ML4DM Lab6 - (Cifar10 Dataset CNN).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"ML4DM Lab5 - (Cifar10 Dataset CNN).ipynb","provenance":[],"collapsed_sections":["odtd_TYjui9E","ZlflH850uvqV"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"odtd_TYjui9E","colab_type":"text"},"source":["#Import"]},{"cell_type":"code","metadata":{"id":"kMlCWG69m_tk","colab_type":"code","outputId":"0ef7aff9-4a45-4df4-a88c-d51245453543","executionInfo":{"status":"ok","timestamp":1574184864403,"user_tz":-330,"elapsed":3175,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":83}},"source":["from __future__ import absolute_import, division, print_function, unicode_literals\n","\n","# TensorFlow and tf.keras\n","import tensorflow as tf\n","from tensorflow import keras\n","\n","# Helper libraries\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","print(tf.__version__)"],"execution_count":1,"outputs":[{"output_type":"display_data","data":{"text/html":["<p style=\"color: red;\">\n","The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n","We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n","or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n","<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["1.15.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ZlflH850uvqV","colab_type":"text"},"source":["# Loading the Dataset"]},{"cell_type":"code","metadata":{"id":"JeDCqS6inBws","colab_type":"code","outputId":"15219d63-59ce-4723-c197-a697b8664869","executionInfo":{"status":"ok","timestamp":1574184903384,"user_tz":-330,"elapsed":10942,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":54}},"source":["cifar10 = keras.datasets.cifar10\n","\n","(train_images, train_labels), (test_images, test_labels)= cifar10.load_data()\n","(val_images, val_labels) = (train_images[0:10000], train_labels[0:10000])\n","(train_images, train_labels) = (train_images[10000:50000], train_labels[10000:50000])"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n","170500096/170498071 [==============================] - 7s 0us/step\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"LU_Vq-fZu1F6","colab_type":"text"},"source":["# Data Preprocessing"]},{"cell_type":"code","metadata":{"id":"c4pMGw3snCZj","colab_type":"code","outputId":"63ac7c5d-90cb-448e-b163-77337106ad3b","executionInfo":{"status":"ok","timestamp":1574184922998,"user_tz":-330,"elapsed":1455,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":127}},"source":["class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer','dog', 'frog', 'horse', 'ship', 'truck']\n","\n","print(\"train_images.shape\", train_images.shape)\n","print(\"length of train_labels\",len(train_labels))\n","\n","print(\"val_images.shape\", val_images.shape)\n","print(\"length of val_labels\",len(val_labels))\n","\n","\n","test_images=test_images[1:9985]\n","print(\"test_images.shape\",test_images.shape)\n","\n","test_labels=test_labels[1:9985]\n","print(\"length of test_labels\",len(test_labels))\n"],"execution_count":3,"outputs":[{"output_type":"stream","text":["train_images.shape (40000, 32, 32, 3)\n","length of train_labels 40000\n","val_images.shape (10000, 32, 32, 3)\n","length of val_labels 10000\n","test_images.shape (9984, 32, 32, 3)\n","length of test_labels 9984\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"9U2b5FYQnFgu","colab_type":"code","outputId":"573612c4-e2ba-4fc4-c31a-0b4e6792e613","executionInfo":{"status":"ok","timestamp":1574184926702,"user_tz":-330,"elapsed":2531,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":692}},"source":["plt.figure(figsize=(15,15))\n","for i in range(20):\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.xlabel(class_names[int(train_labels[i])])\n","plt.show()"],"execution_count":4,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA1MAAAKjCAYAAAD4XVUxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOy9eYxm2Xne955777d/te/VS/U6S/f0\n7MMhh8NNFEWJorVYSqxINiA5imEkiB0YDpL8lX8CGEGABNkAERZix7JiQogd7aZESqQocoazcKZ7\nenp636urqmv5avn25d6bPziaqud9z7Crv4hdPcrzAwjwfHPudu7Zbtf7vI9L01QIIYQQQgghhNwf\nwV7fACGEEEIIIYR8FOHHFCGEEEIIIYT0AT+mCCGEEEIIIaQP+DFFCCGEEEIIIX3AjylCCCGEEEII\n6QN+TBFCCCGEEEJIH0T3UzkMgjQTbR+SeNOqpz+06P1pF+nZnXP3rJOqMzvRx9jrFEslKOfzWXte\ndZi9l3vfW+C5f+fwWzaOe55r48WDUH3/utAcE4b4WxIn9n5UHX13iedeJAh0JVNl5/1ubG5Jo9m8\nd+P8CAkCl4b6vhW7cQfYRff7m4evXXbRDnoc7uYgl957rIq714vyXWcX591BnCSSJOZmHiiFfCYd\nGsht/+C5m3yUgbJv7mg121AO1TGZjJ07Ou0O/uDp+EGEx+m5zTfe2u0WlNPEvgc9t+k63dgz36j3\nOVAsmjp6PoxTOx/qpwzUvzM6T79xGVw+I9W+IiKBulaqyr2uZ55V2PEk0knwuPmlxmqaphP3PNmP\niHw+l5ZL222fJraNE/XssacP6D2F02XPHJArqDU78Fzb4W+6z/osYuwsZsdC3FPPpNZaT1cz602U\nsduwIMRr+Z47CO4xTXmeyan9Qii+PqvqBHr82HHYNf343ovqyur6nvbZXL6UlgZGPiiHkec9qD2a\n87T51lYVyu0WznVRZOdZ/e58e7RU9bcwvPd+sdfD33zzrL2QvvZHzy7JjE0zePt9JjwuTbsf2mfv\n62MqE0Wyf3z7PK1e19TRE6bzTZhqUUwSPWHaa+vJxYf58NATptgO+7EXn4fyseOHTZ1ELQxhmIOy\nb5LVH1y5bN7UyWTwPBsba6ZOt4cbovLAIF4nKptjhoeHoVyr1Uyd8tAIlCPVNtVNey9htoDXblVN\nnXZn+93+83/92+a/P2jCIJDhst1g7US/X9+He6A2iMEDm298HxWeFfqvAZeqTbDnmyJV1048k5T+\nLVHtGSR22snEalF3HVMnDXC+0edNU990pv/RwNd22/e7Xq17/vuDZWggJ3/vF059UE4Duxg/NjUF\n5er6qqlz7r1rUB4ZnoTyzPSYOebWrVtQTj0bjPLoEJQLGdzMDpZxXhMRuXLpIpR7Hbt2lNSHULeN\nG4O765vmmK7g/PjZ558xdQaG8H631IediEhO9fW8w2cKA7txiSZHoTw+ts/UGezhtTrdBpRX7tp5\nVo/vjrNtdWcLj/sn//0bN+2JHhzlUlF+9ic/90G53W6YOs0W/lbVH+4i0mjhszq1Ycxm7Eb+2Als\n96Rsz9sOcVxnc+oDoWPPqz+6I2f/oXVzDZ9pawPLnZadH7s9/G10ctzUKQ2q/pf1jReso6fitGf7\nbF7tF4aiaVMn18H9w2AR9xw1z7q/tLwC5V5q34EEOJf8xle+uqd9tjQwIj/+C//4g/LI6Kipk8vi\nXFYoFkydr3/tG1C+eukKlMcmBswxxSKet7ppx0svxvV4cAjnx62tDXPM2gquA72W5z2ofwRPE6yT\niu1ru/nXxd38E+qPat/kEr13MRU8R+lKdg7Qdbrd2x/aZxnmRwghhBBCCCF9wI8pQgghhBBCCOmD\n+wvzy2Zk/8HtPwvfnL9t6mxu4Z/TI5+mR/3mdvG3Px0+6EWdJlZhf77Y8wEVNjc0YP/U2+ninz1D\nFZ5XUrqrH1wMr3Xrtm2rogprmZqxYSKxio13gfrzf8aG+elwx0zW3p8O0V1fuwPluifksKVCc8KO\nDR/sxtt/3O122ua/k4eX1KmwEM+fxhMTmuz5Y74Ok3Q6jNf353Sj2vPf5H2jx/xHJB58R8hlLrTh\nRUkb23CzYsdisYzhbVEO563b8wvmmEyE1xoYt/PhVrsJ5TDEEM2lig0/aak+kc3aUMChAs5lsQqt\n27cfQxtFRG7MY1hi4glrmd6H7XBsvw3lXlutQLlSUfNfgM8sIrJ6F8Mou56wtZwOMdzE62xU1s0x\nzQTn2W7RrqHN6OEScU6Mjcp/8mt/Z7s8Yd9VLochUp3UhltWNrB91tewvLh4wxxz+fZ5KC+3l02d\nQOmoekqi4NtedGKs005sCPDQOD7T6LgKw+/ZLVa7iXNQvmzDwFyEdbKecNuc1i6qqa04iPciItJQ\noZb5on2mzzz5MpSbFR0GZt9tGjwJ5fmFRVPn6PHHoPwbX/mqqfMg6fZ6snx3OyyusmbDFzfVeD3+\nqJ075o7hvu34iaNQPvXkSXOM1kj9xV+8bq+9ju/m05/EEOa7S/PmmN//vT+AcuzRLqax2n/rRV2H\nzInYVAi+8+5ibdXaxV0J1neB1rRa9YFPP7iba+9+nuVfpgghhBBCCCGkD/gxRQghhBBCCCF9wI8p\nQgghhBBCCOkDfkwRQgghhBBCSB/cVwKKfC4nx44d+aC81bCCvfVNFB97JV7akE79Z5+BnjY50wZm\nPmLlZ6UN60RE8nkUkEahFUbX6yjA3KxvQblQtkJP7YvV8pgzFtUzZD0eBtoUMJvDpBX5IgqcRURW\nV9FroOgRuOr7W9jA9/bmG2+aY+pddS+J9SOQHe3XaDIBxUeJ1N07YUsqegzZMaX9qbTxpt8MWCWl\n8IpDdfaLftwvHn56cSwrG9uJCcLEtvGYEgm3m75kITi9L69jsoOwZxMm7J9C35k4tu23VUcRe0Ml\nXgg8yUOGxtDjKozt3NGpY5KH0RImpOh51oWhAUyu0+vaOSdRnkflrPVcrGfxno8+gvPqwJD15PrW\nm2egvHD7uqkTrWMCj60azrMLyyhwFxEZncV3sNawyS/coCfp0R4SRoGMjmyvM72OvedYvZue2Dp3\nbl2G8u3bmCSl1bUJE7LKA3D/sPVt2uxgO+eUz1SrYcdCTSVQajetF1CkvNAy6l5KkV3Te1msE3rm\nUL2/Kec8CShCbSyN82EpZ69dC/E8acb6X86rxDRJA89bKNqEOF2He8EksON7YdkmvNlLnAskm93e\nT62t2sQ5msNHDpjfghDnpffeuwTlVgv3iyIiBZV8bGbaJvqZGMM5JwhwjTw4ZxOWffLlT0L5zbfO\nmDr1ujJQ72LfTzo2MYw2Pu927HjxmaFbtKmwNjXvb712xt9W3YsnmZb+pvD5294P/MsUIYQQQggh\nhPQBP6YIIYQQQgghpA/4MUUIIYQQQgghfXBfmqkgCKW8wwhyYnza1KnXdZyxjXHOKAO63cRJaoOt\nILDfgTrmUZ830eZkIpKJ0HAy44khLpcxXrRTx9jaxHP/BRWvfPKJJ0wdbUyaejQgOWVkmVOaqa5H\nt5Qq975OYnUE5Sze30ARrxNlrc6qPIRxvVmPriXMbJ83vGmN+x40adp/HO7DgE+31E9or1ODw9sm\nqv+lnunBCY6XNPVophI1VlVMeSDWtNepePAk8em11Jj//xrk/JASp6nUdmgsXdejP7iLxqTtmq2z\nWMW5N1A6kbnxYXNMqYhanGuLdgxvtfG8pUGcK8oZqzttNjHGfmvTmoIPKyPLSMXB1+r2GQdHUa8a\nObsuVDbxfsembB2tCcjncM48fMiudW+cxzn06oVbpk5BSSE226ifmD603xwzfQB1Gee++11TZzRv\n16m9pFFvyOuvv/1BudW26755Nc7qLm7cQnP7uytK55ez7y5fwN8yeauPqHVR79TJ49zm00xtNbG/\n9Tp23kqV+a+oPuw8usROG+e2woDVv5WGUJdUzdu5TmvFkh7qUXJqbyMiIiH22dTZsVrrLeH9qnGX\nZuwztQTHiwusjjzKWo393uJgz7W+ZrVN+w6i1jNJ7bM3t9RztVFzFMZWg3Ts4EEojxTtu4qVzP7O\nHdScVWv2vC99Gg2XF9c2TZ2Ll1HbGSmjdhfYdX9gAOf4as2axLfb2AeSxDNezF5U1elXM5VgYwV6\nb+rZK+xu+0DTXkIIIYQQQgj5kcKPKUIIIYQQQgjpA35MEUIIIYQQQkgf8GOKEEIIIYQQQvrgvhJQ\nhGEoA0PbouXZWWsa5pTKdGnxtqmjvLKkrU0fPcklwgAFwvocIiJximK2VCnMkp4Vt+ULKMAcHrOi\nbC0EHOwpM0ktQhVrUOgT7E1MaFGzRySnjPnCCJ+h1bLXLikT4dhjkJnJ4rWyWRQhBh6zwag0AuV8\nxt5vtOO4ILyv7vUj414JKHRyhgeJvjd9L747S5QhnRbP7wbfGFPaaQk9JtdBqg0b7TvWrz1O0JzT\neQz0UiVEdZ4Bbt/jbp5bGRF/BJKRJEki9eZ2m+UiKxKPVSNn8tZIs5hinVCJ7rue97tZRaG+i2yd\nQhaT4PSU+LdVs8LjMHPvf7drq3cTqbl5ZtAmxckU8LmLgW2rydlHoVwozZg6OolGrXUeyusrK+aY\nkRLOkYMlO2fq3lYsoxlwxmOofvoCmn5WG1Zonq3ZBA97SavdkSs3thNw1Jvrpk6c6OewY/GuMjGu\nNnAdzZfs+y10sV/36tYMuOmwX2ebOL90Pcklqg3s142GTeTUVEkAemqPkcR2fgxU0p7BnqkiIyHO\nkcXEnkfvO1I1gZeLnsQWeeyjrm3rNNpqrAZ4TKtl26oVYwKFYtH261JgjWn3kkajKW+/c/aDcq9r\nX8S+CA2+K5s2oUOsDGxLA7j/2jc7a46ZnURj6VbdjudYGbMfPXoUytdvzptjNquYDGN4zLZ5Rhlh\nu44yns7aeWx4GA2EO7HtAz2VMEj3cxGRbkftdwK1F/AkrdC/Oc+8EZikGXq98e05VLIY327LkxTu\nw+BfpgghhBBCCCGkD/gxRQghhBBCCCF9wI8pQgghhBBCCOmD+xK1pIJRzj6tRqmEMbj5nCfeX4mQ\nikWM0Qw8xotRgDG59YaNMd2qY1y0dfG1560rg7V628ZbFwqoo8op02Ft0iYiEquY0ihTNHVciMaL\nSWrjRTtKe+UifIbIo/cJlcbCZa2eotPB2NpGE9uu17MxpkkL20abvf3gfnY8g8fUdy+4X03Ug9RQ\n+bRLiC+O997n0LqqwGiQPP1Gva/YYzgZx1gnE9o+kKjj0hTHR75kp52Out849ply42+md+2iv3nj\noh820lSCHQac2cEhU6WjNKSFktUo7BvDebXZwzHebav5UkS6atiPjFgNabmIbbiycBfK4x4d0Kg6\nz3rDms52Gng/odKLFjzml1EbdSPTh46YOjPqt2Jg1600g32y0sU2v337mjnmgNLXBk/bsbB6Bw3e\nF+6iMeh7r6+aYzYTHD/jM5OmTrtlx+Ze0otjWdvY1js1PJqpZhv1Jj2PHqGujHJbHVxzWrFHK+aU\nkX3Wal8aTq2tbaWZ8uiKw57SYLdtnY7SD/ViPUfZOUnviFode7/VOv4Wesx1O+p2AqUh7WbtOOwo\nvXcmsuNbImzPQK3zQWKP0SqbXM5qsVzm4TKaDsJAijvMaMOMfVcTs6gVqnk06m+/fQbKc/vRdDvx\naHuXFnHc634jIjIwjLoqp8bL4wXbxmffuwjlrsc8e3YadVSNDdwLTqrrioicOHkCytWmXTvOvHMa\nykt3rc5UnDInVt8CzjMOJcHfgtSjf9JbfbVP8WmxXKq1WL7zUjNFCCGEEEIIIT9S+DFFCCGEEEII\nIX3AjylCCCGEEEII6YP70kw55yST2Y559GmmtF9RPm/jZLtdram4txdMoOLnfXnu2zpffg4jeSOP\nX0uoPFMW1zZMHR3rnYnwmV544XlzTE5pxS5fvmzqNNS3bKlodVUZhzHOzRrGt+Y9Xk5a+1JTx4iI\ntFUsbUd5JWS0LkxEiiPoM1UqZ0ydndqNyHOOB41zfz2aKftb+kOLu0XrnXTfT2wYr6dd7f1qzZQe\nLoknhjhSuqqBkn2/RRULX6/amOxiAeeFz332JSifeBLjr0VE/uX/9UdQvnZ9wdQJAq1Jucc78f72\n8GumoiCUoR0eMbEnRnxd6YuiYY+OQflKjeVQQzE9av2Wkjr2i9ura6ZOt4Ex7EcmD0L5iQOHzTHZ\nLPaJa3dumjrtHPalpL4MZefR8I2OTWGd1OpPIqWhaXXsfPid76Lu4aq6v2PTdq179DjOh0lk9bZO\niUliwTrdpr3fu2voSTgwMWbqjE5YPcxekqSJ1HbojVsd68nUUut+1zO5dXVfD7E/dhOrqdDyscSn\nIU21tgmvkwS+PYfyYszY/UO5rDwnuzhnZjwSEC3dbnm0Gq0Ez9Pu2msHsfLRVPuJuGf9gro9PE+Y\nt3uObE5pAdVzBx5tt5arRpFdO4LA/raXOHHidvSVbMaO8cFBHOOFotVFTk3iPDp3EOdD3z6o0dTe\ni3Zd0vkGcqrK+JBvv3gcygemR0ydjU2cX1bWcI7Pe/ahofId9cgHpar8Yl3OesxWa7hu1aqoIU26\ndo7vqrwBsc+LyugOdR2PmZsaiEm/m7j34V+mCCGEEEIIIaQP+DFFCCGEEEIIIX3AjylCCCGEEEII\n6QN+TBFCCCGEEEJIH9xnhoDUmNGaE0Yo4stkPMauqo4+pxbPi4gMKOPKhkcBV22ieC3KoFi+XLYi\n7RsLaCzWXayYOhKi+O5jL76M553cZw65eBHN0zZjj/C9rcTey3dMlZlJNFAbV4ZqzQ17v4mgYC+b\nt+9A6x21YNSXBKRaReFiklrBZmeH2Wgv9oj+9oAfSQIKT2IIy70FjYE6rz4i8Vyo11VGd577HVHJ\nQjIqMczUpDUCPbgfzfxOPLLf1KltYd+qbm6ZOsUC3vP4OPaTp5+yxqpPnXsCytc9CSg8Lr22zj2q\nPEhD5n4Jg0CGi9sJBooTo6ZOu60SjPiGWorz4T5lMjuetXP5tVsoGj40c8jUGZ1BU8qMMkgsehJm\ndJVo+NDsQVNneekWlOsqGcZQ2ZpUDo9h21xfsKLn2xWcIyfGrQnymYvvQfnSLTSdnTv8cXPM1PGT\nUA6X7LULSszdUa7I3dQaEd+Yx0QclWU7x48M24QEe0kqTuIdCWISn5m3+i0Ru4YHWZ1EQXXsxG5Z\nYpVwwnm2NdqPNavKvdAzV6t9Sj615w1CTEYVZ3Gv0OvaMRYn+M5HPeMl11aJuyKbyKsn+rnVvqpn\nEwn0Euw37cSu4br9soG6tvMsfjqJmDZnFZHE7X1Cqp3EcSybm9vJxdLEttfrr78N5TC068f09ASU\ndRK2+fl5c8z6Cq5v45PTpk63h9eaUfN3NrT3e+IRXFsDZ9davSgurqHp+h/86dfNETev4H623bbt\nsFbBRG2lsieRRRb7RaGE4yX0JFRbXsZERK2WnTMlUHNJF8upJ3mRMen1JLZIfRnAPgT+ZYoQQggh\nhBBC+oAfU4QQQgghhBDSB/yYIoQQQgghhJA+uO8g1p1aGp+2KZvBmMhs1hPjrEIT83kV/+2RNRTL\nKrYy64nJVd+GoYozbnVsXHRLhWQ/9fEXTJ1nPvYpKE/PoEbqzDto+CgisryJ8aNDo1b30IkxjjPw\n6NHePHMOyl/4NOq1hsatoeNqBeNxfWZ5mYIyNFYGzO22NV3shmg054snbe+IZ016P1xf96BwHpPB\nH1rf1wFT9ZuKn3c+fZTD63o8rqXbQRO7rtICDg9YbcTsDOrmnn/haVPn8ccfhfLMNMZkr2+gJkRE\nZGsVNR8vPm3jrW/cQYO/xRXbt6JI6R66aJLaqns0AspQ23n0eKJ+03qzxCuHwms57Zgp0rfh8o8O\nB4aCmcTeYE/10eXVZVNnam4WyjevX4HyrRrqRUVEDu5HE9zP/bg1JD93AecX10CNT2wME62nYr5g\nDUUlwTkno3QsDe3OKiJLKk6/UrWGvHc38Tl7MmvqPHYS+3pxArWAz37iRXNMTy1th48dN3UW1Nx7\n8V00Ay5EVlv0zHHUKnqkL7J42epr9xQXSBpua9qcFiWJ1T+5wM4DkdKk9FrYyEnXrku9FNswSm2b\nZpQuKaM1PuYIEQnx3eVydi4OlW7FhWim3IrsfJOpb0B54oY1xq63cK1tH7PrfGwWFLxWL7ZzczbV\nv/kM35WOV619secYvWY6j1Gtnr/3HIfa8c1NO3esVfBdFT2GtrrzZCLs559/+WPmkEP7nsIfAts2\nYRY1oo0uXmjzru03nR7WGSzbeXZ8FLVXMxO4Nzi6z+pZy1ns1wNDdj/bUpO81oeKiNy6g/qsu8vq\nGTyaqVwB++z8vNVTx0qP6VQ7SOIRFavfeh2rxYo9JsIfBv8yRQghhBBCCCF9wI8pQgghhBBCCOkD\nfkwRQgghhBBCSB/wY4oQQgghhBBC+uC+E1C4HZ9fLrKiuUQZx6WBNVpMtfBLGceFHqV+EJShPDTi\nETNm0Ig0n8djMpG9l+kpFCOPDc/Y+1XmaXcXUdB87p0L5piFBRTJTc/a8/aUCDbnEaumAYpe/+KV\nN6D8pZ/4tDkmyA9CeXFx0dQZLKCgUPJYzpax7UREQqW/zeds9ylktgWPQfAwfKun8iPJMGDMX33X\nQAFmp2PF0xNjKCx+6tQpKJ981IrlZ6bRdFQb9IqItNooplxavAblO3eskeC8MubrVldNnR//0s9C\neaP+jqlTLOI4a9dwrE5MWJPrffvwt2zG9q2eMr7W+U98SUDMLw9dsglLkibSbG8LX6OKTRayUcdE\nJQVPkplsgO1V7+AxgWeePaCE7ufPf8fUef01TGTx7PHHoOwzmg5yKHquNjZMneUK9sneOva/4pA1\nXV+PUDQ+NGHNL8vT6ri4Zup86ac/C+VWD8fP8CgmfRERqTcxScDdO1dMnTNvnoXy4jw+90DOGgg/\ndRzHfL1n2/OVBSs+31tCEUi+YJMUZJT3a+oxq01UspVQJaYJQiskz6hkB1HGrjvjBZyThgM8ZqFh\nx1isjOzFeQxuA5XsQiet8Px79XADn3ts0SY+6KjEAc2sTaoRqvtJVeKIxJNsRyeTSLQpsoikoTJA\nVROtL3FIVyf6uf+t5QPHOSfZzHa7dlJrIh0po2FfTq1QJT/4/GdxTzY2aPedr7/6CpR9CXkOH1Xz\nqnp3zYbtN7FKtjU6ZpPitHU2IHXeT7/4CXNMZUMl+tmw83dP9YGiZw/5yY8/C+VLl69D+ZXXv2+O\nqQ/gxNHxmNjXq/hMXZVZrtPCRF8iInEb5289dkXsX5t+mIXvw7DbJYQQQgghhJCPHPyYIoQQQggh\nhJA+4McUIYQQQgghhPTB/QW2OifhDp1U1mNgVsigXqfWsN9rrSbGM2aVxmdkDLVPIiJ5FX85JDa+\nUcc8BiomO+35NF4Yo33tmo1DvXnrNF5GPVKnaoUYtTXUx2yEdVMnX8D266bWICxJMWa82sK43m9+\n+z1zTJxgnXrDXjvtYcxrQZmlHXwEY1tFRFodPGZ5+Yap0+tuX0vfx0cZo5DSRsDORtPqOk899bip\n85M/8TkoT00q3V/OBmnXaxjf3+rZfpM67KTa8PS7r6KWQ0QkVQHh1YbVAn7qp/42lK/dvGXqHFD6\np+OHDkC5WUdDVBGRwSHs573YGuhpDYDTmgGv2bL96WEnCCIpD2zrfLIebVNZRW9HWTvPdpUGYHwC\n9VDdlm2vq7fRVHFelUVEFhaUCfNGBconHz1hjhmfxX5dU6bmIiLNBsa1d5ShdcdKnaTawH4Sr9nY\n+Ilp7ASz4xOmTtbhXJzNYdv1arYd1hfxt0unrX7whjJkbXaUwWzo0Q2peaO+btck1/E6VO8ZzgUS\n7NAkB55/p81ktfG5ndtirYMMldAq8ZhoBnieIG2aKqNZHENH51CPcvk1qw10gjqWvNIVi4h0lTGp\nlrcVA6uFGVrFdTSTse8ydwDnzNRj7tzZwH4RKo1XkPfp1ZWxr0dXlQ20+S+OhTj2aFOV6WysTVNF\nJAgersk4l83I8UPba9Xlax4zWLWOppF9hrl9OLftVzrob3z7u+aYhWVcw+dm7V56WenuD82hofrc\nPqvDLw2qPuqZX0Rp9UVp5Npi55soi2NsctrqlpbWca7baNt1fijFvf+BadzXv/SM1XhVqnNQfuOd\ni6bOxWtqfo5Un3W+uQbvJXQejaHa7Husfz+Af5kihBBCCCGEkD7gxxQhhBBCCCGE9AE/pgghhBBC\nCCGkD+5LMxW4QLK57djOVtvmmt+qYNxk6vE/GZ5CL5CWw7jiSmrjjBPlVdP2JPzPhvjbYF7F8cY2\n3rpaw986PRu/rON/0x5qkHx+SvkC+pI0rWxJ2nUUATif7kbwmUKlC7t82XoBtVX+/G7P+hulMf7W\nU3n4Q88zhRm8v3bXihh67s72/4/tdT+yKF+pMMJyu2X71gsvYFz+3/2Vv23q6Hj5uIuxvj4tTKuL\n7/fSVetvc/P6HSh/5jM/BuV80cbT16rYB5KM9YmYX8Lx3e7YcXjjxg0oH5zF8d71jMNIz0Se+GXj\nI6Vjv1OPjsRznoedMAxkqLQd+95q2zbOKf3JpkdQNK78T9YrGAu/soR9RERkqYjzy1bF48+hhCFd\n5W+z1bTzWK6Bc0GtaTVxzZb2H1TX6VotTFv1gRXP/epI91NHrXfb4q3zUO70sG20nlBEJHSo5xn0\n+GDtP4QahlaC42dh0eo0mmqx0N1cRCTIe3wW95BUfuCP9lf4NDOJepDI49uUOuyzTmmF0p59v6lg\nX4o8fkGry+hhNjeH+tXepu2Pa7eWofzIi580dXpqeskoL7yyRzY8N4wa8eik1dJeWMf7CT1btc4S\n+l0mygcyGLYeZtr/KfLMj9ozKonVuPTpoUJ8bp/nn4QPl2ZqbHRIfuU/+qkPyl/5za+aOgsrqO30\n+Uy5RI3XDr6XL37hOXNMpYp6ogMzdk5q1XHOXF1HndXdTU8/v479/OicbfNnn0A9XlnlI8g3VEcS\nkVvXbkO5XrNzcS6Puq/hAbt/qFdQX1vMY398+tSj5pjNOva/O0t2z3v5Jj53mFV+dV0712QSpVPz\nCKLSBF/4D9vRfvR2GoQQQitYEu4AACAASURBVAghhBDyEMCPKUIIIYQQQgjpA35MEUIIIYQQQkgf\n8GOKEEIIIYQQQvrgvhJQpCISJ9tCrqbH9HF9A4XQAwNWBDk6hSZnl26jKG1pDcsiIllBsV25ZAX0\nx44fgnIhwGO0AFZEpKJMxC5cnDd1Gi2VgEI9ts8+MVGGwZJ4kksoIX7iEc4miVbF4b10ulY1p032\nxsasMHqgjCLDShMFkVWPILejBOCdxBpZptntd9eLf5jF2cODc/c2wEzV++x0UIo4N4dJFkRE/v7f\n/2UoZyOrXr1yBY1xB5UYtBtbs8ZiGYWTq57x8tSzaLo8MYUGfxMT1hg7yKDY9tAj1kBvs4p9oN31\nKKyV0HhpZQnKB47sN4ecehJF2LP7pkyd+Xl8Tif9iPB9IuiHywBV0kTiHWOtYLJziLRVUorQI4y+\ndhVFw+0KjvF8xrZfZQvn70bNzgOjQ9j/EsE+ulKxiYkaMf67XbNjM/I0ezhfRBHOUTVPkpfhCRwv\nYdZKhGdLKnlR5ZypU1NC6IxK8DA2fsQcMzpxDMqPPGmNLN947RKU//Rbb0M57th1IVRJGFbWbHvW\ntDvsHuMklWCnea6z7yE2iZA8CSgCfA+BSkAReAysnVqfo55NetXM4HkuKEH9f/i5L5hj3noFzVYH\nArvnuK3W9SCPSUnGU0w48oMTYZ8df9yaXH8ig33/4unzps4LH3seyk2HCWYuNO281lb/fh549iU9\nlWUhyOI7CT3vQCf62c2autcMDBTks59+4oNyrf5FU+ePvoZmzj1nzXU/8eLHoHxwH+4F2omdnPM5\n7KO9rv27xnuXFqH8rjKmTQNr2nvzOq6RkyN2j9bt4jy6bxr79VAZE6SIiMxM4Xp8/doNU2ezgmtH\n15M4qVjEfn1gGhO1DZdt0opaA+/3U1tPmzqpSsh0Z1l9UyzYcVhZ1fdrqki37dnffAj8yxQhhBBC\nCCGE9AE/pgghhBBCCCGkD/gxRQghhBBCCCF9cH+aqVSkuyMMslQeM3WKJsTexuRWNzB+MVBGfLkY\nY/tFRPI9jIEcH7Hxomdf/zpeZx3N0+p1jCl+/2pQGh23eo6kibGfiTIb9Jn2pkqb4YsgDrT2ynmM\nIZUYIgjwvDmfWCLF3wqhNfRcW7wB5VoN9TJTk7YdogjjwSs1q3tYq+98Tw9D3LSD+G1fLHccYx8N\nQ/s+ExVbXixhv/mlX/o5e0yMg+HM+bOmTlHFTjeb+K6mZg6ZY1YraN539OgxU+djL2Ac97tnUZs1\nMGDjoq/cQKPSRWUKKSJSWcXfRoeswfbxY4egHKgQ+1bHxiE/duopKD/33JOmzvztb+IP6lX61FCp\n0kmmHmPfhy28P01TMBj3+DbLQAH7TZCx2rq7LRzTsTKZbXSs8eKdJfVbYls1Vv2628H+2PZIJYcT\nfOehRz8oav4LM/iMmxU733QdrgtDZavniDpo8hj2PGbzK6jtXV7G2P1Pf9Fq+LIFXP+uXFkydcrD\nqKP6+f/gZ6F8+o13zTELt2/gvWzY5x6Z1uufb217kKQiOw3mA9sJogxuN3oeE+Yw1OMVO3+raZ+z\npPRjAzk7t9XzOD6u3UKz5C8ePWyO+dWf+TKUv/N7dv6OA7zWxjHsN/vncMyJiFx8FfVaj5ZHTJ3D\n46i7WXzV6vzy4/hMJ0cP4b1dtf38stKFpFZeJoHamISB3u94DnJK6+bZEz1sOBHJ7thP/cLPWc3U\n7CyOsz/5szdNnWtX8H3ODOJcks3ZbXaitIDnr9m19tXvo95po43tXmvg/C4i0mni+NGmuCIir7yK\nc87yIurxDu6zeuovfR4Nqx975KCps7SM68DKpjXYjiLsSw1lUJ4Vuy6MDOH4+MJnXjR1Tp5CzfXi\nCvb91TX7TfGO2hPduWPfweICvoNXL5gqH/Dw93hCCCGEEEIIeQjhxxQhhBBCCCGE9AE/pgghhBBC\nCCGkD/gxRQghhBBCCCF9cF8JKHq9niyvbIt5g8AabOXzShidWqPFVhUFwcM5FOPNHLKJLSZG9kH5\n9FkryFxYQsPdxx99BMob69a46+2334Hy+OysqfPTn/8clFNliqtFdSJWgOkVZBphp62SakGeEtRn\nnX2FtRqKdL/5zT83dQaLKIwdKGBChReee8aet44ivvNXrFlspbpDeOwxSd4L7mUgqP+7Tlrgq/P5\nH0NB5iPHD5hj7ty5BuWRoQFTJ6dE9s06ivtdYvtWdR1Fm2PD46bOmbdOQzkMcYw99wwmfBARKRTR\nkHB51Y6XfAbbJuPps0kPx8fzn3gZysNjdnyHESay6NhpwySPCIxRpCepgcJ5kqI8bAaTcZLIZn1b\nnN8KrHHuzDi+83LomQdUkpQjR3A+fOetM+aYehf7W6tpEwkEak4KHL7v8XGPEWgTxclZq8uXcgHf\nQ7eNz13Ke8ZCo6nq5Eyd8dGiKtukKW+/i89wZ0mZsRataepTz+C1vvXN75s6Fy9fhvKhI0ehPDFq\nkyV0Gyi4Xrhrx+Gzz74A5T9547ap8yBJ0lQ6OxLL+MZZGOAc1EqsQF1UohKnjN+nS3bCCVtYJ/WY\npF545yKUb1y4DuWjHbuWPf2IErUrE1URke44Jq6oT2KfeK9iz/vCSz8G5X0Ttg9cUmaxYcM6in7/\n25hIoFfEvUs2Z02k82NqfHsyUPSUeXYYqjmgZ9fHXoz3F+isQ/LwJaVIk1S69e2+EhVsGx+YxWQM\njx+z63xFJcbZaGAfznRtf1y4i4kNrizZNm2FuE6GOWzTbGITjERZnGePPWb3s4encR9yZO5ZKJ94\nFOcoEZEjh3G9mZ6wBtaDAzivDq3bBDPzK7j3110iDOzaEaTYnlmPafT0IH6LjBbx/sJjc+aYZ0+i\nEXtl0yb0qFYxIdjf/t1/Zup8cJ8f+l8IIYQQQgghhHwo/JgihBBCCCGEkD7gxxQhhBBCCCGE9MF9\naabCMJSRke14y07HxtPXtjDGUBvTioiUyxjfqCUL+w9Yg8SZaYxV/d4bp22dCYxvraxivPLwKJqp\niYiUyhhP32lZc6+jh9FAL01VjHZq4zy1yWviMb9MVGx34jlP3MPv3XYHdQTaGFZE5PrNS1Be37Qx\n98ePPwplbWh8/jJqyURE7i5jXP7y6ryp09vxDA+DZMrJvWO1wxA7oI4ZFxEpFFDk8YlPPAfl9QrG\nQIuIHD+G8fTtpo0hdsqUcm0V46DX16zh8pMnUc+2tubRrinTuuefewLKm5vW/DKvtH9ZT3+cGUM9\nR5LYZ+oorUtRjffhMWsKeOXSLSifPW3NTHNKaBPH+N52o5kKPPqoh6Gf7iQIIyntMHttV+2cdHcN\nY+MDj0blziKOz8ExNKCcO2Zj410e9RvLS7Zv1VfQ3Dkf4XhZX7PzTWFM6dtCe7+FCOuEXex/I4PW\nmFhrRkdGrB5qaATn/ZFxu76sV3H8vnsN++NWd9kc063j/Q7m7foyPoi6h5tKQ+VT66UJ/jo9ZcfL\nZz71GSj/s6/8O8+ZHiQOHWCdNeYuqPZJXdHU0celal4dz9tjqoICyzur9l0F6nbyysS307N6mX//\nDdQa392yGqTyCL6bzCL22Su3bphjtIy8V7NarPkV1G+cOvqIqVPaxDmgO48PuTFoNSDtUaUpTG17\npnqvosqJ2HVB61nj2M7Fek+018RJKtXWdpst3lo1dV5/G8frd161OtPHHkdt3XoLn72yYPtjW81t\nQWj3KEcPY9+an8e5uBDZfcpgCefDqGuf6fjBp6H8sedRPz1QtHNoQZlwu9gzvgexb2kDbhGRYg7n\nw8ihZq/jcXxfUxrxspUYiqi9QNzE8ex6Vhs4NYTPOTFk9bYusJqzD4N/mSKEEEIIIYSQPuDHFCGE\nEEIIIYT0AT+mCCGEEEIIIaQP7kszlc1k5MDMdgyhLy62WcP4/gsXL5o6axWMqW+1UGNx8KDNCV8o\noe5iednGGU+MoyagpeImk66NUM9lMXa6Wbe5+8+/9xaUU0FtScdjiqOfqeXRy2xt4bU2Nuy1dZxx\nu4PPpK8jIrK0iDG6eU+ceeAwnlXfiy/mXceZT3i0L2m8fb+NlZvmvz9wnJNwRzxyJmN1F90uPms+\nb01wnnsOvRjEYWxvq4NxvSIiNaVD63i8QrY2sV+EDu8vm7Gxvqe/j3Hbr7zyqqnzxS9+Acpvfx89\ncC5dsuPy2jV8X/msjSHOJPicWduccuDgfih3lAat1rLj5Su/8X9AuVq1/dqZ6SrRFTzH6PI9PMfu\n8d8fBKmk0k2359aGHYoSBNgWgfP0rS387cJZ9D07cfKUOWZqDN/VoakRU+ftN3D+zjp8D82mvZdG\nA//drpyxXiE51fZZ5RmV9axWB6bQf3B0YMbWmcMGXGvaTvvNtxagvLmpPK6KVu9x7SaOl2zWoz9R\nHinlQazTaNu2WlnGufjxx582dQ7O2jVyLwnDSEqDO3R+LfuyWm3lq+isxszFqMcbUf5kazXrTbU6\nip4yudF9ps7Th1And/gk6n87C/j+RUQGlP5kftD2reuC62jh+hUot+p4/yIif3EO+/kbVoYjrRDb\n5p+8NG3qjCu/vjO3UCu9INYHtNeZgPJoYPUxeYfvKVJ7hZ4duiJKu+j38/Mct4ckqUhtx3T31d+1\nfpx/8LVXoByLnTuScBjKZ8+h3nJnnoG/YnoGfZvGR+yeY3AIG3p0EI/ZspI4OTyLffSnP/+cqXNw\nFvtWWS3zGc/eL1B+l54pXuYXcE8+Mmk1hlPj+FujoXRVzs6zHaXL7nryDwRqHGbVYlH16I6LBXyX\nBc9mpue51ofBv0wRQgghhBBCSB/wY4oQQgghhBBC+oAfU4QQQgghhBDSB/yYIoQQQgghhJA+uK8E\nFM45yQTbQrTQIzI88SgamE1OWYPE1994A8rf/HMU/l27hkJpEZFDR1Fw22rZ5BennkChbiZCsW+a\nWEOwtIfnqVatYPT3f//fQjnu4jeoz+S1p84bx7ZOVxsFetxDczklws6hSM6XUGF4CIWnmciKG9fX\nVqA8O43JJIaGrXi1UMB7KXoSNWTC7fv5V0u/Zf77g8aJyE4/vFzW/vtBqESPB+es0Pjll07ieZXg\nX5s/i4icO4dJHqrrNklFu4n9YnQERaaHDx8yx1xR46OyYfvsN/7sm3id1r372sgIikMHBkumTqiS\nApQ9dcYmUSwdqwQfN6+iSFtE5MKlC1BOxI4XUebLTj1D4Pm3oVgZDyc+8XTq/b97RtztycbdpQ/K\nUcaORZfFxCSNhk1wU61ioo9uG00fcwWbYKTYxhYYG7AN9vhjOD7mb96AcupRmndUwyfOI+TWCUWU\n6aOvHY4cegzKrZrtAxOz2Fbf+8ZVU+faPJpjjw/p8Wzvt6raPBtb8XQU4nN3lUl3pW6V3LUOrh1T\n+w+aOknwMPTUbeIklnrLzkM7STLYPnHPisInVzERRLuLbdyYOmCOyQ/g2pWNbR8op5i4ohzg2Jhf\nxvVQRGRkGM9zasImAzp7HRNmDI2pfjNtE7icvYkJRoZHjpg6jz/6JJT/8NXXTZ27b+Fvm21s327D\nju/JNvatdmrXpCDGdT3Tw21imnrW0MCpsp03kvsQ8z8INrbq8gd/+toH5eUNm/Roah+uZeUR6xhb\nbWIbnnsD17fBYZvkY24Ozzuzb8zUmZhW6/Eozn+z++29jE/gteLAJnsKc/j+Am3I6zNXDrEPNDt2\n/724jnPZnbo1b3/r7bNQHlN7jqdPnTDHlErqmWI7Z2YivD+9tum9q4hIFOF41nt2EZH4Pros/zJF\nCCGEEEIIIX3AjylCCCGEEEII6QN+TBFCCCGEEEJIH9yXZqoX96SyuR3vu7VlY54r6xg37dNzNJWB\n7eQUxo9qs1MRkc1NjGlvt62x2J0FNEt75DjG059527rjxT2Mky16jLuyKrYyV8QYzlzOHlMoYJ1i\nyWpqyuq38oBPp4R18loz5bnfKMLfotAXL6piTHOqK3gd9pSGIbTdJ9hh+Jf1Obo+YKJMJJOT2zqk\ncsnqvKaVMempU8dNnWIB42m7XYzbHSij1klEZEoZip5925oCRgH2rVIRDf42Nq2R8xNPPQHlTs+O\nhds3MZZ/So2xYt5qnQaH8bds3v5by+QEPucnP/0pU+fIMdQAXLuMeqgwtX2rMKC0gQV77UjphJQU\nS5ynz7ZaONf0UqvFSkB7tfdx/aFzUt6hPfTpY5odfK7EE9yt5452C2P7Fxaum2NUeLp0PNK1fftR\nB1vfwL7mPP2xpPQyLrGx/Ho+acRY58Y1u9585icexXNM2H5zdx01DF/7utVMddXt5PN4L0lgz7u2\nhe2ZpDVTZ3ICTVJFGWHPL9m1LnBKmzpo55Y7K3fNb3uJc4FE0baGY33L6iWKOdQ2leN5U2dwC59r\nYRj1eat5NEgVERlv47jPO6sfTOZx7c/GuC8ZzlldS7WLY+rLx6xm6ojSDV+r4TO283ZN7yr94NzY\npKmTrqOp8Na67bO/8ss/CeVX3kKN7vqiHYejEV571aPz63RxrUsbqDdLPHrbfEabiNu5uO0xqN5L\nqrWGfPMvT39QPnTomKnzyTn8baNqtXW1CrbzkSN4zPydG+aYc2fPQ/nuojX2PXgEtZJT+3ENb45b\nzdRr3/4ulP/w39k+8A9+/Zeh/PGnjkJ5vGz3SJ2WMj73OAa/fvo9KP/Zd942dVpNnO9mlS5syKPB\nfuTIISi3m3Z8Z0rYflHhHvtbEen1lJ7ao+mLPfkQPgz+ZYoQQgghhBBC+oAfU4QQQgghhBDSB/yY\nIoQQQgghhJA+4McUIYQQQgghhPTBfZr2BhLuTIAQeUTiORRpFgpWzDY+jgZlQ0NDUO50rGiuo9Tm\nuvwDUCy2sICizYMHrNDzYy+gOd7goBW4llSiiEIBEyv4Ei1EGWyHKPQY3an200khfGiD4NRjsBaG\nGVW295cqEWmcYHvGPY9xm04cEFqR6Y78ExJ4RNsPmsA5ye8wPp6esSbSTzyOCSdmpq2BXlmJMu8s\noXg/8Lxf/fgjo0OmztLCMpR1woRNTwKKWgOF7xPjVqA+MqzMfw8dhnKxYIXRIyMo4jw4N2vqTM+g\nCHZ4wrbVn33tj6EcqX4yd8wKfXNZFEaPjtm26ihPxUYTswaEnv4WJyieTrtWZOp2GPu6hyABRa/b\nk9XlbYF8kLcJZFJlQnj7thVGJ0o0nCqzwwsXrKh9dgYTJjQ8154ZQbH+6BDOmaMZa345GOK8FXcb\npk5JzauVijJpznhMKmfRTHt56byp88qraHJ99ZYdU06ZjnbV+lJr2n7RUEmQfObtQRbnjclZTErT\naHiOUYbGrbZN1tHpeObnPcVJmm6vOyMDdp5Nm9ju6cJlU2d0DueB3DjOA6XQJooIO6tQHqrdMHXy\nAzjP1jv4PqubNjlCqYiJiTZv2PcQVDFZQ3XjXSgffe55c8ynP/YMlO/etWahURvX8PGyPc9Pf+kl\nKM/tQ0Pjr/3Od8wxnTomSUk3reC/pUzsg0ibZ9uxkMQ4fmJPkgrf+NhL2q22XLmwPTcsz1dMncNH\ncN2cnrEmzAdnMYnCmEoYs++g3VNev4Jz752FO6bO+ibOZZ8cwLEwMa6S24hIV/WbM++eM3WuXv0f\nofyZl56D8lOPW5PwkSE87/FHDps6169jop9rl94zdb74WeyzH3sBk2k9eeKQOSZRw8OTn042VDIg\n3decs3uDTAbnGr3PFxEJIms+/WHs/W6XEEIIIYQQQj6C8GOKEEIIIYQQQvqAH1OEEEIIIYQQ0gf3\npZmKokjGx7Y1EkODNoY9UQGOPY+2qadMHXuxCor0yBa0B1wY2ljGSGmQtIYik7HaoYw2mwvseXW8\n5W6kQInSMvk8cO15baVYmeoFJvbTHqO1Sp7wZXOc1lkloY3Jd+qY1P9QO/6vz/j3wRLHsazvMI5u\nt62x5iPHMEZ4Y8Ma0ulGHB3BeOVKxZpTb22gyejE5Kip06yjRmptDeP/222rP6mpuPfJSatPePrp\nZ6G8X8XTe6R2Usjj+BgsWl1Vr4W6ga/8z/+rqXPrJurJPv/jn8PrFG2c/v6DaM45MmbnjWYD54l2\nE+OiR0ZtbPrp029BubZptTpBsD0HVKpWF/GgSV0gabittfF44Eq+hFqmUsGaPm7WVd9Jca67chl1\nJCIiXWXYeeLoAVMnr4xn0xKed6RsdS2zw9iXOi1rvFhVGq/lVdSxlGasjs5l8J1fvH7B1Pmzb6OZ\naRJa89VsFp+708F+0EvtMT01z7qs1Ze1lBFkZR3nllbD9rfxUdRc1Kt2Pip4TCj3kjjuyEZ1W38S\nxvaec0r/GW7dMnW+/N98FsrD06jN2GzgPCEi0nI4/+XrVkOaTVGb0Yrxfd64eMkcc/Y7i1C+Nb9k\n6tRaOF9/8mXUmR4+bOf88xdQx1JPre70137l16BcylkT5Nde/R0of+/Nm1BeaFlt4Kde/gSUiy2r\nFXvrMuovwyKudbmMnb/1fiyXs2NhYMDOUXuJEydhun3f1XUrxjn9Gq4fY6N2z3v8BJrUT+/HPvr8\nC6jrFBE5ehT3HOfO2Xmr2cD5e2QM57rYY0D/zIuorZuds3uDc2fRTPePv/EK/vfz1sz92By+u2df\neNrU+dW/94tQfuzYIVPnMy/gvuTYcWyHWhO1TyIir76B9zs2ZtekQg61qRubuB/z7et1XgPfJj1f\ntGvZh8G/TBFCCCGEEEJIH/BjihBCCCGEEEL6gB9ThBBCCCGEENIH/JgihBBCCCGEkD64LxVrEDgp\n5LeFXnmPyFBruHRCChGRNE1UnXsbZTq5t0mhkY/tIgGCTpLgS2wRhj88oYM2ePSdZzcGttpI13d/\nOSW06/e8sUr6oRNm+NpBJ8hwzmNotiNBxsNg2hvHsWxubieCSBKbhEQbvG1sWCFqq4nvuFDGZ2s0\nMdmEiEiqkoeMjVkx8tx+FFh//U//HM9bt4JMbS7XbNikCrdvz0N5ZQUFzLFnzI0MYpIAF3vMszso\nWL52+ZqpU1IGx8tLKGg+VLPtOziC184V7P0tLeIzHN6/H88xZBNmhJlTUL560YreN9a3hdq+JDAP\nmiQVafa2n39ixCbW0ILawZJ9dtfFRCX1qjIwTuyzFoo4PkqDdonoOTxvkMXxs9KwY+zGMr7zqUmb\nJODKDewn713Hvvb8AZ8pMyZw2Nq0CWbWN/A8mYJdt/JqDkiUiXkqNgFFRhlN9zxr3comJmJYreD9\n+YymiwW81nrFJgpZW7XJEPaSIMjKQGnHXNayBqhmyhmyCUVihwkdZqfwoNmsXXMSZeacptZQVGfc\nCR32iblpm+hn+T3ss0eftvPh+DQakB+bRYH9xTN2rrv6Hgr+9z/5C6ZOFOH9rVT+0NQ5/vhdKG9t\nYZvvO2r7+c//EiZHqPfsO/jn/waNh7/9Fp63a7u5ZJSxrz8BhU3esNfszOflIo95coRz5HrNJvV4\n+50zUB5fQgPe9pM2ycfE5CSUX/qUTejQVklwFu/i2PAlRMkqk/WpCft+X375U1DeVMmzMh6j2kcO\n4/02mzb5xWOH56B88pBNFKG3jIn6Fogydr05dhjPMzxs91FhgHPmwiKOjdt37LqfLah9lGe/Mx7Y\nderD2PvdLiGEEEIIIYR8BOHHFCGEEEIIIYT0AT+mCCGEEEIIIaQP7kszlaYp6IO0zkbE6mR249uq\ndUE+rY01q733eTS70ST5rq01Xfo8fn3R/WuZfPenzx1F+Mp8x+jffO2i69j3Zo/Rv2nT4R/8+HB9\nn6dpCvown4ZPP3u9Zg1FtzZQuzQVotFivWbjopMYY56PHz1u6px87Cko/85Xf19d15o1PvM06oA+\n/uKLps7Zd89DuafGas8jQfyTd9FM8qkTj5o6P/tzfwvKP/XlL5s6ugss3rkN5fV1a3Acqjjtat1q\nLrIF7PthRvXz0L7bY4+ifuLoEfsOzp/fbqtbd98w//1BE4aBlAa3zQKDyI6prjK9HSjbOPLKKsaN\nhwG2z+CAnf7rW6irShKrFeoKxpZv1fH+Ftfs3HH2Isb7ZzJWC7iyhrqVSPXZzQ08h4jI+TPfgnI2\nsVqN0SE0Xry7bjt/ksH+l1FaiVbHxtOnoep/Wfvcmw2cF9p1pRHwzLObdRwf+WLe1BkbnzC/7SXO\nhZLJ7DD2DK2xa3AY9TqtTatH+N3fQz3RgNyA8v6jdu5IB47iD9G0vcEU2zlJcF595TWrP1lSGsNf\nfnm/qdOuou7i5vUFKG+k75ljfuqXUMfSrtp+Xd34KpRH9tvzzM7g/Rx8HNuz59GZFwpo7DuW2nnj\nqcfwuNfP4FhtO2tkmioxTM2zHrY8BsF7Samck5c+ub0eXDhvjZubDdQG9Xp2v9VR6/ytBVzvKpt2\nLZuewbHwyCOPmDpHjqAG6akn0RB6fgHndxGRy1fQaHqxYfcyB8ZQ/3RoFq+Tzdu57qkn8P6OevRQ\nd9U6L4k9z/RBPK6ndOWXL14xx3SVa/3kxKSpM1RWeu8Q17atutWVLy6jRtdFdu3I5uzc+2E8XDtf\nQgghhBBCCPmIwI8pQgghhBBCCOkDfkwRQgghhBBCSB/ct2ZKe/LcC5+MSf+mtTjah0pEREeq7kbT\no/Fpm7R2yPd8WhumdUu7uRefvkz/5juPvmffeTS70WLdS9O1G32Z9ggQEUl34Rn2QHHoHTQ2bmPE\n2y3Uapw+/Y49TYrakU4X4/QPHcZ4ZhGRJMF45W7H9q1cHuPPDx9C35Ljhz9njpmbw2t5bD/kyNEj\nUH79+29BeXqfjf//xMsvQ3lmYsTUee8ixpUHWc8Uorqx9jTL5K0P0c15jLfOZoumTmkAf2vHGIOf\n1G2M9tzhk1CenTpo6hx/fPtdfud7Nm7+QZPJhLJ/arvtl1ett0lXjc8Jj59I7SK2eznE+O/JMdsf\nG22MLb957Y6pExwcgPLqMt7LnWXr9RREeO1Gx84/HaVHcMo7pFKx5/3e638C5RPHT5g6c7NTUF5c\nsp4jJ04+BuVrtzF2tEejgwAAIABJREFUv5Hatuq28X4z4vFA6uJzdtvYRztde96REfTjGRu3GqDh\noYdLM5VKIt14ex5NYs+spMSU4cAhU+X7F9DjaDjE8fi3fgb1KSIiY3OoOcoVHjN1MkoHsr6Jc/6V\n81YvsV9ph8pTVqPS6uFcPH0K+8TJGastCQKc/1aWbL+e3oftVxy2XnNJjH1nvYLapkLOzgmh0o6d\nO3fR1PneX6KeLG2jXiYMrY4kDbCtWi2r1anWrDZsLxkfG5Jf+9Wf+qB8+eLjps7iArZFbcvqvuaX\nsc7yJnqL1WpWr7Om9KxvbNg5/uY19HA89Tjqfw/OWT+18ef3QblZtf6XWeXNN6A8HQsF+37vLqAW\n8O3v23mruoF+eAf3z5g6swHuF3pt7Cdvvon7FBGRwSGcD/fts2PqtdfwuHwBNVRPPfuMOSbKYvsu\n3rX69EzG6gM/DP5lihBCCCGEEEL6gB9ThBBCCCGEENIH/JgihBBCCCGEkD7gxxQhhBBCCCGE9MF9\nJaAQcWDU6jNA1WJzX54DmwhCKdY9yQ924/57L7NaX/IGnXAi9hkRmwQZ907woNvBh04C4TcMvnfC\nCc29EnH4rrWb+9V1vK8psCLsvcSJk3CHgdvkhBVuj0+g2eHmphWMNutaOI4PPztrE1DM35mH8uId\nawy5vIgC0ZMnnoDy9BiKL0VE4h6Kp3/n3/6eqfPzv/iLUH75U5hc4vptvDcRkcEBFG0+/fzzps7N\nm9eh/L/8779p6ty6jXX+6T/9x1DOdKwRbKGAySVWfUkXOigQHR5BgbVztg+/dRqFqZcHrps65dL2\nc7fbLfPfHzRJ3JPG1vazpp7kB40O9r9mz5P0QRkOtpso9t0/je9bRGRlHQ0mK6s2AcXMBPb1fAnP\n02xZoX6okkl0O/c28Gy0ce5b3bBz4fwimpDO7bOGjscOYdKZlufaozM4zq4tYJKAxDPZdbo4TzQ9\n4nSnTI9VzgBxzp63NIhjIZuzBrj1uk3EsNfszG3QSz17gy7ec9uz/UizT0L5W69iX8plbN/67Bfw\nvJMz9j0MDGOb3rqA7+XqTWus+us/hwl4xmbtexiZwTppsAnlwKTOEpEUk10MTNj1Jonxt3bHnqcX\n43yYzeLa22xYg+PVZZzf/vgv7HNfXsSxkBRxfAc5ey9hiuMl8hmgZm1Sir0kl43kyMHttf/grDWR\n1klmksTu0WpNHNTVNpY3NuzcvLqC/eQvv/2qqXPmNBo1L97ABB5Ts2jQKyJy4klMYHXyxD5T58AU\nvpvqBu5B4q59xpxK6LC8YU3Xjxw6DuVjjx4ydXoxtme3g3P6xz/xSXPMlcuXoXxn3u6j8nm8v2IR\nx+rigm2rIZXYYmnZ7jnm79jjPgz+ZYoQQgghhBBC+oAfU4QQQgghhBDSB/yYIoQQQgghhJA+uE/N\nlIik299fPmWONtxNU0+tNFB18D/7dDeJisH2fQUaA96/Jt2SuZdEP6ONIda/ZTJWJ6KvtZvz6GN2\no4/y1bnXc+7OZNiew2eM/DCRzVrDWH3Phw7NmTp/+a03oLy5hfHf169ZDVK1hrHIYyMDps7bb5+F\nci7COtcj+x5+/Auof/r1//jXTJ3vvPYalA8cQRPfdtvqClp5jKWeX7TxwiefegrKh44dNXW+9rWv\nQfnqDdS1ZEt2LAwNofagWrXapbU11EsUS6iDKJXsu222UHuwvrVp6uycTZoPgWYqcE4Kue17ypSs\nVqNbwfdX27LmjKPKoHrpBhovtrt2rOpY825q26tYwHn15qLSZoQe83G1WgSB7deFIi5HeozdXbGm\nzDOT2JcunH/P1DkwOwblT7xgdZM3F9Foc7iAZo31jr3fyOFvm22rjWg38J6TBNs88IzvIMI6qWf+\n3s3a9kBxTtwObWrgkwopTWOva5+hF6I5baOJxqR/8nU0BhURyan2eumT9rxbyzh33LqG4/zFn7Aa\ntNkD2E9q63a8hDnUYMYpvu9mzTbERgXvr96wY7e2qTSRW3artriAGpqFeTzv+qq9316IGpo1OWnq\nJGU0W42VLXwuY9f9YoDjJZuz91sq4/pyztR4sGSijEyNbr/jzQ071wUFfNYoss9VrWFfKjfxmLmJ\n/eaY7IlHoXxiztb5xuxfQvn7F9Bkdn7V6ge//m2c489dsDqwZ06gtml0CLW1pZI17e2pHXc+b/tW\n5ybez1rVauRmR1DbNFDCfjM0aM2powjX9Vze6vGeeRb3JUt3cU36F//nvzHHSIjvcnTc6m1dZPcq\nHwb/MkUIIYQQQgghfcCPKUIIIYQQQgjpA35MEUIIIYQQQkgf8GOKEEIIIYQQQvrg/hNQ7DShcx5R\nbnTvhAiijQpVkoUk9SQ/0G6HvtPeIxmDLwmEuY4n8cK9kkDsBt8xvoQT93se3zPbRBG2Tj/PZM+z\ni3e9xzhB8fvQwJCpk8uioPGQRwzaewnb6+YtFFu++hqaw4qITE1hMonl1VVTp1xEweXiwi0o16vW\nSO6Nd9+F8j/8hzYBxYZKSLBx7h0ox4F9d11B8fTK5oipM7S8AuXx0TFT50s//WUov/nWm1BeWrWJ\nLVLVZycn7Xm1YD0KVfIYseN7QBlO9mJrZNlsbSdz6GdM/nXjAifZ3HafbDasMHpYGeV2Upt8Iylj\n+yT7pqB85Qr2NRGRg/vwnY8OeebMGPtOW72XTs+K+TNKRHzg4JSps7aBfT1bxOs0NmyfXVrAdWEw\naxOIrEfalNLe36AyffzEsyhG3qxawfXtBTQ8vVyz5qs9Na0a89XYk1wiUQkKmjZBgfjMYPeS1Ekc\nb28nfF7z2sh12JOkoKPa6+pVfPbKst0HvPomJmMJU9tnI4cGtzcW8QaHDlih/mvfwvFRLNg2Hyij\nWL/bQdH9xpZNSrK+iueprhdNndoGNkToSQSTV0lS2hVsm9q6FeoX1Hw9ULRJkQZVcpjBPL6nKLZG\n8qnD8ZHN2ToP27/dx71YNle330+lYtfaOME2HR+361K3if0vpxJajY/bhA7ZLLZFLmOT4mxuPQLl\nXh6vM9ewc+jly2iyvrSwYur80W1M4jI8gvc3d9ied3ICx8fwsH2/BdUfM4FNyhW1MdFPPot9q1Cy\nRvKTU9g2N9fs/H339atQjns4h47vQzNjEZGzZ3Ef9e3vvmPqDA7aPdCH8XD1bkIIIYQQQgj5iMCP\nKUIIIYQQQgjpA35MEUIIIYQQQkgf3JdmKkliaba2Yx7j2MYulpQJl/NocQJl9pponUJiY5O7Ki7f\npwPK5TBGWBus+fQQsTI/9JnO6t8Cde3d6JZ819bH+c7T6/XuWUezG/3TvZ7bpx3T1/ZdJ/a8u73E\nOSe5Hf1g//QBU6eyinH5o6Ojps6nPzMN5dNnMEb3L7/zijkmyKDu4uy5y6ZOnGJcfpJgGw8M2xji\nzVXUavzmv7aGdPv347XnZlEHloT2Pc0voeHf8ayNM24ps9/5+SVTZ2wc46uPHj8B5a7H0TOo4nnS\n1GPQOo1Gh6vLqCOorNTt/SrJQjYsmDql4W3dQBT2ISP9a6beaMvrb+8wA3W2LYoDOPfmirZOroxa\njJFxjHO/ddNqS67eQF3fE4/uM3XW6nito0ewn5w6aeP/tZlu5DFebPfUbxml60ztfLO6hTqlASuJ\nk2wejyt5dA9HjqKp9a3bqOu7dPW2OWZpGTtXu+0zOlflGO8ljj1jIdCmvabKw6aYEhE0Fw5D+36D\nLs6zvfWKqROlOL+4GOvsm7BtPDmMeqg4sBqz8VFs07EGavj+4ptW2+QEDayPH7JmwFEbx9TGFq7X\nlZrdTyxX8IWGqR1jg2Vcg9ZXrb4xClHrknZRr3X7jh0M+Q7OG+V99pmSDs6jvbYybC169FAFnDdr\nddueQ4N2Xd1TUhHZMf5yke2zzmEbF7N2PY5jfMeNrtJt6hwBIpI67CdZK6uSEyePQjmJ8F1t1aw+\n9MiM2qe8c8nUWVzCftFo4xy6sGjX9God+0Q+Z284k8X1ZLBk9XilSH0fqJmsE+NYFhHpnEZ9es+z\nx9Q+8dkMzhNjo/a9DU8ehHJyxerLTr9z3fz2YfAvU4QQQgghhBDSB/yYIoQQQgghhJA+4McUIYQQ\nQgghhPTBfQkEgiCUYnE7jjhJrLdJqIMXPTj1DRfqTzqPLsgpHwOfXudeuh+fDkifx3feJFbHKY8e\nrc3azb34fvNd23funfi0WPravjr9eGXp8/j8wB4Cmx4gk8nI9PS2fqhatbHc33vte1CuVGys+ed+\n7GUov3vuLJSfePJxc8zQCGpWgswpU2d0ArVNIyPoazC/iL4RIiJpDzUrMx4NyOVL56Dc7uFzT+63\nXhKh8og7feZtU6f8AuoIIrFzQKGIsedl5cVy/BjGgouIzC9gP++q+H8RkfV1jKcO1cQxOmY9xMbG\n8Tk3a1ansVbZ1gmlnj79oAnDSAZGt/tB3LN6qEoVY9jHCjbefyiP7ZGmOC8ce8T6qV27hFqhN89b\nrdDJJ1B3GKv4/9FBey9TY9hHlyvWc03rh+IulkvDnnUhxrj8luffBy8uordJN2s1H2st1AlsrKMe\noS025r4X4Xtpdnx+fuo3Fe+feOL/9frY6VlvpfV12357SSoiqWyPHSdWK9RVY2/lhvXmyyiPo4EE\nvX9OHbHnfeI4vpuhKTsPDJSwnWeUPi9TtHrLM2dwHQjaJVOntoHjpZmgJuTmHfvuLt5Gjcr4pPUn\n+/jJk/hDaP1urt3Csbm8jH12ZdNqItMI+9bEsEf3rLpkU+ms0sD6MQ0VcR1zzp633rBtvJdEmUgm\ndvgnFYtWTxurvZ/PqzSvfOGaXdT9tVu2D2xtYp183l577iBq6QZLuNZubOC8JiKyto5tnBO7ni1O\n4dyxos6zuGp1S8UcjrtMxs519Rr2i8rdu6aOJDiGXKDyHGRtOxSURs8jM5U4xv1CqYj9b2TCerkt\nKK38wor1cqu2dr8f4F+mCCGEEEIIIaQP+DFFCCGEEEIIIX3AjylCCCGEEEII6QN+TBFCCCGEEEJI\nH9xXAgrnHBjFJc6KQbVBmS+Bgv7NJEPwZDFIjZDu3pkOtDGtNsD9waXwPN2uFXvrOtkQRYhJ4jHz\ni7COFsv/4DhlSrmLpBr6mXxJIPRz7saA15ekQqPvL/acd6eIfDfnfCDsSBhy6bI1sdvcQuHkxpYV\nIr57DhM6xAmKciObh0FyysjwxBPWBHffATSOq9VQjJwpeJKSqL5/5KA1Ik5SPE+3h+LkWtUaW0oW\nr1UesmLQ1QqKSjOpffD1DRS4Hjoyh5fJWxHvE48/CeWrV+176rRQtHto7jCUK2tWGF1ViRoaDZuA\nZOeYeji6bCIi2886OGANEnuCYzzw5P1pNVQSD5UMoZi18/fcHCbsWN2wySRWKti3qhWck27fet0c\nMzuFIuJW184duRyKk4MMPncU2Xm2kFXG56mts7qGAuv3Ls2bOnEPDau7yo8+EdvAG1UcU622vXZW\nTQxJiicOnG2HyhomatjcsAlxalsPVwIKkVTSHetB4nkPaYBtEZWsKLzQxaQKh/bhenzokE3EkFWJ\nZ9K8Z7xEOLBzZez7xZJNeDN/DY8Z9IyxjXWVTKuM9xeGmJhBRCSUZShv1uw8e1klrigHtj0lxvas\nqj4b5zztO4zGuc2eR2CvTKPDHN5fx5M0ZauKYyznSSSg9xx7TZKk0tqxppRKNsGIfi7fXiqMsL2c\nmoybTd++UyVUc3afnFVJHmYn8N1NDttEK+uj2nDZJjcZG8F3s646zsiCPW9DmfYeOWqTSE1OoFn7\nxqbdRy2uYHKL2ws4FioVuz4vraCZbrXWNnXqdXzOwRLOAWtreB0Rka113C+sLNvEG+2m/R74MPiX\nKUIIIYQQQgjpA35MEUIIIYQQQkgf8GOKEEIIIYQQQvrgvjVT4Q69kE8HpLVNgSdOVhsZarNGX2Tt\nbvQ3Wl+U0bGsnm9HrUHyabF0rG+gzuO7t14Xz+uLF9YaJN8jdjsYbxtrfdYuNB6+KveKX/bpt4xp\nr8c9zWdCuZekaSKdzra2YXHJaoV0vxkdHTZ12m2MKy6UUEvSbNlY32pdmfn1rMles4uxvoUiGlCO\nT1hD3kDFbReKVrd0/NgRKL9z7gyUx1R8s4hIRZnudZTOSkRkYAjjys+9/Z6pk8/g/XR7+Iwjoxj7\nLSKy/wDe77Ejj5g6nTbej+7Bw6MDollaRn3MzRtXTZ12e3uMdZVB5V7Q6yayvLDdV4oFG7cd5rCN\n67HVfKRKDxGruSTvMaCcmVZx+QetWe2t26ibayjNlHgMO28tYsy6NsMUEYmUOWNPzS+Fgr3frjKj\n7rVtW5UGsM92rIRBgowyyk2wPQPPM4VGcmafqdnE82Qzeo2yS3CniZqAYtaOb5fx6JX3kDRNpdfb\nHjvOsw7kyzivDtbteB2soTbj06dQczRz0GqmaoIaj1uLnrVLzWVpA8f5ypJHK52g5uj2vB1j9ZbS\n+XXxfZY8c/PAIM69ndBqm26voU5uMLB9K9PBZ6oo7UsvsLolrX8KPGLfrpo3ikW8v65n7FarOA7D\nITtWo+jh+rf7OO7Jxvp2O/vWpYLS33U6Pg2N0tRncW8QiNXw6T1w26O3XF5GXeT4II4Xn4HwiNq7\nPPvcU6bOgcOo0755G+fm/UrHLSLSbOAa7ss/UFSa63DIatDCEPtOGmOfbWzZPdLGKt7fVtVqpjIB\ntkU7xfu7voqaWBGRttIUx127J056u9/PPly9mxBCCCGEEEI+IvBjihBCCCGEEEL6gB9ThBBCCCGE\nENIH/JgihBBCCCGEkD64vwQU4iTakdTBl6TAZ9ZnztOHYaxLlMmZ59r6N22wFgZWtKt/892L/u2e\npsOeY3xmb5rAY4KcOGzPrLp26kkvsZv21Ik3nJLzxz4zP4MV7OG7fQhM+pyTKLstTuzGVjiZqDYc\nnxg3dRpNFPsW8igyLQ9ZEfH+g7NQrjetuLLRRsF1toBi1fn5W+YYnTggSKwo9uaN61AulTCRgC95\nSKGI4uRcwRq2atPjK1cumzpf+uIXoXx3ZRHKPvFqbRNFpY8+bhNQPP7o41D+7a/+FpTTwJ736adP\nQXmgPGjqnD79zgf/Xxsw7gVRFMnU+LZIvdO2gtturJLXeMxf4y62RzGDc8dgweM03UFRbrVqTR+H\nB7Cvnzr+KJQ3lVGyiMhAEa9d3bCGjjMH0AgyX8DrRBmbAKDVREPH+qa937oyXhwYtH2gWMK+/9Zb\np6G8vIJGuiIipTKOQ9+822nje8mGyjzWasilqH5MejYpStczfvcSJyLhjrk/8CSn6qpbrretGfHB\nKeyT6TDOQfXAitp7DufZrrN17izchHLlJs5bvY5dq0L1rnqJfaYtNTSdesg0tnNzTxk3h86+33xe\n7Utie3+bysS8pcpJaLd3sVrHcgW7X4tyeFy7qdaonE2oEKl1y7ff2c0e6EESx7Gs7zDlrbZswiVt\nROtLnNNUc2ZP7Sei0JeYBvtFp2vnrVYL9wtryuR6374Zc0yxjHXyRfuuslUcU5FOhKYdy0UkVomZ\nVlasCW5T9b9u167HG2p+bjSx/8X2FUghxL1L1ZMExGXwPIm6327Trh2xWld9YyxId7+H5V+mCCGE\nEEIIIaQP+DFFCCGEEEIIIX3AjylCCCGEEEII6QO3G33NB5WdWxGRm/esSMgPmEvT1LrDPkDYZ8l9\nwj5LPorsab9lnyV9wD5LPmp8aJ+9r48pQgghhBBCCCE/gGF+hBBCCCGEENIH/JgihBBCCCGEkD7g\nx9T7OOd+zjl34kd8jUPOuXc/5L/95l9d3zl3wzlnzY4I2QXOuX/knDvvnPvtvb4XQnYL+y35qPNh\na7dz7mecc//1XtwTIT8M9tm/Hu7LtPdvOD8nIn8oIu/txcXTNP31vbgu+RvJfyoiP56m6fxf/eCc\ni9I0tS56hDw8sN+Sv5Gkafr7IvL7e30fhOwW9tn742/0X6acc7/rnPu+c+6cc+4fvP9bbcd//0Xn\n3L90zr0kIj8jIv+Dc+60c+6oc+5p59z3nHPvOOf+H+fcyPvHfMs59z855958/19RX3DO/Tvn3GXn\n3H+349z/xDn37vv/+y923FbknPvt94/9v51zxR3nfd7zDH/XOff6+/f1FedcqOsQ8lc4535DRI6I\nyL93zm06537LOfddEfkt51zeOfcvnHNnnXNvO+c+9/4xRefc7zjn3nu/r7/m64uE/KhgvyUfNZxz\nJefcHznnzry/zv+d9//Tf+6ce+v9/vrY+3V/1Tn3v73///+lc+433t9DXHLOfXnPHoL8/wr22R8d\nf6M/pkTk76dp+pyIPC8i/8g5N+arlKbpK/KDL/D/Mk3Tp9M0vSoi/0pE/qs0TZ8UkbMi8t/uOKST\npunzIvIbIvJ7IvKficgTIvKrzrkx59xzIvJrIvKiiHxcRP5f9t4sxtLzzO97vu3sW+1VXb2TTbIp\nkpJIiTPSjDyOZwwncWzPxE4CxwiyIYHtAM6NL5IguQsQOLlILgLbQIIgNgx4geFtDGMy8Yyl0Uii\nJEqiRLLJZu/d1VVd+6mzL9+Si27x1P95XrKrT9hdRfn/uyHfr99ve/evzvt//v+F53lffnTuiyLy\n17MsuywiLXn411gnnuddFpH/QER+JcuyL4lIIiJ/YZqCIP96kGXZXxSRdRH5N0TkfxWRl+XhX/v/\nvDxsp1mWZa+KyJ8Xkb/leV5BHrbB/SzLXhaR/0FE3jiWhyf/2sJ2Sz6H/Jsisp5l2RezLHtFRH7n\n0fGdLMteF5G/ISJ/9RPOPS8ib4rInxSRv/moPRPytGGbfUr8on9M/RXP834qIm+JyBkRuXSUkzzP\nq4tII8uybz069LdE5I8cyvLznz7fFZH3syzbyLJsKCI3H93nV0XkH2dZ1s2yrCMi/0hEvvHonHtZ\nln3n0f//nUd5P4lfl4cLhB96nvfOo/TFo7wDIY/4Z1mW9R/9/6/KwzYnWZZ9KA89Nl54dPzvPTr+\nnoj87Biek5DDsN2Sk867IvLHPc/7a57nfSPLsoNHx//Ro//+SB4uQF38gyzL0izLrsnDdcNLT/dR\nCRERttmnxi+sZsrzvD8qIr8hIl/Lsqzned43RaQgIoeNtab9sh4++m966P9/nn5cmWpjr08z+vJE\n5G9lWfbfPtnjEfIx3eN+AEKmgO2WnGiyLPvI87zXReTfFpH/0fO833v0Tz9fEyTyyeuBJ1kHEPKZ\nwDb79PhF/mWqLg+3gPQe7QH95UfHNz3Pu+x5ni8iv3Uof1tEqiIij77W9z3P+/mvSf+RiHxLjs63\nReQ3H+3pLz+6z7cf/dtZz/O+9uj//0MR+cNPuc7vicif8zxvUUTE87xZz/POPcFzEHKYb8ujbaKe\n570gImdF5KqIfEdE/v1Hx18WkVeP6wEJccB2S04cnuedEpFelmV/R0T+FxF5/QlO//c8z/M9z3tO\nHu42ufo0npGQw7DNPj1+YX+Zkod7Qf+i53kfyMNKf+vR8f9GHkbt2xaRt0Wk8uj43xOR/8PzvL8i\nIn9ORP5jebgvtCQPf9L8T4964yzLfux53v8tIj94dOj/zLLsJ57nnX/0LP+V53n/lzyMHPg3PuU6\nVzzP++9F5HcfffyN5aF+4M5Rn4WQQ/x1Efkbnue9KyKxiPwnWZYNPc/76/JQh3JFRD4UkfdF5OBT\nrkPIs4TtlpxEXpWHQatSeTg3/yUR+YdHPPeuPFwf1ETkL2ZZNng6j0gIwDb7lPCyjL/UEfKvM48i\nREZZlg0e/dXpX4rIi1mWjY750Qj5RNhuyeeRR39o/edZlh11EUvIscI2+3h+kX+ZIoQcjZKI/CvP\n8yJ5qNP7y1yQks8BbLeEEEKOHf4yRQghhBBCCCFT8IscgIIQQgghhBBCnhr8mCKEEEIIIYSQKeDH\nFCGEEEIIIYRMAT+mCCGEEEIIIWQK+DFFCCGEEEIIIVPAjylCCCGEEEIImQJ+TBFCCCGEEELIFPBj\nihBCCCGEEEKmgB9ThBBCCCGEEDIF/JgihBBCCCGEkCngxxQhhBBCCCGETAE/pgghhBBCCCFkCvgx\nRQghhBBCCCFTED5J5mIxn9Wq5Y/TaZqaPMPhGNKuPJ7vQTqfz0O6UiqLplDAPOoSIiIyimNIDwZD\nSMfq30VEwjCCdBTZIvHEcbNDJMnYHEtjPJbL5UyeYrkK6Syz9zlo7UO63e5COvADc06Uw3cYDgcm\nj59hvejreL79zk5UXQ5GtjxHcTLJHyeSpumnF95TplwqZo167eN05mg4yQjryvMcj6yPqfJznTIa\njdQpmckTBqq9qQuND5Xnz0kTPOY73smT7FPzBIGt3zjBc5LE9t00xXtHUWTyBD4ei1X/CDzXvfG6\ncWz7lH7mXB7vk9iiMm3fVU/+obbfH45lNI6Ptc1WKuVsdm724/S0D6PPC0Ls4652o8frfr9v8oxG\nOK5mplnb69YbDXVvO24lqq3HY2wDfuDou6bSbZ5iqYDX8Ww/1HNDZpq+va5uW5ktCMmreUtncQyz\nkgvx4HhkG/aDB1uQ7g3jnSzLFuzVng2zc/PZ6tmzT3iWa9zSB2yZHuU6n/0ZTxHHK2aug8/s3p/F\nZR5/4fd/9s6xttlyqZQ1GvWP00eZ951ZVB92zUOaMMDCcK0xAzU4PG5O/8QHVAxGeB0977suq8et\n1LGWMbfORiaPeHq9MMXvOa5XTPU8hZnSzK6/fQ8Heb2+FbFP92Br+xPb7BN9TNWqZfnzf+7XP053\nB3aRfuPmBqQH/aHJky9igZ49fw7Sv/rGL5lzvnDporqGrYQ1NcFcvX4T0lvbu+acpbklSC8vL5k8\ngVrw6kbT2sV3FhHp7D2A9BnHRPPFN/8IpAeJXZj+P//vP4b0N//VdyFdKc+Yc1ZWZyF95/o1kyc/\n7kF6poIfdkG+ZM5p9bG+r9zbNnnW9g4+/v+9zT3z78+aRr0mf+k/+wsfp5OCXcQ117D+gpytB08t\nRCXGzhtFtoek6eBkAAAgAElEQVTfuXUH0qOe/UCYry+re2Nbe7CDH9MiIp1WE9KVSt7k8TMc0St5\nfP5K3dbvfhcHv+Zez+QZDjqQXlpaMXlqlXlI7zWxnVTzRXPO7n4L0tt7tk/V1TOfOXsK0u2uncVu\n3fwQ0lFk679YnEyo3/npdfPvz5rZuVn5q//df/1xOvLsM3tqdnNNSXqcmpmpQbpYsu2m08H6vXLl\nPZPn9h0cVxP17Zmmtv/8qd/6Tbx3wba/g11sAztbOJ6Xqo6+28RzfN9Oaa998WVI53N2kt/ZxjY6\nGugPT3vdG9evQjp1/FHt4vPP4XXVHyPyOTtunFuqQHr9np23/ue/9r9D+scfbd8xmZ4hq2fPyj/+\n/T/81Dx6seo7viR1aXieXeA87rrOPCodOP5weVy4PsL1MVeez+JeruumajTRNZA5Fp2pua7rD3GY\nfnmlcaxtttGoy1/+L//zj9OuucGLcIwM7dAmEf6tRlotlcHRhGfquC4u6PWFiFTyOA6EPv7Bp1Kw\nY1Kq/rjpO+59dQ3vvdnEuqtGdh4dq2bS7tk8xRD7VBbb6k28RTzg4zzgOf7IJmrM9EPHH9V670I6\nFKy3TmLX38Uc/jDR6dnvmUKGZf4//W9/8xPbLLf5EUIIIYQQQsgUPNEvU0mcyMHe5LP7+t17Js/O\nQRvSc7MNk+dA/fXzD7/zA0hfv/KROefXvv5VSP/xP/EbJs/iIv6q1DzAPxFsbti/drfUNrqZ2brJ\nk1db9PT2rfHQboVJ1FaYRrVi8uhdK9/9nv3LXruH5XnuIv6K1+/ZrXZnzuOvBdWireaDe2uQLqst\nU3c21s05iYdf+/N1ux2z2Z3UbdP1e/EzJhmOpHXz1sfpaM7Wg/6RMxnZX2TSDv7V4tIL+ItS4vj1\n4GAHfwlI8rauKlFR5cGHKRTtz9PDPpbrTM3+lX+2ivfebWM76sWOLYch3sv37K/KvQ4eaxe6Jk+g\n/io06GE/vFS2dVApYjkMHH+1rqtf1+o17Ku9se2HjRn8lXZ+oWrytJqTd3BunXjGeJ4n+dykPJzb\nIodYD6lj70OlrPpngPW7u49tQkRkNMbrnj3/nMlTa+Avj4efVURk44H9xfrK++9Duly0bWBzHX+J\nunkdfyW8/IUL5pySescvfekNk6egfml+/8qPTJ6bN25A+tQK3qtate2m28f3vHD+tMkjPs4ViRoC\nRo6dMN/97vcg/f7P7M6C/WbTHDtePOcvTYfR/+76Rcl2P7N3yN7ZbMU6wi9V6i/2x9/rEf2LkUsu\n8bSu65vyU788O66raz5z7u5/RlsXj4jnZRIGk/Gun9h1UrWijiW2jUcZzkuNkvp1yLe/4uR9LIta\naHdr5NVWwFiV6YH5CUyk38dxYduxW6OsdjN97SKObR/YZbIM+mqLvevXIdUwRrFtKfk8HisEuK7y\n7WYJKZXw3gdNxy9IJXzPXB5/AWsEdvwu+bheeL9r133t2K6tPgn+MkUIIYQQQgghU8CPKUIIIYQQ\nQgiZAn5MEUIIIYQQQsgU8GOKEEIIIYQQQqbgiQJQjEYjuX93Erig1baC7zd/5WuQfvGFcybPvTso\nNP7Zuyiwfe60Cp8oIt/5PoYE9/NWmP/v/uafgfRFFXL9zk0b9rirfFSGQyuoT1JUCQdaoJnawAL6\nWOTwSGk1MXT4/s6WyfONX34T0rduYmCI737nh/Z5VQCFSqVg8oRzGCr/lZdQaL7/B1bgfP3WJqRL\njkACjdKkXh6cADF/Fo9lvDspV88RFrmjhbF9K3Cs91E4OXfxDKR3QyuujQIUleYdUT9zKuhDJ8MA\nAJFD1L26hAEALp21/SWfxwAU7TsYcGR37745xx/jAw4d1gc63nboMNVIuth2ensolM3KGBRCRGS5\niPfuVmxwk6iE7a1awzzbTRuK/9QS3qsxYwPMjFqTNuGfACm653kYitvh6aEF3p7TuwvT3S62rU7X\nETgnwf4ROWwC5uYwJH0uh+PLyikbhvb6NQwj/tZ33zJ5rrz7AaR7KmjK7u5dc87yEgYdOnNq1eSZ\nrWMwiZ3tHZPnQAUrOrWCZd7uWIuCShX79/y8tanIPOzfsQoZ7fJUvKbm1aHD7+2NX34d0rf+2fdN\nnl8MHh+04CgBJ8xVP6NQ458F0zy/yHTvcJTQ6NrjSuc4SWX3/4csi2U4mswZC4uXTZ5mF+fAYuQK\n569sR3xcf+VzdpmdqbVBdcaOs2kHLRHuruE6udm3a9V5dZ1TjrVfsYxRHvZbGGRhMLDr2aIKw+47\nmoAX6AAZdo1eVVYQoYeT1MAR3iROcD3Rb22aPKUqBl2LVTCRXGLXOzc28Htgael5k2e76Vi0fQL8\nZYoQQgghhBBCpoAfU4QQQgghhBAyBfyYIoQQQgghhJApeCLNVLlUlq++MdHw/OD9qybPyincwx44\ntCRBhPsQ5xdQ8/Gr3/gVc865s3jdn/7sA5Pnjde/DOmLZ3AfZaVoHcH21V75gwOrFZqZwb3wQYDf\noL5Y3Uig9n722tZgTXK4d//s8orJEirjs5ki7oE9NWP36RdDfL5BPDZ5ErUPf0lpD15//TVzzt21\nb0J6PLB7dsuH9tZa879nT5ql0h1OjIQbfWuOJzlsF0Xf5klauH959yPUrsWrc/a6autxIbV7iIvK\nCFnvg04dxnezc6iHqjp0LfsJ7vXO57EuKiPbZvf2UBfi2hc9V8OyWa3ZsorVM2+NMZ0P7T7k+Qa+\nU2dsDYPzdcyTU89XcBhQzlRQIzUY2zzJeFI2J0EOECeJNJuT8aLu0Cb6AZZhENqxLfPU8O7hywWR\n3U+fqnErdfy9LVNtNlGmlZUa1pOIyHMXL0L6n/+T3zZ59newj2mz9LZjbO530QD+7//dv2vynD6D\nY1u5Zvvh4gLqwIIA+9TAoR8MfCzfu3es22VJtb8gpzRSDq2b5LC+z1562WS5cAH1wP/wBGimHqf9\nOZK5rqeTj59DjIbHcV3V9I0591Hu87RwlcNRdEmP0z+5rqHvpccREZFMl0Wm0o4mexRT4Wm1YU+L\nzAslzU/m7YJntZSzVZzXew49URTqcscy9fJ2mX1qFufNnFgt99vrH0G6neI67o037Bqtruqz1bK6\n2JsPMM/BENcCp+ftvDBSbuPNtm1b9QLW7zi0DWWmjMeWF3B83O1Y49y1bSyb4cC+U6+H88D8LOqn\n47ydH+cWsW7zeavljubs2vmT4C9ThBBCCCGEEDIF/JgihBBCCCGEkCngxxQhhBBCCCGETAE/pggh\nhBBCCCFkCp4oAEWukJNzz5//OH1jzRph9ffQeHa5boMqzCoNbq+MArhQK/dF5JUvfgnSD/atYO/K\nFQyIcXEVhdAFh/A9r0xRS4H9vgxGKHgbjVCMHI1tIIZMma+2tq04OVZCxU7f3rujDHiLRSybV189\nb84ZpnjvB/esGfDKMgb98EMUXI+HDkGpMi8eOsT8+UOCa+8EmPYmmchBPCnn0obD2HUOyyIe2/a3\nF+O7b22iWF4HBhERyWK8TppZEWQ6QoFjzsN60IaAIiLBGJ+lljgMW1W7jspozLdZsea1zS6apK4W\nqyZPqYDHVko2OMIwRnHqYF4ZHlfRdFhEZF4FUtnuW5FpSQWg2O9gv/McBse5Cgp9B0MbSKB0yHzV\nd/T/Z02WpjLsTd6tlVixb62O5ZU52kmW4bg6Gvd1BnNOIY/tJM1sXxir6zQaaACei2yAh/n5BZW2\nRtP3KmgsXavhs1QqdjwZDbAv3L9njX1vXkezy/PPnTF53lTm6PV6A9KNWdtfWgcHkB46xOkjZYQt\nCfb3yGEifubcC5AuFW0fqzdskI+Tjg6I8NQCEjiNaDXH389PAq4aMAEodCaXYauqy8+Dsa/v+1Io\nTObkkW/HrUppG9LDoTXZHifYx0vK2LcauYxoce66v2UDHez08Xn+rV//Y5D2E7ueuHXtFqRvb9ga\nTtUYf+EMzpGzJfu8zTa+Y85hRDwe4DtkqX2+yJyG59SLtt2M6njSznbJ5AnVhWtF/O5IfRvYoqzm\n+sTRG3QwrU+DIwohhBBCCCGETAE/pgghhBBCCCFkCvgxRQghhBBCCCFT8ESaqTAIpD4z2b/9hZcv\nmTw//SmaB64u2r3my3O4H31pBs2yCpHdu5gonUi5aPdN3rqF++Xvr6FZo6R2T/viDO5HLwY2z7CN\nZpGtfdTdLNXsPtp6Cfehdpv7Js/9HdSofHDPaq8ut1BrsFTT+1CtaerWNmqkZmvW2PfFl3Bf/pUP\nrkD67i2rPdASqDixxq9efOjYCdg27fmh5MqT9pWGdm/y3R7WZ65oyzSt4P7lzQOsz5k9287Pqnbx\n4eYDk2egtExhCTVJBc/uOy75WO4X6lbbtOTh8+6qqmoF1ui3XsbrnHJom3IevtNsuWHyaO2SH+A7\nLC1bvUwQ4t5k36F/GofYAPf20Ah7FNgGN/TxuuWyrdt2caJ9cXmoPms8EQkPGYG7tCXGcDe1fTEe\n4fjieTi25SI7hs7OKC1l4DA5TlAz5av2GPpWvzU7hwaJF563c8f7agxaXMX2lxeH+XgB36nTtnvj\n90fKVDO25fnez96HdLWm5qhTVvtbm8H5peTQGLZa2EbbbTSXTB16x/lZvFeW2TrIhQ7z8RPOcZq2\nHr9697NHF+dnJlM6gkbKPssRDJlPGL4vUq1MxqrMs7rnVhfHl3pj2+RJYpxrU0E94yBDfbWIyLCJ\nus2Ntl13fuHSK5Beu4vj+c7BpjknTbDcF8/Ytd98GZ+3FOF4nToMhMMCjkF1u3yQVF23N7YNJ4xQ\nzx/4uJ7ojewnSbGAN3v95VdMnm4Px8NOE7Vjw9jW2zC+DOlS3Y6zg5SmvYQQQgghhBDyVOHHFCGE\nEEIIIYRMAT+mCCGEEEIIIWQKnkgzNRgM5MZHH32cPnfa+nWk8auQXru9bvLMz+P++YbymFm7e9uc\ns9dET48bH9rrdjq4H/0nP8X9mAUrK5BiQe31HVsfGhnhdetFPCdw7GlXFi8y7Nvr3rmH+zh3Nmye\nZFntb62p/fSe9UZ4+dLLkK7W50yem9fQk+v9n74L6V7P6rfmVL31NnZMnv4hf6A0PXqM/qeF73lS\nCCdlVJy1+6JLC6ch7fIWqGRY5+0NrLtmD9uIiMg55QUzmLPeMNd7uA+6EeDz1Wbt5uRzZ1CrsTo3\na/LstLH+9P7lKHZ4FykdTiG1ZVXO4d5kL7F74/M5fOZfuox7nBtlq6lZa6LObyCOtjPC/dZ91clK\nJasBKitN2qBn/avywWQY9E+AusL3PamA95Utr3iI7calBfOUJqrTQT3R7p7t49c++Cmke/0Dk2dx\nGbVBly+j/nL9/j1zzv17OF63Dqy2aaB8mlLl3zKM7QA+pzyu+kM7L0ioznNUcaJ0VN/+9vcg/Utf\n/5o559y583gNh4a0UkFNbqq0beOx1SfosbfRsLrEOLYai+Pn8N9mHy+0cfU0P9N5HuN55OTJ7320\nXn+Mmi+nIZT27Xr8e+scxlNKHl82RykF3/HAJ0BCDWRZLMNkomfKulbT3OstQ7qZtxqkUgE9REtq\nHt09sNrj/Tau9S6esuu42RKOFd0R9vn5OXuOqDVXMnKsKVM81uujrjOK7HrCD/BTIXVoiQIf8yzM\n2XErUV6WRfUKg7FtN8MejpHLs7YONlN8h60E5y1frE47UX6Ivm8/h0p5q1n/JPjLFCGEEEIIIYRM\nAT+mCCGEEEIIIWQK+DFFCCGEEEIIIVPAjylCCCGEEEIImYInCkDR63bl7R+89XH64JINQPDqq1+G\n9OUXXjZ5Nh6sQbq1j6a4QWhFaDPKNLXk+Ay830Th7k9+ikaMX1aCYRERb4DiNt+zIuKccqstFVC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/3EOiCF0xT5c4jnZxIWJu+/2bTrzrzq00Fsx4EfvIvG5ivnsd3kcrYNaFPwwBGsa6aOgXyK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\nSSa91qQdNPetXmJuDvfKh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size 1080x1080 with 20 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"zaqg2brqu89W","colab_type":"text"},"source":["# CNN Model"]},{"cell_type":"code","metadata":{"id":"jPlnZE8NnP3b","colab_type":"code","outputId":"2e9c4ba3-ce3d-4500-a06e-2e3d290c67da","executionInfo":{"status":"ok","timestamp":1574185535658,"user_tz":-330,"elapsed":393242,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":711}},"source":["multiworker_strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(tf.distribute.experimental.CollectiveCommunication.NCCL)\n","with multiworker_strategy.scope():\n","  model = tf.keras.Sequential([\n","\n","      tf.keras.layers.Conv2D(64, (3,3), padding='same', activation='relu', input_shape=(32,32,3)),\n","      tf.keras.layers.Conv2D(64, (3,3), padding='same', activation='relu'),\n","      tf.keras.layers.BatchNormalization(),\n","      tf.keras.layers.AveragePooling2D((2,2)),\n","\n","      tf.keras.layers.Conv2D(128, (3,3), padding='same', activation='relu'),\n","      tf.keras.layers.Conv2D(128, (3,3), padding='same', activation='relu'),\n","      tf.keras.layers.BatchNormalization(),\n","      tf.keras.layers.MaxPooling2D((2,2)),\n","\n","      tf.keras.layers.Conv2D(256, (3,3), padding='same', activation='relu'),\n","      tf.keras.layers.Conv2D(256, (3,3), padding='same', activation='relu'),\n","      tf.keras.layers.BatchNormalization(),\n","      tf.keras.layers.MaxPooling2D((2,2)),\n","\n","      tf.keras.layers.Conv2D(1024, (3,3), padding='same', activation='relu'),\n","      tf.keras.layers.Conv2D(1024, (3,3), padding='same', activation='relu'),\n","      tf.keras.layers.BatchNormalization(),\n","      tf.keras.layers.MaxPooling2D((2,2)),\n","\n","      tf.keras.layers.Flatten(),\n","      tf.keras.layers.Dense(4096,activation='relu'),\n","      tf.keras.layers.Dense(256,activation='relu'),\n","      tf.keras.layers.Dense(128,activation='relu'),\n","      tf.keras.layers.Dense(64,activation='relu'),\n","      tf.keras.layers.Dense(64,activation='relu'),\n","      tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n","  ])\n","  \n","\n","\n","  model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n","\n","model.fit(train_images, train_labels, epochs=15)"],"execution_count":5,"outputs":[{"output_type":"stream","text":["INFO:tensorflow:Single-worker CollectiveAllReduceStrategy with local_devices = ('/device:GPU:0',), communication = CollectiveCommunication.NCCL\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n","Instructions for updating:\n","If using Keras pass *_constraint arguments to layers.\n","Train on 1250 steps\n","Epoch 1/15\n","1250/1250 [==============================] - 29s 23ms/step - loss: 1.6395 - acc: 0.3903\n","Epoch 2/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 1.1494 - acc: 0.5913\n","Epoch 3/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.9071 - acc: 0.6862\n","Epoch 4/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.7269 - acc: 0.7545\n","Epoch 5/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.5932 - acc: 0.8022\n","Epoch 6/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.4753 - acc: 0.8424\n","Epoch 7/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.3607 - acc: 0.8813\n","Epoch 8/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.2815 - acc: 0.9097\n","Epoch 9/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.2104 - acc: 0.9337\n","Epoch 10/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.1634 - acc: 0.9477\n","Epoch 11/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.1359 - acc: 0.9577\n","Epoch 12/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.1195 - acc: 0.9629\n","Epoch 13/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.0960 - acc: 0.9706\n","Epoch 14/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.0938 - acc: 0.9711\n","Epoch 15/15\n","1250/1250 [==============================] - 25s 20ms/step - loss: 0.0803 - acc: 0.9747\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.keras.callbacks.History at 0x7fde8373b8d0>"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"xQ-7E3hSvlFq","colab_type":"text"},"source":["# Testing Model"]},{"cell_type":"code","metadata":{"id":"so99H02vn7s0","colab_type":"code","outputId":"4054e351-1f77-4f7a-e852-b46631c47a9b","executionInfo":{"status":"ok","timestamp":1574185591898,"user_tz":-330,"elapsed":4309,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":54}},"source":["test_loss, test_acc = model.evaluate(test_images, test_labels)\n","\n","print('Test accuracy:', test_acc)"],"execution_count":6,"outputs":[{"output_type":"stream","text":["312/312 [==============================] - 2s 7ms/step - loss: 0.9120 - acc: 0.7938\n","Test accuracy: 0.79377\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"K9JhmBe8n_eL","colab_type":"code","outputId":"c45c55a7-f5d0-4c92-925c-48e5779b97b3","executionInfo":{"status":"ok","timestamp":1574185610355,"user_tz":-330,"elapsed":4012,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":73}},"source":["predictions = model.predict(test_images)\n","print(predictions[0])\n","print(np.argmax(predictions[0]))\n"],"execution_count":7,"outputs":[{"output_type":"stream","text":["[4.0597873e-04 2.3783259e-04 5.9027849e-07 1.9953732e-06 1.5325882e-06\n"," 4.3922205e-07 4.9694495e-07 1.4243903e-07 9.9934322e-01 7.6979313e-06]\n","8\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"jf0hiTAYveAB","colab_type":"text"},"source":["# Plotting the predictions"]},{"cell_type":"code","metadata":{"id":"FCxu_HjloBrY","colab_type":"code","colab":{}},"source":["def plot_image(i, predictions_array, true_label, img):\n","  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n","  plt.grid(False)\n","  plt.xticks([])\n","  plt.yticks([])\n","  \n","  plt.imshow(img, cmap=plt.cm.binary)\n","  \n","  predicted_label = np.argmax(predictions_array)\n","  if predicted_label == true_label:\n","    color = 'blue'\n","  else:\n","    color = 'red'\n","  \n","  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n","                                100*np.max(predictions_array),\n","                                class_names[true_label]),\n","                                color=color)\n","\n","def plot_value_array(i, predictions_array, true_label):\n","  predictions_array, true_label = predictions_array[i], true_label[i]\n","  plt.grid(False)\n","  plt.xticks([])\n","  plt.yticks([])\n","  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n","  plt.ylim([0, 1])\n","  predicted_label = np.argmax(predictions_array)\n","  \n","  thisplot[predicted_label].set_color('red')\n","  thisplot[true_label].set_color('blue')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Tiu68IwqoG5G","colab_type":"code","outputId":"6dbeefe7-c07d-441a-bce8-e7a89313de6e","executionInfo":{"status":"ok","timestamp":1574185627040,"user_tz":-330,"elapsed":3776,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["\n","num_rows = 3\n","num_cols = 3\n","num_images = num_rows*num_cols\n","plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n","for i in range(num_images):\n","  plt.imshow(test_images[i])\n","  plt.show()\n","  print(test_labels[i])\n","  print(class_names[int(test_labels[i])])\n","  #print(predictions[i,:])\n","  plt.bar(class_names, predictions[i,:], align='center', alpha=0.5)\n","  plt.show()\n","\n"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAWgAAAFlCAYAAADGe3ILAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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size 864x432 with 1 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size 432x288 with 1 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size 432x288 with 1 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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["[1]\n","automobile\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD7CAYAAAB68m/qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAUBUlEQVR4nO3de7BlZX3m8e8DLSIXIdKdlMMlTUgT\n7Ulm1LQII05IhCnACGQUA2OiZow9akBHJVWktBiKydToGGNqAqhgDMZLEImmerBNmxBRQgXs5k43\ngl2g0sSKrSJeUBH4zR/rPbg5nHP27u7dF1++n6pTvS7vWu+71l7r2e9ea6/dqSokST/9dtvZDZAk\nTYeBLkmdMNAlqRMGuiR1wkCXpE4Y6JLUibGBnuQDSb6e5LZ55ifJ/02yMcktSZ4z/WZKksaZpId+\nCXD8AvNPAJa1v5XAe7a9WZKkLbVoXIGq+nySpQsUORn4qxqeULo2yf5Jnl5VX1tovYsXL66lSxda\nrSRptuuvv/4bVbVkrnljA30CBwL3jIxvatMWDPSlS5eybt26KVQvSU8cSb4y37wdelM0ycok65Ks\n27x5846sWpK6N41Avxc4eGT8oDbtcarqoqpaUVUrliyZ8xODJGkrTSPQVwGvaN92ORK4f9z1c0nS\n9I29hp7kr4FjgMVJNgH/A3gSQFW9F1gNnAhsBB4Afm97NVaSNL9JvuVy+pj5BfzB1FokSdoqPikq\nSZ0w0CWpEwa6JHXCQJekTkzjSdEnlHf//Z3bvY43HXf4dq9DUn/soUtSJwx0SeqEgS5JnTDQJakT\nBrokdcJAl6ROGOiS1AkDXZI6YaBLUicMdEnqhIEuSZ0w0CWpEwa6JHXCQJekThjoktQJA12SOmGg\nS1InDHRJ6oSBLkmdMNAlqRMGuiR1wkCXpE4Y6JLUCQNdkjphoEtSJwx0SeqEgS5JnTDQJakTBrok\ndWKiQE9yfJI7kmxMcvYc8w9J8tkkNya5JcmJ02+qJGkhYwM9ye7ABcAJwHLg9CTLZxV7G3BZVT0b\nOA24cNoNlSQtbJIe+hHAxqq6q6oeBC4FTp5VpoCntuH9gH+ZXhMlSZNYNEGZA4F7RsY3Ac+bVeZc\n4DNJzgT2Bo6dSuskSROb1k3R04FLquog4ETgQ0ket+4kK5OsS7Ju8+bNU6pakgSTBfq9wMEj4we1\naaNeDVwGUFX/DOwJLJ69oqq6qKpWVNWKJUuWbF2LJUlzmiTQ1wLLkhyaZA+Gm56rZpX5KvBCgCTP\nZAh0u+CStAONDfSqegg4A1gD3M7wbZb1Sc5LclIr9hbgNUluBv4aeFVV1fZqtCTp8Sa5KUpVrQZW\nz5p2zsjwBuD5022aJGlL+KSoJHXCQJekThjoktQJA12SOmGgS1InDHRJ6oSBLkmdMNAlqRMGuiR1\nwkCXpE4Y6JLUCQNdkjphoEtSJwx0SeqEgS5JnTDQJakTBrokdcJAl6ROGOiS1AkDXZI6YaBLUicM\ndEnqhIEuSZ0w0CWpEwa6JHXCQJekThjoktQJA12SOmGgS1InDHRJ6oSBLkmdMNAlqRMGuiR1wkCX\npE4Y6JLUiYkCPcnxSe5IsjHJ2fOUeVmSDUnWJ/nodJspSRpn0bgCSXYHLgCOAzYBa5OsqqoNI2WW\nAX8EPL+q7kvys9urwZKkuU3SQz8C2FhVd1XVg8ClwMmzyrwGuKCq7gOoqq9Pt5mSpHEmCfQDgXtG\nxje1aaMOBw5Pck2Sa5McP9eKkqxMsi7Jus2bN29diyVJc5rWTdFFwDLgGOB04OIk+88uVFUXVdWK\nqlqxZMmSKVUtSYLJAv1e4OCR8YPatFGbgFVV9eOquhu4kyHgJUk7yCSBvhZYluTQJHsApwGrZpX5\nW4beOUkWM1yCuWuK7ZQkjTE20KvqIeAMYA1wO3BZVa1Pcl6Sk1qxNcA3k2wAPgv8YVV9c3s1WpL0\neGO/tghQVauB1bOmnTMyXMCb258kaSfwSVFJ6oSBLkmdMNAlqRMGuiR1wkCXpE4Y6JLUCQNdkjph\noEtSJwx0SeqEgS5JnTDQJakTBrokdcJAl6ROGOiS1AkDXZI6YaBLUicMdEnqhIEuSZ0w0CWpEwa6\nJHXCQJekThjoktQJA12SOmGgS1InDHRJ6oSBLkmdMNAlqRMGuiR1wkCXpE4Y6JLUCQNdkjphoEtS\nJwx0SeqEgS5JnZgo0JMcn+SOJBuTnL1AuZckqSQrptdESdIkxgZ6kt2BC4ATgOXA6UmWz1FuX+CN\nwHXTbqQkabxJeuhHABur6q6qehC4FDh5jnL/E3gH8MMptk+SNKFJAv1A4J6R8U1t2qOSPAc4uKo+\ntdCKkqxMsi7Jus2bN29xYyVJ89vmm6JJdgP+FHjLuLJVdVFVraiqFUuWLNnWqiVJIyYJ9HuBg0fG\nD2rTZuwL/DJwVZIvA0cCq7wxKkk71iSBvhZYluTQJHsApwGrZmZW1f1VtbiqllbVUuBa4KSqWrdd\nWixJmtPYQK+qh4AzgDXA7cBlVbU+yXlJTtreDZQkTWbRJIWqajWweta0c+Ype8y2N0uStKV8UlSS\nOmGgS1InDHRJ6oSBLkmdMNAlqRMGuiR1wkCXpE4Y6JLUCQNdkjphoEtSJwx0SeqEgS5JnTDQJakT\nBrokdcJAl6ROGOiS1AkDXZI6YaBLUicMdEnqhIEuSZ0w0CWpEwa6JHXCQJekThjoktQJA12SOmGg\nS1InDHRJ6oSBLkmdMNAlqRMGuiR1wkCXpE4Y6JLUCQNdkjphoEtSJyYK9CTHJ7kjycYkZ88x/81J\nNiS5JcmVSX5++k2VJC1kbKAn2R24ADgBWA6cnmT5rGI3Aiuq6t8BlwP/Z9oNlSQtbJIe+hHAxqq6\nq6oeBC4FTh4tUFWfraoH2ui1wEHTbaYkaZxJAv1A4J6R8U1t2nxeDXx6WxolSdpyi6a5siS/A6wA\nfm2e+SuBlQCHHHLINKuWpCe8SXro9wIHj4wf1KY9RpJjgbcCJ1XVj+ZaUVVdVFUrqmrFkiVLtqa9\nkqR5TBLoa4FlSQ5NsgdwGrBqtECSZwPvYwjzr0+/mZKkccYGelU9BJwBrAFuBy6rqvVJzktyUiv2\nTmAf4ONJbkqyap7VSZK2k4muoVfVamD1rGnnjAwfO+V2SZK2kE+KSlInDHRJ6oSBLkmdMNAlqRMG\nuiR1wkCXpE4Y6JLUCQNdkjphoEtSJwx0SeqEgS5JnTDQJakTBrokdcJAl6ROGOiS1AkDXZI6YaBL\nUicMdEnqhIEuSZ0w0CWpEwa6JHXCQJekThjoktQJA12SOmGgS1InDHRJ6oSBLkmdMNAlqRMGuiR1\nYtHOboAk7Ure/fd3bvc63nTc4dtlvfbQJakTBrokdcJAl6ROGOiS1ImJAj3J8UnuSLIxydlzzH9y\nko+1+dclWTrthkqSFjY20JPsDlwAnAAsB05PsnxWsVcD91XVLwLvBt4x7YZKkhY2SQ/9CGBjVd1V\nVQ8ClwInzypzMvDBNnw58MIkmV4zJUnjTBLoBwL3jIxvatPmLFNVDwH3AwdMo4GSpMns0AeLkqwE\nVrbR7yW5YwdWvxj4xg6sb6vrfvNOrHvKrNu6rXsO23iO//x8MyYJ9HuBg0fGD2rT5iqzKckiYD/g\nm7NXVFUXARdNUOfUJVlXVSus27qt27p7qXu2SS65rAWWJTk0yR7AacCqWWVWAa9swy8F/rGqanrN\nlCSNM7aHXlUPJTkDWAPsDnygqtYnOQ9YV1WrgL8APpRkI/AthtCXJO1AE11Dr6rVwOpZ084ZGf4h\ncOp0mzZ1O+VSj3Vbt3Vb944Sr4xIUh989F+SOvFTF+hJVifZfwuXuSTJS7ehzlPmeDp2qpIsTXLb\nPPPeP1N/ki8nWbzQMqPlx9R5TJIrtrXtk2r1/Ycpru/cJGdNa327ehuSvCHJ7Uk+sgPqmvd43NlG\nz4FZ00+a66dJtmL9+yd5/baup61rh55jP3WBXlUnVtW3R6dlsD235RSGnz3YKarq96tqw7aWbz/j\nsDMdA0wt0Kehfc32p8XrgeOq6uUzE3bF9u+sNlXVqqp6+xRWtT/Dvn6MXXFfz7ZLB3qSv01yfZL1\n7aGkR9+dWw/ijiR/BdwGHJzke0ne3cpfmWTJHOs8J8naJN9Jsnlm3UmuSvJgki8kuTPJ21rP/mjg\nt4GPJPlBkrcmeVaSa5PckuSTSX6mrfuqVv+61pN6bpJPJPlSkj8eacObk9zW/v77SPMWJflIW/by\nJHuNrHeu77meAhyW5FtJ7ptZZrR82yfvSnIzcFSGH1r7YpIbgP88pdfpFW1f3JzkQ0lenOFH2m5M\n8g9Jfi7DD7a9FnhTkpuSvGAr63pre33+CfilNu2wJH/XjpWrkzyjTV+S5G/a6702yfPb9HNbO68B\nPjSlNsx3TDy3TbspyTu3tteb5L3ALwCfTnL/aPuT7JnkL5Pc2vb5r7dl9kpyWZINrU3XzXMczWf3\nJBe3c+QzSZ4y5tj/syTrgDcmObUd3zcn+Xwrs3vbB2vb8v9tgu3eO8mn2npuS/LbbdaZSW5o2zzz\ner8qyflt+JIk723n4p1JfnMLtvvtDOfVTa2tVydZBWzIrE8uSc5Kcm4b/sV2vN/c2nbYrG15bnt9\nHjN9qqpql/0Dntb+fQpDaB8AfJnhyaylwCPAkSPlC3h5Gz4HOL8NXwK8dNY6n8ZwMr+krfufgAfb\nvBOBW9p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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]}]}