(1)(Works-Transfer)cifar103ClassAccuracyHighDIScoreCrossCrossVer06.ipynb

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  "metadata": {
    "colab": {
      "name": "(Transfer)cifar103ClassAccuracyHighDIScoreCrossCrossVer06.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preritt/CS231n-/blob/master/(1)(Works-Transfer)cifar103ClassAccuracyHighDIScoreCrossCrossVer06.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "FgwWyMb6iCkO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import gzip\n",
        "%matplotlib inline\n",
        "from keras.models import Model\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.layers import Input,Dense,Flatten,Dropout,merge,Reshape,Conv2D,MaxPooling2D,UpSampling2D,Conv2DTranspose\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.models import Model,Sequential\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.optimizers import Adadelta, RMSprop,SGD,Adam\n",
        "from keras import regularizers\n",
        "from keras import backend as K\n",
        "from keras.utils import to_categorical\n",
        "from keras.datasets import cifar10\n",
        "from sklearn import preprocessing\n",
        "from keras import losses"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "txdR9fEabt2P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "from keras.models import model_from_json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PYdWJtFsiF1a",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.utils import np_utils\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.datasets import cifar10\n",
        "from keras import regularizers\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mrqV7B9nDNUk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "keras.backend.clear_session()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9m3ehGh2iIBA",
        "colab_type": "code",
        "outputId": "02922873-0336-49de-c2b0-f6922df85ea3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot\n",
        "from scipy.misc import toimage\n",
        "from keras.datasets import cifar10\n",
        "def show_imgs(X):\n",
        "    pyplot.figure(1)\n",
        "    k = 0\n",
        "    for i in range(0,4):\n",
        "        for j in range(0,4):\n",
        "            pyplot.subplot2grid((4,4),(i,j))\n",
        "            pyplot.imshow(toimage(X[k]))\n",
        "            k = k+1\n",
        "    # show the plot\n",
        "    pyplot.show()\n",
        " \n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "show_imgs(x_test[:16])\n"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: `toimage` is deprecated!\n",
            "`toimage` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
            "Use Pillow's ``Image.fromarray`` directly instead.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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O8NF2er2620g5XXfgeE5cX60xHA39KCJ8t8/127cBmE7G5EJIU5YlzVZM8RHA\naK2zxNLpZUzA3Xs+tO/v72GlkeH8M8/SW/Lr24QGJdwQRWnJZEzNOBsxTaVhJuujhMjIpWmdm+j1\nlmhEPvwPlKPX9gZ+odMkE4KYsdVkqf+sVkXNsdCMNTeue8jMOHjx2Qv8llT3HGUxNZzikKG2WG3q\nUizHbNrHyVNnOXv2LADfvHObQvD/e3f3uLflIb833niVc9Ik8/TTF1hf96Wenc4CyJSEaVZSZjKm\nJopqvNXiZsOf7xvH5FBVaShgHqIz7Kg7anOZy1zm8pGX9/VolVKngF8F1vHlul92zv1PSqkl4P8A\nzgJXgL/unNv9YedyzrKw5DO0o0nJuKwIvjWnT/laz7dfe4f9sd9F2q3TnHraf/bq21e5ect7RT/2\nY5+rC707x0+wdNyD5Nd23mSSSktda4nuqg+nPtU5yT3xQK5c/W4942pvf8jqqvccFtxtzrT9+2td\nQ6i895blE1pK1eOcH6c8Tt1GSZO/8bf+EwDGZchbF33dqlUhibTI5k6xs1dRII4pJaGjArBIb31/\ngLnjPYdbd++SitdkpwWtpvfoLr1zg8vXrslnQ5ZWvPecpSn7wt61vbWFk2y31hYlLaitRoOeeIZJ\nEjMZTuo6xcctj1O/Sim2tzynw7u7W3X2v7e4yLFjnschK3LyzHvr1pX0xz6pO5mMKQsZ5qhd3V/f\nikISqSluhAFTWdMWS0so+4xSRFJjaoyuoZlpUdQMVgpLLi3UN7Z3GQs5eBDEbBw7eSgz2h6vbsFU\nnmNRgMxa88mnA1yb4mYmSUJHWMyUniUXnXP1kMfB7l2+vSUVC9/9JkvLHjrc2DjFxrGzcp4FliUa\nXl3fqOk+rSspJBornJ0lwxQoiV5caXHWPnAj04NABwXwXznnXlFKdYCXlVL/L/C3ga855/6+UuqX\ngF8C/u4PO5EtchqhV2I6zeq+YaUcK0v+YX1bX+Lujl+g28ay0PaY7nMvLXDpqi9TykvYk6z3hQsX\nuHDOW+Ort/d57bXv+c9uNYli6cFvd7jxmocRNrf7KIEsTNLh2ElvpM8oOC3UgokuSKeChdnQlx8d\nTmL8sel2cWmJxQ2/WX33+zfqMCuzllJCWGc1ppp0gKOsB+I5ZgMXXE2gs7V9h6KQKQEWel2f8c6y\nlJ1tf48whq0tb1zSfEIhnXZllmGk1KaZRMSC15rCkE0rTLak0UoOszHssem3LApu3PAhebPV5Dnp\nUlpaWaMpZW/TyYjdXV8Bk+cpYynRajYTFro+fG/FMQ0xloFSlAIdFEVGLi1jU13UQanWhlIcktxB\nYPzadXbKVAY7bt/bYmvb5ywgtzBnAAAgAElEQVQGgwG7UmLXaraIO8sUh0Mq89h0i3OYqldD2ZoP\nWTk3m/CpFKFg4ZPhgM1N73Tdvr1Jf9+/HxpDRxpAWklCM/Dvl2XJTekwe+fKJabTr/sfUGqWV3zF\nyMc+9gIXznvHbHV1ja5MwY0bXRwyYsFaiqpSTmmyh6CVeV/owDl32zn3irweAG8AJ4C/AvyKHPYr\nwF99oG+cSy1z3R6uzPV7eDLX7cPJQyXDlFJngU8B3wDWnXO35Z828SHED5U0TZHSQhKdYWXsd5Ak\nJInfNTqdNm3p7X7uuWf557/7VQDG+5s0l3ym9+KNu5w66VtJzz37aWLxnJ46fZo96Sl//Y13sLIz\n3tzL6E/EWyhj+nveG17bOMm1bf966dQC23FFvZixJ1uXCxJSm5FxuA0Lj6rb8XjMb/zar/pz0cAI\nY30QxnVxPYRSyA1BpGudh2FIVBFTRw2M85/tRotoiQpyUzKVao3CQSS1sPk4ZTwSmKVIUTIED63J\nxJMqR2NGAx86N6OA1QUZA99MiMIfPJb8ccqj6jcIQl74xCf9a2MIRHeD4ZChzKaL47AO4W2Rc3zd\nw1JxEmEq5jNbMJIZa9P+gD3xgLd37tVk4s8//yyh1B37VlLhUigKUplEcmPzOvekUSTLMsYV4f3e\nPpG0Cg+GQ7729a8zGPQfQlMPL4+qW5QMZQRcEdfk3VaDqj14ixEY4buvvMxQKCKXOk1u3Pavuwtd\nQlnrtpjQbUuCLTREMoI9jFsY7XW1s7fP1SuvA7C/d4NXviUNNlHCqVO+cuP4sdMcO+493ePrp2lJ\nc5RqaJSOeexVB0qpNvCPgf/SOdc/iPs455xS6j3jE6XULwC/ANDtdkE6ZywjVJVtto6+9Dfv7W2x\nvOQX9M9+6c/xyU/4co3f+Cf/FCWlMwsLi5w47sPkdreHKbziljYCjp3zD/p+I+Hb3/XlTreHChd6\n472wsczK077CwQRJ3Q3ylmtxcVM4FoxiItnzcQGFNQzKNx9UVQ8tj0O3ygT8P7/56/76wyaNpkz3\nJMDIuBSHRoeVoVUkUsaVJDFR4hdi0Fwmibx+Ih3WjRoqUVSXkacZqUAEeZ5hK3xNuXoyK9qAjHhZ\naIUstPw1tBsRceiPD1WOKtNDH2fzOPTbWlghkOx9EISUYgyMcgQCi2hFPSlgMsqY7Ps1PRn4jQ1A\nhxonG9Zbb7zOtStXAF9J48QxOH5sgyVpGpmMx0zG0hyyu8f2rs81TLJJXaY4Ho/Z73tjqnE0hSJw\n8/ZtNjc360qQw5DHodu1tdUa6lJFgBYuCAs4mTRhFAyHXp/TScqzz/husE9/8rO8/KrvEvvGt77J\nvkwPLouMtWMeFvjiF79Y37srV6/yb/6N50x48fkX6Iqe72xucueOz2vkecaGlIadO3e2hthGg/0a\nkw2DFtMsf7zDGZVSIV6Z/7tz7p/I23eUUsfk348Bd9/rs865LzvnPuuc+2xDkilzmcnj0q3ST7zJ\n7yMhj23ttjrvdcifanlcul2Q0rQfZXmQqgMF/EPgDefc/3Dgn/4v4D8G/r78/2++37mmpWKrlPrU\ncIrOfHbUWVO34B0/tsaf/XHPEpWEJefO+Pq3v/gf/Bz/5z/9CgBbm/vc3ve7zHR6kUh2vZ1JwcWr\nPtNIluNWfJ/+4lqzngigVIgV782qqK4H3S9DklDqegPFSPmdMQ9DnM0p1eOHDh6nbsPAEEiisSz3\n6MoY60CF9Lc8nDLoj8jLKrRNcQdZs4QuLmqs1d5/oQK0uLTNqFEzSpX5bCQ8sUZJ4WMSBTTEc1hq\ntzjZ9vf65LEVpB+CdDpAO+9hBUbR6zYw5nCwg8e6dtMppeiukURUqtOomh3rzt27jKQdOZ1MKCVi\nK8uCp86fBWB1baXOYodBSGVk4iRCUB2m6ZQ333oL8PXIVdIrL2Yk3qPBoJ5mMR6PyDJ/bXEQ0Jd5\nent7e5T2cAg+H6du4cCw0ANz5IwGq2bTDRoCV/3Zn/rpul02MAHPfNIPeX3pM59DVwEVipVln2B/\n6qmnCSTSOHvh4xw/7e1Co9Go282dc3W7eVla1oTustNZwEiEoK2mtB4Cy7XCqsc7nPEngL8FfE8p\n9R1577/BK/I3lFI/D1wF/vr7nSgtFb/5B74q4JNnVtgQztRmGHBsw/+wYytdnn7KwwK4jNtSlvXL\nv/4VXvmOx1PSaUZd4+40Th6AMu5SisEIaFCIcSx0o8aGcYppJiUaWhEIpmOsxU0Fg8QSShmHUZos\nV7x3APTI8th0i7MstPxiGkyn5KUvFXr2uRdxx7zRvbe1zV3JTg/3SsYSkpZlgS38w9wKFnju476K\n41Z/wD3pI59kIyaCLRoUsWxKrTCkJyVKq70eG8f9fTx/Yp212Ot/OOqzs+NxNBNpmi2Pc7U7DZaX\nFwmCQ/PGH5t+nS2ZDr0udJnVhkGbgFLISd55522GQhMZhQFhRSRjDFYwf11YkM19eWmp5jMdT4ZM\nJv6eXb9+o35faXBSEjLOpuxLRcFoe59Q9FYUeU39OdrrUwjWW5Ylh1Uuw2PUrbWWQd/DAoEzZFKt\nUVBSSAVMWZY1vaFzUAhsorQmk43u+OlzNTewsgotlImXr+0wkY46pTWdhXP19+7u+/MEQUCre9Zf\nkFPs7Pu1fuvOTo0fxzpCCh9Q7YDp7rS+vveT913hzrk/4Acjvj/9QN8yl/eUuW4PV+b6PTyZ6/bh\n5IkCeyWKr73i22jfefcSX/qMZ+N6+vgCly+9A8BPfu4lEunNHmSG3/jtbwLw7ddvMS6kKiBI0FL0\nba2rwXOnFaXsbqnV5GU16ycnRUaPO1cPfzNG02z6LSqipKqbL1VQA+BFXhB1ehx1DLTIMwa7HiKY\n4Bhf9w0FSyZkRRoEwnRMQxoHJsbhXBUWlFQu+3iyxZ+VFugXn/8Y16752tHtvV1SoVXEuno4XkM7\nVgQu6LVa9Xylza1rvLXlk88qieiu+TCu0e3Q7PjrWVpZpr2wUFdCHGXRCjLxOJNAoar+d6PRwrrV\n7bZJJNnYbjUxopdmklBINcY7b77J/o6vNNgfDeq++zAydVItjqJ6Ptt4OuGehLTjdFqTWS92e2RV\nwnYypJAa3INk1iiFUvoQy5QfjwwHA37t1/83AFpBk1JalHNra6irLGeJp7zI6+fcBAHTVFi6SocS\nfokwiFmSaSvtdo+8nDFzVQk7pVTN0qWURkt9fRBEaDV7v8p3KQtK2O9UU6On9+rW6veTJ2o9giBA\nCTHH7d09/lC4CMr8DOB/5OrGSZSMjvijb32fr3zdZwhT2wTpwdez6nrKNKuJHqwt65tROlWHVsoY\nkDKRwJiaKb/TaWPkXNrllK6a8hpSWd2NjQU63QXelfEuR1XCMJiRlaQFyOZz+e232JdpCxoYWf/A\nj4ocW9b4S91rnk4HvPKv/KSGn2q1eUn0M1no1OGvKgqm0gG1X6Y1HHH1zTtsTXz2exoqGjLKZnGj\nR9yViceNiGaFSzZbKBPwURhlA4pSwkSlZlBAmg5q6KARBGhxEiajEemO77u/Ph5iBetSztVUkiZI\nCGXwog5mkyqGuxOmU2/Up9NxrZ1Ea3LhoM0xNZQzmUzqsFppRVFRDpaaKPTG9iiLUpqm2AVjA+LY\nrw+rghrP1lrVcI21Rf2bnCuxQm+qULiKi0AZqsESmozAeN2maYqq7Iei5knI86ImQdLa1Mb4oK3J\nhgOcTGqZGojNNnk+fqDfeLTvwFzmMpe5/AjIE56woOqBjMU04sod7/2kozf4yU8/A0Cjd4x9aX/9\nF9/4FlMJb/MiJ5bkgrW2TuQAGFW18lJj/7EJZuG+nnnSjUajTr7kecFACr1L60iLaiT0CuvHJOxI\nAiaDwf0Z+iMoYRzS6fqk1OjGFpWXOC0LduR3RSogE32Wrqxp4YAZK5GCi696uOb6IGdVC8OXc5Sy\nuw+1ZVMqBy6mY25IH/+4GdA55esP18+dIelJ2Y4OfAoZaLfbNIUyUYcxTumPhENblgXXL3vWt7u3\nIlIhrS+LtG5SyPOijqi0VoShd6mCQM8aRUJTN2gUZc505D+bpiMGMmPMFdCSdnCjNU7uXzoa1xMZ\n9tOsrjoobVm3gtoD9zQIwnqKyZEW5xgOPOzVNDGyFCnR9fSNLB9TSMIWXeDEi83zDFtIUrB0Ndm3\nUrqu0NAanPP3K51OJEkI1rk6GvbRoDQp4WbwghwHYPKcQjzaca/Dxqk2OQ+m3yc8nNHVw9OsSepu\nq7vDlFfe8mHWz44dA+czkDd3B8RCrlGMDVOhiGs2GwSCi03TdIaXKVPDBU4HNRt6GCcMBcPKihGN\nxsx4VMZ1NM1oC6bTW90gK/wD8NabbxLaklzKZ46qmCBgdd13zt2+sTUb4uEglQWUO2pMsOT+jaMu\nVFGQywM82rqHjn2Hkkmn3JLzfIeUi4HorR3SOumrCFaPH2d51TcCxa0mmZzTOUsso0pMYGqjY4IA\nfd/IkaMrRTbl+sXXAJ8Br0qQgjisyUiUUkRSjdFsNuuH1VpbU0EOh3kNEVin0KrCVnMicQbWjh9n\nNPRlYv29XQop5nfFjANhnI3rczrnZrAsirAqj8IyHg9qWOGoSpZPeP31PwSgGUYElTOAAsmtlLbA\nCuwVRrp+XZQFZUWdoVQd/ivl6kkN/n1vF6y1dSmcLW19H7UKUEKfaK2rJ+IeUC05BeWiv0fHP/Y8\nCy0w4YNBinPoYC5zmctcDlme+LjxKlw1JqwZ6EsdcuWu92J/+Te+yp//qc8CcPnWPcZVthBNKEXH\nJopoys4VNRImAx/+53lRh1lhEtQD3zzQXZFTOyYVHZ119fu9xSWWpe1ua3uHPaFY27v2DufPnTv0\nNtFHFa00sWS5w0hT5rIjKyjqImA7K6t06r7fZCuqOaUYigf0ZjZmQcjB35ze4TVpdd7pNlk65WsR\nj509Tk/qdONWGy11jLmzGGFPMmFcTyFQWtWhm1IK/RHIigPgHFpoJW1R1J5QqQO0m0FXqdAnFvkY\nKzFweWA4YhAENQuVCUKCKnlbWBLh7IgbMbvb/jyjwbAmVjdK18TfhZtR9N2XPXeORNb9sL/HeLR/\n9MeNO0XoZH3YoIZflNKeNg5QrqhHkhsV1KiXdkE91QKlarJ5NLUnbwJDKcmz3BVY4QFx+gDBt1ZQ\nsYbhZsm2QFNIlVLn+DonP+YhzkDF7L39PWz+YJHuEzW0JjDEEmaNJhmR8Q9xUVi0YLf/8o9e5fIt\nDyPsj3J2hn5xFxm0WgIjWEssJChBFJE0pNRDGwJx5Us0heAvyrq6j7zMc7K86vBJ6u6RxZVjZAJr\npFHARKoMbBAymk7uw76OojhgNPGbVaeXMB0JhmhtvchKR10s7yPWg33pYmhNwEj70O0Psn2ujqXr\nrqkJ1j25xsaJVc6tephleWEZLfdlhGMqRj0IDIkY/qTZIoiEV6HRJD5AZvPRkVk5nHMFTjYUl7vZ\nxgF1Rrs0pg4r4zieVbcYM9vrbEmZy32aTMjkGZhMRoyG4gwUBSryhmQ6HtdGyI/E8aKUql8HWuOk\n5Gh3+w55NvkIGFpHKQRTuY4piqqMMKBKs1hXoqvyLlti5V7Y0tYTLqIwpiqwsG62GSrtn3sASuvp\nF/EVSBVgrrSFajirmtmOvBmx+KwnmDlx9hRT4UO49ObLJPmQ8gHHMM2hg7nMZS5zOWR5oh6ts45Y\nTHta5oRS21qYWZuhbrS5esu3a+rAUMhAwKKYzaMajUZ1qBTHcT1rqdFI0BJqRElMo+k9rSwr2JIi\ncUtBIM0Oi90W60s+2bOxscSeeIGDvd26lbK3tMTWva0623hUxTmLibyuFldb5G3RbW4RFIHcWpx4\ntNpSJ1aUUrOZaEFIIAMc80ZEuuBhgacW1liUIYHtbkC76b2sOAmYSqY3o8RVNaJh8J6kzSYwhJLI\nNMbgHpij/sMVay1l5U0qNet/D0K0JFq01nVDgTamrrRQWtdVK0VR1MX2eVFipBY2Hw4o5bOtdFrX\n3Wqlaqa0ml8C7hvRXhQFQaV3Y9i543lc8nSEUj4UPtKiwMhIdR06wooyzuo6eW5qynpwqqyrZOLQ\nsNj1yViNqhnNSlvUHBpxHNX1sgpVQwqlLRkMfOTgLDWk0FeWYMWf8/Qzz7C46KO3m29eZPviZQAC\nW5CEquZWeD95oobWWltDB80AbC6jVIwf3wG+PMVWQ+0yhytnYyrq4WnW1oZ2d3eXHTlPt91iYdEb\nhq7RJMKMXtqUQLK7JjZ1aU4cqPr9YrxPMfbvD/e2a+wliUOmB0KMoypKQU/GBLWbuh46V+SWop6k\noNASiyk0ui7KNjV5TBA6GoLxdTot1tuedKMdN2gJXhvFIZlE/cNIM6kWt9IkgZDTmBkWqY2pQ2rn\nXE2JF0U5UXj0u8LAG0sjoX2odb3+3AGMWTlmwwRdAQIplNbWDQ5FntdZ78l0QilTmIvJhJYc01hY\nrisN8mlW3yeYdTVhbYUC4XC0xKiP+rv0hZ8Ch9zvow174YBqui8pTiZZG0KMVB0opbBSqqaUqyk7\nbZEzHnvIzK+xqlrAYquJFbmpSWjUwXJCB6V8F8phBQPurC2y+ozPQWgsb33zGwCkd7cxZQVT+vKx\nB93C5tDBXOYyl7kcsqgHJa59LF+m1D1gBGw9sS99f1nhwa7njHNu9bAv5oPKEdUtPJh+j7RuAZRS\nA+CtD/s6/pjM1+7hymNbu0/U0AIopb7lnPvsE/3SHyJH7XoeRY7ibzmK1/RB5Cj+jqN4TR9UjuJv\neZzXNIcO5jKXuczlkGVuaOcyl7nM5ZDlwzC0X/4QvvOHyVG7nkeRo/hbjuI1fRA5ir/jKF7TB5Wj\n+Fse2zU9cYx2LnOZy1z+tMkjebRKqS8ppd5SSl1USv3S47qouXiZ6/fwZK7bw5O5bt9DqkaAh/0P\nMMC7wFP48QjfBV74Icd/CV8ecxH4pQ/6vY/yH3AK+D3gdeA14L+Q9/9b4CbwHfnvZz+M6/ug+p3r\ndr52j4p+57r9Ad/xCBf3Y8DvHPj77wF/73Eo/xAVegz4tLzuAG8DL4hC/+sP4yY/qn7nuj083c71\nO9ft49Lto7TgngCuH/j7BvCFH3Ds54GLYRS82+z6NtEyy1+LZTCi1pow9q12hXMcZJWq2uiyyZRA\naOSSRlQfY0tXM2tFcVCT/TpX/U/Va67orfbIs4z2YodWr81Ct4m15WsAO9v7NLvJPwBPOShKx4Qh\nzsJ0PCVPsyfZh/ug+v08cLHbjN9ty3hvp9Rrp9d866yBA22hfpig/8NW6sE6O+tKPKD/0rq6p15p\nQ1H161tbj2CuaA6XWw3AsdRqsNhMqn7y18C3K6512163Wtejsf2pHaM0Y5oXR1G3IPpdDHn3TKNq\n4+S1T3TrxltMfeXuIB1Brd/SzWgKDq5up+rRdFg3Y+OyzpDKQUaoLU/GisLByVhzIlIUhKTGvAYw\nKXNaUfgPABo4tKvaWR3KKQbWMa04Gw9fHlq3URy922y3qvde6y17/hHFQT6OgyinwwkJ/YGRXkLY\nLUc4VSvUuRmxvXPuvrVunaPZboKDZqtJo9mojONr1YeTJP4HHPiMvwJ/fUVRUJb2fXV76FwHSqlf\nAP4u0DVG81N/7c8BsH1jk2s3bwDQakRsnD8OwN7UUi/FIKB/y4+4uPH9N1k67ckdnvvYKQLhr+zv\nl2SFJ4Y4+dQqiRhvm0EpfAVxq0k28Ubo1tVrBNKz/6Wf+QxjYbL/R7/6FXorniehEUVYIbNpbRyj\nmJR8+/e++dh186hyULdJGPCf/Tt+qnCuQpZ6fkPrGGAohq1UNFr+t9tyNtJjMp3UPfRFmkNFrjFN\nGaUy977ZZWvsyU22RxOmI98j3gobhNVoFleSCRnKeDrFihVZ6HRYaPvxNe1mi62+jDAqLc5ZfvvV\nNx+7bh6HHNRv0yh+78dlgGBoGU782tLO0ImrSQo506wyAEE9vaOfOkRdFFbVvBKlgv7EHz/Kodpq\nxmWHS0Iws+CGOFmL2zl0xKrvquNcFJ1+b+8OC0J88KIqaaeeN3isCkyh+Wf7D0bl9yTloG6DIOCn\n/9LP1P9WcUEYHIGkkaKoWXNHl26Cdf43xs2y5oKYjEoy2aGKQmOFJyUvbT3ipijy2jjneUYqE3St\ndfX3FsVsZI3Ny5oQSClVH+OcQ6O4ffvuA/3eRzG0N/HYRiUn5b37xDn3ZaXUDvClqJn8fCg8r+1u\nh9aOf71+comGjKDez4YEQkyCDihlrEqgodX2JDF5UaCdNxjTUZ9p5h9cW6ww3ffGYGdzDyOsXqun\nGwSRTHkdZSQN/11JHFJOK67PnGzsF+v68hKJeN45mttXb1HmT5zT8331e1C33Vbj55d7fqPYOLZK\nJg9b1h8wlPHNJmpRisdvs4JEOH3BUtZsUZCnXudNVD2aPTKOXMbX3EvHjKZCrqHCOhpphA06FTFH\nIycR/Ws1IxlPp+PaA9S2+LCIvx967W5E/HxTZlZNcYzFoJYFVFOOytwySavPQiak9UMLI1k+uQVT\nG1rHMPevp4UiVRURUIG2/kT9uKRtvU4j5bgnJNc3uzGv9/09vrw75qmKBzg2JJVHK27yE9bvw9uF\nJP55WxMfuTpasmVOEPk1am1Bv+8Z+KLEEsR+85imE9ot/zx3emE9dy0fTkDGh+syorSz8MLW3NRT\nVMXMU1iccAMra9GuIo9RGLFHYRDihEtZaUWgNPfubj+QUh7F0H4TuKCUOodX5M8Bf/MHHHsTOGWC\nkMnEL46k3aSz6I1Z79gSQ1mgoQ5JxOPMraUQasQoClFCdba7OSSp6BaHg4rFmqZJ6AgJtc01uex0\nJgiwMtROGzUb96wNDRn4uHHqOCdPnQHg2Ik1UjHMN67cYDzZrYmGn6A8qH5vAqccjrX1DQCSWBMa\nGWQ5nvqB9ECjEeKctwqBUTSEmLssciJh7IoaCUOhjitLRyj3YtDfo6Mr0vCUwcjfF0VASMWC72ri\n9V6zSUsMeWkLCnn49/p9Ctm0eu3OfdSCT1Aeeu0WpWOzL9MQ4pCJ8/oliOmLh17mGiGGI3eKQkzc\nxGnG8qAXThHm4mmpklSM6LRUFHKfXGkRlIJBCftFNRMvYiKG50Y2ptzz92DNBiwGXqcdBaHAEXGp\nKZ/8RvbQugXuY9Sq5n4VhatfT9MJtzY9IvH0+Q1aLW+6xtOMqcxg67Q7dHtyHlPMyO8zQ5FV7HEh\n1Rxy5UqUeKuhhqghVJ7GoGVNhibCqEr/arYJ2BIOTGh5P/nAhtY5VyilfhH4HTwU+Ms1rvEn5ZvA\nhQ/6XX8a5SH0O9ftQ8p87R6ezHX73vJIGK1z7qvAVx/guEIp9Yta8ZWueE5rG8fpp54YR4UB6b7f\nfSIdE9qD3KUysRLY3/J4baPVYpqIV7Tcoy2jmQeuZCzhXdmMUbLTTfbHRJEkMkIBv4FYN+iu+dfP\nffI5kGtzDVcnjZqNkE//+Me5d33nA+noUeRB9FsvbOe+UpVF7+4PCGX8SVZ6QnSAdtPUSQFTNnDC\n79luNWYjsPMpUUMmDI8zP/saWFtoE0podebEMbZST86e5eUBb8Qx2BMYJ06JZay4CXSNi8VRUCcs\nAu25sT8Mqt+HXbsZ+ivXjHeX2mGTTLC90bhkPJT1Wiim4q1OlaOQH5Y5RSpepkMRicIKrcj07Jhq\ntFuubD1LLCgC0pUTAMTLJ9i/fdufZ/cOG3KNA20506y4clOQKc96aCk/hHHjD6tb59xXDk7qVQcW\nRDXWpigywrAaqZ4xGHo4bJLugXDKDob3anhRB464WcFbEem0mqkWYiQHsdCN0QLpBCa4zzutcFlD\nSDX/3DlXTx7O85wszR44WnhixN/Oua8uri2SyQ+4fu0qLSFSHm/3KXOvoEiHjPY8cbFuRrOQXyki\nwQKXT/do9XxWvdlp16nHMnfkgkEopxje9cZx/942L3zuWf/ZjcU6GxmHCb2ux3daS10mMrc4x7LY\n9g/V4qmYwXBYhzBHUZxzX71wchUES7pzZ8DxdRmY2EhmwwGdhwxAFrMWLNyVKBkqiFJEMt9rMsno\nT/2CXlxbYtn60Mp1YwrlX2/d63NK5q5FYcD2PX/vQhVRyL2zaJzorxEnJFGVPHJEQXgfsfVRFOfc\nVy+0QzaVXytue8q0Xz3oJdXjaZxmKphfqixlVeChAqyrHnRFIQuwVNw3hSJQMhLbgBEy9ZZpk3zs\nkwC8qxLupV6niy5n0PeOynI75LSs47bTOCF3n6Yj1JGfDOK+2lvqkctML6Wow3bnHOOx13NZFiws\neFhwMNzFaQ9BapOiK2tpHaPxSM6cY2TnaiSLrG+sARAHa2hV5Q4cARWBeFGTued5USfDNGE96DRN\nUwqZx5alKWma4h5wluDRtR5zmctc5vIjIk90lE1pLduyI1z5zvc4ccaXdHVaLXotH8K7FPb3ZVcq\ncmxWhbctzn3iNAAr55drD1MpzZ2rvkTr+hs3WOr4WT8vvvQxvvXaVQD2toa0Ot4D1sbVJR3NXock\n9rtkq5XQcP61Kh0rPc/l+73XXuGt199mNBgfhkoem2gTsDP0Iz0yq0kSr88yz3CiW6yiqCYANyMK\nycpGymJl6moUNWcQSgnZ2EMB+1lKLB7XUqL5zBlfarfbSeryIxc4xpHUPedl7a2NRiM/QwtoNJv1\naxOY2sM+6jLNLe9ekwxz7rBlVcalsFLjGTlFKh6ORqGr+WxGYUR32rna01J6VoFAWYKMLbcaQvFo\n3dIil6Xu6xuXLtPf8dfw7PIyHUlsnjPQUv54My1BnjHnxr6u/Iir2FcaeI/WWXdgdDoMd7wt2N6+\nQ+KRKBZPRhSBVCOpggpz0cpRyDj2ODJ0JBmryxQd+fXdbk8IjH8ehuOSws6Sw6H4nXnqEASSzObk\nEpkV2XQ2tThPUa6cFUu/jzzxmWGjoVdc6gpax33I2bAxpSwOrQxtKaG4t7PLVOplnn7pLGc/dUI+\nm9VjhQe39nn7D78PwEghfYAAACAASURBVHB/SOtZHxaUpHTXJFzQEItRyWPonPAY1t10h47UIrYa\nTQIJjSkspZTdXHr7OnfevUueHvkQjKvXrgFw5szZeqCftrYOzZ2zNJr+twexwclcsVgplAzKzFEU\nUnPYimJS6xelVQZnBAdEYwSrMs2Iyzc3AYjaDSQqYzqdYKz/YzAe1+PhoziumyDCMKQsLTzw5KUP\nT/LSMpJiWOOoR1nH2lBW4Scad9CIVjir0nVJm1GKRCCbAouTTS0NS4xUw+goRJX+Gdjqtnnjttfv\npXfeJJBSvaRc4YLxRr01GZNVs+/SglAMvGE2f+9Ii3N+OqL/o84j2NxSiKPlioLxyNuOKI0oBToI\nSksoGKp2lqhywAKDloqORhgwSv0Gtbs/pdkSeDHoEsmgVu0sw23vsBUTBaJ/h6sNeWBUXdfrlMbm\nFv2Am9gcOpjLXOYyl0OWJ+rRaq3rKoKNEyc5+/RTACw2Frn27mUAbl26ytKq9zJDUrINH/KffG4D\nXe0+U4OScOrSyzcYSXjx7Mef4rkvPA/A7WvX6cps8+c+9wy6K0X1vWXCptTmZXvc2fEemyLC/P/s\nvWmQped13/d73u3ua+8zPSsGg10EQYLgIi7aaEqKyvFSih2XYydKVKmSkzglV3n5kPKnVD6lKl9S\njip24iR2ElmWLcliTNkyLa6gSIAg1pnBYNae3rtv3/2+65MP59y3GzZJDICZQcO+pwrkxcXbdzn3\neZ/nnP/5n//R4yl1XPp9SU12tvdy7txxtiiKWV5cAYRTM9SqbNV38umhvmtI9LGLh4s8DvsjfC1S\nZoHHKJLvnkYxkRLJIzz6sUTJjaKPThunVirR1tHMlbkGI0cih/3RQd4E0Wy38ojWWot3pNjx4TGD\n0fTfMzYf4e0eaZ31jZNHY45x8PWx5zj52krKHsmc+KsUZxSKkkkMSPCU4RGnDtrISD9x2N4Rnxob\nU/PlNVcmIxatMmxsSqYhc2jstEiOk3n5pNzjblPGheu4h9lP2ePsqnaM7s3xxtUXALBpnI/5rpQa\n1IoC+dksJlA/uAbGoUBpjpPhK/E+TicMRpL5BcUmviPv5bkefkmLlCEUFV4I0oxYM7zUpnnk7RR8\nvGKAd5dF8ge70XougVIrHFyqxToApXqZ848JK2Dz1iabW7KwlqtFnv4J2ThPLZ/AKu0rcWLefO0q\nADu3dlg6J3jqo889QW1OHDQeT6jXxImFpTaOP02NE7auCjXp1MUlxsm088zmnSRxZtjdWQegs7dL\nySnfF3/cUzMGV6vNg26XxYb4NvAMuJLy+ialr1hsYi1VHfVdrleItRmkn/qESoXLsohSXdgLaZTQ\n25XfJe5OWJpSt1KD70ua5RfLFOtKqVvrUJp21BQCpjlWllmMO8XCorsmfH/wZnA0AZRKtWK0GMxU\n98GRTRUAa3LIxvcCKi35PSY1n7Su9Yi9ManyvtI0Yaisl8yrEhaFRXAQu1RLwoA5e6ZEKRHM3EtT\nuiq9kY1GeNpun5CQKsHe2OnGf7xBWtdxWZwTzL9er1Ovia9q9Qqthqyz73/3efyb8r08x2KZPi7S\nqAsE6XmGQkHugSicMDgQf6ZOiOMoXcsFm8gGnGRjHFvRz1CmqO9lUhdnpNQwspzVZApFDNNuSgtp\nhuvc3UY7gw5mNrOZzew+24ONaI3Bn568cZqLjhjHUKpIVPTQE4/wwte+A8ClO3d46iefACD0LX5X\nrp+zRfrIKf/ExYeZf3gJAL9SyHl0C2eaBA3lg8bQLskJ+NZLm6zdEiGIn3z0KTJH0q/MgnVU3yDt\nksWSemdpSmYeuM7Bu7YkSbn0hoizfOTxixSVC5tEY8rKPyZNaOqpjSkQOFIUCG2CupY9KrhluaZU\ncWgvq2/7e4wi8VV/dx9ftQ7GNibRSPqgN6GjPOad7pDVpqbFoyHptNXRD/I0MfCnHNrjHXGBBORT\nzqVrYCqGZfL/mUqO6mPHMmXYJl6BgSe+2O5PKHoSRY38MsWW+Lp+eoUz57QF/NTjuG2J0kbf+Cbh\nrvh96/Zt7rz+IgCbS016vkR+3tYuzWnbtE2xGkk7mSE19m4L4x+YFYtFnnxMBJEqlUoOHbiBiy4t\nOt0eVrnIBT/IxY52ewMaFdkLao1Kro1gXQdGsu6tTbEaeWZphOtO7+dD8ZiUkES1PFy/SrEoReNq\nsYDJ5EOkmSXRPSvNUhIOmTXvZA90o/VwiFUBKk0T4lDw2jRJcVTib/XiWTZuCC1rczejcEK+8F7S\nY7Gr/dxpg1ZJNsULP/UztE9IetsddxkYaVII0zHBuirzDDMGJdk4feNy4aMCUxTna+ztSbfZKHap\najdVwU0oakbrGMNg0CfNjvdm2+sN2NHOuUatxu62HCajQZ/Tp4R9US8XD6URM4/9nlyT+ODNCxZ2\n6sTTjLRLb/2t6yRaaa+VKxRU+avXL5KVZIOYWIdUyeb7211efVOw9kliiLNpBd7kCzLJ0ry7xp2m\n18d9JwAMNsdcXePluKw9gtFlGWTmUEbPKnWrF6ckUzW4h5/i0Z/+IgBzJ1dxVB6w0Kgz5bUkaZk9\nxcPPf+KTfPb0BQBee/47/J3vPg/At27cpKaUxc+fewx7S/ye7t0hndKdrCUVvdB76Il7b77v0W4J\nbu24h9oXmSHHmF2vSBQqW8WUqWn34jAd5yIxjmfY3pcmjkK5gKMCVskkITCyARvjkaW6F3iZyCkC\nw3BEogejn1qKSrULvACmUAwiPQmQWpeE+K49O4MOZjazmc3sPtuD5dGmac6jNS50OwLs2zRl8ZR0\nbjulIk9+6iMAPDV5CFcLOePdLkuaFpRTAx1JlTavXcV1hV9bd8q4yn8L44ygo/oJXpld1bW9UK0R\nKqA96U9yScbecI/QynstN8tk2nbnBR4nlha48db6ffHJvbJxlGCqkm62GjVcldmrPHSOuko+9nsd\nQpWXSrOMXZWILBWrNJvi/2q1zmjvBgCeO+H7L74EwN7eDmdPyuuHqYPnytKpV6r0NSvojC0Zqndr\nYzb78ls3i16uRoX1RCoJSNOMzMY5r/Y4m8OhyLRxDEbTWAcnv4kS12KV4J3ZjJFykyvnLzL/Ez8B\nQOHsebY9iURfubLG9pZkFeNOl/5A2pf3OyMOtPX045/8OJ/+9S8AUP2swwuf/CQAv/1H/5zdnuge\nLNbafEKj3lFvgBPLveGRkFh7zONZEffOBb6tIY4OW+GnGr3Ly6u8/oqsrWTiMj8vBfCVxZRSVe7n\narVMmEpxexwN8d0p59XHDwRmScOIJJmmdQmuma7FEZk289TKDbIDeZ0oLlPw5f7BorxvGMcpg3FK\ndncduA92o8VxWF4VzC8MJ6SKj0STkM6mMAEWz56iNSdQQGXfI7wtG9zJoE7s6Jc3MSdOiOPiOCO+\nLYt1J7ZkUz3USpVKSRa0FwQ4yiioFxx2lZgc3ehi27Ixl4MAd7ob+D6hpr1nHznPudMn2dzs3A+P\n3DPzPI+CdoNt7XZzMe5qs0GoC9e6BXyVguv0twk1PVqeP0HgySLu3rlFtC83cLPk8uiFhwD4QRQz\nt7Iqr2MtoTaY+NUy4x1J13rjkEgXcRgl+c5UThIKnuKGjk+ov3ucZLie+6GgeRkMdspOd8GoqLRr\nDa4mhgcmwWcqgVik8eiT8vjMQ/zxjqy5gxvPk6n05GvXrnHrmrBnyjZlQfHajb19QtWS+OznP89w\nqN18lXk+90t/BoBvv/46N26/Ja+zdpugJPeDKTSohQI7tMyHY6PNLIwURjSWnBaYYfNUfbE9x9nV\nswBcv3EFT9P8xdMNTCrr2CaWVk02xZ39AUa76zzj4HhyTZIZrNWuSRuSKWBjzOGgAKdqSEvyO/YG\nY8qO/F7j2DCYqIDNcMxoNM4x23eyGXQws5nNbGb32R4s68B1CCpyUpfqRQI9ZTzXo7MubYaLK8uk\nSjpOeiFxR1Ko7TTCV6HqerVIUQvp5VqdyUiJ96MJVnvQB4M+A09OdtfzYUpAnmtxqiERc5ZFXL0s\n43RaS4uEyisdjCNcdU2p4BHZiOMeF9RKBebK8h1feOkyj18UXYilKCNWoe3JOKKgEnrFao1l5Su2\n2/O5elJv/RbpUKKvxtwi80uiyzx/YolaQ4thvR5BIL/j3tZOzos9ypctV8s4ytbwfIeqyteNJ2mu\n4JYmCb61HwroQBoW5Hu6jovVaCkzNtc6GEeWTMvk/vnz7Gux6rVXXuWgI9zN9vwiSUueT7MIVznL\no34fSlIQ8hsVHn1CFLue+5nPM9HfzxtYfuKZTwHwhZ/5eX7z//4/AbBRwstXhXFS83wWlKueZgkl\nYu4yu/3ALMtS+soWcnO2siwlX2GmUr3Cp56T0WO1YoHdPcliX33xGtWWrMWTp2r4Rf1d0kneXusF\nAY7e20FQgMm0McGCaiwYbM6HHoyHBIH8Rt14wDgVf4axT7+vUzbCCcYm3G2h8YFrHSSaxiYueZdS\nmlo87cEf9foUVQ7Nq9f49Bc+D8B3XnyRb37v+wA8dfFhljTN6u8NaKhk4urSCuOh4FN7B/tMdK4T\nrmVrTzbyci3gzAVhHZhJwjm96W/sb+PVpfI+nETceFPSsutXLrFy9jPcJS/5A7PAc4k7Am+Mo5SM\n6eBLl2AqR8mYvX25ptpuUtGKtx8UKSj9qHV6lb0trb6WK3hKi/MqVWLVBm3Uqrnwx7AYsHJSMPLu\nOKaov2MWJUQ6HaPUbHByek1vxK31wzlLRpb4PffHvTZryO8px5q8xz0yGVFVD6ylR5hMdJLEwjIv\nXNcOJM+j3ZZr5ufqrGljQpTEVOvyvFMpMn/6LAA/9bFn+Zkv/QcALJw8Q6RNDV4xYKJaB0GjwlNP\nCO67efV19say7oetNk8++TH52/GIzit/fMxDBFU3sFOZxEP8v1gsUFBqYhyOaTQlkPipn/ksly69\nDsDuN/eIdSZevdAmTeVAM2mKLmmKpRKBQgpxJk0LAOMsmurvYFwXRz01HA9xq3J9aELGA4HGSIp5\nANYsBfhu4a47w4759jGzmc1sZh9+e+ARbaU8HXoYkxV1llW9SrkiVcQ0TXIJujvdPR5W8vwnnnqG\nF16UU2wUJpS00FUM3HzE+Pr6Vn4Cnjl7FqvNEb7vckrZDhvrW1x9Q17n4hMf5aG2NETsf2eHfYUp\nYlz2epo+t+Y5/9BDfL3wwn3xyb0yF1jRU3urFzJSvvJkEuaV0iRO2e/I93LrVeb0tygWS/Q10g3c\nAq6C/9E4odDUQtpkgtVsJLU2n7u22GqTaWt0fzhgNJGC5dbeASWtyJUrKxRVka3enGdtd1pd7zJf\nC+6HO+6PZUdG2WsxJi4W2G9Kyt++cIGRFgOv7vVZeuwpAG7fvEKqxUBrIkbqxyeefJIvfelLADx8\n/iwnT0qxsb24TKYx0O5+FzTtTaIx/+B//98A+MY/+W2eWjwLwCRx6ChL5rHHnuQnf/bnAfC2tvjm\nay9jiO69L+6hGaCsWGAxKFDUUNTzvbww1unssb0tMx4ff+wiJ88KS+YXKz/L/r5w52vVAtbIfrHf\nWcNmOqgxTbFa6LLGzwtsOAY75ci6ft6mOxknRKkWxkrOtNuaplfBi6YjdBOGwwF3Szt4oButAcoa\nktfnqoRKQQoCh901qXRX5tv01uVxMfB5/nXBnj7zkWf5U3/6TwGwdvMGqS7WYq2Wd+bUqh6pYi7r\na5sEWt3NkhivJD/k0uoC3T3ZdHc317jaFYrZyvJZ1jZvAGCrAacfEYzzxuvX2VzbzeXajqsZoFUS\niKBYatLWKXXWGnylxTWaPjc3xbfd4ZBHNG19/eVX2NWxyU88/CiOdhwNOrtsX5FxT8YrUC3Law6H\nw3xqQz8MeVOhgOs3b7G5L/4cxylOeTrBNDucahEUqOtEhtvbuwTDUd41dtzNUSw2tpZY09tOocor\nY1lzw9fepNQUH9XnlukN5eC+ubE5lZql2Dlg2JH19+t/7c/wH/35Pw9AFMdY3aRHg4hQdVU9C54O\nbfzyP/4nPP8P/xEApd19xgN50ZWlM6yc/CgAz332p1hUcaGgUqfQWMAcey1lh7IexL7j5XoRgetT\nUpy70awxUpGYueUFHp2/CMCll15lWalel69c5uw5gf8CL2Ojew0QHH2iGIHrO4eYtePmw0eTlFx8\nCSci0okjfrEqOCdQzAzxSDbv/e1tDvpd4ujuDrEZdDCzmc1sZvfZHrhM4rQHv9Wex9H5R5NowvYd\nrf5bSGI5uUori+z7Ejl96wff5xe1ddFOxtx6S/iHhVKVUE+VE8vzuXrPQX9AUSM5k8ZsdQTQTgtO\nrqswHvaItbjwR99/kxsjed9q06cxJyfd6iOrzC8t4WmqfFzNMYaCKpR1un2MI1FjoVYn0uFVyWTI\nRH1+++oaTz0ule1Bp8u8qnG159usXZOxzi/+4GUaS5IW723vsbQg0cLuYMQtle7rjias31HS/SjM\ni2E4Lo2KcjuTlHpDCm+UC7Q0AonSS3SjSNtEj7kd0aaOTUamGcPKs5/kFVWb62/2iboa4QQDrr0p\n2VjUn2CVCTDXaOG3pKjTaMyzsSlQzn6/y1jF2k0GLVVfq1aKeYvy8vJJnnpCmnlGnQ6L52SA7PzF\nR6kvCJPGdaCvkzZa5SJZqwWbd+6DQ+6dOTiUC7I+6tUacy35Lssrq7TaoupVrvjML8njS1dfYfmk\ntJXPLTZz7YhX33g9b9ktVcq4I1XyyrLDlmmb5fIEXuCTTcXjEkuk+ibGd4h18kLgwrgnGcjO/pBo\nX36jcTjOxd/vxt5xozXGnAL+D2AJSQB/w1r7Pxlj2sD/C5wFbgC/bK39sax+x3Upt2SBOo7P+k3p\nz44qhkwxrK1ba6yelaaGaDyhrQ59/dsvUfna1wH46JMPM9Eqa1CuMr8sm0Q06hJpijDfniMzU+x2\nk3SKrUQmn06aZikl7Ye+vb2No1Jt+7sdEh0Q+cznPsPy/BJe4d5vtPfSt8YYBhNZHPudfeYn8l0i\nDJRls/Qch4Yu4t/7Z1/j4bOPAvDQ2QukQx1Zc7BPZ1+aR5rVJp/79M8BcPvqFS5dugLA+l6Pq9sd\nfX2XROkvy60mJaVxbXT3KU/lE7F5pbd54gTdZCrSAd3J+L5BB/fSv1gLyroYzS3y3C//BQCKH3uO\nr/6j3wZgcG2XTDFFvxQw6Moaigc9ClprKBcrzC0JA8MtVNjak2sG4yFT7nur3iDU1+ltbVLVUUQf\n/cJPEdQllV7buIOvbJvQGhyl52WTEZmyGm5vrrE17hPfB52Oe+nbQqHIM09/AoCF+Xnq+h09r0io\na9p14emnPw7A1Vtv8vrVywDUPajo2Cm/FLC2KQ1OKydLeBp0TbKQKXaVZSlT8MBzvVzm0PUsqW6u\nrpcQR+KzcDRhpHrXzm6KH8trGifAePauRWXuBjpIgF+31j4OfBL4NWPM48DfAP7QWvsw8If67zN7\ndzbz7f21mX/vn818+y7sHSNaa+0GsKGP+8aYN4CTwJ8EvqCX/X3gXwN//ce9luM4WF/e8vrlqww1\nna+UF4g14hmOh7gaCV27cYvevqRBJ5+6wJf/8BsA9MMen3hKKrrhJKZc1lHlvkdXI9FoPKGkVXXH\nL1EoKcPB9Yj0hA/jmFCJ56fOP8RAdQ+6jqW1JKckhQJbkz2S+xAV3EvfApRLEvmcPnWKoiewSRLF\nODowMUsTHJ1Xtba+w9/5+/8PAL/0Jz7PfFOlEbcHdO+ID+nH9G5I8exkfY6dilxz6fo6Ric4tBeX\noCKpW8mCr8pRbhTlEV26UMq5vNVSiRXNUtqLLXY2Dzm199rutX9DbRxY/fzP8exf/i8B+O6tNeoL\nUnzyK1dzPmgchdKEAGBj4lAysDevv8Wph0TM3ikETDRKjuKYkop9D/sD/uDLvwvAy6+8yIKuxT/x\nxV/koUekrddbOkH/QJXnwhGhjoWPItE7APjm1/41axtrRPG9Zx3cS99WKhUuPiLsH2Nt3hgyHPV5\n/jtyz1svpjEv67s72aHTlXWzVG5y0JNszG1EjEbik2ES5ZM8Arx8lltmUhxV7PLtYXNEfIQobTNL\nlsh+YccZVW1PD90xrrJNXGuxWni/G3tXGK0x5izwUeA7wJI6G2ATSSF+2N/8KvCrAI12g4Fiezcv\nXeapZ8W5rufS17yp2mjkjQZz7Ta3botk4srFM5z7mGhWXr2xxvmzwgp46MwZJkrdSlLL4rKkZetr\nN+n0ZKEHZCTqlM5+n4JWw22WYHVMRVA0DLvy2VbPnebM49Ljf6dzi8Ekuu8yie/XtwvVChVNMced\nHqOu3GzxOCRFcMDuzia3bgkW7jmOUIeA3/zdP6ChOrVLrXbeWeQcdBlp5by+UGdnKP7MCl4uwDPq\n7GCnE26tYUW7nuYbtRyXjJM0Hw20EEaUi7LsWu06nY2tfCzM/bT3vXaNIVYIpnTmIl/5jurCdjs0\nFY4pFAsYBQk379xiEuowwUKBQMetlOvNnAXiuG4+2ilJkimLi9/7nX/K//X3/lcArEkwKrf4+suv\n8F/82n8LwMVHnsToTb+/t89YoZ942OXr//IrALz8nW/R9o6/b0+fPsWUOmSNwVFGxygc8PXv/AEA\ne90tCnXtwEt7lCsqDHXQYRTJgT7MOqCasps7E6xOuw4qPmYKF5qUKTDrZS6ZaiyPJwMihVySMIWJ\nXB/ETg5l9GOXcU/WsZ+Ckzp33Wpz16wDY0wV+MfAX7XW9o7+N2t/tOiltfY3rLUft9Z+vFz9EIyE\n+QDsXvi2USr8sEtmxr3xb+Uusbh/3+xe+HaqxPXvst1VRGuM8RFn/gNr7W/r01vGmBVr7YYxZgV4\nxxwwTdM8nayWfYzy1goFS7ulRZTdMUMtaJ196DSNBYki3nrzLR49I1Gm45WJdKb9aDKkXtax1smE\nSAWTy/UmuwdTCboOdeXjlX0n78FvVUr0U4n8KsMRTVV2bywtshNKQWiQ9MEG9y3mule+5cikgngy\nmWoVM9g/IKurjkGvx96O8mXPrtCYkwW+tr7JrjYy3ByNCCsSfS0EBUYqyH7p9k3e2hKoxxSK9PT1\no3CC6i6zE4bEGhWcbLdzPmScWK5dk3bU+cUTmLoyT2ol/PxT3x+7V/61WLyGrKFvfP8lfu/v/kMA\nfuKZj3DhI8IEKBQKJGNZu6NhD09HiTtBmSefkWLPmQuPUlKIx3XdXATd9312tqWQ85V/9k8pap9+\ne26JsQ7LvHb1Mr/zWwL3/Mk//efzLGHvYBdSWfff/uq/4OU//pZ8HhtRqlRxzPhdeu3u7N6tXUim\nqX2aMhzIfn395i2yVMX4C7WcTzw42GV/T5oUkmifxChbwHUJrPxGw82QUKGbk+fr+FNlRDdBZ2Bi\nIpNroxi/QEUL3n4Skqh+iglTCioyHsy32BhPZUYTXM/JI+V3srthHRjg7wJvWGv/xyP/6XeBvwT8\nD/r/v/NOr5VlaT5W5dM/+1M8+phMwb299xZreueO3xwzVoGJfpywoBqre9kub7wmdJnPPfER5rW/\nvL+3R70tqZtJQrojxaOMhzZ6UKnUKOtoivGwR0GZBpmZMCrIj1oeZZxfEdhhz5vQ6cqm4pcKJOOU\n+6FSfy99ayHHp6vlKr6KvvT3D/Keb4vl7Kp8x4tnLBvrApUU63Uem5cMzw0MNpHNslmrs60H42tr\nW9w6kEPJ2gNcpbv5boCnuG8vSRnqDTCYhCzqhNfyyRV2VbP2+qXLnHtcfveT7RaXPfeuF+u7tXvt\n34nCT7fWbsgwT6Df7+cCO81mkzfXBeqKk4iC1gjKrQVqTVnHw+GIto6pWVxczF/fc12uvCZaHt3u\nPs2aQDmdzj6p8srq1QqvvSSQxcWLj7G8Kn4MgoBrl6UKf+WN1yjowl+o1akUizjO2wLNe2L30rdp\nlnLttjCQrt+4zs2bcigPDg6oloQ9UyrVsEZZNVmfG6ojkQTbuIEGbG6Fxap0jC20F7iyJT559dXb\ntFcF/3ZKGSX9verFBgWVl3QDSCPZsJMwgoFOAYk9Ml/et1yqUKvL63SULXIvRWU+A/xF4BVjzEv6\n3N9CHPmbxphfAW4Cv3xX7zizozbz7f21mX/vn818+y7sblgH3+BHZ3c/867ezPf4+MeeAeDpi2do\nzUuYX2/7BCqQ41UNe1sqdZb1uXVTRajLDfwFOa22x31OaaXbTSypTg1IopAUZSC4HsFUvi5JWFnU\nv92GgRZ1DsZ9JlqBHB8k7IylUGTnlzDa4luoVHEK8X2Juu6lb5MkoaM936snVmlo//3Ng20ONsSH\nZ849xMJZGQC4e+sN7lyWDOFMYwk3k6i0XPCIlQLSG4zItMGh3ZhnZAVaiaP4UEw8NvmY7MTzMZry\nbg1HLNXkNzJewM6WqKfZ8A2KZVl2S605Ll54iJdVFPte2730bwZsa9E1W7CcOy3ykakxuYRmqVTK\nW5PdoECjLdBMa/lMPptqPByyuiqaBo7jMNJJCsYYtra2APB8n4pGtOVqlcFQ3rd30KHfl9/46qVX\nWVG1L2Mst2/cACAZj2hqJlF0He3Fv/fZ2L30befggN/6bWktnoQTadlGi1W6/obDHhNlbgROiVNz\nAiNe302YaKNRqVqiNi+Zq2cSVlYl093rgaMFxaAAfqBQQKmCo9MTMiKKRZX7rFj2NjV7S1JGA51Y\n4Xi02tIHEMUxA50gcjf2wEVl1jakSyWKtzhz7hwAq0vzPHJCpAtdx6MU6IDFMCVU/cdeN+YnLkp/\nc7Hsc7Atae+CV2RNFf7v7O1hfbm5zy8vUdMuJeO6jLW66zkBA+2cSeKYpao2RAzf5LXrkr6cO1Oj\nHGjFfDzh9s1bROHxF+ZYWdTqtxMx7Il/Csahuy9pzpa5RXBKqEjVlROc+ah8p8XWAvt3BJPevL1L\nVel1jVKRrKyTEUo+VWc6bDBiV+GdUZSAqs6TRpQcbVIoFkk0Rdvo9dmeTrXIDJOXJKU7ffY0Z06t\nErzw6n3wyL21i0oL/wAAIABJREFU2MKOyhWOwhRbnBLgMyaTw0nKoTYOeKUKDQ0MVs+cZ74lcIHB\nUtK+/o2NdaxqoBaKhbxxw3g+ruI99UaTJJPfJhlHjPoCA9y8fpULGwJTDIYT7tyWbr4onBBrJ9ko\nCSEo5e9xXG0yGrG3I4eM6zi5NvJkOM4xb99zKeghXvQD6gtyWNWqTfZVArXoR7nw0ZAeQUUnfGQB\nRjtM/cDPYbV6s41flGCvN9gjnMp6VgLmT8oh2b/ZxernGfR7NBX2abSb9Icj7F3GXzOtg5nNbGYz\nu8/2QCPaJE64rZMUXr90i3NbEt1++lPPMt+UEP7M/Go+bvj2wTanHpOIc3utw9Wr3wWg2Vqmrqd2\nf0zODb188zaLc3L9fDlgQQsQrWad2xsCntfLJZrtqQpVmZ2eRM/7wwFd5d1iDGPl125eu0ops5hj\nPm4cwKq6f2iNDF8C5ppNygrgr+1u8+1viR8+9smPk7gSWb3w6utUjc5Uch1ai3Kalz0Ht6skbmtw\n7GFE26hJ5TxzHEaqaDQaDqlMIR3XJdZqeTgMWZoXn59cPsHSCYmqX3/9NVbaLSJVqjrOlhrDREWe\n3UlEUpX1YI3PaCiRULU9x4lzknXV59s8/Kg0Jjxy8QlWl6XY6DnkPO5C4OdSnlhDRUe4O8Yn1Rho\n5eRJFpYkMn7j5ZcZafq8ubXOlddeBmA4GrOjEoJRkjCcxk++C4Fz7Ccs2Cyjr7BXHEU5dFAs+JTK\nms674ChEFY9H9BVyicKYsiZU3Z0DOoGq+i2UKFYkKy2kMEbZAtbJYRzX9fEURsC1TPSaKB5TKMg1\npWqRrCufJ44neTYclMtUapV8r3one8DQgaXoyiJ788YOt65LujDoDXn209KM0G61WJ6XZoRKqcGt\nzg3529UKg6JiVcPbJJp+9TPDeEEWqOedoqN4SuKSi3H0OgfMLclCHw+6dLqSxjpewJ09gR1evHqd\n+ae1imsc1q7I5l0tBwQ2xjnmPEprIdEBlFudPgW91841WjiaOtYKJTqJVP9vXLpBa0kOpbVhmk8b\nLXo+jvJfnNSlpV0x++mQum4Qbb9OqhvEZDJmokwS065TV+nFNEsZjof62Sy+Mx2aGVBRvKwSBGST\n8V1ren6gZgy44kd/NKCu+ql9F6Ke+HR/f48p1208GHLljTcA2Lx1m6qOEPI9Px+Q6WDI9EB3MHR3\nhQmVxTGBdlC+eeVKfjNv72wRxrIZ9PtdvvtN0f4IownhWDYez3XyuoO1Dp7jkh3zCRZJkjBUdksh\nCChral8IDI5R/H88INSOt3Gvz6gvj33fpd2WekRWLLM7lNeZdFOKSuMsxBlJvsRSRpn4an2yTqkt\nrxPaMaFCYCZO0Z8FP02xihNjXMa6v6QWKtUKzmzCwsxmNrOZHQ974DKJqEKOkxq2NqVg84e/8w3q\nDTm1H37qAmVPoqLV2kLOCbycrWEk4yQILVaLU3ExZmleIrPFZIWhCk/3w4iqlTB/FE3wNIqoFAp0\nNNK9vnaNSzdEbpFyiUVVuH/5j77D5z8uSkHPfvZTfP1f/QGee3cpwgdljuPkavGd/oimCp2Hkwk9\njRYGgz4t7ac3seWt16Qo1ShUOKOsjNHwAKu/UWZ9AoUjWuUykUZZvvEZaotvCfBUscv3fcpagIyT\nhEh9nmYZmXJBB4M+197QPvXWPGeXF/n9yzfvh0vuqRljci72kIzNWzJTblwosH5b/Li5vcWwK2vO\nuk5e63cd9zCiMS5GW0wd42CmEpHW4ujo6zgKOX/6hL6vx+6u3CcnV5Z545JkgVkS0e3I8xaLo1CR\nNRZ0uGnmZlhj7m9HyD0wY6CkGULBAScRKCY8GBNpG/O42yfSKr+JEjyN2sutZj6/zi/5VJUZUymU\nSLdljTIa4+mYhMy3pJqd7poR/pxAFsVqiYI2mJi0SDQU2GvcG1PQYY5Fx5AZ+Y2G8QC/UrnrQuOD\nnbDgQKKypH6rzJmm3Nxrb2zyjX/xAwDK9RJl1YutlBwWG5LO++U5bu7KptgbxUxK4rhOd4d+JFXZ\nyXaX8kj+Ns7aHOionKBQI4rkcWewz52BaoD6lrQm1y/Pldi5Lje8F2WcviCYsevt0aw27hqL+aAs\njmPGffleC/USy/OCT0eTEF834Fa5JoKlQKFemz6k4HgUzXS8B1jtwJkQ4+kWUSoVMIqRTQa9XGm+\nXqtQLMk1xnEoThdr4DPWlCszEGsX4BiY0w6r+VaLahDgmuOfWBnHIdD0P3O8fALCZq/PUNP5oFBg\nYUWigeF4nAsReW9bOxameH+W5rKKWZKQaLdjlqW8pvjrIxefYEUx2lu3rjJR8RiDZeo2w+FmalyT\nU+yCcjHXSTjO5hiDq36YjEaMlVkRjQdYFd0hzZjm844VhgeAk1kc1ZcwZFQUdjCTiKQra9RLLInS\n7jInxQ+mkqcpWaT6HcWYTLU5TJblgZwdJGj/DrGTYLWWMYkmTJIsp/O943d8Vx6Z2cxmNrOZvWt7\noBEtZBwon3Ljzg6PPXcWgGhoOdiTk+WrX/keiSOnRHQx4UQsJ91cfY5HlkXtq9Pvsj2SIpZLStmR\nCngYNLnyfRm8uLG9zcqqkJr3r71FpEMDDYbSolTATz/+CK3TUngbTgY5qXluZRFbkvc96A856I1J\njzkX0XEcWlU5zWvlEkFBIvX9TkTgyc/s+kGewts0ZL6p8oZegK8SgDgw0MF0u5OQZCLP14oBWTIV\nRnYo1SW6s+6h2pJxjKSqQLFQRBUoSY0h0aijXKmSWR1njiUajcg+BIyOzBhGGjZGjkexLVH5SrmO\n0Zbuar2Wq8HduH6dsa65YqmEq9CTazJMLM+ncZxfn0QxaSYpcxxFDLUJ5/XLV3AUvul1d3Lepl8I\ncsaCOQIPuK6LpxFbUCiSWXvsoYM0jtm5KTzgeDggU62TwEB5qhfheUTKn8iMzSNJG0VkY723nSxP\n/3vdHq7OYPNSi6vVMC/wMOpEE1vSoUbJXkiUyOu4kBdvTWryAZGhIQ9Nk8wSRcOcIfFO9oDpXSmX\nvivk9MkwxFXmwNypJpESk++8ucvzCIzgl3x6C4Kh1PebnFgUGKFZmyfQ9KhsAhbK0g+9cLbMGZX7\n+6Pnv8f1oVDJdod3mFOY4uTpM6yuSnp36sSpvAd/wIRpB02t1iLMtOsjLbN4Mjr+o2wcQ1XFYLzA\noacV/7XeAb0DOcTmK7V8pIwbOmxpU0O5XKCQp2IusSsbRxSPOVBhDpuUKBemwx9LxLpwjbEEutFY\na/F0UzfG4Co2MYkjqvq31WIx1wN2jSOp4YdglI01hlAbObzWAkur0mFXXjxJrDffcDziQPHUoNKg\n2pbagV8IsDr2xHcdPIVRbJZhVR40nkyIJvKbTSbDnIgRBCWmoxdSGxNqqus45NrC1pIfVr7rUdT0\n2TUumY0egAjl+7M4iuhuCW5fcAyufpeC72ND8VUUxvkUltQxJBowpEmUUy/jKMJTeMdJHRL9W5um\nR7DwBHQDdq3NJzibzMNm045Um/9epDBFB8IswSj+bXEEn71L586gg5nNbGYzu89m7AOMJowxO8AQ\n2H1gb/rONs/dfZ4z1tpjK5x5TH0Ld+ffY+1bAGNMH7j8QX+Of8Nma/f+2j1buw90owUwxnzPWvvx\nB/qmP8aO2+d5P3Ycv8tx/EzvxY7j9ziOn+m92nH8LvfyM82gg5nNbGYzu88222hnNrOZzew+2wex\n0f7GB/CeP86O2+d5P3Ycv8tx/EzvxY7j9ziOn+m92nH8LvfsMz1wjHZmM5vZzP59sxl0MLOZzWxm\n99ne10ZrjPmSMeayMeaqMeZv3KsPNTOxmX/vn818e/9s5tsfYtba9/QP0qn2FnAeCIAfAI//mOu/\nhPAQrwJ/472+7/v5BzgFfBV4HXgN+G/0+b8N3AFe0n9+4YP4fO/VvzPfztbucfHvzLc/4j3ex4f7\nFPCVI//+N4G/eS+cfx8dugI8o49rwBXgcXXoX/sgfuT369+Zb++fb2f+nfn2Xvn2PRfDjDF/FviS\ntfY/13//i8Bz1tq/8kOu/RTwt40xX8wFSKzF177hhXaTsupRZjbN+4/TDDIVknCNxZ32H2OYNhlb\niwhaAjgO2gLNJIwZjUWkI7WWgvbgN+pV/Kl0nE1xmPYu28NBazb/H7Dy3zZ2Djjojx6YPMfd+nfq\n21ar+cWTqyfy54/qZDoqk5imyeHYGAecqXxfZnK9AcdzMc6hWMlUQd5xnNzN4rLpCJbsbcIaJlcw\ncUimqjI2w+rv6DguURTlV7vGYWNzi4OD7rHzrf63TwF/u1CqfLHSnA5Y/Lcu+rff44c/fXef7+h7\nGJO301vsdEKRyCTqv7iOk4vWWAyRDihM5eagf7DHZDh4IP59L76dm5v74pkzIu40mUxyvQzP96cb\nIePBoYBLpVZ956nUh1vEO31g+f+j++CRv+11DnJJzPmlRYzjvO2yGzdusru7+46+ve+iMsaYXwX+\nOlA3xtBsitCGk4as1EX84tf+wn/IRx8R0ZdhNKA/lud7E0Ok4i4VN6ZeFlEP13VzpydJQuaLYIlT\nrBKO5fm3rm/wvVevADCYRJxdkpvkF372M5yYE7UvEw7ykeSxk5I5qmtpyUeM2CQhs5b/5L/7X+6D\nd96fHfVtqVzid37vNwHRk42nYzkSh1JRxGZ6nT1uXH9Tni85VEs6AmTo5KpelbkqhZL403U9yk3x\nVblazFXAjEO+QNM4JFT1JGst3lTUmiJ7HREESrJRrkxVqVa4fkPGBLnGpR7U+Mv/2a/de+fcAzvq\nXy8o8Iu/+remz7/tOufIzTf1S+AYXGOPPD0dL3O4QZojO6poyqraFBafqTiPQ6TPJ9bi6gHqm4TA\nkd+4Wg5oNmQCcoLLjTURUxqGKdbx+O3/+b9/v66453bUt5VKmW99W8byXHnjMvM6s25+aZFED+WX\nnv9jJrrOPvn5z+GpCH1qLZ7ew9bYfD6aY0x+KGVYpjunOXJMWszbNtrDww2MiiZ95bd+h7euXQPg\nP/31/yqfYIy1GGN47hOfvqvv+3422jsItjG1VX3ubWat/Q1jzD7wJcdxfmV6PjuuS3ckN9/67j4f\neVQUtaIkYxTJNbHbgIpIGnb7G4Qjeb7ZKIGKUxsnA1WGMoUSrapsKk9WFuirMvofv/AixZI4qLUw\nhxvogvbMdAwU2DhXTzcWrM4bSo1LliZv+4EekL2jf4/6dmF+4VeqZd04yTDl6aFhCHxRNJqEE6JY\nvsc4jiDV4YnjMmUV7x4PIoZ9+e7LS018q1MFDmKCphyA43FM5k9nVEGqM8bGwwmVgkovlizzc3K4\nZWmd3ljUmcK4z1xTNoXALeEag/Pgp1e867VbKFd/5ch/yQMgA9hpGnVklWTWvj2itYfjyY8+P92k\njWNwppmBTUimr+QYMt1QbWbIMj3IXEslkGvOrjRYWJCZeDdub8NULNs6H4Ry17v27fzCwq9MT5zd\njU0KelgvLi9y+9Z1AC6/+AKuPt998kmqLZGpdMtFettyoAflAE+DsYwj97Y5POiiyThXDffLFd2E\nJUPI8sFiGZ4va7LdbrB9W9530O1QLp2Yfv67Vu6C97fRfhd42BhzDnHknwP+4x9xbe786UZrjMNE\nM8u1rT2GKpM4Go6Y6IZaWmjBdNpq0aezvQ5A0RZp1uSG9l2DN51EiMXXSa0ZEc2SfL3V5TnOXjgH\nQG2ujWdlg7eRSxLloqk4qpPqGQOeal+SYp0U58FPAbhb/94BTjmOS6XcyJ/MdEigYwxWfV6pNen2\nxW+DMCQcyvdd39hm9ZQsoF63xzgShfsTOyFptgFAuepy/pyM+pkMY5ondBqA0yEaqgQgZao6PTRJ\nQ1x3CgdFpDoQbzQ6YL4lr+O7DQY6cO8B27teu2+PYk2+Wf6oFNYa8pv4bffjEShg+lqAaqRO166D\n5RDeSkIZReTi4bsSSFQKHqsrEvm1m1X290Xuc219I58mgHEefHjwnvaFw0PJdVw6uzIx5fYly9d+\n9/cA6N5ZZ/6krNEXv/1NyiqH+uznfpJvffVfAXDhkUd45OmnAIhtCgqBTYZDApWOfOvNq4Qa4D37\n6c8QxnKIBa7H+pZkWrt7e5x/RLSst3Y22LkpY4te+Bdf4af/7J8DwDgurutwt9Dre95orbWJMeav\nAF9BQO2/Z6197Udc/l3g4ff6Xv8+2rvw78y379Jma/f+2cy3P9zeF0Zrrf0y8OW7uC4xxvwVg/l9\nJz/lDZkKF9/Z2mfvQCYvxOGI8VCiycYJn2Jd0kzHVBkO5fTZ3OnQ70naWy0FzM3J6VYqejmmMxoP\nZbgQsHhykRPnBWw35QBfsUbHlkkUyySKQJXU0zjJZzmlJKRx/IGMbL4b/x5Z2L9vj+BQ0wKYMYZU\np1TsbG/zxhuy5vd7BUoVwcv39rfY3JETP5xMGI9FGe7qlZjJRKLbctXwysKevmmfldMSGZ971Gea\n+LcKZQpNibgmoy6JI7+F8d181lWcjIlSEa8ulAqUa+W7Htl8L+3drl3g949Gr9PHR587Gt1k9jCi\n/bdt+n1NLvBtzCFGaPFAZ1OZdExn6wYARddw9rRMGblw9hwLbRliGk3GXFuT36wzTknM9LZ2cJz3\nXpR7r/ZefDst3iaTkK//4VcBqPlQjCXjSYYHXLsia7Fze4OgLuvvkacewQ71msmIWOfUhdZOA1re\n+MFL1Kviq3AwYqRDHtMwJJte77kMejrMdb/Pxlu3AHjtj59nXmHEOy9+j9fOS2Z8+uHHqNeb3C2X\n4IFNWLDWfjnwAzxnClwb0M1ga/+AO9uyUBYaAa7ir9FoyFxBMMKg3KBSlRt9Y22DnZE43bUJSysy\nYaFer0Iq3u30Bjjq3BNLy5SbklZvHXSoKaRQKRbxdbJp4voECrC7QKzD2dw0I4kjzDGesGCt/fIz\nzzzzdqBfV4BxHBIdU1MqlClVdNG8sU57UTBdx1skTnRjdipUdRF7XgHryMbZ74/o67A7x7HcvCnp\n3daGzxd+VqYN1JulfKxNYEpMErkBwskB475AQ7v7fWKFEL2gyFz1zLEffGmt/fL8ybO800b7b/zV\nj0krjzBajjBd8oeOD/m4n4xWRdZeMtxnQadFLy80sFZ8vb61xfq+4u0UMNP6BfCBoLTvwqy1X370\n8cf53f/vXwIQr6/RuSYF2/pii7k5HZJaLbLVk5R/1B/kh9hXf+t3Gd+RtdjZ3OaV78lQy8RajE4V\nXr/1FmW9t2vVOkPFLO9cuYLRordfLzMcyAY83O2gsRj1dMxiWwK5OMl49Q/+OQBba+t87hd+6Qg+\n/+Nt1oI7s5nNbGb32R7suHHAKrfSOC6OHueDScR+Jif1w2fOUejrmGo3zUdROxZqyigolct0dqWK\nXSr47OxJ5HRjbZt6VU6f4XhCe0GKBY8/9CjtOYl6B6NtyHSmkm1hdMCd4xuYjiE2Ma4vYZdjoOS6\nuMc4op3a0QhqGmklUcz+TkefCzh77mMAvPzq91k+IZS6drvJXEui2OEoZBLLb1Fp1ok1/JxMQiYK\n10zClMCX9zrorvHWm5JynVmV3wzAq5YwXfkd97dvkyiTJOxm7CeSjTQaPdrlmHdVvv0A7Si39Z3M\n2rexYd/+Ohpx4ph8UCDW5AWw1IKDZAAuMYuLWqjcjshi8Z0lZWNPIrA37+wztkpxNBlFZxrRWqKM\nY+/ebn/AH37rewBcDGIenZe1WCu4+dyvil+gVZC1dXscMknEV90fvJJz54uDKuWh+C3NItBhi9Vy\niSCT5+24w6Avr9kbDAkVRmidWmF+SfaLG29dpZjJuj+z3Karr1lwfdwD2Xdu2VeJfvrn7n8x7L1Y\nrVZmcVGc2Okc4Ouwu8//9Gd45rOfk2uqhkGsEzHDkFirgsbNaDYFCnjowsME3pSEHzEaCv7X39nH\ncZXqZVysDmErOD6BI5trFsdU60I7Orl8jjjSdLjgE+r0182tG/hKnSmVSvhBCfPgWQfvwY40KWgK\n39nvsbsjkEuz3SCO5LAyjsvZc+LPxy6s0qjKb+H7Dt+/JNff2RxCToR3mVuUQ2w0jjCKrz/y2Dnu\n3JGBm1/72gt88mPPAFAvNxmP5QCsz82RKtPDrzmsbwiTYf3NTVrFBdJpY8MxtyNsocPnftSNZuFH\nJozOlBVgMXqNweQ4QppG+Ipvtyoenk7BLZaq9HpSy9jZ3+fNm/I79cIU35P1HZBw8bRszEkGl2+u\nv7sv+QFYFIbcviMMsNMLVRZ1zQ0DD6MBkjsYUtB7cKndptKS5yu1JibQtVupcnJVGC3FYoFEgwTX\ndTHKsI3DiEfVzxkGO6WSehbPyoa6dukKpidQZuhAV6ds1wOfiid7jYkiosEAO2V4vIN9GHaPmc1s\nZjP7UNsDjWhb7Qb/9V/9SwC8+urrFItyUjz36c9Qq0uklUY9JtPR43duU67KCbV8cglfuXDz8/NU\ntHtpf2+bnS0Bw9Olw+jC87ycCN/t9Si15L0wKYWyfG3rxmQaOcTJgL0DOcX2Orcpl6VIVq2dxg/8\nd275OxY2LdDIKGsAbHrY6eUHbG3t59ce7EuU+c3nb07rkjz3zCr9nqRT3c6QUkWiBWsNifqzUnLp\nKIwQpTUabfmNujsdOtvC+bQNi69NIu3GIn317cb2HcID+X2zzGF3c4ck/nBEtPfKMuVpGMyRKNni\nopxOQk4vSaHyzHKDW1d+AIDnSJEX4MqVqwzC6Vhxn6or6/jRc6ssLQskdOnabZLs2CMHxGHMlR9I\nVnTqwjJnFiRzKtbq+G1pxCiuFimVJBtOiw2sOYRZXC2wR+EIRyG+oFSmpo0xnudypAWPVKNQA3ha\nUIxdS3dHouq5WpVkuAWAkyb4hcOOVE/Xqtvf563vP084Gt7Vd3ygG63rOZx/SGhW84ttisooKJSq\nhz31JiCdzrqPQ0Zjwf8GwwqBP73GUC7JxhmWKxwU5Jpu/w6NpnSSZRhCrSiGUUw4kY1hbr5JXfHI\n2Ib4pSlGljCYyGaQ2BED3ew73TLlSiVvjzzWNgWrjHO4mByHA8Wkrly7zW5HUh/H8fEC2YC7g4Rx\nT27yN67s0R3ITWuxxNo373suU4mCUhAw7XVMophioN177gqZkQNzbvEkBe3SAQsNWWrNZpdmTboA\nG/UmfqWEp2nvvwv2NrmMH2lufpGDYtompah34+pckycuCpMjYMKdVA5Ek8YMh3Jjh+k2bkkwxVq5\nzJNnZUM6e3KR24rJ39nY+kBoie/W4ijK78+Xbt/h9LnnAHj66ecIFmStTOKYXiT3ZDIJydJpC36M\nN4W3soREA4xut8f6+iV5/Tgm0r9tNJpUKnL/7+3sEIfyS6WeYU73goN+H6OQQi2IKWYSPDiRQ6Ka\nLKQJ3/761xkM7q7hZgYdzGxmM5vZfbYHyzowDp4nUVSpCL4+LhSK+Nq6OemNc/WhhYU5qjWJeq3N\nyBTEdhzDZKKcutGIbk85tX6QRxIpkGlUl2YZ/b6cPJVmiaAgp5LrBmC0lTRz8LXf3wwPuXHdYYfa\nsEyaHe/01qZZ/n1d4+Tp1GQc8e1vvAJAakt0D8RvcRKxv3+gf+2Q6Yl/7dpo2gqO4waEY4kWCiWP\nWNkIw0FGrK3O/d6Iel39WZzn9pb4uVrrM6/T7ltzTVwV/nnymU8exliOSAD4Glkfd/th8NHbBGWO\n2qEE39uLZ+R9NLjGUnRlrbUrASeV1/zQygLNmvhkb2uXLFEGggNG12E86lGvSlH3xGKThbrcJ93O\nPjduiajMKEzAPf5smcxm2ETu+ZWzT+KsPgrAsNAgGcvzw8GQJJXHrXYjj1DjKCHRuz6zGSgn+w/+\n+Vf4xtdEqKZWbzJRXvxnPv0ZHn/8cQC+9c3nGWrDQmYNn3tW2ndXTq/ilcX/5ZJHx8o1XpwRjGR9\nT5KI/QTI7q4Y9sDpXaWSLAhjPOJINwbXoEVy0jSlpNhtsdSm2RK8xnGLZNMvZaGrnWTb29t0OpIq\nFYoV6g1JY8fj8ZGbwCWOZEH3DgZEUwJ/4OSpVZZBqSxpL44hzcS5k2jAxvatnOZ0XC2JYyYD2URL\nDQ9Hb/IkDLlwVjrAkqzIG5cEo+3srfH978oNubzyMKXiISl7Wv12gwKZYliDocWqWEkcxxRUxWjU\n79Csi8+L5RKdDcHLB50+n/3cYwDMLzj5IfC2Xv8PARrzo+ydMHtjzA+9xgCufvGAhHltRjjRLrHc\nEAilVS1iploVDsxp5X08HhBqgDEYxTQL8jpVEzEZC6Sw1Rmw25c0HC/AM+aDEER6d2ahqPvCRz7+\nMRp1YcNMRiP8mqxL4xhS3S/u3FnPobFatZpTQF3PZWtLsNWvfe1rPPfsJwA4d/4h9vZl3S8vL7G4\nJP78yZ/+Ap5iummW4Sl8mSYLeKcvApA5Pp7SxNI7NxlvCYsjTWN29rt3XV+YQQczm9nMZnaf7YFG\ntBiLnRKxvYxE04UotiSpgtJxwvyc5JyTyGEwEFjAC9KcL2uszYtnQRBQrckJWK41WT4h4Pnm5iZV\nBb0d1yOJp+GTT6L82jSzGOXjun6JWlWqlPX6Pr2+cBSzLCVJk1wB67halqaMtCLtGpexRj4bN+9Q\nL6kSmetTURZBFPeJpz5PojzmaTZXGQzl9O93N/JINMPiaAWsVCzRrGrrcrrD1vrrANRbVVpNSbnm\nli5SLk+5zvGHhLXxTnaUI3AEFphCBG/7im//vke1TqftoIv1Cg+fljU33L3Ji99+AYDqF75IS2UA\ng6BAWyUQh12HekX8OxwlDAdaBN66SZadBGB3kBIbiYwd15Fi2zF3vbUZrZYyBPwCE6261qrVQ56y\nMexqtLq7vZVDBBcevoBXkQzYdQq8+N2XABiPIpZWhA3z9W8+z8svSWvuz//8zzPSvWBta/toN/Rh\nEd5aCgpnzVWqVJWl1Ko0WFMIIphEUsS9y3X9gDFaQ2ammEhGphut57ns3BZqRTqMWTklwg03N/fZ\n2JDnB6PHUnvBAAAgAElEQVSE6Yo5ceKE8DqA2DqsLIlD5xYWiJQiU6yXKNcEChiOhyjnm5VgWUY3\nACab5Gmy7xSoVxTzWs4YjyUl8HyX+fZ8Tgg/rpZZ2NuQwyEZhmzuyGa5s7bBwb7ALE6hhoN0tnz0\n6U8RW0mhSqUi0UQ2ac9zabdEUCkOM8YjYWIUgwLNhvin2Z6nvSCPB72rxJFAEMmkSxzKot/cXOMH\nL0s622zWaTXkvU6urhIUxJd32yd+HMwArjmUH5xCTo7rUPVU39hGFJSCmOGoQjoUfA+j39X3XBbq\nkg7PN8osaRfUjQ7sabfj7dvXaTREPMb3XcpKa6qVA6ollZ5M4fZNafz4/osvs/byGwCsPvEsnqOa\nrGl6zMODQ1vQLs44jPGq8n2DIMgF/m2aYTXNL8+18sPK9z2s0SYl63DQkbXueQF9DTyuX7tOV6ED\nk2UUCtODyGV3V2DH4XDIJJTgJCiVWJwTvHx+scG8CiWFY4/LXdkXFpKI0tzS4ZSSd7AZdDCzmc1s\nZvfZHix0gMFOFeUNmFTT/8xntCEV8HFnyMWHPwJAe96h0ZTIoT+McpJyuzWXk+pHa+vsKkC9vLiE\n9acKwgmJgtitdguj1d0kigh0VplHQqqfwWYOpUCisbTcolyUE61Sr1CuVPOW1mNrFgJtRRz0h6zf\nvgkIRzEaSoppw5ALkmEyzqq8eVOuL7iWsfKG+/3LeK5EXONJj1Cru+NJnF+zsbUOl7SImPSJVTzd\nGJdCQTKKrfUhd27Jad9qJrhI9PX0MyHPfvKxIx/8mOe1UzMcrl1sXqwqOSl1bRZYrPqcXBXoygnK\n+Mq0KPheXp02NkO7uwnHQ7o7kg6nSZKzL27cfIvTZ6SNtl6pYpUzbpwM42kG5hnmNQpcObFCjwN9\n/QRHW0kNHvGx1+8Srvc5lR90XDNNBMiy9FBC07GcLsk3qZt+3pAztg2GyHp1sSyviP8vXb7MFBcY\n9vtkChdGcURfC+kH+z0uXboKSIF3Wmh0Ap/hgkA3F6ouQ528Mkp9EivR8CQ6YH9/i1iZEO9kD3z3\niFXn1WAw0yaFzKGiyvEH/QMcKylCq73IeCyLuFR18mGLu7u7Qs0CTp0+Q68gKVdnb5OF05KiNssB\nvY4svrMnTjAMBd/d39nlxlVx+vmHz+FPF7FnSVR44qC3T7Uuz9ca1Q9FddxxoFmWm/DWm3d48QUR\n6QhxubgsN+2Fxy8SaPr1W1/5Xk716vbusL0h5O7RsIejuHWtWsv9nKRpPg8sibOchRHHh6NZKrVa\nPm5k2N9k/Y5sHM3mSaqFqU7tbc4/JFDPwlKN7EOCHmQ4ZIo/uWlEW6U2x5tX2NS++LMf/yhzSsvy\nC8Uc5zOOg9EU0zEunsIIYcEnjLRppLub+3E0HrG7I+yNgueTTbUgMkucTEfiGJyCrNEnP/osWVMO\nsrW9AdnRoOBDsHYNMD8vzJWl5XkGKoGKaZLogZbZiNq+BA+lm98j1gai8dMLRIFKKVrDpcu6cSYZ\nbjCdWJGR6Fa3vvv/s/emMZad6X3f7z373W/tW3dX9cZuLsNlhkOOZolmPLLEWIZsJ8rEFiDIwDjK\nFwNy4AAWhHww8klfYlkBAjgCjEROHFiK5VgjSLI8Go1mOBuHO5vsJnthV1d37dutu5/1zYfnvaeK\n45lhNVnVrJHvA5C8vHXuvec85z3P+6z//wZ+XQzz5tYuuw2xEVmcoExfo4pjWobuanu3wZZpH6U6\njh3I/XIixWarS5IOYRKHMpShDOVEyIMvhpkqYnOvieqLnXcsi1pRiisr2Trb2+La18/P0zSA0Ts7\nWxSKprro2LTNAEKlXKU8J+HCK99/HuXI6OLM3BTb96SQtrq0SMVUcbfX1vjuX/5HAC49/BCf/vwX\nAJg+PUvPAALv7mwSmOp84FtEUcIPMj2dNMnSjNtvC+tvq9Vjfdf0/mUx8ZRUrT1bcccwpG5sxSSJ\nRAJjVYuJC0/KMUWX1VXRPwr6fSmwvXvrRXpdM75rK1ITMs3MPESmxbt959rLucecJH1On5bvrJdP\no4qi27DfZ3tTUhmT01W0PlzD90ctCo1vmHwfXphi3jApN4IdCoHkY/xiga010a/n+xRNr3GxXMMe\nIEx5gYQfCB6HZ7yxQlCgWpU1GmdJ3g/qWDb6QK9mwxR42r2IAd1dP85YMaPVTqm+D6SemTTHEevi\nOKRUEj3UalXaPfEgszTNq/pKgTLrLOnvYHvS9UKUYhmoU6003Z7oIYpibEfW4ujkBN2mAfhG0Tb4\nBO1em6oBufddl4KJQLpRSNlwEq60knyNqmibtmHinckUdnz4yfwH3N4FjpnKarZapGYCS7uKybKE\nDucvPUKrIxdTiGJGx6Tto1Kt5cMOm5tbKKRaaFnWfo7Q0iwvSXgxNzWW58KSsMd4TTAWeo0iI+am\nXnv9LVrGkF9+/GEKRQmTozRiak6AOVwk7Dvp7UmWbePVxaBWa4qRcUGp90kom/aX20v3+Ddf+fcA\npGqByUnRyfSEJjA4vjOnLzAyKmGo7xdZWRbj7VtPMT4m96g6UqVtjO6TT34SbTo9Xnjh2/i+AemI\nEqqVcwDMzZ3FL8jDXy17rK+Jgb9waQr7I6Cx+SBi6ZRHT0ve/jMfO0NjWdhZu0rjFwbYGS59M8k0\n4hfwDJZHsVhEmXA+TWM65hidpvgG4MB2bCqmS2a3uceqMdgFPyA0+AYrK+tcuy6/G6YW5x55HAC3\nVMU30J+ZchiQuWoePI3NB5Gg4ON5A0r1LB9A0FmCa9COEg1RIBtRYeo8qZl4S1XGgGRcK4Vv7Itl\nSzcNwM988fMsLQjxYrlUYOmu1HTu3L6LbxyoxAtoGicw6rRoBnIvVqzVAVIohYLNxZLYCMcrUy0l\n2O6w62AoQxnKUE6EvK9Hq5Q6DfwrYArZJH9Ha/3bSqlR4PeABWAR+JLWevd9vi0f3QyKBboGscct\nF1GeeAXj5TKNQVGqtcv4qBS3KuUSgSlclUpligXxwFqtDh2DWD85N8fSTeknXF/byKu+juMSmRFF\nHUZcXBBPa6y+x/qWhMl33n6XuiG7C+M+aV/ClLIbUB2pHUtR4Sh1azk2toFDbOw06BiPM3UUgRnc\nuH1vmWs3xdOdm/UIgscA6MVb3L72KgBXb9xgzDSPnzp1isxwsP383/x5JqbFo23sdfNCZqVSI0nl\nfv3sz/08Sg2GFPrUDLHm9u4uN25KpHHvXsrmuuj5Y0+eZ2yiemxe11HqVymYMEWUnY1VXnvlFQDu\nLW9z8XHxdMdmpyga98cKXDLjFWk3AAMqTxbnPbWWsx8pyZisvO71+vks/8b6Ou9clTW9cm+d5TU5\nzZ1ewuQF8WjrozXigRerYUB0qHUm3HyHZRC8DzlK3XquRdUzfcZJiDIFvzCO5fyBJEnYK0lRd/vs\nOK6J2bVdwrMG2CWaqulRnpycYHRU7kutXM3Hd7Ms5vMXBR3s4ulx7K6kxoLCGElR1mtEhGPSDhOV\nIqNK7JRnw71EbNAr3/pzepZFeoQDCwnwj7XWryilKsDLSqmvAn8f+JrW+jeVUr8O/DrwT37sNymw\nDevZ6NgYnjYAJPU6GjESOtHUi2IY1po7bKxLnjXwa/iGSNF1fIomHHZsh14o3zO3cJYskTzaxsYW\np+cX5LPFMlsbEq42d3epleWzBS9gpCqKqhfrlGzZBNJezNptCZ/b200WLp0j6oeHUNV9y9HpFnJ2\nX9e180m75Z0d1rbN8MLOLuWy5LOV1qwsi9F1XJtmQwxBL1xhZUnCrBvv3OL0Kcl5+8Uqr7z8LgC3\nFxcFOhKo1SbpmU0siXs54WaSxHzqU0Kbc3dpnZe+J10Q1Wod5/wlADrtiPHJH035cgRyZPrVWFxb\nlHBe93bZ3JR1Ftkj3GnK+a9nTYqOGAzfc6jVRKczo2VqrpmqszN0NiDCtOl15SHOsiyfgup2u3lT\n/d7eHsuGfaDdDgkNZkd9dBKnKKF0HyeHB7QOkELqTKbCjmmq8ch0a9mKWs10+VQKbDdkHYepJurt\n42soZfL/yqE/+KxWWF3pHHBsl9ExSaFUq1WmDS5vqeAybtq1tra3OLsga/qTj12ie0MAl+xajVVX\njPSN23dy5ujM8WiabieiHj3TKjkzPUlc0HjPHw60531TB1rrVa31K+Z1C7gGzAF/C/hdc9jvAn/7\nUL84lFyGuj1eGer3+GSo2/uT+yqGKaUWgKeAF4AprfWq+dMaEkL8+M+jcA2nV7FYpmsIEKM0wQtk\nl+8nPZQpbtXLRcKu7CaJDolb8rq512J8UnarguszatB+yvVZZsbF/b/x1huUTdGr2+uyvSW9jv1+\nSNF4zI5bYGpKdsBSsUhoem277TAvJ7aSLjfeukn/eDzaXD6sbnUGtunzLFXLeeSwvLrOi6+KPl3X\nZ9QUzJK4x8pdQe4fmVggMLrKsIgis4NnBe7dE37755//LrWa9L9Wa7MCSQc0mt0csyJNU4NmD65T\n4M03paex0+7i+QOuMulIACmYPahizYfWL7BuwNGtzMMZkwZ7W7k0DVV7c6+LYyD1lNK4OxJpLa9a\nPHpa1tnCZBVtCmNxktHNgaNtVjel2r7TTnhyQcagz0xPcvbMAgCdUHH1lhl3dgLKNQmNewdYFDIN\n2SB1kGmMS3us8mF1a/seoan4765usdsyGA5bi5RMt0axVMI1qTHH97AGPc2WdQBsXeWRwMbGJt/8\n5ncAWJifwjGpyWIxYO/aiwD8+2+/yZIBuW83Guzsyb1odjt577LGoloTb3ikXOC8GZp44rlP4ox6\nBP/v4SA+D21olVJl4A+Af6S1bh6swmuttVI/vNFBKfWrwK8CzM5OYiGutu8VcXzTqtJuUXDkQQyK\nRfqm/aLgeFRGZIHGjmLtjoS0u1urOIFpRrb3ofz6no3jiqKnZ2eJDTngxspdWuY7K9UamNYQ1yvk\nOeNev0fT3OAoSQlMY7LWirW7GznTwHHIUeh2Zmqa2DRP25ZLvSabz9zMKXYMjUypBIFZiDvdJh2D\nY5Bu9QlMPnFk5DQjNdnEiqUqa2vSdfD6699ibEwMbalYpNsdMOLG2Ma4xnHI/GkJy3pRl6v3pEI+\nNT3P+fMCO+dYLlG4az7bfSAQfkeh33J9DG3SIpldyNsUpXlqACqTkQ2OQWHwR4jCHvMz5vdsNw+B\nszTKjWCS2XQT89D6Y0zPnQfgwtlZolB0vRe5dAJ5BnaarXyzs/R+m5E+gIObaWVgKY9Px0eh29pY\njVSL3qJEs7YsXQHf+JM/wzVYJI7rYAWydkvVCnUDhzo6Osr0tNjyYqnEay9Kiqq116bsiz6btQJp\nWwhfx+bO4BtY1e2bV9mxpQaUxiGB2QDLY+PYBkvBsizUoB2PDMdMiUVJiKMPj/V7qK4DJSvjD4B/\nrbX+d+btdaXUjPn7DBi0kh8QrfXvaK2f1lo/PTJaP/SJ/eciR6bb+siDOeGfMDkq/Q48/qHsy1Hp\ntmh6Vv8qy2G6DhTwL4FrWut/duBPXwF+BfhN898/fP+f0znXj+s6eIZ/Z3erRVqWqmCxVqdgRguz\nJMFSgzHdlIIJFwquRxobnnZlkxpusNbeLlYOmKzY25Mk+fr6KkXTDF6uVLBNlVK8kgFp2/4whe/7\ndAYU5q0WvV43/42jlKPUrUaRZqKrOIVWS8LQYsHh1JRgC7z97k1cRzz4SrlOry/6ieOIXl+eh+np\ns0xOyy7/6Mce4RtfF+/i9rtXOWfm7z/3uce5uyye1fZ2j5oJYdvtJp94SirhMzN1/pff+ucA9Ho7\nnD0r4OMbG9tsrG2Z92OS2DqOojhwtPpVB/6t5csP/HUfMlHnx6j8GH0AyUtCXYO7kSWkxtfZ7SYE\nNdHRZDWlUJKIRDkBSShe1Nr2LjtNibokejHwl/q9Hqt+z3+Px5s9at3atjxfoyMV+mYooDY2StyW\n1+1WG2VChFvXb+U99a7rYlsGqN5ROZNKmik2zdDHdOELnKuIN9xqbBMaffm+hzcgynSLecol01nO\nv5fGGYkhCsjSmN682JHNOIRGnzA9HPD3YVIHnwF+GbiilHrNvPcbiCJ/Xyn1ZeAO8KXD/OAgn2fZ\nFsWCgcsjJjEnnGHl4CjK1flURtRuUrQkFJgeHYfygMfexjbtXTg2mcFS6Mdxzrxg2Q6VsngkjuPk\nitNaUzTTZjqLCUwaIcsy9vb2zLlpyuXKoeHQ7lOOTLdaW/QNK2ov7LK7Jw/k9etX+MyzPwXA7PQp\nLFce4CRJaHcNG0K7hWWMwq1332B1dVHe76yzZhrnLdsmjA2spRtQKEqFdsqrUTUGwtpcy0Ou7b02\nfUN81+2v87Wv/an5rSb1shhyjU8UqWMztBzx2n1/UT/0pT5wgbZlocwzkCURoUl7bbQiQoMj61gJ\nsZl2Si2XbiKv17Ya+Wx9hkWaHkhf5Ib/gU0wHpluFRnrO9LRokKfelmew/rICC1lGKuVS8V0ujQa\nTQbBeJpq2m1JC1roPHeLbdNoibP0b//Dd6mb2F3XasxPi2OwG9vsdcRGxGmWM6+kWhPHgzpFhmsM\n+cOXLzD/MUnpbEUNEjTpIcE63tfQaq2/xY/eFr94qF8Zyg+VoW6PV4b6PT4Z6vb+5IGO4GoNaTYg\nTIzAktd+wSYzJIlxmpIaz0nJvg1Aa2OH5XduATB1eoay6YuL+z3sQfOyjnMPuNNq5ojpxVIpr8In\nSZKjgFnKyUkes0xjm2bzVquVe7e+7xNHMZZ1smcZk1TT7Mr1drop3a7osN1t8+oVcTieeuKzXHhI\n8Aeu37hGYdCH2e/lcIhh2KDdkl1+a3M515tfCLhxU/S/ufn7nDkr47ujozW0SVl4ruKdG9Jp8MpL\nr+edGo5rs7YqAwtKWYzWDYV032ZvD9Kjz8ocudy3n3ggK3VQ0jQl64sHlmmLpqG13mrHpJYBl9cZ\nfdO0n1g+qwbHYKvZJRtgCFoO+xR67y0pvmdc/BjDhaOSwHaZL4qXWazX6K/I9QaPPsZVg9+RjiiB\nmwS4/jbJAAVQKdQgLaOsvI+7Vh3NR6C3t/fYNPbF2mzgGz7AX/zFv8O/+N//TwDuLa8KWDsQlIpc\nvHRBzqcQ8M5VYRDZ3t1mZ0eiN7cATgzqkGv3gcMkDhDT0yykbxacFyhSJQYv1RHaMKzGUQ+ViYKu\nv3GV6y9LO9KzX/gM1Tlp40rTmMQ80BohZQTodNp5OBXHaf67YRjmN8lSik0DR6ezhCTZJ2AcEDu2\n2216vV5OBndSJUsz9lpmhj6zePLJTwNQrdVxrUF+0CU1E0rdbo9q1aDaR33CaLDhxCijf02WV7aT\nOGavIzndyakpvvT3/isA5hdmyWI5fm11M8cPXVpa5O49gV50UydnPB4dnaNUNjndVkizmfxEGNrD\nyA/a1oMmLjbOQxj2c5CYUAesGSbibpTkUIpZarPTkmfj6s1Fbt6RPHmEg2UPZv/30wUZCkvvj4bl\nqQqtfyIgZQLLZb5gWtW6fZxINvrZqTO8JUuIs+cXKBkcg+89/3zuRFm2zaDBSyk7x/R1HIuz586Y\nX8i4clWobAKtCDtiL968eiOfoASNNgbY0jFl30w4RhHabHqvfv912ptyv/6HX/vvKZUcvuJ/9VDX\nOMQ6GMpQhjKUY5aPwKOV3TxNY6JIvM8wapEasvtUT+O5pkCFIuqa8DNTFEyxwE51PhLbaTWJu4OB\nPEXLENb1ul06BvUojuO8ayDLdD726PtFmgbUt9tpUSyaJHy9nnvAxWIR3/dzQOuTKplOyRIp4JXL\nVZ791GcAmJs7x7u3ZNS2290C5NrjKKZQkMLY1NRCPqyx3t7LvSClFKZFFsfzqFbkvviBRdeMIm5u\nbBOae9HvZ7meo6hDv9cy7+u8B7dcmcAxGBRxoonj7D3For+aovKIKIxiLFPp3uslrO8082PUIBdg\nWaxtyAz+2vomvcHIru3tN8lkCmWYQsg0Of1aluVrV7poMk46+neGpmki0d3GLkY9XHvzDW5ffwcA\n1/Hy3vA0SXM/3VIq7+jQWuc1wUqlRLcrHQhnzy6wuiIR8Nq9Fd7ZE52/eeNmHiForDzl0u9EvPhd\nwbJI05haXX73C3/tMzz+hOCDTM2fxnbAdg/HJfjgDa1ZcEkcExm2yyjuE5mV0gv7FH0T6mqbxCy+\nmfk5xg3b7dipcXZ2pEWo3dpFm0myXj/Mc679KGZzS9IClUolZ3ZI02x/0Yf9/Tn9JMZ8lH6/T9VM\nm/lBQBSGJz5HqzUUBhBurqa5J/gGUdRFm8ptnGa4Azi6NMwrtI7j550evu/lbW627eEYOLpabYSp\nScmtFgplvvE1ma7x3H0c1YnJKWLTdre5vgaDthvLxjU4tSiLZLAG0owwSn8S0og/Ug7mQ39whai8\nwSrdJ5RRitTcj629Bj0zCKMsex8MRlmERkeWsvMhCKUV+TK0rHxIQXEwXaDQpn1J3jrpSMrCldrs\nG8aEzM0dgIlyn6fOC9bvyuoi198SA3kwjRfHUb5+LMdjsN+Uy2UWF2VgptfvUq1LD//25hbFmqz1\nqemp3NFaW1vPmaPBQhlrn2Yxpww90Zf/u18hMNNpSRqThBzaSRimDoYylKEM5ZjlwXq0OiMyFf84\niTFj4URhmPfRhhE5cLHQOosn5E5WKBpq5navQ2tPEua9dgtldvA4SvJK925zj56BYRwbHyfJUeoV\njqmkC921Kc6lKcr06Vq2nXsBaZrQ6/dzL++kimXbpFr0s7nTodsxu3/WpT5iwNabGbffFSSofhhi\nOSbMD0M8E84XC6W8M8S292fKozhm3RQOS8WIXk/0USnXsZWkFIpBTGCGTQ56qgep2vthh0pFenAd\n1yVNk5/o1MGPAoRXCFkggG+RM0/g+LT7ot/13d18eEFbDmnuueqczjyDvKdAsT+coHWWdxRYWZa/\nn2lrv9NAW5IG0ic7GgNyYHTLdigaONTZuZkc6nB+Z4cNk05pLEznRe8oivLhol6s8/5jdMrlhx8B\nYLfZYnlVhheiNOGvfVYKxfNn5tjdke/sdkNefPFlAO4s3kMz6IjyeOyxywCkUY+miYAdy8ZRFvqQ\nnGEP1NCmaZYPEWhSXG+AOp+yZwAdurUeUVmMoq3SfFAgdSx6oWFkjUNCMyUS9kNcQyDY64d0DDvD\nzk4D90D+ZHAzXNfNW7eUUvlD7gf+/vCC1rQM7oHWmiiK87DupIrWmp0dydEmGUTpgFLGB8OA4NgO\nt25JOBUlGZ7JecdRn0FwExTKOb+9Y7u5/pWy8pAtCnt0Br+bJWhM6qbXoFyWsK9eG2VtXQBp0izJ\nSRjDfgu/IHgIrlei24vynOKJFr0fJh6GbcNSGf6AsVbbbO7KxheF/bx1q93p5+1I6kCIr9A5Dut/\nchoDHIMDsIpaZwfSBZoDTQcGIvFkr10F+CbfbAUeZYN7orKAJBHnYXq8SnpO0ghR2NuvBcQRPeO8\ndTs9Gg2xI2/c2qDdLpjjM5qG+VZlmldfkvzr1deuUCzJMaVyicC0j3mOzjGDL86fZrIu57B652YO\nSFUMArAcyA43GTZMHQxlKEMZyjHLA/Vo4yRma0eKNEHBpeIaxK4goLsiYen66hrjVUHj8b19/DfX\n9cAUtDY3t1helAZ4HUf4pjFZ2Tbd/iA1kTI2ImFHGMU5D7zrujlPVb8fvmdmeuCxNff2cEyoVwgC\nSqXCPr/8CZU01XSNl6+UlXs7XlDCdoynbm2w0xA9e34RA/lAkvaJE9OJEZSwBikUy80pxg+K5wc5\nSlqapfS6AzK9kH4onkMQ+ASD+2KRI3yVSiXOnpNQzPULRLH+CSmG3d9J2jrN+2V7ScLyltybNcvK\nx53jTJPleAg/YsLh4Bkc6JH90a/f6/X+JJCGWWTUXOMZuhZkZhQ+SfIidhLbJKkp6gb74/BKqTwt\nGIcxXTOOWyoWee36PfN+l6mSQUbTKbojazQC0pZhtXAVninCP35+iqoBEZqaHMOJDRNMUsg7nwLL\nwXetQ6v3gRraJElY3xTwkmIpoGvyHbab0DctWivtZeYmTgNQrfp5G1e/32VnU/Isi7fvsHpXYM96\n7TYlQyxYrY8QxrKgi+VKnufqRRHOgZalaICenmV5GNhoNPJBBj8IqJg0gue60mz+E2ANAkMSWCoV\nsMzUXRLFtE3L2/j4RA7R1+s16fXFQCplUTYdHbYF2sC/OU4hD0mTLM47B+q1GoVA9JOkKm99s5TO\ngTncQsbC+UcBiKMQ38AwfuzxJzk7L6kDmxDHyfgRSHonTgabNdZ+K9DB/PJ7IQIzMpPr1somMVNf\nCdaBcFPt51zRh2ZUHfxu3saVaQaZrSzT7zmnn4j8t4LADCOgYXAxCcKQDaBdN6/jxLGTT4NpnYFv\nNpYgpWYILscnx3js4QUAWs02feOEpGma460kcZrfM8excgfM8zxss9aDwMf3BxjaJQKTOvB8H8/z\ncA7pgJ1sN20oQxnKUP4KiHqQO55SahPoAFsP7EffX8Y53PnMa60njvtkPqicUN3C4fR7onULoJRq\nAe981OfxAzJcu8crR7Z2H6ihBVBKvaS1fvqB/uiPkZN2Ph9GTuK1nMRz+iByEq/jJJ7TB5WTeC1H\neU7D1MFQhjKUoRyzDA3tUIYylKEcs3wUhvZ3PoLf/HFy0s7nw8hJvJaTeE4fRE7idZzEc/qgchKv\n5cjO6YHnaIcylKEM5T83GaYOhjKUoQzlmOVDGVql1HNKqXeUUjeVUr9+VMcelyilTiulvq6UuqqU\neksp9Wvm/X+qlFpWSr1m/vkbH8X5/aAcVmdD3d6/DNfusZ7rULc/KIPxvfv9B4HnvQWcAzzgdeCR\nD3vscf4DzAAfN68rwHXgEeCfAv/jgz6fo9DvULfHp9uhfoe6PSrdfhiP9hngptb6Xa11BPwb4G8d\nwbHHJlrrVa31K+Z1C7gGzD3o8zikHFZnQ93evwzX7vHJULc/RD5wMUwp9YvAc1rrf2D+/5eBZ7XW\n/3hPZmAAACAASURBVPAHjvtV4J8AVcdS46WCzBDbjotvcCcd2yJNB+ARcY6SrrHyuedur59DI74X\ne0Pt43VaKidVlOvaB9rAXKfn2dgGki07gNRuWVYOhZgkaf7a9VzSTBPFCUmaPTCEjsPo96BugXHH\nFbAN13Xy6/V9P4eUC8Mwx9XVWr8HJyVfB0oR+AMU+TTHLhAovgFjADgGJMa27f0ZfbUPCZGlWc55\nr7VGDw7SB+fvFUopsiwj0w8ONPWDrN1CYI+fmTbwmpZFEhtdaPaBW7Qmx4ZIMyxDSKktH8tgHfil\n6v73o3LyyyhODtybfVVkeh8/AtIc/7RcDCgVBbTnRz7B5g93lu6wvb31QPT7QXRbLBbHT58V/ItM\nv5dOcnBtYRwRGxuB1j+AK2FweS0r1+FBGEn44XA94m3+4C/94MsD/6P22YYH7Lt72zv0Wp331e2x\ng8porX9HKbUDPFcruV/+7KPTAJQmT3PukScAGB8t09kVsJmNtVW6xv6lVpGkLwvrjdff5N1FAZLJ\nlEIPKIYtF0sNsFQDAl8ehiiJUJZ8No4SSAVI4szpEWoVuexOo50rrlgs5kywO1sNInMSM3MzNLsR\n1+9uHrluPqwc1K1lqS+PTwi4zszMVM52e+7sOfa2BTzm1q13CQ3WbBzH+0YxUzlLq+O4XLwoVMu7\njV26A1CfKCIe4NRaivERQytUr6EGWCvKzhltm3vtHAM4zELSwUEZRIZ6SGuF67g0cwqRkyUH9Xv2\ndPnL/+J/MqhjQYXtddGLnYrTAKCShF5fXu82+xQmzgKQFi/gFQUo6eInvkhmHl7XcXPs38WVTXrR\ngNfOYmAa2mFMNzS0NukufaPTzzz5CM889TH5/lS/B6VLH9xMgZ/+6Z86SrUciRzU7fmHLnz5f/vX\n/wcAvSTCNhi9dqbpm3Xz7voya7sC0q3TGN/bN10D9C6/ENA3gDHtdjsH3dFa4xgboTjAxJ2mpOaz\nOtkHmDlopBOd5bjplmXlpAGu6+I6Hv/X//zbh7reD2Nol4HTB/7/lHnvxxyrci/2zNwUC5NiGHTS\nxzZo/1YtoG0WVi+DFUNeF/ZazEyNAWB7fr5zFUoq5/Py/SBHTOp1Nbu7sihLpYBe20AIJjHthqCG\nNbebnDklPO072xsUS2KkJ+oleo48SBOjRTL4KDjDDqvfZeB0oRDkfGnb2zt4vtzaVrONZyiYn3rq\nKcbHRYdLS3d57TWhbw/DKPeasixld1egLLXW+AYMOQh8GrvGo8gyMIDgrW6fwEAmlsslbOP0Vy2P\nQlm4xOrjNcJYzu327UXCWMCZbcfGcmwIP6CGPrjc99rV2sIyD7dXcJmaM4Zwp0Nn19CEqwTLlvU3\nPQPXe7IZ3VstcWpa3n/xT18iMhucZ9toA8q+1+mRxAOad0VkuMSiJCPOQa5j6p68/+TliEEokemU\nQV1bofigUeoRyX3r1rIszs7MAgLkjfHaN7e2ct6/kUoZbdARw34XL2dksGk2Bfaw1+2x25O11ev1\n8ujWdRwsc7zS+5xjaZrmiGAq0+8xtINjtKVyjjvb3odn1FojJBfHz7DwInBRKXUWUdjfBX7pxx2r\nUUzOSOqj7nusX5MHPY16JKEYvyxNsH2BOisGVdJIDK3npfiGpdYPCpw9dw6Abq/BgI5mfX2DkjGW\no/UiWWS8DiDSoqzGZoOREQm5apVKHtK5boHtLblJk2M1ZqfG5fu7PZbX1nKP7wHKYfX7InDRdV3m\n54VErtfr5KRzb731NpNjgnkxPT3JrVvCS7+3t5ez/mqtxCtCiCwH7BK2bXPq1Fz+egDv12p2yMyD\nvd1oMjslUYpfLDEgcv3Yk4/wxBNPAXDqzCm+8fxfApDob7KzLZ5Jq9WkH4YfBQHAfa/dNE5pbBtY\nT9+i1zEPqA/1CdFF1HNwHHndi1PeuCfe7UavB0oU8/U37qGNB+yrlMEuk6AYLDHbdvJUWpxoEmN4\nuknC5VPC5vq9771Gaogwn/nk4xQ8cWDSNM0NzEck928XtKZalWe+32zTMvCdtg2JiTLdoMRkTTZu\nNTKSp/12d3epGHjQdqtNZBiuldZ4xkmwLTtfY0mS5M9ydoAOSLFvgLXeh6BMM517tGma7kcLmUZh\ncdg97QPfEa11AvxD4M+Q5PHva63fep9jh3JIOax+h7q9fxmu3eOToW5/uHyoHK3W+k+APznssbWK\nT9MgoN/Za6BaEqKemh4nbImn1Ww2CEpCDZz4LexMQv5qxcH2ZF8olT1cx/Au7fUpFg1Tg1tja33L\n/F7KmdPiyXWaPXQkx9QqAZ6hEmvudVlZlXMoBAWSVLaud+9sUTXA3904ItQfDSX2YfWrtf6TYilg\ndXUVkBxoviNHCa2GeKjLy3fzFIFtO7imeBZFYZ7iy7IUx3hcYRgyOTFu/pBy+/a78lqRAymPjk8w\ngLHu9Po8/fSzADz77KeZnT0FQFAocuqMRCBf/geXWVkR5Pvf/l//Od1e9yMhvrzftfvwuSp+UfSS\nqYx2Qzwt7dikoXhIgesSmEKrbSvubkvdYXWrzXRJPNE7SxuUyrIWfZ0QhZLeSrRClSSV5geFQZBG\nlqk8PI2lIAvAmzcXeevaVQBu313iF37hOQDq5TJJOuA2A7IHHzDct24ff5RGU7gE636RLDPPHjEP\nPXQRgHavy8q6rO+9XpdowIbSalIybAinZuewN8WktdttasYD7odhTtTa7/ffE516zsAEqgNA6un+\nmlTqPZx2eeHNcOjpQ2r3gTIsRHHK2+/cBCBIEiZM6DoxloHhUU+ilDCTnEt/d4tCWcL80cChZQxh\nuVxjbVVyN9VqjSiShz5LEyYnJefq+Talonxnr5tgm0o6vkekJeRyXJv1DTG0YSkkM3lY7Tqs78mD\n5Ln71CMnWdIkZWZGrn1tdZvdXdGhpRSOGjz8DoN1ZVk2nieGo9XqkQ7C024Pz+xElgV7Jl975tQc\ntvmeTtgnMzlaq9vn7PwZAJ577jnGxoSGqFSt8fYNude/929+L09B/MZv/DrtjmyqL3z/u3zn29+h\n3T6ZxbCDoiwN9mBL8aiPiu7iOKQdGdaKJGFnR9a0W6oyasvrsdPTBIE4CfWKR6Fg8ov4OCZ/ntkO\nDDa4JKLXlTWtsfL7pElIU5MOKxaEIwj4D3/+Pe4Ylte/8/M/yyVTzLRRJGSHNgYflcRxwo3bNwD4\n+MIlYlPwu7q6xMyMbNbtJCYwesgin9t7xqnQCa4hbfVwODMhz0A6OsauYRZpRD22TB63nyZgNq6J\noMSpUXEkVrc3SQeM2FmSM1Zo2yJV+91LA9JGpTOsH+jc+XEyHMEdylCGMpRjlgfq0Spl0TfJ6rDb\nxzUufGNvD9c27RRZBibJb+sM1/B49bXHugmB74R3KVUlzLIsK+d49wM3rxwqNO227Iy7jT1sR7y0\nbr+PzuT4iWoVpSS94PgeoWmv2W40SU2Rol5x6XT6J57jTmvNzrZ4n563rwe00SnSklI1ejt79mze\npdDtXst7lNM0zd/3PI/NTYkc6tUStZpU0XeabTAtWlPTdb74M38dgMc+9ji9nnzP/PwCt95dBMBx\nvdxDuLu8zOOPPwLA0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c+VRdkF7nXkQczKNn1DO7O2do/z52VeHNvaz/PpmNHaoM0ipbMr\nRpeoz0jF8JAVfCx3QFnhYdmDBnM7z4WlaUpq8oUFr4gdGAg6pSma+f0otlm6KWmKs77LmTOTOVTd\nSZU0y3jtqkzCzM7OctFUXx2LnOvLcx16puF9Ze0ekYHci5OIODZGt9XKYRJt28ZSsvnYls7xaxPf\nyyf2VCbtZADojLbBG+73IjodWfRJltIfTKG12jQN2E8xCCgUiiircxwqOVJJYlhtyNrdCUsUVmXt\n/vxCjaLJC46O1CAR3RUqAVFfdFq6/DBff30FgKs3m/hKrt+12li+GJXU8TBRLAXdpTYjz8DZc5O8\ntS6G8+zCHC0kTeaXijmbBdrCpGvpp0ViA2BTrFQpFvyPgobpvsR2HAKDi2EpC8+W57BUnuSxZ8Xh\neb2xy6bpdPncz32eVkfqNVsrO6TLop9SxWZ0SjZxy7HyNk6fGG1YPebGJjk1Kc5VsrqBuvKOOYsm\nfYMxfOM71/i2I69Xx0dQakArlJCaHGKoHPR7iEZ/vAxTB0MZylCGcszygD1aG21CnL2wzUYsO3tl\nsoYyIWhjb4teV7wuslFGRqW66z38CHZLwoudrT16ffmsoxLGx8wxfoEkZ2Q9UMlW+ztPkiZ5k7iy\n7Bw8WVkKXAPaXCyxekdG+fpxk0baIolPdngbRREvvfIqAH/7zDxBIKmAvd0GFRPy7+w26IeD0OoV\n/uLrfw7Af/13fiHnurp7927eoTE/P0/FjEwr2855wixgfdWEp+02n/ussKyWyiXu3pUixfLyMl1T\nXIzSmPjAvL1rim0jo6PUKjU2G82jVsfRi1LMeXKeQZYx+5ikZqZGi/zHr/6lHNOfZmlJvKiFhYU8\njZDGET/zrAyH3Fh+hw0DgUhikXRFF46VUHfEEx2vZiQmggqKHonBmBgtVfAM3OdYtUDPrN1yqokM\nclvqJ7jGq7Ycj0rBPfHA9anOOH1OCng7S3dpGlTH0CpTNwSfF2vTLN6TNGIw4fPMz34GgOTdHcqm\nF3ZdddnZE53XihUCEyLcW1zklFm7eqfDrTcFy2Ptziq9VH6sWnHpbsnrm75H/6c/DkC/v0NjS9Z0\naFtgxnEjMmzn8DgdD9TQohRt06qyuLVMS8sDbQcOTskQ2bU3aRvgDBXBxLTka0frVXaXhErFS5fw\ndyT/EoYJg2RslLroQVU9aZKadhBHe3k7Eii8PI9rkQ4oX+KEyFCS44HlmCbxXpdilmGd7MKtuT5Z\nTI7jsWoQ9z3HpmowNz3Xyx+6bqfNV7/6VQAefeQhPvFxIVK8c+cOi6bRO8syTs1Ki0wx8PE8yRui\nLd55R0Kuxm6DZ575BADjhXFefFHax65ceXOfbTTbr806tpXPlCdxQqlQ+KjJBA8l7R7smVRAv9+n\nZDq9Go09ksgwr3Y77GxIlfzC6TMsDRrm1zf5zNOfAuDZh8v88fdlnY0WE+Y8WdPvdOfp2rIhNnod\nqgb6rxH26SLr9e52wkMPyzGzE0XWB0bFt3HLJu9QH0Ensjl6vgdoTrp6e1HIzTXZoB6bP8vdVQnz\nNxodnqpIp0FidyhfEGP8ncYS9dOSXjhz5hKPnJbp0TfvvcvbJuW3tHiL0rx0Ds3PnKI0IenFnXbK\nNzckjbPjpQRjsr7bnYTdabFN9XKFkbLUfV5bvc6m2TAt18cdEEHjk9mH56444bdgKEMZylB+8uV9\nPVql1GngXwFTyGDv72itf1spNQr8HrAALAJf0lrvvs+XsWwI6xa3Vuh7ZrRN6xxubPHuHd4x8Inz\npxYoG9g9tzRKeVI8ASsoYa/Kzr5x9xaDonW7HYPxXK00xCmbApjW+VhpoVDAMi5/RkIyGMfd2WZ3\nXbxA3Wowbsb0Kq5Hu9/L+bKOUo5StxoZpQXY2tri9u1FAB65fImKuRY/8HMvE6VYXZNQ7OaNm5w5\nLZHDV77yRzno+bu33+WZT4i3emp2hrExw8fmBtwwfGCNxi67u3JqhUKQ05w7jkM0oHXO9nmrskzn\noMrNVotWq7V/TkcsR6nfTNucvyQV6tmZKpEpzLx6fRsnkPXkBw4/9YxEBlMjI7z9thwTuBbLpmF+\n6W5CGMr7sZXRM2mETMWURkzqKnKompC20t5jftbQbHdHKRdEj/MzFlOm24FME5te5Harj+0OoPz6\ntDs90Eev36PUbTsO+drbQpR7dmKey2cWAPjuqy9ybWkRgMkzM0Smi+VTj32C127LUMO1xbs8VDkL\nwIXJU4wH0oO7OTvLxrrBNMFhdFz6nvfqCfFPPQlAMUo5W5E17SuPDUsMSafX5dXrQny53GrSGyCl\nRSlFM8of2BYqO7xNOEzqIAH+sdb6FaVUBXhZKfVV4O8DX9Na/6ZS6teBXwf+yY/9oiThlrn4ZhoS\nDfImqSYwAwiW0+Zb3/82ADMzs3zGhL1BEKB8MRhO3aJm0gLKCVCetLA0m20y03hfK/rYZkqnM3mS\n+gAAGWlJREFU027nMIm2ZeVzz6mOaRt4xrtvv0lsiAhHfRcMCIVKM7I0wzoeFoAj061tWUxOSJi1\nsrKOa4AwHn34kTw0r9frTBjyylsvvMiu6dy4eu0qn/qUNGtvbm5y7Zp0L7z11lW+YyATz87P59/v\neQW+8x25R7bt8NJLLwEC3vOFL3wBgHa7wx/90R8BcOXaWzm8XKYzbKPLVqvF8vLye1hJj1iOTL+e\nZ1OuiEGdG3W4uy4P/b3dkPOjYvCWlm7jGTCYeyv30O4+mei3TIP9vc0Kf/OT4+Y7fbYbYkQbuymZ\neRpVpthdk3rErtfHNvCJza17rN+VDe7Nl75H3+RuFfs4qdXREUmnAUHBo1Bw6XePpavj6OxClnH1\nnuRNv/3Gazz3pEB2PnzhHK+/8QYA9rqfY3mEnQ5VE9q3/UW+fvP7AEyqMiMV6TqYnVogNJRVvXZI\n5kg65dKlC6RmMrSxu4syFE4zk+PUDc3WW7dvUzI0QfWgyvqq3ItUa5wRuUlulmK56rBMNu+fOtBa\nr2qtXzGvWwhX+xzwt4DfNYf9LvC3D/mbQzEy1O3xylC/xydD3d6f3FcxTCm1ADwFvABMaa1XzZ/W\nkBDix0qYxGwZwrTEO8C3k+m8QDI5O8HaXQnhn3/hG5xfkH7Q6anZvDOh205pGUQjilW6Bocg7m8x\nIAGKIg/VHlBod3JA6lqtliOb2cqmvWcQqe4uMVkQL7lW3G/yTtOMJMvEEz5G+bC6tW0H25aC4tbW\nFiVzDZubW4ya9Ivnee/pkR3gG7zy8iv83M8KweL4+FjuYW5sbNIw6EmdVpMwlK6GleW1nHGiVqvz\nW7/1WwA8+ugjPPfccwBsb2/x+S98Xi5ga4O7pmLsuVaOeGRZFmmWPpAR0Q+rXyzNH39XPvLn397A\nNewJ+HVM0AXtFn/xTfGums02n/2cNNjPTk/w9pas3VAHPH5JPNqZUpLz1+1+r89abKjHlcJ2RcF+\nocP1t6Rg9sqLr3L2nKR4GnttVjckGhup16nU5H4/8dBlmgbnolorcu/OzWPX74fVraUUPdPr/cby\nuzxyTnqIH56e5amPPQ7ArZs3cQZklE4Bry+R6+npSe5tSgHsevMmtUSKW+lKwoQtxduJYsDilnil\nFz/xSR4NBBPkyptv8PzLAgO6vXCGkTHxhmvFKo+dlTRRp9+hWxZ9Lm2vE5n+ccePqQQxRw6TqJQq\nA38A/COtdVMdyFlqrbUaJPb+08/9KvCrAKWST3/wiwrcQQeWcgZQpzgFi+nTEt7eWV7i+i3JlYyO\nVPNugbDVYvGGTMus72yQmRyfZ3u0WtLJECcpykyM7O3t8fgTcsNG6iM5JmvgFLGRELvd7uGbroNT\n05P5xJPn2KiCh+0cX4PGUejWdV32zKaRJGnewra9vUXvlCy+JE3pmdat8+fP8+xPCe7sd57/S15+\n+WUASqVyjmsrhlh+utPp0jcQlFqTpyPa7TatlqQgOt0uzzwjKYgrb77FqTmZpBodHeXuXfPsafJO\nD8UA6u/YDcGH1u/oaJ2//kkxANvtmMxMC2yvXKe3Ixv92soalpkcKFk2N68vAvDYQ5fwt+WYNEn5\nf/6D1CDKjsb1ZeFvdX2skml7K7g5tVO71eT8xQUAHn3iUToGD2FicoxC2UyPZZqSycM7Ks1BZZRu\nY3G8gD1HoVt/pELRbBSLrS2+/pasRY+M8/MLAMwlMXduyYbz6JkFHh2XDgTdSJg8L8/wm2vv8k4o\nTloj7nGmLJ0Gb60uUj4lHQj9tCfstcDMxBRnzglRaJgldAyUp041bTOR5ofw9AVh4t3e3qXTNROO\nxYww6udgPu8nh3LTlFIuosx/rbX+d+btdaXUjPn7DLDxwz6rtf4drfXTWuunA/9kT1d9FHJUunWO\ncSP4SZaj0m/ZAJ0PZV+OSreumQr7qyyH6TpQwL8Ermmt/9mBP30F+BXgN81///D9vivRGbEaoEfp\nvChiWVY+g6+UhWdCeJyUJZMkf/bpZ1Bm98iimFZDdva3bl6l0xXXPnCdPCztR3EO9p1EMY1vS5g1\nMz3Bw5c+Jt+jUqZNn+jCpUssGxDw3TRm0ngLURwdGqHnfuUodZtlWe7RVqs1UlMUvHNniYq5lna3\nz7rprChX6zz2mOzUr7z0Am9fE36vkdERiobfKtM613kYhsQmRaMs8lRKmum8ELO5ucF1w9n06Z/6\nVJ6m2N7b5cqbUlXWaBwzsDAxNka5XKC9tHJYld2XHKV+y6USv/ZL0lHQ7Mfs7MpwQWPTY2lRhje8\npx7CM3rvNhu8eU/ev720TGTWa12PEyeSDlsNPVrGG3YcqJUNBofukxnvancnZnxM1vGZ82e5+rqg\nU83NzeWjtVeuXMvvh6NStHHZAsvG0UkOCn6UcpS6BY0akB4qxSum4Le5ucbjlwWc/typMxTnTVqg\nXGDMkQLkkwUXAwvB+VPn+NNbkrr57sot3kllrRdmFW+H8p131q9yeVy82MmREc6cWgBgcXmJrqFl\nv7e0wrUr8jysrN3l018Ufrypyhh3GtInbUcQ2kmOffB+chg36DPALwNXlFKvmfd+A1Hk7yulvgzc\nAb70fl+kVYbGVJjVPuQYKXm3gOu4gywCge/RbErFVGubxLRTJMCEwYsd27tDst3Pv6haEa1XVbBP\ngpe6bNwRpf/FV/8MIrnsmfkFWi0xTi3tsZOJgXllcZPHzprTjCK8bD/cPWI5Mt1mWZYPZdi2yqe7\n7q2s0o/MQ7u7y7aBKwzjiOW7iwAkcYoykJWnT5+jZNgAms1mzpjgFlz6hoZIkBMNEaQDmTWYbvJZ\nWpJ82cVzF/Kc+vj4ZJ6OUJadp2Gm5maYmp7m3sbOYfV1v3J0a1drMIatZMfEvgF3OXeaP/rDrwFC\nNTNSM3nByXH+//bOJTau6zzA37mv4czwMXxJFinJtGzJ8SONLbtOYAdtnD6QOgHadFEkiwJFu+gm\nQLtM24033bVFFwUCtGh2LYoArdO4sJPWdtLARf2UZFWSpUjWkzRJcUjOkJy5c5+ni/PPJWs0lixr\nhmPrfICA0WCG99z/nvnPOf/zD37vGwCUApct6Q4SZw6bW1IMJoMwMXI/dbHJf59fAODSynX2VKTj\n8MQoroR6DflBkcmYpCklCVMMvIBU7Op7pidpivfcdRTDY+dx3Z5sFG6bbDOtaUk36jRNaYucO1HC\ntZOme8L4+VPsl6iXvWNjVCPze3xs7ggVscUedH1+e8aEbjmey/OXjQmiVco4VTdlD189HjB07y8D\nMD11hFDqcaxdrxcdRF599TWuzpvF3/ehLrbwPeNTrMiCGTdC0ry8XW/iBtxQ0WqtX4WfG8XwKzd3\nGcv/h5Vtb7Hy7R1Wth+Nvhv2XFmtVJajuk4sp0wpMLvJSrlMSWJqy3qIYXFcKSCSVS/LUvZMGw/h\n9JUJYkdqI5BTUZJeq3OiUJoDas20NIK8enGe7y3/GwB+7QBLy8YbGcdR0eRR6Q5Xrpod8GfvO8ih\nPTV+/pwaDMyO1uxqGo1GUaJweKRKIjvR5kaTTJIFdJ4VqZlxknBFdqKf/8IXmN1vnFjN002qUutg\nvFYrokRWV9eLHWoOOLJDnd23H6Q0YHsrZM+McUDcf98RatKqvB12it3X6dNnmL82T7vVvv0Cuc2E\nYZu6pInSaRFKlszskUN88VfN0fLixWU2GmYuXl6o01g3O6FDc7MoSaO9+9BhdGSejecHIGX9Kn6H\n106b+d1Jh0hTI5OxasDRXzBe8rWN1wrT2NUr88Xzy3ROU05m1eoIy2tm15VkKcPVoYFPcc7zvGij\nnqYprnRGCZWDI/e7sLXOOwvGAV7yFFOSDn59q1kkHRzecxf7pNj6V2Z9mm0jk5cXjnO+Y16/dOlV\n1Kox6Xxp7kniq8YEdOLEGyxeNr/5+voGTW3Gs2dsjKUV8/4Dhz+DkqiDk0vLdDbT7cipG9BnRatw\npKldRQVUq0ZY1WCUsjQNdBynKIhSmRii5HdDOhy2IjP5dJYQSi54OfPZVzGCjpMY6TpBliaUpPRf\nRo4r5RlLMxWuLJpj3OLKNbQcxVzXNTYJgNxj47z5MaysvsfGgzO0o8HuAqCUKjojtNvtoovExmaT\n1dXtx1yRbhRpmhZNAifHx1l830QFXF9cZPYuoyAvnr+A200qSTPGJfOsUW8WP/I0z9GiOA8dPECt\nbELJykMlJseNCaK12aDatZeHHXLdXWw1G2uNQvkPMteXV/ibv/4OAPv21oq2PnvPnmXuHpN1NHv0\nIL60VZqZOUBVEhY2m/NFBEwW7yGRgkipM46We4/aLUbFWezXqjiJ8SG5ShdKNNeaQOyvC9fmTVEk\noFItE0sbnMsXrzDimefhlmImpu7lleB8T2RyO4nlDB4lMV63oy/bNZaTJCkKIjmdmDWx6TZObvDr\nD0lnhEqJRQnjmh4b4vcfNIV8qp7PP7797wD8dPM0W4smIae5fJ5K0+iXoeU6JdmYjYw4hKNGzr/z\ntaepuSZ6wUk016XYj/ICVK6LcNIbMdhLncVisXwK6G+ZRK2ouGbHM14ZpiJlCfMoJ251Wyo7ZB2J\nHAgjvBHjLIg6bRzxnobtFovvG2N1EkWUpHRc0kmLrgEldwilJc7QU2Syw19eadGQPPUs0EW8aU5S\nxMQ5jo9OzUp3pb5J4/V32Wh1HW6DS9fLr5Ria8scfVzXoSGN7JQyjRsBSkFApWzkc8+BAxx7w1Td\nOnn8OA89aDy9U7UajXWzQ6jHETMz5lg25DtSGQraYUQYSzRCmpGGUsD9/Xl+6ctfAuD5f32ORt04\nvMrlMiUZZ5qmRFFEOOBmGTDRMMt1c8zc7LQIpNj3iXPnWFqUKnSupjZuTCQH9t3FrPRqGx52mZY+\nWCurIaMV8wzGpiYZkbbWOo6ZkOPq9N4O7S1xvFWrbEo1uyyJkNwTap8ZpVyW3m4uBHLyG3Ib7J00\nJ4lyuYpSbhEVMqho9I7qehTNOrWntivAZVkRbq21oi2fv9Rc4uUzZu520ognZkxEQRalhO+a5/XV\nu55AP2T+5vfffo5jsTktzDdPcVCabM7OjDEnZrL9UUbLl9d5wp5xI/RT587SbhlzkO9rtsJkMDss\nuMpjatRktnjKIZJCGGncYaf7rnsE7nQiEmmB0tpsFsfetZVl3rtggr6HKhVqY1KRveTjSq55pTxC\nJAqgoxM2pGnefKNFW5rd4YLuBnTvCMLPs6zbxB2tAlYjTXqTR4TdIs/zQrnC9r34nkvU3i4kk0n7\nkxSHjkQmfO7hh/iPF18E4NKF89wnR+G5g/t5Z9XYp1SmWRV79nC5QjcwvVIbYk08sQsXLzG333w3\n7oQsvm9smkkccc/cHGAy87otcYIgIMtzfiq1EgaZIPDYN2OUqKNcwtCYn3zfLY69G6sdLl8zsnjz\nrfN4nnT48F08iRCojYwwIZ2Xp8dHmagZc8x6mLG0bL67UfUoS5fnrZlJPN8oTtdJGRs2ch8bm2J6\nwsx71y1THTJjmxybKk6zWeagM9AfofjJrqG3swW7YWtpmhbzTCmFkjntEpDkZh4nJY+Lm2aOrr75\nCiv3GPvrM0efYqTbpXYp4os106jxVGU/x1JjirnqJmyERnEurbbpXDOmgyBMODhl5Pnjl35EajKN\nOTAzy6w0K73c2GCp0ykaZN6IwV7qLBaL5VNAn3e0Ljo2uj1WEMdmFdPp9qqrlNt1XKO0R23M7GKz\nPGFDeowtLC2xLI0dD03UUJLG6HkuVdktVcoVXHEQrF5vcP6q2Y01wxSc7nHNKUwHjlIoZf6OC2hp\n1BbpFJ8yg74maa3pSFRGviPmN0tUMfI8y8jEhhJlIT9++RUA/uzbf8LXnvkNAF584UUuXzDe3Sef\nfJLFK5e732ZiQpo2ui7j4+bIleVZ0RGjWiozNW6iQTrtNvUVc0TrxBHNbjF316HVMTuH0dFRlFI3\nHfS9mziOJpCqz5sbLRob5jQQhimueFEnxnw00sgvrdKWEpxrrRaRdOjYbK0wLycDRznFTrfiOXQz\nJ9e8EhOTZqf7QHI/ccvsmmpVl+qc2cXiZbhD5lqloIIvpRo7+TppKL3dEgcncNA9KJN4u+nuVgPf\nJ5VU+J2zYqfpwHP9ouwmpYBAqvK3ww7/ddUkxtSTFkcPmnoFD941Ryk0v4mn736c7Kr5/FpnhdKw\nkfnqQsj6ppnHw05QRMzocpVkXUxyvouWXnHxhQXiil+k/9+I/udtdvPcXYchsfP5foCWocQpuDIx\nRkdd5u6W45GjuCxBxNr1eeBhE5hcK1eKTCPlqMIU0Nqsk2vz/vXlNVYl1zx3A5zuxNMOqih/qEgk\nT93VCi0ZbJkX40lQ+aDTVbBa6x2te3Jcea20KqIFXKU4/64xv/z5s89SlpC6zz/+GOMSLfDwA/ej\nvm6KL/3whRfIpNZBJ03JJAqjHbYpib1yfG+FWGy0axubfP85k5VZbzRZlU689ea6qcWArHEaOtkn\nQNEqGJLkjbzkoEfMfC0FoLopNplDKFky61GLKDcLn+c55NICxQ22i/k4WhXKIwhchiUVdXS4yqQ0\nvMySNl5uns1EFRwpAxhGEbGUT8ziBrHUWPRLFeLUjCfTDr5fulnH+O6hKUwEaZoWDVkdb3tO53le\nfMbJFEPKKMjc1ZR0t9BLgCPlU0/XL3Nu0dRGeOTAvTz9wOcAeOzwUe6/25gR4rwNojsuf7bJ/Hsm\nYeS+yWmeOmJsvaWSx38eNxuPH37vByxJM8d66JCNjxbhjDdisLdpFovF8imgrztaRymqctwJggBf\nAt1LLkitYjY7Ca42+n9qfJiRkvGMNxZXWJNSe3eN1BgbM8fYvBMXcZhJmhGK80w5irY4wxaX60Xq\nouv6OBKnpx2v2NE6Sm2n2eY5NUnlVa5Ls57S6wpTHxelVLHia62L167jUpMiyZ7nMyQV0GrjNVJx\nRq6t1tkjDS477TYX6sZxePKdd3jsUZPff+HsWRaXjKOhE4b4koBQGRqiJBX98zRjad185sx7F8kl\n5vDIww9SXjIOoNW17WL7iu1xDjqu4zNdM/Ha1DIS2XVlWcJmyzhXwihjfsns3HFKhHE3XtjBF7NU\nphRSzRKdKTxxrlTLZaamjdlrcnKUsYq0LVdNos1uMH9WFHQv+WOMS+OyVCc01o1zLkx8UtXtIALV\nYKQ4lg8yXVNAlqXkcuJM47RwjHueW0QjKHQRl0ySkRcObU2qzXPxSh6JMieKE/NnWbxuHLP3Tc7y\n2BFT42N0tFrU8jgSjPGLR40jtxIE1KSY+/V6neefMwlOr791HEd0RBpUUHk8mFEHnucyXpVOChpU\nV6Bhx2TJAKPDJTpSs3Zza51AFGcWxQxjJmW5VCbbkhoISYbfzehSPiWx0abK4e2zpgBHvbGFW6p2\nR4GrzOdzpXaYDjSObPCVCw8fNkLPtxocW33/ExCAtN3pd+fDzx1FKAvRaGWoaA00VqsRyCSenRxH\nyXeiKCKU6IWfvPQyqQRxK8CXAPna2FiRpafznFSyY9pOh448r9GRKpHYry6d/RmdHeFm3aOszsBB\nsZ0pMrh4QcD0tJkTirQ4C+Y6J4mMvyBOt5jZbxaUrXbC+qa50SiMIZVjvlKUSubLJS/AFaUYJzBa\nM4tgdURR8owJxkGTZN3NQIXugu84kIgPwvUqRaiXchwSMd84ykV3QrjJ7KXdQmtNIlmNuU7I8m5y\nkC6KTWm2lbH2chAFmemcVExPW1kCrplzXuIWRasSlbMYmgVw9WqTYwumDY4CIumwMB1M8NVHvwzA\nRDDChTcuAXD8neOcOGPsvjkaPWyeUTbk4QQOfHjzroLBX+osFovlE47qR3X74mJKrQAtoN63i96Y\nKW5uPHdrrad7PZhbZUBlCzcn34GWLYBSahM4t9vj+AB27vaW2zZ3+6poAZRSb2mtH+/rRT+EQRvP\nx2EQ72UQx3QrDOJ9DOKYbpVBvJfbOSZrOrBYLJYeYxWtxWKx9JjdULR/uwvX/DAGbTwfh0G8l0Ec\n060wiPcxiGO6VQbxXm7bmPpuo7VYLJY7DWs6sFgslh7TN0WrlPqKUuqcUuqCUurb/bruB8ZwQCn1\nY6XUGaXUaaXUH8n7zyqlFpRSJ+TfM7sxvlvFyra3WPn2jjtFtn0xHShTFutnwK8B88CbwDe11md6\nfvH/O459wD6t9TGl1AjwNvBbmE6dW1rrv+jneG4HVra9xcq3d9xJsu3XjvYJ4ILW+qLWOgb+CfjN\nPl27QGu9qLWp4qu13gTeBWb7PY7bjJVtb7Hy7R13jGz7pWhngWs7/j/PLk8SpdQc8Cjwurz1LaXU\nSaXUd5VS47s2sI+OlW1vsfLtHXeMbO9IZ5hSahj4Z+CPtdYbwHeAe4FHgEXgL3dxeJ9orGx7i5Vv\n7+ilbPulaBeAAzv+v1/e6ztKKR8jzH/QWv8LgNZ6WWudadOd8e8wR5pPCla2vcXKt3fcMbLtl6J9\nEzislLpHKRUA3wB+0KdrFyhT/PTvgXe11n+14/19Oz72deBUv8f2MbCy7S1Wvr3jjpFtX+rRaq1T\npdS3gB9hWnJ9V2t9uh/X/gBPAb8L/I9S6oS896fAN5VSj2DKXl4G/nAXxnZLWNn2Fivf3nEnydZm\nhlksFkuPuSOdYRaLxdJPrKK1WCyWHmMVrcVisfQYq2gtFoulx1hFa7FYLD3GKlqLxWLpMVbRWiwW\nS4+xitZisVh6zP8CB+zi+GJTgIQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hVAjMdeSrloa",
        "colab_type": "code",
        "outputId": "1c79e68d-0c5b-470a-e9b2-44b8d5bf66ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "metadata": {
        "id": "ngfK-2Qewhtq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# This function takes the entire traaining data and test data and corresponding categorical label and returns the corresponding data in class indices\n",
        "def getThreeClassesTrainTest(class_indices,train_data,train_labels,test_data,test_labels):\n",
        "  all_train_data = []\n",
        "  all_train_data_label = []\n",
        "  all_test_data = []\n",
        "  all_test_data_label = []\n",
        "  for ind in class_indices:\n",
        "    this_class_location_train =  np.where(train_labels==ind)[0] # train_labels[train_labels == ind]\n",
        "    this_class_data_train = train_data[this_class_location_train,:]\n",
        "    this_class_data_train_label = train_labels[this_class_location_train]\n",
        "    print(this_class_location_train)\n",
        "    \n",
        "    this_class_location_test = np.where(test_labels==ind)[0] #test_labels[test_labels == ind]\n",
        "    this_class_data_test = test_data[this_class_location_test,:]\n",
        "    this_class_data_test_label = test_labels[this_class_location_test]\n",
        "    \n",
        "    all_train_data.append(this_class_data_train)\n",
        "    all_test_data.append(this_class_data_test)\n",
        "    all_train_data_label.append(this_class_data_train_label)\n",
        "    all_test_data_label.append(this_class_data_test_label)\n",
        "    \n",
        "  train_data_three = all_train_data[0]\n",
        "  train_label_three = all_train_data_label[0]\n",
        "  test_data_three = all_test_data[0]\n",
        "  test_label_three = all_test_data_label[0]\n",
        "  \n",
        "  for j in range(1,len(class_indices)):\n",
        "    train_data_three = np.concatenate((train_data_three,all_train_data[j]))\n",
        "    train_label_three = np.concatenate((train_label_three,all_train_data_label[j]))\n",
        "\n",
        "    test_data_three = np.concatenate((test_data_three,all_test_data[j]))\n",
        "    test_label_three = np.concatenate((test_label_three,all_test_data_label[j]))\n",
        "  print(train_label_three.shape)\n",
        "  train_label_three = np.reshape(train_label_three,newshape=(-1,))\n",
        "  test_label_three = np.reshape(test_label_three,newshape=(-1,))\n",
        "  return train_data_three, train_label_three, test_data_three,test_label_three"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3ZJV35fkrj3O",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2ekn-CkLikYJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Get only three classes of data and train the model on it"
      ]
    },
    {
      "metadata": {
        "id": "VPYv1L-uigzL",
        "colab_type": "code",
        "outputId": "d2b71d52-4ab4-4886-c9f9-7be75e5b92b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "cell_type": "code",
      "source": [
        "train_three_data,train_three_label,test_three_data,test_three_label = getThreeClassesTrainTest([0,3,5 ],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[   29    30    35 ... 49941 49992 49994]\n",
            "[    9    17    21 ... 49979 49982 49983]\n",
            "[   27    40    51 ... 49964 49980 49988]\n",
            "(15000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9luxTYeM3aZO",
        "colab_type": "code",
        "outputId": "6ca9e74c-5d1a-41cc-c229-663041dd24d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "plt.hist(train_three_label)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([5000.,    0.,    0.,    0.,    0.,    0., 5000.,    0.,    0.,\n",
              "        5000.]),\n",
              " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PAb8f90RGrIDvJjnYvh1hWazEAKgjSV4LfB34aFX9etzzWW5V9UJVXcTgU/Sbk6zYU35J\n3gMcraqD457LGLyzqi5m8M3Ju9pp3iW3EgOw4NdNaGVo58G/Dny5qr4x7vmMUlU9A9wNbB33XJbR\npcB72/nwfcBlSf59vFMajao60m6PAt9kcGp7ya3EAPh1Ex1ob4jeAhyqqs+Mez6jkGQiyZq2/BoG\nFzr8dLyzWj5VdX1VbaiqSQZ/j++qqr8b87SWXZJz24UNJDkX2AIsyxV+Ky4AVXUSOPV1E4eA/Sv9\n6yaSfAX4T+AtSQ4n2THuOY3ApcCHGPxW+GD7uWrck1pm64C7kzzE4Bed6arq5tLIjqwFfpDkR8B9\nwLer6jvL8UQr7jJQSdJwVtwrAEnScAyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXqfwHd\n/gESZomJ0QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Dni1oqIcyPai",
        "colab_type": "code",
        "outputId": "b5aee0e5-a41d-4cbf-bb75-30695972e628",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "# test_three_label.shape\n",
        "plt.hist(test_three_label)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1000.,    0.,    0.,    0.,    0.,    0., 1000.,    0.,    0.,\n",
              "        1000.]),\n",
              " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qqpuXRnZkDfBwkm8BXwe+VFVfXo4TrbiXgUqSTs2KewYgSTo1BkCSOmUAJKlTBkCSOmUA\nJKlTBkCSOmUAJKlTBkCSOvW/lK9vO4tP9cMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fuM8CeuRsDzW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_three_data, train_three_label = shuffle(train_three_data, train_three_label )\n",
        "test_three_data,test_three_label = shuffle(test_three_data,test_three_label )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EshrdJ78fp7U",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mbK7iwsZx8QW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "le = preprocessing.LabelEncoder()\n",
        "le.fit_transform(train_three_label)\n",
        "train_three_label_cat = le.transform(train_three_label)\n",
        "test_three_label_cat = le.transform(test_three_label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jYJt52bU0ooK",
        "colab_type": "code",
        "outputId": "cd480249-0bef-4823-dbce-149c45e6d4ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "train_three_label_cat.shape"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "metadata": {
        "id": "r3Ydm3cykUFn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_three_label_one_hot = to_categorical(train_three_label_cat)\n",
        "test_three_label_one_hot = to_categorical(test_three_label_cat)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_O4_VLoJ6vSF",
        "colab_type": "code",
        "outputId": "b434f323-3bb2-404e-f691-8c392b393756",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_label_one_hot.shape"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "metadata": {
        "id": "UIrWUPz_lGvF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# split train data and to create validation data\n",
        "train_X,valid_X,train_ground,valid_ground = train_test_split(train_three_data,\n",
        "                                                             train_three_label_one_hot,\n",
        "                                                             test_size=0.2,\n",
        "                                                             random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "H6AkQaz-kRh0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "num_classes = test_three_label_one_hot.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "duFF_pGbj2i1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x, y = 32, 32\n",
        "inChannel = 3\n",
        "input_img = Input(shape = (x, y, inChannel))\n",
        "ep = 10 \n",
        "weight_decay = 1e-4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Pt1coy0mkFrj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def lr_schedule(epoch):\n",
        "    lrate = 0.001\n",
        "    if epoch > 75:\n",
        "        lrate = 0.0005\n",
        "    if epoch > 100:\n",
        "        lrate = 0.0003\n",
        "    return lrate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qNc9zL8RANw6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# keras.backend.clear_session()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4IbtMUdhiscG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def cifar10Classifier(input_img):\n",
        "  \n",
        "  conv1 = (Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:], activation='elu'))(input_img)\n",
        "  conv1 = BatchNormalization()(conv1)\n",
        "  conv1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv1)\n",
        "  conv1 = BatchNormalization()(conv1)\n",
        "  pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) #14 x 14 x 32\n",
        "  drop1= Dropout(.2)(pool1)\n",
        "\n",
        "  conv2 = (Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop1)\n",
        "  conv2 = BatchNormalization()(conv2)\n",
        "  conv2 = (Conv2D(64, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv2)\n",
        "  conv2 = BatchNormalization()(conv2)\n",
        "  pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) #14 x 14 x 32\n",
        "  drop2= Dropout(.3)(pool2)\n",
        "\n",
        "  conv3 = (Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop2)\n",
        "  conv3 = BatchNormalization()(conv3)\n",
        "  conv3 = (Conv2D(128, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv3)\n",
        "  conv3 = BatchNormalization()(conv3)\n",
        "  pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) #14 x 14 x 32\n",
        "  drop3= Dropout(.4)(pool3)\n",
        "\n",
        "  flat = Flatten()(drop3)\n",
        "  out = Dense(num_classes, activation='softmax')(flat)\n",
        "  \n",
        "  return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "St7OPPbkpBRH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "loaded_model_high = keras.models.load_model('high_acc_complete_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qcVCXpeipRb_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# loaded_model_high.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_5TlD_pwo6PU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# loaded_model_high.get_layer('conv2d_1').get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Fmk0PSxIn55H",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = Model(input_img, (cifar10Classifier(input_img)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "94hzQr3eo2t1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# loaded_model_high.get_layer('conv2d_1').get_weights()\n",
        "# model.get_layer[0:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NHnJ3N1xn7AI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.get_layer('conv2d_1').set_weights(loaded_model_high.get_layer('conv2d_1').get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6N8IF4nTC74x",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for l1,l2 in zip(model.layers[0:],loaded_model_high.layers[0:]):\n",
        "    l1.set_weights(l2.get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VAe0CzlD766H",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# model.get_layer('conv2d_1').get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Y1huVOnSqclT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.get_layer('conv2d_1').trainable = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "M9d4nO7Fn_nZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# model.get_layer('conv2d_1').get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-VPIvSX1jytM",
        "colab_type": "code",
        "outputId": "5df355f6-10ad-4b9e-a2b1-1f0d2402903e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "#data augmentation\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True,\n",
        "    )\n",
        "datagen.fit(train_three_data)\n",
        " \n",
        "#training\n",
        "batch_size = 64\n",
        " \n",
        "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
        "# model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "model.compile(loss=losses.categorical_crossentropy, optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "model.summary()\n",
        " "
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 294,947\n",
            "Trainable params: 294,051\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2jabvkmcr1KR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mcp_save = ModelCheckpoint('weights.hdf5', save_best_only=True, monitor='val_loss', mode='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yBK4UwK8lffO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "train_X,valid_X,train_ground,valid_ground"
      ]
    },
    {
      "metadata": {
        "id": "0x-iDMcukCCT",
        "colab_type": "code",
        "outputId": "e8648a9b-461c-4024-bf9d-ef2fb3adacda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        }
      },
      "cell_type": "code",
      "source": [
        "model.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=train_X.shape[0] // batch_size,epochs=20,\\\n",
        "                    verbose=1,validation_data=(valid_X,valid_ground),\n",
        "                    shuffle = True,callbacks=[mcp_save,LearningRateScheduler(lr_schedule)])\n",
        "#save to disk\n",
        "# model_json = model.to_json()\n",
        "# with open('model.json', 'w') as json_file:\n",
        "#     json_file.write(model_json)\n",
        "# model.save_weights('model.h5') \n",
        " \n"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "187/187 [==============================] - 13s 67ms/step - loss: 0.8070 - acc: 0.6930 - val_loss: 0.6682 - val_acc: 0.7313\n",
            "Epoch 2/20\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.6418 - acc: 0.7602 - val_loss: 0.6113 - val_acc: 0.7570\n",
            "Epoch 3/20\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.5745 - acc: 0.7849 - val_loss: 0.6042 - val_acc: 0.7790\n",
            "Epoch 4/20\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.5347 - acc: 0.8032 - val_loss: 0.4984 - val_acc: 0.8263\n",
            "Epoch 5/20\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.5091 - acc: 0.8138 - val_loss: 0.5188 - val_acc: 0.8163\n",
            "Epoch 6/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4949 - acc: 0.8237 - val_loss: 0.4529 - val_acc: 0.8380\n",
            "Epoch 7/20\n",
            "187/187 [==============================] - 11s 58ms/step - loss: 0.4824 - acc: 0.8255 - val_loss: 0.6838 - val_acc: 0.7317\n",
            "Epoch 8/20\n",
            "187/187 [==============================] - 11s 58ms/step - loss: 0.4716 - acc: 0.8314 - val_loss: 0.4909 - val_acc: 0.8173\n",
            "Epoch 9/20\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.4689 - acc: 0.8327 - val_loss: 0.5069 - val_acc: 0.8090\n",
            "Epoch 10/20\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.4480 - acc: 0.8417 - val_loss: 0.4876 - val_acc: 0.8253\n",
            "Epoch 11/20\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.4493 - acc: 0.8453 - val_loss: 0.4803 - val_acc: 0.8097\n",
            "Epoch 12/20\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.4338 - acc: 0.8513 - val_loss: 0.8046 - val_acc: 0.7620\n",
            "Epoch 13/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4265 - acc: 0.8528 - val_loss: 0.4924 - val_acc: 0.8383\n",
            "Epoch 14/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4271 - acc: 0.8529 - val_loss: 0.5489 - val_acc: 0.8390\n",
            "Epoch 15/20\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.4203 - acc: 0.8557 - val_loss: 0.4564 - val_acc: 0.8397\n",
            "Epoch 16/20\n",
            "187/187 [==============================] - 11s 58ms/step - loss: 0.4238 - acc: 0.8552 - val_loss: 0.4300 - val_acc: 0.8500\n",
            "Epoch 17/20\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.4025 - acc: 0.8599 - val_loss: 0.5292 - val_acc: 0.8080\n",
            "Epoch 18/20\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.4147 - acc: 0.8557 - val_loss: 0.4182 - val_acc: 0.8610\n",
            "Epoch 19/20\n",
            "187/187 [==============================] - 11s 58ms/step - loss: 0.4009 - acc: 0.8656 - val_loss: 0.3769 - val_acc: 0.8753\n",
            "Epoch 20/20\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.4009 - acc: 0.8652 - val_loss: 0.4115 - val_acc: 0.8657\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff1e980fac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "metadata": {
        "id": "3KWBGDFQsmm9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# json_file = open('model.json', 'r')\n",
        "# loaded_model_json = json_file.read()\n",
        "# json_file.close()\n",
        "# loaded_model = model_from_json(loaded_model_json)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BXveyoT1ttYC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# loaded_model.compile(loss=losses.categorical_crossentropy, optimizer=opt_rms, metrics=['accuracy'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kUy8WCS4xsYU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.load_weights('weights.hdf5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "W4vixI-GApxU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.save('transferred_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ta_XH9UirtQZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e0197c8d-7d59-4594-d372-c4f5499c2b36"
      },
      "cell_type": "code",
      "source": [
        "model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.37685667928059896, 0.8753333333333333]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "metadata": {
        "id": "w4M2DfGjmT3I",
        "colab_type": "code",
        "outputId": "914a288f-0d79-4b1e-c0b0-b58e4ee797e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "#testing\n",
        "test_eval = model.evaluate(test_three_data, test_three_label_one_hot, verbose=0)\n",
        "print('Test loss:', test_eval[0])\n",
        "print('Test accuracy:', test_eval[1])"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.3659791358311971\n",
            "Test accuracy: 0.8799999998410543\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "M1_boK_fprtB",
        "colab_type": "code",
        "outputId": "5ab93563-6fb8-468c-d3c0-027f38493105",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_data.shape"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "metadata": {
        "id": "ZuH6igT7nbza",
        "colab_type": "code",
        "outputId": "c2c7f582-b14b-40b9-9a8d-d81d36b2b548",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "# show_imgs(test_three_data[:,1])\n",
        "show_imgs(test_three_data[:16])"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: `toimage` is deprecated!\n",
            "`toimage` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
            "Use Pillow's ``Image.fromarray`` directly instead.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Af2ivlWZFtwrHkTJ+R9X48rDpe7dyg7lZ4Ny5cywsyBx67LHH+PjHPw5I7Gwu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\nFhOWpGpKGjKkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cE1UG2RavZW3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def getDIScoreOfLayer(layer_output, true_labels):\n",
        "  # computeScatterMatrix\n",
        "  rho = 1e-6\n",
        "  mu = layer_output.mean(axis=0)\n",
        "#   print(mu.shape)\n",
        "  mu = mu.reshape((1,-1))\n",
        "  centered_data = layer_output - mu\n",
        "  S_bar = np.matmul(centered_data.T,centered_data)\n",
        "  # compute between class matrix\n",
        "  total_classes = true_labels.max()\n",
        "  S_B = np.zeros(shape = (mu.shape[1],mu.shape[1]))\n",
        "  S_W = np.zeros(shape = (mu.shape[1],mu.shape[1]))\n",
        "  unique_classes = np.unique(true_labels)\n",
        "  unique_classes_list = np.ndarray.tolist(unique_classes)\n",
        "  for l in unique_classes_list:\n",
        "    class_l_index = np.where(true_labels == l)\n",
        "    class_l_output = layer_output[class_l_index[0],:]\n",
        "    mu_l = class_l_output.mean(axis=0)\n",
        "    mu_l = mu_l.reshape((1,-1))\n",
        "    total_examples_this_class = class_l_index[0].shape   \n",
        "    ss = mu_l - mu\n",
        "    S_B = S_B + total_examples_this_class*np.matmul((mu_l - mu).T,(mu_l - mu))\n",
        "\n",
        "  DI_Score = np.trace(np.matmul(np.linalg.inv(S_bar + rho*np.eye(S_bar.shape[0])),S_B))\n",
        "  return DI_Score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2_q12H9HU7n-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def getDIScoreOfLayerCNNLayer(cnn_outputs, true_labels):\n",
        "  # computeScatterMatrix\n",
        "  number_of_filters = cnn_outputs.shape[3]\n",
        "  cnn_di_scores = []\n",
        "  for filt in range(0,number_of_filters):\n",
        "    outputs_filter = cnn_outputs[:,:,:,filt]\n",
        "    outputs_filter_reshaped = np.reshape(outputs_filter, newshape=(cnn_outputs.shape[0],-1))\n",
        "    this_filter_score = getDIScoreOfLayer(outputs_filter_reshaped, true_labels)\n",
        "    cnn_di_scores.append(this_filter_score)\n",
        "  return cnn_di_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mPTmOl-QERZN",
        "colab_type": "code",
        "outputId": "8da61757-ecfd-421d-a753-033846df3441",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# layer_name\n",
        "valid_X.shape"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "metadata": {
        "id": "bRQ1YTnsd6zM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# train_three_data, train_three_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pe0g2T0IA7sG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "layer_name = 'conv2d_1'\n",
        "get_cnn1_output_function = K.function([model.layers[0].input],\n",
        "                                  [model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "cnn1_output = get_cnn1_output_function([train_three_data])[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4Yy-y5xIWdz6",
        "colab_type": "code",
        "outputId": "1c1cdabd-ace7-44ff-bf40-7130b56ff381",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cnn1_output.shape"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 32, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "metadata": {
        "id": "EF3q_KOgYTxb",
        "colab_type": "code",
        "outputId": "b4c87199-d784-481d-9a9d-960f487e4c30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cnn1_output.shape"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 32, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "metadata": {
        "id": "B0Hfr4ZwB8dX",
        "colab_type": "code",
        "outputId": "5d7cbd80-07f0-4b49-acb7-3fc8adcf6478",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "np.ndarray.tolist(np.unique(test_three_label))"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 3, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "metadata": {
        "id": "pM8xfShbdHJV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# valid_ground_cat = np.argmax(train_ground, axis=1)\n",
        "# # valid_ground_cat\n",
        "# valid_ground.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jZ1IST7pBZ92",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "di_scores = getDIScoreOfLayerCNNLayer(cnn1_output, train_three_label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RZnzBi-7BcfM",
        "colab_type": "code",
        "outputId": "bdb63c2f-8f5c-4781-840e-dc1a7632e6da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "cell_type": "code",
      "source": [
        "y_pos  = np.arange(len(di_scores))\n",
        "plt.bar(y_pos, di_scores, align='center', alpha=0.5)\n",
        "plt.xlabel('FilterNumber')\n",
        "plt.ylabel('DI Score')"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'DI Score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gFs0BsyhzMXk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "d1LLCxe9aHpX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## DI on different data\n",
        "\n",
        "1.   List item\n",
        "2.   List item\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "WeSV3VJA6lm2",
        "colab_type": "code",
        "outputId": "9e8e8970-cf03-47ba-a2ee-d9f16bebad0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "train_three_data_low,train_three_label_low,test_three_data_low,test_three_label_low = getThreeClassesTrainTest([0,3,5],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[   29    30    35 ... 49941 49992 49994]\n",
            "[    9    17    21 ... 49979 49982 49983]\n",
            "[   27    40    51 ... 49964 49980 49988]\n",
            "(15000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Y3R57M0i6liE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# split train data and to create validation data\n",
        "train_X_low,valid_X_low,train_ground_low,valid_ground_low = train_test_split(train_three_data_low,\n",
        "                                                             train_three_label_low,\n",
        "                                                             test_size=0.2,\n",
        "                                                             random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0IlIKFz86le9",
        "colab_type": "code",
        "outputId": "7784d113-60ea-4e43-a1cb-d1503bd33472",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "layer_name = 'conv2d_1'\n",
        "get_cnn1_output_function_low = K.function([model.layers[0].input],\n",
        "                                  [model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "cnn1_output_low = get_cnn1_output_function_low([train_three_data_low])[0]\n",
        "cnn1_output_low.shape"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 32, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "metadata": {
        "id": "rk6twlNTc2cW",
        "colab_type": "code",
        "outputId": "95401353-2e40-4b12-d406-cffaf6f1d7a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_label_low"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 5, 5, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "metadata": {
        "id": "uZIf7RZ86laf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "di_scores_low = getDIScoreOfLayerCNNLayer(cnn1_output_low, test_three_label_low)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "q9pMSCcD6lUm",
        "colab_type": "code",
        "outputId": "0a50d395-633e-4fe2-d8de-1c0a4b9fc112",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "cell_type": "code",
      "source": [
        "y_pos  = np.arange(len(di_scores_low))\n",
        "plt.bar(y_pos, di_scores_low, align='center', alpha=0.5)\n",
        "plt.xlabel('FilterNumber')\n",
        "plt.ylabel('DI Score')\n",
        "plt.ylim([0,.65])"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 0.65)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAE6dJREFUeJzt3X+QXWd93/H3x7IdqMMUUyspY1mR\nAgIqEgLJxpkSJvVQnBHtVKaNobbrDG5J1M4gQnDD1A0dh6qTGcIvp0lViJo4kLaOsPmRCqrieFKa\nH3WgWoP5IWkMQjG1NNSWjQ0xSTCyv/3jHj2+LLt37wqdvfdq36+ZHd/znGfv/fpc7X72ec45z01V\nIUkSwDmTLkCSND0MBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJas6ddAErddFFF9Wm\nTZsmXYYkzZS77rrrwapav1y/mQuFTZs2MT8/P+kyJGmmJPnSOP2cPpIkNYaCJKkxFCRJjaEgSWoM\nBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJElNr6GQZFuSe5IcSXLDEn1eleRQkoNJbumzHknS\naL0tiJdkHbAbuBw4BhxIsq+qDg312QL8a+DHq+rhJN/TVz2SpOX1OVK4FDhSVUer6jFgL3DFgj4/\nC+yuqocBquqBHuuRJC2jz1C4GLhvaPtY1zbsOcBzkvzvJB9Psq3HeiRJy5j05ymcC2wBLgM2AH+U\n5Aer6pHhTkl2ADsANm7cuNo1StKa0edI4ThwydD2hq5t2DFgX1V9s6r+DPg8g5D4FlW1p6rmqmpu\n/fplPzhIknSa+gyFA8CWJJuTnA9cBexb0Of3GIwSSHIRg+mkoz3WJEkaobdQqKqTwE7gduAwcGtV\nHUyyK8n2rtvtwENJDgEfA95YVQ/1VZMkabRU1aRrWJG5ubnyM5olaWWS3FVVc8v1845mSVJjKEiS\nGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJ\njaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVLTaygk2ZbkniRHktyw\nyP7rkpxIcnf39TN91iNJGu3cvp44yTpgN3A5cAw4kGRfVR1a0PV9VbWzrzokSePrc6RwKXCkqo5W\n1WPAXuCKHl9PkvQd6jMULgbuG9o+1rUt9FNJPpPk/Uku6bEeSdIyJn2i+cPApqp6AXAH8N7FOiXZ\nkWQ+yfyJEydWtUBJWkv6DIXjwPBf/hu6tqaqHqqqb3Sbvwn8yGJPVFV7qmququbWr1/fS7GSpH5D\n4QCwJcnmJOcDVwH7hjskeebQ5nbgcI/1SJKW0dvVR1V1MslO4HZgHXBzVR1MsguYr6p9wM8l2Q6c\nBL4CXNdXPZKk5aWqJl3DiszNzdX8/Pyky5CkmZLkrqqaW67fpE80S5KmiKEgSWoMBUlSYyhIkhpD\nQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2h\nIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKnpNRSSbEtyT5IjSW4Y0e+nklSSuT7rkSSN1lso\nJFkH7AZeDmwFrk6ydZF+TwNeD3yir1okSePpc6RwKXCkqo5W1WPAXuCKRfr9O+BXgL/qsRZJ0hj6\nDIWLgfuGto91bU2SHwYuqar/3mMdkqQxTexEc5JzgHcC/3KMvjuSzCeZP3HiRP/FSdIadW6Pz30c\nuGRoe0PXdsrTgB8A/lcSgL8J7Euyvarmh5+oqvYAewDm5uaqx5olrQE33fH5Jfe94fLnrGIl06fP\nkcIBYEuSzUnOB64C9p3aWVVfraqLqmpTVW0CPg58WyBIklZPb6FQVSeBncDtwGHg1qo6mGRXku19\nva4k6fT1OX1EVe0H9i9ou3GJvpf1WYskaXljjRSSPDXJc/suRpI0WcuGQpJ/ANwNfLTbfmGSfaO/\nS5I0i8YZKbyZwY1ojwBU1d3A5h5rkiRNyDih8M2q+uqCNi8LlaSz0Dgnmg8muQZYl2QL8HPAnf2W\nJUmahHFGCq8Dng98A7gF+Crw830WJUmajJEjhW6l011V9QvAm1anJEnSpIwcKVTV48BLVqkWSdKE\njXNO4VPdJai3AV8/1VhVH+ytKknSRIwTCk8BHgJeOtRWgKEgSWeZZUOhqv7pahQiSZq8ce5o3pDk\nQ0ke6L4+kGTDahQnSVpd40wf/TaDS1Ff2W1f27Vd3ldR0qwatU4/uFa/pt84obC+qn57aPs9SbxP\nQZLOsGn4o2Kcm9ceSnJtknXd17UMTjxLks4y44wU/hnw68BNDK46uhPw5LOkqTMNf2nPunGuPvoS\n4CelSdIaMM7VR+9N8vSh7QuT3NxvWZKkSRhn+ugFVfXIqY2qejjJi3qsaeIcgkpaq8Y50XxOkgtP\nbSR5Bj1/trMkaTLG+eX+DuBPk9wGBLgS+OVeq1qDRo1OHJlIs22WZh/GOdH8O0nmGax9VMA/qqpD\nvVcmSVp1S04fJflrSc4D6ELgDuB84HmrVJskaZWNOqfwUWATQJJnA38KfD/w2iRv6b80SdJqGzV9\ndGFVfaF7/Grgd6vqdUnOB+4Cbui9Op1VZmletW8eC02rUaFQQ49fCrwNoKoeS/LEOE+eZBvw74F1\nwG9W1VsW7P8XwGuBx4FHgR19nq/wB/FJHgtJixkVCp9J8nbgOPBs4PcBhm9kG6X7fOfdDFZTPQYc\nSLJvwS/9W6rq3V3/7cA7gW0r/r+QNHX8w2M2jTqn8LPAgwzOK/xkVf1F174VePsYz30pcKSqjlbV\nY8Be4IrhDlX1taHNC/jW0YkkaZUtOVKoqr8Evu2EclXdyWBRvOVcDNw3tH0M+LGFnZK8FriewZVN\nL124v+uzA9gBsHHjxjFeWlKfHAWcvSZ+Z3JV7QZ2J7kG+DcMTmov7LMH2AMwNzc3FaMJfyg0Lfy3\nqDOpz1A4DlwytL2ha1vKXuBdPdYjaQoZatOlz1A4AGxJsplBGFwFXDPcIcmWocte/z7wBbTmueTH\nkzwWWm1LhkKSDzPixG9VjfyMhao6mWQncDuDS1JvrqqDSXYB81W1D9iZ5GXAN4GHWWTqSJK0ekaN\nFMa5wmikqtoP7F/QduPQ49d/p68hSTpzRl199IenHidZ37WdWI2iJJ15TkVpHCPPKST5JeB1DO5n\nSJKTwK9X1a7VKE7S6vKkr0atkno98BLgR6vqGVV1IYP7DH48yRtWq0BJ0uoZNVL4aeDyqnrwVENV\nHU1yLYMlL27quzjNBv+6lM4eo5a5OG84EE7pziuc119JkqRJGRUKj53mPknSjBo1ffRDSb62SHuA\np/RUjyRpgkZdkrpuNQuRJE3exBfEk/rktfnSyow6pyBJWmMMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1\nXpI6Q1xjSFLfHClIkhpDQZLUGAqSpMZQkCQ1hoIkqfHqo7OQi8BJOl2OFCRJjSMFSVrEWr0vqNeR\nQpJtSe5JciTJDYvsvz7JoSSfSfIHSb6vz3okSaP1FgpJ1gG7gZcDW4Grk2xd0O1TwFxVvQB4P/DW\nvuqRJC2vz5HCpcCRqjpaVY8Be4ErhjtU1ceq6i+6zY8DG3qsR5K0jD5D4WLgvqHtY13bUl4D/I8e\n65EkLWMqTjQnuRaYA/7OEvt3ADsANm7cuIqVSdLa0udI4ThwydD2hq7tWyR5GfAmYHtVfWOxJ6qq\nPVU1V1Vz69ev76VYSVK/oXAA2JJkc5LzgauAfcMdkrwI+A0GgfBAj7VIksbQWyhU1UlgJ3A7cBi4\ntaoOJtmVZHvX7W3AdwO3Jbk7yb4lnk6StAp6PadQVfuB/Qvabhx6/LI+X1+S+na23eTmMheSpMZQ\nkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpmYrPU5Ck5YxaY2jW1hea\nZo4UJEmNIwVpTGfbapjSYhwpSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjZekaiZ5eajUD0cKkqTG\nUJAkNYaCJKkxFCRJTa+hkGRbknuSHElywyL7fyLJJ5OcTHJln7VIkpbXWygkWQfsBl4ObAWuTrJ1\nQbf/C1wH3NJXHZKk8fV5SeqlwJGqOgqQZC9wBXDoVIequrfb90SPdUiSxtTn9NHFwH1D28e6thVL\nsiPJfJL5EydOnJHiJEnfbiZuXquqPcAegLm5uZpwOSviTVaSZkmfI4XjwCVD2xu6NknSlOpzpHAA\n2JJkM4MwuAq4psfXUw8c6UhrS28jhao6CewEbgcOA7dW1cEku5JsB0jyo0mOAa8EfiPJwb7qkSQt\nr9dzClW1H9i/oO3GoccHGEwrSZKmgHc0S5Kambj6SOqT502kJzlSkCQ1hoIkqTEUJEmNoSBJagwF\nSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaC\nJKkxFCRJjaEgSWoMBUlSYyhIkppeQyHJtiT3JDmS5IZF9n9Xkvd1+z+RZFOf9UiSRustFJKsA3YD\nLwe2Alcn2bqg22uAh6vq2cBNwK/0VY8kaXl9jhQuBY5U1dGqegzYC1yxoM8VwHu7x+8H/m6S9FiT\nJGmEPkPhYuC+oe1jXduifarqJPBV4G/0WJMkaYRUVT9PnFwJbKuqn+m2fxr4saraOdTnc12fY932\nF7s+Dy54rh3Ajm7zucA9Z6jMi4AHl+01vax/sqx/cma5dphM/d9XVeuX63RujwUcBy4Z2t7QtS3W\n51iSc4G/Djy08Imqag+w50wXmGS+qubO9POuFuufLOufnFmuHaa7/j6njw4AW5JsTnI+cBWwb0Gf\nfcCru8dXAv+z+hq6SJKW1dtIoapOJtkJ3A6sA26uqoNJdgHzVbUP+C3gPyc5AnyFQXBIkiakz+kj\nqmo/sH9B241Dj/8KeGWfNSzjjE9JrTLrnyzrn5xZrh2muP7eTjRLkmaPy1xIkpo1GwrLLcEx7ZLc\nm+SzSe5OMj/pepaT5OYkD3SXIZ9qe0aSO5J8ofvvhZOscSlL1P7mJMe74393kr83yRpHSXJJko8l\nOZTkYJLXd+2zcvyXqn8m3oMkT0nyf5J8uqv/33btm7vlfY50y/2cP+laYY1OH3VLcHweuJzBTXUH\ngKur6tBEC1uBJPcCcwvv6ZhWSX4CeBT4nar6ga7trcBXquotXTBfWFX/apJ1LmaJ2t8MPFpVb59k\nbeNI8kzgmVX1ySRPA+4CXgFcx2wc/6XqfxUz8B50qzRcUFWPJjkP+BPg9cD1wAeram+SdwOfrqp3\nTbJWWLsjhXGW4NAZVFV/xOAKs2HDy5y8l8EP+tRZovaZUVVfrqpPdo//HDjMYDWBWTn+S9U/E2rg\n0W7zvO6rgJcyWN4Hpuj4r9VQGGcJjmlXwO8nuau743sWfW9Vfbl7/P+A751kMadhZ5LPdNNLUzn1\nslC3EvGLgE8wg8d/Qf0wI+9BknVJ7gYeAO4Avgg80i3vA1P0O2ithsLZ4CVV9cMMVqF9bTfFMbO6\nmxZnaS7zXcCzgBcCXwbeMdlylpfku4EPAD9fVV8b3jcLx3+R+mfmPaiqx6vqhQxWdrgUeN6ES1rS\nWg2FcZbgmGpVdbz77wPAhxj8Q5s193fzxafmjR+YcD1jq6r7ux/0J4D/xJQf/24u+wPAf62qD3bN\nM3P8F6t/1t4DgKp6BPgY8LeBp3fL+8AU/Q5aq6EwzhIcUyvJBd0JN5JcAPwk8LnR3zWVhpc5eTXw\n3yZYy4qc+mXa+YdM8fHvTnT+FnC4qt45tGsmjv9S9c/Ke5BkfZKnd4+fyuACl8MMwuHKrtvUHP81\nefURQHf52q/y5BIcvzzhksaW5PsZjA5gcFf6LdNef5LfBS5jsDrk/cAvAb8H3ApsBL4EvKqqpu6E\n7hK1X8Zg2qKAe4F/PjQ/P1WSvAT4Y+CzwBNd8y8ymJefheO/VP1XMwPvQZIXMDiRvI7BH+K3VtWu\n7ud4L/AM4FPAtVX1jclVOrBmQ0GS9O3W6vSRJGkRhoIkqTEUJEmNoSBJagwFSVJjKOiskuTxoVUz\n706yKclckl/r9l+X5D90j1+RZOtpvMZ1SZ7oLjU81fa5bgmGM/H/8OjyvaR+9PrJa9IE/GW3nMCw\ne4HFlhd/BfARYOzVcYfuQD0GvAn4x6dRY2+SnDu0no60Yo4UdNZLclmSjyxoezGwHXhbN6J4Vvf1\n0W6RwT9O8ryu73uSvDvJJ4C3dk/xEeD5SZ67yOs9OvT4yiTvGXqedyX5eJKjXV03Jzl8qs/Q993U\nrb3/B0nWd20rqU86LYaCzjZPHZo6+tBSnarqTgbLPLyxql5YVV9k8Lm5r6uqHwF+AfiPQ9+yAXhx\nVV3fbT/B4BfwL66wvgsZrHvzhu71bwKeD/xgklMjnAuA+ap6PvCHDO6gZoX1SafF6SOdbRabPlpW\ntwLni4HbBkvtAPBdQ11uq6rHF3zbLcCbkmxewUt9uKoqyWeB+6vqs93rHwQ2AXczCJz3df3/C/DB\n06xPWjFDQRo4h8H69ksFytcXNlTVySTvABZ+Wtnw2jFPWbDv1No2Tww9PrW91M9jnU590ulw+khr\n2Z8DTwPo1uf/sySvhMHKnEl+aIzneA/wMmD9UNv9Sf5WknMYrN65Uufw5OqZ1wB/8h3UJ62IoaC1\nbC/wxiSfSvIs4J8Ar0nyaeAgY3xEa/dxrr8GfM9Q8w0MTkTfyeDDX1bq68ClST7H4CMbd3XtK65P\nWilXSZUkNY4UJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSp+f85tvoP4nFRigAAAABJ\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "emT7m8pI6lP8",
        "colab_type": "code",
        "outputId": "078052da-07ac-4e65-8731-79a6bede029d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        }
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 294,947\n",
            "Trainable params: 294,051\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OcsyCU-caFc9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# # not working\n",
        "# mat_save = {'diScore': di_scores, 'conv2d_1': cnn1_output, 'trueLabel':train_three_label}\n",
        "# savemat('LowAccuracyConv2d_1MatrixMethod', mat_save) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "n7Xmu2bLaFZa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Lxf2mXCG8nW9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "t3vXkMPaaFWT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1WLuuRgeFImF",
        "colab_type": "code",
        "outputId": "17afc009-5768-4401-c6c6-922a7cf01db5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "sum(di_scores)"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.95018826561574"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "metadata": {
        "id": "w27VKTFQUI6d",
        "colab_type": "code",
        "outputId": "de551a4a-c33d-4c84-bfc5-d09191446eb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "max(di_scores)"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6057145859530139"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "metadata": {
        "id": "8VzlOOa1YB0I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from scipy.io import savemat\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HDtz_epHYCT_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}