not_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "from __future__ import print_function\n",
    "import imageio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import tarfile\n",
    "import scipy\n",
    "import skimage\n",
    "import tensorflow as tf\n",
    "from IPython.display import display, Image\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from six.moves import cPickle as pickle\n",
    "\n",
    "# Config the matplotlib backend as plotting inline in IPython\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found and verified ./dataset/notmnist/notMNIST_large.tar.gz\n",
      "Found and verified ./dataset/notmnist/notMNIST_small.tar.gz\n"
     ]
    }
   ],
   "source": [
    "url = 'https://commondatastorage.googleapis.com/books1000/'\n",
    "last_percent_reported = None\n",
    "data_root = './dataset/notmnist' # Change me to store data elsewhere\n",
    "\n",
    "def download_progress_hook(count, blockSize, totalSize):\n",
    "    \"\"\"A hook to report the progress of a download. This is mostly intended for users with\n",
    "    slow internet connections. Reports every 5% change in download progress.\n",
    "    \"\"\"\n",
    "    global last_percent_reported\n",
    "    percent = int(count * blockSize * 100 / totalSize)\n",
    "\n",
    "    if last_percent_reported != percent:\n",
    "        if percent % 5 == 0:\n",
    "            sys.stdout.write(\"%s%%\" % percent)\n",
    "            sys.stdout.flush()\n",
    "        else:\n",
    "            sys.stdout.write(\".\")\n",
    "            sys.stdout.flush()\n",
    "      \n",
    "        last_percent_reported = percent\n",
    "        \n",
    "def maybe_download(filename, expected_bytes, force=False):\n",
    "    \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "    dest_filename = os.path.join(data_root, filename)\n",
    "    if force or not os.path.exists(dest_filename):\n",
    "        print('Attempting to download:', filename) \n",
    "        filename, _ = urlretrieve(url + filename, dest_filename, reporthook=download_progress_hook)\n",
    "        print('\\nDownload Complete!')\n",
    "    statinfo = os.stat(dest_filename)\n",
    "    if statinfo.st_size == expected_bytes:\n",
    "        print('Found and verified', dest_filename)\n",
    "    else:\n",
    "        raise Exception('Failed to verify ' + dest_filename + '. Can you get to it with a browser?')\n",
    "    return dest_filename\n",
    "\n",
    "train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)\n",
    "test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./dataset/notmnist/notMNIST_large already present - Skipping extraction of ./dataset/notmnist/notMNIST_large.tar.gz.\n",
      "['./dataset/notmnist/notMNIST_large/A', './dataset/notmnist/notMNIST_large/B', './dataset/notmnist/notMNIST_large/C', './dataset/notmnist/notMNIST_large/D', './dataset/notmnist/notMNIST_large/E', './dataset/notmnist/notMNIST_large/F', './dataset/notmnist/notMNIST_large/G', './dataset/notmnist/notMNIST_large/H', './dataset/notmnist/notMNIST_large/I', './dataset/notmnist/notMNIST_large/J']\n",
      "./dataset/notmnist/notMNIST_small already present - Skipping extraction of ./dataset/notmnist/notMNIST_small.tar.gz.\n",
      "['./dataset/notmnist/notMNIST_small/A', './dataset/notmnist/notMNIST_small/B', './dataset/notmnist/notMNIST_small/C', './dataset/notmnist/notMNIST_small/D', './dataset/notmnist/notMNIST_small/E', './dataset/notmnist/notMNIST_small/F', './dataset/notmnist/notMNIST_small/G', './dataset/notmnist/notMNIST_small/H', './dataset/notmnist/notMNIST_small/I', './dataset/notmnist/notMNIST_small/J']\n"
     ]
    }
   ],
   "source": [
    "num_classes = 10\n",
    "np.random.seed(133)\n",
    "\n",
    "def maybe_extract(filename, force=False):\n",
    "    root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz\n",
    "    if os.path.isdir(root) and not force:\n",
    "        # You may override by setting force=True.\n",
    "        print('%s already present - Skipping extraction of %s.' % (root, filename))\n",
    "    else:\n",
    "        print('Extracting data for %s. This may take a while. Please wait.' % root)\n",
    "        tar = tarfile.open(filename)\n",
    "        sys.stdout.flush()\n",
    "        tar.extractall(data_root)\n",
    "        tar.close()\n",
    "    data_folders = [\n",
    "        os.path.join(root, d) for d in sorted(os.listdir(root)) if os.path.isdir(os.path.join(root, d))]\n",
    "    if len(data_folders) != num_classes:\n",
    "        raise Exception('Expected %d folders, one per class. Found %d instead.' % (num_classes, len(data_folders)))\n",
    "    print(data_folders)\n",
    "    return data_folders\n",
    "  \n",
    "train_folders = maybe_extract(train_filename)\n",
    "test_folders  = maybe_extract(test_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying images in:  notMNIST_large\n",
      "Label:  A\n"
     ]
    },
    {
     "data": {
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       "<IPython.core.display.Image object>"
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      "image/png": {
       "height": 20,
       "width": 20
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     "output_type": "display_data"
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     "metadata": {
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     "data": {
      "image/png": "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\n",
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label:  B\n"
     ]
    },
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     "data": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label:  C\n"
     ]
    },
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     "text": [
      "Label:  D\n"
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     "name": "stdout",
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     "text": [
      "Label:  E\n"
     ]
    },
    {
     "data": {
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     "text": [
      "Displaying images in:  notMNIST_small\n",
      "Label:  A\n"
     ]
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     "text": [
      "Label:  B\n"
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     "text": [
      "Label:  C\n"
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     "data": {
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      "text/plain": [
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      "image/png": {
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
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     },
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     },
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label:  D\n"
     ]
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     "text": [
      "Label:  E\n"
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     "text": [
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     "text": [
      "Label:  G\n"
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     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 20,
       "width": 20
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 20,
       "width": 20
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABBklEQVR4nO2SvS5EYRiEn+9nF1GJ3UYiEdFouAIRJKtScwEq6yokepWOi9CzVBoRW6xCIZHIipAoNCd7zvuNYpdzEjegMN28z+RNJhnHSE6t5uv2bhEBsNA5bBZUVGNPmSRJA50yhi9ZCFXnCCGW1jypdMKskv2tf/jXYAIiBCfAYRXkyBDR6+cY8srDNyCm2vLqwnT94+n6qhyU/GeXBIuXSpKUdL9JeziwlOsC76Any/OiKPJcD+wrk2Sm3jwO/CTmYwghRo4BZMn5s/VHJ2DlRckKMz3vBNrKlNTd8t8VZ476SRroAObOZf2T1gTeDQt5qbGxtjQ7Vb/rNcY7N7fveDdq9wXwf38qIyHXZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 20,
       "width": 20
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "# Visualise a sample of each label\n",
    "def display_images(filename, samples_per_label):\n",
    "    print ('Displaying images in: ', filename)\n",
    "    folder = os.path.join(data_root, filename)\n",
    "    for label in sorted(os.listdir(folder)):\n",
    "        if(label.endswith('.pickle') == False):\n",
    "            print ('Label: ', label)\n",
    "            label_folder = os.path.join(folder, label)\n",
    "            dirs = os.listdir(label_folder)\n",
    "            random.shuffle(dirs)\n",
    "            for image in dirs[:samples_per_label]:\n",
    "                display(Image(filename=os.path.join(label_folder, image), width=20.0, height=20.0))\n",
    "            \n",
    "display_images('notMNIST_large', 5)\n",
    "display_images('notMNIST_small', 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def saveAsPickle(data,filename):\n",
    "    try:\n",
    "        with open(filename, 'wb') as f:\n",
    "            pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n",
    "            print (filename + \" pickled!\")\n",
    "    except Exception as e:\n",
    "        print('Unable to save data to', filename, ':', e)\n",
    "        \n",
    "def loadPickle(file):\n",
    "    with open(file, 'rb') as pickle_file:\n",
    "        return pickle.load(pickle_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./dataset/notmnist/notMNIST_large/A.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_large/B.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_large/C.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_large/D.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_large/E.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_large/F.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_large/G.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_large/H.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_large/I.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_large/J.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_small/A.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_small/B.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_small/C.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_small/D.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_small/E.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_small/F.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_small/G.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_small/H.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_small/I.pickle already present - Skipping pickling.\n",
      "./dataset/notmnist/notMNIST_small/J.pickle already present - Skipping pickling.\n"
     ]
    }
   ],
   "source": [
    "image_size = 28  # Pixel width and height.\n",
    "pixel_depth = 255.0  # Number of levels per pixel.\n",
    "\n",
    "def load_letter(folder, min_num_images):\n",
    "    \"\"\"Load the data for a single letter label.\"\"\"\n",
    "    image_files = os.listdir(folder)\n",
    "    dataset = np.ndarray(shape=(len(image_files), image_size, image_size), dtype=np.float32)\n",
    "    print(folder)\n",
    "    num_images = 0\n",
    "    for image in image_files:\n",
    "        image_file = os.path.join(folder, image)\n",
    "        try:\n",
    "            image_data = (imageio.imread(image_file).astype(float) - pixel_depth / 2) / pixel_depth\n",
    "            if image_data.shape != (image_size, image_size):\n",
    "                raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n",
    "            dataset[num_images, :, :] = image_data\n",
    "            num_images = num_images + 1\n",
    "        except (IOError, ValueError) as e:\n",
    "            print('Could not read:', image_file, ':', e, '- it\\'s ok, skipping.')\n",
    "    \n",
    "    dataset = dataset[0:num_images, :, :]\n",
    "    if num_images < min_num_images:\n",
    "        raise Exception('Many fewer images than expected: %d < %d' % (num_images, min_num_images))\n",
    "    \n",
    "    print('Full dataset tensor:', dataset.shape)\n",
    "    print('Mean:', np.mean(dataset))\n",
    "    print('Standard deviation:', np.std(dataset))\n",
    "    return dataset\n",
    "        \n",
    "def maybe_pickle(data_folders, min_num_images_per_class, force=False):\n",
    "    dataset_names = []\n",
    "    for folder in data_folders:\n",
    "        set_filename = folder + '.pickle'\n",
    "        dataset_names.append(set_filename)\n",
    "        if os.path.exists(set_filename) and not force:\n",
    "            # You may override by setting force=True.\n",
    "            print('%s already present - Skipping pickling.' % set_filename)\n",
    "        else:\n",
    "            print('Pickling %s.' % set_filename)\n",
    "            dataset = load_letter(folder, min_num_images_per_class)\n",
    "            saveAsPickle(dataset, set_filename)\n",
    "  \n",
    "    return dataset_names\n",
    "\n",
    "train_datasets = maybe_pickle(train_folders, 45000)\n",
    "test_datasets = maybe_pickle(test_folders, 1800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying pickles from train dataset\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 50 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying pickles from test dataset\n"
     ]
    },
    {
     "data": {
      "image/png": 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ja9UimajAqETa9fkrWp6smzz7vFb9DtgueZJ254ek7A7a+XwWHZbsdBEp8vQKA1lqG+wethWN1qnrXfqud78X+Hr6beaEs5J8vEycib+oFgft0jxJc9XT/ejEss0e1yC+iRZWNMq8+0QJXnhswhexIl4NwAFXixI4eL60E+e++T5/+OBkAFbuLnkMMy2hMDv3KoVC4dR5b9n6eOEg+dJHnE+Xv9Sfrk25vq0d3/NzfXUjtW7Zt6ozU58aJMV5/e+Ypk5yaTSWVe+sS8un5HeYt/l9HT9UgCXLpI0bzGJXkXZmCmaCKlKKoiiKoigZkhtFyhhXAXF6Lyf2TPgFBFrIvvOmc2LPrwB4JSTT6V0rOBszvrJpvXvO5ShVrq+G3VtY+nIpXC37FAakR1EVr6VDUGYmLji5PQCl4iYjfhRZUKS8flErLt0DgNPPE1Xp3Qv3IfBx8rqSXs0iJNEP6PWBfK68BY464lIAys+X2UBf7/Q8IDpNKt8dx7/KUaamjLKnxD54PkPGTW3q5SURsWKuGubMdnnx8f2J2P48zox4R02JdIoyethiAP7cR+JjjQqHEz5e9cy+cX4LYFwlYfZe/wZgyN/FL6706sluOUiHpF6ezzfA1DZS3fIUb2f6cr2xe7zqmc8HYejdFcwdI8rYkDy5mbHCZuz9eup3oEDik5xTmpgm1MZWGR01tMuMRBiFTFVrSPhNzqgSRSrPlqO7PDuDNLW8ZPx+SvYzLXuzPwu3l1AETllbH6/m1DMulh37yscDz4lv2hlX/oEhz8nsXrddicXSi8Vk58G77zWjRRm/Z30/ALo+8EXd45QETvtuPyvLo+zGNoo/Xd6G7ClSMU/sN+e8MbtsgidsSCuc9ZyK/I3pxQzzqnrGKbdNSDdHhlTALShmuEi/h7X9yN5YlBQYLx1WxSqT/u8aTM871ZvOYW3nAvDq0H0AsGznNW9eWy0+ubXdT4nC5BiLMWLuy8CUJt+vpuJ3LP/xL3sw+zxx+Nvpz/Ki7/Sx50Vv58PryGd8QzZW3KLgDTF+CsTuYI+TJCbN0dd+4DrRp5r+6wSsc4yt+Uc+yK7TLmzqZSbhNaTeXjYcECf++hyQnbv+p34y5LjgnD5MOEPukxNjrL7pzEEToA3JxtLHp90GwFlvXAS2A3mrJ6lRTjYb4jPn8c+VMj3+od4y3Bcv9NS/NJ0/M8H0kU7UccUfANKGOEOuC2yn8LZf/4Rbu9IdvvSn4xn6fvc+cfD9oEY+SyonJ4cMSeuEnuF+24CqeL03AJNHvuxO9S4yMix56hkXs3p7KXD/ukwmflx4zG8BKJ4+pU5QxLSz4QxDRaOsP1sCnh7WVtwJTj9Z6p+Jf9P46/sl460rdmc3ryzxvGNNev0nk++c15vmFmJA5Rod2lMURVEURcmQnChSJmDczlz5EBlm6mFHcPX29OvD23N/ZP0OSduu6TwnrUBlTm8zYsXcaNPlQyU/bWfWzWurx+7p1bRLXHM8xUBB7foGQqyniV99+elaGc6bfd79XLlyFACdHhNVSXrhm5eJU95jX8+1+HkZcvj4/T488aBM5f5hTxniqrEi7jCf81w32UN84UAe60Y2X8/KiUAPiZAVdbDirgIXXbIUgH7X/cTNz4g69cJTMh3+3l4y9LG5euBcm6O2OfVm+R9q6fl5U6+keXGUjkAbKX9WLE68oiJ5p3iMxZdIENndBm4PwDazZajBom6E8qZnKqE4l42W4Cddg23rtB8T1u0FQGz1miYn6R366vSIHdTQG1agkcPs3qWD5jws92zRyEcBmWziDOnveqWow/EBcP+l9wJwzUEyDdyaJ2PrJi8/Y6db71Bnx5nyzE47RZSogKOWZnliyy+R8PpEW9bUsATe473nBVptIOPWiCpSiqIoiqIoGZI7HymbNdsl97pFQWlYkUooLUFeXSK9MGcx4Gs6z0nant65gkn5aftS3by2VlwfpWpxRF69f8I/J7Ega8BVM/r9x9fLyEByM6GQq0StGi9K1LcX3ANAzDK8/KEoRgPjzrI0ocb3bvzLBdjXGVu7jr4nytqDg545G4D5+z2RFJwTZLkREEfXYXcul5Nc3LgspEM0bgdVRYKnQgNBGm2lzeSHiM2R5X0WHStrNN72tqxpdkXHBfWqqiFfuX5thwlcNOr8zC+imfD6Ay28TpTjF065E4BnN+zKd/vLwrix9XbIAWNgigQbbW8XnaRSk+UesneixbphibpeZcnzc5aG+c88WY+rX/S7jAPZBopEPTS9uxObPd/OgK99acQ5vb6JKy+WOrjoYPG5W2P7jXYOtqX0dVnCZ9Ai8ZW6+d8TuPbYsyW5eRICpbHBRVPinQjzpUj6AUdp864R2ULhbdxJFI5Slm5b7rSHmwkEmRM86lB+RfOoRHnNdN5fArmPbD6sPOnfmGWlFdHcG1157WJpjK2A/dvoxkVHT0rTl59WQVIUYTu+iMc5O24bULFfyYvqg1/dRUVcHq03GrizkvWQidPkwEDCOTRtPMdED5AYUa9fdatsshcXDpoAPT5rhgUtPXFqnGGyIRfKqvQPft6L09stABIG1IxauS//uegAgou+znp+MsZ+WVo1MXe4K7pU4qi9dsMYAK64c0G9ky68w9IAA/OKWHh8+2bLcn1Y3uLpjyMVDIL93NqPXAvIbEWAvA6TuSJ4RN0TOoamEzfHcTBv5iGhyJBN7vcaf+didpH7Na11OJ3YcfmJobLFvxdj7Pmzb+eqXY8CPJGsMzAunPoQ6tObmy99NGlbZ3vCzRMbuzL8FllX8Og3ZZh9/J8vpeQbGVLMigHlJxAk4KzK4MTrakKMnozxx7xq7KShJszKzDreCVrN5GoS8F/uljDRqpXQ+uUWRVEURVGUVkrLKlJOvAZP7+egAbOTdkk3qrl3v7ZLpIcYD6Xe3phzOfmZY/9vRWpbTop28KeXNB3VVjM8vZKy02UY7YbrpVfqrNsFifg3A179LUMutGO4ZGGNvVCP7hx8l6zX1dszUQBgSaSC4u/tVcKdbGdx2roVjbo9aWdI6LZ3jmTcSRK5+d0q6Q3fdIkMaYT/N61ZI5s3Bbenbj+TohdkPGvsJQfwwgAJqpUqZpaDdwi72y4rmjezm8HbQ3bjSDnPO1qD2VmUmEdHPGzv1cBkh3jdMp51UrRFO/Rb4n4v8t3rdp7l4VzVzcGTUX9sKaumhlAvCavwyJni4D2rpkdCiXJ3zLx+/PCH3hxSKOXIu5IBwP03H8/qa6T8zPn6YAAG/ntyxiEOUuJXEKNR4tWxlPuE+vTEqqpuepoN0NZEsHaQBRNNzH4+6bbjjooWDNjn+hwZuG8dNJci1Vzn/SWgipSiKIqiKEqG5KybHuwmU43P6mRHW7QDDfqdaNOh3WIxpWONW+rMxZvm6Z0kqvH1XQ6Tc65endlJMyDodI6dHpGnt+zcr+gg6d0u37OQbY8W9eyd0sQaWgDf127izO/OAqDdfeI3M/jtLxI9sqb4mtjHzrquL292fBtIrNvl+CZNq+mFaeZepzsN3r6mIU+UMbjb2QAMuFvKQ3iK+IJ5HZ5bK36/m28+GQK2IlUf3rJ7Vt/JzZM5H361NzZMQhhUHbsr1SW2D58tsJQNgltOkhAVI/OTlagC0zr8LxzF6NRun7i/OVHyv6qROtj5i4TCGq+2VZx66lGwU0cAKncfxHbXfQvAngVyb3aYeRBdHM27CepwsEsXAP56yIvub44Sde0qUQHbLo9w6u7vA/Du/kPca7Bqs+QTFQgmAuxG7Qjx2w9jyeHitzrwYJHyerSR1Qk+WtSBPg9m/7XjX6tyVDjMO6/9O0tnT6hR/nRygYk1k1O4hjrImNyXCkVRFEVRlC2UFlWkvEsI1IyQNaZ2DGcmI3l74m1/ktk2Vn7CLsx0vb4d8+2gltvJIlShD1e7+W4OnJ4vwOPX3w7AT9eWAFBgRHUpCWxiUJ70FhzVx8vJi/YHYMbbsuZb/xdW0XnO3Dr7JVSPzGdBxfceDcCHh95OxJKp4f7p+UtrO2Ft2pR8YLYdXny9p/i3PzDgVN8+mcxKzBW++9NlehzOlu/1qbTeHvL+hfObI2d18Ptqzd3nSfmyT+r9HT+9iP3M0gm42+w4982KEesh6tExbUVZi1lx9772tGXi/Ac3AlAd60XAJJe9/ECMkD3lqUO+lPs928v0/1OK3/HsKfet8ruOdHGz4cxMbPwlrDhBltc6rfi9Oqrwq8/vDcCmU2q4+yPxjRq8UnwkTV5+k2fpeUMvBLYXX6T1f5f2auJ2j7rK2AGzZHbi/yZLgN54302s3Ck7AYF/qbTYrD0lbVrY2TzR6K8bluy8V59DrRenUXYaujWxSkLrJGaKFZbLqYrXugaKf/9UBE2gTvpO/rp+mJzvbOONFXTCNDsO0HfF8um09Z6RlGhb+3r6VLFzH3GOPayTxN254hwZanvjhFG8vEhia/GpSOy9/7Oc2PxFSWn718hLh/m/lpdgaV6Re8/8L/pji2fwficxuFjnxAdq5qm0xmBC9pRrx+F3C4qg7HfGL1zR+Bddz1BuHGKdtS5XxILk2c7vxXa09x7BQrfuxVvRVGqvAbNuRHHStjiWW6KdyPGvDX6HTKixoNqS+uW0LR1mZWcIpWyPxPC5c26nPSmZL5+nnPI5n527M5CIx9WUxZb9bUbF2N146jZZTy8xyaWQbe8dD0Dcbs5HHigduzU3l1I4xZ5gdEvG2UiLSJbKW6sw/D0Emmlor9mGDH8B6NCeoiiKoihKhuTM2XzDtskqSLoBNON2v8rpI0yq7kJ8saxTFigUOXlyTRsOaBNLuf/m8Kfv5K9rA8c1Fa8i1eFlCaJX/NyktI513OCf2flQAH48THrWN572FNftIhGL2UU+vrmohuM/HwfAkNukJxv/9gfZ2Ij1r67e/S1AlD5HifIHhyzNK2L+OXLnSq+SIJkm2MxrFlpWdoMK5hgTTdysdB1cq+KiELZplhwl8Ku3ez39BwBKr5pMqE9vAKxCGb7ZMLoz210uztYP9W4ZZ/jGsm5Eck+8xoq4KsT2U2Utus53S9sSbRvE+NTDaEGASFt5RtUdpAGJ7SvO1R/uPIGudnBMJ9p48Y/ZmYix+8CEwuzk9571/eQa2kt+AlhY075LPjATpdY/TL7bSACevO0f9A0VJu064MVxFMul8sfxzwPw9K4jAAhvmEashcLJtDYlSdl6UUVKURRFURQlQ1pWkfJIEtsO+ylpU2MCaHpZFysisqf0duJ5co7y+DSgop6j6uJPf9iwpYAdULJZpZQETm+2nR1s0jjLLNSmWPHeiru+Dk6Ps6+98svDNw/jin+Kc+e8Yx8AZDrwgv0fB+CHvSRo3wkPiZLQ+++T0g5YeVzRPACCpm2dbc6SJjErzlMn3Q3A9U+eJr/NXpCVQKBbK36n48reCYfc+vz8vNu+rhVFckxzZpS66m1wU6LuxFZIoEknPEXxnPks/VxUqhc+kFAcY4tEram1ctiP89zLzsPXJG2KeOp74D3xMQx9KCpxXopQGkmeaU4Zv0vK+F5//wNzz5I6+FalqEX5i1fjnKEpgWo75lfW+e2DNTLhpKq7PJPZld0AcZTPxCdycwT/LvdsoCf474MbZL3IQc9t4rqnpa3500W/BUSJAjDhcIssF1NjRXi+vEdWznVS8fIGfXdbkniweRQ9q5nO+0ugRQ0ppwIHCgs5ttv0pG3pzrLzy7Vnt1vF2U89upm905d3/ekf313WZnupcADxqqq0ztFUnLA6/iGqBoesnMjCzqy8mhoGXyQzdAabCwBYeOxDbtTjYfkixX9/sSxwOqjzOAb+fkpaeVwalSLTOcVtdV70NVaEXcK20/4/5KXU/rBYYl0vNaTq4jOSVu+Q+D9qx4cPpqgj3qHrF9bKOO6Y0mbK4+bwtr/OGpD29QTahIn+JJ2Sq6ceB8BY26CvsRo59JKNISF/RHNj2LdH8mzHsEk0i+0XJ3diGuxwuHVQntmgf68FCenG15X9AXtdRedamtBJ+6Gsu3zpmfhtQ409sGufvk0wRSeskXjjsJWfJKsoTNpGYtdVxKvdmYIP3yUz9NadE+WfS2WmYPitRCw3aL419/ydjS9rgjwzor+kmaHh6OR4jQRWAAAgAElEQVR54NxV7FmQOp1c0Fz9j7iOhGaMDu0piqIoiqJkSE6czU3/3pxU/KH9n5j69a103xD+aa6ZOBn60z+hWBw5X+6/D8yqG5OpVeGuTWbfh0DQHSoacpkof/fs14+LO/wISHgISAxnzj/lQXb/clxaSb1XORyAUeEFm+2dhQi6z2TKqJcA2Paa8fS+SYZHmmXF+S0cN1K7zS77/pDWcd619t6bJc+GnbKYscbii1HmrL0HwJrk8Az+eEwNko3Iy574UQDBYYM5tN1LSbsUBvL5xPYHL1wkw5BOCxOvjdQ/NO0PLbAyMWy4uKKT82MiVEcT6sCC720paljit1GdRP2bslDUqvO7fMz/dZbJKLE1a2WnRkwugeThx67jk0OoFAUKuG+DxATs8rW4U0y46lF+f9GFAITtKTHZXGvzl06zDcG19JqyWxGqSCmKoiiKomRIThSpshEd3XH1bIw5Z2Oaq5O+k5/2AfE1KBvRkaJZTT59yxKPQTBZ9bnr7UO5+JTkNfm8fmGlF81J69T/ni9+OFfssmCzvjtBE3B9PxxlatL4f3LQ0ssBKHlSpsG7ylQ08otd58mvzlWesCsA7/R/yC2L9Tm6erd1e8f+fmZz5DQzvErbNg9LcNY9Jov6mV8Ro2DDdN8BKcqB3VMOhMNYTSwnfqf+qtL27NdG/nGU2sJAPm+XyfT+2OwFvvw1zqcpXraRQ2YfDsDiz2S1hH6szMoElr4T7XwflwhAfF1XWS/wlDnidD6pajDz7hbFaMCpokiZgHHbB3edvBSrHXid0yuPt8vl4IeA5PU1J0w4EoDyM+TYe1ceQPhNe+aLTjDJOs3lI9Wccz9S+Ra6gWG3grZfFSlFURRFUZQMyYkitXbbhP1WYy+fUGgyW3MvZsXdmUsOAUzGCpc/P+uGByiq74DWitPjtXvzHWYmxr8d3yivX9ifer2V1mnz35B1AGM7x+udaenef4/C9/KNtwFwTPgKADo9bCtToVCT1v/b4rDvvwnluUpUqHs3AA7780fubvUFk/UGcgW4bd1ASt74vpky3AQ8vc3Y96J6Fnuymaov6lfpll2xOwAPj7uH8TP9Cyo2jQ0DE4qeW/fJ5+OVso5dUXxhyjxtFl/v2opG4YBlAPSzfk7+vYmEJ4rqM2bmyXw+8hUAd427ub+W8CTx8/Zj3ktPADD8pdMlH7/+kXh5uZzEnT1ouf87/lteii5cmvy/PaLwdHknenwm4RUeuuRfAPz+yvEUYa/r14S1BBUPnhvYYrP2svjQWmq9UytNPy9Tk7iJTVW5IUeGVHybxsV4qo+gCTQYtbwpxLapG6tli8IuJOGNntg4tgHkNTbbp7liZddX5WX48OV9OK+9xALzv9S9eIdMe9vrln1x/X0AjOx8ESBxrBzcF1Ys1jijKhD0LIptDye1kGQcCiTurRP7qw7ehtCJ/xWpJdRPhl26Pr8BgGs6y/2NWLF6h6z99/yxFw+mb3l6EfFzhmNAehYBT9XA+teCC+25DoDdCoIM77yySVnwOz2XbZsYegx4GuEVsyQy/yAWOgc2KV2grtGSJUrOq+XOt/sDcFmHxYCEOwHYZdoFHHycjPXe8/TTAPSfWcYhn0vdK/haDK/uX8hiy4FPpyeG++xns/a83flyqMTC8g5/Avz98ZOoGSvX898yWV+z6MUvtqwFw7cEPGWmtqh5nMIjzXRewB0axoBlG9ftv5GJCLG59vC5MU2uG7E2m7cyve1pqDK716pDe4qiKIqiKBmSE0XqyMEz3e9NdRSfWhNhXm33pN+2zV/GqHB4M0fUjz8/hw35nvQmorcyHLXJ7gWX905clzNlPuIx/t+o2AaA8Q2cNrZW1IFHbj+KcTdIL7XG7qXW9yyDJuCqKM6QohMQdPRuJ9PjaslTzBtqwunB1zdM66hW8VjOAn1G45K/MIko9HWGgDy9rWCnjgAsPXsoN457AoCj2kqw1PrUPe92RxFw1lYrvet7Wv2gqH399aoUxrjPNNilCwB/3/ZVd/PijR2zmqUdh3vWq/No2x1nZrHH2lzKqK36RH9aynuHyOoOj969BwBf7PwEAFNveoCR9nqB1135GwDWDg/CUClvFdvY5fUrud5gp44sP1kc1fuMFTXuk4F3EbGDpzrl7t0qUV57v1fGVS88C8CVf5Eo5iVM1iG9ZqSmQ6JsBpoYssB7vPe8QFbL7Wf3iELqVdqHTZC3Td/r7fVYPa4OmVLVpa5Jk2rSTttlnmvNQmgOVaQURVEURVEypEUVKWdl+MNKJrq/NTYQp7/HftH1l9DhX8mryq8afxLTr70/5f4N4c/PkSXTmdf7kEblMWOy1Qk2JrFUh92r6H7Ukjq7edWNf7wlU5jHD23g3HYvuNMjk9n5mLEATNvhBSB5SnQqnGcQ84VGmL7zc3z0utj057xxPgADX6oRfw1wgyemwvGpiu06nOV7ir9HtzHiGFvzoKy11falhM9GtvCWp/26y/qDE8/fi0ix3Pe4XbNi9q2ItIvTcahMP79xm9cAOKjwQ8+9sOqc10/Eirnls8YSJeH566Rstt2Q/WvMBSY/311G5OfTBwNwSOF77vayTanLVtrYapdTbsZ2m+Zu8q632XFmsh9nqwwo6SiwxhD9eTkAPY+Vsn/sLucCMP/Utlw2RtrbnW8VhenVsh35Zp20xes2SZ0pvF4cxvfvvJBnFndK3hbIZ01MfEU7B8WJ/fcPngdA7TEWn1UOARJhTTBGfaOyhdevzq7fkfaJshjM2ksDatsnJhy4aWbJr89p62U0RK6jtsQnVwaafi3rRiTymWoy1Fc1onh1nVru/ub3ycyEFjWkqraVF9sBbRIZb+zsOv+Lpt2Sums3tfspUYkba6j587NfQYQbR/TczN5p4g5R1b+b4+znxtxoqGD5ZF13to0Vd19Gqy4Uqf/LofdSYxdmp1A7Bs81K0cy5Bbb4e/y+pN0tfpAkC5niLPg2NcPAOCFAR8A4pDqDAGkwrnHzpOssSLs10byvvBEiXW15NgKHl8vMaveXiahm1eulBmDoXCUEb1kJtQxXcXYOq7oE/d6Sv8rxtjwzxYDEA0Esz7G4C2HN3aVRaNvvP67Rp2jxoq4lb0+A8oxmmQ/uU8j77Ud9V+yo8WnWEy3XjxGl+ukb/8fDzcyVlIo0XiZoFyPFWucUWfyEmuxhXpJfbvw/Nfq7Fe5MUNDyhfPKL6TlKkdwx+CPS/Xkf4/r44TWiURzbcIc8Cy3LbFjf00VcrioKnwBrLw8sR2vwIgPqQvtZ3kPhYUyn2pKJcFr9/+dD1dOovRdMA7s90kHANqn++OBaDnJ/IievSlBzj9gt8BEMY2Sk2g3s6PUg8+dwa3bkZqCbaTchopyd699RpiznmDxVIWYhs3JiLwuzGf7LYhQ8Mq5imr3YeuqrPdLb/eGFObGb40wWBiP7t+n3fwB+52593vjXf496WHyUa7fjQ2yv/m0KE9RVEURVGUDGlRRWrt8ISzV2OH3PwR0MviMl03f2VFHSfbNssqmxwx3Zs/b74bwhvB1TgrzdvWe6Ckfke6ylFyTa6yYA8nmFCojsN1qvAAXke9ZVeIEjXx4lsBqLHyXZXI6XlftlwWZZt7cl9iqxemc3menkic2HqJVF15gsRAOuHFAwF4aeD7PilX1t/b3LMImzz3eTmxfPqGiriui4SUdz7r4871Q3n1yoMAGPLGVKDl1AR/3jeHM3QUsrW4+iKWR6xYHcfying1O959KQC9b0koUZDBNHNP2YlXJ6u6HXqXud/TqZ+1PROKmRWL1zl/vTh1xFZQA8XF1D4paZ7fXlRHp663D7SBivTrord3H2gjCky8UtSWtSNl2GpgXlGdKf3vlm9HfO36pHM4ihmxVjps5a63af9v99BNMOiGA4ltlOE7vpyJoxfn+5Q6a7eRnPOkKIHHF210T7/z1zKM3/a+9gD86bmHAThowh/p82YTy6KSwGlf7bpveZV0Z3g53ExqX0Hd82Y7lEzYJN6P74x4BoADT7sMgPZPT0kox970/Gk7bUYs5m6b+8COcs5OE9z22FGinPa2Il7NmptLJR/Ya2BmaaRCFSlFURRFUZQMaVFFqnKHTe5376r1mTC9RsbszbqyOtuCa8uZHZEe7rb5smZeYxUqb/4qR2+qd18v3t6Y8z1QIL3hq3cQp8+qeK17fsdHpipey6t7SziBcSeJhV78/BQ5TwNGsxPUcdnh8rndGTN5p9/99tZEXPb/Vkov/A8vnwXAwOvFvyhevbDxjsoe58foCgmSWLG/9HNL7zqfRcdMsHdMnLfKFybBGcMOmoD7XMJ2kayxIq4i4yg3XnXktUq5rj++dAYAg/4xh4K1okQlreHn5LUZcfKebnR+pyx6o/J7lTuQe+MoJBPKxGfoqauPoNdr2en9B0bIrIJ4UT617SWdH4+QtGfucI+9V35K1czbqwR4/4A7ATjusj+6Ua5NzFZTU9x7J/qwlRegtoOEKVm5s+ThglPe5OIOnwIJVbjAk16ovBF9P0/vPl5VlbSp4NjNB/Y8tt3XfDHoHPlnuoRhtyKOSryF+P64CpUnvx7HYSe0xNpDJIJ73mlyPz4b+SRLo+Jof8TcEwFY8t9SInYzcuEdLwHwu1suAKDPQ5NUicoiwc7i6G/1koCw1T3kxm/qFKRsoJT9c3f80N2/PlU7HbzHn7vj5wA8fdn+ALRfEKfNWrsOLpcyYX4Wv6bYmrWNSsepy0FjqIlLOXF8Wh/92x0AHLvt5Qx4VdIJzPlR0tm40X3POD5iVj9pD9fsUEKvs2UkZdFged/UWJE6ir8zCWqvWy+nm62eZnsNSFWkFEVRFEVRMqRFFal9Bs13v9e3Tlsq/OuOTdskY52pLOP42vVMq5ZAhdvmr0p5fEN487fP4Pn17JnMhjNlXbBoAVTKDGPOP+4dAM5tv8LeK7VyMdL+edIdMnPtvzeKgvTcql1ZuUlmUuTZS7l0bVPO/h1kVs3YIlFivDPlbls3EICn5svMt8C7Hejxgiw/UrpGpim7QpcnCGKjcI4JJGaWAAwZP5X93pTp0eYyuf/PbPM0PUINr1qYmNEXcHsUzqy129bJNOsH3x3D0LvFf6Z0sVxLDI9K08Sgbs2NVxUNpvgGEmjz/ucPB6D0blFF2myYmrXe/9X/fQ6AfVJOgqtfWfOrugPz5Ll++8f7iV0hparCqjub1k+RCadUiP3+k97AsXll6c/CdWb/VYzuzfpt5L51O0yWNfp8mKxNF7Pibr1xlMJR4TDnPf86AH/4THyDimbLPp2+i9DmE/HZc/ytthjc9S9jrB8j7cPaQ0Vtj88RBWTUW+MJb5AbXtnTVg73LKOmQpTDFw8T38vOCz3rZKoS1STWnSPvjHFXvMrA/BkADMgTZbdvA21mUwNae4+/trO8T64dN7vOfktslXJhpB0Ac2u78/BtR6edTtIsbl8VdkaN5p79AGVnSnn8skaue0W0hKD9puoSknuyfb58OjNJvYRNHsvtvJ67QOruppulHej2TvOppy1qSF3UzZmamN/osAT+ocDvynsBYEU3ehzUZJ94eTnfVPaV39qtSnl8Q3jzd0G3D+vZM5m8U0UizwPaBeWF8PLSUQA8NlcqjDGph5osK/meFLex4zLl1xIykv8N1VLoflpfwqezJc7OLYukkes4W/YpWlJFYIYYf92rEnHZHVMp5RTTpuCJZyOfAcJv2lOh35SPk4+4nJ/3lXu/x15iGBzd6Rv5v2CZmzcnwvpnGwbz+QwxnHp8JC+A9v+VRmZQ1RTXkdw7jNeaG/QaK0K5PbS5Oib36Yvq/ryxeiQAX30/AIBe78m24ndnuWvnxRzH4Sy+tEoC1fa3gqTwCpAYXkx3GNzr3OkYv+1Nm0Yd6zjq55lgnZeDty7ml5M2I94QY/uWbnUX5E411O/97jhaH3/II7L/wYn9R/9NIjJ3vS/D0BO5wtPxafeMuA1EC6RNWrO75L9seByC0h60+VGeZf8/R4l9L3U26imLoMN52aCyl5Tvc9uv8JTLZAPKcYuAxNB6phOpNkeqSTOOAeQYdL2Csm2/Nsu4s1f67/Chj8hQcE33KO26iqEzrIu8K0e3k85N7/x1DM4XsWHXsHRSijyhkpwhuh+jct1vVHZ128/vV0hopcD0Yvq+JeuWxr+Vd1++Je1Ac8Y306E9RVEURVGUDDFWMzviKoqiKIqibK2oIqUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaoIaUoiqIoipIhakgpiqIoiqJkiBpSiqIoiqIoGaKGlKIoiqIoSoaEWjKxMYETrZZMLwlj5NOyCHbpAsAPN/YHYJtLZmDV1DR4ivfiL5qG9hnzv99ZAO8Me4OIFQMgzwQzynIqYlYcgCgxyuK1APwUzQNg2qZSAN5dM5zps/sD0PN9Sbv927OIbdwoJwnY+YnH6py/oWtM6xkagwlKGlY0mvh9l+0A+PGIIgD67fkTAGN7fsnehQsA6B2Ua1kTr2V2bQcApm+Sa3l+0Q4A1EzuRK+PKyWpSd/WTT4kxdqK2ddnAu61pvUMc1FOfc8kOHgAd77/bwCG5LUF5NkHTcN9n0D3eQ1eY3zF4Ga5Rqd8ZoNNlpTvokABAKNvGk/XeycBjXuOoT69mXNZbwDOHvMRAAcWfQ/AiPyIe/5c4ty3oAmw37nnAfDJm39sel0Et+1z62QsBpYcGmzXDoCyg4cBsHKXACN3nQ/A+T0/AWCn8Do6B9smnbIiXg3A17UF3P3zgQB8O2kwAD0mxyj6VM4RW7su6bjgkIGwWn57e+2EBp/hM/N2sQBOLl6fVnvqLX9OXTnkyNPkur+SZ04gWLft8/xm7TkKgHdffGKz50yFN3/3begDwMVDP2zwGq/69ngL4KZuM5LKQWvEm79rVo4E4ObtX26dbWoqfO3sul/vzml/mAjAZR0W19n95QqpHycO+qrea2xRQyqXeF/sc64eCMDjB04A4JbgbrhP2WNwZcSY5QBsc8MFzDnngUyzu1mcChYkQFfb6Ohql40dwz8DMK7kZ2ID7QJ/hOx/34Y+PH77EQB0emSyHFCPQZURnvM5BlT0gB0BWHdJJf8ZLfejb6goxcHJDXXfQD59QxEADiqcB8CVneSTnWDJBRUAXLLoeAB+fH4g3R/7WpKvrk4+tZWl62thquIh+1MMijhxikzuX/r1kc0XQB7JL8zAwWvg3vSPN3n5AKx+sA3zRz8IQI0lZSps8uy9GtfJycRQrO+e+PNz05ptaDt7VaPT2CyBxPU5dTIwcihzftMegD8e+AYA40o+cfdzytuDG4YC8Nf5h7NynbxQrLi0jz06lwGwU5cl3NPvNfltoNTr2OlxnquQzurfvjtUtpVIJ+7hIf/iqNv+mHb2r/5U6veJh04gQIPva+J2S55nghw17xC53sXSJsfctj3FM7Tibtsfmr8MgBMWiIH40sD3XSOpvtLi5K8iXs3dr0pbe/HVDWaZLy+SDuJ+JbsSLZCyUt5bUorvtwGAL3Z+gsJAfsMnyyI1VoS9posRWvNhZwCKfpZ7l1cZJ7xeygmftmi2MsdjLP/4lz0AuOzE/zBh/l4A3FP2KwCsmDyDV/a9n1fXyLM5cVD9p97qDSlXnbAbkdivdmD2SfcBML1WCkWgWxfii37MToJ2Je1/7WR+feDeADzeV0papgqVt/F2GooAxm2g/Y17FI/hYNuD49r/yIU3iCEzYPvfAjD44i9kY6oeWiPw3+Nghw7Mva8fAN/vKy8weVGkMqCk4fm6VgyEZRFRoaqtPDoGxVjaLl9eLKV5ieMdY+y1we/ID9fCjeOk4Z94434AlHyyCICNu/en8NUvMr6+nBAIMCocznUuGkXMirsqUlbOZxdex8i4Zpu3eaz77mkfv+YsMeK/HP0Aa2KiYBbbL6NMe/6ZGIqp0nJ+K7eNlrDdKXp40r4MWTS10Wn48dbJQIHUrfnX7wLAB6feVqczc8taUZOe+tcY+r4sHbKo3SYWsbBuzbWNjh8si9/0OAGAFUeJIn7guMnc0u0bAMbu/i9AOgEAYVNEeWn6xuiQ33wJwOjXTmPGLs8CqYzhum3rc+UdiBxWLmlXViblOWUn2bJcozO2UtqbqsPFeHzz6wIOL6xOmU6q/Iz8YDyD/2R3VtMwpMzncq/C9h9AWyevd0leRz//G+bs/eRmrz8b+M+7/efn0G/sd3Ym7U5spgJDLvF08CvG7gZAbX95nv/ZdQBdyucA0MV3WO2iAJPmD5B/dmsgiWzlVVEURVEU5ZfG1qtI2Ra9K2cXFgIw+Nbv3d5En+AmAGKdimGRc5xtW2Y4HGRCYs1bkVo+/mJb+TFDRSrV2HzQs21zPZNgCvs4YsWosXu/C49/CIARP48HoNfNk9webGPwK1HW7tsDsOuD03iry/8AqPF0YJzrOWnhQQD88NYQSf+jSvIWr5R91qyzzxkh2N4eTujdA4CyESUALD84wuP7PA7Afm3i7rmv7TwbgCvvEF+IJzf2AmDC3wdQ2Oira2H8ww2r1jLsIXk+8Ty5iZE+NSwc81hL52yz+Mvzm1VFPHSwPFsrlD2/QC9W+Yq0940dud793iHQBmj60OOaWCXzI6LwxOyhnJinvuXbanBhQOpmv5BFkRGdwfErCps8dxjI8T26bZ24Gwy/ZSUer8JG46+TgVHD2fEJURUmdhVFOmYVUhaXtm/npy4HYOD10wHoWT2JqDF1z+m7b67/IXGiK6Tudn5Ins23j+Uz8JZxAK76XxW326pgHqZbw/6oicTt4ba3S2CXze/mL4t/m3UoPStnySnsIV4r0oBaaqvyzj10fErv/HEMhw97XXbBqaebL99d381PGlJtEHtfE0jcd5MveY5XVckPc9uCDHC415ptRcp/XmtOQocMtJH6Y9Um7qEV30LUKbttNaEQyw+X/A+9VHxyY+Xlrm1gRez32I4ysrFL+BvaT7VdKU6vPwlVpBRFURRFUTJkq1WkvMoQwNwbZYbBxF4Puj3DHrafQG1JGMe2d3oF2Zh81HZp0+xUp/dcY0WYVi3Wf7UlOR2RX+n2ZtPx90ilgt3+m4cBuOvfY4j+vKxxmQsE3V6v2Vlm4/3laVFLdisI1lHL9ppxHPm3d5S8vCt+D71Z454uVS88tkEcWrE/i2fKv8XPwW39xZnz11d2B2DS4be7z9O51olrRgBQ8uTkxilujelNZhu7Uxpbv56+f5mUtCk4qBTG5CBPaVIVDxNduDjX2XDpeYn4xhzS5XTWjiwGYOzl7wJwRUe7R7qZmZCpfG4AnjzuJFixGgCrVso48URj4SgJJiyfNUN7MeYeUaTdyRLAgoj4/x02RVTHgdfb6viieQlfnkbgV6JqD94JgL8/8BC7FQSTrml9vJojrv0DAKX/El8eyz7e5OUn1CZ7/6SZt6lwFBVHhbTiDLp8CgAjNl0EwJxfJybe9Ou2Nv0Ls31ywmWNUz9qakIZ3Ucgob7Zx6+tTOjZMcdHqJ5TB2tpnM+pM1vQ+84JJrdBeRUZXksT8KbplIkGy0JrwucTZ4YPom07efe775ZA0FXZnGtbPzQx8anblI1pJaWKlKIoiqIoSoZslYqUCYVcJarm8J0B+HrsHfbWNnXUmU1d88juaLMQ9M3CT4yvp8av4jjTbysv6Iypkesxdi843qGYOedIL3vhCQ+llR/nup10DrI7Wlce05+u96WpSAUSvc5QqczMO+7J9wDcnm+NFWFdTPwgDrrnUgB63jYJWJh0juS4NvXcG7uH6PoQmADRxUsAGHKBfJ6598UMu0Mkq9t6iJJTfkVP+wQJ5SstshUOIsvEO6Se9Zgr/PXowMKlvDVZ1MmgyZ7/RE1Mmqk+hev573/2SPu46I8Sp8z8vJyOX0pv8+migwG44qr7AYlVlU5IiZmbJA5V7Ps5ddSfJByfFrsXnLexnAnvHQDAg4UyvbrrpCCd/yd56//TDDmv03s2pvEzozzqcOxXMl37jgfFN2lUOOwq8E68rEP+9ge62EqUsWeGur4vVgaKg6uoOHHbjOuX1N+evTZsyBkA/LDnv9mmfePDOwQaWSWNoekzzOzjY/Hc6w0me+HZWnWaWcXn7xxtV8BxA6YBMMX7xnfUP7sOrdlRLvybmhr4dm5aSW1dhpTHwdyZ8nv8rSLlP7VRHJv75K3lqLZVSYdVdg/Qrjmy08h67JeNf1jVDRaw9PMAACAASURBVIDeM79PNHhO4NCfggy+RArIdn1OBeC7XZ8BxJCpzxHRn055f4uujcsqWBbmcWl8z20vTqZOg70uHuX0S8SJtedrYtR4h9Zc5/R0DRYrhfRtP2vHCZJPpzP5Ppkav+ORthP7lBlu2o2RpAdPa51hB/oUTMl1Fuqlc7AtT/b7pOEd08Q/ZF0Rr+azWQ3MQ/biBKL0PP/whsxerkHjcTL2Df3Ul3a8usYd5vLilkb/MHJjjHgn/XiMUG+ZWDH2/rcA3NAZZfFNtLcd7UvfOReAIQ9OTjhgpxGIuNFYlscZXRhwhQylPPdOB67v/kH201SUBsj/eT1/6SITkUaPlyH1rvdPwgTtd6u936jtpcP/5yVHY0XSm9ySe1NbURRFURRlC2WrUqS80ctn32k7l3eQ3uD+Z/8GgOF/+46j2iYHZ9zUtXVO4/R2eI2jtjlKTH4e8Wrp9cWnSlgAdpWPQCPt42BjYijaPea15+3Ol0PEgdQ/dLD3TePpaitRjjJYJ9p4U7FVtXi13aMOBAmXi2rQ+QZ7m3PPGjlN995eW1jwzq0UJ/iso9mcuuAYil6w1Z3n0jiBXUasWELKtDL02Y1ZnjrlSKPpDB1ZMVd1CuQnVOK466jehGFkz9DFj3dLG+Cow054g/aBNrxWKUPCw/9PtkWhjmKUdZzrsq/dGYr/1/EH89cjZdLJD39rvuSNsTwuBHKfrFh6k0j8+weyOEyttCApyuBly2USxn+uuhWA33xzIXF7mTFn1MRZGunSZ8+hP6pIKYqiKIqiNCtbhSLld/6sPnIXFh0l6+gNeMVeDuVdURkqbyiuc3y0m0eSaaWLRRJw/DLsT8/02NqS5B6TOLVvvvcV9Pl2dPw+/R6Xc68PGv+568PiKFFjfjgSsMedbR+MrCtRfjw9+rYvyTOu4yPZyOCqjjN+fVRbUeItvFxCwBjX36U14Pdh+qQarrj+AgCsDKtRLB+ihVI+ayTiAN13l4kQkUe6U5RmD3FzZCoueH2kGhNKw4pbroKV9bpgl/3yk3dj5m6yFJMT4qDAJPJ4zZNnAtBnacJfscWmsXsc0AHiM2fT2w5jwt9+12zJxqKJZa8c5T5d/PtH0lSylCySyv8ww/bWDWkUtfj2T6MB6PuYhOAZ/6+XeXj//WRHe78dwh8D0P/NyrTT2PINKWMSa7x1Esn43Nte4fZ1skbO0Gt+AHBXn5u7oQv0TT5F5y7lLZLVxuItN4nYLnajXFlJqI/MJLrwiIlJx21uaM8/BHfeT3sC0O7Z9IeyKo+U9cv+2jURF8YxPOI3ict6wCzNTiCuxuKZUSifmVW8dCIGZzuq8JaIf+htWaQDJU9Obrb0wixutnM3hHdoz50s0UhjJNRDYp5V7tCHtl/JUJcTFbwpi6WX/HaJ+92pi04d/7w6Tv9n7UV7nfw397BeKpzrCgRdF4xs4u8cdiqpIDBquCQdsmf9RtNrk/z7d2uXeD/401GajtsxMYFE9PlU9cA32xsrnlYddOMdhkLkvy2z9ra/VZzNv/3j/fzuz7JyRvf/yXk/rJL3Kl98l3YsslYqvyiKoiiKorR+tnhFygQTMVRm/1NWHz+z3Yf86tfHAJC/8cuk/VetqxvooF/7dTh9jpz01jZDSVtxGA2WtCc2RGS02g4yVXPlLvlccsp/ABhXIqu1O0MteSbofo+SuB6nl/p2lZzjp4vsla2t79KO5v3z0bb65xkC/aPtwBf64Cv5waMStigtGP9pm8cvoGCVE/vH/jHbI32+81Z3tZIiRLdK3JADWVDsfKqmxBtrmeHUgC909bkdRGmb/ekxFIak1xxPY/yyTTBCl3xpXU7u8BoAUzYN4OXh3ZJ3zOC6ovuLOvzC4HuBNna+k/N0/vQz6D1fpnzXG/+qpYjH0g970gj8CvGUUS/BW1lPRpXobOBTeVKVR2f9O4JB4pV2uCJ/vDLvKZ2yXU8bYUWjrstJ9ztlmHvbvU9jz21lxYEv2vYH4JrJxwEw2PrK3b8hVJFSFEVRFEXJkC1WkfL2rspPliB9Cw8Sh8sBr/yWwe+I349j2TqraEc31LUwhxav5MtQ2D2fJJC5z0KmFAaS8/bJdi8BsGlmrRtF2tsjclSnmJ1Fr0rkfvdsO2aeRHWuuVC8eK2Zsio8gWDaas7J20+r89sr00SRGsJUQNSIBlda38IZNGGpGzm7pQj16wO/btEkG48TcmALf/7+9fdK8ySEwEsD32/0uRJO+aIInzLhGHpb9jqKjhKcgUqz6GhpC9oH2mx2gkTok/aNPq+iNBspIvdvPFXe35Vjy7hoG3H0HlMoEcW7BEPMikgdeWDl/gB8+vm2APR7K+KOgnhVrSR1Ctz0gl26EFu7LintwjfbMXWM7L//oDkAzL9quLs93REqVaQURVEURVEyZMtTpDzLwIBYmdff+CgA96yXtd+G/P4b113Fb1HmbajrCzSycAlfl4iqElvTiJXJmxmnV5xqLbAaK+L6Q/jXPItYMZ4ul5kIf/3ycABKHzEEP/padgjI2nOZ+EwcX+L4nCXUs+K5W14xaiqxriWYZTLjKhHAL8WsoEADsz7qCRbqP2+sa0kGOVWygaMqbbLSU9raGDv8B5ZbP69dJesQ9nvgh4TnYhNmt+6ww/w6vzmqdlVc8tllRiLkQmMD0ypK1vCM8DhrtC68VfyVu7STdnTT9O48NPFoAB60JZ7y/tBrtMw6fWCILIH2+CmfysZT4JqVEnj7rcf3AqD7Q1+5yx65yyBFRa2t2a4v6y7tBEC7RyTtNbtGyV8go1Yre8pvoQ9tX99GjNRscW9Ab/RygPl39+SgQrlRN116EADhmmmJKZW+xiO/rO6LbVR4GaadHV/KMaR8Cx7mAqfxrrGidabdBgjUMaAc4sRZWiuhIOLVss/a4fn0WCrO+LH5iwCwnInrjVgodZs8yVPEirnpF6zzHZuL0ActTTTuDl+5EZOz7ETrP2+607eV7FNfpyYVq2ISg+abmhLuXiqLj0d/J3XSWv99k4b0HH7T41P3u9/JfHKNOJ+H569KrOv3S6iXSuvEGV4bVMoP10o96PWsvKPbvCIuJgNYVOewLp7vlxeLa8rGQ2Torer0MqbvLEsc3HSVrKt6+/kDePafsl/Hx5NDseR9+h0bTtoegAP/Ii4qwT/uwNJzxe1n0WsDAeiO00EOpj0xQof2FEVRFEVRMmSLUaT8w1Drz9odgLn7PsCAl+3o5W+Kg7nJy3fVAv/0xfD6hHriRAEekteWWPu2zZj7zHB6wYUm9RRMf4gDxxE9bPK4tvNsAK4+dJac67AAc6+UXvIhb1wOwNA/yT6xDWVphz9wer7e6OmRQp/K11qjw7cEPqVh7sM7A3DazlOIWLItz9jRpwMRXv/HrwDcQJatYoq64rYNjur64IZeALx21v7UdBJVKpVCGLBnfuStlyE18/NqYquX21uXJ3bMgnq5U9hxnK3bdq2OyjBFfN36JqezJRKxYmmtUJAOYZO3WfVfSQ9nzdVFp/dg6I12gFh7ZMQbkLOOaur5LV4uYUSKXvzC/oRDR50KwOxLZHhu0SGPcPnfJETMLiecCEDny6VOxubMZ8hvRYmaYZ++Te8V7F1aBsCyc+10XPeh9MvPL/iNpyiKoiiK0jS2DEXKE+Ax1K8PAHdcdx8At60bzDbXiOri2LJJlqTPwi3YkNoXqLqnWLTh6XaSzvo8LehW4F+7bGqNXMedyw6iJF+Cc3bL3wjA6MLFHFwolrSjRHmP96tVUStGaUh6BQuPfQiA3++2AwCzj+lN9KelaeVxvt0L39aj9FV3btx1bs34y81B28vCYjd2/S7l/s/2kim9rhv5L1nNa8U4PofWtO8Ip6EaJjUbfrU3S750eZ6yEndTlLSWRSTESbyysllDuZi8/IYnVHiJWy0SGiPPBFVFakVsPGoUAN2mRhJKlOMMnm55cIL9epYYin8j7/4h58j/B+1xFqG/rQZg6ugXAfjEXkHtd7dcQNd/fyvH2eGQfriqNwveEef3flWTMssXW4gh5Y1evuROcQrfs0AakT9duD/hcpHrkrz0N7NGTnhD1DUyvNGLK7rLrQg3Q/7TpcaSa3SG8n799dkA9D7+e9aFJWcLauXzi067c+cO4jxecakYVE7BiVlx1xgLekRH57rL4mKU/bOHzOIb80QvAgekl8d3KiSGx8iOCxL5HtjMCxM3lhzEANscyzdJHJ8aK0K1/XydxY4LA3kEt+xwS78YGrtosTtDzoo3W8T9z6rFWDq8sG79KwzIzKXGTCTJhNYSL8w/FHvjmqE8+ZYMm1vOAgRp3gb//icc/Dk3dZuRMh0lPTYMkvdQ37u+SwydNbbsODHqvB0Y//p7k74lJo+d4X+S9fS+vOBO2fXItfBv2RbqLUP1Q7f9CcaKYBHLYEjPzUajj1AURVEURVGAVq5IeR1vV48T5/LvdhVHMtfBfOIXrlWaysL1x04Jr6piY1x6cO0CianMm7q2Podpr6jmhn2wrfLYmrXkvSfOph3eld+GvXgGAD/s+W/X0dIbCd1RqdobmRrt9K7eG/Y6O55/QVp5euT7PQG4fK957vku2elDACYGJEZHkiKYC1XISbMVKFNFIVEGwiYv5TpdaSzXprQCYp4H5fSIcz0hYGqlTNc+vPD7Otu654lKHew0NBEbryn1wXdssESU1nlXDydWIL8F7I685RVrHGEuLF+KFgTpcfukxqffAH6l6KnZO1N61eT6Dkmb53rupIpUE4m2leefNNScDfzr7wWCrl9Fn79JOTv+9TMB6PjdHHc0YMGfJQxC4SuGbqvtIb0mTPTRZlxRFEVRFCVDWqci5ShMTvTyQaU8cdUdANy5figAQ//0AwDxUChhSVp1L8fY1m+8WizWwMYqNsTFYu0QTNiR1V1bX7A6b8fRjdDuseZNvu0TZkdy7XW/KB5Vu9e6EY79Duybo2Tsz2nlqegDmWod3Dvg9s4uKJHVs/+7vzha5X3wdZ3AqS2CXW6CXUQZi61c1XJpb4blVTINfUGkgmq7qx6zffPamiiB1uFiomyBPDtLVmP4y77fE3NVWPnYKbwCgAf792ieIMO20n/UgV+4vpap1BonwrrTHg397Iymp50GhQW1GNuv1G0na9OrbP79w22yE0bhl4yjWmJM874bPP6Ijl0QnzHbTTs4fAgAwYEVAHS7ZlYdeyMTVJFSFEVRFEXJkNapSPlYc1eIkfniz1QSkOnkY2aIIlVoYjROS/qMviEJdeDtLVmdthBpwCNT+XtY+bMkhMGP0SjD8lMH8fTinbV4Temb9rd/1ntM93dFufrq6lpG5UvxybN9f6JXiM9W3vtWy/uYBYIECqQH+sNf+gMw7B6Z1RSbNbfFsuHv1bQ5WYLIXZJ/Ql3fFGPoWfYN4And0UpmQSmtnw4TpR1j37pr7PUOFQGwZlQxnezlMZsU0sVXdmN2cMSZO1rsOXYcAI/cejsAg/LCVNlrErozVO21Odu+X5RB4o0nFg+4Sr2jnqVdt3z7x+NZ9On5hZLXV4JBY1l11r9tLpy22AkGGq+u5ocrZNZ//0ft91M8lnYw6vpodYaUCYXcG7Disj0A+Hb0/ZT+93wABrwgD6G6k2Q90Eg1zlgWh9zwMQDXdJ7j/t65U3nSfi31sOsjFEy0eO70Tu/Ua0citRuMWL9uAPQMpVfxvcN9u4Yr0zomungJACe8cxGLjpoAQIXtvP+/ES8DsNep42n3zBTJty2vu41atvFMf3VigwTbSQPY41Ex+pbuZtWJcF8f6Q6HpnWutesa3klRMqDjy+IAfcsVg7mykwyvx33dyprDy+CR5suDycun6AWp62eeeTYA03Z4gTx7GLswKPXOWXuw20eryH3LqrQ0v+ov5fPH7t2IOi4XzTwZyGnz49Xyflp9we7kyTJ65L+dWEkiG0OMOrSnKIqiKIqSIa1HkfI4fAWHDQbgzctvBeD3y/f5f/bOM8CK6mzAz9yynYWFpdcFFkEUOyo27Mbeo7EEY+89RmOSL+VLYiyxY9dEY48Se+9SRBSsdER6Z2FZdvfemfl+vHPmzr13y93ZC2K+9/nBXe6dOXPmzDln3vO2w1aXSkZSo9koa6s0Gzj+/Yul/KBGakiFZENdYb7YREn0WiKaERZ6QF+p39fxAl/bkibFZ2jN5l4mbdgxUtxk+oNMgpqXbxJy7m6tVdK7/rBbVrHkUHHY6xwVrZPjxTqP/d/b+NUMSTXrTpHQbKuwMGWKbO8KxLKwYnJfRv3uOjZzb5AUGTP2ucu/L4Ctf3MBff+Ye8j1clvaumesrEXtVNoeUfiR3unH5Jj1OTNNBwRSXpgvompi+KEIJuTcElKjgBdKDjx19wFc8xtZ8Ucy1sav7nQfZ+0spjf3U3GLyMsqvIkxHOzCiQy90zWLDgHAnjkH5f8fEW8Wmz9mEL3/KmqhMBnEcyGzXGevHQBoOGAdg06R/ueaALU8WZ62jBlBURRFURTlR8iWo5EKYN8pW5gYh8kvLh1BpEGcco3PTaY2pjX8UMj6elbWlWT9vmO5+P68EZeN43wpuR1bLLhtVCBk7plnwooH/2MMfR6V32Ibvb3ziqPUVMl3g04WZ+pZgx7xz29JE2UwGqQo8Ju5xwDwZv+WzzFaEnvmHA6685cAfHnZ3QCs8TQ52xeW8IsnXgJg7CUnAlDw2uRUGcZfybtP13Fb9ID1/cOM5ifR6D8fs/fi0juLmbWTJGut9RxuyyxxMvzgnBs5cuaVLd9YgPH1vQA4rmyd/12mZqqhewkFn7e+qm/PXo1uhlY0sm5jXv23Nin5TLoX5AdKrlri56mIbxH+k0G6jp3AwccfDsDrw2TcmbHYJ1bG+j/J32WHBE4yDrbt1Ly7yYQ/tx7Xf1qzx70/ZWsAqpnUJn9F5b+Dd5/bCYAHz7mD39+xNwDORvFdak8izEyswkLfahUdvhUAjb8VP9X+lxZie/5Sfv/P03zygwtSmc7IC68dxdfD5MU89AHJtt3/owmpxg7rtBx48dSszxakdi2ZDcBbZSJJ2Gvar260i9L/n6l2b47Ml+Ts0Y/AaK/MFl6kub5k7Sbe7iufFoGEfVuum+nsVixGr7+JuWzQiDMAmLPfw4Ds5XeiF5yz132S/2vvJ69myG3zAUguWtzyRZq5piHSoQPzL9kWgL+OeQSAI0vr/JdHRVSe7/XL5Zj3/zCKjT1zFzz+PvcAAI7e9ll/02fz7Ex8x+Dff8PM+EgACld5fTLf73gji3jlrhpcsuULUIYtYJ9Dn0B/Txa1cFyAYEQrwInlspv5c6ddTadHU46qUrz7g7gCBM38sVPElH/Nq7I57A3dZeG5xq7j4xHPATDoFjHxDb5iYipIw5hBjHDY2n1414x487ZTX8+q03YB4LpKWcjYrkOZlb5raa93Av/ZXDvBt1eYD3l+WxfQOZX5Awz7fF6z700SOrrojAo2PNcNgOKDZfNi0x2seEGqH5ovW5pHAjmp/Do3NODu4W2QfLOY8b6/TnJH2d9MyavQFuRHMisriqIoiqJseWxejZRZBUWs7ND90TsCMPWiOzh34Z4A9P+dF0LfDudII4FaBSnnZLsu/bYTrk113FP59ewqn2vWeOcV5JwRN5MNffOzSm1wE75GxGQNtl0nS1vS2v5PJvOwcUQv8/Ya3OWzE6m819uXamxudXJt22/bwafKar3qHklRYdIiAFRGZV+/WaeO5eVj5XoXvX8qAD3ekvMrpq2GFdLe9sqVgLR7tFcPADYMlRXMwv3l/i4/9GUu7PRhVp2MJurwmT+ROp4q7VKycBK+DvKOy1u9t+Sjcr3ojREaHOl3sYzV6f19P4Z7P261rHwyJ1HLSlvqUREpTvttS9BUFUUSvqmVaPtzsxhcz2HfakiQXLCw7ecHvKDXDUz/rTktcWZ7VsVFxfryn29i0u8kc/5N8w4GoOSMJMmF3s4Am3N/R3ONSJTkUnHg/fJEucFLnpD57vZek/3cUnNOugeAqk5nMfSibwCyg1iCq/xAG2SaM01IOSO35X9/nZ5fYZ1T74/Fj+tFs9Bxkjy3ZBNltYj3znDa2J0iltv+Z2D2FYy2TYPWWGa1LTdR8L3okRlokijb/BpeJ2CBbSr9TlMBMs1hXDHuPu8EPnjsQQBGvnwCAF3PktRDySVLs0+MRLMCdlJaKzdLLlh47SguPu0/ADx55aEAFL4jbiX5SnXQFD/87KsoiqIoivIjZfNqpMxOzUQh6Wmi9hVN1FX3/wsQrcq022Vn5o7uxHZfsikJtFPX2rT/1zj1dIvKHnKLDhZn8x4mG7Ztt2ll4yZT+zIduNsXab+F3TE86Dge9IOKenKw+c58Orh+Yr6gtsr4fRhN1Oivjgag689Xtj1JXjBDrbeiGnLeJwCMmHkBD198KwA7FaaWNYeVyCr2sJ94K1hRHDEnUct3SdlNflFCspEXWDbVBR9mldEcL9cV8cuHJOVCnz+npzqwYrE2rZ46/kv63Y4//Smf7fwUkNLiJQP7lOWqCQyLSXRabMn9H/Da5cRqZMjOOnVsWr2iP8CaKPO+jyhZxwEfP5O38hOu2RNTtBt3rOnPS8Mr2l5QIHtx7x2XpP3U2rMLjimAEivu9+PfbJRxVLBwxibzvciJwP3Zs+YCMHMP8VGquvNs5h12v/zm3cu8Qx7grk9Fc3jfvUcA0Oc5CbZJLliYfQ+BgJtouewdueDcbQC4//w72K0opSUHKImk5qvr50gQS8FC8Y8kEm2bP5l37LJW8rJkPsdzqz/k+YhotNMCh6D5+dz7PTMc/6yhqfkkl7G+eu8GujzY9nsMuo5lau2srVLvrE0132RqZzf2TvUDp95kiQ9nZTHjI/bOFEZdLr56L90ku2jYk+R57PGfKxn0lFwnMv5L/3rNudRFu3Zl2bGDAdjrHNE67VfwJuNOGQ1A4ecpTRRs2rGpGilFURRFUZSQbFaNVGR7CYFdtV1HVuwh0uHrB4vmYki81D8ueqqkkK/x0kOWz9mINaH50NqmMFJo437iwV8zQFZJa/euZ+bOD5oaAen+JuMukySg+1VfAUD3jyNUfLk25+uaaJZZf9qe1/uIT4JZqeVjJdGUL0zmd9HAv4aEa/PnlRLF9tzDowHoeZdEUtjJRLgIFbOy87Q0ps173jKe37x4EgDfXiEavtsOeIwjS+uaLGZQvIxBcW+lU7wy8EvTmqgGN8Hvl0s47bhnxZ9uwAOz6bPMWzlm3EubVyLe+d1PWcx2j54MwLSRT7StjHZiu46vOTT0fwGK3pKUGC8cI1oa06YJ195kK9VciVoRP+VEPki46avfDtGNQBs0UgENhLPHCABe2to4AUo9W2szf2z52pYC7lgjkb09LxXNVJv9fjYFTrp22E1Inx9y9mQOGC2a2iUXy2r/+Z3v48JOCwC48BovdclV0o9+v3xvPl4iflar18qcHI06jOgjPmA39BsHwKD4BwDYrsXEern2r2YfB8DClZ2YvJeUu3iSpBIZgGikrGg0K61HSyy+WrYJe++Yv2G70udjZD8z8xxNnzmv0yIeelHOrTxF3id2jZfOpKmUNoE5I9qlMwBrHpO+dkXnf/vlNtVfTH3MPP/Jfnew12+vzvkeTZRZfdcCGsukv23oJZ9Fe8t8OG2HhwB5h+WS2iYMZr9Gw/hDb2HfpyVyvnCC7FFXusR7l21wKFydu+9wMNq7w1Oi8f/512cCsP4mKWfucfeCdCFerpPx+eDivZi5UnxW41F5BiZR9aWVz/sbIu3zkryvh1w4Gdyv/WsFr70psdzNGKZ8+ec/dUHyI2VOkoa4Fc3Kyr3tpJ/R6xhxjmwx/0ngt8h2wwC490VRa691pFGnN3Zncq1MFMsapHM4boRyz9m8T6E4PR/aQcxy2xcWMuSf0plm//KKVqWNIz+8yAUYV/16i4OvvdiuQ4MrHWSJ7TmTemaxt9cP56Nlco8rPpf993q/l6DwPVGX+ikk/Fwajj+xvOk80+I9Hhg5odUO05RTX6xHd5YeKXVav69M2kcMkfoc0WkqWxeIw2GZ98xXO4181iAD6NmVOwPw4bShAPR9DYr/4+WlCvTfXAZOa/cHcGD0RDez7PUniVBfe1INABcM+YADS2VA94mJ8JzPCS7h2nxcL+Wd88y5AFT9aoL/+/KL5CXx6bV3ApDE9q8f6TGr1Xt0llZvkoHfVGqNsJhgCnNfD9b04OlhYq7J6TnGTpJ7dGyct8WU9eawFwFyHpvfJ8WkMtXri3+ccTiVv5KXnPPVdDmoHbnmWqJdY9ETDKxoNM05FyCyzVAWHyDCQnIv6c9nD5XAiYNLv2FYQXZ6mK8bJbff2BWjAXh5qgimlRNidHvbcySfv8A/3uTwsVZL+U05EufyDP/w1REuwPWV03N6ZsH+Z4Tgg04YI3X5WFJCNGleDHxnBJs3nnmk2TKbIli/u9ZKf7t46Dut3uOvph3nAvy5+xdbfJ64YP2uWyZ94K/b/bv1sRjoq83N04mDdmaepB3k+j0kH9rp5Yuynrdp21veOIyt7pQgC3v2vNQBecqRFqS1vrplPi1FURRFUZQfAZtVI6UoiqIoivLfhGqkFEVRFEVRQqKClKIoiqIoSkhUkFIURVEURQmJClKKoiiKoighUUFKURRFURQlJCpIKYqiKIqihEQFKUVRFEVRlJCoIKUoiqIoihISFaQURVEURVFCooKUoiiKoihKSFSQUhRFURRFCYkKUoqiKIqiKCFRQUpRFEVRFCUkKkgpiqIoiqKERAUpRVEURVGUkKggpSiKoiiKEhIVpBRFURRFUUKigpSiKIqiKEpIVJBSFEVRFEUJiQpSiqIoiqIoIVFBSlEURVEUJSQqSCmKoiiKooREBSlFURRFUZSQqCClKIqiKIoSEhWkFEVRFEVRQqKClKIoiqIoSkhUkFIURVEURQmJClKKoiiKoighPCt0cAAAIABJREFUUUFKURRFURQlJCpIKYqiKIqihEQFKUVRFEVRlJCoIKUoiqIoihISFaQURVEURVFCooKUoiiKoihKSFSQUhRFURRFCYkKUoqiKIqiKCFRQUpRFEVRFCUkKkgpiqIoiqKERAUpRVEURVGUkKggpSiKoiiKEhIVpBRFURRFUUKigpSiKIqiKEpIVJBSFEVRFEUJiQpSiqIoiqIoIVFBSlEURVEUJSQqSCmKoiiKooREBSlFURRFUZSQqCClKIqiKIoSEhWkFEVRFEVRQqKClKIoiqIoSkhUkFIURVEURQmJClKKoiiKoighUUFKURRFURQlJCpIKYqiKIqihEQFKUVRFEVRlJCoIKUoiqIoihISFaQURVEURVFCooKUoiiKoihKSFSQUhRFURRFCYkKUoqiKIqiKCFRQUpRFEVRFCUkKkgpiqIoiqKERAUpRVEURVGUkKggpSiKoiiKEhIVpBRFURRFUUIS25wXOzByghvqxEgUHBuA7387CoAvzr0j9TNW2uEb3UaOPvUCAKLvfQaAFZNbdZPJUFUAeNN5xmrtGHOPa0/fneJTlwCwdG0HqYN3tuuC66YX5bqA9535zXG8/9sWbtKTeRPyaSUsohvl79gGOa6gRg4pWeZQMWUFAPbMOamLRKLexZzARdt2j2GfoRWLZbV9tHs3AJYcPxgOWg3AzwZNBuCoDl/QJxoHYJndCMATNTsB8I9vd6XkgzIAun6+AYDYmjpqt6oAYMHhcn9nj/wQgI9/MojkosU53R/A1O/7ugAjCoqwvbaKWulrDtt12Og2Zp2b8I5fbMtlVtilAMxp7Mabq7eW8hf1ljb5pgN93t0o9Z88AwCnri5VWA7Pqyna0k9bJBLFinvjpqEBgOiwaob/azYAF1dK+653pJ4Rwg3v1kh4672+UYeRj18JwJyrr8jPPTaHN1gjxcXy2bULAMnunWioLAJgYxdpm41dLeq7yqUSldLHy7vWAjCo80qGdlgGwE6l3wGwY+FiesUKAah35fj13vz24JqRvP27PQH46LmrN9lYNDQ5H3r3HvPGp927ErusAAAnJs/CiXtzT22C+CoZg9aadQAkl6/05+tMouXluLb89vr6R1p9hod0PssFsNfVNlsmlkWkpETqtWFDa0VuVnIZiwcVn+oCLDtzJ2pGeuOsQO41WSfPKro2RuFqafP4ejmvYL1LwXqZG+Ib5DOS8D6TDtjSPSyvl7gW/rN1o/JpF0qZydIojaXyd0Mn+a2+q0tDd+kf8Y5Sr8R66bfl38Tpdd80ILfn2K6x2ByW5c+J1s7bADDj7GKO2vlzAE6qmATAVnGp+1Ib+nt9f6GdAOD+VXvy0hw515oq7+luU+U3Ll1BeWE9AC/tdUeL97hZBanQmBcJ0P8vnwIwYYxM3nsXQcKVTmc+yyJFzD1OXsLV723GegZYsYvD3G3GAVDnyAu3JFKwSa8ZfOl/0Sgd4KIZJwOQfKg7HZ6aKAdarfb7vBEUYKOV8jKaddUQAP54zJMAnNThjSbOLPX/qvLa7bpKETau22sG7CW/LUnKC2uFE2NEQVGTddhr19GUPLc45zpfc+QYOe+xz/1rmr4Vt6TfRa0IZVbT1wOoiJq/5JmMLl7KmR2Xejfk/bQnrDxTJv6Ha0YAMPbtAwHY6v61OF9Nl+O859WUMLpJcWyw0/tK3cBOXNP1IwCinoBTFPUm701VDaTtI1aE+Lrc+67pb3W7DkwJPZWplwRAosImUioTZ+cKeRZVnVYxrIM8qxHFshAZEF8JQPdoI92jIlyZvtB2yvy/Ci2Zp4osqcPvun7DIwftGbLcZoik1zPYh2IDBwCw5OCeANSMqqd3t7UAxKPS7l2KllJZKG3TISbzyqCi5QAcUTqTnjG5n4n1cvyTa3Zl9nopL+lIr9i58/cARK1VfHjlbjlX3V5b0/yPZh6zIukLkAwiRTJO3W0Gy+enX6WvajPKs2LxtPNd225eiMsDJW+WA/D54LubXbiFwZTleAucCFa7y/Xrd1iEQ488tH0VDEnwnbL+JOlLd//lNgC2Lyz0j/ukQep62NVXANDpyzXUbN0JgCWj5ZhHfzKWG/cQwYs95CM41w987lz5cq+W6/TjEKQCRPv2AqBH1Kw8Spuc0P7nwH8D8ESJvLT9gRaQYjclbmlq4DV4q86o2/xLwEyo7cEMEtt1GB4X4ePdbaQdon+PUHXgWQAMOUuE0SYnk3xgWeDVxUzaa8bszqXXPg3AKR3ebvbU5bY817mJImxP09grKs+uKl6WdbyZxHsGvltjy/HlEZlAF+0L1c/lXn3nCxFgHn51P647TQSpWkdWNRVRWfnesaY/9z1yGABJI/Ol5H3sIu9F3ckTAjok2LafCHNn934fgAOK11MZlZOvqJgFwNUnyIt75bEb2O3DCwHY6np5sSXnfpeaRLxVfV6fXUZ/iGwzlOWjRMtnBS6zxz+vkvoUu1m/5RWvXKdA/uj8ZYS+94+XL/94eaun1+wrL86Drv+QSm8ZH7fSX4g7FX3HToW5LHAKMj7TJ9yZCem3D66W2bjBSU2t0WDHACJNNNjqRukHH729LUP/+pV8eV4O1WoOIxREo/4YNH1nw9G7UnCBaMsv7C+LmHsWjgag8bUBxCd7AvIMOaZmyVpqMgSJLzoMAuC5ofsz/zBZyf/llH8CcGvPT2noIYKhmdcerOkBwMPXH0XpW5Nyvg1nrx0AiH/1HfaaNWn35vd918by5jurSF6kzoa6lPATlzqsHSpzRcdPIbq1vBfsb2amlwW4iXRNc6SkBKtAymhRsAtJ16LavJcJqfdBWHG/NfqWrtlEJbeMP/cBY373ApAuQN2yeiAAb4wR61WHT0WBYANlX8sxpQPkt0XJCuocsd4kvAVbx4gslHb43wuovsubby5ouU7qI6UoiqIoihKSH4VGyorF/VXC0gNF9zAkLiu4BjfBes90VuFJkgnX5vRyUcXfe9hxAJQ9+0lWWZuSog4N/t+FVsz7TGmdMlW4A9/6BeWTRYOSFKUHVvpCNg03Co3lsopK9pZr7TlEfFdu6vMq3TxNhzErRrGYd+gDAOxwkYjX3e4UaTtvJqOgydBbDc66c1cA5h471v+pwU1frZ4+f28+f07s1L3fkRVfZP5SMHXq3hWANTtVyufRG3h3NynPaKTqnEbfdFoSkXJN2x6y2zQCnmI5E6tL3Y+d4f/z8rJt6HWj137eari1frXR+7yrUrQWN+9Yxbzj5BoPHPAQAPsXm1VREbNHPwLAxLfku4v+fBFdHpgg18ynZipjhR+r6g/AX156xDeX5tPkkCuZ13z50CLufmW/nM8ve0Y0H+OfKQDEzJd5r+PKBzPmU/H1OLFM+l6d0+hruTP9LyFlKjHH3LGmPy+fKuY493NvyWu19bmIVmIAE3DaY3rPONdNJkkcIL6FPX4/F4DXqu7l6FkHA3Dnz0+Q08ZLG/QmZQJPmxEy/PWc9Z6jzuQv6Sdujdx32y4AvPxaI/f3/RiAw2f+RE47WdqhdMmkLFNjS8SnL5Tr1QZ8n0w/9cyS9VVdsBz5rvC7VX79Yr3FemH8Izs+NtEvwv52dta1Yn3Ed3HGZX3kMl5TDn66DiZ+AUCkg2jf3I0b82Zmn7+XtOmuJ55PbR/PP9ZTsBi/19gGl6g3vUSScq92gcXKPWQunXyQmLaMhjtXdppyIgCJdyspWCflGmWqXWSR8IwAiQ7yW8FaqV/5dw4dn/NMYhvZvJg5yLX56xtHAuAc+CIAN3x0KMN+M19+XvZV2mk1p+zGL64XDdY5He/2v2/wHnSJJfLDVg+dD8CAu8ZjBTRdLaEaKUVRFEVRlJD8KDRSQWfzomOWpf00I2Fz/BPis/Ht6XcB4OBgLMOrTxR/mbJnjD29BTVPHulUlnJ+jOawwqy+MwGfSIShv2LL1cHRK3+Zt1I7eb9LuO7+RwDYv1i0JbVOva8BKj3cc3q+Uz6CNudQBJw+5cNi0TPigzB313uB9FV+jacl2/+OSwHo9bfx9EK0O2b9nlYjzy+hfIasIssfhzG7iVat8AZxen2h+jVf0xXL8Ar4Vfe3OG/rX7TvHjOIRRy82A7ff6JJAv3NtLO9UlbN8TdXM+QNueObK0cDcM6vqwH47IS/U4ashnYolDI+/cNYBg0Xx5nBl3ura19TkD9nWGeZ+Awc/cEFVPeW9t2YlHuMWC7RyOYZQwbjU7T8pb70WDS+7QUEfPasiBe1ZLQJEYt+sdXegdKWhVasRc1bg9d/TX++57HD6PO5p530VrBuwiu/jcol8WlKtH5g1okZF/LqP/vmXZh24q0ARLx189ZjL6PvH736WvJ80yL5MucD182eiwJj3opKOxgfpln/swsDfzoUgGHXLgIguUTmHCte0CaLgL1ihf93tFycsu11Eh3Y7XEZRw/3G+f7q31cL/30r/N/wsqH+wHQ6VHRSBmnc6ehIXU/gXarf9hrs63vAQJBTCfa7H7TZQD0HDtFTisszJtGykTEdnp0Ap3aeG5nUWSzz3VXA/D1RaJpaXATWX63we+qHxOty8BfTvB+ndn2erf5jDwR6IvVl8o8OC7mWaqSk8mcCWffIg7pc066x//OWGogFQR29gKxFAy43tP6xwtwG3Prq6qRUhRFURRFCcmWrZHyVttuMkl0sMSMPzD0MQBsV1YXdyzbn8F/Eb+EF4+XFcvRpakoiH/sLCL773seAXgro00VrRaga0nb8pmsG1RKubhxESnKWNW2hqf1MKvh2DtTuHb6sQB8ssMzQLqPz8meU8MLEckTg2OHS4kQiAyC1Ep2xfPVfLnT44BowkBW7yby7ahrJBdQr8dTPlp+lJ/v8xPQemRoEwBcz2ehYR/5f9W9ZzPviPvlO08z5XjPt1+sjMX7V7b9/lrAhHUDuI1yvZxX2sHVfMxbza+WyDyjaTr2pYv4+V3/AfD9/Wqcjcz5qayqBkYlLLf6EvEDapefW8DnAKBh1DAAOpTX0ejliApqoZxmok9d10prl3wR865d+UVDK0c2g+v69+ZmaCsbdxjENgWve/9r3XfHdh3f59HQcV4wXNPrvyHD5d0w5wXy7MX6in+P/Q/p+3OG3sNyr04H3Shh4H1vH5/Szpi+29a+E4iYczO0O4WvTqb6Vfkqafq6mcvD+qdGor4mKrKd9M/re97v/ZiK5t29SPrIa0NfZliVaK2Nlse/R9fN0vpbOw3nsSGiPbddcVINRurW7y7vFPfWBu8+8piGJDiPtuSDmGlNiUZ9bVbf1z1nqovkw3bdLG1o8LtBT0hbmreCVVgITgvvQ3Ntf65w2m/JyAdWuoY5UlSEUy/vnJkP7QzAvENkzmxwE75W1mihEq7t+2JO/6v46ZbgRZW6Ts4ywhYtSPkvaMdm/gmiuhtWUJJ2zPvvjKBqnajirpp8PABHj36EGkc84EYWigPZ4uMkJLLbXcv8PCGb0um8T8la/+9IE4o/47Bqpu7aPhHKvb+NOrHFyc2y/PYxndsko0vutxOPbXO7d6A4HxYFJv/qAqNmF2dMtyHcgMhsx9mPSajynJ0eThOgQBzLd7xbTHl9PAHKn8y9jt8s5iUYlK2Ms7U3+IecO5lBG8TsZVS4K71UChWRYjodkXseqU1OCy8h47gee3sKT5x0EAClT78EwHFlKcF07vEy6Y/4Tl4WPW8Zn2aeaQ9Lz5PJ+ZuRT2y2HGhNkRmUsP3wC+jefOaMnPBNe15fWr1VIWWRtjnUm9+/bfTSbMyu9V9IbksvozwTzKdjHK+rnpKUBXf2TqUY2PVlSRUx5PZUcITjvYDzupg0ZQWdyU1D5zEPk10ifbFDYGFlggKCosZFJ4kD8t1JWUT3eUsc5GMLVmCvWJlWvfWDOviBK6YfmKAVgAGVnvl3U+Tg89qtzcJsQJCxkm0zt1uOHO/328bGzZIWKO+YhJzevOnU17PgN5LaYN4hYuY0c2axVeCP3eDccs2y7QEoGWc0GSkFTq6oaU9RFEVRFCUkW7RGKuh0udOREspoVgvLvaSLA/+93peqe/zbC1UcLeH+QXoe952cf6e7SVWSRjLuX7SqxeMcf+0k0u+GvoHVRUHG6j+4Qvbu300mA2YwkZw3HC+pBn7714f89BCmvYJasVmNkhyvPeppKxbzNVELfi0rgDn7yQog6FhutAnnLtydPn9NOfEBqVVxCPzVQkCTM/gKMYtVlZ0NwLzD7/eP71Wa/0R6ecWsSr02tQoLcaZ+A8BdF0mI8qgHbvVXzYanLrkJgMvfPxt3ytchr52+mu11pzyfQQvOy82j1BtqJYsjFK307sMoJUIsck2+ShOGXT5f5oHur4dwNM8qPH3tuH5g6m+TOLfEal775gSMg5819AUgMvN7bG8V62uJI81rLlynCeftEJgxEK3sQvJ++dtoosy4v2HVMLa6SIJYXN8Mkti02odNmAUcvO1P8ExVGQQDTQ4slcS6T+wtaRmWbpR5r89za7M0hy3kSgZg9mJJwTLYlXQMViwazgyrZBPIKJ/l2hEcr021t689kjkiss1Q3jvnRgAaXBnHxd54Dmqa/b5jwYvPyvurr2tcTVKWsFxRjZSiKIqiKEpItkyNVGb4/8htuaWP+L1ELdG0XL5AEnEF903q8OqXADyyrhtjyiWs16zMHhwk25OcOfxM7K9nNH2dPGBC4asKl6dup4kY6KBEDBDrmvITMpqilmy0kaIi6vcR57jV54sv0Ce7SD6DQiue5e9R62yko5dw7N6Zkjywp/OtV1g09zYI2I/dPcS2/MF56SuAQivm+4AZvvnLCErcgBMf5GdV7PsXJPy6DTlHnOmH/kFCfLt+7lD2uvQNNs1uDHnHbWjwgwfib8iWPqNeuoJ5R98HpLbBMT6D319r0ff4EBcKPHt39+0AuOlhSXa6fWGhHwLeVB/2NTie/9TP5u3Lqj0ytvGA/Gk/8rC9U6Y2Oj5wfZvOD6ZWeX65+ATa61a23Zk6jwEvs68Ywsxh8syMb6jZ5uK52/ajS3JC3q/5Q2I2To4H+lhwPznD4RNk/FedJIEpZTHZ7y+ZTPqJNf3Eoq1opCrez9hXczOl0fmvJqM/Njl2WknrkkpnImXMvq4oKxl1YSSoiTL+bzJnzUxsoOpxL0WHf8m2ywNbpCAVdDIHmHNCWVbG1i9fkjwlfRjv79Bu9tP7/ftHMcaL4FrjTSzGJDLnpM4M+E3T18lL3T2znOSmaT4KKDO31I79FrD4qJEArB0sj8VklW3s5OBWeOaN7uLEfmr/Tziv43ivLOkoCdd82v6EYjpOx0ix37E6359uHrIiVtvnBcui782S18k8Gz9aDtc37d24WvbkKn1xSsC0sAk23g1EZhn6/3aC//ePcdrLNL0Ou201XxwqAveIjKCLl3e5h/N3Ob99F/S65EOrRNAuj9WT8Gx0mfvFAdh+BIz0q6+fHUYPLx+Yr5LPx9jKx4LHjDcTpeU5au/VL5XzPpdNiIMm8v4l4oD8+UE751QFYz4qWrrB38+xPZjotUmn3exHMRsB6unajgB0e2F2Kq/Oj1yAMtgl8uw6RlLCjZnvUnOhjZ1o/nm69eluBQ3lqfk4mZWJCBo6p8/XaXmk/ksE1M1G5p6eXtDR/Ct3ZJufiJLDjK0v1/ai/kYJiip81UuhHwhm8M3b3t6JH+x5px91mRldC9nm+7Omn0rxvHlSrUDwRltR056iKIqiKEpItkiNlHEcM07Jvzj4Hf+3eQmxzfT/t2Q4t8leufcfB/bhngovI7vr0YdPYNrvjeQZIoNwK1gdRWXcMdIAlDR7XGbW2Ser3oGx7zRzdNPY3gKopRB1oyW6Y81A/nWT7HvV+WVPUxMizNOs6Nf8fHde7zc27RrmnoLO5ve9LCH8A5MTct6TLl+Y620xOU/aSob2xJ4xm8tni+P521vLnlHGlFMVL2P2T9u2zxaQZqKIJOTvFz8Tk22sLIFr1ChW9mrbrNHthLceG2Sz7qHctDPN1weIeNdqkD407CbJbm3Pnpe38PNIl84AnFH5KkZz3JT5MpOg1urmnt5OBI98ltM1g+b2qhfOAWDIeV7IdQitxuxTJENSRbTED/Eus2R1/6vxYuetXjU15/K2ZKxoyrm7ZoCMa/MsgvtsmjaOW1F+t4uX/uCV0QCsXCNzM0sKKajx8tZ5zX7uT1/xr9VUuhorc4oMPifVROVOJOrPOSZlxw7PiVb41W53831S3u/9TFBNj8+562YJ6njxLUmB5MsHsbjfJ2acJeO5Z6ysxfdhpiVo7es9Kba+k/+0Yx9R1UgpiqIoiqKEZIvTSAUzNNcfIM6v13S5FyPznTf7JDlwpufbEAnsTeVJm0XvfMEdayWu+bKK74DUvkl/6jaFQ/aW1WDsnSlZ12wvTieRpEvbuB+Z7TpN2uZTv6eveuJW1F+RZYZrz0nUcs+qvQB49nPZ+X3ww0k6fzQh7bhQDpNeG297/pf+6i+ToG2614eBe9rMDpqbS/O1OVn8oWSuZmv5CKb52GaXee0qe+g9EnzwWk9xbm9qv67NgRmrpn/v/ZqM1+LZ81LpBdpKRvZ2u393AHYpTLVfa4k424sZ31Ei9OjfcnqUXDj2wAnN/lb+mZcKJrhrwX+L5qQFxWHwGZodAU7f/tk2FW/6XSLgc+lkKDdy3YNNaZ6Kx8Tf90/dJBBo1BXnUbJU2vWtxx9q9Xw3mSBSIlaf3x2aesbN+TrKrgQyny3xNF99Xl7uv1vbY7VQjZSiKIqiKEpItjiNVJD5J2Zv2bD0JdnRu2fM2+YkFvP3i4p4qQechgZuf+9gAC47RrbSqPF8CCqjpcz9qZQ3pG0uSTmRqBAfhZI2+nI4uE3a5g1N5fdrLjS9T6yQKyo/BOC6gz4GoOyQQm5dI5ENT94ufkuV9wV8pXKMiErutyMAd/QZi+k+RsoP+oBMrJfySieK5tCmfRK/IpTPy9ZMGs7r/V67yv74TklcOHAf0QS7jZFWw8LTyIfCwwViUlC396VfdRqXCt8PqznO3Bpm1baiOY5akZy3hgHp4+a4ixZJAtxXp29NNOYlym0hs6Njy29lnxXT675p8l1bbyTA6RVGI1Wc9VvpksBYy9DG/dhJdMitU5r50fhwBrEzOmuRFWtR+1q45r9Em/cDEYyIazxE5pnHBkhk/fA7ZJurPk+O97Wnw+6R7zqPWkr89i4AFCYkas+khXEbGthwkKQAOr1cooUTrt2sRiqJTdR7x163WPyF7RmzsyJ6w7DlCFKB0Phouew69/c9n/J//rheppze/5SwYTuZnWvJqU81RPU/JBVC3VGiKgyGyv5ltKgB/1Eq9hGzR10+aOwoTZrp5N4auYRe54rjunSLisoz+HK4omIWAFf/jwg3A4fL3nTVl05M3x+rBeYd691fpMCfoEznDJoublkswpq90jNhtCVXlZJGMAtz6dL0l0Iwk/POhatzLzSQUsDeV4TjE698A4D+BWIScdwIEWvzmmMTboxBccnBdnKDpHPo9E/vRysSXhjIEJJqtkr9Hey3zdcrZW4ct0GEsHnHetmuF3ze5hQNWa0awuxWYjV/rYL1qStkCpFB/ICMtLp4QmEb9w0Mbiqe5bgb2JGhvbRw22mYObWtc2vQYd38v2CdClLtItAf5p8k7WvMa/3Hyo4MTizm97l+fwjuYjA3vaxAv1xwVHpnaEmQCm7a/OFHwwEYxMS87L2rpj1FURRFUZSQbDEaKT85pm2z6miRFo8u/QCQFcHNC8VU5/aplOOrJEmXnwMgE+/rf66rAuC8TpK9tM5p5KQOkn35tqO2BaD8iUmp67dzxdTQwXMAD4Re5mIyeLq2I/Mb5d7iOSy5iqwEHaJirhwQl/DwHQrSM01DuvnPrLwdb/U79wTJFr/j9PPpOrZ5x9UgB+7yhf93pikymK196sLeAFQhGqlg+LISnkhj8xqi+jZoNYJaivV9RVV+SYVoexcmJVlhfBNsdN8aCRcqvbEYW5c/LW0mBYPW+X9n7jLQFMG9MZ9eLqaJ5ALZdy1SUpJKwZKjM7wfINMOB/AnaiSQ5LrKGVmmqqCFsSXNUj4DMjZlLIkVT+3tWTM8XSubGdJuWG6LpeGbRkl7EM/KYZBKKjswVkufjH0sg1rIZFHGNaJR2BSJhf9bCXSOUUPEIvLEuhEA2DUyFq1oKjWCMQXKqW5aGan0BzEuHfl22mVa0j4Gf+vzbn6DoFQjpSiKoiiKEpItRiPl47o0Hrcm7atat4EHq/4DQNkrhVmnOBkeBxEi/nd1jkivCTd1nrGB154gknD54/mzfzcGHCFbcmLN/O3e84730zHk6m9hnO6iXXeTaw8Qn43Vw4vhcNEEvb79w4A42RsfkESGn8kvL3uSR18clcPdwcldJvl/t5TAMNm45XWtHytB7dGGXuk+LUH/ntc3DAbg7BzKDDr+d/6iBoBtHrsEALtIxkMTOTg3HWbRGXcp/V76/6DbpgR/Cu9jZ1lZmpeRvb/3/25Oo9Eck+f3B2AgMk+5jY2bZtujVrh//D4AXHfkjKzf6rp5gSDBLwNpEKIVFQDMvUy2mYl41S9e7lK8UuamgvVeQtik6ydr9fdFixsHdrCS8luiXHxN6iuiJEvkWrV95NP0paqxs7GXpfYhzZVg0uXybzz/0yPkw3ZdbNLn05X2BnZ98XIAhl4jaT0ilZK0kaQNMeljzjLR5k+/dTjzDhfnZ7ONSFCDEcl4vJZl5SW24v8LwfmmulSe/6c1/b0fvXHUxBhK+y7jvejsMpzzOz0AgO02n1Q3qFl8o076TsmHMmbyFQT1w7/tMvZfi/Xtw2PbPez9KNEoZv+o5mlKnSffFUbTnb4jWP5ge9QTMn7d51iSCxel1Sesyj1RnvrbbKSZi7K/vjKOUSz7ZsaWVI6ui9sgJhhT94j3WTk+CvdK5zjqxCsBeOKmm/xssaazGWfxkzqs4TcX9cvL8vStAAAgAElEQVShlrBzYZ33V1GLJsuikgyTwebIIeU9O995MA/mky2N2j7Nm1PH1+QuSJk2iZaXc9wTEr56ZkeJhM3M47Q5CC4sjEnm9HclGILJ3obTeQhYiPURk/MxXd71v2spWrapY2LTm9+xYHNS/U9vv7gj0/OJAdQeIZvxdn68ICVEmrnNsrDXyMuroZvMu/OOus8/N3OR932ylvEbJbv04oQIYPWOjLEdS75j7yK5lnEpCDr8mkzVh9/2Syl72fJ2Z6ePNNEFMufa/9QOote7ch2zMbGzwZu7HDtrX7XIhuy+Hnwp13WXvzuZ6zW2vCuG2T9OEVL72rpMWj0AgJN6Slb/x3cQtx3Ltv09KNP2MfTmqsy9cZfsWZq2mwY0nc086N5y+TTZGaLPOnFwz1cOSTXtKYqiKIqihOQH10hlhh4uPL4fwwtEA2VWRlcuHcmL73t7eBnRrzUFR8ZxZx4gK9Brunzr70u1fWGRd83+9Lh1UZP1aSuJ0pT2I+igmknmCmpjZ8vXSPnam9Y0KZkrO28FaUUsrCJZNZc9PRGAw04+hy93fRxoWnV99EETW76WR0ur96CJZJseSwCo8f7f1lDqUBizQ+azC6xqfuzE9khPcZAIZMN/5wsx0zAy9/Lsdeu4YdwxANy/nZg56hplDDSlN4hYLgnby/yckM+yEhlPLeVQag1zbjxms+EjMVH3mTw+/aDQpr1U2gS7h2hUjiyt839uq+atyzdbRtCENUECP6peOts3S5m57fPdRdt++KhziLz/efqJgbEw5HzRCozoeTIAX4x8ghPmiIZg4UOi4az4ppbo9PkA2J52x2gHPrC24/yx4jQ871AxswQ1Uvu8Jua1ITfLs4wUFeHU17frviONrY/lDtGNlC5Mv45vInetrPQMbuC/ZkyZHSNmJjbQ66ParLKa7e+ui1W6ZWgttxhMezuNzF4mQVWnDBET3+kv/8s/7JF13QC48+bjAOhy/4SUSS/DqmHvuo5cMO+lqBWh5OXy9B/ztJuBaqQURVEURVFC8oNrpDIdvQYdMyvLRv/JjTsz+MncNCbN8cwF+wNw3fUzsvatG3TsLDbc2nR92kqyQ6rsXMKqDQ0VIVbzWVoWT2K34jh1suI2yU1PHjTFP6qpRHXXdP0op0t+lZB6jizM9qUIJoe8oKdoAP9Wvicgmo9NsudXUCvn1WPxlZJxuu8jkoDUXrGi3X4ZPwiZ/oP9+/KX4c+nHWL8B1faG+g/zvvyrNwv4Y7ajl47ifawXwfRdjlu8+urpBuhR5GsBD+8T9IAVN7rpcTIU9LVCma1u4wgVsTyF7Nrt8oOcW9JI5WZnLHWqadsjmgnTC/eLNrWpvDG0bAbV3H1zjsAcGOPdO3TiFum8dVO6cc3paHte774TO16wPl0HvcVABXrUxnlM+dM/56dRobdKHumPb13RwBOLKvxdzbo+0r6uHPtkL6SAW1EyYrsMoy/p3lOh5Ys47Y+YnEoyzzYdVv02cz0N1vrFBCt2Qjg639dx21xHrNXtSE57v8DjJVg6eWjmLn33d63Ms9ctkSsTS+9swtjDpb3xqe/HwvAIVNPw/V8JI0G0CSRvWTrlJ9jU2PYjF3jR/VFYz3dXpHknsYryvejbSeqkVIURVEURQnJD6eRyghljGwn/h13DbifqCVrCBOq2OntOTieFGpFRfZzW9FqWGY17x3X6+UFAHz9y41ZPlj3VI3j59vJMt6Z9m2T9csVpzTXLSLS/acaKwL305Tdtgl/KOOn4Ccq87RpbqKRaCdZHa54THa5v67yg6xorKBGaW5S2rdbK/V+cMXeAOzU++MsPy+jmUq4NqO9QMvfHCDPteT5T/KSit/HROgFEqnOfFBW5fMOkRXPv88Wbdz9PzsS99Ov2n/NzYxVIM/ERGfOOrcPh5TI38YXpszb+ujXSw6g8JXJbb7G3GNKmL3NP4DUqr6lPcca3IT/+/DO6c5YQc1Pe8iMqMonq4e3fQ9MSPXxZ2v7EV0iW+j4tdscEalNYNrJnjWXqZdK33/yge8A/KTDN/f8jKr7JY5zyNneXmWxeFZEa3KJRGx2enQpjh/96j0Hx8XXdpt51yROjBfIfmXA7Poe8ltZDa+uF7+p4nHig2Xm07AaADeZ9MsoeV5SsAw86kwA5h70oD+XzUmItvAn4y9kwOKG9DJa0BzG11t84LlUdYnIcRM3yv088N0elHWTd1I8UeWdEEttVWKiyrxIPmfFqrxuO/bfQHRr2ef1zStu5GfzDgdg9lrxlZqwvWwF94q9C/98eV8Arv+5RO858UDspOl720lZp5Z/AMj811TaA+MLbHzdTvr0LPoulfdAvueYH0yQygxlnHuCBJb2DGSXvXiKOEAOWPGFf+NOfW4vYTNkzHnJ+SJIjfnq50ze8WkA1nkvo27RUub8VK5fNa3p+uVKvENq8LYlP02yS2qC8YXFZMAUlik4unZW3UzI7cqTd2DM1S8BcGGn94GmTRjBl8Qf5h8JwCv9W67nO+9uL+ecNoGkvxloZrmO/12Hi6Xd7efc1M7L7TTxBUNWzefMe0Yy75D70o57t8bLkTN/GXaOewluKVjxAl+AsnaQTP9PnHwbIJNC5rP86sYRlDGJtlL9rxqqXdnTzom1kD/KvDOiULhS+me3qfL8649og3d7M3hpYCj9rhZn6jftLi+NwMIkvlW6g2qwrzZF5oLnlZXbkly6zCv3hzUXm75vxWJEPhST3oNnHgWA/eCLAJzSYRUzD5UdDIb/5SIAqq6d4M8Vfhi/EYyi0ayx1fTFszuJE3iZvbF4KADlSBZrK+69uBoass7LGW++M/nztrpAFr3bn3kBG3pLfQY/LgJk9eqV4G1i35TAm+nCMfCGr7h4hWyUW3ywOEGvrikFoOe/CkkWS/mJIZVeAalzLU+giq+XtoxtrAcVpNKYfqEEeVREilh7tgSTVEyXvlH9iCgxvjj1736G/sH/ugKAQeMnpDYpbpR3/5I9ZIFcFilqcfFn5kijQOg5NjsHZb5Q056iKIqiKEpIfjCNlK/i9TQFRx+Wvddb53FeCKmVHa4amue6gGx2TzxQ5imHieZmwm9L0uvXRjqW5Rbam+mIXlqx0f/Nd8gMrPr8LOaVXQBoHNidtYM9DdRusua6aq/XALiw00Rf1Z3wymjKGS+42p7/iqey3rvlelf/U5wovz+5lt7REu9e0p3OC624vwp4ZatXANj2lxfQ62/j0+7Fz1bsOk1rpzLMdwY3mSRSKqvF7x4ZCMC8PVLaqBc2SL3mnSiqeXvF/JSpdkslM5lootFPHrnDw+LMvVNhAWtsCSKo8Nq++r0xAAx8ZlKb7jE2cAAAV/z7KfYv9lIDtJCJf1ORec036uLcuosXoOAljWyvBjOofdit9/w21i99nE5d2Du1f2Q+TdXtIGj2inw0FYB/HTEagHvvcPhgWwlQmHzaLQDsHLmCQb/2zMBGy2NMHbkG2/iBEAn/74pYSguzdLFoIPxg8/Y65AcTM1bLXBWplbHQ/Y7xfh3MPqJW925YbdCoOevX0+M2L93GbfLRMWRVt4zkGK2wmbWpx+0uJt7rl++E/c1M+dLrs9WnfwbAiZ0OBq8fDloZkAdMn/SeWeOe61M/NRHQlamlOuP7vQCIvT0lYGLOr9uAaqQURVEURVFC8sNopAJh0sl9xefmT92MRiHqaxQqXvP2w3Hd8E6KGSusri/M5L1fi/w4ulg0OgnX5vpKcUI7YP9zACh4Q9IFtDWFfPeylLTcUvLKTA3RRUPf44Z7DpM69pWVeL9y+dy2fDE7lIgUPyAuGqGBsZSjcSbBvfSC1zHf17mygjah88fOPpBeN3orgL9e3tLtYX8tz2Sfty5j3iGSgM9Pz2+l0vNnbkMz6ZJbGbXxMsBbQWbirRT8pHm2nUqwmdH+9YePZK8/SX1f7fao//0nDXKtO8fINgDWvKmpsvMQlp93ItGUL14gUAAk+GLrh8Xh8k/dJPx3pb2Byqho4o6dfSAA1Rd6iRKhbU7PdaIBvXb6sQzvIo7GG7yAg0grm+w5Xhhy0mn/OsykWiiJyX1//uYw+q3x+kd702UEAkZivXsBsF9Fuh9Za9vDZI5Ta1ZpuLpsakz/9u7Znin+J8UHw7Dfiu/P2J+Lr9Ss08ZyyC4y18TOkPs3PqRYVoaTOU2PHT/Boo218zYAVBc8FahPnjUegT7gThfndturQ7S8HGejZwnwxoC9bDnRatFWRzuLc7KvCWmKgMbL+I+ZYA973bq2abSb07C3hRw0RkF/tjaTUT8rGs1NGxnyvsqi4huXcKMYf0Pf0uDN+fbamtQJTbS3CaC6c8fH/e8yx6ftOllaqtl/2xqAEial7VuaT34QQSoY3TP3JBkMwQa54pOfAjBojThQtms/HKPqNREuK1dxzienATBzH4lWqnHq/RfU/JPl+OrXw3WY3iVrczouswOc12kR5x15XzNHB0kJT0ZIsTPMdxEs35G8wU0JoCbPkxGgrlsmkTX1Z5WDuyKnepsOvvVvl/D2PvL3/sUy4QT3O/LNQ14zFkYiTL1WoumG7iXt3/seOa9g0nQ/yiXYwaNdxSmxZt9BUv4p0rYTd74ry7nwtbpCbjxXonhiH6eEYNg00V+xiINpWaug+Sg3H9fJfjE5qYABkxtl4S8lB9Yz593EsAJZUNQ4IvRURks5+FuJeIkeI07T9jrPebqN2dsTVWL2XLE0zmcJL5DDE5BaE6TyiblWvZdNvf9L61J+vH4fCicEByfNRH/pS4eWeAID0rYtbbzd1O8V0wNt8wNF67VIhkAF0O8PIpj+7XFxRD/j2i5MOkjsV/Ufyv0c/KDshTfgxql+Djofy8oyr6eCf2yWjRQD3rYFZrP5MgYPXJpeRqSdglVQ0BkoETFWnQhPyUWL03NkGdZ52chzcXAPulEUy/xolXuBT+vW5b7bRBgCi0g3w4zVEsF5bc02bTNEbhjQAYDiadll5UQTfaIl/vGBmNfmHncvh+xwilzzc9nvrsl9CQPuBWZxOf33Ejy0f7G44QQjiI2SIOHa/n57VS970arPyeIpX/vqNYWa9hRFURRFUUKyeTVSVspsY9R0f9/3iazDuryeIaHm0/nVsujygpfkaB/56BDYMfqhvWSPqr91lUzo9qrVbVLrDixe6f/d2mo3SMK1fe1RLrvRBzFpFozzeNyK+wHdtpvaZ8jkWDnwhSsB2Oo6CTN31s/J+R7NKj+5cBF/vOgXUs69fwegj5e6os5pzMqebgdW79P39Mxx4lPMPWt788ZKUb+uT8iz71lSw6ld3wPgoJI3M2qR0gBdtEg0OHPHDCD29abXRBmCZi3XCyHP1ek4UiLaEGebQcw7WtrsqmP+A8A5HcUp03ZTY8BkWh74/Lls9UsxQdsmvDpkvrP6/xFN1rxtn88pf9SmIjO32fa7X0D3T+W3fKrh1w+QMW+c9Nt6z6aeFV/V+CEaP1hG81wI9gdj7pvzHQBDzpzn582bfrFo4v9zhjiiLz6tIxe8MgaAwU+Ixicy+eussWTGGMDaHaQtg6lrTuglY/E/vcV1I7locaou7TSzWw0yztyNoqmN9eiOm/C084GM4vay5W0oNODMXiZtYnf2XOXbFp/Qdrz2CPZz077GvGgVF2EZzY2nAXfKS1i6lzj1X3/JY2lFFlrZr/bgd7+4QbZBuL2n7GnXffwaIitE4+/WiwbPTSZ9R28TGBTMP9aW+XXYLaKhnHp4A8c+LhnJxx21O5AyQzfHgutHATDr+DsBaPDGYoRImhUEZB4Z9M4ZQDBvWhsDKUKgGilFURRFUZSQbFaNVDBT8/wLJMng0aVi7zQaiyV2HZ2niq3dCWiw2kvQ9lzxpuzlZZzajyyt8yXb0Z6/zzlXyM7nVdemEoLlQv+ClEbKZFY1+qGW9vSKW9E270LfHDXORt7dKD4hN845CIB1b/eg37OLAKieJzZjx2gz2rBKDCYBLHxVJP7TzhYn8p/+/VVA/L0M5rkmsX1NW0PGKuK8TovSzmmNhcla9nn2KrmXa8WPzm2YkXdNlF2c0jhk7r91WPevuO9qcdhNGv/jwIrSJJhMlMuXTnmS7j1lxfeLKvFZOaHsLV9DYtrJ9i4ZtSKcu1BWbLN/Jdq66ncm4RpNgPEFCbm6L/yzJKAdPuoCIiYLhbesquvrrUIjm1bjYrkWblSu0eVTabDuD6QCEfKpUbQL0p9fvTc2I0SytKYOrq+xMgEdFy/aTer0dWAfwC3RR6opMv2mrNQODkO8fRmvKRUN/OKzt2PUiaKpPuYQCUv/ZMNAnvlQNL+9ve3NyqeKhsGtLOe83d5Lu9wau45zOooG6oarJNHv4Mvl/1Y8htvQvvncWSlap417y7hYNjJO/5c8R+WVq9pVtlzAG4vlXvBF+0tskfrDJaHt0tPqOW6IBMiMLJNAkyIr4X+av+OWtF/nSCNV8ey9Iw1Ohm+hg0PS+25MuWjrxvxW9rSbl6hlmS1a23pXNF6NbjRwrrTCrAbxrXx31RDmPlOd8z0m54la7+IrL+Gl22Vj20PfEuf/fZ+8GoB+rzUSaZT6rR4uden9s3l8Uy2+tXWOl6k8YEEyY/e9jVK/S+4+j8E3e3OISXXQBr+zsKhGSlEURVEUJSSbVSPlb3mx8zY8fLZEjSTc9Ki9KKRW9k0k28oHtrdq+WJjPwCOLJ3u+xkZif7Vn90IwLlvXEL03c9yLnvrwiXeX0W+TTqXBId1TiM1nqZmvefXVOfI+d8nK/i2XpIzztwge+fNXd+F75d2BiA+V6T38rnSXp1m1hGdJivnsjrZ7bqMuf5WCVk24xCSuptM+uUUvC5OLS/sLdtC3HTtEVx7qNjgz+woK9doQGZvKmS1pTYye2CN+VB8sobc3sDgKROlHuagSDtCgTOIjJD7OP3Q1O7iZZF0reTFFfO5+PK7aR8lvhbk7Y1S/oWfSERL70fjFL4u/S7mTPEqFs3b6iryvmjy+ryf+u77Z7YF4JmdHwRglV1K1Np0WpeEG2Ogl87j9H4/ly8fyF/5QU121zdlRfznK7YC4LrKGV4dUsekIk0dXxM1boOs+GdcIxr0WHJKaL+0H5wm/KaMH5qJmu1x2wRW3Cp966Ge4puy4uAqrP1lEA6+RlIPFEdFOzKm8llGFooGw/Tl8kBalgnH3wzAvitE69Dnz+MJlQwy0N83jhZNVEO53MOAm6bldW87t1xUzEt287Qi77d0dPv5193in9YnVtbKkdGMz4KsI1q2ajT/W1W8jCrfXdDJ+ExxSImnWaqYz5yrnve+bTllDqTeOSXPTeKEZbIlVcmf5V0561TRinFqy2UYTZTRFt+6emsefOkAAKrvlHr1XDT+Bxmfm1eQ2kMcD4+8/21/8AXDFkEcFtfeKA3V+QwRGpLLV7Y/p4yZJC2Y9ydRpb7Q5Xbv2pGsMMpBnsr0rHue574Lj835MkuTElZ6x7KRvD1DJu2imTKxlC2QupesTBKtl04a8dTckUYbK+Htd5XwQuI9Bz+rts4X/twGcRgvoJbBLXhB+kPATJjRqO8omC+BI2jmg5SAOujKVfz7TxKqOvbYYwDYeOg6Tq4WgeCYcnmJV8Wkbg1ukllJaf8XaiTt/PNzRhB/TwISer+4EIDq70SwcGlCGMzjoLnhhUcAGFFQ5L8cmhL+NrrZzuXGfFnrDfZZCZmMpzf0Yl6DmFs/WCZm46Wf96Dbp1J+xwkSlj9w0dRUYZmbx+bR1BVsv1gPGWe/3+4FQDKoAyTc+ryZm5si4TYSt+SltXylOPZ2hPxNhIGQeOPs/NGx8hIeeL1EOvx11LOMLpbfVtnS3s+v24EH35LNU4f+XfpebMEUv6wfnQDVFJkOzoFdBIwTvdnIuOKRpVQ8Ioct9p5NrK/k5fp137Op6yXzW1036fv1XVL7Njre+qPIGyrRiopUxvo24r+MPxQhuNBL/eEE6p8P843l5aOL17a7qJw44GFJPXH8UR9ySidxu+gUkQfzep3kwvpk/UC+3yCO5ctq5R1TU1tEos5zRq/1FBH18gwijfirTMcTkOxSB7fEC+4olQdSWiyfHYoaqCyWG+5XKs9nUNEKBhbKvpLVcZnbe3kpD96v78R1d0l+si9vbv0e094VH8sct9EL9tp/P0lbs3D/AgqHi/tDr3IvvYsbYcEqcUPgW7nvHhM9c+db06hKSD7BoJJgUwYZNYea9hRFURRFUUJiuZvQAUtRFEVRFOW/GdVIKYqiKIqihEQFKUVRFEVRlJCoIKUoiqIoihISFaQURVEURVFCooKUoiiKoihKSFSQUhRFURRFCYkKUoqiKIqiKCFRQUpRFEVRFCUkKkgpiqIoiqKERAUpRVEURVGUkKggpSiKoiiKEhIVpBRFURRFUUKigpSiKIqiKEpIVJBSFEVRFEUJiQpSiqIoiqIoIVFBSlEURVEUJSQqSCmKoiiKooREBSlFURRFUZSQqCClKIqiKIoSEhWkFEVRFEVRQqKClKIoiqIoSkhUkFIURVEURQmJClKKoiiKoighUUFKURRFURQlJCpIKYqiKIqihEQFKUVRFEVRlJCoIKUoiqIoihISFaQURVEURVFCooKUoiiKoihKSFSQUhRFURRFCYkKUoqiKIqiKCFRQUpRFEVRFCUkKkgpiqIoiqKERAUpRVEURVGUkKggpSiKoiiKEhIVpBRFURRFUUKigpSiKIqiKEpIVJBSFEVRFEUJiQpSiqIoiqIoIVFBSlEURVEUJSQqSCmKoiiKooREBSlFURRFUZSQqCClKIqiKIoSEhWkFEVRFEVRQqKClKIoiqIoSkhUkFIURVEURQmJClKKoiiKoighUUFKURRFURQlJCpIKYqiKIqihEQFKUVRFEVRlJCoIKUoiqIoihISFaQURVEURVFCooKUoiiKoihKSFSQUhRFURRFCYkKUoqiKIqiKCFRQUpRFEVRFCUkKkgpiqIoiqKERAUpRVEURVGUkKggpSiKoiiKEhIVpBRFURRFUUKigpSiKIqiKEpIVJBSFEVRFEUJiQpSiqIoiqIoIVFBSlEURVEUJSSxzXmxAyMnuK0eZFngymHRrl0BmHPpYLp8Jd/Vd7IAcCOWf0q0UX5rLJfvyr+3KX12Uqo88MtsD286z1itHeMsrW7/hXKk1qkHYLWTBGCtI49zemN3Xli5AwBTFvUFoGB8B/o8/R0AyUWLpYBIVD4d2y+ztXvM6RkGaeIaBmfP7QFYcGAJ3XZbAsDRvacBsGPxd+xQuAGABtcBYHJDFwDuX7QPX06pAmDgc9IGkY+m+uVaMWkHN5nMumYuz3Dgk//rAlSU19GYlPrX1hbJj03cveta/m9WVA6IxeV+KzrUAdCvfA3DOiwFYETxAgC2LlhK35isZZ5aPwCAJxfvAsDy9WWsX1gu574q91/48uS83WPwOVrxAikr0QjA0stGATDu8r8R9UrqFytrrci8Y3vP3fEaPW5FuWX1QACu2vr1Vu9x11NudgEm3HyPX5YharW8hjTHN7jZ7ZsqI1WFSMaaNIKV03USrvSTuCX9bI1dx+ibrgLgy79f3u6xaMViuLY39sy82r0by48YJNcbLePnJ1t9I58V09iuYCUAXaOFfh3nes3wYV01AOOWyNj9bkof+r0m/Sb+gYxdN5lMjXtDE+M/l346cf4AF2BkYdx/Jq21aXME23qfc8+RunpFfTD2vqxn0VaC9fukIQHAbv2/a9NYbAmrUJ6H29AAwNJLRzHtmrsBaHDlejGifh02BeY6hVac3a4+D4BPHr0yb/eY+b6ODq7i6BflXX5m+UIA5iQ3ArDCLmaFLXPkgkZ5N3zf0JmVjTJXbUjKvOa4qerFIvKMYpZ8FkcTlMakPUsi0o97FtQAcGjZ1zxVsxMA12/zUov3uFkFqZxwXb8xnbVyQ4WrLaINcuNd75nYahFrfr47HQcOACA59zv5Mo8CVXtpcBPYeaiHGfA9o3EA+sXk/yMKajix7D0A7P7e4N4jwhsXynFX3X22nHfLeCkoILzmi6Ze9GtP212ue9ZcAO4beBcA3aKlzZRSnPa/w0pk0j+s+nWQ+ZzlJ4iwddi0M+h0o5QTef/zZuuQCyVTSgDYUFyCu916AObs93CbysjxSv5fUW9gJ2x5ho2JGJTLpBW9XF5sS88eRq/fyfHOtG+B8PcYxAhQhp53fgLARc/+lETfSgA29hRBcuGBLlOPuA2AjhF5PsEX0CPrugFw503HSf0ccJubgiLgyf4kOshBdT0ddh05A4CHB7yRcUKU+77ZA4Crtm79vjo+J/3gpIv248mqd4DUiyDaijLevIhKrILWL9QK5ppmzMetKA7ehE76S3ufW6+i523euPz75W2/mJnnvPq7ySTRrQYD8O0VFQDcu/8jHFSS2bZB0oXmQivOCK8ZRhTIIuDCTvLJMKj9mYzL65ftCcD7D46i+/2fyvVN32phQaXkiJM+R9f1Tv2/3hP4O0bim+TSQQEK4Jpl29Plg4X5v5ARAL05JdGjI4eUzgQgakm/HBSTeWdIPALUyvGl3ifz212FlEBcRvd4TU7nbHmCFPgvdTcpD6/XHZ+y6PKdASjYXyTE2LuigbCiqYnIHN9lyhrWbysTerEvSKU/oB+SQisOrcrw4bFdx19Jm1Wz47rsXywd5IurZBUzcOC5AFRfNCl7BdkOrFjMf7E7e4lmrM+Ns3m939iMI0XwaXAT/ss4SOYqP6gBMBghbPKOT7PkURlM+9//SwD6/tF7IUWibZrAe948Puu7Hf5zEgAf7/RPr85JCi0ZPktseVmcM+tn/G7gCwCc9/AFAJQslr5cVOOQLJL6b6yU+1q3bSMn7igvnBu6S38es804r/wET63vCcD/fHIkAO6GGGc8Li/Ap/73EADKH5eFRbDN24spJ7lwEdbCRXIfXtsPec7lsKpTAPhoxHMA1DqyoquIlhxL7hUAACAASURBVHDTNwcC0PuBCVJYG9ueSJRV3vG7XHEpkOqvANZXHeSP43O4D2/lPuWjHcETpDL7VJCgRmFmQgT0M749DYAOBVJWxHL9VW2Zt5LtWVRDtwIRuAcXLgNgn2LRsFZGS/2XjxnztusQ974z/X77v0t/6XXz+HBjMUOAMm2+4NejePGcvwEwKF7mX9/23sFJ5DjTLg5Oi4u8psZgWUSE7Jt7iAAevf5TLvmFaFZnXDRCDpr4hVQvj/30/xWWlbXgifTf4P8dJ3/zd1OYfmr68riXdmfAggn5v5CZK7x+FvloKif9UjS0S0Z7WmVL+mev/qt4ZZt/AVBmibYuiZ01xs2iJfh9cCGzxhEN18i3LwGg0yRZNcQ2Qte3vgfg7O9brrb6SCmKoiiKooRky9RIGYx6OtFI35dXAVA3QGyiMd/nIZolqTNvAfU7yUrINw5l+EhsTjJt+ztMPom1S72VdYFXr2btH81jReXcgmLRxI3oJb5Pf+r7AkPipVnXNquKBkfaa+6x9wKw49fn03VsO1YXTZgTFl8lfjb/vvhGAIbES/3rm0+zui204qlVew4EtVdBX5aenh/PN+eLBmNQZ7HhD758om8Cy+l2PJ8hqyDuazU2fCNmkZJdPLu741ASkb8nbhDN0fI3+vD0CbsCAW1YE3T0PnsAU702GHWi1HX7q0QzdXfviZxeLia90w94CIArl+zIPc8eCsBvfvcUAGMbRDVT+u/8ahUBea7eM7XingmxoYGGZPNt2TCzPO3/keIi3MZE69cy49OK4Hp/G9PzJT8T7cbtvSbT+du2a5S7TXGpPUXMT0Z70pS/jfHFigJPrJVrlh0iZmi8/mMnjQ4HGrzPVYAVk6f6fkfxSXywsjMAGwd0Ys0Q6Sc120o77LXtDM7q/gEAF959MQC9bpJ7teIF2fNZawS0RP/H3nkHVlFlf/w7r+SlkRASQocEktCkSBNUEAtiRSxrWXsHFdeylt113bWsrr1j770uFhSsoChNikrvvYWSkJ73Zub3x7l35r55L8l7k5cQ/Z3PH+TxZt7MnZl775z7veecKxX6Te/1BgAsO3Qygib1gqofjbzukCkVKTqGP0a1XB5LHeXLOxMydTzekfz5lr8zEwBw+V9pmjLtg7lxtUXDbJqxvnXYJpoZaKpyA7bv8CFdN9jfRVEKE4Hsa2W7mVFF19V1elWTnM9CUUVbvTNH/KX/q+4MYz8nxXjOwA8AADVGCIGIfjCyX6xR2v8/th8DACi8aAFtVKahY9VOWZFiGIZhGIZxSctWpBRK+pEikDWXfA9C0o9KHb1Jf6CKCjmNCl+njrS/jFRrAsfqhlBHugCQ/UgqcmfQiC0RzsLyukvFdV1XcB6Cz9Lxvur9KQAaWdQVjXLe1dPx1cu57k8vRsHyGlY/Nhzr/iT9Wmw/KIlUciRrg+X4xxbyA/p5QzcAgFGSBDOVjtexwz4AwOV5swAAF2Xssn4rR0ypnqQIxWvtWc8AAAYvn4ic52JX3Kw6ZRrWNQX2ho/41JH4ziCpEaEUYF05RY9oPlFG6cOnR1dSZERVq3dp1LVuCs3193ztAqwcSf5Y+3SK/Huow0IMOpucKe/46EwAwFV3fgEA+GpBf4Q2bY35GmPCNAHhSyCVOWgaRnVYE7ZbquLgmrXccYiamrjrtjOKcOqKgwCQIpW5uDiuYwFA5pK92Byi6+gdo++4dP63ylSPimKGQtY16nv20pfib9JKoN10+qqd2H8ngHtbjQQAdCxT/PgQ6fgfM6Ltb32PHMuXHfIGAIrsTREO89IvSzeNCJ8XqdBNrUzG01uOBABs3JdlHb4wm+77zZ2nAQCGJ9d9LMBu772TKKDi9YceAgBcseOasAjbhtCbSDLyiEjvppISEl5uzWP59+r57QEAj3V+FjJgxRm0kCickYyPbiEfSM+sxXEpi43BOo9Ux5NF9GJZGUrKwwOSpO9TPFSEAuITqWyeFFLfzJoamEZstgIrUgzDMAzDMC5p2YqU8OD3pKUhmEoWfmgDuc97+/YEAFR1yUDSNKHuKMpIxjqyLssP7gwASLYUKc8Bj9wzvYpPQ5IYIrvJ++Hw+5LWs75mPXzjyQfrjlkUJ/6vtsvqzJNyddZKvH/qsXGf3qmmrXl4OABg3Z+eifChUP0yZC6gl96gyLNub29GaCOFU/fA3jrP924a5Tx4/ILTceRllFvkoQ4LAURX3OT13nvzC3jsqxPivj6VQEndI5OdQfIL0gyguIL8tNqERH2TI5oGItcsFaaWFIm8s3/DaTNp9PdRwVcASJk6V7jWTR21AgDw3HIKOa/6VwBFFzcQWpIAvK1aYWxmuLonlYjdegWyF5B6KGtmrCO6+jD30IhxXk0Q5uZt8R+geB/KzPjCwnWHj4us43Wqa04fFaU9q5HFdIwgjLKy8N+5SAugtr/1/6XUIqsOochYmWMuRUuy2p2aS0v6RI367VT6YjL53aR9uxxGGan+HbHd2l/Gh92ZQ/3E9jOp/504aQquyKRnoobIyzpRKfwx80XEYOcH12L74bG/dq556BoAwA9/e7he/7b6cIbuF3x3MXp8MT9sn4IZF2HN6Fei7t8QzvKUG9W45qEbAAC/PBHTIRpGeXY7h1MnkOVNjbus8SIjkyW/LaT8fQXY6e6d5YKINuepW+2rLyo35vMJn854lPSWaUg5cj7p/QuQtj0U9t2u4eTMGWylodMC6gT04mLr976FlHtizwUDAADJ0oHsADqdSzTdfrnIF2ciQ4K1QMDqqN97ZzQA4F+TlimyJ90LtRHu7RufFK2GMW+/kRzL155tJ4eTFVo1bvI/piR4vW8hI6DTfprWUK88TMYVz0pOfxkV1J23fXo2lr1MnWrv18nZcPlhr4dNHwJ2ozo2NYgrb2qHxuCvqNsgKAna8nJZJb3020Behkgg20C1i5Zvp3oCTa2s+oKuu4cvxXoxvZX/HQAgfxnlBBvdbwV2inxBTUqndhgYKBH/Cc//Nb2yK7DOYcy5aG/O6a1ed1JbvuOR02HUbI77eMa+fag0pHzfRO3f6S6gDNbMaEZSAvLaWX3G8P6Yde6D4lt6JnI6z6t5Il70a4Pl+NO9NwEA2j4jjGJtAwBKkyLLpvnsl7NMLSOnLnMnU9ud8lYhXn6bjLjZAz4EQIaEbPfSCVoadi93/QEDr7oq5mvMfYrOc8elI/BAe8oL5nSVaAjntFfXVyN/2eVVHzA6+v4N4SzPHbtGWOXGEy5ygUVDqScpx++0PifCcKgL3TSsOrMpRKlluk6ze2uzDneFA4mbqb1EwFN7DMMwDMMwLmmhilR48szSglRkf0GjUq1zJwBAcglZnrnfF6NyaB4AIPA5KVJaUhKMSnLQDZSSJe85iKaFjF9XtKgs502CMlLIXla30mUl3tMAPTnGeyGdYnUd2uC+AIAPrqUUB0GTVCJ1lCQVlMPuvQ5FYpQmS2dNZ4WCShLWepQ5ZaRsVNMIt+tZtLTFjfMH4b/tSa6Xo2E5Es///DL0fkxMGU6I7TKd+KrC748ablwWtJePCQUdTSpe+VuoF5rPB30Z1fmTfqIR/KojXrWmK+W1nTKIRunfb+2BqrOy4ztXDDgDCfYNzLaSoMpnK4MHnt84EoHKDeKHLtuYpkWc0+nAHS9mKIRaU+oFB16RBpDQvmfjDWadz0RVFeS282/5K9q+Q0qUXHZE9hmmrtttMZrju2yDou3qJaXIHE9t8dof7TQVTjcCtU84+M+/xX2NW6taW5+dynpDOKcAk/bVRKz0FNhbU+f+DeEsD5V1X1zHiAVvNuncL/R+Q3yT4no5m3j5tpLcMZKm/2x/yVnqLViRYhiGYRiGcUnLVKSEpSstcMMH6LtFQs5TKeFh+kbyG9FXr0PlYeT/EhCjJTNoqxpZCymp4Z5h5EfV+le0qOVimprKtnWPWNTRTOq2OG1q00Trx8kZVSb/lCNev+a1jj3kAUo42P6pn8IVKLgI9VZGys4Q+VmPjoD/PnI8v3wzrce26i5SzIo+m4fGPmm51mM0KkN2TL1em8CxiajP7d8RqsER9tpvctT817YzAABTVx4EX7+yxJ3bKkP49ZT0VNRG4ZOWCirTplXtUIgN9DPhX+MmnD9ClVQTvvIo2EIG3Hw47DnI1MNO5+AQdGtdwb5fXA0AKHpnTsQCuDHjUKs0f5K9FM991Dffd3sJNldT3+3zhD8vv6ajLBhAvISaMMElgIQqhAktq6Ls7jmBnncv/9eJO36M3DX/RABAARbZ5fqjzui4oOUZUsraXLX98wAAGRvtzriyLVXS9OmUcdgEkFQmnCkLSX7UV621pqD0lZTzJngkGVLe1pnQS2JbiLCpMJWog4Tm4ogiSftP3xXxnZwWksbOr7XV6DyNDE48GLF7OOLZVI0fhun5zwGIzFTu17zo+cMFAIC8RxuRsbkerGOJc2Z/vhIHZ9IUWO5kiuhLNmjtL83nc+UYqUac1WdIVevKMzQSkz9GnWJpNZdyR/1aW43+STSNKO95Z5HN3efX0bVN4qcTnM7itQV2RmO/o75lLm/EGnHiWj39e2HFRIpK6vNvuu7QDulc20Km5VoIG06jqdy+SSkRU2myjQc0P5bW0jPr/TAFCegIH2w2BjNYaz3D9A+ovX37fhrsvO/REH3NAXqcWlCPmNrTQi2zbqnT3PtOJPFAjcKMdxrSLe0+i9/4/f8ET+0xDMMwDMO4pOUpUsoIuDSPrOCc936FJtQm6TwunckBIGMZOaHu75cDAEhbtda25IWCkrGRRmDVQwrg/zpyTZ3mRFOUjgbz08R1YHt0v+FuCkleOYDyyuimYTl8Vpmk5qRrpG6M/+QvKFw2N75zTSxW1rmjKR6Z52VBTS0KbqBRZ0hxTm8SZIqMPXuR+2T4+nYJyRov8NQzYg0aMrUGIhWpenKe1IsShh7aSariEzuPxvNdfgRg33OpQLRpVYEUXwxr2sWDpkXcu8N6rLU+yxXX5ZRuzhJbrdK8cowWJZW4qnLJdiqmh1Zen4pXR5HSec+kYY0qfkKRuaASkBerXuLoizIPtcPgnQ7Pct07Lzy4ds1ZAADf8tUAwlOXxITHG5ELK/p+TbRwXYJZfX4GUo49NOy7qnYtTJGSbiryOWkaJhw0K2wXA2aT5DNX02UsFu0yaw6tmpC4JD1/LFiRYhiGYRiGcUnLUaQUNcXXgdYSkuvlGRUVKBtEa8G1XkJ+ILryO30l+UvVHkKKVHogEOFEmTqf9tlxRhHaSl+9A5Scc0+fZORW9gMABFvRiF1zMdLVk8gOrs2kx7ivkP4/6MRlmJ5nK1EAjS4Mx0reg36mkWrRTYsjfAbqwtO/FwDg3d4vwKuRf47H4Vx5xpdXo2hrAtcSjAU1bF6GcicyyWmwHkVKp/NqiRYrpP+DUCnW7M+xNjm9PLyaiYykpluR3dua1hO8rN1M5ZxUvq8rKTTdt3CNpYsYsv3V55Dq8QIh2m/fhaSgrjv2aeRPuwwAUBSkUGtnYMEBQSqqLcjZ/cJuc6zP9SWR3DKXUsbkwWXme0OPnlT0d8qac5450EVoEGcKkKpThuKkVo+IrRTc42midQhVNfPSJZTwuO22DU1yrj8KrEgxDMMwDMO4pAUpUnZKgoqBXQAArVcKPyhNQ20GWd/68jXWd9bvxGgpZbcYNfUrBH5eQpuFIiLTJ3hrAW8BrRekr1lvH6sJQzmdSdMW3Ta5yc4lkVE86qhlXg19vuTNiQCA/DvJV8wMBSPXCquDrWMorLmDL11Z9V3cY5kA830j5uMlDNNsUtWrPsVQN5VrTaAs5VxeZne5vSSL4aivKb4gUryJ9pFSVpzv1Q0AMCipGkBy2G6bgxQ9VnzWQVEPI3Nh6gHRhkUAUHWOiZ6HbAAATO/xkNg7FenLo/hVNTPt/BTZ6+k/EgAQyhLLAOmJeb6ymsiqYyTRTfJ//0vM9XhM2krxKb3e6K30jfEWLjyS0jxsIHYNTgUAeGvMsHL/Hqkxg3YyYoFX05psvTpXSJ80URe2He6x0sw09fp6dr/uR3AGqeBmUCTEbq4Zht8ZLceQkm8LTUN5JypWzte/0Ff9eyG12CGtR1k7L/0XWjxzz6jOyFQSsKpk/1aOkiGUd6qVZUg170LGlUZtQtcEktM88uXq0TQkC+PGLxrbw3u74/NJowEAed+J9bVEo9C83pgbRuBIciLXTSPCyXyKWLA3MHeVVZaWuB6TK+oxpIwwQyqB53S8HEMh2yD3OAzVkOnBvtrUBJ6cDDnZvPb0o0483ZMcEWp/cQY5oE+44ynX4di6aRtnWasddbGZpuDVAc+E1uRcO2HaO01yLuc9zJ9C61AWfRuKeRDS1ZdS5zY1k3jKnvjunzMH2JpzAlh3Gg3+nJnTG8cNCTiGO7zNPdCLFelkLqfGxXuu20B7sW51RYpEIgfCsj9fHyxHzi9x5hn7fwpP7TEMwzAMw7jkwCtSDhnZ1z0PXumnKkZE+/q3RvYcCvU1ZAJLaypQsz6HtlKmbT3QBd6sLPq8b1/4eRYtR+VwCqvOcJvdt5EkZjRXP8412SZlrcZRLy0HAPxp9pUAgMLbafpCX7PeVvga4JSuvwIQjsYOkea5LaMAAEbZtj/ceoaaXveoXjeaaDziUGLatKqwPnsdw9HqkA+lNXUrFK5Q1KWSIvvrUoPWVssR67sNfOMvAID8W2fD25YS30JxDNfSaD8znRQzoxWNeEsL05F5+WYAwLReUwEA+/RKpG6hKX1Zc8ymTjnQhARN3VJupUpkwLBG/fmfXg4AKLqKklnGMyXuTIFRF55Q4+6fFrLLJLPZJ0JQT2/8IVzRoqbwHDidzM0RFJR0Z/eXIHWPplpfr8akc8rVE/62ZRySfyZXGjmv8IeZYUgwrEgxDMMwDMO4pAUoUuHr3u0f0A5tfhNLuKQKnw+N1tSLlexfy1A9tAcAwP+lCKFW5v3TdtJwyhjSm7b9uLhJk3OqKQgAoNfzV6HNcvoumCpWU3cxwtOFsCUd8SvyqOxHDFmGJzp/BcCe74ZpoF8S3YPVo18BAMyYTuW54+pLkTRtfkznHJa6ts5tG/aQI3oXbGvUWmtuCFsG5kCpYAk+rXP0l51iJ6GVo1KpPJZVB8J9tRJ8/oyee63PqY4Rfe58u/IaQgEO87lzLskkVJeMn01oH1Mbv29uIQDghjYr4CkR62haBWkeHym1nc6pprOf89UE+i6drsfj1eH307bWaZRuIj9jL/qkkxp+SBqN4Ack7QdAqp3zWfm1JPT49mIAQNGV7tOErAnSfRwYiOxj1ASd1Vl0frd6pVqtpBJan4+RD95mW7rEDRdsHIXtlZlh33VILcVr3b4/QCWqm9396akdluxBuVCCrT69iZn7WwGKSsQyWwdo9ub3woE3pGQnKQyZqhwPUj+maBRvFlX2qrYa9MtGxHzI2kwNbVY4IpiUzrj1XHIk3XFcZwBAzo+uSh4zhnjDSkG283fV8M6gBXYTGgUhOrdtpomTTqTFgq965D0AwJnppdZUQJVwGB2dQg1yz+Pv4sXxx8V0ikOS94tPKWEOrQBgJGidubhwZgBWvkuoQdXcKceUSFJPMj2nE9r+Zm2W0xOfVJAhkpoURPFSMa02NgHnBgBDt859aYGdNV5OTa8PlgMAMheQERGCMg0X7UXreLl6UpNhVJDR9Oy3RwMAbvnT6rp/18QBIWo7nV5GUyqxGDrFAGYKM2VWBi2YbXanvqW8eyvs7UktP+3wYtp/ZyZ6XklT5Nb0pYspk8/L+gMABgZWRvQxKqUF9Dcr7jMIPHY7khG6LXl6rCGKJ3aGZ/Gy8O8G9gE+P0AFUlAzmQNA+ajIwVNToJuG1a536dQm285Wzvc7nl5vDlrusIFhGIZhGKaFc+AUKYdq4O1NU3GBUtNSj/S9NE3Q+fkl0Nq0jv3YuoHQ5i302RPuvAePF6GN5OBqeClfla9De4S273B9KfFieu3RupYk5ucaI4U7pz40DwJTaST9eMrZAIAzH3/WGkXKvzKU+fT0/fjrTa1iOlWlmPrMjFLcTm3sKZwmd0p0TMXuu2gEcj5aCgDQ9++Pus/vCjUlR0EeAOC8jBnQTaovcurkrV3DAQC5aeUIzKCp1URGlWvdSFn5U6tvxDd2LqtXSg4BAOhb7NBsqy5GUwMdipI6TdDmN2oTn5wQJYXDAViBwOuYaw9rp6I8lvqm1C+r7gnFI3Ux4LyiLACmlQfPvXr6wYaBAIC/56yMWGtPJfPg3XEd1wyFq/k9X9iPET+JKc7ayNB7Q/RngVK6Dzsvq8KyQ98A0PQ5j9xg+iI7r2jfNTuKCu3r2AEA8PhQO/1GfdnrG4u6bt+n5fQuzpm6ynYyDyU4R90fjBZQexiGYRiGYX6fHEBFKtzvofQgGk1nLt0XkXXW1HWYxXsaPqaHjqn5fPBmZABQRohRaLOSRsT7R3RD6kdCkWoGFUNTsiObtaQKJTRTrKZZ15H+8SIAwG239cPdueRjY48S7cf/t+FfiE+31nvoX2opi3UHX03EnP0NeV8CAJ7KGAa9rMwuC5BQfyVNWUtx11W0ivui2yZj/MXkHBT8MwVWh7Zus8vwO0vDoHm9MIXysfNw8m5J9yRbz67GoPqyeCuto9Ytex9SZy5P2LkBqpN7h1Jm41yR6qDcqLacXd9bNQgA0DUkfLc83rjajVrnc16lLPuTX+8Hs2a9Y8fmf3a6Y/3IBtup0ydM9G+aR1P6OlvlSkR7r5lNbRFDEOGvqKoXj/cmVeOubuMBgBT5+tql4zvjl+XI+KWegjj6zD7/yKln5wOPFjIi4kK00IFZd7UuKgZSuz4ulfo53TSazYH/pY3Up6bvXseZzGOEFSmGYRiGYRiXHDhFSoxe5Mru1a1phNRq1Xr4OnUEAOwZ3RUAEErRoCfHEBEmhhn+cnu8kbaTLOmk6ZFrxiTNWQEA2H3ZAKRaI7SWNTJxhWlCE34LMv3AktKOQO5vYbupI5w/pa+J6dCv7KSopDF5X1t+GUFxu09MpfDc28/ui5znaBmahITNimcj/VTMmhpUnkb+OZ/eer8oQwqmFE4HANzzRU8AwHeTaGTl+X5xzAlH66SeIYdHU/xGEhS4aOq6pQxknm77IElfkxt3kBpU1I4iwTZ+1B3ty35CQlDqxd6+jsSfpm4lUtTXhKdU1LxemC6V3HrTZIhnp3m0ljsydio7Qmlvyu6k2xR69ouvqMFA0c6caRBqzCCGJ1OdWTWR/N2637oZHrG/UV3d8Ik8XmvdRydaIGBFXpZcQJHV03s8bZWjJflG/Z7YOK55z6fOLpR92R4AkI51v7tkuE5lNhbW76fZsHSUuD7vgTGk1KmWAZQyOWWvcOAM1qLyIDKksmdsAqBM0cRI1SnDsLc3XVrFOAofzVtKUmlo8xZLrpQdQNJ+E57+vei7X5bbZQR+d1NCEmcDyApU1rEn4Vy7rS7mz6Pn5c3/NmLhXCuL+g0f4v1PDgYAhHZQRnotELCmR2K6p5pm56ISjo7SGCs/czieu/8RAEBnH73Mg6aOUoPy+vw9h9JnvD2MQuo7zjSh+RprSNXdQL0eUXc1RC5aHG9HpKwhqQ2hRYDf7fWs2JiG3SI0efpGyoF2UDtKPdDx2YUwGmssCtRAgVYHhU+pqzmkspwziY2xGtT656wfRtMbJb9H9OWUKuLs+ZdhxeGvAwBCwj3YK14o6hTfN39+AABw0bfX2fn1hEEF8cxVA97C0O17Lwc1om0aFRVWNvuzbp5u/6SedAxu8LlJtFcHeoo/YryjpybO4EtEWf9y+FcJKEl8yL6lyxR634aAJml0WsTqJFHOoQzmrP4ohkGamzVst22kKfIirKu7PA3AU3sMwzAMwzAuOUCKlB3aXdKLFIXsH8U6eT4fvDVkEVpKlDLSlo6w8DhWyQZQO3YIAOCGB99Cno9G0lLyHjJ2Ip3nxa2APIaYJsiZvw/Fh5BDb7Z0qmymJIAJRZkCk/fF24cUpL+2fxUyt7EcparTAP8r7wYAuKSBUxS8TaOWXWdUINuTYv0esDM3X5SxCzOnVIr9hBK4Zat9EDlVoz5LdUQMAKZpTffI6d/1/x4MAFhw4SNIF+eW56wxg8gU3/X+8XwAQNcHfrLO19gM60Y94dF+oUhBAzSvSwVTPjvp6B3Uod9H6SSko7duGjhv9ZkAgHH5NE076w5Kf5BSPc8e6blFScQJAJ5WrfDPXuFZClM9Sdin07Nt8yuVT47fGjUNoKogzoCPYZQcc39BGjLenhv3obVAAMmeP274dtfHvNg9gtpljlJXAGqbso10Fert7U+/hNtvuAwAkPKxWOPPUkLNiLoIzaO0S3vmAAC87XKR8RH1oze0oRF9jRlM+JRepxR72iXe6RvndGdFlxSkzw7fp7xrSp37N4SzPJ1SShBDaJRyALu+GyNJyT85/QmxsWlXJFSv9ZatFKwT2rDZ3iFRMzKK4ux6el6+N6QDfE1NRIxHrHVD3a/rZ+EHcdOPsSLFMAzDMAzjkuZVpORIx6NBS6GRUzCNvgut30gF6p4HlJGaYir7S5zKgj56EMzbKOHckwVkxZ/8yXXo+bclAIA975G/Va9LyKGj+AXTUj9keYylK1F7LDlKWmkTyisafbl1kbCEnM7EgPL/ikq34U46ft+klIjkeDIhZ6qWhHsWHQ8AuKSogXPOIyXkmAWX4ddhb4cfRywxUGMG8XLXHwAAT31JSU9ffuQktH2HnokhUiPU55jsbZeLbWfR2hZjLyZlaVq7p8XWZGuULf+me5Lx5/VHAgDyLyffOl3xN2os9SXsC3jFSu0ewJcU52jLoc7J+r35H4diWe/JAOxR47SqVBybS/X4hXdoSZ8uU+jeaD5f4x2xHSpsaFABjk/9Vmy0FYbVIfqsbdwe/vtE+FNomq2IieVprnnrfQDA7cvHAW/FP1r0dmyPDE22iUBMcLmTUQAAIABJREFUv7EVLBHg0AKdbi0F8sfFOOSDGwEAa896BgBQY1JdSNWSItb6G50CfP7U4wCAwX++AgDQ8SW6zsCM36z+I1p98uaQP8nW8ymg446rXsP4tHJxTrpnHnjiVnXqYtfVFDDyr9yHASSL48cX0eH01xp88wKs+UYkrxXvgGE3/Vzn/g3hLM+/cmdj5NWxZ8W12r6hY8OJdI09/KREJeo+1oXqUzfrK1J+8wwh1yUybYw8jqah5oShAIBNY+m6cwpIv8tJrUBpDV3/9l2UgDv9l2R0+Z/w2Vq3AYDt2+wZ0BuPHPxe2GkaWkbHuV7h2OUnIfnTeY6yxt+PNa8hJW6mGQqheixJmBkbwiX3YIfW8G+jjOYhUXnUBm0eOgAAsIGWksMLh7yEiYvOBQDcPJzypBTumGtNN+RcQtMPx86kl/i7+YdZRpucMjKDtWi9js5ROpaceNPfn2vtk+iFd5sqj5R0HK0+pj9a30zGxLJCyjAcNHXLgJIdqjR8tofK0eUFURXOaugk1Gl0/lsIy7+gKZ7eSZS7WTWo5Dmubk0y8dV3PI1Pbqb97lp5EgBg9w4yWrVKL8x02r9nHr2cb8/7BMOTwxuF7KhV5DVcsHEUSk6j69NLdtFGdbqikeiBujuyNL94SXuAdJH3Rb7kNK80TqI3NfncpVG57SZ6cSy7erLV6OVzOzG1GpOmjQEAFN4tDCilDjcWtUMHgK2jUqxzqx3QrAp6iZpVVY0+p4V4Vp4kvxVJtuq/lLn7xNQ5AIBHH0yL/tsGqCpsi44+2b5iM6RyfGTse9JyAdidd0vKSaYGBRTcSCsZjO5LfeCMg6YAoOeWolEdUQ0q+VxXjnyNvjucjvW/ijZ4ZStF5m4uoZdZMOhFQS4NVm/v9gkAYFiAMt3rpoGgXBNSTHAkck24J298EgDVO7dGhdOQfLzjfIwaSQakKQbpj3Z41nUWdlkeWb50T7JVbuD6uI7VY9imsP+rBnFToE5xdZ7h6EPU1RUaia8zuXiUvhDArP7PA6jfSLS2HePB2mvJUD/l6ZsBAMnFVN9uu/X1sDxbQPS6pw66pQH1VAkN8L0TA/bi6I0IMOOpPYZhGIZhGJc0qyIl80MVj+mG2gyy/jo8txAAYAhrMJjugyYUI4lx+EDU/JOUpe/7vRq2bczykxH4mpSNrWdmAgBCqT2snFJeIRakeWhUu/bijuh2uzi+nDLUNKRNo9XYVz5Aile3MpIfk6bNj76SfRw4pd8911ei5Bw6PpLk2mTxnUPzmPAl0wiqW1tS8C7qTCrF2emzIxzA/Zo3iqJD1vsRb92E/G8c3pd1nxgAhV5fcT2Ntp555FEANH0IkDIls6bL83ugYVwaKVjjBoXLsdGxyysz3XvFc1BHjEUzLwQA9LhsDYwKhxLVyOz0mkezVN5QWt2j7FSfmNKoBbLFNVpKUwNKo+cgSrux4V90TcsPo+k8NYO4pPdzV6Hw37YDPR3fpRO16lBcRxhyn2NX2eVUxlyDUjYAAL5OplEdhIKk+fxR1320qEcyl1NnRnU19l1I0+xrz6Sp3NFLSGUJzFzkKh9Y8cCkMId9oGFVo2+AgiPe70MKIOaLHGwJHKU3GmW6RNb11DP2AgBGf2ArU85s+OqKBnKbDEA5M70UZ/YMDzAIxx/2O3UVCqkOn7TqeKzfQ1NnS0e8GbZ/3GpPRA7yxKDJZqkEh7jJQVQX8ZTbUpOH98ekrvFNVblFfS8AwH17CpG8lvKSNUWmNu0Nanezij6K7NejTNWq2+Q055JrJzv2MSAnd1RVMITw9inrdqonCVduob5l80XUd+mrViXkfcGKFMMwDMMwjEuaVZHadG4eAKD93Cp4f6QRnuEYsQd2VcE4jHwjqm8nFeq7g17COetpZNj7uasAAB6hNBlJQLCHFYAt/kRauH/7iPyoAv1KI7Jta/4kGOJzr2cozHbvA2Sd7j7oUHR6dJ7zcHHhHP0uUlb0bgpqTB3lBl2POrcuLXNZnu4fXgkAKLx1duwjfZmR3udD6v/Ij2xS2TUAgLOfoPX6rsi0E6ga0nEZOkLis3MtxWhJ1DzwWCNc54DlqZIueOVB8rPKf2m2OAYiwvcbi+pgHEque8wxJHMDAODH7gV4tAeNKMc9Smpdm1+pTMklBqqyRPh1J/qu7WHb8XTPlwHYap4k3ZOMN8vIsfeR+ynlQdcXf0rc2leKv6LzmNUnDwMATO/xXITvQdDUMVoU9Yqb+wAA8v8+W/w+GJ9/geJv5GnVCgCw7paDMOOCB8QONBKtfZEyLQewoc4M2/VR2d/25arP50Qqx7ppWP55O4ZTudrNF0VuRPb2JkNJWSDXuAwcRz5dPR6egN/OIMdy2Z5004BztTn5f8PUrfYo26naPp1+UD5Ns/qTo5dROm7fMZvguy6ffkACgHUsPexYDeOxklsmWJlJ0AoEdeFxkZRz09h0a3UIp1N0onEqUu+sG4zcTavDd0pgMs5nur8vPqVHzDCoSWOtd6XyfJwzKer7Qx5DJof2a157Nkoc63uRvP/ytyci/9+0pqcZFGp7nOuD1gUrUgzDMAzDMC5pXh8psUqJ7+dVgFCFPOnku2Dk0zpQqY/twkcFlB7/7t3kPzL6momW+tEVjVtPrOL0Q7DmblqnrPBflH3TqLSXT9GX0vIimSfQ/2uuyoW3c8dGndNJjRmMUGXixatp1uhQjqSlBR7Q/BG+CLv1Cly27jT6/HgeAKDwQxGZ6CJ0XlUy/F+TlT9lVF8AwH9vG4d3xlEqimEBWQ5lRBnjaFCOyu4pJoXk4/cOBwB0e2Y52uwTPl1qioNERVPJCLLkgBUZtf2Iukdnk1pTIsLrTngBMunp2jMpDB1nNnQy2l8qP++Xkwp1+0dno+BBqovZe8S6hYlIcSCQ/ooVAzqhoh09x7396f69fMozEftH89VYdRH5MN1yPCnI7/08BP5iOpavgh6yvxzW8zZEb6MLRas6V0dOd/Lpua1oKgDgxNSZ8GqkRN1RTIpXxkfkR2kiPFKtIbwFpIpMOvi7eq/D2l+0n0qj1lKsqkeSwgORH9HU9Za5fJTqLwU7ZUzBdXMw/r3LAQDFt5BK/dHAF5BvhdeH9x2ETMQZeRqnj9lTJV3wwuSTAQDtniHl3gSQvYTO5YwQ1k2jyUL5f6/Itnj0SQus75rKN0oSdPgRla1pjVzH+reJjFY/4VGKuPvgLw+gyB+fv2KEX10974/ltZX4zzZ6eS/6lPqPbi+Q0pZXPBumfF8kyI9W0qyGVNsFJDcHPk/HkCxy+A6IfC0jRb4av6ajx7vXAgAKricH8VTMjVyfR9KQ1C+mZ2QYetqHcxHoSSHm1R/TGlFp/lpr4VmfJqaiTLrRR6XPwVcpw+O80voJaP5GS8ulRhVKRSXYq1NFm1dNL45ZJQVYsI2c6cyF5IDf+bsKaLPJoT7NpPXvLIflOF5OKvKlLp+NXkzOioV/Kca/nyALYvOpHQAAKaOLcXY36iiks/IQsf5fiRHCL7U5AIAXt40EACxamo+uwuc1ZdpiuoYgGdG6cs6mWMR292VkuN3y17dgmFRvTk2fJbbSvVYbt9oRyM7B6fAIADt1ernMqMwDAPxcno/payndRuup1LlkT18LAMjfOds6QlNc67I7qPNef8LzEY7Am0IUbjyvJoAkMRUj24cfBrzWZ/p7Ty4ZOveduLjR5So1apCpkaX1+pdHAAB6BIUhGWcqkl2jaUrwuqwNcTk7q87YXxxCxuLEIRMAAObPSxTjvYVN8QH2lK1uT8HjJxow5p5Cu1x10GXYcBo5g7cfSU7153SmucsxqavQ2Uf3X7oHrA758UkpDT7fX0lpazI/F/X1oyXIlYtlK5n1k36gdDPHTKAVJQy/ND7tov74YWMv9o/BvsO7AgCmdvrctVN+vPgdU6Up25X3qovp84bo8DDVkeumX4K1Z1PdazuU3kMXdKP3/MDkjegiosNyvFQHd+tV2KaTYbdDp6CyRaL/nLatD7atpXd45lK6ng5f7YC+mga2nWG/LwAxEFVWzkgkPDRgGIZhGIZxiWa2JHmaYRiGYRjmdwQrUgzDMAzDMC5hQ4phGIZhGMYlbEgxDMMwDMO4hA0phmEYhmEYl7AhxTAMwzAM4xI2pBiGYRiGYVzChhTDMAzDMIxL2JBiGIZhGIZxCRtSDMMwDMMwLmFDimEYhmEYxiVsSDEMwzAMw7iEDSmGYRiGYRiXsCHFMAzDMAzjEjakGIZhGIZhXMKGFMMwDMMwjEvYkGIYhmEYhnEJG1IMwzAMwzAuYUOKYRiGYRjGJWxIMQzDMAzDuIQNKYZhGIZhGJewIcUwDMMwDOMSNqQYhmEYhmFcwoYUwzAMwzCMS9iQYhiGYRiGcQkbUgzDMAzDMC5hQ4phGIZhGMYlbEgxDMMwDMO4hA0phmEYhmEYl7AhxTAMwzAM4xI2pBiGYRiGYVzChhTDMAzDMIxL2JBiGIZhGIZxCRtSDMMwDMMwLmFDimEYhmEYxiVsSDEMwzAMw7iEDSmGYRiGYRiXsCHFMAzDMAzjEjakGIZhGIZhXMKGFMMwDMMwjEvYkGIYhmEYhnEJG1IMwzAMwzAuYUOKYRiGYRjGJWxIMQzDMAzDuIQNKYZhGIZhGJewIcUwDMMwDOMSNqQYhmEYhmFcwoYUwzAMwzCMS9iQYhiGYRiGcQkbUgzDMAzDMC5hQ4phGIZhGMYlbEgxDMMwDMO4hA0phmEYhmEYl7AhxTAMwzAM4xI2pBiGYRiGYVzChhTDMAzDMIxL2JBiGIZhGIZxCRtSDMMwDMMwLmFDimEYhmEYxiVsSDEMwzAMw7iEDSmGYRiGYRiXsCHFMAzDMAzjEjakGIZhGIZhXMKGFMMwDMMwjEvYkGIYhmEYhnEJG1IMwzAMwzAu8TXnycZ4/mTGtKPHS38NHQCgjx6EqW8+CwAIaH76zjQAAF4tui1YYwbD9u/36FUAgI73/wTNnwQAMIO1cZX/K+N9raF9wq5RE7ub9JW3XS4A4B+zp+GwZE9M1xEPu/QKAMC2kA8zKnsCAD7acjBtm9MB3d/bQ+dcupJ+IO8zYN3rhq6x3mcorlfzemGGQmGbPAf1wobT2wAAskfsAACc1nkxAOCY9GXo4qX7ENCoSm7Rg1gXpP0/2UfX8N2GQgCAb24rdJlaTNeyfDUAwJvdBque6AoAOL/vPNp/ZxEAIPkfrWDO/y2m6wOAMd4z6RpNE7uvHEHHH7cbAOD30n3yaiY8Wvit0JTvfBpdT41O17N9XwbMdWkAgJzFtE/WrE0Ibd0Gx0HoT5R7GCtx11MnSr319qV6tHxSJgDA2yoIszgAAEjaS3U2maoVUvYYSNov7k8NXb9mKKcRH/VkqndlXXwoo0eGYNcaAEB+p90Y1GYzAGBo+joAwOgUuke53jT0fZLa8fK7r29UW/R17gQAuPOH/2FwgPqDSoP6A7/mhV9T2kYdBE26VgNGg/s68YGOL9u92g98UpEKABjf4xf3bVGiaYDsW0QbBwBvH2obm0/IoS8OKwEAHN11FY7LpLZS6KcH28bjwboQ1eMVtR0AAHPLugMAPlvSH21+pPvX7otNAIDQlq1h9RhA1LocSz09+rsbTAAoebMz2rw0m47psv9uKpzl2XvJCLQ+dwsA4JsjH27wGqu258X2XowBZ72qi8bUXScpHTY0rr9pIuRzMYb1AQBsOzwVFUX0jAKtqL/xeEzU1lLd1vfT/m1n0z3MfncRjOpqAA3X1WY1pGLFanyi4W85MtkyiGRnl+pJiuuY2ceIF9b9gBkKJqikDSArs6i0wcKOAGAZUYkm15sm/gIDAxsAAJNa08vI28+DLReXAwBG/zAJANDztr0AgND6jeFGVbzIl5S4XjMUgmdAbwDAqpuSAQBfjnwCPfzpAKIZj8kRDbuHLwVFfqrsx6XOod06ib+HAduvpWs54id6sWZnVmDNgFcA2Eb0v9ouAwDc9OzBWDIyLfbrES/b2uOG4qfbHwcQuwFfLyPpj34BHWNODXD98rMAAJ63swEArd9fREWoqYHmo+ZpSmNEeRE2JerLb8t/6DrXD3sOAN1beS+aixrTbutVnRJ0D8SLfYeeAYA6S2nEx/psbWOrEW1HFgfCAIUH9645HgAwvkcjDijbs2lY9aZ27BAAQPmk/XjtoFcAAL2TyGiLXq/TrU+DxeEGB8i4OrcV/X2kw1x4j6XffHMr7XTZV5ei15P7AQDGkhXh5YmzDn/V+1MAQK9uV6GNdW0NvrebF0d5yrppmC/KDTzc4M+buz0Bia27LQHVmF33Xxr8vn/WowCAfkn03qirXcu6L9tgYBw9j4EZV6HdEz/FdP4WaUg5DZ28kRutz14tvNLqphH1BvkcFeS1Xm8AACYUXQh91Vr60jFKTTROg7B4UKq1zamYJRJZMQC7chimiQ5eOv+aI18GAEz5kjrKyRecDm32L/GfyNE5ejPpeMvvK8LCEx4DAGR55TWno9yoDvt5CqjyezVPvQ1bXs9+w37hdfDRuVaNes3aT95TJ+e3mY2/9r8yjgsjNp5tWM9nn14JAEj10P9DZv0vBF3UKXXEJ69R1s3Dkj2YPfBdAID3YKrDd9/cCwDwycNHIusVGoFb9dTjbRZjytTtc+Q+mgIA6DOSDFYtBFTn0jV9OJ6e8cAAKVSqkTWtkr77+7Lx2LerFR0sSNeo1Qola7cHaVvoPrXaTAOkpD3V0MT5199Gx1px+OtWeTrMEB8mxntR4W08tHMXAOCpcePwYJcMAEBFBzrfsKsX4slOc6nI4jnLZxc0devzcStOBADs+F83hISdrtU3wJf2sA+482Lqj05PJ4ND1hdowJ5FpFxjbJzXCNjGtzAUfe3bYf2TbQEAv40gY9ireaCbyWG/2y7q961bTsJPC0iFTNlG15lUCgTFI6zoSc/phkO+AgBMytpotc9RyeK5jXsO64+ngc5Jz9wMAOh8b2wvJCelRhVdV7PrGY1Dljsrhn0v3XS46/NIBbxKp7p7T+fPAABdfekRxrH6rpxSQf3nWzsPAQBk+qthmO4M1Jfbuyx8ExHKoHp42g/USbRaQHW9rEBHn36kmh7ahgSGTF8lKg16D22toaf1yZL+AIDen21FrHMC7CPFMAzDMAzjkpanSGma7cfQrQsA4MHu7wIgZcOpNBkwUS6s/0xPivW9tLylStFVKBibx7dDx/tJkdJ8ZMU311z7/gJ7pF9pCEXKG58iFcvctg9e6/q9iq0sRyhSGRovRtHLnp2DWUd0jKscqjriLSR/id7vbAAAfN7heQTNQNi5/JoX6Z7wUfCqIPl0vbJ3BFaUtQMA7Kuh55ydXIFjspcDAC7IWA9AVbfs5yrvR0DzR0y/hZc39tGWlImvHPy99V26J2BdR13opgFDyA6BKFOlTgkZpv1ZKly35dBUyG33rMCg02jar8PEMtpn67YIxaFJUNQb74yFAIAuM+j/ms9nnfu0NjTiWzfmJQD0DEb9dioAIP0smjZuW7ISbeNQfk2P11Jwe9yaBwDYN5PUkt+CqWj902aXF+U8EZVFX7YKgdX0vJNEPzD1iMGWImW3M6/yf/q8+01y8Gr34k+xPRdFwf3bgPEAgNOFoirrVY0ZRIfZ8auOEecfTqPqs175HBdkkH9fpRESV6LBI/qF/rPPBQB0vUsoqIuXoRBzGzzf1ADJEM9ffSLeuJamr/onUfsuN6rR0UftZemkyQCA7j0vBQAUXrTAKmssJIvpVtPzO5KkNNMqdyxsGV7u/lTWc6e+9OVfhgEgtwbZF8meqNysQaZG78gbPz0PAFBwA017lSUnW/5AcdN4N6tGo77Di66aF75R9D/tTROmsCme/zepgBlZlaha3hoA0OPdUgBA4WLq8+LpYVmRYhiGYRiGcUmLU6TUaKUdYzsDIIfIoMMnRVrZ9+zuh082HQQAWDD4PQD1O8TmjNkK3E+fm9rp3Hn8QQevtT5LX5t4icVJMKoiA1ulS9do5Cgd9/+esxJFtxwVWwGkumDo8LYl34uhH1AU4B1tlwKgEaksZ4pGo/0QdEutqH6bRrM5syh6T1+3CTAoCk+6FZcB+DgtDwAwpQ+Vbf14UhWvPfUzXN2alAk5svagbsXJCzMuPzhvF1LnxrWaAamE1nd8FXnd0kfom/0UMdIrZTvOy6Ayy7pZYwat8quKBEA+MwuHkP/UK9+Qz8xblxwP/ES+bM2iTAGWiiL9/TS/rUilZYSPYHfpFQjcRdF9egmpiFogAOgOhSWKT6P0y9I8GmT1NfeTErcxROeeWjowMsqxsXi8gKO9jO69KmI32aYCmt9SWbNWVtk7yPtT37mkz2SNDkMPb7/y+c+o8iNt3oY4LgCAx+4zQ0cNBgDc+cLzAMgPT7Zz6Uy/z6jCmHtvAgB0nky+S4bihyefdVTkvRLPq8PDP+GGReQ/9+BLTwMABgaSLR8hWdcPLaK+rzi+K/t/QTwKXV2/lc/fW4+Tnletnc4m6PU2qhwtERnwtGMk+T6VH1qJp4a9CQA4NlX2swa8w+hmrP0zKYNn/ofaRs5zs2O+J6xIMQzDMAzDuKRFm6AlI+0Rr1ORkiO4V38YieyFwh6kwZg1ygcifapeKnoTVxddCADh0XtNEbnnOObu/+YDAA5LvRIVHahcM29+CAD5d9UVWq8qbN2/ugQA0O1tLyra0eOryaKRRtUhNE/+9WFPWT5h9YXrq/4+J49p2C8CQFhKhy3PUw6az9tSBI898vVHKDN3/PNiZLxN8/Ep2gZRNuX+OJQPMxSEUUHXg5+XAADy5tP+n93TFZP/cjIA4O0rbP+MupS4eKnoTUpbR1/9KpTz3u4xqnDk0zSa6foYKUfyGpYk5+HdQRSGtek6+t2Kw1+3jqEqHgAAzb6fF2VQhFm/N5/HjVddTft9MZ92a2plSvgrmaJ8ZrDWOud5BfPDdn25ZCA8sxaL8tO9M2tq4jqdGSVyKMdLo8elpR0AbI/reDGdU0a45XcDAFzT7n1IbdRn+UbZ/iZv7Ke8BP5fKfJHB2DW1soLqPM86pUZwehj2Ac3j4W+M8ZrVHyuvAXUt0x8llRMmWKl0qi1UsXIOnbcv/+K3BdJidJExKVUmMxQyPJRqxfxfD3JycB35FNy+Z3XAQDm/+dp+B397s4qCvvzoCS2a/t/RCLbrm7WrY3oUOqms6vU9aZXt5sQtR/ccf2hAIBPr6epJ/kuVFEjvKtEPyvT80y64UMAwHs/HmnnXGyAlmNIyY43FIInlaZT/j7kC2tzXU6+2Qs8aPspSfG776CXVo43LeIlJ29cvj8dm8eTY7PqdN4cDufJ0ylHUCAUgu9kcgpUHeTrQjUGc78UjrHT5iDgkB3NR6khnH7JTZh/N8ns9eXEUaerLsueFcslWC/W4okj8OswOoe8t/IZ+TWvlVRw8pk0nZexaI7lxC2nB9Qwe+uFrXbijvxUmk9OjdSg8z30IrjxR3J4/vfLL1ovD1ke6WivxzgtJ9nfle5rtOAFFTX3DwAc+s5f0UOUy5TPRlyDUV0NbfavAIBuP1GHNvjKiXjjb2RIy3w+ari9fAHK6xkcSMJ/n3oGAHD7eWRQy6m+Jk+NoBjQ5iCSzC9u/YzYSFELLy4dgXzQNdaXiDFWzCoaSG3T6WW/bElXFDaBISWp6ENTqIMDSRH9R8jqeL34cPsgAIBnP03Vqg74saLtJYN5e4imE/yinqycl4fuLq5x3xNUzvFpdDxphKvpYgo+ngAAKHpxtmVAxWvoWsj0HtXVVrtu8zKl6+h1/PlhKSsAWKH1PAXCNAWqIZV5HLUfaUDJ1DUBzWdNb6uuP/KznLKXA9eHx2ajw9LYzs/1mmEYhmEYxiUtSJGyR7xVR/QFAFyaGZnETaoeMnQ+99stCO2mLLvXbaYEeW/kzYhQC1RyxmylD83kdC7RkkT21VAIxf3Db70qwUvUUfFusfxL1hIK0TQAy3kVMvO1kPqzX1+AF28ih+5LM8mhW00kKFFVli6+2Gxqb2tyJr7gmi8itsnjlxpVeOAWSoCZuoimDLVAIP7Rr5wmMR1qlaZZqiVmksp3/veXY92xLwIAyuQUY5ypJSQVnezP9SVOdX5X8F45IiZ2rESLWoSylvPsbExaeQ0A4OFXKExchpCryfPUrP7Dk6mOnPD8TADAV0dT8sTQjp22gtcEaCJ9hGkAuwfSSE9m0pckLU5Xf9DwMRVF1VJ0lKkxOS3694upLvVaug6J1tw0r51uYftwuzz19R8bFlAQTHfEkIpBee5WhnpNQ9HfaQr00v+Mo+/Eth7lCyLrUF2Icpf/6RDMHkBLaFlqrKgLAc2P+/bQ0kq9bqF0IgaUqcgEYDqCCfLuNbD8f6QCSKW1Riwx03JeOMwfFWdiUalCNbQaiu5oedVtY3f3YUWKYRiGYRjGJS1mgKAup7L52HDlpNKoVeb6adu1a8+k/Tfao8K5P5DvBvJmhC25AIT7Gb3ak0IgJ/S8CACgr1zT5MvFAOGjwOrChtUZ1cF1VjX5dWHdlqjHA6CoegaCZsNrKKmK18JaUkJGN/CbrReSWnhd1syIAADJoBlXoeAjW4kCGuGLEQ3ThFEdfrwuH3oRHEPlyXQk/vywdDC8v62L+fC1OfZ1OeuRRFX4niqhJG+eNVssxcQ5SodpRihrWiBgJby8/J/XAwC+u5eWXYk2ekr1JFm+L9dlbaBzPzwaAND9zztjUoFcoxx732Hh916GurefYweHRFx/2LFsf8hYkPeoqRfHSRuw1/pc13MHgLYL4+wjoviuWckP3SZBVGg1cUvEd2rAzZuvjAEAdCgTDuYufLrqRdZn6aeyaClO/J6U1nXHULJWqUjFseIlw7jCuZi8W4w4rKMDb0jJTlU6e2saxo8Kz0zq17z2S1t0bOt/pKzCedhq7ddtGnXwNecFIyJVVKdz6YS2aTw5l3a6b02zZDlnbpclAAAgAElEQVS3pH0Ah/dcHbbNuYYgEJ5V+Y0dw+m7MspSrObncS5qW3nyMExo/VzYsaLlQVKnL+7ZQNOio/PqvwbfMbsjyudcj67owRqr5GawaaPJZP1J/nQehubTYszdz6B7u3gd1ZGeD1bAKFsR86HTciusz9GeCxBuSH207WAAgG/fprii6MyaGsvQbP06Oer2HUVReetPfD7qtGLAkTF52RE0nXns2CuRNP3nBs8ZN872CeDoXuGRLNMraRrZv2C1HQwULYLSMVjZdtOhMMSlyeCBqBG06gK8iRroKNclp4nPyl9obZbP1hlNuSpYgczlyvQ6wtu1c/1JT/9eqH2I6lO/LMqB9cXnQ5H3T3re1mKrqntBjNdYc/xQAMAbBY9AXWBYLf8r+3PR5U0KqpE1sl4jtxGo96HH8/R5xmHU79aG/hiL4zL/f/DGYQrw1B7DMAzDMIxLWoAiZTuZA4A25CDc3k6qKRR+7oEWEQre5VtlekGMLn2zKN/Q4/t64aY2NApzrjek0m6MkMTva2KHcyUbOAB4MzJwQpvfwnbxRLFp1emFBcspT0wRbEVIqh5SBakYTyt5T7j3A2sf5+r1dNxItWDP66TeNDS3d0vP6VHLBwBXbqTcTsbiZfYPEpTbqU4UZ+52j5OqUfE4fVUo8ijHW4LOre1cN9GeCxCuVK3bQMpmETbFPb3mVOz63EOht98fDYxKDl870Kt5LGVVTvHJdrH5whB6TEficbRPb0E+rmv3lthI7fORtccAADLK1tqKnG4rhs5UCNoQWongt+sn47Q1NO1UcU89ZWiKtA7qdRVQ3Z+Q9Y3YmGIpuM7+44PSQTB+WR5+LKWOqy4KALDujNZY2eftsN2nJg2NLI8LpW3raLrXnX3pVn2wC0J/7l9yLLrsoH7RUr+aSnVXg0F+oCCQ+wePpDKmkYoXQjNk42eYBOCpjT14hxUphmEYhmEYlxxwRco5gtt4QisrEaJUnzzwWCPC/5XT6N8/j/w0DCDCv2nyj0fhppNJkXIqMqrT+ctFNLKe0PMicjgHmsbp3DGqN3p2w9jUaWJj3Wu5qf4wFxxCasu771EywLSUGrRJJSff0zsIh+VMOwmnVDFUXw/pEyWVJKlmFMy4CD1EMj28WP+ljEmRyQJTI7b9tp3WqOuKPc23FpzENOtcF8nU9bieZ6/MnQ3uoypVyZsVx/B4FTjpqCvUgtD6jQCAC2deivVj6WFEC8V3+kq9NeJ53NHl1PjOHQNq2gMAKBncDn2TwpPI7llMbTIDa5W6XvdzX3dGK+vzqqkUmt9JqIfNlRw3LJ3DYFqLS/Y7qv+bmogTAN5ffzBysUKUNbKOO8t++kk/Rpy70wxl/0b4K/UYusn6XFfCYu+8DPs/Ta0OW+cxrX5ULyElCvKv20Mq3aP1zvC0DL8rqzzi/1GS8zN/cFiRYhiGYRiGcckBV6Scvkn5ozdE7KOOEO/8jaLLulTQvH+01ds7T/MA5K4TEUbu1TyW0mVF752ai07/JUWqKaL3nKP60oI0ZHlForp6Ej6qCTPvaLs07G80gnLZhrqSb4ohU8BDx+3+NS0zUnT50piTAMpyA2pUIVFTqVyDTBbajP4QiVK/eqXYS3REUwqdJO9JwEkddbjLx16AluaLWC8SiFyyZljAj+KjuiSgIA4c59k5InKXLOESpwUC8LYhdcc06Ho0TYNZQ20p2D8PAPDCmaSc6qaBLtPJH82KgGuiiLIIlOvaPSz8nFSvo6sdtXPaRD+eI1qveCLdqDtyn7SOtUDch7RlO6wIusaoRBd1shMWy3rqrBfZS+3+NSy6MF5cqj+aN7z9uC2DGtFu1ZGmXBIpDpx1NkHR98zviANnSDmm0OSim091fx0ylNd6gWh2RdUWKFI1AE9Ksv0CFY00/etleK6UppmuyKSQ42hO15IOYzYD/xXFaQqnc0fntrdv/NqvLH+03E3qGncSp0P5dr0SE9b9CQCw/1F64RZOEWkmfL6Ys2LXtwjyH4WO/n1x7Z+0v/E9p7MzbjV/C6ZWUj6sE1Mp11C07PSqIV58SOJfLM72UNjfztsmHZw/vedB+qIOh3E5lSzXk8sRGdGv2z4sPDABaLbpJ/V+9+4VmYdJ4hzg/OeS17DhPFqs269F3m+vMAnPyXhYfGMP5B7ZfiwAIKTkvmsMAwIy9UvkNLvsJwJ7EpS/zaXR0tjHWSrqWG1eDcwRAwAAlR2oXXiCLcNiMfxUr1O2Uzutzauxyt3wSqrMH4E/7tuQYRiGYRimiTlgipRzCm3rSR0AAPn+dGs0JeXqgObHLrHWXLdPaB5Fjo+MquoIh12jrAz3f3YKAOCKc2kaQVWknFMlLxa+jQm9L6bjLheJMpsw07mv937rc30ZlO197GGdVCQMGNZvndNP6jptQxdSBvi2t2jQl5KDfopG6++FharHeJ1/ZCVKkuZpeBSv3nNfdeLrSGjrNkzZMxgAcGIqOSxHm3JSnd7zCht2ko8LJTmmNycbAHBr3lRrc9ARvFBX8tJkUWf8jrrzxZo+yMOvdKrmCk5wpCLxdWiPv3T5KmyXaFOpkvFp5UBaeYOn0U1KtFpjhqw2+9OqHgCAQixISCqCbnUEVwBApUnH9e0sTcg0oi+/GwDAaEUai6Y3jxpUrFOdObrnSnxzDqXNMNPl1F6zFKFhRLXWykkpO7rnEqvc7Q9UmZgIgqZuKfjRKDPkyhj0/yiCc5388d+KDMMwDMMwTcQBU6ScPiHmaNsvxZrfV0K8VwTJr6J4GDl7egYJr1dlEGwKs9BfYUR4/KlO5+pyMQA5nW86pS0AoJNQpBLidB5leQ0AOLyLve5bXaN4Fa/mieZubF27VKyi+S/NHPgGAOCeNwdj+uOHAwDavESpDizHzzhUN1XZcyar9PiU4zTGsfUAo8uKFOPqbp5QAq5VPgPFaXlrZWZch5BLkCQKzeu1FKLKYd0BACMCX0KqYgO/pCV5ii5dAADwdewQoShpmgZTrCe3/JECALDSOtSWKYEgzaV0OlKR1PTsiGNTw0epXs0T4VP58F66/g/uOhahZGp4sosxNcBXQ//Z04+2zbuQfKTStYB13Myf7c+J8AVbVEv942HJkdv84hmZKYHIjTGiqoSBV2n5p48KPgYA/FpbLc5jIM3T8LVIdyYdGvxWaMvdDf6uwE9l+GF6fxTeLpbVaYr1OxuBszw/3DkCT1464wCWqAUi3nOazx933XcugRYvMijKAx/SPVEai0BHVfh544ivODCGlKbZWb7bUQ6aJ/vb2X+lAaUaBKPE9c//z9NNUqROY0ROliZ0Ove1p4WHL875HPJlFG0awRnJN3zxGUi/n5zsa9rQd1VtPNgzjF5aHx/7BACgfxLdJHVqT97LO9r+grvvpmzq+cMvBwAUXTGfThhtfbM6WCWMQmcuIQDIySqzPjdb9FUTUGnKl09sdUD3JyBxjDWVbHcyqb6GjXh1ivHwjFWNL0dYmez2t3Mo1TvV2T13hnDEFnVH37kr0pBSFsjt+qH4rYhGhK7cN0/zJN9x5q3bPsLuWFXjSV3nEgCemHsUAKDo3TmR05BK+2k9jYzfL86gYJezW+2z2nO7udQ+TDQygk4wt5IM08OS10VkYJftvjY3HV7pz+8wIuMhO1AZ9v+efq84T90vpkQgp41Nxc7WkoQB3kIGa7I80pAyPYrLBkPI59gMOeKcSOPpw/IM/HUWubqgVlQoj2mLMaLJ+zPpOXZYGLubAU/tMQzDMAzDuOSAKFLqlEHxCeSAKRUnVU2RqE5i1TGOpryWozpdojOfFBCuBr1Y+A4A4IpoTuduRxeOEWCwBznUDw3UP/p2OqBXfJuLzBmUMyZNOKmm6jqyX6DjXn/U1QCAl195DABNVTqn+WrMIKpESO76k54HAPS8ayIAIO+fs+vMCu7ks7L+AIC+2asjpiUvz58FAPio/QCEdgjH5yZ02q8TKSNb6oMZlyxcbUTm9HJiwLRqT01rOl+runePHem43bYtxrVdFLaprnX/JBme6kSUwC6KoiqmDLaTZa0KUuBHm1/Cc0ABiEyjobRlTzBc0k/ardS5A6QuBAfYjuOqIuVsg1k/23XCUkTktXk0S43QsloDANp47eO+up8ctT3rKF2BDiRkau/T7f0AADe0WWcpaLoZnk9qd79ktJshixuez64h1Oe/8TqhfnUmh+/qLDp+ychq/Dz6KQB2jjk1RYtUMKdUUEqbf7x0AXIXUV8+8/OGyyD7GE0ps1lL/diBUDei4lBTNSM2l43/Vwyjurru9HSkbxbv5hIj7K8naKA0n9rZ/lE0zdYmswIZD1LP6v2OVvBo6J1i1JFa/u7HzkPRU/QeTfS6k6xIMQzDMAzDuOSAZzbfd0z4KLrGDCFVC1eP/JrXGtmkJ/DcqlrTWWQ53zienM47K07nbq1Wpz/G7n4p1nnrSxDqXEcta409V6t5pcplQBM+Ub5vydl37FxSmJYf9jpqxFpn8l4GNH+EojH5z88BAB5+6hhbQWqAZxeMAgDccuxq6zt5LZdmUlqFp088Fdkv0vGaIlN8nTiyS4f568SRmblElwkO607MqaYiKC2kkVEO4N5p2qFeVg7LwwUZFJbvXDexLn6upKS2J7grgVIWO0WAHLldXTjT2vzavuG0+ZflYT+LlrpA9TVMWUuqVvevKKN+r5d3WO786n5haTmAxKiZjsAPeY6TC5dYu6jrUkoFu9SgkXH2ErufMqqFk7NUOVX1QZR1dS0Fvh+buhmPLz8SANBpj1iVwONNSFbund93og997e+c7X7/kGq0kxvjrZvKfddm/wLA7n/TRXvKeU7HkNdJEV979MsAnCse0H7/fP4CAEDn+3+KWf1m/jhUdaB33+rzn7YS+UabJZKUG9Te0j3J6Hm0mDn5TmxswNdPdyhSN+04GADQ8cO1VioQVW31daZ2VJtH737/CkqYq+/eE3OialakGIZhGIZhXNK8QwM5KgyF4GlF8573DP0obBe/5o1Qa54p6YQHptHieaZfxhzXdx7AU0vn8nclf44lh74KoOGEkl3GbKTD3ytOk8Dovf2FdqGjKVJOv6altTQabrVou2VJGzLk1zTt9ezkMTeI8eJh0c8vzyXPfbQIvLt9VD7S34tNkeowlapM6TFVyPTQAewkZ3T88274Al9+UkjXVFxMmxI0Cq8LNTrM14kipsqGdgYApG6ugLmg7jUKnWyqyRaftkZEQ0WjaDBFfDbG98WpXpZeYUdAhoRu440y7lHL9/O+bq7OHVkYe8SnH9IHAHByuhwOpuHdZZQotDsW0+71JdNUVA19zXoAQOGF9FePtp+mNU1STsco1pNHyyRd39ZekkpGQKr+bx+UkcrnW7BSbEPkM1avcQuloJg6bgj9DRyKbnt2A4CdGDNB7aDLdKojv15abUXs1jgiTe8a/jHe6jCUzr+dFGNXfp9CgZJ+VtJPzKisROZPwsH1aPqjm2aEepq1Snmm3tjVYeaPQWVb+5nLRL71LXd20rKzAQDaQznIn0G+UVaNbaD9eB2pj87NmgMAOGfC9ej8Hb0bdveld5fn+N24v8+HAICjU+i4Z6+nCN2yM9rbbaYBmtWQsl4WoRDKxlIHfWb6D2H7eKBZ8rS8qQ9MOxkFN9DNiMlJTOkopME2dSF1luPSKiMMFtXp/IWCdwE4nM5dLNip+XwRTohZPfdan6M5Izpf2tKxO7R5W6QcrnkiHHTbHrSr3uNLnEZcTaYW85Rp+vtzAQDDzrgSK0e+BsB2jpcN5LqsDXjpecrz1fEMmh4zdT2x2atlxy7rVLAWJRfQOe+6/QUAsPID7dMrMfy1G2M+9LrKnAb3UevM24XUEM84fCI8s4RxEUuuG02DR+xniFxL+8+habN5Q56CrAnRFrSWqGH6vy6mlz5GNlj8elGNuuJBNM2ZK9bHA4DUuY613WKdMlJzyUAMUqLkz1rzCN2DnIW0f+vXRf4gxViOF6ejdclgmvDqHCUwI2QGIe/9W1uHAQB8lZvsctbTkcvySaOxSZlH6Uz+vPBSLBn+Ztgm2cbPbbUH915AObA63SdXNHDhriCny6OME2JZa9JXqfzwd5wahXGHR7Hv5bSz09hWV/A4sSPV7W+3DrXFA4frRp3nchhSRSI9zfIrJgNX1P07OeX4Tv63AIABZ1+F9o/EZkjx1B7DMAzDMIxLmnlqz7bbto4JH9qoDmgBR7HaLlAOkSRG5w0k8JPqh1FG8veN8ykR17jRr0Q4ZHo1jzU9Fc3pXHMhRZuhEOAYPZ/RzQ5njxbK7lSKXlxKCku+8Svgcag5ysh49xW035z+T4ojRa4nqOIcCSSVxe/MW3BLCV6bRsrNBRk0dSHvYdAEfh1GCVaHThFr/Z1fDH0fqVOWuibqA631J+pDtCkHRzoDaB5rytUM0j3YeMeh+OUySv8gFRxZnixvKt4/9xFxsBsavLadlQ0nMlADBuQUp/+uXdCPEsqHYxSlRamvpmFaSpQ2hMLK7/vPM3QszRs1U70Tea2lRhU6fyfu4aQGi18/ysiwfHh4IsbdegVyFzsyAMeqMERJyudUKavGD8Pas+gejJg3Ib5y14fjHu4aZn92Tp3qpp2kb8tcckTNwyZxGC222VtVxa6vbieAzvdoWPshpVvo4af+S51uv+/ylwAAk9+jTKih9RsTqg57YjiElojs/8zvlqxXSVXu1WMiVlxGSbWdiae9msdyMr+pzVoAwORrj0KRUJE8KTSFbFSKPsk0bWfwet7RUrUPmro9C6OsuSfflZZdALILauPIZ8OKFMMwDMMwjEuaVZGyQo8DAfzl8PAV19XQYzkCX15Llmf2zM22s7VqjdaHY5Sc9aXwrB4dmV4gGt2O3UCHuTc+h3PpG1N53ADoAZFeIYOs5nGtHhJ7pYYt7SFx+jX5/XQNtccNRVU23R9DzPeWd9HQZTSNkn/oSUqMX4tcT1DFGUa/JUSj2MwlJbEvpC5G2qENm/DGJScCAFq/+j5dXxo9m0qj1hoFzB/0HgDgiVnd8P4/aEScMmUeHStKYjXVF8waLUslwwqHD8Hbm5zZd9xH17liyGQExToSUt20DwqMn/oXAMCGiQ1fYmmVveyFc6kQFafz/uc9P8eoaacCANJvoGehL10pLiEyRN6Tmopd5w8AAPznJlINZGJadfQUDedo7uJ14+z72khUleL03ovDtn1e0Q2+uZT2wKozjUgu6VwqpfDWZfiwnJZDynhnbvi+jfCtcf62bZ9i67Mz+aZ633N+camkNGFghcRSlX5eghNeuwkAsPJSGu3La6pBECcKl7ZXX6f2Xn5ylq0OS59T2ce5UM1CKZx8kmkA+d4oqKp/N4e20y3PbqdGBQWORUs3Ytba7+j0pHC/VHlMtV1H8zv1O96ZnjjizA5IQo/akQdhUuvZ4n8e8S/dnBB0S2L/1xaK1Att2Wr/OMaG7uw4cz6jdci+vM1vOSGrUyfOqbAXe5ABcGnvi+0s57Gcd0ARAODjJx+1Mv3a57GddKMZOs6Hu3SEcCAdUf85ddPnOE90odE5pXntBnrpG0tWxJwvQ74gNJ8P+Ilyyzx5EU3feV+l7PAnptrGjIwimpS1EROeok7+lL/Qc939KkWZtZ25DaENZBSGTTWIMnlbU7boyhGUXXnjOA0fjKVpzMEBupYI4wl2npIe316MwqvFSzkGQ6qiKr6FXlWD6vt+/wMAbPmCXlr37DwGAPDj1nxUVJCV1D67FABwbfdvcGY6Zdq1F54OP6YTuZ+zw9n1eHekoTjaT6KjTpeK+iJzlMnpRk//Xrgo6yXxA6q7j60+CjnV1JYi8j2pdUipg3VNjWt+nzUw2nsxVfLpXZ9G/hTS8ovMeWI/l1mIlYAE+VtvNi16fk33GdZu6iBO/n+xmJrNXEoGh9WbaJ4Dk60/Cuo0f94/qT/tnnUlAGDdac8CIINb5sJ6r/s3AIBxU46Ddh5FtIY2b6FjyGvyeGNzZZBT1aEQdg8ON6S9mlZvnjwmnMbk1XJO0Xq1ugc1XnXw7nxFeL1Nmt9rz6U0l75m9NMRg0AVp8gxve/76PMw+Sr0fF6srrCVIsz1sjJ7lYF+NLBec50fy4qeE7/2Rz2mk7ry9PnLo+0dHZ7aYxiGYRiGcckBUaQ2jU2yVBNnltMaI2RJ7D8vJAWiEHviH5WK0aK0svXdZM1e8/M5WDWKwvZVhUbNcg4AHYTT+aZT2qJTHIrUnv70uyxvqnVtcsouWh6g+pCWcgj1TxPUFx4vqTGD1j2WDn17782j36M47qzHZihkj2B+pOmfJ846HQCw5JV5uCV7tXVegJyhpRL2eU+xyNY99GdpbRWe30Mx+2vKyMm/RvehMIMUlqMzKdpgfBqNqL2ax1Lh5Gg7WfOFOV4DQOFrJD8V/G1O7IobgGCNu2bh17x2/RFq5OROlLYD8q/zXI5cKtFG8HIfdbpP/i16ha4x/4PZ8aXpkJK4qgCa4de97ej/a+/eY6Oq8jiAf+9M2+nQlraUFitYl1KIiEJ88FhdopFdotEafIIxEU0k6Gp0N278w83uZsMf6z5MXIi7Pv8wrJoYBYIIaHCzPLSAID4qVgWsi4ICrcAW2srM3P3je869t9NpOz0t7Gb3+/kDSucyM/eec+8993fO+Z1RmFzUM9XB5KpDOJKd2iFXhCayX352N1cky7jN+XXPwyuDl8/aklUXXbsO7ZT96K/qOXh8QekG85tYrwGoccSwvJ0RMts1a4/tf836blGZdPD9Jt7PyOuE7zlQv2X+E8FkCHver564Hls38tjc/TSf9s9d3goASH19AH4eEz/sa6mrLsGK65aaF1kvBuqWlp6GM29a2u/7Op6OngnZp1Q6fXrytxnJI+EH2uuZ7QWK9qBk96YkvELsXcDJJ/+8mSGizZ3syTicKkOZWV+0sZQpC5impef9cKDckTbtkL1/2PyNFXvy79tTREpERETE0X8ks/mcK8IBrL36JSP/rtgdaecNw2rpADByQwnA5eLyGnRe+5P9wKP5v3/b9LBVbyNR+USMcrEt6XwjWTaClYEfDJIuiCR1tE/csx9lCoCadRyf45p13D7BBP30Jnv4xstr8dQyRph2zeHK8OWxZPD9wjQJ/MzzChN4vHYH37S278/rNtt3ZLpR6iWC97V+38Z+8td/xbXN6ldFEjkOYqCy3xnJwms+M98ytNtl7yt/1/MJP+55QfnYp6Jc03Lt+MERsaJggsDcZx4GAIxf4laGtsyO33QpjjaY+mWCDZ11/A5r5j4GgMfXHofnz/07XmvmYPAPTtb1eM8R8W6cTLNcPungWnPN39bi5AFGaYu/5f4UmPki3aN8LGzkk6Rdp/H97m6MajoIADnXxRoUs+L8qYpEOPHjXibFtedWNHoSHSfZWMFr1D8Wc8zRyFYek4KuNIo+ZfbyfNenPCNMfbPlahMYz9l0Lxb+bjWA8Bin/QymJ1jYzQ/8BQCwczEjbQ98ugDffFIDACg+zGOUaPeRKeL2nWNYTysvYrT4hSl/DlIu2DpfGivGF6dYT3+8ghGvSVs/BMBAyFAmDfwvGrfVfQVZm3yyM833uKtyjXmltNeEJnvNBIDHGv8GAHhxxkwAQHlhFzL+cK5k21PJWp5PM3bdgu0XcXKS7bGx6Y5SSPca+xmDF/TI1JleotvLzFgptAXbpf1wlY1ckS5ukwmus1audSHv++w2fq917+Yd5VdESkRERMTRmU1/cBmnei+pfQIAl5ywreboyHk7xqVmJ59qfPSeJp33Z2Y9/YxZsw/bf8mnyxmJMHqQa7kYAHiy4SXcP21R3p93yfnh0hC5km6eTmEEK/zTWrT/cuz59WQAQM0bkSgGMOSp2j2ShIKzKSYu5PpIN1/xUwBA62Ify2c9BwCYVczjnuvJITpmKDtaaaM9Ca8wqCM/2z8XAPDRMxeg+gUmPE12mdle0Vllg5hhVfWuOS0a+XTtImc0sZ9hWvEcP1mrTvBJ7KFtt6BhKY/1OduHVoaf/5Frwe2d/2Q/s2jCaF+0LOaVdJi/d/f9AdXmtfH9f49wtiXHzz3SegPSX3C9y6HWz7UrB15fM7pf0e2uTPJ6tPM3vZMHTvsD6/RZjzMiNZSla4ZN1pg3W/eTq7bjlbcYqV12540AgMvueA9/qt0EIJzBa2e/vj11BTB1MB8cRjFWneDs2l9svhXnLeVU9YYPGBnL5JiyLvRc3ZZhfLewPLLrffTfwTlsZnKebnY85eiF7aj/LaO8L17DaOis4v57Xvr6ffReYdsR/fUcxL1YjqtrmPj4Rx/y/ChfxGtSCsi7J+yMNqRaG3lhromX9JoeG11np6mLJ6TX0grADBZ17dqLLIYKMBz/YAsXRGyaxjXSoikXoiF/gJmCW+dV5v1xPx/7pvkpljNX1HCyx+xgmn0le06xy+Xl9hlYv5trGY5bySIeseY9FKVM99kwNaB6se8Xi8OLc99jmxjSrd/oY8l4VtSDV3OA8XfTeXOae8HHuKaSaytNK2L3w6h4HE2drAfbTkwAALz+1RQAQMfWaozbwAu1t60ZAFCVaULG7pcdGOx4c6t6ll2CE6bcg1fnMUfX2QVm/UdTpjHPC6YTRxvMtjs3lpXWgwPkWV6HTHntTyfwTaocANDcyUV0V3/F7qjDLaMx+n3+36q1TDfQcGRXZL26rNQDgzTuLX6XSbV3IPW9uQwc5UWo8LjJu9Lhoegoz59kG7dPHEsHWao983DjRW6Mftagfj/u4Xgdb9LtU7ndDy5k19j8sTvQWMJ9+9KUVfuzdSjHAad9ymbP5Vwh/YEGQ0e7yYGeXbwjDg3PMIPTKdqgynTwpjlmGRvf+/5agOtnczD617P5oFDxQzYKrx37MRoS/PnCBMvh7LiPfSnWkb2nOBlky3GmeXnto6kYvZnlW72aZTnpyI6g4TTUevr/INqdP1R9dWtls/U5k+Cuzc0AAAEjSURBVH8GwT4lB94kuG6l248GqWiW1HNy0gFzPzg2swsX1zMNzsxKBiQuTraiOs5rfZnHOl1mUm9UxpI599Pu2zEzueJf5jr1ZWoktpxgvX2njetPtjSfg7r1PAYl6/jwn7L3sUEs7q2uPRERERFHnq8wq4iIiIgTRaREREREHKkhJSIiIuJIDSkRERERR2pIiYiIiDhSQ0pERETEkRpSIiIiIo7UkBIRERFxpIaUiIiIiCM1pEREREQcqSElIiIi4kgNKRERERFHakiJiIiIOFJDSkRERMSRGlIiIiIijtSQEhEREXGkhpSIiIiIIzWkRERERBypISUiIiLiSA0pEREREUdqSImIiIg4UkNKRERExJEaUiIiIiKO1JASERERcfRvqafL4gwqJJgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 50 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (10.0, 10.0) # set default size of plots\n",
    "labels = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']\n",
    "num_labels = len(labels)\n",
    "\n",
    "def display_pickle(dataset, samples_per_label):\n",
    "    for y, label in enumerate(labels):\n",
    "        pickles = pickle.load(open(dataset[y], 'rb'))\n",
    "        idxs = np.random.choice(pickles.shape[0], samples_per_label, replace=False)\n",
    "        for i, idx in enumerate(idxs):\n",
    "            plt_idx = i * num_labels + y + 1\n",
    "            plt.subplot(samples_per_label, num_labels, plt_idx)\n",
    "            plt.imshow(pickles[idx, :, :])\n",
    "            plt.axis('off')\n",
    "            if i == 0:\n",
    "                plt.title(label)\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "print ('Displaying pickles from train dataset')\n",
    "display_pickle(train_datasets, 5)\n",
    "print ('Displaying pickles from test dataset')\n",
    "display_pickle(test_datasets, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brunonishimoto/anaconda3/envs/udacity/lib/python3.6/site-packages/skimage/transform/_warps.py:105: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n",
      "/Users/brunonishimoto/anaconda3/envs/udacity/lib/python3.6/site-packages/skimage/transform/_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
      "  warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./dataset/notmnist/train_no_length.pickle pickled!\n",
      "./dataset/notmnist/valid_no_length.pickle pickled!\n",
      "./dataset/notmnist/test_no_length.pickle pickled!\n"
     ]
    }
   ],
   "source": [
    "img_size = image_size\n",
    "new_size = (28, 56)\n",
    "\n",
    "def make_arrays(nb_rows, size):\n",
    "    if nb_rows:\n",
    "        dataset = np.ndarray((nb_rows, size[0], size[1]), dtype=np.float32)\n",
    "        labels = np.ndarray((nb_rows, 5), dtype=np.int32)\n",
    "    else:\n",
    "        dataset, labels = None, None\n",
    "    return dataset, labels\n",
    "\n",
    "def merge_datasets(pickle_files, dataset_size):\n",
    "    dataset, labels = make_arrays(dataset_size, new_size)\n",
    "    pickles = []\n",
    "    for i in range(10):\n",
    "        pickles.append(pickle.load(open(pickle_files[i], 'rb')))\n",
    "\n",
    "    for i in range(dataset_size):\n",
    "        # Generate a random label with max length 5 chars\n",
    "        data_length  = np.random.choice(5, 1)[0] + 1\n",
    "#         data_length = 5\n",
    "        random_chars = np.random.choice(10, data_length)\n",
    "#         random_chars = np.random.choice(9, data_length)\n",
    "        data_label   = np.append(random_chars, np.full(5 - data_length, 10, dtype=int))\n",
    "        \n",
    "        chars = []\n",
    "        \n",
    "        for char in random_chars:\n",
    "            index = np.random.choice(len(pickles[char]), 1)\n",
    "            chars.append(np.reshape(pickles[char][index, :, :], (img_size, img_size)))\n",
    "\n",
    "        image = np.ndarray((img_size, data_length * img_size))\n",
    "        \n",
    "        for idx, char in enumerate(chars):\n",
    "            image[:, img_size*idx:img_size*(idx+1)] = char\n",
    "\n",
    "        image = skimage.transform.resize(image, new_size)\n",
    "\n",
    "        dataset[i] = image\n",
    "        labels[i]  = data_label\n",
    "    return (dataset, labels)\n",
    "\n",
    "if not os.path.exists(os.path.join(data_root, 'train_no_length.pickle')):\n",
    "    train = merge_datasets(train_datasets, 200000)\n",
    "    saveAsPickle(train, os.path.join(data_root, 'train_no_length.pickle'))\n",
    "else:\n",
    "    print('Train dataset already exists')\n",
    "    train = loadPickle(os.path.join(data_root, 'train_no_length.pickle'))\n",
    "if not os.path.exists(os.path.join(data_root, 'valid_no_length.pickle')):\n",
    "    valid = merge_datasets(train_datasets, 5000)\n",
    "    saveAsPickle(valid, os.path.join(data_root, 'valid_no_length.pickle'))\n",
    "else:\n",
    "    print('Validation dataset already exists')\n",
    "    valid = loadPickle(os.path.join(data_root, 'valid_no_length.pickle'))\n",
    "if not os.path.exists(os.path.join(data_root, 'test_no_length.pickle')):\n",
    "    test  = merge_datasets(test_datasets, 10000)\n",
    "    saveAsPickle(test, os.path.join(data_root, 'test_no_length.pickle'))\n",
    "else:\n",
    "    print('Test dataset already exists')\n",
    "    test  = loadPickle(os.path.join(data_root, 'test_no_length.pickle'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def randomize(dataset, labels):\n",
    "    permutation = np.random.permutation(labels.shape[0])\n",
    "    shuffled_dataset = dataset[permutation,:,:]\n",
    "    shuffled_labels = labels[permutation]\n",
    "    return shuffled_dataset, shuffled_labels\n",
    "\n",
    "train_dataset, train_labels = randomize(train[0], train[1])\n",
    "test_dataset, test_labels = randomize(test[0], test[1])\n",
    "valid_dataset, valid_labels = randomize(valid[0], valid[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying training images\n",
      "[[ 2  4  5 10 10]\n",
      " [ 8  7  2  4 10]\n",
      " [ 1  8 10 10 10]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying test images\n",
      "[[ 9  6  0  6  9]\n",
      " [ 6 10 10 10 10]\n",
      " [ 8  1  6  1  2]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying validation images\n",
      "[[ 6  5  5  8 10]\n",
      " [ 3  1  1 10 10]\n",
      " [ 7 10 10 10 10]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def display_dataset(dataset, labels, samples_per_label):\n",
    "    idxs = np.random.choice(dataset.shape[0], samples_per_label, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i + 1\n",
    "        plt.subplot(1, samples_per_label, plt_idx)\n",
    "        plt.imshow(dataset[idx])\n",
    "        plt.axis('off')\n",
    "    print(labels[idxs])\n",
    "    plt.show()\n",
    "    \n",
    "print ('Displaying training images')\n",
    "display_dataset(train_dataset, train_labels, 3)\n",
    "print ('Displaying test images')\n",
    "display_dataset(test_dataset, test_labels, 3)\n",
    "print ('Displaying validation images')\n",
    "display_dataset(valid_dataset, valid_labels, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set (200000, 28, 56, 1) (200000, 5, 11)\n",
      "Validation set (5000, 28, 56, 1) (5000, 5, 11)\n",
      "Test set (10000, 28, 56, 1) (10000, 5, 11)\n"
     ]
    }
   ],
   "source": [
    "new_size = (28, 56)\n",
    "num_labels = 11\n",
    "num_channels = 1 # grayscale\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def reformat(dataset, labels):\n",
    "    dataset = dataset.reshape((-1, new_size[0], new_size[1], num_channels)).astype(np.float32)\n",
    "    labels = np.eye(11)[labels]\n",
    "    \n",
    "    return dataset, labels\n",
    "\n",
    "train_dataset, train_labels = reformat(train_dataset, train_labels)\n",
    "valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n",
    "test_dataset, test_labels = reformat(test_dataset, test_labels)\n",
    "\n",
    "print('Training set', train_dataset.shape, train_labels.shape)\n",
    "print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
    "print('Test set', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(predictions, labels):\n",
    "    predictions = np.transpose(predictions, (1, 0, 2))\n",
    "    global_acc = 100.0 * np.mean(np.prod(np.argmax(predictions, 2) == np.argmax(labels, 2), 1))\n",
    "    individual_acc = 100 * np.mean(np.argmax(predictions, 2) == np.argmax(labels, 2), 0)\n",
    "    return [global_acc, individual_acc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "patch_size = 5\n",
    "depth = 16\n",
    "num_hidden = 64\n",
    "image_size = (28, 56)\n",
    "num_outputs = 5\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "\n",
    "    # Input data.\n",
    "    tf_dataset = tf.placeholder(tf.float32, shape=(None, image_size[0], image_size[1], num_channels), name='data')\n",
    "    tf_labels  = tf.placeholder(tf.float32, shape=(None, num_outputs, num_labels), name='labels')\n",
    "    tf_learning_rate = tf.placeholder(tf.float32, name='learning_rate')\n",
    "  \n",
    "    tf.summary.image('inputs', tf_dataset)\n",
    "    # Variables.\n",
    "    ## First convolutional layer\n",
    "    with tf.name_scope('conv1/'):\n",
    "        with tf.variable_scope('conv1/'):\n",
    "            conv1_weights = tf.get_variable('conv1_w', shape=[patch_size, patch_size, num_channels, depth],\n",
    "                                            initializer=tf.contrib.layers.xavier_initializer())\n",
    "            conv1_biases = tf.get_variable('conv1_b', shape=[depth],\n",
    "                                           initializer=tf.random_uniform_initializer(-0.1, 0.1))\n",
    "\n",
    "        tf.summary.histogram('weights', conv1_weights)\n",
    "        tf.summary.histogram('bias', conv1_biases)\n",
    "    \n",
    "    ## Second convolutional layer\n",
    "    with tf.name_scope('conv2/'):\n",
    "        with tf.variable_scope('conv2/'):\n",
    "            conv2_weights = tf.get_variable('conv2_w', shape=[patch_size, patch_size, depth, depth],\n",
    "                                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "            conv2_biases = tf.get_variable('conv2_b', shape=[depth],\n",
    "                                           initializer=tf.random_uniform_initializer(-0.1, 0.1))\n",
    "\n",
    "        tf.summary.histogram('weights', conv2_weights)\n",
    "        tf.summary.histogram('bias', conv2_biases)\n",
    "    \n",
    "    ## fully connected layer\n",
    "    with tf.name_scope('fc1/'):\n",
    "        with tf.variable_scope('fc1/'):\n",
    "            fc1_weights = tf.get_variable('fc1/fc1_w', shape=[image_size[0] // 4 * image_size[1] // 4 * depth, num_hidden],\n",
    "                                         initializer=tf.contrib.layers.xavier_initializer())\n",
    "            fc1_biases = tf.get_variable('fc1/fc1_b', shape=[num_hidden],\n",
    "                                         initializer=tf.random_uniform_initializer(-0.1, 0.1))\n",
    "\n",
    "        tf.summary.histogram('weights', fc1_weights)\n",
    "        tf.summary.histogram('bias', fc1_biases)\n",
    "                                \n",
    "    with tf.name_scope('fc2/'):\n",
    "        with tf.variable_scope('fc2/'):\n",
    "            fc2_weights = tf.get_variable('fc2_w', shape=[num_hidden, num_hidden],\n",
    "                                         initializer=tf.contrib.layers.xavier_initializer())\n",
    "            fc2_biases = tf.get_variable('fc2_b', shape=[num_hidden],\n",
    "                                         initializer=tf.random_uniform_initializer(-0.1, 0.1))\n",
    "\n",
    "        tf.summary.histogram('weights', fc2_weights)\n",
    "        tf.summary.histogram('bias', fc2_biases)\n",
    "    \n",
    "    ## softmax\n",
    "    with tf.name_scope('digit1/'):\n",
    "        with tf.variable_scope('digit1/'):\n",
    "            softmax1_weights = tf.get_variable('digit1_w', shape=[num_hidden, num_labels],\n",
    "                                              initializer=tf.contrib.layers.xavier_initializer())\n",
    "            softmax1_biases = tf.get_variable('digit1_b', shape=[num_labels],\n",
    "                                              initializer=tf.random_uniform_initializer(-0.1, 0.1))\n",
    "\n",
    "        tf.summary.histogram('weights', softmax1_weights)\n",
    "        tf.summary.histogram('bias', softmax1_biases)\n",
    "    \n",
    "    with tf.name_scope('digit2/'):\n",
    "        with tf.variable_scope('digit2/'):\n",
    "            softmax2_weights = tf.get_variable('digit2_w', shape=[num_hidden, num_labels],\n",
    "                                              initializer=tf.contrib.layers.xavier_initializer())\n",
    "            softmax2_biases = tf.get_variable('digit2_b', shape=[num_labels],\n",
    "                                              initializer=tf.random_uniform_initializer(-0.1, 0.1))\n",
    "\n",
    "        tf.summary.histogram('weights', softmax2_weights)\n",
    "        tf.summary.histogram('bias', softmax2_biases)\n",
    "    \n",
    "    with tf.name_scope('digit3/'):\n",
    "        with tf.variable_scope('digit3/'):\n",
    "            softmax3_weights = tf.get_variable('digit3_w', shape=[num_hidden, num_labels],\n",
    "                                              initializer=tf.contrib.layers.xavier_initializer())\n",
    "            softmax3_biases = tf.get_variable('digit3_b', shape=[num_labels],\n",
    "                                              initializer=tf.random_uniform_initializer(-0.1, 0.1))\n",
    "\n",
    "        tf.summary.histogram('weights', softmax3_weights)\n",
    "        tf.summary.histogram('bias', softmax3_biases)\n",
    "    \n",
    "    with tf.name_scope('digit4/'):\n",
    "        with tf.variable_scope('digit4/'):\n",
    "            softmax4_weights = tf.get_variable('digit4_w', shape=[num_hidden, num_labels],\n",
    "                                              initializer=tf.contrib.layers.xavier_initializer())\n",
    "            softmax4_biases = tf.get_variable('digit4_b', shape=[num_labels],\n",
    "                                              initializer=tf.random_uniform_initializer(-0.1, 0.1))\n",
    "\n",
    "        tf.summary.histogram('weights', softmax4_weights)\n",
    "        tf.summary.histogram('bias', softmax4_biases)\n",
    "    \n",
    "    with tf.name_scope('digit5/'):\n",
    "        with tf.variable_scope('digit5/'):\n",
    "            softmax5_weights = tf.get_variable('digit5_w', shape=[num_hidden, num_labels],\n",
    "                                              initializer=tf.contrib.layers.xavier_initializer())\n",
    "            softmax5_biases = tf.get_variable('digit5_b', shape=[num_labels],\n",
    "                                              initializer=tf.random_uniform_initializer(-0.1, 0.1))\n",
    "\n",
    "        tf.summary.histogram('weights', softmax5_weights)\n",
    "        tf.summary.histogram('bias', softmax5_biases)\n",
    "  \n",
    "    # Model.\n",
    "    def model(data):\n",
    "        # first conv\n",
    "        with tf.name_scope('conv1/'):\n",
    "            conv   = tf.nn.conv2d(data, conv1_weights, [1, 1, 1, 1], padding='SAME')\n",
    "            conv   = tf.nn.relu(conv + conv1_biases)\n",
    "            for i in range(depth):\n",
    "                tf.summary.image(f'conv{i}', tf.reshape(conv[:, :, :, i], [-1, 28, 56, 1]))\n",
    "            hidden = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "        \n",
    "        # second conv\n",
    "        with tf.name_scope('conv2/'):\n",
    "            conv   = tf.nn.conv2d(hidden, conv2_weights, [1, 1, 1, 1], padding='SAME')\n",
    "            conv   = tf.nn.relu(conv + conv2_biases)\n",
    "            hidden = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "        \n",
    "        # flatten\n",
    "        with tf.name_scope('flatten/'):\n",
    "            shape   = hidden.get_shape().as_list()\n",
    "            reshape = tf.reshape(hidden, [-1, shape[1] * shape[2] * shape[3]])\n",
    "\n",
    "        # fc1\n",
    "        with tf.name_scope('fc1/'):\n",
    "            hidden  = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)\n",
    "        \n",
    "        # fc2\n",
    "        with tf.name_scope('fc2/'):\n",
    "            hidden = tf.nn.relu(tf.matmul(hidden, fc2_weights) + fc2_biases)\n",
    "        \n",
    "        # classifiers\n",
    "        with tf.name_scope('digit1/'):\n",
    "            classifier1 = tf.matmul(hidden, softmax1_weights) + softmax1_biases\n",
    "        with tf.name_scope('digit2/'):\n",
    "            classifier2 = tf.matmul(hidden, softmax2_weights) + softmax2_biases\n",
    "        with tf.name_scope('digit3/'):\n",
    "            classifier3 = tf.matmul(hidden, softmax3_weights) + softmax3_biases\n",
    "        with tf.name_scope('digit4/'):\n",
    "            classifier4 = tf.matmul(hidden, softmax4_weights) + softmax4_biases\n",
    "        with tf.name_scope('digit5/'):\n",
    "            classifier5 = tf.matmul(hidden, softmax5_weights) + softmax5_biases\n",
    "        \n",
    "        return [classifier1, classifier2, classifier3, classifier4, classifier5]\n",
    "  \n",
    "    # Training computation.\n",
    "    logits = model(tf_dataset)\n",
    "    \n",
    "    total_loss = 0\n",
    "    for i in range(len(logits)):\n",
    "        with tf.name_scope(f'digit{i+1}/'):\n",
    "            loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n",
    "                labels=tf_labels[:, i], logits=logits[i]))\n",
    "            tf.summary.scalar('loss', loss)\n",
    "            total_loss += loss\n",
    "    tf.summary.scalar('total_loss', total_loss)\n",
    "    \n",
    "    # Optimizer.\n",
    "    optimizer = tf.train.GradientDescentOptimizer(tf_learning_rate)\n",
    "    grads_and_vars = optimizer.compute_gradients(total_loss)\n",
    "    minimize = optimizer.minimize(total_loss)\n",
    "    \n",
    "    # Gradient norm summary\n",
    "    for g,v in grads_and_vars:\n",
    "        if 'fc2_w' in v.name:\n",
    "            with tf.name_scope('gradients'):\n",
    "                tf_last_grad_norm = tf.sqrt(tf.reduce_mean(g**2))\n",
    "                tf.summary.scalar('grad_norm', tf_last_grad_norm)\n",
    "                break\n",
    "  \n",
    "    merged = tf.summary.merge_all()\n",
    "    # Predictions\n",
    "    def prediction(logits):\n",
    "        prediction = []\n",
    "        for i in range(len(logits)):\n",
    "            with tf.name_scope(f'prediction{i+1}'):\n",
    "                prediction.append(tf.nn.softmax(logits[i]))\n",
    "        return tf.stack(prediction)\n",
    "    \n",
    "    predictions = prediction(logits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printStatistics(dataset ,loss, accuracies):\n",
    "    print(f'\\t{dataset} loss: {loss}')\n",
    "    print(f'\\t{dataset} accuracy: global - {accuracies[0]:.2f}% --- average - {np.mean(accuracies[1]):.2f}%')\n",
    "    for i in range(len(accuracies[1])):\n",
    "        print(f'\\tDigit{i}: {accuracies[1][i]:.2f}%')            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "step 0:\n",
      "\tTrain loss: 11.996456146240234\n",
      "\tTrain accuracy: global - 0.00% --- average - 5.62%\n",
      "\tDigit0: 3.12%\n",
      "\tDigit1: 6.25%\n",
      "\tDigit2: 6.25%\n",
      "\tDigit3: 12.50%\n",
      "\tDigit4: 0.00%\n",
      "\tValidation loss: 11.64498519897461\n",
      "\tValidation accuracy: global - 0.00% --- average - 28.96%\n",
      "\tDigit0: 3.10%\n",
      "\tDigit1: 9.70%\n",
      "\tDigit2: 12.50%\n",
      "\tDigit3: 42.52%\n",
      "\tDigit4: 76.98%\n",
      "step 100:\n",
      "\tTrain loss: 6.799890995025635\n",
      "\tTrain accuracy: global - 6.25% --- average - 51.25%\n",
      "\tDigit0: 12.50%\n",
      "\tDigit1: 31.25%\n",
      "\tDigit2: 59.38%\n",
      "\tDigit3: 71.88%\n",
      "\tDigit4: 81.25%\n",
      "\tValidation loss: 7.8702778816223145\n",
      "\tValidation accuracy: global - 1.80% --- average - 43.68%\n",
      "\tDigit0: 12.30%\n",
      "\tDigit1: 25.42%\n",
      "\tDigit2: 41.82%\n",
      "\tDigit3: 59.28%\n",
      "\tDigit4: 79.56%\n",
      "step 200:\n",
      "\tTrain loss: 7.426486015319824\n",
      "\tTrain accuracy: global - 6.25% --- average - 48.12%\n",
      "\tDigit0: 18.75%\n",
      "\tDigit1: 37.50%\n",
      "\tDigit2: 46.88%\n",
      "\tDigit3: 59.38%\n",
      "\tDigit4: 78.12%\n",
      "\tValidation loss: 7.150575637817383\n",
      "\tValidation accuracy: global - 5.46% --- average - 49.38%\n",
      "\tDigit0: 24.46%\n",
      "\tDigit1: 30.82%\n",
      "\tDigit2: 46.62%\n",
      "\tDigit3: 63.64%\n",
      "\tDigit4: 81.36%\n",
      "step 300:\n",
      "\tTrain loss: 6.634085655212402\n",
      "\tTrain accuracy: global - 6.25% --- average - 49.38%\n",
      "\tDigit0: 34.38%\n",
      "\tDigit1: 34.38%\n",
      "\tDigit2: 40.62%\n",
      "\tDigit3: 65.62%\n",
      "\tDigit4: 71.88%\n",
      "\tValidation loss: 6.354475975036621\n",
      "\tValidation accuracy: global - 10.78% --- average - 55.05%\n",
      "\tDigit0: 39.82%\n",
      "\tDigit1: 36.24%\n",
      "\tDigit2: 50.72%\n",
      "\tDigit3: 66.06%\n",
      "\tDigit4: 82.42%\n",
      "step 400:\n",
      "\tTrain loss: 6.422230243682861\n",
      "\tTrain accuracy: global - 12.50% --- average - 54.38%\n",
      "\tDigit0: 46.88%\n",
      "\tDigit1: 25.00%\n",
      "\tDigit2: 56.25%\n",
      "\tDigit3: 59.38%\n",
      "\tDigit4: 84.38%\n",
      "\tValidation loss: 5.770993232727051\n",
      "\tValidation accuracy: global - 17.52% --- average - 60.68%\n",
      "\tDigit0: 51.64%\n",
      "\tDigit1: 44.06%\n",
      "\tDigit2: 55.32%\n",
      "\tDigit3: 68.78%\n",
      "\tDigit4: 83.58%\n",
      "step 500:\n",
      "\tTrain loss: 6.086592674255371\n",
      "\tTrain accuracy: global - 12.50% --- average - 58.75%\n",
      "\tDigit0: 46.88%\n",
      "\tDigit1: 50.00%\n",
      "\tDigit2: 46.88%\n",
      "\tDigit3: 62.50%\n",
      "\tDigit4: 87.50%\n",
      "\tValidation loss: 5.320945739746094\n",
      "\tValidation accuracy: global - 21.78% --- average - 63.95%\n",
      "\tDigit0: 57.58%\n",
      "\tDigit1: 49.66%\n",
      "\tDigit2: 55.82%\n",
      "\tDigit3: 71.34%\n",
      "\tDigit4: 85.34%\n",
      "step 600:\n",
      "\tTrain loss: 4.015072345733643\n",
      "\tTrain accuracy: global - 40.62% --- average - 76.25%\n",
      "\tDigit0: 71.88%\n",
      "\tDigit1: 65.62%\n",
      "\tDigit2: 75.00%\n",
      "\tDigit3: 81.25%\n",
      "\tDigit4: 87.50%\n",
      "\tValidation loss: 4.6361918449401855\n",
      "\tValidation accuracy: global - 26.06% --- average - 68.98%\n",
      "\tDigit0: 63.52%\n",
      "\tDigit1: 56.70%\n",
      "\tDigit2: 64.24%\n",
      "\tDigit3: 74.46%\n",
      "\tDigit4: 85.96%\n",
      "step 700:\n",
      "\tTrain loss: 4.513926029205322\n",
      "\tTrain accuracy: global - 25.00% --- average - 70.00%\n",
      "\tDigit0: 71.88%\n",
      "\tDigit1: 50.00%\n",
      "\tDigit2: 65.62%\n",
      "\tDigit3: 81.25%\n",
      "\tDigit4: 81.25%\n",
      "\tValidation loss: 4.344089984893799\n",
      "\tValidation accuracy: global - 28.30% --- average - 71.82%\n",
      "\tDigit0: 62.80%\n",
      "\tDigit1: 62.40%\n",
      "\tDigit2: 67.32%\n",
      "\tDigit3: 77.84%\n",
      "\tDigit4: 88.74%\n",
      "step 800:\n",
      "\tTrain loss: 4.183811664581299\n",
      "\tTrain accuracy: global - 31.25% --- average - 66.88%\n",
      "\tDigit0: 59.38%\n",
      "\tDigit1: 56.25%\n",
      "\tDigit2: 68.75%\n",
      "\tDigit3: 62.50%\n",
      "\tDigit4: 87.50%\n",
      "\tValidation loss: 4.312882423400879\n",
      "\tValidation accuracy: global - 27.56% --- average - 71.74%\n",
      "\tDigit0: 65.68%\n",
      "\tDigit1: 59.08%\n",
      "\tDigit2: 69.14%\n",
      "\tDigit3: 76.58%\n",
      "\tDigit4: 88.22%\n",
      "step 900:\n",
      "\tTrain loss: 3.6157078742980957\n",
      "\tTrain accuracy: global - 28.12% --- average - 75.00%\n",
      "\tDigit0: 68.75%\n",
      "\tDigit1: 78.12%\n",
      "\tDigit2: 71.88%\n",
      "\tDigit3: 62.50%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 3.7443759441375732\n",
      "\tValidation accuracy: global - 33.74% --- average - 76.09%\n",
      "\tDigit0: 69.62%\n",
      "\tDigit1: 67.54%\n",
      "\tDigit2: 73.22%\n",
      "\tDigit3: 80.94%\n",
      "\tDigit4: 89.14%\n",
      "step 1000:\n",
      "\tTrain loss: 2.611752510070801\n",
      "\tTrain accuracy: global - 43.75% --- average - 85.00%\n",
      "\tDigit0: 75.00%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 81.25%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 3.5459306240081787\n",
      "\tValidation accuracy: global - 33.74% --- average - 77.26%\n",
      "\tDigit0: 71.52%\n",
      "\tDigit1: 68.86%\n",
      "\tDigit2: 75.46%\n",
      "\tDigit3: 80.56%\n",
      "\tDigit4: 89.90%\n",
      "step 1100:\n",
      "\tTrain loss: 3.983724355697632\n",
      "\tTrain accuracy: global - 31.25% --- average - 73.75%\n",
      "\tDigit0: 68.75%\n",
      "\tDigit1: 56.25%\n",
      "\tDigit2: 71.88%\n",
      "\tDigit3: 81.25%\n",
      "\tDigit4: 90.62%\n",
      "\tValidation loss: 3.755586624145508\n",
      "\tValidation accuracy: global - 32.74% --- average - 76.01%\n",
      "\tDigit0: 69.10%\n",
      "\tDigit1: 69.90%\n",
      "\tDigit2: 71.26%\n",
      "\tDigit3: 79.66%\n",
      "\tDigit4: 90.14%\n",
      "step 1200:\n",
      "\tTrain loss: 3.287214994430542\n",
      "\tTrain accuracy: global - 25.00% --- average - 77.50%\n",
      "\tDigit0: 56.25%\n",
      "\tDigit1: 75.00%\n",
      "\tDigit2: 71.88%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 3.386615037918091\n",
      "\tValidation accuracy: global - 37.12% --- average - 78.70%\n",
      "\tDigit0: 74.52%\n",
      "\tDigit1: 72.20%\n",
      "\tDigit2: 74.18%\n",
      "\tDigit3: 81.18%\n",
      "\tDigit4: 91.42%\n",
      "step 1300:\n",
      "\tTrain loss: 2.6830592155456543\n",
      "\tTrain accuracy: global - 40.62% --- average - 83.12%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 68.75%\n",
      "\tDigit2: 81.25%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 3.078864812850952\n",
      "\tValidation accuracy: global - 40.48% --- average - 81.03%\n",
      "\tDigit0: 75.70%\n",
      "\tDigit1: 74.82%\n",
      "\tDigit2: 78.76%\n",
      "\tDigit3: 84.12%\n",
      "\tDigit4: 91.76%\n",
      "step 1400:\n",
      "\tTrain loss: 4.432773590087891\n",
      "\tTrain accuracy: global - 21.88% --- average - 75.00%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 59.38%\n",
      "\tDigit2: 75.00%\n",
      "\tDigit3: 68.75%\n",
      "\tDigit4: 87.50%\n",
      "\tValidation loss: 3.236617088317871\n",
      "\tValidation accuracy: global - 36.68% --- average - 79.73%\n",
      "\tDigit0: 72.10%\n",
      "\tDigit1: 72.24%\n",
      "\tDigit2: 78.10%\n",
      "\tDigit3: 84.04%\n",
      "\tDigit4: 92.18%\n",
      "step 1500:\n",
      "\tTrain loss: 3.209559917449951\n",
      "\tTrain accuracy: global - 28.12% --- average - 77.50%\n",
      "\tDigit0: 68.75%\n",
      "\tDigit1: 78.12%\n",
      "\tDigit2: 78.12%\n",
      "\tDigit3: 75.00%\n",
      "\tDigit4: 87.50%\n",
      "\tValidation loss: 2.940782070159912\n",
      "\tValidation accuracy: global - 41.26% --- average - 82.29%\n",
      "\tDigit0: 77.44%\n",
      "\tDigit1: 74.88%\n",
      "\tDigit2: 79.32%\n",
      "\tDigit3: 86.62%\n",
      "\tDigit4: 93.20%\n",
      "step 1600:\n",
      "\tTrain loss: 3.419083595275879\n",
      "\tTrain accuracy: global - 34.38% --- average - 76.25%\n",
      "\tDigit0: 81.25%\n",
      "\tDigit1: 68.75%\n",
      "\tDigit2: 62.50%\n",
      "\tDigit3: 81.25%\n",
      "\tDigit4: 87.50%\n",
      "\tValidation loss: 2.8892641067504883\n",
      "\tValidation accuracy: global - 41.98% --- average - 82.52%\n",
      "\tDigit0: 76.18%\n",
      "\tDigit1: 74.76%\n",
      "\tDigit2: 81.88%\n",
      "\tDigit3: 86.48%\n",
      "\tDigit4: 93.32%\n",
      "step 1700:\n",
      "\tTrain loss: 2.4465370178222656\n",
      "\tTrain accuracy: global - 46.88% --- average - 85.00%\n",
      "\tDigit0: 75.00%\n",
      "\tDigit1: 68.75%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.922215223312378\n",
      "\tValidation accuracy: global - 40.56% --- average - 82.04%\n",
      "\tDigit0: 76.78%\n",
      "\tDigit1: 71.92%\n",
      "\tDigit2: 82.22%\n",
      "\tDigit3: 86.12%\n",
      "\tDigit4: 93.14%\n",
      "step 1800:\n",
      "\tTrain loss: 3.162048101425171\n",
      "\tTrain accuracy: global - 37.50% --- average - 81.25%\n",
      "\tDigit0: 81.25%\n",
      "\tDigit1: 68.75%\n",
      "\tDigit2: 75.00%\n",
      "\tDigit3: 87.50%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 2.8913650512695312\n",
      "\tValidation accuracy: global - 40.86% --- average - 82.63%\n",
      "\tDigit0: 75.94%\n",
      "\tDigit1: 75.18%\n",
      "\tDigit2: 80.80%\n",
      "\tDigit3: 87.42%\n",
      "\tDigit4: 93.82%\n",
      "step 1900:\n",
      "\tTrain loss: 3.1920993328094482\n",
      "\tTrain accuracy: global - 40.62% --- average - 81.25%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 59.38%\n",
      "\tDigit2: 75.00%\n",
      "\tDigit3: 87.50%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.63547420501709\n",
      "\tValidation accuracy: global - 46.24% --- average - 84.29%\n",
      "\tDigit0: 77.82%\n",
      "\tDigit1: 78.70%\n",
      "\tDigit2: 83.26%\n",
      "\tDigit3: 87.54%\n",
      "\tDigit4: 94.14%\n",
      "step 2000:\n",
      "\tTrain loss: 2.9051077365875244\n",
      "\tTrain accuracy: global - 50.00% --- average - 81.25%\n",
      "\tDigit0: 81.25%\n",
      "\tDigit1: 78.12%\n",
      "\tDigit2: 81.25%\n",
      "\tDigit3: 81.25%\n",
      "\tDigit4: 84.38%\n",
      "\tValidation loss: 2.7508769035339355\n",
      "\tValidation accuracy: global - 44.02% --- average - 83.47%\n",
      "\tDigit0: 79.02%\n",
      "\tDigit1: 76.30%\n",
      "\tDigit2: 79.98%\n",
      "\tDigit3: 87.90%\n",
      "\tDigit4: 94.14%\n",
      "step 2100:\n",
      "\tTrain loss: 2.7685062885284424\n",
      "\tTrain accuracy: global - 50.00% --- average - 86.25%\n",
      "\tDigit0: 81.25%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 81.25%\n",
      "\tDigit4: 90.62%\n",
      "\tValidation loss: 2.58237361907959\n",
      "\tValidation accuracy: global - 45.56% --- average - 84.44%\n",
      "\tDigit0: 78.54%\n",
      "\tDigit1: 79.54%\n",
      "\tDigit2: 83.58%\n",
      "\tDigit3: 86.64%\n",
      "\tDigit4: 93.88%\n",
      "step 2200:\n",
      "\tTrain loss: 2.7348458766937256\n",
      "\tTrain accuracy: global - 40.62% --- average - 85.62%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 62.50%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.626617670059204\n",
      "\tValidation accuracy: global - 45.26% --- average - 84.30%\n",
      "\tDigit0: 79.00%\n",
      "\tDigit1: 76.12%\n",
      "\tDigit2: 82.82%\n",
      "\tDigit3: 88.92%\n",
      "\tDigit4: 94.66%\n",
      "step 2300:\n",
      "\tTrain loss: 2.781325578689575\n",
      "\tTrain accuracy: global - 53.12% --- average - 86.25%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 78.12%\n",
      "\tDigit2: 78.12%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 100.00%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tValidation loss: 2.5910935401916504\n",
      "\tValidation accuracy: global - 45.66% --- average - 84.39%\n",
      "\tDigit0: 78.64%\n",
      "\tDigit1: 77.64%\n",
      "\tDigit2: 83.42%\n",
      "\tDigit3: 88.00%\n",
      "\tDigit4: 94.26%\n",
      "step 2400:\n",
      "\tTrain loss: 2.628216028213501\n",
      "\tTrain accuracy: global - 50.00% --- average - 85.62%\n",
      "\tDigit0: 68.75%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 90.62%\n",
      "\tValidation loss: 2.4412055015563965\n",
      "\tValidation accuracy: global - 48.96% --- average - 85.68%\n",
      "\tDigit0: 78.28%\n",
      "\tDigit1: 80.60%\n",
      "\tDigit2: 85.44%\n",
      "\tDigit3: 89.82%\n",
      "\tDigit4: 94.28%\n",
      "step 2500:\n",
      "\tTrain loss: 2.8047688007354736\n",
      "\tTrain accuracy: global - 53.12% --- average - 85.00%\n",
      "\tDigit0: 71.88%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 78.12%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 2.441404104232788\n",
      "\tValidation accuracy: global - 49.04% --- average - 85.78%\n",
      "\tDigit0: 78.88%\n",
      "\tDigit1: 80.68%\n",
      "\tDigit2: 85.00%\n",
      "\tDigit3: 89.84%\n",
      "\tDigit4: 94.52%\n",
      "step 2600:\n",
      "\tTrain loss: 2.4929301738739014\n",
      "\tTrain accuracy: global - 46.88% --- average - 85.62%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 78.12%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 2.4673428535461426\n",
      "\tValidation accuracy: global - 48.60% --- average - 85.64%\n",
      "\tDigit0: 80.06%\n",
      "\tDigit1: 80.22%\n",
      "\tDigit2: 84.10%\n",
      "\tDigit3: 89.70%\n",
      "\tDigit4: 94.10%\n",
      "step 2700:\n",
      "\tTrain loss: 2.267190933227539\n",
      "\tTrain accuracy: global - 68.75% --- average - 90.00%\n",
      "\tDigit0: 90.62%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 2.4982032775878906\n",
      "\tValidation accuracy: global - 46.72% --- average - 85.06%\n",
      "\tDigit0: 78.08%\n",
      "\tDigit1: 79.12%\n",
      "\tDigit2: 84.34%\n",
      "\tDigit3: 89.24%\n",
      "\tDigit4: 94.50%\n",
      "step 2800:\n",
      "\tTrain loss: 2.1723201274871826\n",
      "\tTrain accuracy: global - 68.75% --- average - 88.75%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 2.4451541900634766\n",
      "\tValidation accuracy: global - 49.56% --- average - 85.90%\n",
      "\tDigit0: 80.82%\n",
      "\tDigit1: 81.94%\n",
      "\tDigit2: 83.88%\n",
      "\tDigit3: 89.02%\n",
      "\tDigit4: 93.84%\n",
      "step 2900:\n",
      "\tTrain loss: 2.1807925701141357\n",
      "\tTrain accuracy: global - 53.12% --- average - 86.88%\n",
      "\tDigit0: 71.88%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 84.38%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 2.401031494140625\n",
      "\tValidation accuracy: global - 49.72% --- average - 86.04%\n",
      "\tDigit0: 79.40%\n",
      "\tDigit1: 80.14%\n",
      "\tDigit2: 86.18%\n",
      "\tDigit3: 89.46%\n",
      "\tDigit4: 95.02%\n",
      "step 3000:\n",
      "\tTrain loss: 2.0937535762786865\n",
      "\tTrain accuracy: global - 50.00% --- average - 87.50%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 78.12%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 2.2490217685699463\n",
      "\tValidation accuracy: global - 51.84% --- average - 86.86%\n",
      "\tDigit0: 80.90%\n",
      "\tDigit1: 81.80%\n",
      "\tDigit2: 85.76%\n",
      "\tDigit3: 90.78%\n",
      "\tDigit4: 95.06%\n",
      "step 3100:\n",
      "\tTrain loss: 2.5749869346618652\n",
      "\tTrain accuracy: global - 40.62% --- average - 85.62%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 71.88%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 87.50%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.3325836658477783\n",
      "\tValidation accuracy: global - 49.98% --- average - 86.44%\n",
      "\tDigit0: 81.48%\n",
      "\tDigit1: 80.24%\n",
      "\tDigit2: 84.68%\n",
      "\tDigit3: 90.90%\n",
      "\tDigit4: 94.92%\n",
      "step 3200:\n",
      "\tTrain loss: 2.4317233562469482\n",
      "\tTrain accuracy: global - 53.12% --- average - 87.50%\n",
      "\tDigit0: 81.25%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 90.62%\n",
      "\tValidation loss: 2.273219108581543\n",
      "\tValidation accuracy: global - 51.24% --- average - 86.70%\n",
      "\tDigit0: 80.58%\n",
      "\tDigit1: 81.12%\n",
      "\tDigit2: 86.44%\n",
      "\tDigit3: 90.74%\n",
      "\tDigit4: 94.60%\n",
      "step 3300:\n",
      "\tTrain loss: 1.8910400867462158\n",
      "\tTrain accuracy: global - 53.12% --- average - 89.38%\n",
      "\tDigit0: 71.88%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 100.00%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.2312557697296143\n",
      "\tValidation accuracy: global - 51.58% --- average - 86.88%\n",
      "\tDigit0: 80.20%\n",
      "\tDigit1: 82.02%\n",
      "\tDigit2: 86.26%\n",
      "\tDigit3: 90.86%\n",
      "\tDigit4: 95.04%\n",
      "step 3400:\n",
      "\tTrain loss: 2.9451117515563965\n",
      "\tTrain accuracy: global - 43.75% --- average - 83.12%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 75.00%\n",
      "\tDigit2: 81.25%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 90.62%\n",
      "\tValidation loss: 2.330958127975464\n",
      "\tValidation accuracy: global - 50.28% --- average - 86.34%\n",
      "\tDigit0: 80.74%\n",
      "\tDigit1: 79.28%\n",
      "\tDigit2: 85.74%\n",
      "\tDigit3: 91.24%\n",
      "\tDigit4: 94.68%\n",
      "step 3500:\n",
      "\tTrain loss: 1.0881379842758179\n",
      "\tTrain accuracy: global - 78.12% --- average - 93.75%\n",
      "\tDigit0: 90.62%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 100.00%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.192822217941284\n",
      "\tValidation accuracy: global - 52.80% --- average - 87.35%\n",
      "\tDigit0: 81.00%\n",
      "\tDigit1: 81.76%\n",
      "\tDigit2: 86.94%\n",
      "\tDigit3: 91.46%\n",
      "\tDigit4: 95.58%\n",
      "step 3600:\n",
      "\tTrain loss: 1.5346046686172485\n",
      "\tTrain accuracy: global - 59.38% --- average - 91.88%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 96.88%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 2.1831181049346924\n",
      "\tValidation accuracy: global - 53.06% --- average - 87.46%\n",
      "\tDigit0: 81.34%\n",
      "\tDigit1: 82.50%\n",
      "\tDigit2: 87.40%\n",
      "\tDigit3: 90.88%\n",
      "\tDigit4: 95.18%\n",
      "step 3700:\n",
      "\tTrain loss: 1.7932262420654297\n",
      "\tTrain accuracy: global - 59.38% --- average - 88.75%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 2.343329906463623\n",
      "\tValidation accuracy: global - 50.90% --- average - 86.68%\n",
      "\tDigit0: 80.14%\n",
      "\tDigit1: 80.90%\n",
      "\tDigit2: 87.32%\n",
      "\tDigit3: 89.40%\n",
      "\tDigit4: 95.62%\n",
      "step 3800:\n",
      "\tTrain loss: 2.8186140060424805\n",
      "\tTrain accuracy: global - 37.50% --- average - 81.88%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 78.12%\n",
      "\tDigit4: 87.50%\n",
      "\tValidation loss: 2.150026798248291\n",
      "\tValidation accuracy: global - 53.14% --- average - 87.38%\n",
      "\tDigit0: 81.34%\n",
      "\tDigit1: 82.52%\n",
      "\tDigit2: 86.74%\n",
      "\tDigit3: 91.44%\n",
      "\tDigit4: 94.86%\n",
      "step 3900:\n",
      "\tTrain loss: 1.7076075077056885\n",
      "\tTrain accuracy: global - 68.75% --- average - 91.88%\n",
      "\tDigit0: 87.50%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 2.1262807846069336\n",
      "\tValidation accuracy: global - 53.88% --- average - 87.71%\n",
      "\tDigit0: 82.24%\n",
      "\tDigit1: 83.14%\n",
      "\tDigit2: 86.58%\n",
      "\tDigit3: 91.16%\n",
      "\tDigit4: 95.42%\n",
      "step 4000:\n",
      "\tTrain loss: 1.5949246883392334\n",
      "\tTrain accuracy: global - 56.25% --- average - 90.00%\n",
      "\tDigit0: 75.00%\n",
      "\tDigit1: 96.88%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 2.081048011779785\n",
      "\tValidation accuracy: global - 53.90% --- average - 87.87%\n",
      "\tDigit0: 81.76%\n",
      "\tDigit1: 83.44%\n",
      "\tDigit2: 86.88%\n",
      "\tDigit3: 91.84%\n",
      "\tDigit4: 95.42%\n",
      "step 4100:\n",
      "\tTrain loss: 1.8672995567321777\n",
      "\tTrain accuracy: global - 50.00% --- average - 86.25%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 71.88%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 2.0787413120269775\n",
      "\tValidation accuracy: global - 54.24% --- average - 88.05%\n",
      "\tDigit0: 81.96%\n",
      "\tDigit1: 83.08%\n",
      "\tDigit2: 87.98%\n",
      "\tDigit3: 91.70%\n",
      "\tDigit4: 95.54%\n",
      "step 4200:\n",
      "\tTrain loss: 2.745250940322876\n",
      "\tTrain accuracy: global - 34.38% --- average - 85.62%\n",
      "\tDigit0: 65.62%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.1495349407196045\n",
      "\tValidation accuracy: global - 53.18% --- average - 87.56%\n",
      "\tDigit0: 81.06%\n",
      "\tDigit1: 82.68%\n",
      "\tDigit2: 86.50%\n",
      "\tDigit3: 91.98%\n",
      "\tDigit4: 95.58%\n",
      "step 4300:\n",
      "\tTrain loss: 1.3199671506881714\n",
      "\tTrain accuracy: global - 62.50% --- average - 90.62%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 100.00%\n",
      "\tDigit3: 87.50%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.04231595993042\n",
      "\tValidation accuracy: global - 55.24% --- average - 88.33%\n",
      "\tDigit0: 82.94%\n",
      "\tDigit1: 83.40%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 92.10%\n",
      "\tDigit4: 95.70%\n",
      "step 4400:\n",
      "\tTrain loss: 2.034759044647217\n",
      "\tTrain accuracy: global - 56.25% --- average - 86.25%\n",
      "\tDigit0: 75.00%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 2.190453052520752\n",
      "\tValidation accuracy: global - 53.60% --- average - 87.72%\n",
      "\tDigit0: 82.76%\n",
      "\tDigit1: 81.76%\n",
      "\tDigit2: 87.88%\n",
      "\tDigit3: 90.98%\n",
      "\tDigit4: 95.20%\n",
      "step 4500:\n",
      "\tTrain loss: 1.9955438375473022\n",
      "\tTrain accuracy: global - 62.50% --- average - 90.00%\n",
      "\tDigit0: 81.25%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 2.1168665885925293\n",
      "\tValidation accuracy: global - 53.76% --- average - 87.90%\n",
      "\tDigit0: 81.22%\n",
      "\tDigit1: 82.56%\n",
      "\tDigit2: 87.86%\n",
      "\tDigit3: 92.12%\n",
      "\tDigit4: 95.74%\n",
      "step 4600:\n",
      "\tTrain loss: 2.5326895713806152\n",
      "\tTrain accuracy: global - 34.38% --- average - 82.50%\n",
      "\tDigit0: 56.25%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 2.1706461906433105\n",
      "\tValidation accuracy: global - 52.68% --- average - 87.54%\n",
      "\tDigit0: 79.36%\n",
      "\tDigit1: 83.42%\n",
      "\tDigit2: 87.14%\n",
      "\tDigit3: 92.06%\n",
      "\tDigit4: 95.70%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 4700:\n",
      "\tTrain loss: 2.1042630672454834\n",
      "\tTrain accuracy: global - 53.12% --- average - 89.38%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 78.12%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 2.0776917934417725\n",
      "\tValidation accuracy: global - 53.56% --- average - 87.90%\n",
      "\tDigit0: 81.86%\n",
      "\tDigit1: 82.06%\n",
      "\tDigit2: 87.88%\n",
      "\tDigit3: 92.16%\n",
      "\tDigit4: 95.54%\n",
      "step 4800:\n",
      "\tTrain loss: 1.9301342964172363\n",
      "\tTrain accuracy: global - 56.25% --- average - 89.38%\n",
      "\tDigit0: 87.50%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 78.12%\n",
      "\tDigit3: 100.00%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.0680503845214844\n",
      "\tValidation accuracy: global - 54.58% --- average - 88.00%\n",
      "\tDigit0: 82.06%\n",
      "\tDigit1: 83.68%\n",
      "\tDigit2: 86.34%\n",
      "\tDigit3: 92.16%\n",
      "\tDigit4: 95.74%\n",
      "step 4900:\n",
      "\tTrain loss: 2.3099353313446045\n",
      "\tTrain accuracy: global - 56.25% --- average - 86.88%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 78.12%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 90.62%\n",
      "\tValidation loss: 2.0623254776000977\n",
      "\tValidation accuracy: global - 54.34% --- average - 87.99%\n",
      "\tDigit0: 82.00%\n",
      "\tDigit1: 83.12%\n",
      "\tDigit2: 87.78%\n",
      "\tDigit3: 91.80%\n",
      "\tDigit4: 95.26%\n",
      "step 5000:\n",
      "\tTrain loss: 1.7437266111373901\n",
      "\tTrain accuracy: global - 65.62% --- average - 92.50%\n",
      "\tDigit0: 87.50%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 100.00%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.0225205421447754\n",
      "\tValidation accuracy: global - 55.30% --- average - 88.42%\n",
      "\tDigit0: 82.26%\n",
      "\tDigit1: 83.40%\n",
      "\tDigit2: 88.70%\n",
      "\tDigit3: 91.76%\n",
      "\tDigit4: 96.00%\n",
      "step 5100:\n",
      "\tTrain loss: 1.724489450454712\n",
      "\tTrain accuracy: global - 56.25% --- average - 90.62%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 93.75%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 2.0374929904937744\n",
      "\tValidation accuracy: global - 54.44% --- average - 88.15%\n",
      "\tDigit0: 81.40%\n",
      "\tDigit1: 83.60%\n",
      "\tDigit2: 87.68%\n",
      "\tDigit3: 92.12%\n",
      "\tDigit4: 95.96%\n",
      "step 5200:\n",
      "\tTrain loss: 2.43938946723938\n",
      "\tTrain accuracy: global - 43.75% --- average - 86.25%\n",
      "\tDigit0: 75.00%\n",
      "\tDigit1: 78.12%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 2.087268590927124\n",
      "\tValidation accuracy: global - 53.80% --- average - 87.91%\n",
      "\tDigit0: 81.98%\n",
      "\tDigit1: 82.62%\n",
      "\tDigit2: 87.56%\n",
      "\tDigit3: 91.96%\n",
      "\tDigit4: 95.44%\n",
      "step 5300:\n",
      "\tTrain loss: 1.6308430433273315\n",
      "\tTrain accuracy: global - 56.25% --- average - 90.00%\n",
      "\tDigit0: 81.25%\n",
      "\tDigit1: 93.75%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 2.011779546737671\n",
      "\tValidation accuracy: global - 55.32% --- average - 88.39%\n",
      "\tDigit0: 82.60%\n",
      "\tDigit1: 84.16%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 91.94%\n",
      "\tDigit4: 95.74%\n",
      "step 5400:\n",
      "\tTrain loss: 1.6135938167572021\n",
      "\tTrain accuracy: global - 65.62% --- average - 91.25%\n",
      "\tDigit0: 90.62%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.0271105766296387\n",
      "\tValidation accuracy: global - 55.34% --- average - 88.36%\n",
      "\tDigit0: 82.32%\n",
      "\tDigit1: 83.36%\n",
      "\tDigit2: 88.68%\n",
      "\tDigit3: 91.58%\n",
      "\tDigit4: 95.88%\n",
      "step 5500:\n",
      "\tTrain loss: 1.1817913055419922\n",
      "\tTrain accuracy: global - 68.75% --- average - 91.25%\n",
      "\tDigit0: 90.62%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 81.25%\n",
      "\tDigit3: 100.00%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 1.9436118602752686\n",
      "\tValidation accuracy: global - 56.80% --- average - 88.94%\n",
      "\tDigit0: 82.80%\n",
      "\tDigit1: 84.42%\n",
      "\tDigit2: 88.66%\n",
      "\tDigit3: 92.82%\n",
      "\tDigit4: 96.02%\n",
      "step 5600:\n",
      "\tTrain loss: 1.9547605514526367\n",
      "\tTrain accuracy: global - 62.50% --- average - 90.62%\n",
      "\tDigit0: 96.88%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 78.12%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 2.0212314128875732\n",
      "\tValidation accuracy: global - 54.80% --- average - 88.18%\n",
      "\tDigit0: 81.76%\n",
      "\tDigit1: 83.74%\n",
      "\tDigit2: 88.06%\n",
      "\tDigit3: 92.24%\n",
      "\tDigit4: 95.10%\n",
      "step 5700:\n",
      "\tTrain loss: 2.301187515258789\n",
      "\tTrain accuracy: global - 46.88% --- average - 86.88%\n",
      "\tDigit0: 81.25%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 81.25%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 1.9619783163070679\n",
      "\tValidation accuracy: global - 56.44% --- average - 88.69%\n",
      "\tDigit0: 82.20%\n",
      "\tDigit1: 84.72%\n",
      "\tDigit2: 88.16%\n",
      "\tDigit3: 92.22%\n",
      "\tDigit4: 96.14%\n",
      "step 5800:\n",
      "\tTrain loss: 2.2978649139404297\n",
      "\tTrain accuracy: global - 46.88% --- average - 85.00%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 78.12%\n",
      "\tDigit3: 87.50%\n",
      "\tDigit4: 84.38%\n",
      "\tValidation loss: 2.062790870666504\n",
      "\tValidation accuracy: global - 55.46% --- average - 88.41%\n",
      "\tDigit0: 82.22%\n",
      "\tDigit1: 83.46%\n",
      "\tDigit2: 88.56%\n",
      "\tDigit3: 92.36%\n",
      "\tDigit4: 95.46%\n",
      "step 5900:\n",
      "\tTrain loss: 2.2671687602996826\n",
      "\tTrain accuracy: global - 43.75% --- average - 86.88%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 81.25%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.956464171409607\n",
      "\tValidation accuracy: global - 57.06% --- average - 88.84%\n",
      "\tDigit0: 82.70%\n",
      "\tDigit1: 84.58%\n",
      "\tDigit2: 88.88%\n",
      "\tDigit3: 91.94%\n",
      "\tDigit4: 96.08%\n",
      "step 6000:\n",
      "\tTrain loss: 2.160102367401123\n",
      "\tTrain accuracy: global - 50.00% --- average - 85.00%\n",
      "\tDigit0: 75.00%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 71.88%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 90.62%\n",
      "\tValidation loss: 1.9982573986053467\n",
      "\tValidation accuracy: global - 55.52% --- average - 88.47%\n",
      "\tDigit0: 81.64%\n",
      "\tDigit1: 83.96%\n",
      "\tDigit2: 88.16%\n",
      "\tDigit3: 92.58%\n",
      "\tDigit4: 96.02%\n",
      "step 6100:\n",
      "\tTrain loss: 1.843817114830017\n",
      "\tTrain accuracy: global - 65.62% --- average - 91.88%\n",
      "\tDigit0: 87.50%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.963394284248352\n",
      "\tValidation accuracy: global - 56.16% --- average - 88.59%\n",
      "\tDigit0: 82.56%\n",
      "\tDigit1: 84.04%\n",
      "\tDigit2: 88.28%\n",
      "\tDigit3: 91.80%\n",
      "\tDigit4: 96.28%\n",
      "step 6200:\n",
      "\tTrain loss: 2.2418835163116455\n",
      "\tTrain accuracy: global - 46.88% --- average - 85.00%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 78.12%\n",
      "\tDigit2: 75.00%\n",
      "\tDigit3: 87.50%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.021822929382324\n",
      "\tValidation accuracy: global - 55.36% --- average - 88.43%\n",
      "\tDigit0: 81.82%\n",
      "\tDigit1: 84.48%\n",
      "\tDigit2: 87.58%\n",
      "\tDigit3: 92.26%\n",
      "\tDigit4: 96.02%\n",
      "step 6300:\n",
      "\tTrain loss: 2.401587963104248\n",
      "\tTrain accuracy: global - 46.88% --- average - 86.25%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 87.50%\n",
      "\tDigit4: 90.62%\n",
      "\tValidation loss: 1.936115026473999\n",
      "\tValidation accuracy: global - 56.74% --- average - 88.78%\n",
      "\tDigit0: 82.70%\n",
      "\tDigit1: 84.28%\n",
      "\tDigit2: 88.26%\n",
      "\tDigit3: 92.50%\n",
      "\tDigit4: 96.16%\n",
      "step 6400:\n",
      "\tTrain loss: 1.9014828205108643\n",
      "\tTrain accuracy: global - 53.12% --- average - 86.88%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 78.12%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 1.8950812816619873\n",
      "\tValidation accuracy: global - 57.60% --- average - 89.27%\n",
      "\tDigit0: 82.80%\n",
      "\tDigit1: 84.86%\n",
      "\tDigit2: 89.34%\n",
      "\tDigit3: 93.26%\n",
      "\tDigit4: 96.08%\n",
      "step 6500:\n",
      "\tTrain loss: 2.0030384063720703\n",
      "\tTrain accuracy: global - 56.25% --- average - 88.12%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 87.50%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 2.0071165561676025\n",
      "\tValidation accuracy: global - 55.84% --- average - 88.54%\n",
      "\tDigit0: 81.12%\n",
      "\tDigit1: 84.20%\n",
      "\tDigit2: 88.56%\n",
      "\tDigit3: 92.90%\n",
      "\tDigit4: 95.90%\n",
      "step 6600:\n",
      "\tTrain loss: 1.6120107173919678\n",
      "\tTrain accuracy: global - 62.50% --- average - 90.62%\n",
      "\tDigit0: 75.00%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.9540879726409912\n",
      "\tValidation accuracy: global - 56.58% --- average - 88.78%\n",
      "\tDigit0: 82.62%\n",
      "\tDigit1: 84.42%\n",
      "\tDigit2: 88.64%\n",
      "\tDigit3: 92.16%\n",
      "\tDigit4: 96.08%\n",
      "step 6700:\n",
      "\tTrain loss: 1.5310559272766113\n",
      "\tTrain accuracy: global - 59.38% --- average - 89.38%\n",
      "\tDigit0: 87.50%\n",
      "\tDigit1: 78.12%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.9206935167312622\n",
      "\tValidation accuracy: global - 56.54% --- average - 88.92%\n",
      "\tDigit0: 82.20%\n",
      "\tDigit1: 84.36%\n",
      "\tDigit2: 89.08%\n",
      "\tDigit3: 93.00%\n",
      "\tDigit4: 95.94%\n",
      "step 6800:\n",
      "\tTrain loss: 1.7213629484176636\n",
      "\tTrain accuracy: global - 56.25% --- average - 88.12%\n",
      "\tDigit0: 75.00%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.926144003868103\n",
      "\tValidation accuracy: global - 56.88% --- average - 88.86%\n",
      "\tDigit0: 81.82%\n",
      "\tDigit1: 84.94%\n",
      "\tDigit2: 89.06%\n",
      "\tDigit3: 92.32%\n",
      "\tDigit4: 96.14%\n",
      "step 6900:\n",
      "\tTrain loss: 2.0470848083496094\n",
      "\tTrain accuracy: global - 65.62% --- average - 90.00%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 90.62%\n",
      "\tValidation loss: 1.9548728466033936\n",
      "\tValidation accuracy: global - 55.90% --- average - 88.63%\n",
      "\tDigit0: 81.30%\n",
      "\tDigit1: 84.64%\n",
      "\tDigit2: 88.66%\n",
      "\tDigit3: 92.36%\n",
      "\tDigit4: 96.20%\n",
      "step 7000:\n",
      "\tTrain loss: 1.774641990661621\n",
      "\tTrain accuracy: global - 56.25% --- average - 90.00%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 93.75%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 100.00%\n",
      "\tDigit4: 93.75%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tValidation loss: 1.9578806161880493\n",
      "\tValidation accuracy: global - 56.20% --- average - 88.64%\n",
      "\tDigit0: 81.94%\n",
      "\tDigit1: 84.50%\n",
      "\tDigit2: 88.70%\n",
      "\tDigit3: 92.72%\n",
      "\tDigit4: 95.32%\n",
      "step 7100:\n",
      "\tTrain loss: 1.1999536752700806\n",
      "\tTrain accuracy: global - 65.62% --- average - 91.25%\n",
      "\tDigit0: 81.25%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 96.88%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.9016731977462769\n",
      "\tValidation accuracy: global - 56.94% --- average - 89.12%\n",
      "\tDigit0: 82.04%\n",
      "\tDigit1: 85.62%\n",
      "\tDigit2: 89.06%\n",
      "\tDigit3: 92.58%\n",
      "\tDigit4: 96.30%\n",
      "step 7200:\n",
      "\tTrain loss: 1.7344304323196411\n",
      "\tTrain accuracy: global - 59.38% --- average - 90.62%\n",
      "\tDigit0: 68.75%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 100.00%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.8494436740875244\n",
      "\tValidation accuracy: global - 57.98% --- average - 89.32%\n",
      "\tDigit0: 82.48%\n",
      "\tDigit1: 85.72%\n",
      "\tDigit2: 89.34%\n",
      "\tDigit3: 92.86%\n",
      "\tDigit4: 96.20%\n",
      "step 7300:\n",
      "\tTrain loss: 1.9477359056472778\n",
      "\tTrain accuracy: global - 53.12% --- average - 87.50%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 96.88%\n",
      "\tDigit3: 81.25%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 1.9291298389434814\n",
      "\tValidation accuracy: global - 56.80% --- average - 88.90%\n",
      "\tDigit0: 82.80%\n",
      "\tDigit1: 84.06%\n",
      "\tDigit2: 89.08%\n",
      "\tDigit3: 92.74%\n",
      "\tDigit4: 95.82%\n",
      "step 7400:\n",
      "\tTrain loss: 1.3818159103393555\n",
      "\tTrain accuracy: global - 65.62% --- average - 92.50%\n",
      "\tDigit0: 87.50%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 100.00%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.9828921556472778\n",
      "\tValidation accuracy: global - 56.38% --- average - 88.83%\n",
      "\tDigit0: 81.70%\n",
      "\tDigit1: 84.56%\n",
      "\tDigit2: 89.00%\n",
      "\tDigit3: 92.76%\n",
      "\tDigit4: 96.12%\n",
      "step 7500:\n",
      "\tTrain loss: 1.4080028533935547\n",
      "\tTrain accuracy: global - 65.62% --- average - 92.50%\n",
      "\tDigit0: 93.75%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 87.50%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.8725239038467407\n",
      "\tValidation accuracy: global - 57.64% --- average - 89.24%\n",
      "\tDigit0: 82.64%\n",
      "\tDigit1: 85.26%\n",
      "\tDigit2: 89.00%\n",
      "\tDigit3: 92.90%\n",
      "\tDigit4: 96.40%\n",
      "step 7600:\n",
      "\tTrain loss: 2.1801977157592773\n",
      "\tTrain accuracy: global - 53.12% --- average - 86.88%\n",
      "\tDigit0: 75.00%\n",
      "\tDigit1: 78.12%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 1.945651888847351\n",
      "\tValidation accuracy: global - 55.70% --- average - 88.64%\n",
      "\tDigit0: 81.78%\n",
      "\tDigit1: 83.04%\n",
      "\tDigit2: 89.50%\n",
      "\tDigit3: 92.44%\n",
      "\tDigit4: 96.44%\n",
      "step 7700:\n",
      "\tTrain loss: 1.637511968612671\n",
      "\tTrain accuracy: global - 62.50% --- average - 90.00%\n",
      "\tDigit0: 81.25%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 96.88%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.9378944635391235\n",
      "\tValidation accuracy: global - 57.00% --- average - 88.94%\n",
      "\tDigit0: 83.00%\n",
      "\tDigit1: 84.82%\n",
      "\tDigit2: 88.10%\n",
      "\tDigit3: 92.88%\n",
      "\tDigit4: 95.88%\n",
      "step 7800:\n",
      "\tTrain loss: 1.411924123764038\n",
      "\tTrain accuracy: global - 62.50% --- average - 91.88%\n",
      "\tDigit0: 87.50%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.898196816444397\n",
      "\tValidation accuracy: global - 57.56% --- average - 89.11%\n",
      "\tDigit0: 82.98%\n",
      "\tDigit1: 85.18%\n",
      "\tDigit2: 89.28%\n",
      "\tDigit3: 91.84%\n",
      "\tDigit4: 96.28%\n",
      "step 7900:\n",
      "\tTrain loss: 2.195880651473999\n",
      "\tTrain accuracy: global - 56.25% --- average - 88.75%\n",
      "\tDigit0: 93.75%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 84.38%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.8856056928634644\n",
      "\tValidation accuracy: global - 57.50% --- average - 89.06%\n",
      "\tDigit0: 83.70%\n",
      "\tDigit1: 84.02%\n",
      "\tDigit2: 89.30%\n",
      "\tDigit3: 92.32%\n",
      "\tDigit4: 95.94%\n",
      "step 8000:\n",
      "\tTrain loss: 1.3206130266189575\n",
      "\tTrain accuracy: global - 68.75% --- average - 91.88%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.8586024045944214\n",
      "\tValidation accuracy: global - 58.52% --- average - 89.38%\n",
      "\tDigit0: 82.98%\n",
      "\tDigit1: 85.38%\n",
      "\tDigit2: 89.32%\n",
      "\tDigit3: 92.96%\n",
      "\tDigit4: 96.28%\n",
      "step 8100:\n",
      "\tTrain loss: 2.9697763919830322\n",
      "\tTrain accuracy: global - 40.62% --- average - 83.12%\n",
      "\tDigit0: 71.88%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 78.12%\n",
      "\tDigit3: 81.25%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 2.14704966545105\n",
      "\tValidation accuracy: global - 54.40% --- average - 87.92%\n",
      "\tDigit0: 82.36%\n",
      "\tDigit1: 83.14%\n",
      "\tDigit2: 87.02%\n",
      "\tDigit3: 91.98%\n",
      "\tDigit4: 95.10%\n",
      "step 8200:\n",
      "\tTrain loss: 1.7171220779418945\n",
      "\tTrain accuracy: global - 65.62% --- average - 90.62%\n",
      "\tDigit0: 90.62%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 90.62%\n",
      "\tValidation loss: 1.9897669553756714\n",
      "\tValidation accuracy: global - 55.62% --- average - 88.41%\n",
      "\tDigit0: 81.90%\n",
      "\tDigit1: 82.76%\n",
      "\tDigit2: 89.12%\n",
      "\tDigit3: 92.42%\n",
      "\tDigit4: 95.84%\n",
      "step 8300:\n",
      "\tTrain loss: 1.5797119140625\n",
      "\tTrain accuracy: global - 53.12% --- average - 87.50%\n",
      "\tDigit0: 87.50%\n",
      "\tDigit1: 75.00%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 87.50%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.8504769802093506\n",
      "\tValidation accuracy: global - 58.04% --- average - 89.26%\n",
      "\tDigit0: 83.32%\n",
      "\tDigit1: 85.04%\n",
      "\tDigit2: 89.48%\n",
      "\tDigit3: 92.42%\n",
      "\tDigit4: 96.04%\n",
      "step 8400:\n",
      "\tTrain loss: 2.048797369003296\n",
      "\tTrain accuracy: global - 56.25% --- average - 88.12%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 75.00%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.854760766029358\n",
      "\tValidation accuracy: global - 58.44% --- average - 89.40%\n",
      "\tDigit0: 83.68%\n",
      "\tDigit1: 84.74%\n",
      "\tDigit2: 89.82%\n",
      "\tDigit3: 92.38%\n",
      "\tDigit4: 96.40%\n",
      "step 8500:\n",
      "\tTrain loss: 1.0099644660949707\n",
      "\tTrain accuracy: global - 71.88% --- average - 93.75%\n",
      "\tDigit0: 87.50%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 100.00%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.8905439376831055\n",
      "\tValidation accuracy: global - 57.96% --- average - 89.34%\n",
      "\tDigit0: 83.50%\n",
      "\tDigit1: 84.98%\n",
      "\tDigit2: 89.16%\n",
      "\tDigit3: 93.20%\n",
      "\tDigit4: 95.86%\n",
      "step 8600:\n",
      "\tTrain loss: 1.4987385272979736\n",
      "\tTrain accuracy: global - 59.38% --- average - 90.62%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.917646050453186\n",
      "\tValidation accuracy: global - 56.42% --- average - 88.79%\n",
      "\tDigit0: 82.88%\n",
      "\tDigit1: 84.20%\n",
      "\tDigit2: 88.44%\n",
      "\tDigit3: 92.36%\n",
      "\tDigit4: 96.06%\n",
      "step 8700:\n",
      "\tTrain loss: 1.8539031744003296\n",
      "\tTrain accuracy: global - 53.12% --- average - 87.50%\n",
      "\tDigit0: 68.75%\n",
      "\tDigit1: 93.75%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.892314076423645\n",
      "\tValidation accuracy: global - 57.74% --- average - 89.21%\n",
      "\tDigit0: 82.76%\n",
      "\tDigit1: 85.60%\n",
      "\tDigit2: 89.24%\n",
      "\tDigit3: 93.14%\n",
      "\tDigit4: 95.30%\n",
      "step 8800:\n",
      "\tTrain loss: 1.7154262065887451\n",
      "\tTrain accuracy: global - 62.50% --- average - 90.62%\n",
      "\tDigit0: 87.50%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.876444697380066\n",
      "\tValidation accuracy: global - 57.34% --- average - 89.21%\n",
      "\tDigit0: 83.34%\n",
      "\tDigit1: 84.36%\n",
      "\tDigit2: 89.56%\n",
      "\tDigit3: 92.44%\n",
      "\tDigit4: 96.34%\n",
      "step 8900:\n",
      "\tTrain loss: 2.125663995742798\n",
      "\tTrain accuracy: global - 43.75% --- average - 85.62%\n",
      "\tDigit0: 65.62%\n",
      "\tDigit1: 93.75%\n",
      "\tDigit2: 81.25%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.8538905382156372\n",
      "\tValidation accuracy: global - 57.88% --- average - 89.32%\n",
      "\tDigit0: 83.14%\n",
      "\tDigit1: 85.50%\n",
      "\tDigit2: 89.10%\n",
      "\tDigit3: 92.70%\n",
      "\tDigit4: 96.14%\n",
      "step 9000:\n",
      "\tTrain loss: 1.3841116428375244\n",
      "\tTrain accuracy: global - 68.75% --- average - 91.88%\n",
      "\tDigit0: 93.75%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.8021012544631958\n",
      "\tValidation accuracy: global - 59.18% --- average - 89.66%\n",
      "\tDigit0: 83.68%\n",
      "\tDigit1: 85.76%\n",
      "\tDigit2: 89.24%\n",
      "\tDigit3: 93.20%\n",
      "\tDigit4: 96.42%\n",
      "step 9100:\n",
      "\tTrain loss: 1.6414536237716675\n",
      "\tTrain accuracy: global - 62.50% --- average - 90.00%\n",
      "\tDigit0: 87.50%\n",
      "\tDigit1: 75.00%\n",
      "\tDigit2: 96.88%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.907913327217102\n",
      "\tValidation accuracy: global - 57.42% --- average - 88.96%\n",
      "\tDigit0: 83.06%\n",
      "\tDigit1: 85.28%\n",
      "\tDigit2: 89.02%\n",
      "\tDigit3: 91.54%\n",
      "\tDigit4: 95.92%\n",
      "step 9200:\n",
      "\tTrain loss: 1.4625263214111328\n",
      "\tTrain accuracy: global - 62.50% --- average - 91.88%\n",
      "\tDigit0: 81.25%\n",
      "\tDigit1: 90.62%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.8026118278503418\n",
      "\tValidation accuracy: global - 58.62% --- average - 89.52%\n",
      "\tDigit0: 83.38%\n",
      "\tDigit1: 85.46%\n",
      "\tDigit2: 89.40%\n",
      "\tDigit3: 93.30%\n",
      "\tDigit4: 96.04%\n",
      "step 9300:\n",
      "\tTrain loss: 2.135082483291626\n",
      "\tTrain accuracy: global - 53.12% --- average - 88.12%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 87.50%\n",
      "\tDigit4: 93.75%\n",
      "\tValidation loss: 1.8519855737686157\n",
      "\tValidation accuracy: global - 57.94% --- average - 89.34%\n",
      "\tDigit0: 83.32%\n",
      "\tDigit1: 84.94%\n",
      "\tDigit2: 88.72%\n",
      "\tDigit3: 93.12%\n",
      "\tDigit4: 96.62%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 9400:\n",
      "\tTrain loss: 1.6285250186920166\n",
      "\tTrain accuracy: global - 68.75% --- average - 91.88%\n",
      "\tDigit0: 84.38%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 96.88%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.811868667602539\n",
      "\tValidation accuracy: global - 58.94% --- average - 89.61%\n",
      "\tDigit0: 83.74%\n",
      "\tDigit1: 85.20%\n",
      "\tDigit2: 89.40%\n",
      "\tDigit3: 93.30%\n",
      "\tDigit4: 96.40%\n",
      "step 9500:\n",
      "\tTrain loss: 1.5364023447036743\n",
      "\tTrain accuracy: global - 53.12% --- average - 89.38%\n",
      "\tDigit0: 68.75%\n",
      "\tDigit1: 87.50%\n",
      "\tDigit2: 96.88%\n",
      "\tDigit3: 93.75%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.853399395942688\n",
      "\tValidation accuracy: global - 58.64% --- average - 89.54%\n",
      "\tDigit0: 83.74%\n",
      "\tDigit1: 85.80%\n",
      "\tDigit2: 89.48%\n",
      "\tDigit3: 92.50%\n",
      "\tDigit4: 96.18%\n",
      "step 9600:\n",
      "\tTrain loss: 1.527566909790039\n",
      "\tTrain accuracy: global - 65.62% --- average - 91.88%\n",
      "\tDigit0: 93.75%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 84.38%\n",
      "\tDigit3: 96.88%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.9618206024169922\n",
      "\tValidation accuracy: global - 57.88% --- average - 89.27%\n",
      "\tDigit0: 83.94%\n",
      "\tDigit1: 85.12%\n",
      "\tDigit2: 88.46%\n",
      "\tDigit3: 92.78%\n",
      "\tDigit4: 96.06%\n",
      "step 9700:\n",
      "\tTrain loss: 2.1058144569396973\n",
      "\tTrain accuracy: global - 53.12% --- average - 87.50%\n",
      "\tDigit0: 78.12%\n",
      "\tDigit1: 78.12%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 90.62%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.9050698280334473\n",
      "\tValidation accuracy: global - 57.38% --- average - 89.14%\n",
      "\tDigit0: 83.42%\n",
      "\tDigit1: 84.36%\n",
      "\tDigit2: 89.64%\n",
      "\tDigit3: 92.94%\n",
      "\tDigit4: 95.36%\n",
      "step 9800:\n",
      "\tTrain loss: 1.7836213111877441\n",
      "\tTrain accuracy: global - 65.62% --- average - 89.38%\n",
      "\tDigit0: 90.62%\n",
      "\tDigit1: 81.25%\n",
      "\tDigit2: 90.62%\n",
      "\tDigit3: 87.50%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.7979199886322021\n",
      "\tValidation accuracy: global - 58.30% --- average - 89.62%\n",
      "\tDigit0: 83.30%\n",
      "\tDigit1: 85.72%\n",
      "\tDigit2: 89.72%\n",
      "\tDigit3: 93.04%\n",
      "\tDigit4: 96.30%\n",
      "step 9900:\n",
      "\tTrain loss: 2.399404287338257\n",
      "\tTrain accuracy: global - 46.88% --- average - 86.25%\n",
      "\tDigit0: 68.75%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 93.75%\n",
      "\tDigit3: 84.38%\n",
      "\tDigit4: 100.00%\n",
      "\tValidation loss: 1.8841472864151\n",
      "\tValidation accuracy: global - 57.62% --- average - 89.11%\n",
      "\tDigit0: 83.20%\n",
      "\tDigit1: 84.62%\n",
      "\tDigit2: 88.54%\n",
      "\tDigit3: 92.94%\n",
      "\tDigit4: 96.26%\n",
      "step 10000:\n",
      "\tTrain loss: 1.7212440967559814\n",
      "\tTrain accuracy: global - 59.38% --- average - 91.25%\n",
      "\tDigit0: 87.50%\n",
      "\tDigit1: 84.38%\n",
      "\tDigit2: 87.50%\n",
      "\tDigit3: 100.00%\n",
      "\tDigit4: 96.88%\n",
      "\tValidation loss: 1.8093773126602173\n",
      "\tValidation accuracy: global - 58.80% --- average - 89.61%\n",
      "\tDigit0: 83.64%\n",
      "\tDigit1: 85.72%\n",
      "\tDigit2: 89.22%\n",
      "\tDigit3: 93.12%\n",
      "\tDigit4: 96.36%\n",
      "\tTest loss: 1.1103163957595825\n",
      "\tTest accuracy: global - 71.62% --- average - 93.39%\n",
      "\tDigit0: 90.34%\n",
      "\tDigit1: 91.02%\n",
      "\tDigit2: 93.16%\n",
      "\tDigit3: 94.73%\n",
      "\tDigit4: 97.71%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 10001\n",
    "train_dir = 'summaries/train/lr=05-xavier'\n",
    "valid_dir = 'summaries/valid/lr=05-xavier'\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    os.makedirs(train_dir, exist_ok=True)\n",
    "    os.makedirs(valid_dir, exist_ok=True)\n",
    "    \n",
    "    train_writer = tf.summary.FileWriter(train_dir, session.graph)\n",
    "    valid_writer = tf.summary.FileWriter(valid_dir, session.graph)\n",
    "    \n",
    "    tf.global_variables_initializer().run()\n",
    "    print('Initialized')\n",
    "    for step in range(num_steps):\n",
    "        offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "        batch_data = train_dataset[offset:(offset + batch_size), :, :, :]\n",
    "        batch_labels = train_labels[offset:(offset + batch_size), :]\n",
    "        feed_dict = {tf_dataset: batch_data, tf_labels: batch_labels, tf_learning_rate: 0.05}\n",
    "        _, t_loss, t_predictions, t_summary = session.run(\n",
    "            [minimize, total_loss, predictions, merged], feed_dict=feed_dict)\n",
    "\n",
    "        if (step % 100 == 0):\n",
    "            print(f'step {step}:')\n",
    "            train_writer.add_summary(t_summary, step)\n",
    "            t_accuracies = accuracy(t_predictions, batch_labels)\n",
    "            printStatistics('Train', t_loss, t_accuracies)\n",
    "\n",
    "            # Validation\n",
    "            feed_dict = {tf_dataset: valid_dataset, tf_labels: valid_labels}\n",
    "            v_loss, v_predictions, v_summary = session.run([total_loss, predictions, merged], feed_dict=feed_dict)\n",
    "            valid_writer.add_summary(v_summary, step)\n",
    "            v_accuracies = accuracy(v_predictions, valid_labels)\n",
    "            printStatistics('Validation', v_loss, v_accuracies)\n",
    "                \n",
    "            \n",
    "    # Test\n",
    "    feed_dict = {tf_dataset: test_dataset, tf_labels: test_labels}\n",
    "    test_loss, test_predictions = session.run([total_loss, predictions], feed_dict=feed_dict)\n",
    "    test_accuracies = accuracy(test_predictions, test_labels)\n",
    "    printStatistics('Test', test_loss, test_accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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