I stated earlier that it’s necessary to freeze the convolution base of VGG16 in order to be able to\n",
    "train a randomly initialized classifier on top. For the same reason, it’s only possible to fine-tune the top\n",
    "layers of the convolutional base once the classifier on top has already been trained. If the classifier isn’t\n",
    "already trained, the error signal propagating through the network during training will be too\n",
    "large, and the representations previously learned by the layers being fine-tuned will be destroyed. Thus\n",
    "the steps for fine-tuning a network are as follows:\n",
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    "The steps for fine-tuning are as follows:\n",
    "\n",
    "1. Add our custom network on top of an already-trained base network.\n",
    "2. Freeze the base network.\n",
    "3. Train the part we added.\n",
    "4. Unfreeze some layers in the base network. (Note that you should not unfreeze “batch normalization” layers, which are not relevant here since there are no such layers in VGG16. )\n",
    "5. Jointly train both these layers and the part we added.\n",
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    "We already completed the first three steps when doing feature extraction. Let’s proceed with step 4:\n",
    "we’ll unfreeze our `conv_base` and then freeze individual layers inside it.\n",
    "\n",
    "As a reminder, this is what our convolutional base looks like:"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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    "id": "cnObzTupGhLV",
    "outputId": "3754b2b3-8885-44b3-cb87-82612d223ec3"
   },
   "outputs": [],
   "source": [
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    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
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    "id": "aDtcl5X2GhLa"
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    "We will fine-tune the last three convolutional layers, which means all layers up to `block4_pool` should be frozen, and the layers `block5_conv1`, `block5_conv2`, and `block5_conv3` should be trainable.\n",
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    "Why not fine-tune more layers? Why not fine-tune the entire convolutional base?\n",
    "You could. But you need to consider the following:\n",
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    "- Earlier layers in the convolutional base encode more generic, reusable features, whereas layers higher up encode more specialized features. It’s more useful to fine-tune the more specialized features, because these are the ones that need to be repurposed on your new problem. There would be fast-decreasing returns in fine-tuning lower layers.\n",
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    "- The more parameters you’re training, the more you’re at risk of overfitting. The convolutional base has 15 million parameters, so it would be risky to attempt to train it on your small dataset. \n",
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    "Thus, in this situation, it’s a good strategy to fine-tune only the top two or three layers in the convolutional base. Let’s set this up, starting from where we left off in the previous example."
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
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    "#### Freezing all layers until the fourth from the last"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tBXYN1t2GhLc",
    "outputId": "b33ae8d1-925b-4e8a-f15d-a62356070896"
   },
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   "outputs": [],
   "source": [
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    "conv_base.trainable = True\n",
    "for layer in conv_base.layers[:-4]:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XWw1mYfUGhLg"
   },
   "source": [
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    "Now we can begin fine-tuning the model. We’ll do this with the `RMSprop` optimizer, using a very low learning rate. The reason for using a low learning rate is that we want to limit the magnitude of the modifications we make to the representations of the three\n",
    "layers we’re fine-tuning. Updates that are too large may harm these representations."
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "#### Fine-tuning the model"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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    "id": "4YBjFhSVGhLh",
    "outputId": "c688820a-0f28-4aa0-b247-15a9684fa08f"
   },
   "outputs": [],
   "source": [
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    "model.compile(loss=\"binary_crossentropy\",\n",
    "        optimizer=keras.optimizers.RMSprop(learning_rate=1e-5),\n",
    "        metrics=[\"accuracy\"])\n",
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    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
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    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(filepath=\"fine_tuning.keras\", save_best_only=True, monitor=\"val_loss\"),\n",
    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "]\n",
    "\n",
    "history = model.fit(train_dataset,\n",
    "                    epochs=30,\n",
    "                    validation_data=validation_dataset,\n",
    "                    callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9rwSMMQaGhLx",
    "outputId": "0a58db5a-0f22-45e8-d1fb-0a664fceaf4d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NE3oWEMYcpaTUw9wN2Xy2fDtfrtxB9oEit0sCoHHNKoy79Syq2RQappxYQBiDEwrzN2bz6fLtfLHCCYXEuGj6tatHuwbVcLupP1qEyzs1oG61BHcLMRHFAsJErFKPMn9jNp8t38YXK3awO88Jhb7t6nF5xwb0blPHmnJMRLOAMBGl1KMs2JTNZ8u28/mKHezOK6RKbDQXtavLFR0b0LtNXarEWSgYAxYQJgJ4PEr65r18tmwb01bsICu3kITYKC5qW5fLOzakT9s6JMbZn4IxR7O/ChOWPB5l4Za9fLZsO9OWb2dXbiHxMd5Q6NSAi9rWtVAw5gRC+hciIgOAZ4Fo4DVVffKo9U2AcUCKd5tRqjpNRJoBq4GfvZvOVdU7Q1mrqfw8HmVxxl4+Xbadz5fvYMf+AuJioujTpg6Xd2pI37Z1SYq3UDAmUCH7axGRaGAM0B/IBBaIyMequspnsz8Bk1X1RRFpD0wDmnnXrVfVLqGqz4QHJxRymLbcOVPYvs8Jhd5n1OGRTm3p264eVS0UjDklofzL6QGsU9UNACIyCRgI+AaEAtW8j6sD20JYjwkTqsqSjJxDzUfb9hUQFx3FBWfU4eEBbenbrq7dbtOYIAhlQDQCMnyeZwI9j9rmMeArEfktkAT081nXXEQWA/uBP6nqd0e/gYiMAEYANGnSJHiVR7CC4lLmbthDbHQUiXHRJMXHkBgXTdX4GBLjYoiLcWd2FlVlaeY+pi3fzmfLtrM15yCx0cIFrevw0CVt6Ne+nl1AZkyQuX3uPRR4U1X/IyK9gLdF5ExgO9BEVfeISHfgQxHpoKr7fXdW1VeAVwDS0tK0vIsPNyWlHka8vZDZa7KOuU1stJAYF+MNjGgS42OoGh9NYlwMSYeeO+uS4mJIjD8cLkm/2N5ZHxvtP3RUleVb9/HZsu18tnw7mXudUDi/dR3+r/8Z9Gtfj+pVLBSMCZVQBsRWoLHP81TvMl+3AwMAVHWOiCQAtVV1F1DoXb5QRNYDZwDpIaw34j0xbTWz12Txx8va0blxCgeKSjhQWEJ+YemhxweKSskvLCGvsJT8osPP9+Tlk1/kLMsrLKGgOPD5i+JiopzwiIshKd45a0mKi2FLdj5bsvOJiRLOa12b+/q25uL29ameaKFgTHkIZUAsAFqLSHOcYBgCDDtqmy1AX+BNEWkHJABZIlIHyFbVUhFpAbQGNoSw1og3ft5m3vhhE7ef15w7Lmhx2q9X6lHyi0rILyol7xghc6Co1Pvcu77ssXd5yzpJ3NOnFRd3qEdKYlwQfktjzMkIWUCoaomI3AN8iTOEdayqrhSR0UC6qn4MPAi8KiIP4HRY36KqKiIXAKNFpBjwAHeqanaoao10P67bzaMfraRPmzr84bJ2QXnN6CghOSGW5IRY6gXlFY0x5U1Uw6PpPi0tTdPTrQXqZG3cfYCrx/xA3eR43r/7HBv9Y0yEEZGFqprmb53dMCiC7TtYzO3jFhAl8PrwsywcjDFHcHsUk3FJSamHeyYsIiM7n3du70mTWolul2SMqWAsICLU45+u4ru1u/nXdZ3o2aKW2+UYYyoga2KKQG/P3cy4OZsZcUELBp/V+MQ7GGMikgVEhPl+7W4e+3glfdvW5eEBbd0uxxhTgVlARJANWXncPX4hrepU5ZkhXYiOcvk+msaYCs0CIkLsyy/m9nHpxEZH8drwNBuxZIw5IeukjgDFpR7unrCQzL35TLjjbBrXtBFLxpgTs4CIAKM/WcUP6/bw1PWdOKtZTbfLMcZUEtbEFObemrOJt+du5jcXtmBQmo1YMsYEzgIijM1ek8VfP1lFv3b1+P0lNmLJGHNyAgoIEXlfRC4XEQuUSmLdrjxGTlhE67o2YskYc2oCPeC/gDNV91oReVJE2oSwJnOacvKL+PW4BcTHOCOW7J7MxphTEVBAqOoMVb0R6AZsAmaIyI8icquI2HjJCqS41MNd7yxiW04BL9/cndQaNmLJGHNqAm4yEpFawC3Ar4HFwLM4gTE9JJWZk6aqPPrxSuZs2MOT13Wke1MbsWSMOXUBtT2IyAdAG+Bt4EpV3e5d9a6I2E0YKohxP25iwrwt3NW7Jdd2S3W7HGNMJRdo4/RzqjrT34pj3WjClK9vf97F6E9XcXH7evzuYusiMsacvkCbmNqLSErZExGpISJ3h6Ykc7LW7crltxMW06Z+NZ6+oQtRNmLJGBMEgQbEHaqaU/ZEVfcCd4SkInNS9h4o4vZx6cTHRvPa8DSSbMSSMSZIAg2IaBE59LVURKKBuNCUZAJVVOLhzncWsn2fM2KpUUoVt0syxoSRQL9ufoHTIf2y9/lvvMuMS5wRSyuYtzGbZ27oQvemNdwuyRgTZgINiIdxQuEu7/PpwGshqcgEZOwPm5g4P4ORfVpydddGbpdjjAlDAQWEqnqAF70/YaWguJRrX/iRfu3rMah7aqWYCnvmz7t44rNVXNKhHg/2txFLxpjQCPQ6iNbAP4D2QELZclVtEaK6ys3e/CLqJMfz/Ddree7rtZzTshaD0xoz4Mz6JMRGu13eL6zZ6YxYamsjlowxIRZoE9MbwKPA00Af4FbCZCbYBtWrMO62HmzLOch7CzOZsjCT+99dQvJHMVzVuSGD0xrTKbU6Pn30rsk+UMTt4xZQJc4ZsZQYZyOWjDGhI6p64o1EFqpqdxFZrqodfZeFvMIApaWlaXr66V/U7fEo8zZmMyU9g2krtlNQ7KFt/WQGpTXm6i4NqVU1PgjVnryiEg83vTaPJZk5vDvibLo2sU5pY8zp8x7L/V7wHGhA/AicB0wFvgG2Ak+qaoVpAA9WQPjaX1DMp0u3Mzk9gyUZOcRGC/3a1WNwWmPOb12bmOjyOYlSVR5+bxmT0zN5dkgXBnaxTmljTHAcLyACbaO4D0gE7gUex2lmGh6c8iquagmxDOvZhGE9m7BmZy6TF2TwweKtfL5iB/WqxXNdt1QGpTWmee2kkNbx+vcbmZyeyb0XtbJwMMaUmxOeQXgvivunqj5UPiWdmlCcQfhTVOLhm592MSU9g5k/78Kj0KNZTQalpXJ5pwZB7xf4evVOfv1WOgM61GfMsG7WKW2MCapgNDHNVdWzg15ZEJVXQPjaub+A9xdtZUp6Bht2HyApLporOzdkUFoq3ZrUOO2O7Z935HLtCz/QvE4Sk3/TyzqljTFBF4yAeBFoBEwBDpQtV9X3T7DfAJz7RkQDr6nqk0etbwKMA1K824xS1WnedY8AtwOlwL2q+uXx3suNgCijqizcvJfJ6Rl8umw7+UWltKyTxOC0xlzTrRF1kxNO/CJH2ZNXyMAxP1BU4uGje86lQXWbRsMYE3zBCIg3/CxWVb3tOPtEA2uA/kAmsAAYqqqrfLZ5BVisqi+KSHtgmqo28z6eCPQAGgIzgDNUtfRY7+dmQPjKKyxh2jKnYzt9816io4Q+beoyOC2VPm3rEhtAx3ZhSSk3vTaPZZn7mPybXnRunBL6wo0xEem0O6lV9dZTeN8ewDpV3eAtYhIwEFjls40C1byPqwPbvI8HApNUtRDYKCLrvK835xTqKFdV42MYfFZjBp/VmPVZeUxJz+S9RZnMWL2T2lXjuLZbKoPTUmlVN9nv/qrKHz9YwYJNe3l+aFcLB2OMawK9kvoNnIP5EY53BoHTJJXh8zwT6HnUNo8BX4nIb4EkoJ/PvnOP2vcXw3dEZAQwAqBJkybH/R3c0LJOVUZd2paHLj6DWWuymJyewdjvN/LK7A10bZLC4LTGXNGpAckJh2/r/crsDUxdmMl9fVtzZeeGLlZvjIl0gfZ6furzOAG4hsPf9k/HUOBNVf2PiPQC3haRMwPdWVVfAV4Bp4kpCPWEREx0FH3b1aNvu3rszivkg0VbmZyewSPvL2f0J6u4rGMDBqelsr+ghCe/+InLOzXgvr6t3S7bGBPhAm1ies/3uYhMBL4/wW5bgcY+z1O9y3zdDgzwvsccEUkAage4b6VUu2o8d1zQgl+f35wlGTlMTs/kk6XbeG9RJgCdUqvz7+s723BWY4zrTnXcZGug7gm2WQC0FpHmOAf3IcCwo7bZAvQF3hSRdjhnJ1nAx8AEEfkvTid1a2D+KdZaIYkIXZvUoGuTGvzlivZ8vmI7czfs4f/6t6FKXMWbJNAYE3kC7YPI5cg+iB0494g4JlUtEZF7gC9xhrCOVdWVIjIaSFfVj4EHgVdF5AHv69+izrCqlSIyGadDuwQYebwRTJVdlbhoru2WyrXdUt0uxRhjDglomGtlUFGGuRpjTGVyvGGuAc02JyLXiEh1n+cpInJ1kOozxhhTAQU6Hemjqrqv7Imq5uDcH8IYY0yYCjQg/G1nEwMZY0wYCzQg0kXkvyLS0vvzX2BhKAszxhjjrkAD4rdAEfAuMAkoAEaGqihjjDHuC/RCuQPAqBDXYowxpgIJdBTTdBFJ8XleQ0SOO/22McaYyi3QJqba3pFLAKjqXk58JbUxxphKLNCA8Hhv7gOAiDTDz+yuxhhjwkegQ1X/CHwvIrMAAc7HO822McaY8BRoJ/UXIpKGEwqLgQ+BgyGsyxhjjMsCnazv18B9ONNuLwHOxrm720Uhq8wYY4yrAu2DuA84C9isqn2ArkBOqIoyxphjykyHb56AogNuVxL2Au2DKFDVAhFBROJV9ScRaRPSyowxxtf+bTDjMVj2rvN85wq44R2IsvunhEqgAZHpvQ7iQ2C6iOwFNoeqKGOMOaT4IPz4PHz/NHhK4bz/gyopMP0v8OUf4NJ/ul1h2Aq0k/oa78PHRGQmUB34ImRVGWOMKqz8AKY/Cvu2QLur4OLHoUYzZ33eLpjzP0hpCr3udrXUcHXSM7Kq6qxQFGKMMYdsWwJfjIItc6BeR7j6U2h+/pHb9H8ccjY7ZxHVU6H9Va6UGs5sym5jTMWRuxO+GQ2Lx0NiLbjiGej2K//9DFFRcO2rMO5KeP8OqNYQUv3eGM2cokBHMRljTOiUFML3z8Dz3WHpu9BrJNy7CNJuPX4ndGwVGDIRkuvDhBsge0O5lRwJLCCMMe5RhdWfwpieMONRaHYejJwHlzwBCdVPvD9A1Tpw43ugpTB+EORnh7bmCGIBYYxxx86V8NZV8O6NEBMPN70PwyZBrZYn/1q1WzlnEjlbYNIwKC4Ifr0RyALCGFO+DuyBT/8PXjoPti+DS5+CO3+AVn1P73Wb9oJrXnI6tj+6Gzye4NQbwayT2hhTPkqLYf6rMOtJKMyDs+6A3qMgsWbw3uPM65yziBmPOcNf+z0avNeOQBYQxpjQW/OVMxx1z1poeRFc8g+o2zY073Xu/bB3M3z/X0hp4nR0m1NiAWGMCZ2sn51gWDcDaraEoe/CGZeASOjeUwQu+zfsy4TPHnSukWjdP3TvF8asD8IYE3wH98Lno+DFcyBjAVz8BNw9F9oMCG04lImOgUFvQL32MOUWp6/DnDQLCGNM8JSWOP0Mz3WD+S9D15ud6xnOuQdi4sq3lvhkGDbFGS47YbBzRmFOigWEMSY41s+El8+HaQ9BvQ7wm9lw5TOQVNu9mqo1gBunOFODjx8MBfvcq6USsoAwxpyePeth4jB4+2rnQDz4bRj+CdTv6HZljnodYPBbsPtnmDzcGU1lAhLSgBCRASLys4isE5FRftY/LSJLvD9rRCTHZ12pz7qPQ1mnMeYUFOyHr/7sXAW9cRb0/QuMnO9Mmlce/Qwno2UfuPJZ2DATPr3fuYLbnFDIRjGJSDQwBugPZAILRORjVV1Vto2qPuCz/W9x7lRX5qCqdglVfcaYU+QphcXvwDePw4Es6HKjEw7J9d2u7Pi63uQMf539L0hpBhf+zu2KKrxQDnPtAaxT1Q0AIjIJGAisOsb2QwG7qsVtHg8c2FXx/9jLy8G9EBXjdHhGOlXY9L0zbHXHMmjcE4ZNhkbd3K4scH3+4FxIN/NvzjUSnW9wu6LTdzAHcneE5LqSUAZEIyDD53km0NPfhiLSFGgOfOOzOEFE0oES4ElV/TBEdZoyqvDJb51vhzVbQuuLoXU/aHoexCa4XV358Hhg+2JYO9352brQmU20SS9nLH2r/lC3XcVrQgmVwlzYMAvWfuVcy7B/K1RrBNe97ly1XNk+BxG46nnn9/hopDNF+NH3magsPKWwaBx88zeoWg/u+jHo/x4V5UK5IcBUVS31WdZUVbeKSAvgGxFZrqrrfXcSkRHACIAmTZqUX7XhavZTTjiceb0z2mPhGzDvRYhNhOYXQKt+zkGy7I5e4SI/G9Z/4wTCuhmQvxsQaNTdmQqipADWznBucTn9L1At1QnO1hdD8wshvqrbv0HwqDoXt62b7oTC5jngKYa4ZGjZ2/k8zrwe4hLdrvTUxcTBDW/D65c4EwXePh3qtHG7qpOzcTZ88YhzX+6m58KAf4QkrEVD1FkjIr2Ax1T1Eu/zRwBU9R9+tl0MjFTVH4/xWm8Cn6rq1GO9X1pamqanpwej9Mi09F34YAR0GuJMeCbi3At40/fOgWLtV7B3k7Nt7TOcg2OrftD0HGcmzsrE43GaSNZ5zxIyF4B6oErNwyHYsi8k1Tpyv31bnQBZNx3WfwtFuRAV63wGrfs7n0ntMyrft+qiA84BZ+1XThDu2+Isr9ve+3lc7DQnlfd1DKG2dzO81g9iEuDXMyC5ntsVnVj2Rpj+Z1j9CVRv4tyCtf3A0/p/TkQWqqrfOy2FMiBigDVAX2ArsAAYpqorj9quLc79rZurtxgRqQHkq2qhiNQG5gADfTu4j2YBcRo2zoa3r4UmZztTLvs7EKg6wxnLvllu+h5KiyA2CVr0dr5Rt+oPKY3LvfyAHMxxRrCUnSXk7XSWN+zqbUq72Hl8vJvT+Copgox53oPqdMha7SxPaeJ8Dq0vdpou4pJC8uucFlXYs87bjPYVbP6hcv1bBtPWRfDm5c4ZxC2fVcx/L3Ca+r77D8wZ43wpOf8B6HWPc8Ok0+RKQHjf+DLgGSAaGKuqT4jIaCBdVT/2bvMYkKCqo3z2Owd4GfDgDMV9RlVfP957WUCcol0/wesXO53St38JVWoEtl/RAdj43eEDZNm3zjrtvN+m+0Pjs9371qnqnH6XfSvOmOfcUCYhxZlWulV/579V6wbn/XIyvOE5AzZ8C8UHIDoemp3rPdvq79znwK2zi6L8w2eD66b7nA22Ofzv1aRX5TsbDIafP3fuIdH6EhgyPvAvCeXB44GlE+Dr0c6Xms5Doe+jzgWAQeJaQJQnC4hTkLvTOcUuKXBOsWs0PbXXUYXdaw6HxeYfj2y3buU9AFVrGNTyf6Fgv3NwLutQzd3uLK/f6fBZQqPuzjw9oVRS6HwG62Y4texe4yyv0fxwU1Sz84Ly7e+49qw/XMOm751/55gq0OLCwx3up/pvHm7mvQKf/w56jIBL/1Uxmgm3zIXPH4btSyD1LBjwT0jtHvS3sYAwv1R0AN64zDl43fJZcIcqFuYe2aa93zsHTr0zDx8gU3uc/oFaFXatPtyXsGUOeEogvrpzYVTr/k4buttDdvduOty0tWEWlBx02r2bnX94pFjNFqf/PsUFsPl75zNf+xVke8d01Gp1OKSbnhs5I9JO1pd/hDn/g0v+7twT2y05Gc7tV1e8B8kNof9foeOgkIWWBYQ5kqcUJt0Ia7+EIROgzaWhe6+yg3jZt/rTPYgX5jlX7R43fM6C6NjQ/U6nI9gH8bLwWTvdCeVQhU8k8HhgynCnA3jwOKfztzwVHYAfnoUfngMUzr3P+Qlxv4gFhDlMFab9Dha86syZ3+OO8n3/QJqBUtMOtwMH0nzVqh9Ub1S+v0ewHGoGmg6bvnOagcqGFftrBjpm81Wzw59feTRfhavigzDuStixHIZ/Co3PCv17qsLyKTD9UcjdBh2udc4aUspn6L4FhDnsx//BV390RkBc8oS7tRzqSPZ+Az66IzmhunMgzDm6AzxMh12WdSSXjRTz7Uhu2cdpejjUAR7nBEFF6AAPNwd2O31zhfudvrlQnoFtXejcNyNzPjToAgOedO6tXY4sIIxj1UfObJbtroRB4yCqgk3m6zsUde1055Q70oZdlvE3FLVq/cMBWVGH0IaL3evg9X7OtTG/nhHc+2YD7N8OX/8Vlk6EpLrOvbM7D3Plb9ICwkDGfOfUuX4nGP5xxW+CUHUuXqtIQw7dVFrifBZ2llB+Ns+BtwY6Azhu/jA4nfvFB52O8O+edppKe42E8x90da6v4wVEBfsKaUJiz3qYOASSG8DQiRU/HMA5EFo4HBYdY+FQ3pr2gmtedAZWfHiX04l9qlRh5Yfwvx7O3Ekt+zhTo/d7rEJPBFlR5mIyoZKfDeMHOd/Gb5zq7t29jKlszrzu8LDTlCZO5/HJ2r7UmTdp8w/OaLurP3EGIVQCFhDhrLgAJg517sU7/GOo3crtioypfM69zxkw8MMzzoiytNsC2y9vl3PPjEVvO30YVzwN3YZXqjNjC4hw5fHAh3dCxly4/g1nniVjzMkTcYaE78uEzx6C6o2dwQLHUlIE816CWf9yrks5+2648PdQJaXcSg4W64MIV1//FVZ+AP3+Cmde63Y1xlRu0TEw6A3n/taThzvNRkdThZ+mwQs9nRlXm54Dd8+FAX+vlOEAFhDhKX2sczqcdptzemyMOX3xyc4d9KrUgPGDnTOKMjtXwdtXw6ShzmyrN70HN06G2q1dKzcYLCDCzZqv4LMHnbHylz5lI1+MCaZqDZwDf3G+M/gje6Pz9/bSubBtiTOh3l0/OFf3hwHrgwgn25fClFuckRLXvxH6WUuNiUT1OsDgt2D89fBcF5BoSLvdud91sC+oc5kdQcJFToZz2lulhnMaHE63wTSmomnZB659xZnY78KHnfuUhyELiHBQsA8mDHZOe2/7Mqg3EzHGHMOZ1zk/YcwCorIrLYbJv3Jm9bzpPajX3u2KjDFhwgKiMlOFT+53Zvi8+kVnYjtjjAkSG8VUmc1+Cpa8AxeOgi7D3K7GGBNmLCAqq6WTYOYTzk3Me49yuxpjTBiygKiMNs6Gj+5xJvy68jm71sEYExIWEJXNrp9g0k3OHcQGvx1+d1UzxlQYFhCVSe5O5+rN2AS4cUqlnd/FGFM52CimyqLogHOtQ/5uuHVaud3Q3BgTuSwgKgNPKUy9HXYsgyEToWFXtysyxkQAC4iKThU+fxjWfO7MSd9mgNsVGRM2iouLyczMpKCgwO1SQi4hIYHU1FRiY2MD3scCoqKbMwYWvArn/BZ63OF2NcaElczMTJKTk2nWrBkSxqMBVZU9e/aQmZlJ8+bNA97POqkrslUfwVd/gvYDod9ot6sxJuwUFBRQq1atsA4HABGhVq1aJ32mZAFRUWXMh/dHQOpZcM3LEGX/VMaEQriHQ5lT+T3tqFMR7VkPE4dAtYYwdBLEVnG7ImNMBAppQIjIABH5WUTWicgv5oMQkadFZIn3Z42I5PisGy4ia70/w0NZZ4WSn+1c66AKN06FpFpuV2SMCZGcnBxeeOGFk97vsssuIycnJ/gFHSVkASEi0cAY4FKgPTBURI6Yi1pVH1DVLqraBXgeeN+7b03gUaAn0AN4VERqhKrWCqO4ACYOde51O3SSc7W0MSZsHSsgSkpKjrvftGnTSElJCVFVh4VyFFMPYJ2qbgAQkUnAQGDVMbYfihMKAJcA01U127vvdGAAMDGE9brLUwof3gUZc2HQm9Ckp9sVGRNR/vrJSlZt2x/U12zfsBqPXtnhmOtHjRrF+vXr6dKlC7GxsSQkJFCjRg1++ukn1qxZw9VXX01GRgYFBQXcd999jBgxAoBmzZqRnp5OXl4el156Keeddx4//vgjjRo14qOPPqJKleA0S4eyiakRkOHzPNO77BdEpCnQHPjmZPYVkREiki4i6VlZWUEp2hUbZ8PLF8DK96H/aOhwjdsVGWPKwZNPPknLli1ZsmQJTz31FIsWLeLZZ59lzZo1AIwdO5aFCxeSnp7Oc889x549e37xGmvXrmXkyJGsXLmSlJQU3nvvvaDVV1GugxgCTFXV0pPZSVVfAV4BSEtL01AUFlLZG2H6n5372lZvAoPGOUNajTHl7njf9MtLjx49jrhO4bnnnuODDz4AICMjg7Vr11Kr1pH9ks2bN6dLly4AdO/enU2bNgWtnlAGxFagsc/zVO8yf4YAI4/at/dR+34bxNrcVZgL3/3HuQguKhYu+hP0usdGKxkT4ZKSkg49/vbbb5kxYwZz5swhMTGR3r17+72OIT4+/tDj6OhoDh48GLR6QhkQC4DWItIc54A/BPjFbc9EpC1QA5jjs/hL4O8+HdMXA4+EsNby4fHA0gnw9WjI2+nc7Kfvo1CtgduVGWNckJycTG5urt91+/bto0aNGiQmJvLTTz8xd+7ccq4uhAGhqiUicg/OwT4aGKuqK0VkNJCuqh97Nx0CTFJV9dk3W0QexwkZgNFlHdaV1pa5zpxK25c4F78NmQip3d2uyhjjolq1anHuuedy5plnUqVKFerVq3do3YABA3jppZdo164dbdq04eyzzy73+sTnuFyppaWlaXp6uttl/FJOBsx4FFa8B8kNof9foeMguwucMRXA6tWradeundtllBt/v6+ILFTVNH/bV5RO6vBTdAB+eNb5AbjwYTj3PohLOv5+xhhTQVhABJsqLJ8C0x+F3G1w5nXQ76+Q0vjE+xpjTAViARFMWxfC56Mgcz406ALXj4WmvdyuyhhjTokFRDDs3w5f/xWWToSkujBwDHQeZjOwGmMqNQuI01F8EOb8D757GjzFcN4DcP6DEJ/sdmXGGHPaLCBOhar3Zj5/hn1boO0VcPHfoGbgd2oyxpiKztpATtb2pfDm5TBlOCRUg+GfwJDxFg7GmJCrWrUqANu2beP666/3u03v3r0J1pB/O4MIVN4u+OZxWPQ2JNaEK56GbsMhKtrtyowxEaZhw4ZMnTo15O9jAXEiJUUw7yWY9S8oOQi9RsIFv4MqKW5XZowJps9HwY7lwX3N+h3h0iePuXrUqFE0btyYkSOdqegee+wxYmJimDlzJnv37qW4uJi//e1vDBx45CSemzZt4oorrmDFihUcPHiQW2+9laVLl9K2bdtKMxdT5aYKP38OX/0RsjdA60vgkiegdmu3KzPGhIkbbriB+++//1BATJ48mS+//JJ7772XatWqsXv3bs4++2yuuuqqY95T+sUXXyQxMZHVq1ezbNkyunXrFrT6LCD82bkKvnwENnwLtdvATe9Bq35uV2WMCaXjfNMPla5du7Jr1y62bdtGVlYWNWrUoH79+jzwwAPMnj2bqKgotm7dys6dO6lfv77f15g9ezb33nsvAJ06daJTp05Bq88Cwld+Nsx8AtLHQnw1GPBPOOt2iI51uzJjTJgaNGgQU6dOZceOHdxwww2MHz+erKwsFi5cSGxsLM2aNfM7zXd5sIAAKC2GBa/Dt/9w7tWQdjv0+YPTGW2MMSF0ww03cMcdd7B7925mzZrF5MmTqVu3LrGxscycOZPNmzcfd/8LLriACRMmcNFFF7FixQqWLVsWtNosIPZugvGDYffP0KIPDPgH1I2c2R2NMe7q0KEDubm5NGrUiAYNGnDjjTdy5ZVX0rFjR9LS0mjbtu1x97/rrru49dZbadeuHe3ataN79+DdRsACIrkh1GgG/R6DNpfaNNzGmHK3fPnh0VO1a9dmzpw5frfLy8sDoFmzZqxYsQKAKlWqMGnSpJDUZQEREwc3Tna7CmOMqXDsSmpjjDF+WUAYYyJauNxV80RO5fe0gDDGRKyEhAT27NkT9iGhquzZs4eEhIST2s/6IIwxESs1NZXMzEyysrLcLiXkEhISSE1NPal9LCCMMRErNjaW5s1tJuZjsSYmY4wxfllAGGOM8csCwhhjjF8SLr33IpIFHH/SkuOrDewOUjmVnX0WR7LP40j2eRwWDp9FU1Wt429F2ATE6RKRdFVNc7uOisA+iyPZ53Ek+zwOC/fPwpqYjDHG+GUBYYwxxi8LiMNecbuACsQ+iyPZ53Ek+zwOC+vPwvogjDHG+GVnEMYYY/yygDDGGONXxAeEiAwQkZ9FZJ2IjHK7HjeJSGMRmSkiq0RkpYjc53ZNbhORaBFZLCKful2L20QkRUSmishPIrJaRHq5XZObROQB79/JChGZKCInN1VqJRDRASEi0cAY4FKgPTBURNq7W5WrSoAHVbU9cDYwMsI/D4D7gNVuF1FBPAt8oaptgc5E8OciIo2Ae4E0VT0TiAaGuFtV8EV0QAA9gHWqukFVi4BJwECXa3KNqm5X1UXex7k4B4BG7lblHhFJBS4HXnO7FreJSHXgAuB1AFUtUtUcV4tyXwxQRURigERgm8v1BF2kB0QjIMPneSYRfED0JSLNgK7APJdLcdMzwO8Bj8t1VATNgSzgDW+T22sikuR2UW5R1a3Av4EtwHZgn6p+5W5VwRfpAWH8EJGqwHvA/aq63+163CAiVwC7VHWh27VUEDFAN+BFVe0KHAAits9ORGrgtDY0BxoCSSJyk7tVBV+kB8RWoLHP81TvsoglIrE44TBeVd93ux4XnQtcJSKbcJoeLxKRd9wtyVWZQKaqlp1RTsUJjEjVD9ioqlmqWgy8D5zjck1BF+kBsQBoLSLNRSQOp5PpY5drco2ICE4b82pV/a/b9bhJVR9R1VRVbYbz/8U3qhp23xADpao7gAwRaeNd1BdY5WJJbtsCnC0iid6/m76EYad9RN9yVFVLROQe4EucUQhjVXWly2W56VzgZmC5iCzxLvuDqk5zryRTgfwWGO/9MrUBuNXlelyjqvNEZCqwCGf032LCcNoNm2rDGGOMX5HexGSMMeYYLCCMMcb4ZQFhjDHGLwsIY4wxfllAGGOM8csCwpgKQER624yxpqKxgDDGGOOXBYQxJ0FEbhKR+SKyRERe9t4vIk9EnvbeG+BrEanj3baLiMwVkWUi8oF3/h5EpJWIzBCRpSKySERael++qs/9FsZ7r9A1xjUWEMYESETaATcA56pqF6AUuBFIAtJVtQMwC3jUu8tbwMOq2glY7rN8PDBGVTvjzN+z3bu8K3A/zr1JWuBc2W6MayJ6qg1jTlJfoDuwwPvlvgqwC2c68He927wDvO+9f0KKqs7yLh8HTBGRZKCRqn4AoKoFAN7Xm6+qmd7nS4BmwPch/62MOQYLCGMCJ8A4VX3kiIUifz5qu1Odv6bQ53Ep9vdpXGZNTMYE7mvgehGpCyAiNUWkKc7f0fXebYYB36vqPmCviJzvXX4zMMt7p75MEbna+xrxIpJYnr+EMYGybyjGBEhVV4nIn4CvRCQKKAZG4tw8p4d33S6cfgqA4cBL3gDwnf30ZuBlERntfY1B5fhrGBMwm83VmNMkInmqWtXtOowJNmtiMsYY45edQRhjjPHLziCMMcb4ZQFhjDHGLwsIY4wxfllAGGOM8csCwhhjjF//D6ane5PQS5yxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='lower right')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper right')\n",
    "plt.show()"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tensorboard"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the TensorBoard notebook extension\n",
    "%load_ext tensorboard\n",
    "%tensorboard --logdir logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix and Missclassified Images"
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": 111,
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   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(validation_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WoDOi_F8GhL5",
    "outputId": "17c21c92-2a5d-4e21-c367-57e818046762"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  2   0   1   0   0   2   0   5 ], angelina jolie\n",
      "[  0   3   0   0   4   0   3   0 ], brad pitt\n",
      "[  0   0   8   0   0   0   0   2 ], catherine deneuve\n",
      "[  0   0   1   6   1   0   1   1 ], johnny depp\n",
      "[  0   2   0   0   7   0   1   0 ], leonardo dicaprio\n",
      "[  2   0   1   0   0   3   0   4 ], marion cotillard\n",
      "[  0   1   0   0   0   0   9   0 ], robert de niro\n",
      "[  1   0   0   0   0   2   0   7 ], sandra bullock\n"
     ]
    }
   ],
   "source": [
    "Y_valid = np.zeros((num_valid_images,1),dtype=int)\n",
    "\n",
    "step = num_valid_images // num_classes\n",
    "for ind in range(num_classes):\n",
    "    Y_valid[ind*step:(ind+1)*step] = ind\n",
    "    \n",
    "confmat = confusion_matrix(val_labels.argmax(axis=1),np.argmax(prediction,axis=1))   \n",
    "\n",
    "for i0 in range(num_classes):\n",
    "    sys.stdout.write('[')\n",
    "    for i1 in range(num_classes):\n",
    "        sys.stdout.write('{:3d} '.format(confmat[i0,i1]))\n",
    "    \n",
    "    sys.stdout.write('], {}\\n'.format(class_names[i0]))\n",
    "    \n",
    "sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nNp0qChLGhL-",
    "outputId": "f22e9bfe-e5da-4d57-fbdc-2ea55d6681e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrong classification for: sandra bullock\n"
      "image/png": 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Eru/wPmQzv01BzhDQAgafsiilpVMuPTvfcXDnJndv36AejdDaplSrTW5VACIJ9zC4PC4rqQG4FOKQtRm+xAqP8LBagIfJo0B7HwUv8LjJd7QSOEsiSSEEIt45FIMSCDkKvw4sCQRSLUFVV2xtbfKZz3yGsq64c/sOX/7y7ywXtw+ROJsvd1tEgYRT54tLszjEgEikbRvu3bvH7/7u77K7e4G+6zieTjm8f4BIxGpFWYwIvif4tMCjqKWS6r3H+4Bznq53y4rFuigTtJmIRxFCKo1u2p5IxOWAZfCBxjXMF1MKY3OuPlAqz+bGhLou6RT0PhBdy/3bN3j91ZdR2lAWFd5HqqpGi4BRyyrKdYW3FElZmnW/P66VHKe/09gMqMfhc8N/b71gvzVWxOOkOM6VwJoMw2aUThPe2KUPqkSwakAEKpSxGeyTdrWdrW12trZxfZ9MfueYTCbpeEnFQNpotNKYXG7a9i1d3+H6NiHhMtR4QBpCIERDTDg8DvcP8K7H9S2FdjmboPHO0TQNzvUYpehDILiednbMaLwBhcL5SO8dolTiMlAl1miQyK07N6mrOlcRBrzvCT5BiLtuRl1XXL50gfmsoWk6jqdz9na2YNrRLDroG7Z3dijqisZ5fuW//0f81mSDj378M2zvPcWFS09y9fkP8+TVpxGdsABFUVAWFbaoaLuW+WyO8z11XSB9RAipniFbSUopAqBEI1rjRSWFQeRRltvJtRoRWQvKvkO3Yx3AdK4EHkM5PWSr3ShF9wujCTm/HQHRNoML0oTp+54m4wjatqXveowxJwpkEmhHTkzsYUdM+P+QU2qrirqiKKnKMkGBCRA8fYxMxvXSbz48PKDrekLw2KIgeo8CqnqCC5EQPb0PoEwqTwbm8wVVVWCMWgYpB3RkAvZEJHMgJJCSxrk2LUwix/OWWBVIaTHK0HlPaBsWXYPyHuk6ju/dYj6d0bXHlJVGCWxub7OxvYXPFopIwLt03yZqhjQgkIOpyfoanpWIWv0sByOmoThjDM8c67cBLX672YI3q1N4XORcCbyJDBWDhTFsbUwS6KbvadoWMavKOQDXO5qmpWkahAQSGlJf8GDRyXpaMMaId9mft+bE+8ZotDbJmlCaYAzWWuraEHI24nA+WyLsoqRgoFGKUVWzaJvkDoRIFJVQjSHSti1p4zIIJ4FOA3IPlRSUcz1dpyB6kmfvWTQdVmusMWhj6b3HRU/XtYx0gYqR+fEBfnqIKMdkc5Ry/O4pyqqg71qMNihR6X6tQZTBu3Z4+g8y82SlvEolnrrf04VcZ0gah7NjC6ehwqfnwsPmyPpnHseg5bkSOEOcc7RtmoyjuuLihQv8gR/6IRTw0iuv8Nnf+gJFUUBQEHN9AYrgI6++8joxRrquoygKmqZJvq8Py+yCVir52zkvH2NEG40xqUovEYQ4+r5LfAExhcrqsmBzc8yTVy4xPbhDUBojiqkpUyDOWKazBcSe3vXcuHs/mbuSXIAUG0gZDSMq7/AdPmQocp7A1lo00PYBURC8Zz6dcuXiHsfTKffuT8FYZouWrnfsbI1QUVMooa5r+t7Qt7C4v8/e7pj5/g2+8YU7XLO/xc7lq1z60Av84O//Q9hLT1JOtmkWcyqpsUXBaDShX8xIFZMdIadhvfdYk+HZMWYzfg3k8S7ldMDx3VoPw+dhlU5+XORcCazJWVr8uWc/xIef/RBPP/UUd+7cwVpDXVeEYZBzqnA+n+OcY3NzkxgjbdclFp4+5caD+CWIyOR0Y8iBRRHJ9f5pOIwxeVcJ9L2DOCeGwOaTV7A2AXiKogKbKuuK4ykyFK7EgHMdXd/Td302qQMxuqVVopRKjEIqEYk432VOgZDdk2SCF9oQvcdYy2QyYv/wmMm45mMffYFXXr2O844uKI5mQtu1lKXh4vYE0YoQIi545vOWWAZMDYEjpD+CxT1+4xf/OVc/9GGuXH2WK898GKNTAVHXNKt7jMklSd9BHrCmZChIeIcWeFyvIX+kiMJZ5z63BL5tZNDkWxsbbG1uUhYFZFahqixpQkylw3kO9X1PCIEmT+Ku75fZhHXzc5gkdV0TJVUL9n2PzjGCyAAuUnivcmGPp5Mu1fMXCaxTlCMUkRA8ovQy9ReDy7yEQ6HRsHjSsQOzT1iSoOQFl4lAjDEEl3kIlMEFj6DRuqDtPOOxoq4rtjdLpnNH04eUUYgaRGg6R2FKjFaEkMBIxGQlYSLRN7jZAXf230C8J3Q948kWk509bFnRn8rAKFHZbZET+33OHJ549c1M+BNFSqtXTwQGZYCMfofJuRJYk2GiDBh+rTVt23J4cMitW7eYHh4QnWNSj/Bdh8PjvKMsyxydd9y5c2dJ3qG1ZjQapckYYmLkIVUpfvzjH6coC3z0fO1rX6Pv06JVZoUsHAJhJt/PeDxmMp4wGlWMx2NiCHRNS987+jYTdeJQMaCBGDwoQWtDWVa0bUPwnuB6EmeIp+8FbVLptCJZJNF3qdgneARF3zuODo+p6g0ODqfcvXubH/vD38+1m/tcu7HP/aOWsR0jWvPKazf47o8+zfbGmMCYycYWXd9zeHzE5MIOTd9z5/qrbIw22L/xCndv3eSrX/8GP/xv/BhXn/0w2qZajSF4mowAtYxrDOMkb8MTeFx99G+1fEcrgXjqr2FXee6Zq3zvd32KUVXx+37gB9ja3MQ5x/HREZtbWzxXVhweHbFoGpq2pWl65lbTtF1i/R2osPJkFlbMQUqpVL2nFM47XN9jtcb1PT56rDZ45yH6VIyTa/BD9Dg8PZ4+BqqioJ3PWTQzVPQoPAGPc9D5QO8CaQnHBH2OjsKoFLxzHrFlsgJEqG1B3zZ47/AScRnMFENAaSEQafoGXSp0YRE94cWXrnPp0gWevHSBL/zWl6gmCluO2dq9SLUxphhXbGyM6doZI1uwuXUJgLbtmHWOw9l+pmgzjNuG3/iFf8q1qx/iT/+7f5ajRUDFhIHw3uEzB6HCJB6GEFAx8RQMWIKTsh7gO/XOqd3+QR0hp94bakROvvcwSYSrydVan1srv+WDlz34jlYCwFpsaTUbRqOa3d0drlx8gicvX2E0qnMFXlrAE22W6LmEeguJ0ksJzvuUz1YphbXyY3NFHOBDYLFYIMSc91/561opgvhUtAOZWBNEIn3X0ncWbzVSGLzr6JpFJiqOCZfvPT4klSaS3IsQAr3rMVkxEWPCKiW4Yi6aMoiAD0mBpYDgUOMQMpBpyNtrDo9m7GxtsDWpefrKBRqvCUooJxNChEXTo9SCotQQPW3TJqh13ydylWaBZF7B2aLl7t17HB8d8vGPf5xqa4fRZJN5XRG1XVpFMfMJnMYGyBkL6+H4/oRbfPhxJ3Cka3/Lm5735Pk+mIv9YXKuBM6QGCNlUfDCCy9Q1BXaWra2t1PwDRCdd/EY8QgueJz3uOBTsC/EBAZcq5lfRZ57ur7j5s2bmLxrHB8fJ8bigRYsFxs55RLPvgajI83RPiWOmoAqDG4xo5keLXcf7z1N26JsidIaE0IOFHpc31OWdpnSHBSUCLR9x3hUo1XN4XHC+qeISFgGCZVStG2XWIm1pmkc167dYHZ8yO//4R/g6y+9wv3DGRPjOTqccnexoGmm/OAPfR+Lecc3X3yJna1tRFJsYv/+PcqiRGnD3bsvY6ua1964wc07+/yJf/tPc/WZD9EspuxeehJT1Ghd0Pl032exDL4fpv/jmPN/N/KulYCIPA38beASabr/zRjj3xCRXeBngWeBV4A/H2Pcf/Rb/dbJiy9+k1/5lV/h2rVr/Mk/9uPUdc1rr7/Oy6+8wv7hAYdHRzRtm/D2GeXXNA1d3xMSnhDIdGRr9QF6wMsHTyDSe0/0nvF4TJ+bcQwZhqIsQEeUd2gihRL2tibUZUlB5PrLL+KDQ2LP0dEBTefofVJK0fllWbTzDlFQGIM1BaZMgKX96Tw5CpJITGSxQGuF1gY3EAvJqsmHiGALTU5AUNZjTCH0Dr7w2S9w9ZmneerSJe7cP2Tj8gUOj6b89u+8wa/+6mfZ2NhkZ+8Kr7/yKhAwRlEUhkXrCL4jirBoOprugEX7Ir/yy7/EJz/1af7IH/2jtPM5zkd0pWi6QGlUYjfKz/KdLvzvlIX9TuRRLAEH/Mcxxs+LyAbwORH5F8B/APx8jPGvS+pG/FeB/+TRb/VbJ33fMZ3NuHHjBl/56u9SVRW3bt/m/sEhh8fH7B8cpgNFETPfgM+UWAMLL6yi00s0W0zRbhl48yU3+8iReRGhKFLnnaoqUQba6RGaSKk1Vilc13Ewm3H/3m0g4eidc8vrex8SJ2H+cZ6seAI+U6IlGL/OiyggWtOHgI8RawyltYQQWSwWFIVZohuL0hLzNaIoOheQGCm2tlLBUdtTVyVVPUoVi33PbL7AR8EFoXUpDamcw5YTogSigDYFfZsrHmXOqy+/jNaay08+yfMf/yRjU+SJuiomWv/5VgYA3x4Y6fGSd60EYow3gBv592MR+SqpB+GfAf5IPuyngV/kMVMCWqfHcnBwwK/82q+hjc0wWsVisWC6WFCWJVqrtJBjWEJZlbBivmGtA09yrpfMvUoprNYoDMfHxyCCKVIGoK5rysJiDbSHPTF4tFFI8BxP59y9e5/jo3sZPZfTbzny6HuHsQpRiQ+wc5KsFefRqie4gBFFoQ29d/gQKKoyZRmCx9qK8XiC957j42NGoxprE+V6VRcEH1OhUZP6LwYjXL76LPfv3uLgaMrTTz9NXVXMCgu5mOlwOuPazTtcvHAhWR+9ZyNqRGmURKyOLNoZ3jvoHS+/8jKHR4f03vE/ufwk9WQzVS6qVS+FAea8rEB8l+CcN48JrOTbOcvwnsQERORZ4HuBXwcuZQUBcJPkLjwekgf60qVLPPfcc3Rty917B/hF6iQ0nc8yP4DG+xzxzdDhBC+2TDZGEDzBB1qXaMV88GnBOI+P4EVRFWVSGD7ReotWVFXF1atXM5+fptaB7VLh2gbftbz48iu4KChdsrVzgRgSOej+/ICM86UuylRYk/n8UnxBEUXRuwEsJGibYMjksmhtDSpEmq5lHMYYY9jd3aXvW6qq4qmnnmJre4PFYsH+wSFvXLtBOZ5Q2oLPf/kbXLl0kXq0wW9/7RX2Rim9+r3f8xl+55uv4PpIMR5z9+CAwlpKa3nt+g02x2NGdZ3cJtGIVoBCK8XR8TG/+uu/xr/xx/44k80NiqrE6gqdawiGuMlAwz7wNsDb343frgJ4u/I4WgHwHigBEZkA/zXwv40xHp1ibYkiZ5V2gJxqSPqBksFmVoKPns45XExlucPOI8SUfsupo5QdUOxu7yLB0/U9d/cPcaxIPbRJrD6RRMUlJMRfBEb1iI0cC7hwcY+ysPTzI+7eukkznxOCo4+R0WSD3b2L7N94HR/CshPyUFmrlEoMwCHS9SsoTQie6BMMWDKbMZLIUsqqxLtUtGS0wccU2djY2uTo6BAQfO+ZHs2YzWZMj6bECG3b43rPqKqYNw7voe8id9tpIhUp61Q1qX2qSlRp0bYxNRhtXCA0qcbCDU1c/BC4FCT2/Ot/9Us0bcMf/jcvogzJlcrMSxFJGIIQEoUbSRkMCmKQhy/Ok2Chk0jCU0e+Ax6CRCz7+CiER1ICImJJCuDvxhj/m/zyLRG5EmO8ISJXgNtnfTZ+EBuS5oH2IUX6Xe6w40Ji/80RPiIRNSw6LeQieQpr2d3aScUzzYK7+4d5oabqQD3AgYksmkWq5/fJL65GNZubGxSl5dLlS4zqijs3AtNFy/HRnCiBIJrxxgZXrlxm/41X8a7Hu0Q+MqS0lU7AmtQaLS8YVj0SU1owUYJrk7gU67pO/niIqRNwsyDEwHhjwnyxIEZoFg3T4+myK3EQRdsl5qSqHjGdNyxEEZxwOJsTCZS1R0m6hvNtyvu71EVpNBrReU/nO6qqypDnBAWOgEZRaM0v/9IvIkrxfd//Q2xfHOW+jol8JIoQZdXTAE7i9h9GQrJqZ3YWTuCdyVluwsCA9LjIo2QHBPhbwFdjjP/Z2ls/B/wE8Nfz///oke7wWygDqUff98vWXsYaSshFLCs/1BhDVZZUZYHvE+hHCCiVgm66SwU6YWhQmouKhgq4ZXNOJWh0LuzxXLlyhe//gR9gb3eH1197hW7ece3113njxnW6MOPe/X1Cl3kNIccWDD4KnkjnWozWaJNeG5h7jFKoXNrcB4+KYJRdIiMv7F2gsJa7d+9yYXePEAL37txlMZ/T5e5AhbF0XUfbdXTO5/qHyGvXXqeuKqw2KTOhUhv1LiZK9IhQ2iq5RRKI0TOdzzGFxVhD7wKKBFxKPRqTNTC0af/NX/01vvjF3+H//dN/h82tbZzriUolopaQS56D/5Zn5r9d4gSPYgn8AeAvAV8Wkd/Kr/010uL/+yLyk8CrwJ9/pDv8FstQsjssDq0GQlGXIuBdR9O0PHnpMhd2t9ne2sT1PcfHxzRNw2wxYzGf0zTJl14sFsTcLCSGkMphVWL+SeCh1EQkZPLSH/mRH+Hpp59mMhmzMZlw87U3WCwavvnyy4gIXduy390H16+CkSplJwir5qaksCQ6d04GCM4vC3IGRaS1pmkaFos5MVRUVUWzaOi6lm7R0DUtkUjbdpRFkZRA32VEXF60IqmUONOi0XuUCKZPsGXvQ8Ip1BUxrmr1h27E+IAWSY1elELpDA0m4QK6tmU6b/l7f+f/y/d83/fzqe/6LrbqGkgZjTi0cpe3xwXwXlGHfbvAkh8lO/CveXgk5cfe7Xl/r2W9CaYxBqsTpVZSBIG5KHzv2Nve4cnLl7l0cY+u69jf3+fg8IAbt29zkEk+bDlKdfshENabi2ZI7kDmUUgi70Dg+eefZ2dnh9FoxM7OLpcvP8m1167hXcIcdH1H2zlGJpF5pnZArJCAkiHK+VIDNiEQIQSIgjaJvGOwSrquYz6bE3xY1kG0iwWu63P60RNiS9t3mRLMUyiD0qmvQIrUe8KaXy+RlN+3Buc8bdtjyyqb32nBxpjKlJMyy5kOQGUlxVAlGAJ91/Hz/+JfYAvL8y88z65cIAqpNUwCNbA8aX6uZ2UMTi/aR1UIp2Jg7/jzHwQ5RwyuyTCg8/mco6MjjDHomFqKJeCMRWLeUUkAnLqscM5RVCVVP0o5dWNQa2W7w2T0a65B27bL1NZivkBbw9HhEV/96lfZ3t7Gbm2xWLTU9YiNjU22trZ47fo+vu1QPlKZMjU29T2ua4kx+8rpiyTFEFaTUotCTMYn5D22ax2un6OUsL9/iFLHbEw2Uv3D0D/BaAiC7zoWXZtM9LIguEid2ZH39/cJAkGSzWGtRcvqO4sknoGu65cxiLbv8DHFXHRWAJKDaQoFIfEJVFWFKSy2tNy7dZO7N29yeO8eT15+iuiTFVDYgoQfCMvr/V7W868TlzwOcq4EzpDZbMb+/n5a+KJz/l9lUFDAx8Br115nOjvmjRvXQeB4OmU2n3M8nTJvFjkvnyapzo1LhMQ90PV9sjiUQqlU1qtCoOtaXnrpJT7ykY9grOXwcErvPPNFw7Vr12iaBUYU1hhc75b1BUZrQhQ8ebPPQEVypHzod1hZS4bfAywtgb7vk5IgWQXb29t477h18xabW4kKbDqbJkgyORlpdG7SohlXNZ3vcT4RpBICIfd1K9f6CwzkIIu+TylJSQE+n+nQFEIgkbRopSmsTUqujyy6nhjgi1/8Ak3f8lf/j/8nlLZJz9liWd8w0Jg/TN4uY9BZn3knO/3jlC08VwJnSO/6JbOQEp0DVgrvV8CU/f192rbh8PgIay2zxTzvoH4ZjIt5Q9ZaU5UlWuvEljNQjpEtWZWi+13Xc+3aNY6Oj9ne2aXr+lR04z1Hx0eIBLRJZm/vXEIFimC0ZoAt+MiyjZew5lsPufWcnVi3SkIIybQnRbYT35/BlqkN+fBdXN5lU3cllmzBNgccoyTQ0uDmiESUTiXKy+KpfD0jkqMWA0VbPFEWJPl7QXKdvHNEFLdu3qTte+7fu8tkcwtrq/wd0gPQazDnJWLzW7QiT8YjHh8tcK4EzpBhh4yZNGSYvKjVzuXa1GDkYDpdfgZW5q/KgaqBm2BrawtjzJJ78OjoCO9Sm/K6SAttsVjwpS9/mT/24z/Ohz70LDs7O9woyyXTriZZI845+jYFHm2Z+vopBBWTSe67LvnEknZVUSq3Ru9SnMMaptOjBDGOyVQnZ0astXRti7GGi5eewPUOYwzbuzt0TVKMxlgOD48IztNrQ12WKEDnlGRQiSxFIsTocTHiuh58wBhLWdb0Gdyj1cAalLABWqdCKmPS69aWRHq09LgIruuYHh3whc99lo9/6tM8+dQzWekG1lPzb5co9L2WlSL4ll/6Xcu5EjhDEptNprdiIPk4aQomM3u12zwQcMqvDb61932G3yrK0lCWBuc9MWaAT0wdAA4ODmjahoODfb7xla/zr375l/jKV75C0/eUeWGk0t8S51PJrs47cYghLVqtQEzOp8flfVhr6fuetm0orSXqdM91XaYdW2nKMmcAmiY1VrEFnfMsFguUTqxKdT3K/QQFrVK8QBtBaYvSht4nJSRKCD5ZJEVR4JxPmREVCa5P1YqicTHklukqpVeF3Mo9UZh3zqfOSSis1RgRfub/9/f493/iJ3jm2Q9hjMa5ZA10bY8tzDtWAG/n+NNBwLM+M1h5j1OM8FwJPESWENQhURXDifcS6OfhnyOyTM1579Iiyn0MQ/AZkCjLRhoqN/r03nM8nTKdTdFKcffuXe7v389B8pjJSQVtFCGSwUZhaZonxFwK/fnBHFYrivOkQ2ImOknBAx9C6rCsFNYWtG6xvE9HTyQF3MzQG8H1jKsCY1WqCNQTZtMFfe/Q2qJdXNKXYfQysNprP2QvicYgKkGarbGJXk1Uxk+AKEAE510uilLgVwrt1Vde4ZvffJHnX3iBZz70wjIwCicX6Nur/3/462cxEj/s/N/qYqb3Ss6VwJvIwAoEK/fgxASAMxVBksTOo1Wi57p//34GC2UI77BASenBqqwoioRNuHXrJpcvXeJTn/oU//Dn/uESCptQgAntZ02xXGjBrVGYy7D7O3oXMoFp6nW47HUACfWXIbpuOqeua1StKWyJ61oSpEHRNHOQlI60WujbBYvjQ566fIWistR1yfPPfYgXX/wm9+7tY01J7xW962majo3N1LY9uLAslw4xEsuSrg84H7BlQfSJt8AYhZJkTqceDl3KOJgS55sM5Gpp+sCv/+qv07Qdf/Hf+wlGow2KskyYiwzoWocPP0oq8DQj8en31s+5+v8dXeL3VM6VwJuI5PD7w0z+Qc4yDTOqOO/GaVIfz2fJAlDDRFm5Ha5PHYQnkwkbowmFMly/9gaH+/ssZonJ2IogOvUhiJmzIBISk1DOvysRui4TgRi9LLAZYg5Doc2qCjKxCBE9Xbvg4P5tJuOaUVnjfM/e5hhJFUn43E24KkuqwnDlyiUuXNjFGqHWzzCfXaBtOsYbG7Rdz/U3bmCLAvKOfzyd5tblPYdHM0qtKY2mdx1Gq9ystWc0Tt2Tk7JM3ysFNu2Sbq2oDTeuX6d3ju/7/h/k+Q9/hL0LF9B6fCI+s477eNdz4NtczpXAO5QT5t9bH7z6X2JegCvCDqWEICBx1X6s73vG4zEi8Bu/9hvcvXOXtm0xSmdTOkX3Q0YbZvXE0LMvvTeAkvIRIYB3aQde3nzINQ+JYyDBnYXSaMZViTGKvhe2Nzap65K6Lrh54wZlYdmcTNjcnHD1qSs8cXEP3zdsFJpmUdM1HWIMzgc2S8N0PsP5SAhC7Of0ylBqRdc0iDIgGqVSYFOQHJsYug3J0q0KIVOdhUjqOBRo5nMWsxkbkwnOdcxnU8b1eImIfKfj+WbydlOJj5srAOdK4KGyNP1ZEVq+mf/4ZuamnDouxlz2Kgo1mPA+4CTFDsbjEa53/OOf+zmuXb9G0zZYY4iEjFWIEP2J6wUyDHkNIOSCz7GLQMjovdXiCkBC9qkYKZShKgxbGyM2xjVGK/pecXF3i729HS5fuohfHKciqZ1tnn76aZ568hIXdrfoZlOOxgXtYk7oHfePjhCl2X32Ki+/8iqzeUPXe3yjcEbhS6Fv28SClIN9ieY8kapE1shCiAn7EH0qy44eHz2CSwrNe65eeZKj+YzDwwM2N7aTa6NWVta5vLmcK4E3kQRaWf394GI/Cbx5mAwpo0EBDJDddUnUXpq+7/lH//AfIQG+8pWvsOg7IhFjBFvYZDJHaJ1bTvRl1Zw6Fb/Ipv+g0IZaAWsMY6sTXl8JWsAaTVkYRpVBx4CJwqgecffGTQ7v3ube9Wt89LnnmExq6lHNxQsXqIzCNXP2JiWlK2jFI77k4qTGxUDXOz7x7JO0nWM6XXDlwibzRc/RtGE+m+GjEEQTRLNoFngfKKzQuYEsROWMRuJj6HMQNFkvJj2vrucf/+Of49Pf891cffoZyrJca95yrgDejpwrgaXIyd/j4HPnnG8kmaGsov4Pfu4hZ16e4PRr5BRhoiiLAWxhePmbL9G3HS56RA/8+4mHwOVCIcntx2MIGKPz7pkWT1xju02Vhokh2OZuy1VRsDkpqIpEJTauCmJwaIHJRo2EZJxriejMcnz/fsPO9gbBJ6YjtbdLXRRM6orCwGSyxagaISES6XHBsWjaBBWWSHSKvoMOhwo9Oxs1vQv0IdL5iCorfAAXSQCi4Olch3cO5z1d8IQMRVYZhRVjpGkafvtLX2Jrd5vxeMyTV67m7Msqij+4Xqsg3vpIvD1FsUz6fBvqlXMlsJR1pMkAYs1Lfn3kB5gfnEC4PRAlZhUtFlkpjhMWQASWwKR0Wmssb1y7RjNfgM6w5awEvPepmCcEKmMhBCTERFMePSGmDsRElZOAqbWXiMYYQ6k0lbWMipLNjYrNcc3GqGZ3c4LrG2JwVFVN34ZcDOQJrqBpPIvFnOOjI4gBJRFcT21TfEBcRzlJsGiJjhBbvO8pC8N0OkXweKdZKLAEFI7dzZqmTUHCaRuwuqQPwvE8NXQlBLq2wfWp6UgfAijJ4dCs4EKgbRu+8bWv8fwLH+bSpcuE4PB+zToStRyDkw/+IbNAHhzTM+cIj28M4LScK4EzZADYACfy/Y8q66Z5yLn5AdILadJ1XUff9TiffF6tdcrNd/0y0q9T/mxZ7jzPaTQEtNjV9UgoQyNQ6RSoqwphVMHORsmlvS0u7u5weW+bjXFNVRbUVbX63iFy8+Yt2q7FB8/Ozl5ug5aqELVRVFWBVTaRgQRPO5+jKRE0hYUnnhjjvafrOvZ2jpnOG47nC9544zr7B0ccTyNFYbh3tKBddDSLliCGEASjS5q+RykYGU3TpaIr0UMJtULpxMWwvbXF3s4OTdMwmdgTC/TbYaG+n3KuBM6UgSIq7aQPvHvCMniwIGUIaJ3VFGP1sQHTf/K6IYQM1EkswIm2PC6tj2XAK/ccTFmHmEk7U12BlkR6rkKk0FBZGFeKKxcmTOqayajm6jNPsr0xYXMy4vLO5tI12NiYJKquCM55dicVvfd03jPZ2M5xh+SSGKNomgXFuE7tzXxCBDqXmHWKokabiEhH38Pm5gRrDYVVGC4zrkr2D464e9TQthrfC1Pf5zbn4GJuxx5CLnKSRJvmO7SohIHIltRvfvY3mc7n/I8vP0lVpRTjiSzD+yTvprjogybnSuAsecTxPI0biGuvw+kg4kkE2gA7HQJ83vsUwZeM+sv/x5hak8WcOku1Cont2ChBI1gV2ZpYxpVhc1Jw9YktJqOkBJ558gkmdcWoLNjdHGOVYI1ma1RgMp7fOU9R1QSg9Z6ynuTsRKoGjH7V9DQhGWPKNWaWZWMUIm5pktd1mUhOJVIajSKildD7SN85gnMcG0Xb9wQPAU1qaJrqJYzRyxqHmLs5q9zh+bVXX0OU5vbt24wnG0tG5/dLvp3wA+dK4E0kseA+WjFISjM+6FLEOLT4SrCiofIsxkhVVxhjWHQtwzQeFAAhEp3HWpNiAIBok84eA4VWFDhGheaJrQmf+dTzXNjZYHerZmdznABCSrF7YYtJXTMuK0qbmJK1Si3FlaRsRTUuULYEpRmpRAYqSqMyJXvSRAmvgGhEgVIGY1IH5+B7nAskFuQq7cq2QI9S8G9vZ4NRXbC9tcXN2/e5f3jM5qjmqy9f52DW4iN0LtCtcT1GEZQ16Pys+q5jPpvx4Y9+hN2dHX72Z3+Wv/yTf4XJZANr7bJacv25P6q8PSjy46MkzpXAWbLE+Ajr4/ngTr6qHzhrYqxcg5OvDSnCGCJR1iLYkqr+vPOpwhAwejVEkkFGog0ieSfMDLlaEvuxch0X97a4sDnmQ09s89TuiI2RplaOSakZjyeMRxPqUeIENFqwolPhjjaIMQm4LwLKoJRFtAGlCbgEVoqp4aYSWaINJfMA+N5BdKQqJ1Axg5zyog14fIyo3ClZqdTV6MoTu4zrknaxYG+7BgV3j7rE6BSTNeVcJGZkk+QqycHlUjGRvexPj5bW1Ko1/NrQijwwju+MJ+Cd1Ro8DnKuBM6UNXCQwAOs6TkKF9+Btl/NjbWIsgy4AZbXW1J0rWUThgzjcuJncFC+uVRGLBEjMNaaC5sjLu9u8OTFLbZHBVWpKbRQF5pxVbA5HmFsLtjJWIFUOanR2pKIlQXEJIWjDCiFipm6TFJAbsifBE9KZRLwMrAyg0SNKJ+Qizm9KjluoZQml/5jjWYyqhBgZ2vE3nSMC5HDWQu5t7IoTXAJRiwZlD0UJhFTkVYMnrqul5RqfZ+YkYb4y3IEToO53pGpt1T7S/TnWcc8Tt7CuRJ4C1n37AfAT/rrpL95JmJQrayAQWGkw5JPrzJ4KIGSDCIakdTpSJRCh1Vjk+Sn58xCTOxGURQsW5rByGpeeHKX5568wKXdTZ69sofRkcJq6lHJqC6pSkNhQSRgcuViwiEkpbD+HYyxaFOAKAIqIQ5zRH4wFmKIuLZNmkB5TGGJPkJM/nsIgUDiINQ5aFkoQ68DPj+3VBzVM6oNH3n+aZSxFMVd7h0ecdQlzIYSQ/SJK3HImmhJLSJCdMznx0R2+aHv/yHG4zq1NA9CpVJV4tAL7tF8+fVzrEb2ZFXhyWPz0+SD7B68F81HNPBZ4I0Y458SkeeAnwH2gM8BfynG2D3qdb6lsp4iFB7YRcIQrX9kdR8zFHgA+qwQgEoEL2CVRjOUBoeTrkUImXI7MNmoubQ94SPPPc3FScFGbQnOUdY1o1HFxuY48fWZFEswa/eulCJK6muoiRRl6haMUohooiTcgS0rlE6KoPctojTaKHQUou/Be3BDcNPnvox2WRuhCKlWImcXyO6NtRbJZdaIcPmJi4guOFwEDr95g26RKOBDfgbBRcrCLFO5Wgn39w8YjSdAXD5DP1hVGdvxrQzmPU6Bw/cifPq/Ab669vf/FfjPY4wvAPvAT74H1/gAipxQ9oM5/04GP4cGU+lrWLkAq0DhADpapSyH02djGGLyh40IpdXsbk/Y3hgxGVVYo6nKIvdHKLHaLIOLas2HTS7I6msl/sMUAEwEoCovfp15ETUxu0MRWb7HAHpaFjRliDS521lcljsuYyPDs9MZEGW0ZlSVTEYjtjYm1FWBNRokKZKQlUdiQE5PzIfAYrFgPk9NU4aU6ln++Tv1/9/OeK6XEb/fKcn3Qx5JCYjIVeDfBv4/+W8BfhT4B/mQnwb+R49yjd8TeZOBXLcQ3koGYNCbUV3FnPIarIuh0nBVZpyozp1zudVZSokZDMlG0FhlkBCQ4NjZGLO3s8He9gab4xGb4zGTuqY0NjUgSatmiQVIPQFcrkdSoBQBIYrC2Gq56I0tGOIgCbWrls1Jl5kLUtFSCJ7gHa7viM5llGFqlrrejOWEC5WVgLWW0mjqwrI5SsVMdVVgjNCH1ECVCE3Xp5SkKFwmbZnP5/jeJ57HjLVID/nk2J2+9lvJmy3qx3Xhr8ujugP/D+B/D2zkv/eAgxjjEJa9RupU/HjJKcQgecc+MXFyam/15zrA6OwYwcl6gawAQkDMeqvtAScQ6fseUWapqZ1zCRWodfKPfbIgTJkWuAYW8yl6c4fKmlSTHz2961BdpM5ltlol/3pQNCYjD4fApB7uVUCMzrt8xPkOgsJ7vfzmMaR+iuQGqMTEfhwl0nu3DNwtefdkoG83qY9iSMQniCwJRQutqa1hMrJsT2oaF+iisOhTF6hBqfYuKYWyLBLzUH5GSimMNmuW1cmxOZeT8q6fioj8KeB2jPFz7/LzPyUinxWRz965c+fd3sb7IvHEgs91+mfhyE8p/3dTw76M/Ofd3xiD1qn9VlmWrOoKTiqhEJPPbWwKfGmVKwJzyzBixGgh5l05+LBUGgwhDdI1QxgM+OGGkunuXI9zHa7PP64j+A5CnwKBwSMxFQR71+O7Dgke1kzyoWtzHNiU1tJzq+Ietcx6LLMHWmGNoi4tVWGwOrVT1wNQikTY4mPM7cgSW/OdO3eSIshVmUOK9lFTdw/b8b/TLYE/APw7IvJvARWwCfwNYFtETLYGrgJvnPXh+EFsSJolLneu7PfL2eb8AAOCt047ncxNx6UhoRgITQWtFUVp8UoIQWELy+J4hg9uzaVInw94tLGpbFalxqjaJHM65HboWhd43xNyYCx4RxCdAQeJCHSZ34/JAlA5oxGCx3cL8ANuILkPGENKUKrhIaQgXN/i2haVCVNj8EhWAgztycKDilREltRpZIUgWiFaMFqoC0NlDUaEQhu8T12HUs4hpVudd4RgaduO1157jb7vUgtzpXmv5XFe7A+Td20JxBj/DzHGqzHGZ4G/APxCjPHfA/4l8OfyYT/BY9SQdCknsgNyhraXBzID72xHWCmPtOBCZgNKtF9FVbN34SI/8iM/ws7ONmVZUFXVMjAoIrhcatu6lqKy6CKZ1NPpnMV8QdcmfH1ZFFhjkmWgNBKzm4FKRCZ95iUg4RAAfOjxrk+L1jlwPbie0LeEviP2PbHvCF2L79ucGeghdMTQ4UNqt66sXbkcxqQOztnKShaPWiqBwRpIlOPpR2vBdQ0qBjbG9TJ4qI1ZK/JK/48nY8qi4KWXXmI+T52Ul30LiMvsxwdqt/mAyPvhJP0nwH8kIi+SYgR/6324xnsnqyD1SrLvukQKsir1jRmqkqCyyc9NPUCHV0PeaYcIfj5dzKnqGNdTzBkAlMzvoRxYaQ1K07V9ajMecyqRZPqmxiMJV5CyFLloSCmc88lCWSL7DFolau+4Ft9YRdqHlkWeGB3ed8Tglw9ldf+pZFlixHuXg3+esEQ3JssiVS5pYkYCkpumIrk9m9EonVuOrbkDSYGmICT5u9Rlxe72JuNRSfQuKYmsf5WsFEqMkfliwXQ+S8zO8xnNYk50fvm8I+tDnb7VckzXfta+8YmfYdxDHomzfk49sTMm2QdTBb0nYKEY4y8Cv5h/fwn4offivO+3PHTPzgpgLWu2BPusZDXkqzOtZQ7O9EEjA6+frJ9DsoGfuQK0tYAwmy3Sog6JCiyS+hP0uYMx2Sog5l4JolLj0gFhF8ISCSgngECR4CNKD8E8vwQ/RZ+BS8Oxma2IGFOH5qwElDbpK4ZIIgrOhU0E0Dp9VR/SYlUKQaHQGSB1Mi4w/D50ehJS9WBdFezubHPUeLh1gFGJHCRxKKSKwhACUWtm80TGWpQlbbOgaxqqYgwqPfJhCZ4crzMH/yGvv90FLKf+X//8B9OVOEcMPqKkzfUkuGhl7p8xceTByTCkCQese1GUWGtp2zYh34YUW67aizEug1/WWKq6Tu3JYk7ZabWEzp5OU6qc8hvwByHEpFS0QSvBmIKirBFlcCGis0kfSG3IAhAHAJFKEc2IQkyBYPCuRdsywYmj4PuQC5I0vkvAJsnBy5MBUkGUYKzGBoVxacE/cWGPLmj25455vEd/vGDe9+n6SxNLEYHeeY6mx6jCYusCdPJohET9rtSaRjiXpZwrgTNk4K2HvFzXd/V1vz8+6GPK2k6wXhdwct2fzD4MxUTDdZUStDEUVYkpCnTXJsackBZhSuWl/oECxBBQyi797AQtjsuAniiNtoaM6l8BjkRSAC6nE0U0ogzBJzchQKILz5gCpXK9gNIoZXISIOb+BUN5L4hOKEFlIsH3DJkU0SbnR5MLItqgELx3qVhJUqWgD4lQtbCGLio2xjVXLu7yyo0bGBxGCX3vE3W7JOwE2V2p6yqVHIvgo0tdkXJjE8mxgXe/Iz+oQIZNADnNHvH4aJpzJfAQeWhK6dTr6xv7yn2QlYe4FjBc57hb0gbkY4a0ZIwx7e5VxWg8wmS8fvRx2VHoRFByUAhqSC/q1c6faxCWKL8Tfm8y9VXuY5DQgMltCNnMjwpiTtkl5ZcDeDkesfSlxZGCAVnpKZ2VU0xKJHtBKJ0tAZMIUZQgUTIhqsoZRZ+qBiNoZbAKRmXJ3vYGk1IzXwhzJbREIqt71kSMVtRViVZpBEIM6XdZVRuyZhWdJQ+rBn2o4hjei6fG5TGScyVwhgyBqsTu8yA/0LqVsHz3dLaAtBFba5bR71Vt+1CBFtcQg6u/L1y4wN7eHpNRzVe//KWUAjt17uGcg2ugtaasqtRclIQEXFdAA3ouQXMNIKk3orXJh1cms/GYFJjMu3d0AdEKZQ3oYvls0k6sEa1SFF7lbsdNg2hSWtA5RAwECCH9rowQRRPajhg9IQ4koMNzDbhocaQ6iaowREkNTD7zsef5xqu36F6/Q+8UXoSQgUe1Uoyqgq26pIgB5R3oIlkq5ADoqTF8L9N9j3Pq8FwJPESUUhRFQZW5+ofefEOp7yowmCPxee9XOS0lkvLcdg2NlxqT+mVbsYTbD0sKsYFa7NKlSzz99NNUhcUUNjUm6fsUoR4CacN9xNSOu7AFVVUymUwoq4QZ8ENNgAjaWCDxAAznCDHiiVilQKsl6i65DGppvYQQEZ/4CiKksmFrSW3IBW1ttmQ8aE2IIbvqGmNLolIEL4An9AO/YomWgChP27Wpcav3dH0gpDKmlBXJCMbaGp7a22U+dxwvPIsbd2gDpNM6OheY1AWf/PBH6BcNx/sHbF0s8+JfK/s+gdd4p3I66j/o/qzYP8AZgDeTcyXwEFEqLeCisDkop1MvQTynIOnJ9M0+oVa5WYlK9Fpam2xCS96dY04nDhyDg6madiZTFtTjMePJBmVR5JZjqWBG8g4eh12aFPWyRlEYTWkMVVmidYIEx4zPiblleMqtpRRcAIJSuT4xR+WHDMLS/FfEmN2YEIjiEYlE0ShtM7txqjwckIEMjUjjoCAz0Egk308k+uwSxEDnAsezlqZt6PukCOaLhr7r6ZqWUV2DJETkRl2zvTlhd2vOa7fuJiKRbIoroNSKvUlNf3CHxf0xW9tbhKJKKdxlRCRmF35oPT88/SwPcQMfllV4/Jb8g3KuBM6QAcBSVQU298gritQnb5hIIaPflsW9cdXzbrWgBpKQVCbs3Cq6H9YQdSqmBJ0pCi48+SSiLa2LYC1IgRKTL6Eg5oWpQVQCH0wqw6TUTApDXZQYCckE9+B9WqiJkUcTjMFrS1AaZQxRpz6Bwz9Er6L/yZFHAkAP9IQgiFdoOyFm6wi6pBC8h65DsCnhGCLOp/oAYuq36PuO4Hu0UhzP5tw/POa1N25xPJ3lrsaG27fv0TQNgZbv+q5PUVdpR69tyd7mhCefaPjC1xxBLFFZlCg2S81OVVDO77P4xq8x724T9rYJ5SbaRopMdhJicj2W2Ym14O5bWwfhgVdWn3h81cG5EniIhBBSD8BRlYJv3i276p42+obCn8HkH3YdUasswOlAo4gslUDMsF3XLPjG136XP/3jf4Lv/Z5PUdcF/7AymBgppMCJWs465x1Wayal5eqlCzx1aY9Lu5tYaxJ+XyLOp5ZlIQoiBmPLHLRTKN8jBCQaxJT4GPAEdJE/HxwxRBTJ+vAEXHArC0H1S7fIz3MmRGLGCjS4mFyY4D0hBoL3zBdz5vOW6XzBzZt3ubd/yNHRlKbr6NuW0lqef+5DVKViOptx685dPv/FL3Pxwh4vfPg5tNGMome375mMavom0DlPcC1dEBY2cO/eXVRzB6ctT7sOIwlIJQRQJrtRDy7m72Q5VwIPkZjhtW2nltx/g/kLZPzMg4VGIYTV7rCWo4eT4Jj0dkAG3yKbp81slqnCPO3RbXbHJTuTMfcOco4+49aEiDWKUVmwOa6Y1CVVWeQzKzyRzkM3bZg2PYezlno8SRkEYykM1FWF1grnc19CPPgOlXH+3gesCsvnEXxGEoogOpOhhMHnzulBozPyMXMl+kDb98wXDTdu3OR43jCdt9y8dYem6fDes725iVQlVWnZ3hhRVUJVJtDQ9dt3EdE4H7G1paxKRnVq4276HuUCwTuMLtAKZrMpe3Wd+iD4XMzEgGdYGf6PcyDvvZZzJXCGDAG7ruvwrnuwJmAtJ7xa4GoZ4V4Bhx7ccdYtA0VMQbooGQqbIMSL40PuvPEad175Ild3N+ivXOL4cJZ2awkEHEaEUWHZHNdsjwrGpaHUKe4QREFQzH3k+OCAplkwbxbs7O1R1SOqesSF3W12d0qksHSdwxqH1p4471GiiCFx9KmiXGUX4srqKcrkHnnnc1wsxSd8r3Chz3gGTdd5jo5n3Lx9j1/9zS8wnS1oe0fTtWxubLCzvcNHP/oClRZKLYxry3yh8JMxT118kp3NPbrg6ZqeyUZNWVaM6p6qsBStx/aBjsB4VDGqS6bTKVvPP8H21iZ4RwwOYrZmcmZmvV35w4hHvpOUxLkSeIjkmFHe+VOVn+SAHHHFzHP2ZHnQ9F+dd7UThSE4iKTMAxGjNb/8L3+em994gh9+4SJXNkqORwbVL5DCLmm5yqJgVNZsVCM2qxITA67v6GzB/cNjDo+mXL95i1FZMKorNjY2eOm1m6n2Thuq0QZKpaDiU0/sceXiNtubIzZri1YprVkWJYv+CCEBmIzWy+Cez7FwFQIh+BXOQQJt45jO5ly/dZs3btzj9v0jrt26x807+5R1xWRzQjWe0PnInfv7/PpnP8dGXTKpKy5f2GVUFCiEvm8ZGU2JJirQXlNIZFRYSmOxuscYTzmuMAqMFi7sbXNxb5fNjTHBd6kRW4AuOIJWiVjlDF4BOQX2eXRF8PgokXMlcIasoLaQcP551xgqURhANDkxtLaw3+7OMryWjlbpOhLRWrGYTzm4J9wyU0ys2BoVXH5ii1cPp/gQl4FHow2FtRTGYLRGicL5yP3DI6azORsbI/a2ttgYj9icbGCNpg8hLWBlmc/nzGYzmkXDwcEhk1HFuFBsjsaM6pLNyZjRyFAYjS0U0ZtERqp0IkMZMmaSXIUQAj44pofHHB4dsX/3Hrdv32L/uKHveyabm9STMaONCUEi89mCZrHgzv4Bk7pme2MDXYypTY9VCqMTqalWOctALj1WmrosKUxHoQNVpRnVBVVZUFrLZDymLktc37GYzzGV5NZpDy7M0xbew7ID387yHa0EHoT8nnw398BksATWDxw4ACBPpJgXdDx13vjg3BrwZ+uuhOR5rrUgMdDOprz+9Ve58vQLbI0tzz97hTe+9HX6GBBVAAkFaI3BGpugv1rT9p57B4eE4PjkR57lyYsXGVcj6qpie2tC6z2dD8wax+3omc2mvPzaXa7legMr8PTFXXY3x1zc2+TKlV3G45IxligaYy1WlwRScVIqMFJ41+N8T9/3HO7fY3//gKODfQ4PD+g9jCcjdrd2sdUIXZRMFw3drGd/4XjttZuM6xG72y2j7Sewoaeyip3tERujItU16CFJl4hTRnVFNWtpXWBjVLExKZmMCsrCsjEeURYFXdMQpseUKCaTyZK4JI3BKWvtrHiBDOMZl4jAE9bCGXPq8dn/V/IdrQQeJjHERL+lBAl62IRSGjAdAVGtYUcGgo0UtFvNjoHVZgCVDB9I0CKJuUA1dgSE4GHuHc63bG5e4A996pP88q99nk6VfOTDH+M3vvIyTd+mHL9P2AEXHaaydD4wn0954+ZddjfGbIxrduqCdjalOZ7Su8CLr1/n4HjK0WwOo5LC1hS24sKTT3Hn7k0W8yO2KsWdg8hicczs+AAVe7a2xritEZvbY5zzNKFjY3OEFDVoQ3QtKvRI39NN7xO6BnxARPP8Cx+h3thkY2ePr7x6nd958RW++o1XKesJtigIwH7rmPmG/eYur937FUql2d2c8Mnnn+Sjz15ioxZqUUjscvpWMakLNquC6D0jDWOt2K4rnrl6BVsIbT/j8OAez3xil6reTNWLIRGRODVgGIZROjFkiYdxDQkcY6qMWNZWrtWOPDB3yPGex8iiOFcCywjfGe8NUNYgqx1dQOIQVBo+vwr2DWO/zBksJ4M8dO5EQlIqJBx9jGCs4cLeHpPJhMOF42j/PqOypPWRhYfCZObgGPDRp0knwqQeY7RAcHTNAnTFdNFy8+4BL71xh1nb0XSexf0DyqKmtBUBuHLlIs889yxPX9xkeucu9I5JXeER2t6xaFqqrqAoEzBJaY0pSsSWhLlLJcsIrutRpkCVkaBbLj5xmWoyRpcl33zlJfb3DxhVBRcu7tL3jrZtuLA54dOf/jSj0YgvfenLLGYN09mMO3fu8tyVXfR4xHg8SriDjM/YnEw4nDb0rmdSWrYnY7Y3JoxGE+ZNi4wjO5cuo3MbNcn9DyKp6Xxk3c0bBjuNR+AkgOjEWL5Nl+Fxoi85VwJvQ1YFJHEJDl2bOicyBSfei6fPkX9fnyCy0hqDPomZB2BzY4PtzU1af8Ts+IhxVdK4QNv0WJNKfyNDgw8QlT7TdjOC65nNIrEQbh9O+frrN7h27xAXNaiC/cNDrJ5jdUHrHE996EM8+fRzfPyFJ7n+jRfpplNqa4ni6H2kbV3OIhQpcKgUxhbosqJfzHIjUvDOp36DpULVHdsXLqCtZdYsuH+wj/eO7Y0xF7c3OD4+Rhxc2tnkB7/7U2xtbfHaN1/Etx0ET7uYIzFQGMN4VNHOF/iQntPGeMy4PqJtNJOqYGtjxNbGmLIomTVzTIDLF55AlGEgWQkr8PADY3IieMuDynzgeuSM+M7jLudK4C3kdFDvROBPVmbECfDQ2zhXzJ9KHvUwCQWD5vhoxtHhMdYontidMJ7UmPEOb9w/SMxD1tK7uGT2ESQhDrViNB5x/6hnOptz4/o+83DIG3fu8cWvvQjVhMnWNpubE4r2mL7p6dqG0cYGX/jib/PyKy9z8P2f4Q9+/2fYqCtuXn+Ddj4lSsQrxWLhsNpTV4IPnlJSCzFbaLqFEKPQBWijophs8bFLH2J7d4drb1zjs5//LH/tP/qP+dKXv8ov/eIv87UvfJaqrtmYTHj22avYbs7s9pQ4vc+HL1/giQu7fOLDT3H14g4b4xEjkzIEUYHXhr3tLe7v32cxFbZHlt2Nms1xjQuBzkdUC9PeUPpIoQJaeaJoHrZ+3yobMGQPvr2Wf5JzJXCWrJWbnlYAZyqEtUDS6ezASYLR00okWQtKJSx/slV1zqM7Iop6VLPoj7l/9ya7mzV9jPQHCzqlMCqxCWtRKZquMjlnVTFvHbcOF+zPFhzPF2xubvH7/+AfZGt7C1tYPvev5yzmDa5zBBX43s98mheee4bnL1/kxa//LqXVfPITn2A+HeP6ltA1hBBwHpzzCIm5qO97og8ZTJVqAiQI1hj2dve4c/cOzfExO/WIl776u7THxzx79SpXr1xOKVjvuXH9NTbrirIsuXxhmyuXdrn8xAWef/oKlQarIfoeg9ADEBiVBaVRFBo2N8YUVqNUpmOnQooRuppAzOnX6KCwPFxFv9WUGKyIt6cGHidr4VwJvAdy0r88ZUc+TAQyw0WGEKfXlDH03tO6QBCNLUqUmtIsjtkYlyx6z/G8R3xa9ELKYCiVcuBGK4rCYgsLyhLiAq0N2xubfOLDz7KzvYVW8M3P11SAs57W93z0uaf5rk99nJrIi7/9eSDwyU98nMnmBl1rmR0HomsTk7FLRKLeB3AuwZ69x4eI1hZ6jwCltRA8lbVcunCR2eEBhTI8c/UpxpMx3jvmsym33ngF1zWUVnH18kWuPLHHExd2uLCziW/mDJTlipQ5gdRfsSwMVWEpbUqRaqUpygIfFGJLdDFKQxECQXxuRyZLiPfw7N/RWL+NKsTHSQHAuRJ4S3lTM3HNPhzM+3jq7Yd9bhl4ihB7R2LIMVSjmr6Z0oRIsGOkrCnHY3ZdRzhsEt1/VNw8aihMpuF2HqXSQqhri48tT2yP+CM/8GnmfeDe/hGvvXGdoxvXqV3L7vYGxjvGRmOrAlVt8vQTe+xtjHn9Gy9yeXeX2eyIX/jv/hk/+uN/nHo8pm0XEHtC5i/ouw4xLRFwXU/fOZyPmGpEP71LN/PcuXuDz3zm0xitWSzm3Lp3TJTUkdi5Hq3Au47tkaI0msIaRqOKUVVSWI210PQR70JqSx6GGEyksIrd7U1i9LRti1K71OMJexd2uHV8hNgCU9agFSo6outTDQWpWOtRwUAPw348jvJISkBEtkktyD5NGp+/DHwN+FngWeAV4M/HGPcf5Tq/Z7JOGyQxV9kNeePMFrSM+J+RYw4PcQXi2mdiBMUSdTefzyF6jhctX/3ma3TzWYq4kxZb1zZ0izn4gCkKisIyMBNB6vC7szlJ6UcPUycUsWO6b5nt3+Pa7Jg7hWV3ZwuJYIzhwpUrzBctb9y4xdbeBZ69ehmrhd57sIa+7zA6AYS8g75PvAohp8KUzuQlMVVNjjY3EGMpRpbb924lRKQPBG3QEtGx5/DgdrJYjOLqpV1Km6jRQnCJUUlrlARc2xL6QPSOGFSO0EdEK6pRzaTvaaZHjDdS6fD21piDtgGBRbNgPJkkgFNMcZN3vezXx4yH7/aPI+T4US2BvwH8sxjjnxORAhgBfw34+RjjXxeRvwr8VRIN+QdT4uqXtOQzElBiDtiRFICwJNWAuFz8q3OsACUnz7uaGA/sFLnuPsISiOKdQyloOsc3Xr/Obg1WIlrp1LU3JBotqwKl1VSFTWZyvqhCKK1NMNoQKKIlBs9svkvb9qm02fc8deUigkIbw1PPPks9qjDaMN7YYHt7TJWVy+17d3BdxCiFj5k8NGZkUySXCQOiiKIILmBtgbIWraBt5gkMhcaUFomp4EcIaEnn3dyc5JQn9K6jKGziZQgeLRotGoVL2P9c3CRKU1c1MUZK7anrgqLQFFalzk2mWBU8ISlNOIwvMKiDBzfvhy/yOHxsDUfwsOkUT06BD3RA8V0rARHZAv4w8B8AxNR+vBORPwP8kXzYT5OoyD+QSuAkSGS5vPPkzL62Xh/AVCGYGHezQgircFFcG/mHBQgfuAcBT6LIShRkPUZZZoueX/7iV/jDn/kQFyZlhsFCURRcqCu4f8hkVLA9qhPfgCyrG1Ci0VphSsW4qtne2eSpJy+yaNr003ZcvnyZgEa05ZkPv4BSlgg0zYIF0LqAVakMt8iUZL1okET3pY1N3zl4vIeIATH43mOMxUSFbltMYdG6SP+XlsW8YbZo2NnepSwLjNEE36NSL1Qqxrm3YSBGodQlYoSoA4uuITpPcBFTF2xvWXZ2NihkO7EjxQYVejY3tmGyTQHE6IjaILpGZ5Rn6tdwVorwTcz5JXgoU7OfNaaycgsT0esw+A8/7QdBHsUSeA64A/xXIvLdwOdIbcovxRhv5GNuApce7RZ/D0RYZgbWmYWXwaQYH4QCL6sDT5qMb2U+hsyhPxwzUJnNm4Y37gacPI8djRhbxbMfeorD2YyD2TGTq0+k3gF4UB4foPcRLeBdj0RFYUuc7zG2YHu8Sd32tH1P1/dMitQMRNuCMD8CnQhNR1olzkEiru8I/ZzoWoh9rnKMCIG+61Zdg0RhrcVbmy0VwRrBaIURQRHAdUSlKI1gNyfJ0lKCSMQYTYw+R+DjEpSFz+3Lls8+MSRpFfEErNZYoxkZRWEM1mhm0yPs5DK6tA+M4Qq5+SYD/wgr9jsxJmCA7wP+wxjjr4vI3yCZ/kuJMUYROfPJiMhPAT8F8MwzzzzCbbwPkkEhw0o/MZHgAbjpwwb/LDDKmx0bwqptdx8DR94jxlKPxmyPKxZNypOLjlSjCYdHx8xm85QC06md+JKxSEW0JJy9NcltMMZQOEvnHKVSaDPs6Kl4SQmY1I+MEDO3QExVgsG5RMaRF6rKrkDiE4gM5CgiuQkRKYA3NCXSkv4WrdNFZO25Rg9RD083NVENEQ+Zk8ADEHJmQJQQnAMjuTW7prAFxiiIjqzFTyz4EzUDb2OtnkUC82bj9zjLoyiBa8C1GOOv57//AUkJ3BKRKzHGGyJyBbh91ofjB7khKScZgU76kqxtGKe3FTlzsrwlCCXX63uf2o0Jqelm4z22GrG1vceVi5scHzcYA9tbNRtbO7zy2uvMpkcpcKcKEgtwj9KC0oJRirIssGVBWRhKY5dkpkRJOX1lQBu0tSitUESInhh6xC0wEggEur5NJB0qgW6NEiQEousREVzf41y6tmTfXUXQaIzSGKMQDaIFMvHq0Fux7+OyOUiMEde2eAlEpej6LnEWkPAJMSZmo65tsCYmdKIYbGGxxqBiQU9EYiCudV1KYzLwDD44Bm85Jx7qzn2Qvf23J4/SkPQm8LqIfCy/9GPAV4CfIzUihce1IelbiDBw2Sc5AQleM/+Hib7qtXcGOYlacdQug0qS3jO25IlLl7jy1BUuXXqCqi7Zu7DLh59/FquEyahmb3eb3keci7g+4jqPjgqT/V6FQkUh+oAltfcuigJbltiywpYVpiixRUFhi8SMHBzK9agQ00/OYiT3RvAu4poW3/UJsSiJNlzlhqF98IlbEAUh5rboPimRkPzyoaY/uc4KawqMsrjWJfJTH5keHS8f1dCdyYVA1/dLBictEGJHWRaU9YgWwakKJxUuJkaikK2Vt2IDllNu4Ltd4Ak+/oHa195UHjU78B8CfzdnBl4C/mckxfL3ReQngVeBP/+I1/g9FQE45UueNTVijhMMR5wGo5yVHRhqDVI3Lzlx4khqJb5YzJnNpkyLSFVX+OCYNwuMEqqiYFSOaGLiEnQ+EoZ8ehAIkrsErQANQ/lvGL6TJCIQRUi7p++REFIHoZjM/IHIZGjhFcLQBk0j6OQGRJalutmzX/4ssRS5y5L4kD8r2dLSpB6oIWcvUrNTlUFUQ2+GmN/3IT0fkRRMdM7RO0dUns4LnRe0B+sDkpWU0bnqI6q3Cgy8I3m7AeAPsjySEogx/hbwA2e89WOPct5vlcQz/lhbx8u03RD/l+UBObUXOWUFnDz/cl3L6rNDEcrJWIKsVSQO50rsvHfu3uHWVskobjOqa6azY/YPD9ibbDEqKxaVo5m3eB9xEgguKwKfyo1jIC/idA0lqVOR9y5lRCQkhRAyA59vIYZc5pwUQAyRGASJmcY8BPreYXMaMbUhY8m2lL3/xMi87EqcfKgQYsI4kKJ/QrKUltZCiPg+0ZZppVPSJvdmGLAJPgSiAmVSWbHvfKKCQ9M4aF1Eu4DpPWJ97qacYg5DZvPEOK3PgVP64Sz37mFYgIeRyn7Q5RwxuCYrBSBEpfLGt1IDJ0z2AVUQVQqKDcE1JEXQh+BhBhxFBBf8CWUja+zBLK+TZ2kMGBP5Z7/waxzd/wSf/PN/CtUcoGMJcZvLzzyFunWH1nUcHs/wwdPFQB8srodeRbq5pioKMIllV0JMO3ZI10owhYgRwKWFXGmL9y0uB+HC8N1UCuBJTKApJSG1E9BCWVb0zhNiB6Jxvk24vEInTob8MLTOSIvoiH2PLkq0TnEK1zUQHUZiakPmI3SO6BzBeZwLydoh4rRgdcHmzi4XL25BP8fqEu8VR21P5yK694TWIyWp9DsqxOhBK6axk6TaV0m/IQ348LjOmy3wE9beY6QIzpXAm+Ryh0X/dqxHydFotfb78PpwsvXfl9deP/kaxmDAxdy5f8y9gymtj1ifug73fZdjBoayLClsQedTw9Km7+iNwvqA8z4HCjXa9GhjEAQXQAxpoaJQxmb0YuIm8DEt/lR4E1YLQ8hcg4ISh+BRknD9Vguh0PRW41shuEDTdBiTuhobm+i9lFK58UhkAEym6H8K+IUQCc7jncO7RHs+rKcQE1gp5pRkVZZUZcUi9ARV0UfhyDl6oyhQ+OggdskKGsJfy6DuoJxzDAc5awqcGMOHZQyWw3fi/ccnYPiuA4Pf7nKSTvwtjs3+6cmf9eDSWrCJk0GnN7tGCMLxvOVw1jBddLR9CrjF6HE5Kl8UReo1kDMMnetxPiSIr/f0vaPve1zXE5zD9w7X9XlhCaBzliB1H3YxmdyBFc/i8lmIIFqhjUYkIJJ21RA8WguFNRRWoyV1We66jq7rc+eloUOyQmmV3IWYFrZfdmdNbMlDC/ahUUvM9+QjBBQRjbWWMvMrhghBGRyW4y7SoVOfQkJOGTqipK7JDwvYxTWk12ma+NPydrI9j5EOOLcEHk1SSm/YVUTUsqIPVr7jQEiRIMlrhBWR1U4kJ4OJEcGYEokdx23gd16+zjOTyNbGiKt7u9y4cYPgk/LROlXGOe/pQ6SLYAPY3qfim87RxnlK7VmLNhZdWEQnmJ73Pl9bAXr5jcgt1ZzzzBZztOiENShrTGXAFjhliCiUKRClKCcj6hCQtqPtWroMKrLWUtZVarLSO5TJzUK9R4nC9x2u62jmC5RzSwxC3/d0ziV2I+fwtkAXCRZd4ClcQzi4xWhPE6Xk7sGUjfIi1huE1LI8KRsNaBTxgfU5hAJOx2WGMTzr97NkcAcGZf+4yLkSeJeyTiUmOVgYYyDKqlR1yHuv76jp+CHU+KDFcQJlSCCK5tqte/zDf/rz/OU/+6NcGE3Y3N6iiwGrDUTh1u19yrpEOU3T9yxcaudVhkjfdyiJWCFV0g335YduQqmhqPcug5UCWmmUsWkBsRgajuN8SD/Bo+wEZWvEVkQxyRXyDmU6vI/43hFcCuY571m0LWidOhnn7xi9J4iGEHBtg+97JHiCT65A6tacLAAXYck4TKRre9r80zQzrr/6InOnqanRoQXf4XuPc4Jxgveg9FDwdfJ5y9o4PmyZP27Bvnci50rgDHnAZIyDyX+Wabh20InfTuIIUu/CeGIfGgJTZ0WVY07NRWM4OJ7xpf27LNyPIqakqEeMuoa6qNCiGY1qnKTmZ03T0jpHYVJ9v+tTo5KoNcG55HcrTfAuU5frVKEYPD6kwh4lApLYjENMab0YE1inz12FkBRL0EVJHzRG5XydpJSd63PLtpg6GXVdjy09OqbgaRSHUh4lGvGR0PeEvgefAoHLno2R5U8kpTdFgYvC/aM5bd9xMG2ZHhzS9JFYXUT5GSq0xOCWRU+5S9oyS7Pung1ZzDdb5t8OoKCHybkSOEvi2Zp/STTJMCkebDF2urvNsBCGVPuJPJQIatgVTymCGAL4jqjGBNE4W3DUOGZ9xGPoe0dpA1VR8JEPv8DXX3mZ6WJO6zxN32O1xvkC16X240ErnFJEDwRSei9VQuFUQwgu4fcTl3FyL4JfLvzO9bSLFhGoS4ufzyk3thjVI47mHSNrwUeOmgXHR0e0bUdVV8QQlnn88cYE73q6viO5TzqRoYhKmYC+p2vmqVBwrT0bGaQUtcIYjSk0Tjb5xS+9yPWbN3nyqQssjueEbsZIXuaTO2NKxqBagg4EFRAVUcovYzLnspJzJbAUGTz7HEnWaeJBqhgUWe7m6yZkVJLSgqQFLWTKakhRdnKaUZEQcyIp/55tz3VVcxqqbHVJo1Ja0QbLf/1P/lum9z/N0xs/gpndJ9Iz7zt+96Vvctz0KF1hTEfwKY+/cD4V8PQBo3pQOjVN8UK7mGOcQ7sO6ducyoNIoPN9vgfB5qYmfevpXGB+94A7t+7g26c4OppTjm4k0hGt6JuGV158ifsHC5Q21ONNFk2XEqRK6LsGIRJCIiFJroFCQsSKQpGUU9v0yYqJgS73SfCAj6ByqlNPChYhcnvaMn39iAsX9phsPYlSCw7vvcqoinzouedZ6JiIRbRGRZddCrN63qddgDPMgYGvYUgqrKuQde8iGVDqoTiCD6qcK4FB5OTQruMBVoDT1S6e7IBVrnm5wyyNBVk/HaxZESdmzgn34BSqUMnyqiEE7t67xe1b29y5+Tqb9YSmdcymx3zxay8zmmxSlRXaFIQcJOx6R68URlL3XuM8KEG04NxAkBJQIaB0qp0OQzfhmAhKBtSed44QYL5YMDs+Zm9rE+c8ZdsmX14ibdsyn885Pp6htGU8mQAeaxX1KLUXH0zxZZFSyFBi0RhJVlTCN6VOzX49W5FdLImgtMZFaHqPm3VsXyyRagtTbtB2L7OY7tMfv4EaX04NXdSACHhvF2dkPcV2aswfEzlXAg+RGBM8deh8E/IiPhHIe0DbD0piLRVIWB6/HihMR5+dfooxEiXSqx7tyxRwpOGZC2P6+T6//rnf5Ed//C9w93jON169zt/7uf+B7/6eT/CR55/mqd0tQjuny+XIlTFoCZjOoaSlwIMKaC14IjF6dHB4l+7FxUDXOyKJtDOh9FKUHhRt23FweMT9+0eI1mhruXPrTu7YHFC25P7+G/S9IxIYjQp293bY29tNi1EEdEItdl2Xz5uedcY456YqgT54fEwEpiFbJkpSp6SoExArQYkTpgLRFFvb9PtH7B81fOMr/5KPXfgEpdlCbMCEUeqYjH/Hc2GIG6wHD9YRn0NacNm1+jEKJJ4rgTNkiOaLDJNr+D2e2OUjed7KklsEDfg4tOtWDwWYvFm0WamErPPRU4tmVCp2N0t+5DMf4Y3rh/ztf/w1/s4v/Q06H2n6nmOBl2/eJWrNR5+5Shv6FE4XaLsWFTy2LDBRIU6gEwiLlC7MJJ1oRQS6rkOMRhmDMQZyf0RRgcODQyYbIy4+8QlefvElglGooqTpekJIwKT79w748EdfYDwZMRpVlGWB1kLTtVS6Sk1KRIgS0UVKKzaLBaHv6DJ/gM9NRgLZDROAgLEaYzRaG7yoZcxFZAVsEqW4+vF/k8vbnmcvv07vbhPmmr58EuvGybrS72ZSkIlKT7sDq7+GFONQE/K4yLkSeFNZuQFDfIDsAsSlP59x/6QiYLWWLVii4nhQEQzglLhMTp9EmyWzV1C6Z29nxPd97DKHBwuu311w7SjQHd0ikIt0FMwWc46Pj1ECVietFCCn1yJ98BTBoGIy/52PmR0pEKNJxUaS3AF9gnknUhSW7e0tUJqiKCnLis3tTUQpZvOG6XyWexIK1WjM5taE8XiELQxlWaQ4QPTLNGnISEmlNGKyaS8u7dLe4aOs1SzJ0uIyWjIRyTquYmWdxQhKGXS9TbAtx7OefvYqlbJs7j6zhul4c3nzdOBJ9+0UZnBlADxGWuBcCbyFRGDAs+UAQBrfwb2Pq+MgmYODtRByROGhgaKHzBjJu4kEjaoarj65x5/5se/h//af/WNeue+YmTFajlNQLSp671g0C+azY6JrKbQgaLyHoBROoPWOIqSeADoE+hjSogseCS4tYqUw1i4XXciFO3VdMR5f5QmXmH0XiwUvfPTDTI/nHE9n3Ll3l7quGY/HXH36KqORwVjJfQBUXrhm1ZsgJpNelCLma/quIzLAlhUh5medlYCIUBh1goVpVaadrLQYQemKTgn3Zi23X73NeHydy2bEE8/+IbzviWtdps8c73dgxg9KQNaG8fGyAZKcK4G3kqEMN2/W6/G9laO4kkRtGSEGhBUa77QSWHcLHlZm7JznD37v0zx7ecI3XnqZ6ws4CoFoZoiP2EKjjSU2Ie+2SeFonYg9nfcJPqwSNsDFXFoLGBMJEvEKCm0TB6AxGGtSjQ3J57bGpB05BpSK2EIhqmI8imxtjglReCE+BwJKK+q6orCC1gNYapU6bZomZ0fAeccQdoUUVVdGYxTELhdaKYuEACoiSi3JSrz3LObzZTwhZRnSPStjqAqhslsE+4N84rkjJhsXMYsFiyIpIBP1YxW9f7/lXAm8XYmnfo1pkaxrgRPLWCCGeCIYuD7xlgU5cXAPZKlhBBINF/AD3/VhKu353Oe/QRN70BEDuV23RrRCaYOPPb0PuCiUqJQX1zH5wJKpx7JLM0TbB60meVcerIEBJ+GDRylZ+rkIGQKc4+y55kAkMSFHItYalF37frl214dsFwWI0Sc6c4Zy5dQYRHRiGBaV6MuR3D9QBJ17Lw7Bt7536ZyDixFSXCCGHokBa2uK8ZNsXJhQmQnKL/CxWIYD3mzHP6tuYBUCXDP58yvC4xcHWJdzJTDI2gie2LlPbxiyMvdFEnpNGEqC1xuSrszYVQBrMGVBRDEsxfRRYUg2DVWIRaH4oe/5KPfu3ue/+O1fpEOhtUL7hMVXSqdraIN3jj5EXEigmlRTkKr+yGZ3FFm6MlkFpOsPCz3fRsx+tg8ezWoxCMmMR9JOb0wuBUYlUtLgMUYjJqUAY6ZHDz7gg88KKMUjEmAo3YGKIXEQ5poLlV0Az6AXBTv0JSBlBJqmTeXLAN4nsFNw4B0SI9aWbO3uUm+OMN7ijg+J8WJyL9aH/QFlcCrQd2qSDBvA+mvh9GGPmZwrgTWRtf8HXy8tdMmU2MMijVRVRWEtpS2o6xrvfULG9T1N0yR6LTUErZI5rbXOP4YQHH3vcK5Pu7Gk9Jc1BmsKChPZKmZs7Uw4XHS0VFS6JCohRBmaAGfG4YgpSop6gi1KCknFNUGlwKHSyXdW1qSdFEiEHllJBY/3knbYCH2XyD1EAqU12aeXBPEl7fxlUWPLCmUKZvMGW49Tjj84bFWn3gjR08/nEKBQBQulcht1x2zeJOJQEeq6ZIi8iNIoSfAB73KWRilMkZuRuJ75ouM3vvhVbh11QEmMiaLcotgd7WBGBXpkqMuKbn+f+8d3uHnvgA99/N9FaUN8kxTh0io7IacCP8OrQ9r49Guk8X5c5FwJnCED+Gf19yoQNQSri6KgsBZrLHVdp7r9vDL7vk8TXKlsAUCM6zyDw1lXmAE1LMj0KnVd8ImP7fHGGzd5/fV9oCRjDUmE2yrX2ufOxpLq9Y/nc8a1otAKpVKKU7JVooxOCzqHO5OLnTAAGlJFofN0bSo1tlZRWp1IRBAsqZbA9z2dtuhCMNqii7TDJ4iwUPdCnPfcuXuXdnqE73uC98zalrZPtOeH82OKqmA0rimfuoxSycIy1tB2fpkeXJZixyF1q/BojqYzmjbig6CHZaiS22C1weTqxtu373B0cJu7+/s887EeRK8h/E6na/O18tCffP1sY/9h8PLHyTU4VwJnyZoZn1/IL2fiECUJTZfrBNbJRNdO8mA9QBxIL2VJl7V8T+IJ37asDC985CluXL/N66/vE4JOCy2GNcUS8449BOFg/+CIXbuZQEIxs/wNMYmMCgQG7QCQOArSF6PziQwEIFJQaEOUiEHQklqQdb1DdRGFIQadYhHO4X2gb3uqBtxswe1Xb9BMj+i7jq5tmS3mLPqehetpo2N7dzOFLNbAV1prYh+WfAaDxMwrGFB4Mcybjt4p4kAblt0JsRqrLFoGJXCbo/2bTKfHxNghFA9doO9FrHDFLPTo5/pWybkSeBMZFnVKa8ly8YUQOT4+pjAWozUHBwdAmgB9ZsKNMeLiySDTsihmeYEhOAcuJhNVEXHWosyYj3z0Cb72uZd49ZX73D+Yo0pBK0GL0IUelV0IJSkV1/U9v/GFL7H1+76XutxB4VeIR98TQpXYjZVglUEbSe3NY1rELgRm84amDcQgKOm4tbhH6HpC0yG9x7c9se3YKcb0QBehjYrNrW0KWzCfzfni4oDedYTeYbSmXTQcHhwyX8zBaGxd8dzHnuO5jz7H5auX8NLROUeMEVWUKC9E39K2C1wfQRRBG0IMtMrSRouLyQoxYuj7RcoslBZTF5RFSWkKRGu+8fWv42d32SwLCHOgBOyZ4/w2ZgSP1ep+m/KoDUn/d8BfIT2ZL5PYhq8APwPskboS/aWYWpQ9NrIezY8ZFeSX4Z+0cL33uN6tTaCTnAGQy1/jA3nF9Qut8sr5cwFouw4R+NCzV/jSr73IvOnRhQFJqbMQVYK+5tpYEWGxaOialjg/4v50xu7WBGMSdZdWCUDUdS0mWKy1SCEYaymMom2b5fdu25ZmHjg6nPHay6+xUYwptaUQjR0ISGPE98d4bfBK0/jI9L5L/QVE6NsFfdfSLFpiiIxGI55//nmiBDwRrxVPPf0Uo3GFDz3KpiYpIUct+65f0YrnQGQUaHvH7eM5bxw0BLGJhSh0EAPaWkxVocpy6Q4obVlMp/iDfSqrqcSjJNKeCgIP3/3NJSmAt6MwhkzK4yKP0ovwKeB/DXwyxrgQkb8P/AXg3wL+8xjjz4jI/wv4SeD/+Z7c7bdK4klTdCgAGvjrh8nghxrBXDgUGfBEA5JtFRgcgn/rc+jEvBs2mZxaNFZz6coFYtA4lzr/BkIyf4NawpSHsFQi34hM+47D2YKjRcN4UqBiyGCW1CCEjL8PRpY+tOoVxIhWqWhIq8QReHD/EDNRSAG2SJ63EkErTdQq/SiNKQx929L3HZOqorQaHQ2+64lKmIxGPHHxAiKBXiJOKba2N7GlJeCX2RWJ4H1K/7ne5Y5GallQ1DnH4XTO7XvHhChLHkQlpL4JZYUuLFpptChEafqmxU2n9BpMdAydj96OnDzunYOIHhd5VHfAALWI9KSOxDeAHwX+Yn7/p4H/M4+ZEgjZ79Y5V0/W7CGEZZMNyJHkIYe+TA6m14a036AIhuNPiiK3N00xhfyqKQxb21s899xzVNUWcBNHB3RILFAUhJiwAIigEWxRJBLQYLl++y6FCmw9d4VJYRBJfQJsFJxvcW1P76EoDWNbU1HjfaAoYLyxRTfz7G5sU0RNbQoKbalNQWhbtAhWK+pqwrzv6Ylcvvo016+9zvzoiM2qYnf7CZTAbNaysbmV8AwR9u/fxo5K6q0aXSp0IWgj+OhTMaGL9E1Lt+hwXaIYw0RCcDQuMGtbjhYNB9OGtksLemgYOx5P2NzcxtRVyrBojdKarmnpjo5oQ0ts5qjSvW+79Hrs5zGqH3r3SiDG+IaI/N+B14AF8N+RzP+DGKPLh10Dnnrku/wWyxDFH3L7K7LQIauff/IOv3otfz5rgSGNt0o5DZPjZMAwRJWbgUQsHhsjanbI0e98kb3RjN3t5FJoipxBaFOTz5hUiBdF6NuUX1fCS9dv0/SOJ564yCWgVpEi9uiYSDpTZZ7QFC0L0RgNxmpEKQKC6ICxsLtV4+cd2idG4e3tHcoisRv3bc9oPEFZi9Wai3sX8Du7PPHERVrf4bsOU85h3tB1CxbdHDU2mM0Ku12iSgENIbtWwYNzKf/fNC3OR4wt6AJ0LrDoWl595WXuNwZ0gdWSCEojFFXFeDJiY1KzoTVVrSkqneI4i0C3cMxjS4wq14CsZwCWozbYVG89P079vV5KPKQG1WqYP/DyrtmGRWQH+DOk7sRPAmPgT7yDz/+UiHxWRD57586dd3sb75OsLeghrXcibZj9yPz76X/Dq6tzrH7OmhmSufNS7h5UDPTzOde/8XVK5RhVOh+X0oCIz24HS9clDhThITKdt9zdP+aV67c5mLfMe0/nSSzEubtP1zmatqdp+yWV15BKjAqUVdQbFaZIzUO97xOngA+pQUhwiAKrNdE7tFIU1qKtJfiY+gR4T9d39KHDq4gel5hxgS4tUSUFEGKiHxvqClJgNTdnUYnIpe06jqdTrE39BkWlVKfKWZGqqhiNakZ1TaEKxGjECtYK2kfEJ0WLkiVYKA6oqRM/Z7x0xiEPzJSVIXhiFrzJxz5Q8ijuwB8FXo4x3gEQkf8G+APAtoiYbA1cBd4468PxA9yQFFam+4nc/imf/s1k3QV4WNBpRU+9yoVnz5/DwyN+43/4VVR1lUlVAUIUDXiIERU1MfcEiGqldHyKrnHv4Ih/9SufZfSHfhi/t0WsUmDRiEcBiyaRfRgjGFNBr9BBUCahC6WylHsbdM7RTRvaaUt/+xalKaiKklhoStdRug5tDIumwYdAcC3d0RTXNCngWIAdl5TbWxS7I3RZJkURPLhIULlQKQo+JvIT0QmXMNCZz6bH3Lj+Bt/7fd9PcXvG/VfvoXRiSRKt2NjeZnN7m43NbQq7RWcsRaHZ3NRUSmglMStHk6HR7yGOZ5ndXP/jMZNHUQKvAb9PREYkd+DHgM8C/xL4c6QMwU/wGDYkXYcNr+f/RZ0KKr2HoJAYU1qxj4mGu2mFduHpu2O66YzYd4SiyLAdlVBy2UKJMRLVymLJYFyClHzud17k1qVdPv3C01yMlkmhqa0GH5k3HYFAWRe44DAGxraiUKlG34XIeHebetMT2p72/hTpAz44wqxFtR2YOUVZYhFsBLfYp2tneAkw0mw/eQEzKpBag1Uggeg7tDYp7x9SL8IYA94nJSbKICHQtx3Hh4cURvjYR16gKkvGE9i7CNpY+tzTcOfCRap6jDEGoyJQcvvGPT7/87/K3W9+nuP796AY07iSMtq3GorvOHmUmMCvi8g/AD4POOALpJ39nwA/IyL/l/za33ovbvRbKQNC8Cx5r/jj4rLkGNZdhIjgEfqgWHSashJKgVpH+kzPNXxQ4vr5cnMtIdclgAuRw+Nj6kK4fW9EzRaaEqtT9aGPnkWTYLhlUQAJMagiGJHU2MMI3mi8Vila3weUC8RZSx/Bi8v8JdmaUYKMLcYq1KjAbpToyiA2ZwDIxUFr5vPQt9CHRGmOpCYkfd9xfHRAPZmwd/EiEairigsXSup6QlQaXRTsXrjAxuZmDo56xDkW+/d59cufp2aB3RrB1lMEyuQWLCs2TqvwuHrpxNun0zgn5YNu7r+VPGpD0v8U+E9PvfwS8EOPct4PkqzM+lW6f0UPdrasuwBn0Ykvz82gCPwqhpCr/bpoOG4tGxPLRmXYq4Q7rcMP2QdRuc13PleIKVMhYHUK9XR9Q4vj6MjzxrXApg6osIVRBaNJRds2dH3HwdGMrUmqDWgXLZVWWCXosqTxPnUYV5q62iA19Qn44xld16W0Y0x+vBJhVI0ZT8bY2lKMaqQYai8EEwfXStF1joHuO0Tyond0vc8xCEfbLDg4uI+2msl4xPGipx5t8tTONkEKyvGEsh5R1mN293apRzVKOWjnLO7c4MaXfp2PbirqC1cpnvlhUGNC1Cmrcmr0Tvd+4JSCTW+cNdjLwVxNksdMzhGDZ8jAtT+kAk/LSQzBw/eBkymjk2nCwRI4ATPNO6SIofOal+901OqY3VHNv/Ojv4+/+8++wP6iwWuh0PWJ8OTyGiKI91itGI0LPvHMZS5uVOyNCl5/9VXujba4sHeFF567lBanKThetFhTIKIQHwgjm6nQfaInRygKi083RySizJixTLK7pBLfoDGM6zHKJDLTxFGYeAPUEMB0qXOyUirzHaQ6iK7zdG3AefC+4/69u/zuV77En/yTP85kcwsXU3q2KAwyqdl54ilGk22qesJ4ssmoTinSZtHx8tc/i55/k3//T32Mr31hwaVPfz/f/Wf/l/SjMnEuvEVM4HFjC35UOVcCD1Hcaa9YW6DrgKDl8otLzX+i9nz5mnpwh2FdiaznqWTtGpFF63npZsv/v703DZYkSe77fh6RmVX1zj6nu+c+dmav2QWwBBaHcMgESiJgkkAaZTLSZCZSoowmE2kmmiSjSPELv9CMFCXKSB0kJRMMAEWC4iEQSwIgAS4BEhC5u9z7nHtmZ7qn7/NdVZkZ4frgEZlZ1fV6emexPf3Qz8fe9HtZWVlRkRke7n93//vJomF1vMdI9ji1VgLKjSYgmb5EMSrxFOhxREStCjA6x9NPHOPU5oQRsCYFW7sNV65fYXXFsbq2Yp2LRJnWM7wTxkVB08bEc6AUYok33nu8RmMrdg58QVkYEYnicKm9WVWOEJ8BVCW2DRqNVTjG0PEFWnch63PQxMi0rtmta/amU65dvkRoaz7w/udYX1+jKL1lZxKIe9cJ7S7lI48iZYmWY8pyZJEJgegbrr3xNfTqC6yXFzn17Ec59sQH0dF63+OBxftw14/FvuceZJXxQCuBfW+c2EIMiRREBLpUHqWre0fnOeuWLfLcpGTxsTKTsw9XdXx5iUF3dxZ5+YLy1CRwciVSSuSJIxuoeHZuzlBtbVcDcKWFCNSUAF1mo+OpRzc4fXSV2V7gyc3HePWtt7nw6iucexuOnzzCJhuMK0P3HbCxukbbBCRAUUoiLfEU3qPKoBzaU43GFGVFRPDVCOcLECM6QdVKlLFuR5EaYjReRBGaAbNQE1r26pqdvSnbW9tcPP82R4+s830/8v2IV5rUhBWNhJ1r1PUe7fs+QqzWCMUKm6ElzKxpisy2uPnGV9m78HXGa1O+5/f9XlYe/TA7tWPF2/K/o8W+LEsweQeSczwWrIS8RxyUvIBFeaCVwH7yjiG9u7oGLD4VuYAoL/jhZ+Xqwi5RSSI32WU2OcHKsZKnj0bWa0FeaXnp4pRmspoai4PEFpf2owZr6DEeO95/uqC+eo6tcAzGj1IXgWefe4wPfOBJ/tlvfoaL589z+cJ5nnrmaZq9mtmoYWWyyqgQCu9APWUVk80RWRmPLCXXOcrxGPEFKp5iNDaKL1/gysqarERjLGq1RqOhltZmwNqOe++ZtYYBbG3vsbW9w/Vrt3j5pVf47g89y8NnHmIy8jQx0AQjc21bAzKvXrvG5/7yn+TMY+/joUef4eYT38XnP/9Zbl0+y/sm22yMImeeeB8PP/+D7D32b1GvnaQIE6omJr37LezbOo8B7Xf/O1D2ACqCQyXwLcqwsCinC+9XhDJXCnubn5mzy+Zdg3xeVCFEz9fP7hLqCcfWNvD1dZ49MaL8vif49RcvMW2Mc9/KgnsAsxTHWuE4swq3rk+JoWX1hGdlJeC8MvGejzz/LG+fO8/ly9c49+Y5NjePsLG+xuVr1zm6vspkXJkZ3raoOuMSaAOusFTItlUKZ3UELqkjVdA2kAoXCW2LRE1cABBaUwBtW1MHZW+vYW/asr3dcuHtS2xvb3H69HFOnznJsWObQF+v4ZwnhBqZHKc6dZzyxo4VMl0/x83t63DlPFVTUzz0EB/4+A9y5PTjyNHH0GJizVXaGa33qSDpDj7/fAT4tpes8HOeej6/eFBdgkMlsI8sLtrF5J9lC34ZmeiycxfFzlscgBCD5/XLMzyep045jrvAo5urPHrmBC+dv8qVrcD2NFJHQCLioBBlrSo5MhFOrAhXr7fsNDUPTRqOrloA0gs88+TDSGyppzPevnANJ0XCF5Sy8DjvGFUljVo2oRMhtNbEVD2ENuKLHPLLbL+RGCLeG5tJaBtyZxCNsVMCIbQ0jbI3bdnZrrl5Y5ub12/ShhlPP/UMx48fYXV1gnacCwlfiYofbzIaHWXjzE1WZJeKPdi+yNGJIx45wZHHP8ij3/NvsXbiEW61nnrWIqHBo7TFBI/RrrHkfin7W4GQswEHJw9dwZwp2kWGDo5KOFQCS2QZqv+tytKFPXgt/TY45uY+2yGM1LPt4YVbe9z47Gv8kR96jhNlw3o4y3/+b7+PT79whc+9dIXXryrBtxQSOTqG5586xiPH1zl9fMLf+tU3ie48H9zb44n19+GLEcF7vDa87+nHePLxx3n5pTf5yte+wcvnznLmsTNo2zI9somcOG4WgVdCE/HREUJN0waKlHMTQqCqrANyjMansEMmX4G2rs3iSbyAUYPVLezV7OxMuXp9i89+9jM8/eTDPPH4Y/yujz1PTGnMYDRjuaNw4R0jD67yfPSHfoKN9VXW11Z55MRxxkeO4UarzNyY3Vlkq22g3mU17BkgSEUTJ+DuvvfIHKg7lwj+zvf+IEUXDpXAPrJfmGieIrxP+FlkEs5g36ISuY1xeHDd+dLiCC5QOTOHbzTKL3/hLN/1xBrf//4jbMRrfORow7Fnxry6ustobcJ4teLo2gShYHt7l1966SKXp4Gy8rx9bZemzU1UHKoRJ0pVCI89doJy9GEuXbnG1196hRiUnd0ZzhUcO7JJWTgKsUzGqiyoRiWjEGlnZl7viljPwKjE1pJ9nLMOwhpCx5bUNA1NG5g1gevb25x9+wKXr17j5KlNnn32CR575LS5Ek4IrdURFEWBBvtdiLSuYFqssTpeIYw2mFUbzFbOMCkKKid4piBKQ6TGsedWE7Dr8DGQ9NHS8G829+cdtD5VPNcJLOr2OSfvAC3+LIdK4FuQ/XoEfKvXgGUKQOaOG+oPPpnbsxa+8fYW1djz0Ol11utblBQ8dmRMnM6YHBkxXhvjyxEXr9VcvTnlhbe3aFxFjML1bSsfDhFre56KlbyDzc0ViqpksjLmjbPnmE5nXL9+k1FVWXfhUUVVAGpZfTH3L3CGBYTUAixGJQYFcTjvqKoSUiagYQGmAHanLZeuXOPq1avs7Nziueee5NSp4xw9utElPWfTfEjaImIU5aKBoigQX4CriL7qzndOKSQQRXECrZjJ4gEXQ5rnvkR8Ga34UBsvQ/3vlDN0EOVQCewjQ5NuWc+A3u+7Q6x5SYLQ0NVYrCqcVwyOoBXjFgSlIXBNp/zW67f4/JtbnPYtH3zqFE8/eoS2DMzciO2p8LWXLvL6xYZbtVCPVhmxS4iRy7cqbkXPehsZzabEcgIieK94iayuFFSjk/z7P/GT/Mt/9RnOnTvPN998i+eff57jx45y/OgGqJn7011oy4KyqvBFQdM0tG1LjJG2jTgv+MLT1lVqaGqKYGdnl71ZZGu34XOf+xLVWDhxcpMf+5EfoBAFtf4DOYpSliO292qCxkRmAivNTSZ7gU1/Gi8tFVMms6u0foPgJmg5Br+Di1NKZqgWKSypiEURUS2WW3qqc7hAvs8HcXf/VuSBVgL7mXUZ7ELVkmNEOvpx8mupyg0i4nyf8IOBZPZLOiK26/Yda/sB9OFCJZEKpGsp3gXCQFGMfImoUqNcxHHr3C2+dHmHtmms+k9grw7MWogOvAQiFV6VUneYBqFWJdLgZAJAUMdoVOFawROJRc1HnnuCx04d5cLVG7x+7ixnz7/NxsYGzz3xCOvjEStVST0p8PW0y4PInYGbOlIUuQZwD8XRhsisbnj9jW9ya2uHnb0pm0fW+eD7n+Tppx7FxdCRoCoZvVdLfRBAUw9CPE4htDU33n6DYw8/zWh9g9aPcOIoUEoNBHG0UhDESFJFFVRoVfCAirksmuZW5xwAK1G225bp5foQ4b7P0oJ16NzBURwPtBIA5nO/93lZFv6285XM8Xd7ztjCRYfVhgufk+sResWggyvkuLaJl9zNKDJTmE5bdK+e27ly1aP5wQqp/NhpSxssAapvRJoaj0aDvbxzeIHN9RW8F6KDG9tb7O7V7O7s8Pbbl5iUJaOqYG3DOg6XpSURzWa1cRTsNZS+SGHObAUobYhcunaDGA2jOHPmJKdPneTEsSPGW5jmJrcgV7A+iYNpceIw/mSl3b7JbOsas8kKR44/lNSozjEXm/K1LAdzf6x4yLIGdYkdd+c+hd+KHKQSgkMlsFR6k33u6F3e2Y5jzvUhwiHT8N2QW1oEat4s7ceRz5h3N7qsw3mEEXCouETeAYojRAvlqUJdt3jxpgi8dRAqR45jmxN+7Ac+xpWrN3nxxdf4+tdeYjqrCaqcfuQ4x44dY2Njg7ZtuXzpCjdv3uL69ZtUfoz3BWXpqOsp3juqUcnq6ipPPfEIzz3zOA8/fJK1lQrVFsF3fQVCCDYGMdcjK7QcepTU2jzWW9x4+zVmOzd59tlnmYkpm4AOvzWqhiE4jfhYItF392QRlxkqgGXn/E6VQyWwVJZHBYbcgoDFwGRZNYoBfYuo8fChWkwkuluZP9ctlCMnd0XmFYMkDoImRGYhMG0FaVrbKcUxHpW2KwZL6xOqRGcOpQqnj25w9GMf5gd+4Hu5ePkqb731Ni+/+Bo71/dwch5xjhBanHM88fBjtGFKCA1tO+P40TXOnHqI9z/7DKdOPURZWFOUUQXemamd8RUnziIATWPUYWB9FYCyEGKrBDEXYcW1wC5st/zWr/wd3vexH+LYmSeQkJSdClqMaOoIUc0NiOZyuSF/5Pxd+7bsgHnc6Nu40D2WQyWwRO50/+ZutB3Y99w7hQfvdH3VZabq7aMcXm7+o2TxVIRsnuekmPzTdzHK7oR3JJryxGIkwsrKiPHaCt4FSteyUholmhOHcx4l4r2wtrZKE2aJxqxlXHmOHtnk4VNH2dycEFOkwKjBUou0IncJTklJ9GNL3zQpM+3cMCeKlwDU1Ls3uPrWK7SzGaee/IAlPqX3JD7opJQFl6yy4U6/TAk/CBZAlkMlsEyWJHssj++/O8dvv4dvuHvfibNoWU1Srs2XTn1Il8cuWOZOzGsocxqmM5umoSoKfOqFWPhA9EAxpm33rJrQKxL3OLruObZxih/42PtTAVGJF2dsQxoI7SwRdwheHPXe1AhbicQwhaAJMPU4Z+Qm5agitgENlvWYjateOQGkoiTL0sc5oRQDT8sSzr/4Ja6ce5NTJ09QTDYRVxJSg7KgObNacAp+oAQWE4IOdKzvXcqhElgieVeEPlQ4/Lk3Y1iuCLpowm1F8T3F6Zx2UFs2MUbaEKjblmktlGVJG4KlGrvSSo/TvlmWDaotdVMjpcdJifeekkiZrITZrCG04ItAVVYoVuknDkJtYGXEEwKIFDhXJpbjhhiNXqysRhSjMXWwFu2kwIhZK6Yoi8JwixgDohb7j8mKwCvihSNjoTpaMG1v8Zlf/Xsce+Rp1k+c4fjDT+OaltAaVZqjIIgSvVso5uqm8IGUQyWwZMPtNtlcFib978rgwbnjQ9ODi0tZhe7KVeh91Ix+50unRzih6imvVgegZDKBXWg5urHCk488RFmogWRYpZ96B1GIkUQYkgA1sRboZSHM6hZN5cEqQlDzrV2I1jhE6aoM88zFkLoxCUbsaXYAdTNDUMqySK3VnaH5zpB8VaFpmjnXRpxlN2pUxCU3ADUgUyNEAw0r73Gi1v3o5nm2ml3q7ZvI5DhUE6ScoNF1blAbW7wUiLh0i11SvMNOU+m+I/NW1zsAu3bP3zWR9z2XB1oJ7LeGNf3nnMu5axa3pn8mlr5vWVHKosl5BzdjcRR9qHkQ0soJLd03yFZBOqKSKmQMH6+oObmxwnd/6Bl8cwsh4hADARNAEFJ7c3EWJhRnJCJl6ZlNa5CIqhApusjoKERUW6KLtBrnvkeIkuZKOwBOiTTNlKosKKsxZVngU06ES+G8qNC0YQDAgngxgmVR8Ib4S8IORAWNStMEqqqg8IJKZLp7jZ2ta1w59ybHHn2Wcv0E5cYJovNQVuk7B/AWaXAxW14pqShnLaqFWDvs5x28v8VksIMiD7QS2E/m2YbvzgVY2Ndvqyn41sNN+0ODjnTj1NqLK45eQzlQZ490u8vzzz3J+544w1OnN7h+aQ8HTJvAZFR2IbmyKCw+HyM+dVjO4FlRFMSo1HWN0lIWhhtYPUB/Tu7SnAlHMkVbXdcURUFVVaytraXMv76Tc/dZIYIq49GI6XRKzJ9ND1jOz0FMuQ5KDA1tYwpnpfC4MKNCmTjl1ltfp6EguBFPf+i7cccfoRidwskYrx4JHog4rEVboDLlA12SUZ8Lku/L7yx5R5tFRH5aRC6JyFcHx46JyK+JyMvp36PpuIjIXxGRV0TkyyLyse/k4L+TMnzw8gK+vfR0wB3IEEdYfr3h74vXy7vIMNln2fslJ8Fki0RAxaHiiSQuflEqDyfWx3z0uSd5+uET6N42hQa8CCJ+bgxt2xJToU8uZBKRuTCaiNjuXRQ2L2Kmui8LnHe9xZzatueFPx6PrQgofTdfeIrKaMlcYW6GSLJARJhOpyg9g9HwXpirYyk/w/kzFyL3hXRdP0VHw8Q3rMiUFd1m+/zLXHzlC7z5jU/jpzcp2poi9WqMIkQRpHMHBE3NUH+ny904Lj/D7Z2F/hTwSVV9Fvhk+hvgJ4Bn088f5YD1ILxbWebn77ewF1+7G3AxL/R9X0//0+5HLD8eQJRR5dlcG/Pk6RM8/cgpHjqyRpzt4lB8t6D6ccVctx9j6kZkC8GSdnrl473t+JJ8eOdtwbmOrGMQZkyKoCxLfOE7Reqc0ZU577qOw/lLaapNyAq1xzeY/51e2QrMWSWK4LzHe4cXZVzASqms+JbZ9fPcuvAq1978Bs2NCzS3rtJs30Jjaw6AgNPQuwODCIoOskLvVGLe39uDozze0R1Q1X8hIk8uHP4p4N9Mv/8s8BvAf5eO/5zaDH1KRI6IyBlVPf/bNuJ7IPML9U5dh945mr94Xbiza3A3boOam2yofzJV23YGMTAR+NAzz/LBpx/nd3/vRwg7t5jubQNYc47CU/p5S6AoipRdZ63Ji8LNLWSReZ+ftHMX2Sqgb9JSFAVtjKZIdHC8TLu6sxqHLgwrYjkFdYOGyMp4kmoI6HpBuqSokrcNEo3NSAAvODyxaamDEtXyDlzpqaoRmklXg1LWUyY6ReMeX/7nv0DjVinXT/DRH/7djDeOUVQVntaqHlUI0aGFR+Tu+/Xd6yjSb4e8W0zg1GBhXwBOpd8fAd4anJcbkh4oJTDU9PleLlYE2sM/n2PWL3LbopybzwVYZiUsfmYHioGxCA+ApqESyfuSB0QjTpW18Zgf/b4P8KFnHufM8U1GOmOrqS08Vq3iU31+5fsYecYFNIFtZVmiGrquzCLOQFHNcGR6yFPBj6QdXZKybGMk5ytkKyN/h7IszTqo7F8Vm5+2blKNs/buDnQLP0dkiqIwkhHNCoTuXhiqHwizKZBclMIiOuIEj6MYr1iERwBfU4c9YnORt7/2WzRaUo7XOP3YUxw59aiRr8RIKuamJ4Xtn4P9koz6uToY8m0Dg6qqIt86vaKI/FHMZeDxxx//dodxT2V54tDwdbos3rlX5/5exBeymS89Gp3J+YYPVffg2eIUoHLK5voGp45v8JFnHuPJh4+zMRkhYUZMpq64AucjRSoSWhTDNxIIrtnktni8VQhqjpuaEsim/G2g3SDLDyMWzYtYBLxPFoR3HXhoAGfq5itCV9eXxpKnynsPGonh9gkXJIGELa6FSHJlXBqJCIXz4CXlHUVc09CGPXavvsXeTHHVCuPS4Z3gR6s0rmS8cQxxRQJfe7aorn7DJquP1iTF2jFB33an7z95t0rgYjbzReQMcCkdPwc8NjjvwDYkXVZgsvg69owBQz/VFk3C7Lty1fQuMqKXFxuKPaj5qceqBQFqDK8WIj7F4RVzAZQRXiKlU06tOP7dH/sePvL+J1mrYFx4nDbMZlO8V4qoFG0LXvA+7d7So/NlYdEFjYHZbJdRaezBGsE7IQoEDaAVooKo68x8VUPl8/wURUEza4y4RMTISb1nNB5RFA7vBeegKByNNR/EF47GtnhEHAGzAJw3CtOcQtyb2hELW+ZYfrLMotI2DdJGyzpsBV8WloUoEDXiXIFzBb5QRuIpY0QJVBqp2+uc/cpv8sZXPkW1ssnRU0/w3Pf9CDLZYCYVEoteEcSA0AAtCLRibc/JpdD+d36ewCewZqN/nvmmo58A/riI/G3g+4GbBw0P2E+Wm39DSrCFVGDF+gvfMV5sZqZ0JcKZadhgPi+hUx9RCkKwluTOOSpteOz0CZ58+CQf//BTPHJinSMrFQUtGkPq7mMhRCdCUXhoQwcMWrKOmSwhhI6y3DljCsqhv6oqrE5KlLqeEaNDtbBwYVlSliV1XXfWQNM0eOmbuI5GowQgZj/Z5mg6nZKrK+u6xovrAEbJVoGzugQjK2l7YHEAVpp14S2jUKCqqttoxe0eRIqyRJyg0XIlpCjx+R5Ka2Qo4pjVLXF2i1tvv8KXf/06jauopeSD3/VxypV1pBixU1viFaqoeOMwCJF6usv62ip+n+5V96O8oxIQkZ/HQMATInIW6z3454G/IyJ/BPgm8B+l038Z+EngFWAX+E+/A2O+p/Kt4js6tF/veE66PvOMxNqZkZYdZ70HpccZRKi88MyZh3jykYd46uGHePqRk6xUQuUUotCGmNp99/6rF7uOc9ae2/z9fjyawS8x2zaHP7uF59xgNyZB5hZRcL5ICzNl3CnkwgWX+hD2YJl2147R2IkHwPttFtgiyCZOeoNqcV4xy2yI7Zub0SsfNPd36KMwqpkERKBwxCCE0BLqbXYu79HgaKXk+tkN3MoGVKu4yTFTWuJRKQh46qZh6+YNXn7xBU6feoiTP/xDd35Y7hO5m+jAH9znpR9fcq4Cf+zbHdT9Jdl0X7aj99GBIWjXPWTJf76bsuF8nohlwWXLAnzK1jPTvHKwMRJ+/7/9g5w6usbGpGTkoPARESWodAsshDQWNXiLwuGSO1AUua6+r0EwN9fQ+AzqDUOEo9EI58T4AdL7YxuYjMadexCdo2majl1JkgLLi1khcRlYH4PYtJTiCAmgFBFcWSzMZ44yxG5ehgBczm1QhRBD56MnA6A7P4QGwSwOy3OQ7pu7hP2Jg6py1iilaZG9PQrzN3j9C79B7cfE0QbPfPSHGK1tUlQrREYEjdTTPa5cvsj/8lf+Ms9/+EP86O8UJfCgyiLgl3fE/RZx1NghyFkBLFcQg2veZrKadAvKexyKiy3SzPjIM6d57vEzPP++x3nq1Brj0lM4cJKtCKibpgMPu508veYLayfmu50ZS5UNSmgNoHMC4iw3v0glvjHauSG05ESjJpUDiwh7e3vdmOdSfsUwAgTaUOPxeFfixFtYsG3RNqF8lgE0p3Qs8xDCIIqRQVPVPrGpKDxtaxGNQiCkFu4ixkEwZ0kEQZzRmeU8iJDzDGKA0CLRWqW3AfCVRTCishKEUYy0cZezX/k0QRyuHHHs4ae4cH2bV795ln/4j36Jy5cv88EPPndXz9n9IIdK4FuU4c6+zOjPfm8Ok71TrbrkJJ/Ba4KkPH6oiKyvjnnk+Emef+oUT5w5waMnN5lU3kC2zjRPCkqtzXcM9vB7l7x9VUO9nUthvfTp6mi1h9v7QMQwtEmHRdg5iieRgCT7W2NGydVM6+w20CvQDkxcmDtVtZqHlE6M5KxJWVCO0s17m5SH7fCxOx6jJGReLbfQOqsmhdumMah1X/YGREZNDVJDILYNTQshQNuoJWJlYBLjYRRtiNMbqAi0FVtXJ7z0wjd56fU3uXzpkqVYd/0g7385VALfgiymDytxPoute+rn3wPzvq09UOlEyQrDxBh9LL++KmDVKU+d2uDf/L7neezkOqvjipVxmYA2Mz5iMvtz+W3bBmP9dR7vkmntFJymDD/X+emqCk1WTM5CepLLd6Pt5PQ7cx5/ThQahgclhTxksd4igXeWG2DXTpkFyW9PbguCK3yvdezDujn0vkg+vS3a/NlmoRjAGkM0HsWszSQpAXHpPdoBndbbIYOjkRgCbdsynQZCIk71he+rHEUQZ81hCwlIUdBK4OKFc3zpc5/llbfOp8rG1DGlU3f55/6UQyVwlzIE7ubcgvTQz52bEWcFZ8RW9L5rejgseN69R5yFw0RbKu85sbnK9374cZ577BQnN1Y4ublC4aCsRozGK1Y+E4K16kqlthrVFj+mRBQDuByOwhf40uFL8+m9zw+mYzQa0dYNbdNQ1zVSlYDvwDMRwXlrCJIl7+pASjDqwb4QYgIEHU0zQ6KjkJIYI64ocL4kqoXz2qZJbEap+3FRdAlCWVlKsjQ0Gu4Blm8QgkU/fOqYnOEN762Fe4x9qK4fX4oUFEWXmRhCmyjJQNyItY3SrAhVCm9NWkRgGgyXcBKpUKIruHRzj7/x87/IlWnBrO1Dpr+jogOHYgu227GWuQNK93Bmk9jEdb9rzL/l5JVsekdL+wuR0guPnzrCY6eOc+roGk89fITTx9ZZn4wYVaXV0jvBeABBUpKNpAWiIaYNSCyt1hkQ6LpQWirucZYF51KmXysB7wWNlgXZNBbnL4oE+KXnOTq1Dj4pGSY/5rdhHQmly9aEoIRWKIuS0hmZyGw6Q1PM3YlP8XfbkUlKNrYhLe7UNSXEHFtAW+32Vw25sxK2eKP91oUqO8ukt8qKwncAoncF0ff3zHmf3DRNvIemr6vS4xJzsw+OmRTMguPKjS2mboWQSq3zT35C5ty9d/EEfqflUAnsI12o6nYHFlgEDrMbkH3TbJ73GWaxexL6TLvcE2tUlBRRWRt53v/YCb772Sd46Og6kwrGY6u9L3IOuwga2zSW1FNLFYImjgDz8yWNsSxLRCBqsDi4c9ZJWLCKQueILUYXpKYE2sbIOnRsO67kDDsRgsQuNKhufpftv6GSk480ffHYBlxZUfiCUTmintbZAbBcgPR1aCPi7ZqhtTh8zE0NUnpzVE1UZDaX1uDEZt6nPAegxyG8FRU51yss730XxhxWBgyjGIsefeEKfBS8Kho9dSvs1MpO3RLK1ohnU1WXJQ7dj0v+djlUAt+CDIEqswpyFZyp/s77S0CbMetlybx+BjR5BQkNI4n86PPv57nHT3Hm2DprvmVlVFJVUK2uUpW5ks9AwGxyo5I69ubW3ymU5oXKl12qbq5xcNIDc3kndJJSe71PrL7CeDwm1MYYFJsWLxMKb9fwJCQ99HOQi47msAFvyjNoTCnCqWoQTwiR2WzWMQh1uIKzgqimriFbTSmpCJL7kXdZDFMwYNJqGGZ1bXkRrqKqKsMlfE5xTot74LYZNtKHQjvQcojddLCERSYKBVVLxIpuxNdfP8uXXn2b6MfgRiAO1cCiZXS/y6ES+BZlHtnPiS4enDHchhgQMRwgaiIl0YjGhoJI6RzjsuDpxx9hc3XEkZWSp04f5aEjFRtjpfRWsy/emHck2awhBCTx7Wk0VFtV0ZggRifmfLiEyidro1+clhvg8usaGcCTtomlxJ7QZfL1RT25gIc2oDFYI5ABDjAEDmWgrDRbDWneQghErZOlRaIP07TiUiiwaSFhL2VRzoGDFnlwfa4+dG3Uo6phKykhSpx0RlcGK7NyzFZeZpDKBUmdokgAbUw4TlQ1Y8mb1bK1p7xx/hovffMC+BFRxZTXAVMAcKgE7igZAxjKohIQFJUiPZj20BiYrDgCk6qgcFAQmEhkUjjWxhUffvwEDx1b5/jGCqtFYFw4Sh9xvkRdQshj6Ba5xki0nmTEaAh3jgi4HGVID73rintI1F3ZKsg1+tqF9Eh/O1JGYQ/Kd76/qqaIhSM6NXDMzZN1dgBiSvvVBMzFqHNTqFEJse3dLRl8TprzGELCYYQi1SVYRaEiYizFtuZyerEgYszCEekWvC3oyGL+QTcW8hRI950X80NydqNma09stq7t7HH+6hbnr9xCZcXwmC7qk69+MORQCXybkjD4lP6qlChlCJQONlZKnnj0OCePbvLoqeOcWB8x9sLYQ+XNDfc+WMJPqnZrVAmhhdAS6l1G4zGFN8ugI/9QCKHpUei0YERc56ejamNy2vH7ZxfYrtMaIp929Jji5ChURYGUjqoqaGY1ohE/GQFYFWI1oizLbqcvy5LZbEaM0ViMmybNh5ifnxapiLeoRkrO8YnQNFsLQa0ngYUCtStndt7jioK6rueibbYg7TxjN5IE0mblYsCpJUtlpWfnWbhQui5R6YLp8qnEObSdteMQ6npGE4Xtacv/94Vv8OblW0xjaUSrqRoyXzuGZU1p7k85VALfhqgIKg40slqVrI1Knj59hEePr3E0mfrrq6usTMasra1QOKEsPGVpdFs4gQzUpcIYQYnR6L5CG6zoJfmt6nzyW40RmAQ8aggp7p0MiLRDFwnWl6gYW2f2tWNadG2PMfTIJb4o8M7G6VPlYQ59WWluX4EIZnmMx2NbOG1LSB2K87jzjp2lSx5Kfn2XLiwOXxQ2brX0YnWptDhqcoeEVrVrSWa5ErHLKjRYMiaYJoGYNqv4FH6MAxfnttqEFJ1ANc1btk7MfWqDsrNX8/kvf4NL20qr3tKgg+UGaKZkW7Ao7md5oJXAosG2eNuyD2m/08V+JPmLo7JgMnKsjSrWRyWbk4oPPHqcJ08f4dj6hI1Jac08CmvjbT6rpyiNvDM6D87MW1tUUGgkhpoQAnVt+EJMSQc5yCWdN9+b0IrmvBjyqF33gFujzsyensNXMT3cMfHsAV3M3ntHURaJFNTO75qWJDs610aIswVszL+Nmfa592ICNUWcWTHd5KcEHM3Xtus5L2RyT0k+ef6ekhKNNL3d8AFQMiA3uIkpRCkJbhhShd0e8en/jtFITPPTEXP+QYwIwvbulAtXbnL+6hZ7OjLLSzPrUV//cYB0wIOtBIDBQzN/yCLooM6hojhaJFqZbOlKKlfy5LEJ7zu9xkfe9yQTD6NC2FyfsLK+QjWqqKrCEGrnca4kRwY0qvXkK8zUBeaoukKoLE5fNsxms8EubQ+6+c+BPuIAFpBQvKeL63uXwDy1ndTyAtJ3TemyMTN0xJ5bECe4QijGhfHq5B0TM3VDCFS+7HgFXXJVgtqOTFSrR4iR0aSiLCoKX1G3BvhlhRPEfsyEd6Q1TdtZDtLt7ln5CIpzQ4JXK7hyYjUKHVEJSWkk3EOdo4nZmxA0ZIWgBEIHpqp468mAEkSYBcwdSi7Ba29e5F986TV23KY9D3GGU2jcGEWQMLstk/J+l0MlcAdxKD5aammUEU7hzNF1Pvj4aZ57aIPV1YrxpOT4xgqVKKVzrG2sUlZlQsoV74uOfismAE6KIoUbDS0fjUZzpJ5g7LfexwHirl1IEFXaYL0QcrPRTMAB5lOba9FnNs4BXqq00Si2c9jTQnjp4fXz7L72Fk0EnrmPQN82PLsu2RVABFc4CJJyFGKnPMwtNxfIrpNDnnTRjJyR2SPttlWbi5EthNiDi0Dbtr2LNfi+Q3M/pt08W0o2fzL4PNCUVpzgAiisuKqJLdNQce7KFq++dYFAkWY+jTC2LDahPShyqATuJAqI7VyFKA+fOMITDx3hmdObPHNyjWJUIlXJpPSUAoV3lGXRk2OIJr/V9ZZG2oqzElisnwe6xbf4AKvav12sPqH53uXzGQBj0n3mEAHPxzLzj6SsOmsQmuLxmTdQBOd8ciEWOipLf22NCzuzAGrx+WwXh8w1mMA3lWSDa17/0p2bv3dfypxdihzy65XTbbdMByE/5pVfX3qcFEmcb0eefYeY4RojcScAbYQXXjvH2UvX2d5rUArLUtQMJmr/PfobfSDkUAncQWIyRwsCKzT8yHc9z2PH1zi17jk+EqT0SMrIq4rCynS9gX3WxaeP0+cmGmYtJ+BqsNDzztcnJM0vOtvpJLHshHQ9c1V8tqPz4zhY/PZLrxByqCubwobAmyVhjUekUwLee8qiMCsmWrFU27S0bYsvJDER59wISQ1IAjGG1BokVx4KbXY7BNRlcM7+Fu/2Xbh95eIg9VfAi+/Di1lZiHTuzbIqxM66yEpAFYmZWj0BlgJRDd9QadMcCnV0/MI/+U3O36qptejunzVfzfyI9J/9bTx391oOlcASySEs5z2FVx47ucmPf9dTPHuiYH0cmIwdzWiFynlKJ4gHX1r3L1e4RCsGqo6mMfM4m7/54fTeG1iVzfFBNl9n2kLXuKNt265m3hRESR/7T0UwqhTiQQatvgchwyyW+OM64o+qKi3fn9T0ozQXBqxkN1sSs+kMsIVZiBDqhjpGQtuTj8QmGGiXahRckVqiKV0ilccSfPJikYTGZ8WUsYai8J2roAlIFEx5BO3Zk4pBRyQvVjvQKYNU6hxTdCPXHnjvaUPTKZBOCSRnQdOxtg1MY8l2nHBpV9maQaMCoSEzFws9p4PgOmvtoMihElgiWY+Ltjx16jhPnzrCQxsrrJRQpR0zpshBVCPgsEQV28GTkZxQ+75ctk9OSXTkSI/i3Wk8C9bC8Fj+rPwQ99TYyQBYcDe6aAGSSmp7ZL7zGCzkYO8dIvpJYoz2/RPg2I8v0X+LLT6XWpNF7QfUf0ZPL54GloyZVBK94A6BdGzDSuzyG2RBueVrZ9G4OPqFz2cwXyjESJQClfy9HW9fvMpnXjzPTg2tCqjiUw9nG6909xXt3Y6DIodKYPjAANn39A4qCXzXk6d58tQm65Wn8lbsIs5QcxSLWYvVCKhGfIx9g80YkQSkxYWHNSsBHSy+oTmsZBxg6CYwuEbuImQD92lnJZWzdt5B/o55oWtyCYSunsAsEekTh/Lumwg57RLaZQkawCcpXJrq/BksPklhw6LolE7ObRj6+0PrJ39vESG0ga7mMs2FqFkMhj8MMiOZX25ZeVhuQR+mzAqRjAukz8e7rLLT65EoZuC30cDQl14/y9/7h5+knpwBHKItTnJ4lqTIw0Fa93NyqAQAIQD9oiQ0HB87fvfHP8hTD004uuLZnKxSVOALQQpHgSc6T/QeKWKisxdoAaeoiwQRPD2A53zvEoQQrGRWoPR9uaoDYwpuWtqmtm44bSS2aumymBnvNPSNPTELwNZgoCqtBDi0zeBbamYSxeHwRd901Eu6Rgb4Brv70Mf2ZZG6FedsQctgbAemr5KqE1NJbx0aRITSpz6J3luC1CAduCiK1P5MrfjHViwaUnoySlSjI+u33L5fYdu2SUFp4jKwduahAys7NDR9FwBHjAmjEbVyZaDxnqLZNreMkn/6hZf5wmtXqP06oak7RedNK5kyTtiAvTbXNP5AyDvaorK8IelfFJEXxJqO/oKIHBm89qfFGpK+KCL/7ndo3N8RMeMw8vCJdZ565DinNsesjQqqwlFIqgkw9iucE0onjJ3g1IAq5wpEKsRXiCuRVANgCTV9ll3Xvdf5rhGnPTMWt9e0wCQthBhDWiQYqKVDBl86gIy8W6f4f1fxCPRG/QDZXwDPbsui014ZQA+s9W7J7d2Cu1z7bMHYG5N1YWPs3C3pw3P59y6MOBzbHLi3JFKR75/0o0CYszTc8LslK8bqpc0F0FSW7VBCaGjaQB0dX3/tHG9euEbAMU/KmjM2ByHOBKDmOT8ocjcZDT/D7Q1Jfw14XlU/CrwE/GkAEfkQ8AeAD6f3/O9iJXUHQqw0JPLIyU2eefgYRyYF47Kg8h4nmZYKowRwjtI7xk4M6BKHcyXOjXCuwvkyUVL3BJw55JZ3sJydZ2LVasZyk+rwU0gwhr5/QfZiXUbymQ+X2cIxk70X7Xz8bMLmBer2UQJ5xx0e698/TMiZN+UhK4vYKQpgoAToxtKPtwdGF68771/3v3duxuBa3fUSRjJUAp0yyFWU9K6BEI0WTCOiLU3bsjtruLI15eU3L3Lh6s1E+95/BtJnk3YLf05BHhw18K4akqrqrw7+/BTwH6bffwr426o6A14XkVeAjwP/6rdnuN8ZsUp58wfRGY8dW6M+fYxjlWXGVd4xLsGPrZBHvKcsR5QilAKt84ivQAoInsK3iDNzMYfyXOL6y2BUTHT4Xew6Wj5/jJGmrjsizaaJHeCGONSBi7ZjSfKNiwG5B2TfG8s0jP0DqSHlBjiHhn73zRRcHeKeEPOyKFN2b1qQGYOQ1AQ0mfAZK+iwCxIGEixU6JzraMOiRgiW29BZKgPcA6CuZ4jQuR12DuQeAiKgqbjHdvKQMBdJHZDtrmZFsKgoIIfxFM3BTLVEp+2tPfbcCl955S1+9hd/nVuyTpQRvrBuSvm9i9eL8eBEAxbltwMT+M+A/yf9/gimFLLkhqT3qdgCST1uAYHYMHKRiRcKcRSJo99XDsoJtTr2GijVsbYyYXUyIoSd5FeDxhneG9AWpcAMob6OXdMiyeChbUqpxZga715I3YOMMWdgVpP2VXHd+Vk6kCwn5WifKjzcQaFXFvnaRu2djg0Auza0iZloiNLTfU6WYWvwDsBMyGTOhFQSJpKq+HRYYqwJxU8JRPl4xgmGnyMiEKWzSOZ2386AMUXbR0e1M+WHWYjZ4GlCpGkjTatMo+fzL73JC29eZldGRFJ0IwO+i0/QQJFBryAWlcT9LN+WEhCRP4NBYX/zXbz3PW9ImjDkDsrJRJ+Fgyrz8RUeXzikdIRixK2dhvPXdqlGJeszZbNRjm1UBhSpgjPaK/HOilHyQ9+FyAYhsWxKa2+ihzakUlQ11tyBR98VCOVLLH6fzmQ15UHUlJk3MKOhC9eh2lXUaTquqinNua/Xz5iG7rPbDR/67uFPLY1y5mFWgimoMshYlH5MXbhz4GsvmNWLnzV0k4bzMO82pLutveKx+5HoyQI0QZk1kVtT5cVvXuS1Czdp/RgNkiIS8Xb8Y/E7z41x6VTdl/KulYCI/GHg3wN+XPtZOGANSc0f1NzuU6CQgB95ypURZeUpRyNcKbQl7GjJF149yyf+6WdYP7JBnN5iIjV/7s/8l4gECA1SrNGMJ0SNTG/dYGVUdMy+Iq7DA0JiCs6hvNC2NHVjVXitEYa2KnOgjZGYSMIR+sfeGmdovwvmTRVSnF9RDbYr2xsIkcQ36KzPn/d9Yk0y4b0viQmEcAJNDMYBkIDNRQsA0oIYLJbhjW3b1gp8NEUbBrUIw54GNo6+I1GPOyRLJ2b+x/47S/d+o1pH+lTrfiwW2nQC6rDSZHG0QZnFwLXdmi+/cZUvffMql29N0WKChGmii+0BzOGun8ec3Y4cqTg4sYF3qQRE5PcAfxL4MVXdHbz0CeBvichfAh4GngU+822P8jssFjmT5PQWBD+iLibs+ZLxeEI58shI+cf/+FO8dmEXnRxjVwtOHV/jyeMr1G1BUNjbmfLFr77I2au3EIGPPXWC555+lLXVCc55ihQKJPbmvz1YPdFGSNV3ISohSreT5h0SQNUy7/pw1ULK7RBNB3JOQG7/ZcogA48WlguJQiuHHHNqv31e/9DHhJ24BFqGEObalS2i+zGEznwpisKQ+AHXQDfmfP0YEDecm345LQKY+ZwhBbxz1m6MBMJqYjkiKbZsFEWs41AUaCj44ouv8+pbF3nx/A7Xd1uClFTeWwej9JkZap23VObHBpm/8HdQFaEsb0j6p4ER8GvpJn1KVf8LVf2aiPwd4OuYm/DHVDUsv/L9IzJAipGSy7emvHF5i0lVsTVtWJmUTNZKXjt7iYtbSiyOpJ53BeoqvvHaBRyRvd1dvvjyWc5dus7KqOSDZ44QFIuLV4UpG7WwX256kVH0EDJrkKbdMm/l6R/pwaxu3As+akatu20oPfPZR1ZNXY7z30kR5Bi78Q30sQaN2hcBDXAJVe0LgpLMLejknw+0CL1Lot33RoXQmdnZtA7WjWhRus8ffD8WEfkByNhNno1HiUhHDCtEFdoIe03Ly+eu8I03L/L62cucu9lQhxQH7r7fIPJBct/y53fznh6fYYTlgMi7bUj6f93h/D8H/LlvZ1D3TuyB8FnHqyJM+PXPvcY/+ZXPsLY6Yb0KHN1Y4clHHuLazDNDaEOgjLtcurTNxYsX+KVPfdHcBu9pdndYrzxPnznByROnkdEKsRpTrI1gawsSjbYrR7Qx0rZNAgRTbQAZByDlI9gu7lMoMvu2gGUvSko8EjqSTClcBwx2j6L0RKBNVOs9kM6v22Dty4c+r2rasQucwfqWV58+ezaruzCniCT2YKMayyiGaqQYZBPGprHwHYD2lYVFUdA2vVIpimIu7TgNp2vjEgeKEOjcBoCmrSlS7YNdzpiGNLkXLkV3VCt2G+Wtyzf5Sz/999nRMa1UFsJNCrqt90itXPtFr9pZV+IXzH4xZEll3g2639XBA54xKICf+1MF1JUEV9JQcPT0w3ht+NI3XmcWKpASIdLUDfgS9SOKiTOSEInMZlM+8tHv4vTxTf7xpz7D6UefpCpKQrPHT/zw86yPHa6dEXYNB7C8gJbQxq4VVgwBRZAurJgWFX2Oug52y6FvOgTCnJsPZbkFXz0n0YTQ4Lu2Ymbyq4BXy4bLC8gwRttpq6oipLZdi8h4pE8yatu2D0VKX0eRx5PHmzMIraOxxWtabVN/ANvNffLL4wDpH+64EaidJ4jDo5QplVcUYvREJ7Q49uqan/+F3+DSzcC13cCsOM6smRFiyygTkWiqC1ni3/dFR8uXd3aRDoocKoEkOjgSVGlCYNoEpi1IhFvTlliMDNASi6lrDBZCCpFGA6IBcY7d6Yyrt7a5dOUmN9rL5o/P9vhd3/0s4keMAWkDmhZ8PWs6xiFNO3hG9vqHLfvIvXkPPSiVz+0XJCxsmEvcB/tfVKMYc5pSp7u8gGRiR6Mhyypk6PNn3z5fOybUX9F9F8nysfTVlZJ6/+XGpiT/W30O5GYfaRjoSIpFfMI01Jq0iNl5tQo3tqZcubXN21dv8PW3LnN9O7LTCMF5BN8Rkg7BxG6Umq2beVBw+P0PUlhwKA+0Erg9uGTHZk3L7qymbgMXr96g9I62WAHJvP3CFCW0M6LOqKc1bVODRlZXxnz15dfxhWeysc6b1y5BBI/y+uVtcPDQxDEJqW1YCOxOp4j4lK2gdJlEBeSMQxJzbs9B0I95SGdV13VX528ltb2vfhvllQiI+feS0pILZ5+ZGX9cAiA1mhHuElcC2mf55d0+uya5TDjnIHRzq4uG/PzMi1hfxJhYe3OtRUw7q3qstdoAsOywgFTsNM45HzHSNDVajpiqYyvAC29f51NfeIHPfPkl2vGYoJ6IR0JgRIGKp1VzyQS6gqosmTdhqHT779ZbN4uv3e/yQCuB/aT0ntI5NAZ2tndT80qH00hsW0K7C5gZ3dQzVqsJsRwD4IoRvqpwRUFLSVH2qb3/x9/4Jd7/+DF+6KNP8fFnzlj1YYi4ckJoLS9AJFJIvxtm8x+RLqXJVNXAtB8g1Tl7zyyWeUbgLCKWWZfDiaPRKNGP5zJe7QDIjOv6MrMhSx9+S5ZLUebFkn7PYyaH7LLF0Fc3+rlGofadcrqzL4y/UDFWH3GOsqpsUdInUA1FxCIapcCsUVopqMsjnLuxzWe/8nV+5dc/ReNLZrFiWh6nafcsrKsthQpE6z+4mEif8yfeSQ7Sol+UB1wJ9CAcZJjQzP0imZoh+bwqQlV4O6iRoBj5lKtQcZSFFQO1w0y46PBOgUDTRq5c22Pkr3NspeT5RzcZlxUEYW9mZquIUCCJmLQ3yyHv5MtZh4YLojNTB9/ytt1LBizKZFM859nb9wPFC109vqoas6705njOK5DYh+xkbsykKkTXZQ5a1uT82HoXp19vGYDLnk9O8u2AOW7zdojANCo3Z4GLN2/w1Tfe5to08uo3L3LxVksx8uAjIhGv0jVgVYTgli1iCwMshiYXswJzf4Q87uG/B0EecCUAyCAbLx1yaQHgHBpbQ/EjlCsj6wwswqyNBEooSlqNjKsRVVEQdras3lwFF5XCB9rYMqunzJqCS1f3+Lq+xfYPPm05/tFz/eYtytEKVVXhSp/otiCQE31Sfb5YcVCM80krkFtu9wt+WKknrq/Ws2N0ykQ1m/jJD0dSrN9KlwOhD8PlxdpRfc0v3n799+HNnPprDVX73oFDF8J6CiZMQbVf7ApSZI5DugxKVR0UPim5+1Orys1pzcWtmi+/+jY//0ufRKtNgnp0tGn8BrSgM0Q9MSYeCA/B27V96NXjcBnnHIpFF2D4c1BxgUMlkBH3wd/WSNSlndd2UA+0TUgPgifUUwMHgaKqmM5qprOa7Z09jo9WGI3GoJE6OKZT2NpqmVQR71q268h1OUazpzQ3bvErn/40q5MNjh89wcd/8HdRSo2XQOWtQUgUT0FJi+JRCqeIS2nF1uKHrjU6pHwAQwazyd80AStkEkQtDi4pguB90dGaOyCkXgfSMfArSOyKoACrwk0hPucKQipdFrGmqIgBnNUoMSvPBfcKoph74RKXQdAc/wDnI1GhDQLBxu2d60uqQ2B1XNBqSxOVWbnC1WnktXPX+Jlf+HXq4Jg1gdZtINEIwzzmavS2XzA6+HTnfdAubyI9BjaXaMdsnN2x23ISFiyDgyYPuBLYh/pBB/8MTMFhdpz3BuQpdGW7IkJZVtSNkXlYe67ArK5T/72Gmpbtvcgnf/PzvP+xRzm9uclbF7Z59JGjjIPj137zszx9+jinj67y9Jl1AwIjBLV01IAQ1EJg/cMY5wbfsw4NcAB69qGc3NL77VbwlE3fzmrocguUnrfQxNyVxNkXY3INTPHE5PirWA5ADrTbeHuMwpFZgPJYlYDi1Ho0lE6JocVJikxo6vTsHLvRsTNzXN3a5df/9efY1YJrOzXXtvaIMRX9iFvI6usdwP67zC/euzXnh6xPQysrPwcHSSE84EpguVg4vNfwi5q+87HTYhq2zy7L0nLkQ8RJkdpw10BL0IBEZdrCv/zXX0NjRfn+TS7frDl+2rHdwKc//2VufPA5PvDkGR4/tZkSC9Wag1CiyUohvnMSytyiHcTqB2f0D6w4xEEM80QiXSRiyFeqfZGRAk2OmzsLbCQj3YDNlCwUVS3HAJKrIanBSwIjndljGiMxcfoXLhBbe1/OZsx+/60be1yfRs5e2uIX/9ln0dEK6svEFBzmvxuDhTn4+vst+OG9vn3ObncDhscPmgKAQyWwVDILTQhhLsQF/UPRpOy3qMpsNqMojHqrrmuqqqIoC4gQQ4PGGpEGX3mqcoXJeBUN8PkXvsmXXniFyeqI18+e5aU3z3J9Bt84fwstx/zAs6coRam8Y1KlPHgx39pWYOjGlGVYCjwcc+F8t3v1IKG9bmnAocMPhpIX9NxRZ0BiVzuAhQKtlXdqPpLCls0gWSgtOawcx5h6TP+k/oFOKJzQzPZQjXhn8xvwNDhitcYbFy7zjdff4u/+o08ykzGxmMCxxwl1g4aAFyVKnIuYDEVSE9ThTn43sl9y0GL05Z0Sie5HOVQCS2RYZ794M/NNzg+1YKmyw1i9LY6WYizWwkqEajwhKMl/xnoAhhZHy9NPPMzbl6+yu7NNVYwRhLYN7O7tsTIaGWpZt1RFgZdokQv65JXFnWtYTDOkNMsuQI9wg2LU6t21JJGSMEjESZ/l3IAaPFv/LocbU0uybP4v7MLzUYNMX9b73LmDsZH5NoQYqaOjdiPeunCVL7/0Ta5s11zfqbmyNeVWnNBKAQ2INEhocEsanw4jFsOEpmV1D4vvyXRneV7hduthGT4wPH4Q5FAJLJMlC2r+5b7ppNI34cgPQE6nbWOTKMmctQYz2CulB7eEEPFeWF8/gly9SR0iK5MS1NJ061bBR3yIOCKbq97anWkiRtU+R2CZEhAZNvXQOQUgKdLQmciaFjXZZO6z/oRkzif3x84duAsp5Vehy6fPSmXoe5OAv7RM+v80++vpxwl7deDCjVvM/CovnrvKp772OuevbTGNjkYLWhnZ1RRcaCmwiIbpgdsTdrrFOYikDO9nPmfRtx9aVnfCC4ZJWQdJAcChErgryTc1uwa9KUlKfPG3nT+dTdnZucnpkw8BwtbWLuubR2nbhp2dbaK2uLLAjSdsz5SZVMRqlbJaoZntMdsb0VYbnLt6nemsJsbIs49WbIwia0VL5StgvnPRMFEoS36Qm7ads2ysEYcturZtzcTPlox9C9vtXc8tGHTB1BXpFGbGUVxWOHnBi10xtJm2LHUTHsxrBh1UHLiCuHKUV8+/xV//v/8x20zYrZXt3ZayGluCUox42i6fQdL7o1pWH/RzMBxvvmfL3IDhTt83hl1eALtoWRx0OVQCA+l832QCtykFFqzVFjHdeNfXwqPMPWSZSdhQ58j1rS3KomS8skLdzLq02sl4zRZOFF5/+wI3d3aMTASoJqtst/APfutzTOtgTT5Dy9FjJynKMZUItXh2ZzVbu3u8+dZbvO/pZ9hcX+OVF77KM08+xubaCsQGWttiQ1SkEDwRp4EQUmpyMv+7HtydJZEAwZgi5hoRTdyFqJUpa+h4ENKliCEgIVGd+yI1TnX4okBJXXucNQ0psCzIKSXX9gJnr13nl//5vyTEVW7t1FzZcdSE5EY5CLE3VAa1hDHS9XBRUnejPKB8puptXYGWxffnUpHT/RxaAvleD5VGhwOQaOdF5qjIhurmflQbh0ogyxA1hi6aPDQVLXbOUq65fE7+1zmf+PQV71KrrOT/WuKJT41FA1vNLqFNfPkoQWFn1vLNC9et8jhGJLa8cf4quzsrbI4d06BsT2tu7e7xxluXmZZHOLox4+XXL6DFiDMnj/D48VVCyP303OB7aUfvhUJKhbCFoxGrUJQFQFAH4dI8STlPoacRV00FUCiIpRZbSUT3Jgt2qDX5bOqWVy9c4XKtvHXlBl948W1CGBHVE1QIadU75puvZFwC7anR8n2cy+qjX9jLzPQ7gXhZCSx7fanZn+//0qvdv3KoBO4g2ZTOeQE5+23R5AY6UzuE0O0eRWHTWxQF4h1H1tfZ3t5mZ2eHOlGJtW3LaDSirIqunPbmzVt45zi2eYRZUxPVlMov/fN/TeWVcQkXb0ypW6FVy8D79IuXqbzgmj2++up5PvjkQ/yR3/djtPU07cRjywR01j+hX6zzaa8SDSzUZAWkhmApbz9Rsi6ETIfuUIyR4EvbNUPAYzTe2mKsQjiUgtoVXJsG3rqyxf/6c/+Axq8SpGIWNjvFkusY0EEkg7yzdpqr+z4wX0w1jAAsu2fDqEo2/XPvR024zGLNBcxjAnNux909VvedHCqBJbIs/pu547JkIouhqZgftiGHvqpS1zVNSiBSVdbX16nr2liFQ2A6nTIeW9eg7e1t1tfXmYzGjIuKqI5Z2zKta8ZrR6Fw1E7xe1fNL24jk1GJIyBNSxFqfvTf+DE+8OQZtnZmjF2kcOC0QbUghIhKtOYo0rsDhtArhS+6hQdmHfSLJ4UXutoBb++NfQ4AiHUk0oDTliKb5AqNVEQ3Ybd2/NWf+7tcbx23ouNiWMfHAodDQiQRF3QYx6Is24WHtRFDluIhaLqYN7C4qPOxIe36oqWw+Ln5+jGxER9EnOBQCdxB7hTy2Q8pvpNpWdd1t1Mt7lLDB7EoCsszcNZ+ixSHN4KP1LNAjStfNLAyXmF1NKH0EKc77OztcOXGDcYUnForE0+hkgskhthHlz3I4oJY+C6DnIL+O9kL8xmLVoAV004aipKIo1bHC29cYLtx3JwpL52/yU5w1FKi0aUGoAEkLN1RexBxMJiF14fKeD+EftG3h9tj+0Ogdb/7mZXF3BgYPhdL33ZfyqESWCLLdoBl4SPod4KOwHMOiZ7fYZqm6ZRA3q0WsxHH47HlHXhHLQ3TZhdwrIxXaOvaQnMCsZmhzRTRyMbKCR575DTrayvcvHqZL371y7z22pgf/6HfxYmNE9ZqXAKuSLt46psnaWcfIuI2JrpFsLgRG5gZB/Z5MrVTKTIiVGqNWndDpHWeVkbsaMkv/MaXePPaLa7sNSgVEizzcRwjuAZcRF2EWALLrQCbpuVh2+Huv+x+ZllGHT50I7IsRn2G93343uH182cfMgsdcMnm4G0kHMybjsO/27alba1Zx1AWY8z52nVddwutbdvONRiNRp37EB14X9ri0gYNs26H2akjRbVKNRpxabvhxmsXTAl5h7br7E0d/+orb3BycxU5NmGtikiwRZo7IiHSx+bTwrac/oQEaK43sO8QIh0Jake6JYIry84dkBS5qMWzV0741X/1ZV4/f5VvXrjBrVlgtymYqsMDBcF6PLpIS0w1TJlObD/fu1cBiy7bogXwTok7i0lWWVEP35vPW7QgFl0MVZ23Tw6QKXA3bMM/jfUXuKSqzy+89t8A/yNwUlWviM3QXwZ+EtgF/rCqfv63f9jfeVncyfOxO8nQRFx8z+IDlEHDxWtmC8Gy8wqL2eewnPe0bWMKBCMNKbD2WLUKzqVW6ArEwPmrt9htoZEi1dGbAoDe0ukSYvJuJn1VZY7794k22KL3vkP7u8WXogkxwMUbe1y8vsXLb17ky6+f58rNPa5v17RRrd9BVFxyT6JY9+CIt2tEwWkP/91+X/J83L3cyUVb3MmH71m8/3fy97s5OkAWQJa7sQR+BvhfgZ8bHhSRx4B/B3hzcPgnsF4DzwLfD/zV9O/9K3dY19kaWFzIdwKmuvx8cakv3mLmnEkm6gwhzBX3tG2LLwrA2qCFEIjOSDbFe5qmZrueUnlH4cBrTIxE5u+HJhCi0qJo03BjGjgZHJu+xGmu4Te3xaXgem5CImIlvcNdrKPpVlBNZr93xJCxAAsDBqCNytbujJfevs43Xj/LJ3/rszTlGuJH+HJMU+8iUanAMACvBKe0xsuOqE/lEC0it3dEttuVcxjmMzSH59i4lwN6i+5ajLGz3pbdp8V7nN+z9Lp6x8fpvpV31ZA0yf+MNSD5xcGxnwJ+Tm1mPiUiR0TkjKqe/20Z7XdY8u3vE376B8N7b9WBC3nmWW5DjTVCox0td/8ZyQ9HcV4onMePJ9SN7fCbR4/0IGAMeG/md9u27G3tUdc1bd2wefw4gCHxDkvyQVMzT7Ec/tE6f+3v/hO++4NP84d+/09QX3qVzUnFic0NKBUVa7CtbehozYmK+DKRf9iOby07I67ocYOyrMiNPWIx4dK1bV544xx/9Wf+Dk2xQZQCHR2nSGHPdjY1t8anCINGS02MildNvIGBQEwZhfNYydxiyxGHhRqA7gYOIhh5zrvcjuRODOnYhpGc4bGh5AjR3D1e3PUPXmAAePcdiH4KOKeqX1qYrEeAtwZ/54ak96US2O+e5Ycvt9oahv6WPZiLO0iXPSZu7r25YGYutpx+z5x/XgwjQKHwnja03Rhy3HoymQy+RAId22YutFXXNbO6oW0jr569yif+2af46KPrlOXIFITTzu/Olk1UtYXuykQl1lf6WUaRJl5BEO8IlNzarfnEr3ySKzuBKzd32ZMVYhBIO7mGPgoyXLCZHWlZVGI4j8PXlt2jOfA1Jw1093eQXDC8X4N7tmgZLN7HdxrLOz0LB0G+ZSUgIivAf4+5Au9a5D5oSLqfLIsr54SaxV1j2fkikurt+3P38zvze7zrW1dlLn+X3IOhEiiKgtFoBCnzLj/hGZj03ppuxKjMZg1+NOLi9V1+4zNf4eTq93DyyAY4j0hAhuPG0mu981AUIN7SbaN2GXlGR26U7NvTmp3gOX9th1/8Z59mty0IUuHH60jbdm3Wl83r4uJdBua90/1ZhtlkTIM0Ld3r9B6OanYp7v7zhrIYhpxb9EP9cID0wLuxBJ4BngKyFfAo8HkR+TgHriHp/jJE8IcAWpaM7OffF2PM+XjOAgwhdG2+82tquXkgdCSi29vblmEI3e8xRqbTaVrkORRJR7e1s7OT3BdhNjOCk6osefih40RXWGGO97x+9ionJgVPHR8zHo8Tw7B13HGpyUcbApOJdfCZ1dMEBygtUEeIlExb5f/5lX/BC6+f561L19mVDaIXUEFns7l5XKYsh3M8lGULcmii7wfMdu/bDwCkT/VeFhG4za1Ydo0lAO7ws7Ob4tSxTOHfz/ItKwFV/QrwUP5bRN4AvjdFBz4B/HER+dsYIHjzoOABQ8kLfJlfmP8d7vrLAKlliPLcggA6qisGFWsKoQ2I9NhEdimK1CWIdH7T1NS1VRiurKxQFAWXL19mbW2NyXhs/IEiNDEyrVuubxXs1hFXjNJ1BFRxRZFaZyniHW1bA5LChp46Wkjy7SvbvPzGWb720jf55rU9rt2csts4ojjz8a0VCzHn8y/M3aJLMJyzRVlmVqtqHvLcXM9dYwlou+iGwe38AIv3cXGXX3QbhjLMbJQ8rgNkCryrhqSqul8vwl/GwoOvYCHC//S3aZz3VBYX8jLZb4fr3vsO78kWQHck/eLS4lPM7x5ed+iOtG1fezD83Bx+9EWBKjiJiEIIkVu7M/aaCH6Ece5nZZTMZAFyIlM6hhO2dmvevHidVy/c4osvvMlnv/QifvUobWsLns4tyYt0eRzvThjKfufdfqy/9J3v0XIcIb9viO8suhV3kttwiAUlf5AWf5Z325B0+PqTg98V+GPf/rDeWxnuAncCh/IOPcwN6HaUDA4Owkpzu0mXnJ+JNkmlOukxUit9resasAYhWQHs7OzQtvWcYrhx4wYxRtbX11G8NeBQYUQLrmA0XuPijRvcmCpttY7Gm6DWdahpGsMwvCA4QoSgQqtCGwq++NIr/Nzf/2Wu1BP2WsdMjjLabfCiWMAgEsRYmmcY+5HockBwOI93mntY7gJYdsI+703zpub4zy3H7joyn8C1qADuRiEsU1Lds5JbyR2gYOFhxqAq0BrNNo4S8LFFQsNMx1Q+WCcfSoKErhTXBZdKi4XSF0ZLDXMEpNEQOvscJ335a3pY7PPzMNKDkx+mwTOYufwy27Gqcvr0aXZ3d9nd3bFyYfGJ5szwAtEIoaXx5vNXRUBj4NL163z666/yI0+vM6rs/CYqeIv/u6Jgr/VcvbnDl77xGp/7yitcuHKLq7OKNjqcwtjFbjG22pOrWMlvgBRRuBMesGht7RcBuO01WX7O/MGFv9Ocx9SqrJvvO+AAi9aKqg5o2ftMS9EERQyatBwkPAAecCWQHwGLhGcizEgpgRGBViNOI05yW+yYymytAQmYDunDf+l6MvyMIQKfdqihxbDwxGbOgkUfNIOLIkJVVd35ltLb1y4457uMv47tXyMhNJTesb27x+vnLvG9T20Sg9A2kbcvXaYOatmJvmAWCq7d3OUrL53jKy+fY2faQjGBNEsqiaEnfX9V7ayXPLF34/svA1zn7s+CErmrxaW36Yn5z7+D0lk2xrmxLLFK7F/hIO38i/JAKwEgPRSOjkA71qy7lqNlQFpDuh0eRwNxZqrfgXNghTgumeV9GrBtjDKvDO6wKy7KfiapqrK2tsbRo0c5e/Ys3tvuX1XVHGiYF4LV2UfaOjILgeObE27tNLz65kWuy3fTTFuuXLnBP/zl3+Li5Rts7Uxp1FEyAjWG39ZVqK/SA193D3uI82tiLnKSxjCUISvvYpht/na80049P693M3f52BBkfad7sDiWxWstnrMILB4keaCVQN6Hc62YGXk13/Xco8Tv/zDbtbeuvNGoxqoi2wrKVuN46/oOb17btiy+2FcFDp/SvDiWgWF38j/zzp+pzIEuSWhrawvvPVVVUVUV29vbzGazbmcdVRXeeTRaz0QQxAuzRqnbKTe3dvnzf/3vYR5DZGt7Rt2OaP2IiNCEnGgjxjGYuvdIKmsGCGEBEBuY9t7dXn23+D2XAWzzFlJ/XpbMvLRsqe1neSxD/O9m/t9JFvGAxVTig6QQHmglsFRUWRkVHF0dsbpa2m6PEjRSYWmyAaHdCfhbg4dM5xd6Nv3tkvv7u/sdW4ZcD8ODOR15v1Ba1IhoNtutcMg6Ixkl2Pb5aW+xpJLhCGgq7Om7Eqdde8CpuCiLi3h4fJksFlntpxyWvWYHl152X3ln62K5DDGJoTtwNwrnIGULHSoBoLth+RkLDU5rViclDa0BXoXg6gZFaHHsbO2w2+x26H13JVngwrsLN2ARABsu/IwDDJUAWIszVctnH2IE+WvYQkt8/kBsA9OYeyk6VEegVngkKKrBfiSCWEMRVSCAJRHkAuPcb6Gfttt28tSGaD9zWnU5d1821YcyjOf3SmB/v354zeV8BHfGAt7pHrGgoPL7FqNABwkbPFQCnVNA4tL3uGpEUY1QoPSFIfuFsuJXQQvqWPCNl1/gxh5A1TEIO7HsvqjG5OvnegAsj4Uv/r4s9px/hjkBsLzQpb/O/Hfr35dTkFvACES8eqKGznqJOlBk2lsLHa8fkInJl8Xq7wSuDZXZMv96cdEvzo0unL94jTsBiHcKVd6VhbCPhbLscw6QN3CoBLq10kXlErOOE5wrsI7dCip4ChSHixFa0JA61qIdqj+4av9AprWoqoPPydIFm5YqgO6Kgwfermugn3bRhiEzUJ+FJCKpq49505oh/ZyzqKnJ55y5azRm0Ldky1hHbxZLP3f0n2mKZLgCdA4jyVZE+qQF70Lnf1tY9IvzMsQQlirZd1qIw8EsWdO3+f2Ycs9zMbSGYjeneVzv8Nn3kTzQSmC4aK0cv/eHRQzgKiTg7DCIhQKlbanE4wg9l/0QwBqg35B2jc6vpGvmGcnNM+jPG47vDk9SVgLd+CUHBgeiYiQjCU6LWQEMt3msqci8ZdLv+M7vnz5t86a9Ek0VfHGg2PrxDvoldhpqfpXG23ba4ef2vQyHY1iGRQxN96GZnxdt/zEDRZGU5fwXzd+y/1MG37NzS+iVTjeeA4QJyP2AYorIZWAHuPJej2UgJzgczzvJ/Tamw/HcWZ5Q1ZOLB+8LJQAgIp9V1e99r8eR5XA87yz325gOx/Pu5HYmzUM5lEN5oORQCRzKoTzgcj8pgf/jvR7AghyO553lfhvT4Xjehdw3mMChHMqhvDdyP1kCh3Ioh/IeyHuuBETk94jIiyLyioj8qfdoDI+JyK+LyNdF5Gsi8l+l439WRM6JyBfTz0/ewzG9ISJfSZ/72XTsmIj8moi8nP49eo/G8v7BHHxRRG6JyJ+41/MjIj8tIpdE5KuDY0vnREz+SnquviwiH7tH4/mLIvJC+sxfEJEj6fiTIrI3mKu/9ts9nnctw/TMe/2DFfC9CjwNVMCXgA+9B+M4A3ws/b4OvAR8CPizwH/7Hs3NG8CJhWP/A/Cn0u9/CvgL79E9uwA8ca/nB/hR4GPAV99pTjCau1/Bcn1+APj0PRrPvwMU6fe/MBjPk8Pz7qef99oS+Djwiqq+pqo18LexBib3VFT1vKZ2aaq6BXwD65dwv8lPAT+bfv9Z4Pe+B2P4ceBVVf3mvf5gVf0XwLWFw/vNyU+RGuGo6qeAIyJy5js9HlX9VVXNXUo+hTFu39fyXiuB/ZqVvGci1m3pe4BPp0N/PJl2P32vzO8kCvyqiHxOrEcDwCnt2ZsvAKfu4Xiy/AHg5wd/v1fzk2W/Obkfnq3/DLNGsjwlIl8QkX8uIj9yj8eyr7zXSuC+EhFZA/4+8CdU9RbWS/EZ4LuxLkr/0z0czg+r6sew/o5/TER+dPiimo15T0M7IlIB/wHwd9Oh93J+bpP3Yk72ExH5M0AL/M106DzwuKp+D/BfA39LRDbeq/EN5b1WAnfdrOQ7LSJSYgrgb6rq/wugqhdVNahV6vyfmPtyT0RVz6V/LwG/kD77YjZp07+X7tV4kvwE8HlVvZjG9p7Nz0D2m5P37NkSkT+MdfL+j5NiQlVnqno1/f45DAt77l6M553kvVYC/xp4VkSeSrvMHwA+ca8HIVZq9n8B31DVvzQ4PvQhfx/w1cX3fofGsyoi6/l3DGz6KjY3fyid9oeYbwZ7L+QPMnAF3qv5WZD95uQTwH+SogQ/wD1qhCMivwdr1PsfqOru4PhJEfHp96exzt2vfafHc1fyXiOTGIr7EqYZ/8x7NIYfxszILwNfTD8/CfwN4Cvp+CeAM/doPE9jkZIvAV/L8wIcBz4JvAz8U+DYPZyjVeAqsDk4dk/nB1NA54EG8/H/yH5zgkUF/rf0XH0F65J1L8bzCoZF5Ofor6Vzf3+6l18EPg/8+/f6Od/v5zBj8FAO5QGX99odOJRDOZT3WA6VwKEcygMuh0rgUA7lAZdDJXAoh/KAy6ESOJRDecDlUAkcyqE84HKoBA7lUB5wOVQCh3IoD7j8/9T5KsbpJ+ctAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matched to: angelina jolie\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matched to: marion cotillard\n"
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matched to: marion cotillard\n"
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose the class label you want to check\n",
    "clbl = 7\n",
    "step = num_valid_images // num_classes\n",
    "pred_labels = np.argmax(prediction[clbl*step:(clbl+1)*step],axis=1)\n",
    "wrong_labels = np.transpose(np.nonzero(pred_labels != clbl))\n",
    "\n",
    "\n",
    "# Get the validation dataset as numpy array\n",
    "\n",
    "import numpy as np\n",
    "def get_images_and_labels(dataset):\n",
    "    all_images = []\n",
    "    all_labels = []\n",
    "    for images, labels in dataset:\n",
    "        all_images.append(images)\n",
    "        all_labels.append(labels)\n",
    "    return np.concatenate(all_images), np.concatenate(all_labels)\n",
    "\n",
    "val_images, val_labels = get_images_and_labels(validation_dataset)\n",
    "\n",
    "\n",
    "print('wrong classification for: {}'.format(class_names[clbl]))\n",
    "\n",
    "for i, i0 in enumerate(wrong_labels):\n",
    "    img = val_images[clbl*step + i0]\n",
    "    img = np.squeeze(img, axis=0)\n",
    "    plt.figure(figsize=(4, 4))\n",
    "    plt.imshow(img.astype(\"uint8\"))\n",
    "    plt.show()\n",
    "    plt.axis(\"off\")\n",
    "    print('matched to: {}'.format(class_names[pred_labels[i0][0]]))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R0dfpdDOGhM2"
   },
   "source": [
    "# Part IV : Object Detection with Mask R-CNN\n",
    "\n",
    "### Please run this section on Colab !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vOAEQt-pGhM3"
   },
   "source": [
    "Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph.\n",
    "\n",
    "It is a challenging problem that involves building upon methods for object recognition (e.g. where are they), object localization (e.g. what are their extent), and object classification (e.g. what are they).\n",
    "\n",
    "In recent years, deep learning techniques have achieved state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions. Most notably is the R-CNN, or Region-Based Convolutional Neural Networks, and the most recent technique called Mask R-CNN that is capable of achieving state-of-the-art results on a range of object detection tasks.\n",
    "\n",
    "In this section, we will discover how to use the __Mask R-CNN__ model to detect objects in new photographs.\n",
    "\n",
    "After completing this tutorial, you will know:\n",
    "\n",
    "- The region-based Convolutional Neural Network family of models for object detection and the most recent variation called Mask R-CNN.\n",
    "\n",
    "- The best-of-breed open source library implementation of the Mask R-CNN for the Keras deep learning library.\n",
    "    \n",
    "- How to use a pre-trained Mask R-CNN to perform object localization and detection on new photographs.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ra-bXlWXGhM4"
   },
   "source": [
    "## Mask R-CNN for Object Detection\n",
    "\n",
    "Object detection is a computer vision task that involves both localizing one or more objects within an image and classifying each object in the image.\n",
    "\n",
    "It is a challenging computer vision task that requires both successful object localization in order to locate and draw a bounding box around each object in an image, and object classification to predict the correct class of object that was localized.\n",
    "\n",
    "An extension of object detection involves marking the specific pixels in the image that belong to each detected object instead of using coarse bounding boxes during object localization. This harder version of the problem is generally referred to as object segmentation or semantic segmentation.\n",
    "\n",
    "The __Region-Based__ Convolutional Neural Network, or R-CNN, is a family of convolutional neural network models designed for object detection, developed by Ross Girshick, et al.\n",
    "\n",
    "There are perhaps four main variations of the approach, resulting in the current pinnacle called Mask R-CNN. The salient aspects of each variation can be summarized as follows:\n",
    "\n",
    "- __R-CNN__: Bounding boxes are proposed by the “selective search” algorithm, each of which is stretched and features are extracted via a deep convolutional neural network, such as AlexNet, before a final set of object classifications are made with linear SVMs.\n",
    "\n",
    "- __Fast R-CNN__: Simplified design with a single model, bounding boxes are still specified as input, but a region-of-interest pooling layer is used after the deep CNN to consolidate regions and the model predicts both class labels and regions of interest directly.\n",
    "    \n",
    "- __Faster R-CNN__: Addition of a Region Proposal Network that interprets features extracted from the deep CNN and learns to propose regions-of-interest directly.\n",
    "    \n",
    "- __Mask R-CNN__: Extension of Faster R-CNN that adds an output model for predicting a mask for each detected object.\n",
    "\n",
    "The Mask R-CNN model introduced in the 2018 paper titled [Mask R-CNN](https://arxiv.org/abs/1703.06870) is the most recent variation of the family models and supports both object detection and object segmentation. The paper provides a nice summary of the model linage to that point:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GlXwuVoOGhM7"
   },
   "source": [
    "### Matterport Mask R-CNN Project\n",
    "\n",
    "Mask R-CNN is a sophisticated model to implement, especially as compared to a simple or even state-of-the-art deep convolutional neural network model.\n",
    "\n",
    "Source code is available for each version of the R-CNN model, provided in separate GitHub repositories with prototype models based on the Caffe deep learning framework. For example:\n",
    "\n",
    "- R-CNN: [Regions with Convolutional Neural Network Features, GitHub](https://github.com/rbgirshick/rcnn)\n",
    "\n",
    "- Fast R-CNN, [GitHub](https://github.com/rbgirshick/fast-rcnn)\n",
    "\n",
    "- Faster R-CNN Python Code, [GitHub](https://github.com/rbgirshick/py-faster-rcnn)\n",
    "\n",
    "- Detectron, Facebook AI, [GitHub](https://github.com/facebookresearch/Detectron)\n",
    "\n",
    "Instead of developing an implementation of the R-CNN or Mask R-CNN model from scratch, we can use a reliable third-party implementation built on top of the Keras deep learning framework.\n",
    "\n",
    "The best of breed third-party implementations of Mask R-CNN is the [Mask R-CNN](https://github.com/matterport/Mask_RCNN) Project developed by Matterport. The project is open source released under a permissive license (i.e. MIT license) and the code has been widely used on a variety of projects and Kaggle competitions.\n",
    "\n",
    "Nevertheless, it is an open source project, subject to the whims of the project developers. As such, I have a fork of the project available, just in case there are major changes to the API in the future.\n",
    "\n",
    "The project is light on API documentation, although it does provide a number of examples in the form of Python Notebooks that you can use to understand how to use the library by example. Two notebooks that may be helpful to review are:\n",
    "\n",
    "- Mask R-CNN Demo, [Notebook](https://github.com/matterport/Mask_RCNN/blob/master/samples/demo.ipynb)\n",
    "\n",
    "- Mask R-CNN – Inspect Trained Model, [Notebook](https://github.com/matterport/Mask_RCNN/blob/master/samples/coco/inspect_model.ipynb)\n",
    "\n",
    "There are perhaps three main use cases for using the Mask R-CNN model with the Matterport library; they are:\n",
    "\n",
    "- __Object Detection Application__: Use a pre-trained model for object detection on new images.\n",
    "\n",
    "- __New Model via Transfer Learning__: Use a pre-trained model as a starting point in developing a model for a new object detection dataset.\n",
    "    \n",
    "- __New Model from Scratch__: Develop a new model from scratch for an object detection dataset.\n",
    "\n",
    "In order to get familiar with the model and the library, we will look at the first example in the next section.\n",
    "\n",
    "#### Object Detection With Mask R-CNN\n",
    "\n",
    "In this section, we will use the Matterport Mask R-CNN library to perform object detection on arbitrary photographs.\n",
    "\n",
    "Much like using a pre-trained deep CNN for image classification, e.g. such as VGG-16 trained on an ImageNet dataset, we can use a pre-trained Mask R-CNN model to detect objects in new photographs. In this case, we will use a Mask R-CNN trained on the [MS COCO object detection problem](http://cocodataset.org/#home).\n",
    "\n",
    "#### Mask R-CNN Installation\n",
    "\n",
    "The first step is to install the library.\n",
    "\n",
    "At the time of writing, there is no distributed version of the library, so we have to install it manually. The good news is that this is very easy.\n",
    "\n",
    "Installation involves cloning the GitHub repository and running the installation script on your workstation. If you are having trouble, see the [installation instructions](https://github.com/matterport/Mask_RCNN#installation) buried in the library’s readme file.\n",
    "\n",
    "#### Step 0. Open Colab and Upload this Notebook\n",
    "\n",
    "#### Step 1. Clone the Mask R-CNN GitHub Repository\n",
    "\n",
    "This is as simple as running the following command from your command line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "colab_type": "code",
    "id": "HGiDmuejGhM8",
    "outputId": "ce5ca013-96e5-4766-d2ed-b4cde9b3ca94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'Mask_RCNN'...\n",
      "remote: Enumerating objects: 956, done.\u001b[K\n",
      "remote: Total 956 (delta 0), reused 0 (delta 0), pack-reused 956\u001b[K\n",
      "Receiving objects: 100% (956/956), 111.84 MiB | 30.52 MiB/s, done.\n",
      "Resolving deltas: 100% (570/570), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/matterport/Mask_RCNN.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S7uXyFVPGhNA"
   },
   "source": [
    "This will create a new local directory with the name Mask_RCNN that looks as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "raw",
    "id": "DhKn5ytcGhNA"
   },
   "source": [
    "Mask_RCNN\n",
    "├── assets\n",
    "├── build\n",
    "│   ├── bdist.macosx-10.13-x86_64\n",
    "│   └── lib\n",
    "│       └── mrcnn\n",
    "├── dist\n",
    "├── images\n",
    "├── mask_rcnn.egg-info\n",
    "├── mrcnn\n",
    "└── samples\n",
    "    ├── balloon\n",
    "    ├── coco\n",
    "    ├── nucleus\n",
    "    └── shapes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WvFlDgvJGhNB"
   },
   "source": [
    "#### Step 2. Install the Mask R-CNN Library\n",
    "\n",
    "The library can be installed directly via pip.\n",
    "\n",
    "Change directory into the _Mask_RCNN_ directory and run the installation script.\n",
    "\n",
    "From the command line, type the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "aEUeZhX5GhNB",
    "outputId": "be5de5a1-e821-477c-ce28-91bb9f8c3194"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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