{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"cd","execution_count":1,"outputs":[{"output_type":"stream","text":"/tmp\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nos.listdir('/tmp/../')","execution_count":2,"outputs":[{"output_type":"execute_result","execution_count":2,"data":{"text/plain":"['proc',\n 'lib64',\n 'tmp',\n 'root',\n 'home',\n 'opt',\n 'sbin',\n 'etc',\n 'dev',\n 'usr',\n 'bin',\n 'boot',\n 'media',\n 'mnt',\n 'run',\n 'var',\n 'srv',\n 'lib',\n 'sys',\n 'kaggle',\n '.dockerenv',\n '.jupyter',\n 'src',\n '.theanorc']"},"metadata":{}}]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport os\nimport cv2\nimport matplotlib.pyplot as plt\nfrom IPython.display import display\nprint('Files in this directory:',os.listdir('../kaggle/input'))","execution_count":3,"outputs":[{"output_type":"stream","text":"Files in this directory: ['train_labels.csv', 'train', 'sample_submission.csv', 'test']\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Files in this directory: ['test', 'train_labels.csv', 'train', 'sample_submission.csv']"},{"metadata":{"trusted":true},"cell_type":"code","source":"def f(x):\n    display(x)","execution_count":4,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"images_list = os.listdir(\"../kaggle/input/train/\")\nprint('Total number of training images:',len(images_list))","execution_count":5,"outputs":[{"output_type":"stream","text":"Total number of training images: 220025\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Total number of training images: 220025"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_labels_df = pd.read_csv(\"../kaggle/input/train_labels.csv\")\nprint(\"Total number of labels for training images: \",len(train_labels_df))","execution_count":6,"outputs":[{"output_type":"stream","text":"Total number of labels for training images:  220025\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Total number of labels for training images:  220025"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_labels_df.columns.tolist()","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"['id', 'label']"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"['id', 'label']"},{"metadata":{"trusted":true},"cell_type":"code","source":"print('First image id in training images:',images_list[0])\nprint(\"First image id in taining_labels csv:\", train_labels_df.iloc[0,0])","execution_count":8,"outputs":[{"output_type":"stream","text":"First image id in training images: 437e629c0ad96457b71d4767761e09d6ef2d6fcb.tif\nFirst image id in taining_labels csv: f38a6374c348f90b587e046aac6079959adf3835\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#Visualize one image"},{"metadata":{"trusted":true},"cell_type":"code","source":"img = cv2.imread('../kaggle/input/train/'+ images_list[0]) #opencv color order BGR\nrgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\nplt.imshow(img)     #opencv BGR format   ","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f93483daf98>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvWeYHOd1JvpW9/RMd09PznkAzGCQEwEikQQYRIoURUqkgmVZwZKtteV8fR3W91nbstfPs2t77bWvdy3TkiVLlpbKlCiJYhQIMCCHQRikGUzOOXT3dKr74z1fncIQAEdmAH2nzp+Z7vqq6quvquuc8573nGPZtg1PPPFkaYnvZk/AE088efvF++F74skSFO+H74knS1C8H74nnixB8X74nniyBMX74XviyRIU74fviSdLUN7QD9+yrHdblnXBsqzLlmX94Zs1KU888eStFevfS+CxLMsP4CKAdwHoBXAEwEds2z735k3PE088eSsk6w3seyuAy7ZtdwCAZVmPA3gYwHV/+MXFxXZtTc0bOOVbJ/aCv0Z8ri+sBdsyC/4CgN/v53GSaQBAlmvjaN8gACA7kMMxsi2VSes5/HJLfDxOcUmp+yMAYGR4nNuKCgAAM5MTzrbsLA6cnZkGAEQiufw8O+OaJS8qIPOAzyff6kksX5bMMc5rzOhCZERZ5EYiAID5VILnKsh3xmQFuL9Zj9nZWRmTp+vRO2wOyHO61sonc7Is/g2FggCA9Hxcr0LGxFNJOWc25xOfd8YEs3mNWTKPubkov5fj8TiWXCsn4LP1TvssS/6aNaKkMu67TglkZ1811xnXmqdkjnKqq54la8Ffc86M67lw1l8GxeZ5jYVFRc6YRDKJwclhTEWnFz6qr5E38sOvAdDj+twLYPvCQZZlfQbAZwCgproaP3ziB2/glG+y+FLOv0m52eYb8xDmZHQNs2XtzVfz4inN2nqDCvP4Y0wPTgEASuZ0/8f++C8BAMvKG7m/PMPjs3POGH9hMacW4o/ow5/8JQBAKKLT/qf//W8AgI8+8iAA4KdPfsvZVldaCAA48NNnAQC337YDAPDygRecMXaGD2FN3TJ+DvBcKUt/lIEQ5zE/fwkAEI3pjyme5Cpt3bUTANAx3A8AuOM9d+s6VPGFlS8vp1cOvMz5PHCnM+aff//veM1RLqwvptcYygnJX/5wV69dCwCY6jrrjMmE+VI7PzQEACiqqgUAdF684oxpqWkAAJQXlAAADh8/BgBYsW6VMyadEwAAJKOcQCitHnCuj9tyg7wBCfnljc/oPUvJ66C2gedft3ENAGDfgeedMWNjAwCAYIDHDtj64siWRyRbjh2WF1hUXpYAMDfPl2taXgrnrrQDAN776CPOmN6BAfzKF/9vLEbeyA9/UWLb9mMAHgOADevXv6MSAyzXbBzNvuBdabs/L5y9eTuk9QWSEm1TXsw38TNPfMPZ1jvQBwAojvAhfO8jHwQAlC1rdsZ8+StfAQB0D/Jh/qu//isAwF/8y+85Y+64bTcA4Dvf4rFX11U42+bjfCDf+9D7AAA/+tETAICYSwvW1VUBAM6ePQUACOVxPqs36Ht7apbnH+3gAzYVizrbfOEwANWQW7fcAgCYnphyxpRVc05njp8EAMyO0Eo5+E19Ad22YxePF+ePobu929m2YnkTAKBvkD+YV08eBwB89P67nDGvnOb8fX7+UAYGOLYgoi+wEqMR5Ye2fEUjACCYG3bGmB9TdW05AGCiZ8DZFo/zB5ecp/aekB9gwqcvh0geX1IXLl8EALRd4stpNqrrMTvL66+s4FoXhvX8NTXVAID9z3Nt8mRbaUmZziPFZ6x/dBQA8L5HPwAA6JOXHgAsX74COWLhvJ68EXCvD0Cd63OtfOeJJ568w+WN/PCPAGi2LGuZZVnZAH4OwDvIjvfEE0+uJ/9uU9+27ZRlWb8O4GkAfgD/Ytv22dfZ7R0lWba+9zJix5sFsY0f4DLvjW+fWYDGVFeoqd3X3gUAWL6cfl5/d5ezrbKW48bmaPb1TtFMK1u32Rlz1wPvAgB89fHvAADS4lt/7zE1kXMETqytoB+e5fZZBBycmKJpWik+bnRGzd+RUZqy5eXEAxJJ+vyvvvhDZ0w0xu9uqaPfmnaBWcua6ZpMTfA6gmU8TnPTamfM+ACBzFMvHwIA5OXQfB1OKeBVtnIj5zrH7+ZcLtP+Y0cAAPfcx/W41E846eildmdMs6xbppM+/YULxCPq62v1HKU0rTsuXwYAZAcFeJvReeQVcszkGEHS5LzOIy+b856S+9C0bh0AYCSmIGM6MQkAaFlO4Do3wn0unD/jjHn3g8Rq/urP/gQAUFmhZryZS88wwc7GxkZeV0jN9oys/869dHXS8qSuXL3OGTMxOYPMIp3pN+Tj27b9YwA/fiPH8MQTT95+ecvBvbdC3qziIT4XcOc3iL1zDvnHpU2d8J3sZ8I+U8MjzpjmWmrY1n0HAADnW08721bVrgAABELUvsFSAk/2uGqY+g3Upi1nV/IcPmqokX61HD7y0EMAgL4LbQCA6cFBZ1tpJQGqWQlbXbzcycvwKbhXKRo6kyRq3N3N4wQDqmEqa6mRwoJCT4wPO9siE/z/gx98GADwveefAQDceq8CbwMHed6qCM8VFgAuFXdFQEJE5efD/O5Um67Vn/zpn/Gf5fxTdIzHseR4AODPZTSisIhzfe8DXLO+9g5nzOV2WgHGootGqV1rVzQ5Y2amCYg2S5Tj0pjOw2fxJ5KVw3t9WO7n2Lyu56//pw8BAMZHaQGdP8uI9q07djpjIBjj6jUbAADVVarxcwXMa5A5BcUqCYU1lJNXwOuenJAwbT6jJZ1XFFaLJxJIJnV9byQeZdcTT5ag/IfU+G+auIkaotj9ouGNdndH8xzfXvazbJJCcv2qKcNhvtr//i/+BwCgLqQaqqq4EgDQ1kXtnTJkjjLXbaBrjdISvtF9kDiyi0nU33UBAPCD73wNANBz6ZKzbfmKFgDAzj33AAAqGmiB5Oa55highdF3hT5oIMBzNDSob2xCc5clXl23vsXZ9ku/8+sAgJgQVqJJar8Xf6QYwZ773g0AuHSQvnpRKfENNxzReYV+d98ofWvDOQCAHz/JMGRtNdesOMRzHTh8zBmzegMxgqISYh3nzjC8F5+edsYEJFRXVsUQZqFc63RMY+QFoj1NODCQle1sy8ujdTY5NgYAWLmK8f/WTrUq/suf0m//iz//HABgYw6xhwtnlcsWP8Y5NdTTqjh/vs3Z1tRMS3DV+k0AgKERWpAhsYgAICk6enyG2MLkFB+UyUkNs46NjSMpeM3riafxPfFkCcqS1vgZlz53MGvDjLwRjCCa3pfh3wKXVscQfcgiS5he4+oLnjtODVDayLd+b3svAGD9pBJnMtN8o+eIv/3D7zJC+quf+qQz5sgLZOVdPENSy/3vepezrb2TxxweZ8TgjnvIpsstUlQ/nEU/cGqIiPC3/+1LAICjxxSFLi0k826mkP7mb/3B7zrbcjZT681e7AQAxKPUnl967PPOmO9/icf8zEc/BgC4cokasr5Rfeszl6j1IsX0d88JSQcAOi/Sitm8mtERn8DVldUustOXvwwAWLeG1khODjV19cplzhhzI/OKidwPTxCBLykqdoYkZsjYK6+iddExOOZsm5zk+IJC4gkTU9xWWuiiUgoK/y2JxHzkY48CAAZ6+50hEaFeRmeokfMjev78fK71uXO0gLKESTg8MumMqaggyWd0mNZRiUQi4lHFh2JTSWTSi8O/PI3viSdLULwfvieeLEFZ0qZ+yvXes8XG9wmn23y+KgfLgIFi4vvSXL5QiZrRX/7PfwsAKIvQfA1lqTuREN63Jckopw8SjLr9znfrnOIEgQ48+1MAQKkAT7UtCq6deoHhs6oyAomDrlCfz29AIIaWGrI518YNSvRIztCE7OzsBAA0r6WrkZ+v4F44i67GB/87Tfx0WsNE010MIQ300a1YWcv9dq1QM7z9DLlcz/zgSQBAdbXw0V/8qTNmUvIHJk+dAABs2bDJ2ZbXWA8AOPvySwCAHZsJ5J13gWq3SZLQunUM4yXmCUTOJzWBZmyCpvHlXu5XVdsIAOju1hBofoBm+CUh+STSCWebbRDYOZrUwQICbkNDGt6sKSdwmZ8nLp94d5atP69EnPtfGeK96u3T/LZ4gtsqhNQz0M913b5dXcDjx7lGK1fQzRro4fzjU0okKisuR5Y/gMWIp/E98WQJyn9Ijf9mEXhMVhYAWAtS73xXpeVRMjA52VdzdgfOKIni+CFmo91SSy20prrR2eYXDXC0jdrQShDo+fx/+xtnTKnQaGeGqLHv3LOHG4KaK18kqb/xON/2sZjms45F+d0ty3nek2dauU9NlTOmfhlzq+azCNyNz/M6sgtU43/0o58EAPRJ7vCy5fXOtpPPUwtX5dLSSZUSFDt36IgzZnaIIanqCm4LSEGBrbcoPfm5/S8CAGzJKHzpmaedbb/2yU8DAMpkqZNCSFq2UtNprQw18ze++XUAwAc+xIxEkzYMAFaI550apJWTM0FwbtOGDc4YA7JeHGY4r/uypvWWCnFm4xZaHIWFvOaSQs2uC4SZnTc4QCtg/7MvybXrmufn8zh9AvjVNSgAOTLE815sY3bfyhYCoN/+2v9xxrSsJB06I1ThIckgLC0udcY0NixHKKh1Bm4knsb3xJMlKG+7xs9kMrCs12rThd9dS6ub73y+67+vFmMNmDFJ11i/+V/CIQEJA2VlqaZNW/zfaH5TIOX7TzzhjNm95w4AQLYQLIKFBc62HCnqsLKJhA1k83PUlZxy+BQJKiVCQ11Wz7Edz6o2jceJPDTUUzPkFyjRIy600bFJEnAahdCTnRtyxoxPEkfIF980KMU/Koq1OtLlfuZ9z0n1ESuqufJhm1ql4wTDk+VBar/zJ5TqeuvWrQCAwmIee2yWGvecWCAAMC/zaKmjBZIoUX/1wjmOKy/lOsxJiKujW338hgZaE7/46Y8DAF45dpDnLNdQmV/2M/n3Jw5zHYMpJel89V+pWffewbDor/3u+51tg+Jvj49Qw6bTtK6yXOhPNnjsdNxUxeGcL17Sua5o5n3IL6TmTyU1zDssyTkf+iCLanxX6izcc+deZ8y0JF2dbSUuZJJxZmY15//I0UOYiyq+cSPxNL4nnixBeds1/kLNfi3tv/B7o6GvN/bfO4esbH3r+6XCSRZEjWf4OZVSbZyQ7+aT8rqVvw998CFnTGCKY9pPnJXDuHxzqZ93qY+aoDxMEoYvpjTL1U1ExnfuouXg9xFdb+9VjTsxSc0YzqVmCYZVm+cX8pbW1TO7ZVgot/2uRJ6SYlohOUKDbRSfsrJEffxZIRI99e2neI13K0moLJsav18Q5eko1yyVUj0yOcttZfU8V0DW8fSLrzhjNq5jOa1S0YI90xqdGJuiJouKBZYvtNzaYq0xd7GDPnGojKj8tl2k/Hb0dDpjApKmvEzSk+eHSDbqvaDpvVvX0n9/8IEHAABTrrp+U0lq+OFpRgfyC7jW0VmlBfsldXdiglZSQQGvOZXR6ECeYAMmySY6r1TbQDbn2C9I/+aNxB++/fjXnTHbthLhf+QjrNr05X8mQaqsvNwZc+nKJSRclsSNxNP4nniyBMX74XviyRKU/5DhvBuJMeMXA/K5SSk+M14KaJoSx+6ont8vLoJY774s7mMqwQJAQKqmtGynGdvfoUSNi0Le6BztBACcPkegqalaTeyKRoarnvwhOfobN9N8HZCS2gCw/dbbAQDHD70KAHjkV3/Z2dZxgsc8203XwCfAX9gV5gnm8AISFs3vpiae/8yJ886YhjqCis21dD0mhpQ3HhFXoUrGXLnA/aqEVw8AiTBN20NnmUkYFpDtjj1aideeoxsyLGXHq2p0HXol7BaWmgVpMfFDlgKZDRIq9Unp7BHJaquq0jDaQDdDrdOyfudPEBwrz9eqSXfdRQKVwYw7uhSUSwshKiwg7ZjMq8TlXvX38b6GxXWanaM5PzY56ozJL6A70tXbCQDoE9AQAFI2Xb3iEl7jqwJshl2A7EuvMPTZ2sZ8ip230RWMp/Q5r2uqRXaruq83Ek/je+LJEpSbovF/VpBuoRZfjFa/Fji4UNJJBV9MBR5D5JH+CfC7OlmkReObd6pPCqK7yyhXlxBsuXCZoa66NQ3Otskkx+VWUxvOz1IjHD/5qjNmvQBFu3cSzBkYYRinoWGFM+app/n2LxfN+70vftXZlic15lY00+J46dhhAMC2Hdv0wqVJRiLG+TQvo6btv+ICOy2CTzXFUnJ6RMs4D0kfgd1S5rt3nNr04Tv2OGMSEqKMzhBMa5TQ4Ys/1HqspVWcv8khj7nCmrlSSWg+xEe0prkRAFBka1bcuu0MGf7j//xrOR73Wd6sa3WLzKn1Ga5ZkTQYqZV1AoB5Ae66LxEsLMrTcxxpo/YNyr3PCHjWO6EWmCU02QEpdf3Q+2mlXXJZDtNztBS2b2cp8icHVOOvEjr10Agtn0Sa6/Ern/2sM+ai0IlrGkikyhViUVe/ksdaNqxG5FlX1uANxNP4nniyBOWmh/Ou9/21tPRiQoGL2c+MyXIRgRyNL0k6pmVRIqOhtoT4UxlD7pF9Cl0aoneYPn35CpJhfEHVopk8aobNu6h9j0pVl+SIq7lCJUkpJ44e5T4+Hjs/ojXaduy+DQAQkxZNPT0a6suVZh2wea41a9ZzPuW6f/t50oorxG994qtfBgC88Iwm0JQUUXtuuu09vA4XpblZEoZOnSFhJyH++0viPwPAurU8b0k1NdSIWAU+V0ugqCTDjMbo6+cXKPFmz4P0u8OmAo8kAp14Qud46sB+AEBBkLhKUCrR/sv//l/OmN4O0m+b6xoBAMkUNfaZM5r7n5FQY4W0uUKRtgIrk9DcmZNMkqmSsFx9qYbRhieILaxdR1ptIkFrac1qTVoaE1quT1hfG9YrHjIklX+a1jOMF5YafOMzWiWotJq4xeWuTu6/ZQuvx9XFKTuS4yRpvZ54Gt8TT5ageD98TzxZgnLTw3k/C3PvZzneYoA/V6o8fNJcw9S0TItpa7tKGdkS6rMkK89YVXMuFldQwnmW5LNnXOBggQBW7S/TbCwvpfk2ELug+0vH2gJpCNm4ktlsiYwexzRgaBbOf4WYwwAwIQ04CwRkDEtd57On9Bz5Qd72UwcJ/B3Zz2yyVbWusGI58+fHhBtfXKZgWEoKVSYF6JqQnPeSMg2jVVZy/7TkoVdW0uSPt2gji9aT5NY3SObb8jXLnW3jCZ7DL41N/aW8/vNntTxYj5TObpTecx3SSHJtgwKqpflcx5ZVZCeefIXnbLuiGXgXztNlae/uBAB8+nd+29n2l3/Pxp61Uk+gWO5LwNW5YlqaW+6QElzj0phj3FV2PTdX8vjn6U50d2mYNyyu4pgUOO2VDL7lTVqmrK2NIVNLuhBHpYdfJF/dkrm5OaQzXnltTzzx5Dpy0zX+WyWLsRhsF4HHaHHb3YgeULUOwOejxs+SThwhiflFwrqMw5KHHi4lQDM6paG+ylpmoaUbaCFcPMeQTnmhauwyKXI5Oc03+plTAphlK3Fl40ZqyAsXqQVaT2tWXH4FNX2gmOGeHCGBHNj/sjNm23rmgrdIxZxLUvp6rF+rypSFaClkUuSq5xQpAHnyEC2EnXeygcbBg9Sid9+uDTWmhxiq7BIC08b1DC+292r4af1t7JY7MM7vRlPKkd+y81YAQEjKjF86Tk0f8ut9nZT8A1u0eqmQlPa9qvkAj36EXWX9xQTlwsUcu3azgmv9lzjHHHD/wy9qe+t37yGQeugVaQUmOfZDPRqOy0imXlYWn4N2KXfufgZNlR7T5svnshjypMGKeXZqJXR7/JR2pNt4C8G8aSEHnT7LQqVXutVyqampwXzc4+p74okn15F3DIHnRuG8fw+BZzHi3t/U2DPfZeSv22dKpU09AH5OSybf2LjSWYukH/vEDEM6OdlKuzQ12NIpaoiCXGrl7Xs17NN9hfTPLqlr5xPNW1So4TjjU3dLFlpBseb8mysyOd7tvTxefb36vdE5atbavcy4K/g+G2EEitztreh3zo5SCyVdOMZddzCL7cI5aqTP/uInAQD7XzzsjGlewdBWSirG9PXxeiprNed/cp7WxFicPvKKJtXCsTQ1V0geixPHWadgSMJZAHDbdloF/ReJX/QPUHN/9pd/yRlT8S4Sodp/TALPu997PwDgue9p849Ykvdqpp/4w4Pvf5+zLSciVYbWk2RTJmW5o8NKx92zm0Smgy/T8jGUYbdFOdRPv91O8btZV9OPjJRrj0ntvSOnaN2E89R/N6Xgu3q4jiVC771zr1KgT7WecMLQryeexvfEkyUoN0Xj36i6zr93/xvJ9SIHAZ8m1xg0306bvlbit/ldlof4cuZtmc7wPyuo/ve8zC2WphYJ5ur+MSEDFTXTD68qo+ZsP3rRGXPkOVZfWbuB2mxYkN6iQkW8p0X7fvyzrEt36ogi9hmbWsMQkcqamfQzMq4I80SMWufYcyTyPPLZ/wIA2L//WWdMewcpovmriSdUrnT5xDa1TUQaa47N0DqIp7Xa8NQ055ErVYdi0jZsZmzAGXNlnFqweCMtnozLOpqVfmXJy9wvNC7U34xaN/MjjGC0C44S2kgUfPXHVWMPCaW1YC23zQxxn5wc1abBAP/PFrKVndGfxTlpez4pdfxseQamXA01YtLIIyGJO/584iHFriSbk4eJOwTlQasrVHry/CwJWJkpXmOlT+aRUnzo7NOkOq9YQ8uj/SIjGHdsu8UZM15QjWyvyq4nnnhyPfF++J54sgTl/7fhvMWI7SqYaJsOuNbVIJ9bHJdhwTZ3Xj9smmmmx7npRAsAqShNfcPFDsQk86xGAa/WHJrLFy8xTFMpZbpjCQ11+QMcMzFMs9VdfDQlhJlUkuMLhFseCuit7u8j8FcgZaS+/n/+DQAQyVOX5YH7ydGfl1zx7l410bNCvLbly2iiP/sjlsV2eUUYGiY4d/YsOfGf/dQvAACe/N43nTHVjQTBZqVFcG6e1gworyBh6PQBhg4vXmb4qiKqY2IWQ55xKWO1XEC1Kcm2AwAIj394lGBcXoJAmpWl6zE5RxPbllJqnX0aqovJvQ2FuFZxKWa5vEHLjXcJGceQdEyP+tmolj3Pk/LaPe3iQoXUzUzKs1ctBKCz5+j6NbdoCe4CCbmeOcsQblpqEPzgiR87Y+67/wGEXHUCbiSexvfEkyUoS1vju8N5DlVXxGmX6wYGxRqwjHXAv+5+6gtLgLuBRVO4Mz9IYGh0kvnb6ZRmAE5OUVNu2kbCxsgUtVlJSN/kYWmrNTREAsuLP1XCSVmplMqWfPOOy9QQO3ZpPn5G8vFLpQx188r7AFxdfHRawk2T49SUt9661dkWm+cqPSU97ANCd97katP1xS9+AQDwkITPXjrOmgOpHF2PeIoaOxgiuFZVrRlvtqxVieSdJ2Ic29CkrcReOs7wWe0yhip3305CkLtAanYWratCKeg5K63FWqTQJwC0nyEtuVTKYrsbcpRUUtPG4rwPw5Jz73MVtYymqX2XL2sEAMxN0eJIxbWgpuUXy2OCIcPciHZYzsmX52GaFkKeIVTN6f5dI7yPTau5xpu2Evw94SJvtZ0/j1hcLcMbiafxPfFkCcrranzLsuoAfAVABajyHrNt++8syyoG8A0AjQA6AXzItu2J6x3nnSgZ1e+whX7ryxjq7o32lGos8t4MuTTlvPjiKcmT9qfdlF+h+Iaohc4J5TQzphqmspZhpwvtDPFEJcf84792pzOm7SI11Nk20nldPT+cpp/l4tun4tQm3338a86YgjJqm6Ji0lH7hVySSqs27uhkGGvXJobzOs51Otv6RogRVEkiUFSSUg4+r7nyt29nFZqA+NjHTjN0WOVqdvHhD5NO+5I0wmjdd8zZtuFehqnqpMlHYYj7Fdcovbl6kpo+p4IWkPGxa1pWOmMmxmixjEq1oxKxhNIp1abL1zBUOTtJjTuXdGE/k/T/i4u4nutW8dj2vGp8MaCQK41FpsdoVbhb1ZvaeCWSvFReq/67mXdumJbPzCznduBlpR43t9BCue2uewAAvXLPNm9VS+6FF17A/PybR9lNAfhd27bXANgB4Ncsy1oD4A8BPG/bdjOA5+WzJ5548h9AXlfj27Y9AGBA/p+xLKsNQA2AhwHslWH/CmAfgD94S2b5lokbnZe3vGh+9fndabnUiAvflmlXpdOUILqWrGzG9dr3i8Y3mt/Umgv4VNPu3MM3+sGjrJa7ZTUJOIGIJsmUSnvsK+089prVSu7xCyU0GZ256u/yRk25HZD6eaPyt66B6PzAkBpspcX0M7PT1EZ+13UkZrhW6Wlqw1xJJS7MVlJLeQGTjdrOEqH+9Gd+FQDQ46LcHn2ViP+zP2ZUwM7SlW39Ka2AqNS2e+ju93LOXVr7z1SoqazkeuQXUJvPDimdNpagBvT5aZUZbdrfrVWLiqXp55nTPOfmbbc627qkkYlJhV7dyESr3k7dv7CRuMOUULfNs5Ofq378xDjJOBs3k0JsIjsAEMwhfjM5zXOcvkCLblmz4hARaY75ze98FwDwgDT/uHBByVvVdTUIZL8FVXYty2oEsBnAIQAV8lIAgEHQFfDEE0/+A8iif/iWZUUAfAfAb9u2Pe3eZhPKviaP1rKsz1iWddSyrKPj4+PXGuKJJ568zbKocJ5lWQHwR/8127a/K18PWZZVZdv2gGVZVQCGr7WvbduPAXgMADasX//mNLZ/C8Q0zsjcaIxtyD0CAMrglKssdEAq7/gDNLkTKQVb0pJ9NREXk1oy/yJhV3HHYprky6M0Y5vXMMttanLMGTMpxR2z/ZzAmZOHnG21UlRzVExcE06cnNIMwknpqDsk+fc+6fa663YFEPe9yPz9HGlgcfakduuNlBFMq5Zc+fgwQUq3+frqiwT61u6maRuLETgrLdV+7i89/xzXo5MEmDyXO9Mn5JcZMfUPijsxPa2hzyIx0YdHeW1zUYb87FkdEwiT8NPTTZejpZKgWtBV36D1FEuhP/zIhwEAY+Oq17ZtI1EnL8z7OtTF42zZrKBa1wSzLiDmAAAgAElEQVRJPeMCJBqyTyCgZndiPiVzpmE8OqJK0PRCnBTX6WMf/wQA4Pl9+50xd9xFsHR8hK7Kr//6rwMAfuO3f9MZk06mXkMuu568rsa3+OR8EUCbbdt/49r0AwCfkP8/AeD7izqjJ554ctNlMRp/N4CPAThtWdZJ+e6PAPw3AN+0LOvTALoAfOitmeJbJ+6QncnOM2rcaZ1luUk+RuNf/dn9/jRVWFJywHQy/Zptc6JRzP5ui8HUmrssrZbmxYPau3evM6bjIrVOWytvx/SIdsL1xWkZFErjiIxcSDKu86ipZIjs9u0M550XevAXP//PzhhDeDnQLW2p6hXCGZ2jtpqNcW6To6S4Zqe0QYlPYlw+8G9uLqnLf/xHf+yMKQ5Qi1flUZt2t7c52yqkVHVaaLnnW2lx+CMaBsuXOgR5ZbRy6mtYueb8YKczpqCCFkZlGa2DkQFaOZarN9o9UpfgyiVm8o1NK9V2s5Sx/t73vgUA2LOTRKa+AbXAgkLACYqGD0gVp9lZLY9tAOBYlJZYdE7PERdL0Dx0V67QAurvU5r0sYOsfRCXGgZ//Vd/CQB46ZUDzpicnBykXGSwG8liUP2XcDV9zS13X+d7Tzzx5B0sS5qyey1xqvssqMjjFtX8rx2TEcJNMvPaN6+pzhOfY9jG+ILJCR3bPUzt+T4ht3ReJJHm1QMHnTGFudR0fmkBtmG1asGBHoaChmap2Yqlkm8spudYtYZaa6if2jwkoa5bRMsCQLloyv5OaesExSryi2TeCVou/izOw2erxo9HpZ98XuiqbVs26Dk6TpCw44vzOFvXaOurpBBs8sPUnvEE9x+fmnPGFEkdu8oy4iKWRX8+EtKc/dlJjs8WjMA09ug7reG4pr0Mh/Z1Sj1AV1iyu5NEmQqpEtzVzfVoatTEqrAkxph7b0g0plISAGT7xRKUKrsFEZ1jkVgIxjo7dpjWzaoVWmW3v5fPRXc3n4fWU1y7D39YDe2UnUHke4pd3Eg8yq4nnixBueka/43WzXtDYimGb8lSmGq7xpE3b3EASJsEHJPCK/Ren09Tb53x8koNutpTm8SXXPEF29upnRsLlFyTV0rf2qSR7t9HdP399z7ojLkideADsnTjQ/16jklqJONfFgvKv3PnTmdMfx/909JSaq1DrzCBJplUbbp2DampOQGSYrJd6Z4T04ayy2jERFQ4q657WSw1ArMsXmtXO/3W3nZNeQ0bDZ3Pc9iuCEhODhH+pDSQ9AkeUBjS+v7LGqkRaxuoseeEcjszrnTcrFzeo77OTh5HSFObVmuyz/wM78vu7UTqO/tciLtUQJqd49zG5hlBWONqgRWd55hBIUR1SbWhHbcqEai0lH58QKo2p1ytr8KS5rz/Rfrra9aQuNPg7g8gNfb23MH6frC4f9tFxUXi8ThiccUObiSexvfEkyUo3g/fE0+WoLztpv5NNe1/BjEgn9/vSn1bYOpn+bh8mZSb9mNy9uVvxpWdZ/FYxvw3ZJayAg2Vdc0RECrKZebb9luZY56Ia8ivrFDytYtpTl9p73S2FRYQmPJLf7BB6dJaU6etq1okw+yZp38CAMjLZTitvkrz4afGaK6OS3nt5s2aa58vhSqjUxKqC9IMDbrio8WFHFNSQHP1tFSVuXXzXmfM6f37AACWhOwGRrStVI50Fs4t4bUaAM0X0jLjBvCrrKbLcuI4e9mXNOp65ke4Hv1SSScqufJTrpBfupSuwcQk71mk2FVdZ4huTWEJswPXrqU7EHO5RbYAlzMxugy7dtOtOtPa6ozJi3A9gtm89/E5zZs39RxapCWaT0LBo8MazisUYPiF554BALR30k3cvlvdiUherpMP8nriaXxPPFmCctM1/mKaZr5VYsA5QMN3Bty7VgEeSdyDT770ZUy23Wur9BgxGXgA4BdLISVjIrnUAm6qa0gALoM7hkRD9PcqSWdNAzVcn4SEZqYVzApIqefObmrPDZukbZerLuDpM8zjT0kf91SOZPTN65i4kHT84DkyCbVqzH+zUk2mJEhAcmxQS3j3DFOz+o6zZdQd9zDr8Ntf+4ozZnKaR9ogGYgbblPK8HFp1pERq6pctPrpC1pxZu/9JN4U19BSSV6iVTHYp2uVTFLjt7cRBMuTdTj9qjb/aM9mdZvmFmrPxpVq3YRGaHmYZh1JSVN55JH3OGMG+1lHb0cVNf2MlPuudjUznZfKOHOz3BYJadjtkhCyVixnlqQtoeC1LgDyR2KdTUsouLSMFki+q2nmqTOtXgUeTzzx5Prytmp826bGv1ZDS2tBHTu3LPzOHWJ7Y/NxN8swPFyh7JoxLg2esU2bbPOZ2sMHFw5gXT0391wNTTQt+wWNpnc16swPUENl5rnfsFBM7Xm1HGwpueMTayCvQBNfZueoUcor6FunpHKsuy5AvrRlXi0hu+4OasMsv4bsKst5zNkoNf6sq3JtIJfnT0slX9Nmat7VbixUINZMIfcfm+a8mtdqGCw/n/O/9U6GqI6cPuFsyyqnFt96C7VwuTQTTYQ05LfsLmrmiTZaJ+Z+VhbpegSyuI4N0gJ8WMJfFaWaKz/Wy9Dp9BT/msrAADA2QQ0fi9Onn5qiPz8ypM0/Ta6+ZUJ0cs+DYQ3zjo3QX8+SQg0jwxqCNbhMShqUJhP8++QPf+CMSUgdwXUSRty2m9hPjxB6AODuu+7BXz/7P7AY8TS+J54sQfF++J54sgTlpjP3bqa4XQgD6vmc0lsSunOX3hJYa2F2XiqtHHXHZcnmcfwuNyAgQFVWigBcJFd6zSV0HqPCBTehPws08XLCeqt6h8h+K5fOs6X9WnrrQhsz9grzGf5paKA5n0hoONBnXV0CvLiEZm/SNcYO0AyfT0uGmauGYzCf886kuK13kObw2Jy6A2XS471yBUNr6RweYDSpZRse/c2PAQA6rxBcu/8zH3S2xWOcS3cbMwf7RmkqJ7JceQ0nWXbKAl2UiLgcBXlqxg8Oc//VK7gOFw6SCTk6rvMYlWKhjXKRpeW6fzzANYoNSifd++8CAEyPq6meJeuYEI5+lrDz8nMVtB2VhiaHDzLnYmWTFgRtFlCv/UonAKCujrkXaVd1iDzp0ms68X7lX77Ec6Z1PapraxGNKtB7I/E0vieeLEF5WzW+Zb0WqFv4eSHY5/7uzRZDwAEA/4J3oGUy71zfO0CfEFWyFjbhgM7b8CiStnsbv5yXEF9KwMJRV0myHCnHPTpBoKm6gaDU9Jx2Th2RbrkPPsyusEl3H/ZBHisgE/BbBNnSrhZcPf0EhCorGW4a6CfHvMjVj312mtor7qfmra5VUkxALIaMgFGTk5OvWYeBSWrE7B5q81VrGbJrXFPnjDl1kZWDTA2CUErPMSl5DfuPsJLPI3vZ9OPskGrBFw+T2/6hh9meq1CITfv27XPG5OWK9vUTQKsoJQHI58qH775MoC6WIIA3n9K1eu+D7wYATMyaOgd8Zl75qVYksqWhxgc/xIzKc6fZ3/5S1xVnzMXztE4MSef2XZo7ceTwUQBAVQUBzWCQ55ju1XteVMIchZdeZFWeRmm3ZUhMANC4YjlycjQ35EbiaXxPPFmC8rb7+Isl5rwd1N6rQm3y1+/k48v318AB/IbIIwQey1UW2jB/0teoPWquKSFv6YSE6CbGtR7eBinVHJ2jxpuQbLveUfUp56R+HQRHWL91h7Ntakz8zDSv7bTkbTc0KJlk7Vpmf5k6fh0dpH/esWuvMyY6y+MYr7+irFrPIa2/gln0rbPyBcdwEZE6JXxXKnn9RRX0UbNm1C4oqqUvffkE/d78Os28yxEcYUx88RJpDfYLn/0lZ8z+H78IABgXiyM2y2seG1WsYfN6atb289TQJSWcT3Od0nJXNa8HACxfyWo7+4+ccbZlHadVUlpKa6jvMu/H2ICrPHchrbJ/+n//AQCQG+S6hMKqffPl/xUNtHhOHDvqbGuo5/41tZzTV/7t6wCAjOvnmS0luDdtZIOTSsF3OiTrEAAGeweRSi6uAo+n8T3xZAnKTdf4/x7N/mZZA5mM2yuVYxqNL38zbkLOwtekScRxI/+Zq7m+7uv1S8vsrAC/M8k67jbX5r+EkDiOnKKm6hnRPPZhwQSMFn7groecbc0tJLU8/yQpnnfcxupow6PqbyYFfTbEk/XrNshnV1uoFGcSluSQ6krVkBNSKwAyJjkvlgzU3zR9J3ftItFkRq5nelabXZw9T/+/rIbXMR3X8xuMIuOsp1gVhVq5NhCUugad7QCAS2e4RsvrG50xc7M873PPsLFoVoaW1J987k+cMR2n6X+be92yqlm39RAPOXKIfzeuIeKeSehcK4VsZEntxBVNjLKMDmnkoKmR62esPdO+HADGxwU/KKZVs3nzZgDA0IhaLvVCQJqbIw5x7BgtuZygUn8zqdSifxuexvfEkyUo3g/fE0+WoLy9XH2QqxJwpbyZ/503kIBSSTdHXrLf0sZsFq66G5zzpZ3uFlcfD2q2w2dCbRJWk57lAJBtglFi7mXJ52xXjnlGwjbIyPnl+yRc5JggzxH38bu4q3R2Yp7bQqmAzEuINFl6HfE6Zrg98zffAACskh52EWhox4rRRB64THArdadmihXVEhwsb6Jp+pLkqG9u0T5slWJap7No4p49T3Ctrlq5+hE/gbrcikYAwI+e3eds2yTNJLKLGPaqlNLZBw4/54y58y6a+Ol5KbpZQJP0zFlX6a1Cni8Vpfla4Xa9pJBoVTlN4lMXyJ9fnbrNGTJ2gWu7aj3N7xX305y+eO6UM2b/C5zTrs10Z/qlC/Dj//RNZ8yu3XsAAK+8zIy97bftdrb1tHPdt69jma9jh0gAqi7XugDdvSTN5GQTQD1xnADgzu23OGPKKgkOnm4jqDc9q2Z8ZRXvx4A05IjJtReXakHOri7mGCQl1Ggy+YI5+nwNTYwCruftRuJpfE88WYJy0ym7RteZN5BJkbeuW8pfNb2Vsd1fXjXm6sw786V11Tb3xZuUep/TaMF31fyuLYaW6yIbyTxsefP6XVOMZNPCyBGLZWyWmm6gWyvPbNrWyH8EOAtkEQC0XdTfikoCPVlSFtuAhXIBAIAzQt1ds4zho+mYhgx9gq+VSa/6zSGCScN9qo1tMCxkEva27tzibMvJJRkmIUV95oXkY4fVglq/gyW8s6qptS620roor1d6sakWOjRCAk1ft4JhpunInj33yWeu1bPPqFURiXBtmpqp6R//ylcBAFs2agnvwTDP39vDc+zeQUtkeEgbYhw+QgA1JiSnggIlMplaCYcP0xpYsYwEnNMnNINvwy5q9mahBe9/jqSjPldDjLAQf8KSh59MK4EoIUBhOMT7OTjI0G3IVag1nMv/M2ne69mZSdlXw3exWHTRmauexvfEkyUoN13jm3BXZiFV9xphCfOWckJm7rebfOe/VgOM65w7y7XBMke30gtGqTZNyxgzVnEJPZCpdZfjjNFtAcELTFlsa56+XHxa6+ElZiQk1ECfsv8KiSIRV0PJiNBQZ0wpZZ/6ddEoNUFhEX3jjbeQnPKq1LcDgHlwP/8sj3NFWletWqYhuwutrICTkISg3/ubv3K2tb1KgkvKR20TKua5iuqUJHROwmBVGSaVBAtoXRiyDQD4hQBVXEQySjqhj2NhmD70M09Qw0cnOOeKcq0LaPJTDkoJ8hJJzunrUMvlzj2s0vO1LzGpZf9L1NzNzdq8o3uQ46vraR1Fk6qNh8dIVpoXDGl6llbBRz72KWfMSIxWxcAw8RlfgGbSgVeUpLNqDe+nnXltmDceJ2V5StamWEJ97nqPtmBOufIcTEmVn0RS17Outh5ZgcX9pD2N74knS1Dedo3vs3FVHTvjUpsad0aHuz0V5+1oKt8aTe/S2IZqa11LvQuKbzt03NfiB07TTKddtkQSXGOcOTkleGSMK0kmWxpIWIbX60qisMQfsyUNNyi+fpFLm+97eh8A4N333Q8A+M5XWKOueplquksDrGM3J7X2/Dn6/g5meEtnRYskJOJwx717nDE/+QnJPeuEuBPKoW8+2tOp1yENPoPitr/8jW852/LKiULPzJJIFC7l/LND6uNv3cNjJ0QhHT/GWnnDw+rH3/sukouSQkG+1HbW2fbk408AAJpqiV5HB3k9dpkSX2ZmqH2jM1zHrg7O/+c+oOm9ly4QW7jr7gcAAFe6WB9vLKYEnKIaYibb99wBABhyJU2FC8VSkcai73uEYxpWKMnHP8b1P3aIfn+1NPiIxjTZ5/wlzm3LLUxWOnR0n7MtEuEzU1jAa2uXcwWDrlqMIY24AEB5FaM9qYQ+8D293Q5e8HriaXxPPFmC4v3wPfFkCcrbbOpbsCzrmhl69gKQD75rmONmrCH9uMb4DeBnbHbXOUyIMLOAyIOFOB4nImOluo7LLcgsdBHMcV15/aa/niluabnereZ/a0HIxe/K7huT4pp1d7LSy1ScwN/T+y44Y4Ji7m7ds/WquQLA6bMk7KxsYVHGVbs3AQCO7XvJGXP4HMc8+OiHAQCF6xi++sl3v+GMya2j+Wn7ef4zhw8522qaaK7uvJu56p1SSHLb2o3OmJPPkkTTLJVmwima45G0ugM9p+iyfOmL/wQAaGnUrr8feBdJSWO9NLtvfxeLbo5FNAxnQNWLp1meumkZAbTzbe3OkPbLDJVu20nS0a13EOxz5xVUSXnunHya1o9/7nPOtvoaAp71zbyOEXGvmku1oGeteGFP/Zhz3baZ63Pxgs7Dn8UwXlp+citbtHT2q4cY/quR6jqlEmadcXUGNnkVNXVazwAAhkbUdQrn5sLnKtx6I/E0vieeLEF5eyvw4BoJbgv60Tva+AZZRk57q6u+W3i81267kWQWjLnW2ReOMUZGIFszxualIUZS2iplB1TDBeT/NKg1ZiXnfnxKwaSk7J9bTJLM5/7qLwAAf/cPn3fGZAsItOVWAk1zMReJQ1ptNUhe/8Blomv9k0r5/cVf+ywAYP8hElfsGV5IdbVqk+eeJpi3voUar/OKK/+8iuDVmVaG9ZKyLnUubXT+AkOEkyGGuAYvc/8G15ivfv4LAICPfvhRAMDyWu0a3HWZ4cBcqXlXv1w0b73uP9rLUFtVMTUlEnwiRoY1xBWN0ap49QitnMJKatOde7SGQU5FvhyPzKbf/c9/pNdxhvToF56hVg5L6e7DLymBZ9kyrrUlcz3VSiBz0y3a3qrzCoHLwn4+K/lFWnq7RRqKxOb4XAyLFq+t0fBqOMx73in594a4UyhhUgCIRCLw+xenyz2N74knS1BuOoHHeLv2df4CrlCd+XtdSo7u566AkzKl8oxPbsg+V1XXoTjU4QWf+b85kNlJ/snS4yRjaTm/DHGRMExvzRnpXT8jtfOS8xr2iUsLpKeeeRoAcP+v/CoA4FO/+ts6kQB90fJqhnSeevpZZ9NzT7HX/e//zp8CAAZGSVXtduWGT6ToL9Y2ksRSnE2t8Z2v/rMzJr+KZJjhOVoudz/0iLPNEn91WvL3y+oY3iuIFDljjkpoKx3nSmzeQKzhie9+1xnzO//XbwEATp4gqWZiTFtfbVhDjOKVKwy/vbz/KQDATL6GqzauI6YQsEhnTQghqrFWrYKJtVzrJ2U9W4IkNE3NKklnro0EngrjW8/otiah4Z4sohbv6ebYO1ztvgJSZ6Egj+ty9CC1e8CvlNtNt2wHAFTX8t7FE1pRKTnKpyxSwDUvkjWLuaoep4TiGymQKkWCE7lDyRnbAm5AdXeLp/E98WQJyqJ/+JZl+S3LOmFZ1g/l8zLLsg5ZlnXZsqxvWJaV/XrH8MQTT94Z8rOY+r8FoA2ASV367wD+1rbtxy3L+jyATwP4x9c7iA9XGyOLKRRkzG7/gs/uDDwnQiffpV3bjImfFhPf7G+5Qh9+863Jkb/GBDWDkGMdtl/a5TKI+xDIovnn9yuIY3qim26qKTFNc0NqEvqzCFQd2P8KAGDTVuafV21c74xJi9WekKYTEyMK3GVLaPHVl7j/3vtvBwA0b/uEM6Z3lMBZXQXDZ3NDnFegWN/duWU8TlMVM/faXfz3ltU0sSekLHdQTONjx7X33T13M2xm1uyk9Iq/5/77nDGnzhEcLJAGFs3L1UT/0hfodsxL+OxSG03+vGp1J86KO5Gco7m7ZT1Ddsmk3tfN21hsc90qAnA9vQQZezq0eGiddB+enaQLNDWqIcPZSbpj27YwO/HiOYYOgy4Q7cgR5grccxfXOpJDl2FifNYZc+Yszf/yKoZgz1+47GwrLuZPynH5pE5EcbHmPsxFuW1kkOHJMik+WlqoBUqnpqY0HP46siiNb1lWLYD3APiCfLYA3AXg2zLkXwG8b1Fn9MQTT266LFbj/08Avw/AEKVLAEzatm3Qh14ANa93EIbzrobmtHGFfL5B11wj2RI+m5amC4D2Cc9IeeF0UgkaOVLuOC28ecNNj8UUxDEhuYzsF5bGBGNj+vbPL6S2CUnrK1NM0d2jPC1hPAjXPZVU8CVH2mIVR6jhzgxSYxa6miCMDvB8WzYy3GQIKN/81j5nzI4dDOOdPmlKRit/vTSXAFMoaNo6kQQyO6FrVVTEMWFRFiNdtBiWrdRw2p3volZve4ng4N677nK2lZTQKlmzgdbAj37Mrq5r169yxhje+bGjEvYSMMpYOwBQXUnmiz+Lj9EXhcjDdeB5N7bQ0ikS3vrkvLaIqpXmGJM2NfXcOEOHDY3anqr3CsOKlUVSQyAlbbaCGmb1S+2Ev/9Ldprdfes2Z9u9e5lP8I2vPc55Sa5Bzvvf74yJSgZf0Cf3OmnmqPc+Hud3c8Lf37nrDmfbiWO0zkwLMEAsw1kFfQ1vv6KCaz85xhBwtk+vIz2f0Rjz68jranzLsh4EMGzb9rFFHfG1+3/GsqyjlmUdHXMlP3jiiSc3Txaj8XcDeMiyrAcABEEf/+8AFFqWlSVavxZA37V2tm37MQCPAcDG9Rts4OoMOnsR0YeFb6eZOfpOkXzVdElpR2Xqlbm1sMld7u2ln1pfT2JEtqupoWNzpK8+W1GR+pRxOXZ0lGGn7BxaCYaIAwDBCLVpVEIxfkuPF53hvCcHqTVCWdw/mFGftCBEzZQt343282Xpd+EIR14m/bZWQm7v+/lHnW0Hvsuw1+QMtdDhl5nPvnKb0mFrdzBzrnc//d0vP8Zc9T/6499yxgSkm9X550gVnujTPuz9Hcwea17FUt7bt/B4L+7/qTPm3vvuAQCsqKMV0X6ZTTtys1VDzUe5Hp09PEfb0ZPOtrt2MPyFaY6pljLSZRWapXjkEPPdN62hVVBcJHXtTim9eMdt9Ls7u/l4Dklbq7ll6uNv30m/e+1KZtyNiSUGAHPSGGR6jPesUKrk/OSrX3bGjI6Tsvwv/8iGGg8/+kkAwPP7XnXGpGxq/x/+6EnOeWOTs82UySsqoE8f8DG/f2RYS5Fn5/D6q/K4LSQh3Zlxrd23sqnlzWuhZdv2f7Ztu9a27UYAPwfgBdu2PwrgpwA+IMM+AeD7izqjJ554ctPljRB4/gDA45Zl/VcAJwB88fV2sPHaZhjXIuwAC6rkyl9jKeSKHxubd+Uem8q54uOPTeibsLiQmnFVE9/oKfH1p2cVdQ36ZSlS3D8mjQtMkwNAq6fMC5peINuuwgGkTtpMlFZAVkCvxOROp8UaKBK6ZSjqqtKboobvvkLN9Jnf+F0AwI+f1lpzRlPOzwjhZVRr9hWGqVlOSuus8Rj93/0HtcrPp+fYhioT5zV/+MGHAQDnJWceAJ478D0AQEuQqt9NK56Nib8+ze9ypc12fb02vezp7QQAlJXyu1qh44ZdOebDfbQ45qeItWxcoRhBcoLrnydF/yJhRkcGo0pPrqmg1jZ1ADq7mRSzYdMGZ8zcHOdYUsTjbCkguj8+7DJQBTfYKXUCv/H1x51Np1oZqRgbpRWwfCOJSFOjahX4LaHPigX59X8jFbmoXCm3c/Jc5cs8Tpxqdba970EmJHVdoVU1PSm4TEzxjBHx6cOCTWzcwGvsszudMWdazyPmqgFwI/mZfvi2be8DsE/+7wBw643Ge+KJJ+9M8Zh7nniyBOWmc/WNXKct3TXFlBB2h+MiRTQ3C0M0nydHFBiJCaiWL8BHVMpa55Uo+SEUIJhmwLiRUYI5cy4evZXN5fJLqeN5iWZmuwg4CSHkG7cgmXB35OX+BYXM8LLDdAfshOZdN69guOb7P9nH88/QZVm+rMoZ89UvMVNv93aCa60HFVR7+mn2iOuSwo+5si4zUTX1932fAGDYJ2WlxpjNdusd2gDCP0/T+lgrm3a0rN3kbMsI0GRn89rqJAy48hYtwT0vbtSZE+cAAD0jdEvcNQ1sWY/2y3Rr8nIizrZ6KYfV3y5ZelKnfNZVWipbgNBQLgGvcQHiggXaT+50K+sCrFzJEN+MmM8jPUpIOnGMINzmj5F0dGuXApmTE1yblDxzg4O8joKglsIqKaM7c04yEGukE++aLbpmG7Yx9NkvhT2nJtUVPXaCoGYOCBxeuEggdNftu5wxfdKdt6yaz0FnJz8vq1PQtmnZGkR+qoD3jcTT+J54sgTlpmj8G4XwjKa/0RtpXsC53Dx9u5kupNkC0hUVaPuh6BjfroZM07ScoZTZadW0sVlpgyTAYbW8xcdcmtIAhyYvf0zChHkuwMocJyiNMKLTqqHm5f+ibGqq+Ly0+3J1Peq4QA21ayfhE9PZdm5Oc8ynZ6l1urt4rbEJzbyLxmROedTYFVUMR5bM6XqMdxGYatlEsHOlNOjoae9wxqQFRCuuoMXwipBMAGDPAySv9E5xHrU+ZtIhT+9aIIehtWAp97/0/AEAwKqVa5wxrZdIW73/PQIutipVZN+rzNibGiGoVV9FLRqs0nBelgkNZnOti6t4HSMTShLKk7CuseBM0coZF1j59DO0gDY/So2/8Va1fJ576hkAgE/60w8O0pIKVynZqV+sKzEkFvkAACAASURBVNO9N1LC+5sV0TBt60Xe1817SYzKadNtyTizJO04H/4dt7Ewat+gApDFGV536zlaUOtXcR2feWG/M2brhm1IJl0P0w3E0/ieeLIE5W3W+DYysG+cmWNK5t1gSJYk1/hcue6GXDMnPdbzAppw0lhNNrFPSiqf2ce3pKm4AgBjE/w/LD7x7Q/cC+Dqpplz4l9a2TxvjuRh+1wNNTLi40fC1BDxhCvXfpqYRFrmnZyXVmBJxQGq6zjXM+eofVs2cp+4q3d8UTG1t2n+mdbdkZBQ4dAMtZAVklp3WRqW9EsSy7JKEkYa7qeGOfm5P3DGpDPiC4/wOFU1ysg2Jbs/+ikm/iRyuA4XLqvF0LycVXpelEYeNZX0TTOucuMFYVpXvT0MR7rrye3czcaVl88zRDcqiTMFEW1Wae5D3LT7klZisXnVeqNj1P4rG2rlOLye6jK1HC72Mze+/VX62ituUd+84TKtoswMrTN7ghZgvqsv/WCC1sS225lQ1bKVWMflAdXYs3OcxwGxfEIuqu2YhOq2b6FP33qaWj0npM9wVQPX5vZ79gIAzp1kglPNcm1JVlxRjayAJoXdSDyN74knS1Dedh9/YUs/8+a5kW9v0l+Nb500iTgpJXOYBoOBEP3tTNSVgCOasf0i66/92R/9PwCAnXXqb8YyPNZMhtpi7Wo2Xsxt1NTIXEm1TYhZEhG/z47rPAKypPOS4mnFNVHDn5LqwAJyhEPEKKbGFGuYl2OlMjzX2Dg1byyuKzchqapVpYxKjE/otX7qP/0GACBHarodF7+567Si2CGL2ubiOSbQPLePhJVIpaLqFyUNNksSRQJBxQhswSYkOxmtJ6kpG5uVhjrUQy0aGyc2sXUdUfXkqKLZk9IsNC3VZbpttQZePkCrbP1m+tvLpS5deVCjG+29RN+zg2IBZnNdj0maLACM9RP93r2ZWvzQS9S4DS2ayJMb5rNz8CDbha/Yqhq/WGrsXUjwmod7iI/YEY0IFa2VxJl5XkdoNa2r9U1qZf3g+z8EAEwJxfbn3/9zzrb+YmIlXWL5BMVaLKrQ/UMF1P6TQsgylmlhTH/Cx86evaqJx43E0/ieeLIExfvhe+LJEpR3DIFnofiuAQCa70w+fcrVUGNScvMLIxH5q6G+Ecm2Guoj2NIsjRvKoOBJuJRgz9FLzN82rkLYryAMBGwZm5FOpWKihlyFvn1S2XOgT7jcaQVbsmVcQPLyCyIMNQ3NdTpjYlI2OW2b3nUC5EXVRN6wkeaviRb93KO/4GzLqScIF52mifvQxz8OADjyxAvOmMFWmpQz0wTMAtl0byantQBkaZVkw82TD5/O1pBltazVnJBbwllco0xUzcxQIcdnyfXUlxCUu3xM++OlJOxW20B3amRGwda6VTTtT0kf+kCQlW9aytY5Y/IkVJgb5D1P+3kdeRHNzNy4l3UE2qXizY5tkvWXo49+3M/naFjSxlNT6rpZEh7Oyab5nRfm/XjvAw85Y14eJZ9/YkZDv4B28wWA4610h8yz+4UvaWrLg3cxnBmUUKNZj3PtulaJMR6seSXdqYCQxjp6zjljrlzoRSzumfqeeOLJdeSmtNByi1bekc8mLd41bGHJ65Tk3vv8qrHzCvkmNo0GJmc0R75W2h29PEqNcpt0RQ1c0fden1RvyS/nG7WxidlP00kNo80lCcKZFuQmmmi7JusT6m/QR00fm3RlEEqyVSqP2jAib/apjjZnSKqd13TrVmq2vh5mcSVd6xGOkMxSLrXisuoVVIum+cYfmOIOo23URlFLrZJn2wh+lRZQMwakaktRngJWuaI5RmJco7J6DX/56qlhq/cw061imhZU/3klEnUcpVWxto4Zb+3ttLZm/Loe/ipqv4m0EG4sV08zCb3WmW610jt+NqWhuuI8zv9SNym/Kxo5trpY72tmXqoklUhdOykN3tmn1k1KQraNxVzXyctKAAqnSOTyB2mx9M0xg3HEGtD1sISKLdZm65O0UsZnlSS0ezkBw4RYkl3SERcAXnicFYxS87xHZRW8ryVV2qbr9nsZ6uvp4LwP7mfNgbpiDYGOpwfh98pre+KJJ9eTd6yPfyMxOf3uJB3TQCNLkmxMzr5724pmkjEO7mPiSUFcfcGRGfq7+dJiqV20SKhaQyrZhdK4QcJOpiHHfFS1WFj8TUMnNX40AJTlU2saGumkaLGt27TGW3eS2iKa4LXlFVELHz+jufI7biO5pX5FIwCgo8NFtU1RU59v4/itm2g5HL6sVV0DMu/olJBRJHw06SqN9qjUlHv+IC0On6sVWEUN/f65CY6PR6VBaJY+Tj39DB92nmMI9Y5drHYbiyvmYZKtzFPo7gGfSkvtw1x+ZxqTzLtqMJgc/7jNax4aojYMZikekSVWhF9CmG0XeF+LXJV80hmey4RwB/vVGujskQo8slb33ktiV8ZlnJjz19XS8pmYJh5TWK7VmzZJlaLhAVoKK1dr7YGxXiEZLeOYuTlaNaOTinmcOUYi0/AIsaNYlHOOBdWnv+P2PfiHM16SjieeeHId8X74nniyBOVtN/X99uIKbN5ITK+ypIv3nSWmoGXKC/v0nTYhob7l0lTh+eeZs55fqOBgaYSAyp0Psud7UJo8ZOer2Thv0QSLi5nll3OGctStMEUlK/NpDte4OsCO9NE0DgnPe1bCXxcvqhl+pwCP3/seS18FCukWbN6mue7Lm8nPNkBiv6sQ5uwM3YdsAUAvnCanO5yl4N4t6wg0haW+wNwU16eyXM3fSQm1rd1CcG7Gr2WgMuLG5EjYLC295s/3qsuRkVDr5lsYegwIr35gSEtWmcegWEqYTY9qBuKwlLE2a3X0OBmIy5r2OGM62mm2B/MFsGvnnPNzlOXYUEFwrr2T4GKBMO4627X77+q1BEeXC+/9R0+96Gyrrl8LADh5giBpXQVduYkZdTkaVzcAAPrHeY4KyQ1Zs0WboEzGucaBArqLzau0LNfJf/gOAKC7n2zFmmrOZ/MmdQe6e7keHR1ch5iU54oFdR5V5RVOI5fXE0/je+LJEpS3VeOzocaia/47spDfbzqEBlyZSD6JsSUli21mTvnv6TiBsjLporpWsqfWNKx2xkQFNGrYToBlQHKhJ6Ku40jDBEPqMACi5eJMhA0pZ4jhwflxtUqKpeJPjmgxyyJgVOPKfOseGpS58jtT0HPn3drQ4vJZavGLl9i7vbhAQcrKEgJKrZ0CBklBy4Ctq7hjK8HEHzzxBACgROYVdlkubedphdz+AZKDVqxUjnzPLLX2ecl9KCmiVi1yVTRKFHI9bWlvZe5HRYUW5Lx4nqSWeclcW72ixdmWnGdYNikceVMSPWMrKyYa4zEz0jxk5217ec09akG1Sc7/pBTvzMnlw7e6Za0z5t77ubbf+Ld/BQBs2KyVbzqE618qQN323Wx0cvilp50xzaaophC8Csu5DrMxfXbGpeKOKdc+2qugb2E5Q4XD3XweTEWe3FwFB1NJPsNRsRJLS7mtvEZDfpPREaQzLtbQDcTT+J54sgTlbffxF75pFmrza32/kMBjwniBHA0xGWKQCRG5M/dyJFwVle+2SjgszxXym5xijbiRCYZb4vLm9GWrb2ycUpMH71TtsTQMlS1hr1Ep6xzKUYwgRyrFmJBUrlTuMccBgCPHmCF21z1sSGFCfj1X2p0xUxIu8pkQpqvAwYzkdo/1MZyWmeOxRyfUf55ror/ZWEffNL+A2iPjujv5Uhewt5++ZfV6JQmtWkbfNZnF80anhTTUo/3tD73C6/j5h9h6oVvCiSlX8YDychKYbKlrOO3qS58l61hUSmJWWppU1tarVdB6kZTW2+5hm6tBybUfn1TqbHE5LZWyCq59yxpq+hMnDzpjINbQCiFEDQ2o/59I8P4XSpuyyRjXsaFF8+DH5B5VVUujFrnnyXm9L/NSQj1Ywmfl6CltMPrKftb8+81f/n0AwPNPs2FKh6uxZjAs85B6gmVVtATrmrQ+QSgnB1nZi9Plnsb3xJMlKO84As+1EP+FmIBpmmlnVHuYJhmGVJNxkUmCkqPfLxqhokzol5NKMfVJO6uoaMiiEiHuWHocp1WWnDcplV5yQ/r+TAjVtUBIOqVh9cGGu6Ry78Ss7EeE+OK5C86YkOT4z4tVY6r8XDintN5AFq8xV2oQZBJq3XRc5LEapUrthFQbjgS0EnCnNG6obWCtt2GxEuZc5JjJKa5Dr1R6ec9vfMjZ1tdFOm7M4prnSNOLHJ+u1V233wkAGJRW2j1S1bapSSmmuSFe23Av8ZRQWMkncak/0NXL/atqqbl9WfowVFZzbXPzpSFmhmtfWaeRlCGxQlY1M/9+34ukK2/Zooh5VKr7PPZP/wsAULdCsZ+NO2lNrFjBtersZyRhy0bN56+TOgbTk1yzgwfY2uvOPfc4Y0xD0dP7mVRz7KdHnG0NpbQUOk4TM9nQxDoRpqIvAPQN8ryFxdT49U2MwFQsV4KZLy8If3BxP2lP43viyRIU74fviSdLUN4xpv6NSD0LtwXFxHVz9c0bLCAmvgn5AUBawJuEEKxN3rRpkAFoaDBbLMlolGabO5vQ9NMrKiC5p0jywZNzGrIrlUzAnjGar+3tCsoV53B8RJoxGCCuqbbBGXPlIk36MTGRSyvolhS7yoUPCg9+ZoqgUkFYwcV8KTk+N6b5+4CGEAEgSxqLdHaTk15Z3wgAKHPlsRdL7/ke6db71Bd+4Gzb+zDN+FdbWQI7L5vnNCW9AOCKgJEfeJQuwtwkQ3bdPcqDr5SwV0kpQTXTzRgAKgT4K6/j3ArF9erv1gKW5v6bZiGjEjLLdmWo1S2jiZ6WRz1PQMvCQnXB/vZv/5LXX0iQMy+k11FVSZM6EJLnSgC7tOuXMyG9E/vFlbMTPP8T3/ihM8Z0Rq4o4Xl3rt/pbBvs5L1uP003bXULwVPbVUZrZpLnqFzGfICW9eJqFCr4nJibg31duPxq8TS+J54sQbkpxTbd7ySjzRfznjJvqbQ0DUi7mgcYwM+0wEq6iAym2UaBkCfMZ9sVWkol+XY1IbeYECWKXd1yDcpoin1OxPkWLs5V4sploewGpCqNO2SYnCV4VhimZo2PEizs69FutzGhz4YbGgEAhw4QjGpuaXbGmNLQM9I5daBHw0/FBdS+finGOCaAXWmlargOab+0ZhOJTDMxuZ5ZtaA6BghCffjhjwAAfnpE23S1n2c1nOgY51q3nMDb6JBmkzWKFZOYopUUlopIXb1KLy4t5c2PFHBugVGtoZCTy/mPT1Obz6R47Gyf0qxrawgUDg5Q0zZJf/verk5njBREwug4jx3J570adZ0rEuZz0SYhtkCu3vO41AXwF3GupvKNz9U2LSkkr4gAuuvvIkHq1ecPOWNW1DP8NyNA4sXz551tu7ayLPepo8yoHJDM0DLXPdsiWZaFNYYmTZkc0TDtyMQo5hMegccTTzy5jrytGt/G1ZV1riXX0vwLCTy2+Ow5LgKPWgPSXMHSd5qxKoymtsVv97svX5JK0tIQ41o4QpYkxZh6AKZsUBqKJ5SV8S091sM3cSjb1VAT1B5Z4oO2X6BPV16kb/bKJmqtoGAV40PUZuH1WmvuouxXWkbtVeiySibGGLKsq2KSUNs5ofWWKeU2UszzVVRRY/qFvNQ3rPn40pcDBULDXbtGzz88Tp90UGoY5mURYwik9X7E48RIhmU+w1L9KJSnJbxTtpTFluYnm27Rrutm/QekD/2sHG9+Wsk54YgkC0lDj3GpJ1BZqdfqT0kev/jLPiEClZZWO2OKC4kn3LZrL8e6cJ0isUb6RLMuk4ScuTlXJSGxNjfuknLtsowrlmlDy+d/8izPW8R7dd999zrbzp2hdRUpkjqFUkd+ekbDzVm5vI7qcmIWbad5X1vbL+lcK8oxP+9pfE888eQ6ctMbavws+9zoLXWjbU6m7gJrI8t1+bbQXk17a8OCzfhcM5auHwY5zYjmN7RaAMiP8I0eCxNHmB9Ri8GQcmJSQK9pOd/eE9J0EQCGBOk3x6yTBJ5LF5Tks3otaaem2WNlpSa+lBTTNz53gZpg6w6p1daraLgtRJuUrFpFJbXf2JTOdVkDNf1+aUCx+15NXAlPEbfI5MgiSQuw6XGtVVcaJg5hNP4mSYxqWK4RjL/5678GAGwXTZ9MudZaNJfzjWjqAlej1LIKavZhIVbNSWvuimJNbukW6nLHZZJhli+jVj4hxCQA2HwLffIXnmbS0m179zrbDJEpX46ZG2J0ZXBIsYrCcq7Hc99iundZHtfzyAFtAloiUYk6aYU1MqtJOr1jnGNS6vFFJO25tEgjOfV1POa3v85EoimxXu97WIlVR0+eQTrhKg10A/E0vieeLEHxfvieeLIE5W0H967XO2/h52u5BOY7/zW2LZRrvdEWGkF+Wy8/bRsznqNMCW+n3rdrBgbcswU1NNltADA7QtMwLeBYSZGrqs0szd6M8NCN6V/kIueMDvZeNR+Tvx6LaQafT+axeQN7rReX6PmHhmnSbpBecX29JMxk5yjJZ/Va8sYHBTjMzaVbEnTx+QNCOGlcxay8dlfIsGo551Ql9cKbGglI/svf/7MzpmuGIcqNG2jij0rR0eKEhj57Bjk3A+llXK5YUnoYWtLsolDIRfFpDRlGxcTfupnA47FT5MiPuLLrujvo8kxP8b7WNdBkfvq0htoCFtdv5WqSYtxVgpZvagQArNlO/v6pcyQm5bhA28IAyU57ttJ1e2UfM/+62rWEdv0dzAidTXDOm1drdZ66FrpzwSDX/ODTzPU//upLzpiTJ/is1AvZ6vwZhlSrg+r6NETKke3zKvB44okn15FFaXzLsgoBfAHAOlBxfwrABQDfANAIoBPAh2zbnrjOIV5XbgjOXeez/xrfmeNc1YLLkIQWgHxXNfe4SrNf66waBjRhPJ8ceNxVKy4xTU2VbfPt3dOpNNTZQQJ3LfUM85y/QHquoYoCQEk5w0cDUob5soRrTHgLAOaEonrnXXsBKL0YAAZEi05M8FbEhMCzao1WnJkQMouhFxdIyGpoUAGvkRFaJ8UrCMYFInqHuvoJFPpFuczKfLZu1zLhba2cd90KqU4jNf9+9PRTzpgGOXa3VAla46rA09xES+PwSWax+YOSdZnWcN6ZU9S+2RHeu3EB3DJJpVCPDHYCAHbsvh8AcP78KQBAMFevJ1fKps+M8Tjzrso5kVwCbU9+bx8AYGCc9+Xuu3c7Ywp9tNjaTjB//oUfEeQzoWUAKC4h6FpQybHDkwroRuMEJS0hkY3N0GobHel0xpRFaLGdOsR6gNWFtBL6XUSggfOdSL7JLbT+DsBPbNteBWAjgDYAfwjgedu2mwE8L5898cST/wDyuhrfsqwCAHcA+CQA2LadAJCwLOthAHtl2L8C2AfgDxZzUt81/rcWKNyFoTdAdXDmWmMWUcfPwQ9krO3S8k6IzhzTRPVck/U5lXuFACQYQXWdEkauTFDrzE1K6G5W38BNy6jFJqSCrAnZ1VQogcfUTMuTRhJpCc/1u5o8ZPs5769/5asAgLpGrdh6/wMPAADOtJEUEhLKcNpVu2B4lOevquV+89K8w1gSABCb47xHpZ/7Xdu3O9sGRFsNDfbIX34+fUYtBuP3m0rA0QQtj3Ub1bdtv0SrYDLDdc12NbK8dIk+7K1SXfgHTzFJaGO5rtXINNfh1ZeoYTuv8Hg+W9Gc82dJof7QBz4GAPjJ018HAKxas8IZUyZaeGiQFtj5y+qbj8d5rx/5+U8BANokrDrao+G4cy/xvGda2XwkLpWEPvGLP++MsSX02dFBq+C+j9/nbLtymhbPCy88BwBYVsZ77s/Se7ZuLec7Ush7l0jQSmk9dtgZ09C4AdmBN8/HXwZgBMCXLMs6YVnWFyzLygVQYdu2aSA2CKDiWjtblvUZy7KOWpZ1dHx87FpDPPHEk7dZFvPDzwKwBcA/2ra9GcAcFpj1NlXnNXWubduP2ba91bbtrcXFJdca4oknnrzNshhwrxdAr23bJv7xbfCHP2RZVpVt2wOWZVUBGL7uEW4gxsRfOJHUNcC5hZK5xhDHnF9E0460+wjW1Y6EMfF97lqbMlmfmKZ+gRcvn9OiiNExmrQRS0xsV8nq2DRBuL/48z8HAKxcRlN71Wot49TZzmMNC4CWlkKUDVWVzhjTY66ziybphVbtq5eRbrIbNm8GANQ0EEB7+ZVXnTElEiLMCRO46rgsoJirIZy57r5eAk0HX9FzrN9KEO7KDIGl5VUsdWU45wCwspFjquoZPntxP7MM99yvoNjKZb8HAHjuBz8BcHXvvpgAXsOv0MUZHOJ6zF445YwZGOX4qmVkw9lxgnIN9er6VBfRDZsS96aoiGHBj/7SJ5wxR16kG3HsBB/xiirl2MflmDFhNQYkEf/kEWXlNUojDlsarvzCJ38OAJDMKOg6LQVAa2q5Hmdeuehsaz1NF2PbVjZTmerlfe3t1tJbA9W8ZwURPk8dZ+lm5RdrMdfC2AzSmTeJuWfb9iCAHsuyDOR6N4BzAH4AwKzeJwB8f1Fn9MQTT266LJbA8xsAvmZZVjaADgC/CL40vmlZ1qcBdAH40A32B6ANNa4F7vluAO6ZTY5OthZscI13QnavNxkoWYfHNAQek3lnDu7K8vv/2vvy4Dju68yvB5jBAINjcN83cfC+D1HUTVuSbVmWr1iO48S5vLVbSTbZZMuupCqb2lRtnHOzW9m4UkklTpzIkSVbd2RJpC6KEsWbBAESxEXc1wwGxwzmRO8f3+t+LVukmMQGoaBfFQsE+tfdv/718a7vfc8C7mRkW5Iz8qR1ssEcapT5MeLWp4Y0KOcR9pWD+4l772inZrpwUbXYlNTmW5o7WwKKpUEF+QxJd1yLaSa0oFnUI5Iu6+phoO03vv7bPE6OBn1yJTVUWU1tmJZlCM06NK4E43JWuF+BAyjilaBmmwTwJkYtQkslsOztoUYrLaOlYjUf6b+k3APJRWpTKx15174D9rZAkOcb6CeF9pHXWd32lY/caY/5wZGjnKuw+8TkZyKo6bhd0hzjwhnW2pcIe9KKo1HKqTPU9Lv3MZA4NqrrYIrpl4oz6Do5QgBRe71e6+vnXgMA3Hkv59/ULoxC4xokbKukFVG1gT8HhtRAzogVkZROwnc89BAAYPyKEqz2X2IQz7JY2rex8UvvoNZHTC4tIL3yo2no95ObevFN0zwHYM/7bLrvps7iiiuurCm5JZx7ztTdexNkP/p34Eehtj+Ricj/DZmJXa3niANYYQArW7SS5vLNz+pX15+0qKbpPwcLgva24UGmbaw+7HkCSlmYUxhqdRXhn5XCeTchPHTTDqrl4kJpMiGpvwpHiitb+PdCAkJ5483XAAAFjqq2pqYmAEBvH7XygrDkVNdrjXpU2o6tpHixwXwFGYVnaGHUbhFtnkWwUo9f4wCBXGqmReE3LCkTzryJCXvMQamKu3SSFs8LL7xgb1sUK6amkb5toTALvfmGMgFF5dhzUqN/cD957BIL6ltHZphJSgrLUBrUqh6fMvncfjvjDrOTjHW0NCvYye9vAgAshHmOHA/Xt7N1kz3mnVHGL0pruEaxDNezvEaD2bPztCIqM9T4Mw7rKsdLa6hAmJxGL9BK/Kkv/5I95hu/c0mOw3k0C6PRvju322MuXZmwod4fJC5k1xVX1qHckhZaTtZcC9SYuokofLb9871a2Snp9/nbe6C5jt8zab38QCE1wOIStbeRJkAi4PBtI4vUvlVl0hhzlFopa05ZZYxljjcgrDbjCqm0gDpjA/yiZ5ULc45HbZrFBWpau65aPs0rho4ZECugpp4xgrij40h6gdomKIU7x48Q4vm5zzxij5mXhpo956mhPdIQY+OmbfaYoBT1+BK0RqZ6ztnbCptoGbz7EucRk2j8gZ3qDf7pn3wDAPDIzzDCnVXFC9lyt/rGkQj9fb+PWvntFxXOa0GeEaf29a8wzRCJ6z3Lz+X6ZQlGKixR8LIaBVQlPHzC5pc51+27aRXEspV74OIy17bz0CcAABuqNCvw+mOM+AdMWnAlBbS2LA5AAGhoIjDr8gCvp30rQUpJB3S4pbKJxztyBgDQ2qjw5J5JAoBeHyCAJxolIOrX/9ev2GMO3v8wAMDIMPZSJanxl57V5p2tDU3IMdx6fFdcceU64r74rriyDmXNNNS4FZJMKo7en6EpmSX1zAlJ30QzmvYpKaZ55TXE7AzRtAvNqnnljdHE75Pe7zXVCrCYkjrvIklVTU7Q5M/NdVCACe1SZEGoq2tptlZUaF2/FXizfmb79RxWqnNB9n/gQZI6WlV7gKYIC6XnnMVSfrlHsfYbN9Fcffc4a9yzq5TQs6KAabzLY3QZHv6lBwAAA89pB9iSCo5PrtA0taoEVxzppoD0yrPIQ6NRXet77mfabnSGboQhQdbicl0HQ8BNzdIncH6OLkNBobpeI9Nc81pJXQ72DQEAhh77Z3tM4y5yF5RLunXZ0cjCSnmGJP22nOYcW6s1ENqcZs2FFQjOkUs8d1rdo9OnaOIfPHAXrz1b06srcTHfJaB7RIBE4+8O2GP80oG3sY5uxV9/85sAgBIHF0QoGr/pdJ6r8V1xZR3Kutb4ZSWaapuPMqhnEWgWCWX1onS2BYC5GaZS4vK97GxmoOrdy6opQ9OElqYT1MZjjiqubVs4finEc1WLVkwlVdNFwjyfjRESAsqSXIX+trYzMJSQeu8pR7ssq4NwhcBy3zlBqG5sSRtI7BDwR1EhtcVynCq/pka16egIwSfFkl7M9mv668oANXR5LcdHBzjnlo/ttMd8Rth5jr7D8z98YDcAwO9gruk+f0bOxTXbt0eDg7OiqZMpasO4aPcdW3bZY+JS9LUiqUu/VDQOXlMizHIJ9M0KP8GQEJt+5aHfsMfUbGUA8ZjAcO/bp7wCF4XAMyEt2fqkNdgDX1IIS14f9jYRPAAAIABJREFUX6OYsOv84MnneX29l+wxRhatxGwJxE4M6xzj0kQlJJq/QlibvvvYE/aYbdt5z194kZWIaUkXF1drIPKRz3wOf3LxH3Ez4mp8V1xZh7KuNX5sSbV5jjSSXBYGEwtG6nc07SjMZypn5CJ9r4jQSXtXtLlCgZ9f9JBJbZxKaJuveRnvE8puUwA48ajWwS8Jt96GFgI9FqQRpFVfDwCl5dTCRfn0ZSccYJByodrevovAjtwANXU0qiCjEeHPK9rCtOTkBGMVFr8fADQ20m/uukrAjS9PLY6xeeHPa6BmyljpyCm1XHyifRukYCY/l3PNC6rGj80z3dXSSEuo55JaTnt20CoZnub5g9JOyiooAoCM1SxznH9LSWHPg5940B4zN8/rnpmkBfHVX/wqAI3lAIBXiq6sJqZOXFffVVo3B3ewzdWwsCidOK7MSuOXaR21baT/HZqlVeF1nGP/beQzKA5yHeprVVN3nWZaNS0go4cfJGR3ZUVTjhcknSqGD3buJRQ55UhfZ3x+mJ6b0+WuxnfFlXUo61rjJ+P6RbWaTcYS1ELLUo5peDViLl2csDTN/5RL6e1cSiGiRkaaM/rFp/MqnHZFHPd4mp9tI83zOy2PafFt6+vom1qsOGPjqmGSghmurmM5an2LlpHeJvDTbC+/6VsD1LAXz56yx+QXCWOtNFjcK+w6Y2PKAxcMWgVJksGYU6skJn53tpDuWQ1G0wtq+filrRakhVVROc/Ze1yLdB771pMAgIdvvwMAUJhQPTQX4ZoGcnlfRqQ99oqjpdme7YTNpoVtt24Lsw2vHnvDHlMjEf9ysZLyBJh0rksLYHzCPbhrC4+XWNT7mVySuI7ESMqLmNnpOqOFVS1SiDQt3ItlEvG/7bA2IUmJBXj8LcY8Cgu06Kq1rQkAcPUSS7ItmHRenrYtD0/x2I2dZOI5eAezHma21o3nVufB43U1viuuuHIdcV98V1xZh7KuTf1AvprxSenV5pH663KpeAsYGtxbHKDZV+plGqxUUlNnh/vtMRPSQdbvo0nY3Kh97bNktWdnGVyziBH9eQo4KSrknMKhKdmHplyzo+ecFS60WIJKS8vtbcXSrbdbaJfflaYMmzcrNjw3wPNZwaeJKQbrAoVqfo5PEove3MBmFfmVWml2/uhzAIBNm1llOCTBxf07tVLsigBe6isZxBo5TQDR0eeP2mN+61e+zmtcpKszeFoDmK0tZCXKFhrsa0dZubdlg1bFRUI0f2PSc25aagYqG2rtMVYtyN5DdIH+6M/+lOtxUM3w2wu45j7B4y8vaCA0LS7fxMgQAMBrcKwnpUFbC2RU3cD7YArBaF6ppotDEQYpdx1gynMxqa5TGnQvw3N0tbq6OOu8PH0+P/2ZnwIAnL7IFGhI3Iq5JZ3r8HgOYovqCt1IXI3viivrUNa1xk+lNBjlMan9LD67jAS+5pccKao0xwQMpsie+BbBEktzQ/aY2bC0yTKpRe+682P2tktXqYUrKpg2C4eoBVY8Co5paaZmtzjvfKLx/Y6vf1jqz8Pz/OrPx7VxQ80EK9SCAj/dtIOAlxPHtR3T4XvvBQD09zItudmC555QiGmp7L97GzXUk/+izGpt9Qww1ZcyqNV1kcCXx/7m2/aY1sYmAEBTMy2ep558FgBw6OA99pg3j7ATb4usR22DUl57c7nW754jfPXAIc65zKOWTyCP+OTXj7Oqr3YDue9SXoWtnpcg3oUnHwcAFIl1dNaROvyoQIUTEnT1O6rq/MKZsCz8AB0bOcdXj2mwtKiO1xiRQOCFfvIcHHxALSBPDi2mpQXes9lxBXa1djYBAAaF1ntqhM9QTY4+F3mSDk0LM9OZkzx/bpE+Fx6fB6mkzv1G4mp8V1xZh7KuNX5uvoJSwtIAoziXX+bIHH3sZ/7hcXvMxCn6qfe1MZWyIKCQ0lKNA8xK08zGJsJApx3gmtIypnkSK9JIo5GAj4unL9hjFheYttsgLaTiknKcm9ZGjgkhyYtEabHkBjWl09TK/X7wMqGdHR3URvd+VFNDk8KCkyesOv2DjEs0b9A4QEpSSv391JgeB3trtdXm6xpjFRvqmgAAT7/1jj1m5AprzHdsl7Zh9UyrBXN1rXxCaRSTazzXrRDXQ3eSv+7zX/4yACAQFM6+U8rg0ztArZ0vFkNCfOupmPq9hx/5JPe7PAQAiEZpHdXmKZBoSFp4FRXzHJWlGs/Yvo338bFv0rq75x42wqisUP89I4SPeaKhMxZ7UVS173f+6e8AAFd6eI0H9ijbcFaGcykTfga/pJCbm9QCCsq2jg7eo7SkBy/0aGHUwsI8EgktMLqRuBrfFVfWoayyxjff07YK+FF2nPfd6zr7OPe1xtzoeNreWpojZvS4VlPKfD+/+qb8nB0ds8cMir/4xhC/qjUF1F4XzupXt6aFmq1IWl+PT6qmrt9A7RsVX3BB2mVv36sR5qkRwjczUhSSFO0eXVY/Pi0MRDWiaasdmuGccLQXCkPLtWFaKZ0dml147VX61ls307ePLyXkHBrzaBHffEQKTfKLVUMG8qjZUjLHcWEEynG0b7JANakYrRuPtL2+ePq4PaaxnpDfyQlpyeVoJBldobZs2Mwo/qTAct9wtIyqb6Km/8JD1OpmPudTVKvFRpAsyf4FsViE6sn5SD3xAuMXM+e5VjubW+xt0yHGTDZvY5bhyFEW4Dzw0GfsMU8/yzkV1YkVIHDv2Kz68WOX6fcvCpy366zy8j/yMFmKui8w5pJfQItqx+1b7DHpRU54SdiB+/p4n/fv1cKolUwK3zylrcpvJK7Gd8WVdSjui++KK+tQ1nVwb9kRCDEyNF+tKq46AYGU5yqoZTBjNdCgSRyeFQroKm1vZdW4L4m594VPf9ze9trbNAn33E7Cx5PvMhjW0KBsLvl5NOkGh2j25UibLmNZg2sFAsC5/S6muGJJTV9Zqb7iYqatlrJpcqdTuv8hwcaPS/OOgoCYqKYGCX0+PhqjMwx85RZocLB9E83es+cZlLQqGfMLNFi6aQPTbhOjNNENg2bvHXdrOi8sePzmDVzrvfF99rYpi3Jc+tNfuEhwz1Q0Yo959COPAgAyPs67uEKATI4KwNGLDDKOCRFmpdS65wQUNPVpcRXiAqq55uj6+7KY5CV+IVgN0x04e0ldjtYm1kxYbc/KBQiVdmD+872cU6FQafuz9NXLEkanHEkl5xdxblcuDdljnnnqKS6Hn+7Upk66IxdPalqxvq4KmZS6hDcSV+O74so6lHWt8a0vLAAsS214dIYapU7AKQGo9sgTVE17C7/w87O0DnLyNVI0NMJg0M9/lf3UJ6a1EcaGdmrKt9/hV9rnpTY7fV7Tee0NtBg6NpIHzqqd9+Wp5dG2kQGvQD5TPBdPaaAombEagTCIFZAGG1Y9PACkgtTwecL7trREzZRwBBDHhSFmUUAttVUbdB1qOcfCIf602p+9O6oVhDMTnHeNUJHHJdj3nW//rT2moYWpqT0HyUP3Mz+vjSwff5xp1KNPsaHmxQvU+M3tTfaY2t0MpIb7qYWPv8mg5UxYA6qLc7yf/ecZDNvcSphx2tGjLSRll9t2kwOguVKDg5/+1KcAAMdeYiOP4XFe1yYoFbkhfASX+2ldfPIhsvMYhr5en/vsFwEATz3zPQBAi6PdWCjMOeYXcq3aOrkuT3//OXtMhVgzrVK1OTLIYGFmWZmV+udDSMTddJ4rrrhyHVnXGt/JSGpx0lmNHK+9y3rruWFNMVnsqSUl1J6xKP3WWEI1ZXEx/dWrwubqL9QU2VSI5yiR+u88YakpKtR5vPoGi1huv41xgMUY929pVXBNsxSwHD9BPzOdUosjV6C9viy//KRW77mk4JgRacVttfKaGKGmTjqvI8g5FhRTq5/r0v3v/jhhyKXlTKdNSnuvn/tFbfkUHuf1X5L0XZ/Alb1eB3ffZWrhjdvIx3fsDYUVP3g/m1s88wwbWuzbQc6ASFzBOeEezvuxf/wWAGBB/P+paWUULhHmoK2yZsszLB76+COajpuVBqGnu3jPY2NqMcQk5mPBnP/+O4Qlnzqlc911kOlUXxHPZVi+uqN4qqiSMY9HhC13cUn9//bthPa+eYS1+keP8hkodMRMyoVDISXgoIxYKRPXtDHnxo5WZN1EehxwNb4rrqxLcV98V1xZh7KuTf1kRmuqC6R/uxlgAG/2Is3IyIhi7ZtqGNRbXKJJuSAoqrwCNelqmmmSR4WyuqBMl7hBAjN5Un+fI0SeCQd1V3MT0zRX+5jOs8w9w/GN7hUcfE83iSAbmhWVNzTAoFypBPAWpEa964J2ss0RNskxwdqHZujOLC1qJWJONk3yskrOefSaujymDEvGBc8vbk2HgzMgJ4/XNjTC+awIsWhRgVKR9Y9MvOc6qhs1gBiTOoSAj9ffc5Yptl/+na/aY579Ns1ur6SwSqSxSPOWHfaYUklDjl/mmoUmaeq/9oL2nLvrU+xL95G72Hyk75LyAoxM0UXp99A9CuTShaqt0etImAyorWTRZRub5Tk2fW6jPQZXuOYnpC7jU/9ZXY1v/wGvY1Zo1yNCl/7wx7Wy81of578wQ7equowuWDSkQdtsj4GbtPRdje+KK+tR1rXG9zioiMtLpQtqiF/rISE3zCwq8KVzD9No02GCQSwK6fmoWg4HGomvjgo4aPcuTfv09TIQYwXDJqSFVmm5gmMCEhi60k0NV1NDcE+3A1RS39AEANi7k7X2CUdwb1RA6FHpmru8yOtobVL8eUwYZqzYZpkEoTraVENNTFHDZ6TXfHxBmV2sLNVihMFKr2D0M9C1ikt7stsOMig3Im2ycnxaP74Q5XirvdXkhAbVwrM8dra0Vv7sw58GAPzOV3/OHpMl23ZvZyOOoDRBWXYEKa9IR+BysbJamklMuuwg7Xz9NQbqtu1jQLW2VslL9/0aqwRf+z5bblmsODMSJASAhI/3NZ3impfJPP7oF/7EHrN5M6v8PBBL6NiwvS0l1mFG2ox98kG2JEssKUuPR3iXbIYmYevZumOzPWZ0ZBgZt4WWK664cj1Z1xp/xcGblpDmBdEIv7L9QnVclq+12SvSziom7bEsUE00qVpsOS7AmQL62EOOdMvY2BAAYLCX8QOLZcdboX6a9SVuFcpsywJwVih6BXxSKmnBt94+aW+bk4qwqoOs9x6R9lJF+QpWWpR2UlkygaA0XmxsUh/bMOjLzi1xbLsjjvDid8l/t2kbrZtcPx+jkWFt8rh3J9Nnv/l//wAAUCm+9vSYVqwdfpBa/IpUru3cf8jeNi4ptaA0GLXgsHketSqiYoWUSaorMkcrwedX0NWunbQGlqQqzmrMuRTXWvkiobp+9mWm0aqCes+rchnraGnj9YSXOf+eIY0D1MlN27iblXJmlByCRY52YYMSY9i0kRr62e8+pdchjVYf+jjh3Xl+WgX9k0pFnk4JFbukLNMZWlRjM5q6rGysRva7WiF5I3E1viuurEO5KY1vGMavA/hFACaAiwC+AqAawHcAlAI4DeBnTNO8OcKvNSLZ2Xr5CWlYmCettKx2Vzu3apPG6Vl+ta1mFVY5f1WdRrOTGWrjYgGOTIypxs+kJfpcTA21Iqw2oVmF9ZriohUVUNPlC1NMc7P6nQND9A89UuiRH1BtniuZgkKBf0KaNF7p0QYSGzupvRZEQ8LD47z++uv2mMZGglLuuI2Wwz852kq3tdAyKBB/t1HYdTZuarLHfO1rbEpZJC2rC2SOZoVqU4tl6PIw/WWLgwAAIhFqNq/8LSTsxQ985F57TG83rbIsyVLkiaYfGFZN6ZEWU6VBrsewxFXqOrXWPSHW1PgMC4M6N2isI19YfYws3nOfRPWtBh0AEJUo/rj46AODPP+27bvtMbkCmQ5N02LIWtHw+ycfpKYvDHA9L3Ux8p/lUX+9pYnP2EA/NX2OHG/IwfBc11IPj+fHBOAxDKMWwK8C2GOa5hYAWQC+AOAbAP7MNM0NAOYA/MJNndEVV1y55XKzpn42gFyDVQd5ACYA3AvA6uP7LQCf+vFPzxVXXPlJyAea+qZpjhmG8ccAhgEsA3gJNO0jpmla0bFRALXXOYQtBgCvBzAd5khaAmYWnVTKsp8dqTark210kYG3gBA2Zjs6ha6ISRiUOuvlqIJRMkJJXCJBrFGpIptLF9tjdkhg61gvqZorm6Uev0E7yF7oYzODwnI2iQjNM/DVuFUr5/Iqae7VtPBv/lyttb9wikG4rCzOJ8vgz+kZJZAsLKWJbUhgKLeYqaElxzd6WVyUPTtJnXXxmgaKmtpodvf3M/0XnqVpu2WjVoNd7mZgaotQb00JIWhZlV7rstxaC/vfskVdjekETeIWL4EysQjN4ZI8Xc+iXCGglB56fUM8x2/+1h/YY149wfXYtI3u1FxIK8sqClkdWRTgfUz76Xrl+PUxiybpRlmZxuwc3vvKaqUiSyV5HVekcq6+Wcx4B3jLt8TreXBHEwCgv+dte1v7YXbeDS3STWvfzhqC80/omm/r4POQkjRiWRndgMjsrM5DrsNyPfbvV+rtPAFpLQmvQlaA1YEJRwCyuIruWekSn/Ouc6xErKtVlyMbCcDxTtxIbsbULwbwMIBmADUAAgAeuKmjc/9fNgzjlGEYp0Lh8Afv4IorrvzE5WaCe4cBDJqmOQMAhmF8D8DtAIKGYWSL1q8DMPZ+O5um+VcA/goAtm3daqZWTKQS+iUzDX57LPjq+0EOfaL9s60glgTFnLXHVmBoVrS735HSsXpXTS0ymFVQSW2yMqPBkwVpSRSSaqySIqbjXj+qAS+/QEJDAqnM8jF14vEoc40FDDl+nFVpzQLzBdTS8ElwsLGJ2uv+vdrP/doY52gBMUaEJacpR2mpKyqoEY4dI/DknnuU1ebiOVaYzYSYvjogfdnfflOrySySTK8c0wIyDQgsFACammkBjUswqqen1952572HASh0+LlnSUBZkK/3Y9dOsumcPUnt+ZWv/DIAwJ+nFWdx0ZB7D3COLz5zxN5WJpDj0mIGOV986SUAwL4dalUEi5nOzBW49ekztHIaG9U6WRBGok2bCL6SYkVkeR3WojxPVsq0rEzhuNY9a+/cIcfjNVrVkwAwPkRwkrWOd93NevyxEQ0yWvcsO4fPYnhOqwzfeIPdfRvE6gwJs5PhwOJc7uZab+7kmK7z78jc9dnrudyHeFyrQW8kN+PjDwM4YBhGnkEK2/sAdAN4FcBnZczPAnj6Ovu74oora0xuxsc/YRjGEwDOgP0az4Ia/HkA3zEM4/flb3/zQcfyeLIQyC1AwqNfJUO+ktbXNpOmNfAe6myBv1p+/LLUIhcUKvAlIF/9sLCZZPk1xZUlTDdW3Xmh/N5Rp9p4pp9f50yUxzYyXJqgo495bi41/sgFpluKaukT19XV2WPyZbwpcy1yaLg77rhDLohWSW09LY9z58/YY+JxadMlgJdlAeAsLip8M0fSPn6xgAaHh+xtRSXUlNY6HhPL447btYHDpYusrb92jQU0hRL7aG1V33hUADO1tW1yjQruicxzTkN9jE0cOEBY64G7D9tjkEuN/6lPUzd0nyG4Z3hE4xnlFbz+eYH+xhwWXLkFmJmlpbFxB4E4saSjsUct1/3sOWr67TuolWemFCTU0NwEAJiU69myTTgIplQbLy0RQLOwYKU3VYsGpOnI7BRTdmmTz8X4uAJnGpu5blYKMiWU6A72dkxOc3+fgHMCkq4FgI3Ci2A1+9i+VWIvExojiEd5/3uvMH33+c9/CQBw6qRapIXFQfh8ahneSG4qj2+a5u8C+N0f+vMAgH3vM9wVV1xZ47K6kN2MCSymYCQ1opojABXr47givZ+NbPVCMhl+CdPSlKFEWhwtLinfWEziBlb56rKjOURUyh2zpNllbZVo+nHVot//Fllc0tP8+k+EqPmzHRRmE3F+tTs3Efwxt8wxpsMXSwmIw2K8mRpRHrq6GhajTAhv2+gUNV5ZpUZmMzO8DqsIZPtuRrx7BxQIVJXH2ECWgEqiUWcxB2XHbmq/86dpTfzLS/9ijykpYqagqkrKhAO8B8WO1lEWH2GhsNLWZrSopedylxxHfGHRcG+/8pI9prubMYVZ0XRfevQ/AQAGHbDendLQMxSmZjvxrkbTg9LCPL+Alt/4NAPDeSvK4BMMUhs3t1DjXupmCe2OHdpkwor9WKxHAwM8f1u7Fi119/B62oUT8UqvgmI8UpHU0UbL5613yG+Y5cg6jU0ynlIpoJ4r/dKM1JFJCUtge2GRz2LGAcHO9lmNWdi0NL2Z25IJtW6ChbQQlmO818+/wLXO9uq7VFtfCRhqrdxIXMiuK66sQ3FffFdcWYeyqqZ+fDGKK6+ewNyCNkWokyq0OjG9CiVgt7yiNdXLYurnBRk4m5M65YCjKcKKNIwwBANtpDU4WFFKkzYgAI+hbprN/+d3f98eMzXMoFN1AcfGwzRtt21UrP61YZrtcXErgpJyCjhouq9eobkZmmTwp6JEu6qOjw4BAHx+mqtWgGje4bJUlxCTvXOHBHimmF6MLSsgKSDBvfA819FcUXPPIrNcmGeAq6mRbkHQ0Rm4+xJx+7c1MyU1MMQgn9/RjGHkGue6nOB+FZUajEonOJemepqypqzH8eNq6l+5yrRiiTT2OHWSLoc3R7kHersZJE0L1j4nW83fymq6HQ3NdDW6+wj2qXZUzllp1KgwIVnYf6vBB6BptPEJ6ULc2gRAA5sAUCtBQit1l5urazU+xudiZpruiCmAM48jcJcvDTTqJY04MPSqrEGfPSZHmn5YHXGdfAAxeZ63bCF7U78Qk2Z51K1JLnO/QD7dst37mAI9feYte0xkIfaeeocbiavxXXFlHcqqavxYZAFnnjmC0WlN6WzdvxcAkJ9NLVYs3WaX0vrVDktAwytaKxqjZsrPVS0kdGewYh3ehEbcApI+m5ti0ObZv/t7jnHAeiss/rsIwRNFAQaDuhzMNzv3MolhSIVWdSPhuFblFwAcn2LaKNuQL++KgpUKpbmFxUeXlmYdI+OKfQrm8LwWS09vP+dc52DQmZL+9lYFXtxhMWQk0njyHQbKgpIWdIJSOjsYDHvyaZZaHL6PPd+92aphDh6iRjl1joHI0nLVEZUlvI7QBDVagbR1mhy9bI8pF+BNUxPhrHNhCYx2VttjxsQqGp/i9QTz9RzbNnOOb77DdJWlISeSytLTtoHBuG01PEePBPeSSV3z1lauW5sE50JhBuJyHVba9Ayto8oqPnsDg9cc+3O//Hyu44wAmvwOq8AKJM8KRLeqSmDaplpQhVYTE3kGZqf1OtICOluO8hq3bOY54zHdf2KKz4PHw/ekt4/XcfDOO+0x4cgsvD69hzcSV+O74so6lFXV+CupNJbHZmEuqDZPTjO9MSzNDaMC48yv0hQXJK2xOEaftlQYVwbPK8R0STjiysQK8CfVxz87SK01dpVf8uQoNYwnqn5WTTHBOOMxftEtjVBRqbX29VIT/8IR+rK33U0f+dixo/YYj3zlcwSW689SZ/DQXQTRPP8C20LFJI7RsUHZbfKkOePLLxG++oUv/TQAICIgEwAYlZpyyeahsU4LVy4LQ2xZUIFHgNZ4A8DuPbRc7n/gIwCArou0aqoqqx178OC11UyZdZ3XJpHtzUxDjg1Sw56/RqskPDVkj2loagIAHHuN/u4D9z8CADhx/FV7zJ49jJ/Mz1LjjY1rqg8G86iTo4zH+KUwyWOoruqWYqP6eqZnA5ICzF1RuPblK4xnFAmTjwWEClRrQVI6zWfQamWVla37Hzp0NwDg9Bly93kE85t2pHBNsbKsVGFawFNbNms6b2mRluToCJ/TWEyttHJJo/ok1pEtxVvzc2oZb+zks5eQeFegkBbH2IQCkdo621yN74orrlxf3BffFVfWoayqqZ/lyUJRfhDBYjXjy4uYbpm5JsGLLAa+Wus1mJUvfeAiYooNdNGkKspVkkv/Cv8/fJrmX8CjpIMFWTTb09KPPSdFN2DJETy5Os1AVZ1g0/MEc1/dpKb+nAQZF5dpdpdspSmXeu0Fe4xFUeCVeoCqcg2qvXOMqZegBOWapffc0y88b4+57zD5TL70ZXaO3SBBqTfe1rRNjVQXVpQxxTN4RSvntkqn1b5eCbSJPxBwkG2ePc9uvQ2NXOPWTp7DmcZKLtPUzmR4HRatMwCsSOC16/wJAEB+Ds/R2qi1D2lBplm4c6uiMsvU1GNC0rrzQhjZVKWVd1MDNNEjsq2smtcczNF03tDQEABgYoxpuNZWpvMSCUVtLiU418J8aQwigbQ8x7VanKumIBBLSrRbbkaQe5YLOjfPZ+Dw4fvtMadOsbouvsxtl6Un4L13awXf2EifzI3z8Tlo3ybH6IIWC6IymeBxhoc1HXi5l+7YI5//HABgZJIB4eIyXbOxsTGkHCnZG4mr8V1xZR3Kqmp8T1YWcguLcM1Rp+yVyrCoVGa1tVKLBuIanBvpHQIAjE/wKzclqY24o5voqAAyWuqZ2tnYqoGVN15hQKk0yC/qhFSIJR0gn+UkNVRLO+u2c6T3/FJcv6CnTlFTfvKzDFSFRKuOTypZ5tAVWhzl+Qwm5WVpsMUjOOoFqW4rLub5ayu1uq9e0nZxASS9eoxMK3n5ClbK8fK2TYxyPayUHaB8AklZT6sWwqo5B4At29jkY0YsKKukO69Q17OihtbIK8/z/CuO+op8WRtDtLoVXIvMac1AWYWw0qSErFJ0TK7fuR4rcmzO1UqPAkCXWCVWqjRX1jEyr2Qu2wXkFBG68KtXJZBXqFqwRSoOR0f5zFmkpeGIBtcScm3hCC25w/c/bG87eYrAo9kZrlVtAy3A8koHW5FYgld7ee/vvYcptsWFOXtMOsWAX1pSjV5T70eBANEWIry2Ugn25eQo7j6Z5lo/8V2Snm7cwnqREkeadmx0DKmkq/FdccWfMP3dAAAOYklEQVSV68jq+vjZ2SgoK0WtR09rQQyLBCDRc4ZQT29KtfFl0aI5kqoICizVG9WvZkMRv8DzE/xqvtyrTRHbO6n9X/rBKwC0Km1iXL+OrUI5XSt91IelFrrOkWoLi2batJ2Vb28dY8otnlSfsq2d5zLEccw40j5Wmqi2ll/pCbuqS9NxVjesC+eZfguIVZM2dT062unLLs4x9Wg1vQSAmLAM7dhFbTgo/m9hsUKHc0WzGwIDnpMUV1mVpvOWhAOh9yrTaXfcttfeNjzI9J3XKxDqeV7/0JC2lVqIMqbQuZlrlRZGoYpqtW6ef5FpUb+kPq00JQBsLON8rbSqheAuL1Mf/9RJphgtdp1FufaxMa2IXFxiqm7bNlbsRYSRZ2RUz1VSyvvhyxVOxjG14K7K9Vvpu4oa3quzF87bY0IhHmvvXvLobd7MZ6jvqsZeeqQlWlU54weF5WoxhKV61GJ9Col1UVPj4Hko4loPSrVnfWMTAKDMEY94+52TSLo+viuuuHI9WVWNn06nMRcKvSfyWFIqXyzRaBn5eVr8aQB2d0evwB5zpH456vDTpqXuOzuH1sD+/QfsbTXCkDMkQJ6RMX6hPQ5W2EceZRR9Rr6+ndsJLnn7XW1P9Zawwn7mUbLKbN3GL/xTTzxuj9naTguhcwOj64NXh3QeFdQWs+ILWxmM3buUcdUUWHJKos8FhaL5Ao5mk8IUs7QkfACOwgyLDainhzxwsTS1cX1Huz0mIxyEVbX0zS0mm3y/QqCf/C7hvKVltALmwkpMUFnNa/MdYOZkPsy4QnhOfdK2jn2yP4+9uMx1raxyMBqdpdZsbqBvn52tcQQIfHgpQqukQp6TdNoBgy3ifH05nMf4GP14J9/ikhTA9A8Q7LUsDSo3bND1aOugv9wt7DbBYrUqyip53tkQtfChQ3wunvj+i/YYn/Do7d/Pa+66SLBP2MGyu7GNluDSIkFjgVxdh7gw505Ni7W2kzGYUETjGWMSxS+QgqAViU9dG1TLZevmXcg7qc/JjcTV+K64sg7FffFdcWUdyqqa+svRKM6fOIGqGk3bWOm3IunRblU99TgCI1ZP8qwsmpJpsSibOtVcGxI6q9pymu9vvaWAF6s+OybY/MZGpmTSkpYCgFyp8w56GEx68aWXeY4WBRJ98Ytf5H/E9Th/jibd7/3e/7THvPnqazz2ikUhpmbnvKTYgkU8V42sw/yiuiwQLLpPApkpwX03Nmo/t5hU41XX0Qwf61eqqAlJLSZTPFdNK9NXlY7A3Yh01E2keB2lwlcwHVGSyj4JalUWcv+yUgeOX1yLtg6avfX1NIdfeF5x+L5suigrWUxV5RRKZ15HGuy2u0jOefxN7ldQqI9jm3TiFW8GXq8EOVd0reob+OzMz9ONKJIAmFWDDwAVUn9wsYtUaK2tDAQmHZ2SLeruDumnNzGllXMRcatKBSgTmueEamr1OhqqaeL39RFwkys07KUOoFp0KS5/ExLVmKP7sdeq56dbNCfNOzw+Ta82iaswJ9caWeKznB9Q92z7jp3Ie1z3uZG4Gt8VV9ahrKrGNwwDOVkemCmtl37lFabdSiTNMbPAL1ppmX4tK6v51a6sIWyzXSCmvT1a/20RVl6UtImTFntuRhoUCODEorwOtnfYYzwCsb14iQE8K1i4b58SCedK3fmKUDxv7uBXuLxEQRT3SbOJVyR12NiilNVlZdJUQbT6yZM8V6VDC+Zn8xx1AlFNCw1pIqmVhDPSnTVbQCBVVVX2tgXhE2hqZcAwS7gD5hwVkXlSwdjQwHX1eWnldJ87a48J+Dhm2xZaVSUOUEz3Ba6xP0CLZVkqIaNx1WJFVVyTJWnwcMe9hK+OTqh1UiIWz56Dd8k6qPbK9loBL4GtZrituV3XarCf21bEAtu9j91pwyFleLKsgBpJI1YK4anhURrqMgkctsnzYKVZAWBeYMUJSeWOCFnorKPt2YzwEuzazpRhclnAOg7CWL+koGenebySUr2OcqlEtfgJaqTRSsbQ9ySxwnWsFYBXbS3HTIyrddJ3deDH2lDDFVdc+Q8mq6rxvT4vyuur4PVqAc2Kl9piOSXFC/J7PK7wz+kp+jwz00zXnHiT9e+FDs49K0VYJb5Y96Ur9ra9Un9ufR1rG5jGat+jfHoJ4Y2rrBAwRzZ90j6p+QaAbVvpZ/+Pr38NAFBUxPO3t2kcwKJj3thJX7LK4VufOMGilkiIaZricvrBkaimbQZPS8GJpAUbNtBiKHJw5qWFVnrwKq+xwK/aKxymxjc99NF9skZLK4okqmvhsWuFcWZkiKmi7rM99pg8g5bC7bcTgPPiM8o5cO991NAXu2khzAo0NeVIK+ZIAVVcQFYXeuhjV9Wp5TAqjDkTk7RgpsKamjooTSUPH/4oAODSaVoKlo8LAKakJcsreMwsabwamdM4gMXGU1LCeI7FbOT16bOzGOOY0hrGXk6ePm1viwj19xce/SkAQJ+0GUsk9PmsqOA6vnWcgKL772NryXBGIbvjAimvb6AFFYmoBbYg/n4sLdTwwhsJn6M1m6R3Uwt8VlqklVZ5tVrGb7z+pm2ZfJC4Gt8VV9ahrKrG9wfysHn/LpvNFABWlqj9B4SR1CpZjYbVd/EkJaIrX2/ra+UPONoFCVBlWWIElRUasY9IO+utu+iDzQkrazKhX92Ra6PyN/rSncLVVl+j/vM5gYjukEYJ1eKHT0wqZ96KzK5cmiu8+eYb9ra4RPUXYlZUntZFPKUaqkbKeBfCjLD3dnE+JRX6Zbc6PFlNQ0OT6m/Gl3htPgGxVNUxg7G5Wa2SuGiUSIjn7TkvpcxZmoG49+67AQBvv8VGHNNTqo2LiqiFM2nOrUBKdnMCuv+AwHorxH8tClLDWk1AAMAr3IOHP8oS1yu92kqsq5sWwo4O3rMVwcyG5tR/LymVMlaJ0C9LdsTv4NOz4NQxKdW1ml5u3qotwTaW8T5ODU/JHFXTFgtLcm4effTcAH92dw/bY/btZmu03Fw+u7Oz0vjUATtvaeEzExMLKBRSiyGW4ppU1jJLUVBCC2ZgTC2wphb69HV1XM+uXmaUrFJ1ADh090Hkv6KWzI3E1fiuuLIOxX3xXXFlHcqqmvo5fj8aN3Yg5TD3FoWtJChBuaUQTdwyR639svQbK5P+YaUS3FqKaYortUgTNwahzPapuWfVyxt+HnNihgGbx196xR6zeydTQVXlNPt8koq5eP6cPcZimEnEaNpeE3M2A01jVVXRJMsWhpVhR6VYuZjxrRukJnyO85h3AGdaKhlgsqrjDNA0DDo6A1uNOFYEtz7oAPC0ikmfLb3drMYR2QGt2U9JALK6kseMLrAOPT9Hcd5lwgbzz0/R1M/zqqvxh3/IRiT7DrJi7//95V8AAJJRNZHvvuMTAIDlOI+diNOsP3lBgVXzYbpa2zYxkFfq6N13pp+mfv4Ozmnndo4ZnFdWmpBg4a8NMsXW2cGgpc9ByBmTZyRH6NM7Ouj6+ByklFZf+yGrP6GDu8DiMZifY1BtUdzGYLGmi3uv8vwVAkKrrGewb9zRGTgsbpXfT9ehMKgp4CoZH1vhXMPSdbfMkebdtJ3B4olJBrjjKbouFZV6nOHRQSSTbjrPFVdcuY4YpqNr50/8ZIYxAyAKYPaDxq4xKcOHb87Ah3Pe7pz/fdJommb5Bw1a1RcfAAzDOGWa5p5VPem/Uz6McwY+nPN257w64pr6rriyDsV98V1xZR3KrXjx/+oWnPPfKx/GOQMfznm7c14FWXUf3xVXXLn14pr6rriyDmXVXnzDMB4wDOOKYRh9hmF8bbXO+68VwzDqDcN41TCMbsMwLhmG8Wvy9xLDMF42DOOq/Cz+oGOtthiGkWUYxlnDMJ6T35sNwzgha/7PhmHcXCvVVRLDMIKGYTxhGMZlwzB6DMO47UOyzr8uz0aXYRiPGYbhX+tr/cOyKi++YRhZAP4CwIMANgF41DCMTatx7n+DpAH8N9M0NwE4AOC/yFy/BuCIaZptAI7I72tNfg1Aj+P3bwD4M9M0NwCYA/ALt2RW15c/B/CiaZqdALaDc1/T62wYRi2AXwWwxzTNLQCyAHwBa3+t3yumaf7E/wG4DcAPHL9/HcDXV+PcP4a5Pw3gIwCuAKiWv1UDuHKr5/ZD86wDX5R7ATwHwABBJdnvdw9u9T8ARQAGIXEmx9/X+jrXAhgBUAJC3p8DcP9aXuv3+7dapr61WJaMyt/WtBiG0QRgJ4ATACpN07TA15MAKq+z262S/w3gvwOwGDdKAURM025Pu9bWvBnADIC/Fffkrw3DCGCNr7NpmmMA/hjAMIAJAPMATmNtr/WPiBvcu44YhpEP4EkA/9U0zQXnNpOf9TWTDjEM4xMApk3TPP2Bg9eOZAPYBeAvTdPcCUK532PWr7V1BgCJOTwMfrhqAAQAPHBLJ/VvkNV68ccA1Dt+r5O/rUkxDMMLvvT/aJrm9+TPU4ZhVMv2agDT19v/FsjtAD5pGMYQgO+A5v6fAwgahmFVYK61NR8FMGqa5gn5/QnwQ7CW1xkADgMYNE1zxjTNFIDvgeu/ltf6R2S1XvyTANok8ukDgyHPrNK5/1ViGIYB4G8A9Jim+aeOTc8A+Fn5/8+Cvv+aENM0v26aZp1pmk3g2h41TfOnAbwK4LMybK3NeRLAiGEYFtXxfQC6sYbXWWQYwAHDMPLkWbHmvWbX+n1lFYMiHwPQC6AfwG/f6uDGDeZ5CDQvLwA4J/8+BvrMRwBcBfAKgJJbPdfrzP9uAM/J/1sAvAugD8B3AeTc6vn90Fx3ADgla/0UgOIPwzoD+D0AlwF0AfgHADlrfa1/+J+L3HPFlXUobnDPFVfWobgvviuurENxX3xXXFmH4r74rriyDsV98V1xZR2K++K74so6FPfFd8WVdSjui++KK+tQ/j+29nzkjkMYggAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('shape of img:',img.shape)","execution_count":10,"outputs":[{"output_type":"stream","text":"shape of img: (96, 96, 3)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(rgb_img) #matplotlib color order RGB","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f9347b68550>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(type(train_labels_df.iloc[0,1]))\ntrain_labels_df.iloc[0,1]","execution_count":12,"outputs":[{"output_type":"stream","text":"<class 'numpy.int64'>\n","name":"stdout"},{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"0"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(type(train_labels_df.iloc[:,1]))\ntrain_labels_df.iloc[:,1]","execution_count":13,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.series.Series'>\n","name":"stdout"},{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"0         0\n1         1\n2         0\n3         0\n4         0\n5         0\n6         1\n7         1\n8         0\n9         0\n10        0\n11        1\n12        0\n13        0\n14        1\n15        0\n16        0\n17        1\n18        0\n19        1\n20        0\n21        0\n22        0\n23        1\n24        1\n25        0\n26        0\n27        0\n28        1\n29        1\n         ..\n219995    1\n219996    0\n219997    1\n219998    1\n219999    0\n220000    0\n220001    1\n220002    1\n220003    0\n220004    0\n220005    1\n220006    0\n220007    1\n220008    0\n220009    1\n220010    1\n220011    0\n220012    1\n220013    0\n220014    1\n220015    0\n220016    0\n220017    0\n220018    0\n220019    0\n220020    0\n220021    1\n220022    0\n220023    0\n220024    1\nName: label, Length: 220025, dtype: int64"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"No. of positive and negative examples"},{"metadata":{"trusted":true},"cell_type":"code","source":"total_images = train_labels_df.iloc[:,0].tolist()\nprint('Total no of images: ', len(total_images))\nnon_tumor_images = train_labels_df[train_labels_df.iloc[:,1] == 0]['id'].tolist()\nprint('No. of non-tumor images:',len(non_tumor_images))\ntumor_images = train_labels_df[train_labels_df.iloc[:,1] == 1]['id'].tolist()\nprint('No. of tumor images:',len(tumor_images))\n","execution_count":14,"outputs":[{"output_type":"stream","text":"Total no of images:  220025\nNo. of non-tumor images: 130908\nNo. of tumor images: 89117\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_labels_df['label'].value_counts()","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"0    130908\n1     89117\nName: label, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# '.tif' is not there at the end of image ids in train_label.csv\ntum_img = cv2.imread('../kaggle/input/train/' + tumor_images[550] + '.tif')\ntum_img_grey = cv2.imread('../kaggle/input/train/' + tumor_images[550] + '.tif', cv2.IMREAD_GRAYSCALE)\ntum_img = cv2.cvtColor(tum_img, cv2.COLOR_BGR2RGB)\n\nnon_tum_img = cv2.imread('../kaggle/input/train/' + non_tumor_images[250] + '.tif')\nnon_tum_img = cv2.cvtColor(non_tum_img, cv2.COLOR_BGR2RGB)\n\nplt.imshow(tum_img)","execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f9347acfac8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(tum_img_grey)","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"<matplotlib.image.AxesImage 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"tum_img_grey.shape","execution_count":18,"outputs":[{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"(96, 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"non_tum_img.shape","execution_count":20,"outputs":[{"output_type":"execute_result","execution_count":20,"data":{"text/plain":"(96, 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#AfterWork-  write functions given an 'img id' it shld return 0/1","execution_count":22,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Issue - training images ends with .tif, train_labels.csv 1st column id doesn't end with .tif"},{"metadata":{"trusted":true},"cell_type":"code","source":"print(images_list[0])\nprint(total_images[0])","execution_count":23,"outputs":[{"output_type":"stream","text":"437e629c0ad96457b71d4767761e09d6ef2d6fcb.tif\nf38a6374c348f90b587e046aac6079959adf3835\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Adding '.tif' at the end of each id in train_labels.csv"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_labels_df.iloc[:,0] = [train_labels_df.iloc[:,0][i] + '.tif' for i in range(len(train_labels_df.iloc[:,0]))]","execution_count":24,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_labels_df.head()","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"                                             id  label\n0  f38a6374c348f90b587e046aac6079959adf3835.tif      0\n1  c18f2d887b7ae4f6742ee445113fa1aef383ed77.tif      1\n2  755db6279dae599ebb4d39a9123cce439965282d.tif      0\n3  bc3f0c64fb968ff4a8bd33af6971ecae77c75e08.tif      0\n4  068aba587a4950175d04c680d38943fd488d6a9d.tif      0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>f38a6374c348f90b587e046aac6079959adf3835.tif</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>c18f2d887b7ae4f6742ee445113fa1aef383ed77.tif</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>755db6279dae599ebb4d39a9123cce439965282d.tif</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>bc3f0c64fb968ff4a8bd33af6971ecae77c75e08.tif</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>068aba587a4950175d04c680d38943fd488d6a9d.tif</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"new_df = train_labels_df.copy()\nnew_df.head()","execution_count":26,"outputs":[{"output_type":"execute_result","execution_count":26,"data":{"text/plain":"                                             id  label\n0  f38a6374c348f90b587e046aac6079959adf3835.tif      0\n1  c18f2d887b7ae4f6742ee445113fa1aef383ed77.tif      1\n2  755db6279dae599ebb4d39a9123cce439965282d.tif      0\n3  bc3f0c64fb968ff4a8bd33af6971ecae77c75e08.tif      0\n4  068aba587a4950175d04c680d38943fd488d6a9d.tif      0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>f38a6374c348f90b587e046aac6079959adf3835.tif</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>c18f2d887b7ae4f6742ee445113fa1aef383ed77.tif</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>755db6279dae599ebb4d39a9123cce439965282d.tif</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>bc3f0c64fb968ff4a8bd33af6971ecae77c75e08.tif</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>068aba587a4950175d04c680d38943fd488d6a9d.tif</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_images = new_df.iloc[:,0].tolist()","execution_count":27,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# column_names = [f'p{i}' for i in range(1, 48*48 +1)]\n# column_names[-1]\n# df1 = pd.DataFrame(columns = column_names)\n# df1.head()","execution_count":29,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# # df1['index'] = [i for i in range(0,220025)]\n# # df1['index'] = list(range(0,120025))\n# df1['index']\n# f(df1.head())\n# f(df1.tail())","execution_count":30,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"p = cv2.imread('../kaggle/input/train/' + train_images[0], cv2.IMREAD_GRAYSCALE)\nplt.imshow(p, cmap='gray')\np.shape","execution_count":31,"outputs":[{"output_type":"execute_result","execution_count":31,"data":{"text/plain":"(96, 96)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(p[31:79,31:79], cmap = 'gray')\np[31:79,31:79].shape","execution_count":32,"outputs":[{"output_type":"execute_result","execution_count":32,"data":{"text/plain":"(48, 48)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def crop(img):\n    image = cv2.imread(img, cv2.IMREAD_GRAYSCALE)\n    crop_img = image[31:79,31:79]\n    return crop_img\n    ","execution_count":33,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x = crop('../kaggle/input/train/'+train_images[0])\nprint('shape of x is:', x.shape)","execution_count":34,"outputs":[{"output_type":"stream","text":"shape of x is: (48, 48)\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"So, we need to convert the shape fr_m (48,48) to (48,48,1)"},{"metadata":{"trusted":true},"cell_type":"code","source":"from numpy import newaxis\ndef crop_image(img):\n    image = cv2.imread(img, cv2.IMREAD_GRAYSCALE)\n    crop_img = image[31:79,31:79]\n    crop_img = crop_img[:,:,newaxis]    \n    return crop_img","execution_count":35,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"eg = crop_image('../kaggle/input/train/' + train_images[0])\nprint('shape of this image is:', eg.shape)\neg","execution_count":36,"outputs":[{"output_type":"stream","text":"shape of this image is: (48, 48, 1)\n","name":"stdout"},{"output_type":"execute_result","execution_count":36,"data":{"text/plain":"array([[[244],\n        [247],\n        [247],\n        ...,\n        [253],\n        [243],\n        [241]],\n\n       [[248],\n        [245],\n        [246],\n        ...,\n        [247],\n        [245],\n        [242]],\n\n       [[247],\n        [245],\n        [246],\n        ...,\n        [248],\n        [247],\n        [244]],\n\n       ...,\n\n       [[242],\n        [250],\n        [216],\n        ...,\n        [245],\n        [245],\n        [246]],\n\n       [[244],\n        [240],\n        [243],\n        ...,\n        [245],\n        [245],\n        [245]],\n\n       [[186],\n        [211],\n        [ 97],\n        ...,\n        [244],\n        [245],\n        [245]]], dtype=uint8)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.models import Sequential\nfrom keras.layers import Convolution2D\nfrom keras.layers import MaxPooling2D\nfrom keras.layers import Flatten\nfrom keras.layers import Dense\nfrom keras.layers import Dropout","execution_count":37,"outputs":[{"output_type":"stream","text":"Using TensorFlow backend.\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nclassifier = Sequential()\nclassifier.add(Convolution2D(32,3,3, input_shape = (48,48,1), activation = 'relu'))\nclassifier.add(MaxPooling2D(pool_size=(2,2)))\nclassifier.add(Convolution2D(64,3,3, activation = 'relu'))\nclassifier.add(MaxPooling2D(pool_size=(2,2)))\nclassifier.add(Convolution2D(128,3,3, activation = 'relu'))\nclassifier.add(MaxPooling2D(pool_size=(2,2)))\nclassifier.add(Convolution2D(256,3,3, activation = 'relu'))\nclassifier.add(MaxPooling2D(pool_size=(2,2)))\nclassifier.add(Flatten())\nclassifier.add(Dense(output_dim = 256, activation = 'relu'))\nclassifier.add(Dense(output_dim = 128, activation = 'relu'))\nclassifier.add(Dense(output_dim = 64, activation = 'relu'))\nclassifier.add(Dense(output_dim = 1, activation = 'sigmoid'))\n\nclassifier.compile(optimizer='adam', loss = 'binary_crossentropy', metrics=['accuracy'])\n","execution_count":38,"outputs":[{"output_type":"stream","text":"WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nColocations handled automatically by placer.\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), input_shape=(48, 48, 1..., activation=\"relu\")`\n  This is separate from the ipykernel package so we can avoid doing imports until\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:5: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\")`\n  \"\"\"\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\")`\n  import sys\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation=\"relu\")`\n  if __name__ == '__main__':\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:12: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=256)`\n  if sys.path[0] == '':\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:13: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=128)`\n  del sys.path[0]\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:14: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=64)`\n  \n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=1)`\n  from ipykernel import kernelapp as app\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# def process(img):\n#     img = cv2.imread(img, cv2.IMREAD_GRAYSCALE)\n#     img.resize((img.shape[0]*img.shape[1]))\n# #     return img","execution_count":39,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# x = process('../kaggle//input/train/' + train_images[0])\n# print('x from (96,96) shape to (96*96): ',x)","execution_count":40,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# for i in range(220025):\n#     row = process('../input/train/' + train_images[i])\n#     df1.loc[i] = row","execution_count":41,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.utils import shuffle\nshuffled_data =shuffle(new_df)","execution_count":42,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"shuffled_data.head()","execution_count":43,"outputs":[{"output_type":"execute_result","execution_count":43,"data":{"text/plain":"                                                  id  label\n33062   c79b186dcadf1fc1708cefabcbd03dcbae0b306c.tif      1\n208200  c01b8da68b2c1888f0e99b718a2b913e17f6aa37.tif      0\n2484    a6849487b033f18b1ddefbcd88b524523fd3cfa2.tif      0\n38379   af5e5f5cc53e0a897016b72c35c62671d0067ce3.tif      1\n145104  2863ff095743791703c8f5c270f3bb4ab40d7787.tif      1","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>33062</th>\n      <td>c79b186dcadf1fc1708cefabcbd03dcbae0b306c.tif</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>208200</th>\n      <td>c01b8da68b2c1888f0e99b718a2b913e17f6aa37.tif</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2484</th>\n      <td>a6849487b033f18b1ddefbcd88b524523fd3cfa2.tif</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>38379</th>\n      <td>af5e5f5cc53e0a897016b72c35c62671d0067ce3.tif</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>145104</th>\n      <td>2863ff095743791703c8f5c270f3bb4ab40d7787.tif</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(shuffled_data)","execution_count":44,"outputs":[{"output_type":"execute_result","execution_count":44,"data":{"text/plain":"220025"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"features = [crop_image('../kaggle/input/train/' + i) for i in  shuffled_data.iloc[:,0].tolist()]\nprint('Input features is a list containing ' + str(len(features)) + ' arrays')","execution_count":45,"outputs":[{"output_type":"stream","text":"Input features is a list containing 220025 arrays\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('shape of each example is:',features[0].shape)","execution_count":46,"outputs":[{"output_type":"stream","text":"shape of each example is: (48, 48, 1)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"labels = shuffled_data.iloc[:,1].tolist()\nprint(\"labels is a list of ouput labels containing 0's and 1's\")","execution_count":47,"outputs":[{"output_type":"stream","text":"labels is a list of ouput labels containing 0's and 1's\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nx_train, x_val, y_train, y_val = train_test_split(features, labels, test_size=0.3)","execution_count":48,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Length of x_train', len(x_train))\nprint('Length of y_train', len(y_train))\nprint('Length of x_val', len(x_val))\nprint('Length of y_val', len(y_val))","execution_count":49,"outputs":[{"output_type":"stream","text":"Length of x_train 154017\nLength of y_train 154017\nLength of x_val 66008\nLength of y_val 66008\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('No. of tumor examples in y_train =', sum(y_train))\nprint('No. of non-tumor examples in y_train =', len(y_train) - sum(y_train))\nprint('No. of tumor examples in y_val =', sum(y_val))\nprint('No. of non-tumor examples in y_val =', len(y_val) - sum(y_val))","execution_count":50,"outputs":[{"output_type":"stream","text":"No. of tumor examples in y_train = 62489\nNo. of non-tumor examples in y_train = 91528\nNo. of tumor examples in y_val = 26628\nNo. of non-tumor examples in y_val = 39380\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('type of x_train is:', type(x_train))\nprint('type of y_train is:', type(y_train))\nprint('type of x_val is:', type(x_val))\nprint('type of y_val is:', type(y_val))","execution_count":51,"outputs":[{"output_type":"stream","text":"type of x_train is: <class 'list'>\ntype of y_train is: <class 'list'>\ntype of x_val is: <class 'list'>\ntype of y_val is: <class 'list'>\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"But shape of data should be (examples, height, width, channels) which means its an array.\n\nList doesn't have any shape. It only has length. \n\nSo, we need to convert list into array."},{"metadata":{},"cell_type":"markdown","source":"Input features shape should be (examples, height, width, channels). For example (220025, 48, 48, 1)"},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train = np.array(x_train)/255\nx_val = np.array(x_val)/255","execution_count":52,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train","execution_count":53,"outputs":[{"output_type":"execute_result","execution_count":53,"data":{"text/plain":"array([[[[0.96470588],\n         [0.92941176],\n         [0.92941176],\n         ...,\n         [0.9254902 ],\n         [0.9254902 ],\n         [0.9254902 ]],\n\n        [[0.8745098 ],\n         [0.94901961],\n         [0.92941176],\n         ...,\n         [0.9254902 ],\n         [0.9254902 ],\n         [0.9254902 ]],\n\n        [[0.84313725],\n         [0.9372549 ],\n         [0.91764706],\n         ...,\n         [0.9254902 ],\n         [0.9254902 ],\n         [0.9254902 ]],\n\n        ...,\n\n        [[0.91764706],\n         [0.91372549],\n         [0.91372549],\n         ...,\n         [0.93333333],\n         [0.9372549 ],\n         [0.92156863]],\n\n        [[0.91764706],\n         [0.91372549],\n         [0.91372549],\n         ...,\n         [0.91372549],\n         [0.9254902 ],\n         [0.93333333]],\n\n        [[0.91372549],\n         [0.90196078],\n         [0.90588235],\n         ...,\n         [0.90588235],\n         [0.91372549],\n         [0.92156863]]],\n\n\n       [[[0.30980392],\n         [0.40784314],\n         [0.57647059],\n         ...,\n         [0.28235294],\n         [0.45098039],\n         [0.59215686]],\n\n        [[0.14901961],\n         [0.20784314],\n         [0.43921569],\n         ...,\n         [0.31372549],\n         [0.34509804],\n         [0.37254902]],\n\n        [[0.54509804],\n         [0.34901961],\n         [0.35294118],\n         ...,\n         [0.29019608],\n         [0.54117647],\n         [0.60784314]],\n\n        ...,\n\n        [[0.63137255],\n         [0.5372549 ],\n         [0.71372549],\n         ...,\n         [0.73333333],\n         [0.97647059],\n         [0.9372549 ]],\n\n        [[0.62745098],\n         [0.64313725],\n         [0.75686275],\n         ...,\n         [0.84705882],\n         [0.82745098],\n         [0.92941176]],\n\n        [[0.62352941],\n         [0.67058824],\n         [0.68235294],\n         ...,\n         [0.81176471],\n         [0.69411765],\n         [0.77647059]]],\n\n\n       [[[0.19607843],\n         [0.58039216],\n         [0.58039216],\n         ...,\n         [0.45882353],\n         [0.96862745],\n         [0.95294118]],\n\n        [[0.39215686],\n         [0.50196078],\n         [0.58039216],\n         ...,\n         [0.32941176],\n         [0.34901961],\n         [0.76078431]],\n\n        [[0.81960784],\n         [0.58039216],\n         [0.07843137],\n         ...,\n         [0.15294118],\n         [0.16862745],\n         [0.10588235]],\n\n        ...,\n\n        [[0.81568627],\n         [0.70588235],\n         [0.48235294],\n         ...,\n         [0.34509804],\n         [0.59607843],\n         [0.9372549 ]],\n\n        [[0.8745098 ],\n         [0.78039216],\n         [0.96470588],\n         ...,\n         [0.23137255],\n         [0.20392157],\n         [0.47843137]],\n\n        [[0.38039216],\n         [0.05882353],\n         [0.60392157],\n         ...,\n         [0.39607843],\n         [0.30588235],\n         [0.38431373]]],\n\n\n       ...,\n\n\n       [[[0.86666667],\n         [0.87058824],\n         [0.87058824],\n         ...,\n         [0.8745098 ],\n         [0.8745098 ],\n         [0.8745098 ]],\n\n        [[0.87843137],\n         [0.8745098 ],\n         [0.8745098 ],\n         ...,\n         [0.8745098 ],\n         [0.8745098 ],\n         [0.8745098 ]],\n\n        [[0.8745098 ],\n         [0.8745098 ],\n         [0.8745098 ],\n         ...,\n         [0.8745098 ],\n         [0.8745098 ],\n         [0.8745098 ]],\n\n        ...,\n\n        [[0.79607843],\n         [0.85098039],\n         [0.85882353],\n         ...,\n         [0.85490196],\n         [0.85490196],\n         [0.85490196]],\n\n        [[0.85098039],\n         [0.82352941],\n         [0.85490196],\n         ...,\n         [0.85882353],\n         [0.85882353],\n         [0.85882353]],\n\n        [[0.83921569],\n         [0.89411765],\n         [0.83529412],\n         ...,\n         [0.86666667],\n         [0.86666667],\n         [0.86666667]]],\n\n\n       [[[0.94509804],\n         [0.57647059],\n         [0.78039216],\n         ...,\n         [0.96470588],\n         [0.69803922],\n         [0.74509804]],\n\n        [[0.7254902 ],\n         [0.85882353],\n         [0.90196078],\n         ...,\n         [0.69411765],\n         [0.98431373],\n         [0.61176471]],\n\n        [[0.81960784],\n         [0.96862745],\n         [0.67058824],\n         ...,\n         [0.68627451],\n         [0.96078431],\n         [0.7372549 ]],\n\n        ...,\n\n        [[0.4627451 ],\n         [0.70980392],\n         [0.82352941],\n         ...,\n         [0.99607843],\n         [0.97254902],\n         [0.99215686]],\n\n        [[0.30588235],\n         [0.74117647],\n         [0.76470588],\n         ...,\n         [0.8       ],\n         [0.94509804],\n         [0.99215686]],\n\n        [[0.6745098 ],\n         [0.60784314],\n         [0.83529412],\n         ...,\n         [0.78431373],\n         [0.81568627],\n         [0.8627451 ]]],\n\n\n       [[[0.8627451 ],\n         [0.98039216],\n         [0.93333333],\n         ...,\n         [0.58431373],\n         [0.60784314],\n         [0.43921569]],\n\n        [[0.68627451],\n         [0.83921569],\n         [0.64313725],\n         ...,\n         [0.54901961],\n         [0.42745098],\n         [0.58823529]],\n\n        [[0.50980392],\n         [0.91372549],\n         [0.89411765],\n         ...,\n         [0.48235294],\n         [0.50980392],\n         [0.67058824]],\n\n        ...,\n\n        [[0.70588235],\n         [0.89019608],\n         [0.75686275],\n         ...,\n         [0.9372549 ],\n         [0.98431373],\n         [0.72941176]],\n\n        [[0.83529412],\n         [0.67058824],\n         [0.92156863],\n         ...,\n         [0.97647059],\n         [0.92156863],\n         [0.69411765]],\n\n        [[0.82745098],\n         [0.74901961],\n         [0.6627451 ],\n         ...,\n         [0.96862745],\n         [0.83137255],\n         [0.62352941]]]])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('shape of x_train is:', x_train.shape)\nprint('shape of x_val is:', x_val.shape)","execution_count":54,"outputs":[{"output_type":"stream","text":"shape of x_train is: (154017, 48, 48, 1)\nshape of x_val is: (66008, 48, 48, 1)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nfrom keras.preprocessing.image import ImageDataGenerator\n\ndatagen = ImageDataGenerator(shear_range = 0.2, zoom_range = 0.2,\n                   horizontal_flip = True)\nclassifier.fit_generator(datagen.flow(x_train, y_train, batch_size=20),\n                    steps_per_epoch=len(x_train) / 20, epochs = 70)","execution_count":null,"outputs":[{"output_type":"stream","text":"WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.cast instead.\nEpoch 1/70\n7701/7700 [==============================] - 96s 12ms/step - loss: 0.5129 - acc: 0.7562\nEpoch 2/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4875 - acc: 0.7739\nEpoch 3/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4773 - acc: 0.7806\nEpoch 4/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4701 - acc: 0.7850\nEpoch 5/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4644 - acc: 0.7880\nEpoch 6/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4589 - acc: 0.7903\nEpoch 7/70\n7701/7700 [==============================] - 92s 12ms/step - loss: 0.4566 - acc: 0.7924\nEpoch 8/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4535 - acc: 0.7938\nEpoch 9/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4493 - acc: 0.7961\nEpoch 10/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4472 - acc: 0.7980\nEpoch 11/70\n7701/7700 [==============================] - 92s 12ms/step - loss: 0.4457 - acc: 0.7975\nEpoch 12/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4416 - acc: 0.8002\nEpoch 13/70\n7701/7700 [==============================] - 92s 12ms/step - loss: 0.4401 - acc: 0.8006\nEpoch 14/70\n7701/7700 [==============================] - 92s 12ms/step - loss: 0.4386 - acc: 0.8025\nEpoch 15/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4382 - acc: 0.8014\nEpoch 16/70\n7701/7700 [==============================] - 92s 12ms/step - loss: 0.4352 - acc: 0.8035\nEpoch 17/70\n7701/7700 [==============================] - 92s 12ms/step - loss: 0.4355 - acc: 0.8031\nEpoch 18/70\n7701/7700 [==============================] - 92s 12ms/step - loss: 0.4330 - acc: 0.8055\nEpoch 19/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4314 - acc: 0.8072\nEpoch 20/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4301 - acc: 0.8068\nEpoch 21/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4296 - acc: 0.8072\nEpoch 22/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4286 - acc: 0.8072\nEpoch 23/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4276 - acc: 0.8083\nEpoch 24/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4263 - acc: 0.8095\nEpoch 25/70\n7701/7700 [==============================] - 95s 12ms/step - loss: 0.4237 - acc: 0.8105\nEpoch 26/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4249 - acc: 0.8093\nEpoch 27/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4219 - acc: 0.8110\nEpoch 28/70\n7701/7700 [==============================] - 92s 12ms/step - loss: 0.4217 - acc: 0.8118\nEpoch 29/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4206 - acc: 0.8120\nEpoch 30/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4217 - acc: 0.8116\nEpoch 31/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4180 - acc: 0.8139\nEpoch 32/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4200 - acc: 0.8124\nEpoch 33/70\n7701/7700 [==============================] - 93s 12ms/step - loss: 0.4180 - acc: 0.8132\nEpoch 34/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4178 - acc: 0.8126\nEpoch 35/70\n7701/7700 [==============================] - 95s 12ms/step - loss: 0.4171 - acc: 0.8140\nEpoch 36/70\n7701/7700 [==============================] - 95s 12ms/step - loss: 0.4176 - acc: 0.8143\nEpoch 37/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4159 - acc: 0.8147\nEpoch 38/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4190 - acc: 0.8135\nEpoch 39/70\n7701/7700 [==============================] - 95s 12ms/step - loss: 0.4175 - acc: 0.8137\nEpoch 40/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4172 - acc: 0.8139\nEpoch 41/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4159 - acc: 0.8142\nEpoch 42/70\n7701/7700 [==============================] - 95s 12ms/step - loss: 0.4145 - acc: 0.8158\nEpoch 43/70\n7701/7700 [==============================] - 95s 12ms/step - loss: 0.4149 - acc: 0.8157\nEpoch 44/70\n7701/7700 [==============================] - 95s 12ms/step - loss: 0.4149 - acc: 0.8151\nEpoch 45/70\n7701/7700 [==============================] - 95s 12ms/step - loss: 0.4164 - acc: 0.8160\nEpoch 46/70\n7701/7700 [==============================] - 95s 12ms/step - loss: 0.4136 - acc: 0.8158\nEpoch 47/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4129 - acc: 0.8164\nEpoch 48/70\n7701/7700 [==============================] - 95s 12ms/step - loss: 0.4114 - acc: 0.8170\nEpoch 49/70\n7701/7700 [==============================] - 94s 12ms/step - loss: 0.4131 - acc: 0.8176\nEpoch 50/70\n7701/7700 [==============================] - 95s 12ms/step - loss: 0.4123 - acc: 0.8161\nEpoch 51/70\n6301/7700 [=======================>......] - ETA: 17s - loss: 0.4121 - acc: 0.8177","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"So, the training accuracy is_ : 81%"},{"metadata":{"trusted":true},"cell_type":"markdown","source":"70 epochs:\n    Epoch 1/70\n7701/7700 [==============================] - 82s 11ms/step - loss: 0.5062 - acc: 0.7616\nEpoch 2/70\n7701/7700 [==============================] - 77s 10ms/step - loss: 0.4801 - acc: 0.7792\nEpoch 3/70\n7701/7700 [==============================] - 76s 10ms/step - loss: 0.4716 - acc: 0.7844\nEpoch 4/70\n7701/7700 [==============================] - 75s 10ms/step - loss: 0.4653 - acc: 0.7875\nEpoch 5/70\n7701/7700 [==============================] - 75s 10ms/step - loss: 0.4597 - acc: 0.7914\nEpoch 6/70\n7701/7700 [==============================] - 75s 10ms/step - loss: 0.4532 - acc: 0.7930\nEpoch 7/70\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4507 - acc: 0.7965\nEpoch 8/70\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4478 - acc: 0.7970\nEpoch 9/70\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4443 - acc: 0.7986\nEpoch 10/70\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4417 - acc: 0.8000\nEpoch 11/70\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4396 - acc: 0.8020\nEpoch 12/70\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4368 - acc: 0.8027\nEpoch 13/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4355 - acc: 0.8034\nEpoch 14/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4331 - acc: 0.8049\nEpoch 15/70\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4326 - acc: 0.8062\nEpoch 16/70\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4308 - acc: 0.8074\nEpoch 17/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4284 - acc: 0.8081\nEpoch 18/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4275 - acc: 0.8079\nEpoch 19/70\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4263 - acc: 0.8102\nEpoch 20/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4253 - acc: 0.8097\nEpoch 21/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4237 - acc: 0.8102\nEpoch 22/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4256 - acc: 0.8103\nEpoch 23/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4219 - acc: 0.8122\nEpoch 24/70\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4224 - acc: 0.8116\nEpoch 25/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4212 - acc: 0.8129\nEpoch 26/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4191 - acc: 0.8143\nEpoch 27/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4233 - acc: 0.8120\nEpoch 28/70\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4212 - acc: 0.8130\nEpoch 29/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4216 - acc: 0.8128\nEpoch 30/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4172 - acc: 0.8150\nEpoch 31/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4169 - acc: 0.8152\nEpoch 32/70\n7701/7700 [==============================] - 73s 10ms/step - loss: 0.4172 - acc: 0.8148\nEpoch 33/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4166 - acc: 0.8150\nEpoch 34/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4148 - acc: 0.8159\nEpoch 35/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4176 - acc: 0.8140\nEpoch 36/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4148 - acc: 0.8172\nEpoch 37/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4170 - acc: 0.8172\nEpoch 38/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4126 - acc: 0.8180\nEpoch 39/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4140 - acc: 0.8167\nEpoch 40/70\n7701/7700 [==============================] - 70s 9ms/step - loss: 0.4122 - acc: 0.8184\nEpoch 41/70\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4107 - acc: 0.8188\nEpoch 42/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4123 - acc: 0.8179\nEpoch 43/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4115 - acc: 0.8192\nEpoch 44/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4089 - acc: 0.8207\nEpoch 45/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4077 - acc: 0.8213\nEpoch 46/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4159 - acc: 0.8165\nEpoch 47/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4083 - acc: 0.8201\nEpoch 48/70\n7701/7700 [==============================] - 70s 9ms/step - loss: 0.4160 - acc: 0.8166\nEpoch 49/70\n7701/7700 [==============================] - 70s 9ms/step - loss: 0.4075 - acc: 0.8206\nEpoch 50/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4052 - acc: 0.8214\nEpoch 51/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4040 - acc: 0.8219\nEpoch 52/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4112 - acc: 0.8196\nEpoch 53/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4110 - acc: 0.8212\nEpoch 54/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4087 - acc: 0.8188\nEpoch 55/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4108 - acc: 0.8180\nEpoch 56/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4030 - acc: 0.8228\nEpoch 57/70\n7701/7700 [==============================] - 69s 9ms/step - loss: 0.4039 - acc: 0.8220\nEpoch 58/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4001 - acc: 0.8241\nEpoch 59/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.3999 - acc: 0.8239\nEpoch 60/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4069 - acc: 0.8219\nEpoch 61/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4046 - acc: 0.8223\nEpoch 62/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4039 - acc: 0.8219\nEpoch 63/70\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4040 - acc: 0.8230\nEpoch 64/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4014 - acc: 0.8239\nEpoch 65/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4095 - acc: 0.8220\nEpoch 66/70\n7701/7700 [==============================] - 70s 9ms/step - loss: 0.4034 - acc: 0.8231\nEpoch 67/70\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.3996 - acc: 0.8233\nEpoch 68/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4069 - acc: 0.8228\nEpoch 69/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4079 - acc: 0.8212\nEpoch 70/70\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4022 - acc: 0.8237"},{"metadata":{"trusted":true},"cell_type":"code","source":"# c = np.reshape(y, y.shape + (1,))  -> (48,48) to (48,48,1)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.cast instead.\nEpoch 1/20\n7701/7700 [==============================] - 79s 10ms/step - loss: 0.5148 - acc: 0.7533\nEpoch 2/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4845 - acc: 0.7763\nEpoch 3/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4765 - acc: 0.7813\nEpoch 4/20\n7701/7700 [==============================] - 75s 10ms/step - loss: 0.4717 - acc: 0.7839\nEpoch 5/20\n7701/7700 [==============================] - 76s 10ms/step - loss: 0.4640 - acc: 0.7881\nEpoch 6/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4588 - acc: 0.7918\nEpoch 7/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4549 - acc: 0.7935\nEpoch 8/20\n7701/7700 [==============================] - 75s 10ms/step - loss: 0.4515 - acc: 0.7958\nEpoch 9/20\n7701/7700 [==============================] - 77s 10ms/step - loss: 0.4471 - acc: 0.7990\nEpoch 10/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4446 - acc: 0.7992\nEpoch 11/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4417 - acc: 0.8008\nEpoch 12/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4387 - acc: 0.8026\nEpoch 13/20\n7701/7700 [==============================] - 76s 10ms/step - loss: 0.4366 - acc: 0.8041\nEpoch 14/20\n7701/7700 [==============================] - 73s 10ms/step - loss: 0.4346 - acc: 0.8042\nEpoch 15/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4335 - acc: 0.8065\nEpoch 16/20\n7701/7700 [==============================] - 75s 10ms/step - loss: 0.4318 - acc: 0.8068\nEpoch 17/20\n7701/7700 [==============================] - 76s 10ms/step - loss: 0.4299 - acc: 0.8077\nEpoch 18/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4273 - acc: 0.8098\nEpoch 19/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4265 - acc: 0.8092\nEpoch 20/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4254 - acc: 0.8100"},{"metadata":{"trusted":true},"cell_type":"markdown","source":"After rescaling:\n    Epoch 1/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.5090 - acc: 0.7600\nEpoch 2/20\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4840 - acc: 0.7770\nEpoch 3/20\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4756 - acc: 0.7820\nEpoch 4/20\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4696 - acc: 0.7858\nEpoch 5/20\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4642 - acc: 0.7887\nEpoch 6/20\n7701/7700 [==============================] - 77s 10ms/step - loss: 0.4602 - acc: 0.7915\nEpoch 7/20\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4551 - acc: 0.7943\nEpoch 8/20\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4519 - acc: 0.7961\nEpoch 9/20\n7701/7700 [==============================] - 71s 9ms/step - loss: 0.4471 - acc: 0.7982\nEpoch 10/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4451 - acc: 0.7990\nEpoch 11/20\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4424 - acc: 0.8010\nEpoch 12/20\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4379 - acc: 0.8037\nEpoch 13/20\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4363 - acc: 0.8040\nEpoch 14/20\n7701/7700 [==============================] - 74s 10ms/step - loss: 0.4348 - acc: 0.8050\nEpoch 15/20\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4319 - acc: 0.8061\nEpoch 16/20\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4286 - acc: 0.8081\nEpoch 17/20\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4279 - acc: 0.8082\nEpoch 18/20\n7701/7700 [==============================] - 73s 9ms/step - loss: 0.4246 - acc: 0.8098\nEpoch 19/20\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4228 - acc: 0.8117\nEpoch 20/20\n7701/7700 [==============================] - 72s 9ms/step - loss: 0.4224 - acc: 0.8110\n    \n    "},{"metadata":{"trusted":true},"cell_type":"code","source":"weights = classifier.weights\nweights","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions = classifier.predict(x_val)\npredictions","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions.resize(66008,)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_val_pred = list(predictions)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_val_predicted = [1  if i > 0.5 else 0 for i in y_val_pred]\ny_val_predicted2 = [1  if i > 0.1 else 0 for i in y_val_pred]\n\nsum(y_val_predicted)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sum(y_val_predicted2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.unique(y_val_pred)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\nconfusion_matrix(y_val, y_val_predicted)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import accuracy_score\naccuracy_score(y_val, y_val_predicted)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_val2 = x_val\ndatagen.fit(x_val2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions2 = classifier.predict(x_val2)\npredictions2","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sum(predictions2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.unique(predictions2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions2.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions2.resize(66008,)\npredictions2.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_val_pred3 = list(predictions2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_val_predicted3 = [1  if i > 0.5 else 0 for i in y_val_pred3]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sum(y_val_predicted3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.unique(y_val_predicted3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\nconfusion_matrix(y_val, y_val_predicted3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import accuracy_score\naccuracy_score(y_val, y_val_predicted3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"classifier2 = Sequential()\n\nclassifier2.add(Convolution2D(32,3,3, input_shape = (48,48,1), activation = 'relu'))\nclassifier2.add(MaxPooling2D(pool_size=(2,2)))\nclassifier2.add(Dropout(0.2))\n\nclassifier2.add(Convolution2D(64,3,3, activation = 'relu'))\nclassifier2.add(MaxPooling2D(pool_size=(2,2)))\nclassifier2.add(Dropout(0.2))\nclassifier2.add(Convolution2D(128,3,3, activation = 'relu'))\nclassifier2.add(MaxPooling2D(pool_size=(2,2)))\nclassifier2.add(Dropout(0.2))\n\nclassifier2.add(Convolution2D(256,3,3, activation = 'relu'))\nclassifier2.add(MaxPooling2D(pool_size=(2,2)))\nclassifier2.add(Dropout(0.2))\n\nclassifier2.add(Flatten())\nclassifier2.add(Dense(output_dim = 256, activation = 'relu'))\nclassifier2.add(Dropout(0.2))\nclassifier2.add(Dense(output_dim = 128, activation = 'relu'))\nclassifier2.add(Dropout(0.2))\nclassifier2.add(Dense(output_dim = 64, activation = 'relu'))\nclassifier2.add(Dropout(0.2))\nclassifier2.add(Dense(output_dim = 1, activation = \"sigmoid\"))\n\nclassifier2.compile(optimizer='adam', loss = 'binary_crossentropy', metrics=['accuracy'])\n\nclassifier2.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.preprocessing.image import ImageDataGenerator\n\ndatagen = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2,\n                   horizontal_flip = True)\ndatagen.fit(x_train)\nclassifier2.fit_generator(datagen.flow(x_train, y_train, batch_size=20),\n                    steps_per_epoch=len(x_train) / 20, epochs = 20)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"If we use dropout:\nEpoch 1/20\n7701/7700 [==============================] - 83s 11ms/step - loss: 0.6758 - acc: 0.5944\nEpoch 2/20\n7701/7700 [==============================] - 79s 10ms/step - loss: 0.6754 - acc: 0.5945\nEpoch 3/20\n7701/7700 [==============================] - 79s 10ms/step - loss: 0.6753 - acc: 0.5945\nEpoch 4/20\n7701/7700 [==============================] - 79s 10ms/step - loss: 0.6753 - acc: 0.5945\nEpoch 5/20\n7701/7700 [==============================] - 80s 10ms/step - loss: 0.6753 - acc: 0.5945\nEpoch 6/20\n7701/7700 [==============================] - 78s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 7/20\n7701/7700 [==============================] - 79s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 8/20\n7701/7700 [==============================] - 78s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 9/20\n7701/7700 [==============================] - 80s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 10/20\n7701/7700 [==============================] - 79s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 11/20\n7701/7700 [==============================] - 78s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 12/20\n7701/7700 [==============================] - 79s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 13/20\n7701/7700 [==============================] - 79s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 14/20\n7701/7700 [==============================] - 79s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 15/20\n7701/7700 [==============================] - 78s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 16/20\n7701/7700 [==============================] - 78s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 17/20\n7701/7700 [==============================] - 80s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 18/20\n7701/7700 [==============================] - 79s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 19/20\n7701/7700 [==============================] - 78s 10ms/step - loss: 0.6752 - acc: 0.5945\nEpoch 20/20\n7701/7700 [==============================] - 79s 10ms/step - loss: 0.6752 - acc: 0.5945\n\n    "},{"metadata":{"trusted":true},"cell_type":"code","source":"weights2 = classifier2.weights\nweights2","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions4 = classifier.predict(x_val)\npredictions4","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions4.resize(66008,)\npredictions4","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_val_pred4 = list(predictions4)\ny_val_predicted4 = [1  if i > 0.5 else 0 for i in y_val_pred4]\nsum(y_val_predicted4)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.unique(y_val_pred4)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\nconfusion_matrix(y_val, y_val_predicted4)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import accuracy_score\naccuracy_score(y_val, y_val_predicted4)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_images = os.listdir(\"../kaggle/input/test/\")\nprint('Total number of training images:',len(test_images))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_features = [crop_image('../kaggle/input/test/' + i) for i in  test_images]\ntest_features = np.array(test_features)\ntest_features = test_features/255\ntest_features[0]\ntest_features.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_preds = classifier.predict(test_features)\ntest_preds.resize(57458,)\ntest_preds = list(test_preds)\ntest_values = [1  if i > 0.5 else 0 for i in test_preds]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sum(test_values)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = {}\ndf['id'] = [i.split('.')[0] for i in test_images]\ndf['label'] = test_preds","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_images[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"DataFrame = pd.DataFrame.from_dict(df)\nDataFrame","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_file = pd.read_csv('../kaggle/input/sample_submission.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = DataFrame\nsubmission.to_csv('predictions.csv', columns=['label']) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission.head()","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}