CLAHE_sgd_0.01.ipynb

{
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  "metadata": {
    "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/CLAHE_sgd_0.01.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": "60dc0f8e-6ee7-4c22-9b29-25237699ce13"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\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": 75,
      "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": "6fb67d94-0a6d-4f18-8a7f-84abb4a8c8f9"
      },
      "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(\"CLAHE\")\n",
        "data = []\n",
        "labels = []"
      ],
      "execution_count": 76,
      "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/CLAHE/Covid/IM00001.tif', -1)\n",
        "im_rgb = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)"
      ],
      "execution_count": 77,
      "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": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rzbolh88VGB2",
        "outputId": "9fa3228c-665a-41c2-cae7-54a0b505eddb"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 79,
      "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": "b1634a49-0412-4bab-d388-c242ee5f586f"
      },
      "source": [
        "\n",
        "print(labels[500])\n",
        "plt.imshow(data[10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7bba4d2950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "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": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FPCyIqcVGB7",
        "outputId": "a99b79f3-ad61-4dbe-f93c-244f95aa52a8"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 81,
      "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": "ab143b27-08b8-4e05-fd27-562059943009"
      },
      "source": [
        "data = np.array(data)\n",
        "data.shape"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ktZ5hNYwVGB9",
        "outputId": "6aed9989-e733-487d-e2b0-8c523f757a8e"
      },
      "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": 83,
      "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": "5918e5f2-0fc0-4920-b14b-b2b603242770"
      },
      "source": [
        "print(trainX[0])"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[0.00807058 0.00807058 0.00807058]\n",
            "  [0.01741401 0.01741401 0.01741401]\n",
            "  [0.02690735 0.02690735 0.02690735]\n",
            "  ...\n",
            "  [0.03169393 0.03169393 0.03169393]\n",
            "  [0.02255028 0.02255028 0.02255028]\n",
            "  [0.00716122 0.00716122 0.00716122]]\n",
            "\n",
            " [[0.02066173 0.02066173 0.02066173]\n",
            "  [0.02131128 0.02131128 0.02131128]\n",
            "  [0.02400939 0.02400939 0.02400939]\n",
            "  ...\n",
            "  [0.03444197 0.03444197 0.03444197]\n",
            "  [0.02739712 0.02739712 0.02739712]\n",
            "  [0.02909573 0.02909573 0.02909573]]\n",
            "\n",
            " [[0.0178637  0.0178637  0.0178637 ]\n",
            "  [0.03115436 0.03115436 0.03115436]\n",
            "  [0.01926272 0.01926272 0.01926272]\n",
            "  ...\n",
            "  [0.02839637 0.02839637 0.02839637]\n",
            "  [0.0293957  0.0293957  0.0293957 ]\n",
            "  [0.03304299 0.03304299 0.03304299]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.02674746 0.02674746 0.02674746]\n",
            "  [0.02674746 0.02674746 0.02674746]\n",
            "  [0.03654058 0.03654058 0.03654058]\n",
            "  ...\n",
            "  [0.02234056 0.02234056 0.02234056]\n",
            "  [0.02443917 0.02443917 0.02443917]\n",
            "  [0.02788658 0.02788658 0.02788658]]\n",
            "\n",
            " [[0.02604782 0.02604782 0.02604782]\n",
            "  [0.02604782 0.02604782 0.02604782]\n",
            "  [0.01765398 0.01765398 0.01765398]\n",
            "  ...\n",
            "  [0.02234056 0.02234056 0.02234056]\n",
            "  [0.03143404 0.03143404 0.03143404]\n",
            "  [0.01814333 0.01814333 0.01814333]]\n",
            "\n",
            " [[0.0157052  0.0157052  0.0157052 ]\n",
            "  [0.02604795 0.02604795 0.02604795]\n",
            "  [0.02324988 0.02324988 0.02324988]\n",
            "  ...\n",
            "  [0.02159104 0.02159104 0.02159104]\n",
            "  [0.01884297 0.01884297 0.01884297]\n",
            "  [0.00680142 0.00680142 0.00680142]]]\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": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTvu4IV0i7Fj",
        "outputId": "eb11bb33-e785-4972-c482-9e83d926d5cc"
      },
      "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": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_5 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rescaling_4 (Rescaling)         (None, 224, 224, 3)  0           input_5[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "normalization_4 (Normalization) (None, 224, 224, 3)  7           rescaling_4[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv_pad (ZeroPadding2D)   (None, 225, 225, 3)  0           normalization_4[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_4 (Glo (None, 1280)         0           top_activation[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout_4 (Dropout)             (None, 1280)         0           global_average_pooling2d_4[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 1280)         5120        dropout_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_8 (Dense)                 (None, 512)          655872      batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dense_9 (Dense)                 (None, 2)            1026        dense_8[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": 87,
      "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": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0TdWxuFPkT3",
        "outputId": "d55bcb25-0dd2-4e06-9d65-2af4f4e3132f"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_4 (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": "7ed9d34d-7c60-41e9-f139-2657eecfee15"
      },
      "source": [
        "# train the model using the Adam optimizer\n",
        "print(\"[INFO] training network...\")\n",
        "opt = SGD(learning_rate=0.01, 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": 90,
      "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_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_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_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 1.2470 - accuracy: 0.5093WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 1.2217 - accuracy: 0.5160 - val_loss: 0.5916 - val_accuracy: 0.5200\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.52000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.6218 - accuracy: 0.6559 - val_loss: 0.3872 - val_accuracy: 0.8720\n",
            "\n",
            "Epoch 00002: val_accuracy improved from 0.52000 to 0.87200, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.4907 - accuracy: 0.7935 - val_loss: 0.2861 - val_accuracy: 0.9040\n",
            "\n",
            "Epoch 00003: val_accuracy improved from 0.87200 to 0.90400, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.3438 - accuracy: 0.8629 - val_loss: 0.5961 - val_accuracy: 0.7200\n",
            "\n",
            "Epoch 00004: val_accuracy did not improve from 0.90400\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.2235 - accuracy: 0.9117 - val_loss: 0.6297 - val_accuracy: 0.6920\n",
            "\n",
            "Epoch 00005: val_accuracy did not improve from 0.90400\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.2340 - accuracy: 0.9189 - val_loss: 0.1178 - val_accuracy: 0.9520\n",
            "\n",
            "Epoch 00006: val_accuracy improved from 0.90400 to 0.95200, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0837 - accuracy: 0.9787 - val_loss: 0.2227 - val_accuracy: 0.8840\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 0.95200\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.0669 - accuracy: 0.9836 - val_loss: 0.0732 - val_accuracy: 0.9760\n",
            "\n",
            "Epoch 00008: val_accuracy improved from 0.95200 to 0.97600, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.1389 - accuracy: 0.9728 - val_loss: 0.1532 - val_accuracy: 0.9720\n",
            "\n",
            "Epoch 00009: val_accuracy did not improve from 0.97600\n",
            "Epoch 10/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0982 - accuracy: 0.9787 - val_loss: 0.1029 - val_accuracy: 0.9480\n",
            "\n",
            "Epoch 00010: val_accuracy did not improve from 0.97600\n",
            "Epoch 11/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.4179 - accuracy: 0.8642 - val_loss: 0.0792 - val_accuracy: 0.9680\n",
            "\n",
            "Epoch 00011: val_accuracy did not improve from 0.97600\n",
            "Epoch 12/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0466 - accuracy: 0.9837 - val_loss: 0.0403 - val_accuracy: 0.9920\n",
            "\n",
            "Epoch 00012: val_accuracy improved from 0.97600 to 0.99200, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 13/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.0225 - accuracy: 1.0000 - val_loss: 0.0304 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00013: val_accuracy improved from 0.99200 to 1.00000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 14/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.0236 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00014: val_accuracy did not improve from 1.00000\n",
            "Epoch 15/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0140 - accuracy: 1.0000 - val_loss: 0.0195 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00015: val_accuracy did not improve from 1.00000\n",
            "Epoch 16/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.0160 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00016: val_accuracy did not improve from 1.00000\n",
            "Epoch 17/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0067 - accuracy: 1.0000 - val_loss: 0.0196 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00017: val_accuracy did not improve from 1.00000\n",
            "Epoch 18/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.0152 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00018: val_accuracy did not improve from 1.00000\n",
            "Epoch 19/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.0116 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00019: val_accuracy did not improve from 1.00000\n",
            "Epoch 20/50\n",
            "24/24 [==============================] - 0s 20ms/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.0101 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00020: val_accuracy did not improve from 1.00000\n",
            "Epoch 21/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.0103 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00021: val_accuracy did not improve from 1.00000\n",
            "Epoch 22/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.0092 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00022: val_accuracy did not improve from 1.00000\n",
            "Epoch 23/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0081 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00023: val_accuracy did not improve from 1.00000\n",
            "Epoch 24/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0074 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00024: val_accuracy did not improve from 1.00000\n",
            "Epoch 25/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0070 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00025: val_accuracy did not improve from 1.00000\n",
            "Epoch 26/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0073 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00026: val_accuracy did not improve from 1.00000\n",
            "Epoch 27/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00027: val_accuracy did not improve from 1.00000\n",
            "Epoch 28/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00028: val_accuracy did not improve from 1.00000\n",
            "Epoch 29/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00029: val_accuracy did not improve from 1.00000\n",
            "Epoch 30/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00030: val_accuracy did not improve from 1.00000\n",
            "Epoch 31/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00031: val_accuracy did not improve from 1.00000\n",
            "Epoch 32/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00032: val_accuracy did not improve from 1.00000\n",
            "Epoch 33/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0048 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00033: val_accuracy did not improve from 1.00000\n",
            "Epoch 34/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0045 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00034: val_accuracy did not improve from 1.00000\n",
            "Epoch 35/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0044 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00035: val_accuracy did not improve from 1.00000\n",
            "Epoch 36/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0044 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00036: val_accuracy did not improve from 1.00000\n",
            "Epoch 37/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00037: val_accuracy did not improve from 1.00000\n",
            "Epoch 38/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00038: val_accuracy did not improve from 1.00000\n",
            "Epoch 39/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00039: val_accuracy did not improve from 1.00000\n",
            "Epoch 40/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00040: val_accuracy did not improve from 1.00000\n",
            "Epoch 41/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00041: val_accuracy did not improve from 1.00000\n",
            "Epoch 42/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 8.8043e-04 - accuracy: 1.0000 - val_loss: 0.0035 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00042: val_accuracy did not improve from 1.00000\n",
            "Epoch 43/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 9.3071e-04 - accuracy: 1.0000 - val_loss: 0.0034 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00043: val_accuracy did not improve from 1.00000\n",
            "Epoch 44/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 9.2930e-04 - accuracy: 1.0000 - val_loss: 0.0033 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00044: val_accuracy did not improve from 1.00000\n",
            "Epoch 45/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 7.5387e-04 - accuracy: 1.0000 - val_loss: 0.0033 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00045: val_accuracy did not improve from 1.00000\n",
            "Epoch 46/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 9.0373e-04 - accuracy: 1.0000 - val_loss: 0.0033 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00046: val_accuracy did not improve from 1.00000\n",
            "Epoch 47/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 8.6222e-04 - accuracy: 1.0000 - val_loss: 0.0031 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00047: val_accuracy did not improve from 1.00000\n",
            "Epoch 48/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 8.5762e-04 - accuracy: 1.0000 - val_loss: 0.0030 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00048: val_accuracy did not improve from 1.00000\n",
            "Epoch 49/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 6.1978e-04 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00049: val_accuracy did not improve from 1.00000\n",
            "Epoch 50/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 7.7696e-04 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00050: val_accuracy did not improve from 1.00000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "98_oXi-lcgux",
        "outputId": "d4073dc9-a52f-45b2-afbc-80800cac5555"
      },
      "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": 91,
      "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": "58b3a175-c71b-401d-fc0f-f2f7d2e8d8c9"
      },
      "source": [
        "predictions = model.predict(testX, batch_size=32)"
      ],
      "execution_count": 92,
      "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_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_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": "a7804527-9988-4e0a-c64e-f29bdbf1801a"
      },
      "source": [
        "print(predictions.argmax(axis=1))"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 0 0 1 0 0 1 1 0 0 0 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 0 1 0 0 0 1 0 1 1 0 1\n",
            " 1 0 1 1 1 0 1 0 0 1 0 0 1 0 1 0 1 1 1 1 0 1 1 0 0 0 1 0 1 0 1 0 1 0 1 0 1\n",
            " 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 0 1 0 0\n",
            " 0 1 1 0 1 0 1 0 1 0 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0\n",
            " 1 0 0 0 0 1 1 1 0 1 1 1 0 0 0 1 0 0 1 1 1 0 1 1 0 1 0 0 1 1 0 0 1 1 0 0 1\n",
            " 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 0 0 1 1 1 0 0 1 1 0 0 1\n",
            " 0 0 1 1 0 1 0 1 1 0 0 1 0 0 1 1 0 1 1 1 0 0 1 1 0 0 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6puOgflYR_e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPAZkmBJVGCD",
        "outputId": "bf213516-3d48-4b0e-abbb-a1bc667b032e"
      },
      "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": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Covid       1.00      1.00      1.00       120\n",
            "      Normal       1.00      1.00      1.00       130\n",
            "\n",
            "    accuracy                           1.00       250\n",
            "   macro avg       1.00      1.00      1.00       250\n",
            "weighted avg       1.00      1.00      1.00       250\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJ-r6keDY5jt",
        "outputId": "c804c8a0-eb7d-44f0-81e2-a0ad75300f28"
      },
      "source": [
        "cf_matrix = confusion_matrix(testY,predictions.argmax(axis=1))\n",
        "print(cf_matrix)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[120   0]\n",
            " [  0 130]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLp1ooZeY90p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "4f2a52c6-11eb-48b1-ca77-9bbb6f393c8b"
      },
      "source": [
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cf_matrix, display_labels=encoder.classes_)\n",
        "disp.plot(cmap=plt.cm.Blues)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f90d08531d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtE5PB_Vcqiu"
      },
      "source": [
        "\n",
        "import io\n",
        "\n",
        "path='/content/drive/MyDrive/Normalized/Covid/ IM00001.tif'\n",
        "save_path='/content/drive/MyDrive/Testing/Covid/covid1.tif'\n",
        "with open(path, 'rb') as f:\n",
        "    tif = Image.open(io.BytesIO(f.read()))\n",
        "array=np.array(tif)\n",
        "max_val=np.amax(array)\n",
        "normalized=(array/max_val)\n",
        "im = Image.fromarray(normalized)\n",
        "im.save(save_path,'TIFF')"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwPAgH02dIS6",
        "outputId": "777c3526-8f8a-4c87-bb73-542551df28c7"
      },
      "source": [
        "\n",
        "width = 224\n",
        "height = 224\n",
        "dsize = (width, height)\n",
        "image_testing = cv2.imread('/content/drive/MyDrive/Testing/Covid/covid1.tif', -1)\n",
        "image_testing = cv2.cvtColor(image_testing, cv2.COLOR_BGR2RGB)\n",
        "image_testing = cv2.resize(image_testing,dsize)\n",
        "image_testing = np.array(image_testing)\n",
        "image_testing.shape"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "wQijrpZ_dPjn",
        "outputId": "cab0509e-e965-4fa1-c709-eb1b0e54544c"
      },
      "source": [
        "plt.imshow(image_testing)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f910616a0d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IRTDbxzsdST7",
        "outputId": "a177a365-5e1e-4c50-8002-3ac993898d14"
      },
      "source": [
        "image_testing = np.expand_dims(image_testing, axis=0)\n",
        "print(image_testing.shape)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 224, 224, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_VQoQn24dUtc",
        "outputId": "52c2d654-0120-424e-c8d9-6478df81d0be"
      },
      "source": [
        "\n",
        "output = model.predict(image_testing, 1)\n",
        "print(output)\n",
        "print(encoder.classes_[output.argmax(axis=1)])"
      ],
      "execution_count": 101,
      "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_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_input'\"), but it was called on an input with incompatible shape (1, 224, 224, 3).\n",
            "[[9.7943485e-01 1.9714976e-02 8.5011590e-04]]\n",
            "['Covid']\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}