(dropout_3_Layer)cifar103ClassAccuracyLowDIScoreCrossCrossVer04.ipynb

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  "metadata": {
    "colab": {
      "name": "(dropout_3_Layer)cifar103ClassAccuracyLowDIScoreCrossCrossVer04.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/KProj/blob/master/(dropout_3_Layer)cifar103ClassAccuracyLowDIScoreCrossCrossVer04.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",
        "outputId": "0f6e2e2c-b13a-4e57-aa8c-b90bf4aac077",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "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": "9m3ehGh2iIBA",
        "colab_type": "code",
        "outputId": "4d47761b-ca66-426a-ccd4-4537194e7194",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "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": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 12s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "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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\nS4+xitZisVh6zP8CB+zi+GJTgIQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hVAjMdeSrloa",
        "colab_type": "code",
        "outputId": "e8e14e79-6822-4724-c5c2-73edf3f3b72d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "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": "76e23a3a-ccce-4656-bb5a-a59ca46dd390",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "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": 7,
      "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": "082f8046-f868-46cb-db65-974962a36276",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "cell_type": "code",
      "source": [
        "plt.hist(train_three_label)"
      ],
      "execution_count": 8,
      "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": 8
        },
        {
          "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": "af633019-b785-422b-e7c9-17583175fc62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "cell_type": "code",
      "source": [
        "# test_three_label.shape\n",
        "plt.hist(test_three_label)"
      ],
      "execution_count": 9,
      "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": 9
        },
        {
          "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": "c62dd974-2637-4138-d44c-8f6042a6b4a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "train_three_label_cat.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "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": "1ffdac83-a653-4b90-e1c9-9e0c153ea8c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_label_one_hot.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "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": "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": "-VPIvSX1jytM",
        "colab_type": "code",
        "outputId": "416788a4-9499-4a9a-8d41-c6165aa1c733",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 972
        }
      },
      "cell_type": "code",
      "source": [
        "model = Model(input_img, (cifar10Classifier(input_img)))\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": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "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('.mdl_wts.hdf5', save_best_only=True, monitor='val_loss', mode='min')"
      ],
      "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": "217c76cd-2cd2-48d8-ea65-115a00328d32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        }
      },
      "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": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/20\n",
            "187/187 [==============================] - 14s 73ms/step - loss: 1.1549 - acc: 0.5989 - val_loss: 0.9712 - val_acc: 0.6083\n",
            "Epoch 2/20\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.8008 - acc: 0.6681 - val_loss: 0.8762 - val_acc: 0.6750\n",
            "Epoch 3/20\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.6811 - acc: 0.7085 - val_loss: 0.6707 - val_acc: 0.7113\n",
            "Epoch 4/20\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.6405 - acc: 0.7303 - val_loss: 0.5778 - val_acc: 0.7760\n",
            "Epoch 5/20\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.5936 - acc: 0.7517 - val_loss: 0.5538 - val_acc: 0.7703\n",
            "Epoch 6/20\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.5757 - acc: 0.7630 - val_loss: 0.5715 - val_acc: 0.7743\n",
            "Epoch 7/20\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.5534 - acc: 0.7757 - val_loss: 0.6812 - val_acc: 0.7397\n",
            "Epoch 8/20\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.5361 - acc: 0.7853 - val_loss: 0.6019 - val_acc: 0.7820\n",
            "Epoch 9/20\n",
            "187/187 [==============================] - 10s 51ms/step - loss: 0.5170 - acc: 0.7911 - val_loss: 0.5835 - val_acc: 0.7917\n",
            "Epoch 10/20\n",
            "187/187 [==============================] - 10s 51ms/step - loss: 0.4989 - acc: 0.8051 - val_loss: 0.5032 - val_acc: 0.8023\n",
            "Epoch 11/20\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.5057 - acc: 0.8056 - val_loss: 0.5254 - val_acc: 0.8120\n",
            "Epoch 12/20\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.4820 - acc: 0.8121 - val_loss: 0.8152 - val_acc: 0.7530\n",
            "Epoch 13/20\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.4877 - acc: 0.8184 - val_loss: 0.5412 - val_acc: 0.8090\n",
            "Epoch 14/20\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.4695 - acc: 0.8194 - val_loss: 0.5003 - val_acc: 0.8143\n",
            "Epoch 15/20\n",
            "187/187 [==============================] - 10s 52ms/step - loss: 0.4851 - acc: 0.8237 - val_loss: 0.4820 - val_acc: 0.8213\n",
            "Epoch 16/20\n",
            "187/187 [==============================] - 10s 53ms/step - loss: 0.4558 - acc: 0.8300 - val_loss: 0.4424 - val_acc: 0.8460\n",
            "Epoch 17/20\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.4504 - acc: 0.8329 - val_loss: 0.4039 - val_acc: 0.8540\n",
            "Epoch 18/20\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.4441 - acc: 0.8355 - val_loss: 0.5838 - val_acc: 0.8027\n",
            "Epoch 19/20\n",
            "187/187 [==============================] - 9s 51ms/step - loss: 0.4405 - acc: 0.8383 - val_loss: 0.4912 - val_acc: 0.8283\n",
            "Epoch 20/20\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.4334 - acc: 0.8418 - val_loss: 0.4433 - val_acc: 0.8437\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "w4M2DfGjmT3I",
        "colab_type": "code",
        "outputId": "474e8304-5cdc-4462-ebe9-ed85ef64c455",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "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": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.4190043207804362\n",
            "Test accuracy: 0.8513333333333334\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "M1_boK_fprtB",
        "colab_type": "code",
        "outputId": "fdb9e764-75ce-41fc-cf25-96963fad6561",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_data.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "ZuH6igT7nbza",
        "colab_type": "code",
        "outputId": "c954a6ac-95cc-4ef9-a79e-e5f26e2a41f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "cell_type": "code",
      "source": [
        "# show_imgs(test_three_data[:,1])\n",
        "show_imgs(test_three_data[:16])"
      ],
      "execution_count": 25,
      "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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Pflas5O3tHn4kz4PJtbNAQ8/DVkwTDxJ3XwyGSlQ51AkLKfCfGGO+p5RqAt9V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\n7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J6KApOXVigXewpCj9yr9IGGFMzrNCHTeUY3ohzZyxgMBnSkvCgxBl/qGI0GpASkGpWY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\nnr0AAAAASUVORK5CYII=\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": "3bc166c8-dd1b-407d-e9c2-aa3c4edf23d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# layer_name\n",
        "valid_X.shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "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 = 'dropout_3'\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": "c1017fc9-7e61-4f19-b7f8-8f986d7fc185",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cnn1_output.shape"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 4, 4, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "EF3q_KOgYTxb",
        "colab_type": "code",
        "outputId": "bfd94d6b-54f9-4f05-e245-c1cb0db42ef6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cnn1_output.shape"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 4, 4, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "B0Hfr4ZwB8dX",
        "colab_type": "code",
        "outputId": "47d0eb91-93a5-4534-d083-8562262ac91e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "np.ndarray.tolist(np.unique(test_three_label))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 3, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "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": "5f35f7d1-44e1-4dd5-e513-731907547746",
        "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": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'DI Score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lSjPzr0qtNl2GwgFgU5KN9MJgB/DS/gWSnAX8AbC1qu7osBZpRRp0T9Fw06A6C4WqOp5k\nF3AtvUNSr6yqg0kuA6aqah/wFuBHgKuTAHy9qrZ1VZOk0TQO3aCrJVg7HVOoqv3A/mltl/Y9f16X\nr6/FGYcPqKRujMRAs7qxWv6ykbR0DAVpmay0kHaPcmXygngjwLOptRqsxPf5Svt9wD0FSY0H85f/\nSvxyXK0MhRXCXXmtFAbMcNl9JElqGQorzErst5W0fAwFSVLLMYWODfMGJlIXfB8uzriM+xkKWnH8\n0lrdxuXLd1TZfaSR4piINFyGgiSpZShoVRqHPZJxqHGxVurvtZSG9f/vmIJGgl8SK9c4/9+uxvEJ\n9xQkSa1VtaewGlN/FI3zX47SStdpKCTZCryd3p3XrqiqN02b//PA7wNnAjuq6pou61lKCw0YA0nL\nZSWH7kr+3eaynN8fnXUfJVkD7AbOBTYD5yXZPG2xrwPnAx/oqg6Np5U8yCqNsi73FLYAh6vqCECS\nvcB24OYTC1TVV5t593VYhyRpQF2Gwlrgtr7po8DTO3y9JWNXz+hzL0LqxlgMNCfZCewEOOOMM5bt\ndQ0HjSpDUV3pMhRuB9b3Ta9r2hasqvYAewAmJyfrwZcmaTUwPBeuy/MUDgCbkmxMcgqwA9jX4etJ\nkh6kzkKhqo4Du4BrgVuAD1fVwSSXJdkGkORnkxwFXgL8QZKDXdUjSZpfp2MKVbUf2D+t7dK+5wfo\ndStJkkaAl7mQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUM\nBUlSy1CQJLUMBUlSy1CQJLUMBUlSq9NQSLI1yaEkh5NcNMP8hyb5UDP/c0k2dFmPJGlunYVCkjXA\nbuBcYDNwXpLN0xa7ALi7qh4HXA68uat6JEnz63JPYQtwuKqOVNW9wF5g+7RltgPvbZ5fAzw3STqs\nSZI0hy5DYS1wW9/00aZtxmWq6jhwD/CjHdYkSZpDqqqbFScvBrZW1aua6V8Bnl5Vu/qW+WKzzNFm\n+svNMndOW9dOYGcz+QTg0IMo7XTgznmXGl3WPzzjXDuMd/3jXDuMRv0/VVUT8y10cocF3A6s75te\n17TNtMzRJCcDjwbumr6iqtoD7FmKopJMVdXkUqxrGKx/eMa5dhjv+se5dhiv+rvsPjoAbEqyMckp\nwA5g37Rl9gGvaJ6/GPhUdbXrIkmaV2d7ClV1PMku4FpgDXBlVR1MchkwVVX7gD8E3pfkMPDX9IJD\nkjQkXXYfUVX7gf3T2i7te/4PwEu6rGEGS9INNUTWPzzjXDuMd/3jXDuMUf2dDTRLksaPl7mQJLVW\nVSjMd9mNUZJkfZJPJ7k5ycEkr23aH5PkuiRfav49bdi1ziXJmiSfT/LxZnpjc0mTw80lTk4Zdo2z\nSXJqkmuS/FWSW5L83Lhs/yQXNu+bLyb5YJKHjfK2T3Jlkjuaw9RPtM24rdPzjub3uCnJ04ZXeVvr\nTPW/pXnv3JTko0lO7Zt3cVP/oST/dDhVz2zVhMKAl90YJceB11fVZuAZwK819V4EfLKqNgGfbKZH\n2WuBW/qm3wxc3lza5G56lzoZVW8HPlFVTwSeQu/3GPntn2Qt8OvAZFU9md6BHjsY7W1/FbB1Wtts\n2/pcYFPz2Am8a5lqnMtVPLD+64AnV9WZwK3AxQDN53gH8KTmZ97ZfD+NhFUTCgx22Y2RUVXfqKr/\n2zz/W3pfSGu5/6VB3gu8aDgVzi/JOuAFwBXNdICz6V3SBEa4/iSPBn6e3hFyVNW9VfVtxmf7nww8\nvDn/5xHANxjhbV9V/5PeEYj9ZtvW24E/qp7rgVOT/MTyVDqzmeqvqj9rrtQAcD29c7WgV//eqvpe\nVX0FOEzv+2kkrKZQGOSyGyOpuXrsWcDngB+vqm80s74J/PiQyhrE7wP/Brivmf5R4Nt9H5RR/j/Y\nCBwD3tN0f12R5JGMwfavqtuBtwJfpxcG9wA3MD7b/oTZtvU4fpZfCfxp83yk619NoTCWkvwI8MfA\nb1TV3/TPa070G8nDx5K8ELijqm4Ydi2LdDLwNOBdVXUW8B2mdRWN6vZv+t630wu2nwQeyQO7NsbK\nqG7rQSS5hF538PuHXcsgVlMoDHLZjZGS5CH0AuH9VfWRpvlbJ3aVm3/vGFZ983gWsC3JV+l11Z1N\nr4/+1KZLA0b7/+AocLSqPtdMX0MvJMZh+z8P+EpVHauq7wMfoff/MS7b/oTZtvXYfJaTnA+8EHhZ\n39UaRrr+1RQKg1x2Y2Q0/e9/CNxSVW/rm9V/aZBXAP91uWsbRFVdXFXrqmoDvW39qap6GfBpepc0\ngdGu/5vAbUme0DQ9F7iZ8dj+XweekeQRzfvoRO1jse37zLat9wG/2hyF9Azgnr5uppGRZCu97tNt\nVfXdvln7gB3p3WRsI70B8/8zjBpnVFWr5gE8n95RAF8GLhl2PfPU+mx6u8s3ATc2j+fT65f/JPAl\n4L8Djxl2rQP8Ls8BPt48/8f0PgCHgauBhw67vjnqfiow1fwf/Alw2rhsf+DfAX8FfBF4H/DQUd72\nwAfpjX98n95e2gWzbWsg9I4k/DLwBXpHWY1i/YfpjR2c+Py+u2/5S5r6DwHnDrv+/odnNEuSWqup\n+0iSNA9DQZLUMhQkSS1DQZLUMhQkSS1DQStKkh8kubHvsSHJZJJ3NPPPT/KfmucvWsxFEZt13Jfk\nzL62LzaXI1mK3+HvlmI90mJ0euc1aQj+vqqeOq3tq/TON5juRcDH6Z3YNZC+M4KP0jvW/F8uosbO\nJDm5fnh9I2nB3FPQipfkOSfu59DX9kxgG/CWZo/isc3jE0luSPK/kjyxWfaqJO9O8jngd5tVfBx4\nUt8Zz/3r/ru+5y9OclXfet6V5PokR5q6rkzvXg1XTVvH5endD+GTSSaatoXUJy2KoaCV5uF9XUcf\nnW2hqvoMvcsNvKGqnlpVX6Z3H93XVNXPAL8JvLPvR9YBz6yq1zXT99H7Av63C6zvNODngAub17+c\n3nX1/0mSE3s4jwSmqupJwJ8Dv9O0L6Q+aVHsPtJKM1P30byaq9E+E7i6d7kgoHdpiBOurqofTPux\nDwCXNNevGdTHqqqSfAH4VlV9oXn9g8AGepdDuA/4ULP8fwE+ssj6pAUzFKSek+jdb2C2QPnO9Iaq\nOp7k94Dfmj6r7/nDps37XvPvfX3PT0zP9nmsxdQnLYbdR1rN/hZ4FED17lXxlSQvgfY+wE8ZYB1X\n0btU9URf27eS/HSSk4B/voi6TuKHVzN9KfAXD6I+aUEMBa1me4E3NHdWeyzwMuCCJH8JHGSA27VW\n79au7wB+rK/5InoD0Z+hd+XMhfoOsCW9m8CfDVzWtC+4PmmhvEqqJKnlnoIkqWUoSJJahoIkqWUo\nSJJahoIkqWUoSJJahoIkqWUoSJJa/x/N8sSlRVL+xwAAAABJRU5ErkJggg==\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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "84bd7f46-eaa1-4b29-a4c7-90e891b2915c"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "train_three_data_low,train_three_label_low,test_three_data_low,test_three_label_low = getThreeClassesTrainTest([1,8,9],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[    4     5    32 ... 49993 49998 49999]\n",
            "[    8    62    69 ... 49968 49976 49985]\n",
            "[    1     2    14 ... 49963 49971 49997]\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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f9629325-103d-4c84-8fda-902c82c7e2af"
      },
      "cell_type": "code",
      "source": [
        "layer_name = 'dropout_3'\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([valid_X_low])[0]\n",
        "cnn1_output_low.shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 4, 4, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "f83e3d3b-70d3-4757-b1ed-a20325893185"
      },
      "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')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'DI Score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "emT7m8pI6lP8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "outputId": "0b757d64-4a67-425b-d4f5-cf50ae224649"
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 42,
      "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": "d952f2e3-2582-4b00-e56c-f04702663abc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "sum(di_scores)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38.141677749945984"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "w27VKTFQUI6d",
        "colab_type": "code",
        "outputId": "aad2ff98-f25d-4911-f78a-aabefc44e8b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "max(di_scores)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8258121827128675"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "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": []
    }
  ]
}