dog_app.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Artificial Intelligence Nanodegree\n",
    "\n",
    "## Convolutional Neural Networks\n",
    "\n",
    "## Project: Write an Algorithm for a Dog Identification App \n",
    "\n",
    "---\n",
    "\n",
    "In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with **'(IMPLEMENTATION)'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully! \n",
    "\n",
    "> **Note**: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission.\n",
    "\n",
    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.\n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut.  Markdown cells can be edited by double-clicking the cell to enter edit mode.\n",
    "\n",
    "The rubric contains _optional_ \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. If you decide to pursue the \"Stand Out Suggestions\", you should include the code in this IPython notebook.\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "### Why We're Here \n",
    "\n",
    "In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app.  At the end of this project, your code will accept any user-supplied image as input.  If a dog is detected in the image, it will provide an estimate of the dog's breed.  If a human is detected, it will provide an estimate of the dog breed that is most resembling.  The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!). \n",
    "\n",
    "![Sample Dog Output](images/sample_dog_output.png)\n",
    "\n",
    "In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed.  There are many points of possible failure, and no perfect algorithm exists.  Your imperfect solution will nonetheless create a fun user experience!\n",
    "\n",
    "### The Road Ahead\n",
    "\n",
    "We break the notebook into separate steps.  Feel free to use the links below to navigate the notebook.\n",
    "\n",
    "* [Step 0](#step0): Import Datasets\n",
    "* [Step 1](#step1): Detect Humans\n",
    "* [Step 2](#step2): Detect Dogs\n",
    "* [Step 3](#step3): Create a CNN to Classify Dog Breeds (from Scratch)\n",
    "* [Step 4](#step4): Use a CNN to Classify Dog Breeds (using Transfer Learning)\n",
    "* [Step 5](#step5): Create a CNN to Classify Dog Breeds (using Transfer Learning)\n",
    "* [Step 6](#step6): Write your Algorithm\n",
    "* [Step 7](#step7): Test Your Algorithm\n",
    "\n",
    "---\n",
    "<a id='step0'></a>\n",
    "## Step 0: Import Datasets\n",
    "\n",
    "### Import Dog Dataset\n",
    "\n",
    "In the code cell below, we import a dataset of dog images.  We populate a few variables through the use of the `load_files` function from the scikit-learn library:\n",
    "- `train_files`, `valid_files`, `test_files` - numpy arrays containing file paths to images\n",
    "- `train_targets`, `valid_targets`, `test_targets` - numpy arrays containing onehot-encoded classification labels \n",
    "- `dog_names` - list of string-valued dog breed names for translating labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 133 total dog categories.\n",
      "There are 8351 total dog images.\n",
      "\n",
      "There are 6680 training dog images.\n",
      "There are 835 validation dog images.\n",
      "There are 836 test dog images.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_files       \n",
    "from keras.utils import np_utils\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "\n",
    "# define function to load train, test, and validation datasets\n",
    "def load_dataset(path):\n",
    "    data = load_files(path)\n",
    "    dog_files = np.array(data['filenames'])\n",
    "    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)\n",
    "    return dog_files, dog_targets\n",
    "\n",
    "# load train, test, and validation datasets\n",
    "train_files, train_targets = load_dataset('dogImages/train')\n",
    "valid_files, valid_targets = load_dataset('dogImages/valid')\n",
    "test_files, test_targets = load_dataset('dogImages/test')\n",
    "\n",
    "# load list of dog names\n",
    "dog_names = [item[20:-1] for item in sorted(glob(\"dogImages/train/*/\"))]\n",
    "\n",
    "# print statistics about the dataset\n",
    "print('There are %d total dog categories.' % len(dog_names))\n",
    "print('There are %s total dog images.\\n' % len(np.hstack([train_files, valid_files, test_files])))\n",
    "print('There are %d training dog images.' % len(train_files))\n",
    "print('There are %d validation dog images.' % len(valid_files))\n",
    "print('There are %d test dog images.'% len(test_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Human Dataset\n",
    "\n",
    "In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array `human_files`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 13233 total human images.\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "random.seed(8675309)\n",
    "\n",
    "# load filenames in shuffled human dataset\n",
    "human_files = np.array(glob(\"lfw/*/*\"))\n",
    "random.shuffle(human_files)\n",
    "\n",
    "# print statistics about the dataset\n",
    "print('There are %d total human images.' % len(human_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step1'></a>\n",
    "## Step 1: Detect Humans\n",
    "\n",
    "We use OpenCV's implementation of [Haar feature-based cascade classifiers](http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html) to detect human faces in images.  OpenCV provides many pre-trained face detectors, stored as XML files on [github](https://github.com/opencv/opencv/tree/master/data/haarcascades).  We have downloaded one of these detectors and stored it in the `haarcascades` directory.\n",
    "\n",
    "In the next code cell, we demonstrate how to use this detector to find human faces in a sample image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of faces detected: 1\n"
     ]
    },
    {
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d/N48dcqdpA9iFiLy4/T6hTHmHwC/D/zcGPO5iPzUGPM58MUT29Jox14mIdky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\niiobEDk0m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JMTwMWj+nJeQWmCBmVF1CeLIGFtiVaJLnS44gIVIvNYEL2HYCHVJPOI3aD5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\nDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3811c5f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2                \n",
    "import matplotlib.pyplot as plt                        \n",
    "%matplotlib inline                               \n",
    "\n",
    "# extract pre-trained face detector\n",
    "face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')\n",
    "\n",
    "# load color (BGR) image\n",
    "img = cv2.imread(human_files[3])\n",
    "# convert BGR image to grayscale\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# find faces in image\n",
    "faces = face_cascade.detectMultiScale(gray)\n",
    "\n",
    "# print number of faces detected in the image\n",
    "print('Number of faces detected:', len(faces))\n",
    "\n",
    "# get bounding box for each detected face\n",
    "for (x,y,w,h) in faces:\n",
    "    # add bounding box to color image\n",
    "    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
    "    \n",
    "# convert BGR image to RGB for plotting\n",
    "cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# display the image, along with bounding box\n",
    "plt.imshow(cv_rgb)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before using any of the face detectors, it is standard procedure to convert the images to grayscale.  The `detectMultiScale` function executes the classifier stored in `face_cascade` and takes the grayscale image as a parameter.  \n",
    "\n",
    "In the above code, `faces` is a numpy array of detected faces, where each row corresponds to a detected face.  Each detected face is a 1D array with four entries that specifies the bounding box of the detected face.  The first two entries in the array (extracted in the above code as `x` and `y`) specify the horizontal and vertical positions of the top left corner of the bounding box.  The last two entries in the array (extracted here as `w` and `h`) specify the width and height of the box.\n",
    "\n",
    "### Write a Human Face Detector\n",
    "\n",
    "We can use this procedure to write a function that returns `True` if a human face is detected in an image and `False` otherwise.  This function, aptly named `face_detector`, takes a string-valued file path to an image as input and appears in the code block below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# returns \"True\" if face is detected in image stored at img_path\n",
    "def face_detector(img_path):\n",
    "    img = cv2.imread(img_path)\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_cascade.detectMultiScale(gray)\n",
    "    return len(faces) > 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Assess the Human Face Detector\n",
    "\n",
    "__Question 1:__ Use the code cell below to test the performance of the `face_detector` function.  \n",
    "- What percentage of the first 100 images in `human_files` have a detected human face?  \n",
    "- What percentage of the first 100 images in `dog_files` have a detected human face? \n",
    "\n",
    "Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face.  You will see that our algorithm falls short of this goal, but still gives acceptable performance.  We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays `human_files_short` and `dog_files_short`.\n",
    "\n",
    "__Answer:__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected human faces:  99 / 100\n",
      "Detected human faces out of dog faces:  12 / 100\n"
     ]
    }
   ],
   "source": [
    "human_files_short = human_files[:100]\n",
    "dog_files_short = train_files[:100]\n",
    "# Do NOT modify the code above this line.\n",
    "\n",
    "## TODO: Test the performance of the face_detector algorithm \n",
    "## on the images in human_files_short and dog_files_short.\n",
    "def test_face_detector_performance():    \n",
    "    detected_human_faces = 0\n",
    "    for path in human_files_short:\n",
    "        if face_detector(path):\n",
    "            detected_human_faces += 1\n",
    "    print(\"Detected human faces: \",detected_human_faces,\"/ 100\")\n",
    "\n",
    "    detected_human_faces = 0\n",
    "    for path in dog_files_short:\n",
    "        if face_detector(path):\n",
    "            detected_human_faces += 1\n",
    "    print(\"Detected human faces out of dog faces: \",detected_human_faces, \"/ 100\")\n",
    "    \n",
    "test_face_detector_performance()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Question 2:__ This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?\n",
    "\n",
    "__Answer:__ **In special cases it is reasonable - for example logging in with face scanning on a device. In general if it is not possible to force the user to pose on camera, a CNN, which is trained with lots of images of humans, could do a good job of detecting people in various poses.**\n",
    "\n",
    "We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :).  Please use the code cell below to design and test your own face detection algorithm.  If you decide to pursue this _optional_ task, report performance on each of the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# copied these functions from Step 2 here because I need them for the optional assignment\n",
    "\n",
    "from keras.preprocessing import image                  \n",
    "from tqdm import tqdm\n",
    "\n",
    "def path_to_tensor(img_path):\n",
    "    # loads RGB image as PIL.Image.Image type\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)\n",
    "    x = image.img_to_array(img)\n",
    "    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor\n",
    "    return np.expand_dims(x, axis=0)\n",
    "\n",
    "def paths_to_tensor(img_paths):\n",
    "    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]\n",
    "    return np.vstack(list_of_tensors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 13233/13233 [02:42<00:00, 81.63it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 8351/8351 [02:25<00:00, 57.45it/s]\n"
     ]
    }
   ],
   "source": [
    "## (Optional) TODO: Report the performance of another  \n",
    "## face detection algorithm on the LFW dataset\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "from PIL import ImageFile                            \n",
    "ImageFile.LOAD_TRUNCATED_IMAGES = True   \n",
    "\n",
    "dog_files = np.array(glob(\"dogImages/*/*/*\"))\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(np.concatenate([paths_to_tensor(human_files).astype('float32')/255, paths_to_tensor(dog_files).astype('float32')/255]),\n",
    "                                                    np_utils.to_categorical(np.concatenate([np.zeros(len(human_files)), np.ones(len(dog_files))]),2), test_size=0.2, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 224, 224, 8)       224       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 112, 112, 8)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 112, 112, 16)      1168      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 56, 56, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 56, 56, 32)        4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 28, 28, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 28, 28, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 14, 14, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 500)               3136500   \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 500)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 250)               125250    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 250)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 502       \n",
      "=================================================================\n",
      "Total params: 3,360,636\n",
      "Trainable params: 3,360,636\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "from keras.models import Sequential\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=8, kernel_size=3, padding='same', activation='relu', \n",
    "                        input_shape=(224, 224, 3)))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=16, kernel_size=3, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=128, kernel_size=3, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(500, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(250, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(2, activation='softmax'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 13813 samples, validate on 3454 samples\n",
      "Epoch 1/20\n",
      "Epoch 00000: val_loss improved from inf to 0.52029, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "65s - loss: 0.6945 - acc: 0.6191 - val_loss: 0.5203 - val_acc: 0.6740\n",
      "Epoch 2/20\n",
      "Epoch 00001: val_loss improved from 0.52029 to 0.15092, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "19s - loss: 0.5063 - acc: 0.8105 - val_loss: 0.1509 - val_acc: 0.9473\n",
      "Epoch 3/20\n",
      "Epoch 00002: val_loss improved from 0.15092 to 0.08657, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "19s - loss: 0.2010 - acc: 0.9188 - val_loss: 0.0866 - val_acc: 0.9696\n",
      "Epoch 4/20\n",
      "Epoch 00003: val_loss did not improve\n",
      "19s - loss: 0.1427 - acc: 0.9495 - val_loss: 0.1576 - val_acc: 0.9461\n",
      "Epoch 5/20\n",
      "Epoch 00004: val_loss improved from 0.08657 to 0.06396, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "19s - loss: 0.1056 - acc: 0.9658 - val_loss: 0.0640 - val_acc: 0.9820\n",
      "Epoch 6/20\n",
      "Epoch 00005: val_loss improved from 0.06396 to 0.04597, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "19s - loss: 0.1289 - acc: 0.9615 - val_loss: 0.0460 - val_acc: 0.9838\n",
      "Epoch 7/20\n",
      "Epoch 00006: val_loss improved from 0.04597 to 0.03764, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "19s - loss: 0.0661 - acc: 0.9773 - val_loss: 0.0376 - val_acc: 0.9867\n",
      "Epoch 8/20\n",
      "Epoch 00007: val_loss did not improve\n",
      "19s - loss: 0.0445 - acc: 0.9848 - val_loss: 0.0570 - val_acc: 0.9818\n",
      "Epoch 9/20\n",
      "Epoch 00008: val_loss improved from 0.03764 to 0.03622, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "19s - loss: 0.0775 - acc: 0.9754 - val_loss: 0.0362 - val_acc: 0.9881\n",
      "Epoch 10/20\n",
      "Epoch 00009: val_loss did not improve\n",
      "19s - loss: 0.0340 - acc: 0.9875 - val_loss: 0.2656 - val_acc: 0.9195\n",
      "Epoch 11/20\n",
      "Epoch 00010: val_loss improved from 0.03622 to 0.02401, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "19s - loss: 0.0526 - acc: 0.9824 - val_loss: 0.0240 - val_acc: 0.9907\n",
      "Epoch 12/20\n",
      "Epoch 00011: val_loss improved from 0.02401 to 0.01371, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "19s - loss: 0.0702 - acc: 0.9702 - val_loss: 0.0137 - val_acc: 0.9945\n",
      "Epoch 13/20\n",
      "Epoch 00012: val_loss did not improve\n",
      "18s - loss: 0.0207 - acc: 0.9930 - val_loss: 0.0170 - val_acc: 0.9936\n",
      "Epoch 14/20\n",
      "Epoch 00013: val_loss improved from 0.01371 to 0.01344, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "18s - loss: 0.0543 - acc: 0.9805 - val_loss: 0.0134 - val_acc: 0.9959\n",
      "Epoch 15/20\n",
      "Epoch 00014: val_loss improved from 0.01344 to 0.01275, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "18s - loss: 0.0199 - acc: 0.9934 - val_loss: 0.0128 - val_acc: 0.9962\n",
      "Epoch 16/20\n",
      "Epoch 00015: val_loss improved from 0.01275 to 0.01259, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "18s - loss: 0.0310 - acc: 0.9894 - val_loss: 0.0126 - val_acc: 0.9965\n",
      "Epoch 17/20\n",
      "Epoch 00016: val_loss improved from 0.01259 to 0.01140, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "18s - loss: 0.0067 - acc: 0.9978 - val_loss: 0.0114 - val_acc: 0.9959\n",
      "Epoch 18/20\n",
      "Epoch 00017: val_loss did not improve\n",
      "18s - loss: 0.2287 - acc: 0.9704 - val_loss: 0.0166 - val_acc: 0.9942\n",
      "Epoch 19/20\n",
      "Epoch 00018: val_loss improved from 0.01140 to 0.01119, saving model to saved_models/weights.best.dog_vs_human.hdf5\n",
      "18s - loss: 0.0068 - acc: 0.9971 - val_loss: 0.0112 - val_acc: 0.9980\n",
      "Epoch 20/20\n",
      "Epoch 00019: val_loss did not improve\n",
      "18s - loss: 0.0278 - acc: 0.9916 - val_loss: 0.0123 - val_acc: 0.9974\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint  \n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.dog_vs_human.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "\n",
    "history = model.fit(X_train, y_train, validation_split=0.2, epochs=20, batch_size=500, callbacks=[checkpointer], verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 99.6062%\n"
     ]
    }
   ],
   "source": [
    "model_predictions = [np.argmax(model.predict(np.expand_dims(feature, axis=0))) for feature in X_test]\n",
    "\n",
    "test_accuracy = 100*np.sum(np.array(model_predictions)==np.argmax(y_test, axis=1))/len(model_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "human detected for file: Richard_Gephardt_0001.jpg\n",
      "dog detected for file: French_bulldog_04824.jpg\n",
      "dog detected for file: Komondor_06380.jpg\n",
      "human detected for file: Julie_Gerberding_0011.jpg\n",
      "dog detected for file: Kerry_blue_terrier_06285.jpg\n",
      "dog detected for file: Elvis_Costello_0001.jpg\n",
      "dog detected for file: Norfolk_terrier_07050.jpg\n",
      "dog detected for file: Boykin_spaniel_02468.jpg\n",
      "human detected for file: Nick_Nolte_0004.jpg\n",
      "human detected for file: Fernando_Henrique_Cardoso_0008.jpg\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def dog_vs_human_cnn_detector(img_path): \n",
    "    if np.argmax(model.predict(path_to_tensor(img_path))) == 0:\n",
    "        return \"human\"\n",
    "    else:\n",
    "        return \"dog\"     \n",
    "   \n",
    "for file in np.random.choice(np.concatenate([dog_files, human_files]), 10):\n",
    "    print(dog_vs_human_cnn_detector(file) + \" detected for file: \" + os.path.basename(file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step2'></a>\n",
    "## Step 2: Detect Dogs\n",
    "\n",
    "In this section, we use a pre-trained [ResNet-50](http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006) model to detect dogs in images.  Our first line of code downloads the ResNet-50 model, along with weights that have been trained on [ImageNet](http://www.image-net.org/), a very large, very popular dataset used for image classification and other vision tasks.  ImageNet contains over 10 million URLs, each linking to an image containing an object from one of [1000 categories](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a).  Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.applications.resnet50 import ResNet50\n",
    "\n",
    "# define ResNet50 model\n",
    "ResNet50_model = ResNet50(weights='imagenet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-process the Data\n",
    "\n",
    "When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape\n",
    "\n",
    "$$\n",
    "(\\text{nb_samples}, \\text{rows}, \\text{columns}, \\text{channels}),\n",
    "$$\n",
    "\n",
    "where `nb_samples` corresponds to the total number of images (or samples), and `rows`, `columns`, and `channels` correspond to the number of rows, columns, and channels for each image, respectively.  \n",
    "\n",
    "The `path_to_tensor` function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN.  The function first loads the image and resizes it to a square image that is $224 \\times 224$ pixels.  Next, the image is converted to an array, which is then resized to a 4D tensor.  In this case, since we are working with color images, each image has three channels.  Likewise, since we are processing a single image (or sample), the returned tensor will always have shape\n",
    "\n",
    "$$\n",
    "(1, 224, 224, 3).\n",
    "$$\n",
    "\n",
    "The `paths_to_tensor` function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape \n",
    "\n",
    "$$\n",
    "(\\text{nb_samples}, 224, 224, 3).\n",
    "$$\n",
    "\n",
    "Here, `nb_samples` is the number of samples, or number of images, in the supplied array of image paths.  It is best to think of `nb_samples` as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing import image                  \n",
    "from tqdm import tqdm\n",
    "\n",
    "def path_to_tensor(img_path):\n",
    "    # loads RGB image as PIL.Image.Image type\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)\n",
    "    x = image.img_to_array(img)\n",
    "    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor\n",
    "    return np.expand_dims(x, axis=0)\n",
    "\n",
    "def paths_to_tensor(img_paths):\n",
    "    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]\n",
    "    return np.vstack(list_of_tensors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions with ResNet-50\n",
    "\n",
    "Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing.  First, the RGB image is converted to BGR by reordering the channels.  All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image.  This is implemented in the imported function `preprocess_input`.  If you're curious, you can check the code for `preprocess_input` [here](https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py).\n",
    "\n",
    "Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions.  This is accomplished with the `predict` method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category.  This is implemented in the `ResNet50_predict_labels` function below.\n",
    "\n",
    "By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this [dictionary](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "\n",
    "def ResNet50_predict_labels(img_path):\n",
    "    # returns prediction vector for image located at img_path\n",
    "    img = preprocess_input(path_to_tensor(img_path))\n",
    "    return np.argmax(ResNet50_model.predict(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write a Dog Detector\n",
    "\n",
    "While looking at the [dictionary](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a), you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from `'Chihuahua'` to `'Mexican hairless'`.  Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the `ResNet50_predict_labels` function above returns a value between 151 and 268 (inclusive).\n",
    "\n",
    "We use these ideas to complete the `dog_detector` function below, which returns `True` if a dog is detected in an image (and `False` if not)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### returns \"True\" if a dog is detected in the image stored at img_path\n",
    "def dog_detector(img_path):\n",
    "    prediction = ResNet50_predict_labels(img_path)\n",
    "    return ((prediction <= 268) & (prediction >= 151)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Assess the Dog Detector\n",
    "\n",
    "__Question 3:__ Use the code cell below to test the performance of your `dog_detector` function.  \n",
    "- What percentage of the images in `human_files_short` have a detected dog?  \n",
    "- What percentage of the images in `dog_files_short` have a detected dog?\n",
    "\n",
    "__Answer:__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected dogs out of human faces:  1 / 100\n",
      "Detected dog faces out of dog faces:  100 / 100\n"
     ]
    }
   ],
   "source": [
    "detected_human_faces = 0\n",
    "for path in human_files_short:\n",
    "    if dog_detector(path):\n",
    "        detected_human_faces += 1\n",
    "print(\"Detected dogs out of human faces: \",detected_human_faces,\"/ 100\")\n",
    "\n",
    "detected_human_faces = 0\n",
    "for path in dog_files_short:\n",
    "    if dog_detector(path):\n",
    "        detected_human_faces += 1\n",
    "print(\"Detected dog faces out of dog faces: \",detected_human_faces, \"/ 100\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step3'></a>\n",
    "## Step 3: Create a CNN to Classify Dog Breeds (from Scratch)\n",
    "\n",
    "Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images.  In this step, you will create a CNN that classifies dog breeds.  You must create your CNN _from scratch_ (so, you can't use transfer learning _yet_!), and you must attain a test accuracy of at least 1%.  In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.\n",
    "\n",
    "Be careful with adding too many trainable layers!  More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process.  Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train. \n",
    "\n",
    "We mention that the task of assigning breed to dogs from images is considered exceptionally challenging.  To see why, consider that *even a human* would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.  \n",
    "\n",
    "Brittany | Welsh Springer Spaniel\n",
    "- | - \n",
    "<img src=\"images/Brittany_02625.jpg\" width=\"100\"> | <img src=\"images/Welsh_springer_spaniel_08203.jpg\" width=\"200\">\n",
    "\n",
    "It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).  \n",
    "\n",
    "Curly-Coated Retriever | American Water Spaniel\n",
    "- | -\n",
    "<img src=\"images/Curly-coated_retriever_03896.jpg\" width=\"200\"> | <img src=\"images/American_water_spaniel_00648.jpg\" width=\"200\">\n",
    "\n",
    "\n",
    "Likewise, recall that labradors come in yellow, chocolate, and black.  Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.  \n",
    "\n",
    "Yellow Labrador | Chocolate Labrador | Black Labrador\n",
    "- | -\n",
    "<img src=\"images/Labrador_retriever_06457.jpg\" width=\"150\"> | <img src=\"images/Labrador_retriever_06455.jpg\" width=\"240\"> | <img src=\"images/Labrador_retriever_06449.jpg\" width=\"220\">\n",
    "\n",
    "We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.  \n",
    "\n",
    "Remember that the practice is far ahead of the theory in deep learning.  Experiment with many different architectures, and trust your intuition.  And, of course, have fun! \n",
    "\n",
    "### Pre-process the Data\n",
    "\n",
    "We rescale the images by dividing every pixel in every image by 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6680/6680 [02:26<00:00, 45.48it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 835/835 [00:13<00:00, 76.58it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 836/836 [00:15<00:00, 55.10it/s]\n"
     ]
    }
   ],
   "source": [
    "# pre-process the data for Keras\n",
    "train_tensors = paths_to_tensor(train_files).astype('float32')/255\n",
    "valid_tensors = paths_to_tensor(valid_files).astype('float32')/255\n",
    "test_tensors = paths_to_tensor(test_files).astype('float32')/255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Model Architecture\n",
    "\n",
    "Create a CNN to classify dog breed.  At the end of your code cell block, summarize the layers of your model by executing the line:\n",
    "    \n",
    "        model.summary()\n",
    "\n",
    "We have imported some Python modules to get you started, but feel free to import as many modules as you need.  If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:\n",
    "\n",
    "![Sample CNN](images/sample_cnn.png)\n",
    "           \n",
    "__Question 4:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.\n",
    "\n",
    "__Answer:__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_6 (Conv2D)            (None, 224, 224, 8)       224       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 112, 112, 8)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 112, 112, 16)      1168      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 56, 56, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 56, 56, 32)        4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 28, 28, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 28, 28, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 14, 14, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 500)               3136500   \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 500)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 250)               125250    \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 250)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 133)               33383     \n",
      "=================================================================\n",
      "Total params: 3,393,517\n",
      "Trainable params: 3,393,517\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "from keras.models import Sequential\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=8, kernel_size=3, padding='same', activation='relu', \n",
    "                        input_shape=(224, 224, 3)))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=16, kernel_size=3, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=128, kernel_size=3, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(500, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(250, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(133, activation='softmax'))\n",
    "\n",
    "\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Train the Model\n",
    "\n",
    "Train your model in the code cell below.  Use model checkpointing to save the model that attains the best validation loss.\n",
    "\n",
    "You are welcome to [augment the training data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html), but this is not a requirement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "Epoch 00000: val_loss improved from inf to 4.87746, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "15s - loss: 4.8889 - acc: 0.0083 - val_loss: 4.8775 - val_acc: 0.0108\n",
      "Epoch 2/300\n",
      "Epoch 00001: val_loss improved from 4.87746 to 4.83848, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 4.8669 - acc: 0.0121 - val_loss: 4.8385 - val_acc: 0.0168\n",
      "Epoch 3/300\n",
      "Epoch 00002: val_loss improved from 4.83848 to 4.70525, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 4.8015 - acc: 0.0146 - val_loss: 4.7053 - val_acc: 0.0263\n",
      "Epoch 4/300\n",
      "Epoch 00003: val_loss improved from 4.70525 to 4.59775, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "13s - loss: 4.7093 - acc: 0.0206 - val_loss: 4.5977 - val_acc: 0.0287\n",
      "Epoch 5/300\n",
      "Epoch 00004: val_loss improved from 4.59775 to 4.59175, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 4.6313 - acc: 0.0301 - val_loss: 4.5917 - val_acc: 0.0347\n",
      "Epoch 6/300\n",
      "Epoch 00005: val_loss improved from 4.59175 to 4.35887, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 4.5185 - acc: 0.0367 - val_loss: 4.3589 - val_acc: 0.0527\n",
      "Epoch 7/300\n",
      "Epoch 00006: val_loss improved from 4.35887 to 4.31871, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 4.4339 - acc: 0.0392 - val_loss: 4.3187 - val_acc: 0.0527\n",
      "Epoch 8/300\n",
      "Epoch 00007: val_loss improved from 4.31871 to 4.17725, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 4.3463 - acc: 0.0483 - val_loss: 4.1772 - val_acc: 0.0659\n",
      "Epoch 9/300\n",
      "Epoch 00008: val_loss improved from 4.17725 to 4.09643, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 4.2818 - acc: 0.0455 - val_loss: 4.0964 - val_acc: 0.0754\n",
      "Epoch 10/300\n",
      "Epoch 00009: val_loss improved from 4.09643 to 4.03980, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 4.2224 - acc: 0.0572 - val_loss: 4.0398 - val_acc: 0.0707\n",
      "Epoch 11/300\n",
      "Epoch 00010: val_loss improved from 4.03980 to 4.02604, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 4.1524 - acc: 0.0587 - val_loss: 4.0260 - val_acc: 0.0671\n",
      "Epoch 12/300\n",
      "Epoch 00011: val_loss did not improve\n",
      "11s - loss: 4.1299 - acc: 0.0620 - val_loss: 4.1446 - val_acc: 0.0731\n",
      "Epoch 13/300\n",
      "Epoch 00012: val_loss did not improve\n",
      "12s - loss: 4.0865 - acc: 0.0711 - val_loss: 4.0407 - val_acc: 0.0778\n",
      "Epoch 14/300\n",
      "Epoch 00013: val_loss improved from 4.02604 to 3.90518, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "12s - loss: 4.0394 - acc: 0.0767 - val_loss: 3.9052 - val_acc: 0.0994\n",
      "Epoch 15/300\n",
      "Epoch 00014: val_loss improved from 3.90518 to 3.84857, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.9779 - acc: 0.0827 - val_loss: 3.8486 - val_acc: 0.1126\n",
      "Epoch 16/300\n",
      "Epoch 00015: val_loss improved from 3.84857 to 3.80213, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.9502 - acc: 0.0849 - val_loss: 3.8021 - val_acc: 0.1006\n",
      "Epoch 17/300\n",
      "Epoch 00016: val_loss improved from 3.80213 to 3.79205, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "12s - loss: 3.8613 - acc: 0.1018 - val_loss: 3.7921 - val_acc: 0.0898\n",
      "Epoch 18/300\n",
      "Epoch 00017: val_loss improved from 3.79205 to 3.75463, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.8361 - acc: 0.0999 - val_loss: 3.7546 - val_acc: 0.0994\n",
      "Epoch 19/300\n",
      "Epoch 00018: val_loss did not improve\n",
      "11s - loss: 3.7896 - acc: 0.1073 - val_loss: 3.7764 - val_acc: 0.1102\n",
      "Epoch 20/300\n",
      "Epoch 00019: val_loss improved from 3.75463 to 3.73335, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.7504 - acc: 0.1163 - val_loss: 3.7333 - val_acc: 0.1138\n",
      "Epoch 21/300\n",
      "Epoch 00020: val_loss did not improve\n",
      "11s - loss: 3.7132 - acc: 0.1179 - val_loss: 3.7790 - val_acc: 0.1018\n",
      "Epoch 22/300\n",
      "Epoch 00021: val_loss did not improve\n",
      "11s - loss: 3.7044 - acc: 0.1213 - val_loss: 3.8302 - val_acc: 0.1030\n",
      "Epoch 23/300\n",
      "Epoch 00022: val_loss improved from 3.73335 to 3.70565, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.6526 - acc: 0.1271 - val_loss: 3.7057 - val_acc: 0.1269\n",
      "Epoch 24/300\n",
      "Epoch 00023: val_loss improved from 3.70565 to 3.68426, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.5936 - acc: 0.1317 - val_loss: 3.6843 - val_acc: 0.1234\n",
      "Epoch 25/300\n",
      "Epoch 00024: val_loss did not improve\n",
      "11s - loss: 3.5654 - acc: 0.1378 - val_loss: 3.6900 - val_acc: 0.1138\n",
      "Epoch 26/300\n",
      "Epoch 00025: val_loss improved from 3.68426 to 3.64056, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.5747 - acc: 0.1368 - val_loss: 3.6406 - val_acc: 0.1138\n",
      "Epoch 27/300\n",
      "Epoch 00026: val_loss did not improve\n",
      "11s - loss: 3.4920 - acc: 0.1507 - val_loss: 3.8128 - val_acc: 0.1066\n",
      "Epoch 28/300\n",
      "Epoch 00027: val_loss improved from 3.64056 to 3.60636, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "12s - loss: 3.4808 - acc: 0.1473 - val_loss: 3.6064 - val_acc: 0.1377\n",
      "Epoch 29/300\n",
      "Epoch 00028: val_loss improved from 3.60636 to 3.55491, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.4449 - acc: 0.1552 - val_loss: 3.5549 - val_acc: 0.1437\n",
      "Epoch 30/300\n",
      "Epoch 00029: val_loss improved from 3.55491 to 3.52320, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "12s - loss: 3.4235 - acc: 0.1565 - val_loss: 3.5232 - val_acc: 0.1401\n",
      "Epoch 31/300\n",
      "Epoch 00030: val_loss did not improve\n",
      "11s - loss: 3.3834 - acc: 0.1705 - val_loss: 3.5316 - val_acc: 0.1425\n",
      "Epoch 32/300\n",
      "Epoch 00031: val_loss improved from 3.52320 to 3.47871, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "12s - loss: 3.3650 - acc: 0.1706 - val_loss: 3.4787 - val_acc: 0.1677\n",
      "Epoch 33/300\n",
      "Epoch 00032: val_loss improved from 3.47871 to 3.42157, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.3143 - acc: 0.1762 - val_loss: 3.4216 - val_acc: 0.1725\n",
      "Epoch 34/300\n",
      "Epoch 00033: val_loss did not improve\n",
      "11s - loss: 3.3141 - acc: 0.1791 - val_loss: 3.5056 - val_acc: 0.1533\n",
      "Epoch 35/300\n",
      "Epoch 00034: val_loss did not improve\n",
      "11s - loss: 3.2773 - acc: 0.1879 - val_loss: 3.7731 - val_acc: 0.1257\n",
      "Epoch 36/300\n",
      "Epoch 00035: val_loss improved from 3.42157 to 3.36759, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.2362 - acc: 0.1941 - val_loss: 3.3676 - val_acc: 0.1737\n",
      "Epoch 37/300\n",
      "Epoch 00036: val_loss did not improve\n",
      "11s - loss: 3.1664 - acc: 0.2045 - val_loss: 3.3724 - val_acc: 0.1665\n",
      "Epoch 38/300\n",
      "Epoch 00037: val_loss did not improve\n",
      "11s - loss: 3.1648 - acc: 0.2073 - val_loss: 3.3747 - val_acc: 0.1689\n",
      "Epoch 39/300\n",
      "Epoch 00038: val_loss improved from 3.36759 to 3.33095, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.1321 - acc: 0.2129 - val_loss: 3.3309 - val_acc: 0.1868\n",
      "Epoch 40/300\n",
      "Epoch 00039: val_loss did not improve\n",
      "11s - loss: 3.1305 - acc: 0.2126 - val_loss: 3.4341 - val_acc: 0.1796\n",
      "Epoch 41/300\n",
      "Epoch 00040: val_loss did not improve\n",
      "11s - loss: 3.0895 - acc: 0.2230 - val_loss: 3.3449 - val_acc: 0.1749\n",
      "Epoch 42/300\n",
      "Epoch 00041: val_loss improved from 3.33095 to 3.28650, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.0747 - acc: 0.2158 - val_loss: 3.2865 - val_acc: 0.2024\n",
      "Epoch 43/300\n",
      "Epoch 00042: val_loss improved from 3.28650 to 3.25121, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 3.0171 - acc: 0.2280 - val_loss: 3.2512 - val_acc: 0.1976\n",
      "Epoch 44/300\n",
      "Epoch 00043: val_loss did not improve\n",
      "11s - loss: 3.0020 - acc: 0.2331 - val_loss: 3.2639 - val_acc: 0.1772\n",
      "Epoch 45/300\n",
      "Epoch 00044: val_loss improved from 3.25121 to 3.23972, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 2.9596 - acc: 0.2399 - val_loss: 3.2397 - val_acc: 0.2120\n",
      "Epoch 46/300\n",
      "Epoch 00045: val_loss did not improve\n",
      "11s - loss: 2.9586 - acc: 0.2324 - val_loss: 3.2969 - val_acc: 0.1940\n",
      "Epoch 47/300\n",
      "Epoch 00046: val_loss did not improve\n",
      "11s - loss: 2.9205 - acc: 0.2491 - val_loss: 3.3441 - val_acc: 0.2060\n",
      "Epoch 48/300\n",
      "Epoch 00047: val_loss improved from 3.23972 to 3.15641, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 2.8984 - acc: 0.2509 - val_loss: 3.1564 - val_acc: 0.2192\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49/300\n",
      "Epoch 00048: val_loss improved from 3.15641 to 3.13637, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 2.8948 - acc: 0.2579 - val_loss: 3.1364 - val_acc: 0.1988\n",
      "Epoch 50/300\n",
      "Epoch 00049: val_loss improved from 3.13637 to 3.11002, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 2.8594 - acc: 0.2567 - val_loss: 3.1100 - val_acc: 0.2204\n",
      "Epoch 51/300\n",
      "Epoch 00050: val_loss improved from 3.11002 to 3.07705, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 2.8590 - acc: 0.2555 - val_loss: 3.0771 - val_acc: 0.2204\n",
      "Epoch 52/300\n",
      "Epoch 00051: val_loss did not improve\n",
      "11s - loss: 2.8069 - acc: 0.2797 - val_loss: 3.1971 - val_acc: 0.2323\n",
      "Epoch 53/300\n",
      "Epoch 00052: val_loss did not improve\n",
      "12s - loss: 2.7812 - acc: 0.2730 - val_loss: 3.1511 - val_acc: 0.2299\n",
      "Epoch 54/300\n",
      "Epoch 00053: val_loss did not improve\n",
      "11s - loss: 2.7442 - acc: 0.2848 - val_loss: 3.1505 - val_acc: 0.2144\n",
      "Epoch 55/300\n",
      "Epoch 00054: val_loss did not improve\n",
      "11s - loss: 2.7355 - acc: 0.2912 - val_loss: 3.1947 - val_acc: 0.2192\n",
      "Epoch 56/300\n",
      "Epoch 00055: val_loss did not improve\n",
      "12s - loss: 2.6858 - acc: 0.2919 - val_loss: 3.5388 - val_acc: 0.1808\n",
      "Epoch 57/300\n",
      "Epoch 00056: val_loss did not improve\n",
      "10s - loss: 2.7145 - acc: 0.2950 - val_loss: 3.1989 - val_acc: 0.2204\n",
      "Epoch 58/300\n",
      "Epoch 00057: val_loss did not improve\n",
      "11s - loss: 2.6391 - acc: 0.3088 - val_loss: 3.1205 - val_acc: 0.2204\n",
      "Epoch 59/300\n",
      "Epoch 00058: val_loss improved from 3.07705 to 3.05982, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "12s - loss: 2.6405 - acc: 0.3018 - val_loss: 3.0598 - val_acc: 0.2419\n",
      "Epoch 60/300\n",
      "Epoch 00059: val_loss improved from 3.05982 to 3.04865, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 2.5776 - acc: 0.3163 - val_loss: 3.0487 - val_acc: 0.2491\n",
      "Epoch 61/300\n",
      "Epoch 00060: val_loss improved from 3.04865 to 3.01281, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "12s - loss: 2.6076 - acc: 0.3041 - val_loss: 3.0128 - val_acc: 0.2431\n",
      "Epoch 62/300\n",
      "Epoch 00061: val_loss did not improve\n",
      "11s - loss: 2.5776 - acc: 0.3109 - val_loss: 3.0702 - val_acc: 0.2335\n",
      "Epoch 63/300\n",
      "Epoch 00062: val_loss did not improve\n",
      "11s - loss: 2.5571 - acc: 0.3222 - val_loss: 3.1113 - val_acc: 0.2383\n",
      "Epoch 64/300\n",
      "Epoch 00063: val_loss did not improve\n",
      "11s - loss: 2.5339 - acc: 0.3207 - val_loss: 3.0219 - val_acc: 0.2611\n",
      "Epoch 65/300\n",
      "Epoch 00064: val_loss improved from 3.01281 to 2.96998, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "12s - loss: 2.5469 - acc: 0.3273 - val_loss: 2.9700 - val_acc: 0.2671\n",
      "Epoch 66/300\n",
      "Epoch 00065: val_loss did not improve\n",
      "11s - loss: 2.4781 - acc: 0.3339 - val_loss: 3.1126 - val_acc: 0.2479\n",
      "Epoch 67/300\n",
      "Epoch 00066: val_loss did not improve\n",
      "11s - loss: 2.4812 - acc: 0.3370 - val_loss: 3.0817 - val_acc: 0.2527\n",
      "Epoch 68/300\n",
      "Epoch 00067: val_loss did not improve\n",
      "11s - loss: 2.4293 - acc: 0.3534 - val_loss: 3.0590 - val_acc: 0.2731\n",
      "Epoch 69/300\n",
      "Epoch 00068: val_loss did not improve\n",
      "12s - loss: 2.4664 - acc: 0.3479 - val_loss: 3.0228 - val_acc: 0.2503\n",
      "Epoch 70/300\n",
      "Epoch 00069: val_loss improved from 2.96998 to 2.93885, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 2.4255 - acc: 0.3464 - val_loss: 2.9389 - val_acc: 0.2719\n",
      "Epoch 71/300\n",
      "Epoch 00070: val_loss did not improve\n",
      "11s - loss: 2.4093 - acc: 0.3470 - val_loss: 2.9904 - val_acc: 0.2659\n",
      "Epoch 72/300\n",
      "Epoch 00071: val_loss did not improve\n",
      "11s - loss: 2.3551 - acc: 0.3613 - val_loss: 3.0234 - val_acc: 0.2683\n",
      "Epoch 73/300\n",
      "Epoch 00072: val_loss did not improve\n",
      "12s - loss: 2.3498 - acc: 0.3658 - val_loss: 2.9599 - val_acc: 0.2683\n",
      "Epoch 74/300\n",
      "Epoch 00073: val_loss did not improve\n",
      "11s - loss: 2.3521 - acc: 0.3717 - val_loss: 3.1662 - val_acc: 0.2443\n",
      "Epoch 75/300\n",
      "Epoch 00074: val_loss did not improve\n",
      "11s - loss: 2.3414 - acc: 0.3647 - val_loss: 2.9761 - val_acc: 0.2599\n",
      "Epoch 76/300\n",
      "Epoch 00075: val_loss did not improve\n",
      "11s - loss: 2.2436 - acc: 0.3808 - val_loss: 3.0421 - val_acc: 0.2575\n",
      "Epoch 77/300\n",
      "Epoch 00076: val_loss did not improve\n",
      "11s - loss: 2.3224 - acc: 0.3730 - val_loss: 2.9432 - val_acc: 0.2766\n",
      "Epoch 78/300\n",
      "Epoch 00077: val_loss did not improve\n",
      "11s - loss: 2.2521 - acc: 0.3790 - val_loss: 3.0224 - val_acc: 0.2695\n",
      "Epoch 79/300\n",
      "Epoch 00078: val_loss did not improve\n",
      "11s - loss: 2.2211 - acc: 0.3842 - val_loss: 3.1181 - val_acc: 0.2575\n",
      "Epoch 80/300\n",
      "Epoch 00079: val_loss did not improve\n",
      "11s - loss: 2.2600 - acc: 0.3794 - val_loss: 3.0292 - val_acc: 0.2599\n",
      "Epoch 81/300\n",
      "Epoch 00080: val_loss did not improve\n",
      "11s - loss: 2.1732 - acc: 0.4027 - val_loss: 3.0492 - val_acc: 0.2838\n",
      "Epoch 82/300\n",
      "Epoch 00081: val_loss did not improve\n",
      "12s - loss: 2.2249 - acc: 0.3787 - val_loss: 2.9392 - val_acc: 0.2659\n",
      "Epoch 83/300\n",
      "Epoch 00082: val_loss did not improve\n",
      "12s - loss: 2.1914 - acc: 0.4040 - val_loss: 2.9947 - val_acc: 0.2587\n",
      "Epoch 84/300\n",
      "Epoch 00083: val_loss did not improve\n",
      "12s - loss: 2.1660 - acc: 0.4106 - val_loss: 2.9404 - val_acc: 0.2707\n",
      "Epoch 85/300\n",
      "Epoch 00084: val_loss did not improve\n",
      "11s - loss: 2.1673 - acc: 0.4096 - val_loss: 2.9592 - val_acc: 0.2874\n",
      "Epoch 86/300\n",
      "Epoch 00085: val_loss did not improve\n",
      "12s - loss: 2.1383 - acc: 0.4079 - val_loss: 3.0558 - val_acc: 0.2707\n",
      "Epoch 87/300\n",
      "Epoch 00086: val_loss did not improve\n",
      "11s - loss: 2.1344 - acc: 0.4170 - val_loss: 3.0128 - val_acc: 0.2754\n",
      "Epoch 88/300\n",
      "Epoch 00087: val_loss did not improve\n",
      "11s - loss: 2.1201 - acc: 0.4175 - val_loss: 2.9905 - val_acc: 0.2790\n",
      "Epoch 89/300\n",
      "Epoch 00088: val_loss did not improve\n",
      "12s - loss: 2.1032 - acc: 0.4166 - val_loss: 2.9828 - val_acc: 0.2647\n",
      "Epoch 90/300\n",
      "Epoch 00089: val_loss did not improve\n",
      "11s - loss: 2.1018 - acc: 0.4191 - val_loss: 3.1734 - val_acc: 0.2647\n",
      "Epoch 91/300\n",
      "Epoch 00090: val_loss did not improve\n",
      "11s - loss: 2.0899 - acc: 0.4267 - val_loss: 2.9699 - val_acc: 0.2958\n",
      "Epoch 92/300\n",
      "Epoch 00091: val_loss improved from 2.93885 to 2.88557, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "12s - loss: 2.0850 - acc: 0.4253 - val_loss: 2.8856 - val_acc: 0.2970\n",
      "Epoch 93/300\n",
      "Epoch 00092: val_loss did not improve\n",
      "11s - loss: 2.0780 - acc: 0.4245 - val_loss: 3.0390 - val_acc: 0.2922\n",
      "Epoch 94/300\n",
      "Epoch 00093: val_loss did not improve\n",
      "11s - loss: 2.0319 - acc: 0.4380 - val_loss: 2.9079 - val_acc: 0.2958\n",
      "Epoch 95/300\n",
      "Epoch 00094: val_loss did not improve\n",
      "11s - loss: 1.9961 - acc: 0.4437 - val_loss: 3.0161 - val_acc: 0.3006\n",
      "Epoch 96/300\n",
      "Epoch 00095: val_loss did not improve\n",
      "11s - loss: 2.0448 - acc: 0.4324 - val_loss: 2.9079 - val_acc: 0.3030\n",
      "Epoch 97/300\n",
      "Epoch 00096: val_loss did not improve\n",
      "11s - loss: 1.9692 - acc: 0.4383 - val_loss: 2.9987 - val_acc: 0.3042\n",
      "Epoch 98/300\n",
      "Epoch 00097: val_loss did not improve\n",
      "11s - loss: 2.0321 - acc: 0.4381 - val_loss: 2.9631 - val_acc: 0.2790\n",
      "Epoch 99/300\n",
      "Epoch 00098: val_loss improved from 2.88557 to 2.86309, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "12s - loss: 1.9800 - acc: 0.4476 - val_loss: 2.8631 - val_acc: 0.3174\n",
      "Epoch 100/300\n",
      "Epoch 00099: val_loss did not improve\n",
      "11s - loss: 1.9702 - acc: 0.4503 - val_loss: 3.0314 - val_acc: 0.2934\n",
      "Epoch 101/300\n",
      "Epoch 00100: val_loss improved from 2.86309 to 2.82439, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 1.9687 - acc: 0.4540 - val_loss: 2.8244 - val_acc: 0.2994\n",
      "Epoch 102/300\n",
      "Epoch 00101: val_loss did not improve\n",
      "12s - loss: 1.9312 - acc: 0.4645 - val_loss: 2.9398 - val_acc: 0.2994\n",
      "Epoch 103/300\n",
      "Epoch 00102: val_loss did not improve\n",
      "11s - loss: 1.9533 - acc: 0.4524 - val_loss: 2.8724 - val_acc: 0.3054\n",
      "Epoch 104/300\n",
      "Epoch 00103: val_loss did not improve\n",
      "11s - loss: 1.9517 - acc: 0.4564 - val_loss: 2.9423 - val_acc: 0.2982\n",
      "Epoch 105/300\n",
      "Epoch 00104: val_loss did not improve\n",
      "11s - loss: 1.9094 - acc: 0.4680 - val_loss: 2.9088 - val_acc: 0.3030\n",
      "Epoch 106/300\n",
      "Epoch 00105: val_loss did not improve\n",
      "13s - loss: 1.8986 - acc: 0.4738 - val_loss: 2.9848 - val_acc: 0.3090\n",
      "Epoch 107/300\n",
      "Epoch 00106: val_loss did not improve\n",
      "10s - loss: 1.8789 - acc: 0.4703 - val_loss: 3.1448 - val_acc: 0.2659\n",
      "Epoch 108/300\n",
      "Epoch 00107: val_loss did not improve\n",
      "13s - loss: 1.8382 - acc: 0.4786 - val_loss: 3.4564 - val_acc: 0.2287\n",
      "Epoch 109/300\n",
      "Epoch 00108: val_loss did not improve\n",
      "10s - loss: 1.8686 - acc: 0.4795 - val_loss: 2.9177 - val_acc: 0.3090\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 110/300\n",
      "Epoch 00109: val_loss did not improve\n",
      "11s - loss: 1.8559 - acc: 0.4868 - val_loss: 2.9547 - val_acc: 0.3066\n",
      "Epoch 111/300\n",
      "Epoch 00110: val_loss did not improve\n",
      "12s - loss: 1.8614 - acc: 0.4727 - val_loss: 2.8264 - val_acc: 0.3090\n",
      "Epoch 112/300\n",
      "Epoch 00111: val_loss did not improve\n",
      "11s - loss: 1.8410 - acc: 0.4828 - val_loss: 2.9354 - val_acc: 0.3018\n",
      "Epoch 113/300\n",
      "Epoch 00112: val_loss did not improve\n",
      "11s - loss: 1.8344 - acc: 0.4856 - val_loss: 2.9519 - val_acc: 0.3030\n",
      "Epoch 114/300\n",
      "Epoch 00113: val_loss did not improve\n",
      "11s - loss: 1.8094 - acc: 0.4855 - val_loss: 2.9131 - val_acc: 0.3078\n",
      "Epoch 115/300\n",
      "Epoch 00114: val_loss did not improve\n",
      "11s - loss: 1.7642 - acc: 0.5014 - val_loss: 3.0036 - val_acc: 0.2850\n",
      "Epoch 116/300\n",
      "Epoch 00115: val_loss did not improve\n",
      "12s - loss: 1.8013 - acc: 0.4875 - val_loss: 2.9510 - val_acc: 0.3174\n",
      "Epoch 117/300\n",
      "Epoch 00116: val_loss did not improve\n",
      "12s - loss: 1.7737 - acc: 0.4951 - val_loss: 2.8719 - val_acc: 0.3174\n",
      "Epoch 118/300\n",
      "Epoch 00117: val_loss did not improve\n",
      "11s - loss: 1.7998 - acc: 0.4925 - val_loss: 2.9070 - val_acc: 0.3030\n",
      "Epoch 119/300\n",
      "Epoch 00118: val_loss did not improve\n",
      "11s - loss: 1.8160 - acc: 0.4898 - val_loss: 2.9080 - val_acc: 0.3066\n",
      "Epoch 120/300\n",
      "Epoch 00119: val_loss did not improve\n",
      "12s - loss: 1.7380 - acc: 0.5167 - val_loss: 2.9544 - val_acc: 0.3030\n",
      "Epoch 121/300\n",
      "Epoch 00120: val_loss did not improve\n",
      "11s - loss: 1.7641 - acc: 0.4956 - val_loss: 2.9764 - val_acc: 0.3006\n",
      "Epoch 122/300\n",
      "Epoch 00121: val_loss did not improve\n",
      "11s - loss: 1.7470 - acc: 0.4979 - val_loss: 2.9280 - val_acc: 0.3102\n",
      "Epoch 123/300\n",
      "Epoch 00122: val_loss did not improve\n",
      "11s - loss: 1.7359 - acc: 0.5141 - val_loss: 3.0759 - val_acc: 0.3162\n",
      "Epoch 124/300\n",
      "Epoch 00123: val_loss did not improve\n",
      "11s - loss: 1.7201 - acc: 0.5111 - val_loss: 3.0610 - val_acc: 0.3246\n",
      "Epoch 125/300\n",
      "Epoch 00124: val_loss did not improve\n",
      "11s - loss: 1.6890 - acc: 0.5152 - val_loss: 2.8995 - val_acc: 0.3269\n",
      "Epoch 126/300\n",
      "Epoch 00125: val_loss did not improve\n",
      "12s - loss: 1.7197 - acc: 0.5078 - val_loss: 2.9599 - val_acc: 0.3257\n",
      "Epoch 127/300\n",
      "Epoch 00126: val_loss did not improve\n",
      "12s - loss: 1.6980 - acc: 0.5139 - val_loss: 2.9216 - val_acc: 0.3365\n",
      "Epoch 128/300\n",
      "Epoch 00127: val_loss did not improve\n",
      "11s - loss: 1.7127 - acc: 0.5107 - val_loss: 2.8624 - val_acc: 0.3054\n",
      "Epoch 129/300\n",
      "Epoch 00128: val_loss did not improve\n",
      "11s - loss: 1.6635 - acc: 0.5271 - val_loss: 2.8734 - val_acc: 0.3222\n",
      "Epoch 130/300\n",
      "Epoch 00129: val_loss improved from 2.82439 to 2.79630, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "12s - loss: 1.6844 - acc: 0.5100 - val_loss: 2.7963 - val_acc: 0.3329\n",
      "Epoch 131/300\n",
      "Epoch 00130: val_loss did not improve\n",
      "11s - loss: 1.6400 - acc: 0.5305 - val_loss: 3.0681 - val_acc: 0.3162\n",
      "Epoch 132/300\n",
      "Epoch 00131: val_loss did not improve\n",
      "11s - loss: 1.6598 - acc: 0.5220 - val_loss: 3.2083 - val_acc: 0.2982\n",
      "Epoch 133/300\n",
      "Epoch 00132: val_loss did not improve\n",
      "12s - loss: 1.6557 - acc: 0.5216 - val_loss: 2.8290 - val_acc: 0.3305\n",
      "Epoch 134/300\n",
      "Epoch 00133: val_loss did not improve\n",
      "11s - loss: 1.6225 - acc: 0.5307 - val_loss: 2.8842 - val_acc: 0.3257\n",
      "Epoch 135/300\n",
      "Epoch 00134: val_loss did not improve\n",
      "12s - loss: 1.6329 - acc: 0.5312 - val_loss: 3.0573 - val_acc: 0.3150\n",
      "Epoch 136/300\n",
      "Epoch 00135: val_loss did not improve\n",
      "11s - loss: 1.6418 - acc: 0.5245 - val_loss: 2.9872 - val_acc: 0.3222\n",
      "Epoch 137/300\n",
      "Epoch 00136: val_loss did not improve\n",
      "12s - loss: 1.6259 - acc: 0.5398 - val_loss: 3.0346 - val_acc: 0.3090\n",
      "Epoch 138/300\n",
      "Epoch 00137: val_loss did not improve\n",
      "11s - loss: 1.5872 - acc: 0.5411 - val_loss: 2.8599 - val_acc: 0.3114\n",
      "Epoch 139/300\n",
      "Epoch 00138: val_loss did not improve\n",
      "12s - loss: 1.6117 - acc: 0.5378 - val_loss: 2.8117 - val_acc: 0.3557\n",
      "Epoch 140/300\n",
      "Epoch 00139: val_loss did not improve\n",
      "12s - loss: 1.6266 - acc: 0.5373 - val_loss: 2.8731 - val_acc: 0.3317\n",
      "Epoch 141/300\n",
      "Epoch 00140: val_loss did not improve\n",
      "11s - loss: 1.6305 - acc: 0.5340 - val_loss: 2.9457 - val_acc: 0.3162\n",
      "Epoch 142/300\n",
      "Epoch 00141: val_loss did not improve\n",
      "11s - loss: 1.6004 - acc: 0.5428 - val_loss: 3.0593 - val_acc: 0.3281\n",
      "Epoch 143/300\n",
      "Epoch 00142: val_loss did not improve\n",
      "12s - loss: 1.5857 - acc: 0.5550 - val_loss: 2.8849 - val_acc: 0.3293\n",
      "Epoch 144/300\n",
      "Epoch 00143: val_loss did not improve\n",
      "12s - loss: 1.5761 - acc: 0.5458 - val_loss: 2.8377 - val_acc: 0.3305\n",
      "Epoch 145/300\n",
      "Epoch 00144: val_loss did not improve\n",
      "11s - loss: 1.5621 - acc: 0.5542 - val_loss: 3.0052 - val_acc: 0.3222\n",
      "Epoch 146/300\n",
      "Epoch 00145: val_loss did not improve\n",
      "11s - loss: 1.5669 - acc: 0.5567 - val_loss: 2.9556 - val_acc: 0.3485\n",
      "Epoch 147/300\n",
      "Epoch 00146: val_loss did not improve\n",
      "11s - loss: 1.5807 - acc: 0.5505 - val_loss: 2.9554 - val_acc: 0.3222\n",
      "Epoch 148/300\n",
      "Epoch 00147: val_loss did not improve\n",
      "12s - loss: 1.5947 - acc: 0.5501 - val_loss: 2.8796 - val_acc: 0.3365\n",
      "Epoch 149/300\n",
      "Epoch 00148: val_loss improved from 2.79630 to 2.76853, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 1.5432 - acc: 0.5579 - val_loss: 2.7685 - val_acc: 0.3521\n",
      "Epoch 150/300\n",
      "Epoch 00149: val_loss did not improve\n",
      "11s - loss: 1.5226 - acc: 0.5658 - val_loss: 2.8500 - val_acc: 0.3437\n",
      "Epoch 151/300\n",
      "Epoch 00150: val_loss did not improve\n",
      "12s - loss: 1.5754 - acc: 0.5498 - val_loss: 2.8755 - val_acc: 0.3437\n",
      "Epoch 152/300\n",
      "Epoch 00151: val_loss did not improve\n",
      "11s - loss: 1.5340 - acc: 0.5615 - val_loss: 2.9018 - val_acc: 0.3473\n",
      "Epoch 153/300\n",
      "Epoch 00152: val_loss did not improve\n",
      "11s - loss: 1.5355 - acc: 0.5563 - val_loss: 2.7819 - val_acc: 0.3413\n",
      "Epoch 154/300\n",
      "Epoch 00153: val_loss did not improve\n",
      "12s - loss: 1.5219 - acc: 0.5730 - val_loss: 2.8435 - val_acc: 0.3497\n",
      "Epoch 155/300\n",
      "Epoch 00154: val_loss improved from 2.76853 to 2.73296, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "11s - loss: 1.5037 - acc: 0.5629 - val_loss: 2.7330 - val_acc: 0.3401\n",
      "Epoch 156/300\n",
      "Epoch 00155: val_loss did not improve\n",
      "11s - loss: 1.5013 - acc: 0.5643 - val_loss: 2.9647 - val_acc: 0.3509\n",
      "Epoch 157/300\n",
      "Epoch 00156: val_loss did not improve\n",
      "11s - loss: 1.5323 - acc: 0.5698 - val_loss: 2.7779 - val_acc: 0.3413\n",
      "Epoch 158/300\n",
      "Epoch 00157: val_loss did not improve\n",
      "12s - loss: 1.5166 - acc: 0.5663 - val_loss: 2.8470 - val_acc: 0.3509\n",
      "Epoch 159/300\n",
      "Epoch 00158: val_loss did not improve\n",
      "13s - loss: 1.4715 - acc: 0.5760 - val_loss: 3.2862 - val_acc: 0.3126\n",
      "Epoch 160/300\n",
      "Epoch 00159: val_loss did not improve\n",
      "10s - loss: 1.4985 - acc: 0.5743 - val_loss: 2.8283 - val_acc: 0.3629\n",
      "Epoch 161/300\n",
      "Epoch 00160: val_loss did not improve\n",
      "12s - loss: 1.4913 - acc: 0.5743 - val_loss: 3.0030 - val_acc: 0.3485\n",
      "Epoch 162/300\n",
      "Epoch 00161: val_loss did not improve\n",
      "11s - loss: 1.4623 - acc: 0.5779 - val_loss: 2.8050 - val_acc: 0.3545\n",
      "Epoch 163/300\n",
      "Epoch 00162: val_loss did not improve\n",
      "11s - loss: 1.5186 - acc: 0.5577 - val_loss: 2.8405 - val_acc: 0.3497\n",
      "Epoch 164/300\n",
      "Epoch 00163: val_loss did not improve\n",
      "11s - loss: 1.4184 - acc: 0.5988 - val_loss: 2.9190 - val_acc: 0.3437\n",
      "Epoch 165/300\n",
      "Epoch 00164: val_loss did not improve\n",
      "11s - loss: 1.4398 - acc: 0.5827 - val_loss: 3.0055 - val_acc: 0.3329\n",
      "Epoch 166/300\n",
      "Epoch 00165: val_loss did not improve\n",
      "11s - loss: 1.4500 - acc: 0.5859 - val_loss: 2.9803 - val_acc: 0.3222\n",
      "Epoch 167/300\n",
      "Epoch 00166: val_loss did not improve\n",
      "11s - loss: 1.4705 - acc: 0.5795 - val_loss: 2.9600 - val_acc: 0.3222\n",
      "Epoch 168/300\n",
      "Epoch 00167: val_loss did not improve\n",
      "11s - loss: 1.4585 - acc: 0.5755 - val_loss: 3.0323 - val_acc: 0.3246\n",
      "Epoch 169/300\n",
      "Epoch 00168: val_loss did not improve\n",
      "11s - loss: 1.4507 - acc: 0.5803 - val_loss: 2.8564 - val_acc: 0.3365\n",
      "Epoch 170/300\n",
      "Epoch 00169: val_loss did not improve\n",
      "11s - loss: 1.4485 - acc: 0.5786 - val_loss: 2.9321 - val_acc: 0.3234\n",
      "Epoch 171/300\n",
      "Epoch 00170: val_loss did not improve\n",
      "11s - loss: 1.4340 - acc: 0.5869 - val_loss: 2.9944 - val_acc: 0.3246\n",
      "Epoch 172/300\n",
      "Epoch 00171: val_loss did not improve\n",
      "11s - loss: 1.4267 - acc: 0.5800 - val_loss: 2.8640 - val_acc: 0.3401\n",
      "Epoch 173/300\n",
      "Epoch 00172: val_loss did not improve\n",
      "12s - loss: 1.4336 - acc: 0.5884 - val_loss: 2.9200 - val_acc: 0.3449\n",
      "Epoch 174/300\n",
      "Epoch 00173: val_loss did not improve\n",
      "11s - loss: 1.3972 - acc: 0.5990 - val_loss: 2.9069 - val_acc: 0.3796\n",
      "Epoch 175/300\n",
      "Epoch 00174: val_loss did not improve\n",
      "11s - loss: 1.4077 - acc: 0.5849 - val_loss: 2.9460 - val_acc: 0.3365\n",
      "Epoch 176/300\n",
      "Epoch 00175: val_loss did not improve\n",
      "11s - loss: 1.4243 - acc: 0.5947 - val_loss: 3.0203 - val_acc: 0.3569\n",
      "Epoch 177/300\n",
      "Epoch 00176: val_loss did not improve\n",
      "11s - loss: 1.4412 - acc: 0.5883 - val_loss: 2.9233 - val_acc: 0.3269\n",
      "Epoch 178/300\n",
      "Epoch 00177: val_loss did not improve\n",
      "11s - loss: 1.3901 - acc: 0.5989 - val_loss: 2.8448 - val_acc: 0.3485\n",
      "Epoch 179/300\n",
      "Epoch 00178: val_loss did not improve\n",
      "11s - loss: 1.3803 - acc: 0.6012 - val_loss: 2.8139 - val_acc: 0.3425\n",
      "Epoch 180/300\n",
      "Epoch 00179: val_loss did not improve\n",
      "11s - loss: 1.3729 - acc: 0.6048 - val_loss: 3.0264 - val_acc: 0.3257\n",
      "Epoch 181/300\n",
      "Epoch 00180: val_loss did not improve\n",
      "11s - loss: 1.4159 - acc: 0.5952 - val_loss: 2.8442 - val_acc: 0.3557\n",
      "Epoch 182/300\n",
      "Epoch 00181: val_loss did not improve\n",
      "11s - loss: 1.3894 - acc: 0.6039 - val_loss: 2.9161 - val_acc: 0.3449\n",
      "Epoch 183/300\n",
      "Epoch 00182: val_loss did not improve\n",
      "11s - loss: 1.3698 - acc: 0.6054 - val_loss: 2.9121 - val_acc: 0.3437\n",
      "Epoch 184/300\n",
      "Epoch 00183: val_loss did not improve\n",
      "11s - loss: 1.3705 - acc: 0.6046 - val_loss: 3.0893 - val_acc: 0.3545\n",
      "Epoch 185/300\n",
      "Epoch 00184: val_loss did not improve\n",
      "11s - loss: 1.4029 - acc: 0.5943 - val_loss: 2.8820 - val_acc: 0.3557\n",
      "Epoch 186/300\n",
      "Epoch 00185: val_loss did not improve\n",
      "11s - loss: 1.3607 - acc: 0.6081 - val_loss: 2.9413 - val_acc: 0.3389\n",
      "Epoch 187/300\n",
      "Epoch 00186: val_loss did not improve\n",
      "11s - loss: 1.3634 - acc: 0.6062 - val_loss: 2.9395 - val_acc: 0.3545\n",
      "Epoch 188/300\n",
      "Epoch 00187: val_loss did not improve\n",
      "11s - loss: 1.3407 - acc: 0.6206 - val_loss: 2.9443 - val_acc: 0.3497\n",
      "Epoch 189/300\n",
      "Epoch 00188: val_loss did not improve\n",
      "11s - loss: 1.3884 - acc: 0.6062 - val_loss: 2.8903 - val_acc: 0.3677\n",
      "Epoch 190/300\n",
      "Epoch 00189: val_loss did not improve\n",
      "12s - loss: 1.3550 - acc: 0.6088 - val_loss: 3.0943 - val_acc: 0.3473\n",
      "Epoch 191/300\n",
      "Epoch 00190: val_loss did not improve\n",
      "11s - loss: 1.3702 - acc: 0.6057 - val_loss: 2.9284 - val_acc: 0.3701\n",
      "Epoch 192/300\n",
      "Epoch 00191: val_loss did not improve\n",
      "11s - loss: 1.3430 - acc: 0.6117 - val_loss: 2.8568 - val_acc: 0.3545\n",
      "Epoch 193/300\n",
      "Epoch 00192: val_loss did not improve\n",
      "11s - loss: 1.2960 - acc: 0.6226 - val_loss: 3.2051 - val_acc: 0.3317\n",
      "Epoch 194/300\n",
      "Epoch 00193: val_loss did not improve\n",
      "11s - loss: 1.3582 - acc: 0.6130 - val_loss: 2.9888 - val_acc: 0.3317\n",
      "Epoch 195/300\n",
      "Epoch 00194: val_loss did not improve\n",
      "11s - loss: 1.3157 - acc: 0.6229 - val_loss: 2.8824 - val_acc: 0.3473\n",
      "Epoch 196/300\n",
      "Epoch 00195: val_loss did not improve\n",
      "11s - loss: 1.3487 - acc: 0.6063 - val_loss: 2.8852 - val_acc: 0.3581\n",
      "Epoch 197/300\n",
      "Epoch 00196: val_loss did not improve\n",
      "12s - loss: 1.3362 - acc: 0.6168 - val_loss: 2.8357 - val_acc: 0.3617\n",
      "Epoch 198/300\n",
      "Epoch 00197: val_loss did not improve\n",
      "12s - loss: 1.3350 - acc: 0.6154 - val_loss: 2.9432 - val_acc: 0.3413\n",
      "Epoch 199/300\n",
      "Epoch 00198: val_loss did not improve\n",
      "11s - loss: 1.3656 - acc: 0.6060 - val_loss: 2.8695 - val_acc: 0.3521\n",
      "Epoch 200/300\n",
      "Epoch 00199: val_loss did not improve\n",
      "12s - loss: 1.2900 - acc: 0.6252 - val_loss: 2.9444 - val_acc: 0.3521\n",
      "Epoch 201/300\n",
      "Epoch 00200: val_loss did not improve\n",
      "12s - loss: 1.3191 - acc: 0.6170 - val_loss: 2.8538 - val_acc: 0.3353\n",
      "Epoch 202/300\n",
      "Epoch 00201: val_loss did not improve\n",
      "12s - loss: 1.3336 - acc: 0.6196 - val_loss: 2.9269 - val_acc: 0.3413\n",
      "Epoch 203/300\n",
      "Epoch 00202: val_loss did not improve\n",
      "12s - loss: 1.3191 - acc: 0.6172 - val_loss: 3.0610 - val_acc: 0.3353\n",
      "Epoch 204/300\n",
      "Epoch 00203: val_loss did not improve\n",
      "12s - loss: 1.3244 - acc: 0.6231 - val_loss: 3.0007 - val_acc: 0.3246\n",
      "Epoch 205/300\n",
      "Epoch 00204: val_loss did not improve\n",
      "11s - loss: 1.3062 - acc: 0.6249 - val_loss: 2.8975 - val_acc: 0.3605\n",
      "Epoch 206/300\n",
      "Epoch 00205: val_loss did not improve\n",
      "11s - loss: 1.3145 - acc: 0.6194 - val_loss: 3.1334 - val_acc: 0.3317\n",
      "Epoch 207/300\n",
      "Epoch 00206: val_loss did not improve\n",
      "11s - loss: 1.3269 - acc: 0.6161 - val_loss: 2.8352 - val_acc: 0.3653\n",
      "Epoch 208/300\n",
      "Epoch 00207: val_loss did not improve\n",
      "11s - loss: 1.3072 - acc: 0.6249 - val_loss: 3.2398 - val_acc: 0.2982\n",
      "Epoch 209/300\n",
      "Epoch 00208: val_loss did not improve\n",
      "13s - loss: 1.2787 - acc: 0.6367 - val_loss: 3.0344 - val_acc: 0.3437\n",
      "Epoch 210/300\n",
      "Epoch 00209: val_loss did not improve\n",
      "12s - loss: 1.2991 - acc: 0.6305 - val_loss: 3.1774 - val_acc: 0.3377\n",
      "Epoch 211/300\n",
      "Epoch 00210: val_loss did not improve\n",
      "11s - loss: 1.2817 - acc: 0.6333 - val_loss: 3.3676 - val_acc: 0.3006\n",
      "Epoch 212/300\n",
      "Epoch 00211: val_loss did not improve\n",
      "11s - loss: 1.2727 - acc: 0.6311 - val_loss: 2.9631 - val_acc: 0.3653\n",
      "Epoch 213/300\n",
      "Epoch 00212: val_loss did not improve\n",
      "11s - loss: 1.2681 - acc: 0.6364 - val_loss: 3.1535 - val_acc: 0.3425\n",
      "Epoch 214/300\n",
      "Epoch 00213: val_loss did not improve\n",
      "11s - loss: 1.2870 - acc: 0.6248 - val_loss: 2.8875 - val_acc: 0.3617\n",
      "Epoch 215/300\n",
      "Epoch 00214: val_loss did not improve\n",
      "11s - loss: 1.2867 - acc: 0.6286 - val_loss: 3.0790 - val_acc: 0.3401\n",
      "Epoch 216/300\n",
      "Epoch 00215: val_loss did not improve\n",
      "11s - loss: 1.2954 - acc: 0.6312 - val_loss: 3.0811 - val_acc: 0.3269\n",
      "Epoch 217/300\n",
      "Epoch 00216: val_loss did not improve\n",
      "12s - loss: 1.2863 - acc: 0.6341 - val_loss: 3.0641 - val_acc: 0.3772\n",
      "Epoch 218/300\n",
      "Epoch 00217: val_loss did not improve\n",
      "11s - loss: 1.3036 - acc: 0.6295 - val_loss: 2.8905 - val_acc: 0.3737\n",
      "Epoch 219/300\n",
      "Epoch 00218: val_loss did not improve\n",
      "12s - loss: 1.2727 - acc: 0.6379 - val_loss: 2.9694 - val_acc: 0.3401\n",
      "Epoch 220/300\n",
      "Epoch 00219: val_loss did not improve\n",
      "12s - loss: 1.2779 - acc: 0.6247 - val_loss: 2.9694 - val_acc: 0.3497\n",
      "Epoch 221/300\n",
      "Epoch 00220: val_loss did not improve\n",
      "11s - loss: 1.2773 - acc: 0.6278 - val_loss: 3.0046 - val_acc: 0.3713\n",
      "Epoch 222/300\n",
      "Epoch 00221: val_loss did not improve\n",
      "11s - loss: 1.2604 - acc: 0.6336 - val_loss: 2.9840 - val_acc: 0.3569\n",
      "Epoch 223/300\n",
      "Epoch 00222: val_loss did not improve\n",
      "11s - loss: 1.2379 - acc: 0.6548 - val_loss: 3.1106 - val_acc: 0.3569\n",
      "Epoch 224/300\n",
      "Epoch 00223: val_loss did not improve\n",
      "11s - loss: 1.2618 - acc: 0.6326 - val_loss: 2.9814 - val_acc: 0.3737\n",
      "Epoch 225/300\n",
      "Epoch 00224: val_loss did not improve\n",
      "11s - loss: 1.2641 - acc: 0.6313 - val_loss: 3.1144 - val_acc: 0.3485\n",
      "Epoch 226/300\n",
      "Epoch 00225: val_loss did not improve\n",
      "11s - loss: 1.2733 - acc: 0.6403 - val_loss: 3.0626 - val_acc: 0.3545\n",
      "Epoch 227/300\n",
      "Epoch 00226: val_loss did not improve\n",
      "11s - loss: 1.2518 - acc: 0.6417 - val_loss: 2.9064 - val_acc: 0.3629\n",
      "Epoch 228/300\n",
      "Epoch 00227: val_loss did not improve\n",
      "11s - loss: 1.2195 - acc: 0.6506 - val_loss: 3.0410 - val_acc: 0.3593\n",
      "Epoch 229/300\n",
      "Epoch 00228: val_loss did not improve\n",
      "11s - loss: 1.2307 - acc: 0.6538 - val_loss: 3.1107 - val_acc: 0.3485\n",
      "Epoch 230/300\n",
      "Epoch 00229: val_loss did not improve\n",
      "11s - loss: 1.2794 - acc: 0.6400 - val_loss: 2.8490 - val_acc: 0.3737\n",
      "Epoch 231/300\n",
      "Epoch 00230: val_loss did not improve\n",
      "11s - loss: 1.2438 - acc: 0.6428 - val_loss: 3.0929 - val_acc: 0.3557\n",
      "Epoch 232/300\n",
      "Epoch 00231: val_loss did not improve\n",
      "11s - loss: 1.2248 - acc: 0.6448 - val_loss: 2.9718 - val_acc: 0.3557\n",
      "Epoch 233/300\n",
      "Epoch 00232: val_loss did not improve\n",
      "11s - loss: 1.2603 - acc: 0.6389 - val_loss: 2.9906 - val_acc: 0.3701\n",
      "Epoch 234/300\n",
      "Epoch 00233: val_loss did not improve\n",
      "11s - loss: 1.2387 - acc: 0.6503 - val_loss: 2.9487 - val_acc: 0.3569\n",
      "Epoch 235/300\n",
      "Epoch 00234: val_loss did not improve\n",
      "12s - loss: 1.2263 - acc: 0.6507 - val_loss: 3.1784 - val_acc: 0.3605\n",
      "Epoch 236/300\n",
      "Epoch 00235: val_loss did not improve\n",
      "11s - loss: 1.2682 - acc: 0.6413 - val_loss: 3.0690 - val_acc: 0.3629\n",
      "Epoch 237/300\n",
      "Epoch 00236: val_loss did not improve\n",
      "11s - loss: 1.2581 - acc: 0.6415 - val_loss: 3.1214 - val_acc: 0.3377\n",
      "Epoch 238/300\n",
      "Epoch 00237: val_loss did not improve\n",
      "11s - loss: 1.2321 - acc: 0.6415 - val_loss: 3.1017 - val_acc: 0.3760\n",
      "Epoch 239/300\n",
      "Epoch 00238: val_loss did not improve\n",
      "11s - loss: 1.2429 - acc: 0.6509 - val_loss: 2.9358 - val_acc: 0.3629\n",
      "Epoch 240/300\n",
      "Epoch 00239: val_loss did not improve\n",
      "11s - loss: 1.2145 - acc: 0.6534 - val_loss: 3.3173 - val_acc: 0.3186\n",
      "Epoch 241/300\n",
      "Epoch 00240: val_loss did not improve\n",
      "11s - loss: 1.2595 - acc: 0.6354 - val_loss: 2.9766 - val_acc: 0.3629\n",
      "Epoch 242/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 00241: val_loss did not improve\n",
      "11s - loss: 1.2608 - acc: 0.6409 - val_loss: 3.0806 - val_acc: 0.3581\n",
      "Epoch 243/300\n",
      "Epoch 00242: val_loss did not improve\n",
      "11s - loss: 1.2225 - acc: 0.6512 - val_loss: 2.9621 - val_acc: 0.3389\n",
      "Epoch 244/300\n",
      "Epoch 00243: val_loss did not improve\n",
      "11s - loss: 1.2436 - acc: 0.6489 - val_loss: 3.0556 - val_acc: 0.3593\n",
      "Epoch 245/300\n",
      "Epoch 00244: val_loss did not improve\n",
      "11s - loss: 1.2356 - acc: 0.6472 - val_loss: 3.1083 - val_acc: 0.3569\n",
      "Epoch 246/300\n",
      "Epoch 00245: val_loss did not improve\n",
      "11s - loss: 1.2236 - acc: 0.6478 - val_loss: 3.0649 - val_acc: 0.3713\n",
      "Epoch 247/300\n",
      "Epoch 00246: val_loss did not improve\n",
      "11s - loss: 1.1890 - acc: 0.6527 - val_loss: 2.9779 - val_acc: 0.3713\n",
      "Epoch 248/300\n",
      "Epoch 00247: val_loss did not improve\n",
      "11s - loss: 1.2498 - acc: 0.6361 - val_loss: 3.0612 - val_acc: 0.3641\n",
      "Epoch 249/300\n",
      "Epoch 00248: val_loss did not improve\n",
      "11s - loss: 1.2281 - acc: 0.6400 - val_loss: 3.2058 - val_acc: 0.3150\n",
      "Epoch 250/300\n",
      "Epoch 00249: val_loss did not improve\n",
      "11s - loss: 1.2282 - acc: 0.6521 - val_loss: 2.9815 - val_acc: 0.3641\n",
      "Epoch 251/300\n",
      "Epoch 00250: val_loss did not improve\n",
      "11s - loss: 1.1934 - acc: 0.6586 - val_loss: 3.1255 - val_acc: 0.3533\n",
      "Epoch 252/300\n",
      "Epoch 00251: val_loss did not improve\n",
      "11s - loss: 1.2041 - acc: 0.6578 - val_loss: 2.9817 - val_acc: 0.3425\n",
      "Epoch 253/300\n",
      "Epoch 00252: val_loss did not improve\n",
      "12s - loss: 1.1772 - acc: 0.6611 - val_loss: 3.0573 - val_acc: 0.3760\n",
      "Epoch 254/300\n",
      "Epoch 00253: val_loss did not improve\n",
      "11s - loss: 1.1912 - acc: 0.6534 - val_loss: 3.0399 - val_acc: 0.3425\n",
      "Epoch 255/300\n",
      "Epoch 00254: val_loss did not improve\n",
      "11s - loss: 1.1988 - acc: 0.6567 - val_loss: 2.9954 - val_acc: 0.3689\n",
      "Epoch 256/300\n",
      "Epoch 00255: val_loss did not improve\n",
      "12s - loss: 1.1786 - acc: 0.6710 - val_loss: 3.0673 - val_acc: 0.3784\n",
      "Epoch 257/300\n",
      "Epoch 00256: val_loss did not improve\n",
      "11s - loss: 1.1557 - acc: 0.6636 - val_loss: 3.1177 - val_acc: 0.3509\n",
      "Epoch 258/300\n",
      "Epoch 00257: val_loss did not improve\n",
      "12s - loss: 1.1996 - acc: 0.6586 - val_loss: 3.1095 - val_acc: 0.3449\n",
      "Epoch 259/300\n",
      "Epoch 00258: val_loss did not improve\n",
      "11s - loss: 1.2113 - acc: 0.6543 - val_loss: 3.0455 - val_acc: 0.3509\n",
      "Epoch 260/300\n",
      "Epoch 00259: val_loss did not improve\n",
      "12s - loss: 1.1787 - acc: 0.6586 - val_loss: 2.9808 - val_acc: 0.3485\n",
      "Epoch 261/300\n",
      "Epoch 00260: val_loss did not improve\n",
      "13s - loss: 1.1950 - acc: 0.6520 - val_loss: 3.1806 - val_acc: 0.3198\n",
      "Epoch 262/300\n",
      "Epoch 00261: val_loss did not improve\n",
      "11s - loss: 1.1730 - acc: 0.6618 - val_loss: 3.1900 - val_acc: 0.3760\n",
      "Epoch 263/300\n",
      "Epoch 00262: val_loss did not improve\n",
      "10s - loss: 1.1597 - acc: 0.6690 - val_loss: 3.1922 - val_acc: 0.3545\n",
      "Epoch 264/300\n",
      "Epoch 00263: val_loss did not improve\n",
      "12s - loss: 1.1756 - acc: 0.6635 - val_loss: 3.0415 - val_acc: 0.3653\n",
      "Epoch 265/300\n",
      "Epoch 00264: val_loss did not improve\n",
      "11s - loss: 1.1377 - acc: 0.6710 - val_loss: 3.2926 - val_acc: 0.3497\n",
      "Epoch 266/300\n",
      "Epoch 00265: val_loss did not improve\n",
      "11s - loss: 1.2089 - acc: 0.6607 - val_loss: 3.0818 - val_acc: 0.3701\n",
      "Epoch 267/300\n",
      "Epoch 00266: val_loss did not improve\n",
      "11s - loss: 1.1712 - acc: 0.6719 - val_loss: 2.9997 - val_acc: 0.3461\n",
      "Epoch 268/300\n",
      "Epoch 00267: val_loss did not improve\n",
      "11s - loss: 1.1739 - acc: 0.6605 - val_loss: 3.0360 - val_acc: 0.3629\n",
      "Epoch 269/300\n",
      "Epoch 00268: val_loss did not improve\n",
      "11s - loss: 1.1872 - acc: 0.6574 - val_loss: 3.2070 - val_acc: 0.3425\n",
      "Epoch 270/300\n",
      "Epoch 00269: val_loss did not improve\n",
      "11s - loss: 1.1385 - acc: 0.6753 - val_loss: 3.3015 - val_acc: 0.3677\n",
      "Epoch 271/300\n",
      "Epoch 00270: val_loss did not improve\n",
      "11s - loss: 1.1858 - acc: 0.6598 - val_loss: 3.2386 - val_acc: 0.3617\n",
      "Epoch 272/300\n",
      "Epoch 00271: val_loss did not improve\n",
      "11s - loss: 1.1895 - acc: 0.6628 - val_loss: 3.0579 - val_acc: 0.3269\n",
      "Epoch 273/300\n",
      "Epoch 00272: val_loss did not improve\n",
      "11s - loss: 1.1644 - acc: 0.6661 - val_loss: 3.2318 - val_acc: 0.3617\n",
      "Epoch 274/300\n",
      "Epoch 00273: val_loss did not improve\n",
      "11s - loss: 1.1793 - acc: 0.6638 - val_loss: 3.0692 - val_acc: 0.3485\n",
      "Epoch 275/300\n",
      "Epoch 00274: val_loss did not improve\n",
      "11s - loss: 1.1830 - acc: 0.6594 - val_loss: 3.1016 - val_acc: 0.3533\n",
      "Epoch 276/300\n",
      "Epoch 00275: val_loss did not improve\n",
      "11s - loss: 1.1554 - acc: 0.6662 - val_loss: 3.2181 - val_acc: 0.3629\n",
      "Epoch 277/300\n",
      "Epoch 00276: val_loss did not improve\n",
      "11s - loss: 1.1732 - acc: 0.6638 - val_loss: 3.0275 - val_acc: 0.3581\n",
      "Epoch 278/300\n",
      "Epoch 00277: val_loss did not improve\n",
      "11s - loss: 1.1262 - acc: 0.6692 - val_loss: 3.3129 - val_acc: 0.3617\n",
      "Epoch 279/300\n",
      "Epoch 00278: val_loss did not improve\n",
      "11s - loss: 1.1689 - acc: 0.6637 - val_loss: 3.1307 - val_acc: 0.3617\n",
      "Epoch 280/300\n",
      "Epoch 00279: val_loss did not improve\n",
      "11s - loss: 1.1733 - acc: 0.6700 - val_loss: 3.3585 - val_acc: 0.3569\n",
      "Epoch 281/300\n",
      "Epoch 00280: val_loss did not improve\n",
      "12s - loss: 1.1459 - acc: 0.6773 - val_loss: 3.3610 - val_acc: 0.3425\n",
      "Epoch 282/300\n",
      "Epoch 00281: val_loss did not improve\n",
      "11s - loss: 1.1339 - acc: 0.6775 - val_loss: 3.0743 - val_acc: 0.3653\n",
      "Epoch 283/300\n",
      "Epoch 00282: val_loss did not improve\n",
      "11s - loss: 1.1902 - acc: 0.6639 - val_loss: 3.0227 - val_acc: 0.3281\n",
      "Epoch 284/300\n",
      "Epoch 00283: val_loss did not improve\n",
      "11s - loss: 1.1691 - acc: 0.6636 - val_loss: 3.5012 - val_acc: 0.3473\n",
      "Epoch 285/300\n",
      "Epoch 00284: val_loss did not improve\n",
      "11s - loss: 1.1791 - acc: 0.6707 - val_loss: 3.3248 - val_acc: 0.3365\n",
      "Epoch 286/300\n",
      "Epoch 00285: val_loss did not improve\n",
      "11s - loss: 1.1619 - acc: 0.6695 - val_loss: 3.2358 - val_acc: 0.3401\n",
      "Epoch 287/300\n",
      "Epoch 00286: val_loss did not improve\n",
      "11s - loss: 1.1379 - acc: 0.6762 - val_loss: 2.9375 - val_acc: 0.3689\n",
      "Epoch 288/300\n",
      "Epoch 00287: val_loss did not improve\n",
      "11s - loss: 1.1924 - acc: 0.6539 - val_loss: 2.9666 - val_acc: 0.3701\n",
      "Epoch 289/300\n",
      "Epoch 00288: val_loss did not improve\n",
      "12s - loss: 1.1567 - acc: 0.6748 - val_loss: 3.0876 - val_acc: 0.3509\n",
      "Epoch 290/300\n",
      "Epoch 00289: val_loss did not improve\n",
      "11s - loss: 1.1602 - acc: 0.6710 - val_loss: 3.1491 - val_acc: 0.3485\n",
      "Epoch 291/300\n",
      "Epoch 00290: val_loss did not improve\n",
      "12s - loss: 1.1285 - acc: 0.6730 - val_loss: 3.1089 - val_acc: 0.3509\n",
      "Epoch 292/300\n",
      "Epoch 00291: val_loss did not improve\n",
      "11s - loss: 1.1221 - acc: 0.6777 - val_loss: 3.0216 - val_acc: 0.3473\n",
      "Epoch 293/300\n",
      "Epoch 00292: val_loss did not improve\n",
      "11s - loss: 1.1100 - acc: 0.6800 - val_loss: 3.1042 - val_acc: 0.3557\n",
      "Epoch 294/300\n",
      "Epoch 00293: val_loss did not improve\n",
      "11s - loss: 1.1449 - acc: 0.6724 - val_loss: 3.0695 - val_acc: 0.3605\n",
      "Epoch 295/300\n",
      "Epoch 00294: val_loss did not improve\n",
      "11s - loss: 1.1583 - acc: 0.6693 - val_loss: 2.8360 - val_acc: 0.3413\n",
      "Epoch 296/300\n",
      "Epoch 00295: val_loss did not improve\n",
      "11s - loss: 1.1706 - acc: 0.6668 - val_loss: 3.0029 - val_acc: 0.3329\n",
      "Epoch 297/300\n",
      "Epoch 00296: val_loss did not improve\n",
      "11s - loss: 1.1503 - acc: 0.6717 - val_loss: 3.1345 - val_acc: 0.3617\n",
      "Epoch 298/300\n",
      "Epoch 00297: val_loss did not improve\n",
      "11s - loss: 1.1946 - acc: 0.6660 - val_loss: 3.1828 - val_acc: 0.3485\n",
      "Epoch 299/300\n",
      "Epoch 00298: val_loss did not improve\n",
      "11s - loss: 1.1495 - acc: 0.6806 - val_loss: 3.1585 - val_acc: 0.3485\n",
      "Epoch 300/300\n",
      "Epoch 00299: val_loss did not improve\n",
      "11s - loss: 1.1550 - acc: 0.6743 - val_loss: 3.1233 - val_acc: 0.3377\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "from keras.callbacks import ModelCheckpoint \n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "\n",
    "epochs = 300\n",
    "batch_size = 133\n",
    "\n",
    "datagen_train = ImageDataGenerator(\n",
    "    width_shift_range=0.2,  \n",
    "    height_shift_range=0.2,  \n",
    "    horizontal_flip=True) \n",
    "\n",
    "datagen_train.fit(train_tensors)\n",
    "\n",
    "history = model.fit_generator(datagen_train.flow(train_tensors, train_targets, batch_size=batch_size),\n",
    "                    steps_per_epoch=train_tensors.shape[0] // batch_size,\n",
    "                    epochs=epochs, verbose=2, callbacks=[checkpointer],\n",
    "                    validation_data=(valid_tensors, valid_targets),\n",
    "                    validation_steps=valid_tensors.shape[0] // batch_size, workers=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Model with the Best Validation Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.load_weights('saved_models/weights.best.from_scratch.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Model\n",
    "\n",
    "Try out your model on the test dataset of dog images.  Ensure that your test accuracy is greater than 1%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 33.6124%\n"
     ]
    },
    {
     "data": {
      "image/png": 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0ycND8PEQrhkY2zRfQgvHiILBYGgyakQhvRBLtZW3lh7kcFYJU8d0Jquogvk7M/jttPWI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x161b242f0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jprXmF1gUZpnzFhwycTsvLwiJc2zfvdRhNQAkjDLPTonoZtxH1v/dFmhe/cMc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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x161b26b0d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]\n",
    "\n",
    "test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)\n",
    "\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "\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', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step4'></a>\n",
    "## Step 4: Use a CNN to Classify Dog Breeds\n",
    "\n",
    "To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning.  In the following step, you will get a chance to use transfer learning to train your own CNN.\n",
    "\n",
    "### Obtain Bottleneck Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')\n",
    "train_VGG16 = bottleneck_features['train']\n",
    "valid_VGG16 = bottleneck_features['valid']\n",
    "test_VGG16 = bottleneck_features['test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture\n",
    "\n",
    "The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model.  We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "global_average_pooling2d_1 ( (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 133)               68229     \n",
      "=================================================================\n",
      "Total params: 68,229\n",
      "Trainable params: 68,229\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "VGG16_model = Sequential()\n",
    "VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))\n",
    "VGG16_model.add(Dropout(0.3))\n",
    "VGG16_model.add(Dense(133, activation='softmax'))\n",
    "\n",
    "VGG16_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/100\n",
      "Epoch 00000: val_loss improved from inf to 12.32647, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "1s - loss: 14.3285 - acc: 0.0370 - val_loss: 12.3265 - val_acc: 0.1030\n",
      "Epoch 2/100\n",
      "Epoch 00001: val_loss improved from 12.32647 to 10.48043, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 12.5295 - acc: 0.1015 - val_loss: 10.4804 - val_acc: 0.1904\n",
      "Epoch 3/100\n",
      "Epoch 00002: val_loss improved from 10.48043 to 9.42599, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 11.1839 - acc: 0.1644 - val_loss: 9.4260 - val_acc: 0.2587\n",
      "Epoch 4/100\n",
      "Epoch 00003: val_loss improved from 9.42599 to 8.82049, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 10.1821 - acc: 0.2290 - val_loss: 8.8205 - val_acc: 0.3162\n",
      "Epoch 5/100\n",
      "Epoch 00004: val_loss improved from 8.82049 to 8.50695, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 9.5129 - acc: 0.2687 - val_loss: 8.5069 - val_acc: 0.3629\n",
      "Epoch 6/100\n",
      "Epoch 00005: val_loss improved from 8.50695 to 8.33651, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 9.1585 - acc: 0.3030 - val_loss: 8.3365 - val_acc: 0.3665\n",
      "Epoch 7/100\n",
      "Epoch 00006: val_loss improved from 8.33651 to 8.05239, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 8.6996 - acc: 0.3449 - val_loss: 8.0524 - val_acc: 0.3988\n",
      "Epoch 8/100\n",
      "Epoch 00007: val_loss improved from 8.05239 to 7.93696, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 8.4459 - acc: 0.3608 - val_loss: 7.9370 - val_acc: 0.4024\n",
      "Epoch 9/100\n",
      "Epoch 00008: val_loss improved from 7.93696 to 7.82658, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 8.1326 - acc: 0.3925 - val_loss: 7.8266 - val_acc: 0.4072\n",
      "Epoch 10/100\n",
      "Epoch 00009: val_loss improved from 7.82658 to 7.63858, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 7.9583 - acc: 0.4036 - val_loss: 7.6386 - val_acc: 0.4263\n",
      "Epoch 11/100\n",
      "Epoch 00010: val_loss improved from 7.63858 to 7.56212, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 7.7774 - acc: 0.4189 - val_loss: 7.5621 - val_acc: 0.4299\n",
      "Epoch 12/100\n",
      "Epoch 00011: val_loss improved from 7.56212 to 7.42660, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 7.5795 - acc: 0.4374 - val_loss: 7.4266 - val_acc: 0.4515\n",
      "Epoch 13/100\n",
      "Epoch 00012: val_loss did not improve\n",
      "0s - loss: 7.4190 - acc: 0.4555 - val_loss: 7.4406 - val_acc: 0.4431\n",
      "Epoch 14/100\n",
      "Epoch 00013: val_loss improved from 7.42660 to 7.38458, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 7.3627 - acc: 0.4659 - val_loss: 7.3846 - val_acc: 0.4539\n",
      "Epoch 15/100\n",
      "Epoch 00014: val_loss improved from 7.38458 to 7.36753, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 7.2867 - acc: 0.4723 - val_loss: 7.3675 - val_acc: 0.4419\n",
      "Epoch 16/100\n",
      "Epoch 00015: val_loss improved from 7.36753 to 7.27132, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 7.1945 - acc: 0.4811 - val_loss: 7.2713 - val_acc: 0.4515\n",
      "Epoch 17/100\n",
      "Epoch 00016: val_loss improved from 7.27132 to 7.20519, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 7.0654 - acc: 0.4921 - val_loss: 7.2052 - val_acc: 0.4527\n",
      "Epoch 18/100\n",
      "Epoch 00017: val_loss improved from 7.20519 to 7.14654, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 7.0090 - acc: 0.4961 - val_loss: 7.1465 - val_acc: 0.4647\n",
      "Epoch 19/100\n",
      "Epoch 00018: val_loss did not improve\n",
      "0s - loss: 6.9238 - acc: 0.5135 - val_loss: 7.1830 - val_acc: 0.4647\n",
      "Epoch 20/100\n",
      "Epoch 00019: val_loss improved from 7.14654 to 7.13518, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.9033 - acc: 0.5139 - val_loss: 7.1352 - val_acc: 0.4599\n",
      "Epoch 21/100\n",
      "Epoch 00020: val_loss improved from 7.13518 to 7.09920, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.8864 - acc: 0.5168 - val_loss: 7.0992 - val_acc: 0.4754\n",
      "Epoch 22/100\n",
      "Epoch 00021: val_loss did not improve\n",
      "0s - loss: 6.8617 - acc: 0.5232 - val_loss: 7.1509 - val_acc: 0.4814\n",
      "Epoch 23/100\n",
      "Epoch 00022: val_loss did not improve\n",
      "0s - loss: 6.8493 - acc: 0.5223 - val_loss: 7.1688 - val_acc: 0.4719\n",
      "Epoch 24/100\n",
      "Epoch 00023: val_loss did not improve\n",
      "0s - loss: 6.8186 - acc: 0.5316 - val_loss: 7.1214 - val_acc: 0.4790\n",
      "Epoch 25/100\n",
      "Epoch 00024: val_loss did not improve\n",
      "0s - loss: 6.8007 - acc: 0.5352 - val_loss: 7.1695 - val_acc: 0.4587\n",
      "Epoch 26/100\n",
      "Epoch 00025: val_loss did not improve\n",
      "0s - loss: 6.7690 - acc: 0.5382 - val_loss: 7.1388 - val_acc: 0.4778\n",
      "Epoch 27/100\n",
      "Epoch 00026: val_loss improved from 7.09920 to 7.09746, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.7860 - acc: 0.5376 - val_loss: 7.0975 - val_acc: 0.4754\n",
      "Epoch 28/100\n",
      "Epoch 00027: val_loss did not improve\n",
      "0s - loss: 6.7462 - acc: 0.5397 - val_loss: 7.1540 - val_acc: 0.4695\n",
      "Epoch 29/100\n",
      "Epoch 00028: val_loss did not improve\n",
      "0s - loss: 6.7646 - acc: 0.5395 - val_loss: 7.1193 - val_acc: 0.4790\n",
      "Epoch 30/100\n",
      "Epoch 00029: val_loss did not improve\n",
      "0s - loss: 6.7196 - acc: 0.5442 - val_loss: 7.1727 - val_acc: 0.4707\n",
      "Epoch 31/100\n",
      "Epoch 00030: val_loss did not improve\n",
      "0s - loss: 6.7321 - acc: 0.5418 - val_loss: 7.1229 - val_acc: 0.4814\n",
      "Epoch 32/100\n",
      "Epoch 00031: val_loss improved from 7.09746 to 7.07457, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.6785 - acc: 0.5481 - val_loss: 7.0746 - val_acc: 0.4778\n",
      "Epoch 33/100\n",
      "Epoch 00032: val_loss improved from 7.07457 to 7.06739, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.6657 - acc: 0.5430 - val_loss: 7.0674 - val_acc: 0.4802\n",
      "Epoch 34/100\n",
      "Epoch 00033: val_loss did not improve\n",
      "0s - loss: 6.6229 - acc: 0.5481 - val_loss: 7.1051 - val_acc: 0.4790\n",
      "Epoch 35/100\n",
      "Epoch 00034: val_loss improved from 7.06739 to 7.01716, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.5763 - acc: 0.5504 - val_loss: 7.0172 - val_acc: 0.4826\n",
      "Epoch 36/100\n",
      "Epoch 00035: val_loss improved from 7.01716 to 6.98150, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.5265 - acc: 0.5572 - val_loss: 6.9815 - val_acc: 0.4731\n",
      "Epoch 37/100\n",
      "Epoch 00036: val_loss improved from 6.98150 to 6.93946, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.4776 - acc: 0.5572 - val_loss: 6.9395 - val_acc: 0.4850\n",
      "Epoch 38/100\n",
      "Epoch 00037: val_loss improved from 6.93946 to 6.87119, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.4565 - acc: 0.5593 - val_loss: 6.8712 - val_acc: 0.4886\n",
      "Epoch 39/100\n",
      "Epoch 00038: val_loss improved from 6.87119 to 6.77000, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.3931 - acc: 0.5606 - val_loss: 6.7700 - val_acc: 0.4922\n",
      "Epoch 40/100\n",
      "Epoch 00039: val_loss did not improve\n",
      "0s - loss: 6.3360 - acc: 0.5675 - val_loss: 6.7782 - val_acc: 0.4874\n",
      "Epoch 41/100\n",
      "Epoch 00040: val_loss improved from 6.77000 to 6.73774, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.3113 - acc: 0.5699 - val_loss: 6.7377 - val_acc: 0.5006\n",
      "Epoch 42/100\n",
      "Epoch 00041: val_loss did not improve\n",
      "0s - loss: 6.2704 - acc: 0.5756 - val_loss: 6.8090 - val_acc: 0.4934\n",
      "Epoch 43/100\n",
      "Epoch 00042: val_loss did not improve\n",
      "0s - loss: 6.2589 - acc: 0.5816 - val_loss: 6.7807 - val_acc: 0.4922\n",
      "Epoch 44/100\n",
      "Epoch 00043: val_loss did not improve\n",
      "0s - loss: 6.2622 - acc: 0.5814 - val_loss: 6.7964 - val_acc: 0.5030\n",
      "Epoch 45/100\n",
      "Epoch 00044: val_loss did not improve\n",
      "0s - loss: 6.2621 - acc: 0.5771 - val_loss: 6.8284 - val_acc: 0.4910\n",
      "Epoch 46/100\n",
      "Epoch 00045: val_loss did not improve\n",
      "0s - loss: 6.2489 - acc: 0.5801 - val_loss: 6.7809 - val_acc: 0.4826\n",
      "Epoch 47/100\n",
      "Epoch 00046: val_loss did not improve\n",
      "0s - loss: 6.2346 - acc: 0.5829 - val_loss: 6.7627 - val_acc: 0.5078\n",
      "Epoch 48/100\n",
      "Epoch 00047: val_loss did not improve\n",
      "0s - loss: 6.2115 - acc: 0.5831 - val_loss: 6.7844 - val_acc: 0.4910\n",
      "Epoch 49/100\n",
      "Epoch 00048: val_loss did not improve\n",
      "0s - loss: 6.2016 - acc: 0.5837 - val_loss: 6.7870 - val_acc: 0.5030\n",
      "Epoch 50/100\n",
      "Epoch 00049: val_loss did not improve\n",
      "0s - loss: 6.1908 - acc: 0.5831 - val_loss: 6.7755 - val_acc: 0.4934\n",
      "Epoch 51/100\n",
      "Epoch 00050: val_loss did not improve\n",
      "0s - loss: 6.1854 - acc: 0.5819 - val_loss: 6.7407 - val_acc: 0.4934\n",
      "Epoch 52/100\n",
      "Epoch 00051: val_loss improved from 6.73774 to 6.68742, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.1805 - acc: 0.5843 - val_loss: 6.6874 - val_acc: 0.5066\n",
      "Epoch 53/100\n",
      "Epoch 00052: val_loss did not improve\n",
      "0s - loss: 6.1699 - acc: 0.5865 - val_loss: 6.7676 - val_acc: 0.4994\n",
      "Epoch 54/100\n",
      "Epoch 00053: val_loss improved from 6.68742 to 6.67464, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.1595 - acc: 0.5907 - val_loss: 6.6746 - val_acc: 0.5042\n",
      "Epoch 55/100\n",
      "Epoch 00054: val_loss did not improve\n",
      "0s - loss: 6.1538 - acc: 0.5891 - val_loss: 6.7375 - val_acc: 0.4934\n",
      "Epoch 56/100\n",
      "Epoch 00055: val_loss did not improve\n",
      "0s - loss: 6.1549 - acc: 0.5892 - val_loss: 6.6920 - val_acc: 0.5078\n",
      "Epoch 57/100\n",
      "Epoch 00056: val_loss did not improve\n",
      "0s - loss: 6.1349 - acc: 0.5931 - val_loss: 6.6820 - val_acc: 0.5018\n",
      "Epoch 58/100\n",
      "Epoch 00057: val_loss did not improve\n",
      "0s - loss: 6.1501 - acc: 0.5892 - val_loss: 6.7170 - val_acc: 0.5030\n",
      "Epoch 59/100\n",
      "Epoch 00058: val_loss did not improve\n",
      "0s - loss: 6.1239 - acc: 0.5937 - val_loss: 6.6965 - val_acc: 0.5042\n",
      "Epoch 60/100\n",
      "Epoch 00059: val_loss did not improve\n",
      "0s - loss: 6.1250 - acc: 0.5930 - val_loss: 6.7326 - val_acc: 0.5078\n",
      "Epoch 61/100\n",
      "Epoch 00060: val_loss did not improve\n",
      "0s - loss: 6.1007 - acc: 0.5978 - val_loss: 6.7463 - val_acc: 0.4946\n",
      "Epoch 62/100\n",
      "Epoch 00061: val_loss did not improve\n",
      "0s - loss: 6.1174 - acc: 0.5946 - val_loss: 6.7253 - val_acc: 0.4994\n",
      "Epoch 63/100\n",
      "Epoch 00062: val_loss did not improve\n",
      "0s - loss: 6.0936 - acc: 0.5918 - val_loss: 6.7273 - val_acc: 0.4946\n",
      "Epoch 64/100\n",
      "Epoch 00063: val_loss improved from 6.67464 to 6.66130, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.0476 - acc: 0.5913 - val_loss: 6.6613 - val_acc: 0.5054\n",
      "Epoch 65/100\n",
      "Epoch 00064: val_loss improved from 6.66130 to 6.61851, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 6.0092 - acc: 0.6012 - val_loss: 6.6185 - val_acc: 0.5102\n",
      "Epoch 66/100\n",
      "Epoch 00065: val_loss improved from 6.61851 to 6.58232, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 5.9942 - acc: 0.6027 - val_loss: 6.5823 - val_acc: 0.5150\n",
      "Epoch 67/100\n",
      "Epoch 00066: val_loss improved from 6.58232 to 6.56200, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 5.9848 - acc: 0.6078 - val_loss: 6.5620 - val_acc: 0.5042\n",
      "Epoch 68/100\n",
      "Epoch 00067: val_loss improved from 6.56200 to 6.56137, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 5.9934 - acc: 0.6015 - val_loss: 6.5614 - val_acc: 0.5090\n",
      "Epoch 69/100\n",
      "Epoch 00068: val_loss did not improve\n",
      "0s - loss: 5.9760 - acc: 0.6072 - val_loss: 6.6183 - val_acc: 0.4994\n",
      "Epoch 70/100\n",
      "Epoch 00069: val_loss did not improve\n",
      "0s - loss: 5.9718 - acc: 0.6066 - val_loss: 6.5707 - val_acc: 0.5126\n",
      "Epoch 71/100\n",
      "Epoch 00070: val_loss did not improve\n",
      "0s - loss: 5.9810 - acc: 0.6045 - val_loss: 6.6335 - val_acc: 0.5102\n",
      "Epoch 72/100\n",
      "Epoch 00071: val_loss did not improve\n",
      "0s - loss: 5.9770 - acc: 0.6064 - val_loss: 6.5943 - val_acc: 0.5054\n",
      "Epoch 73/100\n",
      "Epoch 00072: val_loss did not improve\n",
      "0s - loss: 5.9755 - acc: 0.6063 - val_loss: 6.6650 - val_acc: 0.5006\n",
      "Epoch 74/100\n",
      "Epoch 00073: val_loss improved from 6.56137 to 6.54824, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 5.9598 - acc: 0.6082 - val_loss: 6.5482 - val_acc: 0.5186\n",
      "Epoch 75/100\n",
      "Epoch 00074: val_loss did not improve\n",
      "0s - loss: 5.9668 - acc: 0.6069 - val_loss: 6.6230 - val_acc: 0.5090\n",
      "Epoch 76/100\n",
      "Epoch 00075: val_loss did not improve\n",
      "0s - loss: 5.9552 - acc: 0.6097 - val_loss: 6.5887 - val_acc: 0.5126\n",
      "Epoch 77/100\n",
      "Epoch 00076: val_loss did not improve\n",
      "0s - loss: 5.9657 - acc: 0.6060 - val_loss: 6.6431 - val_acc: 0.5042\n",
      "Epoch 78/100\n",
      "Epoch 00077: val_loss did not improve\n",
      "0s - loss: 5.9615 - acc: 0.6093 - val_loss: 6.6239 - val_acc: 0.5150\n",
      "Epoch 79/100\n",
      "Epoch 00078: val_loss did not improve\n",
      "0s - loss: 5.9595 - acc: 0.6093 - val_loss: 6.6224 - val_acc: 0.5114\n",
      "Epoch 80/100\n",
      "Epoch 00079: val_loss did not improve\n",
      "0s - loss: 5.9586 - acc: 0.6097 - val_loss: 6.6079 - val_acc: 0.5066\n",
      "Epoch 81/100\n",
      "Epoch 00080: val_loss did not improve\n",
      "0s - loss: 5.9461 - acc: 0.6111 - val_loss: 6.5952 - val_acc: 0.5138\n",
      "Epoch 82/100\n",
      "Epoch 00081: val_loss did not improve\n",
      "0s - loss: 5.9629 - acc: 0.6096 - val_loss: 6.5987 - val_acc: 0.5138\n",
      "Epoch 83/100\n",
      "Epoch 00082: val_loss did not improve\n",
      "0s - loss: 5.9633 - acc: 0.6075 - val_loss: 6.6319 - val_acc: 0.5054\n",
      "Epoch 84/100\n",
      "Epoch 00083: val_loss did not improve\n",
      "0s - loss: 5.9383 - acc: 0.6138 - val_loss: 6.5928 - val_acc: 0.5126\n",
      "Epoch 85/100\n",
      "Epoch 00084: val_loss did not improve\n",
      "0s - loss: 5.9461 - acc: 0.6123 - val_loss: 6.5805 - val_acc: 0.5114\n",
      "Epoch 86/100\n",
      "Epoch 00085: val_loss did not improve\n",
      "0s - loss: 5.9443 - acc: 0.6115 - val_loss: 6.6547 - val_acc: 0.5006\n",
      "Epoch 87/100\n",
      "Epoch 00086: val_loss did not improve\n",
      "0s - loss: 5.9379 - acc: 0.6123 - val_loss: 6.5830 - val_acc: 0.5102\n",
      "Epoch 88/100\n",
      "Epoch 00087: val_loss did not improve\n",
      "0s - loss: 5.9640 - acc: 0.6102 - val_loss: 6.6257 - val_acc: 0.5018\n",
      "Epoch 89/100\n",
      "Epoch 00088: val_loss did not improve\n",
      "0s - loss: 5.9393 - acc: 0.6121 - val_loss: 6.6893 - val_acc: 0.5054\n",
      "Epoch 90/100\n",
      "Epoch 00089: val_loss did not improve\n",
      "0s - loss: 5.9422 - acc: 0.6118 - val_loss: 6.6256 - val_acc: 0.5102\n",
      "Epoch 91/100\n",
      "Epoch 00090: val_loss did not improve\n",
      "0s - loss: 5.9258 - acc: 0.6069 - val_loss: 6.5516 - val_acc: 0.5090\n",
      "Epoch 92/100\n",
      "Epoch 00091: val_loss did not improve\n",
      "0s - loss: 5.8383 - acc: 0.6055 - val_loss: 6.5854 - val_acc: 0.5006\n",
      "Epoch 93/100\n",
      "Epoch 00092: val_loss improved from 6.54824 to 6.53443, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 5.7825 - acc: 0.6150 - val_loss: 6.5344 - val_acc: 0.4982\n",
      "Epoch 94/100\n",
      "Epoch 00093: val_loss improved from 6.53443 to 6.44561, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 5.7587 - acc: 0.6108 - val_loss: 6.4456 - val_acc: 0.5102\n",
      "Epoch 95/100\n",
      "Epoch 00094: val_loss did not improve\n",
      "0s - loss: 5.6984 - acc: 0.6180 - val_loss: 6.4666 - val_acc: 0.5042\n",
      "Epoch 96/100\n",
      "Epoch 00095: val_loss improved from 6.44561 to 6.43260, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 5.6785 - acc: 0.6162 - val_loss: 6.4326 - val_acc: 0.5066\n",
      "Epoch 97/100\n",
      "Epoch 00096: val_loss improved from 6.43260 to 6.39903, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 5.6585 - acc: 0.6207 - val_loss: 6.3990 - val_acc: 0.5114\n",
      "Epoch 98/100\n",
      "Epoch 00097: val_loss improved from 6.39903 to 6.39233, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 5.6175 - acc: 0.6263 - val_loss: 6.3923 - val_acc: 0.5126\n",
      "Epoch 99/100\n",
      "Epoch 00098: val_loss improved from 6.39233 to 6.36614, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "0s - loss: 5.6182 - acc: 0.6256 - val_loss: 6.3661 - val_acc: 0.5198\n",
      "Epoch 100/100\n",
      "Epoch 00099: val_loss did not improve\n",
      "0s - loss: 5.5811 - acc: 0.6272 - val_loss: 6.4000 - val_acc: 0.5114\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x161b38069e8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint  \n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "\n",
    "VGG16_model.fit(train_VGG16, train_targets, \n",
    "          validation_data=(valid_VGG16, valid_targets),\n",
    "          epochs=100, batch_size=133, callbacks=[checkpointer], verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Model with the Best Validation Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Model\n",
    "\n",
    "Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images.  We print the test accuracy below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 52.0335%\n"
     ]
    }
   ],
   "source": [
    "VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]\n",
    "\n",
    "test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict Dog Breed with the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Australian_shepherd_00888.jpg Pembroke_welsh_corgi\n",
      "Norwegian_elkhound_07150.jpg Norwegian_elkhound\n",
      "Golden_retriever_05243.jpg Golden_retriever\n",
      "Irish_wolfhound_06077.jpg Irish_wolfhound\n",
      "Norwich_terrier_07234.jpg Norwich_terrier\n",
      "Afghan_hound_00148.jpg Newfoundland\n",
      "Collie_03815.jpg Pembroke_welsh_corgi\n",
      "Smooth_fox_terrier_08119.jpg Smooth_fox_terrier\n",
      "Australian_shepherd_00808.jpg Welsh_springer_spaniel\n",
      "Glen_of_imaal_terrier_05160.jpg Glen_of_imaal_terrier\n"
     ]
    }
   ],
   "source": [
    "from extract_bottleneck_features import *\n",
    "\n",
    "def VGG16_predict_breed(img_path):\n",
    "    bottleneck_feature = extract_VGG16(path_to_tensor(img_path))\n",
    "    predicted_vector = VGG16_model.predict(bottleneck_feature)\n",
    "    return dog_names[np.argmax(predicted_vector)]\n",
    "\n",
    "for file in np.random.choice(dog_files, 10):    \n",
    "    print(os.path.basename(file), VGG16_predict_breed(file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step5'></a>\n",
    "## Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)\n",
    "\n",
    "You will now use transfer learning to create a CNN that can identify dog breed from images.  Your CNN must attain at least 60% accuracy on the test set.\n",
    "\n",
    "In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features.  In this section, you must use the bottleneck features from a different pre-trained model.  To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:\n",
    "- [VGG-19](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz) bottleneck features\n",
    "- [ResNet-50](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz) bottleneck features\n",
    "- [Inception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz) bottleneck features\n",
    "- [Xception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz) bottleneck features\n",
    "\n",
    "The files are encoded as such:\n",
    "\n",
    "    Dog{network}Data.npz\n",
    "    \n",
    "where `{network}`, in the above filename, can be one of `VGG19`, `Resnet50`, `InceptionV3`, or `Xception`.  Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the `bottleneck_features/` folder in the repository.\n",
    "\n",
    "### (IMPLEMENTATION) Obtain Bottleneck Features\n",
    "\n",
    "In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:\n",
    "\n",
    "    bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')\n",
    "    train_{network} = bottleneck_features['train']\n",
    "    valid_{network} = bottleneck_features['valid']\n",
    "    test_{network} = bottleneck_features['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bottleneck_features = np.load('bottleneck_features/DogInceptionV3Data.npz')\n",
    "train_InceptionV3 = bottleneck_features['train']\n",
    "valid_InceptionV3 = bottleneck_features['valid']\n",
    "test_InceptionV3 = bottleneck_features['test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Model Architecture\n",
    "\n",
    "Create a CNN to classify dog breed.  At the end of your code cell block, summarize the layers of your model by executing the line:\n",
    "    \n",
    "        <your model's name>.summary()\n",
    "   \n",
    "__Question 5:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  Describe why you think the architecture is suitable for the current problem.\n",
    "\n",
    "__Answer:__ ***I chose the Inception network to join to a fully connected neural network. The output data from Inception network gets transformed by a global average pooling to a format, which can be pushed into the fully connected network. I was experimenting with adding more and more dense hidden layers but after a point, accuracy was not improved, so I sticked to 3 hidden layers: two of them have relu activation function and the last one operates with softmax, which is exceptinally good with categorization. Adding dropout layers also improved accuracy.***\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "global_average_pooling2d_2 ( (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 500)               1024500   \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 500)               0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 250)               125250    \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 250)               0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 133)               33383     \n",
      "=================================================================\n",
      "Total params: 1,183,133\n",
      "Trainable params: 1,183,133\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "InceptionV3_model = Sequential()\n",
    "InceptionV3_model.add(GlobalAveragePooling2D(input_shape=train_InceptionV3.shape[1:]))\n",
    "InceptionV3_model.add(Dropout(0.4))\n",
    "InceptionV3_model.add(Dense(500, activation='relu'))\n",
    "InceptionV3_model.add(Dropout(0.4))\n",
    "InceptionV3_model.add(Dense(250, activation='relu'))\n",
    "InceptionV3_model.add(Dropout(0.4))\n",
    "InceptionV3_model.add(Dense(133, activation='softmax'))\n",
    "\n",
    "InceptionV3_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "InceptionV3_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Train the Model\n",
    "\n",
    "Train your model in the code cell below.  Use model checkpointing to save the model that attains the best validation loss.  \n",
    "\n",
    "You are welcome to [augment the training data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html), but this is not a requirement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/100\n",
      "Epoch 00000: val_loss improved from inf to 1.46835, saving model to saved_models/weights.best.InceptionV3x.hdf5\n",
      "1s - loss: 3.6501 - acc: 0.2400 - val_loss: 1.4684 - val_acc: 0.6719\n",
      "Epoch 2/100\n",
      "Epoch 00001: val_loss improved from 1.46835 to 0.81036, saving model to saved_models/weights.best.InceptionV3x.hdf5\n",
      "1s - loss: 1.7371 - acc: 0.5624 - val_loss: 0.8104 - val_acc: 0.7557\n",
      "Epoch 3/100\n",
      "Epoch 00002: val_loss improved from 0.81036 to 0.70792, saving model to saved_models/weights.best.InceptionV3x.hdf5\n",
      "1s - loss: 1.2814 - acc: 0.6525 - val_loss: 0.7079 - val_acc: 0.7820\n",
      "Epoch 4/100\n",
      "Epoch 00003: val_loss improved from 0.70792 to 0.58309, saving model to saved_models/weights.best.InceptionV3x.hdf5\n",
      "1s - loss: 1.0495 - acc: 0.7018 - val_loss: 0.5831 - val_acc: 0.8084\n",
      "Epoch 5/100\n",
      "Epoch 00004: val_loss improved from 0.58309 to 0.56733, saving model to saved_models/weights.best.InceptionV3x.hdf5\n",
      "1s - loss: 0.9269 - acc: 0.7344 - val_loss: 0.5673 - val_acc: 0.8216\n",
      "Epoch 6/100\n",
      "Epoch 00005: val_loss did not improve\n",
      "1s - loss: 0.8505 - acc: 0.7542 - val_loss: 0.5740 - val_acc: 0.8323\n",
      "Epoch 7/100\n",
      "Epoch 00006: val_loss did not improve\n",
      "1s - loss: 0.7847 - acc: 0.7743 - val_loss: 0.6063 - val_acc: 0.8251\n",
      "Epoch 8/100\n",
      "Epoch 00007: val_loss did not improve\n",
      "1s - loss: 0.7136 - acc: 0.7897 - val_loss: 0.5887 - val_acc: 0.8216\n",
      "Epoch 9/100\n",
      "Epoch 00008: val_loss did not improve\n",
      "1s - loss: 0.6826 - acc: 0.7903 - val_loss: 0.5836 - val_acc: 0.8132\n",
      "Epoch 10/100\n",
      "Epoch 00009: val_loss improved from 0.56733 to 0.56609, saving model to saved_models/weights.best.InceptionV3x.hdf5\n",
      "1s - loss: 0.6417 - acc: 0.8067 - val_loss: 0.5661 - val_acc: 0.8156\n",
      "Epoch 11/100\n",
      "Epoch 00010: val_loss did not improve\n",
      "1s - loss: 0.5933 - acc: 0.8198 - val_loss: 0.5865 - val_acc: 0.8168\n",
      "Epoch 12/100\n",
      "Epoch 00011: val_loss improved from 0.56609 to 0.54996, saving model to saved_models/weights.best.InceptionV3x.hdf5\n",
      "1s - loss: 0.5662 - acc: 0.8223 - val_loss: 0.5500 - val_acc: 0.8383\n",
      "Epoch 13/100\n",
      "Epoch 00012: val_loss improved from 0.54996 to 0.54003, saving model to saved_models/weights.best.InceptionV3x.hdf5\n",
      "1s - loss: 0.5458 - acc: 0.8271 - val_loss: 0.5400 - val_acc: 0.8359\n",
      "Epoch 14/100\n",
      "Epoch 00013: val_loss did not improve\n",
      "1s - loss: 0.5279 - acc: 0.8329 - val_loss: 0.5643 - val_acc: 0.8371\n",
      "Epoch 15/100\n",
      "Epoch 00014: val_loss did not improve\n",
      "1s - loss: 0.5200 - acc: 0.8337 - val_loss: 0.5557 - val_acc: 0.8419\n",
      "Epoch 16/100\n",
      "Epoch 00015: val_loss improved from 0.54003 to 0.51491, saving model to saved_models/weights.best.InceptionV3x.hdf5\n",
      "1s - loss: 0.4928 - acc: 0.8440 - val_loss: 0.5149 - val_acc: 0.8359\n",
      "Epoch 17/100\n",
      "Epoch 00016: val_loss did not improve\n",
      "1s - loss: 0.4808 - acc: 0.8452 - val_loss: 0.5387 - val_acc: 0.8491\n",
      "Epoch 18/100\n",
      "Epoch 00017: val_loss did not improve\n",
      "1s - loss: 0.4655 - acc: 0.8522 - val_loss: 0.5871 - val_acc: 0.8335\n",
      "Epoch 19/100\n",
      "Epoch 00018: val_loss did not improve\n",
      "1s - loss: 0.4321 - acc: 0.8653 - val_loss: 0.5954 - val_acc: 0.8383\n",
      "Epoch 20/100\n",
      "Epoch 00019: val_loss did not improve\n",
      "1s - loss: 0.4142 - acc: 0.8671 - val_loss: 0.5808 - val_acc: 0.8407\n",
      "Epoch 21/100\n",
      "Epoch 00020: val_loss did not improve\n",
      "1s - loss: 0.4175 - acc: 0.8695 - val_loss: 0.5818 - val_acc: 0.8407\n",
      "Epoch 22/100\n",
      "Epoch 00021: val_loss did not improve\n",
      "1s - loss: 0.4025 - acc: 0.8719 - val_loss: 0.5574 - val_acc: 0.8479\n",
      "Epoch 23/100\n",
      "Epoch 00022: val_loss did not improve\n",
      "1s - loss: 0.4016 - acc: 0.8743 - val_loss: 0.6157 - val_acc: 0.8455\n",
      "Epoch 24/100\n",
      "Epoch 00023: val_loss did not improve\n",
      "1s - loss: 0.3882 - acc: 0.8754 - val_loss: 0.6231 - val_acc: 0.8479\n",
      "Epoch 25/100\n",
      "Epoch 00024: val_loss did not improve\n",
      "1s - loss: 0.3769 - acc: 0.8817 - val_loss: 0.6085 - val_acc: 0.8539\n",
      "Epoch 26/100\n",
      "Epoch 00025: val_loss did not improve\n",
      "1s - loss: 0.3547 - acc: 0.8891 - val_loss: 0.6070 - val_acc: 0.8419\n",
      "Epoch 27/100\n",
      "Epoch 00026: val_loss did not improve\n",
      "1s - loss: 0.3403 - acc: 0.8891 - val_loss: 0.6812 - val_acc: 0.8479\n",
      "Epoch 28/100\n",
      "Epoch 00027: val_loss did not improve\n",
      "1s - loss: 0.3470 - acc: 0.8937 - val_loss: 0.6264 - val_acc: 0.8539\n",
      "Epoch 29/100\n",
      "Epoch 00028: val_loss did not improve\n",
      "1s - loss: 0.3346 - acc: 0.8900 - val_loss: 0.6498 - val_acc: 0.8587\n",
      "Epoch 30/100\n",
      "Epoch 00029: val_loss did not improve\n",
      "1s - loss: 0.3356 - acc: 0.8936 - val_loss: 0.6151 - val_acc: 0.8407\n",
      "Epoch 31/100\n",
      "Epoch 00030: val_loss did not improve\n",
      "1s - loss: 0.3230 - acc: 0.8936 - val_loss: 0.6321 - val_acc: 0.8455\n",
      "Epoch 32/100\n",
      "Epoch 00031: val_loss did not improve\n",
      "1s - loss: 0.3181 - acc: 0.8963 - val_loss: 0.6231 - val_acc: 0.8551\n",
      "Epoch 33/100\n",
      "Epoch 00032: val_loss did not improve\n",
      "1s - loss: 0.3239 - acc: 0.8942 - val_loss: 0.6207 - val_acc: 0.8599\n",
      "Epoch 34/100\n",
      "Epoch 00033: val_loss did not improve\n",
      "1s - loss: 0.3083 - acc: 0.9063 - val_loss: 0.6442 - val_acc: 0.8395\n",
      "Epoch 35/100\n",
      "Epoch 00034: val_loss did not improve\n",
      "1s - loss: 0.3041 - acc: 0.9030 - val_loss: 0.6214 - val_acc: 0.8683\n",
      "Epoch 36/100\n",
      "Epoch 00035: val_loss did not improve\n",
      "1s - loss: 0.3197 - acc: 0.8984 - val_loss: 0.6645 - val_acc: 0.8479\n",
      "Epoch 37/100\n",
      "Epoch 00036: val_loss did not improve\n",
      "1s - loss: 0.3042 - acc: 0.9016 - val_loss: 0.6265 - val_acc: 0.8635\n",
      "Epoch 38/100\n",
      "Epoch 00037: val_loss did not improve\n",
      "1s - loss: 0.3044 - acc: 0.9072 - val_loss: 0.6641 - val_acc: 0.8527\n",
      "Epoch 39/100\n",
      "Epoch 00038: val_loss did not improve\n",
      "1s - loss: 0.2869 - acc: 0.9064 - val_loss: 0.6804 - val_acc: 0.8467\n",
      "Epoch 40/100\n",
      "Epoch 00039: val_loss did not improve\n",
      "1s - loss: 0.2857 - acc: 0.9079 - val_loss: 0.6498 - val_acc: 0.8431\n",
      "Epoch 41/100\n",
      "Epoch 00040: val_loss did not improve\n",
      "1s - loss: 0.2708 - acc: 0.9135 - val_loss: 0.6978 - val_acc: 0.8491\n",
      "Epoch 42/100\n",
      "Epoch 00041: val_loss did not improve\n",
      "1s - loss: 0.2897 - acc: 0.9109 - val_loss: 0.7069 - val_acc: 0.8503\n",
      "Epoch 43/100\n",
      "Epoch 00042: val_loss did not improve\n",
      "1s - loss: 0.2737 - acc: 0.9093 - val_loss: 0.7076 - val_acc: 0.8383\n",
      "Epoch 44/100\n",
      "Epoch 00043: val_loss did not improve\n",
      "1s - loss: 0.2675 - acc: 0.9147 - val_loss: 0.6762 - val_acc: 0.8599\n",
      "Epoch 45/100\n",
      "Epoch 00044: val_loss did not improve\n",
      "1s - loss: 0.2839 - acc: 0.9126 - val_loss: 0.7199 - val_acc: 0.8443\n",
      "Epoch 46/100\n",
      "Epoch 00045: val_loss did not improve\n",
      "1s - loss: 0.2659 - acc: 0.9144 - val_loss: 0.7409 - val_acc: 0.8527\n",
      "Epoch 47/100\n",
      "Epoch 00046: val_loss did not improve\n",
      "1s - loss: 0.2485 - acc: 0.9177 - val_loss: 0.6886 - val_acc: 0.8647\n",
      "Epoch 48/100\n",
      "Epoch 00047: val_loss did not improve\n",
      "1s - loss: 0.2764 - acc: 0.9178 - val_loss: 0.6943 - val_acc: 0.8539\n",
      "Epoch 49/100\n",
      "Epoch 00048: val_loss did not improve\n",
      "1s - loss: 0.2538 - acc: 0.9222 - val_loss: 0.7385 - val_acc: 0.8539\n",
      "Epoch 50/100\n",
      "Epoch 00049: val_loss did not improve\n",
      "1s - loss: 0.2644 - acc: 0.9184 - val_loss: 0.7315 - val_acc: 0.8539\n",
      "Epoch 51/100\n",
      "Epoch 00050: val_loss did not improve\n",
      "1s - loss: 0.2341 - acc: 0.9228 - val_loss: 0.7592 - val_acc: 0.8587\n",
      "Epoch 52/100\n",
      "Epoch 00051: val_loss did not improve\n",
      "1s - loss: 0.2448 - acc: 0.9244 - val_loss: 0.7267 - val_acc: 0.8659\n",
      "Epoch 53/100\n",
      "Epoch 00052: val_loss did not improve\n",
      "1s - loss: 0.2604 - acc: 0.9190 - val_loss: 0.7391 - val_acc: 0.8503\n",
      "Epoch 54/100\n",
      "Epoch 00053: val_loss did not improve\n",
      "1s - loss: 0.2504 - acc: 0.9208 - val_loss: 0.7704 - val_acc: 0.8443\n",
      "Epoch 55/100\n",
      "Epoch 00054: val_loss did not improve\n",
      "1s - loss: 0.2338 - acc: 0.9278 - val_loss: 0.7810 - val_acc: 0.8491\n",
      "Epoch 56/100\n",
      "Epoch 00055: val_loss did not improve\n",
      "1s - loss: 0.2375 - acc: 0.9225 - val_loss: 0.7594 - val_acc: 0.8467\n",
      "Epoch 57/100\n",
      "Epoch 00056: val_loss did not improve\n",
      "1s - loss: 0.2277 - acc: 0.9328 - val_loss: 0.8379 - val_acc: 0.8419\n",
      "Epoch 58/100\n",
      "Epoch 00057: val_loss did not improve\n",
      "1s - loss: 0.2336 - acc: 0.9284 - val_loss: 0.7737 - val_acc: 0.8527\n",
      "Epoch 59/100\n",
      "Epoch 00058: val_loss did not improve\n",
      "1s - loss: 0.2436 - acc: 0.9265 - val_loss: 0.7580 - val_acc: 0.8467\n",
      "Epoch 60/100\n",
      "Epoch 00059: val_loss did not improve\n",
      "1s - loss: 0.2498 - acc: 0.9275 - val_loss: 0.7990 - val_acc: 0.8407\n",
      "Epoch 61/100\n",
      "Epoch 00060: val_loss did not improve\n",
      "1s - loss: 0.2382 - acc: 0.9301 - val_loss: 0.7687 - val_acc: 0.8479\n",
      "Epoch 62/100\n",
      "Epoch 00061: val_loss did not improve\n",
      "1s - loss: 0.2195 - acc: 0.9298 - val_loss: 0.8073 - val_acc: 0.8491\n",
      "Epoch 63/100\n",
      "Epoch 00062: val_loss did not improve\n",
      "1s - loss: 0.2347 - acc: 0.9298 - val_loss: 0.7823 - val_acc: 0.8503\n",
      "Epoch 64/100\n",
      "Epoch 00063: val_loss did not improve\n",
      "1s - loss: 0.2305 - acc: 0.9344 - val_loss: 0.8100 - val_acc: 0.8599\n",
      "Epoch 65/100\n",
      "Epoch 00064: val_loss did not improve\n",
      "1s - loss: 0.2310 - acc: 0.9280 - val_loss: 0.8386 - val_acc: 0.8575\n",
      "Epoch 66/100\n",
      "Epoch 00065: val_loss did not improve\n",
      "1s - loss: 0.2179 - acc: 0.9368 - val_loss: 0.8382 - val_acc: 0.8467\n",
      "Epoch 67/100\n",
      "Epoch 00066: val_loss did not improve\n",
      "1s - loss: 0.2213 - acc: 0.9338 - val_loss: 0.8019 - val_acc: 0.8563\n",
      "Epoch 68/100\n",
      "Epoch 00067: val_loss did not improve\n",
      "1s - loss: 0.2318 - acc: 0.9325 - val_loss: 0.8361 - val_acc: 0.8575\n",
      "Epoch 69/100\n",
      "Epoch 00068: val_loss did not improve\n",
      "1s - loss: 0.2478 - acc: 0.9287 - val_loss: 0.8282 - val_acc: 0.8623\n",
      "Epoch 70/100\n",
      "Epoch 00069: val_loss did not improve\n",
      "1s - loss: 0.2392 - acc: 0.9287 - val_loss: 0.7864 - val_acc: 0.8491\n",
      "Epoch 71/100\n",
      "Epoch 00070: val_loss did not improve\n",
      "1s - loss: 0.2379 - acc: 0.9295 - val_loss: 0.7457 - val_acc: 0.8527\n",
      "Epoch 72/100\n",
      "Epoch 00071: val_loss did not improve\n",
      "1s - loss: 0.2168 - acc: 0.9358 - val_loss: 0.8820 - val_acc: 0.8443\n",
      "Epoch 73/100\n",
      "Epoch 00072: val_loss did not improve\n",
      "1s - loss: 0.2001 - acc: 0.9382 - val_loss: 0.8484 - val_acc: 0.8515\n",
      "Epoch 74/100\n",
      "Epoch 00073: val_loss did not improve\n",
      "1s - loss: 0.2045 - acc: 0.9410 - val_loss: 0.8537 - val_acc: 0.8491\n",
      "Epoch 75/100\n",
      "Epoch 00074: val_loss did not improve\n",
      "1s - loss: 0.2265 - acc: 0.9334 - val_loss: 0.8835 - val_acc: 0.8467\n",
      "Epoch 76/100\n",
      "Epoch 00075: val_loss did not improve\n",
      "1s - loss: 0.2298 - acc: 0.9314 - val_loss: 0.8935 - val_acc: 0.8563\n",
      "Epoch 77/100\n",
      "Epoch 00076: val_loss did not improve\n",
      "1s - loss: 0.2195 - acc: 0.9376 - val_loss: 0.8789 - val_acc: 0.8515\n",
      "Epoch 78/100\n",
      "Epoch 00077: val_loss did not improve\n",
      "1s - loss: 0.2164 - acc: 0.9368 - val_loss: 0.8701 - val_acc: 0.8587\n",
      "Epoch 79/100\n",
      "Epoch 00078: val_loss did not improve\n",
      "1s - loss: 0.2235 - acc: 0.9340 - val_loss: 0.8696 - val_acc: 0.8479\n",
      "Epoch 80/100\n",
      "Epoch 00079: val_loss did not improve\n",
      "1s - loss: 0.2040 - acc: 0.9398 - val_loss: 0.8718 - val_acc: 0.8611\n",
      "Epoch 81/100\n",
      "Epoch 00080: val_loss did not improve\n",
      "1s - loss: 0.2210 - acc: 0.9388 - val_loss: 0.8673 - val_acc: 0.8503\n",
      "Epoch 82/100\n",
      "Epoch 00081: val_loss did not improve\n",
      "1s - loss: 0.2166 - acc: 0.9371 - val_loss: 0.8719 - val_acc: 0.8635\n",
      "Epoch 83/100\n",
      "Epoch 00082: val_loss did not improve\n",
      "1s - loss: 0.2134 - acc: 0.9422 - val_loss: 0.9412 - val_acc: 0.8527\n",
      "Epoch 84/100\n",
      "Epoch 00083: val_loss did not improve\n",
      "1s - loss: 0.2268 - acc: 0.9395 - val_loss: 0.8421 - val_acc: 0.8443\n",
      "Epoch 85/100\n",
      "Epoch 00084: val_loss did not improve\n",
      "1s - loss: 0.1918 - acc: 0.9475 - val_loss: 0.9145 - val_acc: 0.8503\n",
      "Epoch 86/100\n",
      "Epoch 00085: val_loss did not improve\n",
      "1s - loss: 0.2070 - acc: 0.9392 - val_loss: 0.9048 - val_acc: 0.8419\n",
      "Epoch 87/100\n",
      "Epoch 00086: val_loss did not improve\n",
      "1s - loss: 0.2187 - acc: 0.9427 - val_loss: 0.8260 - val_acc: 0.8575\n",
      "Epoch 88/100\n",
      "Epoch 00087: val_loss did not improve\n",
      "1s - loss: 0.2020 - acc: 0.9392 - val_loss: 0.9941 - val_acc: 0.8503\n",
      "Epoch 89/100\n",
      "Epoch 00088: val_loss did not improve\n",
      "1s - loss: 0.2149 - acc: 0.9428 - val_loss: 0.8790 - val_acc: 0.8551\n",
      "Epoch 90/100\n",
      "Epoch 00089: val_loss did not improve\n",
      "1s - loss: 0.2022 - acc: 0.9421 - val_loss: 0.8939 - val_acc: 0.8587\n",
      "Epoch 91/100\n",
      "Epoch 00090: val_loss did not improve\n",
      "1s - loss: 0.1970 - acc: 0.9443 - val_loss: 0.8815 - val_acc: 0.8527\n",
      "Epoch 92/100\n",
      "Epoch 00091: val_loss did not improve\n",
      "1s - loss: 0.2272 - acc: 0.9403 - val_loss: 0.9276 - val_acc: 0.8395\n",
      "Epoch 93/100\n",
      "Epoch 00092: val_loss did not improve\n",
      "1s - loss: 0.1907 - acc: 0.9463 - val_loss: 0.9812 - val_acc: 0.8431\n",
      "Epoch 94/100\n",
      "Epoch 00093: val_loss did not improve\n",
      "1s - loss: 0.2313 - acc: 0.9373 - val_loss: 1.0170 - val_acc: 0.8551\n",
      "Epoch 95/100\n",
      "Epoch 00094: val_loss did not improve\n",
      "1s - loss: 0.2030 - acc: 0.9458 - val_loss: 0.9524 - val_acc: 0.8515\n",
      "Epoch 96/100\n",
      "Epoch 00095: val_loss did not improve\n",
      "1s - loss: 0.2031 - acc: 0.9412 - val_loss: 0.9953 - val_acc: 0.8527\n",
      "Epoch 97/100\n",
      "Epoch 00096: val_loss did not improve\n",
      "1s - loss: 0.2212 - acc: 0.9401 - val_loss: 0.9414 - val_acc: 0.8587\n",
      "Epoch 98/100\n",
      "Epoch 00097: val_loss did not improve\n",
      "1s - loss: 0.2062 - acc: 0.9440 - val_loss: 1.0121 - val_acc: 0.8527\n",
      "Epoch 99/100\n",
      "Epoch 00098: val_loss did not improve\n",
      "1s - loss: 0.2116 - acc: 0.9428 - val_loss: 0.9744 - val_acc: 0.8563\n",
      "Epoch 100/100\n",
      "Epoch 00099: val_loss did not improve\n",
      "1s - loss: 0.2123 - acc: 0.9443 - val_loss: 0.8978 - val_acc: 0.8623\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint  \n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.InceptionV3x.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "\n",
    "history = InceptionV3_model.fit(train_InceptionV3, train_targets, \n",
    "          validation_data=(valid_InceptionV3, valid_targets),\n",
    "          epochs=100, batch_size=133, callbacks=[checkpointer], verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Load the Model with the Best Validation Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "InceptionV3_model.load_weights('saved_models/weights.best.InceptionV3x.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Test the Model\n",
    "\n",
    "Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 80.2632%\n"
     ]
    }
   ],
   "source": [
    "InceptionV3_predictions = [np.argmax(InceptionV3_model.predict(np.expand_dims(feature, axis=0))) for feature in test_InceptionV3]\n",
    "\n",
    "test_accuracy = 100*np.sum(np.array(InceptionV3_predictions)==np.argmax(test_targets, axis=1))/len(InceptionV3_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Predict Dog Breed with the Model\n",
    "\n",
    "Write a function that takes an image path as input and returns the dog breed (`Affenpinscher`, `Afghan_hound`, etc) that is predicted by your model.  \n",
    "\n",
    "Similar to the analogous function in Step 5, your function should have three steps:\n",
    "1. Extract the bottleneck features corresponding to the chosen CNN model.\n",
    "2. Supply the bottleneck features as input to the model to return the predicted vector.  Note that the argmax of this prediction vector gives the index of the predicted dog breed.\n",
    "3. Use the `dog_names` array defined in Step 0 of this notebook to return the corresponding breed.\n",
    "\n",
    "The functions to extract the bottleneck features can be found in `extract_bottleneck_features.py`, and they have been imported in an earlier code cell.  To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function\n",
    "\n",
    "    extract_{network}\n",
    "    \n",
    "where `{network}`, in the above filename, should be one of `VGG19`, `Resnet50`, `InceptionV3`, or `Xception`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anatolian_shepherd_dog_00678.jpg Mastiff\n",
      "Pharaoh_hound_07744.jpg Pharaoh_hound\n",
      "English_toy_spaniel_04526.jpg English_toy_spaniel\n",
      "Saint_bernard_08037.jpg Saint_bernard\n",
      "Border_collie_02021.jpg Border_collie\n",
      "Havanese_05597.jpg Havanese\n",
      "Irish_setter_05828.jpg Irish_setter\n",
      "Tibetan_mastiff_08163.jpg Tibetan_mastiff\n",
      "Alaskan_malamute_00315.jpg Alaskan_malamute\n",
      "Golden_retriever_05233.jpg Golden_retriever\n"
     ]
    }
   ],
   "source": [
    "from extract_bottleneck_features import *\n",
    "\n",
    "def InceptionV3_predict_breed(img_path):\n",
    "    # extract bottleneck features\n",
    "    bottleneck_feature = extract_InceptionV3(path_to_tensor(img_path))\n",
    "    # obtain predicted vector\n",
    "    predicted_vector = InceptionV3_model.predict(bottleneck_feature)\n",
    "    # return dog breed that is predicted by the model\n",
    "    return dog_names[np.argmax(predicted_vector)]\n",
    "\n",
    "for file in np.random.choice(dog_files, 10):  \n",
    "    print(os.path.basename(file), InceptionV3_predict_breed(file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step6'></a>\n",
    "## Step 6: Write your Algorithm\n",
    "\n",
    "Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither.  Then,\n",
    "- if a __dog__ is detected in the image, return the predicted breed.\n",
    "- if a __human__ is detected in the image, return the resembling dog breed.\n",
    "- if __neither__ is detected in the image, provide output that indicates an error.\n",
    "\n",
    "You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the `face_detector` and `dog_detector` functions developed above.  You are __required__ to use your CNN from Step 5 to predict dog breed.  \n",
    "\n",
    "Some sample output for our algorithm is provided below, but feel free to design your own user experience!\n",
    "\n",
    "![Sample Human Output](images/sample_human_output.png)\n",
    "\n",
    "\n",
    "### (IMPLEMENTATION) Write your Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Human, looks like: French_bulldog filename: Colin_Powell_0117.jpg\n",
      "Human, looks like: French_bulldog filename: Andy_Rooney_0001.jpg\n",
      "Human, looks like: Bull_terrier filename: Michelle_Branch_0002.jpg\n",
      "Dog, looks like: Dalmatian filename: Icelandic_sheepdog_05708.jpg\n",
      "Dog, looks like: Bull_terrier filename: Pekingese_07605.jpg\n",
      "Human, looks like: Beauceron filename: Jayson_Williams_0001.jpg\n",
      "Dog, looks like: Bull_terrier filename: Bullmastiff_02914.jpg\n",
      "Human, looks like: Bull_terrier filename: Toni_Braxton_0002.jpg\n",
      "Human, looks like: Curly-coated_retriever filename: Chawki_Armali_0001.jpg\n",
      "Dog, looks like: Bull_terrier filename: Boston_terrier_02277.jpg\n"
     ]
    }
   ],
   "source": [
    "def detector(img_path):\n",
    "    if dog_detector(img_path):\n",
    "        predicted_vector = model.predict(path_to_tensor(img_path))\n",
    "        return \"Dog, looks like: \" + dog_names[np.argmax(predicted_vector)]\n",
    "    elif face_detector(img_path):\n",
    "        predicted_vector = model.predict(path_to_tensor(img_path))\n",
    "        return \"Human, looks like: \" + dog_names[np.argmax(predicted_vector)]\n",
    "    else:\n",
    "        return \"Neither human nor dog\"\n",
    "\n",
    "for file in np.random.choice(np.concatenate([dog_files, human_files]), 10):\n",
    "    print(detector(file) + \" filename: \" +  os.path.basename(file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step7'></a>\n",
    "## Step 7: Test Your Algorithm\n",
    "\n",
    "In this section, you will take your new algorithm for a spin!  What kind of dog does the algorithm think that __you__ look like?  If you have a dog, does it predict your dog's breed accurately?  If you have a cat, does it mistakenly think that your cat is a dog?\n",
    "\n",
    "### (IMPLEMENTATION) Test Your Algorithm on Sample Images!\n",
    "\n",
    "Test your algorithm at least six images on your computer.  Feel free to use any images you like.  Use at least two human and two dog images.  \n",
    "\n",
    "__Question 6:__ Is the output better than you expected :) ?  Or worse :( ?  Provide at least three possible points of improvement for your algorithm.\n",
    "\n",
    "__Answer:__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oQ0MD5IYThMmRGuhFMynijrK7tZmWF54jtfl3DP7hFtoe+AFOJkGhAGEc0ILJ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IyimuK3CdBmVZs5+yfEqapjSDNuPxDvNnO2zlKfmu5d3f1OGlzTGtsE0cN4AJ437B+eUHaT7x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9VmQD1G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\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O57Z/mfuHH3Cz+Rwz9yFdv8lYNrDDEWU25lh/n035PGV6Rq+zzr2H3+GFS7+EdVsEk4/A\nTrp4li3y//WCvgZEfp4UpK6yLLl3796KpXHv3j3W1tbY2dlZmUPXoFFtKJ1l2cpQugYssyzD9/2V\n7yFQJVEt53Tf92m32ysJUe1vBM/6Liml6HQ6WFGxki5fvsze3h5aa6bTKb1eb2VwLYRga2uLS5cu\nkafFUvFgVn44wIopVH/2izK6i/41wApYqqVqNfBzUT5Wy9xqg+yaQVVL7y4ae7uuy9raGmma0u/3\nuXLlCkVRbYwdHh4ymUx44YUXyPOcRqNBFFXP0P+c+nmStP9UKeTfRUkpGQwGRFHEfD5HLoGji2Oi\nlgxaWyWT1cl3tacmaUGRpizyjBxDeOG9a2+iQjz1oaqvSf2+6+vrrO20ePDgEdPJHLkpiZZAY33U\noFV9resxVZYlcVyxk8JGFV9fA0dMU+7fv8/Ozs4qiZDlPZQkCYeHh/R8sQIkgyjCyuIZ0KgGvi6y\nkRqNygbCFQJvaaS+vd1lNpux3lkjmZ2xfuky65cuo/wIjUvUKBgME/aP5mzs3KH90q+QejHzhSYp\nFG3/2euSpilbW1v84Affq8ghlPD3L7Tvb70+EliawYL1mCY5r/dDjJaM54LhIuf09Ix/9ge/j3Lg\n4aMBVmpe/ORL/Opv3OSdB++jZEUH9VWAJxVKSHAVeZqRZSVWuGS6ivROy6RqQKXF5hYjlsZhRuDV\nGvuixFiJKUoKkZMVI6SqoqwXaUqWGtI0AxmQF3ZJx3NwHYfSepSkSOUhcNClIgh8XNfH8Rqk2QQr\nLVJ4CAxlXoAGVYU/4wYuZZrheUG1uM9SfFUBXtoEhGGMzQ1OIOh3q92lxSKjNBpUSZqklEVBnqUY\nLZcMkuWN6FS7G54fg1UEbkRhCtJsQprmFBqUF9JZv0S7u063t4HIDbac0fFbnD/JefnWLgqXfKLJ\nzxSTo5ytVkxZCLZ3epRTh4Pjkk6vSS4XtHZDPvXFNXZeafLKZ0IuXfLpGw+VSH50/xBbwsfeuExe\nFnzrK3s4gcf39w7JNDjSMp8bXAcO3h5z/HCBdV0KY7FasrHm4UqH0+MZX/7X32VwNKIZgFSGbtfH\nGodWwyVJC9aDiEVSMJwuQChOzofIDJ67tkE+bn6YQ39V1mq0BYuDkB7ScZE8pZzW8rNaRlaWBmOr\nJKenkiODxmIMT01Dl1HyUEmfDLb6N3CwwkVJv/IcUhJTiVsQ4ikbpfZYMsYglb9kJuW4qgKegiCi\n21+jv75GGDdpdHq0+n3idpNFmVHmGlu4LHKNzeH87JAinyOVYTo5QeBV0jAtKErLaDrClJo4jFDW\nYMoUJUFgUI5HYQTJfEa2qBLHlHRotltIFZKlMxy/wSIdYbTAFQaFxGTzyh9DeiR5htHLJB7pog3L\n+zcAUfmSsWQzSFkBcpVR9QStLQiFcAKMqM5XOp1jyhxNtXOUFwXkGYURCCVxgpgg6iIcF+G6uI0G\nrhehK/SvAgAcl6wEo1zcuE8QtfA8F+kr4k4fN+wRRJsEzR7K83H8Jn7cQigXFCjlIVS1YxkEAVHc\nJW60cJ1qHvL8Jm7cptnqsbvV4/p6SNsv8WWBEL+QLPys8q1m+GSfdhgS+UFlyFxqXAF6MUSJFK3n\nYCpmYBS1cKWgEQWVlLgsKRYJ6XxMnk4pja6ec1SJUI1Wh7WdbTq9bgWQlFXDWC7jny9fv057rYdy\nPZTjID2X3Gi0eNog14uuOunmrzrqknWSkdVgNcr1CcIGhRE4XsB0MuHGtav0Om1OD5/gKomrKmP+\nRqPB2toaftzED0KazRYCKtPsq5eQYZtcBSyMYLTIUXGLuNPns1/4Ir/5P/wzXvrUZ8ALQMqlnxi8\n/itfJGq1OT4+Zu+9d3nne9/iwVvfYXB0hM6S6vlXFBXjUlTs2o/azvF/bqX5hPcnpyStHpOTQxIr\n2Fy/RZGnXFE7GN/jMy+/hpmdMkkSPhjc4/DoXWbZHCtiut4O42mC6DS5utVmMD4ENthcu41JThif\nTmnLLVqyySzvcff7P+SH3/oOh0++w7cffJvYbeJPEp4cn3Ay0+zPpvRb2+z0Y4J5zN6jcTO5+QAA\nIABJREFU/8D1m69Sqi5lq0PkBqxt3qBYPMFYh9FoxHce3yewAQs7YtttsdbZ5eVXP0uyeMRm5w69\n5ja7zdvcWX+D+dmIYjzj5PyE4dkevfYNoDZwdWnEHZSY8WTvxwyHQ4ajI6bzY/b3jpcpohnvv/OQ\no4P7lPmcRw/f5eF795gMB8znc8bjMSbP8OI2L332M9xev8p7X/k/mYwTmt02UvU5OnjMOuvM0j6h\nE7O98xJ56ZLOCh7c/yEtFTNMJK9d/RzeLGZcFGgtOHryFllvjcPFj3ly8IDDoz2Goyq+OU1TshSK\nMmXv8C1ev/UPePN7X0LbCX0dk0xTxtkQnc8xoycE5SGx7xJ7HY4O9zgbHpPkOdLpcX76Hk4eETU7\nJAdPmA8zfuv5f8Sl9k2cMCezCyTbXN/cotfp8omrv0TLDRgaj0HuU0z38b0ps1yjpM/Hmk3enQ84\nKzVXmpfAkYwG+9zyXvoQR/7TurjQv3hf/6Qn0c+ay/669/ubzIl1f1UDRL1ej2vXrlW+h0tAZTqt\n7CJWsuDlz4CVHKde6F70mfF9f2UAfNHPpf6M9YK+fo/6/WpfmkbUZK23TjNusdZbJwpi+t01Wo02\n16/e4NL2ZS5tX2Z7c4fQj2g0GriuSxiGK3lS7a9Us4NqA+VaElXL1OqF/ze+8Y1ngDzf9ytfyeX6\nSCnFbDarvFyWbKv5fL767DVYV4Mfk8mE4XC4Omf1eR4Oh5yfn3Pp0iWCoJJ4f+nLf/KMPO0/ZX6/\nKHG66HdUAyUftQ2GGmi8KA+En5aS19Kw+vxdTJqzpUYXS09Eo595758FotWvu+irFYbhM9erZsfV\nPUUNfNbgYX1ua/Czllxe9OsKgorkUANVF73Hao+sGoismUU/+Tnrqu+hmtFWjw/HcVZsvpoVN5mO\nSNMUjaDQlkVZMp6NGU3GOH7AKx9/jaSEEon0fbL8pxGhmjl40RfsF/XT9ZFgHAkjGM8KrIXvn5VI\nCa4yOMrn4bggbPRxnBFHewVmqhgPJpS54pOvfYrLV6/QCJpYCRoXpaBcFFVqi5bk2RjPaVPIDIWg\nMBopDBKFNQLhuQiryfOUvFjQiH0W2YLx8Alu1MNogS0URlhms3M8J8SUC1ylsdqQpxmV3YRD7Ltg\ndOVNIxWOrXZw3cClyBKEDDAFGJOTJlOCRkyaKaQf43geZVYifaq4c0dSGFAWssWUIHSrhWpZ0o8b\nDM+PaW2uU+gzimRE6QQoHPIsxVJiraYsLWWRVg8z6VLYBY1Og8kkp8hSfN/D2IJZMqWhOogy4fDw\nFOHmJKMJQrlMzg2z0RjXW3CcQLProbWLPoMoWHC4X5meLY5nSF8SOSPmacG0yDGZ4e6DEbES5Drj\nd3/3U2gLew8HrF9rcn48IpvPOTvPmGrDmiuZTnKurK3zYHBOs+9xdlrw+ic6HA/OmQ0Vr+5aEuNy\nbafHyfkxs6HB4lMKzaUbuxy/+YBiatjpRiRlSVaU3Dua0217fHA255W2z6K0NJXL/dmIS5dj+LB9\nIpcTNeJpOoUtTeVhtARztDFoU1IUOWm6QCkH36sexNgSkFhrEBi0qYAoURkgoXVBIQTGVItTR1Ue\nJkq4S0lLxVwQKKQSKCOxylTpXtKjNBopqhQQgUIKiZBPWU4CgRQO6xtbuL5HtrZFtkg53n+fQiv2\nHt3l45/+DZQuScb7ON0Npkvp2uDkgF6/C1RAl+c7SHJ0YUFaXCqKtaVqqKyGTGtcxRIEs5VRNGCR\nFIXGiZoU85QyL9Amp9vZYroYohjQ7WwzGQ0QKApcHKsQWlOanNIGxEGIRWNNdU6FLChNAcLDlpqs\nyCt/Dc9lsUhw4gZ5nuIqHy9wmc7OKfIZwlrAxToRwmREnTUcsZQQ6gUCB2MFygFdLlCOgzUhC52i\nyxJfSaSoALMgrBIRwSAdB6Vc1DKdovLjqTyxrM0RjodwBb6M8d2AosyqB7QG6zcpzIKu59OMHMqy\nWH7OX9RPlmsNBwdPyLOMZJbhCUNR5kSb62g7xeQp6WJKS5SUhUUiaDaqnd3x+YiNra3VblsVQ+yC\nMThetcWlXA8viIiaGXmek2cZZVHQ7XZxPY+8KBCyYoFYKTBCVPe3dJ5Z/CilKPVPG8H+VbvsciVc\ns+giQ+jKZ0EbzdHRIU6vRZlntBoxVhcMBgP+H/bePFiS5L7v+2TW3efrd8+b+9rZ3Zk9cBGL5QIE\nAREgKEqkbJOyIyzJtMPWHwpblsNBieQf0h+2LIcZphghO8KUQiJlh03ING2ZB0yZJA7i3F0s9p7d\nuecd8+4+667KTP9RXT1vBotLpAkwhIzoeK+7q7vryKrK/P6+R7PdxfUDwjBkbn4B17UZj8f0ej1a\nQYPewjyqUXki+I0W5y5dpDe/QKvbqbbNdigLXQFvWs2qieFowpWn3sPnP/UvSeMJPUtiBR6thl9d\ny4qsAsaVBlNivjdqXX8irem3eXThAnc3XyKetynYoC2fIj8c4DWX8MxpknGKYxeE4YTl5VOUToeF\n5Tny0QGrS6ew0rucXO1xPLjEjY1XmOy8QdDs4nQyWmaRJLeQNmytv4QzJ3n0mR9iaEpOBxfptFZZ\nWzrB85uf5jBKwHM5zEb0t+5w6fhlDkdvcfXtL9IoXNzxNmeaFxmFffz+Ibk9YuXYKXbf2COy9rh9\nWPDUmQ+Q2gWD8SH71+9x4okmr2z2+fNPXiRCsPfCLTprx3HMDpOmRZYPAYWhoNVos7C0wv5Oxng4\nwdiwu36L4N0u0a0tLr/vRxmHu1jFiJvXv8CNG5/h+NqzOI7HZDjCcTyWuitge5iy5MJzP83Z9/8l\nfvdv/TR6e529yTnO93rcHoTo/TFzJy+xs/48npURj7dYO3EKy/FxC8Nj73kOMQHjN9kev04n2qe3\n+n7ifsjuwRaOvorjSoo8oSxXabVaJEnGYJxwemGZ333pH3P2zBnG/QNuH7xNFiuOrc3jZTH7jR2e\nv/4ZfvoDP0MWJsSrC3zhzg5ROqbrzHG4dQDli5w+/RSbhzs8/tRHIUuZXzmBr0qCTo8TzRNs5D0O\n9m7gWg0cZwkrLUmcCU8unOH5W1/lfOci98YbrPdS5u02LdEidAteW7+GyGMWn/5J4Fe/af8UQljA\ni8CWMebHhRDzwCeBM8Ad4KeNMYPpsj8H/EeAAv4zY8zvfcsTwPAAUPDdarZtMxqNZn5zly5dmnoG\nFbPJsOM4s7h4pRRRFOF53mziXUvUaoCoNtmuJ7s1u6cGmY4+r1k9R8NNagPgwWCAlJK1tTV2dnbw\nfZ+lpSWiKMIYw3g8RilVXZfn5maG1UcBk7IsZ2bEwOz7s6y69xwNRbl8+fIMRKgn9nmez/6vpWw1\na6WWpdXfU6fJ1X5OdbDHUdlZLYFSSnHy5MkZMPGFL3xh9tv/Om02dn5I5laDWd9L4JFWCk9Y5GFM\nw3Znx+ubNYGL43hok1MqxcRklCbDLjMoS3LXxnbsak5rWWhLQpwTTP2ImssehetR9MfkKqJwJZ3F\nczhqg6R/l9wa012+TJZVhWJtDMa1sC175vdT+xx5vk+e57huY+Z1lec5nuchVufoZmts3tsimOsw\nEAXdcYkQOboYc+/e26y2n5x5GJGXM7+4WlqplMKSEHTb7A4OOXH5As2Wz/6tdbpOFwdJ89QcwzJH\nDWJ6vsHBIwxjtu7eoTO/wP7BkPHCGkH7DO96/5/jXilpCI2DRRblYBy0LacFPQOWIDEK6To02i2K\naAhaft016uGx1dHnWuvK2/Th422yWTH+wb6o0IbKNPuhJh86FaRbLXPUUuEoWHoUgPt2xoF/nPY9\nMQrTxuBKC8eVOF6JFApLSgQa8pJf+Nmf5+/97D8kvyVQY5fP/s4W116qoold2wJsTKnRJiPPNVLa\njMJxdRGSDkanFOmUNaFtyjRDGY3j2eRpiJQ2RaGQwiPLc7bv3qbZXsYyLlJLEDme26TdmieLB6TZ\nhDiuqJra5FiuQ5QOEFgEzQZYdpUaVVbUVaENiCbSdsmLMdpkYFugBWkyoSzSmUma0JVxscLGcnyS\nTGFZlceMNJLAqzTWdrvJzbdexrIdjHTRZU5RxmTZkCLLMcLFci2QEttysVwH12kRhiW21aDZWsQo\nhVVKbMtHlwXDwQQ3kFiyQdC26e8fMAn75PGE3Eg2bo7Yvptgozh3tsCRgoWgxer8Ir7fYnez4GAv\nJgpLBvsFX3h+lzwqaHccbKfBH33xGtdurhNnY176wiaTuKTd6HKmeYK/8tFH+PjHzxPupuz0DxgP\nYvyGhylytu6Oee+HF+j6EtlqosqM/f2EfpJz5nSToAmf/+2X8BqClmtx7pzPdn9CLhXNVqXBLsIU\nF5urh7usLHRZXeiwvj5gsPWvR4v9k22CKkG7pFCGvJwa5CEBiRYSdFUt0GWVjFUlclWP2hG3uhjd\nP6XrG5Fl+2CqxL4K9c8p84xSZWitMEylcbrE6JKSspKE4VExkMSsWqGNmL5nofW02lFosryS/NmW\nj+O5+I2qUhWORwgs7t25yeH+Fl5nlSQe0mx0UUow1+tSFopSKwpVVn5duMRxSKlSNIayrKJgtSow\nKsEgieMYnRXYjocxqlqXoqhAG23j+i2kXcl64nSE5zbxrA79gx1azR7C8rAtD6UNQlYVxyJNGI0G\naMF9k8LSVMCwtFFlSp5F2K5HYaBQGpUXBJ7POAorw2RtIZgyxoTGdi0yDEpnlGWBylKMEZRFQjGN\ndJbSr0yzbfD9RnXjFBIj5Oy4VOtQs8Dy6hiWZfVXgzH3I8+RNtIYLMfGsm1cxwfHBcfC9ds4rl+l\nz9ke309Vu9+ODtwsnXH3+lVUnt1PbPJd0nhCGUVIlROND8mmsgTbdSpQz/Ho9hZmTNA0TRgM+gij\nsRHorIRCoxVI28GeVognwxFz7c4RqYAzrT67+H5jKnUIyLKMKIpmA626MlYP0OvY5KPpGkdTNqZd\nCmE0uizQ4RCRx5gyA13yl3/q3yZLYigzLEocW3L61AkWet2ZTMKoKilUIjC6YnL/4Ec/xod+4if4\n4F/8i7z7Ix9m6dxZnF6X3LURnRYacAMPDVjy/gCnOdfD8gJW1o4zGAwYDfuM+odM+gekoyGmLFBF\nRlnklEWBLtVskH1/2/50+sefdMuLnNfDXVqWINkcshQ1+cq1LyLmPLaSG9w4vMZrb79AWY758htf\nATS+lXHj+puU0Zhdsc2zjz5LLlyGgwGpZTjVO057zufUqR/g4qNPM56E7EQb9It9FmxNsneHNQL6\nmeLtwz6/+/qvEykI433avks4PGTl2El++7Xf4vT5p3jy0g8wfyxgvvckr937Es78KlEZcWz+IsM0\n5OzJyyw2T9K0W4ySLQ7GdxgOX+TsE5exxrDQ9lhbukB4qHj8zAeYhENOr15hfG9M0zYz2UrN7sjz\nnLm5ObJigFIFT59+jtvmNhvX3pgOyhtgNZiMFHEymkk36kFzPckvs5BUaVrNJka79AKfzc07nGqt\nEix22Nt9nrnFBfrqBt3V0/TaLdYWT9FYOsZXvvpbHI5vsL/5Kl3ZxBSCrZtfRFBy4eL76K26bG3d\nJYpGRPGISThAaMOpkxc4TBJOHX+CV3beYunESZ5+9s/z2MIHMFmbhBHz4zYLwTHu3brLC7e/yo0b\nbyMtzdm1K9wa3eLxK+8ns1PK0TpXHn0vDVuztfk1RA6O8FhtrnBAyivrX+NE7wJRariXDOh25hFJ\nwtt7e3R7j3JruI3yFF/deYF7t28TF2Ns30MoTbt3hv17N7+dLvo3gatHnv8d4A+MMReBP5g+Rwjx\nOPDvApeBHwX+xyno9C2a+bpJWT0Jeicm0uxTD5z73/jkfydm4tH/a7ZD3Yc+9alPEYbhLI2sNvOt\nAZCaSVHLqmqJVp1GVnse1evled5s7FSnjtXX7JpxUodz1FKx+noeRRGTyWTGQPI8jxMnTrC8vDxl\nuDaqRMzpOdNqtWb3/zAMH2CX1Ntdp/IenbzWxsM1uFQnu9XG3zUjCiqT63qf1WBYLWszpoqWr2V9\ndYR6vZ/vJwFXx8R1XS5dusTc3BxKKa5du8ZwOJzth3rS+07smaPH/mhfgPtMmIf7yjfrT9+NVoNt\neV6F8xy9Vz9skF0/pJQzCWSe5wyHQ8IwnKXkHY24r9tRmWHNEKt/33VduiuLGMfCaE00GDE/P8/8\n/PwMJITK1H1xcZH9/f2qsDVNNz66fjVwUffNVqtFOPVkArCaPoeDPkIZ7ly9RhRFs/5Wj1lc152d\nP47j0AjmWF05xbHV0/znf/Nn+cm/8Je49PgTuHNzBEtLbO4OuHp7l/3C4439jI0hvHZnyBvrY77y\n+jaHeYBsnKUUy1iyg0rqflyx5I7u26NJjPV5+nAS4vfb/fY9ARwBPHKqx4V3LSIdjRUIAs8GpSnK\nqtKeZRlIiSstuk2fwHNIoogsLSlNNQmWxkIZQ1rEODgUcY40kOQVlVILTWGySpdre1jCxfGa5GmC\n7booVb3XbLewHUlRjOgtLSGsACEVeTEhDIcY4aJ1SRIPK9YAAtsKkFaJdAPQaSWxMT5aVxRD19a4\nlo3jeKiyMjPO85xWu0eWTCpZmaziBJESz/YQWuG5LaTUONJBCAuERrgW6STB8ywG/X2EMJQIlLEJ\nGpXxXjQZUqRFZafiO+RFjDY5rgPa5EThAUbY+N2AIqwu+F7QpdOZx/ZsHD/A8jUHwyH94ZjJSOE5\nJY0e7O9HhAeK9lwb3QihlKzOr/EDl0+zuLhMMOcy1/O5/GSX5U6XIOuw5HSZ3Cl4/aUhe9sx73pq\nmQXfsHl7TKlSGmuSV1/b5aM/do5P/MhZ/vrPvJ/3XV6hYblcePYM0V7Oj3ziAqtrHo8/skQ4ynnf\nky1OrQRIA8NxQUHGxQtzBK7H8cebrKw4nD4X8OhjTYK5Fk9e7JJMbG4Nx+zmguW5Hlf30m/eMf8U\nWk0NrS6a5UyKplR9gxQzEEEIgWO7ONb0JoGFEWC0wJgp+kyVxiWMjcABXYE89XtG5xR5TJaGZHky\nk7QVZUSWTyjymDSNyfN4ul5T0z5ACzE16a6MurUpydIQVeRVPLypdNiO28ANWrS787Tn1xgN9zCW\nSxxNiKKIaLiHpfp4jS6Nbpuy1GRpTL9/wHjSx7KcymDeBs+3cOyAMCzISwmmxPU9+qMhe7vb2E6D\nrKgGNWGU4ToBSTpBC00rqCqHukwZJ0Mcu8HBcJeiVGRlRRVOswTbqiqKWZ6SJSFFmWK0JM1L8kyT\nJkO0cXDtgPG4jxABEoizlHE4odVoMplMiMb75IXCtgxKQuC3WDp9Be33UMIiz0riOEUpQZIkHB4M\nGI0H5FmCKnO0yiopmwZlPIx0UFpitFWBXY6P41S+Z+ij/gh6ykAxCB1jTIptuzhOgMbgTv1xhLQp\nVBUnLIXCfB84esfmOhadTosojWYJZoWuDEV9t0HD8yvZFJXJtOdVYCK2Q7M1R9DoIHBmqTp5mlVp\nYcpMkxMrn6p6AKdUJZPM83zqJyaRSIQBoQ3STCtK00FwlmWzQRYwkx1848rl/Wq2MBWbEF2SjPah\nCGn4DpYleOSRRwg8h/lelySK8ByHaFLFj7t2BbDmaUTDdav7s9J85GOfYGH1OI2ghSoNtnSwZRWp\na0+3w8ZgARZV2huV7z9JrrAcl9def4NSGQ72++zt94kmY7I0JU8S4jC6D3zVNP2HJo5/FsGjQpVo\nFO3eGqePrxA6yzy19iQbg4hTwRoffvJD/IWn308oFf/Fv/PzbPfvEG2vc9ayWDAec6rLK4PX2Dm4\nyxe3XqE5FFy5+DFkaPGIvUDPNHju7A9Sbg041QsoCoeXd1/j5sYGGdu89LXnaQUnePn6i/RVTNMO\n6GqXu5uvceXJH+Qrb1/H6iySyYBEhAiRMd7dREjN62+/QaBzzraWOOatstpe4SNP/ghd2cLO59mN\ntjFBC4a77G6+ycbkHjcG19nJ3uCtN1/AN4u8+vrLIKr7m+2508l5AylcmqZJpzuPtz/i7MqzOKdt\nCntA3MjYzzfwpOTO7RcodEbQ7DHXXcZxKpcP27YxMsASmmF/txoj3t5le3jIweSAO3dv0nGatE2T\nluWThXeZFx77UYwoFFuTfYZRxEQNeXX3JmdPP8HxhUd59OIzGH8f3/cxVkQymRBHQ5IoprvQ4JnL\nf5kL557ldv82qwtd+skmb939Is684NLKMS6tvp/cmudDF55h0BryxIVnWR/u03bb7A428BwfOzNc\nXn4f9yYZt7a/BNpwcvHdHF8+zWZ/E+ktcevaCzx77Bne6N/kZOMUHVVFrMfDQwozJipDsKFl9zBp\nSfd0g83DayTRhDje4/S5x9iJvzlwJIQ4Afx54J8cefkngF+b/v9rwE8eef3XjTGZMeY2cAP4gW/3\nPPh2JGj/f7SjbJz9/X3W19dJkoQkSSr54dSTJU0r1v5oNHqAWVPLvmoPIWB2/fU8j/F4XMmQpxPr\nekJ/FGAqyyohKs/zKiwhTQnDcCbnqUGdyWQyA5MqI+zmjGFTT7jrYkJtRl3LyWpwqwYQomkASb39\ntm0/ADAkSYKUVYBJFFVGxDXAWy9n2/Zs8n10m+vtTJJkBrLV79X3qjpqvd5vaZryh3/4h7N1r7fr\nO21HAYCjn//TiCf/TlsNzNT97J1AjIcfR5PragDyaKS74zgzW4uHGVc1aHQU+APonVhFTFMqo4MB\ntm2zvLxMo9GY/V4tf2y322xvb88M3YOgut7W/ak+F6SUD4CvSZKQSUOSZ7zy/IucWz0+6+u18bZl\nWXieRxRF9Ho9zp8/T5Zqrl+7QxKX/Kvf+zQWPhcfe5J+VjKxbESji790gpv9kq/cDvnsy1t87daI\nz768wf/5mVcpm2c4/ugT/PNP/jphNMayshnAOZlMvuF+rsHcmvn0/fb17XsCOLIdya17E/bejvB7\nLSgFhVIEvkPQcLGwYerbUmo1jStuEgQetiOo3EZc4jimLCKKtJKoeK0WaRojsMlVWbGOyoxmu4eQ\niqIM0WWJsSUSjcoTijinu7CMbWm8wKfQBY7jUpYZvuNWMhHpIaYD/bSoOqElA9R0Um6URjo2hYnx\nvAaZ0iBd8iKsQAApsHAwWlZ+KzRIxiGmVNiuiylTSpUiJPitJkI4KFVUjAbbQwpn6vcQkMcRRayx\nhU8aRWRJSqkMzUYbQ0GWFShdXZzD0ZAsicjSAmkk4NI/HBF0e5SypCgjwnBINDrEaJtz585hWRYr\ni1VaQ5pUlNxSS67vjtnfHtJrHMdttNja2piZA7rK5sTyGjvrKY22x0TtsXw65fxTbT728RVOHneZ\nHO6yv1Nga81Wf588UXzo3RdoZYK3/yjh7r09LJHwkU+s0JykrJ1cpMxi8jJjyW5z8XKbR9xjNIoW\np90Fnnl8iR86t8Qj3Xm6rkINDdnQEA80hYBmG8Z2xvsf64DW9IeHHD8ueM8HvvsmkdroqYZZzaRD\nlbeQoixzjCmmj2rS6HoV4l9lVkswU1DJ6MogXWeUKqdQOaWuviNXCUaVM5qxUoo8TSiytPLinprp\n2tKqwCWK6c0mQ5fThLepqXJZKpQu0FoisKaVBjGTkWitwSi6vSUOwyGTaIxlN2g0Owgp8fweu/s7\nHIxyDnY3Wb95G9dpYpB05laQwp7KdxKSKCaOhsTxIblOyfMUoTQ7e3v4QQX6JukYz21Q5AbHsRiF\nh9hOk/F4wGQyQTouruvTaS+gpiBbqQpAUmhDo9VlHMUEzS6+16TIS4yCvIgQQlKYDIQNokS4HkHQ\npsgzSgRNPyDThqLMsBwLIT2kY5OmBoNFUpRASaPVxlh2lS6BoFACz+ti+wF5YRiNQ8K4OjfrQUKm\nS6IkIU1j4jycymkrtqRlO0jbQwgLowqKMqMokioRrzTVsc1DtMpQZUqWJ7OBhBQChAPIqRTy++3h\nFkchnW4Ve5vm2Wywn6Yx8+0ew/6IbrsziypOkoRJFFeAnxAoA81Ol0a7TaEVaRwTjyfovEDqqpKN\nroQ69SC+rpZ6nocjrQeqxaooKbOcybiqJtcyh3oAWA9wjlb+jj7UFCAUpmI2qrygKDJ8WWBTIlSJ\n69icPXu2qkxPq8ZZEjE3N/dAdXllYY7xsE+71eR9730/i6vHSApFMY4IpI1rBJYy2KVBKoOlDUIX\nCFPAFGirm+tUDKvHHrs8vYZY7O0fcrC3Q+C5qLJiPakie4BJdbRC+D02J/i2m0CyJBc4N38ao6AR\n9+kf3GLZPcE4HHGy8QhB8zzZ/gBRjJnz1igMjGST9Thj6+4Gl9pnsKRHVFwlDW7yuTd+nZvhXf63\n//eTfOazv89Xrv4rhsR45RxOawk9lnz56hexdmNMfoMSj2X7HJZy0Img1V5he5RxXndxrITRxj3m\nOydJD3cJ8zG3Dr7MvJnjsbPnQLn02svkeKysPcbL916l1Vvm9t6LPHfhObYH1zj/+LO8OriLLArO\nd+f4c2d/mLWTZ9HHSoJmezb5qScoUrg0mx0WV45heQ321T6HUcz4Xp9X1l/GbkPP81m8NM8jjy6g\nTIQ2lUSiZj7UkyutNQudHmVSkNzZ4MqjTzMMC04cm0cXknERkmvF4WjIa9dfwnNc2t0uy+05dHEP\n6bRY68wz11zhxuE2ZZhhxh3OnTxGb9VlnPaZhH3CaERZGiaThKJM6XQbuDTZ2t3BERqyFNMJ2El3\nOXn+OP/zl36Lu/fusB7d5szCAkU2oT8sOLP8JG8PrvObX/4XrKweZ3X1aVy3wVBvo/Ocpc4SB3s3\n8W0P1xFE2wMmWUgkqsl9mkVMDhMWA8EH1t7FnN1hpXmMrnWMQR4To3jssY/z9pt/SF5E36p7/kPg\nZzl6ssKKMWZ7+v8OsDL9/ziwcWS5zelrX9/nhfhPhBAvCiFerM/bh32Hpst9HdPkm8mNvplX0sMM\nlXqZhwHoazfe5nc+9dtkRUqhcuI4JgxDxuPxzOcoCIKZzGs8HjMajWYsjxqEyfMI/2KVAAAgAElE\nQVSc8Xj8gI9Qfa2qQagaUKlYw+UDgE99fY+iaAYWeZ43YxlFUTQDhlzXnXnApGnKYDCg9leq092O\nesskSTKTktX7KU3T2TI106jeJ7Whtud5ZFk225ajTMF6XZIkmUn6lFJsbW0xHo8Jw5Bhv/I0SpKE\nLMsIw5DDwQHD8YBf+Sf/Ezdv33hgXx2Vm71T33infvIwYPLw84c/+91s9brVbLOH79fvxKqqgbZa\nzlcDlzVYVP+t2/2iyn1mUL1Mfbyb3Q5mmu6bxclMkhYEVXJbLYff2NhgbW0N13VnoFGe5zQajWre\nPe37R0HM+hHHVaKjZdt85ctf5geefvcMnKk9lJaXl0nTlCeeeIK1tTWKomBpcRXPbWC05JVXXud/\n/xe/yac/80ecPHcBf67HU+95hjMXL3NjZ0jmzlE6HVqLp+nHkhubA9pLpzlM+vzoT3yU+SWXJKku\nXd9sn9fv15LM7zOO3rl9TwBH2hg0kJJilwa7U0VOZ3mJLgBRoEqDNBaOZeN7bc6eP0+320YKi7LU\n5EVEqbIp1VGjjGYy2aUoCpJ4hCQjLzKksFBFgi4FptQ4LnjSpyxSwuGQTm8Oz/HwW4vYXqvyTUlT\nXDcA6WI5NuicQlUx5r7rUKqM4XibIs1IkxIsTTwJkUYibB9LSLQuK0NdJVF55QukJCidEUcjpOsR\nZzFSeFi2i9aVprXMUyzXQtgORugK3S1zLFtgO0HlOxEfgNIkUZ9JOCSKBwxHB2RZgS40eZJOHekF\no+EhSTohy1PCaJdkGHF4uMXB7gHRZMzgcMhoNOatq6/yyuuvcO7keSa5oTQxKysWWSJxmi3WFldp\nz7fZvrdHuys4dXaOzf6Q3f09bt0b0R9MWF1yGR7GLB9bxQoCtrfGHKxHtO0mB6nCkQWZTnjskXnC\nIq1Q7yhi6bKHFRoC3WBpoUfDcvDzgslhhB7Y7Ed7pFHJ7cEW44OYD/6FLraEP/rSDe70dzh9rsdH\nPnScj/3weY4vtphrePSOSYImZFbKBx+TNOYVx690uP3m5Lvc++/Tsx3LxrUdLHIEJVroqr+UecU+\nMgbk/QuyVgVZkZIV1aSulq4VWUoShVXVPklRZQpl5ZFUqoJ8ymzIi4q1YKiMewUOlu1jOwG25SOE\nNZPO5FlEXiQIU1IWKVmWUOYZSAvHbRA0G/h+Y1pxAGlbCM/n3Nl3VWCW0dy89SpIB8cTBL5Pyw8w\nOmVxZZUsiWk3uxjAsQRKFdV5YPmUxsdqzCOER6kNShuMgP29HSzHJc1L+oNDlDAoZZhMIuIowbEr\nGiyqpD/YJxoeYFvVekXp/YFzHMdYlkUUj3B8B4wGIXHcqqrnOj627VcsLp1iWy5CKDKlKYuUXnce\nKUHnGcJySQqFEgqjBVFRMJpUMjYhXbSx0FTytbQswPaJtEViJMOwYBhlDCcxYQ6O38RpzGHsNkq6\nFEZU8jilyFWBEtV+KFEIpskmWQVeaA1lXlDk1XsCi9JI8rJAqao6arst+DcdODoCOghRSUONEdht\nSRJB2+rQ9hSGHIxNw5NM8pDucpekTBGWxnYERR5XZvbCgClx3IoF1m71ptfxjKLIyPKQrJygZElp\nKSzpY4SL5zawrKqimxYpOJJSl2gqiYFjgVIJkoSyzBFobCOQhQZVybUMglJpLMt+wJhSCIFlFJbO\nKXAp3XlUofHCQ4zOiN0OWJJRLjDphMdPLTPfdJnrdGgvniAuDHPtJgtzTcLxgFGUI7yAzvwix46v\nUUYJOoxIswlZHqJ0ihYl2tIgdfW/sKvHtG9qoysZajQhaDd4+gPPoW2fLI1QakJjYY1JrrBsG60V\nOs/RWV6dYzpDUJl7V9+nH5ze/hlpbcen2zuDK22cls3Fp68QtHucaqecXj3HpFdw4dgSTx+7wus7\nV1maW8aNBT/x8f+U9zz5YS6dOMeVk1dotX2O6WPsjcfI8Zin5s4gFnyK5X3KcJ/Lx57k8vmnWZk/\nx2rzMsdPHkf3HH7s0k9xwuny7/3QXwVnn85yj/mG4hMXP8ILd77AxHZZXOry1t6rGGfCvDXH02ee\nwOl2CeWE9vIp3hq9gtO1UI2MRtAidIe4+OTDbbLtAY0womM6uNLnzXCD37z+VQaxzeGdewz218FI\nHMfCMha27YAuaTTbIJr4nsULb7/IE4+e58NPfZRHT51l9/ZLzJ9e49rV21zd2iYMI1QpZpIepQry\nPEXpihly/L0f4vTp0xzuJvhKsnp6gYWl09zLDtnb2WBw7zYtd46222ayecBbL3+RaDTEsiFMUy6u\nPcHW9suYYsCXb36Oy5ffx53Xb5Gk10EK0izk5PELOKJBp9ngre0v09+6yTiNWFtcxTFz3L5xHbcM\nWG1eotgrufLUM5zrnWe1ewyzeg7pd9gc3CQvExa6XT724b8GWYtyt4QyYW97SLq/zcr8WXReEicF\n25vXcYVh595LjEeHbG6+yUF5iG5oROGxv91n3+zwcnyTg8MdlkyAYwRIw/JCj0B944KZEOLHgT1j\nzFe/0TKmmmV9x5CtMeZXjDHvNca89+E5/FGJzneDIRJFEV/60pf45V/+5RlDo2YgJUlSFaGORIXX\nMq56olmzjmqwok5bq0H+mrlTA0X199Zx9gcHB5RlyWg0mkamV2BALfvK85xOpzNjg9RSutqQuCgK\nVlZWZkBWnucPmFdrrYnjeMYQqSVucN98WWs9W6eHpVRHvYqOsnrq36tlakmS8OlPf5owjhiORxSq\nZBxOCMOQ/f19dnd3GY/HJEnCL/7iL3L16lWSJHngmP9xvI7qdlTqVverep9+t/Gjowyd+vlR9tDR\n4AshBEEQzMDwuq/VPle+789M0OH+eQTMWEE1wDe/ssBuNMJptzi4e4+uEHTmGuiWzWiwSz8c47ou\nq8srqHHMohUQlRluu0FpgdXwZsBp7f91+fQp3nrxBUQUEqiShaCFUBodOOS6pG257F67TTYK2d7e\nJioyXn/5FZrNJt3lBRLbkPo+Z89dYtKPCDWUrRaxk+IG0PMt3DgkGUU0vSaDwYg0zZGNHm/duE3D\nERxuvEVxOGZwb5dmENDtdhGuTZFJ3vWuD7J3WFLSo7RgZ3BAogv8bos0t8kKh7x0KVRlr3Hnzh3C\nMKQoCprN75xYYOuvf2ActLJQpaQsBKqUDzzeqR2Vekop0XkBpUJqg2Xu9++jy/5ptW9pji2E8IHP\nAd50+d8wxvzdP0mjPKPBszWZsigKgVCGYysu7bbL3bs5cQaugM5ci0KVOF6lvZU4FGWKY9toROUR\nDCgnIS0kncYKRVlFjKeRZm5+AaRLoRVaahpem7xQCMbEccjC6hksB/K0kgoJDL7XBl1VhV2vMTXt\n8rBkjtIFk1FEu9PD9xvVgL6IQWf4foNCOQhTTNE5iTCKrKwu4mmcYDvVBSLwmwijoFRkeYTvNCqg\nSQmUzinSDMerItEzO6bMLZROcIImhgLsRQ4P1un0lhgMtkhGI2y/i227SEdT5BIXSbs7T5ZFRIMR\nWCWO62O7FnkqMUqz39/AsVu88eo1BsN9hLJZXk1o2BZzxzrcvRly/ETAoJ/TWLaY9HNKKVnf3CbR\nMf1C0/I9zh9f4HNf2uJDzx1j7ZxFlqZsXku59HiTna2Y9qJNz++h2wXBYos/+P17XD7f4ba9wzCN\ncA8FyThh5Zk5SAs6bZe7b8ekQ4vGnM3JlS5vvrEBwuHEPNx8Y0h/N8btBEiZcn0r5O71PZ58dJWO\n7XHl8ik++dsvs9DwWOgFPH814tTqEm98LaFZfi9MnAWWJZAWCEzFaKNEmRIhbGp8Vxg5lYNVN0Ct\nFMZUunEjFTBlGggQlqwMZY80o6mkSbqiFkthzejHlQ2zwRgq1pPQ0+SjaUyoLmaMJiPAlJqiLNFU\niUtCSCxZJcEpQE9lVEKWWJbDaLjP3FwPoS0OdrZptOa4c/cqreY8hgO00OSZAV0QNDu4bnU+aZVU\no1OVIIyFZRSR9nCtBtHkHrrQtLodpLAYTUIajQDLcihVgskKRmVCp9PBtx1cv8koCplfWCEOQ5QG\n265u0NoY8ryszL+lROmpz5PQWJZbMRctl6LIUKaorkN2g8P9DeaxMKpKJszLnMCxq1RHmYNosnvY\nB1XiBiWB36QwCozDJE/wXBffDQhTELZBK01UhrQxOHaANilZkVZSs6AJskAJgVaSXCmEgSLXCK0w\nZVZJp4SapTYKS+L4XYzR2DjoNCZJMoyJcRz1faHaNzj9XZWTpxPSPKM0CkvYOI5NkmXIUrN4cQln\n7hjaalJoge8YyqmErE4mSZJkRsGOJ0Nct2LLjMMJqydOgtZYlqAsc7yGB6oyP4fqHEYKwiTGdV1a\nfodoNJql3AgDajoYtoWkKEsc35sN4I8yC7XWSMvCti0skyPyBBEfEu+vI8Yj8ijBkwrftdFlysc/\n9iE++X/8LsIWjKOQbrvN8lyLja0t5jq96mokJRfOnKK/c49R/5C7t67jN4JZnG+j3cJ2Hfyph0Gn\nvVD5anluZcxfezFoA7kDZcljj1zi2huv8MwHnmN5oUev1+XlV15ibW2V+dXjGKkQro8xGqMUEonR\nOULYCPt7ogb2nTUJJ862uXe4QyBOcPdggwtnzxLdGeAuOFxLbyHiHDW6h235zGmBf/EpRoNbfPmr\n/4yP/eBf4Y2NLzJZ32Fsd1gdtxkfL3n95gt8+JHnmLcv8Jsv/l/8wrM/yv/z2ivcvfsCicwJZAsn\nO8PyUpurb63z6tYNLq+9m9OdR3nvM+/hv/of/ha7epf2xiG/sb7NleUL7CRbDK2QnVv79LSkuXyZ\nO9e/Rtt3WF1ZY852uXPQZzA5YFm3eOvaW8R2zOGBIl0wNK2AnQOby2s9DsYJ5889Qnk4REpZFQj1\ng0afSZnQjwYY2WN8sMFv3fgax/wlru+8xhd2nufSuR6d7lk8dxoBzX3Jd1XsqPr+qWNnGa2e497B\nOsM8IhsO2B6VGMbkWQPTXEKkQ96882XSuMDyF1leXma3r1nv38C/08AKesjMZi4QxIND9pI+lnua\nxc5J5rrt6e+m7B+MGO9scuL4u7hx7yq37ky4eOI0QcNhrCK29q9ysnOaJavF3SikFY4gHLNkt2me\ne5ZJmpPrBma4Trfw+ND7P8gXX/sqjz92BUXA9mDMaOdtVDNlPRtwafkcb731Eo5waYmARkeRhIcM\nS5cf+shP89XrMcOBRqohmVE8evYZ/uDz/4xeI0Cl33Ri/oPAXxRC/BjgAx0hxP8C7AohjhljtoUQ\nx4C96fJbwMkjnz8xfe1btK9PfaqP48OvP+xxc3T5d/zmI0yTd/p7VD529HviOObWrVv8jb/xNzhz\n6iw/8zM/w+rqKmVZ8uqrr/LDP/zDM6ZFnYpWS4SFEDNGUc3IOJqaFUURjUZjJh2Diu1Tgz5RFNHv\n9+l2u0RRNAOobNum1WrNQCjLslheXkYpRafTmYFHNeDQ6/U4ODjAcRyiKMKfGhnXjKKjjJY0rawa\n6u0AZqwpYMYErDz2POI4njFRHmCvT0Eqy7IYDAa89tprvHH1TX7hF34B3/cZD0d0u11arRYvv/wy\nn/zkJ9k/3HtgnY4e3/o+9jBj6J2O7zfqB/U15SjjppbZfbOCmRDinwI1eHpl+trfA/5jYH+62M8b\nY353+t53bAxf77ejcrKjx+XodtZ9oD4mdTvKNgIe+L6ZJG06B6jZSo7VngKiEXEyQkrJ0tISUla+\noeO9Qxa7PQLb5ZptoT2bhuMSBFUwRqPRwJkmBheykl225nv0+33u3LnDlStXZmy6Wu5YFAXHFpb4\n+//13+XZD32Qzz7/JRYXF9nc3OTevXtcunSJhWMr7F2/w3A4ZPniGbx2kzxKKKf9wPd9MiM4PDzk\n5MVzNDttwrDyp43jmGQyITh1Eek5rJ4+xYUnn2RxbZWBTmZAm1KKNIpI04pIUYGx5QxMrM5V2NnZ\nme3DyWSC5Xw/We3h9u2kqmXAR4wxoRDCAT4vhPgU8G9RGeX9AyHE36Eyyvvb4kGjvDXg94UQjxhj\nvqFY0Baw3HU5mBQszivGoabbgO3tkJPHXHyrwcZ6xGI3Z+XkAkIbhFQYoXEsG61AZymWJyjSjLKw\nWVo+gaFAlRllmdNot9FC48gcY2wsW2FUhi0lWrvMzy8hpEteRNiOh10m2K6DStLKFDsPkcLDtqCw\nQ4TjU+YZvi3I85AiVyhl6M0vYDuCcDKgVENazR6WW2JpgdIlWVKgdIzj+DieR1mm983B/IrWJ1o2\nlhJYFkgrINcCkRbgVJXpveEmjaBDlod4fptAaCyzTJymtFqLBM0ldu+9jWN3MGmJJRtkJsFySrrz\nC+zvbROOJxWt1G8ynowQriRwu7z++oukcUyz5TMej9nciDh93GMhWGFyLMWyl8HeoNPq4TccJtGY\nVBUErse73+1yOCixhObJ90uyIkSMLJaWBJ3HA4ZDm8XFeXZ3Q+JshGvBQqPLlSfm0KUi8FocxhM6\nHcPJcyvcfnUTIyweubzMdj/DtVLWGm2+9OI+ePDxnzzJtZe3iQ80h0nGh95/jFvrBxxbtLD1Movz\nDZIw5nB3h7/6U08yGcd85SvrPP30GQb7ksnmFp7TAMZ/3PPoj9WMMRilUaJAUIE+wpJYukrgE6Ws\n4vWQYCpphgKQleeJoZp/mbIyUXZdH0F1szfCoFGgBVLer+TZtjszXK59uKob7X3p2cxs2Q/QZWXU\nW/tsIQVKF5RJiuNUMgHLlGTFlPapJOPdW9y9dZUsTGg2PMaTSv5lJEzGO9haYpchJgtIs5DSgVaj\nTToZIVugpIeQ1Q0vzAsaQUAYDRG25PmvvsCZ42fYPdjFUA0+gmarinEe7XBq5TxeEKAHE7I4JbEy\n/CxGl4qsNc/CwgnCsI/RDrY9TWOzJUIotCoJ/BaTcEir0yOeHCC0IC0FAolRKRJBngzpdbscHm4j\nDThBE88NSHWJ8HwcbVFKC6TFxsGIdjOj01KUGCbxCC9oUaYFRVGxVYTrY3kutpQkKiceDpAYmoGP\n1jbSdbG8TgUMCh9TluRFQWI5JOkIRwaV9DCN8R1Jb/Uslu3iNpoINI7XIk8i7NEe0aCPzuJ/4wlH\nD7a6iK4px4fsb65jZFVI8ISN0YpSKDq2y2Aw4dhagJEulhRIkeFIC8d2yJOUbrdLGsUIbXAtm4QK\nkFV5gdaKIkuRUpAXFUNCSontOeRFAVWuH0be95NIpl4IdfW5koOKWQVSVCGE6ClQJKaeMbOBpRBo\nBLpIMWmEGt2DZEAZh7hYlURVCkpdcPHsSTrdFlv7Q9rdJfYPdhnsrHPu3AWu39qg02lx8dIj6DIn\nMwrPaLQlibOCIq62ZTwYVhXV6XoE3jaOWzF2FQZrOuj1HJ+8LBCqBG24ePESlhUwGvbptANuXnuT\nXqeBJVYphUJbBgs5lc+KqWRT/pnsx1kaU24WdJ3HuLX9h3Tnl1hSTW41++yPNom3N3BXJa8Mdzh+\nfI3I2uGzr+3xyPJxjp17hpde+V1yu8PiudO8/fbn+Us//h+Qb/fJ7AGhE7E72efZ80/zTz/7q7Qa\nTS6ceC8sRBzuDbk1us1+v+T2cJdH5x/ntTe+zFIr4MY//hX+3PGPs7f4UwTOS2yt73FsdYWXtg9Z\n9ALiwQh9tkUrG5H7NovOCpu7W2x7t7EyjzktWWws4DkWKp2QLZxCTd5iNOwzPLjDC9kteq1VtqKQ\nrq4kMGma4voeXuSAVclkWsawm9zh+OIFxsk+vvDwm/NceewKB9vbzHVPc3A4ZuFkCSKfTR5rpoXr\n2gg84kJx5vIzqPl1zngB/TDi+uYh+1nKIytnGB0MGbYyxqbPuxefZGOwgedqwpHmuNtGD3fRi8cI\nowFzl57mYLKPUBFhGdNym2TZBbQpScupH2B0jFbg8szZ9/LFvc9zZ28HZ+U8zrXP8ANXPs7Vnbdx\nTcqCb9jOIrYPNvncqy/z4x/4MXynge2ATAQqqFj388EcGxsbCFOw0ppjlz5de47heJst3cRqSd7z\n+Ad5+vRHefv1T/N7V3+HCxcu8/mXX8JVfcp8RHvuPAUlUbTBMO5zav5xrIVvPBkyxvwc8HPT/flh\n4L80xvz7Qoj/DvhrwD+Y/v2X04/838D/KoT476nG/BeB5/+458e3AxD967SjptEP/17N4LBtm+3d\ne/w3/+3fn4E/ru3x3HPPzQCXemLszxKmqr5bP6+lZLNJu+OQpulMklbLfmpAoDbQPmpu3Ww2HwAP\navZJnUpZS4lq8EpKyWQyqaTu0wjzMAy/zgPvqNFyDVylaTpLx6oBtfrz9X3HsqwH1rf2eqoLAVEU\n0el0ePXVV1lcXeGXfumXKjBYg+VZs30fRdHsczXb6eHjfBRM/pNoNRhljPlW3o6/Cvwj4J8/9Pov\nGWN+8aHv/I7nu8BMpvgwI+oocFQfp5qZ9DAL62ifgPvgUz1utywLPfX+rPtD4M/hOC5aK/b2N5BS\ncuLECRqNBqmU3Hz1TYpPJLSbLRq9Dpv9fc6dOTPrw9VxmhqmW9Xv7u/v88wzz/A7v/M7PPXUU6RJ\nOjvOdf8eD0I+95nP8h/+9b/G7/3+p/Acj3a7zblz5zh58iS3NtdRqvKQtG17lrBWxilpGOO22iwu\nLDJJ4vtAWpiyvr5eAVqex83Nu6S2xaMfeJalEyd55eobuN0GolBYc/NIIRiPR/i+P5PY2YoZc04I\nwZ07G1Xi89xcVSRWCuvrA8/+jW/fEjiaUlLr6Cln+jBUhngfnr7+a8BngL/NEaM84LYQojbK+9I3\n+o1Sa/IsYWXFpukI0lCzuVugUgGZYj0+xBUBz795jw+UDstn5hn0JzScXTwnwPfBth1GwyHSSBZX\nl6sBsy2IUlPJaBoBlBrpBtiyZnFI3EYTiYW0KkkXxqEoM6RrYxUSjIXj2XjeAlE8QUqHZmcFXZTE\nlCDbiCLBsi1yadi9d5t2dxnLkqRxiEBiO4ZcNpAiI4onaF3iBxIjFK7tI22JYzeIohRhBGVaIF0b\njUaVpmIf2T6uqDwx5ufXKtO9vKAUGZYX4DYComhEqSBORnQ6y7MbkFKKyfiQZqtXxYZ2F9jd3mN7\n5xpSStrdDkWWI22LTrdHOApJwpgoTlgJGpQKPvvm25xozjPkHkFbM47GDMdJZQArDWEW02x1sSQk\nxZhwZNNdthGOhWUkzY7N3bcm7FsJZ864tFOHSViyctpl59AiL2HYLzl3eoXDgzHBTsL582fY3+zz\n4hf3ec8PLvKbv3ET6XqcOhkgSvjc769zbM3nXc+dw2tb3HjrHtHIYscULC83SLOcuBBMtguyYpvt\n9QGdOQ+rlTHvK7onOiSHDi9d23nHfimEOEl181iZ9vlfMcb88p8k2256lk0rEAZEgZyelkaX6LIy\nD7VMA2lZ06j4HGlcBAIhNcaoKjYSXcmpRHUxNNP4d4wBq4rylkIgjcGo6maN0DNTbiGsaRVJAbW+\nt7p5W46LFGCERmJhCXCdihUkpUQbM5Wu1QaQJbevvU46HiFtG104dP0Ge7ubdBoBzZaHbbskpaKc\n7OG15uj1euRJiNtsMxn1Ef8fe28eNMl53/d9nr67555573PvXWB3cRGHCN6iKPFS0aRkk1YsWeU4\nSuRYZce2XElFiR27VLaUSBUrlShF04rLiuwSJVI0RfEmQRIEQXCBXWAPYBd773u/875zdk/f/eSP\nnpl9F1ySAAW5IktP1dT7ztXd0/109+/4HrqGYTogJbpi02lt44UJQehiWCVW1leYnVug3WpjmxZu\nsMPMxAStHYU0i+j3fexyldAP0FIJVgFSlcDvUijW84ArkShqXgyzdItB6GFoBn7gYhoOrttDETqZ\nFKDGSEySIAAkXrcNjoWUKXEGaQq2ZWPKjCSAEMEgdGl1QkSa0h/4dN2QuZlZikWTteYuugqW0NlN\nQrZWr3Kr1eTo3CyNagPdNvD9kE6vS8kxOXbPSfbtm0RqGpZlYeoOiqrn8yTLcq0jCW6/RZqm1KoT\nuV6ObpJlIFWFJIzQ7BKq4RC4PkJ57SKU/zmNUfgoGHW2JYKM1fNnwO8Qk6CaFlqsYpk6A1Oh6lSQ\nlk7Q72LUAxTVBE0fd/eSJBknEqMAUTc0/IGPY4GuKPTbHYrlEp7bRcgMoSkkQ6vbTOZ6RgCVSiUX\nZu10GOHrw4GPZVlDAf10SIswEFISDjUsSFOEgCxLh9uUkeoGRhIhvSa91ZexRIimgKZqpDIhiCMq\nVQU/jiiXHHoDnzhLsHQDw1AJgoBqvUa5WqXv+WiGha0Zeac5U0FTx114YFgkyzvWUTYgjUJQ84A2\nUfPgUygRQs8dC4VMCKOAtbU1jpy4l0KjRmd7g29/7cvMzM2iFmsEkU/BKJJEAVkSEysqumljqToo\n//+yXP5BI5Nwa/sFrq9uMz1nUxxknLr6DSgZbO14hJbgZnOTtx54gGd2m7zx5IM4hTXOrqxwlHkm\nyks0vR47bpv5xgIvvPgcjc2Yd//k3+SZM7+PppYxkgL7D72TdOt5Lsir/MLSB3m6/YcsOhJz7g3o\nu1dppAZvOvCTOB2dN7/zZ3hh/Saff/JfsDh7lH33nuCZZ79OoKiUlVxzol5cIg0Mrq1dIqoKDhzY\nx8blDbKBy0zdRrESlNI0ftjloNMiGyh07TIT9zzM5d0VVncuUpf34tQY62WkWTh2sIqTgK4S8vjB\nN7B4/GGurJ2FTp9zqy9SV8tMLUxy9dJTzMwep9PpjJPokVX0mOKBiru7RtNb5QvPPs+tdpepYpmT\nhx/GffYWl9dv0RkMmNWmmEgmeb59nntOPsiN7hXmnClanYjEEAxeuspDi48R+hGrm+dZnDuI2e6i\nDqkhmqZRKuaCxfXCHC9cOE99bpJ0kPK2xTfSUwUXQofWTpt5Z4YkCLmyfZ3lw8c4cuAxLKtGmGbo\naRezYLO4cA+3bl7AUjRsQxILjylqXGxfpzo9gUDBUudJAji0/51sbm7y8cU/agQAACAASURBVJXf\noDBI+bEHf5Ik61GjxaXNNg8cf4jdlR3qi8c4f/4Jlu0Cwvfod5s/YHbedfxL4ONCiP8SuAn8NQAp\n5QUhxMeBF8lNFv/bH5Q4/6BxN4TRqLiwV/sG7rTDvhs6Za9ezujze9FNe63rFUUZ03BGY1TASGXC\nJz/1CX76Q391jBwKgoBOp4Pj5Lbko4QUGAsFj1A6cFs/RVVVut0u1WqVW7duUS6XiaJonDSrqkq1\nWr0dv/f7SJkjLwYDd4iyu01n0jTtDq2Z0fdGxYQkScb5wEi7qVAoUCqV7nCyGhWY9lKlDON2kXEk\n6DwqJI2MHUbrsiwr17TUh/mPEGRZLkWShLcd50aaUMC48LX32I32+d3mw/d77ZXv3205eUz8vb8n\npfyGEGLf91347fGa89096/mu33A3muZeJObdXr99Pny3a+DeOR9FEbpuDM1VFLxBF2BMfdQ0jXZz\nJ0epFRwsy+LG+iqHhusdzU19GOPvpWtOTk7S6/VotVqkxm1E2mi+f/bTf8w73vEO2t0OtckJ0t0B\nxWJxjO4pFAp0h3RjVVXJhmLfo3kyOo6GYTAxMYFUBF1vl16vl7tqKilpluEOPGpTE1y8/DJH77sP\nbajllG+3OnbtVFUV3/cR8k4drJ2dHaTMneEGHe/PzM7+z/t4NYgjRG6t+RxwCPg/pZTPCCG+n1De\nt/d8/a5CeUKIXwB+AUBTVTI9RSYGQtHI0oxokFKtp4RRxnSpiG8lLDtF2p4L7T7L5Slq05OYdq5J\n4g5cksCjWptGkBLGCWkYYGgKtlMnTWMURSMIBpSKDVQ1Q7eKkEGm5B3gKMhIyDWSkiBCmAZGmhGl\nCapMhjx8FSEkmqVixw5h6OOlMWEEliZJiOl1tmhMTZMmPt3mGpalULBH1pYK/cEAEZo4hZy3qgsL\nYapEoUs6iChP1smwUEjIUo0wGGAUVNx+H1OvIkSApqiYtkk4iEFVQcaYhSqhOyCNJa7fQzVskqiL\n7egkWYoXuZiGRM8E5ckqN69fJRNJ7o6ATq1awjJLGJZNa8ejZCls9Tz0SgkzNjFKOqVaxvq6T7ub\noMn8xA4Tk8Y0nL/c5KETE1y56aKaOjudPsWiQmVpmaefWsFWBFfXAizLwPNSnKJk/sAySmmbp55Y\nZblms3oxQBcKu8UBXltSnzQI1zv8xz+6weyU4J4HGvS22hQnauibuT7RqVNX0UsOJSvi6KEauiVZ\nudHk8LF9fPmTZ9k370DfYvHQIpn06Td9pBSIOGZhX/37Tf0E+IdSytNCiBLwnBDiS8DP8zqh7cbn\ng6oNdYpuBzBJouSiuEmCpgl0w0QRkMkERRUgNRiKZsPwQp3FCCGRmQBFRUGSMRLFEwgxpLMpyvA9\ngZRp3rDPJJCSkQcKkhRVjDAYuc27hkAOEUeKqmMbNkKQU+uymDTJXTta62tsXbtAholVLhIFLpML\nixiKRegNmJw9yMDdQlcMvP4GaiZIzAKZopBJFacymc+tOCaNUpLEo9Pewsfm2o1tWu1tNFJ2dtuc\nOJ4nEFkWc7G9g8hSdpptTEuj1d6mWChTbswgsxihK/hBhGYOC15pQhaFmIZGz+timyZRAqpmkkR9\nLLVMIlLi2ENXNaJwML4hCjS2tnewimWMYoEEk2a7xfr2JssLh+j4Hq1mwNr2On0flqZrrKVdglSQ\nKSq6UcALY85cvcTU9BzCsCgVG5y/1YRbTSaqZWzTYW11kzCO+MIz5/iVf/SPOXJkP4oi0U0LbdgO\n2cuLz+uZtyH5IxeNLMuQBUmhUqHamMP1uuhm8QdNzf+sR0beQpAAMtfN0QS0V29A7CMUBV3XUKI8\n4A2TkCiNSLpNZpb3E/Rb2M4M6BaOaY1ddEYdabgdYGVZOnblUWVGbJrIJEbXVeI4RdFyYXpSCP08\nOA+8Qd4Z0zQ00yAKQ1TVJvIDsmFhOIljFEUlDFLSYeKQo4zy3ygF+fVCqASBj/C6WKokC8Ih7cIg\nlgqaYRGHEYE7IPJ8RJpgmQZ2o4aSxkih4BTLTM8vo+vqmJInhEAoCugaUlGQikKm5gFZJiDO0qET\no8DSTZShNscouUnTiDh06babGLpKtVYnVUD6Ic2tbdYGLqaqAAKhWyDF8LqUoWvGkGSbIl5dOPOq\nxn8Kin6ipFRnDvGe0hL/7onfo7H/BI3aJAVVYUcdoAchh+/7AFG6ziHLpFaY56nvPEtIFUc4TOyb\no//kDtp0nfLEJJvtbdKwxZXTX+Za8yb7Fx9jlyInJyfZSeeovXCOJwvPo4YmJ08+xmDHo3h4nisX\nX+L+wj6eOfstWrt9NvsbKE2fQ288SLh6iQf2zREUpjh3/ixLi/tprSQUnZDFxgR+b5fLKx5TxSXM\nsk2jcogXV58hHKzgyh06/gFmJg6y3bnBixsv8uP3vZlnXxbM2xN84TtfZ2VlhaWlJZIkG7pADuUH\n4oRNr0Pr+ncI/QFaO8Eqq+z0tzizfoPH7jmOm4LcvQyJlt/zhvRQRVGIEgVDi9m3fJSzT3ybw9Vl\nJpQy01aJrrfNW9/4Aa7cuMhzq+fRVIte3GJmYpKbq5c4cvQerp29xqGlg2y2NygbFhdunOPwkYdJ\nFZeSWePrO2eYj1NqC0cJAo8si8hSh2Mn34B2awOZCY5Mn2Ar20Zf63F46SiWpZMWJGbSIGWVza2X\n2X/kEH441FCLutjS5ErnIpZc5/mrT9OOuyxX5nhk+X6MtUucfuk7nLj3IGEhY7K2hNvu4bJDYpi8\nbf8jrPbbBGxxq3eT+xvH6Hgpi8vHubJ9Aa/XpGoWmVk6wlPnvvyqzgMp5dfIm8JIKXeBd36Pz/0q\n8KuvaqGvcoyun6+XfsdeBMveosKrdfBKkoQzZ87w0x/6q2NK8kijZnS99zxvXLwsFAoYhoHneWN6\nVxRFFAqFcZGo3c6drJrN5tiAYGRn3u332NraYnt7m+3tbaIoGKOJSqUSb33r23FdlwsXLuT6R8US\nWZZRrVY5fvz4HU5mo+JTu92mWq2OtxsYixqPEFJ7tXRGscMIFTRCZO11ZRtpRI4oQUEQjAWy9yKG\n9tLG9o5RkeG1OljtNY947Z//oUj6vySE+DngWfK8oM2rzHfhzpx3L80PGBcrRy55wNhZDxhrXu0t\neo6KLKPlJAQINLLYQKBTLDRohTt4vouiaEjNotdvMjMzxc2bNzHNBtvNDeaOHkAWS9h6g80bF/Bb\nG9izdQ4dOEjPD/A7fWq1GorIC5KxpZKpEktXMTQNreQw8DyOHj3KVz//RT70kb/G1tUrzM7O8tL1\na9xzzz08eepr/Nqv/RqnTp3inoUjfOaJ/8AL584yt2+J5X37MIWKT0pHRswnCbVSjb7fQUpJkCWE\nIqO70+LwyYcwzWm2mi2uvXQVHQ3TMlHVmFtbPj//936OP/j3f8CVK1f4wz/8Q3bcHiLNGMgE1+uj\nmjoRGUnok8qUUJGoaUpdNXFdl7VLZxBhFxkIkBmm7XzXvHzl/FWEecdzP81pqHupsLqM71wGd37n\n1YxU5LFczjh4dd95JSX39bqWvqpIa5j4PiCEqAJ/JIQ48Yr3pRDiNZ2JUsqPAh8FKDi6DALIBhFR\nOWZ+2qY+a9Jej2mnEbVMYpsmrh/RmC+x7+B+lpcOEMcpvX4LA4co7qOpDnESIikTRx6pTKhVp/JE\nLwRdT7FMC6GEKKJEFvuougqpRhwFpJkcInxiLLOIZqhg1fCDPkkGqiOJogAyQZz56IaZV3kTiUy6\neAMXS7HY2d1CCIHvetiVClcvX+TA4RPIVOL326SazO2+k0rOAU0yUEFXVNwsQvU9FN/HtgqgCAyz\nRH93Bdd3KRVi7IKBlCpCKCiqpNe5heNMoIqQNAuxyw1SuU0cevjxAK/pUa8t0e5skYiYTrdPwSmx\ntP8QLzz/PO6gRxzHdDoNSo6FadokSoCaQhhDFGfMzNsoapOXX8yoLUgMLMpKhfbuAGmEVB0Nf9Jm\nkOocPFjj+q0+BavM0rzFN567RMGq0mk5pGlEvVZlZXWdxlyNj//OKR770UNM1wskqeQtDy3w4nqb\nXi+idcvnVtSm1enxyGNTVFWTZ7/TZnHZYb7gkE0J5mo2sVD48tcuU5gtsNpssbA4TUE4tNbaWIaJ\nrZZww4Dg+jbbWwH3HipiOjq+TNnY2v5+c3QD2Bj+3xdCvER+U3jd0HbA2NlKyhSkIE0liRzabQ6d\nzOLEGxaUspwqJjIgQaa5QKyqqiQppOnQnU2oqMLK9VkUdUxDyyQoio5Cvgwh5dCVTSLUHN2moCLI\nocdDs+9c6H2ohQQMxd41FEWgKQoyu92l8rtdbr38LKDkwvZRiGEXyJIQrehgWDWyNELGEcXGDKEq\nkErG7s5aniyLHkahTBS6yEwjS2MUxaEfCD779a/Q6w2olktMNSpk3oCdre1cIylK2G1uU6/XWd9a\nZ3pyiigMsYwYt9dC0RWEZmLpCqHvYtsFsjghDH3CKKVgVeh12hSrNZIwJkoFUdhCMQsIRSfNYlRV\nZ9BtEfoeSepTrjbQnQpOoUw3zLh67QqdHY9ecJ1DS4fYHmzQ7GcUbZur2y2KhkW21aJSKHNz4yaF\nQpkwNrh6Y416tYGpGszUpgBwChaOofFympBlClpm8ZknvsT/+MAbgGwcOAhVGaPFctqhQFFAprdt\naEeBWU59MhAKKFo1F/v/Cz5GxaM8EE7RFEHidsnCAT23S6zpTFFCASrVKp12h8MLS+zcOE9p+Tgi\nriFLNXSRU0cFeWdZURSiMMRxnJyypunILEVKQJW4vc7YRadSqeT2z66PqigUTAt3kBeNKpUKmZSk\nQUScZJSmGwwGA4rFMonMRUtNw8p1u8itj8cFKkNHURUUBG6UUjRNum4XPc6DUs3QkaqGputEiUfQ\n7lCrTvOzH/4w/+J/+9/RlAJJlmIaKnNzC7zvpz4MamE49/Jrgj7UB0gMdSxSbFlWrhsgcuRiFAf4\n3oDubovAG9BptUnimDgI6Q+6VMoGQdBjsjaDTAM2d3apVcq8/73v4//4jf+VoOeiZCa6USaRCbZp\nYqqCTqeDWSqjDff76zj+zCn6CI2d3U3q1Fg6uMCaDsupQnNtheP3v53Lzac5tfllTjSO4xRneeFb\nn6GT2BzREyJH8sffOsMRXRA6GtVBRCJV5pb2U54/wPPdazx67BFUt0uzucZ/PPsEv/yWn+LsylU6\nVpFPfPUzfODNP4Zo68Ac/d0mLzdX8Ho3sap19v/Ej3Hp1gXcjTYfee9P8rmXn6fT3eLgvvuZrQXs\nSsF9RpGk4nLu5i32zwquTkxy8dzncfUCuz586E3v4frNTcJgl4paIOjucP3sKno/wDlZ5JGZxygW\ni+PENRrcTjBVfYL7Tk7xtkMf4OOf/2129DOEaY8Mg+OTR7j+0hUOTB+mNrWAbRfHbj4jBAQiptvr\ncuP6LdZ7V9h3cBErKtNoHEXRIjYunGW6NsWJ8lG6ca5XOePsI2hdY3PlFvU5nfOXX2CmXKUTeMwf\n2o/MXBxjjtZgi2OLj3Hx8gu865GF23NdVZmoLnLqapv2zS5KWSESkhPqLNvBDea0BS7f2ubE5IPc\n9JscKE/g91wWyg3WNtsgIlSzRLFawG1PMF9osLHWxF6e5SuXvs70PYe5l5PMFZe4tvFHROkUkzWH\nVhseuvchjB2Li5yhkWg89+Jl2uWY5RP3srL1PMIbsDzzMNfWz/CZr/0hR5YPvr5nyw859nb7947b\n1Pk7dY9eiTqCuxeERsu+29/RsvcWpu62nrshPAqFwrgxEMcxjuPgeR6lUml8Px65r43s6EeUndE6\nfN8fL2OE4BlRbiYmJvB9n6effpp2t4MQAtM0aTQa4zlmmibb29s8/fTTuK57R6FKVVV2dnb43Oc+\nh+u6TE5OMjExgW3b3LhxY6y7tLS0xNTUFEKIHOU3/C0jUfDReTSiJ5mmOUYz9Xq98W8daR+NEE3A\neB+N9u1oP47+3u2YvxIVtvd4fa95s5fS9cr3XnnMv/tzr/lu8dvAPyevOP1z4DeAv/VaFrA359U0\nTe7dHtu2xwW60T6H28WHURFvb4FpJIY9/kVCDKUs8n2p6zq1Wm3shqcoCrZt30FDPPvcGX7qZz6S\nb0O5SDII+eY3v0l5YoL55YNcunGDfrtLvV4fUzBfWcAaURwbjQYrV6/T6XRYW1tjcXGRyclJ3vnI\nI/zGb//fdD2XTBEohs76+vrYha9er6NrGqVSaSzYXiqV6DR3x45/nU6HhdkDNCamiDJB5PVJ4gG9\nXoep6RpGocB//0t/lzAM2dxp8jM/97MIXRv/1hG67XazakjpV1UKTl7INYaaYKPz6S/RRt97vKas\nQUrZEUI8Abyb11EoL8skQqpEUqWSqVxe6VGraWz3fOqWQSgzYhFg2QXi1OXyxWuoGCzs24+hVwgS\nL4faqRq6aeINesgswSlOoSgaURwgEKQKZFIliCAK+xgmhKGCIiySLCKVCqrIsJ0GYTYgiSGTebVW\nzXyEyE9cx3FwjBoDP0TTTTrpNmahmFuXCwWhaqBYKMYAr9Oh1+0Q+6eYmt+PphmkcYhmCpI0YuB3\nsc0qft/FcRpoos/u2ipOuUiv12Zqbokw7CPIuceRHiAJEFKSoWKYJpZaod3eQNfsocPcbl70wiLz\nPXrtFpsbp7ELDgIF1dDY3dyl1dykPlFl99IuaSK5sP4SE/UKC1NTRLFC1S6xdFSlZArWmm2SRDC7\nbIIiaG4NWBv0WJqrYgjBjh8zM1fg20+vUXEUFg8UMdKEz37hJkfutfj22S0KWBxZmGWQDlg+WKGz\nGZKakisX17hyeRc0nZvbfaaLExw7OcELz93CDRLe8CNz9JIYo27xt3/0CKs3W7j9FgtFnVPnVikW\nbY7M1tjc8Zgqq0StHleve+ybKHJ0rsbyG2a4+NRlqrUCbj+g1Usw/IyKpWDIV0dgHUJXHwSe4fvb\n0r7m7kOjViZJIsgEUkAcRUSxC0JFU21UdRQoxWTZUCBR5LDROAlB6LnNdhqSpkCWg9CyNA9WpKKA\nSIjjFKEYaMoQIqsIkAkSEEJFSAVFAbK8sJnKBFUoKKpJJjLMNEOqEplJFGEgFAVF5lb0ghxO6vY8\nLp3+Bl6rTRJmZKrEdhyklGysbTA9s59WcxUYUKxMDNFECZrlIGyDQRTjGBqB18sReqpKd3ebTO1y\n6cY2F65u4g48qlYP2y7iuh12Ol2W9h1ic/1GzgWPm/iDLpYOhWKZwBuQpCnlUgmdGMWoAGruyCRA\nNR2ieECQDXAKZSQ6iAhFGARpSDFLiclIfDd3QIl9kiTBLFTyrqNdIdEVwn6A62psu5I6MS9du4rr\nZcOAzkAkGYnU2G563Fxt4tg2O7vtHGqr5t1BKSCOIyzLAgUiKdg/P8v2TptO1+eZJ5+j+/M96rN1\nhKLkDm+amuu+DZFqeZCgIdQRDD8dh0mjoMLARFN11L/gN0cFELmFJalQUHUTkhB3ZYUsCNEwMFBJ\n7Ai7VKCYZTA5S6fXx48khThClylJFBEpuT0xmkoSDrA0E11AMnDzY6QqaIZOlqQkSYyt27n7pqZi\nmSau56MgiKNk7DIzShbSYcBvGgZBGiMVQSZyZKxt22RJQpQmGKZJkCUomURX9RxRF6YYuk0p2GHt\nuS+xPFlkxx/eM6WBkuZIWdOySA2VIHGZmqhRcyL6/V3sksOxhx7mg3/9v0K3FpBqMA4c7xCY1W93\n7vdaOqtCoCU2muUQRbktdBgEhAOPfrtFueQgohDD0IikQuJLpoopysDlxH3HqTbqtG7dQmhbLBxJ\n6ak2UbVKybJovnyJiYkJ1AMqRmnydaOq/aeg6OtCpTdoUzwwz2L/MG13BabKRDfaBF6P+0o/wku7\nL/O2A2/nyZe/gVi+nxNeiyvnv4PyosfU7ASmNY1y6wZTM0tc8T12psoE233edPTdfO2Zr2JIm/Ky\nijZI+fTTz9K3bhJrJkcXjrK+s4NfsHjjoz/Or/zLf8Df+S9+nj965lNMq/D2+TdzWvkWDy09zsWV\n55Fdh7/1X/8Tvvjxf8WGUqLekJzd0JCGpKuX+WJ4jcZ2kUtbKxyrHaRWnOIr33gW88gRfvbY4/zJ\nN57gw+/9b3j+1JPU58p0Lt/ivp94H/7AJfALt12GNEGqOsjwOquXQz5x8zzF+RkU936CjRu0eJEj\nxTlmlx7j6y98k4Ohz48XbEgzRCZRVT1vvEQZO7tdfDz2TR3ATEOanRb3lOBbK6d47OBBwoqF13NZ\n0Mo0CxM8c/Uy73rXu3j6819hctlgcqpMTSvjpzrb118kLpSIY5VeJyVMX6JqWvSDHSYnHsP321TK\nE8RpjziMEY0C4SBECElnAmYKNS6ubWPZJlvNG5zUZuh0XC5sfodDx05yY32dB+bq7J8+xpXV81St\njG9ffI5j9WPUCxOcaW3Rvxwz6HXpNFeYLd1DQ7MxNYflWpWvfPH/4Wd+5G+jJSYCjYPVBRpzExiW\nTtDdITWa6EWb/mUfPTOwK5XX5Tz58zj2asm8kpIGfM+EcXQ9np2dpdVqYZrmOPkNwxAx1PjRdX38\n+oheM0r6R1pH/pBS7Hke586d44UXXqDV2R1TyUbIoTw2UFEUWFhYYHZ2FlVVOXr0KNeu3eChhx6i\nXq+zuLjI2soqxWJxjFiJkhjTNNnc3OT06dO0Ortcvnw5jxvTaEynLpfLFAqFceNvpKczKmQ4jkOr\n1aJYLI6Ls6urq0xOTo5/txBiXFj49Kc/Pd4ne6lXo2T8bkWjH/Y43m283tpIw3Vtjf4XQvxr4DPD\npz+kMPydyJWRe9rofi+EGJtrjIodo6KfbdvjYt3eoSgKaXK74GRZFoahjVFvYZi7KNdqNXq9HkmS\n8M0vfoVf/MVfZHJmmm6QYjqC06dPs3ToEH/lgYfRNI3dXm/sMJamKWKIaB7t31x0W2Vubo6XXjhH\nFEUcPXqUCxcu8NGPfpRf+fVfZ27fEi+99BIHjh3hwoUL+L6PaZpYlpU7lw2LkyPElWnmCKDd3V08\nz6Ner3Pfw29GUTV21m/httZZX3mZ6ZkGbhrzj/6Hf0ahOsXHPvYxfvHv/RLHjx+n2W2jDOdBEARI\n9U4h+1F+oSgKMk1J0oTd3d0xZfNuc/UvRz5ejavaJBAPi0Y28C7g18gF8V4XoTxFAQyNg7M211fa\nIBUEUDWKeEHI8rTNjhchRECY1KnbDp1uk5pbwVAEhUoZQy+CyEgzFUFMsVyjYJfwfQ/I8uA6TvGi\nPgoC03bIFBVD1cjSDppuIVIFqRnEWYiSqsRJDELJk3CtgKYp1CoG3qADKMRhgttvUbTruL02mnCI\nE5d6Y57QbdNt9fJg37ZA09htbiBJqJZriFKd1m4PVRcMwh4FpUiUdNHtAoOtW9glG12xCbsuSeYR\nhjEyytjqXscsWtSKM0T4pHGQb6/QCLwBhmmwu+2i6xq9zjagIYVK6PcZ9HcZxBmabqGZBnOL+/jU\nn/wJmoBeOAAyup0eXc9jvlYhw2dnoHCtGaBEKoqdsrPa4Z6jZfqDlMcenePpb2xz6OgMmfQ49e0N\nDi9XOXO2jzvoMlWscHC/TeIZLFV1qjVYnpvk68+eZ25W49iBKTZXXIJOgSP3J6xcj1AyOLeyTqiE\nPHCiwPGsSs+FzeYmdmOaJ594GUcxuXBpi2YrZnafQ5oMWFqocKRQZH03YONWj8kJg2u9PnXb4IVv\n3WB6vkTzps9E1cSqWrS3exjFOk8/84O5/kKIIvAJ4O9LKXuv6GK8ZrTd8Hvj7sPywoxMkgwhcxji\nqNMjyFAtE6EpKELLEULkukRZNkIkgaKkoOUdBTIJ34W8zvW8kjRDkXllSRUKiqKDVJAyQQgFNA1Q\nkVlCmoV54oeKKgWKpiKlOizyglBljiaQMXGcc4WzDHY3rhP6PTKZILQ804rigE7XZVbX6bS2CNwO\n9YkqGoIsS9AE+IMeRBFmqYCf+FiaQex6DOItjMoESZrS2mkThSGdQYKQPrc2WyAjBhs9zEKZ/sAn\nDneoFcs4Zt49sJ0ySZZADEGgY5g2kfRQYxtDNxkMXGyrhKUXSKKAgAhTSoRi0W1vUi5X6fa2scwi\nge/lgvh2ldRI0QyLBEBT0TWbzqBJ2/NRNZN+kNAKInTFxA0CLDUFRSUkJUwjDM2gOwhzrQO/g2EY\ntNwOiqJhGSZtt4dtmxSLRUxdRdcF5aJNIlIu3rzEm2fehMpQPJm84Di6yYmRVo9QcxarBCkFUhW5\n6xoKKvlrfy5VhV/HkTHSeFRySmYWo7ptNMsklBm9YSKg60WiIGaqXqc1CGnM7GO2OgO1OYRZyKnQ\nqo5QVRSZgG2TJfl5HAUhhpNDk9M4D/Z83yfwfaIoIuznIqZIhZQchpwNj6Vl2zkNTAgM0ySTkmyQ\nd/iyONfCSJEgUyrFEgkS0zDzQmeSYGi5407kufjdXQqOxdbmBlmaoGUaqZAIJQ9Y4yRG03REJul1\ndvif/un/zG9+9FPc/8Y38e6/8TMMugECMEvFO7r+QuTYablnKo3g3aNr5SCMiIPcITCIfDy/SxxH\nKIoAMuI4wTEtXrxwjpP3PUgmFb755NNMz9R55OEf4bf+1f/FL/3DX+af/rNf5f4T9xNoGgXDoHPl\nEu997/vxb9xk+t6cdvx6dQr/rCn65arDZmeLp1YzXmpdohX4LMT7CKoOaeRRrlc5NrfMmUtPcXHz\nPF5gMnNwGnuqTGgZ1KuLrF4/TX22gF+qsZxpfP7CJzjYX+T4yXtIui3UqVmeeforvPfh93KjeYvm\nhk9DL2LOqjh6CSHrZFsv8o59h7h06izve9NP84XTX+Cpi5/jrW/5CJ/6zG9R1Utcbl7mw/6j6I3D\nZFqXhlfmRvIib3zsPXzl/LMcMRYpNxSuVCZJSipFqbOpmhzN+nzs2c/x2MHH+f0/+B0O1Kd4cvMy\nFU9FO7hJYfZYnmTG0ThJyrIMTzfZ3jzNlFgkosOT57/ETK1C1T7AJvfowwAAIABJREFUZndAtXKQ\nueIDxHo/L6APkxmFfF76QcCzp5+gl71MFitsbLc4se8Qz6x8m8fvfyvbvVXctbNMObPM1Gsk4SaG\nMsna6jbVI7PEkUFRzzi3e4UHKgfoTS8wkAY7G7e42b/O4Zl9hDKiWDLRDUGz2WF56RDL8/ew67sU\nA51KaY5avcQ3nv0KE7UyxeIE0UDjhY0r3HN0jt0L55g/8hCbrV32mTqhJmj1msxY+2h3LlCeqpFE\nXc5sPkNZCmJXkiQaU0dm8Ta2ae90CbKEmczjgbkHeHHlq7T9kExU2b+0yOLxR/j6lz9LaRayuEGv\n32ZyqUHJdtjear8u58ifaojvhwh55f3su13S7vbZ0edHr32vxG+vvszez7wS6bR3+0bb8Aef+Dhv\nfvNbxwhex3HGyKHRskb6RiPx6L3uZKPvjIow09PTPP7440xPT5NlOZK40Wjk7plDZIht25RKpTv2\nwSNveJRSqTQ+Z5aXl9E0bYxKGW374YOHOLj/AIVCYcgs6LC7uzt2dkvTdOxuNnq+V3dor1PoSHx7\nenp6jMxIkmRMoYuSmM9+9rO5Hbq4U4tqtG/vdlxeqUW09xi9co7spRnuRZntLQR+r6LRD1tMGoEk\nhk8/CJwf/v9DCcO/krL3StrdaB+NaO5pmo4pijmy/LspR4qikMgUhscp1y2ymJ6eptVqsbOzw/r6\nOvfeey9CCC5cuMCNi5dp7+xy/OQJnt5o0m32sEoVXnjhBT74s4JGo8H6y1fGoueappENNbRGhRVN\n1/D93L24XC5z/fp1PM/jqaee4p3vfCf79u1jq9NCLzrECjTmZkjTlEKhQLlczqmTQ+fAXq83pmpu\nbm4yGAxwHIf3v//9GOU6na01bl65RHt3m+XlaW5sbPO//OZvUZ5fwDId/Czh8Il7aXY7TE9PE3b6\nCJG73eq2RTAsdKqqSrFYRNXyba8VSuxubo/Rgt9LQP8vRz5ezZ6ZBZ4QQpwFTgFfklJ+hrxg9C4h\nxGXgx4bPkVJeAEZCeZ/nVQjlZRlUNHj5ZY9M0ZmfMclQwUhRFJWdToyOxlSlTBD26Xd7KMJEZgaq\nbiKkCiLN9YeUjCCIkJmCG7SJU0kUh4RhTJLkPOUkS4miAV63Q6/bJkPiewOQMZmMkDK/WGs62FYB\nXTcpWHYO38wUFKHTbm3hDbqUShUcx8ZyimgFnd3dDXQBQeAN6TAGxaKDqmRkaYIgQxEmfr+FFG5+\ncU4zXNclTSBLUvygSxynDII2sYxQNIPBYEAceRiWjt8LaLU2kVFK1+0S+RFZnBHH3dwBwbZx3R5B\nEDDwOpD6WKZCkkp2mzucefY7bK6s0e52kFHC6tYGna6Hogh8Epq7PtudDpmIiNyUoqNyYLnO6lpI\n2bARsU6lUuP5s03uOTzLynoTz404fnCCViujUZU8dGyRrZ5HrVJCmipLC0XarZSLV9eYalisbqVc\nu+LyjQtbfOv8VdZe9FiccVCF4K1vWGRhUdD2FK5d6XJzvcXy9AIvne6QeYLJmSKaVuTxd8xyfKGK\n15JsX01ptTUSqRN0MzZ3Y3wPJudTnDLcXBvQCwIKsw6mpaGYgiyKmZ/7/laLQ5rCJ4Dfk1J+cvjy\n1hBlx58WbTc8Z4jjmDCOiMKMKAqGN4ZcSyeOQ9IszotHSt59iJOQKAqIk7yLkEQxqqKPbzSqqqKo\noGpDhJKSiyQrmolAJ5PqOGCQMs1Ft2WKlDGSFEUBTc07ZciYJHRJ0kFOhSImS0OyNIAsv1GlaUrg\ndlm5/iIikyBUDENFVQRp5GPpBjJL2NlapVIt0Fm/zsDt4rWatHtNsjDGz2DQ65MkKX4iiYRCHCv4\ng5CdHnz5O+do9UNEJhkEIStbTXb6A4Rqc/nKVXzXQ1MdOn2XBGh3XFx3wE6ny85um1a7y63Vm6Sx\njjvwEAqomoGqG+y0N0EFP3Dxo5gk9nAMjd7OOoE3YH3l+riDFg86SCFQdAfTckAz0Q2LrhsThSkl\nu0ChUKJeLFN0TKarDUzboWA7lCyHomWjGDbVchnHcag4ZUqFMqbuYGgmUpAfS3JBYomgWCqgWyZV\n2+Fj/++/o9vujIOLUcCXB08JQtVhhH5hWDTa0+1LorwomCYhUr4+nOc/12NYP4vTFFWmDNpNegOX\nQRSCqqHoGkEQkSQZvU4PWzOwC2UixUSYJTJVH2tB5BTSXH8gTWNkmlPJvL5LEsXEUUQYBGRpOuw6\nK5RKJdI4wTQ0kLfdVoCx4+YoME7TNHcgS+IctRP6SJlro2VZNi4DCgmGlicsvuthmSq6gI2Vm8SB\nj21aCFUlzmKiNMqvI2GETFIUoVErl1DIeN+Hfoof/6m/TpTqWKUKdskiG5o07H1k3LY83mt9HEUR\nvu8T+QFet0ev08b3XFQlw9AyNFMjjAOyLKHX6+G7Hqai0e900VSVWrmCpRs0t7Y4e/oMW6vrNK9f\n5/Jzp7l46hS9ZpOvfvFzPPPkk/nvfn27zKmU8gHy6/ijd6Po8xrFMqSUH5VSPiylfNh2NA4U57kZ\nrPPmk2/D0PrM2HWkVJFpzNfXn2HN3eTqxjXKqmDWSbmxfpGSa1EXsLJ1nX0HHqDV6bEerXLo6L1o\nKlSPTHP2+lV0kQuf3rP/Ub6zdg2nuo+DS8dwrAIXb67zx0/8Hp3mM3zyG+dI90+iLVf47HMfp9sL\n+NCxd/PE536Xkw++nwtbp/ngm97Hn3zhS5jNgPum7ueC0UdrzLC6ssW9M4e4ufkik9YxfumDf5+v\nnHma3VpMI8nYTXzWr2zwmVP/lrkjVba7N/jIT3yIN/zkB3jm25/BciRIFUXNDRrUgoWQCc9e+Cyh\n4XDu5jWe+9oXmLQOULUb/NGnv0A77vOJr36CirKLJYa0CU0F9bammCoFc0Wb9c4pelGbh08+TEfo\nbHfbnFu9wOnVs0zOLmEUFU5fvki/pxDV68wVKmzvtGltXqPZ8zlSPok1NcG1bhPVNCg6MWW7gkeX\nPhkVe4Y4a/LQ/Q+hq5CioRk6q1GL1PR5+swpDk4dYeX6BoWyTS/0KRsmfd/FPlim6DSY0B1mDh3h\n8LE3EiklEjxqs1MUpMqx+Tdx6ckzOJlGZ7CFVazi9jxC32dVtogsuFWwWVw4QIZJQyuwG3URRoNr\nWzd5+yNvRwsLlMwimmKg6AqQ0Shbr9NZ8ud3jK5RY5fKu7x/t9efeOKJ3PmvWMyF/4dW9KPC0Og+\nu7cYMhIRHhVHR5Scer3OAw88wLvf/W4eeOABHnzwQY4fP87CwgK1Wo3JyUlKpdIdVMzRskql0tgB\nznEcCoUChUKBRqPB7Ows09PTzM3NMT09nWvUKAqVSoVDhw5x7733sri4SKVSwXXd8T0GGOsTaZqG\naZrjQsVtgeXcYcvzPJIkodVq5do3cYxt28zPz98V8fO99udrHTndXhujc/LGjj4uZP1phxDiP5Aj\nRY8KIVZFLgb/60KIc8M8+B3Afzfcltec795lfeN9NUIWjdBgI+SWZVljxNdgMBhrue2dZ7rqINBR\nFYMkFpRLDfS5aUpzc2iGw0R9mu3tbXRdZ35+niRJCJM2q2uXObg4z2yxTnulyXxjiotnzxIGLY4c\nnSMMQ1qt1nh94/xiOA9jIZGqQpgl2JUSQdPjK5/+ErPTi9z36BtxZmap20WWJmcoKDq7qxt4vTam\nbaGXahi1OQynQW1mH2ppAk+xKM7vY3PzBoMg45E3/xVqs49Qchp02z1UGaMQcnMr4e/+43/Og2/7\nUdYHfbY2Nnjfe96DTFMqpRL9bpc+MYMsxi44dNvtO2isWZZRNA0UGVGv6Fy88E0sy7qDAvtq5msm\nwzseBgoGCloGaipRkoxQpsRCkqqCTHv1BalRzJckCarkjscrxwhNtffxymXk17xXvfrvOV6Nq9pZ\ncorOK19/XYXyjHJIAY0sEWy0IjqtiIphYJc1TCGxhUM3ivAjHc1QMQwFVVVy+pUQxGmSW5KHPooK\nA7+PodvIoItq5dBQVYEw9lCEwNCtsRZIJkMcu5JbBScCSYznBxTKFWSS65rkYrxyXJlfnD8Gaozb\n6xPFAZZZwNZMCscfJfCbFKMCtclJ/DAhdHdIExXTsECmZCLCH4RY0slvIBWHJFNz9FKpimkUCEMf\nw9SHiYHAtG1UxcB1mwy8Dkls4TgWntdDV1Uifw2hWKSxh2Za6IqB6dh0tteRmYqQuSW774d0ui6b\nz53mnqPHaNSr9EKXTjdAzTI0RUHRMwI/5damQNU96lmNL154meXJKbp+QLDag8Sk1FC4tLZKuajT\nD2Bto8P9x6t0enW81OWxxxu01/tcvOSz0CiiCwVFG2ApGocX68yWbFZ3ekSDmNRKuHy1gylMnnr6\nGoePTVJtZDz6lv188VNXOb+1zVt+YpY/+eQV3CBgeVFjf7XMy1e6lCsWQSL4/JPX2Vct8vhb51i7\n2ePiNRd3oLO4XKK+46GZVXq9GMvwmJxsUCxqtIYOGHcbIr+a/xvgJSnlb+5563VD2+XrAZkJkixD\npiFRPEDK4c0kk2SpikSANtQXSnN656jum8mEOAUR58ggMcxnFHlnR00RGqlUkEQICWmc5ULXSYaq\nSGSW5CJ6UiLQUVQDISRpMgAx6hIpZDLJnbxEgqqYSJEhkXhul9jzkYqKagpknCBEglAyDN0GIEly\ngceCaRF7Hu5gF7s4TTvZIUtSjIJNwyigaAIZhbkTGhpnzp6iHYRoCFJVoBkmzV6HkmswubSAl7gg\ndaIsxdJVFKETpzH9fh9NU7CcMr1eh9mZpfy3pzG+389RW5GHIkyigUuWSpLAJdVtSCJElhJHAbpm\nEocRumGh2yWSFHb7fUzNwC6ZbO5u5gLiuqAb9CFViGWSF9WyjCxNUXUFIZVxRy9Ocu61oWoMegOK\njoVjmEhVkMYRBdsCISnaDpHroWka3TTEv9Wn3WpSqZURIkXKEaR26Fgylnu+ncCPg7hMEgY+ChlR\n6JGmd4r2/UUbcs9fIQSkIVtXztLp9YiiEEUbaikoCv1OFyWO0QcB9zxaZ7WXUCk49Pt9UAR6qUoW\ngmlbuL0uvueRRQmqgH6/j2Naty2R0wxdUQikhCyh1+kR9D00Q0fRDZIsHgoFh+NgzQ88AOwMAt8D\noWIWHYKBS5Rk1Es14ihBUVUMyyRN8qKuqRukoY+ahdSKJpYMCXyPTAp0Q0VJEiytgK5rBJ5PrCUY\nqkVGzIkH30Co6ETSJpUJpghI5e3ARwiBMkIecTsIHs27IAgIgoDmjVXazW3a2+toZMjYJwlDIiSB\nN0BXFELfx/dDVtbXmKqW+b1/+zHmZyepFYo8eO8xPv/pT1Go13jx8iVMp8zN7U0mJyo005C33f/Q\nHV3213WO/BlR9HVdp1i3qETTXPrOGR49+gjbWytkaoE3HT/OUxsLdNvrJE5CjTJ+2UK5fomrxRV0\nfz9VBCW3xMzkA2z5G7xw/hMUtCmcWYsXb3bIlP1cv/kdys4cKCkbF59DLc+hFnSmzCkGasZKplLc\nn3Lx7FUOTaikGykfeM+7+f0v/w73HjvOemsLEaucOf8sH3jjW/n86Se41d+gGvW4trrFG9/9o1QM\nyYWt8xS0Av/6k/+eX3jP3+B8f5eDD97LlbMv8JajR3lpELNy8xQTVYckiplffpi/+RbB6uoq0xMH\nx3O8YhcInAGP1x/ld5/7Jj9y8hD3KSfZFmu4sckvf/gDyJk5HjzxAKWe5AtnvvlddKM0TVEMja++\n+DWins199+3nq1e/Rtg2+NCbf5SbrYs8fvIjZNk2uxsvIR0d1czYN7+E3OiwVDLxFI3paonnLp7i\nhDiMY1TY9T32Vw+y0TxH1Zjh2uolkljwu//mt/kHf+efYNs2Ba1A0Zjg/2PvzYMkue77zk/eWXdV\nV1ff3XMPMAcGNzAACYAkeIAmCUk8ZEoyrbXDkkNaWbvaXXmtkLxry/YfG7Eb4d1VeEOWHBR3SVGm\nKFJcHuABEgQBEPecmAMz0zPTd3fdV1aeL9/+kV2FBgieS4kg5RcxET1ZV1bly8zf+/6+R6QFnDl9\nnr17p8gVy7zv6Ic59/Jl4qCGnt+gXDnOyTMd9hwL6A0Ejugza81R9DI8+tzXOHboZiIjpGN1+MhD\n78PTUmy+fJqcEKSiApoCd+TyfP7Mk4xNZrisLrHUr5G2LRQlT3F6glMnnsPT18llc1xYeorde+5k\nrJBIt7e2Xj9J9m917FjAfC9m0KvuX7yaUfQjfax8JTTitduH77/TVHbnZ0ICrKRSqZHP0ZDd0ev1\nRimYQgiy2ewoyj5pJCQLOc/zyOfzqKrKxMTE6HOGcrYhGDVMLksSNKMRMJJOp0cA1JAZNPw7lUqN\nwKvhQl/Z9jGCV8CybDY7ki7BK4DF8HXD79/v90eMqZ0NgeF7CiHo9/tMTEwQRRGPPvroiLEx/L7D\nRsh3Y5QN3+/1Hn8Vq3XH9tceq+F9Z+d3/o7a5wefH7/0Opv/0/d4/g+93h2BLmE4Aom+3xgen2Fq\n3uvsRzIvttcFvu8znqsQ9V32HtzP8tXr6P1WwrAplZiamsJUQr7yxS/xD/7Rr/Hpj36KNz/4FlpO\nj1Q2w1ce+TIf+pVfIp/P4/s+rVaLqakpPOOVYyClTGpbCaVSiVwuxyNf/xp79+3l/R/4AGv1Lbxu\nH1vX6ff75HI5Njc3R/JN27aT82lmitTKErlMlrnJafyuQ6vZY/8N+zh0+ADOoI3nhXR6bQpTk3gy\n4CPvepiHP/ALBKrKdGWCmUyRubk5pJQjvy3MBGDs9Xqj890wjJEcLggCLMtiY2OD5557jrT1Xzw/\nf5DxhuBiRZFkdVljqzogjiQykIyXbAwMem0PXbHR9Yiu26ZgBYRej7WlDZq1OlevXGZ9aZVarZYs\nXEVMLDTiKMb3XbpOf/tGAVEUUttqEkURjdoGQeChSIGqmORy44RCEroDRJhEjg/6dYQQ1OurZDIp\nKpOzzO47wNTCPrI5m1Qqg522yOZKlMtl9JRJPp8lnytTmd5L2k6RTaVJpfOkUml0I2GQGKqBkAkz\nKvAHdFpdCEPcXps4BjuVxXcDAjcmcHxCt0+32aDb3WLgdBDRgH63Sb22idOuEXg+cRjjdJNkhm59\nlb7XgViQTmcxDBNVt7FTGiL06Q58NNViaWWVYrFA3xWoRHT6Id1eiK3qOHFyQXM9hZWNJnEkWdpq\nYNuwXg+odpogQyxbx85a+K7H7v05ttoBesqn3/QYbEVII8ebj1fYf7jMscNlMnqe7kDl595+M4tb\nNfJmmhsOTGMrJnpepVRR2X+ozOpqm83liKsvVwk8wf7dOa68XOehtx/ipsNF1tZ8Tp1bpbnhMzEu\nabc93nHLLubm85x8vkksdSIiNq4HvPitKpPzedLjacZyFoXyOJ16lwtnmpj+9/Q4ehPwEeBtiqKc\n2v739/gxsu2SkdysddVC0ww0zUBFoqkkBrSqJFaCbYZP4o0VRgnNWNdsZKwR+C6DQR9v4BIGr0Sb\nSrEtO5MBSIEqA4glQsYJ884LcQc+faebyGcClzACoRgIRUGiIhUVRTWQsYGIXkM5RmzL3QTXr75E\n4Pdx3IBYqvhC4IfgeiEijimWKuTzRXLZEhg2vhgQoaEqAYahkSmUSOtpXK+NcF084aMQU+v1Ob/c\nJq9ZxAqoUmEQ+KhCxUhZbHaa1FptQsNkanKe6bm9WJk0pp0BXadYGkfGOkEQUa2uUmvUcToOi4vX\n6HUGXLl8Eb/doN1s4vfbDLodmhuLbC4vU69X8X0VIcG080lnx0yRK5YolcZoDjwuL63gOBLimKmx\ncabLFYa+hYqqoqJh6Ba6qmEYiS+Zsl3gaLpOhIaiaLhhRMdzaPe6BEoSLdodDGi0W6imuW3cbGLr\nOn/yyY8n0sFIEIY+gYgYxvNK4Sc0zu0xlDWO0r3cBm5ni+bWGlEY/OAX6p/BMZTuxTFJopnrEHTq\nyMjD0JKcrsD1UFFIpVK4rktloszVy+exFR817JHWBBnDRFeS67vvDl6hz0uxDUjFiDAijgSGpqMp\nCuzsSMeCdr0BsSTyPaSIiYKQKAhHr5Mi3vYfC/B9n1iEBJ6L0+sT+i79bnebSZZIB4IgAYjjOEYz\n9OScDX0it0votFFlSOT10GVAq76JcAcYesJI8PountPFcxqowkeLE9ATvnsHbmcxaxgGhmmO6OhT\nU1NMTExg2hZRJAj8RB4rRJLCWG82ubR4nVyxzAsnz/DXn/8SUjWxMlkO3niIwliZjjMgWxzDNQz0\nTB6rWMbRdAaGybt+8Vd+vPNCUSpKEgbCDon+RV5pGsB3Ng0+rCiKpSjKHn6ApkEURZzYWqYQ1GDc\nwgsU+m6TutdnY3mLtaUNgriLU1Kp+U1ansPFpQa7cguoL73AcniSjeYmwspw6eKzLA8ke260aF5e\n4/f//q9y7MgxpN/FEB60W0xkC4xZk9ipInLQYmbhHqq9AXfe/CBup0a2spvcWJb9qRnUMYu/fOIp\nhNbihr3H2Wwtcf7KacZFic7aIlgZ3vPwB1luX+P5qy+Qvio5uXSZldbLLC2vooagCJ38sd08tvgU\nh6Z83v3299KMqlQ36lw79TWeWt6klBtHU8BExdB1DHRsI43RNTh+5GYyOYVBtUarKZgdLxH1W5hB\nzEsvLrLibzBZPoamp14li4njGDGIMPOSdx1/Dx23xtt238RtNx9E6n22GlvcefQmDu8/SlM1yKXz\nnFu7TL8huNTahD48e/4ch48e4fium7l6+SqWJak1agxkxHhmnBfOVJkszBAj+a3/+rfBFJw5fRLd\nUrE6GcZSKuVigX4/JlIinn7xNPtKFcrpWcbH3s3pExe4da5C31WII8H8+B6eP3GaC1dOMDaRojA9\nzdzkHLWtLQa5eTw/JA4aBIqF1m/TjzzOri+ze3KOYpTB1yH2JdXVPoWCwUunn6O6dIWT7dPofoiv\nZFjauMza2hq9gcO4Wvyxni8/zWNYy3yvx3cyDzRN4+mnnyaKohHQMpSkeZ4HJMDA0KNmaHjc6yWN\nqqGHUKvVGpn0DsHPoTxt+L6alkSIQ3K98Lyk8dZoNGg2m/R6vZGB8HA/hqbJYRiOwKQhM2cIYg0B\nqJ0+Sjs9a4bysyGTyLbt0f4N983zPHzfp1KpjD77S1/6Ev1+n3Q6PUqdGwJR30s6+P2Oz2uZH6+w\nJ346PWh2AnWpVOoHes0w2e71jLGHwzTNUUKe4zikFJ2pygRqymJ6/25s22ZjYwMhBHNzc5iR5MLp\ns4xPVMhPlMlUxvCJmZye4unHnyAlVSYnJ9F1nZWVlRHYo+v6d8gDu9v1x6/+5q/zrvc/zGZ1i7yV\nxozkCExsNBpUq1UymcyINZfL5Zjcu0BpYpwwCDgwvwu30UYlw5HDRwmjAZoREngd7FyKbGWKG++8\nh4WD+wkVSXVzi0o6T71eR1VVGo3GyEPMtm08z6Ner48M6g3DGJ0D7XYb27b56Ec/+l+kaT/EeEPA\na7qmghIwVs5x5VqXqVIOEUV4IqZg2LQHA9KFHPWaT5y1cJwWu+ZsTp04RWksy6GbjlEczyEjSdtt\nJzR8VUU3kgjiVqNFLp+n1+2QL6WobqwTCR/DypBOZ7GtDKFM5BuRItD0GNOwUVUTr9Njz55D+MIl\njiSxGxIEAqFGCfCkGChKjKLBeGWGMHRRpUoUdlEzY5hWgPR9hB3jOX10AzrdJoVcEcfpoasqmqGj\n6Sq+4zNwuqBquG4N3+lTqkwlrA8SGUwsVFxnQIxAc1WQGhsrV7EtE8Uw0KJ+Qic1VXw3xLQz6KZA\nGxgIGZHNpsmkDDZqdRRUVraq6LFCTyQ3RGKJYsTISOILgR5JUrpNZKnMldI4gc/CtMqVZQ1NNRgf\nU8GFfQeKuJ5HKq1TyWY5t7xJFA6Y25vHdyR1Zwsp8tS7NfaNTfI//69f4CMf2cfytS4b1TY520AK\nyWY9YLwIUtggPB5/ts7uqRyGoaEGKs+fuMqhmyq0Q8Fdx2a5frGObRjcdsim0/AwhclS3MTuwUTO\n4uitZWxFcuLEJrfdPUFXxkRhSGl2DD3bo9fwvuu8lFI+yXc3gfmxse2kjIllgKIkFzRLpogNA13T\nEzYSAmRMLCWKVJKEJSFAjTCMBGyKogAv6GPGaWw7nSjI1Jg4FiPfB1UZAkqvXCCFTIoalAgFA6GA\npsWoUYSm6wmbIFaIY0msxGioKHGMIlWkiImVeKSpzhfGaK4uAYku3nfjxDtJVfDiGBEGIC0KhWna\nvRU0PY9pxqiqRNVtek6XfLaEgZmYLgpBhEOzryAjjUGULDYVJLauY1pQLNikdIViboK9C7NMTk/R\nbjeRoaQwlsW207R6fRRivF6boCuRkcAwLBrVJZzKLJ3OFrZtMj+1QK3fQ9W2F9uKih9EpAs5UoaG\nG7hkMhn8OMJzPdANCrkip84/Qy5dYGmzRhwlkbaa1Chn0ihS4kUhmVR2m3GULLCDKETIxM/AdV2E\nVNA0A8PQ0GRMLpcjk0oTioAoFoR+RKu9iYgjMuk0505dZGtrjenJKRQJiBj0xMsqKajC77gRjjqA\nnkuv06TTaCBeY7D4d22oUiLjGBFBGPl0rl5m9fxZcoZO6PSJpYqeMmm2qqRMiz27F9iqV9mdy+Bs\nXiSOBpCaZO/R43iqimqorxiGSok/cPFEjJQxgUhMpb0gQFe17Q5YjAgiTMNAxhGu4yQ+RZpOJCXh\n0MRxO61M0zQ8K0nMMwx9m9WnoMQKg8Ahb5r4AxfF0BEStr0ykbpKFARoRBC6BE4Hf9DHNlWqzRrZ\nTBFXhOQq04mJd2QQBQG9zStUchVUpUCsJR5smvLqeaW+hg0w6h5LOZIRGAsG2ekxygvT1Dc3WL9+\nlU6rhdptoes25166zPLaGl/8+jcRikTT89hpC0dvcWHpKfzugCO33IfM59jYOMV6bZl7jt/Ff/cv\n/weCWIJiwA+nEPh+Yxr42LbPkQp8Skr5BUVRngY+tS1fWAIcm5UkAAAgAElEQVR+EZKmgaIow6ZB\nxA/QNNBUnQmzwPlWg33zN7Ky/DwP3/8RwjPPcdprMz5rcvnSJsogS2FugYP4hEfvJeg3yehjZAqT\nCDWFZkAmN43rmBzUjjB+ew5DUVm8foo79s5xfW2L+9/0QS48+1UUvYOnpVhtr7KgLmL2db7y+KO8\n96FfYRC3uONoiYAB7zr6FsamXqZR7zJenqU4ZjClTHDw1nH+n0cfJy8neOnKGRZSN6D1Qyp32Pz5\ns3/J773tv+L05TMoisbi0nOYe/Johkd67BBmu8OtCw8ijIj5TA4vbqKZDr1+i8B3kk5xt0Gr3Wbq\nwGE22k9w4bmrvKz2mT8wxqnzF3n4fb/FxZN/zfjYLvoNj1//h/8WQ4uIIvGqhkZrsM7C1BSiYqE2\nTZ48f4o7brgPy9SYKM1z5sufxQ+bjIUeG7U+Dz3wXh757P/LvTfdCkWN+w/cwSc/+VcsTBUpTJW4\n9MxLlMZnaZYl73jLO1n/zBeYKuyi1txg7777eerbXyJj2Ciay0Pv/CD/8n//deZKOe695yHOXDxP\ncdYgbRv06yt4bZ277ngLYXONrK3jhwpnFl/gbXe8gy9+7tMcPnID11YucOn0i7zt732Ywdo1sjPT\nHJ2aph7GSA8y5SKFlMKJxSvkDJc37b6R/XsXeOGFxyimyjy1dYbb77yHK5uX8HJw0DpAz5Wce+kJ\n3vPgw6QqeeArP87z5UcaO31svhsjZeffPyx75PXed+d7vl6i2mvNlXe+frjtU5/6C+5/8330+/1t\nL5lE1rWTOTRkEvm+PzL9HYIocRxTKpVG3kdDkMmyrNHnD/2OTNMcScnEtsR5yCoaLoSFEKRSKVRV\npd1uk8vliOOYwTaj3jCMkaz9td46Q9bFENjZmaw29DcKguBVLKLh30NWqa7rSX2jJP9XVXX0ewx9\nknYmrf0gPlTf69jtPH47JVTD/dv5utdlj70BrB2HrLcha+z7je/3myUSPm3bKzGRicdBSDaToeP0\nUY0EqBky5QqFAu1QMOj1EzB5ahKnvolmJKCK1+vTbbTIZDJ4nke1WmUwGJDJl0cg1s59a3c6Se1r\nmbhhMl8MRSVwXNJZcwTc1Ov1EaA4ZP6opkFleiqZK47LtUtXKJcnyOeLeN6AnG3g9Nss7NnN1MIs\nlxevMLkwS8/poymJ7chQOgqvzGvFMhJGOIzA0eG8GrIFG40G6+vrLEyPIwL3x3Bkf/bHGwI4Aokm\nU6yu9MjnbeI4oOsJynkTR4Q4naRbqypJhGXfC1l/6TJH982hp8p0ewPynS5RDCIM8UwNw7Qg8BGR\nRspK0e11cPtdOh3JwGkwMbk3KVTcFqWxSdxOkHjCaAq2YYOq0m21yRULbKwvY6YLaEj6ThfbSuP0\neqTLY/S7dWIhUBQInB79fhvNMDHSNrKXeCylcyk2N6tk7DRO2MR3FTadTUKvi6np6P0UUbGAYWTo\nN+tk8iWaYiXpMIR9et06tllAxAF+4BCLRNYWBTFWKjFd7XYcdAtMI4WqKPRbPQxTY+BEZDJZyrMV\nSn6e8ckpCoUcz75wlvV6k3bfQVdVQhElnXBd4gcCA51IxqBqYCikNY1qN8SyI9A0FmZsVq+7xHGK\nctri2rUmh28s02sYnLtSxy5YIAKefWaTG/aUWV7zyKY9bGHjiIDbjk1y5ukODV8QRi7vfGCBr32l\njghUqushaz0HSYa7jtxBr1Mll8tRW3Mo5NOcOrGBouS5dLFOqWQRqia23efWGw/w7/79t3n4/eP0\nGh79qokXetSaEZW8xcWTVbqbBsWpmI6jgYyYKb4B7iAy3ja1ToAWw7AAK5FrSUEsEsbcKwyfbYNq\nVSaeNoqKphlIX+B5XYAEUNLldvERJ6CQHMaxS1ASI+0hqKSoOoqmIkl8WRRFR5UQ8wpVWIlDFCSh\nSDpgUgFFxGi6iaLE7D5wlHanxvKFRTRToxXEhAKyaZs9E2XGysXtC3ofNQbD1LANi07fwQ5DlAhU\nGaNJiNEw0nm6ocqV5fPEWsy+XWNksmluO3QjUxPTZNM2oZA4nTau10NV0hioCNfFTltoqsXq6irV\nzRV0dGYmylhZC+F7pAyNhfm9tBpb+O4AP4ip1c+QtnPEmmCsUEQzLErjlcQgXNNQjAxuKDB1MAwF\nPxJUG1Xm5/aysVWn77v4bkAsJaqikJVDbxpw3D4pK00QhRRyWcIw8RDQ9KQIsHWVfNpEVTUavQ6t\nVpKs5YcRa5u1ROKEQhwKBgTomsXv/vM/ZHJPif/xN/+bUVyvijI6PkLIbVozQIznDBD+gCDo0283\nqK5cJQr8n8ycfwMNRUriOAE5Lr98ETUWqEgyqRRyGzycm57B8weYpoGIJJHfo9sfIPU0OavIhXPn\nKc3PkSsUkiJcRETpNL4zwPV8pAhHCwFD04niKKHw2woijrEMg/HSGF3PS0CfHQX+zsWGYRgECDTN\n2PbVUDD1ZOEg4sSgNUaSymWRUmJtF2b+tm+C6w5w61UUERGKGGEkC5oo9ImCEDPvolgWRraEGnn4\noQtxgCYFUiYYivaaqltBASmR6g4ASVUZiekVhVBTMDSb8ZlpKpPjHLr1FqQ7INjcZGtriwM3HGZl\ndZXTFy6wvrVJL7QJooDMeIHZSoV7b72Lhf0H2Xv0KKEcULbG6LabqJqJbRlEISjfEQrwo4+/DYm+\nYZkY+TTz2RvZOn+G2QMHOP31r1IbdAisCM+0mZ89QOgPuHbtGt1CmaXLV/jIe97LJ09+klQ1j1+/\nzNGFu/Eij8kJD4TGtWqbY9P78AaL/PlzVSbKKnPtdY5MT/Inj3+Tm+47Tlo32VuZ5uTiixz/4AfQ\ngojP/fUX+eDxN3Hp6kv89dcf4b//jf+WWnySJxaXUFMtxudyLC1dI5PqQmYev+sztVCk3m2yhuDB\nA/uYKt/A3P6DfO3so8zm9lLrRMhUmrwn6NgGQgxwW8s0UndTmtrD3hv3ETk57HSaTnMLX5UUM0W+\nLZ8jlRsjLOnsL9/B+ZdPkS1bTJVuYonHOdtaYlYICoVSstgNPMLQB9UkDH2efukJVpbWmY5sKrMl\nmi9nsMbH6NWrTEaTmBmdZj0is7/MfD3mmW8/yy27buDShUXWrBa//+Hf5suP/iVrW33a6irvfuh9\nfOvFs6wv13jeO8EdNx0EdYazL73AO+67gS898R85dORtrH1ulermFR647T7Orp7k2088STNsUO7N\ncia/wZi9QCZn0qlVCUJBJT9gZrpIEJboLl5nplhmcXGNd93zEO+65UHObywxYY6x1NmgOHmUc6e/\nxdj0OM8sPkOpV0RqDsX5BU6/fJmet8z+3TdxpV7nthsP4zdq3Dy3l4HWYbW5xnuOvxfPXUcSkrLS\nP/K58bM2dgIQr7d9uGAf/i2lZHx8fJSqNlyEDr1/+v0++XzCgEilUiNGyXBROwR7hkDSTlCl2Wxi\n2/arZG1DAGYIyAAjBpHrujSbTTKZJFI8k8kQx/EI0BoCKoPB4BXLgm0m0/CxoUxq+D2G+yq3gf8h\nSDRkQw3T4YARKOb7PpvVjZEkb5guZ5rmCNDyPO87AJLXM8X+XsdpJzi08z2GDJKfFhaSsm16PgTV\nfpDfYGd4wJB59HrPQWoEMkykiK5PoTzGgRsOcublC0n63toaQgimp6fZUnVWrl1nbW2NPQf3c67X\nwEyn2NzawtR0VhavjUzZFxcXqdfrFOYmR56OUiYAVcayabVazM3N4YQ+mqKgqApfeeTL3HL4KL7v\ns76+zrFjxzh36hS379qFsg0uWttJsLffeQff+OwXOH/mLJ/+87/g3vvfim0nliy+P+Cuu2/n+AMP\n8Fdf+hJWoYidzXD23DluOnIEr+cgVEbn0hAga7Va1Go1FhYWKJfLI59SIQS+7zMzOcFXv/xFKpUK\nrjvA/DHWED/L4w0BHIntC5amaaixIFJ0pCKJI8HAkwhiwtjl8OwU59ebTFZMvMCg0XWoBKCpKv2O\nS9fvUy6NJSeMPyCIYsKwj1XR6LbqSE2j224S+CH+mEtey1PdaFAZi8gXdIIoRoQxMToyAqnHdLqN\nBD0XAT4RqVQKxx2gWdDaWiGVyaPaWSLPRbElWW2GwK2joaHZBQwCAk9jesZCVXWKcQVNUQgY0G/p\nuB2fenOZWE6Tycb4XkL367U7xJEklAlo5mrraEpi8BWFPlJR8DwHGSuEkYeMYqSSgnCAaWTIFXPE\nQUipMsn87oMEYUQQ+vTbDY7f9yZuueNuvvaVr/PUCyeIUXEbbcI4RGzfmPw46ZhksylEkMgtLCTz\nswX6/ZBOxyGV1dmqh6yHDWZnM2xt+Then3xBYXN5QKjA0aNFqs0Bx26t0Ov6RIOYA7vneeSxC+w/\nksVbcbHzKiubguPvrLB42ufytTZTxTyVsSJbaw3MQocTF+o4/Yi52QIZU6dge0SdiKubLscfnCef\nKvK//J/f4t0PzhD1IZNOYc8lxcBAi9D0LKurIfv2mVxe7kMccvRIjqee7fyEZz+gJGwTVTN26LIl\nCgqaahEKH1VNwKVYiV6FnCsoSCI0XWLoJmEUEITbcdlqwnCRIkbRYoRI2CVRpJP430SjDpmqqigy\nRhGgaFHSvFeTtKQ4jomRGJpJHCfm8nEcjxD7TNpE0010PWbvDbcQBrC1sclW38cNFPaloZTOki+U\n8LwBIk5o0oZhE0QOaQNazU1K2Tzd1nUqk/uJ/T6BTNPzfQ4fOsAD99w9opim01niOMIPXKIgwNIh\n9LcjR8MOhh6Ty+RZX7tGvbqBoVk4nSatlEUqNYlu64g4RMrkhiX1FJpuYeoanuNjZdP0uw6FUmJO\nbaVUFMUkY+mAiQgiAqEijQyGnWVjaRln4FJIF4lNQSgibNNIboiBjxdEmGrSTWx2O3iBwAsCMqpG\nezDA0jViTaHqdBNpkVCRUUS1vU4sDKTU0HUtYVnlE6BYVW38SFBbafM7v/8HfOgXf56HH3wvpp2A\nCMQKQoTEUZiARyqI0McfdAhcDyJBp9VIfHD+jo2d3gmRIggjge26bF74NnLtFHv3ztDL+ASuR+h6\ndLtdLl29QLY0juP2sRSNpcuLjE9UCJprdL0eaGmam+P4lVms8Tm6IpEouK6DCAcjEEXGMaEM0baN\nfHEVNAmB8NF0FUOVyCggQo4Ao8BPzltFSaKdM4UiiqohEQy8AR4qtmZARqfba2PoFoO+w/hEBVVX\nWF69hum5ZPwBV86eRLSWSWvJ/VaOTaGkc1iGSRAJsnYKqVuEYQdfzZL3B9Ct0h07SFZ6xGSI1df6\nYiXJbKr6mnJiRy2cxnrlP9r2AiptYe3JM7/7APN3vxkhBB/Z7gwrkpHp/rDQGw6DHL4qSE2VRosJ\nRROvW0i/kYeu6qxfP8vh2+4nuN4ERXLjvhkOlG9h4OmcvXQZH0ilVLxLNdJzk2RSKbxUngff94uc\nuvJt0ntuJWq6vPvN7+DRZ77O1Rc+z30P3E+v5hBFAVlFobtYp33sDH1vkn/7y7/Oxy6/gBW7zBYn\nqB6f5yvf+AR37b6JyQNpXlh9FDPexdEH3s63rj/KTOkW9k4uM7f3YaJul+rGRYJYMDNzE/FWm1PX\nzuP2a6w0X4JcyMv6aTafXWJvaYIXr2/gOXUeuP0hFtdOoM3PUCkVsMz7qHk9ysUiMxM3c/nlM1hm\nNrnWui7tIOD26f28sPQcC5kS/aZHbibLLr1Cmy1eWtnEIUTPlIjjiCAIWF+/yq6FgwRRArB2uk32\nVibZdOvklTy/+tu/w+OPfZl8xuR09QSzhVl6UY9MlCK7Z5737j1E/doKT2+eZ2xmhk5vk/VaB3Wq\nyOHivTx+/hQyFdNodXHyKYyxDGmjRSo9zqf/7D9zdO4BUr06W1zjt377j/nTj/9Tju25k8XlE/yL\nD/8Bf/XYE5xZPkFuz17qPYe4k3TkJQPmph7inmMT7CncgN//Ilq3SdBucSVqYBYybLV6GP2Q8YO7\nubW4QVhJYWxZ3DR7jAO33csjX/0EPbVKoEs6XoqBexI9VaLudbh54W5eOPckd069HW/gs39uhpXW\nGmN2+Sc9/X+k8b08a17LStrpj/NalsZONsvrMZ5e+9jO7cP3/I3f+A3+8A//cMQGdRxnFIMuhBh5\nADmOM2Lt7PTkMQzjVfva6/WI43gksxkyeYY+RN1ud3QPAFhbWxv5IQ0lbUNpmeM4FIvF0TVx+H1s\n2x4xXIZSs+H+DmV0Q1bT8LOG196hdE3TNNLpNN1ukho9BISGqXJDAMd13RFzaQhEDWV1rz2WO6PP\nX89rarh9531gp1fV92KtvZY5lrBhX3cK/a2Nnfv73eb0kK27k5U39Dcafvfhb5kE4KTR1CSJt1RS\niSKPDiE5YoqmxZtvv4tLJ86wtlEjiGBsfAplcpL0Vo2nPvsI93zoPWxeuogaxeRSaZrNJp/5/Od4\n18+/n43NZXbtnmF5ZZG3v+chTp48ycz4BFeuXOHGIwf46le/yu23387GxgaFjobrunz+85/n7rvv\nZveNB3jsiUexpML6hSscmt2DnjaJNZ10PoeVz+CpAfVeh8O338LnPv4X/MI/+DB6PsNAjRnTDXbP\n7WF69zztQQsR9CjYOo3FRZzqFv7uOYShoBop6tUqqUKOWFXpegNk1+Xw3gPEukq726WgmokdgCJR\n0hateoPFi1fIWxZevw/pH16uNvQYG0o4kd9pf6ILYHvqSkAa3990+/XqmVfJNvVXP/63KbV7QwBH\nUio0nAApY1RFEgmBZphsdD00KYhijRvmisSGheMHLK+DqQXMVjKIMGLx4iUqlQql8hhOr4edsRGx\nh+slF671NRdTU+n3B0gRkS6OocUqrX4L3Uy6/u3uAF0LEVKlWd9k4HbIZ/Nolk0cRjhun2xqjP6g\njhIrFEplrjXXibYXXnY2lyTieH10I0scC3zh0e1vkU7lSWcsVCXxNrLtNMgK5dJe+v0qU+F+JBr9\nfpWtWpdcxqNZ72AYBrn8GH4U0KjWKKSziDAicHykDrEfIlImnt8njgWGM8CwbKIMBE3JzMIslqES\nBX3KE7tRNI2gMsX61hrN2ho333ozi1evc3ltHU1PjkPihaJsm5cpNLt9YiQHd+UJBhrNXh9ijb27\nMnT6gnROx++H2KZCpxUhJbRdwfhMnlQmIlAiWh2w9A6WZaPEGk+fvsoDb9rP48+eB2L27J/k1Kk2\nVkFn2sowOW2wWfVZmJyk3eriexO43nXm5ywKGY1sNs+Vl6s4gaQylmLlaptHvn6SY0emuHyth62r\nCOEiI4W6GzI/kePFC+s8+NZprp/qcmy2QoTCeAkqGe/7x579jY+E8SPlK7GiMSJhumzHDMdS2WYe\nhWiKiqJqScoaIOOEIoyqEYWCOAmJJ4W942L2ipmdlAJFFaBAHAtiGaHI7SJGSQCmWIlQ1G2TQRkn\n9FfFT4CjKEKIBNiNFUlo+Khqws4o5krkSrv4xpnrbLUFtgGFfIpcLoOUiWGk7zXJjs3Q6daT7kC2\nwPjkHCKQpOwcvU4dM51D6CaT8xOUSyW07X1XVTXxe5GS2EoTBCZp28R3LSxdwfcHZK1xgsBhPGdT\nSC3g9HvYmsDpNlgHxvNprHQaIVVMPQdaUjRFoYbQIiQqWiaDZqSpt7vojTYiDpmbnsKyVSwzS78X\nstpdJpsdJ5fO0HcGSBEn5862l0DgJh4zKUNHRaHXaRNuexCYpp2YnEsojhUwdI0wjOj2HJarVTRV\nx48idAWyhSyTpQyBP2BuZp61+gb7Z/ZxfXWFvufjugqf+vhn+dM/+Ri/8Zu/xtvuugdFUYhigapo\nKJoOAoRMPKtUFFQ1IJ0yCP8OehztLNikkBhBROvqOVbPvshkLosShlQmJxj0e0SOQ8pW2dw0aWyt\nUK+uYChpZmYnMGSMLnw6m2sUxiYZG8sQh+uE9R7oBdwA+p4ANJRBd1T4JZ1GfbvrG79qvxzHIZvN\nIkQ0Yh3tLJillOj9AdmySRRGyTVDgUAVmEGACH36gx4T4xU2Vq8SdnPUV5dxN5e4fuZJgmunmcxI\n7IyGqqvIIMRX+hiWTak8SatRQ7HSFMZKr0gbhmarUt2Wzn5nPPIPKyF57fEARr4NyYIaJJIw3mZJ\nGq+UKlrMCOz+afYl0GTMwp4bubR6ifJ8mXzBpe/o9DZXONse0Ns8zXuP/WMK6SyPdlaZMCu4cw3E\nIGKrepFew+Dn33uMb549z/kzX0eEKf75L/8u03v2sHL+DG/efz+V4jo3lm8hv5DmP/ynj3KTIfiF\nN7+DJ7/9JVavn8XaaHNw1yG2Tp+lfHCOYqNAq+yjB0ktdKHxDcYLR+g2NtBCm/VwBTJl2tdeIlUq\nMpaHxxvr6IaJZc+gRTkafZdjUzlmd/lsVls88+xTPHD8DsSYytrpDtm4x5mtS9yTeTu+U6fb7zA/\nPsPlpQs8/Lb7eeyJ5/nMFz7Ou3/+56hoLhs1l8aGZPbAJFfOP0vlwAy/855/wUf/6F8RxypoEXt2\n7abdb2MIHU2RLJh5JvbPs/jyk7ipiHzXp+k0uVbr8P73fID/69P/NzdUduPEHrIn8Dcb2HmTe268\nm8hzWW60mKvMEip9Hn3km9z3lkNcOB3yCz/3Vl54+TT37t7HiTOLzGV6XK4tcrm9xrvv/SUmDJ//\n7Y9+k2pzhXJkMfDh1NJp3vame3jwyCFeuPQ8n3v6BX7tne+jtOswV1cvUVtt8s2LLzI9+zx37r4V\nwyuQ0TMUdldYWl5jJdpiIV3iem+VruhjrffYm9lFPD/JV772KYTTJWtPstFbYXrBRTcU6oMq49k5\nrl08w7Q9ww1zB1hce5Hb5u9n9fmvsFpb+UlP/zfMeC2z6LXbXxuVPnxeNpslk8ngOM4I4BleCz3P\nI5vN4jjO6F4zBGyGiWpDFpDneaTT6RGzZ9iUM4xEZlOr1SiVStuNiARUajQaBEEwSl8bgjjDa+iQ\nebRTxqUoysgvaSdzaQhu2bY92j70sBl+p6HH0XB/e73e6DspikI2m6XVao2eP/R/Gkqahu879Hf6\nQaRZP+zx22k4/bMwdhqSD1li8GqP0eFjw3/69nrBtu1RYl6/36dQKFAqldh36Aauri4TEGNkUpTn\nplEkfPXLX+bv/7Nfo1AojMBH13Wp1WqjeTxktSWBMzq+75PJZPjaF77E3Owsa1evo6oqf/yxP6bd\nbvPwww8zMTHBJz7xCaQW8cDxe/l3/9O/Rt/2/UpnckxOThLHMWYYI4XCxz/6Md505928dOYMb3rw\nrezatYtyucxgMKBer48YbEeOHOHFx7/JVq3KsTtuA03n2uVFLMsidP1RmutYOkuj0UBP2xTKY6jb\nSbOWbSEVhae/+TXcfgtLS+GpPll+ML+pv+vjDQEcCSHwAh9iBT/QkDJA9SNEnESME2t863KVWxam\nCSMFqQUYVpq2P+DC4hX2LuzCD336/R6WraGbJn1HMOj3QZFsXF+hHbrsmp7FsEwymRxBHHPiiW/x\ngQ/9I9zA5friFXbt2pMYoHoDDDVD33FIKSr5fB4iFTdyCXwfO5Wh0W0yO38TnW4NEfTwgwGmYaGb\nBlvVFdLpPGa+RCVXwOk00SyDcOBjGjkMNYWRNrAsm2zJQgQCUzcIonkWFg4TI9l94ChxFLG+cgV/\nY4DTabC1vEylMk4YxxCBplr4Tg1vMMD3ffK5IgOnz1x2Gjtj4TseB4/cTrZUxtZSoGmQzaMZFuXy\nBJp2hpuPHiAUMevNOt2Oi6qbCBGiqskNQiFG1TT6boS0IlKxgi41Ll1xSeVUAiKcfkwsVCan84R+\nm/NXYuaNCC8QdOoDZidS9AOVdtehXE5RqaQ5/exVbM1AKCrnTrXI2Srz03muVtt0nIB8ZPLo82c5\ndmA351dX6QmfG+bLXF3qUhi3uOuW3XzzxQ3azZD5Axa33TQFis/UeJmpOZ0XntpEtU16XYfVOGCy\nYvLsc5tkVBtPhhj5mPUlQar4RuhSKwhJkl5GDFJHyphICkQco6kqQsRIESMVUFUJIkJsAz0yVrYZ\nJUFyo0ZHGgpCxsSxRBHDwkXfvpELZByjoCG3mSlhGGLJxMxQkSoyFgh1u4svVRTFII797QJLQ9Nk\nwlBSVFDi7SIj5uLLF/nTv/wqCAtFg7RlUimOAyql8gyN5jqZdJFIOOSzGcJAod2pMVGZJYi7WHoO\nLBtJzPjEFPlsljj0UCWEcYiq6Bg6SWpaLFHVpANjm6lR0kkw6BFFEU46hTdw0DRBOptha20ZhEd3\nAOVUjsAN6Ls9Uqk0ke8Q+z5SN9isbZLtZ2nZKUCl2e2ytlVndnWLg9MT+Cg0ByEdkcYXm+SyRQzD\nouN4DBwfw9CIuv3EN2rbnNiwzCTJSlHxWh6ankiUDF3lYq+NqWrE6Liuh0QlDAIy+Ry2oTM1XqSY\nL9DrurS6fdJmgTD2kATsni5zdX2L0AUDg//w7/8jzq+5vOWOuzCtJB1PN4feCzqxZgIqmmqjyL+Z\nFKqfhjEsuvqDgGLYY/n040hnA7OSJWOnaDcbZDI23V6bUj7Fof3ToMxwdek6q2tdmlshbrtGJpdn\ndm4BbdDk2uUa46UxdM1m4OsMIgNhl9HT+VdYgIpCFAlUPRpFCAMoEqI4QkYCEYTboG486mYFXiIJ\n0BSVdqvBYDBgbLKCounEscT1Iwh8AhlRLBVptjawpCSs99k4e4qNk19EeANmxieAkDiTJVSSNNKU\nnsYddDFtG2IN2zDRDQMt1l71eyVSWRIgeXsMFwQ/8jzaIQMZSfKkHC2A1Nd0zAGMbWbT63mP/DQN\nL/Q5fWWDo4cfwKtepd5q0q5fIbNrDrG4wb1veysXl59Etj12T4+zeu1ljMlpnnnmSWb23cjx+wOq\nGxeIB2vclLmVptZhKb3O5tJlZu0Jrq51ias1KjdOoqPxu//kH9O+cIqnH/sqkQh55NzXeeDt7yRw\nezDnk+8LxueKZIXD2UGfOXuMsfQkT594nr37Q0SnxgD+sAcAACAASURBVN7b76EkTE6ceAoln+Wf\nvPOXCELBS88+jSEkdi9gb7bExdY1IldjrFigVa/z0c9+gTfduJ+zl8+Rm57gznvuZ1fhJjJaHhkG\nfO6xj/MLb3kvj597kWb1PHO3z1OrVfHEOHcdmeXme2/nz/7sz3jLO99Jd3mNpavPc8Ntxwl1DzPW\naVer2Nk8hmGhSoMnrj7LWFPF9ot86K3vp9HexHd6FNMZ1PqA+++4m7VmB02N6HciasXrRN0s5VIJ\nU8/SqDdZr1dZ69VJhzapIEMmX+Mzn/oi9z50P5eXzpIydB5fPM2uQo7Z8Rl27yrwR5/4PyiYZSxM\npm+pcDR1I1947DE++KEbWK6f5p677uPU5TOk4gLNl68jogZVcw1XdpnedRPrl6+jzhksdXscdotc\nX7rGxO5xnGqfF5ce46bcDN+4+CRvv/sBCDpMz05zz43v5ZmLZ6mtV1m/fpXS2AR9r4YYeHzwQ/+Q\nb3z1KT7z9H/m+OE7Of/UYzy47zib2hujYfB6jIvXMnz+/4zv55Pzep/zet5GOwHyoVxKN3T+9b/5\nV/ze7/3eKPXMdd2RBG3IuBmCRSdPnuSWW25JFrfbhtOO44zMqIegiuu6IybNTo+hXq/3Kj+jdrdF\np9cmjpOEtlwuN3r+6L6xDRTtTO16LbtHbl9vh7KjEZt9+/W6rtNqtTBNkyBIghlM02QwGOB5Ho1G\ngwMHDrC+vj6SMA3ZQjt/x+G9bmeM/HC8nlRw+Nzvdlx3vna4/8P93pmo9ipvo5+isRM4Akb1wxBc\nHG4fzkmAfD6PZVkYhkGhUMDdbmKGYchgMKA0WeHwLcfodruEimTu4D6qaxu43QGXz5xjfHycoc9V\nNpslCAJefvllxsbGRvPS930cx8FxHFZXV5mfnEaTCideOMEjjzzCL//Sr2KaJrVajWPHjjE5OYlQ\nghEwqSkqvV6LTHGMiYmJZM4KyT/7zd9m99w8J06d5IMf/CB3HL8b3/epVquMj4+TKua4cuVKYuq9\nDSB5noemafQ9D1MqZA2LS5cu4bouc3NzaJrGWKWCNLSEZJYyiQYeZiypb2xQW7pGMW0iCNBzGvxs\nYI5/4+MNARwZmsZ4Ok3X9TEMHcePt5kvBmN5g04/JIxiXBFja5IgkvS6DpqiEscK11dXWLy2wp13\n3Ijr6eTGMvTbNQQ+Fy6eo9sO2bN7lo3qNYqFSboXn6PdqLJr7iae+tZX6LVr3Pu29zHwPbwgQlMk\nzd46M3MHWVu6hlMq4wcDevVNdh88RrV6HbfvMjG3G98dEAURhhHhhxGRN2BmYQ8KOp12A11q2NkC\nsfCpTM7gh0mCmbLdvU0ZGciaCC8ik5KkLJOB56NoBvlsgdJ4kbndeylXxnnp9DPUmy0UNAzLRlEE\nkRgQC3D6Ll4vZNfeWSrze0mZBuXKHGOVGVJmGqkrSCFRNY0x3aAoyxSKYxy7+36am5usbl7mi5/+\nIs+dPMvyRoMwDNF1FSl1FClp9yIiIZkbt1luOpiqgW0q6KhMV2zGiilOn68ycH1K2Qz1tRZWMcPh\nIyXWNgQpK2RuMs/i9R6Hxue5omxhCp2JjI0yVmLXTMSZC3VsK2bXVJY4sNlVCKlVl5mZ0bGNGS6v\ntZjMjnHpSp0rV3vkUinOXWvReDKi2464YU8Kz60Tk2Jmbw5FkUS+yqGbU1SrfW69Yxera5vUGx02\nr/rs2ZXmvrfewNMnfvLRtAmAs53ApMiE4SMlShwRaxBHYkRZVgwbVAUZB0CMpujEkSSKkg6RYZrE\nQhDH25poJUnWUjUFRdGQhCTR7YmZchSEo06RqiiJV4iSsFNiAagxqiKIhUKSPaUmDDUl8UAiloTB\ngM987Rs89ewFVKmjagJd0UibiUmdZlg4jpMw2XQFTcsixIAwcsmkCjj9PqiSyBBIoaDrBoYmMQhA\nT/T4RBqqFFi6hqoqaDIBsAzDII4EpqFgaeDrGmE0wLIyBFlnO85WoqsKQkgUdDRTo+v6iEhgo5Ky\nMyBD7FSatGlT63apaCk6bsjlpU26XsyVzSVWGgEHF+ZxhIViqIwXy/hBgJARipToSGxdw9vuKmqG\niakbSKnQIYkgFQgMxSIIPcZLeVrdHm4YoUmNQCoYCugpi5yhoxuSXNZis7qEiCSOL1iYm2Z1o8ZY\nbpx6p8NUaYKtuEZeLdAZ9PnYH/85Tzz9JH/w6/8UzdYwwzSalnhcRXFMdzDAdRz8IOAN4RL5ExjD\nwtU2VTob68ioR76UwpeCaNAnjmOq1Spa5JOxU+iKgmro7Nm1QD7nkTItzp49S9q2WLm+yOz8Ap5q\n4dsBUpF0Gw6r9R6F+UOUbQth2Elhvr3AiIMQYoncNrxGUZCRGHWuVUMfFdrD836njKHT6YCqkCuN\noegGqqZi/3/svXeUJNd93/u5Fbuqc0/OYfNi8y64AJEJLAOYJQZRlKxok+KxLZOSKFmmjs7TMf10\nnoIt2dKzApVI2qQYRMIQCYKIu0RcYHOY2dnJebqnc6r8/qjpwWAJkJIeLYKWfuf0menq7uquqltV\n937vN6gKVtNGlWQcyyKdTPDsI49iqApv+JEPk4rqTF18Dru0huaWiMgCNAUUHdPQgTAVTnNfYjeZ\npomzcV0SQiIIvp1C/b3okLdAo9b/QcvAX7S+49s/84M2ELixbNdmf+8gV5/9JrruMbx/O2eXF3i9\n387+fSnKzDKQ2U/nvjiXXrzMnt6D1NNN1tYvsZK7zKWJS7z58IfpaS9TWlfYub2TLzz+OAf8JM1Y\nnsHROHbXDlZr52lXhnjs7P8kahusGl28ae8bWKrmyJEj1jbEY8+c5OieDi4EM3gLHp17R1i5tkK2\ntEB7d5yoYXHP4beTa3icmniYTKaNdeMKv/OHf47nrPOJf/fbLC1e4msPPs7Q3m0EmLz33jfze4/9\nCSM7t7N7dwS/tESys4dd+0Y5c+Yx7v9XP81k9grtyQ727R3giWce5drlc2zbIbE2ucJgewormmI2\nN0Uf/ew9chDdjVCpqzz1/GMUmgE89CBvPfFOotEoyUyGal5QLM7Su7MNcyVPdDBKJtGP4lv09m5n\nfH6eU8tjzBSm2bP7EFFURm/aTb65Rn45R0fbCGfGniWSkvnR+z/A46e/iTiSZH7uDD1mine85b0M\njRzgkW99lYrq0dltkF3P47hFzj7+KL/xsU/y+U//OYnOYf7iwU/xiZ/8GHcdvYtnLpyko81kfn6e\ng72HKa5NUTcNDDWOJeuoZpkzT7/Anbfcw5nZp9m5/VYK5RILlRnMskZ3zxDp5RWqfp7bD59gan2O\nnVYE3xA89dRTkEnT2bmd81ceprvjNkYGh2gvxbl07TxmUke5Ci+OXWD34G4WKjnGC2vf7+b/mq0b\nveW2giw3AhktZpCHvzmIbXkPWZaF53kkEgl83+fYsWMvi7yv1+vhPSgSCX3mNtjIyWQSy7Ko1+tI\nkkS1WqVer2MYBvl8fpOZmk6nsSwLRWoQiUSoVqsh09m2NyVOrd/b8iJqMYxkWaZer28CSi2mTsvz\nyPO8l6Wztf7fCpC1gj16eno2AbIWiPFKBs5bGVlbr/c37vcf9Ov696peUggELzuWW+WAW6V+wMsM\nolvLo9Hopt+VkYix/8ghlpaW8DyPXfv28vxjJ+nt7eXhB7/Gm970JmzbxnEcMpnQ9uXcuXPcd999\nXLlyBc/zWFtbY35+nmQySalUws7ZfPrTn8b3fX7pox/jv/7hn3Do0CE+8pGP8JnPfIapqSmO33aM\nL33pS/i+T63ZoL09Q3t7O+l0mlq9zt9+7stceOEMh44d5ZOf/CTbdu6g3mgwPj6OrusUi0X2HNrP\nk08+yYkTJ1heXub5559n9949SJJELpcjZSYYv3KVeDzOyOBQ6Fu04XPkKxKZzg6Qw3GV67osz8zj\nWXUiCZ31ZgkMiQjatx+If65vq9cEcCSEIGGo1CyHumUTBAGaJGO7Nsk2g2TGQPUdyqUqAonAl+js\n0FnJVogbHng+/Z2dXL4yw4kTHVw8f5m2LoNaKUDxJBKJgPm1capFm23bFGbGp8m097CgLJNdmWVo\naA+rS9OUowlS8RSxZJT5uWUSmTa0SNiBr9cKdPQPUlhdRdI1ekeGWZmfRhYOmbYBao0cupRiYX6S\nSCyBZNtIgYQPOLUSZqodP9BRVB8h+bhuE8OXQQuQPIFsKAjPxZMNYlEd1TAwzQSuU0fRY8Ri7Qzv\n2MVTTzzI/PQitm3hOQ5CEdTKFZpNB10GhIJhxkjGUyTaM9QbLmChoSJLGr7nbsh9fEwzhut4JJJp\nhpXd3HRgifEri8SjFpVqfcMcL2R01Bs2ARIrWRdVkkPAQJbxPcgXbRYXqkTUCLIW+lV1dccQMiwt\nOCyt5elvN1iTEggZnpu8RL3u8tZ7dzM7VWA+N0fd1fCUgJW8Q1+jTmabIBrRyAYSyZTGxPUcvX0x\nsoUCugqSFjJwhnvjpNM6Rp+L1RT4kkDWZK5cKbB9e4x4yqFUUkgkTS5fWGFgMEq7Bs3mOpKs8cyT\nE9/fxt8q4SLJKpIIEBsz6q5j4QdS6LvlgmU1QEgEvoof2EjCR0YQaCqeF2rcvcDHUBRURYdAEARg\n+S4EEpIiQgYfykYCUQAbUqZgI0YcX6DpOgE+UuCiSCFIEwQboJMfICkqiiRwAMkXTFyf4fPffJTy\nuo0UhDN8kiwTjYCpCjo7u5FFwOryDP1923DdGs36GpqiEMFjfX4aOaIRTbUjaypCjpDsGkSXQBYg\n5LBDEY3o+J6DkDSELCMCQiaS4xDICpLroyhRNM3DclVk1UH1FSQ1gu8GyHo4Y6bpBsVaBTkSpVmP\nUKg0qNsexapDUq9gmjGur+RZLlo0vIBosg1PcxCROLlGQHFygUASuE5oahxKv0L/AF2WQuq6JFNp\nNGnaDoaqoGkKslAQmoShaAjFp9mwKcg1ag0XVVfxPUHSjHBwVx+dmV5OnX+WfbsPMjlzjd62bUyt\nTNLf1UchX8S2w4j2WqOKlBDEYqArBu5Kg6orMzM+x0987OP8wod/lv17XdRIMuzg+g6u75HNF2ii\nIiuvBcbdP261JFiO46A1iixcv0JHKo5uajTtBnKzSnGj8xSLRAFo6+wkYhjYnsu2UZ3xq9e47fWv\nw3YCGo5DIENcD30qmq7K5FqDgV2HOXDHfUhGlJn5ecrFEuV8Dl1WiKgSwn+pAyht9LFVVcWxbYTy\nEtPmRolEwwmTIIvreeymE6aRoFByygghk19Zw1B1VlZznHjve4lFTaxYH7oMd9z+NrzCIvkrT5Nf\nmiTwHJrlIqam4CGImAau72FbFpoZp1FaR/vfKAfbOrQQW77nn8LQwXJ9du4YZnF9iiOH7qC8XKAv\n08XowHY+8/RfMCj3MToa4eSLj3LryL08dPJh4sk27jt2L3/0jS/y+//mv/DlbzwKtsLeo7s5+eKT\naBGPRH83i6sBldNXuPtdb2VhbImL184i4ilG+vbQXZP5yjf+FE/zWVlyKTsr7O8dJJIMKFQ8bj10\nLxcmniUqKdx6/09x6vQXcC2Ja1NjeEaNEWMbXRp0bvuXVLpXSXX08uILT/Hi+GkOHztGd3uaz37r\nb1DKcxSyNX74fT/FYy9+kf27bqUUOY9dFdy87+2sry+SatOYW1jgwpmvoiaPoXfpPH/tHLX1Bsqe\nfurT58lisHfXIO1SAaFF2XPsAMePvJ7ZqXV+9APvRRdp7KrOSq7AqdPPceLNb+HolVswb4pSrK8S\nj2o0s8vs2XGAtdVlIqpgsGcQZy7PuFSn1KgSj8boiKfYv3uIWF1mseqwkJvjHbe9kVXH4ljvUbKO\nRNe+g8ga5BoOyVgbyUian3vP+/nMw3+FWrf47Of+CG3HAJ0Fl3feeQ/nr89zrTTLLbvuwRDryCkT\nMRsQa4sxOT/PoaEjrKxcZijZzW33v43x8XEOjJxAj0uI1TUOHziKVW6SjHUhAp+pxTn6nSTFaoFH\nVhdIRDP09fWxfvkK5sh2fFtiYWWWvF3lYNdB2qUaZ+ef5a33v4/HL5/Hsys8fuFvEWb6+938gZeb\nI78SYPBK7KOtTJLvxDp8JcbJjWDFjXWjJ07rXrGV1dF63gJXPv7xj/Mf/v0n0HUdz/PI5XIIITYZ\nES2vIkVRNs2mfd8nm82SSqWo1WqbrKRWcmYLXGolqLXAIl3XWVtbwzTNTUlYa7tc16VarZJKpb6N\n9eO67iZ4sNUbSJZlisUihmFsMlNaJtyt72v5L7XS3VqAWAv8aQFjrdSsG/fn1mN0Y6LbKx2PFkhy\nI/D03QClrZ5ILW+8V2IshZOz33FV/9tr6355NVZVy8BcCLHpl9UCMoMgICIcPGQUNYYnabT1pbGU\ngERbJxE9iqZG0VQf1xFIpo4kdOoNl3S6A0U1WVtbQ9Qb3HbPXVwdH2M+u0rn3qOMLefQjCipzgb5\n1SWsbJZ6KY9uKqwUqqzMTdOdiPHCqW/x9a88QL5W4c477+T9738/n/rUp/jgT/0E+/bexFe++CWy\n0/Pcse8II7sO8s2vfI2UqmDqKkVcjt5yM81mk3MXxvjcn/0eRw7s4sM//xFGDx1ivVhEsqzNBLhM\nJsPZCy+wd+co2dlppsevoUYNBkdHsJsWbqXOycvnQ9DT92laFoVikf7+/hBA8hxyuRw3pTvI9GXw\nXIsHxs4iRyUczyGhxsDlHySh3DqR5vt+GHZ0Q303Sf3WJMdXe+/WdEAhBN/dJenbt+d75QP5mgCO\nJAFCkulLRmlKAuEJBkYlpq7YrC812TOcwqm6VESTW0bSeL0aVFyiKZmb9rajKZAvFhjuMPlfD5/m\n7ltGWK80mZ5eJW7oJPQoi7MWDQIi+gwVu4aoVaj7NvFkiunZJdzA5fjtr2ds7Axz1yd5749/hGJx\nBd3QyK2vkkx1QiAjR2Qcz6aQWyaR7mY9O02pUsS2feZWXqC7bzulQp5YLEYkEWd24iwDQ3vABz+w\n8ZoeRkJBlWNYXhPN07HcKoZhgpCRIi66FsNzfZrVErFEBkW1MOMmGb+D/oFduK5LpZbjsb/9Ehcv\nX8ILBMggRRV0I4lhRBEyGJEksqRhJuIbJrkqzWYNz4OoEaPpOggkIpEYsqxz2113su/IEX7+I79A\nqVojcCWE8NC0cPYC38eRHQJJRglc1pddbD+gu0vHdiR6u1SEopHLWdiWz44dMc6eyaKrGkvrAW3B\nKv3JHtp0i8V8hedemKTWcNFVCashoRsgTIGa0ClkBePVAmvrVY7FOhgcNVlerRE3TcpWEywfIRxk\nX0LzVZyIzORYnsE+DbckY8agmvUpNx1K1QAfj0hC4dp4jWqlwb5tEbI1FxqvhSFKsAEYbchWNqfZ\nZQQbszNS6C0iAgXPcwjw8QMXX5JwLR/HDfACH1UxkGRCkEiW8X1wXRvfd1CBQA516a5nE4iwg6LI\nGq7i4nkOtl3HC0KGgyL7BKqKKikI8ZIZnwg8fF9BCiTyxRJ/+qWvI7kKfpg1j6nItEUkiraDrOm4\nbpNApFEVB8exkSWZIBDokSTCrZDp7SDwBZJhAhpmWy+KpoX+Y6qCobQ6iD4eMggfApCQQqmaHKbP\nSYGCL3m4kkTgSHh+A98BSXHD7Y9EEYqOLKvojk8zcCk3qowtruNaLlMrawx0dCLrFq4bpVoE3YyQ\niESIyS5BzSIIwsG9DDiSj+f7JDf8ClQhYXk+tuPRwEVXdUQgsBwfT/gEXhO/EVCWGhAIzKiB7QeY\n0Sie79LdFsVzJfSIydnxq8QiScavTZBMJCk318nE4sRiEk07IJlpI59dIhZLUbcqxI0OAlx6O9sY\nn1rAdQWaiPBb/+1P+M+/8QlSMRuR7IRAIRZN0dbeTTozjKp9/yOZ/7Gr1XH2PI/582epLMxhWzk6\nBwZo1GySATi2R29vP7mFWXxNRQKiqoGhCxTRZHCoH8v2EUoE1/ep2g4qNeq6QaWqcMe7f4z44E7s\nRCeeLNGJyvA2Fada5vyLp2lUqziyhLdhQBpRw5kuQTg7rG90kj1vg2no+3hu6MVlKwGBFQKHxUKZ\n2dlZBkZG0eMKEVlHV3R8ATcfvw0znWS9nMews0jRFBWtDdHXTbLvMGazSnTmm8xfOgf1AiJwUBWN\nYGO22mo2iWx4XfzjH6Tv8Npr4ZL9Pah41OTy4uPs3n6E5174Bv2GAvE0mb4O4qZCeXWNM8WnqboK\n14oTDI/uxFeKLLlNPvHjH+XHf+eX+eGjt3N9/hrr6iKpuszNe4/w20//Db/9o7/O+nKSx57+c/b1\n30uiP5Tb18sV5EycD7zlX3Dq9Cm+fv4Ub3znm7ip9wRf/qvf4sff95M4ImC/fpTxaxcpl+ZIJYfo\nM9t55OKTuOsWb3/HO1HXI/h2wKdPPcS+vTs4aAxSLpfQ1YAHH/86h47tRbci+KvnmZp4mgFzkDNn\nriB1e0iOwh377mR06BB/8dB/IrdwjXfe92G++vQfsWpkibZ1sKcnwbnL5+nfniAQaSbXLtGV6aRY\nazIw2Me1+SVcSeL581NcH3sAo9LO8RNv5NjRm1hZu4KvwIULFzi+b5TV61dIpg0MO4YyInP//R/m\nf3zhD7j1LUdpzpZ46OJTDOkJbr/9fVzJzXD17KMcOXYbX794mmvVCY4M3kqzOkFJknn65POcOHwf\n/f0ZzmVnYTlL4bM28TadfFsc0Uxy5omn6Tt+DwfSdzC3OoeouOhGnUoliTWfxY9LSIlODm6P06Gr\naHsOs1gtIWk+nuJgiWkiYjueU2f87DhFu8Hxm+8mXemjsNqgs6+L/pqE3L6T3v5DzKxPMnzHXlzX\nRnJfRzOXJ6WlyJhdzM4VWLue5SvlLxDL7KCts42btx3mb1+Y+n43f+DVTZFfi3Uj6AHhb9Z1nY6O\nDrLZLNFoNOwLbLA+WmBSK82p5QmUz+dJp9ObQJIsy5upZbIsb7J/DMPYBG1s2978XAgq1THN0MS4\nxfoxDCOcSNuyL1tG3FsBr9ZrsiwTj8df5qUTBMFmclpLvqZp2qZUqeVvI0kSHR0dANi2TbPZfIm5\n/iqgUGuf/XN992qdBy3A78aSJAnPZ9ODKqIlyaS7NplsIyNDFCrV0Osnk9n0wEokEiSTSSqVCrqi\nsHvfTczPz9Oo1hi/8CKjg/2cO3eO0eFRkFQW7Rkmp6fo6unh2oXLVBbWeeihh1jP5ji4/wAffNfP\nMjg4yP/zX36Xt771rewZ2c7M2DUWFxdxRMDAzm088rWHQukjPrFolI6uTjp7Bpm6fp0vfe6vcLQo\nH//136B3dIRyeQ0jorM4l6NUKoXbFolQqVWJmSann3qG5cUl+gf6iUajTE9Pc/HiRZKJdiKRCJlM\nBtM0SSQSIZipKfQODXCot4fxSxfpSg/x9a9/DUn2+DuhL/9c31avCWfJiCHT26Oz/VCSvfsSaFGL\nfNHittsz7NoRAwRdO+H4sTbsbnDrNms1CxPB4kyB6+caRGST8cs5OtrauPBCiYmrVRpljYPbd1PM\n+WRSKv09oZGiqkq4Uo1KboXx6zMsLV1heW2B558+xeTkJPe/9wPUrRJrc8voSpyGZdO0HRaXp2h4\nHkJWqVRKzFx/kQCJejOPUCQGdxwgFk+wtjjJwuIU2YVFOjqGqNUtGnWLSq1EgEulWsfzfOq2TbUa\nJhU4XiiuVAIDfBB4BJJA0sOoY0ONoxkRZFXBjBl0dQ3xQz/6IX7m5z5MRI9TrrmMX50FyUEEgrau\nHrymTTSmYzWqeI5LvV6lXmsicEMabCDjOk184eN5FqoeoVYq8+EP/QtiuoxhKriOT71mhdIpglAy\n2PQoVS3cwEOXYG2tge008YSKmVLItCsoQmVpoYERNVjONqk2bRaW4ezMKhXHw24K7r17BxawUq3T\n3x5FUVwCLcJq2WKlUqIrqtOo+6yUbGolg2hUZnK6yvR8BSUQuE0HXVYoliwuXy7gqxINy2etUae9\nJ8XQoRTH7u6lI6kTlQV2IaAroVJruPT0ZVhZq5Huem10UvxAhMCib4WPoHXTDwiEIJAlDDOFbpjo\nuoGmRpCEjO8FNC13U5uuadqmxt7BxwtcHMfCcSwsu4brNMIIdj8EAoPABaGiKlFAwnbDRI16vU69\nUQ1ZTE5rAGsT+C6B6+B5Dk88f5rf+pPPIfkqAS6apNBpauwfzXBgdzf33dTFYMpHl218t0kAlCs5\nbNfBDXyavk9DCOLd24l2DWJmOkj3DpHKdNCeShKLxVFVHc/3cVwXx/WRVQlVkdBUGV0VKDJoKmHi\nmOwii7BjoioKIpA2fGE0mp5H3XZZK1YZm1zgzKVpHjj1Ik+fu8b00jpz2RJRM8F6wyJfDhlIHgH5\ncpW51RxruRLleuhH4Ps+vhCokoQuyzQaTSzfpVhr0PSssG06Ho1GHWfDCNJzXKQABBKeC5IaziYV\nSmWKpSz7tw2RK+eJRVQuT06jq9CWSVKolpFkmWx5hUQyxfXZZYb7hsB1kZQUxWKRuNFJuZ6j0Whg\nC4fdw20kEwqS7KNKBh/9tf+bMxPXqJWzeAISnT30DO9E05oYuv7dmub/IeWD3wSngWU5NC0PCUF1\n8Sr1RomGG7C2OE99ZYlzFy5TL1hMXp0m096BGtVJdrfhBB6uF4AaxdLT+LF2AlVBNXT0ZAzLj9KQ\nMmR2HCc2eBO+kiBi10g0ChixCJ6soMbS9I7spCnAChxE4OPaFk2rEabceC6SFvp3CUXFReAJCRuw\nggBHCGRXwrZcGg0L17ORJZifmqCwnMP1BFoiyeCevTRllabrEzVTiEQ3sh4jIrlEAgspcNB1HXfk\nBB3H3opvRFHMyIZ0TcZFQvFrFP0AW9ZRHQsrUJGCjYS4jccrSRL+PiU2pK83PhC8+uP/kAp8B29t\nG+nEIh/94Ie4UimRagbEfA2nXmV49wE6+vdxV9/tvPD439K9PYFYrJBcukRE1jnS1km1ssodR99A\nW8lE7e/m1NhlfvqWN9CZjjE5tUin6OXilUlObRZWCwAAIABJREFUX54may+yzYjx4rMnWaqUMDp3\nsX/XdgrLq8gLebqHEkyU5ihemiZYN6i5OYxsjZ5klLMXnmTnwC7ueveHUOKdVFIBQaPMv3vXR1Eq\nFrPLBdSeDCfPfpXM4CDzxVUWZxY5emw3me4Mi6V53njnfRg5lzsOvY0vPPAX/O7v/iLjV59Hj7bz\n6S//Po2izZC/jYHeYc7KMQxTYXp1GbVUxJUcZN/Gd2yy8wX8Som1lVUmrzzJT//YLzJ0cJQvPPhn\nNByX/vQokyvPc9eBo1yYn8FPy5RLDeaXLtNpdfDM+Sc52DPA7sQuvvzUE+wfuY/UyM28sHaR66cv\n8sO3v5Nnyqc5uu8w2ew8xfU8ZjRNxXJo642x5NeJt3dyy4HXUS2V+a//8b8zqGncs+sIOXeBvh37\niPRpoFfBF3zsRz7EvuQIUcME4TEzN8Vkbpa8U+RitYwd7+bM+WdxSiWuXvk6KWkHTz70VXqHd/K2\nt/8omd40Vy+MYVeaHDt4mFi0jVjbLixN4skrj3JpaQpXF8zMXKOxXiTVO0AjkeNc/nkujV9gV/sw\n2dIqPQmF+flFomYvH3vPG7/fzR/ES9HwW4GYlyUHfaePbwAUrwY0fSffpOC7XLteLUmttXyrkbUk\nSagbbOZarbb5mXq9ztzcHPV6nUgksrldqqrS1ta2KWlr7YNW5H3rr2madHR00N/fTzweJ5FIbKaZ\nhWloJvV6E5+A7HoORVE2GUKt3wgvAQ+yHCa7GobxMsNrSZI2DaVb/ZsgCDZlbC0wSlVVotEosVgs\nZLxEIqFUboNN1dbW9qr769Xq7xNusNXzZyvzZuuyrelWN677Zcf8BwC7ajHqWsyjV3u9BQwqsoGu\nG4CP41jYTm0zMa8FKrY8qFpt10zEUQ2dSCRCXDeYHLtEZ3sbMdPAclyMRAbNDCWSTqPJzPVJzr1w\nhnq1xs3HX8eBI4eIZ1Is59boHRpg1769ZBeXmZ2cxmo0MaImPUMDrC4skYzFsV2HaDpJZ88gejTG\n2toKVy6e5XV3v4H+0VEc3yHAQdPYZOK1TL9bPlZra2sEQcDQ0NAmw8+2bQqFAvl8nmw2SzabZW1t\nje7ubo4fP046nebKlSskElGuTYxxbeIq0dg/lb7v975eE4wjLwgYuinD5MQ0Wqyd4Y4u9JjNwlKV\nvXv30yznODcxTSJtIaNTtmxihoTW1JnJN8Et0u0JYoagmK/SZSRolBp09kY5++w15tZr7NiRZCXr\nYEl1zEiU0nKeW7b18vz0GlLEY3bqCutLWe552ztYmpvGdkFVPeYXZ5m8epUDR46S6WgDZEr5FWwn\nvGCVy1XqtTyZ9iay6KJsldF1g5iZolRcJZLeSbOyjqbGqeWz6AMjKJLAdm2E5yAUDaQAI2JiRHQ8\nIRM4DdymhRFPY9WdjQQmB7tZx/NcdDOBIjQULcL2Xbfxyf98iEJ+lsLaCnPzWcrlAqlGO3gNlKpM\nRIki6TLlUpGIqqEqUfxAIpAChKQjuc7mBcWImnT0dNOWiJCtWqE8KQhnKGTRmh16yfPCCYLQgFlo\nTE0X6PNksqtNIoogEFCt2+iqzp7RKGvZgFjUQpd9ArnJmYvzKGqA4Wucn8qh6WAHTfo7TK7P1Ynq\nGmZEZXTY5PKFLLFEhHSbj+1raIag4oEleTQaAXtHoxgKuE6UbKFEvthgcrLBkf39rKzWsJC55VAn\nz11aYXTAoCE51Cyf5y6Uvq9tv1W+D57rAxsJCpJPSEYMNodTQvhIUijRElKYPBjYVgiWECBrGqqi\nQiCFzwPwAwkhZCSpdaPxQAhkSScIxIaxdgC4yJKKJBQk+eUU8vDmHKbuIQuQAkrFIg8/eRYZCVf4\nyIpMV9zAl2wO7h5msD3D4tIsth0lqkeQRIAkKyiKhu25KBttUJZi+EgEsoZumqCqmKaJqoAiges6\n2LaFa9kkUu0ISUISG+1RkkK1nSPwscCxsZoWgdBpNC0atSqBJNNo2kzMLHBtcp5S1SJbbmB7Lu2p\nJGbKJNOuIPkSkYiG49k0LBfHCwhwUCyoOR6SpCK7AbbjYDvh+aJGdNwNJp/rhh5Hng2KauO5Abbt\nIIvQsDD0sZFgY8YuokZxHIu4rmLoGo1mDadmQYegsL7OzoGjXJgYJxVPMb+yRHeyj1x+HRmZYqGM\nYRgsrSwSi8eIpwSq1oEvyVTyZXbsP4DDGcamcth2HU2o/OEffY7f/+SvY8RkTDOBLsvY5QSe++pu\ngEKICHAS0AnvFV8MguDXhRAZ4PPAMDADvC8IgsLGZ/498DOE4aP/NgiC1walKQjAFzQ9Cc8P0J0K\njcIitWYZ3dBIGial/CrXr08wODiIVCniWE1ya2tUGxV27NiBL1QMI4ZlN9B1A1lSKOebyBEVJIM1\nodO//QjtowepSmpocO05+L6NSyjrtT1BuqOL/pEdXJ8YQ3XqL0u+aXXWZQRS4OPYoSzA9cOOnq7r\nOJaLszHDC9BoNNB1nbHJCe448SaMeBwtlkBSZIS2EVWM8or0f0fVMXpH6Tv2Zmr5FazSOqpjETWT\nVMpFZEUHQqkqgQhZ2K+J6aYf/HJcQTI6x8mJEief+wM0TK7W5rmjvM6b9r2X5kSBQrfGX1/6E97/\nM58gWxln2+FenrlWIjkzxdHRI1jZdWYXJrjlnrv54tcf4K13vpmCs86v/tnH+akTP44kd2IutHP5\nhT8mzXYembhEeucAV8cuYssaXUY3aa2DS8W/5OYjH+WvvvAb/NJP/hpeZY1kpZe8miWh38Rbjr+T\notxA8cq42QLnzz1PdfswlWuncNpMnGQDbUnlZ+/51/zN2AvEHAMlHWV9YpHJa6tQLDLRscSbTnyY\nz5/6b+zoOsiLU9/i9m17ubA2ie+6HDvyJhIRjeWJVd7clWbMvU6n47A0N4dldVH0HIxYF6oUMJ+7\nwluOvptD+3+En/7IXdx18/387Ls/hKlozJdWaDSX0H2Le47ez/zCJbxmA9+tYJg6A8l2npmY4tqX\nv0FCF8yvn6K7cycp/Qi9eyucjjrkpizklTnkQObZ6+fZmxkmnYgwEOviL5/8Y27u2oVVK7N7z1H+\n/Iv/F048ACPFB277IJeunGR5ocGypqOU8zx78XEUpZ14Z5xKKcrb7nwvj515hPb+AZbnrlBq1GlL\ntjOfmyMlhjFNaGgO48tzJCMDLK8uUcpmufV1J8jnx3nL4eN8a+ws1y9d5+0/9D5Ojp1m4sILJNv3\nUnOuMLlwESVmsDy3zF233YG1JHG0t5eZ1SV+6o738OfP/A2nTs58v5v/ZrX6GS32zY0MpNdabQW5\ntkajDw4Osra2tulV1ALEhBCbCWqmab4scayVWBWmGMsb9hDq5kDZtm10Xd+UrbWqXC6zcv06pmnS\n19dHR1v7Zj+j5W8EbE4kwkv7uQVStaLdgc1BeetvS2Z3o9eR53mbg/lWEpxlWbhuOHnZWvfW7/vH\nOo6t49Lqu75W28/ftbaeFy2G17e/Hmwy09rb+uho70TXZWr1MlMzV9m2/Ri9vb2bki9VVzb9t1Kp\nFO0D7SxPzbJz23bmrlxjZvwiyXe/g+OvO8a12SUcG2JtKUpr66TjCXRfMNTbS09HJ4M7t3Hbvfcg\nqwoPP/ww737PD1Nt1Jm6PMb18TEcCQ697hjLhRxew8J2GsRSSYb37KRt5BCW6/PXn/+fjPR08m9+\n5RMULItUKoIIbFaWpikWi5tgpaIoxONxLp85RzqdpqOtnVqtxvr6OkNDQ9x3333IUmRzf4XJxRpz\nc3M8/PDDZLo7GRwdQQ7qfO7TX8Zxa5TLAQr/9Kwavhf1mgCOhBSwNtUgUOM0KhV8T2Jbb5K20SR2\no0TDbnLXod3YDZez03NsG0mjyhqNskx7h8tCfoW667KeF4z2Z1idzbNtWxvL2QK2J9jZ202z6dKh\nQ64REFPiLGRLLLQX2Lk3iZWDSmCRz1d5+MEv09HVSSyaoVLOEouniGgGHR0dCHmYcn6FUjGHhEN7\ndx/l0jJSoJDLrhIIjelrZ9mz9/Vk1+bp7h1gfWmaaKKDSq1CZ28/jUaNWCyB26yhRKLYTp2EmsLz\nAlzfIxACITS0ZDvIOhIB5VIeXQ0ppmayG1mSQQqQFAnZFyhahnZFI5YZpmskBJ2eePR/0ds3jFWv\nEolGWZyYpGdkO0FKh7pGtZInn81Tqxbp6O1E0w1KhQL5XA7LsRndtYP1M5eRFY8gEITjy9ZNPYx/\ndzx3E0zyAodMwmRuqo7ru6S6M5QaTVw3IAhcLo1VkJUA31VZWqmQTqrMLpZxHUFbxqB7wOO50wUy\nkSi5XBMViarlcPOBXuq1Jpm4DIGDFouTbIdyvoYZ16iWLUaG4pQXQcpYXF3Os3/EoOHqKKrHyuIy\nblMwOhrlkWeW6YipIDTGThfY1hMh4qg8lqt/X9t/EAS4vk8QyAg2ZlECF993N2ZIBDIgpFDEFggP\n4UoQ2Js6dU3TCIRE4G8xGZQBSUJWQmNeWYTnWhCEMrdw5iX0DGsNWoWQYUMGF67Ex/Na4EI4qzN+\nfZHPP3QKVVIIfB8jEuHQUCeHDmxntH+AqObjOQ28wEdXNWRV2ejMhB0KRYthN8sgQyBJOChEzDiq\nYYKiYTk2mmLgeWHHJWomURIKTdtBELIhXN9HDUAEAdVKgXqpQnZpBieQcTwXy5WYmFlgNrtOzTMo\nVRtULId0Js6RgWFqTYvseoGy7aDYHvWmhSpLRPQodauOoerEEkkibRF6U0kKpRIruTXK9QaCUL7j\n2w7puBnSufFxfZAD6IgqxGIx5nMVIlqYcCGEwHZcdE2l2WyymisR1TU8X2VoqIO5lRVGhkbIFSp0\nd/Uxu7BAteLg+1XaYmnUqMBZKxGLRcjX8iTIEEukiEUyLK8WaUtFsWo2slbn+Reew1ea7N2W4cpU\ngcD3UQL41d/8TX7xQz/D6GAfmpdHquVeUY+9pSzgDUEQVIUQKvAtIcTXgR8CHg2C4DeFEL8C/Arw\ny0KIvcCPADcBvcAjQoidQevAfx8rCAI8X8L2AlzbxrSrWLlFaNYwDI1ydpX5qeu0xwxKq0tojo2k\nSBTyNn2DfUSMGI4XYDkemhoJzynPJ55Kky1WydkORv9NJAZ3YytRfKRQ0um6BL6LR4AQMkKW8XyJ\neKqTjq46+flJmg0r7ARuMbz0HBvb0UPJwIaZaegj0UBFxXdc8HyqjdA0tVarsX3/TURTCSQ1gqyH\ng4UAiSDwUWXpFTvTQjNwhYcyuA9Fz+CJGZRablOW4LouqUgUJ7wCIST5B2Gy9geivMDh/NIiv3Dv\nB/jq3FmKixVWJ2d56sUHaWtvRx3owLWnuPWm17N49VnaOwfolHZwYM8y+aUJDh2+i1Nff5Qju/Zw\n8eoZPnjHO3nom5/mdXe/n317DlD1bK7N5Lmrf4D3jN7Lk6tjHDx8iOWFq9xx+zu4MP8kA9JRFidn\nsIpJptXnuPvm4/zHT/0677rnPUSi/TRQkdfKRJKdLGRnCPQOctPTyKkkeaExmQ2T20Rc0FjP82DP\nCI89/Tht3Tvoay+zvWuQnrSB5ETJ5af5vS8+yHDXNm7bv4++0Q6urF0n0T+Ij8flsy9y7MAdrDUC\nOtQGV8cu0puEyfUGb7v5MNOL10mkVGRF51B8H9lmjc8++Ak+8q9+n6lrL/LU6cdR1AleuLbMkYMn\nUJNpfu8PPsnP/sS/5NL1CUrrDrfd9waCbJmebccI8tc4vPsE11fGmV+eYnj7Lnrbuvjyk/+dNrmd\nzlg/s9MzZDolOga3YWGjC+jpTHPH7uM8c/Yktxx+P+dffIFUPEIxe52xa0tUjBxqsZN73n4blctp\nLCNMAr10fZJSeYWphQXec8+P8WcP/B4fesuP8bVnH8fymqSjEm+49W6++cTDtLU5fOP0SVLKKKNm\nJ0bK5Oz1F2hXS1yavkiPFtB+7DYmpyfYvWMvpeV2Brd18o3lKrFEgoAZUkqK1ZUiw5lt+BWHgbjg\nW5ce4dzVU3zwLb/G5//fX3rVtimEmAEqhBMAbhAEx77nEwbBt8vTWqBHi0Hyap4jW72Iti7buq5X\nqhsNr7ds73dc/9ZB+43vbYFAEiHjOxqN4jgO5XJ5k+3TMrNugUTlcploNBrGkm/416iqupmI1pIU\ntYyui8Ui6XQaXdep1+ubJtuWZaHrephc5XmbqW6+72PbYZKVEGJT+ub7PpqmvRTCsPHbW/8HQehR\n2mKvb13eCmlosYskSULXdUqlUugXuPG9N6abbd2nrya5gpebY99oCP1K9Wr+VjcynV6L0scWS6hV\nrWPeAgdlWSYSiWwepxYYuZVtVfV8VC0CikYs3Ul7OoahauipHkw1QS6X2/S8SiQSNBoN4vE4ru2Q\nXV9ndPdO0maMcrSIkW5DTqcpraywupLHTMRJpkwUXbC67HHvnfdx5rFTxF2NbH6dTHcnb/+x97PW\nqFBYLHDbHW+gO9XGIw99g7FnTiOZOtHhbo69/hY++6m/oGmX8AQo0Rj9O/ZijrRx+uGTjJ8+z1ee\nfZKKlSMdT+BbHuX1Motzq9i1EhWrQd+2XtYbeSwXKjWbuJmmXmryhT/7DENDQwwPD3P26TNYEZlY\nLEZvby/JZDKUrGVSDCSHaYsl6FBNnnniJFK9STKiUqvnQU3+/z6W3+4j9Ervejm7Sbmh2+0rL7Xz\n1nlwo6fRje1YvuFUeqV+2Y3r+F7VawI4koRMZiTJ3q52nn1umh17erl4fpbe/iQLEw3iEYFlOgx0\nt7HH6CA7WUfqDqgXJOyyRywVI6brNNMBlGyO3jrM9FiB7fu7WZkvkTYD1JyGvC1D8dIs23pV+rq6\nKTgWC1NVursltmcSFNIO9ZpDNLZGJZ9jam4diCCLKjOzUziOw8DwDsqlVTra2tizVxCJ2EjCQFZc\nPKdBLJFhcuIcu/ccxQssZMPA8S0mLz2HfvT1aFpkMx670WigqqEPjaZtGE4HAZIioyhhx99pNDDM\n6AbK/9INTZYUAt/D9wKEFF7cdU1DkWVs4M3v+Eme+NpnmcjmQdjsP3qUiUsnibf3YzfOomgmk1cv\ncODmm0nEM+TyBbILy0xPT2FGdXp7hhjuWWBysbQxmPHxPTmMcw/RC6ymgyQEsiohIcgXq6iqRnvS\nJJGMUCw1EZKL70r4gY8IJBRJx/drROQ4esInFgNJCyiWZEzTJNMbRXVcOlISY5M1mvUlhtsTrNQC\n4hqsrVTQFMFS3iJwAnp7Tc5fWueWQ1Gm5ywyhsL5sTLHj3cxOZFncbnOYCrOpYk8Rw53UVxuYrkN\nglgU0/NZyH5/QaPN8l1EIBCSCIGdzRuEQGykogVIiCDA8WwC3wkljZsXFXkDcPLw3ABJKMiETCNZ\nSHi4IVtJatF73Y2EDBWBvLmOUAPvExBGb4vAwxMBgfCRhI/kSnzp0SdRJCW8YEoSbVGdWDLCwR0j\nJFMmTr1GM3DRdR3TNDEMIwQffR+Ej6bFkRSBphqgqDRtC0MImq5LvVqjoSoobWmQBZbroZsmduDh\n41KqV9FVDc9qULNtbKvG0tws6+s5FheXEJLBYtXCdzwuL9apOQ6a4hBNGRwYGESRZKaXl2hYXugv\ng4QLpIwoluuQK+WQUAgCUJoKvuvzzPwCcgCqrjLQ1s5aMY8VhKlzuXKdke5uzE6FUqVK4HislisU\nLdDkCEJseFMpGrVSnkw6CW4D2VewXY/uthSF9SqarrG8lsXBQRIGi6tZJKFRLNVJxuIsLVRp7xyg\nnM8zumMPS0uTKJ6KFvGJ+x7DfQNcnxtD9QcoVebQpDRtqS5SZoGqpSLJMrVak//xNw/wcx98F6rb\nwHcB8eq3gCDsmVU3nqobjwB4J3D3xvK/BJ4Afnlj+eeCILCAaSHEdeB1wDPf69Pl71NBEOC5YcfX\nd32E74HrMD1xjVREY3FmmoFMgoFkHK9eYXVtEd+I4RNgxsK0Mdt2aW6YlwrZQNU0arUa+arNuqXS\nf/ANBG3baAgZz/IIJAvhubhuE+F7OH6A54Prh7/DbVr4nqDSsMPIZN/FajSJRg0iuo7dqOM4edRI\nGN0sKS9FGdu+TbURDh5sx6FSrfLRX/gYQczE8wMURUaSFJBCqSu8xDQKBwEv7RvPC/AkHUfSUXpT\nqKqBszSBnZ8ODUZFgLQRo+sFfJtU7O8jNfjnenn5vs+b9r6RZ3MrrGebLBbH2TY4jKYbLCzmKPjX\nKZebvP++H6KrfRePPPNZpO0e58a+wWBsH4+f/Cbn809y9eIVdvW9lYevPonUM0SMGrs67mB1bo6m\nCGg4TS4XFjhy9BCyrrF31wHm1haZm1ik5AvefPfdPHDBZ/zFs/zwu36GWk2nkpsi3b+bhttkoXIO\nt7qPZF8azTPp6bsLyyuhizpB4nWsLF6kUFzl4NAQM2On2d+1HTIpBrt6uPbcc6QHb0Ez4P5bb6Zw\nbol2J87lhWdZW6jhqjVu3XcXz68+yv13vp1nzjzNfcfv4K+/9Kd4zTr7T9xPfvpFtOwKRqTG7Ng6\nI3t3cH71OtvaNdZXVIRTIlsvYtsz3DqyhwurD+DHupjPneGXf/4jWFmFQzfdyuP5pxi/fJHxhUsc\nv+WHsJwOTl08jV3Ocve9b8SpK/REu+hI7mSmnGckI7i18w3E0zLlpTx1f5WlbJM9yjbqTQVFivLE\nyc9z4NYjiIbDhewi7z3xFr76lc8xXh/jc19ZYn/bdkQixsLSOB9490/y5a89iGLWOHnhawzFd/DA\n8w/w9n13c7JaJCG6yAZF7jl4O1PuDHPjTxMfrdCf2EGio4fzT/wpw/19vHB1nA7dxejvpe55zDz7\nJHfefjtnz51ECmzWl56nq/MAnZkK2wf2ce78C+zY3s29Q7fyqcc/xb0738H0xbN/lyZ6TxAEuS3P\nf4Xv4YRBwEvSoRbI0PLQaTFfWiyclrTmtVRbgRbP85AVdVN+Vq/XiUajFAqFTVPsQqGAaZoAm3Kx\nrf5B3d3doexNDSeXGo0GyWQSx3GIx+Ob7IuWMfZWcKhWq6EoCul0+mXAz1ZpUguEcBwHx3E2wYit\n1QKWqtXqJlDR8tlTVZVqtbppkt3yb2r5LzWbzU3WEbw6UPSd9mfrc9/p9ddaO/iH1o3b2XreYsy0\njvGrAWiapm3GjRqGgZmIE00mwsmmwCeeTm2u0zTNTfmkXfOIRqObJuJ9fX00S1VW5xdpNptMTU2x\n//Ahkskksqpy1223840vP8DjD3yN/s5ueoaHOPGWN9GeaWN+fJWOVAYzYvCtJ08xfvkqejwKmsKx\nY8fIrqwyd30KIxIBRaatt5toNMr67BJ//Nu/w69+8jfQIjquHzLW4rpBsVgkm83iNW3iyVCiKSkK\ny0s5pAAOHz2CoUe44957NttfIpGg6DRedj62wNK+nj6SEZP1tSyzc9dDj9ggAv/MNvoH12sCOLIt\nj2K5wtKyg24JVqYXyURlKtU6HdsUUpiUFiwmsuvUSg6aK5i/XmNfeztlHLZ3xpnOl9gxGGNhySNn\nF+k/pHL5TJZ6M4k6YKH1qdhzBfr6E6w26xiKia6p7NpZp1CWGbvQINOu40oNAl+gGLB7T4aIIUBK\nYoiA8ek867mr5PN5pqdmaTYdIpEI9brL4FAPni+B69HR1c/kzBgQEDfTyIZgaNtOTEXD8aBZq7Oe\nX6O3ZwBdN7CtBtGogWc7aIYJno8bNDaRwkq5jBGPEY8nIdhIdgj8DZ+acEZbVg0k4aIEAlVSadTq\n3HrP2/jXH/wA2/vaUaNpAr/J4vlzqKpG78B2AkXQP7qX5ZVFFqZnGZ+aolku47iR/4+99w6SJD3P\nO39f+vKuq333uB4/O7tYTxDALhZcEqARJVpQPBIiRJF3cTpRITHuInRHSqcInU6nI4/iiTyABOhB\ngDAEsOASHliswXozfqZnpmemfXd5l5X+uz+yqrZnOLMAyAUBUngjOroqszKr0r/f8z7P87KydgEr\nVeDYjMKLS5vxQz0UoAyc/omBhkhKogBMDRRFxfdDKrU2rUbsgRRKga6qZFMazY6PFHBwX45XzteY\nHEvQ6kUkMxpKqJBIanihR7mQIxQRd9+Z4ey5TXbvS9I579BzFerNEM2EI/sKuP2ASDrcc7zEpatd\nsjmFpGkwNZvg2lKN8ngSuyep9V2mpjQ213sEJMildXTVJpczKe0qsPiJ5rf2ApASIRUQUYwaRzqR\niJDhgCIcOoShTxQBUiGSXmyoKBQUTUURGoKQKJCEoU8oI1Q1QCixbA2hEIvXwoEvSdxtTUgIQx9N\nE8Q30YhIhkQyRMqBHFFREFJBRhGeH/AXX34KVVqEUYBmGeyeKHHfbfMc3jtLKiFJJzMopk6QShJF\nAY7nk8mVYuBLgKIlsO0OSb2EUEEYGmYmT+i6XFur8eVnn2XfZJ47jhyiPDbN4tIZjh89TsLSQCr0\nXYftTp1mu8ETj3+ZwBdcW6/TCS1CXWOqlCIMLVarNTShks2nuP/ofgKh8tLJM0ihoGsGqi5Imxau\n76H4PnbfpdvtxpImJYIwotPuxrYqUhKpGk7f5arrkTQtylmDZreP9CSX1lbIp5M4QcwWMTRj4Ddg\n4LkuIvRQowDDsKjXuwihIZWQuYlJcoUMYRgyUR7j5OIF5sZnqTUr5JJpKt0O+6fnWdtuYGoh1e0t\nthsVvBBC1WV6bAzfkUxNHabTsZkbP0zXqSLkGEZKZbN2kfvuuZvPP/EMIrLQMVi+tsWZU2c5NlfG\nsY2Rt8GtQsQUtBeBBeC3pJTPCiEmpJQbg49sAhOD1zPAMzsWXx1Mu3GdvwD8AsD8/Pxf54r5umOY\nvAZBQOT3idzYqNTtOURmmny5gJ7OsXz2ZVQfAsclUyhiBwGqEOi6iaqqeE4fw9AQSoAQkn7fRmgq\nyXyBUqkIhTkkKmEoiaKQILAhioi8uEuoFwSEcuDLEEYEjkvo+QRhxHalSqfdIpNKYNtdrIFPxebW\nFlNTUyiKQrvRHLECA9ej7zhxkhQGZAtUV2/nAAAgAElEQVR5ep6DGSYwk4nr9/UwNxU7E9VX0R8t\nhocJhUKkaCSKE6heh279CjCopqnEhvRC4e+EOcTfkbBUnY+++Bn++T/97xGdKpuXU2TvnEavqvSz\n64zlS7wtt8DTV58huPg8xbzLZ188iSpU2qLL2ITJQuceJov7yZjX2N48g981+HTlInvueQueZTCv\nTtBeW+LOh36GxuZJLi1f5W1H7mP96pOoQYKH7r+XxXaH9UvPo6hTXK1uMLN7Ad9p0e2eZLmqM1/c\ny/b6RY4vPMzJF57DGKuSTwasVgOWKxc4tPeNPHjX27i0+ArpQMU1m2xce55c4i3s3r2bO950hJdO\nPMOffXgRmXOoqTU8kULx2xw6eJgTLz3P3N37OHH1PPsW5nnPp36DQ3NHOL1R4eWvnqLSWaa/L8Ni\npcX9hw+ycWGRuYVDfObpD3Fbb5L3Rme5LT3DzHyRV5bWySZyBI0nOLl1ipUrJY7s/QE++cSj7J+9\nnYO77me/26VT+QKbwTyZiSILt92G1xC86a338sITnyBJgnt3HWFClPn06T+nUJBMRvexsDDPnz/z\nEf6Pd/825y8+R9X3+AdveTOfX19iyjXY3nyaU/O7uOfg3dSXInIJg723v5HVKy/zQw+8g88+/jnu\nnZzkcxtnyIsi6UyEp2t0+lnuymToySqrzSZH993OB37/T/lf/+Wv8YnH3sulSy+RV/fx0P33ElQl\nndWrlA7NU21t0dzyCPUWT7z8JOWJfewbDwkuSl567knGUmlS5hPcuXuGa5vL/GkjJJHM4qlbhInE\n1z5B/2q8rgUDwfVyL3i1ej/0qhnKvYbA0fD/TjbLzTqsfa34WuykGwfqQwPr4ffurOAPGd+qiH9v\nLpcbDfhTqdSoff2QHdRoNOJOzQMTbUVRyGaz9Ho9SqXSCJRpt9vUajVmZ2dJp9OjbmZDsGgnyLTT\njHq4bcNldF3HH8jre73edVIuz/PiYvDAEHs43TRNut1u/KwcfC9AJhPnK9ls9jpmmGVZZDKZv+IN\nNdxnQ7bSax2L4T6/cR03xnD+znNgp7XCjZ+76bRvAxLSjWys4T62LAtz4D057A53M58jRVFQtdja\nIZ1OoyUt0sU8dLtEmkKqkMNzXFQ1Lsr7vk+9XkdPWiM/qlarRblUYmJigmw2SxAEXLt2jfm9exif\nm8bq9XAqDf7w936PY4ePUt41y4HjR1GTFpcXL6J4AZH0eOmVU5x95SRep4enKuzfu4/jt9/OX37i\nEcqpLI7s44YBR48eRQjBM5/6HOmJCb7/p36Mja0t1ES8vY7jsLq6SqPRIGVYo2tBCoEZxkXwf/Pv\nfhU38Jkan6BUKjE+Po5hGEzOzZBOp5mbm6NQKDAxMUGgKWxvb2OUJ3Ach5XVSyAkMgJVJP82DvPf\ny/i2AI40TZDWHAIniZ90uLTeJ52wKEzoZJMm6xsdrJTBmJogNZ2i1+6xS+jIgsvBO3O4GyqZoo8e\n6cxMSoKuoK/0ODBZZnGjSaDoZFWJITJ4tsPqZo+xKY+ZUoE+SUqaRXahRyvokIxMVpbbHL99Bifs\n47kKK5eqzMzkMTJJArtDIq2jqwFh0OLprzzDgTsnuHR1mce+HCCEiqFDLpFiu9nk6OGDFMcmuO3O\n2+h6VxFSJV/M4fqS9c0ldu0+imGquK6PokDoufiBh2Fm6LS7hF6HwsQUiWQWIkkQeoS+g9t3CGWE\nY/fJF8YQsotqJmPgIIpQLIOEEvLuf/FunnriC7z49Jdo11sYySSeG2E+dZIf+4V3cvKF5zh/+jRW\nLsnq5dPI0GB5u0bf32Z90yZy4fZDY5y71AIEjhsiefWGrqqC4f1PEwqBEgE6kZAIFKbKsbm4bfuk\nMzqZokK74zBVTuJ0JVYmgZAh1ZbD/FyGeqPFaqQyl8vR9vrsmU/Qi/qkU4LymE6ohHjdiHbLwRUa\nB8sF1iptylNpXD9ibo/FyVcqrGy59FwN05Ac3FXCLAS06oLp2SyPfnaR+QkdN/DxOt/6J4hEEkZu\n7DekKhAFA2BQjLpgeF5ssqypBhExS0+KIQgUD/6kjJACZBQfm1cTLJ+hvHC4vigCGUagCBQBihID\nS1KGyGH7WTVmksXGwnBhcYml9SaGoiEVhVTSQlN8ds+Ns7B3LwkLFJGiXW+gKxpj+Ty1VgvfcUnn\nskgBEQLLiOVrbhChKiaOaxNIhbXtKlvbdS5dvsYri1cojE+ycmWJSr3Dnrk52rbD6laV1fUN7J5L\nteXT7jooamw8qcmI9XqTyA8xVI1ULsmde3bRCyPq9SZhKAdj3whN17DtWObT7/dJDCjdwyresHJR\nLORjDX8YoZsmnu/jBx7tbkTaMqm5LoaiEnoRge+Ry2bQDJ0gVGl2G2R1jb4n6LgOuh6zRxKWSRSC\namlsbFY4tH+BzUqNqeI0jmMjIwUfn7SlEwofwwgYK0xRqVSQGKSTGh3b58rKBoYmCcIemgnFQgYp\nLXbtmcfp9jG1EuurK2SNFI7vY/sOiprkg5/7Cv/8HQ+SMLRRJ79bnptx1fgOIUQe+LgQ4tgN86UQ\n4htCE6SUvwP8DsDdd9/9TUEihol7EATxgCMIkG6f0O6jCBVPSbDv9u8mlVQoipDk9Dx2bR3ZrrKx\nvIRR24hlApZJPp+l2aqRVg2QEWGYRNN1bM+nOL0Hz8tyrRWR6G+iqCBlMABpFZAxHT0QcXIuZEgU\n+vj9PnoUYqXSjI9PEoUhnuuyXtmg3W6TzZWoVCosXryCosfsPqEqpFIpdBGxtbWFZhrcdvtx/s2v\n/grtbodQKLh2HyudQxn4VyvEgHR0E2kHgC5DFCJ8IYkigRQqpHL4QR+nb2PkLKqrK+RyC8M9+804\nXP9NRhCF3H/4EM9/9THOnvsqd932ECdOr3Df4bewx6/w2KkzlG6bIkLSq9ugCGqNSxi+ykTR5uXm\nJd4QFjm/uojYlExn387dbz+AakZc2GqgqdvcN3WYz218msc++i+5fe5BSplZUpFBccpETd7BR574\nNMfuvJOffvPPQSqNj8WZta/gLG9TmktzcfM8Ryfuw9xzB83mJkbCJFNQeHDiXt6z9ijveOPP8Pjz\nn+H77voejr3tQfbuOs6JM3/JRz79XsrlEru9BO/9o1/jvkNvZuH2w3z8q7/DAwv/kMvLl7n3wF2c\nO/EUvl+ifkIjVFZQ/UPsOnYbkypsB+e4Z++PkuwJxg/dx4x+gnW/jZnO0bFXeOf3vIvqepOM6POm\nO9/Bcy8/wtbVdRw74MjBhzm+/6fx3B57Dh1Fy1jkohnOr7zEhZcuccdbD7PL77FtjnN4115qFzf4\n8Kf/mDlVcqC8B4lgYXKKl18pcXT+EHv33sVnHnkckbZ4/uLjKB2DfMbhmdpF3EaHRGkfm9U+jz7y\nKN//tgeZySYp7dtNzWkzlZ/hI1/5S/alZggO5Hln4QG+tPgKrX6Vt97+XZx4cpHDBybwrQZJLy4K\nvfsfvps//Nh/4fiB21mp9emtnuD47T/Ec9uPE4gkTz33Esf27+LIvoOE2jS+36RExPmtTUzF4Bd/\n+Cf5zItfJSEzOIGJlZihUC6TXpEUp5PYja9525XEzKEQeO/gnv03KhjA9UWDW4E2w+5fQ2Bip9nx\ncPpQQgXfei+koQeNZcSD31qtNjIltm17BMgM36fTaTY2NigWiwRBMCg+2+TzeWzbJpvNjjpjpdPp\n0bJDyfvQf2gIqmmaNmIFDVlEQ8nT8LcNGVymadLv90dgjuM4o45rQ68iIQSmaQ4MuC28wTk5ZDEN\nTbWHnx2uYwgwDY/L8PcN39+qSDWcfzNWzfAc+PsWN27XThDMMIzRMR5OG7LFdobruhTTWfL5fCxh\nDH0iXUVYBroqKBaLuM3O6LwZys9nZmZIZbP0fBeNGDwaGxtjYmKCdruNqqqsra2xcPQQKAof/f9+\njwP7D3Do2BHGxsfJTozR6nbob22RsCyuLl/k7MnTtLarpHSTw/ffzW1vuINrK8tsr66TUnRCJSBb\nLLBnzx5+5/3v45VHv8DvfupjrLXrmAmTIIpIpBNsrW3EuY2m0el0KE6OUywW+cznPkd1cY2Dx4/x\ngQ9/CDSVdrNFt9tFSsnY2BhywD5yXZder8fp06eZ2rOLsbExgiDgox/9KEIJRl5qupYhove3dsz/\nPsW3BXCkILhwoUk63cWyDHJGioymkmpoOG6XA/PjrFV6pCfBabocnSyyXK3T77uc+JLD/B0BQaDR\ncvsk7RymkLhhktZ2n4XZDCvLXS7LLmYyxdSkyRvmpvGUPiLSuHJumQd3H2VLd+hWTPZMjaPYLkun\nOji2QmbOQ00YWOmIhQmTqxcCUukIzU8wOe9yx8xdrK1XKZbyTBd8IrcNbpKtepus6fP0c08zOVbk\n7PmzzMxMkE6n6fcq7JrfQ65QJplMUSxM4irxg8ePTAgVfK9BGEpQNQw9jefElQNVRPQdF7fXJTc2\njpWMW2nK0MduVdCtBIoiiXxw3A7H7ryfP/jD36XtdWg3AqQXkUokGcsm+MoXvkq7tUGj1WN5tUY+\nl6DS6OKHTbo9j+mZBLlUgn7LZ7KcZWu7QdLQ6Lmv3sDiNvJiVGk31Rj88IKAMBL0ug62ExCEgBpx\n7kKTTEZjopBiZb1DKojQjIDAF2xud9k1lefSUg/cBn0nTgj6QYjvCrzQp5y2oGSSFj5LKx3aMiBh\nWnSbbbqu5ILn0uhEZA2dsbxk6VpEecpj7Zok8hzc0OfeOwtcutIkqEC14X6LzvpXIwYpXBA66qDy\nEIQeYeijKgnCyB8lH6oi0XUNwaACIYmlaepgYChjVH643iiK8Bw3ZvsoGhCNqkxxkmFgmsEosZBh\nhBSD9rCRFttzy5je/Pnnz5PQzJiBpEXkTAtNeOyam0dLp0ipIb1eO+7soaiMp5OMFbOsbVXxHcjm\nSvihREnmCSKfpKbT6tRxPY2V7W2EkuTooaOcu3QJVbHYWK+SSpX55BeeI5W6QCqVQFGUuKtHQmMu\nW6RWq1HI5riyvo6QKpqi4crYU2b/xDjVbo/Tl66SMi1MKxHvs0EiJoRgs1qhlMujSKg2G+hCGXWL\n0jSNVrtDIpFA1zUC1x9ck4MkVtWZKRfYqjXxo4CUoWBZCbJpHQLYbloYmkq7b6MYSVyvTzqVwvc9\nxvOx0aBt6DRrdSqtGrlcDtu2yaRSuF2BUczjuiGlzDg9u0UyYVIopui4Nnv2THNxaZm56X1sNdbZ\nN34AQp92t0o2kaLtbhC6FqEqyBQsjK6KYpgEbkAQ6Hzq1BX+8Tvegfca5tg3nKNNIcSXgbcDW0KI\nKSnlhhBiCtgefGwNmNux2Oxg2t9K7KTGDxPjKIrwXQ8ZOHhBRKDE1d98PqZxK6FPYOkYt383icAh\nsb3M2OReTj7+ITq1Gho6QaMNlkagZlD6Ol5ao4CKVFS6kcrS5iaJVI7QIpZzRTK+1gRIYsBIA8Io\nJIzi/S01CIyIVFZH1yyEn8fp2aR1FbvYx/M8UsYkPadPFA590EJCP6TrdEmnY/r2P3nXuzl79jxC\nVZiZmsZUNTSG1UsFtIFfxGCQpkSv6uWFUCHy6BkKemigCYWOEpJJFrHlJIG7xqwpCaSNEqholgLS\nuo5zJL7jlP3XDolCMlnkyoVVDs+8ldsPH6Wfinjl4pc5MjHBwr67GCtPs7W5zsFjd7C1+Tx6kOLN\nb9nH1Ss+8+kcbzt4N36nxlPPP4ERdZmZeYBHP/5njE9MMLFrP1dOXiQzuZd//aZ/xOXTJ1Dpo1oa\n67UcU2WPH9n/i1y6+kWu6S1yvXFCVTA+lkIrHWd7s8NdswFl3eIvX3iMwEpz7+457p66h//8yPu4\n99BDVNrbvHHmIL/y73+Z3/y/PsgnP/ZvOXz3w4SBQqfb5vlnz5A1LS63zvFQsgRNj43tq+yZmeXl\ny4+j6SpjpTGiSoeW0+ap+lfwXJOrYcje7EE++ImP8Y63PsR2Z5nFc6f4nrd8P0ZJYXO5wtXeVS6d\nu8Cxo3dx4sRn6XfqFHMW+3NTnLtylnRwgQMH5nA7u7lycZHDu+aZSCS570d/hP2HD/DkM8+yW6ui\naiFj0+M4X30Rfd+99Nav0ckJVk4usm/f7SSFxslXniE3m8KspigmxpGWZOtyk335eZ549DPwju+l\nG0W8MTOG34/YM76Ac6FN+q4s3fo6QWhTsxtc+eInOXX6Km+//W3M7TvO5770GPRMdE0w2dfIGmXW\nlmtoYzkiu8fxQ7txjT5feOI89YvrKH2fh+67l+3eEdIiwZFD+3jh3NOcO/sK3tw9BP0eE1Nl/uvH\nPsAb7r6LC9U17p8s8ObD9/Lol/6MVrnAs8+f4/jCka91er5JSrkmhBgHPi+EOH/dufvXKBgMlhsV\nDVQ1dum4lV/R8Bl94wB7p2fHjUyTG58BO2P4/lat4m9cbieraGf3tuFvGGxDnJcNJGFRFI0AINu2\nSSaTdDodLMu6ToJmWRatVot0Oo2qqnS7XYrF4qhwNczRLCtmXfT7fXzfJ51OA68yUYZyt0wmM/p+\nwzDQdX0E5nQ6HfL5PO12m16vN5KcDSVvvu/T6XTIZDID+4y42JFIJHBdd7SdpmmO8sQhcLVTZlgo\nFEbyvZ37fifgsZOtdbOuZzeLG9lkw3XvZKvdjI1z43lz3fd8GxBnh0yv4e/UdX3EDhoCh8CIGQaM\ngDlFUUioJp4P0kwRJdNkUikCz8MyDExdJxrIC2dnZ1leXo6ZSVNjiITBRmUrlqIZSXqOg49DlM9R\nLhi0bIfK9iZ9B2otn3qlypHjt7H32GFSxTwiCtFUlYxQ2Vpc4uRLJ4hUQTvokJrK8cCP/CBu3+Hi\nV76KEcRMtkAISsfnsXstXnz0U/xPv/lb5HfvptFooKdSkFAJZcTG0jXMfkBCU2hqAktTefTDH+VD\nH/oQt00fIp1OU5ydYqtRo35ldZTjKYpCqMWdAsvlMtlslqmpKQwhKWV1PvGRD9KsX8ZQNeKD7wP+\nSFb5uobU/+o05fo8ux9efx2Im9yPdgLjQ9bZdSw19frrR70JDn+zdbwe8W0BHEWBoKCXmcianL7W\nwbICsnmTUEZ0fAWj16foZOgKlzm1SC+IPYC6PR99tslmTbBd97n3njIXXq6wuzBGwQppjft4QjJ7\ne5FsxqK+1aTf8rAUm6WzHQpzcWeA7XSPsG+QT+TZaNUhNNhzPEej0yafSdBZ6ZLKJzh9Yp1779qL\nJqDSaHH5fJXFcIt8LsnSRp3JyCS5x2RjvcLYrizTZpG9vRQWOqv1dWS/z+q2zcX1Go898RyapvHA\nWx9m/75d3H33XYyX51BdH4SCF/WJXJiam8HuNlm+cp5iYZxSaYxWp0mpUKTV6GKkTLrtBrnCGI7b\nYOnCGeb37sdMpfA9j/MvP0N5zKK3XqeYkIhCGj2VY3xyH0L3sYOIarPJZn2N1brExMQL+1iGSW3T\nQZnWEFbAZEojYRWoVj0mSxkubdQQUiEII4RQkDLE0lT6YYD0xYAJE9HpBcRmPOC4EkUR2HbARuhg\nJVQi2cdUkkRKRN8JCIXKRNlgbleal16uoKsaYSiQIsIJYMzUWN/u0jQijh7Jc+ZMjVJZQdOhuRni\n2X0KRZ2UOkatucUbv6vA2obk8kqNO4+myBcsIGKqbKEhKZaSVJ/zvsVXQPzwU0TcsmiYPEQhRFo/\n7qImI4QSdzwbgkajP0KiIIo7r0Gse44kERGhH+D5fcIIdN1CiPhBNdS5e543kKtp191UpBRomkRG\nMRh1+coGmmEgUVEQpJNpNlsVpKqRsFLogSRQBZaVjIEjJHoYIDSTYjaDH4VYOhiahhNBKA0czwUv\n4tzSBdZqfRavXuPgrr3k0iny6TwFmcPxfGZmJ9nY2uYtd93Hl597ivJYjna9QbvdoZjOkzKTTE6N\n06616PZdEIKp8QKtXo+eF5LPZmMTd+LqmGnFLW+b7Q6JhEk6adC1HXRFfbW97OCmrqqxr1dCT2B7\nfVRVoAkFUzPw/QA/jDg4P8H5lQo9P8Rp1pkszXO5skYqkSUQAalkkr4bS52iKMKwdFL5NJt2kyOz\ne1lcvcxYfgrfd5kolGh1bZKZ+Dxt2i1KMxZhTyWbzeJ0IzLFHJtrHTQ9Ta2+ReT5NBtbBNjsmTvI\nxuYlJsePUKmuUsqOkdQy+Lk6S9ea+IrE67W5cLHPtSOXCfxbU8iFEGXAH4BGCeBh4D8BjwDvAv7P\nwf9PDhZ5BPhTIcSvE3td7Aeee50vlluHGOgvI0kYhISBT+QHhL5H4L/qwTCkaSuKgrAsIkVApKPo\nFolJE8MscEBJce3809grz2MrLpnkHvzII1kOsP2AQMsikjkSxWnYWMa2u+AGI2+CncktDAD2wfQg\n8BASdFUjm84RaglMdALPH1WOncCJOy0O6OtyB+Bju4NKpCLJ5tLUm00mxycIQonjeUjdQNXN6yAd\n5RaJcjSUW8gIQtCUEKEEBK0VdLq0mh0UI0NG9YikhVB8hDS+Ocfvv7EwTYtscoqf/4l/xIsrL/EX\nT32BSTFGp7pE8sDd2GtnabcvcnD+INWwxbMnz/Cm+99KZSsgmbfo1ExeOHWRWmiRCsdIWVM8/cwr\nlMwS9bDC+sUORUPh5VfO8BPv+Bd8350/xyc/92/56F/8NrnZGS6ed1jULzG3p0gukWN/+U4+9fKf\nkA8mSColsiWF1ZUKT4aXeONDP8TTzz+FGoZ88eln+Im3fy8vnVrj2S9/ll/60Xdz90Nv5tLGGZyc\nxdmVExx4wx62qovo9zR50HyIxcvrHH3TD1E6+SzZ0GNlexN7zeO+d7yN9YaNZ19kvDRDLazhhlVW\naz2Qbf7jz/8y//ULf8TU7A/z7re/iz9+5I/5pXf/Mi+v/zEP3/dWHrj/YTZrFTbPPMWuwu2cPf84\nG1cXmXrTcZK+Tn9yN0ZkcefcHVysXORNxx5g8/xL/OW1k5xZvsy7fvKXEM0teo7Hg/d/F62WpN1t\n8+nLz/DA7f+A8pjGkxfXmE6EbGsOh/J7WVu/xMryed40fy9/9uVP8JPv/GlSKYue6tIIoEjAIy9+\ngTk1x93hYdy+xwNHHmR1cxstLfmle3+Qxacv81uffJRf/ql30dlepymz1BM9as9fQzs0ydHEAfLZ\nOT7zyGOMHznM//DGn6cZdVCMAwhCcoHDfW9+gI995X1cOHWZ7/mBH2csmydXejuXX36SdHISt+Xy\n4z/0U3Q3t7i0tcFkpkwWje0xhbK4yeBmR0gp1wb/t4UQHyeWnn1bFAyGsqvhgHE4mBqCFq83Q2Un\n2+nG79vpc6Qa5kgOBjEQ0Gq1SCaTMYOi10bVFVbWlikWi6QScVv7IRPEtmO/zbhbc5tMJoPrurTb\n7ZEX0dBw2/f969hEQ0+lYSc03/dHjJVMJkO9Xo8lPztMp7vdLp7njUyT+/3YHsNxHAzDoNPpjLbT\nsqwRi3cnMOQ4DpqmjfxkhmyOnZLDYV55K8Dubzu+1Qy1m8XwPBoyi76e39jtdpmdmGR8PGblpFKp\nke/UUNI4VSpTq9WYm5vji1/8Ig/84PcBkEqlSAzkqqYZN3DJZDJ0zTRG1EeisLWxwuWlZfYcWGBm\n1zweEXanh2WlaTSaXLu8xLXFy6iaIJVMsVDaz7G73kDSMKlvbrO2toYXBghTJ1/Icc9dd/O//S//\niv/467/OoYceuo5plkulOPvKSSr1Gl4U0u+2CS0D1w153/v+gOPHj2O3Yv+lH3jnj3HgwAHWx67S\n6XTo9XrxOZpLYxgG+XyeRCJBJpPB8fu89NJLnDp1KgZEo6+vUPqdeO34tgCOFF1gZCXtoMUd9yRZ\nXnIJpU85X8bwu3h2gDHjkbMsOrisXKlQHs9haT7FySx2x2WimGb9ikeEZMPbYLqfZryUZe1ane++\na46vPnUZwzCYXsiTsNIcySRY3qwyO19ko9ojY3rohT6bi3WslIGimTQqDoVkHjcVUi5McnAv9Dd1\nzi3WiBIKx+/PIwOT1ZUWC3O76bcqHDxwjMuLz3K5ugmigl1Ls2+Py9hUgiunV0HNcmx/AT3UCNIK\nvdazfPyDz5ArlBgvzyB0g16ngU+AgUKz2aK6tsTc7kOEaKxtriP8CktNh5mZKXrtFpqiIYMQK1Eg\nnd7m8tI5SvkyvX4fqZvUWwFdW8Vudzh42GJ83KfZeoGNZQc9YdFrttizq0il3kONYLsZoemSRFJB\nCXystIEXSfS8wqRqsrs8w6W1+tA+GSnjNvF9P0JBIUIiRNxK3htcp6om43GdqgBiQH/VmBhP02z5\n5LMm7a5ga6uFbpk8++wW6CoP3z/NhWtNhBKxtdXHSHbZMz+O3Wtz5nQLM6Fh2xLT0kkkwel79LsS\nZBtFJDl5ps3+fWmO7bUYT6dZqdjUq32EarB7j04YJIBvrceREMSGzzKWmQkh4gfIwABbEqEZcYc9\nopBQvpogBaGDomixzDEaSMwGJtdR4CGDuNOGH0RYVkyDFehomowHqlGA25cEqoqmJ2J6sRp3BnC9\ngCj0qdfafOml01haCikjstkMiYSJIR3efGQ/L596lkIqxcHj95LWVExFIQgium4H3/dJ5gtkdANN\njQEpMwjpuQHdpsOV1SW+8NQp8vkis+VpTi6eI5spcuHqRe4+fhutehsVlYl8mZfOniGTyXNtY43Z\nsWkanVVSSZNLq0vsndrNqtNAAfbummZhfo6Xzy0ipUK73QYpRlUzEUTYfRvd0Egmk7ghsfmx72EY\nGshXE8Rhwug4DpEAGUToOnSdPpZpoikGvUCQtTS6ro5Asra5jYwMfN+l73pAhOcEFHJpHC9golSm\n2+1xZH4PnU6bUmYC3++TSVo4gYftOhhGGtu3mSxMs91okzPzuN0IX/aIbIut1jrzU/PUW02y6SRt\n22du1x6Wl5cZn9xDp7PJxsYGkYjwPIeH3vI2Li8/GjPYlJg5tbi6hplIvdapOQX84cDnSAE+LKX8\nCyHE08CHhRD/FLgG/ASAlPKMEHI56TwAACAASURBVOLDwFkgAP7Hr2WQ+jeNnQlWIGJvKhlGMWss\nCAn9AOkFBGFAv99nenp6ZPQphIi7WUQCS9VRhKQfCZT8FLkjk7xh315e+OQyqfI40tWQkU+t0yBn\nFfHVBKRKlGb2s/34WdIJk0i6qLp20y4WujoElOJKuiRCUQUKOmbSIGEk4q5qg+vaZyCxC+P7ge8F\nI5ZgWoLr9olkiOM45DIpZOghByasUfSq7aOQCsjw1p4OMvYtEkQoQBS4EHgU0grSjYG4ft8mo4VI\nGd/MbyUx+U58Y7G8tNV954/8uws3m/cnv/3/3HSZz/7en7/GGl+65Zzf/7V9N0w5e5NPvf811v1F\nAP7oJnO+9MH/PAZU4U9usWxsxPyrv/LBvzLnPe999jW+Ez7+nv8dgEd//zdG0z76gX8WL8vjt17w\nz9YHLx65YcbvX/fud//vJ4YvB9vwanyU//c1fxvEjcM+8nvvufVH/tOjN5n4kdGrH//4rTqbfa3v\nhgFxB4AP/MF/GL7csR1L/O5v3By3f098ruy62TwhRApQpJSdwevvBf4934SCwdfyJrqRBTQEPYbT\nb/SJ2bnOIcBxI4h/s++9lR/OjV2NhrnZkDmjKAoJM4nverh9B9M0URRlJMtSFIV6sxbnHQOmcz6f\nx7IsunaPUEYk0ylQxHUAkmEYNBqNEQiTSCSwbZuNjQ3S6TRjY2O4rks6nSYIArrd7sjwegj0DMEc\n1419bob/hxIzwzBG2zf87BC8Gu6Per3O+Pg4uq6PuqYNmVDDjm/DQud/949/hkcfffSmTK0bmWM3\nHrud+/fG8+G1pu889juZRzs/ezNG0muFEGKO+FY3QUxP+R0p5X8Rr3dXwR0xPKeHx+fr/b3DZVKp\n1MgbaWi1sHO7a7Ua+Xx+JEsc7s8hO23Y7t6bmCKs1AiCgHa9TrdZw0gmMCyTdqOOY9u0N+rYtk1l\nu0pAnBhqCZPi5Dhz8/N4HZtWtY7vukQCEFAqFlAQtLsdvueHf4AVO6Tb7ZJIJGL5ZKeL27Pp9XrI\nKAAF9u1d4MzpM8zOzKEqGplSms1KhVa9MWIUJRIJ2u12LNlMxay+IbtveP1XKhW63S6FXJbQ+w5w\n9HrE1w0cDQYPLwBrUsoffD0vIlWTlPdqXDuroVQ9EpaGnktQ9esslKa4fG2bUj7JlbMVClMpDh6a\nodnymZ3W6PgeE+kiddfm8G0lnHpEq11jfd1l9948uw8W2DznkRvPoNsdLp2vsXBEIEOYyaRJp1Jx\nJ7VOEq8j2b+3hDRA2B77j+TxvR7jcyqlSOey72JOGBzKJlm8VKPn5NhXztFb7XHfnkOcqrhcubrI\n3N4idtVmKjODv7vK5c0Wm5d6zC9McvFKjXrTQKo9Ns/3mZlNk8+HPPrIRzhy5AiZRJJmbZtWp0M+\nUyKMwA0lXadPr9VicmKGyM+Q0SXN6jaZfA5ds+i0G4RhRKhYVNZPc/HcOeb3LnDp7FncyEHTXMq7\nEugGHDt4lI994itoiooXOUzPG7Rdl10zGWrNPt+1f55uOySVUQgDF8eJsPsh+BI7DPF7PVQF4vHZ\nq1RVIQTR8IFO7J0TMTBhDuKbvCYEKBLPD1EUSb0R4vmSjUpEKgF9z6Ra7+C5kumy4OkTdcBmYizF\nxESClWWHZN7D9TVSWR3X9bGsBPVGh8AXWAmNjqegpSNSpkopY1HZ6jKeK3P6ShU/CpmcTNFqB3S7\nEZrifEMXzDcjpIy7LQkhEIpERAJV0ZFqXOEJpURBBYKBP5FPxKtyk5gxrhKGHpGU6KNRI0Th0EA9\nQsgdlTI5rKLpIGJ2UiQDhJSoUiH0PaR0CUKfxSvXQJqoavzQGRsvYCkebivkuTPnuDPYR/lgBrfV\nRM0kEapKz7HRFAsjlcAwLVTNAFUh8l1UTcX0FTarNV48d5XyWAkZOWzWKuSSWZRAMJadwHd8CmNF\nzl2+yMH5vWy3qiT1FGk01ja3UITOWqNGLlvkyrUllEgQIDi8bx8btQpeEGL3uuSyeTqdDslUAlXE\n7We7dp+EbowMKhudDiKSuI4X+8joOkEQXfcgt3SDMIyTs4RlxRRsIeg5KsVCgcbKVkw7d3xM3aBr\n9zENCzXy0NJJuo5LLmVh213CCLbrTbq+zVg+T6fmUi5N0traYM/0HI5vo4kEvuJi2x3ymTT1VpOJ\nsSmurV5janw3ilTxfJ9kYpJ6Yw27maTv9Wk2YxmvmUizuV1jZnyGC5fOMJbJs7HdIUIhCH1eOHt+\nYHh8q/NSngTecJPpNeBtt1jmPwD/4WbzXq+4kQo/TGaliAZsowDX7qOrGl7fwXUcPOkzOTl5XfXT\n930U6aOhQqDEsitNpx+BVrJwWtPc9pZfpLN8klzCRovyEEyjaX2c3BzZ3cc4v9xkz4HbMFUVVRm0\nkB5WwsWrjCF1B3KjamJE+Y+8aORfNKSjKwi8yBsNPgIvXkfcFU4SCXD6PVRVoJsJJD5eGGAmNNB0\nNEMfdFAcHlsFZZjMD7svxs60MX9QSggGPmjSx+v3CUiy3W5gijQegnEPpKIQqdp3xGmvX1yQUt79\nrf4Rf9MQQrzwd307/j5sA7xu2zFB7GcH8RjhT6WUnxFCPM+3ScHgZvHNYpLsZBztlJcM/97//vfz\nxBNPMDY2NjLzHTKBhoBAu91mYWGB9fV1pJR0Oh2q1ToLCwtUq9VBAwZ3ZDA9ZMdubm6OBtedTofZ\n2Vl6vR7dbpd0Ok2z2SSRSBCGcYFwyDaBVzu3dbtdTNOk1+uNnn+qqtJut0cd2GzbxrKskQ+S67pU\nKhVKpdIIYNj53B2yk1RVHUnrpJQ8/PDDPPXUU9i2fcsCw62khK/n8fobngsB8K+llC8JITLAi0KI\nzwP/hNexq+DO0DQNz/MwTXMkA/xa+yeZTNLr9Uat5z0v9qQaLqsoCp1OB1VVOXHiBO985zuput0R\nYOc4zujcEkLExux9n44X0m028XpNUkpAmEuzdO0qm9dWcLo2ShA3lemHIVauRDoJM3t2MbdvD6XZ\nKa6cvMDKlSsICYEC2XKBg3ffwYf/+AP87M/+LIvVdSIZA55Dvy+nXqWyvsm15WVKpRK5XI7HvvzV\ngc/oJHt2LxBGLhW7zdNfeYLI8di1by/FYpFMJoOUEj2dGLGYwjAuqvU9m7W1tZE5+Hdyl9cnvhHG\n0S8B54Ds4P3r1prT9UKWztZpthXGZ3OY6RDHdlCFwmJ1k9S4yvLpGpHq0dtWOXLbAmtfPUe1HpIo\nJjDHAxRNYXu7z+rlLeb3Ftk/rdDuu1y+2mRsyiOfgDCRI9+2sRSFq50uM8UUTbdLqmDSkR2O3jFL\ntx+xeWUbI6/Q7IbMTBbY3KpztbaEqht4TsjLLyyz77YxJpI5Li2ucLXS4spHP42wJNmMidOFibEM\nZ1YWMdMKY5MpFEOj23QoZATFCYOwJ7jzvl08fvYigSfJ+Ov87m/+FpHSY2piP3N7Z0kcLCA6TcyE\nSaO5SeRKthsVEqZGaAdsbVWYswxMU+IEPlEkcXs9Op0Or7zyHJXqBqdOnuPq0gZjeQO7FrIZRTz2\n2DNIDaanp+g01um4knuOT7O02UbVBM16C1eGJIIsebXMlrqFlRDUuy6WpXG10R1023r1ASCEIJOM\nZUyKpsftOYMgHo/suP8FwVCTHgMericplww8R2BYIY7towhBOqHStcF0+2TGdNo9qNTblEoprq62\nSekRW+2QiYLB2kaVMFLI51J0uiGT4zq6piAVhWa7j21LNitrgMKuOZPdk2meX90mPZ3n1Mub38Al\n8M2JKApHlF8hJKqqgxAIdEIZIUINUIhkQDCQskgZDaRnA/ZWMNC8qwpChCgCNJSBgbaMDXADDxSB\npsRAkqqqCFVBU/S4RWUYggzx/dgkOwx91FCwuF4nkUgThRAqEbunZxkfz+IFIX7o8ulnT6AZOlom\nTSEosO24VFsNDu4/hm4lY9NezyeKXLp9m05li1MXzvDU6Q654gRb6xsITaXebVC08mzX1sjmC1TO\nV5meHGduYoqV7XWmymW2tjbIlCdorVfIFYo0m020FLhCJ4gcDMvi1KWLdNp9Wp0uft9BKgI5eFj2\n+31avR6FZIqe62K3OqiaEldNBoP5IajgBj5hqI6qOLqukkpm8IOAMIow9JhhlLB0ZqfH2Gw0UIRJ\nEPoYqkYw6GYSqgo6CrfNjdFRNOr1OvOzs1Rqdfp2SIsuQhM02w2S6QS222a73sAPQ1Qs5qZ3I6Wk\nXJyg0aiRSefodrZpRhHTk7PUG2sc2Hec9c0VLC3HVrVF5HYIhUapoJEtQOCr3PmGO/jMl76MEoKu\nGniupGf/3TUHvE6uKSVR4EEYG2FrqqDTbSGDENd3yeRzsU/QoNo6qkb7AaoCigCEwAs9DFUgZJ9Q\nMUgUj2JpOt3tl0jnplCjXSj+GqmZBaL0OEnf5fD4DJou0HiVMRQiY6P6oTRg0LUQGMjRYmBI+hHB\nINkPwzA28Q5ClMBBdeNkXFNj5pRAJSAi9Bwcx+HAgQUUTUVKSKaSKAOphKLulKEIbjR0iAb3YylA\nlSoi8pFhAIqOmUjhdZqs2Sbn1/q880d/DrKToCRQpEIUwtfwU/9OfCe+E3+HQ0q5BNx+k+nflILB\nNwog3Mgg2nk/v3H6ay1/K5bRTobLUJ61c95QJqeqKg888MBIigYxaBEEQczsFrHJtB9aWJbFK6+8\nwv79+wnDkEajQT6fH8nFdF1ndnoG3487NV+5coVSqYSmaaPPDDuyGYZBEARcvXr1OgNqwzDo9/uM\njY1h2za+H1sQtFotUqkUruuOGEhDUGloWzCUuw0H3MNObUM2y5At5Xke2oDZOgSohoP1brfLz/3c\nz/Hkk0+O5HRDkEJVVVRVHT3ndrJhhkDPjQyxnR5IN/oZ3fjZnW3rhyycYQe+bzRkbAC/MXjdEUKc\nIzZ7f127Cu6MnVK1ofTqa10Xw2N+9OhRdMvi6sUro2OrKAqmaZIwDBYXF1lYWKDVaoGlxhIux0EI\nQbvdJpWKWedBEDCzey+WZbG9sczayhWeeuzzuMk8SUVnMlNAMUJ8T8VQLfRkGi2XYqqcYPfCPiZ3\nzYIicNs93HYvBjidPnfefy/FqXFeePY53v+B9/HM0imEHR+7Xq+H4zgkNYOnHn+CRrvF/RNlAhUS\niTT7988CA/8nS/Lg9DQnXn4FgpDSxDipVGrEunNkbLsw7ATY6/WwUibXrl2DKKLdblHMpb/eQ/Kd\neI34uoAjIcQs8APED4Z/NZj8ul1EEoXNDZeH336UK5e36fTaoCZptxuEfZWsaTJ5IImRzLKx5PKl\nz7xAZqzMxG6PXt+hXe/j9AxMVFLpNI1GH2mClsyxZ2+S5fUGrUqSXFnDJmT7WhvDVNmuNQeUToe9\nh6d54alVbC/i3tsWUPI+ciNJXrVQpnVUyyLtRLhKwINv/G6qvW02lhq0DY0jBxfoettMjk2xvLJB\nwgpJjivsSZdZ3e7RanjsPZqkvtXETOlEio/UdKpKh2TeIJuWlL00z559hjcc2c2TL3yKzT8PmZya\n53/+5X/G1P6DbC9fo1Qcx263UYp5wp6NldCoV9YwrTRuz2bp2kV0obBy9QqrVzfI5IosLa/G/jVC\ncOBgKfYeijQ026brtYkcjTvummZjo4bwfSank2wSUl/xSBkeXlCj1fdo9zzGCykEUN/qo4iIUAEx\nHIUQ0XcFnh9iqh66phL4EVIo7LRQVJQYKAGF0ANVF9QaPpoq0MwUnX570NZUIVdMUq31SDsKWjFL\n0rJZvNTBNAT9pCTyFCoVj64XUsjqbNc6HDuUQwk8svkcVzdadBoOQjdodCRvfVOB1TWXhh0RRQrn\nTlU5emSS7cdXvp7L4JsWUsoBwyccVQuGD1VVVZFRhJQQRTF7K54nIAqJBvp634tpyIYS69gjGRIi\nCJGjZCYMfdRAA02gIFFUgaoqKDJEKBIZBshIEoU+hCGKlHiORyi12FCa2MOla28zGRZQFZ1dpTmU\nrssXXzjHzMQMoYxoNRxWNpeZmpzFc/ukEhZRFOB4IY3NZT72paepNKDp2jRdBwIfrx9QNlJ0PZds\nNk+v0yaRyXFlfZWJseMYUkUVKuOFSVotm0Q+RaVdIWOlWK9uIbAIg4hcNgZ5enYfEUky2WycVGgK\ngefjej7aoG2ubmgjmramaUhF4PvuyMxRV9RBwrQzcXy1OqQocWLY6dlEmkYpl2Wz0sb1nOuSF9NK\nxEmUmaaytkk5P4HT7RMGgolygWqjSi5XoNlpcnD3ftZr2xQzYwSBhytDVBFRbW7H8lPXYao8SaUW\nMFWeIJFQcHpFOt0qUeSQGS+xVV0hXxwn9PuYRhFF6mw31hBCpVzIsFbtx8lXIHG9b7W/1zceO5P6\n+LyOPR1034tfBzHo5/s+rucxNzeHHBiH7kw04xcWgRCgSqIogEhCIEmrGoqp4+g6RuEYerYE6SwB\nBulgL93/n703j5Pkqq98vzf23Ctr667eV7UWJLEIBAiQscYGyzLYgA0YZgaGN57PMwbPx8uMMfM8\n9jAee+ANNgYen4cZMM+ewYNhbLwARsjYsli072q1pN6ra99yjT3u+yPyRkelqrW0uruq23n6k125\nRGbeGxEZcePc8zunUkLqJttqBWQYIB0dLYwzUibReoNaTYAQ6DniKB30pm1IgpSE7Ha7qSFq1yPy\nfMxui8AOCIOASIuI9YjQDxAyJNa6bN48ztatWwhkjGkV0J0KdrGIZqQXEgnKuDo9VmgyJYqgN0hV\nr0eSKA4xkgSRJHSChFbb58U/+V4u8128ko1jOhCFaLpJFIeAfeE29gADDDDAOiDva5S/r8q8wjDk\nPe95T0ZQhGGYlXNZloXneWkqmg6zs7M4jsOhQ4e49tprMyKm0WhkXkJFJ/WcaTabQErEKLWzpqV+\nrJZlMT8/nybs9kgly7IytVOhUMiIpkajwZYtW9B1ndnZWTRNY2Jigrm5ucy4WymHlEKo2WySJAmO\n41AsFrMENZXUlldawekEvCiKaDQajG8eo1wus7i4uKqccK3ybYU8udNPmGShLWpyqHcOX6v8LEmS\nzGOwP4HsbCGE2EWqur6TF5gqKPoSBW3bzkoRPc+jXq9n7VZpv2rZfLmf6rdvCmzbYN+1V7Hsheya\nW2BhaQVRB7vgsNhq0J1dIggCrrwyNcMPZJz5YynEvTQywzDQhCDohjx56CSTT55geOhy7EpKHHq+\nj6dJwmqqpis4Dps3j7FlfBOl4hDFUp3p5QWOzk0x320iTZ0t27by8pe/nLf/1Fv53kP38fXv302g\nQyXuYBgGs7OzaRsCi5tefzO1Wo1SqUQcx1x2ZYmTTx5h6dQMl+3ei14xsG2bV77uNZRKJY5NT1Kp\nVKhWqykJFaZkkSontRyLyelZNKCghZSLkv7RriI/zyXW/szVs20GqUWJEGmpX6JsRnK/raCP9Ow3\nttb7RHVKPZi/rd22F67MfK6Ko98H/h1QyT13zn5ERVvnypds4aH7nmJqOUKPJTv2xVy+axtVy+CR\nB6aIMPBXIrZvt/G7EQ/e+yRbJ0ZohR1kFFNydBaWQobsIa65cTdzRw4jWjZGSWJqJhOjFkuhz+b6\nEMJxaEzNU91mYegW/nLMiXvnKVFgZCzk6OQJ5h/oUq85jFW3IY2ATqPFwinBkalFTC3kVa+5isce\nbTJW0Yn0DrpXYnZxllgmxFKnXhzi3kOn0AwfRy/wgzsmqdVMug2dREuoliSu34XYYmneJy62qVRt\nEukStGHbdodyaYVf/fWP8N5/8bO8/PrrePCBB0giHyETTk3PEEUR4+PjIHyWZ1aIhUcQwl13PYAX\nN3ngq0fQSLhs/xDlWhnNiek2Y0wzZGzLMH47oD5qc+ypGbZsrnKy1WbqyBJHp7pce/0oXsdnabaD\npgn2bBthqRMwN9NioetSth06XkwkerXcPdNmiFPPmCRBSoEmBInM/wAE9ASDUtBLYxP4SUwUd9BF\netHjhl2WV9KTZceXuHOzOFYF03LxghgpTUxLoOtgSp2llYDhSoHFJcHmoTJhGNJcCdm1b4R7H5jj\nygNDPHhwkd1b6zx4/yxj4xZ6YDK6c/0vggSCWCZEXoTQNUzdQDetNOEsSYjiqEdeJEhkFr8aJ+lJ\nJp9UgZA9f+AEKXSE0DCsIoZuoesmutB6igGBlUikjJFJ1DshJcRxWq6mdAqnFhuUzJSM0nSdrZvG\n2bXtCu586Hvs3LGXlbll7JHNvGjrdv7Xt++lVLZYXmlhiRjbLnHlgcuxTYuTpya55+AxJmeXAQcv\n9hkqVVloNChVikRhB71Qo46g7cVcf80BHnr8BMOlGk8cO8qurds5MnmC/dt2ItsheILhQp1TizMc\n2L6Nux45gq5pbBoZYanRxg3SNBC7d5ItF0u4np8Za3Z9Hz/08Vw/I4J0y8bU9bR+v1cmlM6OBRhG\naghsmBZ6z0DStm26rgdC8uAjTxL6HrpuYZkCUzfxAo+hWi1VliTwxNETaJqBG7cIvZCi7bC43GDT\n8GbcyKNerbOwsoBtmEQyQugBo5U6C0sz7N9zFdOzU+zZtg3fDzEtiWEKGq05tu/cydziHJftvxy/\nK6lVxwn9lBwaHTE5fOwkExNbaTSW2Taxh6XmkdREmZAwWn+zyrOBGjgq4iiKIhI3NQYNopBu6BPF\nMTt276LruiRBSg7mywfS0lCLWCSESUQYRpRMg8j1kZqJZkNsx4TSwR7ZT2IkSNND+DVw9HSWOUhT\nThJDRyYyPd71BgQIAXpqWZ8eG9MTv+j9jiWAoSPQ0GITUxPosUSXkCS9QYDUIPZIohgh0kF1uVhg\n647Ui7ZUKuEUClgFJdPWiGSClju1az2eeS3EcUxCOrAngXbHZXTTBI0QSsOjeDLEjRMKukDGLrox\nEHufQ3z22Re5KHAp9ONS6ANchP3oLz1e63U4c0qaut+/3JmWX6uMSbUhTxDlS35Ugpimadk55D3/\n4r2Ui2mZTBiG6QRgL/VKPTYMg3azg6lbtMI0Xe2+++7jsssuQ0rJ+Ph4Fmc/NXMq82hptVrUajWK\nxSJLS0sp0SQjirJI1+tkF8iO44AmOfTk4+zbs592u43v+5lR8uHDhxkeHqbRaGQlceo7VOy5Wh/H\njh2jWq1Sr9eB04of0zRpNpsUi0XiOM68lZrNJoVCgSBI1bPT09NcffXV/OSbfoo/+7M/o9luZMRS\nHMfZhe8zbQdFIqn3AJmaS03G5Qkp9XxeTaz8C5/P9l8LQogy8FXg30opm/nPk/L5pwrKXKKgYRjS\nstIxpSrzU6SQ2VOqr9GeVWOXMEzL7yuVCk7dxmi7BMeP4wU+XtSh43Y5fuwYb3jDG7Ixkiiaq8gJ\ntZ4U+TlkF3novvt54uDj7N65C891U/V8z1/Rsiwq5dTKoVQqsWXLFirDQ2yamMAyDBYmp5mbm6PV\najE6OsorX/lK7rzzTj7/pT/hju9/jytfei3zy0uM2RYPP/wwSwsLmKZJ3SqjRR4P3v1YqowC5pod\nKqUyB/bvJ0h8jFBmZKYQgsXFRebm5rLfQaleyxIGVTno3MkjdDstKkMlvHNEJg7wHIgjIcQtwJyU\n8l4hxA+ttcwL/RENVUz5wL0LbJtwePUrR1heMnAsHzoWd9w1yZZNFsefmOPqKyf49rcmufJFdV7z\nhp08ftcsljTZvrOOHdu0llrs32XgL4ScnPLYWvaYGLIpVsaJZMzlE8M89fg09kQXkUhsTJzEorzJ\n5OjMPCNb65h2QuPEDNe+dBN+GBAMSbqzbZ56eIX9l42gawUKw6Mszc3SbCxRKBbx2pJyUWe8OMyS\nHrIwNcd3/uFxLr+8wuSxgMUWRIHG8N4CNafKeN1mIWyTuIKlyQb1cZOdHYfpHTqjJYOt2wqEUtCd\nc3nF3ipPPPgt/uov/oqxkVEM0WWx0cXttAlCePvb38zn/+QrNNtdJjZV8DwwCwnzCw2GKzaeC05Z\nxy66zDwcsfOqGvc/sEhlJKG7HKNvqzM0NsJ3/uEwe/fXiIVLvWLQmAs4NtVm/94KhUKB6eMtlmY6\nlBydsfEhTp5qUdOg2YFQQhJrSM1H9A7skpR46B8UpI+VRDU1GUyIEcIgCpPeY8HQUIFuyyUI05Oq\nFmssxIuYmNglhziIiKVGGKfk1PCIRbsZYdg+YZjw1KmQrid56kiHzaNF5qYitk7oNLoQJhoyESzP\nd/nan62v2ggA0TtRJkkqKwJimaALgySJSOIYmYBm5E0fdYRMkAIcq0AsI/SeykHTNSwKaJpA9Dxs\nhARDk0QJCJmWooGeXsKKBCE1Yj3CEKkEWQoBccxyO0JqGjqpL4quJRw5dZQd43uxZZE9O2pMLS1R\nr5ocnV/ANIpUyyDDhL/+zgP83T0HGR8f5fDkHCPVcfwgxCmWCNsentAYrtQAqNTG6Ho+tVqRrr+I\nbglaYZNaZRvLnSXcbpftExM8deo4I0PDzMzPMlIdoWSaBEFCIiCJQyI/otPxsXSDGImfSBw9TbKK\n4pBESEq2RclyODnXztJFdMukpNk9klNmngHFQoGC45CQ4NhOquIKAsIowHU9gjjB0jUs2yLwXKLI\nT02rMDD0dAATRCHCSF1uhkdGiYMO9fIwlYLFgt7obYsEzRREfsi+Xbs4evwYW0f30fAaDNfGsUxo\nuU22bJ1g9ugT7Nt+BVNzU2zZtA0ZJ7SXW0ybszRaM9h2FSOyqVQqLC532bF9P3HUwdALtN0lAj8i\nkunMg3aefAbOK5I0OS2JQ5LARxOS2OvQ9tMo4epInS21Gn63S+AHlJ0CnTgmSWKk1BAywSAhiRM0\naUACuhAU7AJSE1g1ixCTWEqcIP1tpsbbgppeIynoVDQNZEKiy9RJPpJI7fTA2HhGjwKNOIpScVME\nSRyR+Gm5WJj4RIQEMiaSCZIYjZ4aSkswLI1Gs8NLJjbTaLdwCkWEkV4M+NLMLnaSJFUPouKihciO\nx6lgUfQG2h4WMNdYplKpMYvstAAAIABJREFUUC+W0QyDkq4j4pCSpqUyUSEQhsGgSu3coTcGuuhx\nKfTjUugDXDr9WA/kVTGKLFrrOaVouOmmm7LyL6UeyatjVPlXuVzm8OHDaPppIuaRRx5hZGSErVu3\nsnnzZlzXpd1uE8cxlUo65i6VSqvK1KSUq0yO88m41WqVmblput0u9XqdMA6Qnsz8h8IwZOfO1Au9\n2WymEy4yodtuZQqner1OqVSiWCyi63qmYFCEgSoxS/0fIwqFQkaQaZrG8ePH0TSNG264ga9+9auZ\nV5IieBSJ9GxQCiV1X5XHwWnyRN3vVxapx3nSr79cTdO0M4dF9CCEMElJo/8hpVSJBOcsVVDTtExZ\no8oCFflVLBYJguBp6yqf4ieEQO8ZW6v1VB4foea2YW4Rv9OlNbNAvV5n7969LC8vU6/XWYm9VcSR\nWk9KtT157DAzJya5bN9+ltpNaqN1gsXGKtP1oXoVvTfB6jgOsmBhlgp4rQ7t+SVarRa7du0iSRKq\n1Sqf/OQnueZVL+fA1st54K572LVnDw/efS9SSoaKZR577DFue+hBZmZmCMOQkZERbNtmx/Z9GAUb\nyxbEWkjBLKVm9IU0xMcnzvy3KpUK7cCjWq2yuJiae9frdaaOHsI2ddwIYr2EzsAc+1zguSiObgDe\nJIS4GXCAqhDiTziHPyKJoGyYbN85wZMHZyjVDILAwbaWePVrd/L4wyfYtmOUQ08tcvU1NcYminjN\ngNGxzcwtLmHZNp22T32bzvRKjGjMMFweojha4s5Hp7ls7ziP3H+SYEzSETHWssAzJMvNEGdYY36q\nw969dU49tcTCyTb2uM7CXAdNh0cfOEKhkpIjQeDhx0XmnpxF1yvc8Op9uL7HwYeaxKbFt/7+cbZs\nq1LuFLh83Ea3obMSYwy57KmPU7YCGp0mRsXh6MMBL3n5OM3dAY25Nt9dWWYoKtL1Woztq3HqySY3\n/shujh2NKfkr6P4yjbDJZZdvZci3mTyVMvFf+6u/YLnVoazB1EwHu6BjBemGrdUtrn/tCKdOuGzd\nOYy+z+PlB7bQiUPipQR3p8viSpPp+Q71sQKBC3PHYfuBIvMLIX5XoEcmetdlx9bU2qqgFXjq+DxR\nDG1Xsr1eYrkTsux6aGhpOo8QRFGypkldOjNEJruEXvIPieJMMDVBc8UHNIIwwXEkphaRRDGRDiKU\nRHFEwdaIwjhN/ulGaAY0O2lddSJDrthdpBOaeEGY+tOULI4dbtH1fE6cEAi9wLYJOHLUfw4/g/OH\nmfmV9u985i/WTNfZIMiSWp46fPws3q7e89xJujvvfxKAI0fSw8rDB4+tsdRTT2vfiVNzayz3XLHw\n7Is8fzwtrefZceszvKaShP7hLJvzNBw4Vx90wdA7diQ9orndbqMLjXq9TqVSIY5jWisrKeGsaas8\nA1LSVEtT16RMSzmFhqanJGr68QIpRFZiK0RPM9S7kFA+RvnSNyD1fTuDRBhWezIppZQMg7SMLo5J\nwojYj4nC1NMojmM0mSZMOI6TRjO3W+zYsw8/TCiVa6AZSARRLJWQE0EvklrkQgtIkJrIBsxKIv/Y\nQw9hmKlPgmVZ0Et/4wyz9gMMMMAA5wp5r5p+v5r+CUd4dn+i/LJner7/GK2ULPkyLHVBnScq8mVq\n7373u9PEWGW6myMzPM+jWCxmJEan02Hv3r08/sTBbBld15mZmeHEiRPccsstzM/PMzo6SpIkLC8v\nA9BqtfB9PyMSfN+n1WqhaRqzs7MUi0Vs26bZbDI0NJSVOylz5aVWOhng+z5CCFqtVnY+aDabtDpt\nlpeXkVKyZ8+eNPVYpGbaSZIwMjJCuVxO1di9kBClMFIKGUUm5dejruvs3r2bo8ePZMRS/3bJvP/O\ncI3Qv93zJXv95FH/Z6r1/nyi7fu+X5BGTB6UUn4899I5SxVUBIjrukRRlCm/VAmi2ob978nv/1pP\n+Z4SKRYLC3NESVql4HVdFqZm+Kl//k6mpqYolUppqaGjr9r/1XpU6+32b/8dI/VhlldW8ImZWVqg\n6MVZG5WPlm3bGclo1WvotkXz1DQrC4tAOl7Zu3cvX/rSl/jjP/5jxvbt4NOf/DQ/+sM3ces3/5aZ\no09x991388Sjj1IbHWXvxBibR4d58sknKWweZ+f2rZQdi5EtmzCLFlYhVXiHYUilUsG2bQw9Jb4M\nw8j2u6mpqcww2zRNVhZmqZTKNDpdTMvphYAM8ELxrMSRlPJDwIcAeoqjX5FSvlsI8THO1Y9IaFxx\n9W727BvmsTvnmZ9x+bG37OD7dx5jaLSNZdhc+7KtLM9s4YF7D5FIl6HxMsV6wrioIKVOFCWUWiGh\nVmSu2WR4a4W77j/KpvERWos+e3ZXOLywTK0aI4JxnnjiBK+5ZpxQ89htTfD4o0eIYovqliqNlQX0\n2ii6rWGIDvW4wEk34KknXK7cb1IaKVGojTA12yaMEvZsr7Hc7nLzLddz5PE5dMOmtFvQabtcedUQ\nwi6wZfMwkyenOPLIIpFvE2oa06cMxMkiy502o8NlhidMZDdkerJDbVOJxqEGrabPU/NdrrliB7P+\nAounlpg+5fPiA5s4sbDEK1+3hataI5ycajBUTJN82itdtl65lbmHGgRLAT/zoy/nicmT3H7PSXzf\npd1OWFwKeMl1dWZ9j0bcwdQlpojZvtfGskzGx2MmCnWCKGJ62WTxxDTVYQN7xKA2XGJEExyfWmHR\ni7BMjRu2beHuw7NESHRNS9UTscS20oN3IldHpPYPBLLnhUYi03K2dOZeEAYBYS9iXiRAbxY+8GPC\nOEQIDY0E07YZrRdIAp9CtcDxOUlnZZnxiQpdt8ndD6TeIpZuUCwkBH7C5rEhjhxtPc+fzTnHhk7X\nERs8cWYjt28jtw3S9q13G54vQs+j2+2m8cVRyFCtlg4qIp8gCLJygbwngkaCTCSmmZYlSpHORNpG\nqqZJkKsIk8zAU5we6AtDB12D3oxo/6BXaGc2tMxL0aMwxPdTL63Y7yDjGL8bgJQkfgRxQhKmJtl5\nWXrXS41Pt+3am36PJkHT0Qwd07YgjNEByemZck1PT/ExsndhACKRSJkg44TLDlwOpIPsuFdQl18P\nA5wfCCHeCHyCtH7xc1LK313nJq0JsQ7R1OcL4jwmA18oCCGGgM8BLyLdHv8KOMRF1o/zgfxFdd4L\n5kzL9RNJ6mJclaL1E0nqmOr7Pi972csyBY1pmtx1113ccMMNmTooiiI8L03stSwLy7IoFApYlsXS\n0lLmxVMsFrntttsyk2N1Ue66LmEYMj4+niW0QVqavLKyQr1eR0qJ76eTnu12m9HR0awkzHVdytUS\nkoRYRvihR7edkhRBELC8vIwXpB46V199NYuLi9QqQ5mCQ5FQruviOA6u62YqpziOMU0zPX/aNpqm\n0Ww2ufrqq9F1nXK5zLve9S4+8tv/KVPEqDKy54I80dRPlqhrhzy52L+d835IayFJkmezebkB+OfA\nw0KIB3rP/Trpte45SRWUMk3aU2qwer2elSd2u10sy8pSVk3TTM2he6bsar+zOiGtZpNYwHhliJWC\nySIBSytzLC7M8cobryc2BI1GA7NcoFork0SnFWjK+FyVLh48eJBOHBGtdEhkhEWAFicIzaFUtnrj\nnYjYc1lsLOBHHXZesYtdYxO4nQ6R6xMFIbXRCm7UYWRzndGJYbbummBpdoZrrr2C3/mP/4Hb//Lr\nWHbqozVUNCgSoFsVDLvMpi27qdY303YTqiNDJGaJUm2cYrVC2S5QrFWINHANQdFxME2TdruN12lR\nKBps37EZy7Ko14e47bbbiKOQwG1ikxC7Lpjmqu2gaVo21kv9ZNegRMRqsmlNBduzoe8zNHHaIiVJ\nEqSpk5qRkP2vxrHqN9D/uB86Yc8W4fS/MExWvedszOLX7M7zYWNzxNEtQogR4MvADno/IinlUm+5\nD5Oe0CLS+tBvPNPnFhxTfvCXXkdj8giP3RfjTJSo1yXFgkHomQQdjcteNMaya7A0fwJLONilCL/h\n0mhHhG3Ye/UmHn3wOEFH4GoRlYqJrUVcu3U/37z3ISZqVVxiar6JrtlElZDtY8MsT3dYchMqYwnt\nlocvIiwhKZTKhF6H7ft3ES3M4dkahw+2KNQNqkkJpxby4pdexu3fPcjuHTVmFubZtX0z7QWfuU4H\nyyzjlATFSgktcTFNnek5nwd+cIJ/8/brKXQ97vfnGRvfwvTkSZaOOtz/2CxveutWrIJOpBk8cd8p\ntu4eZmnewzA0ljsddM1jszWMOQS+L6hVDBbny9TqEXPzM5RKDnatSHW8TnOuxe6JCkcON/FWQva/\nYjPf+atDXHagxuFTzfQCQhPYJQOv5bF1e43JY8tIC0bqBVpLERgxhDYLzRb7J0Y4NrNAFAq6HRsv\ndpmcamJoOrapc932Mbqhz/1TCyRxry6cMCWQNJBCEMUGq46piUzjqUV6cZNI0fPX0VL/HeXo2vsx\nmbpFlITohoBE4hgGYRSj2QaakCQx2KZOrCVpmVfYS8AwJNvHhplttEnCCNePQVgMl2Fyrnvvel7c\nXwzkwqB9Z4eN3DZY//Zdd9118p57np27UrNxYRgSuh7tdhNN0xiqVTJlTtvtZHJqJXdXAy1dpAb0\nutb7LHR000bn9Oyb1MRpDwWpEyVxdiGhXoPVA4W8v5jWK01cu/2ph0DUKy1Iwig1TA/TJJvYD4mD\nmCSKSaIIL/KJwhAZxQSeT7vdxrQtrnnJi/ETCMN01te0dIpFJ2snWmrILnrJapLVM4tC6NnssR/G\naD3De8N20veLZ48BvlRw3XXXcc8991zwzvYIjCeAHyH1gLwbeKeU8rEL3ZZnQ09NPiFz0dTAT5JG\nUy/J06m6dSmlStX9Emkgyhbg28DziqY+XxBC/BJwHVDtjWE/ysXXhy8C/yil/JwQwgKKpBe1F1U/\nAHRdl07RXqUgUVAX//3Ikzz9yqEzKY7OhLyfjiKL1AV6v/JIvQbwwV/4Rfbt24dpmhSLxaz0bG5u\nLis/Er0yKt/3MzPi2fk5ms0VwjCk0+lQqVRwXTdTBClS6rrrrstK0JQnkeu62Wc2Gg0cx2F+fh4g\ni1TPm1orJZKUMi25LxZpNBoZoaL6VSqV0DQD27TYtWsXc3Nz1Go1hoaGMvWRWq/KvFkRSLquZyrY\nW2+9lQ/+wgcYHR2l2WyysLDAf/iN/4uFhbksxCLvSaQUu8+GvLJIXfyuXcWwmijK71P559V+5XY8\n4jhetxOdEKJFSvheSjgLdf2Gx6XYp53Ah19IWfPzIo7OFy7RH5HCpbjjwaXVr51SyrH1+vL1vnh/\nNgzad/bYyG2D9W/fcyWOkiTB6ymN4qBDwXZ6JqQ+dm9g3fWCNMUmN3usZllEHGAaGkQBfhBhlqpE\nUkPXVl94qEFo1Lu0Mgwj8xBSaWSsMXBNVTpnJl2kTOXege+ThBGB11NHhamhd+gHqWl2LxkuSdKU\njMQP0+hcXWPP/n1EcYzU9N4stoEQkiD0SJIY1/UYHduUXhToBglaRhydnrXVM/NW0BDG6Vl2oRlP\nuxi7lLGOxNGrgN+UUr6h9/hDAFLK37nQbXm+EEJ8DfhU7/ZDOauCv5dSHujvixDib0n7+pyjqc8H\nRJoM/EV6ycA94ugQF1cfasADwB6ZG7hfbP1QOFfEUX/ZUr+aPf+Zaz1WxFG+7GUt4ihJEorFIp/4\nvT/I1Da6rlMsFul2u/zhH/4h1157bVbC1umkyVFJktBoNLAcm5mZKVZWVrLSn0ajkZW7KUPhNLE1\n/e7Nmzdn8erNZhPbtlP1abebkTGO42TtU+SSOvep4A/1PYZh4Pt+piYKgoC9e/ejITLVi1JG2bZN\nq9WiUqlk66PZbFKr1bLvc12XZrvFxz/+cb7w3z/P+Pg4nU6H6elpjp88wac//cksHS5PHCkz5mdL\nP+vftmudn85EHK2FDUQcbehx4dlg0KeLBy+0X881Ve18Y0OX6rwQDHa8AZ4DNrqh5aB9Z4+N3DbY\n+O0DyFI9wjDE1AVhmJpuOpaBrguWlhao1ccyoqg/NSQMQ3Sp0Wm3sJ1iKnM3TITolTQkpz010tnQ\nXvypfloWnChfhtygNP89zzQFIwUZQRMnqQRfRxBpGsQ9H6I4NcNO1UkC4iTr99aJbdlAWxgCz+sS\nBFCwTTQkjZUGUtOIQh+wMQ2TVLvZa3uvsg3y/hKn+5EkCfogMO1CYSurDd8mgevXqS3PGeIcRlOv\nA855MvA6YDcwD3xBCHEtqfrrF7n4+pHhTET1mdRDqjxJ3V+VkHkGskA9vxY5pV7Pl6UJVZqcm3wQ\nQlCr1fjwhz+cKXbUexVR9IpXvIKFhQWq1WpW8qUIFl3XCYIgM5RWpWjKYNg0TQzDYHp6mlqtlkWk\nLywsYFlWVtochiGlUonh4eHsc/KR7kqRNDIyghBp5Hun06FarWalLtVqlSRJ2LVrF51OJ/W2SySz\ns7Ns3749UxXFcZwlqQFpimy3y/DwcNaWMAx54okneO1rX8vY2FhGvjmOw4EDBwiCAMuynrYd1XbT\nNC3zIVK3/HbsP5fn359fZq3ytny5ev++MMAAA5w9BkPFAQZYZ7wQyeCFwKB9Z4+N3DbYGO2Ta9yQ\nMcgYCQRhTBBEREGIY+hEK228ZkTiSmSrw/zRY9RrwzjCJOr4aFGCKUDIGKFrSAFFU9LpNsAwwbBI\n0JFRDEmMLnI147FEJGR+RnkVEj3SKJEiLaXt3RD6s5Z4aehomoFpWRiOjV6wEcXUHwJdQ+g6hmkj\nNQfNKqFHAhlDIGOkJSgNFWm1lgk6DUK3i2UXKZSHiIUBIuHe73+HamUY3SwRC5MgTsPnNJEgiLFj\niS7VhVqqoJIyJIlBJgKBjsY/HbXRAM8Poi+aOv9aT/2y/tL1M0DkkoHPtMxG70MPBvBS4DNSypcA\nHeDX8gtcJP04L1BkgWEYz3hTnjHqsdVLp1IGu2e6ARSLxTOSVC95yUs4fPgw7XZ7VZy6On84jgPA\n8PAwtVqaJlsoFBBCpOXLSZJFiSvSSQhBp9PJyKWRkRHiOGZmZoY4jrPSuJWVlYz4Gh8fx3EcCoUC\n5XKZWq1GkiSMjo4yNjZGrVZjeHiYubm5TDXlOA6WZWVKqnxUfKPRyJRYu3btAsgmMRYWFjh06BDv\nfve7Mc20VNs0TQqFAp1Oh5//+Z/PFFNrrTe1/p/JJybvZ5Qn8/KqsLXUafn0sAEGGODcYaMojgYY\nYIABBhgghUgH6kkCMknw3S6GiJk8cZyhShkv8KkPVTE1WGk2GCpXsEoFDCsd4CZSIJOULNGFxvT0\nNKAxNjGEbphgGD0T/lVfih8Fp0u3+maen9bE50mypMuLbLAspUREvVINCaHvnx4AGxoi0YjimK07\nttHtdul0OtQqZaQQ6HqaqDJcr6IlNqOjoxhWejGUiNTcOm1zOlscypBEauianhp+A0KmpNoAFxzP\nO3l2PSHOczT1BcB5Twa+QJgEJqWUd/Yef4WUOLrY+pEid+x5JrXQM5WYqb/qfn8S1ZlKm+Dp/jn9\nN2UArR7/6i//O2zTOWO5nBCCzZs343le9l6VrmbbqQq0VCjj+z626WSpX8orSfnzzczMZGbYqqzL\ndV3iOMb3fSYmJgiCgG63SxCk5dmaplEulzFNk263m5JjupUSS6U4I3VarRaNRoMtW7awaSwtg1OJ\nW1u2bMlKyFTJm23b2fpSryllUKPV5PjJE0yfmqJgO9n6V2Vupm5w7dUvzhRLQojsfl5hBGQeU6q/\niihSryvVkCKJ8q/JnrGxard6Pm96nFemAbjCXXufvHBY9wm784BBny4evKB+bRQ69lLdOHDp9u1S\n7dcFgxDijUKIQ0KIp0Rqarkebfi8EGJOCPFI7rlhIcStQogne3/rudc+1GvvISHEGy5A+7YLIb4j\nhHhMCPGoEOIXN1IbhRCOEOIuIcSDvfb91kZqX+/7dCHE/UKIv95obQOygqr8TQJxAoHv43U7GEmA\n31giai3jhj6RiJEi5sknH6dYdChVKkgtxCxaaLaJVXBS1VCcQBJRKTgEQYDb9dGMdBAtSFa1Io5j\nTMvCtJynzUqvle5yNuqc/IVGfvZbzZyqmxeFYOhs27mDQqGA7/tohk4iTkfQ2qaJoQmSOGbHzj2r\nkn/ybRZSEhGTaCJNhRPnJlljgLPG3cB+IcRukRocv4M0jXbDQaQ7+TNFU8PTU3XfIYSwhRC7eQ6p\nuucbUsoPSSm3SSl3ka7rv5NSvpuLqA8AUsoZ4KQQ4kDvqZtIk5wuqn48V+SPlc+kHOlfTh2z1bG0\nX2nUf1OvqeUz8ggdU7f4wPs/yO/9t9/PStD6JxJErzy6XC5z8803Mz09namEFNGi7itCqFwuMzEx\nwdDQEIZhZCogy7LYsmVLRugoDyXV1qGhITqdTmZ4XSgUGBkZoVwuU68NU6sMUSlVKRcrFItFkiSh\n3W4TBAELCwu4rsvYWFrSXS6XaTabtNvtzDtJfY/neRlhVavVMn8kTdMIggDf91lZWeHIkSO87W1v\ny4gvtW5s284+56d/+qez19fajv1+Vmo79J9j+8vO+rd/npjKf26edDp9jlzfS9+NoPQ+1xj06eLB\nC+3XhiCOLtWNA5du3y7Vfl0oiDRZ59PAjwFXAu8UaQrKhcYfAW/se+7XgNuklPuB23qP6bXvHcBV\nvff8P71+nE9EwC9LKa8EXgm8v9eOjdJGH/hhKeW1wIuBNwohXrmB2gepD8bB3OON1LYsYSU/eIyj\niDDw8LodosCjYJn4nTaGkAiZYJDg6JD4XXQR47aakMRoJAghSZLeTKYuSKKYxYUFNo+NU65WkInI\nPH7yg8g4SRAivdDIz07m25YfzD6bt8YzoZ98ys/AJkmCZhqESUzJKSDjmOZKA8tIfSdiJFHoE4Qe\nrUaDI0eOUKsPoxk6MZI4SbIIZ43UZNv3upQKNqapr/KSWNWOQZnaBYGUMgJ+Afhb0t/ll6WUj65v\nq84IFU39w0KIB3q3m0mjqX9ECPEk8M96j+n1Q0VTf5PnEE29jrgY+/AB4H8IIR4iPd/8Fy7OfiA4\nrfrJH/sV+dB/fMoT90/7rN7reYVo3uh6FSGUey1PJqhSLaUEKhaLfOxjH+OKK67IItLV5/e3Q5FH\nhmFw8OBBgiAgiiK63S7lcnlVqZwis5QJdX8fy+Uy1WoVy7IYHR3N1KlJkmSeRHmVUaFQoFgsZmVl\niqjyewrWcrmc9WdiYiJrbxRF1Ot1qtUq7XY7S2OD1FOw0+lQKBSy9aPK03Rdp91uc/z4cRqNBq95\nzWtWlfCpJDhFNt14440EQbAqsU5t87xqSEHdV9stPwmSLxvsNzNX78lDrbcwDDO/wNRTaTB5MsAA\nZ4tBqdoAA6wPXgE8JaU8AiCE+FPgzaSDvAsGKeXtIjU9zePNwA/17n8R+Hvg3/ee/1MppQ8cFUI8\nRdqP85bS0jP9nO7dbwkhDpIafG6INvY8Jdq9h2bvJjdK+0SaJvTj9NKEek9viLYpaLnZv8Dz8X0f\nt7uSSvNdH+KYptflG3/1l7zzp9/KkclJapUqs0szLBx5kge/dwevuOnH6Riwc9/ltD0XzSmksncB\nBduiWizi2DZRApqhAamXkZQQi5g4STBNG800kLqGJgSil6LW755wNkRRPxRZ1k+YKZi9iwXbskha\nHpHnE9sBdrmIZhokSZQSaIbJQw89xJt+/BYCKdF7n6XrOlEYYiAgkRR0g9biIrpTxrKcVL2kJcjk\nzAa1A5w/SCm/Dnx9vdvxbJBS3sGqoqJVuOkM7/lt0uPNhoOU8u9Jj3dIKRe5yPogpXwAWCuU5KLq\nx3PBWola/eqUtdCvUlIkeb6MLa8gzRsqV6tVbrzxRl73utdRdEoEQUC73c4IFFX6tNbxUpWmffCD\nH+TWW2+lUChQKBSyNDTVhqGhIe644w5efv11FAqFzPtItd113UxppPyOSqUSvu9n3kO+71MslqnV\n6vh+SKVSQxcp0eL7Pp1Oh+HhYVqtFpCeD8bHxykUCkxOTmYeSOrYrwyvPc/DsiyiKKJUKlGr1TIS\nrNPpoOs6rVaL2dlZWq0WYRiyvLzM2NhY1jZglc+RlJLrr7+e++67L1vfZ1IPqdfz212dI/MEkVqf\nitBSSXTPtH/kS9n+SZqADTDAOcKGUBwNMMA/QayVrLNREk+eKaVl3dosnnuqzwVtY68U7AFSb4lb\nex4UG6V9Kk0oP0rbKG3LkMQxURji+34qk/e7JIELSYhMItrtNM1lenoWEXjc/Y//wJ23f4dgZYWy\nJihpgu7yMicOH8YUGkmQoCPodru0Wi3u/sGdPPTQQ/heSBCkkfeSODe7nc6SanpqYt0/M5rHCylV\ng9PGokEQZDOh/aommST4XRcRJSRRTLfRotVoEocRcRwThiEaCZZhcuDAARLSixLNWF1uIYTA67r4\nzTYijtCl8n4Qq5YZmIgOMMAAFxZrl6LlL/6V6iTzzemVluXfp8gAOE0O5MvX8qVPeSWTlBLTNImi\niJtuuon//J9+m9/8jd/ix3/sFkaHx7LoemVMDavJq/z3qO9VKqC5uTm6XoeO285ej6IoUw/deOON\nmLpFrTKEbTqUCuWMHIGUGFEm1cViMSOcFMk1PDxMrVKlYDuUCkVE7xSlyJ/R0VHiOGZ4eJiRkREm\nJiYwTZMgCPA8D8dxWFhYwDAMXNfN3qvW/8jICPV6Hdd1e0EKp1VMy40lZudnmJqa5Fd/9ZczJZV6\nL5AZbheLRUI/4v/4V/+am2++GSllZpbdv136VUNqnan1rs6RefVR/321bP9+pB6faaLmQkJsAIuK\ns4HY4LYWZwOxwa0wzgbiAthnrLviSAjxRuATgA58Tkr5u+vcpOcMIcR24P8jvfCSwGellJ8QQgwD\n/wvYBRwDfkZKudx7z4eA95G6ln5QSvm369D0Z4VIS1TuAU5JKW+5FPo0wPOHlFIKIdZ9gkb0pfrk\nL9rXu409+f+LhRCwrlskAAAgAElEQVRDwJ8LIV7U9/q6tE/k0oSEED+01jLrve4AYiI8r43X6aax\nw3GI7gfEAohDjCBi8oF7mX7sMa7YupmHH3uExx5+hNe8+lU8+thBlpYWODG/zBOPHUIYNte9+jW8\n+W0/Q3lsnE6QsLy8zO6du9m2fS/LU3Ns2rmbRuBSGa5i+5AkEssy0UwDhEAKCehICUKcHtzmy8n6\n8WwkknqHICVuojAkDAK0MIIwIuqVliWaho6AkonhecydOs4j99/D4kqDm255MzPNFvurFsHKPLML\n0wQjQ2hBBy/wMCVopokbJ+imAVGEnsAjd92JvXSSyvb97Ln+hwkijVhEaCJGN5xUWTWoVBtggAEu\nKGSmGHkm5EkgwzCy+HlFBPST+EmSZERPf8lbnpx40ZVX85a3vIVKpZIRNkKITN3j+z61Wi1T4DxX\nbNu2jc2bN2c+R2EYZmllyv/H87ysJK5SqaSG1rqFZVm0Gm30wunyLA09U9QYhpEpkRR5pZQ27Xab\nWq2WJaWVSqVVhtPKn2jr1q0IIdi0aVOmklJknDL1hpS8UutaCIHneXS76Tm62+3Sbrez9/SvZ0hV\nR7Zt4/s+SZLwzrf/LD/0utfzrW99i69//euEIniaoTWwyqdIbU+1bfJkoXqvgnrt2fan9YQ4bVHx\nI6QTc3cLIf5SSnlBKw3OEn8EfIr0mldB2R78bo8E+zXg34vVtgdbgG8LIS7bSKWyPSgrjPuEEBXg\nXiHErcB7uHj7pewz2iINtrhDCPEN4C2coz6t6zSj2Dg+L2eLje6/8kKwoX1RLgFs5MSTWZGmsyA2\nQEqLeIZUn43SRgAp5QrwHdLfwUZon0oTOgb8KalPSZYmtM5tA0AiCf0Ar9MlCkPingdBKBMSJImA\nRBfc9o//SKTpPDl5km/87W3cd/+D/O+v/jmTx09Q6MnWi47NDa96BaNDFf77//sZvv61r0LosjB9\nkm3jYyTdBk8dfIDO0hT1okV3aXGV38V6wPf9VD3Ul/rieR66rtPpdDBNk1qthtttEwYejzz8MI88\n9DBeu8Xd3/8ed373Dlbm59A1E98L0XUDEMQSQDA8Ns6OHTsYHaoSdZqYpCouSzcGZWoDDDDAukBx\nBer400885Eua8mW9ykNHlXmtCgLIkUOK8M8rWzZv3sz73/9+PvKRj/De976X0dHRzNvHcRxqtVqW\nXmbbNkNDQwAZsaLalW+rUsGo1zRN461vfSvz8/M0m82MXFEEkpQyI3d0XSeOYyzLyryKxsfHcRwH\n27YzEkWphRzHydaLUgKp5LdCoZCdNxTRFccxjuNQLpczUqxWq2Vld8p3SH2HYRgZ4aTaq0yydT0l\nsBYXF5mbm2P37t2rFFfAKmJHqY5UCVsYhkxMTPCOd7yDz3zmM7zpTW96moqov0ytf7/IE4Z5gmmt\n5fPn0w2EzKJCShmQjsvevM5tek6QUt4OLPU9/WZSuwN6f38y9/yfSil9KeVRQNkebChIKaellPf1\n7rdIr3mVFcZF2S+Z4kz2GeekT+utT79of0Rwae50sMoX5XO5py/qPm1AbORknQ2T0iLSM/+GTfUR\nQoz1lEYIIQqkM0mPb4T2yYsgTSiJE9qtFlEYErgeoecTBSFRCH4o6QYxbT/mV37jNxnasY3f//wX\nEHaRYnUYp1xnfnGF6ak5Jk+coNvt8vff+Q5/d9utLM+d4tThQ/zGr36Q+777dyyeOsVnPvFfue+O\nv+Fzf/CfufUrX6IURoRJDJogzrkeXEj9lZSSMAxBCDzfT8vQkhjbSC8Kojjhu9/7ASePH0OXMY/e\nexd/8PGP8nsf/V2++Zd/yfduvY1CLKkLjdtvvZWo08FbbhC5KSHV9l127N1N1/extZij99/OqUfv\noqhJ4ihadcE1wAADDAAghBgSQnxFCPG4EOKgEOJV4jyUbyjSIH/rPx6t5YeTN5vOJ6QpwiJfkqRp\nGm9/+9v57Y/8F37p3/4ye3btZaQ+mpWCSSnTlMqeMbTv+2iaxtDQUGYGrT7n2dLdlKrHcRyWlpYy\nEqrZbGalb8ozKN8+pZJSfxWhpHyIlMpITTCoMi7f97O/YRhiWVb2nCJZlNqoUCik4QpxnJlX51Vb\n6jPDMMxKv6LeOSJJEhYWFlhaWqLRaOC6Lu973/syk+5+gk7dVMmdCpxQkzSlUon3vOc9fOpTn+KN\nb3zjmts9TwLmoQyuL1JsZIuKs8GGsz04W4gNaoVxNhDn2T5jvUvV1mrw9evUlheE57HT/SD3tg25\n03HaF6WSe+5i79OGgpQyEkKoZB0d+Lxch2QdIcSXSI2SR4UQk8B/JE1l+bIQ4n3AceBnem1+VAih\nUloiLkxKi0r1ebh3IAT49Q3Uxgngiz2VnUaakPTXQojvb5D2rYWNsu4QondhEJ72+dEk6DEkUUIQ\nRhCEPH5iGilMKtVhpmaXcHTBcrODFAYt16PtuVhOleX2CvOdBQxD58TMHIZIuO0fbue7t/+AiS3D\nCBkyPF5lamaO7333Lm5885vT8od14k1M2yKIwiyRR+9dGCyvNBBxzOzCPN0g5JHHHmX77j1882/+\nmpddcw0P39Oh2/Hw3YATR47yxT/8HP/7m9/kY7//SfZecTmu10YzNZIgwNIN7rr3QRaXvo1ZMNl9\n+dUcOjHNLW9/NwkD0miAAQZ4Gj4BfFNK+bbexFaR9Lx7zso3JKdLf/OkwZnIg3xpliJQFFkkhMD3\nfUzT5IYbbuD666/nqScO8+pXv5pKpZIRI4VCIVP75NUtlmWhaRrdbpc4jqnVahkZAqwyXs63Ka+U\nUa8VCmkww//5b36eL3/5y5QKafKZMpJW5WaqdC3v4xOG4SojaEW2RFGUlYspUsmTHkEQZISXMtn2\nfT9bh4qwCYLgaQbTsme87boutm0TBMGqBDe1ztrtdkpqETO/OEe73U5NtxOB1iOB8tsvv32SJMGy\nrFXqpWq1iu/7AGyd2Ma/ft/P8RM//iZ+/w9+jyeffJIkSTLyr7+ULV+eli8bzxOQ6n35fWiA84+N\nYHtwthAb2ArjbCDPs33GehNHlwQupZ1OXCS+KJcC5AZI1pFSvvMML22IlBa5wVN9pJQPkRLG/c9v\nqMQeuYHThIQQJOL04NyPQkqxRhKFSD/E7XRYnl9CxBrXXPVilpZWmJ+eYn55haFykRNTU4yM1Gm3\nm1h2AT+KKZoOs40mlg7Ly0vIxOJ422N8U517Dz/MZ7/wa+zYuReXkCDJmWT32tPvs5D3wFCDVfX4\n+cIwDCzLIo5jkijMBu2G0DOzbBmGtFoNipUh9lx+BZ/+9CfRS1UKxQoPHjwEpsOJhSUsTaMzNUvg\nn+LKfbv44mc/xfY9+1hsNHn5K69n3749GJrGF77y52zeNIZmaHSMMr/8279Ao9mgWqsPBtYDDDBA\nBiFEDXgdqc8HvWqAQAhxTtM4ld+bECIzV16jLU8jJPKG/qpszbIsPvD+D7Jr1y7K5TKnTp3iZS97\nGaVSKSOYVGR8fzmUIo3gNDFRLpdxXTfzFur31em/9StjLMtC13UWFhYYHx/HsiyEENRqtczvyDAM\ngiCgVCoRhmFG7ijVkVINqXNFrTKE67oIIYgClyAOMlJGtUNKmRpS99RH6pzqeV6mAFLrEtKSaOVl\npEgs5Q/VarUyMmx5eZmVxgrtdpuFhQU+8IEPZKVypVIpI/DyaXXqsVKDeZ6X+vv1vkNNlgghGBsb\n4zd/47c4duwYH/3oR1laWlq1TfoRRVFGivXvH0BW/r0BsZEtKs4Gs0KICSnltNgglhHPF+IZrDAu\n5n5Bap8hhFhln3Eu+rTexNFFtRHWwiW40ylflJsBB6iKnC/KRdqnAQYYYANC0zQKxSJJGBBISexL\nEiR+HOFFIZ6MiJBYBQcnKCJMnfHNm3qzxyVOnTpJN5F0F5eRQscPIprtFmObN7F3715Gh6v4vseB\nK67hqsuv4PWvvzEtGUgi2qGPXTg9uIbVqTBnwgslWtQMt5SSUJ72bAjDMCsvKJgGoWFyamGBQrXO\n9a95PXfe9yCjwyMkMqJWKtIIQnQZ0O120TSDMauMu9Km89RRitUhHn7kEA89/DiXX34ZRnWIR46e\n5Jd+7cP82C0/QRjFOKXyC+rHAAMMcEliNzAPfEEIcS1wL6nn5QtWnQshfg74ud59gFXKEvW8up8v\nfeo/Rqu/jUaDD3/4w1iGnaVoVqtVHMfBcZzMkFope/LeR/lSNFXGVS6nx0XP80iSJCOf+s2Y+xVS\n/eRSrVbjZS97GTMzMxSLRRzHIQiC7PODIFh1nonjmEqlkhEsqt3dbjdrt+pTnmhTpJSa7FBkmlqv\njUaDSqWC7/tZX6Ioynyd8iSLInaSJMkUXMoMe3l5Gdd1s1Q3pe4Jw3CVB5T622+0rc5tqpRPEYZq\n22qaxp49e/jEJz7B1772Nf70y19aVcKWVx+px3Ecr/K4MgwjK7Xr314bBJlFBel10juAn13fJr0g\nKNuD3+Xptgf/UwjxcVIV4nm3PTgbCPGsVhgXXb+EEGNA2CONlH3Gf+Uc9mm9iaOL+kd0Ke50UsoP\nAR8C6CmOfkVK+W4hxMe4SPs0wAADbExIBE6xQKnoQJyAEMRxiNd1CWWCWbDREoEVQ4KkFXiE7S7d\ntksSxZTKVRIEteE6uCtgmEgEsdAQuoaupYPkSBoUrAKW4RBFIdWiIE48pEg/l75Z6P5ZzvzsZZ+i\n9Hn3WQiBbhgUDQOKDkkQ0G40CcOQ5koDz/MwDQ3T0Ng8sRVpWFz90ldw7Uuv58iRIwRhl0cfeZiV\nxQV8r4uUknK5jFEq0+xEGGGL3UNb+Ol3/Rx79u5CCMFb3vUvceOE2qYthFGMsHQi38M6m402wAAD\nXMowgJcCH5BS3imE+AS9MBSFs1WdSyk/C3wWQNd1qQgAVZ4Ep1U//YqRfg8kIQQ/8RM/wWtf+9qU\nSElE5luk/HaCIMjUO6s62Etnk1LS6XRwXTf1lwtTBejCwsIqUief1tWvNuqHKoMrFArceOON/NEf\n/RFhGOJ5HpZlZSVhShEUxzFJklAul58WlKBK1BTpout6Vjqmvkstr3yMlFeRUrb21jsjIyOZ2ket\n3263mxFoyttJKbnUulHrZWVlBd/3KRaLVKtVQj9dp+o7FYGTb4/qo1IeKW8nz/MolUqZ0klB7Q/v\nete7+OF/9no+9rGPcfz48YzgU5+ltqfaJkp9dDbn4wuJjWJRcTYQG9/W4myw0a0wzgbn3T5jXYmj\ni/lH1MOluNOdCZdinwYYYIB1hAB00TsN9f7oukXBKFDILZckCUhJ2S6QlOpUxk77GEgpiaVEVIaz\n5Y3cYNQwweo9jESIMASJFGhaEYFI8yZEPzl05javVaqWl8yfqZ8p+uXzGpppUB520HSd8rCXGp22\nGhSG01KJ6vAw9XqdTqfD7j3baa50OLD/SlqtVibpr1QqONUyV1xxBS960YuoVqtUq9W07MGyiHVB\n1TAwSDAMgZCSku2cuZMDDDDAP1VMApMyNVQF+AopcXROVedCsEpBkr+/erlUHaSSzlzX5W1vexs/\nctOPUigUVhEUilxQBIQiaFQJsCr7EkLQ6XQyckjXdTzPwzAMPM/DcZzsO/vVUAr9ZXP584ciccIw\nZGrmFIWSg25qOEUbIU+bTkdRhOd5DA0NZUqg/Gd2u90s7l6ddwqFQqYKiuM4I8dqtRpRFBEEAUND\nQ5kZt/JcUqRNGIbA6TJB3/fRdZ1isYjruliWxcrKSvY9HbfNcmMpW2e/8PMfIA7TdaKS2PLrXq3P\nOI4zI2zl7aT6oGkazWaTer2Orqcl2r7vr1JFTWzawv/90f/G1NQUBw8e5Bvf+BtOTJ7MEupU+xX5\npFRh+W2wEbERLCrOBhvd1uJssNGtMM4GF8I+Y70VRxftjwguzZ0uj43sizLAAANcuuifOVxdInC6\nLEAt23/B0X/xoWlnjvh9vsgPSM/JDKdITUaREttxsB0HOVQhkQma0EhkwhW52dQoiLPBsuM42Yy1\nZVp4vZlzNeuuBteWoWft3agD6gEGGGD9IaWcEUKcFEIckFIeIh33Pda7nTPVuRDaKpWRSknLexAp\n5ZCmaVTLNW655RZe/epXMz4+npU8qXKnvNolrxxttVqZx0+3283KtEqlUkbAKIWOKgdTWCu9q/+8\nob67/1yiEt+uvfZalpeXkVKmZXTlWmpu7XmYpkm1WqXb7eI4TuYbpNRGlmWtMotWJJnrutm6UsSS\nIlFs285MtNW6CcMw669SLimSJY7jjJDTNI1Wq5Wtq3a7zcrKCp1Oh8nJyWw9KtKqXq9nZXN5Ukit\nE9kz94bUvDuv2tJ1Hdd1qVQqmQ+SWudK4RVFEbt372bTpk1cddVVhHHEF7/4/7d3rzFyllUcwP9n\nZnbnttttm7YL9sZCG0wttiBBDBES1FAViiSNwVRCiA18AIKJxhT4AMZY/WQ00QpGCE0gklpNJCRi\nCJLwRUGptcqlF8DSsi1NL8vuzOzcjx/e9zz7zHS23S7szDvT/y9pmHlnZud532lo+5/znLMDe/fu\nRbVadlvq/Gqkc/++k+ALIyKalY4HR0RERL7mv4T7/xiwh/xvqc9Z8SOtv9XueA+E8O1VNbwdThqS\nGCAx1AHUw+dJLOwdkZwKgepQxBMJxMP7qUzafdNbr9fRl+BmNCI6b/cDeEaCiWrvArgL4baHT6rq\nPBYTF3L4/w+34KFarSKVSqFarWLz5s1Yd8V6DA0NuccHBwcbeuv4vW/q9boLOTQcSZ9IJFwfIT9g\nsnDdQiqgceuTv42u1Ta1VmGFVRPZdrWdO3dibGzM9Qay51gl0Lx58zA+Po6+vj7Xj8mqoJonoVkz\nbJu2ZkGXHbcKI/+aZjIZ5PN5pNNplMtltwXO76NkwY31OVJVnDx5EnUE4VsikcDKlSvdNjYLumKx\nWEOfoeY/W636ysI568VkQyH8CWqpVMoFQfZcVUUymcTAwAAOHX4fW7ZswbZt2zA6esQFe37DbXtv\n//eTv54g4Ir2ljaiKGNwREREkdIqOLLj8fh0FT9nTl+Z+nmtexS1eg/g7GXu01UZNY8APp9Q6oxq\nKe8t4ra9zY5J2HvDXhIeVxEogFg8Huy+i8WmXjLjlRDRhU5V9wC4usVDn2DVubh+O9bbx69WWbx4\nMTZt2oSRkRFcdNFF0BpcWOE3h/YrXWycvTVIFpGGYMp6/thjNibeKlyaq3dmUlE6XQVSLBZDNpvF\niVOCY8eOIZvNolQq4dSpU1iwYIF7P+v5Y9PN/ODGKo78ddgxq9Ky6iPbhueHTdZPyc6xWCwinQ6+\nXCiVSq76yPocFYtF91mMj4+jv78f77x3EJOTkygUCrjnnnvcYIf+/n4kk0n3GU5XeSUSTKk7efKk\nCwqBqUqnQqGAgYEBVzlrn4EfHokIYhCsvmwVDh8+jNUjl+H06ZPuHOwa+SGbHwgCU38+qypjI6KP\ngcERERF1pem2jTUHP/5jrXoUNR+fjdlUL033jtP+JNVpGzAxHCKibmI9iIrFIsrlsuubc/fdd2Pt\nmiswODjotuHGU3EXKABAoVBw/YesCsYCHwsQLFCwCh17P6s08reB2day5j5GVr0yHQsu/EoiYGqr\n2tDgfHzxuutx9MNR5HI5ZJZkUCjmoTU0VFBZFZCFXrYuC3+s+saCITsHU6vVXNWQXROrBLJqpHq9\n7hpiZ7NZ5PN5lEolFy6JCFKpFCYmJlCplXHi1AlUKhXk83nkxvNI9add9VYymXQVX7Yen4VJfiNv\nO27Xx3ospdPpMx63z9Qqs1SCSW0jIyN48OGHsO/A29ixYwd2797dcN2br4tffdU89IKIzp9EvQs9\nERH1JhGZALCv0+s4i0UATnR6EWfB9c3eSgAPazDpiYjaKJlK6vIVy1xAkk5msHnzZqxatQrDw8Oo\nVqvIZDINzbOr1SoqlYobAQ/AhT/WZNn65YgIyuUycrkcRARDQ0MunKjVai6IARrDHqucKZfLqFQq\nSKfTZzTx9se/+1PKgMZJZzbK/vjx43jyqSewZMkSDAwMIJlMYmhwvguFbFx9PB53t227nd8w2yp9\nrCLJKptsYptt2bIgCIDbDmY9huwaVKtVV5WTSCTc+eZyOdRqNRz9cBSTk5OYmJjA2NgYVixbidtu\nuw2JRAKpVAoLFizAwMCAC338xuQ+245n1UyFQsGFWVYBVqvVXKNzY9fQGmdbMGbPyefzmJiYwOjo\nKB5//HEcGT3sXlMul1EqlRrW4AdHuYk8atUav2shmgVWHBERUafsU9VWWyIiQUT+yfXNXjesD+F4\ncCJqH60Hwcfw8DBuueUWfHbtOiSTSRdAZDKZqWoTL0CwqhgLKSxwsX49fvWPNVjOZDKuB05zjyK7\n3dxLyKqbLKTyH/NfZ/ftNfazLKSxYQW2La5QKLheQxa62LYtC28sEMpkMq5Rtb1PoVBAMpl0z7FJ\naMDUdr1KpeKupT9VzgIz64tk2+dyuZyrDAKCsAkAJicnUSwWUa/XsXHjRrcdzMK55mbm01XdWvWT\nbX+zcMd+XrVadZ9tJpNxn1Nzryb/fRYuXIhsNouhoSE88sgjOPDOfjz22GMoFosoFouIxWKu8qg5\nIGRiRDR7DI6IiIiIiKgt+pP92Lp1K5YvXx5MR4tNVe5Y8GJhi19109fX57YxWU8jAA3b1ix0sNdZ\nCGVb1+w1VsFSq9UaprLNRKvqmmZWnVMqldDX14eJiQksWrQIk5OTiEvCTXVLp9MoFosu0LGtc7Va\nzVUllUolpFIpFzBZdZT1CUqlUhgfH3cVTXZt7LkAXE8i64Vk18iqlfL5PPL5vJs+Z4GNvbd9Nrbl\nzyqNrIqpFX9r2rx581yo4/ci8qfJ5XI5txXOr0Dygzo/bOvv70cqlcK8+YN49NFHcejQIWzfvh35\nfL6h+bnfOJvREdHscS4vERERERG1xdJPLcWqS1cjk8qiL97fEPiUSiWUSiVUKpWGf/j7lT8AGqqE\nmieeWUDU3GTZtqg1b4vyt6PZseZwaCY98fygxN47Ho/j6quvQSqVQi6Xc+uqVCoAgoloNu3MrxKy\niXB23CpzLOixMMQmqWUyGRQKBYyNjaFarbpQKB6Puwqfjz76CCKCbDbr3qNYLOLk6ROYLBUQSwhi\nCXE9pMrlMlasWNFw7SzYsc/AbjdPVvOviV3P5r5Ufj8q/7MrFosYGxtzfZgqlYrbuudXmdk2vKHB\n+bj0kstw1frP4cc/2oabbroJQGOT7JmEfUR0dqw4IiKiTon6NiGu7+Ph+ojoDPv378/dcMMNUe5v\nN1NR7uM2U+c8h53P/r5NS/lYZvpZrJzrhRD1KjbHJiIiIiKitoh6/7OZ6oXz6IVzAHrnPIiijFvV\niIiIiIiIiIioJQZHRETUdiKyQUT2ichBEdnaoTU8KSLHReS/3rGFIvKiiBwI/7vAe+zBcL37ROSm\nOV7bchF5WUTeFJE3ROSBiK0vJSKvici/w/X9MErrC98vLiL/EpHno7Y2IiIiom7C4IiIiNpKROIA\nfgXgqwDWAPiWiKzpwFKeArCh6dhWAC+p6moAL4X3Ea7vdgCfCV+zPTyPuVIF8D1VXQPgWgD3hmuI\nyvpKAG5U1XUA1gPYICLXRmh9APAAgLe8+1FaG9GFrFf6i/XCefTCOQC9cx5EkcXgiIiI2u0aAAdV\n9V1VLQN4FsCt7V6Eqr4C4FTT4VsB7Ahv7wDwDe/4s6paUtX3ABxEcB5ztbajqro7vD2BIABZGqH1\nqarmwrt94S+NyvpEZBmArwP4rXc4EmsjutCpak/8I78XzqMXzgHonfMgijIGR0RE1G5LARz27h8J\nj0XBsKoeDW8fAzAc3u7YmkXkEgBXAng1SusLt4LtAXAcwIuqGqX1/RzADwDUvWNRWRsRERFRV2Fw\nRERE1IIGY0c7OnpURAYA/AHAd1V13H+s0+tT1ZqqrgewDMA1IrK26fGOrE9EbgZwXFVfn+45nb52\nRERERN2EwREREbXbBwCWe/eXhcei4EMRuRgAwv8eD4+3fc0i0ocgNHpGVf8YtfUZVR0D8DKC/kBR\nWN91ADaKyP8QbIO8UUSejsjaiC5YURiKMFNRH1BwPnphUICIzBeRXSLytoi8JSJf6MbzIOpmDI6I\niKjd/gFgtYiMiEg/gsbEz3V4TeY5AHeGt+8E8Cfv+O0ikhSREQCrAbw2V4sQEQHwBIC3VPVnEVzf\nYhGZH95OA/gKgLejsD5VfVBVl6nqJQh+b/1VVb8dhbURXagiNBRhpqI+oOB89MKggF8AeEFVPw1g\nHYLz6cbzIOpaDI6IiKitVLUK4D4Af0Hwl7+dqvpGu9chIr8D8DcAl4vIERH5DoCfAviKiBwA8OXw\nPsL17QTwJoAXANyrqrU5XN51AO5AUC2zJ/z1tQit72IAL4vIXgRB4Iuq+nyE1tdKlNdG1OsiMRRh\npqI+oGCmemFQgIgMAbgewZcpUNVyWOnaVedB1O0k2OZPRERERET0yRORTQA2qOqW8P4dAD6vqvd1\ndmXnFg4oeAXAWgDvq6pVWwqA06o6X0R+CeDvqvp0+NgTAP6sqrs6s+qAiOwC8BMAgwC+r6o3i8hY\nl53DegC/QRDurwPwOoIqqg+66TyIuh0rjoiIiIiIiJpEeUDBufTQoIAEgKsA/FpVrwSQR7gtzXTJ\neRB1NQZHREREREQ0l7quCX23DCg4i14ZFHAEwBFVfTW8vwtBkNRt50HU1RgcERERERHRXIryUIQz\nRH1AwUz0yqAAVT0G4LCIXB4e+hKCbWtddR5E3S7R6QUQEREREVHvUtWqiNhQhDiAJzsxFOE82ICC\n/4jInvDYQwia6u8MhykcAvBNIGiyLyLWZL+KaDfZ78ZzuB/AM2Ho+C6AuxAUQHTbeRB1LTbHJiIi\nIiIiIiKilrhVjYiIiIiIiIiIWmJwRERERERERERELTE4IiIiIiIiIiKilhgcERERERERERFRSwyO\niIiIiIiIiLnjegcAAAAgSURBVIioJQZHRERERERERETUEoMjIiIiIiIiIiJq6f9OgcvEre72aAAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x165684e6ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import imshow\n",
    "from PIL import Image\n",
    "\n",
    "from keras.preprocessing import image               \n",
    "\n",
    "test_filenames = glob(\"test_images/*\")\n",
    "\n",
    "test_images = []\n",
    "\n",
    "for file in test_filenames:\n",
    "    img = Image.open(file, 'r')\n",
    "    test_images.append(np.asarray(img))\n",
    "    \n",
    "plt.figure(figsize=(20,20))\n",
    "columns = 6\n",
    "for i, picture in enumerate(test_images):\n",
    "    plt.subplot(len(test_images) / columns + 1, columns, i + 1).title.set_text(detector(test_filenames[i]))\n",
    "    plt.imshow(picture)"
   ]
  },
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    "## Remarks\n",
    "\n",
    "Provided that I found results in AIND forum with mostly 15-20% accuracy, I am quite happy about the results I have. These ideas could definitely improve the accuracy of the algorithm:\n",
    "* There are many images in the dog dataset, which contain other objects and animals. A cleaner dataset could have provided better accuracy\n",
    "* State of the art CNNs are trained on millions of images while the size of the dog dataset is very small. It is obvious that a much bigger set of data would have given a better algorithm\n",
    "* I was trying to make the CNN more accurate with trying out different random hyperparameters. Searching for an optimal set of hyperparameters with an algorithm could have given an improved CNN"
   ]
  }
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