Siamese-network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implement a Siamese network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This lab will go through creating and training a multi-input model. You will build a basic Siamese Network to find the similarity or dissimilarity between items of clothing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nVHTXTZpkdtM"
   },
   "outputs": [],
   "source": [
    "try:\n",
    "# %tensorflow_version only exists in Colab.\n",
    "  %tensorflow_version 2.x\n",
    "except Exception:\n",
    "  pass\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Flatten, Dense, Dropout, Lambda\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "from tensorflow.python.keras.utils.vis_utils import plot_model\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image, ImageFont, ImageDraw\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the Dataset\n",
    "\n",
    "First define a few utilities for preparing and visualizing your dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iSQMl9cZkgDx"
   },
   "outputs": [],
   "source": [
    "def create_pairs(x, digit_indices):\n",
    "    '''Positive and negative pair creation.\n",
    "    Alternates between positive and negative pairs.\n",
    "    '''\n",
    "    pairs = []\n",
    "    labels = []\n",
    "    n = min([len(digit_indices[d]) for d in range(10)]) - 1\n",
    "    \n",
    "    for d in range(10):\n",
    "        for i in range(n):\n",
    "            z1, z2 = digit_indices[d][i], digit_indices[d][i + 1]\n",
    "            pairs += [[x[z1], x[z2]]]\n",
    "            inc = random.randrange(1, 10)\n",
    "            dn = (d + inc) % 10\n",
    "            z1, z2 = digit_indices[d][i], digit_indices[dn][i]\n",
    "            pairs += [[x[z1], x[z2]]]\n",
    "            labels += [1, 0]\n",
    "            \n",
    "    return np.array(pairs), np.array(labels)\n",
    "\n",
    "\n",
    "def create_pairs_on_set(images, labels):\n",
    "    \n",
    "    digit_indices = [np.where(labels == i)[0] for i in range(10)]\n",
    "    pairs, y = create_pairs(images, digit_indices)\n",
    "    y = y.astype('float32')\n",
    "    \n",
    "    return pairs, y\n",
    "\n",
    "\n",
    "def show_image(image):\n",
    "    plt.figure()\n",
    "    plt.imshow(image)\n",
    "    plt.colorbar()\n",
    "    plt.grid(False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now download and prepare our train and test sets. You will also create pairs of images that will go into the multi-input model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ook7lKQakomz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# load the dataset\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
    "\n",
    "# prepare train and test sets\n",
    "train_images = train_images.astype('float32')\n",
    "test_images = test_images.astype('float32')\n",
    "\n",
    "# normalize values\n",
    "train_images = train_images / 255.0\n",
    "test_images = test_images / 255.0\n",
    "\n",
    "# create pairs on train and test sets\n",
    "tr_pairs, tr_y = create_pairs_on_set(train_images, train_labels)\n",
    "ts_pairs, ts_y = create_pairs_on_set(test_images, test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see a sample pair of images below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BhTpANwipLIk"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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T9GjrVPLUooUkrZW0RdKWUdzpYFYHapR79Mt0E9dngfOAC4A9wKeLFoyI4YhYHRGrh1gwzbczM3vNtBJXRDwfEeMR0QA+B1zY22aZWV/NxVNFSSvann4A2Fq0rJnVTA0657N1XJK+BFwCLJU0AtwEXCLpApo5dyfw8RlsY/VlzvUbmf8Po41MPdJg+g1y8y6mjHcxZyOUqKVKdITk5pMcy2z7aGZexsGj0//L6qquby6oeB1XNnFFxFVTvPz5GWiLmVVF3ROXmb2+iP5eMSzDY86b2UQ97uOStEbSk5K2S7ohsdwvShqX9F9z23TiMrPJenRVUdIgcCtwGXA+cJWk8wuW+xPgvjLNc+Iys8l6Vw5xIbA9InZExFHgLuCKKZb7feBuYG+ZjTpxmdkkx3GquPTYnTGtx9qOTZ0JPNv2fKT12mvvJZ1Js6xqfdn2uXP+mPHpzzF24shoV2+dm55socaS8YONhYWxXLlCriQhN+RObvspCwfS+y03HNC+0ROT8fkHu7g0lhlyZ84r/+Pvi4jVifhUH2Ln1v8S+GREjEvlxgFz4jKziaKnVxVHgLPanq8Edncssxq4q5W0lgKXSxqLiK8VbdSJy8wm690B52ZglaRzgeeAK4GrJ7xVxLnHvpd0O/APqaQFTlxmNoVe3c4TEWOS1tG8WjgIbIiIbZKua8VL92u1c+Iys8l62MXXGmh0U8drUyasiPidMtt04jKzifo88kMZTlxmNoGo/mQZTlxmNokTV01EF3U7i555MRnffbRwZGugRD1Tbv6zLuSGrenWkUbx8DAnDB5OrrtoML1fXhxbnIwv3puuf0tqVPwu45nmxGVmtePEZWa10ufRTctw4jKzyZy4zKxuqj6QoBOXmU3iU0UzqxcXoJpZLTlx1UQX43GNP70jGc/VGy0dOpSMdzP9WE5uvK1upcbrGsz8dSwYSNdhDWbmhZu/v7hOLPd32U1dX925ct7MakmNamcuJy4zm8h9XGZWRz5VNLP6ceIys7rxEZeZ1Y8Tl5nVSm9n+ZkR2cQl6SzgTuBngAYwHBG3SDoN+HvgHGAn8FsRkR6Yaq7K1vykx9PKzW2Y00hsPzfe1kDmNzRXQ5arAxtPrJ8bZyw3TlnuZ4uB6Y9jlpvfr+IHJF2pQx1XmcrGMeAPI+LNwLuA6yWdD9wA3B8Rq4D7W8/NbC6IKPfok2ziiog9EfGj1vcHgSdoTqF9BXBHa7E7gPfPVCPNbHYpyj365bj6uCSdA7wD+AGwPCL2QDO5SVrW89aZ2eybSwWokk4A7gY+EREHcn0AbeutBdYCLCR9z56ZVUPVO+dL3b0raYhm0vpCRNzTevl5SSta8RXA3qnWjYjhiFgdEauHWNCLNpvZDFOj3KNfsolLzUOrzwNPRMRn2kIbgWta318DfL33zTOzWRdUvnO+zKnixcCHgcckPdJ67UbgZuDLkj4K7AI+ODNNrL9uyx1SQ8PM9Hv3c/upUgqAoUy5RGNR8a/3zA0UNDdUvRwim7gi4p8pLkS6tLfNMbNKqHviMrPXlzoUoDpxmdlEER5I0MxqqNp5y4nLzCbzqaKZ1UsAPlU0s9qpdt5y4poN3dRhQb6eKTW8y0xPP5ZrWy7ejVwN2eiS4l9v38OR1stTRUlrgFuAQeC2iLi5I/4h4JOtp4eA/xYRP05t04nLzCbp1VVFSYPArcB7gRFgs6SNEfF422LPAP85Il6UdBkwDFyU2q4LiM1sojiOR96FwPaI2BERR4G7aA6J9drbRfxL2yCkDwErcxv1EZeZTdAsQC19xLVU0pa258MRMdz2/Ezg2bbnI6SPpj4K3Jt7UycuM5usfNfovohYnYhPdbvglFlR0ntoJq53597UicvMJjmOI66cEeCstucrgd2T3k96G3AbcFlEvJDbqPu4zGyi3vZxbQZWSTpX0nzgSppDYv07SWcD9wAfjoinymzUR1xm1qF39ypGxJikdcB9NMshNkTENknXteLrgf8JnA78TWtk5bHM6acTVxVkpwjLTOOVMjTDdVy5OrFUDVuuDqsR3U3rNrao+IQiW8c18Do/GenhIIERsQnY1PHa+rbvfw/4vePZphOXmU00FyaENbPXoT4Oy1yGE5eZTVbtvOXEZWaTqVHtc0UnLjObKDieAtS+cOIyswlE9LIAdUY4cZnZZE5c9RAzOOLjq+Pzk/GZnvuwm/duZHppc+Ntpeq4Xmmk98tAZlCoBZl5FUeXdFGLpenXzs0JTlxmVivu4zKzOvJVRTOrmfCpopnVTODEZWY1VO0zRScuM5vMdVxmVj91T1ySzgLuBH6G5gHkcETcIulTwMeAn7QWvbE17o51ODCWHv0pV890uDGUjKfGrep2TsfRRnfr/3R0UWGs2/G2jmT2S6Z8zopEwHi1zxXLHHGNAX8YET+SdCLwsKRvtWJ/ERF/PnPNM7O+qPsRV0TsAfa0vj8o6QmaUw6Z2VxV8cR1XPdESDoHeAfwg9ZL6yQ9KmmDpFML1lkraYukLaMc6aqxZjYLAmhEuUeflE5ckk4A7gY+EREHgM8C5wEX0Dwi+/RU60XEcESsjojVQ/mRvs2s7wKiUe7RJ6WuKkoaopm0vhAR9wBExPNt8c8B/zAjLTSz2RVUvnM+e8Sl5nxBnweeiIjPtL2+om2xDwBbe988M+uLiHKPPilzxHUx8GHgMUmPtF67EbhK0gU08/NO4OMz0sJZooH0pfnkUfFAumTg3adsT8aXzTuQjO8dPCkZv3Rx8Rya3U6yNZQZ3WV+ZviXlxP9IK9kSjW+9+p5yfgb5/9bMv7Xv1zcp3r6bclVreKd82WuKv4zTDmxn2u2zOYk32RtZnUTgIe1MbPa8RGXmdXL3Ljlx8xeTwKijzVaZThxmdlkfayKL8OJy8wmcx9XPXQ1PVkjPfzKHz/w68n40P50PdP4onTbbp6XiOdm2RpPLzAwll59YDSz/pHi+GDm1tXMqDeMnpzeL8v/dfqfaRx5Hd9XG+GrimZWQz7iMrN6CWK8f5MUl+HEZWYTHRvWpsKcuMxssoqXQ3R7D66ZzTFB82JVmUcZktZIelLSdkk3TBGXpL9qxR+V9M7cNp24zGyi6N1AgpIGgVuBy4DzaY4qc37HYpcBq1qPtTQHKU1y4jKzSWJ8vNSjhAuB7RGxIyKOAncBV3QscwVwZzQ9BJzSMd7fJLPax3WQF/d9O77y/9peWgrsm802FJr8GfSubR//Sk8201KdfTZZpdq2Y+LT42vbaG/bktHL/faz3W7gIC/e9+34ytKSiy+UtKXt+XBEDLc9PxN4tu35CHBRxzamWuZMWpP0TGVWE1dEnNH+XNKWiFg9m20oq6ptq2q7wG2brqq1LSLW9HBzU5URd3aOlVlmAp8qmtlMGgHOanu+Etg9jWUmcOIys5m0GVgl6VxJ84ErgY0dy2wEPtK6uvgu4KXWfK6F+l3HNZxfpG+q2raqtgvctumqctu6EhFjktYB9wGDwIaI2CbpulZ8Pc1h4C8HtgOvANfmtquo+D1JZmadfKpoZrXjxGVmtdOXxJW7BaCfJO2U9JikRzrqU/rRlg2S9kra2vbaaZK+Jenp1tdTK9S2T0l6rrXvHpF0eZ/adpak70h6QtI2SX/Qer2v+y7RrkrstzqZ9T6u1i0ATwHvpXkZdDNwVUQ8PqsNKSBpJ7A6IvpeSCnpl4FDNKuKf7712p8C+yPi5lbSPzUiPlmRtn0KOBQRfz7b7elo2wpgRUT8SNKJwMPA+4HfoY/7LtGu36IC+61O+nHEVeYWAAMi4kFgf8fLVwB3tL6/g+Yv/qwraFslRMSeiPhR6/uDwBM0K7H7uu8S7bLj1I/EVVTeXxUBfFPSw5LW9rsxU1h+rMal9XVZn9vTaV3rDv8N/TqNbSfpHOAdwA+o0L7raBdUbL9VXT8S13GX98+yiyPinTTvWL++dUpk5XwWOA+4gOZ9Zp/uZ2MknQDcDXwiIg70sy3tpmhXpfZbHfQjcR13ef9siojdra97ga/SPLWtkueP3Tnf+rq3z+35dxHxfESMR3NSvs/Rx30naYhmcvhCRNzTernv+26qdlVpv9VFPxJXmVsA+kLSklanKZKWAL8KbE2vNes2Ate0vr8G+Hof2zJBx1AkH6BP+06SgM8DT0TEZ9pCfd13Re2qyn6rk75Uzrcu9/4lr90C8Eez3ogpSHoDzaMsaN4O9cV+tk3Sl4BLaA578jxwE/A14MvA2cAu4IMRMeud5AVtu4Tm6U4AO4GP5+45m6G2vRv4LvAYcGy0uxtp9if1bd8l2nUVFdhvdeJbfsysdlw5b2a148RlZrXjxGVmtePEZWa148RlZrXjxGVmtePEZWa18/8B8RgK9zpRdZsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# array index\n",
    "this_pair = 8\n",
    "\n",
    "# show images at this index\n",
    "show_image(ts_pairs[this_pair][0])\n",
    "show_image(ts_pairs[this_pair][1])\n",
    "\n",
    "# print the label for this pair\n",
    "print(ts_y[this_pair])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lbgAYQW0zT_4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print other pairs\n",
    "\n",
    "show_image(tr_pairs[:,0][0])\n",
    "show_image(tr_pairs[:,0][1])\n",
    "\n",
    "show_image(tr_pairs[:,1][0])\n",
    "show_image(tr_pairs[:,1][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the Model\n",
    "\n",
    "Next, you'll define some utilities for building our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wMo2HbKLkuAa"
   },
   "outputs": [],
   "source": [
    "def initialize_base_network():\n",
    "    input = Input(shape=(28,28,), name=\"base_input\")\n",
    "    x = Flatten(name=\"flatten_input\")(input)\n",
    "    x = Dense(128, activation='relu', name=\"first_base_dense\")(x)\n",
    "    x = Dropout(0.1, name=\"first_dropout\")(x)\n",
    "    x = Dense(128, activation='relu', name=\"second_base_dense\")(x)\n",
    "    x = Dropout(0.1, name=\"second_dropout\")(x)\n",
    "    x = Dense(128, activation='relu', name=\"third_base_dense\")(x)\n",
    "\n",
    "    return Model(inputs=input, outputs=x)\n",
    "\n",
    "\n",
    "def euclidean_distance(vects):\n",
    "    x, y = vects\n",
    "    sum_square = K.sum(K.square(x - y), axis=1, keepdims=True)\n",
    "    return K.sqrt(K.maximum(sum_square, K.epsilon()))\n",
    "\n",
    "\n",
    "def eucl_dist_output_shape(shapes):\n",
    "    shape1, shape2 = shapes\n",
    "    return (shape1[0], 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how our base network looks. This is where the two inputs will pass through to generate an output vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8FjSLg_LoJAy"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_network = initialize_base_network()\n",
    "plot_model(base_network, show_shapes=True, show_layer_names=True, to_file='base-model.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now build the Siamese network. The plot will show two inputs going to the base network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qe4YNz0kkwq5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zMzNRVlam6efv7y/MzMzEtWvXRGlpqUhJSREDBw4U5ubm4s6dO5p+b7/9trCzs9OKu27dOgFA5OTk1HkcBw4cEObm5iI0NPSZx/Laa68JACI/P18vj0UIIVxcXISFhcUzj0UIIeLj48Xy5ctFXl6eePjwoRg0aJDW/N1Tp04V7dq1E3fv3tV63VtvvSX27dun+fez3ldV5ygwMFBERkaKN954Q1y/fv25ctSHdVpaGn1YT4B0Qx/Xaant97mma/2qVatEu3btRGJioiguLhY///yzsLOzE6+++mqN+zxx4oQoKCgQeXl5Yty4ccLY2Fg8fvxY04+16CnWoqeasxbxutow9b2+kH5q9nVaSktLERUVhSlTpmDq1KmwtLTEkiVLYGhoiB07djRFyGfy9PSEUqmEjY0N/Pz88PjxY9y5c0erj1wuh5ubG4yNjeHu7o6oqCioVCqd5Tx+/HgUFhZi6dKljdqPPhxLfXl5eeGTTz6BlZUVrK2tMXHiRDx8+BA5OTkAnq6WW1FRoZVfYWEhzp8/j3HjxgGo3/sqLCwMH374Ifbs2YM//elPzXegRKRzz/P7nJCQgJdffhkTJ06EiYkJXnrpJUyaNAknT57UrL/we56enrCwsICVlRX8/Pzw5MkT/Prrr019KHpx/WYtYi0iaokaPXtYTW7evIni4mL07t1b02ZiYoJOnTrhxo0bTRGyXoyMjAAAarW6zn4DBgyAqampXuRcm5Z6LFXPMlUtXjV8+HD07NkTX331FUJCQiCTyRATEwM/Pz+0a9cOQPO8r3bv3v3Me92pOp6z1sHLy0vqFBqstLQUCoVCq62iogKGhoaaa0htqq5Hz7qO6lpLvX7XpKUei77WIoDX1Ybw9fWFr6+v1GlQE2mSQcvjx48BAEuWLMGSJUu0ttnb2zdFyCZjbGys+QSmpZPyWA4ePIh169YhJSUFhYWF1QqbTCbDrFmz8NFHH+HEiRMYOXIk/vnPf+K7777T9GmO99WgQYMwd+5cneyrLThz5gzCw8M193JTy7VhwwapU2iUcePGYd26dUhMTMTo0aORkpKChIQEvP76688ctLQErEW60VJqEQBeV+vJ19cXQUFB8PDwkDoVaoSqvytq0iSDFhsbGwBPi2BQUFBThGgWarUaBQUF6NKli9SpNFpzH8vJkyfx888/Y+7cubhz5w6mTJmCN954A1999RU6d+6MyMhILFiwQOs106ZNQ0hICLZt24auXbtCqVSiW7dumu3N8b7q0qVLtYd2qW7h4eE8Z61AfHy81Ck0yvLly/Hzzz9j2rRpKCoqgr29PXx8fJp8TZbmwFrUcC21FgHgdbWefH194eHhwfPWCjTroKVr165QKBS4dOlSU+y+2SQnJ0MIgUGDBmna5HJ5s99CoAvNfSw///wzzMzMAABXr16FWq3GnDlz4OzsDKDmr72trKzg6+uLmJgYmJub4/3339fa3lreV0SkeykpKUhPT0dOTg7k8iYpbZJhLWo41iKi1qNJHsRXKBSYPn06du3ahaioKBQWFqKiogKZmZm4d+9ena+1trZGVlYWbt++DZVK1awX5crKSuTn56O8vBxXrlxBUFAQHB0dMW3aNE0fV1dX5OXlISEhAWq1Gjk5Ofjtt9+e6zgOHz7c4Gkm9e1YaqNWq/HgwQMkJydrCoWjoyMA4Pjx4ygtLUVqaqrWlJe/N3v2bDx58gQHDhyotjBbY95XRNS6ffjhh3B0dERRUZFO9sda1DKOpTasRUStUD2mGqvRF198Iezs7AQAYWZmJt544w0hhBBPnjwRCxcuFI6OjkIulwsbGxsxdepUkZKSIm7fvi369+8vAAi5XC5eeuklsXv3biGEEBcuXBDdunUTJiYmYvDgweL+/fvPlUdkZKTo1KmTACBMTU3FxIkTxaZNm4SpqakAIHr06CHS09PF1q1bhVKpFABEt27dxH//+18hxNOpGQ0NDYWDg4OQy+VCqVSKyZMni/T0dK04Dx8+FMOGDRMKhUI4OTmJv/3tb2L+/PkCgHB1ddVM41jTcRw6dEiYm5uLlStX1nocP/74o3jhhReEgYGBACA6deokVq1apVfH8uWXXwoXFxcBoM6fvXv3amItXLhQWFtbC0tLS+Ht7S02btwoAAgXFxetqS+FEKJ///4iODi4xvNT1/tqzZo1wsTERAAQXbt2Fd98883zvHU0OOVx/XFqztZD36Y8ru33uaZrvRBCJCUliQ4dOmhdgwwNDYWbm5vYs2dPtX1WXUe//fZbYWVlJQCILl26iF9++UUIwVrEWiRdLeJ1tWHqc30h/VXXlMc6WaelNfD39xfW1tZSp6ETLf1Yxo0bJ27dutXscTloqb+2er1ojfRt0FJfmzZtEkFBQVptT548EXPnzhXGxsaiuLi4SeLqWku/fv9eSz8WqWoRr6sNw0FL61DXoKV13fjbSFVTHrYGLelY1Gq1ZtrJK1euQKFQwMnJSeKsiKiluH//PgICAqo9Y2BkZARHR0eo1Wqo1WqYmJhIlGH9tKTr97O0pGNhLSLSb03yTIuu3LhxAzKZ7Jk/fn5+UqdKjbBw4UKkpqbiv//9L6ZPn45PP/1U6pSoCc2aNUvr9/edd96p1uf48eMIDg5GZWUlpkyZAkdHRygUCjg4OGDSpEm4cuVKveOGhobC3d0dSqUSxsbGcHV1xYIFC2p8BmLnzp0YOHAgzM3N0a1bN0yfPh33799v0PHqMu6+ffuwZs2aan8IJiQkaJ3Tjh07NijXlsrExASGhobYvn07Hjx4ALVajaysLGzbtg3Lli2Dn58flEplg/fPWtQ2sBa1LaxFLbAW1eNrmVYrODhYGBkZCQCie/fuIj4+XuqUGqwlHsvixYuFgYGB6Nq1q9i3b59kefD2sPpryPWi6paRw4cPi5s3b4rS0lKt7cuWLRMTJkwQhYWFQq1Wiw4dOohTp06Jx48fi1u3bolRo0YJCwsLcffu3XrFHTp0qNi0aZN4+PChKCwsFLGxscLQ0FCMGTNGq19MTIwAINasWSMKCgrExYsXhbOzs+jXr59Qq9X1itkUccPDw8XQoUNFfn6+pq2yslJkZmaKkydPinHjxokOHTrUO8+WfnvYyZMnxciRI4VSqRTt2rUTFhYWwtPTU2zatKlB/29SaInX79q0xGPRl1rUFv8O04X6Xl9Yi/SzFvGZFqLnoA+DluLiYuHh4dFiYjR00OLg4FDjts8++0z07NlTlJSUCCGEUKvV4vXXX9fqc+7cOQFArFq1ql5xx48fL8rLy7XafHx8BACtB3CHDRsmOnfuLCorKzVtVQ/rnj59ul4xmypuQECA8PDwqLFwBQYGtslBC1FroQ9/h7W0WiREwwYtrEX6V4vqGrTo9e1hRG3N9u3bkZ2d3eJjNERaWhqWLl2KFStWQKFQAHi6fsP+/fu1+lWtr5Cenl6v/R84cKDayuhVX10XFxdr2jIyMmBvb6+1fkPXrl0BoMZpWKWIu3z5cly6dKnWBbiIiBqDtYi1SB9rEQctRI0ghMD69evh5uYGY2NjWFlZYfLkybhx44amT0BAAIyMjNCpUydN2wcffAAzMzPIZDLk5uYCAIKCgjBv3jykp6dDJpPB1dUVERERUCgUsLW1xaxZs2Bvbw+FQgFPT0+t9QUaEwMAjhw50mzrNtQmIiICQghMnDixzn4lJSUA0KhnFKrcvXsXJiYmWg/bOjs7VyukVffyVhUpqeNaWVlh6NChCA8PhxBCJzkRUcvFWqQ7rEV6XIvq8bUMUavWkNtjli1bJoyMjMQ333wjCgoKxJUrV8RLL70kOnbsqLWuw9tvvy3s7Oy0Xrtu3ToBQOTk5Gjapk6dKlxcXLT6+fv7CzMzM3Ht2jVRWloqUlJSxMCBA4W5ubnWV7qNiXHgwAFhbm4uQkND63X8urw9zNnZWbi7uz/z9Xv27BEANGs7NdTjx4+Fubm5CAgI0GpPTk4WhoaGIiIiQhQWFopffvlFuLm5iddee61R8XQdNzg4WAAQFy9e1Grn7WFELVtDrqttvRYJobvbw1iLpK1FvD2MqAmUlJRg/fr1eOONN/DOO+/AwsICffr0webNm5Gbm4utW7fqLJZcLtd8gubu7o6oqCioVCrs2LFDJ/sfP348CgsLsXTpUp3sr74eP36MX3/9FS4uLrX2efDgAWJiYhAYGAgPD49nfgr2LKtXr4a9vT1Wrlyp1T506FAsXLgQAQEBUCqV6N27N1QqFbZt29aoeLqO26NHDwDA1atXdZIXEbVMrEW6w1qk37WIgxaiBkpJSUFRUREGDBig1T5w4EAYGRlpfWWuawMGDICpqanWV/8tWXZ2NoQQMDU1rbWPh4cHAgMDMXnyZBw+fFiznkJD7N27F3FxcTh69CjMzc21ti1evBhbt27FiRMnUFRUhFu3bsHT0xMeHh7IyMhocExdx606Vw8ePGhUTkTUsrEW6Q5rkX7XIg5aiBqooKAAANC+fftq2ywtLaFSqZo0vrGxMXJycpo0RnMpLS0F8PSYamNra4ukpCRERkbCwsKiwbFiYmIQFhaG5ORkdO/eXWvbvXv3sGbNGsycORPDhw+HmZkZnJycEB0djaysLKxbt05v4lYtlFh17oiobWIt0h3WIv2uRfImj0DUSllaWgJAjQWhoKAAXbp0abLYarW6yWM0p6qLXl2rZ9vY2GjOeUNFRkbi6NGjSEpKqrHAp6amoqKiAp07d9ZqVyqVsLa2RkpKit7ELSsrA4AWs8o7ETUN1iLdYS3S71rEQQtRA/Xu3Rvt27fHTz/9pNV+9uxZlJWV4eWXX9a0yeVyqNVqncVOTk6GEAKDBg1qshjNydbWFjKZDI8ePaq1zx+nm6wPIQQWLVqE/Px8JCQkQC6v+dJXVXjv3bun1a5SqZCXl6eZ9lEf4ladKzs7u3rlREStC2uR7rAW6Xct4u1hRA2kUCgwb9487N27F99++y0KCwtx9epVzJ49G/b29vD399f0dXV1RV5eHhISEqBWq5GTk1PjPOvW1tbIysrC7du3oVKpNBf+yspK5Ofno7y8HFeuXEFQUBAcHR0xbdo0ncQ4fPiwpNNMmpqawtnZGZmZmTVuT0tLg52dHXx9fatt8/Pzg52dHS5cuFDr/q9du4a1a9ciOjoahoaGkMlkWj+ff/45AMDJyQnDhg1DdHQ0Tp48iZKSEmRkZGj+L2fMmCF53CpV56pPnz61xiei1o+1SHdYi/S7FnHQQtQIn3zyCVavXo3Q0FB07NgRQ4cORffu3ZGcnAwzMzNNvzlz5mDYsGF488030atXL3z66aear1J//3Db7NmzYWtrC3d3d4wbNw55eXkAnt4r2qdPH5iYmGDIkCHo2bMnvv/+e637bhsbQ2rjx49HSkqKZu773xN1zP9eVlaG7OxsJCYm1tqnrtf/nkwmQ3x8PPz8/DBjxgxYWVnB3d0dd+7cwZ49ezBkyBDJ41Y5f/48HBwc0Ldv3+eKQUStF2uR7rAW6XEtqsf8yEStWnOsU9EQ/v7+wtraWuo0aqTLdVpSU1OFXC4X33zzTb32V1FRIYYMGSK2b99er9c1llRxhRAiNzdXKBQK8fnnn1fbxnVaiFo2ff07TJ9rkRC6W6eFtej5NUUt4jotRC1cXQ8FtkQlJSU4evQoUlNTNQ/xubq6IjQ0FKGhoSgqKnqu/VRUVCAhIQEqlQp+fn5NmbJexK2yfPly9OvXDwEBAQCefoqWlZWF06dPIy0trdnzIaK2gbWoZqxFzVOLOGghomaXl5eHMWPGoGfPnnj33Xc17cHBwfD29oafn1+dD0JWSU5Oxp49e3D48OE659XXNaniAsD69etx6dIlHDp0SLM+QGJiIhwcHDBkyBAcPHiwWfMhImqpWIsaTopaxEELkR4LCQnBjh078OjRIzg5OWH37t1Sp9RomzdvhhBC8/Ptt99qbV+1ahUCAgLw2WefPXNfI0aMwHfffYdOnTo1Vbp6FTcxMRFPnjxBcnIyrKysNO2TJ80GUjwAACAASURBVE/WOqe5ubnNmhcRtW6sRXVjLXqqqWsRpzwm0mOrV6/G6tWrpU6j2Y0ePRqjR4+WOg29M2nSJEyaNEnqNIiojWEtot+TqhbxmxYiIiIiItJrHLQQEREREZFe46CFiIiIiIj0GgctRERERESk12p9ED8uLq458yCSXGZmJoC28d4XQkAmkzV6P2fOnAHQNs5Za5eZmYkuXbo0eZyq9wzRs+jqOtXS8LracLy+tHx1/R/KhBDi9w1xcXHw9fVt8qSIiEi/eHl5IT4+vsn23xb/ACUiovr7w/AEAOKrDVqIqHWrrKzEjh07sHz5cuTn52Pu3LmYP38+lEql1KkRURuWkpKCkJAQ7Nu3D6+++irWrl2LgQMHSp0WEemHeD7TQtTGGBgYYMaMGUhPT8f69euxdetWODk5Yc2aNSgtLZU6PSJqYzIzM+Hv748XX3wR6enpiIuLw/fff88BCxFp4TctRG2cSqVCVFQUVq1aBUtLSyxZsgQzZsxAu3btpE6NiFqxvLw8rF27FhEREbCxscHixYt57SGi2vD2MCJ66u7duwgNDcVXX32FHj16YMWKFfDy8uJzCESkU8XFxYiMjERYWBjkcjk+/vhjBAUFwdjYWOrUiEh/8fYwInrKwcEBW7ZswS+//ILevXvD19cXnp6e+Pe//y11akTUCqjVamzduhU9evTAp59+Cn9/f6Snp2PhwoUcsBDRM3HQQkRaevXqhbi4OJw9exampqZ49dVXMWrUKFy+fFnq1IioBRJCID4+Hi+88AI+/PBDvP7660hLS0NYWBgnACGi58ZBCxHVaODAgThx4gSOHTuGvLw8vPTSS/Dx8cGtW7ekTo2IWojjx4/jz3/+M/z8/NCvXz9cv34dW7ZsQadOnaROjYhaGA5aiKhOI0eOxE8//YSYmBhcvHgRbm5u8Pf3x4MHD6ROjYj01C+//AIfHx+MGjUKlpaW+OmnnxAXFwcXFxepUyOiFoqDFiJ6JplMBm9vb1y7dg2RkZHYv38/XF1dsWjRIhQWFkqdHhHpid9++00zffFvv/2GpKQkHDt2DP3795c6NSJq4ThoIaLnZmhoiJkzZyI1NRVLlizB5s2b4eLigjVr1uDJkydSp0dEEsnJycGiRYvQq1cvnDx5EjExMfjxxx8xbNgwqVMjolaCUx4TUYM9fPgQ69atw9///nfY2dkhJCQE7733HgwM+HkIUVtQVFSETZs2YdWqVbCwsMDSpUvx7rvvQi6XS50aEbUuXKeFiBovIyMDK1euxPbt2+Hm5oZly5bB29tb6rSIqImUlZXh66+/xtKlS1FeXo4FCxYgICAAJiYmUqdGRK0T12khosbr2rUrtmzZgitXrsDNzU3zAO5PP/0kdWpEpEOVlZWIj4+Hm5sb5s6di+nTp2vWWuGAhYiaEgctRKQz7u7uiIuLww8//ICysjL8+c9/ho+PD/773/9KnRoRNdLx48fx0ksv4a233sLIkSORmpqKsLAwWFpaSp0aEbUBHLQQkc55eHjg3//+N/71r3/h5s2beOGFF+Dv74+srCypUyOievrxxx81i8za2Njg4sWL2LJlCzp37ix1akTUhnDQQkRNZuTIkbh48SJ27tyJY8eOoUePHli0aBEKCgqkTo2InuH69evw8fGBh4cHysvLcerUKRw7dgy9e/eWOjUiaoM4aCGiJmVgYABvb2/cuHEDGzZswI4dOzTTJJeUlEidHhH9QUZGBvz9/dGnTx9cv34dcXFxOH36NAYPHix1akTUhnH2MCJqVpwilUg/5eXlYe3atZzCnIj0Eac8JiJp5OTk4IsvvsCGDRvg7OyM0NBQeHl5QSaTSZ0aUZtSXFyMyMhIhIWFQS6X4+OPP0ZQUBCMjY2lTo2IqAqnPCYiadjY2CAsLAw3b97EwIED4evrCw8PDyQnJ0udGlGboFarsXXrVri6umLlypXw9/fXTF/MAQsR6RsOWohIUt27d8c///lPnDt3Du3bt8ewYcMwatQoXLp0SerUiFolIQTi4+Pxwgsv4G9/+xsmTJiAtLQ0hIWFQalUSp0eEVGNOGghIr0wYMAAHD9+HMeOHUN+fj5efvll+Pj44NatW1KnRtRqHD9+HAMHDoSfnx/69euH69evY8uWLbCzs5M6NSKiOnHQQkR6ZeTIkTh//jxiYmJw6dIluLm5wd/fHw8ePJA6NaIW6/z58xg5ciRGjRoFKysr/Pzzz4iLi4Ozs7PUqRERPRcOWohI78hkMnh7eyMlJQWRkZHYv38/XF1dsWjRIhQWFkqdHlGLcfv2bfzf//0f/vKXv6CoqAjff/89jh07hn79+kmdGhFRvXDQQkR6y9DQEDNnzkRaWhqWLFmCLVu2aNZ4efLkidTpEemtnJwcLFq0CL169cL58+cRGxuLM2fO4NVXX5U6NSKiBuGUx0TUYnAdCaK6cR0kImqluE4LEbU8GRkZWLlyJbZv3w43NzcsW7YM3t7eUqdFJJmysjJ8/fXXWLp0KcrLy7FgwQIEBATAxMRE6tSIiHSB67QQUcvTtWtXbNmyBVevXoWbmxt8fHwwePBgnD59WurUiJpVZWUl4uPj8ac//Qlz587F9OnTNWutcMBCRK0JBy1E1GK5ubkhLi4OZ86cgVwux5AhQzBq1Cj88ssvUqdG1OSOHz+O/v374+2338aoUaOQmpqKsLAwWFpaSp0aEZHOcdBCRC3eoEGDkJycjGPHjiEnJwf9+/eHv78/srKypE6NSOfOnDmDoUOHYvTo0ejVqxdSUlKwZcsWdO7cWerUiIiaDActRNRqjBw5EhcuXMDOnTtx/Phx9OjRA4sWLUJBQYHUqRE12rVr1+Dj4wNPT08YGRnh3LlziIuLQ48ePaROjYioyXHQQkStioGBAby9vXH9+nVs2LABO3bs0EyTXFJSInV6RPWWkZEBf39/9O3bF9evX0dcXByOHTuGAQMGSJ0aEVGz4exhRNSqcQpYaqkePnyIdevWcYpvIiJOeUxEbUVOTg6++OILhIeHw8nJCaGhofDy8oJMJpM6NSItjx8/xsaNG/HZZ5/B0NAQH3/8MYKCgmBsbCx1akREUuGUx0TUNtjY2CAsLAw3b97EK6+8Aj8/P3h4eOD777+XOjUiAIBarcbWrVvRo0cPrFy5ErNmzdJMX8wBCxG1dRy0EFGb0q1bN2zZsgWXL1+GjY0Nhg8fjlGjRuHixYtSp0ZtlBAC8fHxcHd3x9/+9jdMmDABaWlpCAsLg1KplDo9IiK9wEELEbVJvXv3xv79+3Hs2DHk5+djwIAB8PHxQXp6+nPvg3fXUk3q8744fvw4BgwYAD8/P/Tv3x/Xr1/Hli1bYGdn14QZEhG1PBy0EFGbNnLkSJw/fx4xMTG4dOkS3Nzc4O/vj/v379f5ukOHDiEwMLCZsqSWorCwEGPGjIFKpaqz3/nz5zFixAiMGjUK1tbWuHDhAuLi4uDs7NxMmRIRtSwctBBRmyeTyeDt7Y2UlBRs3LgR+/fvh6urKxYtWoTCwsJq/SsrKzF//nxERkZi7dq1EmRM+qikpARjx47Fv/71L/z973+vsc/Nmzfh4+ODv/zlLyguLtYsivriiy82c7ZERC0LBy1ERP8/Q0NDzJw5E2lpaVi6dCm2bNmiWePlyZMnmn67du3C9evXAQCLFi3Cjh07pEqZ9ER5eTm8vb1x7tw5AMBnn32G3Nxczfa7d+/C398fvXv3xi+//ILY2Fj88MMPGDp0qFQpExG1KO2WL1++XOokiIj0iaGhIQYPHoyZM2eipKQEa9euxVdffQUzMzO4u7tjypQpUKlUmmcX9u/fj969e8Pd3V3izEkKQgi899572Lt3LyoqKjRtT548wf/7f/8P4eHh8PX1RWZmJtasWYPo6Gj07t2b020TET2/a1ynhYjoGe7cuYNPPvkE33zzDZydnXHr1i3NH6fA09vL2rVrh0OHDmHUqFESZkpSmD9/PtavX4/Kykqt9nbt2sHc3BxyuRwhISGYM2cOpy4mImoYLi5JRPS8zp8/j+HDh6OoqKjaNgMDAygUCpw6dQovvfSSBNmRFMLCwhAcHFzjNkNDQ/Tv3x//+te/YGFh0cyZERG1KlxckojoeR08eBClpaU1bqusrERZWRlGjhyJmzdvNnNmJIV//vOfCAkJqXW7Wq3G+fPncfv27eZLioioleI3LUREzyEnJwfdu3dHcXFxnf0MDQ3RoUMHnDt3Dl27dm2m7Ki57du3D1OmTKl2S9gfGRoaYvTo0Thw4EAzZUZE1CrxmxYiouexcuXKWr9l+T21Wo3c3Fy89tprKCgoaIbMqLklJyfD29v7ufqq1WocPHgQ//nPf5o4KyKi1o2DFiKiZ3j48CF27dql+VRdLpfDyMio1v7l5eVIS0vD2LFjUVJS0lxpUjO4ePEixo8fj/Ly8lq/ZTEwMICxsTEMDP5XYjdt2tRcKRIRtUq8PYyI6Dk9ePAA169fx82bN3Hjxg2kpKTg2rVryMrKghACMpkMRkZGKC8v18wuNmbMGOzbtw+GhoYSZ0+NlZqaikGDBiE/Px9CCMjlcshkMqjVagBPbwXr3r07+vbtC3d3d7i5uaFXr17o1asXzMzMJM6eiKhF4+xhRK2Vt7c3du/eLXUaREQ6FxsbCx8fH6nTIKLmEy+XOgMiajqDBg3C3LlzpU6jzRJCIDc3F/fu3YODgwM6dOggSR6+vr4ICgqCh4eHJPFbog0bNgCA5vcnNTUVQgh07twZ7du3lzK1Ns/X11fqFIhIAhy0ELViXbp04aeRBF9fX3h4ePC9UA/x8fEAwHOmhzhoIWqb+CA+ERERERHpNQ5aiIiIiIhIr3HQQkREREREeo2DFiIiIiIi0msctBARERERkV7joIWIiJ7LoUOHYGFhgf3790udit47fvw4goODUVlZiSlTpsDR0REKhQIODg6YNGkSrly5Uu99hoaGwt3dHUqlEsbGxnB1dcWCBQtQVFRUre/OnTsxcOBAmJubo1u3bpg+fTru37/foGPRZdx9+/ZhzZo1msVXiYieFwctRET0XLgW8fP55JNPEBERgZCQEFRWVuLUqVPYuXMn8vLycPr0aZSUlOCVV15BVlZWvfablJSEDz/8ELdv30Zubi5Wr16N8PBweHt7a/WLjY3F22+/DW9vb2RmZiIxMREnT57E2LFjUV5eXu/j0WXciRMnQqFQYMSIESgoKKh3LkTUdnHQQkREz2X8+PF49OgRJkyYIHUqKCkpgaenp9RpVBMWFoaYmBjExcXB3NwcAODh4YHBgwfD1NQUTk5OWLVqFR49eoSvv/66Xvtu3749/P39YW1tDXNzc/j4+GDKlCk4cuQIMjIyNP22bNmCzp07Y/78+bCwsEC/fv3w0Ucf4dKlSzh79my9j0nXcQMDA/Hiiy9i3LhxDRpEEVHbxEELERG1ONu3b0d2drbUaWhJS0vD0qVLsWLFCigUCgCAXC6vdjuds7MzACA9Pb1e+z9w4ADatWun1daxY0cAQHFxsaYtIyMD9vb2kMlkmrauXbsCAH777bd6xWyquMuXL8elS5cQHh5e73yIqG3ioIWIiJ7p9OnTcHR0hEwmw8aNGwEAUVFRMDMzg6mpKRITEzF27FgolUp06dIFu3bt0rw2IiICCoUCtra2mDVrFuzt7aFQKODp6an1CXxAQACMjIzQqVMnTdsHH3wAMzMzyGQy5ObmAgCCgoIwb948pKenQyaTwdXVFQBw5MgRKJVKrFq1qjlOSTUREREQQmDixIl19ispKQEAKJXKRse8e/cuTExM4OTkpGlzdnauNqCreq6kasAkdVwrKysMHToU4eHhvO2QiJ4LBy1ERPRMgwcPxg8//KDVNmfOHMydOxclJSUwNzdHbGws0tPT4ezsjPfffx9qtRrA08HItGnTUFxcjMDAQNy+fRsXLlxAeXk5Ro0apbnFKCIiAj4+PloxNm3ahBUrVmi1hYeHY8KECXBxcYEQAmlpaQCgebi7srKySc7Bsxw8eBC9evWCqalpnf3OnTsH4Ok5bYzi4mIkJSXh/fffh5GRkaY9JCQE9+/fR2RkJFQqFVJSUhAeHo7XXnsNgwYNalRMXcbt378/7t69i8uXLzc6JyJq/ThoISKiRvP09IRSqYSNjQ38/Pzw+PFj3LlzR6uPXC6Hm5sbjI2N4e7ujqioKKhUKuzYsUMnOYwfPx6FhYVYunSpTvZXH48fP8avv/4KFxeXWvs8ePAAMTExCAwMhIeHxzO/kXmW1atXw97eHitXrtRqHzp0KBYuXIiAgAAolUr07t0bKpUK27Zta1Q8Xcft0aMHAODq1as6yYuIWjcOWoiISKeqPn2v+qalNgMGDICpqSlu3LjRHGk1qezsbAgh6vyWxcPDA4GBgZg8eTIOHz4MQ0PDBsfbu3cv4uLicPToUc0D/1UWL16MrVu34sSJEygqKsKtW7fg6ekJDw8PrQfnpY5bda4ePHjQqJyIqG3goIWIiCRjbGyMnJwcqdNotNLSUgBPj6c2tra2SEpKQmRkJCwsLBocKyYmBmFhYUhOTkb37t21tt27dw9r1qzBzJkzMXz4cJiZmcHJyQnR0dHIysrCunXr9CauiYkJgP+dOyKiusilToCIiNomtVqNgoICdOnSRepUGq3qD/C6Fk20sbGBpaVlo+JERkbi6NGjSEpKQvv27attT01NRUVFBTp37qzVrlQqYW1tjZSUFL2JW1ZWBuB/546IqC4ctBARkSSSk5MhhNB6SFsulz/ztjJ9ZGtrC5lMhkePHtXa549TH9eHEAKLFi1Cfn4+EhISIJfXXL6rBoD37t3TalepVMjLy9NMQawPcavOlZ2dXb1yIqK2ibeHERFRs6isrER+fj7Ky8tx5coVBAUFwdHREdOmTdP0cXV1RV5eHhISEqBWq5GTk1Pj2iLW1tbIysrC7du3oVKpoFarcfjwYcmmPDY1NYWzszMyMzNr3J6WlgY7Ozv4+vpW2+bn5wc7OztcuHCh1v1fu3YNa9euRXR0NAwNDSGTybR+Pv/8cwCAk5MThg0bhujoaJw8eRIlJSXIyMiAv78/AGDGjBmSx61Sda769OlTa3wioioctBAR0TNt3LgRAwcOBAAsXLgQkyZNQlRUFDZs2AAA6Nu3L27duoXo6GjMmzcPADBmzBikpqZq9lFaWoo+ffrAxMQEQ4YMQc+ePfH9999rPQcyZ84cDBs2DG+++SZ69eqFTz/9VHP70O8f6J49ezZsbW3h7u6OcePGIS8vr1nOQ13Gjx+PlJQUzTosv1fXWiRlZWXIzs5GYmJirX2edy0TmUyG+Ph4+Pn5YcaMGbCysoK7uzvu3LmDPXv2YMiQIZLHrXL+/Hk4ODigb9++zxWDiNo2meCqTkStkre3NwAgPj5e4kxIajKZDLGxsdXWQGlOs2bNQnx8PB4+fChZDvXRkN+ftLQ0uLm5YceOHXjnnXee+3WVlZV49dVXMW3aNLz77rv1zrWhpIoLAA8fPkSXLl2wcuVKzSD3eenD+5mIml08v2khIqJmUddD6q2Bq6srQkNDERoaiqKioud6TUVFBRISEqBSqeDn59fEGUoft8ry5cvRr18/BAQENHtsImqZOGghIkm99957MDc3h0wmw6VLl57rNZ9//rnmwefNmzfXK96ePXvg7OysuSf/WQsRrl+/HjKZDAYGBvjTn/6EkydP1iveHzX38VLzCg4Ohre3N/z8/Op8KL9KcnIy9uzZg8OHD9e5xouuSRUXePo7denSJRw6dKhRa9UQUdvCQQsRSWrbtm2Ijo6u12s+/vhj/PDDDw2KN3XqVNy6dUuzcvm2bdtqna2qoqICERERAIDhw4fjxo0beOWVVxoUt0pzH68+CAkJwY4dO/Do0SM4OTlh9+7dUqfUpFatWoWAgAB89tlnz+w7YsQIfPfdd+jUqVMzZCZ93MTERDx58gTJycmwsrJq1thE1LJx0EJEbdbLL7+M+/fvIyEhocbte/bsgYODQzNn1fqsXr0aT548gRACv/76K7y8vKROqcmNHj0aYWFhUqehdyZNmoTg4GC0a9dO6lSIqIXhoIWIJCeTySSJO2fOHADAl19+WeP29evX1/sh4ech1fESERG1VBy0EBEAIDw8HGZmZjAwMMDLL78MOzs7GBoawszMDC+99BKGDBmCrl27QqFQwNLSEgsWLNB6vRAC69evh5ubG4yNjWFlZYXJkyfjxo0b1fqtW7cOvXr1grGxMSwsLDB//vxq+VRUVGDZsmVwdHSEiYkJ+vbti9jY2DqP4ciRI/Vap2P48OFwc3PD999/j5s3b2pt+89//oPi4mKMHj26xtfqw/ESERG1FRy0EBEAICgoCPPnz4cQAl9++SV+/fVX3L9/H6+88gouXryI4OBgXLx4EXl5efjrX/+KdevW4fLly5rXL1++HMHBwVi8eDGys7Nx8uRJZGRkYMiQIXjw4IGm39KlS7Fw4UL4+/vjwYMHuH//PhYtWlQtn0WLFmHt2rXYsGED7t27hwkTJuCtt97CTz/9VOsxVM1OVVlZ+dzHPWvWLACo9oD7F198gY8++qjW1+nD8RIREbUVHLQQUTXu7u4wNTVFhw4d8OabbwIAHB0d0bFjR5iammrWoKj6VqGkpATr16/HG2+8gXfeeQcWFhbo06cPNm/ejNzcXGzdulXTb8OGDRg5ciQ++ugjWFpawsTEBNbW1lrxS0tLERUVhSlTpmDq1KmwtLTEkiVLYGhoiB07dtSa9/jx41FYWPjMGcF+769//SvMzMzwj3/8Q7Mo4K1bt3D+/Hm89dZbNb5GX46XiIiorZBLnQAR6TcjIyMAQHl5uaataprSqlm3UlJSUFRUhAEDBmi9duDAgTAyMsLZs2cBPF18r7i4GCNGjKgz5s2bN1FcXIzevXtr2kxMTNCpU6dqt181loWFBd566y1ER0cjJiYG06dPx4YNGzBnzhwYGRmhrKys2mta4vGeOXNGJ/tpKzIzMwEAcXFxEmdCREQABy1EpAMFBQUAgPbt21fbZmlpCZVKBeB/fwja2NjUub/Hjx8DAJYsWYIlS5ZobbO3t290vn80Z84cREdHY/PmzZgyZQri4+Nx/fr1Wvu3xOMNDw9HeHi4TvbVlvj6+kqdAhERgbeHEZEOWFpaAoDmj/XfKygoQJcuXQAACoUCAPDkyZM691f1R/6GDRsghND6aYpvDPr164dBgwbh3Llz8Pf3h7e3d51rSLTE442Nja22b/7U/uPl5QUvLy/J8+BP9R8iaps4aCGiRuvduzfat29f7aHxs2fPoqysDC+//LKmn4GBAf7973/Xub+qWcqed8V4Xaia/nj37t2YO3dunX1bw/ESERG1JBy0EFGjKRQKzJs3D3v37sW3336LwsJCXL16FbNnz4a9vT38/f0BPP1GYerUqdi9eze2b9+OwsJCXLlyRfPg+u/3N336dOzatQtRUVEoLCxERUUFMjMzce/evVrzOHz4cL2mPP49Hx8fdOzYEVOmTIGzs3OLOF4iIqI2QxBRq+Tl5SW8vLyeu394eLgwNTUVAET37t3FqVOnRFhYmLCwsBAAhJ2dnfjuu+9ETEyMsLOzEwCElZWV2LVrlxBCiMrKSrFu3TrRo0cPYWhoKKysrMSUKVPEzZs3teKoVCrx3nvviQ4dOoj27duLwYMHi2XLlgkAokuXLuLy5ctCCCGePHkiFi5cKBwdHYVcLhc2NjZi6tSpIiUlRXzxxReaHMzMzMQbb7whhBDi0KFDwtzcXKxcubLW49y7d69wcXERAETHjh3Fhx9+qNm2YMEC8cMPP2j+vWTJEtGpUycBQBgYGAh3d3dx6tQpvTne5wVAxMbG1us1bV19f3+o+fD9TNQmxcmE4A2iRK2Rt7c3ACA+Pl7iTEhqMpkMsbGx8PHxkTqVFoO/P/qL72eiNimet4cREREREZFe46CFiIiIiIj0GgctREREEjl+/DiCg4NRWVmJKVOmwNHREQqFAg4ODpg0aRKuXLnS4H1XVlZiw4YN8PT0rHF7aGgo3N3doVQqYWxsDFdXVyxYsABFRUXV+u7cuRMDBw6Eubk5unXrhunTp+P+/fua7fv27cOaNWtQUVHR4HyJiOrCQQsREZEEPvnkE0RERCAkJASVlZU4deoUdu7ciby8PJw+fRolJSV45ZVXkJWVVe99p6am4pVXXsFHH32E4uLiGvskJSXhww8/xO3bt5Gbm4vVq1cjPDxc8zxPldjYWLz99tvw9vZGZmYmEhMTcfLkSYwdOxbl5eUAgIkTJ0KhUGDEiBGaxVeJiHSJgxYiImpSJSUltX7a35Ji6FJYWBhiYmIQFxcHc3NzAICHhwcGDx4MU1NTODk5YdWqVXj06BG+/vrreu378uXLWLRoEWbPno1+/frV2q99+/bw9/eHtbU1zM3N4ePjgylTpuDIkSPIyMjQ9NuyZQs6d+6M+fPnw8LCAv369cNHH32ES5cu4ezZs5p+gYGBePHFFzFu3DjNYIaISFc4aCEioia1fft2ZGdnt/gYupKWloalS5dixYoVUCgUAAC5XI79+/dr9ataLyg9Pb1e+3/xxRexZ88evP322zA2Nq6134EDB9CuXTutto4dOwKA1rczGRkZsLe3h0wm07R17doVAPDbb79pvX758uW4dOkSwsPD65UzEdGzcNBCRERahBBYv3493NzcYGxsDCsrK0yePBk3btzQ9AkICICRkRE6deqkafvggw9gZmYGmUyG3NxcAEBQUBDmzZuH9PR0yGQyuLq6IiIiAgqFAra2tpg1axbs7e2hUCjg6emp9cl9Y2IAwJEjRxq82GhTioiIgBACEydOrLNfSUkJAECpVDZHWgCAu3fvwsTEBE5OTpo2Z2fnagPCqudZ/rgQq5WVFYYOHYrw8HBwRQUi0iUOWoiISMvy5csRHByMxYsXIzs7IAl3DQAAIABJREFUGydPnkRGRgaGDBmCBw8eAHj6h/cf18nYtGkTVqxYodUWHh6OCRMmwMXFBUIIpKWlISAgANOmTUNxcTECAwNx+/ZtXLhwAeXl5Rg1apTm1qTGxACgeSi8srJSdydHBw4ePIhevXrB1NS0zn7nzp0DAAwePLg50kJxcTGSkpLw/vvvw8jISNMeEhKC+/fvIzIyEiqVCikpKQgPD8drr72GQYMGVdtP//79cffuXVy+fLlZ8iaitoGDFiIi0igpKcH69evxxhtv4J133oGFhQX69OmDzZs3Izc3F1u3btVZLLlcrvk2x93dHVFRUVCpVNixY4dO9j9+/HgUFhZi6dKlOtmfLjx+/Bi//vorXFxcau3z4MEDxMTEIDAwEB4eHs/8RkZXVq9eDXt7e6xcuVKrfejQoVi4cCECAgKgVCrRu3dvqFQqbNu2rcb99OjRAwBw9erVJs+ZiNoODlqIiEgjJSUFRUVFGDBggFb7wIEDYWRkpHX7lq4NGDAApqamWrehtTbZ2dkQQtT5LYuHhwcCAwMxefJkHD58GIaGhk2e1969exEXF4ejR49qJgaosnjxYmzduhUnTpxAUVERbt26BU9PT3h4eGg9sF+l6tiqvpUjItIFDlqIiEijarra9u3bV9tmaWkJlUrVpPGNjY2Rk5PTpDGkVFpaCgB1PiBva2uLpKQkREZGwsLCoslziomJQVhYGJKTk9G9e3etbffu3cOaNWswc+ZMDB8+HGZmZnByckJ0dDSysrKwbt26avszMTEB8L9jJSLSBbnUCRARkf6wtLQEgBoHJwUFBejSpUuTxVar1U0eQ2pVf9DXtQijjY2N5v+hqUVGRuLo0aNISkqqcaCampqKiooKdO7cWatdqVTC2toaKSkp1V5TVlYG4H/HSkSkCxy0EBGRRu/evdG+fXv89NNPWu1nz55FWVkZXn75ZU2bXC6HWq3WWezk5GQIIbQe7tZ1DKnZ2tpCJpPh0aNHtfb549THTUEIgUWLFiE/Px8JCQmQy2v+c6BqAHnv3j2tdpVKhby8PM3Ux79XdWx2dnY6zpqI2jLeHkZERBoKhQLz5s3D3r178e2336KwsBBXr17F7NmzYW9vD39/f01fV1dX5OXlISEhAWq1Gjk5OdXW7QAAa2trZGVl4fbt21CpVJpBSGVlJfLz81FeXo4rV64gKCgIjo6OmDZtmk5iHD58WO+mPDY1NYWzszP+v/buPaiqcv0D+HflBjYbuaogoiiCNxIvpTNCoJGXk5LiBRSScw5mHcA6QDKpUCaikreAQbMmM86crEDEgUrRM6UcdBpMQ9Gh0QMm3lAQUdgIyob9/v7wx84diGxuewvfz8z+w3e9632ftVwM+2G9l+vXr7d4vLi4GHZ2dliyZEmzYwEBAbCzs0N+fn6H4/jtt9+wdetW7N69G0ZGRpAkSeuzfft2AICTkxO8vb2xe/du5Obmoq6uDteuXdM8B8uXL2/WdtO1ubm5dThOIqImTFqIiEjLunXrEB8fj7i4OPTv3x/Tpk3DsGHDkJOTAzMzM029FStWwNvbG4GBgRg1ahQ2bNigGRL0+CTtsLAw2NrawtXVFXPmzEFlZSWAR3Me3NzcYGpqCi8vL4wcORLHjh3Tmu/R0T4MkY+PDwoLCzX7sDyutb1N6uvrUV5ejqysrFbbz8vLg6enJwYNGoSTJ0+ioKAA9vb2eOmll5Cbm/vUfh4nSRLS09MREBCA5cuXw9raGq6urrh69SoyMjLg5eXV7JxTp07BwcEB48aNa1MfRERtIQnu/kTUI/n7+wMA0tPT9RwJ6ZskSUhLS2u254k+hYaGIj09HXfu3NF3KC3qyp+f4uJijBkzBikpKQgKCmrzeWq1Gi+//DKCg4PxxhtvdHpcneHOnTsYPHgwNm7ciKioqC7pwxCfZyLqcul800JERHrR2mT0nszFxQVxcXGIi4tDTU1Nm85pbGxEZmYmlEolAgICujjC9ouNjcWECRMQHh6u71CIqIdh0kJERNTNoqOj4e/vj4CAgFYn5TfJyclBRkYGsrOzW93jRZ8SEhJw9uxZHDp0qFv2liGi3oVJCxERdauYmBikpKSgqqoKTk5O2L9/v75D0otNmzYhPDwcH3300VPrTp8+HV9//TUGDhzYDZHpLisrCw8fPkROTg6sra31HQ4R9UBc8piIiLpVfHw84uPj9R2GQZg1axZmzZql7zA6zNfXF76+vvoOg4h6ML5pISIiIiIig8akhYiIiIiIDBqTFiIiIiIiMmhMWoiIiIiIyKBxIj5RD5aXl6fZJI96t8TERG40qoO8vDwA4M8PEZGBYNJC1EO5u7vrOwQyEH5+fl3a/q1bt3DmzBnMnj27S/vpTlOmTNF3CPQEfn5+GDJkiL7DIKJuJgkhhL6DICKiZ9e+ffuwZMkS8NcJERF1kXTOaSEiIiIiIoPGpIWIiIiIiAwakxYiIiIiIjJoTFqIiIiIiMigMWkhIiIiIiKDxqSFiIiIiIgMGpMWIiIiIiIyaExaiIiIiIjIoDFpISIiIiIig8akhYiIiIiIDBqTFiIiIiIiMmhMWoiIiIiIyKAxaSEiIiIiIoPGpIWIiIiIiAwakxYiIiIiIjJoTFqIiIiIiMigMWkhIiIiIiKDxqSFiIiIiIgMGpMWIiIiIiIyaExaiIiIiIjIoDFpISIiIiIig8akhYiIiIiIDBqTFiIiIiIiMmhMWoiIiIiIyKAxaSEiIiIiIoPGpIWIiIiIiAwakxYiIiIiIjJoTFqIiIiIiMigMWkhIiIiIiKDxqSFiIiIiIgMGpMWIiIiIiIyaExaiIiIiIjIoMn0HQARET07VCoVampqtMru378PALh7965WuSRJsLKy6rbYiIio52LSQkREbVZZWQkHBwc0NjY2O2ZjY6P1b29vbxw9erS7QiMioh6Mw8OIiKjN7OzsMHXqVDz3XOu/PiRJQmBgYDdFRUREPR2TFiIi0slf//rXp9bp06cPFi5c2A3REBFRb8CkhYiIdLJo0SLIZE8eXdynTx+8+uqr6NevXzdGRUREPRmTFiIi0omFhQVmz579xMRFCIGgoKBujoqIiHoyJi1ERKSzoKCgFifjA4CxsTFee+21bo6IiIh6MiYtRESks9deew0KhaJZuZGRERYsWAAzMzM9REVERD0VkxYiItKZXC7HwoULYWRkpFWuUqmwdOlSPUVFREQ9FZMWIiJql9dffx0qlUqrzMLCAjNnztRTRERE1FMxaSEionaZMWOG1oaSRkZGCAwMhLGxsR6jIiKinohJCxERtYtMJkNgYKBmiJhKpcLrr7+u56iIiKgnYtJCRETtFhgYqBkiZmdnB09PTz1HREREPRGTFiIiajcPDw84ODgAAP72t7/huef4a4WIiDrfk7c0JupB9u3bp+8QiHqsyZMn48aNG+jXrx9/1oi6yJAhQ+Du7q7vMIj0RhJCCH0HQdTVJEnSdwhERETt5ufnh/T0dH2HQaQv6XzTQr1GWloaFi9erO8wiLqdJEld/vzv378ffn5+XdZ+d/P39wcAfkkkg9D0PBL1Zhx8TEREHdaTEhYiIjI8TFqIiIiIiMigMWkhIiIiIiKDxqSFiIiIiIgMGpMWIiIiIiIyaExaiIiIiIjIoDFpISKiNjl06BAsLS3x/fff6zsUgxQaGgpJkjSfoKCgZnV+/PFHREdHQ61WY8GCBXB0dIRcLoeDgwN8fX1x7ty5dvevVquRmJgIDw+PFo/HxcXB1dUVFhYWMDExgYuLC1atWoWamppmdb/55htMnjwZ5ubmGDp0KJYtW4Zbt25pjn/33XfYsmULGhsb2x3v43r6fcnMzNR6Nvr379/u6yHqrZi0EBFRm3Av4qezsbFBdnY2Ll68iD179mgdW7duHZKTkxETEwO1Wo3jx4/jm2++QWVlJU6cOIG6ujpMnToVpaWlOvdbVFSEqVOnYuXKlaitrW2xztGjR/HOO++gpKQEFRUViI+PR1JSUrM9QNLS0rB06VL4+/vj+vXryMrKQm5uLmbPno2GhgYAwLx58yCXyzF9+nTcu3dP53gf1xvui6+vL65fv47c3FzMmTNH5+sgIgCCqBcAINLS0vQdBpFe9MTnv7a2Vri7u3dZ+35+fsLPz0+nc0JCQoSDg0OLxz766CMxcuRIUVdXJ4QQQqVSiddee02rzi+//CIAiE2bNunU79mzZ8XChQvF3r17xYQJE8T48eNbrOfj4yMaGhq0yhYvXiwAiKtXr2rKvL29xaBBg4RardaU7dy5UwAQJ06c0Do/PDxcuLu7C5VKpVPMTXrjfYmIiBD9+vXT6Vra8zwS9TD7+KaFiIieOXv27EF5ebm+w2iT4uJirF27FuvXr4dcLgcAyGSyZsPshg8fDgC4dOmSTu2PHz8eGRkZWLp0KUxMTJ5Y74cffkCfPn20ypqGKT3+FuLatWuwt7eHJEmasiFDhgAArly5onV+bGwszp49i6SkJJ1iBnhfiEg3TFqIiOipTpw4AUdHR0iShJ07dwIAdu3aBTMzMygUCmRlZWH27NmwsLDA4MGD8e2332rOTU5Ohlwuh62tLUJDQ2Fvbw+5XA4PDw+cPHlSUy88PBzGxsYYOHCgpuztt9+GmZkZJElCRUUFACAyMhJRUVG4dOkSJEmCi4sLAODw4cOwsLDApk2buuOWtFlycjKEEJg3b16r9erq6gAAFhYW3REWAODGjRswNTWFk5OTpmz48OHNEsKmeRtNCUQTa2trTJs2DUlJSToPH+R9ISJdMGkhIqKn8vT0xM8//6xVtmLFCrz77ruoq6uDubk50tLScOnSJQwfPhxvvfUWVCoVgEfJSHBwMGpraxEREYGSkhLk5+ejoaEBM2fOxLVr1wA8+hK7ePFirT4++eQTrF+/XqssKSkJc+fOhbOzM4QQKC4uBgDN5Ge1Wt0l96C9Dh48iFGjRkGhULRa75dffgHw6F53h9raWhw9ehRvvfUWjI2NNeUxMTG4desWduzYAaVSicLCQiQlJeEvf/kLpkyZ0qydiRMn4saNGygoKNCpf94XItIFkxYiIuowDw8PWFhYYMCAAQgICMD9+/dx9epVrToymQxjxoyBiYkJXF1dsWvXLiiVSqSkpHRKDD4+PqiursbatWs7pb3OcP/+fVy+fBnOzs5PrFNWVobU1FRERETA3d39qW8eOkt8fDzs7e2xceNGrfJp06Zh9erVCA8Ph4WFBcaOHQulUokvvviixXZGjBgBADh//nyb++Z9ISJdMWkhIqJO1fTX6aY3LU8yadIkKBQKXLhwoTvC0ovy8nIIIVp9m+Du7o6IiAjMnz8f2dnZMDIy6vK4Dhw4gH379uHIkSMwNzfXOvb+++/j888/x08//YSamhr8/vvv8PDwgLu7u+at2OOarq2srKzN/fO+EJGumLQQEZHemJiY4Pbt2/oOo8s8ePAAAFqdCG5ra4ujR49ix44dsLS07PKYUlNTsXnzZuTk5GDYsGFax27evIktW7bgH//4B1555RWYmZnByckJu3fvRmlpKbZt29asPVNTUwB/XGtb8L4Qka5k+g6AiIh6J5VKhXv37mHw4MH6DqXLNH1xbW0TxgEDBsDKyqpb4tmxYweOHDmCo0ePom/fvs2OFxUVobGxEYMGDdIqt7CwgI2NDQoLC5udU19fD+CPa20L3hci0hWTFiIi0oucnBwIIbQmMctksqcOK3uW2NraQpIkVFVVPbHOn5f47QpCCKxZswZ3795FZmYmZLKWf/03JZA3b97UKlcqlaisrNQs8fu4pmuzs7Nrczy8L0SkKw4PIyKibqFWq3H37l00NDTg3LlziIyMhKOjI4KDgzV1XFxcUFlZiczMTKhUKty+fbvZHhjAo53nS0tLUVJSAqVSCZVKhezsbINb8lihUGD48OG4fv16i8eLi4thZ2eHJUuWNDsWEBAAOzs75OfndziO3377DVu3bsXu3bthZGQESZK0Ptu3bwcAODk5wdvbG7t370Zubi7q6upw7do1hISEAACWL1/erO2ma3Nzc2tz3L3xvhBRxzBpISKip9q5cycmT54MAFi9ejV8fX2xa9cuJCYmAgDGjRuH33//Hbt370ZUVBQA4NVXX0VRUZGmjQcPHsDNzQ2mpqbw8vLCyJEjcezYMa15DStWrIC3tzcCAwMxatQobNiwQTO85vEJz2FhYbC1tYWrqyvmzJmDysrKbrkP7eHj44PCwkLNfiOPa20Pj/r6epSXlyMrK6vV9vPy8uDp6YlBgwbh5MmTKCgogL29PV566SXk5uY+tZ/HSZKE9PR0BAQEYPny5bC2toarqyuuXr2KjIwMeHl5NTvn1KlTcHBwwLhx43SKu7fdFyLqIEHUCwAQaWlp+g6DSC8M4fkPCQkRNjY2eo1BF35+fsLPz0+nc0JCQoSDg0Oz8qKiIiGTycRXX32lU3uNjY3Cy8tL7NmzR6fzulNFRYWQy+Vi+/btmrK2xt3b7kuTiIgI0a9fP53aa8/zSNTD7OObFiIi6hatTbruKerq6nDkyBEUFRVpJmK7uLggLi4OcXFxqKmpaVM7jY2NyMzMhFKpREBAQFeG3CGxsbGYMGECwsPDAegWd2+6L0IIlJaW4sSJE5rNUIlIN0xaiHq57du3aybFfvbZZ/oORycZGRkYPny4Zvz5wIEDERQUpJdYJk+ejD59+mDChAk6n/vmm2/C3NwckiTh7NmzXRAddZfKykq8+uqrGDlyJN544w1NeXR0NPz9/REQENDq5PMmOTk5yMjIQHZ29lN3jNeXhIQEnD17FocOHdLsoaJr3L3lvmRlZcHBwQFeXl44ePCgniMkejZJQrRxQCfRM0ySJKSlpWHx4sX6DsUgFRcXY8SIEfj0008RGhqq73B05uLigoqKCty7d0+vccyYMQMVFRXtSjxSU1MRGBiIM2fOtCvxaY2+n/+YmBh8/PHHqK+vx7Bhw7Bt2zb4+fnpJZa28vf3BwCkp6d3arv/+c9/cPToUWzevLlT2+1uWVlZ+O2337Bq1Sr06dOnw+3xvrSuq55HomdIOpc8JuoGdXV1mD59On7++ednug96OkmS9B2CwYmPj0d8fLy+wzAIs2bNwqxZs/QdRof5+vrC19e309rjfSGip+HwMKJusGfPHpSXlz/zfdDTNQ0H0RWTHSIioidj0kLUAiEEEhISMGbMGJiYmMDa2hrz58/HhQsXNHXCw8NhbGyMgQMHasrefvttmJmZQZIkVFRUAAAiIyMRFRWFS5cuQZIkuLi4IDk5GXK5HLa2tggNDYW9vT3kcjk8PDxw8uTJTumjo44fPw5XV1dYWlpCLpfDzc0NR44cAfBoDkbTPBJnZ2ecOXMGALBs2TIoFApYWlriu+++A/Bo4uyHH34IR0dHmJqaYty4cUhLSwMAbN26FQqFAubm5igvL0dUVBQcHBxw8eJFHD58uEv23GjtupKSkmBmZobnnnsOL774Iuzs7GBkZAQzMzO88MIL8PLywpAhQyCXy2FlZYVVq1Y1a7+4uBijR4+GmZmZZmnfEydOaNURQmDbtm0YNWoUTExMYGlpiffee0+nWImIiHoTJi1ELYiNjUV0dDTef/99lJeXIzc3F9euXYOXlxfKysoAAMnJyc3mCHzyySdYv369VllSUhLmzp0LZ2dnCCFQXFyM8PBwBAcHo7a2FhERESgpKUF+fj4aGhowc+ZMzV4UHemjo8rKyrBkyRKUlJSgtLQUffv2xdKlSwEAX3zxBRYtWoQ+ffrg+PHjmDhxIgAgJSUFCxYswN69ezFv3jwAwJo1a7B161YkJibi5s2bmDt3Ll5//XWcPn0aq1atwsqVK1FTU4P4+Hg4OTlhypQpEEJoVppSq9Udvpa2XldkZCTee+89CCHw6aef4vLly7h16xamTp2KM2fOIDo6GmfOnEFlZSX+/ve/Y9u2bSgoKNBq39raGocPH0ZVVRVOnz4NlUqFmTNnau1XsnbtWqxevRohISEoKyvDrVu3sGbNGp1iJSIi6k2YtBD9SV1dHRISErBw4UIEBQXB0tISbm5u+Oyzz1BRUYHPP/+80/qSyWSatzmurq7YtWsXlEolUlJSOq2P9vLz88O6detgbW0NGxsbzJs3D3fu3MHt27cBPNrcr7GxUSvW6upqnDp1CnPmzAHwaDPBXbt2YcGCBVi0aBGsrKzwwQcfwMjIqNk1bt68Ge+88w4yMjIwevRo+Pj4oLq6GmvXru3W62ri6uoKhUKBfv36ITAwEADg6OiI/v37Q6FQaFYpe/ztGwCYm5tj2LBhkMlkeP7557F79248ePBA89zU1dUhMTERM2bMwMqVK2FlZQVTU1PY2Ni0O1YiIqKejhPxif6ksLAQNTU1mDRpklb55MmTYWxsrDV8q7NNmjQJCoWi2RdhQ9A0V6PpDcgrr7yCkSNH4ssvv0RMTAwkSUJqaioCAgI0q+ZcvHgRtbW1GDt2rKYdU1NTDBw40GCu8c/X1RJjY2MAQENDQ7PzVCpVq+27ubnB0tIS586dA/Bo+FhtbS2mT5/eJbE+SWJiIlce0kFeXh6AP1ZtItKnvLw8TJkyRd9hEOkV37QQ/UnTsrl9+/ZtdszKygpKpbJL+zcxMTGIv6QfPHgQL7/8MgYMGAATE5Nm8zckSUJoaCh+//13/PTTTwCAf//731i+fLmmzv379wEAH3zwgWYOjCRJuHLlCmpra7vvYh7ztOvqCkZGRprk5vr16wCAAQMGPPU8fcRKRERkiPimhehPrKysAKDF5OTevXsYPHhwl/WtUqm6vI+2uHr1KhYsWICFCxfiyy+/xKBBg7Bjx45mX5qDg4MRExODL774AkOGDIGFhQWGDh2qOd70xTwxMRGRkZHdeg1NcnNz8euvv+Ldd99t83V1poaGBlRWVsLR0REAIJfLAQAPHz5s9bzOjvXdd9/lPkU64L4YZEj4xo+ISQtRM2PHjkXfvn1x+vRprfKTJ0+ivr4eL774oqZMJpM9dXiQLnJyciCE0BoG0Nl9tMX58+ehUqmwYsUKDB8+HEDLS/JaW1tjyZIlSE1Nhbm5Od566y2t400rbelzl/dff/0VZmZmANp+XZ3p2LFjUKvVeOGFFwA8er6ee+45/Pe//0VYWNgTz9NHrERERIaKw8OI/kQulyMqKgoHDhzA3r17UV1djfPnzyMsLAz29vYICQnR1HVxcUFlZSUyMzOhUqlw+/ZtXLlypVmbNjY2KC0tRUlJCZRKpSYJUavVuHv3LhoaGnDu3DlERkbC0dERwcHBndZHezS9Ffjxxx/x4MEDFBUVPXEuT1hYGB4+fIgffvgBc+fO1Toml8uxbNkyfPvtt9i1axeqq6vR2NiI69ev4+bNm63GkJ2d3aElj1UqFcrKypCTk6NJWnS5rvaqr69HVVUVGhoakJ+fj/DwcAwdOlTzfzpgwAAsWrQI+/fvx549e1BdXY1z5841W+ChO2IlIiJ6ZgiiXgCASEtLa3N9tVottm3bJkaMGCGMjIyEtbW1WLBggbh48aJWvTt37ghvb28hl8uFk5OT+Oc//ynee+89AUC4uLiIq1evCiGEyM/PF0OHDhWmpqbC09NT3Lp1S4SEhAgjIyPh4OAgZDKZsLCwEPPnzxeXLl3qtD7a4uOPPxZ2dnYCgDAzMxMLFy4UQgixevVqYWNjI6ysrIS/v7/YuXOnACCcnZ01fTaZOHGiiI6ObrH9hw8fitWrVwtHR0chk8nEgAEDxKJFi0RhYaHYsmWLMDU1FQDEkCFDxFdffaU579ChQ8Lc3Fxs3LjxibEfOHBAODs7CwCtfg4cOKA5p7XrioqKEgqFQgAQw4YNE8ePHxebN28WlpaWAoCws7MTX3/9tUhNTdXcM2tra/Htt98KIYRISUkR3t7ewtbWVshkMtGvXz8RGBgorly5ohW3UqkUb775pujXr5/o27ev8PT0FB9++KEAIAYPHiwKCgp0/j9oja7PPwnh5+cn/Pz89B0GkRCCzyOREGKfJIQQ3ZolEemBJElIS0szqDH9oaGhSE9Px507d/QdSof5+Phg586dcHJy0nco1AJDfP4NHee0kCHh80iEdA4PI9Kj9ixdawgeH3p27tw5yOVyJixERETUZZi0EPVAFy5c0Fpi+EmfgICAdrW/evVqFBUV4X//+x+WLVuGDRs2dPIVEJEh+PHHHxEdHQ21Wo0FCxbA0dERcrkcDg4O8PX11ew/1B5qtRqJiYnw8PBoduy7777Dli1bntk/7BBR52PSQqQHMTExSElJQVVVFZycnLB///5ObX/06NEQQjz1k5qa2q72FQoFRo8ejRkzZiA2Nhaurq6dGj8R6d+6deuQnJyMmJgYqNVqHD9+HN988w0qKytx4sQJ1NXVYerUqSgtLdW57aKiIkydOhUrV65scc+mefPmQS6XY/r06Zq9s4iod2PSQqQH8fHxePjwIYQQuHz5Mvz8/PQdkk42btyIxsZGXL16tdmKYUR/VldX1+Jf05+1PnqTzZs3IzU1Ffv27YO5uTkAwN3dHZ6enlAoFHBycsKmTZtQVVWFf/3rXzq1XVBQgDVr1iAsLAwTJkx4Yr2IiAiMHz8ec+bMQUNDQ0cuh4h6ACYtRETUpfbs2YPy8vJnvo/eori4GGvXrsX69es1m6HKZDJ8//33WvWa9g+6dOmSTu2PHz8eGRkZWLp0KUxMTFqtGxsbi7NnzyIpKUmnPoio52HSQkREWoQQSEhIwJgxY2BiYgJra2vMnz8fFy5c0NQJDw+HsbExBg4cqCl7++23YWZmBkmSUFFRAQCIjIxEVFQULl26BEmS4OLiguTkZMjlctja2iI0NBT29vaQy+Xw8PDQ2oumI30AwOHDhzu0109vlZycDCEE5s2b12q9uro6AICFhUWXxWJtbY1p06YhKSkJXOyUqHdj0kJERFpiY2MRHR2N999+rt+1AAAFXklEQVR/H+Xl5cjNzcW1a9fg5eWFsrIyAI++2P55CeVPPvkE69ev1ypLSkrC3Llz4ezsDCEEiouLER4ejuDgYNTW1iIiIgIlJSXIz89HQ0MDZs6ciWvXrnW4D+CP1fnUanXn3Zxe4ODBgxg1ahQUCkWr9X755RcAgKenZ5fGM3HiRNy4cQMFBQVd2g8RGTYmLUREpFFXV4eEhAQsXLgQQUFBsLS0hJubGz777DNUVFTg888/77S+ZDKZ5m2Oq6srdu3aBaVSiZSUlE5p38fHB9XV1Vi7dm2ntNcb3L9/H5cvX4azs/MT65SVlSE1NRURERFwd3d/6huZjhoxYgQA4Pz5813aDxEZNpm+AyAiIsNRWFiImpoaTJo0Sat88uTJMDY21hq+1dkmTZoEhUKhNQyNuld5eTmEEK2+ZXF3d8f9+/exePFibNy4EUZGRl0aU1MsTW/5iKh3YtJCREQaTcvL9u3bt9kxKysrKJXKLu3fxMQEt2/f7tI+6MkePHgAAK1OkLe1tcWePXvw/PPPd0tMpqamWrERUe/E4WFERKRhZWUFAC0mJ/fu3cPgwYO7rG+VStXlfVDrmhKE1jZ1HDBggOY56Q719fUA/oiNiHonvmkhIiKNsWPHom/fvjh9+rRW+cmTJ1FfX48XX3xRUyaTyaBSqTqt75ycHAghMGXKlC7rg1pna2sLSZJQVVX1xDp/Xvq4qzXFYmdn1639EpFh4ZsWIiLSkMvliIqKwoEDB7B3715UV1fj/PnzCAsLg729PUJCQjR1XVxcUFlZiczMTKhUKty+fRtXrlxp1qaNjQ1KS0tRUlICpVKpSULUajXu3r2LhoYGnDt3DpGRkXB0dERwcHCn9JGdnc0lj3WkUCgwfPhwXL9+vcXjxcXFsLOzw5IlS5odCwgIgJ2dHfLz8zs1pqZY3NzcOrVdInq2MGkhIiIt69atQ3x8POLi4tC/f39MmzYNw4YNQ05ODszMzDT1VqxYAW9vbwQGBmLUqFHYsGGDZgiPu7u7ZunisLAw2NrawtXVFXPmzEFlZSWAR3MU3NzcYGpqCi8vL4wcORLHjh3Tmk/R0T5Idz4+PigsLNTsw/K41vZKqa+vR3l5ObKyslptPy8vD56enhg0aBBOnjyJgoIC2Nvb46WXXkJubm6z+qdOnYKDgwPGjRun+8UQUY8hCe7WRL2AJElIS0trtucDUW9giM9/aGgo0tPTcefOHX2H0iJ/f38AQHp6up4j6X7FxcUYM2YMUlJSEBQU1Obz1Go1Xn75ZQQHB+ONN97olFju3LmDwYMHY+PGjYiKiuqUNp9Fvfl5JPp/6XzTQkREetHaZG/SHxcXF8TFxSEuLg41NTVtOqexsRGZmZlQKpUICAjotFhiY2MxYcIEhIeHd1qbRPRsYtJCREREWqKjo+Hv74+AgIBWJ+U3ycnJQUZGBrKzs1vd40UXCQkJOHv2LA4dOtTle8EQkeFj0kJERN0qJiYGKSkpqKqqgpOTE/bv36/vkKgFmzZtQnh4OD766KOn1p0+fTq+/vprDBw4sFP6zsrKwsOHD5GTkwNra+tOaZOInm1c8piIiLpVfHw84uPj9R0GtcGsWbMwa9asbu/X19cXvr6+3d4vERkuvmkhIiIiIiKDxqSFiIiIiIgMGpMWIiIiIiIyaExaiIiIiIjIoDFpISIiIiIigyYJIYS+gyDqapIk6TsEIiKidvPz80N6erq+wyDSl3QueUy9Qlpamr5DICIiarchQ4boOwQiveKbFiIiIiIiMmTpnNNCREREREQGjUkLEREREREZNCYtRERERERk0GQAuBQFEREREREZqrz/A82+RWfSVCMYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the left input and point to the base network\n",
    "input_a = Input(shape=(28,28,), name=\"left_input\")\n",
    "vect_output_a = base_network(input_a)\n",
    "\n",
    "# create the right input and point to the base network\n",
    "input_b = Input(shape=(28,28,), name=\"right_input\")\n",
    "vect_output_b = base_network(input_b)\n",
    "\n",
    "# measure the similarity of the two vector outputs\n",
    "output = Lambda(euclidean_distance, name=\"output_layer\", output_shape=eucl_dist_output_shape)([vect_output_a, vect_output_b])\n",
    "\n",
    "# specify the inputs and output of the model\n",
    "model = Model([input_a, input_b], output)\n",
    "\n",
    "# plot model graph\n",
    "plot_model(model, show_shapes=True, show_layer_names=True, to_file='outer-model.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model\n",
    "\n",
    "You can now define the custom loss for our network and start training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HswzRyDAk-V7"
   },
   "outputs": [],
   "source": [
    "def contrastive_loss_with_margin(margin):\n",
    "    def contrastive_loss(y_true, y_pred):\n",
    "        '''Contrastive loss from Hadsell-et-al.'06\n",
    "        http:https://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n",
    "        '''\n",
    "        square_pred = K.square(y_pred)\n",
    "        margin_square = K.square(K.maximum(margin - y_pred, 0))\n",
    "        return (y_true * square_pred + (1 - y_true) * margin_square)\n",
    "    return contrastive_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UIGaA9TMlBCc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 119980 samples, validate on 19980 samples\n",
      "Epoch 1/20\n",
      "119980/119980 [==============================] - 9s 74us/sample - loss: 0.1112 - val_loss: 0.0886\n",
      "Epoch 2/20\n",
      "119980/119980 [==============================] - 8s 69us/sample - loss: 0.0799 - val_loss: 0.0737\n",
      "Epoch 3/20\n",
      "119980/119980 [==============================] - 8s 68us/sample - loss: 0.0707 - val_loss: 0.0701\n",
      "Epoch 4/20\n",
      "119980/119980 [==============================] - 8s 69us/sample - loss: 0.0660 - val_loss: 0.0674\n",
      "Epoch 5/20\n",
      "119980/119980 [==============================] - 8s 70us/sample - loss: 0.0627 - val_loss: 0.0707\n",
      "Epoch 6/20\n",
      "119980/119980 [==============================] - 8s 69us/sample - loss: 0.0612 - val_loss: 0.0673\n",
      "Epoch 7/20\n",
      "119980/119980 [==============================] - 8s 67us/sample - loss: 0.0594 - val_loss: 0.0650\n",
      "Epoch 8/20\n",
      "119980/119980 [==============================] - 8s 68us/sample - loss: 0.0577 - val_loss: 0.0645\n",
      "Epoch 9/20\n",
      "119980/119980 [==============================] - 8s 67us/sample - loss: 0.0570 - val_loss: 0.0643\n",
      "Epoch 10/20\n",
      "119980/119980 [==============================] - 8s 67us/sample - loss: 0.0563 - val_loss: 0.0642\n",
      "Epoch 11/20\n",
      "119980/119980 [==============================] - 8s 67us/sample - loss: 0.0552 - val_loss: 0.0635\n",
      "Epoch 12/20\n",
      "119980/119980 [==============================] - 8s 67us/sample - loss: 0.0545 - val_loss: 0.0643\n",
      "Epoch 13/20\n",
      "119980/119980 [==============================] - 8s 67us/sample - loss: 0.0536 - val_loss: 0.0643\n",
      "Epoch 14/20\n",
      "119980/119980 [==============================] - 8s 68us/sample - loss: 0.0533 - val_loss: 0.0651\n",
      "Epoch 15/20\n",
      "119980/119980 [==============================] - 8s 69us/sample - loss: 0.0531 - val_loss: 0.0662\n",
      "Epoch 16/20\n",
      "119980/119980 [==============================] - 8s 68us/sample - loss: 0.0522 - val_loss: 0.0630\n",
      "Epoch 17/20\n",
      "119980/119980 [==============================] - 8s 66us/sample - loss: 0.0520 - val_loss: 0.0644\n",
      "Epoch 18/20\n",
      "119980/119980 [==============================] - 8s 67us/sample - loss: 0.0517 - val_loss: 0.0652\n",
      "Epoch 19/20\n",
      "119980/119980 [==============================] - 8s 66us/sample - loss: 0.0514 - val_loss: 0.0648\n",
      "Epoch 20/20\n",
      "119980/119980 [==============================] - 8s 66us/sample - loss: 0.0508 - val_loss: 0.0629\n"
     ]
    }
   ],
   "source": [
    "rms = RMSprop()\n",
    "model.compile(loss=contrastive_loss_with_margin(margin=1), optimizer=rms)\n",
    "history = model.fit([tr_pairs[:,0], tr_pairs[:,1]], tr_y, epochs=20, batch_size=128, validation_data=([ts_pairs[:,0], ts_pairs[:,1]], ts_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation\n",
    "\n",
    "As usual, you can evaluate our model by computing the accuracy and observing the metrics during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RYwU4CIhlIE4"
   },
   "outputs": [],
   "source": [
    "def compute_accuracy(y_true, y_pred):\n",
    "    '''Compute classification accuracy with a fixed threshold on distances.\n",
    "    '''\n",
    "    pred = y_pred.ravel() < 0.5\n",
    "    return np.mean(pred == y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IyfJWzjYlKMg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19980/19980 [==============================] - 1s 39us/sample - loss: 0.0629\n",
      "Loss = 0.06286303841346247, Train Accuracy = 0.9421403567261211 Test Accuracy = 0.9138638638638639\n"
     ]
    }
   ],
   "source": [
    "loss = model.evaluate(x=[ts_pairs[:,0],ts_pairs[:,1]], y=ts_y)\n",
    "\n",
    "y_pred_train = model.predict([tr_pairs[:,0], tr_pairs[:,1]])\n",
    "train_accuracy = compute_accuracy(tr_y, y_pred_train)\n",
    "\n",
    "y_pred_test = model.predict([ts_pairs[:,0], ts_pairs[:,1]])\n",
    "test_accuracy = compute_accuracy(ts_y, y_pred_test)\n",
    "\n",
    "print(\"Loss = {}, Train Accuracy = {} Test Accuracy = {}\".format(loss, train_accuracy, test_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3obxy4EBlMyI"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_metrics(metric_name, title, ylim=5):\n",
    "    plt.title(title)\n",
    "    plt.ylim(0,ylim)\n",
    "    plt.plot(history.history[metric_name],color='blue',label=metric_name)\n",
    "    plt.plot(history.history['val_' + metric_name],color='green',label='val_' + metric_name)\n",
    "\n",
    "\n",
    "plot_metrics(metric_name='loss', title=\"Loss\", ylim=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E9KLCFiClP9Q"
   },
   "outputs": [],
   "source": [
    "# Matplotlib config\n",
    "def visualize_images():\n",
    "    plt.rc('image', cmap='gray_r')\n",
    "    plt.rc('grid', linewidth=0)\n",
    "    plt.rc('xtick', top=False, bottom=False, labelsize='large')\n",
    "    plt.rc('ytick', left=False, right=False, labelsize='large')\n",
    "    plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n",
    "    plt.rc('text', color='a8151a')\n",
    "    plt.rc('figure', facecolor='F0F0F0')# Matplotlib fonts\n",
    "\n",
    "\n",
    "# utility to display a row of digits with their predictions\n",
    "def display_images(left, right, predictions, labels, title, n):\n",
    "    plt.figure(figsize=(17,3))\n",
    "    plt.title(title)\n",
    "    plt.yticks([])\n",
    "    plt.xticks([])\n",
    "    plt.grid(None)\n",
    "    left = np.reshape(left, [n, 28, 28])\n",
    "    left = np.swapaxes(left, 0, 1)\n",
    "    left = np.reshape(left, [28, 28*n])\n",
    "    plt.imshow(left)\n",
    "    plt.figure(figsize=(17,3))\n",
    "    plt.yticks([])\n",
    "    plt.xticks([28*x+14 for x in range(n)], predictions)\n",
    "    for i,t in enumerate(plt.gca().xaxis.get_ticklabels()):\n",
    "        if predictions[i] > 0.5: t.set_color('red') # bad predictions in red\n",
    "    plt.grid(None)\n",
    "    right = np.reshape(right, [n, 28, 28])\n",
    "    right = np.swapaxes(right, 0, 1)\n",
    "    right = np.reshape(right, [28, 28*n])\n",
    "    plt.imshow(right)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see sample results for 10 pairs of items below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VRxB-Tmemzt9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1224x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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82ik/09RFIX/qb6cQVdlGWW6jTEqdnh0RabEROmYkZ2E9oFCzr2tCoZb2xUgVj/hQ5bbm9jH+uShzv+Iho/bhcLdlj5WHE7qo+6MUavC1Nb8UaTc8fUVot/yQxuavfvEbIt/fXPj60P7EjleE9qtnybCFFyRz6DUeumJxfK5XhuzyXqDGfcUF5Bpw0Tu+JPKddcd1of21W14n0v722ltD+/MffEtoz/7Kg5jqRJrJNeK5vKyjDJO0F63sOzGPxiMuiS5CtrextAgsGj05pnPJfEeE3FyGfTn+cAn2SBe5TtVIBb6iKIcRfWOsKIqiKIqiKIqizGj0wVhRFEVRFEVRFEWZ0UyulNpZbXp0FslN+IqWYoVqjP9p3q+hy4sFK0gbn+wxiklZDFyeXaglOZm7OmYxzradValz80gG423aPM4znhrUt5NWja/gCgA7BkmONJSTqzKe1rY9tGfFaWXLk5JbAQA5L4eTklvxxODCMG3NMJWTS3cTfffIoukrTT9Y/KyUKyLC2liBesHqHfNENpuk9jsmaffgoyaSh2foGH2jJJEuNkst9d4BkvUt3ihdDY5YqqykapLU1rlM1ZdeHthzKknjUmek4Sd8pM4olR+vzUITK++8HNGMR9/dtWB3aL+0fb3I983fvjy0l365ipSRXZfN5yrnmyKYNJVUKi3HmLrtVFa3/+Eskbb4dBp/FiV60WTjeDY7D0vjckXprc/Ryvf1W2Ufq9tFfadup3RlmOpE2kn6nW2T7bd5HdlDy+VYGo8y16Q8Xb/rXlG3nY6ZniPnwnw9bZuCQTFuMDLX2+cYXDLNIzmIuRSA9Wjbc5osn4fruukL9h4vO2N0Mc0zhc1bcVjxK89Pn/ngD0L77sFjRdqs75N0/dNf+lZon5mQUlw+bj/4h+ND++Nv/73It7VQRA4+thZG8f0nZf+ws+gcdxWpUJ/MzhX51v/Ff4T2FdecJtLW/hXlffV77g3tlV9xBsIpuBJ1/+W0in+z92uRtom5dPnOHWaGD/IsKWOda2a4MmtOypI7S8zIDvKqWrpvuqGTxqbycVIUZRLwnPvSMXcsY8btjlY8bXloZ9qle1f/MnoeG+1wjjeXxsW6OrKbauR4uX03PVcsu/rxiudU8VwP+BOKoiiKoiiKoiiKcgShD8aKoiiKoiiKoijKjEYfjBVFURRFURRFUZQZzeT6GCccn6BR0pMPLiUd++zHpK+Xl5chmsaDKZa3y+EnImX3u+EnhhfRdtMmqYXPtrJQTuM8x6kCD9vSk6oTaak9tJ2KyStrnkM+kLc+SyGERrpKfoKnFWuwavAEXDprTZj2L9so9AQAFHromKtmLQ5t0zL1fSMPOY6bkhdjfvfM/zg3LH00zCilndu4AQBQ13suzm3cgNtGTw3TmpOjob3Dk+03n6GhIdMh+8N0a8+HguLAYGjP+z35rKYXSx/8aIYGl8YtCcReaTDnN6X2X0hShe4+h8r7whOZAyiAZbV7QjtiqM55+DIAiA1WCU/DqeL3M3D12aE962Hpi1ucpBBFyd3U3vJtru9p5c/1pall3vSNq3DNqS34jzuvwq+u/6LI57XSWOJtkq2Z+8TGBslvaep5TO5L6qwloe365Sb72aSXkBNg/zALXRijAs454Y9qepl/8KhIQmKA0qwHeEWL+JCF7/gO8/Ll87Dri2x8Ol6syhIHhRo635gT8qnYTuthYHPlYxxueB/uqu0WaX2foLLvK9K6Dv/Wd5TI94Gl91HaH64K7Z6inJ9PSaSwFUBbJIL6p2S4reWvfS60NxeobBo8WZmrsmTv+NtzRNpggUKknd9A8/1KLMVUZ2gxaytOGvcJdv2DGzwqkLxl69ZA9qOIl0XBWCS8rAjrBAB1LHzTgE91nvblGgoFdsxM6zjH9+lOpbU9XF/WKn783dedE4ZGHOqiweTo/5UDoffAky/qVJUyjNWZtTAJas/P/79TRbalp9P9y/Imegbg4VsBYJSF590z2iDShtm6Ruks3ffmirKtnLeM7l32Llkk0govbCl/HQx9Y6woiqIoiqIoiqLMaPTBWFEURVEURVEURZnRTKqUeniJlAHx8A2p40l6Uny6vLQZALyslFdUklkXE4FcwzA7ILFX6rgK9fQq3yvSObkS7Ewnfa51nfyNoZiYvr85eB4LV+U718FCBnmOJO/lDSSPePRtJHO4569KYSO6zq7DPbefhS//Iy2ffkNKyoBjKfq+nekmzGS8Idk9axaR5C3dR+Vmnd+3vBy17y3ZNgDAUTaKLdk2RFnd9jMpSsSpy+IQS8scuOvCtGScIUaKazeEdmJt5Xy1ALyzW1H784f3SWv8Idm7orKe7/3WuaE9t2MgtH+YOV3ky8yuLC1LvZ5Ctex4JeWrb5W61AvnU198PHuKSGuYJCl13U6qh6ETZdvzmFdNJCuS0LeNZO0LNhcQOc6iaXMBm/KNIp/PwmPFB2Sdc1VXpJ/C1jlK3ylJppXmyWhaznGxEWoDsZrKYaj8aOV5i0W0EdJpQEqhC8mS9Nx6gClULl/uOuW6jfDwTUU5RYh6ZyplRBz5+OhskslLUfHhZfdfS/nxqYkHQvv+oS6R1jNK8mmPXUzGiQN3coJkiNlZLHRTapnI113Yg6Qfx29Tc1HbLfvOh+feHdoPp0mq/d6mp0W+0+/5UGhfftUqkfaaZhovNufbQjvSKPtYcWgIUw2ftaM50XqR9nSO2n3SyP7BZdHdTO6+ILZX5OspNsCiJMV2Qzk1MCk1P37KkVIP+tSIs63TwYHjEFBp3rWV7zsis1rFdvPGAiLHWjRvLCB1BpXhMTfJCXqU1d/9fzxBpHWsYqH6btt3zi6Hf76cM+s+tzO0Vz9LEt6uax7BlMRUkOtXuxdyJe1jxzAGz918Urg70ihDKK3f3BnaG+MUWtBEnLmE3aNGo7INRFlowWSMJp18Uc5bY2FKAWDDv0h3tyVvUSm1oiiKoiiKoiiKolRFH4wVRVEURVEURVGUGY0+GCuKoiiKoiiKoigzmkn1MXZ9h+p3kC68o518VFKz20W+5g3jC91jI/TcH8mOLSlesvN1lFZMymIosnBN0RTp2DOz5O8IRvgfS518ag4dQ3qzTC9GBmU4k1rmo9hSJ8M8cB8p7h8yFqbGegaFpMGIT74HjbPIjw8AhpgPyHCe/G8isf3E2DoCqel2/CaWUf/IpimtUCt9ouID1J6/defFAIDr5zfgW2suxt9c8csw7Yfbzgxt7rsBAEUWvinVKf2lnG4782B+OSbqBP7gflGRCGBMGMLA5ti4xXx4bEF6sC7/f/2hnf4687fdLUeS5kUUQmrXL1aItMsXkY/Uihrye/rd3uNFvt9vXB7aSzcOy0vB5NC6lsaEIRnNDbEROqusE84k2kzl2/BYD7yXdqLhsR0iPAoAmH6qs7o9jvcwq77CZhkea6qTbWbt0r0sFoZpVpMcc/eu7ghtr0BjczQtW0CclX2+1g3DRGnRjIGxQDQjfYoBx6+Yn7sTGirBQi8Zv3LYGh7Wia9RAgCpTpqDJ9LHeGhZ5bnq9udOFNuFEWqLC5f2hfYqu0Tk6y6SD2/LKRRW7ZtPnivyzb8tindc0YLvf+MNaPrlSpH2y+spfMplzK/4B0Ny7IgnqfF8qP1PIu0Tm18f2gMZujfY+TXpx7fs6scx1cg3VvZZTVu612g2ch0GPn7w0FsuSZNHBhZJk0fMWZCG+ynXsvBPvhN/bpi1Z8yVPprTjvH6r/J8hpVHFR/j9EtkOLO6BzfCu3g26h7ciKN+Rf1og/O57o+cFtr+mfL+9VNvvDW0/27hu0O786YHRb65KymE0DvbvyvS3nXfu0J70e3TwEd8nOuqGLYOinu/Yk4+FqitKf1PUrt3q6+B3es3JqkP8HVvACBToO+aWz8o0tIFtrYOCyu7cad8RnywQOPnG1fIseiJpaW2Y7ZXvpPVN8aKoiiKoiiKoijKjEYfjBVFURRFURRFUZQZzaRKqV24hPmkth2hffelDSJf+/30ej27SC7b7o2WD6wRCcI6Gd8iki3uE7KJY1l0qEimcqAOL0vHSM1xQ+ZMAxlFBWIRJocYlU0knaPCmd8sZQ7tHpMgMYlG67qSxDFyrI/WdTkM+1SmTTVSLjTa6EhTx85pBkqpm16QEpPBs6hsohlqe4WCEyqslpV9EEEr0layT7tqc5h2R4Jkfd2QfcyrpTqq7cHMoIpE2kTK/4Zoi7KOLJfCFbKAtbBZJ67QfihufCG0tz5HYZcWHbNb5Ov941zaOFP2xXPr14f2f+24MLTXrF0g8sVaqP/5CdnXK4+Qh5fIRhr7rT9fpMVSTGJbkGdoeJi57TuA3IkobN+BTdkOVKJQI+s1PsDGezcsxRSEhy1JzaOyqd8myybCwhqe0b5VpD20nson10Dt3npO+bK2nWt0pdSObUuhlFzlKZ93q4Va8qOUz1Gbis/lGjyWT55Tvp62I+1SalfsOXyD2sknbRLbvxk5hr53rwzPk9xL82kOZDdFpMxzUZTcKwYfYe15lmyje68eQcH42Ht1CrFFMmxU/1eo/n674CWhfelrZSiZL5x8W2jvLMp5Yc166o+fOv/Xof2uE6TbwRU4DVONthW9FdNqDY3RRWfkGyiSlJrLoJOOXDodyKUjsPtIpGNcLsqmCH48AEhZGoPbWqRry7RjnDJdkc9WHnPtuSeHdnKPvG8s7u0DCoXSf4/dwDtjeOe/kyw6MlvOC/9y4V+G9ujJdE6/2SGluP8zRH3gM9e/V6R1/XKcYZmqnOOUwJHBu/JpzoaPxZFJGmz4WBxg96U2LaXKuRfIoWWkh/LV9shJomkTSa6zPdJN0G5mcxdrN0dje8XzW7hWhlX7xRvOL53Pd8o/awD6xlhRFEVRFEVRFEWZ4eiDsaIoiqIoiqIoijKjmXApNV/drBiXr+sHl1Da3RtotdTioHzl7ddXWWMyOr5nfb6KZqFOFkMhSccYWUgyGneVTlOk8x+VSi3UkRoQkZaW0C7292Oq05AgXVt/zpHkpUkCkl0ky21boans8eJ3PgYA8F7ejvidj+FRJm1M5Zy69T2WRlKMRKyylONIhbdRFz9aJY2pdNrv2gIAiJ7Sjva7tqD4j1SfyQjJVKJRKWd537H3hva3V8ulgef+pvp5TzX4mGPi1Kb8tFx9VKwUnZfaTisVPYeVyLFdob2Qyaf33DtX5CucPBLaF87bLNKuv/3q0I4NUZ8yHVK2deI8WrE6lZbSssprgh5eir0kfcr3y9VHuZw32Sv7wECvXEF/jNc2PCW2b7GXhraXd8b0ybrog8RfPCe0YyNsrHaug8uMayKyMTduobY+Mo/6h7vKM1eORp2uw1ebtpGSDN/4dp85PsY+x+vSXXmaK1Hdla0rnVMsJS86V08DoZ3bJj94iKXUm754dmjftvAmkfamb11PG7OcFVjnUV38vP/00P5I2z0i3xXf+JvQrumnclv13q+KfMf97oPwmwzSgzVY8zc3i7RbhxeG9te++rrQ/tV9p4t8N7/psdD+XO9ykfa2M2ml6zomA757VK78vueDJOPu+Lpc1XeycFe/5fDVpjNW3pPwaBtJU3ki8IwPAwvP+MhzfzwAHtNPZ5hc2s3ns5V26+Lji74yVTmY+14ub86cIN1++P2Q+fOTlQ8yTmlycfcesV3/E9qOjlLEjqNnv0/k+8dzbw/tmtsfrXj8sWgUgBOR4gDOcdxUWtkbkMtDV5O382NUydfzgbPF9pxf+Ii9wmDO7+Ko+9nDFT51cByKu/73Ne0U2/96TKku/GTla9Q3xoqiKIqiKIqiKMqMRh+MFUVRFEVRFEVRlBmNPhgriqIoiqIoiqIoM5oJ9zH2Gmj5f9d3KDWPbLOd/IhNp1zS3ibIB8TLSq1+MUkHtSzkQ6G25MthPYNCbUT4EbtwX6VMC/mAxEekJt1nkRcKDfI8mjcwv74W5ns7DXyMuZ9LYq/0geG+ZR21MpzAsF/ex8+lu9Ac2k6EDdg9VO91HX2hXfSr+JUfqTguEB7zkeK+kF6d9HuK7qI+UNgR+Ffk8ijs2Ilns9TJmmIU8iAeld4cu/ON9F1TMJrAPjD/GK9GtkPuS1wt7AA/RqRL+rb69Qk3dwQRAjgAACAASURBVOm7RpxQTLvIT6k4NFT5uyp8LwD4X6NwBR9dQL7eN37z7SLfzsXlzwkAinV+WTs6KPvzpv5Zod3hnMdUIOacr8fCY9Xvkg1ztKP8dLYh3yK2IyykRCQrO1mkQri/qcrus2hu4RF+XP/gbDMLAxOT43ZiK81Jwwtmh3Yx6bTLDB0zmpHHj6WpXvJ1XhiuyT1GYpDy8XMcnQWZb6iy/zH3TS4mWLgm55q5T+LI0kaRVrsah5TYUirTuRHZLv0YC5/3lLyWN133x9D++sMvDe3b0zLcUQOr2z99+t9C+292XSRPxKP/GwvSp3ZWlNYk+PC1Pw/tn+yUPsZ7ijT+nFK7WaRde+c7Q/u1LyH/yre2rhT5kley0HJfx5SgMUHzXdZZNCLGqiXlVx5X8yykFr9PAoBI4NgfgQ+/yjunpKExJuZMrjE2sbvHnzSqhBbifsQ7375CpHH36cYtVcIwsaKKD1LZJLfIe+XiBhkG7UXjznfMrzbeTz7BKz6+RWS7tZ+v9VHZT7VqmMZx+vOOm3GGvDpYoksWhbbnuNk3PbAZkfNmo+mBzVV9gvlaL1Xvww4BPDzf/wx2irRoT/D8mK/cv/SNsaIoiqIoiqIoijKj0QdjRVEURVEURVEUZUYz4VJqsNfpEUdpkF9AOyLdJGepe0bKaP1YFVkGk0/7kcqvynkYiWS/PJ5hEiQbIT1IbMiRkcwmuYXZIeWbuQZ2HtXCS01BPMMkc05YDrAijXuyPH7XfwLbSqESvXmS09fEpC4j3ke/1fSnqUzj0emg5z20OJEcRLiJLEtL1MgynHtf5Xa/JUthS1rjVEcZJ2zWTx6mcAUdPYdA6nO4YVKifcIwMbo/QmFEcudJSel5C0mqtaLucZHGQ3bsypErwJ5sg8i3M0XS1oZ4HN5AFA33l8p8TTcLrbOSPpe8WIaO+ddFJHP8uxsoVETzTx4S+TqSFDbhvkYp/Y4NUAMpJnkoHVmX+aLTyKYY0RHZlotM85hIS6losrf8Me4YOFnuYIdM7M3IpFHqS9MhclOBRcmJM/lxfNip5zoaV135phmmcaBQZaqKpeiYuXpZL9VckyrBTyPiRDPh87PrchVn3TbZRwfJNstz4MdMt8s0GVzoxTP/y9SP2m6rE2mFxdTGOs/vFmln1LwQ2l3voTFh6z+dI/J99a9JjzzM5Ky/fvQkke9VZ6xG0+4OvGrx0xj24yJtQ5Zk8k1Md39F59Mi37M5Oo+Mc4zlx24L7W2jJKO9bUDKsRsvfx5TjcZ4pmIaD5uUt7LB+UzrG2HS2VYn7FmxaDAMi6QpiHwA4LNBh8ulY0ZKSnlYJzuRUmrPmQe4ZLpKaCEehqlps7yWbVfRdiRL9/Pt/yHnsYrHrpZYRQY9bqp8xnuAwkEdljvPQyGfrkB0wXyxnVvEQtU5z0TevU+M65xSyymMVmxU5uu7eAmKDXH0XbwEjT/c7X6UDl9BPs3DWgHAyBU0X+94hZyFjzmKQi8d17QrtC9pWiPyPZyiwT/ixGBM9JXKwKtSsfrGWFEURVEURVEURZnR6IOxoiiKoiiKoiiKMqOZeCl1O8lv3FfytkjP6UW2EnXbn6TMo1DL5djyNXkl+bQ/JrE2JbtQw1Ym7Zbv1PlK18l+OidXLrakY29ov7B5gUjLM6lZfhYJt6a2cLEEXw2xKJVUiNHClmiMSWnSo7sXhnYr1lc8/vPp9oppXF5X9Km8m5MjIt80EPe+aPI1sr3lCtTufdaQ3DYV30PSSFct8sstJHd/+1JaVdTzZIket5wkc0O/lW170qiyOiZn/TelrO9VJ5FUcFZubWif3SxXuUwz2WDGl9LywSLJ+oeZ3nS0KPMN5UgW1JuuRa4QxQsDrQCA+hoa03qX0fEum7NRHOPj//b+0O743oOoRLaJyfMc95I8Uy35dZRmcrJNJWOU0TgSwqnQxxIDcnu0jc4/7ri25OXCwyF3Pi9XS/XjdGXeiBzDeMSD6UC+kbu9sHHbWVg310hp3BUAACxb6TvXTPniA878zJqOu9q0x9yPimOhBizgORJpvpo1XzXalUtHWbU4SjiRl6+QyqXepfOl7cElzih5iFeFNQ/RMtenfvYakXba22gufEnzCyLt/bdSX++8gtrzBa98QuQ7lcmAN+apjbqrtr+v/V7s2nsW3td+L/6YWi7SuLy30SMp9T+vu0jkq0tQpf3s+O+ItJOX/iS03/WR60N78Bd7MdWptsozl08nnUabKtSH9lkJkg7/R79cOTxvIzimWIuHh07Ge1oeEWnD7F4mz95HRYxse+72hFFlPh0ve4+TnbjpUdoeWUjX1dkml6Av9h5E2zmMUmQAiMxqpQ1ffldxkKJNRJqcSSfCotEwibCtlT4qho25hRdo1WvPkRWDuXL6KemeGFmxLLTXfYDKdOHvZV2OdFI95JpkH5i/jiTSxd0UUSPSKK9r4GgacxKDsjz6jy259Ow90aDxh7TfniPdPDa8k+6vPncRuYttz7WKfHNit4W269aQrrBi/LrsnLL7AeChIelmNufPJVe77SOVnaX0jbGiKIqiKIqiKIoyo9EHY0VRFEVRFEVRFGVGow/GiqIoiqIoiqIoyoxmwn2MTSZXOS1Fevplx1JYg5FGufx47S7yiysmKj/b2yiljfkY28DHOD7CQk80ymJI9DE/KObD7EWlPr8nRWEZ8rPl0v2JPsc5d5qSa5U6/FiKyjRbdPT/WfJDkF4Dkv4c+Vd6VXxqeIgmN9/MC94ERLxxBpDp7qmYNLKGaqa1i/y2c1lZl5v7KN+cAdm2Jw1b+fo3fOWs0K5plr44z37q+NCOD9L40/dNGVqAL+u/J+eEYRqlMEy8LXanpC9OOkv9PhqRrXTvFra+Qiv5DN6+4QSRb/Etlf2KOTw8T8QJezZaS2ncr9hknfBHPm3z8XKqkOyXdZ6ezcb0hBMyqLb8WOL6z+eamF+1Mx9NdR9jN7SFly3vNxkfluU20EXl9oXZT4q0V+KS0OZrSsQqRz3bh0iWyjhf55Xm2ZgR4aQA6R/MfZat0/S4/7HrY1xk9W49svlnXNywg9G55JNW2LEThxI3HM2tn6bQb2/adKlIKyyiceDrb6eQTFfe9yGRb1PHn0L77y57a2g3XCS/uzNSRK+x6IwUsTgu45e1Rmi8/+buC0I7tUOOdbdd+W+h/fKV0l960RtpvYYaSD/aqU5DlNZ4iDorc/CwSRlfht7kfo4tEWrQt37/ZSJf08u6MSefxF27luMf258VaU/lqJ5jLBDccLFyfLRqPtGHnJecKDbTc6kMGtYwH+Bde0Q+m6Px8x1vu0uk3dtLPrAfWvDH0P7fC84W+faeO75T9OroftvE5f21qa0B4jFE581FsZPm2UynHIBy9cxn1xnqs000CI2S6+0+YWX5WJJtdXzER9naRWxqcddQqGEhMFs3baZ8mcohxVyKazeE9u9e853Qfv9vPyry8et0154YOm9JaNfdRnVbHBoS+WLseSk1V7bLxF7AzDNI7DXI/n5xuP+u474l8t2499jQfnxkUWj35OpFvqftPDqnqCz8Rrb4RBtb8KgtKkNv9loa017WLPvitg2lNJOpfF879e6EFEVRFEVRFEVRFGUC0QdjRVEURVEURVEUZUYz4VLqdBeF6nFDATUvotgcu4bpVXhbn3zlXagb32nbccZGciVYlkmmvSKTK2ZlvqH1JNlYfNIukTb46NzQTnXShVaIKDKlyLPQAmaOlHZkR0ia4sqb00OVZUGcvgxJYmKO3JTLVrgUdSQnNSBS7HRkYtwwAT6XkVJaJiU7UnFvX8Vjtj/BXAheQ/3IOHLTQoG1gdw4JdyHAxaiycTofG1WSmzqN1O+xJNSGphvoPPfdQ61o680PyryfaePJF6vbnlcpHU3UIibPBtYnq6Rbh67s9TDH3pkOQqdEQxsKcnSYykaV+668pbQ/sAlfymOIXpElRBVkTzV2UCqSt9jVWsTldvU4Q6BcTC4Yy6/lmyDHOAT/eWlh9luR89bQ+Vok7LvTEU5OccsXyp3eOVtLgsEgIbNZJ/yLx8UaW3HUugeHi7PlTdz/CqeQpGshbGl/8VEZTloLMUk7Y7WkH+35yjeuDQwkqMG4V5zYrDyuJVbQlpJ7xBLqV2uOOocOqc7ZZv98kt+Gto7CjR22Ly8ljom9TUsFptbNmf96jp8fNZsvOdX12HBUdKl5lfHUSyVJ39G7iXLXrlV5DsqSrNrtu/ImWl3jVL5Rows3xhr+EOOvDniavkDFnxng9juubAFFgbZQvX702bmEpV0KrDI5NPuvdHhxEtLl5KROSRvTc3m9+wdIl+UhVz9vy/Isbqmh9rpjZF3sM/I6+q7hsKKuWHgBKwaso6vnh+3yLbHseHDi5DspWOwbgMAyDPV7j5h4Ni4UsMU464MOpam6yzUOmHrWHXW7qYviLqhaVlYWe94uv5tV8gLm7WGLqD+Gen6xcM8xdjEuO0SOcbM/yMdY8h55Ms0Uz+oY/ujczpFvnwDk4i/pF+kvXzBc2jqvgyXvnklimwS+vCO80S+GCvwZuanc2rjVicfC5drZP9oiNBcFTeV+8dgkeb8Lbk2kTYWlspap3EwpvZdgKIoiqIoiqIoiqIcZvTBWFEURVEURVEURZnR6IOxoiiKoiiKoiiKMqOZcB/jfB09i7u6+xUd5Otz//NHh3Z8r/RzHVlKjgKxlHQUqORXzDX9NmKQaaZtPyI/JEI0MR9jU5Df5bNQUbNr5XLhQ0z+XoxP4LL7hwAecmZe24BI28n8Y2Keo/HPje93lmF2/AWNgyKNL39/XCP5MjyxXfpyLh7XN01vXP+81CD5PvGSNgcQGqz5vhdCu79QVzGfG+Jm0mB+tTZb2aekYRul7Xy57Kf1G8h5qH4bXVdXTF7/b/+HfGJWfU225b3vIf/jwWPYsY+R/jbDzNc33ufBazeI95WOZY+nMeJ9G98U2va5jWWuaCyxin83qyK3vmyO2k6hho5hfNmmREgpb8Kng/0y2uaMKez03XAb0VT5Ntu2Sh6j50LyLfLrpT9hsZbKYCr+arzjZS1iOzZcPl+6U9Zzx+PkQxgdkX5bIwtY+DzWHLj/LlA9vBIfqyI5C+OX/kccH1iPu3WxubWqP7NTzzW9bExgn6vrln2Fn5MbFSc1j3bIFQkOPTwEy9PbVoi01gUUcmRnnup23jy5TsSX97w8tNd9lvwQ17/0FpHvLS9cgrr+2Thj3vNY9cgykXbBbz4W2sMrqCL+7+gfiXxfHSD/4y9c/GOR9i9//bbQnv0VCitnYo6vfr5yWM7JYtdQ5RVeIqg8zjZ45UPo+APy3qVQnAVrgUJx38YcQfmxKeb4SUbYui1NcfKnlAEIDz127Sax3TSfekWOreXgrgvE++bIfHndfG0dfl/ux2WH9lhTqdnDw6g6/rusqGq7nTVRag28eQbJXoOGbZXrMs98gt1xxa+0JpFz/26qnEdqDuXNMv/dQlLmq+2hgxQbad2Ti16/SuSreSMVjttWnhmidYw+tf0vQrvhaHnPvqWO2n1Dp0zLZKgQTns/zQNfm/drke9PozRePpaW61z4MIgYHy2xNDKsUI+tlWs3pNnCFD4buN2+wddwcX2M+fHj7N4l5cs1iPgxNqalX/x4etNUnPsVRVEURVEURVEUZcLQB2NFURRFURRFURRlRjPh2rmaPeOT2LS1kEYsMiiX1S7GSeYRc96K+yzUUr6WSRlqSvttBMjVGyHpcqXOPGRHgb2hr9kmdWsdR9FBalzNGIPLsacDXAqUjMrrqquh+muISomRGaeUOp2hQq2f5YTdOZ4kZCvqu0P74cGjxnXsI4l95IV+eUl+om/8v28VumnJ/9VDJE9PJGQ9+/7U+M3Mv/CU0PZGmQS2Rg5dqTkknandIjVRI110bflu+tyfM1Jy9fFrSTb4g3suEWnt//tEaM9i0khz+vEi36u+SVKoB9qOQnS0HbPPK0mKtnYzCeRWCoewDDtQkSohlHi4pn0+xouANZvIsKxXXs92Cnp8jLZXlrF5eZmWGCgvoUv2SwlaTQONOflGKcEqMvcYmTI1cEOM5Jk3gGXSwNjJUuKfuIPaysiyJueYlMblhVWiYexzHpVwxzAe+qRQT18WcW4LCixsS8yRyHt5+nI/Rl+w4yI5JtRtZ+4EjtdITW/l+fpwsmJ+t9iOs+BsOdZpv9T1U5HvLXd9ILSPO2Z7aK+U0ydumP8rbBs5FjfM/xVWLJVhyi5541+GdnoOySY3F2R7WDMyL7Q/NP95kXZTb/mKNxFZ0XZyircqxlQeL+uYZLMzKiXSaVt+JHDl4oObm1GcHcHg7mbgDJm3yAZh3q0GirKO8qzDJCM03x1uKbVXLzsIl0/z8cFzItxwt4ZqIdf4fOSOK1yeXWRFvU+oPjbclzuG9Ur/R1tZWTvuaNw9JOIo5JNDLAQmO0aiX54HHyPd4yeYUrm2h/pKut0JJbeX2o556KnQvm/HcpHv2Ha6X6tzBsn2BLlh8HZz8fz1It+Co+meuj0qn2H6CuSWyuXHN/d3iXy9eXrmct0OPGPhW4OMH0Mt08Vvzc4S+dzQZGMkKuwHIMI/AUADy9saoet3+5HHJqhn+mToqUbIMa0cU+PuV1EURVEURVEURVEmCX0wVhRFURRFURRFUWY0Ey6ltkzqbApSojCYo5XPomz1YxuV0kgufXZXii42ks6ByzfGJBrWlOzEAFv9rkHKIfjK2ck+enXvjUjtxfAoyU/mJeVqb4/XMClYenpJqbPZ8iu/AYDH6mUfSUVufFrMQp4qJuFoc+oTTKrNtC6RoUpLBh657COlZisP+wm20nDh4H7f2jRIUpe6hJTppNjK4YU6OUw4izkeVja9mrRVPjsP95pNjrXLUacdFrgsiuyrH3yPyHbVcSSXXv/JGpG2Yj6tGPva2atD+12N3xX5dhVpJdEf3XUe8vNj2L5+DgCg9qihMK3hp1XWwjVsfGMr5tuC7CseGz+jUdkX8zG+mj5bidNdHZPVu+e4QoxTLXtY8ePyfHOtdFaFAXm+lcafdJscOxpraVzZfbpcqbZ5I413U1FK3Xnzg5UTebs59ViR5D+zJrSzp58t0hKDbK5lRbXvytO0w3Okz9FRp7VYIJL1YZylXvlwzyXRrjSbS6n5Cq4AMDKPxqbm768M7aO2HyPy2Q20Ar/NOprjw4nnzFVsZf3Nv1sikmo/QPcXcaYPbfKc82V1cd2C34f2k5lFItsr69bCGCBpfPxoWK5gvuJmagM7fkFa32u//X6R7/Pv/E5o35eRyxA3/nAlyuGOTVORjnqSXqZ92YAzluYWV/KZLo5vJEj2ePBmGSR79p2P/Qp+Ku49VANbejlTnLjbczs6KrbjLKxKtpnas3uvDENp+XpnFelc+RWm3cgxvHhtlD5Tu0vmy/JIMu6K0jEL61kUai2KbOywTrSGCJsjis7cYirUUTQt97vfzeGHKDBXzsws+V1Nm+ggCeYuNdwnJe2LFpEMek5cPmNweTNvX01RWZcjbEn+vFP4fEVoLj92B/+mCB3T7R+bM7PgWw9DhSRiMWo3DY5WvTdPsu36CI1vfrWQBA5ptvr05lxbaHNJOADkmax/d490Fam8Nj2hb4wVRVEURVEURVGUGY0+GCuKoiiKoiiKoigzGn0wVhRFURRFURRFUWY0E+5jXKhly8A7S67vSZNmfsdm0o/XN0nNPF9y3U/IZ/s88+3l+cZ8ir1iyeb+CoUa6UMQHy7va1Bskvr/wlpSq684fqdI+yVzzXFDUUx1imlqFgUnbE88SpWW9qX/USTz4uO9RJgP8+wYhU2IDc2833Dc0AhejIUpyUYq5hsvIzxsVlL6tHE/80ybdKqZSB/jxo1U7/mXU6iBguNjvGAW+d/UOCHGPOZHs3sJ+aLYrGy/XUkKpfLRU/4o0jaMdoT2s+m5ob181WUiX90T5JvckAW8DoOGLaV+8frLWCinn5Pfi7sCAfcrHi+83wCAl2W+VKxb+klnTYYpEparEvVb5HbmePITHG2W9Zfvl37hY7ghNY5t2RPa9y9pFWltT02v9SAEzFfNrlpTOZtT5bkG2uEzH79YWraVkTmV2yX3P46lijDWwitY5BpleXLf4brtNDFaT9alCCEVdXwX+dok7Jr9Z9ZVPL+JxHhO+EdWjE5EG/SxMCMp5j/3l5/5mDzm6XSQ01mYlgdG5FoFc6MJbIXB3GgCX3zuUpH2sWP+ENrvfe/9oX3VrdeJfLNYGJTPbr5SnnCF0HLTwceYk4e8+UyyUE7rsnNEWkzcqI6gEmMumvsLZcaTXT/MJuafnspP3CoHfkaeR+3jNPDWsbULUJMU+YqtNJ/WOaHvUp3Up5NsTZ+CG+KI+TMXkjSOxEecBwQ2F3jO+kTwLeIdFvPuzclnAiebWJdjWN4nFFkIyNgwu2n3ZYUWGug68w2yQ0eylNdj6564Y1iil55p+NG73v2YyPfkmSeE9k8/KMfIr5/3g9BeEKX7H+6nDgDdzIm7ryh9cZMsTFnK0vEHivJZZ3ee7ldcP+XO+BCiXhGd8SHhf+w5HWFxsrfs97o+y80RCk7W4Ml22WjoPtVjffbR0cUiXz0PHzdw4HesU/uuSFEURVEURVEURVEOM/pgrCiKoiiKoiiKosxoJlxKHRsiyc3IfCm9yObolXfNNibnrZevwrlSwJVjc/k0D+sUhmuKlJacjw+zpeSbpMyBy6yT/SQbKMytFfnm3UNyi863yKXUi0z14E+3SEMsbEttVOrAhwxJadxQS1y+OV4SEXkMLoONGUpzVqCfEQwvltuRCJPmsDAE8aGDk3821JBMJeGE5eLL7g8ukb+fVQk0dMhpeyod2nsupetPDUnZ7MaBztA2TtghHgKChy5yQzl8ft1rQ3vfEA1sXKlhn3NG0NQCVke1RRRrLAZOLLXpJwfn03dnSVbEw+wAgC1S2ZtoZRmQZZ/LF+QgI8ZFJjkyWVk2XE7fHH3xrhCHmubnpcR/L2unrudG/Y7yGsb21WmxHbua8kUb5fhWs5OFkTigM50C8HZkK48JPHQKIEMv8b7ihmDK11Njd903uITQjxpYY+BHzT6h07hEms/r0VFHcs1CnbjnUaiZbhMqkeyV1/mRp98U2iPDNLe2v7lH5PuL2ZtD+9uDK0L76uZHRL4tBYMsLLYUcjilQ8qe50X7Q/tZJhd+yyvvE/lu3HZ5aEfe4dQL3xhne5sqtCZoHEgaOXAPWWqLntPzueyzGvHB0rgbH9x/3jHqnLBcWaa7z7JwTRMdOq64h9pfpIHN+G7IwAEKQRjLybG0cZTJYNl1mbiUBFfCmMrzkZ9z6sT6MJd1IHb344DxxH550MppvEVYU/mdYaSCXY1oTLY3n5dVtX70yNOhuUx2ddwEGgf8808J7S2vlHL3WSeT61BLUt5Iz6mh+vNRubzz7CEm5zzQZIoxdBYSWNm/BKMF6kfpvLx3yRVYuE12TxJz7j2TUXbf78ix62LUXwqWux3INtUUp+tsffrA3//qG2NFURRFURRFURRlRqMPxoqiKIqiKIqiKMqMRh+MFUVRFEVRFEVRlBnNhPsYmyJpxrkPMCBDAcW3MH/CTqkf5z7GtorIn61SHoZksp7ZJzyT66ecr6d0U6DzGHXC1jQ/Rz4U67JzRVquhT6Xa5h6vnvVSPRSoTbEpA9MP9P/Z53YE7HKkQwEkWixYlqcVe6jI0tDu6Zn6vswHWpqT+4T2yMp8h0xCd4JqnRj7r9ijPBh6d4yK7SPW75Nfo6t6r9lgVy6fyLxcnSdIiSR26UiLFRNjWxfkSRt+zx20agcPPx51J+LMXmM/BAfTNgxIrJd1m5mYa6OzQKehVdT6jOPb1oYpi0D+Rh7CelB5obOqISXZ346Tp/KJVh5ML9Rdx0A7utjvan3O2l8jWyXntcS2sVhOR43bqBwXrxWzIOrRT6fNW5+/QDg9Q6yfNOMKn6eJlHZSzGa4T74tD+Slv6ExRqah5m7KgCgUENtJzFQgLEWkawP67jI89BZkVF2fCPn+GIVN0T3vmFcuP6Kh9Enlq8R4NL5o2fF9nHvIx+/cxs2UL6IdFS9N7U8tP/9wUtC+6Ov2izydX3vGnx00Wx8+NaPY+07bhFpv05Tu+ehoXiYOgD4/pPnh/ayHSsrXst08CvmLKilRpv2pY9qnL0jmhWVNzJFN75ZBWr2+vAKFjV7q48cw8xHk6+jAgC1hq1pw0LpTbSPMa/b4tBQlYwHcehsdv+ZsG8Yw/F9yAK2cv+rmnYw+caJzR7a47l49z8R2kvur5zPPYvth+j77Y15ZD/RLd601lfMffCkKux374B5vlnowYEy9e6EFEVRFEVRFEVRFGUC0QdjRVEURVEURVEUZUYz4VJqG6Fn8bwjMZ5XSy/Ac5tIvjm8SIZmkTIrKYfkMmluc8m1jQDDS2jbk6vMi9BAuaYo2y/FHZHt9Io+7TtSsCYmCx+eXuElYiNUbk0xubx7IkJL9/tW1p9bPpXgYYeijo7dM+VlSA1bc2X3H8kMDsrwYH6B+k4sSRKsQu3BSfV5SLQ5p0i51NrM7ND2GscXruJwEN1GfWxxMwvVk5MazUya+p8tyN/7iik2zPEQTY4M2k/RMf2Y02f55zxW3s5Pi1EWGai+IYOI9VHfUJJG1/6kCQeMG26CYZjcLZtz3BqG2In5zDXEOVyOfc440sipIJQs9kgZVKHYRhvOtXiDVPgiWlVE1uVwnoSJvi8r0B84gFgrk8FBSoJ5GbjzHXcdyrewUGG+PHaRyfPd0Fhc1u8VfMAG/91oKeyY3gidiB+V7hoR5k3AZdpAdZl1RSZQ9uuGo+HSUTt/jkjrY64iPEzQ+txskW9JgkKuRPupz6Z9WZlLPvkQEjdejiWffAin7v6wSPv6tV8L7Z155pLg9IGjjpdhno4Ukh7NY6kqeRafWwAAHi1JREFU42rEHfkq3JO4xIeKpXBNQ9VlszF2vGqhoAq+vrdSlMlAe56iKIqiKIqiKIoyo9EHY0VRFEVRFEVRFGVGM+FS6mKCnsWHF0uJSnuSVgNcu3RRaOcdqWjDDpKR5uukTM5dYXqM2EhJHmN8i9iIxfAKOkZkUB4jPkjnmKsnOzkgDz5w/uLQ/s0uuW5g19Jdod3/wILyJzVF4XLQrakWkeaxVVwTnlxRMSpV1xXhakD3GJWIDcqVeqeCzPNww6W9+6SxVakT/QdXGh1PkIxr8PKkSBtIk/tCIjl5UurCLlox1b6b/B+8qxpFvpozSAK7rK1XpHF5forJaLcPSGmzkGP7rmSVbcepzdqizOdfNBDaJ3XsQs3eo3DCrNJY0PPDtSiHnzu48q3ZTuNl12y5TPDTw3SdsVp2fGcV5nMWvRDa22uPFmlT8VfT0QzrE87q42YkjXJ4tdIlIcpWN08kpRTVlV1POQ5SEmziVG6RnDxGuoXacLKbrn8fKfUSGoNHN8vxggco6Hg0BVO0iIzkkG2XsuLRITp+c4LsTKvsR7lmsmudRUUTg2wV7Sh9sS2Mby453Nh85fPwn1knth/cdkJop5hG/DVtT4h8syLU1322yvzrjjofEpJtd970oEjZ+0FaJ3ZBfG9o/+eOi0S+jZs6Q7sLTrSCaUzGpz4Qd1wSiizMQcZZSr3ZKz+uuEQyPoxvEcmMfz37OiPHH4+NurnCFB+LFOUIZSre+yiKoiiKoiiKoijKhKEPxoqiKIqiKIqiKMqMRh+MFUVRFEVRFEVRlBnNhPsYx/6wKrSXPSD9lHYxH6HGM8lh9fc/+67I9/K3vTu0/aj0FRmZU/6SxsI12cCO1NJ3xbZJn5LaPeQjEh8mO9cgf0fgafFLtog0L0nX1pLZXvacpir1O8l3rzYq/R8H0zVu9gMmFqHjp4qJivlEOKjxu+0cMUSHpI9RTRf5rxaY/5Ff2RWZfCaNgYlEhB9evH98IbDqklMjVFZxI/nDzvv8CxXz5WZ3iO3MCQtDu385tbfcQidcUytr69V+MsxRopeWdVR8no7fe+PzKPyzj96/L+N877HP+c7CCMz/zfqVfUr91eSz/MwLp8nDx+mY+QwbE0fl+T6ymnwc598rfRKnIvl+Nmck5KBQ2NOLchSHZCiyDX1doT06Isef4vDwizzDyaOqv+1cCv9TTMo5c3QBy8ujkmWcNSSep5BKuUZ5jMQA8/stWgAWpmhhmuXYMdpB9WcKVH++M23ztULSs51wW2dTf2q8tYpfMfcjncBwTfv05yrMbqL2xue7B4elv/95jetDu/MhuhYeCmp//O2Trw/tbIr8mZcv2iXy1WyuMqFUG7emOHuyFGpyW0FeY5EN+EUrB/+U5X7ycq0TTnJTD0xuPpKberCnmBJpOdAxPBY71PVn3lKg+uzvpnU02it+q6Iohxp9Y6woiqIoiqIoiqLMaPTBWFEURVEURVEURZnRTLiUmuNnKstSzIOrQ/v4r3xQpBUuIrvtKSnn8dhmYoCkWsV4SaZkLBDJArHnSBLc+YiUC+8+jeQtS5lEeutAs8jX9Jry4VeA6tc21Un2jE8664ZaGm0nKViLm5lx4fyNoV0fkVKwvE8yo5oI1YuXlvmml4jr4Oi6RUrw333XvaHdUyBZ2M9vuqTiMWwxKClryQ4wD1Ef66qXaRe3UliR7372yvGf9BSguHuP2I6x7Y4/0H4puD70WKBU7uXkjtVkiFz2acfX0pf95ar9ZzoCMHkaY6LNTpircUo7W2tJijswWCcTJ1Jye4ipFq7IX78ptOdm5PieaZtL9kJKG1rWIPIZFsLMl1GYEM3wNmvD/3ZAZuRuH/lWmoMLMqKW+Mm+Zb0836ZN47xt4XVp3PBrE1fPJkFyfXcs6P0jlX3z5c+H9uobThH5ns6fHNr1v3v4oM5j4RueLrv/NWvleHnrqnkVj+GxsF9+ZnrNwh+ZTYP/CXEpYd5RpHBY81hoLACIiaZTj0rYkRRQ9GFHUuiIyHGlnoVlihn67nRUSq6bPOoIF51Ic/DOit+qKMqhRt8YK4qiKIqiKIqiKDMafTBWFEVRFEVRFEVRZjT6YKwoiqIoiqIoiqLMaCbVx5iHl3Dh/lLzPi/DiETndIb2rtcsEWmpeeQ7lHglhe9oTZR8fqMjHWh92zb0d9MC+HUXd4tjNKbJP2TNswtCu+uDj1Q8X/daqvl7TXVi3YOhnfNlqIxEhK7ryYH5Iq1h2/hiKv35ljNCe+gy6WPT1kT+PcMN5JtVfG4jZhqFLdvE9g3feHvZfHP+XCXMji3j+1eGn95xntjOdZL/ZtetK6ucpaJMHLOeoN9ys611VXJWZvdvaExvLBNJ60iEz0eFTZtF2sJ/kttjRJYtldu5ttAeW7NjDMOGfhv1ABjYqIfYoBPicIA+F3+CfGqXPOP4Ivf3lz33qlTzI55E33Gbq7xmB7+3yXyJ7iGShd0H92VjZWDMuK/55h+8Rmwv3tIT2q4XsS1O37iJf33dtaG9+zR5X+PxtQuc5WGyzVSO+WYqkS7I+8Fi716gUECxdy+O+uO75EH20L2MV2C++jFZR7U7qb/M/20fS1kHRVEmBn1jrCiKoiiKoiiKosxo9MFYURRFURRFURRFmdEYewASI2NMD4At+804tWkD0LvfXMrhQMt+8tCynxy03CcPLfvJQ8t+8tCynzy07CcPLfvJYzqW/SJrbXu5hAN6MD4SMMY8Zq09fbLPYyaiZT95aNlPDlruk4eW/eShZT95aNlPHlr2k4eW/eRxpJW9SqkVRVEURVEURVGUGY0+GCuKoiiKoiiKoigzmpn4YPxfk30CMxgt+8lDy35y0HKfPLTsJw8t+8lDy37y0LKfPLTsJ48jquxnnI+xoiiKoiiKoiiKonBm4htjRVEURVEURVEURQmZ8AdjY8wrjDHPGWM2GmM+WSbdGGO+EqQ/ZYw51UmPGGOeMMbcwfb92BjzZPC32RjzZLD/EmPMKmPM08H/i9ln7gnOY+xzHcH+640xzwbffbcxZlGw/2RjzEPGmDVB2pvYsf43ONYzxphvGWNiwf5PsOM/Y4wpGmNaD3WZHgwHWw/GmAXGmD8ZY9YGZfER9pk3BPt8Y8zpbH/cGPPtoB5WG2MuYmmnBfs3Bt9ngv0XGGMeN8YUjDFXOef2TmPMhuDvnWz//ay8dxpjfnFIC+1QYcwrYMxzMGYjypQ9jGmCMb+CMathzBoY865g/zEw5kn2NwRjPso+d21w3DUw5ovBvjNZ/tUw5rUs/+/Yd3wDxkRY2hthzLNB2g/Z/nfCmA3BX1j2LP2rMGbkxRfSwfNixphKn600xrD0hcaYEWPMx9m+uDHmv4wx640x64wxrw/2fyBo808aYx4wxhzLPvPFoA+tdfrDAY0xxpikMeaRoL+tMcbccCjLeDy8iDFmv+dujPm4McYaY9rYvhMNjdFPG2OSwf6DGWOKrFxvZ/s/HBzH/e5XB9fwpDHmMWPMeYeiDA8F+6sHlu+M4LqvYvs2s7b6GNt/sjFmJbveM4P9MWPMd4PPrDXGfIp9pmw9BGlvNKV5d43h400prdEYs8MY8zW273+C9vGUMeZnxpj6F19ShxljvgVj9sCYZyqkXwRjBtl4/RmWVn7OMOZGGLMOxjwFY/4PxjQH+2Mw5rsw5mkYsxZj9WBMLYz5dfCZNTDm82XO4yoYY8Hm8GCuGAC775qKHOyYw9L3ub8M9l8bHHeNCeZWU+X+kn3udlOmvo0xVwVjCL9P+kIwhj9j5P3lEmPMw6Z0v/NjY0zcOdY+/XayeTH1YIxpDvr0umAMOdv5rBj7TfX7yzcFxw/rLdifCMpyY1C2i1lapTn4ZaY0X4zN20cfwiI7POxvzCnluSgYb9bAmHudtAiMeUL0e2PeEOT1nTHiEhizKhhzVmGsP1Qbc4y5AMY8DmMKcNuvMQthzO+D8etZsDo67FhrJ+wPQATA8wCWAogDWA3gWCfPKwH8FoAB8BIADzvp1wP4IYA7KnzHlwF8JrBPATA3sI8HsIPluwfA6WU+/1IAtYF9DYAfB3YXgGWBPRfALgDN7JxN8HcrgGvKHPdKAH+cyPI+HPUAYA6AUwO7AcD6sc8CWAHgGLdsAXwIwLcDuwPAKgBesP0IgLOD7/ktgMuD/YsBnAjgewCuYsdqBbAp+N8S2C1lrvE2AFdPdlnv8wdELPC8BZZaIG6B1dYpewv8nQW+ENjtFuizQLzMcbptKRYbLPBSC/zBAolguyP4X2uBaGDPscAett0Y/DcWuM0Cbw62l1ngCTtWrnSsVgtsCv63BHYLO6fTLfB9C4xM07a9388G+cIxxmlvPwXwcbbvBgCfC2wPQFtgN7I8fwHgd4F9DoA/B+cRAfAQgIvYOY97jAny1Qd2DMDDAF4yTeqh6rkDWADgTgBbWJlGATwF4KRgexaASGAf0BgTpJVtwyjNKYsBbB777mB/Pcg16UQA6yarDxxoPbB8fwTwG8jxVlwn2/97Vo6vBHBPYL8VwI8Cuzb4/OL91MMyAE8gGEswNt7Qd/07SnP+19g+3of+DcAnJ7us9/sHXGCBUy3wTIX0i2y5+5pqcwZwKRvPv8DmjbfaoB6COWCzBRYH9kuD/XEL3G+Degj2NVjgPgustPz+CHiZBa4se35T5O/FjDksfZ/7S5TuCf+AYG4da5+ocn8Z7HtdcKxnnP0NAO4DsBJBGQN4FYC7gnGsDsBjY20cwE8QzM0AvgE29lfqt9O5HgB8F8B7AzuO4D472C439pe9v0RpDtgKoJ0d92WB/UEA3wjsN4Pu86vNwesBrGCf/85kl/V+//Y/5jRb4FkLLAy2O5z06y3wQ9HvgRUWOMYC9zhjxCk26A8WON6O9YdqY05pTDrRAt+zbvstHf+SwK63wXPZRPxN9BvjMwFstNZustbmAPwIwKudPK8G8D1bYiWAZmPMHAAwxsxHaQD5ZrmDB7/svBGlG0dYa5+w1u4MktcASBpjEtVO0Fr7J2ttOthcCWB+sH+9tXZDYO8EsAdAe7D9m+B8LUqT//wyh37L2HlNAQ66Hqy1u6y1jwOAtXYYwFoA84Lttdba58p837EA7g7y7AEwAOD0oF4brbUPBWX3PQCvCfJtttY+BcB3jnUZgLustX3W2n6UJpNX8AzGmAYAFwOYim+MzwSwEdZuQuWytwAaUGrP9QD6ABScPC8D8Dys3RJsXwPg87A2WzqC3RP8T8Pasc8mg2MH32KHAiuK0gQ0lvZXAG5BqXzpWEHZw9o+uGVfett8I4C/GX9RHBZezBiz38+6Y0yw7zUo/UCzxvmedwP4VwCw1vrW2t7AHmJ56kDlblGqoziABEoPhbuDzxzQGBNkHXtzHwv+bJnPHC5ezBizv3O/CaV2xvddCuApa+1qALDW7rXWFg9yjKlIMKdsLrN/JDg+IOt0shlPPQDAtSj9uLOnTFo5LIDGwG4CsJPtrzPGRAHUAMgBGKpWDwjGm2A8H5sjAJTeMgOYjdKDOH150IeC/liDqVPelbH2PpTG8gOl8pxh7e/Z+B7eryCoBzj1EMwHfwo+mwPwOORY8lkAXwSQcc79bgDDB3HuE8nhur+8BsDnbTC3jrXPaveXgYLhegCfK3Oe5cr4WAD3WmsL1toUSg+Trwja98UAfhbk+y6o3wAH3m8ngoOuB2NMI4ALAPwPAFhrc9baAfa5cmN/2ftLlB7M11tre4J8fwDwevb93w3snwF4WVDWFedgVB7zpi77H3PeCuDnsHZrkJ/aUaX+YO1alLvPt/YJOP0BxiSqjjnWbka5ObikoovC2ruCfCOg57LDzkQ/GM8DsI1tbw/2jTfPzSh1iko3MucD2D32AOvwegBPjA1uAd8OZBH/MCaXcHgPSr9qCUxJNhZH6Vcxvj8G4B0Afufsr0XpAeK2Cuc90bzYegAABPKTU1B6o1ON1QBebYyJGmOWADgNpV/+5gXHrXYeB3PurwVwt/MAMlUYz/l/DaW37zsBPA3gI7DWbfNvhvyhpQvA+TDmYRhzL4w5I0wx5iwYsyY41gfYjRRgzJ0oTarDoMm3C0AXjPkzjFkJY8Z+eKh27h8GcDus3bWf6z/cvJi2PZ7PijHGGFMH4G9RejscYsYkjcBnA/nVT40xs1n6h4wxz6N0g/TXAGCtfQjAn1BSo+wCcKe1dq1z3HGPMYEs8EmU6vcua+3++umh5EWNMZXO3RjzFyj9Er3aOVYXAGuMuTMo77EfaA5mjAFKN7mPmZJc+DX7zw4YY15rjFkH4Nco/SgyFRjPOD4PpTHzG2U+bwH8PpCKvo/t/yiAG40x2wB8CcCYZPpnAFIotd+tAL5kre1D9XroAtBljPlzUN6vCM7LQ0md8YlyF2aM+TaAbgDLAXy1YglML85Gyb3ltzDmuGDfePoSUGpzY/cr+9QDSvVAlMaoKxE8VMCYUwAsgLVTWi5dhcN1f9kF4PxAcnuv4XMr4d5ffhaltitu5k1QxnbfMl4N4HJjTG0gEX4pSvdIswAMWJqz+RhZrd9OJi+mHpYC6EHp3vwJY8w3gzm22thf6f5yI4DlxpjFwQ91rwn2i+8PynYQwKz9zMHvBfAbY8x2lObgfd0Qph9dAFpgzD2B/Plqlra/561qvB7AE5DPW/uOOdXPawDG/DyQct8I7up3mJnoB+NyD5/uL71l8xhjrgCwx1q7qsrxy76VNaUJ5gsA3s92v81aewJKN7rno9TQ+WfejtKvTjc6++cA+D6Ad9l9H1a+DuA+a+39zv4rAfzZuhPT5HHQ9RAmln4RvQ3AR8fxAPotlAa+x1DqbA+i9AZ0POfhMp7PTKW38y7jOf/LADyJkmT/ZABfQ+mX1OAIJo6SBPen7DNRlKTlL0HpRvInGPuxx9qHYe1xAM4A8CkEvpdB2mUoyeMTKP0yPXasZQAuQqksvxkMaOXP3Zi5AN6AqXFz+mLa9sG0rRsA3MTecI4RRelX0T9ba09FSZL1pfCg1t5irT0KpYfqTwNA4LO0IvjcPAAXG2MucI477jHGWlu01p4cHO9MY8zxZa7vcPGixphy5x48/P89gM+U+VwUwHkA3hb8f60x5mXjPI9yLLTWno7SL+o3G2OO2t8HrLX/Z61djtIN2GfH8R0TwXiu/2YAf2utLZbJe27Qfi8H8CHWHq8BcJ21dgGA6xC84UHpbVERpbFrCYCPGWOW7uc89hlvgh+WPgjgN9babWU+C2vtu4LvWQvgTeXyTDMeB7AI1p6E0lg6pnjafx0a8/cozan/G+zZpx5Qqoex/FGUxrGvwNpNKP0IcROAjx2qi5kEDtf95T5zK3+R4t5fGmNOBnC0tfb/xBdXKWNr7e9RkkM/iFK9PIT93yNV67eTyYsZ+6MATgXwH9baU1D6ceeT+xn7y95fBgqUawD8GMD9KLl1jP3AUKkdVJuDr8P/b+9cY+yaogD8HUZpEa2WIo1WVUUqVUqpR9KgQetVJIRiqB8TGikhTbRohD+IEPGMREkbrXpWCEIy9Y7HmDGPRju00Skp9QcTpE2WH2vtnn3PnHPnzoyZO5NZX3Iy5+yz9z777sfae+3HGpgnIhOA59EjHEOdGnQiYT467rybJJmKtQfK61v55OtbXWVO9+k6C7gDHbdOBmp7nJZeMtCKcQfpjA1o5ctuRyjycwZwcZIkW9GtGWcnSbIqeLIZocvQRkDkPgF4HT1vumeFV0S2298/0XMgs6Iw56KN8OJ4hdm2ebwNLLftH/F37kW3Vt+e87uzq3vVpi/lEFatXgVWi8hr3X3MtgfdJiIzROQSYDSw2b4Rb+PKS0eP0p4kyVi0LN/uLl1VopK8vwHd3iKItANb0FWRwAVAAyI7IreOKMyX6CzfOGJ05rMTPQ8Vu/8DrCfd7tQBvInILkS2AN+jA9eitJ8ITAHa0fY5iiRpL58N/UZf6nZ3dStPxpwKPGhyaQlwV5Iki4Hf0dWCMDhah3b4WdaQbo1bAHxh23L/Qld/Tou+3ysZY1vR6skcOehn+iRjApm0H40O8pssvycADUmSHGZxbRCRnaJbrt5B87s3MiYcl0G0A69H63hFiG5fOzqJjHNVkUrK4WRgjeXpFcCTYZU8yodf0boc+snrgSD710XuV6Nn5ndZmE8t/nLl0AG8aWFieTMbWGzpehi4LskYizKlYC3pFsmhi8gfhAk2kXeAfdA6VL4M1QjihcA1pNv5rwbeNRkel0PgWWAzIo/a84Fov1BvMvw0YH2JcZ3BT3+NLzuA12zbb0nfWjC+nA3MtLg+QXdD1BPlcRLlcWJ5LCIP2BhpLqq4bQZ2otuMa3J+U2G7rTJ97YM7ot1Nr6ByvFD2lxlfIiJvicipIjIblSthN+me71veHoRuOc7tg5MkOQS1XxHStRY9jzzU6UDlRCd61Osj4ASsPRC1ByJ9q5CoPRDpW0ZW5nSXrm/t+MhudJIwb/zUP8gAHgRHZwF+RCt4OJQ/LeNnPqWH8r/MiWcOGSMQ6MBpQ8ZttH3j8px0hIP7+6CNr86eT0S3SB+TCTMCXf5fkpOem9BZqpE570KD238g87q/ysGeXwQeLRN/PaXGt0aF3w/MRVe8wruvLP5gkGVeJq6VdDW+tQWdwR1j9wdH7+uAF6qdx4UX1IgarTpKUkMq0zJ+nhJYYffjBbZLbAAH1ojuWIjD1AncZ/dTBbaJGtU6SlLjLBMFfhYYZ8YMDo/StFZgsT2fLyEP1e82gbGiRre2iBreGmP3B+f8xmoa3+pL3S4bNk/GZOJdQanxrTXA2XZfC6yz+2MiPxcBX9v9leg5qBqTSx8CF9m7HskYVIEOxgFHojPmFw6Rcqgo7USGoUwWNJisqbF8nG/veipjxpAa2hmHDqayxmP2fNuep5Aa3zoJ2B6eq3lVUg5FeYGelT4wuv8MON+eN5IapTkH+Mbul6KrKYmFaQOmlysHtF29EOX3NnRbY5yuWsz4loWfEt0/jG7ZrmpeV3SpsZkiQziHSagzMEvgJ5PhxX2Gyuo2MQNDUVxLBZ638Pubn+n27n5RY4t7lUlnqWEddcs3DjZIrr7InIyfOZQa36rD+lZ0i+c2C587vszENYmC8iYaJ6GGnsba/XSgBeu30Ymn2PjWzTlxlciwoVwOqLw/1u5XAA/lfGMrqewvN74MhtLGoLvwptrzLZQa33rZ7nP7YHveGYVfBLxa7byu6Covc44T+NBkzCiBFoHjM36KjAJmjW+NNtnUtT10J3NgpcT1Vw0ONu2RayrLbhmwOjzQhYRao9uEKp/LzK2OVDFNgCfsfTP5lqNLBJe5rQxxRG7L0RWyxug6FO2wv0GtmLaiVi+DBdMP0MP2wf96c18I7MrENcPe7bb0Bvd7ojTUEqxDDqKrt+WAblMUy7vwe8MAZwE60/Ov5eF75j4Jna3baPk7MUrHyWgn8AN6tjYMLk+xuDrR1bfWKMyN6PmRdjIKIra6VO38LXvBPIFNopZGl5lbnYT6C0cIvC/QbIJqYRR2lMDvAgdl4hwhsMr8N4gpZALXCrQKNJr7peY+XuArge/s/eOSKtCJwCM2mGqWYK1a390o0G7XDQW/r2qKcV/qdlHY6F0XGZN5v4JSxXgiOgP7HdrBHmnuj5ncaUTPM00z972BZ6ydtAGPRHH1SMagg6tv7dstZKxoD+ZyqDTtdFVOF1q+tgAPRu49kjHoSkAzOqBrBhZFcd1qYXajKxzPmfvSqEw/B86sZhvoSTnk1PGgGE+2PGiy37Ys8ncm2oc2oTYmZpr7AehAvtXq8J0VlEOCbktss/y+KiddtaSK8V7oCmizxbeayEr1oL3gJYFfBHYJdAgsysj9xSaLm0StQp8ehe3aZ6h7u+jEZaNdT5v7AQLrLL42CeUAEwREYGMU5qactGYHvR8L/Cbwt6X9vKrnZy/qepHMycQxh1LFeASwyupaA+lkZ+74MhPXJCpTjPez+t+GGlGbEfmbjBpcbLe2tW+5djsYrr6UA3p87GtU/r9B/n8d2UqqGE+ieHz5UpSvV0Xu+1letlveTjb3cn3wAtJ+oT6EGdRXdzJH/dxpMqJFchb+JKsYwwKL61+BHWLjfIHlAp2RXGkUOLSszIFTLK5O0XFta/SduaLj02ZRxXnE/54/BVfomBzHcRzHcRzHcRxnWDLQZ4wdx3Ecx3Ecx3EcZ1DhirHjOI7jOI7jOI4zrHHF2HEcx3Ecx3EcxxnWuGLsOI7jOI7jOI7jDGtcMXYcx3Ecx3Ecx3GGNa4YO47jOI7jOI7jOMMaV4wdx3Ecx3Ecx3GcYY0rxo7jOI7jOI7jOM6w5j8U9ZDd3BuhJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1224x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_train = np.squeeze(y_pred_train)\n",
    "indexes = np.random.choice(len(y_pred_train), size=10)\n",
    "display_images(tr_pairs[:, 0][indexes], tr_pairs[:, 1][indexes], y_pred_train[indexes], tr_y[indexes], \"clothes and their dissimilarity\", 10)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "name": "siamese-network.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}