Traffic_Sign_Classifier.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Self-Driving Car Engineer Nanodegree\n",
    "\n",
    "## Deep Learning\n",
    "\n",
    "## Project: Build a Traffic Sign Recognition Classifier\n",
    "\n",
    "In this notebook, a template is provided for you to implement your functionality in stages which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission, if necessary. Sections that begin with **'Implementation'** in the header indicate where you should begin your implementation for your project. Note that some sections of implementation are optional, and will be marked with **'Optional'** in the header.\n",
    "\n",
    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.\n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 0: Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "\n",
    "# TODO: Fill this in based on where you saved the training and testing data\n",
    "\n",
    "training_file = '/home/kazuhiro/Dropbox/MyPython/selfdrivingcar/tensorflow/data/train.p'\n",
    "testing_file = '/home/kazuhiro/Dropbox/MyPython/selfdrivingcar/tensorflow/data/test.p'\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "    \n",
    "X_train, y_train = train['features'], train['labels']\n",
    "X_test, y_test = test['features'], test['labels']\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 1: Dataset Summary & Exploration\n",
    "\n",
    "The pickled data is a dictionary with 4 key/value pairs:\n",
    "\n",
    "- `'features'` is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).\n",
    "- `'labels'` is a 1D array containing the label/class id of the traffic sign. The file `signnames.csv` contains id -> name mappings for each id.\n",
    "- `'sizes'` is a list containing tuples, (width, height) representing the the original width and height the image.\n",
    "- `'coords'` is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. **THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES**\n",
    "\n",
    "Complete the basic data summary below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image Shape: (32, 32, 3)\n",
      "Number of training examples = 39209\n",
      "Number of testing examples = 12630\n",
      "Image data shape = 32\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "### Replace each question mark with the appropriate value.\n",
    "\n",
    "# TODO: Number of training examples\n",
    "n_train = len(X_train)\n",
    "\n",
    "# TODO: Number of testing examples.\n",
    "n_test = len(X_test)\n",
    "\n",
    "# TODO: What's the shape of an traffic sign image?\n",
    "image_shape = 32\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "n_classes = 43\n",
    "\n",
    "print(\"Image Shape: {}\".format(X_train[0].shape))\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of testing examples =\", n_test)\n",
    "print(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.\n",
    "\n",
    "The [Matplotlib](http://matplotlib.org/) [examples](http://matplotlib.org/examples/index.html) and [gallery](http://matplotlib.org/gallery.html) pages are a great resource for doing visualizations in Python.\n",
    "\n",
    "**NOTE:** It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f41a8580a20>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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3NgeW2P6/sr2MePHRi0g519o1tEhSL2A34O66omV12/UvN3qyeu5LSimllFJqENkhT6lr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mAAAg\nAElEQVT4bNNa2lUue/zcvAjpfJfNHSu/X+H16UYkVMzu0xrE3Pk5wBvMG76fJSwsNjVERERERKQk\nZVlvZtz9WzO7l0gu9VrBkOLpZnYtcJWZHUJk876I6KmcRAQQlS7ddCsw2Mz+BgwjElLtDJxNBJ+Z\nW4hkbNeY2QVEUHIJkekdYGViKPcBxFDg3ckNoS5wUarzf8RQ6BOIIDSftG4kkRzsoTS0GXcfb2aP\nAreZ2UAiILqGCJaqzayS870CON3MXiPWnz4HeNPdH4a58/XbeGSrHw3MBK41s8HpnM9j3jre9Vbb\nuaRiM4D1zKySIdCXAkPM7A1gPHH/3k7D7gvNAKrMbKWU4fwR4ObUjtZEkryn3H1cyrJeTl3uO8Dv\nzOxJYorCmcT39Uniu1v4nR0CPGNmLxAPqHYmpm9sR2nlvvfHpOszkTjH+919Ysqynt9vBrBWmos+\nivie3GpmJwKdiVEAo9y9xsyGAQPN7C/E/PbDiP/+REREREQqph7y5ul2orft1iLbjiWW47qLCBy/\nAX6VLcVEhetMu/uXwI5EFvDXiYRVe6cEXfly04FfAmsRc86vJpY9+2sqcjKx1NZ9afsPiORgxVxI\nBP/XEIHZSsTyVPle5xFEj/GIgn33JQKqfxLB0n+JrNeVuoIIqq8k5ifPIpKrZS4hAqvsnLcnEo+N\nSW2+yt0vrEN9hfL3pbZzuZR44HFabcdy91uI3vWhxDVtR0x1KGY4MTc6C/R/n2vHw8T3YNcKz+cU\nKr/vEN/lQ4nruTTxnc2GrM/3nU15DPYjhsO/Tsz338PT0n+F5Yt8VpN7X0N8784i/nv5iEjsVmy/\na4nv+sOpbf2IgP15YiWDB4hs7KQHZf2IZfZeJkaoPFTm/EVEREREFtCqpqai+E1EpF6ydcg9rZsu\nocegX9R06Pqj2gvKIjdt8mdcuNOJ9O69YYMcr23b1nTuvAxTp85g1qw5te8gizXd75ZF97tl0f1u\nWdL9rnT0cf3raewKRERkQdfvdzbV1V8xe7b+h97s9ISqqh61lxMRERFZSArIRaSxaRhOEX369NET\ndhEREZEWTgG5iDQqd9+m3HYzmwPc5u77FnzeHzjd3VdrzPaVaNNqgLn7I41Vx5gxY9RD3kK0adOa\njh2X1v1uIRb1/a6q6kG7du0avR4REWkcCshFpDnY28yudfcnCz5vqt7164gs8I0WkB9089m07/rj\nxjq8iLQA0ydP4QKOarB8ByIisugpIBeR5uAd4Aoz6+nus5q6MVS+fGC9te/6Yzqt9ZPGrkZERERE\nmjEF5CLSHJxMLEl3HHBusQJm9hNijfJtifXbbwP+5O7flSi/K7Hc2arE8ml/dven07YniOUDt0g/\nk4EB7v6Ymd1ALAe4hZltBRxArJt+KnAMcA+xtFx/d/97Ol5bYkm1Pdz9iYW5ECIiIiLScmgdchFp\nDj4ATgdONrNVCjea2feAJ4g1zDcnAuIdibXlF2BmPYEbgcFAD+AW4CEzWz1X7ERiffQqYm32Yenz\nPwLPEeuX75Yr3xfYgHhgcC+we27bdsC3xDB3EREREZGKKCAXkebiUmBC+l3ol8CKwD7uPi7NNT8S\nOMLMflCk/LHANe4+0t0nuvvlxHzww3NlHnT34e4+iehJ72pmK7h7NRFcT3f3L3Llh7j7O+7+NjAC\n2MnMskxKewB3ursyyouIiIhIxTRkXUSaBXefY2aHA8+YWb+CzesAb6ZgOTMa+B6wJvBaQfnuwB5m\ndljus+8xf5K2CbnX1bkypbybe/0YEbTvYGYPAb8GflVmXxERERGRBaiHXESaDXd/DriB6CVfJrfp\n6yLF2xT8zmsL/A3omftZl/l7yL8t2KcVpZO51eTb4O6zgbuA3wA/B7509+dL7CsiIiIiUpQCchFp\nbo4ngvE/5T5zYG0z65T7rC/wHfB2kWM4sFoarj7R3ScChxFD30upKfG6lNuIeez9gDsqKC8iIiIi\nMh8NWReRZsXdPzez44FrieXQIIaITwSGm9lfgOWIXvRbC4axZ4YAT5vZi8CDRNB8FLB1marzveMz\ngLXMbLki27J2/svMZgD9gZ9VeHoiIiIiInOph1xEmtoCvdHufj0xR7wmvZ9DBNUAzxO90/cSvd4L\ncPcXgP2AI4CxwMHAXu7+bKk6Cz67luhNf7hMeYA7gffd/eUS20VERERESmpVU6OkwCIi9WFmtwAT\n3P2Muu67/sBf17Tv+uNGaJWItBTTJ0/hgp2PonfvDZu6KS1W27at6dx5GaZOncGsWXOaujnSyHS/\nW5Z0v0vlF2q4ehq7AhGRJY2ZbQJsBOxCrGNeZ9f9/iSqq79i9mz9D31J16ZNazp2XFr3u4VYpPe7\nF1RV9WjcOkREpFEpIBcRqbsdgGOAv7j7e/U5QJ8+ffSEvYVQj0rLovstIiJ1oSHrIiJNYMyYMTXq\nMW0Z1EPesiwp97uqqgft2rVr6mY0e3oA07LofrcsGrIuIvViZj8A/gLsDqxCZAx/EjjN3cc1YdNK\nMrMngCfcffAirnd34El3/9TMTgO2dPdtFkXdB990Ae27rbAoqhIRqZPp733E+RyuuekiIouAAnKR\nJYiZLQM8C/wAOBp4DegCDARGm1lPd3+3CZvYbJhZN2L98FXTR+cDlyyq+tt3W4FOa3ZdVNWJiIiI\nSDOkgFxkyXIaEYB3d/dp6bPJwIFmtjIx7/mPTdW4ZqY1ueXM3H0mMLPpmiMiIiIiLY0CcpElhJm1\nAvoDf80F43n7AV/kym8OXERkCZ8AnOHu96RtNwCfAz8BdgY+A05091vS9nZEj/Lv0uEeAQa5+1Qz\nWwWYBJxKPAC4xd0HmdmJxHrgPwE+Ba6uZIi6mbUBzgT2J3r+RwGHufvnZrYScCmwTdo2Fhjo7qNz\n7Vg1S7xWMCx9IhGQTzKzA4DVgK3cfetUdlPgPKA38DFwnrtfXcn1ERERERGpROumboCINJg1gOWA\nfxXb6O4fu/s3AGa2PPAP4HpgPeBvwA1mtllulyOBMUTAfjdwlZl1SNvOBTYkso1vBXQE7iyosi+w\nAXCJmf0eGAQcCKwFnAGcbma9Kjivs4iHCf2BnwLLA1elbbcArYBNgF7EaIChuX3LZa3cOP3uA4zM\nlzez7sD/EXPve6f2Xmhmu+T2L3d9RERERERqpR5ykSVHFyKg/Dz7wMy2Bf6ePm8FvOPuPYhg8jF3\nvzIVnWhmGwBHEXPQAV519wvTcU4lhrpXmdmraf8N3X1s2t4f+NTMqoDpaf8h7v5O2r4ycIC7P5m2\nXWNmpxPB7Cu1nNfBwDHu/lg61qHAnmnbvcDd7v5h2nYl8EDtlwqAT9LvT939GzPLb/sD8JK7n5Le\nT0hB+p+B+9JnRa8P8HyF9YuIiIhIC6eAXGTJMZUIujvlPnsW6Jle/wY4PL3uDvQzs/zQ9raA595P\nyF64+7QUsH4PWB1oBzyXhsnnrQ28lF6/m9v/KTPb2MzOSXX3Jnq625Q7ITPrAvwod0zcfTyQDXW/\nCtjLzPoC6xC99g0x8mcd4IWCz0YDh+bel7o+IiIiIiIVUUAusuR4i5jL3Bf4D4C7f03MlcbMpuTK\ntgWGA2cTQXzmu9zrb4vU0Yp5/25sRiyplvcx83rqv84+NLODifnqw4C7gGOJ4eC1+a7UhvQw4J/E\ncPmRwP3AUsTwcSg+XL3Sf/O+LvJZG+Z/gFDq+oiIiIiIVERzyEWWEO4+m5gTfpSZtS9SZOV8cWAt\nd5/k7hPdfSKwK7BPBVW9DcwGuuT2nQZcTPR6F3MokTTuWHe/lRhWvzy1BLDu/iWRAC7r5cfMepnZ\nZGJ4+ObAtu7+V3d/GFgpt/u36fj5ed2r515nw/iLVk3MV8/ry/wjCEREREREFop6yEWWLKcDPyPW\nHD+D6ClfjpgTfQBwayo3FBhoZmcCNxEJzs4mMpmX5e7TzWwYkcTsEGIu9kVAVyKreTcWDHQ/A35u\nZvcTPdpnE//+LFXBOV0KnGlmH6a6LiaG4k8lHgz8Lh1343T+WRb4j4kkb8ela7ElsCPzhr9nvfs9\nzeyzgjqHAoPM7GzgRiIYP5yYOy8iIiIi0iDUQy6yBHH3r4jA82bgZOANYkmylYHd3H3/VO49Yrmu\nXwKvE3Oyj3b3EWUOnx8CfizwGDH8fDTwDfArd68pUhYi4VlHIoHbXen3vcRc8mLl8/4K3EMMS3+G\nmJt+qLt/QATJf07neTwwEJgF9E5tOZAI1McSc+jPyg7q7p8RWdrvAA7KV+juk4GdiCzyrwEnEtfn\n5jLtLHcOIiIiIiILaFVTo78hRUQWtZ4DflvTvtsKTd0MEZEFTH/vI87vdzi9e2/Y1E1p9tq2bU3n\nzsswdeoMZs2a09TNkUam+92ypPvd6PmBNGRdRKQJXNv/T1RXf8Xs2fof+pKuTZvWdOy4tO53C7FE\n3O9eUFXVo6lbISLSIiggFxFpAn369NET9hZCPSoti+63iIjUhQJyWWyZ2TtEAjGI+bszgVeBwe4+\nqomaJbUws62BD929zhnLU/b4Xd19eHo/CTitlrndlR77NGArd9+6grJLEXPafwE8D2wP9Hf3ayut\nb8yYMYt3D1qBqqoetGvXrqmbISIiIrJYUUAui7MaYBCRlKs18EOgP/CgmW3v7o83ZeOkpP8DtqJ+\nS4gdk/Yd3oDtyas0qcYORDDeF/gfsDeR+K3igPzgmy6lfbcV69zA5mj6e//jfA7SfFMRERGROlJA\nLou7anefkl5/BBxvZisCQ8itXS1LjEZPrFGhTsDH7v4KgJnVecWK9t1WpNOaqzR4w0RERERk8bFQ\nAbmZbQF0B24j1iB+091nNUTDRBbCNcBTZra6u080s3WJdbL7At8DxgB/cHc3sy2Jdab/RiwT1olY\nYusgd/8OwMz2Ac4AVgDuI4LC8e4+OG0/BTgM+AHwNDAgLZuFmc0BzgSOAJ5191+b2abAecSSXx8D\n57n71an8DUCNux+YnUw6xlbu/rSZbQNcCKwDfJD2vabYRTCzNYDLiXXJPwMudPfLzGwr4HF3b50r\nO7feNHS7FzHioIpYB/wAd18tV/4Q4E/uvnZa8/t84Hdp8yPAIHefWqRNk9LLJ8zsDHcfXO56FOzb\nHzgtvZ7t7m3SpvXM7FlgA+C/wP7u/loqtzKxpvi26dg3Amfmlmcrycw2J743VcAE4Ax3vye144as\nHcTSatfn3q+WlpUTERERESmrXuuQm1kHMxsNPEn8sbscsVbwq2a2UsM1T6RexhFB87pm1gq4H3gb\nWB/YFGhDBOCZlYg1qn8B7Jpe/x7AzH4GXEd8vzcAZgC/zXY0s4HEcOW9gE2IoG+UmbXJHX+nVO8J\nZrYOMWT7SSIAPQO40Mx2qe2kUi/sHcTc5bWBU4Ar0jELyy4FjAKqgT7AAOBsM/sVMSy7toC0H7FG\n9zZEsLmSmfXObd8NuD29PhfYkBjGvRWx3vidJY7bJ7f/BWbWncqvx0jiYcRo4uFI5qDUhh7A58BV\nuW33EEPKewL7M29oeVlmtgLwD+Lc1yO+LzeY2WbACOAoYHJqxz0F7yfXdnwREREREah/D/m56fca\nwGvp9Z+JnvLzgX0Wsl0iC+PL9LsDsDRwJTDU3b8CMLObgONy5dsCA919PDDOzB4hAsfrgMOBEVmy\nLjM7nEjglTkOONzdn8lt/5AITh9MZa5y97fS9guBl9z9lLRtQgpK/0z0vpezLNFrPSX1wN9uZh8S\nAWeh7YEuRM/2TGB8engwu5Y6Mh+7+7DsjZk9TjyoeNnMOgNbA8eY2dLAkcCG7j42le0PfGpmVdln\nGXf/1MwAprr7TDM7uNLr4e5fm9l04Ft3/yS3aai7P5DqvpT0oMDMtgW6ufvGqdxbZnYc0Ut+di3n\nfwTwmLtfmd5PNLMNgKPcfQ8z+xKYnbWj8L2IiIiISCXqG5DvDOzt7pPSH9e4+3gzOxL4e0M1TqSe\nOqbf1Snouwrob2YbEUO9NyDmm+e9lXtdTQxth+hVn9vj6u6zzexFADNbBlgZGGlm+R7n7wNr5d6/\nm3vdHXihoO7RwKG1nZS7TzWzocC1ZnYqqQfX3b8sUnxtYgrJzNz+N6V2b1lbXcA7Be9HAMcTw/p3\nScceZ2ZVQDvguTQaobANYymv3tcjZ2Lu9ZfE9Ye4113MbFpue2tgKTPrXGxIfUG7+hXs25b6JaIT\nERERESmqvgH5ciwY0ABMBdrXvzkiDaInMST7jRQ0vwhMIYau30YEW8fmdyiS+yALLmexYCKx7H32\n38/uwJsFZT7Pvf66xOtMm/QDBUPJC4a+4+4DzOwK4Nfp5xAz6+fujxYc87si9WSKDVdvW7BPYTvv\nBa5M8/F3I4aPZ/vVAJsRw/nzPi7ThlL1wPzXoxKlev3bEnPK+7HgPSz2EKNw3+FET3p+33LXVURE\nRESkTuo1h5xIirVH7n32B/4A4KWFapHIwjsQ+I+7v0vMaV6BSIp2YVoKbRUqz9Y9lpgfDcydx90L\nIPVMTwFWdPeJ7j6RmD98PmAljufATws+68u8ntdviaH2mTVydS9vZpcDb7n7ue6+CfA4EXAWmgCs\naWbfz+1/gZldnOrIevgzq5doL+lcq4lkbXsSCdJGpE1vEwFxl9w1mAZcDCxf7pjZoSl/PQpVuixZ\nduxuwKe5tq0BDK7gOA6s5e6TcvvuSunpOHVpl4iIiIgIUP8e8r8Aj5nZJsTQ3pNTz9kGzD+/VqSx\nLWtmyxMBdhfgYCJo/Hna/hkxamO3NNR8O2LOc209pJnLiYzgzwD/AgYSAX0WgF0EnGNmnxBB3ClE\nQDm+xPGGAoPM7GxiLnNfYp76kWn7GCKp2TZEsH8R8E3a9jnRO90qzUVfmXg4cFeReh4lRrFck+oy\n4BDi2owleqZPMrNriIdrval9OPZIYBiRYf4tAHefbmbXAlelzOufpDZ3BSaVOM4MIjP6KxVcj2L7\nrmRmq6QHLuWMIqYL3GpmJwKdgauBURVkWR8KDDSzM4GbgI2J3vL9y7Srs5mtCUxy90rn6ouIiIhI\nC1avHnJ3H01kjZ5OzL3dlOgZ3MLdn2yw1onU7mIiidr7wGPE3O2t3f1fAO7+PNEjegXwKpE9/Qjg\nx2m98rLS/kcSy229RAT3z5F6mYELiCD16rS9K7B9bl53TcHxJhNZ13cgEiKeCBzt7jenIsOBu4lc\nDA8Bt5KStqVl2HYmhuS/SvRSD3P364q0ezYx13vF1K4hwLHu/oi7TyMeXOwNvEFkJ7+stmtBzFlv\nxbzs6pljiWt/FzH/+xvgV2WC3kuJUQSnVXA9Ct1LDGcfa2bLUaZn2t3nMG+4+vNE5vcHgD+WP01I\ny5btDPwSeJ34Dh3t7iNK7PI4MVrgNeL+iIiIiIjUqlVNjUZaipRiZn2AL939zdxnbxBrZZcKGkVq\n1XPAfjXtu9X6TGixMP29/3F+v4Po3XvD2gu3QG3btqZz52WYOnUGs2bNaermSCPT/W5ZdL9bFt3v\nliXd70qnuda/nvrslNY4PphYn3epwu3ufuBCtkukudiUGLr8e2II+N7EUPFHmrRVsti7tv8gqqu/\nYvbsJeB/6L2gqqpHU7dCREREZLFT3znkNxEZnl8Bvmq45og0O1cAqxLDyJclvvM7uPuUpmyULP76\n9OmjJ+wiIiIiLVx9A/JfEuuQ39uQjRFpbtJc7GPSj0iDGTNmzJLTQ95MVVX1oF27dk3dDBEREZGS\n6huQf0HtGZlF6s3M3iGWrIJI3DWTSGQ22N1HNVGzpBZmtjXwobvX+d8HM2sP7Oruw9P7SUTit0aZ\nq1+X45vZHGLpvKdLbK/zeR9801Dad12p4vZK3Uyf/CHn01/z2kVERKRZq29AfjZwkZkdkdbnFWlo\nNcAg4A5iNYAfAv2BB81s+7SeuDQ//0es/V6fB3bHpH2HN2B7ytmIWCmiIdT5vNt3XYlOa67WQNWL\niIiIyOKovgH568C5wAQzW2Cju7dZmEaJJNW5udofAcenpcqGoKWllkSNnsUyz90/W5T1iYiIiIgU\nqm9Afh3wJtGTNaPhmiNSq2uAp8xsdXefaGbrAhcBfYHvAWOAP7i7m9mWwI3A34CTgU7APcBBaU1v\nzGwf4AxgBeA+Iigc7+6D0/ZTgMOAHwBPAwPS2tnZMOYziXXNn3X3X5vZpsB5QG/gY2J5tKtT+RuA\nmvwqBPmh0Ga2DXAhsA7wQdr3mmIXwczWAC4HfgZ8Blzo7peZ2VbA4+7eOld2br1mdhrQixhxUEWs\nCX6Au6+WK38I8Cd3X9vM2hFrhv8ubX4EGOTuU4u0aVJ6+YSZneHug8tdj4J9+xNrvWNms3MP9dYz\ns2eBDYD/Avu7+2up3MrAUGDbdOwbgTOLrX9e5Lx3I5JTnubuN5tZK+Ih40Fpl4uB/YnvSjZMfQsz\nu5xY6/4FYD93n1zsvAvrFxEREREppnXtRYpaDfi9uw9195sKfxqygSIFxhFB87opiLofeBtYn1ii\nrA0RgGdWAn4D/ALYNb3+PYCZ/Yx4uPRXIuCbAfw229HMBhLLnO0FbEIEfaPMLD8CZKdU7wlmtg4x\ndPlJIgA9A7jQzHap7aTMrDUxPH8ksDZwCnBFOmZh2aWAUUA10AcYAJxtZr8ihvovEJAW6AfcAmwD\nXA+sZGa9c9t3A25Pr88FNgR2IIZkdwTuLHHcPrn9LzCz7lR+PUYSDyNGEw9HMgelNvQAPgeuym27\nB/gfMVpif+JenVjqpJl33lsD/y7YdiKwL3Gvf07c18Lx5AcDRxJD3Tsz73s233mXqV9EREREZD71\n7SEfQ/QSTWjAtohU4sv0uwOwNHAlMNTdvwIws5uA43Ll2wID3X08MM7MHiECqOuAw4ER7n5t2vdw\nYPvcvscBh7v7M7ntHxLB6YOpzFXu/lbafiHwkrufkrZNSEHpn4ne93KWJXpvp6Qe+NvN7EMi4Cy0\nPdCF6NmeCYxPDw9m11JH5mN3H5a9MbPHiQcVL5tZZyJgPcbMliYC0A3dfWwq2x/41Myqss8y7v5p\nmsIy1d1nmtnBlV4Pd//azKYD37r7J7lNQ939gVT3paQHBWa2LdDN3TdO5d4ys+OIXvKzKzzv/LbD\ngZPc/f9y5zm+YP8zc9+F64BDi513ibpFRERERBZQ34B8OHBD+qP0beC7/MbGyoosQvTQQswvn2lm\nVwH9zWwjYqj3BsR887y3cq+riaHtEL3qc3tc3X22mb0IYGbLACsDI80s3+P8feJhVObd3OvuxFDm\nvNGkwK0cd59qZkOBa83sVOAfwPXu/mWR4msDb+aDv2xkShqmX5t3Ct6PAI4nhvXvko49zsyqgHbA\nc2k0QmEbxlJeva9HTj5p5JfE9Ye4113MbFpue2tgKTPrXGxIPQueNwBm9iNiJMWL2Wfu/qaZFR6j\nVFtEREREROqlvgF5Ngf0hCLbagAF5NJYehLfsTdS0PwiMIUYun4bEQQem9/B3WcVHCMLLmexYCKx\n7H3238buRL6EvM9zr78u8TrTJv1AwVDygqHvuPsAM7sC+HX6OcTM+rn7owXH/I7Sig1Xb1uwT2E7\n7wWuTPPxdyOGj2f71QCbsWCuiI/LtKFUPTD/9ahEqV7/tsSc8n4seA+LPcQo1R6I7wFFjlP4vrAt\nizQJnYiIiIgseeo1h9zdW5f5UYZ1aUwHAv9x93eJOc0rEEnRLkxLoa1C5YHSWGJ+NDB3HncvgNQz\nPQVY0d0npuX9JhMJzhZcWiA48NOCz/oybymsb4mh9pk1cnUvnxKGveXu57r7JsDjRMBZaAKwppl9\nP7f/BWZ2caoj6+HPrF6ivaRzrSaSte1JJEgbkTa9TQShXXLXYBqR8Gz5csfMDk3561Gotrnvhcfu\nBnyaa9sawOA6Hie71x8y/3dhdSIJoIiIiIhIo6lvD3lJZrayu7/f0MeVFmlZM1ueCLC7EEm19iSS\nbkFkF28P7JaGmm9HzHku1UNa6HIiM/YzwL+AgURAnwV0FwHnmNknRAB4ChFQFs4tzgwFBpnZ2cRc\n5r7E3OQj0/YxRFKzbYhg/yLgm7Ttc6J3ulWai74y8XDgriL1PEoMy78m1WXAIcS1GUv0BJ9kZtcA\nexAJ1WpbH3skMIzIMP8WgLtPN7NrgatS5vVPUpu7ApNKHGcGkRn9lQquR7F9VzKzVdIDl3JGEdMF\nbjWzE4kka1cDo4plWa/AZcCZZjaZ+F5dQmUJ8vJtX8/MXkkPOEREREREalWvHnIzW93M7jGzCWY2\nMf1MSkmo3mnYJkoLdjHRc/k+8Bgxd3trd/8XgLs/T/SIXgG8SmRPPwL4cVqvvKy0/5HEclsvEcH9\nc6ReZiJj9jAi0HuJCES3z83rrik43mQiO/cOwGtE5u6jczkVhgN3A38HHgJuJSVtS8uw7UwMyX+V\n6KUe5u7XFWn3bGKu94qpXUOAY939EXefRjy42Bt4g8hOfllt14KYs96KednVM8cS1/4uYv73N8Cv\nygS9lxKjCE6r4HoUupcYzj7WzJajTDDs7nOYN1z9eSLz+wPAH8uf5nzyx7+AuDd3A/8kpkDUMO+7\nUFtgPve861C/iIiIiLRwrWpq6t6ZZGYPEUmd7iT+YL+A6KXbFTg0n8lYpLkysz7Al+7+Zu6zN4i1\nspUHoQUxs+2BF939s/S+CzFPfjV3f68x6uw54ICa9l1XaoxDCzB98oec368/vXtvWHvhRta2bWs6\nd16GqVNnMGvWnKZujjQy3e+WRfe7ZdH9blnS/W70nEH1HbK+GbCLuz9pZjsAf3f3f5vZWcCviF5F\nkeZuU2Cgmf2eGAK+NzFU/JEmbZU0hUOBI83s+PR+MPDvxgrGAa7tfwTV1V8xe7b+h94oekNVVY+m\nboWIiIhIWfUNyJciEj5BzE1dH/g3kV39qQZol8iicAWwKjFMeVngFWAHd5/SlI2SJnEk8X14lhgG\n/09iTn+j6dOnj56wi4iIiLRw9Q3I3wHWI7JOOykzNTH/s0OJfUSalTQX+5j0I/W+oHcAACAASURB\nVICZvUNkL4eYNz2TmNM+2N1HNVAdpxGZ8bcusf0J4Al3H9wQ9VXC3f9HIwfghcaMGaMe8gZQVdWD\ndu3aNXUzREREROqlvgH5TcDwNNT3QSJT9bvAL4g/3kVk8VQDDALuIJI+/hDoDzxoZtunpeUaqp4W\n7eAbr6FD1580dTMWa9Mmf8D57NMs5omLiIiI1Ed9A/K/Al8BrdLc8TOBk4ke830bqnEi0iSqc8P2\nPwKOT1nrhxBZ4KUBdOj6EzqtWXaJeBERERFZwtUrIE9LHl2ce/9XIkgXkSXTNcBTZra6u080s07A\necTSY98nlgkb5O5fmNmWxJDzucsqmtkNQI27H5g+amdmw4DfEUvbnejudxar2MwOBY4HliPWch/k\n7m+UKLscsb78DsTa4De4+0lmtgqxdvqqWaK2/NB5M+sP/IFYH35rYk75MOBH7v51Kv8LYmWJH7v7\nN2Z2CnAY8APgaWBAWupNRERERKQiFQfkZrZFpWXd/en6NUdEmqlxRLKzdYGJxFrq3ydWVWgNXAnc\nQCx9CLUPSe8LjAV6E+uv32Zm/3H3iflCZrYzcCqxtvqbxFrzj5vZWrn14PPuI9YO3xzoCNxhZh8Q\nU2uKtSn/WV/gTOAE4DNgOvBLYn10iDnm96VgfCCRlX8vYnm0PwGPmlmPlJtARERERKRWdekhf5L4\n47XUWmz5P2zb1LdBItIsZcFvBzPrAWwBrOXubwOY2b7Af81srQqP9wFwRApeLzSzHYmg+8SCcscB\n57j7w+n9aansvkRW9LlSuzYht3Z46l1vn4rUto7knFTXN2nfe4DfAPeaWWvg10DWw38ccLi7P5PK\nHk709O9ABP8iIiIiIrWqS0C+WpltaxE9ZKsBlyxUi0SkOeqYflcD3YGpWTAO4O5uZlPTtmI914Ve\nKehJfintW6g7cJ6Z5afELAWsXaSsAZ/n1w53938ApCHrtfXaT8mC8WQE8Hczawv8DPgeMMrMliHW\nqx9pZvljfj+1SwG5iIiIiFSk4oDc3d8t9rmZDQLOAd4HtnT3ZxuobSLSfPQkAto3KJ3YrU36KRb4\ntgW+y70vHNbdmhhqXmy/PwKF2d2ri5T9rshnmVJtyvu64P3TxLD17UhD1919VgrIAXYnhtHnfV6m\nDSIiIiIi82lde5HizGx1M3uKyLw8DOilYFxkiXUg8J/0YM6BTvnh6Wa2LtAhbfs2fbZMbv/CdOLr\nFbzfGPhvkXod6OruE7MfYkWHnxYpOwH4oZnNXUvMzAaloeffEkPWO5Rp0/wVR/LKO4GdgF2IHnPS\n3PUpwIq5Nk0Gzid66UVEREREKlKvLOspodE5xJJIW7r7vxq0VSLSlJY1s+WJALYLMbd7T+DnMHd4\n+iPAzenfgtZEZvOn3H2cmXUgeptPMrNrgD2I5G2eq2NVM7uEmOqSbd+9SFsuAoaZ2QRgNHBoKn92\nYcFU9+PA9WZ2bGr78USito+JoPk4MzsD2BLYkRgqX85I4DFgJvP30l8EnGNmn6TzOoVICje+luOJ\niIiIiMxVpx5yM1vNzJ4kljy7DlhfwbjIEudiIkHZ+0QwuhawdcF/6/sR2db/CTwMvE7KsO7u04gg\nfm9iiHsP4LKCOh4EfkQExHsBO7v7R2nb3OHl7n4HcBIwONWxNbBTfv56gX2JYebPAbcAV7n7Vam3\n+0CiJ34skaztrNouhLu/AHwC3OXuc3KbLiBGBl2dzqEr8IsSmd9FRERERIpqVVNTW56jYGZHEmuN\nfwQcoEBcRKT+eh75h5oOXX9Se0EpadrkDzh/l33o3XvDpm5KWW3btqZz52WYOnUGs2bNqX0HWazp\nfrcsut8ti+53y5Lud22r9Cx8PXUom/VwrQE8ZVZ6qqS7a9kzEZEyrt3/EKqrv2L2bP0Pvd42gKqq\nHk3dChEREZF6q0tAfkCjtUJEpIXp06ePnrCLiIiItHB1WfbspsZsiIg0b2ZWGDl+AtwHHOXuM5ug\nSc2GmfUHTnf31SrdZ8yYMeohb2JVVT1o165dUzdDREREWrB6ZVkHMLOfAZsB7YhszJkadz9zYRsm\nIs3SrkTCtDZEIrNriOW+jmzKRjUTlSXkSA6+8Xo6dF25sdoitZg2+X3Oh2Y//1xERESWbPVd9uwU\n4AzgC6Awq3ANscyQiCx5prr7lPT6f2Z2LnAFCsjrrEPXlem05hpN3QwRERERaUL17SE/HDjJ3c9t\nyMaIyGJnvqHqZjYJOM3db07vtwSecPfWZrYKMAnYiQjiuxDLJw4DbgS6A08Ae7n7DDO7AagGViPW\nQP8vMMDdnzOzE4HfunvPXN3HAv3cfcuCNmX1ngocA9zi7oPMbCfiwWJ3Ygm3U9z93rRPB+ASYq3y\nTmn7Ce5+X9q+InA9sDmx9vhDC3UVRURERKRFqtM65DmdgNsasiEisngxsy7AQGB4LUULh3IfD+xM\nrFU+CLgnfbYdsGn6PHMYsf54L+Bp4CEz+yEwAljPzNbMld0DuL1MO/oCGwCXmNk2wN3Eg4D1iQcD\nI82sdyp7CbH++s+BdVPdw8wse4h5NzFVZyPgb8BRtVwDEREREZEF1LeH/Fnij9t3G7AtItL8PZyS\nu7UCfgB8SgTNdTHY3d8A3jCzS4Db3P1xADP7J7BOruwb7n5S2nYM0I/oQR9qZmOIIPzc1Avem+h9\nL2WIu7+TjnUecKe7Z8s5DjGzjYE/AfsATwIXuPu4VP4i4kHB8mbWCdgE6ObuHwDjzWyj1BYRERER\nkYrVNyC/Dbg8/RE6HvgmvzEbrioiS5yDgH8TAXkXYAAw2szWc/dPK9i/hhg+nvmK+R/sfQUslXv/\nbPbC3WvM7GViiDlEb3h/4FxgT+DJWtqQr6c7cGXB9tHMW95xOPBrMzuUeECQZf5qk/b9PAXjmezh\ngIiIiIhIxeobkF+Xfh9dZFsNoIBcZMn0obtPTK/fNrOXgM+IgHgoCw5PL/ZvzKyC9+XW/fqu4H2b\nXPmRwAVmtgbwG+DqMsepAb7Ovf+6SJk26QciIP9p+j0U+IgI2DOt5t+Vb8vULSIiIiJSVL0Ccnev\n79xzEVmy1BC5KLJ/E74FOuS2L2wa8V7ZCzNrnd4/AODuH5nZk8CBxDzwe+pwXCcC7styn20KeEro\ntjfQx91fSnX/KpVpBbwBdDaz1XMPJzao43mJiIiIiNR/HXIRaZF+aGbLp9cdiTnXrYH702djgINS\noLwckdU8r7BnuTZbmdnRwINEArilgTtz20cQQfUody9cgrFcvUOAZ8zsBSJD+s7EGuvbEb3n04Hd\nzewzYsh6Frgv5e7jzexx4HozGwisTgzdr67juYmIiIhIC1ffdcjnsODQ1My3wPvEsPWz3L1UORFZ\nvNQQ2cUzM4EXgR3c/b302cnADenz8en9yIJjFB6znPuBbYCzgJeAn7t7PvC9m5gLPqKCts/l7v82\ns/2IZc/+RvSY7+HuTwGY2b7AhcRDgEnAmakNvYE3gd8C1xDD2N8FLiZ66kVEREREKlbfHvKjgL8S\nfwg/nT7rS/QSDQWmAn8kkr2dt5BtFJFmwN3bVFDmXSKAzmuT29amoPzqBe8PYH5T3b1coLsc8e/M\n/aUKFKs3fX4HcEeJff4B/KPg4xtz2z8Hdi/YflaZdoqIiIiILKC+AfnewFHufk3us/vMbDxwkLv/\nzMzeAC5AAbmINDAzaw/sABxCLJs2s4mbVGfTJr/f1E1o0aZNfl8z/0VERKTJ1Tcg7wU8XuTzZ4ge\ncoCXga71PL6ISG2GAROIYfGLnWv3P5Dq6q+YPbtcknlpNBtAVVWPpm6FiIiItHD1DcgnEUmQhhR8\nvjMxfxwiu/In9Ty+iLRwRYav57dNBzovwubIEmjs2NcXST1t2rSmY8elqa7+inXWqaJdu3aLpF4R\nERFp/uobkJ8F3GRmGwHPEVmWfwrsARxiZmsT8y3vLnkEEWl2zKwt0eO8H/ATYv3tu4HTUhCMmU1K\n7282syeAJ9x9cFO1uTkws/7A6e6+WqX7/OHGm+jQtVsjtkqam2mT3+O8XXand+8Nm7opIiIi0kzU\ndx3y28ysmljy6BxgFvAqsJO7P2pmWwC3Aqc3VENFZJE4D9gWOAiYSIx0uRRYC+hXpPyuxMoKUnvG\n+Pl06NqNTmuu2VhtEREREZHFQL3XIXf3B4AHSmx7mnnZ10Vk8dEfOMDdn0zv3zOzQ4k1u5d394/z\nhd39i0XdQBERERGRJUXFAbmZnQpc4O4z0+uSWvrwVZHF2BxgGzP7h7tnPb7PAVXAp4WF80PWzewG\noBpYDfg58F9ggLs/l8rOAQ4GTgR+TCxVdqi7z0jbNwcuSnVNAM5w93vSthtSlb2BFYjkkb9x9565\nthwL9HP3LQvauAqR9+JU4BjgFncfZGY7EeuQdydGA5zi7vemfToAlwA7Ap3S9hPc/b60fUXgemBz\nYr31hyq+wiIiIiIiSes6lD0AWDr3utTP/g3YPhFZtC4BBgHvmNlQM9sN+IG7j3f32RXsfxjwOrES\nw9PAQ2b2w9z2M4EBwFbA+sBVAGa2ArHu9/XAesDfgBvMbLPcvvsSwfyOwC3AemaWH/O9B3B7mbb1\nJRa6usTMtiHmxt+Y2nEdMNLMeueuw1rEg4V107kMS3PsSfu2AjZKbT2q/GUREREREVlQxT3k+WRF\n+ddmthywBfCRuz/bsM0TkUXJ3c8ys7eBI4A/EAH2NDP7o7vfWMEh3nD3kwDM7Bhi3vlezFsO8Vx3\nfyRtHwSMMrMjUn2PufuVqdxEM9uACHSzf1fGuPvcnmgzG0ME4eemXvDewE5l2jbE3d9J+54H3Onu\nl2XbzGxjIi/GPsCTxIigcan8RUTv/vJm1gnYBOjm7h8A41OCyz0quD4iIiIiInPVpYccMzvFzD7N\neqXMbFNiaOkdwNNm9piZLV32ICLSrLn77e6+OTGs/HfAG8C1ud7jcuY+lEtD3l8mhoRnRudevwi0\nAdZOZfqZ2bTsBziS6KXOvFNQ1+3MC4L3BJ509wWG1ee8m3vdHXihYPvoXFuHA2Zml5jZo7nzapPK\nfJ6C8cyYMvWKiIiIiBRVcUBuZocAJwHDgCnp4xuAmUAPoBvQATihgdsoIouAmfUwswuy9+4+1d1H\nEMPLPwC2qeAw3xW8b0PMSy+2vU36PYcYrTOcGD7eM/1UATvnyn9dcOyRQA8zWwP4DTCiTLtqCvYv\nPFbWnqxNw4Hzgc+J3v0dC8q2KnivTPMiIiIiUmd16SE/GDjW3f/i7tVpiObawGXuPi71Fp1FDE8V\nkcVPW+AYM+uZ/9DdvyMevE0putf8emUvzKx1ev9qse1AHyKQ9fSzlrtPcveJ7j6RWFJtn1IVuftH\nxNDyA4lA/p4K2jd3d+CnBZ9tCnhK6LY3sKe7n5ESuf0olWlFjBjobGar5/bdoA51i4iIiIgAdVv2\nrDswKvd+G6LXKZ9deCywSgO0S0QWMXd/2cweAO4zs78QQ7hXIBI1LkVlAe9WZnY08CCRHG5p4K7c\n9sFm9i7wDZE47ca0csNQYKCZnQncBGwMnE3tSSJHAJcBo9z9yzLlCnu0hxBLub1A/Bu2M/EAYDui\n93w6sLuZfQask+oAWMrdx5vZ48D1ZjYQWJ1IVFddS1tFREREROZTlx7yVkQAntmCmEeZ7/3qSPSk\nicjiaU9iuPZpxLJlDxBTUbbIlicj/h2oyb3Ou594WPcyMex8O3fPB6o3pZ+HgSeIoB13f48Iin9J\nZGkfDBydhsyXczfxYLG2cvO1093/DewHHJ7q6w/s4e5PpREB+wK7Ew8ZLyCyw/+PSBwH8FtiGbjR\nxIODi2upX0RERERkAXXpIX8d2Ax4K2UZ3hr4e0GZPVI5EVkMufvXwCnpp1SZ1XOvC+eVT3X3A8tU\n8bi7n17iuI8Ty4gV23ZAieMtR/S231+mve8yb254/vM7iISUxfb5B7EMW96Nue2fEwF73lml2lDM\ntMnv1aW4LAGmTX4PNti4qZshIiIizUhdAvLLgavMrBexnu9SxJBTzGwlYq7nccBBDd1IEZE8M2sP\n7AAcAtzm7ovdyJxh+/enuvorZs+eU3thWay1adOajh2Xprr6K9ZZp6qpmyMiIiLNSF3WIb/VzJYi\nhnjOAX6bhn0CnEisWfw3d7+l4ZspIkuAwuHtC2sYseziyQ183EWiT58+TJ06g1mzFJAv6dq2bU3n\nzsvofouIiMgCWtXULPzfyGb2E+Brd/9s4ZsksvDM7B1iKb5MDfAF8AwwwN3fb6R6JwGnufvNjXH8\nJZ2ZbQ186O5uZv2B0919tUaqqz9xr1avoOxpwFbuvnWJ7e2BXd19eKX1jxkzpkY95KGqqgft2rVr\n6mY0GgXkLYvud8ui+92y6H63LOl+FyYGbvh6GuIgackzkeakhkgYls0RbgOsC1xNzAX+edM0S2rx\nf8S6557eN3Svet4IImldpcq15Rii3RUH5IfceBsdunarveASbtrk9/gb0Lv3hk3dFBEREZFFrkEC\ncpFmqtrd82tn/8/MTgWGm1kHd5/WVA2Tpufu3xAJ4RpCnZ+edujajU5rrt1A1YuIiIjI4kgBubQ0\n36bfswHSigHnAf2A7xPZuge5+xdpez/gdKA7sT71w8DBWRIxMzuUyKGwLHB+uYrTcPbzgN8DvYDx\nwIHu/nLavhnwV2ADojf2qbT94zS8+g/AFGKFgyOIhGavAzsSIwCqgE7EGtvbErkebgP+RCxdNgVY\n393HmVlb4EvgPHc/I9V/K/A2seTYlcCv0zV5HDjc3T8scV77A38GVgXeAI5192cqOeci1wfgCTM7\nA3gXaJ2Giw8g/r0a5u5/zu1zKHA8kW19DHHv3ijRzjnE8mVHAM8C95IbEm9mGxLJK3sCLxG99Vvk\nhqm3M7PLieXSviJyZgzJhr6nY8x29wUyuouIiIiIFFOXdchFFmtmtgZwAvBwLiv334H1gV8Rw9i7\nk5a3MrPVgTuJIM2IZf22JQJhzGx7Yv3pvwCbAn2Yf956MacD5wA9iID40nSsjsTw6UdSG7YD1kjH\nzvQlAvBNgFHps/2B3wG7Eg8bngCWBjZP7d2RCLo/B/5DDKsmtfX7xFKGmW1T/QPS/j8HNgTaAxcV\nO5kUjF9GrMXdkwhiHzKzFWs75yL6pN+7EWt/Q1zPtYnreyhwrJn9ItW9M3AqcCQR7D8DPG5my5Y4\nPsBO6VgnpPc16VgdiYctY9J53E5c+/ww9b7EQ5lexIOTC83MgJHAhcSa5CuUqVtEREREZD7qIZcl\n2VVmdkV63ZYIWO8FjgYwsx5E4Lm2u7+dPtsX+K+ZrUUEYwPc/fp0jPfM7P+InmiIJf5ucffb0r4H\nArUli7shrXGNmV1IBPwQQfRgdx+Sq+se5gWpED3e56Sh1kQsyAPu/kJ63w9YEdjI3auBcWZ2JHC/\nmZ1EBPFbAUOBLYgAdHMza0U8lGgHPA/sSfQAv+fuU1PQ/aMS5zMQuNjdb03v/2JmWxJB/Um1nPN8\n3P3TdE5T3X1mev0tcFBaH/0tMzuBCJhHEcssnuPuD6dDnGZmOwL7AlcsUEG4yt3fSm3ZJPf5XsA0\n4I/uXgNMSCMW8gH2++7+p/T64jT9Yf2UgG468K27f1KiXhERERGRBaiHXJZkpxLB28+Int+JwInu\nPjVt7w58kQXjAO7uwFSgewrcHjGzE83sNjN7leh1zoYkrwu8ktv381RHOW/lXlcD30v7fgzcbGZH\nm9lNZjaGGGqeH/48JQvGc97JvV4HeDMF45nRqY41gUeJQJz0+3piDnUvonf8n+4+B7iGCOw/MrNH\niV728SXOpzvw74LPnkuflz3nCn2cgvHMl0TPflb3eWY2LfshHiyUm5j9bonPewAvpWA881xBmUkF\n7/NtERERERGpMwXksiSb4u4T3f1Vote3FdFbnAW5X5fYrw3QxszWB8YSgd9TwIHE8OS8wmRe31Je\n0e1p6cDXifnhLwJHEcOg84q19+tatrfJ/X4O+H46r77EEO9niQcW2XB13H0cMR/8d8CHxHDzR0uc\nT6k68w8Sarsm5cwu8ll2zdsCfyQeumQ/6xDzxEspdc9nseC9LHxfri0iIiIiInWmgFxaBHf/DjiY\n6A0+OvsY6JSGpwNgZusSCdCcSN71lLvv5+5Xu/t/gLWYF4S9QW5IuZl1IHqi6+PXwGfu3s/dL3P3\nZ4k55HUJ+BxYOyWqy/QFvgPedvfZxBzzI4me50+AfxHB+BakgNzM9gP6ufvd7n4A8EvgZ2a2XIk6\nf1rw2U8p3aPekBzomh66THT3icDJRdpTibHEdyNvozrs35jLs4mIiIjIEkpzyKXFcPcXzew64FQz\nuyXN/X2EGCo+kHhAdTkRhI8zs8+A9c2sDzE8+VAiAM+GuF8OPGZmzxC9zacTc8Hr4zOgm5ltQwyN\n3pNIblY4HLycx4gh88PN7C9E5vFLgVtzw9hHEVnYb0nvnyGyoI/NZVFfFjjJzD5NbdmXmBv/aZE6\nLwKuM7P/Ai8Q8+rXJx5m1McMYD0ze6XWklH3MDObQAzNP5SYUnB2Peq9HTjHzIYQc+y3An5LPLCo\nxAxgJTNbxd1LDYsXEREREZmPeshlSVWqx/JEYgj1een974kg9p9EkrPXiYzlEMHsc0Sg+zTQFTgD\n6A3g7v8CDiCycY8BPiI3p7wObQK4gwiS70zH2go4BuhuZqXmXM93vDT/u196+zyx5Nm9wGG5Yo8S\nc7ifSe9fAmYS5565gsg0fzPRc9yT6DFfoP3ufidxTQcDrxI97du5+4RibazApcTycaeV2D73eO5+\nB5E4bjDzhvvvlM8JUGrfQu4+A9iZaP9rxAOFWyg/3D5/vHuJYfpjzaxLmX1EREREROZqVVOjkZYi\n0rKZ2arAT9JUgeyz/2fvzuPsnu4/jr+SiRCpSLR2EYR8JCOIZEitQam2pLXWHmtJk9j5qZakiqCC\nWmqnlNqqRdW+VGIfVEjIx5KQ2IKITESUSeb3x+dcvrm5d+bOmMlkct/Px2Meufe7nHO+3+9IfL7n\nc865BFjW3Q9piTr7DTu2brnuDa2St+SbPW0q5/z85/Tr17+1m9JiOnRoT7dunZk5cw61tfNbuznS\nwvS8y4ued3nR8y4v6Xm3+HxBSlkXEYk0/YfTsnfVxPjx/Ynl0FrElQftS03NXObNK/N/0DfpR2Vl\n39ZuhYiIiEirUEAuImXP3cenNdtHA2sAU4Fj3f3+lqqzqqpKb9hFREREypxS1kVEWkF1dXWdesjL\nQ0VFe7p06aSMiDKh511eKiras9VWA5kz52u9YC0DSlkvL0pZF5E2x8zeBrIDo+uAz4hJ5Ia7+7st\nVO8UYKS739AS5RepcyQwyN23bcr5R/zldpbr3qOZWyUiIovS7GnvcEWXTqy7bp/WboqItFEKyEWk\nOdUBRxGzxkPMPN4HuIKYuf1HrdOsFtPkFKPluveg27rrN2dbRERERKSNUUAuIs2txt0/ynz/wMxO\nI9ZHX87dZ7dWw0REREREFicKyEVkUcit5z0PwMy6EmvBDwaWAe4GjnL3z9L+wcAooDfwJbFO+mHu\n/kXafwSx/vnyxLrlBZnZhcRyZnum778FTgW6uPtXZtaLWD99BWA+sab5Pun7I8Awd3/XzHoAU4DT\niPXhbwRmZOpZOh3/ObEWem1Tb5SIiIiIlI/2rd0AEVmymVlP4GTgvlxADdwJbAj8lEhj702ktGNm\n6wC3A5cABuwJbA/8Ku3/MXAh8Bvgh0AVC45bz3oA2CrzfWviReSA9P1HwDh3n0uk1f+CWO5sILAU\ncFdeeZsDmwB/ylxfO+CW9PUXCsZFREREpFTqIReR5na5mV2aPncgesf/CRwLYGZ9iSC5l7u/lbbt\nD7xmZusR47KHu/u1qYypZvYIUJm+Hwrc6O5/S+ceAhSbLO4/QFcz6wM4EWg/AGwBPEUE5PenHvv9\ngR+7+9hU7n7ANDPbAXg9lXeBu7+d9gO0Ay4GegJbufuXjb5bIiIiIlK21EMuIs3tNGAjYEvgfmAy\ncIq7z0z7ewOf5YJxAHd3YCbQ293fJILkU8zsb2Y2nuglr0iH9wFeypz7aapjIann+wlgENGz/TZw\nL7ClmbVP2+8HehHB9XOZc2cSQXzvTJHv5FXxQ+BIYLa7z2rgvoiIiIiILEABuYg0t4/cfbK7jwf2\nIgLdu80sF1AX60WuACrMbENgIhEIPw4cAtyad2z+mpBfUdyDwLZEuvoTwJNED3kVEUi/2lCbMt/z\nj6shgvoNUk+9iIiIiEjJFJCLSItx96+Bw4CNSSnrRK9z15SeDkBKKV8u7TsAeNzdD3D3K9z9BWA9\nvg3CJxDBdO7c5YB162nGA0QwvgWxHvp4IpX+6LQP4C2glkhpz5X7/VTvpLSp0BJnE9z9CeAM4OyU\n+i4iIiIiUhIF5CLSotz9eeAa4DQzWyWlp98P3GBmA8xsU2JCt8dTb/UMYEMzqzKzXmY2hgjAl05F\nXgLsZWaHWQzkvhLoVE/944kZ1H8CPOHudcT48V+mduDuc4CrgUvMbJvUS38jkaL+cCoqv1c+60Ii\n5X50Y+6NiIiIiJQ3BeQi0pwK9SJDLFH2FbHUGcCBxLjvh4klzV4Bdk37LgKeBh4CxgLdgd8D/QBS\nj/TBxCzr1cCHZMaUF/EQ8KG75yZ/G0f0iD+cOeaEdNzf0/45wA6pl7++a8tlAhwLHGZmmzTQFhER\nERERANrV1RX9f0wREWkhmww7qW657j1auxkiIvIdzJ72DlcctCfrrtuH2tr5rd0caWEdOrSnW7fO\nzJw5R8+7DKTnXV+GZLNQQC4i0gqqq6vramrmMm+e/kFf0lVUtKdLl07oeZcHPe/yUlHRnq22Gsic\nOV8rQCsDCsjLy6IKyLUOuYhIK6iqqtI/6GVC/wNXXvS8y0uHDu3p2LEjc+Z83fDBIiIFKCAXKVNm\ndh0whBgbnf/2rw7Y1t3HNqHcXxGzji8DbErMcJ77/n/AJe5eUbyERtc3W5oJZAAAIABJREFUEtjG\n3bdrrjKL1NMDmAKs5e5Tv2t51dXV6kErE4uqx7Sysi8dO3ZssfJFRESk+SkgFylfRxEBMsDewPHA\nAL4Nzj9tYrnnABcA1wLvExO05b5PB+5oYrnF/BH4UzOXWUyzjfE58vo76dJ97eYqTspczbQpjAb6\n9evf2k0RERGRRlBALlKm3H02MBvAzGYB89z942YoenliCbN3U9kLfAc+aoY6vuHuXwBfNGeZi0KX\n7mvTbd3erd0MEREREWlFCshFpKiU1g6x5NgqwBbEeuDnA5sDSxFLjx3u7m5m84le5EfNbCywTfr+\nmJn9h1i+7DF3b5/K70msK74lsf74GHe/uEA7OgCXAb8gUt8fBYa6+/spZX2Qu2+bjt0ROA/oCTwO\nvAl8z90PSdfzKbA6sEuq8xR3vzGduxqx7Np2wLLARGCEuz9VoE2/TNfTA3gL+K2739W4OywiIiIi\n5UzrkItIQ/Yn1hH/GbF2+N1EALoh8EOggkhTB1iVSHnfjVhXfJX0fde0DVLat5ktDTwI1ABVwHDg\nTDP7aYE2jAC2An4E9Ae+R7wUyMmVuQ5wF3AzsDHxsmBYXlnD0vZKIn3+cjNbLu27MbV3s3T+NODP\n+Y0xsxWBG4AzgV7AdcDfzKxrgbaLiIiIiBSkHnIRaUi1u98LYGbLEj3Vf3b3uWnb9cCJAO4+3cwA\nZrr7Z2n/N9/T55wfAz8ADk5p55PMbAQwr0AbegBzganuPtPMDgK+X+C4w4Bn3X10+j7SzHbIO2a8\nu49JbTsNOJoIzp8B/gnc4e7vp/2XAfcUqGd14u/P99x9GjDGzMYDXxY4VkRERESkIAXkItKQt3Mf\n3P0LM7scGGJmA4D1gU2AD5tQbi/g9RSM58q/vsixVxITz32YUt//CfylwHF9id7vrKeBbpnvb2Tq\nm51eEiyVNl0O7G1mmxPX1p8CmUTu/pKZ/Rt42Myc6JW/2t0VkIuIiIhIyZSyLiIN+SbINLPOwPPA\nPsBrwGmk3vEmKHnRVnd/FVgL2JeYuf0s4IECh9ay8BJu+d+/KnBeOzNrBzwMHAe8A5wLHFhPmwYT\ny7rdDuwMvGBmGzZ0LSIiIiIiOeohF5HGGESMC+/j7rlx2zuxcNBbijeAdc1smVzPspmdB3Rw92Oy\nB5rZAcD/3P024A4z2wx4Ko3lzppITDyX1Z8Y896QPsQ49R+4+6ep3l8XOtCiW/0wdz+ReEFxmplN\nJNLwXy6hLhERERERBeQi0igziAnVdjOz54EdiEnSZjWhrAeIVPcrzexMwIBfAXsVOHZ54Ldm9gkw\nhZhobhrwSd5xVwLHm9lJRFr7nkSQ/WYJ7fmMGL++r5ndTfR+jwIws47pmHaZY4ea2WfATcAGxDj3\nF0uoR0REREQEUMq6iDSCuz8DnA5cCownUrp/DaxkZqumw+ryTsv/nitrHvBzYmb2F4ELgOPd/f4C\nh19KjBm/gegF3wgYnOulz5Q5FdgDOJToqR4I3EnhNPUF2ufu7wFDgZOACcD/EbO71xLLvmWPnU7M\nHL9Has/FwMnu/kg99YiIiIiILKBdXV3B/1cWEWlzzKwSWMrdX8psuwd4zt1Pb72WLaz/8N/Wdem+\ndms3Q5YQNdOmMHrwDvTr17+1m1L2OnRoT7dunZk5cw61tfNbuznSwvS8y4ued3lJz7spwzIbV09L\nVyAisgj1BK41s72B14Edge2Ak1u1VQVcPuQX1NTMZd48/YO+pKuoaE+XLp1a9nn3W5fKyr4tU7aI\niIi0GAXkIrLEcPe7zWwMcA2wIuDAXu4+oXVbtrCqqiq9YS8T6lERERGRYhSQi8gSxd1HA6Nbux0N\nqa6uLpse8srKvnTs2LHhA0VERETKjAJyEWkUM3sbWDOzqZZYVuxyd/9TE8obAoxy9+80oNrMlgKG\nuPvVRfb3IGZoXytN/taqhl5/H12692ztZrS4mmlvcRZobLOIiIhIAQrIRaSx6oCjgNvS96WA7YFr\nzGyGu9/YxDK/q32AU4CCATkwlVhD/eNmqOs769K9JyusW9nazRARERGRVqSAXESaosbdP8p8v8HM\n9gF2A5oSkDeHepdxTEukfVTfMSIiIiIii5ICchFpLrWk9b7N7FTgSGBZYCww3N2npX2rAtcCWwGT\ngHuzhZjZBsBFxBri7wAXuftlad/y6dztiF71fxProG+StmNm84C1geuBV4CfARXAzsTa5Gu5+1Qz\n6wOcD2xO9PJXA4e7u5vZNsS65+cAvwO6Av8ADnX3r4u1w91nN8N9FBEREZEyUW+PkohIQ8ysg5nt\nBuwA3GVmI4j08b2BzYDpwINmVpFOuQNoBwwgAt5jMmUtQwToY4ENgBOAU81sv3TI6cBKwA+BQcBG\nwG+BJ1M504i09HfT8QcB+wK7ArNJqfFm1g64mxj7vmEqryK1J2c1YHdi6bRd0+cDG2iHiIiIiEjJ\n1EMuIk1xuZldmj53AuYA57v7zWY2FRjq7uMAzGwo8D6wk5lNIYL0Nd39PWCSmQ0A9kxl7QdMd/dR\n6ftkMzsLOBa4CegBfA684+5zzWwPoJ2715rZLGCeu3+c6gW4x92fTd97ZNrfCbgM+LO7z037rwdO\nzBzTARjh7pOAV83sfqCKWFKtYDuafjtFREREpBwpIBeRpjgV+Gf6/CXwgbvXmVlnYA3gVjPLTtS2\nDNCLCIQ/TcF4TjXfBuTrAxubWTb1u4KUCg/8CbgT+NjMHgb+Dvytnna+XWiju39hZpcDQ9ILgfWJ\ntPcP8w59M/O5hkhtb0o7REREREQWooBcRJriY3efXGB77u+UPYDX8/Z9CvyIhXuSv8p87gA8TIwL\nX6jH2d0fM7PuwM+JseFXECnlB+Yfm3xZaGN6cfA8Mcnb3UQw3Rs4Pq++2rxT2zWxHSIiIiIiC9EY\nchFpNu4+iwhyV3X3ySlonwb8ETBgAtDNzNbJnLZJtgiiJ/3tzPmbE8usYWbHAAPc/a/uvjdwCDG2\nGxq3dNogYqz5IHcf4+6PEmnoJaWdN9AOEREREZGSqIdcRJrb+cBZZvYxEWCfSgTVk9x9lpk9Alyb\nJn9bBxhOpINDLJk2ErjSzM4DehLp4X9M+9cAfmVmBxM97nsAL6Z9c4hgf11gSpG25QLuGcD3gN3M\n7HliQrphwKwSr7G+doiIiIiIlEQ95CLSWA31RJ8HXEWkcb8IdAd2TL3nAL8EPgGeAs4ELsyd6O6f\nAz8B1gP+m8q4yN3PTof8DngCuCvtXxbYP+17lJg1/WVi1vNC7axL9TxDzJR+KTCeSDX/NbBSWpat\nIafW0w4RERERkZK0q6trTJaniIg0hwHDT6/r0r1nazejxdVMe4uzBm9Nv379W7spraZDh/Z069aZ\nmTPnUFs7v7WbIy1Mz7u86HmXFz3v8pKed4uvoqOUdRGRVnDZkJ9QUzOXefOW8H/Q+61OZWXf1m6F\niIiIyGJJAbmIiLSoiRNfae0mtKqKivZ06dKppBcwlZV96dix4yJqmYiIiLQ2BeQibYiZdSXGL+8K\nrEyss30lMc66Lh0zn5g9fGyB87cBHnX3CjPrQUx+tpa7Ty1w7BBglLuv3VLXszjJ3ptFUd+w6x9j\n+TXXWxRVSRsxa+obnAFlnd4vIiJSbhSQi7QRZrYC8CzwLnAwEYxvClxCzEZ+VAnFPAlkJy1raBKJ\ncppkIv/etKjl11yPFdbdcFFVJyIiIiKLIQXkIm3HOcBcYsbyr9O2d8xsLnCnmV3k7m/WV4C71xLr\nhEse3RsRERERWdQUkIu0AWbWkVgu7PhMMA6Au99jZtsD72Q2b21mlxDLhz0LHODu01Ja9mPuvtCS\nh2m5r2uBrYBJwL2ZfdsAfwHuA/YFznT3P5rZEcD/ASsC1cBR7j4hnTMFOJdYUmzjVOYh7v7fItdY\nRaxhvgkwDRjp7rem1PnDiWB5W2J5sluAE4AjiV7tp4GjM3X/Evg90INYCu237n5X2ncUcByR8j8B\nOMbdn8zem0w6/+7EGuirAw+n+/hZKmdHYom3nsDjwJvAcu5+cKHrExERERHJp3XIRdqGnkBn4PlC\nO9398bxA/TBgGDAA6Eb0rudk09CzSznckb4PSMcfk1dND2BpImC+2cx2AU5L9WwMjAMeNbPlM+eM\nAs4C+gKzgIsKtd/MVgQeJNYt3xgYDfzFzHLTc28OvAIMBB4ARhJB9VFAP2AqcL+ZdUpl3UCscd4L\nuA74m5l1NbN+xEuCIwFLbb6tyL0B+A3xImRroAo4PrV3HWIN8ptTe6vTfSinFH8RERER+Y7UQy7S\nNnRNf84q8fg/uPs4ADO7BjiiyHG5ieAqgc2ANd39PWCSmQ0A9sw79mx3n5zO+Rtwlrvfl/aPNLOf\nAfsDl6Zt17n7v9LxY4Dbi7Rjb2CGux+dvr9hZt2ATun7/FTX/1JZw4H/c/d/p++HEz3h+xPBcQfg\nPXefBowxs/HAl8RLhfnAVHefama/A/5lZsVeTp7m7i+kOm4ignKIFx7PuvvozLXvUKQMEREREZGC\n1EMu0jbMIHqvu5V4/OTM51nAMg0c3xv4NAXjOdUFjsumxfcGzjWz2bkfYEMiTT4nO6a9BliqSP0G\nLJDK7u4Xuvtz6etHmWB8JWAF4LnMsbVE9kBvd38J+DfwsJm9ZmZnA2+7+5dE7/orwAQze4FIe5/k\n7oXWoqqrp/19Wfj+PF3k2kREREREClJALtI2vEUE1gXXQzKzO81su8ymeXmHtKNh+cd8lX+Au2e3\ndQCOBjbK/KwPnFFfGUV83cD+L4t8zqpIP7j7YGIG+tuBnYEXzGxDd5/r7psRY9EfAw5K+4rNrp7f\n/tw9qmXh+1XKPRYRERER+YYCcpE2wN3nEROZDTezBYaapLHcuwDvFTq3RBOAbmlsdM4mDTUL6O7u\nk3M/wO+Icd6N9QbRu/4NM7vVzI5fqFL3GmB6th4zW4p4WeEW/ujuz7v7ae6+AbFU3I/NbKCZnZLG\n3J9AvEDoBGzZyPZOZOGXI1o8WkREREQaRWPIRdqOUcSM6Q+Y2e+JIHNbYpKyC93dm1BmOwB3n2Rm\njwLXmtkIYB1gOJGmXcz5wFVm9gbwFDFOfU8W7CEv1U3A6WZ2DnAVESDvQkzM1q9I3aeb2QdEWvnJ\nxIRztxJ/rw01s89SuRsQY8dfJJaNG2lm04lZ0wcRk+W9DKySV0d9Pd5XAseb2UnAP4nr3ooFU9xF\nREREROqlHnKRNsLdpwNbEOPDbyTGQh9N9EqfkDm0MTN9Z4/dC/iECK7PBC5soD23Ab8FTk9t2RbY\nOTfpW2Pa4e6zgJ8B26SyTgT2cfeXi5wyhgjcryTGjq8GDHL3Gek+7QrsQfRkXwyc7O6PuPt44OBU\n/mtEIL9fkZcZRdvv7lNT+YcSwfxA4E5KT9EXEREREaFdXZ1W6RERaYw0K/1SaQK53LZ7gOfc/fRS\nyth0+Ll1y6+5XsMHStmYNfUNzhi8Kf36afRDW9ahQ3u6devMzJlzqK0tNF+kLEn0vMuLnnd5Sc+7\nxecIUsq6iEjj9STS+/cGXgd2BLYjetxLcumQbampmcu8efoHfUlXUdGeLl06Nfy8N96Uysq+i65h\nIiIi0uoUkIuINJK7353WVb8GWJGY4G4vd59QahlVVVV6w14m1KMiIiIixSggF2lDzCz//+Y/Bu4C\njnX3OemYKcBId7+hBdswyN3HtkT5rcXMhhD3bZ0GDwbcfTQwuqn1VVdXq4c8qazsS8eOHVu7GSIi\nIiKLnAJykbZnV+BpYs3t7sTEZucCw9L+AcDnrdO0Nu0W4J5FVdmoG55gxe69FlV1i62Pp73O0aBx\n0yIiIlKWFJCLtD0z3f2j9PkDMxsNXEoKyN19Rqu1rA1z9/8B/1tU9a3YvRerrrfxoqpORERERBZD\nCshF2r4vsl+yKetmVgH8ATgIWBZ4ABgK7ARcBKzk7vPTebsD57t7DzNbFrgA2J1Y/usfwAh3/yqv\nro7AH4F906b7gaPcfWbafxRwHLAysZzZse7+ZKGLMLOdiOXW1icmSjve3R81s5HAxsAKQCWRIfAc\nsdzaPmn7I8Awd3+3oXrN7Kx0P7oS67oPc/dXU8r6KHdf28y2Af4CnEMsK9c13YND3f3rVM5+wO+J\n9cvvItYtn1TqLOsiIiIiIlqHXKQNM7MfACOAvxY55AzgAGAIsVb2KsDlRADZiZgZPGdP4Ob0+Rpg\nc2BnYAdgy1RWvtFAfyLAHwR0AW5PbetHpNIfCRjwBHBbkeuoBO4G/g5sSKSP32lmK6VDBhNrr28H\nVANXAL8A9k/XtVS6pnrrNbNdgcOJFw2VwAfAtZmmZNeBXC0dtyPxEmB34MBUzpbpHp0NbALMAX5Z\n6NpERERERIpRD7lI23NfmlitHdHr/QkRfBZyGHCcuz8EYGZHELOBzzGzfxFB+MNm1gn4GbCVmXUF\n9gC2c/dn0nm/Inqpv5HOGQb0d/eJadsQ4JMUYPcA5gNT3X2qmf0O+JeZtc/1ymccAjyRJkoDOCf1\n0ndN36e7+1Wpjq5EIP7j3MRyqbd6mpntAHQuVm9q0/+Ad919WupJLzaQuwORFTAJeNXM7geqiEB8\nKHCLu1+d6h8K/LhIOSIiIiIiBamHXKTtORTYKP38kEhDfyr1ln8jff8+8GJum7tnU6pvBn6RAtWd\ngffc/SVgXeLvhux5T7r7pXntWAfoCDxtZrPNbDYwjXhR0Cu16xVggpm9AJxApHQXmlbcgBeyG9x9\npLu/nr6+ndnVK9XxXObYmcTSY72JtPli9d4MzAWmmNk4InPg1QLtyXkz87mG6ImH6MWvztQ/D3i+\nnnJERERERBaigFyk7Xnf3Se7+1vu/izRu7wssFfecV83UM59RC/wICId+9YSz8vJZdhswbcvCDYC\n1gMedPe57r4ZsC3wGDFu+wUzW7VAWQ3V+WWRz1kVQIW7f1msXnefToxR3wV4mQjWnzazZQoV6O61\neZvapT9rM5/z94mIiIiIlEQBuUjbV0f8t7zAf8/uPotIZ98ot83MNjazaWa2dJqg7R/E+OgdiHHb\nAJOBeXnn/dzM8nuA30rH/SC9IJgMzAYuBFY2s4Fmdoq7P+7uJxCBcCdiPHq+N7L1pTqfNLP8lwy5\nemuJseO5Y79PvAjw+uo1s58Ch7v7fe4+jEjDN6BvgXrqM5EYO5+rvz15Kf0iIiIiIg3RGHKRtmcF\nM1s5fe5C9PK2JyZFy3cR8Aczex/4mAiWn0xLfEEE4f8C3nD31wDcfbaZ3QBclMZG1xGzny+wRre7\nf25mVwGXpzHmHwPnE2ujTwGWA0aa2XTgYaInvjPRM53vcmCimR2T2rMX0AcYS6ShZ+udY2ZXA5ek\nemcSs6G/AzyUzitW73rAeWb2IfBfYnb4OcSs7n0KtKuYS4DHUtr7E8TEej1YcFI4EREREZF6qYdc\npG2pA+4A3k8/LxJjqndy96mZY3LOJnrBbwXGEUHrEZn9jxG92jezoGOA8cCDwL+JZcVOLVD+8UQQ\n/HfgKWLCtJ+6e527jwcOBk4EXgNOBvZzd8+/qNS7vjsxPv4VYDdgZ3f/sMh9OCFT7zgiqN7B3b+u\nr153vyddxwVp357A4JRNULI02d0wYCTxDL4HPA18Vd95IiIiIiJZ7erq1KEjUq7MrAux9Felu7/d\nys1pM8ysCpiVmXQOM5sAnOvuN5RSxs9GXFC3YvdiE7yXj4+nvc7Ru/SjX7/+DR/cRnXo0J5u3Toz\nc+YcamsLzWkoSxI97/Ki511e9LzLS3reLT5HkFLWRcqUme1B9EQ/qWC80X4IjDCzA4EPgX2ANYgZ\n3ksy6sAtqamZy7x5Zf4P+sb9qKxs7BB+ERERkSWDAnKR8nUOMbv54NZuSBt0KbAWMXxgeeAlYtjA\nR6UWUFVVpTfsIiIiImVOKesiIq2gurq6Tj3kbVNlZV86duxY8vFKcSwvet7lRc+7vOh5lxelrIss\nocws/2/wj4G7gGPc/YtWaFKLMbNRxARx84E13f3zzL6RwDbuvl0z1fU2MLCeieAWK3+64SlW6W6t\n3QxppA+nOQfCEj3mXURERBYdBeQirWNXYlbuCmKZsCuBPxIzdy8RzKwrcBpwGPBQNhhP/gj8qZnq\n6gnMaSvBOMAq3Y0e6/Vr7WaIiIiISCtSQC7SOmZmxht/YGajiXHJS0xAToytrgMecfdp+TtTNkBz\nZQRsTyzNJiIiIiLSZiggF1k8LBCYmllHogd537TpfmCEu39mZi8BV7r7n9OxDwFLufug9P1w4AB3\n39rMtiDWIt+ECI4fBw5x9+lmNgQ4HPgI2Bb4tbvfbGanAkcCywJjgeGFAupU1+rEmt7bE2npfyPW\nCF8NmJLqnGxm17v7IXnnjgQGufu26ftOwJnA+sDrwPHu/mjatytwBjGR2ivASe4+NlPc9qluzGw7\nYEwq5z1iKbIr0775wAHE2uTrAc+le/VO2t8bOB/YHKhJ9/kPZrYxUA2s4O6zzWw14F3gYHe/Pp37\nJHCNu19b6F6JiIiIiORr39oNECl3ZvYDYATw18zm0UB/YCdgENAFuD3teyBtw8w6AJsBA8ysIu3f\nAbgvrTF+DxHM907bewK/ydSzORHgDgQeMLMRxBJee6dyp6ftFeQxs6WAx4BOwFbAnsDPgHOBqcCm\n6dAq4Ogil1+XyqoE7gb+DmwI3ALcaWYrmdlGwF+A04G+wI3AvWa2TqacbYD/mFl74DbgVqAXcCpw\nqZmtnzl2FDCceEnxAyLQx8y+T7yAeDe1/dfE0mZHu/tLwCfpOnP1zQe2SOd2AQYA9xW5ThERERGR\nhaiHXKR13Jd6a9sRPdGfEL3SmFknInW9v7tPTNuGAJ+kwPVB4KZUTn/gLSKw3MTMnid6u88mAuXT\n3f2CdOxUM/sHESDnzAfOcvf/pXpOBIa6+7j0fSjwPvFi4N951/ATYFVggLvXAK+a2TDgX8Bvicnq\nAD5x99kN3I9DgCfcfXT6fo6ZLQt0BY4neqpvTfsuMbNBwFDgxBSwv+Pus8ysG7AC8FHq1b/ZzN4H\nPsjUNcbdH0/XdxnfDhPYD5gDHOHu8wFP2QKnEWPdHyZehNwLbE0E31ukc7cF3N2z9YiIiIiI1EsB\nuUjrOJRIl25HBNPDgafMbANgZaAj8LSZZZdaaEf0+v4b6GxmfYjAcCyRIr4lMA+Y5+4vApjZDWZ2\nLLAx0AfYCHgiU+ZHmWC8M7AGcKuZZddDXCZTb9b6wOspGM95ivh7ZV1gVmpzKQx4IbvB3UemdvUG\n9jSzIzO7lyJ6/iEzftzdZ5rZn4Grzew04uXAte4+K3Pum5nPNams3PW8kILx7PWsknrAHwCOStu3\nJp7ZA2a2QmrD/YiIiIiINIICcpHW8b67T06f3zKzF4EZwF7Ak2n7FkSPbdZ0d//KzMYSvbJbAzcA\nqxPp1B2IHnTSOOfn089DxEzuOxOp6DlfZj7n/j7YgxjDnfVpgWv4ssC2irw/S/V1Pfs6AOcQ15k1\nN/25PTHuGwB3H25mlwK/SD+/MrPB7v5AOuSrvHJyLw0aup6HgGvSjO6rA/8BJhLPaXti2IGIiIiI\nSMk0hlxk8VBH/PfYnkhBnwf8wN0np8B9NnAh0XsOEXQPIsZ+j0s/WwA78m1P7a7ADHcf7O4Xu/uT\nxBjygr3WqRf5I2DVTL3TiMnlCi2Y7UCvtLxZzuZEcP1W5rpK8QbRe/8NM3vSzPZK9ayda1Nq15HA\nT9IY+k1Jvf5mtrKZXQK86e6j3X0z4FFgcAltcKB/3nj5zYGP3X2mu08HXgVOAp5x97pU7z7E0nXj\nSrxWERERERFAPeQirWUFM8sF112ImcnbA3e7++dmdhVwuZn9ihiLfT4R9E1J5zxI9Bq/6+4fmtlH\nxFj0bYgJ2SB63NdMs45PIXrfdyNS5Ys5HzjLzD4mAtRTiaB0UoFjHwImA381s98AKwIXATe5e00a\nz11qyvrlwEQzO4ZIM9+LSLEfC7wDjEvj4/9NBNfHANsRvf0v59LuiZ783YB2ZjaGSMHfmJgsriE3\nERO+XWFm5xEvIUYBl2SOeZCYoO6M9H0ccDPwb3evr5dfRERERGQh6iEXWfTqgDuIydLeB14kxmjv\n5O5T0zHHEwHv34lxzP8Dfpp6ZXH314gZ0Mel7/OBp4H/uvuMVMZtxIzktxNLdg0CjgN6pxnSCzkP\nuAq4IrWrO7Bj3hhsMnXmep6fIZYd+ydpcrrMtTYo9XrvToytf4UIqnd29w/d/Vlgf2LW84nAYcDe\n7v4EkSr+aKacr4FdiN728cRs7Ve5+zUNtcfdPycmr1s3XftFwPnufnrmsAeIMee5cfi5XnHNri4i\nIiIijdaurq7UjFIREWku+4/4U90q3QuNBJDF2YfTnAN32Yh+/fqXfE6HDu3p1q0zM2fOobZ2fsMn\nSJum511e9LzLi553eUnPu9Rsz6bX09IViIjIwo4+cHNqauYyb57+QW9TNt6Iysq+rd0KERERWUIo\nIBcRaQVVVVV6wy4iIiJS5hSQizSRmd0DfOjuh2W27UOaHCw79tjMfgfs5u6bmNl8YJC7jzWzKcBI\nd89f0gsz60FMxrZWZmx5c7X9OqDO3Q9p5nK3AR5z9zY3P4WZDSGexTqLor7q6mr1kJeJior2dOnS\nSc87T2VlXzp27NjazRAREWlVCshFmm4csF/etkHAe8Qa4dnJwAYS61YDrELhdb0LaalJHo5qoXKh\n5drc0m4B7llUlV19/TOsoTHkZaTQMvfl691pDtCosfgiIiJLIgXkIk03DjjDzJZ19y/Stm2JmcrP\nNrOlM8txDSRmL8fdP1r0TV2Qu89u7TYsbtKz+l+DBzaTNbobPdftt6iqExEREZHFkAJykaarBr4G\n+hPrZK8BrEkE3qcAWwCPmlkvoCvwOEA2ZT1bmJl1AC4glviaDZxdX+VmtkU6ZhOiV/px4BB3n57S\nrw9K24YR/61f6+7Hp3O/SVk3s5HAOsAs4GBi3fMjiKXYTiWWR/xqwb9cAAAgAElEQVSDu1+czu1D\nrFe+ObEEWDVwuLt7gTYeRSy1tjKxnNmx7v5kkevZCTgTWB94HTje3R9N7dsYWAGoBHYl1lI/Hdgn\nbX8EGObu7zZUr5mdle5NV+DZdN6r6Z6Ncve1U+r9X4i13n+Xjv0HcGhuvXEz2w/4PZHxcBex5vqk\nvGXSRERERESKanPjPEUWFykwexbYNG0aBDyfesvHEr3lEL3jE9z9swaKPB34GbAzsCf1pJWbWRci\nvfp+oDewA9AT+E3msM2JoHpzYDhwtJltX6TIXxJp9BsSwe5twI7ANsR63GPM7Ptm1g64G3grHftD\noIIIXPPb2A84l1iX3Ii1u28rcj2Vqdy/p3JvAe40s5XSIYOJNdW3I14AXAH8gnh5MZB4MXBXQ/Wa\n2a7A4cSa55XAB8C1maZk0+1XS8ftSLwE2B04MJWzJXAN374QmZPuoYiIiIhIyRSQi3w3Y/k2IN8W\neCx9/g8LBuSPl1DWocDv3P1Jd38WOLaeYzsBp7v7me4+1d2fJnpwKzPHtCd6rt9w95uA8UBVkfI+\ndvdR7j6F6BnuAhyVer3PI3rY1031Xgac4O5vu/tLwPV59eb0AOYDU9OkdL8D9jezQn/vHAI84e6j\n3f0tdz+HyBbomvZPd/er3P1lYBkiEP+1u4919wnEWH4zsx0aqLcHkZb+brrWXE96IR2AEe7+qrs/\nRLz8yN2/ocAt7n61u7+evr9bpBwRERERkYIUkIt8N+NYMCD/T/r8H6C/mXUkepHrDcjN7AfAikTQ\nnFNNpEEvxN2nAzeY2bFmdr2ZVQMnEL3VOdPdfU7mew3Rk1zIlMznuamOd9Kfudmolk69/5cDQ8zs\najN7Argwr96cB4h08Qlm9kJq3yR3LzTNtAEv5F3jyBTsAryd2dWLuC/PZY6dCTiRLXB/PfXenK5v\nipmNA4YArxa5JwBvZj5n79+GxPPJ1T8PeL6eckREREREFqKAXOS7eQpYzcz6EynOTwK4+0RiTPbW\nQB9K6yGHBQPwr4odZGarEUHntkQgeAwwJu+wQucXDPCB2lIaZ2adU337AK8BpwEnFjrW3ee6+2Z8\nmzlwEPCCma1a4PCvG6j6yyKfsyqACnf/sli96UXG+sAuwMtEsP60mS1T5Bry70vu/tWy8L0sdm9F\nRERERArSpG4i34G7f2FmLxGToD2X6U2G6D0/GHjd3Wc0UM4nZjadSImekDbnJmsrZFdghrsPzm0w\ns6Np+aBwEDGJWR93r0v17lSoXjMbCGzn7mcBj5vZKcB0YEvg9rzD3yAmbsue/yTwpwJteIsIiAcC\nD6Vjvw+sB3h99ZrZHGBNd78cuM/MTifGkfdt5H2YSEzml2tr+9T+lxpZjoiIiIiUMQXkIt/dWGIC\nsQvytv+HmOzsLyWWcwlwuplNJXrXz6/n2BnAmma2HZFuvhewG5k07hYyA/gesJuZPU9MJjeMaG++\nucDI9KLhYSKY70z0TOe7HJhoZscA/yKupw9xb3tnD3T3OWZ2NXCJmf0KmEnc53eIAL1PPfWuB5xn\nZh8C/wX2JSZkez2dV6pLgMdS2vsTwAhifHpbXYNdRERERFqBUtZFvrtxwLJ8O3485z/EJGj52+v4\nNnDLBnBnATcAtxJB6ZX11HkbMev47cRY5kHE5GS9zazYOPHvEizWAbj7M8Rs8JcS490PBH4NrJSf\niu7u44kMgROJ9PaTgf0KLY/m7pOJWcwPJVLxdwN2dvcPi7TnBCL4/jtx/+cAO7j71/XV6+73EEu5\nXZD27QkMdvdCLxSKSvdhGDASeJF4SfE09QwzEBERERHJ166uTh06IiKNYWZVwKzMpHOY2QTgXHe/\noZQyjhh+cd0a3a2lmiiyWHt3mrP74L7069e/4YPbmA4d2tOtW2dmzpxDbW2hOSxlSaLnXV70vMtL\net4tPkeQUtZFRBrvh8AIMzsQ+JCY5G4NYob3khw2ZCA1NXOZN0//oC/pKira06VLJz3vjM369aWy\nsrFTN4iIiCx5FJCLiDTepcBawB3A8sRkbju5+0elFlBVVaU37GVCPSoiIiJSjAJyEZFGSuuOH5d+\nmqS6urpsekwrK/vSsWPH1m6GiIiIyGJHAXmZM7O3gTUL7KoDtnX3sU0ocyQwyN23bcK53wf+AWwK\n3OLuBzfi3B7EjONruftUM5uf2tHoa1hUzGwIMMrd114EdW0DPObui8VkjvnPq5nLvg6oc/dD0u/j\nNu6+XRPKmQKMdPcbzOx7wK7u/tfmaONN1z3Dmmus3xxFLdamvjsJYIkcKywiIiLyXSkglzrgKGLW\n7nyffsdym2J/oCewYRPrb4uzFC7KNi9O92cqsab5xy1czx8pvJ55KQYAn6fPxxGz2TdLQL7mGuvT\na91+zVGUiIiIiLRRCsgFoKYxY19b2PLAG+7+RhPPb/GZEKV5uHsd0OK/d+7+BfBFE8+dkfmq3y0R\nERERaVYKyKVBKW33XGLN6Y2BScAh7v7ftL83sWb2JsRazK81UN7OwO+B3sBk4FR3/2dKLR6ZjplH\ngZR5M1sNuAjYjlj7eyIwwt2fauQ1/TK1oQfwFvBbd78rk0a9H9GzuiyxNvhx7j4/nbsrcAYxqdcr\nwEnZdprZqcCR6dyxwHB3n5b2rQpcC2xF3Md7G2jnYcDxwDpADbFG+Qh3r0tp2Z8CqwO7ADOAU9z9\nxnTucsRz+RnwPnB1PfVsQ6xrfi7xDL4GLnb3s9L+pYBzgL2AlYD3gLPc/aq0fztgDLB+2neuu19Z\n4r3ODjE4gFg3fD3gOeAAd38nldMfuATYiFj7+xFg64aGRmSHUKQhAgcRa5ifAHwJnATMTe1fHrjC\n3U9O505J96Mdmd9Nd6+o75pFREREREqxWIwllTZhFHAW0BeYRQTFmFlH4N/Am0A/YtbpI4oVkoKY\nO4C/EGnp1wC3mlk/IgAeAzxFpDIXCrJvJIKjzYiXA9OAPzfmQsxsRSLIPhPoBVwH/M3MumYOOw3Y\nE9gV2J0IKDGzjVLbTyfuxY3AvWa2Tto/glgCa+/UxunAg2ZWkcq9I7V/ABHgHlNPO7cGLuTbAPUI\n4FDg55nDhgHVQGUq+/IUiANcka5vK2AEEdjXZ2UiIN4+1XWSmR2a9v0G+Em6H73SPbjEzFY0s/bE\nkIdb075TgUvNbP0S7nV+Cv0oYDjxcucHxIsPzKwLcF+61o2Am1ObSk3Bzx73Q2Bt4hncAlxODNvY\nmUhLPyk956xbyPxu1nfNJbZHREREREQ95AJEEHdp3ra33T27SOx17v4vADMbA9yetu8ArAAMdfcv\ngdfNbBCwYpG6hgG3u/vF6fsFZrYpcIK772dmnwNfuXuxccX/BO5w9/dTWy4D7in5SsPqxO/+e6nn\neoyZjSd6S5dPx5zo7k+nOk4FziaCruOBK9391nTcJel6hwInpp+h7j4unTuU6J3eKfW2bgas6e7v\nAZPMbAAR+BfyOXCou9+Vvk81s/8Swfedadt4dx+T6joNOBqoNLNXU7nbuPv4tP90ooe5mAoi82EC\nMN7MLiQC82uIZb0edvfqVNbZRI9xL+BV4nfgo3Q/bzaz94EPiMC3vnudnwY+xt0fT3VcRvy+QLzg\nmA0cnVLd3zCzLYgXN43Vjsgy+NLMriReipyWrnuCmY0mer3H505w9/9lfzfNrFs91ywiIiIiUhL1\nkAtEoLlR3s9P8455M/O5Blgqfe5NjPn+MrO/up66egPP5m17Km0vxeXA9mZ2mZk9RrwYaNTvsbu/\nRPTqP2xmr6Xg8u3MNdSxYO/888CKaQb43sAIM5ud+yF6Vtczs87AGkSPf27fZ0Tg1gvoA3yagvGc\novfK3V8EXjazUWZ2u5lNImafr8gc9kbm+Nnp41KpvvZkgsr66ko+T0Fp9rp7p7LvBpY1s/PM7B4i\n1bwOqHD3mUSWwtVm9raZXUzMSzCrhHudr9jvWV/gxRSM5zzdwPUUMz1T/9x0He9k9s8Flq6vgPqu\nuYltEhEREZEypIBcAD5298l5P9PyjvmqnvPzeznrO7ZQIFbBgkFmQWbWDniYSCt+h2/HtTeauw8m\ngtvbiYD6BTPbMHPI13ntA5hP9PaezYIvL/oQPeS5jJM98vYbkaoNjbhXZvZj4AUilfxeInU+P42/\n0Pntinyu77kA1OZ9ryCuGTM7g5hd/CvgeqKn/5uy3X040XN/BXFfn0ntL+Ve13c9uTpqWfjeNXWS\ntfzrhHSdjVHfNYuIiIiIlEIBuXxXE4BemXHLEGPJi3FgYN62H6btDelDjIfe3t3Pdvf7gNXyjmlw\nTLGFP7r78+5+mrtvALwL5IKpdsT49Jwq4P3UK+rA2tmXF8QEbj9JvaMfAatm9k0jxsYbca+65cab\nJ5vU09TDgGvcfai7X5fq7klpgagTgWdViXUBdDWz7Jr0VcDL6fMRxOR0p7j77UDuebczs5XN7BLg\nTXcf7e6bAY8Cg0u416WayILPBGBRL2z9ze9Wfde8iNskIiIiIm2YxpALwPJmtnKB7bPTklH1eZhY\nT/qaNIZ5IPBL4Jkix18AjDOzZ4le312IicJ2KKGdnwHzgH3N7G6iV3IUfDO5HJQWrH4GDDWzz4Cb\ngA2IGcBfzBzzJzM7HOhGTOh2Uab9Y83seSIVezAxBjk30/f5wFlm9jERFJ8KbA5McvdZZvYIcG2a\n/G0dYgKzmiLtnAFsbmYbEMHgb4gx0/WmU0Okr5vZDcDFZnYIMeP7yAZOawdcZWbHEWOoRwC/yrRl\nFzN7kRiDf2Fq09LETO+7EcH5GCJtf2Pg75R2r0txM3FfLyBSxQcR48qfaGQ5hZTa0z4HWC3NDv8+\nxa9ZRERERKQk6iEXiODq/QI/uRnAi/Y6u3stsazWCkR69RHUM3GYuz9HzOQ9lFgybAiwZ24ir/qk\nsddDiWWqJgD/RwSNtXzbK59ta8F2u/t04iXAHkTP68XAye7+SOawW4mA+yZiErdz0rnPpvb/Op17\nGLC3uz+ZzjsPuIpIY34R6A7smBlb/EvgEyL1/Ezi3hcziuhxfxp4gFhL+zLqz0DIXvOIVM+DRMr8\nRQXPWPDc+4gg9wLinuQmrzuYCDgnEMu23UosS9bP3b8mXqxsRIxZvwW4yt2vKeFeN/i8ANx9Tqpj\na6LX/gBihvuG0vBLkV9vXWZbdt8/iTT+icSEdAWvuRnaIyIiIiJlol1dXamrBoks+VLv52QiLX1q\na7dnUbFYh/xRd29wLH9rMLO1gNUzLz5IKePLuvshrdaw7+CYX19ct+YaS/4qaVPfncRPf9GXfv0W\n9QiDxUeHDu3p1q0zM2fOoba20dMVSBuj511e9LzLi553eUnPu6lzFpVeT0tXINIGtfh/eNJoyxMz\nte9PzBY/ANifSFtvk/Y7eCA1NXOZN2/J/gd9gwF9qazs2/CBIiIiImVIAbnIwpQ2sphx9/FmNgwY\nTYzXngoc6+73t27Lmq6qqkpv2EVERETKnFLWRdooM7uOGINfx8K9+nXAtu4+thXaNR8Y1FDdaXjA\nFGCtYsMDzGwP4D/u/kkT2jEFGOnuNzT23KYo9bpzqqur68qhh1ygoqI9Xbp0arMZEZWVfenYsWPD\nBwqglNZyo+ddXvS8y4tS1kWkIUcRE9tBpG4fT6Ry5/7i+LQ1GkXMBF9q3UXfCKYl2G4D1mqGNi12\n7rj6GdZa3Vq7GbLIfNnaDWiSt9+LFSnLeQ4AERGRlqSAXKSNcvfZwGwAM5sFzHP3j1u3VeDuHzVT\nUe1ZgocPrLW6sX7P+ibMFxEREZElnQJykSVYSmuvy85Enk2tTmndtwIHAh8QvezXAecAvwO6Av8A\nDk3LmxUqH2IptlWALYHXM+UvQyyDtwfx8mAksSRcz3ReO2A3MxsOrEqsa39gWiZuMhGQTzGzg/NT\nz81sqdTOvYCVgPeAs9z9qsxhG5jZk8AmwGvAQe7+cjp/DWJN8+2B6cBfgD+4e13af1i6H+sQa8Xf\nCozI7D8NGJau4eRiz0BEREREpBitQy4i+wI/Ag4iAuDVgN2BHYk1xHcnAvZi9gdOAX7m7m/m7bsY\nGAjsQKzBfhIL/71zIBFUDwL6820a/qbpzyoiGM73G+AnqY29iID6EjNbMXPMocREcH2JNPrLM/v+\nQbyE2Ii49n3SdWBmWxNrxJ8MrAcckcr6edr/K2LIwEHEvTuUJbg3X0RERERahnrIReRGd38VvlmP\nvAPREzwJeNXM7ieC4muKnF/t7vfmbzSzzsABwI/dvTptOwq4L+/QE939xbT/NiJABsil33/i7v8r\nUO9LwMOZss8meuB7Zc79s7vfk/ZfBNycPm8PrOnuuaD/TTM7kQjqzwQ+J7IC7kr7p5rZf4FK4E7g\nMOB8d78vlXcYMLHI/RERERERKUgBuYi8XWBbtqe7BliqkecDrJ/Oez6z7WkWnhF+cubzLGCZeur6\nhrvfbWY/MrPzUl2bEL3UFSWUvT7wAzObndnfHljazLq5+4tmNtfMRhFBeF9gXSC3zFof4PeZtrxm\nZnNKabeIiIiISI5S1kWWbAukUZtZRYFjFpr+2d1r8zbVt+RDsemjc2Vkzy20PNu8RtT1DTM7A/gr\n8BVwPbBZgXPzy87pQIwp35Dokd+ICLp7AbPM7MfAC8DKwL1E2v5TDbRzoTH2IiIiIiL1UUAusmT7\nClgu871nsQNbwJtEkJpdL2kApY+1LrS+etYRwHB3P8Xdb+fb6ywloHdgTSIdfrK7Tybuzemp3sOA\na9x9qLtfl47vmSl7ApHGD4CZrUVMgCciIiIiUjKlrIss2aqBMWa2HfARcD5QaDx2s3P3OWkW9ovM\n7HDiBeCf0u5csF1f8JxLAd/YzGa4e35K+AxgFzN7EVidmIStDli6hOY9CLwD3GRmpwDdiNnfH3T3\nOjObAWxuZhukMn9DzCKfK/ti4FIzG0/MKn8hxXvjRUREREQKUg+5yJLtr8AdxERk9wI3ETOL57TE\nzODZMk8AxhPLmd2e6ofoua+3fnefAdxIzLB+aIFDDgY2Jnqrr03HPUcswdZQ2fOBwcQLgWdS2+4B\njk6HjCJeYDwNPAB8AVyWK9vdbyImkLsYGJuOmVmsPhERERGRQtrV1WmlHhFpGWb2c+Ahd/8ifa8C\nxgGd3b2se5RPPuLiurVWt9Zuhki93n7P2W73vvTr17/hgwWADh3a061bZ2bOnENt7fzWbo60MD3v\n8qLnXV7S8y5pbqPvVE9LVyAiZW0ksHNakqwLcC5wZ7kH4wC7HzaQmpq5zJunf9CXdBUV7enSpVOb\nfN7r0JfKyr6t3QwREZEllgJyEWlJ+xJp3S8Saep3Ase1aosWE1VVVXrDXibUoyIiIiLFKCAXWUKl\nCdWGUHi28jpgW3cf28LN+D9gmrvv0NCBZvZ94B/ApsAtwAhgV3f/a2MrNbNtgMfcfZHMk2H2/+zd\nebxVVf3/8ReDOA+U/kQzBxI/4BUUlVIyRW3QMs0pMwfMIafUHPKrlYJDZuYcmjPmkJqpZeZQ5pio\n4Zyib0VwylkREHEA7u+PtY5sDuecey5wPRfO+/l48Lj37L322mvvfbnw2euz1oqhwHBJq9V7zOjR\no+fLHtNm0dLSnx49ejS6GWZmZraAc0ButuA6mBQQA/wAOJy07FgpOH+3EY2qYVfS0mIDSG07DBhC\nmphuTnzWE2S063w3nXc/vT2GvFMa9z/BTnjctJmZmXU4B+RmCyhJk4HJABExEZgu6a3GtqqmpYHn\nJD0HEBEdPolGI/X+QrDmagPbLmhmZmZmCywH5GZNLKe1t0ras7BtBjBE0j0RMZ60nNjupOXSDgdG\nAr8BfgksQ0oz30vSJ3Wcb1vgRGBV4L/Akfk8w0gTwBER04E9i58ldatQ15qkddUHAwuR1lzfR5IK\nZfYFjiEF+9cC+5baWa0ted+SpDXTv5OvcRxwlKS/5v0rkJZa+xrwDGlJOTMzMzOzdvE65GbWlh8C\nXwf2IKVlrwhsD3wT2DZ/v3tblUTE2sClwPFAf9Ia4zdHRG/gt8BpwCigF3BD2efyuroANwLPk1Lc\nNwS6kV4UlHTJbfsG8D1gR9La5W21BVIw3idf95qktcYvjIjSS8zrcv3r53P+tK3rNzMzMzMr5x5y\nM2vLFZLGwKeTpXUHDpL0DDAmIm4FBgEXt1HP4cAFkq7Jn0dExBBgf0k/i4j3gY9LafXln8ssCvwe\nOFfS1Fz+D8DPCmVac93PA09HxD+BtdtqS67jLuDUwnWfDuwNLB8RywBfAVaW9D/gmYhYnxTwm5mZ\nmZnVzQG5mbXlhQrbxha+n0RKGW9LP2DHiNivsG0h4Nb2NkjSBxFxHjA0B8N9gXWB18uKjit8PxFY\npM62XA58L6e89wVKs3t1y8e+m4PxktE4IDczMzOzdnJAbtbcZpkZPCJmG6sNfFi+QdK0sk31TMDW\nnZTefVnZ9ql1HDuLiFgceAh4k5S6/kdSoHx4WTvLZz4vtbOttlwObJC/nksK9EdVqKfk4/Zeg5mZ\nmZmZA3Kz5vYx8PnC5y914LkErCbp017riDiFNCnaJRXK11pGbAhpbPmapaA7IragvhcDNdsSEdcC\nOwODJD2S9307F+sCPAn0jIjehePXrfO8ZmZmZmafckBu1txGA6dFxGak3ubTgY866FxnAPdExEPA\n34GtSZOhbVql/BRgxYhYRdKLZfveAZYAtsv1fQM4kJSWPrdt+RB4H9ghIt4hpaz/Lh+3sKRnIuIO\n4JKIOAjoDfyElLpvZmZmZlY3z7Ju1twuJ80Y/hfS0l1XkpY3K6nVS90ukh4EdgMOAJ4iTZL2A0n3\nVTnkBtKY7aciYtmyuh4gzZB+DvA4aZb3A4D/l5ckm+O25GXRdgV2yPtOBU4g3ZfSwuE7AW+T0th/\nBZxZxy0wMzMzM5tFl9bWefb/bTMzq9Mfjr2tdc3VBrZd0D5zY8Y/ygobLczAgeu1XbgO3bt3pWfP\nxZkwYQrTps2YJ3Va5+Xn3Vz8vJuLn3dzyc+73uGQc36ejj6BmZnNbtz/1OgmWBXj/idWYECjm2Fm\nZmZNwAG5mVkDbLXfhkyaNJXp0/2GvbNZgQG0tPRvdDPMzMysCTggNzMzK/PUU/+dZ3V169aVpZZa\n1C9gmsSC8rxbWvrTo0ePRjfDzGyB54DczOaJiNiDtHzZXpJGFraPB4ZJKl/zu6PaMQMYIumeeVzv\nsFxvtVnh2+UfI0ax+goxL6qy+cDbfNDoJthnaH5/3mNfE+zCPJtHwczMqnNAbmbzyg+AsaQZz0e2\nUXZ+Nc9mwVx9haD/qp7UzczMzKyZedkzM5trEbEcsDlwHLBxRKzS4CaZmZmZmXV67iE3s3nh+8AE\nSVdGxMmkXvITCvvXioj7gHWBp4E9JD0BEBErAeeSAvo3gEuBEyS15v17A4cDvYFJwDXAQYX9xwIH\nAl2Ao2o1MiKWAU4BtgYWAW4EDpb0XkRsks/9G+CXwDLA9aQU/E/K6nkWOFfSmYVtTwBnFNP1zczM\nzMxqcQ+5mc0LOwF/z9/fSArIi/YCfg30B94Fzivsux54DVgb2APYGfg5QERsDJxJCrT7APvmurbJ\n+38MHJyP+3reVyut/C/AAODbuXw/Zk2vXxHYHvgmsG3+vvxaAK4Cdih9iIh+uX3X1zi3mZmZmdks\nHJCb2VzJPdxfBW7Im64HekfEVwvFzpV0k6SxwNmk4JuI2BxYWdK+ksbmidh+Bhyaj3uf1EP9V0kv\nSboeeBRoyfv3Bk6XdEvucd+b1FNeqZ39ga8Bu0h6RNJDwK7ANhHRJxfrTup9HyPpn8CtwKAK1V0F\nbBARK+bPOwL/kDSxnntmZmZmZgYOyM1s7u0MTAX+kT/fDbwHDC2UGVf4fiIpXRygL7BsREwu/SGl\npC8TET0lPQI8ERHDI+LaiHgG+DLQLR+/JvB4qWJJTwNTqrSzH/CepOcL5QVMyPtKxha+nwQsVF6R\npGeA/zKzl/z7wNVVzmtmZmZmVpEDcjObWz8AFgUmR8QnpOB8GWDHiCgF3tOrHNudNKZ8AKnXfG1S\nWvsawMSI+BbwMLA8cDMphXxUWR3lPeKfUNmHVbZ3Y2aAj6RpbdRfchWwfUT0BVYF/lqlnJmZmZlZ\nRQ7IzWyO5VTvgcBBzAyo1yb1mi9FGoddi4CVgbcljZM0DvgScDxpLPjewMWS9s+TpSnvLwXJT1JI\nKY+IVUkvA6qda5lCejoRsSawZN7XXlcBG5LGmP9d0vy98LCZmZmZfeY8y7qZzY0fAu8AF5bNRD4m\nz34+tPJhn/oH8CJwZUT8HOgJnE8aj90aEe8AgyNiLVKAfjTQC1g4H/874JyIeBx4ljQBXMXeeEmK\niFuByyLiINILyRHA3ZLG5FnW6ybp5Yh4EDiENBbdzMzMzKxd3ENuZnNjJ+Dy8mXBst+TljJbscI+\nACTNIC1B1gV4ALgWuIkU5AIMB94E7gduAz7I9Q7Mx18JDCMF5vfkMhNqtHc30nj224FbSOPA2+rF\nr+UaUor839sqaGZmZmZWrktra60VgszMrJqIOBH4gqQftffYXw09q3X1FaIDWmVmNnfGviYG7bI2\nAweu1+imdHrdu3elZ8/FmTBhCtOmzWh0c6yD+Xk3l/y8q80lNO/O09EnMDNb0OQl1NYF9ge+Oyd1\nfPMng5k0aSrTp/sf9AVdt25dWWqpRf28m8SC8LwHsTYtLf0b3Qwzs6bggNzMrP3WJ62nPkJS+azv\ndRk0aJDfsDcJ96g0Fz9vMzNrDwfkZp1IRCxGmrhsB2AV0pradwHDJI3pgPMtB2wi6c9zePxIoFXS\nnnWUvRO4U9LxVfavBoSkW+ekLe0VEUNJ97V3HWWHAUMkbQqQZ3wfWdi/BLCtpMvrPf/o0aPn6x60\nBUFLS3969OjR6GaYmZlZE3NAbtZJRMTiwH3AYsChwBPAsqQlxUZFxNqSXpzHp/1N/jpHATlw8Lxq\nCHAx6eXDZxKQA1eTJpCrV60JNw4DhgB1B+T3nDaKNXp5DDgutIwAACAASURBVHmjPPu6YE88RtbM\nzMwaygG5WecxjBSA95M0OW97GdgzIlYiBX2HVDt4DnWhdqBZU6Gd86otnxlJHwEfzaPq2t32NXoF\na6+8zjw6vZmZmZnNjxyQm3UCEdGFtGb3yVWC3N2A9wrlNwROIS3/9QZwiqTzq9Q9gLRU2DrAu8AF\nkk7IadhDc5khknpHxAzgBOAA4D5J34uIvYHDgd7AJNJSXwfldcJnSVmPiF2A40hrhf+VFKg+U0hT\nXykibgY2Ja0/foCkO3I9mwAb57ZsVuE6+gGnA4NzOy6QdELeNyxf3+eAFlL6+L1lx89ybcANwHBJ\nq+X965HWJV8beAT4F7BxKU0d6BERI/KzmAr8RtIZpdT3XMd0Sd0qPQczMzMzs3Jeh9ysc/gSsBzw\n70o7Jb2Re3SJiL6kYPEuUkB+HHBaRGxTpe7LSAFmP2Av4MiI2AL4LfAnUoC9fqH8VsCGwFERsTFw\nFnAU0AfYN9cx27kiYiNS2vnJpBnIp5DWKS/aDbgKWBN4iJkp3oeQ1ho/DdiuQt2fJ60z/grwZVJQ\nfVBEFDMGtgauADYD/lPlXnx6bflza65/KdK65KNJAflVpLH8xeyBwcCHpMD/ZNI9D9L9Ow0YRXoR\nYWZmZmZWF/eQm3UOy5KCv3dLGyJic+AveXsX4AVJ/YEfA49IOiYXfS73Hh9J6pUut2qu52VJL0XE\n14Hxkj6IiKmkHu53C+XPkzQ2t2ExYE9JpXpfiohHSb3Qfyk7z/7A1ZIuysfuD3yrrMx1pYnPIuIU\nYOeIWE7SWxHxMfC+pPeY3S6kAH9fSTMARcQxwLGkFwYAb0i6sMKxRcVr+0ph+w+AycAhklpJ9/Sr\nzBpgvyLpiPz9mRFxLDBAkiLifeBjSW+1cX4zMzMzs0+5h9ysc5hACrqXKWy7j9Rbuw5wPLB43t4X\neLDs+FGkHvBKfgUcA7wWERcBi0h6s0ZbPp04TtIjwBMRMTwiro2IZ0g91JXSsgeQephLx04n9YIX\nPV/4fmL+ukiNtpT0BR7OwXjJKKBX7t0GeKGOeqpNitef9JKj2CN+f1mZ8WWfJ1Jf283MzMzMKnJA\nbtY5jAXeIaVFAyDpQ0njJI0DigH0hxWO70blIBlJvyWN/z4ZWA34V0TUWqbs0/oj4lvAw8DywM3A\n9qRAuJJpzD65Wfnn6RX21zMhWrVrLn6tVKaeemDO2l6pjJmZmZlZ3RyQm3UCuTf5EuCneU3rcisV\niwMblO0fnLfPIiIWjogzgU8knSlpc+BCUmANbc+wvjdwsaT989rbIo13rxSIPgV8uoZURHQl9e7X\n0lrl+3IC1ouI4kuHwcBbkia0cY56PMXsbV2/UsEq5nimejMzMzNrXh5DbtZ5DAc2Iq05fhypZ3o5\nYB/gR8CVudy5wMER8SvgUlJguj9wYHmFkj7Kk619MSKOBpYCNgauz0WmAC0RsaKkVyu06R1gcESs\nRQo6jyaNq164QtkRwJ0RcS9pcrqDgFWoHawWA/spQJ/SmPKycleS7s/5EXEqEPnziBp1t8dVwEkR\ncQbp/g4hTUhXcZK9CqYAK0bEKh2wVryZmZmZLaDcQ27WSUiaSlr66zLgl8CTwK2k3vHtJO2Ry71M\nmi18C+AJ4OfAoZIuq1L194HFSDOP30qanf3EvO9y0vjsx/Ln8uB5OCld/n7gNuAD0hJqAyu0/wHS\nS4FhpFndl8jHfVyl7vJtFwFbkmY7L6/7fdL1rp7rPhs4vbCcWj2qvhiQNAX4LullxROk2eCvKLS9\nrfpuIKXOPxURy7ajTWZmZmbWxLq0tjrT0szmXkQMAiZKeraw7UnSGunVXhZ0ChGxKvAFSfcVto0A\nFiutsT6vnfaDs1rX6BUdUbXV4dnXRf8912bgwPXaLjyXunfvSs+eizNhwhSmTZvR9gE2X/Pzbi5+\n3s3Fz7u55Ofd4fMFOWXdzOaVDUlrg+8OvA7sTOrdv7WhrarP0sDtEbEraab49YFdScuhdYiNDx/M\npElTmT7d/6A3Qn/WpqWlf6ObYWZmZk3OAbmZzSvnkNY8v44U4D4GbNHGEmudgqTHI+JA4Neklwgv\nkYYBdNjLhEGDBvkNu5mZmVmTc8q6mVkDjB49unVB6iFvaelPjx49Gt2MTskpjs3Fz7u5+Hk3Fz/v\n5uKUdTObTUQsRprpfAfSDOZTSJO0DZM0pgPOtxywiaQ/z+HxI4HWesZhR8SdwJ3VJmqLiNWA6Khe\n64iYAQyRdE8dZbcmZQT0BLYlTXy3mKT76z3fqFPvJpbvM6fN7VT0xnOwF5/JeGwzMzOzBYkDcrP5\nREQsDtxHmjH9UNJs4MuSlhcbFRFrd8CSW7/JX+coIAcOnlcNAS4mvXzoDGPSjyPNBn888DYwhjQj\nfd0BeSzfh3W+uHaHNM7MzMzM5g8OyM3mH8NIAXg/SZPztpeBPSNiJeAw4JB5fM4u1F5HvKZCO+dV\nWzqLpYH7JL0CEBGdqW1mZmZmNp9wQG42H8gB31Dg5CpB7m7Ae4XyGwKnkNYLf4O09Nj5VeoeQFpb\nfB3gXeACSSdExLB8TiJiiKTeOa37BOAAUkD6vYjYGzgc6A1MAq4BDpLUWp6yHhG7kHqXewF/JQXZ\nzxTS1FeKiJuBTYEXgQMk3ZHr2QTYOLdlswrX0Q84HRic23GBpBPyvmH5+j4HtADbSrq3xv3uAfwW\n+GHedGu+pvciYjywMnBJrvdF0vCBkbltHbJMmpmZmZkteLo2ugFmVpcvAcsB/660U9Ibkj4CiIi+\nwL9I6d0DSQHwaRGxTZW6LwMeAfoBewFHRsQWpID0T6QAe/1C+a1IS5wdFREbA2cBRwF9gH1zHbOd\nKyI2IqWdnwysSxr/vlNZsd2Aq4A1gYeAy/P2Q0jp4KcB21Wo+/PAPcArwJdJLwwOiohixsDWwBXA\nZsB/qtyLkl8D6wFbAEOApZiZtj8I+F9u06Dcnlfy53mdoWBmZmZmCzD3kJvNH5YlpY6/W9oQEZsD\nf8nbuwAvSOoP/Bh4RNIxuehzuff4SFKvdLlVcz0vS3opIr4OjJf0QURMJfVwv1sof56ksbkNiwF7\nSirV+1JEPErqhf5L2Xn2B66WdFE+dn/gW2VlrpN0ed5/CrBzRCwn6a2I+Bh4X9J7zG4XUoC/r6QZ\ngCLiGOBY0gsDgDckXVjh2FlExKLAgcB6kp7K24YCb0dEi6SnImI6MEnSO3l/6fO8TNE3MzMzswWc\ne8jN5g8TSEH3MoVt9wFrk1KxjwcWz9v7Ag+WHT+K1ANeya+AY4DXIuIiYJE21g7/dOI4SY8AT0TE\n8Ii4NiKeIfVQd6tw3ABgdOHY6aRe8KLnC99PzF8XqdGWkr7AwzkYLxkF9IqIpfLnF+qoB1LqfQ/g\n/oiYHBGTSWP1uwBr1FmHmZmZmVmbHJCbzR/GAu+QxkcDIOlDSeMkjSMtu1XyYYXju1E5SEbSb0lB\n6MnAasC/IqLWOOhP64+IbwEPA8sDNwPbkwLhSqYx+8Rs5Z+nV9hfz4Rp1a65+LVSmUpKmUNfJb3w\nKP3pA/yjzjrMzMzMzNrkgNxsPpB7ky8BfhoRS1QoslKxOLBB2f7BefssImLhiDgT+ETSmZI2By4k\nBdbQ9gzrewMXS9pf0sh8ji9ROYh+ijQuu3TurqTe/Vpaq3xfTsB6EVF86TAYeEvShDbOUe550ouB\nZQsvPCYDZ5JePLTVTjMzMzOzungMudn8YziwEWnN8eNIPdPLAfsAPwKuzOXOBQ6OiF8Bl5IC0/1J\n46JnIemjPNnaFyPiaNLkZRsD1+ciU4CWiFhR0qsV2vQOMDgi1iIFpUeTZlBfuELZEcCdEXEvaXK6\ng0izk9cKZouB/RSgT2lMeVm5K0n35/yIOBWI/HlEjborkvR+RFwInBcRPwbeIs3e/kVgfJXDpgB9\nI6LnHLwAMDMzM7Mm5R5ys/mEpKmkpb8uA34JPElajmslYDtJe+RyL5NmQt8CeAL4OXCopMuqVP19\nYDHSzOO3kmZnPzHvu5w0Pvux/Lk8eB5OSpe/H7gN+IC0hNrACu1/gPRSYBhpVvcl8nEfV6m7fNtF\nwJbALRXqfp90vavnus8GTi8sp1aP4rkOB/5Jmll9FPAR8G1JrRXKQnoJ8hNSdoGZmZmZWV26tLY6\n09LMOl5EDAImSnq2sO1J0hrp1V4WLLDO2unU1li+T6ObMU/ojefot9f6DBy4XtuFm1D37l3p2XNx\nJkyYwrRpM9o+wOZrft7Nxc+7ufh5N5f8vOuZy2juztPRJzAzyzYkrQ2+O/A6sDOpd//WhraqQQYf\nsQmTJk1l+vT5/x/0fqxPS0v/RjfDzMzMbL7jgNzMPivnkNY8vw5YmpQGv0UbS6wtsAYNGuQ37GZm\nZmZNzinrZtYhImIGMETSPYVtWwA3Aie2c3z3PBcRQ4FhknrXUXYY6Vo2rbJ/CWBbSZfXe/7Ro0e3\nLig95FZbt25dWWqpRReYjAirzc+7ufh5N5d59bxbWvrTo0ePedgy6whOWTezBUpEfAW4Fvhdo4Px\n7GrgpnaUr/X28jBgCGkSvLqMOu02otfq7Ti9za9mkJYjsObg591c/Lyby7x43np9LOyJ512xTzkg\nN7MOFxFBCn6vlnR4o9sDack30uzp80K7355Gr9VZZ+W15tHpzczMzGx+5IDczDpURKxImrjtDuDH\nZfuWJq0VvjUwmbT++ZGSPsz71yItYbYB8CJwtqTf533DSEuyTQV2Al7Kx/6tSjtmACcABwD3ATcA\nwyWtlvevl9uyNmnptH8BGxfS1HtExAhgt3zO30g6o5T6nuuYLqnbnN8tMzMzM2smXofczDpST9L6\n5D2BPQrreJdcQlqPfEPge8D6pKCYiFgEuBm4B1gLOAI4JiJ2KRy/HSmVfF1gJHBdRPSt0Z6t8rmO\nyp9b87mWIq1vPpoUkF8FHM2saeqDgQ+BdYCTgdNyz/81wGmk9cp7tXVDzMzMzMxKHJCbWUc6D/gA\n6EYKqD8VEb2BbYDdJY2R9BCwL7BHRCwJ7AK8IWm4pHGS/g6cBBxaqOYdYD8lp5CC4j1rtUfSWEnP\nlG3/AamH/hBJz0k6B/hzWZlXJB0habykM4H3gAG5N/994GNJb9V5X8zMzMzMnLJuZh3qdeCbwF7A\nryPiBklP5n39SC8FX00dzbNYnZSOvk5ETC5s7wZ8XPj8kKRPip9zvdW8WGV7f+CRsh78+4FtC5/H\nlx0zEVikxrnMzMzMzGpyD7mZdaTDJE0EzgSeAC6NiNLvne7kXmZSmnjpzxrA03n/7WX71yKlp5cU\ng3FIAXutdUg+rLJ9GrNPzFb+eXqF4zp8KQwzMzMzW3A5IDezjjQdQNIMYG9ScH103idg6bx/nKRx\nwOLAqUCPvH8N4IXC/sHAwYX6B5Sdb31S4N9eT5HGhpfXVa9aS6KZmZmZmVXkgNzMPhOSHgdOB46N\niJY8jvs24I8RsX5ElCZmW0zSJOAKYDHggki+DZxFSoMv6R0Rp0TEGhHxC1Lv+cVz0LyrgKUi4oyI\n6BMR+5Bmbq830J4CrBgRq8zBuc3MzMysSTkgN7OOUimYHU4ax11KXd8NGEdKTf8HKVV9ZwBJ7wNb\nAn2AR4HzScuenVyo70FgOeAxYAdgS0kvtKM95HNNAb4LbEzqYd+N9ELg42rHlNV3Ayld/qmIWLbG\nMWZmZmZmn+rS2upMSzOb/+R1yDeRtNk8qGtV4AuS7itsG0Hqra81a/scO+sHJ7ZGr9U7omozMzPr\npPT6WPrtuREDB67X6KZYG7p370rPnot3+HxBnmXdzCyNZb89InYlrUW+PrAraTm0DjH48G8xadJU\npk+vNQedLQi6devKUkst6ufdJPy8m4ufd3OZF8+7H71oaek/j1tm8zMH5GbW9CQ9HhEHAr8GVgJe\nAg6VdGtHnXPQoEFMmDCFadP8H7gFXX7D7ufdJPy8m4ufd3Px87aO4JR1M7MGGD16dKt7VNrW0tKf\nHj16NLoZc8X/gWsuft7Nxc+7ufh5NxenrJvZAi0iZgBDJN1T2LYFcCNwoqTjG92eGmW3Bs4BegLb\nAm+SxpvfX+/5Rp1xE9Gr95w2tyno9XGwBx5nZ2ZmZgssB+Rm1ilExFeAa4HffdbB+Bw4DrgFOB54\nGxhDmkG+7oA8evVm4MprdkjjzMzMzGz+4IDczBouIgK4Cbha0uGNbk8dlgbuk/QKQER0eDqTmZmZ\nmS14HJCbWUNFxIrArcAdwI/L9i0NjAC2BiYD1wNHSvow718LOBvYgLS++dmSfp/3DQP6AlOBnUgT\ntR0p6W91tKkH8Fvgh3nTrcBBkt6LiPHAysAl+RwvAqsAIyNiSEctk2ZmZmZmC56ujW6AmTW1nsBt\n+esekspnmbwEWALYEPgeaTmyEQARsQhwM3APsBZwBHBMROxSOH47oBVYFxgJXBcRfeto16+B9YAt\ngCHAUsCf875BwP+AQ/L32wGv5M+H1HfZZmZmZmYOyM2ssc4DPgC6kQLqT0VEb2AbYHdJYyQ9BOwL\n7BERSwK7AG9IGi5pnKS/AycBhxaqeQfYT8kpwCigZg92RCwKHAjsK+lhSU8BQ4EhEdEi6W1gOjBJ\n0juSJhQ+T57L+2FmZmZmTcQp62bWSK8D3wT2An4dETdIejLv60d6afhqGmI+i9VJ6ejrREQxCO4G\nfFz4/JCkT4qfc7219AZ6APeXjQ3vAqwBPNXmVZmZmZmZ1cEBuZk10mGSJkbEmcDOwKUR8WVJM0i/\nn94jpY6XT5r2at5/O3BAhf0ln5R97ga0tXBo6ffiV4EpZfveaONYMzMzM7O6OWXdzBppOkAOwPcG\nBgBH530izWZOTkkfBywOnErqwRapx/qFwv7BwMGF+geUnW994Ik22vR8bteyhXonA2cCy1c5pnzs\nu5mZmZlZmxyQm1mnIOlx4HTg2DxW+xnShG9/jIj1I6I0MdtikiYBVwCLARdE8m3gLFIafEnviDgl\nItaIiF+QJne7uI12vA9cCJwXEZtExJrA5cCXgPFVDpsC9I2InnN4+WZmZmbWhByQm1mjVOpVHk5a\nRuzSiOgK7AaMI6Wm/wN4mpTaXgqctwT6AI8C55OWPTu5UN+DwHLAY8AOwJaSXqijPYcD/yTNrD4K\n+Aj4dmEW+PK2nwv8hBTIm5mZmZnVpUtrqzMtzWzBk9cI30TSZo1uSyVn/XBYa/Tq3ehmdGp6fRz9\n9tiUgQPXa3RT5kr37l3p2XNxJkyYwrRpbU1hYPM7P+/m4ufdXPy8m0t+3tXmKZp35+noE5iZ2ewG\nH7oVkyZNZfp0/4NeTT9WoaWlf6ObYWZmZtZhHJCbmTXAoEGD/IbdzMzMrMk5IDcriIgXgJULm1pJ\nS2/dC/xE0isNaNMqpMnEVpX0UhtlNwHulNQh80NExGpASLq1I+qfGxExEmiVtCeApONqlF0C2FbS\n5Z9V+8qNHj3aPeQdrKWlPz169Gh0M8zMzMyqckBuNqtW0rJZf8qfuwFrkiYMuxT4emOa1a5ltTpy\nYoiLgbuATheQt9NhwBDS7OkNMerM64heqzXq9As8vT4ehjLfjz83MzOzBZsDcrPZTZL0ZuHzaxFx\nLHB5RCwpaXKjGtYJdPjEFp+Rhl9H9FqNgav0bXQzzMzMzKyBHJCb1efj/HU6QEQsA5wCbA0sAtwI\nHCzpvUpp48V06jz7dx9gErAL8CFwqqTf5rLdgTOAXYHJQHEZL/K62KcDg4GFgNHAPpJUKPMTYBip\nt/x8SccU9m0FHAf0Iy0pdoykG/K+O3Pbj8+fP02Xz8dsAmwcEUPKZy/P130pcDxwErAw8GvgAeAC\n4AvADZKG5vJLktYN/w6wTG7LUZL+mvfPIC17dlS+X/8BdpP0Yt7/tXx8ADflZkwptOfnwN75vG/n\n+3B8RAzN94aImC6pW0QsnNu9M/A54F/AgZJeKdyDY0k969cDOwJDJf2l8MxeB3aUdCdmZmZmZnXw\nOuRmbYiIL5GCwlskfZA3/wUYAHyblMbeDxhZOKyttPEdgQ+AgcBvgd9ExOp53/GkIHWrXO7gQlu6\nkIL/5/P5NySl1f+mUHcXUqC/ObAncGBE7J6P3wy4jhQ4DyCloF8TEQNrtLV0LYcA9wOnAdtVKbsi\n8D1gY+BEUkB+BrA78ANgp4jYJpc9ixRof500LOAe4MIc3JYMJ63vvS6wbK6TiFgW+BtwG7AOMCbf\nq9J92p103/bM5zgOGB4R6wBX52sYBfTKh5yf270rsAHpRcdfy65tcG7Hr4EbSOual3yD9NLmrir3\nxczMzMxsNg7IzWZ3XkRMzn+mAo8AT5J6a4mI/sDXgF0kPSLpIVIgt01E9KnzHG8DP5M0TtKpwLvA\n+nnfXsAvJd0n6UHg0MJxiwK/B46Q9IKkx4A/AC2FMq3AjyQ9Iekm4Exgv7zvQOBaSb+TNFbSGaQA\n/Ygabe0CIGkSKeh8X9J7Vcp2Bw6T9BxwLul3zO8kjZZ0M/AYUMrTvgvYV9J/JT1P6vX/PLB8ob7T\nJN0taUy+7kF5+07Am5KOlvRcnsBtdOG4F/M9uEvSS5IuIPVgt0j6CHgf+FjSWznbYVfgAEn3SHqS\n9EIjIuIbhTrPyPf8eVJQv1VElGYM2zHf144cv29mZmZmCxgH5GazOxZYG9iINHnZOODnkibk/f2A\n93JgBkBOF5+Q99VjfFnwNhlYKPf8Lgc8Xtg3mplB8QfAecDQiLgoIv5NCri7FcpPkfRM4fMjhXb1\nAx4sa8uodrS7HuNzWz/Mn18s7JtKSmWHNKFaRMRZEXEbcF/eXryWsYXvJ5F6rsntLd4jKATkku4G\n3omIkyLihjx7/vJldZesQbq//ykcPwEQs96X4nX8k/RyYovco/894KoKdZuZmZmZVeWA3Gx2b+ae\n68eB75OCtRsjohTMfVjluG75T6Ve0vL5Gj6uUKZLle8/LRsRiwMPkcY6P016efCzsnrK19HqWqij\nUttL7YbZ2969wraaJJWfv9q6XpeT0vXfJfWmf6dCmfL7VO0ezVI2IvYGbicF/38GNgP+V6UdbT3P\n2cpJmp7r3Z6Ucj9R0gNV6jEzMzMzq8gBuVkNkj4hTQy2DjNTxwUsU0xPzxOtLZn3fZy3LV6oqned\n53sbeIOZqdmQxi2XguIhpHHPQySdJukOYBVmDU6XjIgvFj5/BSj1mIs0Rrpow7yd3PYlC/u+VFZ2\nnqRk5wnddga+L+m4PJHb5/PuemZAfxJYN4+pLymOg98XOE7S4ZKuJAX9yxfqLl7H88A0CvclIj5P\nGnv+TIXyJX8kvUTYmpnL5JmZmZmZ1c2zrJu1QdJDEXExcGxEXCFJEXErcFlEHER6sTUCuFvSmBxs\nfgj8IiIuII0vHsjMoLctI4DjI+IlYCJpbHXJO8ASwHYR8RBpMrEDc7mS1ty2n5LSsQ8ij38nTbB2\nb0Q8CNwMfBfYNtcDKe1794i4hhS8HlfWtilAn4hYTtJbdV5PJR/munaIiHdI48p/l/ctXPWoma4m\nTfh2VkSMALYhDTEYn/e/A3w9Im4ElgJ+Rfp9V6p7CrBiRKwi6cWIuAgYERE/Jg09+A0pRf120kR1\ns70kkPTviJgCDM3nNjMzMzNrF/eQm82qWg/wz0m9x6fkz7uTxpbfDtwC/JcU2JLXKd+b1AP8JNCf\nmcFmPec9CbgMuIY0k/gFpR05Lfp44BzSGOrdgQOA/xcRK+Ri7wJ/J02adhZwbGkpMUn/IQXn++c2\nDyUt1XV3PvZ00pjzu4Er87mKLgK2zNdcj/L72Zrb8Qlp4rQdgKeAU4ETgNeY2dNdtTc+Tyq3BfBl\n0kRxm5Mmtys5hBSIP0ZKLX+MNDN6qe4bSOnoT+Vx+0eQxoX/GbiXFLB/I7ezVluuBV6R9Gi1tpqZ\nmZmZVdOltdWTApuZzYmIuAIozfLeLmftclRr9FqtA1plAHp9PP2GbsHAges1uil0796Vnj0XZ8KE\nKUybVm1KBVtQ+Hk3Fz/v5uLn3Vzy865nKOXcnaejT2BmtqCJiK+QlqnbhlmXnKvb4J9uz6RJU5k+\n3f+gd4R+BC0t/RvdDDMzM7OaHJCbmbXfFsBhwNGSXpqTCgYNGuQ37GZmZmZNzgG5WSeV185eucKu\nVmBTSffMQZ3DSDO0bzoHx34euJ40bvtqST9qbx2dWUQMBYZJanNG/Jyi3u409aLRo0c3TQ95S0t/\nevTo0ehmmJmZmXU6DsjNOq9W4GAqL6n17lzWOyd2JS2DNmAuz99ZXQ3c9Fmd7P6zriZWWOWzOl3D\n6LUXYXc6xVhuMzMzs87GAblZ5zZJ0puNbkS2NGkCs+ca3ZCOIOkj4KPP6nyxwioMXCU+q9OZmZmZ\nWSfkgNxsPhYR40lLse0OrAM8A+xZWoYrIvqRlk1bF7gfeLqN+rYipWL3Iy3rdoykG3Kq+7BcZjoV\nUubraMsXSOugbw7MAP4IHFFYWqxY1zDS2uRTgZ2Al4AjJf0t718ROBvYDFiMtHTaQZJG5f0Hk8Z4\nL09a3u1QSfflfScBewDLAA8CB+b144cCwyWtFhGbAJeS1iP/ZS57PbBXqb0RsUu+V72Av5LWKn9G\nUvlScWZmZmZmFXkdcrP533DS2uX9gYmkQJWI6EFaj3wsaf3t64B9q1USEZvlMpeS0tIvBq6JiIHA\nb4HTgFGkAHRUO9uyEHAnsCjwNWBH4DvMXNe9ku1I6fXrAiOB6yKib953BSkA/gop+H8ZODefa2Cu\ndz8ggH+T0/4jYltgH2B70uzorwGXFM5ZTOdfMZf7JmmN+e1JLxuIiI3y/Tk5t28K6cWBmZmZmVnd\n3ENu1rmdFxHnlG17QVJxPaeRhZ7j04Br8/ZvAJ8D9pf0IfBsRAwBlqtyrgOBayX9Ln8+IyK+TOrF\n3iUi3gc+lvRWjfZWa8uWwArA+pImAWMi4kDgxoj4haQPKtT1DrBf7pE+JSK+DewJHAncAFwn6dV8\nrt8zc/z3KqQe+JckvRQRvwT+FhFd876PgFckvZx7OxLCawAAGrJJREFU0teoci3dSb3uz+T23goM\nIgXi+5Mmtrson39/4Fs17ouZmZmZ2WzcQ27WuR0DrF3259tlZcYWvp8ELJS/70ca8/1hYf/oGufq\nR0rhLhqVt9erWlv6As/mYLxY90LA6lXqeqgsnf2hQlvOAzaPiN9HxJ2kwL/0++w2Upr6kxHxMHAE\nKZV8BnAVKQ1+fETcCwwFxszB9QygcC8lTc/tMzMzMzOrm3vIzTq3tySNa6PMxzX2dWlH2Q8rbOuW\n/9SrWv3V6i5+LVc+trwbMCMiugC3A0sB1wA3AguT0u2RNBX4Sh4H/l3SePH9ImI9Sa/ltPdvAluR\ngvW9c5r7bCRNK9tUup/TmP3eln82MzMzM6vJPeRmC64ngTUiYsnCtoqBZyZgg7JtG+btc0u5LcsU\ntg0mBd3PVzlmQNnn9YEngDVJ49A3l3SypFtI470BiIgNIuLnku6WdASpd35RYKOc9r6PpFskHUga\nfx6kMe/t8RTw6TpeOR1+nXbWYWZmZmZNzj3kZp3b0hGxfIXtk6uMuy66nTQ7+cURcSwp2N4JeKBK\n+TOAeyPiQeBmUu/ytqSx6HPrn6RZ2y+PiKNJ49jPBq4sS2Mv6h0RpwAXkSaBW5e0Fvo0YDrww4i4\nEfgyaTK50kR2U4FhEfEG6R4MARYnBfN9gFMj4nXgUeCHpAnZniUF+vUaAdyZ097/DRxEGp8+p2u8\nm5mZmVkTcg+5Wed2JvBqhT8/zfurBoA53fo7pIndHibNsD6iRvn/ALuRJiz7L2l89Y6S7q6zrbXa\nMgPYOn98gLTk2Q2kmdCreZAUuD8G7ABsKelFSf/LbTySlAXwf6SAeBowUNLjwI+An5GWeTsK2EXJ\nTaRx+WfkfTsCW0uaWOc1lq7nAdIkeMOAR4AlSMvK1RoSYGZmZmY2iy6tre7QMbPOJa9DvomkzRrd\nlkoiYhAwUdKzhW1PAqdIuqyeOs7e9fDWWGGVjmpip6HXXqTv7t9l4MD12i68gOrevSs9ey7OhAlT\nmDZtRqObYx3Mz7u5+Hk3Fz/v5pKfd4fPEeSUdTOz9tsQOCgidgdeB3YGVgJurbuCQ37ApElTmT59\nwf4HvS8DaGlp7xB9MzMzs+bggNzMrP3OAVYlzey+NCmtfgtJb9ZbwaBBg/yG3czMzKzJOWXdzKwB\nRo8e3doMPeQALS396dGjR6Ob0TBOcWwuft7Nxc+7ufh5N5emSVmPiBeAlSvsagU2lXTPHNQ5DBgi\nadM5OPbzwPWkmZuvlvSj9tbRmUXEUGCYpN4dUPf4XHddY2g7QlvPPiLuBO6UdHwb9dRVbn4SEV8i\n9ex+FXgHGCHp1Brl/0qaab2VtMZ2K/BdSTd3cDsXAoZKuqjK/lWA8cCqkl5qZ92bAq9KmhdLuc2V\n+8++jFjhi41uRofTay/DbjT1GHIzMzOzahoekJP+k38w8KcK+96dy3rnxK7Al0hrIM/N+Turq4Gb\nGt2IDlbr2W9LE86EHRFdgL+TZi5fh7T819UR8Yqkq6sc1o+0LNgdhW0TOrShyc7Az0nLnVXyEtAL\neGsO6v4XaRm0hgfkscIXGbhKn0Y3w8zMzMwaqDME5ACT2jP2soMtDTwn6blGN6QjSPoI+KjR7WgU\nSe81ug0Nsjxp3e0DJE0Bno+IfwEbkV7SzCKv570a8FAD/m7WXI5RUivQWX5fmJmZmZnNsc4SkNeU\nU6FPAXYn9e49A+wp6dG8vx9wAbAuaS3gp9uobyvgOFIP4DjgGEk35HTnYbnMdCqkzNfRli+Q1jje\nHJhBWm/5CEmfVGjHMKAvMBXYidTzd6Skv+X9KwJnA5sBiwFPAQdJGpX3HwwcRgq2/gscKum+vO8k\nYA9gGVKv6IGSxuSU9eGSVouITYBLgd8Av8xlrwf2KrU3InbJ96oX8FdS6vIzNVK5+0fEI/ne3g3s\nLemViLgAWF7SNoXr/x2wlKShZfflfuBWSccVto0CbpJ0UkSsle/LBsCLwNmSfl+ookdEjCCtqT0V\n+I2kM3I9s6SiR8RhpDWslwXuA/aV9GL5RUXEvqT1rpcDRgMHS3oy79sMOI30LP9HWvrqgko3p9rP\nB2kN7ZeBX0j6Q6H8y8D/SfpjRHwNOB1oAZ4DjpN0fS43Mh8ykPSsvirp+VI9kkozgZfq/SqwMdXX\nAY/cvnFV9pdf13jgeNL64GsB9wA/zu3dEngW+KGkp3P5iteSfyYvyWWmk14K/IH08/0doBuwFfAE\nOWU9IpYjra++BTAFGCnpF1XaCHBnRBwn6fiI2JD093kg8Abp2Z2fy48kZcl8gZS6/w7wc0lX5P11\nP3czMzMzs0pq9kR1MsOBk4D+wERSQFbqyfs7MJb0n+rrgH2rVZL/E30dKRAdAFwMXBMRA4Hfkv6D\nPYoU1IxqZ1sWAu4EFgW+BuxICiJOqXFd25FSrNcFRgLXRUTfvO8KUgD8FVLw/zJwbj7XwFzvfqTg\n6d/ktP+I2BbYB9ieFPC8Rg5ysmJK94q53DdJ6dzbk142EBEb5ftzcm7fFNKLg1r2y+XXI73wKY0n\nvwr4RkQskevukq/9qgp1XJ33kcuuQBrTf1VELALcTAr41iIFs8fkFwclg4EPSffsZOC0iIjyk+Qg\n+xjgZ7nsJODaCuW+CxwLHJjL3QvcERFLR0RX0n2/Blgj13dO4RkW66n685F7fa8tu+4Ngc8Bf4mI\nXsDfSM9xLdJLlJE5sC7ZlZTq/Z1iMF6hHS+Q7t8o0guYSvrl+3FFRLwaEQ9GxBbV6sxOIL20+Crp\n5+VR4DZgfeAD0t8Z2riW+4Cfkn7WewGv5Lr3IKXPbwtMZtaf4b+SXkp9Dfg+sEdEHFChfYPy1+2A\nU/OLvH8Bd5F+dxxH+lnZpnDMgaQXMC2k3xvnRcSS7XnuZmZmZmbVdJaA/LyImFz2579lZUZK+puk\nsaSgufSf62+Qgpb9JT2be0pvqHGuA4FrJf1O0tjcc3odqRf7A+B94GNJb0maVqWOam3ZElgB2EXS\nGEl35fMdEBGLVanrHWA/JaeQgqQ9874bSD3iz0l6Bvg9KTAAWIXUg/lSntjql8CuOVBYhZSW/oqk\n8aQx+odVOX/3fI4xkv5JWke5dD37kya2u0jSs/nzK1XqKTlH0p8kjQH2AoZExBqkoGcCqacRUu9s\nD+CfFer4E7BmnoQMYAfg0XwtuwBvSBouaZykv5MCvUMLx78i6QhJ4yWdCbxHevlS7sfA6ZL+nAPY\nn5B6TxcpK/cz4CRJt0h6XtIwUjbDrqQhDp8D3pT0sqSrgK+TXoKUa+vn42rSS4vFc/ntgZvzz+UB\nwD8l/T5f9x+BC0nBa8loSTdLerjCuYu2Iz2HgcCZVcr0Jb04uAX4FuklyN8iYt0a9Y6UdGfOFrkD\n+K+kC3Ov+OW5TmpdS/47NxGYnv8OlqYwvUnSg6VMlJKIGEB6YbW7pCck/Zv0Umi2se6S3s7fTsj3\ndG/gEUnH5L9jlwG/A44sHPa4pNMkvUB6KbMY6e9ge567mZmZmVlFnSUgPwZYu+zPt8vKjC18PwlY\nKH/fjzTm+8PC/tE1ztWPlMJdNCpvr1e1tvQFnpU0qazuhYDVq9T1UFk6+0OFtpwHbB4Rv8+p1tcy\n85ndRkrjfTIiHib1FD+TA5irSKna4yPiXmAoMGYOrmcAhXspaXpuXy3F8i+SUn775R7gP5F6hclf\nr891zkLSa6Qe/+3zpmJPel9gneLLG1KmQPH+jmdWE4HyIBtSZsEjhfO+Ken/yn6WID2PU8rOOQDo\nI2kCKWvhooh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yc3VmzgFmAPfUX5yZjwGvVu1G45na8avV44aj7q0kTSAm5JIkSZPb68PU\nDbUsvW+Edm9bhZmZg73ezdwkTWouWZckSZrcnq4ed6NcL05ETAGeBbYFXqu/OCJ2ATYHnqyKVlfP\n2/WbA9NG2Qd3GZY0KZmQS5IkTWKZ+ceIWABcHhEnA8uAsynLyfcA7omIS4F5lE3XLqPswn5H9RZL\ngCMj4gbKUvTzGH72vK0+O/4asE1E7JiZfx7/WUlSb3DJuiRJkk4A7qLsdv4gZWZ8v8x8kLJ52yxg\nKfAr4O6q7o2q7TeB3wO3AQspu6W/5brzIdRnxa8FNgWeiAh3Wpc0aXgfckmSJEmSGuAMuSRJkiRJ\nDTAhlyRJkiSpASbkkiRJkiQ1wIRckiRJkqQGmJBLkiRJktQAE3JJkiRJkhpgQi5JkiRJUgNMyCVJ\nkiRJaoAJuSRJkiRJDTAhlyRJkiSpASbkkiRJkiQ14H8oTq/rWCvjvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41a870fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Data exploration visualization goes here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "\n",
    "sign_df = pd.read_csv('signnames.csv')\n",
    "class_df = pd.DataFrame({'ClassId':y_train})\n",
    "res = pd.merge(sign_df, class_df, how='outer')\n",
    "plt.figure(figsize=(8,8))\n",
    "sns.countplot(y='SignName',data=res,orient='h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## Step 2: Design and Test a Model Architecture\n",
    "\n",
    "Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset).\n",
    "\n",
    "There are various aspects to consider when thinking about this problem:\n",
    "\n",
    "- Neural network architecture\n",
    "- Play around preprocessing techniques (normalization, rgb to grayscale, etc)\n",
    "- Number of examples per label (some have more than others).\n",
    "- Generate fake data.\n",
    "\n",
    "Here is an example of a [published baseline model on this problem](http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf). It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.\n",
    "\n",
    "**NOTE:** The LeNet-5 implementation shown in the [classroom](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 \n",
    "\n",
    "_Describe how you preprocessed the data. Why did you choose that technique?_\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Preprocess the data here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "\n",
    "def rgb2gray(rgb):\n",
    "   r,g,b = rgb[:,:,:,0],rgb[:,:,:,1],rgb[:,:,:,2]\n",
    "   gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n",
    "   gray_ex = gray[:, :, :, np.newaxis]\n",
    "   return gray_ex.astype(np.float32)\n",
    "\n",
    "X_train = rgb2gray(X_train)\n",
    "X_test = rgb2gray(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "Shuffling of the order of data improves the speed of learning because the network learns the fastest from most unexpected data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Generate additional data (OPTIONAL!)\n",
    "### and split the data into training/validation/testing sets here.\n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "_Describe how you set up the training, validation and testing data for your model. **Optional**: If you generated additional data, how did you generate the data? Why did you generate the data? What are the differences in the new dataset (with generated data) from the original dataset?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "I converted color image to gray scale in order to fit it into Lenet architechture. Also the shuffling the training data every epoch is necessary for increasing accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples = 39209\n",
      "Number of testing examples = 6315\n",
      "Number of validation examples = 6315\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "X_test, y_test = shuffle(X_test, y_test)\n",
    "\n",
    "X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5)\n",
    "\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of testing examples =\", len(X_test))\n",
    "print(\"Number of validation examples =\", len(X_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "\n",
    "_What does your final architecture look like? (Type of model, layers, sizes, connectivity, etc.)  For reference on how to build a deep neural network using TensorFlow, see [Deep Neural Network in TensorFlow\n",
    "](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/b516a270-8600-4f93-a0a3-20dfeabe5da6/concepts/83a3a2a2-a9bd-4b7b-95b0-eb924ab14432) from the classroom._\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "** Data **\n",
    "* I split test data into validation and test data by half. The validation set is used for model selection, the test set for final model.\n",
    "\n",
    "** Technique **\n",
    "* I chose ReLU as acttivate function because of two benefits.\n",
    "   * It is efficient for learning.  \n",
    "   * There is no vanishing gradient problem.\n",
    "\n",
    "\n",
    "* I decided to use Convolutional Neural Networks are very effective in areas such as image recognition and classification so I decided to use this for traffic sign recognition problem. it is able to learn to classify local patterns such as shapes and object in an image on its own.\n",
    "\n",
    "\n",
    "* I used Pooling layers because of two reasons.\n",
    "    * decrease size of output\n",
    "    * avoid overfitting\n",
    "\n",
    "\n",
    "* I didn't use Dropout, but Dropout is a technique temporarily for dropping neurons from the network and reducing overfitting. \n",
    "\n",
    "** Final Architechture **\n",
    "* Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.\n",
    "* Activation. ReLU\n",
    "* Pooling. Input = 28x28x6. Output = 14x14x6.\n",
    "* Layer 2: Convolutional. Output = 10x10x16.\n",
    "* Activation. ReLU\n",
    "* Pooling. Output = 5x5x16.\n",
    "* Flatten. Input = 5x5x16. Output = 400.\n",
    "* Layer 3: Fully Connected. Input = 400. Output = 120.\n",
    "* Activation. ReLU\n",
    "* Layer 4: Fully Connected. Input = 120. Output = 84.\n",
    "* Activation. ReLU\n",
    "* Layer 5: Fully Connected (Logits). Input = 84. Output = 43."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Train your model here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "EPOCHS = 10\n",
    "BATCH_SIZE = 128\n",
    "\n",
    "from tensorflow.contrib.layers import flatten\n",
    "\n",
    "def LeNet(x):    \n",
    "    # Hyperparameters\n",
    "    mu = 0\n",
    "    sigma = 0.1\n",
    "    \n",
    "    # Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.\n",
    "    w1 = tf.Variable(tf.truncated_normal(shape=(5,5,1,6),mean=mu,stddev=sigma),name='w1')\n",
    "    b1 = tf.Variable(tf.zeros(6),name='b1')\n",
    "    conv1 = tf.nn.conv2d(x, w1, strides=[1,1,1,1], padding='VALID')\n",
    "    conv1 = tf.nn.bias_add(conv1, b1)\n",
    "\n",
    "    # Activation.\n",
    "    conv1 = tf.nn.relu(conv1)\n",
    "\n",
    "    # Pooling. Input = 28x28x6. Output = 14x14x6.\n",
    "    conv1 = tf.nn.max_pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')\n",
    "    \n",
    "    # Layer 2: Convolutional. Output = 10x10x16.\n",
    "    w2 = tf.Variable(tf.truncated_normal(shape=(5,5,6,16),mean=mu,stddev=sigma),name='w2')\n",
    "    b2 = tf.Variable(tf.zeros(16),name='b2')\n",
    "    conv2 = tf.nn.conv2d(conv1, w2, strides=[1,1,1,1], padding='VALID')\n",
    "    conv2 = tf.nn.bias_add(conv2, b2)\n",
    "    \n",
    "    # Activation.\n",
    "    conv2 = tf.nn.relu(conv2)\n",
    "\n",
    "    # Pooling. Input = 10x10x16. Output = 5x5x16.\n",
    "    conv2 = tf.nn.max_pool(conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')\n",
    "\n",
    "    # Flatten. Input = 5x5x16. Output = 400.\n",
    "    fc0 = flatten(conv2)\n",
    "    \n",
    "    # Layer 3: Fully Connected. Input = 400. Output = 120.\n",
    "    fc1_w = tf.Variable(tf.truncated_normal(shape=(400,120),mean=mu,stddev=sigma),name='fc1_w')\n",
    "    fc1_b = tf.Variable(tf.zeros(120),name='fc1_b')\n",
    "    fc1 = tf.add(tf.matmul(fc0,fc1_w),fc1_b)\n",
    "    \n",
    "    # Activation.\n",
    "    fc1 = tf.nn.relu(fc1)\n",
    "\n",
    "    # Layer 4: Fully Connected. Input = 120. Output = 84.\n",
    "    fc2_w = tf.Variable(tf.truncated_normal(shape=(120,84), mean=mu, stddev=sigma),name='fc2_w')\n",
    "    fc2_b = tf.Variable(tf.zeros(84),name='fc2_b')\n",
    "    fc2 = tf.add(tf.matmul(fc1,fc2_w), fc2_b)\n",
    "    \n",
    "    \n",
    "    # Activation.\n",
    "    fc2 = tf.nn.relu(fc2)\n",
    "\n",
    "    # Layer 5: Fully Connected. Input = 84. Output = 43.\n",
    "    fc3_w = tf.Variable(tf.truncated_normal(shape=(84,43), mean=mu, stddev=sigma),name='fc3_w')\n",
    "    fc3_b = tf.Variable(tf.zeros(43),name='fc3_b')\n",
    "    logits = tf.add(tf.matmul(fc2,fc3_w), fc3_b)\n",
    "    \n",
    "    return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "\n",
      "EPOCH 1 ...\n",
      "Validation Accuracy = 0.647\n",
      "\n",
      "EPOCH 2 ...\n",
      "Validation Accuracy = 0.786\n",
      "\n",
      "EPOCH 3 ...\n",
      "Validation Accuracy = 0.845\n",
      "\n",
      "EPOCH 4 ...\n",
      "Validation Accuracy = 0.847\n",
      "\n",
      "EPOCH 5 ...\n",
      "Validation Accuracy = 0.857\n",
      "\n",
      "EPOCH 6 ...\n",
      "Validation Accuracy = 0.880\n",
      "\n",
      "EPOCH 7 ...\n",
      "Validation Accuracy = 0.873\n",
      "\n",
      "EPOCH 8 ...\n",
      "Validation Accuracy = 0.885\n",
      "\n",
      "EPOCH 9 ...\n",
      "Validation Accuracy = 0.877\n",
      "\n",
      "EPOCH 10 ...\n",
      "Validation Accuracy = 0.885\n",
      "\n",
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "def evaluate(X_data, y_data):\n",
    "    num_examples = len(X_data)\n",
    "    total_accuracy = 0\n",
    "    sess = tf.get_default_session()\n",
    "    for offset in range(0, num_examples, BATCH_SIZE):\n",
    "        batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n",
    "        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y})\n",
    "        total_accuracy += (accuracy * len(batch_x))\n",
    "    return total_accuracy / num_examples\n",
    "\n",
    "x = tf.placeholder(tf.float32, (None, 32, 32, 1))\n",
    "y = tf.placeholder(tf.int32, (None))\n",
    "one_hot_y = tf.one_hot(y, 43)\n",
    "\n",
    "rate = 0.001\n",
    "\n",
    "logits = LeNet(x)\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y)\n",
    "loss_operation = tf.reduce_mean(cross_entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate = rate)\n",
    "training_operation = optimizer.minimize(loss_operation)\n",
    "\n",
    "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))\n",
    "accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.initialize_all_variables())\n",
    "    num_examples = len(X_train)\n",
    "    \n",
    "    print(\"Training...\")\n",
    "    print()\n",
    "    for i in range(EPOCHS):\n",
    "        X_train, y_train = shuffle(X_train, y_train)\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            end = offset + BATCH_SIZE\n",
    "            batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n",
    "            sess.run(training_operation, feed_dict={x: batch_x.astype(np.float32), y: batch_y.astype(np.int32)})\n",
    "            \n",
    "        validation_accuracy = evaluate(X_val, y_val)\n",
    "        print(\"EPOCH {} ...\".format(i+1))\n",
    "        print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "        print()\n",
    "        \n",
    "    saver.save(sess, 'traffic')\n",
    "    print(\"Model saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy = 0.883\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, 'traffic')\n",
    "\n",
    "    test_accuracy = evaluate(X_test, y_test)\n",
    "    print(\"Test Accuracy = {:.3f}\".format(test_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "\n",
    "_How did you train your model? (Type of optimizer, batch size, epochs, hyperparameters, etc.)_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "* Larger batch sizes result in faster progress in training, but don't always converge as fast. Smaller batch sizes train slower, but can converge faster. I set the batch size to 128 considering the size of training data. \n",
    "\n",
    "* The models improve with more epochs of training, to a point. I repeated training some time and thought 10 epochs are appropriate for increasing accuracy. \n",
    "\n",
    "* I selected Gradient Descent as optimizer because it guaranteeds convergence to a local minimum under simple conditions and it's easy to implement. \n",
    "\n",
    "** Prameters **\n",
    "* Batch size : 128\n",
    "* Epochs     :  10\n",
    "\n",
    "** Hyperparameters ** \n",
    "* mean = 0\n",
    "* stddev = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "\n",
    "\n",
    "_What approach did you take in coming up with a solution to this problem? It may have been a process of trial and error, in which case, outline the steps you took to get to the final solution and why you chose those steps. Perhaps your solution involved an already well known implementation or architecture. In this case, discuss why you think this is suitable for the current problem._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "First I started with Lenet archtechture. I thought Lenet would be suitable for this problem because reading handwritten text and traffic signs are similar. I had to preprocess data set as they are fit into Lenet architechture. I converted the image to gray scale and data type. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 3: Test a Model on New Images\n",
    "\n",
    "Take several pictures of traffic signs that you find on the web or around you (at least five), and run them through your classifier on your computer to produce example results. The classifier might not recognize some local signs but it could prove interesting nonetheless.\n",
    "\n",
    "You may find `signnames.csv` useful as it contains mappings from the class id (integer) to the actual sign name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yXS6XvsdyuUx/fz979uxhfHycF154Qb+TrllqpZPmj4A/X/6cpMlkCuAcsMT8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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41a83bc3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xWGyBUTtFval6xNgUq13CzICu/DEliIAs5JkEZQP2QikWi3pHm+3boLX/D6zX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\n0i7jdyj9P1sg0Aodt1jjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41a837fa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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efPNN3njjDfOem56eHvbu3Ws0ermvDARb8gjwIk06OjqMQmmbqK4DSQaNDETb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\nAb9NqQX8NqX/B+rSO8HlFL0hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41a8342a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Uhy6fz5NMJlusgsfjaWHUQKsXrZt3YcyUSiUVzwv5MxqNUq/XGRgYUCyhWq1GJpNhfX1d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41a63720b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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H41EuWLfbTX19vYqjl+XT6TSFQgG3200wGFSEFotF5ufniUQiaihOpVKEw2Ha29vJ5/Pk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41a632fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Load the images and plot them here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "from scipy import misc\n",
    "\n",
    "imgFiles = ['/home/kazuhiro/Dropbox/MyPython/selfdrivingcar/tensorflow/data/roadwork.png',\n",
    "            '/home/kazuhiro/Dropbox/MyPython/selfdrivingcar/tensorflow/data/yield.png',\n",
    "            '/home/kazuhiro/Dropbox/MyPython/selfdrivingcar/tensorflow/data/noentry.png',\n",
    "            '/home/kazuhiro/Dropbox/MyPython/selfdrivingcar/tensorflow/data/speedlimit_30.png',\n",
    "            '/home/kazuhiro/Dropbox/MyPython/selfdrivingcar/tensorflow/data/stop.png']\n",
    "\n",
    "\n",
    "imgs = []\n",
    "for i in range(len(imgFiles)):\n",
    "   img = misc.imread(imgFiles[i])\n",
    "   imgs.append(img)\n",
    "\n",
    "color = np.array(imgs)\n",
    "gray = rgb2gray(color)\n",
    "\n",
    "for i in range(len(color)):\n",
    "    plt.figure(figsize=(1,1))\n",
    "    plt.imshow(gray[i].squeeze(),cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "\n",
    "_Choose five candidate images of traffic signs and provide them in the report. Are there any particular qualities of the image(s) that might make classification difficult? It could be helpful to plot the images in the notebook._\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "I chose \"Roadwork:25\", \"Yield:13\", \"No Entry:17\", \"Speedlimit 30km: 1\", and \"Stop: 14\". The signs needed to be very similar to the training data, otherwise the predictions didn't work well. I think all images are enough brightness and contrast, but the road work sign is somewhat complex sign so if the contrast is not good enough, it might make classification difficult.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25 13 17  1 14]\n"
     ]
    }
   ],
   "source": [
    "### Run the predictions here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "prediction = tf.argmax(logits, 1)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, 'traffic')\n",
    "    print(sess.run(prediction, feed_dict={x: gray.astype(np.float32)}))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "\n",
    "_Is your model able to perform equally well on captured pictures when compared to testing on the dataset? The simplest way to do this check the accuracy of the predictions. For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate._\n",
    "\n",
    "_**NOTE:** You could check the accuracy manually by using `signnames.csv` (same directory). This file has a mapping from the class id (0-42) to the corresponding sign name. So, you could take the class id the model outputs, lookup the name in `signnames.csv` and see if it matches the sign from the image._\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "Yes. It's 100% accurate, but I chose the very nice photos from internet so that the predictions work well. The accuracy on the captured images is 100% while it was 88.3% on the testing set thus It seems the model is good fit. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TopKV2(values=array([[ 61.04610825,  20.42829323,  17.33292389],\n",
      "       [ 71.41280365,  31.28590393,  29.77226257],\n",
      "       [ 63.19551468,  31.75076103,  20.90447044],\n",
      "       [ 89.19376373,  62.79321671,  56.15997314],\n",
      "       [ 48.56686401,   8.48010731,   8.2426548 ]], dtype=float32), indices=array([[25, 31, 22],\n",
      "       [13, 38, 30],\n",
      "       [17, 14, 13],\n",
      "       [ 1,  5,  0],\n",
      "       [14, 38,  3]], dtype=int32))\n"
     ]
    }
   ],
   "source": [
    "### Visualize the softmax probabilities here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "prob = tf.nn.top_k(logits, k=3)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, 'traffic')\n",
    "    print(sess.run(prob, feed_dict={x: gray.astype(np.float32)}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8\n",
    "\n",
    "*Use the model's softmax probabilities to visualize the **certainty** of its predictions, [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. Which predictions is the model certain of? Uncertain? If the model was incorrect in its initial prediction, does the correct prediction appear in the top k? (k should be 5 at most)*\n",
    "\n",
    "`tf.nn.top_k` will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.\n",
    "\n",
    "Take this numpy array as an example:\n",
    "\n",
    "```\n",
    "# (5, 6) array\n",
    "a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,\n",
    "         0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,\n",
    "         0.15899337],\n",
    "       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,\n",
    "         0.23892179],\n",
    "       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,\n",
    "         0.16505091],\n",
    "       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,\n",
    "         0.09155967]])\n",
    "```\n",
    "\n",
    "Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))` produces:\n",
    "\n",
    "```\n",
    "TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.18063401],\n",
    "       [ 0.26076848,  0.23892179,  0.23664738],\n",
    "       [ 0.29198961,  0.26234032,  0.16505091],\n",
    "       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],\n",
    "       [0, 1, 4],\n",
    "       [0, 5, 1],\n",
    "       [1, 3, 5],\n",
    "       [1, 4, 3]], dtype=int32))\n",
    "```\n",
    "\n",
    "Looking just at the first row we get `[ 0.34763842,  0.24879643,  0.12789202]`, you can confirm these are the 3 largest probabilities in `a`. You'll also notice `[3, 0, 5]` are the corresponding indices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "~~~\n",
    "TopKV2(values=array([[ 61.04610825,  20.42829323,  17.33292389],\n",
    "       [ 71.41280365,  31.28590393,  29.77226257],\n",
    "       [ 63.19551468,  31.75076103,  20.90447044],\n",
    "       [ 89.19376373,  62.79321671,  56.15997314],\n",
    "       [ 48.56686401,   8.48010731,   8.2426548 ]], dtype=float32), indices=array([[25, 31, 22],\n",
    "       [13, 38, 30],\n",
    "       [17, 14, 13],\n",
    "       [ 1,  5,  0],\n",
    "       [14, 38,  3]], dtype=int32))\n",
    "~~~\n",
    "\n",
    "From above result, I think if the first value in top 3 values is much bigger than the second value, the model is certain of that prediction. So for Roadwork:25\", \"Yield:13\", \"No Entry:17\",and \"Stop: 14\", the it is very certain of the prediction, but for \"Speedlimit 30km: 1\", the model seems uncertain of prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
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