220079842_assignment1_solution (6).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"220079842_assignment1_solution.ipynb","provenance":[],"collapsed_sections":[]},"file_extension":".py","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.6"},"mimetype":"text/x-python","name":"python","npconvert_exporter":"python","pygments_lexer":"ipython3","version":3},"cells":[{"cell_type":"markdown","metadata":{"id":"v81NrNYlAzIm"},"source":["# SIT744 Assignment 1: Image Classification with Deep Feedforward Neural Network\n","\n","<div class=\"alert-info\">\n","    <p>Due: <strong>8:00 pm (AEST) 19 April 2021</strong>  (Monday)</p>\n","\n","\n","This is an <strong>individual</strong> assignment. It contributes <strong>30%</strong> to your final mark. Read the assignment instruction carefully.\n","\n","<h2> What to submit </h2>\n","\n","<p>\n","This assignment is to be completed individually and submitted to CloudDeakin. <strong>By the due date, you are required to submit the following files to the corresponding Assignment (Dropbox) in CloudDeakin</strong>:\n","\n","<ol>\n","<li>\t<strong>[YourID]_assignment1_solution.ipynp</strong>:  This is your Python notebook solution source file. </li>\n","<li>\t<strong>[YourID]_assingment1_output.html</strong>: This is the output of your Python notebook solution <emph>exported</emph> in HTML format.</li>\n","<li>\tExtra files needed to complete your assignment, if any (e.g., images used in your answers).</li>\n","</ol>\n","</p>\n","\n","<p>\n","For example, if your student ID is: 123456, you will then need to submit the following files:\n","<ul>\n","<li> 123456_assignment1_solution.ipynp </li>\n","<li> 123456_assignment1_output.html</li>\n","</ul>\n","</p>\n","\n","<h2> Marking criteria </h2>\n","\n","<p>\n","Your submission will be marked using the following criteria.\n","\n","<ul>\n","<li> Showing good effort through completed tasks.</li>\n","<li> Applying deep learning theory to design suitable deep learning solutions for the tasks.</li>\n","<li> Critically evaluating and reflecting on the pros and cons of various design decisions.</li>\n","<li> Demonstrating creativity and resourcefulness in providing unique individual solutions. (<em>Warning: Highly similar solutions will be investigated for collusion.</em>)</li>\n","<li> Showing attention to details through a good quality assignment report.</li>\n","</ul>\n","</p>\n","\n","<p>\n","Indicative weights of various tasks are provided below, but the assignment will be marked by the overall quality per the above criteria. \n","</p>\n","</div>"]},{"cell_type":"markdown","metadata":{"id":"JOr2bP8kAzKE"},"source":["## Assignment objective\n","\n","\n","\n","This assignment is for you to demonstrate the knowledge in deep learning that you have acquired from the lectures and practical lab materials. Most tasks in this assignment are straightforward applications of the practical materials in weeks 1-5. Going through these materials before attempting this assignment is highly recommended.\n","\n","In this assignment, you are going to work with the Fashion-MNIST dataset for image recognition. The dataset contains 10 classes of 28x28 grayscale images. You will see some examples in the visualization task below. \n","\n","This assignment consists of five tasks.\n"]},{"cell_type":"markdown","metadata":{"id":"cDqvFgbRAzKG"},"source":["## Task 1 Load the data\n","\n","*(weight ~5%)*\n","\n","Load the Fashion MNIST dataset (https://github.com/zalandoresearch/fashion-mnist). You may get the data via Keras (keras.datasets) or Tensorflow Datasets (tfds). "]},{"cell_type":"code","metadata":{"id":"Krg_ro9M-t_k","executionInfo":{"status":"ok","timestamp":1618805050466,"user_tz":-600,"elapsed":3497,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}}},"source":["import tensorflow as tf\n","import tensorflow_datasets\n","import math\n","import numpy as np\n","\n","# Loading the data using tensorflow\n","fashion_mnist = tf.keras.datasets.fashion_mnist\n","\n","(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n","\n","# class names\n","class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n","               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"],"execution_count":143,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nFbty6i5AzKm"},"source":["## Task 2 Understand the data\n","\n","*(weight ~15%)*\n","\n"]},{"cell_type":"code","metadata":{"id":"Vg6WnS54Cl_G","colab":{"base_uri":"https://localhost:8080/","height":826},"executionInfo":{"status":"ok","timestamp":1618805052759,"user_tz":-600,"elapsed":5689,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"eabf9c3f-9491-4503-ebb0-67bd932b6237"},"source":["import matplotlib.pyplot as plt\n","\n","# 25 images from the train set in the form of 5x5 matrix\n","print(\"25 images from the train set in the form of 5x5 matrix\")\n","plt.figure(figsize=(5,5))\n","for i in range(25):\n","    plt.subplot(5,5,i+1)\n","    plt.xticks([])\n","    plt.yticks([])\n","    plt.grid(False)\n","    plt.imshow(train_images[i])\n","plt.show()\n","\n","# training data\n","print(\"Data type of training data: \", train_images.dtype)\n","print(\"Shape of training data: \", train_images.shape)\n","print(\"Data type of training labels: \", train_labels.dtype)\n","print(\"Shape of training labels: \", train_labels.shape)\n","print(\"Length of training data: \", len(train_labels))\n","\n","# test data\n","print(\"Data type of test data: \", test_images.dtype)\n","print(\"Shape of test data: \", test_images.shape)\n","print(\"Data type of test labels: \", test_labels.dtype)\n","print(\"Shape of test labels: \", test_labels.shape)\n","print(\"Length of test data: \", len(test_images))\n","\n","# unique labels in the data\n","print(\"Labels  in the data: \", np.unique(test_labels))\n","\n","# size of each image\n","print(\"Size of each image\", train_images[0].shape)\n","\n","# memory required for training data\n","print(\"Memory required for training data: \", train_images.size * train_images.itemsize, \"bytes\")\n","\n","\n","# plotting the numeric range of input\n","print(\"\\nPlotting the numeric range of input: [0-255]\")\n","plt.figure()\n","plt.imshow(train_images[100])\n","plt.colorbar()\n","plt.grid(False)\n","plt.show()\n","\n","\n","# The numeric range of input data is in the range [0,255], these values need to be normalized to the range [0,1]. \n","# Normalisation ensures that each input parameter (pixel, in this case) has a similar data distribution. \n","# This makes convergence faster while training the network.\n","\n","# normalisation\n","train_images = train_images / 255.0\n","test_images = test_images / 255.0"],"execution_count":144,"outputs":[{"output_type":"stream","text":["25 images from the train set in the form of 5x5 matrix\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAScAAAEhCAYAAAA9A2ZcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9eZBl133f9znn3O3tr/eenp4NgwFAgCQIgCS4SKYoWpQomlG02VYSKVLJcRIlqVQllXJSUVXyjxJVpSq2JSeqSkWpcjlWZFmRLUu0Ylu0SFEkAZIiiW0wxACYfem9+613O+fkj3Pf7deNATBLz3RT6m9VV8+8rc95997v/S3f3+8nrLUc4hCHOMRBg9zvBRziEIc4xK1wSE6HOMQhDiQOyekQhzjEgcQhOR3iEIc4kDgkp0Mc4hAHEt6dvDgQoY2o3a+13Fd02Vi11s682+vudY9CSWwYkk5IonpC3AuRGajYggVh3W8rBToU6Ai8KCfPFCIThKsp5BprzB3/7dvdI9zbPoXvYUOftCXxBqD6GTbL4B0yvyLwsYGHDiVY8DaHd7VHuP/7FL4HnkdWVxgfvFqGsRKtBV5XIhOLHKbgexhfkjUENjBMVIZYC9pKur0KKgZvbfCO38t+7vNeIJQCT0GacS8Z/5g+qU3ErZ67I3KKqPGs+NRdL2Q/8cf2dy/dzuvuZI/CD5CTbezcJPlEhf58SFYTxNOC5OkenzvzMn904XGGWxFq3UdoEBaEBuNBPpvRnOxzenKVS1sTbHVqVL5TweuDP7BU1jR+Nyc8dw3TH2C63T3Z453us4RUyMDHvP8MW2dqrPxIgncpon4ZKusGmVuEpiRh4wusEFgJwynJcMadg34PFr64gVzeIL9x887WwH3YpxB4i0cxU036J+ukNUleFXQegmxCc+KhZRKtSDKPzQsT+F1BuC7QIegIzCN9Zia6fGTmIj0dspVVeP61U3hLAZOvQNjVBJs5wUsX0Wvr+7fP3dt+6gn6p+poXyAsyMxSf7ODuHAN3euD0TteL6tV5NQk69+3SFYXZFVBZdVQWdNEX3wJk2Zvec+74Xn7hbd97o7I6RA7IZt1sseOcvNDFXqPZPz8s3/KqXCZ2AZMqR5t1effe+Y52jLltF+/5We8kg55bngKPSNpqz4//gPrLOkhb+Z1fuXCX+O1S/M89H8vEl5eh3chp/sNGfiIVpPVp+psPmb5z576EtkHFAMTcL43yyAPGOY+qVFkWjFZGVD1Umoq5XhlnYejJf7nV36E7rU6S0mbqVci5F2Q015DeD7dZxZYe4/Ho585T6x9Eu0x72UYKxhkAVOVAe3WkJ96z+c546/wRFDh1XTA2XSe2Pp0dcS3e8fZSKtsJRWeOn2Z+mMJT3/uMl9cfZQX3lzk0f9tAe6AnO43Ln+2xdM/epaT1TUyq7jQn+KVzz/KiX+eIy9cwcS7yGlygu7TC/z0f/+vWQzWqMmEr/XO8PlLT7Dw0iSsrb/lPfeCQ3K6Q4gwRDabXP25MwwWDMGJHscmVjhe36ChYrZ0ja6O2MidiS2FIRI5k16PhhzSlDGbpoq2Eo1gPT/Kat4AYCOv8TvWQ1tBZj2embzMQm2Lb/4Xx+gtzVC9dIQTv7+KPvvavuzdpBmy18cbQPWa5H///z5N85ENfujYd3myeZW6ipn3tvBFjhKGrq6QWsXAhJwdLPDV1Y+SvdKkuSyQmb1rd2cvoaYmYXqSpQ8q0umcszfnkdIihEUpg7UCrSUrooZSBmM/xHTYo+nFdPKIzaxCbhSx9rjea5HmjpgBrBX8+fVj5LlChZorn25Sf/yjTPyTb2GTZJ93DggIZc5isE5VJjwS3ST4nObsR+fodN+D3vJpvubRP2oRR4dElZTZxk0eja4711VXaKiYKMiwcewspz3EwSQnIRBBAFpj83y/V7MDaqKNPjpN/tEOHzt6mR+efAVfuDUuZW0GJqCnQxLjkVmFsS7nEMqMCX/AhNdnNWuQGA9d5COM3Xa5V7M6ocxpqJjj4RqPVG7ymckX+caJh/jSsYeJv9HEP6fu2HzeExiNTTNkbgk3oXYTluotlmcbPBLd5Ji/xgfCTapCEQqfC/ka6zriYjbNzbjJ+WuztG4IKuuGPBIIs//kxESLZLFFejTFr2QkmxEiMAjPIGSxPiswWoCF894Ml7yJ8phZKzBWkGtJbxBijXSPaYHNJaLrYSsav5EyOJOQtgOm/mUdc0DObYOgrQbMeB1qIuUjCxeoHtWsmJA/6z/Kr8u/yjNPvMkvL36ehnTks6IrrJkasfWRwqCExd6FS/duOHDkJBsNZLtF95kFam92sC+e2/UChZBi3w7s5Z87Tf2TS/wHR18mkhmvDI6ikRgr8IVGI0mM+1qlsEihy39vZFWW0wZSbF+UircGhRPjkZg6W3mlfGza7/FLp7/E33/spzhy42H0q6/vD0EBxnNxJJnBxAuK56+8j60fjvhg+zLrepmqTIhExsXsGJeTKf7oynvonJ1i7juWtGmJ24KwYxH53QXE9xLLn5hn7UMajCHrB+5BYRHS4vvu+9VaYK3C5pK11QZWC8RQYZUFaWF0c/GK/VgBpvitLCKW6EGF4MgAvajZ+KEztM734JsvP/gNj0HFcLXfJm77dE2FK3qKeW+LGdXlUT/nt5JJHv+V65z9+Uf4R//uOj8x8U0aMuWNbBaASGRcSybY6Fdo3GVy452wv+QkioNqrXOXqlX0o8cYTob0jihU3KC6tlBaUHptHazBmrdRQIx93p4v1Q+Q7RbDWcOnZi8RyQyNKKyjItCrNFWRMuen+EK7u8oY+TgS27l2X+RIYTFWFMTmMzABXR3tsKx8oWmrAf0FS+9Mi+rrHjbZH3KS2iKscJmsIVSXLd8+f4LzkzPMNno0/ISql3KpO8Fmv0J8uUFtWaBS4wLkSgAW4RKX+wp/YPG2FHpOg7RYK7Faoo1gZM8a46wgqwVoAUbAWH5J6MKKGlfmWBBmm6SsAK0lOlGEWxo5SNmfo7cNlcBav0pqHQ3ExqdvQhpyiMK4c3J5hWDrOJcGkzABkdDExscXOb40+EKXl91eY5/JqTiYVqMmJ9ALU1z8bI1s0iASi458dLiISix+J0U8t+msBbvrsArhUpsFdlhVhaXFPbrDst0ied9xWIj5QO0yrw4XyI0klDkIZxm11JBpr8Mz0RVaUlMTEl9INJaB0fhC4AtJZrcJKxIKXyhim5NYw/Xc441shlfjBWLjY6xAFQc/tYraExvc8CY4829D9H7ELaxBJZY8EiRNSbhlqKwZ6r8n0GGDzZkWK3VBHkH1pqXVt8xsaXTFktYkxncXthXbBsd+YuLFDYJei2t/0xBVUgadCDtUiFSSV6QjIb29UJEJZzE1spKfbF8gtECkuzZkBTIDE1psaNA9H3/do/KlF9H9/gPb49vB71pWVxqYhwUSQ2Y9F9w3FSAtLXyvbzm/NkNtMWNSQmYVvsipyYSWNyTwcpB7L5ncV3ISsrjjGNALU6y/twmP9Flo9Vjt1OgGVfLIwyqQmU/t9IdpXozxz17CbHW2ScharNajD0VGUfk3TJptP3cvaDdYf0/IzMQSVZlQlSmZcISohEFiGZiAr3TO8Csv/Ch5qjCpQvSL4GjFIFK5fQJbEAasAqss1rPgW6qTA94zu8Snp89yI22TWA9fOivMWMkPLr7Gn6qHIfDvfU93CWHcD4AOBGl9+8T0+xYVg5WgMouVkLSV2+foZbaQVBwAy4nry9SyHHVxlv4RxfyRDZZXm9jNwLlsyiJ8sKlEZNKRkBaYnldaRiUlCRC5QOQCUzFYaZwl5llEYJj4WkDzYnYwguFAtGXwV3wy66GEQSPomxCFYWDXCWWOfPg0WdPtsGNDBtbd5QOh8dFs5RXi1Ie/eG7d9kmdTkT0jgmeOHKD0/VVXvSPckVaBl618OshnlXoKGJ2bRoJ2P4Aq41z9bRGKIXwPORE2xFWniO6vT05GUy9Qu+Y5VStQyQyIpmBcXcRicUXmsR6vN6ZIXqujt+zeANLZTVHWEhaCm9o8QZ5KcQUucF6EqsEOpJkNcXm6RYvPyX563PfYFU0SPDwi7iVRvL9zdfQSM6Hs86N3YeM12j9ANaDPHInr9TgDQ2esQgDOhROzxUJR2b7zkRvhd7YQA6HVK/NkVc8Tj+2xmavSqx8kCCkRXoWnUlHRBowAqWlIyIDJnAkbD23b5k7zw/pbjwIsEYweS7Gf+FN9AEIhAN4fU2wqYithyoOTmYVfRPSN5aqTOmdaZFMWJp+Xrh97pqVwhAITV+H5Jl6pz9z9+u7L596m7BZWv57MO+TPDbkU9PnOBmsEMqcqajPhdoky6/OoBKBPhazPi1Ye7ZJ5cI00bqleTEnXIvxrq8TPzrPYNZn84wk6EDrYk791XVYXoN7lJfER6o88/3f5anmFWLr4wtNJhSJ8ah6zpK6lkxwZWWCh3/rdfLTR+gdr7B1yifsWCb+5avEH3yY1feFNK5qvNigA9+d1BKCjqZ2Lab9pze4yGmyJz18mVNF0FJDAFbyBk9Ur/OR+hu8fOb9hEKQX712bxu7C1jhXDNhLRhHSlaCUZA25DYJjbludmeYBqNGsaf9h4ljjvyTc1R+6BH+ymdeYz2p8no+Q9YLsKmHBkQikanAGxaxJFwcyUqLnxSxwYKIECBjF5OzkSFY8ahfVgSXV8i3Ovu51R0ItlIqyz5beZXUKhS2vPGezaZ5tvYGT/4vl/FFTiQyuqbCS+kRfKEJihvmalIjG/r35Sa579k64Qeo2WmG05LZqQ4NOcRHM+n1GUY+BsHybJNs6BEGOcYIjKfIWhaEQKYeaatGMBXRO+qRtgQmsKQtwcYZD7/bIoR7Jqe8Ivl4+w0aaoixEilcMNCXGoVFYQhl7nQxK2sMP36KlacEeVMTrCuarx9n/fGQzpMpuhIgU0U87e64CIhWFdUln4mvb+ANR369BunuUplV7q5mfVKrGM74+OsNuLoHB+EOYZVTuFspQNjt+NHo963CD2LMcBKFOysFB4KehECvrVO7GvMbr/0VciORyrgF28JV09uuLOCsqBEPFxuTuYtHGa94zAqQFq8naF5Msd3egdB2jSDjHH9gC9mLhxzbYN+E1GTC48ESsVV0TcCarTMwIQMTUjMJWjoZxS0SznuCfScn2W6x9dFjdN6T8dNHX0EJy6apMun1aKkBj1Zv8oOT51jOmvy/Fz7A1mYVsRYggKxu2XoEdMViGwZ0hkgUzXOKwYKl/ZFllsUc7dYUnHvXpbwjsqrkZ5pneTltcDGbRmEJZVaSFMC03yWMnE9+9Ycsr/07/4BVPeTfDE7yP8z8OJ996s/5uwtf5Rff80lS4/Ebx/+QqvTxUPxmZ5H/68LHEP8sdC6s8WnI2P1tq8hQZMbjXLLA5XSKzYcV/qBB+OK97euOISR5JNCRswyEdj+jeFIZR5K3DniPyNj4YJXcf3Iak6Z433qNI//1HJd/8gg849T4wghkUgS8jVu3MBaZiJKITeA2LUfxxPFNCaguWYJ/9c19z87thuwMiFYrbGZVYutimBpJZhWx8VnP63w3XsAXmlBmKAyx9bmRtgCIpFPQ36+DuO/kxOwk137Y8L5HrvBIdIOmjJEYmjIuUvUesfFRvuETi6+zPN1gZbFOphVx7rngZSphqFxKV0D3w0Nmprp8euEcf9CZpfHy6t2vTyq8YwukTcGl3GfTuNSrFAasRInc6XpkxlLWIs/dVXrkS5JH819CJgKVCJrLgj+5+AzvrT1N9YYzIz46+xhWOldAR5ZwXdJOr6BSy/nhHMeidRd4tx7GSgyCgQkBGJzI6S95zip8wLBSbBOP2PmzwzAYd+mMI67ydZKD0RPDmjL5a9MUsbxGtDbP2noFEWmXrEg9RI77KSQCMi8MBgEycxvVYeHWFfsUBsSWhz84ONbSDgxjvH7OqcoqZ4KbfGt4srTQAYyVpSTGWIFh+zmNILWK1HhFgG3vse/klLciPv3kyzzTuMRRb6N8XGFJiy9DYohkxg80z9Gvh/RNSGYVG3mNP+S9rG3W0cPQZVFCwyfPnOeR2hJPVi7zr3rfj37tjbten1CKbHGKtAk3dZOOjsoDNNIoRTIjEplL/Rsna5h4/gb1S21kmmMCj2Q6pHJziLq+Vn62Hcn9pWDw9AmsMi6or2EpaTIbdImEe40urvRRAL4x3yWenLjrfd0LrNi2lEqInXGl8VhTSV527P2iiF3tN8bY1OY5utMh6FpUT0Ijw3qA8BCA1EV2rgiMC0ERWnDfh61RZmHB/fb6Am8P6832EjZNUf2EOX+LYyrhW7CjqmHkFYy0eqOb5EhiYKwkN3KH1GIvse/kJHLDtUGbhXCLo/46XV1BI2nIoUtXipwp1QOgaypIaYhERmYVbTXg509+jYEJ6eoIJQxVmfKJ2jm+Ex/nH1z9QcLNe3OIZa3C+Z+OOPLYTWZUl74J6ZqKIygBDTkozeCBCYiiDPPhx0kaPlldOm2PByaAwXQd7+EaUjvXQCXWxW+UIJ6QWAXJj7+fjSdsWYzZNREL/iax8emKiKpMqauYHz5+jn82/+xeHII7hygsiKSQQxRBfVvElUQRqykx9m+ZHwx90zuiIN8oStFaktnQib59i4rdJo1XfAepU8rbUcLKUggvnYUVbgi83sEkJ72xhZKqdOmksERkpW4PtisYNJJQZEjrCGsUON+MK8iB/AsoJbgFUqswSBd4Kx4bBeoCod3zVpbq6UmvxyS98v2RyJhRKTWZkBvpMkr3AuF0Kha4kk0RG59IZNRkgi80U6rHmq7TNyHGCqZqA65//wTGd2a+rtgi+Gtd3KJIPwsrEDnlhaArFistMhVUFruldkph8UWOFk4lPlKQXxpMugtlHzAil1Ft3A4raoygdpOQFTjLQ+DaqciDyVJCW2QqEQKk3HX+jPRZyunzSlcVSqmB1AKNi0N5A4tK9r9M55YwGvKczCo0BREJV4q1u7IBXKXC7gqHNFelW7vX2HdyskrS9GMaKsZHo4QlM5I1U2cjr7GcNrg6aCOF5WMTb5ZiRKAoD7FUZUJbDYrAsYe2MKl6PDlxja9UFu8pLmPTlMZ5xQ07wz9tfJCnmld4NLrBMW+dhsyYkYLnE1jKWiTG43NHXuIX//PfBsBYi8ZigNRaFOALQSQUcnTAMWTWlMrx61rwSjrPN3oPUfVTwsJlNEKiMLwZz/BGb4azX3uI6Zf3KZaxSyJQxlcYe3yX2zcKHkttMUqQNSCvqv0/AUcYK30KuoZwQ2GMcORUWIoyc1k7CitK5K4PkvGLMrrU3XCEBhqAhcqawesm7lLf57rQW8IaBjpkUBy4URmVz7a2bvSYsbKMSWkkEsMwCe7bTfLBnxtjwkERhuQVj5PVNeoqZmBDYuOXaXRtXRFtPwvxlWbS65Faj428Rl3FhXnptFKbukqj0AN9eXiSrqlQlSmDaUnzkdPw3btbrk0S5r7ep/1GyPmXH+Xl9mPkNUsyrQmmYv7awy8zH27RUkM0kvW8xvNxk4EN6eio1IPANpk6q2j7zmSQ+CInsx7LeZOBCairhHP9eS50plj+8gIydSe9SkAllqOXMipXuw80AyTCEFmvORemyLqNa5re9n1jVpTIQeIa0ZlAIPwAm79zF80HDa+XEW54DKxACIP1LKYQ5LvMHajYCUx16ApoReHOiUL3BZSkfT9cnr2EQaB3mbkadzNUpWbCkKHK2Kd7nyTPFH7GPXXDfDs8WHIqauCs1mAtslolrypORqvUZELfhKX/Gwmn6k6NR2oUQlgmVY9NXQVqJTE1VUzfhGzpkLZyjuDXu6fxpGbW75JMCJJj7bsnpzxHfO0FIiCi6JpQr5E+fITNMzX+wLyPTzz0Op9qn8VYwWZW5ZuDh1jLamxmVSpqp9xgN0a+/aijQTePaHgx036Xi91JLl2c4bG/99Itu2A+6EiGCAJEpVLGl8bxdnGkMv5UBMCFccJN41uMJ1zLX32Lesl9hOpnhJ2InhFI4dxtEzhr0Mdl6qxxcacyDsVYWc8YF432e5Chi0zw3cCYwpq8D3jwlpNSYJyp3PnUI6w8JflwdIGODbmZt2irAdoKDJKqSpgOegStnFDmZRq9ruIiqxASFfqLlupz0l9l01T4/AvvQ4aaY3NO0Gi8vTM7Ta+HHQ7xen1mbkwTdmb5s7/xED8y8RLgrKOGijG4EzuUtzbh1ZjgTVtJQ8VoKxlqn6pMaakhF69OU3/dh2xvm3jdNbIMm6TIzAWBb1mOsjtEM56tY9vd8/oCb6Axw+GBspoAROYU/EbL7V5bY1m4kQgV6cjXG7jnsoZTyeO5fQoB2hfgHQTNxNvDFxq568ApDKq4qY7cuPGuGsZKUquo1hKSZoQQYs+rkx48ORVBVCEF2ndCvlBoAusk8QqLFi7w1lJDVGBpeUMktgyWj6wQhavv0dhSPJZZD+EbTKq4sjzB1KbF7+zhxV0UGdtuFxX4VG62SAZ+KS8AR1CjervRWp3p7A7siJgktrxjSWGKH+f2BSJHDDy8HgcG1lqE0bdfI1fqCnY+VuqezMHohrkbwhhEDkYL8ly55EWRzChhR3ICUWifHGEbz9Ubllk7IQ6GZOJdoN6mx5jCFQSPguKj5NRIg1iPEgaR/d7uSiA8D2tsWU9nUTQuxwzmq2yZEI2gKhKCwmFPreKhYJm2TNk0AV0TcS5ZKOp68rKh2by3xbqus5lVOZceQVvJ33rqK/zTC09hvzDJ9NdX0a+e39N9oJQrJjZFT6JUcTN3qlkprLPqxg7oCEqYkqDeDqO7mLYSORT4fev8eSFK33+/L+iRdODO3gQCu132ctAw/p1qg9AW0/WJc4nMBCoWqKEoNU7BAHRQBMMlYCHctOQVQVYDlYjCujx45Lsb41b8qLfYKC4qi9YZoxiUQbqid5wm6kRzg6WpFvejqdO9k1Nx0YixE24UUxrH7rYlwve48LkK9cfXiK1PbF2jq0imToBpFeu6zsBkpEX5Rk0mrvf22JURW3+b2a2T1H+i/ipfqD3K1nAC8reu5V5gjUUU0R6bJHgrXWR/htXM9QFXGBKzbUkZBBK7w0oCZzWpQiSkgazonunq9ZwrKHOBSu22takUdh+7RwohypNw1PIE3hpvKh+/5YdQZr9cLGq3rHz/YUMPHUkINV6oyQPlOkdUimxcLvD7wsXNgm1ykilY32I8i4lciUvSktSqvjvyY2r0gwY1drRKjdPYb2UNhpH4uPBwrORYZYNXGvP3ZU33Rk6jJm9CghTu5MVdwHCLO7yQCOXITNZrfOKTL/KfzP4JN3XTiRt1hQbCbbwo1UitIigk9JHICtW4W7YZc+WMFQSFQPODoeZItUM3O7r3mRKjGfWKs2mKuLmC159lM6viSY0Urq/Tu1lIu5EUexoVEgOIDEdOUH7H+wopXcxwhHGNz26MKjneEjgvZOSjwyLkwQiGj0kJrFLoQCBDjR/k5MrHhgY84+p5M4kJPKxvsb5FVHOEtOSZ3PFx2lMkbY+85uEXn33gIOSOgt+RSyffJoo/Cj+As5yOh+tM1/sHyK2TRYP9omcS4Hzr0fO7D4JU8MHHGSxWWXlSkcxnNOZ6/GTTEdP5ZB6FdQRkMwymnNoxMGERu3GuXCA0NelcQ20la7pObEYZvoxIpqzrhMfrN/jK9z1M64028vW72uWtMXKvjMZqjRkMkJlgqH1qwqVbY+OjipKbtwQaR+LKXRm8gXH9q1tq6O5MwiCM2FkJv88QnoeIwlJCUEoERilztktTxoPgI1ghENZiS12BGzdlkv13Vcf/vuzHRGsR6kpE6odU1yTWc/EkmRWi2arF5gIbg1xXyNwV/hrfFQL7HXfs8qrraLF/rQHfHt78HPrYLDPeK4RiuxPGuJsXCI2xsmhGJ9GW0njQ1udksMqRaofNA+nWQWlBCc8D30dEEUJJ8H1svYKpR6x8oM7giEA+0eGZuZt8qH2JthoQm6AoagWsIhb+jtjMqPAQthXim7qKFJZA5EyqHoHSZX2bjya2rkPA3MImeW2CYE82eQuMOnAayKws6o606/kt7Q5iKgPfu8hKFmmg8QksckwHVSrcrSndu32D3Hbr3oLdeqd3OVdLZfXI8j4I1tMIvoeOFF5PgBSEGxarBFY5jZlRgrQlyu6e3oCCnMCErrlesAUIyJoWfUD1XLZeJZmKnJ7QUggrLT7O4HDpp1Hcie3HxoTQbTlgOuyx1ZpDpikmjvdsfXdHTrumfoggQE20sVNt8naFzsmIrC5IJgTDJ4Y8eeIqvzTzEg013GEJdU2ExPBodJ03kzkuJ5NsqQq+0FRlypy/xVFvg45xdXNTss+X+o/xx8uP0QqGLFS2+OW5LxKW6mpFbBUDqzgdLPGzJ5/nH7c+u/fkNNYDHOu6H+bGBcFd9ba7MsfvQJnxd9yVZGEpus8YKw9AFHcq+9Yatf2GKPLjo84Cb5ONeyeUbt3o3/fBHbhXDE42WXuvR9Bx5Sf1mzkit8jM4vWc1Z43AvKKQkcCb2hc2UpqyJqKpKEQ1pJVBVnbEE9K2vOz6OUVl0i5j4M47gT5bJPOcY8LyQxTXo+BCVybnrFjGAjtej3tyOBZMgSJ8TnhdXi28Sbf/tDTNM5VYQ9nKt4xOclGAzE/Q+f9M/TnFTpyWYu85oKDOrTQTpGeRSrNwzPrLFY3UcKU7U9ifKSw9HREV0d8pXOGE9E6z9Qu8vJwkYEO8H035SG2Pse9Daoypyosj0Y3uNZqMxt0mfD6XMldQDy2funeKdWnIWOeCK+hg/sfp9Gh5Whlk8R4SGHx5Fj2owiEq9v0z5ymxNU22Vu4RvsNq96FTN4hDiWsddNXinpCBAjlkin2ALivwvOQrSaDaY942hAKifUEfeuVGcrKmuduRhVB2hBkNYE3lGUBtA7dmHJwCnJyQdIWdD54lObXMvKbS/tOSiPEsyG941CV6Y4YqRK2VIzfKvbkqhkU2kou5S5evPIBiUzbRGf3bn13Tk6tJv1Hprj2I4aPP/FdztSWmfT6HAvWyqLcSGb0TcjVdNK1EUHQ1W4G26h7AMBWXuFq3Oab147TPj3kY5UrvBovMNQBsURlXEIAACAASURBVPLLLN6ilzMh3fsf9ZeJGz4L/gYKwxvZDF1dYUu7BnWj3kptGXMy6JZlB/cTJoST0SoX4+myaHLkmspbCNxuhVGGxE1ccSeKlXanRma/r+B3cuvGsYugRrEmYDsWJZyYEW+fq+t2jSdjepJ4SmCmU7IswISCvCLIqxYdunpAmUNegbRtyVoar+cyxXlt1/Eppq+kbcvq+xWNc024ufTg9/g2GMwo9KmYlucqK1wIZWembgRT6px2Pv5aOs+WrlB7ao2tlWki9g53fGb0n1yg/59u8RNHXufJ2mWXZiwC2F1dceUnhdc3YuTEemwV5KStU3631YCnKhf5Ak8gv9Hkd1Y+zNceOsXfOf1HHGtt8mY2jS5UqL+5+QGW0wYvby7QCp07dzx0I5S3dBXYFocpnM5oXVdZ14WSeS8x0hzteIxSOiCFpa6SUnRZklRZU7ctKVBFUbBBEMmMzCgGJtgWdEqwu3vH7+dddyQlKESH4wFxK7clBMAOV09YW5avlE8L0IFENBuIXn//imHHA+EzUyx93wzGB/9qUGqavIELbJvIEHQkMoO8WuzFQLDp/p03XO2digV5xe3Z60tMaEknDMPjLarxCfKLlw+E9TSYE3zsoTdcL7KiL34g8kJ4KcshBrut/pHOECAxPpHI+fETL/KP5j65p+u7I3ISvk/viMdPHH+BhWCDmkx3FAKOQ2GoqaQsaB1ddMmYnqcpY1cwG4LXUVy5MMM35x+iX7nGhWSW2PgkxuPiYIpOFrHSrwEwHfbL7NYow+DEi9tNsTKcitVKEH4A6S2XeXfYfWKNkRNQ6pdulWpTwmDewjjO/fOlxmhHahpZlEkcML+uIKdb4bb7NI1i/BJs6O+UJ+wjbCVkMO8U315fYEJKJbiz9CwyK0ZelRaX2FHKI7RApgJbd+nM7ZIXS1ZXmFbtwCQA8prlsdqSa8mzq03KCL7Ib+naudKsrHiN5r3BGnnV7qlu7Y7IKZ0IWHs25xP1V3l+8DAvJYvMh1tUZcqM12He3yw7N45amczIhJoUfDWe41o2wZV4glDmVGXKufQID0c3+Ye/8Pf5ma/8bRb+Rcjnv/YJfr8Cw1k3qMD4oE71ODq5xS8+/NWyj9Kof1JLDYoBf9vs4+4CbmJE0hbIk4uwd3G6t0Bq2Mqrpck7MMEOpbfBBbr9oqRl1HbCJ0cKQ1hkPzKryAt1ObjWvWlN7L++qYDwPIzvlf3Bd5DRrYLjt/oM43w+YcfI6YAExU09YvhQir/sE24IbOasQRMUtXT+yALcqZIv70GjMpYUaGXIQKN7FbCg+pKkBd6RGtHLByPGljcMn6i/ysVshtj45fk7qmxQxfl6q8oGVQzsa8ohbTXgyWAN09DIMMQkyZ4Q1J1ZThZEIrmSTaER1L3EqaGNV0b6R1qkkcR9yUrQcD6ZYyOruX5NVtEr2t1WZcqU6vHk8at8+4dOIgYKlCWcG1ALU+phypnWCnNhh7YalCbn6Evc/tLGrDgr8UXh4tVdVuJ+khOWHVKAEXYfVDUmYNttKo/KBIBCBS+KYZtjrtB+SwmE2B5cMJ6xu51l2XFZBGW3SCsE8gDUnqmJCbKqh4hVGdyWerTG4kXWFfbuJqOy64Ky5VdhtftydMW6PlA5RcxKMj6vcV8gFTIKsZFmQQ24mLlrKXgbK2lkyZdvL+QEPvBc7zS+0JyZ+jIyyhEnFpFXrmMGg7d8zp3izshJg9eVvDxcpKGcS7alKwx0wHpaYzLoU1cJVZXQkDHSN1wzE3R1xAudY+RWMh30XftZHdHJozKz9bcX/pQTx3+f3976ELHx+SuNcxxVW8ypjIGFrvG5mE+V/Z5GP4nxQTkXbtQh02DwrRslnTUsgyP3fwzAqCnX7oDhbqhblLKMRk05S0u4tr82KMYMOeW9LeI2+wopsUrtsB5uV+qwUxZhkVrwLl/Vg4NUMDNJVvPwuhKhHQl5MY6cFAWhCifGtGMlPOPDQseHNuQCowW2rqGv8GM3sSar7r/SXyiFbDbwqjkLXliec6PeaNqKMiDuNIhyW6DJtuYQ4Lmlk2gj+ZsTzxNWMganJ6iub8GDJieZW/yu+2IvxVMMtU9FZVRkyunqCj0dspFXuTycRArLeW+ufK8vNSE5FZXSUDF1FTPp9Uvld9+EvJlP8kz1Qvmey/kEF3PJtWyCgQlQWNbzGitpnaH2yY0iNdvxikjlVFTGqcoqkczwhSafyNk8fZ9TdsL53e9GHq6vU74dk7oFjHUkl1kFnpNnHBh4CuvLHZbEiHRut1pn9HqhHcGZqo/c55iTkIJstkHSUqWlVLZFoeg0UBDUuDtnfNA1A0VdJ8o660kKvA0PHSvkZILJBFYq8ghXFDw5gdnq7Il1cTeQrQbxE4tMtDpIpIvRWlUS0ij+FMmUlkzo2yENGZUZO3CF+QCPTCyjhKUqcqYbfTrHG9TOVmDl3td5h5aTxe/C5eFEeRFJLEFRqDowAd0sYiuLkMKSGEUo3eDJQOb4wg2eHBUOVmVSXKyWftF5YMHbQAnDSqGf6JqIG2mb3EgqKmMzq7KVVYhzn9xKhrlPphWpVlT8jJqfUism8FZVCp6lSBTed4wLLOH2mniZopDZF5pRYnHU/gVpMQcjVuwgRKlzEnanNXSrnuFvi8LisAryisJ7N+3U/YaQ6KpXauLKwuViWUXZYzHpdxtWWQgKJf+Yntb64MVuYKgINKnvObLznQ6KSoSIkz2xLu4GolKhtxDQimISm+2YuDLSN/lC46MJijIrZynpIpTi+q1pK5kLu4TSXcPNMGZtQmCDvTEG7kxK0B8y+60BXzn1OE9+8A0+PX2WF/vHWE3qvNaZLYmiHQ2peil1L6UiUyoqoypTfJkTiZzYenSLmNNICt/VET0d8h17HGMFuVVoK8iNwivaqKxnNQKZMxt26auQofYZ5j5x7tHpRyShR98LSLTHXKXLk80riIEivMdpv7eL8Vq5cW1TKSUoHhsvyYHt0hYlDH6x19F0lwMx262A9RQ2GF2x21kseHdiKuNUhQRB5E5B3T0eUDlfhdW1d/6A+wwri3IUASIXqNSJi60qSEhYRCZKN05YZ1H5lQwI3HeRC0xkSHxL/U2FNxBMPNVlCch7qkjgCvRkHZVmsLHxzou6T9CzLZY/rnl/fZ3vZpItXd3uDV6QVENuFTMkXc3qqE+/sZJr+QSZcTfQ09EyVZmQWcnx2gavnFrENPZG7XTHOif/+gaz3zjCC+I050/O8MTsTSoq40ilw0ZaoZ+F5EYyyAOWrSBSrotlRWV4UhPKvJjiIMp408iNycfMBE9oPAEVlZUX/cjvDWXOUDt/p+IV6UylCZQmkJqF2hYSy2v9eWqXFZPn9lJHcAsUUoKItxb6jjeYG7V2GQ3kfDsZBmy3UHGDAUaEsM8pnjAgq3nb/Zh2Lf9tradbBM2FcfVoKrWg9z91paNC2S2LdQ2d0NIEttSaicyp28VoP9LiB7l7vmilYisa0dDIrIJIYJj5WOsITlcsQkusJ29PzHqfYEIPvx2zEG1SFTlz/hap9WjImK6JiI3PrOpxTbf47974Ca6uTJBvBfzyD/wLPlS5CEDHRAxMiBSGmkyoCs1CuElteoAJvD0Jj94xOeUXLtG8cIn61afYeLTF0t/o8572Eo9Ub3IxnmY1qbM0bBCnPhum4kSa0hCqHE8aAul+S7FdGGsQpds3IrG6Ssq40c6WDu49S0kTg6DuJ0yGfUKpC8LTvL9+lfPDOZ5bOsn0Sxn+v/7mHnxVbw+hBYnxaNzCBdsWXDpsizUNekzvNCIqid2uzxPcdiznQUDXApK2Kq05K8aElyNV+C2IyAqBwO4UZhpXRBv0DOz3NBLpAtV5RWA8g8xck7/hnCtNsoFFxs6aKgWn2hFOFGSkHk52kAtkRTM/tcUgj/D70BmGGK2wnoXQkINzjffRlTWB4ujUGqfCFWaU5aFg2XXSEG7ceNdWWPRyvh5P0vutBU5/pwNnX+XrXz7Fj9XfYFJ2WDcD1gsBdFUmTCrFI9ENHptZYjM6zl5EI+66dsB/9TJzV+vEF6b59uwRvviwxHtmgx9YfJ3H6jepypRJr4csWum69iGm1EGZYrTMwIZcTKfLz31zOOPcNe2TGo+h9vHGUvAj37iTRSS5Rz8LXGtbabh4aQa14fPlzaeorFgmzicEZy89sEEAIxX4uHxAjrlyqnDtdElYdkfiVo+ydsUVL+QByNCNQWiDzGwZFN6hEB8ZU2bMhQOwLpFStuct3mNC4SbJxAa7z5aTEK5OLq8Cwk24CXouGWGqBpSFWCET4eYPGvD7FjWQxKnvuHrXcVIJeENDMvCxuURYgc0kIhckkwFqq7oPO3Xw1wdc+vYCX2o8yg9W39zxXCQyWqqPwnkz/tAiMo3FFbfH1tI1kmwUoyrqWrdMwivDRV68epRTw7252dw5ORUKUL26BqtreBcuMTk/R7h1nCtTLV6pH6FfD2n7g8IFyzDkxNrJ45HsKNLtmgqv9o+UAfY3OtMMMh9rBUmuSFMPKS1iLO9srUBridESnary9l19I6CybGleSohu9NCvfPeBEJOwbkrMCLczyWL3cMLd/x998A43aZ9LHkSi8QcGUDum/Loni9/mrcaTGBlNY+JF4zvdkxfrt3S52A/oQJR1mM7ldG1SUBY8t2c3rVhgC+W3yAVZ6hGyvS8pLL7SxCPrMpNgipIfXfR4qkhstH81hbIX07gAl5924+zHa0FVUbIiiz7+QluEcU32EuPGZbkW1DvPcW3h6nACfbOCHGzuycCZO/+GbnGB5EvLRP9mg0f/LIIw5IascqM5y7dPPU3aUOhQEHQNxhcMpyThliXoaGRqUMMc/8LNcu5VVXepvsPfegvGhIk2TcFabJaj9YM74b0+vLo5x8xsl6pIi+JdF1/KjZstP2rXO1KIgyOxzLj+zJHMSovL2GIYwgELiHPhCrW1Bp3jp9CRszZkNjanrbCKhLFl7d1upK2iWt+CN7T4565hOvs8xUFKdATWs6jEkVTSlFhpQVrUKCOnAUmxd0Basq0QX4GpAO2UqXaPhxprfG3+KDpSZZzQSosomDyrCky4N3GZu0F+8Qpz/3CZ1z72KEffUwUGbBqP8+lsOWkFnFXkEgDuQN4YNHkzm+SYt0lmFX0bMK86NGTGca/Kc9dOcOYf9xAXr+/JOveGvq0bXKCz7cCz7PaoaENUCTGBhxwk4Ckq7QqqGyN7MWgNSeraSHwPw0oIlSvJqcq0VL6HMisFlpHI8EVeTIvZTtsaJKn1iERKZj0uMENVOi1YpZaQ1cJ9DZ6OwyYJpidpXsxJ2op4Um4LMhVj1pPYQU7j1p9MXSFtZdVQvR5je/239Jd/4BDCVRJUHTlZWWicRu61cJIOK504U+YQrVvXWK4Jo06gcjlk1WtwOUycmtxzUgKbS0QmkQOJ3xNUlzK8jcEDnztYwrgOrtWXKnz26Of41VO/x6TKmFK9sie/QjidYCSwvruZ1vyUSdWjJTUS11RuUqasm4Cfvfgp9Mst1I2L6CTZk2XeN9vSDAaYNy+W/x8bgsquuYPf8zA+TEd9Jrw+NZkgheGYv8a8t0VAMe4JSyQ04S6eySxkCCJh6Y6sqGJY6GJ7i9cmqtvZulF75H2CzXNst0vlT16idmSO9Y8cIau7liI63HZtdltN5QAADdGqJdowNL74mjtH9uhEvhcIpUinNLaWI5eCtxRcC+HElSYAm4AaWJoXUvJKSHLEZfNUDO3vQjeOuMA0laKdb1DJSGMfhoqgI4hWLLVvX8Z03jok9UHj+O9eZ/DCEV779Vl+LFhF2w6xlSRW4QvlevY3BCZyAxrmoi5nvIwJVWfLDKmZAXMq5IXBBK//H49x8pUO+bW9sZpgP+bW/QVEuA7fuHKc1zZmsFbQG4b4fk7o6TJWpqQh14p8bFCjLJp6GSOxFoyRJEMf5RnCKCN9rUn7itgeqnkAYjMAJs1geZXJ55z2Cd8jb1UwkSJteW4eYXFxy9zi9wz+IEf1M2RniBgm6INgMRUwgwGnfzvB+hKv10MkGSI3zHy7Ql7zGMxVqKxpwrUBed1Hpgb/zZss3miRfb2C1+sjMg1pxnSzQt4M8VfWEdaSvFBHaItMY9QgQwxT9MYmNtvnDCVgllaoDmP+7v/4M/xP05L+giVvakQ958OnL3J2ZY65V2O8pU20sfzxt5/gr/cmWaxt8uc3F+lfbFG/KKmsGKafvwkbW3tqDR6S0x7AG1rSzZCVgQ9aIPoeibJ05Zj5IEGkrp3GdmrLOtXxyNKwEORO/DeoRLSuCOo39L5ns94CozH9PuZCv3zIm55C1Guo2Ra66pNXFMK61rXhUg+x1cOsb6AP4IRfm+eo5152cxWNLtuhCGsJazXC08eQK5vo9Q2C40cR2pDfuAk3biJ5qxegxh7zXtl+/IAdRXcM+32av3WTiZkZ+s+eoj+vGM54fDtcJF2PWLyxhu30wBoq1zxe9+e43m6SnW8y+6Jl8k/eJL9x8764qMLewYkihFgBLt2HdTwInLDWzrzbi/4y7BEO9/k9gr8M+3zbPd4ROR3iEIc4xIPCQUpUH+IQhzhEiUNyOsQhDnEgcUhOhzjEIQ4kDsnpEIc4xIHEITkd4hCHOJA4JKdDHOIQBxKH5HSIQxziQOKQnA5xiEMcSNxR+UogQhtRu6s/JHwP63vkNYWuWmqVhEwrciMxmZt6MWp9irK0ophB7rt+OasWMUyw5u4LALpsrN6O2vZe9rjfuN09wt7tU1QiTCCRqQGtsenO+e9CCAgDTKDQgcDfSrHZvc2I34997gf+Muwzpk9qk1u23bgjcoqo8az41Lu+ToQhaqJN9tA8aTsgqyuShiBrCPqLhsrpDh8+cplQ5fTygMvdSTIj0UZSC1IafsJT7Su81Fng1eU51FdaVJcM/tDi9TV+J8N7/Tqm08HeZlX7H9vfvS1p/+3u8SDidvcIt7nPXaOlRRgiKxHm9CLpRETnRMBgXpBMG4IN6TpIdna24zW+IG1B2jbolqb+XZ/qsmXi1R7q5gb5lav7v88Dir8M+3zefuFtn9v7wl8hUBNt4scXufBjPrNnVvn43JvUvQRtZTlBpa4Sfrz15yyolCNevXx7YjPWdcJX4wUaKmY+6tI+PcBYwcXBFC8uLRC/2eDUPz+Kf86giwZzh9hj3KKHlGy3YHqCS59toR/v8Ycf/TXmlKQl3332lraGHE38mZwvDaf4b37nP2Tu6xUqtyKnfW4Nc4iDgT0lJzU1CbNTnPuPphBzCdPtNSYrA27ELQxu4kqs/XL6yotbR92op6jnWodYST8PSI1ikAflQITNrFL2ED8+scH64wmv1ycJlx/hyFdzKpe30Gfv57zxv2TYbTE99QSv/UIdOZ0w0Rzwo0ee40xlCYVlSRuWdJ+qsGQWVkzIpq4ysCEnvTUaMqMhBX1jGVhFVWiOexv85I9+hW985ASv/9RTeJcjqtcF87/5LUwcO2IakeNBvfHc4/rU9BTm2DzywlVMr49aXMD2h+iVPZhGeQCgpqe48vOPUrtpmPzWOvbKDUy3i/AD1yrnNm4+e2s5TbYZnmjz7LPf5dH6Euf7s8Tao5eHJLlHbp3rlmhFrhVb3QrWCsIoK/sZ6dzF6D1fU4tSJisDYqXxhCHyMqajHkerWxxvbnCt12J9Y54p08Q/u6c7OQQ4K3hygs1HGvzHn/wCH6y+yRl/i5ZUGGtZN25MfN8GtGVMZiXX8wmW8yYbeQ2/kjNl+2hiBsanY0O0jGnIjL8z8zxbU1/l+qkKv/bQX+X5CydZ+PwsrKxh+v13X9t+QggQ8o5HdQnPA6Xc79kp+qfqNOJZVKdPemIKf3UAq6sHl5DfDUK4UeetJubkEfj4JisXmoSbbeobHUdOxdQZ+6DJ6fpn5om/r8scgld781zvtVDS4Enj+mIbSWYk1gqEsIRRhjGCLFMYLTFaIJVFeZpKmOIpTaK9cuR4NwvLkVK+0kxEQ6LPXuVGfZGTzzUxgwF2v8cM/UVAcXGoVpPXfv04nz7zHX6h/R1uasXFvE5NpCgsKT4KS1MkGCvwheHp8CaENwHoGkVmJV3j4wvDlBjSNQFdAla0Lcaza3712B9w/UiFv/W//hzeFxeZ+7Wvfu9eoO8A+/R7GB6psHXSI52AeC6n+u8HNKuGUG1w9asLnPzV0DXzO2hu7S5runzYK+Yr5jne3Cz58Vle+y89fuyxF/iN6T/l9x95L//nyY/j9xZQN4pZAbdJ6ntDTlIho5CkDcemNsmNLKeRWCvIjXSz6lTRGVJprBUoaZwllXloI9FaEvg5gadpV4aosTFJ4Br/22L6w+gzj1Q7XJw02IcWkW9eRXc6e7Klv+xQMzOYxVk+cvIin26/TGotmZXbk4ihHHUFrhm+xLl2ElBv0/Z8NFB0N1oy4TMnz/J7J5/l6IljmKUV5+J9j0CEIfLEImIQY/sDmGpj6xX6J+sI7SYcrz3hk0y6bpPWNxAYhr2QYTfCDhST161rLLjfw1N3o7AUhRJYY3cQpzUWGfiomWkGTx5j+Smfj5w6yyea5wA36m1wpYHX67kzxVj3GbeBPSEnGYXI+VnStuF4bYOluEFuJKGXkxeWUsXL3BTfsYGaozhUqr2ScOp+QqQyZoJeMb1EkRgPY93gykHu080iEu2hrSGUOWIu5ubH2hzpx3BITnuC7PFFVt5f4b+d+394b9Dlm8kUshghFAntiIjtaTEAsVV0TbBj7PoIo0GhDZGBKCbPWElsPc6lbkTRfzX9Zb75vuOsf+wok18Fc+nKA971bcJasDstGzU5wfVPz1G7qalf6LH6dJPuSfjpz/4ZnTziYn+Kn539DlNej8+vP8nZjTmuX51k5t8GtM/3EX/+EjZLd88jfXDYHUOTClFOdpb/P3tvHixZltf3fc45d8s9375VdVXX1l29dw/TMz3Ts4/EYAZD2AKBAAskJBBIYdmKkGwF4bAikAgZGYyEDRiwJYwDE2I8Ax5AAzNMD7P3MtN7ddfWtVe9elu+XG/ee885/uPczJev9uXVdA28b8SLt+XNvCdv3t/5Ld/f9wdSIDwPYS2m2904xmhEUKLzxE5Ofo/gk9/5S8TWo2MD/qx7gP/vG49x8FcuYs8tOrZQeuPTt7fEOAnPw1QKmLLm3uIyfePyS55wxsOTGiUsntAUVD5U85K51YPfQ5nhS01RJmgr3Yys/Htq3Dz3vvG42K9grKCgUqbGWizeHzLz7Ns3qPDbGldI7p75UIF3/PXXqciYdWMZV203qtr6xFbhC3PZgFCFRV1pHtQIRg2aRiDzOWkAS8bjh3Y8x6d/6hHW+ruoNNvoRsOd111YwVNjY4h6FaskyVyd5j5DZ0Gytr9Gb9Zgi5r/57XvwCyHFM9L/m1vDwBpGUwAfmDxewaMRS3MYltt9Mrq27OYS0M246IbB+0MlNaXeT1qYpzswE7W/36L791xlNg6k3I2HePnn/k4E88rWFy+YcrPKLYmrPM9dDlEFjPm/Abn/DqpUZS8PjWvR1EmdE2AxFJUffrGH85uG4W2crjbplYNjVIoMnyZodRoiCfpG4+CSpgptmnMFdElf5vyfqsQEqxGeB6yXIIHW/yfuz7HodTQMj51mbhR1aYwvHZqxDMaht/5t8uGLg5+v8L/fZGhkTRMxEeKh/m7+87w+O5/SLVehfUmYBDShfR3RS5KCITnI2oVsilnnOLJADueoKUlM4KomJAmHv6hIqWzlvqxHv6rJ7G9mOwd99HeEdLcLd1Yc1+hp2ooKeHtMk5XgrUbyf8cQilEFLpflILJcdq7Cvz6I79JJDSruogvMhazGpPPKsYPdW451bI1nlOtytp9BWrVVSKZcrB4HnAfOjcWydAyEWtZiW+u38PJ1hgrzRL9lQKyL5F9gdBuHpjsu5lnUjOcFdbbkRFO9Pjxg18DoKUjJoI2vtBM+016OuB0VMNKfyuW81cPI2GKfuphjv4Dy48eeJZTWY9z2QR12eWgJ+maPquUSVBcFn9YNuUHr4WBYXIDHEUeBhqWdJUATU32aO8yNN45R/X8IiaOXfn5LjBMslhEjtXpPLKA6ruhsKrTp3TGMPvHJZYfEdQeWaHRLGLPR9z770+58w58TC/GxH2CI+coBjto7QpZO6Bo7CsiExg7GhGOjFP7luEqtAjhB6jpSWwxwhZDsmpEWvVY2+eTFSErWYwH2UTGuWyMpo54qz/NXNDgjfYcE6+2Uacvcqslqq3xnPoJhVXNuSNj/C/yI+ysrlELYqbDFoeas5xvVVlvR2R9D1o+XlvitQT1dZCpRWQbs85kZtzo5nx6LAKCpkdaqfC/J0+zc3qN90wdZ9zrUZR9N+PN6zFe7KH9Kpf7Y9u4GaRVjw/sOcR90XmSkcR1KHygP5xKDJd7RzeSMBkcM3qsxk1HlhikMCgE1BO6UxHVwcy+u8AwQV6dCgN0QWI9gQkk8XRIUpas75fogmF5sUp41qd4DvSFiwglEZXKxjgo4caTyxSyCExoSasWL/Yojo1h2p2bys3c/qIG77EedgHo+3fRrwX0x/JRXz7EE4Ks6KqMspJSrsR0uyGlKOViVqVrApcXNgEdHSD66W21Km2JccrOnqNw9hz3fW0GMz3Gmx/fT29WU9rZQn2+zuxXmky+fhTT6206ThaLiCBAFAsQ+Fjfw5Qi8CQ68pCpRvZSOH4G02qBEFz86afgJ46zK1imrjosZVXmgnWeGD/N18I5tn2n20O/qvjJmc8T5SFbUfYpyhRwXk5qFcZKENp9Jzc04sbzQRoxPNb9LjFWEomUikipyogds2uc3zU7LFW/LZD5VmfN0DiKYgEb+sjE0q9J+jVJ45GM2V1LvPDw7/KBF38E//8aZ+wv3iI7fyFPAgN55VH4AXa8hvEFfsuiA4GO4MNPvcJnyweZePkee71S7wAAIABJREFUvCNn0Msr37JlCuXGplvjEvt6xxTH/pHi/oWz7Cs0aWUh7TTk49MvU5R9vtHZzWOlU7yn8BY/f/5jLPYqvNpZoKBSIpnSNz5x5rthsPIa7oJUXGum1JZcee/eXSx9YB4duEmpQRO8niK9WKe0YkirIef+6WOkJXeBpQaRgUzzsdUjfp/1NsZbW+mmqRq/jrDgr7vd9lOfeJpPek+DBa8DKgHVt8wcujPzs24LV3KZr8UuvgqfZIjBxb5DyWFhoGUiAtkF4UKvNDckgypckuecIpGSoIaGxozQCjQSlU9qUyMuVXKJb6swxMbHIPFFRoqkbft89/yrfCLzIHgbt5tBSX/ketg0hdwD6k1KOjsNxVMerbdm+I4v/2Nqxwz1F5cx61fOswglyaqRa4CPBNGqJWgKPvvKQaJTATbQbv5fksL6HV+hW1O24d2YZgt12jL1R/dyvrKbC9rtO8LAr1XvxUo3p/GZ6El+oQT7P3aM75p+jZ3BCmfTMU7Ek3zm7EEWT43zwNIZR6i9QivUjZBYt8Zzmqqy/E6DFRahBeMvScKGJVoW+D1LUvc4+OEjPDV+HIVlXRdYzwqc6ozRSiNWuwUyrYY8JnAjoCM/oxz2ef/0UYoy4Ysr+zj8td3s/oMuqpsg4hRW1kC7jvhhifNuwjCZOLL7KneDjhJGhee5r0IB2+9fdS1CugSlHdnNtwzScZhi618esg0eIgxpnnPyRYaxksQqpDBoXKFC5xVWNajo5a1Ho/8DCIRG5c+XWo9IpBgraJmMj5ZfY32+wEve3Nau8WZwpfc3zRBphjAWXQA9kVJ+OaD6Voz88stg9LU3SCnRRY8sdKFSYcni9Q1ZISBsWIyS2EKIqFa+ZcZpdJ2DQZtjf+o+m9fy4ITnsf7hBd5VPMqsctW4Q515Fs/VKZ70MGsNFy2NGiIhhxQFe539dWs8p6Umk8+Wad0riKczVp7MCOsxH9t7iE+/+RDqeIHmJ/Zz3O4nK0BSs2Rlg/UtLiIQWOl+ZjAlVwvawDJw7mvzeF2B3wY1AUf+dojIIkQm8FpTJLMZB/edpfPvDlL5whH06trdk6MYXIhsZPe9lMUuBO3vewdrBxQPf/cbvPwnj7Hz575yxee7Uwx44XmIB/fT3CV5wF+mZT3WTUhJJERC0zYxvrDUZddV7IQhsJrEKlIUKv/wuQrrgC4g0bgc0miuSuXGyhkq5xlFImWP77yNvoXYevTN3TeQWrdaiG6XwmqDXd8oQiHCNltOKiYKsWl2zXyR8Dx6kz46FARNS9AyBK2UuUWL8SVZQZHMVJDjJXgbaV56tZGf8ICAqS5blzWWE8dm+NXKh/nFhc+QWMWZbp1d9yxzKhh3IfmlnDCrh3ZKVauIztWv8ZZcfVsI6U0L+mMGVUvx/IzpWpv9hUV2Ti1wMlFYGSAM6BB0RSPLKb7vGOODdhYp3ReA1nkPjoW+LaCLkqwoSCY11dkWcd8nSxQ6jRChpuglNAWg767A7kpsWG92BgoRplJAl0PSasDyI5Jkd8z3TL7E1/fsgScfRh0+hW5csn0KgQgCx0bu9MjOntuyczUFHxNASQpa2hmPkkjxhUHnBkcJMzQu14O8xgDuAUdqYLQGJs2w8bpVL75ySPB2wlpslmFbLZcHHUAqZCECKa4dmitFVnBeExaEsYjMoloxlEJsxcMEEivf5nUP0gbDFMSVr2V03uPFuQVYcL9nVjJfWmetWgDf2/xeCIEsl5GT4yQ7xskCiXk2uOopbIlxau+v87Ef+iqpVSTG4/W1WQD+bOkBDtYX+Wszb/ATH/kGYzICILWaFM260aSWYVXIIIjzfIafM5EjYZmUbgFHM8MXOvfxueX7ObVeZz0tUjkpsKci3nz5ADtfX7r8Zn67cWluSCrWPnAv7R2S1t6M3fsX+cl7/oKGLhJbn3Npnffef5RX/vkcM//TbsRXXtp0uPBcq8CxH56hdsxS/+0tMk5CYpXEKvDz8r5GMq5SIiHoW4NGobBEYiNHoYTBWLmRVxIGhdkU1g3+Puo9SWHQVhFbn0BoAuE+C2nOHN/nWx4tnuJZuXtr1rfVkGpzqJKH6kIIbM4ZuyKUpF8TIFye1ErhqtJSYgNJWpQIY+8uvp7Rm2yT8Dy36VrDzHMpF7Nx9BOWQGiksDxadTI468USot0ZEjBFEGAP3MOZ99d48G8c4q3mONnPXH0D2xLjFKxn/NHxB7lnfI2F4jqPTZxhNSny6tIcb16cRmeSL83uxZOGpW5pmFcyVjgelDQuohMWPfI/m0urSGGHHlaSKVKt+NDOI4Q7Mj6RPUFU7rNncoWVc7sY7/XJTp29a9jEamYaxmu07h8nHpP0xwTZU01mai3mpGZHqcHx/jQX0woA94SrPFo9zf7SRZ6pvIfL9pWH9tO8t0yyq0/cCLfsPIWSdBYi+uOaUHgMyiiDmySxlm6uQBCJ9DLPZ4OvxGV0g9GE+MBgBehh+DegEJRyaZVVE9AwPRKrSO6dJjCG7PyFLVvrlmBTzs9gM7Px92tBSEwA8aQlm07o10NK5xUTLyaIxOD1DTK1Tln0bsAVPMCBYcJaioeXqVdmOJN5JFZRD7p5gUPQOzhLOFFFNbuc+6552jstWV0zvrDMR8YP8S9f+F5E8+rVvK3JObX6pMernFWGyajDB0oneEPO8+XmXsSFkKAhOXyugMgE0YrbKazMv5TjebjdY7B6clKmQCauUmAF6AiyklNUfPrBw7wnOkvjYJF3VE7w47UTPL73v6Z6tIY4c+5t750cJLjt3CSdXWXOv0ciFnrcN7/Iz97zaaZUjz/r3Mf5tM7JeJzFXhVPanZHy+wJltnprfLZ4vs2jJNUCKVoHqjQOCCZmFyjXQmvX927UShFd1JCPcYXCkmWe0QOLg/k0zEhdc8l6xPjjOMgfBtNohskWDaFgAZJgEYKkzcAb7x8gKYoFLHI6NiAluljrKR9T0StMwF3nXGyl/1s0+QyRvWVro/xIJtK+fDBN/lzcz/GD5j4pkVmG4ZJJnfH5npFjNxc+uhbVOslzuoaqfUYD7rDXOHavoDCmEfYKBJ9fJH/fu9nAajKmFnVpHJMsbZ+dR7UlhgnXQ4Q93QoR30aSYHXejt4fuUeotcLxFOGeH/M/h0XCb2Mfua5/FL+dSku/ZvEkllJX3sst0usn6lROeTzOwee4ujYW3zj4gKvrMzxyfBxxl/XqLcuoLc67zRa+s+NxNWSnsLzkJUKje+8j4vvEDz05HEeqhyi5vVo6YhOFvLp5mOuSpVLwdT9Hi8tL6CN5IlayK+e+iCHj8xz34k2VirU1ATp/nmWHyky9/0neF/9HF/4N+9mbimBdz6EOnZ2a3qyhKuS+sKdl0bQteBfhV3ZtSGRSIlEOjRMvtC5V+U8pIFIoLYbzymBukxIraJrQup+l4pMCEUAZMTWJxKGB8LzrHxPj3i8xvQ3b3953xIMEsCbjNSIl2U0wTqkiz5fLO6hWO/RFSDiFF0MaO3wqB0zeOt3ofTPaP5vVIwwMzzb2cuY12EhXGOnv8qT5eMk//gFYuMT24AFf5WScC1Qr8cL/Oba+6ie1tckaW6JcTKeZLzaoho4RQFfaFKtCNahPw4q0EwV2lT9mNS4svOgQXQjjJPDEA7ItX4MoUxJraKnA0KV0ThfxW9bmv2Itg6Hod46EUFm70w4dx1OhiyVEMUiTNbRlYjWjiLLjwpq96/wsalXmffXaOgii1kNgJ728zVuGOgoV3BY1wV6mQ8GGg9UKE49Tqei6MwqWvcaKknIs8u7qB3rInspuhRsWdJYGIs1m5/L5QMNSU4DgI1k9nD9ef7osvdlxGsaGKxB/6Q/wpkK0PjC0Lcp6Ygd9DHMT6yzVP32E+6/EmSxiCgUCFoWc0HQT0t0ZzPQAl0rkFZ9skggtUV27jK5mKsYJgDRifndQ+/ggblFPjj5JlNek5JIOJzMAFCSfXw0fk5oXE7LvL44y9x6dk2vf2t4TgXFE1NnCGRGUSY8WTrG64VZ0lVDb1qSJIpYe0gRsNovDW/IRLsPtCcHPBgxVCeQwhKplKKXDsXm7imt8VqwgBcrIi9lIVxj3/jyUGLlC9NTlCfHYW39+iSKG8XgogySnEZfpuJnD+ymub/C+Q9YJnev8qsP/BZLusLFrMJLnXt4obWbvlHU/R5VL2YucmXataxEVwd0dMjT08cwVnC0M8V99Yt86L2HefCjZ6jKmIYu8nJ3J19f2Y3+pRmiz7+G8NYgDPGLEabbu/Ssbx7WomKwyaCp16kM9K2iaz1i6xFbf8MbGqmywWai5bUQW2d4fW9DUqUoUyJhOaf1UHIltpK+ddf5LmQUXB9XkFVh3z0ktQLVt3pMPN/BvnWa3ocfpjOrWH6sjMnbRLxGH3vq7Ntz3lfDNYyIPnKcPT8a8NbPfAfv/4kj7PfXOZ0V+YXf+gFk5lp0nvzeV/jx6S8ypTqc6E4gvlElWF675ktuzWUX4AmNsYKuCTA4sTmvn3tB3sbCBlpOUhhMXi71hMGwYZQ2PVYYPOE8Kk9oyAR+d2MHngw7QymWtOR2ICHF1uWcrGtK3bRcz0O/92E6syGN/ZJ4TuON99g12WChtM4r/R00dJG2jih7fQrKkQtDmRHJjWTyUIHBKFZT5x1UvD4FlSCxvNbbwVvdCb7+uQfxW44XE1UsyX/+CCsPu9ydSgQLz0wRvnkObqNwJ8KQ5XdqHj5w2lVTc3KlRhAJTV26MLZBbohukE4wikFlL8254wpLJFP8PDXeNR4aQUX2SJFIYfnB+ef4F7MLt76wtwOX5JnUvnvp3D9FZ05hBUy+3CGrFzFPPUBaVqi+pXoiRUeSpKpQ6x2y5PbGZ90RXKWzwdu5g8M/s5Oxh5fYFSzz4S/9Q7yjBXY+20NkFhNIjnxginjSJxYJbzXHmXgtQ65cW61gy/YkKSyJ8dBGOGKeVqjYfQSV525CYyUSiyd1boSy4bFOjEwOQ4HMKlfJExalMifNKzQYgd827vkxjPsdVC73mhUgrQYEYgsKsQPy2ajg1mCtpQLn3lGgdTDlv33Pn7A/vMCUavGV7n5WsxLPt+4dJgUPls5TlK6UOughuxTGClYTp0U1HbaGmlcvNXbw6tk5DvzyUUyziRCCC3/3CRqPpPz8B3+fxCpe6tzD51bezWxr4jaNU8BTjx7hB6e/Tt+mxDm5EhytY1IpYtsntd5lYd2NYNTb0lairTNOJdknEoZACJo2JEBTEimplfjC8MOV8/zCzF2uKT6KYa7JfY6FUvT2TnD+aUVWNqiuZPJFSzIVsrbfx29bwqYlOnQWChHBXB3bbN011eZRDDsbRhQihB+Q7J7iUz/4i0RCcyKrMf97AYU/+OrwOAUs/dQjuRaYx/JahQPPn8FcJ0+6NSRM6XqkJI7lbaykl/lE6ynC+AThhsCcJ/XwZ/fdXUiTazfJ/G/9zMMTGp1rUw8MmuhLwuU2F+OQdV3MG1EFUlpMALqgHBHuNqAmxjH3ztO4r0xvWtKdsVjfYnyLLWn8YsqemZOMh13OJGOciCdIraKcU/hLXp8J2XY6VEKTWo91XXAGVmbDm3ugixSqLBfVE7QylzT3pOaDk2/ykck3+Nzv348UVcp+nx3yJXypeaGzGyksocxYfzyhN1OF529n0YrvqJ3k/mCJlrEofOqyS0U6Q/WF3jiB0EMZHGzOBr/FjaCbbz6zqklFCvyR5wlzj9Ig+Gpf0V2//uipuwa5p+3Nz5HtnOTY95XIxjL8ahcbe2RSce6DFVQP/JZFWNAB6NkJhLWuSncnuxvy3kwxco/YQQHpOq9rdS46N6KWeezn3kH1gRV+demD/PGzj7HnP6aUXzm60cKTk4aVcsb6K9396JUQfXF5U0/flbClnhO46prGDTIodlPXnCuvHAKYS0h5l2pLuyS5HIYQUljXhBi75LHOZX5VLvtqpWs8vh2IwMfsWWDtYJn1fdCfzqjMtgg9TeBlzBRbTIRd6r4rp/e0TycL6RsPP9QbwxysxAgX5moraekIX2iiXGvbjHgentB4QiERw1BWCUtddZn11tm/cIGi7FOXMa/0F1hMaxztTRPIjDGvS7HWo5vcurcoPNdgO+OvU5fQMXnOD2dIUytZ1WXqqjtMal6KAZ9JYVwb0rBZeINdDhs5qoGaZlFkKATGWlKr8n67DeG6K3madx1Gwh3heYgwJNk3Q3NXhL2nRzFMCTxN80KBoCGRffA7lsKqJotyb7IaIHsZMk7vui6HTcjzJWpmGjs3idzdYe/YCv/p8AOMf1OinvnG5t7CnKA6ME5Hu9OojrwhSZgtTTUOvCJtBXHiM35xDZnVKAbpZbkkYHiDesP/DSa1WIRwp5YYhS83lisMiH6CtRFqEOrh8jYIeyXt/JtCMh5w+L8q8sRjR3jIjymoFE9qMqPoaX/4ezOLhscUVErFd9WVnvZZTUpDzexAupvZWElBpRSUuyi+0JS9Pr7QGCHwvR5SWGpej1CmRCIjNj4nkknSXPpUCjNUEa17XYoqYdpvDi/8rUJOjJPO1Zn1GoRCcs5IijKjIjK6VhFbb0is9PPcoskZ4LChWoowRFgUepOEyqAhOBmp6LVMxLjqUpcGjSC2loYuoZQlEj1S65LyT4YxUeXmJV5vbOHqEjLlzR07aMIWgQ+5traamiTbOcXRH1XMzC+hL9RJpaFS6HPvHyZ4X3ltaHyssciHD5BMFunMhhSWJOHJRUzvDlbqzOC1b+HYkfdp+WN7sd+/QpDC8y/t4/6fPYxptVxZZNRY59rjkZ8RCM2Xz+yhcPHGHIgtM06XaoJrK7Ct9mWe0+jjMpPPqBMbIV428qYNhh6kRrmQkXxD9j2yTLGabS4xW+U8JyHELQvFqxjGXpG8mOzDTCYUyn1may1KfkI96FIShqJMqHmuQubnYv/gEr7aSsb8LkWZUFbuQzaQDinKPpFMHc0fQ0k68bYURceEeaPsCLs6z8P5VufSJd4mGWOnp+7TulChfPzWL6WdnaC1q0BJOMOZIKlgiQR0LaTWo2tCJmijMCRs0Ao0Ajlo9rVwaSpqoN10aY4qtj5d49MVrpycWElF9qjIGB+Ih7wp5Sb23AncjrKDNVgjEdJgkxShJGpsjPjgAsuPhpBmLK1W8IsJ5myR/l+UmThxluxSLW1tHYVjcNn7/TvuOYkwRI2Pke6ZJZ4KqHz9FGa9eXVVj9wQ2yxDjY3ReXo/jfvhQLnF8c/ey9xh4wzToCn9kvfUWtfhoRF0GgXG2zf2nm+NTG9uUIbtKMK6AZmtFlKDrzZyCIMc0+Dx7vtGbx157mnw/wxFZnXe72VAgg080lTRSItUvY0yujNOt7cW1e4z88xFaifGWDsQ0Z0NObE7oFyO8cY1Va+PLzWTXhtfZBSlSxJrJIHI8gkllp3+CguqTWIlSlj8/Gb3hSASCokckh0NhkW9TMdIFnV5yMSO8cE6NvXAGA0KBj0duN+xFM56jB+6ddJePFemvVMSiWwYXkkgEhutKYPw1OXQXE/cAIbL3dVrJcwNkthKOjbAN3powOrK5bh8IdBG5DlIcQeN0208b55bskaCSUCGiKlx1u4L6D3ZQZ0pIlqKwoEG4qRk+n/7yhXlaoXd2HSFtZhevJED2ipcUtyRlTLZrmkuvLtIe49m/4VpvJNsNk6j3o9SCD/vp5uoc+YjksI9TepBj4VnusgvvXhtZ8CYvDVNIhsewbfSOF0LRkHJczuyM15mSD7cNJMuzx8NdfBH5t3F2QZp0XoWE3mYtZAXVxb44OwRlDB09dW7m28WotmmcKhP4aiH9RS2GGICj2ZpJ4uTPklF0p0RZCVLOmaglOGFGZ6vkdLtEp40eMpgLKRa0e2GmExiM4noKkQqULFAJq5Fx++CTEDFFpmBSu2wvWOQRxt8kIWBcE07sl5q2H36PDRufSSWMHYYhWksLVPAqC5SuPxYSSRD5dEA44wmLn80COfc5BVXNY2tR2q9y0iYSrh+Oo2hY0I6efvLwNDt9BpEwhIJD4MdNoHftRjhMqn5WQ7/nWl0wWLXQsRUAk2PmV8I8c+cvrqOtrVYIUiqgmxNoZLk1oymcJUz95zufbdZhnrgAKe/e5LOQzG75ldYapUBKAQxT88c5pHSaT5x/xO88fJu9v2TpY0q4ajAXpZitcabnqS7d4L3v/s1nnnpfo79x/sZP/oWmRDIMHRqDVm22bCFIXJ8DE8aGrpI/Q1B5WTuUORyxVfDlhmnK01TAbcjBOrmdvVLQ8TMymEI6NQxJbIrWe9Fl8t33DZZ2mL7CcQxNu67IYeAVBIVBATTE5hakeJ8iaQi6U0q0pJCFwIyn6Hu+fA8ckpQ0BZugEPqkqHOIFlUalB9g9fViNSg+hoygxjsnkJgfbXxswBhQa20oJ9g49hpCd3Gbqu6GV7HCcwZGPKbRlGULj82qDJuau61Ej9vBt48ZPNy+CKDPIdmkC6stR4SM+Q7DY6/WbrCbSEPXYTnge8jhEC3WjdkKLzZGZId45gdMWQSYgUdj3BZoV54g+x6w0EF6FBg/Nvpk8zfKyk2Sd9mtQLt/Snf8+Ar/O2JL/NCvJtuTnTdH15g3ltn9p51/lX8XaiJcScFc+n55l5iuneO5i6fyaCN1/AYe+EippnLxki5uadwcFa+hy1GSKFpmQKlixpvtXNDirVbE9Zpy4W4Ss2PqXo9fJENXXHrQc2P6WQB2aD6htlkgEYJmI6gaYcETWPFsAdNCVcJsgKChqC9UkTusTm5b9BQfHsfaJtpTLuDLBWQE+MbMrGD3SDTqOUmpVMXKSsJUeiUOEc0a1Cj5VbpjEoU5MYlNzZKYD0nU2J8iQ4ltqCIw9DpSocCowDhptIMpIt14Nbod8pI7Tye4sUU1c3gyvp014X/xmmmknkapsBO0txouApafzAhJSdQxni0TCE3SBubTiA0Ua79RJ6nGjVUMDIhOD9uMKTTJ8NHU5IGBcRWk1r/W1qpU2M1RLGInqyRjkXoSFL44hubNZuuBKk4/bf20rnHAH1KtR7lmT7lf1VFPfs6ZjTHNDKocjA510p3/ZMqpEXJLZMmrL1iBSyt+hzYe56P1l7jkUChOZkz/Q0ruszr/Tn2B4s8OX2K57/zccZebsDLb1z2PMLzOfpTgvt2nOT/fe0xJg45Zvjgc35V5dZSiWS2AjQ4EU9Sef7sDWuQ3b5xyqVdR41NQ5fIssFuD37OUZKX+HCDsvsVT+yS/216nBJ4PZDtjQSxRpKnrJxBuA1YrbFx/qHqX/IW5dNP3dBBg+iLoQFCSafL4w2E8a37n5JYLz+n0R4lY0G5cE3lEhleT2OUm+phldhcfRQuxHPMcDvsYA+WOsj2rVd47OwU7d0l18BrLYlV9K0iJWNSpRRtNpTuHShXqpFhmAMvJ8HpjQ+S+hv0gpweMaAVWDk8Nrb+SL8dKCGGgnOwoUt+RyHd/LWsViCeLjgPRjhiquh5V1UflVGEqFTozlmChQ61Uo/FU+OIw3XGT5+5PPmdYyg5MvhdQFa2ZIVb31hFGKD2HcCUQuSpRczKqguzPMF8aZ266iIRtExEQ5domYjVrOzmSQpD3e+y+AGN361RfPmS5/6Oh1h5pMLju49Q9BLOPbeb2rHejXl5niIruIndPR1Adu1+uk2H3sL7sAnC94beyiCftK4LpGnOJpWWQGabeEyDHdETGgQkxrtMjcDL8xNZ3hA84A6Re0dBy5I0xTCsy4zCSotRWxAKGI2J9XBixqb1hqGbGJN7VNYYROBjA99NkPFGDBFglTNmVgiEtQide5ROwCp/PdykmdRsMjJW5R6Wr1z8ri1WCWfgtEUmGaLXxywukXVunUXdvbfK2gFJRSZooGvCvJcuZUEVads+F/RGInzo8Qw8ICuHYV4HP+8E2Lj5BoZJ5TSDRDhZ3tj6xMb16zl+k3s/EmtHKnx33jgJKUjmqvTHfbpTEhWD37PDa3014yTKJZgeR+7o8vj8GUpeQvsvppn7xSsnv4FNWkiQ5xElZLWMtHTrwxx0wWPtsXF6k5JZQHR72E4XHUr2Fpeoij4GjxVd5kwywan+eM7PcyH6pN/i59//+/zcsR+ieIl65eK7qkz/F6f48bkv8Udrj7Hw6XPY5dUbGyaiFFnBeeIdHWA3DfqQ10zD3L7nlMvQOjKkC8neaM+hmy45Z8VGg+eA+zPEoHl0hMA5+H82UnYbCM+lVoG0ZEVFtGrQwUb1ynyL8hM2SbBphuhtSGKI3GNCOC9KjnpH1wszhzPD8iRmOvKxloP3Z+M5Rp/Nao21FhPfHg+oscdDP9ymLjMMEBufpomomASjLJ9s7+Jf/uF/ybuePsS/WPg0oYDUwrrxh15TnIdhA05WkOenBtC5CG9JpBRFSl3GlERGRQp+r/kALR3xdLROag0tu7k9JvQz1Mw0prF+S2OtrwerNcErJwh9n+ogTM8y9Ora5utx6fv2kf2c/6jmew98k+V+mef/w6PMv3SNIRtXakmxTg2zPtskPjt+y2swvuDiO+HHP/LnfOn79nKuuYNe7LNz8jxPlY5QlBmLOqWlCxRln3eW32LBW2NKdfCFwcdSkoL/+ellzqdPMffFFrrkc+LjAbsePcM/uOcZPtN4mM8eO8C+5RPo9o1thqYY0RuXeFaw0i+xqU/VmmvuPVuTEB+kV3KG8Eq/hOgPwphB79zVQ61BI7B7LrnJ0AyqeiLvv0NadKjwega/MyIPO8CdtlF5hWaUxGbvwh7Nm0FahrmxJhKGkiUGORwJdbg3y9QLlhf3LXB6psp9fpNACuKRrvvUqg1ly5FrOQqNHMovS8AXTl/w1c48a0kROf6K69HcxPfSFIMUOzOO7PfRd8A4YS16ZdVJmpSLEAYgBdIYbLd3Wd5J+AFqcpz1vZIPPvwac8E6hxqzTL3YwT+1fNMTbq2CyXKHk9HYbawBTMGJ9BGzAAAgAElEQVTww7Xn+VD5dVZ0maWsSkX1mFIdtBWs59cjkik7/RV2e21mVIi2FiUEHooPzh/hk+8ostwukxUEB544wXdNv8Z9/kX+zfJfx5wpOpLojfb+eU77XxhJJw0IzY0raGwhCXOjafdkY4xgfdAwu0FCBIhUOsw/bfTZXdLGkv9tENKNPlb4hqTsU11KiIKRcDLv69vGzcMq19+3qAN8YZj114cCcgbDG60Zan/wIkn1cf5O68f4J+/8Mx6OTjMlu0wpS01GfDPRaCRVsWE8tBBDDpPLRynGpaYiPVJreK5f45nWQT7/ucdQsaD9E3+Ij/OuN6q/mkcmzvHljzzBwp9auIMa8emT99P5Z+vsrKxR8hKe+9TDjL+RUfiD5zblSeSuBY7+2CwPvv8Iv7Dwn/jPXv4xGi9PsvfFl8h6NydfY6XEKMFMocXx6NZDWG+ly+wXJL/yzvfzgeob7PeX2e8vu55NBCezMRq6SEX1qMsuu702sRW8mWrqMkPiGnT/2dSX+OdTX6bztKFrBRd0CYVhUZfpfHqWvc+3N3ribkCF1UQeaRlMpljrFpi1Ix7XdY7dOuM0YhnanYigl5+4gb7xaCRORK2fbbzkIJzTVgyP3sR9smJIOg69jNnI7WDaBxVn+K0No2bYkP/dxq2hKDICYS6bmmKsxPT7jB3qgS3wS0vfjS4Z8Cwi0AhlMT13XUWYbyJiRLjOCvezFohAM0wltX28dcn0NwxpURJbiy82KnsAfZsyHzZo7dFkteim9x8RBng7doMxkGnIMke/0AbS1H23xoXIUlAN+jxYOc++aJEvPryPFVFg159XHDkyS/F27aR9/xTVx1a4t7TCl+IZ1r85yeQreXh9s1QAybCgdFtcU2vxe4Zj7SneVz1MXRrO6WAYHjsqiGtEj0RKx2xEKHqE2a/yr0AIUuu03psm4lw2RnFR451dJbvOGoVSIFz/nJUCE4BJPRLrzhMhnJqs1t8antMosmZAOfeEhRH0TMBKr0inH9DrOvXKjWGqAkxO3hFssIHFRmFLeZooTGkUXaFVhwLZjvHNiJZxTuDcNk63BoNgQllkThuQl/LHAPmlF5n4Ekxc4XgZRa5QUK1srkgOPshZhk1TzHrziglm+dgDxFZQk4O2lQyNpGUy9oWL7H3gHMnYLDc70kEXfFoPTyNTi9fTyL7GW+kgkxTb7SGS1OURezHCWnqZa4B+X+EkP/3oF/jN8D2IiTHkyhq6rekcnGHpUY//44Hf5bneHn77/FPs+pMe8tnXLxMhvBnE2kPo23P9VWw52RgjnvWpyYBXkhIpikikTKnO0EPqW7igiy7/JzeuhQEaBmIraZlgmD+8kNU41JundCYmO31m4wWvYqSE57mqdZpgfYkuWEzioRPpctRCIsIQe50ewi2gEuShVc5P0khEXyKTvCqlLCXVdzK0WpEFGaGfEQUp2ki0EWRaDfNKwwUKm48As8Sph6c0db9HVEroj7mmW5FqQulkgUOZYT3rKmV326yzuxy7/qTF6skdnPgfA+7zMyZkh0hkbl6dVWQ3YPFNv4/IMkRyOddmWKEx9uqtGZnhK717eSw8zbhKWdJumkdFepxMJjl6ZI79jet3sl92Xj40d3kkFTChh/HAqqLbCzNQPYFKXZ9yUgWzWuXf66f4ZPg4h0/O4l/0WXuyTDw+T39M0LsvplZv8VtL7+ez33yQya8rpo+/RaY1wg/c+m7CSIlU47cN33z9Xmqnb+9zWzi2QvtTM3zmJx/i+8srPBw4pUklBIm19C00TEBsPRqmSCoSYptSlCnKWqK8cOXjmP6+0EzIPj975PtY+8Isu8/lTPfrhXNy4x60UmB8i+4pRNc1WgvpCkfiEsLopdgyJcxh+wnuQg/aIWQqWE1KpFqRaemGZfr5GCicbbuSYRrFgDPVNx7GCGcPjSutuxK1ozAg8xL9Nm4K4o0TTHbm+MP1J/ho5TXXfJvn8c7rhGY/uj45cDBs8hYnEgutea27wJTXZEYtAy4p3zAZhzszFE96qFbzGmM6r3ZeOYHVh6wAJjLYQh5aGoFIJCLNbyRlsa2AxZ7PorAE53xUT9DaKejOG+Rsj70zK0gsXzmzm+ohj6mvL2PWGq7yJD2EvUkV1sxRSETiIdPbieuA1QYTrxT52und/NHEC1SlR1322OFlKFwE6aqrcsg9G3jIEjcrT+J6Y6PcaiRWcm6xzq5vJNhme+O1bnDqj/ElupAL72U3d3PePs8pV8frZT49HVzGV6ofMTxTfwgTGYQWeE1JR5Zoi42GYSwMmtqHSYVBXiJv/0gV/Hk7wj8eMX5II5pt7HgtJwc6WWDYDutuBabVQhzp89zff4xPffh9/PLf+3VX4UHw3xz7fs6/OMseTlz9CfIcwpCgCkOKCbCZcHgJz2f4FN2YT7zyOI37i3xo4fPMqDbndIX/7szHefYvDrL33750S1Ii/mqP+d94EZRCCJFP5JWIMMDWKmQTJdKKT1JVLu81oYbpgf6EJZ7TFObaTBX6FPyUY2/MUzir2PHZFt75U2Rnz2/IkNxCJVEkKcaX/NOPfpp/Xf4upn7tpp9iCL26hlhvcu9PVfn16l9j6f3zrD0IP/KdX+Dj1Rd5LPC4BzCOzQaAHGaZLseJrMu/W/4gpVcjwj9/Hn0dcbgBrNYbdmHS48EHTnKuWWXNVK/Y4nI13J5xEq4XyaoNOoDCYAPrWLZA5a0eRhXRoUIY10827D+zDJOAg79d6vmI3DgZBf3TBYoXDeWTHWynC+O1TY+1yroxz9u4adgsRZ1cJFopO/lckeELiFSGVRvKhwPpjMuONxbQiCu4rvYqhmrwnBgNSYpcCji9s47BUJIGnUm+cnQP9ZNgbpFkao25YmuF8DxkHOPHfbxiRFgKMaFHspSTa6WgX3cTeOOzNZoerCsYP20pLmd4Z1dcX9ltyunaTpdgNeZPlx9Ardzmhzf3XvXKKmK9yfjrVby4xG977+c/TLybYqVPrdijEvRZKK4zEzaZ9NtMek0imVKVG8b/Ylbha619/Okzj7NwKLu8NeZaXpPWw2sermleO7qAt+xTWRIuv2csIk03fy6ugNsP6woR2neyFgMSpg0NOszlQL/6EvWvXuc5bhLuNgBlZ3N537w1JtToyLvt9pW/krAWvXgRv72HouxTk4Ki8NlbXuKl8i4AZOAjguDyhtiR7vybCmmEcMbOCmyaUjwnObOnTtdqikKQohj7UsjEa1swXeYS2Cxzo+tHqAkCNiXco8uO2sBWTZXTyysoIXnlK/uoH9miJ8Wtj+deofIcVH4v/6NUqIP76M+O8c0D99CZh2RSE032KEZ9pkpuA9BWcnqljjlaZt//8Nzlm9H15HxHHl88usL8Z6aonGijVlpkvdhVR7Psuob99oyTtZjGOtFKxvHFSc41qyhhKR73KZ+781Kjohvza6+8j3q1SyXsE5wNKJ+xcI1Bfdu4Nione/zNz/60K/trQfG0Yu4tM9yV7wisBW0IWpa1bkDDQGolF9K6C/3vpKb2XQDTbLL3E21k48a69W/9hTScPk+0UmD2TAVTDjGRj44CjBcSexuRyEJs8Ndbt60tZS8sUX82xbY7jmoxCIFv4LN0256T7fdRvYy0HZAlCqxgbNUSro8s6iojZW4b/QRztsByotDjgqApCNf15v6dbdwUvLUu1ddK+B2L17MUlhLCiz2X/jP2pho3bwRD194aVB9Mooitomt8Wib6VrTWve2w/T4898qdNUw5dLMJzSZcWASct3g1I7AVb71pta6v7HAViJu5kYUQS8DJW3qltx+7rLVT13vQX4U1wvY6v03wV2GdV13jTRmnbWxjG9v4VmE7c7yNbWzjrsS2cdrGNrZxV2LbOG1jG9u4K7FtnLaxjW3cldg2TtvYxjbuSmwbp21sYxt3JbaN0za2sY27EjfFEA9EaCNKN/ZgAUJK0vECumQpRzGJ8UhbvhtH3LqGEPwlMPUSOoKgmhCnHiKWhGvZxvimG0CLteUbIbTd1BpvEsL3sb5HWpFYCVIzVF7wegaRZm5A5i3iRtcId3addxrb67wc367rjOmQ2P4VtVRuyjhFlHiX+Mg1HyM8D/Ouh+iPBXSnFCuPGyb3rvLwxHnW04gXT+0keKNA9bjBiy0qsXgdjdQGtMUEChNI4rEN6YrVhwTpTMJ77zvGieY45xbr1L8aUj2ZUXzueN63c205jc/a378h9uyNrPFWYd79GEuPF1l/LMEvJWR9z8nXGkF41idcE4y/nmB8SVaUGOUMWLSSEl5oY948lkub2ivq6dzoGuHOrvNOY3udl+PbdZ1ft5+76v+2XKZXFAoc/Zsh0/tW+LHdXx+OhXq1s8COwho/+54/4uy7apxOJniueS9nOnWOnp/CxAoSiayklCtd/tbe5ymrmJLsM++tYZB8vbOXJ6qnqO3q8sIDu/na+V0EjR34p5cxZ85u9VK2HBffWeTRH3iVn575PLu8HpGQFKVPKJxUxqGky9944e8xXW3z9ORJJv0Wa2mJ33/jcYpfnWDu1DknFWN1rotzuS7SNrbxlwVbapy8Pbvp7xrHm+5R8FM+s/QgZa9PQaW0spBGWuDT4lFCmRKJjPvL55kJmyhpOLU2RmetwMJ0g9lSk9QqLqZVujrgi8kBDIJAZmRmnI52M/GmSh2O/sAYU8/vpPZ/34XG6RLvJotgb3GZhinia01RZBidARnjUhMJ+EcHn2HWX2e3t4IUltgq7n/8HL9S/RDn1MMs/PFF9JtHkYGP1eaKI6i3sY2/DNhS45TO1GjuCqmU2njScGa9RjlMKPqJu9G0z+tmjrlondlwnXHVoSgTmuUCzX5E3AuYLTWZL6yTWsVaWmQ1KXGmXcdYwY5yg2YasRYXGIt6lPw+9zx0notLC9T9wI2suVs8CZEP3RyZ7WY9mAsaJFbRMAVSkQxHJklalKTh4+U3qUhFTRbomgSwPBkuc2bny/zGgQ8w++VCPvpc3T1r3cY27gC21Dit7y+y8phFtop04xClDLEyKGkIVUYv81ntFWkkBc4HteEsOoD58jqThQ6B1KwmRS7GFdpZSCsJmS01CWRGNwuGk4XPtypYK6hGffoThvT9DxO+fAq9tLSVS7p1CCfgbg1OVC0ISKqWjxQPs2JCjJVEIqMm+kTCsG58lrIiJ5JJ9gYXeSjo0R0R4/rU6UfY/zsJ6thZtLWg9WYp3G1s4y8ZttQ4ZQUw1QyMQGcSKQ3WCrSR2Fxb2gLaSLpZMJzqEchsOFiznYZIYfCkwViBJ53QvrGSWPukRrmx5sYN3DRWoIuG9kJAdKwAd4ltAjbGlQcBamEOXdGMS0nLGJJ8sEMgjJt8K+wmAR0fhS8MsbW8lWmanYiJTgKehywW88T4Tcv9b2Mb3zbY2rCuJCjWesTdAGPcjamNIDUS30qksESeU8CLtUcjLmCsIFB604BNJSyThTaBzPCEIdYemVU04gKWfABhLgsMoGoJawcLjL9c3srl3Bbc+Bs3fE/Wa6y9c5b6XIMxVeS0jjFG4ot0SDTzMVRFn2mvxbiMKcoivlUYE/PH7Yfor0foYoI3XkMGAdmZsy6sG2hwb2Mbf8mwdcZJuMmexTAl7gUYLejHAUKApzbGzyjpvCIpLKUgwVhBqDJ0PnpcDbylXI9cCo0nDMZmUIDMSPranbbOvacoSulMe+iCf9dMJLeDsMtaRCGiuVtSDxOOpW1OZ5MoLPv8GF84rfVWHqLt9BpM5AMF1k3M8TTid44+iegqVh4uUjkTEC5FiPMX7pxs7ja2cRdgi+bWCYTnY5QbGy4EWCPQWqADic3DLwR40uDl87JC5W6uQGoyK0m1Qkg7NEyeMHhS4w9mawlLnBumfuaBNGgrCP0UUxPY4O4xTptE/wOfeNpQDWPO6jJLWZVIpCjWUPkZp/n3KWUIhaRrErrWclFXaJ6r4PcEvSmB31aoXoAvvg2Gh+ZFAZd7s9se3jZuCltinGS5jNgxS1a2zvj4Gan1sD2Pfs9HZxIlDTKwTAVtstxLKgpXBpfC4llHFRiEd7H2iVSKN6KsPMgxGSvopR6pdl5HMUiZmVilF80TbMWCthimVmTnI+d51/gJjJWcScZZTst8fv1+dkWrPFA4S2x8KqrHg0GH15KML3b38/Hya9zjrYGyFBYFs19t4528iFlvYgYUAnv33vDejgVstUR/pkx4eg195PjbfUrb+DbClhgn4XnoSoSVbEz9zefS2b4izSTdgo8QlkZSGBoYT24kdAfHGetGBRkEWR62SWExVhBrn04a0EkC+qmP1hKlXOJcG4lVws1gv4XhhluKS/hNxpccqF1kR7BKJFJ2BKuUVczpeJxIbrSrxCbgUNrgVDZObD1eSaY52p+leMKnckrjnV/DrDWuy4Z/u6HGxhBjNdaenCUpu+tat3WCpRq62b5hD0p4HmrHPGiDjfuYZvPtv7bb+JZha4xTFNKfjLCeJc7cyHBrAQGyo5B9QceP6Pd9mt3IefvCUgoThLBkWuXpGTH0hjxp8D2Nr6Kh8UkzRbsTkbV8RKSRnkUVEvqpx5IuUYokanyM7MLi28oBEkpttJkAJlC8t3qEA8EiM6rH4xVHBzha2jDO57IaF7Iav3zho8xF6xyIzvNrZz7IoePzHPxfX0U3m1s2K+1OQ9+3k6XHSuz7kcPsKq7yiZefIK1EzPZ2oV4/4SaA3ABkucTiRxdQCZTOJRRePeOu7Tb+SmBrck6eR1JRiEyw3i4gpaVQjtm1c41XT87DhRDT9kkCBeUEKS1KGZLMGaLMuLyUtQKt8ypfXoZX0pBpRaYlrU5EGKZMjrXw83zTSrOEVe5xcV1Smp9ALC2/rcni4bgjIVD797C2O2RvcJGmiTiczLA3uEhJpEgBXePTNBF12UV6hpLXZ0ewyrsLJ/ls9ADCv4vpAiMeohobw+6c4exHx+nOG5h3gzCXkzIzMw2W3lnh6K4i9/3GHLy22TiJMERWynTftYesIMlCgfEExofunCBYh2hNgbfl3Va3BDU5Qfu9ewkbKd5aj/+fvTeNsSzNy/x+73K2u8e+5L5UVlXW3l1FVXVXd9NNAxqYAYPHGhgh0BhGGku2PB8t2ZItY3vkMdZ8QMbDWCCDrDED2ECDaMRAN0M33TTdRXVtXZVVWblvkbHf9Wzv+/rDe+JGZG1dWRWRmbjjkVIRcfPcc897zznP+a/PX/ZGuECTHu6gCoscleilDVy3j1lf/677k/U6/R96GFk4ovUcfeYyZmX1Dqzk3sYukZOiqAmEgyLV1JoZs80+f3/2JS6uTzBYDpEjibMCVxM44XDOkxJAUfjDcA6s3XILBUZXLl61rRlqdC3j0alrAAzKyJNT5RLmTUE2nRDdxjz2PcGW2yIV6eEOgwXBohryomnx+miR1IVMqT4ngmW6NmbZtJhVfaQaEsmS+WCTU0Gdhs4R0kESI0bpvdeqslUBLwSi1WBwuEX9B5d4oLnBYrLJpcEEq1mdE+1VHp68QXCf4cwXHiI4o71lKSQi0MhOGzczyc0nAsqGo0wcVBleNZCokcCpu7zWLUgFnRarpzW1JUX9uibcjDCxZvXhCJU6wr6jpSU6jpB5Xk23ddu1aW774SWUQnbaLD8hkZmgfl0xs9SCe5ichNb+Aez2trdzV8jJhQF5R1DWDXE956dOPs+p+DqPR9d45eAB/gMnGbzZgQIm2wNGeUBe6DGpSGmxlfWklENKi1KWOCiphzn1IKeXR/SXGuSFZj2v8eMz32ZK9WkFKdeGbZaGDVYWHSoPmPuKviduZKEU15+LCB5fJxYQiJJAGq7mE9wULVZNg8wGpE5Tlxm5U6zldQY2wrguf/Kd08Rvxpz9L08w/ZKj+Vt/fbeX9E5UGUPbrjOaUnx2/i02ioRz/WmudluE2vDMgfMcDFeZ1T3++fc/wdTUx+k8v0R2ZJK1+yPWHzfUZgYc6lzi6mab8kqDqeclzcs5ydll3CiFUYoZfnCZnV2BrBhxx8PGfuIRykhx+IsbyI0+brPH4Ln76C8qescNwghkIbj5gwohm8jrM0Srgvp1R+vciGC5j3nzPKpRh7lpuo/OMFhQZIs5lJKiqZh6+d6p13s7VKfN6JlTBN0cvTrAXb6G3aPzsjsxpywnWbaUiWLk6mweT3gjXeCLa49yvjvlg9WBw4a+lQW2CHfLStpmXylt5S1sB8hDWVIPBLJRkA1CvvnGMSbCISdrNxmZgMvdNuvnJ2jeEMTrFuy94QoJJUnnSx6aWmboQOGY0H4efSAMLTkiFzmRCwmEd0MTVYx/19cjGpcda484BnOSxrOPod+4jFldu2trei+IUY7KHYEwJKqgFaRccW3yUrFa1IllQSgM5YmU5SDGRPOkE5LhgoPIUBSKt5amscsx9cuS1qWM6PIG5aWrnhzeRSLmjqxLKWSjjmi3sO0G6ycSnIIWoEOFbNQYTSnylhhL/Djl0KFBB4ZRI0AYhTACYWOiiZC6VthaSD4RUcZVOYgVoC1lx8co74kiESGQtRpydhoz1UTkJWUzZvV0QLyqaTQC4pU1+IDkJLRGHToA1uIGQ+xmD97HhtgVciovXKJ98TJTBxYpFyf5g9lHKFcTHvjf1uk9N8XoQYebygmjklEeUJSKslRobRACrJUI4dDajq/Bsty246fiAZPRgKeOXeTbf34/J/7NJf7qZ57gT4+WxFMj9Dea3P+rL+NGI5wx2LsVDH/Hk1Zy/6mr/PTc3/BWMUFNZjyTvMWMGhEJqAtJ6ixpdbg9pzmeLDOl+gDM/K2j/edvYKL72TxlyX90k/JfHiP4sztITu83Sn5H1s28eY7mdJMLwylON67zVOM8VwdtVgc1/uzy/TTjjOlkwC993+8w/+wmXxme4oXNw7yyPE/yFxO0Lmqa31mFjWuYm8vg3HYRiVQ+yXAnGrt3kqA1yEYde/8Rlj/WYPOUw05nCOVYGWhEVkNmEmFAOEe4phAWhAUzqmGA2sAXJ6fTjv5JC9KhNybBgnCw8FeG5sWUwWJCMWmozw4ok5hgb1f5gSCTBHH0IFd+aIrs2R75zTYusDz98Ov87aVD9F6vc+x8Bz7gw1I2m1z+iQPokaN1saT+4lW48t7b716E0Tns+gbaGKZ//yjBwMLSCrXlCdJpBYcz6nHOKA+wVrzjySCEG2fx/E/flzfMQlITEEqDxCFKgV3fYOGvZsleDyhqdZoXh56Y7oWK6arfTU1NwvwMp9tvcl94kwvFJKEzBDIndYqBE5w1NSbVkBlZctlErJkGRRVcUUKy+g8HrJ1+gNlvlXRPKP7Tw1/l37T/472/cHfeoLdBBnq5yze//CBLzzT5H4+/wo8vvshK0aRvInKrGZmArok5l83yK1/5AZIrmtZFS+vcAL06gJuruNHonZ/prC/n2iti2lnMuiMepE6fYnikxfVPatRIkCwJ8lGEDcHVLTgB0mEi55VfS4GTgHRgBbIEWQqcdIx73KVDGIhXBI0rlvjmCKSgbFhUs6CZpOTtBvHMDNzcm+W+J6Ty122nyeD+abK2ZDQr6R+xxMqiJjOcE3zz3BHsIMAljvTYJDFgzp5/1/OjOm2YmeLmZ+boHYPF77vK9fUWgzcaHFuauEPkBNjBADsY0PytGwAYIF7OiGcSgiSjE4+4NJoYu2ywLeoInpi2ijARrrKwfOW4Dbx7J4z/HPmVF0iAZGs/u7mQD4udjbgTbUaHWtxfu8EhZXk9D4idr2ny5BTyanaAh6KrHFJ91kyDq8UEhVMYJwHDbz716/zbE8/ynT99CKjxs60VfrktadzpWq6qAwApEEJgs+zdiWJljYNfmuStQzNMncz48cYrFAiulU3eyOd5czTHmmnw7d5B5r8iab+2iXvhVQDetfLp7RXwYytu99blf+5MoPggr1CK3qkO66cUR5+5xFt/e4iplyHoQlkTDBckwvnNiR1OOVzgQDlEaHCZwpQCUQhvTe1YoMwFybKj87eefcxUA1c31OoprTBjvSFhsn1HyUlsNZTPTZEeaHDzSU02ZRCdEUr7mHCrOWSUhZgzTawGkzh6BwKwE0SXr73TOBASMTnB8Pgk3c8P+OkHn+dnO9/gz4en+D9qz5F9pfW+x7S7udl3aVcIbmzSCRVXerVbii5FFfQWAozZKiXw53pnUy9OMChCIl1yqLmOfXsJuNyRxrnb7RE7btjRiSmWngyY1V0GztIzCQqHEnasDnogWGdGDWjIiDezea7kExyOVollwdDmnA4sPzXxDX76nzzBI6fO+x3/5CoXjn6M47/06geuF/rQ6xACNTuDPTzH9U806R+zuImcB/+7Vcrz71SQNd0+0TffpPXQw/z8/M/wT498haPBMqkLxsW0fROTqIIbn7I40aT9wo4dSIWMI0QtQSQJBBoXhz4+04nQgxJZGPjm7+zuWquMo2o24eA8LK3g0oyrP16CLOj/6kEWM4PKHKK0qNQQXl0fv9/p7WvQxRG2EbL6cEQ6LRguWFyjJG5lhFaQDwNqNxx6ZClnmuTtkHRSoZMULS2jMiBvCbIDbXj9NtfybhbgB3iPPrBIemqe8z+pcYGFwEFWIkqBWIogF5AL0qyJTRzuviHOSlwuWX1Cs3F/SO2Rj6FGDp16InYKBguCvO0op0pkofh/zz3Gpx99nSnV53MH3uCvmk+/76HteeGIGGXofo41CuPEjlKBHdtURLTTihq/LhylleRG+Zta3BM20vtDCIazAaPjOakNWDG+NaUus6qnzmEQxKIgEJZAKDZNQq+MqScZYWVHBEL5os37L/Bk5xLrZsjTcxf501MxLMyiYM8ISh9YxE61GB5s0l/Q9E4YJo6tc6yzxsrpY9SFoDx34dY3WYPpdmldLLny0jy/Ez3J/a0l5oIuPROTWU0gAhJVMH90lY2lOTpPPozsjhClwSURNtaUtRCTKF/rFArKWJI3BJKmvNcAACAASURBVNGmRqe7e/5lrYaIIggDmJ5g4+EJGp0EvTHCpQqZSupXRgwXY/oLiok3c3Q3fVdyBl+zpRp1Wq3j6JEGJFknILUCVS/8wzgWZB2JUwlpR5B3BHGSo5WlsH74hQ13uRxGKoQUCK0hCBBRCEohtKY4PE3/QEgwM8AaiSk98UDllioo6g4twVYxBecAK3Chw0jLUEpkDir1lqJTkM4ZXM0QNTLyNGA0CnlxdARZPaC3ZJTeC7tLTtVTaKd347IMNchxroaxElMqpLJobcbWkpQ7yckh8Fm6LcsqKzQ9GTG0IcK+bUF321qCbdXLKqsko4i1R+CXnvttXksXuVG2+ZHGq9SEIxaCnnWkTpK6wBOVs1xOJ1hOG8xPbNKWGUoErJkMJeDfnfgTrpQjvpLO8vnOq0w92OePvv/TTL7aQH712x/tuLfwtqEJN370CGvPFHz8vvMc1jkbeY2DtQ06esjLP7uIfHWRw7948V2f0LU//janvhxz7Sce4o3DJ7CP9JhuDTjaWkNFlgPROr9x+j/wx4cf4nef+Rg3Xp4j6AmyGYMLHCI2iK1kifEDIJwRBDcDdF/AFz78km+Bc4hjh0gPNMlbis2jiqkfusab52dILtV54F9vIEY5+UKLq593/Pwnvswf/i+fZXJlOP7+fKB+251xWYbJMoJ/v05bSNqAPrRIfniaC/8gQSymdB/N/CWjLJ3OgJk4ox7kDIqQzVGM+LDJ5veyloRA1muIOIbpDqYZM1pIKOqSIhEMDgrKmqNcjYmXNI1V6B63uNDhBCT3b/BjR1/h0miCy/0JLr+4gLQC6SorSUI5XYwHzQltEcLhCgUWss0YEfn79NfOPEs9zplv9Cr3+b1JeG8sJ6m2C7SE73lzzms7WSuQyhNSWVbsLE0Vh9oOlFfdL4S6xDlBXmpe35xDvz1reZdSzLfAbQUfQM/NsvHpY8T3bfJIdJ1rVRzpzWKKo3qdpoaOdAycfUegxTqJqb4BicQAxkHmCiwQCkPuoKFSusfByYRp9zj6jasfTgH0HYHn7b+DgUNuah5oLtFUKa/2F+gWMRt5wlOHLvG19Dh6bhbb7b2jzsWVBXYE9aWSohnQmegyV+sxGQ54rTvPK3aRi6NpXlpb5OqVSWprgqAP4abCaTCh3qoy8U9h4SWOozXQw90512pulvSRQ6wfDMk6wu9XwqVrU4jEMDruyKcSVBqyeSxClJY/uXaa+lKBXO1yC3+82zW4Q5WiWJzkxjMJZj6l3fSV81pZkqBgsbFJXeWMjHd9+zJCpo5g80PW6QmBmppE1GvYdh0XakysKWoaE0nypsRqgQnBhD6TaLUP0kcrChs4BgfAaYcoBHooGA0j1oo6iSp8W1lXjgnUJwBArHhVEhe4rVsBKf25QzgYKpyFzMYMm4ZsVtMALzf9HtgTcvIxJ189LJTEKgXOlwy4yvKRVZW4EKCETxuPizIrV04rQ6gNeanIS8XVzfY7yQnuIYICOz3B9U85fuzwGxzTMW01YKVscS6boyOHHCEnlhpsMY497YSpFEElAouXUunZcuwGpi7wJvHBlK6MgYT5lTbssjxxvGGIVgIOR6tMqT4X0yne6k6zmcX8Fye+zEpax85OIMpyu85ly4KsEK2MCOY0J1srdIIhgTBcXJtgNIh8TdNaRHJDEa/6qup4zXiXJqhcfwvCOH9TNSTxhkFlH8KseLfrY3qCpaci8gmHSQz1i/4pH14KKU+kzM+uMZqeReWawQGBzARXL0zz4NVNzPLKeDfuu0klC8HgUEL+ZJ+DnR6NMCOUhprO6YQj5sKu/17SSVKj0cpgU9Brg9tfZhAiAg2zU+STNYaLMUUiKBrCq9QGYGL/veoRWIV/CEiQhSBehcFBh5nLoBcgRwI9gHQQcGXYYSbqU1pJ0N8O8JvYE1TQqwgvFqiqL90kDqvBaU9yMvefm00IenFCXQrPYO+BPY85uXaTdL4GlF6JIDCoqjUhCksc220sOxEov/q8VLdk98oE9KGDmJvLPmMl5N2Vq33bhZ/P1vnHn/oazzbe5Eo54oHoOsNgjVXT4K18llezkE8mb9GWlqO6z4bVfCXVKOFohSOGNqLYMYhZ4ZhWCZgRsSj47ZtP8c2LRzjxywa1uoQYptgPU5RZFdiJKMIOh+/I/tW+dpYjF+f4t5/5PlpRystvHGL665qJ14f85i8+S+kk65+YYPZbGlZW0QvzuMk23Qc6ZC1J3hKUdShajr++doT+RoJeDgk2BTXjb5SgC8mqZbAgGc0IhrPS30ARhJugMp+iL2uCvA3qNUnQ+xDlIu/y4BKjjGTZ0bjq4yqyMOPvJbsc008S4sCCEHTOWjZOSNLYQV7g8nx7v+8jWSObTbKnT3H9M47f+r5f4/9c+RQrWZ1nOucZ2pD1osbQhpRWsjRq0SsiSqNIugZ37fYanM1knUv/+ZMc/uxFXj/TIb6uUZknEVmAToER0POEr0dQ1AQ2gqAvcMITTbAp0IOY2nWHygEcehRw/o3jnFMVseVbxabVTwlFHe/WCUfZ8MRXdqrvxoFJKpnuxBC1Mk5Nr7FaO/y+a9pzcrLNmGxCgfBEJKRDyu2SAS9C58YEtCXDGyqLseIWawqgDMC16oi1dVyW3WKl3W2oiQn60wGfb75KR45InaQjc+qiRApLUUywZkJumAYDlxILQ8+GdG2MxBEIy8BGpFWtk3FgEEgEBli1dZ6/cgj9eg356suYXu9DH6uQEjk5gZ1qIYcZoii3b2JTueRFySAP/YNCOHTq0Bsj1vOIqWTI+YcsUbfB5OoRivkOeSdkMK8o65WefBWzGFxrEnQl4YZ334R1WO1dKZWDLD1ZlQljd84psFpUP8FJh3D+xtoNOCWx2n+2E1AmsnJB/N+i9L2awoLKvBXg4q2b7dZjEEHoYydKectFa//QbDcYLIS4sORcPssrawusDWoMy5BRGbA+THDOSwMNejGulFAITm4Uvt7rdiCgrDse6VzjwtQkWVFDpRJR+iC1LBmXNAgrsBqKBpQ1h8p9fVZZ9+dLOG/dyBJMuP2dOFUFuqeq0onKjXPKga408B2+nEI7oponcWu89JEAmo0RM/UBp1o3+cvmEdydjjntNHUHh+qsPSiQ0nnd7MCgpA+Y5aX/+CQsyMutbJ4fjBDqkrQSlJPCy/vWwoK1uiOfqRPeiGAwqNyIu0hM1YUqtKb/6ftYfkLwbJyxaUs2LExKCAScFAXL+jJLZokvdJ9gvajRUBkn4pvcF90gkgUjE3Apn2I+2KCkR4GgqNy8K2XClzZPM/tbCfU/+ta22NyHPewopPfkAVYeqZquqxtTWP+kVZl/Ih5unOdkc5mnn7zA/x0/yfoDUxyPL/KJyXP81k/8Hj9x+j/h7IMHcdWVJKwjWhM0Ljls6PdVX7IM5iSDBVCpJzmEw0lBXhe0LhgQsHFCEW04mpdLRlOKMvHkpDKINiDom3e1gj4MbLvG5gMOG1uogrUqtLRbAwqjKApNupyAgKCTMT/ZZTIektUmt3dS1X+pA/O4JMI0Y4pORNbRlLHARDCaETTOBPzL53+K+k3LbN9QbEwT5ob5UQ7GIIyFdA20L51wN5axt1lQrHs5R/9wxL+/+izmkMNNFXSOr6PVjtFkTtAbRQgBQVBwsrPGkdoaIxsicdR1RlOlNFRKR3k3PBZFVf5Sckhv0BQlM0pjcFjnSCuvJRaSnrMsm5BVU2foIm4UbQqnx3FU6yRDGzKtezwQXeePTnwcE99pt26Hm1XUBUXH64Y75wOBWhmUdORlVdMkLUoKbzXtcPGUdATK+Czf1sCE2DGaCYjCe0vzUmjN0vcpkgc3OFcUnCunuJxPcV90g6ZMaYqCWMAhXfDpxussly0u5tPjPrpPNM9iELRkyqLeJHUOhYOxRHGlvV66XWlqFs6hRpZwE/TAoQrIOgIc6NRhFdhQ8OrZA3wnWMCNNMG61+Z67fWDvNme5aUjBzj75gLtZf8kFs5bHMJCmQj0yFdGjyY1JvQkg6gsIrn9JM5avqBR5j5I2zuoUfk2QVoNJhLI3KJ7t1d8KqIIHn+EweEaKnXIwqIyy2AhvKUwUlQN584JAmUIlKHsKK87VsuwTrCW1hCHm9TcA+NYiROCdCLGhhIbCEwkMAHgPDGHG6Byhx7577RoKMpaAlWmS1RkKwuHCSVFXdJ5SaGuCti8jYVah17aZOYFaF6JKOqarDVNufPedxAX3lW2SnCmPs134irjJsAF3kLdSkA46QPjW1EVF/ikgVM+6C2s8MHvqlJeGOHPf+kVSmTh/29n9Y8wYAOHCWHuJcda/t4Pmz0ip+0PLGoCMZGNs9ZaGYJqlt1WfZMnJ4mx2zVPW69L4RjmcuzemcQynNF04qj6rHukyTcMWXjqOv/RgW/znXyeF4ZHODuYodeOmVQDOmrI6fAGh2XMDySGdXOVP3SKusyxTvK55AYNGWGxDK3/J4FAOJSQhBTUZI7brfIX6wi6BcmypnEtQ/VzuiebAIR9Q9pRlDVofidEpdC8VjKcgqIJyQsKG9T49psPMHHdUb9psNqfH5VbRpOKdFIQ9vzNN5iXSFPFPfBkYwNw0ltGpjqVKnOUdcFwHpoXIOpanBTj2IjKLHLz9gLFNlasPNFg7eMlqqtQqSLoV2Ra+IyUUxKUwVZZ4SQsiHRJPOlryJRwjIqAYRYQHtFk7Q5OCoT1rkwZCx9vqZ4l3vp0yNITvbCAcxR1iQl9DG2LmG2VrJLGu1BF0xFttKkN09sjJxxuaQV59jy1D/gOobXPlhkDSiGjCFeWOGMQwgerRRJvl5xsSb9UMjA7BRU/DGSziRi998Nm98lpK0Bc1YCUdUG7NRwHvXcGt/3m2+OijBWYUo9rnfxrcrxdXipEzTA4qHC12O9vS1fmTmNHQ6w+epjs2DQ/tvgX/EjjVZZNwmebA55raLo2ZsPU+UrvFH8tThDJkkk9YFp3+VRygdRJhk7zO/2TAPzj5jkCIZFCMDSGwgkKZ+i6iGtZG1nuUlbSWZwSjKYlRS1GljFZRyCNt3qKhs++6JG/uYYzirDrqC8Z0gmFyxxh18ctNo8p1AhU4dAj3wgbrzqKupco3irck4UnJvBWmasyRUHfIUp8bENBMBCYBEaBHBOYjwNZL59yG5BpSefNnLwVVRkrR9FwyFIQbgroKpxQ2EhjAxglMYPEICKL0BYpHWFUVKdawA+u0TeKwXqCUA6hfafDO4qDnSAISx6cXSK3ml4eYYoAZyWJ9A/nYCu8YRTXrk+gIsNke0D64hRJcHu3pjMGl+eoVgtRSyAIYCueYyqlDmu3U/fGbGfKtl4PAx97LArcjnDFGJF/ioit9+ysmpbVwA0hfGW/klUtgbuVwIQAJXFKIQcjuHyn3Lq3jeAWWmMVhHK7DmiLnFQVBBfVlBUx3kVl5gqH2dIjh3E7lQwNZcvigupLvgcsp2Jhgo2TESejG8wpyYY11Kvq7xumgXWSSJZkVjMqQjK77Ycr4VDOsV7WMU6ybEqaUhCLraC4qKypGr0iht1arvMBZhOBcAJrfIyIKghqq0yMrDxIn21xY3IUzrsrhfNWiFT+ejWBJzhZOMpEgvCkJAsfzJZVKGXchlQFaWVFTip3uKEYF7pJ47AIhMRn1PKC24IxhDcHNK4EZG2BicU4EB70tm8aE3nlzbImMLHERA4bOox2DLdCCNIRzfUIdclQWy8TrY2/9yol162uBqksWllm4z4jExDKklXq4xjqVhx1S4KaQmKcYFMlzGTOk8RtnU/8KDIpPNFskcNOVMSAddsk5augt3/f2m6cHDG3vh/89jv3vUVK1ZzGMTFp9a7k5KQEvfX+O+3WAUIHyE4bWcJmL2Gy7c3xfhoRBSWRLiutcTl234DxSZNVf52Tlq3WpUBZ2s0hWVxsz6i7E/VNb+8ZfJsC4NXvr/PQj57hRLBKzxpAkzrNwAkeCbvUogE/0bjJ0BYMnOWPB6dYKZr8bu8xFoMNjgbLPJZcomtjfrf3GKfjqzwdrZI7SYFkaAtulk2uDdrIYpfYqTTIYeGzUanPmjnpA9oqB4TDFoJ402ICb0mNpiXppPDWjPTWT9BzNC/bKqXs4y1G+niEHjlU4euXTCwpY0G8WoIQdI/ocdzFCW9RhQPniz9Lhwl9pqx2LaVoBfQXA2R3hNm8vXYdVxrsq2dov662a7CqtgmhFCIMIAgRjRouDLC1CJRfY9n0pCRLi1USpwXQwCpB0vCWghOQLBeotERmBUhfdGziABNFvNh6bPx9bRF7Wquahh0EQ0uUO44PDLqfo1d62OXzlP3+bZ9SV+SYjRw2NreJ5AO98W19Y7dzT32E8WQWcPa9SXiXLaetGxhEGOAmWpgYdLDNvrIaqhnI7dd0ZeZuDd8U1bQVKRxhFRC3rhqiaaX/7iqWfle237X1bFmC1rtB9taaKjU9RfboUYanMj43+TrPp4doqZTjwQoWi0EwsA4jynHBZSgE83qDlhwxo7t05IiOzPnl65/nSq/DdDIgni75TLzBlpkkhSB1vrVhardS6ThEYVCZd+PKWtU3VVlCJvIuVrkptkfJS8YX41ag2gaCMvI1Mr4Xbrv2BTxJZZ3t+qXRlL/hi0YVQLdQxlVwXDNWknRV5rB3sI5wVTYx0B/OUnbuXeV0nBCIokSoDAo/6l31qwCYEKikcmNK462BHZaDS7YTMrI7hKL0MZnKvdGBxmlFnIR+eyEQlQiiC/X4uEReIgoDRYlIc1y39+6yMR9izXu6/Ud93wfA3llOcUQx3aCoO+phMXbXAmWIdElYkZMQEKuCXCkKpTA7eucC5af9pqXGWenrfqohCE6A1IHPXL1Lj9OurKGSCRkXKLpbTVy3OMOVHwj51AOv8sP1M/xPN36Yus54ZPovMMKXAWzagE0gEJZYGCIBh/U6gbA8FCZASOYE33jpJMlVzdLpIUcbqzQmLgJDrBMESDIbMBhGTO+W5eRA5AV65BjNCMq6Qxbb7pSJfR1Lubb9Glu/Vtejk1UwWwjylic3G3j30IYOJ31myGn/k+pvLMihqgLFYCZKglrBoZl1jJUMi4Cs0N7lcYLBekJ0JcSGendvBuczn64APsK4rQ96Rt7tyO9yX8M9jb0jp1qN7tGYYsLSTlK6aYQU0IozTzhG+yks1QTgms5xTpCVGmMFuVFEuqQdjciMJhWavFQo4QjDkrIRE7Qat0rW7rL15Mq3xTe2xLgm21z7oVk2Hyz5bz77e6Qu4Hd7jzET9iic4ldWPs1j9Us8Gl2lLQuUgNQJJP5CzpHjq/KfXv4kX/7LRznxxxlQcuNjBRNVj45FYBAoIRjakGIYIMxtxlzeb31XrjP75zkuCnFxQLrQwCS+VSRvCEwiSKuyni0ZDO/6+b/10BOVkxCt7chOGZ992tou3DSozCCzqtDTgtgRy3BRgIkU6ewCOEgKR1y1VggDc0NDsLaJvHDtbla07eMOY+8qxANN3hS42BCpEmsTnHAkuqieiHIsLieFnxQcKFNZWILCyKowLCdQhsJKQFUBdIeJhNf8uUUwbHcvXZkkiFqCm58BLavsVkI2odn8WMYTJy7xs62rfHHY5G8GJwikd0GXsiYrUYuNYI1JVRAAxY5nZCwMqVP8xUjyF2/dx8LXLOF3rkCnRaD0LYM2t5DaADLpC/ZgV4jYDofYi8NxMWGSHcY2Y3QnImhpikSQTvqgtsrcuGLbB7B9C4QTnpzCvkXljrBbInOLzA2UFpmXsLKOG46w/f74mG85ciFQYUhrZtr/vzE+2ySFH6iZ5bjBAJPuD9T8XsKekZMLNEULVOKbHEsrCZRhIemymtXZyBJPTDsE6LSwhMorFAzSEFEfcCDeYD2rURhFLt2Y0LK2oj7dhms39ixjVzz9AKsPxfzcf/bHPB5fIhAlHZnRlIaa8I25l0rHA2HK0WCNX199DoXlH838DeeyOb7YfZSf7nyDaVUQCcgcpFZyUku+OJziv/+Vn+HIiyn6qy9gygLVaaGVvaUh2CAwzrGcN4lWFDL1CgWejHdpNE/l3pg3zwMQSkEo5Fj50m/ixr9vv23HZ291BVTnYqtLwGy99n7H6RwuyyivXnvfbfbxvYU9qHOq6pkCTd7yqdZySxdb+hL55azBsAiQwhFUQfBQ+vgS1H29k5FI4WjrIbEuiMqAVPrDFcJRBmDjveHWLcnSG4/G9J8c8Xh8iUO6WykDeE2mwjkKvOTuVjzp8folBjbiQj5D4RTTQY/UaYauZEb69xbO8pdpk99ZfpLplzOii2uUOyq+jRXjMgODwFaSvSMTeCGvKiC+Jz2FlTbWLXpcvPvve4J9AtrHDuzq3S12KNu5SFFMGppRQW62yMkyEQw5a2cYpCFRUI5HRSWqIFE5V/odn5UzEi0tM7pHU2ekOqCbR+P92xBMrNFbN+l3UdW7rXVEEWJ6ksHTQ/7VU7/NjBowcJo1U0PuUAJTeCunTklNGP5+/TznypD/felznG5c43R8lYELCazhiBZo/Pfwf918lm+8coL7v/oS5U41AOd8X1dVqWidJyqLrcgJKPejLvv43sDeqhJYP+IpM5pAGUJlGJqQzGifcdO+U7kTjJgJe7T1kJflIgBKG3Tl3mwRgrG+JmpLX1zseNIKIXZP935xjpvPzXFy/hIH9AZvFVMA1KVv9pLC0hKZt/yqXE1RWTvzKuNnZr7GhqmzXLb4evcEiSr4yYlvcVz3mZQhL/+705z6es9Lb7xt7FGWafpVP0ckzNiK6pcR0bpD3G4R4j728XcUe0dOVWOgtb4CVitfWDkwkZ+mYsW4AjxROc2qE9qPHxdVwakjr9rdZTUqyv9eyTO8PdQkdsnNCTRlXfgaKwRDG3lCIkUKW1lM/t+WHriv5PYu3/3BJueEYZCHbBYJIxPQszFnCy/NO/NiCn/zsj9kJRBK4krAOaxRO8ZD+epxiddQ16nbHhh6t9UY9rGPPcbelRK4asackRRVMNw6wVvdaXpphLOSotCUYUHNq1qRWq9xMxpG6KBkWIbcLFpY5+NPxgpU5b7JEmRWjgOvzu1ej5196yILqxu8euI4v5Z8mr838TIzqsu82pbhDITXW7JAUcnrrhpv1QXAA8GAR4Ihj0ZXkcJxUkue/dbPEf8/HaZee2tMK24rpb6lpFmKMTmlTpE6TSR8djMY2rFbt6W97O4FDfV97GMPsIfZOoVtlNSTnFrgywe0tNSDjE5NEWjv5k0mQ6Z1n0ndpy4zJmpeZGuiNuJU6yYPJ1fYLBMGZRWjqtpbfPe325Mgrctz7MYm0y8IvpQ9zp8dfIDJzoBn589zunaNo+EKp8NVLzMK1JzBAFtqyIHwvXGRCJhUI17PJ/gfrnya/NsTLLzWxfVv7azfIlhRGugHrOW+r7wuSqwQXCoLlnoN5lZzXFUsuJtkvI993IvYM3IysWZitsdMfUA7GlVTe0sWk03mkx6FVdR1xoQecjxaYkoOaMuM0xM3WKvXeLBxg+caZ/iBxHAu22SjqNEKffDYUg0qLO3eZJCq1HbnN79ORyr0kYMMHpjli599kr9+aIXPLr7J/ZNfpSEDavL9daVqQvA3wxNc+tf3cfTldeyLr733MRcl0bLi+rANwKSUSAq+mR2gt9Tg0PmrmC1iM+a761fvYx9/h7Gr5OSsQ1TZN9XP2bjQYb3RRMUlpheAFTwvQI4kMqvU8ULHb0w9Q1QrqMUZG2cnUangm/EpfmPiWdoTA7q9BJspxEhBs6DZHtHeMMj1HnbLejB75N5Yg725Qj0vOLEyRfalDn/VeJovNZ+lrAnSKSgbDlOzXsbVCUQq0QOJ7vvJrvGaZer5G7i19ff/qNU1jvzBBOvnDnLi8X9G0BeoVBBuwtGzOXZ1baxf/VG1dPaxj3sduzy3rmqOBWReEm5IykJgY0W4LpGF8GLrQz/iRxjf2Z6uxhTNiPV6jdZ5iR55VcC8FdFvhyhAWa+FnFnIkgI1srjh2/qh9uhm3RqzztVrhEAI1AHVamFPHCKdr5FNKPJmgLAQ9hzJck64MoQz57Fpygfp+rNpCi+8ytTgBGFvmngtQw0K1Fof1+1hdvZ/7RPTPv5/DuFu4yIXQiwDF/fucPYUR5xzM99to++FNcL+Ov+O4Hthne+5xtsip33sYx/7uFPY5YHs+9jHPvaxO9gnp33sYx/3JPbJaR/72Mc9iX1y2sc+9nFPYp+c9rGPfdyT2CenfexjH/ck9slpH/vYxz2J26oQD0XkYup7dSx7ih7rKx+koO0jrbGe+PlszUquwIrxfDKqQQDjx4HzygpWA4GXK1bSEinDYDMmvH57Y7fhg68Rdu9ciq0BiqXB4e7IOJE9X6cAISSEAU5LTCRRuUNkJa4o3rc6X2gFWmNi5UdfdT/8mKc7fT5FNfXXBhITVUNfUzfWTbPVIExZWkRhcFn+nvv6oEgZkLvsXZUib4ucYuo8LX7gIx/Q3cCfud/9QNWzH3aNQmuGP/QxNo5r7HOb5LnCFMrLLlW650mS04wzslKTFpr+Wo2gVtBujkiCglqQ80jnGn/4h89y5L/92m0fwwddI3yEcykE+sAirt0gXWgwmg1IOxKxowk5WbXEqyV5W3vyFX60eRkLgoEj7Fkaz1/Cdnu+LWjHvj/IjfyR17ljlPzO39X0FNljR7GBxCnI2oq8IRguCsINCDcdUc8rYjgBemTRIzOe64eDbFIzmvLDR2UBM9/OEM7P9ItvDJDdIeWFS9uf/T7r3dPzueOzVacNUYSo18gPTTCci9g4IREWJs+UyMyhcstoOvCTdjYN8dIQeWUZu7bhx7N9SHzD/fl7/t/eKmF+L0Ep1k9q+idKPjl/dfzypyfeYF5vcEBvcEgXzKo6bxV9Vm3E5WKK5bLJUtHmwmgKieOpxjl+r/70XVzI+0PogP4TB9g8pkmf6/HJw2f4B1Pf5pHwBm0paMuYuk+mmwAAIABJREFU/+rGU/z+a4/x1LE3mY+7BJW++ifii/xB/2H+4NpjjP7nA8QX1uDs+e2d7+WA1J3YIgapxrrpAOV9B7n48watc5SyFLlGB4b5do9mmNHQGRaBFpZEFby4ssjyzRYq8nql1gimJtd4dOoGy2mDjTThwiMdZGCJ4wL3zQna59u0rlz3N/TbyPGOYud3fWAe04wo6wGD+YDRjGR00EApiNcUeujQqR8XhgQTakzUIK6FBC9nfsrwHmCfnHYRfmIuvLE+Q62yhM4M57muO5xRGTWZE4sCg8Q6r7C5UjRYzptcH7aIVen/v27RBw9glle2B3reDQiBnpsle/AA538sxAUOpx0Ti5vM1Qc8OnGVth5xOZ8id4op1eexsMsj9Sssn2jwI5MvM683AbhpmvxB/2H6Juahzg3+9BfalPkELpum9UpA84qh8YUXtm/aPV4XMCYmEUVs/MMn6B6T1GvrpFlAOgoRAopccGOjxUZYUIty5mo9lvMG596ao/VawNHXC679k4JjM6usDusoaTnfnWJ1UCPPtR8i6gR5rihO5qRzGhN8nPbZIeLrL1bHs8uDKj4g1MQEotMinWvgJAT9AqtD8ha40CKMJOxCtFESbuYIl2AVhD2vBFLWFOrEQVR3CnP2/K6ft31y2iUIITw5SeiPvAZ4oAxrRZ2RDdHCYJ3EImjqlEAYlLB0KyG9tPTTaEJhIDaYmQ6y28PcVXKSmAPTrD0Q8ZOf+Wsm9JCayjgUrBGIksJpbpRtVoomQxvS0wnzqs+k6vNU6yIPhDdoy4ICwYVimlf7B5gJe8xHm/yzh79KJAtiUfAvan+PvJPQ/tYcdm0D2+vdwTUKZBKz9oigXEipa8MoDbCFRGqLQ5AVAaaUFEbRjlJ6WUR8NWDiTEHyjbOoXzjAkcYamdGMCq/mOhxEOCMQ0vk5okbRnBpgOpL1UQuVJzS/znha0d2AqNcwnQZlTSGMQ468Pr2NHCIyOCOwgfBzDUuLyixSCoLNHBspTKzIJ2J0qJHnd3/i9j457RakJF0omTywwYFWl1aQjgc31FTGpBqwVLS5WTQJhCGWBTWZI0NHJAu0NCSqoClHzMx2ufn0NAvrHeh279qSVKvBmX8e8tiRs9yXLLFSNLmed1gpmgAUTlFaSeEUIxPyql3giysPMxGOaOkR39j8YYZlwMqoQaRLIlWymtXGg1S38Pjhy5QHFRufTFj+8iMc/Be3H2+7HQilvFCfM+j5OcyBaTgyZKo5Yr1bAyeQQaUT5sBZQdEPKboh15XBOEE2a1h+PKA+9wBJtMpy2uBmt0GWBdhcjT/LbSVDHPQ3aj7+eGTE8FqNplTbM/12/n6H4GoxZTsi3MiRuUHkJVHXEq0qHnzuKk90LjP5qQG/f/0xzpyfo3EmoH7DUn+tO44x5osdkAJZq2FH6UeKP70d++S0WxACIksnSemEQ1o6o6VHTOoBgSgxVZpOYVHCInHUZFaNfvK64Q2VoXDUw5yVhsBp9V0+dO+gF+Ypj8xyaG6d+5rLfhy6U1gnyJyuxlZ599Q6/7O0itQEDEpDIA2p0aQm8MMfnEAKV23rP6N0ktJKQuUn7RxsbHBlcgF9/Cj2xk3scPj+B/kh4YnJk4+d6jA8UCOMBmhlKi/MIRAgHM5W1o904ASjLPD7SAzZtMCGElUEXB+0KEvlI+Xg57TDdvbSiWpfEASGogH6wAJ2eQWbpnszh/A9ILRGNuq4WoSJJK6uEKUmlALhHHroWKxt8kTtAgf0Bmfa81zrtCnrgR9TP9nAaYkNFYPFECeh5Y6hl3u4a0vYUXpLLO/DYp+cdhFhPWcu6TET9mnplLYeshisYxCcz2bJnPZj1yvLaVL3CYxB4WiotIpJldSDnBt1ILh7p6f/5GGWH9N8fvI1JvWAs8M5tDQEwowHMEA1CQdLpLxJb6tRVt0ipqZzYlXSCPwYLS3smJBK662utAxY6jWxTjBRG2FaJUufW2DuS2DPXdibxe24cYbHWqw+pImDwrvVUUFZKj/UVVqfaUQipJ9EnQ1ChHSEjRzdGSGlY7CZMNhICJLCT6QOTUVGYEvpQ1zC+fiTcGhtSCccvY8v0vymxb7fpOPdhhCemI4skk8mlDVF75ACB7WbGlk6kjXHqdoNnopukjqo64wgKBm2LMJK1h5ukDcFeRvShRKUY+10jc6ZGtNfA3n1xq48WO4ZclInj5EdmeTmxyJMDNEq1FYstesZwcvnMBubd/sQ3x9SEkcFiSq4OJwc34zBhKGjhiwE6wxtxNBGBMIQyQKFQ1Uz+fomrqb7wlzc49W5EhcFd205a/dr6s+sMBv2kMISye15eaVVWAQSNyajrTFfkm2XrbSexLSwWASp0Z6UnGRUBtUIMF/jZY0nq9r0kNWnaky90oJze7/OtKMYzVla2hBISxwWjJyfGrQTzglftyY9yRgjMVvbCIeQ/rUtInKVO0gpcdoitUNqN7aoTN3SO6BpvBr7/d8J2WUh0IsLuHaDbK6OVQKVW+JVgYkEwzlJvGoJho7L6SRLtYCTgeOTjTfIDmm+cOnjOAW9IwJhQBioXdYI69VtEY7+Q9PUWgl6fUB5/uJHWtM9Q07lXJu1ByPiz6ww1+jx+tV5+ldiGhcTFm7OIPPiu7KxjGNQ1ZDKPPeyt3cIQgiS0MeOVkYNykqu+L5GC4WfXKyEGwfCZTV0z1RjpYYmHP8+GQ7QnRwb3j23bjTn+EeHX6KtRhROjeNE1gkKJ6txXdvTX7aIdWcsyeLduVCasbWUW4WxkqzU1Tj6bSvGOMFUY8h8u0feniXc65ICoGgAnRwlHEI44qAkLzWltNsVDTviRkKCEA5nvJu25fYh/WtIV7lowrt4RoDy28hqn+CzYdmkwkXVgIw7EWsSEtdpUrZjirpC5RZhIOxbCicZzgmiDYHKDGd6c7zemOehcIUHwmWK5ht8gY+DhaJj0QNBmAqiNYfKQRiHDQSDWYUo60ShRl76aEHye4achgsRmw8aPtZe42Btg5kTfU4/do2nkvP8/Cd+juSVxzn4r55/19S60BqRJCz/1MN0j0PZsky8JJn59efHk3T3EkJrSGLaUUqiCpZ7dYyRWCv4sjhFI8xYSLocTNY5HK4yqfpYJDeKNpumxlpZH1tbL8cHsU4wN9nFRu273l8UiBIpLDWVU5M5gTB0y5jSSXBybDGBJ6iyIilbxZkAUuNjVBY/HkxLixCOwiqyUpMVAWXpZxPW6kOON1d5eXqR2uIC5bXre3r+so5gcW4DLf3Q1kBa4sDfUHmu/ZDXUoIVnqQqzhHS3WIZCeEtpXHIyFTfS2DH26rAoZRfu4gMeUfhojv3ABJSULZiXCCJlzNMrLChL7aMNg0LX8/RvQw5zOn9r4f4xYePMfMLv4oiQAlL+3XF5JnMr78i7LKucBJUanFS4JSgTARuNqLRbuEGww9tJNwz5JQ3JNFsn1D64ZuTwYBJNWBSDXn2+Hm+Zo+jZmewa+u3VBWLIEQeP0z34SnWHrU0DnWZa/a52DvI/PwsdnVtzwKr42PQGhH6J2BuNVkVNJXCkRtFYZUfvOkkBkkoDLmD89kMvTKmX4ZsZglKWjZMjcIpIl3elc5HNTVJef8hzFRBJAtq0j8M4rBACYupyKew77yptshoJzFt/5TjoDhsW1jGCZS0yNDSjDMS7WccZm2JmZ9ALN3c9RT1GEKAYEyWW4h0iZIWY6QnIIB3abAQwgfPt19w2wHx8WtU1pSPNWlpyUuFVA5Tt7jgDpGTEKAUTgmskqiirGY/Vm1VFmRhfduVENQu9ahPdSicZsNGXMyniTcswVqKrVVJAQEilj67WTqcqoZwC4nVIJoNPxXp7xQ5CeHt4x2BydGs4EeOv8pS1mItrzMXdXm+f5SvbZ7kv178/9h70xi9svS+73e2u7xbraxikc2l2ev0bJpNM6OJZMuKbDlOpCSKERuBYSswYEB2EMRGAAdwEsBBgiSfkg/ZvCQKkABSYsQRJFkRLEsjayYzo1l7ZnqmN/bCbu61v9tdzpIP595bVWx2T5PNIqmYD1Bgsd73rbrnLs95lv/z//8TvrVyhr/38V+k/3IKL1+MH5IKubjAlT+zxr/1V3+PJ9PrLKsJK2rK36j+IuNPnmL4zXD8zmnQxy8MmNuSG8UAt58gcovOazJtyXXNcjIjk/HBG8o51+wi//zaE1in8AG8l2RJzZ7tUXp9JD26n1Z/5ByXftnxp869yLKasqInLMoZF/SMba+46fp8W5yLIFKbkCgb07ZDEZP1skvzWmsL4Vr6A8fkJdYplvsz+qbiwmCTuUvYqXKmjwW2pyNWXtDH45yEQGiDcDAuE5YbMdfSapbzGQNd8qJdYx4EVrZ5XZO2tddGBoQ5uE5ShVibChCQXaeudUwLeYGWnrc3F5HKka+XuCzjfrgnkSTIXo86UXgjkE4inUcUAbIY/ZRLCSpX6LnBvL1FujciEzXfKB7n9288Te9ajdqbUpyM437Cx1QO4qlpI6cgIQiBPbkYHcxd1osffOQkFWrQpxoFPt6/xB/5J5g7QyotpTfMneGleo2+LKn++haX3lxh+Mo6ZhxPzP7n5lw49RZrZp8tN2DX9SjMDuN5xvrYwXHtuodMZBlukOBCReU1hPaG9NQ+PpQKz//8rS8w/E7Kn/vLX+LHB69xbrTDzfmA3XlG7RSiDmzVfcZ1/L95AIK+5tqY7CtrvLiwxr+y/F3qEG+RBZlgRE0m9lk0M0qvKVx8zTZRERyKlDiInqQIEQ/lFUVTCK+cYl4Zqlrz+OIWZ/IdTqZ77NkeNkjqjYrJPGXFmLveed/LRJKgVldwGV0ap4RnkJSspFOGusD6kzgrm0Hg6KDaWcn4NB7dQIQgCrK2KaCPNSfdrzizsouRjtJppPQkiWOUFwSV3/O13c5kr4dYGBJk0zisPEEJhAI9tdGpqKa5kSp0LyPZs/zS7/+7yLEm3ZacdnP8qBfPhwdZx4kBiP/3Cuq+7NK+epSg9rO7PuYH55wanIlQCrEwwg4CTyQ3eEE/hkeQyoPW9GvlGk+m1/jix36VX3t8g3/w+E9ydWuBNKv52mf+HtsevlU8xpvVKoU3GOEoC4Mq7PGlBIfNaFyuu4eurU1I6SMeqCmO919IOfU/fovv/MJj/PjgNU5nuxTWsF9keC+onWJss6arJXkgvbqtHda+MeC1Ty+we66HEZahnNOTEhUEULGgZ+zbDCnig3U4jYv/f2c+GprzUFhN7SSV1dS1wtaaBVNwJttmKAtKb6i9IhtUVIsmsh4cg8k0JSwOcVkg0/EekSIwMCULZs6SnnUdOdHAAETjpIQIbfbTLO4A29ThoyBuUk6gteP8YJu9OouprYyNgJ6psVoc/ywhINKE0MuiQwqRWcALSRACWVqQAm8U3ki8EYTUoGc1q1/uY+YBM7UIG3A904zkBISP2pPRGQW8jvUmVcW11H1Fkt/9XfxgnNOhC6HWT3DlF85iTo25WK1hvUQSqINipOf0dUkdFG9WJ/j1YFjTY/7rp/4R2dMWh+B3Z4+x73P2XM5AFazqMSfNLqPhjNmpJRYu92Bz63iXYzQukRSVwTSKx0IGUu1YSAsS5Xh9uoKeQwghDoRWq+zWPVbSKSvplMuzBVyI0YUUgdzUD6Tm5LZ30d+a8+x/ssavbfwcl/9mzU+ffZULa/+M71VrvFmd4Kn0Oif0mN362a6WNHFxZCeRB5tBpuou/YM4zjOtEkIQjPKCXZ9TThSv7q+yU+VszgdcemWdE1+VnPvhGHX9beze8SDkxdIC2x9bol6KgFEpApmqWUpmDFQZU3AfUzOhYqQU2oJ4EO+sLUGHberqVAA+puylVywlMzJV87ZepLKK6+MBw5Gid+4M9tLlewJcfFczBp8aXBZvKmETVGFR8zp2hYVA+BBTPS8QsxK3lLH5E5bkmmbwtiK76ZHzGjFKYvCoBEE1KVymqIaS+apgdCmgZ55yUeIzc7ty3fuyB57WhWGP3ecsT67sUgd9EDE1xVMjaqTwOARjl9OXJT1ZsygtRRBcswuU3uCCRMkCI+Lw7Cgr2V+RjNpW7XGbiJFP3URJgqbl3NzEmbJN4TFQVIY922PuTJf+aOlJhDvSnn8g5h1+NsO/9gbqeh//3Y/zW3sf5dXzqyynM5aTGeeSm2SiJpUOG2LX7XDkJIXHN/ADiA+zEAFFwAe6SFIpj0wcb7yxxhtWoKaShdclSy+OkRcvY3feW779g1gwmmoowPgjRfpcRTxX4Q/t+LdzRIdrgodeP+K4BI0zi5ivQTIhlba7L5yT1H2JWxki3hZtMnE8pmNnsC2A+0SiyttEbD5urJ05gXQC4QL4iKcQ1oM8SAMBkLHmhIiwAuFiJBW0+OPrnMqNEf/tz/5v1EGz5QasmgmFN8x80gEU22l+JTzX6kXeqlZwxJmuq9UimawZqCKOUyDJRM2To01+75kNVp+/T+R4IVDXilIfnFIpAntlxgIFf3LlZZ7vfwiA6Tzh7WKJ7bLHzWmf8TTjzOouS+mMgY51K+sl+kH7qemUs3/3KxEfA7zw73+W5Gc2+cTgTTJZ09cl82Y8pTUtfUxtOag/CREihED4Lr2bV4ZUO7KlCQv/0wD55e/GXxA8IQSOfYgjMZRLApXFqGlaJyTSsaRnTFzKTTuIKZyMVChdatd8XBxyvIFDeCgvCFZ0aPCg4vtmNuF8toURlj8QTxKanHC6IdDzIQvHVfhvLOSREkVWnqAFdV8iK4WoXeds/CFYQ8gMsnQsPZ+SjD3pbuOQEo0qfUz/EolwcZ0+iXU2VYKqAtIFXBIJ6u42AXigMBq1ukKxonnKbNKXJROXIYUnk9ERGeEYqIMJ/ipoHBEP05clmaixXjJzCROXMXEZY5fjkJzKdknPTrD9+1e5CYEuUoL4cCrpSbVlWU9opz7q/ZTLswXODbbJjMWWmmmVMLMJkoNO1kNhcaQevEM4cF50m8bcGXyQpE1aBEcR4u28XQvgPBwVChFQ0kcQ5qG/cYTj6BjNp4ZyOZBmFbo5Li0dG8ludFY27a6lkKEBXx7UnLp1NDAEIUNM/2QA1XyvAsILQhBI4THCYoTrTitAPQrMVxo20eMwIRBpis9jSheUwKWSYklSjxQ+bzZTHxr21uZLxgWrMjK2QkzfEIc6crIJEiV4I3Ap2B7dfe6NwOYKtbgQsYB3aPf/CWhvPCHgxDLFsuSMjtHO2MXKfnsTG+HIhEU1aOp28DRpUrdM1tRBUXrNzCWMXdYNqG6YXT5y8ir14D7iSA4VhuNuKlAikEjLUBYxrQseNVZsz3ucy7bJtCVUsXNVOt2t3d8ulXgILATRodunNtaZ0kN1psOMAy1FzOHX2odbtg863N4ZHbOD8pnGLlp6aU3SzAUm0nFS72KEY2YN3h84p9jiOvQFsQDerKNzwKp1VBF8SUs60LyuhI/9kuZzduipFgB1PPepUAqRJPhExyFfJXCJoFwUVH2Jux0INIQuZVNVQHgOUtQjkAHR/EzgjMBlApvTMYN6Ay4ViMHg4XZOMssQadr9X2jD63/+BLN/ecKrteBitcb1asTEZZTeNDtw3G3anbp1SJmo2XU9tu2AVFpyVdNTsYs0UAU37YgLyQ3+zmO/RbF4n5xTCHgvsU4iS4kro8MZJCVDU3LTDrsdKLspubE5YllP0MIjKslknHFjPIhjIcQ06GEzl8Bib446FDkYGecE245drmp0w7qgpSORtknpjiZqzgvKWjMtE4S7Za0hHHv3yqeKfGXOSm9KX1ddlLSmIpfUzCbIBtPUPJdNHRGkDEjl47/yIEr0bdGcOPAbrCToeF9cm44AmvJE7NYNshKxUFEtBsQxOePQUECIECKiOxPUucD24oJk5Y+Uz5AQGq7wIMCmAmc4wDMpie2pGIXp6IhcKpickcw2AtWqw2bRcakiOjm3vojIUu7U7ptzCoduNjkcok6fpHp6zufOvMGuzylCdEiuQVIftlt/5pAUwVD4iIfKZE0qbBdl7fscJTxPakm1IFAn3hdH/D1YY1sQBXwseLa1lrHPIjjPB8wEwkTHdFV6gorYGGsVU5d0gMaHzYKGvol8PXHe7p0OVIqDAnM7qnKrY4J4nqyXVFYd0IrcRwtK0M8qero60mGUwlMH1SHgxaFo7+CL7qu11nnFX37LC0Ewr3XT5IlpoFaeTFuSzOJ6sZ5zHCakQIhY0JY2psxB0hSuQZauSc1aMKU44ETnUKDYhXuHF9osV4LNYqQURHSCwofY0ZPgM3NXkeF9K4gfnokLz57n+scG/N3P/B/8bO8S/3R2Fh8kJ9O9mKY1wD8fJHXQ1CGmO4p444xDzp7t4REs62nHqAjRcV0qV1hUU3rSsf+ko//TT8Cv3a+VEl2+E9S16h7Mt4plVAUEz+CKo1yK9bNRUqBGVXe9L02W7uOB3pm5FE73dpn6NI7YSNfBPtq0JZWWuWjGd5r6kxUHfE+qiUZqF2lJbK0Q7v5T1HojOdGfsJTMj2DqimAY26yBPDRvPpy+HepkdbUn3ZTNvACv4jCw9qBadgKYFSlFMCgCiXYkynEinxCC4K1CH1taFwmkNDRMli6J3TdZg5l59O4Mu9zvHFKQscOGlIgAugioMqBK37xHIGyABkYgmmlol8UU1uwr0n2Lnlq8SXBJA+qUD8o53RqSvktIrk6cYPrZx7n2WUXvYzsoPC/WsZsmmwJ44SOCOGJPfLc7K+LrDolsCNt8UPHBCNFxSRG6ulQdNDNfwcgyOXXnIeXdmJBxsDPo2NkJTnTDsGOb0QYQ+dWCbK0HRDoReUuaFII4wpn0sJhPAieSCTOfUHpDX5dYH2t+cLQYftjaDp4NEqNcTOm8xDmBqxTC2/sePAUBPV2hm2J+qixDXXJaTZAE5lV0sG1b/XB37vD/hYhO6TC26UgrvhlpsbWKANOgSJQj1XHsR0n/jkjknpoUiCwDfYDcDhJsHnCJINziFEUItK3SFhXhjcAi0fPomKQLeAleSZyKaZ/wTY0pCVQDhawDqojRmk/kXYFpP1j+0MW38pYvcfQ9UiFMQji1ypWfUnzqT73Ir3/iH+CQ/KA4HYc/8Qch9aEHU3HgeMyh4nhrLsjmM7pxTLKDGUxCTW9UMF+7H3QUAtmkMS2kP3gRRzG8Yq/KYs0pBMyVbXo34zqk8EcegEzFodOH0byGdbPPpGk89GRM8eauHXT2TWf1gOdJCt85YN9M/SfaNZGGJFTy2OtLtzUZnZNpIvJc14z0nHUVo/Taqtt25w4fqmimAIIXR1Hyh7t5DZTAWcnMJ1TNUHeqLKm0seZ4jM6p06LT8lB0BD5t6kj6kAuQdClZaAtsxOtuc4FviuTCNnU2Bc5EnnHhieWJzFH3BTaX6CIiyH2Lgr9D+2CRU3ul3oNaVH7sWcZPjrj+iyUfPn2V/+b0P2bscv6f6dPs2AMM0rYdsFX3u9RgSUfmgSI0UIAAtWidj8CImFIMVUERNNu2T9YQorUPzZaLHEuz4TE/7ErGmaXmZkUGZG5Js5pEWvbrjB9ePsnKbjyOMJlipgfHFJpZvFuLqw+diUihMnYZCs+qiVFU5fVti/iROdNTHuJ6SlUEIc6UpwwCUUmEC/c1chIm6Qq8uapY0HPO9reRIvC/7D3Dd7dPU0wTesMSKT3WqohnChwUyVtwpVXx2oUWAqIITnYcT0p7bKlgrrhWjlgz+zzW3+3Ojw0yPkZav0Oq6p6sNU1xK0NcTxO0wCsggKzoIvnYkDhwXgQQtUeo5n5tak2q9hETlTRAY9/ABgKYSfxsLQ5qiKqKTZTpumKQ3/mM3T1L64RSyEE/hpC9nJAa3DDj5kcH7D8BP//Md/nJ0ct8Mtnk6+Uar5VrHZtiHTRlE/mkTXfONGolDtHNavmmvlEHFRHhbdoXYoG2DarUoUKtOASEOzZriohSxhY5XiAEmKa2UFQGOzaosomoihJZ+Y5/++jMVsTFuNsMlj5oC+IomVxLp2K9PBIhSeKXEu9M85T0aAS6JZnz3Pd1iiztogAjIvXwSBf4INi0AwqrCT5Gwu+2RbQo7xb9/o7c7HDEFQQiCCof7/G1dMzcmQ6KIQSIJEEYTSjvcf1NCnyi8Frgdeys+SQ6FmlDVO7NzTuZXsKB0m9cA+ACwvkYCQHShRhR6ejsZCmQSTwV3sTCuzMC2787Pvx74pxEkqCWl5h+4gz7ZzX7TwCn5/yF577Jnx59j4+aGb83P8nleon/bvp453wWVKSpKIJ+R4cOms4PUB/6WeENO7bHQMUHI3ZWNKXXTEK82G3k1G8IxER9vN2vIOOwZGJiVKDGCp85Eu3oq4rdkGO2NGbSFF6nU/SsZubj8bYEZC3/U+k0lVXcpnzzQC2oBvwqa4yI9MMKz8wmLCZz+rrqUvAWRqBEaMCaonFi0XFlxjKWnvuewAqBXBhh8zbtDA1vVbxnHIJMW+QheH5Lx/ujNDC9a2hSxAElb2Q1CITUMXeGmUv5s6Pv8p3iLP9866l4rozFrS4gqwp3/ca9Xa9UBCOjxHgimG5IXAZBhhi9X98krJ7Fa4maWzASf6g+JAK0ihTSeoQPUa+u9CR7lslGiu0J9DTOH0onsFmEHAQBtici2+hd8OHf8SfUaASn19n7yDKzdUXdj12ceuRxA48cFJxZ2+FUf491s8+Veold1+PVcr0ppMYWumv0utqdK/Jqx4e38HH2TB0qiAPMvD7o5DWKJTu23812tfUoh4zOTyp29vr0rt6H1ryAYVaipWe3BhdAK4eWDhck6Y7AzA5a1sIF9lyODRKlGmIz4gyeC/K4Ost3ZrdOy0ua2cb4IFchFnld63SEbxD8vgEcRkdkpANMsy4XI8QgkMoTUk84LnT0bdckoyRSKkgbwQZFYKjmTH3K1XIpcpu33ExNhy6eBtE5qDZaOoiamkjpltqKVHGeMFjJW+N6qUCJAAAgAElEQVRFEmn5if4rABRtrU4GfKZR5himGYKPNyNNral54tU8DvoKrXFGErSkiRXie40iqDie0uKZvFERmuBj907NLbaXUQ/i7zPTQLYDxbLA9gTpTmjGW6BeyjHra7gbN993pHzHzkksL7L33DJX/5zlp599gc8uvMZps82zZpMXqjWu1EucMjt4JDfsiO/PH+N6GQFospFxbod6Sx8HfdfNfof2nrvY0dgJvei0mhsIOIQgj4urg2K37nW/O5W2e80Iy4LM8TsJwzfvz/68mMWrK+smZZDxmKyXZFsBNa0PaishsFP3OgI2axXeSxJlqbw6UoR9IHabAmaQgZ6IzskhKEJC3XQjpfBHwJldenco3bNediotIYgYMebuaFH2uJclBSFPcKkgV1W3AQ5lQeENV8sFah83jHembUfX19KmtCnereUiAQjp8U0d5ubuINYS1+LrLZZKyhDn2o5DbaehNoFm3KTJrsysKWxrjU/kEWwTEFNBIxuGS4GX4HKFrASyDsjKI2cVNod66El3JGYn0L9aMTuZUQ8C+U06SpVqKUGfXEFsbb/vGcI7OhvCaLY/f4rRX32bnx1ucTLd541ilcvVEt+XZ9izOaXXXK8X8AgmNiVXNY/nm+zYXueUpAhdlKMaxoFb0zofBLOQ4BsuZhsUm2UsmCfS8nhviwU9i9xNTS6/oOf0ZEVPloxkwY6b0X9Dsfy145XeEc0NMDAlfVXxgxVH0qtJtWWgSmqnGF6qUFtj2ssirOft2SIAy/0ZV+tRNy7hm4fiYTM9kfzR7IkuctpzeSTXA4zwjapxPG4tPFo6FJ5SxC5mrutu81DSR1YCFW479H+cFowCCXMXO44n9D6Lasq3Z+f44vPPYkYV/V6Ja66DlBFEHBouJik9qbE4L7BOdd3VWnmqSkdNA+2QMuCciJS8xlPvZNyoFcsfmnFS77GUzqicJtEWm/dIkmOInKSKjk80BWwNegqrz5ek18bgHKrwTY1IdAPJ+Mg+IEIDKWicmggBVXiQYEcZ6S5IG9NZVQXM9gyznwICVXucAxHioLFdzFB3oHB8R84pJIbJGclf3vh2I3OUMAsJpdVMSLtW/lYdV2KDYqBjjaKnqoOd4vAO2xS5W+qQuOPGLciF+Dvi9wItoj5aX1UMVEEmLGUD+Cu9ZuyyrmB+ww55RU1IdwP+5vHyObVhqm4iw5B6jHEd7icAZr+CojzymZ2yx7Adb9HulqHh4z3k92XiqMijcLBnc9IkVgEjVc1B7aZzPO2/RKekfJTJSmT8XW2ZOXarH8BCxcEmIIWPIyXE2pje0djcoZXH1rrr0oVuwzjAO3kvj3QZZZMCuna20DdCmSJEBeEdg6fB8QnX8V0JESLw8ThEVGWD+A7QnH6khWR7jpjH+7EljTuMAI/jLs20g6Cj+8HF2UGvJCjQs4NmgLQBMSsjZKZJVoQHYRu8lJJ3REl8R86pXJLUnx4zVAXbdsCezaNKbUN87xHUXnG9HKGlY9HM40gKggU1R2rfoL7Vka+dut993xJ9GeEYu4z9Zl5rqAoeX7jJGbPFBT3hpXqBy/USF4sTbFc9bsyH7JUZVdP2zZOapWxO/5rDj8d3sswPZFo6ZG47ySMlPLVTqN0ZYX6Q1IvKcnlrgXMndvjw4lXm1lB5RV9X7FeRpveBduuEbCSODn4UDKyaCbXX+EbOygfZpG0HeLTWchWv41zEMaNKRW06DokdCHl8oxvvatYfdHYb91IEw9wnSBsxiFo5ynEeCefa8AFwTZQ0b5xVCDFKUipgzEG6Uk2TqMASQA1qFhemzF7OETuSN+wKu65Hrmp2qx7WKeq+xH8A1sh3NeeQlaelXpKljDCCuvVUt2QsKqoY63FA4FGVp+4rXCLQc4csaopRrxv6zbcdbiKZrUr03BOubxLkSVwWO3Wygt71QLrvYsH9Dkir7jjJ1dqzqKZsq0FXnAbIZI1rEMDr6X6U+ZEVRlpU46DqBiF7eHbJIbsuTkuNArFQvqynzdcEFyQvzE7zu+VzvD1d5LH+Lom0XJ+PmNqEcZlS2aiJplUUSByYkv37DLQ2wqG0w+iIAnYhDgOL6RRfHeo7Ok89S5jX8Ybsm5I8SBbMnD1997zLx2ZSHYkSWiUZKXxM0Q5FS22tqTUjDihVbIPgz7SNHcnb+aVjpq0NRoKEOkiKoDssXe0V0h7UC7O8wlqFrRXIOOibp/EazkuDUjE1bZfgmzqalJ6QWLwX2FqTZTW5sbQyGyfVHlVTX61cPB9eHwzcHqdJG9Mv2nsxMQ1sQHSQbOHpIq4jqR7QUqYIH2JE5MCrEJ1RgwTPdmKTQzZgTWyUjpLVnfFV3VUFblHOWFDTOOfmMiShq0MArOrxEU4fh2TPRsmjfZs1Dkl1KrKlN+Sqoqeadm5oum2N+sdJtc+W6/OrVz7Dq69ssPh9zaufXGe4PEW0f8PJrh6QGcsoLVhLx1xW929XbhHSxkR+6EFDMVxZTRhP8IfTOu8RU8WsGZNYSAokgTUzZlMPjhSXH7g1ODbEwWDsYQGDNl0DurEj1dCDqFvGczr2Al0zVw+AJV3IWOxVUcZr5qIKs5KB0itE8/yk2rLcm1NYzc64hzGORFtODsc4L7kahgyzkmFSUlhD7SV7s5xEW1JjGSXxWm/OemQmSsxvNVnSOT1n1xfMbEJhTWyGaBG7Y8exZkmMTkVDBlcSU7rEHKhKhwi2bKED7QDw4VpTixoPCmQNem5xRiJSie1FFDla07taoucJtteCNQOqcMh5jbuDesUdOadsy5H/xoj/IP23+fG1SzzTu8aH8svUIdZ7HFESaNMOcUF23TgjHGWDZdLSI0OkjGjn4KDBmwjLqWSHrKHm/dVrP863Xz7HytcMvRuOdLfmmf0pam/K7kfWODGYcmM86AqVLSF9paPC7ECV3M8Bf+ubWT8ZyHTN0BTs1jlFYfBleQT9K6xDTySzImHuIpdTqiw9VSKF7xSDHwoTEpGlBBWpduugKLyh9BrVYJpaayEhpWwgEYTuZ4l0zIj1mtLHQnKW1gSZHOt42a0WjMSncK63zbl0k1M60gGPdInLIycTQKpip9U7iZOBWii09PR0hR8KLgw3eaZ3nd++/mF2Zjmz/YwicaRpLPwnyjHKyqazJzCTCFZ8vlrhK9Mn+f7VDVZHU3pZiTeDD0Rp+14mbCBkcYzEGyCA39xCnD1NvdI/imtqKHbbA5F1aByTxPV0xImV0VHZXDcOKzo8bwTh9BpmZ46a1+w8N4obmoV6oBEue0ep4L3szgri84LlH065+MMVvlgmvL2yyOeXX2uUYBuWP+EYICh95MeOcIAY4rblw7a7UXgTB3qloydjkXvX9Zi5lIuzE3zn9TOMvpew/k/fxr5xKR4DYIUAeYL1fMy4TLt2r23Acom2ZKpunNN9uO1FDH1tU09raWlzVbNd9fC1fGeF23nUXFDXuiNlc02hFJoJ94ckeBJSIBJDkBxJ11rk/q1jKy3G6VZr8U9CBLyPD2JqLEGmRx/KWwrx99q8imnUsp4ylAV9WVIHTa4qbD8ydJZWM0jKrqHRdlLbMZ2+rlg0c1b1fqewE6zAS4nTkqJuJLXSKGtVexXFABTs+4ydukdd6tgsUZ7SRLHLe74ltelxU+wOTbHbFwVKiJjiHjIRQoPab0/WwfdeCWSj3hJk/H+LIm+SIFwvwVzfQ1Y1QY2i8woH5HR38jTeeVr3Ry/w1PcyRJ5Rj4b8yl/6Weqn5/zyx/+AZ7IrnNG7XNDgCGw7x7ZPGPuMfZ9RBMOu65M0jmwo56yoCZ9ILGWwbHvPz335b5B8v8e5f/gqz85fJVQV9jYS5Msbe/w761/hlcWTXZdwQcWs/mKxxlqyz0ezt/m/e5GmlHsvfdZZMFGocL/KGOp4rD1dsaRnvLi3DhPzzkJgXdO7Fth+LD4UY5sejOkEGWe2HmBBXMhmoBuiovGg3w00t6alQ3vf0dy2A9cAWriusdGyFrQ/t0JSuxiFpNqCGtynVQFS4HKFzQPrZo++LFEEjCx5Nr/KyWdvMClSNsd9BitN1FNH3JOW/pA4qGO3znmRU+zMcqpKoXKHVA6lPNN5Su0UH1m+ytSm7FY5k6crEHBS77GWjOkPCwREqaxFqIfq2DmMVHXgSERtkYUjaHnAbOlBEDonJXyMooKK4yrCeoJQ+ETgEomZOGQVMNOAtBASCbN5RIW1NawQ2QlkfWd4wzs/F95FOfB5gZwXrH1rjcm1jP/+6p/G53Eq+fTpbRazOSeyCUNdMFAlS2baoXFroVB4Cm94szrB/7WzwldvnufK5WWWvm4YXapxN7fecwhy99Vl/qvkz7Izy/E+tnUh7nDVfho7JR6evFhESeRjtCDiQGUIAk+jpCLjwzmrE0R1G0CjtZhZQNRH1XFv5Zh+KEwpQpbS9oFbTFrLJHE4mmrR+m2Do42W2jrUYciBEBE9fj/ZnISIE/NIeLVYZ2xyFtSUkSrYtEPmlen429u1dMwCh0oQiYyzn/s2bzqQohsI9l5ia40QMNIFSgQKpxFJPE8znzJQBadG+2TKsitz5k16dFwWGjiAngZ04ZFZhlvoU66kJHt1HOg1snFEguBEizCNg8AhpofCR9hDaKSk4muhGyL2SkZiyUOZwt0KCt2do26mp/10SvYbf0QGrLYHYhLKn/k41zc0r1yA6qSlvzLj2RPXWUmnnEr3OujBxKX8cP8kL331PKf/0PL0b3399n/v1oGmEDj5Fdh/fYOkoWWQDvQ8oOpAsmdJr01wL7wU335Xi7wDawjfbZARj6UiMjyVNdPKIMvb3HXOk4w9sjiQ8UZAJqpmmFmAfzioU4RS+EFK0L5znnCQvrVjKsAtdUTfdWCNcA0w06N9KyjgSZRjdh8EDQ4OWlIN4vX67t5pVtJFFsyc0+kOr81X2R/3UNqhtYuEeEGgdBy5aWfstPAdl9VuncfRo2YcB+IG6UoFIrCg50gRmOgEqSO9yq7rMZQFn1x6i9Jr3lJL3DgKQL/nFlSkPMn2PGbiEIM+xVrOdEORjGtEFQiaDucUpIj3tWgck4uRj/CRB0qEBtfkAwHROSBvRCOW0JJCNeDPxukdb1oH7xnRBFuTf/tNei9mrH6zh+8l+DRhKzvPphK8eLh7FkBVnidv7CGvbfGujcbbhBGL/+9bLORppHtoOaedB+8JVU04Bgnrdz082Ux8+9iFhObBJVBZjbzdwoJHlQFRi6alHC/F1XopYl9qhQj+wZad2nqdUpFqVR8g+2+ldWnR4PFjEUiriOdgoIqmQWKYCxOdlopOSxKONWK4nXkdvzJV4xq2gJ6sutlOZxXeScZJgvcSZxUOcDLOBKbKcird6+S92qhdiBCL540TIwguziJFdOV1dFhWcs0udHi/uU8onEG4u48w3tO0xvY1xZKiHsDwrehgwmPrzNYN81XB4qsCGUKsCbXjLs2GIXzAzDzCSVyuInFcI0XeEsl5LVBlwCsolzS9xBBmNnI5JVCOBMJrRIDkuBDi78tCeMdktQTeS9qyVaO5E7NvX77DTxyjNW1a6yWVPwBWOSLEQbijOBGasDfuRBHd7olT+zOfUDkVw+qHJbdTjUaZOkqB4pBHNOvgQAxViohtk01T5DCNzWGxzco/AA5xAcjQILRlR5ZnmqFkaByNP7q+VnPPBdFxh9XNHOStSHfRME2M6xTdyNK3QM5Zo47sg6B0mrk1yJqOxO2eL9eFDvGt5xGU6XJDe6uGpqETcTkceRhFAFkGjPORZqbFODVfreqvtCEyZqYN0t05zCx2nb1pald3OPbwwEU1/39jAXZneYcMn9qEzXpIVRqSiqiC0bKGBhdrTtsFepZSNbUNTxRyKJ2GUnXT5A/clML2FNJYMhGxaG3kVHnN1CYdShyaOTvp2LO9jnFiYlP2bdp9znnJdpkyrwynqvvLIa4KEE6wke3z0v46u2XORrpH6Q0rixNyU5Mqy6RKsV5iFhzey86ZbJd9biRDbJD0dUWW1FQtNa/2hBC7kKk5UK/2QTBYmnUUzG3UdL0Ycn08oH/Vk27d+2jf7+6RvSDIXs8JRsP2HkJJwtKIle9all42MfJPVeTrCpGzCdGg30uHbBxX3dcgwExcpGBJGxJBG5B1oBypKA2VJviyZPQbzyOyFJHnhLqGqsbV1Y844gN75JzugXkdOXLypKZvKmqnSFUkzfNOHAnXO5yH9x2xv24m93XDwJBIB9o/YMnTAxNS4hKJbJDgC3rGgoYFNWfH9rsxJtOkRYdpcBRRJHWoCgZNJ7Mde7Fp7HxVgw3SLMPfh1Q8OEe259DTyAG2kk5JlOW3Lz3HdJ5Q72aIzCGNx001tNzgqUMlno3RPj1dcbVYYGYT5tawkBc4Lylsw6UuAtv7PfZ9znieRQS99Ez3MxBwcbbKcjLjRDLm2nyIc5L+1Rq9OX730sbdrre2+PEEUdcR5T+dQr+PH2R4IztBgxYFHqSIhXHooqm2bNGJZTbfeyO6oe3uPVLg+xlyOMBtbSOsRdQ2MhE8ipzuv7mephoKzvQnrGdjaqe64eTgRDdweSvHurDRa/V1Fbt8wtNTJX1TIjN3fCqw79fam0kpbCZQOs7PnTK7rKgJn0y32XKCbZ91hXJFwAiPwVM33jUTjrE37Po8iqf6HB8ES2bGhtnlHy7/62SDPpTl8aeyztF7e0Z2esS4zniidxMlPD/4jWcYbcXmiu1rXArJbpQ3cikUK5pyxbN4fs5yMuP5rVNdV+/Dy9dIleXSdCmSxwnHjddWMLuSSuZ4Az736HGkc35h6SQfXr3G5wYXuTRfxntJ9oO3sTc27/162+76dNr9SA0HFGtZgwiPUU97rb2RncNp0eI+ldhMIptI3mWyY9aMb4xzdC6NHcF6KSMtlmF7l2DtXcusP3JO98DSGzNGbype/MEZXhzUBCe5sjRipb9B7+WUhdfcAc6pod8N1iJu7pBtrfD85in6SUWua16drXFxexX5doacbd/XNvthC5HrAoho4uWvKFS1wX/+tb9ItRhwWcAtHLxHpg6pAklaI2UEKroQuzNaOcraUJWaemqgkph9hSoEag5nv7eF3xsf6sYeX5cyOIe6eJkTqebLax/j905X6J7l3NcKkutjqG3kVZLyYP5MSkJm8Lnhyhef5LISmLknaQ735XwlIqGrg+L+k1sFsrS0FM5BS2QVMUWzry7x9Y+vcuOnhrz0+gbZpYRQXLrn/OG3Namijp0NqIbZsmMtOIRDaoVOgxRIG2lShIvvtX2FLnwseJsYRek5cTi4EJhxhZgVB9fxLmclHzmne2BiVpJt56Q3UuxUElRgInLqWjG4Gci2DgZ+WwXW4BxhOkXPYH+adUXivTpnNksxYwH2Qbkmmg5og1YvCvwbl1hwnsEbC0zPDqh7gnLBRCFFHUUVg4Eqj5zth8eGggJZCVQJw22BKqIeWrbryG6UcOUG4Q5qER90XW5nB/P2gMWXM/ZcQj3SpG9cwV2+9iOPI7/Nz96t2XP4ceyQAlIxXF7E5k/x1kcXMdcN+Y1AODwUfowm5AG9ibQeYQMuVQ1WyXcHLWsHQuC1RPj4vXA+Kv72FcKBKhxBqEiL0nQbhQdR1geOPf7Ru0L8i3AHHk0IcRN4847/ysNh50IIP1L691+ENcKjdf4xsX8R1vmua7wj5/TIHtkje2T3yx6SftAje2SP7JEdtUfO6ZE9skf2UNoj5/TIHtkjeyjtkXN6ZI/skT2U9sg5PbJH9sgeSnvknB7ZI3tkD6U9ck6P7JE9sofS7gghnog0ZPTv/K8ICMNeR7Buph4m8x/9ucbcSh+vQZVRoz3M73xAdMzO5vsBtN3VGoVACEG4F+RwH4B07P2uET7AtXwXE4khGE09jPNjHV+1J1K4lo5QvJNu+W7sQa7zftr9XKdIDPVCgktBJB6tXMP3TqeiUzqN9Qo/V+hpQO7OfvQv/hFWMKUKt2NjvFP1Ffp8VvzM+3uzEIgkgY89zeRcj52nFfUwYEeehRcUKz8o3kEyFoToZJFFiNPPLhVc+5zEDR3pdU22Bf1rnsU/fAN79dr7PvbfDf/ofaFn72iN7VK1jlS2tUUohcwzfFkSqltGIYREKIVQEqSMnzMatIaqjkOS83mca7sLcOz7XSPc3TpvNyOlVpZheZG9T6wxXxGMz3MwviJB1ILeVcHoTcfwpT14/a04iPoB7NjX+ZDY/Vin0Jrxv/lpxmck44+ULK+OObewQ6Zsp66dyhotPVObsl31+N6NDeYXRyy+JFj/7UsfiFvta+GfvetrxzZbJ7RBLi5w8RcGfPZnXuBsvoMSkTf8S09c4OJnlhCyGZT0LRdooD+cY1SU5l7szTnT3+PnBtcYqII6KEpv2LM539z9FOYOnNNxWrAWrEWkKbLXg7UV5PYefidKDnVCAYmJ/DZZBjrycvthhh0kmJ05cjzHvv7mw0My9z7MXzjN9nMDJv/amNXhlGraYz5N8LPI/YPxqKenXHl7geHZZR77x7PbO6dbqZgf2fHYLedZDvr8xN/+Gv/e6h+yoXJ2fMGWE3y9OEsdNCfNLruuz9hlXBjeYFHNOHOmxHxaUIfAX7j6N8mOifjx2JyTfPIcb/ziCZY+eYPPLrzOG8Uqc5dQes1Gf5/e2ZrNST+yChrL5s0R8kZCnUXNL+uiCvByMmXsMmYuoQ6KtWSfT/Wv808+9XlOuk9h/uD5u6ZkuJcmTMKNX/ok0zMgnppQzUeE4nx8MUSxBSSgAmgf7xEZ0Ikly+ZYqyiurPPM376Bn33wcPnYrIme1NIS5ScucPMTKeNnak4PZijpo+STCojUEwoFhWJ3cwCpY/9Zz/RD6/SlPHDCbTT2yCndHzt0ntXKMmFjjYG6ybYzfLVYJhEOIywXy3VmLuFisdboUQrqoEllzffwnDY7nFT7XPkpxerwcyz8n984eA7v0UZzbM6pXumjPrXLF9Zf44zZ4rX5CUqvmTvDUjLnZDbGh3Wcl4zSgs2tIaopJWnlsE5ipGOgSmY+adgEoxLwSb3H7PGa7XHKyS/rh8Q5aXY+7nj66Sv8rXO/w7YbsOt6UVooCGY+JW2kklqrg2JRzVhUUxSBX9n4Avv6jwdRhBj02X0qYfyE5cKF6wxNSeF01CcUgaA9QTSqJIVCjCpGozmTU0uY8SLq0tsxfX1kD8akguVFiseGZLJmGgw/nJ9m1YxZVDMmNmXiolyZkQ4tHNu2jxKePZtTB0WW1uRP77LlFln63SX8/j7hNjJud2vH8yQIgR0YPnfqFRySL0+e5mS6x5KZ8fpslVRaclXx2ZU3Oony6jHFjcUBP7HxBivJhJlLMMLRU1UjXy7RqmbP9vjW/Dx/5bNf5vfPP43433O4j2IG72pKsXR6j48tXeb54ix1wws+UAWLasZz2WUWZUFfWL5enOGmHXG1WuCGHNGTFXs255XtVdbC1Qe9kne3QzuhXxmx8/mKk+u7nB9s8+HBFa5WC7x6dS1KPiWWC6evI0Xgrd1FlvszNnr7fONnE/af6vPk9/v4yTRuLH8cUzqpIl/RH6djbkz2+4jHNnjr59eoPj1hVY+Z+pRP91+jL6JA7uLCFBckfVlG4VFh+U75GLuuRypr6qB5uTrJz5//Pt9fOsVLxVOsf6Mm/a2vHxXyhLs+R/feOUmF+MSz7D2uGenYkbP+qDabJ/IoD1RJr+Gk3uhFDa8TyZihKjDC4YKk8KYjzW9J5QtvWNAz1npjxuc2UFrjbt6850u5ExNCsNSbs5bsU3tNGTS1V83xS34sfQsfBLshYd/nzHxMceugqH1MV5d777+D+cAtBIIVzMqEq/MRO1XOdtHHjaNYgJWB18UKUnrmk5Sy0kyrBGMc84GPdbd5AV0qcLwqv9A8lFkaWR+lAK0Je/v3hR74YTChNeGTH2K2nrN3XjN5tuLD6zepg2LscxbVNOoLEuiLCicERTAsMmcoHX1Z4oJgKOcd3/1QFTzW2+UHz0654fucrD9N9q3XcZtbH/h477lzkv0eF//8CHlhwkCXnXMpvKH0unkgJUZoVs2EVNasJTWrZoLrSTJZRyFGAjOv2bc5qaxJpWWgCnzjsPZsTJmufWGRlRcy1BcfrHMCON3f5UJyk1fLdWqn2Lc514oha9mEf3X4PV6rl3mx3GDH9im8YdKocCgR+EtLX8WvS35XnXnAq3h/Joqa5Jphvx4ynWbISxmqEOQehI06grIyEKBvwKUZe70B9mxB0IEw7COLAleWsYvZcqsf5zGfPoldHSBrH3nfe5rspWv4uyno3g/Wyntsstfjh38l58LT1/gvz/8Ol+oVbtQjNu2QOmgW1TTqDiIxwlL4nOdn53D5WyzKGyzKGUM5Z1HOGfuMXd/jer3AiWTMf/rJ3+Q7z5zl6z95jvo/O4v8w62jEdTDwIQptMY9VvCRk9dYM/ts1kP2bdY5JogPI8DYZQBsmN3u80ZY6qC5UY2og0IKz0CXpMI2stE1A1WQiRozdHzjJ84hfMaJL97rldyhSUEqbXP8CikCuaqovGa/znilXmHLDuJrRP03HyQ2SGqvqIn1NNZPoHzA7e8/4AW9t4XMUK1ZzKikn1eMdQZ5oF6yqLHCTCShD4iAS8AngZBEeSJRCUIvjTLxHJU+v9cmhECfP8v0uXW2njOUq4H8emxQBAXp2bOk48dIdhvNuixKbXsF+WYNAWxfoQqPLBuHpAQ211EeyYUDXJoAl0ZVElX4A/23Ro77yPlrlXUbWTFvIie3zQRL399DXNmEowprH8j0+bOU51c5fWGTDy1c50uTp7tn0gbFphww80mnDCMJeAQuSL45e5xvzh7HCMdAFTybXqUKMSvYczljm3G9GpFIy6dWLvHbf/IUK6ufpf+b347Mog9FWicESEGSWpaSeafAIUXoUpjWpAjMncGIGC62skKKwLTRcnPITtY6a4rJqeL4zKcAACAASURBVKzJREVfxnTwyY0bvLV07p4u424tlZZEuG6dRrgYNTrNtXqRmU+p/dFTXntF4WI0KYXHLfXQ+z14iJ2TSFNcL8EMK/p5RWos+yrgBaRLBWXIkVbgdVSR9T0Xu5QyCgYIL3CDFJOlzS9sHNNd7rDvaUpRn1pi6znD5NmKwcqMcW+IsAIkzNcFspL0LydR/mgocEmkHu5dlRCgGgn0LCpKR7luqPsCaRutuUMKJC6LlMV6HqlskZHCVtYH64rqu/H3tGDVoKKQQj0MpHtD+vPqnjqnemOJvcdTPr/6FmeybZ7fP9NJxM+dQQtP1TSsShfrpYm0rKdjrpdDNosB6/mYBTMnE3XX6BnbjN0qZ7fKOT/Y5scGl/j1D83Yosfgd/QHol++t84pBNzOHhf+wwEvffrDfO+XNvjEibf5SP8Kb1dL1E41lX+PkQ7bFMNfLddZ0lNW1IRXyzX2XM7cJ+SyIlc1kijQuCBLVvSEk2qPv3/jT/ClNy9w4b+oOXfllQcmBAAxWhR5zpO9N7lgtvlSo+ZqvUQ3TvdqvcjMJcx8wtSmSOFZNDNSmTCXhmtuxGY9ZL6eMdgbwkOC4bq1qClMwv6/8QkmpyR2y7F/LUfWAmWj8ka5lSOcwOYBdIC+5QtPX+TydIGrOyO8F9hFweU/0ePEdwzpm28d3MBC3HMHZRdSrn2uz+RJy9I3Df1rQ3hKMV/3LD25TWkVxTxBvd4DYLYROvHL/SeAEMn762Fcnx1EgKm0ATUXqLmg2HAEHVATGbnSC8HstCeoCEJVFai5aAQBwD47I++VjLKSy1eXkLsmOkIZVVqmJxXCL8NL9+w0sPnxHnv/UsGSmeGDpK9LlAi4IHBNJ3y3yjtx18IZpEgwwjOzSYzuveLV8Ql+/VufQPUs/X7BUys3GZqSUVJQe8XFYo315X2unE5AqR99YO9h974g7h32jUuM+jkXv73K1z8mOXF2wqoZM5MpY5t1rUmIEVQLrtwXOROXNg+1I5WWnqwifJ7AUM15q1rhy+VT/N4PnqX3SgKvfAf3gHFBIkkgS8lELNi3taTDVnjTOCwVI8ZGo64UHo9gyw7YsT1cKgnmg13Ue2pCdioaajRCLC8yOS0pVhpn5WKNqY0eZCkJMsRIyUGoJNfnQ7amPcpJimhEJ8ulwP5ZzdpnPop85RJudy/+gmPofgUNGA8opA3YHHwWOsiKkIFqKJA16Gl0QkFBda6KUKzrCS4JhDQQcgdWoG5oZN3ouuUOmTqcVviZAiFxCxahA+wYQh3VTXwCNg8sjmaMsoKFZM5VvUCQUR7d5gG5XOJNfs+lyV0u6A+KLpsZ6YJ9mzF3pmtWeQ4k5tsm1GHrJOg9eCuwTpIpy4KZHwQcXrKQFlzr14h2Y7tLOzZQjXvhJc7/x4Irf+vzfPnPXODvXPhNXJBcmi+TSMtAlQfS1QT2XM5mPaD0GikCK3pKT8U2ZhEMmag5qff4lctf4LU/PMeHfnUL94OX71jG/DhM9HL8QpxrGnvDlfkCQ12Sq+isfBDMncE1c9aF01GGO6WTw75YrnNlvkCdC0JqHthabrVYqI6dNP/EGcZPDRk/YRF9SyhVvLEblL8IoIqoX+a1R08VYip5fe8xVAn5XFCueELmcUs1O5+Bnc+kPPM/nIFv7B2LY5I2YMYBnGB+QlD3NdWTc5LUsj/LKIsEVyrqC550S7L6fUexIKkWBR958g0AvjF7ErFQsTCc44JgvNVn8WWoe4F6IBDa0+uXZIuWvUlGvZ9y8nScDrixfQJVCJIxTE8F7KLjRH+Clp7CGXyh0aWgHgbcRsnPPfUiX/nyJ0lv3tsNN0gwKpZFBqpgI4Hr5ZDL00WW0hlaHn2SEunQ0pEqi3a+k5Pv6YqljX2ECCTa8UT/Jmtmnxv1iMIbrJecH2xzfXEQO6IfwI4X8RcCp740Ye/aBn/9qb9GebbkP/rcb7Nj+2zb/oEqrPAQYn0pbVRj22hJisCPpZf4XnGGv/a//jKDS4EzrxVw/RgECO/SRJZRDxLqoCmCJpGxziYbFd/DO5A8tCXGqFFS2DiSUzlN3Re4TD80dBGHgZLleo/98zJqnc01wgqCCbjMY7birVQvOORckuzE6C+IGF2pUmD+v/be5Fey7L7v/JzpTjG+ObMyqzJrIqtUpFiWmjRo2JYhyVoYlmHAgNGbBroBL7xtoP+B3vXCMNBoL7oXhrwzZAs2YMMLqy1ZssRJpsgyWWQVq8isyjlfviFejHc853hxbsSLzCpKyqyXWc/2+wIJJjOjXsaNuOd3f8P3+/3NodoMP0vOdJjaaY+LFKrTOZW1SHVm0zBrBMWWIBqUFNpDJWGhqawk6xekWYnseNiESdSDH4bgWnfh7myAkQ6fNUjpKSpDMY1RJzrsebMgG2BqWEhPZ6siTcLevn5UhgcQIGswU4/tOaKNguM8Y5rHLMYpCE+zXbOxPeWlwQnXkiP+OAEXne3RtBHspIEykYiKS/EJd8shx2UniHm9QEuH84Jm+b7bxEG257ByYdBzdTBeZVHLiuDrnZ/yoBnw02KPVNUM0wKuXEIJiV3KuJ4Qz4yECYQn4bd/wOA7gs1Xr/PwVy7x9t+4yQ+LFzmsu2FNc0sboC1v1LI8wK8O8mtmwjuF4JV/ehN7cIgvy8+1x/QJGI2LVTvp0BhpkSJM40KQCsFJ4T4xlWrahnhuDZVT2ARcJM9NcFrPZsqhIt8J0ynRCEQt8LFDZQ1iP2jpRNYgFhF6FprD6Lb8a0BWbTtHhAzL6VDquFhhet2VbOcsaQVeQd33bHRzZG9BbSWjjzfwXkAfuklJPyrZSuZ8tzAgUmwqqLue8SLFKItJGrwX1LVCzBQqD+996bogC4GNNc4L0qgmNg0dUyKaKDTEPegylISDbs6iMuTTBPPQUO/WdIY5v3b1A3ajCXt63Dbkz/YOcBFsxAsUnkhYrugTNsyCRNVUTuO8QvrQ5HaIcCYJD9PlPbysdC6n41XzfMlh/FJ0hBENd6pNjLAMopzp9hbRrAvnKjgtb+h1Fu3RCWaxxyVV8rEKZEMjLBKPkQ2102HE3garRNaoduQeLfe45/mZ0uPPDOJ0Z7wUjo4K68WX1AktHH1drLLEcZ1ivVhJWayXKOFJVB2WVKrPlg4/KzSxwHZtCEDShwNuBXauSU/C9KvaDk9dF4OXoXlc9y1NR1DsClwcok6Thr6UV57JtZg+V9CHR+2mYXlmjXFhw6+X+iMezPuUtSbaW4TBsnRspQsupxNuz4fgBdOriskvlrz18j0+uL9L6Q0bgzmHRz0YG3ZfO0IIz4MXNlAjjRkLklempFHNwc0Nkp2cty7dZ1bHlFYzfPWY416fJo2IOhWJbrg+OGYxjBhdShnEBZmu2C97lE7jYhkcOcxZByfPbjJrg40jEw0vxwfUfcXP5jsUrR2KFI5InsrBTuqUxksi2aBlCFTTOqHxkspqUlWzqWcMpSYRNdZLBipnK16wvxejT7pPvU3vuQm5fFUhLRgCXWA9MC2hhHtEeyZbHxnrfehtnFMtlpfikYAihaO0EaU7bWyXTjNxCbmLkIR13UtC6rIJufp55yU2LQNEa3/jDKA93srA3TEOWypEIdugCviwntomPmRHog1C7TUJJ0LQStvv0kM5EBSbhq6Q8CxyYg+LJmI0TykWEWmnQsngWdQ4ybhOOClSrJXUHaCR3J/2sI1EKk8vLpllMXkTvqumUVDKQEcAqlKHj8kLItNwNTvhnaOrjBYps2mCKBRee5pKMS1irnYdfROCUqJqlPAcFF0WTZjkqgJkdbafg43ghfhkdb4S4UhETSyadnIuV6XdMtNfZU94ImXXsim/qmxCMuFYz/WlcMSyoewHysjT3s7PPji1Tz9f1SsimhENsWzotUrfsU1Xte16cDLChmgMj3Ckzh2kxGuBbWU5jVdMm5hZHdM1ofF/pxjys/E2B6Mev/LKT9mJphxUPWZ1jGvHuY1frnY+B3otIVaSEhFFqJ1t6o5ARBZ5EIUJ01aFmiiiE0l+JYzTAVzicAmYmxpZCqpLHlFL9FziBdjUMbgyYVFEVLOI/JIHoegpiT9rDXfLM7pxsIX7aZd0LPBfq8jiiszUPJx1uVluUs4jfKGoBp7uBwb/vS3c2w3J9oKXuiOud48pneYbP3kVsx+x966n6gaKgfhxhzrz6OsLvrJ3l98cfp//ePt1pjcH7P5J4ESVGwJ/N2E8iTjqzthK5ryYhnJnZmNujjYoS41tFJfuOczB7Ew/BrtR8xu9H/JOcS3IU6TAiKb9iDxaOLQOQUVLu5rgNV6i1OmXIvHEqqG0GonBekntPxlGOrpkeg3S45jsKd/zM26InzYOfFMjrGfRnjuzRlaE06xpGZyWfyeFY+HFik1+LmE0NpbUTq++qGXDULdp9LLpCOGLy1RF6UIgs06u/j6YtJ2D1GlNUqI2hkx/+QpNBn6uiY+C22WhImQdMiVzIlcZk1ceVOD0ANBIRC0QDXgDCEiimrw0UEps7GkyAVK2mr36zCZ3XgYqQb2IoOtwsWA7LUl0OHCRtkBFZBo80Gwr3KRPMvKIRqKV45d6t/jt27/M3Y+3SR5ohIWjL0PTt8huTfSzFDMVVLcyPhzuMN1OmZ5kRCcSrzzFtiD/YkGnXzAwDfM6wihLR1fsxZNA3tUN+SLGH0dh4JBGZ3L9SIXsZMjY0hN1eBAiyIQKKgvZ0DMFqVOtoFdROk2qqlUGtbxvpQhtFi0dkdTEKlB9jGhwj83NE1lTXWoohvq8Bif/yO9l45l7jSNo6Gyb8C1TxMAmby/SKxRhYrdom2+4c5BRfAq8ltgoNBILb1pSm8A6uWLhFl5iXXgaZbIik1WQrzhJ7eRpeSfOUVnXwve7jF7XNJlHLSTRJBx6mwX+jpeeaBL6btUwBCUXeVzs22mdQDQisMaNBwlxaygoK4mPPE3mn4k7gZftr0JB6nB9R2pqoraUSXTopcQ6OD/2o4J3kh46d+AESjpeifd5OOox/KFGNp5yQ9B9Y8Reb8puOuXbt9/CTASdqeDgap+HTR+mGjMPrO9ix/H2y7fZSWY4L3nn4ArzOiKPzEr5EGmLd6BnEnC45GyOpjAa2e+hI0tPuhWdJRam9W6yZLICCQOdr+g8sWxW6oxlcDLSrvrCRhjKVrkRLXunyJYrJclkRX9nRjXYeOr3/lzNg1Th+DeTtxmonK4qGDUdrJcY2ZCIhljWRKIJqaJQKOFIZMVD22W/7MO5YDV9Et6oMLJ2ZuVC0DhF4yVmSbIsOpxMMprjhJmN2Y0mdHTJpA4ZoWuDU5jUnIPo5F3gNwEYTTXwocmtYPyGBRnseNVUYeaCJgkBqd6wCCvAQnW5Do3zUrbZoMB2HD61jPOEOjfoQtD0gsxFJDGiLM/Un0sVkD4UTHccaqRRhebWyWX8sOaNa/cZpjkOwff/4IvIUlD3HXav4d7fb0iTisk043//nf+N9L4gHjv2f7Vhe3fCm1sPVt+tv54z6yRsvSOI3035v0a/idioaC41pFlBVhl+cOcK7jCQc196Y5/rvWP+p/7HfHdynf28hxSefj9n/rJjcTvDzD5J5H0ayG6H5sVtulmxGiwtSzYjGhJRr6RlpdPMbEzlNPOWSGy9WGlh9VrLxfkQiCwCi6TwoRRcJhqxrHl964Af9f4bCU74IPbNZEVMS1BcZk/CrQKTRaLa5lvtNTWawurA8l3nwDwLLdZTIDTEw++XroHrbFuAstE0tUIWYnUzqLaxCKeps1fh550neKNoeh5cGJ07E5rcIrK4SuCq0Bz2CtAOKoVeSOrMhizQBdqBbAetQoaxtHdB0gHtWL7bQRRnG5yE8wgHnWHOvOogrMKMJZUwjMtkVbokBwLhoO6D6Vdc2T5BCs89N8AcCKKJR1WE6aQL30/pApl2eQd6DaqE6FjCbsNOf8ZLvREfTTaZTxOSA4VoYPRSym4WoXB8PN3kYNplpzcjynI6ccUk7XBmLVatqbuGSAcO2TIwScSKR2iRNF61D8jw99afBrLgMBMY9hCCVBAFC0pnmLuYwgeJ2dLWCCBRzWfa7/Rcg5OXsGcmxLKmdKcs6FkbsHqy4F69Qe0VRlgKZzjyXQpvmNUxamCQVY2bTk9LgHMAr2TYDrNGsGycpLLhy7ReUFmFzxV6LjgqO+xE2eoGWEIKjzMedy4yJ7/yV7KdiOzahNn9LtGRQtaKJnPEw4ISqIwmPmiFrl4QjRSdu55FHmGTwIuKxoLkyFNuCWwH0qhmKkPAw4csrLq+TaQU7sZnW4CwDuGg6sH/8ebv8Xt7b/KT4x2a399G55J7yRYPOg1CwM6+o9iSXP3FBwzjnI14wV/u3+A/d1/mm+/+ItEJmLll69uafG+LH33dY1QgLcqPU+KpYLG3LGlhkJVspzOcF2SmptMriPZj0iPHw8GQd65FXEom3Hz/EuZEcuXX7nI5GdNVJb/duXRmDygRRRRbhlhZ5s6vMh3VTkbXH6AOEYjDgGnvZbdGIA70F0dXla2jgeFh1aP2iuM0hJKNNgiWzoSE4jMUO882OC0zG6nga28xeiNeNd0WNl41wes2ai+nXbVXK7e9hYvZ1hO+0H/IH/2tr7L9ww3kH37/XGRMK4gQeOM2TV4ijGYtOGisDBlEI1a1vBLBlsK3r4ukbTOnz/FaHoPMMupIUtcKlKfpePxWhVCecmHwc42aK5qOx2lA+kAVUKGkElZgU48zUA4FLnVI44JbZlpTbSmIHVZKpi/GDMo+3Di79x/WkXm+OX6Ng6KLc5JywyNLQXrL4GKNV5BvC+oe3D0cck8MQHhGlzNujYdEEyg3BfmuIb9s8ZFncX8Qskfl8RuWph/6hbIVA1eNYl7HjMqMWRnTNIrmsqDpKPQU6psZ/3b2NnohsQmk6tTCuck85eCMbgITVAdDU2Eei3fhvOnVtNgIt+oDrxMxH/k82zMKoKUlt6GV0RENU1GjcKu/H5hiNcF9GjyXzEkoxeGXO4xfD9yKhYuY2ZgNE6KsXC/h2uCkcJTeMLUJ16JDvtS5y+/+9Tc4rrts/+HzeNdPABGmbImsSeTplANYyXEaJxE22GwsXT2X121dIGFGqjldq3QeIAQiS7GRDNwe7XGZ48reCZVVHNzaQE8VeiEoLtlWXNvaf0QCVYbMxSZhimcT8IlF6yDpyZIKvxGCcyMV8xc0yXHM2XRbArwO7+cHhy9gXVDcV0NHfKzofexXotvFC9AkHnscI3OJtPCuu4ydGbamnskr4K4V/MKVB4yKlKM/2QsMdwPqpTlJXFM3ivwgw0w1jZXkjWGcJzRWYq2gvtxQ9yWd25J4LBA3NYtLgmprbRAE2DgIkc/m+hVNJsh0xeOV4lJu5R674Vbi37Z0exzL82mEZe4DFSYWoNaC2dJ1w30GmejzCU5GM3rTs/HKMYU3K1kHhAsa6AVSOKY2WdEJ1n9NXYLC8deu3eAbO195Hm/5ieBlMA2LZY1pSYTOh0MXBM6SxTzBjCXxKFx3yAxDf0qsBTOvz1fPSRiDiyRpWlHcSUkfCPb7fbQJHk06FyQHoAqNi4IfkVfBesRFHm889Gv8QqNmChlboih8RpNpirybYDthG0018NRddabBSVahpOxEFceLlHwRk95X2NRz9OsF/jjCTCXxETQp/NpX3+V7D68wGnW5vDmh6GmOvjrADEp2BnPeHt7hbjHkj5O94NNk4R+89Q2uRYf8k49+lXuloSolX9wM3KiHRZeBKdiOZ/zr979CUyW4CBaXPOL6nOZhiiwEf3TrVd7Y3ed/vfwN/vnsV+jfOqO+m9FUPZjVMT+sQstk7mJ+Vs/4YfEWP569QCSb0P9sNa6rz661KgLWAlgoZZUIdKBUVeQ24v89/npozaiCWNZkouLF5BgXPX3m9Gyf0Ws2nW7QsNc9JZapVnsGn4y4608RgMoH47mdaPqZIvEzhVhOMNa80r1oR7IO2/abzCz8ucKvBJZKupW4cqk9OxcQErczpOxLhPDBUmThcVYgpcP0S8oNR7kF1dBTDR3NTkUzsNjUw05JtLdgY3NGtFlgN2v6vZxBllNbhaslsia4UopQ+rkzflwK51EFjPOEIo+whQq+TB3P6y88RG5VNFm4NuFgL56w05nT7ecM4oJhWmD6FUp5FmW04q7ZOJSxXsKOnrKlZswrE9RaxtOPCjbNnN1kxtV0xKvJQxA+GO3F0Gw1fO2lW8itCps58nHCvdmAe/UGshLBxO4M4JXAtZSphQ/bVGqvOHIxh02XURlYSMssXrU6uvX/v471pjmwKkU/mO1yrxyuSrpIBA7UZ2nsP5+GuJTEvZIr2Xi13ilu7Wytl0jlMDgSWTNtwmjdEj7Esq2JITBpP+Fzc142d3govKbwIXpaL/FesKnnzGyMPjKk+57+rYLKhv1fDkGiagZxEXpTllbqcT6ik4wMD/7qJvOrHvKo1XwJTNzw4sYJv7H7HnwpvHbPjLmkT/h6nPMPb/86f/T9N/ifv/SnvJXd5cRmLFzEwsZciw85aHr81vtfD5Ov1CM2KrR2NHWKO2tdoYBo4hn/aCP0wbSnfCPn8s6Y/+XKt/n/6r/O7dk2shHomeA7R9cZRDm/fOkOEs9B2QWgvJ/hTyTfzF4hVg2+X2MbCQ6+NXmVb/jXGf9kM7RZjSdVNdtmyqaerygy9TgmnoQS+MtfuM1vXfs9/p/+6/zB0Rd471sv83C2zT+e/BpxAeXGGR1NKXGRp2tKejJoWhcu4na9xUeLLe7N+rzUOSZVNSd1Gv6TtQTBtdHl8cZ57RU46OoS20h+crjLYhjxQnxCVxVIHB1ZBm+vp8Rzm9b1spKdaIptJwKZKpnZJDgRcKrcP82m3GrRQaAWuODi9xkabM8KXgmWDH7rZWB+O9kuBg1psJ4L4qlDTSoi1dCRJbUL5nO+takIDHF/fso6KXFRS7isFTL1lJuCutLMqphMlo84Ltj2u6ucRpaBEmJEw1AtSERN0g4MerJgtz/jbqVxM42bGpwEEouNz/aWdErgDOhcYKNAYbCVYlLEfG92jdEiBSeYvOqxg4atJPRB501EZcN7ee3SAR/6XWqXBDdPK9EHEXhAwH/44A2k9NiOA+OQieXefID1gqHJ2Y2mJKams7Ng7jvoseL2yZBvleHgb8Xz0EyvBPU4Jq3PkIjrfXDgbIXmRlgWLuL7i2vcnQ+ZFxGpqslkxaELgTg4EMi18xeGOM0azUAiseJUbjZfxMw7EUY2RKI5zbgET22B83x6TkKwmS64HI0pvG4p75aZTULm1EZp6+XqBodTgfAS23oW5A9n6PdzFniU5xQCTW1DgFpOI6MpmKlFThdo4ejJnKolajY+MMQbF6xaz01ZRytBkcHR0mYusL5bAWto6jsckrmLw43vT5hUYRNLbg2113RkiRHNajqbyZJX+4eM84SxjDHjlnJxtcCesdGeV+Ai0dq1iOBfUkrm04TvHb/IfJogrKD/hRG73RmXkgkPix6TOmFWxfTjgl/feZ/aKW7YbarjBDVTZA/Cd+40yMM0kFC/kJOkFb205OGsy+Ei40o/eMFv6ylv7OzzM7VNeXuTk8Muvzv5cujT6CIEpwaYaORZ6gu9XwmUI4LZ3GHd5d3JCzycdinyKCgWWnqAFKGKgbVEQfhHxBnOS1xroWKEDQ+mqSEfGhLRIHHhzwj3szAaXz35jr9n7+fUYhDnDNScohm0NijNJ8S8S56Qa7kY6804JRwDNQ9q940Bbjw5F5t+ITC6naH9ok6V21o6LukT7poNVO6JxhU8PALUyiQelk81F0hrscWeF5teEa7Lxh5hXPBmEp4reydc7Z2cEmbXau2i9QRaBtiwaOiUwAcQCcvL2SHf9S8SH6rA7YohSppVf+TMLsGGJQQ2EdQ9hzPQualxWnPr4DI+c5BZJrOU6Szlxv423U5BN67Ia8OsjPhnJ3+Zqgp+Vcm+RpbQZMFETpXQdML4XylHXStGTcb2YEakLA/nXY7yjHfEVZR0ZHHF5I2cJGn4T/uvcfvGDmasgmlfFtwa1AeG9OiM7m3X+klJR9IKfRcu4mDRCfQQTo0dK6eIJCtvtSXb2z12Tp0X1E4FyYtaMFUhYE8WCVObsKMnZLJk6lJ85FAvXMLtH6z8uv6ieG5lXeNCs3i9+f1oltT6VD82MVhiOX732oeFjLP56ULGzxm+5TmppR9V21QUwtOXBbFo0DnIvMGVJY0P6bMWa/YUy/9G+fOVOUWEPljR3qDKM0xytqNTouQySFnEinPnWV5Ta/Hafu3Lsn6g8jAIaMJBt6kjM01oiJ8h818QDqfTbdkVO7yK2kxF4LTDpDX1wkAtEaVkcdkxSItABygN5UkSPNFFYMd72QajInCa6p7Hpg5qha8k1BLbWxBHJbUNWeZ8EaOUQylHt1cQaRukIQuJWgjqqw0itmjj8MKcWUMcCK6dwq+IlY1TzMsI27Ti+jWVwlLdYNsHzPIkrk/yHEFTaddKRVkJqkpzVHd4JYZE1ExJQXlcJ0XoJw81zyU4ee+5Od7gvd4L7JlJ0OKsbfJdmrBJgiZnqTNbYplB9WWBTyxuq4+YTM+P8VxrNhdGqME7XLWezDtqjhKO7v0adTLDAqMy48D26ZuCwmqkiEI6LW3Qop0XnpOUlBsOrzy9nxhkE8qk8uWgWk9kjfWnRL7H+TKrB07rvljgVq4NsazpxhXTgUe9PGO3P2crXfBBb4jq9XCLxdlkxu0yFWdg8+oJb20/4OMrWyvN2HY6QwrPO99+nfhYkB54jjcMXxg85BvTVygnMdlHpi0PPZe/fo8rnTGF1RwVHSZFzF5SMs4TFu9skt2H7n3L3b/XY/vFOX/z8vv8q4++QvReRu+mxxrI/v4DXuqNeLVzwA+zKfM65u3NO9wv+rx/vEfRy6h7Z5M9i7We09KSKLeG6XEHRPDkWja4q9a/COm72wAAELdJREFUXy97oMKvotNSauVosyag9pKeyslUiZ4JioOE/9R/ja+8dostNefA9pHG0mykmAdPXq4/o7LudGMHhJ6Tko/2jx63Y4DwASj86umr1szolq8TkcP2EvRTROJnjWjZV5E2bJhRgXG7cBHRUY5fhGnJrIo4sRmprEhU84ktF+cBQmtEluJjF/bNuaCwtwlsJXM29M9P0aXwa2Xd6bUtVxEBDNWczFR47SmPU+4XhuxKy65P21XlZ3EdzqOLwEeazhPupkMqq1DSYZTl5niD2SKhc1cEmoQBv9C8e3yZ+WGGnKlTZwMJszJmEiVkuiLVNT4WdExFZRXTKHg3FQOFm8OtkyGJqilKA4mnzgABx/OMVNcMTU6iGiJlV5q0ulGokuCKcBbw4btbnp9EVNReImYKnzpEfGpdtNquwml2VDu1ciOQIkiRTje0qNat1q+WW4wmGbXXJCJkhkJ5bKoxT7Em6tmd8NaoDCFAKRLdrGxSFI5EnC7bCxmUW5lfrUMRJgdWSCQObSzVMEJH54/wJFvWrBY2eN6ooF0aNynqwQg3noDzzMuIUdOhrwtO6qy1oThfAUqkKSJLW7F1CDZNBtXAcSU5YdtM/9yf8WlBt/YaIxq21CwY8RlP8kDjlGK2E7X/dhKaqJ9hIePqOqxH58GbqRrH3GaIMZbYNCS64Xi/T3TfsPFhjTOCk1c1eqK4d3uL+H6bLZpQFjoN41mgulwbHBNJC6Yi0xW5Ntiuo3QSrwRqLpk+6PFupalzAz1HuRHMBOcnKfeEJ9U1/SgnVcHetnKaojJ0Fx49rf+cK/sLos2clpy6pK1MzFhSGY+UjtoprJBrQSfYttjlJN2HRR1GWmqnVrvtlpBt2a5KqEYxhTetY4NESkeTyXNU1q1nTVGESOLwpGobxut9lkcyp7YBJ9uGeHiNe6RcUNrRJBIhz0vtA6IJvRPVmnHNm5hE1Qxbf7zcGvx8jqtqhApLHB+UA4YmZB9KuhDQhAq9mXMQp2S3gx12kVONS9yK8e0Sx83FJqmq+avdDzixGYU/7WLbtSdr7VUrNBWr/uJQLejJnC9HE746vMl7V/aoij7CBo+nuu/JX9shyQs4g32ENpKMvii5/FfuUllF1WiKWlM1mofTLtevHZC+WvP+5ovoqcRMoNlo2NibMOvFlNOY7IahGnjEbsnucEaimxV5EWA7mbGTzrh/aU4uM1Sh4XLBsFME5f40JppKym2HzRyvXdvHSEvlVDvsCbsbN6MFb+494N3XXsOrDL75mS8fnEfa4GjpEHRk2AqTHAnqoSCOG2Y2XlU1p9lTu4G6Ldcbr1ZZ77JHVTnN1CWUzqz0k8DKhw3AGEvZV/TMk4ea53PCpVwLSI+mq09V0pyjhjHQWuue9lignY6omoqwl85X9SpoN5XipE5XPbdILte2O7wP+98+d8QRLjNBVS48NnNBxOnDU7j2KnhHt9/nsvn9abDttHWZGTskhQ9lfiepVkQ9IYLcpe7rp3rSfhqCKZ7nSmdMohtEO0WV0mGtZBAVvNY7IL00o95qcAZk0rCR5ewOZ0T9MvSbYkechAUFWjhqqygbTdloZnXMoonQ2gb9YexJ04phlpOYJgw5AJs5ZLdmEOVhmUV72K0Pa71LG3o+LvLU2bO5yU3rRKBzDy54hjftUttVq+UxycpSGLzEektm7mIKF3pyp0s+PJL2e1cOGxNcTp8Qz75x4zy+abDtjHgZUZcyD7U2sbKItQi+Jm1ZF0VagSo93p0f4zlZu+D1A3REzWY0R9ZhB/2B7XFSZXg7am1IHEwMd2ZDrmdHdHSJi4PMJXcRvpFnvu31aeA7KeVmhN+uMFFDc5BiTiSqElz/2hFvpXeYupTa65UP1zqNwstTaoVD0pM5PVnwh/M3eFAO+Jd1xq35BtNFgioFshbUVp1abJwRS144SO8Lvvn9Lwb7YOO4/tIBkbTkjeGj0Sbv7+9Sl8HewyaeKAlkzMpqtHTc+oJGeMhnMZO4JtbB0reoQxl299YWNO1oy0O1aenqhkhatvojrBMcN/2wARj48GiHLK7YSHIKG7zKPjrZZDpLqUcxGz8VdB+c0SRaheUTur2plq6VsmrXWkm3CopAa5TokTJUOEvnjKrVFdm2BbOcLs9swqRJTkmjLvRejYCpC6VDnQm8Pk89pxbeWmgaGic/Qbh8EiyzEucksnKnm1gea75/bnBws9qmp/KVIBLgdrXFwaLDwB2vXqoWknGetPyS9vNAhilII85FcHKJps4k3gbfYJ9amjrotFQ7dbOtYaDEnTbAxaPZsF36yRAeNP9h/00+OtjE3cnweyWvX3nIB70MtWjvh9OXnw1a3qWaSrzxuFgwiEKz/XCRMT3JYKJRC4kUIThp4SmsCdMtZYnTmuIoRU8UI+1J0opuUuKcDPfjQqGKYJznDNjE0VjVlm0C5yQ0YdblvWQ6TpnJhAPV4+r2CbFqOL4/QOQKswgi8qpzRh+CCDv2ZGuHkoianilpsqBGqO2pV//yf39elfPzHAos8lTz6oMWtvasPNv8kh7yhHgOmZPFV8uRs3qk9PnUl3/qSHpNtW8Fet6APQcn+DG8P78UeB/SkhJIlh/mexyNOwzWXmdmguk8aUs5v2owlk6BFedi+4pNDXVHQCVxkcR0KxrtaWzoBy5cTEeeUjkkyzVBn0TwVQ88qA8+eIHhjzSX/+MRH/29Lf7uL7/DP7q/S6PNasrnlDizzMnLUG6YeVjf1dSevXTCvImZLRLUfkRyIIhPPFVfMH0jZCwnRcowyQMNJC2pZx26HwsmJmY+lMQm3NPeg54JzLTdaNyDaiipak1lFYU1FJVB5hKXAg3ImURWAp0LHn6lYdDJ6dwwyDowzl0MxdbZBCevJTY+3S49kCXb0YyqDyhPVelVGbeOx9stdu1crgIZvvViE7jMhSUWLkynF15RtMHJGUCdx7KuRdUEj+LVIsnHbuMljSDUq8ttuac0eCk8NQpXKsxoiq8++yTnrFANDMUOvJ495JX4IR9X260xvOTfH/wC9ShZZXfeeaIx5OOwvy5TQb6wG02JZUOynVP1up/zFYEqGqKZx4w0TSmxS2ve2LJtZgzUo26VtddMbMKPq42g2t+oyW3Efj1koOYc2y4nLuNX/9KP2X+zx+zvxHwx/hk/mL+IfxgTzQTbX5pxp7NJnUXwFGXAp15H6ckeOmaxDGTEUvC7/+VLwSGgDEsPFlcENg0bZfSJpjnu8YAe0zdHGG0ZT1MQUA0I22YmhuNiiCwkqhSodgONjQNrPD4WNGWXh1GXe0OLKCW6FKhShdVYXYdwYRvN4jCjWETofmgo20FD7yeG5OhsH1CLJuKB7fNL0WGggYiwtbkuNaMyI9GhB9a0gl4pPNILlsXlaR9KAKolVQvuFUPGdYKPLaLURGPJYd1j6iIcAgGnxNonxHMlC4UaNghEH18k+Tgs4hNPYetDeizyMpSL5wReB+awkc0qsCpR01Elh4sOevroQdNzj5qplol7+jQy0gZvp/PQ8LceYT2ylEgVFHS+dX6EpbVGy/wWEghiz4WPiVSDysJtvfSVdu1+s18Zvk8kLC+YEd/Lr/PN0avhgFehr+F9IHqeWc+pcSTHDVXfrJXLpl3BFTYPex0GGrIKXB0zB1l7jjd6YDxyoommElWCGQuMDLWnKoPbp9e0o3S/2jDMcTiU5Tj0q/Dhz72CEomZCaIJ2H2NjRU6FzTCY53AzDzJ6Azvb+EprWZqU4wQGNmEz6IVLi8Xap4uy1y3MPIrAqdrBcDL12oCobOyGplYmGh0HtxDltZ2SjoqyVM5bTw34e+L/TFvpvfCqN3HK2uRJawXWCGIRBO0O6hH/JFqYOFiRC1gkePPUVmXHFR0b6X80dHr3My2+XCyw6v9Q67GI/ZvbTK4I/BL5aR3DD6usGnMzxY7lFYzqYPR/r18gPjTPoOPzojj8hngY4WNg6WJ7VlkpyZOGtK44v3ZJaY24WudG2zpCR1RrZrfA1nzrc4RH3Z2eC3b5xfiu7xqRlReUiNZOMOJy/h347e5mw85yLvUWw02Vrz/p9fIDmUgIJ7VwycviH//B+yuj7LDLvL2t+1iifV/r5XO7P22CYHXWrz34NyjUyfXiln/LNueTzmUQqnw86wFpRBC4L0P70UKfFWf+cN3URseNAMcdzise3TvOBYveF7aHXG9c4yWlnkTrwiXsawfCVKPJxPL5bGprBgaw/RSzI3JJeKRYFonK2skrVy7EOOcBicgTCWcYUvPVoEpkxVGNvRlHixuW//tRNSPBK+iHXMua13/eXs3PQb9cMIwVnzwB6/wXuLRc8GNzovYxLP1rqB755RGgPfE96cMteCP/+BLYWJSC97PrqNK2HuvIbkz/dyXYC37Xs1Gg+lWSOVwTjCbJ3zrp28gPPyL3lfR3Zpup6ATV6SmZiNe8M7tq/jbGb/lv852d85GvKByYez+YNojzyPc3RRZCVQp4HID2yVIqKskPNXPakehB19XT0fo/Ow0q88fPhjcZabmBTMiFjLspTtxxEeaO4dD+nFBompmdRwyIhFIl4+rONb7wcvtQl1TUjSG24dDomNFfOKovSQRNRt6Ti8uGcXgzw2V4LHg4b1nXCYcNj2umFGrUoeeKshkyaaa0ZEliWjIZEnt1SPB6cR2sD5MGj5R652DSZ398Ab6Q7j2+3/B1//4A6Ifwyv/vwrv/7HP6/O/IsA5hIPty2O2sjkH8w7jSQd3HPHm/32X5uZtAPQr16le3GCxO2CSSg77gu0DR/awZPzekON0g2MJsvKoEnZ/lmOO5tj3fwCA0IYP/tEv8cUv7vPl4T1+x/0l5Pfjz9888L8XtBy8rWTO2/E9MpEwb2LSBwv6N3pMbcZP1C5JHGQ2UgbBulznnnmBc+GXX8ugvIduVlLWGvFhh97H0LlXUjnNQJa8aI7YS6fc6Fm8OS/B6XE4x/Gkw08Xu1yNjjm2XUZNh4OqR+0UfyJeJlU1XVWSW7Nqli8jdleF9d1jlaPmEj+dnaueE1KF1d0/T6gqBCKKAufL2lMvqnVPKiHCCnClHn3N5wR54x7DfIcbi5jU1DRW0fluyt535riDo9Xr3P4BZjpn+HECSuFjE3qCecHO7Q4oFRZqWhfKoPEMXxSr4OOtxUwFt0YbTMoEcyNl+J3buMOjn/fWLvAEkEcn7P5pj/987WX+SfI36OuCP7zxGq8fnbD13ZLNH2iqb2V4FQUnhGWF+lgVtqrwHntmOBMhrCcaTZCTHE6mfOe9V/g/+dtUVvH9D6+x/ScKfTDhSZlbz62sq0vNqEqZuzjstHKG4ypj0UTkjSHVNZkOAsrlpG65VfRyOl6JE2UNrig/98O7DiGDfvDPsnARSgXWm3efnuwJiZACoeTPf81zhB2NUN7R1K+s1qv37zrEN//LI5mdm89h/nP2zO3/Bf4h71CloMgjjp0gHrHKyi7w2eHnC9JbU8zhBj86uYwWjuYoCKv9/X1cUZxJEPAEl2mEwBy8xo+391DCYw4MvdvVSvT+JBBP0r8RQhwAN5/4XzkfuOa93/nzXvQ/wjXCxXX+N4L/Ea7z517jEwWnC1zgAhd4Xjg/0v4LXOACF1jDRXC6wAUucC5xEZwucIELnEtcBKcLXOAC5xIXwekCF7jAucRFcLrABS5wLnERnC5wgQucS1wEpwtc4ALnEhfB6QIXuMC5xH8FX1tb2tdbwOgAAAAASUVORK5CYII=\n","text/plain":["<Figure size 360x360 with 25 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Data type of training data:  uint8\n","Shape of training data:  (60000, 28, 28)\n","Data type of training labels:  uint8\n","Shape of training labels:  (60000,)\n","Length of training data:  60000\n","Data type of test data:  uint8\n","Shape of test data:  (10000, 28, 28)\n","Data type of test labels:  uint8\n","Shape of test labels:  (10000,)\n","Length of test data:  10000\n","Labels  in the data:  [0 1 2 3 4 5 6 7 8 9]\n","Size of each image (28, 28)\n","Memory required for training data:  47040000 bytes\n","\n","Plotting the numeric range of input: [0-255]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"PLYAtx-ZAzKo"},"source":["Display 25 images from the train set in the form of 5x5 matrix.\n","\n","Answer the following questions:\n","\n","1. What are the data types and shapes of the features and the label? \n","2. What are the unique labels in this dataset?\n","3. How many training images and how many test images?\n","4. What is the size of each image? How much memory is required for holding the whole training data.\n","5. Find out the numeric range of the input. Do we need to rescale the input? Why?\n"," "]},{"cell_type":"markdown","metadata":{"id":"-tkl-PwsrXip"},"source":["## Task 3 Construct an input pipeline\n","\n","*(weight ~15%)*\n","\n","Creat train/validate/test data splits and construct tf.data pipelines. Make sure that the training data is batched. \n","\n","- How do you determine the batch size?\n","- Do we need to shuffle the training data? If yes, how do you determine the buffer size?\n"]},{"cell_type":"code","metadata":{"id":"hVmNO1PzOU6E","executionInfo":{"status":"ok","timestamp":1618805052761,"user_tz":-600,"elapsed":5688,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}}},"source":["# train, test and validation splits\n","\n","# defining batch size\n","BATCH_SIZE = 512\n","# training dataset\n","fmnist_train_ds = tf.data.Dataset.from_tensor_slices((train_images, train_labels))\n","#fmnist_train_ds = fmnist_train_ds.shuffle(5000).batch(BATCH_SIZE)\n","#fmnist_train_ds = fmnist_train_ds.shuffle(5000)\n","\n","\n","\n","# test dataset\n","fmnist_test_ds = tf.data.Dataset.from_tensor_slices((test_images, test_labels))\n","fmnist_test_ds = fmnist_test_ds.shuffle(5000).batch(BATCH_SIZE)\n","\n","# picking out the validation set from the training dataset\n","fmnist_train = fmnist_train_ds.take(round(len(train_labels)*0.7))\n","fmnist_val = fmnist_train_ds.skip(round(len(train_labels)*0.7))\n","\n","\n","# batch size should be in powers of 2 and the larger the better, provided that the batch fits into the (GPU) memory;\n","# small batches can offer a regularizing effect, perhaps due to the noise they add to the learning process.\n","# larger batches provide a more accurate estimate of the gradient.\n","# the batch size here is 512.\n","# train-data shuffling ensures that the model gets to see all permutauions of input in every batch.\n","\n"],"execution_count":145,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"lnE7ZFS_AzLg"},"source":["## Task 4 Construct a deep forward neural network\n","\n","*(weight ~35%)*"]},{"cell_type":"markdown","metadata":{"id":"ChlhkwhkAzLi"},"source":["### Task 4.1 Setting up a model for training"]},{"cell_type":"markdown","metadata":{"id":"yuxhQM6jAzLl"},"source":["Construct a deep feedforward neural network. You need to decide and report the following configurations:\n","\n","- Output layer: \n","    - How many output nodes?\n","    - Which activation function?\n","- Hidden layers:\n","    - How many hidden layers?\n","    - How many nodes in each layer?\n","    - Which activation function for each layer?\n","- Input layer\n","    - What is the input size?\n","- The loss function\n","- The metrics for model evaluation (which may be different from the loss function)\n","- The optimiser\n","\n","Justify your model design decisions.\n","\n","Plot the model structure `using keras.utils.plot_model` or similar tools.\n","\n","What is the number of trainable parameters in the model. Explain how the total number can be estimated from the model configurations."]},{"cell_type":"code","metadata":{"id":"dpH3urepb_6v","executionInfo":{"status":"ok","timestamp":1618807369523,"user_tz":-600,"elapsed":661,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}}},"source":["# Different layers are appropriate for different tensor formats and different types of data processing. \n","# Simple vector data, stored in 2D tensors of shape (samples, features), is often processed by densely connected layers.\n","\n","# single-label, multiclass classification problem\n","\n","# Layer-1: Flattening: Input Image: 28*28 = 784 pixels, gets converted into a 1D array;\n","# Layer-2: Dense layer: 128 units, relu allows the model to solve nonlinear problems; number of hidden layers = 128\n","# Layer-3: Output layer: 10 units, softmax provides probabilities for each possible output class, as this is a case of single-label, multiclass classification problem;\n","\n","\n","model = tf.keras.Sequential([\n","    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n","    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n","    tf.keras.layers.Dense(256, activation=tf.nn.relu),\n","    tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n","])"],"execution_count":194,"outputs":[]},{"cell_type":"code","metadata":{"id":"jVZawM4BcKJr","executionInfo":{"status":"ok","timestamp":1618807370469,"user_tz":-600,"elapsed":994,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}}},"source":["# Categorical crossentropy loss function is almost always suited for multiclass classification.\n","# It minimizes the distance between the probability distributions output by the network and the true distribution of the targets.\n","# Since, the labels here are encoded as integers, SparseCategoricalCrossentropy() is used.\n","\n","# Accuracy works well to evaluate a classification model as the outputs are discrete labels.\n","\n","model.compile(optimizer='adam',\n","              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n","              metrics=['accuracy'])"],"execution_count":195,"outputs":[]},{"cell_type":"code","metadata":{"id":"2yELI3vhBLTq","colab":{"base_uri":"https://localhost:8080/","height":533},"executionInfo":{"status":"ok","timestamp":1618807370972,"user_tz":-600,"elapsed":881,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"d34253d7-c6ae-4904-d9e4-96e5b7b64bd3"},"source":["from tensorflow.keras.utils import plot_model\n","plot_model(model, show_shapes=True)"],"execution_count":196,"outputs":[{"output_type":"execute_result","data":{"image/png":"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\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{"tags":[]},"execution_count":196}]},{"cell_type":"code","metadata":{"id":"MEaLJ9bZE446","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1618807372138,"user_tz":-600,"elapsed":725,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"8d72ab17-c3df-43a5-c69d-b1827eb13571"},"source":["# saving the initial weights for later experiments\n","init_weights = model.get_weights()\n","\n","names = [weight.name for layer in model.layers for weight in layer.weights]\n","\n","for name, weight in zip(names, init_weights):\n","    print(name, weight.shape)"],"execution_count":197,"outputs":[{"output_type":"stream","text":["dense_87/kernel:0 (784, 128)\n","dense_87/bias:0 (128,)\n","dense_88/kernel:0 (128, 256)\n","dense_88/bias:0 (256,)\n","dense_89/kernel:0 (256, 10)\n","dense_89/bias:0 (10,)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"MZ2BVK5tAzMM"},"source":["### Task 4.2 Fitting the model\n","\n","Before you fit the model. Think about what initialisation method have you chosen? If you did not specify the initialisation method, find out what is the default one. Choose a layer and visualise its initial weights. (Hint: You may use UMAP to visualise a collection of high-dimension vectors.)\n","\n","Decide and report the following training setting:\n","\n","1. The training batch size\n","2. The number of training epochs (at least 1,000 epochs recommended)\n","3. The learning rate. If you used momentum or a learning rate schedule, please report the configuration as well.\n","\n","Now fit the model. Show how the training loss changes. How did you decide when to stop training?\n","\n","After fitting the model, visualise the model weights again. How did the weights change? Why?\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"-F2RvaPdcRdz","executionInfo":{"status":"ok","timestamp":1618807374052,"user_tz":-600,"elapsed":477,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}}},"source":["tf.config.run_functions_eagerly(True)"],"execution_count":198,"outputs":[]},{"cell_type":"code","metadata":{"id":"EsBPCoXMin7k","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1618807468940,"user_tz":-600,"elapsed":92576,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"586396f1-2e5b-45e2-9b93-fe77c36978b4"},"source":["\n","history = model.fit(\n","    train_images, train_labels,\n","    epochs=12, batch_size=128, validation_split=0.33) "],"execution_count":199,"outputs":[{"output_type":"stream","text":["Epoch 1/12\n","\r  1/315 [..............................] - ETA: 36s - loss: 2.2788 - accuracy: 0.0469"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py:3504: UserWarning: Even though the tf.config.experimental_run_functions_eagerly option is set, this option does not apply to tf.data functions. tf.data functions are still traced and executed as graphs.\n","  \"Even though the tf.config.experimental_run_functions_eagerly \"\n"],"name":"stderr"},{"output_type":"stream","text":["315/315 [==============================] - 8s 25ms/step - loss: 0.7919 - accuracy: 0.7271 - val_loss: 0.4456 - val_accuracy: 0.8394\n","Epoch 2/12\n","315/315 [==============================] - 8s 24ms/step - loss: 0.4132 - accuracy: 0.8505 - val_loss: 0.4314 - val_accuracy: 0.8489\n","Epoch 3/12\n","315/315 [==============================] - 8s 24ms/step - loss: 0.3892 - accuracy: 0.8597 - val_loss: 0.3772 - val_accuracy: 0.8645\n","Epoch 4/12\n","315/315 [==============================] - 8s 24ms/step - loss: 0.3391 - accuracy: 0.8736 - val_loss: 0.3429 - val_accuracy: 0.8744\n","Epoch 5/12\n","315/315 [==============================] - 8s 24ms/step - loss: 0.3064 - accuracy: 0.8891 - val_loss: 0.3518 - val_accuracy: 0.8709\n","Epoch 6/12\n","315/315 [==============================] - 8s 24ms/step - loss: 0.2961 - accuracy: 0.8915 - val_loss: 0.3393 - val_accuracy: 0.8770\n","Epoch 7/12\n","315/315 [==============================] - 8s 24ms/step - loss: 0.2773 - accuracy: 0.8967 - val_loss: 0.3591 - val_accuracy: 0.8710\n","Epoch 8/12\n","315/315 [==============================] - 8s 24ms/step - loss: 0.2946 - accuracy: 0.8941 - val_loss: 0.3243 - val_accuracy: 0.8843\n","Epoch 9/12\n","315/315 [==============================] - 8s 24ms/step - loss: 0.2504 - accuracy: 0.9068 - val_loss: 0.3248 - val_accuracy: 0.8819\n","Epoch 10/12\n","315/315 [==============================] - 8s 24ms/step - loss: 0.2445 - accuracy: 0.9089 - val_loss: 0.3195 - val_accuracy: 0.8887\n","Epoch 11/12\n","315/315 [==============================] - 8s 24ms/step - loss: 0.2484 - accuracy: 0.9068 - val_loss: 0.3375 - val_accuracy: 0.8781\n","Epoch 12/12\n","315/315 [==============================] - 8s 24ms/step - loss: 0.2345 - accuracy: 0.9141 - val_loss: 0.3267 - val_accuracy: 0.8870\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ZydRZpuI9zp9","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1618758678047,"user_tz":-600,"elapsed":85554,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"bb0902a8-6dd5-4cce-8565-cdc35c188c61"},"source":["acc = history.history['accuracy']\n","val_acc = history.history['val_accuracy']\n","loss = history.history['loss']\n","val_loss = history.history['val_loss']\n","epochs = range(1, len(acc) + 1)\n","\n","plt.figure(figsize=(15,4))\n","\n","ax1 = plt.subplot(1, 2, 1)\n","plt.plot(epochs, loss, label='Training loss')\n","plt.plot(epochs, val_loss, label='Validation loss')\n","plt.fill_between(epochs, loss,val_loss,color='g',alpha=.1)\n","\n","plt.title('Training and validation loss')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","ax2 = plt.subplot(1, 2, 2)\n","plt.plot(epochs, acc, label='Training accuracy')\n","plt.plot(epochs, val_acc, label='Validation accuracy')\n","plt.fill_between(epochs, acc,val_acc,color='g',alpha=.1)\n","plt.title('Training and validation accuracy')\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"gzOFN785WjWO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1618756840581,"user_tz":-600,"elapsed":81570,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"2d3b0904-8b3e-4c39-fc97-a0846bb5b754"},"source":["# weights after training\n","\n","f_weights = model.get_weights()\n","\n","names = [weight.name for layer in model.layers for weight in layer.weights]\n","\n","for name, weight in zip(names, f_weights):\n","    print(name, weight.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dense_3/kernel:0 (784, 128)\n","dense_3/bias:0 (128,)\n","dense_4/kernel:0 (128, 256)\n","dense_4/bias:0 (256,)\n","dense_5/kernel:0 (256, 10)\n","dense_5/bias:0 (10,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"E45zJVjSdNpI","executionInfo":{"status":"ok","timestamp":1618756841174,"user_tz":-600,"elapsed":81491,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"159f44e1-3f42-4fe2-ee5b-04f7a2c220b8"},"source":["test_loss, test_accuracy = model.evaluate(fmnist_test_ds, steps=math.ceil(len(test_labels)/32))\n","print('Accuracy on test dataset after initial training: ', test_accuracy)"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" 17/313 [>.............................] - ETA: 4s - loss: 0.8828 - accuracy: 0.8843WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 313 batches). You may need to use the repeat() function when building your dataset.\n","313/313 [==============================] - 0s 849us/step - loss: 0.8788 - accuracy: 0.8835\n","Accuracy on test dataset after initial training:  0.8834999799728394\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"9C2VXCM-4Nw9"},"source":["### Task 4.3 Check the training using TensorBoard\n","\n","Use TensorBoard to visualise the training process. Show screenshots of your TensorBoard output.\n","\n","**Optional task:** Record the gradients during training and use TensorBoard to visualise the gradients."]},{"cell_type":"code","metadata":{"id":"gHQVKiwwFYSi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1618758898582,"user_tz":-600,"elapsed":88907,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"55cd499b-f441-42ff-d6ea-dd744ad9bc2f"},"source":["!rm -rf ./logs/\n","#tf.compat.v1.disable_v2_behavior()\n","\n","from datetime import datetime\n","import os\n","\n","root_logdir = \"logs\"\n","run_id = datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n","logdir = os.path.join(root_logdir, run_id)\n","\n","callbacks = [\n","    tf.keras.callbacks.TensorBoard(\n","        log_dir=logdir,\n","        histogram_freq=1,\n","         write_graph=True, \n","         write_images=True,\n","         write_grads=True\n","    )\n","]\n","\n","# tensorboard_callback = tf.compat.v1.keras.callbacks.TensorBoard(log_dir=logdir, histogram_freq=1, write_grads =True)\n","# tbCallBack = tf.keras.callbacks.TensorBoard(log_dir='./Graph', histogram_freq=0, write_graph=True, write_images=True)\n","\n","# def create_model():\n","#   model = tf.keras.Sequential([\n","#     tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n","#     tf.keras.layers.Dense(128, activation=tf.nn.relu),\n","#     tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n","# ])\n","#   return model\n","# model = create_model()\n","\n","# model.compile(optimizer='adam',\n","#               loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n","#               metrics=['accuracy'])\n","\n","# reset the training\n","model.set_weights(init_weights)\n","\n","history = model.fit(train_images, \n","                      train_labels, \n","                      epochs=12, \n","                      batch_size=128,\n","                      callbacks=callbacks, validation_split=0.33)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:`write_grads` will be ignored in TensorFlow 2.0 for the `TensorBoard` Callback.\n","Epoch 1/12\n","  1/315 [..............................] - ETA: 6s - loss: 2.3026 - accuracy: 0.1328"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py:3504: UserWarning: Even though the tf.config.experimental_run_functions_eagerly option is set, this option does not apply to tf.data functions. tf.data functions are still traced and executed as graphs.\n","  \"Even though the tf.config.experimental_run_functions_eagerly \"\n"],"name":"stderr"},{"output_type":"stream","text":["315/315 [==============================] - 8s 24ms/step - loss: 0.5599 - accuracy: 0.8010 - val_loss: 0.4268 - val_accuracy: 0.8444\n","Epoch 2/12\n","315/315 [==============================] - 7s 23ms/step - loss: 0.3811 - accuracy: 0.8636 - val_loss: 0.3775 - val_accuracy: 0.8668\n","Epoch 3/12\n","315/315 [==============================] - 7s 23ms/step - loss: 0.3502 - accuracy: 0.8719 - val_loss: 0.3477 - val_accuracy: 0.8734\n","Epoch 4/12\n","315/315 [==============================] - 7s 23ms/step - loss: 0.3210 - accuracy: 0.8834 - val_loss: 0.3696 - val_accuracy: 0.8647\n","Epoch 5/12\n","315/315 [==============================] - 7s 23ms/step - loss: 0.3019 - accuracy: 0.8893 - val_loss: 0.3392 - val_accuracy: 0.8797\n","Epoch 6/12\n","315/315 [==============================] - 7s 23ms/step - loss: 0.2934 - accuracy: 0.8920 - val_loss: 0.3204 - val_accuracy: 0.8826\n","Epoch 7/12\n","315/315 [==============================] - 7s 23ms/step - loss: 0.2740 - accuracy: 0.8985 - val_loss: 0.3221 - val_accuracy: 0.8802\n","Epoch 8/12\n","315/315 [==============================] - 7s 23ms/step - loss: 0.2565 - accuracy: 0.9051 - val_loss: 0.3288 - val_accuracy: 0.8826\n","Epoch 9/12\n","315/315 [==============================] - 7s 23ms/step - loss: 0.2555 - accuracy: 0.9047 - val_loss: 0.3255 - val_accuracy: 0.8851\n","Epoch 10/12\n","315/315 [==============================] - 7s 23ms/step - loss: 0.2371 - accuracy: 0.9107 - val_loss: 0.3327 - val_accuracy: 0.8815\n","Epoch 11/12\n","315/315 [==============================] - 7s 23ms/step - loss: 0.2360 - accuracy: 0.9112 - val_loss: 0.3251 - val_accuracy: 0.8860\n","Epoch 12/12\n","315/315 [==============================] - 7s 23ms/step - loss: 0.2182 - accuracy: 0.9185 - val_loss: 0.3309 - val_accuracy: 0.8834\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"X5L0t5dKFfMT"},"source":["# The output of this cell is ommited in the .html output, due to file size limitations\n","\n","%reload_ext tensorboard\n","%tensorboard --logdir=logs\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"oxltxSp5jt5T","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1618758976463,"user_tz":-600,"elapsed":1016,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"65e90534-d96d-4f18-bbff-7c0a67819f48"},"source":["test_loss, test_accuracy = model.evaluate(fmnist_test_ds, steps=math.ceil(len(test_labels)/32))\n","print('Accuracy on test dataset:', test_accuracy)"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" 17/313 [>.............................] - ETA: 4s - loss: 0.3540 - accuracy: 0.8767WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 313 batches). You may need to use the repeat() function when building your dataset.\n","313/313 [==============================] - 0s 844us/step - loss: 0.3606 - accuracy: 0.8740\n","Accuracy on test dataset: 0.8740000128746033\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4EgXZCBNAzMs"},"source":["## Task 5 Overfitting and regularisation\n","\n","*(weight ~30%)*\n","\n","Go back to the previous task. Plot the training and validation loss and accuracy. Answer the following questions:\n","\n","1. Do you see overfitting or underfitting? Why?\n","2. If you see overfitting, at which epoch did it happen?\n","\n","Now retrain the model with only 200 training examples. (Make sure that you  reinitialise the weights before retraining.) Do you see overfitting? How did the validation loss and accuracy change?\n","\n","Neural networks are overparametrised models, meaning there can be more parameters than the training examples. Some form of regularisation is almost always necessary to obtain a useful model. Below are some options:\n","\n","1. Add dropout\n","2. Add Batch Normalisation\n","3. Add layer-specific weight regularizations\n","4. Change the learning rate\n","\n","In addition, you may also try changing the weight initialisation method.\n","\n","Apply different regularisation techniques to the model training. You may also try other techniques for improving training such as learning rate scheduling (see https://www.tensorflow.org/guide/keras/train_and_evaluate#using_learning_rate_schedules).\n","\n","Run **five or more** experiments of different training configurations and record the validation accuracy achieved in the Markdown table below. You may modify the table heading to match your experiment design.\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":808},"id":"zPPqsZsQpY7o","executionInfo":{"status":"ok","timestamp":1618758994259,"user_tz":-600,"elapsed":4817,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"1200033b-9c3d-4df3-b7dd-310915fc03d2"},"source":["# It is observed that the accuracy for training and validation follows the same pattern until 12 epochs \n","# and then diverge where only the training accuracy increases whereas the validation accuracy remain still/declines,\n","# this is due to overfitting which is seen from the graph as it starts to decrease and increase. \n","# When the model is trained on only 200 examples, it completely memorises the data resulting in 100% accuracy on the training dataset but a low accuracy on the test dataset.\n","\n","# training the model only on 200 images\n","\n","train_images_200 = train_images[:200]\n","train_labels_200 = train_labels[:200]\n","model.set_weights(init_weights)\n","\n","history = model.fit(train_images_200, \n","                      train_labels_200, \n","                      epochs=12, \n","                      batch_size=32,\n","                      callbacks=callbacks, validation_split=0.33)\n","\n","acc = history.history['accuracy']\n","val_acc = history.history['val_accuracy']\n","loss = history.history['loss']\n","val_loss = history.history['val_loss']\n","epochs = range(1, len(acc) + 1)\n","\n","plt.figure(figsize=(15,4))\n","\n","ax1 = plt.subplot(1, 2, 1)\n","plt.plot(epochs, loss, label='Training loss')\n","plt.plot(epochs, val_loss, label='Validation loss')\n","plt.fill_between(epochs, loss,val_loss,color='g',alpha=.1)\n","\n","plt.title('Training and validation loss')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","ax2 = plt.subplot(1, 2, 2)\n","plt.plot(epochs, acc, label='Training accuracy')\n","plt.plot(epochs, val_acc, label='Validation accuracy')\n","plt.fill_between(epochs, acc,val_acc,color='g',alpha=.1)\n","plt.title('Training and validation accuracy')\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","plt.show()\n","\n","test_loss, test_accuracy = model.evaluate(fmnist_test_ds, steps=math.ceil(len(test_labels)/32))\n","print('Accuracy on test dataset after training on only 200 images: ', test_accuracy)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 1/12\n","\r1/5 [=====>........................] - ETA: 0s - loss: 2.2706 - accuracy: 0.1250"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py:3504: UserWarning: Even though the tf.config.experimental_run_functions_eagerly option is set, this option does not apply to tf.data functions. tf.data functions are still traced and executed as graphs.\n","  \"Even though the tf.config.experimental_run_functions_eagerly \"\n"],"name":"stderr"},{"output_type":"stream","text":["5/5 [==============================] - 0s 119ms/step - loss: 2.2012 - accuracy: 0.1940 - val_loss: 1.7969 - val_accuracy: 0.4697\n","Epoch 2/12\n","5/5 [==============================] - 0s 33ms/step - loss: 1.5117 - accuracy: 0.5373 - val_loss: 1.2357 - val_accuracy: 0.5303\n","Epoch 3/12\n","5/5 [==============================] - 0s 32ms/step - loss: 0.9001 - accuracy: 0.7090 - val_loss: 1.3109 - val_accuracy: 0.5758\n","Epoch 4/12\n","5/5 [==============================] - 0s 34ms/step - loss: 0.7038 - accuracy: 0.7612 - val_loss: 0.9751 - val_accuracy: 0.6667\n","Epoch 5/12\n","5/5 [==============================] - 0s 33ms/step - loss: 0.5627 - accuracy: 0.7985 - val_loss: 1.1302 - val_accuracy: 0.6818\n","Epoch 6/12\n","5/5 [==============================] - 0s 37ms/step - loss: 0.3669 - accuracy: 0.8657 - val_loss: 1.1285 - val_accuracy: 0.6364\n","Epoch 7/12\n","5/5 [==============================] - 0s 36ms/step - loss: 0.3374 - accuracy: 0.8955 - val_loss: 1.2198 - val_accuracy: 0.6364\n","Epoch 8/12\n","5/5 [==============================] - 0s 33ms/step - loss: 0.3073 - accuracy: 0.8731 - val_loss: 1.0367 - val_accuracy: 0.7273\n","Epoch 9/12\n","5/5 [==============================] - 0s 43ms/step - loss: 0.2448 - accuracy: 0.9104 - val_loss: 0.9582 - val_accuracy: 0.7727\n","Epoch 10/12\n","5/5 [==============================] - 0s 35ms/step - loss: 0.1975 - accuracy: 0.9104 - val_loss: 1.3398 - val_accuracy: 0.6667\n","Epoch 11/12\n","5/5 [==============================] - 0s 34ms/step - loss: 0.2105 - accuracy: 0.9403 - val_loss: 1.0502 - val_accuracy: 0.7273\n","Epoch 12/12\n","5/5 [==============================] - 0s 35ms/step - loss: 0.1550 - accuracy: 0.9328 - val_loss: 1.1934 - val_accuracy: 0.7424\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":[" 18/313 [>.............................] - ETA: 3s - loss: 1.1364 - accuracy: 0.6953WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 313 batches). You may need to use the repeat() function when building your dataset.\n","313/313 [==============================] - 0s 875us/step - loss: 1.1480 - accuracy: 0.6942\n","Accuracy on test dataset after training on only 200 images:  0.6941999793052673\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ojtjPPs7Imk1","executionInfo":{"status":"ok","timestamp":1618805380546,"user_tz":-600,"elapsed":779,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}}},"source":["# adding dropout, batch normalisation, changing the learning rate\n","\n","from keras.layers import Dropout\n","from keras.layers import Activation\n","from keras.regularizers import l2\n","from keras.optimizers import SGD\n","from keras.optimizers import RMSprop\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","\n","\n","\n","def optimiser_sgd(learning_rate):\n","  learning_rate = learning_rate\n","  decay_rate = learning_rate / 50\n","  momentum = 0.8\n","\n","  sgd = SGD(lr=learning_rate, momentum=momentum, decay=decay_rate, nesterov=False)\n","  return sgd"],"execution_count":161,"outputs":[]},{"cell_type":"code","metadata":{"id":"TuRPVveRJv5e","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"error","timestamp":1618807164499,"user_tz":-600,"elapsed":297629,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"3b7e9c30-a8e9-4d45-934d-e72a94a3d1bc"},"source":["# The complete output of this cell is ommited in the .html output, due to file size limitations\n","# The results are although compiled in the table below\n","\n","learning_rates = [0.1, 0.01, 0.001, 0.002, 0.02]\n","dropouts = [0.1, 0.2, 0.25, 0.3, 0.15]\n","\n","for i in range(0, 5):\n","  for j in range(0, 5):\n","    \n","    model_s = tf.keras.Sequential([\n","      tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n","      tf.keras.layers.Dense(128, activation=tf.nn.relu),\n","      tf.keras.layers.Dropout(dropouts[i]),\n","      tf.keras.layers.Dense(256, activation=tf.nn.relu),\n","      tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n","    ])\n","    model_s.compile(optimizer=optimiser_sgd(learning_rates[j]),\n","                loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n","                metrics=['accuracy'])\n","    history = model_s.fit(train_images, \n","                      train_labels,\n","                        epochs=50, \n","                        batch_size=128, validation_split=0.33)\n","    acc = history.history['accuracy']\n","    val_acc = history.history['val_accuracy']\n","    loss = history.history['loss']\n","    val_loss = history.history['val_loss']\n","    epochs = range(1, len(acc) + 1)\n","\n","    plt.figure(figsize=(15,4))\n","\n","    ax1 = plt.subplot(1, 2, 1)\n","    plt.plot(epochs, loss, label='Training loss')\n","    plt.plot(epochs, val_loss, label='Validation loss')\n","    plt.fill_between(epochs, loss,val_loss,color='g',alpha=.1)\n","\n","    plt.title('Training and validation loss')\n","    plt.xlabel('Epochs')\n","    plt.ylabel('Loss')\n","    plt.legend()\n","\n","    ax2 = plt.subplot(1, 2, 2)\n","    plt.plot(epochs, acc, label='Training accuracy')\n","    plt.plot(epochs, val_acc, label='Validation accuracy')\n","    plt.fill_between(epochs, acc,val_acc,color='g',alpha=.1)\n","    plt.title('Training and validation accuracy')\n","    plt.xlabel('Epochs')\n","    plt.ylabel('Accuracy')\n","    plt.legend()\n","    plt.show()\n","    test_loss, test_accuracy = model_s.evaluate(fmnist_test_ds)\n","    print('Accuracy on test dataset for:', test_accuracy, \"Learning rate: \"+str(learning_rates[j])+\" Dropout: \"+str(dropouts[i])+\"\\n\\n\")\n","\n"],"execution_count":190,"outputs":[{"output_type":"stream","text":["Epoch 1/50\n","315/315 [==============================] - 3s 8ms/step - loss: 0.8351 - accuracy: 0.6976 - val_loss: 0.5120 - val_accuracy: 0.8193\n","Epoch 2/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4377 - accuracy: 0.8405 - val_loss: 0.3816 - val_accuracy: 0.8630\n","Epoch 3/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3776 - accuracy: 0.8605 - val_loss: 0.3729 - val_accuracy: 0.8636\n","Epoch 4/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3518 - accuracy: 0.8727 - val_loss: 0.3502 - val_accuracy: 0.8731\n","Epoch 5/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3284 - accuracy: 0.8780 - val_loss: 0.3514 - val_accuracy: 0.8708\n","Epoch 6/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3300 - accuracy: 0.8786 - val_loss: 0.3459 - val_accuracy: 0.8724\n","Epoch 7/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3108 - accuracy: 0.8842 - val_loss: 0.3447 - val_accuracy: 0.8755\n","Epoch 8/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3107 - accuracy: 0.8856 - val_loss: 0.3265 - val_accuracy: 0.8791\n","Epoch 9/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2923 - accuracy: 0.8906 - val_loss: 0.3262 - val_accuracy: 0.8825\n","Epoch 10/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2916 - accuracy: 0.8936 - val_loss: 0.3236 - val_accuracy: 0.8822\n","Epoch 11/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2837 - accuracy: 0.8943 - val_loss: 0.3226 - val_accuracy: 0.8822\n","Epoch 12/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2828 - accuracy: 0.8941 - val_loss: 0.3218 - val_accuracy: 0.8828\n","Epoch 13/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2791 - accuracy: 0.8981 - val_loss: 0.3144 - val_accuracy: 0.8854\n","Epoch 14/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2734 - accuracy: 0.9015 - val_loss: 0.3159 - val_accuracy: 0.8842\n","Epoch 15/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2666 - accuracy: 0.9008 - val_loss: 0.3100 - val_accuracy: 0.8889\n","Epoch 16/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2568 - accuracy: 0.9042 - val_loss: 0.3141 - val_accuracy: 0.8854\n","Epoch 17/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2572 - accuracy: 0.9056 - val_loss: 0.3112 - val_accuracy: 0.8870\n","Epoch 18/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2574 - accuracy: 0.9045 - val_loss: 0.3040 - val_accuracy: 0.8891\n","Epoch 19/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2575 - accuracy: 0.9056 - val_loss: 0.3133 - val_accuracy: 0.8869\n","Epoch 20/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2526 - accuracy: 0.9055 - val_loss: 0.3061 - val_accuracy: 0.8892\n","Epoch 21/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2452 - accuracy: 0.9085 - val_loss: 0.3052 - val_accuracy: 0.8902\n","Epoch 22/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2460 - accuracy: 0.9092 - val_loss: 0.3190 - val_accuracy: 0.8838\n","Epoch 23/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2498 - accuracy: 0.9065 - val_loss: 0.3010 - val_accuracy: 0.8909\n","Epoch 24/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2406 - accuracy: 0.9128 - val_loss: 0.3021 - val_accuracy: 0.8912\n","Epoch 25/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2385 - accuracy: 0.9130 - val_loss: 0.3002 - val_accuracy: 0.8917\n","Epoch 26/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2372 - accuracy: 0.9127 - val_loss: 0.3017 - val_accuracy: 0.8924\n","Epoch 27/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2315 - accuracy: 0.9152 - val_loss: 0.2999 - val_accuracy: 0.8920\n","Epoch 28/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2360 - accuracy: 0.9106 - val_loss: 0.2994 - val_accuracy: 0.8914\n","Epoch 29/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2351 - accuracy: 0.9134 - val_loss: 0.3028 - val_accuracy: 0.8902\n","Epoch 30/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2299 - accuracy: 0.9168 - val_loss: 0.2985 - val_accuracy: 0.8926\n","Epoch 31/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2331 - accuracy: 0.9149 - val_loss: 0.3001 - val_accuracy: 0.8923\n","Epoch 32/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2252 - accuracy: 0.9165 - val_loss: 0.3016 - val_accuracy: 0.8920\n","Epoch 33/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2243 - accuracy: 0.9177 - val_loss: 0.3022 - val_accuracy: 0.8925\n","Epoch 34/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2240 - accuracy: 0.9164 - val_loss: 0.3005 - val_accuracy: 0.8919\n","Epoch 35/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2247 - accuracy: 0.9190 - val_loss: 0.2985 - val_accuracy: 0.8934\n","Epoch 36/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2185 - accuracy: 0.9215 - val_loss: 0.2987 - val_accuracy: 0.8922\n","Epoch 37/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2215 - accuracy: 0.9184 - val_loss: 0.2986 - val_accuracy: 0.8924\n","Epoch 38/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2193 - accuracy: 0.9203 - val_loss: 0.2965 - val_accuracy: 0.8934\n","Epoch 39/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2201 - accuracy: 0.9186 - val_loss: 0.2982 - val_accuracy: 0.8926\n","Epoch 40/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2128 - accuracy: 0.9232 - val_loss: 0.2948 - val_accuracy: 0.8938\n","Epoch 41/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2145 - accuracy: 0.9215 - val_loss: 0.3006 - val_accuracy: 0.8920\n","Epoch 42/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2178 - accuracy: 0.9205 - val_loss: 0.2975 - val_accuracy: 0.8938\n","Epoch 43/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2216 - accuracy: 0.9179 - val_loss: 0.2976 - val_accuracy: 0.8941\n","Epoch 44/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2129 - accuracy: 0.9231 - val_loss: 0.2966 - val_accuracy: 0.8938\n","Epoch 45/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2098 - accuracy: 0.9236 - val_loss: 0.2938 - val_accuracy: 0.8950\n","Epoch 46/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2124 - accuracy: 0.9213 - val_loss: 0.2974 - val_accuracy: 0.8946\n","Epoch 47/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2081 - accuracy: 0.9256 - val_loss: 0.2952 - val_accuracy: 0.8943\n","Epoch 48/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2122 - accuracy: 0.9235 - val_loss: 0.2972 - val_accuracy: 0.8933\n","Epoch 49/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2050 - accuracy: 0.9262 - val_loss: 0.2977 - val_accuracy: 0.8932\n","Epoch 50/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2114 - accuracy: 0.9229 - val_loss: 0.2983 - val_accuracy: 0.8929\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["20/20 [==============================] - 0s 9ms/step - loss: 0.3295 - accuracy: 0.8837\n","Accuracy on test dataset for: 0.8837000131607056 Learning rate: 0.1 Dropout: 0.1\n","\n","\n","Epoch 1/50\n","315/315 [==============================] - 3s 7ms/step - loss: 1.2527 - accuracy: 0.5817 - val_loss: 0.5802 - val_accuracy: 0.7931\n","Epoch 2/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.5863 - accuracy: 0.7967 - val_loss: 0.5104 - val_accuracy: 0.8268\n","Epoch 3/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.5080 - accuracy: 0.8243 - val_loss: 0.4863 - val_accuracy: 0.8254\n","Epoch 4/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4669 - accuracy: 0.8355 - val_loss: 0.4671 - val_accuracy: 0.8303\n","Epoch 5/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4420 - accuracy: 0.8430 - val_loss: 0.4488 - val_accuracy: 0.8389\n","Epoch 6/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4223 - accuracy: 0.8525 - val_loss: 0.4051 - val_accuracy: 0.8586\n","Epoch 7/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4030 - accuracy: 0.8581 - val_loss: 0.4126 - val_accuracy: 0.8536\n","Epoch 8/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3992 - accuracy: 0.8599 - val_loss: 0.3973 - val_accuracy: 0.8599\n","Epoch 9/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.3859 - accuracy: 0.8629 - val_loss: 0.3847 - val_accuracy: 0.8622\n","Epoch 10/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3800 - accuracy: 0.8656 - val_loss: 0.3760 - val_accuracy: 0.8672\n","Epoch 11/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3735 - accuracy: 0.8693 - val_loss: 0.3835 - val_accuracy: 0.8624\n","Epoch 12/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3596 - accuracy: 0.8732 - val_loss: 0.3635 - val_accuracy: 0.8712\n","Epoch 13/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3533 - accuracy: 0.8746 - val_loss: 0.3628 - val_accuracy: 0.8690\n","Epoch 14/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3483 - accuracy: 0.8786 - val_loss: 0.3565 - val_accuracy: 0.8725\n","Epoch 15/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3414 - accuracy: 0.8766 - val_loss: 0.3685 - val_accuracy: 0.8673\n","Epoch 16/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.3382 - accuracy: 0.8793 - val_loss: 0.3532 - val_accuracy: 0.8746\n","Epoch 17/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3351 - accuracy: 0.8798 - val_loss: 0.3517 - val_accuracy: 0.8741\n","Epoch 18/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3381 - accuracy: 0.8769 - val_loss: 0.3537 - val_accuracy: 0.8741\n","Epoch 19/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3233 - accuracy: 0.8858 - val_loss: 0.3524 - val_accuracy: 0.8734\n","Epoch 20/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.3247 - accuracy: 0.8873 - val_loss: 0.3483 - val_accuracy: 0.8748\n","Epoch 21/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3228 - accuracy: 0.8840 - val_loss: 0.3386 - val_accuracy: 0.8790\n","Epoch 22/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.3156 - accuracy: 0.8849 - val_loss: 0.3361 - val_accuracy: 0.8807\n","Epoch 23/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3094 - accuracy: 0.8892 - val_loss: 0.3416 - val_accuracy: 0.8789\n","Epoch 24/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3112 - accuracy: 0.8852 - val_loss: 0.3393 - val_accuracy: 0.8789\n","Epoch 25/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3075 - accuracy: 0.8893 - val_loss: 0.3337 - val_accuracy: 0.8825\n","Epoch 26/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3070 - accuracy: 0.8903 - val_loss: 0.3321 - val_accuracy: 0.8828\n","Epoch 27/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.3066 - accuracy: 0.8893 - val_loss: 0.3326 - val_accuracy: 0.8818\n","Epoch 28/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.3002 - accuracy: 0.8909 - val_loss: 0.3398 - val_accuracy: 0.8803\n","Epoch 29/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.3004 - accuracy: 0.8910 - val_loss: 0.3428 - val_accuracy: 0.8798\n","Epoch 30/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.3095 - accuracy: 0.8906 - val_loss: 0.3257 - val_accuracy: 0.8840\n","Epoch 31/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2937 - accuracy: 0.8919 - val_loss: 0.3254 - val_accuracy: 0.8833\n","Epoch 32/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2966 - accuracy: 0.8923 - val_loss: 0.3252 - val_accuracy: 0.8852\n","Epoch 33/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2875 - accuracy: 0.8962 - val_loss: 0.3287 - val_accuracy: 0.8821\n","Epoch 34/50\n","315/315 [==============================] - 2s 8ms/step - loss: 0.2868 - accuracy: 0.8973 - val_loss: 0.3235 - val_accuracy: 0.8848\n","Epoch 35/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2920 - accuracy: 0.8961 - val_loss: 0.3202 - val_accuracy: 0.8861\n","Epoch 36/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2847 - accuracy: 0.8974 - val_loss: 0.3242 - val_accuracy: 0.8857\n","Epoch 37/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2815 - accuracy: 0.8988 - val_loss: 0.3254 - val_accuracy: 0.8842\n","Epoch 38/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2761 - accuracy: 0.9022 - val_loss: 0.3178 - val_accuracy: 0.8873\n","Epoch 39/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2833 - accuracy: 0.8981 - val_loss: 0.3217 - val_accuracy: 0.8852\n","Epoch 40/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2752 - accuracy: 0.8999 - val_loss: 0.3204 - val_accuracy: 0.8860\n","Epoch 41/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2742 - accuracy: 0.9033 - val_loss: 0.3176 - val_accuracy: 0.8881\n","Epoch 42/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2728 - accuracy: 0.9008 - val_loss: 0.3183 - val_accuracy: 0.8869\n","Epoch 43/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2676 - accuracy: 0.9034 - val_loss: 0.3160 - val_accuracy: 0.8878\n","Epoch 44/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2679 - accuracy: 0.9044 - val_loss: 0.3139 - val_accuracy: 0.8886\n","Epoch 45/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2656 - accuracy: 0.9050 - val_loss: 0.3193 - val_accuracy: 0.8854\n","Epoch 46/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2646 - accuracy: 0.9042 - val_loss: 0.3142 - val_accuracy: 0.8884\n","Epoch 47/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2626 - accuracy: 0.9044 - val_loss: 0.3231 - val_accuracy: 0.8848\n","Epoch 48/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2734 - accuracy: 0.9020 - val_loss: 0.3150 - val_accuracy: 0.8884\n","Epoch 49/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.2644 - accuracy: 0.9052 - val_loss: 0.3148 - val_accuracy: 0.8879\n","Epoch 50/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.2602 - accuracy: 0.9068 - val_loss: 0.3153 - val_accuracy: 0.8877\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["20/20 [==============================] - 0s 9ms/step - loss: 0.3448 - accuracy: 0.8773\n","Accuracy on test dataset for: 0.8773000240325928 Learning rate: 0.01 Dropout: 0.1\n","\n","\n","Epoch 1/50\n","315/315 [==============================] - 3s 7ms/step - loss: 2.0892 - accuracy: 0.3039 - val_loss: 1.2801 - val_accuracy: 0.6614\n","Epoch 2/50\n","315/315 [==============================] - 2s 6ms/step - loss: 1.1975 - accuracy: 0.6434 - val_loss: 0.9040 - val_accuracy: 0.7043\n","Epoch 3/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.9134 - accuracy: 0.6913 - val_loss: 0.7773 - val_accuracy: 0.7346\n","Epoch 4/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.8028 - accuracy: 0.7221 - val_loss: 0.7073 - val_accuracy: 0.7599\n","Epoch 5/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.7405 - accuracy: 0.7452 - val_loss: 0.6626 - val_accuracy: 0.7721\n","Epoch 6/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.6920 - accuracy: 0.7637 - val_loss: 0.6293 - val_accuracy: 0.7848\n","Epoch 7/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.6673 - accuracy: 0.7733 - val_loss: 0.6028 - val_accuracy: 0.7994\n","Epoch 8/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.6328 - accuracy: 0.7854 - val_loss: 0.5815 - val_accuracy: 0.8046\n","Epoch 9/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.6095 - accuracy: 0.7896 - val_loss: 0.5666 - val_accuracy: 0.8075\n","Epoch 10/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.5962 - accuracy: 0.7935 - val_loss: 0.5528 - val_accuracy: 0.8111\n","Epoch 11/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.5812 - accuracy: 0.7995 - val_loss: 0.5410 - val_accuracy: 0.8156\n","Epoch 12/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.5684 - accuracy: 0.8033 - val_loss: 0.5308 - val_accuracy: 0.8188\n","Epoch 13/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.5540 - accuracy: 0.8091 - val_loss: 0.5188 - val_accuracy: 0.8221\n","Epoch 14/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.5419 - accuracy: 0.8124 - val_loss: 0.5120 - val_accuracy: 0.8246\n","Epoch 15/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.5297 - accuracy: 0.8173 - val_loss: 0.5036 - val_accuracy: 0.8265\n","Epoch 16/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.5254 - accuracy: 0.8203 - val_loss: 0.5025 - val_accuracy: 0.8261\n","Epoch 17/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.5269 - accuracy: 0.8163 - val_loss: 0.4925 - val_accuracy: 0.8309\n","Epoch 18/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.5148 - accuracy: 0.8210 - val_loss: 0.4856 - val_accuracy: 0.8325\n","Epoch 19/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4993 - accuracy: 0.8263 - val_loss: 0.4810 - val_accuracy: 0.8331\n","Epoch 20/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.5009 - accuracy: 0.8287 - val_loss: 0.4763 - val_accuracy: 0.8353\n","Epoch 21/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4992 - accuracy: 0.8261 - val_loss: 0.4746 - val_accuracy: 0.8330\n","Epoch 22/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4881 - accuracy: 0.8305 - val_loss: 0.4687 - val_accuracy: 0.8354\n","Epoch 23/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4867 - accuracy: 0.8287 - val_loss: 0.4648 - val_accuracy: 0.8371\n","Epoch 24/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4702 - accuracy: 0.8361 - val_loss: 0.4605 - val_accuracy: 0.8395\n","Epoch 25/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4757 - accuracy: 0.8346 - val_loss: 0.4604 - val_accuracy: 0.8386\n","Epoch 26/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4713 - accuracy: 0.8362 - val_loss: 0.4553 - val_accuracy: 0.8408\n","Epoch 27/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4634 - accuracy: 0.8381 - val_loss: 0.4531 - val_accuracy: 0.8414\n","Epoch 28/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4666 - accuracy: 0.8373 - val_loss: 0.4492 - val_accuracy: 0.8435\n","Epoch 29/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4634 - accuracy: 0.8383 - val_loss: 0.4466 - val_accuracy: 0.8443\n","Epoch 30/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4577 - accuracy: 0.8386 - val_loss: 0.4462 - val_accuracy: 0.8444\n","Epoch 31/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4553 - accuracy: 0.8424 - val_loss: 0.4429 - val_accuracy: 0.8447\n","Epoch 32/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4469 - accuracy: 0.8424 - val_loss: 0.4390 - val_accuracy: 0.8464\n","Epoch 33/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4510 - accuracy: 0.8425 - val_loss: 0.4378 - val_accuracy: 0.8452\n","Epoch 34/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4481 - accuracy: 0.8453 - val_loss: 0.4362 - val_accuracy: 0.8467\n","Epoch 35/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4470 - accuracy: 0.8420 - val_loss: 0.4360 - val_accuracy: 0.8474\n","Epoch 36/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4385 - accuracy: 0.8457 - val_loss: 0.4313 - val_accuracy: 0.8491\n","Epoch 37/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4416 - accuracy: 0.8467 - val_loss: 0.4284 - val_accuracy: 0.8502\n","Epoch 38/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4376 - accuracy: 0.8473 - val_loss: 0.4282 - val_accuracy: 0.8491\n","Epoch 39/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4355 - accuracy: 0.8476 - val_loss: 0.4253 - val_accuracy: 0.8504\n","Epoch 40/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4242 - accuracy: 0.8512 - val_loss: 0.4235 - val_accuracy: 0.8514\n","Epoch 41/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4302 - accuracy: 0.8475 - val_loss: 0.4223 - val_accuracy: 0.8508\n","Epoch 42/50\n","315/315 [==============================] - 2s 6ms/step - loss: 0.4282 - accuracy: 0.8504 - val_loss: 0.4205 - val_accuracy: 0.8519\n","Epoch 43/50\n","315/315 [==============================] - 2s 7ms/step - loss: 0.4171 - accuracy: 0.8532 - val_loss: 0.4190 - val_accuracy: 0.8521\n","Epoch 44/50\n"," 11/315 [>.............................] - ETA: 1s - loss: 0.4203 - accuracy: 0.8496"],"name":"stdout"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-190-55da44862642>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m                       \u001b[0mtrain_labels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m                         \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m                         batch_size=128, validation_split=0.33)\n\u001b[0m\u001b[1;32m     21\u001b[0m     \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m 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828\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    829\u001b[0m       \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m 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graph_function._call_flat(\n\u001b[0;32m-> 2943\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2945\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1917\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1918\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1919\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1920\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1921\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    558\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    561\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m           outputs = execute.execute_with_cancellation(\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"markdown","metadata":{"id":"d1LRIBrEgu92"},"source":["\n","|Dropout (rate)   | Batch Normalisation (Y/N)  | Optimiser  | Learning Rate  | Number of Epochs   |  Validation Accuracy |\n","|---|---|---|---|---|---|\n","| 0.1  |  Y |  SGD | 0.1  | 50  |  0.8921 |\n","| 0.1  |  Y |  SGD |  0.01 |  50 | 0.8836  |\n","|  0.1 | Y  |  SGD |  0.001 |  50 | 0.8478  |\n","| 0.1  |  Y |  SGD | 0.002  | 50  |  0.8630 |\n","| 0.1  |  Y |  SGD | 0.02 | 50  |  0.9083 |\n","| 0.2  |  Y |  SGD | 0.1 | 50  |  0.8931 |\n","| 0.2  |  Y |  SGD | 0.01 | 50  |  0.8916 |\n","| 0.2  |  Y |  SGD | 0.001 | 50  |  0.8458 |\n","| 0.2  |  Y |  SGD | 0.002 | 50  |  0.8616 |\n","| 0.2  |  Y |  SGD | 0.02 | 50  |  0.8333 |\n","| 0.25  |  Y |  SGD | 0.1 | 50  |  0.8911 |\n","| 0.25  |  Y |  SGD | 0.01 | 50  |  0.8816 |\n","| 0.25  |  Y |  SGD | 0.001 | 50  |  0.8446 |\n","| 0.25  |  Y |  SGD | 0.002 | 50  |  0.8602 |\n","| 0.25  |  Y |  SGD | 0.02 | 50  |  0.8838 |\n","| 0.3  |  Y |  SGD | 0.1 | 50  |  0.8914 |\n","| 0.3  |  Y |  SGD | 0.01 | 50  |  0.8834 |\n","| 0.3  |  Y |  SGD | 0.001 | 50  |  0.8401 |\n","| 0.3  |  Y |  SGD | 0.002 | 50  |  0.8604 |\n","| 0.3  |  Y |  SGD | 0.02 | 50  |  0.8856 |\n","| 0.15  |  Y |  SGD | 0.1 | 50  |  0.8890 |\n","| 0.15  |  Y |  SGD | 0.01 | 50  |  0.8851 |\n","| 0.15  |  Y |  SGD | 0.001 | 50  |  0.8472 |\n","| 0.15  |  Y |  SGD | 0.002 | 50  |  0.8637 |\n","| 0.15  |  Y |  SGD | 0.02 | 50  |  0.8882 |\n","\n"]},{"cell_type":"markdown","metadata":{"id":"cQ66UDpg6q9P"},"source":["# Layer specific weight regularisation. This structure was applied using the best case of learning rate and dropout combination from the previous experiment.\n"]},{"cell_type":"code","metadata":{"id":"HnkmXXbglGqU","executionInfo":{"status":"ok","timestamp":1618806275938,"user_tz":-600,"elapsed":721,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}}},"source":["from keras.callbacks import LearningRateScheduler\n","from keras.callbacks import History\n","\n","\n","decay_rate = 0.001\n","learning_rate = 0.001\n","\n","def exp_decay(epoch):\n","    lrate = learning_rate * np.exp(-decay_rate*epoch)\n","    return lrate\n","    \n","# learning schedule callback\n","loss_history = History()\n","lr_rate = LearningRateScheduler(exp_decay)\n","callbacks_list = [loss_history, lr_rate]"],"execution_count":181,"outputs":[]},{"cell_type":"code","metadata":{"id":"nujfQsaIllMj","executionInfo":{"status":"ok","timestamp":1618805397235,"user_tz":-600,"elapsed":745,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}}},"source":["from keras.layers.normalization import BatchNormalization\n","from keras.regularizers import l2\n","\n","model_d = tf.keras.Sequential([\n","    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n","    tf.keras.layers.Dense(128, kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01), activation=tf.nn.relu),\n","    tf.keras.layers.BatchNormalization(),\n","    tf.keras.layers.Dropout(0.1),\n","    tf.keras.layers.Dense(256, kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01), activation=tf.nn.relu),\n","    tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n","])"],"execution_count":163,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":754},"id":"Q2Wbul8hlVk_","executionInfo":{"status":"ok","timestamp":1618805398531,"user_tz":-600,"elapsed":1494,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"e1a3a689-8d04-49eb-cd51-f967e240b3c2"},"source":["from tensorflow.keras.utils import plot_model\n","plot_model(model_d, show_shapes=True)"],"execution_count":164,"outputs":[{"output_type":"execute_result","data":{"image/png":"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\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{"tags":[]},"execution_count":164}]},{"cell_type":"code","metadata":{"id":"TPGHTdQ3lq19","executionInfo":{"status":"ok","timestamp":1618805398984,"user_tz":-600,"elapsed":1069,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}}},"source":["model_d.compile(optimizer=optimiser_sgd(0.001),\n","              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n","              metrics=['accuracy'])"],"execution_count":165,"outputs":[]},{"cell_type":"code","metadata":{"id":"qtFIaSl0lMY7","colab":{"base_uri":"https://localhost:8080/","height":703},"executionInfo":{"status":"ok","timestamp":1618807188964,"user_tz":-600,"elapsed":18045,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"bc0c579a-eedf-4ebf-862e-dd223538435a"},"source":["regularised_layers_model_history = model_d.fit(train_images, \n","                                     train_labels,\n","                                     validation_split=0.3,\n","                                     batch_size=512,\n","                                     epochs=12,\n","                                    callbacks = callbacks_list)\n","\n","acc = regularised_layers_model_history.history['accuracy']\n","val_acc = regularised_layers_model_history.history['val_accuracy']\n","loss = regularised_layers_model_history.history['loss']\n","val_loss = regularised_layers_model_history.history['val_loss']\n","epochs = range(1, len(acc) + 1)\n","\n","plt.figure(figsize=(15,4))\n","\n","ax1 = plt.subplot(1, 2, 1)\n","plt.plot(epochs, loss, label='Training loss')\n","plt.plot(epochs, val_loss, label='Validation loss')\n","plt.fill_between(epochs, loss,val_loss,color='g',alpha=.1)\n","\n","plt.title('Training and validation loss')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","ax2 = plt.subplot(1, 2, 2)\n","plt.plot(epochs, acc, label='Training accuracy')\n","plt.plot(epochs, val_acc, label='Validation accuracy')\n","plt.fill_between(epochs, acc,val_acc,color='g',alpha=.1)\n","plt.title('Training and validation accuracy')\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","plt.show()"],"execution_count":191,"outputs":[{"output_type":"stream","text":["Epoch 1/12\n","83/83 [==============================] - 2s 20ms/step - loss: 0.2231 - accuracy: 0.9511 - val_loss: 0.3629 - val_accuracy: 0.8969\n","Epoch 2/12\n","83/83 [==============================] - 1s 17ms/step - loss: 0.2219 - accuracy: 0.9507 - val_loss: 0.3687 - val_accuracy: 0.8958\n","Epoch 3/12\n","83/83 [==============================] - 1s 16ms/step - loss: 0.2213 - accuracy: 0.9512 - val_loss: 0.3642 - val_accuracy: 0.8968\n","Epoch 4/12\n","83/83 [==============================] - 1s 16ms/step - loss: 0.2173 - accuracy: 0.9530 - val_loss: 0.3634 - val_accuracy: 0.8979\n","Epoch 5/12\n","83/83 [==============================] - 1s 16ms/step - loss: 0.2152 - accuracy: 0.9545 - val_loss: 0.3779 - val_accuracy: 0.8926\n","Epoch 6/12\n","83/83 [==============================] - 1s 16ms/step - loss: 0.2187 - accuracy: 0.9524 - val_loss: 0.3647 - val_accuracy: 0.8970\n","Epoch 7/12\n","83/83 [==============================] - 1s 16ms/step - loss: 0.2197 - accuracy: 0.9532 - val_loss: 0.3682 - val_accuracy: 0.8952\n","Epoch 8/12\n","83/83 [==============================] - 1s 17ms/step - loss: 0.2163 - accuracy: 0.9537 - val_loss: 0.3649 - val_accuracy: 0.8975\n","Epoch 9/12\n","83/83 [==============================] - 1s 16ms/step - loss: 0.2167 - accuracy: 0.9533 - val_loss: 0.3653 - val_accuracy: 0.8972\n","Epoch 10/12\n","83/83 [==============================] - 1s 16ms/step - loss: 0.2189 - accuracy: 0.9526 - val_loss: 0.3647 - val_accuracy: 0.8986\n","Epoch 11/12\n","83/83 [==============================] - 1s 16ms/step - loss: 0.2188 - accuracy: 0.9517 - val_loss: 0.3652 - val_accuracy: 0.8979\n","Epoch 12/12\n","83/83 [==============================] - 1s 16ms/step - loss: 0.2157 - accuracy: 0.9543 - val_loss: 0.3678 - val_accuracy: 0.8967\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BSph2pT2baw-","executionInfo":{"status":"ok","timestamp":1618807219480,"user_tz":-600,"elapsed":1168,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"6793186a-9488-4187-b5f1-69c8fa578b23"},"source":["test_loss, test_accuracy = model_d.evaluate(test_images, test_labels, steps=math.ceil(len(test_labels)/32))\n","print('Accuracy on test dataset after layer specific weight regularisation:', test_accuracy)"],"execution_count":193,"outputs":[{"output_type":"stream","text":["313/313 [==============================] - 0s 1ms/step - loss: 0.3912 - accuracy: 0.8915\n","Accuracy on test dataset after layer specific weight regularisation: 0.8914999961853027\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"dLjJA98Qgxg5"},"source":["\n","Answer the following questions:\n","\n","1. Which configuration achieved the best validation accuracy? Report the test accuracy of your final model.\n","2. Which setting had the most impact and which one had the least impact?"]},{"cell_type":"markdown","metadata":{"id":"DKnAsWhyZ9iG"},"source":["# Final Accuracy on test set: 0.8915\n","The batch size, learning rate decay and layer-specific weight regularisations made the most impact on improving the accuracy whereas, batch normalisation has the least effect for the model in this case. After fine-tuning the model, the accuracy on the test set was slightly improved as compared to the initial training accuracy.\n","Configuration:\n","Batch size: 512\n","Learning rate: 0.001\n","Dropout: 0.1\n","decay_rate = 0.001\n","  momentum = 0.8\n"]},{"cell_type":"markdown","metadata":{"id":"UgzZh0jN5ULb"},"source":["## Task 6 Generate HTML output\n","\n","Use *nbconvert* to convert your completed notebook into an HTML file and name it **[YourID]_assingment1_output.html**."]},{"cell_type":"code","metadata":{"id":"Qn6n1hIcyMVG","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1618807617627,"user_tz":-600,"elapsed":83049,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"29c83409-fa7d-4a61-b9f9-bc2da346d139"},"source":["!apt-get update\n","!apt-get install texlive texlive-xetex texlive-latex-extra pandoc --fix-missing\n","!pip install pypandoc"],"execution_count":200,"outputs":[{"output_type":"stream","text":["\r0% [Working]\r            \rGet:1 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n","\r0% [Connecting to archive.ubuntu.com] [1 InRelease 14.2 kB/88.7 kB 16%] [Connec\r                                                                               \rGet:2 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n","Ign:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n","Ign:4 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n","Get:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release [697 B]\n","Hit:6 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n","Get:7 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n","Get:8 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release.gpg [836 B]\n","Hit:9 http://archive.ubuntu.com/ubuntu bionic InRelease\n","Get:10 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n","Hit:11 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n","Get:12 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [395 kB]\n","Get:13 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease [15.9 kB]\n","Get:14 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n","Get:15 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,408 kB]\n","Get:16 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,112 kB]\n","Hit:17 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n","Get:18 http://security.ubuntu.com/ubuntu bionic-security/multiverse amd64 Packages [24.5 kB]\n","Ign:20 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages\n","Get:20 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [696 kB]\n","Get:21 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,755 kB]\n","Get:22 http://archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 Packages [31.4 kB]\n","Get:23 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,179 kB]\n","Get:24 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [898 kB]\n","Get:25 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [2,542 kB]\n","Get:26 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [425 kB]\n","Get:27 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic/main amd64 Packages [39.5 kB]\n","Fetched 12.8 MB in 4s (3,265 kB/s)\n","Reading package lists... Done\n","Reading package lists... Done\n","Building dependency tree       \n","Reading state information... Done\n","pandoc is already the newest version (1.19.2.4~dfsg-1build4).\n","pandoc set to manually installed.\n","The following additional packages will be installed:\n","  fonts-droid-fallback fonts-lato fonts-lmodern fonts-noto-mono fonts-texgyre\n","  javascript-common libcupsfilters1 libcupsimage2 libgs9 libgs9-common\n","  libijs-0.35 libjbig2dec0 libjs-jquery libkpathsea6 libpotrace0 libptexenc1\n","  libruby2.5 libsynctex1 libtexlua52 libtexluajit2 libzzip-0-13 lmodern\n","  poppler-data preview-latex-style rake ruby ruby-did-you-mean ruby-minitest\n","  ruby-net-telnet ruby-power-assert ruby-test-unit ruby2.5\n","  rubygems-integration t1utils tex-common tex-gyre texlive-base\n","  texlive-binaries texlive-fonts-recommended texlive-latex-base\n","  texlive-latex-recommended texlive-pictures texlive-plain-generic tipa\n","Suggested packages:\n","  fonts-noto apache2 | lighttpd | httpd poppler-utils ghostscript\n","  fonts-japanese-mincho | fonts-ipafont-mincho fonts-japanese-gothic\n","  | fonts-ipafont-gothic fonts-arphic-ukai fonts-arphic-uming fonts-nanum ri\n","  ruby-dev bundler debhelper gv | postscript-viewer perl-tk xpdf-reader\n","  | pdf-viewer texlive-fonts-recommended-doc texlive-latex-base-doc\n","  python-pygments icc-profiles libfile-which-perl\n","  libspreadsheet-parseexcel-perl texlive-latex-extra-doc\n","  texlive-latex-recommended-doc texlive-pstricks dot2tex prerex ruby-tcltk\n","  | libtcltk-ruby texlive-pictures-doc vprerex\n","The following NEW packages will be installed:\n","  fonts-droid-fallback fonts-lato fonts-lmodern fonts-noto-mono fonts-texgyre\n","  javascript-common libcupsfilters1 libcupsimage2 libgs9 libgs9-common\n","  libijs-0.35 libjbig2dec0 libjs-jquery libkpathsea6 libpotrace0 libptexenc1\n","  libruby2.5 libsynctex1 libtexlua52 libtexluajit2 libzzip-0-13 lmodern\n","  poppler-data preview-latex-style rake ruby ruby-did-you-mean ruby-minitest\n","  ruby-net-telnet ruby-power-assert ruby-test-unit ruby2.5\n","  rubygems-integration t1utils tex-common tex-gyre texlive texlive-base\n","  texlive-binaries texlive-fonts-recommended texlive-latex-base\n","  texlive-latex-extra texlive-latex-recommended texlive-pictures\n","  texlive-plain-generic texlive-xetex tipa\n","0 upgraded, 47 newly installed, 0 to remove and 57 not upgraded.\n","Need to get 146 MB of archives.\n","After this operation, 460 MB of additional disk space will be used.\n","Get:1 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-droid-fallback all 1:6.0.1r16-1.1 [1,805 kB]\n","Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-lato all 2.0-2 [2,698 kB]\n","Get:3 http://archive.ubuntu.com/ubuntu bionic/main amd64 poppler-data all 0.4.8-2 [1,479 kB]\n","Get:4 http://archive.ubuntu.com/ubuntu bionic/main amd64 tex-common all 6.09 [33.0 kB]\n","Get:5 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-lmodern all 2.004.5-3 [4,551 kB]\n","Get:6 http://archive.ubuntu.com/ubuntu bionic/main amd64 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...\n","Setting up tex-common (6.09) ...\n","update-language: texlive-base not installed and configured, doing nothing!\n","Setting up poppler-data (0.4.8-2) ...\n","Setting up tex-gyre (20160520-1) ...\n","Setting up preview-latex-style (11.91-1ubuntu1) ...\n","Setting up fonts-texgyre (20160520-1) ...\n","Setting up fonts-noto-mono (20171026-2) ...\n","Setting up fonts-lato (2.0-2) ...\n","Setting up libcupsfilters1:amd64 (1.20.2-0ubuntu3.1) ...\n","Setting up libcupsimage2:amd64 (2.2.7-1ubuntu2.8) ...\n","Setting up libjbig2dec0:amd64 (0.13-6) ...\n","Setting up ruby-did-you-mean (1.2.0-2) ...\n","Setting up t1utils (1.41-2) ...\n","Setting up ruby-net-telnet (0.1.1-2) ...\n","Setting up libijs-0.35:amd64 (0.35-13) ...\n","Setting up rubygems-integration (1.11) ...\n","Setting up libpotrace0 (1.14-2) ...\n","Setting up javascript-common (11) ...\n","Setting up ruby-minitest (5.10.3-1) ...\n","Setting up libzzip-0-13:amd64 (0.13.62-3.1ubuntu0.18.04.1) ...\n","Setting up libgs9:amd64 (9.26~dfsg+0-0ubuntu0.18.04.14) ...\n","Setting up libtexluajit2:amd64 (2017.20170613.44572-8ubuntu0.1) ...\n","Setting up fonts-lmodern (2.004.5-3) ...\n","Setting up ruby-power-assert (0.3.0-1) ...\n","Setting up texlive-binaries (2017.20170613.44572-8ubuntu0.1) ...\n","update-alternatives: using /usr/bin/xdvi-xaw to provide /usr/bin/xdvi.bin (xdvi.bin) in auto mode\n","update-alternatives: using /usr/bin/bibtex.original to provide /usr/bin/bibtex (bibtex) in auto mode\n","Setting up texlive-base (2017.20180305-1) ...\n","mktexlsr: Updating /var/lib/texmf/ls-R-TEXLIVEDIST... \n","mktexlsr: Updating /var/lib/texmf/ls-R-TEXMFMAIN... \n","mktexlsr: Updating /var/lib/texmf/ls-R... \n","mktexlsr: Done.\n","tl-paper: setting paper size for dvips to a4: /var/lib/texmf/dvips/config/config-paper.ps\n","tl-paper: setting paper size for dvipdfmx to a4: /var/lib/texmf/dvipdfmx/dvipdfmx-paper.cfg\n","tl-paper: setting paper size for xdvi to a4: /var/lib/texmf/xdvi/XDvi-paper\n","tl-paper: setting paper size for pdftex to a4: /var/lib/texmf/tex/generic/config/pdftexconfig.tex\n","Setting up texlive-fonts-recommended (2017.20180305-1) ...\n","Setting up texlive-plain-generic (2017.20180305-2) ...\n","Setting up texlive-latex-base (2017.20180305-1) ...\n","Setting up lmodern (2.004.5-3) ...\n","Setting up texlive-latex-recommended (2017.20180305-1) ...\n","Setting up texlive-pictures (2017.20180305-1) ...\n","Setting up tipa (2:1.3-20) ...\n","Regenerating '/var/lib/texmf/fmtutil.cnf-DEBIAN'... done.\n","Regenerating '/var/lib/texmf/fmtutil.cnf-TEXLIVEDIST'... done.\n","update-fmtutil has updated the following file(s):\n","\t/var/lib/texmf/fmtutil.cnf-DEBIAN\n","\t/var/lib/texmf/fmtutil.cnf-TEXLIVEDIST\n","If you want to activate the changes in the above file(s),\n","you should run fmtutil-sys or fmtutil.\n","Setting up texlive (2017.20180305-1) ...\n","Setting up texlive-latex-extra (2017.20180305-2) ...\n","Setting up texlive-xetex (2017.20180305-1) ...\n","Setting up ruby2.5 (2.5.1-1ubuntu1.8) ...\n","Setting up ruby (1:2.5.1) ...\n","Setting up ruby-test-unit (3.2.5-1) ...\n","Setting up rake (12.3.1-1ubuntu0.1) ...\n","Setting up libruby2.5:amd64 (2.5.1-1ubuntu1.8) ...\n","Processing triggers for mime-support (3.60ubuntu1) ...\n","Processing triggers for libc-bin (2.27-3ubuntu1.2) ...\n","/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n","\n","Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n","Processing triggers for fontconfig (2.12.6-0ubuntu2) ...\n","Processing triggers for tex-common (6.09) ...\n","Running updmap-sys. This may take some time... done.\n","Running mktexlsr /var/lib/texmf ... done.\n","Building format(s) --all.\n","\tThis may take some time... done.\n","Collecting pypandoc\n","  Downloading https://files.pythonhosted.org/packages/d6/b7/5050dc1769c8a93d3ec7c4bd55be161991c94b8b235f88bf7c764449e708/pypandoc-1.5.tar.gz\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from pypandoc) (54.2.0)\n","Requirement already satisfied: pip>=8.1.0 in /usr/local/lib/python3.7/dist-packages (from pypandoc) (19.3.1)\n","Requirement already satisfied: wheel>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from pypandoc) (0.36.2)\n","Building wheels for collected packages: pypandoc\n","  Building wheel for pypandoc (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for pypandoc: filename=pypandoc-1.5-cp37-none-any.whl size=17036 sha256=ef2706729562854e8ad223b14aebe13ae6edc05f66ed1bd58ca6ddc299fb2a08\n","  Stored in directory: /root/.cache/pip/wheels/bb/7d/d6/2f9af55e800d37e42e546106bcbd36a86e24e725e303d17e04\n","Successfully built pypandoc\n","Installing collected packages: pypandoc\n","Successfully installed pypandoc-1.5\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XYnrANKFyMYm"},"source":["!cp drive/MyDrive/Colab_Notebooks/220079842_assignment1_solution.ipynb ./"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"D2tAsyR2yMcQ"},"source":["!jupyter nbconvert --to html \"Maze.ipynb\""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dCxlJQKKc2w2","executionInfo":{"status":"ok","timestamp":1618807769263,"user_tz":-600,"elapsed":180802,"user":{"displayName":"akriti verma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgLicUqJqSpbaQkVBRSBrAe4jyG-DbkFfGCxA3ztw=s64","userId":"18093050313413537526"}},"outputId":"46d3b34d-a476-4d6c-f095-05fd280d2107"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":201,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"cWbr7jjEAzNb"},"source":["---\n","**END OF ASSIGNMENT ONE**\n","\n"]}]}