gaussian_rRMSprop_0.1.ipynb

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    "accelerator": "GPU",
    "colab": {
      "name": "Copy of scenes_nn_4 layers.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rfadhila07/Test-result/blob/main/gaussian_rRMSprop_0.1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dJvuvIBVLS_",
        "outputId": "47dc2691-e605-4235-b4d6-d692bda4d7a6"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H215ikbNVVZS",
        "outputId": "67bb53a1-9174-404c-b103-b4431f4410ae"
      },
      "source": [
        "cd /content/drive/MyDrive/ "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7VB1d4wVdov",
        "outputId": "64df2b7d-febc-488c-c245-29de35c980b2"
      },
      "source": [
        "ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'1101174267 m rifqi fadhila mixer.pdf'\n",
            "'1101174267_Muhammad Rifqi F_2_TT4107.pdf'\n",
            "'1101174267_MuhammadRifqiFadhila_1&3_TT4107.pdf'\n",
            " 1101174267_MuhammadRifqiFadhila_3.3_TT4107.pdf\n",
            "'1101174267_muhammad rifqi fadhila_Filter2.jpg'\n",
            "'1101174267 muhammad rifqi fadhila OSILATOR.pdf'\n",
            "'1101174267 muhammad rifqi fadhila penguat.pdf'\n",
            "'1101174267_Muhammad Rifqi Fadhila_Proposal Tugas Akhir dan lembar pemeriksaan.pdf'\n",
            " best_weight_scene_EfficientNetB0.h5\n",
            " \u001b[0m\u001b[01;34mCLAHE\u001b[0m/\n",
            " \u001b[01;34mClassroom\u001b[0m/\n",
            " \u001b[01;34mDataset\u001b[0m/\n",
            " DSC_7396.JPG\n",
            " \u001b[01;34mGaussian\u001b[0m/\n",
            "'Getting started.pdf'\n",
            " IMG-20210217-WA0001.jpg\n",
            "\u001b[01;34m'MPTI 2021'\u001b[0m/\n",
            " \u001b[01;34mNormalized\u001b[0m/\n",
            "'PMMB2021_BATCH1_1101174267_MUHAMMAD RIFQI FADHILA.rar'\n",
            " Screenshot_20210217-060246.jpg\n",
            " TT_41_07_101174267_MUHAMMADRIFQIFADHILA.pdf\n",
            " \u001b[01;34mValo\u001b[0m/\n",
            " \u001b[01;34mVisualizable\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IGm26ZzVGBs"
      },
      "source": [
        "# import the necessary packages\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.layers.core import Activation\n",
        "from keras.layers.core import Flatten\n",
        "from keras.layers.core import Dense\n",
        "from keras.optimizers import Adam, SGD, RMSprop\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from PIL import Image\n",
        "from imutils import paths\n",
        "from skimage import io\n",
        "from skimage.filters import gaussian\n",
        "from skimage import exposure\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iF6yEboXxV9J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbbf98c9-9f0f-44c2-ff5a-fd5489e4addb"
      },
      "source": [
        "#Normalized dataset\n",
        "import PIL.Image as pil_image\n",
        "import io\n",
        "path ='/content/drive/MyDrive/Dataset/Normal/'\n",
        "path_no = 1\n",
        "for paths in os.listdir(path) :\n",
        "  if paths.endswith(\".tif\"):\n",
        "    filepath = os.path.join(path, paths)\n",
        "    img = Image.open(filepath)\n",
        "    array=np.array(img)\n",
        "    max_val=np.amax(array)\n",
        "    normalized=(array/max_val)\n",
        "    im = pil_image.fromarray(normalized)\n",
        "    if path_no < 10 :\n",
        "      save_path='/content/drive/MyDrive/Normalized/Normal/ ' + str('IM0000') +str(path_no) + '.tif'\n",
        "    elif path_no <100 :\n",
        "      save_path='/content/drive/MyDrive/Normalized/Normal/' + str('IM000') +str(path_no) + '.tif'\n",
        "    else :\n",
        "      save_path='/content/drive/MyDrive/Normalized/Normal/' + str('IM00') +str(path_no) + '.tif'\n",
        "    im.save(save_path,'TIFF') \n",
        "    path_no +=1\n",
        "print(\"Normalized Success\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normalized Success\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9Uvnrw4rqNh",
        "outputId": "6b80c02f-e9cb-4d0a-e464-0901dbf4c235"
      },
      "source": [
        "#Gaussian Filter\n",
        "import cv2\n",
        "from skimage import io\n",
        "from skimage.filters import gaussian\n",
        "path ='/content/drive/MyDrive/Normalized/Normal/'\n",
        "path_no = 1\n",
        "for paths in os.listdir(path) :\n",
        "  if paths.endswith(\".tif\"):\n",
        "    filepath = os.path.join(path, paths)\n",
        "    img = Image.open(filepath)\n",
        "    img = np.array(img)\n",
        "    gausfilter = gaussian(img, sigma=1, mode='constant', cval=0.0)\n",
        "    im = Image.fromarray(gausfilter)\n",
        "    if path_no < 10 :\n",
        "      save_path='/content/drive/MyDrive/Gaussian/Normal/' + str('IM0000') +str(path_no) + '.tif'\n",
        "    elif path_no <100 :\n",
        "      save_path='/content/drive/MyDrive/Gaussian/Normal/' + str('IM000') +str(path_no) + '.tif'\n",
        "    else :\n",
        "      save_path='/content/drive/MyDrive/Gaussian/Normal/' + str('IM00') +str(path_no) + '.tif'\n",
        "    im.save(save_path,'TIFF') \n",
        "    path_no +=1\n",
        "\n",
        "print(\"Denoissing Success\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Denoissing Success\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6b1PTs21ki0",
        "outputId": "028554f8-d7b6-450b-d05a-def065b74cda"
      },
      "source": [
        "#CLAHE\n",
        "from skimage import exposure\n",
        "path ='/content/drive/MyDrive/Gaussian/Normal/'\n",
        "path_no = 1\n",
        "for paths in os.listdir(path) :\n",
        "  if paths.endswith(\".tif\"):\n",
        "    filepath = os.path.join(path, paths)\n",
        "    img = Image.open(filepath)\n",
        "    img = np.array(img)\n",
        "    clahe = exposure.equalize_adapthist(img)\n",
        "    im = Image.fromarray(clahe)\n",
        "    if path_no < 10 :\n",
        "      save_path='/content/drive/MyDrive/CLAHE/Normal/' + str('IM0000') +str(path_no) + '.tif'\n",
        "    elif path_no <100 :\n",
        "      save_path='/content/drive/MyDrive/CLAHE/Normal/' + str('IM000') +str(path_no) + '.tif'\n",
        "    else :\n",
        "      save_path='/content/drive/MyDrive/CLAHE/Normal/' + str('IM00') +str(path_no) + '.tif'\n",
        "    im.save(save_path,'TIFF') \n",
        "    path_no +=1\n",
        "\n",
        "print(\"CLAHE Success\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CLAHE Success\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HCs86pnwc5WL",
        "outputId": "ff1ff9bc-6b02-4381-aee9-00fcd28c3648"
      },
      "source": [
        "# grab all image paths in the input dataset directory, then initialize\n",
        "# our list of images and corresponding class labels\n",
        "print(\"[INFO] loading images...\")\n",
        "imagePaths = paths.list_images(\"Gaussian\")\n",
        "data = []\n",
        "labels = []"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] loading images...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsbyXzXK0k-4"
      },
      "source": [
        "import cv2\n",
        "\n",
        "im = cv2.imread('/content/drive/MyDrive/Gaussian/Covid/IM00001.tif', -1)\n",
        "im_rgb = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "me1s-uFI7kJi",
        "outputId": "ee65e6ec-f105-4484-ded0-aa7c9f110acc"
      },
      "source": [
        "\n",
        "plt.imshow(im_rgb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f303d83bc10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g731n7VyhnDH4unhjTr1ORlICYd+j4nURbnxcBp2tca0/k9/j3PmcuFvqYBZ3E/+i2tgAg1QMWsUCGGkEDm59WCCU2ANlHuxCSBzpy5TSN2tFHzlSbD6PdJ1rIiuJVsMpa1RK/zm6HpGtxPmjD2VujI/ZxxhjLcxhZETeV5+beB/67fQ8Bjm9CpR+xr85k4ob6eiDK5k/IzKkWDDlIBKZV3TXfC5dlmCC3McBwMECdgRI+rr72sdgNH8vO+j4VbaxAQangEygVLSGrhhuQcrSQlLRd2ejStxv4EITlTsG5/yeURBpbrEjHYxBTkmlCueC4S7KVUIXFT8yqKjM0YJGSxytXbyHz0sEqfi3GFQrYxU+fzFg56DkSurjdYsb54SYkM9dXMuYCscFIX7BmtC/L+p7VOgYS+Fesb6GOXOG4PLyi65xGJvgY6y0Qwj8LT/xzVKuJO5SSMVqw6uUye/DQlBZ6fltf5YHvpxmOhWkxec6MPlzXYldAfhuVCYXcPrh9NOzEF80Zu4hjcDI7xPHFd0PVzi3gg52Ubn9JOYIZDzfgQrgB9yjK+Ssx0GK73ng0vvuPzOGGCfwuWCMuGhlLIJ5jOuEG+txDDdAMcBI/10uI9v0tXgVbWwYQ/RT4yJHRXEBL7Nwjs4IiSuhswsW3BchujV+nVR0IVzZ/bne3H3gOp4RjyRzGlzGQOJ8+Xe+yCrHOYyW9osYTmRocXxxI5D/HIOkPhYHSd8l62wHCx4ZoSt8VH52TcZ58/t6n3zdASj647IYx+1z6f9Tts/1EXCd6ZT10eMk/kyOIHjVbSyAwRfba/JRYibZ3YwywXUaF5XN//lzY3CR5qgtFV0L96f5m9/TA1RllNLH7NbWLW7sC9+JqU0fn1PYMqviVtH7WVbEVfZ8/92tbBTgaE09XuBj8Rb9ap+D+H8MAPJZBD5+doYS3ZvI2lwpsfT+nKuA2A2V95OApo8J2eR3ZwYuU4BijEk5I3xVbSyAAaFiwEymF6swIdGyxSBV2YT55EujQOVVVXFSUcGx2C5UkYrTnBX4wntQLlo9nu0ZksiEokXy77vCRxYTWYX3OVru+Pf4nTLXwu/lc+SxGKfJMb4RrX68rzMwBz6u8/RyGb2P7grXOFsqA0U3ML7G0Rg5o/Pm88EcuFHxdfI9HfG7V6WRXzU4jE2MISJ89FGly7sbJRVAISppWbCPBR8Oh+r3+wULFalcpXLxPsJ+v5+YS9leBFqk3dHS8XOk3FGRojsU2Yuk5LOWxSywMj4uVwoHQeIqVwXvuLfHgMoYhANUrASM2aDIorhHLFTin7ONuD/lqhiIW9joCrif7vMgFQOSZVa9jMWUATZ98D57jKVSqRTeVBUZVpSlyF59PeN6fB1tLBiDdLl6kIX1Q0Rc2NytiIjq93FWwX2jL+sswAWS+5RZ07LiokiDfdHKlIrPsBowmLK0lDMJ/773rWwuXGFd6binX+PWMcZgysDM58nngh2SWEQ+9/46+F41L66YZWAUQcHnztfH++uGJLqF3kdnOQBtlBcff2S0Zfd0VuyMwI2hp7h9PeNzMFqvijWMDTCUbaqJFoC68igE/l2pWMBSJlQ+uTAAJj3GL2AK0bpFtwIr5oJ+XWtFc6FywfLgJ5/F6H0MXjpI+bj92gh8/hzmjiCcPyta4hj8LQsGX7UhKCoPfXcwi/UUrjiMI7ohDsqeyvagoo8jsk5kIsYxItvyPnsrA8rYP/pctlkwskxnDT4Xr6q+YWxciTJq7oE2F+pII33XHwLtllu6LDzQVreQsQ8utFBiaeQ3ez+4pzcULN7b3QR+5/94zFqk1h7EdKsT6aj/HOeCe/vGpjJmE2l/WaEVbgLXei7fn0kf/JnMqd/fGVkZsEXQ/iKL6SDDWvqLiZ1ROAg7iDuIxPHQ35hVKFNUl4Or1hcZimzNgcHXwYGkbG/NX6eNDTD4AtJ8UjixKVpUSZdQ3SffT4DiO3EXYVQcR2f/3ZWM7/FMp6dxX4aDD9fTfGtxZBgRpPjcGUwUmii4Pm/+HfdjvW6EZ0Tr7pWkMRAbFcaZiCugry/z5ZYyAoHPe/w5XuPMyg2JK7AH+aKRiWNwmYrH1/G7P8vnPsZsIrNEBiJYRNbEdSh93NDnTO8qRvZV29i4EtHCRd/KhZagm0d9afFnFsaLkfw5PqFecstC+fHy9MUPLInKGC2Lf8+f7/5i7I9v/ikTdu8fzQXQLRjUOQog94wumFfdxYBaHCff9VJjj5a7Artrx/NiOXmWZYU9IfG5kfrTF77r7qYrsn/PP3NWEIHT0+LxXshgBBYHCcbnbC7OF/eLDMBjNg7qPua4fl/3foqxYQyNRiNNuC+io2zMJ7tix4WXiseUSSrQSLe80WWRiu6JdLkajc/oiwtv2Wd+RHm0Tm6ByiikM45off0zp+XxGX5PxhEVPaYvXdi9itDXxp/lilLGjsrW6CpQjawtzmuZMYhjcIrt8SgfW+yjs1KfW39mGZNxGfRWBgo8h0pO+se1blQii/F++7py7+jOftU2NsDAJBCVdSGMwcUIBJGKuhXn3lJ5KigKnN8byxF9ce+nW4mI7lh+6jKuWvCoAFjuSL397y4APgc+dtwmf26knXzP3SsHtUhrfR4iu4rf83H5PHla0tfIv+PAyHNpzKmXGfv4orK5Unv8w12/6NfHAKszTuYrWvqYRfAxcn+PS5QxAzdY8RAXNwbRBXID83W4FGMDDEyMHwHui+m/R78yChBI7Avuwsa9WCDp8q42riurYfd+lMUsPEVXJoDOZDyIWVYz7wDnVNzH7IBIc4WOzRXDLWnZGOPcORhHJYgWP861C31cT9bND8KJY3DmCBuIrgUy5K5QdAViXMBjLJ5mLWOCniHxsfnPzkSjEYluaRl4sR78HUPgssD4nCHh5n4db7b60hhDlmX/Msuy9SzLfmyfzWVZ9m+zLPvLF//Pvvg8y7Lsf8iy7JMsy/48y7JfvU4nfHKZJKwtAh8RnO/598tqziObiBQ1Wkb/blwIfEYXbITLA04uhJ5X93F6/2PwyQHL++gg4MyHa6IwS5erR3lupKaRQvtnZRYoUusIYoy5TIli1anPjQu1A0603J5ipgEWLkveXx9fZEau8H4P5t3XLCpxZApxfbm+rETe93PE6ki+H+WauWODYQSwyMa+SrtO8PF/kvS3wme/I+mP8jy/L+mPXvwuSX9b0v0X/74n6V9ctyMMjsV1XywuKDQyWk7/vt/Xf+a7bjHLLJMHkOLzY3TYQSYGOp3i85lTSt+n4NQ0UsXIYrAOUdk9pehAE+fYA2NlFisqhoNR2b29/zwrgm8MAPt1DnreTwdJLGek8FFuvIgIJgQYcR93/ViHuE+Ha2NAmO8T8HPjRb94ZnS3IgOM7MzlxefDg7vRtYos7+sIRH4pMOR5/u8kbYePf1PS7734+fck/Zf2+e/nF+3fS+pmWXbjGs9Ig4/0N1ojp5n87vcoq0h0K8DCO73zfzFXz3Oviky79ZVU+IxrnPHEcUSrxrNdQKIARXZQlsOOfqk/11N2ZWON88Y1EWA8DRj77H2IlN6fE12CaJ19TRwkHRCiS1P2mY+ZtfJYCd9zyh7Hz3fi1vGye7sc8r3IwmLcpCzLxvj9u86mvVCPf5zd8NdpXzVduZzn+eqLn59LWn7x8y1JT+y6py8+u9SyLPtelmX/Icuy/+AUsmxCpWKJNAvjaTKUzy1GVJpINSPjuIr6Og30zx2AuF8ZrXPr59/nZ+nymYtRmZkjt8JuKXgGzTMt/pyoUB5TiezB59WVyNfGa/8R/jLm4wwiulBc6wrq1NrZoH8vyy6/sNeNhjMHf5GxdHmrezQ+Lnf+HOQnKrYbJfruz4guhI8nzrvLTSyd9nXjM39BD2Oo1+tXxpiu0/7adQz5RU9eOtKR5/n38zz/dp7n344T5ijP3yL993P3PaAUaZc08tFckf16t5hO66MllS5vd6Y5xS2jrAizxyfc6pZFrZ3J2LwVBAcaHOlsFGju4ylNn4tYi+Bvcor02oWdn2Pa2N2aCHi+Jm4dI0PwPvlc0/+YmnPwitc7cMDinO3En1kv5iEeShxZGvPE/DLXZf2LIB376TJFH3zt/Xdk3OMNkcV+lfZVgWENF+HF/+svPn8m6bZd99aLz768I8EyxcAXQuIA4ukZ9xcjsETfNyK2f+Y00yfZrYSDSQQW93UdFLjOD+/gO/FeTmUjtZRUmBuudaF2AXSmI40UMtbYA2yRgQEupFxjPxxk/PcY63BwjnPiVt3jPg6srHeMq3h8Is5THI/HCby/ZevpwMmhL250rooZeV88TuZs1d2neJ94rzI27XIOM/H19hjHVwWHrwoMfyjpt1/8/NuS/rV9/vezi/afSdozl+MLm1NrX6hYq85k9Pt99fv9wmREKspi+kGx3hxMXEG/yEp5P33SI2UuC5rGWINbMZoDpNNFp8/+HVeOGLcoi5aTEvX0pM87yjIYDNTv9y8djOOB37heETy5f1TcuA701eeB73Ivz8LEdYoKE1mV+/9lwUAH4iiP9Nf7XgbYZYy37DvOhpBRl5/YB5/bMsbmc+LuHEboq+7A/NIIRZZl/4ukvyFpIcuyp5L+maTflfSvsiz7R5IeS/qtF5f/G0l/R9Inko4k/cPrdsStHQIqFWMM/X4/+U5xsHzf010+SX5v/hYXy33j4bD4ejqaW0OvK/BFc0tO32j9fr/0c1dup+U+D+5vR4vqQU6u8z4788ICxv47Y6J/fH56enopMFh2nJuDoK+Dj5N+xH5GtuRK7sG2CDwOcn49n9EHn29/lvc7ggVygAviSu+AHdfJnx2ZK0wXZsn3y5hkDJQ6K4s6AuvCoPi4I+h+WftSYMjz/O9e8ae/WXJtLukfv1QPrMXMgVQ86s39SqiwK5CDCj/7Md9YS+nyDriyugEXFEfpMjchxgh8X310c2gOJpEJeL9cCblHmfKjFPH+Hi8pi1fwfWdFscjM7wlwRaWr1+vp3RzeT1fyyJIiW4pzD5D536+y8K48bhRimjQqVlQ4/4x594KjGFB0Nlg2Xjdu0eVzQxWVP8/z5MI5ODI3FDPFosBolMrY8pe1sal8jBVlHvjyyKz7iQ4AnAkZg0quTL7YTi8jaLhiuCWXRpF2zxC4L+vPjIrKvb0v/qYpB4hYyhx99EgdY20FffXvRLYRszqMx+/POLw/lGt7QE5SYhX8nXmRLruIDpKxX/6y4QimrE0s3IqgUalcBOlcaXzu4ynVboCYqxhQZK0ZTwQanhtLvrlf2XsxIvvhf/88up+eWXEA4n7eCNK/7NuzxwYYypTJGYFbdEf1WHjC/2U0MWYanAm4G8FEAjY+6bAQBzDuF/35ss9ciP2ekf66ssR5wvJw/wg2/I5iRrfEc99X0UwUHwDxQ3KkC4Gr1+saDi9KmX3+vP805tIVMFpN+sbvfjZBtII+P56qK2NJ0VWIgBTZhd/fi+34jlt2z3AwP25oHGwJnjrbctB38AVg/RpfO+T+qqIxADG+Ie26bWy2XfvWV2lUc+D0XLr8zgTPTPA7RR6O3q7gfJfm33f/DArrPn9UvkhBvf/SqHzVi1IivXWryz3cd/Xx+iJHoIsWtqx/tVpNjUYjbSfnPv1+X4PBIFFT+uRMLrp6Hteg32XuFC9RqVQq6RkxHeuuDsLfaDQKdQruSzsTcwX0eY3AGNmLf597XOV2cU9/nrMwN1B5npe+xZrvlu2CdeYWXWJ33ZBtXwcPfEdgPTs7SyD+Mm2sGEMUcgIp/nkM5DjSMgn+SvMYzXbqFf27aLligY0votNM32nnz5FUUGR+jz4633dK79aN+/H36LIwBn9eme/qwuGCFd0Yn+sIOIAJjIq5iiCCsgDUZDgQ5Pj6vnhG5GAwSALN99wtQGHc/3fF97WI7CSCLc+P7NHHxRyi8HzX185l19crumgedIxy5+svqcBO/TN3n8tYSNSXl21jAQyuAGW0USoiLoP1t0ZxTZnv6QtBi1TX2QALedU1ESSkotvjoJDno9eOuV/OuOIr1CKN9UV3t+QqZuBz6PETnxMvomJu3B1za1xm8fjd+xtdJeabdULASXdGF8tjOSga8wutdvYHm3OL6QG6qI5AosUAACAASURBVBA+DjcMPtboDvB9ZCyeSyoVd0GWpYeddfh9Y0woyhHr424ja0ma3tfegYH7RYB8mTYWroQrGQjo9MgnCaFw9PUoOM3pIQsXhcWVg2CiuxxuQaLFjilFhJHmyuEL5wLK2NxH9zLayAicuURWEQGM6yNjoi/9fr+ggF4v4WsSGQn38UBwvCa6VlhdmMJwONTp6alOT08LcRsPsJJ18hRh9OVZI/4OWLiv7qAa14C15X6u1KwzzMaBzeeMWgHGKCl9Hpmer58DD3/DcPjfr3J/PDjvB75kWVaoXXC5eJk2FoxBulz15/4kv7vg8hkL7RYP4XML48Ls/pgruE+uC360iGXBTmcTNAcKFxBXbrdCBPlcKFx4vK9lFNQVH7chgqv3JfaL36Xyw3mdVZUJHoo3OTmZBLbRaBQsb57nl97n4WAvjfL80T1yF8XdGq7xTFKk05FtfdHffP6Jv/hLkHzdeZ4DqssH8RLPVLi7R3mzGyYvxPLro5vhjMLXwo0O333ZTVVjBQzDYfHQk6tSQ06d3eeWijv6IpK7Ql7l43l/ovvBPcqujz6v3yeyCf+Og0n0ld2y8eyy3308LlT87woY3ZWyz8sYmD8vxkGcafmzuJfva0EZJiYmCi4NKcpKpaKTk5PCeNylcFbhgh8NC2NBpjz+4UpWprgeT4iyE90IaWS9XbE9BoWBaTQaklRIUXN/Aooe0D05OSm84g5j5QBIKzuAljn2vl63jQ0wuKWLhRxMSvSZ/AxHFsaDYF65GC1m9A35nMkl+OUWxemw55ARWF+0MlruFl1SQfi5zlu0xjS3pB55B1QdHKI7RN+4T6PRKDAXt9QRRBmrW9hoPd2yViqVdCpTo9FIGQ8PPMZNXWU+uQMM6+pzGt1Oj9l4fMnBOVJ/Yghx3l1+fLOaV7A644vzF9mfu8C+lvzN5wGXhGe5SwvDwGVxVkXQ3t3el3UpxgYYpGJwz6PeHmT04iKUN9ImhCHSRA8Ollkgt4IxXnCVkkf/HgHyv/Fdr4vwAGMZM3CFifUGHhuQRi4L1/p8uqI5tfXPYFceAXfK6nPowT2nrh7HifQepXZL6Wvq43Ll834wX840Iqvze3oMxa2tu2T03VkH8xh9fV9Hl5NopcvmnT773PGc6B74cwBW/9nlxQOWzBtxG+Tvqx7aMjbAwEQzcI/WY1nKFsMtvwfQuN6DlvhazjB88p1S8xz8Yff5vM9S0Td1QHPfUCp/iaz/vaxFXz/GM1yYI3gwLyiUuwtEtp3xwDg8aOvzg0CXxX1ge41GI92bzxF0XuHutL3MekbwdRB1pXFgd4Xi765s0Wo62HufsMbuinjf6C+MlXHEtKLLqoMYroWnQstkz9fW5yiWSHPPyOri2rxsGwtgqFQq6nQ6acdkBAP3h/v9vvI8T/4aBTNxIhGGGKdwaihd3iPhyB+tAH11S+3CECmjC0UZ1XZLFSl0WQCOfjhQxD0ZbiGdHTgYcn/GQ/N7RzrtGZZImQFyXgAsKe2bOD09Tc+OYOiVldEK4xoB/vFcBJ9jfvYAm4OPg4wboHj4iq9lTKm6IroMeNrUgZR+4Q4QxIwMNzIST71yXwenmKpnrsjO+TVRtl+mjQUwVKtVdTod7e/vF/w+QEAqKgB+FM2pmgs8iu1AcXp6miiWVAxmcq0DkbsVEaQQbo8Mu0XhO2V+Ji0qvN+T70drwPUuGLAC5svdihiL4PsIHSDiUW6nuU7vHVyl4lupzs7OUuAwumIojLt7cZ7pq/fbYwUUr/l1w+HoreWx0IfvNRqNND4UmWAfykQ/4vgc/J1xocjORBmXA6y7Ly5Dzh4dFGjRRXW5QdZh1RMTEyl4G9PT6I6DxHXaWAADg6OIxa0Cg8UHLjtHQCqWwroCuR/Ggnjk17/jlgEldqoYkZzm9/bFdNbgzynLVccYh38Haut02YXAx+F99Ki6VKztcD/Xg2f0GyXifowR3537uaLQT6wj6+pgwPe4J3MWXQb65iDuRiPLsmQ4XJFdwd2KO8hE98XX2RkMltnnyO/h1/M8j1lxH387Vqz9cPbF+H1d3TVwdzWOy2W9UinGjNzQXLeNBTDkea5Wq5VoV6/XKwzeTxtCcKJ1pa4egfYgjYOHUy0mKx4T58ehRT+2LGPighaDQ+5L+kI7sDmbKPOn3edFcHxsrhwxTcacOeAgxNE6R3bCWN3/xmLHlKxnZPh7s9kszEV0nwAun0cPXpb52fSTMeBiOKOoVCo6PT1N4OTyQEbJ+876MD43LrBRnyv/vgMZshSBw7/vQM68cx3zzNr4Gp+dnaX9Py5jPh9e2AVoRsZy3TYWwADFn5ycvEQJB4OBDg8PU1CPzT8nJyeFI6+c9vtiuS/oLMEttTSiia4QZT6qU0+3IpF1uOXieR6VdqAbDoepKAgFIXYSA2oulDzfy4V9X4FTW57l7kT07f1QUd9A5vdF+dyV88NBHKAAC6e0zgb9GV4y7qyLe1DNB3vwMRPPoC+Mhc+Pj48Llr4sNuSBVe8fnwPGXi/Aujrwu1GJwBZlrcyFcSYR5TCCl4MqfaB2J4LMaxljGA6HWl1d1fT0dGINLPjx8bGOjo6UZVlSHtAQpfAJAJk9P1+WnnL09XdNxEpEAl6RmkZXILo20apJIzbhMRFJyY3a399XlmVqNptJ8CNgoRxlyuRzw/MAVGdHCI8rBX931uT3ZQ6JYzhTcvCNB8q4IKNcR0dHBZB0xuEuBf9QTq/mhCE0Gg2dnJzo+Pi40H9kwN1DWmRYHnsg9eeWnHgEDKdsf4sX5jmYu7K7InvMzN1V1sVdBGeU1IXQ1+g6n5ycSLqcdSFmd902FsDAgDc2NpTnFyW11WpVU1NTmpqa0tLSUmINKHq9XtfR0ZEqlUoqnvHgFikzBD+6Ey4wCHz0/Vggp+vuhviC+1i8cY88zxPboaEI1WpVp6enSdihhQ5KEYB8LwH9py9c4z66R6x5Nv3DlfICKQdF9+29T3GcMR5RxsYciNxScn/66q6Xj+v8/FyTk5NqtVqq1Wo6PT1NyuCWX1JSohiwdOWNLmV07byfbhR8vPTfrXM8dyLGMTAG0e0D6JEzB1qPx1Sr1RRwZK3J6DlwAUY+L9dpYwEMKLMrfq1WS5ZlenpanU5HW1tbyeKBgLVaTdPT08rzXL1eT0dHRwVhQyh9YWIaD+GLfh1/w5rEgJP7ki4QvihYa0+NcW/+BjNqNBrJXeKe9NcFjeapMmcUHntBKfzcCndBUHaEyi0g/fTxuLC5RXLXwBXaLWuMuqNo8WQjfo+uXK1WU6fT0eTkpCSlLJbvjUE2fL09zuExBwcrB9o4HgcRlxeuj3IULbkHPOnD7OysWq2W8jzXycmJDg8Pk3Fz18HjBvQhbiKMTMTZL4zrtQw+Mli3Ni4UJycnajabyc/k+qmpKU1OTibFYHH9TdEIsKO/p99qtVqhSk66fHgnzYXco8KTk5OF77vbI42yAe7306epqak0TtwfUmwok/uwZdTbrZIDIdbSD8h1q00/vNjJA1vMEbUIjM2FjGvcdYJVUJkqXRz7Vq/XC6cSEZx05ZqamirEJ6KSM5cnJydpjPQvHj4jjba1e9+ZR9aM2BZrBNNgTnxO3eVy8PM1ZQ7oO8ruR/Ajo8zD1NRUIYA9HA51cnKifr+v09PT1H+ezRp6nQj9YL1dJl7LGIM0QnOESrqYiKOjo+RWOF1GgHEf3DeLUWP38xAS7kGpbgwk1uv1pNweyPHDTj3YRMyCVJGfZg3Cs+Bey16tVnV4eFhQysFgkMbrMYOo/N6Xfr+vycnJ9B0PvnmQlkCe98OVPgJotDTuFtAnT/0xXx6riTEWFCfLsgLoSBfR9GazmQDfMwFkGXAxoc8O2MfHx6k/AF6r1UrFc7y+DTbnMSpcVJ7HunomyllldIec+fg1zLsHj8/Pz9VsNtXv93V0dCRpVPw2MTGhVqul09NTHRwcFOYusjLuiwywjg5mX6WNDTBIxcitTyALfnJyUlBGrkNJvMiEFoNEMXDm1/HsycnJZN25J8LNSUQoda1W09TUlFqtVhLoRqORlNQFYjAY6OTkREdHR2k8u7u7BeGRRgJH8CymCD0Y6kDq7kkUTOYXHx4hIkbD993iMWf03xU4uhFO21EM1sUpNpZucnIyARMARKxof38/HT/XaDQ0PT2tVqul6elpNRqNwpFvGIKTkxP1ej3t7+9rb29PvV5PBwcHaY2dceC+EWAlcMn8+E5H5tIzBIzP5Yd4gcd4HHABa8bebDbVbrc1GAx0dHSkXq+XxkTpOOsAOGE0MIZen+CA4ezSXa2XaWMBDFBqrB0LhwJidSOtQ6j8JSjNZjNtJPGATlkmwemzdBGscmvKgmJdEGyo3/T0tObm5rS0tKSZmRm12201m80CcDE+noUQ7+7uant7W9vb29ra2lKv19Px8XHqO89vNBqJHqI8TnEBRlwuZyqxVByQ8iAtFjLGDbBeuCJU10V2EesX3LJ6/t7jJB7k9DhKpVLR5OSkut2uFhYWtLCwoNnZWS0sLCQ3xJ/rLg+fDQYD7e7uamNjQ5ubm1pbW1Ov1yvEOVjjo6OjQjaCeSCT4wHP6E54KhbZ8vmRRkaJ79XrdU1PTyfG41W6zjjOz8+1u7ub+kpMBZaBUYFteVzIZd2Dla9lHUOtVtPi4mISdBaY9BDW1isffTJAY6wMi4oSxpiFH3PuFsXjF0wyLgfgMDk5qdnZWb399tu6ffu25ufnk7Li+6LcoLbn1lH2W7du6datWzo4ONDW1pbW19e1urqqZ8+epYNZ6df5+UUlodNGFMoVVRq90MYj0+6mMJcoK/MhXX4tu+f7p6enk2Xz7IYzDK71+gtnFCinKx3KND09rRs3bujGjRu6efOmut1uYjKsyeHhoaRRUA7g5hqs/dzcnJaXl5Xnuba3t7W6uqqnT59qfX1de3t7Oj09LQTmUG7my9035iqyBY/v+Bwwx077mct2u63JyckUWAd0z87O1Gq1kmvjoEw593A4TJk5V3gyMsgIz6Lv6MprGWNoNBq6f/9+QmYAwpERCt7r9dKxYDQm1xXe6yEicnoWIkaQ+d0Vmn+dTkfvvfeePvjgAy0sLKTA3MbGRnom90Hg/X0XJycnhag3tPL27dtaXFxMzOPRo0fa2dlRp9NRvV5PEWtPu9EQaB+XR6hdcaSRrwrb8ixQjKrX63UtLCzo4OBACwsLyrJMP/zhDxNL8ToKZ0Uoi6Rknb0wyt9qVa/XdfPmTd25c0d37txRp9NJ7tHBwUHynT0F6pacRmbF30kxOTmpTqej+fl53bt3T48fP9Zf/uVf6vHjxzo9PS1UQbryM08AU4yLuBvnAMj3yI54Jop77O/vJ1cCV2pxcVG1Wk2tVktTU1MJDBgzc3x0dKSDg4P0DwaKnHoNBnOGTr1sGwtgqNfrevfddxNdhb4DAEdHR6kCcmdnR5ubm9rd3S0IpMcIfDKxNj7JsQTaqxklpX6w+LVaTSsrK3rw4IHu3LmjycnJBEyeXmIBnF67/09DuAiIQemxlJ1OR5999plOT09VrVZ1fHycMjIuCCgbn3scwKk89N1jBQADIAhLmpiYSPQdy+su3bNnz7S1tVVgIozPabVnmDyrQqtWLwqQ3nnnHb3//vt66623Entxa+1z6Yegcl/GUa/XE1PDHcW9IvL/3nvvaX5+XnNzc/rZz36m3d3dgqt6enqqRqOhZrOZsh5sI2ctYQfRXXT24MwLV63RaKjb7arZbKaUq2+CajabKfNGLIJ59bjC8fFxcpVgFwcHB2neuJb59wzRy7SxAAbpYuJi6oZ/DPrk5ER7e3vqdrt6/vy5Njc302JxrTSyjoPBIPlngINTXg92xjgESlWr1XT79m396q/+qt599131+33t7+8XAn8wBI9beADOQcLRG4uF1Saz8NFHH2lubk6ffvqp1tfXEwDQR57hFZtYY7ce0ohWxgIuBxfYTqfT0dLSku7evat33nlHMzMzKddOLGRqaioBtWePfLdqDA4Drp61mZ6e1nvvvaePPvpI09PThQyLg5pbYl9baVQJSWzKC4WIzUgj91GSut2uvv3tb6vVauknP/mJdnZ20hp6sJg58voRlJ3r4ntI3UhVq1U1m03NzMyo2WxqeXlZy8vLarVaBTaBHPi6wlQkJdnns6mpKbXbbXW7Xe3s7Gh9fT2BGi449wIc6dPLMIexAIaY+kKYmTzPFtTrdc3MzGh+fl6ff/65nj9/rt3d3TQp7udhjaFtHtzxiLZTZ64lAHr37l392q/9mpaXl3V0dJQoKP2FfUijuIf7dGQBaF7nzn4Pt/wHBwdqt9u6fft2unZtbS3V/FPk4zl7PzKNFCV98gi7p92k0R4H/N4PPvhAH3zwgVZWVjQzM5OCX2dnZ1pfX9fz588lXQgnVghW5/QaN8wLb1Difr+v+fl5feMb39CHH35YKGTD9fH9CPTby5L9ZUL0wykz68y6Qu+xuM1mU++//74mJib0k5/8RM+fPy9YehSL58YqRtba6xJiJqnRaGh2dlbz8/NaXl7WzZs31Wq1CvUZrA9sw8vQAe4oS9S9zM7OpgAmf9/b20vz6PPhzOG6bSyAAaR0CxyDglLxLUqtVkvtdlvtdlsbGxtaW1vT3t5e8rtcaQlIkvlwdN/d3dXh4WESXs+N37hxQw8fPlS329XJyUlh0bDYIDGxBHctQHGEIEaLnXo7FSSOsrCwoIcPH6pSqejx48dJeBjDyclJylQ0Gg3t7e0li40wRCoJeCJ0WZbpzp07qlar+vDDD3X37l1NTU3p+Pg4+cpbW1v6+OOP9ejRI21vb0tSUgCoO+vDHHqmgp9PT0/V7Xb18OFDffTRR6pUKur1emks7vOjEK7c7rIBGJ4hoF8wOEDZC5ZYk1arpXv37iUDsr29rcnJyVS7Qv0AbMSBxoOQ9BPmSrq63W5reXlZt27d0vz8fLqvs5GyDJiXwDebzQLbIp3LGs/OzhbYh4MkzdmIx+W+rI0FMKAYXghD7tbjAB4szLJMMzMzqtfrmp+fV7fb1ePHj7W5uVmIzEsXgtRqtdLWboSP6LOXIPd6PZ2fn6vb7erBgwe6ceNGEiYPNLGgXkFH86o2SYU0FpaP5/lJVCwoW4YHg4FmZ2f18OFDDQYDPX/+PNFG8t9kSiYmLg5cpcCH/SZ5frH3BBByXx9wnJ2d1Y0bN1IQ7Pz8XH/+53+unZ0d3bhxQ0+ePNGjR4+0u7ubAIy5cxpeq9XUbDYLQo7iUrT14Ycf6sGDB8qyLFm4GExF8aICuovoVtdTkCiIMylYJH2emJjQ8fGxpqam9O6772pjYyPFJ5i/Wq1WqIhETmEOfM4zSSXiJi0tLemdd97R8vJyAjwACmZB1szL1gFWz6zhgvm8wxinp6dTdmJ/fz8Fqr0OBUPwMm0sgEEaFWY4dcSv9nSX071araaZmZlUTzA9Pa2f/exnWltbK0TOKSaRpK2tLR0cHCSUZWFQcJR4bm5OKysrOjw8TIwA6+FxBPeDcU24vtlsSlLhXIDIjtzaOpsgi+HZkIODA62urqZ4hKd2p6amVK/XdXBwkNgBfabwyl/wwr6MWq2m7e1t3bx5U6enp3r27Jn29/f1ox/9SCcnJ1pfX9fBwUFhL8fh4WESUKL/lDcDfDAwQKxareqdd95JTGFvby9FznHbnBF4itkrRcsAwpXUMwseJMTwUGoOO2u327pz54729/e1s7NTWG9JiTnEbAvrCIhQb9DtdnXz5k3dunVLCwsLqV4CZQcYvF7Hg8A02KC7MB44dpBoNBpaWVlJrJbAPPKFXL5MGytgQJhQLgQHUMD6OAIjWFNTU7p7926atI2NDUkqRHh7vV6a8KOjo4TwfAfrMjU1pZWVFXU6HfV6PUnFwz7pjwuqCw73ch9bKp48hMVjXFh/7s+zKMKZn5/X+++/r9PTUz19+rTAVnq9XnIn5ubmkitCwdTZ2Znm5ubUbrdT8KvdbqvT6Whqakrr6+v6i7/4C/3VX/1VKq4h0Lizs6Pz8/NkSaHJh4eHKeYxOTmZfF8Aj7gFOyHn5uZ07949VatVbWxsFBTLg718BvX37IakBCAwF74LyJHpcdcQt5LYB0aFcuSZmRl1u11tb28nQ5FlmY6PjwvxGXdBHWCpypycnNTy8rLefvttdbvdtJa+q5Z74RogyzHg6nLu43eGAhs4Pz/X/Px8KqHe399PxoE5wqhdt40FMPjke1VcLCKZnJzU8fFxCtj5NeypeOedd1LtA38jyNfr9ZL1ROE9tdlsNrW4uKjl5WXdu3dPnU4nBQmhqJ5x8BQkwhbHw+/EBqTRlmmnd3HrcJaN3h1AKfXy8nLyE1dXVxM4DAYDbW1tJXdpcXFRBwcH2tvbS+MmaNntdjU3N6cbN26o3W7r+PhYvV5PT548ScJ0fHxcqFFAcD04jFuW53kCG4KRziaq1apWVlZ07949LS4uJmD2jE3M8HiRD3OI8sOQJKnZbBbSvlhj7ueGQ1Kqn5iamkr3qFQqCSCZf19H339Cg1HgwsAY5ufndevWLXW73dQP2IDXbnBvj8/QR2eUnmGC4dCQb2SRlPre3p42NzfT4TS+f+Nl2pcCQ5ZltyX9vqRlSbmk7+d5/s+zLJuT9L9KelfSI0m/lef5TnYh7f9c0t+RdCTpH+R5/h+/7DlMFINh8C/6kCaLifbiJVBTurAod+7c0c7OTsEndj+WPDVWfWJiQrdv39b777+fmEKr1UoK6fUMTle5Jwrkn8eoOmNjPJ7qoxgL0CHD4M/DCt+5c0eNRkOdTkfr6+upGo74yGAwSFV0fpT7wsKC7t69q8FgkMDx+fPnybWiXgKAbbfbCbhQVo/Ys4eBvkJbAfCJiYnk4r3zzjtaWlpK/fO9GR5D8AIuwJg+RGZRq9WSAXCmiZy4RfeaFI8xUUvhQCCN3LqZmZn0d1zBvb09HR4eJlCQpHa7rbfeeks3b97U3NxcgeK7i0xQ1LNQzj6QES+QazabCQzpp6SUkfAU9MTEhBYXF7WwsJD66UzsZdp1GMOZpP8mz/P/mGXZtKQ/zbLs30r6B5L+KM/z382y7Hck/Y6k/1bS35Z0/8W/70j6Fy/+v7KB0ggEioIVwheTVKB1TAaKiZUFuff39wuWTLqwzGzXpsR2aWlJ3/rWt7S0tJSoPhMOeKDUBEWpu0DJPajHd1l8hJFiG2dFHhTjdxTAF528ebPZ1P3793Xjxg2trq5qa2sr0Xr8eZ6BclcqlQR4u7u72t3d1f7+fqKezB0nR1ExGA9BcVAgIwO1z/NcnU6nsI9kfn5e8/Pzqtfr6vV66vV6hfF43h0LDHXGKnv1acwAACoAjddMIDe+lr4dH1ZWrV7sbt3f3y9kishkER+g9B3gpIz55s2bunnzppaWltRsNgtpS9gM7hByy9+QKR8PQUnoP9ae2h53V5FfDya3223Nz89rbW0txUekYrXsddqXAkOe56uSVl/83Muy7C8k3ZL0m5L+xovLfk/S/6ULYPhNSb+fX0jAv8+yrJtl2Y0X9yltw+HF24+ZCE+DEXTCMjFhgAD01lNHw+FQ8/PzarVaqbYfwUCwpqenk2Dcu3dPy8vLSUE9GMlCIVAEc1gQzzggWJOTk4XTdLinB48QVvcl3c90S+rPIWYwNTWl+/fv6+2339bR0ZEODw+TIrOzkO3cw+EwbSpCSFESr74kftDpdBLgEfmmTJct5ayBNDp5CUAg7ce67ezsJMvuY2Ne+B/QlZSObHPa7qlgwMLdB2mUacLYUDTmbIQYBWXGns4lYEylLTsvmRtSiHmea2VlRffv39etW7cS1Y9MwPc98L8zAB+Dp9lxpzze5sCHkYkuN3GO1dXVFGvI8zyN47rtpWIMWZa9K+k/kfT/SFo2ZX+uC1dDugCNJ/a1py8+uxIYmETPHTMpTBRWgAl0AZNGNJCAUpZlyaV4+vSppBFF3d/fTz43dJd9DDwHBUVYPfLPIiNgpDPdb2TRCGI5FafPsIXj4+NC6g2hdsBhrNVqNQk1kfCZmRnNzs4mUD08PNTu7q56vZ42Nzd1eHioiYkJbW1t6ezsLGVonHlxf/epp6amtLi4qE6no5mZmaSgAKeng2EOsDeKlsj8ONARt/B5JE7kgInbw3oQYJRGiub1LvjaTsuRCa5j/l2mWq2W7t+/n2Iyx8fHhTgJ7JWzJScmJjQ/P68PPvgggQLr4W6Rp61hg87oYB6wQ68NybKLs0hgKIzZjYVnMTxuw3pVq9W0WcvjG9dp1waGLMvakv53Sf80z/N9D5zleZ5nWfZSTkyWZd+T9D1J6dAN/CmolFcwOj2HVhG4cXej1Wolwet2u2q326pUKtrY2CjsTvPAjmc+sDb4rUTWqYqkXyjt2dlZql/3Wgu3QrAb99lxnTxN6VF4rqN/UHpppAzMC9fz7OFwqJmZGS0sLOjWrVvJ8i0tLSXlo1/0FVcL5W+325qdnU0VdpSWE5vhu5VKJTEVxulKhT8PuHlwzwPIrCO/+5mdzhp9DqJbyTO9GIl/XqXoLiCuxPLysu7fv69PP/1UW1tbBbcM6t/r9ZI7RwUuVaDEEwgKwnBoXhzH+jPnzpLjGvtr/ZAT5s4zJV7rUK1WUxo/7i6+brsWMGRZNqELUPif8zz/P158vIaLkGXZDUnrLz5/Jum2ff2tF58VWp7n35f0fUmam5vLoTpeHcjPHg1nwZgc/NOjo6NkmVwRK5WKlpeX1W630x73g4ODlGZiZ+Px8XGaXO7PQSFUWKJEHvShj9PT04kyI5RkFBBU+sNxXe63SyNGw8/4ul7+7ME491FfrFMh3kGmBbCURq/0c5cFxuPp2na7nQJ3h4eHKb/PHFOvAEPCWhJc9ICwBw0BFa/gZLz+zksYibtfHqdx14J7+jZtrLXPBfMFGFMrghu7tLSk/f39wqsSg2Q6GQAAIABJREFUyZS4SwLIENhmLV25Dw4OLqVKYaQeO/Kxe6DRA68OJswN9/B5cPBlExwp+q8dGLKLO/6Pkv4iz/P/3v70h5J+W9Lvvvj/X9vn/yTLsj/QRdBx74viCy+ekSq4orBz1oJPJpbFlcCFQRotGsrV7XbTSUDHx8eanp5OEVwXGqgjAnd6eprSWzGW4QFGrAV99SPHJBUUj8VzZuD3AQQZL0qIAjtrgS15FJx7Q8+9HwAi383zPIEAgsdcQ50RLGILAJtnggjyItAIKnMqjdw9qLkXQHEv1pA1Zq58XwnzCRBQw+EshbUCkDEuKLUXTBE0nJycTCeSk3Z1FrS4uJgOGwa4HPgBE9aetfL6Cneh6KOn6f3oAHdlkCkCmmSpMCDuapMRarVaX4ktSNdjDP+5pL8n6UdZlv3Zi8/+O10Awr/KsuwfSXos6bde/O3f6CJV+Yku0pX/8Mse4BSLQ0NZNMp5EUyEhOtQRIQaRO10OgU6j6vx9ttva2JiQt1uV5OTk9rc3CxYb4SXhWKCHQxQcHdHEEYvu/UCqLjrDdSHlbgyQUURjOFwVDqNQmKdEVAUS7p8uC4C4wFbFAMAof+shaTk3tFX4gsohAs4wuebpfy5WHrSqLVarVCIwzw64ABCCL9H63HDYrDaA5GkMD04J42yF6yzr/3y8rKeP3+e6iKYA+6L3BGf8hoXlxGvUyEOBXMgwOkZHWm0G5jx4Wq4y8DaT09PF2okXC5wsQH8k5OTgit1nXadrMT/LekqyPmbJdfnkv7xy3SCRWWrLEeU8TffAIO1mZiYSCW+/F8WrGPyUdqjo6OkjDyL4B+C65tWXHC4d5q8WnHbNAoG6iN8bKzBJYiBIwJh0ih1hXI42BEc9Mi7uzMeYHK3gog/8+InFPnnWDdPixEIPj8/1+HhYVJEajyweFhdhBjl8xgC805c5vz8PJ2wDVB5zh9QYG3r9XrK318Vf2JdvcCMOXZFxJA4CEojNuOHzSIDg8Eglcq/++67BfnlHm7tvV6CVxxg3Y+Pj9P8wkgAx/39/XQ/ZAH3jH76OSWVSiXVrjCeyclJLS4uamZmRgcHBy9V9SiNSeWjpCQoUD4PLAEEnu9FWFAq393olJbPsFQI6e7ubpp07od/x8EoUDG/xoNgXrzEfgKCdxy8wXek0UKT9nOFl0b7/1Fm3AO+OxhcHJRKBoXPmSeu92PoHaScUgJoZHAARsqcnd57sNAj7KyBp/D4mUNPmB/YBmCNFaYPKDXj9LWOcoGbiMLEILEXjPX7/RR89QKowWCQ3B8/PVpSUjIMBoHGxcVFdbtdvfXWW6l4jDGQrvXUIvPuLiQGCqaBKwUAIy+AMWANK3KZI0js7h3GDVnwHaYv08YCGNxNcGXx4IvHDvw0IwTed50xedVqNdW7OzUE/QEJp2iR0pMSdBcCgffag4mJiZQBQRjYWBSzDxxT53lplFkqUkyewxg8Wk/AzUHLTxhGWcimMF6CeBR+AYYe3PRrcek8K+B03gN8BE/d56YfsfYBRZydnU3zDd1mPID16elpqpwENPCxsyxLQdapqalC7Ie1ybKLsy48fYjVhiEwj0tLS3r27Fmam1qtprffflsPHz5McuegwPfYwIaLCMh52tTZJezUA5qedpRUMEzS5fd4eIwGmQH0KVbzisnrtrEABppTMqgtfqKktJgctsF1+Oju34PY3W43BZII/MSAjAurpxydgfA9FpWFhT5C81EUXhaCQqPsjImFBsQ46tzrHbA+pAPpmzMAL6SSlH4HNLz4iDTvycmJOp2OBoOLE5UJqLEG/GMsCN309HQh0u5KQh/ILtFX0svUQQAgjMHjMvwDzAFTrObi4qLOz8/TgTnMCUwNN0saRfKHw2FKt05NTWl7ezspl5fVA5TVajW9CoBGyTMpRpclGJ2zIvqMq4ChYy7KirUAQNxRwCZmfJAZdyOlYqbFWQrl7wSRr9vGAhiyLEsluJIKllIalc+ymAgdDMM3jEijiYOueQRbKr6Kjmt9sVAoSnz5Dn1lAVhEhI9+VyqVlN+mX1hQqD2C7/EB/mdb8/7+fkFJqtVqqidwFsI/fGZKm8lAzM7OpnddUPSCbw7QwrSYF+bWS85xj3AxEFReDeiC7O7c9va2Dg4OUrm0VzkSq8Bd9Cg/bCwG9fz9EowDQ3FVhoe+V6tV7e7uJncMQIQdEli8d++etre3dXR0pOnp6bSjlH0lXsPBWmCgAD7PFuAuxcwSgELRmpdBY0S8UEkqbthjjlyWmftms1koF3+ZNhbAEFNJ7sei1G5tUQi32O534lJgnXyDklR8oau7D1BTSnGxlii5Hz7C1mmeSd265849huHVgnwfgHJgODw81NHRkfb29lI//Fiz4XCYfGAUHavplj/P83SG49zcXGIBWXbxDg822ExPT+v09FR7e3tpPcggSEog12q1kpvCOBkra+XWU1LBb8fC7+3tpZJu6L80yhRQQejnXjjoO6i7spPu9uAla46iwvwAJXdFPX7EC4FQQJQbF4Y5IjaAK0L/3V1gLdzl8jQ1/9gK72Pgu4Cu6wbz7/U66Ad6Q0aFWMPLlEWPDTAMBoO06cfpFAVDWEKsITEDlN6zAvikbnmk4gtOoPj+N3w9rL8HOfN8dBKSCwlBRBoC51aD8ZGyQhCky29IxprneZ7e6IyLgrUlzgGDgj57EG52dlbvvPOOut1uIU5BlV6WZQnMEFp/HwaWb2JiIgkX68G19KnVaml7ezuxG0lJyQjceZYF4BkOhylL5JV8PNfjMswta8fceBqPoB7PZ01xFRjr3NxccqGkUSYIxtrv9/X5558no9Dtdi+lI2EtgJevsbt8nokBlPycB+JVzJfLJ4wIpsic0WfG6alI2AffR19Yl+u2sQAGSZeqyrBEoDtBNJSDgAtCjzuQZVnhDUMefMEnRRBRWtCf4BkA4Kk6t/7QX/dJY/pLGgXosHgoZ61WS26O58hRcLY0w0iI4rvFlkYHiU5NTenGjRtJmfx0YqfqlUqlMAfETxBa34vCfOKbA3KAHrUfABUHhbRarRRfIdWHnyspfZciImlU1CMVzynwgCf9Q+EBMWl0LJ6nfd16Q9dZT9Yyy7K0qxLXq9PpaGNjI6XPOZ+DtfMgLnt7uDeWmz7A6Jh3D1Y6WMMCkHs+Qx6IDXlmhXlw99WrPTGmbrRepo0FMFSr1VRwxOKDlJ1OJymr+5QIEuW+HrxBCNle3Gq1UuUjhTUe4MESYClB152dnUS/UWzpwqf2E3nZYOV+LgonjYJLAM/e3l4SJBaOV6EDgigj8+N1AdDXTqej5eVlLS0taWpqKhV+4dPu7OwkgZybm1On00lxDqwWpdzOOlAAafSOjbOzi7MI2KYtKb1ZibdGEfl/+vRpitUQLERoiSex+9KDxsydB0AJ3sFcYCpuXTEgcQ2ZJ2m0PwIwnZmZSezFqf9gMFCn09GDBw90fHysTqejiYmJdLIT/XF312s/SIOimGz9d1nHgMG46B/MBllmjM5QPT7l44V9AfKe7nVjct02FsAgFYt8oH2UlmItmXCEFsVmsbHEWHHOQDw6OtLm5qbm5uZUq9XSMd5Y1LOzs3SIpjQqueatQTCCarWqnZ0dbW9vJ+tK1RmHi3o6jCAm7g2CigXLsixFi1lU/EHAyfPyCNPk5KQWFhbS+yfYZs2ZB+wbwSqyoQYQ293dTa9rw8f3Qhtp5AocHx+n7cbO0Pg79QpLS0sJuNvtduGgV6wd7hincOEf+z2JaaBs0HAMBXPjbgZ03QOxsJHDw8OUckbxPMrvhwExb1NTU2mHose/YJnIpWdnAD7klGe12+3EzgAwsiS4GsPhMBUswRSQS2c+yIIzOmdFxHkALvrysm6ENCbA4NkFSYUgFgro6R0vmyZugCI2m800wex+40CSo6OjZNmcpkPRI1ofHh5qbW1NMzMzKaCzs7NTCGJhiby2HqHd2dlJAhBrM/wsSc/LMx6PqkNBAYU7d+7o/fffV6VS0c9//vO0XZgNT8Qp2FK+uLioZrOZXhpDPGA4HGp3dzfFbnz7OIpKdge67gfCMM6HDx9qcXEx+cGtVisFGVH0w8NDPXr0KO1wRYG9hsNTxL51HQDCYnuQjYwVB89AowFyXDzSwcQvfL+GV0+en59re3u7EMxDPmEhMFZPT6PMk5OT6fVxrGWv10vySUwGwMQo0Dfml8A6YOwZN9wOL2jiM69k9Z9fto0NMODXAwZYAiyaf+ZUyf0pFB4BAzWZ1O3t7RTBJn0ojYJZfjiIp354XwXswANEklLfYzaCfg+Hw8ROiDx7vp858PSUBxazLEsHud65cyfR3CdPnqQ3KUkjmtput1OlHm95ev78uZ48eaKtra10ihM+MvQXH1Ya1ZIQFffqPcYN++j1eoUNarwQB3eAY9pv376tjz/+WD//+c8L27y5nwMGzM/rKFBewAowYS1gnbAHCo4AENxR+s4zKXSCeiMv7saw7jyzUqkkcEVBAUDuwTkYuAzdbjelLGMdA0FvvgeQeWATsOEzT62jO7Bt0voek3iZNhbAgNKhVO5WIBRYFUAEmkpcArT3ABDWAquFFcf6stC+QYntxR4M8pQcFsv3K5ACRFi9OtGzJbVaTXt7e+mIM69a5J4IoFtN2Mj777+vhw8f6vj4WM+ePUv0HMvnLpcfl//zn/9cz58/T1uKB4NBYUci/zx/7qkwt9QufGdnF6cz/dmf/Znu3bunBw8eJFbBdVjE09NTzc/P69d//df1s5/9TD/4wQ+0trZWqOtAiYlLEBwkfkOBG3NELMkzPQ4sVEuS+VhaWiowslrt4qUtjBUGBJgDFCgyn6HQkgpAztzBJKjoPD8/L8QqoPy4KB7Q5PnSKLYEcAJSUvEN1rgSzjSOjo5SERtuysu0sQAGabTxCSVk0FA1aXToqgetEA58Ufx57klrNpuanZ1Vu91OJxmxIP4iGvdpUXSAgoCgB3ocidkogzARz2CxEQKCdB74wv+EydAPouXvvfeevvnNb6rZbGp9fV2VSiX5wWRr8GUJABJHwKcnXoOSoei4Wgg04EqUXlICKn8nh3QBppubm/qTP/kTdbtdvf3226muhCAtSry1taWbN2/qwYMHarVa6aU2ALtbX2IRlEH7PgKO5fPgLMrNenHEHfsdhsNhYn4AKPfwOJBbYqwu4IvB8TiXgwRrwDq22+0kZ4CMxzs8NezpXYwWcu/9ABAAKZd1DBf3wU3i2pdpYwMMUC5PPeG74Tp43loqoqvnuB2JpVHunMUgkDYxMXp5iu9zoPoQSxTTTwSpXDlOT0/VbreT0OKm4HdDFRHmqamp5BfjwyM4fjJTvV7XW2+9pQ8++CC5BTMzMwkQEZL9/X1tbm6mGAIWwivwUADmW1LK+AyHw3T6MRkAounuIlFBCQMjm/Lpp58qz3N997vf1QcffKDl5eV0EhJKylyfn5/rzp07Gg6H+sEPfpB87MFgoOnp6UsR/sPDw8Lr9zg4B2UiJgKbcB8bH59rsixLriQyVqvVUkETJfjIlmcQmGuv40A2OIKeuUN2UXqPofR6vUJanqAt7hOBdGctuMQYHfoBGDibcKbjJ269TBsLYMBS+NZQL3umGo0ADuCArw6Nw8fm+9BDmqd4HLHL0JjgIt/3ijY/BIaUGlHuLMvUarWUZRelwI8ePdLExETyuwEbBGl7ezspK7QPcCPFurKyomq1qqdPn15KN3KGJRkJ0qG8QwJh9HQkNQYu6Ag/QOGK4wVb7OZjXWZmZpRlF+8A/eyzz3R2dqbt7e30HszBYKDPP/88KcPx8bEePXqkSqWi+/fv6/z8XD/60Y/SeGB5sLXB4OK9GBgG0nuAoWcVqNlgLLiFWHvpYhcvoM987+3tpRoMAIrSbo9hSaMdssgMNRzEB+gHoO5ViMgMLg/ZDSpqCXQ6S3aX2mshYE4Av6RC7ILYDSBCnOS6bWyAgcCiFwMx0dA635jkRTM+mUw+VYSegvR3PLKA0N7T09NURHV2NirZpbYARMb/dYvsvinHxgEifkSYvzzX3RDiA16xxjHgnNj86NGjQhAQYOTdGdKFYBBDqdfryQLxHapIq9VqYime+aA/FB/xblD6wHdRFJTQi2keP36s7e1tPXnyRL/yK7+iu3fvan5+Xs+fP08uQaVS0fr6uubn5/XgwQMdHh7qpz/9qfI8T6yC+1cqlbT9HaXy1/EdHh4WXjnAnPu4Patzfn6eXh2PTElK32WtkEmAyCsquVe3200vrHW3AFfNmSBuKnKH8sJSPUhOABNQ5rnu4hG/YuySEkuSlPr0/4vgY0zDSKOtqlBbBunZA2n08hCsDNQTQQZkPNfMkW0Ey87OztJZfdBA3w59dnZxnBbvSiDt50zk8PAwpU0XFxeTVeJQVa+eHAwu3oFBAIwsASf8egAN1rSwsJDy69vb2+mAUld0aD5voMIaI5SAhlsgj6VMTk6q3W6ncmoCgbAilAC/f3d3V9IoHrS3t6cf/OAHevr0qT766CPdu3dP7733XnoO/x8fH2t5eVnvvfee1tfXtbq6mu7tFhfGwmnYRPYJsrIRDDeFMaJYMCKvv8AQeWzLM10TExNp8xQHp3gNxdLSUsoyeGAWtoH1JhMCuAFIyCNg5GBLYZofE4DlJ2gpjcqhnSl78BQDhkF9mTY2wICvh4BiNbyclMCKpCTwHJDiNQJYaQ41RdgnJibSllr8Md5uzWQiNNwHCy8ppckIDvJKONwgkJyiIKLCVM05envwjOpNt8AeJAMEyOcTkKM2A0WjCpF3O/C8Xq9X2KQDyDCXTrVxL2AlCB6brHCxOBTl/PxcS0tLyZfF6vX7fe3u7urHP/6xdnZ29NZbb2llZSXt9ETxidgvLy/rs88+0/Pnz5OyQLVRiFqtVjgEZW5uLrEwwAEGl2VZii0wFldegMlTlKxNp9NJ9wAUkU0/HNiV0Ks33Q0FNPxlQy5HMADcOwwHpfJkxWBOHkMjnUt8BblFhvb399PpTRi367axAYbNzc2CdfdzGKTRjj9Hdyy4b1zhfnNzc0lJpqenk5WFqkoXLIPX0e3u7mptba1QXEXsA4H37AKfY41QZs9wSErBKIJKXkjjVBBfuN1uJ4vIASubm5tXVgl62a8Hz/r9fgItfHM/hZgMBH41DMyPuqOOhMCpW05YFUVXVBTSP48V7O7uql6va39/X7Ozs1pZWdHKykq6N+vAuy13d3fTMXKepsalY40ACa+I5DoqL32jE4YDXx4Z8GAhLhcGAVaB8vNSHU9L+54Vxs38MF+eDmXt/PBed/uQKwwjwAQbAXzINjEHyAE/u+y6Ll2njQUwQPPYsw+qkrqD7jNgJpi/ZVmW3kyd57lu3rypbrerW7duaXFxUZJSFaO7LAhxrVbTrVu31Gw2tbq6WqhlcMuDoEmjw2Wh19yT9KdHgSm6cR8WJkFRFRVxRJIRJt6HQewBgeFZ0mgrLs/1yDyuBo0A4O7ubqEq8fPPP09l3a1WK7lEuBrMswcrCWQCYpISeJCyQ7kGg0HKmOzt7Wlra0srKytaWFhIbg3xjHq9rvX1dZ2fn6dTmXz+GL+zAGc//E/WCVfKldlZhFdpMpc0mCzf8eMByUr4Dl9Pt3tsR9Il95S5xCA2Go1LGQd0w39HL1gHYm30n5+JwXhdznXb2AAD1BP/y/1d9/8lJUsKYkpKqa7l5WXdvXs3WaOdnR1tbW0lhQGRQeh6vZ7y3WwPfvLkSSF7gfLTEAYsI1Ye4fA8M1TSi2QQYtKXXhgD8OR5rq2trURlSRHCjPI8T/QRiutBWMpysTD4sIPBIAVOKc39yU9+oidPnuj09OKo/IcPH+ru3bs6Pj5Oe07oh+fFeTkwz5BGOXdcgH6/n86WYFwUW62vr6fUIa9vJzbUarVSYRKuFGtAvYj759QG+GleZC54dpZlhX0YXkMCsLHmzWZTc3NzBfDHghNQ9JSvZ7gAJ0+Zo9S+N0IavXlMGgV9uRfgDqtjDKwlQW7AjJgPc+YH0ry2MQZJicayj8DRj6g9Sk1QD6GYm5vThx9+qJs3byaf+NmzZ0lwndp7UIcglHSRypqbm9PR0ZG2traSEkM9vW6BeghAoNfrpf5hiSmDxj0iU4IF86yEpMJLX09PT9OBtZ6XlkavxmPXIhZnd3dXtVotlSdzZoXHA6g7wO368Y9/rB/96EcJdJmXd999V7Ozs4V4inSx1ZgdloAPSlKtVtO9PQoOEKEUZGgYI2DoBwJzve+mJKXo8waDwCLCiAAmXB3WgDJzwNTPQ4BlnJ+fp81PjJn0MX4+zYPJsAnuxzpKo30/BKtZO1wE3EJ3xdxF5R8y6HEP1yOAcmNjI70AKcbnrtPGAhgkpRw5G5akkZ8PGGApCR6yuB999JE++OCDtHmHo8QIsEH1jo+P0yEm1BNgeaTR5qyVlZVkIdlDgNVjofEZfY8/lW4sBuDB3xEMqgHZtYilYcMVL6mlj1BUfF+pSHHdQpK+g2FgkWAKHIg6MzOjwWCgx48fp01nzPfa2po+/vhjfetb30rzODc3l1LEZCJ4NjSYICp5c4KdXtwD9QdsqVNAMeJr1QBEWAsv0CHDQx9QKsYBw3C3A1CENUij2JWftkz8hXWk1Nh3oHoq1FkhwEAg2a8jlgRQdjqdgnviNTJcD+iw3s5gYAEOIOjIxsZGYTMXgHndNjbAQGVe9OlAfoQNVpHnuWZnZ3Xr1i1985vfVKVS0dOnT7W6upp8+rm5uaSEpCgBGX95K8UsBNHa7bampqa0s7OTQIrzH7EeVDiyQP1+P1UOguYE53B7qEhEGBA22AN+I9ScFJwfLedHyrnfiHDCEKDHKIBv5sLv39nZ0e7ubnINcIdOTk60ubmZngn7AaimpqZSjQigKo3YBKlXP8yGIi/WmfoANm7t7e2lOhKsKEzIwaFSudjV6O4EYEM9CgE5XAfmzVOF0kiheCZZA/8bWTFkBdBCTp2pEPNiDmCjuDmwXFLls7OzhSAofQZ8fc8NzyQoK+kSgOBKHR8fa319PcljdIWvpY8vrcGvoIGKlJRC4bzc2VNkOzs7Oj8/1+zsrN5//31lWaZPPvlEq6urOj091fT0tCQVioxQfpTUF9UXB8XudDpaXV0tlCfjthBwYzE5Io3yZwScBSQACLgR2+BZ0HUXUOkiWIXfTfAP94XDZrDGzA/j8rQWcwDFhuq6BULBvOCG9K7vn5CUmA7pMIrC2CxFXwHwZrOp+fl5dbvdNC+ANP0lu8R2cS8GYs68zkBScpcAMLab41eTQqW4CIOBpXbFI70nKdW1EAykjsa3grPWjBeqDqP0vR6AH7ENZyweCIcpejahDIRwV+mDf1ar1fTkyRNtbm6mdCyxmJdpYwMMbOJBgQABmtMiLAr729fW1lJ6isCbV/9BSfkOSgTVw2pDU2EbHiVG2KUL+nlwcJAEinvhEiCQ7LjEb4RBsJEJekv6ikUmIOfluFDtk5MTPX/+XFtbW4kG1+sX7+YkB+8xFAdB0qy9Xk/dbldzc3NaWlpKNQCMlXJq5rHf72t7ezsdMELQi/lBCbDaxAqgxo1GQ7Ozswkcnjx5kuYagSb7AEPEegPIXsZ9dnaWyqFZU2mURgUIyWoAaB69d8AnK0JhVKPRSNadNT46OkquHoyAvwOMMDXkBKCAJZ6cnCTwJz7AXHlMhnsyF57CR8m5HrbkTGN9fT09E8PzWmYlpFHghNQYaIclJUeMwkgXluvg4CCd0dfv91P5LgsrjU707ff7KYDE3wAGt5awF17vBXBguZyecvITVhpLCJ0mvQoL8Qg6VsFToLgP/qYm8t2U8z59+jRtoUaB3CJRYk1wDgUgmMdW9eFwqNu3L15MTmorz3PdunVLt27dSgIJ2JFWbbfbiYHgDsVzHnxPR7vd1p07d9K1KNLu7m6hqlUqbnMnrccaUQYNIKIQsAJOa/KTizAI7AOJsQDf7o/VR5kcYHFPACsYlbMzYgfcl9OdcXtOTk6SO8X8+bZt6kVwS3Ax+Z+1hEm4wcOgPXr0KGV9pFE16msJDPit9Xo9FY94vToKhiBwqMrc3Fyh2Mcr41hYLD9+IijqUV6CS1gqBM03tbBARHb5vN1uJ0GAVSCYWXaxuYhnYjE8kOi7Fcn3e+qpWq2mdOhPf/pTPXnyJAEh/Tg/vzhZSrqgpsvLy4Vt6Izz8PAwnU6EtW632/rWt76Vjp/n+/STyP7p6WnyYZk7AE26ANher6ednR3t7e2lwK9nIgA3gqR7e3tpbrg/4AyLcdbgWSlAi6yCp249c4VccD6Fb1jiWVNTU8kwYO07nU5ab/5HbqgwpTiOOg6Y1ebmpg4PD9VsNlOGCEYsjWpgCM4CThgLj80ABKy1VHyJMGyAwC+gAMDS/9c2xsD/fgaCH9cujVKNTBTC22q1UiAMP9M34yDYfs6eVy2S70cA3YoTJfZ0JVQUa+39JHCIElEJiY9HHz19hr9IQ3FJn3GAzObmZspN8yxck36/r62tLVWrVd25cycJLMIhqVAUIykFIxcWFhIzYA4IdLlf3Wq1kv9fr9c1NzeXlHgwGKR4CIqHNT05OdH6+rq2t7eTVfb9A9QDcF8AkRgAICkpsRXuRUUrrOPw8DD56dRlAC7MFy4Llp+19pShl2IzjkqlopWVFS0uLqrVamlycjK5D6xxr9fT2tqanj17psePHyeQjDUYuKcEIpEHZx4Au8eRcB088EtfPvnkE62traXUM3rCWF673ZUID9aXxYD+EXiCCYDeN27cSP6kH3EFvfd6eHxAyn4p7un1eilDgSVgsbHivlsPwKLK0OMClUolHQbiqVAUAbrsljTP8xRchEYSrALU3No5g0GQWHR2gn788ceam5srWCjfRwAYUTyDq0TdBdd6xaP7xlB8Iu3r6+va2trS/v5+ckcYp2+sYrs12QL2kPjZBAh18dJMAAAgAElEQVS+022ehcJ2Op1LewOIp/R6vWQx8cNZP+IRvV6vsH5ci5vjYAS43Lx5M2UR2Kq9sbGRXppDnISTs5eWljQYDPTHf/zHaf5woYij8S8GfZvNZhq3u6I+TxgO2Pb29rYeP36cjsN39whwf5k2FsAgjV5D5xF5lFsavd2IBmICBFTYYfXa7XYh10uKjnMJNzY2CjsF2WxFtR9Rct8UBHITgQdMnGJ6ARGLDt3HV/b6fw9ooqgeJPPj4vDbiXfAgmAHsJbV1VU9efJEb731VmI3u7u7yvM85f+9DgPwQ+A8O4SV4ZRp3yyFS7S2tqadnR3t7+8XUp/S6Nj2w8NDffzxxwVAh8IjwLgPgAPzToEbbgL+MnOD8BNboTIRefJUnldE4g7V63XNzs6mey4sLGh6ejoZoPn5edVqtVTKzVmSXsfAP15VsLKyot/4jd/Q3t6ePv7445QlQfGRCdgMIIAs4gIAAMwTMiiNiqtOTk706aefam1trXCwbJnrcd02FsDgyiGNatBRfmiUW2IYAovjR5ITXXeLTiENFhpE9Z2W0ugtzSiT1xZQu4DCeJUbC0EpLxbOMytYFBaTHDoxBWguY3JQWltbS+cXelGWZ1NwYfb29vTZZ5+lY9B9T0m3200+J74+6VVpxGZgIvPz8wWw8DQoDIx598o8wAPLe3h4qN3dXe3s7OjGjRspngJzIrgJoALi3JMALEA/OTmZTq1aWlpKYAcLIz7hcsPacR8PbMJQl5aWtLi4mN4V2Wg0dHR0lFLXnGGJQrOHh2Dk3t5eyhp94xvf0He/+131er0U22FuXfaRVWfHAKSkJGvIPvOBa/fpp5/q+fPn6QRuafRuU2l0hsTLtLEABvLBUF6nTggO1+FKsLtseXk5pZOImqPc5NE9wo2CcA9qBdhxODs7mw5oOTk50fz8vPr9fgIVAlEe7SVtSt8AB08pTU9Pp1gALMUzLlhZLAnUMtb1Y7Xpb5aNDkyVlBRkY2NDn3/+ebLEVPfhNkGHEUK2Q8NKcMfwx1E62AWpOGg7jIn3ZJDpAWAA5idPnmhhYUGLi4tpTB6bwXqS0cBYUE+BHOAOkQ7udrvJvdjf31etNjpvE2VEFjyliPIz1vn5eR0cHGhnZyeBNRu/yHwQqwJYqFLFCPT7fW1ubqrZbOq9997TgwcP9MMf/jC5K7ixuHaMyVmkB8mZF/rjDGN7e1uffvppkk/mnfQlgPNa1jHQCLwsLCxIGr2/zwMt7jdubW3pxo0bhSAgE4DVJR0G/UKAEUqv6EOAz87O9Pnnn6vZbOqb3/xmOuwU5CYlVpZO89QWviOWnPcLYKEjO8DSETPhM/z4GC+BNtN/3xOyv7+v1dVVTU9Pq9PppP4gvPjGExMTmp+fT8fHUSMwPT2dmJMzMQ+28vJdD54Car57FFpO9efnn3+e6HqlUkkMC5fJaz8AjaOjo8Lbm1utVoo17O7uFjaIwRqw0l43wn39fEbSmLdv304Vp6QskUHqPTyg6rEPmBT1N2dnZ1pfX9fdu3cT6GJAUHzPeuCKwKSQWWdiHp+AMTx69Eirq6spTQwoIM/OGl6mfSkwZFk2KenfSWq8uP5/y/P8n2VZdkfSH0ial/Snkv5enuf9LMsakn5f0q9J2pL0X+V5/uhLnpE6T3Tbq8Sk0SnQCDfU6uzsTPPz8+r1esnS812oLxFh3JOZmRlJSpaNSPfMzIzm5uZSTv873/lOoog3btxI27a91JnCHWoM/MBTjxp7ZNuDTH42gJfxekqVPQ4EP2l+WhTC4C+4IRWa57mWlpYS4FI1ijBSI0JZsgddYWkbGxvJT8f682o7MhYEALknys56UqDFeQueZfIsTKVSSbSYYCNK70G3SqVSSHMy3xSN0S+CySgYBW4oXpZlWl5e1sLCQkqhwsy8HoPYEbJVrVbTQTEouwd2T05OtL29XWCrsAJiXs4KvBG/Yb0Zr3QRL5uZmdHOzo4ePfr/2jvT2Dqz877/z72Xq8TtcpNEUsvMeDaPZuQl4xgOjNhAuhhx0hZp4rZo0yKA0aYfWhhF6qBAkQLph/RDmxRN4xpwASdI6jhNjBhG0zhN/MVAnNiWx5PJaChRFClxE/dLUpRE8vLth3t/5/7fI86MZGukS4MHIEje5X3Pe86z/J//85xzprS2thYRHo4PuQEVvxPpyruSPppl2VYIoUXS10MIfyTpU5L+S5ZlXwghfEbSz0n6jfrvtSzLngohfELSr0j6mbe6Acrg/IDXHDAwcA+SorWdnZ3V4OBgFArWFQDb8KAdHR2RYISs8t2XgPpZVjt74qWXXlJ/f79u3LihYrGol156STdu3Ij7BHR2dqpcLuvEiRMaGRmJuyqxUIXcNB4Pj0GtBrscE4547YaHFYRJvt083g4PjkCQ0w8hxK3ZyG93dHTo9OnTMX02NDSktbW16K0ZU6lRSg5R2tLSounp6fga4RewurW1VSsrK3HMUQ5qSVBIkBSVoxgkT6M5jyQpKq6fQA76OMgL8ixkgrwoyDNX1JTAMQ0MDMQ+47UhhIHlvh4DD54ubyckIMO2urqq/v7+WIvCdnD+ea6F8cKZgUS9xgGjsrOzo2vXrml1dfUeZwknxrVJwT5Ie1vDkNVmaKv+b0v9J5P0UUn/sP765yX9kmqG4Sfrf0vS/5b030IIIXubfAkPRQyIUjtfgBJhPT2ddPbsWa2srMQwge/CprMwyivZENpSqRQ3Pr1586bGxsY0MjISYe/e3p7K5bLOnj2rkZGRaFQQIqlRI8B+ja64wGyEgeeBUPM8O8VbwGpgKsYSkhCBZGs65zvK5bL6+vpULNaW37JqlfAA44QxA2IjlK4I/A2JCbfjSKG1tVXz8/M5TsXrIRBOxhuOBGHFeEI80idkABTGszvpC0rhfpJiiESqj1Qsht/Rm6RoHDmDA8SJjGG0nR8CweEAJEX47yndSqUSCV8MCAQyxp9n8jUmODQvxkKW+vr6NDc3FwlRytl9/QUZMvr/jqQrQwhF1cKFpyT9uqSrktazLIPqnJE0Uv97RNINScqybC+EUFEt3FhOrvlJSZ+UFAfRq+B6e3sjzIRZlvJHniMIm5ubGhsbU3d3d1wRyd58lJ16bQQKiPKRCrt165ZeeOGFSEC98cYbmp6ejsYHhWN14draWlROhL1cLkeLjxVnclBsyDDy6l6b4ISdr/0nbOJ7vocgsff+/r76+vo0PDwc4ffOzo5OnDih3t7eWAdB0Q+Ve4wDsBzhYk4YT2o4+vv7VSzWtrNn3QWKLykSwTwPzHuWZeru7o5napKh4R7E9ayv4FklRY4GFENZOd/3VF5fX5+Ghobi97xAzuth6nKYS39ijLmPL70GaTDfhLe+zR/z5s5BUiSk2TuT2geXa/rBeDMXXnlLRuzatWtxFy5Jua37MFYYR+d97rfdl2HIsqwq6UIIoVfSlyQ9e993ePNrflbSZyWpt7c38w0xJcW1CuR/JeWgGrEX0LSlpUXPPvus+vr6ovf0zUKZAKA38SlVdK2trXruueciUrh69arW1tZyKa5KpRI9AHUShUJBKysrMY7v7e2NUA9ira2tTcPDw9FQ+EKnarUayS7iTn44F+L27dt6/fXXc5WdoCZi9yyrHWnPeZWMFxwEtft4K2AqXtDDFJ4Xb+W8DPtPYugwqK2trdEwg2acZ4EbYMl0uVzWyMiIQqidyoW3RqgheVnqjiJigByNMHZ47CeffDIaPEmx/gA5ITPguX2MAEpOmAkxiAHgnsgr4R/oAgNFoReVjgMDAzlF3t/fj0Qp/BCOkPnhdYwXxnxyclKzs7PRGGHwUX4ITQwn13iQ9kBZiSzL1kMIX5P0QUm9IYRSHTWMSpqtf2xW0pikmRBCSVKPaiTkmzaHngginh/P4DEi8RMKsbe3pxs3bqijo0NnzpyJXAI7QksNAwLiYN0AXv9DH/qQRkZGdPPmTV25ckVbW1vxUBgQADAbr+6poUKhoFOnTsV7sXYA+AfMdxKN70uNsyX6+vrie+TUe3t7NTs7q8XFxSjcxM4OMYeGhnTmzJmo1FyHJbi3b99WV1dXXIyEcqXcDWw22YfW1ladOnVK7e3tunbtWjzij3ANwhLy1rkAF3A25R0dHdWZM2d06tSpyEOAmggPMVgQmVwD+E02hJAOJRgYGNDY2Fhu6XS1Wtuuj7oST4tzT4yIe1knZXEuhBWEBaA25BPv7ugOg8dWdRgAJ0VR5J2dnRimeDh1927tnA9qVNhIyHkh3/qQe5PWftB2P1mJQUm7daPQIenHVCMUvybpp1TLTPyspD+sf+XL9f//vP7+n70dvyBJvb29Wltby50sRWENXgejgVV2cmZvb08TExPa3NyMy3vZlWd7e1uLi4va2NjI7Rngh7PgCZhQYCE1C1wLBcerYWgGBwdVLpf16quvxgUyTBQxL4Lu8FVSrKSEzJQaW4K1tLTozJkz8TBYipwYE7IqAwMDOnHiRBwvBGtlZUXLy8sKIWh0dFQf+chH1N/fH73j/v6+VlZWIn9APh9DhSIi0G1tberp6YmKB2Lp6urSwMBAJF/xXMB7Nibp6+vT+fPn9eyzz6qnpydubc/RdBsbG5qdnY1b5blx3draiieWS4phIUuiIZghskGg/f39OnbsWCTqkBdCBCd6CQ2B9tSzYMC9RNuNKWdxsJktcgFCyLIs9suNO+ODceG6oEuek7GfnJzUzZs3o9HFYeEEPPuBIX2nOIaTkj5f5xkKkr6YZdlXQgivS/pCCOGXJX1H0ufqn/+cpN8KIUxIWpX0ibe7AcrX398foa7UgMJMpMe8DD4kDcUurDwkTeTQzgcry2o7QJHauXnzZmTw8aqeIWBwUTq2OqMP/f39mpub0+XLlyPSgRl3Fpz+4g0kRSSEQQHyo6TEzE8//bRWVla0srISydOuri719fWpXC7HugCECHSFsJC5kBoH9LDgiG3vQEWkQPGAlUpFr7/+ulZXV+O8uVHe328syGIxk6/I7O3tVU9Pj06fPh0XPhGvs6ist7dXnZ2dWlpaynlCoDs1BhhYDCSel0wMcThQfXBwUE899ZQuXrx4T1HV7u5u3BXbN9fxkmypsdlOoVBQb29vHAPGCzSBU6HOADIaR4MRQtkxBE6mg/aQa7JMOzs7mpmZicVWcDmePkVWCTVxpF6Bez/tfrISr0p6zwGvT0p6+YDX70j6+w/Sibt372p6elpPPvlkhPxAI198BAmJJ0cJPE0GIpAa1h0hxCC0tLTk6un392ubW8ApIORY7rW1tSiseGpOmu7v71dXV5cqlYquXLkSj4yDtQfWSYrCC/mF0CNM7EwlKU44jPvGxoYKhdohK2RVKB6izp/xgozEu1NuLEnf+c53NDQ0FGPs5eXlSKRCQlJFODQ0pJaWFq2vr2tmZiYWDEF4At89TQp87+joiMYNT07l58LCQuz39va2lpeX1dLSEjkVV16vTHS+iF2zqDil/sX35KQgbWtrK25Vz/J036+BsPW5556LXtozFzwvisbY45l5HyTKZjbUEsALeTEU9w8hxJoO5BSOzGU8hJA7kMczahDFTsaSgXGe5EFaU1Q+ZlmmiYkJDQ0NqbOzMw4wm4QODw/HNB8WFNTg6SeEYnl5OXrf9Hs0WPbW1tY4cZCJxWJtx2WP45zgKZVKkUMgY7G7uxsPR2WHH/qHQAP7SfmhvBgHrsEWbZCTCwsLmpqairAYogxEJSnG6lIjDAmhtnYDQpaiHbbHT1l9zpQgrib0YlzYDBZeJctq1YWuxL5Rb5Zl6unpifsOYDjm5+ejoqGcKCQnZOMg8JwQiDDrKF6lUolGeH19PYYeVB+y23NnZ6deeOEFvfHGGzGjQwbECU3Ssl6BSqhAaIB3B00w5oSlXgHLpjjOk0mNbep5bhQbFASqIIzd3t6OWSB4B5xdWuVLSTtZHpDIg7SmMAwhhFjF9cILL8QFOqQugeFMIPAIy0usVirVtk5nByG3xEBDUm7FYmNLLwYXy+wlsUyeW2VicWLFubm5uEksSgKSwehQqMTGLfQBg4WirK6u6uTJk7k698XFRS0tLUVvTNbDU3lAeuAzDP3Kyoo6Oztj5WO1WtXc3JxmZ2djtoHqUuJquBP2V+jo6NDQ0FAsbWbrMCm/XwWhAbs8eVk34Y3vZkSVJHUSGxsbmpubi5WRGxsb8XsoM0bHMwRAZ7wsIdbKSo3z3tvbi4u3SqVSJGOpXYBDWFtbi6eXFYvFuIQe5ACR7UaVDAbIk9f4Dud/4oiQd0k5chcjB2JElkAp165d0/z8vDY2NnILsbim17twD3diD73A6VE0vP3MzEwu547Cs8Grk0SScgQhFp8JAVbC6LJyUVJugROKThxOXE7NAbARDgCOAa8BQQrB6UpCCERakKXPnNHgm3kiVLOzsznSlR2RyLJQXIMh5H4YLQ+fGLP+/n699NJLOnHihFpaWjQxMaFKpaKZmRkdP3481g342gAMVmtrq0ZHR3X+/PloGJaWlnTp0iXNz8/ndhuCC2BcGb8Qgnp6enJrHcgMsTiqUqloeno6V/dPaNTe3p47UcrrWHp7e3M1JRjonp4e9fT0RHmhDqK/v1/Dw8NaX1+Py+85hWtpaSnOu1dOIh+QkoVCQT09PdFpIUvHjx+PYR28GWSm94P59vqWnZ2dSIaDNkHFnB5+69atOAeMqxtzUAQkOv2iHw/SmsIwSIoE18TEhC5cuBDhOZ6bij08A2hBaizTZqBAF0tLS7nUUaVSiQuXUCQaaS+pAVOBhb6Yx/dfKJVKMWff1tamgYGBKDwIsIc9ICAU0SsHfcnt3NxcTFuyChKlwHviqYHNwFWuy7qRjo4Ovec979HY2FiEp+fOnVO1WtVXv/rV3HkdUsNAUXMwOjoai74wyFQ6XrlyJRK2/n2UiVAM9EAlIIipUCjEYqGVlRVVKpVowDH6XiTkJc54U65FpScem2pPrwWAd/CKSGRBqqEfX8adZVlEXq5YzD/xf5Zl6u3tzfFF7e3tGhwc1ObmppaWliQpolKvrJQUS7yRc2SezMTS0lIkfXE6fA65J1RDzpyP4b0HaU1hGJggioWWlpb05JNPxv0QUNj9/f0oNMR6bok9NcQgkTuGs9jY2FB7e7uGh4ejwEL4ANtQaP5G+X0PAZTPuQuWNPsOQhgVDx+86tBX4+EJ7969m4u1iYOB4exF4ApC6oxw49ixY9F7MFagnCzLdPbsWV24cEETExOSFEMm0pJ9fX0aGxuLIYTn2X1RmiM1WHYMKkU+hGidnZ2RLIRRxxhubGzEvQ9IxRHH82wYfXgjqbHHJeFJe3u7FhcXNTIyEoleQjsMC7UYzhOgRPAYZ86c0eDgYFztCacD7N/c3NTx48cVQshtrsvCttOnT0uS1tbWoqNx2J/WsSC3Xtbc2lo7PtE3/2WxFClc9INxzqzmxrkb50PupzWFYZAag0KGYmxsTOVyOa7gY1CIsT3OJ44CzmJkSCneuXMnprFgs4vFok6cOBEhNF7ZCTkKpbq7u6OQg1J8oREGBPKuVCppaWkpF2t60VGWZXEbMlAEQsf1iOF9rwcMGKvrEDSMHpkCxoZCoIsXL0pSXH7NWRAvvviiBgcHY909m5qeO3dOp0+fjtugkWIFDbAhLagEw4AxZh6owMQ4eUoPQ8GmsMD8jo6OWIwEyQqqwTsi/JLiYiwKk0IIWlhYULlcVmdnZ9yJulqtamlpSfPz83FRGh4cw4rCsWR9dHQ0GjO4IxAje0u6IyF8Onv2rIrFosbHx3OGjTGEvHVCkHl1w1AqlTQ9PR338nTC3MMSQh2psR4H8hpEdCgRQ1pnsLi4qImJCT333HNxwEk7lUolDQwMxMmmTNaJH6w3g8gkehhx4cIFvfzyy1F5JicndfXq1WhknLBBKKXGaj/ISyw1KUgMAXX0XlmHUMM+ozC+JoR40VftIZR4WUhY0rMYLYSHXYakmoBRTv3ss89G47u0tKSTJ09Gzzg8PKxbt27F073u3r2rGzduaH19PVY2VqtVTU1NaWpqKsaxCDDcS7FYjBWOVBgSFmDY8Zi3b9/W8vKyNjY2opHp6upSZ2dn5IjS8UOZfH1EtVqNBhkS78aNG+rv79fy8rLW1ta0vLyc25KNfhAugLCkRu3A6upqrEZlw2HGglPOGH8Kr0ZGRrS/v6/r169H0pm5IWvEvEPyViqVnPxT7bm+vq7Z2dn4nsu1pGgoMQQ4BcYlLXZ6kNYUhsFTjcViMaKGoaEhlcvlHHyF4OOMy2q1cd4fA+YpTEmxwg5oPTAwoCeeeEKdnZ1xl6Pz58+rtbVVFy9ejCWw1BowoUA/H2wmQWps5Z5lWax89EIm3vNVlpKi0HE/50sIJ4C9IA9CjM3NzbhgjMKe48ePx1OeMSBTU1Pa3NzUqVOn4vbwQGoWNhFara6uampqKipqS0uLNjc3NTExkavnYE5ARBjd5eXaejl4BFJ9i4uLkbzD63rxmBOnfAbjzHMTeztywwiiDKDAGzduxJoY0ADzBcGMAWf+3LtTpIVBI5VerdYOvaWiktJ5kNvCwkJcqOeL/VIZJaTCYPEMOBsyEcgeVaVebEfIQZoTpACyoZKY3bXutzWFYaA5SbS0tKRr167FGNdrClhqTNEMg4BXIRZG2EibMfhsgX779u0ILc+ePasXXnhBd+7c0Te/+c1IYHnhCBObEn5eQOKf9xVyCAmelX7xN/ASHgDPzn0JY5wnyLIsrqSk9oFUrSsBhnJlZSWOz9n6adaQfii31NgkhD0qt7a2Yimu1KhG5G+psRrT1x9wjfb2dq2vr8eiJsIiH0vGD8PR3d0dMwFejORGltBla2srbu5KqhEOBqTl52bifOAzMAhenOTENMZ9fX09PmtbW1skZNl7ks1rCoVCLoUJIkTGPatFWTqvY6xmZ2c1OTmptbW1eMaFk8ROKqbkLPUUzmM8yMpKqUkMAwrB3yHU9jGcmprS0NCQTp8+nSt/JvXENvB+6hLX8jJRBgkPBskJdGfSu7q69Pzzz2t5eVnz8/PRADFhxPCQUXAZ9BtPByPPgSN4DJbukm5k3T1EJlvZkxbFyBBrE8Z0dnbmPCskJOkqUAbKxN+QezMzM9ra2oqrUZeWliKLDcEHZF5cXNTCwoJWV1dj8RMpOq5LbEycz70whNQroOzb29uam5uL6U3CMuYQ9ASxJymHIpxIoy9embi8vByv64u44CJ8dagXGDkE96wH6NPH3A/1ZWdwdh7v6emJaVkU0rkmrudeHu/PPgpTU1PxkBqyU9wfWfMxoYSdBVhUzHr4+iCtKQyDlN+jEWJlZWVFExMT6uvrU09PT/QKwDFSPFtbW5G4cujmTDzCg5FYWFjQwMCARkdHY958dXVVp0+f1okTJzQzM5Or05cUvQzeijoETnHC2gMvmQxqIWDJeY0agJWVFR0/fjwutsLjk/f37cYQRjcceAy+w3NS2eeVn2QCCM/Onz+v4eFh3bhxQ/Pz87E6c3h4WMvLy7p8+XKuEMs5kb6+vtwaFpSTIiupcS4ptQG3bt3S1tZW7HOWZTGGpirUQ0GEmnu6ovN9OBv6AbJgTQMIkuX1VBOCejBEZIcgg6kURQYwFsgp8gdk93GCFMdQYriRT0hgDD681P7+ftz1GafghtKf29eqwIk58sOIeO3P/bamMQyQQlJju+xisRjLgd/73vfm4nZnwrMsi4euUgoNTPeNN/BWCN/S0pKeeeaZCL9Q+LGxMV29ejUKCYroE82EcW1icSYCr4RC450KhULcoKRarcYzBqXGSUikoVjYNDAwEI8/xzg4AQkJCpzHs1ByTR/dI0LSjY+P693vfne8Rwghwv2pqaloqEm5ORqQGkVmWZZFw41X5758F2/W1tamwcHBmI9nbhgfYnkWIfm6ElCJpMgfMM+EBowt5cN4U4yVowAcDQYDBEYYI9XQIBkkStgxuHj+u3fvRq4BA+8cSVo3w6Isrw1pb2/X9evXNTMzExECazOYX4wKssJ8OipEDjyVfChLoqUGy4zQ8aCQXuVyWc8880wufuR7voKQwWbQ2E2HKkAGidLYkZGRGLdiCBz+4wFQyHRrLmJSahS8wMXJQhQFReOnp6cnXo++eWqPFCVxO8rhfQJ1EN972pbsAagF0hNvc/36dXV0dOjpp5+Ouwt1dnbq9ddfj+XbGFnQDsqJctCX1HBQHISike+XlAt5MMq+wtF3usKwoiCEYLD3KDyZEFLboJFKpRJX0mJkUDQfM4xaqVTS5uZmvIbvKoVxdmK5paUlrgmhlsOPMvAUOgaLVcDITXt7u9bW1uJpUhg5ns3rN+g78uicEwvYMJrM36HkGKQGJJQUJ5cHowSXbcs4XAZIx8DwGp6IxUpYdfZTpLGEmTMOtre3Yy06UF9SVC6IPaw7wgs68NWS8BMeVwIX3fBJinsNSI1Vf86TODGF52MNhi+x5hQtr+ZDkCG66Asp1u3tbV27di23wc3Nmzc1PT0dCVBCMS90Ai3hueAJMLow5Tw/ffB9BlBEEBRjC1HohCyL1QqFQoy9QS0YUcbX8/zO/DsxXCwW48bByJCHPyyQk3RPGpqqRo4BbG1tze0AxYlcjAky5JkWvofh2Nzc1Pj4uObn56NBchTtqUrnPFy2CJsJzei7P9v9tqYwDO4FfWNPPMfeXm2HpldeeUUvv/xy3OHYN9Rwtt4JFwpZUCbiL6xqS0tLDFMgkeAc2LIMRWhpacnly0ECKC5W3BlhJpD0FQQm8S1hB/Ew1/EwBOHw1B2CiuC1trbGMxZQDngGFMzTcpB2nvcfHR3V3t6erl27pjt37sR9KeA68FgIN54blOQ7Ke3uNg6rAc3h+ZkX9uMkK4Gy0l+eGT4Hxe7q6soVgSH0LvwYI+aAOXQjAEJxw0SFoaR4junx48dVLpd18uTJeJAvcre3txcPGqa/IEtf96cAACAASURBVKTUS5ONgQ9pa2tTd3e3tre3dfnyZS0sLESn55wBesFYYGSQZYyoy4c7SzJ6D9KayjBI+bMSgOvAzMnJSbW0tOjChQsaGhpSW1tbrjSYCYE04vs0nyzg5vLysqrVqp566imVSiXdvHlTq6ursWwZIXJSzGN8QhkgL/32tJSn2CjSQinpI8iACQU5kMmgodz0h8rQ48ePq6urS/v7+3GBEJkYL6d2oQNxQOB2dXXFSjtPraJUGGziVwhWSFmek3COw3gdpkO0MWagL78fY8HYosw+jp4RSj0mYwQfgGHFkHH4L+TerVu3chmCJ598UmNjYxoeHs4dzYch2tjYiPwFfANzhgw7Z0W/bt++HQu3UPCdnR1NTExoYWEhkpPIKOOGXDuvIDW2+3fCU2psoMN4O4K839YUhkFSbk0EAwexRjiwtram8fFx7e3t6X3ve58GBgZyFhEhIOUGxOP4LiAcBTDA64sXL2pqaipuHkvuHIFw4ioldMi5MzH0G9jJ5MMicx08q/+P4vmKRT7jP8B1Rx7E65Tw+m7ZnqIrFAqxlJdMCdWHGxsbmpmZiXUV1Wo1nvCMkFE7QKjD+RJ4NZACPA19TNcLtLa26uTJkzEu95gYD4iiSMoVN2EgQEZeyOPcCgpGGpQl0GROWKPB8w0PD8dVqCjz1tZWTI2vra1FVENBWGdnZ0RqS0tLMQTxdKKkuJEwstTZ2am2tjbNzc3p+vXrcV8JXzPjlYyMNw20QthGtklSrs4GGX3Q1hSGwdM5FIe4JSY02N+vrc577bXXdPv2bV24cEFnz56N5bRYfQ8t0lWIknKVcNyf2oVqtXFsHb/xzggkhss34QBBSI1in3SBDnA1hYEoD+EJaAJkQTqUxVTFYjFCcdAV98G7cnwbxS6emejr64tpMp6X9DBZGyrttra24o7YkuKWdbu7u1pdXY2HzoAcSqXGjktwGTwr1/CVoMT4vgiMjVoxJr4M3t/HYzqXw/hiYEgr8kySYvmzE3mtra06d+6czp07FytKnXBtbW2NO19RIr2zUzuj8saNG5qeno5hFghTUtzBycvnSe2Sddvc3IzHF/IcPBshJcYBXsILxDAmfI4fxh4depDWNIbB419IPDwktQFY1J2dHY2Pj2ttbU3nz5/X888/HxdMsbWawzU8JV4zzQcjIMBsBBHPSrkxQpSmhLwgBsFlkphYrDcxNl7ZwwVPOYE2fGu77u7uiKDIoQPFnVdBUOg3fQalUOtAHyDhMIy8BlsOc8440me2joOlZ28HMhBs4e7GDwXxUIuxwsCgTF4zguDzXYhmHzf3jPyPMmFgMThDQ0O5/Rrow87OTswweHbIkcjm5qbeeOMNXbp0STdv3oyhliuh1y8QtlH41t7eHjMTkI1puMQ9PfskKRof9MWRpNTYggBy2eedUON+WsAyPc5WKpUyP1oObwnxtru7GzcpQVhQZk6Ieu6553Ty5Mk4aFhpFNMH3pnpEEJMFzqDjLAwIRgToDkIhPy3v+8K7yTk7u5uPCPBJ9Ph3v7+ftxPAiHGo9EfJw5ZxIMygzIwcBgX+sDYsvcB6V/3sIVCIe7HCNSXlIOyCB7GDx6INB/lz769HoYFb+9IiXUGB60XcYTFOJOqJI3HnHrBla+PwGPSb8bUEWlLS23L/pGRkbjLuKS4m9Ts7KxmZmZ08+bNmFJ0YtfRqo8ZBg+E1N3drc3NTb366quamZnJLZTzzAWhIYaBOZUaeztgIHg2DDGG3DNId+/e/XaWZe+/H51sCsNQLBYz4K0rTPrwZAUoPvGimMHBQT377LOxWIcNTohdpcYqSfgMIC+C6IYJC4/BIJb1SQfNwNrjKRBCxpZ8//b2dlwo5LAPQcVoodwICJyJe1k/NZmwxUOHUqkUz0XA07pyOtm2t7cXKwHZDg9D5AqKFwYVedjl6wsISTAOGA9CRGovJMUaAUIlzywRjvg8QKiS9mX8UW7PcKUpSMbKw0LmClKS+J9TwrMsi/wL8uQwHwNHuIZxYg5BgyAXHNz4+LiuXr2a27wYBWa+MF5uePxow2KxmFtZy7xjEFJnuL29fd+GoSlCCW9AKAacSWRggMJSg2Vmg9NKpaKFhQU988wzevrpp9XX1xeZYKwywsIEolx4U+A+gpfCQ4dxCDiTFUKIsSOTwz1RGspuvcIOIWWCQRS+LySpVgQe5h/U4orhiAJOgOszbggZXhclAG1RfEMGxjMSPpY+pnhg3/NRamw64rEzab40U8LzkZ3wUM1jZsg2N+IYWgw1ffZwAMUBccIjwDns7+/HczikxuI3+uyZBkk54+wKiuH3+g36f/XqVU1NTalSqeQQKQ6Cezs3xf8YQYy619FQpcszY1CRmwdpTYEYCoVC5kfFo1C0dLUbxSG+85ErS1dXl8bGxvTcc8/FBVgUJ5FD9uIQhIOJcM9MfAwslxRTU0wOQoenqj9TFGhgLQoKOvAYkv7AVQCpHSnwrJLuCYu4DsJOdgCP6n0DwcAxwLSzcxR99oVQLOZBIT31S8jg6Tu8Js/G5znAhbUaMOkIrxs/uAPGm1Qwr3sIx3UwCG7QnGfiuv7shGcYGOc14HzSuhDfSJjr+n2YD+SV1O74+LguX76sjY2NuD+lyz1y5OlYZMaNscuqOyAnKlmABnrY2Ng4XKFECCHDozDBknJC5ZaTmI342QuAHCoODQ1pbGxMZ86ciQd/UK9PlZ4XIbl38QmTlNvEA4H3eBZ454jHPQBCKDVOacLAoeBMvi+UYuIxVCCJQqFx4rKnLUE7PCNeEw6H5+B6EG58F9TEOKMgbkgdlfieiJ4+BAkQClA3glK7B/d74FV9jQCyITVqJEBCXrsCqnP5kRqVkW70nSz2XL8rna8z4XrczxWTa2N0kBF+9/T06NatW7p8+bKuXLkSt7FjbQlj6sZBahxbSPNQi77BT3mdAkbBQ9T6IrLDFUq413R4j5D5VuG85lV2zg+gCIVCQWtra3E57JkzZ/TEE0+or68vLkgiv+1MvtRIn6bEIwbHhcLr7EEEKFy6ZNiFnectlUox5+8hCv3Z3NyMOXr6h8KjiA5jpUb6jmsiQEBzXy/gbD4befgqQ5CR8z3MF6EDoY8XQdEP0ocQaA7LpcbeBl4Mxji7EnrqmuYrDz1jAgpIMxMYcsaEseMzKCgH47jS8jzu1emfFyS5gYJTWVlZ0eXLlzU9PR03p5EUwwKezZ/XEQOG2FEzsu8/jlrhNrxA7IF0shkQQ7FYjFkJSXGSXAl5cGIxvAtC4GkbYB8FTghauVzW6dOnderUqUjeEWJ43MyAepoTRIDwOeRlDFF2fwaMinsjz7gQj3IfSENPc8FjQNRBMLr38Hn0sKc+vrkQAKNA3YHH/k6woqw8K+NIwZTPDagDMpL3U0ju3poyZxSBVYkeDlUqlXt4J6mxzsIzHa4AcD6EFDynr3tI05yEoxhD55/cMHsoCQfiKIjQrVqtneHBTllch9JoJ5hBWMgwSNgzczgMDJYjXDciKZGO/FUqlcOFGBAujAEDAUmWFn4guA6hPNzAwBBHMwHr6+taWVnR9PS0hoeHNTo6GtcvcD8G32NXN1SeHnJBSXkGL6Emh+7XwuOiCKurq5qbm9P+fm1fAs4o4MxCnplVjdwfBUKgPKuBp0T4fM+CQqEQ14dA9GE4HL0wpjw7BtNDPl/MBXnJ+CHgrsRuDDkE1rkejHsIIab2MDRSfos9DxdABY6EXPFSI0e/POuFgfb+0riWpNxBt4Q2nhVYXFyMR8qxjUCpVFuUhpFHBghR6IOPK2tvJOU4H4q9MHQpYe+oESfyIK1pEINvnMHEeqopjedQQucInNX2CUVpd3d346pI1vpzSjSLY/geSoTXdq+QCouvx0g9iHskhAdFY43Azs6OpqenNTMzo66uLvX392t1dVW7u7XdgKjZR2jxLrdv386dC+GIBJianhvhxC7jSwiAl2XMnfPxsm68GIVkHlY46nM0IClyBSAmoLYkra6u5ghBDr4ljCQD5cQw/QM9OmnpcTooBaODI2C+cEQgHAxxGrd7GhFZ4vtkB1ZWVjQzM6OFhYUYNoDY2EOSRtjhRW/ItmcWMCg8K9wa/XXUxncIsXAoIYTDRz4Wi8WsXC7HCfQ4Tmps98Zkep7Y/8dLSY0ViHghBpXr4R2OHTumzs7Oe4wE3sE9IFZYUg7COmHlYQ8cAsiAz4AmgHyLi4uanp7W2tqazp07p49+9KO6detWPO1JqqXNODGaWg68D4t7HHYSY6aeAsOF4nj/9/f3Y7EUpBcIirCsWCzGkCZl/T2Vx/hguKWakrIoCQO2tbWl+fl5dXd3q1wua3x8XHfu3NGpU6c0ODgY78c8+rZ1zLPDfoyPhyzVajWSdUB45IH3CRd5FpTZDT3jBeHKjkvsEj0/P6+VlRWtr6/n0CPZGMJVZBB04RkHKU+WsvbFV7K6sfVwEgPjz4Qe1I344TIMhUIhY+NVh4ZuORG81GsDDaVGsYqTe7yOInEPh/Qeg3Z2dmpwcFD9/f0ql8txfwPgGB4JT08faN5vVz4EhNDozp07qlQqWl1d1crKSlx119/fr49//ON64okndOvWLV27dk2XLl3S8vJy7B/nUOJNQBKkIUE5DisxBg6jPTNAnQTEm7P2PD/P5lkSoDDj4Qac7AkL2hzlVau1BVh7e3s6ceKETp48qfX1dX35y1/W5uamBgYGNDIyoqGhoTiGpBW5B3NKrO8o07kcL8P2MIgQyhGpr7VwJdzf348cCGHVysqKFhYWtLi4qPX19ciHeAhJP0EkKCohHw25dD7DaxVSVOzKz7g62cy1uHadvDxcHIPUYKkl5Yg4BNmtOw/MhLlxQ/hRYmeU+du9Psrk3nZ9fV1zc3Pq7u6OezFy3oGvS0ComBDvC0bIC2moo6DSjz0fbt++HT387u6uvvWtb6m/v1/d3d1697vfrbGxMU1OTmp6elpLS0uxOIb9Aeg7igDhhlB6HYV7dz7r24rfuXMnR7R6WswJYMhbvDMel7UbEGh4WMg6DvYtFAoaGRlRf39/jKGvX7+u69evR6INjmhwcDDumcCRfo5amFPfd8C9LoYI4+6Iw8lJDCDy4EVroI5KpaLr169rcXExhj8suvIjEDwr4s3l19/3UMU5EWSL/+knz5lmwthnxB0B8vogrWkMg1f/eSrGra17BBQfY5HWxPMa3hPLi3I4pENxSc+VSqVYHuvrCCDYCA2AmfSHxjOgMGmlJKQSP/Rnf7+2mczFixfV1dWlD3/4w+rr69PAwIB6enp07tw5Xbt2TZcvX9b6+rrW1tbiTtTlcjlurMJ4dnd3Ry8JumFsKejxcKilpSUaNOJYJ+2kxj4QHgPzHmPDpq6emtvY2IjQdmRkRIODg7FgqVqtamFhQV//+te1sLAQFcsPc/GzLzDOoBYyIV7WzXg7+ejEK32G3KPMGuPsCI9dmUAIq6uruSXS7rUdafn9eB0uAoOFE/TUON919CkpVwcD8vB1OHwmDadT53k/rWlCCcgmt+RSPiZO05lONqWkGIrvg+QT5vl4Js5fw2P4pPC+8w0eyhw0CWmMjwHyLAqxKgpcKtVO2/rABz6gD3zgA/H8ApSRvQGnpqa0vLwcU5F9fX0ql8uR6UeBEFqUnCXXIAeQE4LoWRlCHzcANIyx1yJgQHk+FrN1dnZGI8fiJBRrenpaX/va1/Td7343nsAFP3Hs2LF4vgWrQn2ZObwFh9j6RrVOFIKKkAH66WHm9vZ2biHXnTt34pLo7e1tVSqVOHfMLXKAwXS470YCGWWukW0MEPOBMjua8BQscsL93JHyfTJoUg25GgH98DmGEEJR0rckzWZZ9uMhhHOSviCpX9K3Jf3jLMt2Qghtkn5T0vskrUj6mSzLpt7q2nAMhAYMLtWBqVHwQXVr6YpJTJvCaJSr/ky5yXCyjs8iZPTpoFjW+4cV5z0MFPG3Gz88LvGs1DCIUq3U+Pnnn9cP/dAP6emnn1ZXV1euGGt3t7YnAqv+VldXI4QnvqdfZET8MF03mhgl+uyhHMYx3R3aMxWMC9zJ3t5eTLn29PTEQ36Yt2q1qvX1dV26dEnf+MY3dOXKlWgUGG/6RvYG1Jfm+EulUjwHg12a2dqfhgH2tSNss7axsZFbyu4I1feLRMGZK+ex/D44MifAPYviJLQrtae/GXufH+TT0bPXs2Dk4RsIt+y674hh+JSk90vqrhuGL0r6gyzLvhBC+Iyk72ZZ9hshhJ+X9GKWZf88hPAJSX83y7KfeatrU+DksRAD5oqZhgn+v8NbIBtsLJPnKMMtLort3+dzqRFwfoPPMym+1Db1uKnAeCjkxGp9AuM1Wffx0ksv6cUXX9TQ0FBcXOVKvLW1paWlJc3NzWl5eVmVSiXuruTj6B7WMx2sxJQapBzPyRgBe/kMaw68xoCKv76+PvX29sYt/b0akj0mX3nllbinAV6N52es0xgZ+M0c05wTQTYwHMydh5soLBwBfWdMUTJfvUhY60gPI4PyefzvIYGnP5078xDCi7t4JowPRgDZYsx9ERUy6Y4VJFgf14drGEIIo5I+L+k/SvqUpI9LWpJ0IsuyvRDCByX9UpZlfzOE8Mf1v/88hFCStCBpMHuLGxWLxQzLx0QyGWku3ev6Pa7z9A+Kz+AwMUAxJt0ZZCaV5tDMLToQ12GeQz+Ux2M95xCYbEcZvmeklC93lWqC1NfXp6eeekovvfSS3vWud8WNSjFiXJ/0KgfoYCTYnswr6PBk/f39sRoU4eKaIYRcuhCSFWTCpiZZlsXNT1i85TtYAddv3ryp119/Xa+99ppmZ2fjQqI0rk4dg8fpvA+S8Co/X0OBcfMl88wZY4+ScV/kwPuBPDjkRwaZU5CVk99kUHAO9JHf7lzog4c5yBGywHMwls4PYcwdVbAeyGT6oWclflXSL0jqqv/fL2k9yzJSCTOSRup/j0i6Ue/IXgihUv/8sl8whPBJSZ9kcrwaEMEFYmNFGVRiKDcaLrROLLrH8ApBNwg+me6p4BWYAFh8BMtDACe08GSu4IQyjkAwIi6wxPJewVetVuNpzXNzcxofH9czzzyj06dPx+pIFAgCsKenR6dOnYqQn70gOFqtUqlobW0tCo4bAB8LjGmpVIrhQEdHh3p7e2Mq16tQ+R9ik/JfTrW6dOmSZmZm4hbrjAfj6egqNaw0j/EJeZgXR12MPyGPVxk6OkFxkZ8UwjOn7qz4nhsswlbvO79dlv37bozcuDvCcvl0bgQeBGeFHiA3GObvpb2tYQgh/LikxSzLvh1C+NHv+U5Jy7Lss5I+K9UQg5Tf9horCgpI+QGpUdvOIHl9ggsJk+sGg7UUWF9eR3nxOFhpV2o8mZNxCLUjA57DuRP3Gm6I8HwuVC6UeKDZ2Vmtrq5qcnJSo6OjevLJJ3X27FkNDg7GTUD4DmPW2dmpgYGBHC/gMSs/wNqUSyDscrLWuRZXRsYTGDs3N6fJyUlduXIlIgSv8OM53WAynmk/uUcIjX0vfP0A/WAFJsrlhCuZBw/nsqyxe5fLnBcRubHw0ArP7N6dz2FEHM2mqMLRBNWw6dofroFc0ieu5U6JGhnfUOZ7afeDGD4k6SdCCB+T1C6pW9KvSeoNIZTqqGFU0mz987OSxiTN1EOJHtVIyLds/sAMkqcYifvcw/gKvTTG8oHHokv5jEZaQeeIhPd8chyhMLlMqhenSMopoF8fQ4GHdogoNVJsXNPhItfb3d3V4uKi1tbWNDk5qaGhIY2OjmpsbEynTp1SX19frubCURTndFDSy5gzji749JuxJrXHNVkjITX2zrxz507cBm1yclLXrl3T8vJybh8GL/Pl/m4I0lWceGUPNbgfc+2EoXNE/M3z+KpJxh75cy7pzWQKyA+C8DDCDRvj5XyCh8kuO4xBWtHLZ5FRjAjP5Y6EMUoLp77X9kDpyjpi+DdZjXz8PUm/nzXIx1ezLPvvIYR/Kel81iAf/16WZT/9VtctFAqZH+dVv1fkEDyVxsSgKL4xh8M1riM1liG7sHu60ckzJhDD4KXWTlr5QiCpQTT59Z37AAHxeQ+PEAYnt3gu5ocxcEKK99gs99ixYyqXyxoZGdGJEydieXd3d3fsI8VH3BcB90If1j8w3jyTe0jiaz99aWFhQbOzs/GA3LR4Kx0Xh9LMuZQvXON5GSv6SZ88hexhoNSouUCBqMtI2X/GxufW54bQyA09z0Poyz0dMfLb5cCbl6O7o/LMhRs2+s6zhBAiue5by5FZSds7wTEc1P6tpC+EEH5Z0nckfa7++uck/VYIYULSqqRPvN2F3Fs6JMKaI5R4GGJaBNnLf906MxFuWVEmBpL7+3cd4nM/Ryh+3ZQ3oO+pkKYe1+GoGzQn/pxj4LpSw4tgDIHJm5ubWlxc1NTUlI4fP67u7m719vaqXC7HLEFXV1duz0Rf4enGGL5BykN9CrTYB3F1dVWLi4taWlqKJ3ixuW5qLHk+DDmfcdjv8DhFfPA99AfjzDV8pSFz6N/3WhSH4j7Wjk48LPX5Zezw8sgJ/Ip/VmoY9dQRpPdy5+QcBJwCc+HP4nzFWxmFB21NU+B07NixnKKSamIAnU/AujpB44PoJBgKzTW5hi+h9vJVPsckQsD5UlzPfjhr7ESlCxrCx6Q6xHQU4lDe419XWF53gXYo62PnxCD5f/ZDbGtri9kD8v4pe8+4+kpOFJ9zPFi2TZjgCKA+t/Fa/po/o/M5MOyMBWNK7MzfjC9jw5oIT0l7+OapTRyMe3UnGt05OBLwrd3StQkYdxCYFzfxPg35ccTD973mgbCKDIWHxK4DXJu1JD7W3h4VYnhozaGfW3m36giRK6Vbco/lpHz4wMDzOpbV4bFnF/BsKGbKNDsZ5ffh/RSKu/A6l8H9mHg3UC5cKePs6bRUwDAEKLSv6MNoORGWFtL4mLniIqi+m5YfA+ihVuqx+c1nUqLOQwHGPd1EhnF01ISB5X83BF4X4kQqz4kSeqjoikx/nNhzw5FyXvQbZ0NI4pvTSHnUx/8+r75ginEE8bp+uIPgmm9lFB60NYVhcAFJJ9Nfw0oyQSm55ALJ5LGxCUIAjIWkce/jk+skkKexXGn9Pt7PFP67x8B7+5FiHpKgNFIDsqYezD0L73mIAinnEJRxAkG5Ah5kCLgP4+PGibnAELk387nw5huQ7O3txcwBKKhare1HQHh4UJgXQsjtCO1emOcqlWqLtoDvHi7wfCgfY4Wx5D7c34lWjAbjBsrB0PFaimQYE7+nI2Bk3RXdkaSHJSlJ60bT6yseRmsKw+AT595UUk4RpYYRkZTL+aNQHp8i+NzDlYTXuB9xdkpUOnJx4tAF1lNODg35Pu/5tZwA9HsiPAiLbzEn5UnONH73ONbfQ7j9O/SRz6IYGOSDVgd6f1E6ns+9W8rUo2y+7NyNqj+7I0Dm0Q2XOwyP/x0xQMjh7T3M89Qgxgn044jLjR1FdK6MjgDpc0qegxT8Mx6+gCaQWT4LOkoNIM9Jc1TmXMfDaE1jGJxUQsAQBjwW77lCuiK6l3JDkqY8vZoO4XJFcMXlN99LDYZ7BifXnGBzyMl94TFI+XmWRGqszZAU38eTI0RSY1ckr2R0ZIHCo5ApHD0I0kvKGTapAY0ZezdG7kXd2PgzHbQjN/PjRsszOG6MPZb3dTKtra25Q3xSGZGUO70JY8Y88Nw+p4wHZHiaXSLu98+kz5SOq5Pk3A905H31kJQ+umzxOXdO9PthtqYwDKkHdtYW6+kkotSI7Z1okxr7IPAdj1NTi+/GxT2pcw4uEKngeI6b5+BzTkR6fOwTm+a1HWa6oEB4ITCpEXQij895/h/h9ewDhGpKoLKFWGoYMJ7p664AGB6MGP2nb3hoaiko63UvmhKuDr+5F2EYdRg+Lj4H3B+i2asj3SPzfH4PJwh9fukv9/T1Cx5ypvdwxU3n2ZEs93YEhZEldeqZmIfJK3hrGsPgENmJGAahUChEgXJo7RDWmX4XWLfSDrdc6J0MTIWRz7pRYOIcCnIt5w4k5RQSo8PfKWR3I+EpPM/X00+p4bF45pRQ9IIpxso3k/V7OUryeZGUU3juBblJ2tNXJmJ8MCjMr4+hlye7weZ5eU54ECpA6TOGhvnwENKNlIcqyAlzxfi6TKQhGmOBsQfBHWS0fKyr1Wo80Yv5Ogg9uvzxGk7RMzTOY4ASnWR9mK0pDIN0b/zkcJIft7SptUWgGKg3I+24hlt17uc8AZ/zeNrjOPfYUh7yuVK75+O3P6MLTNonh8bOU3Dd1PO4gDKenvL1UMM3+0AZgLZeZkw//LO+RwaLlrzmAaV0JEY45wLv27w5xJcaxhTo7nG0z7tXP6aQ342IpJxB8Hl0Z+OKhhzxLMihG1OQD8jV121wfyea3QgxDtyL8MQrXlOk4f3z8X3YrWkMA8KXQlM3BAwEAsRkSIoQ0xXLYRjX84F1RUstPx6C95xLQIkkRSHwvnBfJhavRYrUYaLHy8ViMVer4Z7FPSvNPR7P5vfjOk7a4TH92owFz+2hk0N93qMxzs6qe/rQ+RDGknsBwT1U9Hng+tyTvjDmPH+xWMwd9Y7hctThfWGc3Mj48zihicyh8F727sSipxjT6kafAw/rCIVcbv2+vqcHY4BRBS09bMLRW9MYBqlRwurxOIOJAjlsJyZO41NW0XmmIlVuqSHYKEXKRfAZ5zMQQBcYIHaKThwFuLdOSciU+HQUxL1TtOT8SupJeM2NLcrnHI1nTHifv6X8Vvj0168DocY905gdxXW05aGWj4V/xp9V0j3G0gu9PBXsxsGJW6+ypG8eOnJdDznhJkCujjTdQfBZDwtT2XPZcCRDP0BEKULw1LCP0zttFKQmMgxp6ix9cATUhcuV2JU85QlcuF1AqIZzlOJlq44m0pDABYTvepqJ76XhgF8Pz+NkKZ9xwfW6Ar7LMzlP4qSVCIIGNgAAF9VJREFUGyM8jaMKRzk+jo4anF/gWagP8Hu74fC5YjzTufGx8SyJlCcr0+bP7t4dDsKNBM+Tlq2nRic1ht5fjIOk3FmnbmB8vp0DOAj5OP+AU0uzEo4IPaRk7OFs3kmjIDWJYXBhdgvtMM+9osdgLuw0JwRdsbi+1GD23cP5hB4UciDIrpT++yDj4ZOaphzTlJyHAP45ntGJPfrsHIl7IveC/O+7DyHQ6bN6ypFnd6PqrzmJyn1RTN+NyGsIfJ5R7HTMmAMvHff7+1wWCoW4KYwjNicqDwrB/Joua94HN36EEs45HWT8+N+RKmPmGSxk0I2o38udiRsc5yXeydYUhkFSbnLwZq4wPqiuHEBLjxndKlOoQi2+x53uWXnNvYELssM5RzNeD+CFMC4ovJaSXx428RmvSnQ05ASkjxeG0gui0tDDFTtV7pSTcI/LNT388Pfd+DicT40tffaMAAbJjS7Xwuj7mPOcrqxulA7ayp/MiaScgXGD7uObOg6QK/fitxsNXxvB/Z1T4Ht+hipePw3/uL4/M0bH+aNH0ZrGMEj5WN/JMgbL69ohf9xLIDhSQ8lTNji17FK+gixFKd4vUnOuFC4M7tFS2M6kOuJIvXEK+93Q+AIeruffOShjIeWPaGN83NChlE7KeUu9fJri5D4HQX/PtzPeDt39el7v4ejGkV+K/pCJdGFcmpnheqmz8cbz8J6PM/Pp8sAz+Bb8jIMbSmTVZZyw0UMvD5/dmOLUMBLvdAhBaxrDwKQftK9CGstJeQvuisJrXlXmkI+WDrJnB6T8gi765+iC16SGMKB8bhQQBBcQ5w9oCAZCiPC5YXTPlo5bmt9OiU4Xcve8rmSORmg+Dy6sfN83anF+J4XXjvh80ZCjCeds3iwsIuNAcVQa/qXxPGPkRiJ1Ij4G/tw0PHyatvW/GT8PBT3N7vtfeujl8+Q8B59lPh+lUZCayDA4RHTv4MKaxrRp7OnpQSm/Gs5JKClf4ejewIXE2fx0QRKCTvrJ01neF+/PQcKbQsf0+TFyKXyW8sU4/O+8Bp4yvT6NTXB8Dui3x9Ppd9zYuoIfhERSg4ohgVmnejGtzvT58/gaOJ4akDQ047teho08eVgGSvI5cZLY++/3hvD0Xau5h1cneojs/fLPe0iYZqWkRjXvo2xNYxhcEaV8POyDkrLnDsdTKJ2SfW4YmGT+dkjoRsPvi9B4H91IIYBeGZcaM6khFC6QXj3ItR12Opz3kItn8xg3HT+pQbZ6+tDH3DMBHlq4Z0w9m4d4hUIhbpSSogFXEObP915gTJgHTzczVh5ueCqSMec6KQ/hCCt9JppnKeinG3n3/o5CUmPS2dkZ96fgvp4y5vpuNFPegPoGjKA/76NsTWEYfDKchHOP4SSTCwg/qUJ4IYlPfJqqcjiXQlDp3qXdTkqm4Y1zFvztYVGaxvRjy71ffi+Pwb1oZ2dnR21tbfF+ku4hP7MsyxGuKWyHmHNjx7P5e25MaQ7PfccgL013w4MRdpLPj2vz5ughhBAXSfl1UTDfs5FQFKPpcwVPw3gS9jEnKN9BhgMiMiVImScMc3KGQ5wHxhtDlmZ10n00fQ4eRQbioNY0hsEH1Rl5t7pYcY4bQ5idqExZcybDi1Wc+HEykUl2hUx//PxLF2hHDwgZRsHhqXMpTliBWHyjFfrFsx8E7T2DgYHxvqFETtw6tHfD6fCc/jpZ6sb1II6H8XYYjdFmXHzthsNzn3PnNUBS7DnAvb0wjO3pGCNPjzqK9HnxuXJykz6kXA739NfTsfbP+ljzGV/vAqfkz8BY8jqO43G0pjAMUsN7s3DKlcZjfYe7TrSlMJrfTLx7Jo9JU5jnKIJrpkrgvw8yEPzQNxdiV1DnRJyb4DqOIpzgYkyowXehlvJHmrn3c7ZdalQ28h3vsxsJvCvz5JmeNFRjzrgm4+PbsuGVPUR0ROHjynx4STDP7zwP93P+IVVglNoNAfPq7/vYeBiCwnJvGkaN+fH6DOeKPGxIeQR3FCxGe1xGQWoiwyDlY1EmEHjsO/e4crpSpHEygy4pZ8EdCkr31lC4cr+Z0qdexsmtVCCdX8D4OSpxIUtP5k5DI+9L6n1SdOP9dQHEs2NYHXW4cfDXvL5Aym+TliIUFNnHL/WizB3fYc78PX8GKZ8pcpbeQyeIYp7N++SG1UlOfrtT4LdDefZTYMzSuU/lycfdjRDjCZpyxOUo4nG2pjIMknJxt3tTJt0LVVzQnARyIXAj454MwXV4yjU9DnQFdPidKl9KALpweYYi7Q/GxQtxvD/eJwSKZ6ClPAjKgwHw0MfRAP87CvBreG2BIxnp3jMOeM2Rm/fTlY/7cj++SxzvaMA5AVKU/lyOPFKSjloQFN6NfiojXprOmHJdUIkjSeQxy7LcrtHIAf3y+7l8OjnKd/b29h5r+OCt6QwDyuqTRmiRxv5u0Z0Q9IlIrb7UiDe9us1jyBTW8h2vcpMaJF4ar2PYfMceynadiESJHKLSN4e9aU2EZxIcFbjnZSxp6ZoB6d5CHvpPH7wmI12/4oVPrlRuFBhDqRHepGPqawXSZ/bX3IBIiqc2keN3j814gc6YEzemzuF4CjYlrL2/9IMqRpTYwws3+j7W/jpyyZh7yNYMRkFqQsMg5bkB9x5pbJhCXofTqdemuafgPqnhkfJKzsQhxE5euodNIaELlbc0teY8SrHY2KzWvYsTWY5EuB7K6970INSU8izc24XSwxf6lVZdpqGYPw9j6NfnfxQByO8GiWf1TEyKeBwx8bofoOsKxjyk4aCvEUlR50F/0zyMYay9FN2L5HhW5uggLio1IM65PO7WlIZBaiAHJk5STqjcQ7ngexFJSlK54KeeyEODFMZ77O2hDRNPXz2ccc/ncNe3inf0g3fmXmns6WjIDZ3DfA950gIb91JpfO9e28fDC4q8XNm5Ff53ROGoIS0B9355haaUD0X29xtbyOOhHWnxnGkNgHMDzB3NjeSbhaHeF59L53sOynq44eZ7bqBc/ty5YVSayShITWwYpEbhUTrgUv7syJRYSj2a77mYXstjTy94SmGdhwzSvdWZLiQojHuBdO0H3keqCTRHjvE9F/iUuKKPLvgpi54u/sEAeJEO7/s5D87gO2LxegZHM/Tfw7w3g9Pe8PIoGkbWWfvUq3vNCOEm/ffnYbxTxOJZIsbQUZ1/zg0cBsnH19GCh3+MNfPmSMcND9dqRqMgNblhkPJbvPmZhbyXpr2YqHSyHAW4YkiNApYUVaTf47u85qSh98k5i3RxDa879+FePuUB0hjcnxFIy9kZji7S8MkR2EHkIOOVVuWlHIYrvZ/m5WPp3h2I7ytg+X5ayZgaFCflnF/Z3d2Nm9a69/ffqcd3h+Dj7mPvRslDU57dNwtC9lw+nLdIQwo36h76NqNRkA6BYZDynIN0LyOfVqI5HE7z5p6m8mIoX+TimQIE1PcdTIkpRx4OH6WGJ0fhXEjcwCB8DtPT0EfKx/E8s3tMnsGv7VkHhNXHw78H+uGzUn65tUNwxtDRT2rE3OCmpJ73zyG5H2LjyuXrLVKE4kYlhfdOMrrRcITkfXen4POMMXOvz99sL4fx9TH1vkqNc0yb1ShIh8QwSHlP7IbBPYaUh28eA0t5hWEHY580Vwq3/C4gfn2H7Wka0ZWW7zkc93AmhBAhZUq0IkS+SAuBdjLUMwrFYjG3bsFJW++3l2I7H+GxsD8LyunwGaX15/KCKP9eagBp1HW4EnJfPutj7dvYu4FNn9dlxg2ahxluoHge5wt8DFJ+xsOJarWaMzz87waUepxHvVLye2mHxjBIDc+eZfmTjj3Od6Io3WjVJ9xj1NR48JlUgHjd7839pMYZA644LrRSQ0DSCkOpUWBDXz308OIuh/H0G+ORekwfO6lmDNiijHu6kKIA9NlLtD2F6EbTFcQRgxvVVFFdwVBW7uEOgPFKjf3u7m4ufez3QCGd0MWbc7/UwDsh6O87v+GGhM/xvs+dIwuXg7Qwq5nboTIMUr7OgVw23oPJd+IIofWMgpQ/qh6hQGBduN0Tcl0E2BXfPRjK5DwHQudxtntQ+uOKzrX8WbiXpzCl/JJurodyeJYi3ViV6ziklvKIieuDbByduBHwbICHOs5TuKF0Qi5VGA/TeN9JQwwJn2F8iN39foyjQ30/Vct/6LdnxA5CWshSuqkO8uZ8TQghrro8DEZBOoSGQcpnDZg8n1RX7lThpYP3MeTvNFZlot0goBz8HHSwqwsBKwCdg6DwZn+/lpZrb2+PZN1BGYG0rwdBfpQyJTH9e9VqNVdu7USkw2pf5MNrKLUXabmBSqG2Xy81NAc9h//N90E2HmL5c6WGME3zejGYoxpXfL+/99sVP13ZyTgc1H9fTbm3t6c7d+7cMw/N3gpv/5HmbBiH27dv57aQd+FDsZjAdOVi6ilT/iK9FveUdI8XcSV1wSScQciJ6/0a7vG4pvMS3j+H7qlXRHi9Ci/1tH4qliuuP6PHzFKDQwkhxLSq1DjfwYlHmhtk/nfy1WG7Iy/GjHEiJucZvV6E53WDzrV9AxU3DhjydKwd1TiyYY6QGZcpntf5EfrB+LAJ8GEyCtIhRQzeUgudIgT/jZC695fyipEiDpAIzdflI3TsF5Cms4DeKKsLEOXRCCqlvTwTir6/v59b+JRyFqkiY/h4HvfyPB/cBMuZffWnGzz3hG7sPHPiXtw5gRSVpe/7/KVowkMtPxULY5A6AEckbvBTg5VyCP6sfJ//fb4YPx8L5Ij/fQ8JqbFDVTNnHt6qHVrE4A1Pwo+knKdOw46UPJPuNQpSg1F3YTnIu6fkFQ3B4DMYoxRqcw/3OAitr7TzvuFVPcZ3I+iViu61vSaAe3ndhStdWqPhm+MchDrcAKfel7H107i4rmc1XEm5ri+lTjkPmis912au3JA5+nGU4iEVfUjn2w2zywZOwe9xmI2CdJ+IIYQwJWlTUlXSXpZl7w8hlCX9rqSzkqYk/XSWZWuhNnK/JuljkrYl/dMsyy4+/K7nm8eNWHvfhYjdjtgQxoUqjWv39/dzGQYXSk+78V3geWqAUkjvSuf5fyfYPL6X8uGGIwePg12R0poGlNSVj+t7hiBNLzpsdgV2o8LzeeUkfU4rQb1S0vvjRsg9PQbEr3VQNoS+pQYjXQnrEN/DNx9nnj1V6tToSMqFBz4+oMfDbBSkB0MMH8my7EKWZe+v//9pSX+aZdm7JP1p/X9J+tuS3lX/+aSk33hYnX27hpLcvXs3t0mrp7V8ey0ExuNHBIZNUj2OdZjq30sJMSm/ctDv52XJhAp+L0ctboTgCugPns+NjSuJ8woOefF6KReShgpezyEp5z25h3M7PobpWB3URx8rH0M3rF6YBnJwZOdbnzm68pDN++E8lBtGRwH+21FJigiRCfq/v7+v27dv31OJe1jb9xNK/KSkz9f//rykv2Ov/2ZWa9+Q1BtCOPl93OeB297eXiR9UDyPN9va2nL5ca9xcIH1OBXFQMCkfDghKae47uEwOK4gwGo3JA7NfQWgKzFGwr2XcyEu6K7M9MFhsgu31044Ycvn/LqMTYp+MCjOe4CI0iIyR2luNHh+T9N6Hzs6OuLraZET1/D+pqHkQQSiz40/mz+rc1kYdcYLeTtM6ci3a/drGDJJXw0hfDuE8Mn6a8NZls3X/16QNFz/e0TSDfvuTP21XAshfDKE8K0Qwre+h36/fYfrHsTz9u6xUFwX4nRfBdJ1Hk86CYXiOUR2PiHlCFzpUkHj+7Q0RnZPnxKsIATiXO9LuvIyVXznDxgH/76fz+FQnmXTPJfH6W7sHPm4Qh+kjCkaSw02n3VU4GtJ/Lo+N87d+GdTToj+Ot/ihjENR3i2w84nHNTuNyvxI1mWzYYQhiT9SQjhDX8zy7IshPBApjLLss9K+qwkPeh3H6ThmV0x8OqOCFwYXAF8vX3qJbm+M9pOVnptv3ssz2rwmhsuL96R8uScoxFXuJSYozlHkHppfvNdz8unG7NwrfRQ1/RabiS9L66YzuH4Aia/L4aH//27jCnfx/j5EngvT6ZvaXjH2Do6SMeQeUzRlxOxP4jtvgxDlmWz9d+LIYQvSXpZ0s0Qwsksy+brocJi/eOzksbs66P11x5bo94BpfPXpYb3d/ILhUI58E4ePjiMThl8F2AnEz1UkPKpUyc46Ve6LZx0byGT39M9G57XYbWTpnzHq/a4h3MHKJlvxOp7NNBX7xfow72+34MDWxyxcR1/Tl+d6kaM1zxV6LxRGjK5R0/RD/c9qJrUOQyejezXD0rYcFB721AihHAshNDF35L+hqTXJH1Z0s/WP/azkv6w/veXJf2TUGs/LKliIcdjawgD3ijlDhwJOKTlt5dee8jgguUxZxqHIvwokCuVk4YeroBAXCmcDHSjwbP5/4QCB6XqEGqv/XB47Wgh9bYO29378iz+w30PUiIPpd7M6Dl5SF/8Obx/jJ3vqyHlT7RKQyXG1Os4eB9D4ZmRW7du5Y46+EFt94MYhiV9qT4JJUm/k2XZ/w0hfFPSF0MIPydpWtJP1z//f1RLVU6olq78Zw+9199jY7JRtPb29hyMzLLGxhl4G4Tal1t7egphwoPyecqg+S739Tp791jFYjGGPC60FMqglNK9YQCwO4X97qlpnvXAS4IEDtoJqlBobNnuZKqfmCU10n6eunRlxcg5cnEUgQEkFPD/PVziOs6R8DfPgsHkWQkf3Uh7FsQ/6/s8+Fjv7Ow0zUatj6KFZnjQEMKmpPHH3Y/7bAOSlh93J+6jHZZ+Soenr4eln9LBfT2TZdng/Xy5WUqix7NGfURTtxDCtw5DXw9LP6XD09fD0k/p++/rD0RJ9FE7akft4bYjw3DUjtpRu6c1i2H47OPuwAO0w9LXw9JP6fD09bD0U/o++9oU5ONRO2pHrblasyCGo3bUjloTtcduGEIIfyuEMB5CmAghfPrtv/GO9uV/hhAWQwiv2WvlEMKfhBCu1H/31V8PIYT/Wu/3qyGE9z7ivo6FEL4WQng9hPDXIYR/1Yz9DSG0hxD+MoTw3Xo//0P99XMhhL+o9+d3Qwit9dfb6v9P1N8/+yj6af0thhC+E0L4SpP3cyqE8FchhFdCfb3RQ517L6Z51D+SipKuSnpCUquk70p6/jH258OS3ivpNXvtP0n6dP3vT0v6lfrfH5P0R5KCpB+W9BePuK8nJb23/neXpMuSnm+2/tbvd7z+d4ukv6jf/4uSPlF//TOS/kX975+X9Jn635+Q9LuPeFw/Jel3JH2l/n+z9nNK0kDy2kOb+0f2IG/ycB+U9Mf2/y9K+sXH3KeziWEYl3Sy/vdJ1WouJOl/SPoHB33uMfX7DyX9WDP3V1KnpIuSPqBa8U0plQNJfyzpg/W/S/XPhUfUv1HV9hb5qKSv1BWp6fpZv+dBhuGhzf3jDiXua4n2Y27f1/LyR9HqMPY9qnnjputvHZ6/otpCuz9RDSWuZ1nG+Xbel9jP+vsVSf2Pop+SflXSL0hixVV/k/ZTege2QvDWLJWPh6Jl2YMvL3+nWwjhuKTfl/SvsyzbSNZ+NEV/syyrSroQQuiV9CVJzz7mLt3TQgg/Lmkxy7JvhxB+9HH35z7aQ98KwdvjRgxNt0T7gHYz1HegCk22vDyE0KKaUfjtLMv+oP5y0/Y3y7J1SV9TDZL3hhBwTN6X2M/6+z2SVh5B9z4k6SdCbX/TL6gWTvxaE/ZTUn4rBNWMbdwKod6n72vuH7dh+Kakd9WZ31bVSJwvP+Y+pa0pl5eHGjT4nKRLWZb952btbwhhsI4UFELoUI0HuaSagfipN+kn/f8pSX+W1QPjd7JlWfaLWZaNZll2VjU5/LMsy/5Rs/VTekRbITwqsuQtSJSPqcaoX5X07x5zX/6XpHlJu6rFYT+nWtz4p5KuSPp/ksr1zwZJv17v919Jev8j7uuPqBZnvirplfrPx5qtv5JelPSdej9fk/Tv668/IekvVVue/3uS2uqvt9f/n6i//8RjkIMfVSMr0XT9rPfpu/Wfv0ZvHubcH1U+HrWjdtTuaY87lDhqR+2oNWE7MgxH7agdtXvakWE4akftqN3TjgzDUTtqR+2edmQYjtpRO2r3tCPDcNSO2lG7px0ZhqN21I7aPe3IMBy1o3bU7mn/H5cjhYEanThjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QQBNriY1JrE"
      },
      "source": [
        "\n",
        "width = 224\n",
        "height = 224\n",
        "dsize = (width, height)\n",
        "# loop over our input images\n",
        "for imagePath in imagePaths:\n",
        "  # load the input image from disk, resize it to 64x64 pixels, scale\n",
        "\t# the pixel intensities to the range [0, 1], and then update our\n",
        "\t# images list\n",
        "    image = cv2.imread(imagePath,-1)\n",
        "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "  #image = image.convert('L')\n",
        "    image = cv2.resize(image,dsize)\n",
        "    image = np.array(image)\n",
        "    data.append(image)\n",
        "\n",
        "  # extract the class label from the file path and update the\n",
        "\t# labels list\n",
        "    label = imagePath.split(os.path.sep)[-2]\n",
        "    labels.append(label)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rzbolh88VGB2",
        "outputId": "71aefada-42b4-41ce-bed7-8d9a1bd80966"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 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'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "aGbsj1UcytpV",
        "outputId": "8908891f-dcc7-44bd-e736-d7cd41338b0e"
      },
      "source": [
        "\n",
        "print(labels[500])\n",
        "plt.imshow(data[10])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fbf864c9290>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twd1IeuKVGB5"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# encode class values as integers\n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(labels)\n",
        "labels = encoder.transform(labels)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FPCyIqcVGB7",
        "outputId": "5ea52f1c-f72b-419f-ba04-343d5fde2000"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LynLhwt_pvNc",
        "outputId": "2c472477-9e72-4c84-9024-3b4f4bcf9e66"
      },
      "source": [
        "data = np.array(data)\n",
        "data.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ktZ5hNYwVGB9",
        "outputId": "03af35ad-0329-4793-9bb8-fc3ac2ea38d9"
      },
      "source": [
        "# perform a training and testing split, using 75% of the data for\n",
        "# training and 25% for evaluation\n",
        "(trainX, testX, trainY, testY) = train_test_split(np.array(data),\tnp.array(labels), test_size=0.25)\n",
        "print(trainX.shape)\n",
        "print(testX.shape)\n",
        "print(trainY.shape)\n",
        "print(testY.shape)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(750, 224, 224, 3)\n",
            "(250, 224, 224, 3)\n",
            "(750,)\n",
            "(250,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "akW74c5YfOGq",
        "outputId": "d8c5e53d-3e95-47ff-dcaf-38e185092ddc"
      },
      "source": [
        "print(trainX[0])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  ...\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]]\n",
            "\n",
            " [[0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  ...\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]]\n",
            "\n",
            " [[0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [2.997997e-10 2.997997e-10 2.997997e-10]\n",
            "  ...\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [7.273475e-10 7.273475e-10 7.273475e-10]\n",
            "  ...\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]]\n",
            "\n",
            " [[0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  ...\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]]\n",
            "\n",
            " [[0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  ...\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]\n",
            "  [0.000000e+00 0.000000e+00 0.000000e+00]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1LK97ZhDrRZ"
      },
      "source": [
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras.models import Model\n",
        "from keras.layers import GlobalAveragePooling2D, BatchNormalization, Dropout\n",
        "\n",
        "from keras.applications.efficientnet import EfficientNetB0\n",
        "from keras.layers import Dense, Conv2D, MaxPool2D , Flatten\n",
        "from keras.applications.resnet import ResNet50\n",
        "import tensorflow as tf\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTvu4IV0i7Fj",
        "outputId": "3d706c20-0118-45cf-e149-9586389f3168"
      },
      "source": [
        "base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224,224,3))\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dropout(0.5)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "out = Dense(2, activation='softmax')(x)\n",
        "\n",
        "model = Model(inputs=base_model.input, outputs=out)\n",
        "model.summary()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
            "16711680/16705208 [==============================] - 0s 0us/step\n",
            "16719872/16705208 [==============================] - 0s 0us/step\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rescaling (Rescaling)           (None, 224, 224, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "normalization (Normalization)   (None, 224, 224, 3)  7           rescaling[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv_pad (ZeroPadding2D)   (None, 225, 225, 3)  0           normalization[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv (Conv2D)              (None, 112, 112, 32) 864         stem_conv_pad[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "stem_bn (BatchNormalization)    (None, 112, 112, 32) 128         stem_conv[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "stem_activation (Activation)    (None, 112, 112, 32) 0           stem_bn[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "block1a_dwconv (DepthwiseConv2D (None, 112, 112, 32) 288         stem_activation[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block1a_bn (BatchNormalization) (None, 112, 112, 32) 128         block1a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block1a_activation (Activation) (None, 112, 112, 32) 0           block1a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_squeeze (GlobalAvera (None, 32)           0           block1a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_excite (Multiply)    (None, 112, 112, 32) 0           block1a_activation[0][0]         \n",
            "                                                                 block1a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_project_conv (Conv2D)   (None, 112, 112, 16) 512         block1a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_project_bn (BatchNormal (None, 112, 112, 16) 64          block1a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_conv (Conv2D)    (None, 112, 112, 96) 1536        block1a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_bn (BatchNormali (None, 112, 112, 96) 384         block2a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_activation (Acti (None, 112, 112, 96) 0           block2a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_dwconv_pad (ZeroPadding (None, 113, 113, 96) 0           block2a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block2a_dwconv (DepthwiseConv2D (None, 56, 56, 96)   864         block2a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_bn (BatchNormalization) (None, 56, 56, 96)   384         block2a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block2a_activation (Activation) (None, 56, 56, 96)   0           block2a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_squeeze (GlobalAvera (None, 96)           0           block2a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_reshape (Reshape)    (None, 1, 1, 96)     0           block2a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_reduce (Conv2D)      (None, 1, 1, 4)      388         block2a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_expand (Conv2D)      (None, 1, 1, 96)     480         block2a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_excite (Multiply)    (None, 56, 56, 96)   0           block2a_activation[0][0]         \n",
            "                                                                 block2a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_project_conv (Conv2D)   (None, 56, 56, 24)   2304        block2a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_project_bn (BatchNormal (None, 56, 56, 24)   96          block2a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_conv (Conv2D)    (None, 56, 56, 144)  3456        block2a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_bn (BatchNormali (None, 56, 56, 144)  576         block2b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_activation (Acti (None, 56, 56, 144)  0           block2b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_dwconv (DepthwiseConv2D (None, 56, 56, 144)  1296        block2b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block2b_bn (BatchNormalization) (None, 56, 56, 144)  576         block2b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block2b_activation (Activation) (None, 56, 56, 144)  0           block2b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_squeeze (GlobalAvera (None, 144)          0           block2b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_reshape (Reshape)    (None, 1, 1, 144)    0           block2b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block2b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block2b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_excite (Multiply)    (None, 56, 56, 144)  0           block2b_activation[0][0]         \n",
            "                                                                 block2b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_project_conv (Conv2D)   (None, 56, 56, 24)   3456        block2b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_project_bn (BatchNormal (None, 56, 56, 24)   96          block2b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2b_drop (Dropout)          (None, 56, 56, 24)   0           block2b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_add (Add)               (None, 56, 56, 24)   0           block2b_drop[0][0]               \n",
            "                                                                 block2a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_conv (Conv2D)    (None, 56, 56, 144)  3456        block2b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_bn (BatchNormali (None, 56, 56, 144)  576         block3a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_activation (Acti (None, 56, 56, 144)  0           block3a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_dwconv_pad (ZeroPadding (None, 59, 59, 144)  0           block3a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block3a_dwconv (DepthwiseConv2D (None, 28, 28, 144)  3600        block3a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_bn (BatchNormalization) (None, 28, 28, 144)  576         block3a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block3a_activation (Activation) (None, 28, 28, 144)  0           block3a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_squeeze (GlobalAvera (None, 144)          0           block3a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_reshape (Reshape)    (None, 1, 1, 144)    0           block3a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block3a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block3a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_excite (Multiply)    (None, 28, 28, 144)  0           block3a_activation[0][0]         \n",
            "                                                                 block3a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_project_conv (Conv2D)   (None, 28, 28, 40)   5760        block3a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_project_bn (BatchNormal (None, 28, 28, 40)   160         block3a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_conv (Conv2D)    (None, 28, 28, 240)  9600        block3a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_bn (BatchNormali (None, 28, 28, 240)  960         block3b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_activation (Acti (None, 28, 28, 240)  0           block3b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_dwconv (DepthwiseConv2D (None, 28, 28, 240)  6000        block3b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block3b_bn (BatchNormalization) (None, 28, 28, 240)  960         block3b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block3b_activation (Activation) (None, 28, 28, 240)  0           block3b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_squeeze (GlobalAvera (None, 240)          0           block3b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_reshape (Reshape)    (None, 1, 1, 240)    0           block3b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block3b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block3b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_excite (Multiply)    (None, 28, 28, 240)  0           block3b_activation[0][0]         \n",
            "                                                                 block3b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_project_conv (Conv2D)   (None, 28, 28, 40)   9600        block3b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_project_bn (BatchNormal (None, 28, 28, 40)   160         block3b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block3b_drop (Dropout)          (None, 28, 28, 40)   0           block3b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_add (Add)               (None, 28, 28, 40)   0           block3b_drop[0][0]               \n",
            "                                                                 block3a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_conv (Conv2D)    (None, 28, 28, 240)  9600        block3b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_bn (BatchNormali (None, 28, 28, 240)  960         block4a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_activation (Acti (None, 28, 28, 240)  0           block4a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_dwconv_pad (ZeroPadding (None, 29, 29, 240)  0           block4a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4a_dwconv (DepthwiseConv2D (None, 14, 14, 240)  2160        block4a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_bn (BatchNormalization) (None, 14, 14, 240)  960         block4a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4a_activation (Activation) (None, 14, 14, 240)  0           block4a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_squeeze (GlobalAvera (None, 240)          0           block4a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_reshape (Reshape)    (None, 1, 1, 240)    0           block4a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block4a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block4a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_excite (Multiply)    (None, 14, 14, 240)  0           block4a_activation[0][0]         \n",
            "                                                                 block4a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_project_conv (Conv2D)   (None, 14, 14, 80)   19200       block4a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_project_bn (BatchNormal (None, 14, 14, 80)   320         block4a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_conv (Conv2D)    (None, 14, 14, 480)  38400       block4a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_bn (BatchNormali (None, 14, 14, 480)  1920        block4b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_activation (Acti (None, 14, 14, 480)  0           block4b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_dwconv (DepthwiseConv2D (None, 14, 14, 480)  4320        block4b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4b_bn (BatchNormalization) (None, 14, 14, 480)  1920        block4b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4b_activation (Activation) (None, 14, 14, 480)  0           block4b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_squeeze (GlobalAvera (None, 480)          0           block4b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_excite (Multiply)    (None, 14, 14, 480)  0           block4b_activation[0][0]         \n",
            "                                                                 block4b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_project_conv (Conv2D)   (None, 14, 14, 80)   38400       block4b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_project_bn (BatchNormal (None, 14, 14, 80)   320         block4b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4b_drop (Dropout)          (None, 14, 14, 80)   0           block4b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_add (Add)               (None, 14, 14, 80)   0           block4b_drop[0][0]               \n",
            "                                                                 block4a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_conv (Conv2D)    (None, 14, 14, 480)  38400       block4b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_bn (BatchNormali (None, 14, 14, 480)  1920        block4c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_activation (Acti (None, 14, 14, 480)  0           block4c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_dwconv (DepthwiseConv2D (None, 14, 14, 480)  4320        block4c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4c_bn (BatchNormalization) (None, 14, 14, 480)  1920        block4c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4c_activation (Activation) (None, 14, 14, 480)  0           block4c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_squeeze (GlobalAvera (None, 480)          0           block4c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_excite (Multiply)    (None, 14, 14, 480)  0           block4c_activation[0][0]         \n",
            "                                                                 block4c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_project_conv (Conv2D)   (None, 14, 14, 80)   38400       block4c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_project_bn (BatchNormal (None, 14, 14, 80)   320         block4c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4c_drop (Dropout)          (None, 14, 14, 80)   0           block4c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_add (Add)               (None, 14, 14, 80)   0           block4c_drop[0][0]               \n",
            "                                                                 block4b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_conv (Conv2D)    (None, 14, 14, 480)  38400       block4c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_bn (BatchNormali (None, 14, 14, 480)  1920        block5a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_activation (Acti (None, 14, 14, 480)  0           block5a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_dwconv (DepthwiseConv2D (None, 14, 14, 480)  12000       block5a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5a_bn (BatchNormalization) (None, 14, 14, 480)  1920        block5a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5a_activation (Activation) (None, 14, 14, 480)  0           block5a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_squeeze (GlobalAvera (None, 480)          0           block5a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_reshape (Reshape)    (None, 1, 1, 480)    0           block5a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block5a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block5a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_excite (Multiply)    (None, 14, 14, 480)  0           block5a_activation[0][0]         \n",
            "                                                                 block5a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_project_conv (Conv2D)   (None, 14, 14, 112)  53760       block5a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_project_bn (BatchNormal (None, 14, 14, 112)  448         block5a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_conv (Conv2D)    (None, 14, 14, 672)  75264       block5a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_bn (BatchNormali (None, 14, 14, 672)  2688        block5b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_activation (Acti (None, 14, 14, 672)  0           block5b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_dwconv (DepthwiseConv2D (None, 14, 14, 672)  16800       block5b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5b_bn (BatchNormalization) (None, 14, 14, 672)  2688        block5b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5b_activation (Activation) (None, 14, 14, 672)  0           block5b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_squeeze (GlobalAvera (None, 672)          0           block5b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_excite (Multiply)    (None, 14, 14, 672)  0           block5b_activation[0][0]         \n",
            "                                                                 block5b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_project_conv (Conv2D)   (None, 14, 14, 112)  75264       block5b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_project_bn (BatchNormal (None, 14, 14, 112)  448         block5b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5b_drop (Dropout)          (None, 14, 14, 112)  0           block5b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_add (Add)               (None, 14, 14, 112)  0           block5b_drop[0][0]               \n",
            "                                                                 block5a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_conv (Conv2D)    (None, 14, 14, 672)  75264       block5b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_bn (BatchNormali (None, 14, 14, 672)  2688        block5c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_activation (Acti (None, 14, 14, 672)  0           block5c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_dwconv (DepthwiseConv2D (None, 14, 14, 672)  16800       block5c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5c_bn (BatchNormalization) (None, 14, 14, 672)  2688        block5c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5c_activation (Activation) (None, 14, 14, 672)  0           block5c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_squeeze (GlobalAvera (None, 672)          0           block5c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_excite (Multiply)    (None, 14, 14, 672)  0           block5c_activation[0][0]         \n",
            "                                                                 block5c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_project_conv (Conv2D)   (None, 14, 14, 112)  75264       block5c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_project_bn (BatchNormal (None, 14, 14, 112)  448         block5c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5c_drop (Dropout)          (None, 14, 14, 112)  0           block5c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_add (Add)               (None, 14, 14, 112)  0           block5c_drop[0][0]               \n",
            "                                                                 block5b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_conv (Conv2D)    (None, 14, 14, 672)  75264       block5c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_bn (BatchNormali (None, 14, 14, 672)  2688        block6a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_activation (Acti (None, 14, 14, 672)  0           block6a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_dwconv_pad (ZeroPadding (None, 17, 17, 672)  0           block6a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6a_dwconv (DepthwiseConv2D (None, 7, 7, 672)    16800       block6a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_bn (BatchNormalization) (None, 7, 7, 672)    2688        block6a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6a_activation (Activation) (None, 7, 7, 672)    0           block6a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_squeeze (GlobalAvera (None, 672)          0           block6a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_reshape (Reshape)    (None, 1, 1, 672)    0           block6a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block6a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block6a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_excite (Multiply)    (None, 7, 7, 672)    0           block6a_activation[0][0]         \n",
            "                                                                 block6a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_project_conv (Conv2D)   (None, 7, 7, 192)    129024      block6a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_project_bn (BatchNormal (None, 7, 7, 192)    768         block6a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block6b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_activation (Acti (None, 7, 7, 1152)   0           block6b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   28800       block6b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6b_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block6b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6b_activation (Activation) (None, 7, 7, 1152)   0           block6b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_squeeze (GlobalAvera (None, 1152)         0           block6b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_excite (Multiply)    (None, 7, 7, 1152)   0           block6b_activation[0][0]         \n",
            "                                                                 block6b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_project_conv (Conv2D)   (None, 7, 7, 192)    221184      block6b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_project_bn (BatchNormal (None, 7, 7, 192)    768         block6b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6b_drop (Dropout)          (None, 7, 7, 192)    0           block6b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_add (Add)               (None, 7, 7, 192)    0           block6b_drop[0][0]               \n",
            "                                                                 block6a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block6c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_activation (Acti (None, 7, 7, 1152)   0           block6c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   28800       block6c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6c_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block6c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6c_activation (Activation) (None, 7, 7, 1152)   0           block6c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_squeeze (GlobalAvera (None, 1152)         0           block6c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_excite (Multiply)    (None, 7, 7, 1152)   0           block6c_activation[0][0]         \n",
            "                                                                 block6c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_project_conv (Conv2D)   (None, 7, 7, 192)    221184      block6c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_project_bn (BatchNormal (None, 7, 7, 192)    768         block6c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6c_drop (Dropout)          (None, 7, 7, 192)    0           block6c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_add (Add)               (None, 7, 7, 192)    0           block6c_drop[0][0]               \n",
            "                                                                 block6b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block6d_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_activation (Acti (None, 7, 7, 1152)   0           block6d_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   28800       block6d_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6d_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block6d_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6d_activation (Activation) (None, 7, 7, 1152)   0           block6d_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_squeeze (GlobalAvera (None, 1152)         0           block6d_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6d_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6d_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6d_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_excite (Multiply)    (None, 7, 7, 1152)   0           block6d_activation[0][0]         \n",
            "                                                                 block6d_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_project_conv (Conv2D)   (None, 7, 7, 192)    221184      block6d_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_project_bn (BatchNormal (None, 7, 7, 192)    768         block6d_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6d_drop (Dropout)          (None, 7, 7, 192)    0           block6d_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_add (Add)               (None, 7, 7, 192)    0           block6d_drop[0][0]               \n",
            "                                                                 block6c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6d_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block7a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_activation (Acti (None, 7, 7, 1152)   0           block7a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   10368       block7a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block7a_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block7a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block7a_activation (Activation) (None, 7, 7, 1152)   0           block7a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_squeeze (GlobalAvera (None, 1152)         0           block7a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block7a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block7a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block7a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_excite (Multiply)    (None, 7, 7, 1152)   0           block7a_activation[0][0]         \n",
            "                                                                 block7a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_project_conv (Conv2D)   (None, 7, 7, 320)    368640      block7a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_project_bn (BatchNormal (None, 7, 7, 320)    1280        block7a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "top_conv (Conv2D)               (None, 7, 7, 1280)   409600      block7a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "top_bn (BatchNormalization)     (None, 7, 7, 1280)   5120        top_conv[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "top_activation (Activation)     (None, 7, 7, 1280)   0           top_bn[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d (Globa (None, 1280)         0           top_activation[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 1280)         0           global_average_pooling2d[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 1280)         5120        dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 512)          655872      batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 2)            1026        dense[0][0]                      \n",
            "==================================================================================================\n",
            "Total params: 4,711,589\n",
            "Trainable params: 4,667,006\n",
            "Non-trainable params: 44,583\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gVx0PzEF5p2"
      },
      "source": [
        "es = EarlyStopping(monitor='val_loss',\n",
        "                   patience=5,\n",
        "                   mode='auto',\n",
        "                   restore_best_weights=True)\n",
        "checkpoint = ModelCheckpoint('best_weight_scene_EfficientNetB0.h5',\n",
        "                             monitor='val_accuracy',\n",
        "                             verbose=1,\n",
        "                             save_best_only=True,\n",
        "                             mode='auto',)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sny_9-wU8PGB"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers.core import Flatten\n",
        "from keras.layers.core import Dense\n",
        "\n",
        "model = Sequential(\n",
        "    [\n",
        "        Flatten(input_shape=(224*224*3,)),\n",
        "        Dense(100, activation=\"relu\", name=\"layer1\"),\n",
        "        Dense(16, activation=\"relu\", name=\"layer2\"),\n",
        "        Dense(16, activation=\"relu\", name=\"layer3\"),\n",
        "        Dense(3, activation = \"softmax\", name=\"layer4\"),\n",
        "    ]\n",
        ")"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0TdWxuFPkT3",
        "outputId": "966a2bff-3cf5-4c1d-b36e-9a6ef2e1b755"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 150528)            0         \n",
            "_________________________________________________________________\n",
            "layer1 (Dense)               (None, 100)               15052900  \n",
            "_________________________________________________________________\n",
            "layer2 (Dense)               (None, 16)                1616      \n",
            "_________________________________________________________________\n",
            "layer3 (Dense)               (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "layer4 (Dense)               (None, 3)                 51        \n",
            "=================================================================\n",
            "Total params: 15,054,839\n",
            "Trainable params: 15,054,839\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggcSdj_aVGCB",
        "scrolled": true,
        "outputId": "6c890d05-871c-44fb-fbb6-6c93cec2cc57"
      },
      "source": [
        "# train the model using the Adam optimizer\n",
        "print(\"[INFO] training network...\")\n",
        "opt = RMSprop(learning_rate=0.1, decay=1e-6)\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])\n",
        "H = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=50, batch_size=32, callbacks=[es, checkpoint])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] training network...\n",
            "Epoch 1/50\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_input'), name='flatten_input', description=\"created by layer 'flatten_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_input'), name='flatten_input', description=\"created by layer 'flatten_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 14575.6744 - accuracy: 0.5181WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_input'), name='flatten_input', description=\"created by layer 'flatten_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "24/24 [==============================] - 14s 39ms/step - loss: 13577.1105 - accuracy: 0.5140 - val_loss: 0.7351 - val_accuracy: 0.4920\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.49200, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 0s 21ms/step - loss: 0.7284 - accuracy: 0.4836 - val_loss: 0.7051 - val_accuracy: 0.4920\n",
            "\n",
            "Epoch 00002: val_accuracy did not improve from 0.49200\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 0s 20ms/step - loss: 0.7039 - accuracy: 0.5031 - val_loss: 0.6987 - val_accuracy: 0.4920\n",
            "\n",
            "Epoch 00003: val_accuracy did not improve from 0.49200\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 0s 20ms/step - loss: 0.6944 - accuracy: 0.5281 - val_loss: 0.7143 - val_accuracy: 0.5080\n",
            "\n",
            "Epoch 00004: val_accuracy improved from 0.49200 to 0.50800, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 1s 23ms/step - loss: 0.7042 - accuracy: 0.4815 - val_loss: 0.7002 - val_accuracy: 0.4920\n",
            "\n",
            "Epoch 00005: val_accuracy did not improve from 0.50800\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.6989 - accuracy: 0.5109 - val_loss: 0.6938 - val_accuracy: 0.4920\n",
            "\n",
            "Epoch 00006: val_accuracy did not improve from 0.50800\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.7009 - accuracy: 0.4500 - val_loss: 0.6970 - val_accuracy: 0.4920\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 0.50800\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.6990 - accuracy: 0.5029 - val_loss: 0.6941 - val_accuracy: 0.5080\n",
            "\n",
            "Epoch 00008: val_accuracy did not improve from 0.50800\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.6984 - accuracy: 0.4960 - val_loss: 0.6996 - val_accuracy: 0.5080\n",
            "\n",
            "Epoch 00009: val_accuracy did not improve from 0.50800\n",
            "Epoch 10/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.7001 - accuracy: 0.4973 - val_loss: 0.6931 - val_accuracy: 0.5080\n",
            "\n",
            "Epoch 00010: val_accuracy did not improve from 0.50800\n",
            "Epoch 11/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.6992 - accuracy: 0.4987 - val_loss: 0.7033 - val_accuracy: 0.4920\n",
            "\n",
            "Epoch 00011: val_accuracy did not improve from 0.50800\n",
            "Epoch 12/50\n",
            "24/24 [==============================] - 0s 20ms/step - loss: 0.6989 - accuracy: 0.4887 - val_loss: 0.6948 - val_accuracy: 0.4920\n",
            "\n",
            "Epoch 00012: val_accuracy did not improve from 0.50800\n",
            "Epoch 13/50\n",
            "24/24 [==============================] - 0s 20ms/step - loss: 0.6948 - accuracy: 0.5147 - val_loss: 0.6958 - val_accuracy: 0.5080\n",
            "\n",
            "Epoch 00013: val_accuracy did not improve from 0.50800\n",
            "Epoch 14/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.7004 - accuracy: 0.4914 - val_loss: 0.6947 - val_accuracy: 0.5080\n",
            "\n",
            "Epoch 00014: val_accuracy did not improve from 0.50800\n",
            "Epoch 15/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.7014 - accuracy: 0.5246 - val_loss: 0.7027 - val_accuracy: 0.4920\n",
            "\n",
            "Epoch 00015: val_accuracy did not improve from 0.50800\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "98_oXi-lcgux",
        "outputId": "c749ca6c-be33-4fb7-a364-5d86131cbd6e"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(H.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(H.history['accuracy'])\n",
        "plt.plot(H.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(H.history['loss'])\n",
        "plt.plot(H.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cu5SSxXyiOs3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b53519f4-79db-4b1c-db68-99bf4ea28f88"
      },
      "source": [
        "predictions = model.predict(testX, batch_size=32)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_input'), name='flatten_input', description=\"created by layer 'flatten_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FG6VqJuUYOgi",
        "outputId": "8319d76a-7fad-43fa-8aed-37b7a2e08fc7"
      },
      "source": [
        "print(predictions.argmax(axis=1))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6puOgflYR_e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPAZkmBJVGCD",
        "outputId": "37591f9e-c8cd-4609-9cad-645f75acf7d7"
      },
      "source": [
        "# evaluate the network\n",
        "print(\"[INFO] evaluating network...\")\n",
        "predictions = model.predict(testX, batch_size=32)\n",
        "print(classification_report(testY,predictions.argmax(axis=1),target_names = encoder.classes_))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Covid       0.00      0.00      0.00       123\n",
            "      Normal       0.51      1.00      0.67       127\n",
            "\n",
            "    accuracy                           0.51       250\n",
            "   macro avg       0.25      0.50      0.34       250\n",
            "weighted avg       0.26      0.51      0.34       250\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJ-r6keDY5jt",
        "outputId": "0d916be4-5683-485f-bfcc-6e7e57ed627a"
      },
      "source": [
        "cf_matrix = confusion_matrix(testY,predictions.argmax(axis=1))\n",
        "print(cf_matrix)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  0 123]\n",
            " [  0 127]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLp1ooZeY90p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "941a46f7-6da2-4414-b664-7a0e9f4b0d14"
      },
      "source": [
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cf_matrix, display_labels=encoder.classes_)\n",
        "disp.plot(cmap=plt.cm.Blues)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fbf1593b690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}