Results and Discussion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实验设计如下：  \n",
    "应用目前比较流行的模型，如VGG,Inception,ResNet,Xception,DenseNet及其演化模型对猪进行识别，由于训练样本数据太少，所以采用迁移学习的方式进行训练，为了公平的检验各算法在猪脸识别上的表现，所以采用只去除全连接层，添加一个新的全连接层的方式进行训练，对网络内部的参数不进行任何调整，各模型的参数均选择在ImageNet上进行预训练过的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "from matplotlib import collections\n",
    "from matplotlib.path import Path\n",
    "from matplotlib.spines import Spine\n",
    "from matplotlib.projections.polar import PolarAxes\n",
    "from matplotlib.projections import register_projection\n",
    "from matplotlib.ticker import FuncFormatter, MaxNLocator\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rc('font', family='time new roman', size=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Vgg16参数\n",
    "vgg16epoch1trainacc=0.3635\n",
    "vgg16epoch1valacc=0.5150\n",
    "vgg16epoch1trainloss=5.5050\n",
    "vgg16epoch1valloss=1.6763\n",
    "vgg16epoch1time=678\n",
    "\n",
    "vgg16epoch2trainacc=0.5985\n",
    "vgg16epoch2valacc=0.6336\n",
    "vgg16epoch2trainloss=1.3721\n",
    "vgg16epoch2valloss=1.2542\n",
    "vgg16epoch2time=648\n",
    "\n",
    "vgg16epoch3trainacc=0.6637\n",
    "vgg16epoch3valacc=0.6799\n",
    "vgg16epoch3trainloss=1.1364\n",
    "vgg16epoch3valloss=1.0801\n",
    "vgg16epoch3time=635"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Vgg19参数\n",
    "vgg19epoch1trainacc=0.2857\n",
    "vgg19epoch1valacc=0.4709\n",
    "vgg19epoch1trainloss=7.1869\n",
    "vgg19epoch1valloss=1.8846\n",
    "vgg19epoch1time=598\n",
    "\n",
    "vgg19epoch2trainacc=0.5526\n",
    "vgg19epoch2valacc=0.5819\n",
    "vgg19epoch2trainloss=1.5387\n",
    "vgg19epoch2valloss=1.4257\n",
    "vgg19epoch2time=580\n",
    "\n",
    "vgg19epoch3trainacc=0.6250\n",
    "vgg19epoch3valacc=0.6326\n",
    "vgg19epoch3trainloss=1.2742\n",
    "vgg19epoch3valloss=1.2237\n",
    "vgg19epoch3time=584"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Xception参数\n",
    "Xceptionepoch1trainacc=0.6134\n",
    "Xceptionepoch1valacc=0.7784\n",
    "Xceptionepoch1trainloss=1.3617\n",
    "Xceptionepoch1valloss=0.7612\n",
    "Xceptionepoch1time=3049\n",
    "\n",
    "Xceptionepoch2trainacc=0.7787\n",
    "Xceptionepoch2valacc=0.8438\n",
    "Xceptionepoch2trainloss=0.7317\n",
    "Xceptionepoch2valloss=0.5218\n",
    "Xceptionepoch2time=3040\n",
    "\n",
    "Xceptionepoch3trainacc=0.8272\n",
    "Xceptionepoch3valacc=0.8536\n",
    "Xceptionepoch3trainloss=0.5703\n",
    "Xceptionepoch3valloss=0.4877\n",
    "Xceptionepoch3time=3040"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#iception_v3参数\n",
    "inceptionepoch1trainacc=0.5392\n",
    "inceptionepoch1valacc=0.7474\n",
    "inceptionepoch1trainloss=1.6689\n",
    "inceptionepoch1valloss=0.8734\n",
    "inceptionepoch1time=2106\n",
    "\n",
    "inceptionepoch2trainacc=0.7235\n",
    "inceptionepoch2valacc=0.7518\n",
    "inceptionepoch2trainloss=0.9232\n",
    "inceptionepoch2valloss=0.8210\n",
    "inceptionepoch2time=2085\n",
    "\n",
    "inceptionepoch3trainacc=0.7714\n",
    "inceptionepoch3valacc=0.7937\n",
    "inceptionepoch3trainloss=0.7538\n",
    "inceptionepoch3valloss=0.6706\n",
    "inceptionepoch3time=2085"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#iceptionresnet参数\n",
    "inceptionresnetepoch1trainacc=0.5258\n",
    "inceptionresnetepoch1valacc=0.6696\n",
    "inceptionresnetepoch1trainloss=1.7285\n",
    "inceptionresnetepoch1valloss=1.1528\n",
    "inceptionresnetepoch1time=3911\n",
    "\n",
    "inceptionresnetepoch2trainacc=0.7262\n",
    "inceptionresnetepoch2valacc=0.7676\n",
    "inceptionresnetepoch2trainloss=0.9288\n",
    "inceptionresnetepoch2valloss=0.8084\n",
    "inceptionresnetepoch2time=3882\n",
    "\n",
    "inceptionresnetepoch3trainacc=0.7690\n",
    "inceptionresnetepoch3valacc=0.7921\n",
    "inceptionresnetepoch3trainloss=0.7598\n",
    "inceptionresnetepoch3valloss=0.7224\n",
    "inceptionresnetepoch3time=3880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#ResNet50参数\n",
    "resnetepoch1trainacc=0.7827\n",
    "resnetepoch1valacc=0.8949\n",
    "resnetepoch1trainloss=0.7495\n",
    "resnetepoch1valloss=0.3175\n",
    "resnetepoch1time=1496\n",
    "\n",
    "resnetepoch2trainacc=0.9007\n",
    "resnetepoch2valacc=0.9053\n",
    "resnetepoch2trainloss=0.3119\n",
    "resnetepoch2valloss=0.2525\n",
    "resnetepoch2time=1491\n",
    "\n",
    "resnetepoch3trainacc=0.9238\n",
    "resnetepoch3valacc=0.9624\n",
    "resnetepoch3trainloss=0.2341\n",
    "resnetepoch3valloss=0.1222\n",
    "resnetepoch3time=1491"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#DenseNet169参数\n",
    "densenet169epoch1trainacc=0.7158\n",
    "densenet169epoch1valacc=0.8732\n",
    "densenet169epoch1trainloss=1.3353\n",
    "densenet169epoch1valloss=0.7561\n",
    "densenet169epoch1time=1880\n",
    "\n",
    "densenet169epoch2trainacc=0.8606\n",
    "densenet169epoch2valacc=0.9260\n",
    "densenet169epoch2trainloss=0.7717\n",
    "densenet169epoch2valloss=0.5796\n",
    "densenet169epoch2time=1880\n",
    "\n",
    "densenet169epoch3trainacc=0.8885\n",
    "densenet169epoch3valacc=0.9211\n",
    "densenet169epoch3trainloss=0.6826\n",
    "densenet169epoch3valloss=0.5717\n",
    "densenet169epoch3time=1881"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#DenseNet161参数\n",
    "densenet161epoch1trainacc=0.7411\n",
    "densenet161epoch1valacc=0.9183\n",
    "densenet161epoch1trainloss=1.2861\n",
    "densenet161epoch1valloss=0.6365\n",
    "densenet161epoch1time=3352\n",
    "\n",
    "densenet161epoch2trainacc=0.8790\n",
    "densenet161epoch2valacc=0.9265\n",
    "densenet161epoch2trainloss=0.7634\n",
    "densenet161epoch2valloss=0.6110\n",
    "densenet161epoch2time=3340\n",
    "\n",
    "densenet161epoch3trainacc=0.9016\n",
    "densenet161epoch3valacc=0.9194\n",
    "densenet161epoch3trainloss=0.6760\n",
    "densenet161epoch3valloss=0.5943\n",
    "densenet161epoch3time=3340"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#DenseNet121参数\n",
    "densenet121epoch1trainacc=0.6811\n",
    "densenet121epoch1valacc=0.8274\n",
    "densenet121epoch1trainloss=1.4356\n",
    "densenet121epoch1valloss=0.9118\n",
    "densenet121epoch1time=1581\n",
    "\n",
    "densenet121epoch2trainacc=0.8256\n",
    "densenet121epoch2valacc=0.8873\n",
    "densenet121epoch2trainloss=0.8806\n",
    "densenet121epoch2valloss=0.6978\n",
    "densenet121epoch2time=1574\n",
    "\n",
    "densenet121epoch3trainacc=0.8573\n",
    "densenet121epoch3valacc=0.9020\n",
    "densenet121epoch3trainloss=0.7714\n",
    "densenet121epoch3valloss=0.6142\n",
    "densenet121epoch3time=1577"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abb0106a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\matplotlib\\font_manager.py:1288: UserWarning: findfont: Font family ['time new roman'] not found. Falling back to Bitstream Vera Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
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0YqWDdBmjJWW2LS4pU0QyC/m+xGSDi8uVEaIB+hOz39/u7nPbKdtMpAsib0gX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aMmHbb//E8fyiAna9bY4FwzZ8Jzz5mjtXp1+idZNvr339J52moreyIUUppVV2clEO5oOeXmo4/s\nHg9L0ZYuNSf/8MPhF78wR22PPfxedKrC6tW5nbGwDVmUsLpy+HDYf/90Z2z4cHu0OhVg0yaraw6X\n55+vtkVbUJMOmxNB1ap9Zs6EBx6wEobmZhg1yl5ggwdv6WBlOlr9+vknmpNcNm+GZ55JlaLNmWP3\n/b77whe/aA7ahAnVGzHa6TS0tlr7sFzO2MKF6SOjdO9uTtfw4TByJBx3XLoztuOOXjNUMsLmOqHD\n9fHHbW9HfxfS67rKuMNWizQ2wuOPp5y0BQvsZXXUUXDttXD88bDzztW20nGK5+23UyVojz1mJcSD\nB8Mxx8C559rI0ttvX20rnQ7Gpk3p1ZWZzth776WP0TVgQMr5OvRQmDo19XvECOvA5t/CBaJqTW6K\ncbZWrTJnLVdX1/79rQF7uGy1Fey6a2o7M27gQOvBeMIJlT0HbRDbYRMRAS4AzgJ2Aj4C7gIuUdWC\nXFQRmQKcA+wO9AQWAXcC16hqG3V3nZQFC8w5mznTXmAbN1rD6U99ypbDD7dqIadiuBZKyNq19hES\nlqK9/bZ1QTv4YPjud60U7ZOf9LdfQqkVLaxZk9sZW7TIGvtH3/lDhqScrzFj0p2xESPsve5EaGoq\n3MHKth2dizFKr15bOldDh1pNUqajlbldV1dcd9ZctlSRYkrYrgHOBe4FrgJGAucB+wFHt5VZRH4K\nfA94FPgRsBk4HPgxcDxwcBE2dTw2bYLZs1NO2vz5dtMddhhcfrk5ad5Op9q4FoqltRVefDFVijZ7\ntlXl77KLOWeTJsERR/hgh7VD1bWgau3DsjljYdiqVan03bpZleSIEfYoPfbYdIdsxx07YS17c7M1\nwivG2Vq1Kn1A0yhdu6acqOh6551zl3CF23V1nfBCZCeWwyYio4BvAfeo6smR8AXAdSJyqqrekSd/\nV+B84AVVPSYS9TsRaQGmiMi+qvpyHLs6DEuWwN/+Zg7aww9bqcOQIdZP+/LL4eij/QWWEFwLRbB0\nqd3XDz1knQY++sjaTx55pFXlH3us9SZ2aoqkaGH8+PTelf36pUrCJkyAKVPSS8eGDOlks9CsXGn6\ne/bZ3A5Xrs5pIuY4ZTpWe+7ZdgnXwIHeIa1ExC1hmxKsr8kIvxG4EpgK5BQm0B3oDSzLErckWOcZ\nN6KD0dJiPVHCUrQ5c+ymPugguOgiK0UbPdqrgZKJa6Etmprg6adTpWgvvWThY8bA175mpWjjx/sg\nUbVPIrRw/vlwyCHp1ZWd2kdQhZdfTr1fnnnGSrZ3393GAhk40DzY0aNzO1rh7/79O5l3m0ziOmz7\nA61AWn9XVW0SkbnAuHyZVXWjiMwCjhORi7Di82bgCOCbwK2q+nZMm2qLlSvtBTZzJjz4ICxfblPP\nHHecjVcyaZI1rnaSjmshE1V4441UO7QnnrCeV0OGWOnZRRdZKfG221bbUqe0JEIL9fU+3BNr18Kj\nj6actA8+sKLGY46B3/3O3jPDhlXbSqdI4jpsQ4Hlqro5S9xiYLyIdFPV5jz7mAJMx768rgzCFLhc\nVS+NaU/yyfWVs99+8PWvW3XngQf610vt4VoAq0Z59NFUKdrChVZidsghcOml9gGy776dvKijw+Na\nqBbhR1L4fpk1y+qF99wTTj3VamkOOcRLsTsIcR22PkCuyRs3RtKsyZEGYBPwLibOvwVhnwMuFpEm\nVb0ipk3JI99Xzg032LAb/pVT63ROLbS0wAsvpErRnnvOwvbYA0480Ry0iROtzYrTWeicWqgWjY1W\nej1zpi3vvGON8o84Aq6+2t4vu+xSbSudMhDXYdsAbJMjrlckTVZEpDfwDNa4dEok6i4RaQV+LCJ3\nq+qb+YyYNm0adRnDP9fX11NfX9+W/eVBFd58M+WgPfmkfeXssYd95UyebIP0+FdO1ZkxYwYzZsxI\nC1u9enUxu+o8Wnj//VQJ2t//btX6dXU27t/115uTNmJE6Y7nVATXQg2xYEHKQXvsMXPawmGdJk+2\nYZ369KmykbVLCbVQXlS14AV4EOtu3T1L3NPAsjbynw60AJ/NEndSEHdGnvxjAG1oaNCq09io+uCD\nqueeq7rrrqqg2rOn6nHHqV53nepbb1XbQqdAGhoaFKt+GaOuBdUNG+zenjZNddQou7dFVA88UPWS\nS1Sfflp18+bSH9epOq6FhNDUpPrYY6oXXpjSYLduqkceqXrVVar/+pdqa2u1rezQFKOFci9xS9ie\nB44BDgBmh4Ei0hMbb+eJNvIPDdbZGmx1A4Qkz76waFFqdoHHHrMG1cOHp75yjjzSv3I6Dx1HC6rw\n6qupas5Zs6yH57BhVnp26aXWWWDrrStijlNzdBwtVJNwWKcHHrBhb9autdk8Jk+Gyy7zYZ2c2CK4\nE/g+NqL17Ej4WVi37NvDABEZAtQBi1S1MQj+Fya+LwH3ZOz7y5g3m5wZV8M5DMOqznnzrHPAIYfA\nj35kQho1yhtUd05qWwsrVlj1ZljVuXixtYOZOBF+9jPr1en3tlMYta2FatHSAv/8Z+r98uKLNoTT\nQQfBd75j75f99nMNOv8mlsOmqvNE5NfAOSJyLzATGIWNcP2EqkYrga/EiroPB2YFYX8F/glMFpEn\ngfuC8M8BE4C7VHVukf+lNCxbZsNtPPCAvchWr7ZhCCZPhksusY4DPh9Jp6fmtNDcbANmhqVoL7xg\nJWt77QWnnGIlaYce6tObObGpOS1UkxUrTH8PPGDvmZUrYdAgG27jwgtNh4MGVdtKJ6EUU8x8Ptab\n5yxgMrAcuBbI7Hqt2Ng8qQDVVhE5CpuC5CRMvAq8CVwEXF2EPe2jtdVeXmGDzuefty+aceNsXLTJ\nk21wHx+81tmSZGthwYKUg/boozaR4tZb20fHN79ppWjeW9kpDcnWQrVQtZKz8P3y3HP2zhkzBs4+\n25rTjBvnwzo5BRHbYVNVxQSUV0SqegZwRpbw9cAPg6U6rFplpWcPPGBtBj76yErNJk2Cb33LvnZ8\ncE+nDRKnhXXrrLt/6KS9+aa9CA46KPX1PnasvxyckpM4LVSTNWusDdrMmfZ+WbLEZgo49li46SZ7\nv2y/fbWtdGqQjt+QE+wrZ968VIeBf/zD2g/suy989atWijZ+vM0G7Di1xOuv28vhoYdsGqjNm627\n/6RJcOWV1hHGq/Adp3yowvz5qVK0p56yJgijRsHUqfZ+OfhgH9bJaTcd10NZv96qgUIRvfee9eA8\n+mgbO+r442HHHattpeO0jylT7L4+4gj45S/NUdttN2+o7DjlZMMGePzx1PtlwYL/397dB8tV13cc\nf395aB4KRFsYkFCNjIBmUGkSYvGBZ7UTS+1QClxkGFOVceQxlXaUojhYLSKUgDBgwUFb6QUKqDCm\nahUoJZYxkysomoIUAiMgGpQgJISHfPvHby/Ze917757N3d1zL+/XzJ0Nvz2/s997dj/c754952w5\n/vPQQ+Hii8vfl3nz+l2lppnp1bDdf/+WvWi33QbPPQevex0ceWR5l3PggeVMOGm6uOwyWLoUZszo\ndyXS9PbAA1v+vtx6a7n0zZ57whFHlL8vBx3kSTvqqqndsG3aVK4ZNfwu5777ym7ngw6C884rIdpr\nr35XKXXP4sU2a1I3PPdc+Xhz+LIb994L22+/5dI3S5bA3nu7N1s9MzUbthtvhHPOKdeReuYZ2GOP\nEp7Pf77skt5hh35XKEmaah55ZMsOgO9+t5zIM3du+fty7rnl69h23LHfVeplamo2bJ/9LLztbXDW\nWSVIb3yj73IkSdUMX59wuEm7++5yCae3vhXOPLP8fXnTm/z7olqYmg3bLbeUL7uVJKmKX/2qXLR2\nxYpydvVvfgM771xOFPjYx8rlN/waNtXQ1GzY/D41SVI7Nm+GoaEte9F+8INyKY5Fi+CUU8petEWL\nvD6ham9qNmySJI3lySdHXrz28cdhzpyy9+zDHy5703bdtd9VSpXYsEmSpr777y8nCnzzm7ByZbk4\n+r77wvvfv+Xi6Ntv3+8qpY7ZsEmSpr5jjikXkT7sMLj00rIX7dWv7ndV0qSxYZMkTX2XXFK+atCL\no2ua2qbfBUiStNUOOMBmTdOaDZskSVLN2bBJkiTVnA2bJElSzVVu2KJYFhFrImJjRDwcEedHxOwK\n69g2Ik6NiNUR8XREPNn494eq1iP1i1mQCrMgdV8nZ4kuB04BbgDOB94AnArsBxw+0eSI2B64GTgI\nuBq4rFHHXsBrOqhH6hezIBVmQeqySg1bRMwHTgauz8yjm8bXAhdHxLGZec0Eq/kkcChweGbeXrFe\nqRbMglSYBak3qn4kelzjdvmo8SuADcDx401u7B4/Ffj6cCgjYoeKNUh1YBakwixIPVC1YVsEbAZW\nNQ9m5ibgLmD/Cea/A9gRGIqIiyLiKeCpiPhlRHwmIvz2XU0VZkEqzILUA1WPYdsdWJeZz7e47xHg\ngIjYLjNfGGP+Po3bZcAm4Azg18D7gI831r+0Yk1SP5gFqTALUg9UbdhmUwLVyrNNyzw1xjI7Nm5f\nCczPzPsb/319RNwCnBAR52bmvRXrknrNLEiFWZB6oGrDtgHYZYz7ZjYtM5aNjds7m0I57F8oZwgd\nDIwbzGXLljFnzpwRYwMDAwwMDIw3TWJwcJDBwcERY+vXr+9kVWZBU5pZkIpJzEJXVW3YHgXeEBHb\nt9j9PZeyW3ys3d4AP2/c/qLFfY8BQXmXNa4LL7yQBQsWtFOvNEKr/4EPDQ2xcOHCqqsyC5rSzIJU\nTGIWuqrqSQerGnMWNw9GxAzK9XZWtZrU5AeN2z1a3Dc89suKNUn9YBakwixIPVC1Ybu2cXv6qPET\ngVmUCx4CEBG7RcQ+ETFreCwz1wIrgcURsV/Tsts01vE88J2KNUn9YBakwixIPVCpYcvMe4BLgSMj\n4oaI+EBEXABcANyWmc0fAp8LrOF3T+k+hXI8w/ci4uyIOBm4nXJq+Gcy8+dINWcWpMIsSL3RyVdT\nnQY8SHnnswRYB1wEnD1quaRcm2fkYOZdEfFW4B8a65pJCfD7M/NfO6hH6hezIBVmQeqyyg1bZiZw\nYeNnvOWWMsa1cxrvyP6i6mNLdWIWpMIsSN1X9Rg2SZIk9ZgNmyRJUs3ZsEmSJNWcDZskSVLN2bBJ\nkiTVnA2bJElSzdmwSZIk1ZwNmyRJUs3ZsEmSJNWcDZskSVLN2bBJkiTVnA2bJElSzdmwSZIk1ZwN\nmyRJUs3ZsEmSJNWcDZskSVLNVW7YolgWEWsiYmNEPBwR50fE7E4KiIhrI2JzRPyok/lSv5gFqTAL\nUvd1sodtOXABcA9wMnAdcCpwU9UVRcSfAX8JbOigDqnfzIJUmAWpy7arsnBEzKeE8frMPLppfC1w\ncUQcm5nXtLmu3wcuBS4B3lulDqnfzIJUmAWpN6ruYTuucbt81PgVlHdDx1dY12cbj39WxRqkOjAL\nUmEWpB6otIcNWARsBlY1D2bmpoi4C9i/nZVExGLgJOCYzHw6IiqWIfWdWZAKsyD1QNU9bLsD6zLz\n+Rb3PQLsHBHjNoERsS1wJfCtzLyh4uNLdWEWpMIsSD1QdQ/bbGDTGPc927TMU+Os4++APYEjKj62\nVCdmQSrMgtQDVRu2DcAuY9w3s2mZliLidcAngHMy86GKj/2SZcuWMWfOnBFjAwMDDAwMdLpKvUwM\nDg4yODg4Ymz9+vWdrMosaEozC1IxiVnoqsjM9heO+BZwGDB79O7viLgD2Cszdx1n/jeABcC7gOeG\nh4FbgWeA9wDPZOYvxpi/AFi9evVqFixY0Hbd0niGhoZYuHAhwMLMHGpnjlnQdGQWpKKTLHRb1WPY\nVjXmLG4ejIgZwH6MOui0hVdTjnf4CfCzxs99wFxg78Z//3PFmqR+MAtSYRakHqj6kei1wJnA6cDK\npvETgVnA1cMDEbEbMAd4ODM3NoY/CryixXovAzYCy4CW76KkmjELUmEWpB6o1LBl5j0RcSlwUkTc\nAKwA5gOnALdlZvOHwOcCJwAHA7c35t/Sar0RcQHw28z8WuXfQOoDsyAVZkHqjZb4Kn0AAAycSURB\nVKp72ABOAx6kvHtaAqwDLgLOHrVcUq7N0672D6aT6sEsSIVZkLqscsOW5SyFCxs/4y23FFja5jpf\nW7UOqd/MglSYBan7Ovnyd0mSJPWQDZskSVLN2bBJkiTVnA2bJElSzdmwSZIk1ZwNmyRJUs3ZsEmS\nJNWcDZskSVLN2bBJkiTVnA2bJElSzdmwSZIk1ZwNmyRJUs3ZsEmSJNWcDZskSVLN2bBJkiTVnA2b\nJElSzVVu2KJYFhFrImJjRDwcEedHxOw25r4iIk6LiG835m2IiP+NiC9GxB6d/QpSf5gFqTALUvd1\nsodtOXABcA9wMnAdcCpwUxtz3wKcD2wGvgCcBHwTOB74cUS8voN6pH4xC1JhFqQu267KwhExnxLG\n6zPz6KbxtcDFEXFsZl4zzirWAHtn5oOj1rsC+E/gHODoVhOlOjELUmEWpN6ouoftuMbt8lHjVwAb\nKO+IxpSZD48OZWP8e8CvgX0r1iP1i1mQCrMg9UDVhm0RZbf1qubBzNwE3AXs30kREbETsCPweCfz\npT4wC1JhFqQeqNqw7Q6sy8znW9z3CLBzRFT6mLXhLMrHs1/uYK7UD2ZBKsyC1ANVG7bZwKYx7nu2\naZm2RcRRwEeB/8jMr1SsR+oXsyAVZkHqgarvejYAu4xx38ymZdoSEUuAr1J2pR/b7rxly5YxZ86c\nEWMDAwMMDAy0uwq9TA0ODjI4ODhibP369Z2syixoSjMLUjGJWeiqyMz2F474FnAYMHv07u+IuAPY\nKzN3bXNdfwp8jXIa+OGZOeHWiYgFwOrVq1ezYMGCtuuWxjM0NMTChQsBFmbmUDtzzIKmI7MgFZ1k\noduqfiS6qjFncfNgRMwA9mPUQadjaQrlT4F3thNKqWbMglSYBakHqjZs1zZuTx81fiIwC7h6eCAi\ndouIfSJiVvOCEfEu4EbKtXcOz8wnK9Yg1YFZkAqzIPVApWPYMvOeiLgUOCkibgBWAPOBU4DbMrP5\nQ+BzgROAg4HbASJiIfANICln/iyJiNGPcTVSzZkFqTALUm90cqr1acCDlHdPS4B1wEXA2aOWS8q1\neZrtC/xe498XjrF+g6mpwixIhVmQuqxyw5blLIULGTtYw8stBZaOGvsK4CnamhbMglSYBan7Ovny\nd0mSJPWQDZskSVLN2bBJkiTVnA2bJElSzdmwSZIk1ZwNmyRJUs3ZsEmSJNWcDZskSVLN2bBJkiTV\nnA2bJElSzdmwSZIk1ZwNmyRJUs3ZsEmSJNWcDZskSVLN2bBJkiTVnA2bJElSzVVu2KJYFhFrImJj\nRDwcEedHxOwK61gSESsj4umIeCIirouIeVVrkfrJLEiFWZC6r5M9bMuBC4B7gJOB64BTgZvamRwR\nRwI3AzOAM4DzgAOBOyJitw7qqYXBwcF+l/A76lgT1LeuDpiFFur4/NaxJqhvXR0wCy3U8fmtY01Q\n37rqpFLDFhHzKWG8PjP/KjO/lJlnAH8DHBoRx04wfzvgC8BDwDsy8/LM/BzwbmA34FMd/A61UMcX\nWx1rgvrWVYVZGFsdn9861gT1rasKszC2Oj6/dawJ6ltXnVTdw3Zc43b5qPErgA3A8RPMPwh4FXBl\nZm4cHszMu4HbgGMiYtuKNUn9YBakwixIPVC1YVsEbAZWNQ9m5ibgLmD/CebvDyRwZ4v77gR2Avau\nWJPUD2ZBKsyC1ANVG7bdgXWZ+XyL+x4Bdm7s3h5v/vCyreYDzK1Yk9QPZkEqzILUA+OFqJXZwKYx\n7nu2aZmnxpnPGOt4dtQyrcwEWLNmzTiL9Mf69esZGhrqdxkj1LEmqF9dTa+nmRWmmYUx1O35hXrW\nBPWryyxMrro9v1DPmqB+dXWYha6q2rBtAHYZ476ZTcuMNx/KmUCdzJ8HcPzxEx0S0R8LFy7sdwm/\no441QW3rmgd8v81lzcI46vj81rEmqG1d8zALk6KOz28da4La1jWP9rPQVVUbtkeBN0TE9i12f8+l\n7BZ/YYL5w8ve22I+tN4tPuzbwPuAtWx55yVtrZmUUH67whyzoOnILEhFJ1noqqoN2yrgncBiYOXw\nYETMAPajnNEz0fwADgBuGXXfAZRd5veNNTkznwD+rWLNUjuqvoMyC5quzIJU1GLP2rCqJx1c27g9\nfdT4icAs4OrhgYjYLSL2iYhZTcv9F/AY8MHmK2BHxJspp3Zfl5kvVqxJ6gezIBVmQeqByMxqEyIu\nBk4Cvg6sAOYDpwD/nZmHNS33ZeAE4ODMvL1p/CjgGuBHlOv0zKEE/UVgUWY+thW/j9QzZkEqzILU\nfZ18NdVplK8OmQ9cAhwNXAQcMWq5pFybZ+Rg5vXAn1OONfg88LeUd1hvN5SaYsyCVJgFqcsq72GT\nJElSb3Wyh02SJEk91PeGLSI+HhHXRcT/RcTmiHigw/UsiYiVEfF0RDzRWOe8ftUUEbc15o7+eTEi\nFnSwvr0i4pyI+J+I+GVEPBURP4yIM5sP1G1jPZO2nSarri5sq70j4qsR8dOIeDIinomINRFxQUTs\nVmE9k7qt2ng8s9De+sxC+zWZBbPQznrMQvvr6WkWRjx2vz8SjYjNwBPAEOU76dZn5p4V13Ek8O/A\nD4ErKQesLgNeoByw+os+1HQr5XiO0ymnrDdbkZlPVlzfPwIfAW6ifL/e88AhwDHA3cCfNL67b7x1\nTOp2msS6JntbHQqc2ajn55Tf743AXwPrgf0yc90E65j0bdVG3WahvfWZhfZrMgtmwSwwdbMwQmb2\n9QeY1/TvHwMPVJy/HeWiig8As5rG39zYiJf3uqbGvFs7mTfO+hYAO7YY/zTlTKqP9Ho7TUZd3dhW\n4zzOUZQDns/ox7bq9uvOLJiFCrWahR7X1I3n1yxMyjasdRaaf/r+kWhmrt3KVRwEvAq4MjM3Nq33\nbsoFG4+JiG17XNNLothxa9eTmUOZ+dsWd11Lefex7wSrmPTtNEl1vWSyttU4Hm7cvnKC5bqyrSZi\nFtpjFiaFWTALYBag5llo1veGbRLsTzlV/M4W990J7ATs3dOKtpgLPA2sb3zefUNE7DPJj/FHjdvH\nJ1iu19up3bqGTfq2iogZEfGHETE3It4FXE7ZBismmFrn19R46ly3WTALvVTnus2CWehI1a+mqqPd\nG7etvmtueGwusKY35bzkAeAOyoUgXwTeQrmQ5KER8fbM/MnWPkBEbAN8gnJ8wERfzdKz7VSxLuje\ntvog8IWm/34QOD4zV46x/LC6vqYmUte6zYJZ6LW61m0WzELHpkPDNnzGSasDGJ8dtUzPZOYHRg3d\nGBE3U3ad/hPw7kl4mIsoL+KPZ+bPJli2l9upSl3d3FZfo4RnB+CPKRfm3LmNebV8TbWhlnWbBbPQ\nB7Ws2yyYha0xHRq2DY3bGS3umzlqmb7KzDsi4nbgkIiYkROcJTOeiPg05atgLs/M89qY0pPt1EFd\nLU3GtsrMR4FHG/95U0TcCKyKiNmZ+blxpk6Z19QoU6Zus9A+s9CRKVO3WWjfyz0L0+EYtuENP7fF\nfcNjrXZh9staYFsmPsBxTBHxKeDvgS9l5kfanNb17dRhXeNZy1Zuq2aZ+WPK6dgT1TbVXlPDplrd\nazEL7VqLWahiqtW9FrPQrrW8TLMwHRq2VZSzTg5ocd8BwFPAfT2taHx7U04B/nUnkxsv/k8CV2Xm\nhypM7ep22oq6xrNV22oMs4A/mGCZqfaaGjbV6jYL7TML1Uy1us1C+16+Wej2dUOq/DDBtW2A3YB9\nGHkNlOFrozwIzG4aH742yhf7UNNOwDYtln0P5XovN3dYyycb86+aYLmebqetrGvStxWw6xjjhzR+\n1+/08zXVxdedWejzdjILZqGXz+8kvObMwhTKQh2+6eB44DWUzvVkYHvKAYUAD2XmV5uW/TJwAnBw\nZt7eNH4UcA3lbJIrKFcfPp1yZsmizHyslzVFxHsby99MOdPlBcrBlu8D1gFvz8z7K9Z0EuXMlofY\nEoRmj2fmd8eqqTE+qdtpMurq0ra6kXK9nFsadc0EFgLHUk4RPyQzfzRWTY3xSd9WbdRtFtqrySy0\nX5NZMAtmgambhRG63RG20fXe2vhlW/3cMmrZqyhP3IEt1rME+H5jwz9BuUDfa/tRE/B6ypP6M8pu\n0o2Nf18MvKrDmq4ap6YRdfVqO01GXV3aVkdRvhLlIcpBoM8APwWWA3v04zVlFsyCWTALZsEsbM1P\n3/ewSZIkaXzT4aQDSZKkac2GTZIkqeZs2CRJkmrOhk2SJKnmbNgkSZJqzoZNkiSp5mzYJEmSas6G\nTZIkqeZs2CRJkmrOhk2SJKnmbNgkSZJqzoZNkiSp5v4fJuageGYMA7AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abb0106d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,2,3]\n",
    "plt.figure(1)\n",
    "fig, axes = plt.subplots(3,3)\n",
    "fig.text(0.5, 1.6, 'Accuracy of the Pig Identification',\n",
    "             horizontalalignment='center', color='black', weight='bold',\n",
    "             size='large')\n",
    "fig.subplots_adjust(top=1.48, left=0.07, right=0.97,\n",
    "                    hspace=0.3, wspace=0.3)\n",
    "((ax1, ax2, ax3), (ax4, ax5, ax6), (ax7, ax8, ax9)) = axes \n",
    "ax1.plot(x,[vgg16epoch1trainacc,vgg16epoch2trainacc,vgg16epoch3trainacc],color='red')\n",
    "ax1.plot(x,[vgg16epoch1valacc,vgg16epoch2valacc,vgg16epoch3valacc],color='blue')\n",
    "ax1.axis([1,3,0,1])\n",
    "ax1.set_title('VGG16', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax2.plot(x,[vgg19epoch1trainacc,vgg19epoch2trainacc,vgg19epoch3trainacc],color='red')\n",
    "ax2.plot(x,[vgg19epoch1valacc,vgg19epoch2valacc,vgg19epoch3valacc],color='blue')\n",
    "ax2.axis([1,3,0,1])\n",
    "ax2.set_title('VGG19', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax3.plot(x,[Xceptionepoch1trainacc,Xceptionepoch2trainacc,Xceptionepoch3trainacc],color='red')\n",
    "ax3.plot(x,[Xceptionepoch1valacc,Xceptionepoch2valacc,Xceptionepoch3valacc],color='blue')\n",
    "ax3.axis([1,3,0,1])\n",
    "ax3.set_title('Xception', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax4.plot(x,[inceptionepoch1trainacc,inceptionepoch2trainacc,inceptionepoch3trainacc],color='red')\n",
    "ax4.plot(x,[inceptionepoch1valacc,inceptionepoch2valacc,inceptionepoch3valacc],color='blue')\n",
    "ax4.axis([1,3,0,1])\n",
    "ax4.set_title('Inception_V3', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax5.plot(x,[resnetepoch1trainacc,resnetepoch2trainacc,resnetepoch3trainacc],color='red')\n",
    "ax5.plot(x,[resnetepoch1valacc,resnetepoch2valacc,resnetepoch3valacc],color='blue')\n",
    "ax5.axis([1,3,0,1])\n",
    "ax5.set_title('ResNet', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax6.plot(x,[inceptionresnetepoch1trainacc,inceptionresnetepoch2trainacc,inceptionresnetepoch3trainacc],color='red')\n",
    "ax6.plot(x,[inceptionresnetepoch1valacc,inceptionresnetepoch2valacc,inceptionresnetepoch3valacc],color='blue')\n",
    "ax6.axis([1,3,0,1])\n",
    "ax6.set_title('InceptionResNet_V2', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax7.plot(x,[densenet169epoch1trainacc,densenet169epoch2trainacc,densenet169epoch3trainacc],color='red')\n",
    "ax7.plot(x,[densenet169epoch1valacc,densenet169epoch2valacc,densenet169epoch3valacc],color='blue')\n",
    "ax7.axis([1,3,0,1])\n",
    "ax7.set_title('Densenet169', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax8.plot(x,[densenet161epoch1trainacc,densenet161epoch2trainacc,densenet161epoch3trainacc],color='red')\n",
    "ax8.plot(x,[densenet161epoch1valacc,densenet161epoch2valacc,densenet161epoch3valacc],color='blue')\n",
    "ax8.axis([1,3,0,1])\n",
    "ax8.set_title('Densenet161', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax9.plot(x,[densenet121epoch1trainacc,densenet121epoch2trainacc,densenet121epoch3trainacc],color='red')\n",
    "ax9.plot(x,[densenet121epoch1valacc,densenet121epoch2valacc,densenet121epoch3valacc],color='blue')\n",
    "ax9.axis([1,3,0,1])\n",
    "ax9.set_title('Densenet121', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abb92c400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+WjZXnTmOW8leWdEDMD9cL4QQ5ZNrYnUw1+cZ/DCV4YeKit4TUwghahENIwoh4uRFM/sY\neB7fc1UPbIIfIqoP8jjg7GTEE0KI6qOeLSFEbJjZLPyE5Vy+q5bjPX1rsrIQoscgY0sIERtmdiSw\nB95Z7Zr4YcOFwDv4/faudc69k5iAQgiRADK2hBBCCCEqiFYjCiGEEEJUEBlbQgghhBAVRMaWEEII\nIUQFkbElhBBCCFFBZGwJIYQQQlQQGVtCCCGEEBVExpYQQgghRAWRsSWEEEIIUUFkbAkhhBBCVBAZ\nW0IIIYQQFUTGlhBCCCFEBZGxJYQQQghRQWRsCSGEEEJUEBlbQgghhBAVRMaWEEIIIUQFkbElhBBC\nCFFBZGwJIYQQQlQQGVtCCCGEEBVExpYQQgghRAWRsSWEEEIIUUFkbAkhhBDdGDMbZWbtQZiStDw9\nERlbEczsgVCjvD1PvvGhfLeF4uvM7Htm9lcze8/MFgXhXTN70szOM7Ov5Kizr5kdYWb/NLP3zazV\nzBaY2Vtm9oiZnWFmW0bKbGBmvzOzR81sXkimti7Osy441qNmNtvMlpjZh8F5HV/sdRPdj56gC2Y2\nwMx+bmb/C2RrMbNXzOwcM2so5bqJ2sHMtjCzxaG2ckSWPBeG0j8yszWSkDUfgTF1bhC+kyObC0J7\nFUUTGZxzCqEAHIxvjG1AC9CYJc/6QXom7BbEfw54ISjfHsmTCe3AjCx1jgDeLqDsfyPlvpMlfzvQ\nluccBwKP5jnW80nfB4XkQ3fXBWA14LUcx2gHJgKDk74PCpUNwCmhNjAPGBZK2wFYEWoX+yQtb45z\nODekL3/Jkt4IfD0II5KWtyeG3ogodwPzgcFAP+AA4PpIntGABd+nA48EbzuPAsPwbw8rgJuAfwOz\ngFWAL+EfCOuGKzOzTYGHgzwOaAWuAR7HK/8awEhg/yzytgLjgReB5cBZBZzjlcBOwbHeAy4FJgXn\n+3lg7QLqEN2f7q4LvwG2CI4zFfgpsBg4B9g2kPFi4Mgu6hG1zcXAvnjDqhG4AdjZzAbi23umfd/g\nnLs3EQkLx2WNdG4h8N8qyyLCJG3tpTHgjZHMm874LOmvhtIvCOKuoOPNYimwa576vxz5/UCo7Dxg\nqzxlt86TNipUT663+S1Dss8E1kz6eiukN3RzXZgZkv3wUPzXIvKvkfR9UKhsADbEv1hk2sMpeCM/\n0w7eAwZGygwCfg48H7TVJcA04G/A5yN5twVuA94P2tSn+JeKlXrK8C8WmeOOBY4GXsG/CHwA/Bbo\nG+TdIJQ3W3gvyDcqGhc55r7AfcDHwDL8S9GDwPey5J0aqmtn4FTgreD83wPGJX0/0xgSFyCNIfJn\nuwIYGkrbOvwnDmwK9A6ULdPVfF0Rx1qbzsMXZ5chdyEPmJ+HZL8P/+b2fqDIrwI/Bizpe6CQjtDN\ndWFJ6FjfC8VvFTmv/ZK+DwqVD8BRoXu+NNLuR0XybghMibSTcNg3lPe4oI5sQ9ztwK8idT8WyhMd\n5s6UuS/Iu0GWtPAx3g3yjQqlvRc53qU5ymbirojknxLK82YW+dqA7yd9P9MWNEE+C865Z/CWOvgu\n5ENDyYdlsuHnNr2FH3obFMrzYLg+M9vOzLaPhMxQ3YjgGBYta2a9spTb3swGl3F6Xwx93xP/5jQE\n6IPv9boIuLWM+kU3opvrwqTQsU4ws/XMbBXg9Ei+Dcs4hqgRnHPXAf/Ct4nMFBsHXOqceyKS/Ta8\noeOABfiX2G/hdeJveIMDM9sCb8xYEPcrYHfgh/jeLYAzzGzHSP0ZPRiO78naC//fnJHpW2bWBMwA\nvokf+sykPRDEfRM4MN85m9m+wAl0TJ6/KDjW70Jxx5jZ97IVBzbCzxf7NhC+RifnO26PJGlrL60B\nOIMOK/31IK4OPy8lE39MEP91Or8Z7Bypa1EoPRMyZQ+JlP1cqNzqWcq1A7vnkLmQt/mHIsf7G7AH\nHRMsM28oWY+h0PNCN9aF74aOFa03/Jb+s6TvgUJ1At7QiN7/TSJ5toj8V+6dp74/hPI+CGwfCteG\n0m4NlXksFH97pL57Qml3h+LD/9/ZJshn7dkC/h6K/0ekzF9DafeG4qeE4i8NxX8lpD+zkr6XaQvq\n2crNzfhGY8BwMxsB7ELHhN6lwB3B93nBZ2Zy4uqRutrpeEtwdJ7EOC+SN1o2WibrBMgiWBL6vhw/\nV+VB59wvgWfpeNPfq8zjiO5Dt9QF59zdwPfxc1DCdY/H9xhkmFvOcURtYGb9gUvwbcBCSZdFsm6R\nKYJv+/fnqXaL0PfdgP+EQmbhhQFfyFH+qTy/P5/nuIUyvMBjDSc740Pf54S+r1aOUN0RGVs5cM59\nSOeGNIbOwyb/dM7ND36/BSwM5d0tUtcg51wv/NyosBKDX14efoDsFio3xznXKyhrlG9ogZ/AmWGO\nc64l9Htq6Hs5wzOiG9GNdQHn3J3OuY3xD66v4OeN7UlnQ+/lOI4lUs/vgU2C7zPxq1sN2DUm34PR\nl4xwqFWfbp+Gvq9ITIoaQMZWfm4IfW/CDztkuDHzxTm3Aj+GnxlnH2Nm2xdyAOfcR/ihvUzZn5jZ\nZuWJnZfwuPrqZjYg9Huj0PewUSbEDaHv3UUXwsd+zzk3wTk3Gz9nq1+Q9AF+tZnoxpjZrviJ7BmO\nAH4W+v1bM8sYYm+E4vuSfxRgUuj7bZkXhmjAL8rIRlR3wr/fCX1vD30v5rk+ucBjTUaUhfxs5edu\n/OTHRrx/nwwfEZn4ix8z3wvv5LEP3t/QNcAj+CXFQ8jdWzQOeCZIXxV4zswux3c1L6Zjgu5Kb/Rm\ntjrwjeDnFyJpGU/Crc65h4Pv9+B7FYYB9cCNZnYtfq7NV4M87XQMCwkB3VAXzGwY0Ixv628BA/AT\nfccGeR1wlnMu/CAT3QwzGwRcF/x0wJXOuYeAh4IJ5Lvi28ZNZra9c+4NM3sW2A7fDm81s98BE/A9\not/GG1b34V9Sfow3gA41sxb8JPyleP3YEtgHuADvi66TaMAB5rfXeQI/dP/tUHr4Pzo8hPdNM9sL\nr2sfOefezXP6N+BfnAzYx8z+gNfTUUB4Uvz1eeoQhZD0pLG0Bzr7WslMCvxtjrybAC+x8tLZbMtq\nD4+U3ZbOS2rzld0xVG5UlrzREF3q+3W8IkaPk6lfflIUVgrdTRfI7aMoU/e5SV9zhaq06xtD9/4t\nYEAobQjekMm0i7OC+I3w0y6ytdFsrh+W52jLmfxjQvnDE+RfzJH/gcg5DKfD030479VBelg3os+D\n/8tzHm1kd/2QSd8hFB/Wp5w7mPTUoGHErrmBlcfXb8yW0Tn3Dt679WH4HqQP8RPSl+LnADyJ91q9\nnXPuhkjZF4DNgePxQykz8c7lMo7sHsH3GHzBOfd49NAFhPCx/otfZn9DIONy/B/KA/jtVi4u4LqI\nnscNdC9dmIs3IF/HT85fFsh5B/AN5xeNiG5M0OM5mo6dDg53zrVm0p1z0/HtMNNuzjazLznnpuDd\n6JyLn2u4EN9+ZuB7gSeF6rgc3wt2K356xlL8y+5k/Iq/McBdOUS8NEh/Ga8/0/ErHPcLZ3LOTcbr\n2uuBHNnae67nwcn43q3MDg+Z58EjwIHOuWOzyLVSPfmOIQLnlUIIIYRIHjN7DN8T5YAjnHPR4UVR\ngxTds2VmDWZ2ppm9YmYLzGyWmT1tZmO7Li1E90G6IIRHuiBEfoqaIG9mhu9q3A4/pHApfuJgE3C9\nmQ13zp0Rt5BCpA3pghAe6YIQXVPUMKKZbYffOfwi59ypofje+D2SVnXOyZmZ6PZIF4TwSBfiJRhG\n3AE/jHikhhG7B8W6fsjseTYzHOmcW2Fms/HLvIXoCUgXhPBIF2LEObdT0jKI+CnW2Hoev2rndDOb\nBjyH7y4+HL+67YexSidEepEuCOGRLgjRBUWvRgy8QV8HbBqKXoD3E3JPjLIJkWqkC0J4pAtC5KcU\nD/ItwGvAP/Hj9Kvh/ZA0m9m+zrlHsxUKvDvvgXcEtyRbHiFKoB/eq/iDzrk5XeSNG+mCSBPSBSE8\nSepCdorxgIrfv6kV+EEkvj/wLvAeQW9ZlrKHUJjDQQWFUsIh1fQGjHRBIb1BuqCg4ENVdSFfKLZn\naxx+4807w5HOucVmdh/+TWZDvDv/KFMBbrnlFjbffPMiD1tZxo0bx8UXp8tpehplgvTJNWnSJEaP\nHg1B+6oi0oUqkUaZIH1ySRfiJW33F9IpE6RPrgR1ISfFGlvrBZ+98tSVq84lAJtvvjkjRowo8rCV\nZfDgwZKpQNIqF9UfgpAuVIk0ygTplQvpQiyk8f6mUSZIr1ykaGi6WA/yb+B3Bz88HGlmq+D3apoL\nvBOLZEKkG+mCEB7pghBdUGzP1iX4TTF/a2ZfBJ4GVgeOBtYGjnNOmy2KHoF0QQiPdEGILijK2HLO\nvW9m2wLnALsABwGLgZeAcc65f8YvohDpQ7oghEe6IETXFO36wTk3BTiiArIkRlNTU9IirEQaZYL0\nypUE0oXqkEaZIL1yJYF0oTqkUSZIr1xpotg5W92SNDaUNMoE6ZVLxEMa728aZYL0yiXiIY33N40y\nQXrlShMytoQQQgghKoiMLSGEEEKICiJjSwghhBCigsjYEkIIIYSoIEUbW2Z2rpm15wlLKyGoEGlC\neiBEB9IHIfJTtOsH4O/A21nivwScBtxTlkRC1AbSAyE6kD4IkYdS/Gy9BrwWjTezUfhdtq+LQS4h\nUo30QIgOpA9C5CeWOVtmNgDvNfhD4ME46hSi1ihaD9rbKy2SEImh54IQHcQ1Qf77wCDgeu2BJXow\nxelBc3PFBRIiQfRcECIgLmPrKKAduD6m+oSoRYrTgz/9CV59taICCZEgei4IEVC2sWVmmwLbA486\n56aVL5IQtUdJejBsGBx6KCxZUlHZhKg2ei4I0ZlSViNGORo/AfLaQjKPGzeOwYMHd4pramrS3kqi\nS5qbm2mODL3Nnz8/IWlWoig9ABi3yioMnjgRttzSB6QLojBSrgug54KoEjWgCwBYOUPpZtYLP/mx\nFzDEObc8T94RwIQJEyYwYsSIko8pRJiJEycycuRIgJHOuYlJyFCMHgT5O3ThiSfglFPg4Ydh112r\nIa7opqRBF0DPBZE8adGFMOUOI+4LrA3c3NUDRohuTOl6cPLJ3sgaOxbmzKmIcEJUGT0XhIhQrrF1\nFL6r+C8xyCJErVK6HtTVwQ03+HlbP/whaNGWqH30XBAiQsnGlpmtB+wBPOecez0+kYSoHWLRgyFD\n4Kqr4O9/hxtvjFU+IaqJngtCZKecnq2xQfmCJwQL0Q2JRw8OOAAOPxxOPBHefTcOuYRIAj0XhMhC\nycaWc+43zrlezjl1FYseS6x6cOmlsNZacNhhsGJFDNIJUV30XBAiO3E5NRVClEtjI9x8Mzz3HPz6\n10lLI4QQIiZkbAmRJr7+dfj5z+G88+DZZ5OWRgghRAzI2BIibfz857DNNjB6NCxcmLQ0QgghykTG\nlhBpo74ebrkFPvoIxo1LWhohhBBlImNLiDSyySbwf/8H110Hd9+dtDRCCCHKoCRjy8xWNbMLzext\nM1tsZp+Y2Xgz2z5uAYVIMxXVhSOPhO9+F44+GmbMiEFaISqHngtC5KbojajNbBjwBDAAuA54CxgM\nfBEYEqt0QqSYiuuCGVx9NXzxi3DEEfDAA97jvBApQ88FIfJTtLEF3IrvEdvKOfdJzPIIUUtUXhfW\nWAOuvx6+9S3405/8XopCpA89F4TIQ1GvyWa2A7A98Dvn3Cdm1tvM+ldGNCHSS1V1YY894KST4Kc/\nhddeq8ghhCgVPReE6JpixyT2xG8w+qGZ3QssBlrM7E0zOzR26YRIL9XVhd/+1k+aP/RQWLo09uqF\nKAM9F4TogmKNrc0AA64BVgEOA44AlgI3m9nYeMUTIrVUVxf694dbb4XJk+HMM2OtWogy0XNBiC4o\n1thqDD4XADs55253zt0I7ADMA7THiOgpVF8XvvQlv43PRRfBo4/GXr0QJaLnghBdUOwE+cX47uJm\n59xnO+U65+aZ2T3AYWa2mXPuzVwVjBs3jsGDB3eKa2pqoqmpqUhRRE+jubmZ5ubmTnHz589PSJqE\ndGHcOL8qcexYeOUVWG21cs9D1CDSBSE8KdOF3DjnCg7A5UAbcFyWtN8EadvlKDsCcBMmTHBCxMWE\nCRMc/o9LieETAAAgAElEQVR+hCuiLZcbEtWFDz5wbtVVnTvgAOfa20urQ3Q7eqQuCJGFpHQhXyh2\nGPF5/Nj8+lnShgafWvYregLJ6cL668NVV8Gdd8JNN1XkEEIUgZ4LQnRBscbWP4CFwGgzG5CJNLN1\nge8Abzrn3otRPiHSSrK6cOCBfijxhBPgPamcSBQ9F4TogqKMLefcPOBUvEfg58xsnJn9DHgGqAdO\njF9EIdJHKnTh0kthzTXhsMNgxYqu8wtRAVKhC0KknKL3/nDOXQN8D/8mcx5wBjAJ2NE5pyVSoseQ\nuC4MGgQ33wzPPgu/+U3FDydELhLXBSFSTinb9eCc+we+61iIHk3iurD99nDWWfDLX8Luu8NXv5qY\nKKJnk7guCJFitKutELXO2WfDyJEwejQsWpS0NEIIISLI2BKi1qmvh1tugZkzvR8uIYQQqaL6xtab\nOf3aCSFK5fOfh0sugWuvhX9oJEcIIdJE9Y2t446D11+v+mGF6PYcdRR85ztw9NG+l0sIIUQqKNrY\nMrP2HGFBQRWssQbsuiu8/XbRwgqRJsrWhfgF8j1b9fVwxBHQ3p6IGKLnkTpdECJllLQaEXgSuDoS\nt7ygkldcASedBDvvDE8+CRttVKIIQqSC0nWhEqyxBtxwA3zrW3DZZXCiXByJqpEuXRAiRZRqbL3n\nnLutpJKrrQaPPAI77AC77OINrvWz7fIgRE1Qui5Uij328EbWaaf5l5ott0xaItEzSJ8uCJESSp6z\nZWb1ZtZQUuH11oPx4/0wxy67wEcflSqGEIlTli5Uit/9DjbeGA45BJYuTVoa0UNIpS4IkQJKNbYO\nAFqBhWb2sZldamaDiqph2DB49FHvF2jXXWH27BJFESJRyteFStC/P9x6K0yaBD//edLSiJ5BOnVB\niBRQirH1HHAufmuGMcCjwAnAk+FNSAti4429wTVrlvd+PXduCeIIkRjx6UIl2Hpr+PWv4Y9/9D3J\nQlSOdOuCEAlT9Jwt59zXIlG3mNmrwAXAyUBxm7QNH+7ncO24I+y5Jzz8MDQ2FiuWEFUndl2oBKec\nAvffD2PGwCuv+DmTQsRMTeiCEAkSl5+tPwDLgL1LKr3VVvDQQzB5Muy9N7S0xCSWEFWnPF2Im7o6\nuPFGr1M/+hE4l7REoueQLl0QIkFKXY3YCefcCjObAazRVd5x48YxePDgTnFNTU00NTXBAw/AbrvB\nfvvBvfdCv35xiCe6Cc3NzTQ3N3eKmz9/fkLSZCc2XYiToUPhqqvgoIPg29/2vVyippEuCOGpBV0A\nMBfDm66Z9QUWAs8450blyDMCmDBhwgRGjBiRu7InnvDDiTvvDHfdBX36lC2f6L5MnDiRkSNHAox0\nzk1MWp5YdSFuxo6Fu++Gl1+Wf7tuiHRBCE/adAGKHEY0s1wTPn4F9ALuKVuiUaP83m4PP+yXra9Y\nUXaVQsRNVXQhbv70J1h9dTjsMOmViI2a1AUhqkyxw4g/N7PtgMeA94GBwF7ATsAzwJ9jkWr33eHO\nO2H//f3b+E03Qa9esVQtRExURxfiZNAguPlm/0Lz29/KJYSIi9rTBSGqTLHG1uPA5vilvasDbcDb\nwBnAxc65ZbFJts8+0Nzs55n06wfXXOMn+wqRDh6nWroQJ9/4BpxxBvziF/6l5itfSVoiUfs8Ti3q\nghBVpChjyzl3D9XsEj7gAL+SaswYb3D9+c9+s10hEqbquhAn557rV/+OHg0TJ8LAgUlLJGqYmtYF\nIapE+ruKRo+Gq6+Gyy/3e71p6boQ5VFfD7fcAtOnez9cQgghKkosrh8qztFHw5IlfnPd/v3h/POT\nlkiI2mbTTeHii+GHP/S+7b7znaQlEkKIbkttGFsAJ5wAixfD6ad7g+vMM5OWSIja5gc/8N7ljz4a\nvvpVWGedpCUSQohuSfqHEcOcdhr88pdw1llwySVJSyNEbWPmF5706gVHHKEheiGEqBC1ZWwBnH02\n/OxnMG4cXHll0tIIUdusuSZcfz38+99w2WVJSyOEEN2Sso0tM+tvZu+ZWbuZXRqHUF0cEH79azjp\nJDj2WLjhhoofUohCqLouxMWee/ph+tNOgzfeSFoa0Q2oWV0QokLE0bN1Pt63SvXGIMz8MOIxx8BR\nR8Htt1ft0ELkofq6EBe//73fwufQQ2Hp0qSlEbVP7eqCEBWgLGMr2NfqZOBcoLoOsMzgiiu8a4jR\no/2eb0IkRKK6EAf9+8Ntt8Hrr/uheiFKpOZ1QYgKULKxZWZ1wDXA/UAylk5dHVx3HXzve97T/P33\nJyKG6NmkQhfiYOut4YIL4MIL4bHHkpZG1CDdRheEiJlyerZOATYFTohJltLo3ds7aNxrL7+X4qOP\nJiqO6JGkQxfi4JRT/N6JY8bA3LlJSyNqj+6jC0LESEnGlpltBPwC+KVz7oNYJSqF+nq44w7YaSfY\nd1946qmkJRI9hNTpQrn06uU3fl+0yC9AkTsIUSDdTheEiJFSe7auBN4BLo5RlvLo2xfuuss7Z9xr\nL3j++aQlEj2D9OlCuQwd6t2q3HGH7zUWojC6ny4IERNFG1tmNhrYBTjWOdcWv0hl0L8/3HMPbLUV\n7LEHvPRS0hKJbkyqdaFcDjrILzw5/niYOjVpaUTK6da6IEQMFLVdj5n1Af6In/z4iZltHCStH3wO\nDuJmO+fmZ6tj3LhxDB48uFNcU1MTTU1NRQmek4ED/UT5XXeF3XaDxx+HLbeMp26RKM3NzTQ3N3eK\nmz8/azOrODWhC+Xy5z/Df/4Dhx3m9ahXr6QlEgHSBSE8adKFvDjnCg7AYKAdaAs+w6Et9HlKlrIj\nADdhwgRXFebMce6LX3RunXWce+ut6hxTVJ0JEyY4vC+fEa6ItlxuqCldKIcnn3Surs65Cy5IWhLR\nBdIFITxJ6UK+UOxG1C3AAVni1wSuAB4ArgVeLbLe+FltNXj4YdhxR9h5Z3jySe+0UYh4qB1dKIdv\nftNvj3XuubD77rDNNklLJNJHz9AFIcqgKGPLObcCuCsab2YbBF/fdc6lx7fKWmvBI4/ADjvALrt4\ng2v99bsuJ0QX1JwulMMvfgEPPeS9y0+cCA0NSUskUkSP0gUhSiTOjagz3XbpYr31YPx4aGvzBtdH\nHyUtkej+pFMXSqW+3q9K/PBD+MlPkpZG1BbdSxeEKJFYjC3n3DTnXC/n3Mlx1Bc7w4Z5g2vRIj9x\nfvbspCUS3ZTU60KpbLYZXHQRXHWVX/ErRBd0W10QogTi7NlKNxtv7L3Lz5rl557IO7YQxXHMMbDP\nPn7zd/UQCyFEwfQcYwtg+HA/h2vaNNhzT1i4MGmJhKgdzODaa/2epEceKe/yQghRID3L2ALv8PSh\nh2DyZNh7b2hpSVoiIWqHtdaC66+HBx6Ayy9PWhohhKgJep6xBTBypH9YTJwI++0HS5YkLZEQtcNe\ne3nP8qeeCpMmJS2NEEKknp5pbAF87Wtw333w9NNwwAGwbFnSEglRO/z+97Dhht4dhHRHCCHyUpSx\nZWabmtktZvaGmc0zsxYzm2RmfzSzdSolZMUYNQr+8Q/v/PSQQ2DFiqQlEjVCt9OFYhkwAG69FV57\nDc4+O2lpRIL0eF0QogCK9SC/PrAO3oHdh8AKYCvgGOAgM9vaOVdbfhV23x3uvBP23x/GjoWbbtIe\ncKIQup8uFMuIEXD++XDGGX7ByY47Ji2RSAbpghBdUKwH+fHA+Gi8mf0H+CtwOHBhLJJVk332gdtu\ng4MPhn794Jpr/IorIXLQbXWhWE491c9/HDMGXn4ZVl01aYlElZEuCNE1cVkU7weftftPe+CBcOON\nfqXViSdqWbsoldrXhWLo1cv3Bi9YAMcdJ70RYXqWLgiRh2KHEQEws77AQKAfsCXwW/yWDPfHJ1oC\njB7tVyb+4AfQvz/84Q/et5AQOei2ulAMw4bBFVf4eY/f/rafNC96HNIFIXJTkrEFHA38KfR7CjDa\nOfd0+SIlzNFHe4PrxBO9wXX++UlLJNJN99WFYmhq8qt7jzsOtt/er1QUPQ3pghA5KNXYuhuYhH+L\n+TKwL7BGXEIlzgknwOLFcPrp3uA688ykJRLppXvrQjFcdhn85z9+/tZjj2mhSc9DuiBEDkoytpxz\nM4AZwc97zOwu4AUzG+Cc+11s0iXJaadBayucdZZf5v7jHyctkUghPUIXCmXwYLj5Zr8q8fe/96sU\nRY9BuiBEbkrt2eqEc+5VM/sfcByQV6nGjRvH4MGDO8U1NTXR1NQUhyjxcs45vodr3Di/SvFHP0pa\noh5Nc3Mzzc3NneLmz5+fkDTZ6ba6UCg77AA/+5nXnd12g222SVqibol0QQhPLegCgLmYVg+Z2UvA\nxs65xhzpI4AJEyZMYMSIEbEcsyo453u1Lr3Ur1Q8/PCkJRIhJk6cyMiRIwFGOucmJi0PdGNdKJRl\ny/wODYsW+S2xGhqSlqhHIF0QwpNGXSjWg/zaOeJ3Ar4APBOHUKnCDC65BI45Bo46Cm6/PWmJRAro\nkbpQKH36eO/yH3zg/XCJbo10QYiuKXYY8QozWxfvwG4afonvSOBgYD7wk3jFSwlmfmn7kiXePUTf\nvvDd7yYtlUiWnqkLhTJ8OFx0ERx7rN+4ep99kpZIVA7pghBdUKyxdRswBhgNrIn3oTINuAK40Dn3\nYbzipYi6OrjuOm9wHXSQ31Nxr72SlkokR8/VhUL54Q+9O4ijjoJXX4W1s3aAiNpHuiBEFxS7Xc+d\nwJ0VkiX99O4Nt9wCBxzg91K87z7YZZekpRIJ0ON1oRDM/AvKVlvBkUfCv/4lJ8HdEOmCEF2jDQCL\npb4e/vpXv7x9333hqaeSlkiI9LLWWvCXv8D99/uheCGE6IHI2CqFvn3hrrvgK1/xQ4nPP5+0REKk\nl7339nO3fvITmDQpaWmEEKLqyNgqlQED4N57/RDJHnvASy8lLZEQ6eXCC2GDDfwCk2XLkpZGCCGq\nioytchg40A+PbLKJd+D4+utJSyREOhkwwLuDeOUVOPfcpKURQoiqImOrXAYPhgcfhPXWg113hbff\nTloiIdLJyJF+Y/ff/Q6eeCJpaYQQomoU69T082Z2npk9Y2afmNkCM/ufmZ1pZgMqJWTqWW01ePhh\nWGUV2HlnmDIlaYlEhZEulMhpp8E3vwmHHQbz5iUtjYgB6YIQXVNsz9aRwMnAO8AvgVOBycCvgKfN\nrG+84tUQa60Fjz7qJ8/vsgt8KNcy3RzpQin06gU33QQLFsDxxyctjYgH6YIQXVCssfU3YH3n3GHO\nucucc1c755qAC4AvAkfFLmEtsd563uBqa/MG10cfJS2RqBzShVLZYAO4/HK47TYfRK0jXRCiC4oy\ntpxzE51zC7Mk3QEYfh+sns0GG8D48X4T3l13hdmzk5ZIVADpQpkccogPxx4L06YlLY0oA+mCEF0T\n1wT5ocHnxzHVV9tsvLHv4Zo1C3bfHebOTVoiUT2kC4Vy2WV+nuOYMb43WHQ3pAtCBJRtbJlZHXA2\nsBy/R5YAvxHvI4/4t/Y994SF2V78RHdCulAkq6zi52/95z/whz8kLY2IEemCEJ2Jo2fr/4CvAmc7\n5+T3IMxWW8FDD3mv2XvvDS0tSUskKot0oVhGjYLTT4ezz4aJE5OWRsSHdEGIEEVtRB3FzM4Hjgeu\ndM79vpAy48aNY/DgwZ3impqaaGpqKkeU9DJyJDzwgB9O3G8/73W+X7+kpapJmpubaW5u7hQ3f/78\nhKTpjHShDM47z7tOOeQQb3ANkLeArpAuCOFJsy50wjlXUgB+AbQD1xSYfwTgJkyY4Hokjz3mXL9+\nzu29t3NLlyYtTbdhwoQJDnDACFdiWy43lKoLzz3XQ3UhG5MmOde/v3PHHpu0JDVLLetCj30uiIqQ\nBl2IhpJ6tszsF8A5wPXOuR+UUkePY8cd4Z//hH328W/wt98OvcvqWBQpoBxd2G47GDIEhg6FYcOy\nf66xBphVRPR0MXy43z/x+OP9kPveeyctkSgSPReEyE3RT3szOwevUDc65+Q/pRh23x3uvBP23x/G\njvWTg3v1SloqUSLl6sKZZ3pD6v334YMP4MUX/Wd4n+Z+/fIbY0OH+i06uwXHHuv3Gj3ySHj1Ve8o\nWNQEei4IkZ+ijC0zOx7fTTwNGG9mh0ayfOyceyQm2bon++zjHTkefLB/kl5zDdRpi8paIw5d2H9/\nGDGic1x7u/cY8sEHHUZY5nPSJL/eYuZM8CMwnlVXzW6EZb4PGQL19TGcdKUxg+uu8wtLjjzSz2/s\nEd16tY2eC0J0TbE9W9vgx0GHATdkSX8CyKtUs2f7B0qPti8OPBCWLvX+hfr1gz//WQ+V2qNsXchG\nXR2svbYP22yTPc+yZTBjxsrG2Pvvw1NP+c/wtoNmsO66uXvHhg2DNddMSRNce234y1/8S8lVV8GP\nfpS0RKJrKqILQnQnijK2nHNHAEeUc8A99vBTldZd179xh8P663f+3b9/OUdKOaNHw+LFcMwx/kT/\n8IeUPO1EIcShC6XSpw9suKEPuVi40BtgYWMs8/2ll/zn0qUd+fv2XblHLNpL1thY6TML+Pa3vZF1\nyil+ruPw4VU6sCiFJHVBiFqh6jO0L7rId+ZMn94RJk3yn9HVmquu2rVBVtMTiH/wA1iyBE46yRtc\n55+ftESim9DYCFts4UM2nPO9zNl6x956y/vjnTnT90JnGDw4f+/YkCHeEIyFP/4RHnsMDj0Unnkm\nxoqFEKL6VN3YGjVq5XkqGRYt6myEhcNrr8GDD678AOjTx+//nM8oW289/+aeSk480fdw/fSn3uA6\n88ykJRI9ADM/dLjmmt4VXDaWL/fDldnmjz3zDPz1r/Dpp53rXGed/BP611qrwCkEAwbArbf6JZt7\n7QVf+IJ/+8oX5L9OCJFSUuV7YOBA2GwzH3KxYgV8/HFuo+yll/xn1Fn7Gmt03Uu26qoJ9ZKdfro3\nuM46yz9kfvzjBIQQojP19X5f9Q02yJ2npSW7MfbBB/DKK/5z8eKO/H36eL3LZ5B95tty5Ei49lq/\niOThh/0eo3Pn+t7gbPTr17VBlit06zkLQoikSZWxVQi9e3cYR7lwDhYsyG2QTZgA99wDn3zSeVVX\nv35dG2TrrluhlV3nnOOfSuPGeUE0MVjUAA0NfkpVrmlVzsGcOdkNsnffhccf9zoZ7q0eNChsfI1l\n7Z3H0thIR+i7jEYW0tg+n8a2eTQum0Pj0tk0tM6ibv7cDqNs7lyYMsV7pc/8Dlt+Yfr2Lc9Qq9m5\nDEKIalBzxlYhmPm348GDc89ZAT9MMnPmysbYhx/6z+ef95/hF2kzPxSSzyAbMsQ/MIr6/zWD3/zG\nPwyOPRYmT/YzoAcP9hv2Zk4oHDSPRaQcM9+rvMYa8OUvZ8+zYoXXw2zzx154wbvCWLjQB2+U9QFW\nD0JnBg6ks2HWCI2fC30fsILG3otprGv1Bptb4A22FXNpXDqbgUtm09j6MY2LZtJ/6jTspZc6DLXW\n1uwn0KdP6YbagAEy1KDjzVfXQnRTSnFqegbwZWAksBEw1Tn3ubgFqwb19f7tediw3Hmc8/+z2Yyx\n6dP93JXp0/1k4zANDV33kq29dsSJvBlccokX7Lbb/IqBXH/w4HvAchli+Yy0cKgJB0zppDvpQpL0\n7t2x6jEfzvl3kYzhVUyYPj3zvTcLFzaycGEjLS1r5z1er14h420DaBzYTmO/5TT2WUZj/RIae7XS\naItoZIHvZVsxl8Zln9I4dxaN0z+hsXUGjQtfpHHBdBpbP6IPy1c+SH196YZaQ0NqjJOydWHPPf2F\nznSTbr55x+fGG+t/StQ8pfRsXQDMASYCq8QrTvowg9VW82GrrXLnW7rUTybOZpBNmeL9H82Y0dk7\neF2dn1Dc2SAzhnzpQtbc9UIaGqCh73Ia2hfRsGI+DSvmM3D5XOpb5nlDbF7wGQ3vv985PdccF/Bv\n1sUaauG0QYN68rZDPUoXksbMN9cBA/yLSrm0t/tFOYUbbXUsXNiXhQv7Mmth40pl86kZQJ8+jsYB\nbTT2X+GHQuuX0ti7lca6FhpZROP8+TR+Oo/G5Z/SuHQ2jUtm0dj6Do1LPvG9cKEwkEX06l3X2fhK\n1nlhebpw8cX+z3HyZL88/Z57Opan9+4Nm2yyshG22Wb+/0eIGqCUp+TnnHNTAczsVaAhVolqlL59\nYaONfMhFZrl9NoNs+nR44gn/OXduuFQ9sGoQgph6/1KbCQMHhn4PgoZ1I3F9V9BQt5iBda00uEU0\ntC/0xtvyeTQsm8vApXNoWDKHhtZZ9F441y8xmzKlswEXdsoUpaGheCMtHAYNqtVti6QLNUxdnW96\ncT2vly/vynizoGetNwsX9vssfv5C+DCTp6Uj/4oV+Y83oG45jYuX0rh8MY1zWmHF88Az8ZxM8ZSn\nCzvs0HmZunN+JVTG+Mp83nKLH2POMGTIykbY8OF+cm1Kev2EgBKMrYxCieIJL7ffeuvc+Vpb/aTi\nlpaVw6JF+eMWLoSPPorG96alpZG2tkYgf5dAnz4RA24oNAyHhv5tDOy7nIbey2jotZgGW0yDtdDg\nWhjYvoCGtgXeeFv6KQ2tnzJwziwaWj+moeVVGhZ+RMOCmfRanufVv7GxtOHQmTNLuxkxIF0QYcIj\nguXinH+/yd/TVh+EgSxcCNOmzYEHyj92afLGrAsZPyLrrOMd24ZZtAjefLOzETZ+vN9xYHkwVDto\nUHYj7HOf05CkSIQeO/6TZjJDJXHinO+l78pYyx3fi3ktvfhwUT9aWgatlC+8qjMX/fo5Gvo7Gvq3\n0dBnBQ19ltHQexkDewfGGy2+523BQgbOnUfD8vk0LPuUhiWf0rD4DQa2fEJD+wKfLwitzO/6wELU\nGGZ+Sma/fv7lrBAmToQHEjK2qsrAgd4tSNRB3PLlvkc+2ht2991+eTp4QyvXkGTVtkgQPREZWz0E\nMz/U2bcvrL7yIq6ycM7PV+naiDMWLTJaWupoaamnpaX/Z/nmtMD7mXytHeVbW7sy5Cbi5+QKIXo0\n9fWw6aY+7LtvR7xzvrs/aoTddJOfy5Fh/fWz94ats46GJEXZyNgSZWPmXQ317++X+MdJZgVaLgPu\ntdfkdF8IkYfMTuzrrgs77dQ5beHClYckH3kErriiY9Lc4MG5hyR77uIgUSRVbynjxo1j8Gcuoj1N\nTU00NTVVWxRRA4RXoD3ySDPNzc2d0udHN9SsIaQLolSam6ULsdDYCNts40OY5cvhvfc694a98Qbc\ndVfnIcnPf77D+AoPSQ4cWFm5xWfUii6YK2SyTa7CwaqTQvypmNkIYMKECRMYkWtzRCGKZOLEiYz0\nczdGOucmJiWHdEEkjXShCjjnF+VEhyQnT/ZLyTMMHZq9N2zttTUkWQXSogth1AcqhBBCFIIZrLee\nDzvv3DltwYLOQ5KTJ8NDD8Hll3cMSa6ySnYjbKONNCTZzdHdFUIIIcpl0CDYdlsfwixf7jcCDfeC\nvfYa3HmnnzMG3udOeEgy87nZZt4Hj6h5StmuZzSwAWDAmkC9mZ0VJE9zzt0So3xCpBbpghCecnVh\n1Ci/SjrqWi/sYi/f9379Ujw6V1/fMadrv/064p3z24pEhyT/8hcfn2HYsA7ja+hQb3xlJrJm+575\n7N8/6V0FRIhSeraOAnaIxJ0XfD4B6AEjegrSBSE8ZenCkUf6uerhHcimTYNXXumIW7AgtxuY+vqu\nDbOu0qs+imfWsU/bLrt0TluwoGMoMmOI/fvffr5YSwu0tRV2jP79CzPMiv2e+ezbN8VWbrooxYP8\nTl3nEqL7I10QwlOuLowd23m3nmy0twfbG83vvPVr2ECLxs2Y0Tm9tTV3/QMGFG6YZYsbODDGjqRB\ng+ArX/EhG8uXe6OrtdWHzPdC4zLfP/00d3ohi+fq6joMsVKNuK7Su4nHf83ZEkIIkXrq6joMnFJZ\nvjy3gZbNWJs1C95+u3Ncrj0rzQob8syXXvBwaKYrb5Xi9/wuiIyn6q6Mta7SFyzwvXHZ0rvauT1D\n797FG3GfflqZ61IGMraEEEL0COrrvePlUp0vZ5ws5zPQot+nTOkcl3HTlY0+ffIbZgMGQK9e3vCM\nhmzxhcatHG/U1fWnV6/+1NWt3jnfQKgbVEqdkTjXRt3SxdQtaaXX0lbqlrRSt7jFfy5ppW7xIuoW\nt2KtXRh7s2b5MedwXL6LnBAytoQQQogCCDtZXm+90upoayt+OPTDD/33xYv9cGo4tLUVFpc+egED\ng5Abs+INyBUDJ0JLurZxk7ElhBBCVIlevSo7ApgL5wo3zLLFFZM36fIffgjXXVfd69sVMraEEEKI\nbo5Zz/GbOnFi+oytotdOmGecmU0ys8Vm9r6ZXWhmAyohoBBpRboghEe6IER+SlmoegnwR+A14ATg\nr8BJwD0xylVVoptYpoE0ygTplSshpAtVII0yQXrlSgjpQhVIo0yQXrnSRFHGlpltgVekO51zBzrn\nrnPOnQqcAuxsZgdXQshKk8aGkkaZIL1yVRvpQvVIo0yQXrmqjXSheqRRJkivXGmi2J6tQ4LPSyLx\n1wCtwOiyJRKiNpAuCOGRLgjRBcUaW9sA7cAL4Ujn3FLgJWDbbIWE6IZIF4TwSBeE6IJija31gNnO\nueVZ0qYDa5hZD1nvIHo40gUhPNIFIbqgWAUYACzNkbYklCeb+9Z+AJMmTSrykJVn/vz5TJw4MWkx\nOpFGmSB9coXaU78qH1q6UCXSKBOkTy7pQryk7f5COmWC9MmVoC7kxjlXcABeAWbmSLsDaAN650g/\nBHAKChUKhxTTlssNSBcU0hukCwoKPlRVF/KFYnu2ZgCbm1m9W7nLeAi+KznHNp08CBwKTKXjbUeI\ncukHbIhvX9VEuiDShnRBCE9SupCTYo2tF4DdgK8AT2cizawvsDXweK6Czrk5wG3FiyhEl/w3gWNK\nF0QakS4I4UlCF3JS7AT5O4LPH0fijwH6A7eWLZEQtYF0QQiPdEGILrBg3LzwAmaXAscD/wDuB7YA\nTsdtbH0AACAASURBVAT+45zbJXYJhUgp0gUhPNIFIfJTirFl+DeYY/BjorOB24FznXOtcQsoRFqR\nLgjhkS4IkZ+ijS0hhBBCCFE4pWxE/RlmdoaZ/dXM3jWzdjN7r8R69jKzp81skZnNCercMCmZzOzx\noGw0tJnZiBLq+7yZnWdmz5jZJ2a2wMz+Z2ZnmtmAIuqJ7TrFJVcFrtWmZnaLmb1hZvPMrMXMJpnZ\nH81snSLqifVaFXA86UJh9UkXCpdJuiBdKKQe6ULh9VRVFzodu5yeLTNrB+YAE/FbNsx3zn2uyDr2\nB/4G/A+4FhgMjANWANs45z5KQKbH8HMOfgxYJPl+59y8Iuv7DXAccA/wLLAc2Ak4CHgZ2M75rS3y\n1RHrdYpRrriv1c7AmYE8H+LPbyvgSGA+sLVzbnYXdcR+rQqQW7pQWH3ShcJlki5IF6QL1K4udKJM\nZ3Ybhr6/CrxXZPne+O0c3gP6h+K/FFyAK6stU1DusVLK5alvBNCYJf58vMO/46p9neKQqxLXKs9x\nDsDvv3ZqEteq0u1OuiBdKEJW6UKVZarE/ZUuxHINU60L4VDWMKJzbmo55YFRwLrAtc65xaF6X8b7\nZjnIzHpVWabPME9jufU45yY65xZmSboDb/V/oYsqYr9OMcn1GXFdqzy8H3yu2kW+ilyrrpAuFIZ0\nIRakC9IFkC5AynUhTFnGVgxsi3ep/2yWtGeBQcCmVZWogyHAImB+ML77dzPbLOZjDA0+P+4iX7Wv\nU6FyZYj9WplZXzNb3cyGmNnuwJX4a3B/F0XT3KbykWa5pQvShWqSZrmlC9KFkkh6J/b1gs/pWdIy\ncUOAau9S+h7wFH7Przbgq3ifMTub2Tecc6+XewAzqwPOxo+Hd+VBuWrXqUi5oHLX6mjgT6HfU4DR\nzrmnc+TPkNY21RVplVu6IF2oNmmVW7ogXSiZpI2tzMqGbJPtlkTyVA3n3FGRqLvM7F58d+NFwB4x\nHOb/8A3wDOfc213kreZ1KkauSl6ru/ENfyDwZWBfYI0CyqWyTRVAKuWWLkgXEiCVcksXpAvlkLSx\nlXF21zdLWr9InkRxzj1lZk8CO5lZX9fFaox8mNn5eG/LVzrnfl9AkapcpxLkykoc18o5NwO/wS3A\nPWZ2F/CCmQ1wzv0uT9GaaVMRakZu6ULhSBdKombkli4UTk/XhaTnbGUu2pAsaZm4bN1+STEV6EXX\nk/FyYma/AM4CrnPOHVdgsYpfpxLlysdUyrxWYZxzr+KX7HYlW621qQy1JvdUpAuFMhXpQjHUmtxT\nkS4UylR6qC4kbWy9gF/d8LUsaV8DFgBvVVWi/GyKXyb6aSmFg4Z7DnC9c+4HRRSt6HUqQ658lHWt\nctAfWK2LPLXWpjLUmtzShcKRLhRHrcktXSicnqsLcfmQoAvfJcA6wGZ09nGR8X0xBRgQis/4vrgq\nAZkGAXVZ8u6N9+dxb4mynBOUv76LfFW9TmXKFfu1AtbOEb9TcK4PJdmmKtjupAsJXyfpgnShmvc3\nhjYnXaghXSjXg/xoYAO8xXgCUI+f/AYwzTl3SyjvDcAYYEfn3JOh+APwG5a+AlyD9+r6Y/wKhm2c\nczOrKZOZfSfIfy9+RcUK/MTAQ/Gbq37DOfdOkTIdj19BMY2ORhzmY+fcI7lkCuJjvU5xyFWha3UX\n3h/K+ECufsBI4GD8MuKdnHOv5JIpiI/9WhUgt3ShMJmkC4XLJF2QLkgXqF1d6EQ5lhreS2xbjjA+\nkvd6/EXfIUs9ewH/DS7aHLzztI2SkAkYjr8hb+O7FhcH3y8F1i1RpuvzyNRJrmpdpzjkqtC1OgC/\nTcQ0/ITFFuAN4BJg/STalHRBuiBdkC5IF6QL5YSyeraEEEIIIUR+kp4gL4QQQgjRrZGxlVLM7HEz\naw/CmKTlEUKInoT+g0WcpNrYMrOxocbelrQ8cWJm54bCoCxZXCikCjObFLovv82T791Qvl8HcaPM\nrDmoY46ZLTezeWY20cx+Y2aFeAMWFSaie+HQamZvm9n1ZrZFFeV5PCLHwZH0UeH/CjPrU8axNgjp\n5snlS1+76D84mf/gQKZs+rfIzN4wsz+Z2dCua4pVpqkRWbaLpIfbyoxc9RR4rC+F7s3YEsofFJJl\ncY77i5mNCeVrMbNBZraOmZ1mZv8ys3fMbIGZLQm+X2VmG5V0UtWYGFbGRL2x+JUQbUBb0vLEfG7t\noXMbliV9S+DrQVgjaXkjsv0sJPv7OfJ8PXzvgM8H8edG4jMhcz3eAwYnfY49PUR1L8f9WgB8oUry\nRCc4v0loeTkwKiJvnzKONSrcHpO+F2lqB0nLE/O5pfY/OM//ZPi/ciY5XCJUSKYpETkezdNWZsTU\n7tqJLGAosHxfYG5I1qNz5HsolOfWIO6gLp5R84Gti5Up1T1bISxpASpEzjcm59zrzrn/BmF2NYUq\ngJvwjc6AIWa2U5Y84W7351zHflrTgT8DhwG74fe2uj1Ic/jl2QdUQmhREhnd+wawM3A6/t47oAG/\njL7ashiwCXBEnjxxHCd1vcoJov/g5PgIr3+7ApktaRywFtl1oBoYsKOZ7ZIjLS5K0kHntwK6IyTL\nYdE8ZrYu/j8tk+eGUPIyoBnvrmJ34AK8weXw+zJeWIpQqQ10tm7bQvFTQ/E7A6fivb8uwfeMjMtR\n36jgBrwf5P0U71n21Ei+/viHynN4K3ZJUP8fibzhEHkLBobid0WfjV+e+gTw9VD+G+iwmtuzhDFB\nvsejcaE61gL+ALweHKMVvznnRUSW1kau4XhgC+CfwDz88tf7gI1LuDf/Dp3HXyJp9cH5Z9J/UEB9\nc0P5s94/heqFXLoXpN0bulf3R9KG4JdjTwra5ULgRbw/m96RvIPxf1qZvIsD3XwU/1DpF8r7GJ3f\nNtuD/4H6ID2shyv1bOGXo18LvBscZz7wFDA2km9qF/q5Ug9Idw652gH6D67ofzAdPVsr9a7inbJm\n5L88yzUuqK0HedcDrgryLgnOZQrwAHBuJG+0Z6sNeDbHea7UswVsG9yX9/EbQn8KPAzsE8mX7Z58\nFopou9uFyq0ANoiknxpK/wA+884wnOw9nf8Xyr+oaF1KWplLVPTwTX8z1PDaQt+/H6nrl+G6ImFi\nKN/qkcYc7Ub8IHzT6Dx8MQf4MEuZJcA3g/zXR9Kix8koevjhMiZ0vM2Bj/PI9wmhoR06d+1Oxz/8\nomVeLeHeNIXkn0/nB+N+obRWYFCeelbDb3KakXE5VRqaUihe94K0sLF1dSh+O/wfaLRtZup5hMA4\nCvI/kacdtwFrhfKG9eHp0PeTg/ScxlbQHlvzyHVzKO+ULHJkwgpkbGW7TvoPjvk/mMKNrTMjaV21\n9ZtCeXsD7+Q5j9ZI3VNCecM6+J0sbWVGpOxxeP3JJdcFobxRQ7iTDhbZfieH6jgrkvZSKO3XBdR1\nXEimj4rWpaSVuUxFb8d3950N7Il/a/isMYTy7x65aQ8DBwJ7AD8F7g7lvSOUdwLw/aD8X0Pxj+dQ\n9HbgNWB//FDYpFDapCD/xnTMZ8qkfZfI3AByK/qE0HlMDs5jf7wCZuJfzqHo7cDzeIU8Ef92kUnb\nrch704/OvVEHh9LuDMXflqN8pqGHr8Mk4LtJtzuFlXUP2D5o6z8JdK4N/9b85SB/H7zDwUwbvAP4\nVtC2/5+9O4+TrCzv/v+5ppfp7mGmh51hB9lX7cYVg0FjjDFR8zxGbR0VRX0SNT/SicnvURM0GjUm\nJiQmURRkEbRcAVGJRkXBIKJ0yyKiLMM2bAPI9MBMz9Z9PX9cVdSp6lq6qk51ner+vl+v8+ruc05V\nXV11vlV3nXOf+/w8cT8fyK+/e2Le3fm8/DbRiP9IfvvYM1FPMg8fobhn9SFgiCqNLWAPom9Zoa7/\nIA5fv47YC1JY/7T8+iPEodHkh+NzE/nsW6jXIAtT+XaQmK/34Da+B1PaZ+t+In8vJA5nJRuW+yZu\n0+i2PpqY93OiS8cLgbXEHroby7aFZGPrT4lBRWeBmypsKw8kbncM0dAqfJn+O+BFwFspPQLy2/n1\nnwf8fdk2UHhtnldtW62y/b6n/PXPzz+u7PU/ch739cPE+g1f3qfjYW4h6IV/+hOJ+c9KrP9IYn4y\npD+t8XjD+Y2hsLG+Jr+RFz5oksEodPguf5M/KnF/I2XLTkwsS86vtMtyTtCB42vc3zFly0YrPIdb\ngX0St7kisf47m3h9Pp24/bcSz+F0Yv5Lqtz254nnufAm9Etgbae3O00VPyDKG8bXUXpo5mWJZQ8l\ncnMypXsu1+fXX57I2g3AM4DlNeopb2wlPyjeWyGHhcZW8rFvJN6wC3V9KLEs2TBIfnirg7zegxf8\nPZjSxlal/H0HOLqs3oa2daLfY2HefxN77HprvDbJ1/ztwP9O/P16qje2/qms7uR7w7mJZZ+v8v7T\ncAf5xP3sR+ketWfm5/9jYt6187ifTyT+t7tIfBGc79RL97sy8ftjid+TVwFPnqJ+WY37OgLoITrB\nGdFBrprjiEsQJD3u7r8q/OHuk2Y2TfQ/ADicCEGzjkr8Pu3uT92Xu//SzDYCqxPrTiTWd+BX7v5Q\nYl6152u+LgTeRjxXLzazvYhvbMvzyx8kQlzJm4gLlu5BfFC/JV/z58xsxt1rPfey8DzxuxGZ2j8x\nL5mxvYAfVbgPA9aY2a7u/riZXUh08D2e2FZnzexeoiF3vrtX23Zw9wkzu4zY3t5NNNQrSdZ1PNF3\npVJdx1V7LKlL78G09T3Yy/424svJXsSeu4KGtnV3v8PMvkfsZfodov/ZjJndCVwLnO3u11WpCXf/\nmpn9PF/L+yl23i+XrOvF+alqXWly9/vN7Eri/wN4g5ldT+xBL7ig2u3zQ8h8jti7CtHf7EXu/kij\ntXTL2Yi1/Cbx+86U79trTLuk/FgL4Tdlfyefr4bPIHH3H1N8s+shdlevLSwm+sKUv1EUbnuTu/+P\nu1/m7m+j9IzEP2m0FmkbA9zde4g398/l568ALjSzoyrcplZuktl5G7G9fJE4BLONOBv1NcC3zewP\n69T2t8Q3zWHgr+bxv9SqacU8bi+V6T14/hp9DzbiQtk9RDYKX0D2AL5mZrtXeZz5but/QLzfXkIc\nEp0hGqRvAq4ys5E6/8/f5H8+jfjCXE8nMnhB/qcR7y0vIfZ4QbznfLHCbTCzYeL5fnW+vpuIvfnr\nmiliMTS25iP5rfcVNda7jdjYjHhyj3T3nvIJWOnuF1W4/a5mdmThj/yGOphYnrzSebIRMt/X4VeJ\n3wfN7MTEYx1D8RtV+brtdGHi9zOIXcOVlmGh2mCTs4nfm9nLJm3m7o8B/4fYjQ7RT6swqG3yG/a9\nRN+matm5L39/7u45d3+9u5/o7iuIM9Ag8pH89lmpnluIPR9G9Kuq1LBP1vXjSjUV6kqsl9wWl8p7\nZLvpPbhF7r6e+HIyRdS+K/GFo6Dhbd3dt7v7Oe7+Knc/hmjw/Ht+cR91huFx9/8i9qDNN4NfqFHX\n8Yn10szgJURfNicaqZ8slA9c7u5T5TfIDxh7DXBKfr3vEydYND1Y62I4jDgf5xIbjQHPNLP/Bs4h\nXoATgJPd/ZXuPmVmlxAdHg24wsw+TgR0NfHN4gXAkcTx7aTCRvZVM/u7/O8fTCy/zd1vSPz9GPHC\nA/ypmX2T2MCuc/eK3w7d/WYzmyT6IRjwRTN7P/Hm9P7Eqje7+0Sl+2iDi4j/04ADC6UC1yd35+et\nAtaZ2ZeI073vJcL9MuKYf8FP2lqxNM3dt5vZh4lMGfCH+Q+c7xJniR1A/hu4mZ1DnJm1hugf8mJi\nT+hbIK4wQJz2PgE8QOwdPSXxcAPzKOn9xLfVnirLv0T08doFONnMvkKcfj5FfLs9iujYfSnRrwWK\nh3YM2NfM3kB0MJ5298l51CRz6T24OSWNF3d/zMz+neIepbeZ2Ufd/WEa3NbNbG+iQfE1Ys/yg8T7\n8UmJh5xPBt9HnFlc8SgGsWfpz4lG0+vNbDPwTWKv0v7E4LF/SHT+L+w5Tx5ePcHM/oh4L9mY/5I1\nb+6+Nf9cnE5xLMeCC8vXN7PDiUPj+1Lco/XhfB3JVX/q7jsaKSSzE/PrnHlKYv5BldbPL/sQpZ2x\nk1PytOM9iGP61dadAe5MrP+CxPxHKJ71kZy2Ai8oq+fzFdabJX92CbVPO36wRm0PA8dWeQ6vLKvh\n/MTtzmzhdfpuhTr+pMJ6w1X+58JtCmf37NNsLZram738sl5Kx1n6Wn7+c4g3yUrbZuE1Pi9xP9NV\n1ius+4rEusk8fKSsnrMr3LZ86IfNdeo6M7H+MuKLQPn6t3X6dcnKdoDeg9v6HkztoR92Ixqphdv9\nSzPbOrB3lXUK0zbgpCqv+dvLavp22W0rDf1QOPGhWl3J53g1MQZZ+fr/3eR2fHKFx7yf/NhaNbb5\nWlNDw8B0wy7y5DHdSvPntb67/y3REfBrxDgs24kW/w1E6ArrPUqcUfNuYg/Lxvy69xOdBj9M9V2r\nTxBngFxEfOhMA1cTHequKlv3DOJY8WPEC+f5nzX/R3e/lfgm+M/E7tnp/PRrYjDJE31uy7/ac0iV\neY26oOwxqh0H30J80/w+sRdkC/HcPkycVvsXwIiXdiCVzqmWpZ1EZ9jCsleY2XHu/hPiUMC/EKff\nFwZ7XEf0fRgHzkzc1f8lBne8i8jOTuLb638BL3X3r1eop5IPEhmoVu9lRCfezxB71qaJN/LbiTHD\n/oTioQXcfZb40Lo6/z/Uys9SoPfg0v9jod6Dqz2PvwE+lZj/dstfU7bBbX0jsYfsO8SwLVuIBtH9\nxPA9v+Xu18+z1veV1Vte8yeJL2Ofzz/WNuK1/xVxpuobicN9hfU3EsNxXE80lFvKoLtfQzwHyfup\n2qe4wv9SPpVvJ3VZ9ceS+TCzFxDfgJzoyHhoh0sSEVky9B4s3WCp9NmSech3Xn9mndW2+sL1BxMR\nEXmKmR1GHAKt5TZvYniGdmq4sWVmK4jdr68FDiZ2B95GXLZjTmezJWQxXKh1DZXHR0q6G9A3R5QF\nkYKMZGExvAdLfX9DHHas5TSKne0zoaHGlkVX/G8Tx14vIEZVHSJO0T7fzI5y9/ekXWQX8LKf3aze\n/7AY/seWKQsiISNZWEzvwVJbvb5bmdwGGuqzZWbPAX5MnP3w7sT8XqJz4K7urjGSZNFTFkSCsiBS\nX6OHEVflfz6YnOnuO83sUWKQQ5GlQFkQCcqCSB2NNrZ+Spwu+tdmdg9xDbMh4vjoCDG6tMhSoCyI\nBGVBpI6Gh34ws5OBzxIXDC3YRAxIdnmKtYlkmrIgEpQFkdqaGfphMzFg4deJ4/S7Ae8Ecmb2cnf/\nfqUb5S+Y+RLibLatTVUrMtcAcfbTdzyu3beQlAXJEmVBJHQyC5U1OOT98cQos28rmz8I3EmMFD1n\n+Pv8Oq+j/qismjQ1O72umcs4NDuhLGjK7qQsaNIU04JmodbU6J6tcWA5MZT/U9x92sy+RXyTOZi4\n/Ea5uwEuvvhijj66/PqhnTU+Ps5ZZ53V6TJKZLEmyF5dt956K2vXroX89rWAlIUFksWaIHt1KQvp\nytrrC9msCbJXVwezUFWjja198z97atxXtfvcCnD00UczMjLS4MO21/DwsGqap6zWxcIfglAWFkgW\na4Ls1oWykIosvr5ZrAmyWxcZOjTd6IWof0mM0ntacqaZrSYu3Po4cEcqlYlkm7IgEpQFkToa3bP1\nr8Qw+f9gZicA1wC7A28lrlX0Dndd2VqWBGVBJCgLInU01Nhy93vN7JnAmcCLgNcA08ANwLi7fz39\nEkWyR1kQCcqCSH0ND/3g7ncBb25DLR0zNjbW6RLmyGJNkN26OkFZWBhZrAmyW1cnKAsLI4s1QXbr\nypJG+2wtSlncULJYE2S3LklHFl/fLNYE2a1L0pHF1zeLNUF268oSNbZERERE2kiNLREREZE2UmNL\nREREpI3U2BIRERFpo4YbW2b2fjObrTFta0ehIlmiHIgUKQ8itTU89APwNeD2CvNPBP4KuLylikS6\ng3IgUqQ8iNTQzDhbvwB+UT7fzF5AXGX7synUJZJpyoFIkfIgUlsqfbbMbIgYNXg98J007lOk2ygH\nIkXKg0hRWh3kXw2sAs7XNbBkCVMORIqUB5G8tBpbpwOzwPkp3Z9IN1IORIqUB5G8lhtbZnYEcDLw\nfXe/p/WSRLqPciBSpDyIlGrmbMRybyU6QJ47n5XHx8cZHh4umTc2NqZrK0lduVyOXC5XMm9qaqpD\n1czRUA5AWZDmZTwLoM8FWSBdkAUArJVD6WbWQ3R+7AH2c/cdNdYdASYmJiYYGRlp+jFFkiYnJxkd\nHQUYdffJTtTQSA7y6ysLkrosZAH0uSCdl5UsJLV6GPHlwN7ARfU+YEQWMeVApEh5ECnTamPrdGJX\n8Xkp1CLSrZQDkSLlQaRM040tM9sXeAlwnbvfkl5JIt1DORApUh5EKmtlz9ab8refd4dgkUVIORAp\nUh5EKmi6seXuH3X3HnfXrmJZspQDkSLlQaSytAY1FREREZEK1NgSERERaSM1tkRERETaSI0tERER\nkTZqqrFlZrua2cfN7HYzmzazDWZ2pZmdnHaBIlmmLIgEZUGkuoavjWhmBwJXAUPAZ4HbgGHgBGC/\nVKsTyTBlQSQoCyK1NXMh6s8Te8SOd/cNKdcj0k2UBZGgLIjU0FBjy8xOAU4G3uXuG8ysF+hz9+m2\nVCeSUcqCSFAWROprtM/WS4lrXq03s28A08BmM/u1mb0+9epEsktZEAnKgkgdjTa2jgQMOAdYDbwB\neDOwDbjIzN6UbnkimaUsiARlQaSORvtsrcz/3ASc6u47Aczs68A64CPAhemVJ5JZyoJIUBZE6mh0\nz9Y0sbs4VwgUgLtvBC4H9jGzI1OsTySrlAWRoCyI1NHonq31+Z8PVVj2YP7nrrXuYHx8nOHh4ZJ5\nY2NjjI2NNViKLDW5XI5cLlcyb2pqqkPVKAvSOcqCSMhYFqpz93lPwGnALPCRCssuBmaAQ6vcdgTw\niYkJF0nLxMSEE9+qR7yBbbnVSVmQrFEWREKnslBravQw4mXAE8BaMxsqzDSzNcArgF+7+7oG71Ok\nGykLIkFZEKmjocOI7r7RzN4NnA1cZ2bnAcuBPwH6gD9Lv0SR7FEWRIKyIFJfwyPIu/s5ZvYI8NfA\nB4ndxz8GXuvuP0m5PpHMUhZEgrIgUlszl+vB3S8jdh2LLGnKgkhQFkSqa7TPloiIiIg0QI0tERER\nkTZSY0tERESkjdTYEhEREWkjNbZERERE2qjhsxHNbLbKoifdfVWL9Yh0DWVBJCgLIrU1NfQDcDXw\nmbJ5O1qsRaQbKQsiQVkQqaLZxtY6d/9CqpWIdCdlQSQoCyJVNN1ny8z6zGxFmsWIdCNlQSQoCyKV\nNdvYehWwBXjCzB42s0+YmY7Ly1KkLIgEZUGkimYOI14HfBm4E1gF/D7wLuAUM3ueu29JsT6RLFMW\nRIKyIFJDMxeifm7ZrIvN7Gbgw8AZwEfTKEwk65QFkaAsiNSW1jhb/wRsB16W0v2JdCtlQSQoCyJ5\nzZ6NWMLdd5rZA8Ae9dYdHx9neHi4ZN7Y2BhjY2NplCKLWC6XI5fLlcybmprqUDWVKQuyEJQFkdAN\nWQAwd2/9TsyWA08A17r7C6qsMwJMTExMMDIy0vJjigBMTk4yOjoKMOruk52uR1mQTlEWRELWsgAN\nHkY0s92qLPp7oAe4vOWKRLqAsiASlAWR+ho9jPg3ZvYc4AfAvcAuxFknpwLXAv+RbnkimaUsiARl\nQaSORhtbPwSOBt4I7A7MALcD7wHOcvftqVYnkl0/RFkQAWVBpK6GGlvufjnaJSyiLIjkKQsi9aU1\n9IOIiIiIVKDGloiIiEgbqbElIiIi0kZqbImIiIi0kRpbIiIiIm3UcmPLzAbNbJ2ZzZrZJ9IoSqQb\nKQsiQVkQKZXGnq0PEWOrtH7dH5HupiyIBGVBJKGlxlb+ulZnAO8HLJWKRLqQsiASlAWRuZpubJnZ\nMuAc4Arg0tQqEukyyoJIUBZEKmv0cj1JfwEcAbwSdbSXpU1ZEAnKgkgFTYXBzA4BPgD8nbvfl2pF\nIl1EWRAJyoJIdc1+8zgbuAM4K8VaRLqRsiASlAWRKho+jGhma4EXAb/l7jON3n58fJzh4eGSeWNj\nY4yNjTV6V7LE5HI5crlcybypqakOVaMsSOcoCyIha1moxtznf2aumfUD9wHXAeOJRfsDPwAuAj4I\nPOruU2W3HQEmJiYmGBkZabVuEQAmJycZHR0FGHX3yYV6XGVBskZZEAmdykItjR5GHAT2BF4G3J6Y\nfkCMp/IG4Dbg9BRrFMkiZUEkKAsidTR6GHEz8KoK8/cEPgX8F3AucHOLdYlknbIgEpQFkToaamy5\n+07gkvL5ZnZQ/tc73V1jq8iipyyIBGVBpL40x0FxdGkGEVAWRAqUBRFaG9T0Ke5+D9CTxn2JdDNl\nQSQoCyJFGuFXREREpI3U2BIRERFpIzW2RERERNpIjS0RERGRNlJjS0RERKSNGmpsmdkRZnaxmf3S\nzDaa2WYzu9XM/tnM9mlXkSJZoyyIBGVBpL5Gh37YH9iHGMBuPbATOB54O/AaM3u6uz+abokimaQs\niARlQaSORkeQvxK4sny+mf0I+DJwGvDxVCoTyTBlQSQoCyL1pdVn6978z11Tuj+RbqUsiARlQSSv\nqRHkzWw5sAswABwL/ANxSYYr0itNJPuUBZGgLIhU1+yerbcCjwD3Ad8GhoG17n5NWoWJdAllQSQo\nCyJVNHttxEuBW4lvMc8AXg7skVZRIl1EWRAJyoJIFU01ttz9AeCB/J+Xm9klwM/MbMjdP1brtuPj\n4wwPD5fMGxsbY2xsrJlSZAnJ5XLkcrmSeVNTUx2qJigL0gnKgkjIYhYqMXdP547MrgX2dfeDqiwf\nASYmJiYYGRlJ5TFFJicnGR0dBRh198lO1wPKgnSGsiASspiFNEeQHwR2S/H+RLqVsiASlAUR8q9w\nAQAAIABJREFUGh9Bfu8q808FjgOuTaMokaxTFkSCsiBSX6N9tj5lZmuIAezuIU7xHQVeC0wBf5lu\neSKZpSyIBGVBpI5GG1tfAN4IrAX2JMZQuQf4FPBxd1+fbnkimaUsiARlQaSORi/X81Xgq22qRaRr\nKAsiQVkQqS/NDvIiIiIiUkaNLREREZE2UmNLREREpI3U2BIRERFpIzW2RERERNqo0UFNDzezD5rZ\ntWa2wcw2mdnPzey9ZjbUriJFskZZEAnKgkh9je7ZegtwBnAH8HfAu4FfAX8PXGNmy9MtTySzlAWR\noCyI1NHooKZfAT7i7k8k5n3GzO4A3gucDnwyreJEMkxZEAnKgkgdDe3ZcvfJskAVfAkw4jpYIoue\nsiASlAWR+tLqIH9A/ufDKd2fSLdSFkSCsiCS13Jjy8yWAX8L7CCukSWyJCkLIkFZECnVaJ+tSv4N\neDbwHne/PYX7E+lWyoJIUBZEElpqbJnZh4B3Ame7+z/O5zbj4+MMDw+XzBsbG2NsbKyVUmQJyOVy\n5HK5knlTU1MdqqaUsiALSVkQCVnOQpK5e3M3NPsAcCbwWXd/2zzWHwEmJiYmGBkZaeoxRcpNTk4y\nOjoKMOruk52oQVmQLFAWREIWslCuqT5biUCdP59AiSxWyoJIUBZEqmu4sWVmZxKButDdT0+/JJHu\noCyIBGVBpLaG+myZ2TuBDwD3AFea2evLVnnY3b+XUm0imaUsiIRUsnDDDXDkkbBiRXuKFOmwRjvI\nnwQ4cCBwQYXlVwG1Q/XAA/CMZ4BZgw8tkimtZ0FkcWg9C6efDm97Gxx9NIyOwkknxc+nPx2GdHlF\n6X4NNbbc/c3Am1t6xD/8Q1i9Gk48MYJUmI45Bvr7W7prkYWSShZEFoFUsvD5z8OWLXD99TAxAV/8\nImzfDsuWxWdDsgF24olqgEnXSWOcrcb827/Bk0/GbuMrroi/Afr6IlSFxteJJ8a0224LXqKIiCyg\no46CkRF461vj7+3b4Re/iIbXxEQ0wr7wBdixA3p6ig2wQiPsxBNhcLCz/4NIDQvf2Hr+8yNUBU88\nATffHI2vwvSlL8HWrbH8wANL94A9/elw8ME6DCkislj198fnxMhIHF4E2LZtbgPs858vNsCOPba0\nAXbCCWqASWYsfGOr3MqV8LznxVSwcyfcfntpA+xTn4JHHonlq1aVNr5OPDGCtnx5Z/4HERFpr+XL\ni42pgkIDrHD4cWICLr642AA77ri5DbCBgc79D7Jkdb6xVUlvb3SUPPpoKIwg7A4PPVTaAPv2t+Hf\n/z2WFW5T3gjbfffO/i8iItIe1RpgN99c2gD73OfiS3xvb3wxL/T/Gh1VA0wWRMONLTN7D/AMYBQ4\nBLjb3Q9Nu7AKDwxr1sT00pcW52/ePPcw5Fe/CtPTsfyAA+Z2xj/kkOh4KdKCjmVBJGMylYXly6Mx\nddJJxXlbt8bnROHw48QEXHhhsQFW2ANWaISdcIKOlEiqmtmz9WHgMWASWJ1uOU1YsQKe85yYCmZm\n5h6GPPfc2DMGceiyvAF27LH6diONylYWRDon21kYGIBnPjOmgq1b4aabig2w66+HCy6Iz4++vrkN\nsOOPVwNMmtZMY+tQd78bwMxuBrI3Cl1PT5zdctRR8NrXFuc/9BDceGOxAfa978F//mcchuzpicOQ\n5Y2wPfbo3P8hWZf9LIgsjO7LwsAAPOtZMRVMTxcbYBMT8NOfwvnnFxtgxx8/twGmIYtkHhpubBUC\n1ZX22Seml7ykOG/z5uhgWWiA3XgjXHppjPkCsN9+c8+GPPRQHYaU7s6CSIoWTRYGB+HZz46poNAA\nKxx+vO46OO+8YgPshBNKO+Efd5waYDJHNjvIL6QVK+aGa2YG7ryz9DDkeefBgw/G8l12KY4DVmiA\nHXecTjMWEWnCWWfFTqK99po7dbx3R7UG2I03Fhtg114bXVVmZ6OhVd4AO/ZYNcCWODW2KunpgSOO\niOnVry7Of/jhCFjhUOQPfwhnnx0BW7YsDluWnw25114d+zdERLrB1VfDN78JGzfOXbZqVeVGWGHa\ne+/i77vttkAHHQYH5/YV3rKltAF2zTVwzjmlDbDC4cdCA6yvbwGKlSxQY6sRe+8Nv/u7MRVMT5ce\nhrzhBvj61+PwJMC++87tB3bYYToMKSKSd+mlMX7p9u0xnOKGDTE9/HDx98I0MVFctn176f309EQ3\n20oNsUpTqte9HhqC5z43poLNm6MBVuiE/6MfwWc+Ew2w5ctLG2Cjo2qALWJqbLVqcHDuWS6zs8XD\nkIW9YJ/7HHz0o7F8xYoIWbIBdtxxut6XiCxp/f3RTXa//eqv6w6bNs1tjCWn+++Hn/88fn/ssbn3\nsWLF/PaY7bVXDNnY2+gn5ooVcwft3rw5PhMKDbCrroJPf7rYADvxxNIG2DHHqAG2CJi7N3/j/Fkn\n8xlPxcxGgIlTTjmF4eHhkmVjY2OMFQYvXcweeaT0bMgbboBf/Sr6iC1bFoctk53wV6wonYaGir8P\nDCy5SxblcjlyuVzJvKmpKa6++mqAUXef7EhhKAuysJSFxu3cCY8+WtoYq7TnrDC/MFRjsdZocM1n\nr9nee0fX3nm/RReuF5y8FNGvfhUtyoGBaICNjsLhh1f/TCifenpSe+6yLMtZSFrwxtb73jfB0542\nQk9PbAu9vcz5vdK8RpZXWnfZsoy2TbZuhVtuKW2A3XhjXDOylmXLKgetVvgaWadLgjo5OclojB7d\ndR8wExMTjCSvEyrSAmUhXZs3V2+MlTfUHn00dkwlDQzMf6/ZnntW2HlVaIAV+oBdfz3ce2/xTPl6\n+vub+wyo9/kwNBRHdDL5gRqykoWkBT+M+OEPL/QjFhUaYu1u2DW2fIDe3lF6ekbpPRB6DoGe/z1L\n/8xWhnq2MWTTDDLNEFsY8s0Mzm5maPZJhnZuYnDnEwxt38jybZuwLZvj3WHLluK7xObNc6c0gtpq\nw24J7pUTke6yYkUcYDh0HuPgz8zAb35Te6/Zr38dXbY2bKj8XXrXXcsbYruw117Pj+mVsNfbYXgY\n+ntn6Z/dSt+OLfTv2Ezfzmn6tz9J37aYlk2Xvd9X+hx48skopNKyHTvq/8NmjX/Zb2TZIjxs2szl\netYCBwEG7An0mdn78ovvcfeLa93++uvjKNnOnbGBzszU/72RdTt1u+3bY7tO5zGWsW3bEDMzQ8Cu\n83hN4ovG0FDxS8dTv68pmz/oDPXvZLBnO0O92xhato1B28qQbWHItzDoW6IxN/tkNOZ2bmJoxxSD\n2zYytO1xeqafjHeKao25+QQ1uVeu1T1x995b//HapNUsiCwWSz0LPT2xd2rPPaOPez3T07X7mm3Y\nEBdB2bAhep/s3Jm89TJgKD/NHXS7pye+K/f1zePnMPTtUTa/Z5b+ZTvpYwf9toM+304/2+nzbfTP\nbqNvdls09ma20j8zTf/OLcWG344t9G/YTN+2J6MBuPVx+rfdS9/0Jvq3baJvyyb62Rb3zfaSnz3M\n8NRX8L6+1hpwhWGaMqSZPVunA6eUzftg/udVQM1QmRX37khtO3ZEKLdsiamV3x95pPC7MT3dx5Yt\nfWzZsoLp6bl9E2rp76/QsFud+H1gNhpzvTsY6tvOUM82Bm0bQ8uiQTfo0wx57J0bnN0cjbmdmxjc\nPsXQ9mjQDU79huXT62JvXfKb2ZYt0YchO1rKAi98IRx8MOy/f/Vpl13Sr1okfa1lYYkZHISDDoqp\nntnZGBJjwwaYmorPhR074gt+4Wfy92Z/bt8eO7x27FjG9u397NjRX/e+t29P7y3ZzKOh1ztL37IZ\n+nfupO+JGfqf3Emf7aCfHfSxnX7fHg1A3xoNv5liw69vdiv9bGKKh9IpKkXNjCB/ajsKkbn6+mJa\ntaq9jzM7G13HGmnAVVu+eXoZjzzWz5Yt/UxPryhZZ/Pm2Hs3H4W91E817HaDwUHPN+ZmGOzbyVDf\nDrY8eT386Hfa+wRV0XIW1q6Nd6r16+FnP4vz3zdsKF1n9erajbEDDmj/BiJShz4X2mfZshg/bLfd\nOl1JZTMzrTXwivOM7dt72LGjh+3b+xq6rycKf29zdmybZePjE7DuY51+akpo6Ad56qje0FCcbdNO\nre2tM6ane9iypYctW/p5ZBo2bKx/mDWz3vKWGFwoads2eOABuO++aIQlpxtvhG99K67xmfw6uXJl\n/QbZ6tXqJyciqSscqer4SP9AHMXuYXKyl+gfnx1qbMmCSntv3eQkmQtVS5Yvh0MOiamaHTuiQVbe\nGFu/Hm69Fb773ViePD1qaKh2g2z//WM0SDXIRERSp8aWSLfp66vf4WPnztgDVqlBduedMZDi/feX\n9rxdvrx+g2yvvXT1AxGRBqmxJbIY9fYWG0jVzM7GWaWVGmT33RcX112/vvSaKH19Mbx3rQbZPvvo\nDBgRkQQ1tkSWqmXLYM2amJKXm0pyj1NZKzXI1q+PwRbvuy/OsCjo6YlrgtZqkK1ZsyjH0hERqaSZ\ncbYM+HPg7cDBwCPAl4Ez3X2eI2aKdL8lkQWz4jDX1Ub4do8RHas1yG6+ORpkhYuzQzT09tmndoNs\n333j0KZk3pLIgkgLmtmz9a/AnwFfAz4OHA38f8DTgc6cgy/SGS1l4dRT4wye3t7i1NdX+ne1edla\n1+jt3Z3e/Xan96ATS9Z96jJZ7jFIULUG2fe/Hw2yTZtKn6S9945z3gtnVmR96ulZqicZ6HNBpIaG\nGltmdgzwLuCr7v7qxPy7gU+Y2Wvd/Yvplth+uVwucxf/zWJNkN26FloaWXjTm6ItsXNnTDt2FH9P\nTpXmb98eO4rmu361+XPHPcsB6b6+0fAy+vpW09u7mt7e4yo34g6GXpuhd3Y7fbNb6d25ld4d0zz6\nyCUcuurFDC7bxoDFNGhbGbCtDLCVQaYZ8K0M+hYGfJqB2bgSwsDsFAMzWxiceZKBmc3xc2dMgzs2\n0bdjCzY7z4HfKqj6THWqoffAA03/L63Q58LCyWJNkN26sqTRPVuvy//817L55wD/AKwFFKoUZLEm\nyG5dHdByFk47rfqRuYXiXtr4evWrc3zuc2MNNdjSnd/Dzp2D+Snm3fLDq9n3yHezMT/w7tatxUF4\nkz+T/fjnwwwGBp3BQRhY7gwsdwYHnIH+WQaXzzLQN8tA/wyD/TMM9M4w0LeTwd6dDPTGFRK+fNXb\nefjF/8pgz3YGlsU0uGxb/G5bGbRtxcYgWxnwuM7pwOwWema2F4cCT06FVnSlZdWmwsiO8x0xOH36\nXFggWawJsltXljTa2DoJmAV+lpzp7tvM7AagSi9bkUVnUWTBrLhjBOLnnnt2tqZyL385XH55/fUK\nV0Ko1hirPM/yv1vV22ychq1PzL39hg278DdfPoHp6dIhzeajtzeujjAwUPw55/fVc+dXXXcQ1t83\nAeMnNfckt2ZRZEGknRptbO0LPOrula42fD/wXDPrdfedFZaLLCbKQsYkr4SwEAqNwMLewXoNvGaW\nb9pUe/m2bcmKOtZXTFkQqaPRxtYQsK3Ksq2JdTZVWUdksVAWBCjdO7hy5cI+9uxsNLi2boWf/hR+\n7/cW9vHzlAWROhptbG0Bqh1kGEisU3X5rbfe2uBDtt/U1BSTk5OdLqNEFmuC7NWV2J4W+spcysIC\nyWJNkL26Hn1UWUhT1l5fyGZNkL26Ovi5UJ27z3sCvg3sAPoqLPsf4OEat30d4Jo0tWl6XSPbcqsT\nyoKm7E7KgiZNMS1oFmpNje7Z+hnwYuBZwDWFmWa2nBhP5Yc1bvsd4PXA3RR3LYu0aoAYRPE7C/y4\nyoJkjbIgEjqVhaos/+1ifiubHQfcCFzi7n+cmP9nxGm/a909l3qVIhmjLIgEZUGkvoYaWwBm9gng\nncBlwBXAMcTIwT9y9xelXqFIRikLIkFZEKmtmcZW+TWwHiUGrHu/6xpYsoQoCyJBWRCpreHGloiI\niIjM37JOFyAiIiKymLXU2DKz95jZl83sTjObNbN1Td7P75vZNWb2pJk9lr/PgztVk5n9MH/b8mnG\nzBq+mp2ZHW5mHzSza81sg5ltMrOfm9l7zWze412n+TylVVcbnqsjzOxiM/ulmW00s81mdquZ/bOZ\n7dPA/aT6XM3j8ZSF+d2fsjD/mpQFZWE+96MszP9+FjQLJY/dymFEM5sFHgMmietjTbn7oQ3ex/8C\nvgL8HDgXGAbGgZ3ASe7+UAdq+gHRwfPPmXsNjCvcfWOD9/dR4B3A5cBPiDFpTgVeQ5zF8xx3rzYC\nc+E+Un2eUqwr7efqhcB78/WsJ/6/44G3AFPA09390Tr3kfpzNY+6lYX53Z+yMP+alAVlQVmge7NQ\nosXB7A5O/H4zsK7B2/cS185aBwwm5p+YfwLOXuia8rf7QTO3q3F/I8DKCvM/BMwA71jo5ymNutrx\nXNV4nFcRF7t9dyeeq3Zvd8qCstBArcrCAtfUjtdXWUjlOcx0FpJTS4cR3f3uVm4PvABYA5zr7tOJ\n+72RGAjvNWbWs8A1PcVCy1c7c/dJd3+iwqIvEa3+4+rcRerPU0p1PSWt56qGe/M/d62zXlueq3qU\nhflRFlKhLCgLoCxAxrOQ1OkO8s8khtT/SYVlPwFWAUcsaEVF+wFPAlP547tfM7MjU36MA/I/H66z\n3kI/T/OtqyD158rMlpvZ7ma2n5n9LnA28RxcUeemWd6masly3cqCsrCQsly3sqAsNKXRy/Wkbd/8\nz/srLCvM2w9Y6KuUriOu6XUTsdv02cQAfS80s+e7+y2tPoCZLQP+ljge/oU6qy/Y89RgXdC+5+qt\nwL8n/r6LGIn6mirrF2R1m6onq3UrC8rCQstq3cqCstC0Tje2Cmc2VOpst7VsnQXj7qeXzbrEzL5B\n7G78F+AlKTzMvxEb4Hvc/fY66y7k89RIXe18ri4lNvxdgGcALwf2mMftMrlNzUMm61YWlIUOyGTd\nyoKy0IpON7YKIwsvr7BsoGydjnL3/zGzq4FTzWy51zkboxYz+xBxaYuz3f0f53GTBXmemqirojSe\nK3d/AHgg/+flZnYJ8DMzG3L3j9W4addsU2W6pm5lYf6UhaZ0Td3Kwvwt9Sx0us9W4Unbr8KywrxK\nu/065W6gh/qd8aoysw8A7wM+6+7vmOfN2v48NVlXLXfT4nOV5O43E6fs1qut27apgm6r+26Uhfm6\nG2WhEd1W990oC/N1N0s0C51ubP2MOLvhuRWWPRfYBNy2oBXVdgRxmuhvmrlxfsM9Ezjf3d/WwE3b\n+jy1UFctLT1XVQwCu9VZp9u2qYJuq1tZmD9loTHdVreyMH9LNwtpjSFBnbFLgH2AIykd46Iw9sVd\nwFBifmHsi093oKZVwLIK676MGM/jG03Wcmb+9ufXWW9Bn6cW60r9uQL2rjL/1Pz/+t+d3KbauN0p\nCx1+npQFZWEhX98UtjlloYuy0OoI8muBg4gW47uAPqLzG8A97n5xYt0LgDcCv+3uVyfmv4q4OvxN\nwDnEqK5/TpzBcJK7P7iQNZnZK/Lrf4M4o2In0THw9cSV7J/v7nc0WNM7iTMo7qG4ESc97O7fq1ZT\nfn6qz1MadbXpubqEGA/lynxdA8Ao8FriNOJT3f2majXl56f+XM2jbmVhfjUpC/OvSVlQFpQFujcL\nJVppqRGjxM5Uma4sW/d84kk/pcL9/D7w4/yT9hgxeNohnagJOIp4QW4ndi1O53//BLCmyZrOr1FT\nSV0L9TylUVebnqtXEZeJuIfosLgZ+CXwr8D+ndimlAVlQVlQFpQFZaGVqaU9WyIiIiJSW6c7yIuI\niIgsampsiYiIiLRRJhtbZvYmM5tNTDNmNm1mD5nZ9WZ2tpk9p9N1diMzO8jM3p+fzqiyzvFmdpaZ\n/Y+ZbU68Duvq3PdyM/tzM7vGzH6Tf83uMbP/MrPXVlj/D8zsCjN7xMy2mdn9ZvZ5Mzs2rf+32ykL\n7ZOVLDT7GEuNstA+7ciCmT3dzD5sZlfnt/0tFtdIvMHMzjSzFa0+RldZiI5hTXSGexNxBkS1Dnqz\n+eliEqdxaprXc/uCxPNX8fRn4Iwqz3+906VvSNx3+W2/XLb+R6usO0t0qPy9Tj9XWZiUhbY+t1nJ\nQsOPsRQnZaGtz23qWQA+VWX9wuPcDKxs5TG6aer05XrqMeJK3c8H+oGDgddQvK7S64jTN/+wE8V1\nKcv/rHVmxEbgv4DrietO/ek87vfLwAn5+70J+E/itN+VwDHE2SFRQHz7/P8TNfwtcB3we8BfEq/1\nRWZ2uLtvnNd/tfgpC+nreBZaeIylTFlIX7uy8BjwOeKaiDuJBvOr849zDNG4+vsWH6M7dLq1V6VF\nXPgGMwvMVFj+Pkpbv3+cWDYI/DXx4T1FXGTyNuCfgT1qteaBA4CLiLFAtgBXA6NltxkGPk5cDHML\nsRfmXuD7wMeAgbL1jwLOBe7MrztFXA39TRX+r/MT9ZxJvFn8JP84G4CzSQzU1uhjEJdKKDxvsxWm\nA2u8FrW+wfx+Yp1flD8HFdb/aGL9H5Yt+3Vi2Rmd3hY7PSkLizsLzTzGUp2Uha7LwvOBFWXzjOJe\n3xngm0slCx0voM6TXC1URumH8jfz83cndk1W23V5H3BQ4n5ekFj3ceChCrd5OLnBAFfVuP8ZYK/E\nuq/MB6LSobJZ4HNVQjVDvBFUut0ny25T7zEuSqx7V4V6C9POFkJ1bmKdzwFfJa5FtZm4TMIbytY/\nO7H+N8qWTSSWXdbpbbHTk7KwuLPQzGMs1UlZ6K4s1Hgdv5y47ReXShY6XkAzocqv88nEOg/l530p\n8eJMELsrf7fsxf1hlVDNAnfkb/PGfMgKy96WX3/3xLy7iYHWfhsYAz5CtNj3zK+7BzGgW2Gj/Q/g\nxcQu7nWJ+zmtQqgKyy4CXpq/bWH+NvL9ERp9DGCEGD25MP9+4rpQz8tPfc1s8MBPy57HSuH+aGL9\nMxLzN+efwwHgj4hwF253Q6e3xU5PysKizMJH5vF6L4oPGGVh6WahyuuzO3FosXDbsaWShY4X0EKo\nPpRYZyuxG3dHYgN7DXByfnpBfmMsvHCHl4WqMH+kQmhngH/Kz1ueeIwbgGcAy6vU987E7W/Mb7SF\nej6UWHZNhVDNADcl5hsx4m1h2bEtPEbyjaTuBjyfDZ74tpX8cPkk8Wb2n4nndydwVH79VcRIwMkP\noOTrUJj/q05vi52elIXFnYVmHmOpTspCd2Whwm1WEYcxC7ereghxMWYh6x3ka9kz8ftG4mriPUTH\nOwNyNW57HHH5gKQn3H0y8fdjid93A3D3bWZ2IfBm4Hjyh7zM7F6iL8D57v7f+dsck7j98cRGVs7y\ntVRyZeEXd3czexwYStaTwmOkZWvi9wfc/R0AZvZd4BXAvvk6fo9oQG0ys98i3rh+j2LnzAeJSzD8\nDvE6Pt7muhcLZaFLs9DmWpYiZSE7WSg+mNn+RMf3Y4nX4vvEHsAlI5PjbNVjZkbxA9mJMxfKeY1p\nlwrr/qZsXvKMIUv8/jZgLXHtp5uJb0YHEd+Yvm1mlc6AqVXLnLFG8uZbTyuPkZZ7KJ4hdO9TBcXX\nk3sT6w0nlt3r7n9A7PJ+FvGmeGDZ+je2seZFQVno/ixIOpSFzGUhijI7HriWYkPrS8DL3H1rzRsu\nMl3Z2CLOyDiU4sZ1AcXd94U3uiPdvad8Isb1uKjZB/aQc/fXu/uJ7r6COMuF/OOO5X+/NXGzH1eq\npVBPs7U0+Rizid/Tev2vSvx+YOGX/JvfgYll95Tf0N03uvuEu99JvKZjicVfS6m+xUxZaP4xMpUF\naZmy0PxjtCMLmNmpxNmb+xLPw8fd/XXuviOtx+gWXXEY0cxOBvqIbwpjRB8IiBfvW+7+1fx6lwB/\nTATrCjP7ONG5cXX+ti8AjgSObqGWO4FvEbuKHyB2UZ+SWGUg//NLROfIXYCTzewrwBeI02/3I07L\nfSlwKXEcvRnNPEZhN7gB+5rZG4hOk9OF3eVmdgDRaZLET4AhM3tF/vdH3f2a/O8XAn9DHJPf18w+\nCVxGdHjfN7/Ok8A3CndkZl8gzgL6GdGZ80TizWmQ/G5md/9uc0/L4qUsVNXNWWj0MQRloYZMZMHM\nXkns6evLL8sBl+dft4Kt7j5R+GNRZ6G8E1cWJko7QlaaCp0dLyQxvghxSOrGxPJKt7szsX6yI+S6\nshren7jNeYn503XqekVi3VcSZ9tVqqWw/pmJ9c+vND+/7K7EslNaeIxlxOGM8vVva+D5nwWuLKvv\nfxG7zsvvdyY//4/L1v9Bjdf1OsrGvlmqk7KwJLLQ8GMsxUlZ6K4sUHoWZbWp/PldtFnI8p4tL/t9\nB9HhcT1xLP4Cd7+u5Abuj5rZs4B3EJ3vjiY6Dz5CcYC5S6s8jjNXpWX/lzit9wQixIPEcfQJ4N+8\n2BESd7/MzJ4BjAMvBPYnNuYHic6x36xRTyVevrzRx3D32fw3jn8GTqLYubL8cWvVMWe5u1+SHxn+\n/xLf6HYjnpergH/00k6mEN94jOirtRvxxnAL8Q3sXHcvH2V7KVMWKtezWLLQ8GMsYcpC5XoymYUm\n1m/2Npln+dakiIiIiLRBwx3hzGyFmb3XzG4ys01m9ojFle3f1I4CRbJKWRAJyoJIbQ3t2cqfUXM1\n8BziTI/riN2NY8CzgY+5+3vSL1MkW5QFkaAsiNTXaGPrOcCPgX9x93cn5vcS16Ta1d13q3Z7kcVC\nWRAJyoJIfY12kF+V//lgcqa77zSzR4H+VKoSyT5lQSQoCyJ1NNrY+ilx5sdfm9k9FHcXn0aMifF/\nUq1OJLuUBZGgLIjU0fDZiPkByT5LnLJfsAl4o7tfnmJtIpmmLIgEZUGktmaG5d8M/AL4J2JU5NOJ\n0XhzZvaiFGsTyTplQSQoCyI1NNpB/nhiF/EZ7n5OYv4gETQDnuYV7tTMdgdeAtwNLKkLUEpbDQAH\nA99x98fqrJsaZUEySFkQCR3JQk2NDDcPnEeMQrtrhWWfyC87pMptX0ftK5Br0tTK9LoJ6YX3AAAe\nf0lEQVSFvPQCyoKm7E7KgiZNMS1oFmpNjXaQL1xItafCst6yn+XuBrj44os5+uimr/fZFuPj45x1\n1lmdLqNEFmuC7NV16623snbtWshvXwtIWVggWawJsleXspCurL2+kM2aIHt1dTALVTXa2PolcWX1\n04CPF2aa2WriwpePE8fpK9kKcPTRRzMyMlJllc4YHh5WTfOU1bpY+EMQysICyWJNkN26UBZSkcXX\nN4s1QXbrIkOHphttbP0r8EbgH8zsBOAaYHfgrcDewDvc5x6XF1mElAWRoCyI1NFQY8vd7zWzZwJn\nAi8CXgNMAzcA4+7+9fRLFMkeZUEkKAsi9TW6Zwt3vwt4cxtqEekqyoJIUBZEamtmnK1FZ2xsrNMl\nzJHFmiC7dUk6svj6ZrEmyG5dko4svr5ZrAmyW1eWqLFFNjeULNYE2a1L0pHF1zeLNUF265J0ZPH1\nzWJNkN26skSNLREREZE2UmNLREREpI3U2BIRERFpo4YbW2b2fjObrTFta0ehIlmiHIgUKQ8itTU8\n9APwNeD2CvNPBP4KuLylikS6g3IgUqQ8iNTQzDhbvyCu5F7CzF5AXPjxsynUJZJpyoFIkfIgUlsq\nfbbMbIgYNXg98J007lOk2ygHIkXKg0hRWh3kXw2sAs7XNbBkCVMORIqUB5G8tBpbpwOzwPkp3Z9I\nN1IORIqUB5G8lhtbZnYEcDLwfXe/p/WSRLqPciBSpDyIlEpjz9ZbiQ6Q56ZwXyLdSjkQKVIeRBKa\nGfrhKWbWA7wBeAy4bD63GR8fZ3h4uGTe2NiYrq0kdeVyOXK5XMm8qampDlVT1EwOQFmQ5mU1C6DP\nBVlYWc5CkrXSb9HM/ogYX+Usd//LOuuOABMTExOMjIw0/ZgiSZOTk4yOjgKMuvtkJ2poJAf59ZUF\nSV0WsgD6XJDOy0oWklo9jHg6sav4vBRqEelWyoFIkfIgUqbpxpaZ7Qu8BLjO3W9JrySR7qEciBQp\nDyKVtbJn603526sDpCxlyoFIkfIgUkHTjS13/6i797i7dhXLkqUciBQpDyKVpTWoqYiIiIhUoMaW\niIiISBupsSUiIiLSRmpsiYiIiLSRGlsiIiIibdRUY8vMdjWzj5vZ7WY2bWYbzOxKMzs57QJFskxZ\nEAnKgkh1DV8b0cwOBK4ChoDPArcBw8AJwH6pVieSYcqCSFAWRGpr5kLUnyf2iB3v7htSrkekmygL\nIkFZEKmhocaWmZ0CnAy8y903mFkv0Ofu022pTiSjlAWRoCyI1Ndon62XEhcYXW9m3wCmgc1m9msz\ne33q1Ylkl7IgEpQFkToabWwdCRhwDrAaeAPwZmAbcJGZvSnd8kQyS1kQCcqCSB2N9tlamf+5CTjV\n3XcCmNnXgXXAR4AL0ytPJLOUBZGgLIjU0eierWlid3GuECgAd98IXA7sY2ZHplifSFYpCyJBWRCp\no9E9W+vzPx+qsOzB/M9da93B+Pg4w8PDJfPGxsYYGxtrsBRZanK5HLlcrmTe1NRUh6pRFqRzlAWR\nkLEsVOfu856A04BZ4CMVll0MzACHVrntCOATExMukpaJiQknvlWPeAPbcquTsiBZoyyIhE5lodbU\n6GHEy4AngLVmNlSYaWZrgFcAv3b3dQ3ep0g3UhZEgrIgUkdDhxHdfaOZvRs4G7jOzM4DlgN/AvQB\nf5Z+iSLZoyyIBGVBpL6GR5B393PM7BHgr4EPEruPfwy81t1/knJ9IpmlLIgEZUGktmYu14O7X0bs\nOhZZ0pQFkaAsiFTXaJ8tEREREWmAGlsiIiIibaTGloiIiEgbqbElIiIi0kZqbImIiIi0UcNnI5rZ\nbJVFT7r7qhbrEekayoJIUBZEamtq6AfgauAzZfN2tFiLSDdSFkSCsiBSRbONrXXu/oVUKxHpTsqC\nSFAWRKpous+WmfWZ2Yo0ixHpRsqCSFAWRCprtrH1KmAL8ISZPWxmnzAzHZeXpUhZEAnKgkgVzRxG\nvA74MnAnsAr4feBdwClm9jx335JifSJZpiyIBGVBpIZmLkT93LJZF5vZzcCHgTOAj6ZRmEjWKQsi\nQVkQqc3cvfU7MesFngSud/fnV1lnBJg45ZRTGB4eLlk2NjbG2NhYy3XI4pbL5cjlciXzpqamuPrq\nqwFG3X2yI4UlKAuyEJQFkdANWYCUGlsAZrYO2O7uR1VZPgJMTExMMDIykspjikxOTjI6OgpZCpWy\nIB2gLIiELGYhlRHkzWw5sD/wcBr3J9KtlAWRoCyIFDXU2DKz3aos+nugB7i85YpEuoCyIBKUBZH6\nGu0g/zdm9hzgB8C9wC7EWSenAtcC/5FueSKZpSyIBGVBpI5GG1s/BI4G3gjsDswAtwPvAc5y9+2p\nVieSXT9EWRABZUGkroYaW+5+OdolLKIsiOQpCyL1pdJBXkREREQqU2NLREREpI3U2BIRERFpIzW2\nRERERNpIjS0RERGRNmq5sWVmg2a2zsxmzewTaRQl0o2UBZGgLIiUSmPP1oeIsVXSuciiSPdSFkSC\nsiCS0FJjK38R0TOA9wOWSkUiXUhZEAnKgshcTTe2zGwZcA5wBXBpahWJdBllQSQoCyKVNXq5nqS/\nAI4AXok62svSpiyIBGVBpIKmwmBmhwAfAP7O3e9LtSKRLqIsiARlQaS6Zr95nA3cAZyVYi0i3UhZ\nEAnKgkgVDR9GNLO1wIuA33L3mUZvPz4+zvDwcMm8sbExxsbGGr0rWWJyuRy5XK5k3tTUVIeqURak\nc5QFkZC1LFRj7vM/M9fM+oH7gOuA8cSi/YEfABcBHwQedfepstuOABMTExOMjIy0WrcIAJOTk4yO\njgKMuvvkQj2usiBZoyyIhE5loZZGDyMOAnsCLwNuT0w/IMZTeQNwG3B6ijWKZJGyIBKUBZE6Gj2M\nuBl4VYX5ewKfAv4LOBe4ucW6RLJOWRAJyoJIHQ01ttx9J3BJ+XwzOyj/653urrFVZNFTFkSCsiBS\nX5rjoDi6NIMIKAsiBcqCCK0NavoUd78H6EnjvkS6mbIgEpQFkSKN8CsiIiLSRmpsiYiIiLSRGlsi\nIiIibaTGloiIiEgbqbElIiIi0kYNNbbM7Agzu9jMfmlmG81ss5ndamb/bGb7tKtIkaxRFkSCsiBS\nX6NDP+wP7EMMYLce2AkcD7wdeI2ZPd3dH023RJFMUhZEgrIgUkejI8hfCVxZPt/MfgR8GTgN+Hgq\nlYlkmLIgEpQFkfrS6rN1b/7nrindn0i3UhZEgrIgktfUCPJmthzYBRgAjgX+gbgkwxXplSaSfcqC\nSFAWRKprds/WW4FHgPuAbwPDwFp3vyatwkS6hLIgEpQFkSqavTbipcCtxLeYZwAvB/ZIqyiRLqIs\niARlQaSKphpb7v4A8ED+z8vN7BLgZ2Y25O4fq3Xb8fFxhoeHS+aNjY0xNjbWTCmyhORyOXK5XMm8\nqampDlUTlAXpBGVBJGQxC5WYu6dzR2bXAvu6+0FVlo8AExMTE4yMjKTymCKTk5OMjo4CjLr7ZKfr\nAWVBOkNZEAlZzEKaI8gPAruleH8i3UpZEAnKggiNjyC/d5X5pwLHAdemUZRI1ikLIkFZEKmv0T5b\nnzKzNcQAdvcQp/iOAq8FpoC/TLc8kcxSFkSCsiBSR6ONrS8AbwTWAnsSY6jcA3wK+Li7r0+3PJHM\nUhZEgrIgUkejl+v5KvDVNtUi0jWUBZGgLIjUl2YHeREREREpo8aWiIiISBupsSUiIiLSRmpsiYiI\niLRRo+NsHW5mHzSza81sg5ltMrOfm9l7zWyoXUWKZI2yIBKUBZH6Gt2z9RbgDOAO4O+AdwO/Av4e\nuMbMlqdbnkhmKQsiQVkQqaPRcba+AnzE3Z9IzPuMmd0BvBc4HfhkWsWJZJiyIBKUBZE6Gtqz5e6T\nZYEq+BJgxKUZRBY9ZUEkKAsi9aXVQf6A/M+HU7o/kW6lLIgEZUEkr+XGlpktA/4W2EFctkFkSVIW\nRIKyIFKq0T5blfwb8GzgPe5+ewr3J9KtlAWRoCyIJLS0Z8vMPgS8E/i0u/9jOiWJdB9lQSQoCyJz\nNb1ny8w+ALwP+Ky7v2O+txsfH2d4eLhk3tjYGGNjY82WIktELpcjl8uVzJuamupQNUXKgiw0ZUEk\nZDUL5czdG79RBOpM4Hx3P32etxkBJiYmJhgZGWn4MUUqmZycZHR0FGDU3ScX+vGVBckKZUEkdDoL\nlTR8GNHMziQCdeF8AyWyGCkLIkFZEKmtocOIZvZO4APAPcCVZvb6slUedvfvpVSbSGYpCyJBWRCp\nr9E+WycBDhwIXFBh+VWAQiVLgbIgEpQFkToaamy5+5uBN7epFpGuoSyIBGVBpL60RpAXERERkQrU\n2BIRERFpIzW2RERERNpIjS0REemsjRuhiTEfRbpFGtdGFBERad6LXgSrV8PTngaHHVb8Wfh9zRow\n63SVIk1ruLFlZu8BngGMAocAd7v7ofO+g9tugyOOgF12afShRTKl5SyILBItZ+FjH4PZWbjzTrjj\nDrjmGli/vrh8aCgaXeWNsMMOgwMOgJ6elP8jkXQ1s2frw8BjwCSwuuFbF651tc8+EZTDDy+GpzCt\nWtVEWSILrrUsXHstrFwJBx0E/f1p1yaykFrLwu/8DpRfrmd6Gu66KxpfhUbYHXfApZfC3XfDzEys\n19cHhxwytxF22GFw8MHKlmRCM42tQ939bgAzuxlY0dCtL7ggvoXccQfcfjvccgt8/evwm98U19lr\nr7kNsEKjbHXjORZpk9ay8K53xc9ly2D//eMD49BD5/7ce28dQpGsay0LlQwOwjHHxFRuxw64557S\nRtidd8J3vwuf/jRs2xbrLVsGBx5Y+dDk054We8xEFkDDja1CoJp2/PFzv8FANLbuvDMaYIXw3H47\nXHEFPPpocb3dd5/bACtMu+2mDyVZMC1n4fLL483+rrtg3br4ecst8I1vlG7zg4PVG2KHHKJD8tJx\nLWehUX19xff9l7ykdNnsLNx//9w9YtddB5//PDz5ZHHdNWsq7xF72tP0xV5SlZ0O8rvtFtMznzl3\n2caNpaEpNMi+9z14+OHieqtXVz4sedhhsOeeaohJtuy3X+UvHgBPPBGNr2RDbN262Obvugu2bi2u\nu+eelRtihx4ae8x6sxNzkbZbtiz6cR1wAJx6aukyd9iwYe4esVtuiS8/jz1WXLfwxb5SPzF9nkiD\nuuNdePVqGB2NqdwTT8zdI3bHHXDVVfDAA8X1Vq2qfFjysMN0mEayZ+VKOOGEmMq5w0MPzW2IrVtX\n7FhcOI2+pyf6hFVqiB1ySHygaNuXpcIs3u/33hue97y5yx9/PD5PyhtjP/gBPPhgcb2VKys3wp72\ntPgStUyjKkmp7mhs1bJyJTz96TGV27y5NDSFvWI//nHpmS4rVlTvrL9mjYIj2WIW2+WaNZU/MLZt\ng3vvLTbACo2xiQn4yldiT3HBypVzG2CFnwcfHIcwRZaKXXeFk06KqdzmzZGjZCPsjjsgl4P77ovD\nlwDLl1c/c/Kgg7SneYla3K/6ihXV9w5MT0dwyveI5XLxQVXYMzA4WPmw5OGH6xuMZNPy5bF9Hn54\n5eWPPz53r9hdd0Vfsbvvjs7HBWvWVG6IHXoo7Luvtn9ZOlasiD7Hxx8/d9m2bZGd8j1i3/pWZKuQ\nqd7eaHBV6id2yCEwMLCg/5IsnAVvbI2PjzM8PFwyb2xsjLHCkBALZXAQjj02pnJbtxZPOU7uEfvq\nV+MMmPJvMJX2imnsl9TlcjlyuVzJvKmpqQ5V07qOZWHXXWOq1F9sZiYOv5c3xApnej30UHHd/v7Y\n+1Wt4746GLfNYsvC2rXjrFo1TG9vtEf6+uA1rxnjtNPGumNH0PLlcOSRMZWbmYk9X+V7xK66Cs47\nL774Q+yx3n//6mdOrly5sP9Tl+iWLJi3cImEwim+8xm8zsxGgImJiQlGqnUK7gbbt8c3mGRH/cJ0\n113FsV/6++NDp9JeMe1KTs3k5CSj0Zdv1N0nO1XHksnCli2x/VfqL3bXXaVneu26a/WGmMYWS103\nZwEmgMpZGByMLrerVkV7o/B7o3+vWJHB7onu0Rcs2QhL7h1LNhr22iv2NC9fvvBTb28Gn7zqspKF\nJH3iN6q/P0bAP+KIucsKY78kG2B33BHDV5TvSk4OwpfcM3bwwfG1TiSLhoaqj33kHkNWVGqITUzE\n4fnCl5FqY4sVftdJK0vKVVfFS79pU0xPPFH8vdrf9947d3lheK1KzFprrCX/Xr48pX/cLA7H77sv\nnHJK6TL3GBIp+VnyyCPxTyanLVuia0D5/OS0dWv8bHbnillnGnm1pi7rwtDM5XrWAgcBBuwJ9JnZ\n+/KL73H3i2vd/ktfgptuim8ZK1bEEEGVfu/KL73JsV/K7dxZuis5OXzFpz8de8ygePbYPvvEk1CY\n+vqa/7vVdbtso14orWZh0TGLU+L33BOe9ay5ywsZKG+I1RtbbO+9i9tlpW212vbb7PIu+xafBa1m\nYZddou3dqm3bouE1n8Za8u8HHpi7vNBbpJL+/vqNs/k03FaurNHbxCzOFt59d3j2s1t/ctwjg7Ua\nZWlN9Rp/hSnZP/T/tXd2MZJUZRh+vvnf4WejbCLLKoJEQKIREVQSoi4ETTDRxKyBADEBlAt+zGK4\nASMKXBiNEMQbDJD1Ag2sigQSEg0BREDIJoPsCpu4ugzEBdbsGmbZH5iZ7s+L08X09HR3VXX9Nvs+\nyUnVVJ1z6p3T5+3+6lTVqbSMjfUOxKKTuhoxyMjWFUBHCM4treVfgL6muv328HnHMT7ePyDrFaTF\nrU9PVxQ7RKNZJ54I55+/fF+jESbhiwKwHTvCGczCQgjCFhbCdf25uaW/5+eXUvvf7et5dbjR0WKC\nuDzyts+zVj6ZvHDY0e6BbnTOLRalbdtCv+7Vz6P1JF8sSckazOUdACbZXy218EL0W7tmTbZ63MOA\nUZqRtn37wtfRjh3L9x882P9YRxyRPDibnl7ZDXqtr9xmTEyMMz45ztgRR9bjfKLZDN7NO9jbtSuc\nxNWIQWaQXx+fqzfPPx8e5jhwYCnt359ufW4unIl0y5NklHR6uphAbmJiwBPi0dHwSonjj4fzzhug\ngh40m71/oNIEbWn2tf/99tvp6pmfH3yYuwKyemHr1vAvj4yEZNZ/PW7/oHmj9crpN7dYEtyXn6D0\n6m9Fbjt0KH3ZvE6KKhyBzuqFumG29P2+dm22uhYXw29Te3CWJIDbs2dlmTzPJ6JArH+Aln1//zIj\nTExMMT4+tXL/6t7lY589m5mBhx7Kr7FyoJJ7tsbHw4NKeT+s5B4uTQ8SwEXrb765tN6+v33C7l6M\njuYXvLUHbu3Lbtt6L0cwmwQml2+fBJtKU0+Jy2YD5uexhfBDZAvhx8kWw8iGzb+7tH1hgcbLL8HG\na1d+GEPAZZdVrWA5Uf+qOvjrtS0+GWYTmE2kKJP2GG35J8AmcziGN0NqNkLyJtZcZKTZwLyBNdqW\nUZ7G4or1XXtfgscur7obiQ7GxvL5vYuuAkaDvN3OK9KuD5J3fj78NqatN89A0ax/MFfDq4jvrxvk\nzcKtHqtWZR9G7qTRGHwkbv/+kHbv7p6njh2jOkaBVa2UhNXxWWrK5s1w6qnhS7TZDCluvai8ZRxj\nUD3RetrUeewsdRRbdqSVxjIdd3HxffV1LjowWwoqhpFo4DmPgC9uPXo1Zp2QOxMyOrp07TxP3JfO\nFNqDsOh++eiLNFrXcvlydhZuvpmh5KSTus+PKMQgzMx0f6OZEHUgGo0q4/bCmRm4//7ij5MGBVsV\n0/5E7THHVK1m+JiZGd5gSwghxOGBnukXQgghhCiQ1MGWBa4zs+1mdsjMXjOzn5vZdBEChagr8oIQ\nAXlBiP4MMrJ1B3Ab8A/gGmAz8D3g4Rx1lUrne5XqQB01QX11VYS8UAJ11AT11VUR8kIJ1FET1FdX\nnUgVbJnZaQQj/d7dv+Xu97r79cD3gXPN7KIiRBZNHTtKHTVBfXWVjbxQHnXUBPXVVTbyQnnUURPU\nV1edSDuydXFreUfH9ruBg8ClmRUJMRzIC0IE5AUhYkgbbJ0JNIEt7Rvd/V3g78BZOekSou7IC0IE\n5AUhYkgbbB0H7HH3bm+P3AWsMTNNJyEOB+QFIQLyghAxpDXANPBuj33vtOXZ12X/FMD27dtTHrJ4\n5ubmmJmZqVrGMuqoCeqnq60/TZV8aHmhJOqoCeqnS17Il7p9vlBPTVA/XRV6oTfunjgBW4E3eux7\nAGgAYz32Xwy4klJB6eI0fTlrQl5Qqm+SF5SUQirVC/1S2pGt14FPmNm4rxwyXkcYSu71usk/AZcA\nsyyd7QiRlSngBEL/KhN5QdQNeUGIQFVe6EnaYGsLcD7wOeCZaKOZTQKnA0/2Kujue4HfppcoRCzP\nVnBMeUHUEXlBiEAVXuhJ2hvkH2gtN3ZsvxJYBfwmsyIhhgN5QYiAvCBEDNa6bp68gNmdwNXAQ8Cj\nwGnAtcBf3f283BUKUVPkBSEC8oIQ/Rkk2DLCGcyVhGuie4D7gR+5+8G8BQpRV+QFIQLyghD9SR1s\nCSGEEEKI5AzyImohhBBCCJGQTMGWmd1gZpvN7N9m1jSznQPWc4GZPWNm+81sb6vOE6rSZGZPtsp2\npoaZnTFAfR83s1vM7G9m9l8z22dmL5jZjWY2naKe3NopL10FtNXJZnafmb1sZm+Z2QEz225mt5nZ\nsSnqybWtEhxPXkhWn7yQXJO8IC8kqUdeSF5PqV5YduwslxHNrAnsBWYI78eac/ePpazjm8DvgBeA\ne4DVwHXAInCmu79ZgaYnCDd4bgSsY/ej7v5Wyvp+AlwFPAw8BywA64ELgReBL3h4j1i/OnJtpxx1\n5d1W5wI3tvT8h/D/fQq4HJgDTnf3PTF15N5WCXTLC8nqkxeSa5IX5AV5geH1wjIyzhx8Qtv6NmBn\nyvJjhHdn7QRWtW3/dKsB7ipbU6vcE4OU61PfGcBRXbbfSphd+aqy2ykPXUW0VZ/jbCC87Pb6Ktqq\n6H4nL8gLKbTKCyVrKuLzlRdyacNae6E9ZbqM6O6zWcoDXwLWAve4+6G2el8kTIR3oZmNlqzpPSxw\nVNZ63H3G3d/ususBQtT/yZgqcm+nnHS9R15t1YfXWssPxOQrpK3ikBeSIS/kgrwgL4C8ADX3QjtV\n3yB/FuH9Rc912fcccDRwcqmKllgH7AfmWtd3/2Bmp+R8jI+0lrtj8pXdTkl1ReTeVmY2aWbHmNk6\nM/sKcBehDR6NKVrnPtWPOuuWF+SFMqmzbnlBXhiItK/ryZvjWstdXfZF29YBZb8SfifwNOEFqw3g\n84QJ+s41s3Pc/aWsBzCzEeCHhOvhca+rKK2dUuqC4trqO8Av2/5+BbjU3Z/pkT+irn0qjrrqlhfk\nhbKpq255QV4YmKqDrejJhm43273Tkac03P2Kjk0PmtkjhOHG24Gv5nCYXxA64A3uviMmb5ntlEZX\nkW31R0LHPxL4DPB1YE2CcrXsUwmopW55QV6ogFrqlhfkhSxUHWxFMwtPdtk31ZGnUtz9aTN7Clhv\nZpMe8zRGP8zsVsKrLe5y958lKFJKOw2gqyt5tJW7vw683vrzYTN7ENhiZtPu/tM+RYemT3UwNLrl\nheTICwMxNLrlheQc7l6o+p6tqNHWddkXbes27FcVs8Ao8Tfj9cTMfgz8ALjX3a9KWKzwdhpQVz9m\nydhW7bj7NsIju3Hahq1PRQyb7lnkhaTMIi+kYdh0zyIvJGWWw9QLVQdbWwhPN5zdZd/ZwD7gn6Uq\n6s/JhMdE/zdI4VbHvQnY5O7fTVG00HbKoKsfmdqqB6uAD8bkGbY+FTFsuuWF5MgL6Rg23fJCcg5f\nL+Q1hwQxc5cAxwKnsHyOi2jui1eA6bbt0dwXv6pA09HASJe8XyPM5/HIgFpuapXfFJOv1HbKqCv3\ntgI+1GP7+tb/+ucq+1SB/U5eqLid5AV5oczPN4c+Jy8MkReyziB/KfBRQsR4DTBOuPkN4FV3v68t\n76+BbwNfdven2rZvILwdfitwN2FW142EJxjOdPc3ytRkZt9o5X+E8ETFIuHGwEsIb7I/x93/lVLT\n1YQnKF5lqRO3s9vdH+ulqbU913bKQ1dBbfUgYT6Ux1u6poDPAhcRHiNe7+5be2lqbc+9rRLolheS\naZIXkmuSF+QFeYHh9cIyskRqhFliGz3S4x15NxEa/Ytd6rkAeLbVaHsJk6edWIUm4FTCB7KDMLR4\nqLV+J7B2QE2b+mhapqusdspDV0FttYHwmohXCTcsHgBeBu4APlxFn5IX5AV5QV6QF+SFLCnTyJYQ\nQgghhOhP1TfICyGEEEK8r1GwJYQQQghRIAq2hBBCCCEKRMGWEEIIIUSBKNgSQgghhCgQBVtCCCGE\nEAWiYEsIIYQQokAUbAkhhBBCFIiCLSGEEEKIAlGwJYQQQghRIAq2hBBCCCEKRMGWEEIIIUSB/B9h\n3wYn6/EiOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abb92c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,2,3]\n",
    "plt.figure(2)\n",
    "fig, axes = plt.subplots(3,3)\n",
    "fig.text(0.5, 1.6, 'Loss of the Pig Identification',\n",
    "             horizontalalignment='center', color='black', weight='bold',\n",
    "             size='large')\n",
    "fig.subplots_adjust(top=1.48, left=0.07, right=0.97,\n",
    "                    hspace=0.3, wspace=0.3)\n",
    "((ax1, ax2, ax3), (ax4, ax5, ax6), (ax7, ax8, ax9)) = axes \n",
    "ax1.plot(x,[vgg16epoch1trainloss,vgg16epoch2trainloss,vgg16epoch3trainloss],color='red')\n",
    "ax1.plot(x,[vgg16epoch1valloss,vgg16epoch2valloss,vgg16epoch3valloss],color='blue')\n",
    "ax1.axis([1,3,0,8])\n",
    "ax1.set_title('VGG16', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax2.plot(x,[vgg19epoch1trainloss,vgg19epoch2trainloss,vgg19epoch3trainloss],color='red')\n",
    "ax2.plot(x,[vgg19epoch1valloss,vgg19epoch2valloss,vgg19epoch3valloss],color='blue')\n",
    "ax2.axis([1,3,0,8])\n",
    "ax2.set_title('VGG19', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax3.plot(x,[Xceptionepoch1trainloss,Xceptionepoch2trainloss,Xceptionepoch3trainloss],color='red')\n",
    "ax3.plot(x,[Xceptionepoch1valloss,Xceptionepoch2valloss,Xceptionepoch3valloss],color='blue')\n",
    "ax3.axis([1,3,0,8])\n",
    "ax3.set_title('Xception', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax4.plot(x,[inceptionepoch1trainloss,inceptionepoch2trainloss,inceptionepoch3trainloss],color='red')\n",
    "ax4.plot(x,[inceptionepoch1valloss,inceptionepoch2valloss,inceptionepoch3valloss],color='blue')\n",
    "ax4.axis([1,3,0,8])\n",
    "ax4.set_title('Inception_V3', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax5.plot(x,[resnetepoch1trainloss,resnetepoch2trainloss,resnetepoch3trainloss],color='red')\n",
    "ax5.plot(x,[resnetepoch1valloss,resnetepoch2valloss,resnetepoch3valloss],color='blue')\n",
    "ax5.axis([1,3,0,8])\n",
    "ax5.set_title('ResNet', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax6.plot(x,[inceptionresnetepoch1trainloss,inceptionresnetepoch2trainloss,inceptionresnetepoch3trainloss],color='red')\n",
    "ax6.plot(x,[inceptionresnetepoch1valloss,inceptionresnetepoch2valloss,inceptionresnetepoch3valloss],color='blue')\n",
    "ax6.axis([1,3,0,8])\n",
    "ax6.set_title('InceptionResNet_V2', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax7.plot(x,[densenet169epoch1trainloss,densenet169epoch2trainloss,densenet169epoch3trainloss],color='red')\n",
    "ax7.plot(x,[densenet169epoch1valloss,densenet169epoch2valloss,densenet169epoch3valloss],color='blue')\n",
    "ax7.axis([1,3,0,8])\n",
    "ax7.set_title('Densenet169', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax8.plot(x,[densenet161epoch1trainloss,densenet161epoch2trainloss,densenet161epoch3trainloss],color='red')\n",
    "ax8.plot(x,[densenet161epoch1valloss,densenet161epoch2valloss,densenet161epoch3valloss],color='blue')\n",
    "ax8.axis([1,3,0,8])\n",
    "ax8.set_title('Densenet161', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax9.plot(x,[densenet121epoch1trainloss,densenet121epoch2trainloss,densenet121epoch3trainloss],color='red')\n",
    "ax9.plot(x,[densenet121epoch1valloss,densenet121epoch2valloss,densenet121epoch3valloss],color='blue')\n",
    "ax9.axis([1,3,0,8])\n",
    "ax9.set_title('Densenet121', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OxA/c3AefiNwNdMRPU7005hrTIscNAx7Et6B8DBQ750qbELu0MBs2wEsvwTnn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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abb92c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X=['VGG16','VGG19','Xception','Inception','ResNet','InceptionRes','Dense169','Dense161','Dense121']\n",
    "Y=[vgg16epoch3valacc,vgg19epoch3valacc,Xceptionepoch3valacc,inceptionepoch3valacc,resnetepoch3valacc,inceptionresnetepoch3valacc,densenet169epoch3valacc,densenet161epoch3valacc,densenet121epoch3valacc]\n",
    "x = range(len(X))\n",
    "\n",
    "plt.figure(3)\n",
    "plt.plot(x, Y, 'o-')\n",
    "plt.xticks( np.arange(18), X,rotation=-45 )\n",
    "plt.margins(0.08)\n",
    "plt.subplots_adjust(bottom=0.15)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+Fsxc9fLL8M47aiqJNnQofP65XyguEzgHY8bAySdDUVHQ0Yg0jppLJKe99pqf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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abb684390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X=['VGG16','VGG19','Xception','Inception','ResNet','InceptionRes','Dense169','Dense161','Dense121']\n",
    "Y=[vgg16epoch3trainacc,vgg19epoch3trainacc,Xceptionepoch3trainacc,inceptionepoch3trainacc,resnetepoch3trainacc,inceptionresnetepoch3trainacc,densenet169epoch3trainacc,densenet161epoch3trainacc,densenet121epoch3trainacc]\n",
    "x = range(len(X))\n",
    "\n",
    "plt.figure(4)\n",
    "plt.plot(x, Y, 'o-')\n",
    "plt.xticks( np.arange(18), X,rotation=-45 )\n",
    "plt.margins(0.08)\n",
    "plt.subplots_adjust(bottom=0.15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abb52cb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RNjOzHcysPxseGe7yyPJBHXsZEczsqqQBPK9KuhlJunOT8SuY2cfM7AIzm21m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\nJKkHHNhLkiRJPfA/dGLfEQaFPqsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abb52c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(5)\n",
    "n_groups = 3\n",
    "fig1, axes = plt.subplots(3,3)\n",
    "fig1.text(0.5, 1.6, 'Accuracy of the Pig Identification on train and validation set',\n",
    "             horizontalalignment='center', color='black', weight='bold',\n",
    "             size='large')\n",
    "fig1.subplots_adjust(top=1.48, left=0.07, right=0.97,\n",
    "                    hspace=0.3, wspace=0.3)\n",
    "((ax1, ax2, ax3), (ax4, ax5, ax6), (ax7, ax8, ax9)) = axes\n",
    "vgg16_train = (vgg16epoch1trainacc, vgg16epoch1trainacc, vgg16epoch1trainacc)    \n",
    "vgg16_val = (vgg16epoch1valacc, vgg16epoch1valacc, vgg16epoch1valacc)\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "rects1 = ax1.bar(index, vgg16_train, bar_width,alpha=opacity, color='b',label=    'train') \n",
    "rects2 = ax1.bar(index + bar_width, vgg16_val, bar_width,alpha=opacity,color='r',label='val')\n",
    "ax1.axis([0,3,0,1])\n",
    "ax1.set_title('VGG16', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "vgg19_train = (vgg19epoch1trainacc, vgg19epoch1trainacc, vgg19epoch1trainacc)    \n",
    "vgg19_val = (vgg19epoch1valacc, vgg19epoch1valacc, vgg19epoch1valacc)\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "rects1 = ax2.bar(index, vgg19_train, bar_width,alpha=opacity, color='b',label=    'train') \n",
    "rects2 = ax2.bar(index + bar_width, vgg19_val, bar_width,alpha=opacity,color='r',label='val')\n",
    "ax2.axis([0,3,0,1])\n",
    "ax2.set_title('VGG19', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "xception_train = (Xceptionepoch1trainacc, Xceptionepoch1trainacc, Xceptionepoch1trainacc)    \n",
    "xception_val = (Xceptionepoch1valacc, Xceptionepoch1valacc, Xceptionepoch1valacc)\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "rects1 = ax3.bar(index, xception_train, bar_width,alpha=opacity, color='b',label=    'train') \n",
    "rects2 = ax3.bar(index + bar_width, xception_val, bar_width,alpha=opacity,color='r',label='val')\n",
    "ax3.axis([0,3,0,1])\n",
    "ax3.set_title('Xception', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "inception_train = (inceptionepoch1trainacc, inceptionepoch1trainacc, inceptionepoch1trainacc)    \n",
    "inception_val = (inceptionepoch1valacc, inceptionepoch1valacc, inceptionepoch1valacc)\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "rects1 = ax4.bar(index, inception_train, bar_width,alpha=opacity, color='b',label=    'train') \n",
    "rects2 = ax4.bar(index + bar_width, inception_val, bar_width,alpha=opacity,color='r',label='val')\n",
    "ax4.axis([0,3,0,1])\n",
    "ax4.set_title('Inception_V3', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "resnet_train = (resnetepoch1trainacc, resnetepoch1trainacc, resnetepoch1trainacc)    \n",
    "resnet_val = (resnetepoch1valacc, resnetepoch1valacc, resnetepoch1valacc)\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "rects1 = ax5.bar(index, resnet_train, bar_width,alpha=opacity, color='b',label=    'train') \n",
    "rects2 = ax5.bar(index + bar_width, resnet_val, bar_width,alpha=opacity,color='r',label='val')\n",
    "ax5.axis([0,3,0,1])\n",
    "ax5.set_title('ResNet', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "inceptionresnet_train = (inceptionresnetepoch1trainacc, inceptionresnetepoch1trainacc, inceptionresnetepoch1trainacc)    \n",
    "inceptionresnet_val = (inceptionresnetepoch1valacc, inceptionresnetepoch1valacc, inceptionresnetepoch1valacc)\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "rects1 = ax6.bar(index, inceptionresnet_train, bar_width,alpha=opacity, color='b',label=    'train') \n",
    "rects2 = ax6.bar(index + bar_width, inceptionresnet_val, bar_width,alpha=opacity,color='r',label='val')\n",
    "ax6.axis([0,3,0,1])\n",
    "ax6.set_title('InceptionResNet_V2', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "densenet169_train = (densenet169epoch1trainacc, densenet169epoch1trainacc, densenet169epoch1trainacc)    \n",
    "densenet169_val = (densenet169epoch1valacc, densenet169epoch1valacc, densenet169epoch1valacc)\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "rects1 = ax7.bar(index, densenet169_train, bar_width,alpha=opacity, color='b',label=    'train') \n",
    "rects2 = ax7.bar(index + bar_width, densenet169_val, bar_width,alpha=opacity,color='r',label='val')\n",
    "ax7.axis([0,3,0,1])\n",
    "ax7.set_title('Densenet169', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "densenet161_train = (densenet161epoch1trainacc, densenet161epoch1trainacc, densenet161epoch1trainacc)    \n",
    "densenet161_val = (densenet161epoch1valacc, densenet161epoch1valacc, densenet161epoch1valacc)\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "rects1 = ax8.bar(index, densenet161_train, bar_width,alpha=opacity, color='b',label=    'train') \n",
    "rects2 = ax8.bar(index + bar_width, densenet161_val, bar_width,alpha=opacity,color='r',label='val')\n",
    "ax8.axis([0,3,0,1])\n",
    "ax8.set_title('Densenet161', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "densenet121_train = (densenet121epoch1trainacc, densenet121epoch1trainacc, densenet121epoch1trainacc)    \n",
    "densenet121_val = (densenet121epoch1valacc, densenet121epoch1valacc, densenet121epoch1valacc)\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "rects1 = ax9.bar(index, densenet121_train, bar_width,alpha=opacity, color='b',label=    'train') \n",
    "rects2 = ax9.bar(index + bar_width, densenet121_val, bar_width,alpha=opacity,color='r',label='val')\n",
    "ax9.axis([0,3,0,1])\n",
    "ax9.set_title('Densenet121', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\matplotlib\\font_manager.py:1288: UserWarning: findfont: Font family ['time new roman'] not found. Falling back to Bitstream Vera Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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6gXyzyJMj4rkVFXsB+dzcs8PyfcjvZb8a747GEf9jy2O7S2pzaT+Ia6Pf0hsi/5zKdNHN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\nVcTESpIkqSImVpIkSRUxsZIkSarI/we/XbOh5XeUlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abb6b7e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X=('VGG16','VGG19','Xception','Inception','ResNet','InceptionRes','Dense169','Dense161','Dense121')\n",
    "VGG16_time=vgg16epoch1time+vgg16epoch2time+vgg16epoch3time\n",
    "VGG19_time=vgg19epoch1time+vgg19epoch2time+vgg19epoch3time\n",
    "xception_time=Xceptionepoch1time+Xceptionepoch2time+Xceptionepoch3time\n",
    "inception_time=inceptionepoch1time+inceptionepoch2time+inceptionepoch3time\n",
    "resnet_time=resnetepoch1time+resnetepoch2time+resnetepoch3time\n",
    "inceptionresnet_time=inceptionresnetepoch1time+inceptionresnetepoch2time+inceptionresnetepoch3time\n",
    "densenet169_time=densenet169epoch1time+densenet169epoch2time+densenet169epoch3time\n",
    "densenet161_time=densenet161epoch1time+densenet161epoch2time+densenet161epoch3time\n",
    "densenet121_time=densenet121epoch1time+densenet121epoch2time+densenet121epoch3time\n",
    "\n",
    "Y_time=(VGG16_time,VGG19_time,xception_time,inception_time,resnet_time,inceptionresnet_time,densenet169_time,densenet161_time,densenet121_time)\n",
    "y_pos = np.arange(len(X))  \n",
    "\n",
    "plt.barh(y_pos,Y_time,color='r')\n",
    "plt.yticks(index,X)\n",
    "plt.xlabel('time/s')\n",
    "plt.title('How fast is the running time of each model?')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Xception_120参数\n",
    "Xception120epoch1trainacc=0.8153\n",
    "Xception120epoch1valacc=0.8922\n",
    "Xception120epoch1trainloss=0.6459\n",
    "Xception120epoch1valloss=0.4775\n",
    "Xception120epoch1time=3448\n",
    "\n",
    "Xception120epoch2trainacc=0.9397\n",
    "Xception120epoch2valacc=0.8122\n",
    "Xception120epoch2trainloss=0.2254\n",
    "Xception120epoch2valloss=1.2694\n",
    "Xception120epoch2time=3441\n",
    "\n",
    "Xception120epoch3trainacc=0.9545\n",
    "Xception120epoch3valacc=0.9483\n",
    "Xception120epoch3trainloss=0.1721\n",
    "Xception120epoch3valloss=0.2544\n",
    "Xception120epoch3time=3441"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Xception_100参数\n",
    "Xception100epoch1trainacc=0.8164\n",
    "Xception100epoch1valacc=0.9276\n",
    "Xception100epoch1trainloss=0.6456\n",
    "Xception100epoch1valloss=0.3813\n",
    "Xception100epoch1time=4156\n",
    "\n",
    "Xception100epoch2trainacc=0.9509\n",
    "Xception100epoch2valacc=0.9597\n",
    "Xception100epoch2trainloss=0.1975\n",
    "Xception100epoch2valloss=0.1894\n",
    "Xception100epoch2time=4148\n",
    "\n",
    "Xception100epoch3trainacc=0.9648\n",
    "Xception100epoch3valacc=0.9586\n",
    "Xception100epoch3trainloss=0.1457\n",
    "Xception100epoch3valloss=0.2832\n",
    "Xception100epoch3time=4147"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7mB7/TUfHmtcVy9+qdXUrFlqYYqGhsVC13AmYu++bo+xRwFElpt0E3JR3+a2q\nra2t2U1YSyu2CVq3XXkpFoqr1+fb1QVLloQfjPjHI/taatrKbj032thll7o0q87aGDMGQm+QoRQu\n+GuWucCk3LUUC8UpFvLoH7GQRzM64fdLrZhUtGKboHXbJfWRfr7uYa+21h+NpUvDPIrZYAPYcEPY\naKPC69ixhf83HOlstPJFNnxmDhv9417W/9cjWPsv8I5V+KrVsHoVTtj1jl+LjVurzKAhMGgQPjh5\nHTQYBg/GBw8uTBs0uPtrVLYwbRAMGowPvD4pMxgfmEwbOKh7/TX/J38PTF8HFtrmhfVV7rXctPnz\n4dvpiUPpMcWCYqEcJWAiUtLq1WHPu9IPRKlpHR3F5zt4cPiBiH80Ro2C8eOTH43MD0r8usEGMLDY\nN9crr8Dtt4fhttvguefCgvbYA760O6y/PgwZsvYwdGjx8cWmDxpUOGfSD82dqwSsFMWCYqHelICJ\n9GPusGxZ9Xva2WlvlegGYQYjR679gzB6dOUfjY02Ct/jPbZiBdxzT/iBue02+Pvfw/gddoBDD4X9\n94e99oJ3vKMOC5O+TrGgWGg1SsBE+rBLL4Urryz94/Hmm2HPvZhhw9b+QdhyS/iP/4hOX5T40Rgx\nAtZr9JNku7rgb38r/MjMmhV6F2+xRfiB+drX4CMfCYcPZJ2jWFAs9DVKwET6sP/9X9hkk+4/CGPG\nVP7RGDkynEVoeYsWFX5k/vpXWLw47MXvsw+cc074sRk/vl+fCpHqKBYUC32NEjCRPmzmTGjNm0fX\n6M034Y47wo/M7bfD00+Hwwu77grHHQf77Qcf+lDozyISUSxIX6METESap6MDHnywsGf/0EPh9Mp7\n3xv26M85B/bdNxyuEOnPFAvrHCVgItI47jBvXuFH5q674O23YeONQ5+Vo48Oe/bhhkAi/ZdiYZ2n\nBExEetfLLxcuh7/tNnjhhXDaZM89w3Xe++8PH/hAE3oyizSYYkEiSsBEpL6WL4e77y78yDz2WBi/\n444wZUr4kdlzTxg+vPx8RPo6xYKUoQRMRHqmszPctTDds581K/RnGT06/MB84xvhlMpmmzW7pSK9\nS7EgOSgBE5H8Fiwo7NXPnAmvvx7urr3vvnDuuaHvyvbb65J46f8UC1IjJWAiUtkbb4Qfl3TP/pln\nYMCAcEn8CSeEvfsPfjA8nkSkP1MsSJ0oARORtXV0wP33F/bsH344XBI/dix8/ONhr37ffcNdLEX6\nM8WC9BIchmqzAAAcAklEQVQlYNJvuMOqVfmHJ55odstbgDv84x+Fmz7edVd4cN4mm4Q+K8ceG/bs\nt9yy2S0V6V2KBWkQJWDruFqTlnTo6OhZ/XoOnZ3NXpt9zIsvFk6j3H57+H/IkPDQ3tNPDz8yO+6o\nS+Kl/1MsSBPkTsDMzICTgWOBMcCrwFXA6e6+vMp5TAGOB8YCQ4BngSuBC929xDPnpZQ33oD580Nf\n0Pj1hRcqJ0i9kbQMGlT7MGRI6L/ak3nkHZ58Ej75yfzvs8/FwrJlYW8+/ZF5/PEwfqedYOrUwiXx\nw4bVdbHS/ykWRPKr5QjYhcBXgGuAc4HxwInATsB+lSqb2VnAN4G/AmcCq4B9gO8A/wnsXkOb+rWV\nK8NzWIslWQsWwJIlhbIbbADbbANbbx26JQwZ0thkZsCAvnexT7z+cmrtWOjshDlzCn1X7rsvZN3v\neU/4gfnWt8IplXe9q0eLEUGxIJJbrgTMzCYAJwAz3P3QaPxC4GIzO8zdryhTfwBwEvCwu+8fTfqV\nmXUCU8zsP9z90Tzt6uu6usIR71IJ1vPPF8oOGgRbbRWSrA9+ENraQrKVJl0bbdT3EqCmcA8Pu33x\nxfDMtZxaNhaeeaawVz9zZjg8OmJEyMbPPz/82Iwdq41E6kaxIFKbvEfApiSvF2bGXwKcDUwFSgYa\nMAgYBrxcZNqLyeuynG3qE958MyRTxRKshQuhvb1QdtSo7kex4gRr9OhwlElK6OyEV18NiVV2eOml\n7n+vXNmTJbVGLCxZAjNmFPbsFywIG8iHPgQnnRSu0Np1V10SL71JsSBSg7wJ2M5AFzA7Hunu7Wb2\nCLBLucruvtLM7gY+bmZfJxyuXg3sC3wZ+F93fyZnm1pCR0f504RvvFEou/76hYTqgAO6J1hjxuip\nFEWtXNk9gcomVOnwyivhkGJs001h883DMG4cTJ5c+H/zzWHxYvjMZ/K2qDVi4cMfDq/jxsEnPhH2\n6vfZJ5yLFmkMxYJIDfImYFsAr7n7qiLTngd2M7OB7r66zDymAL8j7BmdnYxz4PvufkbO9jSMe/i9\nL5VgPfdcKANhp2urrUJCNWkSHHJIIcHaZpvwsHsd9SassKVLyydU6fg4g4WwFztqVBg23zzs2cZJ\nVTp+s80q7/HOnVtL61sjFk4/HY45JvRlEWkOxYJIDfImYMOB9hLTVkZllpaZRwewgBBsNyfjDgZO\nM7N2d/9BzjbVzdKl3ZOq7N/xGat3vauQUO25Z/cE693vhoHr8g0+urrgtdeKJ1PZRGvFiu513/GO\n7onUDjt0T6jS4Z3vbPYl4a0RC5/+tH5wpNkUCyI1yJsmLAc2LTFtaFSmKDMbBtxP6Gw5JZp0lZl1\nAd8xs6vd/elyjZg2bRojM3cdbmtro62trWzjV62CZ58tfRRr8eJC2eHDCwnV/vt3T7DGjAmnEdc5\nHR3VnQZ8+eW172+x8caF5GmbbWCPPbonVGmSNWJEr76F6dOnM3369G7jltR2GWSfjgURxYJIUMdY\nyMU8PW9WTWGzW4CPAMOzh5vN7F5gO3cv+Zh3MzsCuBQ4xN2vzUw7CLgaOMbdLy1RfyIwZ86cOUyc\nOHGt6e6hC1CpBOvf/y50D1pvvbCzFCdW8eu73rUOnSZ8663qTgPGGSqEc62bbbZ2IpU9DThqFAwe\n3Jz3VoW5c+cyadIkgEnuXtX5yFaPBZFaKBZEglpiIa+8R8BmA/sDuwKz0pFmNoRwv5c7K9TfInkt\ndh3fQMCqadPTT4dkqthpwuXRftYmm4Rkauutw4UwcYK15ZbrwMUwixeHu7GWOwX44ovhpoSxYcO6\nJ1Ljx699CnDzzcMKXnfvDN0SsSDSAhQLIjXIu1FfCXyLcMfjWdH4YwmXEV+ejjCzUcBI4Fl3Tzv6\nPEEIpiOBGZl5f5HQ6XI2FRx2WHgdOrSQVO27Lxx9dPckq5fPZrWmf/wDrr46DNmHHG60UeGI1JZb\nhhuJFTtitcEG69Dhv5q1RCyItADFgkgNciVg7v64mf0UON7MrgFuAiYQ7oB8p7vHJ1HPBo4g3M34\n7mTcn4GHgAPM7C7gj8n4g4E9gKvc/ZFK7fjNb+A//zOc/Vp3D8Ak3MNjNK6+OtwDZ968kEAdeCCc\ndlq4HDNNroYOrTw/qUqrxIJIsykWRGpTy2HdkwhXqxwLHAC8BlwEZC8VdsK9YQoj3LvM7COER04c\nRAhGB54Gvg5cUE0Ddtop5BTrLHd49NFC0vXUUzByZLgK6Ic/DFcNDBnS7FauC5oeCyItQrEgklPu\nBMxDr/0LqBAU7n4UcFSR8cuAU5NBquUOjzxSOL34r3/BhhuGG4ief354jpmSroZSLIgEigWR/NSx\nsZW5h5uEpke6nnkm3P/qM5+BH/843Pm5ha8uFBERkeKUgLUad5g9OyRcM2aESzs33hg++1n42c/C\n1Qb9/vJNERGR/k0JWCtwhwcfLCRdixaF5xcedFB4jtE++6zjt9YXERHpX/Sr3ixdXfDAA4Wk69//\nDnd/PfjgkHTtvbeSLhERkX5Kv/CN1NUF991XSLqefz7cHiJNuvbaK9xdXkRERPo1JWC9rbMTZs0K\nHemvuSbceX6LLULS9bnPwe67K+kSERFZxygB6w2dnXDPPSHp+uMfw6N/Ro+GQw8NSdduu+kOsiIi\nIuswJWD1sno13H13Iel65ZXwtO+2tpB0ffCDSrpEREQEUALWM6tXw513hqTr2mvh1VfDo38OPzwk\nXbvuqmcqioiIyFqUgOW1ahXccUch6Vq8ODz5+4tfDEnXzjsr6RIREZGylIBVo6MDZs4MSdef/gSv\nvw7bbgtf+lK4enHiRCVdIiIiUjUlYKV0dMDtt4ek67rr4I03YLvt4L/+KyRdO+2kpEtERERqogQs\n1t4Of/lLuEfXddfBkiUwbhwcf3w4vbjDDkq6REREpMeUgK1cCbfeGpKu66+HpUth/Hg46aSQdL3v\nfUq6REREpK7WzQRsxQq45ZaQdN1wA7z1Vki0vvrVkHRNmNDsFoqIiEg/tu4kYMuXw803hz5df/4z\nLFsWTil+7WuhT9f48c1uoYiIiKwjct8Z1IJpZjbPzFaY2bNmdq6ZDc8xjwFmdqKZzTGzt83szeTv\nL+VtT1nLloWE69BDYdNNQ6L11FPwzW/Ck0/Co4/Caacp+ZKa9KlYEOlFigWR/Go5AnYh8BXgGuBc\nYDxwIrATsF+lymY2CLgBmAxcDvw8acd2wFY1tKe7t9+GG28MiddNN4XTjRMnwqmnhgRsu+16vAiR\nRGvHgkjjKBZEcsqVgJnZBOAEYIa7HxqNXwhcbGaHufsVFWZzOvBhYD93vztne4t7661wWvHqq8Np\nxpUrww1RzzwzPPR6223rshiRVMvGgkiDKRZEapP3FOSU5PXCzPhLgOXA1HKVk8PRJwJ/SoPMzNbP\n2YZg6VK4/HL4zGfC6cUpU+D55+F734P582H2bPj615V8SW9pnVgQaS7FgkgN8iZgOwNdwOx4pLu3\nA48Au1SovxcwAphrZheZ2VJgqZm9YmZnmdmAqlpx8skh6Zo6FV5+GX7wA1i4EB58EE45JTwaSKR3\ntUYsiDSfYkGkBnn7gG0BvObuq4pMex7YzcwGuvvqEvXHJa/TgHbgFOB14AvAN5P5H1WxFUuWwDnn\nhNOL73lPzrcgUhetEQsizadYEKlB3gRsOCFAilkZlVlaosyI5HUjYIK7/yv5f4aZzQSOMLOz3f2p\nsq249NLQsV6keVojFkSaT7EgUoO8CdhyYNMS04ZGZUpZkbw+EAVZ6veEK2D2AcoG2rRp0xg5cmS3\ncW1tbbS1tZWrJsL06dOZPn16t3FLliypZVaKBenTFAsiQR1jIZe8CdgLwHgzG1TkcPNowmHoUoeZ\nAZ5LXl8qMu1FwAh7QWVdcMEFTNQRMKlBsS/kuXPnMmnSpLyzUixIn6ZYEAnqGAu55O2EPzups2s8\n0syGEO73MrtYpchDyeu7i0xLx72Ss00izaBYEAkUCyI1yJuAXZm8npwZfywwjHADPQDMbJSZjTOz\nYek4d18IzAJ2NbOdorLrJfNYBfwlZ5tEmkGxIBIoFkRqkCsBc/fHgZ8CB5nZNWZ2tJmdB5wH3Onu\n8UnUs4F5rH0J8lcI/QH+amZnmNkJwN2ES5nPcvfnEGlxigWRQLEgUptaHkV0ErCAsGdyAPAacBFw\nRqacE+4N032k+yNmtjvw/WReQwkB+UV3/98a2iPSLIoFkUCxIJJT7gTM3R24IBnKlTuKEvduSfaY\nPpN32SKtRLEgEigWRPLL2wdMRERERHpICZiIiIhIgykBExEREWkwJWAiIiIiDaYETERERKTBlICJ\niIiINJgSMBEREZEGUwImIiIi0mBKwEREREQaTAmYiIiISIMpARMRERFpMCVgIiIiIg2mBExERESk\nwZSAiYiIiDSYEjARERGRBlMCJiIiItJguRMwC6aZ2TwzW2Fmz5rZuWY2vJYGmNmVZtZlZo/WUl+k\nWRQLIoFiQSS/Wo6AXQicBzwOnABcBZwIXJ93Rmb2SeBgYHkN7RBpNsWCSKBYEMlpYJ7CZjaBEFwz\n3P3QaPxC4GIzO8zdr6hyXu8Afgr8BPh0nnaINJtiQSRQLIjUJu8RsCnJ64WZ8ZcQ9lam5pjXD5Ll\nn5qzDSKtQLEgEigWRGqQ6wgYsDPQBcyOR7p7u5k9AuxSzUzMbFfgeODz7v62meVshkjTKRZEAsWC\nSA3yHgHbAnjN3VcVmfY8sImZlU3qzGwA8GvgFne/JufyRVqFYkEkUCyI1CDvEbDhQHuJaSujMkvL\nzOPrwDbAp3IuW6SVKBZEAsWCSA3yJmDLgU1LTBsalSnKzN4LnAZ8190X5Vz2GtOmTWPkyJHdxrW1\ntdHW1lbrLGUdMX36dKZPn95t3JIlS2qZlWJB+jTFgkhQx1jIxdy9+sJmtwAfAYZnDzeb2b3Adu6+\nWZn61wETgY8CHelo4A5gGfAJYJm7v1Si/kRgzpw5c5g4cWLV7RYpZ+7cuUyaNAlgkrvPraaOYkH6\nI8WCSFBLLOSVtw/Y7KTOrvFIMxsC7ESmE2YRWxL6C/wDeDoZ/gmMBsYm//8qZ5tEmkGxIBIoFkRq\nkPcU5JXAt4CTgVnR+GOBYcDl6QgzGwWMBJ519xXJ6P8GNiwy358DK4BpQNG9HJEWo1gQCRQLIjXI\nlYC5++Nm9lPgeDO7BrgJmAB8BbjT3eOTqGcDRwD7AHcn9WcWm6+ZnQe85e7X5n4HIk2gWBAJFAsi\ntcl7BAzgJGABYe/mAOA14CLgjEw5J9wbplrVd0YTaQ2KBZFAsSCSU+4EzEOv/QuSoVy5o4Cjqpzn\n1nnbIdJsigWRQLEgkl8tD+MWERERkR5QAiYiIiLSYErARERERBpMCZiIiIhIgykBExEREWkwJWAi\nIiIiDaYETERERKTBlICJiIiINJgSMBEREZEGUwImIiIi0mBKwEREREQaTAmYiIiISIMpARMRERFp\nMCVgIiIiIg2mBExERESkwZSAiYiIiDRY7gTMgmlmNs/MVpjZs2Z2rpkNr6LuhmZ2kpndmtRbbmZP\nmtkvzezdtb0FkeZQLIgEigWR/Go5AnYhcB7wOHACcBVwInB9FXU/CJwLdAE/Bo4HbgSmAo+Z2fY1\ntEekWRQLIoFiQSSngXkKm9kEQnDNcPdDo/ELgYvN7DB3v6LMLOYBY919QWa+NwG3Ad8FDi1WUaSV\nKBZEAsWCSG3yHgGbkrxemBl/CbCcsMdSkrs/mw2yZPxfgdeB9+dsj0izKBZEAsWCSA3yJmA7Ew4T\nz45Huns78AiwSy2NMLMNgBHAy7XUF2kCxYJIoFgQqUHeBGwL4DV3X1Vk2vPAJmaW67Rm4lTC6dDf\n1lBXpBkUCyKBYkGkBnkTsOFAe4lpK6MyVTOzQ4D/Bm5299/lbI9IsygWRALFgkgN8u6VLAc2LTFt\naFSmKmZ2AHAZ4dD1YdXWmzZtGiNHjuw2rq2tjba2tmpnIeuo6dOnM3369G7jlixZUsusFAvSpykW\nRII6xkIu5u7VFza7BfgIMDx7uNnM7gW2c/fNqpzXx4FrCZct7+fuFd+tmU0E5syZM4eJEydW3W6R\ncubOncukSZMAJrn73GrqKBakP1IsiAS1xEJeeU9Bzk7q7BqPNLMhwE5kOmGWEgXZE8D+1QSZSItR\nLIgEigWRGuRNwK5MXk/OjD8WGAZcno4ws1FmNs7MhsUFzeyjwB8J937Zz93fzNkGkVagWBAJFAsi\nNcjVB8zdHzeznwLHm9k1wE3ABOArwJ3uHp9EPRs4AtgHuBvAzCYB1wFOuLLlADPLLuNyRFqcYkEk\nUCyI1KaWS4NPAhYQ9m4OAF4DLgLOyJRzwr1hYu8HBid/X1Bi/go06SsUCyKBYkEkp9wJmIde+xdQ\nOlDSckcBR2XG/Q7QJcXSLygWRALFgkh+tTyMW0RERER6QAmYiIiISIMpARMRERFpMCVgIiIiIg2m\nBExERESkwZSAiYiIiDSYEjARERGRBlMCJiIiItJgSsBEREREGkwJmIiIiEiDKQETERERaTAlYCIi\nIiINpgRMREREpMGUgImIiIg0mBIwERERkQZTAiYiIiLSYLkTMAummdk8M1thZs+a2blmNjzHPA4w\ns1lm9raZLTazq8xsTN62iDSTYkEkUCyI5FfLEbALgfOAx4ETgKuAE4Hrq6lsZgcBNwBDgFOAHwJ7\nA/ea2aga2tMSpk+f3uwmrKUV2wSt264aKBaKaMXPtxXbBK3brhooFopoxc+3FdsErduu3pQrATOz\nCYTgmuHun3P337j7KcBXgQ+b2WEV6g8EfgwsAvZy91+4+znAx4BRwJk1vIeW0IobTyu2CVq3XXko\nFkprxc+3FdsErduuPBQLpbXi59uKbYLWbVdvynsEbEryemFm/CXAcmBqhfqTgc2BX7v7inSku/8d\nuBP4vJkNyNkmkWZQLIgEigWRGuRNwHYGuoDZ8Uh3bwceAXapUH8XwIEHikx7ANgAGJuzTSLNoFgQ\nCRQLIjXIm4BtAbzm7quKTHse2CQ5nFyuflq2WH2A0TnbJNIMigWRQLEgUoNyQVHMcKC9xLSVUZml\nZepTYh4rM2WKGQowb968MkWaY8mSJcydO7fZzeimFdsErdeuaHsamqOaYqGEVvt8oTXbBK3XLsVC\nfbXa5wut2SZovXbVGAv5uHvVA/Ao8GKJaVcCncDAMvUvTsqMKzLty8m0/crUn0I4VK1BQ28MUxQL\nGjTgKBY0aEiHqmMh75D3CNgLwHgzG1TkcPNowmHo1RXqp2WfKlIfih+GTt0KfAFYSGHPSKSnhgJj\nCNtXtRQL0h8pFkSCWmIhl7wJ2Gxgf2BXYFY60syGADsRrlipVN+A3YCZmWm7EQ5R/7NUZXdfDPwh\nZ5tFqnFfzvKKBemvFAsiQd5YyCVvJ/wrk9eTM+OPBYYBl6cjzGyUmY0zs2FRubuAF4Fj4jskm9mO\nhEuRr3L3zpxtEmkGxYJIoFgQqYEl59Crr2B2MXA88CfgJmAC8BXgHnf/SFTut8ARwD7ufnc0/hDg\nCkK/gUuAkYTA7QR2dvcXe/B+RBpGsSASKBZE8qvlUUQnER4VMQH4CXAocBHwqUw5J9wbpvtI9xnA\ngYRz9T8CvkbYA9pTQSZ9jGJBJFAsiOSU+wiYiIiIiPRMLUfARERERKQHmp6Amdk3zewqM3vGzLrM\nbH6N8znAzGaZ2dtmtjiZ55hmtcnM7kzqZodOM5tYw/y2M7Pvmtn9ZvaKmS01s7+Z2bfijqtVzKdu\n66le7eqFdTXWzC4zsyfM7E0zW2Zm88zsPDMblWM+dV1XVSxPsVDd/BQL1bdJsaBYqGY+ioXq51O/\nbarZpyDNrAtYDMwlPFNsibtvk3MeBwFXA38Dfk3owDkNWE3owPlSE9p0B6E/xMmES6xjN7n7mznn\n9/+A44DrCc9HWwXsC3we+DvwIQ/PXis3j7qupzq2q97r6sPAt5L2PEd4fzsA/wdYAuzk7q9VmEfd\n11UV7VYsVDc/xUL1bVIsKBYUC7RoLPTWHV6rHYAx0d+PAfNz1h9IuEnffGBYNH7HZKX8otFtSurd\nUUu9MvObCIwoMv57hCuFjmv0eqpHu3pjXZVZziGEDsCnNGNd9fZ2p1hQLORoq2KhwW3qjc9XsVCX\nddi0WGj6KUh3X9jDWUwGNgd+7e4rovn+nXADwM+b2YAGt2kNC0b0dD7uPtfd3yoy6UrC3sH7K8yi\n7uupTu1ao17rqoxnk9eNKpTrlXVViWKhOoqFulAsKBZAsQBNjIWmJ2B1sAvh0uYHikx7ANgAGNvQ\nFhWMBt4GliTni68xs3F1XsZ7kteXK5Rr9Hqqtl2puq8rMxtiZhub2Wgz+yjwC8I6uKlC1Vbepspp\n5XYrFhQLjdTK7VYsKBaA/I8iakVbJK/FnhWWjhsNzCsyvTfNB+4l3FiwE/gg4caEHzazPd39Hz1d\ngJmtB5xGOL9e6VEcDVtPOdsFvbeujgF+HP2/AJjq7rNKlE+16jZVSau2W7GgWGi0Vm23YkGxsEZ/\nSMDSKyqKdehbmSnTMO5+dGbUH83sBsKhyvOBj9VhMRcRNspvuvvTFco2cj3laVdvrqtrCcGwPvAB\nwo0eN6miXktuU1VoyXYrFhQLTdCS7VYsKBZi/SEBW568DikybWimTFO5+71mdjewr5kN8QpXgZRj\nZt8jPPrjF+7+wyqqNGQ91dCuouqxrtz9BeCF5N/rzeyPwGwzG+7u55Sp2me2qYw+027FQvUUCzXp\nM+1WLFSvv8VCf+gDlq7I0UWmpeOKHTJsloXAACp3+CvJzM4Evg38xt2Pq7Jar6+nGttVzkJ6uK5i\n7v4Y4fLhSm3ra9tUqq+1eyGKhWotRLGQR19r90IUC9VaSD+Jhf6QgM0mXFWxW5FpuwFLgX82tEXl\njSVcsvp6LZWTjfl04FJ3/1KOqr26nnrQrnJ6tK5KGAa8s0KZvrZNpfpauxUL1VMs5NPX2q1YqF7/\niYW8963ozYEK91YBRgHj6H4PjvTeHAuA4dH49N4cv2xCmzYA1itS9hOE+43cUGNbTk/qX1qhXEPX\nUw/bVfd1BWxWYvy+yXv9SzO3qV7c7hQLTV5PigXFQiM/3zpsc4qFJsZCK9wJfyqwFSGzPAEYROhg\nB7DI3S+Lyv4WOALYx93vjsYfAlxBuFriEsLdaU8mXDmxs7u/2Mg2mdmnk/I3EK7kWE3ofPgF4DVg\nT3f/V842HU+4cmMRhQ079rK7316qTcn4uq6nerSrl9bVHwn3a5mZtGsoMAk4jHBJ877u/mipNiXj\n676uqmi3YqG6NikWqm+TYkGxoFigRWOh1uy2XgPhbredJYaZmbKXJh/E3kXmcwBwX7IiFxNu+LZ1\nM9oEbJ98SE8TDkuuSP6+GNi8xjZdWqZN3drVqPVUj3b10ro6hPAIjEWETpHLgCeAC4F3N2ObUiwo\nFhQLigXFgmIhHpp+BExERERkXdMfOuGLiIiI9ClKwEREREQaTAmYiIiISIMpARMRERFpMCVgIiIi\nIg2mBExERESkwZSAiYiIiDSYEjARERGRBlMCJiIiItJgSsBEREREGkwJmIiIiEiDKQETERERabD/\nD+yh5Fus9ZKyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abf32a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,2,3]\n",
    "fig, axes = plt.subplots(1,3)\n",
    "fig.text(0.5, 0.6, 'Accuracy of the Pig Identification',\n",
    "             horizontalalignment='center', color='black', weight='bold',\n",
    "             size='large')\n",
    "fig.subplots_adjust(top=0.5, left=0.07, right=0.97,\n",
    "                    hspace=0.3, wspace=0.3)\n",
    "((ax1, ax2, ax3)) = axes \n",
    "ax1.plot(x,[Xceptionepoch1trainacc,Xceptionepoch2trainacc,Xceptionepoch3trainacc],color='red')\n",
    "ax1.plot(x,[Xceptionepoch1valacc,Xceptionepoch2valacc,Xceptionepoch3valacc],color='blue')\n",
    "ax1.axis([1,3,0,1])\n",
    "ax1.set_title('Xception', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax2.plot(x,[Xception120epoch1trainacc,Xception120epoch2trainacc,Xception120epoch3trainacc],color='red')\n",
    "ax2.plot(x,[Xception120epoch1valacc,Xception120epoch2valacc,Xception120epoch3valacc],color='blue')\n",
    "ax2.axis([1,3,0,1])\n",
    "ax2.set_title('Xception120', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax3.plot(x,[Xception100epoch1trainacc,Xception100epoch2trainacc,Xception100epoch3trainacc],color='red')\n",
    "ax3.plot(x,[Xception100epoch1valacc,Xception100epoch2valacc,Xception100epoch3valacc],color='blue')\n",
    "ax3.axis([1,3,0,1])\n",
    "ax3.set_title('Xception100', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7+yNE/76jgYeIsegWJzE/TPxQW8vdH80v29I2W3jdzmy9lRpjcdpeRKR6tNR5O+k79BBx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\nGOPwxdYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abb715908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,2,3]\n",
    "fig, axes = plt.subplots(1,3)\n",
    "fig.text(0.5, 0.6, 'Loss of the Pig Identification',\n",
    "             horizontalalignment='center', color='black', weight='bold',\n",
    "             size='large')\n",
    "fig.subplots_adjust(top=0.5, left=0.07, right=0.97,\n",
    "                    hspace=0.3, wspace=0.3)\n",
    "((ax1, ax2, ax3)) = axes \n",
    "ax1.plot(x,[Xceptionepoch1trainloss,Xceptionepoch2trainloss,Xceptionepoch3trainloss],color='red')\n",
    "ax1.plot(x,[Xceptionepoch1valloss,Xceptionepoch2valloss,Xceptionepoch3valloss],color='blue')\n",
    "ax1.axis([1,3,0,1.5])\n",
    "ax1.set_title('Xception', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax2.plot(x,[Xception120epoch1trainloss,Xception120epoch2trainloss,Xception120epoch3trainloss],color='red')\n",
    "ax2.plot(x,[Xception120epoch1valloss,Xception120epoch2valloss,Xception120epoch3valloss],color='blue')\n",
    "ax2.axis([1,3,0,1.5])\n",
    "ax2.set_title('Xception120', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "ax3.plot(x,[Xception100epoch1trainloss,Xception100epoch2trainloss,Xception100epoch3trainloss],color='red')\n",
    "ax3.plot(x,[Xception100epoch1valloss,Xception100epoch2valloss,Xception100epoch3valloss],color='blue')\n",
    "ax3.axis([1,3,0,1.5])\n",
    "ax3.set_title('Xception100', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cMuzbYDZPhuE+Zk1KpPrbH2a2i5l938zmmtlQtD3nNJn/JDO71cweS5ZZbGa/\nN7NPmdnaDZZ7jZmdYWYLk2WeNrNfmtnO5ZZw7FMs9EensRAt9xEzu9HMnjezl8zsbjM71swmN5hf\nsVBV7q5BQ6kD8GlCA7kSeB54TTRtV2BFMm0l8O6Rzm+DMhyXlGEY+EnO9HWAnZNhh5HOr4ZC973q\nb/n5OznaxvEwp8H8VzaYPy3jHGCtzDI7AAM5yw0n+/DDI72fRvugWOhL/jqKhWSZs6MyZWPhdmCd\nzPyKhQoPOmIv/XAycANghEbzpwBmNgU4KxkP8FN3v2wkMtgBzx3p/qK735wM8/qdKSmV6m/5/gpc\nAnwRuJDaNm3mMeB44J3AvwHXJuMd2A04MJ3RzMYB5wLrJ9MvB94DnJTOAvzAzLborRiVp1goX0ex\nYGYHAR8mlGcJ8Elgf+D+ZJY3Ad+M5lcsVN1I/7LQsGYMwBbAYmpHCD4NnEntqMLDwJTMMusCxwC3\nEY4ODQGPABcAb8jMO53QWD0KLAOeBX5DzlEj4LpovR8BPgb8EVgK/AU4AZiQzLt5NG/e8HAy327Z\ncZl1vofQgD4NLAeeAa4G3pcz78IorT2BzwAPJOV/GDh6pPfnmjao/vav/lI7otrsiP1ewPjMuInA\nU9SOPn43mvauKE/PpdsnmfbraH3fGOm6NtoHxcKoi4Xbo3k+G43fOVr3EmB9xcKaMYx4BjSsOQPw\n0ajRWBY1LiuA3TLzbgEsiObJnpZ8TzTvJ5M08k4rDgNfy6R9bTTPPZnl0mUuT+bdPGdavI6Hkvl2\ni6Y9nFnfaQ2WTcf9IDP/gmie+3PytxLYf6T355o2qP72p/7SRmemybK3RcueEI3/djT+mibrmzvS\n9WwsDIqF0RELwHqZtHaJpo0j/PBI1/dOxcKaMehSHOkbd/8x8CvCqb7x6WjgNHe/PjP7uYSG2IEX\nCEd73kE45XgBoeHBzKYSGltLxn0NeBvwH4QjPQCfM7PdM+lbMmxDOKqzD6HBS/P0DjObCTwB7EJy\nyjmZdmUybhfgA83KbGbvAQ6ndjPWt5N1fSMad6iZvS9vceC1hMb2XUC8jY5qtl4pnurv6K6/ZrYV\nsH006tLo7y2jv5/KLJr+b8Dris5XFSkWRk0svJb6S3VW1W13X0m4jj6dntZtxULVjfQvCw1r1kBo\nCNMjAunn6zPzTKX+yMY7m6T3rWjeq4E3R8OPomk/j5a5Nhp/Xia9S6NpF0fj4yMZeTdc5R7lAS6K\nxv8ys8zcJDvkAAAgAElEQVTsaNpl0fgF0fjTovEzqB0Zemak9+WaOKj+ll9/6eKIPbAZcF+03Pcz\n038TTTsrM+2QKF/LR7qOjZVBsTDysZBsm/gMwuaZ6Y9E0z6vWFgzBh2xl74xs0nAKYQjG/FRhu9l\nZp2aLkI4zXtFk2SnRn+/FbgxGv5PlM52DZb/XZP/39Bkve3aps11bUO+OdHfA9Hff9NLpqRzqr+j\ns/6a2XbALcBWhH3zc+CwzGwvRX9PyEyL/x8sKl9VplgYNbHwUub/duq2YqHi1LGXfvom8Prk7ycJ\nN/QYsLeZZb+Iu+FNhlcWkP5IeDb6e8WI5UJA9bcbpdbf5LKMG4FXE7bTye5+sCeHHyMPR39vkpm2\nafLpwENF57GiFAudKyMWFlDbLhDVbTMbD2wYTUvrtmKh4tSxl74ws70JN0alDgE+G/1/gpmlXxT3\nRuMnEE75NjI/+vtcdx+XNwD/2GD5Nzf5/8/R38PR353EzX1trus+ZNRS/W25rr7X3+S66asIT1xZ\nCRzl7p9pMHt6tNSAHcxsYjRt1+jvawrPaMUoFlquq2+x4O6LgT9QO2uySzT5LYQbaNOzJTcl4xUL\nVTfS1wJpqP5A+OJ9hNp1lt+LpsWP17oZsGT8zdSu9Xse+BzhRqqZhFPt6R3+21F7isIwcDrh5qS3\nEr5wTiQ8jeDgaJ3XRvO/DHwdeDvhKFR8vejMaJlPRuMfInxBvRl4XTK90XWZ747WtYJwHenbk3XG\nT3/YL1omvi5z12j85lFaK0d6v64pg+pv+fUX2BrYNxlmRcv/MRr/pmj+w6P9sTLZTm/ODNtF84+j\n/qkklyfbOX0Z0DDhEoXXjnR9G82DYmFUxsKHo7QGgU8A7wcejJY9XbGw5gwjngEN1R+ofyveA8Dk\naNpm1L8B7wvJ+NcSngEcPxosvkko+4i0l3Pmi+fP+zJYSe0ZwNn5r8yUYRtqb1WM5z0jmb5bNC77\niLRTm5RjJfmPSFPHfpQMqr/l11/q3wbaaPhJzjZoNszJrGMa4XKIvHKsAD4y0nVttA+KhdEXC8ky\nP2uSrzuAdRULa86gS3GkVGa2L3AQ4Zq9FcC/u/uSdLq7P0640S29DvBYM3ujuy8A/onQyM0DXiQ8\nk/cJ4GKi07bu/n3gXwhHfx4hnHZcTDglOhs4GPhFgyyelky/i/DSkMcJR2LeG8/k7vcRjoz8KclH\nfM3nqtlyxuHuRxHejHkV4WUmLxO+AH8LfMDdP5GTr9XSabYOKYfqb1/rr7cxdD2/u99BeAvnjwkv\nL1oOLCI8PWVXdz87J0+SUCyM3lhw94MJNxjfQti+S5PyfYnwbPsXMvMrFiosPVUmssYws2sJR2Uc\nOMTdfzbCWRJpm+qvSKBYEFmdjtj3gZl9zsxmm9lDZjZsZg93mc4+ZnaTmQ2a2UCS5hbF5lakPIoF\nkUCxICJlUMe+P44H9iDcmf9cNwmY2X7AZYQnC3yGcHPQrsDvzCz7yCqR0UqxIBIoFkSkcONbzyIF\n2NLdFwKY2d10+Bze5Hm03yFcc7iLuy9Nxl9FuDHmS8B/FpjfNYGuUx8ZioViqP6OfYqFYigWRCI6\nYt8HaePdg90IL474Udp4J+neBVwHfNDMxvW4jjWGu+/h4ZnI43VNZn8pFnqn+lsNioXeKRZEVqeO\n/dgwnXBE4tacabcSni28VV9zJDIyFAsigWJBRFajjv3Y8Krk8/Gcaem4zfqUF5GRpFgQCRQLIrIa\nXWM/NkxOPpflTBvKzFPHzDYkvB1vYTSvSK8mAlsAV7v7QB/Xq1iQ0UaxIBKMVCxIRB37sSF9CciE\nnGkTM/NkvZ3wsg+RMnwIOLeP61MsyGilWBAJ+h0LElHHfmx4IvncDLg/My091Zp3OhbCERnOOecc\ntt122+Jzljj66KM5+eSTS0tf6xhd65g/fz4HHXQQJPWrjxQLWseoWodiobEq7F+to30jGAsSUcd+\nbJgLGLATMCczbSfgBeCBBssOAWy77bbssMMOpWVwvfXWKzV9rWP0rSPR79P4igWtY9StI6FYyKjK\n/tU6OqbLu0aQbp4dZcxsEzPb2swmRaOvB54EPmZmk6N530h45Nlsd1/Z56yKlEqxIBIoFkSkXTpi\n3wdmdhCwOeHoysbA2mb2hWTyI+5+TjT7CcDBwO7ADQDuvsLMjgLOI7xR8ExgPeBTwNOEF5GIjHqK\nBZFAsSAiZVDHvj8+SnjNd+wryef1QNyAOzCcTcDdLzSz9wDHAN8iPAnht8Bn3f3JwnMsUg7Fgkig\nWBCRwqlj3wfuvkcH8x4CHNJg2hXAFUXlq0gzZ87UOtawdXRDsaB1VHEd3VAsaB1VXIeMPHP3kc6D\nlMjMdgDuuOOOO/p104ysAebNm8e0adMAprn7vJHOTzsUC1IGxYJIMBZjoYp086yIiIiISAWoYy8i\nIiIiUgG6xl5EpEODg4MMDfX2qOaJEycyZcqUgnLUHZWjZjSUYyyqyrZXOWpGQzmke+rYi4h0YHBw\nkNlnnMGKgYGe0hm/4Ybsf+ihI/YFqnLUG+lyjEVV2fYqR72RLof0Rh17EZEODA0NsWJggD0nTWL9\nyZNbL5Dj+SVLmDMwwNDQ0Ih9eaocNaOhHGNRVba9ylEzGsohvVHHXkSkC+tPnsxGvXzxLV1aXGZ6\noHIkRkk5xqKqbHuVIzFKyiHd0c2zIiIiIiIVoI69iIiIiEgFqGMvIiIiIlIB6tiLiIiIiFSAOvYi\nIiIiIhWgjr2IiIiISAWoYy8iIiIiUgHq2IuIiIiIVIA69iIiIiIiFaCOfR9YcLSZzTezpWb2qJmd\naGZtv/PZzA40s5vM7Bkze8HM7jGzY81snTLzLlIkxYJIoFgQkTKoY98fpwAnAfcAhwOzgSOBS9tZ\n2MyOB84BlgBfAj4D/BH4MnB18dkVKY1iQSRQLIhI4caPdAaqzsymEhrtC919/2j8QuA0MzvA3c9r\nsvw44Cjgdnd/azTpDDNbCRxoZv/k7n8spwQixVAsiASKBREpi47Yl+/A5POUzPgzCUdaDmqx/NrA\nJODpnGlPJp8vdZ07kf5RLIgEigURKYWO2JdvR2AYmBuPdPdlZnYnML3Zwu4+ZGY3AO8ws/8GLgJW\nAHsAnwD+190fKiXnIsUaFbHw3HPPsWjRoi6LAAMDAyxfvrzr5YswODjI0NBQT2moHCNKsVCQqtSh\nqpRDRp469uV7FbDI3V/OmfY4sJOZjXf3FU3SOBA4GzghGQAc+Jq7H1dobkXKMypi4dxzr2XOnO5/\nCy9ZMsjiu+fz3g02gClTuk6nW4ODg5xxxmwGBpptptZUjhGlWChAVepQVcoho4M69uWbDCxrMG0o\nmueFJmksBxYQGvErk3HvA441s2Xu/vUiMipSslERCxMmTGfDDd/cXo5zDA8/zFNLf8uKFb19CXdr\naGiIgYEVTJq0J5Mnr991OirHiFIsFKAqdagq5ZDRQR378i0BNm4wbWI0Ty4zmwTcQrhJ6sBo0mwz\nGwa+bGYXuPuDzTJx9NFHs95669WNmzlzJjNnzmyVf1nDzZo1i1mzZtWNW7x4cTdJjYpYuPrqb3DT\nTRvVjZs+fSYzZrQXC4ODA23NV7bJk9dnypSNWs/YgMrROcVCPdWhYo2lchQYC1IwdezL9wSwrZmt\nnXPadTPC6dhmP68/ALwO+H850y4A9gfeAjRtwE8++WR22GGH9nMtksj7AThv3jymTZvWaVKjIhb2\n3ferbLPNXu3nWiShWBAJCowFKZieilO+uYTtPCMeaWYTgO3J3DyV41XJ57icaeMBQz/QZGxQLIgE\nigURKYUCv3znA58HPgXcFI0/lPC4sp+nI8xsE2A94FF3X5qMvpfQSH8EuDCT9r8TbpZq9SVQiCLu\n2p84cSJTRvimHpWjps/lqEwsiPRIsSAipVDHvmTufo+ZfQ84zMwuAq4ApgJHANe5e3yR2gnAwcDu\nwA3JuF8BtwH7mNn1wC+S8e8D3gzMdvc7yy7H4OAgs884gxUDvV3DN37DDdn/0ENHrFOsctTrZzmq\nEgsivVIsiEhZ1LHvj6MITy84FNgHWAScCmQfSeaEZxvXRrgPm9lewOeA/QiNvBOunfxv4ORSc54Y\nGhpixcAAe06axPqTJ3eVxvNLljBnYIChoaER6xCrHDUjVI4xHwsiBVEsiEjh1LHvA3d3QkPbtLF1\n90OAQ3LGvwQckwwjav3Jk9mol07g0qWt5+kDlSPR53JUKRZEeqFYEJEy6OZZEREREZEKUMdeRERE\nRKQC1LEXEREREakAdexFRERERCpAHXsRERERkQpQx15EREREpALUsRcRERERqQB17EVEREREKkAd\nexERERGRClDHXkRERESkAtSxFxERERGpAHXsRUREREQqQB17EREREZEKUMdeRERERKQC1LEXERER\nEakAdexFRERERCpAHfs+sOBoM5tvZkvN7FEzO9HMJneQxjgzO9LM7jCzQTN7Pvn742XmXaRIigWR\nQLEgImUYP9IZWEOcAhwBXAScCGwLHAlsD+zdamEzWxu4DNgN+DnwA8K+ewOweTlZFimFYkEkUCyI\nSOHUsS+ZmU0FDgcudPf9o/ELgdPM7AB3P69FMl8E9gT2dvcbSsusSIkUCyKBYkFEyqJLccp3YPJ5\nSmb8mcAS4KBmCyenZY8Efpk23mY2pehMivSBYkEkUCyISCl0xL58OwLDwNx4pLsvM7M7gektlt8F\nWAeYZ2anAocAU8xsEeFL4IvuvrJVJp577jkWLVrUTf4BGBgYYPny5V0vX4TBwUGGhoZ6SkPlGFGj\nIhZERgHFgoiUQh378r0KWOTuL+dMexzYyczGu/uKBstvnXweDSwDPgM8C3wI+FyS/iGtMnHuudcy\nZ85DneZ9lSVLBll893zeu8EGMKX/B4YGBwc544zZDAw02kztUTlG1KiIBZFRQLEgIqVQx758kwkN\nb56haJ4XGsyzTvK5ATDV3f+c/H+hmc0BDjazE9z9/maZmDBhOhtu+OYOsl1vePhhnlr6W1as6K1D\n2q2hoSEGBlYwadKeTJ68ftfpqBwjalTEgsgooFgQkVKoY1++JcDGDaZNjOZpZGnyeWvUeKd+Rngi\nwu5A0wb86qu/wU03bVQ3bvr0mcyYMbPZYqsMDg60NV/ZJk9enylTNmo9YwMqR+dmzZrFrFmz6sYt\nXry4m6RGRSxccsmxXHPNqXXjOokFWXMpFkSCAmNBCqaOffmeALY1s7VzTrtuRjgd2+yw62PJ51M5\n054EjHDUpql99/0q22yzVzv5Fakzc+ZMZs6s/6KfN28e06ZN6zQpxYKMaYoFkaDAWJCC6ak45ZtL\n2M4z4pFmNoHwvOK5eQtFbks+X50zLR33114yKNInigWRQLEgIqVQx7585yefn8qMPxSYRHixCABm\ntomZbW1mk9Jx7r4QuAmYYWbbR/OulaTxMvDrcrIuUijFgkigWBCRUqhjXzJ3vwf4HrCfmV1kZh81\ns5OAk4Dr3D2+SO0EYD6rP+rsCML1lteY2XFmdjhwA+GRace7+2OIjHKKBZFAsSAiZdE19v1xFLCA\ncCRlH2ARcCpwXGY+JzzbuH6k+51mtjPwtSStiYSG/t/d/X9LzLdI0RQLIoFiQUQKp459H7i7Aycn\nQ7P5DqHBs4eTIzzvLT53Iv2jWBAJFAsiUgZdiiMiIiIiUgHq2IuIiIiIVIA69iIiIiIiFaCOvYiI\niIhIBahjLyIiIiJSAerYi4iIiIhUgDr2IiIiIiIVoI69iIiIiEgFqGMvIiIiIlIB6tiLiIiIiFSA\nOvYiIiIiIhWgjr2IiIiISAWoYy8iIiIiUgHq2IuIiIiIVIA69iIiIiIiFaCOvYiIiIhIBahj3wcW\nHG1m881sqZk9amYnmtnkLtM738yGzeyPRedVpEyKBZFAsSAiZVDHvj9OAU4C7gEOB2YDRwKXdpqQ\nmb0LeB+wpMgMivSJYkEkUCyISOHGj3QGqs7MphIa7Qvdff9o/ELgNDM7wN3PazOtVwLfA74L7FtC\ndkVKo1gQCRQLIlIWHbEv34HJ5ymZ8WcSjq4c1EFaXyfss2MKyJdIvykWRALFgoiUQkfsy7cjMAzM\njUe6+zIzuxOY3k4iZjYDOAz4oLsPmlnhGRUpmWJBJFAsiEgpdMS+fK8CFrn7yznTHgc2MrOmP7DM\nbBzwI+Aqd7+ohDyK9INiQSRQLIhIKXTEvnyTgWUNpg1F87zQJI3/BrYE3l1gvkT6TbEgEigWRKQU\n6tiXbwmwcYNpE6N5cpnZ64Fjga+4+yPdZuKSS47lmmtOrRs3ffpMZsyY2W2SsoaYNWsWs2bNqhu3\nePHibpJSLMiYplgQCQqMBSmYOvblewLY1szWzjntuhnhdOyKJsufBAwAl5jZ65JxRth3r0jGveTu\nTzXLxL77fpVtttmruxLIGm3mzJnMnFn/RT9v3jymTZvWaVKKBRnTFAsiQYGxIAXTNfblm0vYzjPi\nkWY2AdiezM1TOV5DuB7zT8CDyfAAofHfKvn/jGKzLFIKxYJIoFgQkVLoiH35zgc+D3wKuCkafygw\nCfh5OsLMNgHWAx5196XJ6P8C1s9J9wfAUuBooOlRGZFRQrEgEigWRKQU6tiXzN3vMbPvAYeZ2UXA\nFcBU4AjgOnePL1I7ATgY2B24IVl+Tl66ZnYS8KK7X1xi9kUKo1gQCRQLIlIWdez74yhgAeFozD7A\nIuBU4LjMfE54tnG7vJDcifSPYkEkUCyISOHUse8Dd3fg5GRoNt8hwCFtpvnaArIm0leKBZFAsSAi\nZdDNsyIiIiIiFaCOvYiIiIhIBahjLyIiIiJSAerYi4iIiIhUgDr2IiIiIiIVoI69iIiIiEgFqGMv\nIiIiIlIB6tiLiIiIiFSAOvYiIiIiIhWgjr2IiIiISAWoYy8iIiIiUgHq2IuIiIiIVIA69iIiIiIi\nFaCOvYiIiIhIBahjLyIiIiJSAerYi4iIiIhUgDr2fWDB0WY238yWmtmjZnaimU1uY9n1zewoM7s6\nWW6Jmd1nZj80s1f3I/8iRVEsiASKBREpgzr2/XEKcBJwD3A4MBs4Eri0jWX/GTgRGAa+AxwGXA4c\nBNxtZtuUkWGRkigWRALFgogUbvxIZ6DqzGwqodG+0N33j8YvBE4zswPc/bwmScwHtnL3BZl0rwB+\nA3wF2D9vQZHRRLEgEigWRKQsOmJfvgOTz1My488ElhCOsDTk7o9mG+9k/DXAs8B2RWRSpA8UCyKB\nYkFESqGOffl2JJwunRuPdPdlwJ3A9G4SNbN1gXWAp3vNoEifKBZEAsWCiJRCHfvyvQpY5O4v50x7\nHNjIzLq5JOoYwqVUP+0hbyL9pFgQCRQLIlIKdezLNxlY1mDaUDRP28zs/cB/AVe6+9k95E2knxQL\nIoFiQURKoZtny7cE2LjBtInRPG0xs32AcwincA9od7lLLjmWa645tW7c9OkzmTFjZrtJyBpq1qxZ\nzJo1q27c4sWLu0lKsSBjmmJBJCgwFqRg6tiX7wlgWzNbO+e062aE07Er2knIzN4BXATcDbzd3Qfb\nzcS++36VbbbZq93ZRVaZOXMmM2fWf9HPmzePadOmdZqUYkHGNMWCSFBgLEjBdClO+eYStvOMeKSZ\nTQC2J3PzVCNJ430xcC/wVnfXT2MZaxQLIoFiQURKoY59+c5PPj+VGX8oMAn4eTrCzDYxs63NbFI8\no5m9DfgF4dnFe7v78yXmV6QsigWRQLEgIqXQpTglc/d7zOx7wGFmdhFwBTAVOAK4zt3ji9ROAA4G\ndgduADCzacAlgBOedLCPmWXX8XNERjnFgkigWBCRsqhj3x9HAQsIR2P2ARYBpwLHZeZzwrONY9sB\nr0j+PrlB+mrAZaxQLIgEigURKZw69n3g7k5ofBs1wOl8hwCHZMadDejRZVIJigWRQLEgImXQNfYi\nIiIiIhWgjr2IiIiISAWoYy8iIiIiUgHq2IuIiIiIVIA69iIiIiIiFaCOvYiIiIhIBahjLyIiIiJS\nAerYi4iIiIhUgDr2IiIiIiIVoI69iIiIiEgFqGMvIiIiIlIB6tiLiIiIiFSAOvYiIiIiIhWgjr2I\niIiISAWoYy8iIiIiUgHq2IuIiIiIVIA69n1gwdFmNt/MlprZo2Z2oplN7iCNfczsJjMbNLMBM5tt\nZluUl2uR4ikWRALFgoiUQR37/jgFOAm4BzgcmA0cCVzazsJmth9wGTAB+AzwTWBX4HdmtkkZGe7U\nrNtuK30df/pT+etQOUqnWChAVepQVcrRJcVCAapSh6pSDhl56tiXzMymEhrtC939A+7+Y3f/DPBp\nYE8zO6DF8uOB7wCPALu4++nu/g3g7cAmwJdKLUCbZs2dW/o67r23/HWoHOVRLBSnKnWoKuXolGKh\nOFWpQ1Uph4w8dezLd2DyeUpm/JnAEuCgFsvvBmwK/Mjdl6Yj3f0u4Drgg2Y2rpisipRKsSASKBZE\npBTq2JdvR2AYqPup7O7LgDuB6S2Wnw44cGvOtFuBdYGtes+mSOkUCyKBYkFESqGOffleBSxy95dz\npj0ObJScVm22fDpv3vIAm/WQP5F+USyIBIoFESlFs4ZDijEZWNZg2lA0zwtNlqdBGkOZefJMBPjL\nX+5qMktrzz33OM8OLuaWhQt56PnnV5u+aHCQa+67r2kag0NDPLpsGXfddRcbbLBBh+t/jqGhpfz5\nzzcxceKUjpatT0flSPVSjvnz56d/TuxgMcVCQnWoOuVQLKgOgcoBXceCFM3dNZQ4AH8Enmww7Xxg\nJTC+yfKnJfNsnTPtE8m0vZssfyDhlK0GDWUMByoWNGjAUSxo0JAObceChuIHHbEv3xPAtma2tq9+\n2nUzwunYFS2WT+e9P2d5yD8dm7oa+BCwkNqRHJFeTQS2INSvdikWpIoUCyJBN7EgBVPHvnxzgbcC\nM4Cb0pFmNgHYnvAEg1bLG7ATMCczbSfCqdoHGi3s7gPAuZ1mWqQNN3c4v2JBqkqxIBJ0GgtSMN08\nW77zk89PZcYfCkwCfp6OMLNNzGxrM5sUzXc98CTwsfiNhGb2RsIjz2a7+8pSci5SLMWCSKBYEJFS\nWHK9nZTIzE4DDgN+CVwBTAWOAG50972i+X4KHAzs7u43ROPfD5xHuC7zTGA9whfCSmBHd3+yPyUR\n6Y1iQSRQLIhIGXTEvj+OIrzyeyrwXWB/4FTg3Zn5nPBs4/qR7hcC7yFcC/kt4P8Sjti8RY23jDGK\nBZFAsSAihdMRexERERGRCtARexERERGRClDHfgyy4Ggzm29mS83sUTM7Mb6Jqo009jGzm8xs0MwG\nzGy2mW1R1DrM7DozG84ZVprZDsk8n0vW+1Ay7eFOt0WrshSxjhZl2dfMvmJmt5jZX83sBTP7g5l9\nvqj9YWZv6HUdrfaHmW1lZueY2b1m9ryZvZTs+5PMbJMiylEGxUL7ZVEstFUGxYJiod30FQsFlEOK\np0txxiAzO5Vwk9VFwFXAtsCRwA3uvncby+8HXAD8AfgR4aaro4EVhJuunipgHdcSrh39FOGxbLEr\n3P15MxsGBoB5wI7AYnffslXanZSF8LznXtfRrCw7Ah8FLgVuBV4G9gA+CNwF/Iu7N3rDZLtlOAr4\nZI/raLo/gB2AzyfpP5as+x+B/wMsBrZ390W9lMPdn2q2fDcUC+2XBcVCO2VQLCgW2l2HYmGUxsIa\nb6TfkKWhs4EQhCsJjzOLxx9OuMHqgBbLjye8uORhYFI0/o2EQDu913Uk814LPNxini2iv+9uNX+X\nZelpHa3KQmj41skZ/9VkG36ygDL0tI5290eD5d6f7PPP9FqOomIgSlux0FlZFAtt7o8GyykWFAuK\nBR/dsaDBdSnOGHRg8nlKZvyZwBLgoBbL7wZsCvzI3ZemI939LsJLUT5IeCNhL+tYJTl1u07eNHdf\n2G46DbRTlr/0uI5V8sri7vPc/cWc2c8nHAHZrkWy7ZThrh7X0bQMLTyafG7QYr6W5TCzcR2stx2K\nhRrFgmIBFAugWFjTY2GNp4792LMj4Zfy3Hikh1NudwLTWyw/nfD4tFtzpt0KrAvs2uM6UpsBg8Di\n5Nq6i8xs6zaXbUc7ZdmqoHV1Wpa/Tz6fbpFuL2Vodx2plmUwswlmtqGZbWZmbyMcGXLCadmyytEt\nxUKNYqG9daQUC/UUC91RLJRXDunS+JHOgHTsVcAid385Z9rjwE5mNt7dVzRZPp03b3kIgd7LOiCc\nevsd4eUpK4F/JlybuaeZvcXd/9Rk2Xa1W5b5Pa6no7KY2VrAsYRrHlu9tr2rMnS4jk7K8DHgO9Fy\nC4CD3P0mmuvXvsiuU7EQKBYUC4qFQLGwZsfCGk8d+7FnMtDohpihaJ4XmixPgzTS5Sf2uA7c/aOZ\nUb8ws8sIp9++Dby90bIdaKcsbT+BoJEuynIqoYH8nLs/2CL5bsvQyTo6KcPFhEZ2CvAmwgtwNmqV\nPn3aFznrVCzU8gCKBcVC83UqFurn6ZpiYdTGwhpPHfuxZwmwcYNpE6N5mi0PMKHJ8kPApB7Wkcvd\nf2dmNwB7mNkEb3HHfhvaKUvH+WxHo7KY2VcJr4k/3d2/2UZSHZehi3XkyiuDuz9BeGIEwKVm9gtg\nrplNdvdvFFmOAigWahQLigXFQn0eFAtdqEAsrPF0jf3Y8wSwkZmtnTMtPVXa7FToE9G8ectDOEXW\nyzqaWQiMo/VNN+1otyxlWUhUFjP7EvAF4Mfu/sk20+ioDF2uo5mFNNkf7n434TFlrdY1EvtCsVCj\nWOjdQhQLioXeLUSxkBrpfbFGUsd+7JlL2G8z4pFmNgHYnsyNTQ2WN2CnnGk7EU6jXt/jOprZivCY\nq2d7SCPVTlkeKGA9jawqS9KwfhE4y90/3kEabZehh3U0087+mAT8TYt0RmJfKBZqFAu9UywoFoqg\nWKgZ6X2xZvJR8MxNDe0PhEdYrQQuyIw/Ihk/Mxq3CbA19c+PTZ8ruwCYHI1Pnyv7wwLWsS6wVk7e\n38/A2HYAAAHNSURBVEl4qsJlOdOaPku427IUsI6WZSE0qsOEhrXZvuu6DD2uo50y/F2D9PZI8vHr\nIveFYkGxoFhQLCgWFAtFx4IGV8d+LA7AaUlDehHh7XYnAcuBazLz/TQJ0F0z49+fBNU84BPAZ4Gn\nkgDctNd1APsCDxGed3wk4XTd2ck6nwJen8x3EOH04THJ+IHk/y8Q7rjvuSy9rqONshyXLLMA+DDh\nWc/xsHcBZTisl3W0sz+AXwC3AMcDhybznU246WkA+Kci6pViQbGgWFAsNNr2igXFgoYC2oKRzoCG\nLnZaOLV1NOEu9aWEl218i+gXcTLfWUlA7ZqTxj7AzYTn1w4QXmrx2iLWAWwDnAc8SDjVtjT5+7Q4\nkAlvvVvZYJhTRFl6XUersiTLNEq/bh09lKGndbSzPwiN76XAI4SbmV4C7iU0+q8uql4pFhQLigXF\nQqNtr1hQLGjofbBko4uIiIiIyBimm2dFRERERCpAHXsRERERkQpQx15EREREpALUsRcRERERqQB1\n7EVEREREKkAdexERERGRClDHXkRERESkAtSxFxERERGpAHXsRUREREQqQB17EREREZEKUMdeRERE\nRKQC1LEXEREREamA/w/MOYsLYZVrewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abf561cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_groups = 3\n",
    "fig1, axes = plt.subplots(1,3)\n",
    "fig1.text(0.5, 0.6, 'Accuracy of the Pig Identification on train and validation set',\n",
    "             horizontalalignment='center', color='black', weight='bold',\n",
    "             size='large')\n",
    "fig1.subplots_adjust(top=0.5, left=0.07, right=0.97,\n",
    "                    hspace=0.3, wspace=0.3)\n",
    "((ax1, ax2, ax3)) = axes\n",
    "xception_train = (Xceptionepoch1trainacc, Xceptionepoch1trainacc, Xceptionepoch1trainacc)    \n",
    "xception_val = (Xceptionepoch1valacc, Xceptionepoch1valacc, Xceptionepoch1valacc)\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "rects1 = ax1.bar(index, xception_train, bar_width,alpha=opacity, color='b',label=    'train') \n",
    "rects2 = ax1.bar(index + bar_width, xception_val, bar_width,alpha=opacity,color='r',label='val')\n",
    "ax1.axis([0,3,0,1])\n",
    "ax1.set_title('Xception', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "xception120_train = (Xception120epoch1trainacc,Xception120epoch1trainacc, Xception120epoch1trainacc)    \n",
    "xception120_val = (Xception120epoch1valacc, Xception120epoch1valacc, Xception120epoch1valacc)\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "rects1 = ax2.bar(index, xception120_train, bar_width,alpha=opacity, color='b',label=    'train') \n",
    "rects2 = ax2.bar(index + bar_width, xception120_val, bar_width,alpha=opacity,color='r',label='val')\n",
    "ax2.axis([0,3,0,1])\n",
    "ax2.set_title('Xception120', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "xception100_train = (Xception100epoch1trainacc, Xception100epoch1trainacc,Xception100epoch1trainacc)    \n",
    "xception100_val = (Xception100epoch1valacc, Xception100epoch1valacc, Xception100epoch1valacc)\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "rects1 = ax3.bar(index, xception100_train, bar_width,alpha=opacity, color='b',label=    'train') \n",
    "rects2 = ax3.bar(index + bar_width, xception100_val, bar_width,alpha=opacity,color='r',label='val')\n",
    "ax3.axis([0,3,0,1])\n",
    "ax3.set_title('Xception100', weight='bold', size='medium', position=(0.5, 1.05),\n",
    "                     horizontalalignment='center', verticalalignment='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GKeUS6o1OD0yy1Qxt9jjqufmaEct3oV7Ljl3dHa1G+e/V3od1e+7I8Amre+Pc/jr1kVpd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\n9P8B9x6K1Pc78DAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19abfaa2ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X=('Xception','Xception120','Xception100')\n",
    "xception_time=Xceptionepoch1time+Xceptionepoch2time+Xceptionepoch3time\n",
    "xception120_time=Xception120epoch1time+Xception120epoch2time+Xception120epoch3time\n",
    "xception100_time=Xception100epoch1time+Xception100epoch2time+Xception100epoch3time\n",
    "\n",
    "\n",
    "Y_time=(xception_time,xception120_time,xception100_time)\n",
    "y_pos = np.arange(len(X))  \n",
    "\n",
    "plt.barh(y_pos,Y_time,color='r',alpha=0.4,height=0.6)\n",
    "plt.yticks(index,X)\n",
    "plt.xlabel('time/s')\n",
    "plt.title('How fast is the running time of each model?')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "考虑到过拟合的模型没有训练的意义了，所以在准备这个notebook时并没有再跑过拟合的例子，但是在比赛过程中，过拟合的的确确发生了，那个时候我顺手截了两张图片，两张图片代表两种过拟合情况，其中一种情况是将视频逐帧提取，然后训练模型，出现过拟合现象，另外一种情况是在做了图像增强后训练完整模型出现的，因为每类图片有600多张，做完图像增强后，每类图片扩增到2700多张，在InceptionResNet_V2上进行完全训练，出现过拟合现象  \n",
    "<img src=\"images/11.png\" style=\"width:891px;height:198px;\">\n",
    "<caption><center> <u> <font color='purple'> **图 1** </u><font color='purple'>  : **逐帧提取过拟合**<br> </center></caption>  \n",
    "<img src=\"images/22.png\" style=\"width:814px;height:75px;\">\n",
    "<caption><center> <u> <font color='purple'> **图 2** </u><font color='purple'>  : **完全训练过拟合**<br> </center></caption>  "
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}