MalariaDetection .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6b911e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "401a08b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = '/Users/fasihussaini/Desktop/cell_images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "75585071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.DS_Store'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(data_dir)[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4314a181",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.image import imread\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ecf54551",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_path = data_dir+'/test'\n",
    "train_path = data_dir+'/train'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6cfbdb12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.DS_Store', 'parasitized', 'uninfected']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e5a51e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "para_cell = train_path+'/parasitized/'+'C189P150ThinF_IMG_20151203_142224_cell_84.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ac034caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x13334b580>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(imread(para_cell))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "69333d9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C13NThinF_IMG_20150614_131318_cell_179.png'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path+'/uninfected')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f1ba7eff",
   "metadata": {},
   "outputs": [],
   "source": [
    "uninf_cell = train_path+'/uninfected/'+'/C13NThinF_IMG_20150614_131318_cell_179.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8613ebc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1336101c0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V89f2lma7XnpHqPHE2zWd6DKS9my7gtqmRFzJsD3pIPRp3swoXJywHWi0FJsROiUpEoCEnHN1URGwmSvXv1phj7QdTDnj8R91F7LrZlKnaUqTc1I3IpnuOdvi5Srucr3WwZhrOGbPMKurxPX2vVrTP5ftRQSMCBaReGSI6r/Htnqkje2vQSQ41XHDPpbYr7V7CZCyOgSvfgFYbHU1spUi3LZAfLtgtVITg2atxsEFVIBzacKT734sPnx46NO6KTPe/sC7BKGvAtgbojq4+4478YMv/Ae47eZbcHR0hPlIkPLG6UYxOs1wn+xs390ARM40Zo0eKsZVIeIaAE9W50Ec7TzPSERIDaJW7rq0ehLWXmHrkkluwDJDVAySiNf1yG6f8b41EP20MjO7r3Y3ng2HSdxFI0IhUKUCTiz7VkAI1zQlN2AljYiS9mpJHADAJFlFR/NGpjRLXx586H34yn/y/+D33/ue1XFdAbj0higi+mgA/xbAXZC38GJm/g4iugPADwN4AoDfBfDFzHymZmMbEIAbDw5w08E5HFDGjIJizn8zKFmx8ODCcdeMB1BQ16qIeBJza8hCjTvGwHRJ40wpsXKPNva398Fug7XgiF2J9ppraBsMYi0WBCYyWjNqmbptuG76u1xrZuiq41bCx2ZIRspiTIPZFPQeIsJNBzdUpCXGTedu2Co1nDW4GK18A+BvM/PHA/h0AF9DRB8P4IUAXsHMTwHwCv19VcFmI3G+CG4Z8alWy2txjgsvYeqpdMXEQOGvpPqr13qg5AsRqEhnHKww42iecTQXyeLRtLlZ44+tT7ZYexF3hJAxOMK+j8ThMTfH1vsWz2MOWbwQP6/dq3HYFottzzWJobrCSteuxTpXaYZYDHxlNjeZGNVmbX+eGUdHReaM7bml+TNX3dWk514wp2XmdwJ4p35/iIjeANlM+vkAnq2XfT+AVwH4hovq5WUEZqAcVYNTYtu3Va28GxWHC+tiYfDGRDejgSQLQjHcFmXRqIrIMU2Erkah6uc0JLKYYVu8QJuRI/1ecs0RMkedNBq1+vt6zjPSj9ee1U/omjDNDK+MQYEt2zhLqbp+FI9Dr7whF7oZYGJYsXYUBrSOFdSvq1GQYqnOkNo4VxFcEkMUET0BwNMBvBrAXYrQAHA/RHwe3XPFd4LPKeERN9+MwBNw2y23yEJz6lsLe3OBc4haa5i19KclbQdDC0cKblzRxDn278KF5HwObkRmifwxZHf+vSWiqUfUJvgfx4vS8VyvX24Tkfv7hs8YGp0WRwbnjeCo9TuZxBIuNwIA8WpZfLJVfARDXT5aOdL7JEasR958i2zJ6W42xkMPPzzYBvTKw0UboojoVgA/D+BbmPnHiOi9zHxbOP8eZr79mDauiHDyhz7m8fjnf/WvY6KsaXWMhITbb3kEUAhlU5AgUU+Hh0eafgdZBFoiRjZt1uAE1NQ3BiPGFss4xU9rUUZEhMPDQ2w2G79Pks07rmm1lcAudI9ih+0z3m/XxTS8CCNuDLRRU7GN0bXx3NKAFGzGRkBQCYL1q/7ZefMZt/1MyWKLI1Gp+m6ahJsaklOGqyPTQdLUQLhFOmWAUfAHH3y/RGApNz4qR/j6f/2deON9b18unMsDpxMRRUQHAP4DgB9g5h/Tw79PRHcz8zuJ6G4A77qYZ5wmnJsm3H37nTigqQtBFGc8zwy2silWZ1gRVnarq4ENgqNRhDUOF0XTMadaLvToJtH2YXodt2eOQUT7Ha+LCBOvWRN5jzPSHMd5tREEa9Lw+qjnM9hdO+MxhrmiShiZq4HaxOUq5bDWbmWx5hQVpRPhox55u5eDTZlwOB/hYDqbHtGLsR4TgO8B8AZm/qfh1MsAfAWAf6SfP3FRPTxNKIxyWFCyhhF66RcJOSwze0Gx+UizddQ/W+OFpZKgOftL6RGQXW+Vym4tMnjJlhhxBKAwecV94zgmcnNoY1scsd2zmz91/fgu19h41p4VdXQE8dlcUUBLRNTS5ONrCRu5FAPAE+WlkdCvYLcCAYWsmqQYsSgJcUABZiq17hbO9hadF0NKPhPAXwDwG0T0q3rs70GQ9UeI6KsAvA3AF19UD08RGGKnEKsiNCnd/K8SreMhiRYDa0H/xRLN23BCwaslB4vIEw1M9tt0ZxOtU4gwssUsOm43hh0MQmvX9FbiUkoTOWV9sxS/XhzvdddtqpbfGyxOItbWoI9F5cdgqLLgChOnmTROmmq2kNynVSzMyFYsHJRRmEBFn8Os9bdUjJ5Z1BBI274h2hmEi7Ee/1cs3XAGz7nQdi83cGHZd3mGB0kYR5WwxILZX6j5Z7lWkXAQEbbXLV1Ma4RdS18z8c8cJNGzWZG8Qe7OsVm5CrW/+3EOEHft2MjoFKWCep+K7a60KrJRPdt8U7cX98cHYKKy6fIxmixZGS175szKQZMTzIl6y7giYoK/lyhDc9HqXESa3MyYcsZBbhMM2kJ6VwbOptB+uaAAfESScjYTNkeSWE6YIHJVkjC6uQhnZQbxDKvCYMaOFswqbFxMXTgFIFIkNZGYyHcO8PyawHmGrpXF05QRAY1rxTNkwPUe6xe15y2jh1kihrQnAHSHAbfUqsLIKgrLKpeGKTUBIW54ariz9YbVeMX6/DEiVO4JLyObE1XrbzApAGI1tswmCVCROOYpT5imJDqy2hioyPVUVAxngMMeQwfpAP/nn/0KPHx4XoMwpN1v/4n/gF950xsXfb2ccH0jLQCLchKuCk0GsPhfM07ZdZVLBt+AU+toEDEgRK6lt0ROpWJa66Ncfh+Kpt0w4ie8h/KM2m80XKoaubA45zav0Lc4HtMBW6E9iMHBVRRG1fR623jHor7LNMGuJXpp4eL59sZMuQCcuEV0c1ZYvRpiEYlJo81U3H7qYz5GCJMiLYNx2y23Lvp0ueE6R9oQZYOKGJujjbz0WQxLXGxTKm6ucwg6rR0QAwucK0XdtrfaApUzt3odL47Fe/t+RD2Uecs+PN39/TN7ZLFE9Pic5prOMjx0+ywIUA3h7K+x/vd97X3Oni2UzNddcMON5zAdTHqccHBwgFIKjo6OME2kBeTEgkzJKl5QIAIzGITMSbblLEYksejvlYLrGmnNqjubT9Wr/Ye0N0NIM4z4Mf2t+hCABjnqomuPAS1yWT+iONwvzrXIpTWkgD11YMntEaFH0h65ovV2jfODNeqpG0O8N7bfEy6YAY5bghSf189BQwjnoq+knTcrqtfOSo1jrs1XScTymgtKNTH45ruL6bwicF0jLSB5soln8Ey1NrHvFVNjhhfWxBI5p17dcTV50eTK5ghJIhxnCe6RZyvVN+vq8NTSNdP3qw+uiNf1RIVhVTLaZ1g70YBlCGsWaa+vjKVqMQJWS5fp4GI/MCKR9Rg7pzVR18riwHrMLU0T41TVs0W8Jm1DM5D4bGDtdYO0n/T4J+HPP/tz/GUfnd/gtptuRUL2APaiuZfVmgsA6gbg2fNjLfLJEtYNK61tW1RmnTTRLSJcjxAjbhc/t10/Qi6Exd23O7pnjZNu4+bbwJBzjZOP/Ms1uKKzMNlwwP5piCjPkXTDg4MDSNXGwPEZSFl335NbuzblOVYv2QQp4/nElm0kLPfL/9Rz8XlPfybUwAHkhJf90i/iv7z+13eem4uF6wZp777jTnzBMz7DReLzDx+KL9Yydwr71o5RrLXImygyU7VoePsL41JYrGadNRgFILTcZsxxl9zRvzVc2I+Hfow4a7+rwDYReHTc+9Ubs7p7+hDK9SAMCtxvKcZbm/ZbEL9y0pRSWzUScCy0+HH7sPmx0FJmVkyVku8JJEYtN07K3zM+9qlinALL55Tx+t996x5pTwWYAM5gLfmSKWvABFWdFEksiSX5GvSMnmIilehehTHmJMV8vW3mT3usLs5I7fVIXUSo3EcWaQjz6zjGSLQ+Lqpn1P9RcEX8jNAg38D6vXZfD1EHHrUxUgfqs6VGM6Ax3ak1GCXNmtpsNp44Lw2wqDipBl8QEaoArUsgAZsNI6UidZizVLkkMk348sP1g7QGJvtw5WzC3II4zIFK+2e4nmULjiHSrrzGlFIXAaXXd1w0Wpl7cbou3nY8POBy21IwjtOddz1XH4YVXqunB6L9yL1z3LyM+tZblSmaHqgangA0ifPa7Y4IMmwfXemj2iMsmIOV6xKAROrz3U0Xv5RwfSGtJjyXzexJ7GWeIZX9xP1TE8tNAE6anynLUhK5l8gKKPWHvN+4UCP3iqLimh5rxKDX+4YGKDLRveOWAYu2cbtdkDKOdY0j8pa2esSK1uM+L/i4fq5xfCd00YKsoi2RUJUSkJYIIt6qhblQNUx5RRwvTWsPAspGVKjpQCUglGMlmksN1yTS/pEnfxye9QmfVHNfmfGkux4ryKbVE6zUaZkB2YpDE9cLgyAWzUSE2ZOvUUl4sMzaOrUII0UrmJUzimpyKy2+98i40BnjotyyaAcnXK/tr7fP4yzaBiNjmJ8rjIJ2jLG9uD2JgSFrTBjYBtEaHftkw1wSEyG5Fk/tdgpGTRrQMkLCZKv9wRLv5Ts5Ry4W1QZdN0kiyJ71CZ+M22+9FZRFx06J8D/e/Ca86td+9dhxXQhck0j79I99Mv7aF3wR5vOzlBWxVLq5eHJAMQNDqTpkmcXiYOGGAoKtpDJg1TzD4nXxuRcSl2LgyE8KrIuMI+trFCdHoqW30fWmORf6chwh2IbU3ke0BMcgBmLEjKaTWqd7o9namEob7lXzdS2IhkR6kscGX7GJvaTmDxWXCwOJk9g+WA2V0GqPKor/sad9Ip71iZ+EdJBBiTBNCd/3Mz+9R9qTQCms9ZTmmsHDAErncnGiXfUYo8SAvDjZl3WDZsc7BbveKvx7W83nEnrEkz4vt4+0Y6N9ZHtxdbGQO6494ur2F7N4Yn98RCtcrklgXx3tyOpdpYaIwFZWZ5vRa0QEU6JaA4sAEGkdaXEH2WZl0AR6VkXUpSAGxOejXLVIsEhKEszsCSQFSAeyDkoBKCt3J50L3VRtLZb6UsE1ibS2oGogPFXTfYEu6ICEZkxQEqtEFkB4AayvtOGuIxPQbkaekYsFWHLjeP2oLtQaeJB/f3zFGNQTkl5UP9aKHNoZEaVd+r3N8ryul7Mb/yKPNau7tAtIyl5LnC2Qwp+h75PcGKX3my6s1nvy9RL+9Dk1AX+PtCcCLkA5AngWH1xSvaUUxkaDKDCLHrs5EkSVrVGzGBtmRh9SV8Vf+CdDDD620VWt9dRy0NpOK+LFTbSGxp2AACPk6blkBEqEKeVmI+me247CKe24cdFY/sb+hmJu199+TCMjGjPj6Oio6cMITOroQdoFmOMO9X1N5+q7plSQWKOzSsGmbBpubXSckJXLyr5EGwA5JylgXyBF0f2dbzBNCXki36uJckZ1Hl16uCaRVkB1UVYktpKjTgmDaKQirsHIMMR9CBtRs1DrwhzrptJeDYbdxfAT21jrW2+siv2L9/Sc76TPX0OcHqKYvYaIIwmj71uvU/dc18XlFV26thvuWRHio+FK2oWvERC8yHy8vhRIwoEyBObqAuqDaS41XKNIq5ReX+pswRGlqHhMjqwUxCt4mFy/qNs6RLI+2gSAETLFxVVrBo/dHKOX3C/8kUhrn70OygwvgRP7M4KeE8dj0foapYemLVKRksZi9FASCIa20bgNevdX5YwqtYAay3Uca0OogqC0Nt4lsdHrjAubmkTwHOgpZ1iAjvl4Zw3gOS24JpGWtWA1FYLtrqYecaOLmBWBSwkoy9EohEptUTN5KlWuSNRXIqwgK6UuTHM1rFtMt1HotYqKsS/WD+MQcdFHGCFRXOQWZRRF4JGe6wQrH7/n7S5j3CYJRA7c+ruL2xtYbRJE7T2Ry8o1NV4cGlTRqw22520pRfcKslI1+h5Rn2/eiCQUzPfcPQ24NpEW0DpOxlGVTbqIq7qOo2TVUpmrrhSNEYBxNK53+DV91cCR+Dfuay8GHnduF46pIxnm7UY9sxeZ1xC5f0ZPdEopSHlsJDtOVx3p2yMiMRKZnRCp4Uel2soV+/7qh1uZ0era/XfLG7E1YXHqKscFzi3PT+avpuW7vJRwTSKt1KHQNK2im2EV862xUsoEybqa4Q46BCMSt/a/Uuzl6Qvp8kFbQ1ULPcL0i2/tBR8nOm4DZnbOM4IeUfsUus1mUxcvtVtj2iZecTw2XyNxfsStYz96o91ItB6pBX1YqCBaas5FxJwwwSPIxPIIinvlsm2zKe1lSmFt1GfMM+PgIKsozEhJELzMWsaG0Nb+ucRw1SPt4z/qLtz1yNuV2gnCfsydd9XoJDvhyGZcVEXljpPaJyMgaPjuxqtw/RpHOg6iAau3Cu8iQo6O1z4d34dtnPak43FTzoArbmtj7Tl93/o+7wJV9DU9NNT0quIVKsEeSwhOFIztmu0jGKwU77VkFuGu2+7Ap33c06o+DeA9H3gIb7r33p36vg2u+q0u/4//5cvxJc/6LMwbmU/S6BUqwHxkGzHJZkvzxny3YtYHA2UzOwdtDUqAJEXXexxURKrxs1nv2d1q2OvDOg8LrtPDNo4Vj6kttJHLowGnf7antnVBHn1fjXMa99xsNiIeH0yL9nuiNuqzPXMUORXnajRHw3nQuGuTFCLkHAxqYuLw9L4oLsu2mZqgnwjTDdnvKZBInYNzU1PcPGVpDwTNBgJoIsnlTQAy8HOvey3+2otehB3hdHYYOAtARMgp604AgrS+M1upQeTiI3UWCYB9N7qItAZVp7UvgJFM4yjW5pr41vdz2/Ge0464d4RhW8335b3RgHOcjjySAHqrbnMOu4vudk8cR0T4vtZVf/02xGV9VzVBfkn8Ukq+c4TfyzUUU9aDJI8k/10raUILwAmrqdU2Tc1lFoNUpix7DSuBqJUzLg6ueqQVZJMEdttyssysXBWe2CyLKoG4eMXFHmkb0Vcbt2OdkAazBPdnTqKfRj0RaBFjJLL2HMcXNAI58WvW+zEybvVIOupXRIS+L+iQ8Li+GxjyG/cWnXFe3GfnRuJrM7cqC68Z1+J4iupNRfd6cYmXyNeV9LES5pylBI07JGDWbCnDWg1X5JFUBfCIvEsBVz3SSiIAdC8eAPaps8cM3R+VWzGMB+IaQu1ePWWik5dRVdhm4OktoBEh1hZbjDLq/b4Nl9HuUecHZKM8XV8W16AlCD2ntWtyKNI9EqXjMevXhaha0Q/cE5GRnt9z+B6R5WtblSPOR51LAESN6iPvOrnUJdFgwNHhBiknkeiKXcha1kY5A7Hm2BLypIiuVeIIAJXddfHj4OpHWrY/QrUeAeauYSCk44XIJUNoNvoaF659rk2yGTeWiAksF0pchGscJx4jogWni0Ywv6tD6Pi5Zsjp27TntQt6KZJG6CUEDJB+7f41A1Xff/tu1x+3WZjfu4PhSq4DbCZb4lmNUn69WpuZZGuRxOzXlLmAiIHEUtiexOdLbFFTCAXhzgjSElEG8BoA9zHzFxDREwH8EIA7AbwWwF9g5sOLfc4amIA4TVl3vJOMDttzhzUlzxY7AbLT+qFsN5nCHhOGxItFwdpOwzHHtYgNRkjQV7qICDWKkrLrLX7YnPzUte/dPCEl7322sd9r+nf/TBdrYTSzJRp933riZd89EwfwWsVxvmIstM1N319m2R+YQKGsapwXsRRtNrPk1GbLjur1alFYc5ZY9JmruD5vNtjMda8lIpbYY5ItZIQWJN9riGBzloBLFI98KVr5OgBvCL+/FcC3M/OTAbwHwFddgmesg3FV56T1d4nHUDkURc5qXBpCpH1BcWgbNSSw/2u6cgwXhYu3wumLWrW5FK2mMWM+2mDebKSiRmFT2oFSRdBtnMp+C+EqiJtgN/PUzc1obENuBi2AVycsCDmjUMDBNNi4Kqb7HzXSkkkWS0lgW3SYv4PwLtaMbXZlJcQaM6e2EQ9HZHY1K+46wXp90e8FpAUCZW4La3QU25UXDxeFtET0OAD/E4Dv1t8E4LMAvFQv+X4AX3Qxzzi2D6rpl03RjaEB27Fd3D3mczVElReTUhK3Dwhg0UUSSSZHXURQjIUm01t+626Iu/itb5dYuD/PM3ieUTYzNoeH2Jw/xOGHP4zN+UOUow1Qihg3dB+hxADp4rE+jkrfsBKEeVMwb2bEErFxbsrMYgvggOgdh/N2FTnFbFBkz58s5tKmAqJyw9UyMixjgY5FolZkjCZF8CyRMLaNbCIp0BZF5fi8bX+g1jLdqwzVCJbhlRk1EGezmbHZFEf+wlr5ZGaNthOiVZh0GAmMhM0MzKVKCFJcQesbXQK4WPH4RQD+LoCP0N93AngvM2/0970AHju6kYheAOAFF/l8SNxnpYqlWJZFtcETkxNuYy2ybtgjVyI1lvhSQXQTn3qRbptRKf62ZPx5njEFg0tvRAFMSFcrtxITOU3ObYqSeLOA233SpiDgPM+uPo1E3ThOInFJMLZzRyh3dd5EMWh/rIuOxHdWLtSkInbzulQfCNu8JaNwTWZ2qaC3iIvrZpn107+XeZ4lwiln5CxEnVXPqgRB+8AFs8Yemw3PJDZ7PyfUXlbhYjaV/gIA72Lm1xLRs096PzO/GMCLta2dhnPjuXOaVVGPTWmCB2wzq0TZicRkluRofFJOxa3toro2AAQ91s5ZcytjWiBy5GBMtU5vr9ux3KBheK3OXBcXwbZzNHdE8yxULmsQq1o0VujGWotm+UbEbMRl70VrgY7X9nOxELH7n+H8WlIDwzK2IHpjJyqPxmdI28yP/uWc63yvzAmg7qhUj0ld5WAghL2boHoAgEogJqzZTn05JXzEzTc7kQJEann4w+dxErjYTaW/kIg+H8CNAB4B4DsA3EZEk3LbxwG47yKe4UAAvuHP/jk88+OeJgnuRXZnf9Sttykilmp4cqRV3uUqE4dzZCxCONuA8wH1noiM4j46Wt3nxvscDCcppaasqd1jSEWanJ11+4qIbLYjPAdxeA6xwSBfOrUf+mmchoILZGn11rtJrKdS+Gykk4fvpYCt793416zGcl3t22jOFo/08cDzVaXB8f3tsTaryt4Fqag/uteINmm9ZIlwCnvzFkXmItFOeaqbjJbZxhprrtr6I3zaU/8QXvoP/iFsp76cM9507334uu/8ThxqMYBd4GI2lf5GAN+ok/BsAH+Hmb+MiH4UwJ+BWJC/AsBPXOgzWiA8+vY78KRHPxZs4YlHRVUFpXTOSdEusC1rY8QZm3YG160JBqOFWzl39bEaW7NFLJyrjjP2BWw7H4wNYS5ekhhAvAWzXlo/jumvXGPx2i6c6OeAmEHcGlhB2J4IRh2zSjvL/qyK6EFyYixFzRHhoDCxfbtKrx2pbe6EkIa6WwQtTr4Up/25frwlGLU7grg333AjPvbWW2RXexIi8uHDQ9mR8QRwGn7abwDwQ0T0zQBeB+B7LlnLSrWokHMtsbwCs+r5PKNaWo3rerDEUqQywxQzN/Gq5EarkSFD4km3JbPHBWgc1/HLvoTNqQ2KGZlCP80d0pYDhbdt95nOlrOGzw0CDGzOGprGQQdlKCJSg7ALBPT3EbDpGD0/Hl8gczgXI6PWdh0YtRnB5ynR8LwRpmxSFmoRPTFKkeu+dZOwalvwMaPqt80csDES8vdeiay9kygR7g6XBGmZ+VUAXqXf3wrgmZei3cFzFlzVdFhxnUAc3Fy5RsMtQRB9sC5wiS2tAQ22aPyehnMAUKNUHzFkn2uisvfAqL9Gyxg1J1TLqBVENzBRuKhFFWZAUqLCkG1OrIpkTrnpdzeJrRjdXcMMWCV+VtEgLrpmTkKzxdpe4bo+h4pIHnBAbUtROojtjNSW4fg6cI4qF0tfzSCF7dy9EkQx8o2MZFXSYbF7sngXTEK2NRrLHhlxLI0qtztcVRFRrKKi66ilToQsdgAFSDDzfXANwESzihjGgI2qNki7EIeM87R6Us+F4iKIx+R7pNJjr10pBfNm03AZyUQy40qShR30Thub9TdTVonMJAy0GKYLrRHmzDgW5hpoBJTB+whiPcvzrN/OqXriEfTrHojbR63WR17hTKt6eJQGYismrQ2wprVhhBjW2EnWQuwpHLJEAyVG4uet3HaeixLYC0NY4CpDWksIgLm82ASWZLIw4PHDaEIXRaRdlhTJwUpr4XljES9anvtzS26wiJJiwPJ3XQzV41HPNbGKuK4PpqyEAo2RxJ7XV0esu8eVtgPoFvZgLLFNuQEQQ10Cs2U1pUUTFjW0bZuPqN/38wdAjHBoJZYhQplIydBk9lbEHhnC4vEoNRWTePT2mLgAfQxSZQyUpOqFuQUBTc1TAx4rIjMSpkRS7LwwmKsPuBr/CjhEW+0KVxfSql/V9QIAPtu2wFW54CgGKhcwaof4QqJeAqwgbLiFe3fJmpg5WGzGC7VP2pnK9XyR1vNs9xCpQSS5pTg+yzkboujaip09x9wmyrvE0Bm0YiPcdiKwXTRId5z4GX9HTj9qI0pODA1UoW4OuvfQ6uv2rCry2/xG20X7LJPadNuYVNcVCsCpzguzIHhK3jE37sVlB0RPxsngqkJa32hqMFDbsZuoVoUHh7fEAM8aZUPJS7w1HIoyCoQK9susF4H9uQ31Xo+UWlqpVTzXMZne6pX8mLHZ1AWaCEBWAxxZiboqKIq+L+d4hiB5amRiX6RunAt9cw7dxfQSJF85pYQ5qBt9VFKCbAE55UmMP0QeM22IE9PrYkoeUO04i3fezWVEYFcfzKq0BUS1aqWfatFukT0WuiciTF3ROu+/vAjI3k+5egEYgskMceVBxGbT452wEh3X7SFcXUiLiDgyEb4oYFwhoUCoonAnwDlvpLpYec+uA7act0fYBeXvKPzoWqP2PYW1RdAuUHLEBhhF25/RbmbFyjKYINUS0C6ulkNGQrLkcqPPEfREi6iOaY2o9mNccESoTxmBsKyIx0BXJXJAgGI//XlK6Oy6SNR6yQWofVz2o7qvnNyoNCf0I7kg5QIIW//6cMqTY+1VhLTsSeu+mRKEY5VZ2A9RQrZdAnQbCLGokpZVrXpX1WuWCBmhuhxaKr1mvFh70WTCMaE5Z9/nYDE2t4JwT9Uxu4VoxjJbNEQSiCO0ixoCt0TEVGU1E0QG4uVSOhhLENLXmpFEurNzf22vh3cnHWmBdpeD/nmmd9Y2lon5dm2DuDmhcrlAeE0/9jmTP3P11F0azA0UnoHQRilg6sdm5+279qlIOxdSzeJMIu3BNOEvP/fzcfftd2rtXgmmePKjH4eyYWSWAtEopIH+RaNRGDPPmDfyl5JaUUMCPGALrZ1YQsI8S70jq/lrFuWRqT9CXIwtByF/2dI7WRw5ZzBJILmFTE5pkpxNlRri4jW9i+FNNZ9OnEimRayzxcWxBfKAwSiNDilrri58j+TKWS32xbUNEymNx7GJp6o2crGShHV+en1xMYfWMw7jVEMOAOREgajIRloxICLO/Zqu3lr8W0JMbstkQLezdIMXigdYWIBAYZlfEYtJyxxJ2GMmWZeyhYjM23SQKlHQP8Zu23z2cCaRdkoJn/v0Z+APfczjkTlj3jA2RzPOn9+gzAXZVier5RgAdHNf5tn3mbUJjrqcuzbckQYXc/oCYyOO2e9Q13PMGCoHmJXQrkU16lCCRVbJgq575rgc0bUtECwppo+Z+yeRIw8FLXG5MIybuPTtHRR9N4ifnWTgREfQvrpDTEy25/G4QPo2hJKUP44HdQ4lJtslAB1nTyyjtDCUCrRN46ZGkKl2XGOb/QWo1CNdScnsCIZ4Nssmrkg7RnSF1jEoseu4/j549F52gzOJtAB8bTZIBJkQEcUs91EXX9whj9rqCrGkiOgYUe+RVLWZjzBN03AiK5KPJzqWXQGW3DY0JHTb0tFgCAtFDh1b4UX2kS1CQ6ZpmkRE1E9HMEATBqT285rYSFTL2kS/KanbpepslUPFZzMYs7Yri78GnYyJzXjOImL3hd36uTSkjDWkYv/iO4i/S5GNtDiKxJ24v5QCQhhiuE/mZ2kVJ4j/OWXUogAmaneqSO3XuPDccXBmkZZMuQ+TW40e1FwJVH2EldJFDrHMiEH3u7VwLqoydteOoDd6mFjsv0ODDREK59SJAeMucpB8Vz5H2pS0vGfyMp8mOVRO5wLEQnez+VmdexMzQe3CbMRP6a9Ytqld2J06sSaZxPnqn9MTqb6N/r4eCRc6+IKzLZE7zAAcabtx2Rz3z/XbrK2od/g6Xp/zk8CZRVoTf0uZdbDk9YVRADNZRE4UkXM4qdY0czOJZkA5ODhASilUmV83xKyJYP1zql47wBM1kiEEgQCmRQHUxRCb+J0UWU0UNtEU6sNtxs1VD4Npxg3X6LoUo5li1FVAWCdqDGQisOaZ9savngMOqziufMZ2UkrOXXvE7N9NzwH76+pz3Eow7I+3oe9w1ZYRRGyrnzDPBZRbCWkzb5CRNC+3BmRs0/PX4IwiLTVUy3da18VplRKswoBJIXZ9LaRFkOjiviSnvZyWa9vCit/jQm1fOhYLMLZdQY853lR+unX4qUUgQF62IW0UwZi5xv4CKJoj5pzS6h6JbrAYx9auDBCgJ17xmmi46xfkmtgbnzNCym0wSoDv+9/bIYxjxncV+9OMWRaJTp2eDwjfEAyYKlzAxSQVfZfqxza9Whs/dnwjOKNICxji1smO+8FYtQqAC4FLoJmuQhiFrAjVi8nxfHT810VTs2pGJU7kmmjcMuo6eBkBb537rixKIgJZaJxvRJ2cw9aNp8TlxQwPHDCdmRTJJXSTwFz9u/O8TIzox9YSv3pNi7AAUV4g3ELf8/mv1vseodfE2theT2xG+voaJ29UFH0Hio++Bkb3AKg7JgJSYgesBtCWw9vam2erdUXIk3JhksoX1TNgc3ZyxD2zSJsIFhovAyss9YxmYC5W9wie2WNcpZBxyajTxpZ7sa3Gm5o/LhqpduFGEXblYsxw0SpJnh/A6qsMq8uMO5UqF0W6IkXDSvVHk1JykZqrUkv6uaZnrvYRS0NJRGqonxyFNJRvvMmYG7wCIvRSTBQl+x0G4rXLfleuZeOUflRpqrmabTtKQT0jei1zDZIUA4VkB/l4XiQdi1/XENNkfTBJsCCx7BdCSdWZhmCnRf92gTOLtBS/2RjVCmz6X7FKFaW+CFKOZs7+SrGlrUaf4YqYRIR5FveFG1pGDHPHWY7I3yy2oN9WibkuyMTViGT3uE4LDmPX/E8uTsHD07sn1Of0yLMGvT5aj/U6Y7Dah/tGOv/IcNRfO+L4fX/gM0ggim612IfqqultCsZ1a5PcrIN+3NzNY/MMBP9xbS5Yj71XAGrRg3YcJ4Mzi7SCkKxxxIx5w+CZnLOKyKIvjLHgUIAtDHs5QefU9KkIRj2NQ6e09PetGTjiuTUGpgTYXzInodO9LmV1iYSIiG66KbNy0OTlSqGFySa0JW+a/oJcbO77PoozjuOSRdgvYCMiVns4iMMaBWXzaGPp52ck2o7E6Tgmq/Zf+6E6vtXyGliCJZpJzo2IgRH3+s7GBixWCtoQXb9OGYCWqSQ2g+HslnupoFn1XE4mDdV+nBRxzyjStrqCiBrKZbkO1midzHnx8/YCWjHXmx5ygDVdaKSjSZv99fUBPeJWJDI920S4peWwR2Lvq71gRoMQfb8WVF965PPEg/H1COtjo35u7B0cL1q3c9POQ+xn39ZIt1yeD1yL2rbbzypZyTjH/Ru9Xz+Pto+y1loO3OryLYGL7VTjaq+77z6fwJlFWnhhaADqEingopkTSbJ0rMJCIbMow5OOjTMBQEp1IYoeWPWmkQEKWFss2rfFoo1ZIKrnLcZTF74919pac7BHUdL1KN7uOokGJhGfddyoiBgDO3qRtetB027UURuksr9ufiJCrInJI27cz0GPWCnVEkG90aoPdDHkjmAIzf4utnC65hS76Cu/lkhbjVZj7q43eGjuNRVcweF/11V1wSXSrQr1eAJ7tss8a9UAjy1m+A4CIHCpxqaICPJXFi94JNZFA4r3NyzouFBbZIZfMwoaIKoRRf1iXONI/cZdfX9GHMsMIdt0TD3YHVvh6kSYB9yi77+Nu+/Pmh7c699VbDfRf6wD27PaY2hUHkO5qP/2fZakDJVUjGNX94RLx7H/suySGLksrI3V9lDI079FKl7O6S5wZpHWZDlyI3n3klVfMcZhxgWwVA2gPqCAa+aLvZBebzLOPuJwzbPRcqV+cfaLX3QjyO4A6JGwFb8Xm1t5+0ukIFrqo8trAoX3g/BF7Bwrtm8iHkGRex1pXX/lJfeP89Gn5fXzuXbfiLjBesJLO4ZBL9LbXbUtGuLLQoIhk9v8gvYGqte62kaCsLZnAgO6iZeqaoNxnwTOLNISCOAkW2fYFgxzAjNhM6tvMpTusVI0JpqWYq4cYGalq1yU2taYXDNmmJ4BwOsc9ZUPgbGe635j7ZAghCF6uF4pes7m44tbjZiEWVBL45g+br5QQwwOIh0jLkyTVOvuCIO5TfAicEoZRd1IhFklEcuQkomVeWTLEy2qTCblsIG4jRAySh6R4xJJHHPMwJJ317p8ekIodh9f/VIQwJHcuF4/HyY+d7sbAOG+GnUXx5HS5GsK5FYUnT62bvi2JpkIMwMzZt0is3Js6cdSujkJnFmklbWpizEM1A1JVjTad3+S0yODEYcLlufHOpjfy0udbARxcZBKARFh5SJ7fl0s8X47RlaeQsEWYuW40aiyNHxFcc/mrLGA1jtd3OT2ka6SxL7Fc2bcqn1eN2qNOKy0u+Q2x82zz8GirdC5QdtE/S39Oxhzf+ey9t6CTquLUdoQMc+vt8vNW+D3dsO76YZz+KQnPQmHm02Qlgm/9qY3r87BGUba9oXbMeOuxZLaCzR3Ua6J9YUlWKKdKHshpjuaW2XNmmnX9GJo1IXH+vFYD+31tqgXjhZ8f/+FUOaTqE1r4rbS0EaMjn0Z6dM1eXzZfrQRROPRLkSy7V8kyK0Yv12vhxPR0byvqx3hvTADFN93YAImeWies5lWGrpIwJPufgy+74UvBOluBSkTkICP+7N/fnX8Zxppi3NZNJNiu7g7hRsgZbvo61RZCVY7bu9lTZeMutVC30G/gMbjqE9bHlu79jjd7zRghDDRKLUN+XsX1IiztsR3uSdPhH7sy/Pb79k2l94PLN+jXGsSk0lFY2Ju+5TUtVjXm9sb9B9TFaPjtBARzk0HSDkhTQlp6mt7LeHsIq0FV8AMRbW4cztRqKIx2hfTI60UAZe5FZ0KqLG97eLpqf8I0dYWZTzXLLiVBTo6FnXEC+KuFwAj9cCfT8adaHitIeHCHbQC2zhaHPNICokLv+em257XtL/1WqiK0usMcqxyWjQMw6QRA7M+uzHLOe3AOHgCuNj9aW8jopcS0W8T0RuI6DOI6A4iejkRvUk/b7+Qtln12PjC7HvRtD1R/XW/Wdq+GZa36+2PEWxtwUUjSvyM3KNHbkO8atSS4300UGw7tr8uom2HtXu26ZTHItpAmom/R3093g6wLkn0asNIpO45d2+hzjk3WVFt+30/q63gpPPu77BomdUBUyEK8cmw9d2qG8BumsxFIS1kl7yfZuanAfgUyI7wLwTwCmZ+CoBX6O+tcJAz7r7jDjz6dvm767Y7MKVs8otOYphMNhpIfn4NqvqhFPmYviyp+lLc3oaofVttm2NJoG+jf86aCLnLM/sFv/a7v3cx/sGzhmL04Fy8Zg3BR8/dRlT6ue9/b3uWvYs1cdrWmp/za5aGpGbMfg3XT/3X+HS7oTsX3vE9X8z+tI8E8CcAfKU+6BDAIRE9H8Cz9bLvh+zx8w3b2vrYxzwG3/u3vwFlI1EiKMCt0832JBBl5JQku8UsngQAXHccCwNNmj9auVY1JKVJuLO94HlepottQyBrP3L0fqH0urC3PZdGjwpzOby/D9TYFS5EpLZ7tm14JYcGut0x7e567dq9cX4vRlXoRezYko1tRMTaZ0aRI94vnLagEmhKGoBDJPWPQ5CPlOeJYrM9q5UCRnAxOu0TATwA4HuJ6FMAvBbA1wG4i5nfqdfcD+Cu0c0UdoJ/zJ134iMfeZsgrSLufKjUaoaLF8yh3pMZCvSzKPu1AVcqW6meXR8ne7SoItKNFk689zidKj6X0S6AY0V5Ph5BTio+b+vvVrGaebinq7U50oXj/SPi15+L7W2b97U+9sR2NwSP14z7X9eMfWHnmON9bvUqF5stgEWe4VIigPc//DBe+6bfRgGL6JyA48TBi0HaCcCnAvgbzPxqIvoOdKIwMzOtbObKYSf4T3rik3RENeTMFvpc2PXXuRxh3hSgWHaHIifIETlSqko15eKilfasyIroPdRkq9h1PXLtoieuITEzo/AMhIirNQ40WsCjRdw/41LDom2y5dr2c9T3kZgdr9llbrcRgSgN9FLOtucsEzRG0kR3rOmbccjk56TCSD+G0A8t0CC75elYgi/33gfeha//zu/Ehw8PF31Zg4tB2nsB3MvMr9bfL4Ug7e8T0d3M/E4iuhvAu45tiWsgvyCfclU2P6qGgxWJ4Yx7FnkgY+CmPRIaMklFB4mmiXuOAq1vdZT43bt87PiajrVEWlaT2bgu0LYFaOdPCmvIbr/Xqkf0YIRxTUIB12Lq9tvYTdVeRHwES+ihHNd2VTRtCDFBPSrsm5FZ26xtj97HaOxryH8icG4b2vQxpMa+0j537Xnkfyd9txdsiGLm+wG8nYieqoeeA+C3ALwMsgM8sPNO8K21jf3PxItorZUaSO6SsXs7p39rSWyPL7fgGN8HLPXNkc5zzDw1Y4ttja4zWCMGPRzHzfrvo/uP44x6Ema8cfuAIT1By6lQWIrQlwhlNRX5tKXuHvIXT1AkNYT1tqy21vGib/++thGv/r5uBgAzJ3VEVYiZkmIvRBDXyzgMts5AM1s7w8X6af8GgB8gonMA3grgL0IIwY8Q0VcBeBuALz6uEQZCRQbxyc4zN+lsxhlyAg7nQ+dY82YjBaG5LipDPtnZHchZhmlJ7ggEYbM58omNhh9gKe6O9KYetupvmrg+0uPWjGBrYuI2kf1CoO/3iGuZSWABDCeacb76msZ9veL47AgnrVC4pjqsSSvuP8VyjvsspJYd6BEeIfeiV7UfSevI+y4JoT8qZZwELgppmflXATxjcOo5J2nnw4fn8fq33SP5hRuAmPDRtz8aN+QDeQ6qSNSKZWGBwwXdcEp0kJaScXN7fGnx+65cdO33mIKrvEjdNf4s7SvVX9aKSWZNq1wVeA7mDaKaWWJiKBCRv52HqK2alBNtAXaCOxHZxsVmZevmokf+nviMuVDbRvx+3DvpkXbbPTy4biROMzq1dgD2bt3UFLm7ISnZ24k96OZ4RzgTEVFvvf9+fNn/+y3++6aDG/Cvv/rv4il3PVaq5TMByLBtQHMmFy5yzkACNpsZcym64bRMxMHBOdFxZ9WXC6Pmp8tkWvhijJNd00u3Qdwwai2Vz/YNs+SkAni2jVNdqsgq18uL7/HM6X/oV7JtQVICzfaEmm1EJAEHNd63js9K0dYyPNH6zq6XG+FcqA0qzkZxvkeYuPtAL/7HttagErk657HdGEoZg1O8zSQi+KzWoSrCts82N6CpVtNUxdwS+lL1WNsKdCPzkCape5zVGpxY9jwzomsqwMklYwBnBGmZGeePjvx3ta5pzWISVb8oomULvMAarQoiaKdHtox6uz6zDVEjZ+4X6hqFt2v63dD92p7z9EaKwQvuRUPvi3NWOW7WS6LISSvOM3MjvlZEis+CLtIB91Ss3zZncZ62lVDtj/XPsu+jOe712MYe4JhS21py164/9sd1Pv0aWOKIpGbSVOMHnEN3iMmul184nAmk7YGg8r/lRjrVl+T2lKYqxw3WiImEtoeLcRvhgsZR2qJm2/SoHjF6/bY39vQcukfqcZJ7O34Ghosy9qdZxNKQjHsWK7nk5JpxzWpoFc0ZXRKoMteicnU8EVmxuuh7N9kIKeO4Y+bUSM04DmH7+bF32COzzTcza83iCqPrF89mYLZtU7trZH0mn9uErFKRbji9sPNeLLoKnEmkNX2sbpVRwByTwgHnpirOzXNN8copt9X7uFJL+dtNRwKWiLOtBm9cwG7QMFHIjDWqGxLpHj3Syaq0Wlv+GQ0X8C+s7VG4zxk26nh7ZLJjRJXbJwr71aoeZz7tONfoFrd9mlGwtt2KviN7wXFzP5rf0VxHGInahsymn45gG3ePrqURd47dSMpoYpy5mTHEEAXdoKsSwB2W4ALOJNICMCO7focvVP3aLe91Pcqu7xFWjm8Xj4f9GnCFeG+7mKHGIMO6OjoAoJR8I2kEvO0NKtEZb2OAP8fUXleYXAa2hRq5Ul3YwQ2VnKpVv3c/LxxUEf3PCIOJnYR2QY8qRu5q5NsKqvv30Es//bPX0XZ9TcixOsdL/Xs5V7ToHDnyCvGLCH/yuTizSAvIwHI+wDxrjLAaSzYbqSs75eyBFFHENf3M6j6Vmf1YSrkR3XpxKC6qhZ645cU6RS/FI58GRtXFcxqOcoL313Oi+F3UAtQQO+PqXBGVY/K5fpc4WbiBhYmQteyNhNcFC7T+73HdBYsFHffq3cY1RzB6J24oKgWbzewBMna8j5Ra2CiKMIJRRFX/vPhZVawEomr8EqOh7WRoflvUxHdUV5n7pIm03A8Ui4GTIu6ZRVqjWDKuVscwUmsGFzFsELz2Tok6Vk95x8+LLzkak3ZZbAvjRxCNjhPxthmuIvFY6HSrUsVSvKNwn3DAqHpAI81UpyWqC5vU/qwlaCnUlAZawWEbcer7PUSoHebZrvEN2AaI3b/H9h0uReeR/tz3exRgbESLwU7MTAAiABxDuMJLcC2jGf/JCNqZRFqC6FnOuRaLM06oIW32Q5sQMeV3UM1tjRsqr/ZhIGoBy53gl/cl2fk7wIiKRyPIaMf043Tu3ngyOl8NeXDxV66vlSeZGTy3ZV+ICMhCMEvseyjcFg0zozV3HNHbJrVsGyczu0oR24hc3fsXiJ7stEAuom6zS4xEakfupKI/l5Y/ii4kjENtBCLVUHtN+OIc94RwJpHWgMGYywyw+VOFsuWcfKHUtbikom4YmsvieC8ajUSl/ri134OE2pHbCvtl2C+8NQrfc9YRYq4dW5wPonGjj+reR/NctxrkTdBvAa0pzajBHAwwg1KRKNtsuc61D5mSE9dY6dJ+9yrHrgRpyJFVhF/Tj0eSSULCjNZj4CpN4P5L9YgaxDJSZeNIapEmnWu/X0XnRTyFT+dybe0KZxppRfeq1laLGhcOXCmuG1XQvgwiqisVS+rbv9zjkLbXdayPrpp0879mpIrn1/TSHta4wvp96hZhE880AAKken7Yv9ETNcy/qIuOqhAIT3HU6zrRf9SXfh57W8KI221D5jqyaMzZLmbvysl7vbg+C+EF17nxPNnQh74/CwKKSgQJ0Pm81pAWcVCkelgBc6h62G2kZZE+HvnD4qO0matBBkuDU//MXSghEclu6N09I8tpJBTMtcrjqA8jJB1xg+U8daBSSEqprW5vIoqKx/WQzDGK5ncqU2UAnLIW21MO4tH8hELjLTEBdJtntcRsmqaGOPYST+pE8pyz6N+ocxFVjVEpn8WUbCPC3RgYCDHD1OS7UtLoPK1TTdmqKdZIqKSRUUgJsHtHFP4EcKaRlrsvYnRqh1sXvb0kuJUuXNW0yjwWKbdx3aFY3Pd3IPKs3b+NI4y4z0gH3IrwC1VKD8RLuf42DirclqulVSUc1sqDJtpZA2YsPAnHGIrzaMXa46SO+I6H72ZHfXqbKA44TXKuGqU9J/7JiJRrCLU9im2gWTQm4p8UziTSisRmHFLFs/gCBOs07lisxpvNEeaZsTmaMU2yA7eJOgcHFphhObtLrhA/7fuaMciYjPW2aJ/WNlPqN4Ua6bT9974v8Z41sd6uyznLHILF/dTMnWjfOWdBUGbMEFWD59r/gqKKcJIAEQY4K2dV15AZZSLEvoykjT4uO477uNBGk2DyweQ7E/SceVTK1dun6ocYqT0jJPe1kQiUEkqRUirTNCmCFiEgOdhQyqwSdXyHwf2TIJvIZS04d0LEPZNICwDEVClR4K7ys55z838BwCoqoReNxAwjk8YYvJvtItIWbgvAkSK+/Lgr3/Ly7cEgx/VrjUvF2lVO6JyD6mJOCaT1quDcYVZRsMZrM2uYnjXjMrLaFIh9/56eCPVEcNsYj9Pz7VhsixkNwo6IX0NAldUZZ4x97q+JQAQw2Q4PpprJXykkom8iFXtrMFAiC5zgyomj7w2VKxP1Tz0eziTSujinSFgA8Fx9YTZQUj1XAinknpyzMoNxortN2HGi00hvHF3nrQZuCCy3g4zX99X4157Vc4Cos23rW9XrJENKnq+6/pQwz1p6lmTBJcVp1uwX1o28iYGcIRkqDGmLCUQZunmSE6hIpEzP3EWUt2PxeNz1oZ9fI9R9bm7/LDMUNf7RkCvbIDrgemZDCAFUo7iNc6NjFpaZKftzGJY9JIEXJjan0A97LFGCJWWdFM4k0hrUxVz9c6wzzAz1vdmCbqVA4GR6Vg899V5wEbTI1CNSdCXE8aw9a22BAoP9YI/pMytb1F9g0/O157UJdq7llSvn4lZ5K/cCX2hLROr7v0aA1uZntJ1IRPhRexSQoO/LcbBAWNasG92zYzEWtOMimsDELtZ63TH5r/41nDbquwx4YBC1NoYd4UwjLWB6gYlBAVEQkckmVm9q9Jza1hoSj15khOW5AYUI7YyQecRpthlL1gwkI1121NfFmTBPcp5hey/GCCMLaewrachzwxho2f/jRPmhwazTW2M7q1JIt9ZXiWH3uyd823TY+CwExIX1JxBU38ZDy8u4NBjFX0fi2rNIWk8CZxJpRQ0rXujNtvIwLmuctuqSloZmC6pFqojsBtsQ5iSwxgHX6gePEDleH7lq7f86QYntHncfoyAh+9RwKaJeaMkeDgg8bF93dCDlKpSWKXnbnr+GpKNnxXt6qWPbPaNn923EvjgiDvbPMU4bJQzSmtopuYoPq1QnBiY/6uqHaSImNSIB7LHHdGK0PZNICxjOVaORvwMfYctlUWN+6sU7issjK6XBSDSTdzTmfvH3Nu5x3L279Ht0T8uJ2/sJqttGzhrGbGJ18+xoQFFm64tvh/72nHY0xyPpJF63eAZXDhWRUbgcozVcLv3aC4Sl8ThszC1h5uUcwGUQAOSuspTJ0/Wc68ZEgXrjieDMIu1IJJajALiotdhWpUwCma7g3HkUoNA9ZWXxjKDVdXZDphGn7X+v6Ymjz1EbQFuQzBc+hUADwOctuqAWBrtW7kS0ljBJ5o/plJFkOk09hpPa8ShRtBt8t+/Azk1TXaojkTLOiW84Hc4t7A5GgNISqZv7omgMALYzgCI7g0JWD5woUsqYJvmjDGACyH28Y86+K5xZpCVkyPYd7f6xRHGXgRC+iBp1I4txd2Q8ti9rOlOj762Lisfd31874kjHcedlW2Ztr9yAAcnvhexWXlTnsMAmI3iFASoixuVARJp+gRfzujbHI4KybTwjWBXZO4QbdcH62ZT5ITgxWgMjToXFF2vJJiDNKiNCcm6pSQkETIrEzAWFgaTFod5837348f/6Cx4ZRUR48H3vw2azOXb8Ec4k0ooeoROE2Y+JKGMisy1q+R7nP764XZF1F67obZoI1i3YbXu0jsS8Xs/bZmTadZG399mKqohr+/MSqK1DrJKx0kJB3IWUEsRnbjOAQO11axLFSZB1l/P9nPVceNuc8uAZ4/dUwEiobpzkIi+0BBLUb00MUMpAIGrMktb4u/ffj+/+qZ/cOqZd4EwiLRAVdzsgZvVCBENkuy7qbr1eJm3Iwh2d641BF8KVtxmHRuejeCq1mZepZWswWqj9dzHGQav/qXiHBAY7IrpDaICYPVdtz1fk4FK5kXP2AbGJfzbHI2OT3du7fEb9bPrUjKg+dxvIGNqKjj2Y3lpQ52JmBqEAlJESifhLDI94SkDOqQZdJHYX0A4CxU5wZpF2CMpp20O7UOvxIt+Fyvb3rB07DtH7tnsDUG942UVktDGMEIy5IU/yP7sDre3zMTRqdLplsmaG2Z7MP/q9Nufb302VHhpUrYebNkRNaMfBaFWPVZG9kyAk8klbIPJ9fCixmFZc57W+XSJMDXBmkdYoentQPmSS5YAvcF7qM3Xxt1R9G2c87pr+emt3tzFt4RY7EIyIoBGx47G1PjZtamqeE4wCcCEACYlYslM03JGlkRAwYGlpwsM92mcwF71ldzS2HnHW5iLqpd4PsIv5NfJp/Xnu32VspVMt0TRVrR1flB6mSbJ8UtqooYqrYcr6r0h9KeBid4L/m0T0m0T0eiL6QSK6kYieSESvJqI3E9EPk2wZctHgIpkvilhpsA8EH/Z1uBDWnnUcxJfWv8iRgaZfoHFx9elnw7GHBd3H3fZt1xtrX+NCZTakRaObC8dIiz45m9IVb/d5nwM36cXhkTgfDXbbru3nxSqPJGNlPsxlW/38QddJFedbiOJ7DylYmGv1k+pBGL9X1P6Gey4FXDDSEtFjAXwtgGcw8ycCyAC+BMC3Avh2Zn4ygPcA+KqL6aBx1UVdI+4QtOHMcQEfr9uMNuWyF70NgfsFssYdvL1wnb3InHNToKwnBKP27G8taL7XM2u/5JpSpFCeiNBt/61Pghwxftg4jVnvK4KPQgq31RSOfY/X9pt1p9C+lSY1CWCEnGOkDYJxNJFQe32DjGHrShEiDPHa+QFRWJOMpMZTEUzqu805I6dcI6cuEi6K00LE65uIaAJwM4B3AvgsyLaXgOwE/0UnbZTBmHmD2Ws9sQ+YbW8Ni4rS7/7StY3CMwrPYN5Aoqv6BIKlrttcY0SgsKSi+R9rpYfaliHdiLsCdREbjLjLGjU+TuTtF7vXZZZOaNRTWyTcqlSylp6xLUHktxArIqpujn5MamThRCiJUAjYzBvfWiXOZSSIq+/bkBkQP3CWHauKGYEgaDdz/d1z5H7uhpxN36HUom7nvaDIerFNWzSHuKBAdmms28aYOwcoQGbxw2aAMiNlVuOTRKCZeV4IwcWim8AF67TMfB8RfRuA3wPwIQD/GbIb/HuZ2RxP9wJ47Oh+CjvBr7QP5u5la6xsaMUJacfjIGKcVazYYcF0HJGAWgq16XcrTsbPkUGo3reLwWwsmm/V08I1Dbf1uamLXGhQ5NZ1Ok3krffLf5VHm1SjYqFyQOMyhVm50m7x1KNxx72LQJp22XSr7ifUtxE5bNSJa1eqfuBVPMjcNjpPOp6ccj0Gk7jghjYTi73/7R6e2mb9bT7ZSyQdXzjSEtHtAJ4P4IkA3gvgRwE8d9f7OewET4Pd4mXRtBNfuDjDA8xML4vEXCc9Qtmkh37D9OAe2oyTwaYOYdb7iJ5ubIt6SH0bPYKPrl+IiYP2+ucuxyHHbOtQ4awhAEUvbQXo+iNKHq6f5SzF31g3LisFB75Jd53jmK43MlAZp3OurEhqG2qN1I15nj0hfTSnfZsRWukGahgaqyH+SQByckMcQwxgyYhYKZKaXITBVrE7DZ55abD2YqzHnw3gHmZ+QDv3YwA+E8BtRDQpt30cgPsupPF+gusxwHW1RBquONbrXOftkHRJhdt7DOyuk0629WXEdbYh3IW81G1jJ9KyM4Z8VsXC2JfPD+raNe6CVu/z59kz0RPIdn6Jlrm/vfqwGAMQuPd4rlhF2/isCKPfVayljvvVUcm7Xpd27H6fEy1/WofO1SdOwDv+4AH87gP3Y5qyxCDnhNffc8+43RPCxSDt7wH4dCK6GSIePwfAawC8EsCfAfBD2Hkn+CVQSsgpia5AstG0bHGpCAEggTDz8TpTXZEXLqL2x3vLpi3OGDhxEjFx7frjEHnExa1/gHAFy5EVpIWxi6rDKguRsDvRGr0cSiYkNbwUMIgLeMOyLw2A7KtfCMCIs/S5s5GoOXENYxpJCj3xiM/oCV5F1AVVdm4ZmUH0W49EbGEScntKkgiQJ7IJQNJMHtbqFQTGz/3aa/FtP/rv23e15T2eBC5Gp301Eb0UwP8AsAHwOoi4+58A/BARfbMe+54LbD84zqNeqgEWvHQPAKN6THIPd06yyG37l++bTw1mOSJJ3Iwr/u6f4eMZiGvbiM0ah9rlHkDFxHnW8cP/hDlUfkWQ7J8y67Ygbj0l1+Gs7Vb6aRc2MDYA2bxYhcw4ppZbt8g74swx5bFPZ9wm2VBA2DpXaAiGidYuaVhVRdtnVrmq68PGbQPHpURIWsyibHlPFwMXuxP8NwH4pu7wWwE88yTtEIAp166cm6aq7Bs17JCwZ0Cj/Vn0yoVuLO1IXmT9XV96SnUXuWh06KHXn2Nbo+9rYt82A83o3l04uM9TsLSzislwxLVr9Fwp4rhD5JSkYmH/h8U76I1B/VjdUjviuJDgVCcBK+pFFMWj7uwI16gmgKtS1q86o8oT+zGgirnJENcQlKviakicatZPdU1dOv11BGciIuoJj340vuUrvhooBJ4JqSQ87raP0sgdrn7FQsph4bNvLwsrnMxI42iBAfUFLyFy93jPqJ3l7+NE3W1GkpGeatds47Kuw/u4Zsxa7wlzAc8MnqM6IeIEz+F5GurYImdwqdmCtAyD1X60FvkY2xuRyJ+TZAsY05UNwa2diJgliNoAdGO16vaqbfd2i4bP1jkFV25pNbMQUuiSSndgdwkxUjWWOpeFbmeZLir17jg4E0h78w034ukf+2SgZPAG4A1w/uENyqaP+Gktv8aJiaipuHDc4haogRnja1mNMmPYavDY8Z7jrlvTV7c/qyIuAC3SBt/vt5gV2QcfkczSZ0M/7XtP9OI5VC7ei7/1/HaRXpJBKpI5giqyRh2Ytrw7IzKN8Ulng9oud+MJRFN5rW9s7rQ/GsFYJRCuiBvF5GPsJxcDZwJpBYKwpkzOqG6zWZTtjGfiHFpdZsmxAFnILSJIIEB9es8ZwLzw0/ZI17tp1pK4t7UR7+/Pjzb76rnJEhlk8opa1eeNIOpcZE+jzdEGPLMVUwQYmI9q4ECihJzaLSRNjzPR2g0zqItzEzjiaFcAAE0FxdW9a7t5sjmIEVbig14Gt8h9Erk0z6Vao/XTxdhuzn3awrwL51Rrm/n9IQEXtvOCceeUAMpWz7gi7mnBmUFaUiWJEXVZK/0ZgwEYzCoOlwLb9sqE2bioKxICxoFa4wbC9wHSdpyuR6x4fTwWEXjreDtYE4l3QfxWzDfRvxVVnRMWFgWSgz+VDMEmcVMYVsLMQ1XCqbhMft6I7Yj4jOYpzpc9PxqEFu9jy/w1CL2YGlogkHFTC76RpRcMaFFHJUFQUNIqjEGfJ3h0mCCviMU5X7o44xGcCaSVsYvusIHoXKRW4ii6lSJ6LbNwSWLVMTjoJzw2YESkXz69F8MBMDfverRwWgIwTvxeHXO3qK2t3qiy7d4xEtTrfFNt3VWhFAmGoJmBmYyByDNTwjRlHBwc4OCGA0ciLkV0ui19sEihtXkaEZ2ImLYJNlI7J3Gvo6atTgSOucjtfLSW4RY4/HXBD8o+XZefghjsRdpQLcqJQO4aI2StDXVacCaQtoq4OkmUIMZkwhFZRCT7p+1KoXfWWbQFMFzsFNqwlw5YwW1gqUOuLdTRsR7pR1UDR9bV4XNp3RjVt7d2Pup8wl3hvlkRiwuIya+ThO6pLrasge+ZkKYk8cCNulIXJoFkGxGM/ddRH12du3mGJqQu5igSMYuIQkegrX3pmxIRF+XVWOm5rqbGVAmPmj9JSkhmNQ6c1YxT0ySISpO4eFK87pThbCCtUU5T4HVy2LZTAGCRjo0a4i/GUDJQ6sJVMjKq26xv4+SD7lCwoC5Puj7Vc7mop42QdlRWdRs3XSsXOtJpW4RQDmKSBYdxchV0o+RBJOGJnjdLsmCThgzK+1G9rhQAxon6fic91s5N3/9wQ9VP195FL2p317VSTng32ra3YSKt9S+KuFSfZeOq4xMum8ydE6ssZsL5o/M43BwiTRDxOBE+dP7Dy8FcIjgbSAvIxBVGSQBniBicCJwBkeHgUTjQQ/Hl6Y4WkAJvIjZXH62JnAS26KCwCZch06j2cNz93Di57O86K2daj6uNhpno8ojBAMaZ1jjzcVbYWFhc7jFuKgs2q2vicD5CKeJ6kU2jCw4PD8HMOJgOZDtGQt3rR6N8CkQNSVA9MOTdyJpXwsYxGwgAMeZZrsu5EqtmfvRNQfcMily2R3Yb98HBgcYoVzeSiP4zmEvrKjKDGTE8yNqJScI0nXPE9KJs0wTbl6c4G9DdA1NCmixKDOLTpoLvf/l/wn/8xZ+vBJ6Ahx5+ePE+LxWcHaRFjYByY5RFASgQQUt4VqNIg7wdJ41C9PJx7Iar2v4yJK7tIepCi08KC21XA8S258Q2Y7+OM8j4sfDNECvOqem2M88yxcZNXQ9jSNVBgFNbNK818JmYQ3U+0RYkOG58Ueccic+RY3pHgqZT1ZwWvI9RwiLbnM300dZC3iyTIOq6Dhv+4rXv/cBDuPeBd62O9VLDmUHamjImv+V7ge3Q1hgMNFHARL0RRO7ViK+9zpm35ziuWl/Ducg97Lh937ZzQHxG33e7dyRKRyJhGTE1j9bOte2ZdMFccHh0HufPHwIM5JRAU0aesojHxCg8Y6LsuwgYAhtSLzKS7M+OBUQhajfy7scIru6cOFfugjJJxPRp1OO1Lftu+nbxfliORErUVbyIoj3DrMfuXjJR2OKLM/tG0TAask6TThXODNIuoS5QE2FY6xJBa89KcLshitwTuaFH1yD6TwM3R5t+t40zNGC6N1oE7cXZkZi8xo17vW3N/dMbvhZiJNi3UUmJgKwV77MEKmzmWYoLcMHBNGHKB5jOTUhZ51TbmYwAmC7Y2xJ2maaBzhv7vtbWWvsjpG/1+/achmGMK1IQJMhfCQ5Ru25aFxSUaNX70qQEbdp9Pi4VnD2krQRYfjZcrBeZzdjSUvcGadHtzAbTeNesrtt1yLogdhhKWFjDNkacB0ukjfqvwZrvtkor5EjrXJLgCMso4pM9kD83NAV1Q0RT9jmjtT67aKzPp3reDTlhbP65A3I280fjvOOlEU6lFAApWI8bIkKCdLLjfUVM1ooVwiSAaqBSkVrjkSVUER5kcTnhzCCtT4wZOTyev02LYoYYWXQyJV+01CLcVF0TBiOXQ6x6b9fs2k+j4SPL5po/Nx5f25wrXm9lYQB4TSIThaOro4nJLW6Nq8+aEogJBzfeABDh6HCDmw4ybsJNOJjOIecJ6aCmzlFK4srI5MYoE3/XCBVpNpHptN1ZjJDW9o+lnBYEqp8LANWzMLimkXSUqFNKaiQbW49971jT481qbokBxOKrzcB0oOGM6tpJSSsuhsT4ywlnB2lRKbt8R1dHthO1/Fil7LD7oiiHsDXGgEtE41P83GpECW304mqE5e+euciCluN2Iur19pubNupvud8ISe1cp9uSIH4xrprEF55SlmJkidRMDOckjrCDOY/j7asXenTUYsZGc7p9nnmFDKyBWaR9AemxGLmFrkVDZtZIp+Rbdtjao2XCuyG+SX2XVzo+I0hrlNGMNaRZF2DQLBOWJgJmtQHp6k+UwZsC4iI7lrtYWBHXXQpEYlhhksJeA4o+crHE884NAMvxaMS9EbdYIm7M7WX/bsYUZrPWVqstM6EULRnDxkqNiMVVIz7Saco1Q4rF9TWdy8hTwnRwIBtME2FztPEql8iEjEkT31MbLI81ZMRivIOZ23pt1PWj+8t8CaQIaKK3ZfTEII6FXQHtuzAXEdR374TGwxITOFmAiab5EEvRBQtTtOAJ+57Z5+cy4+zZQFpZdpZXyRVxlQqak58ZoMJgNd2TBmAgo4qGGuUjQeOSobm2pkaGnJ7j2nVRrEsQa+aaUWk4xuF1VXTcpZ1tLp+WW0NXmIZuKhulRJgOstsBRISsYYEAAqLas1Rdse6u9GdkhKv9aqxY9VqTWAPCngQar4D/ru/JJBBD4sppK8d1CS4gpAdbuKgMnxtyCcSuufxYeyaQ1uy5bKTVko+5qKFYgixkUyilfiDIrnoEhuyxxwxwEYQVN4O9xECR7ZlqPInlRaPJf+GLREVeKey1TCCoi66TKc2UE8TdeE+P/GuLN4qj/bNHejsl0lIzapyhhJwT5k3xcMCcwh42hqwEFJS6Dy9Vrt/bAKJrpneBtUhc+yQ6jPzOsd7wMUg7MuDFuSlsrh44klKp3wszdB8tJJX9WwMTqmGJ7LuiuqkNWY/nisTXpfX43ne9C3/rX32XI91BnvBXPveL8Ojb7nRjkxmpXNfVly4cWBKjuXjVWkklI6H0Keph+kwxYC3dJiOO4SIbVwpt7Wkv5Gi3o5/db9duc29ETj6Kjurv30WM9z5p4oXMUZG6WhxS6NQQYMSz9t8QabvaIONYIlXU0+23Pc/GMyJCa0jQW9D9OjVaciDQLqVRbXOjUkUyphCMbTRFTmvnZX3lA/J7fuDlP43ffNs9Tfu/8Za3Dvt7WnAmkPa9H/wgfvy//aL/vuncOXzpH/8cR1qx0FVxzXW5Ip9OuF29M7EQw/1T4vI02EYtRwuKGk6LsLiL69OR42zjEvYMs+AeZ4UeIe/4GPyTdVGXUqqOF7lER6wY3PW/bbv9vi3DqSdgrYQR2+rnqm9vFaEpGBvjsw2n1T+bVFOuxqVUETuUlkHi8Bs15jgBr/md38bPvvY1yz5cRjgTSDsEVl3KdDNELmIiZ9RZw74+bNFUIggpgW3q4cYXO1pAEYzCR92J/DlLi3GMfoplUEY7EfT1kiKXjVwo7pZORCHudoREbdtyfc2SSVmMUszwSCMC1KqcG1HYjHrSVOVk/TMaDhfmTaSUubqslAjbeI6OjhrDkkk1o/mz51UE1nmi1Mx7fZ+CpPbsIvoVrHbzNE0gkl0AsvpdUwLSlNUgJ+Kx7CJwYXr3acDZRVozDrjCzy4GkYlCQQRmNrHOWEJoS63N9tU54TGmvwbBGOpIMRfCDvoXhW6ExWTt9oaungjsouf1RrNWn6wLu0rMwWep49LJMB0E0UBUCaS1sTTOiZqh0kfHWbkVgdq+oI6vIThUnzkEbc6b7tuywy7CkorFJLu3Z0u7UwmC4NxV2maASjA8sV9zFuDsIq3pFvHQbC+KPReUy4y56K4vDJlsvb2UFjGkpKhk6VhJEYHBQrQzdmw2Q5ZSCgJivKpBr3fNWkfYkCJWJLRdEey+EYzEwp4jj63grUtkViNN8gAN48KtGG2zQZYg6i/DYpdbA5T3PwhAZhAyzjdNk/4WAxfDOGh4BIwvSjtGiPuYbdvSg1Vlan2uyRHarPspJ81gYo8nPnfDJBk6uSAlMXZigr8jphkFUiFUospYAyp0b54rDGcUacNCJfPXqX7BkOJkXIMQUkoounfKir0EwIrxhtC9eFpsMWLH7Z89W3TFDmEigvV4GBZhr5/GY+tW6fEYGl07ShTKcwxJtrXtkkpPIGCEcskVWx3YntT26ziRMhI9x/sVnT5MQtBgl8YnV3m0sLjlv4KoMgO3FIuxLuq2KScUzHjpL7wKD7zvPbVaBYC33PeO9X5dJjijSAu4uGMRUSB1A7Ebe+ZZnT1B5HMRjrwZe6X1t3Ev6MIzEQqt/tjkvSrbX3CYfkFG0RPN10Yv690jEanWLKlrz14To+eQXWNn+wLr8b7+XLS2j5A2AqMS0TimhigFzlgJVutHH7dd55KZwz5slb1HIm8SDWUpFWO7AlTRV26rkU4mPgM5J6QkUtpLf+GV+M3fvWfRnysNZxZpxRCQ1bcmyCr1jUVcsVq9MJHVXhhQU3eMefhCgeuxvgBLtfYaWIxv9NcW5a4uVmIsupLqWr1BxhFypcRoNLz04m+8fu2+/hrjRCPiMkKoaBCzqomL8QXkj+flvrk5PvouPYJztXrgeHCO34nEAGq4pSMkV4Q0Nw6hxhSH89l2D9DUuzTJsTIn0I59u9xwbH4CEb2EiN5FRK8Px+4gopcT0Zv083Y9TkT0z0h2gf91IvrUC+1YDaOziBVykYeCMhRD1qpohOrEb64N7VCl8IzWGNRzCuESQefSdtoO4wQLkL2t+Bf72nC7FT2yabMzZkWjkX8SfP56PXbErXv1oD++TR9fkxL8/QRRNo67nafYoP71Pt/4PbbpYYdUnwkWEZ4C8kaxOIWSMpc5c+cksEvXvg/LLSxfCOAVzPwUAK/Q3wDwPABP0b8XAPiXF9oxIokmS6H8idWWnaaMaUqgSRLkZ94AVFQ3qaKxLQ4xXJAaWOIGzsl9dZWS270t8tgu4HFxcNKSOFTLCMf9Xxeg1eojotrGyRbwwMDYt8ytH9eCMLwKhW8WbXtXcsNlGDMIjIODLKVSIBZSRs0oipzV3CSNThr6sLYLfZw/u28GY1bhGA2yrd/jcxQJcSQwui7MYOa/tdga6WbP5By2oNCMQjOYZrAZlXIBTYw8Qf4s2mkKKtYZg2PFY2b+BSJ6Qnf4+QCerd+/H8CrAHyDHv+3LG/xl4joNiK6m5nfeZJObcqMV/7a/8Ab3/57HtDOzHjGk56Gj3zEbS7eiiVx9u0c18BE1saV4VxWKXeixjDYi6i9+BqBwz3x+pFVt1mp4YGsli3TDV3/88vDILt+ts0HkTZFbg2fN0AtvMqN7dkEeEpkFVICtyTya61Ho3FVKaftt42pf1XtM+B99YdQnc94zFXaBJh1GFmkCK+kGBCXdGf2lAnJXIhGCOx6jTems4uzF6zT3hUQ8X4Ad+n3xwJ4e7jOdoJfIC1t2Qn+aLPBi378R9rrQXjRV38dHvXI2zUDiDAx6haOicFFXthYjzOfXEDYzuBjmTuRk9Tzydtecw31v0d6bTcHw/ur6CyL3IlMEdcLBsSg55IwCaO3gId+OPfWTCCkrIgbxM5gOHL1QiMuLDxUCEQIL8xZpSPNWeZghONQc4qjaAsVXtkLvY2s0ZHguh5rFuEMsRYnCzsESN01kvLJoMTIE3lcsVvMc5ai5EkCKWib9foKw0UbopiZabCT+w73bd0Jfg2sJqBxhTwJuZyPSqXARDL5ICfd1bIZEChwUxe5VG9OyGHBmw44zmTpv6+Mt/m0expkXdFV3e+bgIQM5Gg9rR+OqK7/LcV8J0QpuVEs5axMkZrmrBCXN58ERyh5B7y9PiZ4aaCzDnVjTBZbvuTecTosSQRg33qSdBFIkIS8I/J0OlsPrFt5GMcl5INUEVrdPikHfZZM9128jjMBF4q0v29iLxHdDcBK0d0H4KPDdRe8E/wIKCxGN6qkhJQZZVOcE0A3SmIyt064P7yJuHyaUqRkIlZU+dvFX/tEwVM45rjxe8/dt/oju+dEBBwaqAb96fu8Jk7Hdv3a8L+cl/9SuK5XIbYiLKAcNYq5VWFd9rzpqLZDzlWjQVHeV4grJoj01RmbTGT2+sVmNQ4Gum6ZnDm4UBvZyyC7vAPtbu8vA/DlakX+dADvO6k+uw0oE/KU1NAgLyJnwpRriZSsfyaxibgUKK3+sVY/ixUAN5vNMLACqIaoUbDA2vs9NkgANbZ2tOBd1A3XxtIzFsvcp7ZFg1WPhCX4Xm081pb93qUA3ehzDWGj0SiK8LHkz3AOFEeZJFPLttzIimDTlDBNSbJwJgJN4rJJmcUQpVU8DVH9evPfHiTkg4x8kPX96su0fNkzCsdyWiL6QYjR6VFEdC9kE+l/BOBHiOirALwNwBfr5T8F4PMBvBnAwwD+4qXsrImulHVCSw17k3NKqc1WQtSw00qVjxdlG1fSFug5zTZLqum5kduOjsV7+u8nAW87/I7H4/NWx0ALr+jwntjPhoDAGKr9Jv/N3b1N3+Vo2w99f5V7qmtGEdxLE5kobdeq2OvZO7GfCdjMG7z+nntwtNnArO6Hmw0++KHT2yXgYmAX6/GXrpx6zuBaBvA1F9upNchKaYkzZmJsyqzpeSriiNlGat2WmsI3z67gNY75iCiR8rtbQzeAkoUsz4lGqn7foDXjkIFZnntONEKi48L/ei46utbcJr3uDrTqQE9E1ojVgoN23H3Yj2BtBtBYxHvEhdsOEF2yem0Vb+UU18R0iB4KAihu6EyMnNhdhJzVzeNGK3n2+x9+GH/rn38X7n/3u5uub8LWnGcJzmxE1Ai4/6e+NiYxKEgQubgy/OUyUKPZ9WWrS6XqVJB6U805QsqTo3h1kSwRrQcp5BcQorC7X3aFNdG0R5JtARCFsSAca9f2RMORsO+LYxVVZ05r5RPDX7jcJSNvVy22xhUJsK04DIktBtgMhsjs/mu2rBsTnaN+myDVJQBFThKf68SaKWb+a+mDrJkZm3k+s0jaw1WItJY1U4BUHHHTlMAF2OiWelKcW+6Kvk5ZXyGIIQW3RimeoULU5qyW7oWOEJeILPHU3SHzLH10cT2OZ8VaPPq9KkYOOJwh7IhhWp9HyLzK2deIk1qgq41dn9HfGy0n3i8ljOZLRiSkcJHWcNYr+9uDchV9aaq+WQ6pdOZdoAmgXIBc/Lj5dmVFzX2vzzRcVUhrVj7jWikVIBMKyGsh5ylLlJJuQsUFoLnId67i1ASpIzWXudHbiMJmWHGtRl2vc3M03ElrLrH6GpGTG4AW4xkhQzCg9pbnGLkU24iBII7coCZksZSB+LxFWhj1sbd82xz0hi9SxLONqZlrkTXxd1ddu5TiRjDWyA45J4hXjWVybC6zR3nJRliafqfjNFeP+WlTruGJmJSGZOPy8izZdOtkktCVhKsIaRkPvu99uPfBB1BmednzZsYjbrwVN5+7EZ7jmgBdskFEMz3KFjRcPHOEpYoA/kSulD8ea3G5E1mbk9ITD0yIgQVYi5gKz46jd4So1tdo4Gr654aZKM1uD+5YO76GtP1nVB/s2bYZNavKkbzkaM3oYCVSZNeG9yH3mEWbwCgoHPRX48ouakPepyGsegysZCwR4ahs8OB73gdGASUJEHn3+z/gSRJXA9CufsJT7cSOwRW33Hgjpmx0RvTPr//CL8bzn/ksD3csG8g2mbPUCubCmA8ZXFxyBXNNkC+bqhta5UIvxwpbTASe5XmyS/1SV4zczn4bctnvYBqCUYMGcVa4rP2O2TcHBweN4WhRDC70rzcwDd1WA325h95wF8e34Y1b3A3JJCtLtiKx51rEkkGBldBJbkuYpgnMMwpvpCSM3ieGwBlIAHuGTki/0++USKKeEgOTJLknYqQD4C3vuA9/+R/9Y3zo/PnaB2Y89PCHdvaZXyZ4LTM/Y3TiKuK0wAc/vDTBH242TnEr94TutKdA8CoYTSQRhCqLsSNcaxQ8IFXSRUVUXRbOxoLhJcI2I1EMEumPEeAxwHaEOytsGXD82Ico7vfPXzNOLRatD1EQxnbnkmOmYxYXxcGG0DG5Aqa+hndUB5rDjnc+73pdkl3F67CVI/vuB6qXWg6t7PKXXWy2GsZWu1hUH8Z7P/DBBmmvNriqkHYIjpCyOtgWux6HugZsRTcbLgGgTChzMIJAjFjGFAVJZaHQKP0GNbPHLKfAwCJryOn/GXDzbL2hRfKyPNZsfK0EpO/dSIy1z6EqEM7B5ig0SjkG7QfrNZkhSBCw5+Qp1/aIQj80os2zk+y8vk+mivhsIrGWf6lhjOZJICCJ0SllQp7gWVWWAeZ671UOVz3SumvAquh7VgcBG6AQg9SfK4YouE4LJkk2YNGtCOSIachaFykEic3AVNAs/LpQFYHtOUY4tI3Ypi0gqyZoz+0/fU+jhCDm2p8800VbmN5dMa6JXw5t1wMaqs8MJkl3TJS83rR33qldzQUmCr3w55jk0Y7TkKyguORSeHaiZe+uaDaO7zIRUgzrca7blwTxWNw6c+27BVNYdFzuxn4VwlWPtIebI3zw8EMoR0WDKggH6QDn8gGKxYwTQl5mrXRhiMpMusePtGl6rFuSGeIbZMhet3P1GUb3iRRNmwVVSuBWHVBYxRQ/R8cC4hCT70dTuTV7WVL5GfTm5jp03FzH3nN5lWU5WHiX91O8WgWMagCTueljkr23Oi4Tr1VCUUIrc1NzghFEWxCjECNZ5JMG++fg8jFRmEl06fOHh9UwNaWrWiw2uKoMUSN41CMeiUfcfIvapYTmf+Ezn4W/8OznYd4APDM2R7MYorxEDTTNTTCZZzFClTkEBeibd1fGXLtYCoNneLGwMovbSAwvBeAqurqhKOisLlKiirYOXHtgH8bFSlFRutNvw9VupW6PwqtPLoVouGGo18GPcwfZdfKlOOHq5H/kHOaC7JhEQBQUEInhKh9MwmlLUXHYNsgiUK46dD4gpAP1wbrxCR6bLP5Z4LVvfCO+6SXf20gx54+OcO+7HjhrRqcRXBuGqBE8+P734cH3v6859u4PvN/1J1YKzmBQsSTzyo2Sysvib1SuGkRit7wCARlQWTjks1nfQW9zMZHtRLyOEHXV3thk4L01zrkFoXrm2pzQz+PCHxfPH+i7/f01HXBpzKr0R0XreCwYrswl5IYr11vtT/XSTJoMAthOAP7b/LKZ8KHD83jLfe+4GhD0RHDVI+0IqhQn4pVV4puZgQ3DMnwICZQmrYsLcOSsaPFr3cepRa7F9e/aXDQ8sRqy+nujy2TtOcPxHcMFR+d6l87ICHVc22vtCi1K6Jm4icUcr0vVwi/IVrOLKKNKA2QIqLWloRuG5VQ5rW2WZZs9ZyBPEmyRc0Ke8qK/1wJcm0hLVONWIWKlM0an8hZ3uqzmuBZA0RqLqu/TcsKjxbiPDV5d8EDTdg+7tDM6vi3o3563ze3TE5P+ObV9IW8et81165JKAAKn9kJrFiuctGypGMAYxQ2JlMRnm5IkgYjYm5EmSb1zsTgnNUxZXqz169risAbXJNJaTLEjoblV0IqaBNLyJ9XWSuEecSeI7tsucDFU1SyfpchoCL0NCXbVG0f3bTu2DdErMrV93fU5bdQXN+p1fK5nTRlhJLPuWhaO3OiicU4obMEj8pdzwnSQkVPGzOxVJ5g0VlgpceXellIJoAwCTq4RuCaR9mdf98t4031vV0OqvMRPecJT8BV/6gsgziF56bKYAOcWuldLmuRQ3Ls2TZBrWOU9Jhxk2+5CFtsygaAVo211V5HS9OVlETkRCXX7kzLmivH6HmIkVnT5zPMM297DuJIRHjOsRbHd+wBuiYBKKyDbwcCIQfIsGtlGA9WnCvboJdsMi1JxpGONqsoTQDlpQnsBZcaBV+Rk/Nh/+Xn8zGt+qbrVKHBX15+Bdz/00DWnzwLXKNK+490P4h3vfrA5dsuNN4qfbkaINooinvj+WGtLWT6uabeki5Q4CSEoFTEMaaMrpwbVYmDA6jhjqZKAtxN0u5GUt+Siwe4diUNos3J/DM7rb68r7a25ClHUcu3qhN0IaKigcFKzJbjuGnyjvoVkMx8McwFJ8TU1LKkLhwhIU1YCUPC2B+7Hf/ut31hOynUC1yTSjoASYToQq0VKAIruIl+UcxZZXImlTq/fp1iWVKYmNUmzxzGzpgGycGZfxFWnptxGCPXJ80Arpkbxkogwq4HLdW1e121je/FzKaa3EVuj7waezWR3DmKQ87S0rJNt7WnuIEVoxoxpyl4t0qqR5JTVizt73mtOCWlKmM4J0rJl7FzHcP0gLZkfTzglkiBtmUmipVR7lcQCVmsvOdKicG0IwjG42OJNWqZTL0FltGt6Zds3WoQlNtcY1wWilO1gIrax5crxYZK/c0svjxp9uNkQuUXwqP9Gzd2+m3jdMV15nhsJDLk1P9ZE2ITFeMlUBp1fiWAy1w7Ve65vnL2ekFYKgRFlDUUkoBDmRCgaXEEEQPUti8EwpC1mHQV8obJx36zXlXY9SWYON0gYrasRYhH03qjU7HinXCwCFxN5W24n8btVF2VIeGKi1CKg0QPmDmHFlWIc3gIjEtXz8scN8vqudFZrOlUklGQprS2sKocVFs9kIxDD1HSQkA4kwydNBDMsXoDt7pqC6wZp3/B79+D/+sGX1IXJhMfc8ZH4sj/5+cJ9oXGpbAkARRLoRSYD5uKL1/5sZ4MEq/1ULcTiC5b6yURadhTVUi1QDVHSTnWTuJEq6KAjC/TIddMk53Mrers7xnXVlkiIoWoGkUY2Waz1wMjWiKmay5qIJPQw6RyCdJd1FYEh7fr+tCbRKMGUdjPSlJAPEl76Cz+H33zbW4NezPi1N79p19d+TcJ1g7T3/sEDeOkvvrI59okf87H4smc/LxSFk+yeNBWwIqquRhQ2BDCkJVnPZkNh5WqAW5crIkJ0PLQiJEFzfGNwQ5KFLy6l1p3UumfM3dLqrrVtFYVLh9hUmzTyESWBUubKtcnEbR0X1R0PxMeKaP9SI1NSpC0a+snuc/VKGrDqEVRTI2WyVD9OYmWeCP/9Db+B/++X//tJXvU1D9cN0o6ASDddMvHNdMCUReQsKtshifuCAaLsrqTEWkpmU9y1hPBHANKkdY7U8iqnLHiDQI3IV/2nDMCoghEJQHRuBmu6nlybjAIouCuVoCIlOyKbqyveUDggYwoclIRbJg2AMFpEgQA5555EFM6ZUAAwkYeIHhxMsPhiy8ypzyhOJAyxD87lusXHdS4Kj+C6RlpxR6DuSaOHU9EkHcMFBjgL8ll1fcsgAsM5DqkVOm4IJrovS41m4s59ww2y2Um7JBaZc4j5wvEhDQSzlAYciH4r58RlY5ZqIyg2J3a+zhGUszJVY5YRhXYfWPJ9cBhQvZVUeqjXpUAYGju968rVfzzwdl33cN0jbZq0lpCVCDJDSWGU2dhmcVcilGO6UURFYENaZrghy6HIhpYVxzrrMCtic+WDYMJm3gCAlJZpTNM9osGRLOrITOyIz7UF/VW/CwetSeIWKGHnrZge6SBzcGFZ+RgK4q7mSGoIYgLlgrpPkvphNaHe61MThVKoMo4yz9dkcMTFwnWNtA+87734tz/708hkflfCLTfehOc94zNw43QOCbWEJ6mnyGRgSoY05MkAEpQBIKnbiAHPTIHtytcirC1cbcDbAeDPb7gqTK9WTqXmX2Pu3rJ+sV3tVcttrL52GXVtCwe1a0PFh04frdchVJIQIgg2Nw+CldnGXKR+F9e6V4UZL3/Nq/HA+9/j9aCYGPe88x0nfKvXPlzXSHv/u/8A3/Ef2y017779Tjz7U/4IbrrhBhAlLw5nSMpe2E2Om4hoyFhmlhzuzjAESsiUVBQn58SbzaYRkb2QnFql5SB7ewRB1KTieqy0YTqzc8HgZvL2iTqXUzRssftRY1nS6Id1TqzctfbbkFZ4ekbynejs3hSqeIjBq5ZePZo3+IFX/jR+/XffvNvLu45hl718XgLgCwC8i5k/UY/9YwD/M4BDAG8B8BeZ+b167hsBfBWAGcDXMvPPnE7XTwnMODURqEhFx1KkYgIzMG80+J0SNpviccGuAqpBy+pMMYAMArheM5vYzcB0Tl4Bq4hs1R7BhElTy+Z5qduaO8dFYeu8tOaDsSMmajtnJOPMmhGlAfbJOK2K8Dki5gRXC+I17rpR/TnFrTkQSvGYuT2M1Wo5Hx4d7kXhHWGcf9XC9wF4bnfs5QA+kZk/GcDvAPhGACCijwfwJQA+Qe/5LhKH31UFlkSdcvLKftm+a5CA63Kh9lDdTVwDBnK1xpobw44Zp0pZwvTkWUn3oknh+Sm4S6zt6vcUndFidVU/7673Mi6mSxNqgrnXUOr+XCRGczxZ4H6qXDdZhUQTg21ObC5y6I+L9eyIW+Z5r7+eAHbZgOsXiOgJ3bH/HH7+EoA/o9+fD+CHmPk8gHuI6M0AngngqnG0ERHygfwBIn5mDXdkBqZzgBmjpDqhxcwCYMY8U60uyAzezEDKkOwXWenpnOi+HsTBAGnO74KKsmzRaOVmkoqgjRGKo6tXSuGwtp1Inz1pFJTLu1W8nXkjCD0p5yZGniYVi+u2H7IlpGyNKbHQUuUQBBRsVJ+V9plYcl9Twg3TgXDVo41asGVOUQAczeKs3oRi53vYCpdCp/1LAH5Yvz8WgsQG9+qxBRDRCwC84BI8/5LChw7P41W/+jrcetPNzXHW+lGswRAE4FOe+BTc8RGPdPFY9FDRP83QbFUznLuZuSgBiUMRcfOhOJjVRr6y7Q7oCnV19xBHa7UacVh9paFShF7e6KgmW5u7xZxFnrZnor8ZmxLL/kksqXeUspbzoUZfljjvvEiIZ2Ycbo7wy294PT744Q/h6FDE4s1mg/c//IGLf4HXAVwU0hLR3wewAfADJ72XmV8M4MXazpmhse956CH87y/5N8del1LCd/31v4M/dvsd8GAKxw5FGtvSLfpkoAjCpsfqdY1VuV7PzEiqb1KJ7fQIHnTILoYYADJsB7pgNIIhe0Uq4/W2MTe0dlNFfAbzBkZAXIVIsh9OSkkycyj5pteYVYpICaXM+MCHHsY3v/R7cd8fPHD8C9nDAi4YaYnoKyEGqudwVUbuA/DR4bLH6bFrDzQlLx8QOG4DYxt/mbWXKCAm4AjmRiTdDiNUOyNt30TuAhGLKVfhmRqkbTqm259UN5D/z3VfWgsSqfnCMX8YmpxeEZyM0lCM5oLrsiYWx0oURhhmNn1VtvWQ+OYLm/Y9XCDSEtFzAfxdAH+SmR8Op14G4N8T0T8F8BgATwHwyxfdyzMKln1StT6zqLKLlDlZrq0eCP7VYHMO5+Hit4nOxEskdetsCNiw5ALLW7A2DGriAZBSrZ1l+/eSFi0XtlgcCd3a7BtfVYLkbh4PnqiGOtO1fQcGlq1EWXSMi5j56xt2cfn8IIBnA3gUEd0L4Jsg1uIbALxcX+AvMfNfYebfJKIfAfBbELH5a5j56tmO7ISQklQEBOBul2QWXq3rmyjJoi1hkTI5F/VDXsZGPyRMSRA29ws8buwlyEWABiwYQQj2qtCmc06LXIIhoXxPWkvYsnosSikl26bDahZTICyQSoo6AE5F8pU3s7jLICVuipa6wdnRhq5K2MV6/KWDw9+z5fpvAfAtF9OpqwEYwD33vwOPvOUWLSIOEWmDWAsIAnNpOR6Y8Mhbb8XjPvKj/BCVymVZkdpSCJslbsQhyYZk97zzHbCUP9sJ0I1L1l74ZJYNs0ChumRo3pCWGbj1phvxxMfcXRPYg0HtvR94CO948EHlnBDLuPY55wk5Z5SNiMXzpsiG3fMMnhnv/+AHcbTZXIrXcF3CVb/DwJWEgzxdcOmT5/3Rz8A3f7UYz40rRqi4F7kxa4SWcMvXvfl38Nf+2bdhc0p7q37aU5+G7/zav4VzBwdIKWHm2UXdl/3if8U3/7vvO1mD7qliHO6R9ji4dncYuJJwNG8k7usCYFM2bbSRfronh2vEkocnqnhrFl8k4PzRIeZTKhW6KbMEhVhwBCe3TDMY54+OTuW5e9gOe6S9QkDqDeqTB8x1aq4XC/avn4ChuATWXxin362TIi6nSfpSi9SkUFdqD5cb9kh7hcAqNBgIwtJCjzUktnNW1QKQ+OhTxVlSI1ia4fZrgm65cYoP3sNW2CPtlQJlkuaqaT+jjiuIaees3hTQcunT66dbtDwM0rJ89nBl4KwYoh4A8EEADx537WWAR+Fs9AM4O33Z96OFy9GPxzPzR45OnAmkBQAies2atex67Adwdvqy78fZ6sfemrCHPVxlsEfaPezhKoOzhLQvvtIdUDgr/QDOTl/2/WjhivbjzOi0e9jDHnaDs8Rp97CHPewAe6Tdwx6uMjgTSEtEzyWiNxLRm4nohZfxuR9NRK8kot8iot8koq/T43cQ0cuJ6E36eftl6k8motcR0U/q7ycS0at1Xn6YiM5dhj7cRkQvJaLfJqI3ENFnXIn5IKK/qe/k9UT0g0R04+WaDyJ6CRG9i4heH44N54AE/pn26deJ6FNPo08RrjjSarXGfwHgeQA+HsCXalXHywEbAH+bmT8ewKcD+Bp99gsBvIKZnwLgFfr7csDXAXhD+P2tAL6dmZ8M4D2Q0rSnDd8B4KeZ+WkAPkX7c1nng4geC+BrATxDy/ZmSJXPyzUf34dlBdK1OXgepNjDUyA1z/7lKfWpQt1O8cr8AfgMAD8Tfn8jgG+8Qn35CQCfA+CNAO7WY3cDeONlePbjdDF8FoCfhAQPPghgGs3TKfXhkQDugRoow/HLOh+QYoBvB3AHJNT2JwF83uWcDwBPAPD64+YAwL8G8KWj607r74pzWtQXZLBawfE0QcvEPh3AqwHcxczv1FP3A7jrMnThRZASPpZndyeA97JUUQMuz7w8EcADAL5XxfTvJqJbcJnng5nvA/BtAH4PwDsBvA/Aa3H55yPC2hxc9vV7FpD2igMR3QrgPwD4emZ+fzzHQj5P1S9GRLaDw2tP8zk7wATgUwH8S2Z+OiQevBGFL9N83A6pof1ESK2xW7AUV68YXI452AZnAWmvaAVHIjqAIOwPMPOP6eHfJ6K79fzdAN51yt34TABfSES/C+CHICLydwC4jYgsE+tyzMu9AO5l5lfr75dCkPhyz8dnA7iHmR9g5iMAPwaZo8s9HxHW5uCyr9+zgLS/AuApahk8BzE4vOxyPJgkt+17ALyBmf9pOPUyAF+h378CouueGjDzNzLz45j5CZDx/xwzfxmAV6Lu3nA5+nE/gLcT0VP10HMgRfou63xAxOJPJ6Kb9R1ZPy7rfHSwNgcvA/DlakX+dADvC2L06cBpKswnUPo/H7In0FsA/P3L+Nw/DhFzfh3Ar+rf50P0yVcAeBOAnwVwx2Xs07MB/KR+fxKkBO2bAfwogBsuw/P/MIDX6Jz8OIDbr8R8APiHAH4bwOsB/DtI9c/LMh8AfhCiSx9BpI+vWpsDiMHwX+ja/Q2IxftU52YfxriHPVxlcBbE4z3sYQ8ngD3S7mEPVxnskXYPe7jKYI+0e9jDVQZ7pN3DHq4y2CPtHvZwlcEeafewh6sM/n9/HfFAzN7nhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(imread(uninf_cell))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fb4c1dcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/fasihussaini/Downloads/TF_2_Notebooks_and_Data/04-CNNs'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "da88faa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12480"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(train_path+'/parasitized'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3680dac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12480"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(train_path+'/uninfected'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "781125f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1300"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(test_path+'/uninfected'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "99f56132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1300"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(test_path+'/parasitized'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b997e231",
   "metadata": {},
   "outputs": [],
   "source": [
    "dim1 =[]\n",
    "dim2 =[]\n",
    "\n",
    "for img_filename in os.listdir(test_path+'/uninfected'):\n",
    "    \n",
    "    img= imread(test_path+\"/uninfected/\"+img_filename)\n",
    "    d1,d2,colors = img.shape\n",
    "    dim1.append(d1)\n",
    "    dim2.append(d2)\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "724f62ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130.92538461538462"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(dim1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ee83824b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130.75"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(dim2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f89fc5f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x133628370>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6zALwjpRyjRBiEYCXAezJvPa0lPLj0wXBShfBIltpYfDoGNqaTm1+69rtkEymsLt/BP0jY+horkFXR7MrFSp01SG8qhox3fGcrJFqbAC+rn7hxrzLHFuVLnr+7Ympn3u/9qlyq3qhXQsrjwS/CeAOAHdnG6SUfzY1shC3AcgtI/CalHKN7RBJINCp1NxQ3KVS0hPVHnBqw35xrMFSu1NU47qlHtTF7MU83KACyiqRIjHtn65SyqcAHFG9JoQQAK4AcI/LcRGfolOpuaG4K/fyO+U+Px2VOm/iPk6ftZwHYFBK+WpO22lCiBeEEL8UQpyn+0UhxDYhxLNCiGeHh4cdhkGKhZeKu3Ivv1Pu89NRqfN2k9z3y2QyWepwSobThHUljHdX/QA6pZRrAVwH4HtCiCbVL0op75RSrpdSro/FYg7DIMVCVyJHVxLJjlKt3MvvlPv8dFTqvN0k9/0yEqlccfeMZy6EiAB4P4Azs21SynEA45nvnxNCvAZgGQAqKsqErEotfz+iq6NJ2a5Tqqk24RfNrccdH1yL3gMjSEkgLIDuBc2eqt1UcQCwJSqxOoZu7cpdzVep8/aKRCKBB294PwAgEg7j7HWrSxxR8bDkh5VR/z2cVQlm2jYBuEFK+e6cthiAI1LKSSHEYgC/AtAtpVTugWWhSjBYOFXceSncsDsPq2WOdLGp2u/44FpMJKVyHoC/1XxeUYkeXjaZkUqwDBWCgBM/LCHEPQB+A2C5EOKAEOJjmZc+ALPY4nwAvUKIFwH8AMDHp0tWJHjoSuRYLZ3jpXDDDnbKHOliU7X3HtDPo1LLC1XqvIm7TPtIUEp5pab9akXbDwH80HlYpJyZiXDDC7m2nTJHuthU7SmJos6DkEqBlS5I0fFSuOFGHKoyR9kyTlZiDgtQZECIB1Su3ISUDLvCjc7Zddg3fMxylQSrVRV0cdRXh3DthiUG4Ue2jJMq5nyhyLp3zcJtl6/B9d+3LjKwI/4gpFJhwiJFp5C3UX575+w6W0IMO1UVCh3vzqf2GX5fFzMATCSlof9tl69BQ00I285fjJQEQgKoihROsPkxFxJuMGmRSsWSStBrqBIkOvYNH8PFX/mVqTr5o9eep9wPstvfq+NtO38xvvLk3hmPce2GJVNJcCbzIIHD8l8hkWiVjDbNTX8vJBZ2vgsA0NHagicfe8Sb6IqLo1qChJSMQlUSVG/cdvt7dTydP5XVMSjcIDqi0Sh6vnC/qb0SbEYouiC+xm6VBKdVFdw6nh1/KtUYFG4QYoYJi/gau95STr2o3DqeHX8q1RjdC7zx6iIkyHAPi/geo0dWLbo6mgp6ZE1MTKL34AgGjo6ho6kG3fOaUVUVtnw8u1UZxsaS2NU/goGj42hvqkZ3R/p4dsZQHTOZTCnnYXc9SCCYsR9WljKqesE9LBJM7HpkJZMpPLTrIG58oG+q/44tcWxZPd/ym7odj6yJiUk81NePmx48dbztPXFsWTXPlj9V/jF187i0qwMP7+53ND9CggqvcOJr7Hop7e4fmXozz/a/8YE+7O4fUfZ3Su/BkalklT3eTQ/2ofegs+Pp5rGryPMjxE8wYRFfY9dLSVdCaWDEG++lAU18g0edHU87j6PjRZ0fIX6CjwSJr8kq6PI/j6RTy2VLJeX3b2/2Rl3XoYmvrcnZ8bTzaKou6vyI/8i1F8lSKTYjvMMirpFKSewbPobfvHYI+4aPIZX/YaQZYFe119XRhB1b4ob+O7bE0dXRrOw/MTGJZ/cfwcO9B/Hc/iOYmJjUxqKaX/e8ZmzvMR5ve08cq+apj2d1jXTz6O5oVravaGtSjqs7XjKZws4338Zjff3Y+eY7SCZTyjjsxOw1fomj1GQ/h5X7tXDB/HL50HBBeIdFXMFOSSQ7FCrjpCISCWHL6vlY2tqAgZExtDfXoKujWSlImJiYxAO9B5WCiXxVYaH5bVk1D4tb6qdq/q3SqBLtlo2aVRc1lHeaVRdFJBIytccaq/DEniHL/mLvWd5qWZji1Xm1i1/iIKWFsnbiCk5LIpWCZ/cfwYe+8Ywp5u987BysXzTH0NeN+dkZQ9f33m3n4s/ufNpSGSdV3+z8VPO+d9u5WL1wtuvzdgO/xOEhjmTtZSRpB5wYOBJiBbviCD9gRzDhxvzsjGHHM0xXxkkv3LAuTPHLefVLHKS0MGERV3BaEqkUdGhiVgkm3JifnTHseIbpyjhpPbw0Y6uEG345r36Jg5QWPhIkruDWHoOdKg66ihZW/bDs7mH9bM+gwfeqe0EzLlzeBsDsW6Vr+9XeIYyenMTx8STqayJorAnjvCWtpv6ds+vwi1eHTMe7YGmrqX3du2bh+HjK8R7W5u55ODBy0hSznfNqde3tYvf68jIOjzzK+EjwFNq1YMIirmG3pFE+yWQKD+x8y5IYQJdsNsc78LNXhy29saVS8lQCmUiiviqCxtp0AlH1Vb1hqpKCzsvqwqUxZVUMXcxVEYFPfO8FS0lo44o2vPH2CdPa685J9g+DrDBlRVuTUrixqasdACydV6+FEVavL6/i8Hh+M7IXmWrLsRnJJ4C2I0xYxP/sfPNtpUhAJQbQCSa+fc3ZuOqu3zoSNnglgtDFpmtXeWrphBROxQfFFpV4iVdxeDw/x7UEdQTw7ouiC+J/7FSp0AoHNJUg7AgbvBJBaKtUaNpVnlq6NXIqPii2qMRLvIrDL/OrZJiwiG9QCQp0YgCtcCBTCSK/3Y6wwZ4IwrqXlS42XbvKU0snpHAqPii2qMRLvIrDL/OrZPhIkPgGu3tYD/YexD/m7Afd3BPH++Id+NW+Q9j11ilRQnx+M/54WSveePuEJRHEHy6O2eprZw/r8T2D2Dt0bCq201sbsHF5m2kP67bL16ChJoTnXn/HJLqwU73eKoX2ZwCzgES3d2RHmOLVB37LfQ+rkh8JstIF8Q12qlSEQgK1VWFDxYfaqjCklHjnRGJqD6kmGsKtl63CT14axPXfNyeQwaMTJhHEb18/hGu++ZxJWTc8OmFKpgCUlTgAKNvCImSI7bbL1yASCaEqIgxzaagJ4dhYytD39ivWAICpb1XE+Ru/rqIIYE8lOJGUppjtWsR4NRenx/JqXGId3mGRQKITaNx9zdn4cJ6Awa4I4s6rzsSH7/ofQ5ud6hA6vKxe4ZWwwSthSplVqXAD3mGdgqILUl7oxAeDCgGDXRHE2ycSir7ObUu8rF7h1ca/V8IUihXITOAjQRJIdPYbbQr7jawIIr+vzqpjdl3UcKzc6hCmMWzYeuisUrJCCisxq/p6ufFvx97FzvwoVpg5KnuRXCLhMBYumD/1c0drSzHCKgq8wyKBRGu/0d5ksvs4vbUBt11utijp7lBbg0TCMPedp7b10NmWqNBZpXR1NJvauxeY23R9C9mtOMWOvYud+XkZc7mjshfJtxp56flnpr4C9qHhgnAPixTEjVI0qjEAtWpMVZopFBIF++YLNE6eTGDXwFEMHh1HW1M1utubUF0dUVZJOH5yHLsHjk317WpvQHU0qiwPNTaWxK7+EQwcHUd7UzW6O5pRU2PvIYWuWoOqPZWSOXGcmp+qJFUkElKukdPyVYVittPXeF71YpoKxrU9rADuWeVDlSCxjxsyXtUYOtm3rsZdrLEKf/Xt500xRCIhrF44G6sXnjpeMpnCIy8NKKXxi2MNhk3+sbEkHukbwk0PGaXxs+qi+F/3vGCK7eHd/ZYk94UIhYQpDlW7TllnZ43sloLSnVddzFbnV2yVIClf+CcO0bL/8PGpNxkgvVF+3X0vYv/h447G6D0wohy39+DI1Btxtv3GB/owenLScgy7+9Vj7O4fMfXd1T8ylayyff/xwT7semvEcmyqcd1At/Z21mhX/8hUssq23/RgH3b1q9ffznl1Yy5eHY+UL0xYRItX5Xr0qj318Y5PJC3HYK+8k/WSSG6oBO2gW3s7a2S3FJQflIaEFIIJi2jxqlyPvnSR+nj1VRFTmy4GO+WdbJVEsuEh5QbaUlA21shuKSivlYbFOh4pXyi6IFrc2sPKL9ez7l2zcGwsZao8oduf6Zxbg6GRhMlDKplMmQQFoZDA7v4jGE8IDI6mBQXVEYmujjmmvaaxsSQe/90g9g4bSyU110Tw3BvGkkjnnx7Dz18dQiIpp+KIhgU2LG+ztYelEzucODmBvoFRg/jjl3uPWN7DUq3ROZ1zXdnDciq88YuXlc+h6OIUFF0Q+7hViia/XE+2Tl5+eaFQSGBWXdTQ3lwbxeDIBP7+B72GN+jx8ST+e/eA6c14Y1cMewZOGoQU2zfHsbhlAo0R41/0VVVhhEMiL7bVODqWNLTt2BJHKiVx9GTSVLvQDro37ncvmWMSf2zfHMeilhrTGk1OplAVMa5ddSSkXKNIJIQtq+ZhcUv91Jv/qoxK0Op5deOPFjvXkdeeWuXAtJ/DEhIr150z9XMA/bC0THuHJYS4C8ClAIaklPFM2/8G8JcAhjPdPiulfDTz2g0APgZgEsC1UsqfTBcE77DKF125HjteT6q+qhJM07WffZrR9M5ObLoyTm6UZtLFrCoRpeurW0+rsdmN2Q+loMoMlmY6haPSTN8EsEnR/q9SyjWZr2yyWgngAwC6Mr/zf4QQYcXvkgpBt+Fux+tJ1VdVgmm6diexaQUMLpRm0sWsKhGl66uM2QVBSLEFExRokEJMm7CklE8BOGJxvB4A/yWlHJdS/h7AXgBnO4iPBBzdhrva60ktmFD1bdMIBwq1O4lNK2CYQWkmqzGrSkTp+ipjdkEQUmzBBAUapBBO9rA+IYT4MIBnAVwvpXwbwHwAT+f0OZBpMyGE2AZgGwB0dnY6CIN4idMN8EVz6/H1q9aZfKSAdEXyXGFDV0cTbr9ijckXqqUxjD9YfA6GR8cRa6zGpJxEd3sTbu6Jm/aUlrXXa9uf3X/EINBYNLcet12+Gtd/f6dhD6uuOoQ7rlxrEFd0dzQrx+3qaFZW1ohGw6ZqGYvm1uOuq89EchI4cjyBOfVRRMJAV3sDbv3TVUbxR6wBkdCpeoJTJaIU896xJY7qSMjQN1s2yk6lC8BcfSRbbil/T6lzdh32DR9zXRihO16ll3HKfb+MRKtKHE3pmGnC+iqAmwHIzL+3AbjGzgBSyjsB3Amk97BmGAfxELdUgiofqebaqCWvp45ZUewdNIsozpwPk0BjVl0UNSIytadzaowqPN43bByjJ45LuloxmZKGvpGwwKHRhCEhbN8cx6ScRFNtBP9y2Wocn0iiviqCaERgPJFQCibmza7CX3zrecOcL1nZjgNvj5uEImvnhTApjcKUL7y/G+s75+JRhT+VSpjy7iUxdG6rM5SpSqUkHug9aEklqKs+snFFm9J/6xevDuET33thxteFDnpOqcl9v6ytq6/Y98sZJSwp5WD2eyHEvwN4OPPjWwByCuVgQaaNBBBdhYIzbGyA6ypPbDt/sWnce7edO/UmmOUPFs81VaO46aE+LGqpmyqflCUrjsiq5bLcfc1Z5jEe7MOiuWfj0z809s33oTp1vLPxyf960XS8u685Wxnff159lmnOnXPqlJUnFs2tww337zK033D/LixuqcfqhbMNa71v+Jhy3o9ee56pTNWz+48oj3fa3Dpl9ZH8eevOSfYPAifXRSHslIIilcWMEpYQokNK2Z/5cSuAvsz3DwH4nhDidgDzACwF8FvHUZKSUGgD3OqbiR0hharv8Kg9cYVKHHHkeMLyGLoqHHZFHoeOjVvuq53LyJghAQH2zom2OoeNeds5f3auCzJzppO155Mvc3dCqSXy0yYsIcQ9AC4A0CKEOADgnwBcIIRYg/Qjwf0A/goApJS7hRD3AXgJQBLA30gpJz2JnHiOHS8kHTrfKrXowny8WKPas0rle5Urjshtn1MftTyGzodKdzxde0uDUeQxkzFUogk750Tr4WVj3jovK9X5ozCiOGTtRUpB79c+VZLjZrGiErxSStkhpYxKKRdIKb8hpbxKStktpVwlpdycc7cFKeUtUsrTpZTLpZQ/9jZ84iV2vJB06HyrVi1oNo2r8k0KhyW2b87zrNqc9r1SxabyuJqUKfMYPXF0tTeY2pe0NuDmfI+szem+Sj8sxRjZPa/8viqvru096XarXlt2zkn3PLXfV7cL/luq81fpwgjiPSzNRAqi8jcC1F5WOkVabnt7ptrC5GTKpKyrrY0qPadSMmUoWxRvb0RdbZWpnFG2XaXaA6A83jsnx/DKwPGp9mXt9YgAeCmnbWV7PRpqa5TeWfW11Rg5OYY9Of2Xt9ejCmHl8VSx1dZGtd5eKlTeUjrPsOza51e6sOO/VegasCqMqNByS3bw7IPDblKkDyGzNBOZGSqfJpVy0I730tc/tA6DRydMyrpL4q14ZPeQYYxbL1uFiWTKpDK8aGWrUp13SbwVP35pyND/yx9YYyqrtGNLHO9dGTOpB2/uiaO2Koy/y5G6b++J4+KuGB7NVxpujuNP4jH8RNE+qz6Ka3M8tbb3xPG+rnatV5fK20uFzluqKiKUqr2qqjDWL5pj6bwW8qxSiSCsCiNYbom4Bau1E1volIN2vJdGxyaVyrrdA8dMY+wdOqb2oRo4ph0jv38iKaeSVe4YewaOK/2wXhkcNc3jJUXfmx5Sj3HTQ32YSKTMazFw1LGnltYn64DZw8upb5lbnlX0wyJuwYRFbKH3abKu2js+niyqas/O8XQKOLsKP5U/VSE1oFXslJNy6ltmd4xSjE0qCyYsYgtd6Rw73kv1NRFlX1XZIZ13lp3STHaOp1PA2S0FpfKn0vV1o7yTU9WelyWRWG6JuAVFF6Qg+ZvlnbPr8ItXhwz+Vt0LmvFHp7Xg8T2D2Dtk9JbauLxNuYc1NDphKnN0cVcrfr3viMFzqr46hGNjk3g1Z9wlrQ24cFkLnnzlkOl4G5a1YO+hUYMfVmONwBtHxk1eVucuno1f7jls8sMKC2Eo13RzTxwXFdjDev71dxAOhabKLU3KFKrDIRw5njQc7/zFLXj05UF87ke7psa4ZWs3elbNQyolTYIVAKa2SCSk3A+qrw7hudeNHl4XLm/T7hGpzmuhPSyn15BXe1hlJOawHHQkWiWjTXOn72h1vHAYCxcoK+iZKNLnsCi6IPbRvdFURYSprFIkEkJYhEy+VyrvpQXNtfjp7wYN5X7qqtL1BUdOJA1J4dY/XWUqW3TL1m6EEMJ4ImVov7knjgSkyQ/ry3+2BiNjSaMgZHMcIQDJVF5JpK3dqKsJG2KrrQojhBCa68ylmcIAhkcTpkQWa4zi736w09A2gUlEw8YyR9GwwEQiiYd3Dxriu7knjupoCJ/O8bja3hPHllXzTOuZTTaqUld2zuvGFW2mUlBuvfmryjs5pVLFHG5/DitI9iO8wyJa3PCyUvkY7XzzbWVflddTfqmkQn117XdcuXYqeVgZww3/LTteVnbi+M7HzjGp/ux6SJWLx1WZeWeVTNbuw4TlyA+LVChueFmpNtZ1fctJdGHHy8peHOb1tCtqKBePK4o5Kg8mLKLFDS8rZckgTd9yEl3Y8bKyF4e+XFN+X52ooVw8rijmqDy4h0UAqDevdd5EVRGBmqjRp0nlZaUr15Mt15T/AdpsmaPc/aDTYw344vu78Zn7TwkVPr+1G93tTfj81m589kfG9mXt9fjyB9YYBBaz6sLY0RPHjXl7RCva6/Gly1YZBB1L2xrR0hDB+nedNSWiACTi7Y1Kz6qV7fXK9slUyrBG2zfHsby9HrdetsokFIm3N2J7T1y5h5U/xqp5zUrBhB0PKd15XdBci51vvm3w8MqtdOHEE80Lj6tC45aRGIPkwIRFCm5eq7yJACg35636GIVCQullVR2NYlGsBv959Vk4dGwcLQ3VaKwNYd/QSZNn1TgmEQkZN/IjmWONnEyaqmXUVhmFFNXRECaRfoSUK1a49bJVOPD2uCGZbt8cx8TCFCYmjX13bIkjCSjbm2qrTAKNKoSVwpSa6ii2rJqHxS31U2+wXW2NePqNw7jzqjPx9okEZtel1YcAHAsmVOdqQXMtHtp10PRHxObueXhiz5AjYYNXHle6cXVrVO5ijEqAogvim014lXDj7mvOwrZvP+dIdGFHuOGGyKMmGsJ/Xn0Wrvz3Zyz1tSNM+c7HzsGHvvGM6+eq2MfzkoCKMUonaxcSCzvf5dp4VphGHk9ZO9Hjhu+VG8dTiTHseFm5IdwohR+Wap11whSdx5XTc1Xs43lJsa/nYlNKexG3mKlNCUUXxDeb8CoxRtbLKr+vHQGDHeGGGyKPmmhhP6z8djvClA6PzpXueO0BFDZQjFG+MGERV3yv3DheVriR2x4JQ+nptKy9XulDpWo/PdaAW7Z2W+6b70011ddGHPl+WIU8vAoJU3L77tgSR/c8tT+V03Ol9S3z6HheUuzrmRQP7mERAGrfK90Gtc73yujTdEplpmpPJlMm36uamgiOnRwzeVFJAC/ntK1or0djbY3Sy2qWph2A4746/y3VGFUIKfuq/L5qaiLKNU2lpLKvHY8yO2o53Xm1c234hQDGHAg/LLeY5sPK3MMihdF5HuUzMTGJB3oPmnyvdF5Pl3Z14OHd/Yb2r35oLYaP5pUz6oljU1cMj+XV6/uPD6/DwXfM3lkb42Yvq0LtsaYo/vo7L0zb99bLVmE8kTLUOdy+OY6L4jH8ePewad4Xd6mP19pUhY9/53nDWlyysh3/vXvANMalXW2m0ky3XrYKiUlpqDuY651lxaNs44o2y/UBUylZUA1o1fvKLwQxZjI9fCRIbNF7UON7pfF62tU/YmoPi5DZQ+rBPvxO4S0VDoWVflOvaHyodO1hEbLUd+/QMZN31k0P9eHlgePKeet8skJCmNdi4KhyjL6BUaUPWDZZ5Y6h8s7S+U3tVniR6Xyo6FlFggATFrGFTjWm9XpStNtR/g2P2lPnOS2V5JZK0GlpJl0cKu8sO6rLrFrO6hgsc0T8BB8JEltkVWr5n3HJKuDy29sV7Vnln5UxYo3qcXXH07UXKpWU2zerEizW8ezGofLOatOck6zyL79dpZbTjUFlnf9IJBJ48Ib3F+14duxHrNLR2jKj36PoogJRbcQD6k37/I34FW0Npv2W7B7WE68MmXyr3ru8Db98bdhQKinWGMHg0YSpRNEFy+biF68cNrT/P0tmY3AkYSp9dP7yuXgq38uqQHtbcxTDR43+VOcsno0nXho27K/9y+WrMZaYNLTdnNlf+0le3x1b4ti0Uu2TtWB2NZ7+/duGkk8blsa0e1hPvDJsmPfKeU04Pj5pKD2V3cMCYBCxrGhrVO4/bVzRhv+7bxijJyen5t1YE8Z5S1qVe1h2qkO4UfqI5ZMM+FZ0UYJq7hRdkDSFPK4+8b0XDG0XLo3hob5+5RtsbhmhVfOaEQoJjCfNJYoA4OjJpEHEkBUU5PcNASaPq5Xz1ihLHwHqkkgAkMjzuPri+7sxOJLAp3/Ya0gqAFAVDhlKNqWkxNyGqLKMU1UklOfpFEJERLCkrRbf+ujZU4q0proQ+g4cM3l1CSHQ1mQs2dRYG0ZIqL295s+qNpWvSqWktoRSfmmmVEpieHTC1DeVksrEYNWzyg0fqkr1siLO4B1WhWHH4+rb15yNqxSlhFSeTLrSPqox7JQ/sutl5YbH1b9cthqfuOcFS31V89OVk9Ktp65dFYeuVNK9287F6oWzkYvunKj62iln5Ebpo4CWT/IS3mGdgn5YJI0djyuVYCItErDucaUaw46wwa6XlRseV8cnkpb72hGV6NZT166KQyd6UYkxtOfEhnDDK4EGRR5kJjBhVRh2PK7ateWIrJcSUo1hp/yRXS8rNzyu6qsilvuq5qcrJ6VbT127Kg5dqSSVGEN7TgoIN/L7FhJoWOmrg+WTyEzgI8GAYWejWieucLqHtTnegYOjY4ZxUymJB3a+pfzgcL7oor46hJPjKfxucHRKZLCsrRHvXjoHP8sTXazpbMbo2KRJoHH+srl4Kq9vtj1fSPGFrd0Ih4RpD2tjPKYcozoSwif/60XDPN6zUt134/I29A68jclJgeHRccQa05YoLx88bti3u7knjvfFO/CzV4dNa3/h0hge3zNoGru5NoK/+vbzhr7vWd6q3MPKfqA4l2QyhR/v7jcJYS7q6jD1tbOn5PUeFqAWAJU5fCR4CoouygG33lTseFzl+zTF25uUb7oXLJmrFCUkZdLkT7UjY06YLzJIwiy6+OqH1ioFCVD0zbbnCynCYYG2pqjRWyqVfhw1oRCKzKmPmOahEoTc3BPHhExi//CYSSW4pLXWJJioqgor1z6ZTCnHPmf5XPU5WT0fS1sbMDAyhvbmGnR1NJsSEJCu9hBS+G/pPMrs+Jk59beilxWZCbzDChDF3hi3E4NXIgg3/LDsCinuvOpMfPiu/3F0PJUflm7tn91/RCmkUIlb7BBEYUMQY3aJkvlhTXs8D/2yNL5YvMMqB+z4/HjlCaQb1ysRhBt+WHaFFE6rVIwl1H5YurXXVw9xJkAIoi9UEGMuNuXgh5XFri8WRRcBotgb43Zi8EoE4YYfll0hRaEqFVbbVX5YurXXeVypxC12CKKwIYgxk+LBR4IBwsuNcatiDt24FyyZi0d2D5qEBhd1xUxCiiWtDZAAPv0DswjimdfeRmIypypGQwRvHBk3jfteTZX0jfEYHldUVY81RvHCGyOmqhj5Ao0dW+Job67Cb3//jqkKx092D5vi+JOuGH6hqKyxYE41frHn8FRb94JmXLi8DamUNFmtpFISD/YeNI19aVc7Xjl0zJJdi2oPy0thg1dVKir4A8W+FV14iUbQwUeC5YBXG+N23yRUFRGqohHMqouahAYhhEzChlu2dGNuQ9RQ8SGaqaowMpY02WzUVoUN42b/Ao/lCylk+lFSc03EMHZ9dQhHT04aYvj81u7MXIwCjcaaCA6NJpRVOLJ7b7lxhCFMlTU+v7UbB9+ZMI0xMTFpslrJVqlQrd0Trwzh73OSus6uRacS9ErY4GVScUPQQcoX3mERV8Qc9247V1lVwanowk5VDDfGcON4uvkVqhyiEl3YGUNVvUKHU2FDBQsjvIR3WKdgpQuix40qB7qqCk5FF27YfdgZwy17ETuVQ3SiC1tjKKpX6HBaZYJVKkipmPaRoBDiLgCXAhiSUsYzbV8C8D4AEwBeA/BRKeU7QohFAF4GsCfz609LKT/uReDEPexYS9i1stBZahQSXeT2dcPuw84YbtmLFKocYlo7zZraGUNVvUKHUysRWpGUlmLbi+Tjpt2IXZuRaR8JCiHOB3AMwN05CWsjgJ9JKZNCiH8GACnlP2QS1sPZflbhI8HS4oaYQ2fHfsGSuXi4b9Agjrg588FhlehCZVk/kUwZ9mx0fbVj/OkqTKYkbsix6tjeE8dGhXBD2XdzHBfH1TYil8Rb8cjuIcO+2809cdREQ5b3nzZ3zzPZg9x2+RqkZMrRHpYb59uL3ydKAvNIsAiVL7RrYWkPq1AiEkJsBXCZlPLPmbBKgxuKrXzfq+55zaiqCuPkyQR2DRzF4NFxtDVVo7u9CeFwSNn3+Mlx7B44NtW3q70B9bXVeOfkGF4ZOD7Vvqy9HgLAnpy25e31aK6tUfYFYGqbpemra7czRhVC6BsYnWqLtzeirrYKR0+O4Xc5fc9or0dTbY1yjSSkcgzdOquUf6mULNg3t9IFAEvKwenOt9XrC0irDCmMcA0mrFN4qhK8BsC9OT+fJoR4AcBRADdKKX+ljEiIbQC2AUBnZ6cLYVQmbvy1m0ymlPXpLlrZikf6hkx3R7XR8JTlR7avzshQdycUa4rir7/zwrR3PDu2xFEVDinrAFq9w7I7xqz6KK695wXT/B5T9L04HsNjCml8VcR8B/m+eDt+vveQ5btTVX3HTV3tiERCWL1wNlYvPHX+VHUcdXdduvOt6l/o+loca6DIokjkvl9GolUljqZ0OBJdCCE+ByAJ4LuZpn4AnVLKtQCuA/A9IUST6nellHdKKddLKdfHYjEnYVQ0+w8fn3ozAdKb39fd9yL2Hz5ueYzd/SNTb17ZMW58oA+7B45NvUFn2//xwT68MjRq6vvSwHFT35se6sMrmvawCBnbHlT3vfGBPuwdPmZ5XDfGmEikLM/vpYHjyrXbO2Q+3q6Bo8pztbt/RNnee2DE0nnVnr/+EXvnW9HfjeuLOCf3/TISqdxPI804YQkhrkZajPHnMvNcUUo5LqU8nPn+OaQFGctciJNocEOx5ZXCr1C7nfJHbhzPKz8sN46nW3/VGKrzasf3ym5/KgKJn5hRwhJCbALwaQCbpZQnctpjQohw5vvFAJYC2OdGoESNG6VsdL5JbnhLuVH+yI3jeeWH5cbxdOuvGkN1Xu34Xtntz1JJxE9YkbXfA+ACAC1CiAMA/gnADQCqAfxUCAGckq+fD2C7ECIBIAXg41LKIx7FXtZYFVIsmluP269YY9pjyHpUWRmjq6MJO7bETXsaXe0N2L45btrDqq8KT8maa6Lpyg4r2+tNfbdvjmNZez2+dNkqkydTQ3UId19zFo4cT2BOfbpKxbL2etzcEzeUKLplazeqwsJwvB1b0uOqYl6miGPHljiqIyHLY1RFzX1181vZXo9btnbjczmqws9v7UZ9dRjXblhiKNfU3d6E2y5fg+u/b1QDdnU04Y4PrkXvgVOlo+LzmyFyJPa55zVfMLGyrVF9/jJiDNX5Vp2TFW1N2Dd8zHC9FLq+nFy3hMwEVrrwITOtAzjT8juplMTP9gxOvWGGBLAqU/tufDxpUMCd0V5vqJ0XEsCSWAPevXwunn/jHYRFyFAqaW3nLPxUUYOvqTZiMEnMiiB+/eoRvJIxdgwJ4MzOWRgZS07VIgxl3lzPXzYXTyniOH/5XLyQF0dzbQivHxm3PMYFy+fi94dOGlR4icQknn7jsGl+Z3fOxs9eOTT15h8SQNe8JpyYmMQN9xuT2MUr2/HTPYOGvktbG/De5W1aufuBkZMm76wHeg+ay1dFw9j1lvn86a6XH/cNmBJndVQt8gCsKQIpd3eEb+1FTMf3wG4kz2bEmazda5iwjLhR+sYr76zf/v6wrRJFTv2p7rhy7ZQicabHu+cvz8FHv/k/tsY4+zTjG4KdeevKO+nKKtkpt6TyztIdT3e96M63qhSUV9ccMREYWbsX5EnlWZopSLix0e1GuSV1X3viA6fChuPjScfHGx61H7OTeevKO+lLM1kvt6Qq46Q7nu560Z1vqyIPHRRoEK9hwvIhbmx0e+WdZVd84FTYUF8TcXy8WKP9mJ3MO1veKb9vu2YMbbtCBKHyztIdT3e96M63VZGHDgo0iNfwkaAPcWMvwC3vrGQyZdjgX95Wh5+/cti0YX/+srl4XLFX9d6umHIPq3NuNcYTMIgu1nbOwjP73kYiecoPa3ZdBIePJwx+WqdnjpffNxoWOGfxbLzw+jsIh0JTYzfXhnDkRBJhETIdT/XB4YvjMTTUGt9kT55M4Kd7hky+VxuWtxj2sMICWDmvCcfGJw1CjEJllQqVbDowctIgYNDtYYWFwPXf32k6f4DZ9wqAYc8y69cFQLmHZdU/DXBmW1Lh8JGghUeClfsJNB/jlieQyrfKTt+JiUk81NevrJOX6/V0c08cVQgrPZ0iAKqjRh+pptoI3jhsNGXcvjldzWvkRNKkSsw/3o4t6r7ZMYZHE4b2W7Z2IxoS+PQPXzD1ba6LmHy5IsL83yIaDSOp8L2KhiMICWN8t12+Bu+Ld2BJrN4g3IhEQtiyej6WtjYY2kMhYVq75toonnptWJlAtqyah8Ut9VPJIt7ehF///pDp/KVSUllBY+OKNkwkjXPJtj/q0D+NXlbES3iHVaa4IbrQiQGselnZFSU49c5ya4xvX3M2zsoTXex8822l35fOy8oNsYJVEYRdjzJdO/2wSgrvsHiHVbkU2gDPf1PR9dWJAZxWnrDrOVXsShcq0YW2OoSNddZhVwRh9fzpYta1W43ZzrVF3MctexE3bUKcYtVmhAmrTHHD40rnvWTVy0rXbtdzyunxZjJGPjq/r3YXvKF0629VBGHXo0zXTj+sYBCNRtHzhfsdj1OEquuuQ5VgmZKtUJBVbWX3GVQVCnR9uzuasT2zh5Rtv7knjiWtDYa27Zvj6G5vUo6RrRCR276ktWFqbyp3jGWKvrrjqfpOteeNfcvWbiyJWR+jW1EhIlsNJLfvji1xrJrXbHmd7Z6rVQuaHZ2/rg71OdG1W43ZzrVFiJtwD8unuFHiRlUBIxQSyrHzK1p0tzehtjaKEycnTL5OAkLZd2wsiV39Ixg4Oo72pmp0dzSjpiai9JFKwbo/FWz0nVVbo/TlSkAq+6q8rKqrI8q1183P6GV1Skhh5/zllltqb6rBqnnpMfLH1XlcTXeurbY7vbbIjCn6HpaP77C4hxUk3CpxEwoJk2eRauy7rj4TB46MW3LT3d4Tx4LZ1bjmm88ZYrtwacykKNzeE8elXW14bPewoV3nF7VJ4U/11Q+txfDRhNr3Km/c7T1xXNJl9vC69U9XYWLS7Fp8abwNv9h72LAWd3xwLSaS0rT271neatktuJCXler86fypYo1V+KtvP29pDNW5nkm7k2uLEK/hI0Ef4qUHkWrs5CSUXk+7B45NJYSp9gf7kJyEKbZd/SPKvn0Do6Z2nV/U7xSeU2ER0vteKY6n8vDaO3zM5P9000Pp2PLXoveAxpvqoNpDqvegMy8rQO9PNXpy0pNrgJCgwoTlQ7wscaMa+8jxhC3FncrLSqcodKrasxubU1WivqySPZWgnTJHOtWeqnwVyxyRSoYJy4d4WeJGNfac+qjyeHa8rHTlhZz6RdmNzU6pJHtlldTnRFUqyY7CD9D7U6nKV1GJRyoZii58yEztRaxs8Cv3sD5yJg68bX0Pa3l7HUZOTE6VOYqEgXXz5+DxPYOmEkrvWRbDE68MG9qXtKb3Pf4+Zw9rx5Y4Nq6M4TEbe1hPvXLYdLwNy1rw5J5DhhJKKzuaMDqedLyH9ZOXB0wlqf5kRTueem3YVOaoriqEI8eSU2WjGmvCOG9Jq3YP68e7+w1jn97agObaiOU9LBJ4imYvkv38VZ6lh5+g6CJI2CnNNBOBRn4ZphOJFN4Xb8dpLXWn1HIZaXdzrbF0UUNNGK8MnDCVVVo1L4mJZMpUQmkSKYwnUqZSTi2N0bxSQiGkYC6VNJGUWN5Rh7s/ejYGR8fQ1liDSFgiCqE8XgoSE5Pm9tNaanDnVWcavKyqohHlOgMwtaVSEuOK4wEwlTm67fI1ODmRMCXkVEpqz8nEpHGMW7Z24w8WtVgqlUQqC6efw/KxOnBaeIcVcOyWybHT/39+f9hUmsluWSWnflg1UXdKM9151Zn48F3/Y2hT+U3psFuaSRWz7ni6se3ERwJP0WTtAUhY9MMqV+wKNOz0Vwkp7JZVcuqHZUegYVsoovCb0mG3NJMqZt3xtGPbiI+QSoAJK+DYFWjY6a8SUtgRMBRqt+qHZUegYVsoovCb0qETRtgRXeiOpxvbTnyEVALcw3KIGxUp7Iyb3945uw5fv2odRk9OGjb4s95Jpyol1KKrowmL5tbjjg+uNYkEOmfXYd/wMcPxujua8dUPrTX4SIVDwMI5cYOA4eae+FQJpnxxxIr2etx62SqTOGJWXRh3XLn2lJdVSGCZou+S1gaEhEBNNGTYD+pqb9Ae7+aeuMl/KxSSuHbDEsO4XYoSTDqypZnyP9zbnSnNlLuHeNvla5CSKXPMmuPpxl7R1mQ6J9zDIpUME5YD3KpIYXXcjSvaTP5GX79qHYaOThjeoHdsiWNiYlJbmUElEvjFq0OmygwXLJmrVOi1NVUZBBO1VWFUhaNYFKvBf159Fg4dG0dLQzXCYYmoiJhEF7ds6caRE0ljpYueOKoQUgop5s2qNvls1VZX4X3xdizKFYpkyirle0u1NlZh5ETKNGc750jnZRWJhJTCjVRKonNOnamv1bFXtDUpK2hQJUgqGYouHOCVL5Adf6M7rlyLv/vBTlNfnZeVHZGAHcGEblw3PLVUx7MjKtEJRfzs30TPqYqDootTUNbuBV75AtnxNzo+ntSIAdTiAzsiATuCCX0lCOdCCl3FB9Uaq9ZOJxTxs38TPaeIDqd+WBEhsXLdOaZ2H38uawomLAd45Quk9zcyt9fXRJR9dV5WHTa8l3R+USrBhM4Xyg1PLTsVH1Rrp/Pf8nPVCHpOER1u+WHl0/u1T7k+ptswYTkg6wuUv8/g1BdIN25XR7NJMDGnPqIWA3Q048sfWINEUp4SNoSFViQQa4pg/bvOMlSviLc3KoUN1dGQQVBwy9ZurJrXjO/95VlIJMXUXk40IrGytdkkgrhlSzeiYaOQYvvmOOLtjfjSZatM1SRam6MGgUZWVGJ17boXNGuFKXZEMyoRSyQS8mSMztl1SnGMl55TXgmICHELJiwH2KlI4ca4gLmqwvL2NdjcPc8kBgCAoyeTJrUcYK50EWuKYP/wmCkxrZ4nEWuMGipEVEeBwaMJw+9HwwITkwnsHTxpGmNZrAHVmT2rqf4RgbamqKnyBJB+7JUvuhgYmTAINApVjVCt3YLmWqV9RzKZsixsSCZTeGDnW5btRZyOcccH12I8YTzXt1+xxtF1VQivBESEuAlFFwHCzka8ncoMd19zFrZ9+zlTX5UIQifyKHalCy+rVDhdTzfGKLZQhCKPklN0A8d8fCTGYKWLcsBOlQo7lRnsWHjoRB7FrnThZZUKp+vpxhiFhCJe4KWlDSFuwYQVIOxUqdBWT3BoL5IVeVjpW6jdaaULN6pU6CxDnK6nG2PoKop4Jbrw0tKGELfgI8EAYWefwc5+yV1Xn4kDR6zZi3z5A2swcjJpshy5uCuGR/OsQbZvjuPieAyPKqzsm2sj+OR/vWiI7eKVbXi4b9Awxo4tcVRFQqY9rC2r52s/iJtPsfef3NrDUtmceLWnxD2skuPYXiRrGzJTfCRr164FE1bAyCq58sUYKnXXxMQkeg+OYODoGNqbarBqXjOqqsJKpdpEIom+gdGpqhHx9kbU1Vbh5MkEdg0cNVSTCIdD6D04MnW87LijJ8fw8sDxqb4r2uvRWFujHCMaDWN3/4ipEsTYWBK7+kcMNieRSEjZd7o1yl2PZDJleS10Y+vW0845yR4vfy6FxiiWvYgqBiarouF4D8tHe1BOYcIqV+yUcdK1Z2vf5fs3XdrVoSzvpLq7GRtL4qFd/aY7rEvjbXj0pUFLY3i1Hrq7Fd0aqe4q7NyB8G6FzAAmrFNQdFGu7D98fOqNEUhvlF9334vY3T9iuf3677+IV4eOGdpufKAPu/pHphJNbvvu/hFTHLv6R6aSVbbvTQ/1oW9g1PIYXq1H7wHra3HdfS9i/+HjlsZ1oy8hxDpMWAHHThmnQu1KJZ6uvJNCoafrq1P+eeX1ZKc0k24tVMo4Oyo6Ku4I8QYmrICjU3dp/Zuabfg3aVR7KoWerq9O+eeV15NqPXSKO90aqZRxdlR0VNwR4g2WKl0IIe4CcCmAISllPNM2B8C9ABYB2A/gCinl20IIAeDLAC4GcALA1VLK590PnQDQ+lt1dTThtsvX4PrvG/equjqs+zfpyjstjzVi55tvG4QK3R3NuPVPV2HvcI7vVawB8fZG3LK1G5/70a6psW/Z2o1lLQ14dv8RDBwdQ0dTDbrzBAy5YgVALWDQrYeqNJO61FWT5dJahcpwqcoq2S3ZxbJIhEyP1dJM3wRwB4C7c9o+A+BJKeUXhRCfyfz8DwAuArA083UOgK9m/iUekV+u6fYr1iCZTGFSpgwlkSZlCslkylSaqSoikJKhvLYQUjKllLD/8rVhkyR9c/c8hEMiz3NqNSLhMKrCwlTK6al9hwxjbO+JY3O8Az97ddiRvLtQWStVCS2rpbUKjasTvTxqsWQXRRqEWMOySlAIsQjAwzl3WHsAXCCl7BdCdAD4hZRyuRDi65nv78nvpxubKsGZoyupo/Oh8sqfSleiSNeuGkMVm9+9rNwoacSySARufA5LSCzsfBcAX32maiZ44ofVlpOEBgC0Zb6fD+DNnH4HMm2GhCWE2AZgGwB0dnY6CKOy0W3w6/2wvPGn0vthqdvVY5iP53cvKzd8q+h9RaYj9/0yEq2a1l4kCFYhM8EV0YVM36bZ+kCXlPJOKeV6KeX6WCzmRhgViW6DXyuYsFH+yE5ZJV2JIl27egzz8YpdosgubggsKNIg05H7fhmJVK7JhpOZDwohOnIeCQ5l2t8CsDCn34JMG/EAnRigu6MZt162CntzvKVOb21At0Z0EQmnH7/l9o23N2J7T9y0hxWNGL2sdmyJY5XCZ+v2K9Zg1bxmk8fV6a0NpjG298SVsekEE37xhXLDE83uGBRokErFyR7WlwAczhFdzJFSfloIcQmATyCtEjwHwFeklGcXGpt7WM7QlfX5cd+ASSV4UbwdgLHkT+fsOvzkpUFl32xJo+ybY7y9Cc+8cThthjiRRH1VBI21YZy3pNU0bqE4NiyLoW/gqKm8U6lLFM1EAOFGSSOrY1CgUba4ai8S8KoXzkozCSHuAXABgBYAgwD+CcADAO4D0AngdaRl7UcysvY7AGxCWtb+USllwWzEhOU+djbyveo7k/6lxu/x+j0+MmOYsE7hTHQhpbxS89IGRV8J4G+sxUW8ws5Gvld9Z9K/1Pg9Xr/HR4iXVO7uXZmT3cjP/0u8UGUGt/vOpH+p8Xu8fo+PeE8ikcCDN7wfgN5SpKO1pdhhFQWWZipTshv5WfWZlSoObvedSf9S4/d4/R4f8Z5oNIqeL9yPni/cj4UL5uOl558xfQX4M1gFob1IidEpvuwowXR9jV5PZu+l3P6plFT2LXQ8qyIDlecUAKUPlR8UcH73hfJ7fGRGzGgPK+B7VTo8+eAwcYhdLys73kt2/LBuv2INqiICn/jeC5bLHy2ONVjaM0mlpGX/LTvuvV5iZ36lwO/xEeIVfCRYQux6WdnxXrLjh3XdfS+i98CIpeO5MUed/1bvQevzJoRUHkxYJcSul5Ud7yU3/LDc8G/Sxaf236KPFCFEDxNWCbHrZWXHe0nvh6VuV5VmckN5potPdbwOligihBSACauE6BRfWZ8mJ6q9ro4m3PHBtbh2wxJ84sIl+OSGJbjjg2u1Y5/5rlmmvm4oz1Tx3Xb5GixtbTC07dgSR3emvBMVcIQQFRRdlJBCHksqzyq7Y6h8slT9O2fX4fGXB019vZxjKiXROacOAyNjaM+oEkMhYXnehFQq2c9hRcJhnL1udanDKSqUtfuQYnss+aXcj1/iIKQE2Ja1l6mkHSiwFnwk6EMKld/xYgw3jucGfomDEOJPmLB8SLE9lvzix+SXOAgh/oQJy4csmluvFEzMxGPJi3JLdkmlJPYNH8NvXjuEfcPHkMrXtBcpDkJIsKHowqeoBBN20IkddJUrrPa1ix3/Ji/jIIQEH95h+RBd9Qq7FR+yJXzOXdyCxbGGgm/8dvrawe5cvIqDEBJ8mLB8SDmJD8ppLoSQ0sKE5UPKSXxQTnMhxA8kEgk8cuPlZet5VQgmLB/iFxGEG/hFSFHMORPiJdFoFAsXzC9bz6tCUHThQ/wignADPwgpij1nQog38A7Lp/hFBOEGpRZSlGLOhBD3YcKqMCpRBFGJcyakHGHCqjAqUQRRiXMmpBxhwqow/CKCKCaVOGdCyhFWay9jUimJ/YePY/DoGNqaTokdsu2VVE2iEudMAoXlizESrZLNLW043P+ml/GUEu1aUCVYpkynjFsca6goy45KnDMpT6LRKNo65pU6jJLAR4JlCpVxhJBygwmrTKEyjhBSbjBhlSlUxhFCyo2yS1hBK8Gji9fpPKiMI4SUG2UlughaCR5dvBtXtOHxlwcdzcMPJZEIIcRNyuoOK2hCA128u/tHiu6HRQghfqesElbQhAa6ePtHgjUPQkjxSCQSePON17Fh0yWlDqXolFXCCprQQBdvR3Ow5kEIKR7RaBSXfP5H6B86VOpQik5ZJSw3hAZ+8Irq6mimYIIQQvIoK9GFU6GBn7yiKJgghBAjM05YQojlAO7NaVoM4CYAswD8JYDhTPtnpZSPzvQ4dnFSgkcngjjj2vM8K+mji5elhAghxMiMHwlKKfdIKddIKdcAOBPACQA/yrz8r9nXipmsnBI00QYhhFQSbu1hbQDwmpTydZfGKwlBE20QQkgl4VbC+gCAe3J+/oQQolcIcZcQYrbqF4QQ24QQzwohnh0eHlZ1KTqsDkEI8SO575cT4+N45MbL0dHaUuqwio5jPywhRBWAgwC6pJSDQog2AIcASAA3A+iQUl5TaAw/+WEV2zdJ51lFCKkoLP+nr62rl6edEcdLzz/jZTylxFM/rIsAPC+lHASA7L8AIIT4dwAPu3CMolFMsUPQSkkRQkgpceOR4JXIeRwohOjIeW0rgD4XjlGWBK2UFCGElBJHCUsIUQ/gvQDuz2m+VQixSwjRC+CPAfytk2OUM1QlEkKIdRw9EpRSHgcwN6/tKkcRVRBZVWJu0qIqkRBC1JRVaaag4ZYqMWgeYIQQMhPKqjRT0HCjBBOFG4SQSoF3WCXGqWcVhRuEVBaJRAKD/QdLHUZJYMIKOBRuEFJZRKNRtHXMK3UYJYEJK+CwnBQhpFJgwgo4LCdFCKkUKLoIOPTOIoRUCkxYZQC9swghlQAfCRJCCAkETFiEEEICARMWIYQEiEr+HBb3sPKgPxUhxM9U8uewmLByYJkjQgjxL3wkmAPLHBFCiH9hwsqBZY4IIcS/MGHlwDJHhBDiX5iwcmCZI0II8S8UXeTAMkeEkCDQ0dpS6hBKAhNWHixzRAjxM5FwCE8+9kipwygJfCRICCEkEDBhEUIICQRMWIQQQgIBExYhhJBAwIRFCCEkEDBhEUIICQRMWIQQEiCqq6tLHULJYMIihJAAMT4+XuoQSgYTFiGEkEDAhEUIISQQMGERQggJBExYhBBCAgETFiGEkEDAhEUIIQEimZzEhk2XlDqMksCERQghQUII9A8dKnUUJYEJixBCSCBwbOAohNgPYBTAJICklHK9EGIOgHsBLAKwH8AVUsq3nR6LEEJI5eLWHdYfSynXSCnXZ37+DIAnpZRLATyZ+ZkQQgiZMV49EuwB8K3M998CsMXtA6RSEvuGj+E3rx3CvuFjSKWk24cghBDiIxw/EgQgATwuhJAAvi6lvBNAm5SyP/P6AIC2/F8SQmwDsA0AOjs7bR0wlZJ4bPcArrvvRYwlUqiJhnD7FWuwqasdoZBwNBlCCPEbue+XkWhViaMpHW7cYf2RlHIdgIsA/I0Q4vzcF6WUEumkhrz2O6WU66WU62OxmK0D7j98fCpZAcBYIoXr7nsR+w8fn/EkCCHEr+S+X0YibtxnBBPHCUtK+Vbm3yEAPwJwNoBBIUQHAGT+HXJ6nFwGj45NJassY4kUhkbH3DwMIYT4jmg0go7WllKHURIcJSwhRL0QojH7PYCNAPoAPATgI5luHwHwoJPj5NPWVIOaqDH0mmgIrY01bh6GEEJ8x7KlS/HkY4+UOoyS4PQOqw3Ar4UQOwH8FsAjUsrHAHwRwHuFEK8CeE/mZ9dYNLcet1+xZippZfewFs2td/MwhBBCfISjh6FSyn0AVivaDwPY4GTsQoRCApu62nHGtedhaHQMrY01WDS3noILQggpYwK7excKCSyONWBxrKHUoRBCCCkCLM1ECCEkEDBhEUIICQRMWIQQQgIBExYhhJBAwIRFCCEkEDBhEUIICQRMWIQQQgIBExYhhJBAwIRFCCEkEDBhEUIICQQibVdV4iCEGAbwukvDtQA45NJYbuHHmAB/xsWYrOPHuPwYE+DPuHJjOiSl3GTll4QQj1ntW274ImG5iRDiWSnl+lLHkYsfYwL8GRdjso4f4/JjTIA/4/JjTH6HjwQJIYQEAiYsQgghgaAcE9adpQ5AgR9jAvwZF2Oyjh/j8mNMgD/j8mNMvqbs9rAIIYSUJ+V4h0UIIaQMYcIihBASCAKdsIQQs4QQPxBC/E4I8bIQ4g+EEHOEED8VQrya+Xd2kWP6WyHEbiFEnxDiHiFEjRDiNCHEM0KIvUKIe4UQVUWI4y4hxJAQoi+nTbk2Is1XMvH1CiHWFTmuL2XOYa8Q4kdCiFk5r92QiWuPEOJPihVTzmvXCyGkEKIl83NR1koXkxDif2XWarcQ4tacds/XSReXEGKNEOJpIcSLQohnhRBnZ9qLtVYLhRA/F0K8lFmXT2baS3a9F4ippNd64JFSBvYLwLcA/EXm+yoAswDcCuAzmbbPAPjnIsYzH8DvAdRmfr4PwNWZfz+QafsagL8uQiznA1gHoC+nTbk2AC4G8GMAAsC5AJ4pclwbAUQy3/9zTlwrAewEUA3gNACvAQgXI6ZM+0IAP0H6Q+0txVwrzTr9MYAnAFRnfm4t5joViOtxABflrM8virxWHQDWZb5vBPBKZk1Kdr0XiKmk13rQvwJ7hyWEaEb6P883AEBKOSGlfAdAD9KJDJl/txQ5tAiAWiFEBEAdgH4AFwL4QTFjklI+BeBIXrNubXoA3C3TPA1glhCio1hxSSkfl1ImMz8+DWBBTlz/JaUcl1L+HsBeAGcXI6YM/wrg0wBylUlFWStNTH8N4ItSyvFMn6GcmDxfpwJxSQBNme+bARzMiasYa9UvpXw+8/0ogJeR/uOxZNe7LqZSX+tBJ7AJC+m/QoYB/KcQ4gUhxH8IIeoBtEkp+zN9BgC0FSsgKeVbAP4FwBtIJ6oRAM8BeCfnIj2A9H+mUqBbm/kA3szpV8oYr0H6r1+ghHEJIXoAvCWl3Jn3UinXahmA8zKPl38phDjLBzEBwKcAfEkI8SbS1/8NpYpLCLEIwFoAz8An13teTLn44loPEkFOWBGkH018VUq5FsBxpG/7p5Dpe+2i6fYzz8h7kE6m8wDUA/Blza9ir40VhBCfA5AE8N0Sx1EH4LMAbiplHAoiAOYg/Rjr7wHcJ4QQpQ0JQPrO72+llAsB/C0yTz2KjRCiAcAPAXxKSnk097VSXe+6mPxyrQeNICesAwAOSCmzf7X8AOkENpi9vc/8O6T5fS94D4DfSymHpZQJAPcD+EOkHzlEMn0WAHiriDHlolubt5Der8lS9BiFEFcDuBTAn2feXEoZ1+lI/9GxUwixP3Pc54UQ7SWMCUhf8/dnHmX9FkAK6QKqpT5/H0H6WgeA7+PUo6yixSWEiCKdGL4rpczGUtLrXROT3671QBHYhCWlHADwphBieaZpA4CXADyE9H8gZP59sIhhvQHgXCFEXeYv32xMPwdwWYliykW3Ng8B+HBGPXUugJGcRymeI4TYhPRe0WYp5Ym8eD8ghKgWQpwGYCmA33odj5Ryl5SyVUq5SEq5COlEsS5zzZVyrR5AWngBIcQypIVGh1CidcrhIIB3Z76/EMCrme+LslaZ/2vfAPCylPL2nJdKdr3rYvLbtR44Sq36cPIFYA2AZwH0Iv2feTaAuQCeRPo/zRMA5hQ5pv8XwO8A9AH4NtKqn8VIX3x7kf4LtLoIcdyD9D5aAuk33I/p1gZptdT/h7QyaReA9UWOay/Sz+9fzHx9Laf/5zJx7UFGiVaMmPJe349TKsGirJVmnaoAfCdzbT0P4MJirlOBuP4I6b3anUjv05xZ5LX6I6Qf9/XmXEMXl/J6LxBTSa/1oH+xNBMhhJBAENhHgoQQQioLJixCCCGBgAmLEEJIIGDCIoQQEgiYsAghhAQCJixCCCGBgAmLEEJIIPj/AWD/IDM+q60oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(dim1,dim2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "68c912ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_shape = (130,130,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "55d5fd8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "93833228",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class ImageDataGenerator in module keras.preprocessing.image:\n",
      "\n",
      "class ImageDataGenerator(keras_preprocessing.image.image_data_generator.ImageDataGenerator)\n",
      " |  ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-06, rotation_range=0, width_shift_range=0.0, height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False, rescale=None, preprocessing_function=None, data_format=None, validation_split=0.0, dtype=None)\n",
      " |  \n",
      " |  Generate batches of tensor image data with real-time data augmentation.\n",
      " |  \n",
      " |   The data will be looped over (in batches).\n",
      " |  \n",
      " |  Args:\n",
      " |      featurewise_center: Boolean.\n",
      " |          Set input mean to 0 over the dataset, feature-wise.\n",
      " |      samplewise_center: Boolean. Set each sample mean to 0.\n",
      " |      featurewise_std_normalization: Boolean.\n",
      " |          Divide inputs by std of the dataset, feature-wise.\n",
      " |      samplewise_std_normalization: Boolean. Divide each input by its std.\n",
      " |      zca_epsilon: epsilon for ZCA whitening. Default is 1e-6.\n",
      " |      zca_whitening: Boolean. Apply ZCA whitening.\n",
      " |      rotation_range: Int. Degree range for random rotations.\n",
      " |      width_shift_range: Float, 1-D array-like or int\n",
      " |          - float: fraction of total width, if < 1, or pixels if >= 1.\n",
      " |          - 1-D array-like: random elements from the array.\n",
      " |          - int: integer number of pixels from interval\n",
      " |              `(-width_shift_range, +width_shift_range)`\n",
      " |          - With `width_shift_range=2` possible values\n",
      " |              are integers `[-1, 0, +1]`,\n",
      " |              same as with `width_shift_range=[-1, 0, +1]`,\n",
      " |              while with `width_shift_range=1.0` possible values are floats\n",
      " |              in the interval [-1.0, +1.0).\n",
      " |      height_shift_range: Float, 1-D array-like or int\n",
      " |          - float: fraction of total height, if < 1, or pixels if >= 1.\n",
      " |          - 1-D array-like: random elements from the array.\n",
      " |          - int: integer number of pixels from interval\n",
      " |              `(-height_shift_range, +height_shift_range)`\n",
      " |          - With `height_shift_range=2` possible values\n",
      " |              are integers `[-1, 0, +1]`,\n",
      " |              same as with `height_shift_range=[-1, 0, +1]`,\n",
      " |              while with `height_shift_range=1.0` possible values are floats\n",
      " |              in the interval [-1.0, +1.0).\n",
      " |      brightness_range: Tuple or list of two floats. Range for picking\n",
      " |          a brightness shift value from.\n",
      " |      shear_range: Float. Shear Intensity\n",
      " |          (Shear angle in counter-clockwise direction in degrees)\n",
      " |      zoom_range: Float or [lower, upper]. Range for random zoom.\n",
      " |          If a float, `[lower, upper] = [1-zoom_range, 1+zoom_range]`.\n",
      " |      channel_shift_range: Float. Range for random channel shifts.\n",
      " |      fill_mode: One of {\"constant\", \"nearest\", \"reflect\" or \"wrap\"}.\n",
      " |          Default is 'nearest'.\n",
      " |          Points outside the boundaries of the input are filled\n",
      " |          according to the given mode:\n",
      " |          - 'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k)\n",
      " |          - 'nearest':  aaaaaaaa|abcd|dddddddd\n",
      " |          - 'reflect':  abcddcba|abcd|dcbaabcd\n",
      " |          - 'wrap':  abcdabcd|abcd|abcdabcd\n",
      " |      cval: Float or Int.\n",
      " |          Value used for points outside the boundaries\n",
      " |          when `fill_mode = \"constant\"`.\n",
      " |      horizontal_flip: Boolean. Randomly flip inputs horizontally.\n",
      " |      vertical_flip: Boolean. Randomly flip inputs vertically.\n",
      " |      rescale: rescaling factor. Defaults to None.\n",
      " |          If None or 0, no rescaling is applied,\n",
      " |          otherwise we multiply the data by the value provided\n",
      " |          (after applying all other transformations).\n",
      " |      preprocessing_function: function that will be applied on each input.\n",
      " |          The function will run after the image is resized and augmented.\n",
      " |          The function should take one argument:\n",
      " |          one image (Numpy tensor with rank 3),\n",
      " |          and should output a Numpy tensor with the same shape.\n",
      " |      data_format: Image data format,\n",
      " |          either \"channels_first\" or \"channels_last\".\n",
      " |          \"channels_last\" mode means that the images should have shape\n",
      " |          `(samples, height, width, channels)`,\n",
      " |          \"channels_first\" mode means that the images should have shape\n",
      " |          `(samples, channels, height, width)`.\n",
      " |          It defaults to the `image_data_format` value found in your\n",
      " |          Keras config file at `~/.keras/keras.json`.\n",
      " |          If you never set it, then it will be \"channels_last\".\n",
      " |      validation_split: Float. Fraction of images reserved for validation\n",
      " |          (strictly between 0 and 1).\n",
      " |      dtype: Dtype to use for the generated arrays.\n",
      " |  \n",
      " |  Raises:\n",
      " |    ValueError: If the value of the argument, `data_format` is other than\n",
      " |          `\"channels_last\"` or `\"channels_first\"`.\n",
      " |    ValueError: If the value of the argument, `validation_split` > 1\n",
      " |          or `validation_split` < 0.\n",
      " |  \n",
      " |  Examples:\n",
      " |  \n",
      " |  Example of using `.flow(x, y)`:\n",
      " |  \n",
      " |  ```python\n",
      " |  (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
      " |  y_train = utils.to_categorical(y_train, num_classes)\n",
      " |  y_test = utils.to_categorical(y_test, num_classes)\n",
      " |  datagen = ImageDataGenerator(\n",
      " |      featurewise_center=True,\n",
      " |      featurewise_std_normalization=True,\n",
      " |      rotation_range=20,\n",
      " |      width_shift_range=0.2,\n",
      " |      height_shift_range=0.2,\n",
      " |      horizontal_flip=True,\n",
      " |      validation_split=0.2)\n",
      " |  # compute quantities required for featurewise normalization\n",
      " |  # (std, mean, and principal components if ZCA whitening is applied)\n",
      " |  datagen.fit(x_train)\n",
      " |  # fits the model on batches with real-time data augmentation:\n",
      " |  model.fit(datagen.flow(x_train, y_train, batch_size=32,\n",
      " |           subset='training'),\n",
      " |           validation_data=datagen.flow(x_train, y_train,\n",
      " |           batch_size=8, subset='validation'),\n",
      " |           steps_per_epoch=len(x_train) / 32, epochs=epochs)\n",
      " |  # here's a more \"manual\" example\n",
      " |  for e in range(epochs):\n",
      " |      print('Epoch', e)\n",
      " |      batches = 0\n",
      " |      for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):\n",
      " |          model.fit(x_batch, y_batch)\n",
      " |          batches += 1\n",
      " |          if batches >= len(x_train) / 32:\n",
      " |              # we need to break the loop by hand because\n",
      " |              # the generator loops indefinitely\n",
      " |              break\n",
      " |  ```\n",
      " |  \n",
      " |  Example of using `.flow_from_directory(directory)`:\n",
      " |  \n",
      " |  ```python\n",
      " |  train_datagen = ImageDataGenerator(\n",
      " |          rescale=1./255,\n",
      " |          shear_range=0.2,\n",
      " |          zoom_range=0.2,\n",
      " |          horizontal_flip=True)\n",
      " |  test_datagen = ImageDataGenerator(rescale=1./255)\n",
      " |  train_generator = train_datagen.flow_from_directory(\n",
      " |          'data/train',\n",
      " |          target_size=(150, 150),\n",
      " |          batch_size=32,\n",
      " |          class_mode='binary')\n",
      " |  validation_generator = test_datagen.flow_from_directory(\n",
      " |          'data/validation',\n",
      " |          target_size=(150, 150),\n",
      " |          batch_size=32,\n",
      " |          class_mode='binary')\n",
      " |  model.fit(\n",
      " |          train_generator,\n",
      " |          steps_per_epoch=2000,\n",
      " |          epochs=50,\n",
      " |          validation_data=validation_generator,\n",
      " |          validation_steps=800)\n",
      " |  ```\n",
      " |  \n",
      " |  Example of transforming images and masks together.\n",
      " |  \n",
      " |  ```python\n",
      " |  # we create two instances with the same arguments\n",
      " |  data_gen_args = dict(featurewise_center=True,\n",
      " |                       featurewise_std_normalization=True,\n",
      " |                       rotation_range=90,\n",
      " |                       width_shift_range=0.1,\n",
      " |                       height_shift_range=0.1,\n",
      " |                       zoom_range=0.2)\n",
      " |  image_datagen = ImageDataGenerator(**data_gen_args)\n",
      " |  mask_datagen = ImageDataGenerator(**data_gen_args)\n",
      " |  # Provide the same seed and keyword arguments to the fit and flow methods\n",
      " |  seed = 1\n",
      " |  image_datagen.fit(images, augment=True, seed=seed)\n",
      " |  mask_datagen.fit(masks, augment=True, seed=seed)\n",
      " |  image_generator = image_datagen.flow_from_directory(\n",
      " |      'data/images',\n",
      " |      class_mode=None,\n",
      " |      seed=seed)\n",
      " |  mask_generator = mask_datagen.flow_from_directory(\n",
      " |      'data/masks',\n",
      " |      class_mode=None,\n",
      " |      seed=seed)\n",
      " |  # combine generators into one which yields image and masks\n",
      " |  train_generator = zip(image_generator, mask_generator)\n",
      " |  model.fit(\n",
      " |      train_generator,\n",
      " |      steps_per_epoch=2000,\n",
      " |      epochs=50)\n",
      " |  ```\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      ImageDataGenerator\n",
      " |      keras_preprocessing.image.image_data_generator.ImageDataGenerator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-06, rotation_range=0, width_shift_range=0.0, height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False, rescale=None, preprocessing_function=None, data_format=None, validation_split=0.0, dtype=None)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  flow(self, x, y=None, batch_size=32, shuffle=True, sample_weight=None, seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None)\n",
      " |      Takes data & label arrays, generates batches of augmented data.\n",
      " |      \n",
      " |      Args:\n",
      " |          x: Input data. Numpy array of rank 4 or a tuple. If tuple, the first\n",
      " |            element should contain the images and the second element another numpy\n",
      " |            array or a list of numpy arrays that gets passed to the output without\n",
      " |            any modifications. Can be used to feed the model miscellaneous data\n",
      " |            along with the images. In case of grayscale data, the channels axis of\n",
      " |            the image array should have value 1, in case of RGB data, it should\n",
      " |            have value 3, and in case of RGBA data, it should have value 4.\n",
      " |          y: Labels.\n",
      " |          batch_size: Int (default: 32).\n",
      " |          shuffle: Boolean (default: True).\n",
      " |          sample_weight: Sample weights.\n",
      " |          seed: Int (default: None).\n",
      " |          save_to_dir: None or str (default: None). This allows you to optionally\n",
      " |            specify a directory to which to save the augmented pictures being\n",
      " |            generated (useful for visualizing what you are doing).\n",
      " |          save_prefix: Str (default: `''`). Prefix to use for filenames of saved\n",
      " |            pictures (only relevant if `save_to_dir` is set).\n",
      " |          save_format: one of \"png\", \"jpeg\", \"bmp\", \"pdf\", \"ppm\", \"gif\",\n",
      " |              \"tif\", \"jpg\"\n",
      " |              (only relevant if `save_to_dir` is set). Default: \"png\".\n",
      " |          subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
      " |            `validation_split` is set in `ImageDataGenerator`.\n",
      " |      \n",
      " |      Returns:\n",
      " |          An `Iterator` yielding tuples of `(x, y)`\n",
      " |              where `x` is a numpy array of image data\n",
      " |              (in the case of a single image input) or a list\n",
      " |              of numpy arrays (in the case with\n",
      " |              additional inputs) and `y` is a numpy array\n",
      " |              of corresponding labels. If 'sample_weight' is not None,\n",
      " |              the yielded tuples are of the form `(x, y, sample_weight)`.\n",
      " |              If `y` is None, only the numpy array `x` is returned.\n",
      " |      Raises:\n",
      " |        ValueError: If the Value of the argument, `subset` is other than\n",
      " |              \"training\" or \"validation\".\n",
      " |  \n",
      " |  flow_from_dataframe(self, dataframe, directory=None, x_col='filename', y_col='class', weight_col=None, target_size=(256, 256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None, interpolation='nearest', validate_filenames=True, **kwargs)\n",
      " |      Takes the dataframe and the path to a directory + generates batches.\n",
      " |      \n",
      " |       The generated batches contain augmented/normalized data.\n",
      " |      \n",
      " |      **A simple tutorial can be found **[here](\n",
      " |                                  http://bit.ly/keras_flow_from_dataframe).\n",
      " |      \n",
      " |      Args:\n",
      " |          dataframe: Pandas dataframe containing the filepaths relative to\n",
      " |            `directory` (or absolute paths if `directory` is None) of the images\n",
      " |            in a string column. It should include other column/s\n",
      " |              depending on the `class_mode`:\n",
      " |              - if `class_mode` is `\"categorical\"` (default value) it must include\n",
      " |                the `y_col` column with the class/es of each image. Values in\n",
      " |                column can be string/list/tuple if a single class or list/tuple if\n",
      " |                multiple classes.\n",
      " |              - if `class_mode` is `\"binary\"` or `\"sparse\"` it must include the\n",
      " |                given `y_col` column with class values as strings.\n",
      " |              - if `class_mode` is `\"raw\"` or `\"multi_output\"` it should contain\n",
      " |                the columns specified in `y_col`.\n",
      " |              - if `class_mode` is `\"input\"` or `None` no extra column is needed.\n",
      " |          directory: string, path to the directory to read images from. If `None`,\n",
      " |            data in `x_col` column should be absolute paths.\n",
      " |          x_col: string, column in `dataframe` that contains the filenames (or\n",
      " |            absolute paths if `directory` is `None`).\n",
      " |          y_col: string or list, column/s in `dataframe` that has the target data.\n",
      " |          weight_col: string, column in `dataframe` that contains the sample\n",
      " |              weights. Default: `None`.\n",
      " |          target_size: tuple of integers `(height, width)`, default: `(256, 256)`.\n",
      " |            The dimensions to which all images found will be resized.\n",
      " |          color_mode: one of \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\". Whether\n",
      " |            the images will be converted to have 1 or 3 color channels.\n",
      " |          classes: optional list of classes (e.g. `['dogs', 'cats']`). Default is\n",
      " |            None. If not provided, the list of classes will be automatically\n",
      " |            inferred from the `y_col`, which will map to the label indices, will\n",
      " |            be alphanumeric). The dictionary containing the mapping from class\n",
      " |            names to class indices can be obtained via the attribute\n",
      " |            `class_indices`.\n",
      " |          class_mode: one of \"binary\", \"categorical\", \"input\", \"multi_output\",\n",
      " |              \"raw\", sparse\" or None. Default: \"categorical\".\n",
      " |              Mode for yielding the targets:\n",
      " |              - `\"binary\"`: 1D numpy array of binary labels,\n",
      " |              - `\"categorical\"`: 2D numpy array of one-hot encoded labels.\n",
      " |                Supports multi-label output.\n",
      " |              - `\"input\"`: images identical to input images (mainly used to work\n",
      " |                with autoencoders),\n",
      " |              - `\"multi_output\"`: list with the values of the different columns,\n",
      " |              - `\"raw\"`: numpy array of values in `y_col` column(s),\n",
      " |              - `\"sparse\"`: 1D numpy array of integer labels,\n",
      " |              - `None`, no targets are returned (the generator will only yield\n",
      " |                batches of image data, which is useful to use in\n",
      " |                `model.predict()`).\n",
      " |          batch_size: size of the batches of data (default: 32).\n",
      " |          shuffle: whether to shuffle the data (default: True)\n",
      " |          seed: optional random seed for shuffling and transformations.\n",
      " |          save_to_dir: None or str (default: None). This allows you to optionally\n",
      " |            specify a directory to which to save the augmented pictures being\n",
      " |            generated (useful for visualizing what you are doing).\n",
      " |          save_prefix: str. Prefix to use for filenames of saved pictures (only\n",
      " |            relevant if `save_to_dir` is set).\n",
      " |          save_format: one of \"png\", \"jpeg\", \"bmp\", \"pdf\", \"ppm\", \"gif\",\n",
      " |              \"tif\", \"jpg\"\n",
      " |              (only relevant if `save_to_dir` is set). Default: \"png\".\n",
      " |          subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
      " |            `validation_split` is set in `ImageDataGenerator`.\n",
      " |          interpolation: Interpolation method used to resample the image if the\n",
      " |            target size is different from that of the loaded image. Supported\n",
      " |            methods are `\"nearest\"`, `\"bilinear\"`, and `\"bicubic\"`. If PIL version\n",
      " |            1.1.3 or newer is installed, `\"lanczos\"` is also supported. If PIL\n",
      " |            version 3.4.0 or newer is installed, `\"box\"` and `\"hamming\"` are also\n",
      " |            supported. By default, `\"nearest\"` is used.\n",
      " |          validate_filenames: Boolean, whether to validate image filenames in\n",
      " |            `x_col`. If `True`, invalid images will be ignored. Disabling this\n",
      " |            option can lead to speed-up in the execution of this function.\n",
      " |            Defaults to `True`.\n",
      " |          **kwargs: legacy arguments for raising deprecation warnings.\n",
      " |      \n",
      " |      Returns:\n",
      " |          A `DataFrameIterator` yielding tuples of `(x, y)`\n",
      " |          where `x` is a numpy array containing a batch\n",
      " |          of images with shape `(batch_size, *target_size, channels)`\n",
      " |          and `y` is a numpy array of corresponding labels.\n",
      " |  \n",
      " |  flow_from_directory(self, directory, target_size=(256, 256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='png', follow_links=False, subset=None, interpolation='nearest')\n",
      " |      Takes the path to a directory & generates batches of augmented data.\n",
      " |      \n",
      " |      Args:\n",
      " |          directory: string, path to the target directory. It should contain one\n",
      " |            subdirectory per class. Any PNG, JPG, BMP, PPM or TIF images inside\n",
      " |            each of the subdirectories directory tree will be included in the\n",
      " |            generator. See [this script](\n",
      " |              https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)\n",
      " |                for more details.\n",
      " |          target_size: Tuple of integers `(height, width)`, defaults to `(256,\n",
      " |            256)`. The dimensions to which all images found will be resized.\n",
      " |          color_mode: One of \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\". Whether\n",
      " |            the images will be converted to have 1, 3, or 4 channels.\n",
      " |          classes: Optional list of class subdirectories\n",
      " |              (e.g. `['dogs', 'cats']`). Default: None. If not provided, the list\n",
      " |                of classes will be automatically inferred from the subdirectory\n",
      " |                names/structure under `directory`, where each subdirectory will be\n",
      " |                treated as a different class (and the order of the classes, which\n",
      " |                will map to the label indices, will be alphanumeric). The\n",
      " |                dictionary containing the mapping from class names to class\n",
      " |                indices can be obtained via the attribute `class_indices`.\n",
      " |          class_mode: One of \"categorical\", \"binary\", \"sparse\",\n",
      " |              \"input\", or None. Default: \"categorical\".\n",
      " |              Determines the type of label arrays that are returned:\n",
      " |              - \"categorical\" will be 2D one-hot encoded labels,\n",
      " |              - \"binary\" will be 1D binary labels,\n",
      " |              - \"sparse\" will be 1D integer labels,\n",
      " |              - \"input\"  will be images identical to input images (mainly used to\n",
      " |                work with autoencoders).\n",
      " |              - If None, no labels are returned (the generator will only yield\n",
      " |                batches of image data, which is useful to use with\n",
      " |                `model.predict()`).\n",
      " |              Please note that in case of class_mode None, the data still needs to\n",
      " |              reside in a subdirectory of `directory` for it to work correctly.\n",
      " |          batch_size: Size of the batches of data (default: 32).\n",
      " |          shuffle: Whether to shuffle the data (default: True) If set to False,\n",
      " |            sorts the data in alphanumeric order.\n",
      " |          seed: Optional random seed for shuffling and transformations.\n",
      " |          save_to_dir: None or str (default: None). This allows you to optionally\n",
      " |            specify a directory to which to save the augmented pictures being\n",
      " |            generated (useful for visualizing what you are doing).\n",
      " |          save_prefix: Str. Prefix to use for filenames of saved pictures (only\n",
      " |            relevant if `save_to_dir` is set).\n",
      " |          save_format: one of \"png\", \"jpeg\", \"bmp\", \"pdf\", \"ppm\", \"gif\",\n",
      " |              \"tif\", \"jpg\"\n",
      " |              (only relevant if `save_to_dir` is set). Default: \"png\".\n",
      " |          follow_links: Whether to follow symlinks inside\n",
      " |              class subdirectories (default: False).\n",
      " |          subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
      " |            `validation_split` is set in `ImageDataGenerator`.\n",
      " |          interpolation: Interpolation method used to resample the image if the\n",
      " |            target size is different from that of the loaded image. Supported\n",
      " |            methods are `\"nearest\"`, `\"bilinear\"`, and `\"bicubic\"`. If PIL version\n",
      " |            1.1.3 or newer is installed, `\"lanczos\"` is also supported. If PIL\n",
      " |            version 3.4.0 or newer is installed, `\"box\"` and `\"hamming\"` are also\n",
      " |            supported. By default, `\"nearest\"` is used.\n",
      " |      \n",
      " |      Returns:\n",
      " |          A `DirectoryIterator` yielding tuples of `(x, y)`\n",
      " |              where `x` is a numpy array containing a batch\n",
      " |              of images with shape `(batch_size, *target_size, channels)`\n",
      " |              and `y` is a numpy array of corresponding labels.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from keras_preprocessing.image.image_data_generator.ImageDataGenerator:\n",
      " |  \n",
      " |  apply_transform(self, x, transform_parameters)\n",
      " |      Applies a transformation to an image according to given parameters.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: 3D tensor, single image.\n",
      " |          transform_parameters: Dictionary with string - parameter pairs\n",
      " |              describing the transformation.\n",
      " |              Currently, the following parameters\n",
      " |              from the dictionary are used:\n",
      " |              - `'theta'`: Float. Rotation angle in degrees.\n",
      " |              - `'tx'`: Float. Shift in the x direction.\n",
      " |              - `'ty'`: Float. Shift in the y direction.\n",
      " |              - `'shear'`: Float. Shear angle in degrees.\n",
      " |              - `'zx'`: Float. Zoom in the x direction.\n",
      " |              - `'zy'`: Float. Zoom in the y direction.\n",
      " |              - `'flip_horizontal'`: Boolean. Horizontal flip.\n",
      " |              - `'flip_vertical'`: Boolean. Vertical flip.\n",
      " |              - `'channel_shift_intensity'`: Float. Channel shift intensity.\n",
      " |              - `'brightness'`: Float. Brightness shift intensity.\n",
      " |      \n",
      " |      # Returns\n",
      " |          A transformed version of the input (same shape).\n",
      " |  \n",
      " |  fit(self, x, augment=False, rounds=1, seed=None)\n",
      " |      Fits the data generator to some sample data.\n",
      " |      \n",
      " |      This computes the internal data stats related to the\n",
      " |      data-dependent transformations, based on an array of sample data.\n",
      " |      \n",
      " |      Only required if `featurewise_center` or\n",
      " |      `featurewise_std_normalization` or `zca_whitening` are set to True.\n",
      " |      \n",
      " |      When `rescale` is set to a value, rescaling is applied to\n",
      " |      sample data before computing the internal data stats.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: Sample data. Should have rank 4.\n",
      " |           In case of grayscale data,\n",
      " |           the channels axis should have value 1, in case\n",
      " |           of RGB data, it should have value 3, and in case\n",
      " |           of RGBA data, it should have value 4.\n",
      " |          augment: Boolean (default: False).\n",
      " |              Whether to fit on randomly augmented samples.\n",
      " |          rounds: Int (default: 1).\n",
      " |              If using data augmentation (`augment=True`),\n",
      " |              this is how many augmentation passes over the data to use.\n",
      " |          seed: Int (default: None). Random seed.\n",
      " |  \n",
      " |  get_random_transform(self, img_shape, seed=None)\n",
      " |      Generates random parameters for a transformation.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          seed: Random seed.\n",
      " |          img_shape: Tuple of integers.\n",
      " |              Shape of the image that is transformed.\n",
      " |      \n",
      " |      # Returns\n",
      " |          A dictionary containing randomly chosen parameters describing the\n",
      " |          transformation.\n",
      " |  \n",
      " |  random_transform(self, x, seed=None)\n",
      " |      Applies a random transformation to an image.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: 3D tensor, single image.\n",
      " |          seed: Random seed.\n",
      " |      \n",
      " |      # Returns\n",
      " |          A randomly transformed version of the input (same shape).\n",
      " |  \n",
      " |  standardize(self, x)\n",
      " |      Applies the normalization configuration in-place to a batch of inputs.\n",
      " |      \n",
      " |      `x` is changed in-place since the function is mainly used internally\n",
      " |      to standardize images and feed them to your network. If a copy of `x`\n",
      " |      would be created instead it would have a significant performance cost.\n",
      " |      If you want to apply this method without changing the input in-place\n",
      " |      you can call the method creating a copy before:\n",
      " |      \n",
      " |      standardize(np.copy(x))\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: Batch of inputs to be normalized.\n",
      " |      \n",
      " |      # Returns\n",
      " |          The inputs, normalized.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from keras_preprocessing.image.image_data_generator.ImageDataGenerator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ImageDataGenerator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "68ddbe69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/fasihussaini/Desktop/cell_images/train/parasitized/C189P150ThinF_IMG_20151203_142224_cell_84.png'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "para_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ebe705a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_gen = ImageDataGenerator(rotation_range=20,\n",
    "                              width_shift_range=0.1,\n",
    "                              height_shift_range=0.1,\n",
    "                              shear_range=0.1,\n",
    "                              zoom_range=0.1,\n",
    "                              horizontal_flip=True,\n",
    "                              fill_mode='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "adc182f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "para_cell=imread(para_cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "aa4d59e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x13df4c5b0>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(para_cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a10dd9f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x13e9c2730>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image_gen.random_transform(para_cell))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "fc8851b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2600 images belonging to 2 classes.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.preprocessing.image.DirectoryIterator at 0x13ea5ce80>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_gen.flow_from_directory(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "6f802c06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential \n",
    "from tensorflow.keras.layers import Conv2D,Dense,MaxPool2D,Dropout,Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "ad2d8184",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-07 04:45:43.489061: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=32,kernel_size=(3,3),input_shape=image_shape,activation='relu'))\n",
    "model.add(MaxPool2D())\n",
    "\n",
    "\n",
    "model.add(Conv2D(filters=64,kernel_size=(3,3),input_shape=image_shape,activation='relu'))\n",
    "model.add(MaxPool2D())\n",
    "\n",
    "\n",
    "model.add(Conv2D(filters=64,kernel_size=(3,3),input_shape=image_shape,activation='relu'))\n",
    "model.add(MaxPool2D())\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(units=128,activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "\n",
    "model.add(Dense(units=1,activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "2eb8164a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 128, 128, 32)      896       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 64, 64, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 62, 62, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 31, 31, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 29, 29, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 14, 14, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 12544)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               1605760   \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 129       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,662,209\n",
      "Trainable params: 1,662,209\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "09708940",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "06e564c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor='val_loss',patience=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "0b1289d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "49cbbb41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/fasihussaini/Desktop/cell_images/train'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "971a6edf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 24958 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_image_gen = image_gen.flow_from_directory(train_path,\n",
    "                                                target_size=image_shape[:2],\n",
    "                                                color_mode='rgb',\n",
    "                                                batch_size=batch_size,\n",
    "                                                class_mode='binary',\n",
    "                                               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "92b566f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2600 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_image_gen = image_gen.flow_from_directory(test_path,\n",
    "                                                target_size=image_shape[:2],\n",
    "                                                color_mode='rgb',\n",
    "                                                batch_size=batch_size,\n",
    "                                                class_mode='binary',\n",
    "                                               shuffle=False\n",
    "                                               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "3560e126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'parasitized': 0, 'uninfected': 1}"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_gen.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "3ad47099",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ys/r1f3d1bn3k11yrhwbf5xl1hh0000gn/T/ipykernel_27789/1509124778.py:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  results = model.fit_generator(train_image_gen,epochs=20,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1560/1560 [==============================] - 274s 175ms/step - loss: 0.7921 - accuracy: 0.5484 - val_loss: 0.6808 - val_accuracy: 0.5673\n",
      "Epoch 2/20\n",
      "1560/1560 [==============================] - 285s 183ms/step - loss: 0.6751 - accuracy: 0.5849 - val_loss: 0.6662 - val_accuracy: 0.6031\n",
      "Epoch 3/20\n",
      "1560/1560 [==============================] - 307s 197ms/step - loss: 0.6311 - accuracy: 0.6357 - val_loss: 0.2916 - val_accuracy: 0.9046\n",
      "Epoch 4/20\n",
      "1560/1560 [==============================] - 360s 231ms/step - loss: 0.2553 - accuracy: 0.9201 - val_loss: 0.2480 - val_accuracy: 0.9258\n",
      "Epoch 5/20\n",
      "1560/1560 [==============================] - 319s 204ms/step - loss: 0.2255 - accuracy: 0.9284 - val_loss: 0.2185 - val_accuracy: 0.9323\n",
      "Epoch 6/20\n",
      "1560/1560 [==============================] - 368s 236ms/step - loss: 0.2165 - accuracy: 0.9314 - val_loss: 0.2123 - val_accuracy: 0.9292\n",
      "Epoch 7/20\n",
      "1560/1560 [==============================] - 345s 221ms/step - loss: 0.2073 - accuracy: 0.9355 - val_loss: 0.2076 - val_accuracy: 0.9365\n",
      "Epoch 8/20\n",
      "1560/1560 [==============================] - 337s 216ms/step - loss: 0.2022 - accuracy: 0.9377 - val_loss: 0.1829 - val_accuracy: 0.9419\n",
      "Epoch 9/20\n",
      "1560/1560 [==============================] - 332s 213ms/step - loss: 0.1997 - accuracy: 0.9403 - val_loss: 0.1891 - val_accuracy: 0.9427\n",
      "Epoch 10/20\n",
      "1560/1560 [==============================] - 334s 214ms/step - loss: 0.1977 - accuracy: 0.9406 - val_loss: 0.1833 - val_accuracy: 0.9404\n"
     ]
    }
   ],
   "source": [
    "results = model.fit_generator(train_image_gen,epochs=20, \n",
    "                              validation_data=test_image_gen,\n",
    "                              callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "1340fa66",
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "4ec17fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses[['loss','val_loss']].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "8ca36f3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f8iGU58PEP5pfthCi2bTKFvopSin+b1xPnr+1L8t3HGPaP7+gtNoZ7GoBUDzzbazJybQZd5O5BWsNa16G5F7Qbay5ZQshmlWrDvRTvjUsnVfqDUAqDPIApNp9+6n6/HPipt6FMvtSxb2fwrEtvhtYGFfEP78QV4wr5jd6Qv/2/Ot+3wCk24M8AKlk1iyUzUb8nXeaX/ialyG6HfSfbH7ZQohmdcUEOsCwrr4BSF7tG4D05f5i0+vgqaigbN482kyYgDWpWWYYPr8jebDvMxj6MFjDzC1bCNHsrqhAB98ApA8f8Q1AmvbPL0wfgFT24Yd4q6uJvzsIsyqu+QvYo2HQfeaXLYRodldcoINvANLch6+lT3tzByBpj4fid2YRcfXVRPTra0qZdUoP+a5uGXQvRMSZW7YQwhRXZKADJETZefdBcwcgVf5vJa7Dh4MzkGjdq6AUDP2u+WULIUxxxQY6+AYgvX73ICYPMmcAUsk7b2Nt146YMWOarYwG1ZT4Rob2uwNi08wtWwhhmlY7sKipbBaD333ddwekUwOQ/vqNqwi3BXYAUu3u3VStWUvyk0+ibLaAHrtROW+Aq8p3qaIQotVqUgtdKTVeKbVTKbVHKfWTBrb/SSmV63/sUkqVBrymzejUAKRf3OIbgPTNfwR+AFLxO7NQYWHE3Wny5YLuWvjideg6Ctr1N7dsIYSpGg10pZQFeAXIBvoAU5VSfervo7V+Ums9UGs9EPgL8GEz1LXZ3XNt8wxA8pSVUfbf/9Jm0kSs8fEBOWaTbXoPKo/BtTIJlxCtXVNa6EOAPVrrfVprJzAbuPUC+08F/h2IygXD2QOQdh27/AFIpXPnoh0OEqaZfDLU6/VdqtiuP3QZaW7ZQgjTNSXQOwCH6y3n+9edQynVGcgAPj3P9oeUUjlKqZyioqKLratphnVN5L3v+AYgff3VyxuApN1uimfNInLwYMJ79QpgLZtg91I4sQuufcJ3hYsQolUL9FUudwFztdaehjZqrWdorbO01lnJyckBLjqw+qSevgPStH9+wdKtlzYAqWLFCtyFR4j/VhAGEq1+GWI7Qt/bzC9bCGG6pgR6AdCx3nKaf11D7iKEu1vO1jHh9ACk775zaQOQSma+jS01lZhRo5qhhheQnwOH1sDQR8Bi8lU1QoigaEqgrwe6K6UylFJ2fKE9/+ydlFK9gHhgbWCrGFynBiCN6JF80QOQHDt2UL1+PfHf/CbKYvI87KtfgvBYuPpb5pYrhAiaRq9D11q7lVKPAksBC/CG1nqrUup5IEdrfSrc7wJm62YebrmnZA87S3bSLqodKZEppESmYGvmFmik3cqMb2Xx9IebeXn5booqavnlrX2xWi78fVj8zjuoiAjivn5Hs9bvHCf3wvYFcN2TEBZtbtkiZLlcLvLz83E4HMGuigDCw8NJS0vDdhHjVpo0sEhrvQhYdNa6Z85afq7JpV6GFYdX8PJXL9ctKxSJEYm0i2xHSlQK7aLanfM8KTIJm3F5oX9qAFLbNmG8smIvJypr+cvU8w9AcpeUUL5gIbG33YYlNvayyr5oa1/xdbNc8x1zyxUhLT8/n5iYGNLT01FyEj2otNacPHmS/Px8MjKafhP3kBspOq3PNEZ3Gs3R6qMcqzrG0aqjHKv2/TxQdoB1R9ZR5ao64zWGMkgKT/K16qN8rfp2Ue3qWvntotqRHJGMxbhwt4hSih+N60XbmHCeW7CVm1/+nK8P6sjEAe3pmBB5xr6lc95H19aaP29L1QnInQUDpkBMO3PLFiHN4XBImLcQSikSExO52KsBQy7QI6wRdInrQpe4Lufdp9JZydGqo6dDv/qob7nqKHtK97CqYBU17jMHDVmUheTI5NNh30CLPykiCUMZ3HNtOqlxEfx1xR5+u2QHv12yg8y0WCYOSOXmAe1pH2Wl5N//JuraYYR169bcH8mZvvw7uB0yzF9cEgnzluNS/i1CLtCbItoeTTd7N7rFNxymWmvKneVntO5PPT9WdYwdxTv47PBn1Hpqz3idVVlpG9m2rmX/wPjrGRg/mo82H+GjTUf41aLt/GrRdr7l2M3Uo0cJ/9E5syQ0L2c1fDkDemRDck9zyxZCBF2rDPTGKKWIDYslNiyWngkNB5/WmrLasrrWff2W/rHqY3x59EuWH1rO/6bcyMMjuvLwiK4cOFHFR5uPkP6LVymMSuTBz51kHV7LxMz2ZPdrT3JMM98lKHcW1BTDcBnmL0JTdHQ0lZWVwa5GyLoiA70plFLEhccRFx5Hr4RzR3jmHs/l7sV3s+LwCiZ1nQRAelIU9yfXcuDoXoxHn+SJXr34aHMhz/x3K8/N38o1GYlMzGzP+L7tSIwOcLh7Pb6ToR2yoNOwwB5bCBESJNAvUWZyJqlRqSzav6gu0AFK3n4bIzKSbvdM5YmYGJ4Y051dxypYmFfIwk1H+Nm8LTzz361c2zWRiQPaM65vO+Ii7Zdfoe0LoGQ/jP2FDPMXl+0XC7ayrbA8oMfsk9qGZyc17U5dWmt+/OMfs3jxYpRSTJ8+nSlTpnDkyBGmTJlCeXk5brebV199lWuvvZZvf/vb5OTkoJTi/vvv58knnwxo3UOFBPolUkoxPmM8M7fOpMRRQnx4PO4TJyhftIi4O+/EEhNTt2+PlBh+cFNPnhzbg+1HKli4yRfuT32wmZ/N28J13ZOYOCCVsX1SiI24hMsrtYY1L0NCF+g1MYDvUojg+PDDD8nNzSUvL48TJ04wePBgbrjhBt59913GjRvHz372MzweD9XV1eTm5lJQUMCWLVsAKC0tDW7lg0gC/TJMyJjAG1ve4JODn3Bnzzspee89tMtF/De/2eD+Sin6pLahT2obfjSuJ1sKylm4uZCFeUf4v/fzsFsMbujhC/fRvdsSE97EcD+4Bgo2wM1/gEYuvRSiKZrakm4uq1atYurUqVgsFlJSUhgxYgTr169n8ODB3H///bhcLm677TYGDhxIly5d2LdvH4899hg333wzN910U1DrHkxX9C3oLleP+B50ie3Cov2L0E4nJbNnE3X99YR1aXwggFKK/mmx/DS7N6ueupF5j1zLt4Z1ZmthOd9/L5dBLyzjO2/nsCCvkGqn+8IHW/MyRCbCwIa/SIRoLW644QZWrlxJhw4duPfee5k5cybx8fHk5eUxcuRIXnvtNR544IFgVzNopIV+GU51u7ya+yqH58/BU3SChP938bMqKqW4qlM8V3WK5+kJvdl4qISFm46waPMRlm49RrjNYHSvFCYOaM+NvdqeOTr1+A7YtQRG/hRsEQF8d0IEz/XXX8/rr7/OPffcQ3FxMStXruTFF1/k4MGDpKWl8eCDD1JbW8vGjRuZMGECdrudO+64g549ezLN7PsOtCAS6JdpQsYE/pb7N4796w0S0tOJGj78so5nGIqs9ASy0hP4+cQ+rD9QzEebjrB4yxE+2nyESLuFMb194T4iuYqwj6eDNQIGPxigdyRE8H3ta19j7dq1ZGZmopTid7/7He3ateNf//oXL774IjabjejoaGbOnElBQQH33XcfXq/vBu+//vWvg1z74FHNPJfWeWVlZemcnJyglB1oP3hlEg/+ZQ8p06eTMK15uj3cHi9f7C9mSe4BXFsXMNH9CddZtuLFoOSaH5GY/XSzlCuuHNu3b6d3797Broaop6F/E6XUBq11VkP7Sws9AL6Wa6c6DMpHX01CM5VhPbGD4btnMnzvbNAlONqksSTq27x4LIvovZ34bzOVK4QIHRLol8l17DhJa3by0dUG0cf/x8PtA9jCqa2ALR/CxplQkAMWu++yxKu/RXjGCMYbBgf/t5dfL97BwZNVdE6MClzZQoiQI1e5XKbS92aD10v+uP4s3r+4yTe/OC+t4fB6+O+j8PuesOBxcFbCuF/DD3bA5Deh641g+P7pJmamArAgr/By34oQIsRJC/0yeJ1OSma/R/TIkQzLGsHyL15gV8mu884Pc0HVxZA329caL9oOtijodztcfQ+kZZ139GeHuAgGp8ezIO8Ij47qfpnvSAgRyqSFfhnKP1qEp7iYhLunMTZ9LBZlYfH+xU0/gNcL+z6DuffDH3rC0p+CPQomvQz/txNu/St0HNzoUP5JmansPFbBzqMVl/eGhBAhrUmBrpQar5TaqZTao5RqcE5YpdSdSqltSqmtSql3A1vNlkdrTfHbM7F360rksGEkhCcwNHUoSw4sabzbpbwQVr4ILw+EmbfCnuWQdT98dw08uBwG3QNhMRc+Rj0T+rfHUNLtIsSVrtFAV0pZgFeAbKAPMFUp1eesfboDPwWGa637At8PfFVblpqNG6ndtp2EaXfXTUQ/IWMCBZUF5BXlnfsCjwt2fATvToE/9YVPX4D4znDHP+GHOyH7t5ByacOtk6LDGN4tifl5hZffhy+ECFlN6UMfAuzRWu8DUErNBm4FttXb50HgFa11CYDW+nigKxps2uuldudOqlavpnLVamo2bMCIjSX2ltMzLY7qOAq7YWfx/sUMbDvQt/LkXvjqbch9FyqPQXQ7382br5rmm0wrQCZlpvLjuZvYlF9GZse4gB1XiNbI7XZjtba+U4hNeUcdgMP1lvOBa87apweAUmo1YAGe01ovOftASqmHgIcAOnXqdCn1NZX7xAmq1qyhctUqqtasxXPiBABhPXoQP20asbfdhhF5+l6i0fZobki7gaUHlvDj8AwsX70DBz4HZYEe4+Dqb0G3sWAJ/H+kcX3bMX3eFubnFUqgi8u3+CdwdHNgj9muP2T/ptHdbrvtNg4fPozD4eCJJ57goYceYsmSJTz99NN4PB6SkpJYvnw5lZWVPPbYY3XT5j777LPccccdZ9wkY+7cuSxcuJC33nqLe++9l/DwcL766iuGDx/OXXfdxRNPPIHD4SAiIoI333yTnj174vF4eOqpp1iyZAmGYfDggw/St29fXn75Zf7zn/8A8Mknn/C3v/2NefPmBfYzukyBShYr0B0YCaQBK5VS/bXWpfV30lrPAGaAb6RogMoOGK/TSc2GDb5W+Oo11G7fDoAlPp6o4cN9j2uvxZbStuEDHN1MdukJljmKWb/4cYZGtIfRz0DmN6BN+2ate2yEjRE9k1m4qZCfTeiNYcic6CI0vfHGGyQkJFBTU8PgwYO59dZbefDBB1m5ciUZGRkUFxcD8Mtf/pLY2Fg2b/Z98ZSUlDR67Pz8fNasWYPFYqG8vJzPP/8cq9XKsmXLePrpp/nggw+YMWMGBw4cIDc3F6vVSnFxMfHx8TzyyCMUFRWRnJzMm2++yf3339+sn8OlaEqgFwAd6y2n+dfVlw98obV2AfuVUrvwBfz6gNSymWitce7b5+9GWUX1l+vRDgfYbERedRXJTz5J1HXDCe/dG2Wc53SDoxy2zPVdblj4FTdYw4jq2J7FV32NoWNfrbte3AyTMlP5ZNsxvjxQzNAuiaaVK1qhJrSkm8vLL79c1/I9fPgwM2bM4IYbbiAjwzeLaUKCbzz2smXLmD17dt3r4uPjGz325MmTsVh8k9uVlZVxzz33sHv3bpRSuFyuuuM+/PDDdV0yp8q7++67eeedd7jvvvtYu3YtM2fODNA7DpymBPp6oLtSKgNfkN8FfOOsff4DTAXeVEol4euC2RfAegaMp7SUqrVrqVy9mqrVa3AfOQKAPT2duK9/najh1xI1ZAhG1AVGXboccGgNbJ4LW+eBqxra9oXs3xHefzKjNvyeT/I/42fajZ0A3I2oicb0bkuEzcKCvEIJdBGSPvvsM5YtW8batWuJjIxk5MiRDBw4kB07djT5GKreZb4Oh+OMbVH1fq9//vOfc+ONNzJv3jwOHDjAyJEjL3jc++67j0mTJhEeHs7kyZNbZB98ozXSWruVUo8CS/H1j7+htd6qlHoeyNFaz/dvu0kptQ3wAD/SWp9szoo3lXa5qNm0qe5kpmPzZtAaIyaGqGHDiHr4YaKGD8ee1uECB9G+k5t7l8OeZbD/c3DXgD0a+k/2XWaYenXd9eLZGdks2LeA1QWrubHTjSa9U4i0WxnTJ4VFm4/w3C19sVlkmIEILWVlZcTHxxMZGcmOHTtYt24dDoeDlStXsn///roul4SEBMaOHcsrr7zCn//8Z8DX5RIfH09KSgrbt2+nZ8+ezJs3j5iYhi8BLisro0MH3+/9W2+9Vbd+7NixvP7669x44411XS4JCQmkpqaSmprKCy+8wLJly5r7o7gkTfqK0VovAhadte6Zes818AP/I+ichw9TtWoVlatXU73uC7yVlWAYRAwYQNL3vkfU8GuJ6N8fdaFv2NoKX3DvWeZ7lB70rU/o6j+5OQbSrwN75DkvHZo6lLiwOBbvX2xqoAPckpnKgrxCVu05wY09z9PXL0QLNX78eF577TV69+5Nz549GTp0KMnJycyYMYPbb78dr9dL27Zt+eSTT5g+fTrf+9736NevHxaLhWeffZbbb7+d3/zmN0ycOJHk5GSysrLqTpCe7cc//jH33HMPL7zwAjfffHPd+gceeIBdu3YxYMAAbDYbDz74II8++igA3/zmNykqKmqxs1K2iulzPZWVVH/xRV0r3HXoEAC21FSirrvOdzJz2FAsbdqc/yBaw7GtpwP80DrwunxD8LuMgG6joetoSGj8bkQAz699noX7FvLZnZ8RaTs39JtLrdvD4BeWMaZPCn+8c6Bp5YrQJ9PnNu7RRx/lqquu4tvf/rYp5V0R0+dqjwfHtm11JzNrcvPA7UZFRhI1ZAgJd99N1HXDsaenn9Gfdo7qYti3wjdSc89yqDzqW5/SD4Y94muFdxwK1ovvB8/OyOb9Xe/zv/z/kZ2RfYnv9OKFWS2M79eORZuP4nB5zry7kRDikg0aNIioqCj+8Ic/BLsq5xVygV4yZw5Ff/wTHv+dvcP79CHx/vuJGj6cyKsGouwXCF+vBwq/Ot0KL9gA2gvhcb4ZDLuN8bXCA3CJ4aCUQbSNbMui/YtMDXSAWzI7MCcnn892Hmd8v+a9XFKIK8WGDRuCXYVGhVyg29q3J3rkSP914ddiTWjklhIVR/0t8GW+1nhNCaCgwyC44ce+EO9wNRiBbckaymB8+nje3fEuZbVlxIbFBvT4FzK0SwJJ0Xbm5xVKoAtxBQm5QI++/nqir7/+/Du4nXD4C38rfDkc8492i2oLPbL9feGjILK57i102oSMCczcNpPlh5Zze/fbm728U6wWg5v7t2f2+sNU1rqJDgu5f2YhxCVoHb/pJQdO94Pv/5/vhhCGFToNgzHP+bpRUvqZOsgHoE9iHzrFdGLx/sWmBjr4Bhn9a+1BPtl2lK9dlWZq2UKI4AjNQHdWw8HVp/vCT+7xrY/tBAPu9F9SeD2EX+CqFhMopRifMZ5/bP4HJ2pOkBSRZFrZV3eKp0NcBAvyjkigC3GFCL2RJ1+8Dr9Nh1lfhw1vQXw6jP8NPJoD398EE/8EvW4OepifMiFjAl7tZemBpaaWaxiKiQPas3JXESVVTlPLFsIM0dHR59124MAB+vXrZ2JtWobQC/S2fWDwAzDtQ3jqAEz7AIZ+F5K6N3pnn2DoGteVHvE9Lu5ORgEyKTMVt1ezZOtR08sWQpgv9LpcMq73PUJIdkY2L218iYLKAjpEX2CKgQDrm9qGLklRzM8tZOqQlj9dsWg5fvvlb9lR3PT5U5qiV0Ivnhry1Hm3/+QnP6Fjx45873vfA+C5557DarWyYsUKSkpKcLlcvPDCC9x6660XVa7D4eC73/0uOTk5WK1W/vjHP3LjjTeydetW7rvvPpxOJ16vlw8++IDU1FTuvPNO8vPz8Xg8/PznP2fKlCmX9b7NFHot9BB06jp0s1vpSikmZaaybv9JjpU7Gn+BEEE0ZcoU5syZU7c8Z84c7rnnHubNm8fGjRtZsWIFP/zhDy/6rlyvvPIKSik2b97Mv//9b+655x4cDgevvfYaTzzxBLm5ueTk5JCWlsaSJUtITU0lLy+PLVu2MH78+EC/zWYVei30ENQhugMDkgewZP8SHuj/gKllT8pM5aXlu/lo0xHuv65p0xYIcaGWdHO56qqrOH78OIWFhRQVFREfH0+7du148sknWblyJYZhUFBQwLFjx2jXrl2Tj7tq1Soee+wxAHr16kXnzp3ZtWsXw4YN41e/+hX5+fncfvvtdO/enf79+/PDH/6Qp556iokTJ3L9hS6RboGkhW6SCRkT2Fmyk32l5s4q3K1tNH3at2G+3EBahIDJkyczd+5c3nvvPaZMmcKsWbMoKipiw4YN5ObmkpKScs6UuJfqG9/4BvPnzyciIoIJEybw6aef0qNHDzZu3Ej//v2ZPn06zz//fEDKMosEuknGpY/DUAaL9i9qfOcAu2VgKrmHSzlcXG162UJcjClTpjB79mzmzp3L5MmTKSsro23btthsNlasWMHBgwcv+pjXX389s2bNAmDXrl0cOnSInj17sm/fPrp06cLjjz/OrbfeyqZNmygsLCQyMpJp06bxox/9iI0bNwb6LTYrCXSTJEUkMbjdYBbvX3zRfYCXa+IA3/B/aaWLlq5v375UVFTQoUMH2rdvzze/+U1ycnLo378/M2fOpFevXhd9zEceeQSv10v//v2ZMmUKb731FmFhYcyZM4d+/foxcOBAtmzZwre+9S02b97MkCFDGDhwIL/4xS+YPn16M7zL5tMqps8NFR/u/pBn1zzL7Jtn0zepr6ll3/HqGqpq3Sz5/g2mlitCh0yf2/Jc7PS50kI30ehOo7Ea1uBckz6gPTuOVrD7WIXpZQshzNGkQFdKjVdK7VRK7VFK/aSB7fcqpYqUUrn+h7mXcoSI2LBYrku9jiUHluDVXlPLnjCgPYaCBdLtIlqRzZs3M3DgwDMe11xzTbCrFTSNXraolLIArwBjgXxgvVJqvtZ621m7vqe1frQZ6tiqZGdk81n+Z2w8tpGsdg3+1dQs2saEM6xrIvPzCnlybI8L3/hDiBDRv39/cnNzg12NFqMpLfQhwB6t9T6ttROYDVzcUC1RZ2THkURYI4LS7XJLZioHTlazpaDc9LKFEM2vKYHeAThcbznfv+5sdyilNiml5iqlOjZ0IKXUQ0qpHKVUTlFR0SVUN/RF2iIZmTaSjw9+jMvrMrXs8X3bY7Mo5ucVmFquEMIcgTopugBI11oPAD4B/tXQTlrrGVrrLK11VnJycoCKDj3ZGdmU1payrnCdqeXGRtoY0SOZhZuO4PUG5+omIUTzaUqgFwD1W9xp/nV1tNYntda1/sV/AIMCU73WaXiH4cTYY1hyYInpZU/KTOVImYOcgyWmly2EaF5NCfT1QHelVIZSyg7cBcyvv4NSqv6NK28Btgeuiq2P3WJnTKcxLD+0HIfb3EmzxvROIdxmSLeLCHkXmg/9StVooGut3cCjwFJ8QT1Ha71VKfW8UuoW/26PK6W2KqXygMeBe5urwq1FdkY2Va4qPi/43NRyo8KsjOmdwqLNR3F7zL10UojWyO12B7sKdZo026LWehGw6Kx1z9R7/lPgp4GtWus2pN0QEsMTWbx/MWM7jzW17EmZqSzcdITVe08yoseVey5DnN/R//f/qN0e2PnQw3r3ot3TT593eyDnQ6+srOTWW29t8HUzZ87k97//PUopBgwYwNtvv82xY8d4+OGH2bfPN3neq6++SmpqKhMnTmTLli0A/P73v6eyspLnnnuOkSNHMnDgQFatWsXUqVPp0aMHL7zwAk6nk8TERGbNmkVKSgqVlZU89thj5OTkoJTi2WefpaysjE2bNvHnP/8ZgL///e9s27aNP/3pT5fz8QIyfW7QWAwL49LHMXfXXCqdlUTbzfvzcWTPZGLCrSzIK5RAFy3GlClT+P73v18X6HPmzGHp0qU8/vjjtGnThhMnTjB06FBuueWWRsdRhIeHM2/evHNet23bNl544QXWrFlDUlISxcXFADz++OOMGDGCefPm4fF4qKyspKTkwueZnE4np6YvKSkpYd26dSil+Mc//sHvfvc7/vCHP/DLX/6S2NhYNm/eXLefzWbjV7/6FS+++CI2m40333yT119//XI/PkACPaiyM7J5d8e7rDi8gkldJ5lWbpjVwri+7Vi65Sgv3NaPcJvFtLJFaLhQS7q5BHI+dK01Tz/99Dmv+/TTT5k8eTJJSb4btickJADw6aefMnPmTAAsFguxsbGNBnr9Oxnl5+czZcoUjhw5gtPpJCPDd++BZcuWMXv27Lr94uPjARg1ahQLFy6kd+/euFwu+vfvf5GfVsNkLpcgykzOJDUqNThT6mamUlHr5n+7rszxAKJlCtR86IGYR91qteL1nj7PdPbro6Ki6p4/9thjPProo2zevJnXX3+90bIeeOAB3nrrLd58803uu+++i6rXhUigB5FSivEZ41lXuI4Sh7mXEV7bNZHEKLtMqStalEDNh36+140aNYr333+fkydPAtR1uYwePZpXX30VAI/HQ1lZGSkpKRw/fpyTJ09SW1vLwoULL1hehw6+8Zb/+tfpYThjx47llVdeqVs+1eq/5pprOHz4MO+++y5Tp05t6sfTKAn0IJuQMQG3dvPJwU9MLddqMZjQvz3Ltx+jqrblnKUXV7ZAzYd+vtf17duXn/3sZ4wYMYLMzEx+8IMfAPDSSy+xYsUK+vfvz6BBg9i2bRs2m41nnnmGIUOGMHbs2AuW/dxzzzF58mQGDRpU150DMH36dEpKSujXrx+ZmZmsWLGibtudd97J8OHD67phAkHmQw8yrTW3/fc24sPjeWv8W6aW/eX+Yu58fS0v3TWQWwc2NJuDuJLIfOjmmjhxIk8++SSjR48+7z4yH3qIUUqRnZHNxmMbOVp11NSyszrH0z42nPm50u0ihFlKS0vp0aMHERERFwzzSyGB3gJkZ2Sj0Sw9sNTUcg1DMXFAe1buLqK02mlq2UIEQijOhx4XF8euXbt4//33A35suWyxBejcpjN9EvuweP9i7ul7j6ll35LZgb9/vp8lW45y15BOppYtWh6tdUjNld+a50O/lO5waaG3EBMyJrD15FYOll/8Xc0vR78ObchIimLBJul2udKFh4dz8uRJ029iLs6ltebkyZOEh4df1Oukhd5CjEsfxx9y/sDi/Yt5OPNh08pVSjFpQHv+umIPxysctI25uP9AovVIS0sjPz+fK/VeBS1NeHg4aWlpF/UaCfQWol1UO65OuZrF+xfznQHfMfXP3kmZqbz86R4WbTrCvcMzTCtXtCw2m61uhKMITdLl0oJMyJjAvrJ97CrZZWq53VNi6NUuRgYZCRHiJNBbkLGdx2JRluDcb3RgKhsPlXK4uNr0soUQgSGB3oLEh8czNHUoSw4sMf3E1KQBqQAs3HTE1HKFEIEjgd7CTMiYQEFlAXlFeaaW2zEhkqs6xbFAul2ECFkS6C3MqI6jCLOEBaXbZdKAVLYdKWfP8UrTyxZCXL4mBbpSarxSaqdSao9S6icX2O8OpZRWSjU4z4BoXLQ9mhvSbmDpgaV4vB5Ty544oD1KISdHhQhRjQa6UsoCvAJkA32AqUqpPg3sFwM8AXwR6EpeabIzsjnpOMn6Y+tNLbdtm3CGZiSyMK9QBpcIEYKa0kIfAuzRWu/TWjuB2UBDN/X7JfBbwNzb2LdC13e4nihbVNCudtl3ooqtheWmly2EuDxNCfQOwOF6y/n+dXWUUlcDHbXWH13oQEqph5RSOUqpHBmNdn7h1nBGdRzFJwc/wekxd9Ks8X3bYTWUnBwVIgRd9klRpZQB/BH4YWP7aq1naK2ztNZZyclyc+ILyc7IpsJZweqC1aaWGx9l54YeySzIK8TrlW4XIUJJUwK9AOhYbznNv+6UGKAf8JlS6gAwFJgvJ0Yvz9DUocSFxQWn2yUzlcIyBxsPmXtbPCHE5WlKoK8HuiulMpRSduAuYP6pjVrrMq11ktY6XWudDqwDbtFay+2ILoPNsHFT55v4LP8zql3mjt4c0yeFMKshV7sIEWIaDXSttRt4FFgKbAfmaK23KqWeV0rd0twVvJJlZ2RT467hf/n/M7Xc6DArY3qnsGjzEdweb+MvEEK0CE3qQ9daL9Ja99Bad9Va/8q/7hmt9fwG9h0prfPAuDrlatpGtmXR/kWmlz0psz0nKp2s3XfS9LKFEJdGRoq2YIYyGJ8+nlUFqyirLTO17JE92xIdZpWrXYQIIRLoLdyEjAm4vW6WH1puarnhNgs39U1h8Zaj1LrNHbEqhLg0EugtXJ/EPnSK6RS0q10qHG5W7jphetlCiIsngd7CKaXIzsjmy6NfcqLG3GAd3i2J+EibXO0iRIiQQA8B2RnZeLWXpQeWmlquzWIwoX97lm07RrXTbWrZQoiLJ4EeArrGdaVHfI/gTKmbmUqNy8Oy7cdNL1sIcXEk0ENEdkY2eUV5FFQWNL5zAA1JT6Bdm3Dm50q3ixAtnQR6iMjOyAYwvZVuGIqJA9rzv13HKat2mVq2EOLiSKCHiA7RHchMzmTJ/iWmlz0pMxWXR7N061HTyxZCNJ0EegjJzshmZ8lO9pXuM7XcAWmxdE6MZMEm6XYRoiWTQA8h49LHYSjD9KkAlFJMGpDK6j0nKKqoNbVsIUTTSaCHkKSIJAa3G8zi/YtNv0XcLQNT8WpYvOWIqeUKIZpOAj3ETMiYwKGKQ2w7uc3UcnukxNAzJUaudhGiBZNADzGjO43GaliDdr/RnIMlFJTWmF62EKJxEughJjYslus6XMeSA0vwanPnKp84oD0AC2UqACFaJAn0EDQhYwLHqo+x8dhGU8vtnBhFZsc4mdtFiBZKAj0EjUgbQYQ1IjhTAQxoz9bCcvYWVZpethDiwpoU6Eqp8UqpnUqpPUqpnzSw/WGl1GalVK5SapVSqk/gqypOibRFMjJtJB8f/BiX19zRm5MyU1EKufGFEC1Qo4GulLIArwDZQB9gagOB/a7Wur/WeiDwO+CPga6oOFN2RjaltaWsK1xnarkpbcK5JiOBBXmFpl86KYS4sKa00IcAe7TW+7TWTmA2cGv9HbTW5fUWowD5TW9mwzsMJ8Yew5IDwZkKYG9RFduOlDe+sxDCNE0J9A7A4XrL+f51Z1BKfU8ptRdfC/3xhg6klHpIKZWjlMopKiq6lPoKP7vFztjOY1l+aDkOt8PUsrP7tcdqKBbkySAjIVqSgJ0U1Vq/orXuCjwFTD/PPjO01lla66zk5ORAFX3Fys7IpspVxecFn5tabkKUneu6J0m3ixAtTFMCvQDoWG85zb/ufGYDt11GnUQTDU4ZTFJEEs+ufpbpq6bzef7nuDzmnCS9JTOVgtIaNh4qNaU8IUTjrE3YZz3QXSmVgS/I7wK+UX8HpVR3rfVu/+LNwG5Es7MYFv42+m+8s/0dPj30Kf/d+19i7DGM7jSacenjuKb9NdgMW7OUPbZPCmFWgwV5hQzqHN8sZQghLk6jga61diulHgWWAhbgDa31VqXU80CO1no+8KhSagzgAkqAe5qz0uK03om9+dV1v8LpcbK2cC0fH/yYZQeX8Z89/6GNvU1duA9pPySg4R4TbmNUr7Ys3HSEn0/sg8VQATu2EOLSqGD1gWZlZemcnJyglN3aOT1O1hSu4eMDH/Pp4U+pclURGxbrC/fO4xjcfnBAwn3R5iM8Mmsjsx64huHdkgJQcyFEY5RSG7TWWQ1ta0qXiwgxdoudkR1HMrLjSGo9tawpWMPSg0tZemApH+7+kLiwOEZ3Gs1N6TcxpN0QrMal/TcY1astUXYL83MLJdCFaAGkhX4FqfXUsrpgNUsPLOWzw59R7a4mPiye0Z1Hc1PnmxjcbvBFh/uT7+WyfPsxcqaPxW6VmSSEaG7SQhcAhFnCGNVpFKM6jcLhdrC60BfuH+37iLm75hIfFs+YzmMYlz6OQSmDmhTut2SmMu+rAlbuKmJMnxQT3oUQ4nwk0K9Q4dZwRncazehOo33h7m+5L9y3kPd3vU9CeAJjOp0Od4thafA4w7slERdpY8GmQgl0IYJMAl34wr3zaEZ3Hk2Nu4ZVBav4+MDHLNi3gDm75pAQnsDYzmMZlz6Oq9tefUa4260G2f3a89/cAmqcHiLsDQe/EKL5SR+6OK8adw2f53/O0gNL+bzgc2rcNSSGJ9Z1y5wK97V7TzL17+uYktWR8f3bcXWneGIjmuf6dyGudBfqQ5dAF01S7arm8wJ/uOd/jsPjICkiibGdxzKm01hmfKxZsfMEHq9GKejRNoarO8eT1TmeQZ3j6ZwYiVJyrboQl0sCXQRUtaualQUr+fjAx6zMX0mtp5bkiGSyUoaQYOmBo7IT+wqj+epQGRUONwBJ0WEM6hzHoM7xDOqcQL8ObQizSveMEBdLAl00m2pXNSvzV/LxwY/ZcGwDxY5iAKJt0fRPGkDHyN4oZzrHj6eQd9jBwZPVgK/vfUCHWAalxzOok68VnxgdFsy3IkRIkEAXptBak1+RT25RLrnHc8ktymV3yW40GkMZdI/rTs+4fkTqblSWdWB7vo2tBeW4PL7/g12Sos7opumaHI0hUwoIcQYJdBE0Fc4KNhdtrgv5TSc2UeWqAiAxPJH+SZm0tffEXd2Zw0cTyD1USXGVE4DYCJu/i8b3yEyLk6toxBVPAl20GB6vhz2le8gryqtrxR+u8N0/xWbY6JvYl/ToPtjcXSg5mcrmw172HPfdkNpqKPqmtmFQ5wQGdY4nKz2elDbhwXw7QphOAl20aCdqTpB3PK+uFb/15Na6m1+nRafRJ2EAsUZ3aso7srcwmk355ThcXt/2+AhfuHeO5+rO8fRq10ZmfhStmgS6CClOj5NtJ7fVteK/Ov4VJx0nAYiyRdE/qT/tw3pDbTrHjrcl95CDY+W1AETaLSTHhBEXYaNNhI24SDtxETZiI2zERfp+xp5aX2853CZdOSI0SKCLkKa1Jr8yn9zjuXUhv7t0N17tRaHoFteN7rH9ifB2oaq8A9U1EVTUGJTXeCmrcVFa7aSsxoX3Av/Vw20GcRF2X8BH2s74EoiLtPu+HOp9KcRF2ImNtBETZpUTt8JUEuii1al0VrL5hO9ka97xPPKK8qh0VZ6xT5gljEhrJJG2SCKtkYRZIrAbEVgIxyAMpcPQ3jC8Hjtutw2Xy4bTZaWm1veoclioqLbgcFrR3jDw2jj7ro2Goi7sfV8G9rrgjzt7OfLMvxisFpmdUlw8mW1RtDrR9miGpQ5jWOowwHeydV/ZPrac2EK5s5xqdzU1rhqq3dVUuaqodlVT7a6m2lVNufsk1a5qqty+9af66+vY/Y8Y3y9IdP1NRjhhRgRWI9z3xaB9Xwxej51Sj50TbjuuEiuOY1YcTgPttaG1Dc75aSXKFkFMWASxEZHER0QRHx5FfFQYcRGnu4Pi/V1DvmXfXxAyTbE4nyYFulJqPPASvlvQ/UNr/Zuztv8AeABwA0XA/VrrgwGuqxDnZTEsdI/vTvf47hf9WpfHVRf2p36eCvu69Y1uO0mVqwqXuxqnqxqtPTRlmFSF/5EPvt+eUgu62IrWdn/4W8/5MrBix24JI8wSRrg1nEhrOJH2CKL9XxAx4ZHEhUUQFxFFSnQCPeK7kxARQ7jdQqTNIn8ZtGKNBrpSygK8AozF9/9uvVJqvtZ6W73dvgKytNbVSqnvAr8DpjRHhYUINJvFRqwlltiw2IAcT2uNy+vC4XHgcDuoddfi8Dio9dT6lj21p7edta7WXUuNu4YKZw2VtTVUOmuodjmocfv2d3iqcHpKcOlaHF4nVTg54XVBrYZaoLKh+ii0MxGPIxVvbSrKmYrd05EIi++6/gibhXCb72eE3VK37tRy3Tab4d9urbfdOPO1/mOFWQ2ZuycImtJCHwLs0VrvA1BKzQZuBeoCXWu9ot7+64BpgaykEKFEKYXdYsdusdPG3qbZy9Na4/Q6cbgcnKyp4kRlJSerqzhRVcnRqiIOVe6moHoPRx37qHBvqnudV8WjVCfwdsTj6Ui1qwPlFfE4nF5qnB5qXL7HqUtEL4ahOOMLwW41sBkGVovCajGwGQqrRWGzGFgN/zqLwurfp25f/7b66877Gv/z+uvO3nZqncXwHdvir4dv+az1/p+h9MXUlEDvAByut5wPXHOB/b8NLG5og1LqIeAhgE6dOjWxikKIC1FKEebvgokNj6VL/Pn3LastY2fxTrYXb6/7ub9sMR6LBywQHRNNz4Se9E7oTa+EXvRK6EV6mwy8XktdwNc4PTj8z6udZy6f+iJw1Hte4/RQ6/Hi9nhxezQur657Xul2+9Z5vLj9610ejdvrPXO9f9uFrlRqLpazAt7302jgC+Gs9WdsP3P91CGduKFHcsDrGtCTokqpaUAWMKKh7VrrGcAM8F3lEsiyhRCNiw2LZUj7IQxpP6RuncPtYE/pHnYU72BH8Q62F2/ng90fUOOuAXwjeLvFdasL+N6JvenRtgdRtsB0UV0Mr1fj8oe978vBe94vBJfHt+zx1vsS8Wo8/i8Ij/+1p5frrfdqPJ7zrPcvext63TnH8+J0e3F7Pb5l//Zyh6vxN3sJmhLoBUDHestp/nVnUEqNAX4GjNBa1wamekKI5hZuDadfUj/6JfWrW+fxejhYcZAdJ3ewo2QHO07u4LPDnzFvzzwAFIpObTrVhfypR1JEUrPW1TAUYYaFsLOSS2tNjbuGandt3dVNHnc1bpf/Kid3NdWeaiwWC5HhkURaI4iwRhBpiyTi1HNrVN3z891ysaVr9Dp0pZQV2AWMxhfk64FvaK231tvnKmAuMF5rvbspBct16EKEFq01x6uP17XiT3XZFFSebt8lRySf0WXTO6E3HWI6YKjTV9Z4tReH29HgJaX1f9a4a8673Rfep19f465BE7g/+sMsYfWC3h/6tnrPz/kyiDzvuvqvC7OEXXaf/GVdh661diulHgWW4rts8Q2t9Val1PNAjtZ6PvAivst13/dX9pDW+pbLqrUQokVRSpESlUJKVAojOp7uVS13lrOzeOcZXTZrC9fi0R7AN11DQnjCGUHdVBZlIdIWSZQtyjdIzD9QLCUypS4oTw0cq9vHv3z29ghrBBpd9wVQ464548uhbl29MQxnrzteffyc1516n01hKIMIawRPDX6Kr3X/WtM//CaSkaJCiICr9dT6+uVP+gK+wllxTuCeas1G2aJOrztru82wteirTE5donq+L4izvxxObR+XPo6BbQdeUpkyUlQIYaowSxh9E/vSN7FvsKvSrOpfohqocQyXQ4aMCSFEKyGBLoQQrYQEuhBCtBIS6EII0UpIoAshRCshgS6EEK2EBLoQQrQSEuhCCNFKBG2kqFKqCLjUuxolAScCWJ1QJ5/HmeTzOE0+izO1hs+js9a6wbl3gxbol0MplXO+oa9XIvk8ziSfx2nyWZyptX8e0uUihBCthAS6EEK0EqEa6DOCXYEWRj6PM8nncZp8Fmdq1Z9HSPahCyGEOFeottCFEEKcRQJdCCFaiZALdKXUeKXUTqXUHqXUT4Jdn2BRSnVUSq1QSm1TSm1VSj0R7Dq1BEopi1LqK6XUwmDXJdiUUnFKqblKqR1Kqe1KqWHBrlOwKKWe9P+ebFFK/VspFR7sOjWHkAp0pZQFeAXIBvoAU5VSfYJbq6BxAz/UWvcBhgLfu4I/i/qeALYHuxItxEvAEq11LyCTK/RzUUp1AB4HsrTW/fDdG/mu4NaqeYRUoANDgD1a631aaycwG7g1yHUKCq31Ea31Rv/zCny/rB2CW6vgUkqlATcD/wh2XYJNKRUL3AD8E0Br7dRalwa1UsFlBSKUUlYgEigMcn2aRagFegfgcL3lfK7wEANQSqUDVwFfBLkqwfZn4MeAN8j1aAkygCLgTX8X1D+UUlHBrlQwaK0LgN8Dh4AjQJnW+uPg1qp5hFqgi7MopaKBD4Dva63Lg12fYFFKTQSOa603BLsuLYQVuBp4VWt9FVAFXJHnnJRS8fj+ks8AUoEopdS04NaqeYRaoBcAHestp/nXXZGUUjZ8YT5La/1hsOsTZMOBW5RSB/B1xY1SSr0T3CoFVT6Qr7U+9VfbXHwBfyUaA+zXWhdprV3Ah8C1Qa5Tswi1QF8PdFdKZSil7PhObMwPcp2CQiml8PWPbtda/zHY9Qk2rfVPtdZpWut0fP8vPtVat8pWWFNorY8Ch5VSPf2rRgPbglilYDoEDFVKRfp/b0bTSk8QW4NdgYuhtXYrpR4FluI7U/2G1nprkKsVLMOBu4HNSqlc/7qntdaLglcl0cI8BszyN372AfcFuT5BobX+Qik1F9iI7+qwr2ilUwDI0H8hhGglQq3LRQghxHlIoAshRCshgS6EEK2EBLoQQrQSEuhCCNFKSKALIUQrIYEuhBCtxP8HDcU/rC/wI9QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "5e815540",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ys/r1f3d1bn3k11yrhwbf5xl1hh0000gn/T/ipykernel_27789/4011314882.py:1: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
      "  model.evaluate_generator(test_image_gen)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1876104325056076, 0.9403846263885498]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate_generator(test_image_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "5a190247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loss', 'accuracy']"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "ecf4806d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ys/r1f3d1bn3k11yrhwbf5xl1hh0000gn/T/ipykernel_27789/2032896581.py:1: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
      "  pred = model.predict_generator(test_image_gen)\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict_generator(test_image_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "2ea72d27",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pred > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "f0968089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2600"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "5ff13d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "ba008a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image_gen.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "297155a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.92      0.94      1300\n",
      "           1       0.93      0.96      0.94      1300\n",
      "\n",
      "    accuracy                           0.94      2600\n",
      "   macro avg       0.94      0.94      0.94      2600\n",
      "weighted avg       0.94      0.94      0.94      2600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_image_gen.classes,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "9ffec577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1200,  100],\n",
       "       [  50, 1250]])"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_image_gen.classes,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "507ce0d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C175P136NThinF_IMG_20151127_142009_cell_241.png'"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(test_path+'/parasitized')[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "be26534c",
   "metadata": {},
   "outputs": [],
   "source": [
    "para_cell= test_path +'/parasitized/'+  os.listdir(test_path+'/parasitized')[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "77b234b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/fasihussaini/Desktop/cell_images/test/parasitized/C175P136NThinF_IMG_20151127_142009_cell_241.png'"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "para_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "88856943",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "a68b8d88",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_image = image.load_img(para_cell,target_size=image_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "687ca0d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6qh5lsbXdP/Gg665HeER2lJed7WL3RRlP2rdK0Yss+asUCFVVUXg2NX1hgn3VI3MNx3Ecx5FCtG00ydVeVyG2u8Ocq4HnKh8mxftppSv5/4Qk2fxBhYy+OwF6aFtUYwM56mm9db5ml1gGwVn3x07TnshaV8Ojc0pn0G/BBnDh0Q1cOU1cmUd/zh5P50++BIW9rvvTn/7KzRACyMePrwgJu9KZReJ5gZTFJ5Hl561thu3aHQ0di4g0F8UkEbnf7xkyFo2SiBWodCkjqbiIdZ7neLmh2YpOx7dWQrGsZbr3tGMdWEzTUcBUReH9wbCs6BY+eUYbi6oRkbvnc6fpv/yf/+RdNlzWas7pZgVOFRc7xZ8oUv8ska+TOwn692W1Epkole/iXw/ANxFkFwJDsLoZd87VleZBYjyR9cuOk8ytjN3HoMlsv0LP7LD7cdwZuc8XdPfcut1/20Wn+qN/CxvySOjUzG1GzkrY8Iu6Q5orrltmC3Celn6yR6t9PbJ3PmGXWQr07vb9ssUe9EjjW+JM0uJC2Trk12W/HnIZjUdZyR4n7MiKzVHXb98m/0+szcYGUt2nu4MXdltUAiCgrKj7K7BBRBBhWTdf+4197RSv0JP7zEeedwf7JLml2niMxLLXCNuaPeHJaHYcjwUGPGrbEwW79iw2yLWn6i2t65+X0CZMMCeaDShX32GCPP+ygR5ChFwF1E6BrEb8djZERJanShCaZlcWdulaN7i+MeF8ysqc442njQ29dYyz/6nYfGlzgJ74VkkMAM55bbnRtiWriyRJPDZkYitlBRSlHFV9wk4Ge3zRn7Cus5GCg+R5vtre7P0gRm8cxtvjWtxf/uXPcofs/T7PcxLqZm6o/l8RoV/tPSV62dIcrQ2JO8CoQKXsTBaq8puEKwrwqLAqM69a8MNiJMl3XXaMMecVUMrGYstV5C6VvH71lmWYUBVc2YDgUypQ1K9n67FcF/mGr0REpMdCryAdK9ocY/yz/+l7T2x40B0ZSl1NKKC7+zjkZeghhE1FHMcLIAn2ZevqGLfS5ZLlKq7Vh5Lx+/0+57zf7wASMKjOxiRNsqfblhl+umWOv+TdSScMCj3PVYfII6NemMe05Vq2ihStKzoqZKWkda29QS9mJqBS4BHmSlEKA/BsEL3i8ooJk/c5XaIu64bzbm4PhreOx0Q/Hgz9ujTXhaKhexVrdtmJjZd1U1NxN1vjcBF6yULDWfv1159wNdDlc2rbCy3cm6sanJCzNGQXlJLoZVKK6FXSsNZ53+Uyj+lGUh4dkogAO3U1l3wWMP06ubIxDzc+AGvvZg8PkVKZv0WCwXKeedzvZ7mmt+3p3Suw9cQXk6L1tZd4FnqB3XQUO89IFkqGjGnKJbcNXjmtPIa8i1u7UVU2mlJMRfNtaNE2W8zajxJ2PMIHxfJOhIhAPEQixc5qfVuA1pdoAxvfzKwC1qq0oIEElVQ3ZYpSpoooaqk9Sqlmk6J+17OiGpsHWo9LUVkYcqZgaGhVXS1/YbtZqh64PyFbT1gJhDR87Ek6ZxiZMwYu1TQz+EOqtCgD5O4mEcmnl7V/i/IewDd+8tNfRkTOjfAc3+Fwd0LNZ0R8PD+WRTqOF7YYqWSHu2qfeVm0VKDx+HJZXei6ObI91iJ9RtoutgPbo9reXFVyV1qlqnJceUayttroe7RdjVX5/8sMNuArHiNgkCAlFifKsT39cCeKAlARBINwd9DH8fKF2lA6RXLhv7uJqrCzkoJaW1nkEq76fw85SrT7xpCS026ySmk6ktNp3Z92x8crbC3jWc+Mx37ebkjxWGfu8lRPiPewxUUoINA0st1iRcPb8kREfmdwj8nQfv7Fhm7vOh3NV6ReqQB38CB740ZxokKFsrDlA4oQKW4FBkgrGudPeg2nhKsfFRSg+U/EirhKbN1d2s7q3gtTX9YDZ4GoK2Lxu3xGyQGanXR3HceTWnc1YhU0IgDmxDIA77MBgM0gFQhNxQF9BiLcqXIcu7U2IipjKoqXKYiGaWeE7nsbSE+Uim25wlo84OkFM+lN7BlAhJNXe9mTArn74MJRyj5UMCYCUQlfFCzJLeLKhiO5Eb00g8Xv2+328ePHMr8Z4FZ5Lh4To/p8CZPkjTxC4H4bL2amxzvg0tiXiIhgXOUkbhShyyxwlezxGHj0H3YD1SUxmu8tkd/nGx8dMsnyC13i0Dzhst2Jt5uLCMKO4zDEcVzQRdmipDUfZ2+UvnI3sPIxjqpIrH6+ZM6edyjVddaVI2JVCtaOrpot8MyG2OWGuLqgCyBbt2zg5QNoXMt4MhedN6WkT2LYbQiw4p1uG4tYTzzutJN9uLvZibIe7gIg4B4VokSLKaQlGf2Oqbslal17osUd+xzUydFAhCRii+xlzkndecObLdR//Z9+NfaCzd1zFGOO8hhjILU40vul7l9wSpEpayw9cmULEHs809nW9UlEssgYkWK4MJJOguJ9RBhCRAYfMDjVYxOXY0hE7G0QF/PKokqDbHs40B2e7HkCaCah2OnuaO0Bl2ruWEBV01GXCGZdWt70BkTEFSmZGfaCly3Oe7if51mIleyMt4hoe/jCUyLaBa0U5a0W759wJ6rXw5XRa6cBgATp8Jx3tU2/qvrmWRruMRgGyjsF7Yfgsv2p31FECo0v3AmtEFTJfGlDrXqF3edJMpUWslaUeyye2WCPeyi2QmXsschku2e9y3WZix7VdeHFoy3yjedERHrC2Ggr21aOrjdveRA5F1dkj2W9ijkjJ8PBCURMAA4QsA1HFh2T+0CQARQzFinHyHAuJUb7otiw55Ktzs7YGUwloapqNkl6rC40yLM2AEg2XIQmmcWG4zjccJ5nOI7jBTtvSjL1JtHS9CdTXgWZ/mXVWyrN7oarMzX2ZJJiwF4tDxEkmrQGV4kIPWOe7F8yF5E4LYjQHXXto+LmsnWdji1qCMDcIxkdj+4a2wzUGosHieCWU3Tz4zgctdJnF52yOLIU7O6vr6/51fk63X11ZiiAFGRZGWJTz2JGJ6W0DaqlQ0+qU8pbf/WNYJNQTQc4gThPEwhJnMjZtqEmoDsEeuB2//8+qlBIu9+PG8eLKJ1KHmLTwsUjQmAeZzgHD9Hl/PK+EYjgDuG6YAGC1iuWLA8PCkVkwmt7fUT4nMdxTLOxI3LQzc+hKqRgIGBmOfO9H+d5jp1tRWkDsAKGhGS4UJQLrirx7Dh2d1BdddDCm7JOFVmVGNb53Jt/zCwbopRKj3AIeH+9Hxj26jDO0+KcdwNf/cxpZIFTMMcMugyVl6E3nTpjgCLHhxto57TTMpm3TFFAjuNIt09/2nh7xQg+V4ZUHlFEcj+k7Zk4WQOOCMmuA/cAdCAiRDMY4dv0TURGH9Uoq7JxoYYR4Q3JQHOzxQy8MeilMd1hoBn6sgBPFqw3oolkYcPNjR6qB7M1zSLMYaQ5JjgNrwHPAcaYCPN7SMhQ/XD78PkLXyIs5MM4MAw4AIucb0MZA0R2Z815mpnioQSd21u94SsliyTDDO5sTScVBJbJLdrmkY1gT2wYaeITjceOl8x8yFGUirgsZjQY/Mmwcu/S6YlCP+pZuxt/AgMW1ErKGObuMyg6X0+EBDycgyNihi9okwE6GKDFnCaguc1wGeIGxt1Jc2II6ae/yudDjxvd0jAs29ge7CnyXk+Wba9yyWKdsPycDNQ0gh2sAxBAyRykefpJ8ri9A69GxNgGxxPZXvdtlZ8sYFSiUD9+skLd8vQb1HPzEdB/+r40oK4Qq42dWz6CZoQyoBSEr75EjwQj4REEyYOa78qQEDsdIpnZcboxjsjx3AveSSkD4BsifFzXpfRdgFaWe03UkNTmtTpCemNAKhMZbffYMxtKB0nmEBr38PQKFJJVY+mmqbvZonvhS3wMQN9lUldVAEk4M49A+Ew/HWEK5OiHyNzenJBVX3EwIhwwC/NpcQ3eIFR1T7VHeBjnULW7Gebx8mIfp6pyxDGG0c/zzFQrAbRmSMXdCzooE+obJSMZDjBkt3Ra26NYBi3TfejaxP/EBpLjOI45X5N1wUslUxZSprpNrIhIWmjxlPW4e2aMpSg96+5x7eWi07lllLJsXahqNqqa0ekaercJcxj8nJzQEHiYeZj95jcfSULlOA4JQQgBnx4CvQ33CIOQbqDnfOE1+tPp4X47jvSxnTp4013UjfAq4BDr3QNJ/Uytwe4VxhjTzjEUb3iw2OC7X6FolLdwQ+Z73SEXHhktGyjfVaFRr492n6F7Pz7aFujNCTM7lTQ70Vq7sq9bBuNkiB+Helh4BGTavN/vPANzisvL7VbM3jKoqiLH8DJ3wSE5SyIckKFuoUN1KEXDApA0OUAIImJewudOVTi4o/ZUvuwl4S5bJVnmtBmWU+BExHd5WMc7nTIRq4V5JQEP1KTXN09J/2UcNzMKQepXL7tUKeWDYW2+IW31E/JYljovThUA3nKfJG5sUVgZZVbH2vAw+iph5SEijIWcsMVs9WDuHuaxX9BBEu5oGy9rXS8vL7VSANmNmD9RkbGnLT5ZoTeBEiKsdmOnKknPp8qQlcYkyXxXaVbJ/gvqxot8Dx1N7/S4R0tc+xOXGtEZVwHSqMAENQEfF3cXQyCTXeQcy1Rc1ds4phuH6P79ZvxlTquqSjJsTXDrDxO5Fmbh7FrLx48fs9WqtLAbiSc/Gnjwkf0gObbSmYiE5SaZtrf78UOnb92pdnPKYxmut1mg7cNhC/tKDLMtfOGsjz3xqQAiiMytGaEYQzwQgRADMDFfjhd3d1vDm7N19zzP7LwK0hGOiGlUXbUgCVGN9PR7JmkR0bNVGBDqZh4Rq4kIe1BOmAPiEZnhQ1sBzhcR9Dgg9M37JzYsaS2ZLXUr63/dfv8AD3vwHnbQF03LkXRl79fpqy0vkkFAZF8UGVwmyAlKqCqb2UpTmCNQZChFOFSOAZVMYcdxQHDaOcOfjHEFFLLbcNxdgGgz0yPWtoUSI0SM2iGanSUch94SkSuLJFD4tWtGVZl8hoHvbKXJY8WwhSzF6pT2lNCIIBa6wA21xh6joHujdnXQbHDw6jAsD//kDEpkyiHVo+/RTCOxlPBAQJWqGoMqYnJiEKcRSoaHxLSZeYSqjuGME5hOkQMqr/M8dKgMj9AxqCKaEa1jb09bVCv4gpxAOEjO80w3myvKMoZt7Cg2ohcbi1RVerg5RO73++12A0FolglJ+dH/8TcON7MPHz6MMZC7jLz2JyEgVzZPj9zQUTNNRSQnWne9AZBTzrrfXm2HLU5drvhxo1zsGLeYJCLH8SIywvzQIRn5DTXYSfPD52G8CQbG5y/xwlex479+kc/kP9t/+Y1/NLU4wmhGi4EYeOU9httwU6NAcr3IYejDIxB0i+znLGFf+3xJyY6bgpblgHPIcehNBGaniEjgpkNB+toTD6rokQQ6wyAyMY/jEMHreZ9ux3F8/vnnGVWo6rA9dgGPXqXElnzeOVQ07RJdlO0i/9SyWPjdUzbebWAZqMJoZRXWdzBmQAAegdBDZAgVx8v4r+RzO6eInDFzRJuoRHhObTrd7MTtg0KR2wdSxWt0ayzM/6HrgHhnaT1JUr2evBJpd6cEudHpbLt60Tln0D/77DNde0MvIoztP9dXmyUPzTl19C6SLund5dazVvghDaDvp3VsCo+BgGSz2wMJdrUySwj087QQTnVTjM+OcSinrElbQobIyHfpYbVcHsPMwi+gXsjb7UZBTrwqfcXlqx6mXzLrRI/VXDOTFURcPSKSxnY9fBYsEEBO19hRiURsI79pdCVfIrLfbpSX8tIJvim3dW0oZ1uq0InOXXAvQnef0XmQPz/PU/cc7x1lpeV0cfHhdPgAb/Jqp1NNYrwMRBw8zvNUHQCUy7HL0OMYJ2Z6MKHfbjeLLKsEyWMbhmixdY6yzoUk0C3y0AuxY5Mlo/WogXB3F4iIilJl2p07hnx5eTGbIvJnP/zDpQ1l5aV14xb5mr1amVTeW9vG0KfCDnbO1R1vL7sX855Uob65vm89tduBAQiDh8TxMkQA+fCb//xfLAzhoUM5Mj/LHmuK5PB/0E183F70kIi43YYq5z3cfb28KauhDUYNX+PV9hPqU9Z5mQcCwAwnmRj1jElyvByqmolnBPb2B0944sEo5fT3lL6MMN08o7W9+z9T36U+IpJdqt3gtGd96DrpYF/3DcXg+lV+2Xmjqvk34ZV+73PiOEZEcIyXz4/sdfD1BkUN4Pj8xcyY4Y1mHr5W6ggdes41Sjt2g5MMOV5u6ZmX7Z5XFASseSloMFpZsL7GaTbGuOkNQAgtPF9t+fJy5Gmff/7ZD/7sH+DxGDuoT1SEADzyPWAWftGlLEkRrrKBouPl2NrJ1UJ5KWy7WpeIitBk7z7rOlR8YuuFJQmR28sBIPxw99hTj2WsFixsXGTOOW5HVvxJ3u/3gIuKyJG+um5ddqkscy2t/optZsuc9B6nHL27wkIFVNzP2uqLN8fI3+9Ma1HHTic5jqUo6Ru8FZW4383lDbCz1phd/KsdNfFmOFuV1Kt8WE7vOA542FzD6Z8URXbDa172tgfS124ffTmyfOTuqiM2j2+ahqrQSXV3m+4eIuIWc7/kZ8lQ6wAyThm00wCqivtUPaIBmrbrz4GQoSCNZ+o0AQtjcIzxftfejASzkufLTOZTnm4hZCSGSnd/fX293W4lj7mRrSiie6BgPnettmTnyRuzhSXVtXiJqi3kii0rLDfWcr1rHFMhuz21TLR/hb+PLa0FjnW/WOLve8dDYQcA0tCjGVIAkvelA8HYr0MWVuNe7Fp0V6YHNnSzvlKEhmKKyHy9797Nd8S5ZLxUeOnOm57O+mtX/GJDWSevl/DsbQd8rC/Fhh/6r7hb0Got2aJiZrreWr/ac9ZK7aqRFKXKgpWgPFmkukUAFHHb1nUnv56b1IcELANOkOMQd2ervrzDhjEkgi6IiHxhJ4BIFcPVH69rs+paebdOPZmIR9/bOdG9SDx65mK5t8kI3rZm7uTmaqXuaaDutx1624da0cHjPkO/ImaIEGUMwfAwMHSoW9T3KVxSK9pCJiI4FlfuNoOeoqPHAMBBc/cwFTXPBkAF8E//6R+95QFWXgEw9zaGlRAs3cA1Uax8zpO1eeqZ7ETvX0arHZY9qc++G9P9zUCyTu6yFf0K9XjdpBSndW/jqUBLajKZu4xhEWgBj5l5GAXm03x6XHhfXx0zxYZTQCLCEwoymMHKJJAMOBhP1HjWhhK0zhogsiRJ0hDurnvLQgFQdaduiOuJzZ6nOMUjTvC0pMozrt1RWEagmFd5e/9tcVdac+0TC8s3pMZc3UR7SzpJ5DuMWwUtNqDSPYdndVIEmQB0+CC2CEfEvLZCUS5L8IVs+PMfLDX59//hJ4HMmSUhkbmXpKp+zmIAdgKpeyMQmp8ob/FkWEtaOzWfXEg0/DxnWtVpfGylLTaU3nhra6xKeD1PnVAu1/Z7lzLGW6apvQ1c9qam5G52vOYb3QUId32UiV1OAknzmSvw8MGBZiHeZ8MDFZSBgGW/0CmZQPmVP+fEgJKvzpir6LgfbrQXrj3l0oUnPhkQaQhHt0toTQj9mt0n70a/q3s+O/75GCCUHSvtkRaVYkNDa1pfQltktInqFVgv1xgBoVBCgkowfO/6Eoio7LYC/5IhMn10yQ6WEIaAc+1BDjEzWz2UV/DXM69uoOufOZyk+1I+xq/df9TCynz34XTdi+AxvnoiaymTqlYppuxMVx02ynYB6oXlngCl2H322WfRUuvdvSIks9HfdvlPlcgGWb/O/+1s4O6ICaVEhCDcwkl4oLYEXf373SXWFZ4cdXmLMk1Plr2Lav9eRCzmEw/6+d3K5ff2BV2z74pL/VNaVt8fu6tR2bGnK8QFo2UPfxNEhEeUZDO9yVcxSkH42jRAkqL0YGRFtjZwz2u6UycE3kj3kyksl/70fZG4ovVaf6fFW8qWhalryp7VwY5LP+IQ73K9s6E/ZyeT7g303f+tpj8hq1EAazBv5V7t8R5yqS9kw1/86R/nh3/7o7+e+fpIgJJzIDynJaTb42Phuh796dI9UqqV9/W/+0Bs5rv7jGhHcSK+eKpt8anHb0886M/T2fBkY/3NEIemBwDc97AkYkEVadAzc9znvzN/4B029EcJjVgDDSML3MnPAMKuoNMf9+mzOdv6Z62zjEM3L8WPpy8rYO1KVot/y/W8Sweankj/9Hj1Zfn5py/76uzxnWnXouj5jA9CszLZC1NI3uK9URkXzd/9tu6UledOsp4K6fXilivJeiJcF//CWJ5o1EkTberGkyuu60RrP6gro4GXpUD9YeRx5lK/e53ZMZIMysu4YWdXxZ6nKyBfsxOBmsqf/32B6PTjHW3wrMQxlMPdDRPq8Gw1NBmHm5vZbQySYVlh36DYdkR27eFd7Cxa6546vwThTYKWx3STx1Lw0+dCf0vPxvWq5yu568V2exztx1YdKRtrbcoV95zpysUejKrqXMNlIKoRUNWVdVNEqW05Gfx/DTaISPr9tdNs74wFQ6AUSFwAp29xsLWH9/KKPa27kJxHLBNN2KUhCiTlvXJ8ZwMfoyA+dMQ+1GKLW9009Ut1W9qfp9SxVArbEjwaiUgciOQxjojg3meuqmSoypcbpXfY8D/+8PsA/t1//AkIsDWEeQA+p7s7JJmUE6/b1pJH+vbPT9LXl4emLhGRXRf2BhDko7ORth+W2xt3G9K5XifgPXvyZL7QfHW1Zj39390NK3tdDUiaW/QoIhR0Lrp7NiR+DTbUsdZMoapcZY3daZG7gEiDQzioZoZY6Ga3xXX7J3+Itq+k5tbk90V3PnqRLo989FgPuMKbimEKZnVOpN3v53d+1E/4GDqTtHC0gaSqmu/KJHM6ruU0GK40wjLDc/cvDla/lA1JAXKZfZKiRFaGXOacHvlygV3jFORbfzr8V8SqHLKn051epTrLIjVku5O7jEZpSX/gL4os64f1oZu7zoZ3g4JUMki2oC6UO4EFILerpuxnL+x6q0aapsbpr2mU8sigBraG4uaWF8FygDJoZ5rmEGIM8Va43htJnze1l3D1Na+3vDzOdyioB00JnoyG7xnwdeV6T2Wd74+96DUnq56q8tB1r7yviLRBScld22gmat9qvpp0j3vIi2QHAjOG3K9a/9PvP/cAfFU2pJSISDbiUkNlwEOEfk6SlCBChZLN7bgErfauApj3c4xx6JhzssHIBf5k32fZq1KmihrxKPX6+K4+dwd96JjnOY4rBHqrKJ03JQpPzkaz/WuDRXk1W/M8m34rHK4jL+IO6NBAGwMZJkCsUQNfpgfrCX/rGf/2P/w4IiJ1Yoa7w4Kk765mTESs15BlU2w+cfUXXxTZL8lh615dBN2P2gW/58P9kcpYr/CsYWrEQ+pet0DbV4mWpj357bEKzmvQ3CqxoBmrPVISgN7WTY/bzd1FWeFTatQPf/iPfyt514p+6xnvWGdhNq+TawOIiCR2Vet8l5Tk9aBlYeoot8kNEHk7inx1cr9LH5+LR/PlDYXtR51ZC4zILr7noSvxCOXW+cXFzDR3Oukb0fvtIl7Hb39RsaxWHHOH5H6LCGSTqwgRixOem6IcCMQ1WbnLGtfLKhb5nt4LgNakFHuP9MX7RwOC3Tww51ztkeuH78BWnWddJjrP6hvuQqyITLcC6ZwOXvQlaVlHUgUjt7kB5eO+DEH6Jmz4Z3+6ynP/249+jFhO6dLlLThl03O5iak8haclyOspPfKCWTPBo4pUWltHJwHajrPby6iCUr9+Z1vJb2dPcaKLeemQiJw2i6ZrbtD65dKDVO5og8cSSf1aPPhKbKhDlSFCd9XD3eERwDxN1ysfIrh8QzYDgQ+zBqOB+x264UY43gJH++3TDwea6rj7cRzV2Emy3gvz1hCxxazdYz+Z3PxsZlTJMSMGk42PUoKMhLOpEJEMjTIzECkc7Ir0PjEbgkBuSgxfJesVPBgFDCG53mdMAMyhTNxTKLpteaIOmiEurep9iU80Kv9cFL+MddthxQZE+i7JdV7WD/Pisqu5H++voq2CFO05d+EfgE0jqTsgrov/+Z//4Venah5fgw3jEJLn3cwNguzAgpKeikyPAFx0MaO64SJn0lGidfPh0VK/RTiqr/R2u9njCx6KVVxIkSLglinrg6mJjXdiZwnxBtorTrtnEe3qUV9xUXo0XsBq6sE5XRmn2W2M4zg2CPe8w/BTs2Fovrgke0kynwxBhqqMCyhNNxWrL/ZqDYqdcHnrtygLU4us/y8/OSf3C2Dq5Lfut3CRp8d+cFrNIdcJj7aOEdHjx7gwxwAAiUAQqwCXvd9UOD3fGqT69bzCeqpv8Jt//+Of+9rDbfP0fFskc16yAUD2uPn9cpXbLAz41ePdjdUTUTonMjcuctdpTRty/VfjDBp7iuVoqvbkuuuzxdUeICLZ+JV6sJ5TmhoJbrfbGEMUAP7Jn31tW1TH19CGOgKTAgJOHtTM6ehEQBLgXc2Hh7mnGQ3mqLI4/YSEYhS5uXvO8MiSgpUyKi3EsLoRinbcUOiTv41d6vDHfs4n6KnzZtM3PBwEs8NSWgvvLlwB0JFaHiJX2+s3O77Vj//3v/wrePi9GaXZNi0lCpayabvD58wXsQcA3WVtPobz2KYgHYzunmI0wApvotuKLLvZidYc1dnw9st1Pp1kzjOiSuTI+a0BeYe6C2VVHf78B3/wbciIb6YNdaiKh4OGyLbyCznIEMLMZqb7xyD1/jq5g+vSBtkvX6vIpD5UHbTsDx4xiaL42/LqUzjkG5jrxu1JpbjfsrCG8jDwACZeEr84IUbGt1ODdXwrNhzHcWJaTgr1oEq2h8CuYT9JgixYjnHQo7Cm0cBXaaX2Im5CRmZW/XdPJ+RPOoaK5l260auL49FLP3Ei2/QW3clKoXtQ0C+Lx7a5b3x8Alb+m//4V/mue0D8TIGNbOCAueSbn7PTErqGucxrMcqViJaYlzuVx0HyvX3/CapCo1THfyJC2k4kAPc9cR/A9Gsv3oypqsdnL9ctDiGz+hZjDBm8DomI+Od/8YNvT708vpU25KGHzmlCuLne1MxEFUL3IHWaHR9eYpqo+gzJbFngezibYRKM3VpYPKjqY7GnQ0NpZMqp4rHCw15CiMjW+dlnzS6wJQx23A4zO+RGcs41K0A1C5mqqlhQ3Zrb0U3fpzo+5eX+zX/6a3pWSyQiJPK9OgCQ74WSQPrtbEm23SWuoOrRRxg9WRU0Kkfr4vJWhiswsb5JZky7q2puv1fVtYdDl/8/jiPdMnILXjYC70pyDiVGFhJ0pYF/sUG2T3h8Am2o43Y75jQVcQNCECCXFWK+ViVIRg6uiEDkDO3YG3+HXtbJQ1Tn/fkN6OWrZR+xsancJpxPsqwTwmyq6jSL1UsR3kALi+mwAIYq4EHffXYhQq4mI+5xR8B7Q6k+yfEp2ZCuUlVNLJzZmJ6G1U5XOfycovn2ieltihYBC5M1AxJAtoDMymZZkRUBIOi+30IGIhN6SDTVjrysCEOYDtUQlDUNwNITvNzu97sMCYkcHzLypRUiEKru2FQSF3hw1J/2+J1c9P/82S/MMgSN3Nu7rMcZlvNTvRVPzNxwjDFP7y0dwHoxt7f6aEacHfnATg68bbaYsaLn3iaScCwV9/s9Z105vcaD5Wu1x7iqh6rKPeRfRP7iT755kvxbj0+pDXVsYtHdbx+OOeeH24c55/18TY1Z7wG5Bu3mawtCB4HLEywAm6kN8MBYGENinsBy1wEEVTzC4ypSok0XWckjLOZKrUma2+12c3hO9R9jtPRjKQEAffPGhU9+/M5vkMePfvYzM/PpsMT6MxcTkvT01TEo0l7QThJ+AeM9Wt+9ObushIcXAaJBqoU4UcXdoWsPK1aTZzpuqZB3Dc+KiIjbywDww+/9DpWgjt+JNrw9yBDBuB1zGpxuYI4lEYZIhAzIngooFCLfl7hceRp6QezKq+/CH0v0d8LV3z7tIcKckZYwkVJlCMkZjq0xHRMUhei659MWyt/p8R2xYYMNqmDMKQzliKCZhZsMUggnPRDgmjbsQYlYm5YArPEFiJyIkylI9XXvLFmgsssCK5QSEUfcjgFEvnM9J4WmOhY0TVKGUIS7gvu78cfvHN/VfR6PH/3sZySnr3ZrWG5oWBtGYvcPuF9VYllVrwcDFXubSX5fMG1vIF9s4N5zGPPKxdIF7LHpJFXln/zRV+1q+YTHd6QNT4fk/ILAGBohkzb8ajhcL3z3fAfc/k1KpmViYVmkvKC6Jk4ennWCjC8ld/KpuBuIYyTVfY/ORQJ5FR19h2S4jt+PNnzJ8e9+9jMsMGPtN+rQ2Qpb9/jYqjGkgAOorecvLy8LQ9wvHNPM0RLIGumW/Yf/6Pcg+2+P3482fMmhgxuZGGZGgA+VUTDfXOwOUo8cqW0Fsn72+Wer7UPydQoCrleHQiAisMgWHxF+lW657+b4+8eGPmY94QpnH0GQRWnVa4JwYRvuTgkVZqPKdhXXBRWUPTEI+E5joS8//n/b47V6+ETGSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=130x130 at 0x147964A30>"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "262aad99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 128, 128, 32)      896       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 64, 64, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 62, 62, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 31, 31, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 29, 29, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 14, 14, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 12544)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               1605760   \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 129       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,662,209\n",
      "Trainable params: 1,662,209\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "8682e498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=130x130 at 0x147964A30>"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "efd866c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_image = image.img_to_array(my_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "73dd541e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14821d9d0>"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAYUklEQVR4nO3df3BX9Z3v8eebQBIS5UcwBgxciK7VYqe3MBFBZe3gWtF1xDujrdyOF0UHe8e93VWnKtfptLcdKFZblx3YsqnK6lZEy/auVOfqdl2qMFYUrlxKBS3qYkIBIZAESDC/3veP7yHmS74h4fvrnHzP6zHznXzP55zvOe/v+X6/75zP55zz+Zi7IyLxNSzsAEQkXEoCIjGnJCASc0oCIjGnJCASc0oCIjGXsyRgZnPN7H0z221mD+VqOyKSGcvFdQJmVgR8AFwDNADvAPPd/b2sb0xEMpKrI4EZwG53/8jd24G1wLwcbUtEMjA8R+utBup7TTcAl/W3sJnpssUsGz9+PNXV1WGHkVP79+9n7969YYcxlBxy98pTC3OVBAZkZouARWFtv9AtXLiQJUuWhB1GTi1btozFixeHHcZQsidVYa6SwF5gUq/piUFZD3evA+pARwIiYcpVm8A7wIVmVmNmxcCtwPocbUtEMpCTIwF37zSzvwJeBYqAp9z9D7nYVtyUl5dTVlY2qOUKXVlZGZWVfaq4g3LixAmOHj2a5YiGppycIjzjIFQdGLSHH36Yb33rWwMud/bZZzN69Og8RBSelpYWWlpa0nrt2rVr+c53vpPliCJvq7vXnloYWsOgpGf06NFMnDgx7DAiYdSoUYwaNSqt144dOzbL0QxdumxYJOZ0JBARt912G1ddddWAy82YMSMP0RS+2bNn88QTTwBw5MgRfvCDH8S2jUBtAhGxatUq7r777rDDiKWGhgamT5/OwYMHww4l11K2Cag6IBJzSgIiMackIBJzahgMwW233cbMmTOTyq688sqQopExY8bwyCOP0NbWdtrljh8/zrJlyzh8+HCeIssPNQyGYPXq1dx+++1hhyFnqLGxkenTp/PJJ5+EHUq61DAoIn2pOpAHxcXFzJgxg5KSEgDOO++8kCOSdIwYMYIrr7ySAwcO9JR1dnby9ttvD1iViDJVB/KgqqqKLVu2MGHCBACGDRuGmYUclZwpd+95nNTc3MyMGTP48MMPQ4xs0HTvQJjMjKKiorDDkAyYWZ/kXQgJXW0CIjGnJCASc6oOZKCkpIRp06YxfPjpd2NFRUVPo6AUlqKiImpraxk/fnxPWVdXF9u2bRsyjYVqGMzAlClT2Lx5MxUVFQMuW1RUNOTrjtKXu9PV1ZVUduzYMWbNmsWuXbtCiqpfahjMtpONfQMdCUjhMrM+n/9QawDWt3eQzIzi4uKksuLiYv13lz7MjJKSkp4qYGdnZ5+jhShREhikSZMm8Ytf/CKpA8+SkpK0u7eSwjVy5EjWrFnDiRMnAFi+fDnPPPNMyFH1T0lgkEpKSvjyl79c8J13SuaKioqYOnVqz/S5554bYjQD0ylCkZhTEhCJOVUH+jFixIjTTosMVlFRUZ/vT1dXF93d3SFFlEzXCaRQU1PDU089RWlpaU9ZaWkpX/rSl3Q6UM7Ynj172LdvX1JZXV0dq1evznco2b1OwMwmAc8AVYADde6+3MwqgOeBKcB/AF939yPpbicMZWVlXHrppbEYyktyb/LkyUyePDmp7OWXXw4pmr4yaRPoBO5396nATOAeM5sKPAS85u4XAq8F0yISUWkfCbj7PmBf8Pyome0EqoF5wFeDxZ4Gfgs8mFGUeTBz5syewT+qqqrUBiA5NXv2bB58MPln8bvf/Y433ngj77FkpYJrZlOAacBmoCpIEAD7SVQXIu+qq65i2bJlYYchBairq4tT297mzJnDnDlzeqaLiopYunTp0EwCZnYW8M/A37h7S+/LaN3d+2v0M7NFwKJMty8SdXV1dTz33HP9zr/oootYuXJlHiNKllESMLMRJBLAs+7+q6D4gJlNcPd9ZjYB+DTVa929DqgL1hOpswMi2fTRRx+xcePGfue3trb2OVLIp7QbBi3xL/9JYKe7/7TXrPXAguD5AuDF9MMTkVzL5EjgCuA24Pdmti0o+5/AMuAFM7sT2AN8PaMIc+Cyyy7T4B8igUzODmwC+ruP9up015sPc+fO5fvf/37YYYj0SNV4mC+6/E0kZB988AE33HADe/bsCWX7BZ8Exo0b16f7r3HjxoUUjUhfR48eZcOGDaFtv+CTwD333MN9992XVKZOP0U+V/BJoKSkRB2BiJyG+hMQiTklAZGYG7LVgXHjxg2qk88xY8bkPhiR06ioqKCmpiap7MiRIzQ1NYUT0CmGbKciP/7xj1m4cOGAy5WVlTFy5Mi04hLJhtbW1j6jEf3oRz/iJz/5Sb5DKazBR8rLy3WqT4aEsrIyysrKksqi9I9JbQIiMTdkjgTKy8t59NFHqapKdE/wla98JdyARDIwf/58pk2bllS2bt26095ynCtDJgkUFxdz7bXXcv7554cdikjGpk6dmjRACcDOnTtDiUXVAZGYUxIQiTklAZGYi2SbwKhRo1i6dGnSKcDi4uLID+wokombbrqJCy64IKns17/+NWvWrMnpdiOZBEpKSrjxxhuZNGlS2KGI5M0ll1zCJZdcklRWX1+f8ySg6oBIzEXiSGDs2LFcffXnPZKNGjUqUldUiYTloosu4uabb04q2717N9u2bcvaNiJx70Btba2/8847fcp7j2EgEkepfp8rVqzg29/+djqri/a9A/rBi/SVj9+F2gREYk5JQCTmlAREYi7jJGBmRWb2rpm9FEzXmNlmM9ttZs+bWXHmYYpIrmTjSOCvgd63Pz0CPO7ufwYcAe7MwjZEJEcySgJmNhH4S+CJYNqAOcC6YJGngZsy2YaI5FamRwJ/CzwAdAfT44Amd+8MphuA6gy3ISI5lPZ1AmZ2A/Cpu281s6+m8fpFwCKAc889l1dffbVnXnFxMbNmzaK0tDTd8ERkkDIdmvxGM7seKAVGAcuBMWY2PDgamAjsTfVid68D6iDR2/B1113XM6+yspItW7boBiKRPEi7OuDui919ortPAW4F/t3dvwlsAE5e7LwAeHGQ60t6iEh+5OI6gQeB+8xsN4k2gidzsA0RyZKs3Dvg7r8Ffhs8/wiYkY31ikju6YpBkZhTEhCJOSUBkZiLTH8CIjI4w4cP7+l5y905ceJEZuvLRlAikj+33HILl19+OZDoiHT+/PkcO3Ys7fUpCYgMMeeccw7nnHMOkBijs6ioKKP1qU1AJOaUBERiTklAJOaUBERiTklAJOaUBERiLhKnCMePH8/ChQt7psvLyzn77LNDjEgkPiKRBKqrq1myZEnYYYjEkqoDIjGnJCASc0oCIjGnJCASc0oCIjGnJCASc0oCIgWuurqaiy++uN/5SgIiBW7p0qW8/fbb/c5XEhApcCNHjjztFbhKAiIxpyQgEnMZJQEzG2Nm68xsl5ntNLNZZlZhZr8xsz8Gf8dmK1gRyb5MjwSWA6+4+8XAfwZ2Ag8Br7n7hcBrwbSIRFTaScDMRgN/TjDgqLu3u3sTMA94OljsaeCmzEIUkVzK5EigBjgIrDazd83sCTMrB6rcfV+wzH6gKtMgRSR3MkkCw4HpwM/cfRpwnFMO/d3dAU/1YjNbZGZbzGzLwYMHMwhDRDKRSRJoABrcfXMwvY5EUjhgZhMAgr+fpnqxu9e5e62711ZWVmYQhohkIu0k4O77gXozuygouhp4D1gPLAjKFgAvZhShiORUpt2L/Q/gWTMrBj4C7iCRWF4wszuBPcDXM9yGiORQRknA3bcBtSlmXZ3JekUkf3TFoEjMKQmIxJySgEjMKQmIxJySgEjMKQmIxJySgEjMKQmIxJySgEjMKQmIxJySgEjMKQmIxJySgEjMKQmIxJySgEjMZdqpiIiEqLKykscee4z29vZ+l5k2bdpp16EkIDKEjR49mrvuuiujdag6IBJzSgIiMackIBJzSgIiMaeGQZEC093dTVtb26CXVxIQKTAHDhzglltuoampaVDLKwmIFJiOjg527dpFY2PjoJbPqE3AzO41sz+Y2Q4ze87MSs2sxsw2m9luM3s+GJ1IRCIq7SMBM6sGvg1Mdfc2M3sBuBW4Hnjc3dea2SrgTuBnWYlWRJI0NjayYsWKpDaAlpYWWltbB72OTKsDw4GRZtYBlAH7gDnAfw3mPw18HyUBkZw4cuQIjz/+OM3NzWmvI5NRifcCjwGfkPjxNwNbgSZ37wwWawCq045ORHIu7SRgZmOBeUANcB5QDsw9g9cvMrMtZrbl4MGD6YYhIhnKpGHwL4CP3f2gu3cAvwKuAMaY2clqxkRgb6oXu3udu9e6e21lZWUGYYhIJjJJAp8AM82szMyMxHDk7wEbgJuDZRYAL2YWoojkUiZtApuBdcD/BX4frKsOeBC4z8x2A+OAJ7MQp4jkSEZnB9z9e8D3Tin+CJiRyXpFJH90xaDIEHPw4EHq6+sBaGhooKurK6P1KQmIDDG//OUvuf/++wFwdz777LOM1qckIDLEdHV1ceLEiaytT/0JiMSckoBIzEWiOtDW1saOHTt6pocPH84FF1zAiBEjQoxKJB4ikQR27drFZZdd1jNdWVnJpk2bmDhxYohRicRDJJJAd3d30q2Pra2tuHuIEYnEh9oERGJOSUAk5pQERGJOSUAk5pQERGIuEmcHUnH3lGcIEl0XiEi2RDIJNDc3c/vttzNy5MiesrPOOovly5czfvz4ECMTKTyRTALt7e1s2LAhqWzs2LFn1I2yiAyO2gREYk5JQCTmlAREYk5JQCTmlAREYk5JQCTmInmKMJX29nbWr1/PydGKLr30Ur7whS+EHJXI0DdkksDx48e59957e6ZXrlypJCCSBQNWB8zsKTP71Mx29CqrMLPfmNkfg79jg3Izs78zs91mtt3MpucyeBHJ3GDaBP6RvqMNPwS85u4XAq8F0wDXARcGj0XAz7ITpojkyoBJwN3fAA6fUjwPeDp4/jRwU6/yZzzhLRIjFE/IUqwikgPptglUufu+4Pl+oCp4Xg3U91quISjbh4icsffff5/t27cnlb377rtZ3UbGDYPu7mZ2xr2CmtkiElUGEenH+vXreeCBB3K6jXSvEzhw8jA/+PtpUL4XmNRruYlBWR/uXufute5em2YMIpIF6R4JrAcWAMuCvy/2Kv8rM1sLXAY096o2ZNWxY8c4dOhQUll5eXlSHwQiQ0lra2uf2+WPHz+e8+3aQP37m9lzwFeBc4ADwPeAfwFeAP4TsAf4ursftkS3PytInE1oBe5w9y0DBpFGdaKiooKzzjorqey73/0ud91115muSiQSVqxYwaOPPppU1tLSQlNTU7Y2sTXVkfeARwLuPr+fWVenWNaBe848tjN3+PBhDh9OPmnR0tKSj02L5ERzczOffPJJ3rerewdEYk5JQCTmhsy9A4PR1tbWp/5UWlpKaWlpOAGJDAEFdSTw+OOPU1tbm/RYvXp12GGJRFpBHQk0NjbS2NiYVHbkyJGQohEZGgrqSEBEzlxBHQmIDBUvv/wyr7zySlLZli0DXlKTE0oCIiF46623WLFiRdhhAKoOiMSekoBIzCkJiMRcwbcJvPLKK31OE15zzTV87WtfCykiiZuXXnqJ119/Pals06ZNIUXTV8EngY0bN7Jx48aksvLyciUByZvXX3+dxx57LOww+qXqgEjMFfyRQCr19fW8+eabSWXV1dVMnjw5pIikkHR1dbF9+3ba2toA+NOf/hRyRKc3YKcieQkijU5FMjFs2DCGDUs+CHrggQdYsmRJPsOQAnX06FEuv/xydu3aBUB3dzfd3d0hRwWk26lIIUr1oXR1dYUUjRSizs5OOjs7ww5jUNQmIBJzSgIiMRfL6kAq+/fv77mBY+TIkVx88cUUFRWFHJVEXWdnJzt37uSzzz7rKWttbeXEiRMhRnVmYtkwmEpRUVHPj37q1Kls2rSJ8vLykKOSqGtqauKKK65g9+7dSeUdHR1E4bd1CjUMnk5XV1dP42BHR0fI0chQ0t7eTnt7e9hhpE1JIAV3p6OjIykZmBlFRUUkhlaQOHL3Pi3+Ef2Pf0aUBFL4+OOPmTt3btK1BJMmTeLJJ5/sM+CJxEdLSwt33HEH+/fv7ynr6upi796UI+0NGUoCKbS1tbF58+akskOHDulagpjr7Oxk69atoQwQkksDniI0s6fM7FMz29Gr7FEz22Vm283sf5vZmF7zFpvZbjN738yuzVHcIpIlg7lO4B9JjC3Y22+AL7n7l4EPgMUAZjYVuBW4JHjN35uZzrOJRNiAScDd3wAOn1L2r+5+soXkLRJDkAPMA9a6+2fu/jGwG5iRxXhD4+50d3f3nEU4+RjqjUKSmrv3+awLtTqYjTaBhcDzwfNqEknhpIagbMjbu3cv8+bNS7qAaMKECaxatYpRo0aFGJnkwpEjR7j77rs5dOhQT1lHRwcHDhwIMarcyCgJmNnDQCfwbBqvXQQsymT7+dTW1tanc5KamhpdU1Cg2tvbefPNNyN/G3A2pJ0EzOx24Abgav/8mHgvMKnXYhODsj7cvQ6oC9alY2qRkKSVBMxsLvAAcJW7t/aatR5YY2Y/Bc4DLgTezjjKiGpububnP//5gJcXl5WV8Y1vfEPXGETUsWPHWLt2bU8nIJC4JuDYsWMhRpVH7n7aB/AcsA/oIFHHv5NEg189sC14rOq1/MPAh8D7wHUDrT94jRfyo6qqyhsaGlyiqb6+3isrK0P/nuThscVT/P4GPBJw9/kpip88zfJLAHXRIzJEqD8BkZhTEhCJOd07kAdtbW2sWbOG0aNH97vM7Nmz+eIXv5jHqOLjvffeO+1gH01NTUmNgrGTqqEg3w/CbzAJ/bFq1arctXzF3MqVK0P/fCPySNkwqOqASMypOhARhw8fTvsW1dGjR5+2qlGompubaW5uHnC5w4cPD7hMnKmPwYgoLy+ntLQ0rdcuXryY+++/P8sRRd/y5cv54Q9/OOByJ06c4Pjx43mIKPLUx2CUHT9+PO0vamtr68ALFaC2tjYaGxvDDmPIU5uASMwpCYjEnJKASMxFpWHwIHAcODTQsjl2TgRigGjEEYUYIBpxRCEGyDyOye5eeWphJJIAgJltSdVyGbcYohJHFGKIShxRiCGXcag6IBJzSgIiMRelJFAXdgBEIwaIRhxRiAGiEUcUYoAcxRGZNgERCUeUjgREJASRSAJmNjcYtmy3mT2Up21OMrMNZvaemf3BzP46KK8ws9+Y2R+Dv2PzEEuRmb1rZi8F0zVmtjnYH8+bWXEeYhhjZuuC4eV2mtmsfO8LM7s3+Cx2mNlzZlaaj33Rz1B7Kd+7JfxdEM92M5uewxjyM9xfqvuL8/kAikh0THo+UAz8P2BqHrY7AZgePD+bxHBqU4EfAw8F5Q8Bj+QhlvuANcBLwfQLwK3B81XAf89DDE8DdwXPi4Ex+dwXJAap+RgY2Wsf3J6PfQH8OTAd2NGrLOV7B64H/g9gwExgcw5j+BowPHj+SK8Ypga/kxKgJvj9FKW97Vx/uQbx5mcBr/aaXgwsDiGOF4FrSPSSPCEomwC8n+PtTgReA+YALwVfrkO9Pvyk/ZOjGEYHP0A7pTxv+yJIAvVABYkb214Crs3XvgCmnPIDTPnegX8A5qdaLtsxnDLvvwDPBs+TfiPAq8CsdLcbherAyQ//pLwPXWZmU4BpwGagyt33BbP2A1U53vzfkhjDoTuYHgc0+edjPeZjf9QAB4HVQbXkCTMrJ4/7wt33Ao8Bn5Do4r4Z2Er+98VJ/b33sL6vC0kcgWQ9higkgVCZ2VnAPwN/4+4tved5Is3m7PSJmd0AfOruW3O1jUEaTuJQ9GfuPo3EJdxJbTN52BdjSQxoW0Ni4Jpy+o6GHYpcv/eBZDLc32BEIQkMeuiybDOzESQSwLPu/qug+ICZTQjmTwA+zWEIVwA3mtl/AGtJVAmWA2PM7GRfD/nYHw1Ag7tvDqbXkUgK+dwXfwF87O4H3b0D+BWJ/ZPvfXFSf+89r9/XXsP9fTNIRlmPIQpJ4B3gwqAVuBi4lcRwZjllZkZiEJWd7v7TXrPWAwuC5wtItBXkhLsvdveJ7j6FxPv+d3f/JrABuDkfMQRx7AfqzeyioOhq4D3yuC9IVANmmllZ8NmcjCGv+6KX/t77euC/BWcJZgLNvaoNWdVruL8bve9wf7eaWYmZ1ZDpcH+5aug5wwaR60m0zn8IPJynbV5J4hBvO58Pp3Y9iTr5a8AfgX8DKvIUz1f5/OzA+cGHuhv4JVCSh+1/BdgS7I9/Acbme18A/wvYBewA/olE63fO9wWph9pL+d5JNNyuDL6rvwdqcxhDVof76++hKwZFYi4K1QERCZGSgEjMKQmIxJySgEjMKQmIxJySgEjMKQmIxJySgEjM/X8a19iq/ASglAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(my_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "7d330493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130, 130, 3)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "6d109c2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_image = np.expand_dims(my_image,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "6fd5bc86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 130, 130, 3)"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_image.shape # because model needs batch size as well "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "a193930a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.34595042]], dtype=float32)"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(my_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7655e9b5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}