data training part .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2,os\n",
    "\n",
    "data_path='Vedic dataset'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories=os.listdir(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Vedic symbols': 0, 'Vedic worshipping  device': 1}\n",
      "['Vedic symbols', 'Vedic worshipping  device']\n",
      "[0, 1]\n"
     ]
    }
   ],
   "source": [
    "labels=[i for i in range(len(categories))]\n",
    "\n",
    "label_dict=dict(zip(categories,labels)) \n",
    "\n",
    "print(label_dict)\n",
    "print(categories)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_size=100\n",
    "data=[]\n",
    "target=[]\n",
    "\n",
    "\n",
    "for category in categories:\n",
    "    folder_path=os.path.join(data_path,category)\n",
    "    img_names=os.listdir(folder_path)\n",
    "        \n",
    "    for img_name in img_names:\n",
    "        img_path=os.path.join(folder_path,img_name)\n",
    "        img=cv2.imread(img_path)\n",
    "\n",
    "        try:\n",
    "            gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)           \n",
    "           \n",
    "            resized=cv2.resize(gray,(img_size,img_size))\n",
    "          \n",
    "            data.append(resized)\n",
    "            target.append(label_dict[category])\n",
    "           \n",
    "        except Exception as e:\n",
    "            print('Exception:',e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "data=np.array(data)/255.0\n",
    "data=np.reshape(data,(data.shape[0],img_size,img_size,1))\n",
    "target=np.array(target)\n",
    "\n",
    "from keras.utils import np_utils\n",
    "\n",
    "new_target=np_utils.to_categorical(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('data',data)\n",
    "np.save('target',new_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "data=np.load('data.npy')\n",
    "target=np.load('target.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3389, 2), (3389, 100, 100, 1))"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.shape, data.shape  #category in target#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "random.shuffle(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zUfQ5mIo+/Nb9jfB0+UzyZXSeBEtyeZYCWibb79FNl/6czg1xZ4oUyZKEw7nC/hNSdN9HJKuK1tsXZhy2HHNe6or9YgVmDwgoEZQ8s4tuNC9LLB6XICHb6lgh/GfnRADAjT+hdIDKtZb90q30f8cYorumA2ju2Fk0N6p/Sz05DrJgdk5ko59JrtJ5JkvPNYln9fT90y77sxm6f/kyAJZJ/IQPwdH/903z/8i50XBTWa/RX1N2/eJGlNemA8jI8PuzrgIAZJTUkbfHk9DcyawyL+VrHsrGhrgVSsGPL99CyUQjXiFpKd3ORhXndslaRM9OZCWbiLeXFUtLPoPrPtK3RUKQ+fIeCanrx9eRsTaB7uF0XzrOIInn5r1/C8C66RLO5+KH8cp3711l0ZDb3hJtArMHBAQEZhed/YWeYk6Rp6xvjT/ml9SDbcw/yXKcL+8jIYSf+M1TKECjs94+dMf820vayHj92uS0znO6kIk+tNtH0rmPOecfAICjal4y+/ze6j6OvIe6w4590tHLmbES+WinV2F4w5TOPmHTbA2x2apDaE2zjr8SgE0WAWxiiujmUr5LAnzSzr2W0N0vXEOSR9tU/jy4jFbDs3SMinVOdQ+hr7x37cLWvL+QtDxnWFp+C304DMxY8bYokXxkrhzLznGt+YCVLmbe8AQA4ODKN8y+3phdpLIx7IbpTZcPzB4QEFC6fnbRed7KkSW3XEJhYx7rUszgqjXkU61/kZhQi16YL9b/TMEJftLXvklzhi20Y1ceSnpYNUVaovYNYnqryxfrl91soe8ZQmMuOuc+AMD+ZSuK1m29B/Re/NZyPY1Pkc4baQ/H6zZ3IRElCZelhNGF9ZOdxDoNszmR54s0zmVrYXBhdFlbIkbCNIy+OycTZThRKE2va99NX99Uh7Xwj/kX3cOEyf2NHlMniklP+eeWOXITYtZWdBwZIpKQe/xc9DuV6qT7/uDZlCF+2/6fMvt+d97VANxQZrp2iSlYmKXvzNq87QYs8QyVib5TaQOzBwSUCMKPPSCgRFByYrwfhrgkS/nPjalWANZdBDj11lgme+3GfQAA1T3RWupRmY038WNUp7iJohRMcaS/MU/TcUQNWPFhctHUzePgi03FYrZkt519FtX9PKyS3Guy6rZCscjZpUUkZFciL7J7KIl/FU3WQOc3gYwzOPUGGdM+io4rXsa6hP2aNeVzkTWIBiSBNx923IAde/O6ctHPQZAbyuJ9yi568bH0f+VSupdj/t3J1xUNalLuweTmbQfqi3XbifuSjXoFDtVNsFF4zCwbhHTuyxTUtP4cMv7+4YDbANgw5nKndoLgjZxUwd2Mbi9IzEVg9oCAEkHJMbtf/TPDBhBh8XZdfEvEODLkLWaJVNRFFjHWyL9iqBFGl0SVXLExTzDuCTIWdjQQK638ILnrRs610sb/fvtXAIADyzYCAFo8Ju9yCtaJuybtGerE4LT+M3Q9o+61VWLTbXleb9T1FrdmXwoQoei8c+6PbF+Vt2wj+fCyJgmF/cCd1NyyZ4JlLtUt4hG/pL0bxoa6vJMWnuig43U20jnf+DK9H/5fupcSWusysEp4LreE91pw7oEY60yJIO8+xUhAvgFQGF7igQsZ5zNrJWlv1E/oe/iV8XRf/ufyOwAAe2U2FB1fDK8dOmmMr3EIzB4QUCIouaAaP8Xyn130BG1IUlUU98nouz9OO49SKTMtrEt77A246arF+4BoKKwfnilMmeTAlQJLEK3jrUtFKs9OPn4RAODK8TY8lta/ZfgVamZefrHZN+wNYhbjVjO182JCSYXMxMt4MbHOXdMoHFT08XQM2UigzSf/cgFPNj64ouMXdcwpk9I+ntgUWQy/l3bUXL9Osf7fOMtOKV/PNhgRJCRQRi7A+Y0YN2hMwFPcWml50YCbPmG+U+LOJLuF6PmrPkhJV1eefruZMiFFqcsdOoUvf3oN5r/cHYJqAgJKGSXL7KK7z+shvTUR80gWlhc9+KSbKRFjwn0UwJIdQy3pc9XFfb98Rjeplk4Ajvm/t/DYOJbgOaveT4EkaW5K86eLqAa8WyNOOq5KyKWkiIqVXE5fl7Q64wfnfAUAMOQuaiOTameLNzOOmyLaMoUkju9efCcAYFqGKrpKME9ZPxi94Rl6LzX0173L8k++QpJaOISXdfhCOYeqMmsneuwcqQxrmFzyYHis2e+sLd1C1y9BRilOsDFJQE5QjBEi/N9NHz8jo6MLfDuIw/hGMuxN9ea5Wec7l7yUCor876Q/4ytHrcSCwOwBAaWNkmN2H891RwtT1CScdFXPij3zJWK9kcdTp1OVYsv9iGFmTs9Y8ttLUQObEkn7i0Iz0bslt6+USy2ppswKSz5NT3pdYS3fjx92XeTayjx1VswHbv82GSsVUcWiL9tdb8VULrDQXIgyV7m3XNfz+4G/kXRU9xytV+5LZjMd3y2+IZ1qmw7gghRDxFMgeq2OvKeN/Go8JVEdWxWiursLGTvkDe7q8zz5ul1pzLDyNjB7f2IWemP2QhnXtndSpZNdJIm0N1bg5Seux+aNywOzBwSUMkqe2ed2kyVWLO51TsVG6cS6Uep+M6v9+LDP04616wEAOuf4hmvIWqrrqWRStp6cwGLFdVMr7SRExgj6KqLgW8LFWt5da3W5TbsTQx382ZcBAN8bQ9UwZQWiP7uJKkIYchSRc6QmfLuzRt/yL8eVY8h7kYgAIPEnquMukYAiSEmnm1SXE8EoXW753N219NoynSflY5JaRFfPet4QTxl22bVyFcdezKAoSrxA9oruEbSWulfseYYtILb303v7ssoLs/eH0c30Xj56Hff9kfNojWdfvhmtm1cGZg8IKGWEH3tAQImg5MJlBeKCm86hinO7pcWQzRMWY1Q992ESQ9TmvUcAAKqbikMX0cmVThdTgkqmicX60TSnZ7jNvfbrpPmhqbK/4ESliOvL1H/T0bHpNqtS1L9IKsnczukAgE8n6PUHZ1G9970ya2muc06pMyviu7CBNEh0A2TEwCfbpA2WjD36lVMAALl/jDBzFBdYSW+OyrQmjNgNY5WoUl5UZRNtGPogjdmwN30+naOd6jlFQUxyMBG76bVsgx14x9fJkCnG2fIZtJZP3EFJORv2s2ttnUxq2eh/033ONHNATi/GVfof246YPHx/X0GpYnXCHbYdlhEQELALoOQNdIK/dZBJalSqzWyr8dIJq/gJel/rXgCARz9CbX912+ZejyvuObnPiWpbO6xnCnUUlJpkfohtISPMbz8jWy21txPafw3LyZOfj7/uXXStPcPoWLOPucbM6WKJpy5JyTFNea6ew/vLHebtkmvylnDiwhMAAO13jwEQbT0tnruyTSyZmJp3tF3cbQCQq+B1s5sx3SFSDSJzXclnzXv5Hkp3HbG38mHLm2i1d55xnZkjLbf9TjPyet/Gg83Yp26h/ztH0HnEsDjmX9zJxq9nF9mGLcL/zOIq6/SF5168KRjoAgJKHf1mdqVUEsAcACu11kcqpSYBuBdAHYDnAZyote7p6xiDkdlFd+/kpS92yFyYXYJERP2T2uefefg8AMCeP1pi5hRaWTJwQlDpRBwQkrJmErn3avRIAEDPmKF0DKkyK4zQn+yWmBBbk4LqJexInfquYbTG9Yfaj236JAoFvmz8QwBsrXzfJQdY16QUovjhSqqltvDWPSNLSzqSSa5MWNpn9ChrA5bZRd/O+yXWTNpw8TWLNCD6tjD69af/EgDQXrBpvVPT5EKV8Ohm3lfLrZXdoKMq/k4cfg/p88lOkSToPJWraFz981baE3dZf1xwBS99emuxvZj9PADznfdXALhWaz0VwCYAp7695QUEBAwE+sXsSqmxAO4E8GMAFwD4NIAmAKO01jml1HsBfF9rfVhfxxmMzC4QhpeUVwCYwKWqapgZm/KSGkpjJdjmhLvON3Om3PQmAEBnuQJtF9eGT/DT3QmK0BJGyVZ+VUFmZ91ITN9d71Rl8GDYvg8GMDYAL8RT3gvDuyGqLZNpW3cdjT3vCOpKOrNmHl1GzHlu3UR67N+u+AAAIN0uscHg49tZYnXPMmsLs6c64z0SgA2uMf3thPANszs2DS84p5Oll19cdgMA27fPhYQCC4P7tdrdgiB+vzbBcTdSL4ECiz4Fx8814hUaW7HG6yjbVwea3sJyt4DtwezXAbgY1k4zHECz1loEqBUAGuMmKqVOV0rNUUrNadrQe32sgICAHYstMrtS6kgAR2itz1RKHQrgIgCnAHhGa70bjxkH4BGt9b59HWswM7vA7er6cg/9X8t6qzwZRYf3dVYA+MzsrwEAJl7SThvWkS9ei87u6PLm3uejD0GV4SSRcu6SOrHB7OsZGlVcEzH93s3xe2N23p6roCvKOr3kJOxWGHL1++l9sovGPHbslWbsUx27AQDu+PZMWq9Y1llHlx517hqFEEW3FqlCzueGDMs+s37xUsgtjLFTJHs4YWc0Dfr+ORRTMJn18jj4zC6QHgIdTvKPWO6l86owvEgD31pyNABg1X0TzZzOkXyNLFSMnEvfp7KN7OlwCma+XV1d0Bez9yeo5hAARymljgDFXAwBMX2tUirF7D4WwKptW2ZAQMCOxBbFeK31pVrrsVrriQCOA/Ck1voEALMAcEYITgLw4A5bZUBAwDZjq4JqRIxn19tkWNfbCwC+pLXu7mv+riDGu+gokLg1n8UvcUMJulh0q0kU+8YebycR954zjwAAlM1fCQAoNLeYMaqc3T/Sclfq44moL+K9W8lkSA0AoGcSGfFMOyaR1J08597EdwlCEfeU24ZZxGsThpuMHssNHR36OoeettC2lOc+M+2TM8VSpcQrae/4kcw/Fd1nRP90NHPNDTrqqaFBZ1/yBwDAu8qXF53bh4RF1yj6oFvY9VaXJIPaRonXdbY1mzruUTHehlZboXnmrLPo+K/QcXMmZJheG55td65Zwnp7N1gWwbllz720bWK8PabWTwF4iv9fDGDG1swPCAjYeQjhsn1A3HHr8hQKuTRHSSz1yc7IOCca1NR9q+cn8kZ2q33sCXLP7XlVqx28am30hMJqZcz43eK2c5g3S5RopIIRlDffNYEq5BQcZrfhpNyVhh/txtVjmBNFMCG7zPoSGON6nERCaDqQXkc9EzU0uoxrD8yn9mqtGanDkQKEwcXAZ8JihdFZcugYYS/g/POJ0fcvp+Agv9221Bp021l/Zf6JAIDNj1D4cooDfiSoJ1oRmF4vu+g3AIBxKarfL4a7pgK5S2sT1s0m7C91Az7yJAVjVc8r4+uy6ytrpjEjXuTw2/4Y7PrJ7CFcNiCgRBCYfSuwOkdKlrC39CdrdlT2PO8T5sh7OZdr89Xm/wuuIjfd6MfIkVFYTyxhgmwy7GZzXHNF7jrR8SQgZ0y9GdsxgSquGOb22FrgsrXpUSdMyzq1BL9IxxjA1sGTUNf2BqKojgbaPnxecQCLzBF9Pl/GUoenuwNulRlZN/h8zOgjadL/nPdbM2dqhiqtCqMLk4urbFWOQpIvvdYGfA5dzKmt64mNpUNLnB0h0UVjO0eT4t1zFrlWf7XnbyLncVt/Sxiu34dA9Py7N9lEm7/e+X4AAAuRqFlOxxnyZlSadGGq4BZ0SIQJCAgIzP62IFb6pVx7rsqxxrcxFQqjtGliO3mau2GWYuX9b+cUAMBD536UxrxGlnsJuYVT404s9bqH94kU4CfeAFDDiMV6xlPxiK4RLCmIyh7ToUSbpJOoBTzFEUTpdldpF+U5ytbCNJt2p/NlWu15KtfSurPV0bReYW3XfmA6s/B5JGCmdSLNveyrdwMA9s6sKboOv2dfF38O5/ziTABAw3MdzonYHpGTevReMoqbXJSMrinPnVq+fesdAID6JFnWXWYXtu8rDFfgh+H+v3ZKo77mvx+ndT9J56teZT1DqVbrBPvva7egtX1VYPaAgFJGYPZtgFjrX+yxzNuuic2qVNQnbyyyMfmqvp7/yVnnAgCm/YzTZV2rPTOKSY81LCSN4uJM6/wZs+W+YzLVuTe15yOVUOMLLRgpIKZ7SVGtdo9XTCwAgOWfIDYb9ioNEokhNu2Ttwmjb26kuZ8/7UkAwEGVbwEA6pO24IjcZ2F2eS+6+szwtyAAACAASURBVI8uORkAULkqmpQSdx2CWAmI77Po9esupOP9bn/qsuuG3nawuT1t1hb9jmRjGN5P2JFjyPY4yQEAjj9yLV57uScwe0BAKaNkC05uT0x3+ms/sJlY8/0VFLnVUog+tSP90/npLDq/6Pt/+NBNAIDyQ4kJTvrRBWZOwxOcgrCBOndKmqwyjmtHchC2Fwv3arJUV66lnmzgohld44aaKSZ9NB9ls74KLvhFMsycfPGkcX+ja+qupWtdezix25gHiblMwQrY/m+dw+kEB3/pBQDA54Y8DwCxvciF0cUaL/d7QfdoAED5emZMd2kJTzn3eq9Huq96UYlyjWVpku7aTCedLWd4thXIZtPjfCeGsH9eJBKR+hJeZ55sDE8noft0ywdmDwgoEYQfe0BAiSCI8VsByXVPsLC0qUCBDk1OG6IDOUyzrcBVYHrJkQas+C7SrrjrJOFG8uYf+q7NIb/i64cCAOZdQKUD0gvITSehtdptIlhgw6FUyeE8eROQs4ZyvMs32uQcqW+fHcZBOl7TyUhorVcBVQxZRSqA2zKKq9ZIpdjx95II2zaOtrdMtWNHzqHjfvbsWQCAY4bOBRDvshL44bFi/NqXE2Ieay8O9DEGRJHipVFlXFVY79oS3FTxoql/o/csbscZYqV9WIZbXeZ5TE2i2FjYxgY5+f5Iko7fRhwodtf1hsDsAQElgsDsWwFh9LlcwSbJtVYlrRGwddWF7a9a/QkAwEtrqWrXfg0rzdj/GfMoAGuEkae2cIKE5VY61HJB/VMAgDV3PAMAOPaJrwMA9ryyGQCg2AgHADoRfZZLII7iNeo8r9sN2lm0FABQxqm0uXEUfpurIcOTS1h+iqwkjBhzV0zarXGndUu4LDPXclpDzTI7dHMjfT0PqFwCwLKdz5rZuEwehlSI3cCS1utfpkSV3e/q6HWOWarnXgNs6qmw/orv0dspaTJ6ChOPSVq2FQmtiROqVuVpDRLo4xrzRBKRdQuDi1tWrtVlc9ct15cjPTB7QECJYKcG1bj13gTCnkl2G+V19Cku2+MgY2WMe3z/uG8H/+aKpaOS9ISWp664WwDgq/93DgDby6xmJa0hxWGmUtEVAFomsbuJi8g+es7PAADd3kfi6/2RfUyfEpZ74YVnm301s16nf/i+RPR5wITaRiDSgLcv0UAM39M4zE7PSBJLdH1FjN7XV6wfKZzrDuQONrW0pvs+93MA9r5UKTeoKV5Y9avCfvrps8y+Pa6izzNfVRadFFM4Qph9/XdIz/71vlTjrpndaPUJOtbuaVvwwv/O9fWdllDsLOv1bQV6lRmSbdub3eK4I9dhXgiqCQgobQwos++3X0Y//sgINOXpGeMyojylTWpikV5W/CRLe8klYunOGIuoHVuX4ASCRDn6C5EMVufJ6i6lhoRJlnAI5kVXf83MqX+REiEkqULSJRFbG5xeJOFjzcG0tgfP/Fmva5Jrkid8D+twcs1Nedsl9rK3PkNzzuFtHHZrkmgELouL5V561InlXqrjZpzqtg1kue8eFw2/NQU04hJJvPRa29WFC1S4JbK8fm0yZu27iemnfXwRAOD73L0GKA5qke+GX0HWtdqLDeCK1dT24MmXqaNNuoZY9oS9Zpuxnx36fOQ8ef4QhyfoOzIt7UkH6J3Z+yNl+mN9qcDHez65AnNf6g7MHhBQyhhQZt9zepm+66FRpoRPR4x+1eWFO/ZArJOkI7nSQNp7avcFST6QJ7A84cWSKZLEqKRNF5Qn4cIsMbikL8r5vnQVdQGpf94WDNTss1Vb6rbqgp/DHWOI2b/0w4cBAB+pej1yPnfdvTGVq8v9t3MSAOD+LxxKS1lEiSNFRTHipA5zQd4FxOj5qoqMDvkp5HHoGEvvk9LlxWFrlYtPfPELVUSOL75tL+VV6t5v2t1+j77/VSpk0ZjaFDmGfL5xPune6sYLXGu/P0aOt0ea7nuZSmNnYsZhyzHnpa7A7AEBpYzwYw8IKBEMaFCNgkZaFYpqcrkoZxFfjCYJNki4YaYCEb/8ljwCV/yS3GERc+X4oiYM5/2rctbAVcNhq1PTNpwUABaxWF+zgs6rnRxy1SPVTqQyau8GFdvWiF4r1tL5rvnLUQCAI4+nMFkJvQVs4I2Y2PyqJ8tztWbsfad9EgCQWraYTiN16rhCbVFbabgBN1KzntefThfP4Uo6hc2kxiTm0XmGrKW8+e7J7K4b6nzNWF4Xcb5IfHdz601+eTTTTAyACc5zr5tvXW8/vfIEAEDz7vT+tqOpRXMdu8TiAnA6+N7J99HPWHNF/zyPFSPtbmm6tp0tvvcHgdkDAkoEA8rsCdBTUlio4DxlpQJHm+m0EW0us5GDFiTEELAhhRJKWOAnvwRWuMcXA41sE1eeJCX449zjdOWTvCYaK4kLlSvJ2BfpwmI6eviGrWi9Nhoc/Udzg7/6/clF1uGXfAFQk5DKpNE1SlDHtV861oxNzqP20YYZJbFD6tjFNJYssicKkwvDuyG4zPYmcUSYl/PlM5tIIsqMshVv23cfTmOKKtzGGO56MR4n/HvrvB+ylJNLltPxz19+BgCg4nC6pzdMuweAlQYBQAJn5ZvlS5xxSSf1STrPrsDogsDsAQElggHX2ZPQ5knpsuhfWvcHADy4bDoAYP0K0j3L1nIgyyrWz9fZOUkOX+0eRsdrnUDPrqr3E7P8bNoD9uT88BdW9pMp5Gk+PGElijWcsFDOuvuGAunzo9gFJ+2TMxtsiqIu38ItdWu4mczKaFjpn/amGuRdWqqS2Dl+k5Xnu8YDAG646nMAgJGLFtq1MGMbaSNGR/chwTS9QeeLXVd2Ml8Hs7+RHJbZ5J+qTdQRRzcQw3eOr+G5coK+Fsf2BDO2eLBpD833efir9Hlml9H5vjKBOvNceuY9Zs7UjNeZpw+I5FmjWB7Y8i0dNAjMHhBQIhjQoJqKUeP0bidcgCx3sUw6OfsV66Pr2DSNXhv/H9dmX0Cpm7ptsx0kqZnClmwZVTXUdaUwtMoMVVlOLNiDQjsnXTwfAHDeqH8AsDq8K22ItV+kAPEIiNV/QU8DAODXXzjCzMlX0pNf9O9Et5M+2htYj9/nxlcBAF8YRuGZ0jE0GtoJPjddx7eu/SoAoGcIr9kxdYz+F1VdTb7JzOrbDdgqr90UV9/67m+PrLsXrvD1e1cakH0sZahKDsiZRH3WuofbcOa+6t4B8RVv7b74OWLG2bC3DWvtqqf5j51I3o8OLzTbDeTybT8iEe6XIelva8KxdwRCUE1AQMDAMvuQ6kY9Y7+v23JGzvNH/NGmFzeHnW7cg56UrZNp3NTbHf1qLZVVgviPO8g6nqhmRi+zT2SdkqZlHM7qJYPMv4Cqj95x5C/NNrHUy9Nc9HnpzS0W3Ze6G82ca398HACg9nUOofVYRzlpppsnkgRy8eWko0/LkK1BijTUev3gAWsZPuFqCtVtH0vHT/LDPF/u2AT4VDUUJYuGWZwIs6YpuiYnXFb3eOf02dvR+1VvYbbyGUrH2bRjB/Dq3gvTi3SRqLWVbrO7033NVrPEZurUe5b7rWF2PxEH1ue/fl9i+5FHUQmrq6dQR9i4EG1hdr/q6xguZDI6ZXv6DSQCswcEBAw8sx88/QzzXveVgNFLV461M+wTU/qDjb2NdF3TB41ZIs6yrGq5s2k5P61Fb+V+5y7jb7qeXq/ag57w8vQWXdpE4znRfS0FOo488aVe+fj0Rp5ryyH5vb/8dEyu2ISlUt0CwBk3U3EKsXsIgxVSxX5qISTZl+ykAzbMpfNUP7OErn2zTeQx4O+FibaTopJOiqsSyUmklRGc6iqSQ5ye733fekulBWDsBol6sqR3j6fIvHx5VKdWTpRib4xeBPf7JeEG3FG2wDe+bSyt7YrzbzVDG1OtkcPYVNfoF3Zyyn7OA8nygdkDAgLCjz0goFTQLzFeKVUL4DYA+4CEnq8AWAjgPgATASwBcIzWelMvhwAQI8bH1PjyIWPEgOeGoeYrScxacjgZ6Mb8m0TAyqfmx10DHU9EfK6hroayz4pFUV1Z7k4CAPSMoTHfuPV3AIDJqY2RY7sJOFvKjXaTKsQQV+M18ZNqspIA87nbLzL7uuv4PuQ4j51dbWKYE1EdAPIVtC3TIrXiomNTbTR27FNW5EwtJOOUGDsN/JZIsIk1zR+nrJPv/ujXAIAffPcUAEDdf6hVld7YbOYYcd3/3vWh0kllnQSfD2PZTTfKC8gBipsz+l/vuIQbMRp6tfRkTMskq7q0TqXXR79IbjoJV/ZVMteod0j5wHHq9hDjrwfwmNZ6GoD9AMwHcAmAJ7TWUwE8we8DAgIGKbbI7EqpIQBeAjBZO4OVUgsBHKq1Xq2UGg3gKa31Hn0dS5hdDHPuk1QMS8qrgGqMeDFSgJ8n0j2cnqYrP0zPsEkPWjdSZu4bNMevqcZuOlXBqa0pN4VTUkGZGdkg9dZxHExz8i8AAEMT0aQdoLhzh2nUF8P4fvCOMPupx5xJc5wQ3KWHR2uciVAghqnIPVHRfbKzkGFjnggZzlegfAONafz7Btqwcg0vUhJi7OC1x1Dk028vvRqArTwk13jX+vcDABafPNGeYBXXtRf3XDtJFXHfQxPeK98BCf/lsQkOyNHjR5k53fXWmBk5Vl+GO4/tlRgnJczYWZuk1zbvRt+FBy4lhl/Ltf98VxxgP9fpmR0fcLOtzD4ZQBOAXyulXlBK3aaUqgLQoLVeDQD8OjJuslLqdKXUHKXUnGw2xuobEBAwIOgPsx8E4L8ADtFaP6uUuh5AK4BztNa1zrhNWuthvR0HAKqGj9N7f+p8DHmzU05u9hm2T3nPH681bpFeRSfnMdHAnE3TbCGKDTOIpfe8khmL9UjjShI3khNEYnqY5aJ6ppaeafz+3Q+8buYcNYTaCku/tjYdrUjrXl1WiiUwC8jYb80knVetXBc5DwDDrO3vJ+Vx9fuE/Xioc3zZJuyfq+J7yfp+prU4uEn0ep2gscL0Dc/Sg3rzeHtPr7ucJBthMbE9bMwTg0naspsieuqLJwEAxn2HpSapeCtSlJtuK5+9uFBlnzC+ML0jDSbq6CspATnSx63AccbStSbWpCK7CtExrm1JUnOTXKSkfRR9b2776bUArK5e6dhhRJ+XIKmJKa7Rtw09DHrDtjL7CgArtNbP8vv7ARwIYC2L7+DXdb3MDwgIGATorzX+nwBO01ovVEp9H4BkmGzQWl+ulLoEQJ3W+uK+jjNtepn+1V8asaCHAk1uuPpzZl/9c8S0+apMdJKnR7k6o+nFlRTdyjuhc22i9647kHReNoRj4j0reAAnOAy1ARCqm57Eqo1YTRhEgkl0J0soTtJI6/smAgBuuYae9DXcqXUFl7uakLJW7jRLJFle5xfP+AYAoPI/LCmkub+aU/bKsJgkr7CUsfL43QAAnQ32mnM1NDbVKj3LaHtC4mS6i+9bIR0tFyW1QlKbacMDp11lxoqO7pfGEsRV/ZUQYylGcsH3qTPL8EfJpiLdaAHrOekzrRaIhsuKNMbSQGIEBeL0jKca9xI44+rwcl+s3YjvgSkbZscqLzRXxiw+lg5y/8dvBAC0Fax+XilFVvh+TM9IabTtnx/bF7P3N5/9HAB3K6UyABYDOAUkFfxeKXUqgGUAvrA9FhsQELBjMKDhsntNz+jfPdxgwktrE/bx+uBmMuTf/Z0jAQBVyyiVtVDG4ZTyxM4XP8X9RIgivd9BglNd8xV03MWfIfYc8zQdY8gr64vmCMMXmkjfT7BvXosu311sjZfknNevHQMAuO+9t9B5HZoYw07yk045DwBQNntR9BiebYAWI2zj2zZY6qiwOvWiM6mvnqnOxYdJdkvsAr0XNgcAqbcpjWmryO2Om79F/dWa89banfEKM1Z6XolCTHFH0WXFhiEdbJZkiXl/feKRZmxyEUldhQ6y2JtYCdHlvffutiJISm0jWe57Gm3CTcGXDH1fvQOf2eV9Tw19Vlf8/CYAUQ+NhFALpmfovpWpYq7dVj0+hMsGBASEH3tAQKlgp4jx4oqoi8nXliohP1/3EQDAW9+IxunofhhL/DZBAIqrtEiIJLcdzlaRSLX0aDtn2s9JlTAuMDmGuHxyxVVo/Cwuce0VJpI4f/K9j5ixN156DACg5p9Ub90Y3cQgxyKd7rIlfYyYWtFLgIZjzCo0U3XX/CH7AgCWfYzmqILcp6gxDoB5/Fcvo9e7Lr0GgK2/J/X3AbeFFompvhgvribXcOeHlWa9ar9upaDj/3M6AGD375HxVvtuul7UOHefrYMvUUfcj6DSqiN6Ernpuhs4wErq2MWJ8V5lWzEOyz1863j6574P31Q0128b3Z8mkFuLIMYHBATsnMaODWz9ceuiS2CJMLswiTRiPPLJcwAAe9xk2UMCJgz8cEfHZVVUMUZqnHvGPNNiGUDXCDK6bNibxkz8yVyaK+wtSTUxRiHF+fGmVvsQStoobLS5QsL+JsGjjOeIVODXgQOgJUdcpItOYn2p5RZxXUnVGan2yu/Xn/RuAEAbV/9xPWaVq+habvkGJfPXKK7nzz64OHeasLLU9BeGl/fSlNOFH0Yc10vADz0987tkyBz+8AIay4a7iDtW7p0E3gjDS60DkQqc1sfyeaoa+oykHl6+itYfl7BV1BdAhAxm+p/ccovZJdVt/FqGk/nzTTklagOzBwQEbDMGlNkP2q9cP/f4OLQUiK03Ovpl1lOQ6vgx1MHrkwfp4px1mXzvW6cBACpXMrsJo8uT+O1cmhs6Ku2XWYdr2o/Ys6yFnszDHniRBroMLP3POC0zKSm0XuIHYNnfsI1slxp6QzjAZ5PTa86rqOu7oVyYYBQJAea1CYOhQPuXnzrNzPn9mRQ0s5zv8xBmZdHPax29fCh3p/ngQ1QPb+hr3DONBZQsL7/MSXyWuu5XXkTMNyZJFXBFcqhyOrX4nX1EUpD229ed8kUAQPrVJfaaWXopsqeIhMXuTHe/3Dv5zBI8JjGMQ28nNZixuep05HjSnUbCvZOddNxf3nujmdPmtSYXKWY/jh8LzB4QELBdsVOYPQ6bC8Qga/PRhBHRrCr5WeWWeWjmInTfXToTANB1AQVmFDJb0ejGSAF8H9z4FUmsYd1f3kugz8pDya4w/rE2O+nFhZHjFtXBiyS1RGuom8q3lRzZIvXw3GOwTm4YXUKGZazD8FLVtcB6vea5sqb1p5Du/pvvXG3m+Lq5Hwq7qMemk15zDXkThr5JbJrqZH3Z3KeYLrFZ2te8O0lJ//OdOwEAU9PrI+cHLKNLIE5zId4D8b03Z5r/K0/nuavZcu+lTMfC99AYmwnr8s79V2OI5XsaifV7K3hx5x3Xm00bC9HvgEgvkhDjIjB7QEDANmOnMnvesYjKEy2r6am6LEd6fd4rAhFXFCDPVn1Jrjj9VqrAOu4flnHFyt5rcYzeyhj1BampUGGf3Cs/SArr5F8Qw0t5J9HL3aSOhFS0ZSaXsYbZJRHEq3EPFPvzfb0cgO2yKjopW6+bTiVGf/g7VHjBDXpdm49WS61nHb2Jfelfu/o8M3bEK7xeTvdU3nfJhDi71nKvIETHaLrWq6+5gZbvuAbSXrXdrNdHXRDnIfjhwdSbvtAarQYbW2NevDZedeJYiK4uvQrYR5+t48+MD3/p7XeaKbUJ/7tMn8shZext2I6proHZAwICBraLq4+4J1qCn36izxT4Ubk6T09Ht4upPCnLmeEncK3ue75GUV/PnzTejL37NOrH1mtZwzhG7yfbiwUWAMb/jdho08epuETHKLrG0TfMAWAtvXR8ZlzpXycWY4/JlTNHd7HO7lmdLdO7uZucoss+eGH0B5nRu/i6uhzftu/3Fly8iJIaa9+wa0t0R7vEFkUpihnE7ThjIs7onNVvtfH5xBpfHFUp1nfp0COML0yfdhJyLjmRlPZU15uRY5hIupgP06Que9tjPRx8qwpi/3iDQg0z7EHp3pskV2FzwH5PRVevNx6N+BJaOwqB2QMCSgThxx4QUCLYqWJ8HHzRXoTJ8dxCR1x0ALCxQGKRiPYSfivhiB+oWGzGvvu3lJgw899UsXXqlSwOp9kok/RE0Tj0lj/vhOUqdqfVLCUxrnoF7Vt95kEAgNo3rchf8feXoseTwBs/iabDBuL4iBXfZR/P33gEJRNJUotvhHMDWWSbvEoQ07L55HKb0umE4/rielH9wJj1mntHr7kaMvxJnnx5qtgYKeJ73haFAwDskaZ7fOxJ59qxL1GVH79BpTFE99GrwDdWxwUsmTBcryElWsgQmKsU1cJ+Hlkui5RXEphE698RNej6QmD2gIASwaBj9t4gbroKZWvUNfBTdmmOnuJ+Nxb3SSZP2vveRy2Zs3+kued/l2qg1c5zAmMEwlTyFO/t2eiG2BpXHq+hm578DbOpjp0baLLs4ncBAMb+g/YlX+ba9uJyE9Zw3HUmwMNzExlXkOM2WvulfQAAf+Xa5ou5QWR9khhRjHBu9xK5dzWcnlqVJOPhhw6eBwBY/qep9mL96kHykvS2x0CaMea4q8/IZMz99yCfrxjzZl5In93QeVaC8xNhtli/rh+IML60oZZAK5aehP0rLlgJAFjlhHVP5qaeks5bnSBpRr7TA8XwgdkDAkoEuwyzxz39kvys2p2DRyTBZgWTm5uA4Nc2l+Cd635ICQu3N32Q5p5qg36MdikJMR5RSYau2zLY6oRemq2k32btWAmzlcq3iy+dDgCYci0H5HDSTMENxJEuKH5CDL9vO2JfM/YX3+RKp7zQukS01bTcE7fGuWxr5iAaef+1kU8BAC6ptEkz5U2c9JPxwmJN4AxLRHF14fg+7XP5y5E1dBSsm9GXMpbnKET16jO/BABoOZDu7frpU8ycSX8i3TmxmPrMoTPas87cN/f71I+eg/5Yo8/zZ9N2GN33O6ZcXTRFEnpq2DayI6rK9geB2QMCSgSDJhHm7UB0Hgm8kYAceb82b5/qLQUue8T7kkzTNR5dL83Z6qznX8Y64Ztc3dQvSRRXtshnfxNEEg0TjYNIAS1TaA091TSn4fa59visixY4uCY5nOqiLz+JdOm/nvszM7aJre71SSkPJXXqo894N/x0QorW90I3BYnUJenaxWLf7Oj3F55J96d8PSfaiLFc9NmYIiKJLmLw3W4m+8TX65/itdL50qo4VLWdpYyfnn0yAGDNDGL/XKWkmdqxcmkV62gxjQ+tpu2rqbRYbCkxT/Loq3qt74lJTKBw2bMeexQAUBNTqEMChXZkvXhBCJcNCAjYtZl9S3ATbTaxPr82H32++WGhbv900Vsfbd0PADDnq/sDKC5lFcvWvr9edD1HPzR9wb1CmSbZgm0B6w6yXWqq1nKhRs75rTuVwjW/P/FBnmrXJrXLpX+8xB9IHfOx3J1mdLI4bFOko7nsVhcLuCsFCItJ95PTnj4ZADDkZTp+eyOdb+aHnzNzPjSESkqNSrVE1puIabMqn8VZF1DyzYa9pTiG3B+5p8X33wggvKthNsc/PM0ej27rh/d98kVhvzFI1JK1/cDHSXI4csiLketwk3NEsmpM7rgeb4LA7AEBAe9sZnfhsjwALMmRLtrDzFLwIscAYEySmGVhlnRoSbj4ygsnAwAafyZps/YeGst0X5F4DL+MlrwmejjBhCWI7BAntuAySvD4SsM/AQCjUmTRFz3clUzEfy5sI/tEL69WxaWMBX7K8cu8Jvf4Yh1PsMQwPBltyS1jV+aGmG1S5iphSklHJSs3qeXMH1NkXMvu9F4612RauGCHsRG4FUei1yGHl752IgWMfcqyedlcZvusF98QgySXqpr21yYAwEX1TwMAluej3otap0z6lHQ1BgqB2QMCAsKPPSCgVFAyYrzAD1F8K0vhoBtZ5K1xAkzaOMBDxNGEl9mRYVH0oc37mG133XA4AGDkfym4o1DJlUpFNHc72nCAjdQn3zyeDF0tR9OafnHAPbxm+0wex4Ytt1YbEF+tRSCi8rvLvAovW2Eokvu2Mm+TchZlyUhVz+K7qA3l7D4TlSiuwaNAgmnEUHrhhWebfakOPueHuBYca2KJLKtc5TFVZ6QUn4TuSkyTiPM9KrKfFkgvEx4llUi9Qg02JblI3GsA8KE/vQoA+Fj1vMg1+jggY695IINoghgfEBBQeswuEKbKQdrnElM+122ZXRjdd1nVeD3MXKNVDUd4SECLsGpdsjjYYlWO6rdLIIm4t4QppZrqOE5GAWwtOGFLv567m9QygV1rI7ejy0cMdoANYlqQjfZ4kwCZcqbZvtoXS1LOp+78JgBg0uUv2ePXDQMA6FZi3PVH7w0AaOVONoWocBOBSAHC6IbxC8WkV8hwoBUb8crJ9oaRs+m8V/7+VjO2y+t+I9coGMdtuEc67syBTGUNzB4QELDrJMJsb8jTNuk972aUWboQd9Z6DrttEZ3Rq6nu6vLtBZojrhdx7TUbfdYy44QU6fWruHCDdEfp8DqhbHTYui/9F4jq5UlFLh/f7bgtSMToqBNStO3Vnni22xhT712ko8tWfBoAMPlaCraJ1KtjRpd03uEPkL5czwEty46lGoM9tU71Wv5GF4TRJQHJY3TnY0Bqs1TBpfcSMS2M7tarl0Ia8iqSm9Q/HJ0aODfb1iIwe0BAiaBfzK6U+gaA00DBh68AOAXAaAD3AqgD8DyAE7XWxaVBBzn6KiAgLCb610hmi3VspV2ak77klslEv08bIuGAE0W63AZHZ7X9uklnF0YXtvD7nQNADxfqqk8Qk+zGPb7TqqJo7I4ujiDHbTJBKNLFNZquWuMEmIg+/0oH2W6aj5TFctGJckenlx540mVVgo84CWjcLcT0qq7WTFl0BlnO5SMxTC8httLYNudWvOV/mNkvO/3uyFr3Sttee1IARD6zOj7R2FRUihroklP9wRZXpJRqBHAugIO01vuAysIdB+AKANdqracC2ATg1B250ICAPLGYugAAC+RJREFUgG1Df3X2FIAKpVQWVOx6NYCPADie998J4PsAbtreC9zR6OsJ3Ns+0ctG891za9m/wZZp6UPnp5PWOPqsJKhUMkvIe1sPn9iuPmHDN0cmU7y2LdccHyh2WZ6n+1FrEmMykf1uspF4Gn561mEAgIzm+u5SyNFNN017X88EjxEbhJSearPeit2upeN17k/6/IqPcJyDWOMlecZxfcv/15x0OwBrcxAvyaq8vR5hdInH2D1N7D+YGV2wxZVprVcCuArAMtCPvAXAXADNWmv5Fq4A0Bg3Xyl1ulJqjlJqTtOGba8HFhAQ8PbQHzF+GICZACYBGAOgCsDhMUNjHfZa61u01gdprQ+qH75zyvEEBAT0T4z/GIC3tNZNAKCU+iOA9wGoVUqlmN3HAli145Y5OOCLavLeDYfcM9O3eJ136+JtUeQrjhqRoJbB8Njs1iTKVrF/Syqq1ielpVPUhQgA3156FACgbBG3VE5HRX5kHRuv7CvKK+erFzF+s822U0kSNiueI3F+6hyau/bzVDu/gzouR8KWzz7mIQDAuFQzAFvHIM9c2Oa43qRyz6RU1J0o+f+D4XPpDf1RMJYBeI9SqlJRnZ6PAngNwCwAn+cxJwF4cMcsMSAgYHtgi8yutX5WKXU/yL2WA/ACgFsA/BXAvUqpH/G223fkQgcDirrVvA1jzLYacHZWZVKBu/7Z3BlSGi/WsjtQgo2kcWSPw3fZi0fQcbpYEJRONsLeKfuVlNrsJr/cq6grBrxEjQ1k0dloQI+0u264nyr29uw7EQCw39UvmjHvKl8CANhQIPdlF7sMq9j12ZiyBkBpOOpjZ38u/UG/rPFa6+8B+J63eTGAGdt9RQEBATsEJRsuG/D24IbeNheI5Syjkz1hI4f/NnACz2s9o8yc1CrqjmLY2utwA6dKjKkTJ9VpU/Ff10hlmd4qBI2qBwAccv2zAIBjhs4xuyTcWVyfHRwKK0k6vbH5robB6xQMCAjYrgjMHrBV6HQiossV68yso7d5CS9SYOPHC6yntqGFdHWVYU+D2ABYt45Y3oXRZUyB4zT84Bo3TVu60HCobWIk2QjyvyAp4/Aa6kDjdlldw2GxAkkpHp/a8dVgBxLvjKsICAjYIgKzB2wVljt97SpNwQxidOl80srvuziMtvuZ4WaOyjRFD9jNyT7C6G6QZRknxWypUq+7n1lYjSGH+rpr6St+x+R7AVgfeptT+ULWLSGw4z0d3S3YsStY3XtDYPaAgBJBYPaArcLS3DDzv1jhpTOMJPkYZmedvWaZUzzD76sm3VjYGh9JcZW01wpO35W+9QXWyztobmKorUuvy8nnv+A7tM57974ZQHExzC4nklFKek1JRdOEbYGTdwYCswcElAjCjz0goEQQxPiArUKdU+lW0MFGrw7Qq7SkWp4jAbhivQ16WX4KJaSMe5ASYSCBMyyyFyJJLSxAS0itl8du3XdWNZh/IQXPXHvI7+h4nIQjAT9SBajWqQI0jav9+AlO7zQEZg8IKBEEZg/oF4Tt3EaMwpaS9ik156QaqxjwclV2zqjnKAS1azzVjVt/JLnIxv5qHp/IMYcx2+tOmmOMd8zkYrh7/Wzbi+C3h90YWZswu6xbAoD2zhTX7BO8U4JofLwzryogIKAIgdkDtgpuS+VsTEccAEiaCrtcaKPL6sCqhwuA8KZRz3CfuA9PAwB0jrD8M/L+BXxAbqvNteYSXDf+tUvHAADuP/x6M6cpT112ahKdses/IBO7uSQQmD0goEQQmD2gT/iluJJw0kmZuUVXF324h4NpmjnVtWCL6EOJZV1F31es6eJXe/h1RxPb175JlvP0c8T0y7+8GwDgH5/6GZ3HqWbrewsksOfADB2/TBV3pykVBGYPCCgRBGYP6BO+ZdpNY5W66r31hh+VpF526/exX7PxKzwftldMUjnpqsPnkT6f6KLzLP72fgCAR04kRpd69K6HwB6WzjMtTceoiOmYU2oIzB4QUCIIP/aAgBJBEOMD+oRvoBvHbaYBoClPorG0q0qbRpVZfiUD3gdmvmDmvPXk7gAccZ1z0RXP1UnLP2K86xxDlWR+dwK52Naw4U+Mb5KPDlh3X32Sto1MUk79YKq3v7MQmD0goEQQmD2gT/gGOved1IsXpL3gGqnDfsKIZ8y2y1sotDVbR+yc6JG6chwC6xjodJp4+Ic33Bo5XwbR87Q6RsNyrjbzrhSNjevaU6oIzB4QUCIIzB6wVWhIWjZ/LRvVmSXpRNxeortLcA0AdI6nUNdMs1d7jnX3QsYy8Btfjva6kzBcgVTIEd0dAA4uk44wtO2dmtTydhDuREBAiSAwe8BWoTJhmX14ggJWejwbtxSz6OFglyqnUMSMn84GALz49ekAAC2FJ5ISP2uDbH79UWof6OvoYv0Xppro9GJLgaQI34sQEJg9IKBkEJg9YKvg1lCXXmhLuC+7JLeIzj6EdXbXaj+jajEA4EUws6eIb1JtxP5vHD/UjJWiGFm2BQijg9+Lrt7gCBaByXtHuDMBASWCwOwBWwXXXz0lTdFpb2aJM8T67peEciPcatJkQVc/2UCvl1GBSLHCX/nZ35ixkugixxEIw++bJuZPoXTTVrcGgdkDAkoE4cceEFAiCGJ8wDZj/7JmAMDsbmrgKMEuvssMAIazG+6c8U8AAK4pPwEAsH4GuczGpTaasc0cbiv58nLcBIvxw5JRN1tA3wjMHhBQIgjMHrDNGJmkFNTJzMpruVVzj4mEtZzSXCC2F6PdN3/5WwDAgu7RAGzbZMC67CRMVpoxTk1L5dgQErs1CHcpIKBEoLTeQqP77XkypZoAtANYP2An3TaMwK6zVmDXWu+utFZg11nvBK11fdyOAf2xA4BSao7W+qABPenbxK60VmDXWu+utFZg11tvHIIYHxBQIgg/9oCAEsHO+LHfshPO+XaxK60V2LXWuyutFdj11luEAdfZAwICdg6CGB8QUCIIP/aAgBLBgP3YlVKfVEotVEq9oZS6ZKDO218opcYppWYppeYrpeYppc7j7XVKqb8rpRbx67CdvVaBUiqplHpBKfUwv5+klHqW13qfUmrQdCNXStUqpe5XSi3ge/zewXpvlVLf4O/Aq0qpe5RS5YP53vYXA/JjV0olAdwI4HAAewH4olJqr4E491YgB+BCrfWeAN4D4Cxe4yUAntBaTwXwBL8fLDgPwHzn/RUAruW1bgJw6k5ZVTyuB/CY1noagP1A6x5091Yp1QjgXAAHaa33ATWROQ6D+972D1rrHf4H4L0AHnfeXwrg0oE49zas+UEAHwewEMBo3jYawMKdvTZey1jQD+QjAB4GFYVaDyAVd8938lqHAHgLbBB2tg+6ewugEcByAHWg3JGHARw2WO/t1vwNlBgvN1CwgrcNSiilJgI4AMCzABq01qsBgF9H7ryVRXAdgIsBU0x9OIBmrXWO3w+mezwZQBOAX7PacZtSqgqD8N5qrVcCuArAMgCrAbQAmIvBe2/7jYH6sauYbYPS56eUqgbwAIDztdatWxq/M6CUOhLAOq31XHdzzNDBco9TAA4EcJPW+gBQfsROF9njwHaDmQAmARgDoAqkfvoYLPe23xioH/sKAOOc92MBrBqgc/cbSqk06Id+t9b6j7x5rVJqNO8fDWDdzlqfg0MAHKWUWgLgXpAofx2AWqWUpC0Ppnu8AsAKrfWz/P5+0I9/MN7bjwF4S2vdpLXOAvgjgPdh8N7bfmOgfuyzAUxli2YGZPD4ywCdu19QSikAtwOYr7W+xtn1FwAn8f8ngXT5nQqt9aVa67Fa64mge/mk1voEALMAfJ6HDYq1AoDWeg2A5UqpPXjTRwG8hkF4b0Hi+3uUUpX8nZC1Dsp7u1UYQMPHEQBeB/AmgO/sbGNFzPreDxLNXgbwIv8dAdKFnwCwiF/rdvZavXUfCuBh/n8ygOcAvAHgDwDKdvb6nHXuD2AO398/Axg2WO8tgB8AWADgVQC/AVA2mO9tf/9CuGxAQIkgRNAFBJQIwo89IKBEEH7sAQElgvBjDwgoEYQfe0BAiSD82AMCSgThxx4QUCL4/9wE/UNMZ3VGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "X1=data.reshape(data.shape[0],img_size,img_size)\n",
    "plt.imshow(X1[100]) \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1., 0.], dtype=float32),\n",
       " array([[1., 1., 1., ..., 1., 1., 1.],\n",
       "        [1., 1., 1., ..., 1., 1., 1.],\n",
       "        [1., 1., 1., ..., 1., 1., 1.],\n",
       "        ...,\n",
       "        [1., 1., 1., ..., 1., 1., 1.],\n",
       "        [1., 1., 1., ..., 1., 1., 1.],\n",
       "        [1., 1., 1., ..., 1., 1., 1.]]))"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target[1],X1[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}