Traffic_Signs_Recognition.ipynb

{
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   "cell_type": "markdown",
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   "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",
    "\n",
    "## Step 1: Dataset Exploration\n",
    "\n",
    "Visualize the German Traffic Signs Dataset. This is open ended, some suggestions include: plotting traffic signs images, plotting the count of each sign, etc. Be creative!\n",
    "\n",
    "\n",
    "The pickled data is a dictionary with 4 key/value pairs:\n",
    "\n",
    "- features -> the images pixel values, (width, height, channels)\n",
    "- labels -> the label of the traffic sign\n",
    "- sizes -> the original width and height of the image, (width, height)\n",
    "- coords -> coordinates of a bounding box around the sign in the image, (x1, y1, x2, y2). Based the original image (not the resized version)."
   ]
  },
  {
   "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",
    "training_file = \"/home/carnd/code/traffic-signs/data/train.p\"\n",
    "testing_file = \"/home/carnd/code/traffic-signs/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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples = 39209\n",
      "Number of testing examples = 12630\n",
      "Image data shape = (32, 32, 3)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "### To start off let's do a basic data summary.\n",
    "import numpy as np\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 image?\n",
    "image_shape = X_train[0].shape\n",
    "\n",
    "# TODO: how many classes are in the dataset\n",
    "n_classes = len(np.unique(y_train))\n",
    "\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": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
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   "outputs": [
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Enmt+sFjYXhChxRL1fU5IntdhnnQ404Xcg/mQxaJ7HytXZkPfWWjluahf6CbL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emYHFNjhsez0fp6XBiTIDFDlUp32RL2lrBzHDrhrjN0G8vhACdrufiA6yxNZ7n0hvsHgy2Wh/ee\np8fA4IRo8gzWLRlHprGOjStsjLDBUM5bHt+94fj0mmG4IcbHOlYJyXpjCy8D2aHtMEmLsaaU6+2/\ntnXMFeC0VmliznvNi7QTVX11j0xRWzUNyKIRHCsRQ69WREm0XcF6TxMsxjaYuvaMtTirUQWk0ARH\n0zjaJuB9o4Z3AWsjU9hius+KKNWu61rubvbcHg60my1N12HbBtO0tPsN25s9tzd7Xt/s2LQvi9w4\nlauDpLliOYkW5cZhjMdmy1gsyXiK82RjiKXOlJ3GTMdY89j1ju0jvH+Efx6F4b6QRrg/BX4chYdU\ncK0hmUBynmQMU4xdBFI0pNFC9lAM4+Doe8sPzpIs/IShE/jdaDlFw4DV919dtUbFloKql0YMEZWa\nXqiQkzf35c6dvw1wU72iapAIroBHFMEbGDOMZcrZuDaS1kb2knWgk6mUH/18O1WGB52lXCYOjXqA\nzJwVsnz+9F01lHrtnWM2xCdT+9r0XYAQcv1vBiqgW5mmL7y1YkpY0YUrJdL3PeOlZ9fe0W0PtF1H\nWxNTZ8NtQXBIiaSxx5UR6yc5xglmfMLmWY3AOpb2KSP8uQE+PW4RSyKQTIvxHS5MNWIWLq72DXIa\nSePI0J/pL2fGcdBkvzXd8Beg++dDvszjAugWMHs1q6ufprlcTIf1exT01lFa20Qrz9WVaMOzDk2V\nsa9N/9Xf5ZOPsjzLVQE284lXvrTJlaG/PPf0e7mK3Czd+MT3G+Z6dH+qXpwwcetVzStTlNefIjEr\nBUbrJajXGSxiAsZlGmNprcd7rcI8KfisurD6YZYjuMZg9Tx5iQcY0By3ScragnFOozdtA03QKGwp\nyvXOrMZONEyzoope9bkCkpwHLudHHh8eeHf/nvcPA9/c3LDr9nTOEwBLA2yQonlsUux8Hqw/y0jC\nSgKxamxOdGFjERsopqUYrTxfSiSnM1Ji7bOhoJ67IaPAJqtUryZmfz7d4IoBJhPQKYzjmdPlPfdP\nWzb3Lduf9rSHHZbMtvFs2oYmhJnmGjZ73KtvgJ4Yj6Qy0o8FSiKYhFeLbgHdqy8XVl8+DdX0KjGI\nODKe0ezo7WtS8zXd5lvazVf45lAjh4lSEmM6c4mPXNKZsYxs9ge+PRz44T/+ndaMeKJKxU+WwS/0\n4mi3ZbV2Z69CpQJpQdxilGI1FdDGVKqI9yoC4rXQsh5qmueWc+JyOfJ0fODp9MTpMpI2qoTpbC2s\nqRxUvYvmunS6totkhEq3MZqjOZ2lYowWfg5K+zEhoAVo1ZCbUqpmBmJZ9k2p56vmNvGifTtvjVUk\nb6K6Tc3UcSzWQAiYzZYsQfMP1NpkSi4U0Z9TVd8qGIqJ5KLUKkVuQk5wkzwH8dzuLIetxbaGbmfp\nvWEIBtsYXNDo0CFAvlje3TY8PiYuQ6Qfpv7We8YUHJlgMo0RWgFiYDhviH1Lig0l28qiKPP4vdTI\n9KEBDMaOsyNwdj8by1zT0Gi03VtL4z3BB0LTYlxQQ96U5ZZc7TkRrX9jUs3nZURlmTWHrqHDupbg\nPNJUIG81PydnAUm0wdM0gdAEVdEs4LIsKq5r55wIziu4ub295esv33K4vWV3uMFXgNNsW7r9hsO+\n43bX0TUvM7NdkaqdJJhS94QUkqhDwGYYi6lOYkvGaNHUOjbAbHtODBuMKpU9nIRyEZ6MULKhT5Zj\ncZzEsRMFJSOGEYjLp1UKtyoZ2wKSLGMxfABGDI1ov5/EcBGlxhWqw3x1r4JVoQuVm6rU8hEhMkvx\nGxa1sRe0vwlwM8k9W6vmiDdCAILXhShjIuXFiJrawsZ5Zl5VMIMx2GrQz6i9LhArthYVVENDrFx9\nsCyWzPTLZ7Bm+neZAdFirP7Mc65CsHaqczMfvi/kVpuC1vdRrJYypKzJb4YVyFst9vlpjMP5jtAd\nIBZSOWKz4KgUFbOM67xB6rtnz/3swS+z8THtyWtgY+Y/mUBkR3YHQtiqAII1KGchq4JYVdjqLyf6\n8xNDfyYn9b6lKi4weW9fJsWwGsKf+c1kh88X7BUHq4JWEdaqL1LD51pY2eO88niDc0iJxDiQy/S6\nQpZSPWPrT57mag1Ep85MXuu1kTtNqlnW5WyYLYihni18Wtj4BU2mHl8DHL0PVrzq+V8mIFf/fmWZ\n1L5ZkPJzQFmwVuodqVSJVDIxRsYYGXPWKt4zGEmAxTg0p6YJmBJoi1UPa72xFrxl5twyPVsW54U8\n6415IcApMYLVPCzrDI21uMaSQ0BCoBiz0F7qfH80zz87GZkYzzw8fM/9ux8pl4G2WBiScqBbfUYl\nsm1IEulzhGxppk27zruh4CSBuJVhbZHsyCeID0IZ9KKSUpAcEcnT25FYkAy5WLJxFAvOBULbYe3n\nXz3LHqnzU+kq49hzOlvePd5j2o5msyE4hysJZy1vb2744rDHhxrBCVvs7iuaktmmyIjh/FgYhsQh\n9yp+YstCtZ2/fb1Y5oN01TlDwtNLR+9ukfZb3ObXNLtvabavsS4gJYFRKt9jfORD/x5H5uvuwBdv\nvuP2q++4++2/8vrO8+EYeX+0DMC6TtRLLoop4j5Ri6Y9aeoaLwayNURniU5lZyd5DjFGJeutA2dr\nzThXcw00umMJCIGMr7TJpLS7PKpBONWgq8BAa39VlSpQKWgps/DMFZI1BqoAiW0abNdiyoiktKgr\n1nt8eo9IqZL7lU5Grdnyi4691fn/3F9E7ed8+KwQ1/ynnn5i6zKyiLHEIpz6DEScCClm+ph5jIm3\nQ8PbocF+2XCzb2m9wXWwsYZxxeDZNnC4gfza8f5tx9Ox8HS+8HTOdf/OK6Fe0krp84Cn4MnYkjAl\nQslcu3mnh/z8wbPeYUphUUOtNonRfDixOl9+AjbO0XlPFxrapqP4luKaGs3LS58meX/9FopALEDU\n3MssmSyJTc60nSrqBeewxuBtIdmaTyOWEAIheHwTcN7jUsHFvBQZn8dNfwreseka7u5u+PqrL3n7\n5Vtef/GGdrvBNUFzfVpH1zo2jSO80ED3NcplTME6g/OFYoRzHxmzYMdErWWNOpPU1fDc9kKnu65J\nwxj1HBit8FhflUVIQMSQBHoRLiL0ouCmqkHjnToGg6GuHTBiGKTSA8VgZQFFCwVx6pDGWHMujEVI\nkUopjSBPIEdgoFbo1S98YfvbADeTF7Ny4NWuXpLPWB9203tm4+3KrGL6rbF2SdTHQP1sfanFVKnD\nxVisvqJPrcP5a58ZXdW4nSMirADM6nfz3+tHadX26rWavUkvO3htpdNNX1WKVnKO48AwnGkbR2ob\nrRZv1wtFx8T5jnZ7S+4FGYVcLiTJeJk09NcUsD9/xE3gZu1hnQ41wZHFkcyG4vYQdlpQ0ds6D0vF\n35S0MvbQn7icj6Q4ElMi5UzOea5zs3TsJQfInz+wr4HNCtTM63KSjVV5X5UTrh5F6/GhI3hP6x05\nOS0mxlTrRr3mUkHkYsPK7LhYYNa1YbXSV/uI9jWNxXr5rZ95Ajl/rXbtTZNlTxVVLCowOY2WMZto\nV/WyU89s0F1ozEfTUurhbXyoKmuuGkLqppWJ6mQqUKwUAsNsG1F3Wc23W8DXAtLq/9cGE8sRshzV\nk831gkEsWfcIGYytUu1ejfJug/W+rrnpEp/GjGc31qeazEacMbDfBlUOksj5+MDN/oBIQ0mOnFtS\nhCSqACWyNmV0pIxoMUco1WtYx0cEGRNyGSHmasfVArLT/OWCGZPmD7iA9y2CxTlPCG1VRvzctuyT\neR5ESCnCcOHp+AS+Zds2bJzFe4v1FlKis5btpiW0HuNaTOfxkumSnjl9HEg5Eco9NmW8N3gzyYBP\nsj/PB3+xbkXUaE3S0nNg9G+xm1/h9r8ibF7jmy1iCiIKbMZ85jweOQ0ntptb7rZ33L3+iu0XX3Dz\nas+rG8e+qxK389etz4DPbKv8Malnu9RDxlTjW0yNslqzihavHBeGxcNujEZ4xIDxmpPT7Ok2A9ut\nMCZDKtCnEZlqjJQyV4dZKJ+GxexlPtc+Re5eXSVzlHf6vVKGylxVvojoGWuqIufaL/X5g/fxX1fm\nhp4xlb5q0AhCHnAuEYLgvGj9JDMpt0KFc3onFmGIKmVvJGnStwiXnBlih5TC7d6SS4sL4Bst6tiy\niqd4EA/9jeXr15bv7z2//amATUjWM0Vdu3ZW/Zq64l2kbc94f4Jywsiw+uTnAOczR84aZZWY9W5R\ncCNW74h5TTlH4wOdb9i1G7abHaPZMtoO7wrGRhZFw6VPgkbAdMWow7BU9cwigPGAFnEPzuKMI1tf\nxQKsOiBDgw9TzTr9M+XPLqqjymzxNXJzc9jxxRd3fPP1G7786ks2uw7XeKy3GK+2eXAvDz603lZH\nn36AdVJr/kRGEdocVTyjqDPMOIdvAsbZK5bEdHQVa6BobCQL9HX8MJMzWyMsSeBSCudS6MUyooqd\nxugzrcHNJMEepUYoJ1NlAvKs9q1MT2KUOioFl4UWaEholk+vczxFkt1/cinoXDXtBdTIqXz4mLPW\nX0krr9W61QNtncw/HZywGsir61BHeYpqTMf32hO+nIUTKFmfYvUFq3D3stlk6tZ1N1c/r+0hg8HY\nyS5+We5ICI0qudhpQRfS2HN8fM8YR3IcKQU2mw3bblMj4xMf2eCaHVvrib4lXjry+IEcH/BloPWC\nN+tx/PjCmQ52MLVglFy/bDa6DYVANFuy3eOaPTZscNYxy3XPoZ/C2J+5nJ6IVVkpxsQwjKSUPjlO\nLzk/lsP2U221eoQVnWwxb7UooNZdQNwMbnQpWaz1OKd1cHSOsh6cUihJjXLV81+tmbIAJlPHwqyS\nl6aL30rRoN2V3qJZLbCJb1vXKc9n7q/Qpn2wnvLqsSxFyDWCVZZEEx2bCgZLzjgCxgVc2OK7ggRR\nRHQ10noY52Iw1uOC1gEytmgis3jEqVxmBkxJtUiqFl6z3uKC05oZjcfVeZtQuIIJFlyzAovy0fmh\n58En8Ndf1Bw6G/NVLBYxDZvuwM3hFZvNDhu85omYCsfE6NrKsngOPmoWCDTNgTdvf8P+8CUxCZch\n8od397z/8M/c3P09mT357DBPlnSJGgVztWOLFQ0UrBHCJNM/7W2TMUScPYF9QExPmT3udU04g80Z\nd7rQZtg2Ww47TSwvQC5VCeszm+X60lx8EyoPOw4Dl+MTjw8b7puWEDyUBDFiMHzx6pbXdk/TaFK6\nbQ/4m2+qkSgMznN5+P8YhoGDyWwt1Vu8jMoKVTHr+wmIeEoJjOy5mC8pza/pbv+B9vAtvumg5rJk\nyfT5xCU9ksceF2H/xVe8/vaf2BwKzvdsupFXB2G3gWAXOsrLM73AOo9QlrNGJrCiik+WgpWMF2VN\neJQepnOf1ZufE7ZkfCkEUS9ysZYshtDsubvzbP2Bjf+RLrxDHp+IvYpNXM4nyjgipYKOialQN5+Z\nmAZUJ+QMFgRTJbzLOJL6C/FyRmI9H2UyaQtJhCzVuJ2+Z1ooFZi95KKYfaLz8CuAn5xEBgU1zoI3\nYGMPpw80hxtudjdsNoJxaQY35mr56nlScORiuIzTnTqSc6K1wr41nC4tw0VorCEE/b7nxpsA2wB3\nN3BzW3BtJJuRYnRGMZakRR7oi+cilosR7PbEzesfePfuB4z5ETgy0Q1nMPZCAz3Xgqoz/XqaB6sq\ndzi1g7yzeOdpvEZs9ts9N4dXnHPDKTcEX7A2YkxS43edn8CC+efrMwpFIqU6vkoRunarimhW1ceC\nDxSUammcRm0wbq69Zq8mahG/CY1ns23Z7VsOh5bDoeH24NjsLD6YqgBa18TLlhwA2+1W5a4HjTIl\nMmNJVXHVQk6YnJAUkZxomobdraPpW5TupxB6EhZAFLIaUcW5eeSsUaqZq1RiKVxy4ZgLZzTZf4M+\nS7DQWmgMNNUxofaO7tl1wFXWzz7fH7oOnCl0Vtgb4RXCQa1DrUfmVdbc2Bb+s9e5kVLAqmQrBoxV\nycWcSlWMYBmp2cZWTxMTLWjlSlnW/PWyunKGrn+/fuUMkla0mvrRH9sUC8CR1fSiVYMAACAASURB\nVP9ntAzX1HKzeHimd5vVP5qPevbnm/eqBGIoWswwRVIciDkxjEONhjmkaAVn772KD6DUO+tbnG90\nzPGaRlwSkgUnSakC00G+euznY2zqAMlHr1MeuuDItBS7B7/Dhw4XAhMFYaZ01eJscbjQX47klMlp\nKuQV58Kd6/Z81j+rGcNVbOpn5nheJXOEjqp4lGodledAV19v6km3BAhWvV1fHHMUUQG92BrRyXVe\nRSMSFq3qYEQwFVhN0SIq9DFmObjk+Zysn2Xpzi9oyxUtk8VU51MrZRtK5VnPlwXTXGds0YMM10Do\n9GIohrUdIpgpMAY4xIZq/OTZwePxc6KoqYVSU9Q+OGdw3uKdU5noutakTKpHy3xhlrlZImLm40em\nrvnPbLZ6E01Rj2q2hlwcTgKtafF28h5WOt3EM0joLZP4maVuAIcPG/Zhw/5GAcrpfOQPH77nw9Pv\nOY9vSBhMNpgBUhTGPOIkEKuF5mBBeqJyxpOPZwbLkiFdMPGITUOlE9WkXWsxpiBpRM4nTEw01tN4\nwDhSzqQ8Vnngzxw7U42XGeAsB7oUIY+R0Z45Hx/50ChPP8dBzy8XECO0QdWQQusxYYvD0wDUopZP\n44UxDgQ54tKZYAthPc2G6wmoY5UlMJYto39Nbn6N2/2aZv81ze4OY5QwIiWRS8+lf+Kpf0CKZdu+\nYnfzNTdf/B3O/4SxPxGazGZnaFpzdX88s+c+b+y80+jSFGiWxcFnLRXcpEorYZHPpWBKghyROMI4\nQBwRD8VkkoEkio7b9obGbJCUyHHg/nLGnSMlDoxDD1k971olviC5UJKoXGwxSFGQelUEUARbCiVF\nBTfDQB4GKJOMtUZnClRwo1GbUgvylmnNmkoFf8GBtzBI1m05FWy9171Ro8+NFzje0x7ecnOAzQ5M\nKIidnHiV/bmyA0BB4pjBjkLjC5emEIuoUIEIKYOvPoEpg+d5awMcDrC7EXxXKL6Q6vha55HQUkIg\nhYYjlvcxceEB2j9g/A9g3gNnPXjmu0OeGyt/cdP7aa20tlB/sRXMonVXnNPcLO8bus2ew+EVMjri\n6HA+YWxfo0CJSXN2cUEu9rNG8ksVHonAZZ4nZ1uC9zhnEaPqLMoEUGAjaDkHZcTU5SJUxVO155rG\ns9127HYd+33Hfh/Y7Rybja11ta5v2ZdesdY7ZBI5kUrJL5MjXot7mxIhjRAjnXPcbDo2wRGM6Jku\n00lpFxbNfKcJUunq1moqg63qnAOGAegFhgqKasYcHgU2wdSyaCIgk2DVtSN1XjYTqBdBKHgnbDzs\nPRyAriRIPVISxgSMazTCbl8mxgB/I+BmOnTkyuNSi26KRnKsA8mqsTW36tmeqUFmQSGLXKAe4tZO\nyWws9qqC1kkwpto2UudfcyI0502FCNThXCkIwEJ1m4w57f/PoaeJcjdRY6aFNxtXL7I09fDIaWTs\nj8ShJ46j8p4x9KdHRe9pJKbIfrdnt9/Psq/69QbrGkJ30CJWzlHGD4zjAzleaD14O/mwp4Rx5jNP\n7WqZVYyWhwYtTuYZaRG3xYUNNrRKk5uOTgGRQi6RsT8zXs7EYQCsPtfQk2J6xsqYfHITOHnBpfVn\n/32tkiYzCJlAjNYIilDCfHAomJku76y1baRo0mxJpBRn3X9jV9LRtuYtlFjXf6Zkh5GElEQqQkar\nKRvrEKmy0jkvRdLQ/k3I4IoiuQZd0/L9BcjmU1FUs/74Gp1RE9toMdjJIBWh5EyKEXKDKZCKIRar\ntTByruClUkWlVClR9NOy0q6s1crQKUkdA6tRH6tqLBrdkiqQAZIyZRj1wpVMSZmclZc/0+NsrYcj\nawoJy9qbRvGFY5fziGDIySPWk6VBBuHyYeD8w5H8ZYdrWryH0Flcb7HJYHqD+WC0BPrmL//uIoV+\nHHi8nLnESAbarVILnp563j0+sH1lyGXPDthCLYkYGCXwFJXGMtZAlzUWyYb8mEg/9ch5xJaMcx2+\ne6PFBKUnxidOlwculyfGsafvR1VMypmYVKnyRW26sOt2nAy8KRpIyQz9madHMGRKSVjvSajjwNvA\nWzHcuh3Ba2K7bTaEw1dgVM9wCIHh8d+Il55D49Rg0fKNq9u6ng7GglhG2fKQXiObv2P3xf/A336H\n6zZIzd1R5a+BEp84vn/Pw/HI6+/+iVff/iO7N2+uio1mZxhbT/J25tDPeUYvBDjOq8VVsixUaKnO\nttnQyXq3iT6dtWi+hERKPBPPjwynjuHYkKKhz2eGUuilwdot+3BHEMc4ajVzRKNCU56rQVRq2ySQ\nSjs+J0bXEJ1hHA39KAxRbQCHYESpjTJG3btRJfessfhatM0YjbwpuFlyEEDWJAul+Lxg284O/Omz\nVktx8ks5q17tzgh+PGEef6T95jtuX1t27zx+4+DEAjDntTud1wtFz1vDYed4+9rz9Vcbvvuu4+ZV\ng2vVD/Sn5xm6HWxuLGHfYDaRlCylGDZdS3fwNK9a3OuWD8byzw8n/u3hB353/698uPxA5qTgQSZY\ns747XtAkw+x8XkV3ARWXqNEcq3LKYh3FeEyzIexuCMERvMOdI4QLEgUxeSnIKs+ndAI6DkFFJIZR\nEzuMKRQim66jdS3OBZwPWOex1jNR9ly10ya65EzxshrRCW3Ddrdld9iyO7RstoHQaGHPyawyq968\n9Jb9cD6iKRS+3j8qxuGcA+PwtuCJmNhDf2Y7NrzJ8EosNw5SFo5SIzfT5Uw1lde9lMl8rmeBteA9\nxTmi0czVMr0uAUkdrGEao2oEikzW0sf22JQ6oedgIQTYbw2bVqm3ZRyJjw+YGPFmj/UbjN1h3H9y\ncDPLJ0tBioaiFWlWZZGJE6guz+WEmYzM6omdDTtYwIYsHhZnpvCczAAIzFzcydS+zPkrUis0199N\nXN5pu8+/n42h2b08N/2GKbkfPcuqh2kyCGc1nBdsg5QSkhN5vJCGixq8FfTlGBkuR6R6SsesoWbn\nPSF4nK8UQNSzOXmNnfNE6+mzHg5WRuXfL0hseb55Kpb6PdPmKROn2DQkdhi3pWk6jRTVN8s8V6kC\ntDP9+ahJ1VnIKRHHUT0W1+jmerR+wfm7vH+arenDF//DHFWbD9UCkvV/peYKYTS5o3L1RRR0SlWF\nkRIpOat8gHVaoNR5PRCz1wJdE4c2lqouVAvgCfWzrUZAarJuydeRG6U6mhmgf0xlqa+7om7+8jad\n/3PEpToXsigos1WCk6J9kqJqZyZlTBayGMR4itHk5tlNhtFcgHmvr8BlvTQ1dWXK1zMY78BanFWP\noZGJCw82Z6iS0VIT92VlYEzgdVbtqye/rJ/0FwxallRt8ETBq3Kgy9j3J5r2gbg7YG4POG8JXcAn\nwfTAJSL3J2TfYe48Uzih1GgnKPVoKkwsRcgpMvQDQxwZcyJJ0ZpaHTQd5DBwjA+MvSWddmQy1lp8\nGZB04XKOXAgY15GsqqJJFmQspMdIfD9gz1G962GD377GOEvJD5z6e94d3/F4eWQYe4ahJ8ZEKlkF\nNT4Og//ZJlc/LGCa+SxV6leKPedzVqM3Z4oxDDnhrKcNG6xzhMaz2zRKG/Edfqt5XHpqGY5Dz+V8\npCkXmjzgrVIpdN1NZ7allImO9oref0fY/obu7h8Ih9ckmxFTS/GWxNgfuRwfGU4DeXB0u6949e0/\n4TtwYUJNVYnOd2Q3kk1auyWuqR6f0ZzTu3Si0pU5363mFaAUb8FQRB0E1llMVoBRxp54emI8dozb\nFgbhGD9wTolTbsAcGDpDazuG84XTMBLrnWxrXSdrdSy8BUcmx5HL8cIJQyuB82nkeI5cYkYI6nQs\nGZMyMgyUvoKbUjDeVFEDMzsLUymzWtSnVtc8b5/ZrNOBn+6AteKlQQ23Cdy0VjDDifHDj9jhA9Zf\n8G0ibCy+gTHbCiCX+34Wfanz4axl07Yc9g23dx2v3rRsDx7bopHtP/EI1kJood1aun1Ls8sMvaNk\nQ9MEtrvA7nZLd9fxKAP/8v6R397/yB8e/g+X/h2FoT7girb0S1p1Pk+1iOZbZ7KFmKIkarAX48jG\nIb7FdFusDXjnce0FG44YnxEbZ8GVaSjW57Opny8V9MaSMSniIlgPPji8eJwNOOfq2tTIZi4rxdhZ\nfk8vD+ssrvG0m47tYcduv2Wz62i7gA9K65rWxNKbl18Xj5czFo83LZYF4GiEy+FMQfJA7s+k04ld\ngq9cww9OODjhaOXq/l+ZTnXvP7OjZl+oioZHYxnFEGvu7BQiNWLwGIKtwuYiXAs8LKDpejDq+Eih\nDYbDzrLtNOJZxpHh8agiF5s7rOkwpvnPH7mZfATTwTN5MKgh5+n+YvKUTQBnGsNKgbFGDZ15oKuH\nGFGPkVQAo7KQ1eNtDBhNizJGk+6dK7VwVFayf+2fepDVOJuK/JmZ5rJyqa1vo6mT5tmvpj3DZFMZ\nXnLwnp4GkAhpIKdM07Rs9weGQSM1aaQCvyekFEwtfrjfH9jv95rjZCfwZRV4GFON74bU74jDT8T4\nSOugscxA7WPv/XpxGwqeUVqKPxCam4rG/QwOpwhQyZk49AzDiTiO5CzEGIlRnyFPkbgFVs5D+Ndv\n045cduak8jYDX8m1JyrrW3Ikp4hxFus8zhdcldmN8TSPyDy91mN9i/cB54KGyxso1pGScq1LSTVE\nq2veuKAKUM5hKDVJd6TkWA3xeozaKeIxGeif8rxdLdBf3swizlFRlRrXWYu2pVKqAp+rifIa9Sop\nUlIkjxHnHWG7h7mmwjpCWi8ykXkuoCo9mef5MbWGBroCp3/Uj8jqCR4SJY4aeRMV+GCKolkDWQ2l\naa9/BBDl5WtvAvOSEqVkxmwo2RDxSPFcXn2BfOmwviXsd7ixxzBQzj+QfvpfuJvfYL78DabdgXWk\n/sLlw0+Aob19Q+h2iEAcIsf397x//yMMmZvdji6E2RsJ4Lue9tUHLsOFx385cdztGLY7bP+e8f1/\ncH/+QLPd0929JWw3up6GgfJ4Ij9eiA8jrs/YYrHdhvbVa8QPXM7/zvcP/87//e63/PbhkWNfGGOu\nxfzKQhX6zFaqI+sq7WjaptUDPNfoKsIw9Dp3zlHE0HqNGOc8Iinyxd0tN4eOJrhqvHSEzZdwC5Iz\nwbfk07/z2P+ebbBsgsrVm5mk4RjynmO/p+z/gd2r/0m4+zV+uwFXqclF3Z3j6cTvf/cjD49Hdq++\n5le/+obbL7/Cb6rxDKiKXYflhmC+wFOwPKG+01/WZilgq0nlgvpnJkCvzEchGU/2nuIzEjQyLSZp\n5P98YTxdGE49EgqX8cJxHHgcYExH7s0RI54UH4jpyDlHkmsImx2Hm1tMvmCz5dgGNt6SxzOP8XvG\no+PRG/rhkfdPD4gxbHceGwJOCnaMlEtPvPTkWKNLlW4hmBoRLMRaT0XFMRbH2RJVf9nY+VXB3OmP\nRRWmVCxFVRyDVxGLczzz/YfvSX/4N863ez7cB7pg2HYNMnpyrvV4WMZ/cuZMED0ViBlitgzFk62r\nnDd9lp9tRdPMnHhudju+uHG4Y2YcEncm8cYU3nY73t4EHsd7/uP3f+SPP/3I/cMHZOjVhnJmtqGm\nIp4vlb6XnFUDvkbuzITrmOHcDHikjmkUYRShz1B8wPstYbsjbHf4nDHjUB3JS87ble1U7SlnrUZT\nnFfwUZ1euX7PZFNMeayTCM7Ur/oEMwgIbct217E/7Lm5u2W739NuNrigNOm1LW+e/fySNsRR76yC\nUn+tPpP3DS54Ss6cj0ce3j+yf/eBzWbDb95s+ONROLyPvM8GE4ElI3Jt1SxzNIMaIWcYU+E8ZE49\nXEbLkGsuq4HGG7oArTMEI+ocMqvM1OeAZjEJqvNTsCbTtlYpm52e2Tlm+lPEG1Wi01wbUdv2he1v\nCNxomxfFBGrMhDilLtoplLnKUBGhZJVEXhK4JsO0RnVqeG4a9znJba4Sr8eXNYK3niSpqm3IzIRb\n5/ZMime2JlGtLKxPPM0nlnd9SJ1wMwOMz23nc4+VjKvCe6HR2HXOj8Sx1wO48ttLTpRcGGMCKQTv\naNoO60OlO1kMQRPgXYNzG4xrGNNIigNGIlayjta895ekzYWha0AshYZkdli/J7Q3yvldSTgLaIQp\naY7NeD6rdzepvG8cB3LOKwN9AbRX4yx8bID+Je1njKwlrDq5Oaa8q+kSKtWZIwgaOSspYm2DtX4G\nOJJGjdzUcuoaeg8zr9iFRr1GRr1/xRo0NbZGhUQQtIK88wHvVU3MiNJtShqvojYTlctYWykmFeDU\nZ9BOLPvirwUQF4rWsn6nyGmeqlIbA9ZrPgbUyIlSUxhH/BzVM5pUCDONxJj1BbRstek0XZ5BvaKT\nsIUWJKw0nALEAYmFKIUYx0pz1XGzFdjYGobPk5Ts80tg1V524UulxI3EsdDLyGAGOCfGXjh+92vi\naLB+Q3N7h5zvEc6Uyzvi+xHeBNzlLdJ1ECw5DvQPH9QT7zak4imlMJwvvP/xnvt3H7DW8Pr2lm3b\nXekGhE1m+2bk/DDw+MczebPH3dzC6Sf6P/6WGITdN99w89U3NNsNSEIuR+TpgfJ0phwH1fskELYd\n/s0dg/sj/eP3/OHD7/jf93/k98eR89gSs6mG58tX3CTlq3l8LMdBXR/WVoozuvZiHDV/EENKWtg1\nixpbroDJBe/e4PadRhZdi+9eY02j3HLXcM4j5/MDVnq8aM6UMyAEDC2D3PEkb2k3/4XDl/+DcLjD\negViZNGoejxzfnjPD7/7noenyH/75r/z1W/+O83rDf6qeLkDOrzZ03FHa454zliTfrkbYi7COimh\nSb17lBmRgVhEpWCNp7iABI0gw6h5kJcL4+nMeO4pAcaYGIaB8yVy6oWUHsjZIBIRW3Cbjnazo9sd\n2N/cYKPDjIZ917FrG47nkcv5HZeceSiFIQ+c00C72bDzDT4EbMnI0JMuPek8kGPRMgdGo96qjiUq\nCZ8TudR4tayBzS9z47StwwFBoEFoUG9zRJWlEoViCiGocMkl9/8/de/1JEmSrff9XIRIUbq7R23P\nzIqLKwAQJI0PfKEZ/2k+8IVGI2kEaTQQ98Ig7l0xomdaVZdMERnh6vDBPSKzemZ3p2sXsKW3VZfM\njAiXR3zn+3i3vmK4esn61ZwuXDCrTli0Ft9pXNAHm1hpY9aYbHi7kNi5xHqXuN8Ki1lk6RNNgKgy\nVbdReg/3OYBpEaFShqNZy/lSQb3Da8czHfnECp8sNE+PLXevO15dvuX69pZ111FHz9xkO2QKLI2B\nhEcuWynIAiV79MPeyfwhWmZycJLgkkBVUdUL6tmCZr5gcAOmMpBCFuFMMr1ucknKeap1pi2vGkNV\naSqbHRylD0RkxzxfIbiQNNZrjRtMEbTV0NSWxWLO8mjJ0fEJ8+Ui09rb6iCg9rD9KfNurDFORcSz\nKoKkM5uD8N454nrN7e0ti+s7nv/sKU9Pa86vHPMmUIecTYpySFp0UGE87Z9TFyBK8F7YDYntoNh5\nnSHJpf6osorGKuoS6B4Jp3I5x5g8YHr/hx2QKfaNSsxaODlSzNuElpTrJTufRXqpUCORwGPhy/yF\nODe5jYu0wMmmn40Y+EN3/+GUyYwuhbnqAJ6CyuJGo0J7mlZqeauDlHnWwMmvMVoYeY2mKOHoXKEz\nJlNneJAqC4JRl+JgNMfMDjDV74xOQN5AZCqQBXn/sX5SC95lisOqycxcMaDUQF0ZvDUlK5wI3hWh\ntGxY3hNxfuDo6JSjo1NUU2dd0Sm7kAvtbLNgdvwRoa4J/S0bv6ZRkap4fGNvHvLPR7EEqUh2ia1P\n0XaONpoxKp9NRp2Lvt2QHZthwLuQszXeZ6cGCsaUon2TJljgf552aJxTIIcH0XbUvn+KgZDHP5DC\nMDHRKa2p6jY7JLGZHIzseBi0KRkbrad6EFRWrLd1i7JVoXcsxgcZqqCVypHU6JDgSCHs1/54wJWb\nH52Lg1J/plqpH92G/5Q+O3QE959UyZzGqBGtMiBch4J4KLS4voedJhqDVDWiTIaJorIGgtpDDx4E\nPd6LP42fdSEZU8KUVc1zJpKSI/me6DPr3lT7pNSU8RoDDInp2HvweKV7c28+YiKq4JBCcb7Z7rjv\nPBuvoDlmsdly+e4Lbu57TpsTFs//mr77De7NDcrt0CmhblaYdx3KLFEnBmNqmtkp3bbn8t0dfbgm\n7Db4YceQIFSK5dEZT49mXCyPmbHf9BfH53ys/4rFoufsesjwg+igbmg//ZLjec3syROWp2csa4uO\na2T7Eu6/Rne3mBBQKqFqQZ8q7CcN133k7fcrXl71vNwJtyEbzROT4jQHP3z+HcY4OHgLNe3HugSc\nyvgU8hE39EjK0CxyuICUFEOMBBGe+ROO5i1NY1FWo9Qcm54AiRR2KKXx/Utuh9e0lWVmQeQIkSPi\n8gvs+S+wZ19i50uMtYjKZ5FOkX675ubyBbdvXrG9eofQYGuhPplhmveP37yWZhgudMUxlhkKK+B4\nMAUf0XfZMUyFmTOmooUm5D07JiREhpDYBagxtKYhGU/SipAiajcwdI5+6zGLhsouaVVFKx1JDYSY\nSEmhzQxbz1icnLE8OePi/JxZO8NUCt1YTvzAEAaqekVlNww7hx88M1uxOHrCfL7k5PiIudHQdwzr\nNa7b4VwgJEVSdT7rbINPAyE5fMqZwQx53O8Q6mC/fiw64vx0jkmJOgZaEY60oTEGrzWDUnTRMyTP\nzGiU1QxN4q4aUHaHMh01C5b1jGVt6G2mQI4p1yLCKEyZT8UIuCjcdwP6yqGVY7freHOm+Ogcjg0c\nGTibtZzOW9pZhZ0blFWgwQjMDCyMMEdYSkDrHVW1468Xhl+etjw5scyX8ApHv95AklyHkoSZdhmu\nG9PBcpVHOzh74pbCAHqwePO2W6DEZWyySKSgo6CT5GBhPWPWzlnM5/ihw/UNrjizUoJXe6igjAc2\nSGZFW85mzNoaa3MWrqoM1pqSkSsoAoQYsmE/DI5dP+BDACVYq6mN5mhec76cc7acczKfM29mVLYq\nTLWKg6P4RzM4H9qqlOd7mGpSIy5mHSSjB3YC1IH65Ir25JLzjz7hvAmoWaJeQBXBOpX5X+Lh4GWj\n88dkTxQ5OOijwgWFj1k7MWXuhczSpxV1+TBjvJc8BtOZWebLaN+oIi1Q6URT6m0ujjVLM6C3HWm7\nQbmYgVkYUJn4ZR9F//D2F+HcjIbkWPcyCjSqwoYyFTxyYMAd4qKkLCKlSgGnLuw9GpEcJQljsX9p\nuryXVhqjzTQKGlVw6wLYkvLOjs2UrdFmf4iWRbSvB2GywpQanbM0pTyzwnh5zjKR8sSQB1jen9qS\nJIyymLqhrjQpDCiVqCpLZW3BuSdiyPzoiED0hODY7jpiFOqqwWiFZqSgzU4eBoya0RqLty0+Qu88\nFB78Mc08RgQZD0oqHC3aLrDtCdpUKPasKamMW4wR73qGvsMNA875ybnZi5rmMULlqIKC6fUHM+jD\nO+6Ptn0kZkxfJyW56O4AhpUdhhJFCsU5rmpsVSNlfPLhIA8yK1nETB+Y6Aq0yU7Me44CwuS8RwlI\nGEjRZzppRkjhWGtzKCibpizZ+GaH81Qe6VDn9xnXoprG6eE4lOvHRAqRVNkclTE6M94UyuwUXJ6S\nVUWs2yzaazRFg/e9IdmbJtPV5CBipvKoTRvqdDuRKD47hK44Nz6U8jg1Rfly9pQybns4yw+BaY9z\nbAAIQ2aP6jdsNytu79bcdx6pTul2gcu377i8XtGeznn6yS8ZXl/hjaB2PbYfiLf3pMt7UrNAtxVa\nVzTtMd2gWN3ccXN3g1tfE0OPOTunPTvn+PScpxdnLNs57cGtLJanLJZzjqo1Z3rF3abjuu9hNme2\nvOD4aMn5yTGzpkKpgGzvkNX3yO23qO0dJsRM3d2CPjaYJxXbbxyvv7vl9dWONz3ce6Z6OTnst8d0\n4MFLDmfbuB6Nzh/jfgogUfCSDRWRRPSBGKFPWbTOapMZCJXJMFGrUZXGzslFximilGVzI3T9BhTU\nlSLJBUmewPGvqM7+jmpxgZ3NM6KgBDwIAbdZc/f2Ndevv8fdb7HLs6xTctQWiNFhU4ChVRWnquVY\nWVoU1Xt/8Zi+GwWrU8rQk5EiV5djNMYEeAZf0Xthpg3BGpKxJKOQECEMuK6n7waaqsZUM9q6Zo5C\nWUUUByhsdUQzO+fs4iNOzp6waCvapsKKxlBxHAZ8chgbMaanM44dQlU1LI+esJgvmDcVxvUM/Y5+\nvWboevwQSUkjxmJNjTIVSXxxamLWbCqZ22wr7OfH/jz+8A3v4myGiYnGOxYpcWYtc1vhrKVXmnvf\ns/aahcn1RGEGm9rRVDsqu2VujziXSNcLQyMkL/QipJAhgkyBtOLcJGHVBbyPODdwe6d5cwwXJ3BR\nwZMKPjs5ghPF8hSaU4VuBAx4BSpBJYk6BebimZmek3bgb06O+NvzGbMji7RCmzxu26GTMG9mNMDc\n6pxRj35PVCPq0fudxHRA+jSGrGUP6Wc6wcoLBBWzF65CpNI6Q+3bGYv5nKGf0e8aZIQ0CyV4uD/X\npjUiCaMUs6pm2baZNdPkbEYOgpXMKomUIARwLtH3Pbu+J8QAJVsxqy3H85aLowVnywUniznztqEu\n2jgCe80h9k7NnxJCrCQiYhCBICoHqUNAyr2vfSBYT3tzxeL8ks6tiJXHtjBfKGZO0XbZy4hjbekP\nfJzDQGdxQ4pzk2GR4/5d7GOVszXZuRmprmX/FlJsgwd7taKALaltYjGH06Xi4kizNA69viVu1uAC\nqlJQhIHRNjN1PLL95Tg3ME10SlZDSqhp+p3akwuMmNW9iGYZAIl7w0up7LioUsh8MPlHZyobNRyM\nb76aLg6PSpmSlQLjmSINkutscjH5yFj1/g6gHs7uB1zUh3C4fcH0h7YnT5+gtaKyFq2EGDJsr657\nnHVZ1DPG6fIxBJwIFoUVxXZ9y6VEjo9POTo+o2naafHvWU402rS08ycYcKvsvgAAIABJREFUU5P6\nWwZ3jxKHSj6rCyuFF41LGrFzTH2Grha5f3kYvSFGYnRZl6Lr6HddYXnLRkgIcWJeG8d+YmKTh10q\nP/LVn7WVyFWmL82GrilMPYKfaJpVCtl4PrgXVcTAZH/Ksp8ThzGs/eeHPCN5g84aJ5mONbme4IYM\nqZrmecafq0IfnSFpxZg5gAAe0kKrB1d+RLeM4Rk1rtl8lf2P84GTp54Qivq50gpV2RwOCkVvRhxx\n6Bi0QtKMmhlUJgswPtjb9iatwIP1PB5u01Y9rc9AdAPB9YS+w/c7gvel3kBP2TRlNCKCj6NQbKGU\nHZf14fRSasrCfmgbdj3OObzzpIn1yQIB+g0vX37Lv/0PJ5i/ec6nz54j8xZ1VGNSpOoien1LePFb\nSI5KfY46WWKbmuOnp6hly8VHJ8ThI5IEdDPDzOYs5jPms5bavq8ZYIAaOz9i9qRGnQTaEEArbG1p\nGktVVxB2pPU70uV3hK+/Ib54DasOLRWxOiM0F3i9RIUt3dt3rP7TN2xe39I7RUh6SmqPUKFpAD+w\naa33gVkO183IcKSwU4H8vuW9OTIMPTGmrM1izSSuq4qeUEqR5aKhrTN1fSYaOAeViLWGkzPaWlHV\nCvQ5oi+oZp/SzD9F66pk8LMmTNgNdFf37FYB23zK6dMjFmZD3cxZVCeZ0vsH53YeHyMn1OkplVxj\nePPQSHqklelcOAho70VbYyzsjdqgxOC9xbmKobYMxuR6JWswkokFvNuwvrvE45jpE8xyztnJE85q\ng61UVni3M2w1o50taNqayqjMtlkCfM1szqm6YDZrOT09ww+OMARIGqNq8IG4u8dtVgyrG4btPcH1\nJIloW3RJbBYRDZJwIRFFobTNFDaq2NPFwxltgTJZPrh9/myJSUKTAnOBY1vRGsMOxU6Eymlabzgx\nmhOrOFvUHM0VzaKnqm54vnzCcXvCk9NTvmpqXlx6vr/ZMqRY1NkPZKYkG8k+FiNvK3if6Hu4W8GN\nhWsLdwvH9WJLu3TYhYUaxIA3it5ornaB+6seHXpOToRPLho++XzOxSdz1gjXbzr6+wHjhRbDUdVw\nXGlOzQydHCkNZa1mCuH4Y+bNT2gpZCj8BJ8ezyPJDo5GTfo/AhniHR2h3zJs76mX57RVYjmvOI1H\neD8w9D0SIPOoDER8OQ8LDR2qENdA6B3b1YY4DJgCW31g75WdRKQcSVHofWDwgSQRW1maumbe1pwd\nHfHR+TlPT485P25Zzi1VlW2lkY788Hz9U50bTaJSkgWJlc19pjVNCZxut9t8v/0d3eZ7hv5XSOg4\nbxr+2VlN6CODiVwnw7UGnyAU+2M6saUEgsbM2YQm0UjSWejZQ6pzINREqJOi1Yq5NdSjHtBI4X5w\nVo7XiGQyFmUi58eaL541/PwcPplFFpsV7t0r/PoK5RxqUWwabUG1/FF6wD/Q/kKcm7HYvzCSqIKw\nn6icC52zAa3MtGHtF5tM0DRJKQ+Mlr3hAhOOPzs2I9azvPpgoo9OVM4YZcdnr84NuhjcpESQmNl4\npsjEvjhuqm04+HZvlo0XLh4q2SB96Pv/tHZ2fjpdRFIkBIBEXTfUVU8InkOxuVx3E6a76NaRvt8S\nQqZz1cpS1XrytsewkjYN7bymqpdsRTF4nwvdYgAUyih8yuJg1iyo2jOMqUqCLU5ZBApUKQw73G5H\n33XsdjuC7wlhIIQsWDUZqqoUfI+GubxXKyIcfvdnadPIjYPHfkNOKtPh5h0tlh0tlftzk/GWj6YG\nVeBnhxbX/t5/eN/5gCtPKNlYkhQgOpJ3JD+QvJ/6IUckM454DMOOxBd7UNr+Y98eb2SOL5QibDZd\n5TArJOXQKDTN3hq8WKzWmMpmOF/MBBdIIg4dKcWcedGWMaOiUJMy+r6HDq/z3jPJ/hcpRlIIBDdM\njo3v+0z/PBo+Oved1pqQImF0bkqk+8e6RykKBe2HH127XU/wHu8CKQpaGyoLxIgaNrx59R2DNvzs\n2YJY/RXMZ+iTOcYFbB9gfUvcRZQIppljtMV+dIxdLFieHzMJf5agxAFZ8o/cTS6PtrMaO1vSAqdj\nPwIiAcQhuw65ek387mvCN9+RXr5FeYtWC0L9EWn5BV7PicOa9dtLbv/pBZu39/QuBzx+MAdF3h+4\nn9S0zoaWSvJgPutydhilSzDl/T0inw9u6On7HQEIhZwjK5YrrLYYbTAaajPLZ4BtsPMzdNOSjo5R\n6TMaq6msQVWnYM/AHoFeEAZH6DaZwCZG/LZndXlP3weqk485nT9HmxW1McztcRahNBTWu3GiFc1v\nOcamC0xaoIsMwZ/avI9TUGXcnShyBxARU5ybUDF4x2AUfW1zLaa16JStSe+3uFUgqIhuKo4WS+aL\nU2anJ4U5qimixXvPTcoalwRJKeqmxTY1cnSc99CY6Z3DrsdtOrZ3t9zf3uPurxnWd7huQ3B5b9VW\nY+sKsYqoMlmJCylLrRaylTHznXXzVAmKPgi/fFD77GKBQWhEaJViYS2VNnQxsQ0R4xStNxxbnT9m\nFYsZNLOe2txycuz5/KMlZ8dnGFUxSMebfkfwmebayN7GGNdHiIqYcq1Yt0usNpmN7dbAtYHr2nHZ\nQtUMqFaRLCQjiFVIYxhIrNyAUYGLE81n5zVPn7csn9RcXW+5vFzTrXqsi7Rojqqa86bmo5nGqAHS\njnz2ZzKOkB61ZLNzk1Ihb0mHRwRj4HCsfxmdGwkO33f023uWYUdtIvNZxYk6oh8cXTcQfHb6pMDu\ncw11oWqXYj9G8M7TrRLuEA2gxiN2n00WhLG6IAuOKJTNNTttU7Oczzk/Pubjiwuenh5xdtSwnFnq\nKi+l94Nt4+dp7T5iEY+2ptaGWme2NIylbhdEpVF9JPiOvr9ns3lF310Thg1n1lCfzOlXiV2VUCmx\ni0JXsjLABHwaSV6R7GWPCQTIupOTc5Pyc5qUa89mWjO3QnWAHJP0sA/GPTgBRgumSpwfW371cc2X\nJ4GPm4HqZoW/eo3f3QMBPWU6THFu/v/OllYOq6SkQL3e+4OS+hpZzpSMm9ZoRFHsGpk2a4Vkw6W4\n6Ydqsw/EMqfI84M7euCB778eswdZw0Ri2Gcjxj+GKbs0hmIOC/4e2OLZwivfjyj/D2tdt87ihcbm\nQ14bbFVjjcHqQ+HE8lTFk0sh4KXHSMJIYre951pphqFjuVzStm0uwNOZaWMqeTKGenaMUhCGe2Jv\ncSnghoDYI+zsGFMv8qE4bhzF94sxkaLHuZ5tt2XXbfHDQPSZISwbnWlycIEJj5v7Lf3Y1PjP3Iob\neLATJBRRZedZ2zFiW+ZBCkhQOWOSCoufNSVDuFfflimDM15h1IM4yAjGgESfmdFC+VyycKO6X67h\nMYxSwVkvKD6cl+/12o+4WY/sF6as6951/xGPQDKTUedyMaSxM0gDWiTDFkIcw3D4TpNiopo1VLHB\nVFWO1BYHhGkZv/9MZW2WyLnESPKe4HzO1gw9wfkCTVUla2vRtiqBkkRIERcyjFP2IYEHjum+Ax8X\nl7vfZpKMYYi4CEoZGqsnbSPXr9ncv2O727ILEX3xEYt//t8Qf/cbXP9r9G6FdR59VyPfW1LcocOn\nqCenmSa6tYzZ4MevjsydRb+G1RXy9nviN79DXn6Pue9Q0eCMJbYt1S8/xf78n/F285I3f/9v+fuv\nv+bvr+94se1xaV/YPZ3+B9HbD23j0+wZNA936bJP/Z7dfDRsRYQQHUO/pdvcs6ktNybDjH2KmUhG\nUoFSGahnKNNQmWO0TdgpKlaB1BA0uCFHHUMkdj1+teH69Q2/fXVFalp+9VennD99Clc9egCtF/ib\nAXtuMe3BEZwSxETvE9deuE/QI0W39U8L4Iz2yzh3D7MZagwkGAUp4F1PV6LcrVK07RxRA0mGIhS9\nw2812+usJRLI53JyS9JiTt3UVE29Zx8szuWYNcoOnCrkKIkUEqnv8asVm9tbNrfXdHfX9Ot7hl1H\n8CEHAQotsNLgJTHERB9jhhgKmXJfFwhMCSGLjLW2B0GYD2yV9yjyquiBwWVIa+8DQwgklbdj8Skz\nRBohmQTdGrWKzGaXHM3foqoWef6Uvp7zWh2xfdPR30fSzkEG0jMW8o9zNZYjW1ImL0AyVGiXInfB\nYXqyfo5OiI4ok9AW6rlleTLj4nTOJ08MZyeaFT3ryxW/efGK3718zdX2msrfMZMebeHICAuVcpbN\n6BKUzdovQUAesZ+EELIcQAkYjRlD9WB/hbGGNQaPN5q+X7PetNSrK6r7E2K9ZDafc3x8ivcaTYVK\nmh5NTyKoDBuftAbLPhAT+JhpzlXkwbYtB/8oNocoUCYjM0xlsVXF2ekpnz654GeffsLPPv2Ui9Nz\n2mqGFYvyCpWKfAHsnbTxLcfpdogH/onNywytaqzS1Co7gUkLiZA14RQImq5z3F5tePPNWy4W37A8\n/ZLT01M+PzWowaLuhc3dgAqWFCviaK5wcEKM9y+5vjl4iD57gbrUoioFyipslXXSGgV2FG06QNbs\nnVbBKMHaDEM7Oa752Znh+VK4sBtm7hozvMWEW1TckZQpxBN/nvaX4dyUdLUoQfReOvHQOcj2ZV4c\nCl3qMA5EuiifCxnB+C6T2iw8OOsfbHHTzv8jv5teNl4kldRppvqV93RtxuLkPfA/M2vpEcZymHHK\nN1w2st/Hzv+H267fZM++aakK65kxdeZCVw9ZzQ6fMcWSdSrX3W1WDMPA4DJ9ai7GrqkqXSj5Vbl9\nSz07xlYtvTKEAGHY4V1PXR9Rz55hjC5aRTJN+lRYSEIIDEPPdruh67YQcl9mKNBDR3GMyO6FUn9f\nLzx2OfzU/n7o4IjKUY3MVlOjCPuUu0Qk5M08GysRU1VYW2icS6YgqX1Eez+/8vurFEk+s6GNH+Oc\nmxYD+UTNWjkj9XPexFOKU4Rmn+F571lFfuynH95/e3/5h+8pZe1Kyk4DYM2cxs5y6RkJxCFk8VJS\nzJSubiCGGZLm2DYzShlbTfPyRx0cEdQYIQw5yxV7Rxgcvu/xw1CgkfkQ03qviC0qkiROWlB7rYzR\niv6hf8Mh1OUD2v12KOsgkhIYZagN+BjxMeF2G+Tuim23oQuR5cUzFh+d0w1bdq/+iWrYUPcd6i4h\nwSPeI1hQFlUZpLGM0N7pPn/KOD74FAEH/R3y7jvSd1+Rvvod8uYdxkdUssSmYjia0f7iU5r/6ldc\n/x9f8e/+4d/y919/y7+5uWPXDZNzMwaeHlztEWHgSSm+HJ7ZoR2fVSPoYlzs624eXEVAyOyMw9DR\nbS0bkxW3dynRS0JbQ1VVGL2gbSqoG3RbUc1n1LMZo4aYOA/OQ+zB78B7CIm47emvbrl+847fvrqi\n+ugpv3p2ytlff0m6DMTbLL4bbgfUHIyYcmuSLTEX2LmYnZso7CRTNI+EDI/uu7ELZNzH1ZRdUeV8\n0pqsxzMklFJEpaC21M0MIWf9SQPiPa6LxJDXa1QZephczHWAyxlGkTO0yhTHZoyMj2Oj0UURXUIk\n7Xrc6p7t9WV2blZ3DN0WNzhSBG1brG1yIEcpgkT6mOhjoI8JLwpRBlQR3i7ncoZUjf31OOalymek\nQwQGEXof6X3EDQMhRNq2YdbUJDKaI5pEMhHZBZTumc3ecbZ4w/zJGfOnz1gvZvxT9FwmwfsN3vns\n7Emew0km2eh8PpSpocjQog64CxFDZNRiERUAh9GeykSeXMx58mTJx0+P+Pgzy8lR5OrymjeXb/jH\nb/4Tv/vqd6haqOqEps9F3kaYa6EyZI2j4tyIUoTJovqwlgV783k4aiuNO/foME4COKKI2hF8tmtQ\nlmp9hbk/oj23zE7OOY4WkRaiRoZM2kEacCrgVT57k8g05kqAmIjp4b2L2gc8JqkBQxbpVBprFVVl\nqZuas9MTPvvkEz795BM+/eRjzk6OaMwMKwblALOnuB7HLFH+S5I/tx9eO+KZUZHRDnVJwgcl7Aj4\nlGGZohS7nUeu1rz55i2n5lt+/rdnfPIxfH6iOcGyEc/rzuMFhmgYZJ/BGfeCw5aSEHwiBYUSXUas\nSKXYzAuU2dJkqrnJNt5B/xbGV62hscLZ0vDpk5rnp8Jny8RF2jBzb8BdYvwtEiPezNBT+udxgYjD\n9hfh3MA4IRTIuOHmtDmSpuL9pJgWR8GqvPcmxbgao9YlWpTEMNEN6jFa9dAI++HdHHx5AKnKqvQF\n/iYPh0DpQrM5GaCFIUQeZofUuBLGC6j82scY6aYIb+4Nmlyv5Lxn5wb8SHf7YKLsr5NSxLtcm2NE\ncN2atdGk4Fksjmhns3zY2xINHjcNbaiaJShDmhWdFztD2dzPqfRrztxk2t8w9Oy6LbvdNsMMioMV\npxqbw3sbDfj993lOTKEBpj//09bAT2x753ZaxDorHihFhpgURrfx74i+LPqiEq3zBkFhVxmzj9Pf\nj1nBNGYGMyRNUpGHL32SU8fFUSpOf5RUolZpwr7u56b8F+qjH2uyX0MxMrhcdNxoobY1SjLDT37m\nRAZTC3FQOEnEwRGqKmteWDsduhw4OVBeH3yh5c7QvehD/ii4bwBRGq3t5BRiyA5VCtmxGalkx+zO\nxNYIh6byY7feUA5fHwtVdtkfQkpZUit64tDx1ddf8b/863/NX//8OX/95XP0Jz9n8d92yIsXhG9f\nIf2Oqn8HVyozMe7W6P5j1JMzZDFHtQ1Ykz9+yhiRwA3Q73LGZnePXL0iff81vH6FuV9nPSpriIsF\n9Rc/p/78S8zHRwzhksu7N/z6+1e8vb5l2A0EX8RoD7I3v3+//WmtzH7Qxfgr7z1R6av9HroHIY8Q\ntX0oQSTlzPXQ020MBl3YyIQaIHh8fEY0hgXQKgXSIy5M8BqCB++RIZJcIO4ccTvw7uqOr797xTdv\nb7hyO84k4vpId9tx99U7+ss1i6OG+VmL2S3IYh45CNKv7+nXK159/2t++/IfeX37hm1w+P2jPLrv\njMlQTymejC57kR4RELnTcgCRhPM9URJWZhnvr2tms3LGEXNBt++Ia/B4uu0d68WS+WLJYnnEfJkj\n7c1sNmlIjVH74Aa8H0h+i/gtw2ZFv1rRre7pViv67RY/9ESfUGIyTFW3RQw6Oyu7GOmCx6WYI9HF\nuBpjhLGs1+lsLpnax7RBDDFlrabBB/rB0Q8+IxFEpsBIIGfZtHhSEM6xGJ3ori652/0H4jYQfOC0\nesa/+HTO3B5zWdes3vak7UDYeYao6BMMMeFS2mf6C4TKimBh+qiUolaKWiUqHVnMNMcnLR9/csQX\nzxc8eVoTJXL5bsv3L17w8rvfcHfzFRK+K8RIGs2GqhZqqzKjnTZoLYWgI59Xlfpxdq0/1lwIBXEj\nhQhof67nkMT+aFMqS3pEHfB+QOktq80N6bZlWVXE+QwXQCRijKdphOA0Eiq0qlFaUNqDSqSYA+ZR\n9k5ungh5okuBHIz7gtJFpNNqqkbRNJqmtcxmNW2jMSrSd2tu3r3Bdbesb2uMVRg7OjVCLDZqxuAo\nUhSSD0gUPv4f/tUH993pl3+FjonKRXTIz6GVIumaEKHyAn2iD8IuRb69vce/es1WLdmsFcdHZxwf\nn/OzE8XfUjG7A3XvuPeaTVREeVBsXs64YpumDOv7QbVE2UZVAj2CjYp9q0dHmwRWUBbOZ4pnc82v\nlolfNoEvjOc8OWbDFWp7iXQ34DYQDEq3B8HLx9nDh+0vy7kRGDGySpGhKgWqZgqfe5w6v7xwKpwp\n/x3CHkq0SIlMbEjZKfpDXvTDKOah0ZkKbCDDXkbmj9HIVlMIbCogL5mZSRBrggHk/8aUpVJgzA8L\nYX9KG/HNanymYki4ENi5TM/5XtLmQUsxK91LyZoMCCkFQvCl5iBDqYyxU5+qYmBX7RG2OZqcvxg9\nIWZHaSouS1IMdZ+Nie2W3a4j+pyNCKFkbA7bgYMoBz/L2aSRGEL2Xfxf1MGRCaGmMLmIdYQhEth3\ntpRMQSBS0tbT3NbTM+6j0SkbFil/3nsn+9qaMUKN0kWN3lJAIflwjXGfxfwv0R1/pB2amhQGxMFp\nfASZVZi2oUJhIDsjMUPKxuJu54fcv9pgqgrTNDn7ZQ6M2an7ItGNdUmOGHzGYo9MXUJ2RnUuklYm\nM7GIzs7NELJzE1PKQRbFRBM/HoZj9kFKZucxLYTsiPoicKpjLHTV+UBUwRF7+N03X/HO9WAtn335\nV5x88iWLp0tc29Ctb5HLa+rtFtXvSKsOWW9QYUA5D08v4KzMsZ/k3GQoL24Lq1u4ewe3b5HX35Fe\nfA03N+ghQ/V8XRHOjln87d/Q/vN/QaBj113y9u4Nv/nuJVfXW4bOEWLZA9LBfsyfNi9HhstRqTOT\nfIDovCYOEm2M36iyVzzYWpOQxv0o5Xu0MeC8z8XsIaDqBjs7QmtNawziMnOSSEDGauYQSFERgiLt\nHHGz4+rqln///Su+u7mno2ImiWGIbG863nz1Pevv3/Hx5+fU7RmpT9DLSNNE/+4Nd+9e8+q7X/Pb\n7/+JV7d3bIOfJDwPUQof2qwpXK4luDLCPHWBLmdYcCzBkURwCfEBg8GaGaauaGYWrRMSdqQ4kJwn\nuR3b7h5101C3c9rZguXyhOXRKUcnpyyPTvLarapSp6bptmt22xWuvyP0d3SrWzZ3t7jdQBxCyRDF\n4pBUKFOTVJOdGxVxEuhiZOcdLuZgTnZwpqN6qp84dG5GyYcPbT2GEGE3ePrO0XU7hsFl2QltCBJz\n5pXMVpa8xw0eq2qWVtj1l8hlh+od0SpOPxH++Se/4MnpEd/rOZe24/7yjhWRtVdEL+wkMUiYznZf\nanwrsnNTCVgUc9HU2lDpxEIHLuYVHz2b8/z5MZ9/Puf4pGZ103F5teHbFy948dW/xw0vkXCJUg2G\nGqMSpqY4N7qQGwjWKCpr0EYj5pHOjQ97SGIeCR5IZFBAtMUxiCkWso0+OwjrawYDadaij48IwZBQ\nWONoaiG1GpUqlKozNA+IRDJD7cjMeuDmjt+Pwi/FqSrxQkylqWpoWs1iXrEoBCNGJ/rdmut3gc2d\norYKrYWs21ayRUoIKteVJaWJPhL6gRQi/+OjnJtfIcOA3K+g2+V9CwWmJgawW49oR+8C3RBwNytu\neM12rele9/zt3/0Vn10c8bOTitTWiATWncMnzS4ZEpk4ZTKnVHZSZBoM2X8c7DuK7NyoyETsNTqq\nQv6FqkC3ivMjzc+PDX+3HPiX7cCJ6VikHXa4Rm3eIt01MmxQtJPUqBo38j+x/UU4N9nuGw0/Qy6y\nL/j6UcjjwPOejAxF3qwnJ6J07tgKQ5oSyZ6o0TnNpvNIPqCVHtsYZRxpdNP4dcFypoQ6FI5SB0bW\nCEUrUZbRNB8dtwltWuBWaTqs9pGED21N05ZrqOn5UyzQh5BTwaM39eASo3F9AGUK3k0LlRLhlhTw\nYU7bttR1TV0VeNCoAi37j5hKhGaM1qaIShHnena7XGMT/JCzODFkbYU9Ru/B/2qM7qgDym2kUJjK\n9LzTxHjMYlDvffFjlsOUWTn4Xan9SSmbHaJz6kaZHPUaGfQOvco8DwEVsw7GmJWarLGD+TZmO6Yb\nUHsHVmnQpqTVPSllxyZNB8jDjWi65d/z88e3A0f9PTjaPhs1nR/70U2BhOBc3sRaraBqmMhAY8ri\nh1CyYOEgMxUIB3VL07BBqQQtma5SC5eNnWLQmUIeYEqm01hCCgTvimOTCl3m/jly3VRhaxwDjjCt\n38e00XEdDwNVatOqUgyvrcXUBhsc8e6Ot1+95T/Mv+XzT+Y8f3aCfvIl7d/1yPFL/Mu36FVAhzVq\n9ZL4oiOt3qLenMPxGRwdw2KOFFn1/NyjsyMTNFXFARV7WN3BzQ2s72Bzj9zfobaBJC2uqUhHS+zP\nnlI9/xhzccRuuON3//QP/Pof/x3/8R/+keubFdtuT7E9TpD9Mt07GY+ZivrAMBUezrl9/x7Ov4f7\n73gfIoJEQUwO9Qfv2K0TQ0iZw9IYTNUQoxCfnsBZZmnywTGbtcxmLSp6VPAQNDoZru5XfPf9a37z\n9hVvd3es4haXDKvultfXl6QQeXXzDevr12zNGe+GY3gtsEjZSfCebr1iu17x9bcveH275n7ncKOT\n+IMn/7DWNk0x/PK5OTo2Sh0E3gqRTibGyV5CCI6u30KyBNG0omiaOVpVKO1RIaEF8G7fl8NA2qxx\nt9dsmhnK5nknJXPkC3th9Fui3+D7jmG3KxnWwqJlKrTOwaOEwRPp40AXPLvk6cPA4D0+SmaBSqU+\nZdRVGc+lsQNSzgroD/dtWJVotJdIUonGahpVY5oZph4zpJoKoSIboDuEHo3zoHRgxoA1DplH2kVg\nPhs4reY8+9xwv5zTfSqsty13Ed4Nka+v73lxu2W7G+h6Rz07YT4/IXQBv/XM25rzZcOTxvC0Ujxt\nEs+ayPlpxcnHc5qTCjdsePnqius3N9y8fU23ekvFDlslVKUxOjPtjrIZRkMs4IhMj16cYJOfTz1C\ncySVyZuRHJBZbWXaQ6fw5ZTGKV+HnEs1via5itjfErt3kGpMtFRqoK2zoqkWi9Y1RgtWaazyOPGE\nElCzupASTUmK/TWzYyMYA9ZGrM7Oo4qJ5CH0kfVd4q3bsq4M7ypNNUL3lEzw+1wxWerjSrZLK43l\nYd3zh7Qvnn8GLiCbHdJ7JGR43YBm1Xt6b9l0wq7PbHDr+1t2LmJXK+TuLaaK6NRgnj3lo6dP8RcV\nFZoX95Hv7gO3ATaSYWqhOClaQJWszeDgdg1v53BWQwpCWMP2VnizSbwchHWkoCEUtSL3n02cnBhO\njw2/WGh+sVB81gyc1vc08Ra1ukG6N6TdmjQMSIgZsKXUFHzkoL72se0vw7kpHmN2cAxK1Sg0UWUm\nrofG0+iJk3+XAbbsGab275sN52Jo6oIfNCZ/VkW48wfQNrJzkkYhq1gyESM0LU+Ag7vf19mokvYf\nK+gP1pNMC3xU0tYlAiyTeq9+RFiurlskpSx6WSATKYXi4MR9P5VRp6NDAAAgAElEQVTPMn6nsrGX\nIU+QJBYnJ01sUkqEGAZ6P9CGBSfLIypjpvcZa4XG6HgomkBjpEalNBWorjdrhr6D4EnRE0O5vz/y\nyKpsDnnED+qS/myGunrv6z/8xtMclETC5/6jBlVhTRagzBo0BcIoD18t8lDPaK8WXAxreTi3D8cq\nwzOyg5PEk8RlOFPcG+Xv96faX+jP3vaZmT/89tmoLD2XcuTbOUWMGmYVprUYpTBaEF/qwCRjdinF\n/SkGxLPPipa3PLzumGQf5dRSYc1RRqNtznbpUveENvjo6F2Pj6kUnfLee+dgxgiLHc/HQ/jTh7Zx\npHIJ3ghjhcoaamsw1mCrKhvOqxWXX7/h3/dfof/llzw5/Zyjiy+ojmeE+YLdEFD+Hc1qg7pfEVdv\n4fUMNT9FHZ3D2TM4PiM1ArVg6iYbY2RPOzpHGAaU22KGDdzfItfX0G1Rg8uGawDMHD9bkp4+pf2b\nX1H/4hOSVew21/zHv/+/+F//5/+J370OXF8HYjIkyRCkvJrG9MnDdfuoWISa8oCHic3yu+k37De5\n/SUTB2M6BhGSTAb5ZtdD7yaXPSTF4DyVfEytAl23pt9teHLxBGueomJAeYeJFpMU13f3/LsX3/LN\nu9e8G+7p0kD0sNre8PLqkl2/4831N6xvv+Pan9LczNmGG3bxjuBCJr4IkRAiN6sVN/dbOp9JJ0pI\njH3p84cv5rZtivJ7jmZbWyhsEyUwNU7wbN0qQEkihoFOhJAqemqOrcY2C6wJ5JxGFqWlsFxF2dJv\n1gxYNspMAocZbKEyZXMR9BPxkBwipXa1BCuVrbC2RptMRZUEfIp00XEXHKvgSSEH8ESy4xpk79xM\nQaUH+2xCkvoRjbQ/3tYkslJ9xCihrQxtXdMcHWEXSzyCV4IusfAUoA+RXhTOC8pGFsbRWA+LhCwi\n0jjC3ONmc4aPavxg6XzkSuD7bkB/vWX1rWN4d0sfVsxPZxw/O2L9rmfnheZkzpOPj/l8afi8Fb5c\naH6+NJydWqqLinsZ+O3lO168fsO771+yunyF9O+o1UBVSyZmGDV2tEaJyWtBSi2ggB0hjFajK/so\n5+YBCOFwbRYEiB7n9BgEOTywJFC5CvGG1N8Qt3OUnmFUTaUFVUe0SNb504JVmQreAiYldt4jKlEb\njdH7zFPec6UAbaRA8MAawZiEkYgKGnERL57VsKW/s+jk0SlgVMzZrpEAqzxbJM/B3GeGpmlYzubU\n1ePM7C+ef4YKCXYJcYnoBB+EPgh3647VOnBzu+N+5Yiuox9u8Pc3SPOGvp1DqondCb/8r2t++cVH\nzIzhorEcmx47OKpeE9MoEJqvOYr6pqjoB7hZwZsGTubgAuxuhdW18G4lvBmEVdQkcl17BcxqYdEk\nPj+1fPHU8nwGn7fwTA8cqxUMb4n9S9LuFroNaXCkopcmSmVv2xSExSOdwrH9RTg3umQ88qNkiISU\neptR/2WKuk3JluJQTCsii5JNIYKDTUyN4cNUpmAa04mH+P3CypX20KBUDj+K8zGpwY/XVNOlp4Ky\nvGhkWrygCsmWTIZMSjnaleFN5e+jkB4xlv2uAxR6TAomR/A9MYZJX+LHTM+xhm90znKmRYjk2gUH\nxVGKE0HDNmUMaV03VFXFSDub4T8ZUqSmg0XwQ4/brem6LX7I+iIpBGIYqXaFg4FnHMnRGM6uH4yK\n0zniMxo5hwQDj7XcH3b4jxoNf8AYG7NuMUUEx+j0QZoiNxj1niW2n9OHvvgEWZzmYtF0Yp81EIoj\nSSIVVqeHWObf93R/wAV57AZykD39fVcemZL2P9qPW0rZMOmHgIil0dDqClVVaC2QMg1xzi6MFKJ6\n/37y8FrT6JX5obRCKwvKoG1R8FZld0khF5V7P2Vs4oFdPD7XD/xoGR36MtKP6LvxvbTK0VF0vtfa\nGupCDBG9I7lAkp438bes79cM7prL1ZbnP1vw/LMZy5PnVL9UmMUb4psr4n0WQSMK2q3h3oHvkNUl\nYkGsgqpGxnWrhBRCEXfoEd9n6MO2Q0IiqQqaCuZzWJ5inn2M+viCfjHn+n7Nd9//mm++/kf+33/4\nR75+OXCzhhD3wII81GrKBEz79gf32EHfjWlAlQMeOSs9rqlYAjOxCBKrvX37/rQvY50DJ5nYIwZH\nCJ71KgcUkgAp0uiAH7YZj28MJ8lgyM640prb+xXXN295cfWKe3/DJlyz2lwyuIClZbu+5cU3v+Wq\nnbO9fYfrt6yTR281fdjSl4Jy7xwhJkJMdMPA1gV8ypnqseNkOmg+vO9Cye6msu+OfCfZ0QNJpQZC\n6cyaJjnolrlxYqbV7SOdzfVvrdLYqs01n3VAhYBKmdJZIgXxEIghr5kRnp8Yay2kOAyqiPUJyhp0\nlSuWva6IonEhMoRAFzzb4NmGyBDDxEQ5OqxjdksOzp9xrIstTZL4qAmYCGiVMLWm1hUzFK2u0G2F\nqjTZXpHCvC6oYJDYEDV4QEyFbWvquYZZxHHPbh1x8QqfFoRoiXEACcys4mmb+JtnW9oq8OaZ5XK9\noDpZUp0e8V1K3L/rODkx/OrnM56ZHYvuGrxjWMFtD+4O3g1bvr685PXVJdu7K9zmBiv3VHSgHUU+\nNNcyk+uGKHaPLlmvIUXwgkkhB1ses3h/4Ew+PLdHWNqU2Ukj3D+hRUFwaN9Dv0E2d9gmYAsbq7GZ\nqQxRqKTQCXQQtEmgAhFHImIxJXOjpjHKAfFM+mxSxEi2yXQCFRVJg/MDQVsGAZUURIdEjyaiVcr6\nRFNQZe+gGZMDVLP5DPGepnkcnfH1i8u8jlwiupRFXX2kd5F1t2O3WaGTxxJpVR47lQTve+4l8Prm\nBdBijjY09Yr2/Alnpxf84olmrjXnAyxD5GqI3A0ZTUHIwVctkJywXkdeAWFQLKzCbxX9VrHpctYm\naJjVCmOEqoInx8LTY8XnxwOfL3ec6p4z6ZnrNabaILEnR8zyfNMolK2I2iKi9+UMf4b2F+HcTBjM\nUaNBAoLKER0Zk36H9sxDvH12LIrlgmZiHpNiQFC+lRE3n831yd5ixPDrybGR90/Gsnf9oOPHeylh\n4zGncWAbTfhfChtcztRkSl+R/P2PG4l/vHXbNcZYatuitcqHdHFu5KDT5OD/8dm15M1FKT1B7xjr\nDlIsmjgF+wwkHxgGx9HREVov0SX6wQQFkhw1L9mJ4HrWq1v6Xeb0jyUyGWKaDqbShQ+7lDHKU8Ze\n9MG9j3+vpkjQn2MtyKNW1FgsnaFT5enRSucaKkzWZRL282osSJ7mdXZM9qxwhTJ6FJEt75qLVoth\nUjKJk7BpuZcfc2f4wU/+TDvHeMWDgMN4nQdK4MURnaLu4zpMgUSkHxTOK6RtqdoWayy60igZUCIE\nJ8Q+TrBO9d4zyoOPbMmIUZl4QNcoXWOqbLCFFIgp4IJncG7K2By+x+jAPHjO0bCUvfG0368e0Wcq\nHx5aAaWup7aayih88HjvcS7hfGJ1d4/wa15e3fIPX/f8q//uV/z3xz/ny9PPeLo8x54+o69+R6ov\nMZstuuuQ3Q62K+huEKWnj2grorH5YFejOLJCpazNMuKrxVakuiG1R6T2HP3sM5pf/or05IR1eMd3\nb3/N//l//+/8m//nf+PV95HLd5oh5qj8fp6NAatxlP60KBxkmKBCTdAiNVqtJcuXUiSkiEJn8Wb2\n47q/iwOjV3LUGikMaiFAkhx8iflnEnq2mzUn5xecnj8hisWq/KF1zd36kv/4zbe8un/NNt7RxxvW\nm1f4QZg3F2zXNd9+/VuMrdF9h3Y94laklDM13ufiaeccIUZ8iESRrAp+uHbkoF8f0XyMJUMzaoKU\noNpYhK8MqFw/iFZoyRpUmW001ygFl+hsJCYY6ppZ3VCrBpsiJgZMDOjgc32SL3WpcdTcyrVspWJz\nH0zUFdpkGmrTWExbE5VhiJqdj2yjoxsGdr6nD44+JFwSmIqh31u/P+LJ/iG47k9pggcNtjG0jWWm\nLY22RGMz7TOJRERbydJR1qKSJiXJDqqpMLOaaq5hFujlls3tJdudIbIgYUjSk4zHzjRnjeHvnjZ8\n/qTm0jVc+hlde0zXHLG53vFCEqfHmn/2i4bF7p7hu9e41T33JQN44z1vtmu+u3nLzeYGUoeRHa31\nGOsQnYVLtBasAs+I1sjnkS4aaTEmfKl7fpB9fWybUAkPRyqVrXQMHitJUx0iwaH8gNptYXuHUdBU\nhqQqfGUZfQsds8CksYLSkaQ8XhxBAhZbtOlU9kaMApWQ6KFkY1Rh9hrLsgUhFLHbGFLeEwrqRBUl\nnNG5mfZRleFvVVXR1DXJOTTgffOo7nr925d5Lwhx2isG5+n7DP3c7QaIjopAq/PqsklIwbMOnte3\n37EdtlhzQ9W95Yt/+TdcfPS3HDdHPF+2nDtF6wLfbRKv14nNJuG2QvIawSBe2G4Ghl3k7bsC7ROD\nJE1KOSiktbCwwqxJHM2FLz9q+PKp5We25zO9RbsbtLulqROqlgyB9okpTqk02Bqvq2Lzj3PlUV32\noP1FODdCQESjyu2MsKZUMJPZAH/P15gwJKkwpJRja4KMZPzwGOw7uFhuo2FS3lTKChupPt//++lw\nPDir9xCZAyO7pET2ZGjvGZVJDqKYpZ5kNJweMaKjtoOoXIvi3VDwy/Hg2fes8lO9gDq8nkwLc4x+\nSUpEIiEEnHNo3ReGuMRWCSHkiERT1wesSNnSDW7A9Tu6zZphVw7uUFTfSzppMn4PrY+xHXqGB1CJ\n0WnML5OJre7RC+EDHJrf/5dlox5F6gREZ8jLAygT5T+TGdZyZu+BKcjo5AC5pubQcBvZ0GR/WP/Q\nIfsxlY8HF3jfE3l0O7yy4of9M+Kq9/2zH3PF6EzmdRcFBudRKKwKGG2wOlIZBdaiG41JCqXyYabe\ne25hfyChZApUoDPOPxQWOR8DPgV8CPiYjbRxG/nB+B5MrDwM+xFCyQ+m7E9ttS1bhCqwVCmEBpK1\nGgY30A89zkecGynSI3fDbzD3Dm3fENK3XD7/iF8+e8ITPaN99gXNydP/r70z+7HkyM77L5bMe29t\nvXAdjoYcCZJhwDLgB79K/78BG4YNS6MZWZ6Fw63J3qrqLpkZEccPJyIyb1U1h305sqlGfESxq+6S\nS+SJiPOdFRdG7Paa8N0z4utXyBSQMWpoQ9BKfkaC6oVWZktm1yHrNcavMH6FvXyEf/oebnWO0LOl\n58vnL/nmyz/y+fPf8Lsvf8Wv/uG3/Pb3e26uHfvJaqGXh4hNXjRrV4vZovTWiCkr27n6pTXkfLdC\nqOe5Vs53JKeLc5ecLGNs/YwkzVecxoHd7gaMYAmkNLFPwj7C0/UZ+6unhP0tw/aGL59/wVfbb/hu\n+x3X2xcMuz02qkU5jBP7eMN+GNXQEQMmTohM6nmNqkCGqEUMYkq1eWyxX5dQoCNDzgkew5LfGPOe\nqtELzOuvFSCBcRg0ht4ZhzGlwEdgmiadOykxxsgYIivn6BB6gya2dxZne2yvHgCTtPpasdHXlOFc\nhVDI+4wEJpOwMjJGw+0hsRsi+6F4WIMSv0LGqoL80Mr8htX6xKXPMbdUSNaSnEO804IkLusaKLFJ\nVp9pFJiMMCJaUtt6sD34Na5b03eGaUoMYUIk0PmA7yPdJuFyFbD9CMNgeL6zGBeRjZBkSwzfIpOB\n8ZK4f8n++hvi61e4IRKGyM0Q2e9uCdevMMMN3k14H+ic4JyuO2GKOVxQmAIaTp7H05gcNluK3ORC\nN6dstEd5T/rC8d9L24ew2Nv09xgicZiY9ntGd4sVhxVLdB3BeMZJmKaoPczGCZkOmLDDxj1OtDG0\nJhI5jNcqmUp0tHBSkjH3j5vU3Jz1OiEXPxWdOyHM4f7VB2nmUGiLepOdtViTSFbLKR8OIyG+9bAB\n8PqrZ0QRhhgZk863MSjBGYM2m55ixHnhbNPRZ2PdfoocQmQKN2wPge9eJP7ILWlzYPBbzh59xOrs\nfdbdhr/crHjS9Xx60bPde/ZbIUwGEa9yYKkGa13UXV1vrYHOC70XzlaJi1Xkw82eD2Tg0f4l3fQS\nZ7c4s8WbDlwHLmnrCqcpIvgOcSBYYkq4FBAZgQFyqCTmNHL4kyA3SQIGWxmMkhpq/KwYUxd7gGJB\n1QU/B01J4c/kkJ48O7hLcO4rhPqPHHk67tu5F2pj3kFlwfJ1gkolWVISNRfflsUJhBI6Y08mNoAu\nmkat/JLLOg/7PSGEqkjMKuWC3NSbmwmDyQRJoxUywQmRaZywZsBkq84URrb7LVcXV9iLy3pfpZrR\nNAzcXr9gv9syDoNaJ3PZzOW5ipJ7pCqaemH5c9mLkZ91Hacy15ab/o/A3X3v/vG+b2csinp+jrXi\nVsKYqMm7OYxFSxmb49uth8k3kiSH+qnHppD84h36cXf5ZzCJLHB8tPujWAjYXe46f0TveRxHphBq\nl/n1Sl3nncu9m3A4m0uel0avVBFHKMEhUsMDNPdDGMeslMVMaiRq8QAxD46GjtJCzkTDR21VoE9/\nBmuPnjcZYhQNRQpJw0FTYH/Ysx/U0zmNEyHkymrb3xJffMV+/2u+/vo9vv53/4Hbv/3P/M1nv+DT\nDz7j7HJF1wPbVxx+/y8cvvoS2e6R7R63HbD7IYdwAA7EGZI1JKdes3R2htlcYs8esf74E1affkbX\nr2G758UX3/JP/+tf+B+//kf+6ff/ld999c9cvx7Z3nSkWMK/jmn68XpThrII/Wnjl6Tkq+Wk89If\nIFfCtOhPrbazeLiVmpaeLKWKXqnMWYQoaaPh/X5LiAEj6u3bTombIfDRxSO2T0dev/iOl999zh9f\nfsW3h2e82D3n9tVLhu0BJz1ehDhGprRF2KrRI0b1jlfPLTWaQHJ/Nln8LEwB95XCt0QISm5SKl6b\n+dkYY1SLsxFKwz7ncM7ktWhST9M0kGTSku7TxNCtWPmOlbOsvSN5r6FlvcUZgzclbEcJkvr/VTkS\nTK7wGAhhzzgGokxIEPZj4NVtYHeITDGXSc/FgY6NET+Q2Jh5nTgFvoiTGCKG6Ayxs4i3GF/6gChR\nSEbzmiYSA4nJJKLJu6PtwZ3ju0vOznolufuElUS/dqzPDGfnhlUHcUzsdiOfv0jsXxkODAybQJiu\nidPXTHsYbq5I18/ZvvqG4fVrzAhpEA4ThP0Ou9/SxwO9h84b7bXmIQVTPYSRQMh5F6Jaeg7rRavm\nlVYNqRCct0P1mjHL9d33lwRHMrG3KU/HkJiGickdGLiFnM8XXUewTgtKTJE4TsgwEccDMh4gHLCy\nx0rMTbWdkm7pcdZjrBDMBEykeCCNI6B5mmqVVM9bXPxo9TWdpcW4GrNUGxG8tVnP06Y0MQrDITCF\n02bu9TffEkU4SOKQMsFJUXObs44RAecNZ+e9hhMHwcig/YXinnHa8/J6jx+/YyvXfHvzLU9/8Uue\nfvpLHj99n8+unvCXF48I/Zp96NjuhXEykDS32tJTdOhiWDFWvefOQudRctMlNm5ktX9Nv/8Ws/2G\nuP2G7gL6c8GaM3Bn4BJ4C95Dp2QHJ6Rk8zgHhAHMAaWOHvg3TG6EAfBYnCrqVSlSoUjZI7AkBvmX\n2eq2sIQfhcTUQgNm/kKNh/o+rTgrT0aJ0xw6R1YyHyBAqYh9JlS5ulVeM2oo0dxiu3zbLH7edvDU\n6jDs9qRxYH97w3AYiEH72xRlrFr5hVwG0dTF5NgDsNhSC4nMYWohTKpURouJgX1u+rbq1/T9mikc\niNOB/e6aw36vTUGnSb02ScfMLslVfUZHp55vbfG6Vhozi6+VsLY/k8J+R+EqxOuNeOA9Wbyn4hbB\nJEQsYm2tTFfkaLYky8zplsrOojT0nAi7VHv+lLwspfTh+/zz4GFqWKxvyvLNvTErMqaf1bjvZBI2\n2TrvfCY21iRcidGvAS6zgIjJx6gzMBcUEEMIocpgTEkz+o6UpAX9zxe1XG3KPNL8gznk7pSRlKgJ\nuyFvyocpME4BRCu9DePAMI4aChGTFo+LgEk4GQm7HdffdnzRf8XG/pqbly/58r1zPnjviiePH3Pe\ne3z/BPfRGh8m3BTwY8BNMVsZjToOjXa7FiNE73PI2orgVjxPhs8//yOH7Q3Ds2/48o9f8T//95f8\n+vd/4I/ffs53L18zTY44LUpPVdKngr1ci4/CSIHZu/6WMDMhUIJbCuuY/JONHaZaPhaTMpduBa2c\nWTzC1mSSq14ETTyPpKDNM7c7zdUYojai/cMXK9YSmOI1w/iSF7vn3Fw/Y3t9ze5mz3AIhEmIQQhR\nK8epbEuNSCjzfblevFlgpG4TP47elJwap/dYQlrN8jqMxtrbOT8mJrXyjzHn7IggIRJkQqIQ3cRk\nLaNzDN6xc47OWry1eGvw1uDUyZErGUqubCaE3Lx5CgNDOCi5MYkxRLb7pD1Sc8PBWiH1e+RmJrCL\ndSUPtD7u08wSn5yfVYOEQcNHvTNYlzAOzo1BjNU+N3ntH0UbbKqDxwAOOQAvR0gJz5rVak0kECXg\n+5G+C6x6Yd1Z0tkFJp5xtbc83Tlc2iDbHR/3E8PH8H5/y/jic+T2m9wn5KAlyQNIMNgY6LMi3hst\n6dz1Ftt1DDIyTVmZjCVcMOd4lo0oG9P013zcE5KCl/vWcgerz8wcG0aqfQ+qV3uaIoeDWvLHCPsx\nEK0j2MU9hAhT0JYC40SMI8iIIWUvpcNicVk2tS+dKOkvgYVJ1ACUlk2xy3pTZKrsOtkLmfOVMaaO\nYxRDSGCjIFOo1XXfFjaNRJFafTWKEptJtOR0CeMSqxE1Ze1SypVyoSlhmAy3NpFeP2MnI7dhy6vd\nc95/8pQPrx5zfvmE1cUT1v0la3eO7c7pzi4wdk0SNZ4br2tFjKr/WjNizIikEcPIKkb6OOHGFzC8\nQMIWJBCTYYoGM0XMMOJS0rYsrnQDBTYeppISsof0Coke/ACpw7hHJ43fT4LcwIAm4q8wWUjm7P0s\n7HVxKzRgtt3WJMLFxlqUOJOLDiw3lvy1+d+7ur3MOuC8N2rVJdWtiiVtVo1kcY5qIC39OMhKUVRS\nMV9GCbn7EeQmDcSQ3bb7vZKKcSBEqdpaXexFlZl6Lpl3gXl8joei1kWTRAwBJOGcw0pkv50YDrdc\nXb3Pqt8wDXt2t8/Z7zKxGSe1yJVKOFATCKuOXe/9vl3n6JqKJywrsUchT28/av+q0Eeb70fQTsKF\n1JSeqjDLKssnnxfS7Aouz+3Eq+A+wTne4H+cwvSGMx8dUuYXzYJk3/9WnaeRxJBEKw2hhRlMrryk\n+V9lbI9LC0vZlPPvuvjP8//44h722uhbx0SsGCc07GBR5OEEhKAl0IcxcRgnDoeBYRo1BynNOWmq\nvxelw9P5jm7V422PGTyvvnnFr3f/yBf/5zecrRMfvPeEX/zir/n5J5/y8ccf8/4HP6frLX1n6ZzD\nW6ceMJvDsAy6aYoSvxAi+yEwHQJfffEHfvPPv+Kr3/6GV7/7Z549+5Y/3gS+3U7sxj3jZOu41gE6\nErP5qSgpVE96UQJqnuLbwpAJjhoMivemcwaX12bJBHi5mpRTlTBRjHazN7lkq6RSuEQqQbYpkEJk\nv48MYWSKWjb8t3HP65dfcv6k4+xJx/X4iu3LZ+xeHdhthXHMpDRKVszLfL57IwXzZmPufNAs5s4i\nEusklDwXa0HEaj5l9V5JHgcBKzXBOyKMMTKMEzEkUu6AYkSQEAgSCRhGA3urFalsjkawxuCdxXtL\n3zn6zhFGVTyHKTDkeRBFrbUpaSFdLfgDMdnseVUlcrmnf+99lnDFPFzFjlcI8Cly98vLS1IyjEmV\nViVhkc5GOhPpXUfnPLskbEXvYpci3hlW3tI5ixULu0h6toN0ib06o1914BNRRrBbnBeci7je4y6f\nkvqPeRJ6Ppl6OjMht7f8cpN4/7M1F3bH9OIW2b/CxQOOmD1bBiMWL8IK7SDfW0PnHF3fYXuLhAPB\nemJUb7aW3FYSpmWLVZm2JdxfjOZYxLcXwLJDf8+qfzQbipyX5p4xClOIYCZC2msfr+2eZAyxRJoI\nGnJbGkGnlHvnRaxRrxPisXQ44/FWQ6OIIDmUkEyCYgkhu2+Lq2bIqnvmoj8lPUGM0evCEPI1uZBO\nJjedVx1gilGLGGTCXHJ7JSdzC3kdj2ooi1HJDSI6fikxiBD31+zCjuvdC7qvf8eLiwteXV7y9PFj\nnjx9yqOnH/Lo6SdcPvkZ5xcfYVdPmKJFnKM7M2CFYZ8I04Q1t0i64XC4ZjzcElNkiBN22uOmPU5G\nfOeZMMRgYBTMbqI3wsoqsTGug95prxwbkSEgsoP4PMcEXoLpgc9OGr+fBLlRUqO9bZYem6XUL1WN\nY91pVnKXCcd1MSxWcsmEqTQArWZaqpFPjyfLl+tRCxcwRmeeSYvvLJiQHNmW9F5Mjjkv1t9CgMrn\nZ2X97RfecbfTZNjdlmG/Z5w0pyAWk9xDJO7o9XnEaghRJg7HYXTK2FMqlePyoFnDfneLM47hcMtw\nOGRio67RWLqIl2GS71nq6nUtV5bFk6/E09QxKyEWJ+n/9TmbO2e6e1GLD5+Aml+yzHcxC1JbdcGZ\nMFO/c/9K7l7Nm14/fm8hCGa+4x8TYnX/fG+4WMo0/T4yuqQpOfwkQqk+aEyuZlNDw3KYXlUe56Mu\nf5u9e9RcnYeix4t3oVgu5Y4MlnMWGT1J3oDtbk+MSmyGIRc2yI3uUrUUWqxxOO9zv48e3zu6TvO1\nZDqwux453L7kpUs4F3n21QXPnt3wh8+/5v0PP+Lx08dsVp517+k6r4pN53F9zyc/+xk/++QTZHhB\n3D/nu6+/45svvuP19YGb7cSXX33Jb3/7L3z75R+4+eYLrq9veBUsu2hyMQszi22ZQ/Xf+b3i5aqE\nosjeieRm1bkjebPO0HnP2apj1XmGIAxhYQdebCOliaNzSmy8ePpVj7WWlBuOLXt2Cao8pKCGtdF5\nnHXs1x272PPe+SW/+Pn7jDcv+HIaScNADJ6Uq5ApOb0/+5YRY+4AABhuSURBVFjM+Sxqda/S6AM5\nCk87ziu8x5J+MErzyjLvrXXVULQMG9Jx0PYHkVzpk5zjZHXjUkPe8fNLol3hteygwVqLNz09c4z+\nlDS/6DCNHMYph5uRvVrFmFnyPo/3Q722h80yZiGMdXhN/vxyb2dh5HgLfJAgGsPoPAcLuxAIKXFm\nhXPgHGFj4LU1rDCMzrK1hpV3bHpPb622RTBg+h677vGrDrvqcSKlgDTWGbwPGOuhP8Pbc54+3vAZ\nax7HPR/GPaN9xHg+ELeviftrwgjRav6C8xYCuMngMaxE6EisvaHvcwUx0ep3zuk6I6bTinfOU1pY\npJiI05RLpRtt1hjlJCX9uPT2XYpfpDET/OIxLyqaGKLoeWWKxDSBiYjVglNzn0CKUM4vlNxe8lqE\n1L4+zmkYa6xkd5G3bCBnWR0Za8paZ3N7gSJYpax0kceE5CbNgWTAiZxUQhtgH3aElDTfLGkhjUEM\nOwwjFls8rSGQQtB8nBgIWScq8851K7qzcy7Pz7i42LBZb9is1lytNzzarLlan3HpHBcinIeBzbBl\ntdUwR0kjst7Qn6/AGwIHUhpZ9QnXOTwdk13rWItg0gYbA070B2+0R5KbwAUs6lEj907Co6HFIWn/\nPzMBB5ADyIofo3f9JMiNdecgVq2U2ZpUO7KYuafKrJgthBjuWbvM4q9KcEQwYhc7xlIdWhKio90k\nv2pqmeei6GCLoFPfT0Zq+FRdmgtByMTImgWxWYYp2FlBeBscbna6YRwOjONAyKVty32X67t7n1W5\nNZDrwNXN7t7AmsWPoJ3khVrVa7/dMg3anFM7yweGXNo0SakENp+zDsvyHEANUbunoecY+3w/2tHY\nZKW3bIRvv/AunvoDWJDsE45775tFpornrGzUdl7k6wdLyMjdr9/5++g85g0fPFYHq7JZEuN/HLW5\nf1FL+nJ0Xw8917tXeuTFK8reQsmpVv9yzGK5KyE2M1EvorRUglRpyq8uJludHXWpUNmar2G+Jyh7\n6GmycbPdEWNkGA4M40QIal0TUzZUbZXt/YrOr+hzyKdGuAphGpjCLeNhIk5BrZIGnvuXfPnlt/Sr\nX+FXK/q+p+s8vXeV3JjNCn9+xt/9/d/zd1cXpNe/Y3r5K/7hv/2K//5ffsWzb3e83gk3u5Hb3V7X\nlGFgCo4gpubi1TFbEJy7RL3E7R8rnlnZPKFLPMDZqtdqgUE9Ld45+s5zcbbifN3zejsy5uZ/hTQU\nYmONWtD7lVoMxfY5n8sTJFS9qDRurcKQ56wm/kdM19FfXfEXn3zCf/qbz4jPv+N/Y7AxZTK1kKuF\nclTGBGbZKzmKS9Jfp0mORJhH+3Rio0fI587HsE7z1ySzsZTJZymokySp0iHgnbamrAE8eX+xpqwn\nBtBiDIUsJTRvx/Q9ttNy7NYFjBlnA0LVRw2qULp5FJab4QP3XQP7ZlbNYpPJd3yHDC0Np2+BR8NI\ncp6p8+ysIUliSJFzsTwRy2PgUoSVsXhj2TnHa+/ZdB1nKyV4Nk6YzmKeXmCeXmJXHeKcFsTIXeKN\n8Vg35TWgw3WOp488Z5uOKVjGsCKMhmnouX654tVzw3UauNnfEILBiMNEcEPCi6GXiLGB8x76Hg5M\nhClgGHEuKSGix1uPsx1hHJnGA2kKhGFAomidiaxEn1Ac8sHRfnAbKJ595h1Ay/aDSeS2BwFNaNfJ\nPa/My7OYxdzXMEJrJCf6m+q9NqVVA6p3CtqPSa3PRRZhJjbFY2PVuFPK21vJ5ohIKgXPRZAoWl46\nneilBq73r5UopcCQhH1ybMVxK5ZBHL2xeCwSIiloEYExxpzva6pne7U+4+zqCT/76CP+4uMPeXr1\niMcXV6y7npXz9M6x8pber1iZNd1+wkyviWYisoPLK+TiMThPGPeEcVDD2eqcVb/SMuK+NIhWgmxi\nwMSgbNIbiDeQbjDTDYzXGH/ITFOYa3CnOpaz9+DfOLkBZX6Sq27dNYsWS5rJTNtUgqLvH+0hRamR\nkvSVt4ey02UDUVnIi7t+Ppn+ryye2TA8f2Sxft7nAWXznv+dS8bOJKdct9RjiFoazNsn7O12O2KK\nTCEQItWqWqd3WfCXN7FQQGooz3Kcjj/E/N+830t5XiIMYWKYTXpM48JjY5bbzFLNXI5fucZS9lRf\nsvn1eUsyR7/Vb588CR74zp1wpIVKm99eCEL97a683jnLcdfOmeCQxyYrOPPo3CeAy6uqjsI7l1+H\n4Y6SpeNrKcUZrHU5fOT0paMq/ffcZst7PSaui4iweyj9fY6+a+7cQz6OySWyU3EJyjyskglOnb8P\nbYCLaXBM9uopKB7hKntZ5ssMvVcF6AfidrvXym01Fw0Ei3Me53q8X+F9j3O6CVtrEAmkSYtLTNOB\nadKeUWEKxGwpxxi2ZlfHUZM+Hd7lY3eeeLbBXF7yi3//t/zHQfDTCNMtr18944vPf88XX9/ycisc\nAjn8pniSls/MLCV3Xu+KHNeN9a6uWV63R3lLb4OLviPExCFNJIGzbsXlZs1HZ2uuNh6ZIrf78nyO\nxUzQSo0hRIwY7SKOwRnDGKZF363y+bzGZTOy5DwRf3bO+XsfcvXBhzz94CMuLi/pnM0BW/MYYeaR\nml82i38KbZ8Hd0m+jVmsKuVeTlSS5juar6Mai+p6hNIXEVJUKY9lgxJyNcxFrg7kSAY9dg3pyyWs\nrRiSsYj14FxunmuP7pq61Re5Waq2CzK3GK/jO8rjVr2D+d7KpzOJ1tsoZdZOmbUOzbnJ349R+0Nl\nJdtYqwnW2QfTWcPae3rr8GKwKUIMxN1rxpdfMdnA2PfQr/B9j+88vjM4Z0h4EI/JnrV1b+g7q/2M\no2E8dIyux+574qojdJ7BOW3s3veMASYz4UVJmGPi3CY2ztCHiT5GNlYIK0e0Hcn2gMeYjtFZRmcY\n0SaYmIAJMbPwE62vR3hoR334E+VHCY4yK82ni3U/vb+2L2fgIu+PkmO3UPqdxfuO1CVSrw1eJRkk\nGlQtLk0ky/eUCGkDbaNhk2gejMhUf8jGESETs3j6sL1KWhTKeEtIMA0wTbm3XcpF4IAUhRR1zQ6U\nglZC7z191/P48WP+4uOf89mnn/LZp5/y5PFTri4f0blOG1RjcEZwWJxYLA5jPCIdnhWceazXnn3d\n2mOdaIh0X4imaP6Mz1VtRSD3Pcwd6yE4CJDiiJQy7jb/5OHGSf4p+oCtc/gU/CTITaKjdCOvTZyK\nAs0syEVQzfdMkJSV41JdClnG26LKskCpu1UmwL1jLcMTACMlqfgo20fXfpPJEtTJUzZ9a+cmX2VB\nP5rjhUxJ5BQ7+na/mw9jbC5ZOSthizV/oaQYloOhfQ/K5xfKc1VgTLXE2qKIi5BS6QcRculpqUoE\npQhDuYo6yHeIwOKXqo6a+bxlI5x/ZqVzSXt+LOSBa/vXQxEAc/xSuZBFFa8fqkK/UXKKQmUtxnps\n7v7trFvEWJ+G45n5J67zzu0eX58+a5uZc5lTRpZzpigSZU6J9uEQrVK0UGnevJtU8lKIul38rida\nztH6e14vwFRjyamSt90dyMZxkuQkXuvo+hXr9Rnr9SXr1blW8EkD4zBw2GkluWmKNT8h1X5czHNt\neS/REE0gGoMxDnHaOyTIhld7w23oubRrztZn+H6NGPXODEkY0zJELqvfS7JZ17i50thMcFiM5zxO\nJq+HStjcSeTmqusYTUSmQDKGq9WaD84v+PS858nKstsOfGNVKS+Rx3WmieSQ3UQylmQnuq4j9D0h\nTDVhvqzhQqnMVRR/SFj6i0uuPv6E1dMP4OoKs17jvcHZnJdFObfJfapMXVfyozoaJyuVRzOfjUo8\nUtlr6jyB75ntb0autDhXrLN52km9akEbcNbCAbUzZr6FVJrp6jVYyL2N8n4bc/6DUYUmiSXlUTE2\nG1fyxJLZFlb3Sn0r1X11iZIvJctxemBNMeQ1gmKVp55k8TjfCnvf1/yjQwqEoPlYI8JehLV1eC8c\nkjCq3Zx119EJyKT5K8TI+Pxrrv85cP3sETebC+T8grOLS87PLzi7uGC92eC8x3mP9wbjNCTXGVVW\nnUtMQyCGEUSb1frsLbLW0rsNbhJGA6NYnCQ6GTkHrhBGmZhSQvoe6Ve1gMgknkkcQ+cZVytC1xO6\njnTYI4eD6p+91zyVH43v3yvmfc/MeWZ1sS1Kypy4utQfilzW/2fdz2WjeKG2xhpcTma3WS6t7bSx\nbTBgOjA+K9Ym750Wk5tCx2SI0RDSBHEkxr0aBGLQMDqJMxGWdIrIAfASzZfcrHsQ7WEYDxM2gE9J\n5xiiRWeS0f0E9a5GI6z6jn6z4cP3P+BvPv2MX/zVX/Pzv/przh49YXV+qaGI2LpOzkZo/cuKxUWr\nxom1B2c4v7pAJGmOo83PcqlEF++VtYsxdGDWYFcgDoJQ++mqcEOXPTylD5HTVg5Yx6n4aZCbUlA8\nC3Edq6MFXxabpqmKj4Ha7KyaC8mLrK7IukGLPdpwCxZfOUL5+hEHKb/J8XvzscrGkRfYYlEqcZoL\nskS59uMz/rABWyCmUhXNZs8WdUOdK0KVRWFe5Oel4YEbqVfHkaJoijJjjGpnRSGsBGkONViWgH3w\nuEcnPd6lFk82O70Wz7aGxRxf9Gk6ujzw6/G13L/a73tG9yRCX5WjP+89gaMjPChzS0mZD/YgV8j/\nk/qZTE6tx3UrfL+mX200Qd3nmv+noJrGF1WwltfwIH7oOw9Vk5oVM2OL91YtcjNBe0j5y6QkE2ZN\nNpZ6/ct1+aFnXEdZ9Hv1nRNJ4RRSnke+elR817Hqelb9Cu8sSCTGiXE8aPjaYahJ/8vGrdUvVUUj\nz5MkVZEuqnmKwmgDsYsMB2E4WB7Rcb7asPGe3uSmvqIeiiKH8/0u7rsQm2yJX3pOl+Smeq6tKl+2\nFjVwnBKa1llLMgmXn6VWPnL03rHpHee956JzHKJlf9RbouTQJEQ0fKRYPTtnSEmr1ZUKbHefrJE5\ndt9Zh/cdrvPQOaw3OCva0LgOUSGZs8wuV5XZm/7wWlLGLdX+ElBKNGMWMvnWkLou1PQJmc2FGpaX\ncj+y0ruMmX3JgnQYk3OK9L3S56wa0LKypeE+xeq/yKeRhRJaZKiuJ/M0LkpXKQag+4uOakLD69xC\n6TZ5z6geIqnBGvMB3xLfjWqcTCRCitrzJMIoiW0CawIBw87CaHPZ+AQ25ciRGCCMxK0wPg/shhte\nnV+SDleEsMNIYN332PUFhjXGnAErTAlVpSw3grX6o71oVO60J43onhyVMPu8Xq6M4cLAE4uW6XYO\nWa1I6zUHeg6mYxstu6jKasRgu55V0sp61nlNjVj5bFT6c+DNz+F4d9diBrEQm4Wxa1kr81hPXB5M\niY14lyuVziu4MQbn57wzax0xKiE3pssER+VWCy6oki5Gc/S0ONsIk4ExINOoUTN1Hqin8NRQSIBX\no9Anw+Q6LI5DFC1UgMFhiNGiXXccyViMs3jntSqhRLp1z+bijCfvPeXjn3/C+z/7hEcf/oz+/Aq3\n3uQWGw+teHn4klZqRqiOLFuKah2NuN6j5J58ItNC/+gxxmVi2AEeSZbSR8VYO+fleIOpxKaEaP9b\n99yECdCGX8A9WTAP/l7sxUURykpctrppVYllid00N+Izs26me6+Zm1DmYxeRPNJLy4ZfqqVJUcOO\ndR2T430qccKgOUUzASjK20PE6m1QFpx5z8kKRUqz5fuBU4iUkL1ZyeOBz+reZrLrXzeZlJUotXgI\nzrkallA2nxq2UxSxe5N89oAd0Qkpi1guD2HKNFko7lK+X45y6gDeYR1vHK0/EwS0apQ5kpfja/iR\nAnH3eMaAcVjX4/sNq80F67MLVqs1q76rndxPQlb432iFfyP7euBA8wEffnehVBWSgqGGG5j8XhlY\nVaKyZGUPjbcW77S/RkqLsnUPnH6pxFWyeCQuJ6qYOTTQ+xXdas16c063WmElYSUSxj1DuGYYtJln\naeIpouuOhkrVO5zNAHlOzPmKMr+WVTObRHNDBiFuBb9xXKx6Lr3nAmFFKrbuOjb3Hm312GSLpzmW\n5VI4RT29GhKnCqjLoXZKcE7x3BRrqM2kIaTIIQT2dBycoe897216Xg4wpHltOT6IqRuAEcn3m3A5\np+Se0STfkxWNmGCaSPudNguUCWMCzgaciVUu5nGz9ZRFoGyR1yxTxwnX8zpny7VL9QPNxPGtR27x\nHaNSE5JWlbOZCKRCBJIaG1PMuUelTPZyAuT7MaWvV13z8z1bssdvmXyd5+Ud0mwKz8k3aAxa/KfM\nMUOtvlbCqOsxDPSrnr7vKQaLGCJpCpTtZvlMTy3a8/ntFmcMnbP4TNyMWMaoVbmmNHEbItJ3pFWn\nOWFTzMu9wcQIYQQfkVGIgzDaRPTCqod4tsEZw6rbYLorjL/IBKdnzv3QG7LO0HVaGKNUxLIIIU6M\ngzAdgCj6LetY2Y4La3jqLJedYeMc06pn6nqug+F1MNpwNiT2UUvlO7Gs/IqN6zjbnNE5cJ09LTH+\nzta6sP8e/7LwCuvH9VlpYQEe0C+oMvmwjUC/H53VKZ8cMbdUKPqhAfVG9R7nXSY0+mNsl3NwcrVO\na3NrAQ03SxGGSTBjxDivJD57dAoRm/OvT9opeH2bcC6xHcBbS5x6EI+zSrpCiIwxYZySAue1yIoJ\nB6Y0sj5bc3Z5zsXTxzz66CPOnrxHd36F7ddoLew/cV1lIVo+p3soszkBgZQOpLSvurFzFzjX6YFM\nhxiP5NBLzW3SynXGW70Pb2aPzYnzteAnQW5EZgvVnJ5wV+Fcqn9lAkhd8Gbl94cNRiUXlSTdf/PB\nOVOU7+X7i2cwz9V5Qa/KF4tSuA/osKc8xhK+MG/lc6hNyclYcJijk5bvFItg/Wx5765slY25LhBz\nOIy1ti46qZJUJUFHydc/4L5ntS3/de/h3PnzR3OBH3KA+/L4Jz/60OEXBAfDvf49R4fJG/wPSV6f\nNwtz9LfkFUobiHq87+j6Ff1qQ7/q8e7/wRJw5/4evt37E84sZOeIANe5ZpZ85ojczPEumbwbW5NJ\ndUM8LjV/pLc9dK3LCyi/n+K9yeTAOof3Pf1KPWnEkTQNpHRgGnMp9WGs4WHHF3uMSnCQGs5b3zOF\nEOVqNiKYCGkEu9YSsb019Ai+bMhlIL6PnWbjBsvxX1xJ8ZRpKJqSG58JTmlqexIkD2FWcqKUvg9a\nDnrjHbdTqlUp4Xg86mWSiVj1yiyIzQPXVn0wMSEh5H4SCUPCkrIRJi+yi2S4Kk/Fm3P/naNrvOvx\nNkeCZ36U2JWZpMR/9mhYTA0/rCRFFqSFO2Inel3HCufizTes1/dF1xzJj1m8Vl6xi/2lrAfFIykG\nvNdiGUiRcZhizAlDiy3sRyhKN2PAW8NGHCtn8UKu5KV5aUIkSMI6p9WrkmhJ4vK8UmaNUbToThxJ\nwROngRi0EI8FvO3ArcCtgawQ3rlmawzWZeMOzDKbtMJZCgaSyUV3LN44OiwbY7n0jsuuY+o6hs4T\nRRhCoiNX9UpCioJHw91W3nNmofcG63PT3LfEMod2fq1cO8e/LAlOXctNlS8pY3uH6LzhzBRZimLn\nUMsizYtTWWx2Emh1SmwPtstvFkOOoTxGk7MnogkEsdiginkhAnLnKk6asCh5slGIAt4abLLaq6de\njzYXtS4TMGdx3hAJWrGs0/Lf/WbN6vycbrPBdqscjvan58MPnzJSf4RAStO8Htg4H6yEqGW5rmkP\nNoe4lR4hi2d/6pwFMKdW/WloaGhoaGhoaGhoaPgp4c8VRNnQ0NDQ0NDQ0NDQ0PD/FY3cNDQ0NDQ0\nNDQ0NDS8E2jkpqGhoaGhoaGhoaHhnUAjNw0NDQ0NDQ0NDQ0N7wQauWloaGhoaGhoaGhoeCfQyE1D\nQ0NDQ0NDQ0NDwzuBRm4aGhoaGhoaGhoaGt4JNHLT0NDQ0NDQ0NDQ0PBOoJGbhoaGhoaGhoaGhoZ3\nAo3cNDQ0NDQ0NDQ0NDS8E2jkpqGhoaGhoaGhoaHhnUAjNw0NDQ0NDQ0NDQ0N7wQauWloaGhoaGho\naGhoeCfQyE1DQ0NDQ0NDQ0NDwzuBRm4aGhoaGhoaGhoaGt4JNHLT0NDQ0NDQ0NDQ0PBOoJGbhoaG\nhoaGhoaGhoZ3Ao3cNDQ0NDQ0NDQ0NDS8E2jkpqGhoaGhoaGhoaHhnUAjNw0NDQ0NDQ0NDQ0N7wQa\nuWloaGhoaGhoaGhoeCfQyE1DQ0NDQ0NDQ0NDwzuBRm4aGhoaGhoaGhoaGt4JNHLT0NDQ0NDQ0NDQ\n0PBOoJGbhoaGhoaGhoaGhoZ3Ao3cNDQ0NDQ0NDQ0NDS8E2jkpqGhoaGhoaGhoaHhnUAjNw0NDQ0N\nDQ0NDQ0N7wQauWloaGhoaGhoaGhoeCfwfwFjeOqaAnJg+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa13478a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Data exploration visualization goes here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Number of random images to get\n",
    "n_images = 10\n",
    "\n",
    "# Generate a list of 10 random indices within X_train range\n",
    "indices = np.random.randint(0, n_train - 1, size=n_images)\n",
    "\n",
    "f = plt.figure(figsize=(n_images, 1))\n",
    "\n",
    "# Display 10 random images from the training set\n",
    "for i, idx in enumerate(indices):\n",
    "    ax = f.add_subplot(1, n_images, (i + 1))\n",
    "    ax.axis('off')\n",
    "    ax.imshow(X_train[idx])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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",
    "- Your model can be derived from a deep feedforward net or a deep convolutional network.\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."
   ]
  },
  {
   "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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing training images\n",
      "Preparing test images\n",
      "Preparing training labels\n",
      "Preparing test labels\n"
     ]
    }
   ],
   "source": [
    "### Preprocess the data here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelBinarizer\n",
    "\n",
    "def grayscale(images):\n",
    "    \"\"\"Convert to from RGB to Grayscale\"\"\"\n",
    "    gray_images = []\n",
    "\n",
    "    for image in images:\n",
    "        gray_image = np.dot(image, [0.299, 0.587, 0.114])\n",
    "        gray_images.append(gray_image)\n",
    "        \n",
    "    return np.array(gray_images)\n",
    "\n",
    "def normalize(images):\n",
    "    \"\"\"Divide image date by 255 so range is between 0 and 1\"\"\"\n",
    "    return images / 255.\n",
    "\n",
    "def flatten(images):\n",
    "    \"\"\"Flatten the image data\"\"\"\n",
    "    return images.reshape(-1, np.prod(images[0].shape))\n",
    "\n",
    "def prepare_images(images):\n",
    "    \"\"\"Run images through preprocessing steps\"\"\"\n",
    "    images = images.astype(np.float32)\n",
    "    images = grayscale(images)\n",
    "    images = normalize(images)\n",
    "    images = flatten(images)\n",
    "    return images\n",
    "\n",
    "def prepare_labels(labels, encoder):\n",
    "    labels = encoder.transform(labels)\n",
    "    labels = labels.astype(np.float32)\n",
    "    return labels\n",
    "\n",
    "# Save the original shape of the images\n",
    "image_shape = X_train[0].shape\n",
    "\n",
    "# Preprocess training and test data\n",
    "print(\"Preparing training images\")\n",
    "images_train = prepare_images(X_train)\n",
    "\n",
    "print(\"Preparing test images\")\n",
    "images_test = prepare_images(X_test)\n",
    "\n",
    "# Preprocess training and test labels\n",
    "encoder = LabelBinarizer()\n",
    "encoder.fit(y_train)\n",
    "\n",
    "print(\"Preparing training labels\")\n",
    "labels_train = prepare_labels(y_train, encoder)\n",
    "\n",
    "print(\"Preparing test labels\")\n",
    "labels_test = prepare_labels(y_test, encoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 \n",
    "\n",
    "_Describe the techniques used to preprocess the data._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "For this step I made the images grayscale, scaled the values to be between 0 and 1 and flatten the images array.\n",
    "\n",
    "For the `grayscale` method, I'm using a formula I found for converting rgb images to greyscale here: http://stackoverflow.com/questions/12201577/how-can-i-convert-an-rgb-image-into-grayscale-in-python. I convert the images to greyscale to reduce the number of features being passed through the neural network.\n",
    "\n",
    "For the `normalize` method, I'm simply dividing each value by 255. Normalized values make the optimizer's job easier. Well conditioned values make it so the optimizer doesn't need to search to widely for correct values.\n",
    "\n",
    "In `flatten` I convert the array from `(32, 32, 1)` to just a 1-D array with `1024` indices. Flatten is used because a fully-connected network, like the one I'm using, requires a flat array. If I was using a convolutional neural network, I would not use the flatten method through the convolutions or pooling layers. I would only flatten the array at the end if there are fully connected layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New number of training examples = 37248\n",
      "Number of validation examples = 1961\n"
     ]
    }
   ],
   "source": [
    "### Generate data additional (if you want to!)\n",
    "### and split the data into training/validation/testing sets here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "def split(features, labels):\n",
    "    return train_test_split(\n",
    "        features,\n",
    "        labels,\n",
    "        test_size=0.05,\n",
    "        random_state=42)\n",
    "\n",
    "def randomize(features, labels):\n",
    "    return shuffle(\n",
    "        features,\n",
    "        labels,\n",
    "        random_state=42)\n",
    "\n",
    "# Rename variables to be clearer\n",
    "train_features, train_labels = randomize(images_train, labels_train)\n",
    "\n",
    "test_features = images_test\n",
    "test_labels = labels_test\n",
    "\n",
    "train_features, valid_features, train_labels, valid_labels = split(\n",
    "    train_features,\n",
    "    train_labels)\n",
    "\n",
    "print(\"New number of training examples =\", len(train_features))\n",
    "print(\"Number of validation examples =\", len(valid_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "_Describe how you set up the training, validation and testing data for your model. If you generated additional data, why?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "I've renamed my variables to be a little easier to read. I generated validation data from the training data so it can be used for cross-validation. I also shuffled the data  so the model doesn't train in any particular order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "def hidden_layer_network(features, features_count, labels_count):\n",
    "    n_hidden_layer_1 = 128\n",
    "\n",
    "    weights = {\n",
    "        'hidden_layer_1': tf.Variable(tf.truncated_normal((features_count, n_hidden_layer_1), stddev=0.01), name='W_hidden_layer_1'),\n",
    "        'out': tf.Variable(tf.truncated_normal((n_hidden_layer_1, labels_count), stddev=0.01), name='W_out')}\n",
    "\n",
    "    biases = {\n",
    "        'hidden_layer_1': tf.Variable(tf.zeros((n_hidden_layer_1,)), name='b_hidden_layer_1'),\n",
    "        'out': tf.Variable(tf.zeros((labels_count,)), name='b_out')}\n",
    "\n",
    "\n",
    "    hidden_layer_1 = tf.add(tf.matmul(features, weights['hidden_layer_1']), biases['hidden_layer_1'])\n",
    "    hidden_layer_1 = tf.nn.relu(hidden_layer_1)\n",
    "\n",
    "    return tf.add(tf.matmul(hidden_layer_1, weights['out']), biases['out'])\n",
    "\n",
    "def setup_network():\n",
    "    features_count = np.prod(train_features[0].shape)\n",
    "    labels_count = n_classes\n",
    "    counts = (features_count, labels_count)\n",
    "\n",
    "    features = tf.placeholder(tf.float32)\n",
    "    labels = tf.placeholder(tf.float32)\n",
    "\n",
    "    train_feed_dict = {features: train_features, labels: train_labels}\n",
    "    valid_feed_dict = {features: valid_features, labels: valid_labels}\n",
    "    test_feed_dict = {features: test_features, labels: test_labels}\n",
    "    feed_dicts = (train_feed_dict, valid_feed_dict, test_feed_dict)\n",
    "    \n",
    "    return (features, labels, counts, feed_dicts)\n",
    "    \n",
    "def setup_accuracy(logits, labels):\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, labels))\n",
    "\n",
    "    # Determine if the predictions are correct\n",
    "    is_correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))\n",
    "\n",
    "    # Calculate the accuracy of the predictions\n",
    "    accuracy = tf.reduce_mean(tf.cast(is_correct_prediction, tf.float32))\n",
    "    \n",
    "    return (loss, accuracy)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow.contrib.layers import flatten\n",
    "\n",
    "# Code inspired heavily by code in LeNet lesson\n",
    "def LeNet(x):\n",
    "    # Reshape from 2D to 4D. This prepares the data for\n",
    "    # convolutional and pooling layers.\n",
    "    x = tf.reshape(x, (-1, 32, 32, 1))\n",
    "    \n",
    "    # Resize to 32x32.\n",
    "    x = tf.image.resize_images(x, (32, 32))\n",
    "\n",
    "    # TODO: Define the LeNet architecture.\n",
    "    # Return the result of the last fully connected layer.\n",
    "\n",
    "    # 28x28x6 Convolution\n",
    "    conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6)))\n",
    "    conv1_b = tf.Variable(tf.constant(0.0, shape=(6,)))\n",
    "    conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b\n",
    "\n",
    "    # Activation (ReLU)\n",
    "    conv1 = tf.nn.relu(conv1)\n",
    "\n",
    "    # 14x16x6 Max pooling\n",
    "    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "\n",
    "    # 10x10x16 Convolution\n",
    "    conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16)))\n",
    "    conv2_b = tf.Variable(tf.constant(0.0, shape=(16,)))\n",
    "    conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b\n",
    "\n",
    "    # Activation (ReLU)\n",
    "    conv2 = tf.nn.relu(conv2)\n",
    "\n",
    "    # 5x5x6 Max pooling\n",
    "    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "\n",
    "    # Flatten (tf.contrib.layers.flatten\n",
    "    fc1 = flatten(conv2)\n",
    "\n",
    "    # Fully-connected layer (120 outputs)\n",
    "    fc1_outputs = 120\n",
    "    fc1_shape = (fc1.get_shape().as_list()[-1], fc1_outputs)\n",
    "    fc1_W = tf.Variable(tf.truncated_normal(shape=fc1_shape))\n",
    "    fc1_b = tf.Variable(tf.constant(0.0, shape=(fc1_outputs,)))\n",
    "    fc1 = tf.matmul(fc1, fc1_W) + fc1_b\n",
    "\n",
    "    # Activation (ReLU)\n",
    "    fc1 = tf.nn.relu(fc1)\n",
    "\n",
    "    # Fully-connected layer (43 outputs) = (43 classes)\n",
    "    fc2_outputs = 43\n",
    "    fc2_shape = (fc1_outputs, fc2_outputs)\n",
    "    fc2_W = tf.Variable(tf.truncated_normal(shape=fc2_shape))\n",
    "    fc2_b = tf.Variable(tf.constant(0.0, shape=(fc2_outputs,)))\n",
    "    fc2 = tf.matmul(fc1, fc2_W) + fc2_b\n",
    "\n",
    "    return fc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Define your architecture here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "\n",
    "features, labels, counts, feed_dicts = setup_network()\n",
    "features_count, labels_count = counts\n",
    "train_feed_dict, valid_feed_dict, test_feed_dict = feed_dicts\n",
    "\n",
    "# Hidden layer neural network\n",
    "logits = hidden_layer_network(features, features_count, labels_count)\n",
    "\n",
    "# LeNet CNN\n",
    "# logits = LeNet(features)\n",
    "\n",
    "loss, accuracy = setup_accuracy(logits, labels)\n"
   ]
  },
  {
   "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",
    "For the final architecture I used a deep neural network with a single hidden layer having a depth of 128.\n",
    "\n",
    "I also attempted the LeNet architecture we learned in the lecture. The architecture has the following layers and sizes.\n",
    "\n",
    "    * 28x28x6 Convolution\n",
    "    * Activation (ReLU)\n",
    "    * 14x16x6 Max pooling\n",
    "    * 10x10x16 Convolution\n",
    "    * Activation (ReLU)\n",
    "    * 5x5x6 Max pooling\n",
    "    * Flatten\n",
    "    * Fully-connected layer (120 outputs)\n",
    "    * Activation (ReLU)\n",
    "    * Fully-connected layer (43 outputs) = (43 classes)\n",
    "    \n",
    "I opted for the deep neural network because I was getting better validation and test accuracy results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-22-b0ede29b3014>:22 in <module>.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "[Epoch  5/50] validation_accuracy=0.84447, elapsed_time=6.47621\n",
      "[Epoch 10/50] validation_accuracy=0.88985, elapsed_time=12.40452\n",
      "[Epoch 15/50] validation_accuracy=0.90872, elapsed_time=18.33386\n",
      "[Epoch 20/50] validation_accuracy=0.92045, elapsed_time=24.25081\n",
      "[Epoch 25/50] validation_accuracy=0.92861, elapsed_time=30.12547\n",
      "[Epoch 30/50] validation_accuracy=0.92555, elapsed_time=36.00616\n",
      "[Epoch 35/50] validation_accuracy=0.92708, elapsed_time=41.88147\n",
      "[Epoch 40/50] validation_accuracy=0.93065, elapsed_time=47.83227\n",
      "[Epoch 45/50] validation_accuracy=0.93269, elapsed_time=53.75562\n",
      "[Epoch 50/50] validation_accuracy=0.93728, elapsed_time=59.67652\n",
      "Model saved in file: ./model.ckpt\n",
      "Training accuracy at 0.9649102687835693\n",
      "Validation accuracy at 0.9372768998146057\n",
      "Test accuracy at 0.8385593891143799\n",
      "Elapsed time 60.779251\n"
     ]
    }
   ],
   "source": [
    "### Train your model here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import math\n",
    "import time\n",
    "\n",
    "epochs = 50\n",
    "batch_size = 128\n",
    "\n",
    "# Gradient Descent\n",
    "optimizer = tf.train.AdamOptimizer().minimize(loss)\n",
    "\n",
    "# The accuracy measured against the validation set\n",
    "validation_accuracy = 0.0\n",
    "training_accuracy = 0.0\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "start_time = time.clock()\n",
    "\n",
    "# Code inspired heavily by code in the tensorflow lab.\n",
    "with tf.Session() as session:\n",
    "    session.run(tf.initialize_all_variables())\n",
    "    batch_count = int(math.ceil(len(train_features)/batch_size))\n",
    "\n",
    "    for epoch_i in range(epochs):\n",
    "        # The training cycle\n",
    "        for batch_i in range(batch_count):\n",
    "            # Get a batch of training features and labels\n",
    "            batch_start = batch_i*batch_size\n",
    "            batch_features = train_features[batch_start:batch_start + batch_size]\n",
    "            batch_labels = train_labels[batch_start:batch_start + batch_size]\n",
    "\n",
    "            # Run optimizer and get loss\n",
    "            _, l = session.run(\n",
    "                [optimizer, loss],\n",
    "                feed_dict={features: batch_features, labels: batch_labels})\n",
    "        \n",
    "        if epoch_i % 5 == 4:\n",
    "            # Check accuracy against Validation data\n",
    "            validation_accuracy = session.run(accuracy, feed_dict=valid_feed_dict)\n",
    "            print('[Epoch {:>2}/{:>2}] validation_accuracy={:>.5f}, elapsed_time={:>.5f}'.format(epoch_i+1, epochs, validation_accuracy,(time.clock() - start_time)))\n",
    "\n",
    "    # Training accuracy\n",
    "    training_accuracy = session.run(accuracy, feed_dict=train_feed_dict)\n",
    "    \n",
    "    # Check accuracy against test data\n",
    "    test_accuracy = session.run(accuracy, feed_dict=test_feed_dict)\n",
    "    \n",
    "    # Save checkoint\n",
    "    save_path = saver.save(session, \"./model.ckpt\")\n",
    "    print(\"Model saved in file: {}\".format(save_path))\n",
    "\n",
    "print('Training accuracy at {}'.format(training_accuracy))\n",
    "print('Validation accuracy at {}'.format(validation_accuracy))\n",
    "print('Test accuracy at {}'.format(test_accuracy))\n",
    "\n",
    "end_time = time.clock()\n",
    "print('Elapsed time {}'.format(end_time - start_time))"
   ]
  },
  {
   "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",
    "I used the AdamOptimizer in order to reduce the number of hyperparameters. The AdamOptimizer doesn't require a learning rate parameter. I ran the optimizer over 250 epochs with a batch size of 250.\n",
    "\n",
    "Edit:\n",
    "\n",
    "I had a typo before, the batch_size in my code was actually 128. I chose this number mainly based on speed. Training speed seemed faster at this batch size. \n",
    "\n",
    "I've decreased the number of epochs to 50 from 250. This too was for speed, as my training sessions were taking too long. But I also changed this number down to 50 because I noticed my test accuracy was actually not getting much better with the larger number of epochs. This could be due to the model architecture, I'm sure a better architecture would gain more accuracy with higher number of epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "\n",
    "\n",
    "_What approach did you take in coming up with a solution to this problem?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "Not sure if this is the best solution to be honest. I tested a deep neural network as well which actually gave me better test accuracy. I initially started with creating an architecture, running it over a few epochs, then increasing, and keeping the results in a spreadsheet. Then I realized, through reading the forums, that I could just print out the validation accuracy after every epoch so gauge progress, so I switched to using that approach."
   ]
  },
  {
   "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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Load the images and plot them here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "from scipy.misc import imread\n",
    "import numpy as np\n",
    "import csv\n",
    "\n",
    "def load_image(filename, path=\"./signs/\"):\n",
    "    image = imread(\"{}{}\".format(path, filename))\n",
    "    image = image.astype(np.float32)\n",
    "    image = np.dot(image, [0.299, 0.587, 0.114])\n",
    "    image /= 255.\n",
    "    image = image.reshape(np.prod(image.shape))\n",
    "    \n",
    "    return np.array(image)\n",
    "\n",
    "def load_images(filenames):\n",
    "    images = []\n",
    "    \n",
    "    for filename in filenames:\n",
    "        images.append(load_image(filename))\n",
    "        \n",
    "    return np.array(images)\n",
    "\n",
    "\n",
    "label_names = {}\n",
    "\n",
    "with open('signnames.csv') as csvfile:\n",
    "    reader = csv.reader(csvfile, delimiter=',')\n",
    "    for (label, name) in reader:\n",
    "        label_names[label] = name\n",
    "        \n",
    "\n",
    "def label_text(label):\n",
    "    label = str(label)\n",
    "    return label_names[label]\n",
    "    "
   ]
  },
  {
   "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 would be helpful to plot the images in the notebook._\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5hMNhqe/Qew5/ZytNPWESqnwu5a60G/P5HN1uF/1+H/1+X4JNrk+uRZ1R0Kw/1xv1+Lzx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ZAAAH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\nhQlVpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0cbd72978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model restored.\n",
      "filename                      label   prediction          \n",
      "bicycles-crossing.jpg         15      No vechiles         \n",
      "children-crossing.jpg         10      No passing for vechiles over 3.5 metric tons\n",
      "road-work.jpg                 25      Road work           \n",
      "speed-limit-20.jpg            3       Speed limit (60km/h)\n",
      "speed-limit-30.jpg            13      Yield               \n"
     ]
    }
   ],
   "source": [
    "### Run the predictions here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import tensorflow as tf\n",
    "\n",
    "filenames = [\n",
    "    'bicycles-crossing.jpg',\n",
    "    'children-crossing.jpg',\n",
    "    'road-work.jpg',\n",
    "    'speed-limit-20.jpg',\n",
    "    'speed-limit-30.jpg']\n",
    "\n",
    "images = load_images(filenames)\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "f = plt.figure(figsize=(n_images, 1))\n",
    "\n",
    "for i, image in enumerate(images):\n",
    "    image = image.reshape(32, 32)\n",
    "    ax = f.add_subplot(1, 5, (i + 1))\n",
    "    ax.axis('off')\n",
    "    ax.imshow(image, cmap='gray')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "with tf.Session() as session:\n",
    "    saver.restore(session, \"./model.ckpt\")\n",
    "    print(\"Model restored.\")\n",
    "    \n",
    "    prediction_op = tf.argmax(logits, 1)\n",
    "    predictions = session.run(prediction_op, feed_dict={features: images})\n",
    "    \n",
    "    print(\"{:30}{:8}{:20}\".format(\"filename\", \"label\", \"prediction\"))\n",
    "    for idx, prediction in enumerate(predictions):\n",
    "        print(\"{:30}{:<8}{:20}\".format(filenames[idx], prediction, label_text(prediction)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "\n",
    "_Is your model able to perform equally well on captured pictures or a live camera stream when compared to testing on the dataset?_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "No, I don't get nearly as a good accuracy as I do from the test data. One one of the 5 images I found online was classified properly. A big reason for this could be the angle at which my five photos were taken. The test set appears to be straight on, while my photos are a little askew. Another issue might be that my photos show more of its surroundings whereas the test photos crop almost everything except the traffic sign out."
   ]
  },
  {
   "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/r0.11/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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "Unfortunately, the correct labels don't appear to show up in the top 5 of the incorrectly labeled predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model restored.\n",
      "Validation accuracy at 0.9372768998146057\n",
      "Test accuracy at 0.8385593891143799\n",
      "5 image test accuracy at 0.20000000298023224\n",
      "Predicted (15) No vechiles, Actual (29) bicycles-crossing.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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HX8/xkCRJvcXgIUmSijF4SJKkYgwekiSpGIOHJEkqxuAhSZKKMXhIkqRiDB6S\nJKkYg4ckSSrG4CFJkooxeEiSpGL+P0AAf5r3jSXTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa11396d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted (10) No passing for vechiles over 3.5 metric tons, Actual (28) children-crossing.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0b4fdf780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted (25) Road work, Actual (25) road-work.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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C\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa134425940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted (3) Speed limit (60km/h), Actual (0) speed-limit-20.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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jwMuAgzLzVw1v3QU8LCIeOWaVrh6bMf359VYWv4Hq568rxyczH8jMX2TmcGae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\n/w9bOsjFAnv9ZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1343a7710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted (13) Yield, Actual (1) speed-limit-30.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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xeEiSpGIMHpIkqRiDhyRJKsbgIUmSijF4SJKkYgwekiSpGIOHJEkqxuAhSZKK\n+f/RXlCl2rQ3KwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa134295e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Visualize the softmax probabilities here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Original version of this plotting function is from a response on the CarND forums.\n",
    "def plot_top_k_probabilities(labels, probabilities, title):\n",
    "    print(title)\n",
    "    plt.plot(list(labels), list(probabilities), 'ro')\n",
    "    plt.ylabel('Probability') \n",
    "    plt.show()\n",
    "\n",
    "filenames = [\n",
    "    'bicycles-crossing.jpg',\n",
    "    'children-crossing.jpg',\n",
    "    'road-work.jpg',\n",
    "    'speed-limit-20.jpg',\n",
    "    'speed-limit-30.jpg']\n",
    "\n",
    "filename_labels = [\n",
    "    29,\n",
    "    28,\n",
    "    25,\n",
    "    0,\n",
    "    1]\n",
    "\n",
    "encoded_labels = encoder.transform(filename_labels)\n",
    "\n",
    "images = load_images(filenames)\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "with tf.Session() as session:\n",
    "    saver.restore(session, \"./model.ckpt\")\n",
    "    print(\"Model restored.\")\n",
    "    \n",
    "    prediction_op = tf.nn.softmax(logits)\n",
    "    top_five = tf.nn.top_k(prediction_op, k=5, sorted=True, name=None)\n",
    "    predictions, acc = session.run([top_five, accuracy], feed_dict={features: images, labels: encoded_labels})\n",
    "    prediction_values, prediction_indices = predictions\n",
    "    \n",
    "    print('Validation accuracy at {}'.format(validation_accuracy))\n",
    "    print('Test accuracy at {}'.format(test_accuracy))\n",
    "    print('5 image test accuracy at {}'.format(acc))\n",
    "    \n",
    "    for idx, (probabilities, labels) in enumerate(zip(prediction_values, prediction_indices)):\n",
    "        title = \"Predicted ({}) {}, Actual ({}) {}\".format(labels[0], label_text(labels[0]), filename_labels[idx], filenames[idx])\n",
    "        plot_top_k_probabilities(labels, probabilities, title)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 9\n",
    "_If necessary, provide documentation for how an interface was built for your model to load and classify newly-acquired images._\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**"
   ]
  },
  {
   "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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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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