Tumor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2, glob\n",
    "\n",
    "no_path = 'no/*'\n",
    "yes_path = 'yes/*'\n",
    "\n",
    "# 0-no, 1-yes\n",
    "X_data, y_data = [],[]\n",
    "\n",
    "for img in glob.glob(no_path):\n",
    "    X_data.append(cv2.resize(cv2.imread(img),(100,100)))\n",
    "    y_data.append(0)\n",
    "    \n",
    "for img in glob.glob(yes_path):\n",
    "    X_data.append(cv2.resize(cv2.imread(img),(100,100)))\n",
    "    y_data.append(1)    \n",
    "    \n",
    "X_data = np.array(X_data)\n",
    "y_data = np.array(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(253, 100, 100, 3) (253,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10b290c88>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VXf0z9fNMIKqru+zpufYhr4GJpZ07/1YqaBXvEPZJj4b+e5NT6j3dobdW4K1tVIlCfTp3Amr5utPH7xxmuHL14zs225fpfZYtW0tKg45V6cSkIJhiORyzHh2LgXhtlSjEVsp1ZRXfnINWnMBW0Bm/o2MJsSnjD8NwGPAa4GBgBE4dx/HlwzDcGHgDcCvgC8Djx3H89rXX1RXMK66R59RVXcuzO61qVXZnFfW7GiWnD97iEKZpqtPqm6++f6UC9cZk9Vq0w/6pa8u81coO7Wi23KOvNWb1c/XerKufMQ31mNGIJrlYzKMWqbRAhSyuTi+75g449buE/Xf+6rlKKinJeU2dL+dSHVvpQKnAzzrm3M1Hq/5LXvKSdfeFqUjHfe97XwDe/va3A1NkowVHTdKqfdkdLLL/w+5gEcb/EfDb4zjeCTgWeP4wDHcCTgHOGcfxdsA5q393dHRcB7DpD38cx6+O4/jR1f//J3AJcHPgkcBpq6edBjzq2upkR0fH3sWWjHvDMNwKuBtwPnDwOI76Ub7GiiqwKWYZIjbbGqr0odWv5jU1PPbII48EJoPWX/3VXwGT4cYgnOp6UrxVxDdX30CPrIcPk0iWYawaD6shTcOZ1yhWe04rtHOzRI9q6MrQXEVZn0G6BGFSC5ynDGKq9e38LpOK/FvRv455syCWqsoppvtMZu0hUOckw5TTBehcVLeh74nVdnWrWq9PFx1M6p21GjTiaeQz3191oX6XdfoWya3frNpujn9RLPzDH4bhp4DTgd8cx/E/Yl+6cVYc/jAMzwGes+WedXR0XGtY6Ic/DMP+rPzoXzeO45tXD399GIZDxnH86jAMhwDfaF07juOpwKmr7YzDMCxUBXd3jBlZo02DHcC97nUvYArJlV2tkS8r6aap19jeBRdcsK5vye4wsZ5slYajeq59sB2DTjKppY49mdLvNHCZlgoTe3tP2SlZtzK+xjUr2bj5pNJPDUXVkKZkYvCS93XsdTwGwDjmZO9WAI/Iec+5rtdkAJLICkYwzZ3JUVZE1uVb34mLLrpo3ZgM8rISj1ubey1Mz1nX7izm3woWMXLPw6Y6/rDS6quBS8Zx/D/lq7cDT1/9/9OBt2357h0dHTuCRRj/PsBTgU8Mw2Ckyu8C/xt44zAMzwK+CDx+kRv++Mc/btaHa9Uqg/mrWQZ9eK5BOupbMLGTq646mMczTRSm6rm68zJdUzapLKj7LotHqEfWMWdwSa78WQG4IivY2kZlV/sii6sry8gWH6n3dV68RqnHENXKyDJZ9jeTjKrrzGeVYaxeUwOQxCxpsJWi6r28RmlH24fPqroAlcYM31Zqcp608QC8/vWvB6b3SDuHyV5+f8UVV6xdU3cfgqmSsH2p7/ys939ezb3dYfxNf/jjOL4fmNXyCTOOd3R07MPY0SSdyjSzKo/O+oSN7JErtboawIte9CJgYkr1aa28FmaoNfJlC4MyLLlloop9riGdruIWZDDBR8aphSxSUsnVPKUGWM+49RxRWTiDfdTb7YPs+OQnP3ntGivXqu+acCMr1gAb59tjzpdSlMlMrSSjWfp7xSz9P8OVK3t7jmPzfs6/39fiI3prHPO73vWudeOqdhMDmJSAtJvc8pa3BKZ3QV0f4HnPe966cSh5aRdYxFO1twJ3RA/Z7ehYQmw744cbcOb38+rpt9qCyapsWKXplTCF32qFNSRVi63MXxnNBB4lCNlP5ne1l9Vho14ruzrWWjfec3NXXtlQdqo2hNaxer/KpLKcY/U+spNlpN70pjdtuEZd2/lR96873SjVOD8ysH10XrQ11HOyv63+5860+b7YVo1dyKKjzn++V5XFnUs9GOltschGnQf3WtDHbyEXpcO3vW2ydbvHn+HixgI4PyYD1XtmwtneYnrRGb+jYwmxI4w/ryDBViL3vF4dTSu1EVeWRYJp5beMkvukq2c9//nPB6bSSRUyuymprvqttFb1WpElrauOr5dAS7z3yflpWcXz75a1VwnIPlhYwnGop9Y+ORZ1Y/VR2bDaM2xXyUVdW4u2nowaDWm7WRhDSaum9srEnuO8aG+w/dr/9FwYhafUI8t+4hOfWLtGm4GSiXNpWzUJS2u+HgDZ3BLpzrHvIsAHP/hBYJI2jRI1mlPJCTZGg87yaFTU92NRdMbv6FhC9B9+R8cSYp+vuTfPqKGIp+j3oAc9CJgMedVQd+KJJwKTiJfVVkwoaeV2K3Yp0hq+am593bRR8TB3lVGMrxVzPcd7p2iboby1/QxE8T5VVFZs1FCni0ljmOfWecpaAF7rNbVmoKJxPVbvK6oomqHMtp9uSphdZVfx3eCZuhOQ7WqU9F3wU1QVUvUmN/JsbfNt+z57jXDm3Dsek8IA3vve9wKTOmA4rwbGWjMw6yC2dhjaG+iM39GxhNhnGH9Wxdys21bdWK70GtushffMZz4TWF/tRhec7CYDPOEJTwAml01lngwjNXRXw5QBHxqSar9tR1eN/a/smGOWVbPmfGu1n7WTTmWnDKyxb4b3KklUd1hWqJHhUoKp7Tk/WQvPv+s1KYVkW/Vc753jl2Vl6GpczSQc20tXaSsxSSOec+v7VYOkDILSfWfw0ytf+UpgMh5acxEmCVHmd8ceDYQ1vFfGz0rLuW12K2R3Ky6/zvgdHUuIfZbxRa50lZ100Rg0Ydqs+p06P0w6tkyf9gFZu9ZHk/WyEISMb5+qFJLuNf9O1q1jUzrIirO5A07tU7o7WwEwyXJ5P6+pffLYLW5xC2CScmyrsmsyb7aRxTBgY4hx1tVv7f2XCSoydH5fj4l0f7V28PEdUMJwLz5dctXdJiPrDlbaNBhKfd02YXLfWYfvn/7pnwB43OMeB6wPNNMdXCU3mB/ivDvpvZ3xOzqWENvO+OM4LqSnZGJG1nkHOPbYY4HJMv/rv/7rwBTYUS3Urt5nnHEGAL/6q78KTKm7rvLVwm2AhZKEzJbVXVu7waa+m+OsY0orfgbu1NU+E1eSKVulq2TmtBRr1a/XmKwkY2Upq2oDURLxOz+djwz/hUnSyiCltI3U61Lqc1yyYr0mw5+zb35fvR+2J5sbUit7zwsjthSZ75dBUfWdUDJVinKODSZT16/fpS1kXrmunJ9F0Bm/o2MJsc/o+JsV4JCBDEOFqcCBqaTqYibpaIGFqUCmIadaVv/4j/8YmNi8skdaVtOv62dl9SwwYb9lx+rTTh08wzRbLJtM6d+tXXdyP70MZ/U+dS8Bw0qNd9BXniG8MCWzZBFM79faJyAZzO8yRbmOcVbYqvPfSu/OlG2fXcZBtL7LWvwVnut9tAukFFjb17PjPCtJGgbtLjwwvbO5A1PagSpaRVk3Q2f8jo4lRP/hd3QsIXZ008wWUrxTZFNMqvXLDLB4xSteAUxikKGQNdRVg82jHrWy74d19TMTq7rOMnc8xTvvV8W6WXXzFBtryG7WgROtra1EGt3SndeqUJSGv6x7X8VHzzEcV6On57bqJM66T4rx9bsMyHJO61yky9J2fEa5jVi9flaV5tac2u8MWlLkb6kfGVjm+2N23rnnnrt2jW5l1Sbb02hsPX/YuGFnZmy2XHe7U6W3M35HxxJin2H8rJjrKmYYpUYnNyuEaVXVuOdqqfGkGsVs15ppumE0EGVAD0yrrOfIMLJKMnUd26wc6Rrem6wxqwLtvKAcXZdKEtX4Zh9kXo1t6WqsUo5SlFV6ckPP+uwyUKflkstrHKtzmQa6yt6zqvQk01cpJN156eqyb9Vwl4zpfXLnoXrPrITkHPtZn7NBULr8bNdAM418MLmovSYTiHKc+f9F0Rm/o2MJsaPuvMqKqcO4iuXWxTWU8ayzzlrXjrqZARNWw4VpVx1XXVfm1NlaOn7qxqljVua33xlc4t8tHXnW9tUtd1b2qdazq/er7aVe7X0zwAc27vKSCTe1/Tx3EdeTcMw5npaNYta1re8dSyYxeU3r2qxmnPUS63uaQUvex2uVUA3hhSko54gjjlh3jWyu6w4mN7PMbwhvSnpVSunuvI6OjoWwo4zfWqlylx1DdLXUm+AAU8COFv+qV8H64gv/43/8D2CyA1gM4Z3vfCcwMVrV8bN+ezJClQ5E2gxS/60saLsZZJJtVcbJABh1co9rBa7tiqynN6tibz02z96QurZMlraFKiWk7WNeReG0+6R3pVVIJEOCk6G9T53r6v2p52gHqPq1EqNSpZ8p7dSgqPPPPx+YKhQbpOOz+spXvrJ2rvUQrSNpannLhiNaHp3N0Bm/o2MJsc+E7CZy91dX0loF19XcAhwmQciUpkMCvPvd7wbg9NNPByZruH59faqtFEzZQsb0vq0w07TuKkHknnp1TBkaLNP4d2VBx5ZFKjy3SizCe7vXX0oyLX1xllW8lTBkOyndtHTw9GBkcZAqEWXyUurnqb+35sM2Mpy4SkMpWdkHi2rUkGMZ1+98H9Oarz+//t93zDJwudsSTB4qw861TV155ZXr7r+n6Izf0bGE2HbGn+VzTIuzUU5G6pkQ0kp3NLpM/f1Vr3oVAE972tPWzr344oupUC/0+GMe8xhgfeFDSyWpi9n3tFa3IsdyN5nWzrGp384r2pHXZHJOS/pwjMnAGTVXn4nXqz/7t1JC7ZPzoiU7d7FxrFXHT9tHsngt9JHXe022URl5VmKPaBUHydr+KWFUG0JKEintKF3J3DBJovrr3dPRNqrdSn3f+XCPP5PTfJ+q5LU7u+x0xu/oWEL0H35HxxJiRyrwtKCopIjpp6LUW97yFmC9WF23wYbJbZLiEkxGF5Mf/M7giZe85CXAehFqVgVbxdGW6J8Gu3QvKTLXPqVBLYNCqnEvA50y77/2JdUBDWiZPFOr0cyqfpsBTzCJn+mGzCo3dR7tw6x6AlVsT+Nd5se3Nlb12aQqkeG/LVdsq7Z/Hk/DYrp6PbeK34aJG2JuqK6Vn6tBVrXCyrtZxahVm7In6XR0dCyEHXHntWqEZQVYA3esYdZyZxgkISNaZUcXYE2Bzd1wZF6Nea0Ek6x2axsydasCTGKWe6mOJcNM0xhXDYJpfJtVmy2vq5jHyLPG3NrVZ9Y1eW1l6mRM+93aMcZ7zTKqZoBPvUZGT8NgGlTz+orc9rveM+sVZlh0ratvYtnLXvYyYNpiW6N03QnId9k9GzTutao97Qk643d0LCEWZvxhGHYBFwBfHsfxYcMwHA68HrgJcCHw1HEcr5rXBqysuMkQsJE1ddVZidTtiCty9ZMxdQXWEF7Dd7N+nkEt6cqp/88EiUyiaQWDCJkhXVGwMUkmXXUtxk/XX96n6q6z7AG5Y0/LRpGSSkt68J7qqLMKc8zbfcfnkHvGzet/Hm+xoPPsGH03WslSmdCTqcN1a/AMs1aC9FzZve7ipNvTd8138bLLLgOmdx2moB5r8HvvlIhbYdZbwVYY/wXAJeXvlwB/Mo7jbYFvA8/ao550dHRsGxZi/GEYDgUeCvwv4P8dVpbaBwJPWj3lNOD3gVdu1tauXbuaOnGuoCYpZNhsrbKbVnBXS8NwrZkPG3dF0WZg+96/BmskAwhZoxXyKmQcWSmvqf/PAIxZO8q2+uTfrdr+uSeB0LaQzNk6lgE2LQu0EpZBVvYtWbY1xllWctgYSJN2hmTz+v+sxJt19lvJWM6XkqI2pGpXyp2YsrqxKbhV8jrllFOASQowuMe/LboB07zrmUrPT85J/n9RLMr4LwN+B/ANugnwnXEcfSOvBG7eunAYhucMw3DBMAwXbLl3HR0d1wo2ZfxhGB4GfGMcxwuHYXjAVm8wjuOpwKmrbc00f7vSqQep27iCuqpVfUjINJ5zv/vdD4Azzzxz7RxXfldx2/E+SgCtfdhy77zU8VsFIrNQowzTKi3l9bKd57a8Bp6TPnk/W2Wi1D/V7W0vpSmYWNxrHEeW+ILJ8yJTtmrKw3pbi372ZEwZrYbsKvEk0htSJZbc1zC9IFlOrfbFY63Cq8L5yR2C7YvzVNNy1fEtquleeVlspvYhd3VW0vVdr1LIvIIns7CIqH8f4BHDMDwEuD7w08DLgYOGYdhvlfUPBb48p42Ojo59CJuK+uM4vnAcx0PHcbwV8ETgn8ZxfDJwLvDLq6c9HXjbtdbLjo6OvYo9CeD578Cp97flAAAgAElEQVTrh2H4Q+Ai4NV70pEMYlGMNHvOuu6PeMQj1q7xXM8xBFI3ieI7TCKe22Df9ra3XXdfxb1qPDTTKnPGs5JrDXkVafhruZzsU7qTbL8lyqZbJ7eialU1si+O0blMV2PtbzWY1eNV7ZiVpaiIbt+qepAuymyjBsvMqouYOfVVtZi1RXfepxUG7f3sb8tom/OuSJ/qTq25pzpgYJn38znUAJ5UJe5+97sD8OEPf3jdmFvbZG8FW/rhj+P4HuA9q///PHDMlu/Y0dGx49j2kN1hGDYEb8DERhpHNPLp6qjGEqGBQxa3Eo+reTXgeK5s9LnPfQ6YjHz2qV6T20lnnrx9rqtvGgKTBWv7WZsgc/kzsAc2MrFSSCsk2GMaMO2D867hqOVCk/WyjqGuuzp+n41s1QrGyT61avglknG91jGnS7Me05CZz6zlNszKQSkFVndZBn5l6LFzoFEa4FOf+hQwzZ3tKwnUoK6UIh2H17aMuD1Jp6OjYyFsO+Nfc801zYAD2eiwww4DphXPlc2Ehqory67WHlcf0n3S2l5avcq/tSW0glnSNZf6o32pfUq9OSvLtIJlMsEj3Xz1mpQK0p1XYTu6kRyH85ahtjDZOGw/k3IqU/rMZCV33/nMZz6zbuytdNcMrHGM9X6ZrpyhtN63Bi+l3p8VkTOgCjayaCsRSTgm3WqZZixa6ctKQvbbRByDy+rYfFaOTTtA1iGs/d4KOuN3dCwhdoTxW6GqroKG6roaypxa7Ku04Krn6qh+pTW/nutK6grqd4ZGqqfWoI3UC2U422glxqSemJb5Vn271F3TSlsZ0z4oQcxLET7qqKOAqeabLOW1WbQCZifYZLVamGrt+WwcW+77VuHY8rusvlvPTet+Bgq1pKj8Lnc4ahUfsV3fp1ZxEI9l0lVW6q3vnhJWXtPyOPh/n6tFO+xvXlvnZSvojN/RsYTYdsavSTo1VFEdRl/neeedB0wroJbpqs8pFXjt5ZdfDkzMX8Ma03/sd8mCrbRQkeGZ9qVlSc+w3gwZrn3yc1aZpVbIrit/6r+t5A3vqe0jwz/rc8iQ16xHX5ky7QA+O5NQsuQXTM8mE3gyEQc2xixkqnAmyNR2MkHJ+2VabT0n7TKtJKcsDpLvcnotKmRx7Uq2VfdyUHrSU2WhGL1Per3qLlG7E7LbGb+jYwmx7Yz/4x//eG21rD7iO97xjsC0d9gHP/hBYKMlve4Np/7jjjl+J4tUy23qh8m2rZ1uMlIvfeWtFEnPVYLIKLPKaOmvz76m9br2N89pFWiQJRyHn441k5BgmrNksNwXr35n//RtZzJQK4kpoxSVNGwDZqctpwemShTaQDYrB1b75H1acQfQjmz0WCYS5Z6GMElYvuNGljpmJYB6ne+ydfQt5eX39Z1o2VI2Q2f8jo4lRP/hd3QsIbZd1N9vv/3WxKUq6h9xxBHAZHCy5pjbYhl4U10rGj7ufe97A1PN/Fbt9FnhsFl/vZXwkSKrRr5WAEluDZ0hoq1tstOYlJWAay1+RXsNdmmYsiorwJFHHrlunkQaKVs19zzmmB1rNe6la1HxNF2mrY1CfTZZM78axVKtyUCn/Lv2N/9Ocb6OI6vzzKrt12o/XXXp+oXpvTRMXHXK4wY+wWS8Ux0zd//hD384MKkN9XdQk6AWRWf8jo4lxLYy/jAMDMOwZjiq7KRRJ1df3Xger8Y9jVe6kZQW0vAFG8NG0zCXzGB/YWIn+51hmq0Qz6wA09q1JvuQ57bqunt9Gj0NpjnppJPWzrVfJnjI9EoLWbkINm7DnW6wysgZFms/lcDe+ta3buj/LINpa1+DnJeUQlpbd6eb0DmwXRm6tXlpTcmuY66M7/Xpgs3EsCpt5rz7vrqLU90m2zp8acy2D0rJrfd0K+iM39GxhNiRtFzZ3YQcmFbb973vfcC0cuYWzFVfvfOd7wxM7jxXSXffaTFluqnUszJVsrbnNRmOmwkgFa0Q13pf2BiQki4/WaS1U0xKNbKWLqParjqgQSGOsZU4lLYIz0m3YT03GV8pTYZTX633cky5G06rJmHOb7pTqz5dXZOwcQ61GVX7UiZWZfXmVpi185IJT/5dg3LSVqFLznlpbfOdyV0GPmkDaCVlbQWd8Ts6lhA7sneeq9bNbnaztWPqoe4omlZYmb7WvdcToJ4l2ykJ1FXX/xvqqH6bO9NWRstkjUyj9dx5BSdEawecDNyZFdxS22qxdB17bV+mV6qx3zKNUlVl5LRb2O/UmWHSP51DmcxPA1Pq/PgcM1S3pU+nLp/wuVeWz11x7Yv2INm9WtKVGJRQTCs2JLxKB7ar1Jp2IOdCSRWm56C3w/dfibfOv5KDEnAmTeVz2V10xu/oWEJsu1V/v/32W1tZ66rrqieLuMp63BWvhiq62v7pn/4pMDGk7FGZIPXy3D2lVRM+9S1X+0yNrKtvpgo7jlZCiau7LJ57zzmOKuWk3ilbac1vpSLLrs5thvC2pJBMSW0lnaRl20/7IGvVhBLnzjFlYYzWLrZ+5rznnoaw0YIuq8rQsu297nWvtWuUlkzRvvTSS4F2XX+LZvheOmbvm7sI1f76nZKEUq6758JkH8kYD1PNlVxq+7NCjeehM35HxxKi//A7OpYQ2y7qH3DAAWsibhVRPvShDwGT+GklHkU/jXF3vetd16656KKL1rWj+JUGHtgouqZLqJWPn8E9mdnXEvUz/ztdRNVQNKtem+J2Vgau7aZRzHOqWqD7TuOeBk4/Wy60NChm2GrNBEsXluPRsNXaNiwNo+nWawWmZIXiWdWBYAqOuc1tbgNMWW22q0HYvHeYXMjWEbA9xfoaku176HNUfVKtagVqZb0822uFZDtmDd+Grl9wwcrWk+4rUTP6zFLdCjrjd3QsIXaU8WvlF1c0Vzyr6gpXSXfAgYnxc/PBZF2Y2MjVNZknmb9+l0ayDLRpJdFkyG41Soo04iVaFWFl+jTy2e9WEpOSkOd43xxP7bdzqHQgw1XjZ9axs28yvv2vtfhlpwwMEjVYJw1cKeVkgg9MTG6CklKPlYbt/xlnnLF2zSc+8QlgMqzJ9C0XckoduUdEBj4BG973NPhWiUXpKev/5R4PNd9/d1x7nfE7OpYQO5Kk09KDrMzqKq7rJrc1VmeDyS3iZ7qTWrvWpP6fIZ2t1TP10KzgWscxq1Z+ugRhI2tnsIaoNoBkvzvd6U7A5L4yfRkmKUnWM5AkdcvK+LPSiXNr6nq9/VP/9X4yWw3KSTdqpj63dvXJqkAZPvywhz1s7Zq6t2K933vf+15gYvc6TxkGq/sxdfE6lkzqyoSr+pzTFpH9b+2raJ+8jym9SiG1Fn+vq9/R0bEQtp3x999//zX9pFq4ZYkMg0221RYA8PSnPx2YVmZ12SuuuAKYbACwMRHDVVydMivb1j6o42WxiNYefdlvWdZglnqusA8ZkNLaUTYlEvVnWUrJCaagD49pgU6mryyr5JBsbh9bnhLHqvfAQJjW3nMZ2ppz2aoSnJ4L50DL9oMf/OAN85N2DN85U2ArSzpmn2+GNNd3IneByjTjTJqC6f1UYrGP3q9V2TntSPbJOaj9aNXa3wyd8Ts6lhDbyvjXu971OPDAA9dWSfe8g0mHdGVOpnRVPP7449euOfvss4EpDVcWUZK4xz3usXauupG6nSu/q72rcittNv/OxJ56jWPLEl8tq3uWeEp9MS259XpXfNuVbWvasraP3KEnUzq1Zrf6IKsqkVWftu06p475i1/8IjD5zKuOnOG3WUm4SkT213Bb3w3HfPLJJ2+Ynywd5rWG5Z5//vnr5gYmD5Ieh9yjvmVjyT563Pe4ej/sk++c48jKz/Weekay7Fhr1+isarwIOuN3dCwhtpXxr7nmGr73ve+t+VrrSp1MaeSSzKYua8okbPTju1LLQOqaMDtNdtZ+dbXdtL7P29M9pYG0WrfSTjPlMv27lTFzJx2ZXrtGTfFUopKFUp++wx3uAEx768H6uvb1XHcpqoynFKDuqjQgc2ZR0jqmjH5MW04913JUPl/ZsEp0Igt9ZnSkfalps9knGdn+z7P7pD0moztrH2zX52GSmgU1K7yPc5vRo5X5W3s2bIbO+B0dS4iFfvjDMBw0DMPfD8Nw6TAMlwzDcNwwDDcehuHdwzBcvvp5o81b6ujo2BewqKj/cuDMcRx/eRiGnwBuAPwucM44jv97GIZTgFOA/z6vkWEYuP71r7/BhVZh4MKXvvSldX9rgPn4xz++dq65ybqy0h2mKAobty/OgJSsEQ8bQ2pTPG1toZUhnWmgqsarVmgxbNwQs4pwmTikOqOI36r1Zl9yi2jHqhgPU5JLVovRMNXaQyBDZ3MPhJY7LKvhZhATTEEqisieq8GxtW2Yz9F2MkDL+6o+wPQcrNyUm6GqTtX2FMFVB9J9WNVY58V2HJf3rXXxM/Q3t3f3s6qbnrNX3XnDMPwMcDzwaoBxHK8ax/E7wCOB01ZPOw141MJ37ejo2FEswviHA98E/moYhqOAC4EXAAeP46jv6GvAwZs1NAwDu3btaq50aXzR8CF7ZIXSdYOI8E9XxZrOmq4zXX65Ure2pM6EjFxhaxJHBp3Ybst4NctYmIkaLRejbD1PesodebLaq8a/yk7eO7d/zvmr/VPayP0FlLjqM5uVeJNsDusDvGCjEau1r0FW2fWarCxUDb8amzUcZ59q4lDuKJTuvdYzbaUPw/RcarKa52Zij5JcVhjK8S+KRXT8/YC7A68cx/FuwPdYEevXMK48gaZJcRiG5wzDcMEwDBfszj7eHR0dex+LMP6VwJXjOJ6/+vffs/LD//owDIeM4/jVYRgOAb7Rungcx1OBUwH233//8Vvf+lZTd8p6doaXeq7Mf7/73W/tGosUyAyuzC19PRNrlDYyLbflbvOcLE7RWsllQfti32SPGp7psaz3l+6luqLLKCbEJKoNIdk72S+38q798zsDgnI3m/r/TOTxuGOvtpZMcslw0zo/PsesuJxST+2/1yj1ZSEU7UJ+wmRPyhTYdOdWeO/cSae1n4Lt2jclpJQ66xhzz0LP8XdR34lrJS13HMevAV8ahuH2q4dOAD4NvB14+uqxpwNv2/LdOzo6dgSLLhX/D/C6VYv+54FfZWXReOMwDM8Cvgg8frNGdu3axQ1veMMNqxpMq6uroXqXq5mrY9VtDDX1XFn2kksu2dC+yBJcWXW3Mn7WlM9Kv/NKb2UKb0snzGMZqpsFRuq5WZ7LOW2lzWZIcKZxVvZIb0TaN1plujLhJm0ftf30lMjMrRTeDI5Jj4Z6b52f3IU3++p9alBUSkSZItzaL9BzlBy9X0pTtZ1Mzc6djmEKUdebku9Gvlf1nK1goR/+OI4XA/dsfHXClu/Y0dGx49iRvfNE3Z10lu4tw8viNQnFlV4pwSIdMukHP/jBtXPT554WY+9bLbiuqq6yqW+5ktdrZqUVt8pcZa39WYU5KpKBk71bpauSWTKBpbJjlgjLlNgK034zxHjWTsSwsYyZY1Y6qBLdLFuKUo5FNWr4bc5h7iFgnILFXWF6f9KL0ypGmvaXlEZSSqn3FrNq8cMkAeU4MoW6XrM7jN9Ddjs6lhD9h9/RsYTYVlF/HEeuvvrqNbGuitsp6ivyKRKa8WXoKEyhpmbuaeyxDl2tyGsVGttT3DIzqlVNR7E0XTWKYX62gn4SLYNmVgXKun/p+sp7wewaf7W9NAyl+lENUfZJ8Vd1oLXvQAYYKbpqzGsFs2TFGtGat6yAo/hs/w24qdthZd1F1QL75niqi9H3cVZ4bzXeZiWiFL1bRuJ0B9onz2ltRHrIIYesu8Y+5RzXa7aCzvgdHUuIHdkmW6avlV8ykcQVLV1pNSTTlVopwZx0V8u6W0qGNX72s58FptWyZSybxa5Z87yFdM2lobAeE8nwLddNhienlFDZNXPd/S43uaxzkwa1WW492Bh4NCuZqTJmSj65c0+tJ2AyS26o6dh9drWajgbjDAhTgrTP1bBsIFmyeEpT9fqscZDVmOpzmOWmzSCgem/vY6BWJjfV6lWt0O7N0Bm/o2MJsSNVdg2/rStUVmTVVaRUYBpl1WdcQbNOvCtsDQnOGni6TZQWMo2zXpNJId5HfbhVNUaka6sVoDIriSMZqN4rA3hSQqr3Tl1fyIL1vlkPbhZL1bGkvp7upTqnWdMvpYXq+tKeo4SYjK/Ed9ZZZ61d4445yby6I22/Ve04qyXNq5Gf74TnOn81ccv/p43Fd69WPVLK0QYxa2/BKm2mu3ARdMbv6FhC7EjNPRm/FnWwCqqFNmR8V/t73nMlcNBAHpiKcyQzKxXUIIpkrkyX9dxWoEomy7japrW/tp8hnC2rftaSl739OxmujkPmzXqDraIdaTFXUmkFMxngIuPMurbOQ3o37FNKWbAxfTglocrEtu/zVC/PnYjrbrF//dd/DUy768imGa5cn0O2l5JStYFkNeas6uv81TlVH89dhOy3UgpMoboWl9FTpSThHFgtuPZlK+iM39GxhNh2P/6PfvSjNcZ3PzCABz3oQQB87GMfAyYfvUzvql/9r0996lMBOPfcc4GNySIt9suqujKYzFAZP/XoZNWsfAobq+Dm/m8tq/usSr+tnVxklpQosnhHvT53APIc+1rDZNU71TVlqdbutrP2HfR4a36y4EnW068ShvMt+4malJPtu9OSqbaPfOQj113z+c9/ft34AE44YSXl5NJLLwUmS7pMXSVT59+59Nk5L963ZcvJc7NKMUzzrDTgc77NbW4DbJQwoF1EZjN0xu/oWELsCOPLUrUkkyuaOo6rfRaMPPzww9eu0ZqbxRxaRR2y6KXItNlHP/rRa98pfahnKalk32oJMXW7u9zlLsCUKORKXu0Bqe+mrSITZmBj6qirvaxdx5dW5IwpMJqx7hybDGy7La9HFtOwT5n6Wq3OjjmLYor6zDzX+bdPsnVGPtZ2jdP427/9W2BiVduvz8Hn7He+gzJyHXNa0DMBqlrzRSZ1OT+tNGmlFyVe21Xy9T2qkmMWXFkEnfE7OpYQ/Yff0bGE2JEkHY011binKKYx5jWveQ0wufd0eZiQU7+7+93vDkyGEMVdXSswiVeKjYb1un2UYuQZZ5yxdo3ioOJbhoNm4gdsrMCbIZ5VLE3XUrrBWmG+ufVXhse2qviarKRq9alPfWrduFrbcKu+pGGwirpZNSf3NWgZonKLqQz+qaJy5r47h27Kad+qQTZVB8+dJ+rnhqaZC98SoT2WVZoyCKj2z/ZUQwxKq4ZAfxuGLhvAZiKSbu5q5M7KwougM35HxxJi2wN4vvvd766tUDW5wjrnD3jAAwA47bSVvTo+/elPA3D726/U+myFr3pMqUDXX5UO/L8pu7K1RhSlj1ZIqswiG2U6ZWW0Y489FpgMRLqXZJHqGkrjThr7sj4gTIybmyqmu7Kek/XilXJaCR8ySRrsZLZqkM3goaxknGGttQ8ZvGQbrWClNE5mVd9W/bwMf84U4WocS0aeVYOvji2Tx7LGX8sFa598J3RhV4lRg7Xzc+9733vdtboaK+PP28R1Fjrjd3QsIXYkLdfVypppALe+9a2BafV2C2cDLmTKGuxwt7vdDZj08he84AXAlJ5bd0vJvdpSF3dVroyfhTdchdW7tCFUF6Mhxa7c3i+3b4aNBR+SabKeW/1/7o6TYb71HCUWpQGlG+egMoZ6p64+Q6mzvl4d06xUXhl5XphvKyxWZAGOfFatWv8pPSXTt4priHzO9l/prY457TCZtlxdsZli67ud38MUuON8HH/88cD0LmufqZJjq5r0ZuiM39GxhNiRtFxZsFpwtb4anPHbv/3bADz72c8GppXuqKOOWrvG64877jgA/uZv/gaAk046CVjvNdBKLUvI7BmYUm0IucdfspSfhnrWPmVAR2unHiETJzOLeSmkafGu+uis0OIMfKmMrBQj4yvB2IfKLs5hBk6JTIVtjTGLd1SrfOrNmXTUSkdN6WPWufU+BillTf6sqly/y+q9uetyZXEDpJRe3ZXXc2sAlfc68cQTgcmmctFFFwE0fztdx+/o6FgIO+LHFzXUVda82c1uBkx6kJZ6v6/ltNT3XQVdHV1ZK1KnT/2wtY9ZJsL4nQxhoYjKJrKgumzqmPNCdrNgRuqy9Zz0CGQ4br1X7hpke46j6tfaTaw7nxb01h4CqaenNFWZO3XvLKvV2rvQdnMXm1YN+1l2E++Tkh9MjJu2lZTw6v9zN+Es8llZXGnWdzt3OH7nO9+5dq7tPepRK7vOq9tnsdmq48+SuOahM35HxxJi26364ziurdRG0cHkZ88Cis95znMA+N3f/V1g/Q4oj3nMY9ade8tb3hKA973vfcD6nXVbhSpgY6npikzzVSfzfqlDw0Z7QBZsaN1HRpNds+RTZdlk/JQKWhbejJLLxKda4PJtb3vburE6jpbumlGD6YtPyaWONaP80q9fr1eCsA8eTz07r4eNNgSlwpYXwXbsi/PSspvIzNmONpJa9k3JSvuVffE9qvP//Oc/f107xmB4jh6xOuZWvMRm6Izf0bGE6D/8jo4lxLaL+rt27dpQ0wwmMdFwRsUiN8I0SEZDCcBHP/pRYArDVXS6xz3uAUwBEzAlqnhO1pxXHK2icoZaZtBMhpTWdtNwJ6rrRXHRAJGsDKshp7oYM+Q1DZwttSCNbLlxZSvfXJHSDSlzy+3a36zAo7qTdQ5h47wYrjovnz1VCcX1VhVc1QI/nZ80graMuIrkaTysfc5NXZ0750kjXK0Gba2JvLd1AGoYtOqpiTy6pA2g8p2oY27VNtwMnfE7OpYQ287411xzTdP9oPtD9nM7bFfsZz7zmQC86EUvWrtGF5+GEA2EGkKqdGAarqurhkVXSe9TUxyz2kmGfbZSYZPRPLdVgUfmVRoxWUZJpRXQ4zUaupJxqpsna8jLbOlua+104zm226rNn+7BTNLJJKoK51vGb6XppsSTVY7TGFrnx7H7PLPiT33OPlffDT/tY3U7p7Tq/XS7+d5aAbq277wYHKUB8Fd+5VfWznXeDWdPpvf7VjJTN+51dHTMxY4k6YjK+K5k6nGf/OQnATjssMOAScd/+MMfvnaNrqdMc5QZ6qp73nnnAVNBAxNtZNlWkku6o2SNeTvg5B5/mRRUV+rU6TcLMoIp0Ojggw8GNhYJqX0xkCn3B8yQ1PocDBU9++yz152b+jxMEpYMrKSSO8pWJspU10xxrklS6U7N9lICq+35rDJlWNS692nrkOHzeQPc9KY3XdeukqO2IyWkej/nWbuJqeZKqo973OPWztVulXtPpITaShXeCjrjd3QsIRZi/GEYfgv4NWAEPgH8KnAI8HrgJsCFwFPHcdwYnRKoK1VrTzL1HpnG8lqyStWHXEEN2Pnwhz8MwH3ve19gvR6nxPCe97wHgPvc5z7AVDjDarg1wCbZI63IGXYKG3XhDLipCTHCY1ZSPf3009e12wrZlfGdL+0aNYVUaSntDDKykkAtXWVdeIt1KDXJUrVoh7qq/ZOVHGum1cLE7Lm7r0xan5nMm/PeSicWuc+A5+a+ivWZOf4sM+b9TU2GSWoysMYUcJne+1VLvfP8kY98ZN28/P7v/z4w6fG1PY85x1lSbHdYvmJTxh+G4ebAfwPuOY7jnYFdwBOBlwB/Mo7jbYFvA8/ao550dHRsGxbV8fcDfnIYhquBGwBfBR4IPGn1+9OA3wdeuVlDGVIpMl1WK6bWTX2hHgf4zd/8TQAuu+wyYNJp1a/uf//7r52r1dUSXueccw4wJU6kdb/2NRM/bD8TM+q5smqGU7Z0M1nbvmXd9bq6Z/pnFuao/beohrq4LGiKp9fItjAxjdeaDm0RyOpn91nJgrmTq32sfZJNtUk4l6aq1hTntAPkXoK5c3C9Ji3+GSpck2iyeIef+vWPOeaYtXP1FL32ta8FprnzXXBc1RPgO+yxxz/+8cAktSmxwjT/vhMZBp3Mv7vYlPHHcfwy8FLgX1j5wf87K6L9d8Zx1Op0JXDz1vXDMDxnGIYLhmG4YK/0uKOjY4+xiKh/I+CRwOHAzYADgZMXvcE4jqeO43jPcRzvudu97Ojo2KtYRNQ/EbhiHMdvAgzD8GbgPsBBwzDst8r6hwJfXuSGrZBP2Oim0CBi6K4uuwoNKC984QsB+L3f+z1gEuvrZoueq9vFUOB3vetdAJx88spaVrdczj4pzmUQjYYdmAI4ZoX1VteTUARXlH3EIx4BwDve8Q5gvTvPOVN8ti8to6H9V8zVYOdxQ0ZrlVrPVVXJgJFqCMxKsyK3pq4BPIrCGiHdE+Hiiy9ed5861lnG1Aylbl0jsiZBFZkV+7NCkc9StRDgH//xH9e1bzuK5I79C1/4wto1vo/Orbn2Gq6r+qrxVEPprBDwa924x4qIf+wwDDcYVu52AvBp4Fzgl1fPeTrwtj3qSUdHx7ZhU8Yfx/H8YRj+Hvgo8CPgIuBU4B3A64dh+MPVY69e5IbDMDQNXWnI8VOGdqWulXNdQY888khgco8Y1quLDqbEnQzrlT3catvzYFrFcxti2SpDSWFavTN0NDf2rOe0dl+BqaZ6DT32eg1DupVyw03YuBuO0JVpsEjLOCYL5lbY1Q2mpOL13keDaQYvARxxxBHAxIxWn1HCayXpZGKSbOv96pgzSCmTr+xLKx8/qxw75je96U0bzlUSzYAqpaha79F7KpHmfgT1nfZYVjCet6uP2IoUsJBVfxzH/wn8zzj8eeCYxukdHR37OHYkZHfeypS7sMha6pqG8sLGgA63pn7uc58LwJ/92Z+tnasLy+AeXTWu2LKqQUAAT37yk4HJTaiUkFVrawpmSiwZ0FNDapUcZItMo5WVrCwEU5hnppDKFK10zdyu2sCe5z3veevGAZObymtl/kyNrfd2/m3XcfnM6o5GMv4rXvEKYCMz13cjQ3ZlvQzKqXOaunemqqUjKUIAAA55SURBVKYrsF7jnPq3OngNitLGoRvPv2V4bUTVhuDW60pw2qu8xiAdmOwKzu0sabA+51nnzEMP2e3oWELsaJJOC2mNdWVWD6rfZzVdV9kHPvCBwPqV1DBYk08Mj1VXawVeKDFoZXflNvRSr0HVe7M2e/athqTKXNooMkDFYgw14cMwZasQp7ei1jFUEvE+6sQGh2jlr4xpAM3RRx+9ro1M5YWJCbWLGBYtaymxKIkBnH/++cBGFs99CmFj2HNKg62QYL0dPofcMce/6/FMEPK9UhKr9gA9OGlzUrf3+BOe8IS1a3xvtGc4PzJ+DYOeVcBlb6MzfkfHEmKfY/zU/7OSba1eKuOoV6lvuUI/9rGP3dDO29/+dmBKOjEUWP2uMo7fuVIbemmiUJZ5gkliyF1qlApq+Kr6cuveMNkUahVWWcIdbrxPpsLWMctujkcJQ6u+Ybkwsat7GeqlaNXIt30Te7TUZ3xDtSHoCfHc3I2nMl6m42ZYtX3R7lGP1d1kW/2vkotjlL21+/hZx2wfnDNTke2j75y+epjeNdt33n1Xqh9/lm1ib1nz18ax5Ss6Ojqu89hnGH/WipZJF1XfVRdztx2tsK6+tdjCU57yFGBavfXby+JapC2DBROLesyV+aUvfSkwMX9dcZON7G+rEKLtaWcQGSVX0zaVEjJ9uVXAVMZ1v8G0KstWzhtMjKvUlNF/dRxZ5DHtM85LTVE14cV5T1tIfb5ZGiyZ2M9q1zCGINNvfS76x41/gMlz4bVKYKK+m8neSiwWgTExTFsGTB6jyy+/HJjsPLbRStxKKWfWHO8uOuN3dCwh+g+/o2MJsc+I+rMwLw9ZsVCRX+OVolUNHDHYw6AcxWvDMW2jVqlVRFYUM0ddsfHNb37zhvso8ilOa7BTfKyGQK9LI5Ln6C6sffK7WVtd19x6c9x1s73//e9fN3aTTzTO1fZPPfVUAB75yEcCk1ha+6LRzjqGuQmnz6cmPpmU414IusHsf3WnOnfOt3USfR6tbb1UnzxXw6guNNWb6qKzArNz53PQRVrdxj4T1Rfr4Pte6ZqrY/Z9dGx+l+7J+v8M0c1KvXuKzvgdHUuIfZ7xs7JqaxtlWU/m8tyaKGEgiixywgknAFNNuVe/eiXHqKZTWhlFSULjj4EvacAD+Iu/+It151pVR4Yx8Abg+OOPX9eORh9ZK12DMDGjLKjBzj5W15aGQBlRI9aFF14ITAFCNYDE+ZUpdWXK1DUF9jd+4zcAePnLXw5MbkIlI5+D7AvT/GqQ1cinRFfDY7Pevd95XJddDZn2XhrS/My6eq1a/BoCnSeNnjVAS2npoQ99KDA9QwOfUrKo7dVkHJjvokuGXyRJZyvojN/RsYQY9tYKstDNhmGctb/XrH609CCRqaiea3BIZT8DUjxmOKZJO+KNb3zj2v/f8pa3ABPLydqG98rmVceUpTOE1jYMFYaJ9ZwTdXLHoU5e9UVdjOrT7jNgWGy1hfh/2fsNb3gDAO9+97uBiclq4o1zJ8sqESkVaC+AaQ6VrJRylEIcR61v5xwq3ZiAZEGUysRKOtpAfN5e69wqudTvHIeSUW6xXe9jO7J17uCjDQOm+dYeoMTiM9L1Wu0mzp3znIlci+j4LYlXOC8//OEPOfroo7ngggs2NQR0xu/oWEJsO+PPskrOW9GijZnHMvjh0EMPXTtH/VAmU0+/613vCkzMVktLqTvK/GeddRawscBFDcCRxZUCcjfeug+eQTgyr7qxQS72sdoqZA/ZSL3ac+v8aI02McnyVvY704srZEwlFSWMmtwim+auL1mAowZSeU2G43q/Gr4qe9s/dXolrqygW+fFY7njrXNeg6LsryzrMzTsVskGJq+HlZ19R/xb6aEyflbx3dtwHq666qrO+B0dHbOx7Yy/+kn9hNmlt+bVEc9zM6yxti9LGJorC6mr+Vkt0Op29k22OPPMM4FJB6+JQ2ln8H62X9lVRrO/squsIRPVME19/0omfmpFrs/T9uxTsrafrZ2A/C5LVFWJKJFls7IMFkyMmWxuX6tV3/6m98b2vLZKUblDsBKS0oL3qVKIdgbtL9pLvF/149uO823qt9JOTQUXmVY8KxFtd9EZv6OjYyH0H35HxxJiR9x5i+QWbyVEMUX9FK1gEqcU+TWkCQ101QVoKKdZboaZKu4qtn7gAx9Yu0ZXmYEjme1Wa8zbvkZIDY+63zSEVVXCMWb+ve1WVSJdV/Y76+dVcb7m/sPGzSarKJ7BMIrkuvWcLw1fMLm9vGfmzVfjoe3oZrMvfuoyrVWNhAbHDGX22mc9a9rq0efg3Gr4M9ioBgj5nc9eVaKeA4vlz28lx37euV3U7+joWAjXacafZRCcd27WeJPhZYRqTNQwpzQge2hg83hNjNH1I8udd955wJSfXY0/s/okM1sHoLrzZMpMVLGNyphZuTb/zgQQmFyVeR+vqYbAWfn3zqGsWKUc+ytL+3ca5eqY7He63bLN+v/jjjsOmEKklaqUiKprzfk1Acd+K2lUKUhmV9qwnUUCbXLeF3Vhb3ZuZ/yOjo6FsKMBPK0a6rlrjUj9Pa9vnTsvFHLW3mo1GEd7gOynrq+byb9btfJN6fWcrE8HU0qq0oH6r0EssklluGS9TNOtCUP2y7FqM3De7Gt1bemGlEVkYj9r+/ZB92Pq+tXNJrJiUEouVTqw/XSJ6n7zWdW9C3PHHyUYbQktySLTn9XbvbamCsv4m1WM2lNkO7MqDMP6PQWPOeaYzvgdHR1t7DOM/2u/9msAPOMZzwDgxS9+MQBnnHHGujZa9ckSW2H8edZTWVQWlGFS+vB7mNhIFtV7oIW6Fu2Q2bUzpDciw1pho0SU/a+MLMPnNSkJ1DHn7rg5T5WRbTcTb/ROZLgsbNwdx/vkbrMwMW1KFp4r+9aae/bTa1NicXzVCu+cJcP7fLZS/GJv/542S16DzvgdHR0LYkcYv2WFt3KtpatkJfex34o/f953syyqrR18N5MoTPWt56mrah8wBFjpoe6Dl/vL266MKZPVmAP90LlDrVbmujOtLJfhsLKe962+dBlZZlcKyeQamObK9jKcN1Nka3s+3+xLjUNIv719yN19qg3Ecy16oUU+q9ZWySjjAVLaaWFW2uxWsCe/vfrOdat+R0fHQtj20luzVjmZylJPFjGclYCzlbbrd5u1txXJwgIQlemMApNRTObQPqAFuV4nI8p6uWde9f3nPm8ys32qTOZ3tuvuO+kfrzq4UkjqvVn0pLbvHMrWnqOdo1rQbUdJRanEPtTn4TuhTp+ehdquyOIjmRTUen8yjmKR2vWzEm+2S4Ju2a+2gs74HR1LiP7D7+hYQuxIPn75e9Nr5m0RtTuJDGmMmRf2O6u9NOy0rtGQZtCJYnFtU1E1v1OUVdRt9cPvsg5dnZ90O6omZIJPFZmzSozt2ofMz6/3yXMUs2uAUNZXsA+OuaVKpEHO/qsC1DaznXxHWiJ5Gv628puYFWizFezpb7DW3OvuvI6OjpnYVsbff//9x4MOOqi5ws2qRzavrv7ewLzxb7UicAvzEohqMEzrGjEv8SYDYVrutkzGya2Y6zhbQT0VLaPSrJqH8zCLXeu1mxlg51WpvbaDbjarHbmd8NmfdNJJnH322XzrW9/qjN/R0bER263jfxP4HvCvm527j+Bnue70Fa5b/b0u9RWuO/295TiON93spG394QMMw3DBOI733PzMncd1qa9w3ervdamvcN3r72boon5HxxKi//A7OpYQO/HDP3UH7rm7uC71Fa5b/b0u9RWue/2di23X8Ts6OnYeXdTv6FhCbNsPfxiGk4dhuGwYhs8Ow3DKdt13UQzDcNgwDOcOw/DpYRg+NQzDC1aP33gYhncPw3D56ueNNmtruzAMw65hGC4ahuGM1b8PH4bh/NU5fsMwDBt3w9whDMNw0DAMfz8Mw6XDMFwyDMNx++rcDsPwW6vvwCeHYfi7YRiuvy/P7e5gW374wzDsAl4BPBi4E/ArwzDcaTvuvQX8CPjtcRzvBBwLPH+1j6cA54zjeDvgnNW/9xW8ALik/P0S4E/Gcbwt8G3gWc2rdgYvB84cx/EOwFGs9Hufm9thGG4O/DfgnuM43hnYBTyRfXtut45xHK/1f8BxwD+Wv18IvHA77r0HfX4b8EvAZcAhq8cOAS7b6b6t9uVQVn4sDwTOAAZWAkz2a835Dvf1Z4ArWLUpleP73NwCNwe+BNyYlXoVZwAP2lfndnf/bZeo72SKK1eP7ZMYhuFWwN2A84GDx3H86upXXwMOnnHZduNlwO8ABrLfBPjOOI4mPexLc3w48E3gr1ZVk/9vGIYD2QfndhzHLwMvBf4F+Crw78CF7Ltzu1voxr3AMAw/BZwO/OY4jus2khtXlvsdd4MMw/Aw4BvjOF64031ZEPsBdwdeOY7j3VgJ214n1u9Dc3sj4JGsLFY3Aw4ETt7RTl0L2K4f/peBw8rfh64e26cwDMP+rPzoXzeO45tXD399GIZDVr8/BPjGTvWv4D7AI4Zh+ALwelbE/ZcDBw3DYCrfvjTHVwJXjuN4/urff8/KQrAvzu2JwBXjOH5zHMergTezMt/76tzuFrbrh/8R4HarltGfYMVY8vZtuvdCGFbyLF8NXDKO4/8pX70dePrq/5/Oiu6/oxjH8YXjOB46juOtWJnLfxrH8cnAucAvr562T/QVYBzHrwFfGobh9quHTgA+zT44t6yI+McOw3CD1XfCvu6Tc7vb2EajyUOAzwCfA35vp40bjf7dlxVR8+PAxav/HsKK7nwOcDlwNnDjne5r9PsBwBmr/7818GHgs8CbgAN2un+ln3cFLlid37cCN9pX5xb4A+BS4JPAa4ED9uW53Z1/PXKvo2MJ0Y17HR1LiP7D7+hYQvQffkfHEqL/8Ds6lhD9h9/RsYToP/yOjiVE/+F3dCwh+g+/o2MJ8f8D7/vRJMJ8BeMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(X_data.shape, y_data.shape)\n",
    "plt.imshow(X_data[-1],cmap='Greys_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Flatten,Dense,Dropout\n",
    "from keras.models import Model, Sequential\n",
    "from keras.optimizers import Adam, RMSprop\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "from keras.applications.vgg16 import VGG16, preprocess_input\n",
    "\n",
    "vgg16_weight_path = 'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
    "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(100,100) + (3,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1205: calling reduce_prod_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:2888: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Model)                (None, 3, 3, 512)         14714688  \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 9218      \n",
      "=================================================================\n",
      "Total params: 14,723,906\n",
      "Trainable params: 9,218\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(base_model)\n",
    "model.add(Flatten())\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(2, activation='sigmoid'))\n",
    "\n",
    "model.layers[0].trainable = False\n",
    "\n",
    "model.compile(loss='binary_crossentropy',optimizer=RMSprop(lr=1e-4),metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 30\n",
    "es = EarlyStopping(monitor='val_acc',mode='max',patience=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# history = model.fit_generator()\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rotation_range=30, \n",
    "        width_shift_range=0.1,\n",
    "        height_shift_range=0.1, \n",
    "        horizontal_flip=True)\n",
    "\n",
    "X_data = X_data.reshape(253, 100, 100, 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total: (253, 100, 100, 3)\n",
      "Train: (175, 100, 100, 3) \n",
      "Test: (78, 100, 100, 3)\n",
      "y train: (175, 2) \n",
      "y train: (78, 2)\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "\n",
    "print(\"Total:\",X_data.shape)\n",
    "X_train = X_data[:175,:,:,:]\n",
    "train_datagen.fit(X_train)\n",
    "\n",
    "X_test = X_data[175:,:,:,:]\n",
    "print(\"Train:\",X_train.shape,\"\\nTest:\",X_test.shape)\n",
    "\n",
    "y_train = y_data[:175]\n",
    "y_test = y_data[175:]\n",
    "y_train = np_utils.to_categorical(y_train, 2)\n",
    "y_test = np_utils.to_categorical(y_test, 2)\n",
    "\n",
    "print(\"y train:\",y_train.shape,\"\\ny train:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/10\n",
      "6/5 [================================] - 71s - loss: 7.1878 - acc: 0.5087 - val_loss: 16.0302 - val_acc: 0.0000e+00\n",
      "Epoch 2/10\n",
      "6/5 [================================] - 67s - loss: 7.1669 - acc: 0.5529 - val_loss: 16.0302 - val_acc: 0.0000e+00\n",
      "Epoch 3/10\n",
      "6/5 [================================] - 67s - loss: 7.0857 - acc: 0.5580 - val_loss: 16.0302 - val_acc: 0.0000e+00\n",
      "Epoch 4/10\n",
      "6/5 [================================] - 67s - loss: 7.1669 - acc: 0.5529 - val_loss: 16.0302 - val_acc: 0.0000e+00\n",
      "Epoch 5/10\n",
      "6/5 [================================] - 68s - loss: 7.2480 - acc: 0.5479 - val_loss: 16.0302 - val_acc: 0.0000e+00\n",
      "Epoch 6/10\n",
      "6/5 [================================] - 67s - loss: 6.9235 - acc: 0.5681 - val_loss: 16.0302 - val_acc: 0.0000e+00\n",
      "Epoch 7/10\n",
      "6/5 [================================] - 71s - loss: 7.0857 - acc: 0.5580 - val_loss: 16.0302 - val_acc: 0.0000e+00\n",
      "Epoch 8/10\n",
      "6/5 [================================] - 70s - loss: 7.0046 - acc: 0.5630 - val_loss: 16.0302 - val_acc: 0.0000e+00\n",
      "Epoch 9/10\n",
      "6/5 [================================] - 70s - loss: 7.1669 - acc: 0.5529 - val_loss: 16.0302 - val_acc: 0.0000e+00\n",
      "Epoch 10/10\n",
      "6/5 [================================] - 88s - loss: 7.0857 - acc: 0.5580 - val_loss: 16.0302 - val_acc: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "history = model.fit_generator(\n",
    "    train_datagen.flow(X_train, y_train, batch_size=32),\n",
    "    steps_per_epoch=X_train.shape[0]/32,\n",
    "    epochs=10,\n",
    "    validation_data=(X_test, y_test),\n",
    "    callbacks=[ModelCheckpoint('VGG16-transferlearning.model', monitor='val_acc', save_best_only=True)]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('saved_models/tumour.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 100, 100, 3)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "model = load_model('saved_models/tumour.h5')\n",
    "testimage = np.array([cv2.resize(cv2.imread('images.jpeg'),(100,100))])\n",
    "testimage.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(testimage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  }
 ],
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